diff --git a/repositories/BLIP/BLIP.gif b/repositories/BLIP/BLIP.gif
deleted file mode 100644
index f97959778a4d3a9c1d5c06793c96d96204fe2081..0000000000000000000000000000000000000000
--- a/repositories/BLIP/BLIP.gif
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:7757a1a1133807158ec4e696a8187f289e64c30a86aa470d8e0a93948a02be22
-size 6707660
diff --git a/repositories/BLIP/CODEOWNERS b/repositories/BLIP/CODEOWNERS
deleted file mode 100644
index 522fa4a0f715cd0328b9b9dbacae00e060193f43..0000000000000000000000000000000000000000
--- a/repositories/BLIP/CODEOWNERS
+++ /dev/null
@@ -1,2 +0,0 @@
-# Comment line immediately above ownership line is reserved for related gus information. Please be careful while editing.
-#ECCN:Open Source
diff --git a/repositories/BLIP/CODE_OF_CONDUCT.md b/repositories/BLIP/CODE_OF_CONDUCT.md
deleted file mode 100644
index b6724718c9512d730bb7f1bcc5848cd420241407..0000000000000000000000000000000000000000
--- a/repositories/BLIP/CODE_OF_CONDUCT.md
+++ /dev/null
@@ -1,105 +0,0 @@
-# Salesforce Open Source Community Code of Conduct
-
-## About the Code of Conduct
-
-Equality is a core value at Salesforce. We believe a diverse and inclusive
-community fosters innovation and creativity, and are committed to building a
-culture where everyone feels included.
-
-Salesforce open-source projects are committed to providing a friendly, safe, and
-welcoming environment for all, regardless of gender identity and expression,
-sexual orientation, disability, physical appearance, body size, ethnicity, nationality, 
-race, age, religion, level of experience, education, socioeconomic status, or 
-other similar personal characteristics.
-
-The goal of this code of conduct is to specify a baseline standard of behavior so
-that people with different social values and communication styles can work
-together effectively, productively, and respectfully in our open source community.
-It also establishes a mechanism for reporting issues and resolving conflicts.
-
-All questions and reports of abusive, harassing, or otherwise unacceptable behavior
-in a Salesforce open-source project may be reported by contacting the Salesforce
-Open Source Conduct Committee at ossconduct@salesforce.com.
-
-## Our Pledge
-
-In the interest of fostering an open and welcoming environment, we as
-contributors and maintainers pledge to making participation in our project and
-our community a harassment-free experience for everyone, regardless of gender 
-identity and expression, sexual orientation, disability, physical appearance, 
-body size, ethnicity, nationality, race, age, religion, level of experience, education, 
-socioeconomic status, or other similar personal characteristics.
-
-## Our Standards
-
-Examples of behavior that contributes to creating a positive environment
-include:
-
-* Using welcoming and inclusive language
-* Being respectful of differing viewpoints and experiences
-* Gracefully accepting constructive criticism
-* Focusing on what is best for the community
-* Showing empathy toward other community members
-
-Examples of unacceptable behavior by participants include:
-
-* The use of sexualized language or imagery and unwelcome sexual attention or
-advances
-* Personal attacks, insulting/derogatory comments, or trolling
-* Public or private harassment
-* Publishing, or threatening to publish, others' private information—such as
-a physical or electronic address—without explicit permission
-* Other conduct which could reasonably be considered inappropriate in a
-professional setting
-* Advocating for or encouraging any of the above behaviors
-
-## Our Responsibilities
-
-Project maintainers are responsible for clarifying the standards of acceptable
-behavior and are expected to take appropriate and fair corrective action in
-response to any instances of unacceptable behavior.
-
-Project maintainers have the right and responsibility to remove, edit, or
-reject comments, commits, code, wiki edits, issues, and other contributions
-that are not aligned with this Code of Conduct, or to ban temporarily or
-permanently any contributor for other behaviors that they deem inappropriate,
-threatening, offensive, or harmful.
-
-## Scope
-
-This Code of Conduct applies both within project spaces and in public spaces
-when an individual is representing the project or its community. Examples of
-representing a project or community include using an official project email
-address, posting via an official social media account, or acting as an appointed
-representative at an online or offline event. Representation of a project may be
-further defined and clarified by project maintainers.
-
-## Enforcement
-
-Instances of abusive, harassing, or otherwise unacceptable behavior may be
-reported by contacting the Salesforce Open Source Conduct Committee 
-at ossconduct@salesforce.com. All complaints will be reviewed and investigated 
-and will result in a response that is deemed necessary and appropriate to the 
-circumstances. The committee is obligated to maintain confidentiality with 
-regard to the reporter of an incident. Further details of specific enforcement 
-policies may be posted separately.
-
-Project maintainers who do not follow or enforce the Code of Conduct in good
-faith may face temporary or permanent repercussions as determined by other
-members of the project's leadership and the Salesforce Open Source Conduct 
-Committee.
-
-## Attribution
-
-This Code of Conduct is adapted from the [Contributor Covenant][contributor-covenant-home],
-version 1.4, available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html. 
-It includes adaptions and additions from [Go Community Code of Conduct][golang-coc], 
-[CNCF Code of Conduct][cncf-coc], and [Microsoft Open Source Code of Conduct][microsoft-coc].
-
-This Code of Conduct is licensed under the [Creative Commons Attribution 3.0 License][cc-by-3-us].
-
-[contributor-covenant-home]: https://www.contributor-covenant.org (https://www.contributor-covenant.org/)
-[golang-coc]: https://golang.org/conduct
-[cncf-coc]: https://github.com/cncf/foundation/blob/master/code-of-conduct.md
-[microsoft-coc]: https://opensource.microsoft.com/codeofconduct/
-[cc-by-3-us]: https://creativecommons.org/licenses/by/3.0/us/
diff --git a/repositories/BLIP/LICENSE.txt b/repositories/BLIP/LICENSE.txt
deleted file mode 100644
index a63e87f4e1e90c96861648a16a7304d97d3c3f7b..0000000000000000000000000000000000000000
--- a/repositories/BLIP/LICENSE.txt
+++ /dev/null
@@ -1,12 +0,0 @@
-Copyright (c) 2022, Salesforce.com, Inc.
-All rights reserved.
-
-Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
-
-* Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
-
-* Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
-
-* Neither the name of Salesforce.com nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
-
-THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
diff --git a/repositories/BLIP/README.md b/repositories/BLIP/README.md
deleted file mode 100644
index 7a86ebfc21536b97fde1789f0c9ec4a53d2bdd77..0000000000000000000000000000000000000000
--- a/repositories/BLIP/README.md
+++ /dev/null
@@ -1,114 +0,0 @@
-## BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation
-
-<img src="BLIP.gif" width="700">
-
-This is the PyTorch code of the <a href="https://arxiv.org/abs/2201.12086">BLIP paper</a> [[blog](https://blog.salesforceairesearch.com/blip-bootstrapping-language-image-pretraining/)]. The code has been tested on PyTorch 1.10.
-To install the dependencies, run <pre/>pip install -r requirements.txt</pre> 
-
-Catalog:
-- [x] Inference demo
-- [x] Pre-trained and finetuned checkpoints
-- [x] Finetuning code for Image-Text Retrieval, Image Captioning, VQA, and NLVR2
-- [x] Pre-training code
-- [x] Zero-shot video-text retrieval
-- [x] Download of bootstrapped pre-training datasets 
-
-
-### Inference demo:
-Run our interactive demo using [Colab notebook](https://colab.research.google.com/github/salesforce/BLIP/blob/main/demo.ipynb) (no GPU needed).
-The demo includes code for: 
-1. Image captioning
-2. Open-ended visual question answering
-3. Multimodal / unimodal feature extraction
-4. Image-text matching
-
-Try out the [Web demo](https://huggingface.co/spaces/Salesforce/BLIP), integrated into [Huggingface Spaces 🤗](https://huggingface.co/spaces) using [Gradio](https://github.com/gradio-app/gradio). 
-
-Replicate web demo and Docker image is also available at [![Replicate](https://replicate.com/salesforce/blip/badge)](https://replicate.com/salesforce/blip)
-
-### Pre-trained checkpoints:
-Num. pre-train images | BLIP w/ ViT-B | BLIP w/ ViT-B and CapFilt-L | BLIP w/ ViT-L 
---- | :---: | :---: | :---: 
-14M | <a href="https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_14M.pth">Download</a>| - | -
-129M | <a href="https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base.pth">Download</a>| <a href="https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth">Download</a> | <a href="https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_large.pth">Download</a>
-
-### Finetuned checkpoints:
-Task | BLIP w/ ViT-B | BLIP w/ ViT-B and CapFilt-L | BLIP w/ ViT-L 
---- | :---: | :---: | :---:
-Image-Text Retrieval (COCO) | <a href="https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth">Download</a>| - | <a href="https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_large_retrieval_coco.pth">Download</a>
-Image-Text Retrieval (Flickr30k) | <a href="https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_flickr.pth">Download</a>|  - | <a href="https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_large_retrieval_flickr.pth">Download</a>
-Image Captioning (COCO) | - | <a href="https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_caption_capfilt_large.pth">Download</a>| <a href="https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_large_caption.pth">Download</a> | 
-VQA | <a href="https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_vqa.pth">Download</a>| <a href="https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth">Download</a> | - 
-NLVR2 | <a href="https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_nlvr.pth">Download</a>| - | - 
-
-
-### Image-Text Retrieval:
-1. Download COCO and Flickr30k datasets from the original websites, and set 'image_root' in configs/retrieval_{dataset}.yaml accordingly.
-2. To evaluate the finetuned BLIP model on COCO, run:
-<pre>python -m torch.distributed.run --nproc_per_node=8 train_retrieval.py \
---config ./configs/retrieval_coco.yaml \
---output_dir output/retrieval_coco \
---evaluate</pre> 
-3. To finetune the pre-trained checkpoint using 8 A100 GPUs, first set 'pretrained' in configs/retrieval_coco.yaml as "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base.pth". Then run:
-<pre>python -m torch.distributed.run --nproc_per_node=8 train_retrieval.py \
---config ./configs/retrieval_coco.yaml \
---output_dir output/retrieval_coco </pre> 
-
-### Image-Text Captioning:
-1. Download COCO and NoCaps datasets from the original websites, and set 'image_root' in configs/caption_coco.yaml and configs/nocaps.yaml accordingly.
-2. To evaluate the finetuned BLIP model on COCO, run:
-<pre>python -m torch.distributed.run --nproc_per_node=8 train_caption.py --evaluate</pre> 
-3. To evaluate the finetuned BLIP model on NoCaps, generate results with: (evaluation needs to be performed on official server)
-<pre>python -m torch.distributed.run --nproc_per_node=8 eval_nocaps.py </pre> 
-4. To finetune the pre-trained checkpoint using 8 A100 GPUs, first set 'pretrained' in configs/caption_coco.yaml as "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth". Then run:
-<pre>python -m torch.distributed.run --nproc_per_node=8 train_caption.py </pre> 
-
-### VQA:
-1. Download VQA v2 dataset and Visual Genome dataset from the original websites, and set 'vqa_root' and 'vg_root' in configs/vqa.yaml.
-2. To evaluate the finetuned BLIP model, generate results with: (evaluation needs to be performed on official server)
-<pre>python -m torch.distributed.run --nproc_per_node=8 train_vqa.py --evaluate</pre> 
-3. To finetune the pre-trained checkpoint using 16 A100 GPUs, first set 'pretrained' in configs/vqa.yaml as "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth". Then run:
-<pre>python -m torch.distributed.run --nproc_per_node=16 train_vqa.py </pre> 
-
-### NLVR2:
-1. Download NLVR2 dataset from the original websites, and set 'image_root' in configs/nlvr.yaml.
-2. To evaluate the finetuned BLIP model, run
-<pre>python -m torch.distributed.run --nproc_per_node=8 train_nlvr.py --evaluate</pre> 
-3. To finetune the pre-trained checkpoint using 16 A100 GPUs, first set 'pretrained' in configs/nlvr.yaml as "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base.pth". Then run:
-<pre>python -m torch.distributed.run --nproc_per_node=16 train_nlvr.py </pre> 
-
-### Finetune with ViT-L:
-In order to finetune a model with ViT-L, simply change the config file to set 'vit' as large. Batch size and learning rate may also need to be adjusted accordingly (please see the paper's appendix for hyper-parameter details). <a href="https://github.com/facebookresearch/fairscale">Gradient checkpoint</a> can also be activated in the config file to reduce GPU memory usage. 
-
-### Pre-train:
-1. Prepare training json files where each json file contains a list. Each item in the list is a dictonary with two key-value pairs: {'image': path_of_image, 'caption': text_of_image}. 
-2. In configs/pretrain.yaml, set 'train_file' as the paths for the json files .
-3. Pre-train the model using 8 A100 GPUs:
-<pre>python -m torch.distributed.run --nproc_per_node=8 pretrain.py --config ./configs/Pretrain.yaml --output_dir output/Pretrain </pre> 
-
-### Zero-shot video-text retrieval:
-1. Download MSRVTT dataset following the instructions from https://github.com/salesforce/ALPRO, and set 'video_root' accordingly in configs/retrieval_msrvtt.yaml.
-2. Install [decord](https://github.com/dmlc/decord) with <pre>pip install decord</pre> 
-3. To perform zero-shot evaluation, run
-<pre>python -m torch.distributed.run --nproc_per_node=8 eval_retrieval_video.py</pre> 
-
-### Pre-training datasets download:
-We provide bootstrapped pre-training datasets as json files. Each json file contains a list. Each item in the list is a dictonary with two key-value pairs: {'url': url_of_image, 'caption': text_of_image}. 
-
-Image source | Filtered web caption | Filtered synthetic caption by ViT-B | Filtered synthetic caption by ViT-L
---- | :---: | :---: | :---:
-CC3M+CC12M+SBU |  <a href="https://storage.googleapis.com/sfr-vision-language-research/BLIP/datasets/ccs_filtered.json">Download</a>|  <a href="https://storage.googleapis.com/sfr-vision-language-research/BLIP/datasets/ccs_synthetic_filtered.json">Download</a>|  <a href="https://storage.googleapis.com/sfr-vision-language-research/BLIP/datasets/ccs_synthetic_filtered_large.json">Download</a>
-LAION115M | <a href="https://storage.googleapis.com/sfr-vision-language-research/BLIP/datasets/laion_filtered.json">Download</a>|  <a href="https://storage.googleapis.com/sfr-vision-language-research/BLIP/datasets/laion_synthetic_filtered.json">Download</a>|  <a href="https://storage.googleapis.com/sfr-vision-language-research/BLIP/datasets/laion_synthetic_filtered_large.json">Download</a>
-
-### Citation
-If you find this code to be useful for your research, please consider citing.
-<pre>
-@inproceedings{li2022blip,
-      title={BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation}, 
-      author={Junnan Li and Dongxu Li and Caiming Xiong and Steven Hoi},
-      year={2022},
-      booktitle={ICML},
-}</pre>
-
-### Acknowledgement
-The implementation of BLIP relies on resources from <a href="https://github.com/salesforce/ALBEF">ALBEF</a>, <a href="https://github.com/huggingface/transformers">Huggingface Transformers</a>, and <a href="https://github.com/rwightman/pytorch-image-models/tree/master/timm">timm</a>. We thank the original authors for their open-sourcing.
diff --git a/repositories/BLIP/SECURITY.md b/repositories/BLIP/SECURITY.md
deleted file mode 100644
index 8249025739809035264e7776583b2f3ec100553c..0000000000000000000000000000000000000000
--- a/repositories/BLIP/SECURITY.md
+++ /dev/null
@@ -1,7 +0,0 @@
-## Security
-
-Please report any security issue to [security@salesforce.com](mailto:security@salesforce.com)
-as soon as it is discovered. This library limits its runtime dependencies in
-order to reduce the total cost of ownership as much as can be, but all consumers
-should remain vigilant and have their security stakeholders review all third-party
-products (3PP) like this one and their dependencies.
diff --git a/repositories/BLIP/cog.yaml b/repositories/BLIP/cog.yaml
deleted file mode 100644
index c1dfcc430a4cab0fdd2a60a682336219a61c4a4f..0000000000000000000000000000000000000000
--- a/repositories/BLIP/cog.yaml
+++ /dev/null
@@ -1,17 +0,0 @@
-build:
-  gpu: true
-  cuda: "11.1"
-  python_version: "3.8"
-  system_packages:
-    - "libgl1-mesa-glx"
-    - "libglib2.0-0"
-  python_packages:
-    - "ipython==7.30.1"
-    - "torchvision==0.11.1"
-    - "torch==1.10.0"
-    - "timm==0.4.12"
-    - "transformers==4.15.0"
-    - "fairscale==0.4.4"
-    - "pycocoevalcap==1.2"
-
-predict: "predict.py:Predictor"
diff --git a/repositories/BLIP/configs/bert_config.json b/repositories/BLIP/configs/bert_config.json
deleted file mode 100644
index 3ef38aabc7f966b53079e9d559dc59e459cc0051..0000000000000000000000000000000000000000
--- a/repositories/BLIP/configs/bert_config.json
+++ /dev/null
@@ -1,21 +0,0 @@
-{
-  "architectures": [
-    "BertModel"
-  ],
-  "attention_probs_dropout_prob": 0.1,
-  "hidden_act": "gelu",
-  "hidden_dropout_prob": 0.1,
-  "hidden_size": 768,
-  "initializer_range": 0.02,
-  "intermediate_size": 3072,
-  "layer_norm_eps": 1e-12,
-  "max_position_embeddings": 512,
-  "model_type": "bert",
-  "num_attention_heads": 12,
-  "num_hidden_layers": 12,
-  "pad_token_id": 0,
-  "type_vocab_size": 2,
-  "vocab_size": 30522,
-  "encoder_width": 768,
-  "add_cross_attention": true   
-}
diff --git a/repositories/BLIP/configs/caption_coco.yaml b/repositories/BLIP/configs/caption_coco.yaml
deleted file mode 100644
index 42eab7030c0310ba2f265baf36fa1400aa6e5846..0000000000000000000000000000000000000000
--- a/repositories/BLIP/configs/caption_coco.yaml
+++ /dev/null
@@ -1,33 +0,0 @@
-image_root: '/export/share/datasets/vision/coco/images/'
-ann_root: 'annotation'
-coco_gt_root: 'annotation/coco_gt'
-
-# set pretrained as a file path or an url
-pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_caption_capfilt_large.pth'
-
-# size of vit model; base or large
-vit: 'base'
-vit_grad_ckpt: False
-vit_ckpt_layer: 0
-batch_size: 32
-init_lr: 1e-5
-
-# vit: 'large'
-# vit_grad_ckpt: True
-# vit_ckpt_layer: 5
-# batch_size: 16
-# init_lr: 2e-6
-
-image_size: 384
-
-# generation configs
-max_length: 20  
-min_length: 5
-num_beams: 3
-prompt: 'a picture of '
-
-# optimizer
-weight_decay: 0.05
-min_lr: 0
-max_epoch: 5
-
diff --git a/repositories/BLIP/configs/med_config.json b/repositories/BLIP/configs/med_config.json
deleted file mode 100644
index 0ffad0a6f3c2f9f11b8faa84529d9860bb70327a..0000000000000000000000000000000000000000
--- a/repositories/BLIP/configs/med_config.json
+++ /dev/null
@@ -1,21 +0,0 @@
-{
-  "architectures": [
-    "BertModel"
-  ],
-  "attention_probs_dropout_prob": 0.1,
-  "hidden_act": "gelu",
-  "hidden_dropout_prob": 0.1,
-  "hidden_size": 768,
-  "initializer_range": 0.02,
-  "intermediate_size": 3072,
-  "layer_norm_eps": 1e-12,
-  "max_position_embeddings": 512,
-  "model_type": "bert",
-  "num_attention_heads": 12,
-  "num_hidden_layers": 12,
-  "pad_token_id": 0,
-  "type_vocab_size": 2,
-  "vocab_size": 30524,
-  "encoder_width": 768,
-  "add_cross_attention": true   
-}
diff --git a/repositories/BLIP/configs/nlvr.yaml b/repositories/BLIP/configs/nlvr.yaml
deleted file mode 100644
index 2d1122aadb1a776bd347068233096b0c984f648b..0000000000000000000000000000000000000000
--- a/repositories/BLIP/configs/nlvr.yaml
+++ /dev/null
@@ -1,21 +0,0 @@
-image_root: '/export/share/datasets/vision/NLVR2/' 
-ann_root: 'annotation'
-
-# set pretrained as a file path or an url
-pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_nlvr.pth'
-
-#size of vit model; base or large
-vit: 'base'
-batch_size_train: 16 
-batch_size_test: 64 
-vit_grad_ckpt: False
-vit_ckpt_layer: 0
-max_epoch: 15
-
-image_size: 384
-
-# optimizer
-weight_decay: 0.05
-init_lr: 3e-5
-min_lr: 0
-
diff --git a/repositories/BLIP/configs/nocaps.yaml b/repositories/BLIP/configs/nocaps.yaml
deleted file mode 100644
index 9028135859b94aef5324c85c80e376c609d8a089..0000000000000000000000000000000000000000
--- a/repositories/BLIP/configs/nocaps.yaml
+++ /dev/null
@@ -1,15 +0,0 @@
-image_root: '/export/share/datasets/vision/nocaps/'
-ann_root: 'annotation'
-
-# set pretrained as a file path or an url
-pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_caption_capfilt_large.pth'
-
-vit: 'base'
-batch_size: 32
-
-image_size: 384
-
-max_length: 20
-min_length: 5
-num_beams: 3
-prompt: 'a picture of '
\ No newline at end of file
diff --git a/repositories/BLIP/configs/pretrain.yaml b/repositories/BLIP/configs/pretrain.yaml
deleted file mode 100644
index 02355ee0228932803c661616485bf315e862b826..0000000000000000000000000000000000000000
--- a/repositories/BLIP/configs/pretrain.yaml
+++ /dev/null
@@ -1,27 +0,0 @@
-train_file: ['/export/share/junnan-li/VL_pretrain/annotation/coco_karpathy_train.json',
-             '/export/share/junnan-li/VL_pretrain/annotation/vg_caption.json',
-             ]
-laion_path: ''   
-
-# size of vit model; base or large
-vit: 'base'
-vit_grad_ckpt: False
-vit_ckpt_layer: 0
-
-image_size: 224
-batch_size: 75
-
-queue_size: 57600
-alpha: 0.4
-
-# optimizer
-weight_decay: 0.05
-init_lr: 3e-4
-min_lr: 1e-6
-warmup_lr: 1e-6
-lr_decay_rate: 0.9
-max_epoch: 20
-warmup_steps: 3000
-
-
-
diff --git a/repositories/BLIP/configs/retrieval_coco.yaml b/repositories/BLIP/configs/retrieval_coco.yaml
deleted file mode 100644
index a8569e9b67112fe3605ac25e4fdc0231f7975378..0000000000000000000000000000000000000000
--- a/repositories/BLIP/configs/retrieval_coco.yaml
+++ /dev/null
@@ -1,34 +0,0 @@
-image_root: '/export/share/datasets/vision/coco/images/'
-ann_root: 'annotation'
-dataset: 'coco'
-
-# set pretrained as a file path or an url
-pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'
-
-# size of vit model; base or large
-
-vit: 'base'
-batch_size_train: 32
-batch_size_test: 64
-vit_grad_ckpt: True
-vit_ckpt_layer: 4
-init_lr: 1e-5
-
-# vit: 'large'
-# batch_size_train: 16
-# batch_size_test: 32
-# vit_grad_ckpt: True
-# vit_ckpt_layer: 12
-# init_lr: 5e-6
-
-image_size: 384
-queue_size: 57600
-alpha: 0.4
-k_test: 256
-negative_all_rank: True
-
-# optimizer
-weight_decay: 0.05
-min_lr: 0
-max_epoch: 6
-
diff --git a/repositories/BLIP/configs/retrieval_flickr.yaml b/repositories/BLIP/configs/retrieval_flickr.yaml
deleted file mode 100644
index d75ea4eed87c9a001523c5e5914998c5e737594d..0000000000000000000000000000000000000000
--- a/repositories/BLIP/configs/retrieval_flickr.yaml
+++ /dev/null
@@ -1,34 +0,0 @@
-image_root: '/export/share/datasets/vision/flickr30k/'
-ann_root: 'annotation'
-dataset: 'flickr'
-
-# set pretrained as a file path or an url
-pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_flickr.pth'
-
-# size of vit model; base or large
-
-vit: 'base'
-batch_size_train: 32
-batch_size_test: 64
-vit_grad_ckpt: True
-vit_ckpt_layer: 4
-init_lr: 1e-5
-
-# vit: 'large'
-# batch_size_train: 16
-# batch_size_test: 32
-# vit_grad_ckpt: True
-# vit_ckpt_layer: 10
-# init_lr: 5e-6
-
-image_size: 384
-queue_size: 57600
-alpha: 0.4
-k_test: 128
-negative_all_rank: False
-
-# optimizer
-weight_decay: 0.05
-min_lr: 0
-max_epoch: 6
-
diff --git a/repositories/BLIP/configs/retrieval_msrvtt.yaml b/repositories/BLIP/configs/retrieval_msrvtt.yaml
deleted file mode 100644
index 395f62542bb22d706b8e19e2455d2c7298984d0b..0000000000000000000000000000000000000000
--- a/repositories/BLIP/configs/retrieval_msrvtt.yaml
+++ /dev/null
@@ -1,12 +0,0 @@
-video_root: '/export/share/dongxuli/data/msrvtt_retrieval/videos'
-ann_root: 'annotation'
-
-# set pretrained as a file path or an url
-pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'
-
-# size of vit model; base or large
-vit: 'base'
-batch_size: 64
-k_test: 128
-image_size: 384
-num_frm_test: 8
\ No newline at end of file
diff --git a/repositories/BLIP/configs/vqa.yaml b/repositories/BLIP/configs/vqa.yaml
deleted file mode 100644
index 74327e6d0a34672023b44569558fe8beeb052548..0000000000000000000000000000000000000000
--- a/repositories/BLIP/configs/vqa.yaml
+++ /dev/null
@@ -1,25 +0,0 @@
-vqa_root: '/export/share/datasets/vision/VQA/Images/mscoco/' #followed by train2014/
-vg_root: '/export/share/datasets/vision/visual-genome/'  #followed by image/
-train_files: ['vqa_train','vqa_val','vg_qa']
-ann_root: 'annotation'
-
-# set pretrained as a file path or an url
-pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth'
-
-# size of vit model; base or large
-vit: 'base'
-batch_size_train: 16 
-batch_size_test: 32 
-vit_grad_ckpt: False
-vit_ckpt_layer: 0
-init_lr: 2e-5
-
-image_size: 480
-
-k_test: 128
-inference: 'rank'
-
-# optimizer
-weight_decay: 0.05
-min_lr: 0
-max_epoch: 10
\ No newline at end of file
diff --git a/repositories/BLIP/data/__init__.py b/repositories/BLIP/data/__init__.py
deleted file mode 100644
index 0be209acf415855ea6ef753efedf903b5decb6b9..0000000000000000000000000000000000000000
--- a/repositories/BLIP/data/__init__.py
+++ /dev/null
@@ -1,101 +0,0 @@
-import torch
-from torch.utils.data import DataLoader
-from torchvision import transforms
-from torchvision.transforms.functional import InterpolationMode
-
-from data.coco_karpathy_dataset import coco_karpathy_train, coco_karpathy_caption_eval, coco_karpathy_retrieval_eval
-from data.nocaps_dataset import nocaps_eval
-from data.flickr30k_dataset import flickr30k_train, flickr30k_retrieval_eval
-from data.vqa_dataset import vqa_dataset
-from data.nlvr_dataset import nlvr_dataset
-from data.pretrain_dataset import pretrain_dataset
-from transform.randaugment import RandomAugment
-
-def create_dataset(dataset, config, min_scale=0.5):
-    
-    normalize = transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))
-
-    transform_train = transforms.Compose([                        
-            transforms.RandomResizedCrop(config['image_size'],scale=(min_scale, 1.0),interpolation=InterpolationMode.BICUBIC),
-            transforms.RandomHorizontalFlip(),
-            RandomAugment(2,5,isPIL=True,augs=['Identity','AutoContrast','Brightness','Sharpness','Equalize',
-                                              'ShearX', 'ShearY', 'TranslateX', 'TranslateY', 'Rotate']),     
-            transforms.ToTensor(),
-            normalize,
-        ])        
-    transform_test = transforms.Compose([
-        transforms.Resize((config['image_size'],config['image_size']),interpolation=InterpolationMode.BICUBIC),
-        transforms.ToTensor(),
-        normalize,
-        ])  
-        
-    if dataset=='pretrain':
-        dataset = pretrain_dataset(config['train_file'], config['laion_path'], transform_train)              
-        return dataset  
-    
-    elif dataset=='caption_coco':   
-        train_dataset = coco_karpathy_train(transform_train, config['image_root'], config['ann_root'], prompt=config['prompt'])
-        val_dataset = coco_karpathy_caption_eval(transform_test, config['image_root'], config['ann_root'], 'val')
-        test_dataset = coco_karpathy_caption_eval(transform_test, config['image_root'], config['ann_root'], 'test')   
-        return train_dataset, val_dataset, test_dataset
-    
-    elif dataset=='nocaps':   
-        val_dataset = nocaps_eval(transform_test, config['image_root'], config['ann_root'], 'val')
-        test_dataset = nocaps_eval(transform_test, config['image_root'], config['ann_root'], 'test')   
-        return val_dataset, test_dataset   
-    
-    elif dataset=='retrieval_coco':          
-        train_dataset = coco_karpathy_train(transform_train, config['image_root'], config['ann_root'])
-        val_dataset = coco_karpathy_retrieval_eval(transform_test, config['image_root'], config['ann_root'], 'val') 
-        test_dataset = coco_karpathy_retrieval_eval(transform_test, config['image_root'], config['ann_root'], 'test')          
-        return train_dataset, val_dataset, test_dataset    
-    
-    elif dataset=='retrieval_flickr':          
-        train_dataset = flickr30k_train(transform_train, config['image_root'], config['ann_root'])
-        val_dataset = flickr30k_retrieval_eval(transform_test, config['image_root'], config['ann_root'], 'val') 
-        test_dataset = flickr30k_retrieval_eval(transform_test, config['image_root'], config['ann_root'], 'test')          
-        return train_dataset, val_dataset, test_dataset     
-    
-    elif dataset=='vqa': 
-        train_dataset = vqa_dataset(transform_train, config['ann_root'], config['vqa_root'], config['vg_root'], 
-                                    train_files = config['train_files'], split='train') 
-        test_dataset = vqa_dataset(transform_test, config['ann_root'], config['vqa_root'], config['vg_root'], split='test')
-        return train_dataset, test_dataset
-    
-    elif dataset=='nlvr': 
-        train_dataset = nlvr_dataset(transform_train, config['image_root'], config['ann_root'],'train')
-        val_dataset = nlvr_dataset(transform_test, config['image_root'], config['ann_root'],'val')
-        test_dataset = nlvr_dataset(transform_test, config['image_root'], config['ann_root'],'test')     
-        return train_dataset, val_dataset, test_dataset   
-    
-    
-def create_sampler(datasets, shuffles, num_tasks, global_rank):
-    samplers = []
-    for dataset,shuffle in zip(datasets,shuffles):
-        sampler = torch.utils.data.DistributedSampler(dataset, num_replicas=num_tasks, rank=global_rank, shuffle=shuffle)
-        samplers.append(sampler)
-    return samplers     
-
-
-def create_loader(datasets, samplers, batch_size, num_workers, is_trains, collate_fns):
-    loaders = []
-    for dataset,sampler,bs,n_worker,is_train,collate_fn in zip(datasets,samplers,batch_size,num_workers,is_trains,collate_fns):
-        if is_train:
-            shuffle = (sampler is None)
-            drop_last = True
-        else:
-            shuffle = False
-            drop_last = False
-        loader = DataLoader(
-            dataset,
-            batch_size=bs,
-            num_workers=n_worker,
-            pin_memory=True,
-            sampler=sampler,
-            shuffle=shuffle,
-            collate_fn=collate_fn,
-            drop_last=drop_last,
-        )              
-        loaders.append(loader)
-    return loaders    
-
diff --git a/repositories/BLIP/data/coco_karpathy_dataset.py b/repositories/BLIP/data/coco_karpathy_dataset.py
deleted file mode 100644
index a34d29205f42aa09695b160ac9c91958ba041bb3..0000000000000000000000000000000000000000
--- a/repositories/BLIP/data/coco_karpathy_dataset.py
+++ /dev/null
@@ -1,126 +0,0 @@
-import os
-import json
-
-from torch.utils.data import Dataset
-from torchvision.datasets.utils import download_url
-
-from PIL import Image
-
-from data.utils import pre_caption
-
-class coco_karpathy_train(Dataset):
-    def __init__(self, transform, image_root, ann_root, max_words=30, prompt=''):        
-        '''
-        image_root (string): Root directory of images (e.g. coco/images/)
-        ann_root (string): directory to store the annotation file
-        '''        
-        url = 'https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_train.json'
-        filename = 'coco_karpathy_train.json'
-
-        download_url(url,ann_root)
-        
-        self.annotation = json.load(open(os.path.join(ann_root,filename),'r'))
-        self.transform = transform
-        self.image_root = image_root
-        self.max_words = max_words      
-        self.prompt = prompt
-        
-        self.img_ids = {}  
-        n = 0
-        for ann in self.annotation:
-            img_id = ann['image_id']
-            if img_id not in self.img_ids.keys():
-                self.img_ids[img_id] = n
-                n += 1    
-        
-    def __len__(self):
-        return len(self.annotation)
-    
-    def __getitem__(self, index):    
-        
-        ann = self.annotation[index]
-        
-        image_path = os.path.join(self.image_root,ann['image'])        
-        image = Image.open(image_path).convert('RGB')   
-        image = self.transform(image)
-        
-        caption = self.prompt+pre_caption(ann['caption'], self.max_words) 
-
-        return image, caption, self.img_ids[ann['image_id']] 
-    
-    
-class coco_karpathy_caption_eval(Dataset):
-    def __init__(self, transform, image_root, ann_root, split):  
-        '''
-        image_root (string): Root directory of images (e.g. coco/images/)
-        ann_root (string): directory to store the annotation file
-        split (string): val or test
-        '''
-        urls = {'val':'https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_val.json',
-                'test':'https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_test.json'}
-        filenames = {'val':'coco_karpathy_val.json','test':'coco_karpathy_test.json'}
-        
-        download_url(urls[split],ann_root)
-        
-        self.annotation = json.load(open(os.path.join(ann_root,filenames[split]),'r'))
-        self.transform = transform
-        self.image_root = image_root
-        
-    def __len__(self):
-        return len(self.annotation)
-    
-    def __getitem__(self, index):    
-        
-        ann = self.annotation[index]
-        
-        image_path = os.path.join(self.image_root,ann['image'])        
-        image = Image.open(image_path).convert('RGB')   
-        image = self.transform(image)          
-        
-        img_id = ann['image'].split('/')[-1].strip('.jpg').split('_')[-1]
-        
-        return image, int(img_id)   
-    
-    
-class coco_karpathy_retrieval_eval(Dataset):
-    def __init__(self, transform, image_root, ann_root, split, max_words=30):  
-        '''
-        image_root (string): Root directory of images (e.g. coco/images/)
-        ann_root (string): directory to store the annotation file
-        split (string): val or test
-        '''
-        urls = {'val':'https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_val.json',
-                'test':'https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_test.json'}
-        filenames = {'val':'coco_karpathy_val.json','test':'coco_karpathy_test.json'}
-        
-        download_url(urls[split],ann_root)
-        
-        self.annotation = json.load(open(os.path.join(ann_root,filenames[split]),'r'))
-        self.transform = transform
-        self.image_root = image_root
-        
-        self.text = []
-        self.image = []
-        self.txt2img = {}
-        self.img2txt = {}
-        
-        txt_id = 0
-        for img_id, ann in enumerate(self.annotation):
-            self.image.append(ann['image'])
-            self.img2txt[img_id] = []
-            for i, caption in enumerate(ann['caption']):
-                self.text.append(pre_caption(caption,max_words))
-                self.img2txt[img_id].append(txt_id)
-                self.txt2img[txt_id] = img_id
-                txt_id += 1
-                                    
-    def __len__(self):
-        return len(self.annotation)
-    
-    def __getitem__(self, index):    
-        
-        image_path = os.path.join(self.image_root, self.annotation[index]['image'])        
-        image = Image.open(image_path).convert('RGB')    
-        image = self.transform(image)  
-
-        return image, index
\ No newline at end of file
diff --git a/repositories/BLIP/data/flickr30k_dataset.py b/repositories/BLIP/data/flickr30k_dataset.py
deleted file mode 100644
index 018ab387014ddaf554c4d3184cfc0e2ba8b2d487..0000000000000000000000000000000000000000
--- a/repositories/BLIP/data/flickr30k_dataset.py
+++ /dev/null
@@ -1,93 +0,0 @@
-import os
-import json
-
-from torch.utils.data import Dataset
-from torchvision.datasets.utils import download_url
-
-from PIL import Image
-
-from data.utils import pre_caption
-
-class flickr30k_train(Dataset):
-    def __init__(self, transform, image_root, ann_root, max_words=30, prompt=''):        
-        '''
-        image_root (string): Root directory of images (e.g. flickr30k/)
-        ann_root (string): directory to store the annotation file
-        '''        
-        url = 'https://storage.googleapis.com/sfr-vision-language-research/datasets/flickr30k_train.json'
-        filename = 'flickr30k_train.json'
-
-        download_url(url,ann_root)
-        
-        self.annotation = json.load(open(os.path.join(ann_root,filename),'r'))
-        self.transform = transform
-        self.image_root = image_root
-        self.max_words = max_words      
-        self.prompt = prompt
-        
-        self.img_ids = {}  
-        n = 0
-        for ann in self.annotation:
-            img_id = ann['image_id']
-            if img_id not in self.img_ids.keys():
-                self.img_ids[img_id] = n
-                n += 1    
-        
-    def __len__(self):
-        return len(self.annotation)
-    
-    def __getitem__(self, index):    
-        
-        ann = self.annotation[index]
-        
-        image_path = os.path.join(self.image_root,ann['image'])        
-        image = Image.open(image_path).convert('RGB')   
-        image = self.transform(image)
-        
-        caption = self.prompt+pre_caption(ann['caption'], self.max_words) 
-
-        return image, caption, self.img_ids[ann['image_id']] 
-    
-    
-class flickr30k_retrieval_eval(Dataset):
-    def __init__(self, transform, image_root, ann_root, split, max_words=30):  
-        '''
-        image_root (string): Root directory of images (e.g. flickr30k/)
-        ann_root (string): directory to store the annotation file
-        split (string): val or test
-        '''
-        urls = {'val':'https://storage.googleapis.com/sfr-vision-language-research/datasets/flickr30k_val.json',
-                'test':'https://storage.googleapis.com/sfr-vision-language-research/datasets/flickr30k_test.json'}
-        filenames = {'val':'flickr30k_val.json','test':'flickr30k_test.json'}
-        
-        download_url(urls[split],ann_root)
-        
-        self.annotation = json.load(open(os.path.join(ann_root,filenames[split]),'r'))
-        self.transform = transform
-        self.image_root = image_root
-        
-        self.text = []
-        self.image = []
-        self.txt2img = {}
-        self.img2txt = {}
-        
-        txt_id = 0
-        for img_id, ann in enumerate(self.annotation):
-            self.image.append(ann['image'])
-            self.img2txt[img_id] = []
-            for i, caption in enumerate(ann['caption']):
-                self.text.append(pre_caption(caption,max_words))
-                self.img2txt[img_id].append(txt_id)
-                self.txt2img[txt_id] = img_id
-                txt_id += 1
-                                    
-    def __len__(self):
-        return len(self.annotation)
-    
-    def __getitem__(self, index):    
-        
-        image_path = os.path.join(self.image_root, self.annotation[index]['image'])        
-        image = Image.open(image_path).convert('RGB')    
-        image = self.transform(image)  
-
-        return image, index    
\ No newline at end of file
diff --git a/repositories/BLIP/data/nlvr_dataset.py b/repositories/BLIP/data/nlvr_dataset.py
deleted file mode 100644
index a8d6b2d7cd8d3260bd279c7dca80de53bacc691a..0000000000000000000000000000000000000000
--- a/repositories/BLIP/data/nlvr_dataset.py
+++ /dev/null
@@ -1,78 +0,0 @@
-import os
-import json
-import random
-
-from torch.utils.data import Dataset
-from torchvision.datasets.utils import download_url
-
-from PIL import Image
-
-from data.utils import pre_caption
-
-class nlvr_dataset(Dataset):
-    def __init__(self, transform, image_root, ann_root, split):  
-        '''
-        image_root (string): Root directory of images 
-        ann_root (string): directory to store the annotation file
-        split (string): train, val or test
-        '''
-        urls = {'train':'https://storage.googleapis.com/sfr-vision-language-research/datasets/nlvr_train.json',
-                'val':'https://storage.googleapis.com/sfr-vision-language-research/datasets/nlvr_dev.json',
-                'test':'https://storage.googleapis.com/sfr-vision-language-research/datasets/nlvr_test.json'}
-        filenames = {'train':'nlvr_train.json','val':'nlvr_dev.json','test':'nlvr_test.json'}
-        
-        download_url(urls[split],ann_root)
-        self.annotation = json.load(open(os.path.join(ann_root,filenames[split]),'r'))
-        
-        self.transform = transform
-        self.image_root = image_root
-
-        
-    def __len__(self):
-        return len(self.annotation)
-    
-
-    def __getitem__(self, index):    
-        
-        ann = self.annotation[index]
-        
-        image0_path = os.path.join(self.image_root,ann['images'][0])        
-        image0 = Image.open(image0_path).convert('RGB')   
-        image0 = self.transform(image0)   
-        
-        image1_path = os.path.join(self.image_root,ann['images'][1])              
-        image1 = Image.open(image1_path).convert('RGB')     
-        image1 = self.transform(image1)          
-
-        sentence = pre_caption(ann['sentence'], 40)
-        
-        if ann['label']=='True':
-            label = 1
-        else:
-            label = 0
-            
-        words = sentence.split(' ')
-        
-        if 'left' not in words and 'right' not in words:
-            if random.random()<0.5:
-                return image0, image1, sentence, label
-            else:
-                return image1, image0, sentence, label
-        else:
-            if random.random()<0.5:
-                return image0, image1, sentence, label
-            else:
-                new_words = []
-                for word in words:
-                    if word=='left':
-                        new_words.append('right')
-                    elif word=='right':
-                        new_words.append('left')        
-                    else:
-                        new_words.append(word)                    
-                        
-                sentence = ' '.join(new_words)
-                return image1, image0, sentence, label
-            
-            
-        
\ No newline at end of file
diff --git a/repositories/BLIP/data/nocaps_dataset.py b/repositories/BLIP/data/nocaps_dataset.py
deleted file mode 100644
index ba0bed06d8af3dbaccf18a56e725f101e585503e..0000000000000000000000000000000000000000
--- a/repositories/BLIP/data/nocaps_dataset.py
+++ /dev/null
@@ -1,32 +0,0 @@
-import os
-import json
-
-from torch.utils.data import Dataset
-from torchvision.datasets.utils import download_url
-
-from PIL import Image
-
-class nocaps_eval(Dataset):
-    def __init__(self, transform, image_root, ann_root, split):   
-        urls = {'val':'https://storage.googleapis.com/sfr-vision-language-research/datasets/nocaps_val.json',
-                'test':'https://storage.googleapis.com/sfr-vision-language-research/datasets/nocaps_test.json'}
-        filenames = {'val':'nocaps_val.json','test':'nocaps_test.json'}
-        
-        download_url(urls[split],ann_root)
-        
-        self.annotation = json.load(open(os.path.join(ann_root,filenames[split]),'r'))
-        self.transform = transform
-        self.image_root = image_root
-        
-    def __len__(self):
-        return len(self.annotation)
-    
-    def __getitem__(self, index):  
-        
-        ann = self.annotation[index]
-        
-        image_path = os.path.join(self.image_root,ann['image'])        
-        image = Image.open(image_path).convert('RGB')   
-        image = self.transform(image)          
-        
-        return image, int(ann['img_id'])    
\ No newline at end of file
diff --git a/repositories/BLIP/data/pretrain_dataset.py b/repositories/BLIP/data/pretrain_dataset.py
deleted file mode 100644
index 703d543ab5267fdc6fe2b7c84ef6a631d8af90ad..0000000000000000000000000000000000000000
--- a/repositories/BLIP/data/pretrain_dataset.py
+++ /dev/null
@@ -1,59 +0,0 @@
-import json
-import os
-import random
-
-from torch.utils.data import Dataset
-
-from PIL import Image
-from PIL import ImageFile
-ImageFile.LOAD_TRUNCATED_IMAGES = True
-Image.MAX_IMAGE_PIXELS = None
-
-from data.utils import pre_caption
-import os,glob
-
-class pretrain_dataset(Dataset):
-    def __init__(self, ann_file, laion_path, transform): 
-
-        self.ann_pretrain = []
-        for f in ann_file:
-            print('loading '+f)
-            ann = json.load(open(f,'r'))
-            self.ann_pretrain += ann
-        
-        self.laion_path = laion_path
-        if self.laion_path:
-            self.laion_files = glob.glob(os.path.join(laion_path,'*.json'))
-
-            print('loading '+self.laion_files[0])
-            with open(self.laion_files[0],'r') as f:
-                self.ann_laion = json.load(f)  
-
-            self.annotation = self.ann_pretrain + self.ann_laion
-        else:
-            self.annotation = self.ann_pretrain
-            
-        self.transform = transform
-
-
-    def reload_laion(self, epoch):
-        n = epoch%len(self.laion_files)
-        print('loading '+self.laion_files[n])
-        with open(self.laion_files[n],'r') as f:
-            self.ann_laion = json.load(f)      
-        
-        self.annotation = self.ann_pretrain + self.ann_laion    
-        
-    
-    def __len__(self):
-        return len(self.annotation)
-    
-    def __getitem__(self, index):    
-        
-        ann = self.annotation[index]   
-      
-        image = Image.open(ann['image']).convert('RGB')   
-        image = self.transform(image)
-        caption = pre_caption(ann['caption'],30)
-        
-        return image, caption
\ No newline at end of file
diff --git a/repositories/BLIP/data/utils.py b/repositories/BLIP/data/utils.py
deleted file mode 100644
index 628894844becd462d444584b8b2b01a84ee4b8f7..0000000000000000000000000000000000000000
--- a/repositories/BLIP/data/utils.py
+++ /dev/null
@@ -1,112 +0,0 @@
-import re
-import json
-import os
-
-import torch
-import torch.distributed as dist
-
-import utils
-
-def pre_caption(caption,max_words=50):
-    caption = re.sub(
-        r"([.!\"()*#:;~])",       
-        ' ',
-        caption.lower(),
-    )
-    caption = re.sub(
-        r"\s{2,}",
-        ' ',
-        caption,
-    )
-    caption = caption.rstrip('\n') 
-    caption = caption.strip(' ')
-
-    #truncate caption
-    caption_words = caption.split(' ')
-    if len(caption_words)>max_words:
-        caption = ' '.join(caption_words[:max_words])
-            
-    return caption
-
-def pre_question(question,max_ques_words=50):
-    question = re.sub(
-        r"([.!\"()*#:;~])",
-        '',
-        question.lower(),
-    ) 
-    question = question.rstrip(' ')
-    
-    #truncate question
-    question_words = question.split(' ')
-    if len(question_words)>max_ques_words:
-        question = ' '.join(question_words[:max_ques_words])
-            
-    return question
-
-
-def save_result(result, result_dir, filename, remove_duplicate=''):
-    result_file = os.path.join(result_dir, '%s_rank%d.json'%(filename,utils.get_rank()))
-    final_result_file = os.path.join(result_dir, '%s.json'%filename)
-    
-    json.dump(result,open(result_file,'w'))
-
-    dist.barrier()
-
-    if utils.is_main_process():   
-        # combine results from all processes
-        result = []
-
-        for rank in range(utils.get_world_size()):
-            result_file = os.path.join(result_dir, '%s_rank%d.json'%(filename,rank))
-            res = json.load(open(result_file,'r'))
-            result += res
-
-        if remove_duplicate:
-            result_new = []
-            id_list = []    
-            for res in result:
-                if res[remove_duplicate] not in id_list:
-                    id_list.append(res[remove_duplicate])
-                    result_new.append(res)
-            result = result_new             
-                
-        json.dump(result,open(final_result_file,'w'))            
-        print('result file saved to %s'%final_result_file)
-
-    return final_result_file
-
-
-
-from pycocotools.coco import COCO
-from pycocoevalcap.eval import COCOEvalCap
-from torchvision.datasets.utils import download_url
-
-def coco_caption_eval(coco_gt_root, results_file, split):
-    urls = {'val':'https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_val_gt.json',
-            'test':'https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_test_gt.json'}
-    filenames = {'val':'coco_karpathy_val_gt.json','test':'coco_karpathy_test_gt.json'}    
-    
-    download_url(urls[split],coco_gt_root)
-    annotation_file = os.path.join(coco_gt_root,filenames[split])
-    
-    # create coco object and coco_result object
-    coco = COCO(annotation_file)
-    coco_result = coco.loadRes(results_file)
-
-    # create coco_eval object by taking coco and coco_result
-    coco_eval = COCOEvalCap(coco, coco_result)
-
-    # evaluate on a subset of images by setting
-    # coco_eval.params['image_id'] = coco_result.getImgIds()
-    # please remove this line when evaluating the full validation set
-    # coco_eval.params['image_id'] = coco_result.getImgIds()
-
-    # evaluate results
-    # SPICE will take a few minutes the first time, but speeds up due to caching
-    coco_eval.evaluate()
-
-    # print output evaluation scores
-    for metric, score in coco_eval.eval.items():
-        print(f'{metric}: {score:.3f}')
-    
-    return coco_eval
\ No newline at end of file
diff --git a/repositories/BLIP/data/video_dataset.py b/repositories/BLIP/data/video_dataset.py
deleted file mode 100644
index 0a6f8a61105bbd4285f98b3abe9445b73fd4c7ef..0000000000000000000000000000000000000000
--- a/repositories/BLIP/data/video_dataset.py
+++ /dev/null
@@ -1,110 +0,0 @@
-from torch.utils.data import Dataset
-from torchvision.datasets.utils import download_url
-
-from PIL import Image
-import torch
-import numpy as np
-import random
-import decord
-from decord import VideoReader
-import json
-import os
-from data.utils import pre_caption
-
-decord.bridge.set_bridge("torch")
-
-class ImageNorm(object):
-    """Apply Normalization to Image Pixels on GPU
-    """
-    def __init__(self, mean, std):
-        self.mean = torch.tensor(mean).view(1, 3, 1, 1)
-        self.std = torch.tensor(std).view(1, 3, 1, 1)
-        
-    def __call__(self, img):
-
-        if torch.max(img) > 1 and self.mean.max() <= 1:
-            img.div_(255.)
-        return img.sub_(self.mean).div_(self.std)
-
-def load_jsonl(filename):
-    with open(filename, "r") as f:
-        return [json.loads(l.strip("\n")) for l in f.readlines()]
-    
-    
-class VideoDataset(Dataset):
-
-    def __init__(self, video_root, ann_root, num_frm=4, frm_sampling_strategy="rand", max_img_size=384, video_fmt='.mp4'):
-        '''
-        image_root (string): Root directory of video
-        ann_root (string): directory to store the annotation file
-        '''        
-        url = 'https://storage.googleapis.com/sfr-vision-language-research/datasets/msrvtt_test.jsonl'
-        filename = 'msrvtt_test.jsonl'
-
-        download_url(url,ann_root)
-        self.annotation = load_jsonl(os.path.join(ann_root,filename))
-        
-        self.num_frm = num_frm
-        self.frm_sampling_strategy = frm_sampling_strategy
-        self.max_img_size = max_img_size
-        self.video_root = video_root
-        self.video_fmt = video_fmt
-        self.img_norm = ImageNorm(mean=(0.48145466, 0.4578275, 0.40821073), std=(0.26862954, 0.26130258, 0.27577711))
-
-        self.text = [pre_caption(ann['caption'],40) for ann in self.annotation]
-        self.txt2video = [i for i in range(len(self.annotation))]
-        self.video2txt = self.txt2video               
-            
-            
-    def __len__(self):
-        return len(self.annotation)
-
-    def __getitem__(self, index):
-
-        ann = self.annotation[index]  
-
-        video_path = os.path.join(self.video_root, ann['clip_name'] + self.video_fmt) 
-
-        vid_frm_array = self._load_video_from_path_decord(video_path, height=self.max_img_size, width=self.max_img_size)
-
-        video = self.img_norm(vid_frm_array.float())
-        
-        return video, ann['clip_name']
-
-        
-
-    def _load_video_from_path_decord(self, video_path, height=None, width=None, start_time=None, end_time=None, fps=-1):
-        try:
-            if not height or not width:
-                vr = VideoReader(video_path)
-            else:
-                vr = VideoReader(video_path, width=width, height=height)
-
-            vlen = len(vr)
-
-            if start_time or end_time:
-                assert fps > 0, 'must provide video fps if specifying start and end time.'
-
-                start_idx = min(int(start_time * fps), vlen)
-                end_idx = min(int(end_time * fps), vlen)
-            else:
-                start_idx, end_idx = 0, vlen
-
-            if self.frm_sampling_strategy == 'uniform':
-                frame_indices = np.arange(start_idx, end_idx, vlen / self.num_frm, dtype=int)
-            elif self.frm_sampling_strategy == 'rand':
-                frame_indices = sorted(random.sample(range(vlen), self.num_frm))
-            elif self.frm_sampling_strategy == 'headtail':
-                frame_indices_head = sorted(random.sample(range(vlen // 2), self.num_frm // 2))
-                frame_indices_tail = sorted(random.sample(range(vlen // 2, vlen), self.num_frm // 2))
-                frame_indices = frame_indices_head + frame_indices_tail
-            else:
-                raise NotImplementedError('Invalid sampling strategy {} '.format(self.frm_sampling_strategy))
-
-            raw_sample_frms = vr.get_batch(frame_indices)
-        except Exception as e:
-            return None
-
-        raw_sample_frms = raw_sample_frms.permute(0, 3, 1, 2)
-
-        return raw_sample_frms
diff --git a/repositories/BLIP/data/vqa_dataset.py b/repositories/BLIP/data/vqa_dataset.py
deleted file mode 100644
index 92ec1df429b3910316ddd554bfea01c6e7922cae..0000000000000000000000000000000000000000
--- a/repositories/BLIP/data/vqa_dataset.py
+++ /dev/null
@@ -1,88 +0,0 @@
-import os
-import json
-import random
-from PIL import Image
-
-import torch
-from torch.utils.data import Dataset
-from data.utils import pre_question
-
-from torchvision.datasets.utils import download_url
-
-class vqa_dataset(Dataset):
-    def __init__(self, transform, ann_root, vqa_root, vg_root, train_files=[], split="train"):
-        self.split = split        
-
-        self.transform = transform
-        self.vqa_root = vqa_root
-        self.vg_root = vg_root
-        
-        if split=='train':
-            urls = {'vqa_train':'https://storage.googleapis.com/sfr-vision-language-research/datasets/vqa_train.json',
-                    'vqa_val':'https://storage.googleapis.com/sfr-vision-language-research/datasets/vqa_val.json',
-                    'vg_qa':'https://storage.googleapis.com/sfr-vision-language-research/datasets/vg_qa.json'}
-        
-            self.annotation = []
-            for f in train_files:
-                download_url(urls[f],ann_root)
-                self.annotation += json.load(open(os.path.join(ann_root,'%s.json'%f),'r'))
-        else:
-            download_url('https://storage.googleapis.com/sfr-vision-language-research/datasets/vqa_test.json',ann_root)
-            self.annotation = json.load(open(os.path.join(ann_root,'vqa_test.json'),'r'))    
-            
-            download_url('https://storage.googleapis.com/sfr-vision-language-research/datasets/answer_list.json',ann_root)
-            self.answer_list = json.load(open(os.path.join(ann_root,'answer_list.json'),'r'))    
-                
-        
-    def __len__(self):
-        return len(self.annotation)
-    
-    def __getitem__(self, index):    
-        
-        ann = self.annotation[index]
-        
-        if ann['dataset']=='vqa':
-            image_path = os.path.join(self.vqa_root,ann['image'])    
-        elif ann['dataset']=='vg':
-            image_path = os.path.join(self.vg_root,ann['image'])  
-            
-        image = Image.open(image_path).convert('RGB')   
-        image = self.transform(image)          
-        
-        if self.split == 'test':
-            question = pre_question(ann['question'])   
-            question_id = ann['question_id']            
-            return image, question, question_id
-
-
-        elif self.split=='train':                       
-            
-            question = pre_question(ann['question'])        
-            
-            if ann['dataset']=='vqa':               
-                answer_weight = {}
-                for answer in ann['answer']:
-                    if answer in answer_weight.keys():
-                        answer_weight[answer] += 1/len(ann['answer'])
-                    else:
-                        answer_weight[answer] = 1/len(ann['answer'])
-
-                answers = list(answer_weight.keys())
-                weights = list(answer_weight.values())
-
-            elif ann['dataset']=='vg':
-                answers = [ann['answer']]
-                weights = [0.2]  
-
-            return image, question, answers, weights
-        
-        
-def vqa_collate_fn(batch):
-    image_list, question_list, answer_list, weight_list, n = [], [], [], [], []
-    for image, question, answer, weights in batch:
-        image_list.append(image)
-        question_list.append(question)
-        weight_list += weights       
-        answer_list += answer
-        n.append(len(answer))
-    return torch.stack(image_list,dim=0), question_list, answer_list, torch.Tensor(weight_list), n        
\ No newline at end of file
diff --git a/repositories/BLIP/demo.ipynb b/repositories/BLIP/demo.ipynb
deleted file mode 100644
index 3077a1a42c584f3ef535903f64cf6b3bb722490e..0000000000000000000000000000000000000000
--- a/repositories/BLIP/demo.ipynb
+++ /dev/null
@@ -1,301 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "2b949f9f",
-   "metadata": {},
-   "source": [
-    "# BLIP: Inference Demo\n",
-    " - [Image Captioning](#Image-Captioning)\n",
-    " - [VQA](#VQA)\n",
-    " - [Feature Extraction](#Feature-Extraction)\n",
-    " - [Image Text Matching](#Image-Text-Matching)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "cbcb066b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# install requirements\n",
-    "import sys\n",
-    "if 'google.colab' in sys.modules:\n",
-    "    print('Running in Colab.')\n",
-    "    !pip3 install transformers==4.15.0 timm==0.4.12 fairscale==0.4.4\n",
-    "    !git clone https://github.com/salesforce/BLIP\n",
-    "    %cd BLIP"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "a811a65f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from PIL import Image\n",
-    "import requests\n",
-    "import torch\n",
-    "from torchvision import transforms\n",
-    "from torchvision.transforms.functional import InterpolationMode\n",
-    "\n",
-    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
-    "\n",
-    "def load_demo_image(image_size,device):\n",
-    "    img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg' \n",
-    "    raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')   \n",
-    "\n",
-    "    w,h = raw_image.size\n",
-    "    display(raw_image.resize((w//5,h//5)))\n",
-    "    \n",
-    "    transform = transforms.Compose([\n",
-    "        transforms.Resize((image_size,image_size),interpolation=InterpolationMode.BICUBIC),\n",
-    "        transforms.ToTensor(),\n",
-    "        transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))\n",
-    "        ]) \n",
-    "    image = transform(raw_image).unsqueeze(0).to(device)   \n",
-    "    return image"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f72f4406",
-   "metadata": {},
-   "source": [
-    "# Image Captioning\n",
-    "Perform image captioning using finetuned BLIP model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "6835daef",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<PIL.Image.Image image mode=RGB size=409x273 at 0x7F9205179040>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "load checkpoint from https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model*_base_caption.pth\n",
-      "caption: a woman sitting on the beach with a dog\n"
-     ]
-    }
-   ],
-   "source": [
-    "from models.blip import blip_decoder\n",
-    "\n",
-    "image_size = 384\n",
-    "image = load_demo_image(image_size=image_size, device=device)\n",
-    "\n",
-    "model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth'\n",
-    "    \n",
-    "model = blip_decoder(pretrained=model_url, image_size=image_size, vit='base')\n",
-    "model.eval()\n",
-    "model = model.to(device)\n",
-    "\n",
-    "with torch.no_grad():\n",
-    "    # beam search\n",
-    "    caption = model.generate(image, sample=False, num_beams=3, max_length=20, min_length=5) \n",
-    "    # nucleus sampling\n",
-    "    # caption = model.generate(image, sample=True, top_p=0.9, max_length=20, min_length=5) \n",
-    "    print('caption: '+caption[0])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fac320a2",
-   "metadata": {},
-   "source": [
-    "# VQA\n",
-    "Perform visual question answering using finetuned BLIP model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "5e6f3fb1",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<PIL.Image.Image image mode=RGB size=409x273 at 0x7F92049303D0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "load checkpoint from https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model*_vqa.pth\n",
-      "answer: on beach\n"
-     ]
-    }
-   ],
-   "source": [
-    "from models.blip_vqa import blip_vqa\n",
-    "\n",
-    "image_size = 480\n",
-    "image = load_demo_image(image_size=image_size, device=device)     \n",
-    "\n",
-    "model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth'\n",
-    "    \n",
-    "model = blip_vqa(pretrained=model_url, image_size=image_size, vit='base')\n",
-    "model.eval()\n",
-    "model = model.to(device)\n",
-    "\n",
-    "question = 'where is the woman sitting?'\n",
-    "\n",
-    "with torch.no_grad():\n",
-    "    answer = model(image, question, train=False, inference='generate') \n",
-    "    print('answer: '+answer[0])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6100e519",
-   "metadata": {},
-   "source": [
-    "# Feature Extraction"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "4f8f21ed",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<PIL.Image.Image image mode=RGB size=409x273 at 0x7FBCE3506550>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "load checkpoint from https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base.pth\n"
-     ]
-    }
-   ],
-   "source": [
-    "from models.blip import blip_feature_extractor\n",
-    "\n",
-    "image_size = 224\n",
-    "image = load_demo_image(image_size=image_size, device=device)     \n",
-    "\n",
-    "model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base.pth'\n",
-    "    \n",
-    "model = blip_feature_extractor(pretrained=model_url, image_size=image_size, vit='base')\n",
-    "model.eval()\n",
-    "model = model.to(device)\n",
-    "\n",
-    "caption = 'a woman sitting on the beach with a dog'\n",
-    "\n",
-    "multimodal_feature = model(image, caption, mode='multimodal')[0,0]\n",
-    "image_feature = model(image, caption, mode='image')[0,0]\n",
-    "text_feature = model(image, caption, mode='text')[0,0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "201e1146",
-   "metadata": {},
-   "source": [
-    "# Image-Text Matching"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "49ba5906",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<PIL.Image.Image image mode=RGB size=409x273 at 0x7FBCE357CCA0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "load checkpoint from https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth\n",
-      "text: a woman sitting on the beach with a dog\n",
-      "The image and text is matched with a probability of 0.9960\n",
-      "The image feature and text feature has a cosine similarity of 0.5262\n"
-     ]
-    }
-   ],
-   "source": [
-    "from models.blip_itm import blip_itm\n",
-    "\n",
-    "image_size = 384\n",
-    "image = load_demo_image(image_size=image_size,device=device)\n",
-    "\n",
-    "model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'\n",
-    "    \n",
-    "model = blip_itm(pretrained=model_url, image_size=image_size, vit='base')\n",
-    "model.eval()\n",
-    "model = model.to(device='cpu')\n",
-    "\n",
-    "caption = 'a woman sitting on the beach with a dog'\n",
-    "\n",
-    "print('text: %s' %caption)\n",
-    "\n",
-    "itm_output = model(image,caption,match_head='itm')\n",
-    "itm_score = torch.nn.functional.softmax(itm_output,dim=1)[:,1]\n",
-    "print('The image and text is matched with a probability of %.4f'%itm_score)\n",
-    "\n",
-    "itc_score = model(image,caption,match_head='itc')\n",
-    "print('The image feature and text feature has a cosine similarity of %.4f'%itc_score)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/repositories/BLIP/eval_nocaps.py b/repositories/BLIP/eval_nocaps.py
deleted file mode 100644
index 3cbb09a8cc7771605c013583d721aa95d9413b42..0000000000000000000000000000000000000000
--- a/repositories/BLIP/eval_nocaps.py
+++ /dev/null
@@ -1,118 +0,0 @@
-'''
- * Copyright (c) 2022, salesforce.com, inc.
- * All rights reserved.
- * SPDX-License-Identifier: BSD-3-Clause
- * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
- * By Junnan Li
-'''
-import argparse
-import os
-import ruamel_yaml as yaml
-import numpy as np
-import random
-import time
-import datetime
-import json
-from pathlib import Path
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.backends.cudnn as cudnn
-import torch.distributed as dist
-from torch.utils.data import DataLoader
-
-from models.blip import blip_decoder
-import utils
-from data import create_dataset, create_sampler, create_loader
-from data.utils import save_result
-
-@torch.no_grad()
-def evaluate(model, data_loader, device, config):
-    # evaluate
-    model.eval() 
-    
-    metric_logger = utils.MetricLogger(delimiter="  ")
-    header = 'Evaluation:'
-    print_freq = 10
-
-    result = []
-    for image, image_id in metric_logger.log_every(data_loader, print_freq, header): 
-        
-        image = image.to(device)       
-        
-        captions = model.generate(image, sample=False, num_beams=config['num_beams'], max_length=config['max_length'], 
-                                  min_length=config['min_length'], repetition_penalty=1.1)
-        
-        for caption, img_id in zip(captions, image_id):
-            result.append({"image_id": img_id.item(), "caption": caption})
-  
-    return result
-
-
-def main(args, config):
-    utils.init_distributed_mode(args)    
-    
-    device = torch.device(args.device)
-
-    # fix the seed for reproducibility
-    seed = args.seed + utils.get_rank()
-    torch.manual_seed(seed)
-    np.random.seed(seed)
-    random.seed(seed)
-    cudnn.benchmark = True
-
-    #### Dataset #### 
-    print("Creating captioning dataset")
-    val_dataset, test_dataset = create_dataset('nocaps', config)  
-
-    if args.distributed:
-        num_tasks = utils.get_world_size()
-        global_rank = utils.get_rank()            
-        samplers = create_sampler([val_dataset,test_dataset], [False,False], num_tasks, global_rank)         
-    else:
-        samplers = [None,None]
-    
-    val_loader, test_loader = create_loader([val_dataset, test_dataset],samplers, 
-                                            batch_size=[config['batch_size']]*2,num_workers=[4,4],
-                                            is_trains=[False, False], collate_fns=[None,None])        
-
-    #### Model #### 
-    print("Creating model")
-    model = blip_decoder(pretrained=config['pretrained'], image_size=config['image_size'], vit=config['vit'], 
-                           prompt=config['prompt'])
-
-    model = model.to(device)   
-    
-    model_without_ddp = model
-    if args.distributed:
-        model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
-        model_without_ddp = model.module    
-    
-    val_result = evaluate(model_without_ddp, val_loader, device, config)  
-    val_result_file = save_result(val_result, args.result_dir, 'val', remove_duplicate='image_id')   
-    test_result = evaluate(model_without_ddp, test_loader, device, config)  
-    test_result_file = save_result(test_result, args.result_dir, 'test', remove_duplicate='image_id') 
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--config', default='./configs/nocaps.yaml')
-    parser.add_argument('--output_dir', default='output/NoCaps')        
-    parser.add_argument('--device', default='cuda')
-    parser.add_argument('--seed', default=42, type=int)
-    parser.add_argument('--world_size', default=1, type=int, help='number of distributed processes')    
-    parser.add_argument('--dist_url', default='env://', help='url used to set up distributed training')
-    parser.add_argument('--distributed', default=True, type=bool)
-    args = parser.parse_args()
-
-    config = yaml.load(open(args.config, 'r'), Loader=yaml.Loader)
-
-    args.result_dir = os.path.join(args.output_dir, 'result')
-
-    Path(args.output_dir).mkdir(parents=True, exist_ok=True)
-    Path(args.result_dir).mkdir(parents=True, exist_ok=True)
-        
-    yaml.dump(config, open(os.path.join(args.output_dir, 'config.yaml'), 'w'))    
-    
-    main(args, config)
\ No newline at end of file
diff --git a/repositories/BLIP/eval_retrieval_video.py b/repositories/BLIP/eval_retrieval_video.py
deleted file mode 100644
index 07ebab7f41f6466f6f46130002e2e0df1266486a..0000000000000000000000000000000000000000
--- a/repositories/BLIP/eval_retrieval_video.py
+++ /dev/null
@@ -1,250 +0,0 @@
-'''
- * Copyright (c) 2022, salesforce.com, inc.
- * All rights reserved.
- * SPDX-License-Identifier: BSD-3-Clause
- * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
- * By Junnan Li
-'''
-import argparse
-import os
-import ruamel_yaml as yaml
-import numpy as np
-import random
-import time
-import datetime
-import json
-from pathlib import Path
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.backends.cudnn as cudnn
-import torch.distributed as dist
-from torch.utils.data import DataLoader
-
-from models.blip_retrieval import blip_retrieval
-import utils
-from data.video_dataset import VideoDataset
-
-
-@torch.no_grad()
-def evaluation(model, data_loader, tokenizer, device, config):
-    # test
-    model.eval() 
-    
-    metric_logger = utils.MetricLogger(delimiter="  ")
-    header = 'Evaluation:'    
-    
-    print('Computing features for evaluation...')
-    start_time = time.time()  
-
-    texts = data_loader.dataset.text   
-    num_text = len(texts)
-    text_bs = 256
-    text_ids = []
-    text_embeds = []  
-    text_atts = []
-    for i in range(0, num_text, text_bs):
-        text = texts[i: min(num_text, i+text_bs)]
-        text_input = tokenizer(text, padding='max_length', truncation=True, max_length=35, return_tensors="pt").to(device) 
-        text_output = model.text_encoder(text_input.input_ids, attention_mask = text_input.attention_mask, mode='text')  
-        text_embed = F.normalize(model.text_proj(text_output.last_hidden_state[:,0,:]))
-        text_embeds.append(text_embed)   
-        text_ids.append(text_input.input_ids)
-        text_atts.append(text_input.attention_mask)
-    
-    text_embeds = torch.cat(text_embeds,dim=0)
-    text_ids = torch.cat(text_ids,dim=0)
-    text_atts = torch.cat(text_atts,dim=0)
-    text_ids[:,0] = tokenizer.additional_special_tokens_ids[0]
-    
-    video_feats = []
-    video_embeds = []
-    for video, video_id in data_loader: 
-
-        B,N,C,W,H = video.size()
-        video = video.view(-1,C,W,H)
-        video = video.to(device,non_blocking=True) 
-        video_feat = model.visual_encoder(video)        
-        video_embed = model.vision_proj(video_feat[:,0,:])   
-        video_embed = video_embed.view(B,N,-1).mean(dim=1)
-        video_embed = F.normalize(video_embed,dim=-1)  
-       
-        video_feat = video_feat.view(B,-1,video_feat.shape[-1])
-        video_feats.append(video_feat.cpu())
-        video_embeds.append(video_embed)
-     
-    video_feats = torch.cat(video_feats,dim=0)
-    video_embeds = torch.cat(video_embeds,dim=0)
-    
-    sims_matrix = video_embeds @ text_embeds.t()
-    score_matrix_v2t = torch.full((len(texts),len(texts)),-100.0).to(device) 
-    
-    num_tasks = utils.get_world_size()
-    rank = utils.get_rank() 
-    step = sims_matrix.size(0)//num_tasks + 1
-    start = rank*step
-    end = min(sims_matrix.size(0),start+step)
-
-    for i,sims in enumerate(metric_logger.log_every(sims_matrix[start:end], 50, header)): 
-        topk_sim, topk_idx = sims.topk(k=config['k_test'], dim=0)
-        
-        encoder_output = video_feats[start+i].repeat(config['k_test'],1,1).to(device,non_blocking=True) 
-        encoder_att = torch.ones(encoder_output.size()[:-1],dtype=torch.long).to(device,non_blocking=True) 
-        output = model.text_encoder(text_ids[topk_idx], 
-                                    attention_mask = text_atts[topk_idx],
-                                    encoder_hidden_states = encoder_output,
-                                    encoder_attention_mask = encoder_att,                             
-                                    return_dict = True,
-                                   )
-        score = model.itm_head(output.last_hidden_state[:,0,:])[:,1]
-        score_matrix_v2t[start+i,topk_idx] = score + topk_sim
-        
-    sims_matrix = sims_matrix.t()
-    score_matrix_t2v = torch.full((len(texts),len(texts)),-100.0).to(device) 
-    
-    step = sims_matrix.size(0)//num_tasks + 1
-    start = rank*step
-    end = min(sims_matrix.size(0),start+step)    
-    
-    for i,sims in enumerate(metric_logger.log_every(sims_matrix[start:end], 50, header)): 
-        
-        topk_sim, topk_idx = sims.topk(k=config['k_test'], dim=0)
-        encoder_output = video_feats[topk_idx].to(device,non_blocking=True) 
-        encoder_att = torch.ones(encoder_output.size()[:-1],dtype=torch.long).to(device,non_blocking=True) 
-        output = model.text_encoder(text_ids[start+i].repeat(config['k_test'],1), 
-                                    attention_mask = text_atts[start+i].repeat(config['k_test'],1),
-                                    encoder_hidden_states = encoder_output,
-                                    encoder_attention_mask = encoder_att,                             
-                                    return_dict = True,
-                                   )
-        score = model.itm_head(output.last_hidden_state[:,0,:])[:,1]
-        score_matrix_t2v[start+i,topk_idx] = score + topk_sim
-
-    if args.distributed:
-        dist.barrier()   
-        torch.distributed.all_reduce(score_matrix_v2t, op=torch.distributed.ReduceOp.SUM) 
-        torch.distributed.all_reduce(score_matrix_t2v, op=torch.distributed.ReduceOp.SUM)        
-        
-    total_time = time.time() - start_time
-    total_time_str = str(datetime.timedelta(seconds=int(total_time)))
-    print('Evaluation time {}'.format(total_time_str)) 
-
-    return score_matrix_v2t.cpu().numpy(), score_matrix_t2v.cpu().numpy()
-
-
-            
-@torch.no_grad()
-def itm_eval(scores_v2t, scores_t2v, txt2vmg, vid2txt):
-    
-    #Video->Text 
-    ranks = np.zeros(scores_v2t.shape[0])
-    for index,score in enumerate(scores_v2t):
-        inds = np.argsort(score)[::-1]
-        ranks[index] = np.where(inds == vid2txt[index])[0][0]
-
-    # Compute metrics
-    tr1 = 100.0 * len(np.where(ranks < 1)[0]) / len(ranks)
-    tr5 = 100.0 * len(np.where(ranks < 5)[0]) / len(ranks)
-    tr10 = 100.0 * len(np.where(ranks < 10)[0]) / len(ranks)
-  
-    #Text->Video 
-    ranks = np.zeros(scores_t2v.shape[0])
-    
-    for index,score in enumerate(scores_t2v):
-        inds = np.argsort(score)[::-1]
-        ranks[index] = np.where(inds == txt2vmg[index])[0][0]
-    
-    mdR = np.median(ranks+1)
-        
-    # Compute metrics
-    vr1 = 100.0 * len(np.where(ranks < 1)[0]) / len(ranks)
-    vr5 = 100.0 * len(np.where(ranks < 5)[0]) / len(ranks)
-    vr10 = 100.0 * len(np.where(ranks < 10)[0]) / len(ranks)        
-
-    tr_mean = (tr1 + tr5 + tr10) / 3
-    vr_mean = (vr1 + vr5 + vr10) / 3
-    r_mean = (tr_mean + vr_mean) / 2
-
-    eval_result =  {'txt_r1': tr1,
-                    'txt_r5': tr5,
-                    'txt_r10': tr10,
-                    'txt_r_mean': tr_mean,
-                    'vid_r1': vr1,
-                    'vid_r5': vr5,
-                    'vid_r10': vr10,
-                    'vid_r_mean': vr_mean,
-                    'vid_mdR': mdR,
-                    'r_mean': r_mean}
-    return eval_result
-
-
-
-
-def main(args, config):
-    utils.init_distributed_mode(args)    
-    
-    device = torch.device(args.device)
-
-    # fix the seed for reproducibility
-    seed = args.seed + utils.get_rank()
-    torch.manual_seed(seed)
-    np.random.seed(seed)
-    random.seed(seed)
-    cudnn.benchmark = True
-
-    #### Dataset #### 
-    print("Creating retrieval dataset")
-    test_dataset = VideoDataset(config['video_root'],config['ann_root'],num_frm=config['num_frm_test'],
-                                max_img_size=config['image_size'], frm_sampling_strategy='uniform')
-
-    test_loader = DataLoader(
-        test_dataset,
-        batch_size=config['batch_size'],
-        num_workers=4,
-        pin_memory=True,
-        drop_last=False,
-        shuffle=False,
-    )  
-
-    #### Model #### 
-    print("Creating model")
-    model = blip_retrieval(pretrained=config['pretrained'], image_size=config['image_size'], vit=config['vit'])
-    
-    model = model.to(device)   
-    
-    model_without_ddp = model
-    if args.distributed:
-        model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
-        model_without_ddp = model.module   
-    
-    score_v2t, score_t2v, = evaluation(model_without_ddp, test_loader, model_without_ddp.tokenizer, device, config)
-
-    if utils.is_main_process():  
-
-        test_result = itm_eval(score_v2t, score_t2v, test_loader.dataset.txt2video, test_loader.dataset.video2txt)  
-        print(test_result)
-
-        log_stats = {**{f'{k}': v for k, v in test_result.items()},}
-        with open(os.path.join(args.output_dir, "test_result.txt"),"a") as f:
-            f.write(json.dumps(log_stats) + "\n")     
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()     
-    parser.add_argument('--config', default='./configs/retrieval_msrvtt.yaml')
-    parser.add_argument('--output_dir', default='output/Retrieval_msrvtt')        
-    parser.add_argument('--device', default='cuda')
-    parser.add_argument('--seed', default=42, type=int)
-    parser.add_argument('--world_size', default=1, type=int, help='number of distributed processes')    
-    parser.add_argument('--dist_url', default='env://', help='url used to set up distributed training')
-    parser.add_argument('--distributed', default=True, type=bool)
-    args = parser.parse_args()
-
-    config = yaml.load(open(args.config, 'r'), Loader=yaml.Loader)
-
-    Path(args.output_dir).mkdir(parents=True, exist_ok=True)
-        
-    yaml.dump(config, open(os.path.join(args.output_dir, 'config.yaml'), 'w'))    
-    
-    main(args, config)
\ No newline at end of file
diff --git a/repositories/BLIP/models/__init__.py b/repositories/BLIP/models/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/repositories/BLIP/models/blip.py b/repositories/BLIP/models/blip.py
deleted file mode 100644
index 38678f65ea2c276b351c2c97d429ebc2525ddcf7..0000000000000000000000000000000000000000
--- a/repositories/BLIP/models/blip.py
+++ /dev/null
@@ -1,238 +0,0 @@
-'''
- * Copyright (c) 2022, salesforce.com, inc.
- * All rights reserved.
- * SPDX-License-Identifier: BSD-3-Clause
- * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
- * By Junnan Li
-'''
-import warnings
-warnings.filterwarnings("ignore")
-
-from models.vit import VisionTransformer, interpolate_pos_embed
-from models.med import BertConfig, BertModel, BertLMHeadModel
-from transformers import BertTokenizer
-
-import torch
-from torch import nn
-import torch.nn.functional as F
-
-import os
-from urllib.parse import urlparse
-from timm.models.hub import download_cached_file
-
-class BLIP_Base(nn.Module):
-    def __init__(self,                 
-                 med_config = 'configs/med_config.json',  
-                 image_size = 224,
-                 vit = 'base',
-                 vit_grad_ckpt = False,
-                 vit_ckpt_layer = 0,                 
-                 ):
-        """
-        Args:
-            med_config (str): path for the mixture of encoder-decoder model's configuration file
-            image_size (int): input image size
-            vit (str): model size of vision transformer
-        """               
-        super().__init__()
-        
-        self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer)
-        self.tokenizer = init_tokenizer()   
-        med_config = BertConfig.from_json_file(med_config)
-        med_config.encoder_width = vision_width
-        self.text_encoder = BertModel(config=med_config, add_pooling_layer=False)  
-
-        
-    def forward(self, image, caption, mode):
-        
-        assert mode in ['image', 'text', 'multimodal'], "mode parameter must be image, text, or multimodal"
-        text = self.tokenizer(caption, return_tensors="pt").to(image.device) 
-        
-        if mode=='image':    
-            # return image features
-            image_embeds = self.visual_encoder(image)             
-            return image_embeds
-        
-        elif mode=='text':
-            # return text features
-            text_output = self.text_encoder(text.input_ids, attention_mask = text.attention_mask,                      
-                                            return_dict = True, mode = 'text')  
-            return text_output.last_hidden_state
-        
-        elif mode=='multimodal':
-            # return multimodel features
-            image_embeds = self.visual_encoder(image)    
-            image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device)      
-            
-            text.input_ids[:,0] = self.tokenizer.enc_token_id
-            output = self.text_encoder(text.input_ids,
-                                       attention_mask = text.attention_mask,
-                                       encoder_hidden_states = image_embeds,
-                                       encoder_attention_mask = image_atts,      
-                                       return_dict = True,
-                                      )              
-            return output.last_hidden_state
-        
-        
-        
-class BLIP_Decoder(nn.Module):
-    def __init__(self,                 
-                 med_config = 'configs/med_config.json',  
-                 image_size = 384,
-                 vit = 'base',
-                 vit_grad_ckpt = False,
-                 vit_ckpt_layer = 0,
-                 prompt = 'a picture of ',
-                 ):
-        """
-        Args:
-            med_config (str): path for the mixture of encoder-decoder model's configuration file
-            image_size (int): input image size
-            vit (str): model size of vision transformer
-        """            
-        super().__init__()
-        
-        self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer)
-        self.tokenizer = init_tokenizer()   
-        med_config = BertConfig.from_json_file(med_config)
-        med_config.encoder_width = vision_width
-        self.text_decoder = BertLMHeadModel(config=med_config)    
-        
-        self.prompt = prompt
-        self.prompt_length = len(self.tokenizer(self.prompt).input_ids)-1
-
-        
-    def forward(self, image, caption):
-        
-        image_embeds = self.visual_encoder(image) 
-        image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device)
-        
-        text = self.tokenizer(caption, padding='longest', truncation=True, max_length=40, return_tensors="pt").to(image.device) 
-        
-        text.input_ids[:,0] = self.tokenizer.bos_token_id
-        
-        decoder_targets = text.input_ids.masked_fill(text.input_ids == self.tokenizer.pad_token_id, -100)         
-        decoder_targets[:,:self.prompt_length] = -100
-     
-        decoder_output = self.text_decoder(text.input_ids, 
-                                           attention_mask = text.attention_mask, 
-                                           encoder_hidden_states = image_embeds,
-                                           encoder_attention_mask = image_atts,                  
-                                           labels = decoder_targets,
-                                           return_dict = True,   
-                                          )   
-        loss_lm = decoder_output.loss
-        
-        return loss_lm
-        
-    def generate(self, image, sample=False, num_beams=3, max_length=30, min_length=10, top_p=0.9, repetition_penalty=1.0):
-        image_embeds = self.visual_encoder(image)
-
-        if not sample:
-            image_embeds = image_embeds.repeat_interleave(num_beams,dim=0)
-            
-        image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device)
-        model_kwargs = {"encoder_hidden_states": image_embeds, "encoder_attention_mask":image_atts}
-        
-        prompt = [self.prompt] * image.size(0)
-        input_ids = self.tokenizer(prompt, return_tensors="pt").input_ids.to(image.device) 
-        input_ids[:,0] = self.tokenizer.bos_token_id
-        input_ids = input_ids[:, :-1] 
-
-        if sample:
-            #nucleus sampling
-            outputs = self.text_decoder.generate(input_ids=input_ids,
-                                                  max_length=max_length,
-                                                  min_length=min_length,
-                                                  do_sample=True,
-                                                  top_p=top_p,
-                                                  num_return_sequences=1,
-                                                  eos_token_id=self.tokenizer.sep_token_id,
-                                                  pad_token_id=self.tokenizer.pad_token_id, 
-                                                  repetition_penalty=1.1,                                            
-                                                  **model_kwargs)
-        else:
-            #beam search
-            outputs = self.text_decoder.generate(input_ids=input_ids,
-                                                  max_length=max_length,
-                                                  min_length=min_length,
-                                                  num_beams=num_beams,
-                                                  eos_token_id=self.tokenizer.sep_token_id,
-                                                  pad_token_id=self.tokenizer.pad_token_id,     
-                                                  repetition_penalty=repetition_penalty,
-                                                  **model_kwargs)            
-            
-        captions = []    
-        for output in outputs:
-            caption = self.tokenizer.decode(output, skip_special_tokens=True)    
-            captions.append(caption[len(self.prompt):])
-        return captions
-    
-
-def blip_decoder(pretrained='',**kwargs):
-    model = BLIP_Decoder(**kwargs)
-    if pretrained:
-        model,msg = load_checkpoint(model,pretrained)
-        assert(len(msg.missing_keys)==0)
-    return model    
-    
-def blip_feature_extractor(pretrained='',**kwargs):
-    model = BLIP_Base(**kwargs)
-    if pretrained:
-        model,msg = load_checkpoint(model,pretrained)
-        assert(len(msg.missing_keys)==0)
-    return model        
-
-def init_tokenizer():
-    tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
-    tokenizer.add_special_tokens({'bos_token':'[DEC]'})
-    tokenizer.add_special_tokens({'additional_special_tokens':['[ENC]']})       
-    tokenizer.enc_token_id = tokenizer.additional_special_tokens_ids[0]  
-    return tokenizer
-
-
-def create_vit(vit, image_size, use_grad_checkpointing=False, ckpt_layer=0, drop_path_rate=0):
-        
-    assert vit in ['base', 'large'], "vit parameter must be base or large"
-    if vit=='base':
-        vision_width = 768
-        visual_encoder = VisionTransformer(img_size=image_size, patch_size=16, embed_dim=vision_width, depth=12, 
-                                           num_heads=12, use_grad_checkpointing=use_grad_checkpointing, ckpt_layer=ckpt_layer,
-                                           drop_path_rate=0 or drop_path_rate
-                                          )   
-    elif vit=='large':
-        vision_width = 1024
-        visual_encoder = VisionTransformer(img_size=image_size, patch_size=16, embed_dim=vision_width, depth=24, 
-                                           num_heads=16, use_grad_checkpointing=use_grad_checkpointing, ckpt_layer=ckpt_layer,
-                                           drop_path_rate=0.1 or drop_path_rate
-                                          )   
-    return visual_encoder, vision_width
-
-def is_url(url_or_filename):
-    parsed = urlparse(url_or_filename)
-    return parsed.scheme in ("http", "https")
-
-def load_checkpoint(model,url_or_filename):
-    if is_url(url_or_filename):
-        cached_file = download_cached_file(url_or_filename, check_hash=False, progress=True)
-        checkpoint = torch.load(cached_file, map_location='cpu') 
-    elif os.path.isfile(url_or_filename):        
-        checkpoint = torch.load(url_or_filename, map_location='cpu') 
-    else:
-        raise RuntimeError('checkpoint url or path is invalid')
-        
-    state_dict = checkpoint['model']
-    
-    state_dict['visual_encoder.pos_embed'] = interpolate_pos_embed(state_dict['visual_encoder.pos_embed'],model.visual_encoder) 
-    if 'visual_encoder_m.pos_embed' in model.state_dict().keys():
-        state_dict['visual_encoder_m.pos_embed'] = interpolate_pos_embed(state_dict['visual_encoder_m.pos_embed'],
-                                                                         model.visual_encoder_m)    
-    for key in model.state_dict().keys():
-        if key in state_dict.keys():
-            if state_dict[key].shape!=model.state_dict()[key].shape:
-                del state_dict[key]
-    
-    msg = model.load_state_dict(state_dict,strict=False)
-    print('load checkpoint from %s'%url_or_filename)  
-    return model,msg
-    
diff --git a/repositories/BLIP/models/blip_itm.py b/repositories/BLIP/models/blip_itm.py
deleted file mode 100644
index cf354c829564bf5a1f56089a2d745093d51e0fa2..0000000000000000000000000000000000000000
--- a/repositories/BLIP/models/blip_itm.py
+++ /dev/null
@@ -1,76 +0,0 @@
-from models.med import BertConfig, BertModel
-from transformers import BertTokenizer
-
-import torch
-from torch import nn
-import torch.nn.functional as F
-
-from models.blip import create_vit, init_tokenizer, load_checkpoint
-
-class BLIP_ITM(nn.Module):
-    def __init__(self,                 
-                 med_config = 'configs/med_config.json',  
-                 image_size = 384,
-                 vit = 'base',
-                 vit_grad_ckpt = False,
-                 vit_ckpt_layer = 0,                      
-                 embed_dim = 256,     
-                 ):
-        """
-        Args:
-            med_config (str): path for the mixture of encoder-decoder model's configuration file
-            image_size (int): input image size
-            vit (str): model size of vision transformer
-        """               
-        super().__init__()
-        
-        self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer)
-        self.tokenizer = init_tokenizer()   
-        med_config = BertConfig.from_json_file(med_config)
-        med_config.encoder_width = vision_width
-        self.text_encoder = BertModel(config=med_config, add_pooling_layer=False)          
-
-        text_width = self.text_encoder.config.hidden_size
-        
-        self.vision_proj = nn.Linear(vision_width, embed_dim)
-        self.text_proj = nn.Linear(text_width, embed_dim)
-
-        self.itm_head = nn.Linear(text_width, 2) 
-        
-        
-    def forward(self, image, caption, match_head='itm'):
-
-        image_embeds = self.visual_encoder(image) 
-        image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device)        
-      
-        text = self.tokenizer(caption, padding='max_length', truncation=True, max_length=35, 
-                              return_tensors="pt").to(image.device) 
-
-                 
-        if match_head=='itm':
-            output = self.text_encoder(text.input_ids,
-                                       attention_mask = text.attention_mask,
-                                       encoder_hidden_states = image_embeds,
-                                       encoder_attention_mask = image_atts,      
-                                       return_dict = True,
-                                      )
-            itm_output = self.itm_head(output.last_hidden_state[:,0,:])     
-            return itm_output
-            
-        elif match_head=='itc':
-            text_output = self.text_encoder(text.input_ids, attention_mask = text.attention_mask,                      
-                                            return_dict = True, mode = 'text')                     
-            image_feat = F.normalize(self.vision_proj(image_embeds[:,0,:]),dim=-1)   
-            text_feat = F.normalize(self.text_proj(text_output.last_hidden_state[:,0,:]),dim=-1)    
-            
-            sim = image_feat @ text_feat.t()
-            return sim
-        
-        
-def blip_itm(pretrained='',**kwargs):
-    model = BLIP_ITM(**kwargs)
-    if pretrained:
-        model,msg = load_checkpoint(model,pretrained)
-        assert(len(msg.missing_keys)==0)
-    return model         
-            
\ No newline at end of file
diff --git a/repositories/BLIP/models/blip_nlvr.py b/repositories/BLIP/models/blip_nlvr.py
deleted file mode 100644
index 84837167bfa6874d3c3e41fb9b37271113910b7f..0000000000000000000000000000000000000000
--- a/repositories/BLIP/models/blip_nlvr.py
+++ /dev/null
@@ -1,103 +0,0 @@
-from models.med import BertConfig
-from models.nlvr_encoder import BertModel
-from models.vit import interpolate_pos_embed
-from models.blip import create_vit, init_tokenizer, is_url
-
-from timm.models.hub import download_cached_file
-
-import torch
-from torch import nn
-import torch.nn.functional as F
-from transformers import BertTokenizer
-import numpy as np
-
-class BLIP_NLVR(nn.Module):
-    def __init__(self,                 
-                 med_config = 'configs/med_config.json',  
-                 image_size = 480,
-                 vit = 'base',
-                 vit_grad_ckpt = False,
-                 vit_ckpt_layer = 0,                   
-                 ):
-        """
-        Args:
-            med_config (str): path for the mixture of encoder-decoder model's configuration file
-            image_size (int): input image size
-            vit (str): model size of vision transformer
-        """               
-        super().__init__()
-        
-        self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer, drop_path_rate=0.1)
-        self.tokenizer = init_tokenizer()   
-        med_config = BertConfig.from_json_file(med_config)
-        med_config.encoder_width = vision_width
-        self.text_encoder = BertModel(config=med_config, add_pooling_layer=False) 
-                    
-        self.cls_head = nn.Sequential(
-                  nn.Linear(self.text_encoder.config.hidden_size, self.text_encoder.config.hidden_size),
-                  nn.ReLU(),
-                  nn.Linear(self.text_encoder.config.hidden_size, 2)
-                )  
-
-    def forward(self, image, text, targets, train=True):
-        
-        image_embeds = self.visual_encoder(image) 
-        image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device)        
-        image0_embeds, image1_embeds = torch.split(image_embeds,targets.size(0))     
-
-        text = self.tokenizer(text, padding='longest', return_tensors="pt").to(image.device) 
-        text.input_ids[:,0] = self.tokenizer.enc_token_id        
-
-        output = self.text_encoder(text.input_ids, 
-                                   attention_mask = text.attention_mask, 
-                                   encoder_hidden_states = [image0_embeds,image1_embeds],
-                                   encoder_attention_mask = [image_atts[:image0_embeds.size(0)],
-                                                             image_atts[image0_embeds.size(0):]],        
-                                   return_dict = True,
-                                  )  
-        hidden_state = output.last_hidden_state[:,0,:]        
-        prediction = self.cls_head(hidden_state)
-
-        if train:            
-            loss = F.cross_entropy(prediction, targets)   
-            return loss
-        else:
-            return prediction
-    
-def blip_nlvr(pretrained='',**kwargs):
-    model = BLIP_NLVR(**kwargs)
-    if pretrained:
-        model,msg = load_checkpoint(model,pretrained)
-        print("missing keys:")
-        print(msg.missing_keys)
-    return model  
-
-        
-def load_checkpoint(model,url_or_filename):
-    if is_url(url_or_filename):
-        cached_file = download_cached_file(url_or_filename, check_hash=False, progress=True)
-        checkpoint = torch.load(cached_file, map_location='cpu') 
-    elif os.path.isfile(url_or_filename):        
-        checkpoint = torch.load(url_or_filename, map_location='cpu') 
-    else:
-        raise RuntimeError('checkpoint url or path is invalid')
-    state_dict = checkpoint['model']
-    
-    state_dict['visual_encoder.pos_embed'] = interpolate_pos_embed(state_dict['visual_encoder.pos_embed'],model.visual_encoder) 
-    
-    for key in list(state_dict.keys()):
-        if 'crossattention.self.' in key:
-            new_key0 = key.replace('self','self0')
-            new_key1 = key.replace('self','self1')
-            state_dict[new_key0] = state_dict[key]
-            state_dict[new_key1] = state_dict[key]
-        elif 'crossattention.output.dense.' in key:
-            new_key0 = key.replace('dense','dense0')
-            new_key1 = key.replace('dense','dense1')
-            state_dict[new_key0] = state_dict[key]
-            state_dict[new_key1] = state_dict[key]  
-                
-    msg = model.load_state_dict(state_dict,strict=False)
-    print('load checkpoint from %s'%url_or_filename)  
-    return model,msg
-            
\ No newline at end of file
diff --git a/repositories/BLIP/models/blip_pretrain.py b/repositories/BLIP/models/blip_pretrain.py
deleted file mode 100644
index e42ce5f998b0a51e6f731ee6b5c8bae6d02a8664..0000000000000000000000000000000000000000
--- a/repositories/BLIP/models/blip_pretrain.py
+++ /dev/null
@@ -1,339 +0,0 @@
-'''
- * Copyright (c) 2022, salesforce.com, inc.
- * All rights reserved.
- * SPDX-License-Identifier: BSD-3-Clause
- * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
- * By Junnan Li
-'''
-from models.med import BertConfig, BertModel, BertLMHeadModel
-from transformers import BertTokenizer
-import transformers
-transformers.logging.set_verbosity_error()
-
-import torch
-from torch import nn
-import torch.nn.functional as F
-
-from models.blip import create_vit, init_tokenizer, load_checkpoint
-
-class BLIP_Pretrain(nn.Module):
-    def __init__(self,                 
-                 med_config = 'configs/bert_config.json',  
-                 image_size = 224,
-                 vit = 'base',
-                 vit_grad_ckpt = False,
-                 vit_ckpt_layer = 0,                    
-                 embed_dim = 256,     
-                 queue_size = 57600,
-                 momentum = 0.995,
-                 ):
-        """
-        Args:
-            med_config (str): path for the mixture of encoder-decoder model's configuration file
-            image_size (int): input image size
-            vit (str): model size of vision transformer
-        """               
-        super().__init__()
-        
-        self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer, 0)
-        
-        if vit=='base':
-            checkpoint = torch.hub.load_state_dict_from_url(
-                url="https://dl.fbaipublicfiles.com/deit/deit_base_patch16_224-b5f2ef4d.pth",
-                map_location="cpu", check_hash=True)
-            state_dict = checkpoint["model"]     
-            msg = self.visual_encoder.load_state_dict(state_dict,strict=False)
-        elif vit=='large':
-            from timm.models.helpers import load_custom_pretrained
-            from timm.models.vision_transformer import default_cfgs
-            load_custom_pretrained(self.visual_encoder,default_cfgs['vit_large_patch16_224_in21k'])        
-               
-        self.tokenizer = init_tokenizer()   
-        encoder_config = BertConfig.from_json_file(med_config)
-        encoder_config.encoder_width = vision_width
-        self.text_encoder = BertModel.from_pretrained('bert-base-uncased',config=encoder_config, add_pooling_layer=False)
-        self.text_encoder.resize_token_embeddings(len(self.tokenizer)) 
-
-        text_width = self.text_encoder.config.hidden_size
-        
-        self.vision_proj = nn.Linear(vision_width, embed_dim)
-        self.text_proj = nn.Linear(text_width, embed_dim)
-
-        self.itm_head = nn.Linear(text_width, 2) 
-        
-        # create momentum encoders  
-        self.visual_encoder_m, vision_width = create_vit(vit,image_size)              
-        self.vision_proj_m = nn.Linear(vision_width, embed_dim)
-        self.text_encoder_m = BertModel(config=encoder_config, add_pooling_layer=False)      
-        self.text_proj_m = nn.Linear(text_width, embed_dim)
-        
-        self.model_pairs = [[self.visual_encoder,self.visual_encoder_m],
-                            [self.vision_proj,self.vision_proj_m],
-                            [self.text_encoder,self.text_encoder_m],
-                            [self.text_proj,self.text_proj_m],
-                           ]       
-        self.copy_params()
-
-        # create the queue
-        self.register_buffer("image_queue", torch.randn(embed_dim, queue_size))
-        self.register_buffer("text_queue", torch.randn(embed_dim, queue_size))
-        self.register_buffer("queue_ptr", torch.zeros(1, dtype=torch.long))  
-
-        self.image_queue = nn.functional.normalize(self.image_queue, dim=0)
-        self.text_queue = nn.functional.normalize(self.text_queue, dim=0)
-        
-        self.queue_size = queue_size
-        self.momentum = momentum
-        self.temp = nn.Parameter(0.07*torch.ones([]))   
-        
-        # create the decoder
-        decoder_config = BertConfig.from_json_file(med_config)
-        decoder_config.encoder_width = vision_width        
-        self.text_decoder = BertLMHeadModel.from_pretrained('bert-base-uncased',config=decoder_config)    
-        self.text_decoder.resize_token_embeddings(len(self.tokenizer)) 
-        tie_encoder_decoder_weights(self.text_encoder,self.text_decoder.bert,'','/attention')
-        
-        
-    def forward(self, image, caption, alpha):
-        with torch.no_grad():
-            self.temp.clamp_(0.001,0.5)
-        
-        image_embeds = self.visual_encoder(image) 
-        image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device)        
-        image_feat = F.normalize(self.vision_proj(image_embeds[:,0,:]),dim=-1)          
-        
-        text = self.tokenizer(caption, padding='max_length', truncation=True, max_length=30, 
-                              return_tensors="pt").to(image.device)  
-        text_output = self.text_encoder(text.input_ids, attention_mask = text.attention_mask,                      
-                                        return_dict = True, mode = 'text')            
-        text_feat = F.normalize(self.text_proj(text_output.last_hidden_state[:,0,:]),dim=-1)                 
-             
-        # get momentum features
-        with torch.no_grad():
-            self._momentum_update()
-            image_embeds_m = self.visual_encoder_m(image) 
-            image_feat_m = F.normalize(self.vision_proj_m(image_embeds_m[:,0,:]),dim=-1)  
-            image_feat_all = torch.cat([image_feat_m.t(),self.image_queue.clone().detach()],dim=1)                   
-            
-            text_output_m = self.text_encoder_m(text.input_ids, attention_mask = text.attention_mask,                      
-                                                return_dict = True, mode = 'text')    
-            text_feat_m = F.normalize(self.text_proj_m(text_output_m.last_hidden_state[:,0,:]),dim=-1) 
-            text_feat_all = torch.cat([text_feat_m.t(),self.text_queue.clone().detach()],dim=1)
-
-            sim_i2t_m = image_feat_m @ text_feat_all / self.temp  
-            sim_t2i_m = text_feat_m @ image_feat_all / self.temp 
-
-            sim_targets = torch.zeros(sim_i2t_m.size()).to(image.device)
-            sim_targets.fill_diagonal_(1)          
-
-            sim_i2t_targets = alpha * F.softmax(sim_i2t_m, dim=1) + (1 - alpha) * sim_targets
-            sim_t2i_targets = alpha * F.softmax(sim_t2i_m, dim=1) + (1 - alpha) * sim_targets        
-
-        sim_i2t = image_feat @ text_feat_all / self.temp
-        sim_t2i = text_feat @ image_feat_all / self.temp
-                             
-        loss_i2t = -torch.sum(F.log_softmax(sim_i2t, dim=1)*sim_i2t_targets,dim=1).mean()
-        loss_t2i = -torch.sum(F.log_softmax(sim_t2i, dim=1)*sim_t2i_targets,dim=1).mean() 
-
-        loss_ita = (loss_i2t+loss_t2i)/2
-
-        self._dequeue_and_enqueue(image_feat_m, text_feat_m)        
-
-        ###============== Image-text Matching ===================###
-        encoder_input_ids = text.input_ids.clone()
-        encoder_input_ids[:,0] = self.tokenizer.enc_token_id
-        
-        # forward the positve image-text pair
-        bs = image.size(0)
-        output_pos = self.text_encoder(encoder_input_ids,
-                                       attention_mask = text.attention_mask,
-                                       encoder_hidden_states = image_embeds,
-                                       encoder_attention_mask = image_atts,      
-                                       return_dict = True,
-                                      )            
-        with torch.no_grad():       
-            weights_t2i = F.softmax(sim_t2i[:,:bs],dim=1)+1e-4 
-            weights_t2i.fill_diagonal_(0)            
-            weights_i2t = F.softmax(sim_i2t[:,:bs],dim=1)+1e-4  
-            weights_i2t.fill_diagonal_(0)   
-            
-        # select a negative image for each text
-        image_embeds_neg = []    
-        for b in range(bs):
-            neg_idx = torch.multinomial(weights_t2i[b], 1).item()
-            image_embeds_neg.append(image_embeds[neg_idx])
-        image_embeds_neg = torch.stack(image_embeds_neg,dim=0)   
-
-        # select a negative text for each image
-        text_ids_neg = []
-        text_atts_neg = []
-        for b in range(bs):
-            neg_idx = torch.multinomial(weights_i2t[b], 1).item()
-            text_ids_neg.append(encoder_input_ids[neg_idx])
-            text_atts_neg.append(text.attention_mask[neg_idx])
-
-        text_ids_neg = torch.stack(text_ids_neg,dim=0)   
-        text_atts_neg = torch.stack(text_atts_neg,dim=0)      
-
-        text_ids_all = torch.cat([encoder_input_ids, text_ids_neg],dim=0)     
-        text_atts_all = torch.cat([text.attention_mask, text_atts_neg],dim=0)     
-
-        image_embeds_all = torch.cat([image_embeds_neg,image_embeds],dim=0)
-        image_atts_all = torch.cat([image_atts,image_atts],dim=0)
-
-        output_neg = self.text_encoder(text_ids_all,
-                                       attention_mask = text_atts_all,
-                                       encoder_hidden_states = image_embeds_all,
-                                       encoder_attention_mask = image_atts_all,      
-                                       return_dict = True,
-                                      )                            
-
-        vl_embeddings = torch.cat([output_pos.last_hidden_state[:,0,:], output_neg.last_hidden_state[:,0,:]],dim=0)
-        vl_output = self.itm_head(vl_embeddings)            
-
-        itm_labels = torch.cat([torch.ones(bs,dtype=torch.long),torch.zeros(2*bs,dtype=torch.long)],
-                               dim=0).to(image.device)
-        loss_itm = F.cross_entropy(vl_output, itm_labels)  
-        
-        ##================= LM ========================##     
-        decoder_input_ids = text.input_ids.clone()      
-        decoder_input_ids[:,0] = self.tokenizer.bos_token_id
-        decoder_targets = decoder_input_ids.masked_fill(decoder_input_ids == self.tokenizer.pad_token_id, -100) 
-
-        decoder_output = self.text_decoder(decoder_input_ids, 
-                                           attention_mask = text.attention_mask, 
-                                           encoder_hidden_states = image_embeds,
-                                           encoder_attention_mask = image_atts,                  
-                                           labels = decoder_targets,
-                                           return_dict = True,   
-                                          )   
-          
-        loss_lm = decoder_output.loss                
-        return loss_ita, loss_itm, loss_lm
- 
-
-
-    @torch.no_grad()    
-    def copy_params(self):
-        for model_pair in self.model_pairs:           
-            for param, param_m in zip(model_pair[0].parameters(), model_pair[1].parameters()):
-                param_m.data.copy_(param.data)  # initialize
-                param_m.requires_grad = False  # not update by gradient    
-
-            
-    @torch.no_grad()        
-    def _momentum_update(self):
-        for model_pair in self.model_pairs:           
-            for param, param_m in zip(model_pair[0].parameters(), model_pair[1].parameters()):
-                param_m.data = param_m.data * self.momentum + param.data * (1. - self.momentum)
-
-                        
-    @torch.no_grad()
-    def _dequeue_and_enqueue(self, image_feat, text_feat):
-        # gather keys before updating queue
-        image_feats = concat_all_gather(image_feat)
-        text_feats = concat_all_gather(text_feat)
-
-        batch_size = image_feats.shape[0]
-
-        ptr = int(self.queue_ptr)
-        assert self.queue_size % batch_size == 0  # for simplicity
-
-        # replace the keys at ptr (dequeue and enqueue)
-        self.image_queue[:, ptr:ptr + batch_size] = image_feats.T
-        self.text_queue[:, ptr:ptr + batch_size] = text_feats.T
-        ptr = (ptr + batch_size) % self.queue_size  # move pointer
-
-        self.queue_ptr[0] = ptr 
-
-
-def blip_pretrain(**kwargs):
-    model = BLIP_Pretrain(**kwargs)
-    return model 
-
-
-@torch.no_grad()
-def concat_all_gather(tensor):
-    """
-    Performs all_gather operation on the provided tensors.
-    *** Warning ***: torch.distributed.all_gather has no gradient.
-    """
-    tensors_gather = [torch.ones_like(tensor)
-        for _ in range(torch.distributed.get_world_size())]
-    torch.distributed.all_gather(tensors_gather, tensor, async_op=False)
-
-    output = torch.cat(tensors_gather, dim=0)
-    return output     
-
-
-from typing import List
-def tie_encoder_decoder_weights(encoder: nn.Module, decoder: nn.Module, base_model_prefix: str, skip_key:str):
-    uninitialized_encoder_weights: List[str] = []
-    if decoder.__class__ != encoder.__class__:
-        logger.info(
-            f"{decoder.__class__} and {encoder.__class__} are not equal. In this case make sure that all encoder weights are correctly initialized."
-        )
-
-    def tie_encoder_to_decoder_recursively(
-        decoder_pointer: nn.Module,
-        encoder_pointer: nn.Module,
-        module_name: str,
-        uninitialized_encoder_weights: List[str],
-        skip_key: str,
-        depth=0,
-    ):
-        assert isinstance(decoder_pointer, nn.Module) and isinstance(
-            encoder_pointer, nn.Module
-        ), f"{decoder_pointer} and {encoder_pointer} have to be of type torch.nn.Module"
-        if hasattr(decoder_pointer, "weight") and skip_key not in module_name:
-            assert hasattr(encoder_pointer, "weight")
-            encoder_pointer.weight = decoder_pointer.weight
-            if hasattr(decoder_pointer, "bias"):
-                assert hasattr(encoder_pointer, "bias")
-                encoder_pointer.bias = decoder_pointer.bias                
-            print(module_name+' is tied')    
-            return
-
-        encoder_modules = encoder_pointer._modules
-        decoder_modules = decoder_pointer._modules
-        if len(decoder_modules) > 0:
-            assert (
-                len(encoder_modules) > 0
-            ), f"Encoder module {encoder_pointer} does not match decoder module {decoder_pointer}"
-
-            all_encoder_weights = set([module_name + "/" + sub_name for sub_name in encoder_modules.keys()])
-            encoder_layer_pos = 0
-            for name, module in decoder_modules.items():
-                if name.isdigit():
-                    encoder_name = str(int(name) + encoder_layer_pos)
-                    decoder_name = name
-                    if not isinstance(decoder_modules[decoder_name], type(encoder_modules[encoder_name])) and len(
-                        encoder_modules
-                    ) != len(decoder_modules):
-                        # this can happen if the name corresponds to the position in a list module list of layers
-                        # in this case the decoder has added a cross-attention that the encoder does not have
-                        # thus skip this step and subtract one layer pos from encoder
-                        encoder_layer_pos -= 1
-                        continue
-                elif name not in encoder_modules:
-                    continue
-                elif depth > 500:
-                    raise ValueError(
-                        "Max depth of recursive function `tie_encoder_to_decoder` reached. It seems that there is a circular dependency between two or more `nn.Modules` of your model."
-                    )
-                else:
-                    decoder_name = encoder_name = name
-                tie_encoder_to_decoder_recursively(
-                    decoder_modules[decoder_name],
-                    encoder_modules[encoder_name],
-                    module_name + "/" + name,
-                    uninitialized_encoder_weights,
-                    skip_key,
-                    depth=depth + 1,
-                )
-                all_encoder_weights.remove(module_name + "/" + encoder_name)
-
-            uninitialized_encoder_weights += list(all_encoder_weights)
-
-    # tie weights recursively
-    tie_encoder_to_decoder_recursively(decoder, encoder, base_model_prefix, uninitialized_encoder_weights, skip_key)  
diff --git a/repositories/BLIP/models/blip_retrieval.py b/repositories/BLIP/models/blip_retrieval.py
deleted file mode 100644
index 1debe7e2e664f8dd603f8d4c537e3599c68638d7..0000000000000000000000000000000000000000
--- a/repositories/BLIP/models/blip_retrieval.py
+++ /dev/null
@@ -1,319 +0,0 @@
-from models.med import BertConfig, BertModel
-from transformers import BertTokenizer
-
-import torch
-from torch import nn
-import torch.nn.functional as F
-
-from models.blip import create_vit, init_tokenizer, load_checkpoint
-
-class BLIP_Retrieval(nn.Module):
-    def __init__(self,                 
-                 med_config = 'configs/med_config.json',  
-                 image_size = 384,
-                 vit = 'base',
-                 vit_grad_ckpt = False,
-                 vit_ckpt_layer = 0,                      
-                 embed_dim = 256,     
-                 queue_size = 57600,
-                 momentum = 0.995,
-                 negative_all_rank = False,
-                 ):
-        """
-        Args:
-            med_config (str): path for the mixture of encoder-decoder model's configuration file
-            image_size (int): input image size
-            vit (str): model size of vision transformer
-        """               
-        super().__init__()
-        
-        self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer)
-        self.tokenizer = init_tokenizer()   
-        med_config = BertConfig.from_json_file(med_config)
-        med_config.encoder_width = vision_width
-        self.text_encoder = BertModel(config=med_config, add_pooling_layer=False)          
-
-        text_width = self.text_encoder.config.hidden_size
-        
-        self.vision_proj = nn.Linear(vision_width, embed_dim)
-        self.text_proj = nn.Linear(text_width, embed_dim)
-
-        self.itm_head = nn.Linear(text_width, 2) 
-        
-        # create momentum encoders  
-        self.visual_encoder_m, vision_width = create_vit(vit,image_size)              
-        self.vision_proj_m = nn.Linear(vision_width, embed_dim)
-        self.text_encoder_m = BertModel(config=med_config, add_pooling_layer=False)    
-        self.text_proj_m = nn.Linear(text_width, embed_dim)
-        
-        self.model_pairs = [[self.visual_encoder,self.visual_encoder_m],
-                            [self.vision_proj,self.vision_proj_m],
-                            [self.text_encoder,self.text_encoder_m],
-                            [self.text_proj,self.text_proj_m],
-                           ]       
-        self.copy_params()
-
-        # create the queue
-        self.register_buffer("image_queue", torch.randn(embed_dim, queue_size))
-        self.register_buffer("text_queue", torch.randn(embed_dim, queue_size))
-        self.register_buffer("idx_queue", torch.full((1,queue_size),-100))
-        self.register_buffer("ptr_queue", torch.zeros(1, dtype=torch.long))  
-
-        self.image_queue = nn.functional.normalize(self.image_queue, dim=0)
-        self.text_queue = nn.functional.normalize(self.text_queue, dim=0)
-        
-        self.queue_size = queue_size
-        self.momentum = momentum
-        self.temp = nn.Parameter(0.07*torch.ones([]))   
-        
-        self.negative_all_rank = negative_all_rank
-        
-        
-    def forward(self, image, caption, alpha, idx):
-        with torch.no_grad():
-            self.temp.clamp_(0.001,0.5)
-        
-        image_embeds = self.visual_encoder(image) 
-        image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device)        
-        image_feat = F.normalize(self.vision_proj(image_embeds[:,0,:]),dim=-1)    
-        
-        text = self.tokenizer(caption, padding='max_length', truncation=True, max_length=35, 
-                              return_tensors="pt").to(image.device) 
-        
-        text_output = self.text_encoder(text.input_ids, attention_mask = text.attention_mask,                      
-                                        return_dict = True, mode = 'text')            
-        text_feat = F.normalize(self.text_proj(text_output.last_hidden_state[:,0,:]),dim=-1)        
-        
-        ###============== Image-text Contrastive Learning ===================###
-        idx = idx.view(-1,1)
-        idx_all = torch.cat([idx.t(), self.idx_queue.clone().detach()],dim=1)  
-        pos_idx = torch.eq(idx, idx_all).float()       
-        sim_targets = pos_idx / pos_idx.sum(1,keepdim=True)   
-        
-        # get momentum features
-        with torch.no_grad():
-            self._momentum_update()
-            image_embeds_m = self.visual_encoder_m(image) 
-            image_feat_m = F.normalize(self.vision_proj_m(image_embeds_m[:,0,:]),dim=-1)  
-            image_feat_m_all = torch.cat([image_feat_m.t(),self.image_queue.clone().detach()],dim=1)                   
-            
-            text_output_m = self.text_encoder_m(text.input_ids, attention_mask = text.attention_mask,                      
-                                                return_dict = True, mode = 'text')    
-            text_feat_m = F.normalize(self.text_proj_m(text_output_m.last_hidden_state[:,0,:]),dim=-1) 
-            text_feat_m_all = torch.cat([text_feat_m.t(),self.text_queue.clone().detach()],dim=1)
-
-            sim_i2t_m = image_feat_m @ text_feat_m_all / self.temp  
-            sim_t2i_m = text_feat_m @ image_feat_m_all / self.temp   
-
-            sim_i2t_targets = alpha * F.softmax(sim_i2t_m, dim=1) + (1 - alpha) * sim_targets
-            sim_t2i_targets = alpha * F.softmax(sim_t2i_m, dim=1) + (1 - alpha) * sim_targets        
-
-        sim_i2t = image_feat @ text_feat_m_all / self.temp 
-        sim_t2i = text_feat @ image_feat_m_all / self.temp 
-                             
-        loss_i2t = -torch.sum(F.log_softmax(sim_i2t, dim=1)*sim_i2t_targets,dim=1).mean()
-        loss_t2i = -torch.sum(F.log_softmax(sim_t2i, dim=1)*sim_t2i_targets,dim=1).mean() 
-
-        loss_ita = (loss_i2t+loss_t2i)/2
-        
-        idxs = concat_all_gather(idx)
-        self._dequeue_and_enqueue(image_feat_m, text_feat_m, idxs)        
-
-        ###============== Image-text Matching ===================###
-        encoder_input_ids = text.input_ids.clone()
-        encoder_input_ids[:,0] = self.tokenizer.enc_token_id
-
-        # forward the positve image-text pair
-        bs = image.size(0)
-        output_pos = self.text_encoder(encoder_input_ids,
-                                       attention_mask = text.attention_mask,
-                                       encoder_hidden_states = image_embeds,
-                                       encoder_attention_mask = image_atts,      
-                                       return_dict = True,
-                                      )  
-        
-        
-        if self.negative_all_rank:    
-            # compute sample similarity
-            with torch.no_grad():                
-                mask = torch.eq(idx, idxs.t())
-
-                image_feat_world = concat_all_gather(image_feat)
-                text_feat_world = concat_all_gather(text_feat)
-
-                sim_i2t = image_feat @ text_feat_world.t() / self.temp 
-                sim_t2i = text_feat @ image_feat_world.t() / self.temp 
-
-                weights_i2t = F.softmax(sim_i2t,dim=1)
-                weights_i2t.masked_fill_(mask, 0)            
-
-                weights_t2i = F.softmax(sim_t2i,dim=1)
-                weights_t2i.masked_fill_(mask, 0)     
-
-            image_embeds_world = all_gather_with_grad(image_embeds) 
-
-            # select a negative image (from all ranks) for each text
-            image_embeds_neg = []    
-            for b in range(bs):
-                neg_idx = torch.multinomial(weights_t2i[b], 1).item()
-                image_embeds_neg.append(image_embeds_world[neg_idx])
-            image_embeds_neg = torch.stack(image_embeds_neg,dim=0)   
-
-            # select a negative text (from all ranks) for each image
-            input_ids_world = concat_all_gather(encoder_input_ids)
-            att_mask_world = concat_all_gather(text.attention_mask)        
-
-            text_ids_neg = []
-            text_atts_neg = []
-            for b in range(bs):
-                neg_idx = torch.multinomial(weights_i2t[b], 1).item()
-                text_ids_neg.append(input_ids_world[neg_idx])
-                text_atts_neg.append(att_mask_world[neg_idx])
-                
-        else:
-            with torch.no_grad():                
-                mask = torch.eq(idx, idx.t())
-                
-                sim_i2t = image_feat @ text_feat.t() / self.temp 
-                sim_t2i = text_feat @ image_feat.t() / self.temp 
-
-                weights_i2t = F.softmax(sim_i2t,dim=1)
-                weights_i2t.masked_fill_(mask, 0)            
-
-                weights_t2i = F.softmax(sim_t2i,dim=1)
-                weights_t2i.masked_fill_(mask, 0)     
-
-            # select a negative image (from same rank) for each text
-            image_embeds_neg = []    
-            for b in range(bs):
-                neg_idx = torch.multinomial(weights_t2i[b], 1).item()
-                image_embeds_neg.append(image_embeds[neg_idx])
-            image_embeds_neg = torch.stack(image_embeds_neg,dim=0)   
-
-            # select a negative text (from same rank) for each image    
-            text_ids_neg = []
-            text_atts_neg = []
-            for b in range(bs):
-                neg_idx = torch.multinomial(weights_i2t[b], 1).item()
-                text_ids_neg.append(encoder_input_ids[neg_idx])
-                text_atts_neg.append(text.attention_mask[neg_idx])            
-            
-        text_ids_neg = torch.stack(text_ids_neg,dim=0)   
-        text_atts_neg = torch.stack(text_atts_neg,dim=0)      
-
-        text_ids_all = torch.cat([encoder_input_ids, text_ids_neg],dim=0)     
-        text_atts_all = torch.cat([text.attention_mask, text_atts_neg],dim=0)     
-
-        image_embeds_all = torch.cat([image_embeds_neg,image_embeds],dim=0)
-        image_atts_all = torch.cat([image_atts,image_atts],dim=0)
-
-        output_neg = self.text_encoder(text_ids_all,
-                                       attention_mask = text_atts_all,
-                                       encoder_hidden_states = image_embeds_all,
-                                       encoder_attention_mask = image_atts_all,      
-                                       return_dict = True,
-                                      )                         
-          
-
-        vl_embeddings = torch.cat([output_pos.last_hidden_state[:,0,:], output_neg.last_hidden_state[:,0,:]],dim=0)
-        vl_output = self.itm_head(vl_embeddings)            
-
-        itm_labels = torch.cat([torch.ones(bs,dtype=torch.long),torch.zeros(2*bs,dtype=torch.long)],
-                               dim=0).to(image.device)
-        loss_itm = F.cross_entropy(vl_output, itm_labels)     
-
-        return loss_ita, loss_itm 
- 
-
-    @torch.no_grad()    
-    def copy_params(self):
-        for model_pair in self.model_pairs:           
-            for param, param_m in zip(model_pair[0].parameters(), model_pair[1].parameters()):
-                param_m.data.copy_(param.data)  # initialize
-                param_m.requires_grad = False  # not update by gradient    
-
-            
-    @torch.no_grad()        
-    def _momentum_update(self):
-        for model_pair in self.model_pairs:           
-            for param, param_m in zip(model_pair[0].parameters(), model_pair[1].parameters()):
-                param_m.data = param_m.data * self.momentum + param.data * (1. - self.momentum)
-                
-                
-    @torch.no_grad()
-    def _dequeue_and_enqueue(self, image_feat, text_feat, idxs):
-        # gather keys before updating queue
-        image_feats = concat_all_gather(image_feat)
-        text_feats = concat_all_gather(text_feat)
-        
-
-        batch_size = image_feats.shape[0]
-
-        ptr = int(self.ptr_queue)
-        assert self.queue_size % batch_size == 0  # for simplicity
-
-        # replace the keys at ptr (dequeue and enqueue)
-        self.image_queue[:, ptr:ptr + batch_size] = image_feats.T
-        self.text_queue[:, ptr:ptr + batch_size] = text_feats.T
-        self.idx_queue[:, ptr:ptr + batch_size] = idxs.T
-        ptr = (ptr + batch_size) % self.queue_size # move pointer
-
-        self.ptr_queue[0] = ptr  
-
-
-def blip_retrieval(pretrained='',**kwargs):
-    model = BLIP_Retrieval(**kwargs)
-    if pretrained:
-        model,msg = load_checkpoint(model,pretrained)
-        print("missing keys:")
-        print(msg.missing_keys)
-    return model 
-
-
-@torch.no_grad()
-def concat_all_gather(tensor):
-    """
-    Performs all_gather operation on the provided tensors.
-    *** Warning ***: torch.distributed.all_gather has no gradient.
-    """
-    tensors_gather = [torch.ones_like(tensor)
-        for _ in range(torch.distributed.get_world_size())]
-    torch.distributed.all_gather(tensors_gather, tensor, async_op=False)
-
-    output = torch.cat(tensors_gather, dim=0)
-    return output      
-
-
-class GatherLayer(torch.autograd.Function):
-    """
-    Gather tensors from all workers with support for backward propagation:
-    This implementation does not cut the gradients as torch.distributed.all_gather does.
-    """
-
-    @staticmethod
-    def forward(ctx, x):
-        output = [torch.zeros_like(x) for _ in range(torch.distributed.get_world_size())]
-        torch.distributed.all_gather(output, x)
-        return tuple(output)
-
-    @staticmethod
-    def backward(ctx, *grads):
-        all_gradients = torch.stack(grads)
-        torch.distributed.all_reduce(all_gradients)
-        return all_gradients[torch.distributed.get_rank()]
-
-
-def all_gather_with_grad(tensors):
-    """
-    Performs all_gather operation on the provided tensors.
-    Graph remains connected for backward grad computation.
-    """
-    # Queue the gathered tensors
-    world_size = torch.distributed.get_world_size()
-    # There is no need for reduction in the single-proc case
-    if world_size == 1:
-        return tensors
-
-    tensor_all = GatherLayer.apply(tensors)
-
-    return torch.cat(tensor_all, dim=0)
diff --git a/repositories/BLIP/models/blip_vqa.py b/repositories/BLIP/models/blip_vqa.py
deleted file mode 100644
index d4cb3688fad03888f8568ec65437ee20452c6cb8..0000000000000000000000000000000000000000
--- a/repositories/BLIP/models/blip_vqa.py
+++ /dev/null
@@ -1,186 +0,0 @@
-from models.med import BertConfig, BertModel, BertLMHeadModel
-from models.blip import create_vit, init_tokenizer, load_checkpoint
-
-import torch
-from torch import nn
-import torch.nn.functional as F
-from transformers import BertTokenizer
-import numpy as np
-
-class BLIP_VQA(nn.Module):
-    def __init__(self,                 
-                 med_config = 'configs/med_config.json',  
-                 image_size = 480,
-                 vit = 'base',
-                 vit_grad_ckpt = False,
-                 vit_ckpt_layer = 0,                   
-                 ):
-        """
-        Args:
-            med_config (str): path for the mixture of encoder-decoder model's configuration file
-            image_size (int): input image size
-            vit (str): model size of vision transformer
-        """               
-        super().__init__()
-        
-        self.visual_encoder, vision_width = create_vit(vit, image_size, vit_grad_ckpt, vit_ckpt_layer, drop_path_rate=0.1)
-        self.tokenizer = init_tokenizer()  
-        
-        encoder_config = BertConfig.from_json_file(med_config)
-        encoder_config.encoder_width = vision_width
-        self.text_encoder = BertModel(config=encoder_config, add_pooling_layer=False) 
-        
-        decoder_config = BertConfig.from_json_file(med_config)        
-        self.text_decoder = BertLMHeadModel(config=decoder_config)          
-
-
-    def forward(self, image, question, answer=None, n=None, weights=None, train=True, inference='rank', k_test=128):
-        
-        image_embeds = self.visual_encoder(image) 
-        image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device)
-        
-        question = self.tokenizer(question, padding='longest', truncation=True, max_length=35, 
-                                  return_tensors="pt").to(image.device) 
-        question.input_ids[:,0] = self.tokenizer.enc_token_id
-        
-        if train:               
-            '''
-            n: number of answers for each question
-            weights: weight for each answer
-            '''                     
-            answer = self.tokenizer(answer, padding='longest', return_tensors="pt").to(image.device) 
-            answer.input_ids[:,0] = self.tokenizer.bos_token_id
-            answer_targets = answer.input_ids.masked_fill(answer.input_ids == self.tokenizer.pad_token_id, -100)      
-
-            question_output = self.text_encoder(question.input_ids, 
-                                                attention_mask = question.attention_mask, 
-                                                encoder_hidden_states = image_embeds,
-                                                encoder_attention_mask = image_atts,                             
-                                                return_dict = True)    
-
-            question_states = []                
-            question_atts = []  
-            for b, n in enumerate(n):
-                question_states += [question_output.last_hidden_state[b]]*n
-                question_atts += [question.attention_mask[b]]*n                
-            question_states = torch.stack(question_states,0)    
-            question_atts = torch.stack(question_atts,0)     
-
-            answer_output = self.text_decoder(answer.input_ids, 
-                                              attention_mask = answer.attention_mask, 
-                                              encoder_hidden_states = question_states,
-                                              encoder_attention_mask = question_atts,                  
-                                              labels = answer_targets,
-                                              return_dict = True,   
-                                              reduction = 'none',
-                                             )      
-            
-            loss = weights * answer_output.loss
-            loss = loss.sum()/image.size(0)
-
-            return loss
-            
-
-        else: 
-            question_output = self.text_encoder(question.input_ids, 
-                                                attention_mask = question.attention_mask, 
-                                                encoder_hidden_states = image_embeds,
-                                                encoder_attention_mask = image_atts,                                    
-                                                return_dict = True) 
-            
-            if inference=='generate':
-                num_beams = 3
-                question_states = question_output.last_hidden_state.repeat_interleave(num_beams,dim=0)
-                question_atts = torch.ones(question_states.size()[:-1],dtype=torch.long).to(question_states.device)
-                model_kwargs = {"encoder_hidden_states": question_states, "encoder_attention_mask":question_atts}
-                
-                bos_ids = torch.full((image.size(0),1),fill_value=self.tokenizer.bos_token_id,device=image.device)
-                
-                outputs = self.text_decoder.generate(input_ids=bos_ids,
-                                                     max_length=10,
-                                                     min_length=1,
-                                                     num_beams=num_beams,
-                                                     eos_token_id=self.tokenizer.sep_token_id,
-                                                     pad_token_id=self.tokenizer.pad_token_id, 
-                                                     **model_kwargs)
-                
-                answers = []    
-                for output in outputs:
-                    answer = self.tokenizer.decode(output, skip_special_tokens=True)    
-                    answers.append(answer)
-                return answers
-            
-            elif inference=='rank':
-                max_ids = self.rank_answer(question_output.last_hidden_state, question.attention_mask, 
-                                           answer.input_ids, answer.attention_mask, k_test) 
-                return max_ids
- 
-                
-                
-    def rank_answer(self, question_states, question_atts, answer_ids, answer_atts, k):
-        
-        num_ques = question_states.size(0)
-        start_ids = answer_ids[0,0].repeat(num_ques,1) # bos token
-        
-        start_output = self.text_decoder(start_ids, 
-                                         encoder_hidden_states = question_states,
-                                         encoder_attention_mask = question_atts,                                      
-                                         return_dict = True,
-                                         reduction = 'none')              
-        logits = start_output.logits[:,0,:] # first token's logit
-        
-        # topk_probs: top-k probability 
-        # topk_ids: [num_question, k]        
-        answer_first_token = answer_ids[:,1]
-        prob_first_token = F.softmax(logits,dim=1).index_select(dim=1, index=answer_first_token) 
-        topk_probs, topk_ids = prob_first_token.topk(k,dim=1) 
-        
-        # answer input: [num_question*k, answer_len]                 
-        input_ids = []
-        input_atts = []
-        for b, topk_id in enumerate(topk_ids):
-            input_ids.append(answer_ids.index_select(dim=0, index=topk_id))
-            input_atts.append(answer_atts.index_select(dim=0, index=topk_id))
-        input_ids = torch.cat(input_ids,dim=0)  
-        input_atts = torch.cat(input_atts,dim=0)  
-
-        targets_ids = input_ids.masked_fill(input_ids == self.tokenizer.pad_token_id, -100)
-
-        # repeat encoder's output for top-k answers
-        question_states = tile(question_states, 0, k)
-        question_atts = tile(question_atts, 0, k)
-        
-        output = self.text_decoder(input_ids, 
-                                   attention_mask = input_atts, 
-                                   encoder_hidden_states = question_states,
-                                   encoder_attention_mask = question_atts,     
-                                   labels = targets_ids,
-                                   return_dict = True, 
-                                   reduction = 'none')   
-        
-        log_probs_sum = -output.loss
-        log_probs_sum = log_probs_sum.view(num_ques,k)
-
-        max_topk_ids = log_probs_sum.argmax(dim=1) 
-        max_ids = topk_ids[max_topk_ids>=0,max_topk_ids]
-
-        return max_ids
-    
-    
-def blip_vqa(pretrained='',**kwargs):
-    model = BLIP_VQA(**kwargs)
-    if pretrained:
-        model,msg = load_checkpoint(model,pretrained)
-#         assert(len(msg.missing_keys)==0)
-    return model  
-
-
-def tile(x, dim, n_tile):
-    init_dim = x.size(dim)
-    repeat_idx = [1] * x.dim()
-    repeat_idx[dim] = n_tile
-    x = x.repeat(*(repeat_idx))
-    order_index = torch.LongTensor(np.concatenate([init_dim * np.arange(n_tile) + i for i in range(init_dim)]))
-    return torch.index_select(x, dim, order_index.to(x.device))    
-        
-        
\ No newline at end of file
diff --git a/repositories/BLIP/models/med.py b/repositories/BLIP/models/med.py
deleted file mode 100644
index 7b00a35450b736180a805d4f4664b4fb95aeba01..0000000000000000000000000000000000000000
--- a/repositories/BLIP/models/med.py
+++ /dev/null
@@ -1,955 +0,0 @@
-'''
- * Copyright (c) 2022, salesforce.com, inc.
- * All rights reserved.
- * SPDX-License-Identifier: BSD-3-Clause
- * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
- * By Junnan Li
- * Based on huggingface code base
- * https://github.com/huggingface/transformers/blob/v4.15.0/src/transformers/models/bert
-'''
-
-import math
-import os
-import warnings
-from dataclasses import dataclass
-from typing import Optional, Tuple
-
-import torch
-from torch import Tensor, device, dtype, nn
-import torch.utils.checkpoint
-from torch import nn
-from torch.nn import CrossEntropyLoss
-import torch.nn.functional as F
-
-from transformers.activations import ACT2FN
-from transformers.file_utils import (
-    ModelOutput,
-)
-from transformers.modeling_outputs import (
-    BaseModelOutputWithPastAndCrossAttentions,
-    BaseModelOutputWithPoolingAndCrossAttentions,
-    CausalLMOutputWithCrossAttentions,
-    MaskedLMOutput,
-    MultipleChoiceModelOutput,
-    NextSentencePredictorOutput,
-    QuestionAnsweringModelOutput,
-    SequenceClassifierOutput,
-    TokenClassifierOutput,
-)
-from transformers.modeling_utils import (
-    PreTrainedModel,
-    apply_chunking_to_forward,
-    find_pruneable_heads_and_indices,
-    prune_linear_layer,
-)
-from transformers.utils import logging
-from transformers.models.bert.configuration_bert import BertConfig
-
-
-logger = logging.get_logger(__name__)
-
-
-class BertEmbeddings(nn.Module):
-    """Construct the embeddings from word and position embeddings."""
-
-    def __init__(self, config):
-        super().__init__()
-        self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
-        self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
-
-        # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
-        # any TensorFlow checkpoint file
-        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
-        # position_ids (1, len position emb) is contiguous in memory and exported when serialized
-        self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))
-        self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
-        
-        self.config = config
-
-    def forward(
-        self, input_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0
-    ):
-        if input_ids is not None:
-            input_shape = input_ids.size()
-        else:
-            input_shape = inputs_embeds.size()[:-1]
-
-        seq_length = input_shape[1]
-
-        if position_ids is None:
-            position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length]
-
-        if inputs_embeds is None:
-            inputs_embeds = self.word_embeddings(input_ids)
-
-        embeddings = inputs_embeds
-
-        if self.position_embedding_type == "absolute":
-            position_embeddings = self.position_embeddings(position_ids)
-            embeddings += position_embeddings
-        embeddings = self.LayerNorm(embeddings)
-        embeddings = self.dropout(embeddings)
-        return embeddings
-
-
-class BertSelfAttention(nn.Module):
-    def __init__(self, config, is_cross_attention):
-        super().__init__()
-        self.config = config
-        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
-            raise ValueError(
-                "The hidden size (%d) is not a multiple of the number of attention "
-                "heads (%d)" % (config.hidden_size, config.num_attention_heads)
-            )
-        
-        self.num_attention_heads = config.num_attention_heads
-        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
-        self.all_head_size = self.num_attention_heads * self.attention_head_size
-
-        self.query = nn.Linear(config.hidden_size, self.all_head_size)
-        if is_cross_attention:
-            self.key = nn.Linear(config.encoder_width, self.all_head_size)
-            self.value = nn.Linear(config.encoder_width, self.all_head_size)
-        else:
-            self.key = nn.Linear(config.hidden_size, self.all_head_size)
-            self.value = nn.Linear(config.hidden_size, self.all_head_size)
-
-        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
-        self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
-        if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
-            self.max_position_embeddings = config.max_position_embeddings
-            self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)
-        self.save_attention = False   
-            
-    def save_attn_gradients(self, attn_gradients):
-        self.attn_gradients = attn_gradients
-        
-    def get_attn_gradients(self):
-        return self.attn_gradients
-    
-    def save_attention_map(self, attention_map):
-        self.attention_map = attention_map
-        
-    def get_attention_map(self):
-        return self.attention_map
-    
-    def transpose_for_scores(self, x):
-        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
-        x = x.view(*new_x_shape)
-        return x.permute(0, 2, 1, 3)
-
-    def forward(
-        self,
-        hidden_states,
-        attention_mask=None,
-        head_mask=None,
-        encoder_hidden_states=None,
-        encoder_attention_mask=None,
-        past_key_value=None,
-        output_attentions=False,
-    ):
-        mixed_query_layer = self.query(hidden_states)
-
-        # If this is instantiated as a cross-attention module, the keys
-        # and values come from an encoder; the attention mask needs to be
-        # such that the encoder's padding tokens are not attended to.
-        is_cross_attention = encoder_hidden_states is not None
-
-        if is_cross_attention:
-            key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
-            value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
-            attention_mask = encoder_attention_mask
-        elif past_key_value is not None:
-            key_layer = self.transpose_for_scores(self.key(hidden_states))
-            value_layer = self.transpose_for_scores(self.value(hidden_states))
-            key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
-            value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
-        else:
-            key_layer = self.transpose_for_scores(self.key(hidden_states))
-            value_layer = self.transpose_for_scores(self.value(hidden_states))
-
-        query_layer = self.transpose_for_scores(mixed_query_layer)
-
-        past_key_value = (key_layer, value_layer)
-
-        # Take the dot product between "query" and "key" to get the raw attention scores.
-        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
-
-        if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
-            seq_length = hidden_states.size()[1]
-            position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
-            position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
-            distance = position_ids_l - position_ids_r
-            positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
-            positional_embedding = positional_embedding.to(dtype=query_layer.dtype)  # fp16 compatibility
-
-            if self.position_embedding_type == "relative_key":
-                relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
-                attention_scores = attention_scores + relative_position_scores
-            elif self.position_embedding_type == "relative_key_query":
-                relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
-                relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
-                attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
-
-        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
-        if attention_mask is not None:
-            # Apply the attention mask is (precomputed for all layers in BertModel forward() function)
-            attention_scores = attention_scores + attention_mask
-
-        # Normalize the attention scores to probabilities.
-        attention_probs = nn.Softmax(dim=-1)(attention_scores)
-        
-        if is_cross_attention and self.save_attention:
-            self.save_attention_map(attention_probs)
-            attention_probs.register_hook(self.save_attn_gradients)         
-
-        # This is actually dropping out entire tokens to attend to, which might
-        # seem a bit unusual, but is taken from the original Transformer paper.
-        attention_probs_dropped = self.dropout(attention_probs)
-
-        # Mask heads if we want to
-        if head_mask is not None:
-            attention_probs_dropped = attention_probs_dropped * head_mask
-
-        context_layer = torch.matmul(attention_probs_dropped, value_layer)
-
-        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
-        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
-        context_layer = context_layer.view(*new_context_layer_shape)
-
-        outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
-
-        outputs = outputs + (past_key_value,)
-        return outputs
-
-
-class BertSelfOutput(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
-    def forward(self, hidden_states, input_tensor):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.dropout(hidden_states)
-        hidden_states = self.LayerNorm(hidden_states + input_tensor)
-        return hidden_states
-
-
-class BertAttention(nn.Module):
-    def __init__(self, config, is_cross_attention=False):
-        super().__init__()
-        self.self = BertSelfAttention(config, is_cross_attention)
-        self.output = BertSelfOutput(config)
-        self.pruned_heads = set()
-
-    def prune_heads(self, heads):
-        if len(heads) == 0:
-            return
-        heads, index = find_pruneable_heads_and_indices(
-            heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
-        )
-
-        # Prune linear layers
-        self.self.query = prune_linear_layer(self.self.query, index)
-        self.self.key = prune_linear_layer(self.self.key, index)
-        self.self.value = prune_linear_layer(self.self.value, index)
-        self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
-
-        # Update hyper params and store pruned heads
-        self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
-        self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
-        self.pruned_heads = self.pruned_heads.union(heads)
-
-    def forward(
-        self,
-        hidden_states,
-        attention_mask=None,
-        head_mask=None,
-        encoder_hidden_states=None,
-        encoder_attention_mask=None,
-        past_key_value=None,
-        output_attentions=False,
-    ):
-        self_outputs = self.self(
-            hidden_states,
-            attention_mask,
-            head_mask,
-            encoder_hidden_states,
-            encoder_attention_mask,
-            past_key_value,
-            output_attentions,
-        )
-        attention_output = self.output(self_outputs[0], hidden_states)
-        outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them
-        return outputs
-
-
-class BertIntermediate(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
-        if isinstance(config.hidden_act, str):
-            self.intermediate_act_fn = ACT2FN[config.hidden_act]
-        else:
-            self.intermediate_act_fn = config.hidden_act
-
-    def forward(self, hidden_states):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.intermediate_act_fn(hidden_states)
-        return hidden_states
-
-
-class BertOutput(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
-        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
-    def forward(self, hidden_states, input_tensor):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.dropout(hidden_states)
-        hidden_states = self.LayerNorm(hidden_states + input_tensor)
-        return hidden_states
-
-
-class BertLayer(nn.Module):
-    def __init__(self, config, layer_num):
-        super().__init__()
-        self.config = config
-        self.chunk_size_feed_forward = config.chunk_size_feed_forward
-        self.seq_len_dim = 1
-        self.attention = BertAttention(config)      
-        self.layer_num = layer_num          
-        if self.config.add_cross_attention:
-            self.crossattention = BertAttention(config, is_cross_attention=self.config.add_cross_attention)
-        self.intermediate = BertIntermediate(config)
-        self.output = BertOutput(config)
-
-    def forward(
-        self,
-        hidden_states,
-        attention_mask=None,
-        head_mask=None,
-        encoder_hidden_states=None,
-        encoder_attention_mask=None,
-        past_key_value=None,
-        output_attentions=False,
-        mode=None,
-    ):
-        # decoder uni-directional self-attention cached key/values tuple is at positions 1,2
-        self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
-        self_attention_outputs = self.attention(
-            hidden_states,
-            attention_mask,
-            head_mask,
-            output_attentions=output_attentions,
-            past_key_value=self_attn_past_key_value,
-        )
-        attention_output = self_attention_outputs[0]
-
-        outputs = self_attention_outputs[1:-1]
-        present_key_value = self_attention_outputs[-1]
-
-        if mode=='multimodal':
-            assert encoder_hidden_states is not None, "encoder_hidden_states must be given for cross-attention layers"
-
-            cross_attention_outputs = self.crossattention(
-                attention_output,
-                attention_mask,
-                head_mask,
-                encoder_hidden_states,
-                encoder_attention_mask,
-                output_attentions=output_attentions,
-            )
-            attention_output = cross_attention_outputs[0]
-            outputs = outputs + cross_attention_outputs[1:-1]  # add cross attentions if we output attention weights                               
-        layer_output = apply_chunking_to_forward(
-            self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
-        )
-        outputs = (layer_output,) + outputs
-
-        outputs = outputs + (present_key_value,)
-
-        return outputs
-
-    def feed_forward_chunk(self, attention_output):
-        intermediate_output = self.intermediate(attention_output)
-        layer_output = self.output(intermediate_output, attention_output)
-        return layer_output
-
-
-class BertEncoder(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.config = config
-        self.layer = nn.ModuleList([BertLayer(config,i) for i in range(config.num_hidden_layers)])
-        self.gradient_checkpointing = False
-
-    def forward(
-        self,
-        hidden_states,
-        attention_mask=None,
-        head_mask=None,
-        encoder_hidden_states=None,
-        encoder_attention_mask=None,
-        past_key_values=None,
-        use_cache=None,
-        output_attentions=False,
-        output_hidden_states=False,
-        return_dict=True,
-        mode='multimodal',
-    ):
-        all_hidden_states = () if output_hidden_states else None
-        all_self_attentions = () if output_attentions else None
-        all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
-
-        next_decoder_cache = () if use_cache else None
-               
-        for i in range(self.config.num_hidden_layers):
-            layer_module = self.layer[i]
-            if output_hidden_states:
-                all_hidden_states = all_hidden_states + (hidden_states,)
-
-            layer_head_mask = head_mask[i] if head_mask is not None else None
-            past_key_value = past_key_values[i] if past_key_values is not None else None
-
-            if self.gradient_checkpointing and self.training:
-
-                if use_cache:
-                    logger.warn(
-                        "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
-                    )
-                    use_cache = False
-
-                def create_custom_forward(module):
-                    def custom_forward(*inputs):
-                        return module(*inputs, past_key_value, output_attentions)
-
-                    return custom_forward
-
-                layer_outputs = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(layer_module),
-                    hidden_states,
-                    attention_mask,
-                    layer_head_mask,
-                    encoder_hidden_states,
-                    encoder_attention_mask,
-                    mode=mode,
-                )
-            else:
-                layer_outputs = layer_module(
-                    hidden_states,
-                    attention_mask,
-                    layer_head_mask,
-                    encoder_hidden_states,
-                    encoder_attention_mask,
-                    past_key_value,
-                    output_attentions,
-                    mode=mode,
-                )
-
-            hidden_states = layer_outputs[0]
-            if use_cache:
-                next_decoder_cache += (layer_outputs[-1],)
-            if output_attentions:
-                all_self_attentions = all_self_attentions + (layer_outputs[1],)
-
-        if output_hidden_states:
-            all_hidden_states = all_hidden_states + (hidden_states,)
-
-        if not return_dict:
-            return tuple(
-                v
-                for v in [
-                    hidden_states,
-                    next_decoder_cache,
-                    all_hidden_states,
-                    all_self_attentions,
-                    all_cross_attentions,
-                ]
-                if v is not None
-            )
-        return BaseModelOutputWithPastAndCrossAttentions(
-            last_hidden_state=hidden_states,
-            past_key_values=next_decoder_cache,
-            hidden_states=all_hidden_states,
-            attentions=all_self_attentions,
-            cross_attentions=all_cross_attentions,
-        )
-
-
-class BertPooler(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        self.activation = nn.Tanh()
-
-    def forward(self, hidden_states):
-        # We "pool" the model by simply taking the hidden state corresponding
-        # to the first token.
-        first_token_tensor = hidden_states[:, 0]
-        pooled_output = self.dense(first_token_tensor)
-        pooled_output = self.activation(pooled_output)
-        return pooled_output
-
-
-class BertPredictionHeadTransform(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        if isinstance(config.hidden_act, str):
-            self.transform_act_fn = ACT2FN[config.hidden_act]
-        else:
-            self.transform_act_fn = config.hidden_act
-        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-
-    def forward(self, hidden_states):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.transform_act_fn(hidden_states)
-        hidden_states = self.LayerNorm(hidden_states)
-        return hidden_states
-
-
-class BertLMPredictionHead(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.transform = BertPredictionHeadTransform(config)
-
-        # The output weights are the same as the input embeddings, but there is
-        # an output-only bias for each token.
-        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
-
-        self.bias = nn.Parameter(torch.zeros(config.vocab_size))
-
-        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
-        self.decoder.bias = self.bias
-
-    def forward(self, hidden_states):
-        hidden_states = self.transform(hidden_states)
-        hidden_states = self.decoder(hidden_states)
-        return hidden_states
-
-
-class BertOnlyMLMHead(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.predictions = BertLMPredictionHead(config)
-
-    def forward(self, sequence_output):
-        prediction_scores = self.predictions(sequence_output)
-        return prediction_scores
-
-
-class BertPreTrainedModel(PreTrainedModel):
-    """
-    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
-    models.
-    """
-
-    config_class = BertConfig
-    base_model_prefix = "bert"
-    _keys_to_ignore_on_load_missing = [r"position_ids"]
-
-    def _init_weights(self, module):
-        """ Initialize the weights """
-        if isinstance(module, (nn.Linear, nn.Embedding)):
-            # Slightly different from the TF version which uses truncated_normal for initialization
-            # cf https://github.com/pytorch/pytorch/pull/5617
-            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
-        elif isinstance(module, nn.LayerNorm):
-            module.bias.data.zero_()
-            module.weight.data.fill_(1.0)
-        if isinstance(module, nn.Linear) and module.bias is not None:
-            module.bias.data.zero_()
-
-
-class BertModel(BertPreTrainedModel):
-    """
-    The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
-    cross-attention is added between the self-attention layers, following the architecture described in `Attention is
-    all you need <https://arxiv.org/abs/1706.03762>`__ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
-    Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
-    argument and :obj:`add_cross_attention` set to :obj:`True`; an :obj:`encoder_hidden_states` is then expected as an
-    input to the forward pass.
-    """
-
-    def __init__(self, config, add_pooling_layer=True):
-        super().__init__(config)
-        self.config = config
-
-        self.embeddings = BertEmbeddings(config)
-        
-        self.encoder = BertEncoder(config)
-
-        self.pooler = BertPooler(config) if add_pooling_layer else None
-
-        self.init_weights()
- 
-
-    def get_input_embeddings(self):
-        return self.embeddings.word_embeddings
-
-    def set_input_embeddings(self, value):
-        self.embeddings.word_embeddings = value
-
-    def _prune_heads(self, heads_to_prune):
-        """
-        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
-        class PreTrainedModel
-        """
-        for layer, heads in heads_to_prune.items():
-            self.encoder.layer[layer].attention.prune_heads(heads)
-
-    
-    def get_extended_attention_mask(self, attention_mask: Tensor, input_shape: Tuple[int], device: device, is_decoder: bool) -> Tensor:
-        """
-        Makes broadcastable attention and causal masks so that future and masked tokens are ignored.
-
-        Arguments:
-            attention_mask (:obj:`torch.Tensor`):
-                Mask with ones indicating tokens to attend to, zeros for tokens to ignore.
-            input_shape (:obj:`Tuple[int]`):
-                The shape of the input to the model.
-            device: (:obj:`torch.device`):
-                The device of the input to the model.
-
-        Returns:
-            :obj:`torch.Tensor` The extended attention mask, with a the same dtype as :obj:`attention_mask.dtype`.
-        """
-        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
-        # ourselves in which case we just need to make it broadcastable to all heads.
-        if attention_mask.dim() == 3:
-            extended_attention_mask = attention_mask[:, None, :, :]
-        elif attention_mask.dim() == 2:
-            # Provided a padding mask of dimensions [batch_size, seq_length]
-            # - if the model is a decoder, apply a causal mask in addition to the padding mask
-            # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length]
-            if is_decoder:
-                batch_size, seq_length = input_shape
-
-                seq_ids = torch.arange(seq_length, device=device)
-                causal_mask = seq_ids[None, None, :].repeat(batch_size, seq_length, 1) <= seq_ids[None, :, None]
-                # in case past_key_values are used we need to add a prefix ones mask to the causal mask
-                # causal and attention masks must have same type with pytorch version < 1.3
-                causal_mask = causal_mask.to(attention_mask.dtype)
-   
-                if causal_mask.shape[1] < attention_mask.shape[1]:
-                    prefix_seq_len = attention_mask.shape[1] - causal_mask.shape[1]
-                    causal_mask = torch.cat(
-                        [
-                            torch.ones((batch_size, seq_length, prefix_seq_len), device=device, dtype=causal_mask.dtype),
-                            causal_mask,
-                        ],
-                        axis=-1,
-                    )                     
-
-                extended_attention_mask = causal_mask[:, None, :, :] * attention_mask[:, None, None, :]
-            else:
-                extended_attention_mask = attention_mask[:, None, None, :]
-        else:
-            raise ValueError(
-                "Wrong shape for input_ids (shape {}) or attention_mask (shape {})".format(
-                    input_shape, attention_mask.shape
-                )
-            )
-
-        # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
-        # masked positions, this operation will create a tensor which is 0.0 for
-        # positions we want to attend and -10000.0 for masked positions.
-        # Since we are adding it to the raw scores before the softmax, this is
-        # effectively the same as removing these entirely.
-        extended_attention_mask = extended_attention_mask.to(dtype=self.dtype)  # fp16 compatibility
-        extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
-        return extended_attention_mask
-    
-    def forward(
-        self,
-        input_ids=None,
-        attention_mask=None,
-        position_ids=None,
-        head_mask=None,
-        inputs_embeds=None,
-        encoder_embeds=None,
-        encoder_hidden_states=None,
-        encoder_attention_mask=None,
-        past_key_values=None,
-        use_cache=None,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
-        is_decoder=False,
-        mode='multimodal',
-    ):
-        r"""
-        encoder_hidden_states  (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
-            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
-            the model is configured as a decoder.
-        encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
-            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
-            the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:
-            - 1 for tokens that are **not masked**,
-            - 0 for tokens that are **masked**.
-        past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
-            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
-            If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
-            (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
-            instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
-        use_cache (:obj:`bool`, `optional`):
-            If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
-            decoding (see :obj:`past_key_values`).
-        """
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        if is_decoder:
-            use_cache = use_cache if use_cache is not None else self.config.use_cache
-        else:
-            use_cache = False
-
-        if input_ids is not None and inputs_embeds is not None:
-            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
-        elif input_ids is not None:
-            input_shape = input_ids.size()
-            batch_size, seq_length = input_shape
-            device = input_ids.device
-        elif inputs_embeds is not None:
-            input_shape = inputs_embeds.size()[:-1]
-            batch_size, seq_length = input_shape
-            device = inputs_embeds.device
-        elif encoder_embeds is not None:    
-            input_shape = encoder_embeds.size()[:-1]
-            batch_size, seq_length = input_shape 
-            device = encoder_embeds.device
-        else:
-            raise ValueError("You have to specify either input_ids or inputs_embeds or encoder_embeds")
-
-        # past_key_values_length
-        past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
-
-        if attention_mask is None:
-            attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device)
-            
-        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
-        # ourselves in which case we just need to make it broadcastable to all heads.
-        extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape, 
-                                                                                 device, is_decoder)
-
-        # If a 2D or 3D attention mask is provided for the cross-attention
-        # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
-        if encoder_hidden_states is not None:
-            if type(encoder_hidden_states) == list:
-                encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states[0].size()
-            else:
-                encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
-            encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
-            
-            if type(encoder_attention_mask) == list:
-                encoder_extended_attention_mask = [self.invert_attention_mask(mask) for mask in encoder_attention_mask]
-            elif encoder_attention_mask is None:
-                encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
-                encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
-            else:    
-                encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
-        else:
-            encoder_extended_attention_mask = None
-
-        # Prepare head mask if needed
-        # 1.0 in head_mask indicate we keep the head
-        # attention_probs has shape bsz x n_heads x N x N
-        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
-        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
-        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
-        
-        if encoder_embeds is None:
-            embedding_output = self.embeddings(
-                input_ids=input_ids,
-                position_ids=position_ids,
-                inputs_embeds=inputs_embeds,
-                past_key_values_length=past_key_values_length,
-            )
-        else:
-            embedding_output = encoder_embeds
-            
-        encoder_outputs = self.encoder(
-            embedding_output,
-            attention_mask=extended_attention_mask,
-            head_mask=head_mask,
-            encoder_hidden_states=encoder_hidden_states,
-            encoder_attention_mask=encoder_extended_attention_mask,
-            past_key_values=past_key_values,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-            mode=mode,
-        )
-        sequence_output = encoder_outputs[0]
-        pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
-
-        if not return_dict:
-            return (sequence_output, pooled_output) + encoder_outputs[1:]
-
-        return BaseModelOutputWithPoolingAndCrossAttentions(
-            last_hidden_state=sequence_output,
-            pooler_output=pooled_output,
-            past_key_values=encoder_outputs.past_key_values,
-            hidden_states=encoder_outputs.hidden_states,
-            attentions=encoder_outputs.attentions,
-            cross_attentions=encoder_outputs.cross_attentions,
-        )
-
-
-
-class BertLMHeadModel(BertPreTrainedModel):
-
-    _keys_to_ignore_on_load_unexpected = [r"pooler"]
-    _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]
-
-    def __init__(self, config):
-        super().__init__(config)
-
-        self.bert = BertModel(config, add_pooling_layer=False)
-        self.cls = BertOnlyMLMHead(config)
-
-        self.init_weights()
-
-    def get_output_embeddings(self):
-        return self.cls.predictions.decoder
-
-    def set_output_embeddings(self, new_embeddings):
-        self.cls.predictions.decoder = new_embeddings
-
-    def forward(
-        self,
-        input_ids=None,
-        attention_mask=None,
-        position_ids=None,
-        head_mask=None,
-        inputs_embeds=None,
-        encoder_hidden_states=None,
-        encoder_attention_mask=None,
-        labels=None,
-        past_key_values=None,
-        use_cache=None,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
-        return_logits=False,            
-        is_decoder=True,
-        reduction='mean',
-        mode='multimodal', 
-    ):
-        r"""
-        encoder_hidden_states  (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
-            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
-            the model is configured as a decoder.
-        encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
-            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
-            the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:
-            - 1 for tokens that are **not masked**,
-            - 0 for tokens that are **masked**.
-        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
-            Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
-            ``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are
-            ignored (masked), the loss is only computed for the tokens with labels n ``[0, ..., config.vocab_size]``
-        past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
-            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
-            If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
-            (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
-            instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
-        use_cache (:obj:`bool`, `optional`):
-            If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
-            decoding (see :obj:`past_key_values`).
-        Returns:
-        Example::
-            >>> from transformers import BertTokenizer, BertLMHeadModel, BertConfig
-            >>> import torch
-            >>> tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
-            >>> config = BertConfig.from_pretrained("bert-base-cased")
-            >>> model = BertLMHeadModel.from_pretrained('bert-base-cased', config=config)
-            >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
-            >>> outputs = model(**inputs)
-            >>> prediction_logits = outputs.logits
-        """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-        if labels is not None:
-            use_cache = False
-
-        outputs = self.bert(
-            input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            head_mask=head_mask,
-            inputs_embeds=inputs_embeds,
-            encoder_hidden_states=encoder_hidden_states,
-            encoder_attention_mask=encoder_attention_mask,
-            past_key_values=past_key_values,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-            is_decoder=is_decoder,
-            mode=mode,
-        )
-        
-        sequence_output = outputs[0]
-        prediction_scores = self.cls(sequence_output)
-        
-        if return_logits:
-            return prediction_scores[:, :-1, :].contiguous()  
-
-        lm_loss = None
-        if labels is not None:
-            # we are doing next-token prediction; shift prediction scores and input ids by one
-            shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous()
-            labels = labels[:, 1:].contiguous()
-            loss_fct = CrossEntropyLoss(reduction=reduction, label_smoothing=0.1) 
-            lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
-            if reduction=='none':
-                lm_loss = lm_loss.view(prediction_scores.size(0),-1).sum(1)               
-
-        if not return_dict:
-            output = (prediction_scores,) + outputs[2:]
-            return ((lm_loss,) + output) if lm_loss is not None else output
-
-        return CausalLMOutputWithCrossAttentions(
-            loss=lm_loss,
-            logits=prediction_scores,
-            past_key_values=outputs.past_key_values,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-            cross_attentions=outputs.cross_attentions,
-        )
-
-    def prepare_inputs_for_generation(self, input_ids, past=None, attention_mask=None, **model_kwargs):
-        input_shape = input_ids.shape
-        # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
-        if attention_mask is None:
-            attention_mask = input_ids.new_ones(input_shape)
-
-        # cut decoder_input_ids if past is used
-        if past is not None:
-            input_ids = input_ids[:, -1:]
-
-        return {
-            "input_ids": input_ids, 
-            "attention_mask": attention_mask, 
-            "past_key_values": past,
-            "encoder_hidden_states": model_kwargs.get("encoder_hidden_states", None),
-            "encoder_attention_mask": model_kwargs.get("encoder_attention_mask", None),
-            "is_decoder": True,
-        }
-
-    def _reorder_cache(self, past, beam_idx):
-        reordered_past = ()
-        for layer_past in past:
-            reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
-        return reordered_past
diff --git a/repositories/BLIP/models/nlvr_encoder.py b/repositories/BLIP/models/nlvr_encoder.py
deleted file mode 100644
index 1946bb4a300f75afa4848f6622839445903c34a9..0000000000000000000000000000000000000000
--- a/repositories/BLIP/models/nlvr_encoder.py
+++ /dev/null
@@ -1,843 +0,0 @@
-import math
-import os
-import warnings
-from dataclasses import dataclass
-from typing import Optional, Tuple
-
-import torch
-from torch import Tensor, device, dtype, nn
-import torch.utils.checkpoint
-from torch import nn
-from torch.nn import CrossEntropyLoss
-import torch.nn.functional as F
-
-from transformers.activations import ACT2FN
-from transformers.file_utils import (
-    ModelOutput,
-)
-from transformers.modeling_outputs import (
-    BaseModelOutputWithPastAndCrossAttentions,
-    BaseModelOutputWithPoolingAndCrossAttentions,
-    CausalLMOutputWithCrossAttentions,
-    MaskedLMOutput,
-    MultipleChoiceModelOutput,
-    NextSentencePredictorOutput,
-    QuestionAnsweringModelOutput,
-    SequenceClassifierOutput,
-    TokenClassifierOutput,
-)
-from transformers.modeling_utils import (
-    PreTrainedModel,
-    apply_chunking_to_forward,
-    find_pruneable_heads_and_indices,
-    prune_linear_layer,
-)
-from transformers.utils import logging
-from transformers.models.bert.configuration_bert import BertConfig
-
-
-logger = logging.get_logger(__name__)
-
-
-class BertEmbeddings(nn.Module):
-    """Construct the embeddings from word and position embeddings."""
-
-    def __init__(self, config):
-        super().__init__()
-        self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
-        self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
-
-        # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
-        # any TensorFlow checkpoint file
-        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
-        # position_ids (1, len position emb) is contiguous in memory and exported when serialized
-        self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))
-        self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
-        
-        self.config = config
-
-    def forward(
-        self, input_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0
-    ):
-        if input_ids is not None:
-            input_shape = input_ids.size()
-        else:
-            input_shape = inputs_embeds.size()[:-1]
-
-        seq_length = input_shape[1]
-
-        if position_ids is None:
-            position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length]
-
-        if inputs_embeds is None:
-            inputs_embeds = self.word_embeddings(input_ids)
-
-        embeddings = inputs_embeds
-
-        if self.position_embedding_type == "absolute":
-            position_embeddings = self.position_embeddings(position_ids)
-            embeddings += position_embeddings
-        embeddings = self.LayerNorm(embeddings)
-        embeddings = self.dropout(embeddings)
-        return embeddings
-
-
-class BertSelfAttention(nn.Module):
-    def __init__(self, config, is_cross_attention):
-        super().__init__()
-        self.config = config
-        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
-            raise ValueError(
-                "The hidden size (%d) is not a multiple of the number of attention "
-                "heads (%d)" % (config.hidden_size, config.num_attention_heads)
-            )
-        
-        self.num_attention_heads = config.num_attention_heads
-        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
-        self.all_head_size = self.num_attention_heads * self.attention_head_size
-
-        self.query = nn.Linear(config.hidden_size, self.all_head_size)
-        if is_cross_attention:
-            self.key = nn.Linear(config.encoder_width, self.all_head_size)
-            self.value = nn.Linear(config.encoder_width, self.all_head_size)
-        else:
-            self.key = nn.Linear(config.hidden_size, self.all_head_size)
-            self.value = nn.Linear(config.hidden_size, self.all_head_size)
-
-        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
-        self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
-        if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
-            self.max_position_embeddings = config.max_position_embeddings
-            self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)
-        self.save_attention = False   
-            
-    def save_attn_gradients(self, attn_gradients):
-        self.attn_gradients = attn_gradients
-        
-    def get_attn_gradients(self):
-        return self.attn_gradients
-    
-    def save_attention_map(self, attention_map):
-        self.attention_map = attention_map
-        
-    def get_attention_map(self):
-        return self.attention_map
-    
-    def transpose_for_scores(self, x):
-        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
-        x = x.view(*new_x_shape)
-        return x.permute(0, 2, 1, 3)
-
-    def forward(
-        self,
-        hidden_states,
-        attention_mask=None,
-        head_mask=None,
-        encoder_hidden_states=None,
-        encoder_attention_mask=None,
-        past_key_value=None,
-        output_attentions=False,
-    ):
-        mixed_query_layer = self.query(hidden_states)
-
-        # If this is instantiated as a cross-attention module, the keys
-        # and values come from an encoder; the attention mask needs to be
-        # such that the encoder's padding tokens are not attended to.
-        is_cross_attention = encoder_hidden_states is not None
-
-        if is_cross_attention:
-            key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
-            value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
-            attention_mask = encoder_attention_mask
-        elif past_key_value is not None:
-            key_layer = self.transpose_for_scores(self.key(hidden_states))
-            value_layer = self.transpose_for_scores(self.value(hidden_states))
-            key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
-            value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
-        else:
-            key_layer = self.transpose_for_scores(self.key(hidden_states))
-            value_layer = self.transpose_for_scores(self.value(hidden_states))
-
-        query_layer = self.transpose_for_scores(mixed_query_layer)
-
-        past_key_value = (key_layer, value_layer)
-
-        # Take the dot product between "query" and "key" to get the raw attention scores.
-        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
-
-        if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
-            seq_length = hidden_states.size()[1]
-            position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
-            position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
-            distance = position_ids_l - position_ids_r
-            positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
-            positional_embedding = positional_embedding.to(dtype=query_layer.dtype)  # fp16 compatibility
-
-            if self.position_embedding_type == "relative_key":
-                relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
-                attention_scores = attention_scores + relative_position_scores
-            elif self.position_embedding_type == "relative_key_query":
-                relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
-                relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
-                attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
-
-        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
-        if attention_mask is not None:
-            # Apply the attention mask is (precomputed for all layers in BertModel forward() function)
-            attention_scores = attention_scores + attention_mask
-
-        # Normalize the attention scores to probabilities.
-        attention_probs = nn.Softmax(dim=-1)(attention_scores)
-        
-        if is_cross_attention and self.save_attention:
-            self.save_attention_map(attention_probs)
-            attention_probs.register_hook(self.save_attn_gradients)         
-
-        # This is actually dropping out entire tokens to attend to, which might
-        # seem a bit unusual, but is taken from the original Transformer paper.
-        attention_probs_dropped = self.dropout(attention_probs)
-
-        # Mask heads if we want to
-        if head_mask is not None:
-            attention_probs_dropped = attention_probs_dropped * head_mask
-
-        context_layer = torch.matmul(attention_probs_dropped, value_layer)
-
-        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
-        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
-        context_layer = context_layer.view(*new_context_layer_shape)
-
-        outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
-
-        outputs = outputs + (past_key_value,)
-        return outputs
-
-
-class BertSelfOutput(nn.Module):
-    def __init__(self, config, twin=False, merge=False):     
-        super().__init__()
-        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)        
-        if twin:
-            self.dense0 = nn.Linear(config.hidden_size, config.hidden_size)
-            self.dense1 = nn.Linear(config.hidden_size, config.hidden_size)         
-        else:
-            self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        if merge:
-            self.act =  ACT2FN[config.hidden_act]
-            self.merge_layer = nn.Linear(config.hidden_size * 2, config.hidden_size)
-            self.merge = True
-        else:
-            self.merge = False
-
-    def forward(self, hidden_states, input_tensor):
-        if type(hidden_states) == list:
-            hidden_states0 = self.dense0(hidden_states[0])
-            hidden_states1 = self.dense1(hidden_states[1])        
-            if self.merge:  
-                #hidden_states = self.merge_layer(self.act(torch.cat([hidden_states0,hidden_states1],dim=-1)))
-                hidden_states = self.merge_layer(torch.cat([hidden_states0,hidden_states1],dim=-1))
-            else:
-                hidden_states = (hidden_states0+hidden_states1)/2
-        else:    
-            hidden_states = self.dense(hidden_states)
-        hidden_states = self.dropout(hidden_states)
-        hidden_states = self.LayerNorm(hidden_states + input_tensor)
-        return hidden_states
-
-
-class BertAttention(nn.Module):
-    def __init__(self, config, is_cross_attention=False, layer_num=-1):
-        super().__init__()
-        if is_cross_attention:
-            self.self0 = BertSelfAttention(config, is_cross_attention)
-            self.self1 = BertSelfAttention(config, is_cross_attention)
-        else:    
-            self.self = BertSelfAttention(config, is_cross_attention)
-        self.output = BertSelfOutput(config, twin=is_cross_attention, merge=(is_cross_attention and layer_num>=6))
-        self.pruned_heads = set()
-
-    def prune_heads(self, heads):
-        if len(heads) == 0:
-            return
-        heads, index = find_pruneable_heads_and_indices(
-            heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
-        )
-
-        # Prune linear layers
-        self.self.query = prune_linear_layer(self.self.query, index)
-        self.self.key = prune_linear_layer(self.self.key, index)
-        self.self.value = prune_linear_layer(self.self.value, index)
-        self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
-
-        # Update hyper params and store pruned heads
-        self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
-        self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
-        self.pruned_heads = self.pruned_heads.union(heads)
-
-    def forward(
-        self,
-        hidden_states,
-        attention_mask=None,
-        head_mask=None,
-        encoder_hidden_states=None,
-        encoder_attention_mask=None,
-        past_key_value=None,
-        output_attentions=False,
-    ):        
-        if type(encoder_hidden_states)==list:   
-            self_outputs0 = self.self0(
-                hidden_states,
-                attention_mask,
-                head_mask,
-                encoder_hidden_states[0],
-                encoder_attention_mask[0],
-                past_key_value,
-                output_attentions,
-            )
-            self_outputs1 = self.self1(
-                hidden_states,
-                attention_mask,
-                head_mask,
-                encoder_hidden_states[1],
-                encoder_attention_mask[1],
-                past_key_value,
-                output_attentions,
-            )                        
-            attention_output = self.output([self_outputs0[0],self_outputs1[0]], hidden_states)
-    
-            outputs = (attention_output,) + self_outputs0[1:]  # add attentions if we output them
-        else:        
-            self_outputs = self.self(
-                hidden_states,
-                attention_mask,
-                head_mask,
-                encoder_hidden_states,
-                encoder_attention_mask,
-                past_key_value,
-                output_attentions,
-            )
-            attention_output = self.output(self_outputs[0], hidden_states)
-            outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them
-        return outputs
-
-
-class BertIntermediate(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
-        if isinstance(config.hidden_act, str):
-            self.intermediate_act_fn = ACT2FN[config.hidden_act]
-        else:
-            self.intermediate_act_fn = config.hidden_act
-
-    def forward(self, hidden_states):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.intermediate_act_fn(hidden_states)
-        return hidden_states
-
-
-class BertOutput(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
-        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
-    def forward(self, hidden_states, input_tensor):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.dropout(hidden_states)
-        hidden_states = self.LayerNorm(hidden_states + input_tensor)
-        return hidden_states
-
-
-class BertLayer(nn.Module):
-    def __init__(self, config, layer_num):
-        super().__init__()
-        self.config = config
-        self.chunk_size_feed_forward = config.chunk_size_feed_forward
-        self.seq_len_dim = 1
-        self.attention = BertAttention(config)      
-        self.layer_num = layer_num          
-        if self.config.add_cross_attention:
-            self.crossattention = BertAttention(config, is_cross_attention=self.config.add_cross_attention, layer_num=layer_num)
-        self.intermediate = BertIntermediate(config)
-        self.output = BertOutput(config)
-
-    def forward(
-        self,
-        hidden_states,
-        attention_mask=None,
-        head_mask=None,
-        encoder_hidden_states=None,
-        encoder_attention_mask=None,
-        past_key_value=None,
-        output_attentions=False,
-        mode=None,
-    ):
-        # decoder uni-directional self-attention cached key/values tuple is at positions 1,2
-        self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
-        self_attention_outputs = self.attention(
-            hidden_states,
-            attention_mask,
-            head_mask,
-            output_attentions=output_attentions,
-            past_key_value=self_attn_past_key_value,
-        )
-        attention_output = self_attention_outputs[0]
-
-        outputs = self_attention_outputs[1:-1]
-        present_key_value = self_attention_outputs[-1]
-
-        if mode=='multimodal':
-            assert encoder_hidden_states is not None, "encoder_hidden_states must be given for cross-attention layers"
-            cross_attention_outputs = self.crossattention(
-                attention_output,
-                attention_mask,
-                head_mask,
-                encoder_hidden_states,
-                encoder_attention_mask,
-                output_attentions=output_attentions,
-            )
-            attention_output = cross_attention_outputs[0]
-            outputs = outputs + cross_attention_outputs[1:-1]  # add cross attentions if we output attention weights                               
-        layer_output = apply_chunking_to_forward(
-            self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
-        )
-        outputs = (layer_output,) + outputs
-
-        outputs = outputs + (present_key_value,)
-
-        return outputs
-
-    def feed_forward_chunk(self, attention_output):
-        intermediate_output = self.intermediate(attention_output)
-        layer_output = self.output(intermediate_output, attention_output)
-        return layer_output
-
-
-class BertEncoder(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.config = config
-        self.layer = nn.ModuleList([BertLayer(config,i) for i in range(config.num_hidden_layers)])
-        self.gradient_checkpointing = False
-
-    def forward(
-        self,
-        hidden_states,
-        attention_mask=None,
-        head_mask=None,
-        encoder_hidden_states=None,
-        encoder_attention_mask=None,
-        past_key_values=None,
-        use_cache=None,
-        output_attentions=False,
-        output_hidden_states=False,
-        return_dict=True,
-        mode='multimodal',
-    ):
-        all_hidden_states = () if output_hidden_states else None
-        all_self_attentions = () if output_attentions else None
-        all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
-
-        next_decoder_cache = () if use_cache else None
-               
-        for i in range(self.config.num_hidden_layers):
-            layer_module = self.layer[i]
-            if output_hidden_states:
-                all_hidden_states = all_hidden_states + (hidden_states,)
-
-            layer_head_mask = head_mask[i] if head_mask is not None else None
-            past_key_value = past_key_values[i] if past_key_values is not None else None
-
-            if self.gradient_checkpointing and self.training:
-
-                if use_cache:
-                    logger.warn(
-                        "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
-                    )
-                    use_cache = False
-
-                def create_custom_forward(module):
-                    def custom_forward(*inputs):
-                        return module(*inputs, past_key_value, output_attentions)
-
-                    return custom_forward
-
-                layer_outputs = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(layer_module),
-                    hidden_states,
-                    attention_mask,
-                    layer_head_mask,
-                    encoder_hidden_states,
-                    encoder_attention_mask,
-                    mode=mode,
-                )
-            else:
-                layer_outputs = layer_module(
-                    hidden_states,
-                    attention_mask,
-                    layer_head_mask,
-                    encoder_hidden_states,
-                    encoder_attention_mask,
-                    past_key_value,
-                    output_attentions,
-                    mode=mode,
-                )
-
-            hidden_states = layer_outputs[0]
-            if use_cache:
-                next_decoder_cache += (layer_outputs[-1],)
-            if output_attentions:
-                all_self_attentions = all_self_attentions + (layer_outputs[1],)
-
-        if output_hidden_states:
-            all_hidden_states = all_hidden_states + (hidden_states,)
-
-        if not return_dict:
-            return tuple(
-                v
-                for v in [
-                    hidden_states,
-                    next_decoder_cache,
-                    all_hidden_states,
-                    all_self_attentions,
-                    all_cross_attentions,
-                ]
-                if v is not None
-            )
-        return BaseModelOutputWithPastAndCrossAttentions(
-            last_hidden_state=hidden_states,
-            past_key_values=next_decoder_cache,
-            hidden_states=all_hidden_states,
-            attentions=all_self_attentions,
-            cross_attentions=all_cross_attentions,
-        )
-
-
-class BertPooler(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        self.activation = nn.Tanh()
-
-    def forward(self, hidden_states):
-        # We "pool" the model by simply taking the hidden state corresponding
-        # to the first token.
-        first_token_tensor = hidden_states[:, 0]
-        pooled_output = self.dense(first_token_tensor)
-        pooled_output = self.activation(pooled_output)
-        return pooled_output
-
-
-class BertPredictionHeadTransform(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        if isinstance(config.hidden_act, str):
-            self.transform_act_fn = ACT2FN[config.hidden_act]
-        else:
-            self.transform_act_fn = config.hidden_act
-        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-
-    def forward(self, hidden_states):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.transform_act_fn(hidden_states)
-        hidden_states = self.LayerNorm(hidden_states)
-        return hidden_states
-
-
-class BertLMPredictionHead(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.transform = BertPredictionHeadTransform(config)
-
-        # The output weights are the same as the input embeddings, but there is
-        # an output-only bias for each token.
-        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
-
-        self.bias = nn.Parameter(torch.zeros(config.vocab_size))
-
-        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
-        self.decoder.bias = self.bias
-
-    def forward(self, hidden_states):
-        hidden_states = self.transform(hidden_states)
-        hidden_states = self.decoder(hidden_states)
-        return hidden_states
-
-
-class BertOnlyMLMHead(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.predictions = BertLMPredictionHead(config)
-
-    def forward(self, sequence_output):
-        prediction_scores = self.predictions(sequence_output)
-        return prediction_scores
-
-
-class BertPreTrainedModel(PreTrainedModel):
-    """
-    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
-    models.
-    """
-
-    config_class = BertConfig
-    base_model_prefix = "bert"
-    _keys_to_ignore_on_load_missing = [r"position_ids"]
-
-    def _init_weights(self, module):
-        """ Initialize the weights """
-        if isinstance(module, (nn.Linear, nn.Embedding)):
-            # Slightly different from the TF version which uses truncated_normal for initialization
-            # cf https://github.com/pytorch/pytorch/pull/5617
-            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
-        elif isinstance(module, nn.LayerNorm):
-            module.bias.data.zero_()
-            module.weight.data.fill_(1.0)
-        if isinstance(module, nn.Linear) and module.bias is not None:
-            module.bias.data.zero_()
-
-
-class BertModel(BertPreTrainedModel):
-    """
-    The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
-    cross-attention is added between the self-attention layers, following the architecture described in `Attention is
-    all you need <https://arxiv.org/abs/1706.03762>`__ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
-    Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
-    argument and :obj:`add_cross_attention` set to :obj:`True`; an :obj:`encoder_hidden_states` is then expected as an
-    input to the forward pass.
-    """
-
-    def __init__(self, config, add_pooling_layer=True):
-        super().__init__(config)
-        self.config = config
-
-        self.embeddings = BertEmbeddings(config)
-        
-        self.encoder = BertEncoder(config)
-
-        self.pooler = BertPooler(config) if add_pooling_layer else None
-
-        self.init_weights()
- 
-
-    def get_input_embeddings(self):
-        return self.embeddings.word_embeddings
-
-    def set_input_embeddings(self, value):
-        self.embeddings.word_embeddings = value
-
-    def _prune_heads(self, heads_to_prune):
-        """
-        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
-        class PreTrainedModel
-        """
-        for layer, heads in heads_to_prune.items():
-            self.encoder.layer[layer].attention.prune_heads(heads)
-
-    
-    def get_extended_attention_mask(self, attention_mask: Tensor, input_shape: Tuple[int], device: device, is_decoder: bool) -> Tensor:
-        """
-        Makes broadcastable attention and causal masks so that future and masked tokens are ignored.
-
-        Arguments:
-            attention_mask (:obj:`torch.Tensor`):
-                Mask with ones indicating tokens to attend to, zeros for tokens to ignore.
-            input_shape (:obj:`Tuple[int]`):
-                The shape of the input to the model.
-            device: (:obj:`torch.device`):
-                The device of the input to the model.
-
-        Returns:
-            :obj:`torch.Tensor` The extended attention mask, with a the same dtype as :obj:`attention_mask.dtype`.
-        """
-        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
-        # ourselves in which case we just need to make it broadcastable to all heads.
-        if attention_mask.dim() == 3:
-            extended_attention_mask = attention_mask[:, None, :, :]
-        elif attention_mask.dim() == 2:
-            # Provided a padding mask of dimensions [batch_size, seq_length]
-            # - if the model is a decoder, apply a causal mask in addition to the padding mask
-            # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length]
-            if is_decoder:
-                batch_size, seq_length = input_shape
-
-                seq_ids = torch.arange(seq_length, device=device)
-                causal_mask = seq_ids[None, None, :].repeat(batch_size, seq_length, 1) <= seq_ids[None, :, None]
-                # in case past_key_values are used we need to add a prefix ones mask to the causal mask
-                # causal and attention masks must have same type with pytorch version < 1.3
-                causal_mask = causal_mask.to(attention_mask.dtype)
-   
-                if causal_mask.shape[1] < attention_mask.shape[1]:
-                    prefix_seq_len = attention_mask.shape[1] - causal_mask.shape[1]
-                    causal_mask = torch.cat(
-                        [
-                            torch.ones((batch_size, seq_length, prefix_seq_len), device=device, dtype=causal_mask.dtype),
-                            causal_mask,
-                        ],
-                        axis=-1,
-                    )                     
-
-                extended_attention_mask = causal_mask[:, None, :, :] * attention_mask[:, None, None, :]
-            else:
-                extended_attention_mask = attention_mask[:, None, None, :]
-        else:
-            raise ValueError(
-                "Wrong shape for input_ids (shape {}) or attention_mask (shape {})".format(
-                    input_shape, attention_mask.shape
-                )
-            )
-
-        # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
-        # masked positions, this operation will create a tensor which is 0.0 for
-        # positions we want to attend and -10000.0 for masked positions.
-        # Since we are adding it to the raw scores before the softmax, this is
-        # effectively the same as removing these entirely.
-        extended_attention_mask = extended_attention_mask.to(dtype=self.dtype)  # fp16 compatibility
-        extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
-        return extended_attention_mask
-    
-    def forward(
-        self,
-        input_ids=None,
-        attention_mask=None,
-        position_ids=None,
-        head_mask=None,
-        inputs_embeds=None,
-        encoder_embeds=None,
-        encoder_hidden_states=None,
-        encoder_attention_mask=None,
-        past_key_values=None,
-        use_cache=None,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
-        is_decoder=False,
-        mode='multimodal',
-    ):
-        r"""
-        encoder_hidden_states  (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
-            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
-            the model is configured as a decoder.
-        encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
-            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
-            the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:
-            - 1 for tokens that are **not masked**,
-            - 0 for tokens that are **masked**.
-        past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
-            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
-            If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
-            (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
-            instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
-        use_cache (:obj:`bool`, `optional`):
-            If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
-            decoding (see :obj:`past_key_values`).
-        """
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        if is_decoder:
-            use_cache = use_cache if use_cache is not None else self.config.use_cache
-        else:
-            use_cache = False
-
-        if input_ids is not None and inputs_embeds is not None:
-            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
-        elif input_ids is not None:
-            input_shape = input_ids.size()
-            batch_size, seq_length = input_shape
-            device = input_ids.device
-        elif inputs_embeds is not None:
-            input_shape = inputs_embeds.size()[:-1]
-            batch_size, seq_length = input_shape
-            device = inputs_embeds.device
-        elif encoder_embeds is not None:    
-            input_shape = encoder_embeds.size()[:-1]
-            batch_size, seq_length = input_shape 
-            device = encoder_embeds.device
-        else:
-            raise ValueError("You have to specify either input_ids or inputs_embeds or encoder_embeds")
-
-        # past_key_values_length
-        past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
-
-        if attention_mask is None:
-            attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device)
-            
-        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
-        # ourselves in which case we just need to make it broadcastable to all heads.
-        extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape, 
-                                                                                 device, is_decoder)
-
-        # If a 2D or 3D attention mask is provided for the cross-attention
-        # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
-        if encoder_hidden_states is not None:
-            if type(encoder_hidden_states) == list:
-                encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states[0].size()
-            else:
-                encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
-            encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
-            
-            if type(encoder_attention_mask) == list:
-                encoder_extended_attention_mask = [self.invert_attention_mask(mask) for mask in encoder_attention_mask]
-            elif encoder_attention_mask is None:
-                encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
-                encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
-            else:    
-                encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
-        else:
-            encoder_extended_attention_mask = None
-
-        # Prepare head mask if needed
-        # 1.0 in head_mask indicate we keep the head
-        # attention_probs has shape bsz x n_heads x N x N
-        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
-        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
-        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
-        
-        if encoder_embeds is None:
-            embedding_output = self.embeddings(
-                input_ids=input_ids,
-                position_ids=position_ids,
-                inputs_embeds=inputs_embeds,
-                past_key_values_length=past_key_values_length,
-            )
-        else:
-            embedding_output = encoder_embeds
-            
-        encoder_outputs = self.encoder(
-            embedding_output,
-            attention_mask=extended_attention_mask,
-            head_mask=head_mask,
-            encoder_hidden_states=encoder_hidden_states,
-            encoder_attention_mask=encoder_extended_attention_mask,
-            past_key_values=past_key_values,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-            mode=mode,
-        )
-        sequence_output = encoder_outputs[0]
-        pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
-
-        if not return_dict:
-            return (sequence_output, pooled_output) + encoder_outputs[1:]
-
-        return BaseModelOutputWithPoolingAndCrossAttentions(
-            last_hidden_state=sequence_output,
-            pooler_output=pooled_output,
-            past_key_values=encoder_outputs.past_key_values,
-            hidden_states=encoder_outputs.hidden_states,
-            attentions=encoder_outputs.attentions,
-            cross_attentions=encoder_outputs.cross_attentions,
-        )
-
diff --git a/repositories/BLIP/models/vit.py b/repositories/BLIP/models/vit.py
deleted file mode 100644
index cec3d8e08ed4451d65392feb2e9f4848d1ef3899..0000000000000000000000000000000000000000
--- a/repositories/BLIP/models/vit.py
+++ /dev/null
@@ -1,305 +0,0 @@
-'''
- * Copyright (c) 2022, salesforce.com, inc.
- * All rights reserved.
- * SPDX-License-Identifier: BSD-3-Clause
- * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
- * By Junnan Li
- * Based on timm code base
- * https://github.com/rwightman/pytorch-image-models/tree/master/timm
-'''
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from functools import partial
-
-from timm.models.vision_transformer import _cfg, PatchEmbed
-from timm.models.registry import register_model
-from timm.models.layers import trunc_normal_, DropPath
-from timm.models.helpers import named_apply, adapt_input_conv
-
-from fairscale.nn.checkpoint.checkpoint_activations import checkpoint_wrapper
-
-class Mlp(nn.Module):
-    """ MLP as used in Vision Transformer, MLP-Mixer and related networks
-    """
-    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
-        super().__init__()
-        out_features = out_features or in_features
-        hidden_features = hidden_features or in_features
-        self.fc1 = nn.Linear(in_features, hidden_features)
-        self.act = act_layer()
-        self.fc2 = nn.Linear(hidden_features, out_features)
-        self.drop = nn.Dropout(drop)
-
-    def forward(self, x):
-        x = self.fc1(x)
-        x = self.act(x)
-        x = self.drop(x)
-        x = self.fc2(x)
-        x = self.drop(x)
-        return x
-
-
-class Attention(nn.Module):
-    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
-        super().__init__()
-        self.num_heads = num_heads
-        head_dim = dim // num_heads
-        # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
-        self.scale = qk_scale or head_dim ** -0.5
-        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
-        self.attn_drop = nn.Dropout(attn_drop)
-        self.proj = nn.Linear(dim, dim)
-        self.proj_drop = nn.Dropout(proj_drop)
-        self.attn_gradients = None
-        self.attention_map = None
-        
-    def save_attn_gradients(self, attn_gradients):
-        self.attn_gradients = attn_gradients
-        
-    def get_attn_gradients(self):
-        return self.attn_gradients
-    
-    def save_attention_map(self, attention_map):
-        self.attention_map = attention_map
-        
-    def get_attention_map(self):
-        return self.attention_map
-    
-    def forward(self, x, register_hook=False):
-        B, N, C = x.shape
-        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
-        q, k, v = qkv[0], qkv[1], qkv[2]   # make torchscript happy (cannot use tensor as tuple)
-
-        attn = (q @ k.transpose(-2, -1)) * self.scale
-        attn = attn.softmax(dim=-1)
-        attn = self.attn_drop(attn)
-                
-        if register_hook:
-            self.save_attention_map(attn)
-            attn.register_hook(self.save_attn_gradients)        
-
-        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
-        x = self.proj(x)
-        x = self.proj_drop(x)
-        return x
-
-
-class Block(nn.Module):
-
-    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
-                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, use_grad_checkpointing=False):
-        super().__init__()
-        self.norm1 = norm_layer(dim)
-        self.attn = Attention(
-            dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
-        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
-        self.norm2 = norm_layer(dim)
-        mlp_hidden_dim = int(dim * mlp_ratio)
-        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
-
-        if use_grad_checkpointing:
-            self.attn = checkpoint_wrapper(self.attn)
-            self.mlp = checkpoint_wrapper(self.mlp)
-
-    def forward(self, x, register_hook=False):
-        x = x + self.drop_path(self.attn(self.norm1(x), register_hook=register_hook))
-        x = x + self.drop_path(self.mlp(self.norm2(x)))
-        return x
-
-    
-class VisionTransformer(nn.Module):
-    """ Vision Transformer
-    A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale`  -
-        https://arxiv.org/abs/2010.11929
-    """
-    def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12,
-                 num_heads=12, mlp_ratio=4., qkv_bias=True, qk_scale=None, representation_size=None,
-                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0., norm_layer=None, 
-                 use_grad_checkpointing=False, ckpt_layer=0):
-        """
-        Args:
-            img_size (int, tuple): input image size
-            patch_size (int, tuple): patch size
-            in_chans (int): number of input channels
-            num_classes (int): number of classes for classification head
-            embed_dim (int): embedding dimension
-            depth (int): depth of transformer
-            num_heads (int): number of attention heads
-            mlp_ratio (int): ratio of mlp hidden dim to embedding dim
-            qkv_bias (bool): enable bias for qkv if True
-            qk_scale (float): override default qk scale of head_dim ** -0.5 if set
-            representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set
-            drop_rate (float): dropout rate
-            attn_drop_rate (float): attention dropout rate
-            drop_path_rate (float): stochastic depth rate
-            norm_layer: (nn.Module): normalization layer
-        """
-        super().__init__()
-        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
-        norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
-
-        self.patch_embed = PatchEmbed(
-            img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
-
-        num_patches = self.patch_embed.num_patches
-
-        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
-        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))
-        self.pos_drop = nn.Dropout(p=drop_rate)
-
-        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
-        self.blocks = nn.ModuleList([
-            Block(
-                dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
-                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer,
-                use_grad_checkpointing=(use_grad_checkpointing and i>=depth-ckpt_layer)
-            )
-            for i in range(depth)])
-        self.norm = norm_layer(embed_dim)
-
-        trunc_normal_(self.pos_embed, std=.02)
-        trunc_normal_(self.cls_token, std=.02)
-        self.apply(self._init_weights)
-
-    def _init_weights(self, m):
-        if isinstance(m, nn.Linear):
-            trunc_normal_(m.weight, std=.02)
-            if isinstance(m, nn.Linear) and m.bias is not None:
-                nn.init.constant_(m.bias, 0)
-        elif isinstance(m, nn.LayerNorm):
-            nn.init.constant_(m.bias, 0)
-            nn.init.constant_(m.weight, 1.0)
-
-    @torch.jit.ignore
-    def no_weight_decay(self):
-        return {'pos_embed', 'cls_token'}
-
-    def forward(self, x, register_blk=-1):
-        B = x.shape[0]
-        x = self.patch_embed(x)
-
-        cls_tokens = self.cls_token.expand(B, -1, -1)  # stole cls_tokens impl from Phil Wang, thanks
-        x = torch.cat((cls_tokens, x), dim=1)
-  
-        x = x + self.pos_embed[:,:x.size(1),:]
-        x = self.pos_drop(x)
-
-        for i,blk in enumerate(self.blocks):
-            x = blk(x, register_blk==i)
-        x = self.norm(x)
-        
-        return x
-
-    @torch.jit.ignore()
-    def load_pretrained(self, checkpoint_path, prefix=''):
-        _load_weights(self, checkpoint_path, prefix)
-        
-
-@torch.no_grad()
-def _load_weights(model: VisionTransformer, checkpoint_path: str, prefix: str = ''):
-    """ Load weights from .npz checkpoints for official Google Brain Flax implementation
-    """
-    import numpy as np
-
-    def _n2p(w, t=True):
-        if w.ndim == 4 and w.shape[0] == w.shape[1] == w.shape[2] == 1:
-            w = w.flatten()
-        if t:
-            if w.ndim == 4:
-                w = w.transpose([3, 2, 0, 1])
-            elif w.ndim == 3:
-                w = w.transpose([2, 0, 1])
-            elif w.ndim == 2:
-                w = w.transpose([1, 0])
-        return torch.from_numpy(w)
-
-    w = np.load(checkpoint_path)
-    if not prefix and 'opt/target/embedding/kernel' in w:
-        prefix = 'opt/target/'
-
-    if hasattr(model.patch_embed, 'backbone'):
-        # hybrid
-        backbone = model.patch_embed.backbone
-        stem_only = not hasattr(backbone, 'stem')
-        stem = backbone if stem_only else backbone.stem
-        stem.conv.weight.copy_(adapt_input_conv(stem.conv.weight.shape[1], _n2p(w[f'{prefix}conv_root/kernel'])))
-        stem.norm.weight.copy_(_n2p(w[f'{prefix}gn_root/scale']))
-        stem.norm.bias.copy_(_n2p(w[f'{prefix}gn_root/bias']))
-        if not stem_only:
-            for i, stage in enumerate(backbone.stages):
-                for j, block in enumerate(stage.blocks):
-                    bp = f'{prefix}block{i + 1}/unit{j + 1}/'
-                    for r in range(3):
-                        getattr(block, f'conv{r + 1}').weight.copy_(_n2p(w[f'{bp}conv{r + 1}/kernel']))
-                        getattr(block, f'norm{r + 1}').weight.copy_(_n2p(w[f'{bp}gn{r + 1}/scale']))
-                        getattr(block, f'norm{r + 1}').bias.copy_(_n2p(w[f'{bp}gn{r + 1}/bias']))
-                    if block.downsample is not None:
-                        block.downsample.conv.weight.copy_(_n2p(w[f'{bp}conv_proj/kernel']))
-                        block.downsample.norm.weight.copy_(_n2p(w[f'{bp}gn_proj/scale']))
-                        block.downsample.norm.bias.copy_(_n2p(w[f'{bp}gn_proj/bias']))
-        embed_conv_w = _n2p(w[f'{prefix}embedding/kernel'])
-    else:
-        embed_conv_w = adapt_input_conv(
-            model.patch_embed.proj.weight.shape[1], _n2p(w[f'{prefix}embedding/kernel']))
-    model.patch_embed.proj.weight.copy_(embed_conv_w)
-    model.patch_embed.proj.bias.copy_(_n2p(w[f'{prefix}embedding/bias']))
-    model.cls_token.copy_(_n2p(w[f'{prefix}cls'], t=False))
-    pos_embed_w = _n2p(w[f'{prefix}Transformer/posembed_input/pos_embedding'], t=False)
-    if pos_embed_w.shape != model.pos_embed.shape:
-        pos_embed_w = resize_pos_embed(  # resize pos embedding when different size from pretrained weights
-            pos_embed_w, model.pos_embed, getattr(model, 'num_tokens', 1), model.patch_embed.grid_size)
-    model.pos_embed.copy_(pos_embed_w)
-    model.norm.weight.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/scale']))
-    model.norm.bias.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/bias']))
-#     if isinstance(model.head, nn.Linear) and model.head.bias.shape[0] == w[f'{prefix}head/bias'].shape[-1]:
-#         model.head.weight.copy_(_n2p(w[f'{prefix}head/kernel']))
-#         model.head.bias.copy_(_n2p(w[f'{prefix}head/bias']))
-#     if isinstance(getattr(model.pre_logits, 'fc', None), nn.Linear) and f'{prefix}pre_logits/bias' in w:
-#         model.pre_logits.fc.weight.copy_(_n2p(w[f'{prefix}pre_logits/kernel']))
-#         model.pre_logits.fc.bias.copy_(_n2p(w[f'{prefix}pre_logits/bias']))
-    for i, block in enumerate(model.blocks.children()):
-        block_prefix = f'{prefix}Transformer/encoderblock_{i}/'
-        mha_prefix = block_prefix + 'MultiHeadDotProductAttention_1/'
-        block.norm1.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/scale']))
-        block.norm1.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/bias']))
-        block.attn.qkv.weight.copy_(torch.cat([
-            _n2p(w[f'{mha_prefix}{n}/kernel'], t=False).flatten(1).T for n in ('query', 'key', 'value')]))
-        block.attn.qkv.bias.copy_(torch.cat([
-            _n2p(w[f'{mha_prefix}{n}/bias'], t=False).reshape(-1) for n in ('query', 'key', 'value')]))
-        block.attn.proj.weight.copy_(_n2p(w[f'{mha_prefix}out/kernel']).flatten(1))
-        block.attn.proj.bias.copy_(_n2p(w[f'{mha_prefix}out/bias']))
-        for r in range(2):
-            getattr(block.mlp, f'fc{r + 1}').weight.copy_(_n2p(w[f'{block_prefix}MlpBlock_3/Dense_{r}/kernel']))
-            getattr(block.mlp, f'fc{r + 1}').bias.copy_(_n2p(w[f'{block_prefix}MlpBlock_3/Dense_{r}/bias']))
-        block.norm2.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_2/scale']))
-        block.norm2.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_2/bias']))
-
-            
-def interpolate_pos_embed(pos_embed_checkpoint, visual_encoder):        
-    # interpolate position embedding
-    embedding_size = pos_embed_checkpoint.shape[-1]
-    num_patches = visual_encoder.patch_embed.num_patches
-    num_extra_tokens = visual_encoder.pos_embed.shape[-2] - num_patches
-    # height (== width) for the checkpoint position embedding
-    orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5)
-    # height (== width) for the new position embedding
-    new_size = int(num_patches ** 0.5)
-
-    if orig_size!=new_size:
-        # class_token and dist_token are kept unchanged
-        extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens]
-        # only the position tokens are interpolated
-        pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:]
-        pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2)
-        pos_tokens = torch.nn.functional.interpolate(
-            pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False)
-        pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)
-        new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)
-        print('reshape position embedding from %d to %d'%(orig_size ** 2,new_size ** 2))
-        
-        return new_pos_embed    
-    else:
-        return pos_embed_checkpoint
\ No newline at end of file
diff --git a/repositories/BLIP/predict.py b/repositories/BLIP/predict.py
deleted file mode 100644
index 35426cadcbb3bf8c3d8cb9c910511c154e451f4e..0000000000000000000000000000000000000000
--- a/repositories/BLIP/predict.py
+++ /dev/null
@@ -1,98 +0,0 @@
-"""
-Download the weights in ./checkpoints beforehand for fast inference
-wget https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model*_base_caption.pth
-wget https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model*_vqa.pth
-wget https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth
-"""
-
-from pathlib import Path
-
-from PIL import Image
-import torch
-from torchvision import transforms
-from torchvision.transforms.functional import InterpolationMode
-import cog
-
-from models.blip import blip_decoder
-from models.blip_vqa import blip_vqa
-from models.blip_itm import blip_itm
-
-
-class Predictor(cog.Predictor):
-    def setup(self):
-        self.device = "cuda:0"
-
-        self.models = {
-            'image_captioning': blip_decoder(pretrained='checkpoints/model*_base_caption.pth',
-                                             image_size=384, vit='base'),
-            'visual_question_answering': blip_vqa(pretrained='checkpoints/model*_vqa.pth',
-                                                  image_size=480, vit='base'),
-            'image_text_matching': blip_itm(pretrained='checkpoints/model_base_retrieval_coco.pth',
-                                            image_size=384, vit='base')
-        }
-
-    @cog.input(
-        "image",
-        type=Path,
-        help="input image",
-    )
-    @cog.input(
-        "task",
-        type=str,
-        default='image_captioning',
-        options=['image_captioning', 'visual_question_answering', 'image_text_matching'],
-        help="Choose a task.",
-    )
-    @cog.input(
-        "question",
-        type=str,
-        default=None,
-        help="Type question for the input image for visual question answering task.",
-    )
-    @cog.input(
-        "caption",
-        type=str,
-        default=None,
-        help="Type caption for the input image for image text matching task.",
-    )
-    def predict(self, image, task, question, caption):
-        if task == 'visual_question_answering':
-            assert question is not None, 'Please type a question for visual question answering task.'
-        if task == 'image_text_matching':
-            assert caption is not None, 'Please type a caption for mage text matching task.'
-
-        im = load_image(image, image_size=480 if task == 'visual_question_answering' else 384, device=self.device)
-        model = self.models[task]
-        model.eval()
-        model = model.to(self.device)
-
-        if task == 'image_captioning':
-            with torch.no_grad():
-                caption = model.generate(im, sample=False, num_beams=3, max_length=20, min_length=5)
-                return 'Caption: ' + caption[0]
-
-        if task == 'visual_question_answering':
-            with torch.no_grad():
-                answer = model(im, question, train=False, inference='generate')
-                return 'Answer: ' + answer[0]
-
-        # image_text_matching
-        itm_output = model(im, caption, match_head='itm')
-        itm_score = torch.nn.functional.softmax(itm_output, dim=1)[:, 1]
-        itc_score = model(im, caption, match_head='itc')
-        return f'The image and text is matched with a probability of {itm_score.item():.4f}.\n' \
-               f'The image feature and text feature has a cosine similarity of {itc_score.item():.4f}.'
-
-
-def load_image(image, image_size, device):
-    raw_image = Image.open(str(image)).convert('RGB')
-
-    w, h = raw_image.size
-
-    transform = transforms.Compose([
-        transforms.Resize((image_size, image_size), interpolation=InterpolationMode.BICUBIC),
-        transforms.ToTensor(),
-        transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))
-    ])
-    image = transform(raw_image).unsqueeze(0).to(device)
-    return image
diff --git a/repositories/BLIP/pretrain.py b/repositories/BLIP/pretrain.py
deleted file mode 100644
index c9490ec8eb8ff5f074b5772ada55cd27ec673a12..0000000000000000000000000000000000000000
--- a/repositories/BLIP/pretrain.py
+++ /dev/null
@@ -1,173 +0,0 @@
-'''
- * Copyright (c) 2022, salesforce.com, inc.
- * All rights reserved.
- * SPDX-License-Identifier: BSD-3-Clause
- * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
- * By Junnan Li
-'''
-import argparse
-import os
-import ruamel_yaml as yaml
-import numpy as np
-import random
-import time
-import datetime
-import json
-from pathlib import Path
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.backends.cudnn as cudnn
-import torch.distributed as dist
-from torch.utils.data import DataLoader
-
-from models.blip_pretrain import blip_pretrain
-import utils
-from utils import warmup_lr_schedule, step_lr_schedule
-from data import create_dataset, create_sampler, create_loader
-
-def train(model, data_loader, optimizer, epoch, device, config):
-    # train
-    model.train()  
-    
-    metric_logger = utils.MetricLogger(delimiter="  ")
-    metric_logger.add_meter('lr', utils.SmoothedValue(window_size=50, fmt='{value:.6f}'))
-    metric_logger.add_meter('loss_ita', utils.SmoothedValue(window_size=50, fmt='{value:.4f}'))
-    metric_logger.add_meter('loss_itm', utils.SmoothedValue(window_size=50, fmt='{value:.4f}'))    
-    metric_logger.add_meter('loss_lm', utils.SmoothedValue(window_size=50, fmt='{value:.4f}'))
-    
-    header = 'Train Epoch: [{}]'.format(epoch)
-    print_freq = 50   
-
-    if config['laion_path']:
-        data_loader.dataset.reload_laion(epoch)
-    
-    data_loader.sampler.set_epoch(epoch)
-
-    for i, (image, caption) in enumerate(metric_logger.log_every(data_loader, print_freq, header)):
-        
-        if epoch==0:
-            warmup_lr_schedule(optimizer, i, config['warmup_steps'], config['warmup_lr'], config['init_lr'])
-            
-        optimizer.zero_grad()
-        
-        image = image.to(device,non_blocking=True)
-        
-        # ramp up alpha in the first 2 epochs
-        alpha = config['alpha']*min(1,(epoch*len(data_loader)+i)/(2*len(data_loader))) 
-
-        loss_ita, loss_itm, loss_lm = model(image, caption, alpha = alpha)  
-        loss = loss_ita + loss_itm + loss_lm  
-
-        loss.backward()
-        optimizer.step()    
-
-        metric_logger.update(loss_ita=loss_ita.item())
-        metric_logger.update(loss_itm=loss_itm.item())
-        metric_logger.update(loss_lm=loss_lm.item())
-        metric_logger.update(lr=optimizer.param_groups[0]["lr"])  
-
-        
-    # gather the stats from all processes
-    metric_logger.synchronize_between_processes()
-    print("Averaged stats:", metric_logger.global_avg())     
-    return {k: "{:.3f}".format(meter.global_avg) for k, meter in metric_logger.meters.items()}  
-
-
-def main(args, config):
-    utils.init_distributed_mode(args)    
-    
-    device = torch.device(args.device)
-
-    # fix the seed for reproducibility
-    seed = args.seed + utils.get_rank()
-    torch.manual_seed(seed)
-    np.random.seed(seed)
-    random.seed(seed)
-    cudnn.benchmark = True
-
-    #### Dataset #### 
-    print("Creating dataset")
-    datasets = [create_dataset('pretrain', config, min_scale=0.2)]
-    print('number of training samples: %d'%len(datasets[0]))
-
-    num_tasks = utils.get_world_size()
-    global_rank = utils.get_rank()            
-    samplers = create_sampler(datasets, [True], num_tasks, global_rank)         
-
-    data_loader = create_loader(datasets,samplers,batch_size=[config['batch_size']], num_workers=[4], is_trains=[True], collate_fns=[None])[0]      
-
-    #### Model #### 
-    print("Creating model")
-    model = blip_pretrain(image_size=config['image_size'], vit=config['vit'], vit_grad_ckpt=config['vit_grad_ckpt'], 
-                            vit_ckpt_layer=config['vit_ckpt_layer'], queue_size=config['queue_size'])
-
-    model = model.to(device)   
-
-    optimizer = torch.optim.AdamW(params=model.parameters(), lr=config['init_lr'], weight_decay=config['weight_decay'])
-    
-    start_epoch = 0
-    if args.checkpoint:    
-        checkpoint = torch.load(args.checkpoint, map_location='cpu') 
-        state_dict = checkpoint['model']    
-        model.load_state_dict(state_dict)    
-        
-        optimizer.load_state_dict(checkpoint['optimizer'])
-        start_epoch = checkpoint['epoch']+1                
-        print('resume checkpoint from %s'%args.checkpoint)    
-    
-    model_without_ddp = model
-    if args.distributed:
-        model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
-        model_without_ddp = model.module    
-        
-    print("Start training")
-    start_time = time.time()    
-    for epoch in range(start_epoch, config['max_epoch']):
-        
-        step_lr_schedule(optimizer, epoch, config['init_lr'], config['min_lr'], config['lr_decay_rate'])
-                
-        train_stats = train(model, data_loader, optimizer, epoch, device, config) 
-        if utils.is_main_process():  
-            log_stats = {**{f'train_{k}': v for k, v in train_stats.items()},
-                         'epoch': epoch,
-                        }                     
-            save_obj = {
-                'model': model_without_ddp.state_dict(),
-                'optimizer': optimizer.state_dict(),
-                'config': config,
-                'epoch': epoch,
-            }
-            torch.save(save_obj, os.path.join(args.output_dir, 'checkpoint_%02d.pth'%epoch))  
-            
-            with open(os.path.join(args.output_dir, "log.txt"),"a") as f:
-                f.write(json.dumps(log_stats) + "\n")
-
-        dist.barrier()        
-                
-    total_time = time.time() - start_time
-    total_time_str = str(datetime.timedelta(seconds=int(total_time)))
-    print('Training time {}'.format(total_time_str)) 
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--config', default='./configs/pretrain.yaml')
-    parser.add_argument('--output_dir', default='output/Pretrain')  
-    parser.add_argument('--checkpoint', default='')    
-    parser.add_argument('--evaluate', action='store_true')    
-    parser.add_argument('--device', default='cuda')
-    parser.add_argument('--seed', default=42, type=int)
-    parser.add_argument('--world_size', default=1, type=int, help='number of distributed processes')    
-    parser.add_argument('--dist_url', default='env://', help='url used to set up distributed training')
-    parser.add_argument('--distributed', default=True, type=bool)
-    args = parser.parse_args()
-
-    config = yaml.load(open(args.config, 'r'), Loader=yaml.Loader)
-
-    Path(args.output_dir).mkdir(parents=True, exist_ok=True)
-        
-    yaml.dump(config, open(os.path.join(args.output_dir, 'config.yaml'), 'w'))    
-    
-    main(args, config)
\ No newline at end of file
diff --git a/repositories/BLIP/requirements.txt b/repositories/BLIP/requirements.txt
deleted file mode 100644
index d897bc6a08712f4beb2f78ca2592dcbe06a3e2db..0000000000000000000000000000000000000000
--- a/repositories/BLIP/requirements.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-timm==0.4.12
-transformers==4.15.0
-fairscale==0.4.4
-pycocoevalcap
diff --git a/repositories/BLIP/train_caption.py b/repositories/BLIP/train_caption.py
deleted file mode 100644
index 7c639ac646b9a1b8074b6e9c2343b961de76db05..0000000000000000000000000000000000000000
--- a/repositories/BLIP/train_caption.py
+++ /dev/null
@@ -1,206 +0,0 @@
-'''
- * Copyright (c) 2022, salesforce.com, inc.
- * All rights reserved.
- * SPDX-License-Identifier: BSD-3-Clause
- * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
- * By Junnan Li
-'''
-import argparse
-import os
-import ruamel_yaml as yaml
-import numpy as np
-import random
-import time
-import datetime
-import json
-from pathlib import Path
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.backends.cudnn as cudnn
-import torch.distributed as dist
-from torch.utils.data import DataLoader
-
-from models.blip import blip_decoder
-import utils
-from utils import cosine_lr_schedule
-from data import create_dataset, create_sampler, create_loader
-from data.utils import save_result, coco_caption_eval
-
-def train(model, data_loader, optimizer, epoch, device):
-    # train
-    model.train()  
-    
-    metric_logger = utils.MetricLogger(delimiter="  ")
-    metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
-    metric_logger.add_meter('loss', utils.SmoothedValue(window_size=1, fmt='{value:.4f}'))
-    header = 'Train Caption Epoch: [{}]'.format(epoch)
-    print_freq = 50
-
-    for i, (image, caption, _) in enumerate(metric_logger.log_every(data_loader, print_freq, header)):
-        image = image.to(device)       
-        
-        loss = model(image, caption)      
-        
-        optimizer.zero_grad()
-        loss.backward()
-        optimizer.step()    
-        
-        metric_logger.update(loss=loss.item())
-        metric_logger.update(lr=optimizer.param_groups[0]["lr"])
-
-    # gather the stats from all processes
-    metric_logger.synchronize_between_processes()
-    print("Averaged stats:", metric_logger.global_avg())     
-    return {k: "{:.3f}".format(meter.global_avg) for k, meter in metric_logger.meters.items()}  
-
-
-@torch.no_grad()
-def evaluate(model, data_loader, device, config):
-    # evaluate
-    model.eval() 
-    
-    metric_logger = utils.MetricLogger(delimiter="  ")
-    header = 'Caption generation:'
-    print_freq = 10
-
-    result = []
-    for image, image_id in metric_logger.log_every(data_loader, print_freq, header): 
-        
-        image = image.to(device)       
-        
-        captions = model.generate(image, sample=False, num_beams=config['num_beams'], max_length=config['max_length'], 
-                                  min_length=config['min_length'])
-        
-        for caption, img_id in zip(captions, image_id):
-            result.append({"image_id": img_id.item(), "caption": caption})
-  
-    return result
-
-
-def main(args, config):
-    utils.init_distributed_mode(args)    
-    
-    device = torch.device(args.device)
-
-    # fix the seed for reproducibility
-    seed = args.seed + utils.get_rank()
-    torch.manual_seed(seed)
-    np.random.seed(seed)
-    random.seed(seed)
-    cudnn.benchmark = True
-
-    #### Dataset #### 
-    print("Creating captioning dataset")
-    train_dataset, val_dataset, test_dataset = create_dataset('caption_coco', config)  
-
-    if args.distributed:
-        num_tasks = utils.get_world_size()
-        global_rank = utils.get_rank()            
-        samplers = create_sampler([train_dataset,val_dataset,test_dataset], [True,False,False], num_tasks, global_rank)         
-    else:
-        samplers = [None, None, None]
-    
-    train_loader, val_loader, test_loader = create_loader([train_dataset, val_dataset, test_dataset],samplers,
-                                                          batch_size=[config['batch_size']]*3,num_workers=[4,4,4],
-                                                          is_trains=[True, False, False], collate_fns=[None,None,None])         
-
-    #### Model #### 
-    print("Creating model")
-    model = blip_decoder(pretrained=config['pretrained'], image_size=config['image_size'], vit=config['vit'], 
-                           vit_grad_ckpt=config['vit_grad_ckpt'], vit_ckpt_layer=config['vit_ckpt_layer'], 
-                           prompt=config['prompt'])
-
-    model = model.to(device)   
-    
-    model_without_ddp = model
-    if args.distributed:
-        model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
-        model_without_ddp = model.module    
-    
-    optimizer = torch.optim.AdamW(params=model.parameters(), lr=config['init_lr'], weight_decay=config['weight_decay'])
-            
-    best = 0
-    best_epoch = 0
-
-    print("Start training")
-    start_time = time.time()    
-    for epoch in range(0, config['max_epoch']):
-        if not args.evaluate:        
-            if args.distributed:
-                train_loader.sampler.set_epoch(epoch)
-                
-            cosine_lr_schedule(optimizer, epoch, config['max_epoch'], config['init_lr'], config['min_lr'])
-                
-            train_stats = train(model, train_loader, optimizer, epoch, device) 
-        
-        val_result = evaluate(model_without_ddp, val_loader, device, config)  
-        val_result_file = save_result(val_result, args.result_dir, 'val_epoch%d'%epoch, remove_duplicate='image_id')        
-  
-        test_result = evaluate(model_without_ddp, test_loader, device, config)  
-        test_result_file = save_result(test_result, args.result_dir, 'test_epoch%d'%epoch, remove_duplicate='image_id')  
-
-        if utils.is_main_process():   
-            coco_val = coco_caption_eval(config['coco_gt_root'],val_result_file,'val')
-            coco_test = coco_caption_eval(config['coco_gt_root'],test_result_file,'test')
-            
-            if args.evaluate:            
-                log_stats = {**{f'val_{k}': v for k, v in coco_val.eval.items()},
-                             **{f'test_{k}': v for k, v in coco_test.eval.items()},                       
-                            }
-                with open(os.path.join(args.output_dir, "evaluate.txt"),"a") as f:
-                    f.write(json.dumps(log_stats) + "\n")                   
-            else:             
-                save_obj = {
-                    'model': model_without_ddp.state_dict(),
-                    'optimizer': optimizer.state_dict(),
-                    'config': config,
-                    'epoch': epoch,
-                }
-
-                if coco_val.eval['CIDEr'] + coco_val.eval['Bleu_4'] > best:
-                    best = coco_val.eval['CIDEr'] + coco_val.eval['Bleu_4']
-                    best_epoch = epoch                
-                    torch.save(save_obj, os.path.join(args.output_dir, 'checkpoint_best.pth')) 
-                    
-                log_stats = {**{f'train_{k}': v for k, v in train_stats.items()},
-                             **{f'val_{k}': v for k, v in coco_val.eval.items()},
-                             **{f'test_{k}': v for k, v in coco_test.eval.items()},                       
-                             'epoch': epoch,
-                             'best_epoch': best_epoch,
-                            }
-                with open(os.path.join(args.output_dir, "log.txt"),"a") as f:
-                    f.write(json.dumps(log_stats) + "\n")     
-                    
-        if args.evaluate: 
-            break
-        dist.barrier()     
-
-    total_time = time.time() - start_time
-    total_time_str = str(datetime.timedelta(seconds=int(total_time)))
-    print('Training time {}'.format(total_time_str)) 
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--config', default='./configs/caption_coco.yaml')
-    parser.add_argument('--output_dir', default='output/Caption_coco')        
-    parser.add_argument('--evaluate', action='store_true')    
-    parser.add_argument('--device', default='cuda')
-    parser.add_argument('--seed', default=42, type=int)
-    parser.add_argument('--world_size', default=1, type=int, help='number of distributed processes')    
-    parser.add_argument('--dist_url', default='env://', help='url used to set up distributed training')
-    parser.add_argument('--distributed', default=True, type=bool)
-    args = parser.parse_args()
-
-    config = yaml.load(open(args.config, 'r'), Loader=yaml.Loader)
-
-    args.result_dir = os.path.join(args.output_dir, 'result')
-
-    Path(args.output_dir).mkdir(parents=True, exist_ok=True)
-    Path(args.result_dir).mkdir(parents=True, exist_ok=True)
-        
-    yaml.dump(config, open(os.path.join(args.output_dir, 'config.yaml'), 'w'))    
-    
-    main(args, config)
\ No newline at end of file
diff --git a/repositories/BLIP/train_nlvr.py b/repositories/BLIP/train_nlvr.py
deleted file mode 100644
index 84b247bda2334c1fd894b6c11d33ef48c8e7df28..0000000000000000000000000000000000000000
--- a/repositories/BLIP/train_nlvr.py
+++ /dev/null
@@ -1,213 +0,0 @@
-'''
- * Copyright (c) 2022, salesforce.com, inc.
- * All rights reserved.
- * SPDX-License-Identifier: BSD-3-Clause
- * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
- * By Junnan Li
-'''
-import argparse
-import os
-import ruamel_yaml as yaml
-import numpy as np
-import random
-import time
-import datetime
-import json
-from pathlib import Path
-import json
-import pickle
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch.utils.data import DataLoader
-import torch.backends.cudnn as cudnn
-import torch.distributed as dist
-
-from models.blip_nlvr import blip_nlvr
-
-import utils
-from utils import cosine_lr_schedule, warmup_lr_schedule
-from data import create_dataset, create_sampler, create_loader
-
-def train(model, data_loader, optimizer, epoch, device, config):
-    # train
-    model.train()  
-    
-    metric_logger = utils.MetricLogger(delimiter="  ")
-    metric_logger.add_meter('lr', utils.SmoothedValue(window_size=50, fmt='{value:.6f}'))
-    metric_logger.add_meter('loss', utils.SmoothedValue(window_size=50, fmt='{value:.4f}'))
-
-    header = 'Train Epoch: [{}]'.format(epoch)
-    print_freq = 50   
-    step_size = 10
- 
-    for i,(image0, image1, text, targets) in enumerate(metric_logger.log_every(data_loader, print_freq, header)):
-  
-        images = torch.cat([image0, image1], dim=0)
-        images, targets = images.to(device), targets.to(device)   
-
-        loss = model(images, text, targets=targets, train=True)    
-        
-        optimizer.zero_grad()
-        loss.backward()
-        optimizer.step()    
-               
-        metric_logger.update(lr=optimizer.param_groups[0]["lr"])
-        metric_logger.update(loss=loss.item())  
-        
-    # gather the stats from all processes
-    metric_logger.synchronize_between_processes()
-    print("Averaged stats:", metric_logger.global_avg())     
-    return {k: "{:.4f}".format(meter.global_avg) for k, meter in metric_logger.meters.items()}    
-
-
-@torch.no_grad()
-def evaluate(model, data_loader, device, config):
-    # test
-    model.eval()
-            
-    metric_logger = utils.MetricLogger(delimiter="  ")
-
-    header = 'Evaluation:'
-    print_freq = 50
-
-    for image0, image1, text, targets in metric_logger.log_every(data_loader, print_freq, header):
-        images = torch.cat([image0, image1], dim=0)
-        images, targets = images.to(device), targets.to(device)   
-        
-        prediction = model(images, text, targets=targets, train=False)  
- 
-        _, pred_class = prediction.max(1)
-        accuracy = (targets==pred_class).sum() / targets.size(0)
-        
-        metric_logger.meters['acc'].update(accuracy.item(), n=image0.size(0))
-
-    # gather the stats from all processes
-    metric_logger.synchronize_between_processes()
-
-    print("Averaged stats:", metric_logger.global_avg())   
-    return {k: "{:.4f}".format(meter.global_avg) for k, meter in metric_logger.meters.items()}
-
-
-        
-def main(args, config):
-    utils.init_distributed_mode(args)    
-    
-    device = torch.device(args.device)
-
-    # fix the seed for reproducibility
-    seed = args.seed + utils.get_rank()
-    torch.manual_seed(seed)
-    np.random.seed(seed)
-    random.seed(seed)
-    cudnn.benchmark = True
-
-    #### Dataset #### 
-    print("Creating dataset")
-    datasets = create_dataset('nlvr', config) 
-    
-    if args.distributed:
-        num_tasks = utils.get_world_size()
-        global_rank = utils.get_rank()            
-        samplers = create_sampler(datasets, [True,False,False], num_tasks, global_rank)
-    else:
-        samplers = [None, None, None]
-    
-    batch_size=[config['batch_size_train'],config['batch_size_test'],config['batch_size_test']]
-    train_loader, val_loader, test_loader = create_loader(datasets,samplers,batch_size=batch_size,
-                                                          num_workers=[4,4,4],is_trains=[True,False,False], 
-                                                          collate_fns=[None,None,None])
-
-    #### Model #### 
-    print("Creating model")
-    model = blip_nlvr(pretrained=config['pretrained'], image_size=config['image_size'], 
-                         vit=config['vit'], vit_grad_ckpt=config['vit_grad_ckpt'], vit_ckpt_layer=config['vit_ckpt_layer'])
-
-    model = model.to(device)   
-    
-    model_without_ddp = model
-    if args.distributed:
-        model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
-        model_without_ddp = model.module    
-            
-    optimizer = torch.optim.AdamW(params=model.parameters(), lr=config['init_lr'], weight_decay=config['weight_decay'])
-
-    print("Start training")
-    start_time = time.time()
-    best = 0
-    best_epoch = 0
-
-    for epoch in range(0, config['max_epoch']):
-        if not args.evaluate:
-            if args.distributed:
-                train_loader.sampler.set_epoch(epoch)
-                
-            cosine_lr_schedule(optimizer, epoch, config['max_epoch'], config['init_lr'], config['min_lr'])
-            
-            train_stats = train(model, train_loader, optimizer, epoch,  device, config) 
-            
-        val_stats = evaluate(model, val_loader, device, config)
-        test_stats = evaluate(model, test_loader, device, config)  
-        
-        if utils.is_main_process():  
-            if args.evaluate:                
-                log_stats = {**{f'val_{k}': v for k, v in val_stats.items()},
-                             **{f'test_{k}': v for k, v in test_stats.items()},
-                            }
-                with open(os.path.join(args.output_dir, "log.txt"),"a") as f:
-                    f.write(json.dumps(log_stats) + "\n")   
-                
-            else:       
-                log_stats = {**{f'train_{k}': v for k, v in train_stats.items()},
-                             **{f'val_{k}': v for k, v in val_stats.items()},
-                             **{f'test_{k}': v for k, v in test_stats.items()},
-                             'epoch': epoch,
-                            }
-
-                if float(val_stats['acc'])>best:
-                    save_obj = {
-                        'model': model_without_ddp.state_dict(),
-                        'optimizer': optimizer.state_dict(),
-                        'config': config,
-                        'epoch': epoch,
-                    }
-                    torch.save(save_obj, os.path.join(args.output_dir, 'checkpoint_best.pth')) 
-                    best = float(val_stats['acc'])
-                    best_epoch = epoch
-
-                with open(os.path.join(args.output_dir, "log.txt"),"a") as f:
-                    f.write(json.dumps(log_stats) + "\n")
-        if args.evaluate:             
-            break            
-         
-        dist.barrier()   
-    
-    if utils.is_main_process():   
-        with open(os.path.join(args.output_dir, "log.txt"),"a") as f:
-            f.write("best epoch: %d"%best_epoch)      
-            
-    total_time = time.time() - start_time
-    total_time_str = str(datetime.timedelta(seconds=int(total_time)))
-    print('Training time {}'.format(total_time_str)) 
-        
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--config', default='./configs/nlvr.yaml')
-    parser.add_argument('--output_dir', default='output/NLVR')
-    parser.add_argument('--evaluate', action='store_true')      
-    parser.add_argument('--device', default='cuda')
-    parser.add_argument('--seed', default=42, type=int)
-    parser.add_argument('--world_size', default=1, type=int, help='number of distributed processes')    
-    parser.add_argument('--dist_url', default='env://', help='url used to set up distributed training')
-    parser.add_argument('--distributed', default=True, type=bool)
-    args = parser.parse_args()
-
-    config = yaml.load(open(args.config, 'r'), Loader=yaml.Loader)
-
-    Path(args.output_dir).mkdir(parents=True, exist_ok=True)
-        
-    yaml.dump(config, open(os.path.join(args.output_dir, 'config.yaml'), 'w'))    
-    
-    main(args, config)
\ No newline at end of file
diff --git a/repositories/BLIP/train_retrieval.py b/repositories/BLIP/train_retrieval.py
deleted file mode 100644
index 574f03382cc8197b97971a11ae54b632bcfe6655..0000000000000000000000000000000000000000
--- a/repositories/BLIP/train_retrieval.py
+++ /dev/null
@@ -1,345 +0,0 @@
-'''
- * Copyright (c) 2022, salesforce.com, inc.
- * All rights reserved.
- * SPDX-License-Identifier: BSD-3-Clause
- * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
- * By Junnan Li
-'''
-import argparse
-import os
-import ruamel_yaml as yaml
-import numpy as np
-import random
-import time
-import datetime
-import json
-from pathlib import Path
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.backends.cudnn as cudnn
-import torch.distributed as dist
-from torch.utils.data import DataLoader
-
-from models.blip_retrieval import blip_retrieval
-import utils
-from utils import cosine_lr_schedule
-from data import create_dataset, create_sampler, create_loader
-
-
-def train(model, data_loader, optimizer, epoch, device, config):
-    # train
-    model.train()  
-    
-    metric_logger = utils.MetricLogger(delimiter="  ")
-    metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
-    metric_logger.add_meter('loss_itm', utils.SmoothedValue(window_size=1, fmt='{value:.4f}'))
-    metric_logger.add_meter('loss_ita', utils.SmoothedValue(window_size=1, fmt='{value:.4f}'))
-    header = 'Train Epoch: [{}]'.format(epoch)
-    print_freq = 50
-
-    for i,(image, caption, idx) in enumerate(metric_logger.log_every(data_loader, print_freq, header)):
-        image = image.to(device,non_blocking=True)   
-        idx = idx.to(device,non_blocking=True)   
-       
-        if epoch>0:
-            alpha = config['alpha']
-        else:
-            alpha = config['alpha']*min(1,i/len(data_loader))
-
-        loss_ita, loss_itm = model(image, caption, alpha=alpha, idx=idx)                  
-        loss = loss_ita + loss_itm
-        
-        optimizer.zero_grad()
-        loss.backward()
-        optimizer.step()    
-        
-        metric_logger.update(loss_itm=loss_itm.item())
-        metric_logger.update(loss_ita=loss_ita.item())
-        metric_logger.update(lr=optimizer.param_groups[0]["lr"])
-
-    # gather the stats from all processes
-    metric_logger.synchronize_between_processes()
-    print("Averaged stats:", metric_logger.global_avg())     
-    return {k: "{:.3f}".format(meter.global_avg) for k, meter in metric_logger.meters.items()}  
-
-
-@torch.no_grad()
-def evaluation(model, data_loader, device, config):
-    # test
-    model.eval() 
-    
-    metric_logger = utils.MetricLogger(delimiter="  ")
-    header = 'Evaluation:'    
-    
-    print('Computing features for evaluation...')
-    start_time = time.time()  
-
-    texts = data_loader.dataset.text   
-    num_text = len(texts)
-    text_bs = 256
-    text_ids = []
-    text_embeds = []  
-    text_atts = []
-    for i in range(0, num_text, text_bs):
-        text = texts[i: min(num_text, i+text_bs)]
-        text_input = model.tokenizer(text, padding='max_length', truncation=True, max_length=35, return_tensors="pt").to(device) 
-        text_output = model.text_encoder(text_input.input_ids, attention_mask = text_input.attention_mask, mode='text')  
-        text_embed = F.normalize(model.text_proj(text_output.last_hidden_state[:,0,:]))
-        text_embeds.append(text_embed)   
-        text_ids.append(text_input.input_ids)
-        text_atts.append(text_input.attention_mask)
-    
-    text_embeds = torch.cat(text_embeds,dim=0)
-    text_ids = torch.cat(text_ids,dim=0)
-    text_atts = torch.cat(text_atts,dim=0)
-    text_ids[:,0] = model.tokenizer.enc_token_id
-    
-    image_feats = []
-    image_embeds = []
-    for image, img_id in data_loader: 
-        image = image.to(device) 
-        image_feat = model.visual_encoder(image)   
-        image_embed = model.vision_proj(image_feat[:,0,:])            
-        image_embed = F.normalize(image_embed,dim=-1)      
-        
-        image_feats.append(image_feat.cpu())
-        image_embeds.append(image_embed)
-     
-    image_feats = torch.cat(image_feats,dim=0)
-    image_embeds = torch.cat(image_embeds,dim=0)
-    
-    sims_matrix = image_embeds @ text_embeds.t()
-    score_matrix_i2t = torch.full((len(data_loader.dataset.image),len(texts)),-100.0).to(device)
-    
-    num_tasks = utils.get_world_size()
-    rank = utils.get_rank() 
-    step = sims_matrix.size(0)//num_tasks + 1
-    start = rank*step
-    end = min(sims_matrix.size(0),start+step)
-
-    for i,sims in enumerate(metric_logger.log_every(sims_matrix[start:end], 50, header)): 
-        topk_sim, topk_idx = sims.topk(k=config['k_test'], dim=0)
-
-        encoder_output = image_feats[start+i].repeat(config['k_test'],1,1).to(device)
-        encoder_att = torch.ones(encoder_output.size()[:-1],dtype=torch.long).to(device)
-        output = model.text_encoder(text_ids[topk_idx], 
-                                    attention_mask = text_atts[topk_idx],
-                                    encoder_hidden_states = encoder_output,
-                                    encoder_attention_mask = encoder_att,                             
-                                    return_dict = True,
-                                   )
-        score = model.itm_head(output.last_hidden_state[:,0,:])[:,1]
-        score_matrix_i2t[start+i,topk_idx] = score + topk_sim
-        
-    sims_matrix = sims_matrix.t()
-    score_matrix_t2i = torch.full((len(texts),len(data_loader.dataset.image)),-100.0).to(device)
-    
-    step = sims_matrix.size(0)//num_tasks + 1
-    start = rank*step
-    end = min(sims_matrix.size(0),start+step)    
-    
-    for i,sims in enumerate(metric_logger.log_every(sims_matrix[start:end], 50, header)): 
-        
-        topk_sim, topk_idx = sims.topk(k=config['k_test'], dim=0)
-        encoder_output = image_feats[topk_idx].to(device)
-        encoder_att = torch.ones(encoder_output.size()[:-1],dtype=torch.long).to(device)
-        output = model.text_encoder(text_ids[start+i].repeat(config['k_test'],1), 
-                                    attention_mask = text_atts[start+i].repeat(config['k_test'],1),
-                                    encoder_hidden_states = encoder_output,
-                                    encoder_attention_mask = encoder_att,                             
-                                    return_dict = True,
-                                   )
-        score = model.itm_head(output.last_hidden_state[:,0,:])[:,1]
-        score_matrix_t2i[start+i,topk_idx] = score + topk_sim
-
-    if args.distributed:
-        dist.barrier()   
-        torch.distributed.all_reduce(score_matrix_i2t, op=torch.distributed.ReduceOp.SUM) 
-        torch.distributed.all_reduce(score_matrix_t2i, op=torch.distributed.ReduceOp.SUM)        
-        
-    total_time = time.time() - start_time
-    total_time_str = str(datetime.timedelta(seconds=int(total_time)))
-    print('Evaluation time {}'.format(total_time_str)) 
-
-    return score_matrix_i2t.cpu().numpy(), score_matrix_t2i.cpu().numpy()
-
-
-            
-@torch.no_grad()
-def itm_eval(scores_i2t, scores_t2i, txt2img, img2txt):
-    
-    #Images->Text 
-    ranks = np.zeros(scores_i2t.shape[0])
-    for index,score in enumerate(scores_i2t):
-        inds = np.argsort(score)[::-1]
-        # Score
-        rank = 1e20
-        for i in img2txt[index]:
-            tmp = np.where(inds == i)[0][0]
-            if tmp < rank:
-                rank = tmp
-        ranks[index] = rank
-
-    # Compute metrics
-    tr1 = 100.0 * len(np.where(ranks < 1)[0]) / len(ranks)
-    tr5 = 100.0 * len(np.where(ranks < 5)[0]) / len(ranks)
-    tr10 = 100.0 * len(np.where(ranks < 10)[0]) / len(ranks)
-  
-    #Text->Images 
-    ranks = np.zeros(scores_t2i.shape[0])
-    
-    for index,score in enumerate(scores_t2i):
-        inds = np.argsort(score)[::-1]
-        ranks[index] = np.where(inds == txt2img[index])[0][0]
-
-    # Compute metrics
-    ir1 = 100.0 * len(np.where(ranks < 1)[0]) / len(ranks)
-    ir5 = 100.0 * len(np.where(ranks < 5)[0]) / len(ranks)
-    ir10 = 100.0 * len(np.where(ranks < 10)[0]) / len(ranks)        
-
-    tr_mean = (tr1 + tr5 + tr10) / 3
-    ir_mean = (ir1 + ir5 + ir10) / 3
-    r_mean = (tr_mean + ir_mean) / 2
-
-    eval_result =  {'txt_r1': tr1,
-                    'txt_r5': tr5,
-                    'txt_r10': tr10,
-                    'txt_r_mean': tr_mean,
-                    'img_r1': ir1,
-                    'img_r5': ir5,
-                    'img_r10': ir10,
-                    'img_r_mean': ir_mean,
-                    'r_mean': r_mean}
-    return eval_result
-
-
-def main(args, config):
-    utils.init_distributed_mode(args)    
-    
-    device = torch.device(args.device)
-
-    # fix the seed for reproducibility
-    seed = args.seed + utils.get_rank()
-    torch.manual_seed(seed)
-    np.random.seed(seed)
-    random.seed(seed)
-    cudnn.benchmark = True
-
-    #### Dataset #### 
-    print("Creating retrieval dataset")
-    train_dataset, val_dataset, test_dataset = create_dataset('retrieval_%s'%config['dataset'], config)  
-
-    if args.distributed:
-        num_tasks = utils.get_world_size()
-        global_rank = utils.get_rank()            
-        samplers = create_sampler([train_dataset], [True], num_tasks, global_rank) + [None, None]
-    else:
-        samplers = [None, None, None]
-    
-    train_loader, val_loader, test_loader = create_loader([train_dataset, val_dataset, test_dataset],samplers,
-                                                          batch_size=[config['batch_size_train']]+[config['batch_size_test']]*2,
-                                                          num_workers=[4,4,4],
-                                                          is_trains=[True, False, False], 
-                                                          collate_fns=[None,None,None])   
-   
-
-    #### Model #### 
-    print("Creating model")
-    model = blip_retrieval(pretrained=config['pretrained'], image_size=config['image_size'], vit=config['vit'], 
-                             vit_grad_ckpt=config['vit_grad_ckpt'], vit_ckpt_layer=config['vit_ckpt_layer'], 
-                             queue_size=config['queue_size'], negative_all_rank=config['negative_all_rank'])
-
-    model = model.to(device)   
-    
-    model_without_ddp = model
-    if args.distributed:
-        model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
-        model_without_ddp = model.module   
-
-    optimizer = torch.optim.AdamW(params=model.parameters(), lr=config['init_lr'], weight_decay=config['weight_decay']) 
-    
-    best = 0
-    best_epoch = 0
-
-    print("Start training")
-    start_time = time.time()    
-
-    for epoch in range(0, config['max_epoch']):    
-        if not args.evaluate:        
-            if args.distributed:
-                train_loader.sampler.set_epoch(epoch)
-                
-            cosine_lr_schedule(optimizer, epoch, config['max_epoch'], config['init_lr'], config['min_lr'])
-            
-            train_stats = train(model, train_loader, optimizer, epoch, device, config)  
-            
-        score_val_i2t, score_val_t2i, = evaluation(model_without_ddp, val_loader, device, config)
-        score_test_i2t, score_test_t2i = evaluation(model_without_ddp, test_loader, device, config)
-    
-        if utils.is_main_process():  
-      
-            val_result = itm_eval(score_val_i2t, score_val_t2i, val_loader.dataset.txt2img, val_loader.dataset.img2txt)  
-            print(val_result)
-                                
-            if val_result['r_mean']>best:
-                save_obj = {
-                    'model': model_without_ddp.state_dict(),
-                    'optimizer': optimizer.state_dict(),
-                    'config': config,
-                    'epoch': epoch,
-                }
-                torch.save(save_obj, os.path.join(args.output_dir, 'checkpoint_best.pth'))  
-                best = val_result['r_mean']        
-                best_epoch = epoch  
-                
-                test_result = itm_eval(score_test_i2t, score_test_t2i, test_loader.dataset.txt2img, test_loader.dataset.img2txt) 
-                print(test_result)
-            
-            if args.evaluate:                
-                log_stats = {**{f'val_{k}': v for k, v in val_result.items()},
-                             **{f'test_{k}': v for k, v in test_result.items()},                  
-                            }
-                with open(os.path.join(args.output_dir, "evaluate.txt"),"a") as f:
-                    f.write(json.dumps(log_stats) + "\n")     
-            else:
-                log_stats = {**{f'train_{k}': v for k, v in train_stats.items()},
-                             **{f'val_{k}': v for k, v in val_result.items()},
-                             **{f'test_{k}': v for k, v in test_result.items()},  
-                             'epoch': epoch,
-                             'best_epoch': best_epoch,
-                            }
-                with open(os.path.join(args.output_dir, "log.txt"),"a") as f:
-                    f.write(json.dumps(log_stats) + "\n")   
-                    
-        if args.evaluate: 
-            break
-
-        dist.barrier()     
-        torch.cuda.empty_cache()
-
-    total_time = time.time() - start_time
-    total_time_str = str(datetime.timedelta(seconds=int(total_time)))
-    print('Training time {}'.format(total_time_str)) 
-
-    
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()     
-    parser.add_argument('--config', default='./configs/retrieval_flickr.yaml')
-    parser.add_argument('--output_dir', default='output/Retrieval_flickr')        
-    parser.add_argument('--evaluate', action='store_true')
-    parser.add_argument('--device', default='cuda')
-    parser.add_argument('--seed', default=42, type=int)
-    parser.add_argument('--world_size', default=1, type=int, help='number of distributed processes')    
-    parser.add_argument('--dist_url', default='env://', help='url used to set up distributed training')
-    parser.add_argument('--distributed', default=True, type=bool)
-    args = parser.parse_args()
-
-    config = yaml.load(open(args.config, 'r'), Loader=yaml.Loader)
-
-    Path(args.output_dir).mkdir(parents=True, exist_ok=True)
-        
-    yaml.dump(config, open(os.path.join(args.output_dir, 'config.yaml'), 'w'))    
-    
-    main(args, config)
\ No newline at end of file
diff --git a/repositories/BLIP/train_vqa.py b/repositories/BLIP/train_vqa.py
deleted file mode 100644
index 89eb7490862e517cc660f842396033c21d441a20..0000000000000000000000000000000000000000
--- a/repositories/BLIP/train_vqa.py
+++ /dev/null
@@ -1,202 +0,0 @@
-'''
- * Copyright (c) 2022, salesforce.com, inc.
- * All rights reserved.
- * SPDX-License-Identifier: BSD-3-Clause
- * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
- * By Junnan Li
-'''
-import argparse
-import os
-import ruamel_yaml as yaml
-import numpy as np
-import random
-import time
-import datetime
-import json
-from pathlib import Path
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch.utils.data import DataLoader
-import torch.backends.cudnn as cudnn
-import torch.distributed as dist
-
-from models.blip_vqa import blip_vqa
-import utils
-from utils import cosine_lr_schedule
-from data import create_dataset, create_sampler, create_loader
-from data.vqa_dataset import vqa_collate_fn
-from data.utils import save_result
-
-
-def train(model, data_loader, optimizer, epoch, device):
-    # train
-    model.train()  
-    
-    metric_logger = utils.MetricLogger(delimiter="  ")
-    metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
-    metric_logger.add_meter('loss', utils.SmoothedValue(window_size=1, fmt='{value:.4f}'))
-
-    header = 'Train Epoch: [{}]'.format(epoch)
-    print_freq = 50    
-    
-    for i,(image, question, answer, weights, n) in enumerate(metric_logger.log_every(data_loader, print_freq, header)):
-        image, weights = image.to(device,non_blocking=True), weights.to(device,non_blocking=True)      
-
-        loss = model(image, question, answer, train=True, n=n, weights=weights)        
-        
-        optimizer.zero_grad()
-        loss.backward()
-        optimizer.step()    
-        
-        metric_logger.update(loss=loss.item())
-        metric_logger.update(lr=optimizer.param_groups[0]["lr"])
-
-    # gather the stats from all processes
-    metric_logger.synchronize_between_processes()
-    print("Averaged stats:", metric_logger.global_avg())     
-    return {k: "{:.3f}".format(meter.global_avg) for k, meter in metric_logger.meters.items()} 
-
-
-@torch.no_grad()
-def evaluation(model, data_loader, device, config) :
-    # test
-    model.eval()
-            
-    metric_logger = utils.MetricLogger(delimiter="  ")
-    header = 'Generate VQA test result:'
-    print_freq = 50
-    
-    result = []
-    
-    if config['inference']=='rank':   
-        answer_list = data_loader.dataset.answer_list
-        answer_candidates = model.tokenizer(answer_list, padding='longest', return_tensors='pt').to(device)    
-        answer_candidates.input_ids[:,0] = model.tokenizer.bos_token_id
-        
-    for n, (image, question, question_id) in enumerate(metric_logger.log_every(data_loader, print_freq, header)):        
-        image = image.to(device,non_blocking=True)             
-
-        if config['inference']=='generate':
-            answers = model(image, question, train=False, inference='generate') 
-            
-            for answer, ques_id in zip(answers, question_id):
-                ques_id = int(ques_id.item())       
-                result.append({"question_id":ques_id, "answer":answer})             
-            
-        elif config['inference']=='rank':    
-            answer_ids = model(image, question, answer_candidates, train=False, inference='rank', k_test=config['k_test'])      
-
-            for ques_id, answer_id in zip(question_id, answer_ids):
-                result.append({"question_id":int(ques_id.item()), "answer":answer_list[answer_id]})   
-
-    return result
-
-
-def main(args, config):
-    utils.init_distributed_mode(args)    
-    
-    device = torch.device(args.device)
-
-    # fix the seed for reproducibility
-    seed = args.seed + utils.get_rank()
-    torch.manual_seed(seed)
-    np.random.seed(seed)
-    random.seed(seed)
-    cudnn.benchmark = True
-    
-    #### Dataset #### 
-    print("Creating vqa datasets")
-    datasets = create_dataset('vqa', config)   
-    
-    if args.distributed:
-        num_tasks = utils.get_world_size()
-        global_rank = utils.get_rank()            
-        samplers = create_sampler(datasets, [True, False], num_tasks, global_rank)         
-    else:
-        samplers = [None, None]
-    
-    train_loader, test_loader = create_loader(datasets,samplers,
-                                              batch_size=[config['batch_size_train'],config['batch_size_test']],
-                                              num_workers=[4,4],is_trains=[True, False], 
-                                              collate_fns=[vqa_collate_fn,None]) 
-    #### Model #### 
-    print("Creating model")
-    model = blip_vqa(pretrained=config['pretrained'], image_size=config['image_size'], 
-                       vit=config['vit'], vit_grad_ckpt=config['vit_grad_ckpt'], vit_ckpt_layer=config['vit_ckpt_layer'])
-
-    model = model.to(device)   
-    
-    model_without_ddp = model
-    if args.distributed:
-        model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
-        model_without_ddp = model.module    
-    
-    optimizer = torch.optim.AdamW(params=model.parameters(), lr=config['init_lr'], weight_decay=config['weight_decay'])
-
-    best = 0
-    best_epoch = 0 
-       
-    print("Start training")
-    start_time = time.time()    
-    for epoch in range(0, config['max_epoch']):
-        if not args.evaluate:        
-            if args.distributed:
-                train_loader.sampler.set_epoch(epoch)
-                
-            cosine_lr_schedule(optimizer, epoch, config['max_epoch'], config['init_lr'], config['min_lr'])
-                
-            train_stats = train(model, train_loader, optimizer, epoch, device) 
-
-        else:         
-            break        
-        
-        if utils.is_main_process():     
-            log_stats = {**{f'train_{k}': v for k, v in train_stats.items()},
-                         'epoch': epoch,
-                        }                
-            with open(os.path.join(args.output_dir, "log.txt"),"a") as f:
-                f.write(json.dumps(log_stats) + "\n")                        
-                    
-            save_obj = {
-                'model': model_without_ddp.state_dict(),
-                'optimizer': optimizer.state_dict(),
-                'config': config,
-                'epoch': epoch,
-            }
-            torch.save(save_obj, os.path.join(args.output_dir, 'checkpoint_%02d.pth'%epoch))  
-
-        dist.barrier()         
-
-    vqa_result = evaluation(model_without_ddp, test_loader, device, config)        
-    result_file = save_result(vqa_result, args.result_dir, 'vqa_result')  
-                      
-    total_time = time.time() - start_time
-    total_time_str = str(datetime.timedelta(seconds=int(total_time)))
-    print('Training time {}'.format(total_time_str)) 
-    
-            
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--config', default='./configs/vqa.yaml') 
-    parser.add_argument('--output_dir', default='output/VQA')
-    parser.add_argument('--evaluate', action='store_true')      
-    parser.add_argument('--device', default='cuda')
-    parser.add_argument('--seed', default=42, type=int)
-    parser.add_argument('--world_size', default=1, type=int, help='number of distributed processes')    
-    parser.add_argument('--dist_url', default='env://', help='url used to set up distributed training')
-    parser.add_argument('--distributed', default=True, type=bool)
-    args = parser.parse_args()
-
-    config = yaml.load(open(args.config, 'r'), Loader=yaml.Loader)
-
-    args.result_dir = os.path.join(args.output_dir, 'result')
-
-    Path(args.output_dir).mkdir(parents=True, exist_ok=True)
-    Path(args.result_dir).mkdir(parents=True, exist_ok=True)
-        
-    yaml.dump(config, open(os.path.join(args.output_dir, 'config.yaml'), 'w'))    
-    
-    main(args, config)
\ No newline at end of file
diff --git a/repositories/BLIP/transform/randaugment.py b/repositories/BLIP/transform/randaugment.py
deleted file mode 100644
index 094d9f4cacc93146d2bab7311d9dc04feb07032c..0000000000000000000000000000000000000000
--- a/repositories/BLIP/transform/randaugment.py
+++ /dev/null
@@ -1,340 +0,0 @@
-import cv2
-import numpy as np
-
-
-## aug functions
-def identity_func(img):
-    return img
-
-
-def autocontrast_func(img, cutoff=0):
-    '''
-        same output as PIL.ImageOps.autocontrast
-    '''
-    n_bins = 256
-
-    def tune_channel(ch):
-        n = ch.size
-        cut = cutoff * n // 100
-        if cut == 0:
-            high, low = ch.max(), ch.min()
-        else:
-            hist = cv2.calcHist([ch], [0], None, [n_bins], [0, n_bins])
-            low = np.argwhere(np.cumsum(hist) > cut)
-            low = 0 if low.shape[0] == 0 else low[0]
-            high = np.argwhere(np.cumsum(hist[::-1]) > cut)
-            high = n_bins - 1 if high.shape[0] == 0 else n_bins - 1 - high[0]
-        if high <= low:
-            table = np.arange(n_bins)
-        else:
-            scale = (n_bins - 1) / (high - low)
-            offset = -low * scale
-            table = np.arange(n_bins) * scale + offset
-            table[table < 0] = 0
-            table[table > n_bins - 1] = n_bins - 1
-        table = table.clip(0, 255).astype(np.uint8)
-        return table[ch]
-
-    channels = [tune_channel(ch) for ch in cv2.split(img)]
-    out = cv2.merge(channels)
-    return out
-
-
-def equalize_func(img):
-    '''
-        same output as PIL.ImageOps.equalize
-        PIL's implementation is different from cv2.equalize
-    '''
-    n_bins = 256
-
-    def tune_channel(ch):
-        hist = cv2.calcHist([ch], [0], None, [n_bins], [0, n_bins])
-        non_zero_hist = hist[hist != 0].reshape(-1)
-        step = np.sum(non_zero_hist[:-1]) // (n_bins - 1)
-        if step == 0: return ch
-        n = np.empty_like(hist)
-        n[0] = step // 2
-        n[1:] = hist[:-1]
-        table = (np.cumsum(n) // step).clip(0, 255).astype(np.uint8)
-        return table[ch]
-
-    channels = [tune_channel(ch) for ch in cv2.split(img)]
-    out = cv2.merge(channels)
-    return out
-
-
-def rotate_func(img, degree, fill=(0, 0, 0)):
-    '''
-    like PIL, rotate by degree, not radians
-    '''
-    H, W = img.shape[0], img.shape[1]
-    center = W / 2, H / 2
-    M = cv2.getRotationMatrix2D(center, degree, 1)
-    out = cv2.warpAffine(img, M, (W, H), borderValue=fill)
-    return out
-
-
-def solarize_func(img, thresh=128):
-    '''
-        same output as PIL.ImageOps.posterize
-    '''
-    table = np.array([el if el < thresh else 255 - el for el in range(256)])
-    table = table.clip(0, 255).astype(np.uint8)
-    out = table[img]
-    return out
-
-
-def color_func(img, factor):
-    '''
-        same output as PIL.ImageEnhance.Color
-    '''
-    ## implementation according to PIL definition, quite slow
-    #  degenerate = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)[:, :, np.newaxis]
-    #  out = blend(degenerate, img, factor)
-    #  M = (
-    #      np.eye(3) * factor
-    #      + np.float32([0.114, 0.587, 0.299]).reshape(3, 1) * (1. - factor)
-    #  )[np.newaxis, np.newaxis, :]
-    M = (
-            np.float32([
-                [0.886, -0.114, -0.114],
-                [-0.587, 0.413, -0.587],
-                [-0.299, -0.299, 0.701]]) * factor
-            + np.float32([[0.114], [0.587], [0.299]])
-    )
-    out = np.matmul(img, M).clip(0, 255).astype(np.uint8)
-    return out
-
-
-def contrast_func(img, factor):
-    """
-        same output as PIL.ImageEnhance.Contrast
-    """
-    mean = np.sum(np.mean(img, axis=(0, 1)) * np.array([0.114, 0.587, 0.299]))
-    table = np.array([(
-        el - mean) * factor + mean
-        for el in range(256)
-    ]).clip(0, 255).astype(np.uint8)
-    out = table[img]
-    return out
-
-
-def brightness_func(img, factor):
-    '''
-        same output as PIL.ImageEnhance.Contrast
-    '''
-    table = (np.arange(256, dtype=np.float32) * factor).clip(0, 255).astype(np.uint8)
-    out = table[img]
-    return out
-
-
-def sharpness_func(img, factor):
-    '''
-    The differences the this result and PIL are all on the 4 boundaries, the center
-    areas are same
-    '''
-    kernel = np.ones((3, 3), dtype=np.float32)
-    kernel[1][1] = 5
-    kernel /= 13
-    degenerate = cv2.filter2D(img, -1, kernel)
-    if factor == 0.0:
-        out = degenerate
-    elif factor == 1.0:
-        out = img
-    else:
-        out = img.astype(np.float32)
-        degenerate = degenerate.astype(np.float32)[1:-1, 1:-1, :]
-        out[1:-1, 1:-1, :] = degenerate + factor * (out[1:-1, 1:-1, :] - degenerate)
-        out = out.astype(np.uint8)
-    return out
-
-
-def shear_x_func(img, factor, fill=(0, 0, 0)):
-    H, W = img.shape[0], img.shape[1]
-    M = np.float32([[1, factor, 0], [0, 1, 0]])
-    out = cv2.warpAffine(img, M, (W, H), borderValue=fill, flags=cv2.INTER_LINEAR).astype(np.uint8)
-    return out
-
-
-def translate_x_func(img, offset, fill=(0, 0, 0)):
-    '''
-        same output as PIL.Image.transform
-    '''
-    H, W = img.shape[0], img.shape[1]
-    M = np.float32([[1, 0, -offset], [0, 1, 0]])
-    out = cv2.warpAffine(img, M, (W, H), borderValue=fill, flags=cv2.INTER_LINEAR).astype(np.uint8)
-    return out
-
-
-def translate_y_func(img, offset, fill=(0, 0, 0)):
-    '''
-        same output as PIL.Image.transform
-    '''
-    H, W = img.shape[0], img.shape[1]
-    M = np.float32([[1, 0, 0], [0, 1, -offset]])
-    out = cv2.warpAffine(img, M, (W, H), borderValue=fill, flags=cv2.INTER_LINEAR).astype(np.uint8)
-    return out
-
-
-def posterize_func(img, bits):
-    '''
-        same output as PIL.ImageOps.posterize
-    '''
-    out = np.bitwise_and(img, np.uint8(255 << (8 - bits)))
-    return out
-
-
-def shear_y_func(img, factor, fill=(0, 0, 0)):
-    H, W = img.shape[0], img.shape[1]
-    M = np.float32([[1, 0, 0], [factor, 1, 0]])
-    out = cv2.warpAffine(img, M, (W, H), borderValue=fill, flags=cv2.INTER_LINEAR).astype(np.uint8)
-    return out
-
-
-def cutout_func(img, pad_size, replace=(0, 0, 0)):
-    replace = np.array(replace, dtype=np.uint8)
-    H, W = img.shape[0], img.shape[1]
-    rh, rw = np.random.random(2)
-    pad_size = pad_size // 2
-    ch, cw = int(rh * H), int(rw * W)
-    x1, x2 = max(ch - pad_size, 0), min(ch + pad_size, H)
-    y1, y2 = max(cw - pad_size, 0), min(cw + pad_size, W)
-    out = img.copy()
-    out[x1:x2, y1:y2, :] = replace
-    return out
-
-
-### level to args
-def enhance_level_to_args(MAX_LEVEL):
-    def level_to_args(level):
-        return ((level / MAX_LEVEL) * 1.8 + 0.1,)
-    return level_to_args
-
-
-def shear_level_to_args(MAX_LEVEL, replace_value):
-    def level_to_args(level):
-        level = (level / MAX_LEVEL) * 0.3
-        if np.random.random() > 0.5: level = -level
-        return (level, replace_value)
-
-    return level_to_args
-
-
-def translate_level_to_args(translate_const, MAX_LEVEL, replace_value):
-    def level_to_args(level):
-        level = (level / MAX_LEVEL) * float(translate_const)
-        if np.random.random() > 0.5: level = -level
-        return (level, replace_value)
-
-    return level_to_args
-
-
-def cutout_level_to_args(cutout_const, MAX_LEVEL, replace_value):
-    def level_to_args(level):
-        level = int((level / MAX_LEVEL) * cutout_const)
-        return (level, replace_value)
-
-    return level_to_args
-
-
-def solarize_level_to_args(MAX_LEVEL):
-    def level_to_args(level):
-        level = int((level / MAX_LEVEL) * 256)
-        return (level, )
-    return level_to_args
-
-
-def none_level_to_args(level):
-    return ()
-
-
-def posterize_level_to_args(MAX_LEVEL):
-    def level_to_args(level):
-        level = int((level / MAX_LEVEL) * 4)
-        return (level, )
-    return level_to_args
-
-
-def rotate_level_to_args(MAX_LEVEL, replace_value):
-    def level_to_args(level):
-        level = (level / MAX_LEVEL) * 30
-        if np.random.random() < 0.5:
-            level = -level
-        return (level, replace_value)
-
-    return level_to_args
-
-
-func_dict = {
-    'Identity': identity_func,
-    'AutoContrast': autocontrast_func,
-    'Equalize': equalize_func,
-    'Rotate': rotate_func,
-    'Solarize': solarize_func,
-    'Color': color_func,
-    'Contrast': contrast_func,
-    'Brightness': brightness_func,
-    'Sharpness': sharpness_func,
-    'ShearX': shear_x_func,
-    'TranslateX': translate_x_func,
-    'TranslateY': translate_y_func,
-    'Posterize': posterize_func,
-    'ShearY': shear_y_func,
-}
-
-translate_const = 10
-MAX_LEVEL = 10
-replace_value = (128, 128, 128)
-arg_dict = {
-    'Identity': none_level_to_args,
-    'AutoContrast': none_level_to_args,
-    'Equalize': none_level_to_args,
-    'Rotate': rotate_level_to_args(MAX_LEVEL, replace_value),
-    'Solarize': solarize_level_to_args(MAX_LEVEL),
-    'Color': enhance_level_to_args(MAX_LEVEL),
-    'Contrast': enhance_level_to_args(MAX_LEVEL),
-    'Brightness': enhance_level_to_args(MAX_LEVEL),
-    'Sharpness': enhance_level_to_args(MAX_LEVEL),
-    'ShearX': shear_level_to_args(MAX_LEVEL, replace_value),
-    'TranslateX': translate_level_to_args(
-        translate_const, MAX_LEVEL, replace_value
-    ),
-    'TranslateY': translate_level_to_args(
-        translate_const, MAX_LEVEL, replace_value
-    ),
-    'Posterize': posterize_level_to_args(MAX_LEVEL),
-    'ShearY': shear_level_to_args(MAX_LEVEL, replace_value),
-}
-
-
-class RandomAugment(object):
-
-    def __init__(self, N=2, M=10, isPIL=False, augs=[]):
-        self.N = N
-        self.M = M
-        self.isPIL = isPIL
-        if augs:
-            self.augs = augs       
-        else:
-            self.augs = list(arg_dict.keys())
-
-    def get_random_ops(self):
-        sampled_ops = np.random.choice(self.augs, self.N)
-        return [(op, 0.5, self.M) for op in sampled_ops]
-
-    def __call__(self, img):
-        if self.isPIL:
-            img = np.array(img)            
-        ops = self.get_random_ops()
-        for name, prob, level in ops:
-            if np.random.random() > prob:
-                continue
-            args = arg_dict[name](level)
-            img = func_dict[name](img, *args) 
-        return img
-
-
-if __name__ == '__main__':
-    a = RandomAugment()
-    img = np.random.randn(32, 32, 3)
-    a(img)
\ No newline at end of file
diff --git a/repositories/BLIP/utils.py b/repositories/BLIP/utils.py
deleted file mode 100644
index ebe0e1dc2f5d200156d5dd1acc305a8b7b7b98da..0000000000000000000000000000000000000000
--- a/repositories/BLIP/utils.py
+++ /dev/null
@@ -1,278 +0,0 @@
-import math
-def cosine_lr_schedule(optimizer, epoch, max_epoch, init_lr, min_lr):
-    """Decay the learning rate"""
-    lr = (init_lr - min_lr) * 0.5 * (1. + math.cos(math.pi * epoch / max_epoch)) + min_lr
-    for param_group in optimizer.param_groups:
-        param_group['lr'] = lr
-        
-def warmup_lr_schedule(optimizer, step, max_step, init_lr, max_lr):
-    """Warmup the learning rate"""
-    lr = min(max_lr, init_lr + (max_lr - init_lr) * step / max_step)
-    for param_group in optimizer.param_groups:
-        param_group['lr'] = lr    
-
-def step_lr_schedule(optimizer, epoch, init_lr, min_lr, decay_rate):        
-    """Decay the learning rate"""
-    lr = max(min_lr, init_lr * (decay_rate**epoch))
-    for param_group in optimizer.param_groups:
-        param_group['lr'] = lr    
-        
-import numpy as np
-import io
-import os
-import time
-from collections import defaultdict, deque
-import datetime
-
-import torch
-import torch.distributed as dist
-
-class SmoothedValue(object):
-    """Track a series of values and provide access to smoothed values over a
-    window or the global series average.
-    """
-
-    def __init__(self, window_size=20, fmt=None):
-        if fmt is None:
-            fmt = "{median:.4f} ({global_avg:.4f})"
-        self.deque = deque(maxlen=window_size)
-        self.total = 0.0
-        self.count = 0
-        self.fmt = fmt
-
-    def update(self, value, n=1):
-        self.deque.append(value)
-        self.count += n
-        self.total += value * n
-
-    def synchronize_between_processes(self):
-        """
-        Warning: does not synchronize the deque!
-        """
-        if not is_dist_avail_and_initialized():
-            return
-        t = torch.tensor([self.count, self.total], dtype=torch.float64, device='cuda')
-        dist.barrier()
-        dist.all_reduce(t)
-        t = t.tolist()
-        self.count = int(t[0])
-        self.total = t[1]
-
-    @property
-    def median(self):
-        d = torch.tensor(list(self.deque))
-        return d.median().item()
-
-    @property
-    def avg(self):
-        d = torch.tensor(list(self.deque), dtype=torch.float32)
-        return d.mean().item()
-
-    @property
-    def global_avg(self):
-        return self.total / self.count
-
-    @property
-    def max(self):
-        return max(self.deque)
-
-    @property
-    def value(self):
-        return self.deque[-1]
-
-    def __str__(self):
-        return self.fmt.format(
-            median=self.median,
-            avg=self.avg,
-            global_avg=self.global_avg,
-            max=self.max,
-            value=self.value)
-
-
-class MetricLogger(object):
-    def __init__(self, delimiter="\t"):
-        self.meters = defaultdict(SmoothedValue)
-        self.delimiter = delimiter
-
-    def update(self, **kwargs):
-        for k, v in kwargs.items():
-            if isinstance(v, torch.Tensor):
-                v = v.item()
-            assert isinstance(v, (float, int))
-            self.meters[k].update(v)
-
-    def __getattr__(self, attr):
-        if attr in self.meters:
-            return self.meters[attr]
-        if attr in self.__dict__:
-            return self.__dict__[attr]
-        raise AttributeError("'{}' object has no attribute '{}'".format(
-            type(self).__name__, attr))
-
-    def __str__(self):
-        loss_str = []
-        for name, meter in self.meters.items():
-            loss_str.append(
-                "{}: {}".format(name, str(meter))
-            )
-        return self.delimiter.join(loss_str)
-
-    def global_avg(self):
-        loss_str = []
-        for name, meter in self.meters.items():
-            loss_str.append(
-                "{}: {:.4f}".format(name, meter.global_avg)
-            )
-        return self.delimiter.join(loss_str)    
-    
-    def synchronize_between_processes(self):
-        for meter in self.meters.values():
-            meter.synchronize_between_processes()
-
-    def add_meter(self, name, meter):
-        self.meters[name] = meter
-
-    def log_every(self, iterable, print_freq, header=None):
-        i = 0
-        if not header:
-            header = ''
-        start_time = time.time()
-        end = time.time()
-        iter_time = SmoothedValue(fmt='{avg:.4f}')
-        data_time = SmoothedValue(fmt='{avg:.4f}')
-        space_fmt = ':' + str(len(str(len(iterable)))) + 'd'
-        log_msg = [
-            header,
-            '[{0' + space_fmt + '}/{1}]',
-            'eta: {eta}',
-            '{meters}',
-            'time: {time}',
-            'data: {data}'
-        ]
-        if torch.cuda.is_available():
-            log_msg.append('max mem: {memory:.0f}')
-        log_msg = self.delimiter.join(log_msg)
-        MB = 1024.0 * 1024.0
-        for obj in iterable:
-            data_time.update(time.time() - end)
-            yield obj
-            iter_time.update(time.time() - end)
-            if i % print_freq == 0 or i == len(iterable) - 1:
-                eta_seconds = iter_time.global_avg * (len(iterable) - i)
-                eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
-                if torch.cuda.is_available():
-                    print(log_msg.format(
-                        i, len(iterable), eta=eta_string,
-                        meters=str(self),
-                        time=str(iter_time), data=str(data_time),
-                        memory=torch.cuda.max_memory_allocated() / MB))
-                else:
-                    print(log_msg.format(
-                        i, len(iterable), eta=eta_string,
-                        meters=str(self),
-                        time=str(iter_time), data=str(data_time)))
-            i += 1
-            end = time.time()
-        total_time = time.time() - start_time
-        total_time_str = str(datetime.timedelta(seconds=int(total_time)))
-        print('{} Total time: {} ({:.4f} s / it)'.format(
-            header, total_time_str, total_time / len(iterable)))
-        
-
-class AttrDict(dict):
-    def __init__(self, *args, **kwargs):
-        super(AttrDict, self).__init__(*args, **kwargs)
-        self.__dict__ = self
-
-
-def compute_acc(logits, label, reduction='mean'):
-    ret = (torch.argmax(logits, dim=1) == label).float()
-    if reduction == 'none':
-        return ret.detach()
-    elif reduction == 'mean':
-        return ret.mean().item()
-
-def compute_n_params(model, return_str=True):
-    tot = 0
-    for p in model.parameters():
-        w = 1
-        for x in p.shape:
-            w *= x
-        tot += w
-    if return_str:
-        if tot >= 1e6:
-            return '{:.1f}M'.format(tot / 1e6)
-        else:
-            return '{:.1f}K'.format(tot / 1e3)
-    else:
-        return tot
-
-def setup_for_distributed(is_master):
-    """
-    This function disables printing when not in master process
-    """
-    import builtins as __builtin__
-    builtin_print = __builtin__.print
-
-    def print(*args, **kwargs):
-        force = kwargs.pop('force', False)
-        if is_master or force:
-            builtin_print(*args, **kwargs)
-
-    __builtin__.print = print
-
-
-def is_dist_avail_and_initialized():
-    if not dist.is_available():
-        return False
-    if not dist.is_initialized():
-        return False
-    return True
-
-
-def get_world_size():
-    if not is_dist_avail_and_initialized():
-        return 1
-    return dist.get_world_size()
-
-
-def get_rank():
-    if not is_dist_avail_and_initialized():
-        return 0
-    return dist.get_rank()
-
-
-def is_main_process():
-    return get_rank() == 0
-
-
-def save_on_master(*args, **kwargs):
-    if is_main_process():
-        torch.save(*args, **kwargs)
-
-
-def init_distributed_mode(args):
-    if 'RANK' in os.environ and 'WORLD_SIZE' in os.environ:
-        args.rank = int(os.environ["RANK"])
-        args.world_size = int(os.environ['WORLD_SIZE'])
-        args.gpu = int(os.environ['LOCAL_RANK'])
-    elif 'SLURM_PROCID' in os.environ:
-        args.rank = int(os.environ['SLURM_PROCID'])
-        args.gpu = args.rank % torch.cuda.device_count()
-    else:
-        print('Not using distributed mode')
-        args.distributed = False
-        return
-
-    args.distributed = True
-
-    torch.cuda.set_device(args.gpu)
-    args.dist_backend = 'nccl'
-    print('| distributed init (rank {}, word {}): {}'.format(
-        args.rank, args.world_size, args.dist_url), flush=True)
-    torch.distributed.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
-                                         world_size=args.world_size, rank=args.rank)
-    torch.distributed.barrier()
-    setup_for_distributed(args.rank == 0)        
-        
-        
\ No newline at end of file
diff --git a/repositories/CodeFormer/.gitignore b/repositories/CodeFormer/.gitignore
deleted file mode 100644
index 15f690d1628f48e9d7d755a9472a44975fa06839..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/.gitignore
+++ /dev/null
@@ -1,128 +0,0 @@
-.vscode
-
-# ignored files
-version.py
-
-# ignored files with suffix
-*.html
-# *.png
-# *.jpeg
-# *.jpg
-*.pt
-*.gif
-*.pth
-*.dat
-*.zip
-
-# template
-
-# Byte-compiled / optimized / DLL files
-__pycache__/
-*.py[cod]
-*$py.class
-
-# C extensions
-*.so
-
-# Distribution / packaging
-.Python
-build/
-develop-eggs/
-dist/
-downloads/
-eggs/
-.eggs/
-lib/
-lib64/
-parts/
-sdist/
-var/
-wheels/
-*.egg-info/
-.installed.cfg
-*.egg
-MANIFEST
-
-# PyInstaller
-#  Usually these files are written by a python script from a template
-#  before PyInstaller builds the exe, so as to inject date/other infos into it.
-*.manifest
-*.spec
-
-# Installer logs
-pip-log.txt
-pip-delete-this-directory.txt
-
-# Unit test / coverage reports
-htmlcov/
-.tox/
-.coverage
-.coverage.*
-.cache
-nosetests.xml
-coverage.xml
-*.cover
-.hypothesis/
-.pytest_cache/
-
-# Translations
-*.mo
-*.pot
-
-# Django stuff:
-*.log
-local_settings.py
-db.sqlite3
-
-# Flask stuff:
-instance/
-.webassets-cache
-
-# Scrapy stuff:
-.scrapy
-
-# Sphinx documentation
-docs/_build/
-
-# PyBuilder
-target/
-
-# Jupyter Notebook
-.ipynb_checkpoints
-
-# pyenv
-.python-version
-
-# celery beat schedule file
-celerybeat-schedule
-
-# SageMath parsed files
-*.sage.py
-
-# Environments
-.env
-.venv
-env/
-venv/
-ENV/
-env.bak/
-venv.bak/
-
-# Spyder project settings
-.spyderproject
-.spyproject
-
-# Rope project settings
-.ropeproject
-
-# mkdocs documentation
-/site
-
-# mypy
-.mypy_cache/
-
-# project
-results/
-dlib/
-*_old*
-
diff --git a/repositories/CodeFormer/README.md b/repositories/CodeFormer/README.md
deleted file mode 100644
index bb5e1fe6d881c0862835828700ed2cfc8e999463..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/README.md
+++ /dev/null
@@ -1,123 +0,0 @@
-<p align="center">
-  <img src="assets/CodeFormer_logo.png" height=110>
-</p>
-
-## Towards Robust Blind Face Restoration with Codebook Lookup Transformer
-
-[Paper](https://arxiv.org/abs/2206.11253) | [Project Page](https://shangchenzhou.com/projects/CodeFormer/) | [Video](https://youtu.be/d3VDpkXlueI)
-
-
-<a href="https://colab.research.google.com/drive/1m52PNveE4PBhYrecj34cnpEeiHcC5LTb?usp=sharing"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="google colab logo"></a> [![Replicate](https://img.shields.io/badge/Demo-%F0%9F%9A%80%20Replicate-blue)](https://replicate.com/sczhou/codeformer) ![visitors](https://visitor-badge.glitch.me/badge?page_id=sczhou/CodeFormer)
-
-[Shangchen Zhou](https://shangchenzhou.com/), [Kelvin C.K. Chan](https://ckkelvinchan.github.io/), [Chongyi Li](https://li-chongyi.github.io/), [Chen Change Loy](https://www.mmlab-ntu.com/person/ccloy/) 
-
-S-Lab, Nanyang Technological University
-
-<img src="assets/network.jpg" width="800px"/>
-
-
-:star: If CodeFormer is helpful to your images or projects, please help star this repo. Thanks! :hugs: 
-
-### Update
-
-- **2022.09.09**: Integrated to [Replicate](https://replicate.com/). Try out online demo! [![Replicate](https://img.shields.io/badge/Demo-%F0%9F%9A%80%20Replicate-blue)](https://replicate.com/sczhou/codeformer)
-- **2022.09.04**: Add face upsampling `--face_upsample` for high-resolution AI-created face enhancement.
-- **2022.08.23**: Some modifications on face detection and fusion for better AI-created face enhancement.
-- **2022.08.07**: Integrate [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN) to support background image enhancement.
-- **2022.07.29**: Integrate new face detectors of `['RetinaFace'(default), 'YOLOv5']`. 
-- **2022.07.17**: Add Colab demo of CodeFormer. <a href="https://colab.research.google.com/drive/1m52PNveE4PBhYrecj34cnpEeiHcC5LTb?usp=sharing"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="google colab logo"></a>
-- **2022.07.16**: Release inference code for face restoration. :blush:
-- **2022.06.21**: This repo is created.
-
-### TODO
-- [ ] Add checkpoint for face inpainting
-- [ ] Add training code and config files
-- [x] ~~Add background image enhancement~~
-
-#### Face Restoration
-
-<img src="assets/restoration_result1.png" width="400px"/> <img src="assets/restoration_result2.png" width="400px"/>
-<img src="assets/restoration_result3.png" width="400px"/> <img src="assets/restoration_result4.png" width="400px"/>
-
-#### Face Color Enhancement and Restoration
-
-<img src="assets/color_enhancement_result1.png" width="400px"/> <img src="assets/color_enhancement_result2.png" width="400px"/>
-
-#### Face Inpainting
-
-<img src="assets/inpainting_result1.png" width="400px"/> <img src="assets/inpainting_result2.png" width="400px"/>
-
-
-
-### Dependencies and Installation
-
-- Pytorch >= 1.7.1
-- CUDA >= 10.1
-- Other required packages in `requirements.txt`
-```
-# git clone this repository
-git clone https://github.com/sczhou/CodeFormer
-cd CodeFormer
-
-# create new anaconda env
-conda create -n codeformer python=3.8 -y
-conda activate codeformer
-
-# install python dependencies
-pip3 install -r requirements.txt
-python basicsr/setup.py develop
-```
-<!-- conda install -c conda-forge dlib -->
-
-### Quick Inference
-
-##### Download Pre-trained Models:
-Download the facelib pretrained models from [[Google Drive](https://drive.google.com/drive/folders/1b_3qwrzY_kTQh0-SnBoGBgOrJ_PLZSKm?usp=sharing) | [OneDrive](https://entuedu-my.sharepoint.com/:f:/g/personal/s200094_e_ntu_edu_sg/EvDxR7FcAbZMp_MA9ouq7aQB8XTppMb3-T0uGZ_2anI2mg?e=DXsJFo)] to the `weights/facelib` folder. You can manually download the pretrained models OR download by runing the following command.
-```
-python scripts/download_pretrained_models.py facelib
-```
-
-Download the CodeFormer pretrained models from [[Google Drive](https://drive.google.com/drive/folders/1CNNByjHDFt0b95q54yMVp6Ifo5iuU6QS?usp=sharing) | [OneDrive](https://entuedu-my.sharepoint.com/:f:/g/personal/s200094_e_ntu_edu_sg/EoKFj4wo8cdIn2-TY2IV6CYBhZ0pIG4kUOeHdPR_A5nlbg?e=AO8UN9)] to the `weights/CodeFormer` folder. You can manually download the pretrained models OR download by runing the following command.
-```
-python scripts/download_pretrained_models.py CodeFormer
-```
-
-##### Prepare Testing Data:
-You can put the testing images in the `inputs/TestWhole` folder. If you would like to test on cropped and aligned faces, you can put them in the `inputs/cropped_faces` folder.
-
-
-##### Testing on Face Restoration:
-```
-# For cropped and aligned faces
-python inference_codeformer.py --w 0.5 --has_aligned --test_path [input folder]
-
-# For the whole images
-# Add '--bg_upsampler realesrgan' to enhance the background regions with Real-ESRGAN
-# Add '--face_upsample' to further upsample restorated face with Real-ESRGAN
-python inference_codeformer.py --w 0.7 --test_path [input folder]
-```
-
-NOTE that *w* is in [0, 1]. Generally, smaller *w* tends to produce a higher-quality result, while larger *w* yields a higher-fidelity result. 
-
-The results will be saved in the `results` folder.
-
-### Citation
-If our work is useful for your research, please consider citing:
-
-    @article{zhou2022codeformer,
-        author = {Zhou, Shangchen and Chan, Kelvin C.K. and Li, Chongyi and Loy, Chen Change},
-        title = {Towards Robust Blind Face Restoration with Codebook Lookup TransFormer},
-        journal = {arXiv preprint arXiv:2206.11253},
-        year = {2022}
-    }
-
-### License
-
-<a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.
-
-### Acknowledgement
-
-This project is based on [BasicSR](https://github.com/XPixelGroup/BasicSR). We also borrow some codes from [Unleashing Transformers](https://github.com/samb-t/unleashing-transformers), [YOLOv5-face](https://github.com/deepcam-cn/yolov5-face), and [FaceXLib](https://github.com/xinntao/facexlib). Thanks for their awesome works.
-
-### Contact
-If you have any question, please feel free to reach me out at `shangchenzhou@gmail.com`.
\ No newline at end of file
diff --git a/repositories/CodeFormer/assets/CodeFormer_logo.png b/repositories/CodeFormer/assets/CodeFormer_logo.png
deleted file mode 100644
index 024cb724f43c2b5cff7039c69b78f261a5a4898c..0000000000000000000000000000000000000000
Binary files a/repositories/CodeFormer/assets/CodeFormer_logo.png and /dev/null differ
diff --git a/repositories/CodeFormer/assets/color_enhancement_result1.png b/repositories/CodeFormer/assets/color_enhancement_result1.png
deleted file mode 100644
index 34433db6378b37cb47a1e544217e4d7f679f7038..0000000000000000000000000000000000000000
Binary files a/repositories/CodeFormer/assets/color_enhancement_result1.png and /dev/null differ
diff --git a/repositories/CodeFormer/assets/color_enhancement_result2.png b/repositories/CodeFormer/assets/color_enhancement_result2.png
deleted file mode 100644
index 228690ac9b1453e67e0212ab2952bea887543a09..0000000000000000000000000000000000000000
Binary files a/repositories/CodeFormer/assets/color_enhancement_result2.png and /dev/null differ
diff --git a/repositories/CodeFormer/assets/inpainting_result1.png b/repositories/CodeFormer/assets/inpainting_result1.png
deleted file mode 100644
index 2c6fa68ad4340c0281e096f7928d28be831c00c1..0000000000000000000000000000000000000000
Binary files a/repositories/CodeFormer/assets/inpainting_result1.png and /dev/null differ
diff --git a/repositories/CodeFormer/assets/inpainting_result2.png b/repositories/CodeFormer/assets/inpainting_result2.png
deleted file mode 100644
index 2945f9f91c93c329c5e66d4e8519dbb3f90fa1b5..0000000000000000000000000000000000000000
Binary files a/repositories/CodeFormer/assets/inpainting_result2.png and /dev/null differ
diff --git a/repositories/CodeFormer/assets/network.jpg b/repositories/CodeFormer/assets/network.jpg
deleted file mode 100644
index 5aaa6bd1b0f71bf28e5f175c2cda1e7b34b8aa5f..0000000000000000000000000000000000000000
Binary files a/repositories/CodeFormer/assets/network.jpg and /dev/null differ
diff --git a/repositories/CodeFormer/assets/restoration_result1.png b/repositories/CodeFormer/assets/restoration_result1.png
deleted file mode 100644
index 8fd3b67ec9a5c9b7606ea0515a5b071c1e7a1118..0000000000000000000000000000000000000000
Binary files a/repositories/CodeFormer/assets/restoration_result1.png and /dev/null differ
diff --git a/repositories/CodeFormer/assets/restoration_result2.png b/repositories/CodeFormer/assets/restoration_result2.png
deleted file mode 100644
index a2ff282701b6c66a612b3b669512e8d99595ee9f..0000000000000000000000000000000000000000
Binary files a/repositories/CodeFormer/assets/restoration_result2.png and /dev/null differ
diff --git a/repositories/CodeFormer/assets/restoration_result3.png b/repositories/CodeFormer/assets/restoration_result3.png
deleted file mode 100644
index 022d764266b4d43f4ffea6b1f7ccca63b32e180c..0000000000000000000000000000000000000000
Binary files a/repositories/CodeFormer/assets/restoration_result3.png and /dev/null differ
diff --git a/repositories/CodeFormer/assets/restoration_result4.png b/repositories/CodeFormer/assets/restoration_result4.png
deleted file mode 100644
index 5e965076c7b5fae051dc2df354f74c0864ec4214..0000000000000000000000000000000000000000
Binary files a/repositories/CodeFormer/assets/restoration_result4.png and /dev/null differ
diff --git a/repositories/CodeFormer/basicsr/VERSION b/repositories/CodeFormer/basicsr/VERSION
deleted file mode 100644
index 1892b926767774e9ba91f1e584fa71b4c56abb69..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/basicsr/VERSION
+++ /dev/null
@@ -1 +0,0 @@
-1.3.2
diff --git a/repositories/CodeFormer/basicsr/__init__.py b/repositories/CodeFormer/basicsr/__init__.py
deleted file mode 100644
index c7ffcccd7fc0f33b59d99d73d0436d60e561b0fc..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/basicsr/__init__.py
+++ /dev/null
@@ -1,11 +0,0 @@
-# https://github.com/xinntao/BasicSR
-# flake8: noqa
-from .archs import *
-from .data import *
-from .losses import *
-from .metrics import *
-from .models import *
-from .ops import *
-from .train import *
-from .utils import *
-from .version import __gitsha__, __version__
diff --git a/repositories/CodeFormer/basicsr/archs/__init__.py b/repositories/CodeFormer/basicsr/archs/__init__.py
deleted file mode 100644
index cfb1e4d7bb221c429082bd389d9140e5b1cc07b0..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/basicsr/archs/__init__.py
+++ /dev/null
@@ -1,25 +0,0 @@
-import importlib
-from copy import deepcopy
-from os import path as osp
-
-from basicsr.utils import get_root_logger, scandir
-from basicsr.utils.registry import ARCH_REGISTRY
-
-__all__ = ['build_network']
-
-# automatically scan and import arch modules for registry
-# scan all the files under the 'archs' folder and collect files ending with
-# '_arch.py'
-arch_folder = osp.dirname(osp.abspath(__file__))
-arch_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(arch_folder) if v.endswith('_arch.py')]
-# import all the arch modules
-_arch_modules = [importlib.import_module(f'basicsr.archs.{file_name}') for file_name in arch_filenames]
-
-
-def build_network(opt):
-    opt = deepcopy(opt)
-    network_type = opt.pop('type')
-    net = ARCH_REGISTRY.get(network_type)(**opt)
-    logger = get_root_logger()
-    logger.info(f'Network [{net.__class__.__name__}] is created.')
-    return net
diff --git a/repositories/CodeFormer/basicsr/archs/arcface_arch.py b/repositories/CodeFormer/basicsr/archs/arcface_arch.py
deleted file mode 100644
index fe5afb7bd2b359e0c2b7efdf628ab10b63964d87..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/basicsr/archs/arcface_arch.py
+++ /dev/null
@@ -1,245 +0,0 @@
-import torch.nn as nn
-from basicsr.utils.registry import ARCH_REGISTRY
-
-
-def conv3x3(inplanes, outplanes, stride=1):
-    """A simple wrapper for 3x3 convolution with padding.
-
-    Args:
-        inplanes (int): Channel number of inputs.
-        outplanes (int): Channel number of outputs.
-        stride (int): Stride in convolution. Default: 1.
-    """
-    return nn.Conv2d(inplanes, outplanes, kernel_size=3, stride=stride, padding=1, bias=False)
-
-
-class BasicBlock(nn.Module):
-    """Basic residual block used in the ResNetArcFace architecture.
-
-    Args:
-        inplanes (int): Channel number of inputs.
-        planes (int): Channel number of outputs.
-        stride (int): Stride in convolution. Default: 1.
-        downsample (nn.Module): The downsample module. Default: None.
-    """
-    expansion = 1  # output channel expansion ratio
-
-    def __init__(self, inplanes, planes, stride=1, downsample=None):
-        super(BasicBlock, self).__init__()
-        self.conv1 = conv3x3(inplanes, planes, stride)
-        self.bn1 = nn.BatchNorm2d(planes)
-        self.relu = nn.ReLU(inplace=True)
-        self.conv2 = conv3x3(planes, planes)
-        self.bn2 = nn.BatchNorm2d(planes)
-        self.downsample = downsample
-        self.stride = stride
-
-    def forward(self, x):
-        residual = x
-
-        out = self.conv1(x)
-        out = self.bn1(out)
-        out = self.relu(out)
-
-        out = self.conv2(out)
-        out = self.bn2(out)
-
-        if self.downsample is not None:
-            residual = self.downsample(x)
-
-        out += residual
-        out = self.relu(out)
-
-        return out
-
-
-class IRBlock(nn.Module):
-    """Improved residual block (IR Block) used in the ResNetArcFace architecture.
-
-    Args:
-        inplanes (int): Channel number of inputs.
-        planes (int): Channel number of outputs.
-        stride (int): Stride in convolution. Default: 1.
-        downsample (nn.Module): The downsample module. Default: None.
-        use_se (bool): Whether use the SEBlock (squeeze and excitation block). Default: True.
-    """
-    expansion = 1  # output channel expansion ratio
-
-    def __init__(self, inplanes, planes, stride=1, downsample=None, use_se=True):
-        super(IRBlock, self).__init__()
-        self.bn0 = nn.BatchNorm2d(inplanes)
-        self.conv1 = conv3x3(inplanes, inplanes)
-        self.bn1 = nn.BatchNorm2d(inplanes)
-        self.prelu = nn.PReLU()
-        self.conv2 = conv3x3(inplanes, planes, stride)
-        self.bn2 = nn.BatchNorm2d(planes)
-        self.downsample = downsample
-        self.stride = stride
-        self.use_se = use_se
-        if self.use_se:
-            self.se = SEBlock(planes)
-
-    def forward(self, x):
-        residual = x
-        out = self.bn0(x)
-        out = self.conv1(out)
-        out = self.bn1(out)
-        out = self.prelu(out)
-
-        out = self.conv2(out)
-        out = self.bn2(out)
-        if self.use_se:
-            out = self.se(out)
-
-        if self.downsample is not None:
-            residual = self.downsample(x)
-
-        out += residual
-        out = self.prelu(out)
-
-        return out
-
-
-class Bottleneck(nn.Module):
-    """Bottleneck block used in the ResNetArcFace architecture.
-
-    Args:
-        inplanes (int): Channel number of inputs.
-        planes (int): Channel number of outputs.
-        stride (int): Stride in convolution. Default: 1.
-        downsample (nn.Module): The downsample module. Default: None.
-    """
-    expansion = 4  # output channel expansion ratio
-
-    def __init__(self, inplanes, planes, stride=1, downsample=None):
-        super(Bottleneck, self).__init__()
-        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
-        self.bn1 = nn.BatchNorm2d(planes)
-        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
-        self.bn2 = nn.BatchNorm2d(planes)
-        self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False)
-        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
-        self.relu = nn.ReLU(inplace=True)
-        self.downsample = downsample
-        self.stride = stride
-
-    def forward(self, x):
-        residual = x
-
-        out = self.conv1(x)
-        out = self.bn1(out)
-        out = self.relu(out)
-
-        out = self.conv2(out)
-        out = self.bn2(out)
-        out = self.relu(out)
-
-        out = self.conv3(out)
-        out = self.bn3(out)
-
-        if self.downsample is not None:
-            residual = self.downsample(x)
-
-        out += residual
-        out = self.relu(out)
-
-        return out
-
-
-class SEBlock(nn.Module):
-    """The squeeze-and-excitation block (SEBlock) used in the IRBlock.
-
-    Args:
-        channel (int): Channel number of inputs.
-        reduction (int): Channel reduction ration. Default: 16.
-    """
-
-    def __init__(self, channel, reduction=16):
-        super(SEBlock, self).__init__()
-        self.avg_pool = nn.AdaptiveAvgPool2d(1)  # pool to 1x1 without spatial information
-        self.fc = nn.Sequential(
-            nn.Linear(channel, channel // reduction), nn.PReLU(), nn.Linear(channel // reduction, channel),
-            nn.Sigmoid())
-
-    def forward(self, x):
-        b, c, _, _ = x.size()
-        y = self.avg_pool(x).view(b, c)
-        y = self.fc(y).view(b, c, 1, 1)
-        return x * y
-
-
-@ARCH_REGISTRY.register()
-class ResNetArcFace(nn.Module):
-    """ArcFace with ResNet architectures.
-
-    Ref: ArcFace: Additive Angular Margin Loss for Deep Face Recognition.
-
-    Args:
-        block (str): Block used in the ArcFace architecture.
-        layers (tuple(int)): Block numbers in each layer.
-        use_se (bool): Whether use the SEBlock (squeeze and excitation block). Default: True.
-    """
-
-    def __init__(self, block, layers, use_se=True):
-        if block == 'IRBlock':
-            block = IRBlock
-        self.inplanes = 64
-        self.use_se = use_se
-        super(ResNetArcFace, self).__init__()
-
-        self.conv1 = nn.Conv2d(1, 64, kernel_size=3, padding=1, bias=False)
-        self.bn1 = nn.BatchNorm2d(64)
-        self.prelu = nn.PReLU()
-        self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2)
-        self.layer1 = self._make_layer(block, 64, layers[0])
-        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
-        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
-        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
-        self.bn4 = nn.BatchNorm2d(512)
-        self.dropout = nn.Dropout()
-        self.fc5 = nn.Linear(512 * 8 * 8, 512)
-        self.bn5 = nn.BatchNorm1d(512)
-
-        # initialization
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                nn.init.xavier_normal_(m.weight)
-            elif isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.BatchNorm1d):
-                nn.init.constant_(m.weight, 1)
-                nn.init.constant_(m.bias, 0)
-            elif isinstance(m, nn.Linear):
-                nn.init.xavier_normal_(m.weight)
-                nn.init.constant_(m.bias, 0)
-
-    def _make_layer(self, block, planes, num_blocks, stride=1):
-        downsample = None
-        if stride != 1 or self.inplanes != planes * block.expansion:
-            downsample = nn.Sequential(
-                nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False),
-                nn.BatchNorm2d(planes * block.expansion),
-            )
-        layers = []
-        layers.append(block(self.inplanes, planes, stride, downsample, use_se=self.use_se))
-        self.inplanes = planes
-        for _ in range(1, num_blocks):
-            layers.append(block(self.inplanes, planes, use_se=self.use_se))
-
-        return nn.Sequential(*layers)
-
-    def forward(self, x):
-        x = self.conv1(x)
-        x = self.bn1(x)
-        x = self.prelu(x)
-        x = self.maxpool(x)
-
-        x = self.layer1(x)
-        x = self.layer2(x)
-        x = self.layer3(x)
-        x = self.layer4(x)
-        x = self.bn4(x)
-        x = self.dropout(x)
-        x = x.view(x.size(0), -1)
-        x = self.fc5(x)
-        x = self.bn5(x)
-
-        return x
\ No newline at end of file
diff --git a/repositories/CodeFormer/basicsr/archs/arch_util.py b/repositories/CodeFormer/basicsr/archs/arch_util.py
deleted file mode 100644
index bad45ab34e901c47fb539152fca714a3795b0de2..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/basicsr/archs/arch_util.py
+++ /dev/null
@@ -1,318 +0,0 @@
-import collections.abc
-import math
-import torch
-import torchvision
-import warnings
-from distutils.version import LooseVersion
-from itertools import repeat
-from torch import nn as nn
-from torch.nn import functional as F
-from torch.nn import init as init
-from torch.nn.modules.batchnorm import _BatchNorm
-
-from basicsr.ops.dcn import ModulatedDeformConvPack, modulated_deform_conv
-from basicsr.utils import get_root_logger
-
-
-@torch.no_grad()
-def default_init_weights(module_list, scale=1, bias_fill=0, **kwargs):
-    """Initialize network weights.
-
-    Args:
-        module_list (list[nn.Module] | nn.Module): Modules to be initialized.
-        scale (float): Scale initialized weights, especially for residual
-            blocks. Default: 1.
-        bias_fill (float): The value to fill bias. Default: 0
-        kwargs (dict): Other arguments for initialization function.
-    """
-    if not isinstance(module_list, list):
-        module_list = [module_list]
-    for module in module_list:
-        for m in module.modules():
-            if isinstance(m, nn.Conv2d):
-                init.kaiming_normal_(m.weight, **kwargs)
-                m.weight.data *= scale
-                if m.bias is not None:
-                    m.bias.data.fill_(bias_fill)
-            elif isinstance(m, nn.Linear):
-                init.kaiming_normal_(m.weight, **kwargs)
-                m.weight.data *= scale
-                if m.bias is not None:
-                    m.bias.data.fill_(bias_fill)
-            elif isinstance(m, _BatchNorm):
-                init.constant_(m.weight, 1)
-                if m.bias is not None:
-                    m.bias.data.fill_(bias_fill)
-
-
-def make_layer(basic_block, num_basic_block, **kwarg):
-    """Make layers by stacking the same blocks.
-
-    Args:
-        basic_block (nn.module): nn.module class for basic block.
-        num_basic_block (int): number of blocks.
-
-    Returns:
-        nn.Sequential: Stacked blocks in nn.Sequential.
-    """
-    layers = []
-    for _ in range(num_basic_block):
-        layers.append(basic_block(**kwarg))
-    return nn.Sequential(*layers)
-
-
-class ResidualBlockNoBN(nn.Module):
-    """Residual block without BN.
-
-    It has a style of:
-        ---Conv-ReLU-Conv-+-
-         |________________|
-
-    Args:
-        num_feat (int): Channel number of intermediate features.
-            Default: 64.
-        res_scale (float): Residual scale. Default: 1.
-        pytorch_init (bool): If set to True, use pytorch default init,
-            otherwise, use default_init_weights. Default: False.
-    """
-
-    def __init__(self, num_feat=64, res_scale=1, pytorch_init=False):
-        super(ResidualBlockNoBN, self).__init__()
-        self.res_scale = res_scale
-        self.conv1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=True)
-        self.conv2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=True)
-        self.relu = nn.ReLU(inplace=True)
-
-        if not pytorch_init:
-            default_init_weights([self.conv1, self.conv2], 0.1)
-
-    def forward(self, x):
-        identity = x
-        out = self.conv2(self.relu(self.conv1(x)))
-        return identity + out * self.res_scale
-
-
-class Upsample(nn.Sequential):
-    """Upsample module.
-
-    Args:
-        scale (int): Scale factor. Supported scales: 2^n and 3.
-        num_feat (int): Channel number of intermediate features.
-    """
-
-    def __init__(self, scale, num_feat):
-        m = []
-        if (scale & (scale - 1)) == 0:  # scale = 2^n
-            for _ in range(int(math.log(scale, 2))):
-                m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
-                m.append(nn.PixelShuffle(2))
-        elif scale == 3:
-            m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
-            m.append(nn.PixelShuffle(3))
-        else:
-            raise ValueError(f'scale {scale} is not supported. Supported scales: 2^n and 3.')
-        super(Upsample, self).__init__(*m)
-
-
-def flow_warp(x, flow, interp_mode='bilinear', padding_mode='zeros', align_corners=True):
-    """Warp an image or feature map with optical flow.
-
-    Args:
-        x (Tensor): Tensor with size (n, c, h, w).
-        flow (Tensor): Tensor with size (n, h, w, 2), normal value.
-        interp_mode (str): 'nearest' or 'bilinear'. Default: 'bilinear'.
-        padding_mode (str): 'zeros' or 'border' or 'reflection'.
-            Default: 'zeros'.
-        align_corners (bool): Before pytorch 1.3, the default value is
-            align_corners=True. After pytorch 1.3, the default value is
-            align_corners=False. Here, we use the True as default.
-
-    Returns:
-        Tensor: Warped image or feature map.
-    """
-    assert x.size()[-2:] == flow.size()[1:3]
-    _, _, h, w = x.size()
-    # create mesh grid
-    grid_y, grid_x = torch.meshgrid(torch.arange(0, h).type_as(x), torch.arange(0, w).type_as(x))
-    grid = torch.stack((grid_x, grid_y), 2).float()  # W(x), H(y), 2
-    grid.requires_grad = False
-
-    vgrid = grid + flow
-    # scale grid to [-1,1]
-    vgrid_x = 2.0 * vgrid[:, :, :, 0] / max(w - 1, 1) - 1.0
-    vgrid_y = 2.0 * vgrid[:, :, :, 1] / max(h - 1, 1) - 1.0
-    vgrid_scaled = torch.stack((vgrid_x, vgrid_y), dim=3)
-    output = F.grid_sample(x, vgrid_scaled, mode=interp_mode, padding_mode=padding_mode, align_corners=align_corners)
-
-    # TODO, what if align_corners=False
-    return output
-
-
-def resize_flow(flow, size_type, sizes, interp_mode='bilinear', align_corners=False):
-    """Resize a flow according to ratio or shape.
-
-    Args:
-        flow (Tensor): Precomputed flow. shape [N, 2, H, W].
-        size_type (str): 'ratio' or 'shape'.
-        sizes (list[int | float]): the ratio for resizing or the final output
-            shape.
-            1) The order of ratio should be [ratio_h, ratio_w]. For
-            downsampling, the ratio should be smaller than 1.0 (i.e., ratio
-            < 1.0). For upsampling, the ratio should be larger than 1.0 (i.e.,
-            ratio > 1.0).
-            2) The order of output_size should be [out_h, out_w].
-        interp_mode (str): The mode of interpolation for resizing.
-            Default: 'bilinear'.
-        align_corners (bool): Whether align corners. Default: False.
-
-    Returns:
-        Tensor: Resized flow.
-    """
-    _, _, flow_h, flow_w = flow.size()
-    if size_type == 'ratio':
-        output_h, output_w = int(flow_h * sizes[0]), int(flow_w * sizes[1])
-    elif size_type == 'shape':
-        output_h, output_w = sizes[0], sizes[1]
-    else:
-        raise ValueError(f'Size type should be ratio or shape, but got type {size_type}.')
-
-    input_flow = flow.clone()
-    ratio_h = output_h / flow_h
-    ratio_w = output_w / flow_w
-    input_flow[:, 0, :, :] *= ratio_w
-    input_flow[:, 1, :, :] *= ratio_h
-    resized_flow = F.interpolate(
-        input=input_flow, size=(output_h, output_w), mode=interp_mode, align_corners=align_corners)
-    return resized_flow
-
-
-# TODO: may write a cpp file
-def pixel_unshuffle(x, scale):
-    """ Pixel unshuffle.
-
-    Args:
-        x (Tensor): Input feature with shape (b, c, hh, hw).
-        scale (int): Downsample ratio.
-
-    Returns:
-        Tensor: the pixel unshuffled feature.
-    """
-    b, c, hh, hw = x.size()
-    out_channel = c * (scale**2)
-    assert hh % scale == 0 and hw % scale == 0
-    h = hh // scale
-    w = hw // scale
-    x_view = x.view(b, c, h, scale, w, scale)
-    return x_view.permute(0, 1, 3, 5, 2, 4).reshape(b, out_channel, h, w)
-
-
-class DCNv2Pack(ModulatedDeformConvPack):
-    """Modulated deformable conv for deformable alignment.
-
-    Different from the official DCNv2Pack, which generates offsets and masks
-    from the preceding features, this DCNv2Pack takes another different
-    features to generate offsets and masks.
-
-    Ref:
-        Delving Deep into Deformable Alignment in Video Super-Resolution.
-    """
-
-    def forward(self, x, feat):
-        out = self.conv_offset(feat)
-        o1, o2, mask = torch.chunk(out, 3, dim=1)
-        offset = torch.cat((o1, o2), dim=1)
-        mask = torch.sigmoid(mask)
-
-        offset_absmean = torch.mean(torch.abs(offset))
-        if offset_absmean > 50:
-            logger = get_root_logger()
-            logger.warning(f'Offset abs mean is {offset_absmean}, larger than 50.')
-
-        if LooseVersion(torchvision.__version__) >= LooseVersion('0.9.0'):
-            return torchvision.ops.deform_conv2d(x, offset, self.weight, self.bias, self.stride, self.padding,
-                                                 self.dilation, mask)
-        else:
-            return modulated_deform_conv(x, offset, mask, self.weight, self.bias, self.stride, self.padding,
-                                         self.dilation, self.groups, self.deformable_groups)
-
-
-def _no_grad_trunc_normal_(tensor, mean, std, a, b):
-    # From: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/weight_init.py
-    # Cut & paste from PyTorch official master until it's in a few official releases - RW
-    # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
-    def norm_cdf(x):
-        # Computes standard normal cumulative distribution function
-        return (1. + math.erf(x / math.sqrt(2.))) / 2.
-
-    if (mean < a - 2 * std) or (mean > b + 2 * std):
-        warnings.warn(
-            'mean is more than 2 std from [a, b] in nn.init.trunc_normal_. '
-            'The distribution of values may be incorrect.',
-            stacklevel=2)
-
-    with torch.no_grad():
-        # Values are generated by using a truncated uniform distribution and
-        # then using the inverse CDF for the normal distribution.
-        # Get upper and lower cdf values
-        low = norm_cdf((a - mean) / std)
-        up = norm_cdf((b - mean) / std)
-
-        # Uniformly fill tensor with values from [low, up], then translate to
-        # [2l-1, 2u-1].
-        tensor.uniform_(2 * low - 1, 2 * up - 1)
-
-        # Use inverse cdf transform for normal distribution to get truncated
-        # standard normal
-        tensor.erfinv_()
-
-        # Transform to proper mean, std
-        tensor.mul_(std * math.sqrt(2.))
-        tensor.add_(mean)
-
-        # Clamp to ensure it's in the proper range
-        tensor.clamp_(min=a, max=b)
-        return tensor
-
-
-def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
-    r"""Fills the input Tensor with values drawn from a truncated
-    normal distribution.
-
-    From: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/weight_init.py
-
-    The values are effectively drawn from the
-    normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
-    with values outside :math:`[a, b]` redrawn until they are within
-    the bounds. The method used for generating the random values works
-    best when :math:`a \leq \text{mean} \leq b`.
-
-    Args:
-        tensor: an n-dimensional `torch.Tensor`
-        mean: the mean of the normal distribution
-        std: the standard deviation of the normal distribution
-        a: the minimum cutoff value
-        b: the maximum cutoff value
-
-    Examples:
-        >>> w = torch.empty(3, 5)
-        >>> nn.init.trunc_normal_(w)
-    """
-    return _no_grad_trunc_normal_(tensor, mean, std, a, b)
-
-
-# From PyTorch
-def _ntuple(n):
-
-    def parse(x):
-        if isinstance(x, collections.abc.Iterable):
-            return x
-        return tuple(repeat(x, n))
-
-    return parse
-
-
-to_1tuple = _ntuple(1)
-to_2tuple = _ntuple(2)
-to_3tuple = _ntuple(3)
-to_4tuple = _ntuple(4)
-to_ntuple = _ntuple
\ No newline at end of file
diff --git a/repositories/CodeFormer/basicsr/archs/codeformer_arch.py b/repositories/CodeFormer/basicsr/archs/codeformer_arch.py
deleted file mode 100644
index 4d0d8027c8c4ffb26af6f4ba361514e93e320e8d..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/basicsr/archs/codeformer_arch.py
+++ /dev/null
@@ -1,276 +0,0 @@
-import math
-import numpy as np
-import torch
-from torch import nn, Tensor
-import torch.nn.functional as F
-from typing import Optional, List
-
-from basicsr.archs.vqgan_arch import *
-from basicsr.utils import get_root_logger
-from basicsr.utils.registry import ARCH_REGISTRY
-
-def calc_mean_std(feat, eps=1e-5):
-    """Calculate mean and std for adaptive_instance_normalization.
-
-    Args:
-        feat (Tensor): 4D tensor.
-        eps (float): A small value added to the variance to avoid
-            divide-by-zero. Default: 1e-5.
-    """
-    size = feat.size()
-    assert len(size) == 4, 'The input feature should be 4D tensor.'
-    b, c = size[:2]
-    feat_var = feat.view(b, c, -1).var(dim=2) + eps
-    feat_std = feat_var.sqrt().view(b, c, 1, 1)
-    feat_mean = feat.view(b, c, -1).mean(dim=2).view(b, c, 1, 1)
-    return feat_mean, feat_std
-
-
-def adaptive_instance_normalization(content_feat, style_feat):
-    """Adaptive instance normalization.
-
-    Adjust the reference features to have the similar color and illuminations
-    as those in the degradate features.
-
-    Args:
-        content_feat (Tensor): The reference feature.
-        style_feat (Tensor): The degradate features.
-    """
-    size = content_feat.size()
-    style_mean, style_std = calc_mean_std(style_feat)
-    content_mean, content_std = calc_mean_std(content_feat)
-    normalized_feat = (content_feat - content_mean.expand(size)) / content_std.expand(size)
-    return normalized_feat * style_std.expand(size) + style_mean.expand(size)
-
-
-class PositionEmbeddingSine(nn.Module):
-    """
-    This is a more standard version of the position embedding, very similar to the one
-    used by the Attention is all you need paper, generalized to work on images.
-    """
-
-    def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None):
-        super().__init__()
-        self.num_pos_feats = num_pos_feats
-        self.temperature = temperature
-        self.normalize = normalize
-        if scale is not None and normalize is False:
-            raise ValueError("normalize should be True if scale is passed")
-        if scale is None:
-            scale = 2 * math.pi
-        self.scale = scale
-
-    def forward(self, x, mask=None):
-        if mask is None:
-            mask = torch.zeros((x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool)
-        not_mask = ~mask
-        y_embed = not_mask.cumsum(1, dtype=torch.float32)
-        x_embed = not_mask.cumsum(2, dtype=torch.float32)
-        if self.normalize:
-            eps = 1e-6
-            y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
-            x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
-
-        dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
-        dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)
-
-        pos_x = x_embed[:, :, :, None] / dim_t
-        pos_y = y_embed[:, :, :, None] / dim_t
-        pos_x = torch.stack(
-            (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4
-        ).flatten(3)
-        pos_y = torch.stack(
-            (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4
-        ).flatten(3)
-        pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
-        return pos
-
-def _get_activation_fn(activation):
-    """Return an activation function given a string"""
-    if activation == "relu":
-        return F.relu
-    if activation == "gelu":
-        return F.gelu
-    if activation == "glu":
-        return F.glu
-    raise RuntimeError(F"activation should be relu/gelu, not {activation}.")
-
-
-class TransformerSALayer(nn.Module):
-    def __init__(self, embed_dim, nhead=8, dim_mlp=2048, dropout=0.0, activation="gelu"):
-        super().__init__()
-        self.self_attn = nn.MultiheadAttention(embed_dim, nhead, dropout=dropout)
-        # Implementation of Feedforward model - MLP
-        self.linear1 = nn.Linear(embed_dim, dim_mlp)
-        self.dropout = nn.Dropout(dropout)
-        self.linear2 = nn.Linear(dim_mlp, embed_dim)
-
-        self.norm1 = nn.LayerNorm(embed_dim)
-        self.norm2 = nn.LayerNorm(embed_dim)
-        self.dropout1 = nn.Dropout(dropout)
-        self.dropout2 = nn.Dropout(dropout)
-
-        self.activation = _get_activation_fn(activation)
-
-    def with_pos_embed(self, tensor, pos: Optional[Tensor]):
-        return tensor if pos is None else tensor + pos
-
-    def forward(self, tgt,
-                tgt_mask: Optional[Tensor] = None,
-                tgt_key_padding_mask: Optional[Tensor] = None,
-                query_pos: Optional[Tensor] = None):
-        
-        # self attention
-        tgt2 = self.norm1(tgt)
-        q = k = self.with_pos_embed(tgt2, query_pos)
-        tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask,
-                              key_padding_mask=tgt_key_padding_mask)[0]
-        tgt = tgt + self.dropout1(tgt2)
-
-        # ffn
-        tgt2 = self.norm2(tgt)
-        tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))
-        tgt = tgt + self.dropout2(tgt2)
-        return tgt
-
-class Fuse_sft_block(nn.Module):
-    def __init__(self, in_ch, out_ch):
-        super().__init__()
-        self.encode_enc = ResBlock(2*in_ch, out_ch)
-
-        self.scale = nn.Sequential(
-                    nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1),
-                    nn.LeakyReLU(0.2, True),
-                    nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1))
-
-        self.shift = nn.Sequential(
-                    nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1),
-                    nn.LeakyReLU(0.2, True),
-                    nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1))
-
-    def forward(self, enc_feat, dec_feat, w=1):
-        enc_feat = self.encode_enc(torch.cat([enc_feat, dec_feat], dim=1))
-        scale = self.scale(enc_feat)
-        shift = self.shift(enc_feat)
-        residual = w * (dec_feat * scale + shift)
-        out = dec_feat + residual
-        return out
-
-
-@ARCH_REGISTRY.register()
-class CodeFormer(VQAutoEncoder):
-    def __init__(self, dim_embd=512, n_head=8, n_layers=9, 
-                codebook_size=1024, latent_size=256,
-                connect_list=['32', '64', '128', '256'],
-                fix_modules=['quantize','generator']):
-        super(CodeFormer, self).__init__(512, 64, [1, 2, 2, 4, 4, 8], 'nearest',2, [16], codebook_size)
-
-        if fix_modules is not None:
-            for module in fix_modules:
-                for param in getattr(self, module).parameters():
-                    param.requires_grad = False
-
-        self.connect_list = connect_list
-        self.n_layers = n_layers
-        self.dim_embd = dim_embd
-        self.dim_mlp = dim_embd*2
-
-        self.position_emb = nn.Parameter(torch.zeros(latent_size, self.dim_embd))
-        self.feat_emb = nn.Linear(256, self.dim_embd)
-
-        # transformer
-        self.ft_layers = nn.Sequential(*[TransformerSALayer(embed_dim=dim_embd, nhead=n_head, dim_mlp=self.dim_mlp, dropout=0.0) 
-                                    for _ in range(self.n_layers)])
-
-        # logits_predict head
-        self.idx_pred_layer = nn.Sequential(
-            nn.LayerNorm(dim_embd),
-            nn.Linear(dim_embd, codebook_size, bias=False))
-        
-        self.channels = {
-            '16': 512,
-            '32': 256,
-            '64': 256,
-            '128': 128,
-            '256': 128,
-            '512': 64,
-        }
-
-        # after second residual block for > 16, before attn layer for ==16
-        self.fuse_encoder_block = {'512':2, '256':5, '128':8, '64':11, '32':14, '16':18}
-        # after first residual block for > 16, before attn layer for ==16
-        self.fuse_generator_block = {'16':6, '32': 9, '64':12, '128':15, '256':18, '512':21}
-
-        # fuse_convs_dict
-        self.fuse_convs_dict = nn.ModuleDict()
-        for f_size in self.connect_list:
-            in_ch = self.channels[f_size]
-            self.fuse_convs_dict[f_size] = Fuse_sft_block(in_ch, in_ch)
-
-    def _init_weights(self, module):
-        if isinstance(module, (nn.Linear, nn.Embedding)):
-            module.weight.data.normal_(mean=0.0, std=0.02)
-            if isinstance(module, nn.Linear) and module.bias is not None:
-                module.bias.data.zero_()
-        elif isinstance(module, nn.LayerNorm):
-            module.bias.data.zero_()
-            module.weight.data.fill_(1.0)
-
-    def forward(self, x, w=0, detach_16=True, code_only=False, adain=False):
-        # ################### Encoder #####################
-        enc_feat_dict = {}
-        out_list = [self.fuse_encoder_block[f_size] for f_size in self.connect_list]
-        for i, block in enumerate(self.encoder.blocks):
-            x = block(x) 
-            if i in out_list:
-                enc_feat_dict[str(x.shape[-1])] = x.clone()
-
-        lq_feat = x
-        # ################# Transformer ###################
-        # quant_feat, codebook_loss, quant_stats = self.quantize(lq_feat)
-        pos_emb = self.position_emb.unsqueeze(1).repeat(1,x.shape[0],1)
-        # BCHW -> BC(HW) -> (HW)BC
-        feat_emb = self.feat_emb(lq_feat.flatten(2).permute(2,0,1))
-        query_emb = feat_emb
-        # Transformer encoder
-        for layer in self.ft_layers:
-            query_emb = layer(query_emb, query_pos=pos_emb)
-
-        # output logits
-        logits = self.idx_pred_layer(query_emb) # (hw)bn
-        logits = logits.permute(1,0,2) # (hw)bn -> b(hw)n
-
-        if code_only: # for training stage II
-          # logits doesn't need softmax before cross_entropy loss
-            return logits, lq_feat
-
-        # ################# Quantization ###################
-        # if self.training:
-        #     quant_feat = torch.einsum('btn,nc->btc', [soft_one_hot, self.quantize.embedding.weight])
-        #     # b(hw)c -> bc(hw) -> bchw
-        #     quant_feat = quant_feat.permute(0,2,1).view(lq_feat.shape)
-        # ------------
-        soft_one_hot = F.softmax(logits, dim=2)
-        _, top_idx = torch.topk(soft_one_hot, 1, dim=2)
-        quant_feat = self.quantize.get_codebook_feat(top_idx, shape=[x.shape[0],16,16,256])
-        # preserve gradients
-        # quant_feat = lq_feat + (quant_feat - lq_feat).detach()
-
-        if detach_16:
-            quant_feat = quant_feat.detach() # for training stage III
-        if adain:
-            quant_feat = adaptive_instance_normalization(quant_feat, lq_feat)
-
-        # ################## Generator ####################
-        x = quant_feat
-        fuse_list = [self.fuse_generator_block[f_size] for f_size in self.connect_list]
-
-        for i, block in enumerate(self.generator.blocks):
-            x = block(x) 
-            if i in fuse_list: # fuse after i-th block
-                f_size = str(x.shape[-1])
-                if w>0:
-                    x = self.fuse_convs_dict[f_size](enc_feat_dict[f_size].detach(), x, w)
-        out = x
-        # logits doesn't need softmax before cross_entropy loss
-        return out, logits, lq_feat
\ No newline at end of file
diff --git a/repositories/CodeFormer/basicsr/archs/rrdbnet_arch.py b/repositories/CodeFormer/basicsr/archs/rrdbnet_arch.py
deleted file mode 100644
index 49a2d6c204557cba53ada7550deb587541855cfb..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/basicsr/archs/rrdbnet_arch.py
+++ /dev/null
@@ -1,119 +0,0 @@
-import torch
-from torch import nn as nn
-from torch.nn import functional as F
-
-from basicsr.utils.registry import ARCH_REGISTRY
-from .arch_util import default_init_weights, make_layer, pixel_unshuffle
-
-
-class ResidualDenseBlock(nn.Module):
-    """Residual Dense Block.
-
-    Used in RRDB block in ESRGAN.
-
-    Args:
-        num_feat (int): Channel number of intermediate features.
-        num_grow_ch (int): Channels for each growth.
-    """
-
-    def __init__(self, num_feat=64, num_grow_ch=32):
-        super(ResidualDenseBlock, self).__init__()
-        self.conv1 = nn.Conv2d(num_feat, num_grow_ch, 3, 1, 1)
-        self.conv2 = nn.Conv2d(num_feat + num_grow_ch, num_grow_ch, 3, 1, 1)
-        self.conv3 = nn.Conv2d(num_feat + 2 * num_grow_ch, num_grow_ch, 3, 1, 1)
-        self.conv4 = nn.Conv2d(num_feat + 3 * num_grow_ch, num_grow_ch, 3, 1, 1)
-        self.conv5 = nn.Conv2d(num_feat + 4 * num_grow_ch, num_feat, 3, 1, 1)
-
-        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
-
-        # initialization
-        default_init_weights([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1)
-
-    def forward(self, x):
-        x1 = self.lrelu(self.conv1(x))
-        x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))
-        x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))
-        x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1)))
-        x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
-        # Emperically, we use 0.2 to scale the residual for better performance
-        return x5 * 0.2 + x
-
-
-class RRDB(nn.Module):
-    """Residual in Residual Dense Block.
-
-    Used in RRDB-Net in ESRGAN.
-
-    Args:
-        num_feat (int): Channel number of intermediate features.
-        num_grow_ch (int): Channels for each growth.
-    """
-
-    def __init__(self, num_feat, num_grow_ch=32):
-        super(RRDB, self).__init__()
-        self.rdb1 = ResidualDenseBlock(num_feat, num_grow_ch)
-        self.rdb2 = ResidualDenseBlock(num_feat, num_grow_ch)
-        self.rdb3 = ResidualDenseBlock(num_feat, num_grow_ch)
-
-    def forward(self, x):
-        out = self.rdb1(x)
-        out = self.rdb2(out)
-        out = self.rdb3(out)
-        # Emperically, we use 0.2 to scale the residual for better performance
-        return out * 0.2 + x
-
-
-@ARCH_REGISTRY.register()
-class RRDBNet(nn.Module):
-    """Networks consisting of Residual in Residual Dense Block, which is used
-    in ESRGAN.
-
-    ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks.
-
-    We extend ESRGAN for scale x2 and scale x1.
-    Note: This is one option for scale 1, scale 2 in RRDBNet.
-    We first employ the pixel-unshuffle (an inverse operation of pixelshuffle to reduce the spatial size
-    and enlarge the channel size before feeding inputs into the main ESRGAN architecture.
-
-    Args:
-        num_in_ch (int): Channel number of inputs.
-        num_out_ch (int): Channel number of outputs.
-        num_feat (int): Channel number of intermediate features.
-            Default: 64
-        num_block (int): Block number in the trunk network. Defaults: 23
-        num_grow_ch (int): Channels for each growth. Default: 32.
-    """
-
-    def __init__(self, num_in_ch, num_out_ch, scale=4, num_feat=64, num_block=23, num_grow_ch=32):
-        super(RRDBNet, self).__init__()
-        self.scale = scale
-        if scale == 2:
-            num_in_ch = num_in_ch * 4
-        elif scale == 1:
-            num_in_ch = num_in_ch * 16
-        self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)
-        self.body = make_layer(RRDB, num_block, num_feat=num_feat, num_grow_ch=num_grow_ch)
-        self.conv_body = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-        # upsample
-        self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-        self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-        self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-        self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-
-        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
-
-    def forward(self, x):
-        if self.scale == 2:
-            feat = pixel_unshuffle(x, scale=2)
-        elif self.scale == 1:
-            feat = pixel_unshuffle(x, scale=4)
-        else:
-            feat = x
-        feat = self.conv_first(feat)
-        body_feat = self.conv_body(self.body(feat))
-        feat = feat + body_feat
-        # upsample
-        feat = self.lrelu(self.conv_up1(F.interpolate(feat, scale_factor=2, mode='nearest')))
-        feat = self.lrelu(self.conv_up2(F.interpolate(feat, scale_factor=2, mode='nearest')))
-        out = self.conv_last(self.lrelu(self.conv_hr(feat)))
-        return out
\ No newline at end of file
diff --git a/repositories/CodeFormer/basicsr/archs/vgg_arch.py b/repositories/CodeFormer/basicsr/archs/vgg_arch.py
deleted file mode 100644
index 23bb0103c8b14ef2588028f7177753db9af62cae..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/basicsr/archs/vgg_arch.py
+++ /dev/null
@@ -1,161 +0,0 @@
-import os
-import torch
-from collections import OrderedDict
-from torch import nn as nn
-from torchvision.models import vgg as vgg
-
-from basicsr.utils.registry import ARCH_REGISTRY
-
-VGG_PRETRAIN_PATH = 'experiments/pretrained_models/vgg19-dcbb9e9d.pth'
-NAMES = {
-    'vgg11': [
-        'conv1_1', 'relu1_1', 'pool1', 'conv2_1', 'relu2_1', 'pool2', 'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2',
-        'pool3', 'conv4_1', 'relu4_1', 'conv4_2', 'relu4_2', 'pool4', 'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2',
-        'pool5'
-    ],
-    'vgg13': [
-        'conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1', 'conv2_1', 'relu2_1', 'conv2_2', 'relu2_2', 'pool2',
-        'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', 'pool3', 'conv4_1', 'relu4_1', 'conv4_2', 'relu4_2', 'pool4',
-        'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2', 'pool5'
-    ],
-    'vgg16': [
-        'conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1', 'conv2_1', 'relu2_1', 'conv2_2', 'relu2_2', 'pool2',
-        'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', 'conv3_3', 'relu3_3', 'pool3', 'conv4_1', 'relu4_1', 'conv4_2',
-        'relu4_2', 'conv4_3', 'relu4_3', 'pool4', 'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2', 'conv5_3', 'relu5_3',
-        'pool5'
-    ],
-    'vgg19': [
-        'conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1', 'conv2_1', 'relu2_1', 'conv2_2', 'relu2_2', 'pool2',
-        'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', 'conv3_3', 'relu3_3', 'conv3_4', 'relu3_4', 'pool3', 'conv4_1',
-        'relu4_1', 'conv4_2', 'relu4_2', 'conv4_3', 'relu4_3', 'conv4_4', 'relu4_4', 'pool4', 'conv5_1', 'relu5_1',
-        'conv5_2', 'relu5_2', 'conv5_3', 'relu5_3', 'conv5_4', 'relu5_4', 'pool5'
-    ]
-}
-
-
-def insert_bn(names):
-    """Insert bn layer after each conv.
-
-    Args:
-        names (list): The list of layer names.
-
-    Returns:
-        list: The list of layer names with bn layers.
-    """
-    names_bn = []
-    for name in names:
-        names_bn.append(name)
-        if 'conv' in name:
-            position = name.replace('conv', '')
-            names_bn.append('bn' + position)
-    return names_bn
-
-
-@ARCH_REGISTRY.register()
-class VGGFeatureExtractor(nn.Module):
-    """VGG network for feature extraction.
-
-    In this implementation, we allow users to choose whether use normalization
-    in the input feature and the type of vgg network. Note that the pretrained
-    path must fit the vgg type.
-
-    Args:
-        layer_name_list (list[str]): Forward function returns the corresponding
-            features according to the layer_name_list.
-            Example: {'relu1_1', 'relu2_1', 'relu3_1'}.
-        vgg_type (str): Set the type of vgg network. Default: 'vgg19'.
-        use_input_norm (bool): If True, normalize the input image. Importantly,
-            the input feature must in the range [0, 1]. Default: True.
-        range_norm (bool): If True, norm images with range [-1, 1] to [0, 1].
-            Default: False.
-        requires_grad (bool): If true, the parameters of VGG network will be
-            optimized. Default: False.
-        remove_pooling (bool): If true, the max pooling operations in VGG net
-            will be removed. Default: False.
-        pooling_stride (int): The stride of max pooling operation. Default: 2.
-    """
-
-    def __init__(self,
-                 layer_name_list,
-                 vgg_type='vgg19',
-                 use_input_norm=True,
-                 range_norm=False,
-                 requires_grad=False,
-                 remove_pooling=False,
-                 pooling_stride=2):
-        super(VGGFeatureExtractor, self).__init__()
-
-        self.layer_name_list = layer_name_list
-        self.use_input_norm = use_input_norm
-        self.range_norm = range_norm
-
-        self.names = NAMES[vgg_type.replace('_bn', '')]
-        if 'bn' in vgg_type:
-            self.names = insert_bn(self.names)
-
-        # only borrow layers that will be used to avoid unused params
-        max_idx = 0
-        for v in layer_name_list:
-            idx = self.names.index(v)
-            if idx > max_idx:
-                max_idx = idx
-
-        if os.path.exists(VGG_PRETRAIN_PATH):
-            vgg_net = getattr(vgg, vgg_type)(pretrained=False)
-            state_dict = torch.load(VGG_PRETRAIN_PATH, map_location=lambda storage, loc: storage)
-            vgg_net.load_state_dict(state_dict)
-        else:
-            vgg_net = getattr(vgg, vgg_type)(pretrained=True)
-
-        features = vgg_net.features[:max_idx + 1]
-
-        modified_net = OrderedDict()
-        for k, v in zip(self.names, features):
-            if 'pool' in k:
-                # if remove_pooling is true, pooling operation will be removed
-                if remove_pooling:
-                    continue
-                else:
-                    # in some cases, we may want to change the default stride
-                    modified_net[k] = nn.MaxPool2d(kernel_size=2, stride=pooling_stride)
-            else:
-                modified_net[k] = v
-
-        self.vgg_net = nn.Sequential(modified_net)
-
-        if not requires_grad:
-            self.vgg_net.eval()
-            for param in self.parameters():
-                param.requires_grad = False
-        else:
-            self.vgg_net.train()
-            for param in self.parameters():
-                param.requires_grad = True
-
-        if self.use_input_norm:
-            # the mean is for image with range [0, 1]
-            self.register_buffer('mean', torch.Tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1))
-            # the std is for image with range [0, 1]
-            self.register_buffer('std', torch.Tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1))
-
-    def forward(self, x):
-        """Forward function.
-
-        Args:
-            x (Tensor): Input tensor with shape (n, c, h, w).
-
-        Returns:
-            Tensor: Forward results.
-        """
-        if self.range_norm:
-            x = (x + 1) / 2
-        if self.use_input_norm:
-            x = (x - self.mean) / self.std
-        output = {}
-
-        for key, layer in self.vgg_net._modules.items():
-            x = layer(x)
-            if key in self.layer_name_list:
-                output[key] = x.clone()
-
-        return output
diff --git a/repositories/CodeFormer/basicsr/archs/vqgan_arch.py b/repositories/CodeFormer/basicsr/archs/vqgan_arch.py
deleted file mode 100644
index f6dfcf4c9983b431f0a978701e5ddd9598faf381..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/basicsr/archs/vqgan_arch.py
+++ /dev/null
@@ -1,435 +0,0 @@
-'''
-VQGAN code, adapted from the original created by the Unleashing Transformers authors:
-https://github.com/samb-t/unleashing-transformers/blob/master/models/vqgan.py
-
-'''
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import copy
-from basicsr.utils import get_root_logger
-from basicsr.utils.registry import ARCH_REGISTRY
-
-def normalize(in_channels):
-    return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
-    
-
-@torch.jit.script
-def swish(x):
-    return x*torch.sigmoid(x)
-
-
-#  Define VQVAE classes
-class VectorQuantizer(nn.Module):
-    def __init__(self, codebook_size, emb_dim, beta):
-        super(VectorQuantizer, self).__init__()
-        self.codebook_size = codebook_size  # number of embeddings
-        self.emb_dim = emb_dim  # dimension of embedding
-        self.beta = beta  # commitment cost used in loss term, beta * ||z_e(x)-sg[e]||^2
-        self.embedding = nn.Embedding(self.codebook_size, self.emb_dim)
-        self.embedding.weight.data.uniform_(-1.0 / self.codebook_size, 1.0 / self.codebook_size)
-
-    def forward(self, z):
-        # reshape z -> (batch, height, width, channel) and flatten
-        z = z.permute(0, 2, 3, 1).contiguous()
-        z_flattened = z.view(-1, self.emb_dim)
-
-        # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
-        d = (z_flattened ** 2).sum(dim=1, keepdim=True) + (self.embedding.weight**2).sum(1) - \
-            2 * torch.matmul(z_flattened, self.embedding.weight.t())
-
-        mean_distance = torch.mean(d)
-        # find closest encodings
-        # min_encoding_indices = torch.argmin(d, dim=1).unsqueeze(1)
-        min_encoding_scores, min_encoding_indices = torch.topk(d, 1, dim=1, largest=False)
-        # [0-1], higher score, higher confidence
-        min_encoding_scores = torch.exp(-min_encoding_scores/10)
-
-        min_encodings = torch.zeros(min_encoding_indices.shape[0], self.codebook_size).to(z)
-        min_encodings.scatter_(1, min_encoding_indices, 1)
-
-        # get quantized latent vectors
-        z_q = torch.matmul(min_encodings, self.embedding.weight).view(z.shape)
-        # compute loss for embedding
-        loss = torch.mean((z_q.detach()-z)**2) + self.beta * torch.mean((z_q - z.detach()) ** 2)
-        # preserve gradients
-        z_q = z + (z_q - z).detach()
-
-        # perplexity
-        e_mean = torch.mean(min_encodings, dim=0)
-        perplexity = torch.exp(-torch.sum(e_mean * torch.log(e_mean + 1e-10)))
-        # reshape back to match original input shape
-        z_q = z_q.permute(0, 3, 1, 2).contiguous()
-
-        return z_q, loss, {
-            "perplexity": perplexity,
-            "min_encodings": min_encodings,
-            "min_encoding_indices": min_encoding_indices,
-            "min_encoding_scores": min_encoding_scores,
-            "mean_distance": mean_distance
-            }
-
-    def get_codebook_feat(self, indices, shape):
-        # input indices: batch*token_num -> (batch*token_num)*1
-        # shape: batch, height, width, channel
-        indices = indices.view(-1,1)
-        min_encodings = torch.zeros(indices.shape[0], self.codebook_size).to(indices)
-        min_encodings.scatter_(1, indices, 1)
-        # get quantized latent vectors
-        z_q = torch.matmul(min_encodings.float(), self.embedding.weight)
-
-        if shape is not None:  # reshape back to match original input shape
-            z_q = z_q.view(shape).permute(0, 3, 1, 2).contiguous()
-
-        return z_q
-
-
-class GumbelQuantizer(nn.Module):
-    def __init__(self, codebook_size, emb_dim, num_hiddens, straight_through=False, kl_weight=5e-4, temp_init=1.0):
-        super().__init__()
-        self.codebook_size = codebook_size  # number of embeddings
-        self.emb_dim = emb_dim  # dimension of embedding
-        self.straight_through = straight_through
-        self.temperature = temp_init
-        self.kl_weight = kl_weight
-        self.proj = nn.Conv2d(num_hiddens, codebook_size, 1)  # projects last encoder layer to quantized logits
-        self.embed = nn.Embedding(codebook_size, emb_dim)
-
-    def forward(self, z):
-        hard = self.straight_through if self.training else True
-
-        logits = self.proj(z)
-
-        soft_one_hot = F.gumbel_softmax(logits, tau=self.temperature, dim=1, hard=hard)
-
-        z_q = torch.einsum("b n h w, n d -> b d h w", soft_one_hot, self.embed.weight)
-
-        # + kl divergence to the prior loss
-        qy = F.softmax(logits, dim=1)
-        diff = self.kl_weight * torch.sum(qy * torch.log(qy * self.codebook_size + 1e-10), dim=1).mean()
-        min_encoding_indices = soft_one_hot.argmax(dim=1)
-
-        return z_q, diff, {
-            "min_encoding_indices": min_encoding_indices
-        }
-
-
-class Downsample(nn.Module):
-    def __init__(self, in_channels):
-        super().__init__()
-        self.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0)
-
-    def forward(self, x):
-        pad = (0, 1, 0, 1)
-        x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
-        x = self.conv(x)
-        return x
-
-
-class Upsample(nn.Module):
-    def __init__(self, in_channels):
-        super().__init__()
-        self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
-
-    def forward(self, x):
-        x = F.interpolate(x, scale_factor=2.0, mode="nearest")
-        x = self.conv(x)
-
-        return x
-
-
-class ResBlock(nn.Module):
-    def __init__(self, in_channels, out_channels=None):
-        super(ResBlock, self).__init__()
-        self.in_channels = in_channels
-        self.out_channels = in_channels if out_channels is None else out_channels
-        self.norm1 = normalize(in_channels)
-        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
-        self.norm2 = normalize(out_channels)
-        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
-        if self.in_channels != self.out_channels:
-            self.conv_out = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
-
-    def forward(self, x_in):
-        x = x_in
-        x = self.norm1(x)
-        x = swish(x)
-        x = self.conv1(x)
-        x = self.norm2(x)
-        x = swish(x)
-        x = self.conv2(x)
-        if self.in_channels != self.out_channels:
-            x_in = self.conv_out(x_in)
-
-        return x + x_in
-
-
-class AttnBlock(nn.Module):
-    def __init__(self, in_channels):
-        super().__init__()
-        self.in_channels = in_channels
-
-        self.norm = normalize(in_channels)
-        self.q = torch.nn.Conv2d(
-            in_channels,
-            in_channels,
-            kernel_size=1,
-            stride=1,
-            padding=0
-        )
-        self.k = torch.nn.Conv2d(
-            in_channels,
-            in_channels,
-            kernel_size=1,
-            stride=1,
-            padding=0
-        )
-        self.v = torch.nn.Conv2d(
-            in_channels,
-            in_channels,
-            kernel_size=1,
-            stride=1,
-            padding=0
-        )
-        self.proj_out = torch.nn.Conv2d(
-            in_channels,
-            in_channels,
-            kernel_size=1,
-            stride=1,
-            padding=0
-        )
-
-    def forward(self, x):
-        h_ = x
-        h_ = self.norm(h_)
-        q = self.q(h_)
-        k = self.k(h_)
-        v = self.v(h_)
-
-        # compute attention
-        b, c, h, w = q.shape
-        q = q.reshape(b, c, h*w)
-        q = q.permute(0, 2, 1)   
-        k = k.reshape(b, c, h*w)
-        w_ = torch.bmm(q, k) 
-        w_ = w_ * (int(c)**(-0.5))
-        w_ = F.softmax(w_, dim=2)
-
-        # attend to values
-        v = v.reshape(b, c, h*w)
-        w_ = w_.permute(0, 2, 1) 
-        h_ = torch.bmm(v, w_)
-        h_ = h_.reshape(b, c, h, w)
-
-        h_ = self.proj_out(h_)
-
-        return x+h_
-
-
-class Encoder(nn.Module):
-    def __init__(self, in_channels, nf, emb_dim, ch_mult, num_res_blocks, resolution, attn_resolutions):
-        super().__init__()
-        self.nf = nf
-        self.num_resolutions = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        self.resolution = resolution
-        self.attn_resolutions = attn_resolutions
-
-        curr_res = self.resolution
-        in_ch_mult = (1,)+tuple(ch_mult)
-
-        blocks = []
-        # initial convultion
-        blocks.append(nn.Conv2d(in_channels, nf, kernel_size=3, stride=1, padding=1))
-
-        # residual and downsampling blocks, with attention on smaller res (16x16)
-        for i in range(self.num_resolutions):
-            block_in_ch = nf * in_ch_mult[i]
-            block_out_ch = nf * ch_mult[i]
-            for _ in range(self.num_res_blocks):
-                blocks.append(ResBlock(block_in_ch, block_out_ch))
-                block_in_ch = block_out_ch
-                if curr_res in attn_resolutions:
-                    blocks.append(AttnBlock(block_in_ch))
-
-            if i != self.num_resolutions - 1:
-                blocks.append(Downsample(block_in_ch))
-                curr_res = curr_res // 2
-
-        # non-local attention block
-        blocks.append(ResBlock(block_in_ch, block_in_ch))
-        blocks.append(AttnBlock(block_in_ch))
-        blocks.append(ResBlock(block_in_ch, block_in_ch))
-
-        # normalise and convert to latent size
-        blocks.append(normalize(block_in_ch))
-        blocks.append(nn.Conv2d(block_in_ch, emb_dim, kernel_size=3, stride=1, padding=1))
-        self.blocks = nn.ModuleList(blocks)
-
-    def forward(self, x):
-        for block in self.blocks:
-            x = block(x)
-            
-        return x
-
-
-class Generator(nn.Module):
-    def __init__(self, nf, emb_dim, ch_mult, res_blocks, img_size, attn_resolutions):
-        super().__init__()
-        self.nf = nf 
-        self.ch_mult = ch_mult 
-        self.num_resolutions = len(self.ch_mult)
-        self.num_res_blocks = res_blocks
-        self.resolution = img_size 
-        self.attn_resolutions = attn_resolutions
-        self.in_channels = emb_dim
-        self.out_channels = 3
-        block_in_ch = self.nf * self.ch_mult[-1]
-        curr_res = self.resolution // 2 ** (self.num_resolutions-1)
-
-        blocks = []
-        # initial conv
-        blocks.append(nn.Conv2d(self.in_channels, block_in_ch, kernel_size=3, stride=1, padding=1))
-
-        # non-local attention block
-        blocks.append(ResBlock(block_in_ch, block_in_ch))
-        blocks.append(AttnBlock(block_in_ch))
-        blocks.append(ResBlock(block_in_ch, block_in_ch))
-
-        for i in reversed(range(self.num_resolutions)):
-            block_out_ch = self.nf * self.ch_mult[i]
-
-            for _ in range(self.num_res_blocks):
-                blocks.append(ResBlock(block_in_ch, block_out_ch))
-                block_in_ch = block_out_ch
-
-                if curr_res in self.attn_resolutions:
-                    blocks.append(AttnBlock(block_in_ch))
-
-            if i != 0:
-                blocks.append(Upsample(block_in_ch))
-                curr_res = curr_res * 2
-
-        blocks.append(normalize(block_in_ch))
-        blocks.append(nn.Conv2d(block_in_ch, self.out_channels, kernel_size=3, stride=1, padding=1))
-
-        self.blocks = nn.ModuleList(blocks)
-   
-
-    def forward(self, x):
-        for block in self.blocks:
-            x = block(x)
-            
-        return x
-
-  
-@ARCH_REGISTRY.register()
-class VQAutoEncoder(nn.Module):
-    def __init__(self, img_size, nf, ch_mult, quantizer="nearest", res_blocks=2, attn_resolutions=[16], codebook_size=1024, emb_dim=256,
-                beta=0.25, gumbel_straight_through=False, gumbel_kl_weight=1e-8, model_path=None):
-        super().__init__()
-        logger = get_root_logger()
-        self.in_channels = 3 
-        self.nf = nf 
-        self.n_blocks = res_blocks 
-        self.codebook_size = codebook_size
-        self.embed_dim = emb_dim
-        self.ch_mult = ch_mult
-        self.resolution = img_size
-        self.attn_resolutions = attn_resolutions
-        self.quantizer_type = quantizer
-        self.encoder = Encoder(
-            self.in_channels,
-            self.nf,
-            self.embed_dim,
-            self.ch_mult,
-            self.n_blocks,
-            self.resolution,
-            self.attn_resolutions
-        )
-        if self.quantizer_type == "nearest":
-            self.beta = beta #0.25
-            self.quantize = VectorQuantizer(self.codebook_size, self.embed_dim, self.beta)
-        elif self.quantizer_type == "gumbel":
-            self.gumbel_num_hiddens = emb_dim
-            self.straight_through = gumbel_straight_through
-            self.kl_weight = gumbel_kl_weight
-            self.quantize = GumbelQuantizer(
-                self.codebook_size,
-                self.embed_dim,
-                self.gumbel_num_hiddens,
-                self.straight_through,
-                self.kl_weight
-            )
-        self.generator = Generator(
-            self.nf, 
-            self.embed_dim,
-            self.ch_mult, 
-            self.n_blocks, 
-            self.resolution, 
-            self.attn_resolutions
-        )
-
-        if model_path is not None:
-            chkpt = torch.load(model_path, map_location='cpu')
-            if 'params_ema' in chkpt:
-                self.load_state_dict(torch.load(model_path, map_location='cpu')['params_ema'])
-                logger.info(f'vqgan is loaded from: {model_path} [params_ema]')
-            elif 'params' in chkpt:
-                self.load_state_dict(torch.load(model_path, map_location='cpu')['params'])
-                logger.info(f'vqgan is loaded from: {model_path} [params]')
-            else:
-                raise ValueError(f'Wrong params!')
-
-
-    def forward(self, x):
-        x = self.encoder(x)
-        quant, codebook_loss, quant_stats = self.quantize(x)
-        x = self.generator(quant)
-        return x, codebook_loss, quant_stats
-
-
-
-# patch based discriminator
-@ARCH_REGISTRY.register()
-class VQGANDiscriminator(nn.Module):
-    def __init__(self, nc=3, ndf=64, n_layers=4, model_path=None):
-        super().__init__()
-
-        layers = [nn.Conv2d(nc, ndf, kernel_size=4, stride=2, padding=1), nn.LeakyReLU(0.2, True)]
-        ndf_mult = 1
-        ndf_mult_prev = 1
-        for n in range(1, n_layers):  # gradually increase the number of filters
-            ndf_mult_prev = ndf_mult
-            ndf_mult = min(2 ** n, 8)
-            layers += [
-                nn.Conv2d(ndf * ndf_mult_prev, ndf * ndf_mult, kernel_size=4, stride=2, padding=1, bias=False),
-                nn.BatchNorm2d(ndf * ndf_mult),
-                nn.LeakyReLU(0.2, True)
-            ]
-
-        ndf_mult_prev = ndf_mult
-        ndf_mult = min(2 ** n_layers, 8)
-
-        layers += [
-            nn.Conv2d(ndf * ndf_mult_prev, ndf * ndf_mult, kernel_size=4, stride=1, padding=1, bias=False),
-            nn.BatchNorm2d(ndf * ndf_mult),
-            nn.LeakyReLU(0.2, True)
-        ]
-
-        layers += [
-            nn.Conv2d(ndf * ndf_mult, 1, kernel_size=4, stride=1, padding=1)]  # output 1 channel prediction map
-        self.main = nn.Sequential(*layers)
-
-        if model_path is not None:
-            chkpt = torch.load(model_path, map_location='cpu')
-            if 'params_d' in chkpt:
-                self.load_state_dict(torch.load(model_path, map_location='cpu')['params_d'])
-            elif 'params' in chkpt:
-                self.load_state_dict(torch.load(model_path, map_location='cpu')['params'])
-            else:
-                raise ValueError(f'Wrong params!')
-
-    def forward(self, x):
-        return self.main(x)
\ No newline at end of file
diff --git a/repositories/CodeFormer/basicsr/data/__init__.py b/repositories/CodeFormer/basicsr/data/__init__.py
deleted file mode 100644
index c6adb4bb6a926af7a46aaec4794eee95fda02a33..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/basicsr/data/__init__.py
+++ /dev/null
@@ -1,100 +0,0 @@
-import importlib
-import numpy as np
-import random
-import torch
-import torch.utils.data
-from copy import deepcopy
-from functools import partial
-from os import path as osp
-
-from basicsr.data.prefetch_dataloader import PrefetchDataLoader
-from basicsr.utils import get_root_logger, scandir
-from basicsr.utils.dist_util import get_dist_info
-from basicsr.utils.registry import DATASET_REGISTRY
-
-__all__ = ['build_dataset', 'build_dataloader']
-
-# automatically scan and import dataset modules for registry
-# scan all the files under the data folder with '_dataset' in file names
-data_folder = osp.dirname(osp.abspath(__file__))
-dataset_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(data_folder) if v.endswith('_dataset.py')]
-# import all the dataset modules
-_dataset_modules = [importlib.import_module(f'basicsr.data.{file_name}') for file_name in dataset_filenames]
-
-
-def build_dataset(dataset_opt):
-    """Build dataset from options.
-
-    Args:
-        dataset_opt (dict): Configuration for dataset. It must constain:
-            name (str): Dataset name.
-            type (str): Dataset type.
-    """
-    dataset_opt = deepcopy(dataset_opt)
-    dataset = DATASET_REGISTRY.get(dataset_opt['type'])(dataset_opt)
-    logger = get_root_logger()
-    logger.info(f'Dataset [{dataset.__class__.__name__}] - {dataset_opt["name"]} ' 'is built.')
-    return dataset
-
-
-def build_dataloader(dataset, dataset_opt, num_gpu=1, dist=False, sampler=None, seed=None):
-    """Build dataloader.
-
-    Args:
-        dataset (torch.utils.data.Dataset): Dataset.
-        dataset_opt (dict): Dataset options. It contains the following keys:
-            phase (str): 'train' or 'val'.
-            num_worker_per_gpu (int): Number of workers for each GPU.
-            batch_size_per_gpu (int): Training batch size for each GPU.
-        num_gpu (int): Number of GPUs. Used only in the train phase.
-            Default: 1.
-        dist (bool): Whether in distributed training. Used only in the train
-            phase. Default: False.
-        sampler (torch.utils.data.sampler): Data sampler. Default: None.
-        seed (int | None): Seed. Default: None
-    """
-    phase = dataset_opt['phase']
-    rank, _ = get_dist_info()
-    if phase == 'train':
-        if dist:  # distributed training
-            batch_size = dataset_opt['batch_size_per_gpu']
-            num_workers = dataset_opt['num_worker_per_gpu']
-        else:  # non-distributed training
-            multiplier = 1 if num_gpu == 0 else num_gpu
-            batch_size = dataset_opt['batch_size_per_gpu'] * multiplier
-            num_workers = dataset_opt['num_worker_per_gpu'] * multiplier
-        dataloader_args = dict(
-            dataset=dataset,
-            batch_size=batch_size,
-            shuffle=False,
-            num_workers=num_workers,
-            sampler=sampler,
-            drop_last=True)
-        if sampler is None:
-            dataloader_args['shuffle'] = True
-        dataloader_args['worker_init_fn'] = partial(
-            worker_init_fn, num_workers=num_workers, rank=rank, seed=seed) if seed is not None else None
-    elif phase in ['val', 'test']:  # validation
-        dataloader_args = dict(dataset=dataset, batch_size=1, shuffle=False, num_workers=0)
-    else:
-        raise ValueError(f'Wrong dataset phase: {phase}. ' "Supported ones are 'train', 'val' and 'test'.")
-
-    dataloader_args['pin_memory'] = dataset_opt.get('pin_memory', False)
-
-    prefetch_mode = dataset_opt.get('prefetch_mode')
-    if prefetch_mode == 'cpu':  # CPUPrefetcher
-        num_prefetch_queue = dataset_opt.get('num_prefetch_queue', 1)
-        logger = get_root_logger()
-        logger.info(f'Use {prefetch_mode} prefetch dataloader: ' f'num_prefetch_queue = {num_prefetch_queue}')
-        return PrefetchDataLoader(num_prefetch_queue=num_prefetch_queue, **dataloader_args)
-    else:
-        # prefetch_mode=None: Normal dataloader
-        # prefetch_mode='cuda': dataloader for CUDAPrefetcher
-        return torch.utils.data.DataLoader(**dataloader_args)
-
-
-def worker_init_fn(worker_id, num_workers, rank, seed):
-    # Set the worker seed to num_workers * rank + worker_id + seed
-    worker_seed = num_workers * rank + worker_id + seed
-    np.random.seed(worker_seed)
-    random.seed(worker_seed)
diff --git a/repositories/CodeFormer/basicsr/data/data_sampler.py b/repositories/CodeFormer/basicsr/data/data_sampler.py
deleted file mode 100644
index 575452d9f844a928f7f42296c81635cfbadec7c2..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/basicsr/data/data_sampler.py
+++ /dev/null
@@ -1,48 +0,0 @@
-import math
-import torch
-from torch.utils.data.sampler import Sampler
-
-
-class EnlargedSampler(Sampler):
-    """Sampler that restricts data loading to a subset of the dataset.
-
-    Modified from torch.utils.data.distributed.DistributedSampler
-    Support enlarging the dataset for iteration-based training, for saving
-    time when restart the dataloader after each epoch
-
-    Args:
-        dataset (torch.utils.data.Dataset): Dataset used for sampling.
-        num_replicas (int | None): Number of processes participating in
-            the training. It is usually the world_size.
-        rank (int | None): Rank of the current process within num_replicas.
-        ratio (int): Enlarging ratio. Default: 1.
-    """
-
-    def __init__(self, dataset, num_replicas, rank, ratio=1):
-        self.dataset = dataset
-        self.num_replicas = num_replicas
-        self.rank = rank
-        self.epoch = 0
-        self.num_samples = math.ceil(len(self.dataset) * ratio / self.num_replicas)
-        self.total_size = self.num_samples * self.num_replicas
-
-    def __iter__(self):
-        # deterministically shuffle based on epoch
-        g = torch.Generator()
-        g.manual_seed(self.epoch)
-        indices = torch.randperm(self.total_size, generator=g).tolist()
-
-        dataset_size = len(self.dataset)
-        indices = [v % dataset_size for v in indices]
-
-        # subsample
-        indices = indices[self.rank:self.total_size:self.num_replicas]
-        assert len(indices) == self.num_samples
-
-        return iter(indices)
-
-    def __len__(self):
-        return self.num_samples
-
-    def set_epoch(self, epoch):
-        self.epoch = epoch
diff --git a/repositories/CodeFormer/basicsr/data/data_util.py b/repositories/CodeFormer/basicsr/data/data_util.py
deleted file mode 100644
index 63b1bce8e089485182c962e830a163d6d0059da8..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/basicsr/data/data_util.py
+++ /dev/null
@@ -1,305 +0,0 @@
-import cv2
-import numpy as np
-import torch
-from os import path as osp
-from torch.nn import functional as F
-
-from basicsr.data.transforms import mod_crop
-from basicsr.utils import img2tensor, scandir
-
-
-def read_img_seq(path, require_mod_crop=False, scale=1):
-    """Read a sequence of images from a given folder path.
-
-    Args:
-        path (list[str] | str): List of image paths or image folder path.
-        require_mod_crop (bool): Require mod crop for each image.
-            Default: False.
-        scale (int): Scale factor for mod_crop. Default: 1.
-
-    Returns:
-        Tensor: size (t, c, h, w), RGB, [0, 1].
-    """
-    if isinstance(path, list):
-        img_paths = path
-    else:
-        img_paths = sorted(list(scandir(path, full_path=True)))
-    imgs = [cv2.imread(v).astype(np.float32) / 255. for v in img_paths]
-    if require_mod_crop:
-        imgs = [mod_crop(img, scale) for img in imgs]
-    imgs = img2tensor(imgs, bgr2rgb=True, float32=True)
-    imgs = torch.stack(imgs, dim=0)
-    return imgs
-
-
-def generate_frame_indices(crt_idx, max_frame_num, num_frames, padding='reflection'):
-    """Generate an index list for reading `num_frames` frames from a sequence
-    of images.
-
-    Args:
-        crt_idx (int): Current center index.
-        max_frame_num (int): Max number of the sequence of images (from 1).
-        num_frames (int): Reading num_frames frames.
-        padding (str): Padding mode, one of
-            'replicate' | 'reflection' | 'reflection_circle' | 'circle'
-            Examples: current_idx = 0, num_frames = 5
-            The generated frame indices under different padding mode:
-            replicate: [0, 0, 0, 1, 2]
-            reflection: [2, 1, 0, 1, 2]
-            reflection_circle: [4, 3, 0, 1, 2]
-            circle: [3, 4, 0, 1, 2]
-
-    Returns:
-        list[int]: A list of indices.
-    """
-    assert num_frames % 2 == 1, 'num_frames should be an odd number.'
-    assert padding in ('replicate', 'reflection', 'reflection_circle', 'circle'), f'Wrong padding mode: {padding}.'
-
-    max_frame_num = max_frame_num - 1  # start from 0
-    num_pad = num_frames // 2
-
-    indices = []
-    for i in range(crt_idx - num_pad, crt_idx + num_pad + 1):
-        if i < 0:
-            if padding == 'replicate':
-                pad_idx = 0
-            elif padding == 'reflection':
-                pad_idx = -i
-            elif padding == 'reflection_circle':
-                pad_idx = crt_idx + num_pad - i
-            else:
-                pad_idx = num_frames + i
-        elif i > max_frame_num:
-            if padding == 'replicate':
-                pad_idx = max_frame_num
-            elif padding == 'reflection':
-                pad_idx = max_frame_num * 2 - i
-            elif padding == 'reflection_circle':
-                pad_idx = (crt_idx - num_pad) - (i - max_frame_num)
-            else:
-                pad_idx = i - num_frames
-        else:
-            pad_idx = i
-        indices.append(pad_idx)
-    return indices
-
-
-def paired_paths_from_lmdb(folders, keys):
-    """Generate paired paths from lmdb files.
-
-    Contents of lmdb. Taking the `lq.lmdb` for example, the file structure is:
-
-    lq.lmdb
-    ├── data.mdb
-    ├── lock.mdb
-    ├── meta_info.txt
-
-    The data.mdb and lock.mdb are standard lmdb files and you can refer to
-    https://lmdb.readthedocs.io/en/release/ for more details.
-
-    The meta_info.txt is a specified txt file to record the meta information
-    of our datasets. It will be automatically created when preparing
-    datasets by our provided dataset tools.
-    Each line in the txt file records
-    1)image name (with extension),
-    2)image shape,
-    3)compression level, separated by a white space.
-    Example: `baboon.png (120,125,3) 1`
-
-    We use the image name without extension as the lmdb key.
-    Note that we use the same key for the corresponding lq and gt images.
-
-    Args:
-        folders (list[str]): A list of folder path. The order of list should
-            be [input_folder, gt_folder].
-        keys (list[str]): A list of keys identifying folders. The order should
-            be in consistent with folders, e.g., ['lq', 'gt'].
-            Note that this key is different from lmdb keys.
-
-    Returns:
-        list[str]: Returned path list.
-    """
-    assert len(folders) == 2, ('The len of folders should be 2 with [input_folder, gt_folder]. '
-                               f'But got {len(folders)}')
-    assert len(keys) == 2, ('The len of keys should be 2 with [input_key, gt_key]. ' f'But got {len(keys)}')
-    input_folder, gt_folder = folders
-    input_key, gt_key = keys
-
-    if not (input_folder.endswith('.lmdb') and gt_folder.endswith('.lmdb')):
-        raise ValueError(f'{input_key} folder and {gt_key} folder should both in lmdb '
-                         f'formats. But received {input_key}: {input_folder}; '
-                         f'{gt_key}: {gt_folder}')
-    # ensure that the two meta_info files are the same
-    with open(osp.join(input_folder, 'meta_info.txt')) as fin:
-        input_lmdb_keys = [line.split('.')[0] for line in fin]
-    with open(osp.join(gt_folder, 'meta_info.txt')) as fin:
-        gt_lmdb_keys = [line.split('.')[0] for line in fin]
-    if set(input_lmdb_keys) != set(gt_lmdb_keys):
-        raise ValueError(f'Keys in {input_key}_folder and {gt_key}_folder are different.')
-    else:
-        paths = []
-        for lmdb_key in sorted(input_lmdb_keys):
-            paths.append(dict([(f'{input_key}_path', lmdb_key), (f'{gt_key}_path', lmdb_key)]))
-        return paths
-
-
-def paired_paths_from_meta_info_file(folders, keys, meta_info_file, filename_tmpl):
-    """Generate paired paths from an meta information file.
-
-    Each line in the meta information file contains the image names and
-    image shape (usually for gt), separated by a white space.
-
-    Example of an meta information file:
-    ```
-    0001_s001.png (480,480,3)
-    0001_s002.png (480,480,3)
-    ```
-
-    Args:
-        folders (list[str]): A list of folder path. The order of list should
-            be [input_folder, gt_folder].
-        keys (list[str]): A list of keys identifying folders. The order should
-            be in consistent with folders, e.g., ['lq', 'gt'].
-        meta_info_file (str): Path to the meta information file.
-        filename_tmpl (str): Template for each filename. Note that the
-            template excludes the file extension. Usually the filename_tmpl is
-            for files in the input folder.
-
-    Returns:
-        list[str]: Returned path list.
-    """
-    assert len(folders) == 2, ('The len of folders should be 2 with [input_folder, gt_folder]. '
-                               f'But got {len(folders)}')
-    assert len(keys) == 2, ('The len of keys should be 2 with [input_key, gt_key]. ' f'But got {len(keys)}')
-    input_folder, gt_folder = folders
-    input_key, gt_key = keys
-
-    with open(meta_info_file, 'r') as fin:
-        gt_names = [line.split(' ')[0] for line in fin]
-
-    paths = []
-    for gt_name in gt_names:
-        basename, ext = osp.splitext(osp.basename(gt_name))
-        input_name = f'{filename_tmpl.format(basename)}{ext}'
-        input_path = osp.join(input_folder, input_name)
-        gt_path = osp.join(gt_folder, gt_name)
-        paths.append(dict([(f'{input_key}_path', input_path), (f'{gt_key}_path', gt_path)]))
-    return paths
-
-
-def paired_paths_from_folder(folders, keys, filename_tmpl):
-    """Generate paired paths from folders.
-
-    Args:
-        folders (list[str]): A list of folder path. The order of list should
-            be [input_folder, gt_folder].
-        keys (list[str]): A list of keys identifying folders. The order should
-            be in consistent with folders, e.g., ['lq', 'gt'].
-        filename_tmpl (str): Template for each filename. Note that the
-            template excludes the file extension. Usually the filename_tmpl is
-            for files in the input folder.
-
-    Returns:
-        list[str]: Returned path list.
-    """
-    assert len(folders) == 2, ('The len of folders should be 2 with [input_folder, gt_folder]. '
-                               f'But got {len(folders)}')
-    assert len(keys) == 2, ('The len of keys should be 2 with [input_key, gt_key]. ' f'But got {len(keys)}')
-    input_folder, gt_folder = folders
-    input_key, gt_key = keys
-
-    input_paths = list(scandir(input_folder))
-    gt_paths = list(scandir(gt_folder))
-    assert len(input_paths) == len(gt_paths), (f'{input_key} and {gt_key} datasets have different number of images: '
-                                               f'{len(input_paths)}, {len(gt_paths)}.')
-    paths = []
-    for gt_path in gt_paths:
-        basename, ext = osp.splitext(osp.basename(gt_path))
-        input_name = f'{filename_tmpl.format(basename)}{ext}'
-        input_path = osp.join(input_folder, input_name)
-        assert input_name in input_paths, (f'{input_name} is not in ' f'{input_key}_paths.')
-        gt_path = osp.join(gt_folder, gt_path)
-        paths.append(dict([(f'{input_key}_path', input_path), (f'{gt_key}_path', gt_path)]))
-    return paths
-
-
-def paths_from_folder(folder):
-    """Generate paths from folder.
-
-    Args:
-        folder (str): Folder path.
-
-    Returns:
-        list[str]: Returned path list.
-    """
-
-    paths = list(scandir(folder))
-    paths = [osp.join(folder, path) for path in paths]
-    return paths
-
-
-def paths_from_lmdb(folder):
-    """Generate paths from lmdb.
-
-    Args:
-        folder (str): Folder path.
-
-    Returns:
-        list[str]: Returned path list.
-    """
-    if not folder.endswith('.lmdb'):
-        raise ValueError(f'Folder {folder}folder should in lmdb format.')
-    with open(osp.join(folder, 'meta_info.txt')) as fin:
-        paths = [line.split('.')[0] for line in fin]
-    return paths
-
-
-def generate_gaussian_kernel(kernel_size=13, sigma=1.6):
-    """Generate Gaussian kernel used in `duf_downsample`.
-
-    Args:
-        kernel_size (int): Kernel size. Default: 13.
-        sigma (float): Sigma of the Gaussian kernel. Default: 1.6.
-
-    Returns:
-        np.array: The Gaussian kernel.
-    """
-    from scipy.ndimage import filters as filters
-    kernel = np.zeros((kernel_size, kernel_size))
-    # set element at the middle to one, a dirac delta
-    kernel[kernel_size // 2, kernel_size // 2] = 1
-    # gaussian-smooth the dirac, resulting in a gaussian filter
-    return filters.gaussian_filter(kernel, sigma)
-
-
-def duf_downsample(x, kernel_size=13, scale=4):
-    """Downsamping with Gaussian kernel used in the DUF official code.
-
-    Args:
-        x (Tensor): Frames to be downsampled, with shape (b, t, c, h, w).
-        kernel_size (int): Kernel size. Default: 13.
-        scale (int): Downsampling factor. Supported scale: (2, 3, 4).
-            Default: 4.
-
-    Returns:
-        Tensor: DUF downsampled frames.
-    """
-    assert scale in (2, 3, 4), f'Only support scale (2, 3, 4), but got {scale}.'
-
-    squeeze_flag = False
-    if x.ndim == 4:
-        squeeze_flag = True
-        x = x.unsqueeze(0)
-    b, t, c, h, w = x.size()
-    x = x.view(-1, 1, h, w)
-    pad_w, pad_h = kernel_size // 2 + scale * 2, kernel_size // 2 + scale * 2
-    x = F.pad(x, (pad_w, pad_w, pad_h, pad_h), 'reflect')
-
-    gaussian_filter = generate_gaussian_kernel(kernel_size, 0.4 * scale)
-    gaussian_filter = torch.from_numpy(gaussian_filter).type_as(x).unsqueeze(0).unsqueeze(0)
-    x = F.conv2d(x, gaussian_filter, stride=scale)
-    x = x[:, :, 2:-2, 2:-2]
-    x = x.view(b, t, c, x.size(2), x.size(3))
-    if squeeze_flag:
-        x = x.squeeze(0)
-    return x
diff --git a/repositories/CodeFormer/basicsr/data/prefetch_dataloader.py b/repositories/CodeFormer/basicsr/data/prefetch_dataloader.py
deleted file mode 100644
index 5088425050d4cc98114a9b93eb50ea60273f35a0..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/basicsr/data/prefetch_dataloader.py
+++ /dev/null
@@ -1,125 +0,0 @@
-import queue as Queue
-import threading
-import torch
-from torch.utils.data import DataLoader
-
-
-class PrefetchGenerator(threading.Thread):
-    """A general prefetch generator.
-
-    Ref:
-    https://stackoverflow.com/questions/7323664/python-generator-pre-fetch
-
-    Args:
-        generator: Python generator.
-        num_prefetch_queue (int): Number of prefetch queue.
-    """
-
-    def __init__(self, generator, num_prefetch_queue):
-        threading.Thread.__init__(self)
-        self.queue = Queue.Queue(num_prefetch_queue)
-        self.generator = generator
-        self.daemon = True
-        self.start()
-
-    def run(self):
-        for item in self.generator:
-            self.queue.put(item)
-        self.queue.put(None)
-
-    def __next__(self):
-        next_item = self.queue.get()
-        if next_item is None:
-            raise StopIteration
-        return next_item
-
-    def __iter__(self):
-        return self
-
-
-class PrefetchDataLoader(DataLoader):
-    """Prefetch version of dataloader.
-
-    Ref:
-    https://github.com/IgorSusmelj/pytorch-styleguide/issues/5#
-
-    TODO:
-    Need to test on single gpu and ddp (multi-gpu). There is a known issue in
-    ddp.
-
-    Args:
-        num_prefetch_queue (int): Number of prefetch queue.
-        kwargs (dict): Other arguments for dataloader.
-    """
-
-    def __init__(self, num_prefetch_queue, **kwargs):
-        self.num_prefetch_queue = num_prefetch_queue
-        super(PrefetchDataLoader, self).__init__(**kwargs)
-
-    def __iter__(self):
-        return PrefetchGenerator(super().__iter__(), self.num_prefetch_queue)
-
-
-class CPUPrefetcher():
-    """CPU prefetcher.
-
-    Args:
-        loader: Dataloader.
-    """
-
-    def __init__(self, loader):
-        self.ori_loader = loader
-        self.loader = iter(loader)
-
-    def next(self):
-        try:
-            return next(self.loader)
-        except StopIteration:
-            return None
-
-    def reset(self):
-        self.loader = iter(self.ori_loader)
-
-
-class CUDAPrefetcher():
-    """CUDA prefetcher.
-
-    Ref:
-    https://github.com/NVIDIA/apex/issues/304#
-
-    It may consums more GPU memory.
-
-    Args:
-        loader: Dataloader.
-        opt (dict): Options.
-    """
-
-    def __init__(self, loader, opt):
-        self.ori_loader = loader
-        self.loader = iter(loader)
-        self.opt = opt
-        self.stream = torch.cuda.Stream()
-        self.device = torch.device('cuda' if opt['num_gpu'] != 0 else 'cpu')
-        self.preload()
-
-    def preload(self):
-        try:
-            self.batch = next(self.loader)  # self.batch is a dict
-        except StopIteration:
-            self.batch = None
-            return None
-        # put tensors to gpu
-        with torch.cuda.stream(self.stream):
-            for k, v in self.batch.items():
-                if torch.is_tensor(v):
-                    self.batch[k] = self.batch[k].to(device=self.device, non_blocking=True)
-
-    def next(self):
-        torch.cuda.current_stream().wait_stream(self.stream)
-        batch = self.batch
-        self.preload()
-        return batch
-
-    def reset(self):
-        self.loader = iter(self.ori_loader)
-        self.preload()
diff --git a/repositories/CodeFormer/basicsr/data/transforms.py b/repositories/CodeFormer/basicsr/data/transforms.py
deleted file mode 100644
index aead9dc73ed063e1c5865040eaa2652b26aa3ad3..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/basicsr/data/transforms.py
+++ /dev/null
@@ -1,165 +0,0 @@
-import cv2
-import random
-
-
-def mod_crop(img, scale):
-    """Mod crop images, used during testing.
-
-    Args:
-        img (ndarray): Input image.
-        scale (int): Scale factor.
-
-    Returns:
-        ndarray: Result image.
-    """
-    img = img.copy()
-    if img.ndim in (2, 3):
-        h, w = img.shape[0], img.shape[1]
-        h_remainder, w_remainder = h % scale, w % scale
-        img = img[:h - h_remainder, :w - w_remainder, ...]
-    else:
-        raise ValueError(f'Wrong img ndim: {img.ndim}.')
-    return img
-
-
-def paired_random_crop(img_gts, img_lqs, gt_patch_size, scale, gt_path):
-    """Paired random crop.
-
-    It crops lists of lq and gt images with corresponding locations.
-
-    Args:
-        img_gts (list[ndarray] | ndarray): GT images. Note that all images
-            should have the same shape. If the input is an ndarray, it will
-            be transformed to a list containing itself.
-        img_lqs (list[ndarray] | ndarray): LQ images. Note that all images
-            should have the same shape. If the input is an ndarray, it will
-            be transformed to a list containing itself.
-        gt_patch_size (int): GT patch size.
-        scale (int): Scale factor.
-        gt_path (str): Path to ground-truth.
-
-    Returns:
-        list[ndarray] | ndarray: GT images and LQ images. If returned results
-            only have one element, just return ndarray.
-    """
-
-    if not isinstance(img_gts, list):
-        img_gts = [img_gts]
-    if not isinstance(img_lqs, list):
-        img_lqs = [img_lqs]
-
-    h_lq, w_lq, _ = img_lqs[0].shape
-    h_gt, w_gt, _ = img_gts[0].shape
-    lq_patch_size = gt_patch_size // scale
-
-    if h_gt != h_lq * scale or w_gt != w_lq * scale:
-        raise ValueError(f'Scale mismatches. GT ({h_gt}, {w_gt}) is not {scale}x ',
-                         f'multiplication of LQ ({h_lq}, {w_lq}).')
-    if h_lq < lq_patch_size or w_lq < lq_patch_size:
-        raise ValueError(f'LQ ({h_lq}, {w_lq}) is smaller than patch size '
-                         f'({lq_patch_size}, {lq_patch_size}). '
-                         f'Please remove {gt_path}.')
-
-    # randomly choose top and left coordinates for lq patch
-    top = random.randint(0, h_lq - lq_patch_size)
-    left = random.randint(0, w_lq - lq_patch_size)
-
-    # crop lq patch
-    img_lqs = [v[top:top + lq_patch_size, left:left + lq_patch_size, ...] for v in img_lqs]
-
-    # crop corresponding gt patch
-    top_gt, left_gt = int(top * scale), int(left * scale)
-    img_gts = [v[top_gt:top_gt + gt_patch_size, left_gt:left_gt + gt_patch_size, ...] for v in img_gts]
-    if len(img_gts) == 1:
-        img_gts = img_gts[0]
-    if len(img_lqs) == 1:
-        img_lqs = img_lqs[0]
-    return img_gts, img_lqs
-
-
-def augment(imgs, hflip=True, rotation=True, flows=None, return_status=False):
-    """Augment: horizontal flips OR rotate (0, 90, 180, 270 degrees).
-
-    We use vertical flip and transpose for rotation implementation.
-    All the images in the list use the same augmentation.
-
-    Args:
-        imgs (list[ndarray] | ndarray): Images to be augmented. If the input
-            is an ndarray, it will be transformed to a list.
-        hflip (bool): Horizontal flip. Default: True.
-        rotation (bool): Ratotation. Default: True.
-        flows (list[ndarray]: Flows to be augmented. If the input is an
-            ndarray, it will be transformed to a list.
-            Dimension is (h, w, 2). Default: None.
-        return_status (bool): Return the status of flip and rotation.
-            Default: False.
-
-    Returns:
-        list[ndarray] | ndarray: Augmented images and flows. If returned
-            results only have one element, just return ndarray.
-
-    """
-    hflip = hflip and random.random() < 0.5
-    vflip = rotation and random.random() < 0.5
-    rot90 = rotation and random.random() < 0.5
-
-    def _augment(img):
-        if hflip:  # horizontal
-            cv2.flip(img, 1, img)
-        if vflip:  # vertical
-            cv2.flip(img, 0, img)
-        if rot90:
-            img = img.transpose(1, 0, 2)
-        return img
-
-    def _augment_flow(flow):
-        if hflip:  # horizontal
-            cv2.flip(flow, 1, flow)
-            flow[:, :, 0] *= -1
-        if vflip:  # vertical
-            cv2.flip(flow, 0, flow)
-            flow[:, :, 1] *= -1
-        if rot90:
-            flow = flow.transpose(1, 0, 2)
-            flow = flow[:, :, [1, 0]]
-        return flow
-
-    if not isinstance(imgs, list):
-        imgs = [imgs]
-    imgs = [_augment(img) for img in imgs]
-    if len(imgs) == 1:
-        imgs = imgs[0]
-
-    if flows is not None:
-        if not isinstance(flows, list):
-            flows = [flows]
-        flows = [_augment_flow(flow) for flow in flows]
-        if len(flows) == 1:
-            flows = flows[0]
-        return imgs, flows
-    else:
-        if return_status:
-            return imgs, (hflip, vflip, rot90)
-        else:
-            return imgs
-
-
-def img_rotate(img, angle, center=None, scale=1.0):
-    """Rotate image.
-
-    Args:
-        img (ndarray): Image to be rotated.
-        angle (float): Rotation angle in degrees. Positive values mean
-            counter-clockwise rotation.
-        center (tuple[int]): Rotation center. If the center is None,
-            initialize it as the center of the image. Default: None.
-        scale (float): Isotropic scale factor. Default: 1.0.
-    """
-    (h, w) = img.shape[:2]
-
-    if center is None:
-        center = (w // 2, h // 2)
-
-    matrix = cv2.getRotationMatrix2D(center, angle, scale)
-    rotated_img = cv2.warpAffine(img, matrix, (w, h))
-    return rotated_img
diff --git a/repositories/CodeFormer/basicsr/losses/__init__.py b/repositories/CodeFormer/basicsr/losses/__init__.py
deleted file mode 100644
index 2b184e74c861e6fca0c548692a9a949a6100b0aa..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/basicsr/losses/__init__.py
+++ /dev/null
@@ -1,26 +0,0 @@
-from copy import deepcopy
-
-from basicsr.utils import get_root_logger
-from basicsr.utils.registry import LOSS_REGISTRY
-from .losses import (CharbonnierLoss, GANLoss, L1Loss, MSELoss, PerceptualLoss, WeightedTVLoss, g_path_regularize,
-                     gradient_penalty_loss, r1_penalty)
-
-__all__ = [
-    'L1Loss', 'MSELoss', 'CharbonnierLoss', 'WeightedTVLoss', 'PerceptualLoss', 'GANLoss', 'gradient_penalty_loss',
-    'r1_penalty', 'g_path_regularize'
-]
-
-
-def build_loss(opt):
-    """Build loss from options.
-
-    Args:
-        opt (dict): Configuration. It must constain:
-            type (str): Model type.
-    """
-    opt = deepcopy(opt)
-    loss_type = opt.pop('type')
-    loss = LOSS_REGISTRY.get(loss_type)(**opt)
-    logger = get_root_logger()
-    logger.info(f'Loss [{loss.__class__.__name__}] is created.')
-    return loss
diff --git a/repositories/CodeFormer/basicsr/losses/loss_util.py b/repositories/CodeFormer/basicsr/losses/loss_util.py
deleted file mode 100644
index 744eeb46d1f3b5a7b4553ca23237ddd9c899a698..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/basicsr/losses/loss_util.py
+++ /dev/null
@@ -1,95 +0,0 @@
-import functools
-from torch.nn import functional as F
-
-
-def reduce_loss(loss, reduction):
-    """Reduce loss as specified.
-
-    Args:
-        loss (Tensor): Elementwise loss tensor.
-        reduction (str): Options are 'none', 'mean' and 'sum'.
-
-    Returns:
-        Tensor: Reduced loss tensor.
-    """
-    reduction_enum = F._Reduction.get_enum(reduction)
-    # none: 0, elementwise_mean:1, sum: 2
-    if reduction_enum == 0:
-        return loss
-    elif reduction_enum == 1:
-        return loss.mean()
-    else:
-        return loss.sum()
-
-
-def weight_reduce_loss(loss, weight=None, reduction='mean'):
-    """Apply element-wise weight and reduce loss.
-
-    Args:
-        loss (Tensor): Element-wise loss.
-        weight (Tensor): Element-wise weights. Default: None.
-        reduction (str): Same as built-in losses of PyTorch. Options are
-            'none', 'mean' and 'sum'. Default: 'mean'.
-
-    Returns:
-        Tensor: Loss values.
-    """
-    # if weight is specified, apply element-wise weight
-    if weight is not None:
-        assert weight.dim() == loss.dim()
-        assert weight.size(1) == 1 or weight.size(1) == loss.size(1)
-        loss = loss * weight
-
-    # if weight is not specified or reduction is sum, just reduce the loss
-    if weight is None or reduction == 'sum':
-        loss = reduce_loss(loss, reduction)
-    # if reduction is mean, then compute mean over weight region
-    elif reduction == 'mean':
-        if weight.size(1) > 1:
-            weight = weight.sum()
-        else:
-            weight = weight.sum() * loss.size(1)
-        loss = loss.sum() / weight
-
-    return loss
-
-
-def weighted_loss(loss_func):
-    """Create a weighted version of a given loss function.
-
-    To use this decorator, the loss function must have the signature like
-    `loss_func(pred, target, **kwargs)`. The function only needs to compute
-    element-wise loss without any reduction. This decorator will add weight
-    and reduction arguments to the function. The decorated function will have
-    the signature like `loss_func(pred, target, weight=None, reduction='mean',
-    **kwargs)`.
-
-    :Example:
-
-    >>> import torch
-    >>> @weighted_loss
-    >>> def l1_loss(pred, target):
-    >>>     return (pred - target).abs()
-
-    >>> pred = torch.Tensor([0, 2, 3])
-    >>> target = torch.Tensor([1, 1, 1])
-    >>> weight = torch.Tensor([1, 0, 1])
-
-    >>> l1_loss(pred, target)
-    tensor(1.3333)
-    >>> l1_loss(pred, target, weight)
-    tensor(1.5000)
-    >>> l1_loss(pred, target, reduction='none')
-    tensor([1., 1., 2.])
-    >>> l1_loss(pred, target, weight, reduction='sum')
-    tensor(3.)
-    """
-
-    @functools.wraps(loss_func)
-    def wrapper(pred, target, weight=None, reduction='mean', **kwargs):
-        # get element-wise loss
-        loss = loss_func(pred, target, **kwargs)
-        loss = weight_reduce_loss(loss, weight, reduction)
-        return loss
-
-    return wrapper
diff --git a/repositories/CodeFormer/basicsr/losses/losses.py b/repositories/CodeFormer/basicsr/losses/losses.py
deleted file mode 100644
index 1bcf272cfb756d99451a3005567ea4d4c9059067..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/basicsr/losses/losses.py
+++ /dev/null
@@ -1,455 +0,0 @@
-import math
-import lpips
-import torch
-from torch import autograd as autograd
-from torch import nn as nn
-from torch.nn import functional as F
-
-from basicsr.archs.vgg_arch import VGGFeatureExtractor
-from basicsr.utils.registry import LOSS_REGISTRY
-from .loss_util import weighted_loss
-
-_reduction_modes = ['none', 'mean', 'sum']
-
-
-@weighted_loss
-def l1_loss(pred, target):
-    return F.l1_loss(pred, target, reduction='none')
-
-
-@weighted_loss
-def mse_loss(pred, target):
-    return F.mse_loss(pred, target, reduction='none')
-
-
-@weighted_loss
-def charbonnier_loss(pred, target, eps=1e-12):
-    return torch.sqrt((pred - target)**2 + eps)
-
-
-@LOSS_REGISTRY.register()
-class L1Loss(nn.Module):
-    """L1 (mean absolute error, MAE) loss.
-
-    Args:
-        loss_weight (float): Loss weight for L1 loss. Default: 1.0.
-        reduction (str): Specifies the reduction to apply to the output.
-            Supported choices are 'none' | 'mean' | 'sum'. Default: 'mean'.
-    """
-
-    def __init__(self, loss_weight=1.0, reduction='mean'):
-        super(L1Loss, self).__init__()
-        if reduction not in ['none', 'mean', 'sum']:
-            raise ValueError(f'Unsupported reduction mode: {reduction}. ' f'Supported ones are: {_reduction_modes}')
-
-        self.loss_weight = loss_weight
-        self.reduction = reduction
-
-    def forward(self, pred, target, weight=None, **kwargs):
-        """
-        Args:
-            pred (Tensor): of shape (N, C, H, W). Predicted tensor.
-            target (Tensor): of shape (N, C, H, W). Ground truth tensor.
-            weight (Tensor, optional): of shape (N, C, H, W). Element-wise
-                weights. Default: None.
-        """
-        return self.loss_weight * l1_loss(pred, target, weight, reduction=self.reduction)
-
-
-@LOSS_REGISTRY.register()
-class MSELoss(nn.Module):
-    """MSE (L2) loss.
-
-    Args:
-        loss_weight (float): Loss weight for MSE loss. Default: 1.0.
-        reduction (str): Specifies the reduction to apply to the output.
-            Supported choices are 'none' | 'mean' | 'sum'. Default: 'mean'.
-    """
-
-    def __init__(self, loss_weight=1.0, reduction='mean'):
-        super(MSELoss, self).__init__()
-        if reduction not in ['none', 'mean', 'sum']:
-            raise ValueError(f'Unsupported reduction mode: {reduction}. ' f'Supported ones are: {_reduction_modes}')
-
-        self.loss_weight = loss_weight
-        self.reduction = reduction
-
-    def forward(self, pred, target, weight=None, **kwargs):
-        """
-        Args:
-            pred (Tensor): of shape (N, C, H, W). Predicted tensor.
-            target (Tensor): of shape (N, C, H, W). Ground truth tensor.
-            weight (Tensor, optional): of shape (N, C, H, W). Element-wise
-                weights. Default: None.
-        """
-        return self.loss_weight * mse_loss(pred, target, weight, reduction=self.reduction)
-
-
-@LOSS_REGISTRY.register()
-class CharbonnierLoss(nn.Module):
-    """Charbonnier loss (one variant of Robust L1Loss, a differentiable
-    variant of L1Loss).
-
-    Described in "Deep Laplacian Pyramid Networks for Fast and Accurate
-        Super-Resolution".
-
-    Args:
-        loss_weight (float): Loss weight for L1 loss. Default: 1.0.
-        reduction (str): Specifies the reduction to apply to the output.
-            Supported choices are 'none' | 'mean' | 'sum'. Default: 'mean'.
-        eps (float): A value used to control the curvature near zero.
-            Default: 1e-12.
-    """
-
-    def __init__(self, loss_weight=1.0, reduction='mean', eps=1e-12):
-        super(CharbonnierLoss, self).__init__()
-        if reduction not in ['none', 'mean', 'sum']:
-            raise ValueError(f'Unsupported reduction mode: {reduction}. ' f'Supported ones are: {_reduction_modes}')
-
-        self.loss_weight = loss_weight
-        self.reduction = reduction
-        self.eps = eps
-
-    def forward(self, pred, target, weight=None, **kwargs):
-        """
-        Args:
-            pred (Tensor): of shape (N, C, H, W). Predicted tensor.
-            target (Tensor): of shape (N, C, H, W). Ground truth tensor.
-            weight (Tensor, optional): of shape (N, C, H, W). Element-wise
-                weights. Default: None.
-        """
-        return self.loss_weight * charbonnier_loss(pred, target, weight, eps=self.eps, reduction=self.reduction)
-
-
-@LOSS_REGISTRY.register()
-class WeightedTVLoss(L1Loss):
-    """Weighted TV loss.
-
-        Args:
-            loss_weight (float): Loss weight. Default: 1.0.
-    """
-
-    def __init__(self, loss_weight=1.0):
-        super(WeightedTVLoss, self).__init__(loss_weight=loss_weight)
-
-    def forward(self, pred, weight=None):
-        y_diff = super(WeightedTVLoss, self).forward(pred[:, :, :-1, :], pred[:, :, 1:, :], weight=weight[:, :, :-1, :])
-        x_diff = super(WeightedTVLoss, self).forward(pred[:, :, :, :-1], pred[:, :, :, 1:], weight=weight[:, :, :, :-1])
-
-        loss = x_diff + y_diff
-
-        return loss
-
-
-@LOSS_REGISTRY.register()
-class PerceptualLoss(nn.Module):
-    """Perceptual loss with commonly used style loss.
-
-    Args:
-        layer_weights (dict): The weight for each layer of vgg feature.
-            Here is an example: {'conv5_4': 1.}, which means the conv5_4
-            feature layer (before relu5_4) will be extracted with weight
-            1.0 in calculting losses.
-        vgg_type (str): The type of vgg network used as feature extractor.
-            Default: 'vgg19'.
-        use_input_norm (bool):  If True, normalize the input image in vgg.
-            Default: True.
-        range_norm (bool): If True, norm images with range [-1, 1] to [0, 1].
-            Default: False.
-        perceptual_weight (float): If `perceptual_weight > 0`, the perceptual
-            loss will be calculated and the loss will multiplied by the
-            weight. Default: 1.0.
-        style_weight (float): If `style_weight > 0`, the style loss will be
-            calculated and the loss will multiplied by the weight.
-            Default: 0.
-        criterion (str): Criterion used for perceptual loss. Default: 'l1'.
-    """
-
-    def __init__(self,
-                 layer_weights,
-                 vgg_type='vgg19',
-                 use_input_norm=True,
-                 range_norm=False,
-                 perceptual_weight=1.0,
-                 style_weight=0.,
-                 criterion='l1'):
-        super(PerceptualLoss, self).__init__()
-        self.perceptual_weight = perceptual_weight
-        self.style_weight = style_weight
-        self.layer_weights = layer_weights
-        self.vgg = VGGFeatureExtractor(
-            layer_name_list=list(layer_weights.keys()),
-            vgg_type=vgg_type,
-            use_input_norm=use_input_norm,
-            range_norm=range_norm)
-
-        self.criterion_type = criterion
-        if self.criterion_type == 'l1':
-            self.criterion = torch.nn.L1Loss()
-        elif self.criterion_type == 'l2':
-            self.criterion = torch.nn.L2loss()
-        elif self.criterion_type == 'mse':
-            self.criterion = torch.nn.MSELoss(reduction='mean')
-        elif self.criterion_type == 'fro':
-            self.criterion = None
-        else:
-            raise NotImplementedError(f'{criterion} criterion has not been supported.')
-
-    def forward(self, x, gt):
-        """Forward function.
-
-        Args:
-            x (Tensor): Input tensor with shape (n, c, h, w).
-            gt (Tensor): Ground-truth tensor with shape (n, c, h, w).
-
-        Returns:
-            Tensor: Forward results.
-        """
-        # extract vgg features
-        x_features = self.vgg(x)
-        gt_features = self.vgg(gt.detach())
-
-        # calculate perceptual loss
-        if self.perceptual_weight > 0:
-            percep_loss = 0
-            for k in x_features.keys():
-                if self.criterion_type == 'fro':
-                    percep_loss += torch.norm(x_features[k] - gt_features[k], p='fro') * self.layer_weights[k]
-                else:
-                    percep_loss += self.criterion(x_features[k], gt_features[k]) * self.layer_weights[k]
-            percep_loss *= self.perceptual_weight
-        else:
-            percep_loss = None
-
-        # calculate style loss
-        if self.style_weight > 0:
-            style_loss = 0
-            for k in x_features.keys():
-                if self.criterion_type == 'fro':
-                    style_loss += torch.norm(
-                        self._gram_mat(x_features[k]) - self._gram_mat(gt_features[k]), p='fro') * self.layer_weights[k]
-                else:
-                    style_loss += self.criterion(self._gram_mat(x_features[k]), self._gram_mat(
-                        gt_features[k])) * self.layer_weights[k]
-            style_loss *= self.style_weight
-        else:
-            style_loss = None
-
-        return percep_loss, style_loss
-
-    def _gram_mat(self, x):
-        """Calculate Gram matrix.
-
-        Args:
-            x (torch.Tensor): Tensor with shape of (n, c, h, w).
-
-        Returns:
-            torch.Tensor: Gram matrix.
-        """
-        n, c, h, w = x.size()
-        features = x.view(n, c, w * h)
-        features_t = features.transpose(1, 2)
-        gram = features.bmm(features_t) / (c * h * w)
-        return gram
-
-
-@LOSS_REGISTRY.register()
-class LPIPSLoss(nn.Module):
-    def __init__(self, 
-            loss_weight=1.0, 
-            use_input_norm=True,
-            range_norm=False,):
-        super(LPIPSLoss, self).__init__()
-        self.perceptual = lpips.LPIPS(net="vgg", spatial=False).eval()
-        self.loss_weight = loss_weight
-        self.use_input_norm = use_input_norm
-        self.range_norm = range_norm
-
-        if self.use_input_norm:
-            # the mean is for image with range [0, 1]
-            self.register_buffer('mean', torch.Tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1))
-            # the std is for image with range [0, 1]
-            self.register_buffer('std', torch.Tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1))
-
-    def forward(self, pred, target):
-        if self.range_norm:
-            pred   = (pred + 1) / 2
-            target = (target + 1) / 2
-        if self.use_input_norm:
-            pred   = (pred - self.mean) / self.std
-            target = (target - self.mean) / self.std
-        lpips_loss = self.perceptual(target.contiguous(), pred.contiguous())
-        return self.loss_weight * lpips_loss.mean()
-
-
-@LOSS_REGISTRY.register()
-class GANLoss(nn.Module):
-    """Define GAN loss.
-
-    Args:
-        gan_type (str): Support 'vanilla', 'lsgan', 'wgan', 'hinge'.
-        real_label_val (float): The value for real label. Default: 1.0.
-        fake_label_val (float): The value for fake label. Default: 0.0.
-        loss_weight (float): Loss weight. Default: 1.0.
-            Note that loss_weight is only for generators; and it is always 1.0
-            for discriminators.
-    """
-
-    def __init__(self, gan_type, real_label_val=1.0, fake_label_val=0.0, loss_weight=1.0):
-        super(GANLoss, self).__init__()
-        self.gan_type = gan_type
-        self.loss_weight = loss_weight
-        self.real_label_val = real_label_val
-        self.fake_label_val = fake_label_val
-
-        if self.gan_type == 'vanilla':
-            self.loss = nn.BCEWithLogitsLoss()
-        elif self.gan_type == 'lsgan':
-            self.loss = nn.MSELoss()
-        elif self.gan_type == 'wgan':
-            self.loss = self._wgan_loss
-        elif self.gan_type == 'wgan_softplus':
-            self.loss = self._wgan_softplus_loss
-        elif self.gan_type == 'hinge':
-            self.loss = nn.ReLU()
-        else:
-            raise NotImplementedError(f'GAN type {self.gan_type} is not implemented.')
-
-    def _wgan_loss(self, input, target):
-        """wgan loss.
-
-        Args:
-            input (Tensor): Input tensor.
-            target (bool): Target label.
-
-        Returns:
-            Tensor: wgan loss.
-        """
-        return -input.mean() if target else input.mean()
-
-    def _wgan_softplus_loss(self, input, target):
-        """wgan loss with soft plus. softplus is a smooth approximation to the
-        ReLU function.
-
-        In StyleGAN2, it is called:
-            Logistic loss for discriminator;
-            Non-saturating loss for generator.
-
-        Args:
-            input (Tensor): Input tensor.
-            target (bool): Target label.
-
-        Returns:
-            Tensor: wgan loss.
-        """
-        return F.softplus(-input).mean() if target else F.softplus(input).mean()
-
-    def get_target_label(self, input, target_is_real):
-        """Get target label.
-
-        Args:
-            input (Tensor): Input tensor.
-            target_is_real (bool): Whether the target is real or fake.
-
-        Returns:
-            (bool | Tensor): Target tensor. Return bool for wgan, otherwise,
-                return Tensor.
-        """
-
-        if self.gan_type in ['wgan', 'wgan_softplus']:
-            return target_is_real
-        target_val = (self.real_label_val if target_is_real else self.fake_label_val)
-        return input.new_ones(input.size()) * target_val
-
-    def forward(self, input, target_is_real, is_disc=False):
-        """
-        Args:
-            input (Tensor): The input for the loss module, i.e., the network
-                prediction.
-            target_is_real (bool): Whether the targe is real or fake.
-            is_disc (bool): Whether the loss for discriminators or not.
-                Default: False.
-
-        Returns:
-            Tensor: GAN loss value.
-        """
-        if self.gan_type == 'hinge':
-            if is_disc:  # for discriminators in hinge-gan
-                input = -input if target_is_real else input
-                loss = self.loss(1 + input).mean()
-            else:  # for generators in hinge-gan
-                loss = -input.mean()
-        else:  # other gan types
-            target_label = self.get_target_label(input, target_is_real)
-            loss = self.loss(input, target_label)
-
-        # loss_weight is always 1.0 for discriminators
-        return loss if is_disc else loss * self.loss_weight
-
-
-def r1_penalty(real_pred, real_img):
-    """R1 regularization for discriminator. The core idea is to
-        penalize the gradient on real data alone: when the
-        generator distribution produces the true data distribution
-        and the discriminator is equal to 0 on the data manifold, the
-        gradient penalty ensures that the discriminator cannot create
-        a non-zero gradient orthogonal to the data manifold without
-        suffering a loss in the GAN game.
-
-        Ref:
-        Eq. 9 in Which training methods for GANs do actually converge.
-        """
-    grad_real = autograd.grad(outputs=real_pred.sum(), inputs=real_img, create_graph=True)[0]
-    grad_penalty = grad_real.pow(2).view(grad_real.shape[0], -1).sum(1).mean()
-    return grad_penalty
-
-
-def g_path_regularize(fake_img, latents, mean_path_length, decay=0.01):
-    noise = torch.randn_like(fake_img) / math.sqrt(fake_img.shape[2] * fake_img.shape[3])
-    grad = autograd.grad(outputs=(fake_img * noise).sum(), inputs=latents, create_graph=True)[0]
-    path_lengths = torch.sqrt(grad.pow(2).sum(2).mean(1))
-
-    path_mean = mean_path_length + decay * (path_lengths.mean() - mean_path_length)
-
-    path_penalty = (path_lengths - path_mean).pow(2).mean()
-
-    return path_penalty, path_lengths.detach().mean(), path_mean.detach()
-
-
-def gradient_penalty_loss(discriminator, real_data, fake_data, weight=None):
-    """Calculate gradient penalty for wgan-gp.
-
-    Args:
-        discriminator (nn.Module): Network for the discriminator.
-        real_data (Tensor): Real input data.
-        fake_data (Tensor): Fake input data.
-        weight (Tensor): Weight tensor. Default: None.
-
-    Returns:
-        Tensor: A tensor for gradient penalty.
-    """
-
-    batch_size = real_data.size(0)
-    alpha = real_data.new_tensor(torch.rand(batch_size, 1, 1, 1))
-
-    # interpolate between real_data and fake_data
-    interpolates = alpha * real_data + (1. - alpha) * fake_data
-    interpolates = autograd.Variable(interpolates, requires_grad=True)
-
-    disc_interpolates = discriminator(interpolates)
-    gradients = autograd.grad(
-        outputs=disc_interpolates,
-        inputs=interpolates,
-        grad_outputs=torch.ones_like(disc_interpolates),
-        create_graph=True,
-        retain_graph=True,
-        only_inputs=True)[0]
-
-    if weight is not None:
-        gradients = gradients * weight
-
-    gradients_penalty = ((gradients.norm(2, dim=1) - 1)**2).mean()
-    if weight is not None:
-        gradients_penalty /= torch.mean(weight)
-
-    return gradients_penalty
diff --git a/repositories/CodeFormer/basicsr/metrics/__init__.py b/repositories/CodeFormer/basicsr/metrics/__init__.py
deleted file mode 100644
index 19d55cc8321f124c918d78465b053aef67f13a33..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/basicsr/metrics/__init__.py
+++ /dev/null
@@ -1,19 +0,0 @@
-from copy import deepcopy
-
-from basicsr.utils.registry import METRIC_REGISTRY
-from .psnr_ssim import calculate_psnr, calculate_ssim
-
-__all__ = ['calculate_psnr', 'calculate_ssim']
-
-
-def calculate_metric(data, opt):
-    """Calculate metric from data and options.
-
-    Args:
-        opt (dict): Configuration. It must constain:
-            type (str): Model type.
-    """
-    opt = deepcopy(opt)
-    metric_type = opt.pop('type')
-    metric = METRIC_REGISTRY.get(metric_type)(**data, **opt)
-    return metric
diff --git a/repositories/CodeFormer/basicsr/metrics/metric_util.py b/repositories/CodeFormer/basicsr/metrics/metric_util.py
deleted file mode 100644
index 4d18f0f7816431bed6af9d58319c6435bdf5c971..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/basicsr/metrics/metric_util.py
+++ /dev/null
@@ -1,45 +0,0 @@
-import numpy as np
-
-from basicsr.utils.matlab_functions import bgr2ycbcr
-
-
-def reorder_image(img, input_order='HWC'):
-    """Reorder images to 'HWC' order.
-
-    If the input_order is (h, w), return (h, w, 1);
-    If the input_order is (c, h, w), return (h, w, c);
-    If the input_order is (h, w, c), return as it is.
-
-    Args:
-        img (ndarray): Input image.
-        input_order (str): Whether the input order is 'HWC' or 'CHW'.
-            If the input image shape is (h, w), input_order will not have
-            effects. Default: 'HWC'.
-
-    Returns:
-        ndarray: reordered image.
-    """
-
-    if input_order not in ['HWC', 'CHW']:
-        raise ValueError(f'Wrong input_order {input_order}. Supported input_orders are ' "'HWC' and 'CHW'")
-    if len(img.shape) == 2:
-        img = img[..., None]
-    if input_order == 'CHW':
-        img = img.transpose(1, 2, 0)
-    return img
-
-
-def to_y_channel(img):
-    """Change to Y channel of YCbCr.
-
-    Args:
-        img (ndarray): Images with range [0, 255].
-
-    Returns:
-        (ndarray): Images with range [0, 255] (float type) without round.
-    """
-    img = img.astype(np.float32) / 255.
-    if img.ndim == 3 and img.shape[2] == 3:
-        img = bgr2ycbcr(img, y_only=True)
-        img = img[..., None]
-    return img * 255.
diff --git a/repositories/CodeFormer/basicsr/metrics/psnr_ssim.py b/repositories/CodeFormer/basicsr/metrics/psnr_ssim.py
deleted file mode 100644
index bbd950699c2495880236883861d9e199f900eae8..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/basicsr/metrics/psnr_ssim.py
+++ /dev/null
@@ -1,128 +0,0 @@
-import cv2
-import numpy as np
-
-from basicsr.metrics.metric_util import reorder_image, to_y_channel
-from basicsr.utils.registry import METRIC_REGISTRY
-
-
-@METRIC_REGISTRY.register()
-def calculate_psnr(img1, img2, crop_border, input_order='HWC', test_y_channel=False):
-    """Calculate PSNR (Peak Signal-to-Noise Ratio).
-
-    Ref: https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio
-
-    Args:
-        img1 (ndarray): Images with range [0, 255].
-        img2 (ndarray): Images with range [0, 255].
-        crop_border (int): Cropped pixels in each edge of an image. These
-            pixels are not involved in the PSNR calculation.
-        input_order (str): Whether the input order is 'HWC' or 'CHW'.
-            Default: 'HWC'.
-        test_y_channel (bool): Test on Y channel of YCbCr. Default: False.
-
-    Returns:
-        float: psnr result.
-    """
-
-    assert img1.shape == img2.shape, (f'Image shapes are differnet: {img1.shape}, {img2.shape}.')
-    if input_order not in ['HWC', 'CHW']:
-        raise ValueError(f'Wrong input_order {input_order}. Supported input_orders are ' '"HWC" and "CHW"')
-    img1 = reorder_image(img1, input_order=input_order)
-    img2 = reorder_image(img2, input_order=input_order)
-    img1 = img1.astype(np.float64)
-    img2 = img2.astype(np.float64)
-
-    if crop_border != 0:
-        img1 = img1[crop_border:-crop_border, crop_border:-crop_border, ...]
-        img2 = img2[crop_border:-crop_border, crop_border:-crop_border, ...]
-
-    if test_y_channel:
-        img1 = to_y_channel(img1)
-        img2 = to_y_channel(img2)
-
-    mse = np.mean((img1 - img2)**2)
-    if mse == 0:
-        return float('inf')
-    return 20. * np.log10(255. / np.sqrt(mse))
-
-
-def _ssim(img1, img2):
-    """Calculate SSIM (structural similarity) for one channel images.
-
-    It is called by func:`calculate_ssim`.
-
-    Args:
-        img1 (ndarray): Images with range [0, 255] with order 'HWC'.
-        img2 (ndarray): Images with range [0, 255] with order 'HWC'.
-
-    Returns:
-        float: ssim result.
-    """
-
-    C1 = (0.01 * 255)**2
-    C2 = (0.03 * 255)**2
-
-    img1 = img1.astype(np.float64)
-    img2 = img2.astype(np.float64)
-    kernel = cv2.getGaussianKernel(11, 1.5)
-    window = np.outer(kernel, kernel.transpose())
-
-    mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5]
-    mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5]
-    mu1_sq = mu1**2
-    mu2_sq = mu2**2
-    mu1_mu2 = mu1 * mu2
-    sigma1_sq = cv2.filter2D(img1**2, -1, window)[5:-5, 5:-5] - mu1_sq
-    sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq
-    sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2
-
-    ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2))
-    return ssim_map.mean()
-
-
-@METRIC_REGISTRY.register()
-def calculate_ssim(img1, img2, crop_border, input_order='HWC', test_y_channel=False):
-    """Calculate SSIM (structural similarity).
-
-    Ref:
-    Image quality assessment: From error visibility to structural similarity
-
-    The results are the same as that of the official released MATLAB code in
-    https://ece.uwaterloo.ca/~z70wang/research/ssim/.
-
-    For three-channel images, SSIM is calculated for each channel and then
-    averaged.
-
-    Args:
-        img1 (ndarray): Images with range [0, 255].
-        img2 (ndarray): Images with range [0, 255].
-        crop_border (int): Cropped pixels in each edge of an image. These
-            pixels are not involved in the SSIM calculation.
-        input_order (str): Whether the input order is 'HWC' or 'CHW'.
-            Default: 'HWC'.
-        test_y_channel (bool): Test on Y channel of YCbCr. Default: False.
-
-    Returns:
-        float: ssim result.
-    """
-
-    assert img1.shape == img2.shape, (f'Image shapes are differnet: {img1.shape}, {img2.shape}.')
-    if input_order not in ['HWC', 'CHW']:
-        raise ValueError(f'Wrong input_order {input_order}. Supported input_orders are ' '"HWC" and "CHW"')
-    img1 = reorder_image(img1, input_order=input_order)
-    img2 = reorder_image(img2, input_order=input_order)
-    img1 = img1.astype(np.float64)
-    img2 = img2.astype(np.float64)
-
-    if crop_border != 0:
-        img1 = img1[crop_border:-crop_border, crop_border:-crop_border, ...]
-        img2 = img2[crop_border:-crop_border, crop_border:-crop_border, ...]
-
-    if test_y_channel:
-        img1 = to_y_channel(img1)
-        img2 = to_y_channel(img2)
-
-    ssims = []
-    for i in range(img1.shape[2]):
-        ssims.append(_ssim(img1[..., i], img2[..., i]))
-    return np.array(ssims).mean()
diff --git a/repositories/CodeFormer/basicsr/models/__init__.py b/repositories/CodeFormer/basicsr/models/__init__.py
deleted file mode 100644
index 00bde45f003698a5b15d3517ae47b59ef1d86e0c..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/basicsr/models/__init__.py
+++ /dev/null
@@ -1,30 +0,0 @@
-import importlib
-from copy import deepcopy
-from os import path as osp
-
-from basicsr.utils import get_root_logger, scandir
-from basicsr.utils.registry import MODEL_REGISTRY
-
-__all__ = ['build_model']
-
-# automatically scan and import model modules for registry
-# scan all the files under the 'models' folder and collect files ending with
-# '_model.py'
-model_folder = osp.dirname(osp.abspath(__file__))
-model_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(model_folder) if v.endswith('_model.py')]
-# import all the model modules
-_model_modules = [importlib.import_module(f'basicsr.models.{file_name}') for file_name in model_filenames]
-
-
-def build_model(opt):
-    """Build model from options.
-
-    Args:
-        opt (dict): Configuration. It must constain:
-            model_type (str): Model type.
-    """
-    opt = deepcopy(opt)
-    model = MODEL_REGISTRY.get(opt['model_type'])(opt)
-    logger = get_root_logger()
-    logger.info(f'Model [{model.__class__.__name__}] is created.')
-    return model
diff --git a/repositories/CodeFormer/basicsr/ops/__init__.py b/repositories/CodeFormer/basicsr/ops/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/repositories/CodeFormer/basicsr/ops/dcn/__init__.py b/repositories/CodeFormer/basicsr/ops/dcn/__init__.py
deleted file mode 100644
index 32e3592f896d61b4127e09d0476381b9d55e32ff..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/basicsr/ops/dcn/__init__.py
+++ /dev/null
@@ -1,7 +0,0 @@
-from .deform_conv import (DeformConv, DeformConvPack, ModulatedDeformConv, ModulatedDeformConvPack, deform_conv,
-                          modulated_deform_conv)
-
-__all__ = [
-    'DeformConv', 'DeformConvPack', 'ModulatedDeformConv', 'ModulatedDeformConvPack', 'deform_conv',
-    'modulated_deform_conv'
-]
diff --git a/repositories/CodeFormer/basicsr/ops/dcn/deform_conv.py b/repositories/CodeFormer/basicsr/ops/dcn/deform_conv.py
deleted file mode 100644
index 734154f9ed9447d585eae7df6886acb136f8a3cf..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/basicsr/ops/dcn/deform_conv.py
+++ /dev/null
@@ -1,377 +0,0 @@
-import math
-import torch
-from torch import nn as nn
-from torch.autograd import Function
-from torch.autograd.function import once_differentiable
-from torch.nn import functional as F
-from torch.nn.modules.utils import _pair, _single
-
-try:
-    from . import deform_conv_ext
-except ImportError:
-    import os
-    BASICSR_JIT = os.getenv('BASICSR_JIT')
-    if BASICSR_JIT == 'True':
-        from torch.utils.cpp_extension import load
-        module_path = os.path.dirname(__file__)
-        deform_conv_ext = load(
-            'deform_conv',
-            sources=[
-                os.path.join(module_path, 'src', 'deform_conv_ext.cpp'),
-                os.path.join(module_path, 'src', 'deform_conv_cuda.cpp'),
-                os.path.join(module_path, 'src', 'deform_conv_cuda_kernel.cu'),
-            ],
-        )
-
-
-class DeformConvFunction(Function):
-
-    @staticmethod
-    def forward(ctx,
-                input,
-                offset,
-                weight,
-                stride=1,
-                padding=0,
-                dilation=1,
-                groups=1,
-                deformable_groups=1,
-                im2col_step=64):
-        if input is not None and input.dim() != 4:
-            raise ValueError(f'Expected 4D tensor as input, got {input.dim()}' 'D tensor instead.')
-        ctx.stride = _pair(stride)
-        ctx.padding = _pair(padding)
-        ctx.dilation = _pair(dilation)
-        ctx.groups = groups
-        ctx.deformable_groups = deformable_groups
-        ctx.im2col_step = im2col_step
-
-        ctx.save_for_backward(input, offset, weight)
-
-        output = input.new_empty(DeformConvFunction._output_size(input, weight, ctx.padding, ctx.dilation, ctx.stride))
-
-        ctx.bufs_ = [input.new_empty(0), input.new_empty(0)]  # columns, ones
-
-        if not input.is_cuda:
-            raise NotImplementedError
-        else:
-            cur_im2col_step = min(ctx.im2col_step, input.shape[0])
-            assert (input.shape[0] % cur_im2col_step) == 0, 'im2col step must divide batchsize'
-            deform_conv_ext.deform_conv_forward(input, weight,
-                                                offset, output, ctx.bufs_[0], ctx.bufs_[1], weight.size(3),
-                                                weight.size(2), ctx.stride[1], ctx.stride[0], ctx.padding[1],
-                                                ctx.padding[0], ctx.dilation[1], ctx.dilation[0], ctx.groups,
-                                                ctx.deformable_groups, cur_im2col_step)
-        return output
-
-    @staticmethod
-    @once_differentiable
-    def backward(ctx, grad_output):
-        input, offset, weight = ctx.saved_tensors
-
-        grad_input = grad_offset = grad_weight = None
-
-        if not grad_output.is_cuda:
-            raise NotImplementedError
-        else:
-            cur_im2col_step = min(ctx.im2col_step, input.shape[0])
-            assert (input.shape[0] % cur_im2col_step) == 0, 'im2col step must divide batchsize'
-
-            if ctx.needs_input_grad[0] or ctx.needs_input_grad[1]:
-                grad_input = torch.zeros_like(input)
-                grad_offset = torch.zeros_like(offset)
-                deform_conv_ext.deform_conv_backward_input(input, offset, grad_output, grad_input,
-                                                           grad_offset, weight, ctx.bufs_[0], weight.size(3),
-                                                           weight.size(2), ctx.stride[1], ctx.stride[0], ctx.padding[1],
-                                                           ctx.padding[0], ctx.dilation[1], ctx.dilation[0], ctx.groups,
-                                                           ctx.deformable_groups, cur_im2col_step)
-
-            if ctx.needs_input_grad[2]:
-                grad_weight = torch.zeros_like(weight)
-                deform_conv_ext.deform_conv_backward_parameters(input, offset, grad_output, grad_weight,
-                                                                ctx.bufs_[0], ctx.bufs_[1], weight.size(3),
-                                                                weight.size(2), ctx.stride[1], ctx.stride[0],
-                                                                ctx.padding[1], ctx.padding[0], ctx.dilation[1],
-                                                                ctx.dilation[0], ctx.groups, ctx.deformable_groups, 1,
-                                                                cur_im2col_step)
-
-        return (grad_input, grad_offset, grad_weight, None, None, None, None, None)
-
-    @staticmethod
-    def _output_size(input, weight, padding, dilation, stride):
-        channels = weight.size(0)
-        output_size = (input.size(0), channels)
-        for d in range(input.dim() - 2):
-            in_size = input.size(d + 2)
-            pad = padding[d]
-            kernel = dilation[d] * (weight.size(d + 2) - 1) + 1
-            stride_ = stride[d]
-            output_size += ((in_size + (2 * pad) - kernel) // stride_ + 1, )
-        if not all(map(lambda s: s > 0, output_size)):
-            raise ValueError('convolution input is too small (output would be ' f'{"x".join(map(str, output_size))})')
-        return output_size
-
-
-class ModulatedDeformConvFunction(Function):
-
-    @staticmethod
-    def forward(ctx,
-                input,
-                offset,
-                mask,
-                weight,
-                bias=None,
-                stride=1,
-                padding=0,
-                dilation=1,
-                groups=1,
-                deformable_groups=1):
-        ctx.stride = stride
-        ctx.padding = padding
-        ctx.dilation = dilation
-        ctx.groups = groups
-        ctx.deformable_groups = deformable_groups
-        ctx.with_bias = bias is not None
-        if not ctx.with_bias:
-            bias = input.new_empty(1)  # fake tensor
-        if not input.is_cuda:
-            raise NotImplementedError
-        if weight.requires_grad or mask.requires_grad or offset.requires_grad \
-                or input.requires_grad:
-            ctx.save_for_backward(input, offset, mask, weight, bias)
-        output = input.new_empty(ModulatedDeformConvFunction._infer_shape(ctx, input, weight))
-        ctx._bufs = [input.new_empty(0), input.new_empty(0)]
-        deform_conv_ext.modulated_deform_conv_forward(input, weight, bias, ctx._bufs[0], offset, mask, output,
-                                                      ctx._bufs[1], weight.shape[2], weight.shape[3], ctx.stride,
-                                                      ctx.stride, ctx.padding, ctx.padding, ctx.dilation, ctx.dilation,
-                                                      ctx.groups, ctx.deformable_groups, ctx.with_bias)
-        return output
-
-    @staticmethod
-    @once_differentiable
-    def backward(ctx, grad_output):
-        if not grad_output.is_cuda:
-            raise NotImplementedError
-        input, offset, mask, weight, bias = ctx.saved_tensors
-        grad_input = torch.zeros_like(input)
-        grad_offset = torch.zeros_like(offset)
-        grad_mask = torch.zeros_like(mask)
-        grad_weight = torch.zeros_like(weight)
-        grad_bias = torch.zeros_like(bias)
-        deform_conv_ext.modulated_deform_conv_backward(input, weight, bias, ctx._bufs[0], offset, mask, ctx._bufs[1],
-                                                       grad_input, grad_weight, grad_bias, grad_offset, grad_mask,
-                                                       grad_output, weight.shape[2], weight.shape[3], ctx.stride,
-                                                       ctx.stride, ctx.padding, ctx.padding, ctx.dilation, ctx.dilation,
-                                                       ctx.groups, ctx.deformable_groups, ctx.with_bias)
-        if not ctx.with_bias:
-            grad_bias = None
-
-        return (grad_input, grad_offset, grad_mask, grad_weight, grad_bias, None, None, None, None, None)
-
-    @staticmethod
-    def _infer_shape(ctx, input, weight):
-        n = input.size(0)
-        channels_out = weight.size(0)
-        height, width = input.shape[2:4]
-        kernel_h, kernel_w = weight.shape[2:4]
-        height_out = (height + 2 * ctx.padding - (ctx.dilation * (kernel_h - 1) + 1)) // ctx.stride + 1
-        width_out = (width + 2 * ctx.padding - (ctx.dilation * (kernel_w - 1) + 1)) // ctx.stride + 1
-        return n, channels_out, height_out, width_out
-
-
-deform_conv = DeformConvFunction.apply
-modulated_deform_conv = ModulatedDeformConvFunction.apply
-
-
-class DeformConv(nn.Module):
-
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 kernel_size,
-                 stride=1,
-                 padding=0,
-                 dilation=1,
-                 groups=1,
-                 deformable_groups=1,
-                 bias=False):
-        super(DeformConv, self).__init__()
-
-        assert not bias
-        assert in_channels % groups == 0, \
-            f'in_channels {in_channels} is not divisible by groups {groups}'
-        assert out_channels % groups == 0, \
-            f'out_channels {out_channels} is not divisible ' \
-            f'by groups {groups}'
-
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.kernel_size = _pair(kernel_size)
-        self.stride = _pair(stride)
-        self.padding = _pair(padding)
-        self.dilation = _pair(dilation)
-        self.groups = groups
-        self.deformable_groups = deformable_groups
-        # enable compatibility with nn.Conv2d
-        self.transposed = False
-        self.output_padding = _single(0)
-
-        self.weight = nn.Parameter(torch.Tensor(out_channels, in_channels // self.groups, *self.kernel_size))
-
-        self.reset_parameters()
-
-    def reset_parameters(self):
-        n = self.in_channels
-        for k in self.kernel_size:
-            n *= k
-        stdv = 1. / math.sqrt(n)
-        self.weight.data.uniform_(-stdv, stdv)
-
-    def forward(self, x, offset):
-        # To fix an assert error in deform_conv_cuda.cpp:128
-        # input image is smaller than kernel
-        input_pad = (x.size(2) < self.kernel_size[0] or x.size(3) < self.kernel_size[1])
-        if input_pad:
-            pad_h = max(self.kernel_size[0] - x.size(2), 0)
-            pad_w = max(self.kernel_size[1] - x.size(3), 0)
-            x = F.pad(x, (0, pad_w, 0, pad_h), 'constant', 0).contiguous()
-            offset = F.pad(offset, (0, pad_w, 0, pad_h), 'constant', 0).contiguous()
-        out = deform_conv(x, offset, self.weight, self.stride, self.padding, self.dilation, self.groups,
-                          self.deformable_groups)
-        if input_pad:
-            out = out[:, :, :out.size(2) - pad_h, :out.size(3) - pad_w].contiguous()
-        return out
-
-
-class DeformConvPack(DeformConv):
-    """A Deformable Conv Encapsulation that acts as normal Conv layers.
-
-    Args:
-        in_channels (int): Same as nn.Conv2d.
-        out_channels (int): Same as nn.Conv2d.
-        kernel_size (int or tuple[int]): Same as nn.Conv2d.
-        stride (int or tuple[int]): Same as nn.Conv2d.
-        padding (int or tuple[int]): Same as nn.Conv2d.
-        dilation (int or tuple[int]): Same as nn.Conv2d.
-        groups (int): Same as nn.Conv2d.
-        bias (bool or str): If specified as `auto`, it will be decided by the
-            norm_cfg. Bias will be set as True if norm_cfg is None, otherwise
-            False.
-    """
-
-    _version = 2
-
-    def __init__(self, *args, **kwargs):
-        super(DeformConvPack, self).__init__(*args, **kwargs)
-
-        self.conv_offset = nn.Conv2d(
-            self.in_channels,
-            self.deformable_groups * 2 * self.kernel_size[0] * self.kernel_size[1],
-            kernel_size=self.kernel_size,
-            stride=_pair(self.stride),
-            padding=_pair(self.padding),
-            dilation=_pair(self.dilation),
-            bias=True)
-        self.init_offset()
-
-    def init_offset(self):
-        self.conv_offset.weight.data.zero_()
-        self.conv_offset.bias.data.zero_()
-
-    def forward(self, x):
-        offset = self.conv_offset(x)
-        return deform_conv(x, offset, self.weight, self.stride, self.padding, self.dilation, self.groups,
-                           self.deformable_groups)
-
-
-class ModulatedDeformConv(nn.Module):
-
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 kernel_size,
-                 stride=1,
-                 padding=0,
-                 dilation=1,
-                 groups=1,
-                 deformable_groups=1,
-                 bias=True):
-        super(ModulatedDeformConv, self).__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.kernel_size = _pair(kernel_size)
-        self.stride = stride
-        self.padding = padding
-        self.dilation = dilation
-        self.groups = groups
-        self.deformable_groups = deformable_groups
-        self.with_bias = bias
-        # enable compatibility with nn.Conv2d
-        self.transposed = False
-        self.output_padding = _single(0)
-
-        self.weight = nn.Parameter(torch.Tensor(out_channels, in_channels // groups, *self.kernel_size))
-        if bias:
-            self.bias = nn.Parameter(torch.Tensor(out_channels))
-        else:
-            self.register_parameter('bias', None)
-        self.init_weights()
-
-    def init_weights(self):
-        n = self.in_channels
-        for k in self.kernel_size:
-            n *= k
-        stdv = 1. / math.sqrt(n)
-        self.weight.data.uniform_(-stdv, stdv)
-        if self.bias is not None:
-            self.bias.data.zero_()
-
-    def forward(self, x, offset, mask):
-        return modulated_deform_conv(x, offset, mask, self.weight, self.bias, self.stride, self.padding, self.dilation,
-                                     self.groups, self.deformable_groups)
-
-
-class ModulatedDeformConvPack(ModulatedDeformConv):
-    """A ModulatedDeformable Conv Encapsulation that acts as normal Conv layers.
-
-    Args:
-        in_channels (int): Same as nn.Conv2d.
-        out_channels (int): Same as nn.Conv2d.
-        kernel_size (int or tuple[int]): Same as nn.Conv2d.
-        stride (int or tuple[int]): Same as nn.Conv2d.
-        padding (int or tuple[int]): Same as nn.Conv2d.
-        dilation (int or tuple[int]): Same as nn.Conv2d.
-        groups (int): Same as nn.Conv2d.
-        bias (bool or str): If specified as `auto`, it will be decided by the
-            norm_cfg. Bias will be set as True if norm_cfg is None, otherwise
-            False.
-    """
-
-    _version = 2
-
-    def __init__(self, *args, **kwargs):
-        super(ModulatedDeformConvPack, self).__init__(*args, **kwargs)
-
-        self.conv_offset = nn.Conv2d(
-            self.in_channels,
-            self.deformable_groups * 3 * self.kernel_size[0] * self.kernel_size[1],
-            kernel_size=self.kernel_size,
-            stride=_pair(self.stride),
-            padding=_pair(self.padding),
-            dilation=_pair(self.dilation),
-            bias=True)
-        self.init_weights()
-
-    def init_weights(self):
-        super(ModulatedDeformConvPack, self).init_weights()
-        if hasattr(self, 'conv_offset'):
-            self.conv_offset.weight.data.zero_()
-            self.conv_offset.bias.data.zero_()
-
-    def forward(self, x):
-        out = self.conv_offset(x)
-        o1, o2, mask = torch.chunk(out, 3, dim=1)
-        offset = torch.cat((o1, o2), dim=1)
-        mask = torch.sigmoid(mask)
-        return modulated_deform_conv(x, offset, mask, self.weight, self.bias, self.stride, self.padding, self.dilation,
-                                     self.groups, self.deformable_groups)
diff --git a/repositories/CodeFormer/basicsr/ops/dcn/src/deform_conv_cuda.cpp b/repositories/CodeFormer/basicsr/ops/dcn/src/deform_conv_cuda.cpp
deleted file mode 100644
index 5d9424908ed2dbd4ac3cdb98d13e09287a4d2f2d..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/basicsr/ops/dcn/src/deform_conv_cuda.cpp
+++ /dev/null
@@ -1,685 +0,0 @@
-// modify from
-// https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/blob/mmdetection/mmdet/ops/dcn/src/deform_conv_cuda.c
-
-#include <torch/extension.h>
-#include <ATen/DeviceGuard.h>
-
-#include <cmath>
-#include <vector>
-
-void deformable_im2col(const at::Tensor data_im, const at::Tensor data_offset,
-                       const int channels, const int height, const int width,
-                       const int ksize_h, const int ksize_w, const int pad_h,
-                       const int pad_w, const int stride_h, const int stride_w,
-                       const int dilation_h, const int dilation_w,
-                       const int parallel_imgs, const int deformable_group,
-                       at::Tensor data_col);
-
-void deformable_col2im(const at::Tensor data_col, const at::Tensor data_offset,
-                       const int channels, const int height, const int width,
-                       const int ksize_h, const int ksize_w, const int pad_h,
-                       const int pad_w, const int stride_h, const int stride_w,
-                       const int dilation_h, const int dilation_w,
-                       const int parallel_imgs, const int deformable_group,
-                       at::Tensor grad_im);
-
-void deformable_col2im_coord(
-    const at::Tensor data_col, const at::Tensor data_im,
-    const at::Tensor data_offset, const int channels, const int height,
-    const int width, const int ksize_h, const int ksize_w, const int pad_h,
-    const int pad_w, const int stride_h, const int stride_w,
-    const int dilation_h, const int dilation_w, const int parallel_imgs,
-    const int deformable_group, at::Tensor grad_offset);
-
-void modulated_deformable_im2col_cuda(
-    const at::Tensor data_im, const at::Tensor data_offset,
-    const at::Tensor data_mask, const int batch_size, const int channels,
-    const int height_im, const int width_im, const int height_col,
-    const int width_col, const int kernel_h, const int kenerl_w,
-    const int pad_h, const int pad_w, const int stride_h, const int stride_w,
-    const int dilation_h, const int dilation_w, const int deformable_group,
-    at::Tensor data_col);
-
-void modulated_deformable_col2im_cuda(
-    const at::Tensor data_col, const at::Tensor data_offset,
-    const at::Tensor data_mask, const int batch_size, const int channels,
-    const int height_im, const int width_im, const int height_col,
-    const int width_col, const int kernel_h, const int kenerl_w,
-    const int pad_h, const int pad_w, const int stride_h, const int stride_w,
-    const int dilation_h, const int dilation_w, const int deformable_group,
-    at::Tensor grad_im);
-
-void modulated_deformable_col2im_coord_cuda(
-    const at::Tensor data_col, const at::Tensor data_im,
-    const at::Tensor data_offset, const at::Tensor data_mask,
-    const int batch_size, const int channels, const int height_im,
-    const int width_im, const int height_col, const int width_col,
-    const int kernel_h, const int kenerl_w, const int pad_h, const int pad_w,
-    const int stride_h, const int stride_w, const int dilation_h,
-    const int dilation_w, const int deformable_group, at::Tensor grad_offset,
-    at::Tensor grad_mask);
-
-void shape_check(at::Tensor input, at::Tensor offset, at::Tensor *gradOutput,
-                 at::Tensor weight, int kH, int kW, int dH, int dW, int padH,
-                 int padW, int dilationH, int dilationW, int group,
-                 int deformable_group) {
-  TORCH_CHECK(weight.ndimension() == 4,
-           "4D weight tensor (nOutputPlane,nInputPlane,kH,kW) expected, "
-           "but got: %s",
-           weight.ndimension());
-
-  TORCH_CHECK(weight.is_contiguous(), "weight tensor has to be contiguous");
-
-  TORCH_CHECK(kW > 0 && kH > 0,
-           "kernel size should be greater than zero, but got kH: %d kW: %d", kH,
-           kW);
-
-  TORCH_CHECK((weight.size(2) == kH && weight.size(3) == kW),
-           "kernel size should be consistent with weight, ",
-           "but got kH: %d kW: %d weight.size(2): %d, weight.size(3): %d", kH,
-           kW, weight.size(2), weight.size(3));
-
-  TORCH_CHECK(dW > 0 && dH > 0,
-           "stride should be greater than zero, but got dH: %d dW: %d", dH, dW);
-
-  TORCH_CHECK(
-      dilationW > 0 && dilationH > 0,
-      "dilation should be greater than 0, but got dilationH: %d dilationW: %d",
-      dilationH, dilationW);
-
-  int ndim = input.ndimension();
-  int dimf = 0;
-  int dimh = 1;
-  int dimw = 2;
-
-  if (ndim == 4) {
-    dimf++;
-    dimh++;
-    dimw++;
-  }
-
-  TORCH_CHECK(ndim == 3 || ndim == 4, "3D or 4D input tensor expected but got: %s",
-           ndim);
-
-  long nInputPlane = weight.size(1) * group;
-  long inputHeight = input.size(dimh);
-  long inputWidth = input.size(dimw);
-  long nOutputPlane = weight.size(0);
-  long outputHeight =
-      (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1;
-  long outputWidth =
-      (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1;
-
-  TORCH_CHECK(nInputPlane % deformable_group == 0,
-           "input channels must divide deformable group size");
-
-  if (outputWidth < 1 || outputHeight < 1)
-    AT_ERROR(
-        "Given input size: (%ld x %ld x %ld). "
-        "Calculated output size: (%ld x %ld x %ld). Output size is too small",
-        nInputPlane, inputHeight, inputWidth, nOutputPlane, outputHeight,
-        outputWidth);
-
-  TORCH_CHECK(input.size(1) == nInputPlane,
-           "invalid number of input planes, expected: %d, but got: %d",
-           nInputPlane, input.size(1));
-
-  TORCH_CHECK((inputHeight >= kH && inputWidth >= kW),
-           "input image is smaller than kernel");
-
-  TORCH_CHECK((offset.size(2) == outputHeight && offset.size(3) == outputWidth),
-           "invalid spatial size of offset, expected height: %d width: %d, but "
-           "got height: %d width: %d",
-           outputHeight, outputWidth, offset.size(2), offset.size(3));
-
-  TORCH_CHECK((offset.size(1) == deformable_group * 2 * kH * kW),
-           "invalid number of channels of offset");
-
-  if (gradOutput != NULL) {
-    TORCH_CHECK(gradOutput->size(dimf) == nOutputPlane,
-             "invalid number of gradOutput planes, expected: %d, but got: %d",
-             nOutputPlane, gradOutput->size(dimf));
-
-    TORCH_CHECK((gradOutput->size(dimh) == outputHeight &&
-              gradOutput->size(dimw) == outputWidth),
-             "invalid size of gradOutput, expected height: %d width: %d , but "
-             "got height: %d width: %d",
-             outputHeight, outputWidth, gradOutput->size(dimh),
-             gradOutput->size(dimw));
-  }
-}
-
-int deform_conv_forward_cuda(at::Tensor input, at::Tensor weight,
-                             at::Tensor offset, at::Tensor output,
-                             at::Tensor columns, at::Tensor ones, int kW,
-                             int kH, int dW, int dH, int padW, int padH,
-                             int dilationW, int dilationH, int group,
-                             int deformable_group, int im2col_step) {
-  // todo: resize columns to include im2col: done
-  // todo: add im2col_step as input
-  // todo: add new output buffer and transpose it to output (or directly
-  // transpose output) todo: possibly change data indexing because of
-  // parallel_imgs
-
-  shape_check(input, offset, NULL, weight, kH, kW, dH, dW, padH, padW,
-              dilationH, dilationW, group, deformable_group);
-  at::DeviceGuard guard(input.device());
-
-  input = input.contiguous();
-  offset = offset.contiguous();
-  weight = weight.contiguous();
-
-  int batch = 1;
-  if (input.ndimension() == 3) {
-    // Force batch
-    batch = 0;
-    input.unsqueeze_(0);
-    offset.unsqueeze_(0);
-  }
-
-  // todo: assert batchsize dividable by im2col_step
-
-  long batchSize = input.size(0);
-  long nInputPlane = input.size(1);
-  long inputHeight = input.size(2);
-  long inputWidth = input.size(3);
-
-  long nOutputPlane = weight.size(0);
-
-  long outputWidth =
-      (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1;
-  long outputHeight =
-      (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1;
-
-  TORCH_CHECK((offset.size(0) == batchSize), "invalid batch size of offset");
-
-  output = output.view({batchSize / im2col_step, im2col_step, nOutputPlane,
-                        outputHeight, outputWidth});
-  columns = at::zeros(
-      {nInputPlane * kW * kH, im2col_step * outputHeight * outputWidth},
-      input.options());
-
-  if (ones.ndimension() != 2 ||
-      ones.size(0) * ones.size(1) < outputHeight * outputWidth) {
-    ones = at::ones({outputHeight, outputWidth}, input.options());
-  }
-
-  input = input.view({batchSize / im2col_step, im2col_step, nInputPlane,
-                      inputHeight, inputWidth});
-  offset =
-      offset.view({batchSize / im2col_step, im2col_step,
-                   deformable_group * 2 * kH * kW, outputHeight, outputWidth});
-
-  at::Tensor output_buffer =
-      at::zeros({batchSize / im2col_step, nOutputPlane,
-                 im2col_step * outputHeight, outputWidth},
-                output.options());
-
-  output_buffer = output_buffer.view(
-      {output_buffer.size(0), group, output_buffer.size(1) / group,
-       output_buffer.size(2), output_buffer.size(3)});
-
-  for (int elt = 0; elt < batchSize / im2col_step; elt++) {
-    deformable_im2col(input[elt], offset[elt], nInputPlane, inputHeight,
-                      inputWidth, kH, kW, padH, padW, dH, dW, dilationH,
-                      dilationW, im2col_step, deformable_group, columns);
-
-    columns = columns.view({group, columns.size(0) / group, columns.size(1)});
-    weight = weight.view({group, weight.size(0) / group, weight.size(1),
-                          weight.size(2), weight.size(3)});
-
-    for (int g = 0; g < group; g++) {
-      output_buffer[elt][g] = output_buffer[elt][g]
-                                  .flatten(1)
-                                  .addmm_(weight[g].flatten(1), columns[g])
-                                  .view_as(output_buffer[elt][g]);
-    }
-  }
-
-  output_buffer = output_buffer.view(
-      {output_buffer.size(0), output_buffer.size(1) * output_buffer.size(2),
-       output_buffer.size(3), output_buffer.size(4)});
-
-  output_buffer = output_buffer.view({batchSize / im2col_step, nOutputPlane,
-                                      im2col_step, outputHeight, outputWidth});
-  output_buffer.transpose_(1, 2);
-  output.copy_(output_buffer);
-  output = output.view({batchSize, nOutputPlane, outputHeight, outputWidth});
-
-  input = input.view({batchSize, nInputPlane, inputHeight, inputWidth});
-  offset = offset.view(
-      {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth});
-
-  if (batch == 0) {
-    output = output.view({nOutputPlane, outputHeight, outputWidth});
-    input = input.view({nInputPlane, inputHeight, inputWidth});
-    offset = offset.view({offset.size(1), offset.size(2), offset.size(3)});
-  }
-
-  return 1;
-}
-
-int deform_conv_backward_input_cuda(at::Tensor input, at::Tensor offset,
-                                    at::Tensor gradOutput, at::Tensor gradInput,
-                                    at::Tensor gradOffset, at::Tensor weight,
-                                    at::Tensor columns, int kW, int kH, int dW,
-                                    int dH, int padW, int padH, int dilationW,
-                                    int dilationH, int group,
-                                    int deformable_group, int im2col_step) {
-  shape_check(input, offset, &gradOutput, weight, kH, kW, dH, dW, padH, padW,
-              dilationH, dilationW, group, deformable_group);
-  at::DeviceGuard guard(input.device());
-
-  input = input.contiguous();
-  offset = offset.contiguous();
-  gradOutput = gradOutput.contiguous();
-  weight = weight.contiguous();
-
-  int batch = 1;
-
-  if (input.ndimension() == 3) {
-    // Force batch
-    batch = 0;
-    input = input.view({1, input.size(0), input.size(1), input.size(2)});
-    offset = offset.view({1, offset.size(0), offset.size(1), offset.size(2)});
-    gradOutput = gradOutput.view(
-        {1, gradOutput.size(0), gradOutput.size(1), gradOutput.size(2)});
-  }
-
-  long batchSize = input.size(0);
-  long nInputPlane = input.size(1);
-  long inputHeight = input.size(2);
-  long inputWidth = input.size(3);
-
-  long nOutputPlane = weight.size(0);
-
-  long outputWidth =
-      (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1;
-  long outputHeight =
-      (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1;
-
-  TORCH_CHECK((offset.size(0) == batchSize), 3, "invalid batch size of offset");
-  gradInput = gradInput.view({batchSize, nInputPlane, inputHeight, inputWidth});
-  columns = at::zeros(
-      {nInputPlane * kW * kH, im2col_step * outputHeight * outputWidth},
-      input.options());
-
-  // change order of grad output
-  gradOutput = gradOutput.view({batchSize / im2col_step, im2col_step,
-                                nOutputPlane, outputHeight, outputWidth});
-  gradOutput.transpose_(1, 2);
-
-  gradInput = gradInput.view({batchSize / im2col_step, im2col_step, nInputPlane,
-                              inputHeight, inputWidth});
-  input = input.view({batchSize / im2col_step, im2col_step, nInputPlane,
-                      inputHeight, inputWidth});
-  gradOffset = gradOffset.view({batchSize / im2col_step, im2col_step,
-                                deformable_group * 2 * kH * kW, outputHeight,
-                                outputWidth});
-  offset =
-      offset.view({batchSize / im2col_step, im2col_step,
-                   deformable_group * 2 * kH * kW, outputHeight, outputWidth});
-
-  for (int elt = 0; elt < batchSize / im2col_step; elt++) {
-    // divide into groups
-    columns = columns.view({group, columns.size(0) / group, columns.size(1)});
-    weight = weight.view({group, weight.size(0) / group, weight.size(1),
-                          weight.size(2), weight.size(3)});
-    gradOutput = gradOutput.view(
-        {gradOutput.size(0), group, gradOutput.size(1) / group,
-         gradOutput.size(2), gradOutput.size(3), gradOutput.size(4)});
-
-    for (int g = 0; g < group; g++) {
-      columns[g] = columns[g].addmm_(weight[g].flatten(1).transpose(0, 1),
-                                     gradOutput[elt][g].flatten(1), 0.0f, 1.0f);
-    }
-
-    columns =
-        columns.view({columns.size(0) * columns.size(1), columns.size(2)});
-    gradOutput = gradOutput.view(
-        {gradOutput.size(0), gradOutput.size(1) * gradOutput.size(2),
-         gradOutput.size(3), gradOutput.size(4), gradOutput.size(5)});
-
-    deformable_col2im_coord(columns, input[elt], offset[elt], nInputPlane,
-                            inputHeight, inputWidth, kH, kW, padH, padW, dH, dW,
-                            dilationH, dilationW, im2col_step, deformable_group,
-                            gradOffset[elt]);
-
-    deformable_col2im(columns, offset[elt], nInputPlane, inputHeight,
-                      inputWidth, kH, kW, padH, padW, dH, dW, dilationH,
-                      dilationW, im2col_step, deformable_group, gradInput[elt]);
-  }
-
-  gradOutput.transpose_(1, 2);
-  gradOutput =
-      gradOutput.view({batchSize, nOutputPlane, outputHeight, outputWidth});
-
-  gradInput = gradInput.view({batchSize, nInputPlane, inputHeight, inputWidth});
-  input = input.view({batchSize, nInputPlane, inputHeight, inputWidth});
-  gradOffset = gradOffset.view(
-      {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth});
-  offset = offset.view(
-      {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth});
-
-  if (batch == 0) {
-    gradOutput = gradOutput.view({nOutputPlane, outputHeight, outputWidth});
-    input = input.view({nInputPlane, inputHeight, inputWidth});
-    gradInput = gradInput.view({nInputPlane, inputHeight, inputWidth});
-    offset = offset.view({offset.size(1), offset.size(2), offset.size(3)});
-    gradOffset =
-        gradOffset.view({offset.size(1), offset.size(2), offset.size(3)});
-  }
-
-  return 1;
-}
-
-int deform_conv_backward_parameters_cuda(
-    at::Tensor input, at::Tensor offset, at::Tensor gradOutput,
-    at::Tensor gradWeight,  // at::Tensor gradBias,
-    at::Tensor columns, at::Tensor ones, int kW, int kH, int dW, int dH,
-    int padW, int padH, int dilationW, int dilationH, int group,
-    int deformable_group, float scale, int im2col_step) {
-  // todo: transpose and reshape outGrad
-  // todo: reshape columns
-  // todo: add im2col_step as input
-
-  shape_check(input, offset, &gradOutput, gradWeight, kH, kW, dH, dW, padH,
-              padW, dilationH, dilationW, group, deformable_group);
-  at::DeviceGuard guard(input.device());
-
-  input = input.contiguous();
-  offset = offset.contiguous();
-  gradOutput = gradOutput.contiguous();
-
-  int batch = 1;
-
-  if (input.ndimension() == 3) {
-    // Force batch
-    batch = 0;
-    input = input.view(
-        at::IntList({1, input.size(0), input.size(1), input.size(2)}));
-    gradOutput = gradOutput.view(
-        {1, gradOutput.size(0), gradOutput.size(1), gradOutput.size(2)});
-  }
-
-  long batchSize = input.size(0);
-  long nInputPlane = input.size(1);
-  long inputHeight = input.size(2);
-  long inputWidth = input.size(3);
-
-  long nOutputPlane = gradWeight.size(0);
-
-  long outputWidth =
-      (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1;
-  long outputHeight =
-      (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1;
-
-  TORCH_CHECK((offset.size(0) == batchSize), "invalid batch size of offset");
-
-  columns = at::zeros(
-      {nInputPlane * kW * kH, im2col_step * outputHeight * outputWidth},
-      input.options());
-
-  gradOutput = gradOutput.view({batchSize / im2col_step, im2col_step,
-                                nOutputPlane, outputHeight, outputWidth});
-  gradOutput.transpose_(1, 2);
-
-  at::Tensor gradOutputBuffer = at::zeros_like(gradOutput);
-  gradOutputBuffer =
-      gradOutputBuffer.view({batchSize / im2col_step, nOutputPlane, im2col_step,
-                             outputHeight, outputWidth});
-  gradOutputBuffer.copy_(gradOutput);
-  gradOutputBuffer =
-      gradOutputBuffer.view({batchSize / im2col_step, nOutputPlane,
-                             im2col_step * outputHeight, outputWidth});
-
-  gradOutput.transpose_(1, 2);
-  gradOutput =
-      gradOutput.view({batchSize, nOutputPlane, outputHeight, outputWidth});
-
-  input = input.view({batchSize / im2col_step, im2col_step, nInputPlane,
-                      inputHeight, inputWidth});
-  offset =
-      offset.view({batchSize / im2col_step, im2col_step,
-                   deformable_group * 2 * kH * kW, outputHeight, outputWidth});
-
-  for (int elt = 0; elt < batchSize / im2col_step; elt++) {
-    deformable_im2col(input[elt], offset[elt], nInputPlane, inputHeight,
-                      inputWidth, kH, kW, padH, padW, dH, dW, dilationH,
-                      dilationW, im2col_step, deformable_group, columns);
-
-    // divide into group
-    gradOutputBuffer = gradOutputBuffer.view(
-        {gradOutputBuffer.size(0), group, gradOutputBuffer.size(1) / group,
-         gradOutputBuffer.size(2), gradOutputBuffer.size(3)});
-    columns = columns.view({group, columns.size(0) / group, columns.size(1)});
-    gradWeight =
-        gradWeight.view({group, gradWeight.size(0) / group, gradWeight.size(1),
-                         gradWeight.size(2), gradWeight.size(3)});
-
-    for (int g = 0; g < group; g++) {
-      gradWeight[g] = gradWeight[g]
-                          .flatten(1)
-                          .addmm_(gradOutputBuffer[elt][g].flatten(1),
-                                  columns[g].transpose(1, 0), 1.0, scale)
-                          .view_as(gradWeight[g]);
-    }
-    gradOutputBuffer = gradOutputBuffer.view(
-        {gradOutputBuffer.size(0),
-         gradOutputBuffer.size(1) * gradOutputBuffer.size(2),
-         gradOutputBuffer.size(3), gradOutputBuffer.size(4)});
-    columns =
-        columns.view({columns.size(0) * columns.size(1), columns.size(2)});
-    gradWeight = gradWeight.view({gradWeight.size(0) * gradWeight.size(1),
-                                  gradWeight.size(2), gradWeight.size(3),
-                                  gradWeight.size(4)});
-  }
-
-  input = input.view({batchSize, nInputPlane, inputHeight, inputWidth});
-  offset = offset.view(
-      {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth});
-
-  if (batch == 0) {
-    gradOutput = gradOutput.view({nOutputPlane, outputHeight, outputWidth});
-    input = input.view({nInputPlane, inputHeight, inputWidth});
-  }
-
-  return 1;
-}
-
-void modulated_deform_conv_cuda_forward(
-    at::Tensor input, at::Tensor weight, at::Tensor bias, at::Tensor ones,
-    at::Tensor offset, at::Tensor mask, at::Tensor output, at::Tensor columns,
-    int kernel_h, int kernel_w, const int stride_h, const int stride_w,
-    const int pad_h, const int pad_w, const int dilation_h,
-    const int dilation_w, const int group, const int deformable_group,
-    const bool with_bias) {
-  TORCH_CHECK(input.is_contiguous(), "input tensor has to be contiguous");
-  TORCH_CHECK(weight.is_contiguous(), "weight tensor has to be contiguous");
-  at::DeviceGuard guard(input.device());
-
-  const int batch = input.size(0);
-  const int channels = input.size(1);
-  const int height = input.size(2);
-  const int width = input.size(3);
-
-  const int channels_out = weight.size(0);
-  const int channels_kernel = weight.size(1);
-  const int kernel_h_ = weight.size(2);
-  const int kernel_w_ = weight.size(3);
-
-  if (kernel_h_ != kernel_h || kernel_w_ != kernel_w)
-    AT_ERROR("Input shape and kernel shape wont match: (%d x %d vs %d x %d).",
-             kernel_h_, kernel_w, kernel_h_, kernel_w_);
-  if (channels != channels_kernel * group)
-    AT_ERROR("Input shape and kernel channels wont match: (%d vs %d).",
-             channels, channels_kernel * group);
-
-  const int height_out =
-      (height + 2 * pad_h - (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1;
-  const int width_out =
-      (width + 2 * pad_w - (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1;
-
-  if (ones.ndimension() != 2 ||
-      ones.size(0) * ones.size(1) < height_out * width_out) {
-    // Resize plane and fill with ones...
-    ones = at::ones({height_out, width_out}, input.options());
-  }
-
-  // resize output
-  output = output.view({batch, channels_out, height_out, width_out}).zero_();
-  // resize temporary columns
-  columns =
-      at::zeros({channels * kernel_h * kernel_w, 1 * height_out * width_out},
-                input.options());
-
-  output = output.view({output.size(0), group, output.size(1) / group,
-                        output.size(2), output.size(3)});
-
-  for (int b = 0; b < batch; b++) {
-    modulated_deformable_im2col_cuda(
-        input[b], offset[b], mask[b], 1, channels, height, width, height_out,
-        width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w,
-        dilation_h, dilation_w, deformable_group, columns);
-
-    // divide into group
-    weight = weight.view({group, weight.size(0) / group, weight.size(1),
-                          weight.size(2), weight.size(3)});
-    columns = columns.view({group, columns.size(0) / group, columns.size(1)});
-
-    for (int g = 0; g < group; g++) {
-      output[b][g] = output[b][g]
-                         .flatten(1)
-                         .addmm_(weight[g].flatten(1), columns[g])
-                         .view_as(output[b][g]);
-    }
-
-    weight = weight.view({weight.size(0) * weight.size(1), weight.size(2),
-                          weight.size(3), weight.size(4)});
-    columns =
-        columns.view({columns.size(0) * columns.size(1), columns.size(2)});
-  }
-
-  output = output.view({output.size(0), output.size(1) * output.size(2),
-                        output.size(3), output.size(4)});
-
-  if (with_bias) {
-    output += bias.view({1, bias.size(0), 1, 1});
-  }
-}
-
-void modulated_deform_conv_cuda_backward(
-    at::Tensor input, at::Tensor weight, at::Tensor bias, at::Tensor ones,
-    at::Tensor offset, at::Tensor mask, at::Tensor columns,
-    at::Tensor grad_input, at::Tensor grad_weight, at::Tensor grad_bias,
-    at::Tensor grad_offset, at::Tensor grad_mask, at::Tensor grad_output,
-    int kernel_h, int kernel_w, int stride_h, int stride_w, int pad_h,
-    int pad_w, int dilation_h, int dilation_w, int group, int deformable_group,
-    const bool with_bias) {
-  TORCH_CHECK(input.is_contiguous(), "input tensor has to be contiguous");
-  TORCH_CHECK(weight.is_contiguous(), "weight tensor has to be contiguous");
-  at::DeviceGuard guard(input.device());
-
-  const int batch = input.size(0);
-  const int channels = input.size(1);
-  const int height = input.size(2);
-  const int width = input.size(3);
-
-  const int channels_kernel = weight.size(1);
-  const int kernel_h_ = weight.size(2);
-  const int kernel_w_ = weight.size(3);
-  if (kernel_h_ != kernel_h || kernel_w_ != kernel_w)
-    AT_ERROR("Input shape and kernel shape wont match: (%d x %d vs %d x %d).",
-             kernel_h_, kernel_w, kernel_h_, kernel_w_);
-  if (channels != channels_kernel * group)
-    AT_ERROR("Input shape and kernel channels wont match: (%d vs %d).",
-             channels, channels_kernel * group);
-
-  const int height_out =
-      (height + 2 * pad_h - (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1;
-  const int width_out =
-      (width + 2 * pad_w - (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1;
-
-  if (ones.ndimension() != 2 ||
-      ones.size(0) * ones.size(1) < height_out * width_out) {
-    // Resize plane and fill with ones...
-    ones = at::ones({height_out, width_out}, input.options());
-  }
-
-  grad_input = grad_input.view({batch, channels, height, width});
-  columns = at::zeros({channels * kernel_h * kernel_w, height_out * width_out},
-                      input.options());
-
-  grad_output =
-      grad_output.view({grad_output.size(0), group, grad_output.size(1) / group,
-                        grad_output.size(2), grad_output.size(3)});
-
-  for (int b = 0; b < batch; b++) {
-    // divide int group
-    columns = columns.view({group, columns.size(0) / group, columns.size(1)});
-    weight = weight.view({group, weight.size(0) / group, weight.size(1),
-                          weight.size(2), weight.size(3)});
-
-    for (int g = 0; g < group; g++) {
-      columns[g].addmm_(weight[g].flatten(1).transpose(0, 1),
-                        grad_output[b][g].flatten(1), 0.0f, 1.0f);
-    }
-
-    columns =
-        columns.view({columns.size(0) * columns.size(1), columns.size(2)});
-    weight = weight.view({weight.size(0) * weight.size(1), weight.size(2),
-                          weight.size(3), weight.size(4)});
-
-    // gradient w.r.t. input coordinate data
-    modulated_deformable_col2im_coord_cuda(
-        columns, input[b], offset[b], mask[b], 1, channels, height, width,
-        height_out, width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h,
-        stride_w, dilation_h, dilation_w, deformable_group, grad_offset[b],
-        grad_mask[b]);
-    // gradient w.r.t. input data
-    modulated_deformable_col2im_cuda(
-        columns, offset[b], mask[b], 1, channels, height, width, height_out,
-        width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w,
-        dilation_h, dilation_w, deformable_group, grad_input[b]);
-
-    // gradient w.r.t. weight, dWeight should accumulate across the batch and
-    // group
-    modulated_deformable_im2col_cuda(
-        input[b], offset[b], mask[b], 1, channels, height, width, height_out,
-        width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w,
-        dilation_h, dilation_w, deformable_group, columns);
-
-    columns = columns.view({group, columns.size(0) / group, columns.size(1)});
-    grad_weight = grad_weight.view({group, grad_weight.size(0) / group,
-                                    grad_weight.size(1), grad_weight.size(2),
-                                    grad_weight.size(3)});
-    if (with_bias)
-      grad_bias = grad_bias.view({group, grad_bias.size(0) / group});
-
-    for (int g = 0; g < group; g++) {
-      grad_weight[g] =
-          grad_weight[g]
-              .flatten(1)
-              .addmm_(grad_output[b][g].flatten(1), columns[g].transpose(0, 1))
-              .view_as(grad_weight[g]);
-      if (with_bias) {
-        grad_bias[g] =
-            grad_bias[g]
-                .view({-1, 1})
-                .addmm_(grad_output[b][g].flatten(1), ones.view({-1, 1}))
-                .view(-1);
-      }
-    }
-
-    columns =
-        columns.view({columns.size(0) * columns.size(1), columns.size(2)});
-    grad_weight = grad_weight.view({grad_weight.size(0) * grad_weight.size(1),
-                                    grad_weight.size(2), grad_weight.size(3),
-                                    grad_weight.size(4)});
-    if (with_bias)
-      grad_bias = grad_bias.view({grad_bias.size(0) * grad_bias.size(1)});
-  }
-  grad_output = grad_output.view({grad_output.size(0) * grad_output.size(1),
-                                  grad_output.size(2), grad_output.size(3),
-                                  grad_output.size(4)});
-}
diff --git a/repositories/CodeFormer/basicsr/ops/dcn/src/deform_conv_cuda_kernel.cu b/repositories/CodeFormer/basicsr/ops/dcn/src/deform_conv_cuda_kernel.cu
deleted file mode 100644
index 98752dccf8c58817ca1a952554dd3f33188a2d34..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/basicsr/ops/dcn/src/deform_conv_cuda_kernel.cu
+++ /dev/null
@@ -1,867 +0,0 @@
-/*!
- ******************* BEGIN Caffe Copyright Notice and Disclaimer ****************
- *
- * COPYRIGHT
- *
- * All contributions by the University of California:
- * Copyright (c) 2014-2017 The Regents of the University of California (Regents)
- * All rights reserved.
- *
- * All other contributions:
- * Copyright (c) 2014-2017, the respective contributors
- * All rights reserved.
- *
- * Caffe uses a shared copyright model: each contributor holds copyright over
- * their contributions to Caffe. The project versioning records all such
- * contribution and copyright details. If a contributor wants to further mark
- * their specific copyright on a particular contribution, they should indicate
- * their copyright solely in the commit message of the change when it is
- * committed.
- *
- * LICENSE
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *
- * 1. Redistributions of source code must retain the above copyright notice, this
- * list of conditions and the following disclaimer.
- * 2. Redistributions in binary form must reproduce the above copyright notice,
- * this list of conditions and the following disclaimer in the documentation
- * and/or other materials provided with the distribution.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
- * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- * CONTRIBUTION AGREEMENT
- *
- * By contributing to the BVLC/caffe repository through pull-request, comment,
- * or otherwise, the contributor releases their content to the
- * license and copyright terms herein.
- *
- ***************** END Caffe Copyright Notice and Disclaimer ********************
- *
- * Copyright (c) 2018 Microsoft
- * Licensed under The MIT License [see LICENSE for details]
- * \file modulated_deformable_im2col.cuh
- * \brief Function definitions of converting an image to
- * column matrix based on kernel, padding, dilation, and offset.
- * These functions are mainly used in deformable convolution operators.
- * \ref: https://arxiv.org/abs/1703.06211
- * \author Yuwen Xiong, Haozhi Qi, Jifeng Dai, Xizhou Zhu, Han Hu, Dazhi Cheng
- */
-
-// modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/blob/mmdetection/mmdet/ops/dcn/src/deform_conv_cuda_kernel.cu
-
-#include <ATen/ATen.h>
-#include <ATen/cuda/CUDAContext.h>
-#include <THC/THCAtomics.cuh>
-#include <stdio.h>
-#include <math.h>
-#include <float.h>
-
-using namespace at;
-
-#define CUDA_KERNEL_LOOP(i, n)                                 \
-  for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < (n); \
-       i += blockDim.x * gridDim.x)
-
-const int CUDA_NUM_THREADS = 1024;
-const int kMaxGridNum = 65535;
-
-inline int GET_BLOCKS(const int N)
-{
-  return std::min(kMaxGridNum, (N + CUDA_NUM_THREADS - 1) / CUDA_NUM_THREADS);
-}
-
-template <typename scalar_t>
-__device__ scalar_t deformable_im2col_bilinear(const scalar_t *bottom_data, const int data_width,
-                                               const int height, const int width, scalar_t h, scalar_t w)
-{
-
-  int h_low = floor(h);
-  int w_low = floor(w);
-  int h_high = h_low + 1;
-  int w_high = w_low + 1;
-
-  scalar_t lh = h - h_low;
-  scalar_t lw = w - w_low;
-  scalar_t hh = 1 - lh, hw = 1 - lw;
-
-  scalar_t v1 = 0;
-  if (h_low >= 0 && w_low >= 0)
-    v1 = bottom_data[h_low * data_width + w_low];
-  scalar_t v2 = 0;
-  if (h_low >= 0 && w_high <= width - 1)
-    v2 = bottom_data[h_low * data_width + w_high];
-  scalar_t v3 = 0;
-  if (h_high <= height - 1 && w_low >= 0)
-    v3 = bottom_data[h_high * data_width + w_low];
-  scalar_t v4 = 0;
-  if (h_high <= height - 1 && w_high <= width - 1)
-    v4 = bottom_data[h_high * data_width + w_high];
-
-  scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw;
-
-  scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4);
-  return val;
-}
-
-template <typename scalar_t>
-__device__ scalar_t get_gradient_weight(scalar_t argmax_h, scalar_t argmax_w,
-                                        const int h, const int w, const int height, const int width)
-{
-
-  if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || argmax_w >= width)
-  {
-    //empty
-    return 0;
-  }
-
-  int argmax_h_low = floor(argmax_h);
-  int argmax_w_low = floor(argmax_w);
-  int argmax_h_high = argmax_h_low + 1;
-  int argmax_w_high = argmax_w_low + 1;
-
-  scalar_t weight = 0;
-  if (h == argmax_h_low && w == argmax_w_low)
-    weight = (h + 1 - argmax_h) * (w + 1 - argmax_w);
-  if (h == argmax_h_low && w == argmax_w_high)
-    weight = (h + 1 - argmax_h) * (argmax_w + 1 - w);
-  if (h == argmax_h_high && w == argmax_w_low)
-    weight = (argmax_h + 1 - h) * (w + 1 - argmax_w);
-  if (h == argmax_h_high && w == argmax_w_high)
-    weight = (argmax_h + 1 - h) * (argmax_w + 1 - w);
-  return weight;
-}
-
-template <typename scalar_t>
-__device__ scalar_t get_coordinate_weight(scalar_t argmax_h, scalar_t argmax_w,
-                                          const int height, const int width, const scalar_t *im_data,
-                                          const int data_width, const int bp_dir)
-{
-
-  if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || argmax_w >= width)
-  {
-    //empty
-    return 0;
-  }
-
-  int argmax_h_low = floor(argmax_h);
-  int argmax_w_low = floor(argmax_w);
-  int argmax_h_high = argmax_h_low + 1;
-  int argmax_w_high = argmax_w_low + 1;
-
-  scalar_t weight = 0;
-
-  if (bp_dir == 0)
-  {
-    if (argmax_h_low >= 0 && argmax_w_low >= 0)
-      weight += -1 * (argmax_w_low + 1 - argmax_w) * im_data[argmax_h_low * data_width + argmax_w_low];
-    if (argmax_h_low >= 0 && argmax_w_high <= width - 1)
-      weight += -1 * (argmax_w - argmax_w_low) * im_data[argmax_h_low * data_width + argmax_w_high];
-    if (argmax_h_high <= height - 1 && argmax_w_low >= 0)
-      weight += (argmax_w_low + 1 - argmax_w) * im_data[argmax_h_high * data_width + argmax_w_low];
-    if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1)
-      weight += (argmax_w - argmax_w_low) * im_data[argmax_h_high * data_width + argmax_w_high];
-  }
-  else if (bp_dir == 1)
-  {
-    if (argmax_h_low >= 0 && argmax_w_low >= 0)
-      weight += -1 * (argmax_h_low + 1 - argmax_h) * im_data[argmax_h_low * data_width + argmax_w_low];
-    if (argmax_h_low >= 0 && argmax_w_high <= width - 1)
-      weight += (argmax_h_low + 1 - argmax_h) * im_data[argmax_h_low * data_width + argmax_w_high];
-    if (argmax_h_high <= height - 1 && argmax_w_low >= 0)
-      weight += -1 * (argmax_h - argmax_h_low) * im_data[argmax_h_high * data_width + argmax_w_low];
-    if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1)
-      weight += (argmax_h - argmax_h_low) * im_data[argmax_h_high * data_width + argmax_w_high];
-  }
-
-  return weight;
-}
-
-template <typename scalar_t>
-__global__ void deformable_im2col_gpu_kernel(const int n, const scalar_t *data_im, const scalar_t *data_offset,
-                                             const int height, const int width, const int kernel_h, const int kernel_w,
-                                             const int pad_h, const int pad_w, const int stride_h, const int stride_w,
-                                             const int dilation_h, const int dilation_w, const int channel_per_deformable_group,
-                                             const int batch_size, const int num_channels, const int deformable_group,
-                                             const int height_col, const int width_col,
-                                             scalar_t *data_col)
-{
-  CUDA_KERNEL_LOOP(index, n)
-  {
-    // index index of output matrix
-    const int w_col = index % width_col;
-    const int h_col = (index / width_col) % height_col;
-    const int b_col = (index / width_col / height_col) % batch_size;
-    const int c_im = (index / width_col / height_col) / batch_size;
-    const int c_col = c_im * kernel_h * kernel_w;
-
-    // compute deformable group index
-    const int deformable_group_index = c_im / channel_per_deformable_group;
-
-    const int h_in = h_col * stride_h - pad_h;
-    const int w_in = w_col * stride_w - pad_w;
-    scalar_t *data_col_ptr = data_col + ((c_col * batch_size + b_col) * height_col + h_col) * width_col + w_col;
-    //const scalar_t* data_im_ptr = data_im + ((b_col * num_channels + c_im) * height + h_in) * width + w_in;
-    const scalar_t *data_im_ptr = data_im + (b_col * num_channels + c_im) * height * width;
-    const scalar_t *data_offset_ptr = data_offset + (b_col * deformable_group + deformable_group_index) * 2 * kernel_h * kernel_w * height_col * width_col;
-
-    for (int i = 0; i < kernel_h; ++i)
-    {
-      for (int j = 0; j < kernel_w; ++j)
-      {
-        const int data_offset_h_ptr = ((2 * (i * kernel_w + j)) * height_col + h_col) * width_col + w_col;
-        const int data_offset_w_ptr = ((2 * (i * kernel_w + j) + 1) * height_col + h_col) * width_col + w_col;
-        const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr];
-        const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr];
-        scalar_t val = static_cast<scalar_t>(0);
-        const scalar_t h_im = h_in + i * dilation_h + offset_h;
-        const scalar_t w_im = w_in + j * dilation_w + offset_w;
-        if (h_im > -1 && w_im > -1 && h_im < height && w_im < width)
-        {
-          //const scalar_t map_h = i * dilation_h + offset_h;
-          //const scalar_t map_w = j * dilation_w + offset_w;
-          //const int cur_height = height - h_in;
-          //const int cur_width = width - w_in;
-          //val = deformable_im2col_bilinear(data_im_ptr, width, cur_height, cur_width, map_h, map_w);
-          val = deformable_im2col_bilinear(data_im_ptr, width, height, width, h_im, w_im);
-        }
-        *data_col_ptr = val;
-        data_col_ptr += batch_size * height_col * width_col;
-      }
-    }
-  }
-}
-
-void deformable_im2col(
-    const at::Tensor data_im, const at::Tensor data_offset, const int channels,
-    const int height, const int width, const int ksize_h, const int ksize_w,
-    const int pad_h, const int pad_w, const int stride_h, const int stride_w,
-    const int dilation_h, const int dilation_w, const int parallel_imgs,
-    const int deformable_group, at::Tensor data_col)
-{
-  // num_axes should be smaller than block size
-  // todo: check parallel_imgs is correctly passed in
-  int height_col = (height + 2 * pad_h - (dilation_h * (ksize_h - 1) + 1)) / stride_h + 1;
-  int width_col = (width + 2 * pad_w - (dilation_w * (ksize_w - 1) + 1)) / stride_w + 1;
-  int num_kernels = channels * height_col * width_col * parallel_imgs;
-  int channel_per_deformable_group = channels / deformable_group;
-
-  AT_DISPATCH_FLOATING_TYPES_AND_HALF(
-      data_im.scalar_type(), "deformable_im2col_gpu", ([&] {
-        const scalar_t *data_im_ = data_im.data_ptr<scalar_t>();
-        const scalar_t *data_offset_ = data_offset.data_ptr<scalar_t>();
-        scalar_t *data_col_ = data_col.data_ptr<scalar_t>();
-
-        deformable_im2col_gpu_kernel<<<GET_BLOCKS(num_kernels), CUDA_NUM_THREADS, 0, at::cuda::getCurrentCUDAStream()>>>(
-            num_kernels, data_im_, data_offset_, height, width, ksize_h, ksize_w,
-            pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w,
-            channel_per_deformable_group, parallel_imgs, channels, deformable_group,
-            height_col, width_col, data_col_);
-      }));
-
-  cudaError_t err = cudaGetLastError();
-  if (err != cudaSuccess)
-  {
-    printf("error in deformable_im2col: %s\n", cudaGetErrorString(err));
-  }
-}
-
-template <typename scalar_t>
-__global__ void deformable_col2im_gpu_kernel(
-    const int n, const scalar_t *data_col, const scalar_t *data_offset,
-    const int channels, const int height, const int width,
-    const int kernel_h, const int kernel_w,
-    const int pad_h, const int pad_w,
-    const int stride_h, const int stride_w,
-    const int dilation_h, const int dilation_w,
-    const int channel_per_deformable_group,
-    const int batch_size, const int deformable_group,
-    const int height_col, const int width_col,
-    scalar_t *grad_im)
-{
-  CUDA_KERNEL_LOOP(index, n)
-  {
-    const int j = (index / width_col / height_col / batch_size) % kernel_w;
-    const int i = (index / width_col / height_col / batch_size / kernel_w) % kernel_h;
-    const int c = index / width_col / height_col / batch_size / kernel_w / kernel_h;
-    // compute the start and end of the output
-
-    const int deformable_group_index = c / channel_per_deformable_group;
-
-    int w_out = index % width_col;
-    int h_out = (index / width_col) % height_col;
-    int b = (index / width_col / height_col) % batch_size;
-    int w_in = w_out * stride_w - pad_w;
-    int h_in = h_out * stride_h - pad_h;
-
-    const scalar_t *data_offset_ptr = data_offset + (b * deformable_group + deformable_group_index) *
-                                                        2 * kernel_h * kernel_w * height_col * width_col;
-    const int data_offset_h_ptr = ((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out;
-    const int data_offset_w_ptr = ((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out;
-    const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr];
-    const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr];
-    const scalar_t cur_inv_h_data = h_in + i * dilation_h + offset_h;
-    const scalar_t cur_inv_w_data = w_in + j * dilation_w + offset_w;
-
-    const scalar_t cur_top_grad = data_col[index];
-    const int cur_h = (int)cur_inv_h_data;
-    const int cur_w = (int)cur_inv_w_data;
-    for (int dy = -2; dy <= 2; dy++)
-    {
-      for (int dx = -2; dx <= 2; dx++)
-      {
-        if (cur_h + dy >= 0 && cur_h + dy < height &&
-            cur_w + dx >= 0 && cur_w + dx < width &&
-            abs(cur_inv_h_data - (cur_h + dy)) < 1 &&
-            abs(cur_inv_w_data - (cur_w + dx)) < 1)
-        {
-          int cur_bottom_grad_pos = ((b * channels + c) * height + cur_h + dy) * width + cur_w + dx;
-          scalar_t weight = get_gradient_weight(cur_inv_h_data, cur_inv_w_data, cur_h + dy, cur_w + dx, height, width);
-          atomicAdd(grad_im + cur_bottom_grad_pos, weight * cur_top_grad);
-        }
-      }
-    }
-  }
-}
-
-void deformable_col2im(
-    const at::Tensor data_col, const at::Tensor data_offset, const int channels,
-    const int height, const int width, const int ksize_h,
-    const int ksize_w, const int pad_h, const int pad_w,
-    const int stride_h, const int stride_w,
-    const int dilation_h, const int dilation_w,
-    const int parallel_imgs, const int deformable_group,
-    at::Tensor grad_im)
-{
-
-  // todo: make sure parallel_imgs is passed in correctly
-  int height_col = (height + 2 * pad_h - (dilation_h * (ksize_h - 1) + 1)) / stride_h + 1;
-  int width_col = (width + 2 * pad_w - (dilation_w * (ksize_w - 1) + 1)) / stride_w + 1;
-  int num_kernels = channels * ksize_h * ksize_w * height_col * width_col * parallel_imgs;
-  int channel_per_deformable_group = channels / deformable_group;
-
-  AT_DISPATCH_FLOATING_TYPES_AND_HALF(
-      data_col.scalar_type(), "deformable_col2im_gpu", ([&] {
-        const scalar_t *data_col_ = data_col.data_ptr<scalar_t>();
-        const scalar_t *data_offset_ = data_offset.data_ptr<scalar_t>();
-        scalar_t *grad_im_ = grad_im.data_ptr<scalar_t>();
-
-        deformable_col2im_gpu_kernel<<<GET_BLOCKS(num_kernels), CUDA_NUM_THREADS, 0, at::cuda::getCurrentCUDAStream()>>>(
-            num_kernels, data_col_, data_offset_, channels, height, width, ksize_h,
-            ksize_w, pad_h, pad_w, stride_h, stride_w,
-            dilation_h, dilation_w, channel_per_deformable_group,
-            parallel_imgs, deformable_group, height_col, width_col, grad_im_);
-      }));
-
-  cudaError_t err = cudaGetLastError();
-  if (err != cudaSuccess)
-  {
-    printf("error in deformable_col2im: %s\n", cudaGetErrorString(err));
-  }
-}
-
-template <typename scalar_t>
-__global__ void deformable_col2im_coord_gpu_kernel(const int n, const scalar_t *data_col,
-                                                   const scalar_t *data_im, const scalar_t *data_offset,
-                                                   const int channels, const int height, const int width,
-                                                   const int kernel_h, const int kernel_w,
-                                                   const int pad_h, const int pad_w,
-                                                   const int stride_h, const int stride_w,
-                                                   const int dilation_h, const int dilation_w,
-                                                   const int channel_per_deformable_group,
-                                                   const int batch_size, const int offset_channels, const int deformable_group,
-                                                   const int height_col, const int width_col, scalar_t *grad_offset)
-{
-  CUDA_KERNEL_LOOP(index, n)
-  {
-    scalar_t val = 0;
-    int w = index % width_col;
-    int h = (index / width_col) % height_col;
-    int c = (index / width_col / height_col) % offset_channels;
-    int b = (index / width_col / height_col) / offset_channels;
-    // compute the start and end of the output
-
-    const int deformable_group_index = c / (2 * kernel_h * kernel_w);
-    const int col_step = kernel_h * kernel_w;
-    int cnt = 0;
-    const scalar_t *data_col_ptr = data_col + deformable_group_index * channel_per_deformable_group *
-                                                  batch_size * width_col * height_col;
-    const scalar_t *data_im_ptr = data_im + (b * deformable_group + deformable_group_index) *
-                                                channel_per_deformable_group / kernel_h / kernel_w * height * width;
-    const scalar_t *data_offset_ptr = data_offset + (b * deformable_group + deformable_group_index) * 2 *
-                                                        kernel_h * kernel_w * height_col * width_col;
-
-    const int offset_c = c - deformable_group_index * 2 * kernel_h * kernel_w;
-
-    for (int col_c = (offset_c / 2); col_c < channel_per_deformable_group; col_c += col_step)
-    {
-      const int col_pos = (((col_c * batch_size + b) * height_col) + h) * width_col + w;
-      const int bp_dir = offset_c % 2;
-
-      int j = (col_pos / width_col / height_col / batch_size) % kernel_w;
-      int i = (col_pos / width_col / height_col / batch_size / kernel_w) % kernel_h;
-      int w_out = col_pos % width_col;
-      int h_out = (col_pos / width_col) % height_col;
-      int w_in = w_out * stride_w - pad_w;
-      int h_in = h_out * stride_h - pad_h;
-      const int data_offset_h_ptr = (((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out);
-      const int data_offset_w_ptr = (((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out);
-      const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr];
-      const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr];
-      scalar_t inv_h = h_in + i * dilation_h + offset_h;
-      scalar_t inv_w = w_in + j * dilation_w + offset_w;
-      if (inv_h <= -1 || inv_w <= -1 || inv_h >= height || inv_w >= width)
-      {
-        inv_h = inv_w = -2;
-      }
-      const scalar_t weight = get_coordinate_weight(
-          inv_h, inv_w,
-          height, width, data_im_ptr + cnt * height * width, width, bp_dir);
-      val += weight * data_col_ptr[col_pos];
-      cnt += 1;
-    }
-
-    grad_offset[index] = val;
-  }
-}
-
-void deformable_col2im_coord(
-    const at::Tensor data_col, const at::Tensor data_im, const at::Tensor data_offset,
-    const int channels, const int height, const int width, const int ksize_h,
-    const int ksize_w, const int pad_h, const int pad_w, const int stride_h,
-    const int stride_w, const int dilation_h, const int dilation_w,
-    const int parallel_imgs, const int deformable_group, at::Tensor grad_offset)
-{
-
-  int height_col = (height + 2 * pad_h - (dilation_h * (ksize_h - 1) + 1)) / stride_h + 1;
-  int width_col = (width + 2 * pad_w - (dilation_w * (ksize_w - 1) + 1)) / stride_w + 1;
-  int num_kernels = height_col * width_col * 2 * ksize_h * ksize_w * deformable_group * parallel_imgs;
-  int channel_per_deformable_group = channels * ksize_h * ksize_w / deformable_group;
-
-  AT_DISPATCH_FLOATING_TYPES_AND_HALF(
-      data_col.scalar_type(), "deformable_col2im_coord_gpu", ([&] {
-        const scalar_t *data_col_ = data_col.data_ptr<scalar_t>();
-        const scalar_t *data_im_ = data_im.data_ptr<scalar_t>();
-        const scalar_t *data_offset_ = data_offset.data_ptr<scalar_t>();
-        scalar_t *grad_offset_ = grad_offset.data_ptr<scalar_t>();
-
-        deformable_col2im_coord_gpu_kernel<<<GET_BLOCKS(num_kernels), CUDA_NUM_THREADS, 0, at::cuda::getCurrentCUDAStream()>>>(
-            num_kernels, data_col_, data_im_, data_offset_, channels, height, width,
-            ksize_h, ksize_w, pad_h, pad_w, stride_h, stride_w,
-            dilation_h, dilation_w, channel_per_deformable_group,
-            parallel_imgs, 2 * ksize_h * ksize_w * deformable_group, deformable_group,
-            height_col, width_col, grad_offset_);
-      }));
-}
-
-template <typename scalar_t>
-__device__ scalar_t dmcn_im2col_bilinear(const scalar_t *bottom_data, const int data_width,
-                                         const int height, const int width, scalar_t h, scalar_t w)
-{
-  int h_low = floor(h);
-  int w_low = floor(w);
-  int h_high = h_low + 1;
-  int w_high = w_low + 1;
-
-  scalar_t lh = h - h_low;
-  scalar_t lw = w - w_low;
-  scalar_t hh = 1 - lh, hw = 1 - lw;
-
-  scalar_t v1 = 0;
-  if (h_low >= 0 && w_low >= 0)
-    v1 = bottom_data[h_low * data_width + w_low];
-  scalar_t v2 = 0;
-  if (h_low >= 0 && w_high <= width - 1)
-    v2 = bottom_data[h_low * data_width + w_high];
-  scalar_t v3 = 0;
-  if (h_high <= height - 1 && w_low >= 0)
-    v3 = bottom_data[h_high * data_width + w_low];
-  scalar_t v4 = 0;
-  if (h_high <= height - 1 && w_high <= width - 1)
-    v4 = bottom_data[h_high * data_width + w_high];
-
-  scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw;
-
-  scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4);
-  return val;
-}
-
-template <typename scalar_t>
-__device__ scalar_t dmcn_get_gradient_weight(scalar_t argmax_h, scalar_t argmax_w,
-                                             const int h, const int w, const int height, const int width)
-{
-  if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || argmax_w >= width)
-  {
-    //empty
-    return 0;
-  }
-
-  int argmax_h_low = floor(argmax_h);
-  int argmax_w_low = floor(argmax_w);
-  int argmax_h_high = argmax_h_low + 1;
-  int argmax_w_high = argmax_w_low + 1;
-
-  scalar_t weight = 0;
-  if (h == argmax_h_low && w == argmax_w_low)
-    weight = (h + 1 - argmax_h) * (w + 1 - argmax_w);
-  if (h == argmax_h_low && w == argmax_w_high)
-    weight = (h + 1 - argmax_h) * (argmax_w + 1 - w);
-  if (h == argmax_h_high && w == argmax_w_low)
-    weight = (argmax_h + 1 - h) * (w + 1 - argmax_w);
-  if (h == argmax_h_high && w == argmax_w_high)
-    weight = (argmax_h + 1 - h) * (argmax_w + 1 - w);
-  return weight;
-}
-
-template <typename scalar_t>
-__device__ scalar_t dmcn_get_coordinate_weight(scalar_t argmax_h, scalar_t argmax_w,
-                                               const int height, const int width, const scalar_t *im_data,
-                                               const int data_width, const int bp_dir)
-{
-  if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || argmax_w >= width)
-  {
-    //empty
-    return 0;
-  }
-
-  int argmax_h_low = floor(argmax_h);
-  int argmax_w_low = floor(argmax_w);
-  int argmax_h_high = argmax_h_low + 1;
-  int argmax_w_high = argmax_w_low + 1;
-
-  scalar_t weight = 0;
-
-  if (bp_dir == 0)
-  {
-    if (argmax_h_low >= 0 && argmax_w_low >= 0)
-      weight += -1 * (argmax_w_low + 1 - argmax_w) * im_data[argmax_h_low * data_width + argmax_w_low];
-    if (argmax_h_low >= 0 && argmax_w_high <= width - 1)
-      weight += -1 * (argmax_w - argmax_w_low) * im_data[argmax_h_low * data_width + argmax_w_high];
-    if (argmax_h_high <= height - 1 && argmax_w_low >= 0)
-      weight += (argmax_w_low + 1 - argmax_w) * im_data[argmax_h_high * data_width + argmax_w_low];
-    if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1)
-      weight += (argmax_w - argmax_w_low) * im_data[argmax_h_high * data_width + argmax_w_high];
-  }
-  else if (bp_dir == 1)
-  {
-    if (argmax_h_low >= 0 && argmax_w_low >= 0)
-      weight += -1 * (argmax_h_low + 1 - argmax_h) * im_data[argmax_h_low * data_width + argmax_w_low];
-    if (argmax_h_low >= 0 && argmax_w_high <= width - 1)
-      weight += (argmax_h_low + 1 - argmax_h) * im_data[argmax_h_low * data_width + argmax_w_high];
-    if (argmax_h_high <= height - 1 && argmax_w_low >= 0)
-      weight += -1 * (argmax_h - argmax_h_low) * im_data[argmax_h_high * data_width + argmax_w_low];
-    if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1)
-      weight += (argmax_h - argmax_h_low) * im_data[argmax_h_high * data_width + argmax_w_high];
-  }
-
-  return weight;
-}
-
-template <typename scalar_t>
-__global__ void modulated_deformable_im2col_gpu_kernel(const int n,
-                                                       const scalar_t *data_im, const scalar_t *data_offset, const scalar_t *data_mask,
-                                                       const int height, const int width, const int kernel_h, const int kernel_w,
-                                                       const int pad_h, const int pad_w,
-                                                       const int stride_h, const int stride_w,
-                                                       const int dilation_h, const int dilation_w,
-                                                       const int channel_per_deformable_group,
-                                                       const int batch_size, const int num_channels, const int deformable_group,
-                                                       const int height_col, const int width_col,
-                                                       scalar_t *data_col)
-{
-  CUDA_KERNEL_LOOP(index, n)
-  {
-    // index index of output matrix
-    const int w_col = index % width_col;
-    const int h_col = (index / width_col) % height_col;
-    const int b_col = (index / width_col / height_col) % batch_size;
-    const int c_im = (index / width_col / height_col) / batch_size;
-    const int c_col = c_im * kernel_h * kernel_w;
-
-    // compute deformable group index
-    const int deformable_group_index = c_im / channel_per_deformable_group;
-
-    const int h_in = h_col * stride_h - pad_h;
-    const int w_in = w_col * stride_w - pad_w;
-
-    scalar_t *data_col_ptr = data_col + ((c_col * batch_size + b_col) * height_col + h_col) * width_col + w_col;
-    //const float* data_im_ptr = data_im + ((b_col * num_channels + c_im) * height + h_in) * width + w_in;
-    const scalar_t *data_im_ptr = data_im + (b_col * num_channels + c_im) * height * width;
-    const scalar_t *data_offset_ptr = data_offset + (b_col * deformable_group + deformable_group_index) * 2 * kernel_h * kernel_w * height_col * width_col;
-
-    const scalar_t *data_mask_ptr = data_mask + (b_col * deformable_group + deformable_group_index) * kernel_h * kernel_w * height_col * width_col;
-
-    for (int i = 0; i < kernel_h; ++i)
-    {
-      for (int j = 0; j < kernel_w; ++j)
-      {
-        const int data_offset_h_ptr = ((2 * (i * kernel_w + j)) * height_col + h_col) * width_col + w_col;
-        const int data_offset_w_ptr = ((2 * (i * kernel_w + j) + 1) * height_col + h_col) * width_col + w_col;
-        const int data_mask_hw_ptr = ((i * kernel_w + j) * height_col + h_col) * width_col + w_col;
-        const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr];
-        const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr];
-        const scalar_t mask = data_mask_ptr[data_mask_hw_ptr];
-        scalar_t val = static_cast<scalar_t>(0);
-        const scalar_t h_im = h_in + i * dilation_h + offset_h;
-        const scalar_t w_im = w_in + j * dilation_w + offset_w;
-        //if (h_im >= 0 && w_im >= 0 && h_im < height && w_im < width) {
-        if (h_im > -1 && w_im > -1 && h_im < height && w_im < width)
-        {
-          //const float map_h = i * dilation_h + offset_h;
-          //const float map_w = j * dilation_w + offset_w;
-          //const int cur_height = height - h_in;
-          //const int cur_width = width - w_in;
-          //val = dmcn_im2col_bilinear(data_im_ptr, width, cur_height, cur_width, map_h, map_w);
-          val = dmcn_im2col_bilinear(data_im_ptr, width, height, width, h_im, w_im);
-        }
-        *data_col_ptr = val * mask;
-        data_col_ptr += batch_size * height_col * width_col;
-        //data_col_ptr += height_col * width_col;
-      }
-    }
-  }
-}
-
-template <typename scalar_t>
-__global__ void modulated_deformable_col2im_gpu_kernel(const int n,
-                                                       const scalar_t *data_col, const scalar_t *data_offset, const scalar_t *data_mask,
-                                                       const int channels, const int height, const int width,
-                                                       const int kernel_h, const int kernel_w,
-                                                       const int pad_h, const int pad_w,
-                                                       const int stride_h, const int stride_w,
-                                                       const int dilation_h, const int dilation_w,
-                                                       const int channel_per_deformable_group,
-                                                       const int batch_size, const int deformable_group,
-                                                       const int height_col, const int width_col,
-                                                       scalar_t *grad_im)
-{
-  CUDA_KERNEL_LOOP(index, n)
-  {
-    const int j = (index / width_col / height_col / batch_size) % kernel_w;
-    const int i = (index / width_col / height_col / batch_size / kernel_w) % kernel_h;
-    const int c = index / width_col / height_col / batch_size / kernel_w / kernel_h;
-    // compute the start and end of the output
-
-    const int deformable_group_index = c / channel_per_deformable_group;
-
-    int w_out = index % width_col;
-    int h_out = (index / width_col) % height_col;
-    int b = (index / width_col / height_col) % batch_size;
-    int w_in = w_out * stride_w - pad_w;
-    int h_in = h_out * stride_h - pad_h;
-
-    const scalar_t *data_offset_ptr = data_offset + (b * deformable_group + deformable_group_index) * 2 * kernel_h * kernel_w * height_col * width_col;
-    const scalar_t *data_mask_ptr = data_mask + (b * deformable_group + deformable_group_index) * kernel_h * kernel_w * height_col * width_col;
-    const int data_offset_h_ptr = ((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out;
-    const int data_offset_w_ptr = ((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out;
-    const int data_mask_hw_ptr = ((i * kernel_w + j) * height_col + h_out) * width_col + w_out;
-    const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr];
-    const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr];
-    const scalar_t mask = data_mask_ptr[data_mask_hw_ptr];
-    const scalar_t cur_inv_h_data = h_in + i * dilation_h + offset_h;
-    const scalar_t cur_inv_w_data = w_in + j * dilation_w + offset_w;
-
-    const scalar_t cur_top_grad = data_col[index] * mask;
-    const int cur_h = (int)cur_inv_h_data;
-    const int cur_w = (int)cur_inv_w_data;
-    for (int dy = -2; dy <= 2; dy++)
-    {
-      for (int dx = -2; dx <= 2; dx++)
-      {
-        if (cur_h + dy >= 0 && cur_h + dy < height &&
-            cur_w + dx >= 0 && cur_w + dx < width &&
-            abs(cur_inv_h_data - (cur_h + dy)) < 1 &&
-            abs(cur_inv_w_data - (cur_w + dx)) < 1)
-        {
-          int cur_bottom_grad_pos = ((b * channels + c) * height + cur_h + dy) * width + cur_w + dx;
-          scalar_t weight = dmcn_get_gradient_weight(cur_inv_h_data, cur_inv_w_data, cur_h + dy, cur_w + dx, height, width);
-          atomicAdd(grad_im + cur_bottom_grad_pos, weight * cur_top_grad);
-        }
-      }
-    }
-  }
-}
-
-template <typename scalar_t>
-__global__ void modulated_deformable_col2im_coord_gpu_kernel(const int n,
-                                                             const scalar_t *data_col, const scalar_t *data_im,
-                                                             const scalar_t *data_offset, const scalar_t *data_mask,
-                                                             const int channels, const int height, const int width,
-                                                             const int kernel_h, const int kernel_w,
-                                                             const int pad_h, const int pad_w,
-                                                             const int stride_h, const int stride_w,
-                                                             const int dilation_h, const int dilation_w,
-                                                             const int channel_per_deformable_group,
-                                                             const int batch_size, const int offset_channels, const int deformable_group,
-                                                             const int height_col, const int width_col,
-                                                             scalar_t *grad_offset, scalar_t *grad_mask)
-{
-  CUDA_KERNEL_LOOP(index, n)
-  {
-    scalar_t val = 0, mval = 0;
-    int w = index % width_col;
-    int h = (index / width_col) % height_col;
-    int c = (index / width_col / height_col) % offset_channels;
-    int b = (index / width_col / height_col) / offset_channels;
-    // compute the start and end of the output
-
-    const int deformable_group_index = c / (2 * kernel_h * kernel_w);
-    const int col_step = kernel_h * kernel_w;
-    int cnt = 0;
-    const scalar_t *data_col_ptr = data_col + deformable_group_index * channel_per_deformable_group * batch_size * width_col * height_col;
-    const scalar_t *data_im_ptr = data_im + (b * deformable_group + deformable_group_index) * channel_per_deformable_group / kernel_h / kernel_w * height * width;
-    const scalar_t *data_offset_ptr = data_offset + (b * deformable_group + deformable_group_index) * 2 * kernel_h * kernel_w * height_col * width_col;
-    const scalar_t *data_mask_ptr = data_mask + (b * deformable_group + deformable_group_index) * kernel_h * kernel_w * height_col * width_col;
-
-    const int offset_c = c - deformable_group_index * 2 * kernel_h * kernel_w;
-
-    for (int col_c = (offset_c / 2); col_c < channel_per_deformable_group; col_c += col_step)
-    {
-      const int col_pos = (((col_c * batch_size + b) * height_col) + h) * width_col + w;
-      const int bp_dir = offset_c % 2;
-
-      int j = (col_pos / width_col / height_col / batch_size) % kernel_w;
-      int i = (col_pos / width_col / height_col / batch_size / kernel_w) % kernel_h;
-      int w_out = col_pos % width_col;
-      int h_out = (col_pos / width_col) % height_col;
-      int w_in = w_out * stride_w - pad_w;
-      int h_in = h_out * stride_h - pad_h;
-      const int data_offset_h_ptr = (((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out);
-      const int data_offset_w_ptr = (((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out);
-      const int data_mask_hw_ptr = (((i * kernel_w + j) * height_col + h_out) * width_col + w_out);
-      const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr];
-      const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr];
-      const scalar_t mask = data_mask_ptr[data_mask_hw_ptr];
-      scalar_t inv_h = h_in + i * dilation_h + offset_h;
-      scalar_t inv_w = w_in + j * dilation_w + offset_w;
-      if (inv_h <= -1 || inv_w <= -1 || inv_h >= height || inv_w >= width)
-      {
-        inv_h = inv_w = -2;
-      }
-      else
-      {
-        mval += data_col_ptr[col_pos] * dmcn_im2col_bilinear(data_im_ptr + cnt * height * width, width, height, width, inv_h, inv_w);
-      }
-      const scalar_t weight = dmcn_get_coordinate_weight(
-          inv_h, inv_w,
-          height, width, data_im_ptr + cnt * height * width, width, bp_dir);
-      val += weight * data_col_ptr[col_pos] * mask;
-      cnt += 1;
-    }
-    // KERNEL_ASSIGN(grad_offset[index], offset_req, val);
-    grad_offset[index] = val;
-    if (offset_c % 2 == 0)
-      // KERNEL_ASSIGN(grad_mask[(((b * deformable_group + deformable_group_index) * kernel_h * kernel_w + offset_c / 2) * height_col + h) * width_col + w], mask_req, mval);
-      grad_mask[(((b * deformable_group + deformable_group_index) * kernel_h * kernel_w + offset_c / 2) * height_col + h) * width_col + w] = mval;
-  }
-}
-
-void modulated_deformable_im2col_cuda(
-    const at::Tensor data_im, const at::Tensor data_offset, const at::Tensor data_mask,
-    const int batch_size, const int channels, const int height_im, const int width_im,
-    const int height_col, const int width_col, const int kernel_h, const int kenerl_w,
-    const int pad_h, const int pad_w, const int stride_h, const int stride_w,
-    const int dilation_h, const int dilation_w,
-    const int deformable_group, at::Tensor data_col)
-{
-  // num_axes should be smaller than block size
-  const int channel_per_deformable_group = channels / deformable_group;
-  const int num_kernels = channels * batch_size * height_col * width_col;
-
-  AT_DISPATCH_FLOATING_TYPES_AND_HALF(
-      data_im.scalar_type(), "modulated_deformable_im2col_gpu", ([&] {
-        const scalar_t *data_im_ = data_im.data_ptr<scalar_t>();
-        const scalar_t *data_offset_ = data_offset.data_ptr<scalar_t>();
-        const scalar_t *data_mask_ = data_mask.data_ptr<scalar_t>();
-        scalar_t *data_col_ = data_col.data_ptr<scalar_t>();
-
-        modulated_deformable_im2col_gpu_kernel<<<GET_BLOCKS(num_kernels), CUDA_NUM_THREADS, 0, at::cuda::getCurrentCUDAStream()>>>(
-            num_kernels, data_im_, data_offset_, data_mask_, height_im, width_im, kernel_h, kenerl_w,
-            pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w, channel_per_deformable_group,
-            batch_size, channels, deformable_group, height_col, width_col, data_col_);
-      }));
-
-  cudaError_t err = cudaGetLastError();
-  if (err != cudaSuccess)
-  {
-    printf("error in modulated_deformable_im2col_cuda: %s\n", cudaGetErrorString(err));
-  }
-}
-
-void modulated_deformable_col2im_cuda(
-    const at::Tensor data_col, const at::Tensor data_offset, const at::Tensor data_mask,
-    const int batch_size, const int channels, const int height_im, const int width_im,
-    const int height_col, const int width_col, const int kernel_h, const int kernel_w,
-    const int pad_h, const int pad_w, const int stride_h, const int stride_w,
-    const int dilation_h, const int dilation_w,
-    const int deformable_group, at::Tensor grad_im)
-{
-
-  const int channel_per_deformable_group = channels / deformable_group;
-  const int num_kernels = channels * kernel_h * kernel_w * batch_size * height_col * width_col;
-
-  AT_DISPATCH_FLOATING_TYPES_AND_HALF(
-      data_col.scalar_type(), "modulated_deformable_col2im_gpu", ([&] {
-        const scalar_t *data_col_ = data_col.data_ptr<scalar_t>();
-        const scalar_t *data_offset_ = data_offset.data_ptr<scalar_t>();
-        const scalar_t *data_mask_ = data_mask.data_ptr<scalar_t>();
-        scalar_t *grad_im_ = grad_im.data_ptr<scalar_t>();
-
-        modulated_deformable_col2im_gpu_kernel<<<GET_BLOCKS(num_kernels), CUDA_NUM_THREADS, 0, at::cuda::getCurrentCUDAStream()>>>(
-            num_kernels, data_col_, data_offset_, data_mask_, channels, height_im, width_im,
-            kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w,
-            dilation_h, dilation_w, channel_per_deformable_group,
-            batch_size, deformable_group, height_col, width_col, grad_im_);
-      }));
-
-  cudaError_t err = cudaGetLastError();
-  if (err != cudaSuccess)
-  {
-    printf("error in modulated_deformable_col2im_cuda: %s\n", cudaGetErrorString(err));
-  }
-}
-
-void modulated_deformable_col2im_coord_cuda(
-    const at::Tensor data_col, const at::Tensor data_im, const at::Tensor data_offset, const at::Tensor data_mask,
-    const int batch_size, const int channels, const int height_im, const int width_im,
-    const int height_col, const int width_col, const int kernel_h, const int kernel_w,
-    const int pad_h, const int pad_w, const int stride_h, const int stride_w,
-    const int dilation_h, const int dilation_w,
-    const int deformable_group,
-    at::Tensor grad_offset, at::Tensor grad_mask)
-{
-  const int num_kernels = batch_size * height_col * width_col * 2 * kernel_h * kernel_w * deformable_group;
-  const int channel_per_deformable_group = channels * kernel_h * kernel_w / deformable_group;
-
-  AT_DISPATCH_FLOATING_TYPES_AND_HALF(
-      data_col.scalar_type(), "modulated_deformable_col2im_coord_gpu", ([&] {
-        const scalar_t *data_col_ = data_col.data_ptr<scalar_t>();
-        const scalar_t *data_im_ = data_im.data_ptr<scalar_t>();
-        const scalar_t *data_offset_ = data_offset.data_ptr<scalar_t>();
-        const scalar_t *data_mask_ = data_mask.data_ptr<scalar_t>();
-        scalar_t *grad_offset_ = grad_offset.data_ptr<scalar_t>();
-        scalar_t *grad_mask_ = grad_mask.data_ptr<scalar_t>();
-
-        modulated_deformable_col2im_coord_gpu_kernel<<<GET_BLOCKS(num_kernels), CUDA_NUM_THREADS, 0, at::cuda::getCurrentCUDAStream()>>>(
-            num_kernels, data_col_, data_im_, data_offset_, data_mask_, channels, height_im, width_im,
-            kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w,
-            dilation_h, dilation_w, channel_per_deformable_group,
-            batch_size, 2 * kernel_h * kernel_w * deformable_group, deformable_group, height_col, width_col,
-            grad_offset_, grad_mask_);
-      }));
-  cudaError_t err = cudaGetLastError();
-  if (err != cudaSuccess)
-  {
-    printf("error in modulated_deformable_col2im_coord_cuda: %s\n", cudaGetErrorString(err));
-  }
-}
diff --git a/repositories/CodeFormer/basicsr/ops/dcn/src/deform_conv_ext.cpp b/repositories/CodeFormer/basicsr/ops/dcn/src/deform_conv_ext.cpp
deleted file mode 100644
index 41c6df6f721bd95a525fd6a03dd9882e863de042..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/basicsr/ops/dcn/src/deform_conv_ext.cpp
+++ /dev/null
@@ -1,164 +0,0 @@
-// modify from
-// https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/blob/mmdetection/mmdet/ops/dcn/src/deform_conv_cuda.c
-
-#include <torch/extension.h>
-#include <ATen/DeviceGuard.h>
-
-#include <cmath>
-#include <vector>
-
-#define WITH_CUDA  // always use cuda
-#ifdef WITH_CUDA
-int deform_conv_forward_cuda(at::Tensor input, at::Tensor weight,
-                             at::Tensor offset, at::Tensor output,
-                             at::Tensor columns, at::Tensor ones, int kW,
-                             int kH, int dW, int dH, int padW, int padH,
-                             int dilationW, int dilationH, int group,
-                             int deformable_group, int im2col_step);
-
-int deform_conv_backward_input_cuda(at::Tensor input, at::Tensor offset,
-                                    at::Tensor gradOutput, at::Tensor gradInput,
-                                    at::Tensor gradOffset, at::Tensor weight,
-                                    at::Tensor columns, int kW, int kH, int dW,
-                                    int dH, int padW, int padH, int dilationW,
-                                    int dilationH, int group,
-                                    int deformable_group, int im2col_step);
-
-int deform_conv_backward_parameters_cuda(
-    at::Tensor input, at::Tensor offset, at::Tensor gradOutput,
-    at::Tensor gradWeight,  // at::Tensor gradBias,
-    at::Tensor columns, at::Tensor ones, int kW, int kH, int dW, int dH,
-    int padW, int padH, int dilationW, int dilationH, int group,
-    int deformable_group, float scale, int im2col_step);
-
-void modulated_deform_conv_cuda_forward(
-    at::Tensor input, at::Tensor weight, at::Tensor bias, at::Tensor ones,
-    at::Tensor offset, at::Tensor mask, at::Tensor output, at::Tensor columns,
-    int kernel_h, int kernel_w, const int stride_h, const int stride_w,
-    const int pad_h, const int pad_w, const int dilation_h,
-    const int dilation_w, const int group, const int deformable_group,
-    const bool with_bias);
-
-void modulated_deform_conv_cuda_backward(
-    at::Tensor input, at::Tensor weight, at::Tensor bias, at::Tensor ones,
-    at::Tensor offset, at::Tensor mask, at::Tensor columns,
-    at::Tensor grad_input, at::Tensor grad_weight, at::Tensor grad_bias,
-    at::Tensor grad_offset, at::Tensor grad_mask, at::Tensor grad_output,
-    int kernel_h, int kernel_w, int stride_h, int stride_w, int pad_h,
-    int pad_w, int dilation_h, int dilation_w, int group, int deformable_group,
-    const bool with_bias);
-#endif
-
-int deform_conv_forward(at::Tensor input, at::Tensor weight,
-                             at::Tensor offset, at::Tensor output,
-                             at::Tensor columns, at::Tensor ones, int kW,
-                             int kH, int dW, int dH, int padW, int padH,
-                             int dilationW, int dilationH, int group,
-                             int deformable_group, int im2col_step) {
-  if (input.device().is_cuda()) {
-#ifdef WITH_CUDA
-    return deform_conv_forward_cuda(input, weight, offset, output, columns,
-        ones, kW, kH, dW, dH, padW, padH, dilationW, dilationH, group,
-        deformable_group, im2col_step);
-#else
-    AT_ERROR("deform conv is not compiled with GPU support");
-#endif
-  }
-  AT_ERROR("deform conv is not implemented on CPU");
-}
-
-int deform_conv_backward_input(at::Tensor input, at::Tensor offset,
-                                    at::Tensor gradOutput, at::Tensor gradInput,
-                                    at::Tensor gradOffset, at::Tensor weight,
-                                    at::Tensor columns, int kW, int kH, int dW,
-                                    int dH, int padW, int padH, int dilationW,
-                                    int dilationH, int group,
-                                    int deformable_group, int im2col_step) {
-  if (input.device().is_cuda()) {
-#ifdef WITH_CUDA
-    return deform_conv_backward_input_cuda(input, offset, gradOutput,
-        gradInput, gradOffset, weight, columns, kW, kH, dW, dH, padW, padH,
-        dilationW, dilationH, group, deformable_group, im2col_step);
-#else
-    AT_ERROR("deform conv is not compiled with GPU support");
-#endif
-  }
-  AT_ERROR("deform conv is not implemented on CPU");
-}
-
-int deform_conv_backward_parameters(
-    at::Tensor input, at::Tensor offset, at::Tensor gradOutput,
-    at::Tensor gradWeight,  // at::Tensor gradBias,
-    at::Tensor columns, at::Tensor ones, int kW, int kH, int dW, int dH,
-    int padW, int padH, int dilationW, int dilationH, int group,
-    int deformable_group, float scale, int im2col_step) {
-  if (input.device().is_cuda()) {
-#ifdef WITH_CUDA
-    return deform_conv_backward_parameters_cuda(input, offset, gradOutput,
-        gradWeight, columns, ones, kW, kH, dW, dH, padW, padH, dilationW,
-        dilationH, group, deformable_group, scale, im2col_step);
-#else
-    AT_ERROR("deform conv is not compiled with GPU support");
-#endif
-  }
-  AT_ERROR("deform conv is not implemented on CPU");
-}
-
-void modulated_deform_conv_forward(
-    at::Tensor input, at::Tensor weight, at::Tensor bias, at::Tensor ones,
-    at::Tensor offset, at::Tensor mask, at::Tensor output, at::Tensor columns,
-    int kernel_h, int kernel_w, const int stride_h, const int stride_w,
-    const int pad_h, const int pad_w, const int dilation_h,
-    const int dilation_w, const int group, const int deformable_group,
-    const bool with_bias) {
-  if (input.device().is_cuda()) {
-#ifdef WITH_CUDA
-    return modulated_deform_conv_cuda_forward(input, weight, bias, ones,
-        offset, mask, output, columns, kernel_h, kernel_w, stride_h,
-        stride_w, pad_h, pad_w, dilation_h, dilation_w, group,
-        deformable_group, with_bias);
-#else
-    AT_ERROR("modulated deform conv is not compiled with GPU support");
-#endif
-  }
-  AT_ERROR("modulated deform conv is not implemented on CPU");
-}
-
-void modulated_deform_conv_backward(
-    at::Tensor input, at::Tensor weight, at::Tensor bias, at::Tensor ones,
-    at::Tensor offset, at::Tensor mask, at::Tensor columns,
-    at::Tensor grad_input, at::Tensor grad_weight, at::Tensor grad_bias,
-    at::Tensor grad_offset, at::Tensor grad_mask, at::Tensor grad_output,
-    int kernel_h, int kernel_w, int stride_h, int stride_w, int pad_h,
-    int pad_w, int dilation_h, int dilation_w, int group, int deformable_group,
-    const bool with_bias) {
-  if (input.device().is_cuda()) {
-#ifdef WITH_CUDA
-    return modulated_deform_conv_cuda_backward(input, weight, bias, ones,
-        offset, mask, columns, grad_input, grad_weight, grad_bias, grad_offset,
-        grad_mask, grad_output, kernel_h, kernel_w, stride_h, stride_w,
-        pad_h, pad_w, dilation_h, dilation_w, group, deformable_group,
-        with_bias);
-#else
-    AT_ERROR("modulated deform conv is not compiled with GPU support");
-#endif
-  }
-  AT_ERROR("modulated deform conv is not implemented on CPU");
-}
-
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
-  m.def("deform_conv_forward", &deform_conv_forward,
-        "deform forward");
-  m.def("deform_conv_backward_input", &deform_conv_backward_input,
-        "deform_conv_backward_input");
-  m.def("deform_conv_backward_parameters",
-        &deform_conv_backward_parameters,
-        "deform_conv_backward_parameters");
-  m.def("modulated_deform_conv_forward",
-        &modulated_deform_conv_forward,
-        "modulated deform conv forward");
-  m.def("modulated_deform_conv_backward",
-        &modulated_deform_conv_backward,
-        "modulated deform conv backward");
-}
diff --git a/repositories/CodeFormer/basicsr/ops/fused_act/__init__.py b/repositories/CodeFormer/basicsr/ops/fused_act/__init__.py
deleted file mode 100644
index 241dc0754fae7d88dbbd9a02e665ca30a73c7422..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/basicsr/ops/fused_act/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-from .fused_act import FusedLeakyReLU, fused_leaky_relu
-
-__all__ = ['FusedLeakyReLU', 'fused_leaky_relu']
diff --git a/repositories/CodeFormer/basicsr/ops/fused_act/fused_act.py b/repositories/CodeFormer/basicsr/ops/fused_act/fused_act.py
deleted file mode 100644
index 588f815e596ab0fc83ab0f9d21426c22ec5ed7c3..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/basicsr/ops/fused_act/fused_act.py
+++ /dev/null
@@ -1,89 +0,0 @@
-# modify from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/fused_act.py # noqa:E501
-
-import torch
-from torch import nn
-from torch.autograd import Function
-
-try:
-    from . import fused_act_ext
-except ImportError:
-    import os
-    BASICSR_JIT = os.getenv('BASICSR_JIT')
-    if BASICSR_JIT == 'True':
-        from torch.utils.cpp_extension import load
-        module_path = os.path.dirname(__file__)
-        fused_act_ext = load(
-            'fused',
-            sources=[
-                os.path.join(module_path, 'src', 'fused_bias_act.cpp'),
-                os.path.join(module_path, 'src', 'fused_bias_act_kernel.cu'),
-            ],
-        )
-
-
-class FusedLeakyReLUFunctionBackward(Function):
-
-    @staticmethod
-    def forward(ctx, grad_output, out, negative_slope, scale):
-        ctx.save_for_backward(out)
-        ctx.negative_slope = negative_slope
-        ctx.scale = scale
-
-        empty = grad_output.new_empty(0)
-
-        grad_input = fused_act_ext.fused_bias_act(grad_output, empty, out, 3, 1, negative_slope, scale)
-
-        dim = [0]
-
-        if grad_input.ndim > 2:
-            dim += list(range(2, grad_input.ndim))
-
-        grad_bias = grad_input.sum(dim).detach()
-
-        return grad_input, grad_bias
-
-    @staticmethod
-    def backward(ctx, gradgrad_input, gradgrad_bias):
-        out, = ctx.saved_tensors
-        gradgrad_out = fused_act_ext.fused_bias_act(gradgrad_input, gradgrad_bias, out, 3, 1, ctx.negative_slope,
-                                                    ctx.scale)
-
-        return gradgrad_out, None, None, None
-
-
-class FusedLeakyReLUFunction(Function):
-
-    @staticmethod
-    def forward(ctx, input, bias, negative_slope, scale):
-        empty = input.new_empty(0)
-        out = fused_act_ext.fused_bias_act(input, bias, empty, 3, 0, negative_slope, scale)
-        ctx.save_for_backward(out)
-        ctx.negative_slope = negative_slope
-        ctx.scale = scale
-
-        return out
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        out, = ctx.saved_tensors
-
-        grad_input, grad_bias = FusedLeakyReLUFunctionBackward.apply(grad_output, out, ctx.negative_slope, ctx.scale)
-
-        return grad_input, grad_bias, None, None
-
-
-class FusedLeakyReLU(nn.Module):
-
-    def __init__(self, channel, negative_slope=0.2, scale=2**0.5):
-        super().__init__()
-
-        self.bias = nn.Parameter(torch.zeros(channel))
-        self.negative_slope = negative_slope
-        self.scale = scale
-
-    def forward(self, input):
-        return fused_leaky_relu(input, self.bias, self.negative_slope, self.scale)
-
-
-def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2**0.5):
-    return FusedLeakyReLUFunction.apply(input, bias, negative_slope, scale)
diff --git a/repositories/CodeFormer/basicsr/ops/fused_act/src/fused_bias_act.cpp b/repositories/CodeFormer/basicsr/ops/fused_act/src/fused_bias_act.cpp
deleted file mode 100644
index 85ed0a79fb9c75f83470ac834090f03608d998ee..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/basicsr/ops/fused_act/src/fused_bias_act.cpp
+++ /dev/null
@@ -1,26 +0,0 @@
-// from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/fused_bias_act.cpp
-#include <torch/extension.h>
-
-
-torch::Tensor fused_bias_act_op(const torch::Tensor& input,
-                                const torch::Tensor& bias,
-                                const torch::Tensor& refer,
-                                int act, int grad, float alpha, float scale);
-
-#define CHECK_CUDA(x) TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor")
-#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
-#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
-
-torch::Tensor fused_bias_act(const torch::Tensor& input,
-                             const torch::Tensor& bias,
-                             const torch::Tensor& refer,
-                             int act, int grad, float alpha, float scale) {
-    CHECK_CUDA(input);
-    CHECK_CUDA(bias);
-
-    return fused_bias_act_op(input, bias, refer, act, grad, alpha, scale);
-}
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
-    m.def("fused_bias_act", &fused_bias_act, "fused bias act (CUDA)");
-}
diff --git a/repositories/CodeFormer/basicsr/ops/fused_act/src/fused_bias_act_kernel.cu b/repositories/CodeFormer/basicsr/ops/fused_act/src/fused_bias_act_kernel.cu
deleted file mode 100644
index 54c7ff53ce8306db2b3c582ec7fa6696a38b4df0..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/basicsr/ops/fused_act/src/fused_bias_act_kernel.cu
+++ /dev/null
@@ -1,100 +0,0 @@
-// from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/fused_bias_act_kernel.cu
-// Copyright (c) 2019, NVIDIA Corporation. All rights reserved.
-//
-// This work is made available under the Nvidia Source Code License-NC.
-// To view a copy of this license, visit
-// https://nvlabs.github.io/stylegan2/license.html
-
-#include <torch/types.h>
-
-#include <ATen/ATen.h>
-#include <ATen/AccumulateType.h>
-#include <ATen/cuda/CUDAContext.h>
-#include <ATen/cuda/CUDAApplyUtils.cuh>
-
-#include <cuda.h>
-#include <cuda_runtime.h>
-
-
-template <typename scalar_t>
-static __global__ void fused_bias_act_kernel(scalar_t* out, const scalar_t* p_x, const scalar_t* p_b, const scalar_t* p_ref,
-    int act, int grad, scalar_t alpha, scalar_t scale, int loop_x, int size_x, int step_b, int size_b, int use_bias, int use_ref) {
-    int xi = blockIdx.x * loop_x * blockDim.x + threadIdx.x;
-
-    scalar_t zero = 0.0;
-
-    for (int loop_idx = 0; loop_idx < loop_x && xi < size_x; loop_idx++, xi += blockDim.x) {
-        scalar_t x = p_x[xi];
-
-        if (use_bias) {
-            x += p_b[(xi / step_b) % size_b];
-        }
-
-        scalar_t ref = use_ref ? p_ref[xi] : zero;
-
-        scalar_t y;
-
-        switch (act * 10 + grad) {
-            default:
-            case 10: y = x; break;
-            case 11: y = x; break;
-            case 12: y = 0.0; break;
-
-            case 30: y = (x > 0.0) ? x : x * alpha; break;
-            case 31: y = (ref > 0.0) ? x : x * alpha; break;
-            case 32: y = 0.0; break;
-        }
-
-        out[xi] = y * scale;
-    }
-}
-
-
-torch::Tensor fused_bias_act_op(const torch::Tensor& input, const torch::Tensor& bias, const torch::Tensor& refer,
-    int act, int grad, float alpha, float scale) {
-    int curDevice = -1;
-    cudaGetDevice(&curDevice);
-    cudaStream_t stream = at::cuda::getCurrentCUDAStream(curDevice);
-
-    auto x = input.contiguous();
-    auto b = bias.contiguous();
-    auto ref = refer.contiguous();
-
-    int use_bias = b.numel() ? 1 : 0;
-    int use_ref = ref.numel() ? 1 : 0;
-
-    int size_x = x.numel();
-    int size_b = b.numel();
-    int step_b = 1;
-
-    for (int i = 1 + 1; i < x.dim(); i++) {
-        step_b *= x.size(i);
-    }
-
-    int loop_x = 4;
-    int block_size = 4 * 32;
-    int grid_size = (size_x - 1) / (loop_x * block_size) + 1;
-
-    auto y = torch::empty_like(x);
-
-    AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "fused_bias_act_kernel", [&] {
-        fused_bias_act_kernel<scalar_t><<<grid_size, block_size, 0, stream>>>(
-            y.data_ptr<scalar_t>(),
-            x.data_ptr<scalar_t>(),
-            b.data_ptr<scalar_t>(),
-            ref.data_ptr<scalar_t>(),
-            act,
-            grad,
-            alpha,
-            scale,
-            loop_x,
-            size_x,
-            step_b,
-            size_b,
-            use_bias,
-            use_ref
-        );
-    });
-
-    return y;
-}
diff --git a/repositories/CodeFormer/basicsr/ops/upfirdn2d/__init__.py b/repositories/CodeFormer/basicsr/ops/upfirdn2d/__init__.py
deleted file mode 100644
index 397e85bea063e97fc4c12ad4d3e15669b69290bd..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/basicsr/ops/upfirdn2d/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-from .upfirdn2d import upfirdn2d
-
-__all__ = ['upfirdn2d']
diff --git a/repositories/CodeFormer/basicsr/ops/upfirdn2d/src/upfirdn2d.cpp b/repositories/CodeFormer/basicsr/ops/upfirdn2d/src/upfirdn2d.cpp
deleted file mode 100644
index 43d0b6783a5b512b55815a291fcac2bebeea31e0..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/basicsr/ops/upfirdn2d/src/upfirdn2d.cpp
+++ /dev/null
@@ -1,24 +0,0 @@
-// from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/upfirdn2d.cpp
-#include <torch/extension.h>
-
-
-torch::Tensor upfirdn2d_op(const torch::Tensor& input, const torch::Tensor& kernel,
-                            int up_x, int up_y, int down_x, int down_y,
-                            int pad_x0, int pad_x1, int pad_y0, int pad_y1);
-
-#define CHECK_CUDA(x) TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor")
-#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
-#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
-
-torch::Tensor upfirdn2d(const torch::Tensor& input, const torch::Tensor& kernel,
-                        int up_x, int up_y, int down_x, int down_y,
-                        int pad_x0, int pad_x1, int pad_y0, int pad_y1) {
-    CHECK_CUDA(input);
-    CHECK_CUDA(kernel);
-
-    return upfirdn2d_op(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1);
-}
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
-    m.def("upfirdn2d", &upfirdn2d, "upfirdn2d (CUDA)");
-}
diff --git a/repositories/CodeFormer/basicsr/ops/upfirdn2d/src/upfirdn2d_kernel.cu b/repositories/CodeFormer/basicsr/ops/upfirdn2d/src/upfirdn2d_kernel.cu
deleted file mode 100644
index 8870063bae4468deab2e721f0978fe9facfb01b1..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/basicsr/ops/upfirdn2d/src/upfirdn2d_kernel.cu
+++ /dev/null
@@ -1,370 +0,0 @@
-// from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/upfirdn2d_kernel.cu
-// Copyright (c) 2019, NVIDIA Corporation. All rights reserved.
-//
-// This work is made available under the Nvidia Source Code License-NC.
-// To view a copy of this license, visit
-// https://nvlabs.github.io/stylegan2/license.html
-
-#include <torch/types.h>
-
-#include <ATen/ATen.h>
-#include <ATen/AccumulateType.h>
-#include <ATen/cuda/CUDAApplyUtils.cuh>
-#include <ATen/cuda/CUDAContext.h>
-
-#include <cuda.h>
-#include <cuda_runtime.h>
-
-static __host__ __device__ __forceinline__ int floor_div(int a, int b) {
-  int c = a / b;
-
-  if (c * b > a) {
-    c--;
-  }
-
-  return c;
-}
-
-struct UpFirDn2DKernelParams {
-  int up_x;
-  int up_y;
-  int down_x;
-  int down_y;
-  int pad_x0;
-  int pad_x1;
-  int pad_y0;
-  int pad_y1;
-
-  int major_dim;
-  int in_h;
-  int in_w;
-  int minor_dim;
-  int kernel_h;
-  int kernel_w;
-  int out_h;
-  int out_w;
-  int loop_major;
-  int loop_x;
-};
-
-template <typename scalar_t>
-__global__ void upfirdn2d_kernel_large(scalar_t *out, const scalar_t *input,
-                                       const scalar_t *kernel,
-                                       const UpFirDn2DKernelParams p) {
-  int minor_idx = blockIdx.x * blockDim.x + threadIdx.x;
-  int out_y = minor_idx / p.minor_dim;
-  minor_idx -= out_y * p.minor_dim;
-  int out_x_base = blockIdx.y * p.loop_x * blockDim.y + threadIdx.y;
-  int major_idx_base = blockIdx.z * p.loop_major;
-
-  if (out_x_base >= p.out_w || out_y >= p.out_h ||
-      major_idx_base >= p.major_dim) {
-    return;
-  }
-
-  int mid_y = out_y * p.down_y + p.up_y - 1 - p.pad_y0;
-  int in_y = min(max(floor_div(mid_y, p.up_y), 0), p.in_h);
-  int h = min(max(floor_div(mid_y + p.kernel_h, p.up_y), 0), p.in_h) - in_y;
-  int kernel_y = mid_y + p.kernel_h - (in_y + 1) * p.up_y;
-
-  for (int loop_major = 0, major_idx = major_idx_base;
-       loop_major < p.loop_major && major_idx < p.major_dim;
-       loop_major++, major_idx++) {
-    for (int loop_x = 0, out_x = out_x_base;
-         loop_x < p.loop_x && out_x < p.out_w; loop_x++, out_x += blockDim.y) {
-      int mid_x = out_x * p.down_x + p.up_x - 1 - p.pad_x0;
-      int in_x = min(max(floor_div(mid_x, p.up_x), 0), p.in_w);
-      int w = min(max(floor_div(mid_x + p.kernel_w, p.up_x), 0), p.in_w) - in_x;
-      int kernel_x = mid_x + p.kernel_w - (in_x + 1) * p.up_x;
-
-      const scalar_t *x_p =
-          &input[((major_idx * p.in_h + in_y) * p.in_w + in_x) * p.minor_dim +
-                 minor_idx];
-      const scalar_t *k_p = &kernel[kernel_y * p.kernel_w + kernel_x];
-      int x_px = p.minor_dim;
-      int k_px = -p.up_x;
-      int x_py = p.in_w * p.minor_dim;
-      int k_py = -p.up_y * p.kernel_w;
-
-      scalar_t v = 0.0f;
-
-      for (int y = 0; y < h; y++) {
-        for (int x = 0; x < w; x++) {
-          v += static_cast<scalar_t>(*x_p) * static_cast<scalar_t>(*k_p);
-          x_p += x_px;
-          k_p += k_px;
-        }
-
-        x_p += x_py - w * x_px;
-        k_p += k_py - w * k_px;
-      }
-
-      out[((major_idx * p.out_h + out_y) * p.out_w + out_x) * p.minor_dim +
-          minor_idx] = v;
-    }
-  }
-}
-
-template <typename scalar_t, int up_x, int up_y, int down_x, int down_y,
-          int kernel_h, int kernel_w, int tile_out_h, int tile_out_w>
-__global__ void upfirdn2d_kernel(scalar_t *out, const scalar_t *input,
-                                 const scalar_t *kernel,
-                                 const UpFirDn2DKernelParams p) {
-  const int tile_in_h = ((tile_out_h - 1) * down_y + kernel_h - 1) / up_y + 1;
-  const int tile_in_w = ((tile_out_w - 1) * down_x + kernel_w - 1) / up_x + 1;
-
-  __shared__ volatile float sk[kernel_h][kernel_w];
-  __shared__ volatile float sx[tile_in_h][tile_in_w];
-
-  int minor_idx = blockIdx.x;
-  int tile_out_y = minor_idx / p.minor_dim;
-  minor_idx -= tile_out_y * p.minor_dim;
-  tile_out_y *= tile_out_h;
-  int tile_out_x_base = blockIdx.y * p.loop_x * tile_out_w;
-  int major_idx_base = blockIdx.z * p.loop_major;
-
-  if (tile_out_x_base >= p.out_w | tile_out_y >= p.out_h |
-      major_idx_base >= p.major_dim) {
-    return;
-  }
-
-  for (int tap_idx = threadIdx.x; tap_idx < kernel_h * kernel_w;
-       tap_idx += blockDim.x) {
-    int ky = tap_idx / kernel_w;
-    int kx = tap_idx - ky * kernel_w;
-    scalar_t v = 0.0;
-
-    if (kx < p.kernel_w & ky < p.kernel_h) {
-      v = kernel[(p.kernel_h - 1 - ky) * p.kernel_w + (p.kernel_w - 1 - kx)];
-    }
-
-    sk[ky][kx] = v;
-  }
-
-  for (int loop_major = 0, major_idx = major_idx_base;
-       loop_major < p.loop_major & major_idx < p.major_dim;
-       loop_major++, major_idx++) {
-    for (int loop_x = 0, tile_out_x = tile_out_x_base;
-         loop_x < p.loop_x & tile_out_x < p.out_w;
-         loop_x++, tile_out_x += tile_out_w) {
-      int tile_mid_x = tile_out_x * down_x + up_x - 1 - p.pad_x0;
-      int tile_mid_y = tile_out_y * down_y + up_y - 1 - p.pad_y0;
-      int tile_in_x = floor_div(tile_mid_x, up_x);
-      int tile_in_y = floor_div(tile_mid_y, up_y);
-
-      __syncthreads();
-
-      for (int in_idx = threadIdx.x; in_idx < tile_in_h * tile_in_w;
-           in_idx += blockDim.x) {
-        int rel_in_y = in_idx / tile_in_w;
-        int rel_in_x = in_idx - rel_in_y * tile_in_w;
-        int in_x = rel_in_x + tile_in_x;
-        int in_y = rel_in_y + tile_in_y;
-
-        scalar_t v = 0.0;
-
-        if (in_x >= 0 & in_y >= 0 & in_x < p.in_w & in_y < p.in_h) {
-          v = input[((major_idx * p.in_h + in_y) * p.in_w + in_x) *
-                        p.minor_dim +
-                    minor_idx];
-        }
-
-        sx[rel_in_y][rel_in_x] = v;
-      }
-
-      __syncthreads();
-      for (int out_idx = threadIdx.x; out_idx < tile_out_h * tile_out_w;
-           out_idx += blockDim.x) {
-        int rel_out_y = out_idx / tile_out_w;
-        int rel_out_x = out_idx - rel_out_y * tile_out_w;
-        int out_x = rel_out_x + tile_out_x;
-        int out_y = rel_out_y + tile_out_y;
-
-        int mid_x = tile_mid_x + rel_out_x * down_x;
-        int mid_y = tile_mid_y + rel_out_y * down_y;
-        int in_x = floor_div(mid_x, up_x);
-        int in_y = floor_div(mid_y, up_y);
-        int rel_in_x = in_x - tile_in_x;
-        int rel_in_y = in_y - tile_in_y;
-        int kernel_x = (in_x + 1) * up_x - mid_x - 1;
-        int kernel_y = (in_y + 1) * up_y - mid_y - 1;
-
-        scalar_t v = 0.0;
-
-#pragma unroll
-        for (int y = 0; y < kernel_h / up_y; y++)
-#pragma unroll
-          for (int x = 0; x < kernel_w / up_x; x++)
-            v += sx[rel_in_y + y][rel_in_x + x] *
-                 sk[kernel_y + y * up_y][kernel_x + x * up_x];
-
-        if (out_x < p.out_w & out_y < p.out_h) {
-          out[((major_idx * p.out_h + out_y) * p.out_w + out_x) * p.minor_dim +
-              minor_idx] = v;
-        }
-      }
-    }
-  }
-}
-
-torch::Tensor upfirdn2d_op(const torch::Tensor &input,
-                           const torch::Tensor &kernel, int up_x, int up_y,
-                           int down_x, int down_y, int pad_x0, int pad_x1,
-                           int pad_y0, int pad_y1) {
-  int curDevice = -1;
-  cudaGetDevice(&curDevice);
-  cudaStream_t stream = at::cuda::getCurrentCUDAStream(curDevice);
-
-  UpFirDn2DKernelParams p;
-
-  auto x = input.contiguous();
-  auto k = kernel.contiguous();
-
-  p.major_dim = x.size(0);
-  p.in_h = x.size(1);
-  p.in_w = x.size(2);
-  p.minor_dim = x.size(3);
-  p.kernel_h = k.size(0);
-  p.kernel_w = k.size(1);
-  p.up_x = up_x;
-  p.up_y = up_y;
-  p.down_x = down_x;
-  p.down_y = down_y;
-  p.pad_x0 = pad_x0;
-  p.pad_x1 = pad_x1;
-  p.pad_y0 = pad_y0;
-  p.pad_y1 = pad_y1;
-
-  p.out_h = (p.in_h * p.up_y + p.pad_y0 + p.pad_y1 - p.kernel_h + p.down_y) /
-            p.down_y;
-  p.out_w = (p.in_w * p.up_x + p.pad_x0 + p.pad_x1 - p.kernel_w + p.down_x) /
-            p.down_x;
-
-  auto out =
-      at::empty({p.major_dim, p.out_h, p.out_w, p.minor_dim}, x.options());
-
-  int mode = -1;
-
-  int tile_out_h = -1;
-  int tile_out_w = -1;
-
-  if (p.up_x == 1 && p.up_y == 1 && p.down_x == 1 && p.down_y == 1 &&
-      p.kernel_h <= 4 && p.kernel_w <= 4) {
-    mode = 1;
-    tile_out_h = 16;
-    tile_out_w = 64;
-  }
-
-  if (p.up_x == 1 && p.up_y == 1 && p.down_x == 1 && p.down_y == 1 &&
-      p.kernel_h <= 3 && p.kernel_w <= 3) {
-    mode = 2;
-    tile_out_h = 16;
-    tile_out_w = 64;
-  }
-
-  if (p.up_x == 2 && p.up_y == 2 && p.down_x == 1 && p.down_y == 1 &&
-      p.kernel_h <= 4 && p.kernel_w <= 4) {
-    mode = 3;
-    tile_out_h = 16;
-    tile_out_w = 64;
-  }
-
-  if (p.up_x == 2 && p.up_y == 2 && p.down_x == 1 && p.down_y == 1 &&
-      p.kernel_h <= 2 && p.kernel_w <= 2) {
-    mode = 4;
-    tile_out_h = 16;
-    tile_out_w = 64;
-  }
-
-  if (p.up_x == 1 && p.up_y == 1 && p.down_x == 2 && p.down_y == 2 &&
-      p.kernel_h <= 4 && p.kernel_w <= 4) {
-    mode = 5;
-    tile_out_h = 8;
-    tile_out_w = 32;
-  }
-
-  if (p.up_x == 1 && p.up_y == 1 && p.down_x == 2 && p.down_y == 2 &&
-      p.kernel_h <= 2 && p.kernel_w <= 2) {
-    mode = 6;
-    tile_out_h = 8;
-    tile_out_w = 32;
-  }
-
-  dim3 block_size;
-  dim3 grid_size;
-
-  if (tile_out_h > 0 && tile_out_w > 0) {
-    p.loop_major = (p.major_dim - 1) / 16384 + 1;
-    p.loop_x = 1;
-    block_size = dim3(32 * 8, 1, 1);
-    grid_size = dim3(((p.out_h - 1) / tile_out_h + 1) * p.minor_dim,
-                     (p.out_w - 1) / (p.loop_x * tile_out_w) + 1,
-                     (p.major_dim - 1) / p.loop_major + 1);
-  } else {
-    p.loop_major = (p.major_dim - 1) / 16384 + 1;
-    p.loop_x = 4;
-    block_size = dim3(4, 32, 1);
-    grid_size = dim3((p.out_h * p.minor_dim - 1) / block_size.x + 1,
-                     (p.out_w - 1) / (p.loop_x * block_size.y) + 1,
-                     (p.major_dim - 1) / p.loop_major + 1);
-  }
-
-  AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "upfirdn2d_cuda", [&] {
-    switch (mode) {
-    case 1:
-      upfirdn2d_kernel<scalar_t, 1, 1, 1, 1, 4, 4, 16, 64>
-          <<<grid_size, block_size, 0, stream>>>(out.data_ptr<scalar_t>(),
-                                                 x.data_ptr<scalar_t>(),
-                                                 k.data_ptr<scalar_t>(), p);
-
-      break;
-
-    case 2:
-      upfirdn2d_kernel<scalar_t, 1, 1, 1, 1, 3, 3, 16, 64>
-          <<<grid_size, block_size, 0, stream>>>(out.data_ptr<scalar_t>(),
-                                                 x.data_ptr<scalar_t>(),
-                                                 k.data_ptr<scalar_t>(), p);
-
-      break;
-
-    case 3:
-      upfirdn2d_kernel<scalar_t, 2, 2, 1, 1, 4, 4, 16, 64>
-          <<<grid_size, block_size, 0, stream>>>(out.data_ptr<scalar_t>(),
-                                                 x.data_ptr<scalar_t>(),
-                                                 k.data_ptr<scalar_t>(), p);
-
-      break;
-
-    case 4:
-      upfirdn2d_kernel<scalar_t, 2, 2, 1, 1, 2, 2, 16, 64>
-          <<<grid_size, block_size, 0, stream>>>(out.data_ptr<scalar_t>(),
-                                                 x.data_ptr<scalar_t>(),
-                                                 k.data_ptr<scalar_t>(), p);
-
-      break;
-
-    case 5:
-      upfirdn2d_kernel<scalar_t, 1, 1, 2, 2, 4, 4, 8, 32>
-          <<<grid_size, block_size, 0, stream>>>(out.data_ptr<scalar_t>(),
-                                                 x.data_ptr<scalar_t>(),
-                                                 k.data_ptr<scalar_t>(), p);
-
-      break;
-
-    case 6:
-      upfirdn2d_kernel<scalar_t, 1, 1, 2, 2, 4, 4, 8, 32>
-          <<<grid_size, block_size, 0, stream>>>(out.data_ptr<scalar_t>(),
-                                                 x.data_ptr<scalar_t>(),
-                                                 k.data_ptr<scalar_t>(), p);
-
-      break;
-
-    default:
-      upfirdn2d_kernel_large<scalar_t><<<grid_size, block_size, 0, stream>>>(
-          out.data_ptr<scalar_t>(), x.data_ptr<scalar_t>(),
-          k.data_ptr<scalar_t>(), p);
-    }
-  });
-
-  return out;
-}
diff --git a/repositories/CodeFormer/basicsr/ops/upfirdn2d/upfirdn2d.py b/repositories/CodeFormer/basicsr/ops/upfirdn2d/upfirdn2d.py
deleted file mode 100644
index 667f96e1ded35d48f163f37e21d1ed8ff191aac3..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/basicsr/ops/upfirdn2d/upfirdn2d.py
+++ /dev/null
@@ -1,186 +0,0 @@
-# modify from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/upfirdn2d.py  # noqa:E501
-
-import torch
-from torch.autograd import Function
-from torch.nn import functional as F
-
-try:
-    from . import upfirdn2d_ext
-except ImportError:
-    import os
-    BASICSR_JIT = os.getenv('BASICSR_JIT')
-    if BASICSR_JIT == 'True':
-        from torch.utils.cpp_extension import load
-        module_path = os.path.dirname(__file__)
-        upfirdn2d_ext = load(
-            'upfirdn2d',
-            sources=[
-                os.path.join(module_path, 'src', 'upfirdn2d.cpp'),
-                os.path.join(module_path, 'src', 'upfirdn2d_kernel.cu'),
-            ],
-        )
-
-
-class UpFirDn2dBackward(Function):
-
-    @staticmethod
-    def forward(ctx, grad_output, kernel, grad_kernel, up, down, pad, g_pad, in_size, out_size):
-
-        up_x, up_y = up
-        down_x, down_y = down
-        g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1 = g_pad
-
-        grad_output = grad_output.reshape(-1, out_size[0], out_size[1], 1)
-
-        grad_input = upfirdn2d_ext.upfirdn2d(
-            grad_output,
-            grad_kernel,
-            down_x,
-            down_y,
-            up_x,
-            up_y,
-            g_pad_x0,
-            g_pad_x1,
-            g_pad_y0,
-            g_pad_y1,
-        )
-        grad_input = grad_input.view(in_size[0], in_size[1], in_size[2], in_size[3])
-
-        ctx.save_for_backward(kernel)
-
-        pad_x0, pad_x1, pad_y0, pad_y1 = pad
-
-        ctx.up_x = up_x
-        ctx.up_y = up_y
-        ctx.down_x = down_x
-        ctx.down_y = down_y
-        ctx.pad_x0 = pad_x0
-        ctx.pad_x1 = pad_x1
-        ctx.pad_y0 = pad_y0
-        ctx.pad_y1 = pad_y1
-        ctx.in_size = in_size
-        ctx.out_size = out_size
-
-        return grad_input
-
-    @staticmethod
-    def backward(ctx, gradgrad_input):
-        kernel, = ctx.saved_tensors
-
-        gradgrad_input = gradgrad_input.reshape(-1, ctx.in_size[2], ctx.in_size[3], 1)
-
-        gradgrad_out = upfirdn2d_ext.upfirdn2d(
-            gradgrad_input,
-            kernel,
-            ctx.up_x,
-            ctx.up_y,
-            ctx.down_x,
-            ctx.down_y,
-            ctx.pad_x0,
-            ctx.pad_x1,
-            ctx.pad_y0,
-            ctx.pad_y1,
-        )
-        # gradgrad_out = gradgrad_out.view(ctx.in_size[0], ctx.out_size[0],
-        #                                  ctx.out_size[1], ctx.in_size[3])
-        gradgrad_out = gradgrad_out.view(ctx.in_size[0], ctx.in_size[1], ctx.out_size[0], ctx.out_size[1])
-
-        return gradgrad_out, None, None, None, None, None, None, None, None
-
-
-class UpFirDn2d(Function):
-
-    @staticmethod
-    def forward(ctx, input, kernel, up, down, pad):
-        up_x, up_y = up
-        down_x, down_y = down
-        pad_x0, pad_x1, pad_y0, pad_y1 = pad
-
-        kernel_h, kernel_w = kernel.shape
-        batch, channel, in_h, in_w = input.shape
-        ctx.in_size = input.shape
-
-        input = input.reshape(-1, in_h, in_w, 1)
-
-        ctx.save_for_backward(kernel, torch.flip(kernel, [0, 1]))
-
-        out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1
-        out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1
-        ctx.out_size = (out_h, out_w)
-
-        ctx.up = (up_x, up_y)
-        ctx.down = (down_x, down_y)
-        ctx.pad = (pad_x0, pad_x1, pad_y0, pad_y1)
-
-        g_pad_x0 = kernel_w - pad_x0 - 1
-        g_pad_y0 = kernel_h - pad_y0 - 1
-        g_pad_x1 = in_w * up_x - out_w * down_x + pad_x0 - up_x + 1
-        g_pad_y1 = in_h * up_y - out_h * down_y + pad_y0 - up_y + 1
-
-        ctx.g_pad = (g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1)
-
-        out = upfirdn2d_ext.upfirdn2d(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1)
-        # out = out.view(major, out_h, out_w, minor)
-        out = out.view(-1, channel, out_h, out_w)
-
-        return out
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        kernel, grad_kernel = ctx.saved_tensors
-
-        grad_input = UpFirDn2dBackward.apply(
-            grad_output,
-            kernel,
-            grad_kernel,
-            ctx.up,
-            ctx.down,
-            ctx.pad,
-            ctx.g_pad,
-            ctx.in_size,
-            ctx.out_size,
-        )
-
-        return grad_input, None, None, None, None
-
-
-def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)):
-    if input.device.type == 'cpu':
-        out = upfirdn2d_native(input, kernel, up, up, down, down, pad[0], pad[1], pad[0], pad[1])
-    else:
-        out = UpFirDn2d.apply(input, kernel, (up, up), (down, down), (pad[0], pad[1], pad[0], pad[1]))
-
-    return out
-
-
-def upfirdn2d_native(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1):
-    _, channel, in_h, in_w = input.shape
-    input = input.reshape(-1, in_h, in_w, 1)
-
-    _, in_h, in_w, minor = input.shape
-    kernel_h, kernel_w = kernel.shape
-
-    out = input.view(-1, in_h, 1, in_w, 1, minor)
-    out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1])
-    out = out.view(-1, in_h * up_y, in_w * up_x, minor)
-
-    out = F.pad(out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)])
-    out = out[:, max(-pad_y0, 0):out.shape[1] - max(-pad_y1, 0), max(-pad_x0, 0):out.shape[2] - max(-pad_x1, 0), :, ]
-
-    out = out.permute(0, 3, 1, 2)
-    out = out.reshape([-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1])
-    w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w)
-    out = F.conv2d(out, w)
-    out = out.reshape(
-        -1,
-        minor,
-        in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1,
-        in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1,
-    )
-    out = out.permute(0, 2, 3, 1)
-    out = out[:, ::down_y, ::down_x, :]
-
-    out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1
-    out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1
-
-    return out.view(-1, channel, out_h, out_w)
diff --git a/repositories/CodeFormer/basicsr/setup.py b/repositories/CodeFormer/basicsr/setup.py
deleted file mode 100644
index 382a2aa1006e581eaf31dbb3155d4b0ba3b31140..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/basicsr/setup.py
+++ /dev/null
@@ -1,165 +0,0 @@
-#!/usr/bin/env python
-
-from setuptools import find_packages, setup
-
-import os
-import subprocess
-import sys
-import time
-import torch
-from torch.utils.cpp_extension import BuildExtension, CppExtension, CUDAExtension
-
-version_file = './basicsr/version.py'
-
-
-def readme():
-    with open('README.md', encoding='utf-8') as f:
-        content = f.read()
-    return content
-
-
-def get_git_hash():
-
-    def _minimal_ext_cmd(cmd):
-        # construct minimal environment
-        env = {}
-        for k in ['SYSTEMROOT', 'PATH', 'HOME']:
-            v = os.environ.get(k)
-            if v is not None:
-                env[k] = v
-        # LANGUAGE is used on win32
-        env['LANGUAGE'] = 'C'
-        env['LANG'] = 'C'
-        env['LC_ALL'] = 'C'
-        out = subprocess.Popen(cmd, stdout=subprocess.PIPE, env=env).communicate()[0]
-        return out
-
-    try:
-        out = _minimal_ext_cmd(['git', 'rev-parse', 'HEAD'])
-        sha = out.strip().decode('ascii')
-    except OSError:
-        sha = 'unknown'
-
-    return sha
-
-
-def get_hash():
-    if os.path.exists('.git'):
-        sha = get_git_hash()[:7]
-    elif os.path.exists(version_file):
-        try:
-            from version import __version__
-            sha = __version__.split('+')[-1]
-        except ImportError:
-            raise ImportError('Unable to get git version')
-    else:
-        sha = 'unknown'
-
-    return sha
-
-
-def write_version_py():
-    content = """# GENERATED VERSION FILE
-# TIME: {}
-__version__ = '{}'
-__gitsha__ = '{}'
-version_info = ({})
-"""
-    sha = get_hash()
-    with open('./basicsr/VERSION', 'r') as f:
-        SHORT_VERSION = f.read().strip()
-    VERSION_INFO = ', '.join([x if x.isdigit() else f'"{x}"' for x in SHORT_VERSION.split('.')])
-
-    version_file_str = content.format(time.asctime(), SHORT_VERSION, sha, VERSION_INFO)
-    with open(version_file, 'w') as f:
-        f.write(version_file_str)
-
-
-def get_version():
-    with open(version_file, 'r') as f:
-        exec(compile(f.read(), version_file, 'exec'))
-    return locals()['__version__']
-
-
-def make_cuda_ext(name, module, sources, sources_cuda=None):
-    if sources_cuda is None:
-        sources_cuda = []
-    define_macros = []
-    extra_compile_args = {'cxx': []}
-
-    if torch.cuda.is_available() or os.getenv('FORCE_CUDA', '0') == '1':
-        define_macros += [('WITH_CUDA', None)]
-        extension = CUDAExtension
-        extra_compile_args['nvcc'] = [
-            '-D__CUDA_NO_HALF_OPERATORS__',
-            '-D__CUDA_NO_HALF_CONVERSIONS__',
-            '-D__CUDA_NO_HALF2_OPERATORS__',
-        ]
-        sources += sources_cuda
-    else:
-        print(f'Compiling {name} without CUDA')
-        extension = CppExtension
-
-    return extension(
-        name=f'{module}.{name}',
-        sources=[os.path.join(*module.split('.'), p) for p in sources],
-        define_macros=define_macros,
-        extra_compile_args=extra_compile_args)
-
-
-def get_requirements(filename='requirements.txt'):
-    with open(os.path.join('.', filename), 'r') as f:
-        requires = [line.replace('\n', '') for line in f.readlines()]
-    return requires
-
-
-if __name__ == '__main__':
-    if '--cuda_ext' in sys.argv:
-        ext_modules = [
-            make_cuda_ext(
-                name='deform_conv_ext',
-                module='ops.dcn',
-                sources=['src/deform_conv_ext.cpp'],
-                sources_cuda=['src/deform_conv_cuda.cpp', 'src/deform_conv_cuda_kernel.cu']),
-            make_cuda_ext(
-                name='fused_act_ext',
-                module='ops.fused_act',
-                sources=['src/fused_bias_act.cpp'],
-                sources_cuda=['src/fused_bias_act_kernel.cu']),
-            make_cuda_ext(
-                name='upfirdn2d_ext',
-                module='ops.upfirdn2d',
-                sources=['src/upfirdn2d.cpp'],
-                sources_cuda=['src/upfirdn2d_kernel.cu']),
-        ]
-        sys.argv.remove('--cuda_ext')
-    else:
-        ext_modules = []
-
-    write_version_py()
-    setup(
-        name='basicsr',
-        version=get_version(),
-        description='Open Source Image and Video Super-Resolution Toolbox',
-        long_description=readme(),
-        long_description_content_type='text/markdown',
-        author='Xintao Wang',
-        author_email='xintao.wang@outlook.com',
-        keywords='computer vision, restoration, super resolution',
-        url='https://github.com/xinntao/BasicSR',
-        include_package_data=True,
-        packages=find_packages(exclude=('options', 'datasets', 'experiments', 'results', 'tb_logger', 'wandb')),
-        classifiers=[
-            'Development Status :: 4 - Beta',
-            'License :: OSI Approved :: Apache Software License',
-            'Operating System :: OS Independent',
-            'Programming Language :: Python :: 3',
-            'Programming Language :: Python :: 3.7',
-            'Programming Language :: Python :: 3.8',
-        ],
-        license='Apache License 2.0',
-        setup_requires=['cython', 'numpy'],
-        install_requires=get_requirements(),
-        ext_modules=ext_modules,
-        cmdclass={'build_ext': BuildExtension},
-        zip_safe=False)
diff --git a/repositories/CodeFormer/basicsr/train.py b/repositories/CodeFormer/basicsr/train.py
deleted file mode 100644
index a01c0dfccdb8b02283100ec5b792c33afaf22f5e..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/basicsr/train.py
+++ /dev/null
@@ -1,225 +0,0 @@
-import argparse
-import datetime
-import logging
-import math
-import copy
-import random
-import time
-import torch
-from os import path as osp
-
-from basicsr.data import build_dataloader, build_dataset
-from basicsr.data.data_sampler import EnlargedSampler
-from basicsr.data.prefetch_dataloader import CPUPrefetcher, CUDAPrefetcher
-from basicsr.models import build_model
-from basicsr.utils import (MessageLogger, check_resume, get_env_info, get_root_logger, init_tb_logger,
-                           init_wandb_logger, make_exp_dirs, mkdir_and_rename, set_random_seed)
-from basicsr.utils.dist_util import get_dist_info, init_dist
-from basicsr.utils.options import dict2str, parse
-
-import warnings
-# ignore UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`.
-warnings.filterwarnings("ignore", category=UserWarning)
-
-def parse_options(root_path, is_train=True):
-    parser = argparse.ArgumentParser()
-    parser.add_argument('-opt', type=str, required=True, help='Path to option YAML file.')
-    parser.add_argument('--launcher', choices=['none', 'pytorch', 'slurm'], default='none', help='job launcher')
-    parser.add_argument('--local_rank', type=int, default=0)
-    args = parser.parse_args()
-    opt = parse(args.opt, root_path, is_train=is_train)
-
-    # distributed settings
-    if args.launcher == 'none':
-        opt['dist'] = False
-        print('Disable distributed.', flush=True)
-    else:
-        opt['dist'] = True
-        if args.launcher == 'slurm' and 'dist_params' in opt:
-            init_dist(args.launcher, **opt['dist_params'])
-        else:
-            init_dist(args.launcher)
-
-    opt['rank'], opt['world_size'] = get_dist_info()
-
-    # random seed
-    seed = opt.get('manual_seed')
-    if seed is None:
-        seed = random.randint(1, 10000)
-        opt['manual_seed'] = seed
-    set_random_seed(seed + opt['rank'])
-
-    return opt
-
-
-def init_loggers(opt):
-    log_file = osp.join(opt['path']['log'], f"train_{opt['name']}.log")
-    logger = get_root_logger(logger_name='basicsr', log_level=logging.INFO, log_file=log_file)
-    logger.info(get_env_info())
-    logger.info(dict2str(opt))
-
-    # initialize wandb logger before tensorboard logger to allow proper sync:
-    if (opt['logger'].get('wandb') is not None) and (opt['logger']['wandb'].get('project') is not None):
-        assert opt['logger'].get('use_tb_logger') is True, ('should turn on tensorboard when using wandb')
-        init_wandb_logger(opt)
-    tb_logger = None
-    if opt['logger'].get('use_tb_logger'):
-        tb_logger = init_tb_logger(log_dir=osp.join('tb_logger', opt['name']))
-    return logger, tb_logger
-
-
-def create_train_val_dataloader(opt, logger):
-    # create train and val dataloaders
-    train_loader, val_loader = None, None
-    for phase, dataset_opt in opt['datasets'].items():
-        if phase == 'train':
-            dataset_enlarge_ratio = dataset_opt.get('dataset_enlarge_ratio', 1)
-            train_set = build_dataset(dataset_opt)
-            train_sampler = EnlargedSampler(train_set, opt['world_size'], opt['rank'], dataset_enlarge_ratio)
-            train_loader = build_dataloader(
-                train_set,
-                dataset_opt,
-                num_gpu=opt['num_gpu'],
-                dist=opt['dist'],
-                sampler=train_sampler,
-                seed=opt['manual_seed'])
-
-            num_iter_per_epoch = math.ceil(
-                len(train_set) * dataset_enlarge_ratio / (dataset_opt['batch_size_per_gpu'] * opt['world_size']))
-            total_iters = int(opt['train']['total_iter'])
-            total_epochs = math.ceil(total_iters / (num_iter_per_epoch))
-            logger.info('Training statistics:'
-                        f'\n\tNumber of train images: {len(train_set)}'
-                        f'\n\tDataset enlarge ratio: {dataset_enlarge_ratio}'
-                        f'\n\tBatch size per gpu: {dataset_opt["batch_size_per_gpu"]}'
-                        f'\n\tWorld size (gpu number): {opt["world_size"]}'
-                        f'\n\tRequire iter number per epoch: {num_iter_per_epoch}'
-                        f'\n\tTotal epochs: {total_epochs}; iters: {total_iters}.')
-
-        elif phase == 'val':
-            val_set = build_dataset(dataset_opt)
-            val_loader = build_dataloader(
-                val_set, dataset_opt, num_gpu=opt['num_gpu'], dist=opt['dist'], sampler=None, seed=opt['manual_seed'])
-            logger.info(f'Number of val images/folders in {dataset_opt["name"]}: ' f'{len(val_set)}')
-        else:
-            raise ValueError(f'Dataset phase {phase} is not recognized.')
-
-    return train_loader, train_sampler, val_loader, total_epochs, total_iters
-
-
-def train_pipeline(root_path):
-    # parse options, set distributed setting, set ramdom seed
-    opt = parse_options(root_path, is_train=True)
-
-    torch.backends.cudnn.benchmark = True
-    # torch.backends.cudnn.deterministic = True
-
-    # load resume states if necessary
-    if opt['path'].get('resume_state'):
-        device_id = torch.cuda.current_device()
-        resume_state = torch.load(
-            opt['path']['resume_state'], map_location=lambda storage, loc: storage.cuda(device_id))
-    else:
-        resume_state = None
-
-    # mkdir for experiments and logger
-    if resume_state is None:
-        make_exp_dirs(opt)
-        if opt['logger'].get('use_tb_logger') and opt['rank'] == 0:
-            mkdir_and_rename(osp.join('tb_logger', opt['name']))
-
-    # initialize loggers
-    logger, tb_logger = init_loggers(opt)
-    
-    # create train and validation dataloaders
-    result = create_train_val_dataloader(opt, logger)
-    train_loader, train_sampler, val_loader, total_epochs, total_iters = result
-
-    # create model
-    if resume_state:  # resume training
-        check_resume(opt, resume_state['iter'])
-        model = build_model(opt)
-        model.resume_training(resume_state)  # handle optimizers and schedulers
-        logger.info(f"Resuming training from epoch: {resume_state['epoch']}, " f"iter: {resume_state['iter']}.")
-        start_epoch = resume_state['epoch']
-        current_iter = resume_state['iter']
-    else:
-        model = build_model(opt)
-        start_epoch = 0
-        current_iter = 0
-
-    # create message logger (formatted outputs)
-    msg_logger = MessageLogger(opt, current_iter, tb_logger)
-
-    # dataloader prefetcher
-    prefetch_mode = opt['datasets']['train'].get('prefetch_mode')
-    if prefetch_mode is None or prefetch_mode == 'cpu':
-        prefetcher = CPUPrefetcher(train_loader)
-    elif prefetch_mode == 'cuda':
-        prefetcher = CUDAPrefetcher(train_loader, opt)
-        logger.info(f'Use {prefetch_mode} prefetch dataloader')
-        if opt['datasets']['train'].get('pin_memory') is not True:
-            raise ValueError('Please set pin_memory=True for CUDAPrefetcher.')
-    else:
-        raise ValueError(f'Wrong prefetch_mode {prefetch_mode}.' "Supported ones are: None, 'cuda', 'cpu'.")
-
-    # training
-    logger.info(f'Start training from epoch: {start_epoch}, iter: {current_iter+1}')
-    data_time, iter_time = time.time(), time.time()
-    start_time = time.time()
-
-    for epoch in range(start_epoch, total_epochs + 1):
-        train_sampler.set_epoch(epoch)
-        prefetcher.reset()
-        train_data = prefetcher.next()
-
-        while train_data is not None:
-            data_time = time.time() - data_time
-
-            current_iter += 1
-            if current_iter > total_iters:
-                break
-            # update learning rate
-            model.update_learning_rate(current_iter, warmup_iter=opt['train'].get('warmup_iter', -1))
-            # training
-            model.feed_data(train_data)
-            model.optimize_parameters(current_iter)
-            iter_time = time.time() - iter_time
-            # log
-            if current_iter % opt['logger']['print_freq'] == 0:
-                log_vars = {'epoch': epoch, 'iter': current_iter}
-                log_vars.update({'lrs': model.get_current_learning_rate()})
-                log_vars.update({'time': iter_time, 'data_time': data_time})
-                log_vars.update(model.get_current_log())
-                msg_logger(log_vars)
-
-            # save models and training states
-            if current_iter % opt['logger']['save_checkpoint_freq'] == 0:
-                logger.info('Saving models and training states.')
-                model.save(epoch, current_iter)
-
-            # validation
-            if opt.get('val') is not None and opt['datasets'].get('val') is not None \
-                and (current_iter % opt['val']['val_freq'] == 0):
-                model.validation(val_loader, current_iter, tb_logger, opt['val']['save_img'])
-
-            data_time = time.time()
-            iter_time = time.time()
-            train_data = prefetcher.next()
-        # end of iter
-
-    # end of epoch
-
-    consumed_time = str(datetime.timedelta(seconds=int(time.time() - start_time)))
-    logger.info(f'End of training. Time consumed: {consumed_time}')
-    logger.info('Save the latest model.')
-    model.save(epoch=-1, current_iter=-1)  # -1 stands for the latest
-    if opt.get('val') is not None and opt['datasets'].get('val'):
-        model.validation(val_loader, current_iter, tb_logger, opt['val']['save_img'])
-    if tb_logger:
-        tb_logger.close()
-
-
-if __name__ == '__main__':
-    root_path = osp.abspath(osp.join(__file__, osp.pardir, osp.pardir))
-    train_pipeline(root_path)
diff --git a/repositories/CodeFormer/basicsr/utils/__init__.py b/repositories/CodeFormer/basicsr/utils/__init__.py
deleted file mode 100644
index 5fcc1d540462712387523d1e326d1dfc2bcfbf32..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/basicsr/utils/__init__.py
+++ /dev/null
@@ -1,29 +0,0 @@
-from .file_client import FileClient
-from .img_util import crop_border, imfrombytes, img2tensor, imwrite, tensor2img
-from .logger import MessageLogger, get_env_info, get_root_logger, init_tb_logger, init_wandb_logger
-from .misc import check_resume, get_time_str, make_exp_dirs, mkdir_and_rename, scandir, set_random_seed, sizeof_fmt
-
-__all__ = [
-    # file_client.py
-    'FileClient',
-    # img_util.py
-    'img2tensor',
-    'tensor2img',
-    'imfrombytes',
-    'imwrite',
-    'crop_border',
-    # logger.py
-    'MessageLogger',
-    'init_tb_logger',
-    'init_wandb_logger',
-    'get_root_logger',
-    'get_env_info',
-    # misc.py
-    'set_random_seed',
-    'get_time_str',
-    'mkdir_and_rename',
-    'make_exp_dirs',
-    'scandir',
-    'check_resume',
-    'sizeof_fmt'
-]
diff --git a/repositories/CodeFormer/basicsr/utils/dist_util.py b/repositories/CodeFormer/basicsr/utils/dist_util.py
deleted file mode 100644
index 0fab887b2cb1ce8533d2e8fdee72ae0c24f68fd0..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/basicsr/utils/dist_util.py
+++ /dev/null
@@ -1,82 +0,0 @@
-# Modified from https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/dist_utils.py  # noqa: E501
-import functools
-import os
-import subprocess
-import torch
-import torch.distributed as dist
-import torch.multiprocessing as mp
-
-
-def init_dist(launcher, backend='nccl', **kwargs):
-    if mp.get_start_method(allow_none=True) is None:
-        mp.set_start_method('spawn')
-    if launcher == 'pytorch':
-        _init_dist_pytorch(backend, **kwargs)
-    elif launcher == 'slurm':
-        _init_dist_slurm(backend, **kwargs)
-    else:
-        raise ValueError(f'Invalid launcher type: {launcher}')
-
-
-def _init_dist_pytorch(backend, **kwargs):
-    rank = int(os.environ['RANK'])
-    num_gpus = torch.cuda.device_count()
-    torch.cuda.set_device(rank % num_gpus)
-    dist.init_process_group(backend=backend, **kwargs)
-
-
-def _init_dist_slurm(backend, port=None):
-    """Initialize slurm distributed training environment.
-
-    If argument ``port`` is not specified, then the master port will be system
-    environment variable ``MASTER_PORT``. If ``MASTER_PORT`` is not in system
-    environment variable, then a default port ``29500`` will be used.
-
-    Args:
-        backend (str): Backend of torch.distributed.
-        port (int, optional): Master port. Defaults to None.
-    """
-    proc_id = int(os.environ['SLURM_PROCID'])
-    ntasks = int(os.environ['SLURM_NTASKS'])
-    node_list = os.environ['SLURM_NODELIST']
-    num_gpus = torch.cuda.device_count()
-    torch.cuda.set_device(proc_id % num_gpus)
-    addr = subprocess.getoutput(f'scontrol show hostname {node_list} | head -n1')
-    # specify master port
-    if port is not None:
-        os.environ['MASTER_PORT'] = str(port)
-    elif 'MASTER_PORT' in os.environ:
-        pass  # use MASTER_PORT in the environment variable
-    else:
-        # 29500 is torch.distributed default port
-        os.environ['MASTER_PORT'] = '29500'
-    os.environ['MASTER_ADDR'] = addr
-    os.environ['WORLD_SIZE'] = str(ntasks)
-    os.environ['LOCAL_RANK'] = str(proc_id % num_gpus)
-    os.environ['RANK'] = str(proc_id)
-    dist.init_process_group(backend=backend)
-
-
-def get_dist_info():
-    if dist.is_available():
-        initialized = dist.is_initialized()
-    else:
-        initialized = False
-    if initialized:
-        rank = dist.get_rank()
-        world_size = dist.get_world_size()
-    else:
-        rank = 0
-        world_size = 1
-    return rank, world_size
-
-
-def master_only(func):
-
-    @functools.wraps(func)
-    def wrapper(*args, **kwargs):
-        rank, _ = get_dist_info()
-        if rank == 0:
-            return func(*args, **kwargs)
-
-    return wrapper
diff --git a/repositories/CodeFormer/basicsr/utils/download_util.py b/repositories/CodeFormer/basicsr/utils/download_util.py
deleted file mode 100644
index 2a267915743ee3f3232bc8fe992466b52468979a..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/basicsr/utils/download_util.py
+++ /dev/null
@@ -1,95 +0,0 @@
-import math
-import os
-import requests
-from torch.hub import download_url_to_file, get_dir
-from tqdm import tqdm
-from urllib.parse import urlparse
-
-from .misc import sizeof_fmt
-
-
-def download_file_from_google_drive(file_id, save_path):
-    """Download files from google drive.
-    Ref:
-    https://stackoverflow.com/questions/25010369/wget-curl-large-file-from-google-drive  # noqa E501
-    Args:
-        file_id (str): File id.
-        save_path (str): Save path.
-    """
-
-    session = requests.Session()
-    URL = 'https://docs.google.com/uc?export=download'
-    params = {'id': file_id}
-
-    response = session.get(URL, params=params, stream=True)
-    token = get_confirm_token(response)
-    if token:
-        params['confirm'] = token
-        response = session.get(URL, params=params, stream=True)
-
-    # get file size
-    response_file_size = session.get(URL, params=params, stream=True, headers={'Range': 'bytes=0-2'})
-    print(response_file_size)
-    if 'Content-Range' in response_file_size.headers:
-        file_size = int(response_file_size.headers['Content-Range'].split('/')[1])
-    else:
-        file_size = None
-
-    save_response_content(response, save_path, file_size)
-
-
-def get_confirm_token(response):
-    for key, value in response.cookies.items():
-        if key.startswith('download_warning'):
-            return value
-    return None
-
-
-def save_response_content(response, destination, file_size=None, chunk_size=32768):
-    if file_size is not None:
-        pbar = tqdm(total=math.ceil(file_size / chunk_size), unit='chunk')
-
-        readable_file_size = sizeof_fmt(file_size)
-    else:
-        pbar = None
-
-    with open(destination, 'wb') as f:
-        downloaded_size = 0
-        for chunk in response.iter_content(chunk_size):
-            downloaded_size += chunk_size
-            if pbar is not None:
-                pbar.update(1)
-                pbar.set_description(f'Download {sizeof_fmt(downloaded_size)} / {readable_file_size}')
-            if chunk:  # filter out keep-alive new chunks
-                f.write(chunk)
-        if pbar is not None:
-            pbar.close()
-
-
-def load_file_from_url(url, model_dir=None, progress=True, file_name=None):
-    """Load file form http url, will download models if necessary.
-    Ref:https://github.com/1adrianb/face-alignment/blob/master/face_alignment/utils.py
-    Args:
-        url (str): URL to be downloaded.
-        model_dir (str): The path to save the downloaded model. Should be a full path. If None, use pytorch hub_dir.
-            Default: None.
-        progress (bool): Whether to show the download progress. Default: True.
-        file_name (str): The downloaded file name. If None, use the file name in the url. Default: None.
-    Returns:
-        str: The path to the downloaded file.
-    """
-    if model_dir is None:  # use the pytorch hub_dir
-        hub_dir = get_dir()
-        model_dir = os.path.join(hub_dir, 'checkpoints')
-
-    os.makedirs(model_dir, exist_ok=True)
-
-    parts = urlparse(url)
-    filename = os.path.basename(parts.path)
-    if file_name is not None:
-        filename = file_name
-    cached_file = os.path.abspath(os.path.join(model_dir, filename))
-    if not os.path.exists(cached_file):
-        print(f'Downloading: "{url}" to {cached_file}\n')
-        download_url_to_file(url, cached_file, hash_prefix=None, progress=progress)
-    return cached_file
\ No newline at end of file
diff --git a/repositories/CodeFormer/basicsr/utils/file_client.py b/repositories/CodeFormer/basicsr/utils/file_client.py
deleted file mode 100644
index 7f38d9796da3899048924f2f803d1088927966b0..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/basicsr/utils/file_client.py
+++ /dev/null
@@ -1,167 +0,0 @@
-# Modified from https://github.com/open-mmlab/mmcv/blob/master/mmcv/fileio/file_client.py  # noqa: E501
-from abc import ABCMeta, abstractmethod
-
-
-class BaseStorageBackend(metaclass=ABCMeta):
-    """Abstract class of storage backends.
-
-    All backends need to implement two apis: ``get()`` and ``get_text()``.
-    ``get()`` reads the file as a byte stream and ``get_text()`` reads the file
-    as texts.
-    """
-
-    @abstractmethod
-    def get(self, filepath):
-        pass
-
-    @abstractmethod
-    def get_text(self, filepath):
-        pass
-
-
-class MemcachedBackend(BaseStorageBackend):
-    """Memcached storage backend.
-
-    Attributes:
-        server_list_cfg (str): Config file for memcached server list.
-        client_cfg (str): Config file for memcached client.
-        sys_path (str | None): Additional path to be appended to `sys.path`.
-            Default: None.
-    """
-
-    def __init__(self, server_list_cfg, client_cfg, sys_path=None):
-        if sys_path is not None:
-            import sys
-            sys.path.append(sys_path)
-        try:
-            import mc
-        except ImportError:
-            raise ImportError('Please install memcached to enable MemcachedBackend.')
-
-        self.server_list_cfg = server_list_cfg
-        self.client_cfg = client_cfg
-        self._client = mc.MemcachedClient.GetInstance(self.server_list_cfg, self.client_cfg)
-        # mc.pyvector servers as a point which points to a memory cache
-        self._mc_buffer = mc.pyvector()
-
-    def get(self, filepath):
-        filepath = str(filepath)
-        import mc
-        self._client.Get(filepath, self._mc_buffer)
-        value_buf = mc.ConvertBuffer(self._mc_buffer)
-        return value_buf
-
-    def get_text(self, filepath):
-        raise NotImplementedError
-
-
-class HardDiskBackend(BaseStorageBackend):
-    """Raw hard disks storage backend."""
-
-    def get(self, filepath):
-        filepath = str(filepath)
-        with open(filepath, 'rb') as f:
-            value_buf = f.read()
-        return value_buf
-
-    def get_text(self, filepath):
-        filepath = str(filepath)
-        with open(filepath, 'r') as f:
-            value_buf = f.read()
-        return value_buf
-
-
-class LmdbBackend(BaseStorageBackend):
-    """Lmdb storage backend.
-
-    Args:
-        db_paths (str | list[str]): Lmdb database paths.
-        client_keys (str | list[str]): Lmdb client keys. Default: 'default'.
-        readonly (bool, optional): Lmdb environment parameter. If True,
-            disallow any write operations. Default: True.
-        lock (bool, optional): Lmdb environment parameter. If False, when
-            concurrent access occurs, do not lock the database. Default: False.
-        readahead (bool, optional): Lmdb environment parameter. If False,
-            disable the OS filesystem readahead mechanism, which may improve
-            random read performance when a database is larger than RAM.
-            Default: False.
-
-    Attributes:
-        db_paths (list): Lmdb database path.
-        _client (list): A list of several lmdb envs.
-    """
-
-    def __init__(self, db_paths, client_keys='default', readonly=True, lock=False, readahead=False, **kwargs):
-        try:
-            import lmdb
-        except ImportError:
-            raise ImportError('Please install lmdb to enable LmdbBackend.')
-
-        if isinstance(client_keys, str):
-            client_keys = [client_keys]
-
-        if isinstance(db_paths, list):
-            self.db_paths = [str(v) for v in db_paths]
-        elif isinstance(db_paths, str):
-            self.db_paths = [str(db_paths)]
-        assert len(client_keys) == len(self.db_paths), ('client_keys and db_paths should have the same length, '
-                                                        f'but received {len(client_keys)} and {len(self.db_paths)}.')
-
-        self._client = {}
-        for client, path in zip(client_keys, self.db_paths):
-            self._client[client] = lmdb.open(path, readonly=readonly, lock=lock, readahead=readahead, **kwargs)
-
-    def get(self, filepath, client_key):
-        """Get values according to the filepath from one lmdb named client_key.
-
-        Args:
-            filepath (str | obj:`Path`): Here, filepath is the lmdb key.
-            client_key (str): Used for distinguishing differnet lmdb envs.
-        """
-        filepath = str(filepath)
-        assert client_key in self._client, (f'client_key {client_key} is not ' 'in lmdb clients.')
-        client = self._client[client_key]
-        with client.begin(write=False) as txn:
-            value_buf = txn.get(filepath.encode('ascii'))
-        return value_buf
-
-    def get_text(self, filepath):
-        raise NotImplementedError
-
-
-class FileClient(object):
-    """A general file client to access files in different backend.
-
-    The client loads a file or text in a specified backend from its path
-    and return it as a binary file. it can also register other backend
-    accessor with a given name and backend class.
-
-    Attributes:
-        backend (str): The storage backend type. Options are "disk",
-            "memcached" and "lmdb".
-        client (:obj:`BaseStorageBackend`): The backend object.
-    """
-
-    _backends = {
-        'disk': HardDiskBackend,
-        'memcached': MemcachedBackend,
-        'lmdb': LmdbBackend,
-    }
-
-    def __init__(self, backend='disk', **kwargs):
-        if backend not in self._backends:
-            raise ValueError(f'Backend {backend} is not supported. Currently supported ones'
-                             f' are {list(self._backends.keys())}')
-        self.backend = backend
-        self.client = self._backends[backend](**kwargs)
-
-    def get(self, filepath, client_key='default'):
-        # client_key is used only for lmdb, where different fileclients have
-        # different lmdb environments.
-        if self.backend == 'lmdb':
-            return self.client.get(filepath, client_key)
-        else:
-            return self.client.get(filepath)
-
-    def get_text(self, filepath):
-        return self.client.get_text(filepath)
diff --git a/repositories/CodeFormer/basicsr/utils/img_util.py b/repositories/CodeFormer/basicsr/utils/img_util.py
deleted file mode 100644
index d409a132ff216e6943a276fb5d8cd5f410824883..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/basicsr/utils/img_util.py
+++ /dev/null
@@ -1,170 +0,0 @@
-import cv2
-import math
-import numpy as np
-import os
-import torch
-from torchvision.utils import make_grid
-
-
-def img2tensor(imgs, bgr2rgb=True, float32=True):
-    """Numpy array to tensor.
-
-    Args:
-        imgs (list[ndarray] | ndarray): Input images.
-        bgr2rgb (bool): Whether to change bgr to rgb.
-        float32 (bool): Whether to change to float32.
-
-    Returns:
-        list[tensor] | tensor: Tensor images. If returned results only have
-            one element, just return tensor.
-    """
-
-    def _totensor(img, bgr2rgb, float32):
-        if img.shape[2] == 3 and bgr2rgb:
-            if img.dtype == 'float64':
-                img = img.astype('float32')
-            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-        img = torch.from_numpy(img.transpose(2, 0, 1))
-        if float32:
-            img = img.float()
-        return img
-
-    if isinstance(imgs, list):
-        return [_totensor(img, bgr2rgb, float32) for img in imgs]
-    else:
-        return _totensor(imgs, bgr2rgb, float32)
-
-
-def tensor2img(tensor, rgb2bgr=True, out_type=np.uint8, min_max=(0, 1)):
-    """Convert torch Tensors into image numpy arrays.
-
-    After clamping to [min, max], values will be normalized to [0, 1].
-
-    Args:
-        tensor (Tensor or list[Tensor]): Accept shapes:
-            1) 4D mini-batch Tensor of shape (B x 3/1 x H x W);
-            2) 3D Tensor of shape (3/1 x H x W);
-            3) 2D Tensor of shape (H x W).
-            Tensor channel should be in RGB order.
-        rgb2bgr (bool): Whether to change rgb to bgr.
-        out_type (numpy type): output types. If ``np.uint8``, transform outputs
-            to uint8 type with range [0, 255]; otherwise, float type with
-            range [0, 1]. Default: ``np.uint8``.
-        min_max (tuple[int]): min and max values for clamp.
-
-    Returns:
-        (Tensor or list): 3D ndarray of shape (H x W x C) OR 2D ndarray of
-        shape (H x W). The channel order is BGR.
-    """
-    if not (torch.is_tensor(tensor) or (isinstance(tensor, list) and all(torch.is_tensor(t) for t in tensor))):
-        raise TypeError(f'tensor or list of tensors expected, got {type(tensor)}')
-
-    if torch.is_tensor(tensor):
-        tensor = [tensor]
-    result = []
-    for _tensor in tensor:
-        _tensor = _tensor.squeeze(0).float().detach().cpu().clamp_(*min_max)
-        _tensor = (_tensor - min_max[0]) / (min_max[1] - min_max[0])
-
-        n_dim = _tensor.dim()
-        if n_dim == 4:
-            img_np = make_grid(_tensor, nrow=int(math.sqrt(_tensor.size(0))), normalize=False).numpy()
-            img_np = img_np.transpose(1, 2, 0)
-            if rgb2bgr:
-                img_np = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR)
-        elif n_dim == 3:
-            img_np = _tensor.numpy()
-            img_np = img_np.transpose(1, 2, 0)
-            if img_np.shape[2] == 1:  # gray image
-                img_np = np.squeeze(img_np, axis=2)
-            else:
-                if rgb2bgr:
-                    img_np = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR)
-        elif n_dim == 2:
-            img_np = _tensor.numpy()
-        else:
-            raise TypeError('Only support 4D, 3D or 2D tensor. ' f'But received with dimension: {n_dim}')
-        if out_type == np.uint8:
-            # Unlike MATLAB, numpy.unit8() WILL NOT round by default.
-            img_np = (img_np * 255.0).round()
-        img_np = img_np.astype(out_type)
-        result.append(img_np)
-    if len(result) == 1:
-        result = result[0]
-    return result
-
-
-def tensor2img_fast(tensor, rgb2bgr=True, min_max=(0, 1)):
-    """This implementation is slightly faster than tensor2img.
-    It now only supports torch tensor with shape (1, c, h, w).
-
-    Args:
-        tensor (Tensor): Now only support torch tensor with (1, c, h, w).
-        rgb2bgr (bool): Whether to change rgb to bgr. Default: True.
-        min_max (tuple[int]): min and max values for clamp.
-    """
-    output = tensor.squeeze(0).detach().clamp_(*min_max).permute(1, 2, 0)
-    output = (output - min_max[0]) / (min_max[1] - min_max[0]) * 255
-    output = output.type(torch.uint8).cpu().numpy()
-    if rgb2bgr:
-        output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR)
-    return output
-
-
-def imfrombytes(content, flag='color', float32=False):
-    """Read an image from bytes.
-
-    Args:
-        content (bytes): Image bytes got from files or other streams.
-        flag (str): Flags specifying the color type of a loaded image,
-            candidates are `color`, `grayscale` and `unchanged`.
-        float32 (bool): Whether to change to float32., If True, will also norm
-            to [0, 1]. Default: False.
-
-    Returns:
-        ndarray: Loaded image array.
-    """
-    img_np = np.frombuffer(content, np.uint8)
-    imread_flags = {'color': cv2.IMREAD_COLOR, 'grayscale': cv2.IMREAD_GRAYSCALE, 'unchanged': cv2.IMREAD_UNCHANGED}
-    img = cv2.imdecode(img_np, imread_flags[flag])
-    if float32:
-        img = img.astype(np.float32) / 255.
-    return img
-
-
-def imwrite(img, file_path, params=None, auto_mkdir=True):
-    """Write image to file.
-
-    Args:
-        img (ndarray): Image array to be written.
-        file_path (str): Image file path.
-        params (None or list): Same as opencv's :func:`imwrite` interface.
-        auto_mkdir (bool): If the parent folder of `file_path` does not exist,
-            whether to create it automatically.
-
-    Returns:
-        bool: Successful or not.
-    """
-    if auto_mkdir:
-        dir_name = os.path.abspath(os.path.dirname(file_path))
-        os.makedirs(dir_name, exist_ok=True)
-    return cv2.imwrite(file_path, img, params)
-
-
-def crop_border(imgs, crop_border):
-    """Crop borders of images.
-
-    Args:
-        imgs (list[ndarray] | ndarray): Images with shape (h, w, c).
-        crop_border (int): Crop border for each end of height and weight.
-
-    Returns:
-        list[ndarray]: Cropped images.
-    """
-    if crop_border == 0:
-        return imgs
-    else:
-        if isinstance(imgs, list):
-            return [v[crop_border:-crop_border, crop_border:-crop_border, ...] for v in imgs]
-        else:
-            return imgs[crop_border:-crop_border, crop_border:-crop_border, ...]
diff --git a/repositories/CodeFormer/basicsr/utils/lmdb_util.py b/repositories/CodeFormer/basicsr/utils/lmdb_util.py
deleted file mode 100644
index e0a10f60ffca2e36ac5f5564aafd70e79d06a723..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/basicsr/utils/lmdb_util.py
+++ /dev/null
@@ -1,196 +0,0 @@
-import cv2
-import lmdb
-import sys
-from multiprocessing import Pool
-from os import path as osp
-from tqdm import tqdm
-
-
-def make_lmdb_from_imgs(data_path,
-                        lmdb_path,
-                        img_path_list,
-                        keys,
-                        batch=5000,
-                        compress_level=1,
-                        multiprocessing_read=False,
-                        n_thread=40,
-                        map_size=None):
-    """Make lmdb from images.
-
-    Contents of lmdb. The file structure is:
-    example.lmdb
-    ├── data.mdb
-    ├── lock.mdb
-    ├── meta_info.txt
-
-    The data.mdb and lock.mdb are standard lmdb files and you can refer to
-    https://lmdb.readthedocs.io/en/release/ for more details.
-
-    The meta_info.txt is a specified txt file to record the meta information
-    of our datasets. It will be automatically created when preparing
-    datasets by our provided dataset tools.
-    Each line in the txt file records 1)image name (with extension),
-    2)image shape, and 3)compression level, separated by a white space.
-
-    For example, the meta information could be:
-    `000_00000000.png (720,1280,3) 1`, which means:
-    1) image name (with extension): 000_00000000.png;
-    2) image shape: (720,1280,3);
-    3) compression level: 1
-
-    We use the image name without extension as the lmdb key.
-
-    If `multiprocessing_read` is True, it will read all the images to memory
-    using multiprocessing. Thus, your server needs to have enough memory.
-
-    Args:
-        data_path (str): Data path for reading images.
-        lmdb_path (str): Lmdb save path.
-        img_path_list (str): Image path list.
-        keys (str): Used for lmdb keys.
-        batch (int): After processing batch images, lmdb commits.
-            Default: 5000.
-        compress_level (int): Compress level when encoding images. Default: 1.
-        multiprocessing_read (bool): Whether use multiprocessing to read all
-            the images to memory. Default: False.
-        n_thread (int): For multiprocessing.
-        map_size (int | None): Map size for lmdb env. If None, use the
-            estimated size from images. Default: None
-    """
-
-    assert len(img_path_list) == len(keys), ('img_path_list and keys should have the same length, '
-                                             f'but got {len(img_path_list)} and {len(keys)}')
-    print(f'Create lmdb for {data_path}, save to {lmdb_path}...')
-    print(f'Totoal images: {len(img_path_list)}')
-    if not lmdb_path.endswith('.lmdb'):
-        raise ValueError("lmdb_path must end with '.lmdb'.")
-    if osp.exists(lmdb_path):
-        print(f'Folder {lmdb_path} already exists. Exit.')
-        sys.exit(1)
-
-    if multiprocessing_read:
-        # read all the images to memory (multiprocessing)
-        dataset = {}  # use dict to keep the order for multiprocessing
-        shapes = {}
-        print(f'Read images with multiprocessing, #thread: {n_thread} ...')
-        pbar = tqdm(total=len(img_path_list), unit='image')
-
-        def callback(arg):
-            """get the image data and update pbar."""
-            key, dataset[key], shapes[key] = arg
-            pbar.update(1)
-            pbar.set_description(f'Read {key}')
-
-        pool = Pool(n_thread)
-        for path, key in zip(img_path_list, keys):
-            pool.apply_async(read_img_worker, args=(osp.join(data_path, path), key, compress_level), callback=callback)
-        pool.close()
-        pool.join()
-        pbar.close()
-        print(f'Finish reading {len(img_path_list)} images.')
-
-    # create lmdb environment
-    if map_size is None:
-        # obtain data size for one image
-        img = cv2.imread(osp.join(data_path, img_path_list[0]), cv2.IMREAD_UNCHANGED)
-        _, img_byte = cv2.imencode('.png', img, [cv2.IMWRITE_PNG_COMPRESSION, compress_level])
-        data_size_per_img = img_byte.nbytes
-        print('Data size per image is: ', data_size_per_img)
-        data_size = data_size_per_img * len(img_path_list)
-        map_size = data_size * 10
-
-    env = lmdb.open(lmdb_path, map_size=map_size)
-
-    # write data to lmdb
-    pbar = tqdm(total=len(img_path_list), unit='chunk')
-    txn = env.begin(write=True)
-    txt_file = open(osp.join(lmdb_path, 'meta_info.txt'), 'w')
-    for idx, (path, key) in enumerate(zip(img_path_list, keys)):
-        pbar.update(1)
-        pbar.set_description(f'Write {key}')
-        key_byte = key.encode('ascii')
-        if multiprocessing_read:
-            img_byte = dataset[key]
-            h, w, c = shapes[key]
-        else:
-            _, img_byte, img_shape = read_img_worker(osp.join(data_path, path), key, compress_level)
-            h, w, c = img_shape
-
-        txn.put(key_byte, img_byte)
-        # write meta information
-        txt_file.write(f'{key}.png ({h},{w},{c}) {compress_level}\n')
-        if idx % batch == 0:
-            txn.commit()
-            txn = env.begin(write=True)
-    pbar.close()
-    txn.commit()
-    env.close()
-    txt_file.close()
-    print('\nFinish writing lmdb.')
-
-
-def read_img_worker(path, key, compress_level):
-    """Read image worker.
-
-    Args:
-        path (str): Image path.
-        key (str): Image key.
-        compress_level (int): Compress level when encoding images.
-
-    Returns:
-        str: Image key.
-        byte: Image byte.
-        tuple[int]: Image shape.
-    """
-
-    img = cv2.imread(path, cv2.IMREAD_UNCHANGED)
-    if img.ndim == 2:
-        h, w = img.shape
-        c = 1
-    else:
-        h, w, c = img.shape
-    _, img_byte = cv2.imencode('.png', img, [cv2.IMWRITE_PNG_COMPRESSION, compress_level])
-    return (key, img_byte, (h, w, c))
-
-
-class LmdbMaker():
-    """LMDB Maker.
-
-    Args:
-        lmdb_path (str): Lmdb save path.
-        map_size (int): Map size for lmdb env. Default: 1024 ** 4, 1TB.
-        batch (int): After processing batch images, lmdb commits.
-            Default: 5000.
-        compress_level (int): Compress level when encoding images. Default: 1.
-    """
-
-    def __init__(self, lmdb_path, map_size=1024**4, batch=5000, compress_level=1):
-        if not lmdb_path.endswith('.lmdb'):
-            raise ValueError("lmdb_path must end with '.lmdb'.")
-        if osp.exists(lmdb_path):
-            print(f'Folder {lmdb_path} already exists. Exit.')
-            sys.exit(1)
-
-        self.lmdb_path = lmdb_path
-        self.batch = batch
-        self.compress_level = compress_level
-        self.env = lmdb.open(lmdb_path, map_size=map_size)
-        self.txn = self.env.begin(write=True)
-        self.txt_file = open(osp.join(lmdb_path, 'meta_info.txt'), 'w')
-        self.counter = 0
-
-    def put(self, img_byte, key, img_shape):
-        self.counter += 1
-        key_byte = key.encode('ascii')
-        self.txn.put(key_byte, img_byte)
-        # write meta information
-        h, w, c = img_shape
-        self.txt_file.write(f'{key}.png ({h},{w},{c}) {self.compress_level}\n')
-        if self.counter % self.batch == 0:
-            self.txn.commit()
-            self.txn = self.env.begin(write=True)
-
-    def close(self):
-        self.txn.commit()
-        self.env.close()
-        self.txt_file.close()
diff --git a/repositories/CodeFormer/basicsr/utils/logger.py b/repositories/CodeFormer/basicsr/utils/logger.py
deleted file mode 100644
index 9714bf59c30fc82de24c1ee58d9118d0864b3572..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/basicsr/utils/logger.py
+++ /dev/null
@@ -1,169 +0,0 @@
-import datetime
-import logging
-import time
-
-from .dist_util import get_dist_info, master_only
-
-initialized_logger = {}
-
-
-class MessageLogger():
-    """Message logger for printing.
-    Args:
-        opt (dict): Config. It contains the following keys:
-            name (str): Exp name.
-            logger (dict): Contains 'print_freq' (str) for logger interval.
-            train (dict): Contains 'total_iter' (int) for total iters.
-            use_tb_logger (bool): Use tensorboard logger.
-        start_iter (int): Start iter. Default: 1.
-        tb_logger (obj:`tb_logger`): Tensorboard logger. Default： None.
-    """
-
-    def __init__(self, opt, start_iter=1, tb_logger=None):
-        self.exp_name = opt['name']
-        self.interval = opt['logger']['print_freq']
-        self.start_iter = start_iter
-        self.max_iters = opt['train']['total_iter']
-        self.use_tb_logger = opt['logger']['use_tb_logger']
-        self.tb_logger = tb_logger
-        self.start_time = time.time()
-        self.logger = get_root_logger()
-
-    @master_only
-    def __call__(self, log_vars):
-        """Format logging message.
-        Args:
-            log_vars (dict): It contains the following keys:
-                epoch (int): Epoch number.
-                iter (int): Current iter.
-                lrs (list): List for learning rates.
-                time (float): Iter time.
-                data_time (float): Data time for each iter.
-        """
-        # epoch, iter, learning rates
-        epoch = log_vars.pop('epoch')
-        current_iter = log_vars.pop('iter')
-        lrs = log_vars.pop('lrs')
-
-        message = (f'[{self.exp_name[:5]}..][epoch:{epoch:3d}, ' f'iter:{current_iter:8,d}, lr:(')
-        for v in lrs:
-            message += f'{v:.3e},'
-        message += ')] '
-
-        # time and estimated time
-        if 'time' in log_vars.keys():
-            iter_time = log_vars.pop('time')
-            data_time = log_vars.pop('data_time')
-
-            total_time = time.time() - self.start_time
-            time_sec_avg = total_time / (current_iter - self.start_iter + 1)
-            eta_sec = time_sec_avg * (self.max_iters - current_iter - 1)
-            eta_str = str(datetime.timedelta(seconds=int(eta_sec)))
-            message += f'[eta: {eta_str}, '
-            message += f'time (data): {iter_time:.3f} ({data_time:.3f})] '
-
-        # other items, especially losses
-        for k, v in log_vars.items():
-            message += f'{k}: {v:.4e} '
-            # tensorboard logger
-            if self.use_tb_logger:
-                if k.startswith('l_'):
-                    self.tb_logger.add_scalar(f'losses/{k}', v, current_iter)
-                else:
-                    self.tb_logger.add_scalar(k, v, current_iter)
-        self.logger.info(message)
-
-
-@master_only
-def init_tb_logger(log_dir):
-    from torch.utils.tensorboard import SummaryWriter
-    tb_logger = SummaryWriter(log_dir=log_dir)
-    return tb_logger
-
-
-@master_only
-def init_wandb_logger(opt):
-    """We now only use wandb to sync tensorboard log."""
-    import wandb
-    logger = logging.getLogger('basicsr')
-
-    project = opt['logger']['wandb']['project']
-    resume_id = opt['logger']['wandb'].get('resume_id')
-    if resume_id:
-        wandb_id = resume_id
-        resume = 'allow'
-        logger.warning(f'Resume wandb logger with id={wandb_id}.')
-    else:
-        wandb_id = wandb.util.generate_id()
-        resume = 'never'
-
-    wandb.init(id=wandb_id, resume=resume, name=opt['name'], config=opt, project=project, sync_tensorboard=True)
-
-    logger.info(f'Use wandb logger with id={wandb_id}; project={project}.')
-
-
-def get_root_logger(logger_name='basicsr', log_level=logging.INFO, log_file=None):
-    """Get the root logger.
-    The logger will be initialized if it has not been initialized. By default a
-    StreamHandler will be added. If `log_file` is specified, a FileHandler will
-    also be added.
-    Args:
-        logger_name (str): root logger name. Default: 'basicsr'.
-        log_file (str | None): The log filename. If specified, a FileHandler
-            will be added to the root logger.
-        log_level (int): The root logger level. Note that only the process of
-            rank 0 is affected, while other processes will set the level to
-            "Error" and be silent most of the time.
-    Returns:
-        logging.Logger: The root logger.
-    """
-    logger = logging.getLogger(logger_name)
-    # if the logger has been initialized, just return it
-    if logger_name in initialized_logger:
-        return logger
-
-    format_str = '%(asctime)s %(levelname)s: %(message)s'
-    stream_handler = logging.StreamHandler()
-    stream_handler.setFormatter(logging.Formatter(format_str))
-    logger.addHandler(stream_handler)
-    logger.propagate = False
-    rank, _ = get_dist_info()
-    if rank != 0:
-        logger.setLevel('ERROR')
-    elif log_file is not None:
-        logger.setLevel(log_level)
-        # add file handler
-        # file_handler = logging.FileHandler(log_file, 'w')
-        file_handler = logging.FileHandler(log_file, 'a') #Shangchen: keep the previous log
-        file_handler.setFormatter(logging.Formatter(format_str))
-        file_handler.setLevel(log_level)
-        logger.addHandler(file_handler)
-    initialized_logger[logger_name] = True
-    return logger
-
-
-def get_env_info():
-    """Get environment information.
-    Currently, only log the software version.
-    """
-    import torch
-    import torchvision
-
-    from basicsr.version import __version__
-    msg = r"""
-                ____                _       _____  ____
-               / __ ) ____ _ _____ (_)_____/ ___/ / __ \
-              / __  |/ __ `// ___// // ___/\__ \ / /_/ /
-             / /_/ // /_/ /(__  )/ // /__ ___/ // _, _/
-            /_____/ \__,_//____//_/ \___//____//_/ |_|
-     ______                   __   __                 __      __
-    / ____/____   ____   ____/ /  / /   __  __ _____ / /__   / /
-   / / __ / __ \ / __ \ / __  /  / /   / / / // ___// //_/  / /
-  / /_/ // /_/ // /_/ // /_/ /  / /___/ /_/ // /__ / /<    /_/
-  \____/ \____/ \____/ \____/  /_____/\____/ \___//_/|_|  (_)
-    """
-    msg += ('\nVersion Information: '
-            f'\n\tBasicSR: {__version__}'
-            f'\n\tPyTorch: {torch.__version__}'
-            f'\n\tTorchVision: {torchvision.__version__}')
-    return msg
\ No newline at end of file
diff --git a/repositories/CodeFormer/basicsr/utils/matlab_functions.py b/repositories/CodeFormer/basicsr/utils/matlab_functions.py
deleted file mode 100644
index c6ce1004a2c9f8521505c4b5889d3c24a909c70d..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/basicsr/utils/matlab_functions.py
+++ /dev/null
@@ -1,347 +0,0 @@
-import math
-import numpy as np
-import torch
-
-
-def cubic(x):
-    """cubic function used for calculate_weights_indices."""
-    absx = torch.abs(x)
-    absx2 = absx**2
-    absx3 = absx**3
-    return (1.5 * absx3 - 2.5 * absx2 + 1) * (
-        (absx <= 1).type_as(absx)) + (-0.5 * absx3 + 2.5 * absx2 - 4 * absx + 2) * (((absx > 1) *
-                                                                                     (absx <= 2)).type_as(absx))
-
-
-def calculate_weights_indices(in_length, out_length, scale, kernel, kernel_width, antialiasing):
-    """Calculate weights and indices, used for imresize function.
-
-    Args:
-        in_length (int): Input length.
-        out_length (int): Output length.
-        scale (float): Scale factor.
-        kernel_width (int): Kernel width.
-        antialisaing (bool): Whether to apply anti-aliasing when downsampling.
-    """
-
-    if (scale < 1) and antialiasing:
-        # Use a modified kernel (larger kernel width) to simultaneously
-        # interpolate and antialias
-        kernel_width = kernel_width / scale
-
-    # Output-space coordinates
-    x = torch.linspace(1, out_length, out_length)
-
-    # Input-space coordinates. Calculate the inverse mapping such that 0.5
-    # in output space maps to 0.5 in input space, and 0.5 + scale in output
-    # space maps to 1.5 in input space.
-    u = x / scale + 0.5 * (1 - 1 / scale)
-
-    # What is the left-most pixel that can be involved in the computation?
-    left = torch.floor(u - kernel_width / 2)
-
-    # What is the maximum number of pixels that can be involved in the
-    # computation?  Note: it's OK to use an extra pixel here; if the
-    # corresponding weights are all zero, it will be eliminated at the end
-    # of this function.
-    p = math.ceil(kernel_width) + 2
-
-    # The indices of the input pixels involved in computing the k-th output
-    # pixel are in row k of the indices matrix.
-    indices = left.view(out_length, 1).expand(out_length, p) + torch.linspace(0, p - 1, p).view(1, p).expand(
-        out_length, p)
-
-    # The weights used to compute the k-th output pixel are in row k of the
-    # weights matrix.
-    distance_to_center = u.view(out_length, 1).expand(out_length, p) - indices
-
-    # apply cubic kernel
-    if (scale < 1) and antialiasing:
-        weights = scale * cubic(distance_to_center * scale)
-    else:
-        weights = cubic(distance_to_center)
-
-    # Normalize the weights matrix so that each row sums to 1.
-    weights_sum = torch.sum(weights, 1).view(out_length, 1)
-    weights = weights / weights_sum.expand(out_length, p)
-
-    # If a column in weights is all zero, get rid of it. only consider the
-    # first and last column.
-    weights_zero_tmp = torch.sum((weights == 0), 0)
-    if not math.isclose(weights_zero_tmp[0], 0, rel_tol=1e-6):
-        indices = indices.narrow(1, 1, p - 2)
-        weights = weights.narrow(1, 1, p - 2)
-    if not math.isclose(weights_zero_tmp[-1], 0, rel_tol=1e-6):
-        indices = indices.narrow(1, 0, p - 2)
-        weights = weights.narrow(1, 0, p - 2)
-    weights = weights.contiguous()
-    indices = indices.contiguous()
-    sym_len_s = -indices.min() + 1
-    sym_len_e = indices.max() - in_length
-    indices = indices + sym_len_s - 1
-    return weights, indices, int(sym_len_s), int(sym_len_e)
-
-
-@torch.no_grad()
-def imresize(img, scale, antialiasing=True):
-    """imresize function same as MATLAB.
-
-    It now only supports bicubic.
-    The same scale applies for both height and width.
-
-    Args:
-        img (Tensor | Numpy array):
-            Tensor: Input image with shape (c, h, w), [0, 1] range.
-            Numpy: Input image with shape (h, w, c), [0, 1] range.
-        scale (float): Scale factor. The same scale applies for both height
-            and width.
-        antialisaing (bool): Whether to apply anti-aliasing when downsampling.
-            Default: True.
-
-    Returns:
-        Tensor: Output image with shape (c, h, w), [0, 1] range, w/o round.
-    """
-    if type(img).__module__ == np.__name__:  # numpy type
-        numpy_type = True
-        img = torch.from_numpy(img.transpose(2, 0, 1)).float()
-    else:
-        numpy_type = False
-
-    in_c, in_h, in_w = img.size()
-    out_h, out_w = math.ceil(in_h * scale), math.ceil(in_w * scale)
-    kernel_width = 4
-    kernel = 'cubic'
-
-    # get weights and indices
-    weights_h, indices_h, sym_len_hs, sym_len_he = calculate_weights_indices(in_h, out_h, scale, kernel, kernel_width,
-                                                                             antialiasing)
-    weights_w, indices_w, sym_len_ws, sym_len_we = calculate_weights_indices(in_w, out_w, scale, kernel, kernel_width,
-                                                                             antialiasing)
-    # process H dimension
-    # symmetric copying
-    img_aug = torch.FloatTensor(in_c, in_h + sym_len_hs + sym_len_he, in_w)
-    img_aug.narrow(1, sym_len_hs, in_h).copy_(img)
-
-    sym_patch = img[:, :sym_len_hs, :]
-    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(1, inv_idx)
-    img_aug.narrow(1, 0, sym_len_hs).copy_(sym_patch_inv)
-
-    sym_patch = img[:, -sym_len_he:, :]
-    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(1, inv_idx)
-    img_aug.narrow(1, sym_len_hs + in_h, sym_len_he).copy_(sym_patch_inv)
-
-    out_1 = torch.FloatTensor(in_c, out_h, in_w)
-    kernel_width = weights_h.size(1)
-    for i in range(out_h):
-        idx = int(indices_h[i][0])
-        for j in range(in_c):
-            out_1[j, i, :] = img_aug[j, idx:idx + kernel_width, :].transpose(0, 1).mv(weights_h[i])
-
-    # process W dimension
-    # symmetric copying
-    out_1_aug = torch.FloatTensor(in_c, out_h, in_w + sym_len_ws + sym_len_we)
-    out_1_aug.narrow(2, sym_len_ws, in_w).copy_(out_1)
-
-    sym_patch = out_1[:, :, :sym_len_ws]
-    inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(2, inv_idx)
-    out_1_aug.narrow(2, 0, sym_len_ws).copy_(sym_patch_inv)
-
-    sym_patch = out_1[:, :, -sym_len_we:]
-    inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(2, inv_idx)
-    out_1_aug.narrow(2, sym_len_ws + in_w, sym_len_we).copy_(sym_patch_inv)
-
-    out_2 = torch.FloatTensor(in_c, out_h, out_w)
-    kernel_width = weights_w.size(1)
-    for i in range(out_w):
-        idx = int(indices_w[i][0])
-        for j in range(in_c):
-            out_2[j, :, i] = out_1_aug[j, :, idx:idx + kernel_width].mv(weights_w[i])
-
-    if numpy_type:
-        out_2 = out_2.numpy().transpose(1, 2, 0)
-    return out_2
-
-
-def rgb2ycbcr(img, y_only=False):
-    """Convert a RGB image to YCbCr image.
-
-    This function produces the same results as Matlab's `rgb2ycbcr` function.
-    It implements the ITU-R BT.601 conversion for standard-definition
-    television. See more details in
-    https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion.
-
-    It differs from a similar function in cv2.cvtColor: `RGB <-> YCrCb`.
-    In OpenCV, it implements a JPEG conversion. See more details in
-    https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion.
-
-    Args:
-        img (ndarray): The input image. It accepts:
-            1. np.uint8 type with range [0, 255];
-            2. np.float32 type with range [0, 1].
-        y_only (bool): Whether to only return Y channel. Default: False.
-
-    Returns:
-        ndarray: The converted YCbCr image. The output image has the same type
-            and range as input image.
-    """
-    img_type = img.dtype
-    img = _convert_input_type_range(img)
-    if y_only:
-        out_img = np.dot(img, [65.481, 128.553, 24.966]) + 16.0
-    else:
-        out_img = np.matmul(
-            img, [[65.481, -37.797, 112.0], [128.553, -74.203, -93.786], [24.966, 112.0, -18.214]]) + [16, 128, 128]
-    out_img = _convert_output_type_range(out_img, img_type)
-    return out_img
-
-
-def bgr2ycbcr(img, y_only=False):
-    """Convert a BGR image to YCbCr image.
-
-    The bgr version of rgb2ycbcr.
-    It implements the ITU-R BT.601 conversion for standard-definition
-    television. See more details in
-    https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion.
-
-    It differs from a similar function in cv2.cvtColor: `BGR <-> YCrCb`.
-    In OpenCV, it implements a JPEG conversion. See more details in
-    https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion.
-
-    Args:
-        img (ndarray): The input image. It accepts:
-            1. np.uint8 type with range [0, 255];
-            2. np.float32 type with range [0, 1].
-        y_only (bool): Whether to only return Y channel. Default: False.
-
-    Returns:
-        ndarray: The converted YCbCr image. The output image has the same type
-            and range as input image.
-    """
-    img_type = img.dtype
-    img = _convert_input_type_range(img)
-    if y_only:
-        out_img = np.dot(img, [24.966, 128.553, 65.481]) + 16.0
-    else:
-        out_img = np.matmul(
-            img, [[24.966, 112.0, -18.214], [128.553, -74.203, -93.786], [65.481, -37.797, 112.0]]) + [16, 128, 128]
-    out_img = _convert_output_type_range(out_img, img_type)
-    return out_img
-
-
-def ycbcr2rgb(img):
-    """Convert a YCbCr image to RGB image.
-
-    This function produces the same results as Matlab's ycbcr2rgb function.
-    It implements the ITU-R BT.601 conversion for standard-definition
-    television. See more details in
-    https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion.
-
-    It differs from a similar function in cv2.cvtColor: `YCrCb <-> RGB`.
-    In OpenCV, it implements a JPEG conversion. See more details in
-    https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion.
-
-    Args:
-        img (ndarray): The input image. It accepts:
-            1. np.uint8 type with range [0, 255];
-            2. np.float32 type with range [0, 1].
-
-    Returns:
-        ndarray: The converted RGB image. The output image has the same type
-            and range as input image.
-    """
-    img_type = img.dtype
-    img = _convert_input_type_range(img) * 255
-    out_img = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621], [0, -0.00153632, 0.00791071],
-                              [0.00625893, -0.00318811, 0]]) * 255.0 + [-222.921, 135.576, -276.836]  # noqa: E126
-    out_img = _convert_output_type_range(out_img, img_type)
-    return out_img
-
-
-def ycbcr2bgr(img):
-    """Convert a YCbCr image to BGR image.
-
-    The bgr version of ycbcr2rgb.
-    It implements the ITU-R BT.601 conversion for standard-definition
-    television. See more details in
-    https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion.
-
-    It differs from a similar function in cv2.cvtColor: `YCrCb <-> BGR`.
-    In OpenCV, it implements a JPEG conversion. See more details in
-    https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion.
-
-    Args:
-        img (ndarray): The input image. It accepts:
-            1. np.uint8 type with range [0, 255];
-            2. np.float32 type with range [0, 1].
-
-    Returns:
-        ndarray: The converted BGR image. The output image has the same type
-            and range as input image.
-    """
-    img_type = img.dtype
-    img = _convert_input_type_range(img) * 255
-    out_img = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621], [0.00791071, -0.00153632, 0],
-                              [0, -0.00318811, 0.00625893]]) * 255.0 + [-276.836, 135.576, -222.921]  # noqa: E126
-    out_img = _convert_output_type_range(out_img, img_type)
-    return out_img
-
-
-def _convert_input_type_range(img):
-    """Convert the type and range of the input image.
-
-    It converts the input image to np.float32 type and range of [0, 1].
-    It is mainly used for pre-processing the input image in colorspace
-    convertion functions such as rgb2ycbcr and ycbcr2rgb.
-
-    Args:
-        img (ndarray): The input image. It accepts:
-            1. np.uint8 type with range [0, 255];
-            2. np.float32 type with range [0, 1].
-
-    Returns:
-        (ndarray): The converted image with type of np.float32 and range of
-            [0, 1].
-    """
-    img_type = img.dtype
-    img = img.astype(np.float32)
-    if img_type == np.float32:
-        pass
-    elif img_type == np.uint8:
-        img /= 255.
-    else:
-        raise TypeError('The img type should be np.float32 or np.uint8, ' f'but got {img_type}')
-    return img
-
-
-def _convert_output_type_range(img, dst_type):
-    """Convert the type and range of the image according to dst_type.
-
-    It converts the image to desired type and range. If `dst_type` is np.uint8,
-    images will be converted to np.uint8 type with range [0, 255]. If
-    `dst_type` is np.float32, it converts the image to np.float32 type with
-    range [0, 1].
-    It is mainly used for post-processing images in colorspace convertion
-    functions such as rgb2ycbcr and ycbcr2rgb.
-
-    Args:
-        img (ndarray): The image to be converted with np.float32 type and
-            range [0, 255].
-        dst_type (np.uint8 | np.float32): If dst_type is np.uint8, it
-            converts the image to np.uint8 type with range [0, 255]. If
-            dst_type is np.float32, it converts the image to np.float32 type
-            with range [0, 1].
-
-    Returns:
-        (ndarray): The converted image with desired type and range.
-    """
-    if dst_type not in (np.uint8, np.float32):
-        raise TypeError('The dst_type should be np.float32 or np.uint8, ' f'but got {dst_type}')
-    if dst_type == np.uint8:
-        img = img.round()
-    else:
-        img /= 255.
-    return img.astype(dst_type)
diff --git a/repositories/CodeFormer/basicsr/utils/misc.py b/repositories/CodeFormer/basicsr/utils/misc.py
deleted file mode 100644
index 3b444ff3b950e38f43a5451d1330ff1b65951a9e..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/basicsr/utils/misc.py
+++ /dev/null
@@ -1,134 +0,0 @@
-import numpy as np
-import os
-import random
-import time
-import torch
-from os import path as osp
-
-from .dist_util import master_only
-from .logger import get_root_logger
-
-
-def set_random_seed(seed):
-    """Set random seeds."""
-    random.seed(seed)
-    np.random.seed(seed)
-    torch.manual_seed(seed)
-    torch.cuda.manual_seed(seed)
-    torch.cuda.manual_seed_all(seed)
-
-
-def get_time_str():
-    return time.strftime('%Y%m%d_%H%M%S', time.localtime())
-
-
-def mkdir_and_rename(path):
-    """mkdirs. If path exists, rename it with timestamp and create a new one.
-
-    Args:
-        path (str): Folder path.
-    """
-    if osp.exists(path):
-        new_name = path + '_archived_' + get_time_str()
-        print(f'Path already exists. Rename it to {new_name}', flush=True)
-        os.rename(path, new_name)
-    os.makedirs(path, exist_ok=True)
-
-
-@master_only
-def make_exp_dirs(opt):
-    """Make dirs for experiments."""
-    path_opt = opt['path'].copy()
-    if opt['is_train']:
-        mkdir_and_rename(path_opt.pop('experiments_root'))
-    else:
-        mkdir_and_rename(path_opt.pop('results_root'))
-    for key, path in path_opt.items():
-        if ('strict_load' not in key) and ('pretrain_network' not in key) and ('resume' not in key):
-            os.makedirs(path, exist_ok=True)
-
-
-def scandir(dir_path, suffix=None, recursive=False, full_path=False):
-    """Scan a directory to find the interested files.
-
-    Args:
-        dir_path (str): Path of the directory.
-        suffix (str | tuple(str), optional): File suffix that we are
-            interested in. Default: None.
-        recursive (bool, optional): If set to True, recursively scan the
-            directory. Default: False.
-        full_path (bool, optional): If set to True, include the dir_path.
-            Default: False.
-
-    Returns:
-        A generator for all the interested files with relative pathes.
-    """
-
-    if (suffix is not None) and not isinstance(suffix, (str, tuple)):
-        raise TypeError('"suffix" must be a string or tuple of strings')
-
-    root = dir_path
-
-    def _scandir(dir_path, suffix, recursive):
-        for entry in os.scandir(dir_path):
-            if not entry.name.startswith('.') and entry.is_file():
-                if full_path:
-                    return_path = entry.path
-                else:
-                    return_path = osp.relpath(entry.path, root)
-
-                if suffix is None:
-                    yield return_path
-                elif return_path.endswith(suffix):
-                    yield return_path
-            else:
-                if recursive:
-                    yield from _scandir(entry.path, suffix=suffix, recursive=recursive)
-                else:
-                    continue
-
-    return _scandir(dir_path, suffix=suffix, recursive=recursive)
-
-
-def check_resume(opt, resume_iter):
-    """Check resume states and pretrain_network paths.
-
-    Args:
-        opt (dict): Options.
-        resume_iter (int): Resume iteration.
-    """
-    logger = get_root_logger()
-    if opt['path']['resume_state']:
-        # get all the networks
-        networks = [key for key in opt.keys() if key.startswith('network_')]
-        flag_pretrain = False
-        for network in networks:
-            if opt['path'].get(f'pretrain_{network}') is not None:
-                flag_pretrain = True
-        if flag_pretrain:
-            logger.warning('pretrain_network path will be ignored during resuming.')
-        # set pretrained model paths
-        for network in networks:
-            name = f'pretrain_{network}'
-            basename = network.replace('network_', '')
-            if opt['path'].get('ignore_resume_networks') is None or (basename
-                                                                     not in opt['path']['ignore_resume_networks']):
-                opt['path'][name] = osp.join(opt['path']['models'], f'net_{basename}_{resume_iter}.pth')
-                logger.info(f"Set {name} to {opt['path'][name]}")
-
-
-def sizeof_fmt(size, suffix='B'):
-    """Get human readable file size.
-
-    Args:
-        size (int): File size.
-        suffix (str): Suffix. Default: 'B'.
-
-    Return:
-        str: Formated file siz.
-    """
-    for unit in ['', 'K', 'M', 'G', 'T', 'P', 'E', 'Z']:
-        if abs(size) < 1024.0:
-            return f'{size:3.1f} {unit}{suffix}'
-        size /= 1024.0
-    return f'{size:3.1f} Y{suffix}'
diff --git a/repositories/CodeFormer/basicsr/utils/options.py b/repositories/CodeFormer/basicsr/utils/options.py
deleted file mode 100644
index db490e4aa52e26fde31959fd74c2cef3af2ecf76..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/basicsr/utils/options.py
+++ /dev/null
@@ -1,108 +0,0 @@
-import yaml
-import time
-from collections import OrderedDict
-from os import path as osp
-from basicsr.utils.misc import get_time_str
-
-def ordered_yaml():
-    """Support OrderedDict for yaml.
-
-    Returns:
-        yaml Loader and Dumper.
-    """
-    try:
-        from yaml import CDumper as Dumper
-        from yaml import CLoader as Loader
-    except ImportError:
-        from yaml import Dumper, Loader
-
-    _mapping_tag = yaml.resolver.BaseResolver.DEFAULT_MAPPING_TAG
-
-    def dict_representer(dumper, data):
-        return dumper.represent_dict(data.items())
-
-    def dict_constructor(loader, node):
-        return OrderedDict(loader.construct_pairs(node))
-
-    Dumper.add_representer(OrderedDict, dict_representer)
-    Loader.add_constructor(_mapping_tag, dict_constructor)
-    return Loader, Dumper
-
-
-def parse(opt_path, root_path, is_train=True):
-    """Parse option file.
-
-    Args:
-        opt_path (str): Option file path.
-        is_train (str): Indicate whether in training or not. Default: True.
-
-    Returns:
-        (dict): Options.
-    """
-    with open(opt_path, mode='r') as f:
-        Loader, _ = ordered_yaml()
-        opt = yaml.load(f, Loader=Loader)
-
-    opt['is_train'] = is_train
-
-    # opt['name'] = f"{get_time_str()}_{opt['name']}"
-    if opt['path'].get('resume_state', None): # Shangchen added
-        resume_state_path = opt['path'].get('resume_state')
-        opt['name'] = resume_state_path.split("/")[-3]
-    else:
-        opt['name'] = f"{get_time_str()}_{opt['name']}"
-
-
-    # datasets
-    for phase, dataset in opt['datasets'].items():
-        # for several datasets, e.g., test_1, test_2
-        phase = phase.split('_')[0]
-        dataset['phase'] = phase
-        if 'scale' in opt:
-            dataset['scale'] = opt['scale']
-        if dataset.get('dataroot_gt') is not None:
-            dataset['dataroot_gt'] = osp.expanduser(dataset['dataroot_gt'])
-        if dataset.get('dataroot_lq') is not None:
-            dataset['dataroot_lq'] = osp.expanduser(dataset['dataroot_lq'])
-
-    # paths
-    for key, val in opt['path'].items():
-        if (val is not None) and ('resume_state' in key or 'pretrain_network' in key):
-            opt['path'][key] = osp.expanduser(val)
-
-    if is_train:
-        experiments_root = osp.join(root_path, 'experiments', opt['name'])
-        opt['path']['experiments_root'] = experiments_root
-        opt['path']['models'] = osp.join(experiments_root, 'models')
-        opt['path']['training_states'] = osp.join(experiments_root, 'training_states')
-        opt['path']['log'] = experiments_root
-        opt['path']['visualization'] = osp.join(experiments_root, 'visualization')
-
-    else:  # test
-        results_root = osp.join(root_path, 'results', opt['name'])
-        opt['path']['results_root'] = results_root
-        opt['path']['log'] = results_root
-        opt['path']['visualization'] = osp.join(results_root, 'visualization')
-
-    return opt
-
-
-def dict2str(opt, indent_level=1):
-    """dict to string for printing options.
-
-    Args:
-        opt (dict): Option dict.
-        indent_level (int): Indent level. Default: 1.
-
-    Return:
-        (str): Option string for printing.
-    """
-    msg = '\n'
-    for k, v in opt.items():
-        if isinstance(v, dict):
-            msg += ' ' * (indent_level * 2) + k + ':['
-            msg += dict2str(v, indent_level + 1)
-            msg += ' ' * (indent_level * 2) + ']\n'
-        else:
-            msg += ' ' * (indent_level * 2) + k + ': ' + str(v) + '\n'
-    return msg
diff --git a/repositories/CodeFormer/basicsr/utils/realesrgan_utils.py b/repositories/CodeFormer/basicsr/utils/realesrgan_utils.py
deleted file mode 100644
index ff94523b7ddd61f0b72280950fd36e1b8133bf4c..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/basicsr/utils/realesrgan_utils.py
+++ /dev/null
@@ -1,296 +0,0 @@
-import cv2
-import math
-import numpy as np
-import os
-import queue
-import threading
-import torch
-from basicsr.utils.download_util import load_file_from_url
-from torch.nn import functional as F
-
-# ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-
-
-class RealESRGANer():
-    """A helper class for upsampling images with RealESRGAN.
-
-    Args:
-        scale (int): Upsampling scale factor used in the networks. It is usually 2 or 4.
-        model_path (str): The path to the pretrained model. It can be urls (will first download it automatically).
-        model (nn.Module): The defined network. Default: None.
-        tile (int): As too large images result in the out of GPU memory issue, so this tile option will first crop
-            input images into tiles, and then process each of them. Finally, they will be merged into one image.
-            0 denotes for do not use tile. Default: 0.
-        tile_pad (int): The pad size for each tile, to remove border artifacts. Default: 10.
-        pre_pad (int): Pad the input images to avoid border artifacts. Default: 10.
-        half (float): Whether to use half precision during inference. Default: False.
-    """
-
-    def __init__(self,
-                 scale,
-                 model_path,
-                 model=None,
-                 tile=0,
-                 tile_pad=10,
-                 pre_pad=10,
-                 half=False,
-                 device=None,
-                 gpu_id=None):
-        self.scale = scale
-        self.tile_size = tile
-        self.tile_pad = tile_pad
-        self.pre_pad = pre_pad
-        self.mod_scale = None
-        self.half = half
-
-        # initialize model
-        if gpu_id:
-            self.device = torch.device(
-                f'cuda:{gpu_id}' if torch.cuda.is_available() else 'cpu') if device is None else device
-        else:
-            self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') if device is None else device
-        # if the model_path starts with https, it will first download models to the folder: realesrgan/weights
-        if model_path.startswith('https://'):
-            model_path = load_file_from_url(
-                url=model_path, model_dir=os.path.join('weights/realesrgan'), progress=True, file_name=None)
-        loadnet = torch.load(model_path, map_location=torch.device('cpu'))
-        # prefer to use params_ema
-        if 'params_ema' in loadnet:
-            keyname = 'params_ema'
-        else:
-            keyname = 'params'
-        model.load_state_dict(loadnet[keyname], strict=True)
-        model.eval()
-        self.model = model.to(self.device)
-        if self.half:
-            self.model = self.model.half()
-
-    def pre_process(self, img):
-        """Pre-process, such as pre-pad and mod pad, so that the images can be divisible
-        """
-        img = torch.from_numpy(np.transpose(img, (2, 0, 1))).float()
-        self.img = img.unsqueeze(0).to(self.device)
-        if self.half:
-            self.img = self.img.half()
-
-        # pre_pad
-        if self.pre_pad != 0:
-            self.img = F.pad(self.img, (0, self.pre_pad, 0, self.pre_pad), 'reflect')
-        # mod pad for divisible borders
-        if self.scale == 2:
-            self.mod_scale = 2
-        elif self.scale == 1:
-            self.mod_scale = 4
-        if self.mod_scale is not None:
-            self.mod_pad_h, self.mod_pad_w = 0, 0
-            _, _, h, w = self.img.size()
-            if (h % self.mod_scale != 0):
-                self.mod_pad_h = (self.mod_scale - h % self.mod_scale)
-            if (w % self.mod_scale != 0):
-                self.mod_pad_w = (self.mod_scale - w % self.mod_scale)
-            self.img = F.pad(self.img, (0, self.mod_pad_w, 0, self.mod_pad_h), 'reflect')
-
-    def process(self):
-        # model inference
-        self.output = self.model(self.img)
-
-    def tile_process(self):
-        """It will first crop input images to tiles, and then process each tile.
-        Finally, all the processed tiles are merged into one images.
-
-        Modified from: https://github.com/ata4/esrgan-launcher
-        """
-        batch, channel, height, width = self.img.shape
-        output_height = height * self.scale
-        output_width = width * self.scale
-        output_shape = (batch, channel, output_height, output_width)
-
-        # start with black image
-        self.output = self.img.new_zeros(output_shape)
-        tiles_x = math.ceil(width / self.tile_size)
-        tiles_y = math.ceil(height / self.tile_size)
-
-        # loop over all tiles
-        for y in range(tiles_y):
-            for x in range(tiles_x):
-                # extract tile from input image
-                ofs_x = x * self.tile_size
-                ofs_y = y * self.tile_size
-                # input tile area on total image
-                input_start_x = ofs_x
-                input_end_x = min(ofs_x + self.tile_size, width)
-                input_start_y = ofs_y
-                input_end_y = min(ofs_y + self.tile_size, height)
-
-                # input tile area on total image with padding
-                input_start_x_pad = max(input_start_x - self.tile_pad, 0)
-                input_end_x_pad = min(input_end_x + self.tile_pad, width)
-                input_start_y_pad = max(input_start_y - self.tile_pad, 0)
-                input_end_y_pad = min(input_end_y + self.tile_pad, height)
-
-                # input tile dimensions
-                input_tile_width = input_end_x - input_start_x
-                input_tile_height = input_end_y - input_start_y
-                tile_idx = y * tiles_x + x + 1
-                input_tile = self.img[:, :, input_start_y_pad:input_end_y_pad, input_start_x_pad:input_end_x_pad]
-
-                # upscale tile
-                try:
-                    with torch.no_grad():
-                        output_tile = self.model(input_tile)
-                except RuntimeError as error:
-                    print('Error', error)
-                # print(f'\tTile {tile_idx}/{tiles_x * tiles_y}')
-
-                # output tile area on total image
-                output_start_x = input_start_x * self.scale
-                output_end_x = input_end_x * self.scale
-                output_start_y = input_start_y * self.scale
-                output_end_y = input_end_y * self.scale
-
-                # output tile area without padding
-                output_start_x_tile = (input_start_x - input_start_x_pad) * self.scale
-                output_end_x_tile = output_start_x_tile + input_tile_width * self.scale
-                output_start_y_tile = (input_start_y - input_start_y_pad) * self.scale
-                output_end_y_tile = output_start_y_tile + input_tile_height * self.scale
-
-                # put tile into output image
-                self.output[:, :, output_start_y:output_end_y,
-                            output_start_x:output_end_x] = output_tile[:, :, output_start_y_tile:output_end_y_tile,
-                                                                       output_start_x_tile:output_end_x_tile]
-
-    def post_process(self):
-        # remove extra pad
-        if self.mod_scale is not None:
-            _, _, h, w = self.output.size()
-            self.output = self.output[:, :, 0:h - self.mod_pad_h * self.scale, 0:w - self.mod_pad_w * self.scale]
-        # remove prepad
-        if self.pre_pad != 0:
-            _, _, h, w = self.output.size()
-            self.output = self.output[:, :, 0:h - self.pre_pad * self.scale, 0:w - self.pre_pad * self.scale]
-        return self.output
-
-    @torch.no_grad()
-    def enhance(self, img, outscale=None, alpha_upsampler='realesrgan'):
-        h_input, w_input = img.shape[0:2]
-        # img: numpy
-        img = img.astype(np.float32)
-        if np.max(img) > 256:  # 16-bit image
-            max_range = 65535
-            print('\tInput is a 16-bit image')
-        else:
-            max_range = 255
-        img = img / max_range
-        if len(img.shape) == 2:  # gray image
-            img_mode = 'L'
-            img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
-        elif img.shape[2] == 4:  # RGBA image with alpha channel
-            img_mode = 'RGBA'
-            alpha = img[:, :, 3]
-            img = img[:, :, 0:3]
-            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-            if alpha_upsampler == 'realesrgan':
-                alpha = cv2.cvtColor(alpha, cv2.COLOR_GRAY2RGB)
-        else:
-            img_mode = 'RGB'
-            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-
-        # ------------------- process image (without the alpha channel) ------------------- #
-        with torch.no_grad():
-            self.pre_process(img)
-            if self.tile_size > 0:
-                self.tile_process()
-            else:
-                self.process()
-            output_img_t = self.post_process()
-            output_img = output_img_t.data.squeeze().float().cpu().clamp_(0, 1).numpy()
-            output_img = np.transpose(output_img[[2, 1, 0], :, :], (1, 2, 0))
-            if img_mode == 'L':
-                output_img = cv2.cvtColor(output_img, cv2.COLOR_BGR2GRAY)
-        del output_img_t
-        torch.cuda.empty_cache()        
-
-        # ------------------- process the alpha channel if necessary ------------------- #
-        if img_mode == 'RGBA':
-            if alpha_upsampler == 'realesrgan':
-                self.pre_process(alpha)
-                if self.tile_size > 0:
-                    self.tile_process()
-                else:
-                    self.process()
-                output_alpha = self.post_process()
-                output_alpha = output_alpha.data.squeeze().float().cpu().clamp_(0, 1).numpy()
-                output_alpha = np.transpose(output_alpha[[2, 1, 0], :, :], (1, 2, 0))
-                output_alpha = cv2.cvtColor(output_alpha, cv2.COLOR_BGR2GRAY)
-            else:  # use the cv2 resize for alpha channel
-                h, w = alpha.shape[0:2]
-                output_alpha = cv2.resize(alpha, (w * self.scale, h * self.scale), interpolation=cv2.INTER_LINEAR)
-
-            # merge the alpha channel
-            output_img = cv2.cvtColor(output_img, cv2.COLOR_BGR2BGRA)
-            output_img[:, :, 3] = output_alpha
-
-        # ------------------------------ return ------------------------------ #
-        if max_range == 65535:  # 16-bit image
-            output = (output_img * 65535.0).round().astype(np.uint16)
-        else:
-            output = (output_img * 255.0).round().astype(np.uint8)
-
-        if outscale is not None and outscale != float(self.scale):
-            output = cv2.resize(
-                output, (
-                    int(w_input * outscale),
-                    int(h_input * outscale),
-                ), interpolation=cv2.INTER_LANCZOS4)
-
-        return output, img_mode
-
-
-class PrefetchReader(threading.Thread):
-    """Prefetch images.
-
-    Args:
-        img_list (list[str]): A image list of image paths to be read.
-        num_prefetch_queue (int): Number of prefetch queue.
-    """
-
-    def __init__(self, img_list, num_prefetch_queue):
-        super().__init__()
-        self.que = queue.Queue(num_prefetch_queue)
-        self.img_list = img_list
-
-    def run(self):
-        for img_path in self.img_list:
-            img = cv2.imread(img_path, cv2.IMREAD_UNCHANGED)
-            self.que.put(img)
-
-        self.que.put(None)
-
-    def __next__(self):
-        next_item = self.que.get()
-        if next_item is None:
-            raise StopIteration
-        return next_item
-
-    def __iter__(self):
-        return self
-
-
-class IOConsumer(threading.Thread):
-
-    def __init__(self, opt, que, qid):
-        super().__init__()
-        self._queue = que
-        self.qid = qid
-        self.opt = opt
-
-    def run(self):
-        while True:
-            msg = self._queue.get()
-            if isinstance(msg, str) and msg == 'quit':
-                break
-
-            output = msg['output']
-            save_path = msg['save_path']
-            cv2.imwrite(save_path, output)
-        print(f'IO worker {self.qid} is done.')
\ No newline at end of file
diff --git a/repositories/CodeFormer/basicsr/utils/registry.py b/repositories/CodeFormer/basicsr/utils/registry.py
deleted file mode 100644
index 655753b3b9cbd0cfe73fe93a77cf1fcc3db6d827..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/basicsr/utils/registry.py
+++ /dev/null
@@ -1,82 +0,0 @@
-# Modified from: https://github.com/facebookresearch/fvcore/blob/master/fvcore/common/registry.py  # noqa: E501
-
-
-class Registry():
-    """
-    The registry that provides name -> object mapping, to support third-party
-    users' custom modules.
-
-    To create a registry (e.g. a backbone registry):
-
-    .. code-block:: python
-
-        BACKBONE_REGISTRY = Registry('BACKBONE')
-
-    To register an object:
-
-    .. code-block:: python
-
-        @BACKBONE_REGISTRY.register()
-        class MyBackbone():
-            ...
-
-    Or:
-
-    .. code-block:: python
-
-        BACKBONE_REGISTRY.register(MyBackbone)
-    """
-
-    def __init__(self, name):
-        """
-        Args:
-            name (str): the name of this registry
-        """
-        self._name = name
-        self._obj_map = {}
-
-    def _do_register(self, name, obj):
-        assert (name not in self._obj_map), (f"An object named '{name}' was already registered "
-                                             f"in '{self._name}' registry!")
-        self._obj_map[name] = obj
-
-    def register(self, obj=None):
-        """
-        Register the given object under the the name `obj.__name__`.
-        Can be used as either a decorator or not.
-        See docstring of this class for usage.
-        """
-        if obj is None:
-            # used as a decorator
-            def deco(func_or_class):
-                name = func_or_class.__name__
-                self._do_register(name, func_or_class)
-                return func_or_class
-
-            return deco
-
-        # used as a function call
-        name = obj.__name__
-        self._do_register(name, obj)
-
-    def get(self, name):
-        ret = self._obj_map.get(name)
-        if ret is None:
-            raise KeyError(f"No object named '{name}' found in '{self._name}' registry!")
-        return ret
-
-    def __contains__(self, name):
-        return name in self._obj_map
-
-    def __iter__(self):
-        return iter(self._obj_map.items())
-
-    def keys(self):
-        return self._obj_map.keys()
-
-
-DATASET_REGISTRY = Registry('dataset')
-ARCH_REGISTRY = Registry('arch')
-MODEL_REGISTRY = Registry('model')
-LOSS_REGISTRY = Registry('loss')
-METRIC_REGISTRY = Registry('metric')
diff --git a/repositories/CodeFormer/cog.yaml b/repositories/CodeFormer/cog.yaml
deleted file mode 100644
index 236299ed059180ebceee87963742ec0d555fceec..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/cog.yaml
+++ /dev/null
@@ -1,25 +0,0 @@
-build:
-  gpu: true
-  cuda: "11.3"
-  python_version: "3.8"
-  system_packages:
-    - "libgl1-mesa-glx"
-    - "libglib2.0-0"
-  python_packages:
-    - "ipython==8.4.0"
-    - "future==0.18.2"
-    - "lmdb==1.3.0"
-    - "scikit-image==0.19.3"
-    - "torch==1.11.0 --extra-index-url=https://download.pytorch.org/whl/cu113"
-    - "torchvision==0.12.0 --extra-index-url=https://download.pytorch.org/whl/cu113"
-    - "scipy==1.9.0"
-    - "gdown==4.5.1"
-    - "pyyaml==6.0"
-    - "tb-nightly==2.11.0a20220906"
-    - "tqdm==4.64.1"
-    - "yapf==0.32.0"
-    - "lpips==0.1.4"
-    - "Pillow==9.2.0"
-    - "opencv-python==4.6.0.66"
-
-predict: "predict.py:Predictor"
diff --git a/repositories/CodeFormer/facelib/detection/__init__.py b/repositories/CodeFormer/facelib/detection/__init__.py
deleted file mode 100644
index 296262d4e2e29eaa2afba7bda1f0399d77da24f6..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/facelib/detection/__init__.py
+++ /dev/null
@@ -1,100 +0,0 @@
-import os
-import torch
-from torch import nn
-from copy import deepcopy
-
-from facelib.utils import load_file_from_url
-from facelib.utils import download_pretrained_models
-from facelib.detection.yolov5face.models.common import Conv
-
-from .retinaface.retinaface import RetinaFace
-from .yolov5face.face_detector import YoloDetector
-
-
-def init_detection_model(model_name, half=False, device='cuda'):
-    if 'retinaface' in model_name:
-        model = init_retinaface_model(model_name, half, device)
-    elif 'YOLOv5' in model_name:
-        model = init_yolov5face_model(model_name, device)
-    else:
-        raise NotImplementedError(f'{model_name} is not implemented.')
-
-    return model
-
-
-def init_retinaface_model(model_name, half=False, device='cuda'):
-    if model_name == 'retinaface_resnet50':
-        model = RetinaFace(network_name='resnet50', half=half)
-        model_url = 'https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_Resnet50_Final.pth'
-    elif model_name == 'retinaface_mobile0.25':
-        model = RetinaFace(network_name='mobile0.25', half=half)
-        model_url = 'https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_mobilenet0.25_Final.pth'
-    else:
-        raise NotImplementedError(f'{model_name} is not implemented.')
-
-    model_path = load_file_from_url(url=model_url, model_dir='weights/facelib', progress=True, file_name=None)
-    load_net = torch.load(model_path, map_location=lambda storage, loc: storage)
-    # remove unnecessary 'module.'
-    for k, v in deepcopy(load_net).items():
-        if k.startswith('module.'):
-            load_net[k[7:]] = v
-            load_net.pop(k)
-    model.load_state_dict(load_net, strict=True)
-    model.eval()
-    model = model.to(device)
-
-    return model
-
-
-def init_yolov5face_model(model_name, device='cuda'):
-    if model_name == 'YOLOv5l':
-        model = YoloDetector(config_name='facelib/detection/yolov5face/models/yolov5l.yaml', device=device)
-        model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/yolov5l-face.pth'
-    elif model_name == 'YOLOv5n':
-        model = YoloDetector(config_name='facelib/detection/yolov5face/models/yolov5n.yaml', device=device)
-        model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/yolov5n-face.pth'
-    else:
-        raise NotImplementedError(f'{model_name} is not implemented.')
-    
-    model_path = load_file_from_url(url=model_url, model_dir='weights/facelib', progress=True, file_name=None)
-    load_net = torch.load(model_path, map_location=lambda storage, loc: storage)
-    model.detector.load_state_dict(load_net, strict=True)
-    model.detector.eval()
-    model.detector = model.detector.to(device).float()
-
-    for m in model.detector.modules():
-        if type(m) in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU]:
-            m.inplace = True  # pytorch 1.7.0 compatibility
-        elif isinstance(m, Conv):
-            m._non_persistent_buffers_set = set()  # pytorch 1.6.0 compatibility
-
-    return model
-
-
-# Download from Google Drive
-# def init_yolov5face_model(model_name, device='cuda'):
-#     if model_name == 'YOLOv5l':
-#         model = YoloDetector(config_name='facelib/detection/yolov5face/models/yolov5l.yaml', device=device)
-#         f_id = {'yolov5l-face.pth': '131578zMA6B2x8VQHyHfa6GEPtulMCNzV'}
-#     elif model_name == 'YOLOv5n':
-#         model = YoloDetector(config_name='facelib/detection/yolov5face/models/yolov5n.yaml', device=device)
-#         f_id = {'yolov5n-face.pth': '1fhcpFvWZqghpGXjYPIne2sw1Fy4yhw6o'}
-#     else:
-#         raise NotImplementedError(f'{model_name} is not implemented.')
-
-#     model_path = os.path.join('weights/facelib', list(f_id.keys())[0])
-#     if not os.path.exists(model_path):
-#         download_pretrained_models(file_ids=f_id, save_path_root='weights/facelib')
-
-#     load_net = torch.load(model_path, map_location=lambda storage, loc: storage)
-#     model.detector.load_state_dict(load_net, strict=True)
-#     model.detector.eval()
-#     model.detector = model.detector.to(device).float()
-
-#     for m in model.detector.modules():
-#         if type(m) in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU]:
-#             m.inplace = True  # pytorch 1.7.0 compatibility
-#         elif isinstance(m, Conv):
-#             m._non_persistent_buffers_set = set()  # pytorch 1.6.0 compatibility
-
-#     return model
\ No newline at end of file
diff --git a/repositories/CodeFormer/facelib/detection/__pycache__/__init__.cpython-310.pyc b/repositories/CodeFormer/facelib/detection/__pycache__/__init__.cpython-310.pyc
deleted file mode 100644
index 31f27c23432d6ad4e9f51b1a528db2cbdc3e457a..0000000000000000000000000000000000000000
Binary files a/repositories/CodeFormer/facelib/detection/__pycache__/__init__.cpython-310.pyc and /dev/null differ
diff --git a/repositories/CodeFormer/facelib/detection/__pycache__/align_trans.cpython-310.pyc b/repositories/CodeFormer/facelib/detection/__pycache__/align_trans.cpython-310.pyc
deleted file mode 100644
index fa0b1b8859427a02c39b0b51604b550ec7d24561..0000000000000000000000000000000000000000
Binary files a/repositories/CodeFormer/facelib/detection/__pycache__/align_trans.cpython-310.pyc and /dev/null differ
diff --git a/repositories/CodeFormer/facelib/detection/__pycache__/matlab_cp2tform.cpython-310.pyc b/repositories/CodeFormer/facelib/detection/__pycache__/matlab_cp2tform.cpython-310.pyc
deleted file mode 100644
index 7e11eae0df41124be9a91209344410c72e3a99b7..0000000000000000000000000000000000000000
Binary files a/repositories/CodeFormer/facelib/detection/__pycache__/matlab_cp2tform.cpython-310.pyc and /dev/null differ
diff --git a/repositories/CodeFormer/facelib/detection/align_trans.py b/repositories/CodeFormer/facelib/detection/align_trans.py
deleted file mode 100644
index 07f1eb365462c2ec5bbac6d1854c786b6fd6be90..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/facelib/detection/align_trans.py
+++ /dev/null
@@ -1,219 +0,0 @@
-import cv2
-import numpy as np
-
-from .matlab_cp2tform import get_similarity_transform_for_cv2
-
-# reference facial points, a list of coordinates (x,y)
-REFERENCE_FACIAL_POINTS = [[30.29459953, 51.69630051], [65.53179932, 51.50139999], [48.02519989, 71.73660278],
-                           [33.54930115, 92.3655014], [62.72990036, 92.20410156]]
-
-DEFAULT_CROP_SIZE = (96, 112)
-
-
-class FaceWarpException(Exception):
-
-    def __str__(self):
-        return 'In File {}:{}'.format(__file__, super.__str__(self))
-
-
-def get_reference_facial_points(output_size=None, inner_padding_factor=0.0, outer_padding=(0, 0), default_square=False):
-    """
-    Function:
-    ----------
-        get reference 5 key points according to crop settings:
-        0. Set default crop_size:
-            if default_square:
-                crop_size = (112, 112)
-            else:
-                crop_size = (96, 112)
-        1. Pad the crop_size by inner_padding_factor in each side;
-        2. Resize crop_size into (output_size - outer_padding*2),
-            pad into output_size with outer_padding;
-        3. Output reference_5point;
-    Parameters:
-    ----------
-        @output_size: (w, h) or None
-            size of aligned face image
-        @inner_padding_factor: (w_factor, h_factor)
-            padding factor for inner (w, h)
-        @outer_padding: (w_pad, h_pad)
-            each row is a pair of coordinates (x, y)
-        @default_square: True or False
-            if True:
-                default crop_size = (112, 112)
-            else:
-                default crop_size = (96, 112);
-        !!! make sure, if output_size is not None:
-                (output_size - outer_padding)
-                = some_scale * (default crop_size * (1.0 +
-                inner_padding_factor))
-    Returns:
-    ----------
-        @reference_5point: 5x2 np.array
-            each row is a pair of transformed coordinates (x, y)
-    """
-
-    tmp_5pts = np.array(REFERENCE_FACIAL_POINTS)
-    tmp_crop_size = np.array(DEFAULT_CROP_SIZE)
-
-    # 0) make the inner region a square
-    if default_square:
-        size_diff = max(tmp_crop_size) - tmp_crop_size
-        tmp_5pts += size_diff / 2
-        tmp_crop_size += size_diff
-
-    if (output_size and output_size[0] == tmp_crop_size[0] and output_size[1] == tmp_crop_size[1]):
-
-        return tmp_5pts
-
-    if (inner_padding_factor == 0 and outer_padding == (0, 0)):
-        if output_size is None:
-            return tmp_5pts
-        else:
-            raise FaceWarpException('No paddings to do, output_size must be None or {}'.format(tmp_crop_size))
-
-    # check output size
-    if not (0 <= inner_padding_factor <= 1.0):
-        raise FaceWarpException('Not (0 <= inner_padding_factor <= 1.0)')
-
-    if ((inner_padding_factor > 0 or outer_padding[0] > 0 or outer_padding[1] > 0) and output_size is None):
-        output_size = tmp_crop_size * \
-            (1 + inner_padding_factor * 2).astype(np.int32)
-        output_size += np.array(outer_padding)
-    if not (outer_padding[0] < output_size[0] and outer_padding[1] < output_size[1]):
-        raise FaceWarpException('Not (outer_padding[0] < output_size[0] and outer_padding[1] < output_size[1])')
-
-    # 1) pad the inner region according inner_padding_factor
-    if inner_padding_factor > 0:
-        size_diff = tmp_crop_size * inner_padding_factor * 2
-        tmp_5pts += size_diff / 2
-        tmp_crop_size += np.round(size_diff).astype(np.int32)
-
-    # 2) resize the padded inner region
-    size_bf_outer_pad = np.array(output_size) - np.array(outer_padding) * 2
-
-    if size_bf_outer_pad[0] * tmp_crop_size[1] != size_bf_outer_pad[1] * tmp_crop_size[0]:
-        raise FaceWarpException('Must have (output_size - outer_padding)'
-                                '= some_scale * (crop_size * (1.0 + inner_padding_factor)')
-
-    scale_factor = size_bf_outer_pad[0].astype(np.float32) / tmp_crop_size[0]
-    tmp_5pts = tmp_5pts * scale_factor
-    #    size_diff = tmp_crop_size * (scale_factor - min(scale_factor))
-    #    tmp_5pts = tmp_5pts + size_diff / 2
-    tmp_crop_size = size_bf_outer_pad
-
-    # 3) add outer_padding to make output_size
-    reference_5point = tmp_5pts + np.array(outer_padding)
-    tmp_crop_size = output_size
-
-    return reference_5point
-
-
-def get_affine_transform_matrix(src_pts, dst_pts):
-    """
-    Function:
-    ----------
-        get affine transform matrix 'tfm' from src_pts to dst_pts
-    Parameters:
-    ----------
-        @src_pts: Kx2 np.array
-            source points matrix, each row is a pair of coordinates (x, y)
-        @dst_pts: Kx2 np.array
-            destination points matrix, each row is a pair of coordinates (x, y)
-    Returns:
-    ----------
-        @tfm: 2x3 np.array
-            transform matrix from src_pts to dst_pts
-    """
-
-    tfm = np.float32([[1, 0, 0], [0, 1, 0]])
-    n_pts = src_pts.shape[0]
-    ones = np.ones((n_pts, 1), src_pts.dtype)
-    src_pts_ = np.hstack([src_pts, ones])
-    dst_pts_ = np.hstack([dst_pts, ones])
-
-    A, res, rank, s = np.linalg.lstsq(src_pts_, dst_pts_)
-
-    if rank == 3:
-        tfm = np.float32([[A[0, 0], A[1, 0], A[2, 0]], [A[0, 1], A[1, 1], A[2, 1]]])
-    elif rank == 2:
-        tfm = np.float32([[A[0, 0], A[1, 0], 0], [A[0, 1], A[1, 1], 0]])
-
-    return tfm
-
-
-def warp_and_crop_face(src_img, facial_pts, reference_pts=None, crop_size=(96, 112), align_type='smilarity'):
-    """
-    Function:
-    ----------
-        apply affine transform 'trans' to uv
-    Parameters:
-    ----------
-        @src_img: 3x3 np.array
-            input image
-        @facial_pts: could be
-            1)a list of K coordinates (x,y)
-        or
-            2) Kx2 or 2xK np.array
-            each row or col is a pair of coordinates (x, y)
-        @reference_pts: could be
-            1) a list of K coordinates (x,y)
-        or
-            2) Kx2 or 2xK np.array
-            each row or col is a pair of coordinates (x, y)
-        or
-            3) None
-            if None, use default reference facial points
-        @crop_size: (w, h)
-            output face image size
-        @align_type: transform type, could be one of
-            1) 'similarity': use similarity transform
-            2) 'cv2_affine': use the first 3 points to do affine transform,
-                    by calling cv2.getAffineTransform()
-            3) 'affine': use all points to do affine transform
-    Returns:
-    ----------
-        @face_img: output face image with size (w, h) = @crop_size
-    """
-
-    if reference_pts is None:
-        if crop_size[0] == 96 and crop_size[1] == 112:
-            reference_pts = REFERENCE_FACIAL_POINTS
-        else:
-            default_square = False
-            inner_padding_factor = 0
-            outer_padding = (0, 0)
-            output_size = crop_size
-
-            reference_pts = get_reference_facial_points(output_size, inner_padding_factor, outer_padding,
-                                                        default_square)
-
-    ref_pts = np.float32(reference_pts)
-    ref_pts_shp = ref_pts.shape
-    if max(ref_pts_shp) < 3 or min(ref_pts_shp) != 2:
-        raise FaceWarpException('reference_pts.shape must be (K,2) or (2,K) and K>2')
-
-    if ref_pts_shp[0] == 2:
-        ref_pts = ref_pts.T
-
-    src_pts = np.float32(facial_pts)
-    src_pts_shp = src_pts.shape
-    if max(src_pts_shp) < 3 or min(src_pts_shp) != 2:
-        raise FaceWarpException('facial_pts.shape must be (K,2) or (2,K) and K>2')
-
-    if src_pts_shp[0] == 2:
-        src_pts = src_pts.T
-
-    if src_pts.shape != ref_pts.shape:
-        raise FaceWarpException('facial_pts and reference_pts must have the same shape')
-
-    if align_type == 'cv2_affine':
-        tfm = cv2.getAffineTransform(src_pts[0:3], ref_pts[0:3])
-    elif align_type == 'affine':
-        tfm = get_affine_transform_matrix(src_pts, ref_pts)
-    else:
-        tfm = get_similarity_transform_for_cv2(src_pts, ref_pts)
-
-    face_img = cv2.warpAffine(src_img, tfm, (crop_size[0], crop_size[1]))
-
-    return face_img
diff --git a/repositories/CodeFormer/facelib/detection/matlab_cp2tform.py b/repositories/CodeFormer/facelib/detection/matlab_cp2tform.py
deleted file mode 100644
index b2a8b54a91709c71437e15c68d3be9a9b0a20a34..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/facelib/detection/matlab_cp2tform.py
+++ /dev/null
@@ -1,317 +0,0 @@
-import numpy as np
-from numpy.linalg import inv, lstsq
-from numpy.linalg import matrix_rank as rank
-from numpy.linalg import norm
-
-
-class MatlabCp2tormException(Exception):
-
-    def __str__(self):
-        return 'In File {}:{}'.format(__file__, super.__str__(self))
-
-
-def tformfwd(trans, uv):
-    """
-    Function:
-    ----------
-        apply affine transform 'trans' to uv
-
-    Parameters:
-    ----------
-        @trans: 3x3 np.array
-            transform matrix
-        @uv: Kx2 np.array
-            each row is a pair of coordinates (x, y)
-
-    Returns:
-    ----------
-        @xy: Kx2 np.array
-            each row is a pair of transformed coordinates (x, y)
-    """
-    uv = np.hstack((uv, np.ones((uv.shape[0], 1))))
-    xy = np.dot(uv, trans)
-    xy = xy[:, 0:-1]
-    return xy
-
-
-def tforminv(trans, uv):
-    """
-    Function:
-    ----------
-        apply the inverse of affine transform 'trans' to uv
-
-    Parameters:
-    ----------
-        @trans: 3x3 np.array
-            transform matrix
-        @uv: Kx2 np.array
-            each row is a pair of coordinates (x, y)
-
-    Returns:
-    ----------
-        @xy: Kx2 np.array
-            each row is a pair of inverse-transformed coordinates (x, y)
-    """
-    Tinv = inv(trans)
-    xy = tformfwd(Tinv, uv)
-    return xy
-
-
-def findNonreflectiveSimilarity(uv, xy, options=None):
-    options = {'K': 2}
-
-    K = options['K']
-    M = xy.shape[0]
-    x = xy[:, 0].reshape((-1, 1))  # use reshape to keep a column vector
-    y = xy[:, 1].reshape((-1, 1))  # use reshape to keep a column vector
-
-    tmp1 = np.hstack((x, y, np.ones((M, 1)), np.zeros((M, 1))))
-    tmp2 = np.hstack((y, -x, np.zeros((M, 1)), np.ones((M, 1))))
-    X = np.vstack((tmp1, tmp2))
-
-    u = uv[:, 0].reshape((-1, 1))  # use reshape to keep a column vector
-    v = uv[:, 1].reshape((-1, 1))  # use reshape to keep a column vector
-    U = np.vstack((u, v))
-
-    # We know that X * r = U
-    if rank(X) >= 2 * K:
-        r, _, _, _ = lstsq(X, U, rcond=-1)
-        r = np.squeeze(r)
-    else:
-        raise Exception('cp2tform:twoUniquePointsReq')
-    sc = r[0]
-    ss = r[1]
-    tx = r[2]
-    ty = r[3]
-
-    Tinv = np.array([[sc, -ss, 0], [ss, sc, 0], [tx, ty, 1]])
-    T = inv(Tinv)
-    T[:, 2] = np.array([0, 0, 1])
-
-    return T, Tinv
-
-
-def findSimilarity(uv, xy, options=None):
-    options = {'K': 2}
-
-    #    uv = np.array(uv)
-    #    xy = np.array(xy)
-
-    # Solve for trans1
-    trans1, trans1_inv = findNonreflectiveSimilarity(uv, xy, options)
-
-    # Solve for trans2
-
-    # manually reflect the xy data across the Y-axis
-    xyR = xy
-    xyR[:, 0] = -1 * xyR[:, 0]
-
-    trans2r, trans2r_inv = findNonreflectiveSimilarity(uv, xyR, options)
-
-    # manually reflect the tform to undo the reflection done on xyR
-    TreflectY = np.array([[-1, 0, 0], [0, 1, 0], [0, 0, 1]])
-
-    trans2 = np.dot(trans2r, TreflectY)
-
-    # Figure out if trans1 or trans2 is better
-    xy1 = tformfwd(trans1, uv)
-    norm1 = norm(xy1 - xy)
-
-    xy2 = tformfwd(trans2, uv)
-    norm2 = norm(xy2 - xy)
-
-    if norm1 <= norm2:
-        return trans1, trans1_inv
-    else:
-        trans2_inv = inv(trans2)
-        return trans2, trans2_inv
-
-
-def get_similarity_transform(src_pts, dst_pts, reflective=True):
-    """
-    Function:
-    ----------
-        Find Similarity Transform Matrix 'trans':
-            u = src_pts[:, 0]
-            v = src_pts[:, 1]
-            x = dst_pts[:, 0]
-            y = dst_pts[:, 1]
-            [x, y, 1] = [u, v, 1] * trans
-
-    Parameters:
-    ----------
-        @src_pts: Kx2 np.array
-            source points, each row is a pair of coordinates (x, y)
-        @dst_pts: Kx2 np.array
-            destination points, each row is a pair of transformed
-            coordinates (x, y)
-        @reflective: True or False
-            if True:
-                use reflective similarity transform
-            else:
-                use non-reflective similarity transform
-
-    Returns:
-    ----------
-       @trans: 3x3 np.array
-            transform matrix from uv to xy
-        trans_inv: 3x3 np.array
-            inverse of trans, transform matrix from xy to uv
-    """
-
-    if reflective:
-        trans, trans_inv = findSimilarity(src_pts, dst_pts)
-    else:
-        trans, trans_inv = findNonreflectiveSimilarity(src_pts, dst_pts)
-
-    return trans, trans_inv
-
-
-def cvt_tform_mat_for_cv2(trans):
-    """
-    Function:
-    ----------
-        Convert Transform Matrix 'trans' into 'cv2_trans' which could be
-        directly used by cv2.warpAffine():
-            u = src_pts[:, 0]
-            v = src_pts[:, 1]
-            x = dst_pts[:, 0]
-            y = dst_pts[:, 1]
-            [x, y].T = cv_trans * [u, v, 1].T
-
-    Parameters:
-    ----------
-        @trans: 3x3 np.array
-            transform matrix from uv to xy
-
-    Returns:
-    ----------
-        @cv2_trans: 2x3 np.array
-            transform matrix from src_pts to dst_pts, could be directly used
-            for cv2.warpAffine()
-    """
-    cv2_trans = trans[:, 0:2].T
-
-    return cv2_trans
-
-
-def get_similarity_transform_for_cv2(src_pts, dst_pts, reflective=True):
-    """
-    Function:
-    ----------
-        Find Similarity Transform Matrix 'cv2_trans' which could be
-        directly used by cv2.warpAffine():
-            u = src_pts[:, 0]
-            v = src_pts[:, 1]
-            x = dst_pts[:, 0]
-            y = dst_pts[:, 1]
-            [x, y].T = cv_trans * [u, v, 1].T
-
-    Parameters:
-    ----------
-        @src_pts: Kx2 np.array
-            source points, each row is a pair of coordinates (x, y)
-        @dst_pts: Kx2 np.array
-            destination points, each row is a pair of transformed
-            coordinates (x, y)
-        reflective: True or False
-            if True:
-                use reflective similarity transform
-            else:
-                use non-reflective similarity transform
-
-    Returns:
-    ----------
-        @cv2_trans: 2x3 np.array
-            transform matrix from src_pts to dst_pts, could be directly used
-            for cv2.warpAffine()
-    """
-    trans, trans_inv = get_similarity_transform(src_pts, dst_pts, reflective)
-    cv2_trans = cvt_tform_mat_for_cv2(trans)
-
-    return cv2_trans
-
-
-if __name__ == '__main__':
-    """
-    u = [0, 6, -2]
-    v = [0, 3, 5]
-    x = [-1, 0, 4]
-    y = [-1, -10, 4]
-
-    # In Matlab, run:
-    #
-    #   uv = [u'; v'];
-    #   xy = [x'; y'];
-    #   tform_sim=cp2tform(uv,xy,'similarity');
-    #
-    #   trans = tform_sim.tdata.T
-    #   ans =
-    #       -0.0764   -1.6190         0
-    #        1.6190   -0.0764         0
-    #       -3.2156    0.0290    1.0000
-    #   trans_inv = tform_sim.tdata.Tinv
-    #    ans =
-    #
-    #       -0.0291    0.6163         0
-    #       -0.6163   -0.0291         0
-    #       -0.0756    1.9826    1.0000
-    #    xy_m=tformfwd(tform_sim, u,v)
-    #
-    #    xy_m =
-    #
-    #       -3.2156    0.0290
-    #        1.1833   -9.9143
-    #        5.0323    2.8853
-    #    uv_m=tforminv(tform_sim, x,y)
-    #
-    #    uv_m =
-    #
-    #        0.5698    1.3953
-    #        6.0872    2.2733
-    #       -2.6570    4.3314
-    """
-    u = [0, 6, -2]
-    v = [0, 3, 5]
-    x = [-1, 0, 4]
-    y = [-1, -10, 4]
-
-    uv = np.array((u, v)).T
-    xy = np.array((x, y)).T
-
-    print('\n--->uv:')
-    print(uv)
-    print('\n--->xy:')
-    print(xy)
-
-    trans, trans_inv = get_similarity_transform(uv, xy)
-
-    print('\n--->trans matrix:')
-    print(trans)
-
-    print('\n--->trans_inv matrix:')
-    print(trans_inv)
-
-    print('\n---> apply transform to uv')
-    print('\nxy_m = uv_augmented * trans')
-    uv_aug = np.hstack((uv, np.ones((uv.shape[0], 1))))
-    xy_m = np.dot(uv_aug, trans)
-    print(xy_m)
-
-    print('\nxy_m = tformfwd(trans, uv)')
-    xy_m = tformfwd(trans, uv)
-    print(xy_m)
-
-    print('\n---> apply inverse transform to xy')
-    print('\nuv_m = xy_augmented * trans_inv')
-    xy_aug = np.hstack((xy, np.ones((xy.shape[0], 1))))
-    uv_m = np.dot(xy_aug, trans_inv)
-    print(uv_m)
-
-    print('\nuv_m = tformfwd(trans_inv, xy)')
-    uv_m = tformfwd(trans_inv, xy)
-    print(uv_m)
-
-    uv_m = tforminv(trans, xy)
-    print('\nuv_m = tforminv(trans, xy)')
-    print(uv_m)
diff --git a/repositories/CodeFormer/facelib/detection/retinaface/__pycache__/retinaface.cpython-310.pyc b/repositories/CodeFormer/facelib/detection/retinaface/__pycache__/retinaface.cpython-310.pyc
deleted file mode 100644
index c6cf13892384a2cfe6798a209d69ac496f389e42..0000000000000000000000000000000000000000
Binary files a/repositories/CodeFormer/facelib/detection/retinaface/__pycache__/retinaface.cpython-310.pyc and /dev/null differ
diff --git a/repositories/CodeFormer/facelib/detection/retinaface/__pycache__/retinaface_net.cpython-310.pyc b/repositories/CodeFormer/facelib/detection/retinaface/__pycache__/retinaface_net.cpython-310.pyc
deleted file mode 100644
index 5948816edff40a89cbdf4f126ffb4a1b73c98a50..0000000000000000000000000000000000000000
Binary files a/repositories/CodeFormer/facelib/detection/retinaface/__pycache__/retinaface_net.cpython-310.pyc and /dev/null differ
diff --git a/repositories/CodeFormer/facelib/detection/retinaface/__pycache__/retinaface_utils.cpython-310.pyc b/repositories/CodeFormer/facelib/detection/retinaface/__pycache__/retinaface_utils.cpython-310.pyc
deleted file mode 100644
index ca0d79e0695ccbd69918060ab4807cbdcb80ab20..0000000000000000000000000000000000000000
Binary files a/repositories/CodeFormer/facelib/detection/retinaface/__pycache__/retinaface_utils.cpython-310.pyc and /dev/null differ
diff --git a/repositories/CodeFormer/facelib/detection/retinaface/retinaface.py b/repositories/CodeFormer/facelib/detection/retinaface/retinaface.py
deleted file mode 100644
index 02593556d88a90232bbe55a062875f4af4520621..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/facelib/detection/retinaface/retinaface.py
+++ /dev/null
@@ -1,370 +0,0 @@
-import cv2
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from PIL import Image
-from torchvision.models._utils import IntermediateLayerGetter as IntermediateLayerGetter
-
-from facelib.detection.align_trans import get_reference_facial_points, warp_and_crop_face
-from facelib.detection.retinaface.retinaface_net import FPN, SSH, MobileNetV1, make_bbox_head, make_class_head, make_landmark_head
-from facelib.detection.retinaface.retinaface_utils import (PriorBox, batched_decode, batched_decode_landm, decode, decode_landm,
-                                                 py_cpu_nms)
-
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-
-
-def generate_config(network_name):
-
-    cfg_mnet = {
-        'name': 'mobilenet0.25',
-        'min_sizes': [[16, 32], [64, 128], [256, 512]],
-        'steps': [8, 16, 32],
-        'variance': [0.1, 0.2],
-        'clip': False,
-        'loc_weight': 2.0,
-        'gpu_train': True,
-        'batch_size': 32,
-        'ngpu': 1,
-        'epoch': 250,
-        'decay1': 190,
-        'decay2': 220,
-        'image_size': 640,
-        'return_layers': {
-            'stage1': 1,
-            'stage2': 2,
-            'stage3': 3
-        },
-        'in_channel': 32,
-        'out_channel': 64
-    }
-
-    cfg_re50 = {
-        'name': 'Resnet50',
-        'min_sizes': [[16, 32], [64, 128], [256, 512]],
-        'steps': [8, 16, 32],
-        'variance': [0.1, 0.2],
-        'clip': False,
-        'loc_weight': 2.0,
-        'gpu_train': True,
-        'batch_size': 24,
-        'ngpu': 4,
-        'epoch': 100,
-        'decay1': 70,
-        'decay2': 90,
-        'image_size': 840,
-        'return_layers': {
-            'layer2': 1,
-            'layer3': 2,
-            'layer4': 3
-        },
-        'in_channel': 256,
-        'out_channel': 256
-    }
-
-    if network_name == 'mobile0.25':
-        return cfg_mnet
-    elif network_name == 'resnet50':
-        return cfg_re50
-    else:
-        raise NotImplementedError(f'network_name={network_name}')
-
-
-class RetinaFace(nn.Module):
-
-    def __init__(self, network_name='resnet50', half=False, phase='test'):
-        super(RetinaFace, self).__init__()
-        self.half_inference = half
-        cfg = generate_config(network_name)
-        self.backbone = cfg['name']
-
-        self.model_name = f'retinaface_{network_name}'
-        self.cfg = cfg
-        self.phase = phase
-        self.target_size, self.max_size = 1600, 2150
-        self.resize, self.scale, self.scale1 = 1., None, None
-        self.mean_tensor = torch.tensor([[[[104.]], [[117.]], [[123.]]]]).to(device)
-        self.reference = get_reference_facial_points(default_square=True)
-        # Build network.
-        backbone = None
-        if cfg['name'] == 'mobilenet0.25':
-            backbone = MobileNetV1()
-            self.body = IntermediateLayerGetter(backbone, cfg['return_layers'])
-        elif cfg['name'] == 'Resnet50':
-            import torchvision.models as models
-            backbone = models.resnet50(pretrained=False)
-            self.body = IntermediateLayerGetter(backbone, cfg['return_layers'])
-
-        in_channels_stage2 = cfg['in_channel']
-        in_channels_list = [
-            in_channels_stage2 * 2,
-            in_channels_stage2 * 4,
-            in_channels_stage2 * 8,
-        ]
-
-        out_channels = cfg['out_channel']
-        self.fpn = FPN(in_channels_list, out_channels)
-        self.ssh1 = SSH(out_channels, out_channels)
-        self.ssh2 = SSH(out_channels, out_channels)
-        self.ssh3 = SSH(out_channels, out_channels)
-
-        self.ClassHead = make_class_head(fpn_num=3, inchannels=cfg['out_channel'])
-        self.BboxHead = make_bbox_head(fpn_num=3, inchannels=cfg['out_channel'])
-        self.LandmarkHead = make_landmark_head(fpn_num=3, inchannels=cfg['out_channel'])
-
-        self.to(device)
-        self.eval()
-        if self.half_inference:
-            self.half()
-
-    def forward(self, inputs):
-        out = self.body(inputs)
-
-        if self.backbone == 'mobilenet0.25' or self.backbone == 'Resnet50':
-            out = list(out.values())
-        # FPN
-        fpn = self.fpn(out)
-
-        # SSH
-        feature1 = self.ssh1(fpn[0])
-        feature2 = self.ssh2(fpn[1])
-        feature3 = self.ssh3(fpn[2])
-        features = [feature1, feature2, feature3]
-
-        bbox_regressions = torch.cat([self.BboxHead[i](feature) for i, feature in enumerate(features)], dim=1)
-        classifications = torch.cat([self.ClassHead[i](feature) for i, feature in enumerate(features)], dim=1)
-        tmp = [self.LandmarkHead[i](feature) for i, feature in enumerate(features)]
-        ldm_regressions = (torch.cat(tmp, dim=1))
-
-        if self.phase == 'train':
-            output = (bbox_regressions, classifications, ldm_regressions)
-        else:
-            output = (bbox_regressions, F.softmax(classifications, dim=-1), ldm_regressions)
-        return output
-
-    def __detect_faces(self, inputs):
-        # get scale
-        height, width = inputs.shape[2:]
-        self.scale = torch.tensor([width, height, width, height], dtype=torch.float32).to(device)
-        tmp = [width, height, width, height, width, height, width, height, width, height]
-        self.scale1 = torch.tensor(tmp, dtype=torch.float32).to(device)
-
-        # forawrd
-        inputs = inputs.to(device)
-        if self.half_inference:
-            inputs = inputs.half()
-        loc, conf, landmarks = self(inputs)
-
-        # get priorbox
-        priorbox = PriorBox(self.cfg, image_size=inputs.shape[2:])
-        priors = priorbox.forward().to(device)
-
-        return loc, conf, landmarks, priors
-
-    # single image detection
-    def transform(self, image, use_origin_size):
-        # convert to opencv format
-        if isinstance(image, Image.Image):
-            image = cv2.cvtColor(np.asarray(image), cv2.COLOR_RGB2BGR)
-        image = image.astype(np.float32)
-
-        # testing scale
-        im_size_min = np.min(image.shape[0:2])
-        im_size_max = np.max(image.shape[0:2])
-        resize = float(self.target_size) / float(im_size_min)
-
-        # prevent bigger axis from being more than max_size
-        if np.round(resize * im_size_max) > self.max_size:
-            resize = float(self.max_size) / float(im_size_max)
-        resize = 1 if use_origin_size else resize
-
-        # resize
-        if resize != 1:
-            image = cv2.resize(image, None, None, fx=resize, fy=resize, interpolation=cv2.INTER_LINEAR)
-
-        # convert to torch.tensor format
-        # image -= (104, 117, 123)
-        image = image.transpose(2, 0, 1)
-        image = torch.from_numpy(image).unsqueeze(0)
-
-        return image, resize
-
-    def detect_faces(
-        self,
-        image,
-        conf_threshold=0.8,
-        nms_threshold=0.4,
-        use_origin_size=True,
-    ):
-        """
-        Params:
-            imgs: BGR image
-        """
-        image, self.resize = self.transform(image, use_origin_size)
-        image = image.to(device)
-        if self.half_inference:
-            image = image.half()
-        image = image - self.mean_tensor
-
-        loc, conf, landmarks, priors = self.__detect_faces(image)
-
-        boxes = decode(loc.data.squeeze(0), priors.data, self.cfg['variance'])
-        boxes = boxes * self.scale / self.resize
-        boxes = boxes.cpu().numpy()
-
-        scores = conf.squeeze(0).data.cpu().numpy()[:, 1]
-
-        landmarks = decode_landm(landmarks.squeeze(0), priors, self.cfg['variance'])
-        landmarks = landmarks * self.scale1 / self.resize
-        landmarks = landmarks.cpu().numpy()
-
-        # ignore low scores
-        inds = np.where(scores > conf_threshold)[0]
-        boxes, landmarks, scores = boxes[inds], landmarks[inds], scores[inds]
-
-        # sort
-        order = scores.argsort()[::-1]
-        boxes, landmarks, scores = boxes[order], landmarks[order], scores[order]
-
-        # do NMS
-        bounding_boxes = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32, copy=False)
-        keep = py_cpu_nms(bounding_boxes, nms_threshold)
-        bounding_boxes, landmarks = bounding_boxes[keep, :], landmarks[keep]
-        # self.t['forward_pass'].toc()
-        # print(self.t['forward_pass'].average_time)
-        # import sys
-        # sys.stdout.flush()
-        return np.concatenate((bounding_boxes, landmarks), axis=1)
-
-    def __align_multi(self, image, boxes, landmarks, limit=None):
-
-        if len(boxes) < 1:
-            return [], []
-
-        if limit:
-            boxes = boxes[:limit]
-            landmarks = landmarks[:limit]
-
-        faces = []
-        for landmark in landmarks:
-            facial5points = [[landmark[2 * j], landmark[2 * j + 1]] for j in range(5)]
-
-            warped_face = warp_and_crop_face(np.array(image), facial5points, self.reference, crop_size=(112, 112))
-            faces.append(warped_face)
-
-        return np.concatenate((boxes, landmarks), axis=1), faces
-
-    def align_multi(self, img, conf_threshold=0.8, limit=None):
-
-        rlt = self.detect_faces(img, conf_threshold=conf_threshold)
-        boxes, landmarks = rlt[:, 0:5], rlt[:, 5:]
-
-        return self.__align_multi(img, boxes, landmarks, limit)
-
-    # batched detection
-    def batched_transform(self, frames, use_origin_size):
-        """
-        Arguments:
-            frames: a list of PIL.Image, or torch.Tensor(shape=[n, h, w, c],
-                type=np.float32, BGR format).
-            use_origin_size: whether to use origin size.
-        """
-        from_PIL = True if isinstance(frames[0], Image.Image) else False
-
-        # convert to opencv format
-        if from_PIL:
-            frames = [cv2.cvtColor(np.asarray(frame), cv2.COLOR_RGB2BGR) for frame in frames]
-            frames = np.asarray(frames, dtype=np.float32)
-
-        # testing scale
-        im_size_min = np.min(frames[0].shape[0:2])
-        im_size_max = np.max(frames[0].shape[0:2])
-        resize = float(self.target_size) / float(im_size_min)
-
-        # prevent bigger axis from being more than max_size
-        if np.round(resize * im_size_max) > self.max_size:
-            resize = float(self.max_size) / float(im_size_max)
-        resize = 1 if use_origin_size else resize
-
-        # resize
-        if resize != 1:
-            if not from_PIL:
-                frames = F.interpolate(frames, scale_factor=resize)
-            else:
-                frames = [
-                    cv2.resize(frame, None, None, fx=resize, fy=resize, interpolation=cv2.INTER_LINEAR)
-                    for frame in frames
-                ]
-
-        # convert to torch.tensor format
-        if not from_PIL:
-            frames = frames.transpose(1, 2).transpose(1, 3).contiguous()
-        else:
-            frames = frames.transpose((0, 3, 1, 2))
-            frames = torch.from_numpy(frames)
-
-        return frames, resize
-
-    def batched_detect_faces(self, frames, conf_threshold=0.8, nms_threshold=0.4, use_origin_size=True):
-        """
-        Arguments:
-            frames: a list of PIL.Image, or np.array(shape=[n, h, w, c],
-                type=np.uint8, BGR format).
-            conf_threshold: confidence threshold.
-            nms_threshold: nms threshold.
-            use_origin_size: whether to use origin size.
-        Returns:
-            final_bounding_boxes: list of np.array ([n_boxes, 5],
-                type=np.float32).
-            final_landmarks: list of np.array ([n_boxes, 10], type=np.float32).
-        """
-        # self.t['forward_pass'].tic()
-        frames, self.resize = self.batched_transform(frames, use_origin_size)
-        frames = frames.to(device)
-        frames = frames - self.mean_tensor
-
-        b_loc, b_conf, b_landmarks, priors = self.__detect_faces(frames)
-
-        final_bounding_boxes, final_landmarks = [], []
-
-        # decode
-        priors = priors.unsqueeze(0)
-        b_loc = batched_decode(b_loc, priors, self.cfg['variance']) * self.scale / self.resize
-        b_landmarks = batched_decode_landm(b_landmarks, priors, self.cfg['variance']) * self.scale1 / self.resize
-        b_conf = b_conf[:, :, 1]
-
-        # index for selection
-        b_indice = b_conf > conf_threshold
-
-        # concat
-        b_loc_and_conf = torch.cat((b_loc, b_conf.unsqueeze(-1)), dim=2).float()
-
-        for pred, landm, inds in zip(b_loc_and_conf, b_landmarks, b_indice):
-
-            # ignore low scores
-            pred, landm = pred[inds, :], landm[inds, :]
-            if pred.shape[0] == 0:
-                final_bounding_boxes.append(np.array([], dtype=np.float32))
-                final_landmarks.append(np.array([], dtype=np.float32))
-                continue
-
-            # sort
-            # order = score.argsort(descending=True)
-            # box, landm, score = box[order], landm[order], score[order]
-
-            # to CPU
-            bounding_boxes, landm = pred.cpu().numpy(), landm.cpu().numpy()
-
-            # NMS
-            keep = py_cpu_nms(bounding_boxes, nms_threshold)
-            bounding_boxes, landmarks = bounding_boxes[keep, :], landm[keep]
-
-            # append
-            final_bounding_boxes.append(bounding_boxes)
-            final_landmarks.append(landmarks)
-        # self.t['forward_pass'].toc(average=True)
-        # self.batch_time += self.t['forward_pass'].diff
-        # self.total_frame += len(frames)
-        # print(self.batch_time / self.total_frame)
-
-        return final_bounding_boxes, final_landmarks
diff --git a/repositories/CodeFormer/facelib/detection/retinaface/retinaface_net.py b/repositories/CodeFormer/facelib/detection/retinaface/retinaface_net.py
deleted file mode 100644
index ab6aa82d3e9055a838f1f9076b12f05fdfc154d0..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/facelib/detection/retinaface/retinaface_net.py
+++ /dev/null
@@ -1,196 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-def conv_bn(inp, oup, stride=1, leaky=0):
-    return nn.Sequential(
-        nn.Conv2d(inp, oup, 3, stride, 1, bias=False), nn.BatchNorm2d(oup),
-        nn.LeakyReLU(negative_slope=leaky, inplace=True))
-
-
-def conv_bn_no_relu(inp, oup, stride):
-    return nn.Sequential(
-        nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
-        nn.BatchNorm2d(oup),
-    )
-
-
-def conv_bn1X1(inp, oup, stride, leaky=0):
-    return nn.Sequential(
-        nn.Conv2d(inp, oup, 1, stride, padding=0, bias=False), nn.BatchNorm2d(oup),
-        nn.LeakyReLU(negative_slope=leaky, inplace=True))
-
-
-def conv_dw(inp, oup, stride, leaky=0.1):
-    return nn.Sequential(
-        nn.Conv2d(inp, inp, 3, stride, 1, groups=inp, bias=False),
-        nn.BatchNorm2d(inp),
-        nn.LeakyReLU(negative_slope=leaky, inplace=True),
-        nn.Conv2d(inp, oup, 1, 1, 0, bias=False),
-        nn.BatchNorm2d(oup),
-        nn.LeakyReLU(negative_slope=leaky, inplace=True),
-    )
-
-
-class SSH(nn.Module):
-
-    def __init__(self, in_channel, out_channel):
-        super(SSH, self).__init__()
-        assert out_channel % 4 == 0
-        leaky = 0
-        if (out_channel <= 64):
-            leaky = 0.1
-        self.conv3X3 = conv_bn_no_relu(in_channel, out_channel // 2, stride=1)
-
-        self.conv5X5_1 = conv_bn(in_channel, out_channel // 4, stride=1, leaky=leaky)
-        self.conv5X5_2 = conv_bn_no_relu(out_channel // 4, out_channel // 4, stride=1)
-
-        self.conv7X7_2 = conv_bn(out_channel // 4, out_channel // 4, stride=1, leaky=leaky)
-        self.conv7x7_3 = conv_bn_no_relu(out_channel // 4, out_channel // 4, stride=1)
-
-    def forward(self, input):
-        conv3X3 = self.conv3X3(input)
-
-        conv5X5_1 = self.conv5X5_1(input)
-        conv5X5 = self.conv5X5_2(conv5X5_1)
-
-        conv7X7_2 = self.conv7X7_2(conv5X5_1)
-        conv7X7 = self.conv7x7_3(conv7X7_2)
-
-        out = torch.cat([conv3X3, conv5X5, conv7X7], dim=1)
-        out = F.relu(out)
-        return out
-
-
-class FPN(nn.Module):
-
-    def __init__(self, in_channels_list, out_channels):
-        super(FPN, self).__init__()
-        leaky = 0
-        if (out_channels <= 64):
-            leaky = 0.1
-        self.output1 = conv_bn1X1(in_channels_list[0], out_channels, stride=1, leaky=leaky)
-        self.output2 = conv_bn1X1(in_channels_list[1], out_channels, stride=1, leaky=leaky)
-        self.output3 = conv_bn1X1(in_channels_list[2], out_channels, stride=1, leaky=leaky)
-
-        self.merge1 = conv_bn(out_channels, out_channels, leaky=leaky)
-        self.merge2 = conv_bn(out_channels, out_channels, leaky=leaky)
-
-    def forward(self, input):
-        # names = list(input.keys())
-        # input = list(input.values())
-
-        output1 = self.output1(input[0])
-        output2 = self.output2(input[1])
-        output3 = self.output3(input[2])
-
-        up3 = F.interpolate(output3, size=[output2.size(2), output2.size(3)], mode='nearest')
-        output2 = output2 + up3
-        output2 = self.merge2(output2)
-
-        up2 = F.interpolate(output2, size=[output1.size(2), output1.size(3)], mode='nearest')
-        output1 = output1 + up2
-        output1 = self.merge1(output1)
-
-        out = [output1, output2, output3]
-        return out
-
-
-class MobileNetV1(nn.Module):
-
-    def __init__(self):
-        super(MobileNetV1, self).__init__()
-        self.stage1 = nn.Sequential(
-            conv_bn(3, 8, 2, leaky=0.1),  # 3
-            conv_dw(8, 16, 1),  # 7
-            conv_dw(16, 32, 2),  # 11
-            conv_dw(32, 32, 1),  # 19
-            conv_dw(32, 64, 2),  # 27
-            conv_dw(64, 64, 1),  # 43
-        )
-        self.stage2 = nn.Sequential(
-            conv_dw(64, 128, 2),  # 43 + 16 = 59
-            conv_dw(128, 128, 1),  # 59 + 32 = 91
-            conv_dw(128, 128, 1),  # 91 + 32 = 123
-            conv_dw(128, 128, 1),  # 123 + 32 = 155
-            conv_dw(128, 128, 1),  # 155 + 32 = 187
-            conv_dw(128, 128, 1),  # 187 + 32 = 219
-        )
-        self.stage3 = nn.Sequential(
-            conv_dw(128, 256, 2),  # 219 +3 2 = 241
-            conv_dw(256, 256, 1),  # 241 + 64 = 301
-        )
-        self.avg = nn.AdaptiveAvgPool2d((1, 1))
-        self.fc = nn.Linear(256, 1000)
-
-    def forward(self, x):
-        x = self.stage1(x)
-        x = self.stage2(x)
-        x = self.stage3(x)
-        x = self.avg(x)
-        # x = self.model(x)
-        x = x.view(-1, 256)
-        x = self.fc(x)
-        return x
-
-
-class ClassHead(nn.Module):
-
-    def __init__(self, inchannels=512, num_anchors=3):
-        super(ClassHead, self).__init__()
-        self.num_anchors = num_anchors
-        self.conv1x1 = nn.Conv2d(inchannels, self.num_anchors * 2, kernel_size=(1, 1), stride=1, padding=0)
-
-    def forward(self, x):
-        out = self.conv1x1(x)
-        out = out.permute(0, 2, 3, 1).contiguous()
-
-        return out.view(out.shape[0], -1, 2)
-
-
-class BboxHead(nn.Module):
-
-    def __init__(self, inchannels=512, num_anchors=3):
-        super(BboxHead, self).__init__()
-        self.conv1x1 = nn.Conv2d(inchannels, num_anchors * 4, kernel_size=(1, 1), stride=1, padding=0)
-
-    def forward(self, x):
-        out = self.conv1x1(x)
-        out = out.permute(0, 2, 3, 1).contiguous()
-
-        return out.view(out.shape[0], -1, 4)
-
-
-class LandmarkHead(nn.Module):
-
-    def __init__(self, inchannels=512, num_anchors=3):
-        super(LandmarkHead, self).__init__()
-        self.conv1x1 = nn.Conv2d(inchannels, num_anchors * 10, kernel_size=(1, 1), stride=1, padding=0)
-
-    def forward(self, x):
-        out = self.conv1x1(x)
-        out = out.permute(0, 2, 3, 1).contiguous()
-
-        return out.view(out.shape[0], -1, 10)
-
-
-def make_class_head(fpn_num=3, inchannels=64, anchor_num=2):
-    classhead = nn.ModuleList()
-    for i in range(fpn_num):
-        classhead.append(ClassHead(inchannels, anchor_num))
-    return classhead
-
-
-def make_bbox_head(fpn_num=3, inchannels=64, anchor_num=2):
-    bboxhead = nn.ModuleList()
-    for i in range(fpn_num):
-        bboxhead.append(BboxHead(inchannels, anchor_num))
-    return bboxhead
-
-
-def make_landmark_head(fpn_num=3, inchannels=64, anchor_num=2):
-    landmarkhead = nn.ModuleList()
-    for i in range(fpn_num):
-        landmarkhead.append(LandmarkHead(inchannels, anchor_num))
-    return landmarkhead
diff --git a/repositories/CodeFormer/facelib/detection/retinaface/retinaface_utils.py b/repositories/CodeFormer/facelib/detection/retinaface/retinaface_utils.py
deleted file mode 100644
index 8c357757741c6d9bd7ce4d8ce740fefd51850fbf..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/facelib/detection/retinaface/retinaface_utils.py
+++ /dev/null
@@ -1,421 +0,0 @@
-import numpy as np
-import torch
-import torchvision
-from itertools import product as product
-from math import ceil
-
-
-class PriorBox(object):
-
-    def __init__(self, cfg, image_size=None, phase='train'):
-        super(PriorBox, self).__init__()
-        self.min_sizes = cfg['min_sizes']
-        self.steps = cfg['steps']
-        self.clip = cfg['clip']
-        self.image_size = image_size
-        self.feature_maps = [[ceil(self.image_size[0] / step), ceil(self.image_size[1] / step)] for step in self.steps]
-        self.name = 's'
-
-    def forward(self):
-        anchors = []
-        for k, f in enumerate(self.feature_maps):
-            min_sizes = self.min_sizes[k]
-            for i, j in product(range(f[0]), range(f[1])):
-                for min_size in min_sizes:
-                    s_kx = min_size / self.image_size[1]
-                    s_ky = min_size / self.image_size[0]
-                    dense_cx = [x * self.steps[k] / self.image_size[1] for x in [j + 0.5]]
-                    dense_cy = [y * self.steps[k] / self.image_size[0] for y in [i + 0.5]]
-                    for cy, cx in product(dense_cy, dense_cx):
-                        anchors += [cx, cy, s_kx, s_ky]
-
-        # back to torch land
-        output = torch.Tensor(anchors).view(-1, 4)
-        if self.clip:
-            output.clamp_(max=1, min=0)
-        return output
-
-
-def py_cpu_nms(dets, thresh):
-    """Pure Python NMS baseline."""
-    keep = torchvision.ops.nms(
-        boxes=torch.Tensor(dets[:, :4]),
-        scores=torch.Tensor(dets[:, 4]),
-        iou_threshold=thresh,
-    )
-
-    return list(keep)
-
-
-def point_form(boxes):
-    """ Convert prior_boxes to (xmin, ymin, xmax, ymax)
-    representation for comparison to point form ground truth data.
-    Args:
-        boxes: (tensor) center-size default boxes from priorbox layers.
-    Return:
-        boxes: (tensor) Converted xmin, ymin, xmax, ymax form of boxes.
-    """
-    return torch.cat(
-        (
-            boxes[:, :2] - boxes[:, 2:] / 2,  # xmin, ymin
-            boxes[:, :2] + boxes[:, 2:] / 2),
-        1)  # xmax, ymax
-
-
-def center_size(boxes):
-    """ Convert prior_boxes to (cx, cy, w, h)
-    representation for comparison to center-size form ground truth data.
-    Args:
-        boxes: (tensor) point_form boxes
-    Return:
-        boxes: (tensor) Converted xmin, ymin, xmax, ymax form of boxes.
-    """
-    return torch.cat(
-        (boxes[:, 2:] + boxes[:, :2]) / 2,  # cx, cy
-        boxes[:, 2:] - boxes[:, :2],
-        1)  # w, h
-
-
-def intersect(box_a, box_b):
-    """ We resize both tensors to [A,B,2] without new malloc:
-    [A,2] -> [A,1,2] -> [A,B,2]
-    [B,2] -> [1,B,2] -> [A,B,2]
-    Then we compute the area of intersect between box_a and box_b.
-    Args:
-      box_a: (tensor) bounding boxes, Shape: [A,4].
-      box_b: (tensor) bounding boxes, Shape: [B,4].
-    Return:
-      (tensor) intersection area, Shape: [A,B].
-    """
-    A = box_a.size(0)
-    B = box_b.size(0)
-    max_xy = torch.min(box_a[:, 2:].unsqueeze(1).expand(A, B, 2), box_b[:, 2:].unsqueeze(0).expand(A, B, 2))
-    min_xy = torch.max(box_a[:, :2].unsqueeze(1).expand(A, B, 2), box_b[:, :2].unsqueeze(0).expand(A, B, 2))
-    inter = torch.clamp((max_xy - min_xy), min=0)
-    return inter[:, :, 0] * inter[:, :, 1]
-
-
-def jaccard(box_a, box_b):
-    """Compute the jaccard overlap of two sets of boxes.  The jaccard overlap
-    is simply the intersection over union of two boxes.  Here we operate on
-    ground truth boxes and default boxes.
-    E.g.:
-        A ∩ B / A ∪ B = A ∩ B / (area(A) + area(B) - A ∩ B)
-    Args:
-        box_a: (tensor) Ground truth bounding boxes, Shape: [num_objects,4]
-        box_b: (tensor) Prior boxes from priorbox layers, Shape: [num_priors,4]
-    Return:
-        jaccard overlap: (tensor) Shape: [box_a.size(0), box_b.size(0)]
-    """
-    inter = intersect(box_a, box_b)
-    area_a = ((box_a[:, 2] - box_a[:, 0]) * (box_a[:, 3] - box_a[:, 1])).unsqueeze(1).expand_as(inter)  # [A,B]
-    area_b = ((box_b[:, 2] - box_b[:, 0]) * (box_b[:, 3] - box_b[:, 1])).unsqueeze(0).expand_as(inter)  # [A,B]
-    union = area_a + area_b - inter
-    return inter / union  # [A,B]
-
-
-def matrix_iou(a, b):
-    """
-    return iou of a and b, numpy version for data augenmentation
-    """
-    lt = np.maximum(a[:, np.newaxis, :2], b[:, :2])
-    rb = np.minimum(a[:, np.newaxis, 2:], b[:, 2:])
-
-    area_i = np.prod(rb - lt, axis=2) * (lt < rb).all(axis=2)
-    area_a = np.prod(a[:, 2:] - a[:, :2], axis=1)
-    area_b = np.prod(b[:, 2:] - b[:, :2], axis=1)
-    return area_i / (area_a[:, np.newaxis] + area_b - area_i)
-
-
-def matrix_iof(a, b):
-    """
-    return iof of a and b, numpy version for data augenmentation
-    """
-    lt = np.maximum(a[:, np.newaxis, :2], b[:, :2])
-    rb = np.minimum(a[:, np.newaxis, 2:], b[:, 2:])
-
-    area_i = np.prod(rb - lt, axis=2) * (lt < rb).all(axis=2)
-    area_a = np.prod(a[:, 2:] - a[:, :2], axis=1)
-    return area_i / np.maximum(area_a[:, np.newaxis], 1)
-
-
-def match(threshold, truths, priors, variances, labels, landms, loc_t, conf_t, landm_t, idx):
-    """Match each prior box with the ground truth box of the highest jaccard
-    overlap, encode the bounding boxes, then return the matched indices
-    corresponding to both confidence and location preds.
-    Args:
-        threshold: (float) The overlap threshold used when matching boxes.
-        truths: (tensor) Ground truth boxes, Shape: [num_obj, 4].
-        priors: (tensor) Prior boxes from priorbox layers, Shape: [n_priors,4].
-        variances: (tensor) Variances corresponding to each prior coord,
-            Shape: [num_priors, 4].
-        labels: (tensor) All the class labels for the image, Shape: [num_obj].
-        landms: (tensor) Ground truth landms, Shape [num_obj, 10].
-        loc_t: (tensor) Tensor to be filled w/ encoded location targets.
-        conf_t: (tensor) Tensor to be filled w/ matched indices for conf preds.
-        landm_t: (tensor) Tensor to be filled w/ encoded landm targets.
-        idx: (int) current batch index
-    Return:
-        The matched indices corresponding to 1)location 2)confidence
-        3)landm preds.
-    """
-    # jaccard index
-    overlaps = jaccard(truths, point_form(priors))
-    # (Bipartite Matching)
-    # [1,num_objects] best prior for each ground truth
-    best_prior_overlap, best_prior_idx = overlaps.max(1, keepdim=True)
-
-    # ignore hard gt
-    valid_gt_idx = best_prior_overlap[:, 0] >= 0.2
-    best_prior_idx_filter = best_prior_idx[valid_gt_idx, :]
-    if best_prior_idx_filter.shape[0] <= 0:
-        loc_t[idx] = 0
-        conf_t[idx] = 0
-        return
-
-    # [1,num_priors] best ground truth for each prior
-    best_truth_overlap, best_truth_idx = overlaps.max(0, keepdim=True)
-    best_truth_idx.squeeze_(0)
-    best_truth_overlap.squeeze_(0)
-    best_prior_idx.squeeze_(1)
-    best_prior_idx_filter.squeeze_(1)
-    best_prior_overlap.squeeze_(1)
-    best_truth_overlap.index_fill_(0, best_prior_idx_filter, 2)  # ensure best prior
-    # TODO refactor: index  best_prior_idx with long tensor
-    # ensure every gt matches with its prior of max overlap
-    for j in range(best_prior_idx.size(0)):  # 判别此anchor是预测哪一个boxes
-        best_truth_idx[best_prior_idx[j]] = j
-    matches = truths[best_truth_idx]  # Shape: [num_priors,4] 此处为每一个anchor对应的bbox取出来
-    conf = labels[best_truth_idx]  # Shape: [num_priors]      此处为每一个anchor对应的label取出来
-    conf[best_truth_overlap < threshold] = 0  # label as background   overlap<0.35的全部作为负样本
-    loc = encode(matches, priors, variances)
-
-    matches_landm = landms[best_truth_idx]
-    landm = encode_landm(matches_landm, priors, variances)
-    loc_t[idx] = loc  # [num_priors,4] encoded offsets to learn
-    conf_t[idx] = conf  # [num_priors] top class label for each prior
-    landm_t[idx] = landm
-
-
-def encode(matched, priors, variances):
-    """Encode the variances from the priorbox layers into the ground truth boxes
-    we have matched (based on jaccard overlap) with the prior boxes.
-    Args:
-        matched: (tensor) Coords of ground truth for each prior in point-form
-            Shape: [num_priors, 4].
-        priors: (tensor) Prior boxes in center-offset form
-            Shape: [num_priors,4].
-        variances: (list[float]) Variances of priorboxes
-    Return:
-        encoded boxes (tensor), Shape: [num_priors, 4]
-    """
-
-    # dist b/t match center and prior's center
-    g_cxcy = (matched[:, :2] + matched[:, 2:]) / 2 - priors[:, :2]
-    # encode variance
-    g_cxcy /= (variances[0] * priors[:, 2:])
-    # match wh / prior wh
-    g_wh = (matched[:, 2:] - matched[:, :2]) / priors[:, 2:]
-    g_wh = torch.log(g_wh) / variances[1]
-    # return target for smooth_l1_loss
-    return torch.cat([g_cxcy, g_wh], 1)  # [num_priors,4]
-
-
-def encode_landm(matched, priors, variances):
-    """Encode the variances from the priorbox layers into the ground truth boxes
-    we have matched (based on jaccard overlap) with the prior boxes.
-    Args:
-        matched: (tensor) Coords of ground truth for each prior in point-form
-            Shape: [num_priors, 10].
-        priors: (tensor) Prior boxes in center-offset form
-            Shape: [num_priors,4].
-        variances: (list[float]) Variances of priorboxes
-    Return:
-        encoded landm (tensor), Shape: [num_priors, 10]
-    """
-
-    # dist b/t match center and prior's center
-    matched = torch.reshape(matched, (matched.size(0), 5, 2))
-    priors_cx = priors[:, 0].unsqueeze(1).expand(matched.size(0), 5).unsqueeze(2)
-    priors_cy = priors[:, 1].unsqueeze(1).expand(matched.size(0), 5).unsqueeze(2)
-    priors_w = priors[:, 2].unsqueeze(1).expand(matched.size(0), 5).unsqueeze(2)
-    priors_h = priors[:, 3].unsqueeze(1).expand(matched.size(0), 5).unsqueeze(2)
-    priors = torch.cat([priors_cx, priors_cy, priors_w, priors_h], dim=2)
-    g_cxcy = matched[:, :, :2] - priors[:, :, :2]
-    # encode variance
-    g_cxcy /= (variances[0] * priors[:, :, 2:])
-    # g_cxcy /= priors[:, :, 2:]
-    g_cxcy = g_cxcy.reshape(g_cxcy.size(0), -1)
-    # return target for smooth_l1_loss
-    return g_cxcy
-
-
-# Adapted from https://github.com/Hakuyume/chainer-ssd
-def decode(loc, priors, variances):
-    """Decode locations from predictions using priors to undo
-    the encoding we did for offset regression at train time.
-    Args:
-        loc (tensor): location predictions for loc layers,
-            Shape: [num_priors,4]
-        priors (tensor): Prior boxes in center-offset form.
-            Shape: [num_priors,4].
-        variances: (list[float]) Variances of priorboxes
-    Return:
-        decoded bounding box predictions
-    """
-
-    boxes = torch.cat((priors[:, :2] + loc[:, :2] * variances[0] * priors[:, 2:],
-                       priors[:, 2:] * torch.exp(loc[:, 2:] * variances[1])), 1)
-    boxes[:, :2] -= boxes[:, 2:] / 2
-    boxes[:, 2:] += boxes[:, :2]
-    return boxes
-
-
-def decode_landm(pre, priors, variances):
-    """Decode landm from predictions using priors to undo
-    the encoding we did for offset regression at train time.
-    Args:
-        pre (tensor): landm predictions for loc layers,
-            Shape: [num_priors,10]
-        priors (tensor): Prior boxes in center-offset form.
-            Shape: [num_priors,4].
-        variances: (list[float]) Variances of priorboxes
-    Return:
-        decoded landm predictions
-    """
-    tmp = (
-        priors[:, :2] + pre[:, :2] * variances[0] * priors[:, 2:],
-        priors[:, :2] + pre[:, 2:4] * variances[0] * priors[:, 2:],
-        priors[:, :2] + pre[:, 4:6] * variances[0] * priors[:, 2:],
-        priors[:, :2] + pre[:, 6:8] * variances[0] * priors[:, 2:],
-        priors[:, :2] + pre[:, 8:10] * variances[0] * priors[:, 2:],
-    )
-    landms = torch.cat(tmp, dim=1)
-    return landms
-
-
-def batched_decode(b_loc, priors, variances):
-    """Decode locations from predictions using priors to undo
-    the encoding we did for offset regression at train time.
-    Args:
-        b_loc (tensor): location predictions for loc layers,
-            Shape: [num_batches,num_priors,4]
-        priors (tensor): Prior boxes in center-offset form.
-            Shape: [1,num_priors,4].
-        variances: (list[float]) Variances of priorboxes
-    Return:
-        decoded bounding box predictions
-    """
-    boxes = (
-        priors[:, :, :2] + b_loc[:, :, :2] * variances[0] * priors[:, :, 2:],
-        priors[:, :, 2:] * torch.exp(b_loc[:, :, 2:] * variances[1]),
-    )
-    boxes = torch.cat(boxes, dim=2)
-
-    boxes[:, :, :2] -= boxes[:, :, 2:] / 2
-    boxes[:, :, 2:] += boxes[:, :, :2]
-    return boxes
-
-
-def batched_decode_landm(pre, priors, variances):
-    """Decode landm from predictions using priors to undo
-    the encoding we did for offset regression at train time.
-    Args:
-        pre (tensor): landm predictions for loc layers,
-            Shape: [num_batches,num_priors,10]
-        priors (tensor): Prior boxes in center-offset form.
-            Shape: [1,num_priors,4].
-        variances: (list[float]) Variances of priorboxes
-    Return:
-        decoded landm predictions
-    """
-    landms = (
-        priors[:, :, :2] + pre[:, :, :2] * variances[0] * priors[:, :, 2:],
-        priors[:, :, :2] + pre[:, :, 2:4] * variances[0] * priors[:, :, 2:],
-        priors[:, :, :2] + pre[:, :, 4:6] * variances[0] * priors[:, :, 2:],
-        priors[:, :, :2] + pre[:, :, 6:8] * variances[0] * priors[:, :, 2:],
-        priors[:, :, :2] + pre[:, :, 8:10] * variances[0] * priors[:, :, 2:],
-    )
-    landms = torch.cat(landms, dim=2)
-    return landms
-
-
-def log_sum_exp(x):
-    """Utility function for computing log_sum_exp while determining
-    This will be used to determine unaveraged confidence loss across
-    all examples in a batch.
-    Args:
-        x (Variable(tensor)): conf_preds from conf layers
-    """
-    x_max = x.data.max()
-    return torch.log(torch.sum(torch.exp(x - x_max), 1, keepdim=True)) + x_max
-
-
-# Original author: Francisco Massa:
-# https://github.com/fmassa/object-detection.torch
-# Ported to PyTorch by Max deGroot (02/01/2017)
-def nms(boxes, scores, overlap=0.5, top_k=200):
-    """Apply non-maximum suppression at test time to avoid detecting too many
-    overlapping bounding boxes for a given object.
-    Args:
-        boxes: (tensor) The location preds for the img, Shape: [num_priors,4].
-        scores: (tensor) The class predscores for the img, Shape:[num_priors].
-        overlap: (float) The overlap thresh for suppressing unnecessary boxes.
-        top_k: (int) The Maximum number of box preds to consider.
-    Return:
-        The indices of the kept boxes with respect to num_priors.
-    """
-
-    keep = torch.Tensor(scores.size(0)).fill_(0).long()
-    if boxes.numel() == 0:
-        return keep
-    x1 = boxes[:, 0]
-    y1 = boxes[:, 1]
-    x2 = boxes[:, 2]
-    y2 = boxes[:, 3]
-    area = torch.mul(x2 - x1, y2 - y1)
-    v, idx = scores.sort(0)  # sort in ascending order
-    # I = I[v >= 0.01]
-    idx = idx[-top_k:]  # indices of the top-k largest vals
-    xx1 = boxes.new()
-    yy1 = boxes.new()
-    xx2 = boxes.new()
-    yy2 = boxes.new()
-    w = boxes.new()
-    h = boxes.new()
-
-    # keep = torch.Tensor()
-    count = 0
-    while idx.numel() > 0:
-        i = idx[-1]  # index of current largest val
-        # keep.append(i)
-        keep[count] = i
-        count += 1
-        if idx.size(0) == 1:
-            break
-        idx = idx[:-1]  # remove kept element from view
-        # load bboxes of next highest vals
-        torch.index_select(x1, 0, idx, out=xx1)
-        torch.index_select(y1, 0, idx, out=yy1)
-        torch.index_select(x2, 0, idx, out=xx2)
-        torch.index_select(y2, 0, idx, out=yy2)
-        # store element-wise max with next highest score
-        xx1 = torch.clamp(xx1, min=x1[i])
-        yy1 = torch.clamp(yy1, min=y1[i])
-        xx2 = torch.clamp(xx2, max=x2[i])
-        yy2 = torch.clamp(yy2, max=y2[i])
-        w.resize_as_(xx2)
-        h.resize_as_(yy2)
-        w = xx2 - xx1
-        h = yy2 - yy1
-        # check sizes of xx1 and xx2.. after each iteration
-        w = torch.clamp(w, min=0.0)
-        h = torch.clamp(h, min=0.0)
-        inter = w * h
-        # IoU = i / (area(a) + area(b) - i)
-        rem_areas = torch.index_select(area, 0, idx)  # load remaining areas)
-        union = (rem_areas - inter) + area[i]
-        IoU = inter / union  # store result in iou
-        # keep only elements with an IoU <= overlap
-        idx = idx[IoU.le(overlap)]
-    return keep, count
diff --git a/repositories/CodeFormer/facelib/detection/yolov5face/__init__.py b/repositories/CodeFormer/facelib/detection/yolov5face/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/repositories/CodeFormer/facelib/detection/yolov5face/__pycache__/__init__.cpython-310.pyc b/repositories/CodeFormer/facelib/detection/yolov5face/__pycache__/__init__.cpython-310.pyc
deleted file mode 100644
index bf04c897c7ed046c9710dea842ace1bfd9c280cb..0000000000000000000000000000000000000000
Binary files a/repositories/CodeFormer/facelib/detection/yolov5face/__pycache__/__init__.cpython-310.pyc and /dev/null differ
diff --git a/repositories/CodeFormer/facelib/detection/yolov5face/__pycache__/face_detector.cpython-310.pyc b/repositories/CodeFormer/facelib/detection/yolov5face/__pycache__/face_detector.cpython-310.pyc
deleted file mode 100644
index d8ca4eff970a317f959c34cbc8a7b8817bd55de5..0000000000000000000000000000000000000000
Binary files a/repositories/CodeFormer/facelib/detection/yolov5face/__pycache__/face_detector.cpython-310.pyc and /dev/null differ
diff --git a/repositories/CodeFormer/facelib/detection/yolov5face/face_detector.py b/repositories/CodeFormer/facelib/detection/yolov5face/face_detector.py
deleted file mode 100644
index 2282b283e4446915731180e1d2dff748e8e46ec2..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/facelib/detection/yolov5face/face_detector.py
+++ /dev/null
@@ -1,142 +0,0 @@
-import copy
-import os
-from pathlib import Path
-
-import cv2
-import numpy as np
-import torch
-from torch import nn
-
-from facelib.detection.yolov5face.models.common import Conv
-from facelib.detection.yolov5face.models.yolo import Model
-from facelib.detection.yolov5face.utils.datasets import letterbox
-from facelib.detection.yolov5face.utils.general import (
-    check_img_size,
-    non_max_suppression_face,
-    scale_coords,
-    scale_coords_landmarks,
-)
-
-IS_HIGH_VERSION = tuple(map(int, torch.__version__.split('+')[0].split('.'))) >= (1, 9, 0)
-
-
-def isListempty(inList):
-    if isinstance(inList, list): # Is a list
-        return all(map(isListempty, inList))
-    return False # Not a list
-
-class YoloDetector:
-    def __init__(
-        self,
-        config_name,
-        min_face=10,
-        target_size=None,
-        device='cuda',
-    ):
-        """
-        config_name: name of .yaml config with network configuration from models/ folder.
-        min_face : minimal face size in pixels.
-        target_size : target size of smaller image axis (choose lower for faster work). e.g. 480, 720, 1080.
-                    None for original resolution.
-        """
-        self._class_path = Path(__file__).parent.absolute()
-        self.target_size = target_size
-        self.min_face = min_face
-        self.detector = Model(cfg=config_name)
-        self.device = device
-
-
-    def _preprocess(self, imgs):
-        """
-        Preprocessing image before passing through the network. Resize and conversion to torch tensor.
-        """
-        pp_imgs = []
-        for img in imgs:
-            h0, w0 = img.shape[:2]  # orig hw
-            if self.target_size:
-                r = self.target_size / min(h0, w0)  # resize image to img_size
-                if r < 1:
-                    img = cv2.resize(img, (int(w0 * r), int(h0 * r)), interpolation=cv2.INTER_LINEAR)
-
-            imgsz = check_img_size(max(img.shape[:2]), s=self.detector.stride.max())  # check img_size
-            img = letterbox(img, new_shape=imgsz)[0]
-            pp_imgs.append(img)
-        pp_imgs = np.array(pp_imgs)
-        pp_imgs = pp_imgs.transpose(0, 3, 1, 2)
-        pp_imgs = torch.from_numpy(pp_imgs).to(self.device)
-        pp_imgs = pp_imgs.float()  # uint8 to fp16/32
-        return pp_imgs / 255.0  # 0 - 255 to 0.0 - 1.0
-
-    def _postprocess(self, imgs, origimgs, pred, conf_thres, iou_thres):
-        """
-        Postprocessing of raw pytorch model output.
-        Returns:
-            bboxes: list of arrays with 4 coordinates of bounding boxes with format x1,y1,x2,y2.
-            points: list of arrays with coordinates of 5 facial keypoints (eyes, nose, lips corners).
-        """
-        bboxes = [[] for _ in range(len(origimgs))]
-        landmarks = [[] for _ in range(len(origimgs))]
-
-        pred = non_max_suppression_face(pred, conf_thres, iou_thres)
-
-        for image_id, origimg in enumerate(origimgs):
-            img_shape = origimg.shape
-            image_height, image_width = img_shape[:2]
-            gn = torch.tensor(img_shape)[[1, 0, 1, 0]]  # normalization gain whwh
-            gn_lks = torch.tensor(img_shape)[[1, 0, 1, 0, 1, 0, 1, 0, 1, 0]]  # normalization gain landmarks
-            det = pred[image_id].cpu()
-            scale_coords(imgs[image_id].shape[1:], det[:, :4], img_shape).round()
-            scale_coords_landmarks(imgs[image_id].shape[1:], det[:, 5:15], img_shape).round()
-
-            for j in range(det.size()[0]):
-                box = (det[j, :4].view(1, 4) / gn).view(-1).tolist()
-                box = list(
-                    map(int, [box[0] * image_width, box[1] * image_height, box[2] * image_width, box[3] * image_height])
-                )
-                if box[3] - box[1] < self.min_face:
-                    continue
-                lm = (det[j, 5:15].view(1, 10) / gn_lks).view(-1).tolist()
-                lm = list(map(int, [i * image_width if j % 2 == 0 else i * image_height for j, i in enumerate(lm)]))
-                lm = [lm[i : i + 2] for i in range(0, len(lm), 2)]
-                bboxes[image_id].append(box)
-                landmarks[image_id].append(lm)
-        return bboxes, landmarks
-
-    def detect_faces(self, imgs, conf_thres=0.7, iou_thres=0.5):
-        """
-        Get bbox coordinates and keypoints of faces on original image.
-        Params:
-            imgs: image or list of images to detect faces on with BGR order (convert to RGB order for inference)
-            conf_thres: confidence threshold for each prediction
-            iou_thres: threshold for NMS (filter of intersecting bboxes)
-        Returns:
-            bboxes: list of arrays with 4 coordinates of bounding boxes with format x1,y1,x2,y2.
-            points: list of arrays with coordinates of 5 facial keypoints (eyes, nose, lips corners).
-        """
-        # Pass input images through face detector
-        images = imgs if isinstance(imgs, list) else [imgs]
-        images = [cv2.cvtColor(img, cv2.COLOR_BGR2RGB) for img in images]
-        origimgs = copy.deepcopy(images)
-
-        images = self._preprocess(images)
-        
-        if IS_HIGH_VERSION:
-            with torch.inference_mode():  # for pytorch>=1.9 
-                pred = self.detector(images)[0]
-        else:
-            with torch.no_grad():  # for pytorch<1.9
-                pred = self.detector(images)[0]
-
-        bboxes, points = self._postprocess(images, origimgs, pred, conf_thres, iou_thres)
-
-        # return bboxes, points
-        if not isListempty(points):
-            bboxes = np.array(bboxes).reshape(-1,4)
-            points = np.array(points).reshape(-1,10)
-            padding = bboxes[:,0].reshape(-1,1)
-            return np.concatenate((bboxes, padding, points), axis=1)
-        else:
-            return None
-
-    def __call__(self, *args):
-        return self.predict(*args)
diff --git a/repositories/CodeFormer/facelib/detection/yolov5face/models/__init__.py b/repositories/CodeFormer/facelib/detection/yolov5face/models/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/repositories/CodeFormer/facelib/detection/yolov5face/models/__pycache__/__init__.cpython-310.pyc b/repositories/CodeFormer/facelib/detection/yolov5face/models/__pycache__/__init__.cpython-310.pyc
deleted file mode 100644
index 61cd26a506879707e9f0450ea68b7c7bc59badf7..0000000000000000000000000000000000000000
Binary files a/repositories/CodeFormer/facelib/detection/yolov5face/models/__pycache__/__init__.cpython-310.pyc and /dev/null differ
diff --git a/repositories/CodeFormer/facelib/detection/yolov5face/models/__pycache__/common.cpython-310.pyc b/repositories/CodeFormer/facelib/detection/yolov5face/models/__pycache__/common.cpython-310.pyc
deleted file mode 100644
index cf6ee5935750b973dc1f0b588981070bc8a578c5..0000000000000000000000000000000000000000
Binary files a/repositories/CodeFormer/facelib/detection/yolov5face/models/__pycache__/common.cpython-310.pyc and /dev/null differ
diff --git a/repositories/CodeFormer/facelib/detection/yolov5face/models/__pycache__/experimental.cpython-310.pyc b/repositories/CodeFormer/facelib/detection/yolov5face/models/__pycache__/experimental.cpython-310.pyc
deleted file mode 100644
index 52e3041cf89db989d9bb38469e22a756e21974ab..0000000000000000000000000000000000000000
Binary files a/repositories/CodeFormer/facelib/detection/yolov5face/models/__pycache__/experimental.cpython-310.pyc and /dev/null differ
diff --git a/repositories/CodeFormer/facelib/detection/yolov5face/models/__pycache__/yolo.cpython-310.pyc b/repositories/CodeFormer/facelib/detection/yolov5face/models/__pycache__/yolo.cpython-310.pyc
deleted file mode 100644
index 66b5385f46c049fedf39d360870b83cc16e3e75a..0000000000000000000000000000000000000000
Binary files a/repositories/CodeFormer/facelib/detection/yolov5face/models/__pycache__/yolo.cpython-310.pyc and /dev/null differ
diff --git a/repositories/CodeFormer/facelib/detection/yolov5face/models/common.py b/repositories/CodeFormer/facelib/detection/yolov5face/models/common.py
deleted file mode 100644
index 497a00444c4c59725001993a63fe4617e9d323c8..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/facelib/detection/yolov5face/models/common.py
+++ /dev/null
@@ -1,299 +0,0 @@
-# This file contains modules common to various models
-
-import math
-
-import numpy as np
-import torch
-from torch import nn
-
-from facelib.detection.yolov5face.utils.datasets import letterbox
-from facelib.detection.yolov5face.utils.general import (
-    make_divisible,
-    non_max_suppression,
-    scale_coords,
-    xyxy2xywh,
-)
-
-
-def autopad(k, p=None):  # kernel, padding
-    # Pad to 'same'
-    if p is None:
-        p = k // 2 if isinstance(k, int) else [x // 2 for x in k]  # auto-pad
-    return p
-
-
-def channel_shuffle(x, groups):
-    batchsize, num_channels, height, width = x.data.size()
-    channels_per_group = torch.div(num_channels, groups, rounding_mode="trunc")
-
-    # reshape
-    x = x.view(batchsize, groups, channels_per_group, height, width)
-    x = torch.transpose(x, 1, 2).contiguous()
-
-    # flatten
-    return x.view(batchsize, -1, height, width)
-
-
-def DWConv(c1, c2, k=1, s=1, act=True):
-    # Depthwise convolution
-    return Conv(c1, c2, k, s, g=math.gcd(c1, c2), act=act)
-
-
-class Conv(nn.Module):
-    # Standard convolution
-    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):  # ch_in, ch_out, kernel, stride, padding, groups
-        super().__init__()
-        self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False)
-        self.bn = nn.BatchNorm2d(c2)
-        self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity())
-
-    def forward(self, x):
-        return self.act(self.bn(self.conv(x)))
-
-    def fuseforward(self, x):
-        return self.act(self.conv(x))
-
-
-class StemBlock(nn.Module):
-    def __init__(self, c1, c2, k=3, s=2, p=None, g=1, act=True):
-        super().__init__()
-        self.stem_1 = Conv(c1, c2, k, s, p, g, act)
-        self.stem_2a = Conv(c2, c2 // 2, 1, 1, 0)
-        self.stem_2b = Conv(c2 // 2, c2, 3, 2, 1)
-        self.stem_2p = nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True)
-        self.stem_3 = Conv(c2 * 2, c2, 1, 1, 0)
-
-    def forward(self, x):
-        stem_1_out = self.stem_1(x)
-        stem_2a_out = self.stem_2a(stem_1_out)
-        stem_2b_out = self.stem_2b(stem_2a_out)
-        stem_2p_out = self.stem_2p(stem_1_out)
-        return self.stem_3(torch.cat((stem_2b_out, stem_2p_out), 1))
-
-
-class Bottleneck(nn.Module):
-    # Standard bottleneck
-    def __init__(self, c1, c2, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, shortcut, groups, expansion
-        super().__init__()
-        c_ = int(c2 * e)  # hidden channels
-        self.cv1 = Conv(c1, c_, 1, 1)
-        self.cv2 = Conv(c_, c2, 3, 1, g=g)
-        self.add = shortcut and c1 == c2
-
-    def forward(self, x):
-        return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
-
-
-class BottleneckCSP(nn.Module):
-    # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
-    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
-        super().__init__()
-        c_ = int(c2 * e)  # hidden channels
-        self.cv1 = Conv(c1, c_, 1, 1)
-        self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False)
-        self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False)
-        self.cv4 = Conv(2 * c_, c2, 1, 1)
-        self.bn = nn.BatchNorm2d(2 * c_)  # applied to cat(cv2, cv3)
-        self.act = nn.LeakyReLU(0.1, inplace=True)
-        self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))
-
-    def forward(self, x):
-        y1 = self.cv3(self.m(self.cv1(x)))
-        y2 = self.cv2(x)
-        return self.cv4(self.act(self.bn(torch.cat((y1, y2), dim=1))))
-
-
-class C3(nn.Module):
-    # CSP Bottleneck with 3 convolutions
-    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
-        super().__init__()
-        c_ = int(c2 * e)  # hidden channels
-        self.cv1 = Conv(c1, c_, 1, 1)
-        self.cv2 = Conv(c1, c_, 1, 1)
-        self.cv3 = Conv(2 * c_, c2, 1)  # act=FReLU(c2)
-        self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))
-
-    def forward(self, x):
-        return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), dim=1))
-
-
-class ShuffleV2Block(nn.Module):
-    def __init__(self, inp, oup, stride):
-        super().__init__()
-
-        if not 1 <= stride <= 3:
-            raise ValueError("illegal stride value")
-        self.stride = stride
-
-        branch_features = oup // 2
-
-        if self.stride > 1:
-            self.branch1 = nn.Sequential(
-                self.depthwise_conv(inp, inp, kernel_size=3, stride=self.stride, padding=1),
-                nn.BatchNorm2d(inp),
-                nn.Conv2d(inp, branch_features, kernel_size=1, stride=1, padding=0, bias=False),
-                nn.BatchNorm2d(branch_features),
-                nn.SiLU(),
-            )
-        else:
-            self.branch1 = nn.Sequential()
-
-        self.branch2 = nn.Sequential(
-            nn.Conv2d(
-                inp if (self.stride > 1) else branch_features,
-                branch_features,
-                kernel_size=1,
-                stride=1,
-                padding=0,
-                bias=False,
-            ),
-            nn.BatchNorm2d(branch_features),
-            nn.SiLU(),
-            self.depthwise_conv(branch_features, branch_features, kernel_size=3, stride=self.stride, padding=1),
-            nn.BatchNorm2d(branch_features),
-            nn.Conv2d(branch_features, branch_features, kernel_size=1, stride=1, padding=0, bias=False),
-            nn.BatchNorm2d(branch_features),
-            nn.SiLU(),
-        )
-
-    @staticmethod
-    def depthwise_conv(i, o, kernel_size, stride=1, padding=0, bias=False):
-        return nn.Conv2d(i, o, kernel_size, stride, padding, bias=bias, groups=i)
-
-    def forward(self, x):
-        if self.stride == 1:
-            x1, x2 = x.chunk(2, dim=1)
-            out = torch.cat((x1, self.branch2(x2)), dim=1)
-        else:
-            out = torch.cat((self.branch1(x), self.branch2(x)), dim=1)
-        out = channel_shuffle(out, 2)
-        return out
-
-
-class SPP(nn.Module):
-    # Spatial pyramid pooling layer used in YOLOv3-SPP
-    def __init__(self, c1, c2, k=(5, 9, 13)):
-        super().__init__()
-        c_ = c1 // 2  # hidden channels
-        self.cv1 = Conv(c1, c_, 1, 1)
-        self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1)
-        self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k])
-
-    def forward(self, x):
-        x = self.cv1(x)
-        return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1))
-
-
-class Focus(nn.Module):
-    # Focus wh information into c-space
-    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):  # ch_in, ch_out, kernel, stride, padding, groups
-        super().__init__()
-        self.conv = Conv(c1 * 4, c2, k, s, p, g, act)
-
-    def forward(self, x):  # x(b,c,w,h) -> y(b,4c,w/2,h/2)
-        return self.conv(torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1))
-
-
-class Concat(nn.Module):
-    # Concatenate a list of tensors along dimension
-    def __init__(self, dimension=1):
-        super().__init__()
-        self.d = dimension
-
-    def forward(self, x):
-        return torch.cat(x, self.d)
-
-
-class NMS(nn.Module):
-    # Non-Maximum Suppression (NMS) module
-    conf = 0.25  # confidence threshold
-    iou = 0.45  # IoU threshold
-    classes = None  # (optional list) filter by class
-
-    def forward(self, x):
-        return non_max_suppression(x[0], conf_thres=self.conf, iou_thres=self.iou, classes=self.classes)
-
-
-class AutoShape(nn.Module):
-    # input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS
-    img_size = 640  # inference size (pixels)
-    conf = 0.25  # NMS confidence threshold
-    iou = 0.45  # NMS IoU threshold
-    classes = None  # (optional list) filter by class
-
-    def __init__(self, model):
-        super().__init__()
-        self.model = model.eval()
-
-    def autoshape(self):
-        print("autoShape already enabled, skipping... ")  # model already converted to model.autoshape()
-        return self
-
-    def forward(self, imgs, size=640, augment=False, profile=False):
-        # Inference from various sources. For height=720, width=1280, RGB images example inputs are:
-        #   OpenCV:          = cv2.imread('image.jpg')[:,:,::-1]  # HWC BGR to RGB x(720,1280,3)
-        #   PIL:             = Image.open('image.jpg')  # HWC x(720,1280,3)
-        #   numpy:           = np.zeros((720,1280,3))  # HWC
-        #   torch:           = torch.zeros(16,3,720,1280)  # BCHW
-        #   multiple:        = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...]  # list of images
-
-        p = next(self.model.parameters())  # for device and type
-        if isinstance(imgs, torch.Tensor):  # torch
-            return self.model(imgs.to(p.device).type_as(p), augment, profile)  # inference
-
-        # Pre-process
-        n, imgs = (len(imgs), imgs) if isinstance(imgs, list) else (1, [imgs])  # number of images, list of images
-        shape0, shape1 = [], []  # image and inference shapes
-        for i, im in enumerate(imgs):
-            im = np.array(im)  # to numpy
-            if im.shape[0] < 5:  # image in CHW
-                im = im.transpose((1, 2, 0))  # reverse dataloader .transpose(2, 0, 1)
-            im = im[:, :, :3] if im.ndim == 3 else np.tile(im[:, :, None], 3)  # enforce 3ch input
-            s = im.shape[:2]  # HWC
-            shape0.append(s)  # image shape
-            g = size / max(s)  # gain
-            shape1.append([y * g for y in s])
-            imgs[i] = im  # update
-        shape1 = [make_divisible(x, int(self.stride.max())) for x in np.stack(shape1, 0).max(0)]  # inference shape
-        x = [letterbox(im, new_shape=shape1, auto=False)[0] for im in imgs]  # pad
-        x = np.stack(x, 0) if n > 1 else x[0][None]  # stack
-        x = np.ascontiguousarray(x.transpose((0, 3, 1, 2)))  # BHWC to BCHW
-        x = torch.from_numpy(x).to(p.device).type_as(p) / 255.0  # uint8 to fp16/32
-
-        # Inference
-        with torch.no_grad():
-            y = self.model(x, augment, profile)[0]  # forward
-        y = non_max_suppression(y, conf_thres=self.conf, iou_thres=self.iou, classes=self.classes)  # NMS
-
-        # Post-process
-        for i in range(n):
-            scale_coords(shape1, y[i][:, :4], shape0[i])
-
-        return Detections(imgs, y, self.names)
-
-
-class Detections:
-    # detections class for YOLOv5 inference results
-    def __init__(self, imgs, pred, names=None):
-        super().__init__()
-        d = pred[0].device  # device
-        gn = [torch.tensor([*(im.shape[i] for i in [1, 0, 1, 0]), 1.0, 1.0], device=d) for im in imgs]  # normalizations
-        self.imgs = imgs  # list of images as numpy arrays
-        self.pred = pred  # list of tensors pred[0] = (xyxy, conf, cls)
-        self.names = names  # class names
-        self.xyxy = pred  # xyxy pixels
-        self.xywh = [xyxy2xywh(x) for x in pred]  # xywh pixels
-        self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)]  # xyxy normalized
-        self.xywhn = [x / g for x, g in zip(self.xywh, gn)]  # xywh normalized
-        self.n = len(self.pred)
-
-    def __len__(self):
-        return self.n
-
-    def tolist(self):
-        # return a list of Detections objects, i.e. 'for result in results.tolist():'
-        x = [Detections([self.imgs[i]], [self.pred[i]], self.names) for i in range(self.n)]
-        for d in x:
-            for k in ["imgs", "pred", "xyxy", "xyxyn", "xywh", "xywhn"]:
-                setattr(d, k, getattr(d, k)[0])  # pop out of list
-        return x
diff --git a/repositories/CodeFormer/facelib/detection/yolov5face/models/experimental.py b/repositories/CodeFormer/facelib/detection/yolov5face/models/experimental.py
deleted file mode 100644
index 37ba4c4420789c92dc0e2aaeb3d5b64859ec728c..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/facelib/detection/yolov5face/models/experimental.py
+++ /dev/null
@@ -1,45 +0,0 @@
-# # This file contains experimental modules
-
-import numpy as np
-import torch
-from torch import nn
-
-from facelib.detection.yolov5face.models.common import Conv
-
-
-class CrossConv(nn.Module):
-    # Cross Convolution Downsample
-    def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False):
-        # ch_in, ch_out, kernel, stride, groups, expansion, shortcut
-        super().__init__()
-        c_ = int(c2 * e)  # hidden channels
-        self.cv1 = Conv(c1, c_, (1, k), (1, s))
-        self.cv2 = Conv(c_, c2, (k, 1), (s, 1), g=g)
-        self.add = shortcut and c1 == c2
-
-    def forward(self, x):
-        return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
-
-
-class MixConv2d(nn.Module):
-    # Mixed Depthwise Conv https://arxiv.org/abs/1907.09595
-    def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True):
-        super().__init__()
-        groups = len(k)
-        if equal_ch:  # equal c_ per group
-            i = torch.linspace(0, groups - 1e-6, c2).floor()  # c2 indices
-            c_ = [(i == g).sum() for g in range(groups)]  # intermediate channels
-        else:  # equal weight.numel() per group
-            b = [c2] + [0] * groups
-            a = np.eye(groups + 1, groups, k=-1)
-            a -= np.roll(a, 1, axis=1)
-            a *= np.array(k) ** 2
-            a[0] = 1
-            c_ = np.linalg.lstsq(a, b, rcond=None)[0].round()  # solve for equal weight indices, ax = b
-
-        self.m = nn.ModuleList([nn.Conv2d(c1, int(c_[g]), k[g], s, k[g] // 2, bias=False) for g in range(groups)])
-        self.bn = nn.BatchNorm2d(c2)
-        self.act = nn.LeakyReLU(0.1, inplace=True)
-
-    def forward(self, x):
-        return x + self.act(self.bn(torch.cat([m(x) for m in self.m], 1)))
diff --git a/repositories/CodeFormer/facelib/detection/yolov5face/models/yolo.py b/repositories/CodeFormer/facelib/detection/yolov5face/models/yolo.py
deleted file mode 100644
index 70845d972f0bcfd3632fcbac096b23e1b4d4d779..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/facelib/detection/yolov5face/models/yolo.py
+++ /dev/null
@@ -1,235 +0,0 @@
-import math
-from copy import deepcopy
-from pathlib import Path
-
-import torch
-import yaml  # for torch hub
-from torch import nn
-
-from facelib.detection.yolov5face.models.common import (
-    C3,
-    NMS,
-    SPP,
-    AutoShape,
-    Bottleneck,
-    BottleneckCSP,
-    Concat,
-    Conv,
-    DWConv,
-    Focus,
-    ShuffleV2Block,
-    StemBlock,
-)
-from facelib.detection.yolov5face.models.experimental import CrossConv, MixConv2d
-from facelib.detection.yolov5face.utils.autoanchor import check_anchor_order
-from facelib.detection.yolov5face.utils.general import make_divisible
-from facelib.detection.yolov5face.utils.torch_utils import copy_attr, fuse_conv_and_bn
-
-
-class Detect(nn.Module):
-    stride = None  # strides computed during build
-    export = False  # onnx export
-
-    def __init__(self, nc=80, anchors=(), ch=()):  # detection layer
-        super().__init__()
-        self.nc = nc  # number of classes
-        self.no = nc + 5 + 10  # number of outputs per anchor
-
-        self.nl = len(anchors)  # number of detection layers
-        self.na = len(anchors[0]) // 2  # number of anchors
-        self.grid = [torch.zeros(1)] * self.nl  # init grid
-        a = torch.tensor(anchors).float().view(self.nl, -1, 2)
-        self.register_buffer("anchors", a)  # shape(nl,na,2)
-        self.register_buffer("anchor_grid", a.clone().view(self.nl, 1, -1, 1, 1, 2))  # shape(nl,1,na,1,1,2)
-        self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch)  # output conv
-
-    def forward(self, x):
-        z = []  # inference output
-        if self.export:
-            for i in range(self.nl):
-                x[i] = self.m[i](x[i])
-            return x
-        for i in range(self.nl):
-            x[i] = self.m[i](x[i])  # conv
-            bs, _, ny, nx = x[i].shape  # x(bs,255,20,20) to x(bs,3,20,20,85)
-            x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()
-
-            if not self.training:  # inference
-                if self.grid[i].shape[2:4] != x[i].shape[2:4]:
-                    self.grid[i] = self._make_grid(nx, ny).to(x[i].device)
-
-                y = torch.full_like(x[i], 0)
-                y[..., [0, 1, 2, 3, 4, 15]] = x[i][..., [0, 1, 2, 3, 4, 15]].sigmoid()
-                y[..., 5:15] = x[i][..., 5:15]
-
-                y[..., 0:2] = (y[..., 0:2] * 2.0 - 0.5 + self.grid[i].to(x[i].device)) * self.stride[i]  # xy
-                y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]  # wh
-
-                y[..., 5:7] = (
-                    y[..., 5:7] * self.anchor_grid[i] + self.grid[i].to(x[i].device) * self.stride[i]
-                )  # landmark x1 y1
-                y[..., 7:9] = (
-                    y[..., 7:9] * self.anchor_grid[i] + self.grid[i].to(x[i].device) * self.stride[i]
-                )  # landmark x2 y2
-                y[..., 9:11] = (
-                    y[..., 9:11] * self.anchor_grid[i] + self.grid[i].to(x[i].device) * self.stride[i]
-                )  # landmark x3 y3
-                y[..., 11:13] = (
-                    y[..., 11:13] * self.anchor_grid[i] + self.grid[i].to(x[i].device) * self.stride[i]
-                )  # landmark x4 y4
-                y[..., 13:15] = (
-                    y[..., 13:15] * self.anchor_grid[i] + self.grid[i].to(x[i].device) * self.stride[i]
-                )  # landmark x5 y5
-
-                z.append(y.view(bs, -1, self.no))
-
-        return x if self.training else (torch.cat(z, 1), x)
-
-    @staticmethod
-    def _make_grid(nx=20, ny=20):
-        # yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)], indexing="ij") # for pytorch>=1.10
-        yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)])
-        return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float()
-
-
-class Model(nn.Module):
-    def __init__(self, cfg="yolov5s.yaml", ch=3, nc=None):  # model, input channels, number of classes
-        super().__init__()
-        self.yaml_file = Path(cfg).name
-        with Path(cfg).open(encoding="utf8") as f:
-            self.yaml = yaml.safe_load(f)  # model dict
-
-        # Define model
-        ch = self.yaml["ch"] = self.yaml.get("ch", ch)  # input channels
-        if nc and nc != self.yaml["nc"]:
-            self.yaml["nc"] = nc  # override yaml value
-
-        self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch])  # model, savelist
-        self.names = [str(i) for i in range(self.yaml["nc"])]  # default names
-
-        # Build strides, anchors
-        m = self.model[-1]  # Detect()
-        if isinstance(m, Detect):
-            s = 128  # 2x min stride
-            m.stride = torch.tensor([s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))])  # forward
-            m.anchors /= m.stride.view(-1, 1, 1)
-            check_anchor_order(m)
-            self.stride = m.stride
-            self._initialize_biases()  # only run once
-
-    def forward(self, x):
-        return self.forward_once(x)  # single-scale inference, train
-
-    def forward_once(self, x):
-        y = []  # outputs
-        for m in self.model:
-            if m.f != -1:  # if not from previous layer
-                x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f]  # from earlier layers
-
-            x = m(x)  # run
-            y.append(x if m.i in self.save else None)  # save output
-
-        return x
-
-    def _initialize_biases(self, cf=None):  # initialize biases into Detect(), cf is class frequency
-        # https://arxiv.org/abs/1708.02002 section 3.3
-        m = self.model[-1]  # Detect() module
-        for mi, s in zip(m.m, m.stride):  # from
-            b = mi.bias.view(m.na, -1)  # conv.bias(255) to (3,85)
-            b.data[:, 4] += math.log(8 / (640 / s) ** 2)  # obj (8 objects per 640 image)
-            b.data[:, 5:] += math.log(0.6 / (m.nc - 0.99)) if cf is None else torch.log(cf / cf.sum())  # cls
-            mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
-
-    def _print_biases(self):
-        m = self.model[-1]  # Detect() module
-        for mi in m.m:  # from
-            b = mi.bias.detach().view(m.na, -1).T  # conv.bias(255) to (3,85)
-            print(("%6g Conv2d.bias:" + "%10.3g" * 6) % (mi.weight.shape[1], *b[:5].mean(1).tolist(), b[5:].mean()))
-
-    def fuse(self):  # fuse model Conv2d() + BatchNorm2d() layers
-        print("Fusing layers... ")
-        for m in self.model.modules():
-            if isinstance(m, Conv) and hasattr(m, "bn"):
-                m.conv = fuse_conv_and_bn(m.conv, m.bn)  # update conv
-                delattr(m, "bn")  # remove batchnorm
-                m.forward = m.fuseforward  # update forward
-            elif type(m) is nn.Upsample:
-                m.recompute_scale_factor = None  # torch 1.11.0 compatibility
-        return self
-
-    def nms(self, mode=True):  # add or remove NMS module
-        present = isinstance(self.model[-1], NMS)  # last layer is NMS
-        if mode and not present:
-            print("Adding NMS... ")
-            m = NMS()  # module
-            m.f = -1  # from
-            m.i = self.model[-1].i + 1  # index
-            self.model.add_module(name=str(m.i), module=m)  # add
-            self.eval()
-        elif not mode and present:
-            print("Removing NMS... ")
-            self.model = self.model[:-1]  # remove
-        return self
-
-    def autoshape(self):  # add autoShape module
-        print("Adding autoShape... ")
-        m = AutoShape(self)  # wrap model
-        copy_attr(m, self, include=("yaml", "nc", "hyp", "names", "stride"), exclude=())  # copy attributes
-        return m
-
-
-def parse_model(d, ch):  # model_dict, input_channels(3)
-    anchors, nc, gd, gw = d["anchors"], d["nc"], d["depth_multiple"], d["width_multiple"]
-    na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors  # number of anchors
-    no = na * (nc + 5)  # number of outputs = anchors * (classes + 5)
-
-    layers, save, c2 = [], [], ch[-1]  # layers, savelist, ch out
-    for i, (f, n, m, args) in enumerate(d["backbone"] + d["head"]):  # from, number, module, args
-        m = eval(m) if isinstance(m, str) else m  # eval strings
-        for j, a in enumerate(args):
-            try:
-                args[j] = eval(a) if isinstance(a, str) else a  # eval strings
-            except:
-                pass
-
-        n = max(round(n * gd), 1) if n > 1 else n  # depth gain
-        if m in [
-            Conv,
-            Bottleneck,
-            SPP,
-            DWConv,
-            MixConv2d,
-            Focus,
-            CrossConv,
-            BottleneckCSP,
-            C3,
-            ShuffleV2Block,
-            StemBlock,
-        ]:
-            c1, c2 = ch[f], args[0]
-
-            c2 = make_divisible(c2 * gw, 8) if c2 != no else c2
-
-            args = [c1, c2, *args[1:]]
-            if m in [BottleneckCSP, C3]:
-                args.insert(2, n)
-                n = 1
-        elif m is nn.BatchNorm2d:
-            args = [ch[f]]
-        elif m is Concat:
-            c2 = sum(ch[-1 if x == -1 else x + 1] for x in f)
-        elif m is Detect:
-            args.append([ch[x + 1] for x in f])
-            if isinstance(args[1], int):  # number of anchors
-                args[1] = [list(range(args[1] * 2))] * len(f)
-        else:
-            c2 = ch[f]
-
-        m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args)  # module
-        t = str(m)[8:-2].replace("__main__.", "")  # module type
-        np = sum(x.numel() for x in m_.parameters())  # number params
-        m_.i, m_.f, m_.type, m_.np = i, f, t, np  # attach index, 'from' index, type, number params
-        save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1)  # append to savelist
-        layers.append(m_)
-        ch.append(c2)
-    return nn.Sequential(*layers), sorted(save)
diff --git a/repositories/CodeFormer/facelib/detection/yolov5face/models/yolov5l.yaml b/repositories/CodeFormer/facelib/detection/yolov5face/models/yolov5l.yaml
deleted file mode 100644
index 0532b0e22fa7f59349b178146ffddcfdb368aba6..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/facelib/detection/yolov5face/models/yolov5l.yaml
+++ /dev/null
@@ -1,47 +0,0 @@
-# parameters
-nc: 1  # number of classes
-depth_multiple: 1.0  # model depth multiple
-width_multiple: 1.0  # layer channel multiple
-
-# anchors
-anchors:
-  - [4,5,  8,10,  13,16]  # P3/8
-  - [23,29,  43,55,  73,105]  # P4/16
-  - [146,217,  231,300,  335,433]  # P5/32
-
-# YOLOv5 backbone
-backbone:
-  # [from, number, module, args]
-  [[-1, 1, StemBlock, [64, 3, 2]],  # 0-P1/2
-   [-1, 3, C3, [128]],
-   [-1, 1, Conv, [256, 3, 2]],      # 2-P3/8
-   [-1, 9, C3, [256]],
-   [-1, 1, Conv, [512, 3, 2]],      # 4-P4/16
-   [-1, 9, C3, [512]],
-   [-1, 1, Conv, [1024, 3, 2]],     # 6-P5/32
-   [-1, 1, SPP, [1024, [3,5,7]]],
-   [-1, 3, C3, [1024, False]],      # 8
-  ]
-
-# YOLOv5 head
-head:
-  [[-1, 1, Conv, [512, 1, 1]],
-   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
-   [[-1, 5], 1, Concat, [1]],  # cat backbone P4
-   [-1, 3, C3, [512, False]],  # 12
-
-   [-1, 1, Conv, [256, 1, 1]],
-   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
-   [[-1, 3], 1, Concat, [1]],  # cat backbone P3
-   [-1, 3, C3, [256, False]],  # 16 (P3/8-small)
-
-   [-1, 1, Conv, [256, 3, 2]],
-   [[-1, 13], 1, Concat, [1]],  # cat head P4
-   [-1, 3, C3, [512, False]],  # 19 (P4/16-medium)
-
-   [-1, 1, Conv, [512, 3, 2]],
-   [[-1, 9], 1, Concat, [1]],  # cat head P5
-   [-1, 3, C3, [1024, False]],  # 22 (P5/32-large)
-
-   [[16, 19, 22], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)
-  ]
\ No newline at end of file
diff --git a/repositories/CodeFormer/facelib/detection/yolov5face/models/yolov5n.yaml b/repositories/CodeFormer/facelib/detection/yolov5face/models/yolov5n.yaml
deleted file mode 100644
index caba6bed674aa2213b110f19e04eb352ffbeaf1e..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/facelib/detection/yolov5face/models/yolov5n.yaml
+++ /dev/null
@@ -1,45 +0,0 @@
-# parameters
-nc: 1  # number of classes
-depth_multiple: 1.0  # model depth multiple
-width_multiple: 1.0  # layer channel multiple
-
-# anchors
-anchors:
-  - [4,5,  8,10,  13,16]  # P3/8
-  - [23,29,  43,55,  73,105]  # P4/16
-  - [146,217,  231,300,  335,433]  # P5/32
-
-# YOLOv5 backbone
-backbone:
-  # [from, number, module, args]
-  [[-1, 1, StemBlock, [32, 3, 2]],    # 0-P2/4
-   [-1, 1, ShuffleV2Block, [128, 2]], # 1-P3/8
-   [-1, 3, ShuffleV2Block, [128, 1]], # 2
-   [-1, 1, ShuffleV2Block, [256, 2]], # 3-P4/16
-   [-1, 7, ShuffleV2Block, [256, 1]], # 4
-   [-1, 1, ShuffleV2Block, [512, 2]], # 5-P5/32
-   [-1, 3, ShuffleV2Block, [512, 1]], # 6
-  ]
-
-# YOLOv5 head
-head:
-  [[-1, 1, Conv, [128, 1, 1]],
-   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
-   [[-1, 4], 1, Concat, [1]],  # cat backbone P4
-   [-1, 1, C3, [128, False]],  # 10
-
-   [-1, 1, Conv, [128, 1, 1]],
-   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
-   [[-1, 2], 1, Concat, [1]],  # cat backbone P3
-   [-1, 1, C3, [128, False]],  # 14 (P3/8-small)
-
-   [-1, 1, Conv, [128, 3, 2]],
-   [[-1, 11], 1, Concat, [1]],  # cat head P4
-   [-1, 1, C3, [128, False]],  # 17 (P4/16-medium)
-
-   [-1, 1, Conv, [128, 3, 2]],
-   [[-1, 7], 1, Concat, [1]],  # cat head P5
-   [-1, 1, C3, [128, False]],  # 20 (P5/32-large)
-
-   [[14, 17, 20], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)
-  ]
diff --git a/repositories/CodeFormer/facelib/detection/yolov5face/utils/__init__.py b/repositories/CodeFormer/facelib/detection/yolov5face/utils/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/repositories/CodeFormer/facelib/detection/yolov5face/utils/__pycache__/__init__.cpython-310.pyc b/repositories/CodeFormer/facelib/detection/yolov5face/utils/__pycache__/__init__.cpython-310.pyc
deleted file mode 100644
index 530e39b0a8e715a9adcc09af9f72cd0af5077e75..0000000000000000000000000000000000000000
Binary files a/repositories/CodeFormer/facelib/detection/yolov5face/utils/__pycache__/__init__.cpython-310.pyc and /dev/null differ
diff --git a/repositories/CodeFormer/facelib/detection/yolov5face/utils/__pycache__/autoanchor.cpython-310.pyc b/repositories/CodeFormer/facelib/detection/yolov5face/utils/__pycache__/autoanchor.cpython-310.pyc
deleted file mode 100644
index 128dca55b6be06350703eabf677edb25164b32da..0000000000000000000000000000000000000000
Binary files a/repositories/CodeFormer/facelib/detection/yolov5face/utils/__pycache__/autoanchor.cpython-310.pyc and /dev/null differ
diff --git a/repositories/CodeFormer/facelib/detection/yolov5face/utils/__pycache__/datasets.cpython-310.pyc b/repositories/CodeFormer/facelib/detection/yolov5face/utils/__pycache__/datasets.cpython-310.pyc
deleted file mode 100644
index 74eb15b8f61626a5fd2377f044a28006631b468a..0000000000000000000000000000000000000000
Binary files a/repositories/CodeFormer/facelib/detection/yolov5face/utils/__pycache__/datasets.cpython-310.pyc and /dev/null differ
diff --git a/repositories/CodeFormer/facelib/detection/yolov5face/utils/__pycache__/general.cpython-310.pyc b/repositories/CodeFormer/facelib/detection/yolov5face/utils/__pycache__/general.cpython-310.pyc
deleted file mode 100644
index 03642136afaab2d5968858e757cf2c7d1762675c..0000000000000000000000000000000000000000
Binary files a/repositories/CodeFormer/facelib/detection/yolov5face/utils/__pycache__/general.cpython-310.pyc and /dev/null differ
diff --git a/repositories/CodeFormer/facelib/detection/yolov5face/utils/__pycache__/torch_utils.cpython-310.pyc b/repositories/CodeFormer/facelib/detection/yolov5face/utils/__pycache__/torch_utils.cpython-310.pyc
deleted file mode 100644
index ffeb58f780c87d4b178339fc36f7e9b59af89c78..0000000000000000000000000000000000000000
Binary files a/repositories/CodeFormer/facelib/detection/yolov5face/utils/__pycache__/torch_utils.cpython-310.pyc and /dev/null differ
diff --git a/repositories/CodeFormer/facelib/detection/yolov5face/utils/autoanchor.py b/repositories/CodeFormer/facelib/detection/yolov5face/utils/autoanchor.py
deleted file mode 100644
index a4eba3e94888709be7d2a7c7499fbcc1808b4a88..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/facelib/detection/yolov5face/utils/autoanchor.py
+++ /dev/null
@@ -1,12 +0,0 @@
-# Auto-anchor utils
-
-
-def check_anchor_order(m):
-    # Check anchor order against stride order for YOLOv5 Detect() module m, and correct if necessary
-    a = m.anchor_grid.prod(-1).view(-1)  # anchor area
-    da = a[-1] - a[0]  # delta a
-    ds = m.stride[-1] - m.stride[0]  # delta s
-    if da.sign() != ds.sign():  # same order
-        print("Reversing anchor order")
-        m.anchors[:] = m.anchors.flip(0)
-        m.anchor_grid[:] = m.anchor_grid.flip(0)
diff --git a/repositories/CodeFormer/facelib/detection/yolov5face/utils/datasets.py b/repositories/CodeFormer/facelib/detection/yolov5face/utils/datasets.py
deleted file mode 100644
index e672b136f56fd6b05038e24377908361a54fe519..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/facelib/detection/yolov5face/utils/datasets.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import cv2
-import numpy as np
-
-
-def letterbox(img, new_shape=(640, 640), color=(114, 114, 114), auto=True, scale_fill=False, scaleup=True):
-    # Resize image to a 32-pixel-multiple rectangle https://github.com/ultralytics/yolov3/issues/232
-    shape = img.shape[:2]  # current shape [height, width]
-    if isinstance(new_shape, int):
-        new_shape = (new_shape, new_shape)
-
-    # Scale ratio (new / old)
-    r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
-    if not scaleup:  # only scale down, do not scale up (for better test mAP)
-        r = min(r, 1.0)
-
-    # Compute padding
-    ratio = r, r  # width, height ratios
-    new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
-    dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1]  # wh padding
-    if auto:  # minimum rectangle
-        dw, dh = np.mod(dw, 64), np.mod(dh, 64)  # wh padding
-    elif scale_fill:  # stretch
-        dw, dh = 0.0, 0.0
-        new_unpad = (new_shape[1], new_shape[0])
-        ratio = new_shape[1] / shape[1], new_shape[0] / shape[0]  # width, height ratios
-
-    dw /= 2  # divide padding into 2 sides
-    dh /= 2
-
-    if shape[::-1] != new_unpad:  # resize
-        img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
-    top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
-    left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
-    img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)  # add border
-    return img, ratio, (dw, dh)
diff --git a/repositories/CodeFormer/facelib/detection/yolov5face/utils/extract_ckpt.py b/repositories/CodeFormer/facelib/detection/yolov5face/utils/extract_ckpt.py
deleted file mode 100644
index 4b8b631348f2d0cdea4e5a3594bb59f3e8f34a0f..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/facelib/detection/yolov5face/utils/extract_ckpt.py
+++ /dev/null
@@ -1,5 +0,0 @@
-import torch
-import sys
-sys.path.insert(0,'./facelib/detection/yolov5face')
-model = torch.load('facelib/detection/yolov5face/yolov5n-face.pt', map_location='cpu')['model']
-torch.save(model.state_dict(),'weights/facelib/yolov5n-face.pth')
\ No newline at end of file
diff --git a/repositories/CodeFormer/facelib/detection/yolov5face/utils/general.py b/repositories/CodeFormer/facelib/detection/yolov5face/utils/general.py
deleted file mode 100644
index 1c8e14f56a107ec3a4269c382cfc5168ad780ffc..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/facelib/detection/yolov5face/utils/general.py
+++ /dev/null
@@ -1,271 +0,0 @@
-import math
-import time
-
-import numpy as np
-import torch
-import torchvision
-
-
-def check_img_size(img_size, s=32):
-    # Verify img_size is a multiple of stride s
-    new_size = make_divisible(img_size, int(s))  # ceil gs-multiple
-    # if new_size != img_size:
-    #     print(f"WARNING: --img-size {img_size:g} must be multiple of max stride {s:g}, updating to {new_size:g}")
-    return new_size
-
-
-def make_divisible(x, divisor):
-    # Returns x evenly divisible by divisor
-    return math.ceil(x / divisor) * divisor
-
-
-def xyxy2xywh(x):
-    # Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] where xy1=top-left, xy2=bottom-right
-    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
-    y[:, 0] = (x[:, 0] + x[:, 2]) / 2  # x center
-    y[:, 1] = (x[:, 1] + x[:, 3]) / 2  # y center
-    y[:, 2] = x[:, 2] - x[:, 0]  # width
-    y[:, 3] = x[:, 3] - x[:, 1]  # height
-    return y
-
-
-def xywh2xyxy(x):
-    # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
-    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
-    y[:, 0] = x[:, 0] - x[:, 2] / 2  # top left x
-    y[:, 1] = x[:, 1] - x[:, 3] / 2  # top left y
-    y[:, 2] = x[:, 0] + x[:, 2] / 2  # bottom right x
-    y[:, 3] = x[:, 1] + x[:, 3] / 2  # bottom right y
-    return y
-
-
-def scale_coords(img1_shape, coords, img0_shape, ratio_pad=None):
-    # Rescale coords (xyxy) from img1_shape to img0_shape
-    if ratio_pad is None:  # calculate from img0_shape
-        gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1])  # gain  = old / new
-        pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2  # wh padding
-    else:
-        gain = ratio_pad[0][0]
-        pad = ratio_pad[1]
-
-    coords[:, [0, 2]] -= pad[0]  # x padding
-    coords[:, [1, 3]] -= pad[1]  # y padding
-    coords[:, :4] /= gain
-    clip_coords(coords, img0_shape)
-    return coords
-
-
-def clip_coords(boxes, img_shape):
-    # Clip bounding xyxy bounding boxes to image shape (height, width)
-    boxes[:, 0].clamp_(0, img_shape[1])  # x1
-    boxes[:, 1].clamp_(0, img_shape[0])  # y1
-    boxes[:, 2].clamp_(0, img_shape[1])  # x2
-    boxes[:, 3].clamp_(0, img_shape[0])  # y2
-
-
-def box_iou(box1, box2):
-    # https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py
-    """
-    Return intersection-over-union (Jaccard index) of boxes.
-    Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
-    Arguments:
-        box1 (Tensor[N, 4])
-        box2 (Tensor[M, 4])
-    Returns:
-        iou (Tensor[N, M]): the NxM matrix containing the pairwise
-            IoU values for every element in boxes1 and boxes2
-    """
-
-    def box_area(box):
-        return (box[2] - box[0]) * (box[3] - box[1])
-
-    area1 = box_area(box1.T)
-    area2 = box_area(box2.T)
-
-    inter = (torch.min(box1[:, None, 2:], box2[:, 2:]) - torch.max(box1[:, None, :2], box2[:, :2])).clamp(0).prod(2)
-    return inter / (area1[:, None] + area2 - inter)
-
-
-def non_max_suppression_face(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, labels=()):
-    """Performs Non-Maximum Suppression (NMS) on inference results
-    Returns:
-         detections with shape: nx6 (x1, y1, x2, y2, conf, cls)
-    """
-
-    nc = prediction.shape[2] - 15  # number of classes
-    xc = prediction[..., 4] > conf_thres  # candidates
-
-    # Settings
-    # (pixels) maximum box width and height
-    max_wh = 4096
-    time_limit = 10.0  # seconds to quit after
-    redundant = True  # require redundant detections
-    multi_label = nc > 1  # multiple labels per box (adds 0.5ms/img)
-    merge = False  # use merge-NMS
-
-    t = time.time()
-    output = [torch.zeros((0, 16), device=prediction.device)] * prediction.shape[0]
-    for xi, x in enumerate(prediction):  # image index, image inference
-        # Apply constraints
-        x = x[xc[xi]]  # confidence
-
-        # Cat apriori labels if autolabelling
-        if labels and len(labels[xi]):
-            label = labels[xi]
-            v = torch.zeros((len(label), nc + 15), device=x.device)
-            v[:, :4] = label[:, 1:5]  # box
-            v[:, 4] = 1.0  # conf
-            v[range(len(label)), label[:, 0].long() + 15] = 1.0  # cls
-            x = torch.cat((x, v), 0)
-
-        # If none remain process next image
-        if not x.shape[0]:
-            continue
-
-        # Compute conf
-        x[:, 15:] *= x[:, 4:5]  # conf = obj_conf * cls_conf
-
-        # Box (center x, center y, width, height) to (x1, y1, x2, y2)
-        box = xywh2xyxy(x[:, :4])
-
-        # Detections matrix nx6 (xyxy, conf, landmarks, cls)
-        if multi_label:
-            i, j = (x[:, 15:] > conf_thres).nonzero(as_tuple=False).T
-            x = torch.cat((box[i], x[i, j + 15, None], x[:, 5:15], j[:, None].float()), 1)
-        else:  # best class only
-            conf, j = x[:, 15:].max(1, keepdim=True)
-            x = torch.cat((box, conf, x[:, 5:15], j.float()), 1)[conf.view(-1) > conf_thres]
-
-        # Filter by class
-        if classes is not None:
-            x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)]
-
-        # If none remain process next image
-        n = x.shape[0]  # number of boxes
-        if not n:
-            continue
-
-        # Batched NMS
-        c = x[:, 15:16] * (0 if agnostic else max_wh)  # classes
-        boxes, scores = x[:, :4] + c, x[:, 4]  # boxes (offset by class), scores
-        i = torchvision.ops.nms(boxes, scores, iou_thres)  # NMS
-
-        if merge and (1 < n < 3e3):  # Merge NMS (boxes merged using weighted mean)
-            # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)
-            iou = box_iou(boxes[i], boxes) > iou_thres  # iou matrix
-            weights = iou * scores[None]  # box weights
-            x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True)  # merged boxes
-            if redundant:
-                i = i[iou.sum(1) > 1]  # require redundancy
-
-        output[xi] = x[i]
-        if (time.time() - t) > time_limit:
-            break  # time limit exceeded
-
-    return output
-
-
-def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, labels=()):
-    """Performs Non-Maximum Suppression (NMS) on inference results
-
-    Returns:
-         detections with shape: nx6 (x1, y1, x2, y2, conf, cls)
-    """
-
-    nc = prediction.shape[2] - 5  # number of classes
-    xc = prediction[..., 4] > conf_thres  # candidates
-
-    # Settings
-    # (pixels) maximum box width and height
-    max_wh = 4096
-    time_limit = 10.0  # seconds to quit after
-    redundant = True  # require redundant detections
-    multi_label = nc > 1  # multiple labels per box (adds 0.5ms/img)
-    merge = False  # use merge-NMS
-
-    t = time.time()
-    output = [torch.zeros((0, 6), device=prediction.device)] * prediction.shape[0]
-    for xi, x in enumerate(prediction):  # image index, image inference
-        x = x[xc[xi]]  # confidence
-
-        # Cat apriori labels if autolabelling
-        if labels and len(labels[xi]):
-            label_id = labels[xi]
-            v = torch.zeros((len(label_id), nc + 5), device=x.device)
-            v[:, :4] = label_id[:, 1:5]  # box
-            v[:, 4] = 1.0  # conf
-            v[range(len(label_id)), label_id[:, 0].long() + 5] = 1.0  # cls
-            x = torch.cat((x, v), 0)
-
-        # If none remain process next image
-        if not x.shape[0]:
-            continue
-
-        # Compute conf
-        x[:, 5:] *= x[:, 4:5]  # conf = obj_conf * cls_conf
-
-        # Box (center x, center y, width, height) to (x1, y1, x2, y2)
-        box = xywh2xyxy(x[:, :4])
-
-        # Detections matrix nx6 (xyxy, conf, cls)
-        if multi_label:
-            i, j = (x[:, 5:] > conf_thres).nonzero(as_tuple=False).T
-            x = torch.cat((box[i], x[i, j + 5, None], j[:, None].float()), 1)
-        else:  # best class only
-            conf, j = x[:, 5:].max(1, keepdim=True)
-            x = torch.cat((box, conf, j.float()), 1)[conf.view(-1) > conf_thres]
-
-        # Filter by class
-        if classes is not None:
-            x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)]
-
-        # Check shape
-        n = x.shape[0]  # number of boxes
-        if not n:  # no boxes
-            continue
-
-        x = x[x[:, 4].argsort(descending=True)]  # sort by confidence
-
-        # Batched NMS
-        c = x[:, 5:6] * (0 if agnostic else max_wh)  # classes
-        boxes, scores = x[:, :4] + c, x[:, 4]  # boxes (offset by class), scores
-        i = torchvision.ops.nms(boxes, scores, iou_thres)  # NMS
-        if merge and (1 < n < 3e3):  # Merge NMS (boxes merged using weighted mean)
-            # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)
-            iou = box_iou(boxes[i], boxes) > iou_thres  # iou matrix
-            weights = iou * scores[None]  # box weights
-            x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True)  # merged boxes
-            if redundant:
-                i = i[iou.sum(1) > 1]  # require redundancy
-
-        output[xi] = x[i]
-        if (time.time() - t) > time_limit:
-            print(f"WARNING: NMS time limit {time_limit}s exceeded")
-            break  # time limit exceeded
-
-    return output
-
-
-def scale_coords_landmarks(img1_shape, coords, img0_shape, ratio_pad=None):
-    # Rescale coords (xyxy) from img1_shape to img0_shape
-    if ratio_pad is None:  # calculate from img0_shape
-        gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1])  # gain  = old / new
-        pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2  # wh padding
-    else:
-        gain = ratio_pad[0][0]
-        pad = ratio_pad[1]
-
-    coords[:, [0, 2, 4, 6, 8]] -= pad[0]  # x padding
-    coords[:, [1, 3, 5, 7, 9]] -= pad[1]  # y padding
-    coords[:, :10] /= gain
-    coords[:, 0].clamp_(0, img0_shape[1])  # x1
-    coords[:, 1].clamp_(0, img0_shape[0])  # y1
-    coords[:, 2].clamp_(0, img0_shape[1])  # x2
-    coords[:, 3].clamp_(0, img0_shape[0])  # y2
-    coords[:, 4].clamp_(0, img0_shape[1])  # x3
-    coords[:, 5].clamp_(0, img0_shape[0])  # y3
-    coords[:, 6].clamp_(0, img0_shape[1])  # x4
-    coords[:, 7].clamp_(0, img0_shape[0])  # y4
-    coords[:, 8].clamp_(0, img0_shape[1])  # x5
-    coords[:, 9].clamp_(0, img0_shape[0])  # y5
-    return coords
diff --git a/repositories/CodeFormer/facelib/detection/yolov5face/utils/torch_utils.py b/repositories/CodeFormer/facelib/detection/yolov5face/utils/torch_utils.py
deleted file mode 100644
index af2d06587b2d07b2eab199a8484380fde1de5c3c..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/facelib/detection/yolov5face/utils/torch_utils.py
+++ /dev/null
@@ -1,40 +0,0 @@
-import torch
-from torch import nn
-
-
-def fuse_conv_and_bn(conv, bn):
-    # Fuse convolution and batchnorm layers https://tehnokv.com/posts/fusing-batchnorm-and-conv/
-    fusedconv = (
-        nn.Conv2d(
-            conv.in_channels,
-            conv.out_channels,
-            kernel_size=conv.kernel_size,
-            stride=conv.stride,
-            padding=conv.padding,
-            groups=conv.groups,
-            bias=True,
-        )
-        .requires_grad_(False)
-        .to(conv.weight.device)
-    )
-
-    # prepare filters
-    w_conv = conv.weight.clone().view(conv.out_channels, -1)
-    w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var)))
-    fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.size()))
-
-    # prepare spatial bias
-    b_conv = torch.zeros(conv.weight.size(0), device=conv.weight.device) if conv.bias is None else conv.bias
-    b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps))
-    fusedconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn)
-
-    return fusedconv
-
-
-def copy_attr(a, b, include=(), exclude=()):
-    # Copy attributes from b to a, options to only include [...] and to exclude [...]
-    for k, v in b.__dict__.items():
-        if (include and k not in include) or k.startswith("_") or k in exclude:
-            continue
-
-        setattr(a, k, v)
diff --git a/repositories/CodeFormer/facelib/parsing/__init__.py b/repositories/CodeFormer/facelib/parsing/__init__.py
deleted file mode 100644
index 72656e4b5f61df8cd0838588b0c6488fcc886e16..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/facelib/parsing/__init__.py
+++ /dev/null
@@ -1,23 +0,0 @@
-import torch
-
-from facelib.utils import load_file_from_url
-from .bisenet import BiSeNet
-from .parsenet import ParseNet
-
-
-def init_parsing_model(model_name='bisenet', half=False, device='cuda'):
-    if model_name == 'bisenet':
-        model = BiSeNet(num_class=19)
-        model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/parsing_bisenet.pth'
-    elif model_name == 'parsenet':
-        model = ParseNet(in_size=512, out_size=512, parsing_ch=19)
-        model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/parsing_parsenet.pth'
-    else:
-        raise NotImplementedError(f'{model_name} is not implemented.')
-
-    model_path = load_file_from_url(url=model_url, model_dir='weights/facelib', progress=True, file_name=None)
-    load_net = torch.load(model_path, map_location=lambda storage, loc: storage)
-    model.load_state_dict(load_net, strict=True)
-    model.eval()
-    model = model.to(device)
-    return model
diff --git a/repositories/CodeFormer/facelib/parsing/__pycache__/__init__.cpython-310.pyc b/repositories/CodeFormer/facelib/parsing/__pycache__/__init__.cpython-310.pyc
deleted file mode 100644
index 509652d2431e1c894144f97c3eef91caf4ddb0ce..0000000000000000000000000000000000000000
Binary files a/repositories/CodeFormer/facelib/parsing/__pycache__/__init__.cpython-310.pyc and /dev/null differ
diff --git a/repositories/CodeFormer/facelib/parsing/__pycache__/bisenet.cpython-310.pyc b/repositories/CodeFormer/facelib/parsing/__pycache__/bisenet.cpython-310.pyc
deleted file mode 100644
index 04800eecf3ec6d3e8d620307fb1a5c5cdbd1dd9f..0000000000000000000000000000000000000000
Binary files a/repositories/CodeFormer/facelib/parsing/__pycache__/bisenet.cpython-310.pyc and /dev/null differ
diff --git a/repositories/CodeFormer/facelib/parsing/__pycache__/parsenet.cpython-310.pyc b/repositories/CodeFormer/facelib/parsing/__pycache__/parsenet.cpython-310.pyc
deleted file mode 100644
index 48539a82f97a67c6dd611d82a0195895726dec6c..0000000000000000000000000000000000000000
Binary files a/repositories/CodeFormer/facelib/parsing/__pycache__/parsenet.cpython-310.pyc and /dev/null differ
diff --git a/repositories/CodeFormer/facelib/parsing/__pycache__/resnet.cpython-310.pyc b/repositories/CodeFormer/facelib/parsing/__pycache__/resnet.cpython-310.pyc
deleted file mode 100644
index 0ca6999bd50f45a3ddd55d8acd97d6446866dd12..0000000000000000000000000000000000000000
Binary files a/repositories/CodeFormer/facelib/parsing/__pycache__/resnet.cpython-310.pyc and /dev/null differ
diff --git a/repositories/CodeFormer/facelib/parsing/bisenet.py b/repositories/CodeFormer/facelib/parsing/bisenet.py
deleted file mode 100644
index 3898cab76ae5876459cd4899c54cafa14234971d..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/facelib/parsing/bisenet.py
+++ /dev/null
@@ -1,140 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from .resnet import ResNet18
-
-
-class ConvBNReLU(nn.Module):
-
-    def __init__(self, in_chan, out_chan, ks=3, stride=1, padding=1):
-        super(ConvBNReLU, self).__init__()
-        self.conv = nn.Conv2d(in_chan, out_chan, kernel_size=ks, stride=stride, padding=padding, bias=False)
-        self.bn = nn.BatchNorm2d(out_chan)
-
-    def forward(self, x):
-        x = self.conv(x)
-        x = F.relu(self.bn(x))
-        return x
-
-
-class BiSeNetOutput(nn.Module):
-
-    def __init__(self, in_chan, mid_chan, num_class):
-        super(BiSeNetOutput, self).__init__()
-        self.conv = ConvBNReLU(in_chan, mid_chan, ks=3, stride=1, padding=1)
-        self.conv_out = nn.Conv2d(mid_chan, num_class, kernel_size=1, bias=False)
-
-    def forward(self, x):
-        feat = self.conv(x)
-        out = self.conv_out(feat)
-        return out, feat
-
-
-class AttentionRefinementModule(nn.Module):
-
-    def __init__(self, in_chan, out_chan):
-        super(AttentionRefinementModule, self).__init__()
-        self.conv = ConvBNReLU(in_chan, out_chan, ks=3, stride=1, padding=1)
-        self.conv_atten = nn.Conv2d(out_chan, out_chan, kernel_size=1, bias=False)
-        self.bn_atten = nn.BatchNorm2d(out_chan)
-        self.sigmoid_atten = nn.Sigmoid()
-
-    def forward(self, x):
-        feat = self.conv(x)
-        atten = F.avg_pool2d(feat, feat.size()[2:])
-        atten = self.conv_atten(atten)
-        atten = self.bn_atten(atten)
-        atten = self.sigmoid_atten(atten)
-        out = torch.mul(feat, atten)
-        return out
-
-
-class ContextPath(nn.Module):
-
-    def __init__(self):
-        super(ContextPath, self).__init__()
-        self.resnet = ResNet18()
-        self.arm16 = AttentionRefinementModule(256, 128)
-        self.arm32 = AttentionRefinementModule(512, 128)
-        self.conv_head32 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1)
-        self.conv_head16 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1)
-        self.conv_avg = ConvBNReLU(512, 128, ks=1, stride=1, padding=0)
-
-    def forward(self, x):
-        feat8, feat16, feat32 = self.resnet(x)
-        h8, w8 = feat8.size()[2:]
-        h16, w16 = feat16.size()[2:]
-        h32, w32 = feat32.size()[2:]
-
-        avg = F.avg_pool2d(feat32, feat32.size()[2:])
-        avg = self.conv_avg(avg)
-        avg_up = F.interpolate(avg, (h32, w32), mode='nearest')
-
-        feat32_arm = self.arm32(feat32)
-        feat32_sum = feat32_arm + avg_up
-        feat32_up = F.interpolate(feat32_sum, (h16, w16), mode='nearest')
-        feat32_up = self.conv_head32(feat32_up)
-
-        feat16_arm = self.arm16(feat16)
-        feat16_sum = feat16_arm + feat32_up
-        feat16_up = F.interpolate(feat16_sum, (h8, w8), mode='nearest')
-        feat16_up = self.conv_head16(feat16_up)
-
-        return feat8, feat16_up, feat32_up  # x8, x8, x16
-
-
-class FeatureFusionModule(nn.Module):
-
-    def __init__(self, in_chan, out_chan):
-        super(FeatureFusionModule, self).__init__()
-        self.convblk = ConvBNReLU(in_chan, out_chan, ks=1, stride=1, padding=0)
-        self.conv1 = nn.Conv2d(out_chan, out_chan // 4, kernel_size=1, stride=1, padding=0, bias=False)
-        self.conv2 = nn.Conv2d(out_chan // 4, out_chan, kernel_size=1, stride=1, padding=0, bias=False)
-        self.relu = nn.ReLU(inplace=True)
-        self.sigmoid = nn.Sigmoid()
-
-    def forward(self, fsp, fcp):
-        fcat = torch.cat([fsp, fcp], dim=1)
-        feat = self.convblk(fcat)
-        atten = F.avg_pool2d(feat, feat.size()[2:])
-        atten = self.conv1(atten)
-        atten = self.relu(atten)
-        atten = self.conv2(atten)
-        atten = self.sigmoid(atten)
-        feat_atten = torch.mul(feat, atten)
-        feat_out = feat_atten + feat
-        return feat_out
-
-
-class BiSeNet(nn.Module):
-
-    def __init__(self, num_class):
-        super(BiSeNet, self).__init__()
-        self.cp = ContextPath()
-        self.ffm = FeatureFusionModule(256, 256)
-        self.conv_out = BiSeNetOutput(256, 256, num_class)
-        self.conv_out16 = BiSeNetOutput(128, 64, num_class)
-        self.conv_out32 = BiSeNetOutput(128, 64, num_class)
-
-    def forward(self, x, return_feat=False):
-        h, w = x.size()[2:]
-        feat_res8, feat_cp8, feat_cp16 = self.cp(x)  # return res3b1 feature
-        feat_sp = feat_res8  # replace spatial path feature with res3b1 feature
-        feat_fuse = self.ffm(feat_sp, feat_cp8)
-
-        out, feat = self.conv_out(feat_fuse)
-        out16, feat16 = self.conv_out16(feat_cp8)
-        out32, feat32 = self.conv_out32(feat_cp16)
-
-        out = F.interpolate(out, (h, w), mode='bilinear', align_corners=True)
-        out16 = F.interpolate(out16, (h, w), mode='bilinear', align_corners=True)
-        out32 = F.interpolate(out32, (h, w), mode='bilinear', align_corners=True)
-
-        if return_feat:
-            feat = F.interpolate(feat, (h, w), mode='bilinear', align_corners=True)
-            feat16 = F.interpolate(feat16, (h, w), mode='bilinear', align_corners=True)
-            feat32 = F.interpolate(feat32, (h, w), mode='bilinear', align_corners=True)
-            return out, out16, out32, feat, feat16, feat32
-        else:
-            return out, out16, out32
diff --git a/repositories/CodeFormer/facelib/parsing/parsenet.py b/repositories/CodeFormer/facelib/parsing/parsenet.py
deleted file mode 100644
index e178ebe43a1ef666aaea0bc0faf629485c22a24f..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/facelib/parsing/parsenet.py
+++ /dev/null
@@ -1,194 +0,0 @@
-"""Modified from https://github.com/chaofengc/PSFRGAN
-"""
-import numpy as np
-import torch.nn as nn
-from torch.nn import functional as F
-
-
-class NormLayer(nn.Module):
-    """Normalization Layers.
-
-    Args:
-        channels: input channels, for batch norm and instance norm.
-        input_size: input shape without batch size, for layer norm.
-    """
-
-    def __init__(self, channels, normalize_shape=None, norm_type='bn'):
-        super(NormLayer, self).__init__()
-        norm_type = norm_type.lower()
-        self.norm_type = norm_type
-        if norm_type == 'bn':
-            self.norm = nn.BatchNorm2d(channels, affine=True)
-        elif norm_type == 'in':
-            self.norm = nn.InstanceNorm2d(channels, affine=False)
-        elif norm_type == 'gn':
-            self.norm = nn.GroupNorm(32, channels, affine=True)
-        elif norm_type == 'pixel':
-            self.norm = lambda x: F.normalize(x, p=2, dim=1)
-        elif norm_type == 'layer':
-            self.norm = nn.LayerNorm(normalize_shape)
-        elif norm_type == 'none':
-            self.norm = lambda x: x * 1.0
-        else:
-            assert 1 == 0, f'Norm type {norm_type} not support.'
-
-    def forward(self, x, ref=None):
-        if self.norm_type == 'spade':
-            return self.norm(x, ref)
-        else:
-            return self.norm(x)
-
-
-class ReluLayer(nn.Module):
-    """Relu Layer.
-
-    Args:
-        relu type: type of relu layer, candidates are
-            - ReLU
-            - LeakyReLU: default relu slope 0.2
-            - PRelu
-            - SELU
-            - none: direct pass
-    """
-
-    def __init__(self, channels, relu_type='relu'):
-        super(ReluLayer, self).__init__()
-        relu_type = relu_type.lower()
-        if relu_type == 'relu':
-            self.func = nn.ReLU(True)
-        elif relu_type == 'leakyrelu':
-            self.func = nn.LeakyReLU(0.2, inplace=True)
-        elif relu_type == 'prelu':
-            self.func = nn.PReLU(channels)
-        elif relu_type == 'selu':
-            self.func = nn.SELU(True)
-        elif relu_type == 'none':
-            self.func = lambda x: x * 1.0
-        else:
-            assert 1 == 0, f'Relu type {relu_type} not support.'
-
-    def forward(self, x):
-        return self.func(x)
-
-
-class ConvLayer(nn.Module):
-
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 kernel_size=3,
-                 scale='none',
-                 norm_type='none',
-                 relu_type='none',
-                 use_pad=True,
-                 bias=True):
-        super(ConvLayer, self).__init__()
-        self.use_pad = use_pad
-        self.norm_type = norm_type
-        if norm_type in ['bn']:
-            bias = False
-
-        stride = 2 if scale == 'down' else 1
-
-        self.scale_func = lambda x: x
-        if scale == 'up':
-            self.scale_func = lambda x: nn.functional.interpolate(x, scale_factor=2, mode='nearest')
-
-        self.reflection_pad = nn.ReflectionPad2d(int(np.ceil((kernel_size - 1.) / 2)))
-        self.conv2d = nn.Conv2d(in_channels, out_channels, kernel_size, stride, bias=bias)
-
-        self.relu = ReluLayer(out_channels, relu_type)
-        self.norm = NormLayer(out_channels, norm_type=norm_type)
-
-    def forward(self, x):
-        out = self.scale_func(x)
-        if self.use_pad:
-            out = self.reflection_pad(out)
-        out = self.conv2d(out)
-        out = self.norm(out)
-        out = self.relu(out)
-        return out
-
-
-class ResidualBlock(nn.Module):
-    """
-    Residual block recommended in: http://torch.ch/blog/2016/02/04/resnets.html
-    """
-
-    def __init__(self, c_in, c_out, relu_type='prelu', norm_type='bn', scale='none'):
-        super(ResidualBlock, self).__init__()
-
-        if scale == 'none' and c_in == c_out:
-            self.shortcut_func = lambda x: x
-        else:
-            self.shortcut_func = ConvLayer(c_in, c_out, 3, scale)
-
-        scale_config_dict = {'down': ['none', 'down'], 'up': ['up', 'none'], 'none': ['none', 'none']}
-        scale_conf = scale_config_dict[scale]
-
-        self.conv1 = ConvLayer(c_in, c_out, 3, scale_conf[0], norm_type=norm_type, relu_type=relu_type)
-        self.conv2 = ConvLayer(c_out, c_out, 3, scale_conf[1], norm_type=norm_type, relu_type='none')
-
-    def forward(self, x):
-        identity = self.shortcut_func(x)
-
-        res = self.conv1(x)
-        res = self.conv2(res)
-        return identity + res
-
-
-class ParseNet(nn.Module):
-
-    def __init__(self,
-                 in_size=128,
-                 out_size=128,
-                 min_feat_size=32,
-                 base_ch=64,
-                 parsing_ch=19,
-                 res_depth=10,
-                 relu_type='LeakyReLU',
-                 norm_type='bn',
-                 ch_range=[32, 256]):
-        super().__init__()
-        self.res_depth = res_depth
-        act_args = {'norm_type': norm_type, 'relu_type': relu_type}
-        min_ch, max_ch = ch_range
-
-        ch_clip = lambda x: max(min_ch, min(x, max_ch))  # noqa: E731
-        min_feat_size = min(in_size, min_feat_size)
-
-        down_steps = int(np.log2(in_size // min_feat_size))
-        up_steps = int(np.log2(out_size // min_feat_size))
-
-        # =============== define encoder-body-decoder ====================
-        self.encoder = []
-        self.encoder.append(ConvLayer(3, base_ch, 3, 1))
-        head_ch = base_ch
-        for i in range(down_steps):
-            cin, cout = ch_clip(head_ch), ch_clip(head_ch * 2)
-            self.encoder.append(ResidualBlock(cin, cout, scale='down', **act_args))
-            head_ch = head_ch * 2
-
-        self.body = []
-        for i in range(res_depth):
-            self.body.append(ResidualBlock(ch_clip(head_ch), ch_clip(head_ch), **act_args))
-
-        self.decoder = []
-        for i in range(up_steps):
-            cin, cout = ch_clip(head_ch), ch_clip(head_ch // 2)
-            self.decoder.append(ResidualBlock(cin, cout, scale='up', **act_args))
-            head_ch = head_ch // 2
-
-        self.encoder = nn.Sequential(*self.encoder)
-        self.body = nn.Sequential(*self.body)
-        self.decoder = nn.Sequential(*self.decoder)
-        self.out_img_conv = ConvLayer(ch_clip(head_ch), 3)
-        self.out_mask_conv = ConvLayer(ch_clip(head_ch), parsing_ch)
-
-    def forward(self, x):
-        feat = self.encoder(x)
-        x = feat + self.body(feat)
-        x = self.decoder(x)
-        out_img = self.out_img_conv(x)
-        out_mask = self.out_mask_conv(x)
-        return out_mask, out_img
diff --git a/repositories/CodeFormer/facelib/parsing/resnet.py b/repositories/CodeFormer/facelib/parsing/resnet.py
deleted file mode 100644
index fec8e82cf64469fb51be21ad5130217052addbda..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/facelib/parsing/resnet.py
+++ /dev/null
@@ -1,69 +0,0 @@
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-def conv3x3(in_planes, out_planes, stride=1):
-    """3x3 convolution with padding"""
-    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
-
-
-class BasicBlock(nn.Module):
-
-    def __init__(self, in_chan, out_chan, stride=1):
-        super(BasicBlock, self).__init__()
-        self.conv1 = conv3x3(in_chan, out_chan, stride)
-        self.bn1 = nn.BatchNorm2d(out_chan)
-        self.conv2 = conv3x3(out_chan, out_chan)
-        self.bn2 = nn.BatchNorm2d(out_chan)
-        self.relu = nn.ReLU(inplace=True)
-        self.downsample = None
-        if in_chan != out_chan or stride != 1:
-            self.downsample = nn.Sequential(
-                nn.Conv2d(in_chan, out_chan, kernel_size=1, stride=stride, bias=False),
-                nn.BatchNorm2d(out_chan),
-            )
-
-    def forward(self, x):
-        residual = self.conv1(x)
-        residual = F.relu(self.bn1(residual))
-        residual = self.conv2(residual)
-        residual = self.bn2(residual)
-
-        shortcut = x
-        if self.downsample is not None:
-            shortcut = self.downsample(x)
-
-        out = shortcut + residual
-        out = self.relu(out)
-        return out
-
-
-def create_layer_basic(in_chan, out_chan, bnum, stride=1):
-    layers = [BasicBlock(in_chan, out_chan, stride=stride)]
-    for i in range(bnum - 1):
-        layers.append(BasicBlock(out_chan, out_chan, stride=1))
-    return nn.Sequential(*layers)
-
-
-class ResNet18(nn.Module):
-
-    def __init__(self):
-        super(ResNet18, self).__init__()
-        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
-        self.bn1 = nn.BatchNorm2d(64)
-        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
-        self.layer1 = create_layer_basic(64, 64, bnum=2, stride=1)
-        self.layer2 = create_layer_basic(64, 128, bnum=2, stride=2)
-        self.layer3 = create_layer_basic(128, 256, bnum=2, stride=2)
-        self.layer4 = create_layer_basic(256, 512, bnum=2, stride=2)
-
-    def forward(self, x):
-        x = self.conv1(x)
-        x = F.relu(self.bn1(x))
-        x = self.maxpool(x)
-
-        x = self.layer1(x)
-        feat8 = self.layer2(x)  # 1/8
-        feat16 = self.layer3(feat8)  # 1/16
-        feat32 = self.layer4(feat16)  # 1/32
-        return feat8, feat16, feat32
diff --git a/repositories/CodeFormer/facelib/utils/__init__.py b/repositories/CodeFormer/facelib/utils/__init__.py
deleted file mode 100644
index f03b1c2bafcd7759cb7e8722a0c6715f201a46dc..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/facelib/utils/__init__.py
+++ /dev/null
@@ -1,7 +0,0 @@
-from .face_utils import align_crop_face_landmarks, compute_increased_bbox, get_valid_bboxes, paste_face_back
-from .misc import img2tensor, load_file_from_url, download_pretrained_models, scandir
-
-__all__ = [
-    'align_crop_face_landmarks', 'compute_increased_bbox', 'get_valid_bboxes', 'load_file_from_url', 
-    'download_pretrained_models', 'paste_face_back', 'img2tensor', 'scandir'
-]
diff --git a/repositories/CodeFormer/facelib/utils/__pycache__/__init__.cpython-310.pyc b/repositories/CodeFormer/facelib/utils/__pycache__/__init__.cpython-310.pyc
deleted file mode 100644
index c133301c05597b9e9a314830389b99af3fd3fffd..0000000000000000000000000000000000000000
Binary files a/repositories/CodeFormer/facelib/utils/__pycache__/__init__.cpython-310.pyc and /dev/null differ
diff --git a/repositories/CodeFormer/facelib/utils/__pycache__/face_restoration_helper.cpython-310.pyc b/repositories/CodeFormer/facelib/utils/__pycache__/face_restoration_helper.cpython-310.pyc
deleted file mode 100644
index 4baec8c6742f2416e40350939fdb00c4358d7091..0000000000000000000000000000000000000000
Binary files a/repositories/CodeFormer/facelib/utils/__pycache__/face_restoration_helper.cpython-310.pyc and /dev/null differ
diff --git a/repositories/CodeFormer/facelib/utils/__pycache__/face_utils.cpython-310.pyc b/repositories/CodeFormer/facelib/utils/__pycache__/face_utils.cpython-310.pyc
deleted file mode 100644
index f1777654eceac179ab948995c7a480ab7323daa4..0000000000000000000000000000000000000000
Binary files a/repositories/CodeFormer/facelib/utils/__pycache__/face_utils.cpython-310.pyc and /dev/null differ
diff --git a/repositories/CodeFormer/facelib/utils/__pycache__/misc.cpython-310.pyc b/repositories/CodeFormer/facelib/utils/__pycache__/misc.cpython-310.pyc
deleted file mode 100644
index 10455825cce2d70e020dd78af1a0d57cd47f4408..0000000000000000000000000000000000000000
Binary files a/repositories/CodeFormer/facelib/utils/__pycache__/misc.cpython-310.pyc and /dev/null differ
diff --git a/repositories/CodeFormer/facelib/utils/face_restoration_helper.py b/repositories/CodeFormer/facelib/utils/face_restoration_helper.py
deleted file mode 100644
index 6b7644ccd3d9978aea7997a76f7c6fdb0ccde8b1..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/facelib/utils/face_restoration_helper.py
+++ /dev/null
@@ -1,455 +0,0 @@
-import cv2
-import numpy as np
-import os
-import torch
-from torchvision.transforms.functional import normalize
-
-from facelib.detection import init_detection_model
-from facelib.parsing import init_parsing_model
-from facelib.utils.misc import img2tensor, imwrite
-
-
-def get_largest_face(det_faces, h, w):
-
-    def get_location(val, length):
-        if val < 0:
-            return 0
-        elif val > length:
-            return length
-        else:
-            return val
-
-    face_areas = []
-    for det_face in det_faces:
-        left = get_location(det_face[0], w)
-        right = get_location(det_face[2], w)
-        top = get_location(det_face[1], h)
-        bottom = get_location(det_face[3], h)
-        face_area = (right - left) * (bottom - top)
-        face_areas.append(face_area)
-    largest_idx = face_areas.index(max(face_areas))
-    return det_faces[largest_idx], largest_idx
-
-
-def get_center_face(det_faces, h=0, w=0, center=None):
-    if center is not None:
-        center = np.array(center)
-    else:
-        center = np.array([w / 2, h / 2])
-    center_dist = []
-    for det_face in det_faces:
-        face_center = np.array([(det_face[0] + det_face[2]) / 2, (det_face[1] + det_face[3]) / 2])
-        dist = np.linalg.norm(face_center - center)
-        center_dist.append(dist)
-    center_idx = center_dist.index(min(center_dist))
-    return det_faces[center_idx], center_idx
-
-
-class FaceRestoreHelper(object):
-    """Helper for the face restoration pipeline (base class)."""
-
-    def __init__(self,
-                 upscale_factor,
-                 face_size=512,
-                 crop_ratio=(1, 1),
-                 det_model='retinaface_resnet50',
-                 save_ext='png',
-                 template_3points=False,
-                 pad_blur=False,
-                 use_parse=False,
-                 device=None):
-        self.template_3points = template_3points  # improve robustness
-        self.upscale_factor = upscale_factor
-        # the cropped face ratio based on the square face
-        self.crop_ratio = crop_ratio  # (h, w)
-        assert (self.crop_ratio[0] >= 1 and self.crop_ratio[1] >= 1), 'crop ration only supports >=1'
-        self.face_size = (int(face_size * self.crop_ratio[1]), int(face_size * self.crop_ratio[0]))
-
-        if self.template_3points:
-            self.face_template = np.array([[192, 240], [319, 240], [257, 371]])
-        else:
-            # standard 5 landmarks for FFHQ faces with 512 x 512 
-            # facexlib
-            self.face_template = np.array([[192.98138, 239.94708], [318.90277, 240.1936], [256.63416, 314.01935],
-                                           [201.26117, 371.41043], [313.08905, 371.15118]])
-
-            # dlib: left_eye: 36:41  right_eye: 42:47  nose: 30,32,33,34  left mouth corner: 48  right mouth corner: 54
-            # self.face_template = np.array([[193.65928, 242.98541], [318.32558, 243.06108], [255.67984, 328.82894],
-            #                                 [198.22603, 372.82502], [313.91018, 372.75659]])
-
-
-        self.face_template = self.face_template * (face_size / 512.0)
-        if self.crop_ratio[0] > 1:
-            self.face_template[:, 1] += face_size * (self.crop_ratio[0] - 1) / 2
-        if self.crop_ratio[1] > 1:
-            self.face_template[:, 0] += face_size * (self.crop_ratio[1] - 1) / 2
-        self.save_ext = save_ext
-        self.pad_blur = pad_blur
-        if self.pad_blur is True:
-            self.template_3points = False
-
-        self.all_landmarks_5 = []
-        self.det_faces = []
-        self.affine_matrices = []
-        self.inverse_affine_matrices = []
-        self.cropped_faces = []
-        self.restored_faces = []
-        self.pad_input_imgs = []
-
-        if device is None:
-            self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-        else:
-            self.device = device
-
-        # init face detection model
-        self.face_det = init_detection_model(det_model, half=False, device=self.device)
-
-        # init face parsing model
-        self.use_parse = use_parse
-        self.face_parse = init_parsing_model(model_name='parsenet', device=self.device)
-
-    def set_upscale_factor(self, upscale_factor):
-        self.upscale_factor = upscale_factor
-
-    def read_image(self, img):
-        """img can be image path or cv2 loaded image."""
-        # self.input_img is Numpy array, (h, w, c), BGR, uint8, [0, 255]
-        if isinstance(img, str):
-            img = cv2.imread(img)
-
-        if np.max(img) > 256:  # 16-bit image
-            img = img / 65535 * 255
-        if len(img.shape) == 2:  # gray image
-            img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
-        elif img.shape[2] == 4:  # BGRA image with alpha channel
-            img = img[:, :, 0:3]
-
-        self.input_img = img
-
-        if min(self.input_img.shape[:2])<512:
-            f = 512.0/min(self.input_img.shape[:2])
-            self.input_img = cv2.resize(self.input_img, (0,0), fx=f, fy=f, interpolation=cv2.INTER_LINEAR)
-
-    def get_face_landmarks_5(self,
-                             only_keep_largest=False,
-                             only_center_face=False,
-                             resize=None,
-                             blur_ratio=0.01,
-                             eye_dist_threshold=None):
-        if resize is None:
-            scale = 1
-            input_img = self.input_img
-        else:
-            h, w = self.input_img.shape[0:2]
-            scale = resize / min(h, w)
-            scale = max(1, scale) # always scale up
-            h, w = int(h * scale), int(w * scale)
-            interp = cv2.INTER_AREA if scale < 1 else cv2.INTER_LINEAR
-            input_img = cv2.resize(self.input_img, (w, h), interpolation=interp)
-
-        with torch.no_grad():
-            bboxes = self.face_det.detect_faces(input_img)
-
-        if bboxes is None or bboxes.shape[0] == 0:
-            return 0
-        else:
-            bboxes = bboxes / scale
-
-        for bbox in bboxes:
-            # remove faces with too small eye distance: side faces or too small faces
-            eye_dist = np.linalg.norm([bbox[6] - bbox[8], bbox[7] - bbox[9]])
-            if eye_dist_threshold is not None and (eye_dist < eye_dist_threshold):
-                continue
-
-            if self.template_3points:
-                landmark = np.array([[bbox[i], bbox[i + 1]] for i in range(5, 11, 2)])
-            else:
-                landmark = np.array([[bbox[i], bbox[i + 1]] for i in range(5, 15, 2)])
-            self.all_landmarks_5.append(landmark)
-            self.det_faces.append(bbox[0:5])
-            
-        if len(self.det_faces) == 0:
-            return 0
-        if only_keep_largest:
-            h, w, _ = self.input_img.shape
-            self.det_faces, largest_idx = get_largest_face(self.det_faces, h, w)
-            self.all_landmarks_5 = [self.all_landmarks_5[largest_idx]]
-        elif only_center_face:
-            h, w, _ = self.input_img.shape
-            self.det_faces, center_idx = get_center_face(self.det_faces, h, w)
-            self.all_landmarks_5 = [self.all_landmarks_5[center_idx]]
-
-        # pad blurry images
-        if self.pad_blur:
-            self.pad_input_imgs = []
-            for landmarks in self.all_landmarks_5:
-                # get landmarks
-                eye_left = landmarks[0, :]
-                eye_right = landmarks[1, :]
-                eye_avg = (eye_left + eye_right) * 0.5
-                mouth_avg = (landmarks[3, :] + landmarks[4, :]) * 0.5
-                eye_to_eye = eye_right - eye_left
-                eye_to_mouth = mouth_avg - eye_avg
-
-                # Get the oriented crop rectangle
-                # x: half width of the oriented crop rectangle
-                x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1]
-                #  - np.flipud(eye_to_mouth) * [-1, 1]: rotate 90 clockwise
-                # norm with the hypotenuse: get the direction
-                x /= np.hypot(*x)  # get the hypotenuse of a right triangle
-                rect_scale = 1.5
-                x *= max(np.hypot(*eye_to_eye) * 2.0 * rect_scale, np.hypot(*eye_to_mouth) * 1.8 * rect_scale)
-                # y: half height of the oriented crop rectangle
-                y = np.flipud(x) * [-1, 1]
-
-                # c: center
-                c = eye_avg + eye_to_mouth * 0.1
-                # quad: (left_top, left_bottom, right_bottom, right_top)
-                quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y])
-                # qsize: side length of the square
-                qsize = np.hypot(*x) * 2
-                border = max(int(np.rint(qsize * 0.1)), 3)
-
-                # get pad
-                # pad: (width_left, height_top, width_right, height_bottom)
-                pad = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))),
-                       int(np.ceil(max(quad[:, 1]))))
-                pad = [
-                    max(-pad[0] + border, 1),
-                    max(-pad[1] + border, 1),
-                    max(pad[2] - self.input_img.shape[0] + border, 1),
-                    max(pad[3] - self.input_img.shape[1] + border, 1)
-                ]
-
-                if max(pad) > 1:
-                    # pad image
-                    pad_img = np.pad(self.input_img, ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect')
-                    # modify landmark coords
-                    landmarks[:, 0] += pad[0]
-                    landmarks[:, 1] += pad[1]
-                    # blur pad images
-                    h, w, _ = pad_img.shape
-                    y, x, _ = np.ogrid[:h, :w, :1]
-                    mask = np.maximum(1.0 - np.minimum(np.float32(x) / pad[0],
-                                                       np.float32(w - 1 - x) / pad[2]),
-                                      1.0 - np.minimum(np.float32(y) / pad[1],
-                                                       np.float32(h - 1 - y) / pad[3]))
-                    blur = int(qsize * blur_ratio)
-                    if blur % 2 == 0:
-                        blur += 1
-                    blur_img = cv2.boxFilter(pad_img, 0, ksize=(blur, blur))
-                    # blur_img = cv2.GaussianBlur(pad_img, (blur, blur), 0)
-
-                    pad_img = pad_img.astype('float32')
-                    pad_img += (blur_img - pad_img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0)
-                    pad_img += (np.median(pad_img, axis=(0, 1)) - pad_img) * np.clip(mask, 0.0, 1.0)
-                    pad_img = np.clip(pad_img, 0, 255)  # float32, [0, 255]
-                    self.pad_input_imgs.append(pad_img)
-                else:
-                    self.pad_input_imgs.append(np.copy(self.input_img))
-
-        return len(self.all_landmarks_5)
-
-    def align_warp_face(self, save_cropped_path=None, border_mode='constant'):
-        """Align and warp faces with face template.
-        """
-        if self.pad_blur:
-            assert len(self.pad_input_imgs) == len(
-                self.all_landmarks_5), f'Mismatched samples: {len(self.pad_input_imgs)} and {len(self.all_landmarks_5)}'
-        for idx, landmark in enumerate(self.all_landmarks_5):
-            # use 5 landmarks to get affine matrix
-            # use cv2.LMEDS method for the equivalence to skimage transform
-            # ref: https://blog.csdn.net/yichxi/article/details/115827338
-            affine_matrix = cv2.estimateAffinePartial2D(landmark, self.face_template, method=cv2.LMEDS)[0]
-            self.affine_matrices.append(affine_matrix)
-            # warp and crop faces
-            if border_mode == 'constant':
-                border_mode = cv2.BORDER_CONSTANT
-            elif border_mode == 'reflect101':
-                border_mode = cv2.BORDER_REFLECT101
-            elif border_mode == 'reflect':
-                border_mode = cv2.BORDER_REFLECT
-            if self.pad_blur:
-                input_img = self.pad_input_imgs[idx]
-            else:
-                input_img = self.input_img
-            cropped_face = cv2.warpAffine(
-                input_img, affine_matrix, self.face_size, borderMode=border_mode, borderValue=(135, 133, 132))  # gray
-            self.cropped_faces.append(cropped_face)
-            # save the cropped face
-            if save_cropped_path is not None:
-                path = os.path.splitext(save_cropped_path)[0]
-                save_path = f'{path}_{idx:02d}.{self.save_ext}'
-                imwrite(cropped_face, save_path)
-
-    def get_inverse_affine(self, save_inverse_affine_path=None):
-        """Get inverse affine matrix."""
-        for idx, affine_matrix in enumerate(self.affine_matrices):
-            inverse_affine = cv2.invertAffineTransform(affine_matrix)
-            inverse_affine *= self.upscale_factor
-            self.inverse_affine_matrices.append(inverse_affine)
-            # save inverse affine matrices
-            if save_inverse_affine_path is not None:
-                path, _ = os.path.splitext(save_inverse_affine_path)
-                save_path = f'{path}_{idx:02d}.pth'
-                torch.save(inverse_affine, save_path)
-
-
-    def add_restored_face(self, face):
-        self.restored_faces.append(face)
-
-
-    def paste_faces_to_input_image(self, save_path=None, upsample_img=None, draw_box=False, face_upsampler=None):
-        h, w, _ = self.input_img.shape
-        h_up, w_up = int(h * self.upscale_factor), int(w * self.upscale_factor)
-
-        if upsample_img is None:
-            # simply resize the background
-            # upsample_img = cv2.resize(self.input_img, (w_up, h_up), interpolation=cv2.INTER_LANCZOS4)
-            upsample_img = cv2.resize(self.input_img, (w_up, h_up), interpolation=cv2.INTER_LINEAR)
-        else:
-            upsample_img = cv2.resize(upsample_img, (w_up, h_up), interpolation=cv2.INTER_LANCZOS4)
-
-        assert len(self.restored_faces) == len(
-            self.inverse_affine_matrices), ('length of restored_faces and affine_matrices are different.')
-        
-        inv_mask_borders = []
-        for restored_face, inverse_affine in zip(self.restored_faces, self.inverse_affine_matrices):
-            if face_upsampler is not None:
-                restored_face = face_upsampler.enhance(restored_face, outscale=self.upscale_factor)[0]
-                inverse_affine /= self.upscale_factor
-                inverse_affine[:, 2] *= self.upscale_factor
-                face_size = (self.face_size[0]*self.upscale_factor, self.face_size[1]*self.upscale_factor)
-            else:
-                # Add an offset to inverse affine matrix, for more precise back alignment
-                if self.upscale_factor > 1:
-                    extra_offset = 0.5 * self.upscale_factor
-                else:
-                    extra_offset = 0
-                inverse_affine[:, 2] += extra_offset
-                face_size = self.face_size
-            inv_restored = cv2.warpAffine(restored_face, inverse_affine, (w_up, h_up))
-
-            # if draw_box or not self.use_parse:  # use square parse maps
-            #     mask = np.ones(face_size, dtype=np.float32)
-            #     inv_mask = cv2.warpAffine(mask, inverse_affine, (w_up, h_up))
-            #     # remove the black borders
-            #     inv_mask_erosion = cv2.erode(
-            #         inv_mask, np.ones((int(2 * self.upscale_factor), int(2 * self.upscale_factor)), np.uint8))
-            #     pasted_face = inv_mask_erosion[:, :, None] * inv_restored
-            #     total_face_area = np.sum(inv_mask_erosion)  # // 3
-            #     # add border
-            #     if draw_box:
-            #         h, w = face_size
-            #         mask_border = np.ones((h, w, 3), dtype=np.float32)
-            #         border = int(1400/np.sqrt(total_face_area))
-            #         mask_border[border:h-border, border:w-border,:] = 0
-            #         inv_mask_border = cv2.warpAffine(mask_border, inverse_affine, (w_up, h_up))
-            #         inv_mask_borders.append(inv_mask_border)
-            #     if not self.use_parse:
-            #         # compute the fusion edge based on the area of face
-            #         w_edge = int(total_face_area**0.5) // 20
-            #         erosion_radius = w_edge * 2
-            #         inv_mask_center = cv2.erode(inv_mask_erosion, np.ones((erosion_radius, erosion_radius), np.uint8))
-            #         blur_size = w_edge * 2
-            #         inv_soft_mask = cv2.GaussianBlur(inv_mask_center, (blur_size + 1, blur_size + 1), 0)
-            #         if len(upsample_img.shape) == 2:  # upsample_img is gray image
-            #             upsample_img = upsample_img[:, :, None]
-            #         inv_soft_mask = inv_soft_mask[:, :, None]
-
-            # always use square mask
-            mask = np.ones(face_size, dtype=np.float32)
-            inv_mask = cv2.warpAffine(mask, inverse_affine, (w_up, h_up))
-            # remove the black borders
-            inv_mask_erosion = cv2.erode(
-                inv_mask, np.ones((int(2 * self.upscale_factor), int(2 * self.upscale_factor)), np.uint8))
-            pasted_face = inv_mask_erosion[:, :, None] * inv_restored
-            total_face_area = np.sum(inv_mask_erosion)  # // 3
-            # add border
-            if draw_box:
-                h, w = face_size
-                mask_border = np.ones((h, w, 3), dtype=np.float32)
-                border = int(1400/np.sqrt(total_face_area))
-                mask_border[border:h-border, border:w-border,:] = 0
-                inv_mask_border = cv2.warpAffine(mask_border, inverse_affine, (w_up, h_up))
-                inv_mask_borders.append(inv_mask_border)
-            # compute the fusion edge based on the area of face
-            w_edge = int(total_face_area**0.5) // 20
-            erosion_radius = w_edge * 2
-            inv_mask_center = cv2.erode(inv_mask_erosion, np.ones((erosion_radius, erosion_radius), np.uint8))
-            blur_size = w_edge * 2
-            inv_soft_mask = cv2.GaussianBlur(inv_mask_center, (blur_size + 1, blur_size + 1), 0)
-            if len(upsample_img.shape) == 2:  # upsample_img is gray image
-                upsample_img = upsample_img[:, :, None]
-            inv_soft_mask = inv_soft_mask[:, :, None]
-
-            # parse mask
-            if self.use_parse:
-                # inference
-                face_input = cv2.resize(restored_face, (512, 512), interpolation=cv2.INTER_LINEAR)
-                face_input = img2tensor(face_input.astype('float32') / 255., bgr2rgb=True, float32=True)
-                normalize(face_input, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
-                face_input = torch.unsqueeze(face_input, 0).to(self.device)
-                with torch.no_grad():
-                    out = self.face_parse(face_input)[0]
-                out = out.argmax(dim=1).squeeze().cpu().numpy()
-
-                parse_mask = np.zeros(out.shape)
-                MASK_COLORMAP = [0, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 0, 255, 0, 0, 0]
-                for idx, color in enumerate(MASK_COLORMAP):
-                    parse_mask[out == idx] = color
-                #  blur the mask
-                parse_mask = cv2.GaussianBlur(parse_mask, (101, 101), 11)
-                parse_mask = cv2.GaussianBlur(parse_mask, (101, 101), 11)
-                # remove the black borders
-                thres = 10
-                parse_mask[:thres, :] = 0
-                parse_mask[-thres:, :] = 0
-                parse_mask[:, :thres] = 0
-                parse_mask[:, -thres:] = 0
-                parse_mask = parse_mask / 255.
-
-                parse_mask = cv2.resize(parse_mask, face_size)
-                parse_mask = cv2.warpAffine(parse_mask, inverse_affine, (w_up, h_up), flags=3)
-                inv_soft_parse_mask = parse_mask[:, :, None]
-                # pasted_face = inv_restored
-                fuse_mask = (inv_soft_parse_mask<inv_soft_mask).astype('int')
-                inv_soft_mask = inv_soft_parse_mask*fuse_mask + inv_soft_mask*(1-fuse_mask)
-
-            if len(upsample_img.shape) == 3 and upsample_img.shape[2] == 4:  # alpha channel
-                alpha = upsample_img[:, :, 3:]
-                upsample_img = inv_soft_mask * pasted_face + (1 - inv_soft_mask) * upsample_img[:, :, 0:3]
-                upsample_img = np.concatenate((upsample_img, alpha), axis=2)
-            else:
-                upsample_img = inv_soft_mask * pasted_face + (1 - inv_soft_mask) * upsample_img
-
-        if np.max(upsample_img) > 256:  # 16-bit image
-            upsample_img = upsample_img.astype(np.uint16)
-        else:
-            upsample_img = upsample_img.astype(np.uint8)
-
-        # draw bounding box
-        if draw_box:
-            # upsample_input_img = cv2.resize(input_img, (w_up, h_up))
-            img_color = np.ones([*upsample_img.shape], dtype=np.float32)
-            img_color[:,:,0] = 0
-            img_color[:,:,1] = 255
-            img_color[:,:,2] = 0
-            for inv_mask_border in inv_mask_borders:
-                upsample_img = inv_mask_border * img_color + (1 - inv_mask_border) * upsample_img
-                # upsample_input_img = inv_mask_border * img_color + (1 - inv_mask_border) * upsample_input_img
-
-        if save_path is not None:
-            path = os.path.splitext(save_path)[0]
-            save_path = f'{path}.{self.save_ext}'
-            imwrite(upsample_img, save_path)
-        return upsample_img
-
-    def clean_all(self):
-        self.all_landmarks_5 = []
-        self.restored_faces = []
-        self.affine_matrices = []
-        self.cropped_faces = []
-        self.inverse_affine_matrices = []
-        self.det_faces = []
-        self.pad_input_imgs = []
\ No newline at end of file
diff --git a/repositories/CodeFormer/facelib/utils/face_utils.py b/repositories/CodeFormer/facelib/utils/face_utils.py
deleted file mode 100644
index f1474a2a4419b6b62fab8a919ef805b802556464..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/facelib/utils/face_utils.py
+++ /dev/null
@@ -1,248 +0,0 @@
-import cv2
-import numpy as np
-import torch
-
-
-def compute_increased_bbox(bbox, increase_area, preserve_aspect=True):
-    left, top, right, bot = bbox
-    width = right - left
-    height = bot - top
-
-    if preserve_aspect:
-        width_increase = max(increase_area, ((1 + 2 * increase_area) * height - width) / (2 * width))
-        height_increase = max(increase_area, ((1 + 2 * increase_area) * width - height) / (2 * height))
-    else:
-        width_increase = height_increase = increase_area
-    left = int(left - width_increase * width)
-    top = int(top - height_increase * height)
-    right = int(right + width_increase * width)
-    bot = int(bot + height_increase * height)
-    return (left, top, right, bot)
-
-
-def get_valid_bboxes(bboxes, h, w):
-    left = max(bboxes[0], 0)
-    top = max(bboxes[1], 0)
-    right = min(bboxes[2], w)
-    bottom = min(bboxes[3], h)
-    return (left, top, right, bottom)
-
-
-def align_crop_face_landmarks(img,
-                              landmarks,
-                              output_size,
-                              transform_size=None,
-                              enable_padding=True,
-                              return_inverse_affine=False,
-                              shrink_ratio=(1, 1)):
-    """Align and crop face with landmarks.
-
-    The output_size and transform_size are based on width. The height is
-    adjusted based on shrink_ratio_h/shring_ration_w.
-
-    Modified from:
-    https://github.com/NVlabs/ffhq-dataset/blob/master/download_ffhq.py
-
-    Args:
-        img (Numpy array): Input image.
-        landmarks (Numpy array): 5 or 68 or 98 landmarks.
-        output_size (int): Output face size.
-        transform_size (ing): Transform size. Usually the four time of
-            output_size.
-        enable_padding (float): Default: True.
-        shrink_ratio (float | tuple[float] | list[float]): Shring the whole
-            face for height and width (crop larger area). Default: (1, 1).
-
-    Returns:
-        (Numpy array): Cropped face.
-    """
-    lm_type = 'retinaface_5'  # Options: dlib_5, retinaface_5
-
-    if isinstance(shrink_ratio, (float, int)):
-        shrink_ratio = (shrink_ratio, shrink_ratio)
-    if transform_size is None:
-        transform_size = output_size * 4
-
-    # Parse landmarks
-    lm = np.array(landmarks)
-    if lm.shape[0] == 5 and lm_type == 'retinaface_5':
-        eye_left = lm[0]
-        eye_right = lm[1]
-        mouth_avg = (lm[3] + lm[4]) * 0.5
-    elif lm.shape[0] == 5 and lm_type == 'dlib_5':
-        lm_eye_left = lm[2:4]
-        lm_eye_right = lm[0:2]
-        eye_left = np.mean(lm_eye_left, axis=0)
-        eye_right = np.mean(lm_eye_right, axis=0)
-        mouth_avg = lm[4]
-    elif lm.shape[0] == 68:
-        lm_eye_left = lm[36:42]
-        lm_eye_right = lm[42:48]
-        eye_left = np.mean(lm_eye_left, axis=0)
-        eye_right = np.mean(lm_eye_right, axis=0)
-        mouth_avg = (lm[48] + lm[54]) * 0.5
-    elif lm.shape[0] == 98:
-        lm_eye_left = lm[60:68]
-        lm_eye_right = lm[68:76]
-        eye_left = np.mean(lm_eye_left, axis=0)
-        eye_right = np.mean(lm_eye_right, axis=0)
-        mouth_avg = (lm[76] + lm[82]) * 0.5
-
-    eye_avg = (eye_left + eye_right) * 0.5
-    eye_to_eye = eye_right - eye_left
-    eye_to_mouth = mouth_avg - eye_avg
-
-    # Get the oriented crop rectangle
-    # x: half width of the oriented crop rectangle
-    x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1]
-    #  - np.flipud(eye_to_mouth) * [-1, 1]: rotate 90 clockwise
-    # norm with the hypotenuse: get the direction
-    x /= np.hypot(*x)  # get the hypotenuse of a right triangle
-    rect_scale = 1  # TODO: you can edit it to get larger rect
-    x *= max(np.hypot(*eye_to_eye) * 2.0 * rect_scale, np.hypot(*eye_to_mouth) * 1.8 * rect_scale)
-    # y: half height of the oriented crop rectangle
-    y = np.flipud(x) * [-1, 1]
-
-    x *= shrink_ratio[1]  # width
-    y *= shrink_ratio[0]  # height
-
-    # c: center
-    c = eye_avg + eye_to_mouth * 0.1
-    # quad: (left_top, left_bottom, right_bottom, right_top)
-    quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y])
-    # qsize: side length of the square
-    qsize = np.hypot(*x) * 2
-
-    quad_ori = np.copy(quad)
-    # Shrink, for large face
-    # TODO: do we really need shrink
-    shrink = int(np.floor(qsize / output_size * 0.5))
-    if shrink > 1:
-        h, w = img.shape[0:2]
-        rsize = (int(np.rint(float(w) / shrink)), int(np.rint(float(h) / shrink)))
-        img = cv2.resize(img, rsize, interpolation=cv2.INTER_AREA)
-        quad /= shrink
-        qsize /= shrink
-
-    # Crop
-    h, w = img.shape[0:2]
-    border = max(int(np.rint(qsize * 0.1)), 3)
-    crop = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))),
-            int(np.ceil(max(quad[:, 1]))))
-    crop = (max(crop[0] - border, 0), max(crop[1] - border, 0), min(crop[2] + border, w), min(crop[3] + border, h))
-    if crop[2] - crop[0] < w or crop[3] - crop[1] < h:
-        img = img[crop[1]:crop[3], crop[0]:crop[2], :]
-        quad -= crop[0:2]
-
-    # Pad
-    # pad: (width_left, height_top, width_right, height_bottom)
-    h, w = img.shape[0:2]
-    pad = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))),
-           int(np.ceil(max(quad[:, 1]))))
-    pad = (max(-pad[0] + border, 0), max(-pad[1] + border, 0), max(pad[2] - w + border, 0), max(pad[3] - h + border, 0))
-    if enable_padding and max(pad) > border - 4:
-        pad = np.maximum(pad, int(np.rint(qsize * 0.3)))
-        img = np.pad(img, ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect')
-        h, w = img.shape[0:2]
-        y, x, _ = np.ogrid[:h, :w, :1]
-        mask = np.maximum(1.0 - np.minimum(np.float32(x) / pad[0],
-                                           np.float32(w - 1 - x) / pad[2]),
-                          1.0 - np.minimum(np.float32(y) / pad[1],
-                                           np.float32(h - 1 - y) / pad[3]))
-        blur = int(qsize * 0.02)
-        if blur % 2 == 0:
-            blur += 1
-        blur_img = cv2.boxFilter(img, 0, ksize=(blur, blur))
-
-        img = img.astype('float32')
-        img += (blur_img - img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0)
-        img += (np.median(img, axis=(0, 1)) - img) * np.clip(mask, 0.0, 1.0)
-        img = np.clip(img, 0, 255)  # float32, [0, 255]
-        quad += pad[:2]
-
-    # Transform use cv2
-    h_ratio = shrink_ratio[0] / shrink_ratio[1]
-    dst_h, dst_w = int(transform_size * h_ratio), transform_size
-    template = np.array([[0, 0], [0, dst_h], [dst_w, dst_h], [dst_w, 0]])
-    # use cv2.LMEDS method for the equivalence to skimage transform
-    # ref: https://blog.csdn.net/yichxi/article/details/115827338
-    affine_matrix = cv2.estimateAffinePartial2D(quad, template, method=cv2.LMEDS)[0]
-    cropped_face = cv2.warpAffine(
-        img, affine_matrix, (dst_w, dst_h), borderMode=cv2.BORDER_CONSTANT, borderValue=(135, 133, 132))  # gray
-
-    if output_size < transform_size:
-        cropped_face = cv2.resize(
-            cropped_face, (output_size, int(output_size * h_ratio)), interpolation=cv2.INTER_LINEAR)
-
-    if return_inverse_affine:
-        dst_h, dst_w = int(output_size * h_ratio), output_size
-        template = np.array([[0, 0], [0, dst_h], [dst_w, dst_h], [dst_w, 0]])
-        # use cv2.LMEDS method for the equivalence to skimage transform
-        # ref: https://blog.csdn.net/yichxi/article/details/115827338
-        affine_matrix = cv2.estimateAffinePartial2D(
-            quad_ori, np.array([[0, 0], [0, output_size], [dst_w, dst_h], [dst_w, 0]]), method=cv2.LMEDS)[0]
-        inverse_affine = cv2.invertAffineTransform(affine_matrix)
-    else:
-        inverse_affine = None
-    return cropped_face, inverse_affine
-
-
-def paste_face_back(img, face, inverse_affine):
-    h, w = img.shape[0:2]
-    face_h, face_w = face.shape[0:2]
-    inv_restored = cv2.warpAffine(face, inverse_affine, (w, h))
-    mask = np.ones((face_h, face_w, 3), dtype=np.float32)
-    inv_mask = cv2.warpAffine(mask, inverse_affine, (w, h))
-    # remove the black borders
-    inv_mask_erosion = cv2.erode(inv_mask, np.ones((2, 2), np.uint8))
-    inv_restored_remove_border = inv_mask_erosion * inv_restored
-    total_face_area = np.sum(inv_mask_erosion) // 3
-    # compute the fusion edge based on the area of face
-    w_edge = int(total_face_area**0.5) // 20
-    erosion_radius = w_edge * 2
-    inv_mask_center = cv2.erode(inv_mask_erosion, np.ones((erosion_radius, erosion_radius), np.uint8))
-    blur_size = w_edge * 2
-    inv_soft_mask = cv2.GaussianBlur(inv_mask_center, (blur_size + 1, blur_size + 1), 0)
-    img = inv_soft_mask * inv_restored_remove_border + (1 - inv_soft_mask) * img
-    # float32, [0, 255]
-    return img
-
-
-if __name__ == '__main__':
-    import os
-
-    from facelib.detection import init_detection_model
-    from facelib.utils.face_restoration_helper import get_largest_face
-
-    img_path = '/home/wxt/datasets/ffhq/ffhq_wild/00009.png'
-    img_name = os.splitext(os.path.basename(img_path))[0]
-
-    # initialize model
-    det_net = init_detection_model('retinaface_resnet50', half=False)
-    img_ori = cv2.imread(img_path)
-    h, w = img_ori.shape[0:2]
-    # if larger than 800, scale it
-    scale = max(h / 800, w / 800)
-    if scale > 1:
-        img = cv2.resize(img_ori, (int(w / scale), int(h / scale)), interpolation=cv2.INTER_LINEAR)
-
-    with torch.no_grad():
-        bboxes = det_net.detect_faces(img, 0.97)
-    if scale > 1:
-        bboxes *= scale  # the score is incorrect
-    bboxes = get_largest_face(bboxes, h, w)[0]
-
-    landmarks = np.array([[bboxes[i], bboxes[i + 1]] for i in range(5, 15, 2)])
-
-    cropped_face, inverse_affine = align_crop_face_landmarks(
-        img_ori,
-        landmarks,
-        output_size=512,
-        transform_size=None,
-        enable_padding=True,
-        return_inverse_affine=True,
-        shrink_ratio=(1, 1))
-
-    cv2.imwrite(f'tmp/{img_name}_cropeed_face.png', cropped_face)
-    img = paste_face_back(img_ori, cropped_face, inverse_affine)
-    cv2.imwrite(f'tmp/{img_name}_back.png', img)
diff --git a/repositories/CodeFormer/facelib/utils/misc.py b/repositories/CodeFormer/facelib/utils/misc.py
deleted file mode 100644
index 0918283c297a927fc0216670bbe78079087c6312..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/facelib/utils/misc.py
+++ /dev/null
@@ -1,141 +0,0 @@
-import cv2
-import os
-import os.path as osp
-import torch
-from torch.hub import download_url_to_file, get_dir
-from urllib.parse import urlparse
-# from basicsr.utils.download_util import download_file_from_google_drive
-import gdown
-
-
-ROOT_DIR = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-
-
-def download_pretrained_models(file_ids, save_path_root):
-    os.makedirs(save_path_root, exist_ok=True)
-
-    for file_name, file_id in file_ids.items():
-        file_url = 'https://drive.google.com/uc?id='+file_id
-        save_path = osp.abspath(osp.join(save_path_root, file_name))
-        if osp.exists(save_path):
-            user_response = input(f'{file_name} already exist. Do you want to cover it? Y/N\n')
-            if user_response.lower() == 'y':
-                print(f'Covering {file_name} to {save_path}')
-                gdown.download(file_url, save_path, quiet=False)
-                # download_file_from_google_drive(file_id, save_path)
-            elif user_response.lower() == 'n':
-                print(f'Skipping {file_name}')
-            else:
-                raise ValueError('Wrong input. Only accepts Y/N.')
-        else:
-            print(f'Downloading {file_name} to {save_path}')
-            gdown.download(file_url, save_path, quiet=False)
-            # download_file_from_google_drive(file_id, save_path)
-
-
-def imwrite(img, file_path, params=None, auto_mkdir=True):
-    """Write image to file.
-
-    Args:
-        img (ndarray): Image array to be written.
-        file_path (str): Image file path.
-        params (None or list): Same as opencv's :func:`imwrite` interface.
-        auto_mkdir (bool): If the parent folder of `file_path` does not exist,
-            whether to create it automatically.
-
-    Returns:
-        bool: Successful or not.
-    """
-    if auto_mkdir:
-        dir_name = os.path.abspath(os.path.dirname(file_path))
-        os.makedirs(dir_name, exist_ok=True)
-    return cv2.imwrite(file_path, img, params)
-
-
-def img2tensor(imgs, bgr2rgb=True, float32=True):
-    """Numpy array to tensor.
-
-    Args:
-        imgs (list[ndarray] | ndarray): Input images.
-        bgr2rgb (bool): Whether to change bgr to rgb.
-        float32 (bool): Whether to change to float32.
-
-    Returns:
-        list[tensor] | tensor: Tensor images. If returned results only have
-            one element, just return tensor.
-    """
-
-    def _totensor(img, bgr2rgb, float32):
-        if img.shape[2] == 3 and bgr2rgb:
-            if img.dtype == 'float64':
-                img = img.astype('float32')
-            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-        img = torch.from_numpy(img.transpose(2, 0, 1))
-        if float32:
-            img = img.float()
-        return img
-
-    if isinstance(imgs, list):
-        return [_totensor(img, bgr2rgb, float32) for img in imgs]
-    else:
-        return _totensor(imgs, bgr2rgb, float32)
-
-
-def load_file_from_url(url, model_dir=None, progress=True, file_name=None):
-    """Ref:https://github.com/1adrianb/face-alignment/blob/master/face_alignment/utils.py
-    """
-    if model_dir is None:
-        hub_dir = get_dir()
-        model_dir = os.path.join(hub_dir, 'checkpoints')
-
-    os.makedirs(os.path.join(ROOT_DIR, model_dir), exist_ok=True)
-
-    parts = urlparse(url)
-    filename = os.path.basename(parts.path)
-    if file_name is not None:
-        filename = file_name
-    cached_file = os.path.abspath(os.path.join(ROOT_DIR, model_dir, filename))
-    if not os.path.exists(cached_file):
-        print(f'Downloading: "{url}" to {cached_file}\n')
-        download_url_to_file(url, cached_file, hash_prefix=None, progress=progress)
-    return cached_file
-
-
-def scandir(dir_path, suffix=None, recursive=False, full_path=False):
-    """Scan a directory to find the interested files.
-    Args:
-        dir_path (str): Path of the directory.
-        suffix (str | tuple(str), optional): File suffix that we are
-            interested in. Default: None.
-        recursive (bool, optional): If set to True, recursively scan the
-            directory. Default: False.
-        full_path (bool, optional): If set to True, include the dir_path.
-            Default: False.
-    Returns:
-        A generator for all the interested files with relative paths.
-    """
-
-    if (suffix is not None) and not isinstance(suffix, (str, tuple)):
-        raise TypeError('"suffix" must be a string or tuple of strings')
-
-    root = dir_path
-
-    def _scandir(dir_path, suffix, recursive):
-        for entry in os.scandir(dir_path):
-            if not entry.name.startswith('.') and entry.is_file():
-                if full_path:
-                    return_path = entry.path
-                else:
-                    return_path = osp.relpath(entry.path, root)
-
-                if suffix is None:
-                    yield return_path
-                elif return_path.endswith(suffix):
-                    yield return_path
-            else:
-                if recursive:
-                    yield from _scandir(entry.path, suffix=suffix, recursive=recursive)
-                else:
-                    continue
-
-    return _scandir(dir_path, suffix=suffix, recursive=recursive)
diff --git a/repositories/CodeFormer/inference_codeformer.py b/repositories/CodeFormer/inference_codeformer.py
deleted file mode 100644
index fdfe8b301cc7c20c2fb653618e379d243603a108..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/inference_codeformer.py
+++ /dev/null
@@ -1,189 +0,0 @@
-# Modified by Shangchen Zhou from: https://github.com/TencentARC/GFPGAN/blob/master/inference_gfpgan.py
-import os
-import cv2
-import argparse
-import glob
-import torch
-from torchvision.transforms.functional import normalize
-from basicsr.utils import imwrite, img2tensor, tensor2img
-from basicsr.utils.download_util import load_file_from_url
-from facelib.utils.face_restoration_helper import FaceRestoreHelper
-import torch.nn.functional as F
-
-from basicsr.utils.registry import ARCH_REGISTRY
-
-pretrain_model_url = {
-    'restoration': 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth',
-}
-
-def set_realesrgan():
-    if not torch.cuda.is_available():  # CPU
-        import warnings
-        warnings.warn('The unoptimized RealESRGAN is slow on CPU. We do not use it. '
-                        'If you really want to use it, please modify the corresponding codes.',
-                        category=RuntimeWarning)
-        bg_upsampler = None
-    else:
-        from basicsr.archs.rrdbnet_arch import RRDBNet
-        from basicsr.utils.realesrgan_utils import RealESRGANer
-        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
-        bg_upsampler = RealESRGANer(
-            scale=2,
-            model_path='https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth',
-            model=model,
-            tile=args.bg_tile,
-            tile_pad=40,
-            pre_pad=0,
-            half=True)  # need to set False in CPU mode
-    return bg_upsampler
-
-if __name__ == '__main__':
-    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument('--w', type=float, default=0.5, help='Balance the quality and fidelity')
-    parser.add_argument('--upscale', type=int, default=2, help='The final upsampling scale of the image. Default: 2')
-    parser.add_argument('--test_path', type=str, default='./inputs/cropped_faces')
-    parser.add_argument('--has_aligned', action='store_true', help='Input are cropped and aligned faces')
-    parser.add_argument('--only_center_face', action='store_true', help='Only restore the center face')
-    # large det_model: 'YOLOv5l', 'retinaface_resnet50'
-    # small det_model: 'YOLOv5n', 'retinaface_mobile0.25'
-    parser.add_argument('--detection_model', type=str, default='retinaface_resnet50')
-    parser.add_argument('--draw_box', action='store_true')
-    parser.add_argument('--bg_upsampler', type=str, default='None', help='background upsampler. Optional: realesrgan')
-    parser.add_argument('--face_upsample', action='store_true', help='face upsampler after enhancement.')
-    parser.add_argument('--bg_tile', type=int, default=400, help='Tile size for background sampler. Default: 400')
-
-    args = parser.parse_args()
-
-    # ------------------------ input & output ------------------------
-    if args.test_path.endswith('/'):  # solve when path ends with /
-        args.test_path = args.test_path[:-1]
-
-    w = args.w
-    result_root = f'results/{os.path.basename(args.test_path)}_{w}'
-
-    # ------------------ set up background upsampler ------------------
-    if args.bg_upsampler == 'realesrgan':
-        bg_upsampler = set_realesrgan()
-    else:
-        bg_upsampler = None
-
-    # ------------------ set up face upsampler ------------------
-    if args.face_upsample:
-        if bg_upsampler is not None:
-            face_upsampler = bg_upsampler
-        else:
-            face_upsampler = set_realesrgan()
-    else:
-        face_upsampler = None
-
-    # ------------------ set up CodeFormer restorer -------------------
-    net = ARCH_REGISTRY.get('CodeFormer')(dim_embd=512, codebook_size=1024, n_head=8, n_layers=9, 
-                                            connect_list=['32', '64', '128', '256']).to(device)
-    
-    # ckpt_path = 'weights/CodeFormer/codeformer.pth'
-    ckpt_path = load_file_from_url(url=pretrain_model_url['restoration'], 
-                                    model_dir='weights/CodeFormer', progress=True, file_name=None)
-    checkpoint = torch.load(ckpt_path)['params_ema']
-    net.load_state_dict(checkpoint)
-    net.eval()
-
-    # ------------------ set up FaceRestoreHelper -------------------
-    # large det_model: 'YOLOv5l', 'retinaface_resnet50'
-    # small det_model: 'YOLOv5n', 'retinaface_mobile0.25'
-    if not args.has_aligned: 
-        print(f'Face detection model: {args.detection_model}')
-    if bg_upsampler is not None: 
-        print(f'Background upsampling: True, Face upsampling: {args.face_upsample}')
-    else:
-        print(f'Background upsampling: False, Face upsampling: {args.face_upsample}')
-
-    face_helper = FaceRestoreHelper(
-        args.upscale,
-        face_size=512,
-        crop_ratio=(1, 1),
-        det_model = args.detection_model,
-        save_ext='png',
-        use_parse=True,
-        device=device)
-
-    # -------------------- start to processing ---------------------
-    # scan all the jpg and png images
-    for img_path in sorted(glob.glob(os.path.join(args.test_path, '*.[jp][pn]g'))):
-        # clean all the intermediate results to process the next image
-        face_helper.clean_all()
-        
-        img_name = os.path.basename(img_path)
-        print(f'Processing: {img_name}')
-        basename, ext = os.path.splitext(img_name)
-        img = cv2.imread(img_path, cv2.IMREAD_COLOR)
-
-        if args.has_aligned: 
-            # the input faces are already cropped and aligned
-            img = cv2.resize(img, (512, 512), interpolation=cv2.INTER_LINEAR)
-            face_helper.cropped_faces = [img]
-        else:
-            face_helper.read_image(img)
-            # get face landmarks for each face
-            num_det_faces = face_helper.get_face_landmarks_5(
-                only_center_face=args.only_center_face, resize=640, eye_dist_threshold=5)
-            print(f'\tdetect {num_det_faces} faces')
-            # align and warp each face
-            face_helper.align_warp_face()
-
-        # face restoration for each cropped face
-        for idx, cropped_face in enumerate(face_helper.cropped_faces):
-            # prepare data
-            cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True)
-            normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
-            cropped_face_t = cropped_face_t.unsqueeze(0).to(device)
-
-            try:
-                with torch.no_grad():
-                    output = net(cropped_face_t, w=w, adain=True)[0]
-                    restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1))
-                del output
-                torch.cuda.empty_cache()
-            except Exception as error:
-                print(f'\tFailed inference for CodeFormer: {error}')
-                restored_face = tensor2img(cropped_face_t, rgb2bgr=True, min_max=(-1, 1))
-
-            restored_face = restored_face.astype('uint8')
-            face_helper.add_restored_face(restored_face)
-
-        # paste_back
-        if not args.has_aligned:
-            # upsample the background
-            if bg_upsampler is not None:
-                # Now only support RealESRGAN for upsampling background
-                bg_img = bg_upsampler.enhance(img, outscale=args.upscale)[0]
-            else:
-                bg_img = None
-            face_helper.get_inverse_affine(None)
-            # paste each restored face to the input image
-            if args.face_upsample and face_upsampler is not None: 
-                restored_img = face_helper.paste_faces_to_input_image(upsample_img=bg_img, draw_box=args.draw_box, face_upsampler=face_upsampler)
-            else:
-                restored_img = face_helper.paste_faces_to_input_image(upsample_img=bg_img, draw_box=args.draw_box)
-
-        # save faces
-        for idx, (cropped_face, restored_face) in enumerate(zip(face_helper.cropped_faces, face_helper.restored_faces)):
-            # save cropped face
-            if not args.has_aligned: 
-                save_crop_path = os.path.join(result_root, 'cropped_faces', f'{basename}_{idx:02d}.png')
-                imwrite(cropped_face, save_crop_path)
-            # save restored face
-            if args.has_aligned:
-                save_face_name = f'{basename}.png'
-            else:
-                save_face_name = f'{basename}_{idx:02d}.png'
-            save_restore_path = os.path.join(result_root, 'restored_faces', save_face_name)
-            imwrite(restored_face, save_restore_path)
-
-        # save restored img
-        if not args.has_aligned and restored_img is not None:
-            save_restore_path = os.path.join(result_root, 'final_results', f'{basename}.png')
-            imwrite(restored_img, save_restore_path)
-
-    print(f'\nAll results are saved in {result_root}')
diff --git a/repositories/CodeFormer/inputs/cropped_faces/0143.png b/repositories/CodeFormer/inputs/cropped_faces/0143.png
deleted file mode 100644
index 065b3f001f43e7767308d3c2d2e5496cbe3660c7..0000000000000000000000000000000000000000
Binary files a/repositories/CodeFormer/inputs/cropped_faces/0143.png and /dev/null differ
diff --git a/repositories/CodeFormer/inputs/cropped_faces/0240.png b/repositories/CodeFormer/inputs/cropped_faces/0240.png
deleted file mode 100644
index 7a117017a27defc68c875f97322fdf2850c0e50b..0000000000000000000000000000000000000000
Binary files a/repositories/CodeFormer/inputs/cropped_faces/0240.png and /dev/null differ
diff --git a/repositories/CodeFormer/inputs/cropped_faces/0342.png b/repositories/CodeFormer/inputs/cropped_faces/0342.png
deleted file mode 100644
index 8f5aeeae1886ee3ec89dff52d30bcd2a8ad0ad4f..0000000000000000000000000000000000000000
Binary files a/repositories/CodeFormer/inputs/cropped_faces/0342.png and /dev/null differ
diff --git a/repositories/CodeFormer/inputs/cropped_faces/0345.png b/repositories/CodeFormer/inputs/cropped_faces/0345.png
deleted file mode 100644
index b8f71b6d8e437c1ffef045182ca0fcfb2f058e5c..0000000000000000000000000000000000000000
Binary files a/repositories/CodeFormer/inputs/cropped_faces/0345.png and /dev/null differ
diff --git a/repositories/CodeFormer/inputs/cropped_faces/0368.png b/repositories/CodeFormer/inputs/cropped_faces/0368.png
deleted file mode 100644
index 262778a98196bd3f19f71ad49a9c81db417eae8c..0000000000000000000000000000000000000000
Binary files a/repositories/CodeFormer/inputs/cropped_faces/0368.png and /dev/null differ
diff --git a/repositories/CodeFormer/inputs/cropped_faces/0412.png b/repositories/CodeFormer/inputs/cropped_faces/0412.png
deleted file mode 100644
index c4a63b13df67ec8392c14daa204f167d2c664c98..0000000000000000000000000000000000000000
Binary files a/repositories/CodeFormer/inputs/cropped_faces/0412.png and /dev/null differ
diff --git a/repositories/CodeFormer/inputs/cropped_faces/0444.png b/repositories/CodeFormer/inputs/cropped_faces/0444.png
deleted file mode 100644
index 9028dd0e3b65286a79e2a7538b5fb38b0f7a92ff..0000000000000000000000000000000000000000
Binary files a/repositories/CodeFormer/inputs/cropped_faces/0444.png and /dev/null differ
diff --git a/repositories/CodeFormer/inputs/cropped_faces/0478.png b/repositories/CodeFormer/inputs/cropped_faces/0478.png
deleted file mode 100644
index f0924061b574a9e2124c686db8e37995ebed850c..0000000000000000000000000000000000000000
Binary files a/repositories/CodeFormer/inputs/cropped_faces/0478.png and /dev/null differ
diff --git a/repositories/CodeFormer/inputs/cropped_faces/0500.png b/repositories/CodeFormer/inputs/cropped_faces/0500.png
deleted file mode 100644
index 7a7146b473ba7cbec6eb65e7db6891ad1ceb62b7..0000000000000000000000000000000000000000
Binary files a/repositories/CodeFormer/inputs/cropped_faces/0500.png and /dev/null differ
diff --git a/repositories/CodeFormer/inputs/cropped_faces/0599.png b/repositories/CodeFormer/inputs/cropped_faces/0599.png
deleted file mode 100644
index ff26ccdaf1ef441e1366fe2d1b4fa7ea1b8f13a2..0000000000000000000000000000000000000000
Binary files a/repositories/CodeFormer/inputs/cropped_faces/0599.png and /dev/null differ
diff --git a/repositories/CodeFormer/inputs/cropped_faces/0717.png b/repositories/CodeFormer/inputs/cropped_faces/0717.png
deleted file mode 100644
index 9342b5e55fd7d1698936628a7684d0c7ca2d8349..0000000000000000000000000000000000000000
Binary files a/repositories/CodeFormer/inputs/cropped_faces/0717.png and /dev/null differ
diff --git a/repositories/CodeFormer/inputs/cropped_faces/0720.png b/repositories/CodeFormer/inputs/cropped_faces/0720.png
deleted file mode 100644
index af384dce912c081073e4e5fed381dd2385159567..0000000000000000000000000000000000000000
Binary files a/repositories/CodeFormer/inputs/cropped_faces/0720.png and /dev/null differ
diff --git a/repositories/CodeFormer/inputs/cropped_faces/0729.png b/repositories/CodeFormer/inputs/cropped_faces/0729.png
deleted file mode 100644
index 4f70f46e134775659d5ff40098acbafe6c1111c4..0000000000000000000000000000000000000000
Binary files a/repositories/CodeFormer/inputs/cropped_faces/0729.png and /dev/null differ
diff --git a/repositories/CodeFormer/inputs/cropped_faces/0763.png b/repositories/CodeFormer/inputs/cropped_faces/0763.png
deleted file mode 100644
index 1263df7b03de9947281263ac79aaa7e8a1806860..0000000000000000000000000000000000000000
Binary files a/repositories/CodeFormer/inputs/cropped_faces/0763.png and /dev/null differ
diff --git a/repositories/CodeFormer/inputs/cropped_faces/0770.png b/repositories/CodeFormer/inputs/cropped_faces/0770.png
deleted file mode 100644
index 40a64e832d7701c1bfcdc29e89eb1989487d009b..0000000000000000000000000000000000000000
Binary files a/repositories/CodeFormer/inputs/cropped_faces/0770.png and /dev/null differ
diff --git a/repositories/CodeFormer/inputs/cropped_faces/0777.png b/repositories/CodeFormer/inputs/cropped_faces/0777.png
deleted file mode 100644
index c72cb26ff64b7e06767e0252caa6c3f3f0538d27..0000000000000000000000000000000000000000
Binary files a/repositories/CodeFormer/inputs/cropped_faces/0777.png and /dev/null differ
diff --git a/repositories/CodeFormer/inputs/cropped_faces/0885.png b/repositories/CodeFormer/inputs/cropped_faces/0885.png
deleted file mode 100644
index f3ea2632f7671749c15981a966289fafb1765aa3..0000000000000000000000000000000000000000
Binary files a/repositories/CodeFormer/inputs/cropped_faces/0885.png and /dev/null differ
diff --git a/repositories/CodeFormer/inputs/cropped_faces/0934.png b/repositories/CodeFormer/inputs/cropped_faces/0934.png
deleted file mode 100644
index bf82c2d3a36260d589687d21325700e9cf48a889..0000000000000000000000000000000000000000
Binary files a/repositories/CodeFormer/inputs/cropped_faces/0934.png and /dev/null differ
diff --git a/repositories/CodeFormer/inputs/cropped_faces/Solvay_conference_1927_0018.png b/repositories/CodeFormer/inputs/cropped_faces/Solvay_conference_1927_0018.png
deleted file mode 100644
index 0f79547a80af7684e5f3b8bef8160ad9f1c85773..0000000000000000000000000000000000000000
Binary files a/repositories/CodeFormer/inputs/cropped_faces/Solvay_conference_1927_0018.png and /dev/null differ
diff --git a/repositories/CodeFormer/inputs/cropped_faces/Solvay_conference_1927_2_16.png b/repositories/CodeFormer/inputs/cropped_faces/Solvay_conference_1927_2_16.png
deleted file mode 100644
index f75b9602f3a8b2210fc459b22d9a67011404709e..0000000000000000000000000000000000000000
Binary files a/repositories/CodeFormer/inputs/cropped_faces/Solvay_conference_1927_2_16.png and /dev/null differ
diff --git a/repositories/CodeFormer/inputs/whole_imgs/00.jpg b/repositories/CodeFormer/inputs/whole_imgs/00.jpg
deleted file mode 100644
index d6e323e56d782d566a024a1d25282996904ffebd..0000000000000000000000000000000000000000
Binary files a/repositories/CodeFormer/inputs/whole_imgs/00.jpg and /dev/null differ
diff --git a/repositories/CodeFormer/inputs/whole_imgs/01.jpg b/repositories/CodeFormer/inputs/whole_imgs/01.jpg
deleted file mode 100644
index 485fc6a51a066a15ff8164150afef4028a304242..0000000000000000000000000000000000000000
Binary files a/repositories/CodeFormer/inputs/whole_imgs/01.jpg and /dev/null differ
diff --git a/repositories/CodeFormer/inputs/whole_imgs/02.png b/repositories/CodeFormer/inputs/whole_imgs/02.png
deleted file mode 100644
index 378e7b159223e49e9967de53d0c121558b9a56ef..0000000000000000000000000000000000000000
Binary files a/repositories/CodeFormer/inputs/whole_imgs/02.png and /dev/null differ
diff --git a/repositories/CodeFormer/inputs/whole_imgs/03.jpg b/repositories/CodeFormer/inputs/whole_imgs/03.jpg
deleted file mode 100644
index b6c8428105e821801c07e6d4c8dbbc3c72814a58..0000000000000000000000000000000000000000
Binary files a/repositories/CodeFormer/inputs/whole_imgs/03.jpg and /dev/null differ
diff --git a/repositories/CodeFormer/inputs/whole_imgs/04.jpg b/repositories/CodeFormer/inputs/whole_imgs/04.jpg
deleted file mode 100644
index bb94681a7ff03ef57e0951a6c1fbfe6329571950..0000000000000000000000000000000000000000
Binary files a/repositories/CodeFormer/inputs/whole_imgs/04.jpg and /dev/null differ
diff --git a/repositories/CodeFormer/inputs/whole_imgs/05.jpg b/repositories/CodeFormer/inputs/whole_imgs/05.jpg
deleted file mode 100644
index 4dc33735f9ad7c02b591810cc38c033f21be6c81..0000000000000000000000000000000000000000
Binary files a/repositories/CodeFormer/inputs/whole_imgs/05.jpg and /dev/null differ
diff --git a/repositories/CodeFormer/inputs/whole_imgs/06.png b/repositories/CodeFormer/inputs/whole_imgs/06.png
deleted file mode 100644
index 49c2fff2c655aaaddcd38920a063307c7ecc5152..0000000000000000000000000000000000000000
Binary files a/repositories/CodeFormer/inputs/whole_imgs/06.png and /dev/null differ
diff --git a/repositories/CodeFormer/predict.py b/repositories/CodeFormer/predict.py
deleted file mode 100644
index 01ecc6799dee747257167516bfcd66b98efec925..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/predict.py
+++ /dev/null
@@ -1,188 +0,0 @@
-"""
-download checkpoints to ./weights beforehand 
-python scripts/download_pretrained_models.py facelib
-python scripts/download_pretrained_models.py CodeFormer
-wget 'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth'
-"""
-
-import tempfile
-import cv2
-import torch
-from torchvision.transforms.functional import normalize
-from cog import BasePredictor, Input, Path
-
-from basicsr.utils import imwrite, img2tensor, tensor2img
-from basicsr.archs.rrdbnet_arch import RRDBNet
-from basicsr.utils.realesrgan_utils import RealESRGANer
-from basicsr.utils.registry import ARCH_REGISTRY
-from facelib.utils.face_restoration_helper import FaceRestoreHelper
-
-
-class Predictor(BasePredictor):
-    def setup(self):
-        """Load the model into memory to make running multiple predictions efficient"""
-        self.device = "cuda:0"
-        self.bg_upsampler = set_realesrgan()
-        self.net = ARCH_REGISTRY.get("CodeFormer")(
-            dim_embd=512,
-            codebook_size=1024,
-            n_head=8,
-            n_layers=9,
-            connect_list=["32", "64", "128", "256"],
-        ).to(self.device)
-        ckpt_path = "weights/CodeFormer/codeformer.pth"
-        checkpoint = torch.load(ckpt_path)[
-            "params_ema"
-        ]  # update file permission if cannot load
-        self.net.load_state_dict(checkpoint)
-        self.net.eval()
-
-    def predict(
-        self,
-        image: Path = Input(description="Input image"),
-        codeformer_fidelity: float = Input(
-            default=0.5,
-            ge=0,
-            le=1,
-            description="Balance the quality (lower number) and fidelity (higher number).",
-        ),
-        background_enhance: bool = Input(
-            description="Enhance background image with Real-ESRGAN", default=True
-        ),
-        face_upsample: bool = Input(
-            description="Upsample restored faces for high-resolution AI-created images",
-            default=True,
-        ),
-        upscale: int = Input(
-            description="The final upsampling scale of the image",
-            default=2,
-        ),
-    ) -> Path:
-        """Run a single prediction on the model"""
-
-        # take the default setting for the demo
-        has_aligned = False
-        only_center_face = False
-        draw_box = False
-        detection_model = "retinaface_resnet50"
-
-        self.face_helper = FaceRestoreHelper(
-            upscale,
-            face_size=512,
-            crop_ratio=(1, 1),
-            det_model=detection_model,
-            save_ext="png",
-            use_parse=True,
-            device=self.device,
-        )
-
-        bg_upsampler = self.bg_upsampler if background_enhance else None
-        face_upsampler = self.bg_upsampler if face_upsample else None
-
-        img = cv2.imread(str(image), cv2.IMREAD_COLOR)
-
-        if has_aligned:
-            # the input faces are already cropped and aligned
-            img = cv2.resize(img, (512, 512), interpolation=cv2.INTER_LINEAR)
-            self.face_helper.cropped_faces = [img]
-        else:
-            self.face_helper.read_image(img)
-            # get face landmarks for each face
-            num_det_faces = self.face_helper.get_face_landmarks_5(
-                only_center_face=only_center_face, resize=640, eye_dist_threshold=5
-            )
-            print(f"\tdetect {num_det_faces} faces")
-            # align and warp each face
-            self.face_helper.align_warp_face()
-
-        # face restoration for each cropped face
-        for idx, cropped_face in enumerate(self.face_helper.cropped_faces):
-            # prepare data
-            cropped_face_t = img2tensor(
-                cropped_face / 255.0, bgr2rgb=True, float32=True
-            )
-            normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
-            cropped_face_t = cropped_face_t.unsqueeze(0).to(self.device)
-
-            try:
-                with torch.no_grad():
-                    output = self.net(
-                        cropped_face_t, w=codeformer_fidelity, adain=True
-                    )[0]
-                    restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1))
-                del output
-                torch.cuda.empty_cache()
-            except Exception as error:
-                print(f"\tFailed inference for CodeFormer: {error}")
-                restored_face = tensor2img(
-                    cropped_face_t, rgb2bgr=True, min_max=(-1, 1)
-                )
-
-            restored_face = restored_face.astype("uint8")
-            self.face_helper.add_restored_face(restored_face)
-
-        # paste_back
-        if not has_aligned:
-            # upsample the background
-            if bg_upsampler is not None:
-                # Now only support RealESRGAN for upsampling background
-                bg_img = bg_upsampler.enhance(img, outscale=upscale)[0]
-            else:
-                bg_img = None
-            self.face_helper.get_inverse_affine(None)
-            # paste each restored face to the input image
-            if face_upsample and face_upsampler is not None:
-                restored_img = self.face_helper.paste_faces_to_input_image(
-                    upsample_img=bg_img,
-                    draw_box=draw_box,
-                    face_upsampler=face_upsampler,
-                )
-            else:
-                restored_img = self.face_helper.paste_faces_to_input_image(
-                    upsample_img=bg_img, draw_box=draw_box
-                )
-
-        # save restored img
-        out_path = Path(tempfile.mkdtemp()) / "output.png"
-
-        if not has_aligned and restored_img is not None:
-            imwrite(restored_img, str(out_path))
-
-        return out_path
-
-
-def imread(img_path):
-    img = cv2.imread(img_path)
-    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-    return img
-
-
-def set_realesrgan():
-    if not torch.cuda.is_available():  # CPU
-        import warnings
-
-        warnings.warn(
-            "The unoptimized RealESRGAN is slow on CPU. We do not use it. "
-            "If you really want to use it, please modify the corresponding codes.",
-            category=RuntimeWarning,
-        )
-        bg_upsampler = None
-    else:
-        model = RRDBNet(
-            num_in_ch=3,
-            num_out_ch=3,
-            num_feat=64,
-            num_block=23,
-            num_grow_ch=32,
-            scale=2,
-        )
-        bg_upsampler = RealESRGANer(
-            scale=2,
-            model_path="./weights/RealESRGAN_x2plus.pth",
-            model=model,
-            tile=400,
-            tile_pad=40,
-            pre_pad=0,
-            half=True,
-        )
-    return bg_upsampler
diff --git a/repositories/CodeFormer/requirements.txt b/repositories/CodeFormer/requirements.txt
deleted file mode 100644
index f97dfde85ebe83708fc1f6f7234a0ef69f18bde5..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/requirements.txt
+++ /dev/null
@@ -1,20 +0,0 @@
-addict
-future
-lmdb
-numpy
-opencv-python
-Pillow
-pyyaml
-requests
-scikit-image
-scipy
-tb-nightly
-torch>=1.7.1
-torchvision
-tqdm
-yapf
-lpips
-gdown # supports downloading the large file from Google Drive
-# cmake
-# dlib
-# conda install -c conda-forge dlib
\ No newline at end of file
diff --git a/repositories/CodeFormer/scripts/crop_align_face.py b/repositories/CodeFormer/scripts/crop_align_face.py
deleted file mode 100644
index 31e66266ac0e5f818fa18b6409993151086bbc8b..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/scripts/crop_align_face.py
+++ /dev/null
@@ -1,192 +0,0 @@
-"""
-brief: face alignment with FFHQ method (https://github.com/NVlabs/ffhq-dataset)
-author: lzhbrian (https://lzhbrian.me)
-link: https://gist.github.com/lzhbrian/bde87ab23b499dd02ba4f588258f57d5
-date: 2020.1.5
-note: code is heavily borrowed from
-    https://github.com/NVlabs/ffhq-dataset
-    http://dlib.net/face_landmark_detection.py.html
-requirements:
-    conda install Pillow numpy scipy
-    conda install -c conda-forge dlib
-    # download face landmark model from:
-    # http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2
-"""
-
-import cv2
-import dlib
-import glob
-import numpy as np
-import os
-import PIL
-import PIL.Image
-import scipy
-import scipy.ndimage
-import sys
-import argparse
-
-# download model from: http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2
-predictor = dlib.shape_predictor('weights/dlib/shape_predictor_68_face_landmarks-fbdc2cb8.dat')
-
-
-def get_landmark(filepath, only_keep_largest=True):
-    """get landmark with dlib
-    :return: np.array shape=(68, 2)
-    """
-    detector = dlib.get_frontal_face_detector()
-
-    img = dlib.load_rgb_image(filepath)
-    dets = detector(img, 1)
-
-    # Shangchen modified
-    print("Number of faces detected: {}".format(len(dets)))
-    if only_keep_largest:
-        print('Detect several faces and only keep the largest.')
-        face_areas = []
-        for k, d in enumerate(dets):
-            face_area = (d.right() - d.left()) * (d.bottom() - d.top())
-            face_areas.append(face_area)
-
-        largest_idx = face_areas.index(max(face_areas))
-        d = dets[largest_idx]
-        shape = predictor(img, d)
-        print("Part 0: {}, Part 1: {} ...".format(
-            shape.part(0), shape.part(1)))
-    else:
-        for k, d in enumerate(dets):
-            print("Detection {}: Left: {} Top: {} Right: {} Bottom: {}".format(
-                k, d.left(), d.top(), d.right(), d.bottom()))
-            # Get the landmarks/parts for the face in box d.
-            shape = predictor(img, d)
-            print("Part 0: {}, Part 1: {} ...".format(
-                shape.part(0), shape.part(1)))
-
-    t = list(shape.parts())
-    a = []
-    for tt in t:
-        a.append([tt.x, tt.y])
-    lm = np.array(a)
-    # lm is a shape=(68,2) np.array
-    return lm
-
-def align_face(filepath, out_path):
-    """
-    :param filepath: str
-    :return: PIL Image
-    """
-    try:
-        lm = get_landmark(filepath)
-    except:
-        print('No landmark ...')
-        return
-
-    lm_chin = lm[0:17]  # left-right
-    lm_eyebrow_left = lm[17:22]  # left-right
-    lm_eyebrow_right = lm[22:27]  # left-right
-    lm_nose = lm[27:31]  # top-down
-    lm_nostrils = lm[31:36]  # top-down
-    lm_eye_left = lm[36:42]  # left-clockwise
-    lm_eye_right = lm[42:48]  # left-clockwise
-    lm_mouth_outer = lm[48:60]  # left-clockwise
-    lm_mouth_inner = lm[60:68]  # left-clockwise
-
-    # Calculate auxiliary vectors.
-    eye_left = np.mean(lm_eye_left, axis=0)
-    eye_right = np.mean(lm_eye_right, axis=0)
-    eye_avg = (eye_left + eye_right) * 0.5
-    eye_to_eye = eye_right - eye_left
-    mouth_left = lm_mouth_outer[0]
-    mouth_right = lm_mouth_outer[6]
-    mouth_avg = (mouth_left + mouth_right) * 0.5
-    eye_to_mouth = mouth_avg - eye_avg
-
-    # Choose oriented crop rectangle.
-    x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1]
-    x /= np.hypot(*x)
-    x *= max(np.hypot(*eye_to_eye) * 2.0, np.hypot(*eye_to_mouth) * 1.8)
-    y = np.flipud(x) * [-1, 1]
-    c = eye_avg + eye_to_mouth * 0.1
-    quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y])
-    qsize = np.hypot(*x) * 2
-
-    # read image
-    img = PIL.Image.open(filepath)
-
-    output_size = 512
-    transform_size = 4096
-    enable_padding = False
-
-    # Shrink.
-    shrink = int(np.floor(qsize / output_size * 0.5))
-    if shrink > 1:
-        rsize = (int(np.rint(float(img.size[0]) / shrink)),
-                 int(np.rint(float(img.size[1]) / shrink)))
-        img = img.resize(rsize, PIL.Image.ANTIALIAS)
-        quad /= shrink
-        qsize /= shrink
- 
-    # Crop.
-    border = max(int(np.rint(qsize * 0.1)), 3)
-    crop = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))),
-            int(np.ceil(max(quad[:, 0]))), int(np.ceil(max(quad[:, 1]))))
-    crop = (max(crop[0] - border, 0), max(crop[1] - border, 0),
-            min(crop[2] + border,
-                img.size[0]), min(crop[3] + border, img.size[1]))
-    if crop[2] - crop[0] < img.size[0] or crop[3] - crop[1] < img.size[1]:
-        img = img.crop(crop)
-        quad -= crop[0:2]
-
-    # Pad.
-    pad = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))),
-           int(np.ceil(max(quad[:, 0]))), int(np.ceil(max(quad[:, 1]))))
-    pad = (max(-pad[0] + border,
-               0), max(-pad[1] + border,
-                       0), max(pad[2] - img.size[0] + border,
-                               0), max(pad[3] - img.size[1] + border, 0))
-    if enable_padding and max(pad) > border - 4:
-        pad = np.maximum(pad, int(np.rint(qsize * 0.3)))
-        img = np.pad(
-            np.float32(img), ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)),
-            'reflect')
-        h, w, _ = img.shape
-        y, x, _ = np.ogrid[:h, :w, :1]
-        mask = np.maximum(
-            1.0 -
-            np.minimum(np.float32(x) / pad[0],
-                       np.float32(w - 1 - x) / pad[2]), 1.0 -
-            np.minimum(np.float32(y) / pad[1],
-                       np.float32(h - 1 - y) / pad[3]))
-        blur = qsize * 0.02
-        img += (scipy.ndimage.gaussian_filter(img, [blur, blur, 0]) -
-                img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0)
-        img += (np.median(img, axis=(0, 1)) - img) * np.clip(mask, 0.0, 1.0)
-        img = PIL.Image.fromarray(
-            np.uint8(np.clip(np.rint(img), 0, 255)), 'RGB')
-        quad += pad[:2]
-
-    img = img.transform((transform_size, transform_size), PIL.Image.QUAD,
-                        (quad + 0.5).flatten(), PIL.Image.BILINEAR)
-
-    if output_size < transform_size:
-        img = img.resize((output_size, output_size), PIL.Image.ANTIALIAS)
-
-    # Save aligned image.
-    print('saveing: ', out_path)
-    img.save(out_path)
-
-    return img, np.max(quad[:, 0]) - np.min(quad[:, 0])
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--in_dir', type=str, default='./inputs/whole_imgs')
-    parser.add_argument('--out_dir', type=str, default='./inputs/cropped_faces')
-    args = parser.parse_args()
-
-    img_list = sorted(glob.glob(f'{args.in_dir}/*.png'))
-    img_list = sorted(img_list)
-
-    for in_path in img_list:
-        out_path = os.path.join(args.out_dir, in_path.split("/")[-1])        
-        out_path = out_path.replace('.jpg', '.png')
-        size_ = align_face(in_path, out_path)
\ No newline at end of file
diff --git a/repositories/CodeFormer/scripts/download_pretrained_models.py b/repositories/CodeFormer/scripts/download_pretrained_models.py
deleted file mode 100644
index daa6e8ca14ea91c89a318e85d9f182eb7d1bf025..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/scripts/download_pretrained_models.py
+++ /dev/null
@@ -1,40 +0,0 @@
-import argparse
-import os
-from os import path as osp
-
-from basicsr.utils.download_util import load_file_from_url
-
-
-def download_pretrained_models(method, file_urls):
-    save_path_root = f'./weights/{method}'
-    os.makedirs(save_path_root, exist_ok=True)
-
-    for file_name, file_url in file_urls.items():
-        save_path = load_file_from_url(url=file_url, model_dir=save_path_root, progress=True, file_name=file_name)
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument(
-        'method',
-        type=str,
-        help=("Options: 'CodeFormer' 'facelib'. Set to 'all' to download all the models."))
-    args = parser.parse_args()
-
-    file_urls = {
-        'CodeFormer': {
-            'codeformer.pth': 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth'
-        },
-        'facelib': {
-            # 'yolov5l-face.pth': 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/yolov5l-face.pth',
-            'detection_Resnet50_Final.pth': 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/detection_Resnet50_Final.pth',
-            'parsing_parsenet.pth': 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/parsing_parsenet.pth'
-        }
-    }
-
-    if args.method == 'all':
-        for method in file_urls.keys():
-            download_pretrained_models(method, file_urls[method])
-    else:
-        download_pretrained_models(args.method, file_urls[args.method])
\ No newline at end of file
diff --git a/repositories/CodeFormer/scripts/download_pretrained_models_from_gdrive.py b/repositories/CodeFormer/scripts/download_pretrained_models_from_gdrive.py
deleted file mode 100644
index 7df5be6fc260394ee9bbd0a7ae377e2ca657fe83..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/scripts/download_pretrained_models_from_gdrive.py
+++ /dev/null
@@ -1,60 +0,0 @@
-import argparse
-import os
-from os import path as osp
-
-# from basicsr.utils.download_util import download_file_from_google_drive
-import gdown
-
-
-def download_pretrained_models(method, file_ids):
-    save_path_root = f'./weights/{method}'
-    os.makedirs(save_path_root, exist_ok=True)
-
-    for file_name, file_id in file_ids.items():
-        file_url = 'https://drive.google.com/uc?id='+file_id
-        save_path = osp.abspath(osp.join(save_path_root, file_name))
-        if osp.exists(save_path):
-            user_response = input(f'{file_name} already exist. Do you want to cover it? Y/N\n')
-            if user_response.lower() == 'y':
-                print(f'Covering {file_name} to {save_path}')
-                gdown.download(file_url, save_path, quiet=False)
-                # download_file_from_google_drive(file_id, save_path)
-            elif user_response.lower() == 'n':
-                print(f'Skipping {file_name}')
-            else:
-                raise ValueError('Wrong input. Only accepts Y/N.')
-        else:
-            print(f'Downloading {file_name} to {save_path}')
-            gdown.download(file_url, save_path, quiet=False)
-            # download_file_from_google_drive(file_id, save_path)
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument(
-        'method',
-        type=str,
-        help=("Options: 'CodeFormer' 'facelib'. Set to 'all' to download all the models."))
-    args = parser.parse_args()
-
-    # file name: file id
-    # 'dlib': {
-    #     'mmod_human_face_detector-4cb19393.dat': '1qD-OqY8M6j4PWUP_FtqfwUPFPRMu6ubX',
-    #     'shape_predictor_5_face_landmarks-c4b1e980.dat': '1vF3WBUApw4662v9Pw6wke3uk1qxnmLdg',
-    #     'shape_predictor_68_face_landmarks-fbdc2cb8.dat': '1tJyIVdCHaU6IDMDx86BZCxLGZfsWB8yq'
-    # }
-    file_ids = {
-        'CodeFormer': {
-            'codeformer.pth': '1v_E_vZvP-dQPF55Kc5SRCjaKTQXDz-JB'
-        },
-        'facelib': {
-            'yolov5l-face.pth': '131578zMA6B2x8VQHyHfa6GEPtulMCNzV',
-            'parsing_parsenet.pth': '16pkohyZZ8ViHGBk3QtVqxLZKzdo466bK'
-        }
-    }
-
-    if args.method == 'all':
-        for method in file_ids.keys():
-            download_pretrained_models(method, file_ids[method])
-    else:
-        download_pretrained_models(args.method, file_ids[args.method])
\ No newline at end of file
diff --git a/repositories/CodeFormer/weights/CodeFormer/.gitkeep b/repositories/CodeFormer/weights/CodeFormer/.gitkeep
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/repositories/CodeFormer/weights/README.md b/repositories/CodeFormer/weights/README.md
deleted file mode 100644
index 67ad334bd672eeb9f82813cd54e8885331bbb2f2..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/weights/README.md
+++ /dev/null
@@ -1,3 +0,0 @@
-# Weights
-
-Put the downloaded pre-trained models to this folder.
\ No newline at end of file
diff --git a/repositories/CodeFormer/weights/facelib/.gitkeep b/repositories/CodeFormer/weights/facelib/.gitkeep
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/repositories/k-diffusion/.github/workflows/python-publish.yml b/repositories/k-diffusion/.github/workflows/python-publish.yml
deleted file mode 100644
index 9638b6b4575cb6544bf45429ad86899262a50762..0000000000000000000000000000000000000000
--- a/repositories/k-diffusion/.github/workflows/python-publish.yml
+++ /dev/null
@@ -1,37 +0,0 @@
-name: Release
-
-on:
-  push:
-    branches:
-    - master
-jobs:
-  deploy:
-    runs-on: ubuntu-latest
-    steps:
-    - uses: actions/checkout@v2
-    - uses: actions-ecosystem/action-regex-match@v2
-      id: regex-match
-      with:
-        text: ${{ github.event.head_commit.message }}
-        regex: '^Release ([^ ]+)'
-    - name: Set up Python
-      uses: actions/setup-python@v2
-      with:
-        python-version: '3.8'
-    - name: Install dependencies
-      run: |
-        python -m pip install --upgrade pip
-        pip install setuptools wheel twine
-    - name: Release
-      if: ${{ steps.regex-match.outputs.match != '' }}
-      uses: softprops/action-gh-release@v1
-      with:
-        tag_name: v${{ steps.regex-match.outputs.group1 }}
-    - name: Build and publish
-      if: ${{ steps.regex-match.outputs.match != '' }}
-      env:
-        TWINE_USERNAME: __token__
-        TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }}
-      run: |
-        python setup.py sdist bdist_wheel
-        twine upload dist/*
diff --git a/repositories/k-diffusion/.gitignore b/repositories/k-diffusion/.gitignore
deleted file mode 100644
index e647ac957b0cfb9b4d10af4829e84f0f1ca0b2f0..0000000000000000000000000000000000000000
--- a/repositories/k-diffusion/.gitignore
+++ /dev/null
@@ -1,10 +0,0 @@
-venv*
-__pycache__
-.ipynb_checkpoints
-*.pth
-*.egg-info
-data
-*_demo_*.png
-wandb/*
-*.csv
-.env
\ No newline at end of file
diff --git a/repositories/k-diffusion/LICENSE b/repositories/k-diffusion/LICENSE
deleted file mode 100644
index 37a42365ab6670d39334fc9b650975c819fee3d1..0000000000000000000000000000000000000000
--- a/repositories/k-diffusion/LICENSE
+++ /dev/null
@@ -1,19 +0,0 @@
-Copyright (c) 2022 Katherine Crowson
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in
-all copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
-THE SOFTWARE.
diff --git a/repositories/k-diffusion/README.md b/repositories/k-diffusion/README.md
deleted file mode 100644
index 4f7c92f9a947da69dedecd8cc1149b3713ab2e80..0000000000000000000000000000000000000000
--- a/repositories/k-diffusion/README.md
+++ /dev/null
@@ -1,61 +0,0 @@
-# k-diffusion
-
-An implementation of [Elucidating the Design Space of Diffusion-Based Generative Models](https://arxiv.org/abs/2206.00364) (Karras et al., 2022) for PyTorch. The patching method in [Improving Diffusion Model Efficiency Through Patching](https://arxiv.org/abs/2207.04316) is implemented as well.
-
-## Installation
-
-`k-diffusion` can be installed via PyPI (`pip install k-diffusion`) but it will not include training and inference scripts, only library code that others can depend on. To run the training and inference scripts, clone this repository and run `pip install -e <path to repository>`.
-
-## Training:
-
-To train models:
-
-```sh
-$ ./train.py --config CONFIG_FILE --name RUN_NAME
-```
-
-For instance, to train a model on MNIST:
-
-```sh
-$ ./train.py --config configs/config_mnist.json --name RUN_NAME
-```
-
-The configuration file allows you to specify the dataset type. Currently supported types are `"imagefolder"` (finds all images in that folder and its subfolders, recursively), `"cifar10"` (CIFAR-10), and `"mnist"` (MNIST). `"huggingface"` [Hugging Face Datasets](https://huggingface.co/docs/datasets/index) is also supported.
-
-Multi-GPU and multi-node training is supported with [Hugging Face Accelerate](https://huggingface.co/docs/accelerate/index). You can configure Accelerate by running:
-
-```sh
-$ accelerate config
-```
-
-on all nodes, then running:
-
-```sh
-$ accelerate launch train.py --config CONFIG_FILE --name RUN_NAME
-```
-
-on all nodes.
-
-## Enhancements/additional features:
-
-- k-diffusion supports an experimental model output type, an isotropic Gaussian, which seems to have a lower gradient noise scale and to train faster than Karras et al. (2022) diffusion models.
-
-- k-diffusion has wrappers for [v-diffusion-pytorch](https://github.com/crowsonkb/v-diffusion-pytorch), [OpenAI diffusion](https://github.com/openai/guided-diffusion), and [CompVis diffusion](https://github.com/CompVis/latent-diffusion) models allowing them to be used with its samplers and ODE/SDE.
-
-- k-diffusion models support progressive growing.
-
-- k-diffusion implements [DPM-Solver](https://arxiv.org/abs/2206.00927), which produces higher quality samples at the same number of function evalutions as Karras Algorithm 2, as well as supporting adaptive step size control. [DPM-Solver++(2S) and (2M)](https://arxiv.org/abs/2211.01095) are implemented now too for improved quality with low numbers of steps.
-
-- k-diffusion supports [CLIP](https://openai.com/blog/clip/) guided sampling from unconditional diffusion models (see `sample_clip_guided.py`).
-
-- k-diffusion supports log likelihood calculation (not a variational lower bound) for native models and all wrapped models.
-
-- k-diffusion can calculate, during training, the [FID](https://papers.nips.cc/paper/2017/file/8a1d694707eb0fefe65871369074926d-Paper.pdf) and [KID](https://arxiv.org/abs/1801.01401) vs the training set.
-
-- k-diffusion can calculate, during training, the gradient noise scale (1 / SNR), from _An Empirical Model of Large-Batch Training_, https://arxiv.org/abs/1812.06162).
-
-## To do:
-
-- Anything except unconditional image diffusion models
-
-- Latent diffusion
diff --git a/repositories/k-diffusion/configs/config_32x32_small.json b/repositories/k-diffusion/configs/config_32x32_small.json
deleted file mode 100644
index 77d8873ae4db5a04c7712baa0a996b0dfef46e05..0000000000000000000000000000000000000000
--- a/repositories/k-diffusion/configs/config_32x32_small.json
+++ /dev/null
@@ -1,43 +0,0 @@
-{
-    "model": {
-        "type": "image_v1",
-        "input_channels": 3,
-        "input_size": [32, 32],
-        "patch_size": 1,
-        "mapping_out": 256,
-        "depths": [2, 4, 4],
-        "channels": [128, 256, 512],
-        "self_attn_depths": [false, true, true],
-        "has_variance": true,
-        "dropout_rate": 0.05,
-        "augment_wrapper": true,
-        "augment_prob": 0.12,
-        "sigma_data": 0.5,
-        "sigma_min": 1e-2,
-        "sigma_max": 80,
-        "sigma_sample_density": {
-            "type": "lognormal",
-            "mean": -1.2,
-            "std": 1.2
-        }
-    },
-    "dataset": {
-        "type": "imagefolder",
-        "location": "/path/to/dataset"
-    },
-    "optimizer": {
-        "type": "adamw",
-        "lr": 1e-4,
-        "betas": [0.95, 0.999],
-        "eps": 1e-6,
-        "weight_decay": 1e-3
-    },
-    "lr_sched": {
-        "type": "constant"
-    },
-    "ema_sched": {
-        "type": "inverse",
-        "power": 0.6667,
-        "max_value": 0.9999
-    }
-}
diff --git a/repositories/k-diffusion/configs/config_32x32_small_butterflies.json b/repositories/k-diffusion/configs/config_32x32_small_butterflies.json
deleted file mode 100644
index 35a4c8c82fdbe6af93ae336c2f458d1e7340fe91..0000000000000000000000000000000000000000
--- a/repositories/k-diffusion/configs/config_32x32_small_butterflies.json
+++ /dev/null
@@ -1,44 +0,0 @@
-{
-    "model": {
-        "type": "image_v1",
-        "input_channels": 3,
-        "input_size": [32, 32],
-        "patch_size": 1,
-        "mapping_out": 256,
-        "depths": [2, 4, 4],
-        "channels": [128, 256, 512],
-        "self_attn_depths": [false, true, true],
-        "has_variance": true,
-        "dropout_rate": 0.05,
-        "augment_wrapper": true,
-        "augment_prob": 0.12,
-        "sigma_data": 0.5,
-        "sigma_min": 1e-2,
-        "sigma_max": 80,
-        "sigma_sample_density": {
-            "type": "lognormal",
-            "mean": -1.2,
-            "std": 1.2
-        }
-    },
-    "dataset": {
-        "type": "huggingface",
-        "location": "huggan/smithsonian_butterflies_subset",
-        "image_key": "image"
-    },
-    "optimizer": {
-        "type": "adamw",
-        "lr": 1e-4,
-        "betas": [0.95, 0.999],
-        "eps": 1e-6,
-        "weight_decay": 1e-3
-    },
-    "lr_sched": {
-        "type": "constant"
-    },
-    "ema_sched": {
-        "type": "inverse",
-        "power": 0.6667,
-        "max_value": 0.9999
-    }
-}
diff --git a/repositories/k-diffusion/configs/config_cifar10.json b/repositories/k-diffusion/configs/config_cifar10.json
deleted file mode 100644
index 3f88975ec9d28e2b72178d42fe272594ee8a3211..0000000000000000000000000000000000000000
--- a/repositories/k-diffusion/configs/config_cifar10.json
+++ /dev/null
@@ -1,43 +0,0 @@
-{
-    "model": {
-        "type": "image_v1",
-        "input_channels": 3,
-        "input_size": [32, 32],
-        "patch_size": 1,
-        "mapping_out": 256,
-        "depths": [2, 4, 4],
-        "channels": [128, 256, 512],
-        "self_attn_depths": [false, true, true],
-        "has_variance": true,
-        "dropout_rate": 0.05,
-        "augment_wrapper": true,
-        "augment_prob": 0.12,
-        "sigma_data": 0.5,
-        "sigma_min": 1e-2,
-        "sigma_max": 80,
-        "sigma_sample_density": {
-            "type": "lognormal",
-            "mean": -1.2,
-            "std": 1.2
-        }
-    },
-    "dataset": {
-        "type": "cifar10",
-        "location": "data"
-    },
-    "optimizer": {
-        "type": "adamw",
-        "lr": 1e-4,
-        "betas": [0.95, 0.999],
-        "eps": 1e-6,
-        "weight_decay": 1e-3
-    },
-    "lr_sched": {
-        "type": "constant"
-    },
-    "ema_sched": {
-        "type": "inverse",
-        "power": 0.6667,
-        "max_value": 0.9999
-    }
-}
diff --git a/repositories/k-diffusion/configs/config_mnist.json b/repositories/k-diffusion/configs/config_mnist.json
deleted file mode 100644
index 7e19b88e04c36e152e7d99e8bd984d0e2bef2a5c..0000000000000000000000000000000000000000
--- a/repositories/k-diffusion/configs/config_mnist.json
+++ /dev/null
@@ -1,43 +0,0 @@
-{
-    "model": {
-        "type": "image_v1",
-        "input_channels": 1,
-        "input_size": [28, 28],
-        "patch_size": 1,
-        "mapping_out": 256,
-        "depths": [2, 4, 4],
-        "channels": [128, 128, 256],
-        "self_attn_depths": [false, false, true],
-        "has_variance": true,
-        "dropout_rate": 0.05,
-        "augment_wrapper": true,
-        "augment_prob": 0.12,
-        "sigma_data": 0.6162,
-        "sigma_min": 1e-2,
-        "sigma_max": 80,
-        "sigma_sample_density": {
-            "type": "lognormal",
-            "mean": -1.2,
-            "std": 1.2
-        }
-    },
-    "dataset": {
-        "type": "mnist",
-        "location": "data"
-    },
-    "optimizer": {
-        "type": "adamw",
-        "lr": 2e-4,
-        "betas": [0.95, 0.999],
-        "eps": 1e-6,
-        "weight_decay": 1e-3
-    },
-    "lr_sched": {
-        "type": "constant"
-    },
-    "ema_sched": {
-        "type": "inverse",
-        "power": 0.6667,
-        "max_value": 0.9999
-    }
-}
diff --git a/repositories/k-diffusion/k_diffusion/__init__.py b/repositories/k-diffusion/k_diffusion/__init__.py
deleted file mode 100644
index 5de9decab9fef99f2dd152f16b82b5806508ffdf..0000000000000000000000000000000000000000
--- a/repositories/k-diffusion/k_diffusion/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from . import augmentation, config, evaluation, external, gns, layers, models, sampling, utils
-from .layers import Denoiser
diff --git a/repositories/k-diffusion/k_diffusion/__pycache__/__init__.cpython-310.pyc b/repositories/k-diffusion/k_diffusion/__pycache__/__init__.cpython-310.pyc
deleted file mode 100644
index e4455112c0e8787539b02dfe879a2cc2541f2d1e..0000000000000000000000000000000000000000
Binary files a/repositories/k-diffusion/k_diffusion/__pycache__/__init__.cpython-310.pyc and /dev/null differ
diff --git a/repositories/k-diffusion/k_diffusion/__pycache__/augmentation.cpython-310.pyc b/repositories/k-diffusion/k_diffusion/__pycache__/augmentation.cpython-310.pyc
deleted file mode 100644
index 5fa8bef22cae22a7e770bcaf72049d728c942d31..0000000000000000000000000000000000000000
Binary files a/repositories/k-diffusion/k_diffusion/__pycache__/augmentation.cpython-310.pyc and /dev/null differ
diff --git a/repositories/k-diffusion/k_diffusion/__pycache__/config.cpython-310.pyc b/repositories/k-diffusion/k_diffusion/__pycache__/config.cpython-310.pyc
deleted file mode 100644
index e1cb7e0dca07f2e75620973cc18914ee38a46e9a..0000000000000000000000000000000000000000
Binary files a/repositories/k-diffusion/k_diffusion/__pycache__/config.cpython-310.pyc and /dev/null differ
diff --git a/repositories/k-diffusion/k_diffusion/__pycache__/evaluation.cpython-310.pyc b/repositories/k-diffusion/k_diffusion/__pycache__/evaluation.cpython-310.pyc
deleted file mode 100644
index 3debe4fdfb14c31b5e12830cf7c9b91ae60f5195..0000000000000000000000000000000000000000
Binary files a/repositories/k-diffusion/k_diffusion/__pycache__/evaluation.cpython-310.pyc and /dev/null differ
diff --git a/repositories/k-diffusion/k_diffusion/__pycache__/external.cpython-310.pyc b/repositories/k-diffusion/k_diffusion/__pycache__/external.cpython-310.pyc
deleted file mode 100644
index ef90f6d39503b26bb98f19f3c3335ec03b11cde9..0000000000000000000000000000000000000000
Binary files a/repositories/k-diffusion/k_diffusion/__pycache__/external.cpython-310.pyc and /dev/null differ
diff --git a/repositories/k-diffusion/k_diffusion/__pycache__/gns.cpython-310.pyc b/repositories/k-diffusion/k_diffusion/__pycache__/gns.cpython-310.pyc
deleted file mode 100644
index 3158ec8bd48184bdca64d5347a59657c12563034..0000000000000000000000000000000000000000
Binary files a/repositories/k-diffusion/k_diffusion/__pycache__/gns.cpython-310.pyc and /dev/null differ
diff --git a/repositories/k-diffusion/k_diffusion/__pycache__/layers.cpython-310.pyc b/repositories/k-diffusion/k_diffusion/__pycache__/layers.cpython-310.pyc
deleted file mode 100644
index edeb0d07c780d7b7eb645458980de7346a672008..0000000000000000000000000000000000000000
Binary files a/repositories/k-diffusion/k_diffusion/__pycache__/layers.cpython-310.pyc and /dev/null differ
diff --git a/repositories/k-diffusion/k_diffusion/__pycache__/sampling.cpython-310.pyc b/repositories/k-diffusion/k_diffusion/__pycache__/sampling.cpython-310.pyc
deleted file mode 100644
index d5ec97f3931a37ad443b7fd9cc892f9ff680156c..0000000000000000000000000000000000000000
Binary files a/repositories/k-diffusion/k_diffusion/__pycache__/sampling.cpython-310.pyc and /dev/null differ
diff --git a/repositories/k-diffusion/k_diffusion/__pycache__/utils.cpython-310.pyc b/repositories/k-diffusion/k_diffusion/__pycache__/utils.cpython-310.pyc
deleted file mode 100644
index e99f9adc5ab2566e7b5baca23335f5f5d844591a..0000000000000000000000000000000000000000
Binary files a/repositories/k-diffusion/k_diffusion/__pycache__/utils.cpython-310.pyc and /dev/null differ
diff --git a/repositories/k-diffusion/k_diffusion/augmentation.py b/repositories/k-diffusion/k_diffusion/augmentation.py
deleted file mode 100644
index 7dd17c686300c8ecba7fac134aa54f01619c3d46..0000000000000000000000000000000000000000
--- a/repositories/k-diffusion/k_diffusion/augmentation.py
+++ /dev/null
@@ -1,105 +0,0 @@
-from functools import reduce
-import math
-import operator
-
-import numpy as np
-from skimage import transform
-import torch
-from torch import nn
-
-
-def translate2d(tx, ty):
-    mat = [[1, 0, tx],
-           [0, 1, ty],
-           [0, 0,  1]]
-    return torch.tensor(mat, dtype=torch.float32)
-
-
-def scale2d(sx, sy):
-    mat = [[sx,  0, 0],
-           [ 0, sy, 0],
-           [ 0,  0, 1]]
-    return torch.tensor(mat, dtype=torch.float32)
-
-
-def rotate2d(theta):
-    mat = [[torch.cos(theta), torch.sin(-theta), 0],
-           [torch.sin(theta),  torch.cos(theta), 0],
-           [               0,                 0, 1]]
-    return torch.tensor(mat, dtype=torch.float32)
-
-
-class KarrasAugmentationPipeline:
-    def __init__(self, a_prob=0.12, a_scale=2**0.2, a_aniso=2**0.2, a_trans=1/8):
-        self.a_prob = a_prob
-        self.a_scale = a_scale
-        self.a_aniso = a_aniso
-        self.a_trans = a_trans
-
-    def __call__(self, image):
-        h, w = image.size
-        mats = [translate2d(h / 2 - 0.5, w / 2 - 0.5)]
-
-        # x-flip
-        a0 = torch.randint(2, []).float()
-        mats.append(scale2d(1 - 2 * a0, 1))
-        # y-flip
-        do = (torch.rand([]) < self.a_prob).float()
-        a1 = torch.randint(2, []).float() * do
-        mats.append(scale2d(1, 1 - 2 * a1))
-        # scaling
-        do = (torch.rand([]) < self.a_prob).float()
-        a2 = torch.randn([]) * do
-        mats.append(scale2d(self.a_scale ** a2, self.a_scale ** a2))
-        # rotation
-        do = (torch.rand([]) < self.a_prob).float()
-        a3 = (torch.rand([]) * 2 * math.pi - math.pi) * do
-        mats.append(rotate2d(-a3))
-        # anisotropy
-        do = (torch.rand([]) < self.a_prob).float()
-        a4 = (torch.rand([]) * 2 * math.pi - math.pi) * do
-        a5 = torch.randn([]) * do
-        mats.append(rotate2d(a4))
-        mats.append(scale2d(self.a_aniso ** a5, self.a_aniso ** -a5))
-        mats.append(rotate2d(-a4))
-        # translation
-        do = (torch.rand([]) < self.a_prob).float()
-        a6 = torch.randn([]) * do
-        a7 = torch.randn([]) * do
-        mats.append(translate2d(self.a_trans * w * a6, self.a_trans * h * a7))
-
-        # form the transformation matrix and conditioning vector
-        mats.append(translate2d(-h / 2 + 0.5, -w / 2 + 0.5))
-        mat = reduce(operator.matmul, mats)
-        cond = torch.stack([a0, a1, a2, a3.cos() - 1, a3.sin(), a5 * a4.cos(), a5 * a4.sin(), a6, a7])
-
-        # apply the transformation
-        image_orig = np.array(image, dtype=np.float32) / 255
-        if image_orig.ndim == 2:
-            image_orig = image_orig[..., None]
-        tf = transform.AffineTransform(mat.numpy())
-        image = transform.warp(image_orig, tf.inverse, order=3, mode='reflect', cval=0.5, clip=False, preserve_range=True)
-        image_orig = torch.as_tensor(image_orig).movedim(2, 0) * 2 - 1
-        image = torch.as_tensor(image).movedim(2, 0) * 2 - 1
-        return image, image_orig, cond
-
-
-class KarrasAugmentWrapper(nn.Module):
-    def __init__(self, model):
-        super().__init__()
-        self.inner_model = model
-    
-    def forward(self, input, sigma, aug_cond=None, mapping_cond=None, **kwargs):
-        if aug_cond is None:
-            aug_cond = input.new_zeros([input.shape[0], 9])
-        if mapping_cond is None:
-            mapping_cond = aug_cond
-        else:
-            mapping_cond = torch.cat([aug_cond, mapping_cond], dim=1)
-        return self.inner_model(input, sigma, mapping_cond=mapping_cond, **kwargs)
-
-    def set_skip_stages(self, skip_stages):
-        return self.inner_model.set_skip_stages(skip_stages)
-
-    def set_patch_size(self, patch_size):
-        return self.inner_model.set_patch_size(patch_size)
diff --git a/repositories/k-diffusion/k_diffusion/config.py b/repositories/k-diffusion/k_diffusion/config.py
deleted file mode 100644
index f9de7bc203216b0a4e26a6d18c913fedc84dbe46..0000000000000000000000000000000000000000
--- a/repositories/k-diffusion/k_diffusion/config.py
+++ /dev/null
@@ -1,115 +0,0 @@
-from functools import partial
-import json
-import math
-import warnings
-
-from jsonmerge import merge
-
-from . import augmentation, layers, models, utils
-
-
-def load_config(file):
-    defaults = {
-        'model': {
-            'sigma_data': 1.,
-            'patch_size': 1,
-            'dropout_rate': 0.,
-            'augment_wrapper': True,
-            'augment_prob': 0.,
-            'mapping_cond_dim': 0,
-            'unet_cond_dim': 0,
-            'cross_cond_dim': 0,
-            'cross_attn_depths': None,
-            'skip_stages': 0,
-            'has_variance': False,
-            'loss_config': 'karras',
-        },
-        'dataset': {
-            'type': 'imagefolder',
-        },
-        'optimizer': {
-            'type': 'adamw',
-            'lr': 1e-4,
-            'betas': [0.95, 0.999],
-            'eps': 1e-6,
-            'weight_decay': 1e-3,
-        },
-        'lr_sched': {
-            'type': 'constant',
-        },
-        'ema_sched': {
-            'type': 'inverse',
-            'power': 0.6667,
-            'max_value': 0.9999
-        },
-    }
-    config = json.load(file)
-    return merge(defaults, config)
-
-
-def make_model(config):
-    config = config['model']
-    assert config['type'] == 'image_v1'
-    model = models.ImageDenoiserModelV1(
-        config['input_channels'],
-        config['mapping_out'],
-        config['depths'],
-        config['channels'],
-        config['self_attn_depths'],
-        config['cross_attn_depths'],
-        patch_size=config['patch_size'],
-        dropout_rate=config['dropout_rate'],
-        mapping_cond_dim=config['mapping_cond_dim'] + (9 if config['augment_wrapper'] else 0),
-        unet_cond_dim=config['unet_cond_dim'],
-        cross_cond_dim=config['cross_cond_dim'],
-        skip_stages=config['skip_stages'],
-        has_variance=config['has_variance'],
-    )
-    if config['augment_wrapper']:
-        model = augmentation.KarrasAugmentWrapper(model)
-    return model
-
-
-def make_denoiser_wrapper(config):
-    config = config['model']
-    sigma_data = config.get('sigma_data', 1.)
-    has_variance = config.get('has_variance', False)
-    loss_config = config.get('loss_config', 'karras')
-    if loss_config == 'karras':
-        if not has_variance:
-            return partial(layers.Denoiser, sigma_data=sigma_data)
-        return partial(layers.DenoiserWithVariance, sigma_data=sigma_data)
-    if loss_config == 'simple':
-        if has_variance:
-            raise ValueError('Simple loss config does not support a variance output')
-        return partial(layers.SimpleLossDenoiser, sigma_data=sigma_data)
-    raise ValueError('Unknown loss config type')
-
-
-def make_sample_density(config):
-    sd_config = config['sigma_sample_density']
-    sigma_data = config['sigma_data']
-    if sd_config['type'] == 'lognormal':
-        loc = sd_config['mean'] if 'mean' in sd_config else sd_config['loc']
-        scale = sd_config['std'] if 'std' in sd_config else sd_config['scale']
-        return partial(utils.rand_log_normal, loc=loc, scale=scale)
-    if sd_config['type'] == 'loglogistic':
-        loc = sd_config['loc'] if 'loc' in sd_config else math.log(sigma_data)
-        scale = sd_config['scale'] if 'scale' in sd_config else 0.5
-        min_value = sd_config['min_value'] if 'min_value' in sd_config else 0.
-        max_value = sd_config['max_value'] if 'max_value' in sd_config else float('inf')
-        return partial(utils.rand_log_logistic, loc=loc, scale=scale, min_value=min_value, max_value=max_value)
-    if sd_config['type'] == 'loguniform':
-        min_value = sd_config['min_value'] if 'min_value' in sd_config else config['sigma_min']
-        max_value = sd_config['max_value'] if 'max_value' in sd_config else config['sigma_max']
-        return partial(utils.rand_log_uniform, min_value=min_value, max_value=max_value)
-    if sd_config['type'] in {'v-diffusion', 'cosine'}:
-        min_value = sd_config['min_value'] if 'min_value' in sd_config else 1e-3
-        max_value = sd_config['max_value'] if 'max_value' in sd_config else 1e3
-        return partial(utils.rand_v_diffusion, sigma_data=sigma_data, min_value=min_value, max_value=max_value)
-    if sd_config['type'] == 'split-lognormal':
-        loc = sd_config['mean'] if 'mean' in sd_config else sd_config['loc']
-        scale_1 = sd_config['std_1'] if 'std_1' in sd_config else sd_config['scale_1']
-        scale_2 = sd_config['std_2'] if 'std_2' in sd_config else sd_config['scale_2']
-        return partial(utils.rand_split_log_normal, loc=loc, scale_1=scale_1, scale_2=scale_2)
-    raise ValueError('Unknown sample density type')
diff --git a/repositories/k-diffusion/k_diffusion/evaluation.py b/repositories/k-diffusion/k_diffusion/evaluation.py
deleted file mode 100644
index 2c34bbf1656854d9cf233b7620b684e44b30de82..0000000000000000000000000000000000000000
--- a/repositories/k-diffusion/k_diffusion/evaluation.py
+++ /dev/null
@@ -1,134 +0,0 @@
-import math
-import os
-from pathlib import Path
-
-from cleanfid.inception_torchscript import InceptionV3W
-import clip
-from resize_right import resize
-import torch
-from torch import nn
-from torch.nn import functional as F
-from torchvision import transforms
-from tqdm.auto import trange
-
-from . import utils
-
-
-class InceptionV3FeatureExtractor(nn.Module):
-    def __init__(self, device='cpu'):
-        super().__init__()
-        path = Path(os.environ.get('XDG_CACHE_HOME', Path.home() / '.cache')) / 'k-diffusion'
-        url = 'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/inception-2015-12-05.pt'
-        digest = 'f58cb9b6ec323ed63459aa4fb441fe750cfe39fafad6da5cb504a16f19e958f4'
-        utils.download_file(path / 'inception-2015-12-05.pt', url, digest)
-        self.model = InceptionV3W(str(path), resize_inside=False).to(device)
-        self.size = (299, 299)
-
-    def forward(self, x):
-        if x.shape[2:4] != self.size:
-            x = resize(x, out_shape=self.size, pad_mode='reflect')
-        if x.shape[1] == 1:
-            x = torch.cat([x] * 3, dim=1)
-        x = (x * 127.5 + 127.5).clamp(0, 255)
-        return self.model(x)
-
-
-class CLIPFeatureExtractor(nn.Module):
-    def __init__(self, name='ViT-L/14@336px', device='cpu'):
-        super().__init__()
-        self.model = clip.load(name, device=device)[0].eval().requires_grad_(False)
-        self.normalize = transforms.Normalize(mean=(0.48145466, 0.4578275, 0.40821073),
-                                              std=(0.26862954, 0.26130258, 0.27577711))
-        self.size = (self.model.visual.input_resolution, self.model.visual.input_resolution)
-
-    def forward(self, x):
-        if x.shape[2:4] != self.size:
-            x = resize(x.add(1).div(2), out_shape=self.size, pad_mode='reflect').clamp(0, 1)
-        x = self.normalize(x)
-        x = self.model.encode_image(x).float()
-        x = F.normalize(x) * x.shape[1] ** 0.5
-        return x
-
-
-def compute_features(accelerator, sample_fn, extractor_fn, n, batch_size):
-    n_per_proc = math.ceil(n / accelerator.num_processes)
-    feats_all = []
-    try:
-        for i in trange(0, n_per_proc, batch_size, disable=not accelerator.is_main_process):
-            cur_batch_size = min(n - i, batch_size)
-            samples = sample_fn(cur_batch_size)[:cur_batch_size]
-            feats_all.append(accelerator.gather(extractor_fn(samples)))
-    except StopIteration:
-        pass
-    return torch.cat(feats_all)[:n]
-
-
-def polynomial_kernel(x, y):
-    d = x.shape[-1]
-    dot = x @ y.transpose(-2, -1)
-    return (dot / d + 1) ** 3
-
-
-def squared_mmd(x, y, kernel=polynomial_kernel):
-    m = x.shape[-2]
-    n = y.shape[-2]
-    kxx = kernel(x, x)
-    kyy = kernel(y, y)
-    kxy = kernel(x, y)
-    kxx_sum = kxx.sum([-1, -2]) - kxx.diagonal(dim1=-1, dim2=-2).sum(-1)
-    kyy_sum = kyy.sum([-1, -2]) - kyy.diagonal(dim1=-1, dim2=-2).sum(-1)
-    kxy_sum = kxy.sum([-1, -2])
-    term_1 = kxx_sum / m / (m - 1)
-    term_2 = kyy_sum / n / (n - 1)
-    term_3 = kxy_sum * 2 / m / n
-    return term_1 + term_2 - term_3
-
-
-@utils.tf32_mode(matmul=False)
-def kid(x, y, max_size=5000):
-    x_size, y_size = x.shape[0], y.shape[0]
-    n_partitions = math.ceil(max(x_size / max_size, y_size / max_size))
-    total_mmd = x.new_zeros([])
-    for i in range(n_partitions):
-        cur_x = x[round(i * x_size / n_partitions):round((i + 1) * x_size / n_partitions)]
-        cur_y = y[round(i * y_size / n_partitions):round((i + 1) * y_size / n_partitions)]
-        total_mmd = total_mmd + squared_mmd(cur_x, cur_y)
-    return total_mmd / n_partitions
-
-
-class _MatrixSquareRootEig(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, a):
-        vals, vecs = torch.linalg.eigh(a)
-        ctx.save_for_backward(vals, vecs)
-        return vecs @ vals.abs().sqrt().diag_embed() @ vecs.transpose(-2, -1)
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        vals, vecs = ctx.saved_tensors
-        d = vals.abs().sqrt().unsqueeze(-1).repeat_interleave(vals.shape[-1], -1)
-        vecs_t = vecs.transpose(-2, -1)
-        return vecs @ (vecs_t @ grad_output @ vecs / (d + d.transpose(-2, -1))) @ vecs_t
-
-
-def sqrtm_eig(a):
-    if a.ndim < 2:
-        raise RuntimeError('tensor of matrices must have at least 2 dimensions')
-    if a.shape[-2] != a.shape[-1]:
-        raise RuntimeError('tensor must be batches of square matrices')
-    return _MatrixSquareRootEig.apply(a)
-
-
-@utils.tf32_mode(matmul=False)
-def fid(x, y, eps=1e-8):
-    x_mean = x.mean(dim=0)
-    y_mean = y.mean(dim=0)
-    mean_term = (x_mean - y_mean).pow(2).sum()
-    x_cov = torch.cov(x.T)
-    y_cov = torch.cov(y.T)
-    eps_eye = torch.eye(x_cov.shape[0], device=x_cov.device, dtype=x_cov.dtype) * eps
-    x_cov = x_cov + eps_eye
-    y_cov = y_cov + eps_eye
-    x_cov_sqrt = sqrtm_eig(x_cov)
-    cov_term = torch.trace(x_cov + y_cov - 2 * sqrtm_eig(x_cov_sqrt @ y_cov @ x_cov_sqrt))
-    return mean_term + cov_term
diff --git a/repositories/k-diffusion/k_diffusion/external.py b/repositories/k-diffusion/k_diffusion/external.py
deleted file mode 100644
index 79b51cec41c52f054775f26c26cf63414d588aef..0000000000000000000000000000000000000000
--- a/repositories/k-diffusion/k_diffusion/external.py
+++ /dev/null
@@ -1,177 +0,0 @@
-import math
-
-import torch
-from torch import nn
-
-from . import sampling, utils
-
-
-class VDenoiser(nn.Module):
-    """A v-diffusion-pytorch model wrapper for k-diffusion."""
-
-    def __init__(self, inner_model):
-        super().__init__()
-        self.inner_model = inner_model
-        self.sigma_data = 1.
-
-    def get_scalings(self, sigma):
-        c_skip = self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2)
-        c_out = -sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5
-        c_in = 1 / (sigma ** 2 + self.sigma_data ** 2) ** 0.5
-        return c_skip, c_out, c_in
-
-    def sigma_to_t(self, sigma):
-        return sigma.atan() / math.pi * 2
-
-    def t_to_sigma(self, t):
-        return (t * math.pi / 2).tan()
-
-    def loss(self, input, noise, sigma, **kwargs):
-        c_skip, c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)]
-        noised_input = input + noise * utils.append_dims(sigma, input.ndim)
-        model_output = self.inner_model(noised_input * c_in, self.sigma_to_t(sigma), **kwargs)
-        target = (input - c_skip * noised_input) / c_out
-        return (model_output - target).pow(2).flatten(1).mean(1)
-
-    def forward(self, input, sigma, **kwargs):
-        c_skip, c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)]
-        return self.inner_model(input * c_in, self.sigma_to_t(sigma), **kwargs) * c_out + input * c_skip
-
-
-class DiscreteSchedule(nn.Module):
-    """A mapping between continuous noise levels (sigmas) and a list of discrete noise
-    levels."""
-
-    def __init__(self, sigmas, quantize):
-        super().__init__()
-        self.register_buffer('sigmas', sigmas)
-        self.register_buffer('log_sigmas', sigmas.log())
-        self.quantize = quantize
-
-    @property
-    def sigma_min(self):
-        return self.sigmas[0]
-
-    @property
-    def sigma_max(self):
-        return self.sigmas[-1]
-
-    def get_sigmas(self, n=None):
-        if n is None:
-            return sampling.append_zero(self.sigmas.flip(0))
-        t_max = len(self.sigmas) - 1
-        t = torch.linspace(t_max, 0, n, device=self.sigmas.device)
-        return sampling.append_zero(self.t_to_sigma(t))
-
-    def sigma_to_t(self, sigma, quantize=None):
-        quantize = self.quantize if quantize is None else quantize
-        log_sigma = sigma.log()
-        dists = log_sigma - self.log_sigmas[:, None]
-        if quantize:
-            return dists.abs().argmin(dim=0).view(sigma.shape)
-        low_idx = dists.ge(0).cumsum(dim=0).argmax(dim=0).clamp(max=self.log_sigmas.shape[0] - 2)
-        high_idx = low_idx + 1
-        low, high = self.log_sigmas[low_idx], self.log_sigmas[high_idx]
-        w = (low - log_sigma) / (low - high)
-        w = w.clamp(0, 1)
-        t = (1 - w) * low_idx + w * high_idx
-        return t.view(sigma.shape)
-
-    def t_to_sigma(self, t):
-        t = t.float()
-        low_idx, high_idx, w = t.floor().long(), t.ceil().long(), t.frac()
-        log_sigma = (1 - w) * self.log_sigmas[low_idx] + w * self.log_sigmas[high_idx]
-        return log_sigma.exp()
-
-
-class DiscreteEpsDDPMDenoiser(DiscreteSchedule):
-    """A wrapper for discrete schedule DDPM models that output eps (the predicted
-    noise)."""
-
-    def __init__(self, model, alphas_cumprod, quantize):
-        super().__init__(((1 - alphas_cumprod) / alphas_cumprod) ** 0.5, quantize)
-        self.inner_model = model
-        self.sigma_data = 1.
-
-    def get_scalings(self, sigma):
-        c_out = -sigma
-        c_in = 1 / (sigma ** 2 + self.sigma_data ** 2) ** 0.5
-        return c_out, c_in
-
-    def get_eps(self, *args, **kwargs):
-        return self.inner_model(*args, **kwargs)
-
-    def loss(self, input, noise, sigma, **kwargs):
-        c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)]
-        noised_input = input + noise * utils.append_dims(sigma, input.ndim)
-        eps = self.get_eps(noised_input * c_in, self.sigma_to_t(sigma), **kwargs)
-        return (eps - noise).pow(2).flatten(1).mean(1)
-
-    def forward(self, input, sigma, **kwargs):
-        c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)]
-        eps = self.get_eps(input * c_in, self.sigma_to_t(sigma), **kwargs)
-        return input + eps * c_out
-
-
-class OpenAIDenoiser(DiscreteEpsDDPMDenoiser):
-    """A wrapper for OpenAI diffusion models."""
-
-    def __init__(self, model, diffusion, quantize=False, has_learned_sigmas=True, device='cpu'):
-        alphas_cumprod = torch.tensor(diffusion.alphas_cumprod, device=device, dtype=torch.float32)
-        super().__init__(model, alphas_cumprod, quantize=quantize)
-        self.has_learned_sigmas = has_learned_sigmas
-
-    def get_eps(self, *args, **kwargs):
-        model_output = self.inner_model(*args, **kwargs)
-        if self.has_learned_sigmas:
-            return model_output.chunk(2, dim=1)[0]
-        return model_output
-
-
-class CompVisDenoiser(DiscreteEpsDDPMDenoiser):
-    """A wrapper for CompVis diffusion models."""
-
-    def __init__(self, model, quantize=False, device='cpu'):
-        super().__init__(model, model.alphas_cumprod, quantize=quantize)
-
-    def get_eps(self, *args, **kwargs):
-        return self.inner_model.apply_model(*args, **kwargs)
-
-
-class DiscreteVDDPMDenoiser(DiscreteSchedule):
-    """A wrapper for discrete schedule DDPM models that output v."""
-
-    def __init__(self, model, alphas_cumprod, quantize):
-        super().__init__(((1 - alphas_cumprod) / alphas_cumprod) ** 0.5, quantize)
-        self.inner_model = model
-        self.sigma_data = 1.
-
-    def get_scalings(self, sigma):
-        c_skip = self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2)
-        c_out = -sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5
-        c_in = 1 / (sigma ** 2 + self.sigma_data ** 2) ** 0.5
-        return c_skip, c_out, c_in
-
-    def get_v(self, *args, **kwargs):
-        return self.inner_model(*args, **kwargs)
-
-    def loss(self, input, noise, sigma, **kwargs):
-        c_skip, c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)]
-        noised_input = input + noise * utils.append_dims(sigma, input.ndim)
-        model_output = self.get_v(noised_input * c_in, self.sigma_to_t(sigma), **kwargs)
-        target = (input - c_skip * noised_input) / c_out
-        return (model_output - target).pow(2).flatten(1).mean(1)
-
-    def forward(self, input, sigma, **kwargs):
-        c_skip, c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)]
-        return self.get_v(input * c_in, self.sigma_to_t(sigma), **kwargs) * c_out + input * c_skip
-
-
-class CompVisVDenoiser(DiscreteVDDPMDenoiser):
-    """A wrapper for CompVis diffusion models that output v."""
-
-    def __init__(self, model, quantize=False, device='cpu'):
-        super().__init__(model, model.alphas_cumprod, quantize=quantize)
-
-    def get_v(self, x, t, cond, **kwargs):
-        return self.inner_model.apply_model(x, t, cond)
diff --git a/repositories/k-diffusion/k_diffusion/gns.py b/repositories/k-diffusion/k_diffusion/gns.py
deleted file mode 100644
index dcb7b8d8a9aeae38a7f961c63f66cca4ef90a9e7..0000000000000000000000000000000000000000
--- a/repositories/k-diffusion/k_diffusion/gns.py
+++ /dev/null
@@ -1,99 +0,0 @@
-import torch
-from torch import nn
-
-
-class DDPGradientStatsHook:
-    def __init__(self, ddp_module):
-        try:
-            ddp_module.register_comm_hook(self, self._hook_fn)
-        except AttributeError:
-            raise ValueError('DDPGradientStatsHook does not support non-DDP wrapped modules')
-        self._clear_state()
-
-    def _clear_state(self):
-        self.bucket_sq_norms_small_batch = []
-        self.bucket_sq_norms_large_batch = []
-
-    @staticmethod
-    def _hook_fn(self, bucket):
-        buf = bucket.buffer()
-        self.bucket_sq_norms_small_batch.append(buf.pow(2).sum())
-        fut = torch.distributed.all_reduce(buf, op=torch.distributed.ReduceOp.AVG, async_op=True).get_future()
-        def callback(fut):
-            buf = fut.value()[0]
-            self.bucket_sq_norms_large_batch.append(buf.pow(2).sum())
-            return buf
-        return fut.then(callback)
-
-    def get_stats(self):
-        sq_norm_small_batch = sum(self.bucket_sq_norms_small_batch)
-        sq_norm_large_batch = sum(self.bucket_sq_norms_large_batch)
-        self._clear_state()
-        stats = torch.stack([sq_norm_small_batch, sq_norm_large_batch])
-        torch.distributed.all_reduce(stats, op=torch.distributed.ReduceOp.AVG)
-        return stats[0].item(), stats[1].item()
-
-
-class GradientNoiseScale:
-    """Calculates the gradient noise scale (1 / SNR), or critical batch size,
-    from _An Empirical Model of Large-Batch Training_,
-    https://arxiv.org/abs/1812.06162).
-
-    Args:
-        beta (float): The decay factor for the exponential moving averages used to
-            calculate the gradient noise scale.
-            Default: 0.9998
-        eps (float): Added for numerical stability.
-            Default: 1e-8
-    """
-
-    def __init__(self, beta=0.9998, eps=1e-8):
-        self.beta = beta
-        self.eps = eps
-        self.ema_sq_norm = 0.
-        self.ema_var = 0.
-        self.beta_cumprod = 1.
-        self.gradient_noise_scale = float('nan')
-
-    def state_dict(self):
-        """Returns the state of the object as a :class:`dict`."""
-        return dict(self.__dict__.items())
-
-    def load_state_dict(self, state_dict):
-        """Loads the object's state.
-        Args:
-            state_dict (dict): object state. Should be an object returned
-                from a call to :meth:`state_dict`.
-        """
-        self.__dict__.update(state_dict)
-
-    def update(self, sq_norm_small_batch, sq_norm_large_batch, n_small_batch, n_large_batch):
-        """Updates the state with a new batch's gradient statistics, and returns the
-        current gradient noise scale.
-
-        Args:
-            sq_norm_small_batch (float): The mean of the squared 2-norms of microbatch or
-                per sample gradients.
-            sq_norm_large_batch (float): The squared 2-norm of the mean of the microbatch or
-                per sample gradients.
-            n_small_batch (int): The batch size of the individual microbatch or per sample
-                gradients (1 if per sample).
-            n_large_batch (int): The total batch size of the mean of the microbatch or
-                per sample gradients.
-        """
-        est_sq_norm = (n_large_batch * sq_norm_large_batch - n_small_batch * sq_norm_small_batch) / (n_large_batch - n_small_batch)
-        est_var = (sq_norm_small_batch - sq_norm_large_batch) / (1 / n_small_batch - 1 / n_large_batch)
-        self.ema_sq_norm = self.beta * self.ema_sq_norm + (1 - self.beta) * est_sq_norm
-        self.ema_var = self.beta * self.ema_var + (1 - self.beta) * est_var
-        self.beta_cumprod *= self.beta
-        self.gradient_noise_scale = max(self.ema_var, self.eps) / max(self.ema_sq_norm, self.eps)
-        return self.gradient_noise_scale
-
-    def get_gns(self):
-        """Returns the current gradient noise scale."""
-        return self.gradient_noise_scale
-
-    def get_stats(self):
-        """Returns the current (debiased) estimates of the squared mean gradient
-        and gradient variance."""
-        return self.ema_sq_norm / (1 - self.beta_cumprod), self.ema_var / (1 - self.beta_cumprod)
diff --git a/repositories/k-diffusion/k_diffusion/layers.py b/repositories/k-diffusion/k_diffusion/layers.py
deleted file mode 100644
index aa647bd3c1e0bef91e475f2376b4a79f6bb0823d..0000000000000000000000000000000000000000
--- a/repositories/k-diffusion/k_diffusion/layers.py
+++ /dev/null
@@ -1,256 +0,0 @@
-import math
-
-from einops import rearrange, repeat
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from . import sampling, utils
-
-# Karras et al. preconditioned denoiser
-
-class Denoiser(nn.Module):
-    """A Karras et al. preconditioner for denoising diffusion models."""
-
-    def __init__(self, inner_model, sigma_data=1.):
-        super().__init__()
-        self.inner_model = inner_model
-        self.sigma_data = sigma_data
-
-    def get_scalings(self, sigma):
-        c_skip = self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2)
-        c_out = sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5
-        c_in = 1 / (sigma ** 2 + self.sigma_data ** 2) ** 0.5
-        return c_skip, c_out, c_in
-
-    def loss(self, input, noise, sigma, **kwargs):
-        c_skip, c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)]
-        noised_input = input + noise * utils.append_dims(sigma, input.ndim)
-        model_output = self.inner_model(noised_input * c_in, sigma, **kwargs)
-        target = (input - c_skip * noised_input) / c_out
-        return (model_output - target).pow(2).flatten(1).mean(1)
-
-    def forward(self, input, sigma, **kwargs):
-        c_skip, c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)]
-        return self.inner_model(input * c_in, sigma, **kwargs) * c_out + input * c_skip
-
-
-class DenoiserWithVariance(Denoiser):
-    def loss(self, input, noise, sigma, **kwargs):
-        c_skip, c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)]
-        noised_input = input + noise * utils.append_dims(sigma, input.ndim)
-        model_output, logvar = self.inner_model(noised_input * c_in, sigma, return_variance=True, **kwargs)
-        logvar = utils.append_dims(logvar, model_output.ndim)
-        target = (input - c_skip * noised_input) / c_out
-        losses = ((model_output - target) ** 2 / logvar.exp() + logvar) / 2
-        return losses.flatten(1).mean(1)
-
-
-class SimpleLossDenoiser(Denoiser):
-    """L_simple with the Karras et al. preconditioner."""
-
-    def loss(self, input, noise, sigma, **kwargs):
-        noised_input = input + noise * utils.append_dims(sigma, input.ndim)
-        denoised = self(noised_input, sigma, **kwargs)
-        eps = sampling.to_d(noised_input, sigma, denoised)
-        return (eps - noise).pow(2).flatten(1).mean(1)
-
-
-# Residual blocks
-
-class ResidualBlock(nn.Module):
-    def __init__(self, *main, skip=None):
-        super().__init__()
-        self.main = nn.Sequential(*main)
-        self.skip = skip if skip else nn.Identity()
-
-    def forward(self, input):
-        return self.main(input) + self.skip(input)
-
-
-# Noise level (and other) conditioning
-
-class ConditionedModule(nn.Module):
-    pass
-
-
-class UnconditionedModule(ConditionedModule):
-    def __init__(self, module):
-        super().__init__()
-        self.module = module
-
-    def forward(self, input, cond=None):
-        return self.module(input)
-
-
-class ConditionedSequential(nn.Sequential, ConditionedModule):
-    def forward(self, input, cond):
-        for module in self:
-            if isinstance(module, ConditionedModule):
-                input = module(input, cond)
-            else:
-                input = module(input)
-        return input
-
-
-class ConditionedResidualBlock(ConditionedModule):
-    def __init__(self, *main, skip=None):
-        super().__init__()
-        self.main = ConditionedSequential(*main)
-        self.skip = skip if skip else nn.Identity()
-
-    def forward(self, input, cond):
-        skip = self.skip(input, cond) if isinstance(self.skip, ConditionedModule) else self.skip(input)
-        return self.main(input, cond) + skip
-
-
-class AdaGN(ConditionedModule):
-    def __init__(self, feats_in, c_out, num_groups, eps=1e-5, cond_key='cond'):
-        super().__init__()
-        self.num_groups = num_groups
-        self.eps = eps
-        self.cond_key = cond_key
-        self.mapper = nn.Linear(feats_in, c_out * 2)
-
-    def forward(self, input, cond):
-        weight, bias = self.mapper(cond[self.cond_key]).chunk(2, dim=-1)
-        input = F.group_norm(input, self.num_groups, eps=self.eps)
-        return torch.addcmul(utils.append_dims(bias, input.ndim), input, utils.append_dims(weight, input.ndim) + 1)
-
-
-# Attention
-
-class SelfAttention2d(ConditionedModule):
-    def __init__(self, c_in, n_head, norm, dropout_rate=0.):
-        super().__init__()
-        assert c_in % n_head == 0
-        self.norm_in = norm(c_in)
-        self.n_head = n_head
-        self.qkv_proj = nn.Conv2d(c_in, c_in * 3, 1)
-        self.out_proj = nn.Conv2d(c_in, c_in, 1)
-        self.dropout = nn.Dropout(dropout_rate)
-
-    def forward(self, input, cond):
-        n, c, h, w = input.shape
-        qkv = self.qkv_proj(self.norm_in(input, cond))
-        qkv = qkv.view([n, self.n_head * 3, c // self.n_head, h * w]).transpose(2, 3)
-        q, k, v = qkv.chunk(3, dim=1)
-        scale = k.shape[3] ** -0.25
-        att = ((q * scale) @ (k.transpose(2, 3) * scale)).softmax(3)
-        att = self.dropout(att)
-        y = (att @ v).transpose(2, 3).contiguous().view([n, c, h, w])
-        return input + self.out_proj(y)
-
-
-class CrossAttention2d(ConditionedModule):
-    def __init__(self, c_dec, c_enc, n_head, norm_dec, dropout_rate=0.,
-                 cond_key='cross', cond_key_padding='cross_padding'):
-        super().__init__()
-        assert c_dec % n_head == 0
-        self.cond_key = cond_key
-        self.cond_key_padding = cond_key_padding
-        self.norm_enc = nn.LayerNorm(c_enc)
-        self.norm_dec = norm_dec(c_dec)
-        self.n_head = n_head
-        self.q_proj = nn.Conv2d(c_dec, c_dec, 1)
-        self.kv_proj = nn.Linear(c_enc, c_dec * 2)
-        self.out_proj = nn.Conv2d(c_dec, c_dec, 1)
-        self.dropout = nn.Dropout(dropout_rate)
-
-    def forward(self, input, cond):
-        n, c, h, w = input.shape
-        q = self.q_proj(self.norm_dec(input, cond))
-        q = q.view([n, self.n_head, c // self.n_head, h * w]).transpose(2, 3)
-        kv = self.kv_proj(self.norm_enc(cond[self.cond_key]))
-        kv = kv.view([n, -1, self.n_head * 2, c // self.n_head]).transpose(1, 2)
-        k, v = kv.chunk(2, dim=1)
-        scale = k.shape[3] ** -0.25
-        att = ((q * scale) @ (k.transpose(2, 3) * scale))
-        att = att - (cond[self.cond_key_padding][:, None, None, :]) * 10000
-        att = att.softmax(3)
-        att = self.dropout(att)
-        y = (att @ v).transpose(2, 3)
-        y = y.contiguous().view([n, c, h, w])
-        return input + self.out_proj(y)
-
-
-# Downsampling/upsampling
-
-_kernels = {
-    'linear':
-        [1 / 8, 3 / 8, 3 / 8, 1 / 8],
-    'cubic': 
-        [-0.01171875, -0.03515625, 0.11328125, 0.43359375,
-        0.43359375, 0.11328125, -0.03515625, -0.01171875],
-    'lanczos3': 
-        [0.003689131001010537, 0.015056144446134567, -0.03399861603975296,
-        -0.066637322306633, 0.13550527393817902, 0.44638532400131226,
-        0.44638532400131226, 0.13550527393817902, -0.066637322306633,
-        -0.03399861603975296, 0.015056144446134567, 0.003689131001010537]
-}
-_kernels['bilinear'] = _kernels['linear']
-_kernels['bicubic'] = _kernels['cubic']
-
-
-class Downsample2d(nn.Module):
-    def __init__(self, kernel='linear', pad_mode='reflect'):
-        super().__init__()
-        self.pad_mode = pad_mode
-        kernel_1d = torch.tensor([_kernels[kernel]])
-        self.pad = kernel_1d.shape[1] // 2 - 1
-        self.register_buffer('kernel', kernel_1d.T @ kernel_1d)
-
-    def forward(self, x):
-        x = F.pad(x, (self.pad,) * 4, self.pad_mode)
-        weight = x.new_zeros([x.shape[1], x.shape[1], self.kernel.shape[0], self.kernel.shape[1]])
-        indices = torch.arange(x.shape[1], device=x.device)
-        weight[indices, indices] = self.kernel.to(weight)
-        return F.conv2d(x, weight, stride=2)
-
-
-class Upsample2d(nn.Module):
-    def __init__(self, kernel='linear', pad_mode='reflect'):
-        super().__init__()
-        self.pad_mode = pad_mode
-        kernel_1d = torch.tensor([_kernels[kernel]]) * 2
-        self.pad = kernel_1d.shape[1] // 2 - 1
-        self.register_buffer('kernel', kernel_1d.T @ kernel_1d)
-
-    def forward(self, x):
-        x = F.pad(x, ((self.pad + 1) // 2,) * 4, self.pad_mode)
-        weight = x.new_zeros([x.shape[1], x.shape[1], self.kernel.shape[0], self.kernel.shape[1]])
-        indices = torch.arange(x.shape[1], device=x.device)
-        weight[indices, indices] = self.kernel.to(weight)
-        return F.conv_transpose2d(x, weight, stride=2, padding=self.pad * 2 + 1)
-
-
-# Embeddings
-
-class FourierFeatures(nn.Module):
-    def __init__(self, in_features, out_features, std=1.):
-        super().__init__()
-        assert out_features % 2 == 0
-        self.register_buffer('weight', torch.randn([out_features // 2, in_features]) * std)
-
-    def forward(self, input):
-        f = 2 * math.pi * input @ self.weight.T
-        return torch.cat([f.cos(), f.sin()], dim=-1)
-
-
-# U-Nets
-
-class UNet(ConditionedModule):
-    def __init__(self, d_blocks, u_blocks, skip_stages=0):
-        super().__init__()
-        self.d_blocks = nn.ModuleList(d_blocks)
-        self.u_blocks = nn.ModuleList(u_blocks)
-        self.skip_stages = skip_stages
-
-    def forward(self, input, cond):
-        skips = []
-        for block in self.d_blocks[self.skip_stages:]:
-            input = block(input, cond)
-            skips.append(input)
-        for i, (block, skip) in enumerate(zip(self.u_blocks, reversed(skips))):
-            input = block(input, cond, skip if i > 0 else None)
-        return input
diff --git a/repositories/k-diffusion/k_diffusion/models/__init__.py b/repositories/k-diffusion/k_diffusion/models/__init__.py
deleted file mode 100644
index 82608ff1de6137b31eeaf8de6814df6a7e35606a..0000000000000000000000000000000000000000
--- a/repositories/k-diffusion/k_diffusion/models/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .image_v1 import ImageDenoiserModelV1
diff --git a/repositories/k-diffusion/k_diffusion/models/__pycache__/__init__.cpython-310.pyc b/repositories/k-diffusion/k_diffusion/models/__pycache__/__init__.cpython-310.pyc
deleted file mode 100644
index b77b451a6788749725b4d5367b0db7c126472c33..0000000000000000000000000000000000000000
Binary files a/repositories/k-diffusion/k_diffusion/models/__pycache__/__init__.cpython-310.pyc and /dev/null differ
diff --git a/repositories/k-diffusion/k_diffusion/models/__pycache__/image_v1.cpython-310.pyc b/repositories/k-diffusion/k_diffusion/models/__pycache__/image_v1.cpython-310.pyc
deleted file mode 100644
index 8f1ff6fb17d3b50231dd74e08c3d766df9f0b0dd..0000000000000000000000000000000000000000
Binary files a/repositories/k-diffusion/k_diffusion/models/__pycache__/image_v1.cpython-310.pyc and /dev/null differ
diff --git a/repositories/k-diffusion/k_diffusion/models/image_v1.py b/repositories/k-diffusion/k_diffusion/models/image_v1.py
deleted file mode 100644
index 9ffd5f2c4d6c9d086107d5fac67452419696c723..0000000000000000000000000000000000000000
--- a/repositories/k-diffusion/k_diffusion/models/image_v1.py
+++ /dev/null
@@ -1,156 +0,0 @@
-import math
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from .. import layers, utils
-
-
-def orthogonal_(module):
-    nn.init.orthogonal_(module.weight)
-    return module
-
-
-class ResConvBlock(layers.ConditionedResidualBlock):
-    def __init__(self, feats_in, c_in, c_mid, c_out, group_size=32, dropout_rate=0.):
-        skip = None if c_in == c_out else orthogonal_(nn.Conv2d(c_in, c_out, 1, bias=False))
-        super().__init__(
-            layers.AdaGN(feats_in, c_in, max(1, c_in // group_size)),
-            nn.GELU(),
-            nn.Conv2d(c_in, c_mid, 3, padding=1),
-            nn.Dropout2d(dropout_rate, inplace=True),
-            layers.AdaGN(feats_in, c_mid, max(1, c_mid // group_size)),
-            nn.GELU(),
-            nn.Conv2d(c_mid, c_out, 3, padding=1),
-            nn.Dropout2d(dropout_rate, inplace=True),
-            skip=skip)
-
-
-class DBlock(layers.ConditionedSequential):
-    def __init__(self, n_layers, feats_in, c_in, c_mid, c_out, group_size=32, head_size=64, dropout_rate=0., downsample=False, self_attn=False, cross_attn=False, c_enc=0):
-        modules = [nn.Identity()]
-        for i in range(n_layers):
-            my_c_in = c_in if i == 0 else c_mid
-            my_c_out = c_mid if i < n_layers - 1 else c_out
-            modules.append(ResConvBlock(feats_in, my_c_in, c_mid, my_c_out, group_size, dropout_rate))
-            if self_attn:
-                norm = lambda c_in: layers.AdaGN(feats_in, c_in, max(1, my_c_out // group_size))
-                modules.append(layers.SelfAttention2d(my_c_out, max(1, my_c_out // head_size), norm, dropout_rate))
-            if cross_attn:
-                norm = lambda c_in: layers.AdaGN(feats_in, c_in, max(1, my_c_out // group_size))
-                modules.append(layers.CrossAttention2d(my_c_out, c_enc, max(1, my_c_out // head_size), norm, dropout_rate))
-        super().__init__(*modules)
-        self.set_downsample(downsample)
-
-    def set_downsample(self, downsample):
-        self[0] = layers.Downsample2d() if downsample else nn.Identity()
-        return self
-
-
-class UBlock(layers.ConditionedSequential):
-    def __init__(self, n_layers, feats_in, c_in, c_mid, c_out, group_size=32, head_size=64, dropout_rate=0., upsample=False, self_attn=False, cross_attn=False, c_enc=0):
-        modules = []
-        for i in range(n_layers):
-            my_c_in = c_in if i == 0 else c_mid
-            my_c_out = c_mid if i < n_layers - 1 else c_out
-            modules.append(ResConvBlock(feats_in, my_c_in, c_mid, my_c_out, group_size, dropout_rate))
-            if self_attn:
-                norm = lambda c_in: layers.AdaGN(feats_in, c_in, max(1, my_c_out // group_size))
-                modules.append(layers.SelfAttention2d(my_c_out, max(1, my_c_out // head_size), norm, dropout_rate))
-            if cross_attn:
-                norm = lambda c_in: layers.AdaGN(feats_in, c_in, max(1, my_c_out // group_size))
-                modules.append(layers.CrossAttention2d(my_c_out, c_enc, max(1, my_c_out // head_size), norm, dropout_rate))
-        modules.append(nn.Identity())
-        super().__init__(*modules)
-        self.set_upsample(upsample)
-
-    def forward(self, input, cond, skip=None):
-        if skip is not None:
-            input = torch.cat([input, skip], dim=1)
-        return super().forward(input, cond)
-
-    def set_upsample(self, upsample):
-        self[-1] = layers.Upsample2d() if upsample else nn.Identity()
-        return self
-
-
-class MappingNet(nn.Sequential):
-    def __init__(self, feats_in, feats_out, n_layers=2):
-        layers = []
-        for i in range(n_layers):
-            layers.append(orthogonal_(nn.Linear(feats_in if i == 0 else feats_out, feats_out)))
-            layers.append(nn.GELU())
-        super().__init__(*layers)
-
-
-class ImageDenoiserModelV1(nn.Module):
-    def __init__(self, c_in, feats_in, depths, channels, self_attn_depths, cross_attn_depths=None, mapping_cond_dim=0, unet_cond_dim=0, cross_cond_dim=0, dropout_rate=0., patch_size=1, skip_stages=0, has_variance=False):
-        super().__init__()
-        self.c_in = c_in
-        self.channels = channels
-        self.unet_cond_dim = unet_cond_dim
-        self.patch_size = patch_size
-        self.has_variance = has_variance
-        self.timestep_embed = layers.FourierFeatures(1, feats_in)
-        if mapping_cond_dim > 0:
-            self.mapping_cond = nn.Linear(mapping_cond_dim, feats_in, bias=False)
-        self.mapping = MappingNet(feats_in, feats_in)
-        self.proj_in = nn.Conv2d((c_in + unet_cond_dim) * self.patch_size ** 2, channels[max(0, skip_stages - 1)], 1)
-        self.proj_out = nn.Conv2d(channels[max(0, skip_stages - 1)], c_in * self.patch_size ** 2 + (1 if self.has_variance else 0), 1)
-        nn.init.zeros_(self.proj_out.weight)
-        nn.init.zeros_(self.proj_out.bias)
-        if cross_cond_dim == 0:
-            cross_attn_depths = [False] * len(self_attn_depths)
-        d_blocks, u_blocks = [], []
-        for i in range(len(depths)):
-            my_c_in = channels[max(0, i - 1)]
-            d_blocks.append(DBlock(depths[i], feats_in, my_c_in, channels[i], channels[i], downsample=i > skip_stages, self_attn=self_attn_depths[i], cross_attn=cross_attn_depths[i], c_enc=cross_cond_dim, dropout_rate=dropout_rate))
-        for i in range(len(depths)):
-            my_c_in = channels[i] * 2 if i < len(depths) - 1 else channels[i]
-            my_c_out = channels[max(0, i - 1)]
-            u_blocks.append(UBlock(depths[i], feats_in, my_c_in, channels[i], my_c_out, upsample=i > skip_stages, self_attn=self_attn_depths[i], cross_attn=cross_attn_depths[i], c_enc=cross_cond_dim, dropout_rate=dropout_rate))
-        self.u_net = layers.UNet(d_blocks, reversed(u_blocks), skip_stages=skip_stages)
-
-    def forward(self, input, sigma, mapping_cond=None, unet_cond=None, cross_cond=None, cross_cond_padding=None, return_variance=False):
-        c_noise = sigma.log() / 4
-        timestep_embed = self.timestep_embed(utils.append_dims(c_noise, 2))
-        mapping_cond_embed = torch.zeros_like(timestep_embed) if mapping_cond is None else self.mapping_cond(mapping_cond)
-        mapping_out = self.mapping(timestep_embed + mapping_cond_embed)
-        cond = {'cond': mapping_out}
-        if unet_cond is not None:
-            input = torch.cat([input, unet_cond], dim=1)
-        if cross_cond is not None:
-            cond['cross'] = cross_cond
-            cond['cross_padding'] = cross_cond_padding
-        if self.patch_size > 1:
-            input = F.pixel_unshuffle(input, self.patch_size)
-        input = self.proj_in(input)
-        input = self.u_net(input, cond)
-        input = self.proj_out(input)
-        if self.has_variance:
-            input, logvar = input[:, :-1], input[:, -1].flatten(1).mean(1)
-        if self.patch_size > 1:
-            input = F.pixel_shuffle(input, self.patch_size)
-        if self.has_variance and return_variance:
-            return input, logvar
-        return input
-
-    def set_skip_stages(self, skip_stages):
-        self.proj_in = nn.Conv2d(self.proj_in.in_channels, self.channels[max(0, skip_stages - 1)], 1)
-        self.proj_out = nn.Conv2d(self.channels[max(0, skip_stages - 1)], self.proj_out.out_channels, 1)
-        nn.init.zeros_(self.proj_out.weight)
-        nn.init.zeros_(self.proj_out.bias)
-        self.u_net.skip_stages = skip_stages
-        for i, block in enumerate(self.u_net.d_blocks):
-            block.set_downsample(i > skip_stages)
-        for i, block in enumerate(reversed(self.u_net.u_blocks)):
-            block.set_upsample(i > skip_stages)
-        return self
-
-    def set_patch_size(self, patch_size):
-        self.patch_size = patch_size
-        self.proj_in = nn.Conv2d((self.c_in + self.unet_cond_dim) * self.patch_size ** 2, self.channels[max(0, self.u_net.skip_stages - 1)], 1)
-        self.proj_out = nn.Conv2d(self.channels[max(0, self.u_net.skip_stages - 1)], self.c_in * self.patch_size ** 2 + (1 if self.has_variance else 0), 1)
-        nn.init.zeros_(self.proj_out.weight)
-        nn.init.zeros_(self.proj_out.bias)
diff --git a/repositories/k-diffusion/k_diffusion/sampling.py b/repositories/k-diffusion/k_diffusion/sampling.py
deleted file mode 100644
index 13ec143b1685a34ac35bbe23874da3dc518e32c2..0000000000000000000000000000000000000000
--- a/repositories/k-diffusion/k_diffusion/sampling.py
+++ /dev/null
@@ -1,687 +0,0 @@
-import math
-
-from scipy import integrate
-import torch
-from torch import nn
-from torchdiffeq import odeint
-import torchsde
-from tqdm.auto import trange, tqdm
-
-from . import utils
-
-
-def append_zero(x):
-    return torch.cat([x, x.new_zeros([1])])
-
-
-def get_sigmas_karras(n, sigma_min, sigma_max, rho=7., device='cpu'):
-    """Constructs the noise schedule of Karras et al. (2022)."""
-    ramp = torch.linspace(0, 1, n)
-    min_inv_rho = sigma_min ** (1 / rho)
-    max_inv_rho = sigma_max ** (1 / rho)
-    sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
-    return append_zero(sigmas).to(device)
-
-
-def get_sigmas_exponential(n, sigma_min, sigma_max, device='cpu'):
-    """Constructs an exponential noise schedule."""
-    sigmas = torch.linspace(math.log(sigma_max), math.log(sigma_min), n, device=device).exp()
-    return append_zero(sigmas)
-
-
-def get_sigmas_polyexponential(n, sigma_min, sigma_max, rho=1., device='cpu'):
-    """Constructs an polynomial in log sigma noise schedule."""
-    ramp = torch.linspace(1, 0, n, device=device) ** rho
-    sigmas = torch.exp(ramp * (math.log(sigma_max) - math.log(sigma_min)) + math.log(sigma_min))
-    return append_zero(sigmas)
-
-
-def get_sigmas_vp(n, beta_d=19.9, beta_min=0.1, eps_s=1e-3, device='cpu'):
-    """Constructs a continuous VP noise schedule."""
-    t = torch.linspace(1, eps_s, n, device=device)
-    sigmas = torch.sqrt(torch.exp(beta_d * t ** 2 / 2 + beta_min * t) - 1)
-    return append_zero(sigmas)
-
-
-def to_d(x, sigma, denoised):
-    """Converts a denoiser output to a Karras ODE derivative."""
-    return (x - denoised) / utils.append_dims(sigma, x.ndim)
-
-
-def get_ancestral_step(sigma_from, sigma_to, eta=1.):
-    """Calculates the noise level (sigma_down) to step down to and the amount
-    of noise to add (sigma_up) when doing an ancestral sampling step."""
-    if not eta:
-        return sigma_to, 0.
-    sigma_up = min(sigma_to, eta * (sigma_to ** 2 * (sigma_from ** 2 - sigma_to ** 2) / sigma_from ** 2) ** 0.5)
-    sigma_down = (sigma_to ** 2 - sigma_up ** 2) ** 0.5
-    return sigma_down, sigma_up
-
-
-def default_noise_sampler(x):
-    return lambda sigma, sigma_next: torch.randn_like(x)
-
-
-class BatchedBrownianTree:
-    """A wrapper around torchsde.BrownianTree that enables batches of entropy."""
-
-    def __init__(self, x, t0, t1, seed=None, **kwargs):
-        t0, t1, self.sign = self.sort(t0, t1)
-        w0 = kwargs.get('w0', torch.zeros_like(x))
-        if seed is None:
-            seed = torch.randint(0, 2 ** 63 - 1, []).item()
-        self.batched = True
-        try:
-            assert len(seed) == x.shape[0]
-            w0 = w0[0]
-        except TypeError:
-            seed = [seed]
-            self.batched = False
-        self.trees = [torchsde.BrownianTree(t0, w0, t1, entropy=s, **kwargs) for s in seed]
-
-    @staticmethod
-    def sort(a, b):
-        return (a, b, 1) if a < b else (b, a, -1)
-
-    def __call__(self, t0, t1):
-        t0, t1, sign = self.sort(t0, t1)
-        w = torch.stack([tree(t0, t1) for tree in self.trees]) * (self.sign * sign)
-        return w if self.batched else w[0]
-
-
-class BrownianTreeNoiseSampler:
-    """A noise sampler backed by a torchsde.BrownianTree.
-
-    Args:
-        x (Tensor): The tensor whose shape, device and dtype to use to generate
-            random samples.
-        sigma_min (float): The low end of the valid interval.
-        sigma_max (float): The high end of the valid interval.
-        seed (int or List[int]): The random seed. If a list of seeds is
-            supplied instead of a single integer, then the noise sampler will
-            use one BrownianTree per batch item, each with its own seed.
-        transform (callable): A function that maps sigma to the sampler's
-            internal timestep.
-    """
-
-    def __init__(self, x, sigma_min, sigma_max, seed=None, transform=lambda x: x):
-        self.transform = transform
-        t0, t1 = self.transform(torch.as_tensor(sigma_min)), self.transform(torch.as_tensor(sigma_max))
-        self.tree = BatchedBrownianTree(x, t0, t1, seed)
-
-    def __call__(self, sigma, sigma_next):
-        t0, t1 = self.transform(torch.as_tensor(sigma)), self.transform(torch.as_tensor(sigma_next))
-        return self.tree(t0, t1) / (t1 - t0).abs().sqrt()
-
-
-@torch.no_grad()
-def sample_euler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.):
-    """Implements Algorithm 2 (Euler steps) from Karras et al. (2022)."""
-    extra_args = {} if extra_args is None else extra_args
-    s_in = x.new_ones([x.shape[0]])
-    for i in trange(len(sigmas) - 1, disable=disable):
-        gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.
-        eps = torch.randn_like(x) * s_noise
-        sigma_hat = sigmas[i] * (gamma + 1)
-        if gamma > 0:
-            x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5
-        denoised = model(x, sigma_hat * s_in, **extra_args)
-        d = to_d(x, sigma_hat, denoised)
-        if callback is not None:
-            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})
-        dt = sigmas[i + 1] - sigma_hat
-        # Euler method
-        x = x + d * dt
-    return x
-
-
-@torch.no_grad()
-def sample_euler_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
-    """Ancestral sampling with Euler method steps."""
-    extra_args = {} if extra_args is None else extra_args
-    noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
-    s_in = x.new_ones([x.shape[0]])
-    for i in trange(len(sigmas) - 1, disable=disable):
-        denoised = model(x, sigmas[i] * s_in, **extra_args)
-        sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)
-        if callback is not None:
-            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
-        d = to_d(x, sigmas[i], denoised)
-        # Euler method
-        dt = sigma_down - sigmas[i]
-        x = x + d * dt
-        if sigmas[i + 1] > 0:
-            x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
-    return x
-
-
-@torch.no_grad()
-def sample_heun(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.):
-    """Implements Algorithm 2 (Heun steps) from Karras et al. (2022)."""
-    extra_args = {} if extra_args is None else extra_args
-    s_in = x.new_ones([x.shape[0]])
-    for i in trange(len(sigmas) - 1, disable=disable):
-        gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.
-        eps = torch.randn_like(x) * s_noise
-        sigma_hat = sigmas[i] * (gamma + 1)
-        if gamma > 0:
-            x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5
-        denoised = model(x, sigma_hat * s_in, **extra_args)
-        d = to_d(x, sigma_hat, denoised)
-        if callback is not None:
-            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})
-        dt = sigmas[i + 1] - sigma_hat
-        if sigmas[i + 1] == 0:
-            # Euler method
-            x = x + d * dt
-        else:
-            # Heun's method
-            x_2 = x + d * dt
-            denoised_2 = model(x_2, sigmas[i + 1] * s_in, **extra_args)
-            d_2 = to_d(x_2, sigmas[i + 1], denoised_2)
-            d_prime = (d + d_2) / 2
-            x = x + d_prime * dt
-    return x
-
-
-@torch.no_grad()
-def sample_dpm_2(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.):
-    """A sampler inspired by DPM-Solver-2 and Algorithm 2 from Karras et al. (2022)."""
-    extra_args = {} if extra_args is None else extra_args
-    s_in = x.new_ones([x.shape[0]])
-    for i in trange(len(sigmas) - 1, disable=disable):
-        gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.
-        eps = torch.randn_like(x) * s_noise
-        sigma_hat = sigmas[i] * (gamma + 1)
-        if gamma > 0:
-            x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5
-        denoised = model(x, sigma_hat * s_in, **extra_args)
-        d = to_d(x, sigma_hat, denoised)
-        if callback is not None:
-            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})
-        if sigmas[i + 1] == 0:
-            # Euler method
-            dt = sigmas[i + 1] - sigma_hat
-            x = x + d * dt
-        else:
-            # DPM-Solver-2
-            sigma_mid = sigma_hat.log().lerp(sigmas[i + 1].log(), 0.5).exp()
-            dt_1 = sigma_mid - sigma_hat
-            dt_2 = sigmas[i + 1] - sigma_hat
-            x_2 = x + d * dt_1
-            denoised_2 = model(x_2, sigma_mid * s_in, **extra_args)
-            d_2 = to_d(x_2, sigma_mid, denoised_2)
-            x = x + d_2 * dt_2
-    return x
-
-
-@torch.no_grad()
-def sample_dpm_2_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
-    """Ancestral sampling with DPM-Solver second-order steps."""
-    extra_args = {} if extra_args is None else extra_args
-    noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
-    s_in = x.new_ones([x.shape[0]])
-    for i in trange(len(sigmas) - 1, disable=disable):
-        denoised = model(x, sigmas[i] * s_in, **extra_args)
-        sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)
-        if callback is not None:
-            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
-        d = to_d(x, sigmas[i], denoised)
-        if sigma_down == 0:
-            # Euler method
-            dt = sigma_down - sigmas[i]
-            x = x + d * dt
-        else:
-            # DPM-Solver-2
-            sigma_mid = sigmas[i].log().lerp(sigma_down.log(), 0.5).exp()
-            dt_1 = sigma_mid - sigmas[i]
-            dt_2 = sigma_down - sigmas[i]
-            x_2 = x + d * dt_1
-            denoised_2 = model(x_2, sigma_mid * s_in, **extra_args)
-            d_2 = to_d(x_2, sigma_mid, denoised_2)
-            x = x + d_2 * dt_2
-            x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
-    return x
-
-
-def linear_multistep_coeff(order, t, i, j):
-    if order - 1 > i:
-        raise ValueError(f'Order {order} too high for step {i}')
-    def fn(tau):
-        prod = 1.
-        for k in range(order):
-            if j == k:
-                continue
-            prod *= (tau - t[i - k]) / (t[i - j] - t[i - k])
-        return prod
-    return integrate.quad(fn, t[i], t[i + 1], epsrel=1e-4)[0]
-
-
-@torch.no_grad()
-def sample_lms(model, x, sigmas, extra_args=None, callback=None, disable=None, order=4):
-    extra_args = {} if extra_args is None else extra_args
-    s_in = x.new_ones([x.shape[0]])
-    sigmas_cpu = sigmas.detach().cpu().numpy()
-    ds = []
-    for i in trange(len(sigmas) - 1, disable=disable):
-        denoised = model(x, sigmas[i] * s_in, **extra_args)
-        d = to_d(x, sigmas[i], denoised)
-        ds.append(d)
-        if len(ds) > order:
-            ds.pop(0)
-        if callback is not None:
-            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
-        cur_order = min(i + 1, order)
-        coeffs = [linear_multistep_coeff(cur_order, sigmas_cpu, i, j) for j in range(cur_order)]
-        x = x + sum(coeff * d for coeff, d in zip(coeffs, reversed(ds)))
-    return x
-
-
-@torch.no_grad()
-def log_likelihood(model, x, sigma_min, sigma_max, extra_args=None, atol=1e-4, rtol=1e-4):
-    extra_args = {} if extra_args is None else extra_args
-    s_in = x.new_ones([x.shape[0]])
-    v = torch.randint_like(x, 2) * 2 - 1
-    fevals = 0
-    def ode_fn(sigma, x):
-        nonlocal fevals
-        with torch.enable_grad():
-            x = x[0].detach().requires_grad_()
-            denoised = model(x, sigma * s_in, **extra_args)
-            d = to_d(x, sigma, denoised)
-            fevals += 1
-            grad = torch.autograd.grad((d * v).sum(), x)[0]
-            d_ll = (v * grad).flatten(1).sum(1)
-        return d.detach(), d_ll
-    x_min = x, x.new_zeros([x.shape[0]])
-    t = x.new_tensor([sigma_min, sigma_max])
-    sol = odeint(ode_fn, x_min, t, atol=atol, rtol=rtol, method='dopri5')
-    latent, delta_ll = sol[0][-1], sol[1][-1]
-    ll_prior = torch.distributions.Normal(0, sigma_max).log_prob(latent).flatten(1).sum(1)
-    return ll_prior + delta_ll, {'fevals': fevals}
-
-
-class PIDStepSizeController:
-    """A PID controller for ODE adaptive step size control."""
-    def __init__(self, h, pcoeff, icoeff, dcoeff, order=1, accept_safety=0.81, eps=1e-8):
-        self.h = h
-        self.b1 = (pcoeff + icoeff + dcoeff) / order
-        self.b2 = -(pcoeff + 2 * dcoeff) / order
-        self.b3 = dcoeff / order
-        self.accept_safety = accept_safety
-        self.eps = eps
-        self.errs = []
-
-    def limiter(self, x):
-        return 1 + math.atan(x - 1)
-
-    def propose_step(self, error):
-        inv_error = 1 / (float(error) + self.eps)
-        if not self.errs:
-            self.errs = [inv_error, inv_error, inv_error]
-        self.errs[0] = inv_error
-        factor = self.errs[0] ** self.b1 * self.errs[1] ** self.b2 * self.errs[2] ** self.b3
-        factor = self.limiter(factor)
-        accept = factor >= self.accept_safety
-        if accept:
-            self.errs[2] = self.errs[1]
-            self.errs[1] = self.errs[0]
-        self.h *= factor
-        return accept
-
-
-class DPMSolver(nn.Module):
-    """DPM-Solver. See https://arxiv.org/abs/2206.00927."""
-
-    def __init__(self, model, extra_args=None, eps_callback=None, info_callback=None):
-        super().__init__()
-        self.model = model
-        self.extra_args = {} if extra_args is None else extra_args
-        self.eps_callback = eps_callback
-        self.info_callback = info_callback
-
-    def t(self, sigma):
-        return -sigma.log()
-
-    def sigma(self, t):
-        return t.neg().exp()
-
-    def eps(self, eps_cache, key, x, t, *args, **kwargs):
-        if key in eps_cache:
-            return eps_cache[key], eps_cache
-        sigma = self.sigma(t) * x.new_ones([x.shape[0]])
-        eps = (x - self.model(x, sigma, *args, **self.extra_args, **kwargs)) / self.sigma(t)
-        if self.eps_callback is not None:
-            self.eps_callback()
-        return eps, {key: eps, **eps_cache}
-
-    def dpm_solver_1_step(self, x, t, t_next, eps_cache=None):
-        eps_cache = {} if eps_cache is None else eps_cache
-        h = t_next - t
-        eps, eps_cache = self.eps(eps_cache, 'eps', x, t)
-        x_1 = x - self.sigma(t_next) * h.expm1() * eps
-        return x_1, eps_cache
-
-    def dpm_solver_2_step(self, x, t, t_next, r1=1 / 2, eps_cache=None):
-        eps_cache = {} if eps_cache is None else eps_cache
-        h = t_next - t
-        eps, eps_cache = self.eps(eps_cache, 'eps', x, t)
-        s1 = t + r1 * h
-        u1 = x - self.sigma(s1) * (r1 * h).expm1() * eps
-        eps_r1, eps_cache = self.eps(eps_cache, 'eps_r1', u1, s1)
-        x_2 = x - self.sigma(t_next) * h.expm1() * eps - self.sigma(t_next) / (2 * r1) * h.expm1() * (eps_r1 - eps)
-        return x_2, eps_cache
-
-    def dpm_solver_3_step(self, x, t, t_next, r1=1 / 3, r2=2 / 3, eps_cache=None):
-        eps_cache = {} if eps_cache is None else eps_cache
-        h = t_next - t
-        eps, eps_cache = self.eps(eps_cache, 'eps', x, t)
-        s1 = t + r1 * h
-        s2 = t + r2 * h
-        u1 = x - self.sigma(s1) * (r1 * h).expm1() * eps
-        eps_r1, eps_cache = self.eps(eps_cache, 'eps_r1', u1, s1)
-        u2 = x - self.sigma(s2) * (r2 * h).expm1() * eps - self.sigma(s2) * (r2 / r1) * ((r2 * h).expm1() / (r2 * h) - 1) * (eps_r1 - eps)
-        eps_r2, eps_cache = self.eps(eps_cache, 'eps_r2', u2, s2)
-        x_3 = x - self.sigma(t_next) * h.expm1() * eps - self.sigma(t_next) / r2 * (h.expm1() / h - 1) * (eps_r2 - eps)
-        return x_3, eps_cache
-
-    def dpm_solver_fast(self, x, t_start, t_end, nfe, eta=0., s_noise=1., noise_sampler=None):
-        noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
-        if not t_end > t_start and eta:
-            raise ValueError('eta must be 0 for reverse sampling')
-
-        m = math.floor(nfe / 3) + 1
-        ts = torch.linspace(t_start, t_end, m + 1, device=x.device)
-
-        if nfe % 3 == 0:
-            orders = [3] * (m - 2) + [2, 1]
-        else:
-            orders = [3] * (m - 1) + [nfe % 3]
-
-        for i in range(len(orders)):
-            eps_cache = {}
-            t, t_next = ts[i], ts[i + 1]
-            if eta:
-                sd, su = get_ancestral_step(self.sigma(t), self.sigma(t_next), eta)
-                t_next_ = torch.minimum(t_end, self.t(sd))
-                su = (self.sigma(t_next) ** 2 - self.sigma(t_next_) ** 2) ** 0.5
-            else:
-                t_next_, su = t_next, 0.
-
-            eps, eps_cache = self.eps(eps_cache, 'eps', x, t)
-            denoised = x - self.sigma(t) * eps
-            if self.info_callback is not None:
-                self.info_callback({'x': x, 'i': i, 't': ts[i], 't_up': t, 'denoised': denoised})
-
-            if orders[i] == 1:
-                x, eps_cache = self.dpm_solver_1_step(x, t, t_next_, eps_cache=eps_cache)
-            elif orders[i] == 2:
-                x, eps_cache = self.dpm_solver_2_step(x, t, t_next_, eps_cache=eps_cache)
-            else:
-                x, eps_cache = self.dpm_solver_3_step(x, t, t_next_, eps_cache=eps_cache)
-
-            x = x + su * s_noise * noise_sampler(self.sigma(t), self.sigma(t_next))
-
-        return x
-
-    def dpm_solver_adaptive(self, x, t_start, t_end, order=3, rtol=0.05, atol=0.0078, h_init=0.05, pcoeff=0., icoeff=1., dcoeff=0., accept_safety=0.81, eta=0., s_noise=1., noise_sampler=None):
-        noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
-        if order not in {2, 3}:
-            raise ValueError('order should be 2 or 3')
-        forward = t_end > t_start
-        if not forward and eta:
-            raise ValueError('eta must be 0 for reverse sampling')
-        h_init = abs(h_init) * (1 if forward else -1)
-        atol = torch.tensor(atol)
-        rtol = torch.tensor(rtol)
-        s = t_start
-        x_prev = x
-        accept = True
-        pid = PIDStepSizeController(h_init, pcoeff, icoeff, dcoeff, 1.5 if eta else order, accept_safety)
-        info = {'steps': 0, 'nfe': 0, 'n_accept': 0, 'n_reject': 0}
-
-        while s < t_end - 1e-5 if forward else s > t_end + 1e-5:
-            eps_cache = {}
-            t = torch.minimum(t_end, s + pid.h) if forward else torch.maximum(t_end, s + pid.h)
-            if eta:
-                sd, su = get_ancestral_step(self.sigma(s), self.sigma(t), eta)
-                t_ = torch.minimum(t_end, self.t(sd))
-                su = (self.sigma(t) ** 2 - self.sigma(t_) ** 2) ** 0.5
-            else:
-                t_, su = t, 0.
-
-            eps, eps_cache = self.eps(eps_cache, 'eps', x, s)
-            denoised = x - self.sigma(s) * eps
-
-            if order == 2:
-                x_low, eps_cache = self.dpm_solver_1_step(x, s, t_, eps_cache=eps_cache)
-                x_high, eps_cache = self.dpm_solver_2_step(x, s, t_, eps_cache=eps_cache)
-            else:
-                x_low, eps_cache = self.dpm_solver_2_step(x, s, t_, r1=1 / 3, eps_cache=eps_cache)
-                x_high, eps_cache = self.dpm_solver_3_step(x, s, t_, eps_cache=eps_cache)
-            delta = torch.maximum(atol, rtol * torch.maximum(x_low.abs(), x_prev.abs()))
-            error = torch.linalg.norm((x_low - x_high) / delta) / x.numel() ** 0.5
-            accept = pid.propose_step(error)
-            if accept:
-                x_prev = x_low
-                x = x_high + su * s_noise * noise_sampler(self.sigma(s), self.sigma(t))
-                s = t
-                info['n_accept'] += 1
-            else:
-                info['n_reject'] += 1
-            info['nfe'] += order
-            info['steps'] += 1
-
-            if self.info_callback is not None:
-                self.info_callback({'x': x, 'i': info['steps'] - 1, 't': s, 't_up': s, 'denoised': denoised, 'error': error, 'h': pid.h, **info})
-
-        return x, info
-
-
-@torch.no_grad()
-def sample_dpm_fast(model, x, sigma_min, sigma_max, n, extra_args=None, callback=None, disable=None, eta=0., s_noise=1., noise_sampler=None):
-    """DPM-Solver-Fast (fixed step size). See https://arxiv.org/abs/2206.00927."""
-    if sigma_min <= 0 or sigma_max <= 0:
-        raise ValueError('sigma_min and sigma_max must not be 0')
-    with tqdm(total=n, disable=disable) as pbar:
-        dpm_solver = DPMSolver(model, extra_args, eps_callback=pbar.update)
-        if callback is not None:
-            dpm_solver.info_callback = lambda info: callback({'sigma': dpm_solver.sigma(info['t']), 'sigma_hat': dpm_solver.sigma(info['t_up']), **info})
-        return dpm_solver.dpm_solver_fast(x, dpm_solver.t(torch.tensor(sigma_max)), dpm_solver.t(torch.tensor(sigma_min)), n, eta, s_noise, noise_sampler)
-
-
-@torch.no_grad()
-def sample_dpm_adaptive(model, x, sigma_min, sigma_max, extra_args=None, callback=None, disable=None, order=3, rtol=0.05, atol=0.0078, h_init=0.05, pcoeff=0., icoeff=1., dcoeff=0., accept_safety=0.81, eta=0., s_noise=1., noise_sampler=None, return_info=False):
-    """DPM-Solver-12 and 23 (adaptive step size). See https://arxiv.org/abs/2206.00927."""
-    if sigma_min <= 0 or sigma_max <= 0:
-        raise ValueError('sigma_min and sigma_max must not be 0')
-    with tqdm(disable=disable) as pbar:
-        dpm_solver = DPMSolver(model, extra_args, eps_callback=pbar.update)
-        if callback is not None:
-            dpm_solver.info_callback = lambda info: callback({'sigma': dpm_solver.sigma(info['t']), 'sigma_hat': dpm_solver.sigma(info['t_up']), **info})
-        x, info = dpm_solver.dpm_solver_adaptive(x, dpm_solver.t(torch.tensor(sigma_max)), dpm_solver.t(torch.tensor(sigma_min)), order, rtol, atol, h_init, pcoeff, icoeff, dcoeff, accept_safety, eta, s_noise, noise_sampler)
-    if return_info:
-        return x, info
-    return x
-
-
-@torch.no_grad()
-def sample_dpmpp_2s_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
-    """Ancestral sampling with DPM-Solver++(2S) second-order steps."""
-    extra_args = {} if extra_args is None else extra_args
-    noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
-    s_in = x.new_ones([x.shape[0]])
-    sigma_fn = lambda t: t.neg().exp()
-    t_fn = lambda sigma: sigma.log().neg()
-
-    for i in trange(len(sigmas) - 1, disable=disable):
-        denoised = model(x, sigmas[i] * s_in, **extra_args)
-        sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)
-        if callback is not None:
-            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
-        if sigma_down == 0:
-            # Euler method
-            d = to_d(x, sigmas[i], denoised)
-            dt = sigma_down - sigmas[i]
-            x = x + d * dt
-        else:
-            # DPM-Solver++(2S)
-            t, t_next = t_fn(sigmas[i]), t_fn(sigma_down)
-            r = 1 / 2
-            h = t_next - t
-            s = t + r * h
-            x_2 = (sigma_fn(s) / sigma_fn(t)) * x - (-h * r).expm1() * denoised
-            denoised_2 = model(x_2, sigma_fn(s) * s_in, **extra_args)
-            x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised_2
-        # Noise addition
-        if sigmas[i + 1] > 0:
-            x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
-    return x
-
-
-@torch.no_grad()
-def sample_dpmpp_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1 / 2):
-    """DPM-Solver++ (stochastic)."""
-    sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
-    noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max) if noise_sampler is None else noise_sampler
-    extra_args = {} if extra_args is None else extra_args
-    s_in = x.new_ones([x.shape[0]])
-    sigma_fn = lambda t: t.neg().exp()
-    t_fn = lambda sigma: sigma.log().neg()
-
-    for i in trange(len(sigmas) - 1, disable=disable):
-        denoised = model(x, sigmas[i] * s_in, **extra_args)
-        if callback is not None:
-            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
-        if sigmas[i + 1] == 0:
-            # Euler method
-            d = to_d(x, sigmas[i], denoised)
-            dt = sigmas[i + 1] - sigmas[i]
-            x = x + d * dt
-        else:
-            # DPM-Solver++
-            t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1])
-            h = t_next - t
-            s = t + h * r
-            fac = 1 / (2 * r)
-
-            # Step 1
-            sd, su = get_ancestral_step(sigma_fn(t), sigma_fn(s), eta)
-            s_ = t_fn(sd)
-            x_2 = (sigma_fn(s_) / sigma_fn(t)) * x - (t - s_).expm1() * denoised
-            x_2 = x_2 + noise_sampler(sigma_fn(t), sigma_fn(s)) * s_noise * su
-            denoised_2 = model(x_2, sigma_fn(s) * s_in, **extra_args)
-
-            # Step 2
-            sd, su = get_ancestral_step(sigma_fn(t), sigma_fn(t_next), eta)
-            t_next_ = t_fn(sd)
-            denoised_d = (1 - fac) * denoised + fac * denoised_2
-            x = (sigma_fn(t_next_) / sigma_fn(t)) * x - (t - t_next_).expm1() * denoised_d
-            x = x + noise_sampler(sigma_fn(t), sigma_fn(t_next)) * s_noise * su
-    return x
-
-
-@torch.no_grad()
-def sample_dpmpp_2m(model, x, sigmas, extra_args=None, callback=None, disable=None):
-    """DPM-Solver++(2M)."""
-    extra_args = {} if extra_args is None else extra_args
-    s_in = x.new_ones([x.shape[0]])
-    sigma_fn = lambda t: t.neg().exp()
-    t_fn = lambda sigma: sigma.log().neg()
-    old_denoised = None
-
-    for i in trange(len(sigmas) - 1, disable=disable):
-        denoised = model(x, sigmas[i] * s_in, **extra_args)
-        if callback is not None:
-            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
-        t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1])
-        h = t_next - t
-        if old_denoised is None or sigmas[i + 1] == 0:
-            x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised
-        else:
-            h_last = t - t_fn(sigmas[i - 1])
-            r = h_last / h
-            denoised_d = (1 + 1 / (2 * r)) * denoised - (1 / (2 * r)) * old_denoised
-            x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised_d
-        old_denoised = denoised
-    return x
-
-
-@torch.no_grad()
-def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='midpoint'):
-    """DPM-Solver++(2M) SDE."""
-
-    if solver_type not in {'heun', 'midpoint'}:
-        raise ValueError('solver_type must be \'heun\' or \'midpoint\'')
-
-    sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
-    noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max) if noise_sampler is None else noise_sampler
-    extra_args = {} if extra_args is None else extra_args
-    s_in = x.new_ones([x.shape[0]])
-
-    old_denoised = None
-    h_last = None
-
-    for i in trange(len(sigmas) - 1, disable=disable):
-        denoised = model(x, sigmas[i] * s_in, **extra_args)
-        if callback is not None:
-            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
-        if sigmas[i + 1] == 0:
-            # Denoising step
-            x = denoised
-        else:
-            # DPM-Solver++(2M) SDE
-            t, s = -sigmas[i].log(), -sigmas[i + 1].log()
-            h = s - t
-            eta_h = eta * h
-
-            x = sigmas[i + 1] / sigmas[i] * (-eta_h).exp() * x + (-h - eta_h).expm1().neg() * denoised
-
-            if old_denoised is not None:
-                r = h_last / h
-                if solver_type == 'heun':
-                    x = x + ((-h - eta_h).expm1().neg() / (-h - eta_h) + 1) * (1 / r) * (denoised - old_denoised)
-                elif solver_type == 'midpoint':
-                    x = x + 0.5 * (-h - eta_h).expm1().neg() * (1 / r) * (denoised - old_denoised)
-
-            x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * eta_h).expm1().neg().sqrt() * s_noise
-
-        old_denoised = denoised
-        h_last = h
-    return x
-
-
-@torch.no_grad()
-def sample_dpmpp_2m_test(model, x, sigmas, extra_args=None, callback=None, disable=None):
-    """DPM-Solver++(2M)."""
-    extra_args = {} if extra_args is None else extra_args
-    s_in = x.new_ones([x.shape[0]])
-    sigma_fn = lambda t: t.neg().exp()
-    t_fn = lambda sigma: sigma.log().neg()
-    old_denoised = None
-
-    for i in trange(len(sigmas) - 1, disable=disable):
-        denoised = model(x, sigmas[i] * s_in, **extra_args)
-        if callback is not None:
-            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
-        t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1])
-        h = t_next - t
-        
-        t_min = min(sigma_fn(t_next), sigma_fn(t))
-        t_max = max(sigma_fn(t_next), sigma_fn(t))
-
-        if old_denoised is None or sigmas[i + 1] == 0:
-            x = (t_min / t_max) * x - (-h).expm1() * denoised
-        else:
-            h_last = t - t_fn(sigmas[i - 1])
-
-            h_min = min(h_last, h)
-            h_max = max(h_last, h)
-            r = h_max / h_min
-
-            h_d = (h_max + h_min) / 2
-            denoised_d = (1 + 1 / (2 * r)) * denoised - (1 / (2 * r)) * old_denoised
-            x = (t_min / t_max) * x - (-h_d).expm1() * denoised_d
-
-        old_denoised = denoised
-    return x
diff --git a/repositories/k-diffusion/k_diffusion/utils.py b/repositories/k-diffusion/k_diffusion/utils.py
deleted file mode 100644
index 9afedb99276d55d5b923a04ffb62d403c9dfae93..0000000000000000000000000000000000000000
--- a/repositories/k-diffusion/k_diffusion/utils.py
+++ /dev/null
@@ -1,329 +0,0 @@
-from contextlib import contextmanager
-import hashlib
-import math
-from pathlib import Path
-import shutil
-import urllib
-import warnings
-
-from PIL import Image
-import torch
-from torch import nn, optim
-from torch.utils import data
-from torchvision.transforms import functional as TF
-
-
-def from_pil_image(x):
-    """Converts from a PIL image to a tensor."""
-    x = TF.to_tensor(x)
-    if x.ndim == 2:
-        x = x[..., None]
-    return x * 2 - 1
-
-
-def to_pil_image(x):
-    """Converts from a tensor to a PIL image."""
-    if x.ndim == 4:
-        assert x.shape[0] == 1
-        x = x[0]
-    if x.shape[0] == 1:
-        x = x[0]
-    return TF.to_pil_image((x.clamp(-1, 1) + 1) / 2)
-
-
-def hf_datasets_augs_helper(examples, transform, image_key, mode='RGB'):
-    """Apply passed in transforms for HuggingFace Datasets."""
-    images = [transform(image.convert(mode)) for image in examples[image_key]]
-    return {image_key: images}
-
-
-def append_dims(x, target_dims):
-    """Appends dimensions to the end of a tensor until it has target_dims dimensions."""
-    dims_to_append = target_dims - x.ndim
-    if dims_to_append < 0:
-        raise ValueError(f'input has {x.ndim} dims but target_dims is {target_dims}, which is less')
-    return x[(...,) + (None,) * dims_to_append]
-
-
-def n_params(module):
-    """Returns the number of trainable parameters in a module."""
-    return sum(p.numel() for p in module.parameters())
-
-
-def download_file(path, url, digest=None):
-    """Downloads a file if it does not exist, optionally checking its SHA-256 hash."""
-    path = Path(path)
-    path.parent.mkdir(parents=True, exist_ok=True)
-    if not path.exists():
-        with urllib.request.urlopen(url) as response, open(path, 'wb') as f:
-            shutil.copyfileobj(response, f)
-    if digest is not None:
-        file_digest = hashlib.sha256(open(path, 'rb').read()).hexdigest()
-        if digest != file_digest:
-            raise OSError(f'hash of {path} (url: {url}) failed to validate')
-    return path
-
-
-@contextmanager
-def train_mode(model, mode=True):
-    """A context manager that places a model into training mode and restores
-    the previous mode on exit."""
-    modes = [module.training for module in model.modules()]
-    try:
-        yield model.train(mode)
-    finally:
-        for i, module in enumerate(model.modules()):
-            module.training = modes[i]
-
-
-def eval_mode(model):
-    """A context manager that places a model into evaluation mode and restores
-    the previous mode on exit."""
-    return train_mode(model, False)
-
-
-@torch.no_grad()
-def ema_update(model, averaged_model, decay):
-    """Incorporates updated model parameters into an exponential moving averaged
-    version of a model. It should be called after each optimizer step."""
-    model_params = dict(model.named_parameters())
-    averaged_params = dict(averaged_model.named_parameters())
-    assert model_params.keys() == averaged_params.keys()
-
-    for name, param in model_params.items():
-        averaged_params[name].mul_(decay).add_(param, alpha=1 - decay)
-
-    model_buffers = dict(model.named_buffers())
-    averaged_buffers = dict(averaged_model.named_buffers())
-    assert model_buffers.keys() == averaged_buffers.keys()
-
-    for name, buf in model_buffers.items():
-        averaged_buffers[name].copy_(buf)
-
-
-class EMAWarmup:
-    """Implements an EMA warmup using an inverse decay schedule.
-    If inv_gamma=1 and power=1, implements a simple average. inv_gamma=1, power=2/3 are
-    good values for models you plan to train for a million or more steps (reaches decay
-    factor 0.999 at 31.6K steps, 0.9999 at 1M steps), inv_gamma=1, power=3/4 for models
-    you plan to train for less (reaches decay factor 0.999 at 10K steps, 0.9999 at
-    215.4k steps).
-    Args:
-        inv_gamma (float): Inverse multiplicative factor of EMA warmup. Default: 1.
-        power (float): Exponential factor of EMA warmup. Default: 1.
-        min_value (float): The minimum EMA decay rate. Default: 0.
-        max_value (float): The maximum EMA decay rate. Default: 1.
-        start_at (int): The epoch to start averaging at. Default: 0.
-        last_epoch (int): The index of last epoch. Default: 0.
-    """
-
-    def __init__(self, inv_gamma=1., power=1., min_value=0., max_value=1., start_at=0,
-                 last_epoch=0):
-        self.inv_gamma = inv_gamma
-        self.power = power
-        self.min_value = min_value
-        self.max_value = max_value
-        self.start_at = start_at
-        self.last_epoch = last_epoch
-
-    def state_dict(self):
-        """Returns the state of the class as a :class:`dict`."""
-        return dict(self.__dict__.items())
-
-    def load_state_dict(self, state_dict):
-        """Loads the class's state.
-        Args:
-            state_dict (dict): scaler state. Should be an object returned
-                from a call to :meth:`state_dict`.
-        """
-        self.__dict__.update(state_dict)
-
-    def get_value(self):
-        """Gets the current EMA decay rate."""
-        epoch = max(0, self.last_epoch - self.start_at)
-        value = 1 - (1 + epoch / self.inv_gamma) ** -self.power
-        return 0. if epoch < 0 else min(self.max_value, max(self.min_value, value))
-
-    def step(self):
-        """Updates the step count."""
-        self.last_epoch += 1
-
-
-class InverseLR(optim.lr_scheduler._LRScheduler):
-    """Implements an inverse decay learning rate schedule with an optional exponential
-    warmup. When last_epoch=-1, sets initial lr as lr.
-    inv_gamma is the number of steps/epochs required for the learning rate to decay to
-    (1 / 2)**power of its original value.
-    Args:
-        optimizer (Optimizer): Wrapped optimizer.
-        inv_gamma (float): Inverse multiplicative factor of learning rate decay. Default: 1.
-        power (float): Exponential factor of learning rate decay. Default: 1.
-        warmup (float): Exponential warmup factor (0 <= warmup < 1, 0 to disable)
-            Default: 0.
-        min_lr (float): The minimum learning rate. Default: 0.
-        last_epoch (int): The index of last epoch. Default: -1.
-        verbose (bool): If ``True``, prints a message to stdout for
-            each update. Default: ``False``.
-    """
-
-    def __init__(self, optimizer, inv_gamma=1., power=1., warmup=0., min_lr=0.,
-                 last_epoch=-1, verbose=False):
-        self.inv_gamma = inv_gamma
-        self.power = power
-        if not 0. <= warmup < 1:
-            raise ValueError('Invalid value for warmup')
-        self.warmup = warmup
-        self.min_lr = min_lr
-        super().__init__(optimizer, last_epoch, verbose)
-
-    def get_lr(self):
-        if not self._get_lr_called_within_step:
-            warnings.warn("To get the last learning rate computed by the scheduler, "
-                          "please use `get_last_lr()`.")
-
-        return self._get_closed_form_lr()
-
-    def _get_closed_form_lr(self):
-        warmup = 1 - self.warmup ** (self.last_epoch + 1)
-        lr_mult = (1 + self.last_epoch / self.inv_gamma) ** -self.power
-        return [warmup * max(self.min_lr, base_lr * lr_mult)
-                for base_lr in self.base_lrs]
-
-
-class ExponentialLR(optim.lr_scheduler._LRScheduler):
-    """Implements an exponential learning rate schedule with an optional exponential
-    warmup. When last_epoch=-1, sets initial lr as lr. Decays the learning rate
-    continuously by decay (default 0.5) every num_steps steps.
-    Args:
-        optimizer (Optimizer): Wrapped optimizer.
-        num_steps (float): The number of steps to decay the learning rate by decay in.
-        decay (float): The factor by which to decay the learning rate every num_steps
-            steps. Default: 0.5.
-        warmup (float): Exponential warmup factor (0 <= warmup < 1, 0 to disable)
-            Default: 0.
-        min_lr (float): The minimum learning rate. Default: 0.
-        last_epoch (int): The index of last epoch. Default: -1.
-        verbose (bool): If ``True``, prints a message to stdout for
-            each update. Default: ``False``.
-    """
-
-    def __init__(self, optimizer, num_steps, decay=0.5, warmup=0., min_lr=0.,
-                 last_epoch=-1, verbose=False):
-        self.num_steps = num_steps
-        self.decay = decay
-        if not 0. <= warmup < 1:
-            raise ValueError('Invalid value for warmup')
-        self.warmup = warmup
-        self.min_lr = min_lr
-        super().__init__(optimizer, last_epoch, verbose)
-
-    def get_lr(self):
-        if not self._get_lr_called_within_step:
-            warnings.warn("To get the last learning rate computed by the scheduler, "
-                          "please use `get_last_lr()`.")
-
-        return self._get_closed_form_lr()
-
-    def _get_closed_form_lr(self):
-        warmup = 1 - self.warmup ** (self.last_epoch + 1)
-        lr_mult = (self.decay ** (1 / self.num_steps)) ** self.last_epoch
-        return [warmup * max(self.min_lr, base_lr * lr_mult)
-                for base_lr in self.base_lrs]
-
-
-def rand_log_normal(shape, loc=0., scale=1., device='cpu', dtype=torch.float32):
-    """Draws samples from an lognormal distribution."""
-    return (torch.randn(shape, device=device, dtype=dtype) * scale + loc).exp()
-
-
-def rand_log_logistic(shape, loc=0., scale=1., min_value=0., max_value=float('inf'), device='cpu', dtype=torch.float32):
-    """Draws samples from an optionally truncated log-logistic distribution."""
-    min_value = torch.as_tensor(min_value, device=device, dtype=torch.float64)
-    max_value = torch.as_tensor(max_value, device=device, dtype=torch.float64)
-    min_cdf = min_value.log().sub(loc).div(scale).sigmoid()
-    max_cdf = max_value.log().sub(loc).div(scale).sigmoid()
-    u = torch.rand(shape, device=device, dtype=torch.float64) * (max_cdf - min_cdf) + min_cdf
-    return u.logit().mul(scale).add(loc).exp().to(dtype)
-
-
-def rand_log_uniform(shape, min_value, max_value, device='cpu', dtype=torch.float32):
-    """Draws samples from an log-uniform distribution."""
-    min_value = math.log(min_value)
-    max_value = math.log(max_value)
-    return (torch.rand(shape, device=device, dtype=dtype) * (max_value - min_value) + min_value).exp()
-
-
-def rand_v_diffusion(shape, sigma_data=1., min_value=0., max_value=float('inf'), device='cpu', dtype=torch.float32):
-    """Draws samples from a truncated v-diffusion training timestep distribution."""
-    min_cdf = math.atan(min_value / sigma_data) * 2 / math.pi
-    max_cdf = math.atan(max_value / sigma_data) * 2 / math.pi
-    u = torch.rand(shape, device=device, dtype=dtype) * (max_cdf - min_cdf) + min_cdf
-    return torch.tan(u * math.pi / 2) * sigma_data
-
-
-def rand_split_log_normal(shape, loc, scale_1, scale_2, device='cpu', dtype=torch.float32):
-    """Draws samples from a split lognormal distribution."""
-    n = torch.randn(shape, device=device, dtype=dtype).abs()
-    u = torch.rand(shape, device=device, dtype=dtype)
-    n_left = n * -scale_1 + loc
-    n_right = n * scale_2 + loc
-    ratio = scale_1 / (scale_1 + scale_2)
-    return torch.where(u < ratio, n_left, n_right).exp()
-
-
-class FolderOfImages(data.Dataset):
-    """Recursively finds all images in a directory. It does not support
-    classes/targets."""
-
-    IMG_EXTENSIONS = {'.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif', '.tiff', '.webp'}
-
-    def __init__(self, root, transform=None):
-        super().__init__()
-        self.root = Path(root)
-        self.transform = nn.Identity() if transform is None else transform
-        self.paths = sorted(path for path in self.root.rglob('*') if path.suffix.lower() in self.IMG_EXTENSIONS)
-
-    def __repr__(self):
-        return f'FolderOfImages(root="{self.root}", len: {len(self)})'
-
-    def __len__(self):
-        return len(self.paths)
-
-    def __getitem__(self, key):
-        path = self.paths[key]
-        with open(path, 'rb') as f:
-            image = Image.open(f).convert('RGB')
-        image = self.transform(image)
-        return image,
-
-
-class CSVLogger:
-    def __init__(self, filename, columns):
-        self.filename = Path(filename)
-        self.columns = columns
-        if self.filename.exists():
-            self.file = open(self.filename, 'a')
-        else:
-            self.file = open(self.filename, 'w')
-            self.write(*self.columns)
-
-    def write(self, *args):
-        print(*args, sep=',', file=self.file, flush=True)
-
-
-@contextmanager
-def tf32_mode(cudnn=None, matmul=None):
-    """A context manager that sets whether TF32 is allowed on cuDNN or matmul."""
-    cudnn_old = torch.backends.cudnn.allow_tf32
-    matmul_old = torch.backends.cuda.matmul.allow_tf32
-    try:
-        if cudnn is not None:
-            torch.backends.cudnn.allow_tf32 = cudnn
-        if matmul is not None:
-            torch.backends.cuda.matmul.allow_tf32 = matmul
-        yield
-    finally:
-        if cudnn is not None:
-            torch.backends.cudnn.allow_tf32 = cudnn_old
-        if matmul is not None:
-            torch.backends.cuda.matmul.allow_tf32 = matmul_old
diff --git a/repositories/k-diffusion/make_grid.py b/repositories/k-diffusion/make_grid.py
deleted file mode 100644
index 0c6616843cac1a69fdb94df804822cf07b533543..0000000000000000000000000000000000000000
--- a/repositories/k-diffusion/make_grid.py
+++ /dev/null
@@ -1,46 +0,0 @@
-#!/usr/bin/env python3
-
-"""Assembles images into a grid."""
-
-import argparse
-import math
-import sys
-
-from PIL import Image
-
-
-def main():
-    p = argparse.ArgumentParser(description=__doc__)
-    p.add_argument('images', type=str, nargs='+', metavar='image',
-                   help='the input images')
-    p.add_argument('--output', '-o', type=str, default='out.png',
-                   help='the output image')
-    p.add_argument('--nrow', type=int,
-                   help='the number of images per row')
-    args = p.parse_args()
-
-    images = [Image.open(image) for image in args.images]
-    mode = images[0].mode
-    size = images[0].size
-    for image, name in zip(images, args.images):
-        if image.mode != mode:
-            print(f'Error: Image {name} had mode {image.mode}, expected {mode}', file=sys.stderr)
-            sys.exit(1)
-        if image.size != size:
-            print(f'Error: Image {name} had size {image.size}, expected {size}', file=sys.stderr)
-            sys.exit(1)
-
-    n = len(images)
-    x = args.nrow if args.nrow else math.ceil(n**0.5)
-    y = math.ceil(n / x)
-
-    output = Image.new(mode, (size[0] * x, size[1] * y))
-    for i, image in enumerate(images):
-        cur_x, cur_y = i % x, i // x
-        output.paste(image, (size[0] * cur_x, size[1] * cur_y))
-
-    output.save(args.output)
-
-
-if __name__ == '__main__':
-    main()
diff --git a/repositories/k-diffusion/pyproject.toml b/repositories/k-diffusion/pyproject.toml
deleted file mode 100644
index fed528d4a7a148fd0bf0b0198a6461f8c91b87e9..0000000000000000000000000000000000000000
--- a/repositories/k-diffusion/pyproject.toml
+++ /dev/null
@@ -1,3 +0,0 @@
-[build-system]
-requires = ["setuptools"]
-build-backend = "setuptools.build_meta"
diff --git a/repositories/k-diffusion/requirements.txt b/repositories/k-diffusion/requirements.txt
deleted file mode 100644
index 497553b0c7d493cf11d0278f5855ff296ad5e02a..0000000000000000000000000000000000000000
--- a/repositories/k-diffusion/requirements.txt
+++ /dev/null
@@ -1,16 +0,0 @@
-accelerate
-clean-fid
-clip-anytorch
-einops
-jsonmerge
-kornia
-Pillow
-resize-right
-scikit-image
-scipy
-torch
-torchdiffeq
-torchsde
-torchvision
-tqdm
-wandb
diff --git a/repositories/k-diffusion/sample.py b/repositories/k-diffusion/sample.py
deleted file mode 100644
index 21e0dc3c9ca055f7de73b7df7aa2841025187c18..0000000000000000000000000000000000000000
--- a/repositories/k-diffusion/sample.py
+++ /dev/null
@@ -1,73 +0,0 @@
-#!/usr/bin/env python3
-
-"""Samples from k-diffusion models."""
-
-import argparse
-import math
-
-import accelerate
-import torch
-from tqdm import trange, tqdm
-
-import k_diffusion as K
-
-
-def main():
-    p = argparse.ArgumentParser(description=__doc__,
-                                formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-    p.add_argument('--batch-size', type=int, default=64,
-                   help='the batch size')
-    p.add_argument('--checkpoint', type=str, required=True,
-                   help='the checkpoint to use')
-    p.add_argument('--config', type=str, required=True,
-                   help='the model config')
-    p.add_argument('-n', type=int, default=64,
-                   help='the number of images to sample')
-    p.add_argument('--prefix', type=str, default='out',
-                   help='the output prefix')
-    p.add_argument('--steps', type=int, default=50,
-                   help='the number of denoising steps')
-    args = p.parse_args()
-
-    config = K.config.load_config(open(args.config))
-    model_config = config['model']
-    # TODO: allow non-square input sizes
-    assert len(model_config['input_size']) == 2 and model_config['input_size'][0] == model_config['input_size'][1]
-    size = model_config['input_size']
-
-    accelerator = accelerate.Accelerator()
-    device = accelerator.device
-    print('Using device:', device, flush=True)
-
-    inner_model = K.config.make_model(config).eval().requires_grad_(False).to(device)
-    inner_model.load_state_dict(torch.load(args.checkpoint, map_location='cpu')['model_ema'])
-    accelerator.print('Parameters:', K.utils.n_params(inner_model))
-    model = K.Denoiser(inner_model, sigma_data=model_config['sigma_data'])
-
-    sigma_min = model_config['sigma_min']
-    sigma_max = model_config['sigma_max']
-
-    @torch.no_grad()
-    @K.utils.eval_mode(model)
-    def run():
-        if accelerator.is_local_main_process:
-            tqdm.write('Sampling...')
-        sigmas = K.sampling.get_sigmas_karras(args.steps, sigma_min, sigma_max, rho=7., device=device)
-        def sample_fn(n):
-            x = torch.randn([n, model_config['input_channels'], size[0], size[1]], device=device) * sigma_max
-            x_0 = K.sampling.sample_lms(model, x, sigmas, disable=not accelerator.is_local_main_process)
-            return x_0
-        x_0 = K.evaluation.compute_features(accelerator, sample_fn, lambda x: x, args.n, args.batch_size)
-        if accelerator.is_main_process:
-            for i, out in enumerate(x_0):
-                filename = f'{args.prefix}_{i:05}.png'
-                K.utils.to_pil_image(out).save(filename)
-
-    try:
-        run()
-    except KeyboardInterrupt:
-        pass
-
-
-if __name__ == '__main__':
-    main()
diff --git a/repositories/k-diffusion/sample_clip_guided.py b/repositories/k-diffusion/sample_clip_guided.py
deleted file mode 100644
index 592350196fbbac8479563be5be9e138248d94c86..0000000000000000000000000000000000000000
--- a/repositories/k-diffusion/sample_clip_guided.py
+++ /dev/null
@@ -1,131 +0,0 @@
-#!/usr/bin/env python3
-
-"""CLIP guided sampling from k-diffusion models."""
-
-import argparse
-import math
-
-import accelerate
-import clip
-from kornia import augmentation as KA
-from resize_right import resize
-import torch
-from torch.nn import functional as F
-from torchvision import transforms
-from tqdm import trange, tqdm
-
-import k_diffusion as K
-
-
-def spherical_dist_loss(x, y):
-    x = F.normalize(x, dim=-1)
-    y = F.normalize(y, dim=-1)
-    return (x - y).norm(dim=-1).div(2).arcsin().pow(2).mul(2)
-
-
-def make_cond_model_fn(model, cond_fn):
-    def model_fn(x, sigma, **kwargs):
-        with torch.enable_grad():
-            x = x.detach().requires_grad_()
-            denoised = model(x, sigma, **kwargs)
-            cond_grad = cond_fn(x, sigma, denoised=denoised, **kwargs).detach()
-            cond_denoised = denoised.detach() + cond_grad * K.utils.append_dims(sigma**2, x.ndim)
-        return cond_denoised
-    return model_fn
-
-
-def make_static_thresh_model_fn(model, value=1.):
-    def model_fn(x, sigma, **kwargs):
-        return model(x, sigma, **kwargs).clamp(-value, value)
-    return model_fn
-
-
-def main():
-    p = argparse.ArgumentParser(description=__doc__,
-                                formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-    p.add_argument('prompt', type=str,
-                   default='the prompt to use')
-    p.add_argument('--batch-size', type=int, default=16,
-                   help='the batch size')
-    p.add_argument('--checkpoint', type=str, required=True,
-                   help='the checkpoint to use')
-    p.add_argument('--clip-guidance-scale', '-cgs', type=float, default=500.,
-                   help='the CLIP guidance scale')
-    p.add_argument('--clip-model', type=str, default='ViT-B/16', choices=clip.available_models(),
-                   help='the CLIP model to use')
-    p.add_argument('--config', type=str, required=True,
-                   help='the model config')
-    p.add_argument('-n', type=int, default=64,
-                   help='the number of images to sample')
-    p.add_argument('--prefix', type=str, default='out',
-                   help='the output prefix')
-    p.add_argument('--steps', type=int, default=100,
-                   help='the number of denoising steps')
-    args = p.parse_args()
-
-    config = K.config.load_config(open(args.config))
-    model_config = config['model']
-    # TODO: allow non-square input sizes
-    assert len(model_config['input_size']) == 2 and model_config['input_size'][0] == model_config['input_size'][1]
-    size = model_config['input_size']
-
-    accelerator = accelerate.Accelerator()
-    device = accelerator.device
-    print('Using device:', device, flush=True)
-
-    inner_model = K.config.make_model(config).eval().requires_grad_(False).to(device)
-    inner_model.load_state_dict(torch.load(args.checkpoint, map_location='cpu')['model_ema'])
-    accelerator.print('Parameters:', K.utils.n_params(inner_model))
-    model = K.Denoiser(inner_model, sigma_data=model_config['sigma_data'])
-
-    sigma_min = model_config['sigma_min']
-    sigma_max = model_config['sigma_max']
-
-    clip_model = clip.load(args.clip_model, device=device)[0].eval().requires_grad_(False)
-    clip_normalize = transforms.Normalize(mean=(0.48145466, 0.4578275, 0.40821073),
-                                          std=(0.26862954, 0.26130258, 0.27577711))
-    clip_size = (clip_model.visual.input_resolution, clip_model.visual.input_resolution)
-    aug = KA.RandomAffine(0, (1/14, 1/14), p=1, padding_mode='border')
-
-    def get_image_embed(x):
-        if x.shape[2:4] != clip_size:
-            x = resize(x, out_shape=clip_size, pad_mode='reflect')
-        x = clip_normalize(x)
-        x = clip_model.encode_image(x).float()
-        return F.normalize(x)
-
-    target_embed = F.normalize(clip_model.encode_text(clip.tokenize(args.prompt, truncate=True).to(device)).float())
-
-    def cond_fn(x, t, denoised):
-        image_embed = get_image_embed(aug(denoised.add(1).div(2)))
-        loss = spherical_dist_loss(image_embed, target_embed).sum() * args.clip_guidance_scale
-        grad = -torch.autograd.grad(loss, x)[0]
-        return grad
-
-    model_fn = make_cond_model_fn(model, cond_fn)
-    model_fn = make_static_thresh_model_fn(model_fn)
-
-    @torch.no_grad()
-    @K.utils.eval_mode(model)
-    def run():
-        if accelerator.is_local_main_process:
-            tqdm.write('Sampling...')
-        sigmas = K.sampling.get_sigmas_karras(args.steps, sigma_min, sigma_max, rho=7., device=device)
-        def sample_fn(n):
-            x = torch.randn([n, model_config['input_channels'], size[0], size[1]], device=device) * sigmas[0]
-            x_0 = K.sampling.sample_dpmpp_2s_ancestral(model_fn, x, sigmas, eta=1., disable=not accelerator.is_local_main_process)
-            return x_0
-        x_0 = K.evaluation.compute_features(accelerator, sample_fn, lambda x: x, args.n, args.batch_size)
-        if accelerator.is_main_process:
-            for i, out in enumerate(x_0):
-                filename = f'{args.prefix}_{i:05}.png'
-                K.utils.to_pil_image(out).save(filename)
-
-    try:
-        run()
-    except KeyboardInterrupt:
-        pass
-
-
-if __name__ == '__main__':
-    main()
diff --git a/repositories/k-diffusion/setup.cfg b/repositories/k-diffusion/setup.cfg
deleted file mode 100644
index 6678e78268ce97bf7d02c757615df514ab9e2dc0..0000000000000000000000000000000000000000
--- a/repositories/k-diffusion/setup.cfg
+++ /dev/null
@@ -1,30 +0,0 @@
-[metadata]
-name = k-diffusion
-version = 0.0.15
-author = Katherine Crowson
-author_email = crowsonkb@gmail.com
-url = https://github.com/crowsonkb/k-diffusion
-description = Karras et al. (2022) diffusion models for PyTorch
-long_description = file: README.md
-long_description_content_type = text/markdown
-license = MIT
-
-[options]
-packages = find:
-install_requires = 
-    accelerate
-    clean-fid
-    clip-anytorch
-    einops
-    jsonmerge
-    kornia
-    Pillow
-    resize-right
-    scikit-image
-    scipy
-    torch
-    torchdiffeq
-    torchsde
-    torchvision
-    tqdm
-    wandb
diff --git a/repositories/k-diffusion/setup.py b/repositories/k-diffusion/setup.py
deleted file mode 100644
index 0ae4555937eb30e6632281a2326726826a41fe88..0000000000000000000000000000000000000000
--- a/repositories/k-diffusion/setup.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from setuptools import setup
-
-
-if __name__ == '__main__':
-    setup()
diff --git a/repositories/k-diffusion/train.py b/repositories/k-diffusion/train.py
deleted file mode 100644
index 1ba614c6a8edf66755384cc95ddcb2475832234c..0000000000000000000000000000000000000000
--- a/repositories/k-diffusion/train.py
+++ /dev/null
@@ -1,356 +0,0 @@
-#!/usr/bin/env python3
-
-"""Trains Karras et al. (2022) diffusion models."""
-
-import argparse
-from copy import deepcopy
-from functools import partial
-import math
-import json
-from pathlib import Path
-
-import accelerate
-import torch
-from torch import nn, optim
-from torch import multiprocessing as mp
-from torch.utils import data
-from torchvision import datasets, transforms, utils
-from tqdm.auto import trange, tqdm
-
-import k_diffusion as K
-
-
-def main():
-    p = argparse.ArgumentParser(description=__doc__,
-                                formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-    p.add_argument('--batch-size', type=int, default=64,
-                   help='the batch size')
-    p.add_argument('--config', type=str, required=True,
-                   help='the configuration file')
-    p.add_argument('--demo-every', type=int, default=500,
-                   help='save a demo grid every this many steps')
-    p.add_argument('--evaluate-every', type=int, default=10000,
-                   help='save a demo grid every this many steps')
-    p.add_argument('--evaluate-n', type=int, default=2000,
-                   help='the number of samples to draw to evaluate')
-    p.add_argument('--gns', action='store_true',
-                   help='measure the gradient noise scale (DDP only)')
-    p.add_argument('--grad-accum-steps', type=int, default=1,
-                   help='the number of gradient accumulation steps')
-    p.add_argument('--grow', type=str,
-                   help='the checkpoint to grow from')
-    p.add_argument('--grow-config', type=str,
-                   help='the configuration file of the model to grow from')
-    p.add_argument('--lr', type=float,
-                   help='the learning rate')
-    p.add_argument('--name', type=str, default='model',
-                   help='the name of the run')
-    p.add_argument('--num-workers', type=int, default=8,
-                   help='the number of data loader workers')
-    p.add_argument('--resume', type=str,
-                   help='the checkpoint to resume from')
-    p.add_argument('--sample-n', type=int, default=64,
-                   help='the number of images to sample for demo grids')
-    p.add_argument('--save-every', type=int, default=10000,
-                   help='save every this many steps')
-    p.add_argument('--seed', type=int,
-                   help='the random seed')
-    p.add_argument('--start-method', type=str, default='spawn',
-                   choices=['fork', 'forkserver', 'spawn'],
-                   help='the multiprocessing start method')
-    p.add_argument('--wandb-entity', type=str,
-                   help='the wandb entity name')
-    p.add_argument('--wandb-group', type=str,
-                   help='the wandb group name')
-    p.add_argument('--wandb-project', type=str,
-                   help='the wandb project name (specify this to enable wandb)')
-    p.add_argument('--wandb-save-model', action='store_true',
-                   help='save model to wandb')
-    args = p.parse_args()
-
-    mp.set_start_method(args.start_method)
-    torch.backends.cuda.matmul.allow_tf32 = True
-
-    config = K.config.load_config(open(args.config))
-    model_config = config['model']
-    dataset_config = config['dataset']
-    opt_config = config['optimizer']
-    sched_config = config['lr_sched']
-    ema_sched_config = config['ema_sched']
-
-    # TODO: allow non-square input sizes
-    assert len(model_config['input_size']) == 2 and model_config['input_size'][0] == model_config['input_size'][1]
-    size = model_config['input_size']
-
-    ddp_kwargs = accelerate.DistributedDataParallelKwargs(find_unused_parameters=model_config['skip_stages'] > 0)
-    accelerator = accelerate.Accelerator(kwargs_handlers=[ddp_kwargs], gradient_accumulation_steps=args.grad_accum_steps)
-    device = accelerator.device
-    print(f'Process {accelerator.process_index} using device: {device}', flush=True)
-
-    if args.seed is not None:
-        seeds = torch.randint(-2 ** 63, 2 ** 63 - 1, [accelerator.num_processes], generator=torch.Generator().manual_seed(args.seed))
-        torch.manual_seed(seeds[accelerator.process_index])
-
-    inner_model = K.config.make_model(config)
-    inner_model_ema = deepcopy(inner_model)
-    if accelerator.is_main_process:
-        print('Parameters:', K.utils.n_params(inner_model))
-
-    # If logging to wandb, initialize the run
-    use_wandb = accelerator.is_main_process and args.wandb_project
-    if use_wandb:
-        import wandb
-        log_config = vars(args)
-        log_config['config'] = config
-        log_config['parameters'] = K.utils.n_params(inner_model)
-        wandb.init(project=args.wandb_project, entity=args.wandb_entity, group=args.wandb_group, config=log_config, save_code=True)
-
-    if opt_config['type'] == 'adamw':
-        opt = optim.AdamW(inner_model.parameters(),
-                          lr=opt_config['lr'] if args.lr is None else args.lr,
-                          betas=tuple(opt_config['betas']),
-                          eps=opt_config['eps'],
-                          weight_decay=opt_config['weight_decay'])
-    elif opt_config['type'] == 'sgd':
-        opt = optim.SGD(inner_model.parameters(),
-                        lr=opt_config['lr'] if args.lr is None else args.lr,
-                        momentum=opt_config.get('momentum', 0.),
-                        nesterov=opt_config.get('nesterov', False),
-                        weight_decay=opt_config.get('weight_decay', 0.))
-    else:
-        raise ValueError('Invalid optimizer type')
-
-    if sched_config['type'] == 'inverse':
-        sched = K.utils.InverseLR(opt,
-                                  inv_gamma=sched_config['inv_gamma'],
-                                  power=sched_config['power'],
-                                  warmup=sched_config['warmup'])
-    elif sched_config['type'] == 'exponential':
-        sched = K.utils.ExponentialLR(opt,
-                                      num_steps=sched_config['num_steps'],
-                                      decay=sched_config['decay'],
-                                      warmup=sched_config['warmup'])
-    elif sched_config['type'] == 'constant':
-        sched = optim.lr_scheduler.LambdaLR(opt, lambda _: 1.0)
-    else:
-        raise ValueError('Invalid schedule type')
-
-    assert ema_sched_config['type'] == 'inverse'
-    ema_sched = K.utils.EMAWarmup(power=ema_sched_config['power'],
-                                  max_value=ema_sched_config['max_value'])
-
-    tf = transforms.Compose([
-        transforms.Resize(size[0], interpolation=transforms.InterpolationMode.LANCZOS),
-        transforms.CenterCrop(size[0]),
-        K.augmentation.KarrasAugmentationPipeline(model_config['augment_prob']),
-    ])
-
-    if dataset_config['type'] == 'imagefolder':
-        train_set = K.utils.FolderOfImages(dataset_config['location'], transform=tf)
-    elif dataset_config['type'] == 'cifar10':
-        train_set = datasets.CIFAR10(dataset_config['location'], train=True, download=True, transform=tf)
-    elif dataset_config['type'] == 'mnist':
-        train_set = datasets.MNIST(dataset_config['location'], train=True, download=True, transform=tf)
-    elif dataset_config['type'] == 'huggingface':
-        from datasets import load_dataset
-        train_set = load_dataset(dataset_config['location'])
-        train_set.set_transform(partial(K.utils.hf_datasets_augs_helper, transform=tf, image_key=dataset_config['image_key']))
-        train_set = train_set['train']
-    else:
-        raise ValueError('Invalid dataset type')
-
-    if accelerator.is_main_process:
-        try:
-            print('Number of items in dataset:', len(train_set))
-        except TypeError:
-            pass
-
-    image_key = dataset_config.get('image_key', 0)
-
-    train_dl = data.DataLoader(train_set, args.batch_size, shuffle=True, drop_last=True,
-                               num_workers=args.num_workers, persistent_workers=True)
-
-    if args.grow:
-        if not args.grow_config:
-            raise ValueError('--grow requires --grow-config')
-        ckpt = torch.load(args.grow, map_location='cpu')
-        old_config = K.config.load_config(open(args.grow_config))
-        old_inner_model = K.config.make_model(old_config)
-        old_inner_model.load_state_dict(ckpt['model_ema'])
-        if old_config['model']['skip_stages'] != model_config['skip_stages']:
-            old_inner_model.set_skip_stages(model_config['skip_stages'])
-        if old_config['model']['patch_size'] != model_config['patch_size']:
-            old_inner_model.set_patch_size(model_config['patch_size'])
-        inner_model.load_state_dict(old_inner_model.state_dict())
-        del ckpt, old_inner_model
-
-    inner_model, inner_model_ema, opt, train_dl = accelerator.prepare(inner_model, inner_model_ema, opt, train_dl)
-    if use_wandb:
-        wandb.watch(inner_model)
-    if args.gns:
-        gns_stats_hook = K.gns.DDPGradientStatsHook(inner_model)
-        gns_stats = K.gns.GradientNoiseScale()
-    else:
-        gns_stats = None
-    sigma_min = model_config['sigma_min']
-    sigma_max = model_config['sigma_max']
-    sample_density = K.config.make_sample_density(model_config)
-
-    model = K.config.make_denoiser_wrapper(config)(inner_model)
-    model_ema = K.config.make_denoiser_wrapper(config)(inner_model_ema)
-
-    state_path = Path(f'{args.name}_state.json')
-
-    if state_path.exists() or args.resume:
-        if args.resume:
-            ckpt_path = args.resume
-        if not args.resume:
-            state = json.load(open(state_path))
-            ckpt_path = state['latest_checkpoint']
-        if accelerator.is_main_process:
-            print(f'Resuming from {ckpt_path}...')
-        ckpt = torch.load(ckpt_path, map_location='cpu')
-        accelerator.unwrap_model(model.inner_model).load_state_dict(ckpt['model'])
-        accelerator.unwrap_model(model_ema.inner_model).load_state_dict(ckpt['model_ema'])
-        opt.load_state_dict(ckpt['opt'])
-        sched.load_state_dict(ckpt['sched'])
-        ema_sched.load_state_dict(ckpt['ema_sched'])
-        epoch = ckpt['epoch'] + 1
-        step = ckpt['step'] + 1
-        if args.gns and ckpt.get('gns_stats', None) is not None:
-            gns_stats.load_state_dict(ckpt['gns_stats'])
-
-        del ckpt
-    else:
-        epoch = 0
-        step = 0
-
-    evaluate_enabled = args.evaluate_every > 0 and args.evaluate_n > 0
-    if evaluate_enabled:
-        extractor = K.evaluation.InceptionV3FeatureExtractor(device=device)
-        train_iter = iter(train_dl)
-        if accelerator.is_main_process:
-            print('Computing features for reals...')
-        reals_features = K.evaluation.compute_features(accelerator, lambda x: next(train_iter)[image_key][1], extractor, args.evaluate_n, args.batch_size)
-        if accelerator.is_main_process:
-            metrics_log = K.utils.CSVLogger(f'{args.name}_metrics.csv', ['step', 'fid', 'kid'])
-        del train_iter
-
-    @torch.no_grad()
-    @K.utils.eval_mode(model_ema)
-    def demo():
-        if accelerator.is_main_process:
-            tqdm.write('Sampling...')
-        filename = f'{args.name}_demo_{step:08}.png'
-        n_per_proc = math.ceil(args.sample_n / accelerator.num_processes)
-        x = torch.randn([n_per_proc, model_config['input_channels'], size[0], size[1]], device=device) * sigma_max
-        sigmas = K.sampling.get_sigmas_karras(50, sigma_min, sigma_max, rho=7., device=device)
-        x_0 = K.sampling.sample_dpmpp_2m(model_ema, x, sigmas, disable=not accelerator.is_main_process)
-        x_0 = accelerator.gather(x_0)[:args.sample_n]
-        if accelerator.is_main_process:
-            grid = utils.make_grid(x_0, nrow=math.ceil(args.sample_n ** 0.5), padding=0)
-            K.utils.to_pil_image(grid).save(filename)
-            if use_wandb:
-                wandb.log({'demo_grid': wandb.Image(filename)}, step=step)
-
-    @torch.no_grad()
-    @K.utils.eval_mode(model_ema)
-    def evaluate():
-        if not evaluate_enabled:
-            return
-        if accelerator.is_main_process:
-            tqdm.write('Evaluating...')
-        sigmas = K.sampling.get_sigmas_karras(50, sigma_min, sigma_max, rho=7., device=device)
-        def sample_fn(n):
-            x = torch.randn([n, model_config['input_channels'], size[0], size[1]], device=device) * sigma_max
-            x_0 = K.sampling.sample_dpmpp_2m(model_ema, x, sigmas, disable=True)
-            return x_0
-        fakes_features = K.evaluation.compute_features(accelerator, sample_fn, extractor, args.evaluate_n, args.batch_size)
-        if accelerator.is_main_process:
-            fid = K.evaluation.fid(fakes_features, reals_features)
-            kid = K.evaluation.kid(fakes_features, reals_features)
-            print(f'FID: {fid.item():g}, KID: {kid.item():g}')
-            if accelerator.is_main_process:
-                metrics_log.write(step, fid.item(), kid.item())
-            if use_wandb:
-                wandb.log({'FID': fid.item(), 'KID': kid.item()}, step=step)
-
-    def save():
-        accelerator.wait_for_everyone()
-        filename = f'{args.name}_{step:08}.pth'
-        if accelerator.is_main_process:
-            tqdm.write(f'Saving to {filename}...')
-        obj = {
-            'model': accelerator.unwrap_model(model.inner_model).state_dict(),
-            'model_ema': accelerator.unwrap_model(model_ema.inner_model).state_dict(),
-            'opt': opt.state_dict(),
-            'sched': sched.state_dict(),
-            'ema_sched': ema_sched.state_dict(),
-            'epoch': epoch,
-            'step': step,
-            'gns_stats': gns_stats.state_dict() if gns_stats is not None else None,
-        }
-        accelerator.save(obj, filename)
-        if accelerator.is_main_process:
-            state_obj = {'latest_checkpoint': filename}
-            json.dump(state_obj, open(state_path, 'w'))
-        if args.wandb_save_model and use_wandb:
-            wandb.save(filename)
-
-    try:
-        while True:
-            for batch in tqdm(train_dl, disable=not accelerator.is_main_process):
-                with accelerator.accumulate(model):
-                    reals, _, aug_cond = batch[image_key]
-                    noise = torch.randn_like(reals)
-                    sigma = sample_density([reals.shape[0]], device=device)
-                    losses = model.loss(reals, noise, sigma, aug_cond=aug_cond)
-                    losses_all = accelerator.gather(losses)
-                    loss = losses_all.mean()
-                    accelerator.backward(losses.mean())
-                    if args.gns:
-                        sq_norm_small_batch, sq_norm_large_batch = gns_stats_hook.get_stats()
-                        gns_stats.update(sq_norm_small_batch, sq_norm_large_batch, reals.shape[0], reals.shape[0] * accelerator.num_processes)
-                    opt.step()
-                    sched.step()
-                    opt.zero_grad()
-                    if accelerator.sync_gradients:
-                        ema_decay = ema_sched.get_value()
-                        K.utils.ema_update(model, model_ema, ema_decay)
-                        ema_sched.step()
-
-                if accelerator.is_main_process:
-                    if step % 25 == 0:
-                        if args.gns:
-                            tqdm.write(f'Epoch: {epoch}, step: {step}, loss: {loss.item():g}, gns: {gns_stats.get_gns():g}')
-                        else:
-                            tqdm.write(f'Epoch: {epoch}, step: {step}, loss: {loss.item():g}')
-
-                if use_wandb:
-                    log_dict = {
-                        'epoch': epoch,
-                        'loss': loss.item(),
-                        'lr': sched.get_last_lr()[0],
-                        'ema_decay': ema_decay,
-                    }
-                    if args.gns:
-                        log_dict['gradient_noise_scale'] = gns_stats.get_gns()
-                    wandb.log(log_dict, step=step)
-
-                if step % args.demo_every == 0:
-                    demo()
-
-                if evaluate_enabled and step > 0 and step % args.evaluate_every == 0:
-                    evaluate()
-
-                if step > 0 and step % args.save_every == 0:
-                    save()
-
-                step += 1
-            epoch += 1
-    except KeyboardInterrupt:
-        pass
-
-
-if __name__ == '__main__':
-    main()
diff --git a/repositories/stable-diffusion-stability-ai/.gitignore b/repositories/stable-diffusion-stability-ai/.gitignore
deleted file mode 100644
index 3b33f062894825a067995b8b9e8224ba4d9ff708..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/.gitignore
+++ /dev/null
@@ -1,165 +0,0 @@
-# Generated by project
-outputs/
-
-# Byte-compiled / optimized / DLL files
-__pycache__/
-*.py[cod]
-*$py.class
-
-# C extensions
-*.so
-
-# General MacOS
-.DS_Store
-.AppleDouble
-.LSOverride
-
-# Distribution / packaging
-.Python
-build/
-develop-eggs/
-dist/
-downloads/
-eggs/
-.eggs/
-lib/
-lib64/
-parts/
-sdist/
-var/
-wheels/
-share/python-wheels/
-*.egg-info/
-.installed.cfg
-*.egg
-MANIFEST
-
-# PyInstaller
-#  Usually these files are written by a python script from a template
-#  before PyInstaller builds the exe, so as to inject date/other infos into it.
-*.manifest
-*.spec
-
-# Installer logs
-pip-log.txt
-pip-delete-this-directory.txt
-
-# Unit test / coverage reports
-htmlcov/
-.tox/
-.nox/
-.coverage
-.coverage.*
-.cache
-nosetests.xml
-coverage.xml
-*.cover
-*.py,cover
-.hypothesis/
-.pytest_cache/
-cover/
-
-# Translations
-*.mo
-*.pot
-
-# Django stuff:
-*.log
-local_settings.py
-db.sqlite3
-db.sqlite3-journal
-
-# Flask stuff:
-instance/
-.webassets-cache
-
-# Scrapy stuff:
-.scrapy
-
-# Sphinx documentation
-docs/_build/
-
-# PyBuilder
-.pybuilder/
-target/
-
-# Jupyter Notebook
-.ipynb_checkpoints
-
-# IPython
-profile_default/
-ipython_config.py
-
-# pyenv
-#   For a library or package, you might want to ignore these files since the code is
-#   intended to run in multiple environments; otherwise, check them in:
-# .python-version
-
-# pipenv
-#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
-#   However, in case of collaboration, if having platform-specific dependencies or dependencies
-#   having no cross-platform support, pipenv may install dependencies that don't work, or not
-#   install all needed dependencies.
-#Pipfile.lock
-
-# poetry
-#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
-#   This is especially recommended for binary packages to ensure reproducibility, and is more
-#   commonly ignored for libraries.
-#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
-#poetry.lock
-
-# pdm
-#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
-#pdm.lock
-#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
-#   in version control.
-#   https://pdm.fming.dev/#use-with-ide
-.pdm.toml
-
-# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
-__pypackages__/
-
-# Celery stuff
-celerybeat-schedule
-celerybeat.pid
-
-# SageMath parsed files
-*.sage.py
-
-# Environments
-.env
-.venv
-env/
-venv/
-ENV/
-env.bak/
-venv.bak/
-
-# Spyder project settings
-.spyderproject
-.spyproject
-
-# Rope project settings
-.ropeproject
-
-# mkdocs documentation
-/site
-
-# mypy
-.mypy_cache/
-.dmypy.json
-dmypy.json
-
-# Pyre type checker
-.pyre/
-
-# pytype static type analyzer
-.pytype/
-
-# Cython debug symbols
-cython_debug/
-
-# IDEs
-.idea/
-.vscode/
diff --git a/repositories/stable-diffusion-stability-ai/LICENSE b/repositories/stable-diffusion-stability-ai/LICENSE
deleted file mode 100644
index 58a49c99b2b9151af5e1fee0dbd20307671f47ab..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/LICENSE
+++ /dev/null
@@ -1,21 +0,0 @@
-MIT License
-
-Copyright (c) 2022 Stability AI
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
diff --git a/repositories/stable-diffusion-stability-ai/LICENSE-MODEL b/repositories/stable-diffusion-stability-ai/LICENSE-MODEL
deleted file mode 100644
index 9684533d88e7d853a55cabf6caa2f1e4a3e6fdc4..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/LICENSE-MODEL
+++ /dev/null
@@ -1,84 +0,0 @@
-Copyright (c) 2022 Stability AI and contributors
-
-CreativeML Open RAIL++-M License
-dated November 24, 2022
-
-Section I: PREAMBLE
-
-Multimodal generative models are being widely adopted and used, and have the potential to transform the way artists, among other individuals, conceive and benefit from AI or ML technologies as a tool for content creation.
-
-Notwithstanding the current and potential benefits that these artifacts can bring to society at large, there are also concerns about potential misuses of them, either due to their technical limitations or ethical considerations.
-
-In short, this license strives for both the open and responsible downstream use of the accompanying model. When it comes to the open character, we took inspiration from open source permissive licenses regarding the grant of IP rights. Referring to the downstream responsible use, we added use-based restrictions not permitting the use of the Model in very specific scenarios, in order for the licensor to be able to enforce the license in case potential misuses of the Model may occur. At the same time, we strive to promote open and responsible research on generative models for art and content generation.
-
-Even though downstream derivative versions of the model could be released under different licensing terms, the latter will always have to include - at minimum - the same use-based restrictions as the ones in the original license (this license). We believe in the intersection between open and responsible AI development; thus, this License aims to strike a balance between both in order to enable responsible open-science in the field of AI.
-
-This License governs the use of the model (and its derivatives) and is informed by the model card associated with the model.
-
-NOW THEREFORE, You and Licensor agree as follows:
-
-1. Definitions
-
-- "License" means the terms and conditions for use, reproduction, and Distribution as defined in this document.
-- "Data" means a collection of information and/or content extracted from the dataset used with the Model, including to train, pretrain, or otherwise evaluate the Model. The Data is not licensed under this License.
-- "Output" means the results of operating a Model as embodied in informational content resulting therefrom.
-- "Model" means any accompanying machine-learning based assemblies (including checkpoints), consisting of learnt weights, parameters (including optimizer states), corresponding to the model architecture as embodied in the Complementary Material, that have been trained or tuned, in whole or in part on the Data, using the Complementary Material.
-- "Derivatives of the Model" means all modifications to the Model, works based on the Model, or any other model which is created or initialized by transfer of patterns of the weights, parameters, activations or output of the Model, to the other model, in order to cause the other model to perform similarly to the Model, including - but not limited to - distillation methods entailing the use of intermediate data representations or methods based on the generation of synthetic data by the Model for training the other model.
-- "Complementary Material" means the accompanying source code and scripts used to define, run, load, benchmark or evaluate the Model, and used to prepare data for training or evaluation, if any. This includes any accompanying documentation, tutorials, examples, etc, if any.
-- "Distribution" means any transmission, reproduction, publication or other sharing of the Model or Derivatives of the Model to a third party, including providing the Model as a hosted service made available by electronic or other remote means - e.g. API-based or web access.
-- "Licensor" means the copyright owner or entity authorized by the copyright owner that is granting the License, including the persons or entities that may have rights in the Model and/or distributing the Model.
-- "You" (or "Your") means an individual or Legal Entity exercising permissions granted by this License and/or making use of the Model for whichever purpose and in any field of use, including usage of the Model in an end-use application - e.g. chatbot, translator, image generator.
-- "Third Parties" means individuals or legal entities that are not under common control with Licensor or You.
-- "Contribution" means any work of authorship, including the original version of the Model and any modifications or additions to that Model or Derivatives of the Model thereof, that is intentionally submitted to Licensor for inclusion in the Model by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, "submitted" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Model, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as "Not a Contribution."
-- "Contributor" means Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Model.
-
-Section II: INTELLECTUAL PROPERTY RIGHTS
-
-Both copyright and patent grants apply to the Model, Derivatives of the Model and Complementary Material. The Model and Derivatives of the Model are subject to additional terms as described in Section III.
-
-2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare, publicly display, publicly perform, sublicense, and distribute the Complementary Material, the Model, and Derivatives of the Model.
-3. Grant of Patent License. Subject to the terms and conditions of this License and where and as applicable, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this paragraph) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Model and the Complementary Material, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Model to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Model and/or Complementary Material or a Contribution incorporated within the Model and/or Complementary Material constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for the Model and/or Work shall terminate as of the date such litigation is asserted or filed.
-
-Section III: CONDITIONS OF USAGE, DISTRIBUTION AND REDISTRIBUTION
-
-4. Distribution and Redistribution. You may host for Third Party remote access purposes (e.g. software-as-a-service), reproduce and distribute copies of the Model or Derivatives of the Model thereof in any medium, with or without modifications, provided that You meet the following conditions:
-Use-based restrictions as referenced in paragraph 5 MUST be included as an enforceable provision by You in any type of legal agreement (e.g. a license) governing the use and/or distribution of the Model or Derivatives of the Model, and You shall give notice to subsequent users You Distribute to, that the Model or Derivatives of the Model are subject to paragraph 5. This provision does not apply to the use of Complementary Material.
-You must give any Third Party recipients of the Model or Derivatives of the Model a copy of this License;
-You must cause any modified files to carry prominent notices stating that You changed the files;
-You must retain all copyright, patent, trademark, and attribution notices excluding those notices that do not pertain to any part of the Model, Derivatives of the Model.
-You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions - respecting paragraph 4.a. - for use, reproduction, or Distribution of Your modifications, or for any such Derivatives of the Model as a whole, provided Your use, reproduction, and Distribution of the Model otherwise complies with the conditions stated in this License.
-5. Use-based restrictions. The restrictions set forth in Attachment A are considered Use-based restrictions. Therefore You cannot use the Model and the Derivatives of the Model for the specified restricted uses. You may use the Model subject to this License, including only for lawful purposes and in accordance with the License. Use may include creating any content with, finetuning, updating, running, training, evaluating and/or reparametrizing the Model. You shall require all of Your users who use the Model or a Derivative of the Model to comply with the terms of this paragraph (paragraph 5).
-6. The Output You Generate. Except as set forth herein, Licensor claims no rights in the Output You generate using the Model. You are accountable for the Output you generate and its subsequent uses. No use of the output can contravene any provision as stated in the License.
-
-Section IV: OTHER PROVISIONS
-
-7. Updates and Runtime Restrictions. To the maximum extent permitted by law, Licensor reserves the right to restrict (remotely or otherwise) usage of the Model in violation of this License.
-8. Trademarks and related. Nothing in this License permits You to make use of Licensors’ trademarks, trade names, logos or to otherwise suggest endorsement or misrepresent the relationship between the parties; and any rights not expressly granted herein are reserved by the Licensors.
-9. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Model and the Complementary Material (and each Contributor provides its Contributions) on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Model, Derivatives of the Model, and the Complementary Material and assume any risks associated with Your exercise of permissions under this License.
-10. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Model and the Complementary Material (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages.
-11. Accepting Warranty or Additional Liability. While redistributing the Model, Derivatives of the Model and the Complementary Material thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability.
-12. If any provision of this License is held to be invalid, illegal or unenforceable, the remaining provisions shall be unaffected thereby and remain valid as if such provision had not been set forth herein.
-
-END OF TERMS AND CONDITIONS
-
-
-
-
-Attachment A
-
-Use Restrictions
-
-You agree not to use the Model or Derivatives of the Model:
-
-- In any way that violates any applicable national, federal, state, local or international law or regulation;
-- For the purpose of exploiting, harming or attempting to exploit or harm minors in any way;
-- To generate or disseminate verifiably false information and/or content with the purpose of harming others;
-- To generate or disseminate personal identifiable information that can be used to harm an individual;
-- To defame, disparage or otherwise harass others;
-- For fully automated decision making that adversely impacts an individual’s legal rights or otherwise creates or modifies a binding, enforceable obligation;
-- For any use intended to or which has the effect of discriminating against or harming individuals or groups based on online or offline social behavior or known or predicted personal or personality characteristics;
-- To exploit any of the vulnerabilities of a specific group of persons based on their age, social, physical or mental characteristics, in order to materially distort the behavior of a person pertaining to that group in a manner that causes or is likely to cause that person or another person physical or psychological harm;
-- For any use intended to or which has the effect of discriminating against individuals or groups based on legally protected characteristics or categories;
-- To provide medical advice and medical results interpretation;
-- To generate or disseminate information for the purpose to be used for administration of justice, law enforcement, immigration or asylum processes, such as predicting an individual will commit fraud/crime commitment (e.g. by text profiling, drawing causal relationships between assertions made in documents, indiscriminate and arbitrarily-targeted use).
-
diff --git a/repositories/stable-diffusion-stability-ai/README.md b/repositories/stable-diffusion-stability-ai/README.md
deleted file mode 100644
index 2dfddca33cf71da343a937cf0fb5e32150f16fe3..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/README.md
+++ /dev/null
@@ -1,302 +0,0 @@
-# Stable Diffusion Version 2
-![t2i](assets/stable-samples/txt2img/768/merged-0006.png)
-![t2i](assets/stable-samples/txt2img/768/merged-0002.png)
-![t2i](assets/stable-samples/txt2img/768/merged-0005.png)
-
-This repository contains [Stable Diffusion](https://github.com/CompVis/stable-diffusion) models trained from scratch and will be continuously updated with
-new checkpoints. The following list provides an overview of all currently available models. More coming soon.
-
-## News
-
-
-**March 24, 2023**
-
-*Stable UnCLIP 2.1*
-
-- New stable diffusion finetune (_Stable unCLIP 2.1_, [Hugging Face](https://huggingface.co/stabilityai/)) at 768x768 resolution,  based on SD2.1-768. This model allows for image variations and mixing operations as described in [*Hierarchical Text-Conditional Image Generation with CLIP Latents*](https://arxiv.org/abs/2204.06125), and, thanks to its modularity, can be combined with other models such as [KARLO](https://github.com/kakaobrain/karlo). Comes in two variants: [*Stable unCLIP-L*](https://huggingface.co/stabilityai/stable-diffusion-2-1-unclip/blob/main/sd21-unclip-l.ckpt) and [*Stable unCLIP-H*](https://huggingface.co/stabilityai/stable-diffusion-2-1-unclip/blob/main/sd21-unclip-h.ckpt), which are conditioned on CLIP ViT-L and ViT-H image embeddings, respectively. Instructions are available [here](doc/UNCLIP.MD).
-
-- A public demo of SD-unCLIP is already available at [clipdrop.co/stable-diffusion-reimagine](https://clipdrop.co/stable-diffusion-reimagine)
-
-
-**December 7, 2022**
-
-*Version 2.1*
-
-- New stable diffusion model (_Stable Diffusion 2.1-v_, [Hugging Face](https://huggingface.co/stabilityai/stable-diffusion-2-1)) at 768x768 resolution and (_Stable Diffusion 2.1-base_, [HuggingFace](https://huggingface.co/stabilityai/stable-diffusion-2-1-base)) at 512x512 resolution, both based on the same number of parameters and architecture as 2.0 and fine-tuned on 2.0, on a less restrictive NSFW filtering of the [LAION-5B](https://laion.ai/blog/laion-5b/) dataset.
-Per default, the attention operation of the model is evaluated at full precision when `xformers` is not installed. To enable fp16 (which can cause numerical instabilities with the vanilla attention module on the v2.1 model) , run your script with `ATTN_PRECISION=fp16 python <thescript.py>`
-
-**November 24, 2022**
-
-*Version 2.0*
-
-- New stable diffusion model (_Stable Diffusion 2.0-v_) at 768x768 resolution. Same number of parameters in the U-Net as 1.5, but uses [OpenCLIP-ViT/H](https://github.com/mlfoundations/open_clip) as the text encoder and is trained from scratch. _SD 2.0-v_ is a so-called [v-prediction](https://arxiv.org/abs/2202.00512) model. 
-- The above model is finetuned from _SD 2.0-base_, which was trained as a standard noise-prediction model on 512x512 images and is also made available.
-- Added a [x4 upscaling latent text-guided diffusion model](#image-upscaling-with-stable-diffusion).
-- New [depth-guided stable diffusion model](#depth-conditional-stable-diffusion), finetuned from _SD 2.0-base_. The model is conditioned on monocular depth estimates inferred via [MiDaS](https://github.com/isl-org/MiDaS) and can be used for structure-preserving img2img and shape-conditional synthesis.
-
-  ![d2i](assets/stable-samples/depth2img/depth2img01.png)
-- A [text-guided inpainting model](#image-inpainting-with-stable-diffusion), finetuned from SD _2.0-base_.
-
-We follow the [original repository](https://github.com/CompVis/stable-diffusion) and provide basic inference scripts to sample from the models.
-
-________________
-*The original Stable Diffusion model was created in a collaboration with [CompVis](https://arxiv.org/abs/2202.00512) and [RunwayML](https://runwayml.com/) and builds upon the work:*
-
-[**High-Resolution Image Synthesis with Latent Diffusion Models**](https://ommer-lab.com/research/latent-diffusion-models/)<br/>
-[Robin Rombach](https://github.com/rromb)\*,
-[Andreas Blattmann](https://github.com/ablattmann)\*,
-[Dominik Lorenz](https://github.com/qp-qp)\,
-[Patrick Esser](https://github.com/pesser),
-[Björn Ommer](https://hci.iwr.uni-heidelberg.de/Staff/bommer)<br/>
-_[CVPR '22 Oral](https://openaccess.thecvf.com/content/CVPR2022/html/Rombach_High-Resolution_Image_Synthesis_With_Latent_Diffusion_Models_CVPR_2022_paper.html) |
-[GitHub](https://github.com/CompVis/latent-diffusion) | [arXiv](https://arxiv.org/abs/2112.10752) | [Project page](https://ommer-lab.com/research/latent-diffusion-models/)_
-
-and [many others](#shout-outs).
-
-Stable Diffusion is a latent text-to-image diffusion model.
-________________________________
-  
-## Requirements
-
-You can update an existing [latent diffusion](https://github.com/CompVis/latent-diffusion) environment by running
-
-```
-conda install pytorch==1.12.1 torchvision==0.13.1 -c pytorch
-pip install transformers==4.19.2 diffusers invisible-watermark
-pip install -e .
-``` 
-#### xformers efficient attention
-For more efficiency and speed on GPUs, 
-we highly recommended installing the [xformers](https://github.com/facebookresearch/xformers)
-library.
-
-Tested on A100 with CUDA 11.4.
-Installation needs a somewhat recent version of nvcc and gcc/g++, obtain those, e.g., via 
-```commandline
-export CUDA_HOME=/usr/local/cuda-11.4
-conda install -c nvidia/label/cuda-11.4.0 cuda-nvcc
-conda install -c conda-forge gcc
-conda install -c conda-forge gxx_linux-64==9.5.0
-```
-
-Then, run the following (compiling takes up to 30 min).
-
-```commandline
-cd ..
-git clone https://github.com/facebookresearch/xformers.git
-cd xformers
-git submodule update --init --recursive
-pip install -r requirements.txt
-pip install -e .
-cd ../stablediffusion
-```
-Upon successful installation, the code will automatically default to [memory efficient attention](https://github.com/facebookresearch/xformers)
-for the self- and cross-attention layers in the U-Net and autoencoder.
-
-## General Disclaimer
-Stable Diffusion models are general text-to-image diffusion models and therefore mirror biases and (mis-)conceptions that are present
-in their training data. Although efforts were made to reduce the inclusion of explicit pornographic material, **we do not recommend using the provided weights for services or products without additional safety mechanisms and considerations.
-The weights are research artifacts and should be treated as such.**
-Details on the training procedure and data, as well as the intended use of the model can be found in the corresponding [model card](https://huggingface.co/stabilityai/stable-diffusion-2).
-The weights are available via [the StabilityAI organization at Hugging Face](https://huggingface.co/StabilityAI) under the [CreativeML Open RAIL++-M License](LICENSE-MODEL). 
-
-
-
-## Stable Diffusion v2
-
-Stable Diffusion v2 refers to a specific configuration of the model
-architecture that uses a downsampling-factor 8 autoencoder with an 865M UNet
-and OpenCLIP ViT-H/14 text encoder for the diffusion model. The _SD 2-v_ model produces 768x768 px outputs. 
-
-Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0,
-5.0, 6.0, 7.0, 8.0) and 50 DDIM sampling steps show the relative improvements of the checkpoints:
-
-![sd evaluation results](assets/model-variants.jpg)
-
-
-
-### Text-to-Image
-![txt2img-stable2](assets/stable-samples/txt2img/merged-0003.png)
-![txt2img-stable2](assets/stable-samples/txt2img/merged-0001.png)
-
-Stable Diffusion 2 is a latent diffusion model conditioned on the penultimate text embeddings of a CLIP ViT-H/14 text encoder.
-We provide a [reference script for sampling](#reference-sampling-script).
-#### Reference Sampling Script
-
-This script incorporates an [invisible watermarking](https://github.com/ShieldMnt/invisible-watermark) of the outputs, to help viewers [identify the images as machine-generated](scripts/tests/test_watermark.py).
-We provide the configs for the _SD2-v_ (768px) and _SD2-base_ (512px) model.
-
-First, download the weights for [_SD2.1-v_](https://huggingface.co/stabilityai/stable-diffusion-2-1) and [_SD2.1-base_](https://huggingface.co/stabilityai/stable-diffusion-2-1-base). 
-
-To sample from the _SD2.1-v_ model, run the following:
-
-```
-python scripts/txt2img.py --prompt "a professional photograph of an astronaut riding a horse" --ckpt <path/to/768model.ckpt/> --config configs/stable-diffusion/v2-inference-v.yaml --H 768 --W 768  
-```
-or try out the Web Demo: [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/stabilityai/stable-diffusion).
-
-To sample from the base model, use
-```
-python scripts/txt2img.py --prompt "a professional photograph of an astronaut riding a horse" --ckpt <path/to/model.ckpt/> --config <path/to/config.yaml/>  
-```
-
-By default, this uses the [DDIM sampler](https://arxiv.org/abs/2010.02502), and renders images of size 768x768 (which it was trained on) in 50 steps. 
-Empirically, the v-models can be sampled with higher guidance scales.
-
-Note: The inference config for all model versions is designed to be used with EMA-only checkpoints. 
-For this reason `use_ema=False` is set in the configuration, otherwise the code will try to switch from
-non-EMA to EMA weights. 
-
-#### Enable Intel® Extension for PyTorch* optimizations in Text-to-Image script
-
-If you're planning on running Text-to-Image on Intel® CPU, try to sample an image with TorchScript and Intel® Extension for PyTorch* optimizations. Intel® Extension for PyTorch* extends PyTorch by enabling up-to-date features optimizations for an extra performance boost on Intel® hardware. It can optimize memory layout of the operators to Channel Last memory format, which is generally beneficial for Intel CPUs, take advantage of the most advanced instruction set available on a machine, optimize operators and many more.
-
-**Prerequisites**
-
-Before running the script, make sure you have all needed libraries installed. (the optimization was checked on `Ubuntu 20.04`). Install [jemalloc](https://github.com/jemalloc/jemalloc), [numactl](https://linux.die.net/man/8/numactl), Intel® OpenMP and Intel® Extension for PyTorch*.
-
-```bash
-apt-get install numactl libjemalloc-dev
-pip install intel-openmp
-pip install intel_extension_for_pytorch -f https://software.intel.com/ipex-whl-stable
-```
-
-To sample from the _SD2.1-v_ model with TorchScript+IPEX optimizations, run the following. Remember to specify desired number of instances you want to run the program on ([more](https://github.com/intel/intel-extension-for-pytorch/blob/master/intel_extension_for_pytorch/cpu/launch.py#L48)).
-
-```
-MALLOC_CONF=oversize_threshold:1,background_thread:true,metadata_thp:auto,dirty_decay_ms:9000000000,muzzy_decay_ms:9000000000 python -m intel_extension_for_pytorch.cpu.launch --ninstance <number of an instance> --enable_jemalloc scripts/txt2img.py --prompt \"a corgi is playing guitar, oil on canvas\" --ckpt <path/to/768model.ckpt/> --config configs/stable-diffusion/intel/v2-inference-v-fp32.yaml  --H 768 --W 768 --precision full --device cpu --torchscript --ipex
-```
-
-To sample from the base model with IPEX optimizations, use
-
-```
-MALLOC_CONF=oversize_threshold:1,background_thread:true,metadata_thp:auto,dirty_decay_ms:9000000000,muzzy_decay_ms:9000000000 python -m intel_extension_for_pytorch.cpu.launch --ninstance <number of an instance> --enable_jemalloc scripts/txt2img.py --prompt \"a corgi is playing guitar, oil on canvas\" --ckpt <path/to/model.ckpt/> --config configs/stable-diffusion/intel/v2-inference-fp32.yaml  --n_samples 1 --n_iter 4 --precision full --device cpu --torchscript --ipex
-```
-
-If you're using a CPU that supports `bfloat16`, consider sample from the model with bfloat16 enabled for a performance boost, like so
-
-```bash
-# SD2.1-v
-MALLOC_CONF=oversize_threshold:1,background_thread:true,metadata_thp:auto,dirty_decay_ms:9000000000,muzzy_decay_ms:9000000000 python -m intel_extension_for_pytorch.cpu.launch --ninstance <number of an instance> --enable_jemalloc scripts/txt2img.py --prompt \"a corgi is playing guitar, oil on canvas\" --ckpt <path/to/768model.ckpt/> --config configs/stable-diffusion/intel/v2-inference-v-bf16.yaml --H 768 --W 768 --precision full --device cpu --torchscript --ipex --bf16
-# SD2.1-base
-MALLOC_CONF=oversize_threshold:1,background_thread:true,metadata_thp:auto,dirty_decay_ms:9000000000,muzzy_decay_ms:9000000000 python -m intel_extension_for_pytorch.cpu.launch --ninstance <number of an instance> --enable_jemalloc scripts/txt2img.py --prompt \"a corgi is playing guitar, oil on canvas\" --ckpt <path/to/model.ckpt/> --config configs/stable-diffusion/intel/v2-inference-bf16.yaml --precision full --device cpu --torchscript --ipex --bf16
-```
-
-### Image Modification with Stable Diffusion
-
-![depth2img-stable2](assets/stable-samples/depth2img/merged-0000.png)
-#### Depth-Conditional Stable Diffusion
-
-To augment the well-established [img2img](https://github.com/CompVis/stable-diffusion#image-modification-with-stable-diffusion) functionality of Stable Diffusion, we provide a _shape-preserving_ stable diffusion model.
-
-
-Note that the original method for image modification introduces significant semantic changes w.r.t. the initial image.
-If that is not desired, download our [depth-conditional stable diffusion](https://huggingface.co/stabilityai/stable-diffusion-2-depth) model and the `dpt_hybrid` MiDaS [model weights](https://github.com/intel-isl/DPT/releases/download/1_0/dpt_hybrid-midas-501f0c75.pt), place the latter in a folder `midas_models` and sample via 
-```
-python scripts/gradio/depth2img.py configs/stable-diffusion/v2-midas-inference.yaml <path-to-ckpt>
-```
-
-or
-
-```
-streamlit run scripts/streamlit/depth2img.py configs/stable-diffusion/v2-midas-inference.yaml <path-to-ckpt>
-```
-
-This method can be used on the samples of the base model itself.
-For example, take [this sample](assets/stable-samples/depth2img/old_man.png) generated by an anonymous discord user.
-Using the [gradio](https://gradio.app) or [streamlit](https://streamlit.io/) script `depth2img.py`, the MiDaS model first infers a monocular depth estimate given this input, 
-and the diffusion model is then conditioned on the (relative) depth output.
-
-<p align="center">
-<b> depth2image </b><br/>
-<img src=assets/stable-samples/depth2img/d2i.gif>
-</p>
-
-This model is particularly useful for a photorealistic style; see the [examples](assets/stable-samples/depth2img).
-For a maximum strength of 1.0, the model removes all pixel-based information and only relies on the text prompt and the inferred monocular depth estimate.
-
-![depth2img-stable3](assets/stable-samples/depth2img/merged-0005.png)
-
-#### Classic Img2Img
-
-For running the "classic" img2img, use
-```
-python scripts/img2img.py --prompt "A fantasy landscape, trending on artstation" --init-img <path-to-img.jpg> --strength 0.8 --ckpt <path/to/model.ckpt>
-```
-and adapt the checkpoint and config paths accordingly.
-
-### Image Upscaling with Stable Diffusion
-![upscaling-x4](assets/stable-samples/upscaling/merged-dog.png)
-After [downloading the weights](https://huggingface.co/stabilityai/stable-diffusion-x4-upscaler), run
-```
-python scripts/gradio/superresolution.py configs/stable-diffusion/x4-upscaling.yaml <path-to-checkpoint>
-```
-
-or
-
-```
-streamlit run scripts/streamlit/superresolution.py -- configs/stable-diffusion/x4-upscaling.yaml <path-to-checkpoint>
-```
-
-for a Gradio or Streamlit demo of the text-guided x4 superresolution model.  
-This model can be used both on real inputs and on synthesized examples. For the latter, we recommend setting a higher 
-`noise_level`, e.g. `noise_level=100`.
-
-### Image Inpainting with Stable Diffusion
-
-![inpainting-stable2](assets/stable-inpainting/merged-leopards.png)
-
-[Download the SD 2.0-inpainting checkpoint](https://huggingface.co/stabilityai/stable-diffusion-2-inpainting) and run
-
-```
-python scripts/gradio/inpainting.py configs/stable-diffusion/v2-inpainting-inference.yaml <path-to-checkpoint>
-```
-
-or
-
-```
-streamlit run scripts/streamlit/inpainting.py -- configs/stable-diffusion/v2-inpainting-inference.yaml <path-to-checkpoint>
-```
-
-for a Gradio or Streamlit demo of the inpainting model. 
-This scripts adds invisible watermarking to the demo in the [RunwayML](https://github.com/runwayml/stable-diffusion/blob/main/scripts/inpaint_st.py) repository, but both should work interchangeably with the checkpoints/configs.  
-
-
-
-## Shout-Outs
-- Thanks to [Hugging Face](https://huggingface.co/) and in particular [Apolinário](https://github.com/apolinario)  for support with our model releases!
-- Stable Diffusion would not be possible without [LAION](https://laion.ai/) and their efforts to create open, large-scale datasets.
-- The [DeepFloyd team](https://twitter.com/deepfloydai) at Stability AI, for creating the subset of [LAION-5B](https://laion.ai/blog/laion-5b/) dataset used to train the model.
-- Stable Diffusion 2.0 uses [OpenCLIP](https://laion.ai/blog/large-openclip/), trained by [Romain Beaumont](https://github.com/rom1504).  
-- Our codebase for the diffusion models builds heavily on [OpenAI's ADM codebase](https://github.com/openai/guided-diffusion)
-and [https://github.com/lucidrains/denoising-diffusion-pytorch](https://github.com/lucidrains/denoising-diffusion-pytorch). 
-Thanks for open-sourcing!
-- [CompVis](https://github.com/CompVis/stable-diffusion) initial stable diffusion release
-- [Patrick](https://github.com/pesser)'s [implementation](https://github.com/runwayml/stable-diffusion/blob/main/scripts/inpaint_st.py) of the streamlit demo for inpainting.
-- `img2img` is an application of [SDEdit](https://arxiv.org/abs/2108.01073) by [Chenlin Meng](https://cs.stanford.edu/~chenlin/) from the [Stanford AI Lab](https://cs.stanford.edu/~ermon/website/). 
-- [Kat's implementation]((https://github.com/CompVis/latent-diffusion/pull/51)) of the [PLMS](https://arxiv.org/abs/2202.09778) sampler, and [more](https://github.com/crowsonkb/k-diffusion).
-- [DPMSolver](https://arxiv.org/abs/2206.00927) [integration](https://github.com/CompVis/stable-diffusion/pull/440) by [Cheng Lu](https://github.com/LuChengTHU).
-- Facebook's [xformers](https://github.com/facebookresearch/xformers) for efficient attention computation.
-- [MiDaS](https://github.com/isl-org/MiDaS) for monocular depth estimation.
-
-
-## License
-
-The code in this repository is released under the MIT License.
-
-The weights are available via [the StabilityAI organization at Hugging Face](https://huggingface.co/StabilityAI), and released under the [CreativeML Open RAIL++-M License](LICENSE-MODEL) License.
-
-## BibTeX
-
-```
-@misc{rombach2021highresolution,
-      title={High-Resolution Image Synthesis with Latent Diffusion Models}, 
-      author={Robin Rombach and Andreas Blattmann and Dominik Lorenz and Patrick Esser and Björn Ommer},
-      year={2021},
-      eprint={2112.10752},
-      archivePrefix={arXiv},
-      primaryClass={cs.CV}
-}
-```
-
-
diff --git a/repositories/stable-diffusion-stability-ai/assets/model-variants.jpg b/repositories/stable-diffusion-stability-ai/assets/model-variants.jpg
deleted file mode 100644
index de5bb3fc8af5d60d0c0fac8c0c866e9f8d857ba3..0000000000000000000000000000000000000000
Binary files a/repositories/stable-diffusion-stability-ai/assets/model-variants.jpg and /dev/null differ
diff --git a/repositories/stable-diffusion-stability-ai/assets/modelfigure.png b/repositories/stable-diffusion-stability-ai/assets/modelfigure.png
deleted file mode 100644
index 6b1d3e6b9d59fd8d38468e7bce47c903a4e1c932..0000000000000000000000000000000000000000
Binary files a/repositories/stable-diffusion-stability-ai/assets/modelfigure.png and /dev/null differ
diff --git a/repositories/stable-diffusion-stability-ai/assets/rick.jpeg b/repositories/stable-diffusion-stability-ai/assets/rick.jpeg
deleted file mode 100644
index 995486061ba50bd0ae2e213c72de87a27326632f..0000000000000000000000000000000000000000
Binary files a/repositories/stable-diffusion-stability-ai/assets/rick.jpeg and /dev/null differ
diff --git a/repositories/stable-diffusion-stability-ai/assets/stable-inpainting/inpainting.gif b/repositories/stable-diffusion-stability-ai/assets/stable-inpainting/inpainting.gif
deleted file mode 100644
index 4f511347bc057f858db95015725488560ec1f676..0000000000000000000000000000000000000000
Binary files a/repositories/stable-diffusion-stability-ai/assets/stable-inpainting/inpainting.gif and /dev/null differ
diff --git a/repositories/stable-diffusion-stability-ai/assets/stable-inpainting/merged-leopards.png b/repositories/stable-diffusion-stability-ai/assets/stable-inpainting/merged-leopards.png
deleted file mode 100644
index b877158fff2a15d4016135ce2307e19ece877b59..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/assets/stable-inpainting/merged-leopards.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:94a05d717a340d7b240283e72e91984e82093750ba066aa05ab0759188467e69
-size 4958974
diff --git a/repositories/stable-diffusion-stability-ai/assets/stable-samples/depth2img/d2i.gif b/repositories/stable-diffusion-stability-ai/assets/stable-samples/depth2img/d2i.gif
deleted file mode 100644
index 265c1d1ae56feb60c5aabbf88526a6ed47fda383..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/assets/stable-samples/depth2img/d2i.gif
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:7236fc7f4349740c537ef9c8730590c15d198aaf42925a46755ded26bc436bc4
-size 1140400
diff --git a/repositories/stable-diffusion-stability-ai/assets/stable-samples/depth2img/depth2fantasy.jpeg b/repositories/stable-diffusion-stability-ai/assets/stable-samples/depth2img/depth2fantasy.jpeg
deleted file mode 100644
index 8c0c94ee7354cc19e4c86134900397c1ed13eb01..0000000000000000000000000000000000000000
Binary files a/repositories/stable-diffusion-stability-ai/assets/stable-samples/depth2img/depth2fantasy.jpeg and /dev/null differ
diff --git a/repositories/stable-diffusion-stability-ai/assets/stable-samples/depth2img/depth2img01.png b/repositories/stable-diffusion-stability-ai/assets/stable-samples/depth2img/depth2img01.png
deleted file mode 100644
index f062b45a32bf71ce830b4ab2401475d421417d03..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/assets/stable-samples/depth2img/depth2img01.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:60cb68c273602eae8e2fb769a2848e55844d812196260ada112a9aecc604f735
-size 3324111
diff --git a/repositories/stable-diffusion-stability-ai/assets/stable-samples/depth2img/depth2img02.png b/repositories/stable-diffusion-stability-ai/assets/stable-samples/depth2img/depth2img02.png
deleted file mode 100644
index f91cad5b401955221d8875529578504762ca6bfc..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/assets/stable-samples/depth2img/depth2img02.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:adabff92594b17a2554408257d2ab61eb9b89270d5917eafd44a9b75740aab04
-size 1775470
diff --git a/repositories/stable-diffusion-stability-ai/assets/stable-samples/depth2img/merged-0000.png b/repositories/stable-diffusion-stability-ai/assets/stable-samples/depth2img/merged-0000.png
deleted file mode 100644
index fce6c7a0b76bd34a2028a20efa5e5a497953cc7e..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/assets/stable-samples/depth2img/merged-0000.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:b19da6052f01a3b115ac3315ef5db1b7dcdb58091879c0dfe3895a7765a491ac
-size 2129264
diff --git a/repositories/stable-diffusion-stability-ai/assets/stable-samples/depth2img/merged-0004.png b/repositories/stable-diffusion-stability-ai/assets/stable-samples/depth2img/merged-0004.png
deleted file mode 100644
index 41641d3e2d07575fb5fa0e87609770ab1bae8126..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/assets/stable-samples/depth2img/merged-0004.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:0d3ae25218f696375aa953e55d12d034da3bd7abce68616a3af916bdae01cc86
-size 1448945
diff --git a/repositories/stable-diffusion-stability-ai/assets/stable-samples/depth2img/merged-0005.png b/repositories/stable-diffusion-stability-ai/assets/stable-samples/depth2img/merged-0005.png
deleted file mode 100644
index 46634965fdf77e2134e2ac574fe02c12cd34d96e..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/assets/stable-samples/depth2img/merged-0005.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:935252e202a3a6cafa476443f3a0ae3ac95cf85c37c0133f4f32af2aafb8f9ab
-size 4546641
diff --git a/repositories/stable-diffusion-stability-ai/assets/stable-samples/depth2img/midas.jpeg b/repositories/stable-diffusion-stability-ai/assets/stable-samples/depth2img/midas.jpeg
deleted file mode 100644
index 4890ab7c6ca378f27dcf1eef6012848798713c94..0000000000000000000000000000000000000000
Binary files a/repositories/stable-diffusion-stability-ai/assets/stable-samples/depth2img/midas.jpeg and /dev/null differ
diff --git a/repositories/stable-diffusion-stability-ai/assets/stable-samples/depth2img/old_man.png b/repositories/stable-diffusion-stability-ai/assets/stable-samples/depth2img/old_man.png
deleted file mode 100644
index 44bbc0051b5f12743b1c7ef92f23ed5758eed4e2..0000000000000000000000000000000000000000
Binary files a/repositories/stable-diffusion-stability-ai/assets/stable-samples/depth2img/old_man.png and /dev/null differ
diff --git a/repositories/stable-diffusion-stability-ai/assets/stable-samples/img2img/mountains-1.png b/repositories/stable-diffusion-stability-ai/assets/stable-samples/img2img/mountains-1.png
deleted file mode 100644
index d01b8350743e3bd4fdf653d1563ee7d5c2153323..0000000000000000000000000000000000000000
Binary files a/repositories/stable-diffusion-stability-ai/assets/stable-samples/img2img/mountains-1.png and /dev/null differ
diff --git a/repositories/stable-diffusion-stability-ai/assets/stable-samples/img2img/mountains-2.png b/repositories/stable-diffusion-stability-ai/assets/stable-samples/img2img/mountains-2.png
deleted file mode 100644
index e9f4e708535f0e5b53372a3a39e6aa31dce383fd..0000000000000000000000000000000000000000
Binary files a/repositories/stable-diffusion-stability-ai/assets/stable-samples/img2img/mountains-2.png and /dev/null differ
diff --git a/repositories/stable-diffusion-stability-ai/assets/stable-samples/img2img/mountains-3.png b/repositories/stable-diffusion-stability-ai/assets/stable-samples/img2img/mountains-3.png
deleted file mode 100644
index 017de3012c2f03e4f87cce21b4d3342713b9ae95..0000000000000000000000000000000000000000
Binary files a/repositories/stable-diffusion-stability-ai/assets/stable-samples/img2img/mountains-3.png and /dev/null differ
diff --git a/repositories/stable-diffusion-stability-ai/assets/stable-samples/img2img/sketch-mountains-input.jpg b/repositories/stable-diffusion-stability-ai/assets/stable-samples/img2img/sketch-mountains-input.jpg
deleted file mode 100644
index 79d652b8003bbcd1d0c0ba2d984dbbe299ac5916..0000000000000000000000000000000000000000
Binary files a/repositories/stable-diffusion-stability-ai/assets/stable-samples/img2img/sketch-mountains-input.jpg and /dev/null differ
diff --git a/repositories/stable-diffusion-stability-ai/assets/stable-samples/img2img/upscaling-in.png b/repositories/stable-diffusion-stability-ai/assets/stable-samples/img2img/upscaling-in.png
deleted file mode 100644
index 6a16bf53a95850a4eb7730105cea61af7c435c5e..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/assets/stable-samples/img2img/upscaling-in.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:16e043b62bdfcc5be7d0eca5c747878b78e4e6ffaeb3cd1257568cbc2b5e6f7a
-size 1167237
diff --git a/repositories/stable-diffusion-stability-ai/assets/stable-samples/img2img/upscaling-out.png b/repositories/stable-diffusion-stability-ai/assets/stable-samples/img2img/upscaling-out.png
deleted file mode 100644
index b7926bc81099736f7c8df32cadc4481c07eddbd6..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/assets/stable-samples/img2img/upscaling-out.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:c171218814d141f87884672cb00ae07c3ed0e14ce7f7023f2041678e01d93f59
-size 1317941
diff --git a/repositories/stable-diffusion-stability-ai/assets/stable-samples/stable-unclip/houses_out.jpeg b/repositories/stable-diffusion-stability-ai/assets/stable-samples/stable-unclip/houses_out.jpeg
deleted file mode 100644
index b4b8fb9e9cb3087d02b4ebac9591dbc9d44fcd50..0000000000000000000000000000000000000000
Binary files a/repositories/stable-diffusion-stability-ai/assets/stable-samples/stable-unclip/houses_out.jpeg and /dev/null differ
diff --git a/repositories/stable-diffusion-stability-ai/assets/stable-samples/stable-unclip/oldcar000.jpeg b/repositories/stable-diffusion-stability-ai/assets/stable-samples/stable-unclip/oldcar000.jpeg
deleted file mode 100644
index d61c85e0d832a2fa9592d375bf5a1ca1d9bcab09..0000000000000000000000000000000000000000
Binary files a/repositories/stable-diffusion-stability-ai/assets/stable-samples/stable-unclip/oldcar000.jpeg and /dev/null differ
diff --git a/repositories/stable-diffusion-stability-ai/assets/stable-samples/stable-unclip/oldcar500.jpeg b/repositories/stable-diffusion-stability-ai/assets/stable-samples/stable-unclip/oldcar500.jpeg
deleted file mode 100644
index 175d2825101fd64f5a11456c2f86e15f1b281a83..0000000000000000000000000000000000000000
Binary files a/repositories/stable-diffusion-stability-ai/assets/stable-samples/stable-unclip/oldcar500.jpeg and /dev/null differ
diff --git a/repositories/stable-diffusion-stability-ai/assets/stable-samples/stable-unclip/oldcar800.jpeg b/repositories/stable-diffusion-stability-ai/assets/stable-samples/stable-unclip/oldcar800.jpeg
deleted file mode 100644
index d4d91130c0fb4a97389b27cdc8824c0df4d9c485..0000000000000000000000000000000000000000
Binary files a/repositories/stable-diffusion-stability-ai/assets/stable-samples/stable-unclip/oldcar800.jpeg and /dev/null differ
diff --git a/repositories/stable-diffusion-stability-ai/assets/stable-samples/stable-unclip/panda.jpg b/repositories/stable-diffusion-stability-ai/assets/stable-samples/stable-unclip/panda.jpg
deleted file mode 100644
index 49aa1ba4d5a818df96988f3dc2ade49200c767ab..0000000000000000000000000000000000000000
Binary files a/repositories/stable-diffusion-stability-ai/assets/stable-samples/stable-unclip/panda.jpg and /dev/null differ
diff --git a/repositories/stable-diffusion-stability-ai/assets/stable-samples/stable-unclip/plates_out.jpeg b/repositories/stable-diffusion-stability-ai/assets/stable-samples/stable-unclip/plates_out.jpeg
deleted file mode 100644
index ecbc11c3c281ad14a98457d0b531c1df19f6f9a3..0000000000000000000000000000000000000000
Binary files a/repositories/stable-diffusion-stability-ai/assets/stable-samples/stable-unclip/plates_out.jpeg and /dev/null differ
diff --git a/repositories/stable-diffusion-stability-ai/assets/stable-samples/stable-unclip/unclip-variations.png b/repositories/stable-diffusion-stability-ai/assets/stable-samples/stable-unclip/unclip-variations.png
deleted file mode 100644
index 80c656c49da2ffca9c2394423dc600d48b55588a..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/assets/stable-samples/stable-unclip/unclip-variations.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:e9bc45418c5c4ded4fe8ef054c6fc85fa23efe9bab4cdbc42d3ec55f2a57bc39
-size 1774803
diff --git a/repositories/stable-diffusion-stability-ai/assets/stable-samples/stable-unclip/unclip-variations_noise.png b/repositories/stable-diffusion-stability-ai/assets/stable-samples/stable-unclip/unclip-variations_noise.png
deleted file mode 100644
index 9f155786eb36901255c663ec843dafac09d89d67..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/assets/stable-samples/stable-unclip/unclip-variations_noise.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:83e2cdb18e95cb074db4d6d78dc7c2333936ab641e27945c358b2c4160eeb6da
-size 1541201
diff --git a/repositories/stable-diffusion-stability-ai/assets/stable-samples/txt2img/000002025.png b/repositories/stable-diffusion-stability-ai/assets/stable-samples/txt2img/000002025.png
deleted file mode 100644
index 66891c142e9cfe3e0b0193d16b5a45d7b72a7d8a..0000000000000000000000000000000000000000
Binary files a/repositories/stable-diffusion-stability-ai/assets/stable-samples/txt2img/000002025.png and /dev/null differ
diff --git a/repositories/stable-diffusion-stability-ai/assets/stable-samples/txt2img/000002035.png b/repositories/stable-diffusion-stability-ai/assets/stable-samples/txt2img/000002035.png
deleted file mode 100644
index c707c130932a13fae76584324368fedc33faeba4..0000000000000000000000000000000000000000
Binary files a/repositories/stable-diffusion-stability-ai/assets/stable-samples/txt2img/000002035.png and /dev/null differ
diff --git a/repositories/stable-diffusion-stability-ai/assets/stable-samples/txt2img/768/merged-0001.png b/repositories/stable-diffusion-stability-ai/assets/stable-samples/txt2img/768/merged-0001.png
deleted file mode 100644
index a3260bf98645b5f78e7eaf6d08b417a53c310fc2..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/assets/stable-samples/txt2img/768/merged-0001.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:ed10e1df0f4c0f83794310e59a77098b4836d96a2b12cc809ddf39e77b1b6c94
-size 4627059
diff --git a/repositories/stable-diffusion-stability-ai/assets/stable-samples/txt2img/768/merged-0002.png b/repositories/stable-diffusion-stability-ai/assets/stable-samples/txt2img/768/merged-0002.png
deleted file mode 100644
index 297dbfd110876b8e9783e72bb6cf650cf1c760fd..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/assets/stable-samples/txt2img/768/merged-0002.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:b4a009d112633ac788fbbe7a7176d4002e95407b64672afa8104755534bb4641
-size 3458875
diff --git a/repositories/stable-diffusion-stability-ai/assets/stable-samples/txt2img/768/merged-0003.png b/repositories/stable-diffusion-stability-ai/assets/stable-samples/txt2img/768/merged-0003.png
deleted file mode 100644
index 987e2a6dd7caff691af2656e115d9d5723b418dd..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/assets/stable-samples/txt2img/768/merged-0003.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:84adec03ab2e2d54990950a113af97750eab90135596461689efe5ebfa1ebf92
-size 3834227
diff --git a/repositories/stable-diffusion-stability-ai/assets/stable-samples/txt2img/768/merged-0004.png b/repositories/stable-diffusion-stability-ai/assets/stable-samples/txt2img/768/merged-0004.png
deleted file mode 100644
index f70da62c63f24b9d87347f8c8613dd1cf2993886..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/assets/stable-samples/txt2img/768/merged-0004.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:8ac938c03b4b554e1c475a4b3c5df50b72a890eec21542fa7911a8ff01bf13f4
-size 4097005
diff --git a/repositories/stable-diffusion-stability-ai/assets/stable-samples/txt2img/768/merged-0005.png b/repositories/stable-diffusion-stability-ai/assets/stable-samples/txt2img/768/merged-0005.png
deleted file mode 100644
index 067259de9ef2422adac2793d3417245fe2671b24..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/assets/stable-samples/txt2img/768/merged-0005.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:849b877a80752b4578afc2ece4ea0726768809298ee9a38284ba4e159d0a817c
-size 2166197
diff --git a/repositories/stable-diffusion-stability-ai/assets/stable-samples/txt2img/768/merged-0006.png b/repositories/stable-diffusion-stability-ai/assets/stable-samples/txt2img/768/merged-0006.png
deleted file mode 100644
index 0002baa798eaae545f6f2fc205bf6cb965a2b97c..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/assets/stable-samples/txt2img/768/merged-0006.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:2d31dbbf76633677be3b8eba933e9eec82825925535ef9c557a3003daf16ad42
-size 4371148
diff --git a/repositories/stable-diffusion-stability-ai/assets/stable-samples/txt2img/merged-0001.png b/repositories/stable-diffusion-stability-ai/assets/stable-samples/txt2img/merged-0001.png
deleted file mode 100644
index c76fd405d99ba9a83ea93c974d6fa12bd9555a8f..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/assets/stable-samples/txt2img/merged-0001.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:71ca5f77befffa10a2ef6d4b69f8bb721e7ebd7ea03538e2c359dc44f526b0e8
-size 2409358
diff --git a/repositories/stable-diffusion-stability-ai/assets/stable-samples/txt2img/merged-0003.png b/repositories/stable-diffusion-stability-ai/assets/stable-samples/txt2img/merged-0003.png
deleted file mode 100644
index cdc0e618cf42d225d417901b64b1203732eeb879..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/assets/stable-samples/txt2img/merged-0003.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:9fde5a40c512d61e2390e70d9f14b0d33f0af84cbde2dcd9d86e1f9b38072266
-size 2273528
diff --git a/repositories/stable-diffusion-stability-ai/assets/stable-samples/txt2img/merged-0005.png b/repositories/stable-diffusion-stability-ai/assets/stable-samples/txt2img/merged-0005.png
deleted file mode 100644
index 88fcd04df9f576070d97f5655733b7a185352676..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/assets/stable-samples/txt2img/merged-0005.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:a417aadc1d91b91531ca6bbf89840a36f432d8e9382aaa953610bedce22ff76f
-size 2576264
diff --git a/repositories/stable-diffusion-stability-ai/assets/stable-samples/txt2img/merged-0006.png b/repositories/stable-diffusion-stability-ai/assets/stable-samples/txt2img/merged-0006.png
deleted file mode 100644
index 831c2096d8a99cb50323e48e17cc3f4dbba60f8c..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/assets/stable-samples/txt2img/merged-0006.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:1d55ba7d103da275b4612976e93f405fcb593f7e6a6fda31f2e180b41c8e4f59
-size 2638534
diff --git a/repositories/stable-diffusion-stability-ai/assets/stable-samples/txt2img/merged-0007.png b/repositories/stable-diffusion-stability-ai/assets/stable-samples/txt2img/merged-0007.png
deleted file mode 100644
index 05e0f3fd7c8b6db82f15026053687558906e9e30..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/assets/stable-samples/txt2img/merged-0007.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:920ccf908b7fa5073a7c5cd3f4e109b5e66f7e29517ef5462ca55e931d0b5689
-size 2406799
diff --git a/repositories/stable-diffusion-stability-ai/assets/stable-samples/upscaling/merged-dog.png b/repositories/stable-diffusion-stability-ai/assets/stable-samples/upscaling/merged-dog.png
deleted file mode 100644
index 5af3eff5d6e42150b29a2c5a6cad5b3b025212d6..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/assets/stable-samples/upscaling/merged-dog.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:d85d15bd51b3fa162f2b020ccac5a64b10ced728c6f22dcba183dc65ab6e8b5a
-size 1823141
diff --git a/repositories/stable-diffusion-stability-ai/assets/stable-samples/upscaling/sampled-bear-x4.png b/repositories/stable-diffusion-stability-ai/assets/stable-samples/upscaling/sampled-bear-x4.png
deleted file mode 100644
index 00dc7730d2504b40de6958d41411252852a246cd..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/assets/stable-samples/upscaling/sampled-bear-x4.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:d4f2aaa8eb3054cda0a6e8577170d09c1494809cceca21973497602e17a22f1e
-size 3155724
diff --git a/repositories/stable-diffusion-stability-ai/assets/stable-samples/upscaling/snow-leopard-x4.png b/repositories/stable-diffusion-stability-ai/assets/stable-samples/upscaling/snow-leopard-x4.png
deleted file mode 100644
index 30a30a0929d898a5ea71b0b5e6403f54cb992da8..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/assets/stable-samples/upscaling/snow-leopard-x4.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:fe8231dddcf77ada4b46f6949b4ea7757ff2d006253e1807ba6e1168077aad19
-size 3887284
diff --git a/repositories/stable-diffusion-stability-ai/checkpoints/checkpoints.txt b/repositories/stable-diffusion-stability-ai/checkpoints/checkpoints.txt
deleted file mode 100644
index d92df3108fcbd2608ee4f9901d6f2a0eb081674f..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/checkpoints/checkpoints.txt
+++ /dev/null
@@ -1 +0,0 @@
-Put unCLIP checkpoints here.
\ No newline at end of file
diff --git a/repositories/stable-diffusion-stability-ai/configs/karlo/decoder_900M_vit_l.yaml b/repositories/stable-diffusion-stability-ai/configs/karlo/decoder_900M_vit_l.yaml
deleted file mode 100644
index 02a35303aaa3c3556c649768643814cbd7c8fe66..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/configs/karlo/decoder_900M_vit_l.yaml
+++ /dev/null
@@ -1,37 +0,0 @@
-model:
-  type: t2i-decoder
-  diffusion_sampler: uniform
-  hparams:
-    image_size: 64
-    num_channels: 320
-    num_res_blocks: 3
-    channel_mult: ''
-    attention_resolutions: 32,16,8
-    num_heads: -1
-    num_head_channels: 64
-    num_heads_upsample: -1
-    use_scale_shift_norm: true
-    dropout: 0.1
-    clip_dim: 768
-    clip_emb_mult: 4
-    text_ctx: 77
-    xf_width: 1536
-    xf_layers: 0
-    xf_heads: 0
-    xf_final_ln: false
-    resblock_updown: true
-    learn_sigma: true
-    text_drop: 0.3
-    clip_emb_type: image
-    clip_emb_drop: 0.1
-    use_plm: true
-
-diffusion:
-  steps: 1000
-  learn_sigma: true
-  sigma_small: false
-  noise_schedule: squaredcos_cap_v2
-  use_kl: false
-  predict_xstart: false
-  rescale_learned_sigmas: true
-  timestep_respacing: ''
diff --git a/repositories/stable-diffusion-stability-ai/configs/karlo/improved_sr_64_256_1.4B.yaml b/repositories/stable-diffusion-stability-ai/configs/karlo/improved_sr_64_256_1.4B.yaml
deleted file mode 100644
index 282d3cb0db6a51e076e9848f8d26e55e7ae406bb..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/configs/karlo/improved_sr_64_256_1.4B.yaml
+++ /dev/null
@@ -1,27 +0,0 @@
-model:
-  type: improved_sr_64_256
-  diffusion_sampler: uniform
-  hparams:
-    channels: 320
-    depth: 3
-    channels_multiple:
-    - 1
-    - 2
-    - 3
-    - 4
-    dropout: 0.0
-
-diffusion:
-  steps: 1000
-  learn_sigma: false
-  sigma_small: true
-  noise_schedule: squaredcos_cap_v2
-  use_kl: false
-  predict_xstart: false
-  rescale_learned_sigmas: true
-  timestep_respacing: '7'
-
-
-sampling:
-  timestep_respacing: '7' # fix
-  clip_denoise: true
diff --git a/repositories/stable-diffusion-stability-ai/configs/karlo/prior_1B_vit_l.yaml b/repositories/stable-diffusion-stability-ai/configs/karlo/prior_1B_vit_l.yaml
deleted file mode 100644
index 159330d3085527ae142f122b182cfec7a560a3db..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/configs/karlo/prior_1B_vit_l.yaml
+++ /dev/null
@@ -1,21 +0,0 @@
-model:
-  type: prior
-  diffusion_sampler: uniform
-  hparams:
-    text_ctx: 77
-    xf_width: 2048
-    xf_layers: 20
-    xf_heads: 32
-    xf_final_ln: true
-    text_drop: 0.2
-    clip_dim: 768
-
-diffusion:
-  steps: 1000
-  learn_sigma: false
-  sigma_small: true
-  noise_schedule: squaredcos_cap_v2
-  use_kl: false
-  predict_xstart: true
-  rescale_learned_sigmas: false
-  timestep_respacing: ''
diff --git a/repositories/stable-diffusion-stability-ai/configs/stable-diffusion/intel/v2-inference-bf16.yaml b/repositories/stable-diffusion-stability-ai/configs/stable-diffusion/intel/v2-inference-bf16.yaml
deleted file mode 100644
index 66f0dbd8331d84a78a3448fdb2a69dcd7020eb59..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/configs/stable-diffusion/intel/v2-inference-bf16.yaml
+++ /dev/null
@@ -1,71 +0,0 @@
-# Copyright (C) 2022 Intel Corporation
-# SPDX-License-Identifier: MIT
-
-model:
-  base_learning_rate: 1.0e-4
-  target: ldm.models.diffusion.ddpm.LatentDiffusion
-  params:
-    linear_start: 0.00085
-    linear_end: 0.0120
-    num_timesteps_cond: 1
-    log_every_t: 200
-    timesteps: 1000
-    first_stage_key: "jpg"
-    cond_stage_key: "txt"
-    image_size: 64
-    channels: 4
-    cond_stage_trainable: false
-    conditioning_key: crossattn
-    monitor: val/loss_simple_ema
-    scale_factor: 0.18215
-    use_ema: False # we set this to false because this is an inference only config
-
-    unet_config:
-      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
-      params:
-        use_checkpoint: False
-        use_fp16: False
-        use_bf16: True
-        image_size: 32 # unused
-        in_channels: 4
-        out_channels: 4
-        model_channels: 320
-        attention_resolutions: [ 4, 2, 1 ]
-        num_res_blocks: 2
-        channel_mult: [ 1, 2, 4, 4 ]
-        num_head_channels: 64 # need to fix for flash-attn
-        use_spatial_transformer: True
-        use_linear_in_transformer: True
-        transformer_depth: 1
-        context_dim: 1024
-        legacy: False
-
-    first_stage_config:
-      target: ldm.models.autoencoder.AutoencoderKL
-      params:
-        embed_dim: 4
-        monitor: val/rec_loss
-        ddconfig:
-          #attn_type: "vanilla-xformers"
-          double_z: true
-          z_channels: 4
-          resolution: 256
-          in_channels: 3
-          out_ch: 3
-          ch: 128
-          ch_mult:
-          - 1
-          - 2
-          - 4
-          - 4
-          num_res_blocks: 2
-          attn_resolutions: []
-          dropout: 0.0
-        lossconfig:
-          target: torch.nn.Identity
-
-    cond_stage_config:
-      target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder
-      params:
-        freeze: True
-        layer: "penultimate"
diff --git a/repositories/stable-diffusion-stability-ai/configs/stable-diffusion/intel/v2-inference-fp32.yaml b/repositories/stable-diffusion-stability-ai/configs/stable-diffusion/intel/v2-inference-fp32.yaml
deleted file mode 100644
index 7b66ac85830e8884b26cb9e70e094172c9ee5e62..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/configs/stable-diffusion/intel/v2-inference-fp32.yaml
+++ /dev/null
@@ -1,70 +0,0 @@
-# Copyright (C) 2022 Intel Corporation
-# SPDX-License-Identifier: MIT
-
-model:
-  base_learning_rate: 1.0e-4
-  target: ldm.models.diffusion.ddpm.LatentDiffusion
-  params:
-    linear_start: 0.00085
-    linear_end: 0.0120
-    num_timesteps_cond: 1
-    log_every_t: 200
-    timesteps: 1000
-    first_stage_key: "jpg"
-    cond_stage_key: "txt"
-    image_size: 64
-    channels: 4
-    cond_stage_trainable: false
-    conditioning_key: crossattn
-    monitor: val/loss_simple_ema
-    scale_factor: 0.18215
-    use_ema: False # we set this to false because this is an inference only config
-
-    unet_config:
-      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
-      params:
-        use_checkpoint: False
-        use_fp16: False
-        image_size: 32 # unused
-        in_channels: 4
-        out_channels: 4
-        model_channels: 320
-        attention_resolutions: [ 4, 2, 1 ]
-        num_res_blocks: 2
-        channel_mult: [ 1, 2, 4, 4 ]
-        num_head_channels: 64 # need to fix for flash-attn
-        use_spatial_transformer: True
-        use_linear_in_transformer: True
-        transformer_depth: 1
-        context_dim: 1024
-        legacy: False
-
-    first_stage_config:
-      target: ldm.models.autoencoder.AutoencoderKL
-      params:
-        embed_dim: 4
-        monitor: val/rec_loss
-        ddconfig:
-          #attn_type: "vanilla-xformers"
-          double_z: true
-          z_channels: 4
-          resolution: 256
-          in_channels: 3
-          out_ch: 3
-          ch: 128
-          ch_mult:
-          - 1
-          - 2
-          - 4
-          - 4
-          num_res_blocks: 2
-          attn_resolutions: []
-          dropout: 0.0
-        lossconfig:
-          target: torch.nn.Identity
-
-    cond_stage_config:
-      target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder
-      params:
-        freeze: True
-        layer: "penultimate"
diff --git a/repositories/stable-diffusion-stability-ai/configs/stable-diffusion/intel/v2-inference-v-bf16.yaml b/repositories/stable-diffusion-stability-ai/configs/stable-diffusion/intel/v2-inference-v-bf16.yaml
deleted file mode 100644
index 2b4b0e6fabce186a0e5cd29d4efa29eebee152f7..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/configs/stable-diffusion/intel/v2-inference-v-bf16.yaml
+++ /dev/null
@@ -1,72 +0,0 @@
-# Copyright (C) 2022 Intel Corporation
-# SPDX-License-Identifier: MIT
-
-model:
-  base_learning_rate: 1.0e-4
-  target: ldm.models.diffusion.ddpm.LatentDiffusion
-  params:
-    parameterization: "v"
-    linear_start: 0.00085
-    linear_end: 0.0120
-    num_timesteps_cond: 1
-    log_every_t: 200
-    timesteps: 1000
-    first_stage_key: "jpg"
-    cond_stage_key: "txt"
-    image_size: 64
-    channels: 4
-    cond_stage_trainable: false
-    conditioning_key: crossattn
-    monitor: val/loss_simple_ema
-    scale_factor: 0.18215
-    use_ema: False # we set this to false because this is an inference only config
-
-    unet_config:
-      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
-      params:
-        use_checkpoint: False
-        use_fp16: False
-        use_bf16: True
-        image_size: 32 # unused
-        in_channels: 4
-        out_channels: 4
-        model_channels: 320
-        attention_resolutions: [ 4, 2, 1 ]
-        num_res_blocks: 2
-        channel_mult: [ 1, 2, 4, 4 ]
-        num_head_channels: 64 # need to fix for flash-attn
-        use_spatial_transformer: True
-        use_linear_in_transformer: True
-        transformer_depth: 1
-        context_dim: 1024
-        legacy: False
-
-    first_stage_config:
-      target: ldm.models.autoencoder.AutoencoderKL
-      params:
-        embed_dim: 4
-        monitor: val/rec_loss
-        ddconfig:
-          #attn_type: "vanilla-xformers"
-          double_z: true
-          z_channels: 4
-          resolution: 256
-          in_channels: 3
-          out_ch: 3
-          ch: 128
-          ch_mult:
-          - 1
-          - 2
-          - 4
-          - 4
-          num_res_blocks: 2
-          attn_resolutions: []
-          dropout: 0.0
-        lossconfig:
-          target: torch.nn.Identity
-
-    cond_stage_config:
-      target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder
-      params:
-        freeze: True
-        layer: "penultimate"
diff --git a/repositories/stable-diffusion-stability-ai/configs/stable-diffusion/intel/v2-inference-v-fp32.yaml b/repositories/stable-diffusion-stability-ai/configs/stable-diffusion/intel/v2-inference-v-fp32.yaml
deleted file mode 100644
index 8ccd92e295d71681750f749bb7972a62cc44b3cb..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/configs/stable-diffusion/intel/v2-inference-v-fp32.yaml
+++ /dev/null
@@ -1,71 +0,0 @@
-# Copyright (C) 2022 Intel Corporation
-# SPDX-License-Identifier: MIT
-
-model:
-  base_learning_rate: 1.0e-4
-  target: ldm.models.diffusion.ddpm.LatentDiffusion
-  params:
-    parameterization: "v"
-    linear_start: 0.00085
-    linear_end: 0.0120
-    num_timesteps_cond: 1
-    log_every_t: 200
-    timesteps: 1000
-    first_stage_key: "jpg"
-    cond_stage_key: "txt"
-    image_size: 64
-    channels: 4
-    cond_stage_trainable: false
-    conditioning_key: crossattn
-    monitor: val/loss_simple_ema
-    scale_factor: 0.18215
-    use_ema: False # we set this to false because this is an inference only config
-
-    unet_config:
-      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
-      params:
-        use_checkpoint: False
-        use_fp16: False
-        image_size: 32 # unused
-        in_channels: 4
-        out_channels: 4
-        model_channels: 320
-        attention_resolutions: [ 4, 2, 1 ]
-        num_res_blocks: 2
-        channel_mult: [ 1, 2, 4, 4 ]
-        num_head_channels: 64 # need to fix for flash-attn
-        use_spatial_transformer: True
-        use_linear_in_transformer: True
-        transformer_depth: 1
-        context_dim: 1024
-        legacy: False
-
-    first_stage_config:
-      target: ldm.models.autoencoder.AutoencoderKL
-      params:
-        embed_dim: 4
-        monitor: val/rec_loss
-        ddconfig:
-          #attn_type: "vanilla-xformers"
-          double_z: true
-          z_channels: 4
-          resolution: 256
-          in_channels: 3
-          out_ch: 3
-          ch: 128
-          ch_mult:
-          - 1
-          - 2
-          - 4
-          - 4
-          num_res_blocks: 2
-          attn_resolutions: []
-          dropout: 0.0
-        lossconfig:
-          target: torch.nn.Identity
-
-    cond_stage_config:
-      target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder
-      params:
-        freeze: True
-        layer: "penultimate"
diff --git a/repositories/stable-diffusion-stability-ai/configs/stable-diffusion/v2-1-stable-unclip-h-inference.yaml b/repositories/stable-diffusion-stability-ai/configs/stable-diffusion/v2-1-stable-unclip-h-inference.yaml
deleted file mode 100644
index 1bd0c64d3cdf2e3027fcd68b986b3236d4b4101c..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/configs/stable-diffusion/v2-1-stable-unclip-h-inference.yaml
+++ /dev/null
@@ -1,80 +0,0 @@
-model:
-  base_learning_rate: 1.0e-04
-  target: ldm.models.diffusion.ddpm.ImageEmbeddingConditionedLatentDiffusion
-  params:
-    embedding_dropout: 0.25
-    parameterization: "v"
-    linear_start: 0.00085
-    linear_end: 0.0120
-    log_every_t: 200
-    timesteps: 1000
-    first_stage_key: "jpg"
-    cond_stage_key: "txt"
-    image_size: 96
-    channels: 4
-    cond_stage_trainable: false
-    conditioning_key: crossattn-adm
-    scale_factor: 0.18215
-    monitor: val/loss_simple_ema
-    use_ema: False
-
-    embedder_config:
-      target: ldm.modules.encoders.modules.FrozenOpenCLIPImageEmbedder
-
-    noise_aug_config:
-      target: ldm.modules.encoders.modules.CLIPEmbeddingNoiseAugmentation
-      params:
-        timestep_dim: 1024
-        noise_schedule_config:
-          timesteps: 1000
-          beta_schedule: squaredcos_cap_v2
-
-    unet_config:
-      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
-      params:
-        num_classes: "sequential"
-        adm_in_channels: 2048
-        use_checkpoint: True
-        image_size: 32 # unused
-        in_channels: 4
-        out_channels: 4
-        model_channels: 320
-        attention_resolutions: [ 4, 2, 1 ]
-        num_res_blocks: 2
-        channel_mult: [ 1, 2, 4, 4 ]
-        num_head_channels: 64 # need to fix for flash-attn
-        use_spatial_transformer: True
-        use_linear_in_transformer: True
-        transformer_depth: 1
-        context_dim: 1024
-        legacy: False
-
-    first_stage_config:
-      target: ldm.models.autoencoder.AutoencoderKL
-      params:
-        embed_dim: 4
-        monitor: val/rec_loss
-        ddconfig:
-          attn_type: "vanilla-xformers"
-          double_z: true
-          z_channels: 4
-          resolution: 256
-          in_channels: 3
-          out_ch: 3
-          ch: 128
-          ch_mult:
-            - 1
-            - 2
-            - 4
-            - 4
-          num_res_blocks: 2
-          attn_resolutions: [ ]
-          dropout: 0.0
-        lossconfig:
-          target: torch.nn.Identity
-
-    cond_stage_config:
-      target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder
-      params:
-        freeze: True
-        layer: "penultimate"
diff --git a/repositories/stable-diffusion-stability-ai/configs/stable-diffusion/v2-1-stable-unclip-l-inference.yaml b/repositories/stable-diffusion-stability-ai/configs/stable-diffusion/v2-1-stable-unclip-l-inference.yaml
deleted file mode 100644
index 335fd61f3e559ba093320b67c950dec5494bf489..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/configs/stable-diffusion/v2-1-stable-unclip-l-inference.yaml
+++ /dev/null
@@ -1,83 +0,0 @@
-model:
-  base_learning_rate: 1.0e-04
-  target: ldm.models.diffusion.ddpm.ImageEmbeddingConditionedLatentDiffusion
-  params:
-    embedding_dropout: 0.25
-    parameterization: "v"
-    linear_start: 0.00085
-    linear_end: 0.0120
-    log_every_t: 200
-    timesteps: 1000
-    first_stage_key: "jpg"
-    cond_stage_key: "txt"
-    image_size: 96
-    channels: 4
-    cond_stage_trainable: false
-    conditioning_key: crossattn-adm
-    scale_factor: 0.18215
-    monitor: val/loss_simple_ema
-    use_ema: False
-
-    embedder_config:
-      target: ldm.modules.encoders.modules.ClipImageEmbedder
-      params:
-        model: "ViT-L/14"
-
-    noise_aug_config:
-      target: ldm.modules.encoders.modules.CLIPEmbeddingNoiseAugmentation
-      params:
-        clip_stats_path: "checkpoints/karlo_models/ViT-L-14_stats.th"
-        timestep_dim: 768
-        noise_schedule_config:
-          timesteps: 1000
-          beta_schedule: squaredcos_cap_v2
-
-    unet_config:
-      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
-      params:
-        num_classes: "sequential"
-        adm_in_channels: 1536
-        use_checkpoint: True
-        image_size: 32 # unused
-        in_channels: 4
-        out_channels: 4
-        model_channels: 320
-        attention_resolutions: [ 4, 2, 1 ]
-        num_res_blocks: 2
-        channel_mult: [ 1, 2, 4, 4 ]
-        num_head_channels: 64 # need to fix for flash-attn
-        use_spatial_transformer: True
-        use_linear_in_transformer: True
-        transformer_depth: 1
-        context_dim: 1024
-        legacy: False
-
-    first_stage_config:
-      target: ldm.models.autoencoder.AutoencoderKL
-      params:
-        embed_dim: 4
-        monitor: val/rec_loss
-        ddconfig:
-          attn_type: "vanilla-xformers"
-          double_z: true
-          z_channels: 4
-          resolution: 256
-          in_channels: 3
-          out_ch: 3
-          ch: 128
-          ch_mult:
-            - 1
-            - 2
-            - 4
-            - 4
-          num_res_blocks: 2
-          attn_resolutions: [ ]
-          dropout: 0.0
-        lossconfig:
-          target: torch.nn.Identity
-
-    cond_stage_config:
-      target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder
-      params:
-        freeze: True
-        layer: "penultimate"
\ No newline at end of file
diff --git a/repositories/stable-diffusion-stability-ai/configs/stable-diffusion/v2-inference-v.yaml b/repositories/stable-diffusion-stability-ai/configs/stable-diffusion/v2-inference-v.yaml
deleted file mode 100644
index 8ec8dfbfefe94ae8522c93017668fea78d580acf..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/configs/stable-diffusion/v2-inference-v.yaml
+++ /dev/null
@@ -1,68 +0,0 @@
-model:
-  base_learning_rate: 1.0e-4
-  target: ldm.models.diffusion.ddpm.LatentDiffusion
-  params:
-    parameterization: "v"
-    linear_start: 0.00085
-    linear_end: 0.0120
-    num_timesteps_cond: 1
-    log_every_t: 200
-    timesteps: 1000
-    first_stage_key: "jpg"
-    cond_stage_key: "txt"
-    image_size: 64
-    channels: 4
-    cond_stage_trainable: false
-    conditioning_key: crossattn
-    monitor: val/loss_simple_ema
-    scale_factor: 0.18215
-    use_ema: False # we set this to false because this is an inference only config
-
-    unet_config:
-      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
-      params:
-        use_checkpoint: True
-        use_fp16: True
-        image_size: 32 # unused
-        in_channels: 4
-        out_channels: 4
-        model_channels: 320
-        attention_resolutions: [ 4, 2, 1 ]
-        num_res_blocks: 2
-        channel_mult: [ 1, 2, 4, 4 ]
-        num_head_channels: 64 # need to fix for flash-attn
-        use_spatial_transformer: True
-        use_linear_in_transformer: True
-        transformer_depth: 1
-        context_dim: 1024
-        legacy: False
-
-    first_stage_config:
-      target: ldm.models.autoencoder.AutoencoderKL
-      params:
-        embed_dim: 4
-        monitor: val/rec_loss
-        ddconfig:
-          #attn_type: "vanilla-xformers"
-          double_z: true
-          z_channels: 4
-          resolution: 256
-          in_channels: 3
-          out_ch: 3
-          ch: 128
-          ch_mult:
-          - 1
-          - 2
-          - 4
-          - 4
-          num_res_blocks: 2
-          attn_resolutions: []
-          dropout: 0.0
-        lossconfig:
-          target: torch.nn.Identity
-
-    cond_stage_config:
-      target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder
-      params:
-        freeze: True
-        layer: "penultimate"
diff --git a/repositories/stable-diffusion-stability-ai/configs/stable-diffusion/v2-inference.yaml b/repositories/stable-diffusion-stability-ai/configs/stable-diffusion/v2-inference.yaml
deleted file mode 100644
index 152c4f3c2b36c3b246a9cb10eb8166134b0d2e1c..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/configs/stable-diffusion/v2-inference.yaml
+++ /dev/null
@@ -1,67 +0,0 @@
-model:
-  base_learning_rate: 1.0e-4
-  target: ldm.models.diffusion.ddpm.LatentDiffusion
-  params:
-    linear_start: 0.00085
-    linear_end: 0.0120
-    num_timesteps_cond: 1
-    log_every_t: 200
-    timesteps: 1000
-    first_stage_key: "jpg"
-    cond_stage_key: "txt"
-    image_size: 64
-    channels: 4
-    cond_stage_trainable: false
-    conditioning_key: crossattn
-    monitor: val/loss_simple_ema
-    scale_factor: 0.18215
-    use_ema: False # we set this to false because this is an inference only config
-
-    unet_config:
-      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
-      params:
-        use_checkpoint: True
-        use_fp16: True
-        image_size: 32 # unused
-        in_channels: 4
-        out_channels: 4
-        model_channels: 320
-        attention_resolutions: [ 4, 2, 1 ]
-        num_res_blocks: 2
-        channel_mult: [ 1, 2, 4, 4 ]
-        num_head_channels: 64 # need to fix for flash-attn
-        use_spatial_transformer: True
-        use_linear_in_transformer: True
-        transformer_depth: 1
-        context_dim: 1024
-        legacy: False
-
-    first_stage_config:
-      target: ldm.models.autoencoder.AutoencoderKL
-      params:
-        embed_dim: 4
-        monitor: val/rec_loss
-        ddconfig:
-          #attn_type: "vanilla-xformers"
-          double_z: true
-          z_channels: 4
-          resolution: 256
-          in_channels: 3
-          out_ch: 3
-          ch: 128
-          ch_mult:
-          - 1
-          - 2
-          - 4
-          - 4
-          num_res_blocks: 2
-          attn_resolutions: []
-          dropout: 0.0
-        lossconfig:
-          target: torch.nn.Identity
-
-    cond_stage_config:
-      target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder
-      params:
-        freeze: True
-        layer: "penultimate"
diff --git a/repositories/stable-diffusion-stability-ai/configs/stable-diffusion/v2-inpainting-inference.yaml b/repositories/stable-diffusion-stability-ai/configs/stable-diffusion/v2-inpainting-inference.yaml
deleted file mode 100644
index 32a9471d71b828c51bcbbabfe34c5f6c8282c803..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/configs/stable-diffusion/v2-inpainting-inference.yaml
+++ /dev/null
@@ -1,158 +0,0 @@
-model:
-  base_learning_rate: 5.0e-05
-  target: ldm.models.diffusion.ddpm.LatentInpaintDiffusion
-  params:
-    linear_start: 0.00085
-    linear_end: 0.0120
-    num_timesteps_cond: 1
-    log_every_t: 200
-    timesteps: 1000
-    first_stage_key: "jpg"
-    cond_stage_key: "txt"
-    image_size: 64
-    channels: 4
-    cond_stage_trainable: false
-    conditioning_key: hybrid
-    scale_factor: 0.18215
-    monitor: val/loss_simple_ema
-    finetune_keys: null
-    use_ema: False
-
-    unet_config:
-      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
-      params:
-        use_checkpoint: True
-        image_size: 32 # unused
-        in_channels: 9
-        out_channels: 4
-        model_channels: 320
-        attention_resolutions: [ 4, 2, 1 ]
-        num_res_blocks: 2
-        channel_mult: [ 1, 2, 4, 4 ]
-        num_head_channels: 64 # need to fix for flash-attn
-        use_spatial_transformer: True
-        use_linear_in_transformer: True
-        transformer_depth: 1
-        context_dim: 1024
-        legacy: False
-
-    first_stage_config:
-      target: ldm.models.autoencoder.AutoencoderKL
-      params:
-        embed_dim: 4
-        monitor: val/rec_loss
-        ddconfig:
-          #attn_type: "vanilla-xformers"
-          double_z: true
-          z_channels: 4
-          resolution: 256
-          in_channels: 3
-          out_ch: 3
-          ch: 128
-          ch_mult:
-            - 1
-            - 2
-            - 4
-            - 4
-          num_res_blocks: 2
-          attn_resolutions: [ ]
-          dropout: 0.0
-        lossconfig:
-          target: torch.nn.Identity
-
-    cond_stage_config:
-      target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder
-      params:
-        freeze: True
-        layer: "penultimate"
-
-
-data:
-  target: ldm.data.laion.WebDataModuleFromConfig
-  params:
-    tar_base: null  # for concat as in LAION-A
-    p_unsafe_threshold: 0.1
-    filter_word_list: "data/filters.yaml"
-    max_pwatermark: 0.45
-    batch_size: 8
-    num_workers: 6
-    multinode: True
-    min_size: 512
-    train:
-      shards:
-        - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-0/{00000..18699}.tar -"
-        - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-1/{00000..18699}.tar -"
-        - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-2/{00000..18699}.tar -"
-        - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-3/{00000..18699}.tar -"
-        - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-4/{00000..18699}.tar -"  #{00000-94333}.tar"
-      shuffle: 10000
-      image_key: jpg
-      image_transforms:
-      - target: torchvision.transforms.Resize
-        params:
-          size: 512
-          interpolation: 3
-      - target: torchvision.transforms.RandomCrop
-        params:
-          size: 512
-      postprocess:
-        target: ldm.data.laion.AddMask
-        params:
-          mode: "512train-large"
-          p_drop: 0.25
-    # NOTE use enough shards to avoid empty validation loops in workers
-    validation:
-      shards:
-        - "pipe:aws s3 cp s3://deep-floyd-s3/datasets/laion_cleaned-part5/{93001..94333}.tar - "
-      shuffle: 0
-      image_key: jpg
-      image_transforms:
-      - target: torchvision.transforms.Resize
-        params:
-          size: 512
-          interpolation: 3
-      - target: torchvision.transforms.CenterCrop
-        params:
-          size: 512
-      postprocess:
-        target: ldm.data.laion.AddMask
-        params:
-          mode: "512train-large"
-          p_drop: 0.25
-
-lightning:
-  find_unused_parameters: True
-  modelcheckpoint:
-    params:
-      every_n_train_steps: 5000
-
-  callbacks:
-    metrics_over_trainsteps_checkpoint:
-      params:
-        every_n_train_steps: 10000
-
-    image_logger:
-      target: main.ImageLogger
-      params:
-        enable_autocast: False
-        disabled: False
-        batch_frequency: 1000
-        max_images: 4
-        increase_log_steps: False
-        log_first_step: False
-        log_images_kwargs:
-          use_ema_scope: False
-          inpaint: False
-          plot_progressive_rows: False
-          plot_diffusion_rows: False
-          N: 4
-          unconditional_guidance_scale: 5.0
-          unconditional_guidance_label: [""]
-          ddim_steps: 50  # todo check these out for depth2img,
-          ddim_eta: 0.0   # todo check these out for depth2img,
-
-  trainer:
-    benchmark: True
-    val_check_interval: 5000000
-    num_sanity_val_steps: 0
-    accumulate_grad_batches: 1
diff --git a/repositories/stable-diffusion-stability-ai/configs/stable-diffusion/v2-midas-inference.yaml b/repositories/stable-diffusion-stability-ai/configs/stable-diffusion/v2-midas-inference.yaml
deleted file mode 100644
index f20c30f618b81091e31c2c4cf15325fa38638af4..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/configs/stable-diffusion/v2-midas-inference.yaml
+++ /dev/null
@@ -1,74 +0,0 @@
-model:
-  base_learning_rate: 5.0e-07
-  target: ldm.models.diffusion.ddpm.LatentDepth2ImageDiffusion
-  params:
-    linear_start: 0.00085
-    linear_end: 0.0120
-    num_timesteps_cond: 1
-    log_every_t: 200
-    timesteps: 1000
-    first_stage_key: "jpg"
-    cond_stage_key: "txt"
-    image_size: 64
-    channels: 4
-    cond_stage_trainable: false
-    conditioning_key: hybrid
-    scale_factor: 0.18215
-    monitor: val/loss_simple_ema
-    finetune_keys: null
-    use_ema: False
-
-    depth_stage_config:
-      target: ldm.modules.midas.api.MiDaSInference
-      params:
-        model_type: "dpt_hybrid"
-
-    unet_config:
-      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
-      params:
-        use_checkpoint: True
-        image_size: 32 # unused
-        in_channels: 5
-        out_channels: 4
-        model_channels: 320
-        attention_resolutions: [ 4, 2, 1 ]
-        num_res_blocks: 2
-        channel_mult: [ 1, 2, 4, 4 ]
-        num_head_channels: 64 # need to fix for flash-attn
-        use_spatial_transformer: True
-        use_linear_in_transformer: True
-        transformer_depth: 1
-        context_dim: 1024
-        legacy: False
-
-    first_stage_config:
-      target: ldm.models.autoencoder.AutoencoderKL
-      params:
-        embed_dim: 4
-        monitor: val/rec_loss
-        ddconfig:
-          #attn_type: "vanilla-xformers"
-          double_z: true
-          z_channels: 4
-          resolution: 256
-          in_channels: 3
-          out_ch: 3
-          ch: 128
-          ch_mult:
-            - 1
-            - 2
-            - 4
-            - 4
-          num_res_blocks: 2
-          attn_resolutions: [ ]
-          dropout: 0.0
-        lossconfig:
-          target: torch.nn.Identity
-
-    cond_stage_config:
-      target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder
-      params:
-        freeze: True
-        layer: "penultimate"
-
-
diff --git a/repositories/stable-diffusion-stability-ai/configs/stable-diffusion/x4-upscaling.yaml b/repositories/stable-diffusion-stability-ai/configs/stable-diffusion/x4-upscaling.yaml
deleted file mode 100644
index 2db0964af699f86d1891c761710a2d53f59b842c..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/configs/stable-diffusion/x4-upscaling.yaml
+++ /dev/null
@@ -1,76 +0,0 @@
-model:
-  base_learning_rate: 1.0e-04
-  target: ldm.models.diffusion.ddpm.LatentUpscaleDiffusion
-  params:
-    parameterization: "v"
-    low_scale_key: "lr"
-    linear_start: 0.0001
-    linear_end: 0.02
-    num_timesteps_cond: 1
-    log_every_t: 200
-    timesteps: 1000
-    first_stage_key: "jpg"
-    cond_stage_key: "txt"
-    image_size: 128
-    channels: 4
-    cond_stage_trainable: false
-    conditioning_key: "hybrid-adm"
-    monitor: val/loss_simple_ema
-    scale_factor: 0.08333
-    use_ema: False
-
-    low_scale_config:
-      target: ldm.modules.diffusionmodules.upscaling.ImageConcatWithNoiseAugmentation
-      params:
-        noise_schedule_config: # image space
-          linear_start: 0.0001
-          linear_end: 0.02
-        max_noise_level: 350
-
-    unet_config:
-      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
-      params:
-        use_checkpoint: True
-        num_classes: 1000  # timesteps for noise conditioning (here constant, just need one)
-        image_size: 128
-        in_channels: 7
-        out_channels: 4
-        model_channels: 256
-        attention_resolutions: [ 2,4,8]
-        num_res_blocks: 2
-        channel_mult: [ 1, 2, 2, 4]
-        disable_self_attentions: [True, True, True, False]
-        disable_middle_self_attn: False
-        num_heads: 8
-        use_spatial_transformer: True
-        transformer_depth: 1
-        context_dim: 1024
-        legacy: False
-        use_linear_in_transformer: True
-
-    first_stage_config:
-      target: ldm.models.autoencoder.AutoencoderKL
-      params:
-        embed_dim: 4
-        ddconfig:
-          # attn_type: "vanilla-xformers" this model needs efficient attention to be feasible on HR data, also the decoder seems to break in half precision (UNet is fine though)
-          double_z: True
-          z_channels: 4
-          resolution: 256
-          in_channels: 3
-          out_ch: 3
-          ch: 128
-          ch_mult: [ 1,2,4 ]  # num_down = len(ch_mult)-1
-          num_res_blocks: 2
-          attn_resolutions: [ ]
-          dropout: 0.0
-
-        lossconfig:
-          target: torch.nn.Identity
-
-    cond_stage_config:
-      target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder
-      params:
-        freeze: True
-        layer: "penultimate"
-
diff --git a/repositories/stable-diffusion-stability-ai/doc/UNCLIP.MD b/repositories/stable-diffusion-stability-ai/doc/UNCLIP.MD
deleted file mode 100644
index 989533b99599725cc9d61ae6055fa97f9a885f23..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/doc/UNCLIP.MD
+++ /dev/null
@@ -1,88 +0,0 @@
-### Stable unCLIP
-
-[unCLIP](https://openai.com/dall-e-2/) is the approach behind OpenAI's [DALL·E 2](https://openai.com/dall-e-2/), 
-trained to invert CLIP image embeddings. 
-We finetuned SD 2.1 to accept a CLIP ViT-L/14 image embedding in addition to the text encodings.
-This means that the model can be used to produce image variations, but can also be combined with a text-to-image
-embedding prior to yield a full text-to-image model at 768x768 resolution.
-
-If you would like to try a demo of this model on the web, please visit https://clipdrop.co/stable-diffusion-reimagine
-
-We provide two models, trained on OpenAI CLIP-L and OpenCLIP-H image embeddings, respectively, 
-available from [https://huggingface.co/stabilityai/stable-diffusion-2-1-unclip](https://huggingface.co/stabilityai/stable-diffusion-2-1-unclip/tree/main).
-To use them, download from Hugging Face, and put and the weights into the `checkpoints` folder.
-
-#### Image Variations
-![image-variations-l-1](../assets/stable-samples/stable-unclip/unclip-variations.png)
-
-Diffusers integration 
-Stable UnCLIP Image Variations is integrated with the [🧨 diffusers](https://github.com/huggingface/diffusers) library
-```python
-#pip install git+https://github.com/huggingface/diffusers.git transformers accelerate
-import requests
-import torch
-from PIL import Image
-from io import BytesIO
-
-from diffusers import StableUnCLIPImg2ImgPipeline
-
-#Start the StableUnCLIP Image variations pipeline
-pipe = StableUnCLIPImg2ImgPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-2-1-unclip", torch_dtype=torch.float16, variation="fp16"
-)
-pipe = pipe.to("cuda")
-
-#Get image from URL
-url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/tarsila_do_amaral.png"
-response = requests.get(url)
-init_image = Image.open(BytesIO(response.content)).convert("RGB")
-
-#Pipe to make the variation
-images = pipe(init_image).images
-images[0].save("tarsila_variation.png")
-```
-Check out the [Stable UnCLIP pipeline docs here](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_unclip)
-
-Streamlit UI demo
-
-```
-streamlit run scripts/streamlit/stableunclip.py
-```
-to launch a streamlit script than can be used to make image variations with both models (CLIP-L and OpenCLIP-H).
-These models can process a `noise_level`, which specifies an amount of Gaussian noise added to the CLIP embeddings. 
-This can be used to increase output variance as in the following examples.
-
-![image-variations-noise](../assets/stable-samples/stable-unclip/unclip-variations_noise.png)
-
-
-### Stable Diffusion Meets Karlo
-![panda](../assets/stable-samples/stable-unclip/panda.jpg) 
-
-Recently, [KakaoBrain](https://kakaobrain.com/) openly released [Karlo](https://github.com/kakaobrain/karlo), a pretrained, large-scale replication of [unCLIP](https://arxiv.org/abs/2204.06125).
-We introduce _Stable Karlo_, a combination of the Karlo CLIP image embedding prior, and Stable Diffusion v2.1-768.
-
-To run the model, first download the KARLO checkpoints
-```shell
-mkdir -p checkpoints/karlo_models
-cd checkpoints/karlo_models
-wget https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/096db1af569b284eb76b3881534822d9/ViT-L-14.pt
-wget https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/0b62380a75e56f073e2844ab5199153d/ViT-L-14_stats.th
-wget https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/85626483eaca9f581e2a78d31ff905ca/prior-ckpt-step%3D01000000-of-01000000.ckpt
-cd ../../
-```
-and the finetuned SD2.1 unCLIP-L checkpoint from [here](https://huggingface.co/stabilityai/stable-diffusion-2-1-unclip/blob/main/sd21-unclip-l.ckpt), and put the ckpt into the `checkpoints folder` 
-
-Then, run
-
-```
-streamlit run scripts/streamlit/stableunclip.py
-```
-and pick the `use_karlo` option in the GUI.
-The script optionally supports sampling from the full Karlo model. To use it, download the 64x64 decoder and 64->256 upscaler 
-via 
-```shell
-cd checkpoints/karlo_models
-wget https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/efdf6206d8ed593961593dc029a8affa/decoder-ckpt-step%3D01000000-of-01000000.ckpt
-wget https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/4226b831ae0279020d134281f3c31590/improved-sr-ckpt-step%3D1.2M.ckpt
-cd ../../
-```
diff --git a/repositories/stable-diffusion-stability-ai/environment.yaml b/repositories/stable-diffusion-stability-ai/environment.yaml
deleted file mode 100644
index 4687b309b60ae2d6040fcb3f90a380cf6fb11b21..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/environment.yaml
+++ /dev/null
@@ -1,29 +0,0 @@
-name: ldm
-channels:
-  - pytorch
-  - defaults
-dependencies:
-  - python=3.8.5
-  - pip=20.3
-  - cudatoolkit=11.3
-  - pytorch=1.12.1
-  - torchvision=0.13.1
-  - numpy=1.23.1
-  - pip:
-    - albumentations==1.3.0
-    - opencv-python==4.6.0.66
-    - imageio==2.9.0
-    - imageio-ffmpeg==0.4.2
-    - pytorch-lightning==1.4.2
-    - omegaconf==2.1.1
-    - test-tube>=0.7.5
-    - streamlit==1.12.1
-    - einops==0.3.0
-    - transformers==4.19.2
-    - webdataset==0.2.5
-    - kornia==0.6
-    - open_clip_torch==2.0.2
-    - invisible-watermark>=0.1.5
-    - streamlit-drawable-canvas==0.8.0
-    - torchmetrics==0.6.0
-    - -e .
diff --git a/repositories/stable-diffusion-stability-ai/ldm/__pycache__/util.cpython-310.pyc b/repositories/stable-diffusion-stability-ai/ldm/__pycache__/util.cpython-310.pyc
deleted file mode 100644
index c04914de352cc76e1655a159fefae05301339171..0000000000000000000000000000000000000000
Binary files a/repositories/stable-diffusion-stability-ai/ldm/__pycache__/util.cpython-310.pyc and /dev/null differ
diff --git a/repositories/stable-diffusion-stability-ai/ldm/data/__init__.py b/repositories/stable-diffusion-stability-ai/ldm/data/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/repositories/stable-diffusion-stability-ai/ldm/data/__pycache__/__init__.cpython-310.pyc b/repositories/stable-diffusion-stability-ai/ldm/data/__pycache__/__init__.cpython-310.pyc
deleted file mode 100644
index 2e86acae9c53fc40243f303127c3fb20946f2eaf..0000000000000000000000000000000000000000
Binary files a/repositories/stable-diffusion-stability-ai/ldm/data/__pycache__/__init__.cpython-310.pyc and /dev/null differ
diff --git a/repositories/stable-diffusion-stability-ai/ldm/data/__pycache__/util.cpython-310.pyc b/repositories/stable-diffusion-stability-ai/ldm/data/__pycache__/util.cpython-310.pyc
deleted file mode 100644
index 7ee7cfe87f7dc9ca8670cc4b96acc98b8c52596c..0000000000000000000000000000000000000000
Binary files a/repositories/stable-diffusion-stability-ai/ldm/data/__pycache__/util.cpython-310.pyc and /dev/null differ
diff --git a/repositories/stable-diffusion-stability-ai/ldm/data/util.py b/repositories/stable-diffusion-stability-ai/ldm/data/util.py
deleted file mode 100644
index 5b60ceb2349e3bd7900ff325740e2022d2903b1c..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/ldm/data/util.py
+++ /dev/null
@@ -1,24 +0,0 @@
-import torch
-
-from ldm.modules.midas.api import load_midas_transform
-
-
-class AddMiDaS(object):
-    def __init__(self, model_type):
-        super().__init__()
-        self.transform = load_midas_transform(model_type)
-
-    def pt2np(self, x):
-        x = ((x + 1.0) * .5).detach().cpu().numpy()
-        return x
-
-    def np2pt(self, x):
-        x = torch.from_numpy(x) * 2 - 1.
-        return x
-
-    def __call__(self, sample):
-        # sample['jpg'] is tensor hwc in [-1, 1] at this point
-        x = self.pt2np(sample['jpg'])
-        x = self.transform({"image": x})["image"]
-        sample['midas_in'] = x
-        return sample
\ No newline at end of file
diff --git a/repositories/stable-diffusion-stability-ai/ldm/models/__pycache__/autoencoder.cpython-310.pyc b/repositories/stable-diffusion-stability-ai/ldm/models/__pycache__/autoencoder.cpython-310.pyc
deleted file mode 100644
index 693ecf9887ad73fbe6ddebbc3f6dcb27bc4280e7..0000000000000000000000000000000000000000
Binary files a/repositories/stable-diffusion-stability-ai/ldm/models/__pycache__/autoencoder.cpython-310.pyc and /dev/null differ
diff --git a/repositories/stable-diffusion-stability-ai/ldm/models/autoencoder.py b/repositories/stable-diffusion-stability-ai/ldm/models/autoencoder.py
deleted file mode 100644
index d122549995ce2cd64092c81a58419ed4a15a02fd..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/ldm/models/autoencoder.py
+++ /dev/null
@@ -1,219 +0,0 @@
-import torch
-import pytorch_lightning as pl
-import torch.nn.functional as F
-from contextlib import contextmanager
-
-from ldm.modules.diffusionmodules.model import Encoder, Decoder
-from ldm.modules.distributions.distributions import DiagonalGaussianDistribution
-
-from ldm.util import instantiate_from_config
-from ldm.modules.ema import LitEma
-
-
-class AutoencoderKL(pl.LightningModule):
-    def __init__(self,
-                 ddconfig,
-                 lossconfig,
-                 embed_dim,
-                 ckpt_path=None,
-                 ignore_keys=[],
-                 image_key="image",
-                 colorize_nlabels=None,
-                 monitor=None,
-                 ema_decay=None,
-                 learn_logvar=False
-                 ):
-        super().__init__()
-        self.learn_logvar = learn_logvar
-        self.image_key = image_key
-        self.encoder = Encoder(**ddconfig)
-        self.decoder = Decoder(**ddconfig)
-        self.loss = instantiate_from_config(lossconfig)
-        assert ddconfig["double_z"]
-        self.quant_conv = torch.nn.Conv2d(2*ddconfig["z_channels"], 2*embed_dim, 1)
-        self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
-        self.embed_dim = embed_dim
-        if colorize_nlabels is not None:
-            assert type(colorize_nlabels)==int
-            self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
-        if monitor is not None:
-            self.monitor = monitor
-
-        self.use_ema = ema_decay is not None
-        if self.use_ema:
-            self.ema_decay = ema_decay
-            assert 0. < ema_decay < 1.
-            self.model_ema = LitEma(self, decay=ema_decay)
-            print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
-
-        if ckpt_path is not None:
-            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
-
-    def init_from_ckpt(self, path, ignore_keys=list()):
-        sd = torch.load(path, map_location="cpu")["state_dict"]
-        keys = list(sd.keys())
-        for k in keys:
-            for ik in ignore_keys:
-                if k.startswith(ik):
-                    print("Deleting key {} from state_dict.".format(k))
-                    del sd[k]
-        self.load_state_dict(sd, strict=False)
-        print(f"Restored from {path}")
-
-    @contextmanager
-    def ema_scope(self, context=None):
-        if self.use_ema:
-            self.model_ema.store(self.parameters())
-            self.model_ema.copy_to(self)
-            if context is not None:
-                print(f"{context}: Switched to EMA weights")
-        try:
-            yield None
-        finally:
-            if self.use_ema:
-                self.model_ema.restore(self.parameters())
-                if context is not None:
-                    print(f"{context}: Restored training weights")
-
-    def on_train_batch_end(self, *args, **kwargs):
-        if self.use_ema:
-            self.model_ema(self)
-
-    def encode(self, x):
-        h = self.encoder(x)
-        moments = self.quant_conv(h)
-        posterior = DiagonalGaussianDistribution(moments)
-        return posterior
-
-    def decode(self, z):
-        z = self.post_quant_conv(z)
-        dec = self.decoder(z)
-        return dec
-
-    def forward(self, input, sample_posterior=True):
-        posterior = self.encode(input)
-        if sample_posterior:
-            z = posterior.sample()
-        else:
-            z = posterior.mode()
-        dec = self.decode(z)
-        return dec, posterior
-
-    def get_input(self, batch, k):
-        x = batch[k]
-        if len(x.shape) == 3:
-            x = x[..., None]
-        x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()
-        return x
-
-    def training_step(self, batch, batch_idx, optimizer_idx):
-        inputs = self.get_input(batch, self.image_key)
-        reconstructions, posterior = self(inputs)
-
-        if optimizer_idx == 0:
-            # train encoder+decoder+logvar
-            aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
-                                            last_layer=self.get_last_layer(), split="train")
-            self.log("aeloss", aeloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
-            self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=False)
-            return aeloss
-
-        if optimizer_idx == 1:
-            # train the discriminator
-            discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
-                                                last_layer=self.get_last_layer(), split="train")
-
-            self.log("discloss", discloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
-            self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=False)
-            return discloss
-
-    def validation_step(self, batch, batch_idx):
-        log_dict = self._validation_step(batch, batch_idx)
-        with self.ema_scope():
-            log_dict_ema = self._validation_step(batch, batch_idx, postfix="_ema")
-        return log_dict
-
-    def _validation_step(self, batch, batch_idx, postfix=""):
-        inputs = self.get_input(batch, self.image_key)
-        reconstructions, posterior = self(inputs)
-        aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, 0, self.global_step,
-                                        last_layer=self.get_last_layer(), split="val"+postfix)
-
-        discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, 1, self.global_step,
-                                            last_layer=self.get_last_layer(), split="val"+postfix)
-
-        self.log(f"val{postfix}/rec_loss", log_dict_ae[f"val{postfix}/rec_loss"])
-        self.log_dict(log_dict_ae)
-        self.log_dict(log_dict_disc)
-        return self.log_dict
-
-    def configure_optimizers(self):
-        lr = self.learning_rate
-        ae_params_list = list(self.encoder.parameters()) + list(self.decoder.parameters()) + list(
-            self.quant_conv.parameters()) + list(self.post_quant_conv.parameters())
-        if self.learn_logvar:
-            print(f"{self.__class__.__name__}: Learning logvar")
-            ae_params_list.append(self.loss.logvar)
-        opt_ae = torch.optim.Adam(ae_params_list,
-                                  lr=lr, betas=(0.5, 0.9))
-        opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
-                                    lr=lr, betas=(0.5, 0.9))
-        return [opt_ae, opt_disc], []
-
-    def get_last_layer(self):
-        return self.decoder.conv_out.weight
-
-    @torch.no_grad()
-    def log_images(self, batch, only_inputs=False, log_ema=False, **kwargs):
-        log = dict()
-        x = self.get_input(batch, self.image_key)
-        x = x.to(self.device)
-        if not only_inputs:
-            xrec, posterior = self(x)
-            if x.shape[1] > 3:
-                # colorize with random projection
-                assert xrec.shape[1] > 3
-                x = self.to_rgb(x)
-                xrec = self.to_rgb(xrec)
-            log["samples"] = self.decode(torch.randn_like(posterior.sample()))
-            log["reconstructions"] = xrec
-            if log_ema or self.use_ema:
-                with self.ema_scope():
-                    xrec_ema, posterior_ema = self(x)
-                    if x.shape[1] > 3:
-                        # colorize with random projection
-                        assert xrec_ema.shape[1] > 3
-                        xrec_ema = self.to_rgb(xrec_ema)
-                    log["samples_ema"] = self.decode(torch.randn_like(posterior_ema.sample()))
-                    log["reconstructions_ema"] = xrec_ema
-        log["inputs"] = x
-        return log
-
-    def to_rgb(self, x):
-        assert self.image_key == "segmentation"
-        if not hasattr(self, "colorize"):
-            self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
-        x = F.conv2d(x, weight=self.colorize)
-        x = 2.*(x-x.min())/(x.max()-x.min()) - 1.
-        return x
-
-
-class IdentityFirstStage(torch.nn.Module):
-    def __init__(self, *args, vq_interface=False, **kwargs):
-        self.vq_interface = vq_interface
-        super().__init__()
-
-    def encode(self, x, *args, **kwargs):
-        return x
-
-    def decode(self, x, *args, **kwargs):
-        return x
-
-    def quantize(self, x, *args, **kwargs):
-        if self.vq_interface:
-            return x, None, [None, None, None]
-        return x
-
-    def forward(self, x, *args, **kwargs):
-        return x
-
diff --git a/repositories/stable-diffusion-stability-ai/ldm/models/diffusion/__init__.py b/repositories/stable-diffusion-stability-ai/ldm/models/diffusion/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/repositories/stable-diffusion-stability-ai/ldm/models/diffusion/__pycache__/__init__.cpython-310.pyc b/repositories/stable-diffusion-stability-ai/ldm/models/diffusion/__pycache__/__init__.cpython-310.pyc
deleted file mode 100644
index c1e6a2364a942ae196fccf15d4e13bbf65eb4906..0000000000000000000000000000000000000000
Binary files a/repositories/stable-diffusion-stability-ai/ldm/models/diffusion/__pycache__/__init__.cpython-310.pyc and /dev/null differ
diff --git a/repositories/stable-diffusion-stability-ai/ldm/models/diffusion/__pycache__/ddim.cpython-310.pyc b/repositories/stable-diffusion-stability-ai/ldm/models/diffusion/__pycache__/ddim.cpython-310.pyc
deleted file mode 100644
index 731a7279c3b7bf9df2c9c79113caa892c983a37d..0000000000000000000000000000000000000000
Binary files a/repositories/stable-diffusion-stability-ai/ldm/models/diffusion/__pycache__/ddim.cpython-310.pyc and /dev/null differ
diff --git a/repositories/stable-diffusion-stability-ai/ldm/models/diffusion/__pycache__/ddpm.cpython-310.pyc b/repositories/stable-diffusion-stability-ai/ldm/models/diffusion/__pycache__/ddpm.cpython-310.pyc
deleted file mode 100644
index 83e5588276e7772394eac893b9c988f54b1f3fcf..0000000000000000000000000000000000000000
Binary files a/repositories/stable-diffusion-stability-ai/ldm/models/diffusion/__pycache__/ddpm.cpython-310.pyc and /dev/null differ
diff --git a/repositories/stable-diffusion-stability-ai/ldm/models/diffusion/__pycache__/plms.cpython-310.pyc b/repositories/stable-diffusion-stability-ai/ldm/models/diffusion/__pycache__/plms.cpython-310.pyc
deleted file mode 100644
index deb2242ae369340923d92087ec4ca6fc8482c21e..0000000000000000000000000000000000000000
Binary files a/repositories/stable-diffusion-stability-ai/ldm/models/diffusion/__pycache__/plms.cpython-310.pyc and /dev/null differ
diff --git a/repositories/stable-diffusion-stability-ai/ldm/models/diffusion/__pycache__/sampling_util.cpython-310.pyc b/repositories/stable-diffusion-stability-ai/ldm/models/diffusion/__pycache__/sampling_util.cpython-310.pyc
deleted file mode 100644
index 524b42a13c96a63e5c6d5cb08fcd779566286b7b..0000000000000000000000000000000000000000
Binary files a/repositories/stable-diffusion-stability-ai/ldm/models/diffusion/__pycache__/sampling_util.cpython-310.pyc and /dev/null differ
diff --git a/repositories/stable-diffusion-stability-ai/ldm/models/diffusion/ddim.py b/repositories/stable-diffusion-stability-ai/ldm/models/diffusion/ddim.py
deleted file mode 100644
index c6cfd5712281a72c086e592ed57fc0730c5a5a3b..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/ldm/models/diffusion/ddim.py
+++ /dev/null
@@ -1,337 +0,0 @@
-"""SAMPLING ONLY."""
-
-import torch
-import numpy as np
-from tqdm import tqdm
-
-from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like, extract_into_tensor
-
-
-class DDIMSampler(object):
-    def __init__(self, model, schedule="linear", device=torch.device("cuda"), **kwargs):
-        super().__init__()
-        self.model = model
-        self.ddpm_num_timesteps = model.num_timesteps
-        self.schedule = schedule
-        self.device = device
-
-    def register_buffer(self, name, attr):
-        if type(attr) == torch.Tensor:
-            if attr.device != self.device:
-                attr = attr.to(self.device)
-        setattr(self, name, attr)
-
-    def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True):
-        self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,
-                                                  num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose)
-        alphas_cumprod = self.model.alphas_cumprod
-        assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'
-        to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)
-
-        self.register_buffer('betas', to_torch(self.model.betas))
-        self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
-        self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev))
-
-        # calculations for diffusion q(x_t | x_{t-1}) and others
-        self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu())))
-        self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu())))
-        self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu())))
-        self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu())))
-        self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))
-
-        # ddim sampling parameters
-        ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(),
-                                                                                   ddim_timesteps=self.ddim_timesteps,
-                                                                                   eta=ddim_eta,verbose=verbose)
-        self.register_buffer('ddim_sigmas', ddim_sigmas)
-        self.register_buffer('ddim_alphas', ddim_alphas)
-        self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)
-        self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas))
-        sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
-            (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (
-                        1 - self.alphas_cumprod / self.alphas_cumprod_prev))
-        self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps)
-
-    @torch.no_grad()
-    def sample(self,
-               S,
-               batch_size,
-               shape,
-               conditioning=None,
-               callback=None,
-               normals_sequence=None,
-               img_callback=None,
-               quantize_x0=False,
-               eta=0.,
-               mask=None,
-               x0=None,
-               temperature=1.,
-               noise_dropout=0.,
-               score_corrector=None,
-               corrector_kwargs=None,
-               verbose=True,
-               x_T=None,
-               log_every_t=100,
-               unconditional_guidance_scale=1.,
-               unconditional_conditioning=None, # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
-               dynamic_threshold=None,
-               ucg_schedule=None,
-               **kwargs
-               ):
-        if conditioning is not None:
-            if isinstance(conditioning, dict):
-                ctmp = conditioning[list(conditioning.keys())[0]]
-                while isinstance(ctmp, list): ctmp = ctmp[0]
-                cbs = ctmp.shape[0]
-                if cbs != batch_size:
-                    print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
-
-            elif isinstance(conditioning, list):
-                for ctmp in conditioning:
-                    if ctmp.shape[0] != batch_size:
-                        print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
-
-            else:
-                if conditioning.shape[0] != batch_size:
-                    print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
-
-        self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
-        # sampling
-        C, H, W = shape
-        size = (batch_size, C, H, W)
-        print(f'Data shape for DDIM sampling is {size}, eta {eta}')
-
-        samples, intermediates = self.ddim_sampling(conditioning, size,
-                                                    callback=callback,
-                                                    img_callback=img_callback,
-                                                    quantize_denoised=quantize_x0,
-                                                    mask=mask, x0=x0,
-                                                    ddim_use_original_steps=False,
-                                                    noise_dropout=noise_dropout,
-                                                    temperature=temperature,
-                                                    score_corrector=score_corrector,
-                                                    corrector_kwargs=corrector_kwargs,
-                                                    x_T=x_T,
-                                                    log_every_t=log_every_t,
-                                                    unconditional_guidance_scale=unconditional_guidance_scale,
-                                                    unconditional_conditioning=unconditional_conditioning,
-                                                    dynamic_threshold=dynamic_threshold,
-                                                    ucg_schedule=ucg_schedule
-                                                    )
-        return samples, intermediates
-
-    @torch.no_grad()
-    def ddim_sampling(self, cond, shape,
-                      x_T=None, ddim_use_original_steps=False,
-                      callback=None, timesteps=None, quantize_denoised=False,
-                      mask=None, x0=None, img_callback=None, log_every_t=100,
-                      temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
-                      unconditional_guidance_scale=1., unconditional_conditioning=None, dynamic_threshold=None,
-                      ucg_schedule=None):
-        device = self.model.betas.device
-        b = shape[0]
-        if x_T is None:
-            img = torch.randn(shape, device=device)
-        else:
-            img = x_T
-
-        if timesteps is None:
-            timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps
-        elif timesteps is not None and not ddim_use_original_steps:
-            subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1
-            timesteps = self.ddim_timesteps[:subset_end]
-
-        intermediates = {'x_inter': [img], 'pred_x0': [img]}
-        time_range = reversed(range(0,timesteps)) if ddim_use_original_steps else np.flip(timesteps)
-        total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
-        print(f"Running DDIM Sampling with {total_steps} timesteps")
-
-        iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps)
-
-        for i, step in enumerate(iterator):
-            index = total_steps - i - 1
-            ts = torch.full((b,), step, device=device, dtype=torch.long)
-
-            if mask is not None:
-                assert x0 is not None
-                img_orig = self.model.q_sample(x0, ts)  # TODO: deterministic forward pass?
-                img = img_orig * mask + (1. - mask) * img
-
-            if ucg_schedule is not None:
-                assert len(ucg_schedule) == len(time_range)
-                unconditional_guidance_scale = ucg_schedule[i]
-
-            outs = self.p_sample_ddim(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps,
-                                      quantize_denoised=quantize_denoised, temperature=temperature,
-                                      noise_dropout=noise_dropout, score_corrector=score_corrector,
-                                      corrector_kwargs=corrector_kwargs,
-                                      unconditional_guidance_scale=unconditional_guidance_scale,
-                                      unconditional_conditioning=unconditional_conditioning,
-                                      dynamic_threshold=dynamic_threshold)
-            img, pred_x0 = outs
-            if callback: callback(i)
-            if img_callback: img_callback(pred_x0, i)
-
-            if index % log_every_t == 0 or index == total_steps - 1:
-                intermediates['x_inter'].append(img)
-                intermediates['pred_x0'].append(pred_x0)
-
-        return img, intermediates
-
-    @torch.no_grad()
-    def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
-                      temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
-                      unconditional_guidance_scale=1., unconditional_conditioning=None,
-                      dynamic_threshold=None):
-        b, *_, device = *x.shape, x.device
-
-        if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
-            model_output = self.model.apply_model(x, t, c)
-        else:
-            x_in = torch.cat([x] * 2)
-            t_in = torch.cat([t] * 2)
-            if isinstance(c, dict):
-                assert isinstance(unconditional_conditioning, dict)
-                c_in = dict()
-                for k in c:
-                    if isinstance(c[k], list):
-                        c_in[k] = [torch.cat([
-                            unconditional_conditioning[k][i],
-                            c[k][i]]) for i in range(len(c[k]))]
-                    else:
-                        c_in[k] = torch.cat([
-                                unconditional_conditioning[k],
-                                c[k]])
-            elif isinstance(c, list):
-                c_in = list()
-                assert isinstance(unconditional_conditioning, list)
-                for i in range(len(c)):
-                    c_in.append(torch.cat([unconditional_conditioning[i], c[i]]))
-            else:
-                c_in = torch.cat([unconditional_conditioning, c])
-            model_uncond, model_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
-            model_output = model_uncond + unconditional_guidance_scale * (model_t - model_uncond)
-
-        if self.model.parameterization == "v":
-            e_t = self.model.predict_eps_from_z_and_v(x, t, model_output)
-        else:
-            e_t = model_output
-
-        if score_corrector is not None:
-            assert self.model.parameterization == "eps", 'not implemented'
-            e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
-
-        alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
-        alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
-        sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
-        sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
-        # select parameters corresponding to the currently considered timestep
-        a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
-        a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
-        sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
-        sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
-
-        # current prediction for x_0
-        if self.model.parameterization != "v":
-            pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
-        else:
-            pred_x0 = self.model.predict_start_from_z_and_v(x, t, model_output)
-
-        if quantize_denoised:
-            pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
-
-        if dynamic_threshold is not None:
-            raise NotImplementedError()
-
-        # direction pointing to x_t
-        dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
-        noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
-        if noise_dropout > 0.:
-            noise = torch.nn.functional.dropout(noise, p=noise_dropout)
-        x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
-        return x_prev, pred_x0
-
-    @torch.no_grad()
-    def encode(self, x0, c, t_enc, use_original_steps=False, return_intermediates=None,
-               unconditional_guidance_scale=1.0, unconditional_conditioning=None, callback=None):
-        num_reference_steps = self.ddpm_num_timesteps if use_original_steps else self.ddim_timesteps.shape[0]
-
-        assert t_enc <= num_reference_steps
-        num_steps = t_enc
-
-        if use_original_steps:
-            alphas_next = self.alphas_cumprod[:num_steps]
-            alphas = self.alphas_cumprod_prev[:num_steps]
-        else:
-            alphas_next = self.ddim_alphas[:num_steps]
-            alphas = torch.tensor(self.ddim_alphas_prev[:num_steps])
-
-        x_next = x0
-        intermediates = []
-        inter_steps = []
-        for i in tqdm(range(num_steps), desc='Encoding Image'):
-            t = torch.full((x0.shape[0],), i, device=self.model.device, dtype=torch.long)
-            if unconditional_guidance_scale == 1.:
-                noise_pred = self.model.apply_model(x_next, t, c)
-            else:
-                assert unconditional_conditioning is not None
-                e_t_uncond, noise_pred = torch.chunk(
-                    self.model.apply_model(torch.cat((x_next, x_next)), torch.cat((t, t)),
-                                           torch.cat((unconditional_conditioning, c))), 2)
-                noise_pred = e_t_uncond + unconditional_guidance_scale * (noise_pred - e_t_uncond)
-
-            xt_weighted = (alphas_next[i] / alphas[i]).sqrt() * x_next
-            weighted_noise_pred = alphas_next[i].sqrt() * (
-                    (1 / alphas_next[i] - 1).sqrt() - (1 / alphas[i] - 1).sqrt()) * noise_pred
-            x_next = xt_weighted + weighted_noise_pred
-            if return_intermediates and i % (
-                    num_steps // return_intermediates) == 0 and i < num_steps - 1:
-                intermediates.append(x_next)
-                inter_steps.append(i)
-            elif return_intermediates and i >= num_steps - 2:
-                intermediates.append(x_next)
-                inter_steps.append(i)
-            if callback: callback(i)
-
-        out = {'x_encoded': x_next, 'intermediate_steps': inter_steps}
-        if return_intermediates:
-            out.update({'intermediates': intermediates})
-        return x_next, out
-
-    @torch.no_grad()
-    def stochastic_encode(self, x0, t, use_original_steps=False, noise=None):
-        # fast, but does not allow for exact reconstruction
-        # t serves as an index to gather the correct alphas
-        if use_original_steps:
-            sqrt_alphas_cumprod = self.sqrt_alphas_cumprod
-            sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod
-        else:
-            sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas)
-            sqrt_one_minus_alphas_cumprod = self.ddim_sqrt_one_minus_alphas
-
-        if noise is None:
-            noise = torch.randn_like(x0)
-        return (extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 +
-                extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) * noise)
-
-    @torch.no_grad()
-    def decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None,
-               use_original_steps=False, callback=None):
-
-        timesteps = np.arange(self.ddpm_num_timesteps) if use_original_steps else self.ddim_timesteps
-        timesteps = timesteps[:t_start]
-
-        time_range = np.flip(timesteps)
-        total_steps = timesteps.shape[0]
-        print(f"Running DDIM Sampling with {total_steps} timesteps")
-
-        iterator = tqdm(time_range, desc='Decoding image', total=total_steps)
-        x_dec = x_latent
-        for i, step in enumerate(iterator):
-            index = total_steps - i - 1
-            ts = torch.full((x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long)
-            x_dec, _ = self.p_sample_ddim(x_dec, cond, ts, index=index, use_original_steps=use_original_steps,
-                                          unconditional_guidance_scale=unconditional_guidance_scale,
-                                          unconditional_conditioning=unconditional_conditioning)
-            if callback: callback(i)
-        return x_dec
\ No newline at end of file
diff --git a/repositories/stable-diffusion-stability-ai/ldm/models/diffusion/ddpm.py b/repositories/stable-diffusion-stability-ai/ldm/models/diffusion/ddpm.py
deleted file mode 100644
index 3350c032f40b9b04e0b652aa296ad4ae4b2bd9ff..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/ldm/models/diffusion/ddpm.py
+++ /dev/null
@@ -1,1873 +0,0 @@
-"""
-wild mixture of
-https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
-https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
-https://github.com/CompVis/taming-transformers
--- merci
-"""
-
-import torch
-import torch.nn as nn
-import numpy as np
-import pytorch_lightning as pl
-from torch.optim.lr_scheduler import LambdaLR
-from einops import rearrange, repeat
-from contextlib import contextmanager, nullcontext
-from functools import partial
-import itertools
-from tqdm import tqdm
-from torchvision.utils import make_grid
-from pytorch_lightning.utilities.distributed import rank_zero_only
-from omegaconf import ListConfig
-
-from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
-from ldm.modules.ema import LitEma
-from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
-from ldm.models.autoencoder import IdentityFirstStage, AutoencoderKL
-from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
-from ldm.models.diffusion.ddim import DDIMSampler
-
-
-__conditioning_keys__ = {'concat': 'c_concat',
-                         'crossattn': 'c_crossattn',
-                         'adm': 'y'}
-
-
-def disabled_train(self, mode=True):
-    """Overwrite model.train with this function to make sure train/eval mode
-    does not change anymore."""
-    return self
-
-
-def uniform_on_device(r1, r2, shape, device):
-    return (r1 - r2) * torch.rand(*shape, device=device) + r2
-
-
-class DDPM(pl.LightningModule):
-    # classic DDPM with Gaussian diffusion, in image space
-    def __init__(self,
-                 unet_config,
-                 timesteps=1000,
-                 beta_schedule="linear",
-                 loss_type="l2",
-                 ckpt_path=None,
-                 ignore_keys=[],
-                 load_only_unet=False,
-                 monitor="val/loss",
-                 use_ema=True,
-                 first_stage_key="image",
-                 image_size=256,
-                 channels=3,
-                 log_every_t=100,
-                 clip_denoised=True,
-                 linear_start=1e-4,
-                 linear_end=2e-2,
-                 cosine_s=8e-3,
-                 given_betas=None,
-                 original_elbo_weight=0.,
-                 v_posterior=0.,  # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
-                 l_simple_weight=1.,
-                 conditioning_key=None,
-                 parameterization="eps",  # all assuming fixed variance schedules
-                 scheduler_config=None,
-                 use_positional_encodings=False,
-                 learn_logvar=False,
-                 logvar_init=0.,
-                 make_it_fit=False,
-                 ucg_training=None,
-                 reset_ema=False,
-                 reset_num_ema_updates=False,
-                 ):
-        super().__init__()
-        assert parameterization in ["eps", "x0", "v"], 'currently only supporting "eps" and "x0" and "v"'
-        self.parameterization = parameterization
-        print(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode")
-        self.cond_stage_model = None
-        self.clip_denoised = clip_denoised
-        self.log_every_t = log_every_t
-        self.first_stage_key = first_stage_key
-        self.image_size = image_size  # try conv?
-        self.channels = channels
-        self.use_positional_encodings = use_positional_encodings
-        self.model = DiffusionWrapper(unet_config, conditioning_key)
-        count_params(self.model, verbose=True)
-        self.use_ema = use_ema
-        if self.use_ema:
-            self.model_ema = LitEma(self.model)
-            print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
-
-        self.use_scheduler = scheduler_config is not None
-        if self.use_scheduler:
-            self.scheduler_config = scheduler_config
-
-        self.v_posterior = v_posterior
-        self.original_elbo_weight = original_elbo_weight
-        self.l_simple_weight = l_simple_weight
-
-        if monitor is not None:
-            self.monitor = monitor
-        self.make_it_fit = make_it_fit
-        if reset_ema: assert exists(ckpt_path)
-        if ckpt_path is not None:
-            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet)
-            if reset_ema:
-                assert self.use_ema
-                print(f"Resetting ema to pure model weights. This is useful when restoring from an ema-only checkpoint.")
-                self.model_ema = LitEma(self.model)
-        if reset_num_ema_updates:
-            print(" +++++++++++ WARNING: RESETTING NUM_EMA UPDATES TO ZERO +++++++++++ ")
-            assert self.use_ema
-            self.model_ema.reset_num_updates()
-
-        self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps,
-                               linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
-
-        self.loss_type = loss_type
-
-        self.learn_logvar = learn_logvar
-        self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,))
-        if self.learn_logvar:
-            self.logvar = nn.Parameter(self.logvar, requires_grad=True)
-
-        self.ucg_training = ucg_training or dict()
-        if self.ucg_training:
-            self.ucg_prng = np.random.RandomState()
-
-    def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
-                          linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
-        if exists(given_betas):
-            betas = given_betas
-        else:
-            betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end,
-                                       cosine_s=cosine_s)
-        alphas = 1. - betas
-        alphas_cumprod = np.cumprod(alphas, axis=0)
-        alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
-
-        timesteps, = betas.shape
-        self.num_timesteps = int(timesteps)
-        self.linear_start = linear_start
-        self.linear_end = linear_end
-        assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'
-
-        to_torch = partial(torch.tensor, dtype=torch.float32)
-
-        self.register_buffer('betas', to_torch(betas))
-        self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
-        self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
-
-        # calculations for diffusion q(x_t | x_{t-1}) and others
-        self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
-        self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
-        self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
-        self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
-        self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
-
-        # calculations for posterior q(x_{t-1} | x_t, x_0)
-        posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / (
-                1. - alphas_cumprod) + self.v_posterior * betas
-        # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
-        self.register_buffer('posterior_variance', to_torch(posterior_variance))
-        # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
-        self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20))))
-        self.register_buffer('posterior_mean_coef1', to_torch(
-            betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)))
-        self.register_buffer('posterior_mean_coef2', to_torch(
-            (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod)))
-
-        if self.parameterization == "eps":
-            lvlb_weights = self.betas ** 2 / (
-                    2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod))
-        elif self.parameterization == "x0":
-            lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod))
-        elif self.parameterization == "v":
-            lvlb_weights = torch.ones_like(self.betas ** 2 / (
-                    2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod)))
-        else:
-            raise NotImplementedError("mu not supported")
-        lvlb_weights[0] = lvlb_weights[1]
-        self.register_buffer('lvlb_weights', lvlb_weights, persistent=False)
-        assert not torch.isnan(self.lvlb_weights).all()
-
-    @contextmanager
-    def ema_scope(self, context=None):
-        if self.use_ema:
-            self.model_ema.store(self.model.parameters())
-            self.model_ema.copy_to(self.model)
-            if context is not None:
-                print(f"{context}: Switched to EMA weights")
-        try:
-            yield None
-        finally:
-            if self.use_ema:
-                self.model_ema.restore(self.model.parameters())
-                if context is not None:
-                    print(f"{context}: Restored training weights")
-
-    @torch.no_grad()
-    def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
-        sd = torch.load(path, map_location="cpu")
-        if "state_dict" in list(sd.keys()):
-            sd = sd["state_dict"]
-        keys = list(sd.keys())
-        for k in keys:
-            for ik in ignore_keys:
-                if k.startswith(ik):
-                    print("Deleting key {} from state_dict.".format(k))
-                    del sd[k]
-        if self.make_it_fit:
-            n_params = len([name for name, _ in
-                            itertools.chain(self.named_parameters(),
-                                            self.named_buffers())])
-            for name, param in tqdm(
-                    itertools.chain(self.named_parameters(),
-                                    self.named_buffers()),
-                    desc="Fitting old weights to new weights",
-                    total=n_params
-            ):
-                if not name in sd:
-                    continue
-                old_shape = sd[name].shape
-                new_shape = param.shape
-                assert len(old_shape) == len(new_shape)
-                if len(new_shape) > 2:
-                    # we only modify first two axes
-                    assert new_shape[2:] == old_shape[2:]
-                # assumes first axis corresponds to output dim
-                if not new_shape == old_shape:
-                    new_param = param.clone()
-                    old_param = sd[name]
-                    if len(new_shape) == 1:
-                        for i in range(new_param.shape[0]):
-                            new_param[i] = old_param[i % old_shape[0]]
-                    elif len(new_shape) >= 2:
-                        for i in range(new_param.shape[0]):
-                            for j in range(new_param.shape[1]):
-                                new_param[i, j] = old_param[i % old_shape[0], j % old_shape[1]]
-
-                        n_used_old = torch.ones(old_shape[1])
-                        for j in range(new_param.shape[1]):
-                            n_used_old[j % old_shape[1]] += 1
-                        n_used_new = torch.zeros(new_shape[1])
-                        for j in range(new_param.shape[1]):
-                            n_used_new[j] = n_used_old[j % old_shape[1]]
-
-                        n_used_new = n_used_new[None, :]
-                        while len(n_used_new.shape) < len(new_shape):
-                            n_used_new = n_used_new.unsqueeze(-1)
-                        new_param /= n_used_new
-
-                    sd[name] = new_param
-
-        missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
-            sd, strict=False)
-        print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
-        if len(missing) > 0:
-            print(f"Missing Keys:\n {missing}")
-        if len(unexpected) > 0:
-            print(f"\nUnexpected Keys:\n {unexpected}")
-
-    def q_mean_variance(self, x_start, t):
-        """
-        Get the distribution q(x_t | x_0).
-        :param x_start: the [N x C x ...] tensor of noiseless inputs.
-        :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
-        :return: A tuple (mean, variance, log_variance), all of x_start's shape.
-        """
-        mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start)
-        variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
-        log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape)
-        return mean, variance, log_variance
-
-    def predict_start_from_noise(self, x_t, t, noise):
-        return (
-                extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
-                extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
-        )
-
-    def predict_start_from_z_and_v(self, x_t, t, v):
-        # self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
-        # self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
-        return (
-                extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * x_t -
-                extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * v
-        )
-
-    def predict_eps_from_z_and_v(self, x_t, t, v):
-        return (
-                extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * v +
-                extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * x_t
-        )
-
-    def q_posterior(self, x_start, x_t, t):
-        posterior_mean = (
-                extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start +
-                extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
-        )
-        posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape)
-        posterior_log_variance_clipped = extract_into_tensor(self.posterior_log_variance_clipped, t, x_t.shape)
-        return posterior_mean, posterior_variance, posterior_log_variance_clipped
-
-    def p_mean_variance(self, x, t, clip_denoised: bool):
-        model_out = self.model(x, t)
-        if self.parameterization == "eps":
-            x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
-        elif self.parameterization == "x0":
-            x_recon = model_out
-        if clip_denoised:
-            x_recon.clamp_(-1., 1.)
-
-        model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
-        return model_mean, posterior_variance, posterior_log_variance
-
-    @torch.no_grad()
-    def p_sample(self, x, t, clip_denoised=True, repeat_noise=False):
-        b, *_, device = *x.shape, x.device
-        model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised)
-        noise = noise_like(x.shape, device, repeat_noise)
-        # no noise when t == 0
-        nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
-        return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
-
-    @torch.no_grad()
-    def p_sample_loop(self, shape, return_intermediates=False):
-        device = self.betas.device
-        b = shape[0]
-        img = torch.randn(shape, device=device)
-        intermediates = [img]
-        for i in tqdm(reversed(range(0, self.num_timesteps)), desc='Sampling t', total=self.num_timesteps):
-            img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long),
-                                clip_denoised=self.clip_denoised)
-            if i % self.log_every_t == 0 or i == self.num_timesteps - 1:
-                intermediates.append(img)
-        if return_intermediates:
-            return img, intermediates
-        return img
-
-    @torch.no_grad()
-    def sample(self, batch_size=16, return_intermediates=False):
-        image_size = self.image_size
-        channels = self.channels
-        return self.p_sample_loop((batch_size, channels, image_size, image_size),
-                                  return_intermediates=return_intermediates)
-
-    def q_sample(self, x_start, t, noise=None):
-        noise = default(noise, lambda: torch.randn_like(x_start))
-        return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
-                extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)
-
-    def get_v(self, x, noise, t):
-        return (
-                extract_into_tensor(self.sqrt_alphas_cumprod, t, x.shape) * noise -
-                extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x.shape) * x
-        )
-
-    def get_loss(self, pred, target, mean=True):
-        if self.loss_type == 'l1':
-            loss = (target - pred).abs()
-            if mean:
-                loss = loss.mean()
-        elif self.loss_type == 'l2':
-            if mean:
-                loss = torch.nn.functional.mse_loss(target, pred)
-            else:
-                loss = torch.nn.functional.mse_loss(target, pred, reduction='none')
-        else:
-            raise NotImplementedError("unknown loss type '{loss_type}'")
-
-        return loss
-
-    def p_losses(self, x_start, t, noise=None):
-        noise = default(noise, lambda: torch.randn_like(x_start))
-        x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
-        model_out = self.model(x_noisy, t)
-
-        loss_dict = {}
-        if self.parameterization == "eps":
-            target = noise
-        elif self.parameterization == "x0":
-            target = x_start
-        elif self.parameterization == "v":
-            target = self.get_v(x_start, noise, t)
-        else:
-            raise NotImplementedError(f"Parameterization {self.parameterization} not yet supported")
-
-        loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3])
-
-        log_prefix = 'train' if self.training else 'val'
-
-        loss_dict.update({f'{log_prefix}/loss_simple': loss.mean()})
-        loss_simple = loss.mean() * self.l_simple_weight
-
-        loss_vlb = (self.lvlb_weights[t] * loss).mean()
-        loss_dict.update({f'{log_prefix}/loss_vlb': loss_vlb})
-
-        loss = loss_simple + self.original_elbo_weight * loss_vlb
-
-        loss_dict.update({f'{log_prefix}/loss': loss})
-
-        return loss, loss_dict
-
-    def forward(self, x, *args, **kwargs):
-        # b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size
-        # assert h == img_size and w == img_size, f'height and width of image must be {img_size}'
-        t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
-        return self.p_losses(x, t, *args, **kwargs)
-
-    def get_input(self, batch, k):
-        x = batch[k]
-        if len(x.shape) == 3:
-            x = x[..., None]
-        x = rearrange(x, 'b h w c -> b c h w')
-        x = x.to(memory_format=torch.contiguous_format).float()
-        return x
-
-    def shared_step(self, batch):
-        x = self.get_input(batch, self.first_stage_key)
-        loss, loss_dict = self(x)
-        return loss, loss_dict
-
-    def training_step(self, batch, batch_idx):
-        for k in self.ucg_training:
-            p = self.ucg_training[k]["p"]
-            val = self.ucg_training[k]["val"]
-            if val is None:
-                val = ""
-            for i in range(len(batch[k])):
-                if self.ucg_prng.choice(2, p=[1 - p, p]):
-                    batch[k][i] = val
-
-        loss, loss_dict = self.shared_step(batch)
-
-        self.log_dict(loss_dict, prog_bar=True,
-                      logger=True, on_step=True, on_epoch=True)
-
-        self.log("global_step", self.global_step,
-                 prog_bar=True, logger=True, on_step=True, on_epoch=False)
-
-        if self.use_scheduler:
-            lr = self.optimizers().param_groups[0]['lr']
-            self.log('lr_abs', lr, prog_bar=True, logger=True, on_step=True, on_epoch=False)
-
-        return loss
-
-    @torch.no_grad()
-    def validation_step(self, batch, batch_idx):
-        _, loss_dict_no_ema = self.shared_step(batch)
-        with self.ema_scope():
-            _, loss_dict_ema = self.shared_step(batch)
-            loss_dict_ema = {key + '_ema': loss_dict_ema[key] for key in loss_dict_ema}
-        self.log_dict(loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
-        self.log_dict(loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
-
-    def on_train_batch_end(self, *args, **kwargs):
-        if self.use_ema:
-            self.model_ema(self.model)
-
-    def _get_rows_from_list(self, samples):
-        n_imgs_per_row = len(samples)
-        denoise_grid = rearrange(samples, 'n b c h w -> b n c h w')
-        denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
-        denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
-        return denoise_grid
-
-    @torch.no_grad()
-    def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs):
-        log = dict()
-        x = self.get_input(batch, self.first_stage_key)
-        N = min(x.shape[0], N)
-        n_row = min(x.shape[0], n_row)
-        x = x.to(self.device)[:N]
-        log["inputs"] = x
-
-        # get diffusion row
-        diffusion_row = list()
-        x_start = x[:n_row]
-
-        for t in range(self.num_timesteps):
-            if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
-                t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
-                t = t.to(self.device).long()
-                noise = torch.randn_like(x_start)
-                x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
-                diffusion_row.append(x_noisy)
-
-        log["diffusion_row"] = self._get_rows_from_list(diffusion_row)
-
-        if sample:
-            # get denoise row
-            with self.ema_scope("Plotting"):
-                samples, denoise_row = self.sample(batch_size=N, return_intermediates=True)
-
-            log["samples"] = samples
-            log["denoise_row"] = self._get_rows_from_list(denoise_row)
-
-        if return_keys:
-            if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
-                return log
-            else:
-                return {key: log[key] for key in return_keys}
-        return log
-
-    def configure_optimizers(self):
-        lr = self.learning_rate
-        params = list(self.model.parameters())
-        if self.learn_logvar:
-            params = params + [self.logvar]
-        opt = torch.optim.AdamW(params, lr=lr)
-        return opt
-
-
-class LatentDiffusion(DDPM):
-    """main class"""
-
-    def __init__(self,
-                 first_stage_config,
-                 cond_stage_config,
-                 num_timesteps_cond=None,
-                 cond_stage_key="image",
-                 cond_stage_trainable=False,
-                 concat_mode=True,
-                 cond_stage_forward=None,
-                 conditioning_key=None,
-                 scale_factor=1.0,
-                 scale_by_std=False,
-                 force_null_conditioning=False,
-                 *args, **kwargs):
-        self.force_null_conditioning = force_null_conditioning
-        self.num_timesteps_cond = default(num_timesteps_cond, 1)
-        self.scale_by_std = scale_by_std
-        assert self.num_timesteps_cond <= kwargs['timesteps']
-        # for backwards compatibility after implementation of DiffusionWrapper
-        if conditioning_key is None:
-            conditioning_key = 'concat' if concat_mode else 'crossattn'
-        if cond_stage_config == '__is_unconditional__' and not self.force_null_conditioning:
-            conditioning_key = None
-        ckpt_path = kwargs.pop("ckpt_path", None)
-        reset_ema = kwargs.pop("reset_ema", False)
-        reset_num_ema_updates = kwargs.pop("reset_num_ema_updates", False)
-        ignore_keys = kwargs.pop("ignore_keys", [])
-        super().__init__(conditioning_key=conditioning_key, *args, **kwargs)
-        self.concat_mode = concat_mode
-        self.cond_stage_trainable = cond_stage_trainable
-        self.cond_stage_key = cond_stage_key
-        try:
-            self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
-        except:
-            self.num_downs = 0
-        if not scale_by_std:
-            self.scale_factor = scale_factor
-        else:
-            self.register_buffer('scale_factor', torch.tensor(scale_factor))
-        self.instantiate_first_stage(first_stage_config)
-        self.instantiate_cond_stage(cond_stage_config)
-        self.cond_stage_forward = cond_stage_forward
-        self.clip_denoised = False
-        self.bbox_tokenizer = None
-
-        self.restarted_from_ckpt = False
-        if ckpt_path is not None:
-            self.init_from_ckpt(ckpt_path, ignore_keys)
-            self.restarted_from_ckpt = True
-            if reset_ema:
-                assert self.use_ema
-                print(
-                    f"Resetting ema to pure model weights. This is useful when restoring from an ema-only checkpoint.")
-                self.model_ema = LitEma(self.model)
-        if reset_num_ema_updates:
-            print(" +++++++++++ WARNING: RESETTING NUM_EMA UPDATES TO ZERO +++++++++++ ")
-            assert self.use_ema
-            self.model_ema.reset_num_updates()
-
-    def make_cond_schedule(self, ):
-        self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long)
-        ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long()
-        self.cond_ids[:self.num_timesteps_cond] = ids
-
-    @rank_zero_only
-    @torch.no_grad()
-    def on_train_batch_start(self, batch, batch_idx, dataloader_idx):
-        # only for very first batch
-        if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0 and not self.restarted_from_ckpt:
-            assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously'
-            # set rescale weight to 1./std of encodings
-            print("### USING STD-RESCALING ###")
-            x = super().get_input(batch, self.first_stage_key)
-            x = x.to(self.device)
-            encoder_posterior = self.encode_first_stage(x)
-            z = self.get_first_stage_encoding(encoder_posterior).detach()
-            del self.scale_factor
-            self.register_buffer('scale_factor', 1. / z.flatten().std())
-            print(f"setting self.scale_factor to {self.scale_factor}")
-            print("### USING STD-RESCALING ###")
-
-    def register_schedule(self,
-                          given_betas=None, beta_schedule="linear", timesteps=1000,
-                          linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
-        super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s)
-
-        self.shorten_cond_schedule = self.num_timesteps_cond > 1
-        if self.shorten_cond_schedule:
-            self.make_cond_schedule()
-
-    def instantiate_first_stage(self, config):
-        model = instantiate_from_config(config)
-        self.first_stage_model = model.eval()
-        self.first_stage_model.train = disabled_train
-        for param in self.first_stage_model.parameters():
-            param.requires_grad = False
-
-    def instantiate_cond_stage(self, config):
-        if not self.cond_stage_trainable:
-            if config == "__is_first_stage__":
-                print("Using first stage also as cond stage.")
-                self.cond_stage_model = self.first_stage_model
-            elif config == "__is_unconditional__":
-                print(f"Training {self.__class__.__name__} as an unconditional model.")
-                self.cond_stage_model = None
-                # self.be_unconditional = True
-            else:
-                model = instantiate_from_config(config)
-                self.cond_stage_model = model.eval()
-                self.cond_stage_model.train = disabled_train
-                for param in self.cond_stage_model.parameters():
-                    param.requires_grad = False
-        else:
-            assert config != '__is_first_stage__'
-            assert config != '__is_unconditional__'
-            model = instantiate_from_config(config)
-            self.cond_stage_model = model
-
-    def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False):
-        denoise_row = []
-        for zd in tqdm(samples, desc=desc):
-            denoise_row.append(self.decode_first_stage(zd.to(self.device),
-                                                       force_not_quantize=force_no_decoder_quantization))
-        n_imgs_per_row = len(denoise_row)
-        denoise_row = torch.stack(denoise_row)  # n_log_step, n_row, C, H, W
-        denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w')
-        denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
-        denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
-        return denoise_grid
-
-    def get_first_stage_encoding(self, encoder_posterior):
-        if isinstance(encoder_posterior, DiagonalGaussianDistribution):
-            z = encoder_posterior.sample()
-        elif isinstance(encoder_posterior, torch.Tensor):
-            z = encoder_posterior
-        else:
-            raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented")
-        return self.scale_factor * z
-
-    def get_learned_conditioning(self, c):
-        if self.cond_stage_forward is None:
-            if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode):
-                c = self.cond_stage_model.encode(c)
-                if isinstance(c, DiagonalGaussianDistribution):
-                    c = c.mode()
-            else:
-                c = self.cond_stage_model(c)
-        else:
-            assert hasattr(self.cond_stage_model, self.cond_stage_forward)
-            c = getattr(self.cond_stage_model, self.cond_stage_forward)(c)
-        return c
-
-    def meshgrid(self, h, w):
-        y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1)
-        x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1)
-
-        arr = torch.cat([y, x], dim=-1)
-        return arr
-
-    def delta_border(self, h, w):
-        """
-        :param h: height
-        :param w: width
-        :return: normalized distance to image border,
-         wtith min distance = 0 at border and max dist = 0.5 at image center
-        """
-        lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2)
-        arr = self.meshgrid(h, w) / lower_right_corner
-        dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0]
-        dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0]
-        edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0]
-        return edge_dist
-
-    def get_weighting(self, h, w, Ly, Lx, device):
-        weighting = self.delta_border(h, w)
-        weighting = torch.clip(weighting, self.split_input_params["clip_min_weight"],
-                               self.split_input_params["clip_max_weight"], )
-        weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device)
-
-        if self.split_input_params["tie_braker"]:
-            L_weighting = self.delta_border(Ly, Lx)
-            L_weighting = torch.clip(L_weighting,
-                                     self.split_input_params["clip_min_tie_weight"],
-                                     self.split_input_params["clip_max_tie_weight"])
-
-            L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device)
-            weighting = weighting * L_weighting
-        return weighting
-
-    def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1):  # todo load once not every time, shorten code
-        """
-        :param x: img of size (bs, c, h, w)
-        :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])
-        """
-        bs, nc, h, w = x.shape
-
-        # number of crops in image
-        Ly = (h - kernel_size[0]) // stride[0] + 1
-        Lx = (w - kernel_size[1]) // stride[1] + 1
-
-        if uf == 1 and df == 1:
-            fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
-            unfold = torch.nn.Unfold(**fold_params)
-
-            fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params)
-
-            weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly, Lx, x.device).to(x.dtype)
-            normalization = fold(weighting).view(1, 1, h, w)  # normalizes the overlap
-            weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx))
-
-        elif uf > 1 and df == 1:
-            fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
-            unfold = torch.nn.Unfold(**fold_params)
-
-            fold_params2 = dict(kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf),
-                                dilation=1, padding=0,
-                                stride=(stride[0] * uf, stride[1] * uf))
-            fold = torch.nn.Fold(output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2)
-
-            weighting = self.get_weighting(kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device).to(x.dtype)
-            normalization = fold(weighting).view(1, 1, h * uf, w * uf)  # normalizes the overlap
-            weighting = weighting.view((1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx))
-
-        elif df > 1 and uf == 1:
-            fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
-            unfold = torch.nn.Unfold(**fold_params)
-
-            fold_params2 = dict(kernel_size=(kernel_size[0] // df, kernel_size[0] // df),
-                                dilation=1, padding=0,
-                                stride=(stride[0] // df, stride[1] // df))
-            fold = torch.nn.Fold(output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2)
-
-            weighting = self.get_weighting(kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device).to(x.dtype)
-            normalization = fold(weighting).view(1, 1, h // df, w // df)  # normalizes the overlap
-            weighting = weighting.view((1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx))
-
-        else:
-            raise NotImplementedError
-
-        return fold, unfold, normalization, weighting
-
-    @torch.no_grad()
-    def get_input(self, batch, k, return_first_stage_outputs=False, force_c_encode=False,
-                  cond_key=None, return_original_cond=False, bs=None, return_x=False):
-        x = super().get_input(batch, k)
-        if bs is not None:
-            x = x[:bs]
-        x = x.to(self.device)
-        encoder_posterior = self.encode_first_stage(x)
-        z = self.get_first_stage_encoding(encoder_posterior).detach()
-
-        if self.model.conditioning_key is not None and not self.force_null_conditioning:
-            if cond_key is None:
-                cond_key = self.cond_stage_key
-            if cond_key != self.first_stage_key:
-                if cond_key in ['caption', 'coordinates_bbox', "txt"]:
-                    xc = batch[cond_key]
-                elif cond_key in ['class_label', 'cls']:
-                    xc = batch
-                else:
-                    xc = super().get_input(batch, cond_key).to(self.device)
-            else:
-                xc = x
-            if not self.cond_stage_trainable or force_c_encode:
-                if isinstance(xc, dict) or isinstance(xc, list):
-                    c = self.get_learned_conditioning(xc)
-                else:
-                    c = self.get_learned_conditioning(xc.to(self.device))
-            else:
-                c = xc
-            if bs is not None:
-                c = c[:bs]
-
-            if self.use_positional_encodings:
-                pos_x, pos_y = self.compute_latent_shifts(batch)
-                ckey = __conditioning_keys__[self.model.conditioning_key]
-                c = {ckey: c, 'pos_x': pos_x, 'pos_y': pos_y}
-
-        else:
-            c = None
-            xc = None
-            if self.use_positional_encodings:
-                pos_x, pos_y = self.compute_latent_shifts(batch)
-                c = {'pos_x': pos_x, 'pos_y': pos_y}
-        out = [z, c]
-        if return_first_stage_outputs:
-            xrec = self.decode_first_stage(z)
-            out.extend([x, xrec])
-        if return_x:
-            out.extend([x])
-        if return_original_cond:
-            out.append(xc)
-        return out
-
-    @torch.no_grad()
-    def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
-        if predict_cids:
-            if z.dim() == 4:
-                z = torch.argmax(z.exp(), dim=1).long()
-            z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
-            z = rearrange(z, 'b h w c -> b c h w').contiguous()
-
-        z = 1. / self.scale_factor * z
-        return self.first_stage_model.decode(z)
-
-    @torch.no_grad()
-    def encode_first_stage(self, x):
-        return self.first_stage_model.encode(x)
-
-    def shared_step(self, batch, **kwargs):
-        x, c = self.get_input(batch, self.first_stage_key)
-        loss = self(x, c)
-        return loss
-
-    def forward(self, x, c, *args, **kwargs):
-        t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
-        if self.model.conditioning_key is not None:
-            assert c is not None
-            if self.cond_stage_trainable:
-                c = self.get_learned_conditioning(c)
-            if self.shorten_cond_schedule:  # TODO: drop this option
-                tc = self.cond_ids[t].to(self.device)
-                c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float()))
-        return self.p_losses(x, c, t, *args, **kwargs)
-
-    def apply_model(self, x_noisy, t, cond, return_ids=False):
-        if isinstance(cond, dict):
-            # hybrid case, cond is expected to be a dict
-            pass
-        else:
-            if not isinstance(cond, list):
-                cond = [cond]
-            key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn'
-            cond = {key: cond}
-
-        x_recon = self.model(x_noisy, t, **cond)
-
-        if isinstance(x_recon, tuple) and not return_ids:
-            return x_recon[0]
-        else:
-            return x_recon
-
-    def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
-        return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \
-               extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
-
-    def _prior_bpd(self, x_start):
-        """
-        Get the prior KL term for the variational lower-bound, measured in
-        bits-per-dim.
-        This term can't be optimized, as it only depends on the encoder.
-        :param x_start: the [N x C x ...] tensor of inputs.
-        :return: a batch of [N] KL values (in bits), one per batch element.
-        """
-        batch_size = x_start.shape[0]
-        t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
-        qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
-        kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0)
-        return mean_flat(kl_prior) / np.log(2.0)
-
-    def p_losses(self, x_start, cond, t, noise=None):
-        noise = default(noise, lambda: torch.randn_like(x_start))
-        x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
-        model_output = self.apply_model(x_noisy, t, cond)
-
-        loss_dict = {}
-        prefix = 'train' if self.training else 'val'
-
-        if self.parameterization == "x0":
-            target = x_start
-        elif self.parameterization == "eps":
-            target = noise
-        elif self.parameterization == "v":
-            target = self.get_v(x_start, noise, t)
-        else:
-            raise NotImplementedError()
-
-        loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3])
-        loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()})
-
-        logvar_t = self.logvar[t].to(self.device)
-        loss = loss_simple / torch.exp(logvar_t) + logvar_t
-        # loss = loss_simple / torch.exp(self.logvar) + self.logvar
-        if self.learn_logvar:
-            loss_dict.update({f'{prefix}/loss_gamma': loss.mean()})
-            loss_dict.update({'logvar': self.logvar.data.mean()})
-
-        loss = self.l_simple_weight * loss.mean()
-
-        loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3))
-        loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean()
-        loss_dict.update({f'{prefix}/loss_vlb': loss_vlb})
-        loss += (self.original_elbo_weight * loss_vlb)
-        loss_dict.update({f'{prefix}/loss': loss})
-
-        return loss, loss_dict
-
-    def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False,
-                        return_x0=False, score_corrector=None, corrector_kwargs=None):
-        t_in = t
-        model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids)
-
-        if score_corrector is not None:
-            assert self.parameterization == "eps"
-            model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs)
-
-        if return_codebook_ids:
-            model_out, logits = model_out
-
-        if self.parameterization == "eps":
-            x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
-        elif self.parameterization == "x0":
-            x_recon = model_out
-        else:
-            raise NotImplementedError()
-
-        if clip_denoised:
-            x_recon.clamp_(-1., 1.)
-        if quantize_denoised:
-            x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon)
-        model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
-        if return_codebook_ids:
-            return model_mean, posterior_variance, posterior_log_variance, logits
-        elif return_x0:
-            return model_mean, posterior_variance, posterior_log_variance, x_recon
-        else:
-            return model_mean, posterior_variance, posterior_log_variance
-
-    @torch.no_grad()
-    def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False,
-                 return_codebook_ids=False, quantize_denoised=False, return_x0=False,
-                 temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None):
-        b, *_, device = *x.shape, x.device
-        outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised,
-                                       return_codebook_ids=return_codebook_ids,
-                                       quantize_denoised=quantize_denoised,
-                                       return_x0=return_x0,
-                                       score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
-        if return_codebook_ids:
-            raise DeprecationWarning("Support dropped.")
-            model_mean, _, model_log_variance, logits = outputs
-        elif return_x0:
-            model_mean, _, model_log_variance, x0 = outputs
-        else:
-            model_mean, _, model_log_variance = outputs
-
-        noise = noise_like(x.shape, device, repeat_noise) * temperature
-        if noise_dropout > 0.:
-            noise = torch.nn.functional.dropout(noise, p=noise_dropout)
-        # no noise when t == 0
-        nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
-
-        if return_codebook_ids:
-            return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=1)
-        if return_x0:
-            return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0
-        else:
-            return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
-
-    @torch.no_grad()
-    def progressive_denoising(self, cond, shape, verbose=True, callback=None, quantize_denoised=False,
-                              img_callback=None, mask=None, x0=None, temperature=1., noise_dropout=0.,
-                              score_corrector=None, corrector_kwargs=None, batch_size=None, x_T=None, start_T=None,
-                              log_every_t=None):
-        if not log_every_t:
-            log_every_t = self.log_every_t
-        timesteps = self.num_timesteps
-        if batch_size is not None:
-            b = batch_size if batch_size is not None else shape[0]
-            shape = [batch_size] + list(shape)
-        else:
-            b = batch_size = shape[0]
-        if x_T is None:
-            img = torch.randn(shape, device=self.device)
-        else:
-            img = x_T
-        intermediates = []
-        if cond is not None:
-            if isinstance(cond, dict):
-                cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
-                list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
-            else:
-                cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
-
-        if start_T is not None:
-            timesteps = min(timesteps, start_T)
-        iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation',
-                        total=timesteps) if verbose else reversed(
-            range(0, timesteps))
-        if type(temperature) == float:
-            temperature = [temperature] * timesteps
-
-        for i in iterator:
-            ts = torch.full((b,), i, device=self.device, dtype=torch.long)
-            if self.shorten_cond_schedule:
-                assert self.model.conditioning_key != 'hybrid'
-                tc = self.cond_ids[ts].to(cond.device)
-                cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
-
-            img, x0_partial = self.p_sample(img, cond, ts,
-                                            clip_denoised=self.clip_denoised,
-                                            quantize_denoised=quantize_denoised, return_x0=True,
-                                            temperature=temperature[i], noise_dropout=noise_dropout,
-                                            score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
-            if mask is not None:
-                assert x0 is not None
-                img_orig = self.q_sample(x0, ts)
-                img = img_orig * mask + (1. - mask) * img
-
-            if i % log_every_t == 0 or i == timesteps - 1:
-                intermediates.append(x0_partial)
-            if callback: callback(i)
-            if img_callback: img_callback(img, i)
-        return img, intermediates
-
-    @torch.no_grad()
-    def p_sample_loop(self, cond, shape, return_intermediates=False,
-                      x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False,
-                      mask=None, x0=None, img_callback=None, start_T=None,
-                      log_every_t=None):
-
-        if not log_every_t:
-            log_every_t = self.log_every_t
-        device = self.betas.device
-        b = shape[0]
-        if x_T is None:
-            img = torch.randn(shape, device=device)
-        else:
-            img = x_T
-
-        intermediates = [img]
-        if timesteps is None:
-            timesteps = self.num_timesteps
-
-        if start_T is not None:
-            timesteps = min(timesteps, start_T)
-        iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed(
-            range(0, timesteps))
-
-        if mask is not None:
-            assert x0 is not None
-            assert x0.shape[2:3] == mask.shape[2:3]  # spatial size has to match
-
-        for i in iterator:
-            ts = torch.full((b,), i, device=device, dtype=torch.long)
-            if self.shorten_cond_schedule:
-                assert self.model.conditioning_key != 'hybrid'
-                tc = self.cond_ids[ts].to(cond.device)
-                cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
-
-            img = self.p_sample(img, cond, ts,
-                                clip_denoised=self.clip_denoised,
-                                quantize_denoised=quantize_denoised)
-            if mask is not None:
-                img_orig = self.q_sample(x0, ts)
-                img = img_orig * mask + (1. - mask) * img
-
-            if i % log_every_t == 0 or i == timesteps - 1:
-                intermediates.append(img)
-            if callback: callback(i)
-            if img_callback: img_callback(img, i)
-
-        if return_intermediates:
-            return img, intermediates
-        return img
-
-    @torch.no_grad()
-    def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None,
-               verbose=True, timesteps=None, quantize_denoised=False,
-               mask=None, x0=None, shape=None, **kwargs):
-        if shape is None:
-            shape = (batch_size, self.channels, self.image_size, self.image_size)
-        if cond is not None:
-            if isinstance(cond, dict):
-                cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
-                list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
-            else:
-                cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
-        return self.p_sample_loop(cond,
-                                  shape,
-                                  return_intermediates=return_intermediates, x_T=x_T,
-                                  verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised,
-                                  mask=mask, x0=x0)
-
-    @torch.no_grad()
-    def sample_log(self, cond, batch_size, ddim, ddim_steps, **kwargs):
-        if ddim:
-            ddim_sampler = DDIMSampler(self)
-            shape = (self.channels, self.image_size, self.image_size)
-            samples, intermediates = ddim_sampler.sample(ddim_steps, batch_size,
-                                                         shape, cond, verbose=False, **kwargs)
-
-        else:
-            samples, intermediates = self.sample(cond=cond, batch_size=batch_size,
-                                                 return_intermediates=True, **kwargs)
-
-        return samples, intermediates
-
-    @torch.no_grad()
-    def get_unconditional_conditioning(self, batch_size, null_label=None):
-        if null_label is not None:
-            xc = null_label
-            if isinstance(xc, ListConfig):
-                xc = list(xc)
-            if isinstance(xc, dict) or isinstance(xc, list):
-                c = self.get_learned_conditioning(xc)
-            else:
-                if hasattr(xc, "to"):
-                    xc = xc.to(self.device)
-                c = self.get_learned_conditioning(xc)
-        else:
-            if self.cond_stage_key in ["class_label", "cls"]:
-                xc = self.cond_stage_model.get_unconditional_conditioning(batch_size, device=self.device)
-                return self.get_learned_conditioning(xc)
-            else:
-                raise NotImplementedError("todo")
-        if isinstance(c, list):  # in case the encoder gives us a list
-            for i in range(len(c)):
-                c[i] = repeat(c[i], '1 ... -> b ...', b=batch_size).to(self.device)
-        else:
-            c = repeat(c, '1 ... -> b ...', b=batch_size).to(self.device)
-        return c
-
-    @torch.no_grad()
-    def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=50, ddim_eta=0., return_keys=None,
-                   quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True,
-                   plot_diffusion_rows=True, unconditional_guidance_scale=1., unconditional_guidance_label=None,
-                   use_ema_scope=True,
-                   **kwargs):
-        ema_scope = self.ema_scope if use_ema_scope else nullcontext
-        use_ddim = ddim_steps is not None
-
-        log = dict()
-        z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key,
-                                           return_first_stage_outputs=True,
-                                           force_c_encode=True,
-                                           return_original_cond=True,
-                                           bs=N)
-        N = min(x.shape[0], N)
-        n_row = min(x.shape[0], n_row)
-        log["inputs"] = x
-        log["reconstruction"] = xrec
-        if self.model.conditioning_key is not None:
-            if hasattr(self.cond_stage_model, "decode"):
-                xc = self.cond_stage_model.decode(c)
-                log["conditioning"] = xc
-            elif self.cond_stage_key in ["caption", "txt"]:
-                xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[self.cond_stage_key], size=x.shape[2] // 25)
-                log["conditioning"] = xc
-            elif self.cond_stage_key in ['class_label', "cls"]:
-                try:
-                    xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"], size=x.shape[2] // 25)
-                    log['conditioning'] = xc
-                except KeyError:
-                    # probably no "human_label" in batch
-                    pass
-            elif isimage(xc):
-                log["conditioning"] = xc
-            if ismap(xc):
-                log["original_conditioning"] = self.to_rgb(xc)
-
-        if plot_diffusion_rows:
-            # get diffusion row
-            diffusion_row = list()
-            z_start = z[:n_row]
-            for t in range(self.num_timesteps):
-                if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
-                    t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
-                    t = t.to(self.device).long()
-                    noise = torch.randn_like(z_start)
-                    z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
-                    diffusion_row.append(self.decode_first_stage(z_noisy))
-
-            diffusion_row = torch.stack(diffusion_row)  # n_log_step, n_row, C, H, W
-            diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
-            diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
-            diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
-            log["diffusion_row"] = diffusion_grid
-
-        if sample:
-            # get denoise row
-            with ema_scope("Sampling"):
-                samples, z_denoise_row = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,
-                                                         ddim_steps=ddim_steps, eta=ddim_eta)
-                # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
-            x_samples = self.decode_first_stage(samples)
-            log["samples"] = x_samples
-            if plot_denoise_rows:
-                denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
-                log["denoise_row"] = denoise_grid
-
-            if quantize_denoised and not isinstance(self.first_stage_model, AutoencoderKL) and not isinstance(
-                    self.first_stage_model, IdentityFirstStage):
-                # also display when quantizing x0 while sampling
-                with ema_scope("Plotting Quantized Denoised"):
-                    samples, z_denoise_row = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,
-                                                             ddim_steps=ddim_steps, eta=ddim_eta,
-                                                             quantize_denoised=True)
-                    # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True,
-                    #                                      quantize_denoised=True)
-                x_samples = self.decode_first_stage(samples.to(self.device))
-                log["samples_x0_quantized"] = x_samples
-
-        if unconditional_guidance_scale > 1.0:
-            uc = self.get_unconditional_conditioning(N, unconditional_guidance_label)
-            if self.model.conditioning_key == "crossattn-adm":
-                uc = {"c_crossattn": [uc], "c_adm": c["c_adm"]}
-            with ema_scope("Sampling with classifier-free guidance"):
-                samples_cfg, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,
-                                                 ddim_steps=ddim_steps, eta=ddim_eta,
-                                                 unconditional_guidance_scale=unconditional_guidance_scale,
-                                                 unconditional_conditioning=uc,
-                                                 )
-                x_samples_cfg = self.decode_first_stage(samples_cfg)
-                log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg
-
-        if inpaint:
-            # make a simple center square
-            b, h, w = z.shape[0], z.shape[2], z.shape[3]
-            mask = torch.ones(N, h, w).to(self.device)
-            # zeros will be filled in
-            mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0.
-            mask = mask[:, None, ...]
-            with ema_scope("Plotting Inpaint"):
-                samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, eta=ddim_eta,
-                                             ddim_steps=ddim_steps, x0=z[:N], mask=mask)
-            x_samples = self.decode_first_stage(samples.to(self.device))
-            log["samples_inpainting"] = x_samples
-            log["mask"] = mask
-
-            # outpaint
-            mask = 1. - mask
-            with ema_scope("Plotting Outpaint"):
-                samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, eta=ddim_eta,
-                                             ddim_steps=ddim_steps, x0=z[:N], mask=mask)
-            x_samples = self.decode_first_stage(samples.to(self.device))
-            log["samples_outpainting"] = x_samples
-
-        if plot_progressive_rows:
-            with ema_scope("Plotting Progressives"):
-                img, progressives = self.progressive_denoising(c,
-                                                               shape=(self.channels, self.image_size, self.image_size),
-                                                               batch_size=N)
-            prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation")
-            log["progressive_row"] = prog_row
-
-        if return_keys:
-            if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
-                return log
-            else:
-                return {key: log[key] for key in return_keys}
-        return log
-
-    def configure_optimizers(self):
-        lr = self.learning_rate
-        params = list(self.model.parameters())
-        if self.cond_stage_trainable:
-            print(f"{self.__class__.__name__}: Also optimizing conditioner params!")
-            params = params + list(self.cond_stage_model.parameters())
-        if self.learn_logvar:
-            print('Diffusion model optimizing logvar')
-            params.append(self.logvar)
-        opt = torch.optim.AdamW(params, lr=lr)
-        if self.use_scheduler:
-            assert 'target' in self.scheduler_config
-            scheduler = instantiate_from_config(self.scheduler_config)
-
-            print("Setting up LambdaLR scheduler...")
-            scheduler = [
-                {
-                    'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule),
-                    'interval': 'step',
-                    'frequency': 1
-                }]
-            return [opt], scheduler
-        return opt
-
-    @torch.no_grad()
-    def to_rgb(self, x):
-        x = x.float()
-        if not hasattr(self, "colorize"):
-            self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x)
-        x = nn.functional.conv2d(x, weight=self.colorize)
-        x = 2. * (x - x.min()) / (x.max() - x.min()) - 1.
-        return x
-
-
-class DiffusionWrapper(pl.LightningModule):
-    def __init__(self, diff_model_config, conditioning_key):
-        super().__init__()
-        self.sequential_cross_attn = diff_model_config.pop("sequential_crossattn", False)
-        self.diffusion_model = instantiate_from_config(diff_model_config)
-        self.conditioning_key = conditioning_key
-        assert self.conditioning_key in [None, 'concat', 'crossattn', 'hybrid', 'adm', 'hybrid-adm', 'crossattn-adm']
-
-    def forward(self, x, t, c_concat: list = None, c_crossattn: list = None, c_adm=None):
-        if self.conditioning_key is None:
-            out = self.diffusion_model(x, t)
-        elif self.conditioning_key == 'concat':
-            xc = torch.cat([x] + c_concat, dim=1)
-            out = self.diffusion_model(xc, t)
-        elif self.conditioning_key == 'crossattn':
-            if not self.sequential_cross_attn:
-                cc = torch.cat(c_crossattn, 1)
-            else:
-                cc = c_crossattn
-            if hasattr(self, "scripted_diffusion_model"):
-                # TorchScript changes names of the arguments
-                # with argument cc defined as context=cc scripted model will produce
-                # an error: RuntimeError: forward() is missing value for argument 'argument_3'.
-                out = self.scripted_diffusion_model(x, t, cc)
-            else:
-                out = self.diffusion_model(x, t, context=cc)
-        elif self.conditioning_key == 'hybrid':
-            xc = torch.cat([x] + c_concat, dim=1)
-            cc = torch.cat(c_crossattn, 1)
-            out = self.diffusion_model(xc, t, context=cc)
-        elif self.conditioning_key == 'hybrid-adm':
-            assert c_adm is not None
-            xc = torch.cat([x] + c_concat, dim=1)
-            cc = torch.cat(c_crossattn, 1)
-            out = self.diffusion_model(xc, t, context=cc, y=c_adm)
-        elif self.conditioning_key == 'crossattn-adm':
-            assert c_adm is not None
-            cc = torch.cat(c_crossattn, 1)
-            out = self.diffusion_model(x, t, context=cc, y=c_adm)
-        elif self.conditioning_key == 'adm':
-            cc = c_crossattn[0]
-            out = self.diffusion_model(x, t, y=cc)
-        else:
-            raise NotImplementedError()
-
-        return out
-
-
-class LatentUpscaleDiffusion(LatentDiffusion):
-    def __init__(self, *args, low_scale_config, low_scale_key="LR", noise_level_key=None, **kwargs):
-        super().__init__(*args, **kwargs)
-        # assumes that neither the cond_stage nor the low_scale_model contain trainable params
-        assert not self.cond_stage_trainable
-        self.instantiate_low_stage(low_scale_config)
-        self.low_scale_key = low_scale_key
-        self.noise_level_key = noise_level_key
-
-    def instantiate_low_stage(self, config):
-        model = instantiate_from_config(config)
-        self.low_scale_model = model.eval()
-        self.low_scale_model.train = disabled_train
-        for param in self.low_scale_model.parameters():
-            param.requires_grad = False
-
-    @torch.no_grad()
-    def get_input(self, batch, k, cond_key=None, bs=None, log_mode=False):
-        if not log_mode:
-            z, c = super().get_input(batch, k, force_c_encode=True, bs=bs)
-        else:
-            z, c, x, xrec, xc = super().get_input(batch, self.first_stage_key, return_first_stage_outputs=True,
-                                                  force_c_encode=True, return_original_cond=True, bs=bs)
-        x_low = batch[self.low_scale_key][:bs]
-        x_low = rearrange(x_low, 'b h w c -> b c h w')
-        x_low = x_low.to(memory_format=torch.contiguous_format).float()
-        zx, noise_level = self.low_scale_model(x_low)
-        if self.noise_level_key is not None:
-            # get noise level from batch instead, e.g. when extracting a custom noise level for bsr
-            raise NotImplementedError('TODO')
-
-        all_conds = {"c_concat": [zx], "c_crossattn": [c], "c_adm": noise_level}
-        if log_mode:
-            # TODO: maybe disable if too expensive
-            x_low_rec = self.low_scale_model.decode(zx)
-            return z, all_conds, x, xrec, xc, x_low, x_low_rec, noise_level
-        return z, all_conds
-
-    @torch.no_grad()
-    def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None,
-                   plot_denoise_rows=False, plot_progressive_rows=True, plot_diffusion_rows=True,
-                   unconditional_guidance_scale=1., unconditional_guidance_label=None, use_ema_scope=True,
-                   **kwargs):
-        ema_scope = self.ema_scope if use_ema_scope else nullcontext
-        use_ddim = ddim_steps is not None
-
-        log = dict()
-        z, c, x, xrec, xc, x_low, x_low_rec, noise_level = self.get_input(batch, self.first_stage_key, bs=N,
-                                                                          log_mode=True)
-        N = min(x.shape[0], N)
-        n_row = min(x.shape[0], n_row)
-        log["inputs"] = x
-        log["reconstruction"] = xrec
-        log["x_lr"] = x_low
-        log[f"x_lr_rec_@noise_levels{'-'.join(map(lambda x: str(x), list(noise_level.cpu().numpy())))}"] = x_low_rec
-        if self.model.conditioning_key is not None:
-            if hasattr(self.cond_stage_model, "decode"):
-                xc = self.cond_stage_model.decode(c)
-                log["conditioning"] = xc
-            elif self.cond_stage_key in ["caption", "txt"]:
-                xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[self.cond_stage_key], size=x.shape[2] // 25)
-                log["conditioning"] = xc
-            elif self.cond_stage_key in ['class_label', 'cls']:
-                xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"], size=x.shape[2] // 25)
-                log['conditioning'] = xc
-            elif isimage(xc):
-                log["conditioning"] = xc
-            if ismap(xc):
-                log["original_conditioning"] = self.to_rgb(xc)
-
-        if plot_diffusion_rows:
-            # get diffusion row
-            diffusion_row = list()
-            z_start = z[:n_row]
-            for t in range(self.num_timesteps):
-                if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
-                    t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
-                    t = t.to(self.device).long()
-                    noise = torch.randn_like(z_start)
-                    z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
-                    diffusion_row.append(self.decode_first_stage(z_noisy))
-
-            diffusion_row = torch.stack(diffusion_row)  # n_log_step, n_row, C, H, W
-            diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
-            diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
-            diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
-            log["diffusion_row"] = diffusion_grid
-
-        if sample:
-            # get denoise row
-            with ema_scope("Sampling"):
-                samples, z_denoise_row = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,
-                                                         ddim_steps=ddim_steps, eta=ddim_eta)
-                # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
-            x_samples = self.decode_first_stage(samples)
-            log["samples"] = x_samples
-            if plot_denoise_rows:
-                denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
-                log["denoise_row"] = denoise_grid
-
-        if unconditional_guidance_scale > 1.0:
-            uc_tmp = self.get_unconditional_conditioning(N, unconditional_guidance_label)
-            # TODO explore better "unconditional" choices for the other keys
-            # maybe guide away from empty text label and highest noise level and maximally degraded zx?
-            uc = dict()
-            for k in c:
-                if k == "c_crossattn":
-                    assert isinstance(c[k], list) and len(c[k]) == 1
-                    uc[k] = [uc_tmp]
-                elif k == "c_adm":  # todo: only run with text-based guidance?
-                    assert isinstance(c[k], torch.Tensor)
-                    #uc[k] = torch.ones_like(c[k]) * self.low_scale_model.max_noise_level
-                    uc[k] = c[k]
-                elif isinstance(c[k], list):
-                    uc[k] = [c[k][i] for i in range(len(c[k]))]
-                else:
-                    uc[k] = c[k]
-
-            with ema_scope("Sampling with classifier-free guidance"):
-                samples_cfg, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,
-                                                 ddim_steps=ddim_steps, eta=ddim_eta,
-                                                 unconditional_guidance_scale=unconditional_guidance_scale,
-                                                 unconditional_conditioning=uc,
-                                                 )
-                x_samples_cfg = self.decode_first_stage(samples_cfg)
-                log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg
-
-        if plot_progressive_rows:
-            with ema_scope("Plotting Progressives"):
-                img, progressives = self.progressive_denoising(c,
-                                                               shape=(self.channels, self.image_size, self.image_size),
-                                                               batch_size=N)
-            prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation")
-            log["progressive_row"] = prog_row
-
-        return log
-
-
-class LatentFinetuneDiffusion(LatentDiffusion):
-    """
-         Basis for different finetunas, such as inpainting or depth2image
-         To disable finetuning mode, set finetune_keys to None
-    """
-
-    def __init__(self,
-                 concat_keys: tuple,
-                 finetune_keys=("model.diffusion_model.input_blocks.0.0.weight",
-                                "model_ema.diffusion_modelinput_blocks00weight"
-                                ),
-                 keep_finetune_dims=4,
-                 # if model was trained without concat mode before and we would like to keep these channels
-                 c_concat_log_start=None,  # to log reconstruction of c_concat codes
-                 c_concat_log_end=None,
-                 *args, **kwargs
-                 ):
-        ckpt_path = kwargs.pop("ckpt_path", None)
-        ignore_keys = kwargs.pop("ignore_keys", list())
-        super().__init__(*args, **kwargs)
-        self.finetune_keys = finetune_keys
-        self.concat_keys = concat_keys
-        self.keep_dims = keep_finetune_dims
-        self.c_concat_log_start = c_concat_log_start
-        self.c_concat_log_end = c_concat_log_end
-        if exists(self.finetune_keys): assert exists(ckpt_path), 'can only finetune from a given checkpoint'
-        if exists(ckpt_path):
-            self.init_from_ckpt(ckpt_path, ignore_keys)
-
-    def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
-        sd = torch.load(path, map_location="cpu")
-        if "state_dict" in list(sd.keys()):
-            sd = sd["state_dict"]
-        keys = list(sd.keys())
-        for k in keys:
-            for ik in ignore_keys:
-                if k.startswith(ik):
-                    print("Deleting key {} from state_dict.".format(k))
-                    del sd[k]
-
-            # make it explicit, finetune by including extra input channels
-            if exists(self.finetune_keys) and k in self.finetune_keys:
-                new_entry = None
-                for name, param in self.named_parameters():
-                    if name in self.finetune_keys:
-                        print(
-                            f"modifying key '{name}' and keeping its original {self.keep_dims} (channels) dimensions only")
-                        new_entry = torch.zeros_like(param)  # zero init
-                assert exists(new_entry), 'did not find matching parameter to modify'
-                new_entry[:, :self.keep_dims, ...] = sd[k]
-                sd[k] = new_entry
-
-        missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
-            sd, strict=False)
-        print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
-        if len(missing) > 0:
-            print(f"Missing Keys: {missing}")
-        if len(unexpected) > 0:
-            print(f"Unexpected Keys: {unexpected}")
-
-    @torch.no_grad()
-    def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None,
-                   quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True,
-                   plot_diffusion_rows=True, unconditional_guidance_scale=1., unconditional_guidance_label=None,
-                   use_ema_scope=True,
-                   **kwargs):
-        ema_scope = self.ema_scope if use_ema_scope else nullcontext
-        use_ddim = ddim_steps is not None
-
-        log = dict()
-        z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key, bs=N, return_first_stage_outputs=True)
-        c_cat, c = c["c_concat"][0], c["c_crossattn"][0]
-        N = min(x.shape[0], N)
-        n_row = min(x.shape[0], n_row)
-        log["inputs"] = x
-        log["reconstruction"] = xrec
-        if self.model.conditioning_key is not None:
-            if hasattr(self.cond_stage_model, "decode"):
-                xc = self.cond_stage_model.decode(c)
-                log["conditioning"] = xc
-            elif self.cond_stage_key in ["caption", "txt"]:
-                xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[self.cond_stage_key], size=x.shape[2] // 25)
-                log["conditioning"] = xc
-            elif self.cond_stage_key in ['class_label', 'cls']:
-                xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"], size=x.shape[2] // 25)
-                log['conditioning'] = xc
-            elif isimage(xc):
-                log["conditioning"] = xc
-            if ismap(xc):
-                log["original_conditioning"] = self.to_rgb(xc)
-
-        if not (self.c_concat_log_start is None and self.c_concat_log_end is None):
-            log["c_concat_decoded"] = self.decode_first_stage(c_cat[:, self.c_concat_log_start:self.c_concat_log_end])
-
-        if plot_diffusion_rows:
-            # get diffusion row
-            diffusion_row = list()
-            z_start = z[:n_row]
-            for t in range(self.num_timesteps):
-                if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
-                    t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
-                    t = t.to(self.device).long()
-                    noise = torch.randn_like(z_start)
-                    z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
-                    diffusion_row.append(self.decode_first_stage(z_noisy))
-
-            diffusion_row = torch.stack(diffusion_row)  # n_log_step, n_row, C, H, W
-            diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
-            diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
-            diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
-            log["diffusion_row"] = diffusion_grid
-
-        if sample:
-            # get denoise row
-            with ema_scope("Sampling"):
-                samples, z_denoise_row = self.sample_log(cond={"c_concat": [c_cat], "c_crossattn": [c]},
-                                                         batch_size=N, ddim=use_ddim,
-                                                         ddim_steps=ddim_steps, eta=ddim_eta)
-                # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
-            x_samples = self.decode_first_stage(samples)
-            log["samples"] = x_samples
-            if plot_denoise_rows:
-                denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
-                log["denoise_row"] = denoise_grid
-
-        if unconditional_guidance_scale > 1.0:
-            uc_cross = self.get_unconditional_conditioning(N, unconditional_guidance_label)
-            uc_cat = c_cat
-            uc_full = {"c_concat": [uc_cat], "c_crossattn": [uc_cross]}
-            with ema_scope("Sampling with classifier-free guidance"):
-                samples_cfg, _ = self.sample_log(cond={"c_concat": [c_cat], "c_crossattn": [c]},
-                                                 batch_size=N, ddim=use_ddim,
-                                                 ddim_steps=ddim_steps, eta=ddim_eta,
-                                                 unconditional_guidance_scale=unconditional_guidance_scale,
-                                                 unconditional_conditioning=uc_full,
-                                                 )
-                x_samples_cfg = self.decode_first_stage(samples_cfg)
-                log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg
-
-        return log
-
-
-class LatentInpaintDiffusion(LatentFinetuneDiffusion):
-    """
-    can either run as pure inpainting model (only concat mode) or with mixed conditionings,
-    e.g. mask as concat and text via cross-attn.
-    To disable finetuning mode, set finetune_keys to None
-     """
-
-    def __init__(self,
-                 concat_keys=("mask", "masked_image"),
-                 masked_image_key="masked_image",
-                 *args, **kwargs
-                 ):
-        super().__init__(concat_keys, *args, **kwargs)
-        self.masked_image_key = masked_image_key
-        assert self.masked_image_key in concat_keys
-
-    @torch.no_grad()
-    def get_input(self, batch, k, cond_key=None, bs=None, return_first_stage_outputs=False):
-        # note: restricted to non-trainable encoders currently
-        assert not self.cond_stage_trainable, 'trainable cond stages not yet supported for inpainting'
-        z, c, x, xrec, xc = super().get_input(batch, self.first_stage_key, return_first_stage_outputs=True,
-                                              force_c_encode=True, return_original_cond=True, bs=bs)
-
-        assert exists(self.concat_keys)
-        c_cat = list()
-        for ck in self.concat_keys:
-            cc = rearrange(batch[ck], 'b h w c -> b c h w').to(memory_format=torch.contiguous_format).float()
-            if bs is not None:
-                cc = cc[:bs]
-                cc = cc.to(self.device)
-            bchw = z.shape
-            if ck != self.masked_image_key:
-                cc = torch.nn.functional.interpolate(cc, size=bchw[-2:])
-            else:
-                cc = self.get_first_stage_encoding(self.encode_first_stage(cc))
-            c_cat.append(cc)
-        c_cat = torch.cat(c_cat, dim=1)
-        all_conds = {"c_concat": [c_cat], "c_crossattn": [c]}
-        if return_first_stage_outputs:
-            return z, all_conds, x, xrec, xc
-        return z, all_conds
-
-    @torch.no_grad()
-    def log_images(self, *args, **kwargs):
-        log = super(LatentInpaintDiffusion, self).log_images(*args, **kwargs)
-        log["masked_image"] = rearrange(args[0]["masked_image"],
-                                        'b h w c -> b c h w').to(memory_format=torch.contiguous_format).float()
-        return log
-
-
-class LatentDepth2ImageDiffusion(LatentFinetuneDiffusion):
-    """
-    condition on monocular depth estimation
-    """
-
-    def __init__(self, depth_stage_config, concat_keys=("midas_in",), *args, **kwargs):
-        super().__init__(concat_keys=concat_keys, *args, **kwargs)
-        self.depth_model = instantiate_from_config(depth_stage_config)
-        self.depth_stage_key = concat_keys[0]
-
-    @torch.no_grad()
-    def get_input(self, batch, k, cond_key=None, bs=None, return_first_stage_outputs=False):
-        # note: restricted to non-trainable encoders currently
-        assert not self.cond_stage_trainable, 'trainable cond stages not yet supported for depth2img'
-        z, c, x, xrec, xc = super().get_input(batch, self.first_stage_key, return_first_stage_outputs=True,
-                                              force_c_encode=True, return_original_cond=True, bs=bs)
-
-        assert exists(self.concat_keys)
-        assert len(self.concat_keys) == 1
-        c_cat = list()
-        for ck in self.concat_keys:
-            cc = batch[ck]
-            if bs is not None:
-                cc = cc[:bs]
-                cc = cc.to(self.device)
-            cc = self.depth_model(cc)
-            cc = torch.nn.functional.interpolate(
-                cc,
-                size=z.shape[2:],
-                mode="bicubic",
-                align_corners=False,
-            )
-
-            depth_min, depth_max = torch.amin(cc, dim=[1, 2, 3], keepdim=True), torch.amax(cc, dim=[1, 2, 3],
-                                                                                           keepdim=True)
-            cc = 2. * (cc - depth_min) / (depth_max - depth_min + 0.001) - 1.
-            c_cat.append(cc)
-        c_cat = torch.cat(c_cat, dim=1)
-        all_conds = {"c_concat": [c_cat], "c_crossattn": [c]}
-        if return_first_stage_outputs:
-            return z, all_conds, x, xrec, xc
-        return z, all_conds
-
-    @torch.no_grad()
-    def log_images(self, *args, **kwargs):
-        log = super().log_images(*args, **kwargs)
-        depth = self.depth_model(args[0][self.depth_stage_key])
-        depth_min, depth_max = torch.amin(depth, dim=[1, 2, 3], keepdim=True), \
-                               torch.amax(depth, dim=[1, 2, 3], keepdim=True)
-        log["depth"] = 2. * (depth - depth_min) / (depth_max - depth_min) - 1.
-        return log
-
-
-class LatentUpscaleFinetuneDiffusion(LatentFinetuneDiffusion):
-    """
-        condition on low-res image (and optionally on some spatial noise augmentation)
-    """
-    def __init__(self, concat_keys=("lr",), reshuffle_patch_size=None,
-                 low_scale_config=None, low_scale_key=None, *args, **kwargs):
-        super().__init__(concat_keys=concat_keys, *args, **kwargs)
-        self.reshuffle_patch_size = reshuffle_patch_size
-        self.low_scale_model = None
-        if low_scale_config is not None:
-            print("Initializing a low-scale model")
-            assert exists(low_scale_key)
-            self.instantiate_low_stage(low_scale_config)
-            self.low_scale_key = low_scale_key
-
-    def instantiate_low_stage(self, config):
-        model = instantiate_from_config(config)
-        self.low_scale_model = model.eval()
-        self.low_scale_model.train = disabled_train
-        for param in self.low_scale_model.parameters():
-            param.requires_grad = False
-
-    @torch.no_grad()
-    def get_input(self, batch, k, cond_key=None, bs=None, return_first_stage_outputs=False):
-        # note: restricted to non-trainable encoders currently
-        assert not self.cond_stage_trainable, 'trainable cond stages not yet supported for upscaling-ft'
-        z, c, x, xrec, xc = super().get_input(batch, self.first_stage_key, return_first_stage_outputs=True,
-                                              force_c_encode=True, return_original_cond=True, bs=bs)
-
-        assert exists(self.concat_keys)
-        assert len(self.concat_keys) == 1
-        # optionally make spatial noise_level here
-        c_cat = list()
-        noise_level = None
-        for ck in self.concat_keys:
-            cc = batch[ck]
-            cc = rearrange(cc, 'b h w c -> b c h w')
-            if exists(self.reshuffle_patch_size):
-                assert isinstance(self.reshuffle_patch_size, int)
-                cc = rearrange(cc, 'b c (p1 h) (p2 w) -> b (p1 p2 c) h w',
-                               p1=self.reshuffle_patch_size, p2=self.reshuffle_patch_size)
-            if bs is not None:
-                cc = cc[:bs]
-                cc = cc.to(self.device)
-            if exists(self.low_scale_model) and ck == self.low_scale_key:
-                cc, noise_level = self.low_scale_model(cc)
-            c_cat.append(cc)
-        c_cat = torch.cat(c_cat, dim=1)
-        if exists(noise_level):
-            all_conds = {"c_concat": [c_cat], "c_crossattn": [c], "c_adm": noise_level}
-        else:
-            all_conds = {"c_concat": [c_cat], "c_crossattn": [c]}
-        if return_first_stage_outputs:
-            return z, all_conds, x, xrec, xc
-        return z, all_conds
-
-    @torch.no_grad()
-    def log_images(self, *args, **kwargs):
-        log = super().log_images(*args, **kwargs)
-        log["lr"] = rearrange(args[0]["lr"], 'b h w c -> b c h w')
-        return log
-
-
-class ImageEmbeddingConditionedLatentDiffusion(LatentDiffusion):
-    def __init__(self, embedder_config, embedding_key="jpg", embedding_dropout=0.5,
-                 freeze_embedder=True, noise_aug_config=None, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.embed_key = embedding_key
-        self.embedding_dropout = embedding_dropout
-        self._init_embedder(embedder_config, freeze_embedder)
-        self._init_noise_aug(noise_aug_config)
-
-    def _init_embedder(self, config, freeze=True):
-        embedder = instantiate_from_config(config)
-        if freeze:
-            self.embedder = embedder.eval()
-            self.embedder.train = disabled_train
-            for param in self.embedder.parameters():
-                param.requires_grad = False
-
-    def _init_noise_aug(self, config):
-        if config is not None:
-            # use the KARLO schedule for noise augmentation on CLIP image embeddings
-            noise_augmentor = instantiate_from_config(config)
-            assert isinstance(noise_augmentor, nn.Module)
-            noise_augmentor = noise_augmentor.eval()
-            noise_augmentor.train = disabled_train
-            self.noise_augmentor = noise_augmentor
-        else:
-            self.noise_augmentor = None
-
-    def get_input(self, batch, k, cond_key=None, bs=None, **kwargs):
-        outputs = LatentDiffusion.get_input(self, batch, k, bs=bs, **kwargs)
-        z, c = outputs[0], outputs[1]
-        img = batch[self.embed_key][:bs]
-        img = rearrange(img, 'b h w c -> b c h w')
-        c_adm = self.embedder(img)
-        if self.noise_augmentor is not None:
-            c_adm, noise_level_emb = self.noise_augmentor(c_adm)
-            # assume this gives embeddings of noise levels
-            c_adm = torch.cat((c_adm, noise_level_emb), 1)
-        if self.training:
-            c_adm = torch.bernoulli((1. - self.embedding_dropout) * torch.ones(c_adm.shape[0],
-                                                                               device=c_adm.device)[:, None]) * c_adm
-        all_conds = {"c_crossattn": [c], "c_adm": c_adm}
-        noutputs = [z, all_conds]
-        noutputs.extend(outputs[2:])
-        return noutputs
-
-    @torch.no_grad()
-    def log_images(self, batch, N=8, n_row=4, **kwargs):
-        log = dict()
-        z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key, bs=N, return_first_stage_outputs=True,
-                                           return_original_cond=True)
-        log["inputs"] = x
-        log["reconstruction"] = xrec
-        assert self.model.conditioning_key is not None
-        assert self.cond_stage_key in ["caption", "txt"]
-        xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[self.cond_stage_key], size=x.shape[2] // 25)
-        log["conditioning"] = xc
-        uc = self.get_unconditional_conditioning(N, kwargs.get('unconditional_guidance_label', ''))
-        unconditional_guidance_scale = kwargs.get('unconditional_guidance_scale', 5.)
-
-        uc_ = {"c_crossattn": [uc], "c_adm": c["c_adm"]}
-        ema_scope = self.ema_scope if kwargs.get('use_ema_scope', True) else nullcontext
-        with ema_scope(f"Sampling"):
-            samples_cfg, _ = self.sample_log(cond=c, batch_size=N, ddim=True,
-                                             ddim_steps=kwargs.get('ddim_steps', 50), eta=kwargs.get('ddim_eta', 0.),
-                                             unconditional_guidance_scale=unconditional_guidance_scale,
-                                             unconditional_conditioning=uc_, )
-            x_samples_cfg = self.decode_first_stage(samples_cfg)
-            log[f"samplescfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg
-        return log
diff --git a/repositories/stable-diffusion-stability-ai/ldm/models/diffusion/dpm_solver/__init__.py b/repositories/stable-diffusion-stability-ai/ldm/models/diffusion/dpm_solver/__init__.py
deleted file mode 100644
index 7427f38c07530afbab79154ea8aaf88c4bf70a08..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/ldm/models/diffusion/dpm_solver/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .sampler import DPMSolverSampler
\ No newline at end of file
diff --git a/repositories/stable-diffusion-stability-ai/ldm/models/diffusion/dpm_solver/dpm_solver.py b/repositories/stable-diffusion-stability-ai/ldm/models/diffusion/dpm_solver/dpm_solver.py
deleted file mode 100644
index da8d41f9c5e93d2f9e07a22aeef9aeb06d0b7dd3..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/ldm/models/diffusion/dpm_solver/dpm_solver.py
+++ /dev/null
@@ -1,1163 +0,0 @@
-import torch
-import torch.nn.functional as F
-import math
-from tqdm import tqdm
-
-
-class NoiseScheduleVP:
-    def __init__(
-            self,
-            schedule='discrete',
-            betas=None,
-            alphas_cumprod=None,
-            continuous_beta_0=0.1,
-            continuous_beta_1=20.,
-    ):
-        """Create a wrapper class for the forward SDE (VP type).
-        ***
-        Update: We support discrete-time diffusion models by implementing a picewise linear interpolation for log_alpha_t.
-                We recommend to use schedule='discrete' for the discrete-time diffusion models, especially for high-resolution images.
-        ***
-        The forward SDE ensures that the condition distribution q_{t|0}(x_t | x_0) = N ( alpha_t * x_0, sigma_t^2 * I ).
-        We further define lambda_t = log(alpha_t) - log(sigma_t), which is the half-logSNR (described in the DPM-Solver paper).
-        Therefore, we implement the functions for computing alpha_t, sigma_t and lambda_t. For t in [0, T], we have:
-            log_alpha_t = self.marginal_log_mean_coeff(t)
-            sigma_t = self.marginal_std(t)
-            lambda_t = self.marginal_lambda(t)
-        Moreover, as lambda(t) is an invertible function, we also support its inverse function:
-            t = self.inverse_lambda(lambda_t)
-        ===============================================================
-        We support both discrete-time DPMs (trained on n = 0, 1, ..., N-1) and continuous-time DPMs (trained on t in [t_0, T]).
-        1. For discrete-time DPMs:
-            For discrete-time DPMs trained on n = 0, 1, ..., N-1, we convert the discrete steps to continuous time steps by:
-                t_i = (i + 1) / N
-            e.g. for N = 1000, we have t_0 = 1e-3 and T = t_{N-1} = 1.
-            We solve the corresponding diffusion ODE from time T = 1 to time t_0 = 1e-3.
-            Args:
-                betas: A `torch.Tensor`. The beta array for the discrete-time DPM. (See the original DDPM paper for details)
-                alphas_cumprod: A `torch.Tensor`. The cumprod alphas for the discrete-time DPM. (See the original DDPM paper for details)
-            Note that we always have alphas_cumprod = cumprod(betas). Therefore, we only need to set one of `betas` and `alphas_cumprod`.
-            **Important**:  Please pay special attention for the args for `alphas_cumprod`:
-                The `alphas_cumprod` is the \hat{alpha_n} arrays in the notations of DDPM. Specifically, DDPMs assume that
-                    q_{t_n | 0}(x_{t_n} | x_0) = N ( \sqrt{\hat{alpha_n}} * x_0, (1 - \hat{alpha_n}) * I ).
-                Therefore, the notation \hat{alpha_n} is different from the notation alpha_t in DPM-Solver. In fact, we have
-                    alpha_{t_n} = \sqrt{\hat{alpha_n}},
-                and
-                    log(alpha_{t_n}) = 0.5 * log(\hat{alpha_n}).
-        2. For continuous-time DPMs:
-            We support two types of VPSDEs: linear (DDPM) and cosine (improved-DDPM). The hyperparameters for the noise
-            schedule are the default settings in DDPM and improved-DDPM:
-            Args:
-                beta_min: A `float` number. The smallest beta for the linear schedule.
-                beta_max: A `float` number. The largest beta for the linear schedule.
-                cosine_s: A `float` number. The hyperparameter in the cosine schedule.
-                cosine_beta_max: A `float` number. The hyperparameter in the cosine schedule.
-                T: A `float` number. The ending time of the forward process.
-        ===============================================================
-        Args:
-            schedule: A `str`. The noise schedule of the forward SDE. 'discrete' for discrete-time DPMs,
-                    'linear' or 'cosine' for continuous-time DPMs.
-        Returns:
-            A wrapper object of the forward SDE (VP type).
-
-        ===============================================================
-        Example:
-        # For discrete-time DPMs, given betas (the beta array for n = 0, 1, ..., N - 1):
-        >>> ns = NoiseScheduleVP('discrete', betas=betas)
-        # For discrete-time DPMs, given alphas_cumprod (the \hat{alpha_n} array for n = 0, 1, ..., N - 1):
-        >>> ns = NoiseScheduleVP('discrete', alphas_cumprod=alphas_cumprod)
-        # For continuous-time DPMs (VPSDE), linear schedule:
-        >>> ns = NoiseScheduleVP('linear', continuous_beta_0=0.1, continuous_beta_1=20.)
-        """
-
-        if schedule not in ['discrete', 'linear', 'cosine']:
-            raise ValueError(
-                "Unsupported noise schedule {}. The schedule needs to be 'discrete' or 'linear' or 'cosine'".format(
-                    schedule))
-
-        self.schedule = schedule
-        if schedule == 'discrete':
-            if betas is not None:
-                log_alphas = 0.5 * torch.log(1 - betas).cumsum(dim=0)
-            else:
-                assert alphas_cumprod is not None
-                log_alphas = 0.5 * torch.log(alphas_cumprod)
-            self.total_N = len(log_alphas)
-            self.T = 1.
-            self.t_array = torch.linspace(0., 1., self.total_N + 1)[1:].reshape((1, -1))
-            self.log_alpha_array = log_alphas.reshape((1, -1,))
-        else:
-            self.total_N = 1000
-            self.beta_0 = continuous_beta_0
-            self.beta_1 = continuous_beta_1
-            self.cosine_s = 0.008
-            self.cosine_beta_max = 999.
-            self.cosine_t_max = math.atan(self.cosine_beta_max * (1. + self.cosine_s) / math.pi) * 2. * (
-                        1. + self.cosine_s) / math.pi - self.cosine_s
-            self.cosine_log_alpha_0 = math.log(math.cos(self.cosine_s / (1. + self.cosine_s) * math.pi / 2.))
-            self.schedule = schedule
-            if schedule == 'cosine':
-                # For the cosine schedule, T = 1 will have numerical issues. So we manually set the ending time T.
-                # Note that T = 0.9946 may be not the optimal setting. However, we find it works well.
-                self.T = 0.9946
-            else:
-                self.T = 1.
-
-    def marginal_log_mean_coeff(self, t):
-        """
-        Compute log(alpha_t) of a given continuous-time label t in [0, T].
-        """
-        if self.schedule == 'discrete':
-            return interpolate_fn(t.reshape((-1, 1)), self.t_array.to(t.device),
-                                  self.log_alpha_array.to(t.device)).reshape((-1))
-        elif self.schedule == 'linear':
-            return -0.25 * t ** 2 * (self.beta_1 - self.beta_0) - 0.5 * t * self.beta_0
-        elif self.schedule == 'cosine':
-            log_alpha_fn = lambda s: torch.log(torch.cos((s + self.cosine_s) / (1. + self.cosine_s) * math.pi / 2.))
-            log_alpha_t = log_alpha_fn(t) - self.cosine_log_alpha_0
-            return log_alpha_t
-
-    def marginal_alpha(self, t):
-        """
-        Compute alpha_t of a given continuous-time label t in [0, T].
-        """
-        return torch.exp(self.marginal_log_mean_coeff(t))
-
-    def marginal_std(self, t):
-        """
-        Compute sigma_t of a given continuous-time label t in [0, T].
-        """
-        return torch.sqrt(1. - torch.exp(2. * self.marginal_log_mean_coeff(t)))
-
-    def marginal_lambda(self, t):
-        """
-        Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T].
-        """
-        log_mean_coeff = self.marginal_log_mean_coeff(t)
-        log_std = 0.5 * torch.log(1. - torch.exp(2. * log_mean_coeff))
-        return log_mean_coeff - log_std
-
-    def inverse_lambda(self, lamb):
-        """
-        Compute the continuous-time label t in [0, T] of a given half-logSNR lambda_t.
-        """
-        if self.schedule == 'linear':
-            tmp = 2. * (self.beta_1 - self.beta_0) * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb))
-            Delta = self.beta_0 ** 2 + tmp
-            return tmp / (torch.sqrt(Delta) + self.beta_0) / (self.beta_1 - self.beta_0)
-        elif self.schedule == 'discrete':
-            log_alpha = -0.5 * torch.logaddexp(torch.zeros((1,)).to(lamb.device), -2. * lamb)
-            t = interpolate_fn(log_alpha.reshape((-1, 1)), torch.flip(self.log_alpha_array.to(lamb.device), [1]),
-                               torch.flip(self.t_array.to(lamb.device), [1]))
-            return t.reshape((-1,))
-        else:
-            log_alpha = -0.5 * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb))
-            t_fn = lambda log_alpha_t: torch.arccos(torch.exp(log_alpha_t + self.cosine_log_alpha_0)) * 2. * (
-                        1. + self.cosine_s) / math.pi - self.cosine_s
-            t = t_fn(log_alpha)
-            return t
-
-
-def model_wrapper(
-        model,
-        noise_schedule,
-        model_type="noise",
-        model_kwargs={},
-        guidance_type="uncond",
-        condition=None,
-        unconditional_condition=None,
-        guidance_scale=1.,
-        classifier_fn=None,
-        classifier_kwargs={},
-):
-    """Create a wrapper function for the noise prediction model.
-    DPM-Solver needs to solve the continuous-time diffusion ODEs. For DPMs trained on discrete-time labels, we need to
-    firstly wrap the model function to a noise prediction model that accepts the continuous time as the input.
-    We support four types of the diffusion model by setting `model_type`:
-        1. "noise": noise prediction model. (Trained by predicting noise).
-        2. "x_start": data prediction model. (Trained by predicting the data x_0 at time 0).
-        3. "v": velocity prediction model. (Trained by predicting the velocity).
-            The "v" prediction is derivation detailed in Appendix D of [1], and is used in Imagen-Video [2].
-            [1] Salimans, Tim, and Jonathan Ho. "Progressive distillation for fast sampling of diffusion models."
-                arXiv preprint arXiv:2202.00512 (2022).
-            [2] Ho, Jonathan, et al. "Imagen Video: High Definition Video Generation with Diffusion Models."
-                arXiv preprint arXiv:2210.02303 (2022).
-
-        4. "score": marginal score function. (Trained by denoising score matching).
-            Note that the score function and the noise prediction model follows a simple relationship:
-            ```
-                noise(x_t, t) = -sigma_t * score(x_t, t)
-            ```
-    We support three types of guided sampling by DPMs by setting `guidance_type`:
-        1. "uncond": unconditional sampling by DPMs.
-            The input `model` has the following format:
-            ``
-                model(x, t_input, **model_kwargs) -> noise | x_start | v | score
-            ``
-        2. "classifier": classifier guidance sampling [3] by DPMs and another classifier.
-            The input `model` has the following format:
-            ``
-                model(x, t_input, **model_kwargs) -> noise | x_start | v | score
-            ``
-            The input `classifier_fn` has the following format:
-            ``
-                classifier_fn(x, t_input, cond, **classifier_kwargs) -> logits(x, t_input, cond)
-            ``
-            [3] P. Dhariwal and A. Q. Nichol, "Diffusion models beat GANs on image synthesis,"
-                in Advances in Neural Information Processing Systems, vol. 34, 2021, pp. 8780-8794.
-        3. "classifier-free": classifier-free guidance sampling by conditional DPMs.
-            The input `model` has the following format:
-            ``
-                model(x, t_input, cond, **model_kwargs) -> noise | x_start | v | score
-            ``
-            And if cond == `unconditional_condition`, the model output is the unconditional DPM output.
-            [4] Ho, Jonathan, and Tim Salimans. "Classifier-free diffusion guidance."
-                arXiv preprint arXiv:2207.12598 (2022).
-
-    The `t_input` is the time label of the model, which may be discrete-time labels (i.e. 0 to 999)
-    or continuous-time labels (i.e. epsilon to T).
-    We wrap the model function to accept only `x` and `t_continuous` as inputs, and outputs the predicted noise:
-    ``
-        def model_fn(x, t_continuous) -> noise:
-            t_input = get_model_input_time(t_continuous)
-            return noise_pred(model, x, t_input, **model_kwargs)
-    ``
-    where `t_continuous` is the continuous time labels (i.e. epsilon to T). And we use `model_fn` for DPM-Solver.
-    ===============================================================
-    Args:
-        model: A diffusion model with the corresponding format described above.
-        noise_schedule: A noise schedule object, such as NoiseScheduleVP.
-        model_type: A `str`. The parameterization type of the diffusion model.
-                    "noise" or "x_start" or "v" or "score".
-        model_kwargs: A `dict`. A dict for the other inputs of the model function.
-        guidance_type: A `str`. The type of the guidance for sampling.
-                    "uncond" or "classifier" or "classifier-free".
-        condition: A pytorch tensor. The condition for the guided sampling.
-                    Only used for "classifier" or "classifier-free" guidance type.
-        unconditional_condition: A pytorch tensor. The condition for the unconditional sampling.
-                    Only used for "classifier-free" guidance type.
-        guidance_scale: A `float`. The scale for the guided sampling.
-        classifier_fn: A classifier function. Only used for the classifier guidance.
-        classifier_kwargs: A `dict`. A dict for the other inputs of the classifier function.
-    Returns:
-        A noise prediction model that accepts the noised data and the continuous time as the inputs.
-    """
-
-    def get_model_input_time(t_continuous):
-        """
-        Convert the continuous-time `t_continuous` (in [epsilon, T]) to the model input time.
-        For discrete-time DPMs, we convert `t_continuous` in [1 / N, 1] to `t_input` in [0, 1000 * (N - 1) / N].
-        For continuous-time DPMs, we just use `t_continuous`.
-        """
-        if noise_schedule.schedule == 'discrete':
-            return (t_continuous - 1. / noise_schedule.total_N) * 1000.
-        else:
-            return t_continuous
-
-    def noise_pred_fn(x, t_continuous, cond=None):
-        if t_continuous.reshape((-1,)).shape[0] == 1:
-            t_continuous = t_continuous.expand((x.shape[0]))
-        t_input = get_model_input_time(t_continuous)
-        if cond is None:
-            output = model(x, t_input, **model_kwargs)
-        else:
-            output = model(x, t_input, cond, **model_kwargs)
-        if model_type == "noise":
-            return output
-        elif model_type == "x_start":
-            alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous)
-            dims = x.dim()
-            return (x - expand_dims(alpha_t, dims) * output) / expand_dims(sigma_t, dims)
-        elif model_type == "v":
-            alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous)
-            dims = x.dim()
-            return expand_dims(alpha_t, dims) * output + expand_dims(sigma_t, dims) * x
-        elif model_type == "score":
-            sigma_t = noise_schedule.marginal_std(t_continuous)
-            dims = x.dim()
-            return -expand_dims(sigma_t, dims) * output
-
-    def cond_grad_fn(x, t_input):
-        """
-        Compute the gradient of the classifier, i.e. nabla_{x} log p_t(cond | x_t).
-        """
-        with torch.enable_grad():
-            x_in = x.detach().requires_grad_(True)
-            log_prob = classifier_fn(x_in, t_input, condition, **classifier_kwargs)
-            return torch.autograd.grad(log_prob.sum(), x_in)[0]
-
-    def model_fn(x, t_continuous):
-        """
-        The noise predicition model function that is used for DPM-Solver.
-        """
-        if t_continuous.reshape((-1,)).shape[0] == 1:
-            t_continuous = t_continuous.expand((x.shape[0]))
-        if guidance_type == "uncond":
-            return noise_pred_fn(x, t_continuous)
-        elif guidance_type == "classifier":
-            assert classifier_fn is not None
-            t_input = get_model_input_time(t_continuous)
-            cond_grad = cond_grad_fn(x, t_input)
-            sigma_t = noise_schedule.marginal_std(t_continuous)
-            noise = noise_pred_fn(x, t_continuous)
-            return noise - guidance_scale * expand_dims(sigma_t, dims=cond_grad.dim()) * cond_grad
-        elif guidance_type == "classifier-free":
-            if guidance_scale == 1. or unconditional_condition is None:
-                return noise_pred_fn(x, t_continuous, cond=condition)
-            else:
-                x_in = torch.cat([x] * 2)
-                t_in = torch.cat([t_continuous] * 2)
-                if isinstance(condition, dict):
-                    assert isinstance(unconditional_condition, dict)
-                    c_in = dict()
-                    for k in condition:
-                        if isinstance(condition[k], list):
-                            c_in[k] = [torch.cat([unconditional_condition[k][i], condition[k][i]]) for i in range(len(condition[k]))]
-                        else:
-                            c_in[k] = torch.cat([unconditional_condition[k], condition[k]])
-                else:
-                    c_in = torch.cat([unconditional_condition, condition])
-                noise_uncond, noise = noise_pred_fn(x_in, t_in, cond=c_in).chunk(2)
-                return noise_uncond + guidance_scale * (noise - noise_uncond)
-
-    assert model_type in ["noise", "x_start", "v"]
-    assert guidance_type in ["uncond", "classifier", "classifier-free"]
-    return model_fn
-
-
-class DPM_Solver:
-    def __init__(self, model_fn, noise_schedule, predict_x0=False, thresholding=False, max_val=1.):
-        """Construct a DPM-Solver.
-        We support both the noise prediction model ("predicting epsilon") and the data prediction model ("predicting x0").
-        If `predict_x0` is False, we use the solver for the noise prediction model (DPM-Solver).
-        If `predict_x0` is True, we use the solver for the data prediction model (DPM-Solver++).
-            In such case, we further support the "dynamic thresholding" in [1] when `thresholding` is True.
-            The "dynamic thresholding" can greatly improve the sample quality for pixel-space DPMs with large guidance scales.
-        Args:
-            model_fn: A noise prediction model function which accepts the continuous-time input (t in [epsilon, T]):
-                ``
-                def model_fn(x, t_continuous):
-                    return noise
-                ``
-            noise_schedule: A noise schedule object, such as NoiseScheduleVP.
-            predict_x0: A `bool`. If true, use the data prediction model; else, use the noise prediction model.
-            thresholding: A `bool`. Valid when `predict_x0` is True. Whether to use the "dynamic thresholding" in [1].
-            max_val: A `float`. Valid when both `predict_x0` and `thresholding` are True. The max value for thresholding.
-
-        [1] Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S Sara Mahdavi, Rapha Gontijo Lopes, et al. Photorealistic text-to-image diffusion models with deep language understanding. arXiv preprint arXiv:2205.11487, 2022b.
-        """
-        self.model = model_fn
-        self.noise_schedule = noise_schedule
-        self.predict_x0 = predict_x0
-        self.thresholding = thresholding
-        self.max_val = max_val
-
-    def noise_prediction_fn(self, x, t):
-        """
-        Return the noise prediction model.
-        """
-        return self.model(x, t)
-
-    def data_prediction_fn(self, x, t):
-        """
-        Return the data prediction model (with thresholding).
-        """
-        noise = self.noise_prediction_fn(x, t)
-        dims = x.dim()
-        alpha_t, sigma_t = self.noise_schedule.marginal_alpha(t), self.noise_schedule.marginal_std(t)
-        x0 = (x - expand_dims(sigma_t, dims) * noise) / expand_dims(alpha_t, dims)
-        if self.thresholding:
-            p = 0.995  # A hyperparameter in the paper of "Imagen" [1].
-            s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1)
-            s = expand_dims(torch.maximum(s, self.max_val * torch.ones_like(s).to(s.device)), dims)
-            x0 = torch.clamp(x0, -s, s) / s
-        return x0
-
-    def model_fn(self, x, t):
-        """
-        Convert the model to the noise prediction model or the data prediction model.
-        """
-        if self.predict_x0:
-            return self.data_prediction_fn(x, t)
-        else:
-            return self.noise_prediction_fn(x, t)
-
-    def get_time_steps(self, skip_type, t_T, t_0, N, device):
-        """Compute the intermediate time steps for sampling.
-        Args:
-            skip_type: A `str`. The type for the spacing of the time steps. We support three types:
-                - 'logSNR': uniform logSNR for the time steps.
-                - 'time_uniform': uniform time for the time steps. (**Recommended for high-resolutional data**.)
-                - 'time_quadratic': quadratic time for the time steps. (Used in DDIM for low-resolutional data.)
-            t_T: A `float`. The starting time of the sampling (default is T).
-            t_0: A `float`. The ending time of the sampling (default is epsilon).
-            N: A `int`. The total number of the spacing of the time steps.
-            device: A torch device.
-        Returns:
-            A pytorch tensor of the time steps, with the shape (N + 1,).
-        """
-        if skip_type == 'logSNR':
-            lambda_T = self.noise_schedule.marginal_lambda(torch.tensor(t_T).to(device))
-            lambda_0 = self.noise_schedule.marginal_lambda(torch.tensor(t_0).to(device))
-            logSNR_steps = torch.linspace(lambda_T.cpu().item(), lambda_0.cpu().item(), N + 1).to(device)
-            return self.noise_schedule.inverse_lambda(logSNR_steps)
-        elif skip_type == 'time_uniform':
-            return torch.linspace(t_T, t_0, N + 1).to(device)
-        elif skip_type == 'time_quadratic':
-            t_order = 2
-            t = torch.linspace(t_T ** (1. / t_order), t_0 ** (1. / t_order), N + 1).pow(t_order).to(device)
-            return t
-        else:
-            raise ValueError(
-                "Unsupported skip_type {}, need to be 'logSNR' or 'time_uniform' or 'time_quadratic'".format(skip_type))
-
-    def get_orders_and_timesteps_for_singlestep_solver(self, steps, order, skip_type, t_T, t_0, device):
-        """
-        Get the order of each step for sampling by the singlestep DPM-Solver.
-        We combine both DPM-Solver-1,2,3 to use all the function evaluations, which is named as "DPM-Solver-fast".
-        Given a fixed number of function evaluations by `steps`, the sampling procedure by DPM-Solver-fast is:
-            - If order == 1:
-                We take `steps` of DPM-Solver-1 (i.e. DDIM).
-            - If order == 2:
-                - Denote K = (steps // 2). We take K or (K + 1) intermediate time steps for sampling.
-                - If steps % 2 == 0, we use K steps of DPM-Solver-2.
-                - If steps % 2 == 1, we use K steps of DPM-Solver-2 and 1 step of DPM-Solver-1.
-            - If order == 3:
-                - Denote K = (steps // 3 + 1). We take K intermediate time steps for sampling.
-                - If steps % 3 == 0, we use (K - 2) steps of DPM-Solver-3, and 1 step of DPM-Solver-2 and 1 step of DPM-Solver-1.
-                - If steps % 3 == 1, we use (K - 1) steps of DPM-Solver-3 and 1 step of DPM-Solver-1.
-                - If steps % 3 == 2, we use (K - 1) steps of DPM-Solver-3 and 1 step of DPM-Solver-2.
-        ============================================
-        Args:
-            order: A `int`. The max order for the solver (2 or 3).
-            steps: A `int`. The total number of function evaluations (NFE).
-            skip_type: A `str`. The type for the spacing of the time steps. We support three types:
-                - 'logSNR': uniform logSNR for the time steps.
-                - 'time_uniform': uniform time for the time steps. (**Recommended for high-resolutional data**.)
-                - 'time_quadratic': quadratic time for the time steps. (Used in DDIM for low-resolutional data.)
-            t_T: A `float`. The starting time of the sampling (default is T).
-            t_0: A `float`. The ending time of the sampling (default is epsilon).
-            device: A torch device.
-        Returns:
-            orders: A list of the solver order of each step.
-        """
-        if order == 3:
-            K = steps // 3 + 1
-            if steps % 3 == 0:
-                orders = [3, ] * (K - 2) + [2, 1]
-            elif steps % 3 == 1:
-                orders = [3, ] * (K - 1) + [1]
-            else:
-                orders = [3, ] * (K - 1) + [2]
-        elif order == 2:
-            if steps % 2 == 0:
-                K = steps // 2
-                orders = [2, ] * K
-            else:
-                K = steps // 2 + 1
-                orders = [2, ] * (K - 1) + [1]
-        elif order == 1:
-            K = 1
-            orders = [1, ] * steps
-        else:
-            raise ValueError("'order' must be '1' or '2' or '3'.")
-        if skip_type == 'logSNR':
-            # To reproduce the results in DPM-Solver paper
-            timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, K, device)
-        else:
-            timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, steps, device)[
-                torch.cumsum(torch.tensor([0, ] + orders)).to(device)]
-        return timesteps_outer, orders
-
-    def denoise_to_zero_fn(self, x, s):
-        """
-        Denoise at the final step, which is equivalent to solve the ODE from lambda_s to infty by first-order discretization.
-        """
-        return self.data_prediction_fn(x, s)
-
-    def dpm_solver_first_update(self, x, s, t, model_s=None, return_intermediate=False):
-        """
-        DPM-Solver-1 (equivalent to DDIM) from time `s` to time `t`.
-        Args:
-            x: A pytorch tensor. The initial value at time `s`.
-            s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
-            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
-            model_s: A pytorch tensor. The model function evaluated at time `s`.
-                If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it.
-            return_intermediate: A `bool`. If true, also return the model value at time `s`.
-        Returns:
-            x_t: A pytorch tensor. The approximated solution at time `t`.
-        """
-        ns = self.noise_schedule
-        dims = x.dim()
-        lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t)
-        h = lambda_t - lambda_s
-        log_alpha_s, log_alpha_t = ns.marginal_log_mean_coeff(s), ns.marginal_log_mean_coeff(t)
-        sigma_s, sigma_t = ns.marginal_std(s), ns.marginal_std(t)
-        alpha_t = torch.exp(log_alpha_t)
-
-        if self.predict_x0:
-            phi_1 = torch.expm1(-h)
-            if model_s is None:
-                model_s = self.model_fn(x, s)
-            x_t = (
-                    expand_dims(sigma_t / sigma_s, dims) * x
-                    - expand_dims(alpha_t * phi_1, dims) * model_s
-            )
-            if return_intermediate:
-                return x_t, {'model_s': model_s}
-            else:
-                return x_t
-        else:
-            phi_1 = torch.expm1(h)
-            if model_s is None:
-                model_s = self.model_fn(x, s)
-            x_t = (
-                    expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
-                    - expand_dims(sigma_t * phi_1, dims) * model_s
-            )
-            if return_intermediate:
-                return x_t, {'model_s': model_s}
-            else:
-                return x_t
-
-    def singlestep_dpm_solver_second_update(self, x, s, t, r1=0.5, model_s=None, return_intermediate=False,
-                                            solver_type='dpm_solver'):
-        """
-        Singlestep solver DPM-Solver-2 from time `s` to time `t`.
-        Args:
-            x: A pytorch tensor. The initial value at time `s`.
-            s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
-            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
-            r1: A `float`. The hyperparameter of the second-order solver.
-            model_s: A pytorch tensor. The model function evaluated at time `s`.
-                If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it.
-            return_intermediate: A `bool`. If true, also return the model value at time `s` and `s1` (the intermediate time).
-            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
-                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
-        Returns:
-            x_t: A pytorch tensor. The approximated solution at time `t`.
-        """
-        if solver_type not in ['dpm_solver', 'taylor']:
-            raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type))
-        if r1 is None:
-            r1 = 0.5
-        ns = self.noise_schedule
-        dims = x.dim()
-        lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t)
-        h = lambda_t - lambda_s
-        lambda_s1 = lambda_s + r1 * h
-        s1 = ns.inverse_lambda(lambda_s1)
-        log_alpha_s, log_alpha_s1, log_alpha_t = ns.marginal_log_mean_coeff(s), ns.marginal_log_mean_coeff(
-            s1), ns.marginal_log_mean_coeff(t)
-        sigma_s, sigma_s1, sigma_t = ns.marginal_std(s), ns.marginal_std(s1), ns.marginal_std(t)
-        alpha_s1, alpha_t = torch.exp(log_alpha_s1), torch.exp(log_alpha_t)
-
-        if self.predict_x0:
-            phi_11 = torch.expm1(-r1 * h)
-            phi_1 = torch.expm1(-h)
-
-            if model_s is None:
-                model_s = self.model_fn(x, s)
-            x_s1 = (
-                    expand_dims(sigma_s1 / sigma_s, dims) * x
-                    - expand_dims(alpha_s1 * phi_11, dims) * model_s
-            )
-            model_s1 = self.model_fn(x_s1, s1)
-            if solver_type == 'dpm_solver':
-                x_t = (
-                        expand_dims(sigma_t / sigma_s, dims) * x
-                        - expand_dims(alpha_t * phi_1, dims) * model_s
-                        - (0.5 / r1) * expand_dims(alpha_t * phi_1, dims) * (model_s1 - model_s)
-                )
-            elif solver_type == 'taylor':
-                x_t = (
-                        expand_dims(sigma_t / sigma_s, dims) * x
-                        - expand_dims(alpha_t * phi_1, dims) * model_s
-                        + (1. / r1) * expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * (
-                                    model_s1 - model_s)
-                )
-        else:
-            phi_11 = torch.expm1(r1 * h)
-            phi_1 = torch.expm1(h)
-
-            if model_s is None:
-                model_s = self.model_fn(x, s)
-            x_s1 = (
-                    expand_dims(torch.exp(log_alpha_s1 - log_alpha_s), dims) * x
-                    - expand_dims(sigma_s1 * phi_11, dims) * model_s
-            )
-            model_s1 = self.model_fn(x_s1, s1)
-            if solver_type == 'dpm_solver':
-                x_t = (
-                        expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
-                        - expand_dims(sigma_t * phi_1, dims) * model_s
-                        - (0.5 / r1) * expand_dims(sigma_t * phi_1, dims) * (model_s1 - model_s)
-                )
-            elif solver_type == 'taylor':
-                x_t = (
-                        expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
-                        - expand_dims(sigma_t * phi_1, dims) * model_s
-                        - (1. / r1) * expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * (model_s1 - model_s)
-                )
-        if return_intermediate:
-            return x_t, {'model_s': model_s, 'model_s1': model_s1}
-        else:
-            return x_t
-
-    def singlestep_dpm_solver_third_update(self, x, s, t, r1=1. / 3., r2=2. / 3., model_s=None, model_s1=None,
-                                           return_intermediate=False, solver_type='dpm_solver'):
-        """
-        Singlestep solver DPM-Solver-3 from time `s` to time `t`.
-        Args:
-            x: A pytorch tensor. The initial value at time `s`.
-            s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
-            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
-            r1: A `float`. The hyperparameter of the third-order solver.
-            r2: A `float`. The hyperparameter of the third-order solver.
-            model_s: A pytorch tensor. The model function evaluated at time `s`.
-                If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it.
-            model_s1: A pytorch tensor. The model function evaluated at time `s1` (the intermediate time given by `r1`).
-                If `model_s1` is None, we evaluate the model at `s1`; otherwise we directly use it.
-            return_intermediate: A `bool`. If true, also return the model value at time `s`, `s1` and `s2` (the intermediate times).
-            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
-                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
-        Returns:
-            x_t: A pytorch tensor. The approximated solution at time `t`.
-        """
-        if solver_type not in ['dpm_solver', 'taylor']:
-            raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type))
-        if r1 is None:
-            r1 = 1. / 3.
-        if r2 is None:
-            r2 = 2. / 3.
-        ns = self.noise_schedule
-        dims = x.dim()
-        lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t)
-        h = lambda_t - lambda_s
-        lambda_s1 = lambda_s + r1 * h
-        lambda_s2 = lambda_s + r2 * h
-        s1 = ns.inverse_lambda(lambda_s1)
-        s2 = ns.inverse_lambda(lambda_s2)
-        log_alpha_s, log_alpha_s1, log_alpha_s2, log_alpha_t = ns.marginal_log_mean_coeff(
-            s), ns.marginal_log_mean_coeff(s1), ns.marginal_log_mean_coeff(s2), ns.marginal_log_mean_coeff(t)
-        sigma_s, sigma_s1, sigma_s2, sigma_t = ns.marginal_std(s), ns.marginal_std(s1), ns.marginal_std(
-            s2), ns.marginal_std(t)
-        alpha_s1, alpha_s2, alpha_t = torch.exp(log_alpha_s1), torch.exp(log_alpha_s2), torch.exp(log_alpha_t)
-
-        if self.predict_x0:
-            phi_11 = torch.expm1(-r1 * h)
-            phi_12 = torch.expm1(-r2 * h)
-            phi_1 = torch.expm1(-h)
-            phi_22 = torch.expm1(-r2 * h) / (r2 * h) + 1.
-            phi_2 = phi_1 / h + 1.
-            phi_3 = phi_2 / h - 0.5
-
-            if model_s is None:
-                model_s = self.model_fn(x, s)
-            if model_s1 is None:
-                x_s1 = (
-                        expand_dims(sigma_s1 / sigma_s, dims) * x
-                        - expand_dims(alpha_s1 * phi_11, dims) * model_s
-                )
-                model_s1 = self.model_fn(x_s1, s1)
-            x_s2 = (
-                    expand_dims(sigma_s2 / sigma_s, dims) * x
-                    - expand_dims(alpha_s2 * phi_12, dims) * model_s
-                    + r2 / r1 * expand_dims(alpha_s2 * phi_22, dims) * (model_s1 - model_s)
-            )
-            model_s2 = self.model_fn(x_s2, s2)
-            if solver_type == 'dpm_solver':
-                x_t = (
-                        expand_dims(sigma_t / sigma_s, dims) * x
-                        - expand_dims(alpha_t * phi_1, dims) * model_s
-                        + (1. / r2) * expand_dims(alpha_t * phi_2, dims) * (model_s2 - model_s)
-                )
-            elif solver_type == 'taylor':
-                D1_0 = (1. / r1) * (model_s1 - model_s)
-                D1_1 = (1. / r2) * (model_s2 - model_s)
-                D1 = (r2 * D1_0 - r1 * D1_1) / (r2 - r1)
-                D2 = 2. * (D1_1 - D1_0) / (r2 - r1)
-                x_t = (
-                        expand_dims(sigma_t / sigma_s, dims) * x
-                        - expand_dims(alpha_t * phi_1, dims) * model_s
-                        + expand_dims(alpha_t * phi_2, dims) * D1
-                        - expand_dims(alpha_t * phi_3, dims) * D2
-                )
-        else:
-            phi_11 = torch.expm1(r1 * h)
-            phi_12 = torch.expm1(r2 * h)
-            phi_1 = torch.expm1(h)
-            phi_22 = torch.expm1(r2 * h) / (r2 * h) - 1.
-            phi_2 = phi_1 / h - 1.
-            phi_3 = phi_2 / h - 0.5
-
-            if model_s is None:
-                model_s = self.model_fn(x, s)
-            if model_s1 is None:
-                x_s1 = (
-                        expand_dims(torch.exp(log_alpha_s1 - log_alpha_s), dims) * x
-                        - expand_dims(sigma_s1 * phi_11, dims) * model_s
-                )
-                model_s1 = self.model_fn(x_s1, s1)
-            x_s2 = (
-                    expand_dims(torch.exp(log_alpha_s2 - log_alpha_s), dims) * x
-                    - expand_dims(sigma_s2 * phi_12, dims) * model_s
-                    - r2 / r1 * expand_dims(sigma_s2 * phi_22, dims) * (model_s1 - model_s)
-            )
-            model_s2 = self.model_fn(x_s2, s2)
-            if solver_type == 'dpm_solver':
-                x_t = (
-                        expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
-                        - expand_dims(sigma_t * phi_1, dims) * model_s
-                        - (1. / r2) * expand_dims(sigma_t * phi_2, dims) * (model_s2 - model_s)
-                )
-            elif solver_type == 'taylor':
-                D1_0 = (1. / r1) * (model_s1 - model_s)
-                D1_1 = (1. / r2) * (model_s2 - model_s)
-                D1 = (r2 * D1_0 - r1 * D1_1) / (r2 - r1)
-                D2 = 2. * (D1_1 - D1_0) / (r2 - r1)
-                x_t = (
-                        expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
-                        - expand_dims(sigma_t * phi_1, dims) * model_s
-                        - expand_dims(sigma_t * phi_2, dims) * D1
-                        - expand_dims(sigma_t * phi_3, dims) * D2
-                )
-
-        if return_intermediate:
-            return x_t, {'model_s': model_s, 'model_s1': model_s1, 'model_s2': model_s2}
-        else:
-            return x_t
-
-    def multistep_dpm_solver_second_update(self, x, model_prev_list, t_prev_list, t, solver_type="dpm_solver"):
-        """
-        Multistep solver DPM-Solver-2 from time `t_prev_list[-1]` to time `t`.
-        Args:
-            x: A pytorch tensor. The initial value at time `s`.
-            model_prev_list: A list of pytorch tensor. The previous computed model values.
-            t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],)
-            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
-            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
-                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
-        Returns:
-            x_t: A pytorch tensor. The approximated solution at time `t`.
-        """
-        if solver_type not in ['dpm_solver', 'taylor']:
-            raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type))
-        ns = self.noise_schedule
-        dims = x.dim()
-        model_prev_1, model_prev_0 = model_prev_list
-        t_prev_1, t_prev_0 = t_prev_list
-        lambda_prev_1, lambda_prev_0, lambda_t = ns.marginal_lambda(t_prev_1), ns.marginal_lambda(
-            t_prev_0), ns.marginal_lambda(t)
-        log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t)
-        sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t)
-        alpha_t = torch.exp(log_alpha_t)
-
-        h_0 = lambda_prev_0 - lambda_prev_1
-        h = lambda_t - lambda_prev_0
-        r0 = h_0 / h
-        D1_0 = expand_dims(1. / r0, dims) * (model_prev_0 - model_prev_1)
-        if self.predict_x0:
-            if solver_type == 'dpm_solver':
-                x_t = (
-                        expand_dims(sigma_t / sigma_prev_0, dims) * x
-                        - expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0
-                        - 0.5 * expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * D1_0
-                )
-            elif solver_type == 'taylor':
-                x_t = (
-                        expand_dims(sigma_t / sigma_prev_0, dims) * x
-                        - expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0
-                        + expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * D1_0
-                )
-        else:
-            if solver_type == 'dpm_solver':
-                x_t = (
-                        expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
-                        - expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0
-                        - 0.5 * expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * D1_0
-                )
-            elif solver_type == 'taylor':
-                x_t = (
-                        expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
-                        - expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0
-                        - expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * D1_0
-                )
-        return x_t
-
-    def multistep_dpm_solver_third_update(self, x, model_prev_list, t_prev_list, t, solver_type='dpm_solver'):
-        """
-        Multistep solver DPM-Solver-3 from time `t_prev_list[-1]` to time `t`.
-        Args:
-            x: A pytorch tensor. The initial value at time `s`.
-            model_prev_list: A list of pytorch tensor. The previous computed model values.
-            t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],)
-            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
-            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
-                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
-        Returns:
-            x_t: A pytorch tensor. The approximated solution at time `t`.
-        """
-        ns = self.noise_schedule
-        dims = x.dim()
-        model_prev_2, model_prev_1, model_prev_0 = model_prev_list
-        t_prev_2, t_prev_1, t_prev_0 = t_prev_list
-        lambda_prev_2, lambda_prev_1, lambda_prev_0, lambda_t = ns.marginal_lambda(t_prev_2), ns.marginal_lambda(
-            t_prev_1), ns.marginal_lambda(t_prev_0), ns.marginal_lambda(t)
-        log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t)
-        sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t)
-        alpha_t = torch.exp(log_alpha_t)
-
-        h_1 = lambda_prev_1 - lambda_prev_2
-        h_0 = lambda_prev_0 - lambda_prev_1
-        h = lambda_t - lambda_prev_0
-        r0, r1 = h_0 / h, h_1 / h
-        D1_0 = expand_dims(1. / r0, dims) * (model_prev_0 - model_prev_1)
-        D1_1 = expand_dims(1. / r1, dims) * (model_prev_1 - model_prev_2)
-        D1 = D1_0 + expand_dims(r0 / (r0 + r1), dims) * (D1_0 - D1_1)
-        D2 = expand_dims(1. / (r0 + r1), dims) * (D1_0 - D1_1)
-        if self.predict_x0:
-            x_t = (
-                    expand_dims(sigma_t / sigma_prev_0, dims) * x
-                    - expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0
-                    + expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * D1
-                    - expand_dims(alpha_t * ((torch.exp(-h) - 1. + h) / h ** 2 - 0.5), dims) * D2
-            )
-        else:
-            x_t = (
-                    expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
-                    - expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0
-                    - expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * D1
-                    - expand_dims(sigma_t * ((torch.exp(h) - 1. - h) / h ** 2 - 0.5), dims) * D2
-            )
-        return x_t
-
-    def singlestep_dpm_solver_update(self, x, s, t, order, return_intermediate=False, solver_type='dpm_solver', r1=None,
-                                     r2=None):
-        """
-        Singlestep DPM-Solver with the order `order` from time `s` to time `t`.
-        Args:
-            x: A pytorch tensor. The initial value at time `s`.
-            s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
-            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
-            order: A `int`. The order of DPM-Solver. We only support order == 1 or 2 or 3.
-            return_intermediate: A `bool`. If true, also return the model value at time `s`, `s1` and `s2` (the intermediate times).
-            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
-                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
-            r1: A `float`. The hyperparameter of the second-order or third-order solver.
-            r2: A `float`. The hyperparameter of the third-order solver.
-        Returns:
-            x_t: A pytorch tensor. The approximated solution at time `t`.
-        """
-        if order == 1:
-            return self.dpm_solver_first_update(x, s, t, return_intermediate=return_intermediate)
-        elif order == 2:
-            return self.singlestep_dpm_solver_second_update(x, s, t, return_intermediate=return_intermediate,
-                                                            solver_type=solver_type, r1=r1)
-        elif order == 3:
-            return self.singlestep_dpm_solver_third_update(x, s, t, return_intermediate=return_intermediate,
-                                                           solver_type=solver_type, r1=r1, r2=r2)
-        else:
-            raise ValueError("Solver order must be 1 or 2 or 3, got {}".format(order))
-
-    def multistep_dpm_solver_update(self, x, model_prev_list, t_prev_list, t, order, solver_type='dpm_solver'):
-        """
-        Multistep DPM-Solver with the order `order` from time `t_prev_list[-1]` to time `t`.
-        Args:
-            x: A pytorch tensor. The initial value at time `s`.
-            model_prev_list: A list of pytorch tensor. The previous computed model values.
-            t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],)
-            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
-            order: A `int`. The order of DPM-Solver. We only support order == 1 or 2 or 3.
-            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
-                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
-        Returns:
-            x_t: A pytorch tensor. The approximated solution at time `t`.
-        """
-        if order == 1:
-            return self.dpm_solver_first_update(x, t_prev_list[-1], t, model_s=model_prev_list[-1])
-        elif order == 2:
-            return self.multistep_dpm_solver_second_update(x, model_prev_list, t_prev_list, t, solver_type=solver_type)
-        elif order == 3:
-            return self.multistep_dpm_solver_third_update(x, model_prev_list, t_prev_list, t, solver_type=solver_type)
-        else:
-            raise ValueError("Solver order must be 1 or 2 or 3, got {}".format(order))
-
-    def dpm_solver_adaptive(self, x, order, t_T, t_0, h_init=0.05, atol=0.0078, rtol=0.05, theta=0.9, t_err=1e-5,
-                            solver_type='dpm_solver'):
-        """
-        The adaptive step size solver based on singlestep DPM-Solver.
-        Args:
-            x: A pytorch tensor. The initial value at time `t_T`.
-            order: A `int`. The (higher) order of the solver. We only support order == 2 or 3.
-            t_T: A `float`. The starting time of the sampling (default is T).
-            t_0: A `float`. The ending time of the sampling (default is epsilon).
-            h_init: A `float`. The initial step size (for logSNR).
-            atol: A `float`. The absolute tolerance of the solver. For image data, the default setting is 0.0078, followed [1].
-            rtol: A `float`. The relative tolerance of the solver. The default setting is 0.05.
-            theta: A `float`. The safety hyperparameter for adapting the step size. The default setting is 0.9, followed [1].
-            t_err: A `float`. The tolerance for the time. We solve the diffusion ODE until the absolute error between the
-                current time and `t_0` is less than `t_err`. The default setting is 1e-5.
-            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
-                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
-        Returns:
-            x_0: A pytorch tensor. The approximated solution at time `t_0`.
-        [1] A. Jolicoeur-Martineau, K. Li, R. Piché-Taillefer, T. Kachman, and I. Mitliagkas, "Gotta go fast when generating data with score-based models," arXiv preprint arXiv:2105.14080, 2021.
-        """
-        ns = self.noise_schedule
-        s = t_T * torch.ones((x.shape[0],)).to(x)
-        lambda_s = ns.marginal_lambda(s)
-        lambda_0 = ns.marginal_lambda(t_0 * torch.ones_like(s).to(x))
-        h = h_init * torch.ones_like(s).to(x)
-        x_prev = x
-        nfe = 0
-        if order == 2:
-            r1 = 0.5
-            lower_update = lambda x, s, t: self.dpm_solver_first_update(x, s, t, return_intermediate=True)
-            higher_update = lambda x, s, t, **kwargs: self.singlestep_dpm_solver_second_update(x, s, t, r1=r1,
-                                                                                               solver_type=solver_type,
-                                                                                               **kwargs)
-        elif order == 3:
-            r1, r2 = 1. / 3., 2. / 3.
-            lower_update = lambda x, s, t: self.singlestep_dpm_solver_second_update(x, s, t, r1=r1,
-                                                                                    return_intermediate=True,
-                                                                                    solver_type=solver_type)
-            higher_update = lambda x, s, t, **kwargs: self.singlestep_dpm_solver_third_update(x, s, t, r1=r1, r2=r2,
-                                                                                              solver_type=solver_type,
-                                                                                              **kwargs)
-        else:
-            raise ValueError("For adaptive step size solver, order must be 2 or 3, got {}".format(order))
-        while torch.abs((s - t_0)).mean() > t_err:
-            t = ns.inverse_lambda(lambda_s + h)
-            x_lower, lower_noise_kwargs = lower_update(x, s, t)
-            x_higher = higher_update(x, s, t, **lower_noise_kwargs)
-            delta = torch.max(torch.ones_like(x).to(x) * atol, rtol * torch.max(torch.abs(x_lower), torch.abs(x_prev)))
-            norm_fn = lambda v: torch.sqrt(torch.square(v.reshape((v.shape[0], -1))).mean(dim=-1, keepdim=True))
-            E = norm_fn((x_higher - x_lower) / delta).max()
-            if torch.all(E <= 1.):
-                x = x_higher
-                s = t
-                x_prev = x_lower
-                lambda_s = ns.marginal_lambda(s)
-            h = torch.min(theta * h * torch.float_power(E, -1. / order).float(), lambda_0 - lambda_s)
-            nfe += order
-        print('adaptive solver nfe', nfe)
-        return x
-
-    def sample(self, x, steps=20, t_start=None, t_end=None, order=3, skip_type='time_uniform',
-               method='singlestep', lower_order_final=True, denoise_to_zero=False, solver_type='dpm_solver',
-               atol=0.0078, rtol=0.05,
-               ):
-        """
-        Compute the sample at time `t_end` by DPM-Solver, given the initial `x` at time `t_start`.
-        =====================================================
-        We support the following algorithms for both noise prediction model and data prediction model:
-            - 'singlestep':
-                Singlestep DPM-Solver (i.e. "DPM-Solver-fast" in the paper), which combines different orders of singlestep DPM-Solver.
-                We combine all the singlestep solvers with order <= `order` to use up all the function evaluations (steps).
-                The total number of function evaluations (NFE) == `steps`.
-                Given a fixed NFE == `steps`, the sampling procedure is:
-                    - If `order` == 1:
-                        - Denote K = steps. We use K steps of DPM-Solver-1 (i.e. DDIM).
-                    - If `order` == 2:
-                        - Denote K = (steps // 2) + (steps % 2). We take K intermediate time steps for sampling.
-                        - If steps % 2 == 0, we use K steps of singlestep DPM-Solver-2.
-                        - If steps % 2 == 1, we use (K - 1) steps of singlestep DPM-Solver-2 and 1 step of DPM-Solver-1.
-                    - If `order` == 3:
-                        - Denote K = (steps // 3 + 1). We take K intermediate time steps for sampling.
-                        - If steps % 3 == 0, we use (K - 2) steps of singlestep DPM-Solver-3, and 1 step of singlestep DPM-Solver-2 and 1 step of DPM-Solver-1.
-                        - If steps % 3 == 1, we use (K - 1) steps of singlestep DPM-Solver-3 and 1 step of DPM-Solver-1.
-                        - If steps % 3 == 2, we use (K - 1) steps of singlestep DPM-Solver-3 and 1 step of singlestep DPM-Solver-2.
-            - 'multistep':
-                Multistep DPM-Solver with the order of `order`. The total number of function evaluations (NFE) == `steps`.
-                We initialize the first `order` values by lower order multistep solvers.
-                Given a fixed NFE == `steps`, the sampling procedure is:
-                    Denote K = steps.
-                    - If `order` == 1:
-                        - We use K steps of DPM-Solver-1 (i.e. DDIM).
-                    - If `order` == 2:
-                        - We firstly use 1 step of DPM-Solver-1, then use (K - 1) step of multistep DPM-Solver-2.
-                    - If `order` == 3:
-                        - We firstly use 1 step of DPM-Solver-1, then 1 step of multistep DPM-Solver-2, then (K - 2) step of multistep DPM-Solver-3.
-            - 'singlestep_fixed':
-                Fixed order singlestep DPM-Solver (i.e. DPM-Solver-1 or singlestep DPM-Solver-2 or singlestep DPM-Solver-3).
-                We use singlestep DPM-Solver-`order` for `order`=1 or 2 or 3, with total [`steps` // `order`] * `order` NFE.
-            - 'adaptive':
-                Adaptive step size DPM-Solver (i.e. "DPM-Solver-12" and "DPM-Solver-23" in the paper).
-                We ignore `steps` and use adaptive step size DPM-Solver with a higher order of `order`.
-                You can adjust the absolute tolerance `atol` and the relative tolerance `rtol` to balance the computatation costs
-                (NFE) and the sample quality.
-                    - If `order` == 2, we use DPM-Solver-12 which combines DPM-Solver-1 and singlestep DPM-Solver-2.
-                    - If `order` == 3, we use DPM-Solver-23 which combines singlestep DPM-Solver-2 and singlestep DPM-Solver-3.
-        =====================================================
-        Some advices for choosing the algorithm:
-            - For **unconditional sampling** or **guided sampling with small guidance scale** by DPMs:
-                Use singlestep DPM-Solver ("DPM-Solver-fast" in the paper) with `order = 3`.
-                e.g.
-                    >>> dpm_solver = DPM_Solver(model_fn, noise_schedule, predict_x0=False)
-                    >>> x_sample = dpm_solver.sample(x, steps=steps, t_start=t_start, t_end=t_end, order=3,
-                            skip_type='time_uniform', method='singlestep')
-            - For **guided sampling with large guidance scale** by DPMs:
-                Use multistep DPM-Solver with `predict_x0 = True` and `order = 2`.
-                e.g.
-                    >>> dpm_solver = DPM_Solver(model_fn, noise_schedule, predict_x0=True)
-                    >>> x_sample = dpm_solver.sample(x, steps=steps, t_start=t_start, t_end=t_end, order=2,
-                            skip_type='time_uniform', method='multistep')
-        We support three types of `skip_type`:
-            - 'logSNR': uniform logSNR for the time steps. **Recommended for low-resolutional images**
-            - 'time_uniform': uniform time for the time steps. **Recommended for high-resolutional images**.
-            - 'time_quadratic': quadratic time for the time steps.
-        =====================================================
-        Args:
-            x: A pytorch tensor. The initial value at time `t_start`
-                e.g. if `t_start` == T, then `x` is a sample from the standard normal distribution.
-            steps: A `int`. The total number of function evaluations (NFE).
-            t_start: A `float`. The starting time of the sampling.
-                If `T` is None, we use self.noise_schedule.T (default is 1.0).
-            t_end: A `float`. The ending time of the sampling.
-                If `t_end` is None, we use 1. / self.noise_schedule.total_N.
-                e.g. if total_N == 1000, we have `t_end` == 1e-3.
-                For discrete-time DPMs:
-                    - We recommend `t_end` == 1. / self.noise_schedule.total_N.
-                For continuous-time DPMs:
-                    - We recommend `t_end` == 1e-3 when `steps` <= 15; and `t_end` == 1e-4 when `steps` > 15.
-            order: A `int`. The order of DPM-Solver.
-            skip_type: A `str`. The type for the spacing of the time steps. 'time_uniform' or 'logSNR' or 'time_quadratic'.
-            method: A `str`. The method for sampling. 'singlestep' or 'multistep' or 'singlestep_fixed' or 'adaptive'.
-            denoise_to_zero: A `bool`. Whether to denoise to time 0 at the final step.
-                Default is `False`. If `denoise_to_zero` is `True`, the total NFE is (`steps` + 1).
-                This trick is firstly proposed by DDPM (https://arxiv.org/abs/2006.11239) and
-                score_sde (https://arxiv.org/abs/2011.13456). Such trick can improve the FID
-                for diffusion models sampling by diffusion SDEs for low-resolutional images
-                (such as CIFAR-10). However, we observed that such trick does not matter for
-                high-resolutional images. As it needs an additional NFE, we do not recommend
-                it for high-resolutional images.
-            lower_order_final: A `bool`. Whether to use lower order solvers at the final steps.
-                Only valid for `method=multistep` and `steps < 15`. We empirically find that
-                this trick is a key to stabilizing the sampling by DPM-Solver with very few steps
-                (especially for steps <= 10). So we recommend to set it to be `True`.
-            solver_type: A `str`. The taylor expansion type for the solver. `dpm_solver` or `taylor`. We recommend `dpm_solver`.
-            atol: A `float`. The absolute tolerance of the adaptive step size solver. Valid when `method` == 'adaptive'.
-            rtol: A `float`. The relative tolerance of the adaptive step size solver. Valid when `method` == 'adaptive'.
-        Returns:
-            x_end: A pytorch tensor. The approximated solution at time `t_end`.
-        """
-        t_0 = 1. / self.noise_schedule.total_N if t_end is None else t_end
-        t_T = self.noise_schedule.T if t_start is None else t_start
-        device = x.device
-        if method == 'adaptive':
-            with torch.no_grad():
-                x = self.dpm_solver_adaptive(x, order=order, t_T=t_T, t_0=t_0, atol=atol, rtol=rtol,
-                                             solver_type=solver_type)
-        elif method == 'multistep':
-            assert steps >= order
-            timesteps = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=steps, device=device)
-            assert timesteps.shape[0] - 1 == steps
-            with torch.no_grad():
-                vec_t = timesteps[0].expand((x.shape[0]))
-                model_prev_list = [self.model_fn(x, vec_t)]
-                t_prev_list = [vec_t]
-                # Init the first `order` values by lower order multistep DPM-Solver.
-                for init_order in tqdm(range(1, order), desc="DPM init order"):
-                    vec_t = timesteps[init_order].expand(x.shape[0])
-                    x = self.multistep_dpm_solver_update(x, model_prev_list, t_prev_list, vec_t, init_order,
-                                                         solver_type=solver_type)
-                    model_prev_list.append(self.model_fn(x, vec_t))
-                    t_prev_list.append(vec_t)
-                # Compute the remaining values by `order`-th order multistep DPM-Solver.
-                for step in tqdm(range(order, steps + 1), desc="DPM multistep"):
-                    vec_t = timesteps[step].expand(x.shape[0])
-                    if lower_order_final and steps < 15:
-                        step_order = min(order, steps + 1 - step)
-                    else:
-                        step_order = order
-                    x = self.multistep_dpm_solver_update(x, model_prev_list, t_prev_list, vec_t, step_order,
-                                                         solver_type=solver_type)
-                    for i in range(order - 1):
-                        t_prev_list[i] = t_prev_list[i + 1]
-                        model_prev_list[i] = model_prev_list[i + 1]
-                    t_prev_list[-1] = vec_t
-                    # We do not need to evaluate the final model value.
-                    if step < steps:
-                        model_prev_list[-1] = self.model_fn(x, vec_t)
-        elif method in ['singlestep', 'singlestep_fixed']:
-            if method == 'singlestep':
-                timesteps_outer, orders = self.get_orders_and_timesteps_for_singlestep_solver(steps=steps, order=order,
-                                                                                              skip_type=skip_type,
-                                                                                              t_T=t_T, t_0=t_0,
-                                                                                              device=device)
-            elif method == 'singlestep_fixed':
-                K = steps // order
-                orders = [order, ] * K
-                timesteps_outer = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=K, device=device)
-            for i, order in enumerate(orders):
-                t_T_inner, t_0_inner = timesteps_outer[i], timesteps_outer[i + 1]
-                timesteps_inner = self.get_time_steps(skip_type=skip_type, t_T=t_T_inner.item(), t_0=t_0_inner.item(),
-                                                      N=order, device=device)
-                lambda_inner = self.noise_schedule.marginal_lambda(timesteps_inner)
-                vec_s, vec_t = t_T_inner.tile(x.shape[0]), t_0_inner.tile(x.shape[0])
-                h = lambda_inner[-1] - lambda_inner[0]
-                r1 = None if order <= 1 else (lambda_inner[1] - lambda_inner[0]) / h
-                r2 = None if order <= 2 else (lambda_inner[2] - lambda_inner[0]) / h
-                x = self.singlestep_dpm_solver_update(x, vec_s, vec_t, order, solver_type=solver_type, r1=r1, r2=r2)
-        if denoise_to_zero:
-            x = self.denoise_to_zero_fn(x, torch.ones((x.shape[0],)).to(device) * t_0)
-        return x
-
-
-#############################################################
-# other utility functions
-#############################################################
-
-def interpolate_fn(x, xp, yp):
-    """
-    A piecewise linear function y = f(x), using xp and yp as keypoints.
-    We implement f(x) in a differentiable way (i.e. applicable for autograd).
-    The function f(x) is well-defined for all x-axis. (For x beyond the bounds of xp, we use the outmost points of xp to define the linear function.)
-    Args:
-        x: PyTorch tensor with shape [N, C], where N is the batch size, C is the number of channels (we use C = 1 for DPM-Solver).
-        xp: PyTorch tensor with shape [C, K], where K is the number of keypoints.
-        yp: PyTorch tensor with shape [C, K].
-    Returns:
-        The function values f(x), with shape [N, C].
-    """
-    N, K = x.shape[0], xp.shape[1]
-    all_x = torch.cat([x.unsqueeze(2), xp.unsqueeze(0).repeat((N, 1, 1))], dim=2)
-    sorted_all_x, x_indices = torch.sort(all_x, dim=2)
-    x_idx = torch.argmin(x_indices, dim=2)
-    cand_start_idx = x_idx - 1
-    start_idx = torch.where(
-        torch.eq(x_idx, 0),
-        torch.tensor(1, device=x.device),
-        torch.where(
-            torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx,
-        ),
-    )
-    end_idx = torch.where(torch.eq(start_idx, cand_start_idx), start_idx + 2, start_idx + 1)
-    start_x = torch.gather(sorted_all_x, dim=2, index=start_idx.unsqueeze(2)).squeeze(2)
-    end_x = torch.gather(sorted_all_x, dim=2, index=end_idx.unsqueeze(2)).squeeze(2)
-    start_idx2 = torch.where(
-        torch.eq(x_idx, 0),
-        torch.tensor(0, device=x.device),
-        torch.where(
-            torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx,
-        ),
-    )
-    y_positions_expanded = yp.unsqueeze(0).expand(N, -1, -1)
-    start_y = torch.gather(y_positions_expanded, dim=2, index=start_idx2.unsqueeze(2)).squeeze(2)
-    end_y = torch.gather(y_positions_expanded, dim=2, index=(start_idx2 + 1).unsqueeze(2)).squeeze(2)
-    cand = start_y + (x - start_x) * (end_y - start_y) / (end_x - start_x)
-    return cand
-
-
-def expand_dims(v, dims):
-    """
-    Expand the tensor `v` to the dim `dims`.
-    Args:
-        `v`: a PyTorch tensor with shape [N].
-        `dim`: a `int`.
-    Returns:
-        a PyTorch tensor with shape [N, 1, 1, ..., 1] and the total dimension is `dims`.
-    """
-    return v[(...,) + (None,) * (dims - 1)]
\ No newline at end of file
diff --git a/repositories/stable-diffusion-stability-ai/ldm/models/diffusion/dpm_solver/sampler.py b/repositories/stable-diffusion-stability-ai/ldm/models/diffusion/dpm_solver/sampler.py
deleted file mode 100644
index e4d0d0a387548a280d872b60344d0a74dac5e1f0..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/ldm/models/diffusion/dpm_solver/sampler.py
+++ /dev/null
@@ -1,96 +0,0 @@
-"""SAMPLING ONLY."""
-import torch
-
-from .dpm_solver import NoiseScheduleVP, model_wrapper, DPM_Solver
-
-MODEL_TYPES = {
-    "eps": "noise",
-    "v": "v"
-}
-
-
-class DPMSolverSampler(object):
-    def __init__(self, model, device=torch.device("cuda"), **kwargs):
-        super().__init__()
-        self.model = model
-        self.device = device
-        to_torch = lambda x: x.clone().detach().to(torch.float32).to(model.device)
-        self.register_buffer('alphas_cumprod', to_torch(model.alphas_cumprod))
-
-    def register_buffer(self, name, attr):
-        if type(attr) == torch.Tensor:
-            if attr.device != self.device:
-                attr = attr.to(self.device)
-        setattr(self, name, attr)
-
-    @torch.no_grad()
-    def sample(self,
-               S,
-               batch_size,
-               shape,
-               conditioning=None,
-               callback=None,
-               normals_sequence=None,
-               img_callback=None,
-               quantize_x0=False,
-               eta=0.,
-               mask=None,
-               x0=None,
-               temperature=1.,
-               noise_dropout=0.,
-               score_corrector=None,
-               corrector_kwargs=None,
-               verbose=True,
-               x_T=None,
-               log_every_t=100,
-               unconditional_guidance_scale=1.,
-               unconditional_conditioning=None,
-               # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
-               **kwargs
-               ):
-        if conditioning is not None:
-            if isinstance(conditioning, dict):
-                ctmp = conditioning[list(conditioning.keys())[0]]
-                while isinstance(ctmp, list): ctmp = ctmp[0]
-                if isinstance(ctmp, torch.Tensor):
-                    cbs = ctmp.shape[0]
-                    if cbs != batch_size:
-                        print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
-            elif isinstance(conditioning, list):
-                for ctmp in conditioning:
-                    if ctmp.shape[0] != batch_size:
-                        print(f"Warning: Got {ctmp.shape[0]} conditionings but batch-size is {batch_size}")
-            else:
-                if isinstance(conditioning, torch.Tensor):
-                    if conditioning.shape[0] != batch_size:
-                        print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
-
-        # sampling
-        C, H, W = shape
-        size = (batch_size, C, H, W)
-
-        print(f'Data shape for DPM-Solver sampling is {size}, sampling steps {S}')
-
-        device = self.model.betas.device
-        if x_T is None:
-            img = torch.randn(size, device=device)
-        else:
-            img = x_T
-
-        ns = NoiseScheduleVP('discrete', alphas_cumprod=self.alphas_cumprod)
-
-        model_fn = model_wrapper(
-            lambda x, t, c: self.model.apply_model(x, t, c),
-            ns,
-            model_type=MODEL_TYPES[self.model.parameterization],
-            guidance_type="classifier-free",
-            condition=conditioning,
-            unconditional_condition=unconditional_conditioning,
-            guidance_scale=unconditional_guidance_scale,
-        )
-
-        dpm_solver = DPM_Solver(model_fn, ns, predict_x0=True, thresholding=False)
-        x = dpm_solver.sample(img, steps=S, skip_type="time_uniform", method="multistep", order=2,
-                              lower_order_final=True)
-
-        return x.to(device), None
diff --git a/repositories/stable-diffusion-stability-ai/ldm/models/diffusion/plms.py b/repositories/stable-diffusion-stability-ai/ldm/models/diffusion/plms.py
deleted file mode 100644
index 9d31b3994ed283e9d97ed0ae275d89046442cc89..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/ldm/models/diffusion/plms.py
+++ /dev/null
@@ -1,245 +0,0 @@
-"""SAMPLING ONLY."""
-
-import torch
-import numpy as np
-from tqdm import tqdm
-from functools import partial
-
-from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like
-from ldm.models.diffusion.sampling_util import norm_thresholding
-
-
-class PLMSSampler(object):
-    def __init__(self, model, schedule="linear", device=torch.device("cuda"), **kwargs):
-        super().__init__()
-        self.model = model
-        self.ddpm_num_timesteps = model.num_timesteps
-        self.schedule = schedule
-        self.device = device
-
-    def register_buffer(self, name, attr):
-        if type(attr) == torch.Tensor:
-            if attr.device != self.device:
-                attr = attr.to(self.device)
-        setattr(self, name, attr)
-
-    def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True):
-        if ddim_eta != 0:
-            raise ValueError('ddim_eta must be 0 for PLMS')
-        self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,
-                                                  num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose)
-        alphas_cumprod = self.model.alphas_cumprod
-        assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'
-        to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)
-
-        self.register_buffer('betas', to_torch(self.model.betas))
-        self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
-        self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev))
-
-        # calculations for diffusion q(x_t | x_{t-1}) and others
-        self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu())))
-        self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu())))
-        self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu())))
-        self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu())))
-        self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))
-
-        # ddim sampling parameters
-        ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(),
-                                                                                   ddim_timesteps=self.ddim_timesteps,
-                                                                                   eta=ddim_eta,verbose=verbose)
-        self.register_buffer('ddim_sigmas', ddim_sigmas)
-        self.register_buffer('ddim_alphas', ddim_alphas)
-        self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)
-        self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas))
-        sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
-            (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (
-                        1 - self.alphas_cumprod / self.alphas_cumprod_prev))
-        self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps)
-
-    @torch.no_grad()
-    def sample(self,
-               S,
-               batch_size,
-               shape,
-               conditioning=None,
-               callback=None,
-               normals_sequence=None,
-               img_callback=None,
-               quantize_x0=False,
-               eta=0.,
-               mask=None,
-               x0=None,
-               temperature=1.,
-               noise_dropout=0.,
-               score_corrector=None,
-               corrector_kwargs=None,
-               verbose=True,
-               x_T=None,
-               log_every_t=100,
-               unconditional_guidance_scale=1.,
-               unconditional_conditioning=None,
-               # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
-               dynamic_threshold=None,
-               **kwargs
-               ):
-        if conditioning is not None:
-            if isinstance(conditioning, dict):
-                cbs = conditioning[list(conditioning.keys())[0]].shape[0]
-                if cbs != batch_size:
-                    print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
-            else:
-                if conditioning.shape[0] != batch_size:
-                    print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
-
-        self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
-        # sampling
-        C, H, W = shape
-        size = (batch_size, C, H, W)
-        print(f'Data shape for PLMS sampling is {size}')
-
-        samples, intermediates = self.plms_sampling(conditioning, size,
-                                                    callback=callback,
-                                                    img_callback=img_callback,
-                                                    quantize_denoised=quantize_x0,
-                                                    mask=mask, x0=x0,
-                                                    ddim_use_original_steps=False,
-                                                    noise_dropout=noise_dropout,
-                                                    temperature=temperature,
-                                                    score_corrector=score_corrector,
-                                                    corrector_kwargs=corrector_kwargs,
-                                                    x_T=x_T,
-                                                    log_every_t=log_every_t,
-                                                    unconditional_guidance_scale=unconditional_guidance_scale,
-                                                    unconditional_conditioning=unconditional_conditioning,
-                                                    dynamic_threshold=dynamic_threshold,
-                                                    )
-        return samples, intermediates
-
-    @torch.no_grad()
-    def plms_sampling(self, cond, shape,
-                      x_T=None, ddim_use_original_steps=False,
-                      callback=None, timesteps=None, quantize_denoised=False,
-                      mask=None, x0=None, img_callback=None, log_every_t=100,
-                      temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
-                      unconditional_guidance_scale=1., unconditional_conditioning=None,
-                      dynamic_threshold=None):
-        device = self.model.betas.device
-        b = shape[0]
-        if x_T is None:
-            img = torch.randn(shape, device=device)
-        else:
-            img = x_T
-
-        if timesteps is None:
-            timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps
-        elif timesteps is not None and not ddim_use_original_steps:
-            subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1
-            timesteps = self.ddim_timesteps[:subset_end]
-
-        intermediates = {'x_inter': [img], 'pred_x0': [img]}
-        time_range = list(reversed(range(0,timesteps))) if ddim_use_original_steps else np.flip(timesteps)
-        total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
-        print(f"Running PLMS Sampling with {total_steps} timesteps")
-
-        iterator = tqdm(time_range, desc='PLMS Sampler', total=total_steps)
-        old_eps = []
-
-        for i, step in enumerate(iterator):
-            index = total_steps - i - 1
-            ts = torch.full((b,), step, device=device, dtype=torch.long)
-            ts_next = torch.full((b,), time_range[min(i + 1, len(time_range) - 1)], device=device, dtype=torch.long)
-
-            if mask is not None:
-                assert x0 is not None
-                img_orig = self.model.q_sample(x0, ts)  # TODO: deterministic forward pass?
-                img = img_orig * mask + (1. - mask) * img
-
-            outs = self.p_sample_plms(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps,
-                                      quantize_denoised=quantize_denoised, temperature=temperature,
-                                      noise_dropout=noise_dropout, score_corrector=score_corrector,
-                                      corrector_kwargs=corrector_kwargs,
-                                      unconditional_guidance_scale=unconditional_guidance_scale,
-                                      unconditional_conditioning=unconditional_conditioning,
-                                      old_eps=old_eps, t_next=ts_next,
-                                      dynamic_threshold=dynamic_threshold)
-            img, pred_x0, e_t = outs
-            old_eps.append(e_t)
-            if len(old_eps) >= 4:
-                old_eps.pop(0)
-            if callback: callback(i)
-            if img_callback: img_callback(pred_x0, i)
-
-            if index % log_every_t == 0 or index == total_steps - 1:
-                intermediates['x_inter'].append(img)
-                intermediates['pred_x0'].append(pred_x0)
-
-        return img, intermediates
-
-    @torch.no_grad()
-    def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
-                      temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
-                      unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None,
-                      dynamic_threshold=None):
-        b, *_, device = *x.shape, x.device
-
-        def get_model_output(x, t):
-            if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
-                e_t = self.model.apply_model(x, t, c)
-            else:
-                x_in = torch.cat([x] * 2)
-                t_in = torch.cat([t] * 2)
-                c_in = torch.cat([unconditional_conditioning, c])
-                e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
-                e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
-
-            if score_corrector is not None:
-                assert self.model.parameterization == "eps"
-                e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
-
-            return e_t
-
-        alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
-        alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
-        sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
-        sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
-
-        def get_x_prev_and_pred_x0(e_t, index):
-            # select parameters corresponding to the currently considered timestep
-            a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
-            a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
-            sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
-            sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
-
-            # current prediction for x_0
-            pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
-            if quantize_denoised:
-                pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
-            if dynamic_threshold is not None:
-                pred_x0 = norm_thresholding(pred_x0, dynamic_threshold)
-            # direction pointing to x_t
-            dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
-            noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
-            if noise_dropout > 0.:
-                noise = torch.nn.functional.dropout(noise, p=noise_dropout)
-            x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
-            return x_prev, pred_x0
-
-        e_t = get_model_output(x, t)
-        if len(old_eps) == 0:
-            # Pseudo Improved Euler (2nd order)
-            x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index)
-            e_t_next = get_model_output(x_prev, t_next)
-            e_t_prime = (e_t + e_t_next) / 2
-        elif len(old_eps) == 1:
-            # 2nd order Pseudo Linear Multistep (Adams-Bashforth)
-            e_t_prime = (3 * e_t - old_eps[-1]) / 2
-        elif len(old_eps) == 2:
-            # 3nd order Pseudo Linear Multistep (Adams-Bashforth)
-            e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12
-        elif len(old_eps) >= 3:
-            # 4nd order Pseudo Linear Multistep (Adams-Bashforth)
-            e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24
-
-        x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index)
-
-        return x_prev, pred_x0, e_t
diff --git a/repositories/stable-diffusion-stability-ai/ldm/models/diffusion/sampling_util.py b/repositories/stable-diffusion-stability-ai/ldm/models/diffusion/sampling_util.py
deleted file mode 100644
index 7eff02be6d7c54d43ee6680636ac0698dd3b3f33..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/ldm/models/diffusion/sampling_util.py
+++ /dev/null
@@ -1,22 +0,0 @@
-import torch
-import numpy as np
-
-
-def append_dims(x, target_dims):
-    """Appends dimensions to the end of a tensor until it has target_dims dimensions.
-    From https://github.com/crowsonkb/k-diffusion/blob/master/k_diffusion/utils.py"""
-    dims_to_append = target_dims - x.ndim
-    if dims_to_append < 0:
-        raise ValueError(f'input has {x.ndim} dims but target_dims is {target_dims}, which is less')
-    return x[(...,) + (None,) * dims_to_append]
-
-
-def norm_thresholding(x0, value):
-    s = append_dims(x0.pow(2).flatten(1).mean(1).sqrt().clamp(min=value), x0.ndim)
-    return x0 * (value / s)
-
-
-def spatial_norm_thresholding(x0, value):
-    # b c h w
-    s = x0.pow(2).mean(1, keepdim=True).sqrt().clamp(min=value)
-    return x0 * (value / s)
\ No newline at end of file
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/__pycache__/attention.cpython-310.pyc b/repositories/stable-diffusion-stability-ai/ldm/modules/__pycache__/attention.cpython-310.pyc
deleted file mode 100644
index 2e6d643d7ba5b06e3f2bbcb0a5400a9f36512fac..0000000000000000000000000000000000000000
Binary files a/repositories/stable-diffusion-stability-ai/ldm/modules/__pycache__/attention.cpython-310.pyc and /dev/null differ
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/__pycache__/ema.cpython-310.pyc b/repositories/stable-diffusion-stability-ai/ldm/modules/__pycache__/ema.cpython-310.pyc
deleted file mode 100644
index 5d3284403e7e22eca2c8374b1b0d832eab2c56a1..0000000000000000000000000000000000000000
Binary files a/repositories/stable-diffusion-stability-ai/ldm/modules/__pycache__/ema.cpython-310.pyc and /dev/null differ
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/attention.py b/repositories/stable-diffusion-stability-ai/ldm/modules/attention.py
deleted file mode 100644
index 509cd873768f0dd75a75ab3fcdd652822b12b59f..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/ldm/modules/attention.py
+++ /dev/null
@@ -1,341 +0,0 @@
-from inspect import isfunction
-import math
-import torch
-import torch.nn.functional as F
-from torch import nn, einsum
-from einops import rearrange, repeat
-from typing import Optional, Any
-
-from ldm.modules.diffusionmodules.util import checkpoint
-
-
-try:
-    import xformers
-    import xformers.ops
-    XFORMERS_IS_AVAILBLE = True
-except:
-    XFORMERS_IS_AVAILBLE = False
-
-# CrossAttn precision handling
-import os
-_ATTN_PRECISION = os.environ.get("ATTN_PRECISION", "fp32")
-
-def exists(val):
-    return val is not None
-
-
-def uniq(arr):
-    return{el: True for el in arr}.keys()
-
-
-def default(val, d):
-    if exists(val):
-        return val
-    return d() if isfunction(d) else d
-
-
-def max_neg_value(t):
-    return -torch.finfo(t.dtype).max
-
-
-def init_(tensor):
-    dim = tensor.shape[-1]
-    std = 1 / math.sqrt(dim)
-    tensor.uniform_(-std, std)
-    return tensor
-
-
-# feedforward
-class GEGLU(nn.Module):
-    def __init__(self, dim_in, dim_out):
-        super().__init__()
-        self.proj = nn.Linear(dim_in, dim_out * 2)
-
-    def forward(self, x):
-        x, gate = self.proj(x).chunk(2, dim=-1)
-        return x * F.gelu(gate)
-
-
-class FeedForward(nn.Module):
-    def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
-        super().__init__()
-        inner_dim = int(dim * mult)
-        dim_out = default(dim_out, dim)
-        project_in = nn.Sequential(
-            nn.Linear(dim, inner_dim),
-            nn.GELU()
-        ) if not glu else GEGLU(dim, inner_dim)
-
-        self.net = nn.Sequential(
-            project_in,
-            nn.Dropout(dropout),
-            nn.Linear(inner_dim, dim_out)
-        )
-
-    def forward(self, x):
-        return self.net(x)
-
-
-def zero_module(module):
-    """
-    Zero out the parameters of a module and return it.
-    """
-    for p in module.parameters():
-        p.detach().zero_()
-    return module
-
-
-def Normalize(in_channels):
-    return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
-
-
-class SpatialSelfAttention(nn.Module):
-    def __init__(self, in_channels):
-        super().__init__()
-        self.in_channels = in_channels
-
-        self.norm = Normalize(in_channels)
-        self.q = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.k = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.v = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.proj_out = torch.nn.Conv2d(in_channels,
-                                        in_channels,
-                                        kernel_size=1,
-                                        stride=1,
-                                        padding=0)
-
-    def forward(self, x):
-        h_ = x
-        h_ = self.norm(h_)
-        q = self.q(h_)
-        k = self.k(h_)
-        v = self.v(h_)
-
-        # compute attention
-        b,c,h,w = q.shape
-        q = rearrange(q, 'b c h w -> b (h w) c')
-        k = rearrange(k, 'b c h w -> b c (h w)')
-        w_ = torch.einsum('bij,bjk->bik', q, k)
-
-        w_ = w_ * (int(c)**(-0.5))
-        w_ = torch.nn.functional.softmax(w_, dim=2)
-
-        # attend to values
-        v = rearrange(v, 'b c h w -> b c (h w)')
-        w_ = rearrange(w_, 'b i j -> b j i')
-        h_ = torch.einsum('bij,bjk->bik', v, w_)
-        h_ = rearrange(h_, 'b c (h w) -> b c h w', h=h)
-        h_ = self.proj_out(h_)
-
-        return x+h_
-
-
-class CrossAttention(nn.Module):
-    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.):
-        super().__init__()
-        inner_dim = dim_head * heads
-        context_dim = default(context_dim, query_dim)
-
-        self.scale = dim_head ** -0.5
-        self.heads = heads
-
-        self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
-        self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
-        self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
-
-        self.to_out = nn.Sequential(
-            nn.Linear(inner_dim, query_dim),
-            nn.Dropout(dropout)
-        )
-
-    def forward(self, x, context=None, mask=None):
-        h = self.heads
-
-        q = self.to_q(x)
-        context = default(context, x)
-        k = self.to_k(context)
-        v = self.to_v(context)
-
-        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
-
-        # force cast to fp32 to avoid overflowing
-        if _ATTN_PRECISION =="fp32":
-            with torch.autocast(enabled=False, device_type = 'cuda'):
-                q, k = q.float(), k.float()
-                sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
-        else:
-            sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
-        
-        del q, k
-    
-        if exists(mask):
-            mask = rearrange(mask, 'b ... -> b (...)')
-            max_neg_value = -torch.finfo(sim.dtype).max
-            mask = repeat(mask, 'b j -> (b h) () j', h=h)
-            sim.masked_fill_(~mask, max_neg_value)
-
-        # attention, what we cannot get enough of
-        sim = sim.softmax(dim=-1)
-
-        out = einsum('b i j, b j d -> b i d', sim, v)
-        out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
-        return self.to_out(out)
-
-
-class MemoryEfficientCrossAttention(nn.Module):
-    # https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223
-    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0):
-        super().__init__()
-        print(f"Setting up {self.__class__.__name__}. Query dim is {query_dim}, context_dim is {context_dim} and using "
-              f"{heads} heads.")
-        inner_dim = dim_head * heads
-        context_dim = default(context_dim, query_dim)
-
-        self.heads = heads
-        self.dim_head = dim_head
-
-        self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
-        self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
-        self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
-
-        self.to_out = nn.Sequential(nn.Linear(inner_dim, query_dim), nn.Dropout(dropout))
-        self.attention_op: Optional[Any] = None
-
-    def forward(self, x, context=None, mask=None):
-        q = self.to_q(x)
-        context = default(context, x)
-        k = self.to_k(context)
-        v = self.to_v(context)
-
-        b, _, _ = q.shape
-        q, k, v = map(
-            lambda t: t.unsqueeze(3)
-            .reshape(b, t.shape[1], self.heads, self.dim_head)
-            .permute(0, 2, 1, 3)
-            .reshape(b * self.heads, t.shape[1], self.dim_head)
-            .contiguous(),
-            (q, k, v),
-        )
-
-        # actually compute the attention, what we cannot get enough of
-        out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=self.attention_op)
-
-        if exists(mask):
-            raise NotImplementedError
-        out = (
-            out.unsqueeze(0)
-            .reshape(b, self.heads, out.shape[1], self.dim_head)
-            .permute(0, 2, 1, 3)
-            .reshape(b, out.shape[1], self.heads * self.dim_head)
-        )
-        return self.to_out(out)
-
-
-class BasicTransformerBlock(nn.Module):
-    ATTENTION_MODES = {
-        "softmax": CrossAttention,  # vanilla attention
-        "softmax-xformers": MemoryEfficientCrossAttention
-    }
-    def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True,
-                 disable_self_attn=False):
-        super().__init__()
-        attn_mode = "softmax-xformers" if XFORMERS_IS_AVAILBLE else "softmax"
-        assert attn_mode in self.ATTENTION_MODES
-        attn_cls = self.ATTENTION_MODES[attn_mode]
-        self.disable_self_attn = disable_self_attn
-        self.attn1 = attn_cls(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout,
-                              context_dim=context_dim if self.disable_self_attn else None)  # is a self-attention if not self.disable_self_attn
-        self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
-        self.attn2 = attn_cls(query_dim=dim, context_dim=context_dim,
-                              heads=n_heads, dim_head=d_head, dropout=dropout)  # is self-attn if context is none
-        self.norm1 = nn.LayerNorm(dim)
-        self.norm2 = nn.LayerNorm(dim)
-        self.norm3 = nn.LayerNorm(dim)
-        self.checkpoint = checkpoint
-
-    def forward(self, x, context=None):
-        return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint)
-
-    def _forward(self, x, context=None):
-        x = self.attn1(self.norm1(x), context=context if self.disable_self_attn else None) + x
-        x = self.attn2(self.norm2(x), context=context) + x
-        x = self.ff(self.norm3(x)) + x
-        return x
-
-
-class SpatialTransformer(nn.Module):
-    """
-    Transformer block for image-like data.
-    First, project the input (aka embedding)
-    and reshape to b, t, d.
-    Then apply standard transformer action.
-    Finally, reshape to image
-    NEW: use_linear for more efficiency instead of the 1x1 convs
-    """
-    def __init__(self, in_channels, n_heads, d_head,
-                 depth=1, dropout=0., context_dim=None,
-                 disable_self_attn=False, use_linear=False,
-                 use_checkpoint=True):
-        super().__init__()
-        if exists(context_dim) and not isinstance(context_dim, list):
-            context_dim = [context_dim]
-        self.in_channels = in_channels
-        inner_dim = n_heads * d_head
-        self.norm = Normalize(in_channels)
-        if not use_linear:
-            self.proj_in = nn.Conv2d(in_channels,
-                                     inner_dim,
-                                     kernel_size=1,
-                                     stride=1,
-                                     padding=0)
-        else:
-            self.proj_in = nn.Linear(in_channels, inner_dim)
-
-        self.transformer_blocks = nn.ModuleList(
-            [BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim[d],
-                                   disable_self_attn=disable_self_attn, checkpoint=use_checkpoint)
-                for d in range(depth)]
-        )
-        if not use_linear:
-            self.proj_out = zero_module(nn.Conv2d(inner_dim,
-                                                  in_channels,
-                                                  kernel_size=1,
-                                                  stride=1,
-                                                  padding=0))
-        else:
-            self.proj_out = zero_module(nn.Linear(in_channels, inner_dim))
-        self.use_linear = use_linear
-
-    def forward(self, x, context=None):
-        # note: if no context is given, cross-attention defaults to self-attention
-        if not isinstance(context, list):
-            context = [context]
-        b, c, h, w = x.shape
-        x_in = x
-        x = self.norm(x)
-        if not self.use_linear:
-            x = self.proj_in(x)
-        x = rearrange(x, 'b c h w -> b (h w) c').contiguous()
-        if self.use_linear:
-            x = self.proj_in(x)
-        for i, block in enumerate(self.transformer_blocks):
-            x = block(x, context=context[i])
-        if self.use_linear:
-            x = self.proj_out(x)
-        x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w).contiguous()
-        if not self.use_linear:
-            x = self.proj_out(x)
-        return x + x_in
-
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/diffusionmodules/__init__.py b/repositories/stable-diffusion-stability-ai/ldm/modules/diffusionmodules/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/diffusionmodules/__pycache__/__init__.cpython-310.pyc b/repositories/stable-diffusion-stability-ai/ldm/modules/diffusionmodules/__pycache__/__init__.cpython-310.pyc
deleted file mode 100644
index 1db00802538e5e6b4f00f9f5310fa6edbaedd956..0000000000000000000000000000000000000000
Binary files a/repositories/stable-diffusion-stability-ai/ldm/modules/diffusionmodules/__pycache__/__init__.cpython-310.pyc and /dev/null differ
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/diffusionmodules/__pycache__/model.cpython-310.pyc b/repositories/stable-diffusion-stability-ai/ldm/modules/diffusionmodules/__pycache__/model.cpython-310.pyc
deleted file mode 100644
index e12a8b011026a082440d90b49a1bb2ba0539b140..0000000000000000000000000000000000000000
Binary files a/repositories/stable-diffusion-stability-ai/ldm/modules/diffusionmodules/__pycache__/model.cpython-310.pyc and /dev/null differ
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/diffusionmodules/__pycache__/openaimodel.cpython-310.pyc b/repositories/stable-diffusion-stability-ai/ldm/modules/diffusionmodules/__pycache__/openaimodel.cpython-310.pyc
deleted file mode 100644
index d38b9c2b96877afec29d48cf2e028fecbd6c4887..0000000000000000000000000000000000000000
Binary files a/repositories/stable-diffusion-stability-ai/ldm/modules/diffusionmodules/__pycache__/openaimodel.cpython-310.pyc and /dev/null differ
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/diffusionmodules/__pycache__/upscaling.cpython-310.pyc b/repositories/stable-diffusion-stability-ai/ldm/modules/diffusionmodules/__pycache__/upscaling.cpython-310.pyc
deleted file mode 100644
index 0b3b77768b40b3076f01cbc109037927f48f2dd5..0000000000000000000000000000000000000000
Binary files a/repositories/stable-diffusion-stability-ai/ldm/modules/diffusionmodules/__pycache__/upscaling.cpython-310.pyc and /dev/null differ
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/diffusionmodules/__pycache__/util.cpython-310.pyc b/repositories/stable-diffusion-stability-ai/ldm/modules/diffusionmodules/__pycache__/util.cpython-310.pyc
deleted file mode 100644
index 5ec53954dc421b85454d3af6b27773d181bcefd3..0000000000000000000000000000000000000000
Binary files a/repositories/stable-diffusion-stability-ai/ldm/modules/diffusionmodules/__pycache__/util.cpython-310.pyc and /dev/null differ
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/diffusionmodules/model.py b/repositories/stable-diffusion-stability-ai/ldm/modules/diffusionmodules/model.py
deleted file mode 100644
index b089eebbe1676d8249005bb9def002ff5180715b..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/ldm/modules/diffusionmodules/model.py
+++ /dev/null
@@ -1,852 +0,0 @@
-# pytorch_diffusion + derived encoder decoder
-import math
-import torch
-import torch.nn as nn
-import numpy as np
-from einops import rearrange
-from typing import Optional, Any
-
-from ldm.modules.attention import MemoryEfficientCrossAttention
-
-try:
-    import xformers
-    import xformers.ops
-    XFORMERS_IS_AVAILBLE = True
-except:
-    XFORMERS_IS_AVAILBLE = False
-    print("No module 'xformers'. Proceeding without it.")
-
-
-def get_timestep_embedding(timesteps, embedding_dim):
-    """
-    This matches the implementation in Denoising Diffusion Probabilistic Models:
-    From Fairseq.
-    Build sinusoidal embeddings.
-    This matches the implementation in tensor2tensor, but differs slightly
-    from the description in Section 3.5 of "Attention Is All You Need".
-    """
-    assert len(timesteps.shape) == 1
-
-    half_dim = embedding_dim // 2
-    emb = math.log(10000) / (half_dim - 1)
-    emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb)
-    emb = emb.to(device=timesteps.device)
-    emb = timesteps.float()[:, None] * emb[None, :]
-    emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
-    if embedding_dim % 2 == 1:  # zero pad
-        emb = torch.nn.functional.pad(emb, (0,1,0,0))
-    return emb
-
-
-def nonlinearity(x):
-    # swish
-    return x*torch.sigmoid(x)
-
-
-def Normalize(in_channels, num_groups=32):
-    return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True)
-
-
-class Upsample(nn.Module):
-    def __init__(self, in_channels, with_conv):
-        super().__init__()
-        self.with_conv = with_conv
-        if self.with_conv:
-            self.conv = torch.nn.Conv2d(in_channels,
-                                        in_channels,
-                                        kernel_size=3,
-                                        stride=1,
-                                        padding=1)
-
-    def forward(self, x):
-        x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
-        if self.with_conv:
-            x = self.conv(x)
-        return x
-
-
-class Downsample(nn.Module):
-    def __init__(self, in_channels, with_conv):
-        super().__init__()
-        self.with_conv = with_conv
-        if self.with_conv:
-            # no asymmetric padding in torch conv, must do it ourselves
-            self.conv = torch.nn.Conv2d(in_channels,
-                                        in_channels,
-                                        kernel_size=3,
-                                        stride=2,
-                                        padding=0)
-
-    def forward(self, x):
-        if self.with_conv:
-            pad = (0,1,0,1)
-            x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
-            x = self.conv(x)
-        else:
-            x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)
-        return x
-
-
-class ResnetBlock(nn.Module):
-    def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False,
-                 dropout, temb_channels=512):
-        super().__init__()
-        self.in_channels = in_channels
-        out_channels = in_channels if out_channels is None else out_channels
-        self.out_channels = out_channels
-        self.use_conv_shortcut = conv_shortcut
-
-        self.norm1 = Normalize(in_channels)
-        self.conv1 = torch.nn.Conv2d(in_channels,
-                                     out_channels,
-                                     kernel_size=3,
-                                     stride=1,
-                                     padding=1)
-        if temb_channels > 0:
-            self.temb_proj = torch.nn.Linear(temb_channels,
-                                             out_channels)
-        self.norm2 = Normalize(out_channels)
-        self.dropout = torch.nn.Dropout(dropout)
-        self.conv2 = torch.nn.Conv2d(out_channels,
-                                     out_channels,
-                                     kernel_size=3,
-                                     stride=1,
-                                     padding=1)
-        if self.in_channels != self.out_channels:
-            if self.use_conv_shortcut:
-                self.conv_shortcut = torch.nn.Conv2d(in_channels,
-                                                     out_channels,
-                                                     kernel_size=3,
-                                                     stride=1,
-                                                     padding=1)
-            else:
-                self.nin_shortcut = torch.nn.Conv2d(in_channels,
-                                                    out_channels,
-                                                    kernel_size=1,
-                                                    stride=1,
-                                                    padding=0)
-
-    def forward(self, x, temb):
-        h = x
-        h = self.norm1(h)
-        h = nonlinearity(h)
-        h = self.conv1(h)
-
-        if temb is not None:
-            h = h + self.temb_proj(nonlinearity(temb))[:,:,None,None]
-
-        h = self.norm2(h)
-        h = nonlinearity(h)
-        h = self.dropout(h)
-        h = self.conv2(h)
-
-        if self.in_channels != self.out_channels:
-            if self.use_conv_shortcut:
-                x = self.conv_shortcut(x)
-            else:
-                x = self.nin_shortcut(x)
-
-        return x+h
-
-
-class AttnBlock(nn.Module):
-    def __init__(self, in_channels):
-        super().__init__()
-        self.in_channels = in_channels
-
-        self.norm = Normalize(in_channels)
-        self.q = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.k = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.v = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.proj_out = torch.nn.Conv2d(in_channels,
-                                        in_channels,
-                                        kernel_size=1,
-                                        stride=1,
-                                        padding=0)
-
-    def forward(self, x):
-        h_ = x
-        h_ = self.norm(h_)
-        q = self.q(h_)
-        k = self.k(h_)
-        v = self.v(h_)
-
-        # compute attention
-        b,c,h,w = q.shape
-        q = q.reshape(b,c,h*w)
-        q = q.permute(0,2,1)   # b,hw,c
-        k = k.reshape(b,c,h*w) # b,c,hw
-        w_ = torch.bmm(q,k)     # b,hw,hw    w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
-        w_ = w_ * (int(c)**(-0.5))
-        w_ = torch.nn.functional.softmax(w_, dim=2)
-
-        # attend to values
-        v = v.reshape(b,c,h*w)
-        w_ = w_.permute(0,2,1)   # b,hw,hw (first hw of k, second of q)
-        h_ = torch.bmm(v,w_)     # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
-        h_ = h_.reshape(b,c,h,w)
-
-        h_ = self.proj_out(h_)
-
-        return x+h_
-
-class MemoryEfficientAttnBlock(nn.Module):
-    """
-        Uses xformers efficient implementation,
-        see https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223
-        Note: this is a single-head self-attention operation
-    """
-    #
-    def __init__(self, in_channels):
-        super().__init__()
-        self.in_channels = in_channels
-
-        self.norm = Normalize(in_channels)
-        self.q = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.k = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.v = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.proj_out = torch.nn.Conv2d(in_channels,
-                                        in_channels,
-                                        kernel_size=1,
-                                        stride=1,
-                                        padding=0)
-        self.attention_op: Optional[Any] = None
-
-    def forward(self, x):
-        h_ = x
-        h_ = self.norm(h_)
-        q = self.q(h_)
-        k = self.k(h_)
-        v = self.v(h_)
-
-        # compute attention
-        B, C, H, W = q.shape
-        q, k, v = map(lambda x: rearrange(x, 'b c h w -> b (h w) c'), (q, k, v))
-
-        q, k, v = map(
-            lambda t: t.unsqueeze(3)
-            .reshape(B, t.shape[1], 1, C)
-            .permute(0, 2, 1, 3)
-            .reshape(B * 1, t.shape[1], C)
-            .contiguous(),
-            (q, k, v),
-        )
-        out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=self.attention_op)
-
-        out = (
-            out.unsqueeze(0)
-            .reshape(B, 1, out.shape[1], C)
-            .permute(0, 2, 1, 3)
-            .reshape(B, out.shape[1], C)
-        )
-        out = rearrange(out, 'b (h w) c -> b c h w', b=B, h=H, w=W, c=C)
-        out = self.proj_out(out)
-        return x+out
-
-
-class MemoryEfficientCrossAttentionWrapper(MemoryEfficientCrossAttention):
-    def forward(self, x, context=None, mask=None):
-        b, c, h, w = x.shape
-        x = rearrange(x, 'b c h w -> b (h w) c')
-        out = super().forward(x, context=context, mask=mask)
-        out = rearrange(out, 'b (h w) c -> b c h w', h=h, w=w, c=c)
-        return x + out
-
-
-def make_attn(in_channels, attn_type="vanilla", attn_kwargs=None):
-    assert attn_type in ["vanilla", "vanilla-xformers", "memory-efficient-cross-attn", "linear", "none"], f'attn_type {attn_type} unknown'
-    if XFORMERS_IS_AVAILBLE and attn_type == "vanilla":
-        attn_type = "vanilla-xformers"
-    print(f"making attention of type '{attn_type}' with {in_channels} in_channels")
-    if attn_type == "vanilla":
-        assert attn_kwargs is None
-        return AttnBlock(in_channels)
-    elif attn_type == "vanilla-xformers":
-        print(f"building MemoryEfficientAttnBlock with {in_channels} in_channels...")
-        return MemoryEfficientAttnBlock(in_channels)
-    elif type == "memory-efficient-cross-attn":
-        attn_kwargs["query_dim"] = in_channels
-        return MemoryEfficientCrossAttentionWrapper(**attn_kwargs)
-    elif attn_type == "none":
-        return nn.Identity(in_channels)
-    else:
-        raise NotImplementedError()
-
-
-class Model(nn.Module):
-    def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
-                 attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
-                 resolution, use_timestep=True, use_linear_attn=False, attn_type="vanilla"):
-        super().__init__()
-        if use_linear_attn: attn_type = "linear"
-        self.ch = ch
-        self.temb_ch = self.ch*4
-        self.num_resolutions = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        self.resolution = resolution
-        self.in_channels = in_channels
-
-        self.use_timestep = use_timestep
-        if self.use_timestep:
-            # timestep embedding
-            self.temb = nn.Module()
-            self.temb.dense = nn.ModuleList([
-                torch.nn.Linear(self.ch,
-                                self.temb_ch),
-                torch.nn.Linear(self.temb_ch,
-                                self.temb_ch),
-            ])
-
-        # downsampling
-        self.conv_in = torch.nn.Conv2d(in_channels,
-                                       self.ch,
-                                       kernel_size=3,
-                                       stride=1,
-                                       padding=1)
-
-        curr_res = resolution
-        in_ch_mult = (1,)+tuple(ch_mult)
-        self.down = nn.ModuleList()
-        for i_level in range(self.num_resolutions):
-            block = nn.ModuleList()
-            attn = nn.ModuleList()
-            block_in = ch*in_ch_mult[i_level]
-            block_out = ch*ch_mult[i_level]
-            for i_block in range(self.num_res_blocks):
-                block.append(ResnetBlock(in_channels=block_in,
-                                         out_channels=block_out,
-                                         temb_channels=self.temb_ch,
-                                         dropout=dropout))
-                block_in = block_out
-                if curr_res in attn_resolutions:
-                    attn.append(make_attn(block_in, attn_type=attn_type))
-            down = nn.Module()
-            down.block = block
-            down.attn = attn
-            if i_level != self.num_resolutions-1:
-                down.downsample = Downsample(block_in, resamp_with_conv)
-                curr_res = curr_res // 2
-            self.down.append(down)
-
-        # middle
-        self.mid = nn.Module()
-        self.mid.block_1 = ResnetBlock(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=dropout)
-        self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
-        self.mid.block_2 = ResnetBlock(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=dropout)
-
-        # upsampling
-        self.up = nn.ModuleList()
-        for i_level in reversed(range(self.num_resolutions)):
-            block = nn.ModuleList()
-            attn = nn.ModuleList()
-            block_out = ch*ch_mult[i_level]
-            skip_in = ch*ch_mult[i_level]
-            for i_block in range(self.num_res_blocks+1):
-                if i_block == self.num_res_blocks:
-                    skip_in = ch*in_ch_mult[i_level]
-                block.append(ResnetBlock(in_channels=block_in+skip_in,
-                                         out_channels=block_out,
-                                         temb_channels=self.temb_ch,
-                                         dropout=dropout))
-                block_in = block_out
-                if curr_res in attn_resolutions:
-                    attn.append(make_attn(block_in, attn_type=attn_type))
-            up = nn.Module()
-            up.block = block
-            up.attn = attn
-            if i_level != 0:
-                up.upsample = Upsample(block_in, resamp_with_conv)
-                curr_res = curr_res * 2
-            self.up.insert(0, up) # prepend to get consistent order
-
-        # end
-        self.norm_out = Normalize(block_in)
-        self.conv_out = torch.nn.Conv2d(block_in,
-                                        out_ch,
-                                        kernel_size=3,
-                                        stride=1,
-                                        padding=1)
-
-    def forward(self, x, t=None, context=None):
-        #assert x.shape[2] == x.shape[3] == self.resolution
-        if context is not None:
-            # assume aligned context, cat along channel axis
-            x = torch.cat((x, context), dim=1)
-        if self.use_timestep:
-            # timestep embedding
-            assert t is not None
-            temb = get_timestep_embedding(t, self.ch)
-            temb = self.temb.dense[0](temb)
-            temb = nonlinearity(temb)
-            temb = self.temb.dense[1](temb)
-        else:
-            temb = None
-
-        # downsampling
-        hs = [self.conv_in(x)]
-        for i_level in range(self.num_resolutions):
-            for i_block in range(self.num_res_blocks):
-                h = self.down[i_level].block[i_block](hs[-1], temb)
-                if len(self.down[i_level].attn) > 0:
-                    h = self.down[i_level].attn[i_block](h)
-                hs.append(h)
-            if i_level != self.num_resolutions-1:
-                hs.append(self.down[i_level].downsample(hs[-1]))
-
-        # middle
-        h = hs[-1]
-        h = self.mid.block_1(h, temb)
-        h = self.mid.attn_1(h)
-        h = self.mid.block_2(h, temb)
-
-        # upsampling
-        for i_level in reversed(range(self.num_resolutions)):
-            for i_block in range(self.num_res_blocks+1):
-                h = self.up[i_level].block[i_block](
-                    torch.cat([h, hs.pop()], dim=1), temb)
-                if len(self.up[i_level].attn) > 0:
-                    h = self.up[i_level].attn[i_block](h)
-            if i_level != 0:
-                h = self.up[i_level].upsample(h)
-
-        # end
-        h = self.norm_out(h)
-        h = nonlinearity(h)
-        h = self.conv_out(h)
-        return h
-
-    def get_last_layer(self):
-        return self.conv_out.weight
-
-
-class Encoder(nn.Module):
-    def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
-                 attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
-                 resolution, z_channels, double_z=True, use_linear_attn=False, attn_type="vanilla",
-                 **ignore_kwargs):
-        super().__init__()
-        if use_linear_attn: attn_type = "linear"
-        self.ch = ch
-        self.temb_ch = 0
-        self.num_resolutions = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        self.resolution = resolution
-        self.in_channels = in_channels
-
-        # downsampling
-        self.conv_in = torch.nn.Conv2d(in_channels,
-                                       self.ch,
-                                       kernel_size=3,
-                                       stride=1,
-                                       padding=1)
-
-        curr_res = resolution
-        in_ch_mult = (1,)+tuple(ch_mult)
-        self.in_ch_mult = in_ch_mult
-        self.down = nn.ModuleList()
-        for i_level in range(self.num_resolutions):
-            block = nn.ModuleList()
-            attn = nn.ModuleList()
-            block_in = ch*in_ch_mult[i_level]
-            block_out = ch*ch_mult[i_level]
-            for i_block in range(self.num_res_blocks):
-                block.append(ResnetBlock(in_channels=block_in,
-                                         out_channels=block_out,
-                                         temb_channels=self.temb_ch,
-                                         dropout=dropout))
-                block_in = block_out
-                if curr_res in attn_resolutions:
-                    attn.append(make_attn(block_in, attn_type=attn_type))
-            down = nn.Module()
-            down.block = block
-            down.attn = attn
-            if i_level != self.num_resolutions-1:
-                down.downsample = Downsample(block_in, resamp_with_conv)
-                curr_res = curr_res // 2
-            self.down.append(down)
-
-        # middle
-        self.mid = nn.Module()
-        self.mid.block_1 = ResnetBlock(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=dropout)
-        self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
-        self.mid.block_2 = ResnetBlock(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=dropout)
-
-        # end
-        self.norm_out = Normalize(block_in)
-        self.conv_out = torch.nn.Conv2d(block_in,
-                                        2*z_channels if double_z else z_channels,
-                                        kernel_size=3,
-                                        stride=1,
-                                        padding=1)
-
-    def forward(self, x):
-        # timestep embedding
-        temb = None
-
-        # downsampling
-        hs = [self.conv_in(x)]
-        for i_level in range(self.num_resolutions):
-            for i_block in range(self.num_res_blocks):
-                h = self.down[i_level].block[i_block](hs[-1], temb)
-                if len(self.down[i_level].attn) > 0:
-                    h = self.down[i_level].attn[i_block](h)
-                hs.append(h)
-            if i_level != self.num_resolutions-1:
-                hs.append(self.down[i_level].downsample(hs[-1]))
-
-        # middle
-        h = hs[-1]
-        h = self.mid.block_1(h, temb)
-        h = self.mid.attn_1(h)
-        h = self.mid.block_2(h, temb)
-
-        # end
-        h = self.norm_out(h)
-        h = nonlinearity(h)
-        h = self.conv_out(h)
-        return h
-
-
-class Decoder(nn.Module):
-    def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
-                 attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
-                 resolution, z_channels, give_pre_end=False, tanh_out=False, use_linear_attn=False,
-                 attn_type="vanilla", **ignorekwargs):
-        super().__init__()
-        if use_linear_attn: attn_type = "linear"
-        self.ch = ch
-        self.temb_ch = 0
-        self.num_resolutions = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        self.resolution = resolution
-        self.in_channels = in_channels
-        self.give_pre_end = give_pre_end
-        self.tanh_out = tanh_out
-
-        # compute in_ch_mult, block_in and curr_res at lowest res
-        in_ch_mult = (1,)+tuple(ch_mult)
-        block_in = ch*ch_mult[self.num_resolutions-1]
-        curr_res = resolution // 2**(self.num_resolutions-1)
-        self.z_shape = (1,z_channels,curr_res,curr_res)
-        print("Working with z of shape {} = {} dimensions.".format(
-            self.z_shape, np.prod(self.z_shape)))
-
-        # z to block_in
-        self.conv_in = torch.nn.Conv2d(z_channels,
-                                       block_in,
-                                       kernel_size=3,
-                                       stride=1,
-                                       padding=1)
-
-        # middle
-        self.mid = nn.Module()
-        self.mid.block_1 = ResnetBlock(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=dropout)
-        self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
-        self.mid.block_2 = ResnetBlock(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=dropout)
-
-        # upsampling
-        self.up = nn.ModuleList()
-        for i_level in reversed(range(self.num_resolutions)):
-            block = nn.ModuleList()
-            attn = nn.ModuleList()
-            block_out = ch*ch_mult[i_level]
-            for i_block in range(self.num_res_blocks+1):
-                block.append(ResnetBlock(in_channels=block_in,
-                                         out_channels=block_out,
-                                         temb_channels=self.temb_ch,
-                                         dropout=dropout))
-                block_in = block_out
-                if curr_res in attn_resolutions:
-                    attn.append(make_attn(block_in, attn_type=attn_type))
-            up = nn.Module()
-            up.block = block
-            up.attn = attn
-            if i_level != 0:
-                up.upsample = Upsample(block_in, resamp_with_conv)
-                curr_res = curr_res * 2
-            self.up.insert(0, up) # prepend to get consistent order
-
-        # end
-        self.norm_out = Normalize(block_in)
-        self.conv_out = torch.nn.Conv2d(block_in,
-                                        out_ch,
-                                        kernel_size=3,
-                                        stride=1,
-                                        padding=1)
-
-    def forward(self, z):
-        #assert z.shape[1:] == self.z_shape[1:]
-        self.last_z_shape = z.shape
-
-        # timestep embedding
-        temb = None
-
-        # z to block_in
-        h = self.conv_in(z)
-
-        # middle
-        h = self.mid.block_1(h, temb)
-        h = self.mid.attn_1(h)
-        h = self.mid.block_2(h, temb)
-
-        # upsampling
-        for i_level in reversed(range(self.num_resolutions)):
-            for i_block in range(self.num_res_blocks+1):
-                h = self.up[i_level].block[i_block](h, temb)
-                if len(self.up[i_level].attn) > 0:
-                    h = self.up[i_level].attn[i_block](h)
-            if i_level != 0:
-                h = self.up[i_level].upsample(h)
-
-        # end
-        if self.give_pre_end:
-            return h
-
-        h = self.norm_out(h)
-        h = nonlinearity(h)
-        h = self.conv_out(h)
-        if self.tanh_out:
-            h = torch.tanh(h)
-        return h
-
-
-class SimpleDecoder(nn.Module):
-    def __init__(self, in_channels, out_channels, *args, **kwargs):
-        super().__init__()
-        self.model = nn.ModuleList([nn.Conv2d(in_channels, in_channels, 1),
-                                     ResnetBlock(in_channels=in_channels,
-                                                 out_channels=2 * in_channels,
-                                                 temb_channels=0, dropout=0.0),
-                                     ResnetBlock(in_channels=2 * in_channels,
-                                                out_channels=4 * in_channels,
-                                                temb_channels=0, dropout=0.0),
-                                     ResnetBlock(in_channels=4 * in_channels,
-                                                out_channels=2 * in_channels,
-                                                temb_channels=0, dropout=0.0),
-                                     nn.Conv2d(2*in_channels, in_channels, 1),
-                                     Upsample(in_channels, with_conv=True)])
-        # end
-        self.norm_out = Normalize(in_channels)
-        self.conv_out = torch.nn.Conv2d(in_channels,
-                                        out_channels,
-                                        kernel_size=3,
-                                        stride=1,
-                                        padding=1)
-
-    def forward(self, x):
-        for i, layer in enumerate(self.model):
-            if i in [1,2,3]:
-                x = layer(x, None)
-            else:
-                x = layer(x)
-
-        h = self.norm_out(x)
-        h = nonlinearity(h)
-        x = self.conv_out(h)
-        return x
-
-
-class UpsampleDecoder(nn.Module):
-    def __init__(self, in_channels, out_channels, ch, num_res_blocks, resolution,
-                 ch_mult=(2,2), dropout=0.0):
-        super().__init__()
-        # upsampling
-        self.temb_ch = 0
-        self.num_resolutions = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        block_in = in_channels
-        curr_res = resolution // 2 ** (self.num_resolutions - 1)
-        self.res_blocks = nn.ModuleList()
-        self.upsample_blocks = nn.ModuleList()
-        for i_level in range(self.num_resolutions):
-            res_block = []
-            block_out = ch * ch_mult[i_level]
-            for i_block in range(self.num_res_blocks + 1):
-                res_block.append(ResnetBlock(in_channels=block_in,
-                                         out_channels=block_out,
-                                         temb_channels=self.temb_ch,
-                                         dropout=dropout))
-                block_in = block_out
-            self.res_blocks.append(nn.ModuleList(res_block))
-            if i_level != self.num_resolutions - 1:
-                self.upsample_blocks.append(Upsample(block_in, True))
-                curr_res = curr_res * 2
-
-        # end
-        self.norm_out = Normalize(block_in)
-        self.conv_out = torch.nn.Conv2d(block_in,
-                                        out_channels,
-                                        kernel_size=3,
-                                        stride=1,
-                                        padding=1)
-
-    def forward(self, x):
-        # upsampling
-        h = x
-        for k, i_level in enumerate(range(self.num_resolutions)):
-            for i_block in range(self.num_res_blocks + 1):
-                h = self.res_blocks[i_level][i_block](h, None)
-            if i_level != self.num_resolutions - 1:
-                h = self.upsample_blocks[k](h)
-        h = self.norm_out(h)
-        h = nonlinearity(h)
-        h = self.conv_out(h)
-        return h
-
-
-class LatentRescaler(nn.Module):
-    def __init__(self, factor, in_channels, mid_channels, out_channels, depth=2):
-        super().__init__()
-        # residual block, interpolate, residual block
-        self.factor = factor
-        self.conv_in = nn.Conv2d(in_channels,
-                                 mid_channels,
-                                 kernel_size=3,
-                                 stride=1,
-                                 padding=1)
-        self.res_block1 = nn.ModuleList([ResnetBlock(in_channels=mid_channels,
-                                                     out_channels=mid_channels,
-                                                     temb_channels=0,
-                                                     dropout=0.0) for _ in range(depth)])
-        self.attn = AttnBlock(mid_channels)
-        self.res_block2 = nn.ModuleList([ResnetBlock(in_channels=mid_channels,
-                                                     out_channels=mid_channels,
-                                                     temb_channels=0,
-                                                     dropout=0.0) for _ in range(depth)])
-
-        self.conv_out = nn.Conv2d(mid_channels,
-                                  out_channels,
-                                  kernel_size=1,
-                                  )
-
-    def forward(self, x):
-        x = self.conv_in(x)
-        for block in self.res_block1:
-            x = block(x, None)
-        x = torch.nn.functional.interpolate(x, size=(int(round(x.shape[2]*self.factor)), int(round(x.shape[3]*self.factor))))
-        x = self.attn(x)
-        for block in self.res_block2:
-            x = block(x, None)
-        x = self.conv_out(x)
-        return x
-
-
-class MergedRescaleEncoder(nn.Module):
-    def __init__(self, in_channels, ch, resolution, out_ch, num_res_blocks,
-                 attn_resolutions, dropout=0.0, resamp_with_conv=True,
-                 ch_mult=(1,2,4,8), rescale_factor=1.0, rescale_module_depth=1):
-        super().__init__()
-        intermediate_chn = ch * ch_mult[-1]
-        self.encoder = Encoder(in_channels=in_channels, num_res_blocks=num_res_blocks, ch=ch, ch_mult=ch_mult,
-                               z_channels=intermediate_chn, double_z=False, resolution=resolution,
-                               attn_resolutions=attn_resolutions, dropout=dropout, resamp_with_conv=resamp_with_conv,
-                               out_ch=None)
-        self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=intermediate_chn,
-                                       mid_channels=intermediate_chn, out_channels=out_ch, depth=rescale_module_depth)
-
-    def forward(self, x):
-        x = self.encoder(x)
-        x = self.rescaler(x)
-        return x
-
-
-class MergedRescaleDecoder(nn.Module):
-    def __init__(self, z_channels, out_ch, resolution, num_res_blocks, attn_resolutions, ch, ch_mult=(1,2,4,8),
-                 dropout=0.0, resamp_with_conv=True, rescale_factor=1.0, rescale_module_depth=1):
-        super().__init__()
-        tmp_chn = z_channels*ch_mult[-1]
-        self.decoder = Decoder(out_ch=out_ch, z_channels=tmp_chn, attn_resolutions=attn_resolutions, dropout=dropout,
-                               resamp_with_conv=resamp_with_conv, in_channels=None, num_res_blocks=num_res_blocks,
-                               ch_mult=ch_mult, resolution=resolution, ch=ch)
-        self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=z_channels, mid_channels=tmp_chn,
-                                       out_channels=tmp_chn, depth=rescale_module_depth)
-
-    def forward(self, x):
-        x = self.rescaler(x)
-        x = self.decoder(x)
-        return x
-
-
-class Upsampler(nn.Module):
-    def __init__(self, in_size, out_size, in_channels, out_channels, ch_mult=2):
-        super().__init__()
-        assert out_size >= in_size
-        num_blocks = int(np.log2(out_size//in_size))+1
-        factor_up = 1.+ (out_size % in_size)
-        print(f"Building {self.__class__.__name__} with in_size: {in_size} --> out_size {out_size} and factor {factor_up}")
-        self.rescaler = LatentRescaler(factor=factor_up, in_channels=in_channels, mid_channels=2*in_channels,
-                                       out_channels=in_channels)
-        self.decoder = Decoder(out_ch=out_channels, resolution=out_size, z_channels=in_channels, num_res_blocks=2,
-                               attn_resolutions=[], in_channels=None, ch=in_channels,
-                               ch_mult=[ch_mult for _ in range(num_blocks)])
-
-    def forward(self, x):
-        x = self.rescaler(x)
-        x = self.decoder(x)
-        return x
-
-
-class Resize(nn.Module):
-    def __init__(self, in_channels=None, learned=False, mode="bilinear"):
-        super().__init__()
-        self.with_conv = learned
-        self.mode = mode
-        if self.with_conv:
-            print(f"Note: {self.__class__.__name} uses learned downsampling and will ignore the fixed {mode} mode")
-            raise NotImplementedError()
-            assert in_channels is not None
-            # no asymmetric padding in torch conv, must do it ourselves
-            self.conv = torch.nn.Conv2d(in_channels,
-                                        in_channels,
-                                        kernel_size=4,
-                                        stride=2,
-                                        padding=1)
-
-    def forward(self, x, scale_factor=1.0):
-        if scale_factor==1.0:
-            return x
-        else:
-            x = torch.nn.functional.interpolate(x, mode=self.mode, align_corners=False, scale_factor=scale_factor)
-        return x
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/diffusionmodules/openaimodel.py b/repositories/stable-diffusion-stability-ai/ldm/modules/diffusionmodules/openaimodel.py
deleted file mode 100644
index cc3875c63c86fb2e33a83de70a9601cba6b39148..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/ldm/modules/diffusionmodules/openaimodel.py
+++ /dev/null
@@ -1,807 +0,0 @@
-from abc import abstractmethod
-import math
-
-import numpy as np
-import torch as th
-import torch.nn as nn
-import torch.nn.functional as F
-
-from ldm.modules.diffusionmodules.util import (
-    checkpoint,
-    conv_nd,
-    linear,
-    avg_pool_nd,
-    zero_module,
-    normalization,
-    timestep_embedding,
-)
-from ldm.modules.attention import SpatialTransformer
-from ldm.util import exists
-
-
-# dummy replace
-def convert_module_to_f16(x):
-    pass
-
-def convert_module_to_f32(x):
-    pass
-
-
-## go
-class AttentionPool2d(nn.Module):
-    """
-    Adapted from CLIP: https://github.com/openai/CLIP/blob/main/clip/model.py
-    """
-
-    def __init__(
-        self,
-        spacial_dim: int,
-        embed_dim: int,
-        num_heads_channels: int,
-        output_dim: int = None,
-    ):
-        super().__init__()
-        self.positional_embedding = nn.Parameter(th.randn(embed_dim, spacial_dim ** 2 + 1) / embed_dim ** 0.5)
-        self.qkv_proj = conv_nd(1, embed_dim, 3 * embed_dim, 1)
-        self.c_proj = conv_nd(1, embed_dim, output_dim or embed_dim, 1)
-        self.num_heads = embed_dim // num_heads_channels
-        self.attention = QKVAttention(self.num_heads)
-
-    def forward(self, x):
-        b, c, *_spatial = x.shape
-        x = x.reshape(b, c, -1)  # NC(HW)
-        x = th.cat([x.mean(dim=-1, keepdim=True), x], dim=-1)  # NC(HW+1)
-        x = x + self.positional_embedding[None, :, :].to(x.dtype)  # NC(HW+1)
-        x = self.qkv_proj(x)
-        x = self.attention(x)
-        x = self.c_proj(x)
-        return x[:, :, 0]
-
-
-class TimestepBlock(nn.Module):
-    """
-    Any module where forward() takes timestep embeddings as a second argument.
-    """
-
-    @abstractmethod
-    def forward(self, x, emb):
-        """
-        Apply the module to `x` given `emb` timestep embeddings.
-        """
-
-
-class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
-    """
-    A sequential module that passes timestep embeddings to the children that
-    support it as an extra input.
-    """
-
-    def forward(self, x, emb, context=None):
-        for layer in self:
-            if isinstance(layer, TimestepBlock):
-                x = layer(x, emb)
-            elif isinstance(layer, SpatialTransformer):
-                x = layer(x, context)
-            else:
-                x = layer(x)
-        return x
-
-
-class Upsample(nn.Module):
-    """
-    An upsampling layer with an optional convolution.
-    :param channels: channels in the inputs and outputs.
-    :param use_conv: a bool determining if a convolution is applied.
-    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
-                 upsampling occurs in the inner-two dimensions.
-    """
-
-    def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1):
-        super().__init__()
-        self.channels = channels
-        self.out_channels = out_channels or channels
-        self.use_conv = use_conv
-        self.dims = dims
-        if use_conv:
-            self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=padding)
-
-    def forward(self, x):
-        assert x.shape[1] == self.channels
-        if self.dims == 3:
-            x = F.interpolate(
-                x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest"
-            )
-        else:
-            x = F.interpolate(x, scale_factor=2, mode="nearest")
-        if self.use_conv:
-            x = self.conv(x)
-        return x
-
-class TransposedUpsample(nn.Module):
-    'Learned 2x upsampling without padding'
-    def __init__(self, channels, out_channels=None, ks=5):
-        super().__init__()
-        self.channels = channels
-        self.out_channels = out_channels or channels
-
-        self.up = nn.ConvTranspose2d(self.channels,self.out_channels,kernel_size=ks,stride=2)
-
-    def forward(self,x):
-        return self.up(x)
-
-
-class Downsample(nn.Module):
-    """
-    A downsampling layer with an optional convolution.
-    :param channels: channels in the inputs and outputs.
-    :param use_conv: a bool determining if a convolution is applied.
-    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
-                 downsampling occurs in the inner-two dimensions.
-    """
-
-    def __init__(self, channels, use_conv, dims=2, out_channels=None,padding=1):
-        super().__init__()
-        self.channels = channels
-        self.out_channels = out_channels or channels
-        self.use_conv = use_conv
-        self.dims = dims
-        stride = 2 if dims != 3 else (1, 2, 2)
-        if use_conv:
-            self.op = conv_nd(
-                dims, self.channels, self.out_channels, 3, stride=stride, padding=padding
-            )
-        else:
-            assert self.channels == self.out_channels
-            self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)
-
-    def forward(self, x):
-        assert x.shape[1] == self.channels
-        return self.op(x)
-
-
-class ResBlock(TimestepBlock):
-    """
-    A residual block that can optionally change the number of channels.
-    :param channels: the number of input channels.
-    :param emb_channels: the number of timestep embedding channels.
-    :param dropout: the rate of dropout.
-    :param out_channels: if specified, the number of out channels.
-    :param use_conv: if True and out_channels is specified, use a spatial
-        convolution instead of a smaller 1x1 convolution to change the
-        channels in the skip connection.
-    :param dims: determines if the signal is 1D, 2D, or 3D.
-    :param use_checkpoint: if True, use gradient checkpointing on this module.
-    :param up: if True, use this block for upsampling.
-    :param down: if True, use this block for downsampling.
-    """
-
-    def __init__(
-        self,
-        channels,
-        emb_channels,
-        dropout,
-        out_channels=None,
-        use_conv=False,
-        use_scale_shift_norm=False,
-        dims=2,
-        use_checkpoint=False,
-        up=False,
-        down=False,
-    ):
-        super().__init__()
-        self.channels = channels
-        self.emb_channels = emb_channels
-        self.dropout = dropout
-        self.out_channels = out_channels or channels
-        self.use_conv = use_conv
-        self.use_checkpoint = use_checkpoint
-        self.use_scale_shift_norm = use_scale_shift_norm
-
-        self.in_layers = nn.Sequential(
-            normalization(channels),
-            nn.SiLU(),
-            conv_nd(dims, channels, self.out_channels, 3, padding=1),
-        )
-
-        self.updown = up or down
-
-        if up:
-            self.h_upd = Upsample(channels, False, dims)
-            self.x_upd = Upsample(channels, False, dims)
-        elif down:
-            self.h_upd = Downsample(channels, False, dims)
-            self.x_upd = Downsample(channels, False, dims)
-        else:
-            self.h_upd = self.x_upd = nn.Identity()
-
-        self.emb_layers = nn.Sequential(
-            nn.SiLU(),
-            linear(
-                emb_channels,
-                2 * self.out_channels if use_scale_shift_norm else self.out_channels,
-            ),
-        )
-        self.out_layers = nn.Sequential(
-            normalization(self.out_channels),
-            nn.SiLU(),
-            nn.Dropout(p=dropout),
-            zero_module(
-                conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1)
-            ),
-        )
-
-        if self.out_channels == channels:
-            self.skip_connection = nn.Identity()
-        elif use_conv:
-            self.skip_connection = conv_nd(
-                dims, channels, self.out_channels, 3, padding=1
-            )
-        else:
-            self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)
-
-    def forward(self, x, emb):
-        """
-        Apply the block to a Tensor, conditioned on a timestep embedding.
-        :param x: an [N x C x ...] Tensor of features.
-        :param emb: an [N x emb_channels] Tensor of timestep embeddings.
-        :return: an [N x C x ...] Tensor of outputs.
-        """
-        return checkpoint(
-            self._forward, (x, emb), self.parameters(), self.use_checkpoint
-        )
-
-
-    def _forward(self, x, emb):
-        if self.updown:
-            in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
-            h = in_rest(x)
-            h = self.h_upd(h)
-            x = self.x_upd(x)
-            h = in_conv(h)
-        else:
-            h = self.in_layers(x)
-        emb_out = self.emb_layers(emb).type(h.dtype)
-        while len(emb_out.shape) < len(h.shape):
-            emb_out = emb_out[..., None]
-        if self.use_scale_shift_norm:
-            out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
-            scale, shift = th.chunk(emb_out, 2, dim=1)
-            h = out_norm(h) * (1 + scale) + shift
-            h = out_rest(h)
-        else:
-            h = h + emb_out
-            h = self.out_layers(h)
-        return self.skip_connection(x) + h
-
-
-class AttentionBlock(nn.Module):
-    """
-    An attention block that allows spatial positions to attend to each other.
-    Originally ported from here, but adapted to the N-d case.
-    https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
-    """
-
-    def __init__(
-        self,
-        channels,
-        num_heads=1,
-        num_head_channels=-1,
-        use_checkpoint=False,
-        use_new_attention_order=False,
-    ):
-        super().__init__()
-        self.channels = channels
-        if num_head_channels == -1:
-            self.num_heads = num_heads
-        else:
-            assert (
-                channels % num_head_channels == 0
-            ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
-            self.num_heads = channels // num_head_channels
-        self.use_checkpoint = use_checkpoint
-        self.norm = normalization(channels)
-        self.qkv = conv_nd(1, channels, channels * 3, 1)
-        if use_new_attention_order:
-            # split qkv before split heads
-            self.attention = QKVAttention(self.num_heads)
-        else:
-            # split heads before split qkv
-            self.attention = QKVAttentionLegacy(self.num_heads)
-
-        self.proj_out = zero_module(conv_nd(1, channels, channels, 1))
-
-    def forward(self, x):
-        return checkpoint(self._forward, (x,), self.parameters(), True)   # TODO: check checkpoint usage, is True # TODO: fix the .half call!!!
-        #return pt_checkpoint(self._forward, x)  # pytorch
-
-    def _forward(self, x):
-        b, c, *spatial = x.shape
-        x = x.reshape(b, c, -1)
-        qkv = self.qkv(self.norm(x))
-        h = self.attention(qkv)
-        h = self.proj_out(h)
-        return (x + h).reshape(b, c, *spatial)
-
-
-def count_flops_attn(model, _x, y):
-    """
-    A counter for the `thop` package to count the operations in an
-    attention operation.
-    Meant to be used like:
-        macs, params = thop.profile(
-            model,
-            inputs=(inputs, timestamps),
-            custom_ops={QKVAttention: QKVAttention.count_flops},
-        )
-    """
-    b, c, *spatial = y[0].shape
-    num_spatial = int(np.prod(spatial))
-    # We perform two matmuls with the same number of ops.
-    # The first computes the weight matrix, the second computes
-    # the combination of the value vectors.
-    matmul_ops = 2 * b * (num_spatial ** 2) * c
-    model.total_ops += th.DoubleTensor([matmul_ops])
-
-
-class QKVAttentionLegacy(nn.Module):
-    """
-    A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping
-    """
-
-    def __init__(self, n_heads):
-        super().__init__()
-        self.n_heads = n_heads
-
-    def forward(self, qkv):
-        """
-        Apply QKV attention.
-        :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs.
-        :return: an [N x (H * C) x T] tensor after attention.
-        """
-        bs, width, length = qkv.shape
-        assert width % (3 * self.n_heads) == 0
-        ch = width // (3 * self.n_heads)
-        q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1)
-        scale = 1 / math.sqrt(math.sqrt(ch))
-        weight = th.einsum(
-            "bct,bcs->bts", q * scale, k * scale
-        )  # More stable with f16 than dividing afterwards
-        weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
-        a = th.einsum("bts,bcs->bct", weight, v)
-        return a.reshape(bs, -1, length)
-
-    @staticmethod
-    def count_flops(model, _x, y):
-        return count_flops_attn(model, _x, y)
-
-
-class QKVAttention(nn.Module):
-    """
-    A module which performs QKV attention and splits in a different order.
-    """
-
-    def __init__(self, n_heads):
-        super().__init__()
-        self.n_heads = n_heads
-
-    def forward(self, qkv):
-        """
-        Apply QKV attention.
-        :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs.
-        :return: an [N x (H * C) x T] tensor after attention.
-        """
-        bs, width, length = qkv.shape
-        assert width % (3 * self.n_heads) == 0
-        ch = width // (3 * self.n_heads)
-        q, k, v = qkv.chunk(3, dim=1)
-        scale = 1 / math.sqrt(math.sqrt(ch))
-        weight = th.einsum(
-            "bct,bcs->bts",
-            (q * scale).view(bs * self.n_heads, ch, length),
-            (k * scale).view(bs * self.n_heads, ch, length),
-        )  # More stable with f16 than dividing afterwards
-        weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
-        a = th.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length))
-        return a.reshape(bs, -1, length)
-
-    @staticmethod
-    def count_flops(model, _x, y):
-        return count_flops_attn(model, _x, y)
-
-
-class Timestep(nn.Module):
-    def __init__(self, dim):
-        super().__init__()
-        self.dim = dim
-
-    def forward(self, t):
-        return timestep_embedding(t, self.dim)
-
-
-class UNetModel(nn.Module):
-    """
-    The full UNet model with attention and timestep embedding.
-    :param in_channels: channels in the input Tensor.
-    :param model_channels: base channel count for the model.
-    :param out_channels: channels in the output Tensor.
-    :param num_res_blocks: number of residual blocks per downsample.
-    :param attention_resolutions: a collection of downsample rates at which
-        attention will take place. May be a set, list, or tuple.
-        For example, if this contains 4, then at 4x downsampling, attention
-        will be used.
-    :param dropout: the dropout probability.
-    :param channel_mult: channel multiplier for each level of the UNet.
-    :param conv_resample: if True, use learned convolutions for upsampling and
-        downsampling.
-    :param dims: determines if the signal is 1D, 2D, or 3D.
-    :param num_classes: if specified (as an int), then this model will be
-        class-conditional with `num_classes` classes.
-    :param use_checkpoint: use gradient checkpointing to reduce memory usage.
-    :param num_heads: the number of attention heads in each attention layer.
-    :param num_heads_channels: if specified, ignore num_heads and instead use
-                               a fixed channel width per attention head.
-    :param num_heads_upsample: works with num_heads to set a different number
-                               of heads for upsampling. Deprecated.
-    :param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
-    :param resblock_updown: use residual blocks for up/downsampling.
-    :param use_new_attention_order: use a different attention pattern for potentially
-                                    increased efficiency.
-    """
-
-    def __init__(
-        self,
-        image_size,
-        in_channels,
-        model_channels,
-        out_channels,
-        num_res_blocks,
-        attention_resolutions,
-        dropout=0,
-        channel_mult=(1, 2, 4, 8),
-        conv_resample=True,
-        dims=2,
-        num_classes=None,
-        use_checkpoint=False,
-        use_fp16=False,
-        use_bf16=False,
-        num_heads=-1,
-        num_head_channels=-1,
-        num_heads_upsample=-1,
-        use_scale_shift_norm=False,
-        resblock_updown=False,
-        use_new_attention_order=False,
-        use_spatial_transformer=False,    # custom transformer support
-        transformer_depth=1,              # custom transformer support
-        context_dim=None,                 # custom transformer support
-        n_embed=None,                     # custom support for prediction of discrete ids into codebook of first stage vq model
-        legacy=True,
-        disable_self_attentions=None,
-        num_attention_blocks=None,
-        disable_middle_self_attn=False,
-        use_linear_in_transformer=False,
-        adm_in_channels=None,
-    ):
-        super().__init__()
-        if use_spatial_transformer:
-            assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...'
-
-        if context_dim is not None:
-            assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
-            from omegaconf.listconfig import ListConfig
-            if type(context_dim) == ListConfig:
-                context_dim = list(context_dim)
-
-        if num_heads_upsample == -1:
-            num_heads_upsample = num_heads
-
-        if num_heads == -1:
-            assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set'
-
-        if num_head_channels == -1:
-            assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'
-
-        self.image_size = image_size
-        self.in_channels = in_channels
-        self.model_channels = model_channels
-        self.out_channels = out_channels
-        if isinstance(num_res_blocks, int):
-            self.num_res_blocks = len(channel_mult) * [num_res_blocks]
-        else:
-            if len(num_res_blocks) != len(channel_mult):
-                raise ValueError("provide num_res_blocks either as an int (globally constant) or "
-                                 "as a list/tuple (per-level) with the same length as channel_mult")
-            self.num_res_blocks = num_res_blocks
-        if disable_self_attentions is not None:
-            # should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not
-            assert len(disable_self_attentions) == len(channel_mult)
-        if num_attention_blocks is not None:
-            assert len(num_attention_blocks) == len(self.num_res_blocks)
-            assert all(map(lambda i: self.num_res_blocks[i] >= num_attention_blocks[i], range(len(num_attention_blocks))))
-            print(f"Constructor of UNetModel received num_attention_blocks={num_attention_blocks}. "
-                  f"This option has LESS priority than attention_resolutions {attention_resolutions}, "
-                  f"i.e., in cases where num_attention_blocks[i] > 0 but 2**i not in attention_resolutions, "
-                  f"attention will still not be set.")
-
-        self.attention_resolutions = attention_resolutions
-        self.dropout = dropout
-        self.channel_mult = channel_mult
-        self.conv_resample = conv_resample
-        self.num_classes = num_classes
-        self.use_checkpoint = use_checkpoint
-        self.dtype = th.float16 if use_fp16 else th.float32
-        self.dtype = th.bfloat16 if use_bf16 else self.dtype
-        self.num_heads = num_heads
-        self.num_head_channels = num_head_channels
-        self.num_heads_upsample = num_heads_upsample
-        self.predict_codebook_ids = n_embed is not None
-
-        time_embed_dim = model_channels * 4
-        self.time_embed = nn.Sequential(
-            linear(model_channels, time_embed_dim),
-            nn.SiLU(),
-            linear(time_embed_dim, time_embed_dim),
-        )
-
-        if self.num_classes is not None:
-            if isinstance(self.num_classes, int):
-                self.label_emb = nn.Embedding(num_classes, time_embed_dim)
-            elif self.num_classes == "continuous":
-                print("setting up linear c_adm embedding layer")
-                self.label_emb = nn.Linear(1, time_embed_dim)
-            elif self.num_classes == "sequential":
-                assert adm_in_channels is not None
-                self.label_emb = nn.Sequential(
-                    nn.Sequential(
-                        linear(adm_in_channels, time_embed_dim),
-                        nn.SiLU(),
-                        linear(time_embed_dim, time_embed_dim),
-                    )
-                )
-            else:
-                raise ValueError()
-
-        self.input_blocks = nn.ModuleList(
-            [
-                TimestepEmbedSequential(
-                    conv_nd(dims, in_channels, model_channels, 3, padding=1)
-                )
-            ]
-        )
-        self._feature_size = model_channels
-        input_block_chans = [model_channels]
-        ch = model_channels
-        ds = 1
-        for level, mult in enumerate(channel_mult):
-            for nr in range(self.num_res_blocks[level]):
-                layers = [
-                    ResBlock(
-                        ch,
-                        time_embed_dim,
-                        dropout,
-                        out_channels=mult * model_channels,
-                        dims=dims,
-                        use_checkpoint=use_checkpoint,
-                        use_scale_shift_norm=use_scale_shift_norm,
-                    )
-                ]
-                ch = mult * model_channels
-                if ds in attention_resolutions:
-                    if num_head_channels == -1:
-                        dim_head = ch // num_heads
-                    else:
-                        num_heads = ch // num_head_channels
-                        dim_head = num_head_channels
-                    if legacy:
-                        #num_heads = 1
-                        dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
-                    if exists(disable_self_attentions):
-                        disabled_sa = disable_self_attentions[level]
-                    else:
-                        disabled_sa = False
-
-                    if not exists(num_attention_blocks) or nr < num_attention_blocks[level]:
-                        layers.append(
-                            AttentionBlock(
-                                ch,
-                                use_checkpoint=use_checkpoint,
-                                num_heads=num_heads,
-                                num_head_channels=dim_head,
-                                use_new_attention_order=use_new_attention_order,
-                            ) if not use_spatial_transformer else SpatialTransformer(
-                                ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
-                                disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,
-                                use_checkpoint=use_checkpoint
-                            )
-                        )
-                self.input_blocks.append(TimestepEmbedSequential(*layers))
-                self._feature_size += ch
-                input_block_chans.append(ch)
-            if level != len(channel_mult) - 1:
-                out_ch = ch
-                self.input_blocks.append(
-                    TimestepEmbedSequential(
-                        ResBlock(
-                            ch,
-                            time_embed_dim,
-                            dropout,
-                            out_channels=out_ch,
-                            dims=dims,
-                            use_checkpoint=use_checkpoint,
-                            use_scale_shift_norm=use_scale_shift_norm,
-                            down=True,
-                        )
-                        if resblock_updown
-                        else Downsample(
-                            ch, conv_resample, dims=dims, out_channels=out_ch
-                        )
-                    )
-                )
-                ch = out_ch
-                input_block_chans.append(ch)
-                ds *= 2
-                self._feature_size += ch
-
-        if num_head_channels == -1:
-            dim_head = ch // num_heads
-        else:
-            num_heads = ch // num_head_channels
-            dim_head = num_head_channels
-        if legacy:
-            #num_heads = 1
-            dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
-        self.middle_block = TimestepEmbedSequential(
-            ResBlock(
-                ch,
-                time_embed_dim,
-                dropout,
-                dims=dims,
-                use_checkpoint=use_checkpoint,
-                use_scale_shift_norm=use_scale_shift_norm,
-            ),
-            AttentionBlock(
-                ch,
-                use_checkpoint=use_checkpoint,
-                num_heads=num_heads,
-                num_head_channels=dim_head,
-                use_new_attention_order=use_new_attention_order,
-            ) if not use_spatial_transformer else SpatialTransformer(  # always uses a self-attn
-                            ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
-                            disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer,
-                            use_checkpoint=use_checkpoint
-                        ),
-            ResBlock(
-                ch,
-                time_embed_dim,
-                dropout,
-                dims=dims,
-                use_checkpoint=use_checkpoint,
-                use_scale_shift_norm=use_scale_shift_norm,
-            ),
-        )
-        self._feature_size += ch
-
-        self.output_blocks = nn.ModuleList([])
-        for level, mult in list(enumerate(channel_mult))[::-1]:
-            for i in range(self.num_res_blocks[level] + 1):
-                ich = input_block_chans.pop()
-                layers = [
-                    ResBlock(
-                        ch + ich,
-                        time_embed_dim,
-                        dropout,
-                        out_channels=model_channels * mult,
-                        dims=dims,
-                        use_checkpoint=use_checkpoint,
-                        use_scale_shift_norm=use_scale_shift_norm,
-                    )
-                ]
-                ch = model_channels * mult
-                if ds in attention_resolutions:
-                    if num_head_channels == -1:
-                        dim_head = ch // num_heads
-                    else:
-                        num_heads = ch // num_head_channels
-                        dim_head = num_head_channels
-                    if legacy:
-                        #num_heads = 1
-                        dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
-                    if exists(disable_self_attentions):
-                        disabled_sa = disable_self_attentions[level]
-                    else:
-                        disabled_sa = False
-
-                    if not exists(num_attention_blocks) or i < num_attention_blocks[level]:
-                        layers.append(
-                            AttentionBlock(
-                                ch,
-                                use_checkpoint=use_checkpoint,
-                                num_heads=num_heads_upsample,
-                                num_head_channels=dim_head,
-                                use_new_attention_order=use_new_attention_order,
-                            ) if not use_spatial_transformer else SpatialTransformer(
-                                ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
-                                disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,
-                                use_checkpoint=use_checkpoint
-                            )
-                        )
-                if level and i == self.num_res_blocks[level]:
-                    out_ch = ch
-                    layers.append(
-                        ResBlock(
-                            ch,
-                            time_embed_dim,
-                            dropout,
-                            out_channels=out_ch,
-                            dims=dims,
-                            use_checkpoint=use_checkpoint,
-                            use_scale_shift_norm=use_scale_shift_norm,
-                            up=True,
-                        )
-                        if resblock_updown
-                        else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch)
-                    )
-                    ds //= 2
-                self.output_blocks.append(TimestepEmbedSequential(*layers))
-                self._feature_size += ch
-
-        self.out = nn.Sequential(
-            normalization(ch),
-            nn.SiLU(),
-            zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)),
-        )
-        if self.predict_codebook_ids:
-            self.id_predictor = nn.Sequential(
-            normalization(ch),
-            conv_nd(dims, model_channels, n_embed, 1),
-            #nn.LogSoftmax(dim=1)  # change to cross_entropy and produce non-normalized logits
-        )
-
-    def convert_to_fp16(self):
-        """
-        Convert the torso of the model to float16.
-        """
-        self.input_blocks.apply(convert_module_to_f16)
-        self.middle_block.apply(convert_module_to_f16)
-        self.output_blocks.apply(convert_module_to_f16)
-
-    def convert_to_fp32(self):
-        """
-        Convert the torso of the model to float32.
-        """
-        self.input_blocks.apply(convert_module_to_f32)
-        self.middle_block.apply(convert_module_to_f32)
-        self.output_blocks.apply(convert_module_to_f32)
-
-    def forward(self, x, timesteps=None, context=None, y=None,**kwargs):
-        """
-        Apply the model to an input batch.
-        :param x: an [N x C x ...] Tensor of inputs.
-        :param timesteps: a 1-D batch of timesteps.
-        :param context: conditioning plugged in via crossattn
-        :param y: an [N] Tensor of labels, if class-conditional.
-        :return: an [N x C x ...] Tensor of outputs.
-        """
-        assert (y is not None) == (
-            self.num_classes is not None
-        ), "must specify y if and only if the model is class-conditional"
-        hs = []
-        t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
-        emb = self.time_embed(t_emb)
-
-        if self.num_classes is not None:
-            assert y.shape[0] == x.shape[0]
-            emb = emb + self.label_emb(y)
-
-        h = x.type(self.dtype)
-        for module in self.input_blocks:
-            h = module(h, emb, context)
-            hs.append(h)
-        h = self.middle_block(h, emb, context)
-        for module in self.output_blocks:
-            h = th.cat([h, hs.pop()], dim=1)
-            h = module(h, emb, context)
-        h = h.type(x.dtype)
-        if self.predict_codebook_ids:
-            return self.id_predictor(h)
-        else:
-            return self.out(h)
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/diffusionmodules/upscaling.py b/repositories/stable-diffusion-stability-ai/ldm/modules/diffusionmodules/upscaling.py
deleted file mode 100644
index 03816662098ce1ffac79bd939b892e867ab91988..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/ldm/modules/diffusionmodules/upscaling.py
+++ /dev/null
@@ -1,81 +0,0 @@
-import torch
-import torch.nn as nn
-import numpy as np
-from functools import partial
-
-from ldm.modules.diffusionmodules.util import extract_into_tensor, make_beta_schedule
-from ldm.util import default
-
-
-class AbstractLowScaleModel(nn.Module):
-    # for concatenating a downsampled image to the latent representation
-    def __init__(self, noise_schedule_config=None):
-        super(AbstractLowScaleModel, self).__init__()
-        if noise_schedule_config is not None:
-            self.register_schedule(**noise_schedule_config)
-
-    def register_schedule(self, beta_schedule="linear", timesteps=1000,
-                          linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
-        betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end,
-                                   cosine_s=cosine_s)
-        alphas = 1. - betas
-        alphas_cumprod = np.cumprod(alphas, axis=0)
-        alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
-
-        timesteps, = betas.shape
-        self.num_timesteps = int(timesteps)
-        self.linear_start = linear_start
-        self.linear_end = linear_end
-        assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'
-
-        to_torch = partial(torch.tensor, dtype=torch.float32)
-
-        self.register_buffer('betas', to_torch(betas))
-        self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
-        self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
-
-        # calculations for diffusion q(x_t | x_{t-1}) and others
-        self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
-        self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
-        self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
-        self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
-        self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
-
-    def q_sample(self, x_start, t, noise=None):
-        noise = default(noise, lambda: torch.randn_like(x_start))
-        return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
-                extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)
-
-    def forward(self, x):
-        return x, None
-
-    def decode(self, x):
-        return x
-
-
-class SimpleImageConcat(AbstractLowScaleModel):
-    # no noise level conditioning
-    def __init__(self):
-        super(SimpleImageConcat, self).__init__(noise_schedule_config=None)
-        self.max_noise_level = 0
-
-    def forward(self, x):
-        # fix to constant noise level
-        return x, torch.zeros(x.shape[0], device=x.device).long()
-
-
-class ImageConcatWithNoiseAugmentation(AbstractLowScaleModel):
-    def __init__(self, noise_schedule_config, max_noise_level=1000, to_cuda=False):
-        super().__init__(noise_schedule_config=noise_schedule_config)
-        self.max_noise_level = max_noise_level
-
-    def forward(self, x, noise_level=None):
-        if noise_level is None:
-            noise_level = torch.randint(0, self.max_noise_level, (x.shape[0],), device=x.device).long()
-        else:
-            assert isinstance(noise_level, torch.Tensor)
-        z = self.q_sample(x, noise_level)
-        return z, noise_level
-
-
-
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/diffusionmodules/util.py b/repositories/stable-diffusion-stability-ai/ldm/modules/diffusionmodules/util.py
deleted file mode 100644
index daf35da7bae05b9a2880dbc923b70e129e2c904d..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/ldm/modules/diffusionmodules/util.py
+++ /dev/null
@@ -1,278 +0,0 @@
-# adopted from
-# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
-# and
-# https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
-# and
-# https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py
-#
-# thanks!
-
-
-import os
-import math
-import torch
-import torch.nn as nn
-import numpy as np
-from einops import repeat
-
-from ldm.util import instantiate_from_config
-
-
-def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
-    if schedule == "linear":
-        betas = (
-                torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2
-        )
-
-    elif schedule == "cosine":
-        timesteps = (
-                torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s
-        )
-        alphas = timesteps / (1 + cosine_s) * np.pi / 2
-        alphas = torch.cos(alphas).pow(2)
-        alphas = alphas / alphas[0]
-        betas = 1 - alphas[1:] / alphas[:-1]
-        betas = np.clip(betas, a_min=0, a_max=0.999)
-
-    elif schedule == "squaredcos_cap_v2":  # used for karlo prior
-        # return early
-        return betas_for_alpha_bar(
-            n_timestep,
-            lambda t: math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2,
-        )
-
-    elif schedule == "sqrt_linear":
-        betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64)
-    elif schedule == "sqrt":
-        betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5
-    else:
-        raise ValueError(f"schedule '{schedule}' unknown.")
-    return betas.numpy()
-
-
-def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True):
-    if ddim_discr_method == 'uniform':
-        c = num_ddpm_timesteps // num_ddim_timesteps
-        ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c)))
-    elif ddim_discr_method == 'quad':
-        ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int)
-    else:
-        raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"')
-
-    # assert ddim_timesteps.shape[0] == num_ddim_timesteps
-    # add one to get the final alpha values right (the ones from first scale to data during sampling)
-    steps_out = ddim_timesteps + 1
-    if verbose:
-        print(f'Selected timesteps for ddim sampler: {steps_out}')
-    return steps_out
-
-
-def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True):
-    # select alphas for computing the variance schedule
-    alphas = alphacums[ddim_timesteps]
-    alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist())
-
-    # according the the formula provided in https://arxiv.org/abs/2010.02502
-    sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev))
-    if verbose:
-        print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}')
-        print(f'For the chosen value of eta, which is {eta}, '
-              f'this results in the following sigma_t schedule for ddim sampler {sigmas}')
-    return sigmas, alphas, alphas_prev
-
-
-def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):
-    """
-    Create a beta schedule that discretizes the given alpha_t_bar function,
-    which defines the cumulative product of (1-beta) over time from t = [0,1].
-    :param num_diffusion_timesteps: the number of betas to produce.
-    :param alpha_bar: a lambda that takes an argument t from 0 to 1 and
-                      produces the cumulative product of (1-beta) up to that
-                      part of the diffusion process.
-    :param max_beta: the maximum beta to use; use values lower than 1 to
-                     prevent singularities.
-    """
-    betas = []
-    for i in range(num_diffusion_timesteps):
-        t1 = i / num_diffusion_timesteps
-        t2 = (i + 1) / num_diffusion_timesteps
-        betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
-    return np.array(betas)
-
-
-def extract_into_tensor(a, t, x_shape):
-    b, *_ = t.shape
-    out = a.gather(-1, t)
-    return out.reshape(b, *((1,) * (len(x_shape) - 1)))
-
-
-def checkpoint(func, inputs, params, flag):
-    """
-    Evaluate a function without caching intermediate activations, allowing for
-    reduced memory at the expense of extra compute in the backward pass.
-    :param func: the function to evaluate.
-    :param inputs: the argument sequence to pass to `func`.
-    :param params: a sequence of parameters `func` depends on but does not
-                   explicitly take as arguments.
-    :param flag: if False, disable gradient checkpointing.
-    """
-    if flag:
-        args = tuple(inputs) + tuple(params)
-        return CheckpointFunction.apply(func, len(inputs), *args)
-    else:
-        return func(*inputs)
-
-
-class CheckpointFunction(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, run_function, length, *args):
-        ctx.run_function = run_function
-        ctx.input_tensors = list(args[:length])
-        ctx.input_params = list(args[length:])
-        ctx.gpu_autocast_kwargs = {"enabled": torch.is_autocast_enabled(),
-                                   "dtype": torch.get_autocast_gpu_dtype(),
-                                   "cache_enabled": torch.is_autocast_cache_enabled()}
-        with torch.no_grad():
-            output_tensors = ctx.run_function(*ctx.input_tensors)
-        return output_tensors
-
-    @staticmethod
-    def backward(ctx, *output_grads):
-        ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors]
-        with torch.enable_grad(), \
-                torch.cuda.amp.autocast(**ctx.gpu_autocast_kwargs):
-            # Fixes a bug where the first op in run_function modifies the
-            # Tensor storage in place, which is not allowed for detach()'d
-            # Tensors.
-            shallow_copies = [x.view_as(x) for x in ctx.input_tensors]
-            output_tensors = ctx.run_function(*shallow_copies)
-        input_grads = torch.autograd.grad(
-            output_tensors,
-            ctx.input_tensors + ctx.input_params,
-            output_grads,
-            allow_unused=True,
-        )
-        del ctx.input_tensors
-        del ctx.input_params
-        del output_tensors
-        return (None, None) + input_grads
-
-
-def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False):
-    """
-    Create sinusoidal timestep embeddings.
-    :param timesteps: a 1-D Tensor of N indices, one per batch element.
-                      These may be fractional.
-    :param dim: the dimension of the output.
-    :param max_period: controls the minimum frequency of the embeddings.
-    :return: an [N x dim] Tensor of positional embeddings.
-    """
-    if not repeat_only:
-        half = dim // 2
-        freqs = torch.exp(
-            -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
-        ).to(device=timesteps.device)
-        args = timesteps[:, None].float() * freqs[None]
-        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
-        if dim % 2:
-            embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
-    else:
-        embedding = repeat(timesteps, 'b -> b d', d=dim)
-    return embedding
-
-
-def zero_module(module):
-    """
-    Zero out the parameters of a module and return it.
-    """
-    for p in module.parameters():
-        p.detach().zero_()
-    return module
-
-
-def scale_module(module, scale):
-    """
-    Scale the parameters of a module and return it.
-    """
-    for p in module.parameters():
-        p.detach().mul_(scale)
-    return module
-
-
-def mean_flat(tensor):
-    """
-    Take the mean over all non-batch dimensions.
-    """
-    return tensor.mean(dim=list(range(1, len(tensor.shape))))
-
-
-def normalization(channels):
-    """
-    Make a standard normalization layer.
-    :param channels: number of input channels.
-    :return: an nn.Module for normalization.
-    """
-    return GroupNorm32(32, channels)
-
-
-# PyTorch 1.7 has SiLU, but we support PyTorch 1.5.
-class SiLU(nn.Module):
-    def forward(self, x):
-        return x * torch.sigmoid(x)
-
-
-class GroupNorm32(nn.GroupNorm):
-    def forward(self, x):
-        return super().forward(x.float()).type(x.dtype)
-
-
-def conv_nd(dims, *args, **kwargs):
-    """
-    Create a 1D, 2D, or 3D convolution module.
-    """
-    if dims == 1:
-        return nn.Conv1d(*args, **kwargs)
-    elif dims == 2:
-        return nn.Conv2d(*args, **kwargs)
-    elif dims == 3:
-        return nn.Conv3d(*args, **kwargs)
-    raise ValueError(f"unsupported dimensions: {dims}")
-
-
-def linear(*args, **kwargs):
-    """
-    Create a linear module.
-    """
-    return nn.Linear(*args, **kwargs)
-
-
-def avg_pool_nd(dims, *args, **kwargs):
-    """
-    Create a 1D, 2D, or 3D average pooling module.
-    """
-    if dims == 1:
-        return nn.AvgPool1d(*args, **kwargs)
-    elif dims == 2:
-        return nn.AvgPool2d(*args, **kwargs)
-    elif dims == 3:
-        return nn.AvgPool3d(*args, **kwargs)
-    raise ValueError(f"unsupported dimensions: {dims}")
-
-
-class HybridConditioner(nn.Module):
-
-    def __init__(self, c_concat_config, c_crossattn_config):
-        super().__init__()
-        self.concat_conditioner = instantiate_from_config(c_concat_config)
-        self.crossattn_conditioner = instantiate_from_config(c_crossattn_config)
-
-    def forward(self, c_concat, c_crossattn):
-        c_concat = self.concat_conditioner(c_concat)
-        c_crossattn = self.crossattn_conditioner(c_crossattn)
-        return {'c_concat': [c_concat], 'c_crossattn': [c_crossattn]}
-
-
-def noise_like(shape, device, repeat=False):
-    repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1)))
-    noise = lambda: torch.randn(shape, device=device)
-    return repeat_noise() if repeat else noise()
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/distributions/__init__.py b/repositories/stable-diffusion-stability-ai/ldm/modules/distributions/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/distributions/__pycache__/__init__.cpython-310.pyc b/repositories/stable-diffusion-stability-ai/ldm/modules/distributions/__pycache__/__init__.cpython-310.pyc
deleted file mode 100644
index b2e865ac338ebb98df4bb3faa584b11f755bca79..0000000000000000000000000000000000000000
Binary files a/repositories/stable-diffusion-stability-ai/ldm/modules/distributions/__pycache__/__init__.cpython-310.pyc and /dev/null differ
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/distributions/__pycache__/distributions.cpython-310.pyc b/repositories/stable-diffusion-stability-ai/ldm/modules/distributions/__pycache__/distributions.cpython-310.pyc
deleted file mode 100644
index 874b58dbff72e4f4bc0df6303cc49e2f0ae40596..0000000000000000000000000000000000000000
Binary files a/repositories/stable-diffusion-stability-ai/ldm/modules/distributions/__pycache__/distributions.cpython-310.pyc and /dev/null differ
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/distributions/distributions.py b/repositories/stable-diffusion-stability-ai/ldm/modules/distributions/distributions.py
deleted file mode 100644
index f2b8ef901130efc171aa69742ca0244d94d3f2e9..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/ldm/modules/distributions/distributions.py
+++ /dev/null
@@ -1,92 +0,0 @@
-import torch
-import numpy as np
-
-
-class AbstractDistribution:
-    def sample(self):
-        raise NotImplementedError()
-
-    def mode(self):
-        raise NotImplementedError()
-
-
-class DiracDistribution(AbstractDistribution):
-    def __init__(self, value):
-        self.value = value
-
-    def sample(self):
-        return self.value
-
-    def mode(self):
-        return self.value
-
-
-class DiagonalGaussianDistribution(object):
-    def __init__(self, parameters, deterministic=False):
-        self.parameters = parameters
-        self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)
-        self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
-        self.deterministic = deterministic
-        self.std = torch.exp(0.5 * self.logvar)
-        self.var = torch.exp(self.logvar)
-        if self.deterministic:
-            self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device)
-
-    def sample(self):
-        x = self.mean + self.std * torch.randn(self.mean.shape).to(device=self.parameters.device)
-        return x
-
-    def kl(self, other=None):
-        if self.deterministic:
-            return torch.Tensor([0.])
-        else:
-            if other is None:
-                return 0.5 * torch.sum(torch.pow(self.mean, 2)
-                                       + self.var - 1.0 - self.logvar,
-                                       dim=[1, 2, 3])
-            else:
-                return 0.5 * torch.sum(
-                    torch.pow(self.mean - other.mean, 2) / other.var
-                    + self.var / other.var - 1.0 - self.logvar + other.logvar,
-                    dim=[1, 2, 3])
-
-    def nll(self, sample, dims=[1,2,3]):
-        if self.deterministic:
-            return torch.Tensor([0.])
-        logtwopi = np.log(2.0 * np.pi)
-        return 0.5 * torch.sum(
-            logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,
-            dim=dims)
-
-    def mode(self):
-        return self.mean
-
-
-def normal_kl(mean1, logvar1, mean2, logvar2):
-    """
-    source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12
-    Compute the KL divergence between two gaussians.
-    Shapes are automatically broadcasted, so batches can be compared to
-    scalars, among other use cases.
-    """
-    tensor = None
-    for obj in (mean1, logvar1, mean2, logvar2):
-        if isinstance(obj, torch.Tensor):
-            tensor = obj
-            break
-    assert tensor is not None, "at least one argument must be a Tensor"
-
-    # Force variances to be Tensors. Broadcasting helps convert scalars to
-    # Tensors, but it does not work for torch.exp().
-    logvar1, logvar2 = [
-        x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor)
-        for x in (logvar1, logvar2)
-    ]
-
-    return 0.5 * (
-        -1.0
-        + logvar2
-        - logvar1
-        + torch.exp(logvar1 - logvar2)
-        + ((mean1 - mean2) ** 2) * torch.exp(-logvar2)
-    )
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/ema.py b/repositories/stable-diffusion-stability-ai/ldm/modules/ema.py
deleted file mode 100644
index bded25019b9bcbcd0260f0b8185f8c7859ca58c4..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/ldm/modules/ema.py
+++ /dev/null
@@ -1,80 +0,0 @@
-import torch
-from torch import nn
-
-
-class LitEma(nn.Module):
-    def __init__(self, model, decay=0.9999, use_num_upates=True):
-        super().__init__()
-        if decay < 0.0 or decay > 1.0:
-            raise ValueError('Decay must be between 0 and 1')
-
-        self.m_name2s_name = {}
-        self.register_buffer('decay', torch.tensor(decay, dtype=torch.float32))
-        self.register_buffer('num_updates', torch.tensor(0, dtype=torch.int) if use_num_upates
-        else torch.tensor(-1, dtype=torch.int))
-
-        for name, p in model.named_parameters():
-            if p.requires_grad:
-                # remove as '.'-character is not allowed in buffers
-                s_name = name.replace('.', '')
-                self.m_name2s_name.update({name: s_name})
-                self.register_buffer(s_name, p.clone().detach().data)
-
-        self.collected_params = []
-
-    def reset_num_updates(self):
-        del self.num_updates
-        self.register_buffer('num_updates', torch.tensor(0, dtype=torch.int))
-
-    def forward(self, model):
-        decay = self.decay
-
-        if self.num_updates >= 0:
-            self.num_updates += 1
-            decay = min(self.decay, (1 + self.num_updates) / (10 + self.num_updates))
-
-        one_minus_decay = 1.0 - decay
-
-        with torch.no_grad():
-            m_param = dict(model.named_parameters())
-            shadow_params = dict(self.named_buffers())
-
-            for key in m_param:
-                if m_param[key].requires_grad:
-                    sname = self.m_name2s_name[key]
-                    shadow_params[sname] = shadow_params[sname].type_as(m_param[key])
-                    shadow_params[sname].sub_(one_minus_decay * (shadow_params[sname] - m_param[key]))
-                else:
-                    assert not key in self.m_name2s_name
-
-    def copy_to(self, model):
-        m_param = dict(model.named_parameters())
-        shadow_params = dict(self.named_buffers())
-        for key in m_param:
-            if m_param[key].requires_grad:
-                m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data)
-            else:
-                assert not key in self.m_name2s_name
-
-    def store(self, parameters):
-        """
-        Save the current parameters for restoring later.
-        Args:
-          parameters: Iterable of `torch.nn.Parameter`; the parameters to be
-            temporarily stored.
-        """
-        self.collected_params = [param.clone() for param in parameters]
-
-    def restore(self, parameters):
-        """
-        Restore the parameters stored with the `store` method.
-        Useful to validate the model with EMA parameters without affecting the
-        original optimization process. Store the parameters before the
-        `copy_to` method. After validation (or model saving), use this to
-        restore the former parameters.
-        Args:
-          parameters: Iterable of `torch.nn.Parameter`; the parameters to be
-            updated with the stored parameters.
-        """
-        for c_param, param in zip(self.collected_params, parameters):
-            param.data.copy_(c_param.data)
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/encoders/__init__.py b/repositories/stable-diffusion-stability-ai/ldm/modules/encoders/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/encoders/__pycache__/__init__.cpython-310.pyc b/repositories/stable-diffusion-stability-ai/ldm/modules/encoders/__pycache__/__init__.cpython-310.pyc
deleted file mode 100644
index adcda188e57cb5048909de965d07c0c0c77eee85..0000000000000000000000000000000000000000
Binary files a/repositories/stable-diffusion-stability-ai/ldm/modules/encoders/__pycache__/__init__.cpython-310.pyc and /dev/null differ
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/encoders/__pycache__/modules.cpython-310.pyc b/repositories/stable-diffusion-stability-ai/ldm/modules/encoders/__pycache__/modules.cpython-310.pyc
deleted file mode 100644
index f21e7a38e8e868a487a3968f18669e5b9a8f2884..0000000000000000000000000000000000000000
Binary files a/repositories/stable-diffusion-stability-ai/ldm/modules/encoders/__pycache__/modules.cpython-310.pyc and /dev/null differ
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/encoders/modules.py b/repositories/stable-diffusion-stability-ai/ldm/modules/encoders/modules.py
deleted file mode 100644
index 523a7d8535b7a1741bc3d8bbadf59ac63b0de8ab..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/ldm/modules/encoders/modules.py
+++ /dev/null
@@ -1,350 +0,0 @@
-import torch
-import torch.nn as nn
-import kornia
-from torch.utils.checkpoint import checkpoint
-
-from transformers import T5Tokenizer, T5EncoderModel, CLIPTokenizer, CLIPTextModel
-
-import open_clip
-from ldm.util import default, count_params, autocast
-
-
-class AbstractEncoder(nn.Module):
-    def __init__(self):
-        super().__init__()
-
-    def encode(self, *args, **kwargs):
-        raise NotImplementedError
-
-
-class IdentityEncoder(AbstractEncoder):
-
-    def encode(self, x):
-        return x
-
-
-class ClassEmbedder(nn.Module):
-    def __init__(self, embed_dim, n_classes=1000, key='class', ucg_rate=0.1):
-        super().__init__()
-        self.key = key
-        self.embedding = nn.Embedding(n_classes, embed_dim)
-        self.n_classes = n_classes
-        self.ucg_rate = ucg_rate
-
-    def forward(self, batch, key=None, disable_dropout=False):
-        if key is None:
-            key = self.key
-        # this is for use in crossattn
-        c = batch[key][:, None]
-        if self.ucg_rate > 0. and not disable_dropout:
-            mask = 1. - torch.bernoulli(torch.ones_like(c) * self.ucg_rate)
-            c = mask * c + (1 - mask) * torch.ones_like(c) * (self.n_classes - 1)
-            c = c.long()
-        c = self.embedding(c)
-        return c
-
-    def get_unconditional_conditioning(self, bs, device="cuda"):
-        uc_class = self.n_classes - 1  # 1000 classes --> 0 ... 999, one extra class for ucg (class 1000)
-        uc = torch.ones((bs,), device=device) * uc_class
-        uc = {self.key: uc}
-        return uc
-
-
-def disabled_train(self, mode=True):
-    """Overwrite model.train with this function to make sure train/eval mode
-    does not change anymore."""
-    return self
-
-
-class FrozenT5Embedder(AbstractEncoder):
-    """Uses the T5 transformer encoder for text"""
-
-    def __init__(self, version="google/t5-v1_1-large", device="cuda", max_length=77,
-                 freeze=True):  # others are google/t5-v1_1-xl and google/t5-v1_1-xxl
-        super().__init__()
-        self.tokenizer = T5Tokenizer.from_pretrained(version)
-        self.transformer = T5EncoderModel.from_pretrained(version)
-        self.device = device
-        self.max_length = max_length  # TODO: typical value?
-        if freeze:
-            self.freeze()
-
-    def freeze(self):
-        self.transformer = self.transformer.eval()
-        # self.train = disabled_train
-        for param in self.parameters():
-            param.requires_grad = False
-
-    def forward(self, text):
-        batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
-                                        return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
-        tokens = batch_encoding["input_ids"].to(self.device)
-        outputs = self.transformer(input_ids=tokens)
-
-        z = outputs.last_hidden_state
-        return z
-
-    def encode(self, text):
-        return self(text)
-
-
-class FrozenCLIPEmbedder(AbstractEncoder):
-    """Uses the CLIP transformer encoder for text (from huggingface)"""
-    LAYERS = [
-        "last",
-        "pooled",
-        "hidden"
-    ]
-
-    def __init__(self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77,
-                 freeze=True, layer="last", layer_idx=None):  # clip-vit-base-patch32
-        super().__init__()
-        assert layer in self.LAYERS
-        self.tokenizer = CLIPTokenizer.from_pretrained(version)
-        self.transformer = CLIPTextModel.from_pretrained(version)
-        self.device = device
-        self.max_length = max_length
-        if freeze:
-            self.freeze()
-        self.layer = layer
-        self.layer_idx = layer_idx
-        if layer == "hidden":
-            assert layer_idx is not None
-            assert 0 <= abs(layer_idx) <= 12
-
-    def freeze(self):
-        self.transformer = self.transformer.eval()
-        # self.train = disabled_train
-        for param in self.parameters():
-            param.requires_grad = False
-
-    def forward(self, text):
-        batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
-                                        return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
-        tokens = batch_encoding["input_ids"].to(self.device)
-        outputs = self.transformer(input_ids=tokens, output_hidden_states=self.layer == "hidden")
-        if self.layer == "last":
-            z = outputs.last_hidden_state
-        elif self.layer == "pooled":
-            z = outputs.pooler_output[:, None, :]
-        else:
-            z = outputs.hidden_states[self.layer_idx]
-        return z
-
-    def encode(self, text):
-        return self(text)
-
-
-class ClipImageEmbedder(nn.Module):
-    def __init__(
-            self,
-            model,
-            jit=False,
-            device='cuda' if torch.cuda.is_available() else 'cpu',
-            antialias=True,
-            ucg_rate=0.
-    ):
-        super().__init__()
-        from clip import load as load_clip
-        self.model, _ = load_clip(name=model, device=device, jit=jit)
-
-        self.antialias = antialias
-
-        self.register_buffer('mean', torch.Tensor([0.48145466, 0.4578275, 0.40821073]), persistent=False)
-        self.register_buffer('std', torch.Tensor([0.26862954, 0.26130258, 0.27577711]), persistent=False)
-        self.ucg_rate = ucg_rate
-
-    def preprocess(self, x):
-        # normalize to [0,1]
-        x = kornia.geometry.resize(x, (224, 224),
-                                   interpolation='bicubic', align_corners=True,
-                                   antialias=self.antialias)
-        x = (x + 1.) / 2.
-        # re-normalize according to clip
-        x = kornia.enhance.normalize(x, self.mean, self.std)
-        return x
-
-    def forward(self, x, no_dropout=False):
-        # x is assumed to be in range [-1,1]
-        out = self.model.encode_image(self.preprocess(x))
-        out = out.to(x.dtype)
-        if self.ucg_rate > 0. and not no_dropout:
-            out = torch.bernoulli((1. - self.ucg_rate) * torch.ones(out.shape[0], device=out.device))[:, None] * out
-        return out
-
-
-class FrozenOpenCLIPEmbedder(AbstractEncoder):
-    """
-    Uses the OpenCLIP transformer encoder for text
-    """
-    LAYERS = [
-        # "pooled",
-        "last",
-        "penultimate"
-    ]
-
-    def __init__(self, arch="ViT-H-14", version="laion2b_s32b_b79k", device="cuda", max_length=77,
-                 freeze=True, layer="last"):
-        super().__init__()
-        assert layer in self.LAYERS
-        model, _, _ = open_clip.create_model_and_transforms(arch, device=torch.device('cpu'), pretrained=version)
-        del model.visual
-        self.model = model
-
-        self.device = device
-        self.max_length = max_length
-        if freeze:
-            self.freeze()
-        self.layer = layer
-        if self.layer == "last":
-            self.layer_idx = 0
-        elif self.layer == "penultimate":
-            self.layer_idx = 1
-        else:
-            raise NotImplementedError()
-
-    def freeze(self):
-        self.model = self.model.eval()
-        for param in self.parameters():
-            param.requires_grad = False
-
-    def forward(self, text):
-        tokens = open_clip.tokenize(text)
-        z = self.encode_with_transformer(tokens.to(self.device))
-        return z
-
-    def encode_with_transformer(self, text):
-        x = self.model.token_embedding(text)  # [batch_size, n_ctx, d_model]
-        x = x + self.model.positional_embedding
-        x = x.permute(1, 0, 2)  # NLD -> LND
-        x = self.text_transformer_forward(x, attn_mask=self.model.attn_mask)
-        x = x.permute(1, 0, 2)  # LND -> NLD
-        x = self.model.ln_final(x)
-        return x
-
-    def text_transformer_forward(self, x: torch.Tensor, attn_mask=None):
-        for i, r in enumerate(self.model.transformer.resblocks):
-            if i == len(self.model.transformer.resblocks) - self.layer_idx:
-                break
-            if self.model.transformer.grad_checkpointing and not torch.jit.is_scripting():
-                x = checkpoint(r, x, attn_mask)
-            else:
-                x = r(x, attn_mask=attn_mask)
-        return x
-
-    def encode(self, text):
-        return self(text)
-
-
-class FrozenOpenCLIPImageEmbedder(AbstractEncoder):
-    """
-    Uses the OpenCLIP vision transformer encoder for images
-    """
-
-    def __init__(self, arch="ViT-H-14", version="laion2b_s32b_b79k", device="cuda", max_length=77,
-                 freeze=True, layer="pooled", antialias=True, ucg_rate=0.):
-        super().__init__()
-        model, _, _ = open_clip.create_model_and_transforms(arch, device=torch.device('cpu'),
-                                                            pretrained=version, )
-        del model.transformer
-        self.model = model
-
-        self.device = device
-        self.max_length = max_length
-        if freeze:
-            self.freeze()
-        self.layer = layer
-        if self.layer == "penultimate":
-            raise NotImplementedError()
-            self.layer_idx = 1
-
-        self.antialias = antialias
-
-        self.register_buffer('mean', torch.Tensor([0.48145466, 0.4578275, 0.40821073]), persistent=False)
-        self.register_buffer('std', torch.Tensor([0.26862954, 0.26130258, 0.27577711]), persistent=False)
-        self.ucg_rate = ucg_rate
-
-    def preprocess(self, x):
-        # normalize to [0,1]
-        x = kornia.geometry.resize(x, (224, 224),
-                                   interpolation='bicubic', align_corners=True,
-                                   antialias=self.antialias)
-        x = (x + 1.) / 2.
-        # renormalize according to clip
-        x = kornia.enhance.normalize(x, self.mean, self.std)
-        return x
-
-    def freeze(self):
-        self.model = self.model.eval()
-        for param in self.parameters():
-            param.requires_grad = False
-
-    @autocast
-    def forward(self, image, no_dropout=False):
-        z = self.encode_with_vision_transformer(image)
-        if self.ucg_rate > 0. and not no_dropout:
-            z = torch.bernoulli((1. - self.ucg_rate) * torch.ones(z.shape[0], device=z.device))[:, None] * z
-        return z
-
-    def encode_with_vision_transformer(self, img):
-        img = self.preprocess(img)
-        x = self.model.visual(img)
-        return x
-
-    def encode(self, text):
-        return self(text)
-
-
-class FrozenCLIPT5Encoder(AbstractEncoder):
-    def __init__(self, clip_version="openai/clip-vit-large-patch14", t5_version="google/t5-v1_1-xl", device="cuda",
-                 clip_max_length=77, t5_max_length=77):
-        super().__init__()
-        self.clip_encoder = FrozenCLIPEmbedder(clip_version, device, max_length=clip_max_length)
-        self.t5_encoder = FrozenT5Embedder(t5_version, device, max_length=t5_max_length)
-        print(f"{self.clip_encoder.__class__.__name__} has {count_params(self.clip_encoder) * 1.e-6:.2f} M parameters, "
-              f"{self.t5_encoder.__class__.__name__} comes with {count_params(self.t5_encoder) * 1.e-6:.2f} M params.")
-
-    def encode(self, text):
-        return self(text)
-
-    def forward(self, text):
-        clip_z = self.clip_encoder.encode(text)
-        t5_z = self.t5_encoder.encode(text)
-        return [clip_z, t5_z]
-
-
-from ldm.modules.diffusionmodules.upscaling import ImageConcatWithNoiseAugmentation
-from ldm.modules.diffusionmodules.openaimodel import Timestep
-
-
-class CLIPEmbeddingNoiseAugmentation(ImageConcatWithNoiseAugmentation):
-    def __init__(self, *args, clip_stats_path=None, timestep_dim=256, **kwargs):
-        super().__init__(*args, **kwargs)
-        if clip_stats_path is None:
-            clip_mean, clip_std = torch.zeros(timestep_dim), torch.ones(timestep_dim)
-        else:
-            clip_mean, clip_std = torch.load(clip_stats_path, map_location="cpu")
-        self.register_buffer("data_mean", clip_mean[None, :], persistent=False)
-        self.register_buffer("data_std", clip_std[None, :], persistent=False)
-        self.time_embed = Timestep(timestep_dim)
-
-    def scale(self, x):
-        # re-normalize to centered mean and unit variance
-        x = (x - self.data_mean) * 1. / self.data_std
-        return x
-
-    def unscale(self, x):
-        # back to original data stats
-        x = (x * self.data_std) + self.data_mean
-        return x
-
-    def forward(self, x, noise_level=None):
-        if noise_level is None:
-            noise_level = torch.randint(0, self.max_noise_level, (x.shape[0],), device=x.device).long()
-        else:
-            assert isinstance(noise_level, torch.Tensor)
-        x = self.scale(x)
-        z = self.q_sample(x, noise_level)
-        z = self.unscale(z)
-        noise_level = self.time_embed(noise_level)
-        return z, noise_level
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/image_degradation/__init__.py b/repositories/stable-diffusion-stability-ai/ldm/modules/image_degradation/__init__.py
deleted file mode 100644
index 7836cada81f90ded99c58d5942eea4c3477f58fc..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/ldm/modules/image_degradation/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from ldm.modules.image_degradation.bsrgan import degradation_bsrgan_variant as degradation_fn_bsr
-from ldm.modules.image_degradation.bsrgan_light import degradation_bsrgan_variant as degradation_fn_bsr_light
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/image_degradation/bsrgan.py b/repositories/stable-diffusion-stability-ai/ldm/modules/image_degradation/bsrgan.py
deleted file mode 100644
index 32ef56169978e550090261cddbcf5eb611a6173b..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/ldm/modules/image_degradation/bsrgan.py
+++ /dev/null
@@ -1,730 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-# --------------------------------------------
-# Super-Resolution
-# --------------------------------------------
-#
-# Kai Zhang (cskaizhang@gmail.com)
-# https://github.com/cszn
-# From 2019/03--2021/08
-# --------------------------------------------
-"""
-
-import numpy as np
-import cv2
-import torch
-
-from functools import partial
-import random
-from scipy import ndimage
-import scipy
-import scipy.stats as ss
-from scipy.interpolate import interp2d
-from scipy.linalg import orth
-import albumentations
-
-import ldm.modules.image_degradation.utils_image as util
-
-
-def modcrop_np(img, sf):
-    '''
-    Args:
-        img: numpy image, WxH or WxHxC
-        sf: scale factor
-    Return:
-        cropped image
-    '''
-    w, h = img.shape[:2]
-    im = np.copy(img)
-    return im[:w - w % sf, :h - h % sf, ...]
-
-
-"""
-# --------------------------------------------
-# anisotropic Gaussian kernels
-# --------------------------------------------
-"""
-
-
-def analytic_kernel(k):
-    """Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)"""
-    k_size = k.shape[0]
-    # Calculate the big kernels size
-    big_k = np.zeros((3 * k_size - 2, 3 * k_size - 2))
-    # Loop over the small kernel to fill the big one
-    for r in range(k_size):
-        for c in range(k_size):
-            big_k[2 * r:2 * r + k_size, 2 * c:2 * c + k_size] += k[r, c] * k
-    # Crop the edges of the big kernel to ignore very small values and increase run time of SR
-    crop = k_size // 2
-    cropped_big_k = big_k[crop:-crop, crop:-crop]
-    # Normalize to 1
-    return cropped_big_k / cropped_big_k.sum()
-
-
-def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6):
-    """ generate an anisotropic Gaussian kernel
-    Args:
-        ksize : e.g., 15, kernel size
-        theta : [0,  pi], rotation angle range
-        l1    : [0.1,50], scaling of eigenvalues
-        l2    : [0.1,l1], scaling of eigenvalues
-        If l1 = l2, will get an isotropic Gaussian kernel.
-    Returns:
-        k     : kernel
-    """
-
-    v = np.dot(np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]), np.array([1., 0.]))
-    V = np.array([[v[0], v[1]], [v[1], -v[0]]])
-    D = np.array([[l1, 0], [0, l2]])
-    Sigma = np.dot(np.dot(V, D), np.linalg.inv(V))
-    k = gm_blur_kernel(mean=[0, 0], cov=Sigma, size=ksize)
-
-    return k
-
-
-def gm_blur_kernel(mean, cov, size=15):
-    center = size / 2.0 + 0.5
-    k = np.zeros([size, size])
-    for y in range(size):
-        for x in range(size):
-            cy = y - center + 1
-            cx = x - center + 1
-            k[y, x] = ss.multivariate_normal.pdf([cx, cy], mean=mean, cov=cov)
-
-    k = k / np.sum(k)
-    return k
-
-
-def shift_pixel(x, sf, upper_left=True):
-    """shift pixel for super-resolution with different scale factors
-    Args:
-        x: WxHxC or WxH
-        sf: scale factor
-        upper_left: shift direction
-    """
-    h, w = x.shape[:2]
-    shift = (sf - 1) * 0.5
-    xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0)
-    if upper_left:
-        x1 = xv + shift
-        y1 = yv + shift
-    else:
-        x1 = xv - shift
-        y1 = yv - shift
-
-    x1 = np.clip(x1, 0, w - 1)
-    y1 = np.clip(y1, 0, h - 1)
-
-    if x.ndim == 2:
-        x = interp2d(xv, yv, x)(x1, y1)
-    if x.ndim == 3:
-        for i in range(x.shape[-1]):
-            x[:, :, i] = interp2d(xv, yv, x[:, :, i])(x1, y1)
-
-    return x
-
-
-def blur(x, k):
-    '''
-    x: image, NxcxHxW
-    k: kernel, Nx1xhxw
-    '''
-    n, c = x.shape[:2]
-    p1, p2 = (k.shape[-2] - 1) // 2, (k.shape[-1] - 1) // 2
-    x = torch.nn.functional.pad(x, pad=(p1, p2, p1, p2), mode='replicate')
-    k = k.repeat(1, c, 1, 1)
-    k = k.view(-1, 1, k.shape[2], k.shape[3])
-    x = x.view(1, -1, x.shape[2], x.shape[3])
-    x = torch.nn.functional.conv2d(x, k, bias=None, stride=1, padding=0, groups=n * c)
-    x = x.view(n, c, x.shape[2], x.shape[3])
-
-    return x
-
-
-def gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]), min_var=0.6, max_var=10., noise_level=0):
-    """"
-    # modified version of https://github.com/assafshocher/BlindSR_dataset_generator
-    # Kai Zhang
-    # min_var = 0.175 * sf  # variance of the gaussian kernel will be sampled between min_var and max_var
-    # max_var = 2.5 * sf
-    """
-    # Set random eigen-vals (lambdas) and angle (theta) for COV matrix
-    lambda_1 = min_var + np.random.rand() * (max_var - min_var)
-    lambda_2 = min_var + np.random.rand() * (max_var - min_var)
-    theta = np.random.rand() * np.pi  # random theta
-    noise = -noise_level + np.random.rand(*k_size) * noise_level * 2
-
-    # Set COV matrix using Lambdas and Theta
-    LAMBDA = np.diag([lambda_1, lambda_2])
-    Q = np.array([[np.cos(theta), -np.sin(theta)],
-                  [np.sin(theta), np.cos(theta)]])
-    SIGMA = Q @ LAMBDA @ Q.T
-    INV_SIGMA = np.linalg.inv(SIGMA)[None, None, :, :]
-
-    # Set expectation position (shifting kernel for aligned image)
-    MU = k_size // 2 - 0.5 * (scale_factor - 1)  # - 0.5 * (scale_factor - k_size % 2)
-    MU = MU[None, None, :, None]
-
-    # Create meshgrid for Gaussian
-    [X, Y] = np.meshgrid(range(k_size[0]), range(k_size[1]))
-    Z = np.stack([X, Y], 2)[:, :, :, None]
-
-    # Calcualte Gaussian for every pixel of the kernel
-    ZZ = Z - MU
-    ZZ_t = ZZ.transpose(0, 1, 3, 2)
-    raw_kernel = np.exp(-0.5 * np.squeeze(ZZ_t @ INV_SIGMA @ ZZ)) * (1 + noise)
-
-    # shift the kernel so it will be centered
-    # raw_kernel_centered = kernel_shift(raw_kernel, scale_factor)
-
-    # Normalize the kernel and return
-    # kernel = raw_kernel_centered / np.sum(raw_kernel_centered)
-    kernel = raw_kernel / np.sum(raw_kernel)
-    return kernel
-
-
-def fspecial_gaussian(hsize, sigma):
-    hsize = [hsize, hsize]
-    siz = [(hsize[0] - 1.0) / 2.0, (hsize[1] - 1.0) / 2.0]
-    std = sigma
-    [x, y] = np.meshgrid(np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1))
-    arg = -(x * x + y * y) / (2 * std * std)
-    h = np.exp(arg)
-    h[h < scipy.finfo(float).eps * h.max()] = 0
-    sumh = h.sum()
-    if sumh != 0:
-        h = h / sumh
-    return h
-
-
-def fspecial_laplacian(alpha):
-    alpha = max([0, min([alpha, 1])])
-    h1 = alpha / (alpha + 1)
-    h2 = (1 - alpha) / (alpha + 1)
-    h = [[h1, h2, h1], [h2, -4 / (alpha + 1), h2], [h1, h2, h1]]
-    h = np.array(h)
-    return h
-
-
-def fspecial(filter_type, *args, **kwargs):
-    '''
-    python code from:
-    https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py
-    '''
-    if filter_type == 'gaussian':
-        return fspecial_gaussian(*args, **kwargs)
-    if filter_type == 'laplacian':
-        return fspecial_laplacian(*args, **kwargs)
-
-
-"""
-# --------------------------------------------
-# degradation models
-# --------------------------------------------
-"""
-
-
-def bicubic_degradation(x, sf=3):
-    '''
-    Args:
-        x: HxWxC image, [0, 1]
-        sf: down-scale factor
-    Return:
-        bicubicly downsampled LR image
-    '''
-    x = util.imresize_np(x, scale=1 / sf)
-    return x
-
-
-def srmd_degradation(x, k, sf=3):
-    ''' blur + bicubic downsampling
-    Args:
-        x: HxWxC image, [0, 1]
-        k: hxw, double
-        sf: down-scale factor
-    Return:
-        downsampled LR image
-    Reference:
-        @inproceedings{zhang2018learning,
-          title={Learning a single convolutional super-resolution network for multiple degradations},
-          author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
-          booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
-          pages={3262--3271},
-          year={2018}
-        }
-    '''
-    x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')  # 'nearest' | 'mirror'
-    x = bicubic_degradation(x, sf=sf)
-    return x
-
-
-def dpsr_degradation(x, k, sf=3):
-    ''' bicubic downsampling + blur
-    Args:
-        x: HxWxC image, [0, 1]
-        k: hxw, double
-        sf: down-scale factor
-    Return:
-        downsampled LR image
-    Reference:
-        @inproceedings{zhang2019deep,
-          title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels},
-          author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
-          booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
-          pages={1671--1681},
-          year={2019}
-        }
-    '''
-    x = bicubic_degradation(x, sf=sf)
-    x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
-    return x
-
-
-def classical_degradation(x, k, sf=3):
-    ''' blur + downsampling
-    Args:
-        x: HxWxC image, [0, 1]/[0, 255]
-        k: hxw, double
-        sf: down-scale factor
-    Return:
-        downsampled LR image
-    '''
-    x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
-    # x = filters.correlate(x, np.expand_dims(np.flip(k), axis=2))
-    st = 0
-    return x[st::sf, st::sf, ...]
-
-
-def add_sharpening(img, weight=0.5, radius=50, threshold=10):
-    """USM sharpening. borrowed from real-ESRGAN
-    Input image: I; Blurry image: B.
-    1. K = I + weight * (I - B)
-    2. Mask = 1 if abs(I - B) > threshold, else: 0
-    3. Blur mask:
-    4. Out = Mask * K + (1 - Mask) * I
-    Args:
-        img (Numpy array): Input image, HWC, BGR; float32, [0, 1].
-        weight (float): Sharp weight. Default: 1.
-        radius (float): Kernel size of Gaussian blur. Default: 50.
-        threshold (int):
-    """
-    if radius % 2 == 0:
-        radius += 1
-    blur = cv2.GaussianBlur(img, (radius, radius), 0)
-    residual = img - blur
-    mask = np.abs(residual) * 255 > threshold
-    mask = mask.astype('float32')
-    soft_mask = cv2.GaussianBlur(mask, (radius, radius), 0)
-
-    K = img + weight * residual
-    K = np.clip(K, 0, 1)
-    return soft_mask * K + (1 - soft_mask) * img
-
-
-def add_blur(img, sf=4):
-    wd2 = 4.0 + sf
-    wd = 2.0 + 0.2 * sf
-    if random.random() < 0.5:
-        l1 = wd2 * random.random()
-        l2 = wd2 * random.random()
-        k = anisotropic_Gaussian(ksize=2 * random.randint(2, 11) + 3, theta=random.random() * np.pi, l1=l1, l2=l2)
-    else:
-        k = fspecial('gaussian', 2 * random.randint(2, 11) + 3, wd * random.random())
-    img = ndimage.filters.convolve(img, np.expand_dims(k, axis=2), mode='mirror')
-
-    return img
-
-
-def add_resize(img, sf=4):
-    rnum = np.random.rand()
-    if rnum > 0.8:  # up
-        sf1 = random.uniform(1, 2)
-    elif rnum < 0.7:  # down
-        sf1 = random.uniform(0.5 / sf, 1)
-    else:
-        sf1 = 1.0
-    img = cv2.resize(img, (int(sf1 * img.shape[1]), int(sf1 * img.shape[0])), interpolation=random.choice([1, 2, 3]))
-    img = np.clip(img, 0.0, 1.0)
-
-    return img
-
-
-# def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
-#     noise_level = random.randint(noise_level1, noise_level2)
-#     rnum = np.random.rand()
-#     if rnum > 0.6:  # add color Gaussian noise
-#         img += np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
-#     elif rnum < 0.4:  # add grayscale Gaussian noise
-#         img += np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
-#     else:  # add  noise
-#         L = noise_level2 / 255.
-#         D = np.diag(np.random.rand(3))
-#         U = orth(np.random.rand(3, 3))
-#         conv = np.dot(np.dot(np.transpose(U), D), U)
-#         img += np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
-#     img = np.clip(img, 0.0, 1.0)
-#     return img
-
-def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
-    noise_level = random.randint(noise_level1, noise_level2)
-    rnum = np.random.rand()
-    if rnum > 0.6:  # add color Gaussian noise
-        img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
-    elif rnum < 0.4:  # add grayscale Gaussian noise
-        img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
-    else:  # add  noise
-        L = noise_level2 / 255.
-        D = np.diag(np.random.rand(3))
-        U = orth(np.random.rand(3, 3))
-        conv = np.dot(np.dot(np.transpose(U), D), U)
-        img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
-    img = np.clip(img, 0.0, 1.0)
-    return img
-
-
-def add_speckle_noise(img, noise_level1=2, noise_level2=25):
-    noise_level = random.randint(noise_level1, noise_level2)
-    img = np.clip(img, 0.0, 1.0)
-    rnum = random.random()
-    if rnum > 0.6:
-        img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
-    elif rnum < 0.4:
-        img += img * np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
-    else:
-        L = noise_level2 / 255.
-        D = np.diag(np.random.rand(3))
-        U = orth(np.random.rand(3, 3))
-        conv = np.dot(np.dot(np.transpose(U), D), U)
-        img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
-    img = np.clip(img, 0.0, 1.0)
-    return img
-
-
-def add_Poisson_noise(img):
-    img = np.clip((img * 255.0).round(), 0, 255) / 255.
-    vals = 10 ** (2 * random.random() + 2.0)  # [2, 4]
-    if random.random() < 0.5:
-        img = np.random.poisson(img * vals).astype(np.float32) / vals
-    else:
-        img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114])
-        img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255.
-        noise_gray = np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray
-        img += noise_gray[:, :, np.newaxis]
-    img = np.clip(img, 0.0, 1.0)
-    return img
-
-
-def add_JPEG_noise(img):
-    quality_factor = random.randint(30, 95)
-    img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR)
-    result, encimg = cv2.imencode('.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor])
-    img = cv2.imdecode(encimg, 1)
-    img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB)
-    return img
-
-
-def random_crop(lq, hq, sf=4, lq_patchsize=64):
-    h, w = lq.shape[:2]
-    rnd_h = random.randint(0, h - lq_patchsize)
-    rnd_w = random.randint(0, w - lq_patchsize)
-    lq = lq[rnd_h:rnd_h + lq_patchsize, rnd_w:rnd_w + lq_patchsize, :]
-
-    rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf)
-    hq = hq[rnd_h_H:rnd_h_H + lq_patchsize * sf, rnd_w_H:rnd_w_H + lq_patchsize * sf, :]
-    return lq, hq
-
-
-def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
-    """
-    This is the degradation model of BSRGAN from the paper
-    "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
-    ----------
-    img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf)
-    sf: scale factor
-    isp_model: camera ISP model
-    Returns
-    -------
-    img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
-    hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
-    """
-    isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
-    sf_ori = sf
-
-    h1, w1 = img.shape[:2]
-    img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...]  # mod crop
-    h, w = img.shape[:2]
-
-    if h < lq_patchsize * sf or w < lq_patchsize * sf:
-        raise ValueError(f'img size ({h1}X{w1}) is too small!')
-
-    hq = img.copy()
-
-    if sf == 4 and random.random() < scale2_prob:  # downsample1
-        if np.random.rand() < 0.5:
-            img = cv2.resize(img, (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])),
-                             interpolation=random.choice([1, 2, 3]))
-        else:
-            img = util.imresize_np(img, 1 / 2, True)
-        img = np.clip(img, 0.0, 1.0)
-        sf = 2
-
-    shuffle_order = random.sample(range(7), 7)
-    idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
-    if idx1 > idx2:  # keep downsample3 last
-        shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
-
-    for i in shuffle_order:
-
-        if i == 0:
-            img = add_blur(img, sf=sf)
-
-        elif i == 1:
-            img = add_blur(img, sf=sf)
-
-        elif i == 2:
-            a, b = img.shape[1], img.shape[0]
-            # downsample2
-            if random.random() < 0.75:
-                sf1 = random.uniform(1, 2 * sf)
-                img = cv2.resize(img, (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])),
-                                 interpolation=random.choice([1, 2, 3]))
-            else:
-                k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
-                k_shifted = shift_pixel(k, sf)
-                k_shifted = k_shifted / k_shifted.sum()  # blur with shifted kernel
-                img = ndimage.filters.convolve(img, np.expand_dims(k_shifted, axis=2), mode='mirror')
-                img = img[0::sf, 0::sf, ...]  # nearest downsampling
-            img = np.clip(img, 0.0, 1.0)
-
-        elif i == 3:
-            # downsample3
-            img = cv2.resize(img, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
-            img = np.clip(img, 0.0, 1.0)
-
-        elif i == 4:
-            # add Gaussian noise
-            img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25)
-
-        elif i == 5:
-            # add JPEG noise
-            if random.random() < jpeg_prob:
-                img = add_JPEG_noise(img)
-
-        elif i == 6:
-            # add processed camera sensor noise
-            if random.random() < isp_prob and isp_model is not None:
-                with torch.no_grad():
-                    img, hq = isp_model.forward(img.copy(), hq)
-
-    # add final JPEG compression noise
-    img = add_JPEG_noise(img)
-
-    # random crop
-    img, hq = random_crop(img, hq, sf_ori, lq_patchsize)
-
-    return img, hq
-
-
-# todo no isp_model?
-def degradation_bsrgan_variant(image, sf=4, isp_model=None):
-    """
-    This is the degradation model of BSRGAN from the paper
-    "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
-    ----------
-    sf: scale factor
-    isp_model: camera ISP model
-    Returns
-    -------
-    img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
-    hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
-    """
-    image = util.uint2single(image)
-    isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
-    sf_ori = sf
-
-    h1, w1 = image.shape[:2]
-    image = image.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...]  # mod crop
-    h, w = image.shape[:2]
-
-    hq = image.copy()
-
-    if sf == 4 and random.random() < scale2_prob:  # downsample1
-        if np.random.rand() < 0.5:
-            image = cv2.resize(image, (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])),
-                               interpolation=random.choice([1, 2, 3]))
-        else:
-            image = util.imresize_np(image, 1 / 2, True)
-        image = np.clip(image, 0.0, 1.0)
-        sf = 2
-
-    shuffle_order = random.sample(range(7), 7)
-    idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
-    if idx1 > idx2:  # keep downsample3 last
-        shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
-
-    for i in shuffle_order:
-
-        if i == 0:
-            image = add_blur(image, sf=sf)
-
-        elif i == 1:
-            image = add_blur(image, sf=sf)
-
-        elif i == 2:
-            a, b = image.shape[1], image.shape[0]
-            # downsample2
-            if random.random() < 0.75:
-                sf1 = random.uniform(1, 2 * sf)
-                image = cv2.resize(image, (int(1 / sf1 * image.shape[1]), int(1 / sf1 * image.shape[0])),
-                                   interpolation=random.choice([1, 2, 3]))
-            else:
-                k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
-                k_shifted = shift_pixel(k, sf)
-                k_shifted = k_shifted / k_shifted.sum()  # blur with shifted kernel
-                image = ndimage.filters.convolve(image, np.expand_dims(k_shifted, axis=2), mode='mirror')
-                image = image[0::sf, 0::sf, ...]  # nearest downsampling
-            image = np.clip(image, 0.0, 1.0)
-
-        elif i == 3:
-            # downsample3
-            image = cv2.resize(image, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
-            image = np.clip(image, 0.0, 1.0)
-
-        elif i == 4:
-            # add Gaussian noise
-            image = add_Gaussian_noise(image, noise_level1=2, noise_level2=25)
-
-        elif i == 5:
-            # add JPEG noise
-            if random.random() < jpeg_prob:
-                image = add_JPEG_noise(image)
-
-        # elif i == 6:
-        #     # add processed camera sensor noise
-        #     if random.random() < isp_prob and isp_model is not None:
-        #         with torch.no_grad():
-        #             img, hq = isp_model.forward(img.copy(), hq)
-
-    # add final JPEG compression noise
-    image = add_JPEG_noise(image)
-    image = util.single2uint(image)
-    example = {"image":image}
-    return example
-
-
-# TODO incase there is a pickle error one needs to replace a += x with a = a + x in add_speckle_noise etc...
-def degradation_bsrgan_plus(img, sf=4, shuffle_prob=0.5, use_sharp=True, lq_patchsize=64, isp_model=None):
-    """
-    This is an extended degradation model by combining
-    the degradation models of BSRGAN and Real-ESRGAN
-    ----------
-    img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf)
-    sf: scale factor
-    use_shuffle: the degradation shuffle
-    use_sharp: sharpening the img
-    Returns
-    -------
-    img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
-    hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
-    """
-
-    h1, w1 = img.shape[:2]
-    img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...]  # mod crop
-    h, w = img.shape[:2]
-
-    if h < lq_patchsize * sf or w < lq_patchsize * sf:
-        raise ValueError(f'img size ({h1}X{w1}) is too small!')
-
-    if use_sharp:
-        img = add_sharpening(img)
-    hq = img.copy()
-
-    if random.random() < shuffle_prob:
-        shuffle_order = random.sample(range(13), 13)
-    else:
-        shuffle_order = list(range(13))
-        # local shuffle for noise, JPEG is always the last one
-        shuffle_order[2:6] = random.sample(shuffle_order[2:6], len(range(2, 6)))
-        shuffle_order[9:13] = random.sample(shuffle_order[9:13], len(range(9, 13)))
-
-    poisson_prob, speckle_prob, isp_prob = 0.1, 0.1, 0.1
-
-    for i in shuffle_order:
-        if i == 0:
-            img = add_blur(img, sf=sf)
-        elif i == 1:
-            img = add_resize(img, sf=sf)
-        elif i == 2:
-            img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25)
-        elif i == 3:
-            if random.random() < poisson_prob:
-                img = add_Poisson_noise(img)
-        elif i == 4:
-            if random.random() < speckle_prob:
-                img = add_speckle_noise(img)
-        elif i == 5:
-            if random.random() < isp_prob and isp_model is not None:
-                with torch.no_grad():
-                    img, hq = isp_model.forward(img.copy(), hq)
-        elif i == 6:
-            img = add_JPEG_noise(img)
-        elif i == 7:
-            img = add_blur(img, sf=sf)
-        elif i == 8:
-            img = add_resize(img, sf=sf)
-        elif i == 9:
-            img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25)
-        elif i == 10:
-            if random.random() < poisson_prob:
-                img = add_Poisson_noise(img)
-        elif i == 11:
-            if random.random() < speckle_prob:
-                img = add_speckle_noise(img)
-        elif i == 12:
-            if random.random() < isp_prob and isp_model is not None:
-                with torch.no_grad():
-                    img, hq = isp_model.forward(img.copy(), hq)
-        else:
-            print('check the shuffle!')
-
-    # resize to desired size
-    img = cv2.resize(img, (int(1 / sf * hq.shape[1]), int(1 / sf * hq.shape[0])),
-                     interpolation=random.choice([1, 2, 3]))
-
-    # add final JPEG compression noise
-    img = add_JPEG_noise(img)
-
-    # random crop
-    img, hq = random_crop(img, hq, sf, lq_patchsize)
-
-    return img, hq
-
-
-if __name__ == '__main__':
-	print("hey")
-	img = util.imread_uint('utils/test.png', 3)
-	print(img)
-	img = util.uint2single(img)
-	print(img)
-	img = img[:448, :448]
-	h = img.shape[0] // 4
-	print("resizing to", h)
-	sf = 4
-	deg_fn = partial(degradation_bsrgan_variant, sf=sf)
-	for i in range(20):
-		print(i)
-		img_lq = deg_fn(img)
-		print(img_lq)
-		img_lq_bicubic = albumentations.SmallestMaxSize(max_size=h, interpolation=cv2.INTER_CUBIC)(image=img)["image"]
-		print(img_lq.shape)
-		print("bicubic", img_lq_bicubic.shape)
-		print(img_hq.shape)
-		lq_nearest = cv2.resize(util.single2uint(img_lq), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
-		                        interpolation=0)
-		lq_bicubic_nearest = cv2.resize(util.single2uint(img_lq_bicubic), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
-		                        interpolation=0)
-		img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1)
-		util.imsave(img_concat, str(i) + '.png')
-
-
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/image_degradation/bsrgan_light.py b/repositories/stable-diffusion-stability-ai/ldm/modules/image_degradation/bsrgan_light.py
deleted file mode 100644
index 808c7f882cb75e2ba2340d5b55881d11927351f0..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/ldm/modules/image_degradation/bsrgan_light.py
+++ /dev/null
@@ -1,651 +0,0 @@
-# -*- coding: utf-8 -*-
-import numpy as np
-import cv2
-import torch
-
-from functools import partial
-import random
-from scipy import ndimage
-import scipy
-import scipy.stats as ss
-from scipy.interpolate import interp2d
-from scipy.linalg import orth
-import albumentations
-
-import ldm.modules.image_degradation.utils_image as util
-
-"""
-# --------------------------------------------
-# Super-Resolution
-# --------------------------------------------
-#
-# Kai Zhang (cskaizhang@gmail.com)
-# https://github.com/cszn
-# From 2019/03--2021/08
-# --------------------------------------------
-"""
-
-def modcrop_np(img, sf):
-    '''
-    Args:
-        img: numpy image, WxH or WxHxC
-        sf: scale factor
-    Return:
-        cropped image
-    '''
-    w, h = img.shape[:2]
-    im = np.copy(img)
-    return im[:w - w % sf, :h - h % sf, ...]
-
-
-"""
-# --------------------------------------------
-# anisotropic Gaussian kernels
-# --------------------------------------------
-"""
-
-
-def analytic_kernel(k):
-    """Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)"""
-    k_size = k.shape[0]
-    # Calculate the big kernels size
-    big_k = np.zeros((3 * k_size - 2, 3 * k_size - 2))
-    # Loop over the small kernel to fill the big one
-    for r in range(k_size):
-        for c in range(k_size):
-            big_k[2 * r:2 * r + k_size, 2 * c:2 * c + k_size] += k[r, c] * k
-    # Crop the edges of the big kernel to ignore very small values and increase run time of SR
-    crop = k_size // 2
-    cropped_big_k = big_k[crop:-crop, crop:-crop]
-    # Normalize to 1
-    return cropped_big_k / cropped_big_k.sum()
-
-
-def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6):
-    """ generate an anisotropic Gaussian kernel
-    Args:
-        ksize : e.g., 15, kernel size
-        theta : [0,  pi], rotation angle range
-        l1    : [0.1,50], scaling of eigenvalues
-        l2    : [0.1,l1], scaling of eigenvalues
-        If l1 = l2, will get an isotropic Gaussian kernel.
-    Returns:
-        k     : kernel
-    """
-
-    v = np.dot(np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]), np.array([1., 0.]))
-    V = np.array([[v[0], v[1]], [v[1], -v[0]]])
-    D = np.array([[l1, 0], [0, l2]])
-    Sigma = np.dot(np.dot(V, D), np.linalg.inv(V))
-    k = gm_blur_kernel(mean=[0, 0], cov=Sigma, size=ksize)
-
-    return k
-
-
-def gm_blur_kernel(mean, cov, size=15):
-    center = size / 2.0 + 0.5
-    k = np.zeros([size, size])
-    for y in range(size):
-        for x in range(size):
-            cy = y - center + 1
-            cx = x - center + 1
-            k[y, x] = ss.multivariate_normal.pdf([cx, cy], mean=mean, cov=cov)
-
-    k = k / np.sum(k)
-    return k
-
-
-def shift_pixel(x, sf, upper_left=True):
-    """shift pixel for super-resolution with different scale factors
-    Args:
-        x: WxHxC or WxH
-        sf: scale factor
-        upper_left: shift direction
-    """
-    h, w = x.shape[:2]
-    shift = (sf - 1) * 0.5
-    xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0)
-    if upper_left:
-        x1 = xv + shift
-        y1 = yv + shift
-    else:
-        x1 = xv - shift
-        y1 = yv - shift
-
-    x1 = np.clip(x1, 0, w - 1)
-    y1 = np.clip(y1, 0, h - 1)
-
-    if x.ndim == 2:
-        x = interp2d(xv, yv, x)(x1, y1)
-    if x.ndim == 3:
-        for i in range(x.shape[-1]):
-            x[:, :, i] = interp2d(xv, yv, x[:, :, i])(x1, y1)
-
-    return x
-
-
-def blur(x, k):
-    '''
-    x: image, NxcxHxW
-    k: kernel, Nx1xhxw
-    '''
-    n, c = x.shape[:2]
-    p1, p2 = (k.shape[-2] - 1) // 2, (k.shape[-1] - 1) // 2
-    x = torch.nn.functional.pad(x, pad=(p1, p2, p1, p2), mode='replicate')
-    k = k.repeat(1, c, 1, 1)
-    k = k.view(-1, 1, k.shape[2], k.shape[3])
-    x = x.view(1, -1, x.shape[2], x.shape[3])
-    x = torch.nn.functional.conv2d(x, k, bias=None, stride=1, padding=0, groups=n * c)
-    x = x.view(n, c, x.shape[2], x.shape[3])
-
-    return x
-
-
-def gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]), min_var=0.6, max_var=10., noise_level=0):
-    """"
-    # modified version of https://github.com/assafshocher/BlindSR_dataset_generator
-    # Kai Zhang
-    # min_var = 0.175 * sf  # variance of the gaussian kernel will be sampled between min_var and max_var
-    # max_var = 2.5 * sf
-    """
-    # Set random eigen-vals (lambdas) and angle (theta) for COV matrix
-    lambda_1 = min_var + np.random.rand() * (max_var - min_var)
-    lambda_2 = min_var + np.random.rand() * (max_var - min_var)
-    theta = np.random.rand() * np.pi  # random theta
-    noise = -noise_level + np.random.rand(*k_size) * noise_level * 2
-
-    # Set COV matrix using Lambdas and Theta
-    LAMBDA = np.diag([lambda_1, lambda_2])
-    Q = np.array([[np.cos(theta), -np.sin(theta)],
-                  [np.sin(theta), np.cos(theta)]])
-    SIGMA = Q @ LAMBDA @ Q.T
-    INV_SIGMA = np.linalg.inv(SIGMA)[None, None, :, :]
-
-    # Set expectation position (shifting kernel for aligned image)
-    MU = k_size // 2 - 0.5 * (scale_factor - 1)  # - 0.5 * (scale_factor - k_size % 2)
-    MU = MU[None, None, :, None]
-
-    # Create meshgrid for Gaussian
-    [X, Y] = np.meshgrid(range(k_size[0]), range(k_size[1]))
-    Z = np.stack([X, Y], 2)[:, :, :, None]
-
-    # Calcualte Gaussian for every pixel of the kernel
-    ZZ = Z - MU
-    ZZ_t = ZZ.transpose(0, 1, 3, 2)
-    raw_kernel = np.exp(-0.5 * np.squeeze(ZZ_t @ INV_SIGMA @ ZZ)) * (1 + noise)
-
-    # shift the kernel so it will be centered
-    # raw_kernel_centered = kernel_shift(raw_kernel, scale_factor)
-
-    # Normalize the kernel and return
-    # kernel = raw_kernel_centered / np.sum(raw_kernel_centered)
-    kernel = raw_kernel / np.sum(raw_kernel)
-    return kernel
-
-
-def fspecial_gaussian(hsize, sigma):
-    hsize = [hsize, hsize]
-    siz = [(hsize[0] - 1.0) / 2.0, (hsize[1] - 1.0) / 2.0]
-    std = sigma
-    [x, y] = np.meshgrid(np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1))
-    arg = -(x * x + y * y) / (2 * std * std)
-    h = np.exp(arg)
-    h[h < scipy.finfo(float).eps * h.max()] = 0
-    sumh = h.sum()
-    if sumh != 0:
-        h = h / sumh
-    return h
-
-
-def fspecial_laplacian(alpha):
-    alpha = max([0, min([alpha, 1])])
-    h1 = alpha / (alpha + 1)
-    h2 = (1 - alpha) / (alpha + 1)
-    h = [[h1, h2, h1], [h2, -4 / (alpha + 1), h2], [h1, h2, h1]]
-    h = np.array(h)
-    return h
-
-
-def fspecial(filter_type, *args, **kwargs):
-    '''
-    python code from:
-    https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py
-    '''
-    if filter_type == 'gaussian':
-        return fspecial_gaussian(*args, **kwargs)
-    if filter_type == 'laplacian':
-        return fspecial_laplacian(*args, **kwargs)
-
-
-"""
-# --------------------------------------------
-# degradation models
-# --------------------------------------------
-"""
-
-
-def bicubic_degradation(x, sf=3):
-    '''
-    Args:
-        x: HxWxC image, [0, 1]
-        sf: down-scale factor
-    Return:
-        bicubicly downsampled LR image
-    '''
-    x = util.imresize_np(x, scale=1 / sf)
-    return x
-
-
-def srmd_degradation(x, k, sf=3):
-    ''' blur + bicubic downsampling
-    Args:
-        x: HxWxC image, [0, 1]
-        k: hxw, double
-        sf: down-scale factor
-    Return:
-        downsampled LR image
-    Reference:
-        @inproceedings{zhang2018learning,
-          title={Learning a single convolutional super-resolution network for multiple degradations},
-          author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
-          booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
-          pages={3262--3271},
-          year={2018}
-        }
-    '''
-    x = ndimage.convolve(x, np.expand_dims(k, axis=2), mode='wrap')  # 'nearest' | 'mirror'
-    x = bicubic_degradation(x, sf=sf)
-    return x
-
-
-def dpsr_degradation(x, k, sf=3):
-    ''' bicubic downsampling + blur
-    Args:
-        x: HxWxC image, [0, 1]
-        k: hxw, double
-        sf: down-scale factor
-    Return:
-        downsampled LR image
-    Reference:
-        @inproceedings{zhang2019deep,
-          title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels},
-          author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
-          booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
-          pages={1671--1681},
-          year={2019}
-        }
-    '''
-    x = bicubic_degradation(x, sf=sf)
-    x = ndimage.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
-    return x
-
-
-def classical_degradation(x, k, sf=3):
-    ''' blur + downsampling
-    Args:
-        x: HxWxC image, [0, 1]/[0, 255]
-        k: hxw, double
-        sf: down-scale factor
-    Return:
-        downsampled LR image
-    '''
-    x = ndimage.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
-    # x = filters.correlate(x, np.expand_dims(np.flip(k), axis=2))
-    st = 0
-    return x[st::sf, st::sf, ...]
-
-
-def add_sharpening(img, weight=0.5, radius=50, threshold=10):
-    """USM sharpening. borrowed from real-ESRGAN
-    Input image: I; Blurry image: B.
-    1. K = I + weight * (I - B)
-    2. Mask = 1 if abs(I - B) > threshold, else: 0
-    3. Blur mask:
-    4. Out = Mask * K + (1 - Mask) * I
-    Args:
-        img (Numpy array): Input image, HWC, BGR; float32, [0, 1].
-        weight (float): Sharp weight. Default: 1.
-        radius (float): Kernel size of Gaussian blur. Default: 50.
-        threshold (int):
-    """
-    if radius % 2 == 0:
-        radius += 1
-    blur = cv2.GaussianBlur(img, (radius, radius), 0)
-    residual = img - blur
-    mask = np.abs(residual) * 255 > threshold
-    mask = mask.astype('float32')
-    soft_mask = cv2.GaussianBlur(mask, (radius, radius), 0)
-
-    K = img + weight * residual
-    K = np.clip(K, 0, 1)
-    return soft_mask * K + (1 - soft_mask) * img
-
-
-def add_blur(img, sf=4):
-    wd2 = 4.0 + sf
-    wd = 2.0 + 0.2 * sf
-
-    wd2 = wd2/4
-    wd = wd/4
-
-    if random.random() < 0.5:
-        l1 = wd2 * random.random()
-        l2 = wd2 * random.random()
-        k = anisotropic_Gaussian(ksize=random.randint(2, 11) + 3, theta=random.random() * np.pi, l1=l1, l2=l2)
-    else:
-        k = fspecial('gaussian', random.randint(2, 4) + 3, wd * random.random())
-    img = ndimage.convolve(img, np.expand_dims(k, axis=2), mode='mirror')
-
-    return img
-
-
-def add_resize(img, sf=4):
-    rnum = np.random.rand()
-    if rnum > 0.8:  # up
-        sf1 = random.uniform(1, 2)
-    elif rnum < 0.7:  # down
-        sf1 = random.uniform(0.5 / sf, 1)
-    else:
-        sf1 = 1.0
-    img = cv2.resize(img, (int(sf1 * img.shape[1]), int(sf1 * img.shape[0])), interpolation=random.choice([1, 2, 3]))
-    img = np.clip(img, 0.0, 1.0)
-
-    return img
-
-
-# def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
-#     noise_level = random.randint(noise_level1, noise_level2)
-#     rnum = np.random.rand()
-#     if rnum > 0.6:  # add color Gaussian noise
-#         img += np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
-#     elif rnum < 0.4:  # add grayscale Gaussian noise
-#         img += np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
-#     else:  # add  noise
-#         L = noise_level2 / 255.
-#         D = np.diag(np.random.rand(3))
-#         U = orth(np.random.rand(3, 3))
-#         conv = np.dot(np.dot(np.transpose(U), D), U)
-#         img += np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
-#     img = np.clip(img, 0.0, 1.0)
-#     return img
-
-def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
-    noise_level = random.randint(noise_level1, noise_level2)
-    rnum = np.random.rand()
-    if rnum > 0.6:  # add color Gaussian noise
-        img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
-    elif rnum < 0.4:  # add grayscale Gaussian noise
-        img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
-    else:  # add  noise
-        L = noise_level2 / 255.
-        D = np.diag(np.random.rand(3))
-        U = orth(np.random.rand(3, 3))
-        conv = np.dot(np.dot(np.transpose(U), D), U)
-        img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
-    img = np.clip(img, 0.0, 1.0)
-    return img
-
-
-def add_speckle_noise(img, noise_level1=2, noise_level2=25):
-    noise_level = random.randint(noise_level1, noise_level2)
-    img = np.clip(img, 0.0, 1.0)
-    rnum = random.random()
-    if rnum > 0.6:
-        img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
-    elif rnum < 0.4:
-        img += img * np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
-    else:
-        L = noise_level2 / 255.
-        D = np.diag(np.random.rand(3))
-        U = orth(np.random.rand(3, 3))
-        conv = np.dot(np.dot(np.transpose(U), D), U)
-        img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
-    img = np.clip(img, 0.0, 1.0)
-    return img
-
-
-def add_Poisson_noise(img):
-    img = np.clip((img * 255.0).round(), 0, 255) / 255.
-    vals = 10 ** (2 * random.random() + 2.0)  # [2, 4]
-    if random.random() < 0.5:
-        img = np.random.poisson(img * vals).astype(np.float32) / vals
-    else:
-        img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114])
-        img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255.
-        noise_gray = np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray
-        img += noise_gray[:, :, np.newaxis]
-    img = np.clip(img, 0.0, 1.0)
-    return img
-
-
-def add_JPEG_noise(img):
-    quality_factor = random.randint(80, 95)
-    img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR)
-    result, encimg = cv2.imencode('.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor])
-    img = cv2.imdecode(encimg, 1)
-    img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB)
-    return img
-
-
-def random_crop(lq, hq, sf=4, lq_patchsize=64):
-    h, w = lq.shape[:2]
-    rnd_h = random.randint(0, h - lq_patchsize)
-    rnd_w = random.randint(0, w - lq_patchsize)
-    lq = lq[rnd_h:rnd_h + lq_patchsize, rnd_w:rnd_w + lq_patchsize, :]
-
-    rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf)
-    hq = hq[rnd_h_H:rnd_h_H + lq_patchsize * sf, rnd_w_H:rnd_w_H + lq_patchsize * sf, :]
-    return lq, hq
-
-
-def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
-    """
-    This is the degradation model of BSRGAN from the paper
-    "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
-    ----------
-    img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf)
-    sf: scale factor
-    isp_model: camera ISP model
-    Returns
-    -------
-    img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
-    hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
-    """
-    isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
-    sf_ori = sf
-
-    h1, w1 = img.shape[:2]
-    img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...]  # mod crop
-    h, w = img.shape[:2]
-
-    if h < lq_patchsize * sf or w < lq_patchsize * sf:
-        raise ValueError(f'img size ({h1}X{w1}) is too small!')
-
-    hq = img.copy()
-
-    if sf == 4 and random.random() < scale2_prob:  # downsample1
-        if np.random.rand() < 0.5:
-            img = cv2.resize(img, (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])),
-                             interpolation=random.choice([1, 2, 3]))
-        else:
-            img = util.imresize_np(img, 1 / 2, True)
-        img = np.clip(img, 0.0, 1.0)
-        sf = 2
-
-    shuffle_order = random.sample(range(7), 7)
-    idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
-    if idx1 > idx2:  # keep downsample3 last
-        shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
-
-    for i in shuffle_order:
-
-        if i == 0:
-            img = add_blur(img, sf=sf)
-
-        elif i == 1:
-            img = add_blur(img, sf=sf)
-
-        elif i == 2:
-            a, b = img.shape[1], img.shape[0]
-            # downsample2
-            if random.random() < 0.75:
-                sf1 = random.uniform(1, 2 * sf)
-                img = cv2.resize(img, (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])),
-                                 interpolation=random.choice([1, 2, 3]))
-            else:
-                k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
-                k_shifted = shift_pixel(k, sf)
-                k_shifted = k_shifted / k_shifted.sum()  # blur with shifted kernel
-                img = ndimage.convolve(img, np.expand_dims(k_shifted, axis=2), mode='mirror')
-                img = img[0::sf, 0::sf, ...]  # nearest downsampling
-            img = np.clip(img, 0.0, 1.0)
-
-        elif i == 3:
-            # downsample3
-            img = cv2.resize(img, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
-            img = np.clip(img, 0.0, 1.0)
-
-        elif i == 4:
-            # add Gaussian noise
-            img = add_Gaussian_noise(img, noise_level1=2, noise_level2=8)
-
-        elif i == 5:
-            # add JPEG noise
-            if random.random() < jpeg_prob:
-                img = add_JPEG_noise(img)
-
-        elif i == 6:
-            # add processed camera sensor noise
-            if random.random() < isp_prob and isp_model is not None:
-                with torch.no_grad():
-                    img, hq = isp_model.forward(img.copy(), hq)
-
-    # add final JPEG compression noise
-    img = add_JPEG_noise(img)
-
-    # random crop
-    img, hq = random_crop(img, hq, sf_ori, lq_patchsize)
-
-    return img, hq
-
-
-# todo no isp_model?
-def degradation_bsrgan_variant(image, sf=4, isp_model=None, up=False):
-    """
-    This is the degradation model of BSRGAN from the paper
-    "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
-    ----------
-    sf: scale factor
-    isp_model: camera ISP model
-    Returns
-    -------
-    img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
-    hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
-    """
-    image = util.uint2single(image)
-    isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
-    sf_ori = sf
-
-    h1, w1 = image.shape[:2]
-    image = image.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...]  # mod crop
-    h, w = image.shape[:2]
-
-    hq = image.copy()
-
-    if sf == 4 and random.random() < scale2_prob:  # downsample1
-        if np.random.rand() < 0.5:
-            image = cv2.resize(image, (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])),
-                               interpolation=random.choice([1, 2, 3]))
-        else:
-            image = util.imresize_np(image, 1 / 2, True)
-        image = np.clip(image, 0.0, 1.0)
-        sf = 2
-
-    shuffle_order = random.sample(range(7), 7)
-    idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
-    if idx1 > idx2:  # keep downsample3 last
-        shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
-
-    for i in shuffle_order:
-
-        if i == 0:
-            image = add_blur(image, sf=sf)
-
-        # elif i == 1:
-        #     image = add_blur(image, sf=sf)
-
-        if i == 0:
-            pass
-
-        elif i == 2:
-            a, b = image.shape[1], image.shape[0]
-            # downsample2
-            if random.random() < 0.8:
-                sf1 = random.uniform(1, 2 * sf)
-                image = cv2.resize(image, (int(1 / sf1 * image.shape[1]), int(1 / sf1 * image.shape[0])),
-                                   interpolation=random.choice([1, 2, 3]))
-            else:
-                k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
-                k_shifted = shift_pixel(k, sf)
-                k_shifted = k_shifted / k_shifted.sum()  # blur with shifted kernel
-                image = ndimage.convolve(image, np.expand_dims(k_shifted, axis=2), mode='mirror')
-                image = image[0::sf, 0::sf, ...]  # nearest downsampling
-
-            image = np.clip(image, 0.0, 1.0)
-
-        elif i == 3:
-            # downsample3
-            image = cv2.resize(image, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
-            image = np.clip(image, 0.0, 1.0)
-
-        elif i == 4:
-            # add Gaussian noise
-            image = add_Gaussian_noise(image, noise_level1=1, noise_level2=2)
-
-        elif i == 5:
-            # add JPEG noise
-            if random.random() < jpeg_prob:
-                image = add_JPEG_noise(image)
-        #
-        # elif i == 6:
-        #     # add processed camera sensor noise
-        #     if random.random() < isp_prob and isp_model is not None:
-        #         with torch.no_grad():
-        #             img, hq = isp_model.forward(img.copy(), hq)
-
-    # add final JPEG compression noise
-    image = add_JPEG_noise(image)
-    image = util.single2uint(image)
-    if up:
-        image = cv2.resize(image, (w1, h1), interpolation=cv2.INTER_CUBIC)  # todo: random, as above? want to condition on it then
-    example = {"image": image}
-    return example
-
-
-
-
-if __name__ == '__main__':
-    print("hey")
-    img = util.imread_uint('utils/test.png', 3)
-    img = img[:448, :448]
-    h = img.shape[0] // 4
-    print("resizing to", h)
-    sf = 4
-    deg_fn = partial(degradation_bsrgan_variant, sf=sf)
-    for i in range(20):
-        print(i)
-        img_hq = img
-        img_lq = deg_fn(img)["image"]
-        img_hq, img_lq = util.uint2single(img_hq), util.uint2single(img_lq)
-        print(img_lq)
-        img_lq_bicubic = albumentations.SmallestMaxSize(max_size=h, interpolation=cv2.INTER_CUBIC)(image=img_hq)["image"]
-        print(img_lq.shape)
-        print("bicubic", img_lq_bicubic.shape)
-        print(img_hq.shape)
-        lq_nearest = cv2.resize(util.single2uint(img_lq), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
-                                interpolation=0)
-        lq_bicubic_nearest = cv2.resize(util.single2uint(img_lq_bicubic),
-                                        (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
-                                        interpolation=0)
-        img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1)
-        util.imsave(img_concat, str(i) + '.png')
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/image_degradation/utils/test.png b/repositories/stable-diffusion-stability-ai/ldm/modules/image_degradation/utils/test.png
deleted file mode 100644
index 4249b43de0f22707758d13c240268a401642f6e6..0000000000000000000000000000000000000000
Binary files a/repositories/stable-diffusion-stability-ai/ldm/modules/image_degradation/utils/test.png and /dev/null differ
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/image_degradation/utils_image.py b/repositories/stable-diffusion-stability-ai/ldm/modules/image_degradation/utils_image.py
deleted file mode 100644
index 0175f155ad900ae33c3c46ed87f49b352e3faf98..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/ldm/modules/image_degradation/utils_image.py
+++ /dev/null
@@ -1,916 +0,0 @@
-import os
-import math
-import random
-import numpy as np
-import torch
-import cv2
-from torchvision.utils import make_grid
-from datetime import datetime
-#import matplotlib.pyplot as plt   # TODO: check with Dominik, also bsrgan.py vs bsrgan_light.py
-
-
-os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
-
-
-'''
-# --------------------------------------------
-# Kai Zhang (github: https://github.com/cszn)
-# 03/Mar/2019
-# --------------------------------------------
-# https://github.com/twhui/SRGAN-pyTorch
-# https://github.com/xinntao/BasicSR
-# --------------------------------------------
-'''
-
-
-IMG_EXTENSIONS = ['.jpg', '.JPG', '.jpeg', '.JPEG', '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP', '.tif']
-
-
-def is_image_file(filename):
-    return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)
-
-
-def get_timestamp():
-    return datetime.now().strftime('%y%m%d-%H%M%S')
-
-
-def imshow(x, title=None, cbar=False, figsize=None):
-    plt.figure(figsize=figsize)
-    plt.imshow(np.squeeze(x), interpolation='nearest', cmap='gray')
-    if title:
-        plt.title(title)
-    if cbar:
-        plt.colorbar()
-    plt.show()
-
-
-def surf(Z, cmap='rainbow', figsize=None):
-    plt.figure(figsize=figsize)
-    ax3 = plt.axes(projection='3d')
-
-    w, h = Z.shape[:2]
-    xx = np.arange(0,w,1)
-    yy = np.arange(0,h,1)
-    X, Y = np.meshgrid(xx, yy)
-    ax3.plot_surface(X,Y,Z,cmap=cmap)
-    #ax3.contour(X,Y,Z, zdim='z',offset=-2，cmap=cmap)
-    plt.show()
-
-
-'''
-# --------------------------------------------
-# get image pathes
-# --------------------------------------------
-'''
-
-
-def get_image_paths(dataroot):
-    paths = None  # return None if dataroot is None
-    if dataroot is not None:
-        paths = sorted(_get_paths_from_images(dataroot))
-    return paths
-
-
-def _get_paths_from_images(path):
-    assert os.path.isdir(path), '{:s} is not a valid directory'.format(path)
-    images = []
-    for dirpath, _, fnames in sorted(os.walk(path)):
-        for fname in sorted(fnames):
-            if is_image_file(fname):
-                img_path = os.path.join(dirpath, fname)
-                images.append(img_path)
-    assert images, '{:s} has no valid image file'.format(path)
-    return images
-
-
-'''
-# --------------------------------------------
-# split large images into small images 
-# --------------------------------------------
-'''
-
-
-def patches_from_image(img, p_size=512, p_overlap=64, p_max=800):
-    w, h = img.shape[:2]
-    patches = []
-    if w > p_max and h > p_max:
-        w1 = list(np.arange(0, w-p_size, p_size-p_overlap, dtype=np.int))
-        h1 = list(np.arange(0, h-p_size, p_size-p_overlap, dtype=np.int))
-        w1.append(w-p_size)
-        h1.append(h-p_size)
-#        print(w1)
-#        print(h1)
-        for i in w1:
-            for j in h1:
-                patches.append(img[i:i+p_size, j:j+p_size,:])
-    else:
-        patches.append(img)
-
-    return patches
-
-
-def imssave(imgs, img_path):
-    """
-    imgs: list, N images of size WxHxC
-    """
-    img_name, ext = os.path.splitext(os.path.basename(img_path))
-
-    for i, img in enumerate(imgs):
-        if img.ndim == 3:
-            img = img[:, :, [2, 1, 0]]
-        new_path = os.path.join(os.path.dirname(img_path), img_name+str('_s{:04d}'.format(i))+'.png')
-        cv2.imwrite(new_path, img)
-
-
-def split_imageset(original_dataroot, taget_dataroot, n_channels=3, p_size=800, p_overlap=96, p_max=1000):
-    """
-    split the large images from original_dataroot into small overlapped images with size (p_size)x(p_size),
-    and save them into taget_dataroot; only the images with larger size than (p_max)x(p_max)
-    will be splitted.
-    Args:
-        original_dataroot:
-        taget_dataroot:
-        p_size: size of small images
-        p_overlap: patch size in training is a good choice
-        p_max: images with smaller size than (p_max)x(p_max) keep unchanged.
-    """
-    paths = get_image_paths(original_dataroot)
-    for img_path in paths:
-        # img_name, ext = os.path.splitext(os.path.basename(img_path))
-        img = imread_uint(img_path, n_channels=n_channels)
-        patches = patches_from_image(img, p_size, p_overlap, p_max)
-        imssave(patches, os.path.join(taget_dataroot,os.path.basename(img_path)))
-        #if original_dataroot == taget_dataroot:
-        #del img_path
-
-'''
-# --------------------------------------------
-# makedir
-# --------------------------------------------
-'''
-
-
-def mkdir(path):
-    if not os.path.exists(path):
-        os.makedirs(path)
-
-
-def mkdirs(paths):
-    if isinstance(paths, str):
-        mkdir(paths)
-    else:
-        for path in paths:
-            mkdir(path)
-
-
-def mkdir_and_rename(path):
-    if os.path.exists(path):
-        new_name = path + '_archived_' + get_timestamp()
-        print('Path already exists. Rename it to [{:s}]'.format(new_name))
-        os.rename(path, new_name)
-    os.makedirs(path)
-
-
-'''
-# --------------------------------------------
-# read image from path
-# opencv is fast, but read BGR numpy image
-# --------------------------------------------
-'''
-
-
-# --------------------------------------------
-# get uint8 image of size HxWxn_channles (RGB)
-# --------------------------------------------
-def imread_uint(path, n_channels=3):
-    #  input: path
-    # output: HxWx3(RGB or GGG), or HxWx1 (G)
-    if n_channels == 1:
-        img = cv2.imread(path, 0)  # cv2.IMREAD_GRAYSCALE
-        img = np.expand_dims(img, axis=2)  # HxWx1
-    elif n_channels == 3:
-        img = cv2.imread(path, cv2.IMREAD_UNCHANGED)  # BGR or G
-        if img.ndim == 2:
-            img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)  # GGG
-        else:
-            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)  # RGB
-    return img
-
-
-# --------------------------------------------
-# matlab's imwrite
-# --------------------------------------------
-def imsave(img, img_path):
-    img = np.squeeze(img)
-    if img.ndim == 3:
-        img = img[:, :, [2, 1, 0]]
-    cv2.imwrite(img_path, img)
-
-def imwrite(img, img_path):
-    img = np.squeeze(img)
-    if img.ndim == 3:
-        img = img[:, :, [2, 1, 0]]
-    cv2.imwrite(img_path, img)
-
-
-
-# --------------------------------------------
-# get single image of size HxWxn_channles (BGR)
-# --------------------------------------------
-def read_img(path):
-    # read image by cv2
-    # return: Numpy float32, HWC, BGR, [0,1]
-    img = cv2.imread(path, cv2.IMREAD_UNCHANGED)  # cv2.IMREAD_GRAYSCALE
-    img = img.astype(np.float32) / 255.
-    if img.ndim == 2:
-        img = np.expand_dims(img, axis=2)
-    # some images have 4 channels
-    if img.shape[2] > 3:
-        img = img[:, :, :3]
-    return img
-
-
-'''
-# --------------------------------------------
-# image format conversion
-# --------------------------------------------
-# numpy(single) <--->  numpy(unit)
-# numpy(single) <--->  tensor
-# numpy(unit)   <--->  tensor
-# --------------------------------------------
-'''
-
-
-# --------------------------------------------
-# numpy(single) [0, 1] <--->  numpy(unit)
-# --------------------------------------------
-
-
-def uint2single(img):
-
-    return np.float32(img/255.)
-
-
-def single2uint(img):
-
-    return np.uint8((img.clip(0, 1)*255.).round())
-
-
-def uint162single(img):
-
-    return np.float32(img/65535.)
-
-
-def single2uint16(img):
-
-    return np.uint16((img.clip(0, 1)*65535.).round())
-
-
-# --------------------------------------------
-# numpy(unit) (HxWxC or HxW) <--->  tensor
-# --------------------------------------------
-
-
-# convert uint to 4-dimensional torch tensor
-def uint2tensor4(img):
-    if img.ndim == 2:
-        img = np.expand_dims(img, axis=2)
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.).unsqueeze(0)
-
-
-# convert uint to 3-dimensional torch tensor
-def uint2tensor3(img):
-    if img.ndim == 2:
-        img = np.expand_dims(img, axis=2)
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.)
-
-
-# convert 2/3/4-dimensional torch tensor to uint
-def tensor2uint(img):
-    img = img.data.squeeze().float().clamp_(0, 1).cpu().numpy()
-    if img.ndim == 3:
-        img = np.transpose(img, (1, 2, 0))
-    return np.uint8((img*255.0).round())
-
-
-# --------------------------------------------
-# numpy(single) (HxWxC) <--->  tensor
-# --------------------------------------------
-
-
-# convert single (HxWxC) to 3-dimensional torch tensor
-def single2tensor3(img):
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float()
-
-
-# convert single (HxWxC) to 4-dimensional torch tensor
-def single2tensor4(img):
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().unsqueeze(0)
-
-
-# convert torch tensor to single
-def tensor2single(img):
-    img = img.data.squeeze().float().cpu().numpy()
-    if img.ndim == 3:
-        img = np.transpose(img, (1, 2, 0))
-
-    return img
-
-# convert torch tensor to single
-def tensor2single3(img):
-    img = img.data.squeeze().float().cpu().numpy()
-    if img.ndim == 3:
-        img = np.transpose(img, (1, 2, 0))
-    elif img.ndim == 2:
-        img = np.expand_dims(img, axis=2)
-    return img
-
-
-def single2tensor5(img):
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float().unsqueeze(0)
-
-
-def single32tensor5(img):
-    return torch.from_numpy(np.ascontiguousarray(img)).float().unsqueeze(0).unsqueeze(0)
-
-
-def single42tensor4(img):
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float()
-
-
-# from skimage.io import imread, imsave
-def tensor2img(tensor, out_type=np.uint8, min_max=(0, 1)):
-    '''
-    Converts a torch Tensor into an image Numpy array of BGR channel order
-    Input: 4D(B,(3/1),H,W), 3D(C,H,W), or 2D(H,W), any range, RGB channel order
-    Output: 3D(H,W,C) or 2D(H,W), [0,255], np.uint8 (default)
-    '''
-    tensor = tensor.squeeze().float().cpu().clamp_(*min_max)  # squeeze first, then clamp
-    tensor = (tensor - min_max[0]) / (min_max[1] - min_max[0])  # to range [0,1]
-    n_dim = tensor.dim()
-    if n_dim == 4:
-        n_img = len(tensor)
-        img_np = make_grid(tensor, nrow=int(math.sqrt(n_img)), normalize=False).numpy()
-        img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0))  # HWC, BGR
-    elif n_dim == 3:
-        img_np = tensor.numpy()
-        img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0))  # HWC, BGR
-    elif n_dim == 2:
-        img_np = tensor.numpy()
-    else:
-        raise TypeError(
-            'Only support 4D, 3D and 2D tensor. But received with dimension: {:d}'.format(n_dim))
-    if out_type == np.uint8:
-        img_np = (img_np * 255.0).round()
-        # Important. Unlike matlab, numpy.unit8() WILL NOT round by default.
-    return img_np.astype(out_type)
-
-
-'''
-# --------------------------------------------
-# Augmentation, flipe and/or rotate
-# --------------------------------------------
-# The following two are enough.
-# (1) augmet_img: numpy image of WxHxC or WxH
-# (2) augment_img_tensor4: tensor image 1xCxWxH
-# --------------------------------------------
-'''
-
-
-def augment_img(img, mode=0):
-    '''Kai Zhang (github: https://github.com/cszn)
-    '''
-    if mode == 0:
-        return img
-    elif mode == 1:
-        return np.flipud(np.rot90(img))
-    elif mode == 2:
-        return np.flipud(img)
-    elif mode == 3:
-        return np.rot90(img, k=3)
-    elif mode == 4:
-        return np.flipud(np.rot90(img, k=2))
-    elif mode == 5:
-        return np.rot90(img)
-    elif mode == 6:
-        return np.rot90(img, k=2)
-    elif mode == 7:
-        return np.flipud(np.rot90(img, k=3))
-
-
-def augment_img_tensor4(img, mode=0):
-    '''Kai Zhang (github: https://github.com/cszn)
-    '''
-    if mode == 0:
-        return img
-    elif mode == 1:
-        return img.rot90(1, [2, 3]).flip([2])
-    elif mode == 2:
-        return img.flip([2])
-    elif mode == 3:
-        return img.rot90(3, [2, 3])
-    elif mode == 4:
-        return img.rot90(2, [2, 3]).flip([2])
-    elif mode == 5:
-        return img.rot90(1, [2, 3])
-    elif mode == 6:
-        return img.rot90(2, [2, 3])
-    elif mode == 7:
-        return img.rot90(3, [2, 3]).flip([2])
-
-
-def augment_img_tensor(img, mode=0):
-    '''Kai Zhang (github: https://github.com/cszn)
-    '''
-    img_size = img.size()
-    img_np = img.data.cpu().numpy()
-    if len(img_size) == 3:
-        img_np = np.transpose(img_np, (1, 2, 0))
-    elif len(img_size) == 4:
-        img_np = np.transpose(img_np, (2, 3, 1, 0))
-    img_np = augment_img(img_np, mode=mode)
-    img_tensor = torch.from_numpy(np.ascontiguousarray(img_np))
-    if len(img_size) == 3:
-        img_tensor = img_tensor.permute(2, 0, 1)
-    elif len(img_size) == 4:
-        img_tensor = img_tensor.permute(3, 2, 0, 1)
-
-    return img_tensor.type_as(img)
-
-
-def augment_img_np3(img, mode=0):
-    if mode == 0:
-        return img
-    elif mode == 1:
-        return img.transpose(1, 0, 2)
-    elif mode == 2:
-        return img[::-1, :, :]
-    elif mode == 3:
-        img = img[::-1, :, :]
-        img = img.transpose(1, 0, 2)
-        return img
-    elif mode == 4:
-        return img[:, ::-1, :]
-    elif mode == 5:
-        img = img[:, ::-1, :]
-        img = img.transpose(1, 0, 2)
-        return img
-    elif mode == 6:
-        img = img[:, ::-1, :]
-        img = img[::-1, :, :]
-        return img
-    elif mode == 7:
-        img = img[:, ::-1, :]
-        img = img[::-1, :, :]
-        img = img.transpose(1, 0, 2)
-        return img
-
-
-def augment_imgs(img_list, hflip=True, rot=True):
-    # horizontal flip OR rotate
-    hflip = hflip and random.random() < 0.5
-    vflip = rot and random.random() < 0.5
-    rot90 = rot and random.random() < 0.5
-
-    def _augment(img):
-        if hflip:
-            img = img[:, ::-1, :]
-        if vflip:
-            img = img[::-1, :, :]
-        if rot90:
-            img = img.transpose(1, 0, 2)
-        return img
-
-    return [_augment(img) for img in img_list]
-
-
-'''
-# --------------------------------------------
-# modcrop and shave
-# --------------------------------------------
-'''
-
-
-def modcrop(img_in, scale):
-    # img_in: Numpy, HWC or HW
-    img = np.copy(img_in)
-    if img.ndim == 2:
-        H, W = img.shape
-        H_r, W_r = H % scale, W % scale
-        img = img[:H - H_r, :W - W_r]
-    elif img.ndim == 3:
-        H, W, C = img.shape
-        H_r, W_r = H % scale, W % scale
-        img = img[:H - H_r, :W - W_r, :]
-    else:
-        raise ValueError('Wrong img ndim: [{:d}].'.format(img.ndim))
-    return img
-
-
-def shave(img_in, border=0):
-    # img_in: Numpy, HWC or HW
-    img = np.copy(img_in)
-    h, w = img.shape[:2]
-    img = img[border:h-border, border:w-border]
-    return img
-
-
-'''
-# --------------------------------------------
-# image processing process on numpy image
-# channel_convert(in_c, tar_type, img_list):
-# rgb2ycbcr(img, only_y=True):
-# bgr2ycbcr(img, only_y=True):
-# ycbcr2rgb(img):
-# --------------------------------------------
-'''
-
-
-def rgb2ycbcr(img, only_y=True):
-    '''same as matlab rgb2ycbcr
-    only_y: only return Y channel
-    Input:
-        uint8, [0, 255]
-        float, [0, 1]
-    '''
-    in_img_type = img.dtype
-    img.astype(np.float32)
-    if in_img_type != np.uint8:
-        img *= 255.
-    # convert
-    if only_y:
-        rlt = np.dot(img, [65.481, 128.553, 24.966]) / 255.0 + 16.0
-    else:
-        rlt = np.matmul(img, [[65.481, -37.797, 112.0], [128.553, -74.203, -93.786],
-                              [24.966, 112.0, -18.214]]) / 255.0 + [16, 128, 128]
-    if in_img_type == np.uint8:
-        rlt = rlt.round()
-    else:
-        rlt /= 255.
-    return rlt.astype(in_img_type)
-
-
-def ycbcr2rgb(img):
-    '''same as matlab ycbcr2rgb
-    Input:
-        uint8, [0, 255]
-        float, [0, 1]
-    '''
-    in_img_type = img.dtype
-    img.astype(np.float32)
-    if in_img_type != np.uint8:
-        img *= 255.
-    # convert
-    rlt = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621], [0, -0.00153632, 0.00791071],
-                          [0.00625893, -0.00318811, 0]]) * 255.0 + [-222.921, 135.576, -276.836]
-    if in_img_type == np.uint8:
-        rlt = rlt.round()
-    else:
-        rlt /= 255.
-    return rlt.astype(in_img_type)
-
-
-def bgr2ycbcr(img, only_y=True):
-    '''bgr version of rgb2ycbcr
-    only_y: only return Y channel
-    Input:
-        uint8, [0, 255]
-        float, [0, 1]
-    '''
-    in_img_type = img.dtype
-    img.astype(np.float32)
-    if in_img_type != np.uint8:
-        img *= 255.
-    # convert
-    if only_y:
-        rlt = np.dot(img, [24.966, 128.553, 65.481]) / 255.0 + 16.0
-    else:
-        rlt = np.matmul(img, [[24.966, 112.0, -18.214], [128.553, -74.203, -93.786],
-                              [65.481, -37.797, 112.0]]) / 255.0 + [16, 128, 128]
-    if in_img_type == np.uint8:
-        rlt = rlt.round()
-    else:
-        rlt /= 255.
-    return rlt.astype(in_img_type)
-
-
-def channel_convert(in_c, tar_type, img_list):
-    # conversion among BGR, gray and y
-    if in_c == 3 and tar_type == 'gray':  # BGR to gray
-        gray_list = [cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) for img in img_list]
-        return [np.expand_dims(img, axis=2) for img in gray_list]
-    elif in_c == 3 and tar_type == 'y':  # BGR to y
-        y_list = [bgr2ycbcr(img, only_y=True) for img in img_list]
-        return [np.expand_dims(img, axis=2) for img in y_list]
-    elif in_c == 1 and tar_type == 'RGB':  # gray/y to BGR
-        return [cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) for img in img_list]
-    else:
-        return img_list
-
-
-'''
-# --------------------------------------------
-# metric, PSNR and SSIM
-# --------------------------------------------
-'''
-
-
-# --------------------------------------------
-# PSNR
-# --------------------------------------------
-def calculate_psnr(img1, img2, border=0):
-    # img1 and img2 have range [0, 255]
-    #img1 = img1.squeeze()
-    #img2 = img2.squeeze()
-    if not img1.shape == img2.shape:
-        raise ValueError('Input images must have the same dimensions.')
-    h, w = img1.shape[:2]
-    img1 = img1[border:h-border, border:w-border]
-    img2 = img2[border:h-border, border:w-border]
-
-    img1 = img1.astype(np.float64)
-    img2 = img2.astype(np.float64)
-    mse = np.mean((img1 - img2)**2)
-    if mse == 0:
-        return float('inf')
-    return 20 * math.log10(255.0 / math.sqrt(mse))
-
-
-# --------------------------------------------
-# SSIM
-# --------------------------------------------
-def calculate_ssim(img1, img2, border=0):
-    '''calculate SSIM
-    the same outputs as MATLAB's
-    img1, img2: [0, 255]
-    '''
-    #img1 = img1.squeeze()
-    #img2 = img2.squeeze()
-    if not img1.shape == img2.shape:
-        raise ValueError('Input images must have the same dimensions.')
-    h, w = img1.shape[:2]
-    img1 = img1[border:h-border, border:w-border]
-    img2 = img2[border:h-border, border:w-border]
-
-    if img1.ndim == 2:
-        return ssim(img1, img2)
-    elif img1.ndim == 3:
-        if img1.shape[2] == 3:
-            ssims = []
-            for i in range(3):
-                ssims.append(ssim(img1[:,:,i], img2[:,:,i]))
-            return np.array(ssims).mean()
-        elif img1.shape[2] == 1:
-            return ssim(np.squeeze(img1), np.squeeze(img2))
-    else:
-        raise ValueError('Wrong input image dimensions.')
-
-
-def ssim(img1, img2):
-    C1 = (0.01 * 255)**2
-    C2 = (0.03 * 255)**2
-
-    img1 = img1.astype(np.float64)
-    img2 = img2.astype(np.float64)
-    kernel = cv2.getGaussianKernel(11, 1.5)
-    window = np.outer(kernel, kernel.transpose())
-
-    mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5]  # valid
-    mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5]
-    mu1_sq = mu1**2
-    mu2_sq = mu2**2
-    mu1_mu2 = mu1 * mu2
-    sigma1_sq = cv2.filter2D(img1**2, -1, window)[5:-5, 5:-5] - mu1_sq
-    sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq
-    sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2
-
-    ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) *
-                                                            (sigma1_sq + sigma2_sq + C2))
-    return ssim_map.mean()
-
-
-'''
-# --------------------------------------------
-# matlab's bicubic imresize (numpy and torch) [0, 1]
-# --------------------------------------------
-'''
-
-
-# matlab 'imresize' function, now only support 'bicubic'
-def cubic(x):
-    absx = torch.abs(x)
-    absx2 = absx**2
-    absx3 = absx**3
-    return (1.5*absx3 - 2.5*absx2 + 1) * ((absx <= 1).type_as(absx)) + \
-        (-0.5*absx3 + 2.5*absx2 - 4*absx + 2) * (((absx > 1)*(absx <= 2)).type_as(absx))
-
-
-def calculate_weights_indices(in_length, out_length, scale, kernel, kernel_width, antialiasing):
-    if (scale < 1) and (antialiasing):
-        # Use a modified kernel to simultaneously interpolate and antialias- larger kernel width
-        kernel_width = kernel_width / scale
-
-    # Output-space coordinates
-    x = torch.linspace(1, out_length, out_length)
-
-    # Input-space coordinates. Calculate the inverse mapping such that 0.5
-    # in output space maps to 0.5 in input space, and 0.5+scale in output
-    # space maps to 1.5 in input space.
-    u = x / scale + 0.5 * (1 - 1 / scale)
-
-    # What is the left-most pixel that can be involved in the computation?
-    left = torch.floor(u - kernel_width / 2)
-
-    # What is the maximum number of pixels that can be involved in the
-    # computation?  Note: it's OK to use an extra pixel here; if the
-    # corresponding weights are all zero, it will be eliminated at the end
-    # of this function.
-    P = math.ceil(kernel_width) + 2
-
-    # The indices of the input pixels involved in computing the k-th output
-    # pixel are in row k of the indices matrix.
-    indices = left.view(out_length, 1).expand(out_length, P) + torch.linspace(0, P - 1, P).view(
-        1, P).expand(out_length, P)
-
-    # The weights used to compute the k-th output pixel are in row k of the
-    # weights matrix.
-    distance_to_center = u.view(out_length, 1).expand(out_length, P) - indices
-    # apply cubic kernel
-    if (scale < 1) and (antialiasing):
-        weights = scale * cubic(distance_to_center * scale)
-    else:
-        weights = cubic(distance_to_center)
-    # Normalize the weights matrix so that each row sums to 1.
-    weights_sum = torch.sum(weights, 1).view(out_length, 1)
-    weights = weights / weights_sum.expand(out_length, P)
-
-    # If a column in weights is all zero, get rid of it. only consider the first and last column.
-    weights_zero_tmp = torch.sum((weights == 0), 0)
-    if not math.isclose(weights_zero_tmp[0], 0, rel_tol=1e-6):
-        indices = indices.narrow(1, 1, P - 2)
-        weights = weights.narrow(1, 1, P - 2)
-    if not math.isclose(weights_zero_tmp[-1], 0, rel_tol=1e-6):
-        indices = indices.narrow(1, 0, P - 2)
-        weights = weights.narrow(1, 0, P - 2)
-    weights = weights.contiguous()
-    indices = indices.contiguous()
-    sym_len_s = -indices.min() + 1
-    sym_len_e = indices.max() - in_length
-    indices = indices + sym_len_s - 1
-    return weights, indices, int(sym_len_s), int(sym_len_e)
-
-
-# --------------------------------------------
-# imresize for tensor image [0, 1]
-# --------------------------------------------
-def imresize(img, scale, antialiasing=True):
-    # Now the scale should be the same for H and W
-    # input: img: pytorch tensor, CHW or HW [0,1]
-    # output: CHW or HW [0,1] w/o round
-    need_squeeze = True if img.dim() == 2 else False
-    if need_squeeze:
-        img.unsqueeze_(0)
-    in_C, in_H, in_W = img.size()
-    out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale)
-    kernel_width = 4
-    kernel = 'cubic'
-
-    # Return the desired dimension order for performing the resize.  The
-    # strategy is to perform the resize first along the dimension with the
-    # smallest scale factor.
-    # Now we do not support this.
-
-    # get weights and indices
-    weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices(
-        in_H, out_H, scale, kernel, kernel_width, antialiasing)
-    weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices(
-        in_W, out_W, scale, kernel, kernel_width, antialiasing)
-    # process H dimension
-    # symmetric copying
-    img_aug = torch.FloatTensor(in_C, in_H + sym_len_Hs + sym_len_He, in_W)
-    img_aug.narrow(1, sym_len_Hs, in_H).copy_(img)
-
-    sym_patch = img[:, :sym_len_Hs, :]
-    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(1, inv_idx)
-    img_aug.narrow(1, 0, sym_len_Hs).copy_(sym_patch_inv)
-
-    sym_patch = img[:, -sym_len_He:, :]
-    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(1, inv_idx)
-    img_aug.narrow(1, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv)
-
-    out_1 = torch.FloatTensor(in_C, out_H, in_W)
-    kernel_width = weights_H.size(1)
-    for i in range(out_H):
-        idx = int(indices_H[i][0])
-        for j in range(out_C):
-            out_1[j, i, :] = img_aug[j, idx:idx + kernel_width, :].transpose(0, 1).mv(weights_H[i])
-
-    # process W dimension
-    # symmetric copying
-    out_1_aug = torch.FloatTensor(in_C, out_H, in_W + sym_len_Ws + sym_len_We)
-    out_1_aug.narrow(2, sym_len_Ws, in_W).copy_(out_1)
-
-    sym_patch = out_1[:, :, :sym_len_Ws]
-    inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(2, inv_idx)
-    out_1_aug.narrow(2, 0, sym_len_Ws).copy_(sym_patch_inv)
-
-    sym_patch = out_1[:, :, -sym_len_We:]
-    inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(2, inv_idx)
-    out_1_aug.narrow(2, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv)
-
-    out_2 = torch.FloatTensor(in_C, out_H, out_W)
-    kernel_width = weights_W.size(1)
-    for i in range(out_W):
-        idx = int(indices_W[i][0])
-        for j in range(out_C):
-            out_2[j, :, i] = out_1_aug[j, :, idx:idx + kernel_width].mv(weights_W[i])
-    if need_squeeze:
-        out_2.squeeze_()
-    return out_2
-
-
-# --------------------------------------------
-# imresize for numpy image [0, 1]
-# --------------------------------------------
-def imresize_np(img, scale, antialiasing=True):
-    # Now the scale should be the same for H and W
-    # input: img: Numpy, HWC or HW [0,1]
-    # output: HWC or HW [0,1] w/o round
-    img = torch.from_numpy(img)
-    need_squeeze = True if img.dim() == 2 else False
-    if need_squeeze:
-        img.unsqueeze_(2)
-
-    in_H, in_W, in_C = img.size()
-    out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale)
-    kernel_width = 4
-    kernel = 'cubic'
-
-    # Return the desired dimension order for performing the resize.  The
-    # strategy is to perform the resize first along the dimension with the
-    # smallest scale factor.
-    # Now we do not support this.
-
-    # get weights and indices
-    weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices(
-        in_H, out_H, scale, kernel, kernel_width, antialiasing)
-    weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices(
-        in_W, out_W, scale, kernel, kernel_width, antialiasing)
-    # process H dimension
-    # symmetric copying
-    img_aug = torch.FloatTensor(in_H + sym_len_Hs + sym_len_He, in_W, in_C)
-    img_aug.narrow(0, sym_len_Hs, in_H).copy_(img)
-
-    sym_patch = img[:sym_len_Hs, :, :]
-    inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(0, inv_idx)
-    img_aug.narrow(0, 0, sym_len_Hs).copy_(sym_patch_inv)
-
-    sym_patch = img[-sym_len_He:, :, :]
-    inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(0, inv_idx)
-    img_aug.narrow(0, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv)
-
-    out_1 = torch.FloatTensor(out_H, in_W, in_C)
-    kernel_width = weights_H.size(1)
-    for i in range(out_H):
-        idx = int(indices_H[i][0])
-        for j in range(out_C):
-            out_1[i, :, j] = img_aug[idx:idx + kernel_width, :, j].transpose(0, 1).mv(weights_H[i])
-
-    # process W dimension
-    # symmetric copying
-    out_1_aug = torch.FloatTensor(out_H, in_W + sym_len_Ws + sym_len_We, in_C)
-    out_1_aug.narrow(1, sym_len_Ws, in_W).copy_(out_1)
-
-    sym_patch = out_1[:, :sym_len_Ws, :]
-    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(1, inv_idx)
-    out_1_aug.narrow(1, 0, sym_len_Ws).copy_(sym_patch_inv)
-
-    sym_patch = out_1[:, -sym_len_We:, :]
-    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(1, inv_idx)
-    out_1_aug.narrow(1, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv)
-
-    out_2 = torch.FloatTensor(out_H, out_W, in_C)
-    kernel_width = weights_W.size(1)
-    for i in range(out_W):
-        idx = int(indices_W[i][0])
-        for j in range(out_C):
-            out_2[:, i, j] = out_1_aug[:, idx:idx + kernel_width, j].mv(weights_W[i])
-    if need_squeeze:
-        out_2.squeeze_()
-
-    return out_2.numpy()
-
-
-if __name__ == '__main__':
-    print('---')
-#    img = imread_uint('test.bmp', 3)
-#    img = uint2single(img)
-#    img_bicubic = imresize_np(img, 1/4)
\ No newline at end of file
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/karlo/__init__.py b/repositories/stable-diffusion-stability-ai/ldm/modules/karlo/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/karlo/diffusers_pipeline.py b/repositories/stable-diffusion-stability-ai/ldm/modules/karlo/diffusers_pipeline.py
deleted file mode 100644
index 07f72b35a6e05390430880f190ed425985a4a5ef..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/ldm/modules/karlo/diffusers_pipeline.py
+++ /dev/null
@@ -1,512 +0,0 @@
-# Copyright 2022 Kakao Brain and The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import inspect
-from typing import List, Optional, Tuple, Union
-
-import torch
-from torch.nn import functional as F
-
-from transformers import CLIPTextModelWithProjection, CLIPTokenizer
-from transformers.models.clip.modeling_clip import CLIPTextModelOutput
-
-from ...models import PriorTransformer, UNet2DConditionModel, UNet2DModel
-from ...pipelines import DiffusionPipeline, ImagePipelineOutput
-from ...schedulers import UnCLIPScheduler
-from ...utils import is_accelerate_available, logging, randn_tensor
-from .text_proj import UnCLIPTextProjModel
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-class UnCLIPPipeline(DiffusionPipeline):
-    """
-    Pipeline for text-to-image generation using unCLIP
-    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
-    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
-    Args:
-        text_encoder ([`CLIPTextModelWithProjection`]):
-            Frozen text-encoder.
-        tokenizer (`CLIPTokenizer`):
-            Tokenizer of class
-            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
-        prior ([`PriorTransformer`]):
-            The canonincal unCLIP prior to approximate the image embedding from the text embedding.
-        text_proj ([`UnCLIPTextProjModel`]):
-            Utility class to prepare and combine the embeddings before they are passed to the decoder.
-        decoder ([`UNet2DConditionModel`]):
-            The decoder to invert the image embedding into an image.
-        super_res_first ([`UNet2DModel`]):
-            Super resolution unet. Used in all but the last step of the super resolution diffusion process.
-        super_res_last ([`UNet2DModel`]):
-            Super resolution unet. Used in the last step of the super resolution diffusion process.
-        prior_scheduler ([`UnCLIPScheduler`]):
-            Scheduler used in the prior denoising process. Just a modified DDPMScheduler.
-        decoder_scheduler ([`UnCLIPScheduler`]):
-            Scheduler used in the decoder denoising process. Just a modified DDPMScheduler.
-        super_res_scheduler ([`UnCLIPScheduler`]):
-            Scheduler used in the super resolution denoising process. Just a modified DDPMScheduler.
-    """
-
-    prior: PriorTransformer
-    decoder: UNet2DConditionModel
-    text_proj: UnCLIPTextProjModel
-    text_encoder: CLIPTextModelWithProjection
-    tokenizer: CLIPTokenizer
-    super_res_first: UNet2DModel
-    super_res_last: UNet2DModel
-
-    prior_scheduler: UnCLIPScheduler
-    decoder_scheduler: UnCLIPScheduler
-    super_res_scheduler: UnCLIPScheduler
-
-    def __init__(
-        self,
-        prior: PriorTransformer,
-        decoder: UNet2DConditionModel,
-        text_encoder: CLIPTextModelWithProjection,
-        tokenizer: CLIPTokenizer,
-        text_proj: UnCLIPTextProjModel,
-        super_res_first: UNet2DModel,
-        super_res_last: UNet2DModel,
-        prior_scheduler: UnCLIPScheduler,
-        decoder_scheduler: UnCLIPScheduler,
-        super_res_scheduler: UnCLIPScheduler,
-    ):
-        super().__init__()
-
-        self.register_modules(
-            prior=prior,
-            decoder=decoder,
-            text_encoder=text_encoder,
-            tokenizer=tokenizer,
-            text_proj=text_proj,
-            super_res_first=super_res_first,
-            super_res_last=super_res_last,
-            prior_scheduler=prior_scheduler,
-            decoder_scheduler=decoder_scheduler,
-            super_res_scheduler=super_res_scheduler,
-        )
-
-    def prepare_latents(self, shape, dtype, device, generator, latents, scheduler):
-        if latents is None:
-            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-        else:
-            if latents.shape != shape:
-                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
-            latents = latents.to(device)
-
-        latents = latents * scheduler.init_noise_sigma
-        return latents
-
-    def _encode_prompt(
-        self,
-        prompt,
-        device,
-        num_images_per_prompt,
-        do_classifier_free_guidance,
-        text_model_output: Optional[Union[CLIPTextModelOutput, Tuple]] = None,
-        text_attention_mask: Optional[torch.Tensor] = None,
-    ):
-        if text_model_output is None:
-            batch_size = len(prompt) if isinstance(prompt, list) else 1
-            # get prompt text embeddings
-            text_inputs = self.tokenizer(
-                prompt,
-                padding="max_length",
-                max_length=self.tokenizer.model_max_length,
-                return_tensors="pt",
-            )
-            text_input_ids = text_inputs.input_ids
-            text_mask = text_inputs.attention_mask.bool().to(device)
-
-            if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
-                removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
-                logger.warning(
-                    "The following part of your input was truncated because CLIP can only handle sequences up to"
-                    f" {self.tokenizer.model_max_length} tokens: {removed_text}"
-                )
-                text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
-
-            text_encoder_output = self.text_encoder(text_input_ids.to(device))
-
-            text_embeddings = text_encoder_output.text_embeds
-            text_encoder_hidden_states = text_encoder_output.last_hidden_state
-
-        else:
-            batch_size = text_model_output[0].shape[0]
-            text_embeddings, text_encoder_hidden_states = text_model_output[0], text_model_output[1]
-            text_mask = text_attention_mask
-
-        text_embeddings = text_embeddings.repeat_interleave(num_images_per_prompt, dim=0)
-        text_encoder_hidden_states = text_encoder_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
-        text_mask = text_mask.repeat_interleave(num_images_per_prompt, dim=0)
-
-        if do_classifier_free_guidance:
-            uncond_tokens = [""] * batch_size
-
-            uncond_input = self.tokenizer(
-                uncond_tokens,
-                padding="max_length",
-                max_length=self.tokenizer.model_max_length,
-                truncation=True,
-                return_tensors="pt",
-            )
-            uncond_text_mask = uncond_input.attention_mask.bool().to(device)
-            uncond_embeddings_text_encoder_output = self.text_encoder(uncond_input.input_ids.to(device))
-
-            uncond_embeddings = uncond_embeddings_text_encoder_output.text_embeds
-            uncond_text_encoder_hidden_states = uncond_embeddings_text_encoder_output.last_hidden_state
-
-            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
-
-            seq_len = uncond_embeddings.shape[1]
-            uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt)
-            uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len)
-
-            seq_len = uncond_text_encoder_hidden_states.shape[1]
-            uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.repeat(1, num_images_per_prompt, 1)
-            uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.view(
-                batch_size * num_images_per_prompt, seq_len, -1
-            )
-            uncond_text_mask = uncond_text_mask.repeat_interleave(num_images_per_prompt, dim=0)
-
-            # done duplicates
-
-            # For classifier free guidance, we need to do two forward passes.
-            # Here we concatenate the unconditional and text embeddings into a single batch
-            # to avoid doing two forward passes
-            text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
-            text_encoder_hidden_states = torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states])
-
-            text_mask = torch.cat([uncond_text_mask, text_mask])
-
-        return text_embeddings, text_encoder_hidden_states, text_mask
-
-    def enable_sequential_cpu_offload(self, gpu_id=0):
-        r"""
-        Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, the pipeline's
-        models have their state dicts saved to CPU and then are moved to a `torch.device('meta') and loaded to GPU only
-        when their specific submodule has its `forward` method called.
-        """
-        if is_accelerate_available():
-            from accelerate import cpu_offload
-        else:
-            raise ImportError("Please install accelerate via `pip install accelerate`")
-
-        device = torch.device(f"cuda:{gpu_id}")
-
-        # TODO: self.prior.post_process_latents is not covered by the offload hooks, so it fails if added to the list
-        models = [
-            self.decoder,
-            self.text_proj,
-            self.text_encoder,
-            self.super_res_first,
-            self.super_res_last,
-        ]
-        for cpu_offloaded_model in models:
-            if cpu_offloaded_model is not None:
-                cpu_offload(cpu_offloaded_model, device)
-
-    @property
-    def _execution_device(self):
-        r"""
-        Returns the device on which the pipeline's models will be executed. After calling
-        `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
-        hooks.
-        """
-        if self.device != torch.device("meta") or not hasattr(self.decoder, "_hf_hook"):
-            return self.device
-        for module in self.decoder.modules():
-            if (
-                hasattr(module, "_hf_hook")
-                and hasattr(module._hf_hook, "execution_device")
-                and module._hf_hook.execution_device is not None
-            ):
-                return torch.device(module._hf_hook.execution_device)
-        return self.device
-
-    @torch.no_grad()
-    def __call__(
-        self,
-        prompt: Optional[Union[str, List[str]]] = None,
-        num_images_per_prompt: int = 1,
-        prior_num_inference_steps: int = 25,
-        decoder_num_inference_steps: int = 25,
-        super_res_num_inference_steps: int = 7,
-        generator: Optional[torch.Generator] = None,
-        prior_latents: Optional[torch.FloatTensor] = None,
-        decoder_latents: Optional[torch.FloatTensor] = None,
-        super_res_latents: Optional[torch.FloatTensor] = None,
-        text_model_output: Optional[Union[CLIPTextModelOutput, Tuple]] = None,
-        text_attention_mask: Optional[torch.Tensor] = None,
-        prior_guidance_scale: float = 4.0,
-        decoder_guidance_scale: float = 8.0,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-    ):
-        """
-        Function invoked when calling the pipeline for generation.
-        Args:
-            prompt (`str` or `List[str]`):
-                The prompt or prompts to guide the image generation. This can only be left undefined if
-                `text_model_output` and `text_attention_mask` is passed.
-            num_images_per_prompt (`int`, *optional*, defaults to 1):
-                The number of images to generate per prompt.
-            prior_num_inference_steps (`int`, *optional*, defaults to 25):
-                The number of denoising steps for the prior. More denoising steps usually lead to a higher quality
-                image at the expense of slower inference.
-            decoder_num_inference_steps (`int`, *optional*, defaults to 25):
-                The number of denoising steps for the decoder. More denoising steps usually lead to a higher quality
-                image at the expense of slower inference.
-            super_res_num_inference_steps (`int`, *optional*, defaults to 7):
-                The number of denoising steps for super resolution. More denoising steps usually lead to a higher
-                quality image at the expense of slower inference.
-            generator (`torch.Generator`, *optional*):
-                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
-                to make generation deterministic.
-            prior_latents (`torch.FloatTensor` of shape (batch size, embeddings dimension), *optional*):
-                Pre-generated noisy latents to be used as inputs for the prior.
-            decoder_latents (`torch.FloatTensor` of shape (batch size, channels, height, width), *optional*):
-                Pre-generated noisy latents to be used as inputs for the decoder.
-            super_res_latents (`torch.FloatTensor` of shape (batch size, channels, super res height, super res width), *optional*):
-                Pre-generated noisy latents to be used as inputs for the decoder.
-            prior_guidance_scale (`float`, *optional*, defaults to 4.0):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
-                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
-                usually at the expense of lower image quality.
-            decoder_guidance_scale (`float`, *optional*, defaults to 4.0):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
-                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
-                usually at the expense of lower image quality.
-            text_model_output (`CLIPTextModelOutput`, *optional*):
-                Pre-defined CLIPTextModel outputs that can be derived from the text encoder. Pre-defined text outputs
-                can be passed for tasks like text embedding interpolations. Make sure to also pass
-                `text_attention_mask` in this case. `prompt` can the be left to `None`.
-            text_attention_mask (`torch.Tensor`, *optional*):
-                Pre-defined CLIP text attention mask that can be derived from the tokenizer. Pre-defined text attention
-                masks are necessary when passing `text_model_output`.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generated image. Choose between
-                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
-        """
-        if prompt is not None:
-            if isinstance(prompt, str):
-                batch_size = 1
-            elif isinstance(prompt, list):
-                batch_size = len(prompt)
-            else:
-                raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
-        else:
-            batch_size = text_model_output[0].shape[0]
-
-        device = self._execution_device
-
-        batch_size = batch_size * num_images_per_prompt
-
-        do_classifier_free_guidance = prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0
-
-        text_embeddings, text_encoder_hidden_states, text_mask = self._encode_prompt(
-            prompt, device, num_images_per_prompt, do_classifier_free_guidance, text_model_output, text_attention_mask
-        )
-
-        # prior
-
-        self.prior_scheduler.set_timesteps(prior_num_inference_steps, device=device)
-        prior_timesteps_tensor = self.prior_scheduler.timesteps
-
-        embedding_dim = self.prior.config.embedding_dim
-
-        prior_latents = self.prepare_latents(
-            (batch_size, embedding_dim),
-            text_embeddings.dtype,
-            device,
-            generator,
-            prior_latents,
-            self.prior_scheduler,
-        )
-
-        for i, t in enumerate(self.progress_bar(prior_timesteps_tensor)):
-            # expand the latents if we are doing classifier free guidance
-            latent_model_input = torch.cat([prior_latents] * 2) if do_classifier_free_guidance else prior_latents
-
-            predicted_image_embedding = self.prior(
-                latent_model_input,
-                timestep=t,
-                proj_embedding=text_embeddings,
-                encoder_hidden_states=text_encoder_hidden_states,
-                attention_mask=text_mask,
-            ).predicted_image_embedding
-
-            if do_classifier_free_guidance:
-                predicted_image_embedding_uncond, predicted_image_embedding_text = predicted_image_embedding.chunk(2)
-                predicted_image_embedding = predicted_image_embedding_uncond + prior_guidance_scale * (
-                    predicted_image_embedding_text - predicted_image_embedding_uncond
-                )
-
-            if i + 1 == prior_timesteps_tensor.shape[0]:
-                prev_timestep = None
-            else:
-                prev_timestep = prior_timesteps_tensor[i + 1]
-
-            prior_latents = self.prior_scheduler.step(
-                predicted_image_embedding,
-                timestep=t,
-                sample=prior_latents,
-                generator=generator,
-                prev_timestep=prev_timestep,
-            ).prev_sample
-
-        prior_latents = self.prior.post_process_latents(prior_latents)
-
-        image_embeddings = prior_latents
-
-        # done prior
-
-        # decoder
-
-        text_encoder_hidden_states, additive_clip_time_embeddings = self.text_proj(
-            image_embeddings=image_embeddings,
-            text_embeddings=text_embeddings,
-            text_encoder_hidden_states=text_encoder_hidden_states,
-            do_classifier_free_guidance=do_classifier_free_guidance,
-        )
-
-        decoder_text_mask = F.pad(text_mask, (self.text_proj.clip_extra_context_tokens, 0), value=1)
-
-        self.decoder_scheduler.set_timesteps(decoder_num_inference_steps, device=device)
-        decoder_timesteps_tensor = self.decoder_scheduler.timesteps
-
-        num_channels_latents = self.decoder.in_channels
-        height = self.decoder.sample_size
-        width = self.decoder.sample_size
-
-        decoder_latents = self.prepare_latents(
-            (batch_size, num_channels_latents, height, width),
-            text_encoder_hidden_states.dtype,
-            device,
-            generator,
-            decoder_latents,
-            self.decoder_scheduler,
-        )
-
-        for i, t in enumerate(self.progress_bar(decoder_timesteps_tensor)):
-            # expand the latents if we are doing classifier free guidance
-            latent_model_input = torch.cat([decoder_latents] * 2) if do_classifier_free_guidance else decoder_latents
-
-            noise_pred = self.decoder(
-                sample=latent_model_input,
-                timestep=t,
-                encoder_hidden_states=text_encoder_hidden_states,
-                class_labels=additive_clip_time_embeddings,
-                attention_mask=decoder_text_mask,
-            ).sample
-
-            if do_classifier_free_guidance:
-                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                noise_pred_uncond, _ = noise_pred_uncond.split(latent_model_input.shape[1], dim=1)
-                noise_pred_text, predicted_variance = noise_pred_text.split(latent_model_input.shape[1], dim=1)
-                noise_pred = noise_pred_uncond + decoder_guidance_scale * (noise_pred_text - noise_pred_uncond)
-                noise_pred = torch.cat([noise_pred, predicted_variance], dim=1)
-
-            if i + 1 == decoder_timesteps_tensor.shape[0]:
-                prev_timestep = None
-            else:
-                prev_timestep = decoder_timesteps_tensor[i + 1]
-
-            # compute the previous noisy sample x_t -> x_t-1
-            decoder_latents = self.decoder_scheduler.step(
-                noise_pred, t, decoder_latents, prev_timestep=prev_timestep, generator=generator
-            ).prev_sample
-
-        decoder_latents = decoder_latents.clamp(-1, 1)
-
-        image_small = decoder_latents
-
-        # done decoder
-
-        # super res
-
-        self.super_res_scheduler.set_timesteps(super_res_num_inference_steps, device=device)
-        super_res_timesteps_tensor = self.super_res_scheduler.timesteps
-
-        channels = self.super_res_first.in_channels // 2
-        height = self.super_res_first.sample_size
-        width = self.super_res_first.sample_size
-
-        super_res_latents = self.prepare_latents(
-            (batch_size, channels, height, width),
-            image_small.dtype,
-            device,
-            generator,
-            super_res_latents,
-            self.super_res_scheduler,
-        )
-
-        interpolate_antialias = {}
-        if "antialias" in inspect.signature(F.interpolate).parameters:
-            interpolate_antialias["antialias"] = True
-
-        image_upscaled = F.interpolate(
-            image_small, size=[height, width], mode="bicubic", align_corners=False, **interpolate_antialias
-        )
-
-        for i, t in enumerate(self.progress_bar(super_res_timesteps_tensor)):
-            # no classifier free guidance
-
-            if i == super_res_timesteps_tensor.shape[0] - 1:
-                unet = self.super_res_last
-            else:
-                unet = self.super_res_first
-
-            latent_model_input = torch.cat([super_res_latents, image_upscaled], dim=1)
-
-            noise_pred = unet(
-                sample=latent_model_input,
-                timestep=t,
-            ).sample
-
-            if i + 1 == super_res_timesteps_tensor.shape[0]:
-                prev_timestep = None
-            else:
-                prev_timestep = super_res_timesteps_tensor[i + 1]
-
-            # compute the previous noisy sample x_t -> x_t-1
-            super_res_latents = self.super_res_scheduler.step(
-                noise_pred, t, super_res_latents, prev_timestep=prev_timestep, generator=generator
-            ).prev_sample
-
-        image = super_res_latents
-        # done super res
-
-        # post processing
-
-        image = image * 0.5 + 0.5
-        image = image.clamp(0, 1)
-        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
-
-        if output_type == "pil":
-            image = self.numpy_to_pil(image)
-
-        if not return_dict:
-            return (image,)
-
-        return ImagePipelineOutput(images=image)
\ No newline at end of file
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/karlo/kakao/__init__.py b/repositories/stable-diffusion-stability-ai/ldm/modules/karlo/kakao/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/karlo/kakao/models/__init__.py b/repositories/stable-diffusion-stability-ai/ldm/modules/karlo/kakao/models/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/karlo/kakao/models/clip.py b/repositories/stable-diffusion-stability-ai/ldm/modules/karlo/kakao/models/clip.py
deleted file mode 100644
index 961d81502a069ae29b6752478f048c979b27fb9b..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/ldm/modules/karlo/kakao/models/clip.py
+++ /dev/null
@@ -1,182 +0,0 @@
-# ------------------------------------------------------------------------------------
-# Karlo-v1.0.alpha
-# Copyright (c) 2022 KakaoBrain. All Rights Reserved.
-# ------------------------------------------------------------------------------------
-# ------------------------------------------------------------------------------------
-# Adapted from OpenAI's CLIP (https://github.com/openai/CLIP/)
-# ------------------------------------------------------------------------------------
-
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import clip
-
-from clip.model import CLIP, convert_weights
-from clip.simple_tokenizer import SimpleTokenizer, default_bpe
-
-
-"""===== Monkey-Patching original CLIP for JIT compile ====="""
-
-
-class LayerNorm(nn.LayerNorm):
-    """Subclass torch's LayerNorm to handle fp16."""
-
-    def forward(self, x: torch.Tensor):
-        orig_type = x.dtype
-        ret = F.layer_norm(
-            x.type(torch.float32),
-            self.normalized_shape,
-            self.weight,
-            self.bias,
-            self.eps,
-        )
-        return ret.type(orig_type)
-
-
-clip.model.LayerNorm = LayerNorm
-delattr(clip.model.CLIP, "forward")
-
-"""===== End of Monkey-Patching ====="""
-
-
-class CustomizedCLIP(CLIP):
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-    @torch.jit.export
-    def encode_image(self, image):
-        return self.visual(image)
-
-    @torch.jit.export
-    def encode_text(self, text):
-        # re-define this function to return unpooled text features
-
-        x = self.token_embedding(text).type(self.dtype)  # [batch_size, n_ctx, d_model]
-
-        x = x + self.positional_embedding.type(self.dtype)
-        x = x.permute(1, 0, 2)  # NLD -> LND
-        x = self.transformer(x)
-        x = x.permute(1, 0, 2)  # LND -> NLD
-        x = self.ln_final(x).type(self.dtype)
-
-        x_seq = x
-        # x.shape = [batch_size, n_ctx, transformer.width]
-        # take features from the eot embedding (eot_token is the highest number in each sequence)
-        x_out = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection
-
-        return x_out, x_seq
-
-    @torch.jit.ignore
-    def forward(self, image, text):
-        super().forward(image, text)
-
-    @classmethod
-    def load_from_checkpoint(cls, ckpt_path: str):
-        state_dict = torch.load(ckpt_path, map_location="cpu").state_dict()
-
-        vit = "visual.proj" in state_dict
-        if vit:
-            vision_width = state_dict["visual.conv1.weight"].shape[0]
-            vision_layers = len(
-                [
-                    k
-                    for k in state_dict.keys()
-                    if k.startswith("visual.") and k.endswith(".attn.in_proj_weight")
-                ]
-            )
-            vision_patch_size = state_dict["visual.conv1.weight"].shape[-1]
-            grid_size = round(
-                (state_dict["visual.positional_embedding"].shape[0] - 1) ** 0.5
-            )
-            image_resolution = vision_patch_size * grid_size
-        else:
-            counts: list = [
-                len(
-                    set(
-                        k.split(".")[2]
-                        for k in state_dict
-                        if k.startswith(f"visual.layer{b}")
-                    )
-                )
-                for b in [1, 2, 3, 4]
-            ]
-            vision_layers = tuple(counts)
-            vision_width = state_dict["visual.layer1.0.conv1.weight"].shape[0]
-            output_width = round(
-                (state_dict["visual.attnpool.positional_embedding"].shape[0] - 1) ** 0.5
-            )
-            vision_patch_size = None
-            assert (
-                output_width**2 + 1
-                == state_dict["visual.attnpool.positional_embedding"].shape[0]
-            )
-            image_resolution = output_width * 32
-
-        embed_dim = state_dict["text_projection"].shape[1]
-        context_length = state_dict["positional_embedding"].shape[0]
-        vocab_size = state_dict["token_embedding.weight"].shape[0]
-        transformer_width = state_dict["ln_final.weight"].shape[0]
-        transformer_heads = transformer_width // 64
-        transformer_layers = len(
-            set(
-                k.split(".")[2]
-                for k in state_dict
-                if k.startswith("transformer.resblocks")
-            )
-        )
-
-        model = cls(
-            embed_dim,
-            image_resolution,
-            vision_layers,
-            vision_width,
-            vision_patch_size,
-            context_length,
-            vocab_size,
-            transformer_width,
-            transformer_heads,
-            transformer_layers,
-        )
-
-        for key in ["input_resolution", "context_length", "vocab_size"]:
-            if key in state_dict:
-                del state_dict[key]
-
-        convert_weights(model)
-        model.load_state_dict(state_dict)
-        model.eval()
-        model.float()
-        return model
-
-
-class CustomizedTokenizer(SimpleTokenizer):
-    def __init__(self):
-        super().__init__(bpe_path=default_bpe())
-
-        self.sot_token = self.encoder["<|startoftext|>"]
-        self.eot_token = self.encoder["<|endoftext|>"]
-
-    def padded_tokens_and_mask(self, texts, text_ctx):
-        assert isinstance(texts, list) and all(
-            isinstance(elem, str) for elem in texts
-        ), "texts should be a list of strings"
-
-        all_tokens = [
-            [self.sot_token] + self.encode(text) + [self.eot_token] for text in texts
-        ]
-
-        mask = [
-            [True] * min(text_ctx, len(tokens))
-            + [False] * max(text_ctx - len(tokens), 0)
-            for tokens in all_tokens
-        ]
-        mask = torch.tensor(mask, dtype=torch.bool)
-        result = torch.zeros(len(all_tokens), text_ctx, dtype=torch.int)
-        for i, tokens in enumerate(all_tokens):
-            if len(tokens) > text_ctx:
-                tokens = tokens[:text_ctx]
-                tokens[-1] = self.eot_token
-            result[i, : len(tokens)] = torch.tensor(tokens)
-
-        return result, mask
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/karlo/kakao/models/decoder_model.py b/repositories/stable-diffusion-stability-ai/ldm/modules/karlo/kakao/models/decoder_model.py
deleted file mode 100644
index 84e96c9b2f3f54561a5d950097d5d069ce4ce3a0..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/ldm/modules/karlo/kakao/models/decoder_model.py
+++ /dev/null
@@ -1,193 +0,0 @@
-# ------------------------------------------------------------------------------------
-# Karlo-v1.0.alpha
-# Copyright (c) 2022 KakaoBrain. All Rights Reserved.
-# ------------------------------------------------------------------------------------
-
-import copy
-import torch
-
-from ldm.modules.karlo.kakao.modules import create_gaussian_diffusion
-from ldm.modules.karlo.kakao.modules.unet import PLMImUNet
-
-
-class Text2ImProgressiveModel(torch.nn.Module):
-    """
-    A decoder that generates 64x64px images based on the text prompt.
-
-    :param config: yaml config to define the decoder.
-    :param tokenizer: tokenizer used in clip.
-    """
-
-    def __init__(
-        self,
-        config,
-        tokenizer,
-    ):
-        super().__init__()
-
-        self._conf = config
-        self._model_conf = config.model.hparams
-        self._diffusion_kwargs = dict(
-            steps=config.diffusion.steps,
-            learn_sigma=config.diffusion.learn_sigma,
-            sigma_small=config.diffusion.sigma_small,
-            noise_schedule=config.diffusion.noise_schedule,
-            use_kl=config.diffusion.use_kl,
-            predict_xstart=config.diffusion.predict_xstart,
-            rescale_learned_sigmas=config.diffusion.rescale_learned_sigmas,
-            timestep_respacing=config.diffusion.timestep_respacing,
-        )
-        self._tokenizer = tokenizer
-
-        self.model = self.create_plm_dec_model()
-
-        cf_token, cf_mask = self.set_cf_text_tensor()
-        self.register_buffer("cf_token", cf_token, persistent=False)
-        self.register_buffer("cf_mask", cf_mask, persistent=False)
-
-    @classmethod
-    def load_from_checkpoint(cls, config, tokenizer, ckpt_path, strict: bool = True):
-        ckpt = torch.load(ckpt_path, map_location="cpu")["state_dict"]
-
-        model = cls(config, tokenizer)
-        model.load_state_dict(ckpt, strict=strict)
-        return model
-
-    def create_plm_dec_model(self):
-        image_size = self._model_conf.image_size
-        if self._model_conf.channel_mult == "":
-            if image_size == 256:
-                channel_mult = (1, 1, 2, 2, 4, 4)
-            elif image_size == 128:
-                channel_mult = (1, 1, 2, 3, 4)
-            elif image_size == 64:
-                channel_mult = (1, 2, 3, 4)
-            else:
-                raise ValueError(f"unsupported image size: {image_size}")
-        else:
-            channel_mult = tuple(
-                int(ch_mult) for ch_mult in self._model_conf.channel_mult.split(",")
-            )
-            assert 2 ** (len(channel_mult) + 2) == image_size
-
-        attention_ds = []
-        for res in self._model_conf.attention_resolutions.split(","):
-            attention_ds.append(image_size // int(res))
-
-        return PLMImUNet(
-            text_ctx=self._model_conf.text_ctx,
-            xf_width=self._model_conf.xf_width,
-            in_channels=3,
-            model_channels=self._model_conf.num_channels,
-            out_channels=6 if self._model_conf.learn_sigma else 3,
-            num_res_blocks=self._model_conf.num_res_blocks,
-            attention_resolutions=tuple(attention_ds),
-            dropout=self._model_conf.dropout,
-            channel_mult=channel_mult,
-            num_heads=self._model_conf.num_heads,
-            num_head_channels=self._model_conf.num_head_channels,
-            num_heads_upsample=self._model_conf.num_heads_upsample,
-            use_scale_shift_norm=self._model_conf.use_scale_shift_norm,
-            resblock_updown=self._model_conf.resblock_updown,
-            clip_dim=self._model_conf.clip_dim,
-            clip_emb_mult=self._model_conf.clip_emb_mult,
-            clip_emb_type=self._model_conf.clip_emb_type,
-            clip_emb_drop=self._model_conf.clip_emb_drop,
-        )
-
-    def set_cf_text_tensor(self):
-        return self._tokenizer.padded_tokens_and_mask([""], self.model.text_ctx)
-
-    def get_sample_fn(self, timestep_respacing):
-        use_ddim = timestep_respacing.startswith(("ddim", "fast"))
-
-        diffusion_kwargs = copy.deepcopy(self._diffusion_kwargs)
-        diffusion_kwargs.update(timestep_respacing=timestep_respacing)
-        diffusion = create_gaussian_diffusion(**diffusion_kwargs)
-        sample_fn = (
-            diffusion.ddim_sample_loop_progressive
-            if use_ddim
-            else diffusion.p_sample_loop_progressive
-        )
-
-        return sample_fn
-
-    def forward(
-        self,
-        txt_feat,
-        txt_feat_seq,
-        tok,
-        mask,
-        img_feat=None,
-        cf_guidance_scales=None,
-        timestep_respacing=None,
-    ):
-        # cfg should be enabled in inference
-        assert cf_guidance_scales is not None and all(cf_guidance_scales > 0.0)
-        assert img_feat is not None
-
-        bsz = txt_feat.shape[0]
-        img_sz = self._model_conf.image_size
-
-        def guided_model_fn(x_t, ts, **kwargs):
-            half = x_t[: len(x_t) // 2]
-            combined = torch.cat([half, half], dim=0)
-            model_out = self.model(combined, ts, **kwargs)
-            eps, rest = model_out[:, :3], model_out[:, 3:]
-            cond_eps, uncond_eps = torch.split(eps, len(eps) // 2, dim=0)
-            half_eps = uncond_eps + cf_guidance_scales.view(-1, 1, 1, 1) * (
-                cond_eps - uncond_eps
-            )
-            eps = torch.cat([half_eps, half_eps], dim=0)
-            return torch.cat([eps, rest], dim=1)
-
-        cf_feat = self.model.cf_param.unsqueeze(0)
-        cf_feat = cf_feat.expand(bsz // 2, -1)
-        feat = torch.cat([img_feat, cf_feat.to(txt_feat.device)], dim=0)
-
-        cond = {
-            "y": feat,
-            "txt_feat": txt_feat,
-            "txt_feat_seq": txt_feat_seq,
-            "mask": mask,
-        }
-        sample_fn = self.get_sample_fn(timestep_respacing)
-        sample_outputs = sample_fn(
-            guided_model_fn,
-            (bsz, 3, img_sz, img_sz),
-            noise=None,
-            device=txt_feat.device,
-            clip_denoised=True,
-            model_kwargs=cond,
-        )
-
-        for out in sample_outputs:
-            sample = out["sample"]
-            yield sample if cf_guidance_scales is None else sample[
-                : sample.shape[0] // 2
-            ]
-
-
-class Text2ImModel(Text2ImProgressiveModel):
-    def forward(
-        self,
-        txt_feat,
-        txt_feat_seq,
-        tok,
-        mask,
-        img_feat=None,
-        cf_guidance_scales=None,
-        timestep_respacing=None,
-    ):
-        last_out = None
-        for out in super().forward(
-            txt_feat,
-            txt_feat_seq,
-            tok,
-            mask,
-            img_feat,
-            cf_guidance_scales,
-            timestep_respacing,
-        ):
-            last_out = out
-        return last_out
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/karlo/kakao/models/prior_model.py b/repositories/stable-diffusion-stability-ai/ldm/modules/karlo/kakao/models/prior_model.py
deleted file mode 100644
index 03ef230d2ac5e7f785c138f710337980e6754022..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/ldm/modules/karlo/kakao/models/prior_model.py
+++ /dev/null
@@ -1,138 +0,0 @@
-# ------------------------------------------------------------------------------------
-# Karlo-v1.0.alpha
-# Copyright (c) 2022 KakaoBrain. All Rights Reserved.
-# ------------------------------------------------------------------------------------
-
-import copy
-import torch
-
-from ldm.modules.karlo.kakao.modules import create_gaussian_diffusion
-from ldm.modules.karlo.kakao.modules.xf import PriorTransformer
-
-
-class PriorDiffusionModel(torch.nn.Module):
-    """
-    A prior that generates clip image feature based on the text prompt.
-
-    :param config: yaml config to define the decoder.
-    :param tokenizer: tokenizer used in clip.
-    :param clip_mean: mean to normalize the clip image feature (zero-mean, unit variance).
-    :param clip_std: std to noramlize the clip image feature (zero-mean, unit variance).
-    """
-
-    def __init__(self, config, tokenizer, clip_mean, clip_std):
-        super().__init__()
-
-        self._conf = config
-        self._model_conf = config.model.hparams
-        self._diffusion_kwargs = dict(
-            steps=config.diffusion.steps,
-            learn_sigma=config.diffusion.learn_sigma,
-            sigma_small=config.diffusion.sigma_small,
-            noise_schedule=config.diffusion.noise_schedule,
-            use_kl=config.diffusion.use_kl,
-            predict_xstart=config.diffusion.predict_xstart,
-            rescale_learned_sigmas=config.diffusion.rescale_learned_sigmas,
-            timestep_respacing=config.diffusion.timestep_respacing,
-        )
-        self._tokenizer = tokenizer
-
-        self.register_buffer("clip_mean", clip_mean[None, :], persistent=False)
-        self.register_buffer("clip_std", clip_std[None, :], persistent=False)
-
-        causal_mask = self.get_causal_mask()
-        self.register_buffer("causal_mask", causal_mask, persistent=False)
-
-        self.model = PriorTransformer(
-            text_ctx=self._model_conf.text_ctx,
-            xf_width=self._model_conf.xf_width,
-            xf_layers=self._model_conf.xf_layers,
-            xf_heads=self._model_conf.xf_heads,
-            xf_final_ln=self._model_conf.xf_final_ln,
-            clip_dim=self._model_conf.clip_dim,
-        )
-
-        cf_token, cf_mask = self.set_cf_text_tensor()
-        self.register_buffer("cf_token", cf_token, persistent=False)
-        self.register_buffer("cf_mask", cf_mask, persistent=False)
-
-    @classmethod
-    def load_from_checkpoint(
-        cls, config, tokenizer, clip_mean, clip_std, ckpt_path, strict: bool = True
-    ):
-        ckpt = torch.load(ckpt_path, map_location="cpu")["state_dict"]
-
-        model = cls(config, tokenizer, clip_mean, clip_std)
-        model.load_state_dict(ckpt, strict=strict)
-        return model
-
-    def set_cf_text_tensor(self):
-        return self._tokenizer.padded_tokens_and_mask([""], self.model.text_ctx)
-
-    def get_sample_fn(self, timestep_respacing):
-        use_ddim = timestep_respacing.startswith(("ddim", "fast"))
-
-        diffusion_kwargs = copy.deepcopy(self._diffusion_kwargs)
-        diffusion_kwargs.update(timestep_respacing=timestep_respacing)
-        diffusion = create_gaussian_diffusion(**diffusion_kwargs)
-        sample_fn = diffusion.ddim_sample_loop if use_ddim else diffusion.p_sample_loop
-
-        return sample_fn
-
-    def get_causal_mask(self):
-        seq_len = self._model_conf.text_ctx + 4
-        mask = torch.empty(seq_len, seq_len)
-        mask.fill_(float("-inf"))
-        mask.triu_(1)
-        mask = mask[None, ...]
-        return mask
-
-    def forward(
-        self,
-        txt_feat,
-        txt_feat_seq,
-        mask,
-        cf_guidance_scales=None,
-        timestep_respacing=None,
-        denoised_fn=True,
-    ):
-        # cfg should be enabled in inference
-        assert cf_guidance_scales is not None and all(cf_guidance_scales > 0.0)
-
-        bsz_ = txt_feat.shape[0]
-        bsz = bsz_ // 2
-
-        def guided_model_fn(x_t, ts, **kwargs):
-            half = x_t[: len(x_t) // 2]
-            combined = torch.cat([half, half], dim=0)
-            model_out = self.model(combined, ts, **kwargs)
-            eps, rest = (
-                model_out[:, : int(x_t.shape[1])],
-                model_out[:, int(x_t.shape[1]) :],
-            )
-            cond_eps, uncond_eps = torch.split(eps, len(eps) // 2, dim=0)
-            half_eps = uncond_eps + cf_guidance_scales.view(-1, 1) * (
-                cond_eps - uncond_eps
-            )
-            eps = torch.cat([half_eps, half_eps], dim=0)
-            return torch.cat([eps, rest], dim=1)
-
-        cond = {
-            "text_emb": txt_feat,
-            "text_enc": txt_feat_seq,
-            "mask": mask,
-            "causal_mask": self.causal_mask,
-        }
-        sample_fn = self.get_sample_fn(timestep_respacing)
-        sample = sample_fn(
-            guided_model_fn,
-            (bsz_, self.model.clip_dim),
-            noise=None,
-            device=txt_feat.device,
-            clip_denoised=False,
-            denoised_fn=lambda x: torch.clamp(x, -10, 10),
-            model_kwargs=cond,
-        )
-        sample = (sample * self.clip_std) + self.clip_mean
-
-        return sample[:bsz]
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/karlo/kakao/models/sr_256_1k.py b/repositories/stable-diffusion-stability-ai/ldm/modules/karlo/kakao/models/sr_256_1k.py
deleted file mode 100644
index 1e874f6f1b8286d85456ae335316880cb5c15336..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/ldm/modules/karlo/kakao/models/sr_256_1k.py
+++ /dev/null
@@ -1,10 +0,0 @@
-# ------------------------------------------------------------------------------------
-# Karlo-v1.0.alpha
-# Copyright (c) 2022 KakaoBrain. All Rights Reserved.
-# ------------------------------------------------------------------------------------
-
-from ldm.modules.karlo.kakao.models.sr_64_256 import SupRes64to256Progressive
-
-
-class SupRes256to1kProgressive(SupRes64to256Progressive):
-    pass  # no difference currently
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/karlo/kakao/models/sr_64_256.py b/repositories/stable-diffusion-stability-ai/ldm/modules/karlo/kakao/models/sr_64_256.py
deleted file mode 100644
index 32687afe38134c4eed06083fc9c345b1dbe958a9..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/ldm/modules/karlo/kakao/models/sr_64_256.py
+++ /dev/null
@@ -1,88 +0,0 @@
-# ------------------------------------------------------------------------------------
-# Karlo-v1.0.alpha
-# Copyright (c) 2022 KakaoBrain. All Rights Reserved.
-# ------------------------------------------------------------------------------------
-
-import copy
-import torch
-
-from ldm.modules.karlo.kakao.modules.unet import SuperResUNetModel
-from ldm.modules.karlo.kakao.modules import create_gaussian_diffusion
-
-
-class ImprovedSupRes64to256ProgressiveModel(torch.nn.Module):
-    """
-    ImprovedSR model fine-tunes the pretrained DDPM-based SR model by using adversarial and perceptual losses.
-    In specific, the low-resolution sample is iteratively recovered by 6 steps with the frozen pretrained SR model.
-    In the following additional one step, a seperate fine-tuned model recovers high-frequency details.
-    This approach greatly improves the fidelity of images of 256x256px, even with small number of reverse steps.
-    """
-
-    def __init__(self, config):
-        super().__init__()
-
-        self._config = config
-        self._diffusion_kwargs = dict(
-            steps=config.diffusion.steps,
-            learn_sigma=config.diffusion.learn_sigma,
-            sigma_small=config.diffusion.sigma_small,
-            noise_schedule=config.diffusion.noise_schedule,
-            use_kl=config.diffusion.use_kl,
-            predict_xstart=config.diffusion.predict_xstart,
-            rescale_learned_sigmas=config.diffusion.rescale_learned_sigmas,
-        )
-
-        self.model_first_steps = SuperResUNetModel(
-            in_channels=3,  # auto-changed to 6 inside the model
-            model_channels=config.model.hparams.channels,
-            out_channels=3,
-            num_res_blocks=config.model.hparams.depth,
-            attention_resolutions=(),  # no attention
-            dropout=config.model.hparams.dropout,
-            channel_mult=config.model.hparams.channels_multiple,
-            resblock_updown=True,
-            use_middle_attention=False,
-        )
-        self.model_last_step = SuperResUNetModel(
-            in_channels=3,  # auto-changed to 6 inside the model
-            model_channels=config.model.hparams.channels,
-            out_channels=3,
-            num_res_blocks=config.model.hparams.depth,
-            attention_resolutions=(),  # no attention
-            dropout=config.model.hparams.dropout,
-            channel_mult=config.model.hparams.channels_multiple,
-            resblock_updown=True,
-            use_middle_attention=False,
-        )
-
-    @classmethod
-    def load_from_checkpoint(cls, config, ckpt_path, strict: bool = True):
-        ckpt = torch.load(ckpt_path, map_location="cpu")["state_dict"]
-
-        model = cls(config)
-        model.load_state_dict(ckpt, strict=strict)
-        return model
-
-    def get_sample_fn(self, timestep_respacing):
-        diffusion_kwargs = copy.deepcopy(self._diffusion_kwargs)
-        diffusion_kwargs.update(timestep_respacing=timestep_respacing)
-        diffusion = create_gaussian_diffusion(**diffusion_kwargs)
-        return diffusion.p_sample_loop_progressive_for_improved_sr
-
-    def forward(self, low_res, timestep_respacing="7", **kwargs):
-        assert (
-            timestep_respacing == "7"
-        ), "different respacing method may work, but no guaranteed"
-
-        sample_fn = self.get_sample_fn(timestep_respacing)
-        sample_outputs = sample_fn(
-            self.model_first_steps,
-            self.model_last_step,
-            shape=low_res.shape,
-            clip_denoised=True,
-            model_kwargs=dict(low_res=low_res),
-            **kwargs,
-        )
-        for x in sample_outputs:
-            sample = x["sample"]
-            yield sample
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/karlo/kakao/modules/__init__.py b/repositories/stable-diffusion-stability-ai/ldm/modules/karlo/kakao/modules/__init__.py
deleted file mode 100644
index 11d4358a6475243b8b789efb7595566993ccd9e1..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/ldm/modules/karlo/kakao/modules/__init__.py
+++ /dev/null
@@ -1,49 +0,0 @@
-# ------------------------------------------------------------------------------------
-# Adapted from Guided-Diffusion repo (https://github.com/openai/guided-diffusion)
-# ------------------------------------------------------------------------------------
-
-
-from .diffusion import gaussian_diffusion as gd
-from .diffusion.respace import (
-    SpacedDiffusion,
-    space_timesteps,
-)
-
-
-def create_gaussian_diffusion(
-    steps,
-    learn_sigma,
-    sigma_small,
-    noise_schedule,
-    use_kl,
-    predict_xstart,
-    rescale_learned_sigmas,
-    timestep_respacing,
-):
-    betas = gd.get_named_beta_schedule(noise_schedule, steps)
-    if use_kl:
-        loss_type = gd.LossType.RESCALED_KL
-    elif rescale_learned_sigmas:
-        loss_type = gd.LossType.RESCALED_MSE
-    else:
-        loss_type = gd.LossType.MSE
-    if not timestep_respacing:
-        timestep_respacing = [steps]
-
-    return SpacedDiffusion(
-        use_timesteps=space_timesteps(steps, timestep_respacing),
-        betas=betas,
-        model_mean_type=(
-            gd.ModelMeanType.EPSILON if not predict_xstart else gd.ModelMeanType.START_X
-        ),
-        model_var_type=(
-            (
-                gd.ModelVarType.FIXED_LARGE
-                if not sigma_small
-                else gd.ModelVarType.FIXED_SMALL
-            )
-            if not learn_sigma
-            else gd.ModelVarType.LEARNED_RANGE
-        ),
-        loss_type=loss_type,
-    )
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/karlo/kakao/modules/diffusion/gaussian_diffusion.py b/repositories/stable-diffusion-stability-ai/ldm/modules/karlo/kakao/modules/diffusion/gaussian_diffusion.py
deleted file mode 100644
index 6a111aa09ea84157735cc2a4a66a68c4faaf42f4..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/ldm/modules/karlo/kakao/modules/diffusion/gaussian_diffusion.py
+++ /dev/null
@@ -1,828 +0,0 @@
-# ------------------------------------------------------------------------------------
-# Adapted from Guided-Diffusion repo (https://github.com/openai/guided-diffusion)
-# ------------------------------------------------------------------------------------
-
-import enum
-import math
-
-import numpy as np
-import torch as th
-
-
-def _warmup_beta(beta_start, beta_end, num_diffusion_timesteps, warmup_frac):
-    betas = beta_end * np.ones(num_diffusion_timesteps, dtype=np.float64)
-    warmup_time = int(num_diffusion_timesteps * warmup_frac)
-    betas[:warmup_time] = np.linspace(
-        beta_start, beta_end, warmup_time, dtype=np.float64
-    )
-    return betas
-
-
-def get_beta_schedule(beta_schedule, *, beta_start, beta_end, num_diffusion_timesteps):
-    """
-    This is the deprecated API for creating beta schedules.
-    See get_named_beta_schedule() for the new library of schedules.
-    """
-    if beta_schedule == "quad":
-        betas = (
-            np.linspace(
-                beta_start**0.5,
-                beta_end**0.5,
-                num_diffusion_timesteps,
-                dtype=np.float64,
-            )
-            ** 2
-        )
-    elif beta_schedule == "linear":
-        betas = np.linspace(
-            beta_start, beta_end, num_diffusion_timesteps, dtype=np.float64
-        )
-    elif beta_schedule == "warmup10":
-        betas = _warmup_beta(beta_start, beta_end, num_diffusion_timesteps, 0.1)
-    elif beta_schedule == "warmup50":
-        betas = _warmup_beta(beta_start, beta_end, num_diffusion_timesteps, 0.5)
-    elif beta_schedule == "const":
-        betas = beta_end * np.ones(num_diffusion_timesteps, dtype=np.float64)
-    elif beta_schedule == "jsd":  # 1/T, 1/(T-1), 1/(T-2), ..., 1
-        betas = 1.0 / np.linspace(
-            num_diffusion_timesteps, 1, num_diffusion_timesteps, dtype=np.float64
-        )
-    else:
-        raise NotImplementedError(beta_schedule)
-    assert betas.shape == (num_diffusion_timesteps,)
-    return betas
-
-
-def get_named_beta_schedule(schedule_name, num_diffusion_timesteps):
-    """
-    Get a pre-defined beta schedule for the given name.
-    The beta schedule library consists of beta schedules which remain similar
-    in the limit of num_diffusion_timesteps.
-    Beta schedules may be added, but should not be removed or changed once
-    they are committed to maintain backwards compatibility.
-    """
-    if schedule_name == "linear":
-        # Linear schedule from Ho et al, extended to work for any number of
-        # diffusion steps.
-        scale = 1000 / num_diffusion_timesteps
-        return get_beta_schedule(
-            "linear",
-            beta_start=scale * 0.0001,
-            beta_end=scale * 0.02,
-            num_diffusion_timesteps=num_diffusion_timesteps,
-        )
-    elif schedule_name == "squaredcos_cap_v2":
-        return betas_for_alpha_bar(
-            num_diffusion_timesteps,
-            lambda t: math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2,
-        )
-    else:
-        raise NotImplementedError(f"unknown beta schedule: {schedule_name}")
-
-
-def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):
-    """
-    Create a beta schedule that discretizes the given alpha_t_bar function,
-    which defines the cumulative product of (1-beta) over time from t = [0,1].
-    :param num_diffusion_timesteps: the number of betas to produce.
-    :param alpha_bar: a lambda that takes an argument t from 0 to 1 and
-                      produces the cumulative product of (1-beta) up to that
-                      part of the diffusion process.
-    :param max_beta: the maximum beta to use; use values lower than 1 to
-                     prevent singularities.
-    """
-    betas = []
-    for i in range(num_diffusion_timesteps):
-        t1 = i / num_diffusion_timesteps
-        t2 = (i + 1) / num_diffusion_timesteps
-        betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
-    return np.array(betas)
-
-
-class ModelMeanType(enum.Enum):
-    """
-    Which type of output the model predicts.
-    """
-
-    PREVIOUS_X = enum.auto()  # the model predicts x_{t-1}
-    START_X = enum.auto()  # the model predicts x_0
-    EPSILON = enum.auto()  # the model predicts epsilon
-
-
-class ModelVarType(enum.Enum):
-    """
-    What is used as the model's output variance.
-    The LEARNED_RANGE option has been added to allow the model to predict
-    values between FIXED_SMALL and FIXED_LARGE, making its job easier.
-    """
-
-    LEARNED = enum.auto()
-    FIXED_SMALL = enum.auto()
-    FIXED_LARGE = enum.auto()
-    LEARNED_RANGE = enum.auto()
-
-
-class LossType(enum.Enum):
-    MSE = enum.auto()  # use raw MSE loss (and KL when learning variances)
-    RESCALED_MSE = (
-        enum.auto()
-    )  # use raw MSE loss (with RESCALED_KL when learning variances)
-    KL = enum.auto()  # use the variational lower-bound
-    RESCALED_KL = enum.auto()  # like KL, but rescale to estimate the full VLB
-
-    def is_vb(self):
-        return self == LossType.KL or self == LossType.RESCALED_KL
-
-
-class GaussianDiffusion(th.nn.Module):
-    """
-    Utilities for training and sampling diffusion models.
-    Original ported from this codebase:
-    https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/diffusion_utils_2.py#L42
-    :param betas: a 1-D numpy array of betas for each diffusion timestep,
-                  starting at T and going to 1.
-    """
-
-    def __init__(
-        self,
-        *,
-        betas,
-        model_mean_type,
-        model_var_type,
-        loss_type,
-    ):
-        super(GaussianDiffusion, self).__init__()
-        self.model_mean_type = model_mean_type
-        self.model_var_type = model_var_type
-        self.loss_type = loss_type
-
-        # Use float64 for accuracy.
-        betas = np.array(betas, dtype=np.float64)
-        assert len(betas.shape) == 1, "betas must be 1-D"
-        assert (betas > 0).all() and (betas <= 1).all()
-
-        self.num_timesteps = int(betas.shape[0])
-
-        alphas = 1.0 - betas
-        alphas_cumprod = np.cumprod(alphas, axis=0)
-        alphas_cumprod_prev = np.append(1.0, alphas_cumprod[:-1])
-        alphas_cumprod_next = np.append(alphas_cumprod[1:], 0.0)
-        assert alphas_cumprod_prev.shape == (self.num_timesteps,)
-
-        # calculations for diffusion q(x_t | x_{t-1}) and others
-        sqrt_alphas_cumprod = np.sqrt(alphas_cumprod)
-        sqrt_one_minus_alphas_cumprod = np.sqrt(1.0 - alphas_cumprod)
-        log_one_minus_alphas_cumprod = np.log(1.0 - alphas_cumprod)
-        sqrt_recip_alphas_cumprod = np.sqrt(1.0 / alphas_cumprod)
-        sqrt_recipm1_alphas_cumprod = np.sqrt(1.0 / alphas_cumprod - 1)
-
-        # calculations for posterior q(x_{t-1} | x_t, x_0)
-        posterior_variance = (
-            betas * (1.0 - alphas_cumprod_prev) / (1.0 - alphas_cumprod)
-        )
-        # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
-        posterior_log_variance_clipped = np.log(
-            np.append(posterior_variance[1], posterior_variance[1:])
-        )
-        posterior_mean_coef1 = (
-            betas * np.sqrt(alphas_cumprod_prev) / (1.0 - alphas_cumprod)
-        )
-        posterior_mean_coef2 = (
-            (1.0 - alphas_cumprod_prev) * np.sqrt(alphas) / (1.0 - alphas_cumprod)
-        )
-
-        self.register_buffer("betas", th.from_numpy(betas), persistent=False)
-        self.register_buffer(
-            "alphas_cumprod", th.from_numpy(alphas_cumprod), persistent=False
-        )
-        self.register_buffer(
-            "alphas_cumprod_prev", th.from_numpy(alphas_cumprod_prev), persistent=False
-        )
-        self.register_buffer(
-            "alphas_cumprod_next", th.from_numpy(alphas_cumprod_next), persistent=False
-        )
-
-        self.register_buffer(
-            "sqrt_alphas_cumprod", th.from_numpy(sqrt_alphas_cumprod), persistent=False
-        )
-        self.register_buffer(
-            "sqrt_one_minus_alphas_cumprod",
-            th.from_numpy(sqrt_one_minus_alphas_cumprod),
-            persistent=False,
-        )
-        self.register_buffer(
-            "log_one_minus_alphas_cumprod",
-            th.from_numpy(log_one_minus_alphas_cumprod),
-            persistent=False,
-        )
-        self.register_buffer(
-            "sqrt_recip_alphas_cumprod",
-            th.from_numpy(sqrt_recip_alphas_cumprod),
-            persistent=False,
-        )
-        self.register_buffer(
-            "sqrt_recipm1_alphas_cumprod",
-            th.from_numpy(sqrt_recipm1_alphas_cumprod),
-            persistent=False,
-        )
-
-        self.register_buffer(
-            "posterior_variance", th.from_numpy(posterior_variance), persistent=False
-        )
-        self.register_buffer(
-            "posterior_log_variance_clipped",
-            th.from_numpy(posterior_log_variance_clipped),
-            persistent=False,
-        )
-        self.register_buffer(
-            "posterior_mean_coef1",
-            th.from_numpy(posterior_mean_coef1),
-            persistent=False,
-        )
-        self.register_buffer(
-            "posterior_mean_coef2",
-            th.from_numpy(posterior_mean_coef2),
-            persistent=False,
-        )
-
-    def q_mean_variance(self, x_start, t):
-        """
-        Get the distribution q(x_t | x_0).
-        :param x_start: the [N x C x ...] tensor of noiseless inputs.
-        :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
-        :return: A tuple (mean, variance, log_variance), all of x_start's shape.
-        """
-        mean = (
-            _extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
-        )
-        variance = _extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
-        log_variance = _extract_into_tensor(
-            self.log_one_minus_alphas_cumprod, t, x_start.shape
-        )
-        return mean, variance, log_variance
-
-    def q_sample(self, x_start, t, noise=None):
-        """
-        Diffuse the data for a given number of diffusion steps.
-        In other words, sample from q(x_t | x_0).
-        :param x_start: the initial data batch.
-        :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
-        :param noise: if specified, the split-out normal noise.
-        :return: A noisy version of x_start.
-        """
-        if noise is None:
-            noise = th.randn_like(x_start)
-        assert noise.shape == x_start.shape
-        return (
-            _extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
-            + _extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape)
-            * noise
-        )
-
-    def q_posterior_mean_variance(self, x_start, x_t, t):
-        """
-        Compute the mean and variance of the diffusion posterior:
-            q(x_{t-1} | x_t, x_0)
-        """
-        assert x_start.shape == x_t.shape
-        posterior_mean = (
-            _extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start
-            + _extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
-        )
-        posterior_variance = _extract_into_tensor(self.posterior_variance, t, x_t.shape)
-        posterior_log_variance_clipped = _extract_into_tensor(
-            self.posterior_log_variance_clipped, t, x_t.shape
-        )
-        assert (
-            posterior_mean.shape[0]
-            == posterior_variance.shape[0]
-            == posterior_log_variance_clipped.shape[0]
-            == x_start.shape[0]
-        )
-        return posterior_mean, posterior_variance, posterior_log_variance_clipped
-
-    def p_mean_variance(
-        self,
-        model,
-        x,
-        t,
-        clip_denoised=True,
-        denoised_fn=None,
-        model_kwargs=None,
-        **ignore_kwargs,
-    ):
-        """
-        Apply the model to get p(x_{t-1} | x_t), as well as a prediction of
-        the initial x, x_0.
-        :param model: the model, which takes a signal and a batch of timesteps
-                      as input.
-        :param x: the [N x C x ...] tensor at time t.
-        :param t: a 1-D Tensor of timesteps.
-        :param clip_denoised: if True, clip the denoised signal into [-1, 1].
-        :param denoised_fn: if not None, a function which applies to the
-            x_start prediction before it is used to sample. Applies before
-            clip_denoised.
-        :param model_kwargs: if not None, a dict of extra keyword arguments to
-            pass to the model. This can be used for conditioning.
-        :return: a dict with the following keys:
-                 - 'mean': the model mean output.
-                 - 'variance': the model variance output.
-                 - 'log_variance': the log of 'variance'.
-                 - 'pred_xstart': the prediction for x_0.
-        """
-        if model_kwargs is None:
-            model_kwargs = {}
-
-        B, C = x.shape[:2]
-        assert t.shape == (B,)
-        model_output = model(x, t, **model_kwargs)
-        if isinstance(model_output, tuple):
-            model_output, extra = model_output
-        else:
-            extra = None
-
-        if self.model_var_type in [ModelVarType.LEARNED, ModelVarType.LEARNED_RANGE]:
-            assert model_output.shape == (B, C * 2, *x.shape[2:])
-            model_output, model_var_values = th.split(model_output, C, dim=1)
-            if self.model_var_type == ModelVarType.LEARNED:
-                model_log_variance = model_var_values
-                model_variance = th.exp(model_log_variance)
-            else:
-                min_log = _extract_into_tensor(
-                    self.posterior_log_variance_clipped, t, x.shape
-                )
-                max_log = _extract_into_tensor(th.log(self.betas), t, x.shape)
-                # The model_var_values is [-1, 1] for [min_var, max_var].
-                frac = (model_var_values + 1) / 2
-                model_log_variance = frac * max_log + (1 - frac) * min_log
-                model_variance = th.exp(model_log_variance)
-        else:
-            model_variance, model_log_variance = {
-                # for fixedlarge, we set the initial (log-)variance like so
-                # to get a better decoder log likelihood.
-                ModelVarType.FIXED_LARGE: (
-                    th.cat([self.posterior_variance[1][None], self.betas[1:]]),
-                    th.log(th.cat([self.posterior_variance[1][None], self.betas[1:]])),
-                ),
-                ModelVarType.FIXED_SMALL: (
-                    self.posterior_variance,
-                    self.posterior_log_variance_clipped,
-                ),
-            }[self.model_var_type]
-            model_variance = _extract_into_tensor(model_variance, t, x.shape)
-            model_log_variance = _extract_into_tensor(model_log_variance, t, x.shape)
-
-        def process_xstart(x):
-            if denoised_fn is not None:
-                x = denoised_fn(x)
-            if clip_denoised:
-                return x.clamp(-1, 1)
-            return x
-
-        if self.model_mean_type == ModelMeanType.PREVIOUS_X:
-            pred_xstart = process_xstart(
-                self._predict_xstart_from_xprev(x_t=x, t=t, xprev=model_output)
-            )
-            model_mean = model_output
-        elif self.model_mean_type in [ModelMeanType.START_X, ModelMeanType.EPSILON]:
-            if self.model_mean_type == ModelMeanType.START_X:
-                pred_xstart = process_xstart(model_output)
-            else:
-                pred_xstart = process_xstart(
-                    self._predict_xstart_from_eps(x_t=x, t=t, eps=model_output)
-                )
-            model_mean, _, _ = self.q_posterior_mean_variance(
-                x_start=pred_xstart, x_t=x, t=t
-            )
-        else:
-            raise NotImplementedError(self.model_mean_type)
-
-        assert (
-            model_mean.shape == model_log_variance.shape == pred_xstart.shape == x.shape
-        )
-        return {
-            "mean": model_mean,
-            "variance": model_variance,
-            "log_variance": model_log_variance,
-            "pred_xstart": pred_xstart,
-        }
-
-    def _predict_xstart_from_eps(self, x_t, t, eps):
-        assert x_t.shape == eps.shape
-        return (
-            _extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t
-            - _extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * eps
-        )
-
-    def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
-        return (
-            _extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t
-            - pred_xstart
-        ) / _extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
-
-    def condition_mean(self, cond_fn, p_mean_var, x, t, model_kwargs=None):
-        """
-        Compute the mean for the previous step, given a function cond_fn that
-        computes the gradient of a conditional log probability with respect to
-        x. In particular, cond_fn computes grad(log(p(y|x))), and we want to
-        condition on y.
-        This uses the conditioning strategy from Sohl-Dickstein et al. (2015).
-        """
-        gradient = cond_fn(x, t, **model_kwargs)
-        new_mean = (
-            p_mean_var["mean"].float() + p_mean_var["variance"] * gradient.float()
-        )
-        return new_mean
-
-    def condition_score(self, cond_fn, p_mean_var, x, t, model_kwargs=None):
-        """
-        Compute what the p_mean_variance output would have been, should the
-        model's score function be conditioned by cond_fn.
-        See condition_mean() for details on cond_fn.
-        Unlike condition_mean(), this instead uses the conditioning strategy
-        from Song et al (2020).
-        """
-        alpha_bar = _extract_into_tensor(self.alphas_cumprod, t, x.shape)
-
-        eps = self._predict_eps_from_xstart(x, t, p_mean_var["pred_xstart"])
-        eps = eps - (1 - alpha_bar).sqrt() * cond_fn(x, t, **model_kwargs)
-
-        out = p_mean_var.copy()
-        out["pred_xstart"] = self._predict_xstart_from_eps(x, t, eps)
-        out["mean"], _, _ = self.q_posterior_mean_variance(
-            x_start=out["pred_xstart"], x_t=x, t=t
-        )
-        return out
-
-    def p_sample(
-        self,
-        model,
-        x,
-        t,
-        clip_denoised=True,
-        denoised_fn=None,
-        cond_fn=None,
-        model_kwargs=None,
-    ):
-        """
-        Sample x_{t-1} from the model at the given timestep.
-        :param model: the model to sample from.
-        :param x: the current tensor at x_{t-1}.
-        :param t: the value of t, starting at 0 for the first diffusion step.
-        :param clip_denoised: if True, clip the x_start prediction to [-1, 1].
-        :param denoised_fn: if not None, a function which applies to the
-            x_start prediction before it is used to sample.
-        :param cond_fn: if not None, this is a gradient function that acts
-                        similarly to the model.
-        :param model_kwargs: if not None, a dict of extra keyword arguments to
-            pass to the model. This can be used for conditioning.
-        :return: a dict containing the following keys:
-                 - 'sample': a random sample from the model.
-                 - 'pred_xstart': a prediction of x_0.
-        """
-        out = self.p_mean_variance(
-            model,
-            x,
-            t,
-            clip_denoised=clip_denoised,
-            denoised_fn=denoised_fn,
-            model_kwargs=model_kwargs,
-        )
-        noise = th.randn_like(x)
-        nonzero_mask = (
-            (t != 0).float().view(-1, *([1] * (len(x.shape) - 1)))
-        )  # no noise when t == 0
-        if cond_fn is not None:
-            out["mean"] = self.condition_mean(
-                cond_fn, out, x, t, model_kwargs=model_kwargs
-            )
-        sample = out["mean"] + nonzero_mask * th.exp(0.5 * out["log_variance"]) * noise
-        return {"sample": sample, "pred_xstart": out["pred_xstart"]}
-
-    def p_sample_loop(
-        self,
-        model,
-        shape,
-        noise=None,
-        clip_denoised=True,
-        denoised_fn=None,
-        cond_fn=None,
-        model_kwargs=None,
-        device=None,
-        progress=False,
-    ):
-        """
-        Generate samples from the model.
-        :param model: the model module.
-        :param shape: the shape of the samples, (N, C, H, W).
-        :param noise: if specified, the noise from the encoder to sample.
-                      Should be of the same shape as `shape`.
-        :param clip_denoised: if True, clip x_start predictions to [-1, 1].
-        :param denoised_fn: if not None, a function which applies to the
-            x_start prediction before it is used to sample.
-        :param cond_fn: if not None, this is a gradient function that acts
-                        similarly to the model.
-        :param model_kwargs: if not None, a dict of extra keyword arguments to
-            pass to the model. This can be used for conditioning.
-        :param device: if specified, the device to create the samples on.
-                       If not specified, use a model parameter's device.
-        :param progress: if True, show a tqdm progress bar.
-        :return: a non-differentiable batch of samples.
-        """
-        final = None
-        for sample in self.p_sample_loop_progressive(
-            model,
-            shape,
-            noise=noise,
-            clip_denoised=clip_denoised,
-            denoised_fn=denoised_fn,
-            cond_fn=cond_fn,
-            model_kwargs=model_kwargs,
-            device=device,
-            progress=progress,
-        ):
-            final = sample
-        return final["sample"]
-
-    def p_sample_loop_progressive(
-        self,
-        model,
-        shape,
-        noise=None,
-        clip_denoised=True,
-        denoised_fn=None,
-        cond_fn=None,
-        model_kwargs=None,
-        device=None,
-        progress=False,
-    ):
-        """
-        Generate samples from the model and yield intermediate samples from
-        each timestep of diffusion.
-        Arguments are the same as p_sample_loop().
-        Returns a generator over dicts, where each dict is the return value of
-        p_sample().
-        """
-        if device is None:
-            device = next(model.parameters()).device
-        assert isinstance(shape, (tuple, list))
-        if noise is not None:
-            img = noise
-        else:
-            img = th.randn(*shape, device=device)
-        indices = list(range(self.num_timesteps))[::-1]
-
-        if progress:
-            # Lazy import so that we don't depend on tqdm.
-            from tqdm.auto import tqdm
-
-            indices = tqdm(indices)
-
-        for idx, i in enumerate(indices):
-            t = th.tensor([i] * shape[0], device=device)
-            with th.no_grad():
-                out = self.p_sample(
-                    model,
-                    img,
-                    t,
-                    clip_denoised=clip_denoised,
-                    denoised_fn=denoised_fn,
-                    cond_fn=cond_fn,
-                    model_kwargs=model_kwargs,
-                )
-                yield out
-                img = out["sample"]
-
-    def p_sample_loop_progressive_for_improved_sr(
-        self,
-        model,
-        model_aux,
-        shape,
-        noise=None,
-        clip_denoised=True,
-        denoised_fn=None,
-        cond_fn=None,
-        model_kwargs=None,
-        device=None,
-        progress=False,
-    ):
-        """
-        Modified version of p_sample_loop_progressive for sampling from the improved sr model
-        """
-
-        if device is None:
-            device = next(model.parameters()).device
-        assert isinstance(shape, (tuple, list))
-        if noise is not None:
-            img = noise
-        else:
-            img = th.randn(*shape, device=device)
-        indices = list(range(self.num_timesteps))[::-1]
-
-        if progress:
-            # Lazy import so that we don't depend on tqdm.
-            from tqdm.auto import tqdm
-
-            indices = tqdm(indices)
-
-        for idx, i in enumerate(indices):
-            t = th.tensor([i] * shape[0], device=device)
-            with th.no_grad():
-                out = self.p_sample(
-                    model_aux if len(indices) - 1 == idx else model,
-                    img,
-                    t,
-                    clip_denoised=clip_denoised,
-                    denoised_fn=denoised_fn,
-                    cond_fn=cond_fn,
-                    model_kwargs=model_kwargs,
-                )
-                yield out
-                img = out["sample"]
-
-    def ddim_sample(
-        self,
-        model,
-        x,
-        t,
-        clip_denoised=True,
-        denoised_fn=None,
-        cond_fn=None,
-        model_kwargs=None,
-        eta=0.0,
-    ):
-        """
-        Sample x_{t-1} from the model using DDIM.
-        Same usage as p_sample().
-        """
-        out = self.p_mean_variance(
-            model,
-            x,
-            t,
-            clip_denoised=clip_denoised,
-            denoised_fn=denoised_fn,
-            model_kwargs=model_kwargs,
-        )
-        if cond_fn is not None:
-            out = self.condition_score(cond_fn, out, x, t, model_kwargs=model_kwargs)
-
-        # Usually our model outputs epsilon, but we re-derive it
-        # in case we used x_start or x_prev prediction.
-        eps = self._predict_eps_from_xstart(x, t, out["pred_xstart"])
-
-        alpha_bar = _extract_into_tensor(self.alphas_cumprod, t, x.shape)
-        alpha_bar_prev = _extract_into_tensor(self.alphas_cumprod_prev, t, x.shape)
-        sigma = (
-            eta
-            * th.sqrt((1 - alpha_bar_prev) / (1 - alpha_bar))
-            * th.sqrt(1 - alpha_bar / alpha_bar_prev)
-        )
-        # Equation 12.
-        noise = th.randn_like(x)
-        mean_pred = (
-            out["pred_xstart"] * th.sqrt(alpha_bar_prev)
-            + th.sqrt(1 - alpha_bar_prev - sigma**2) * eps
-        )
-        nonzero_mask = (
-            (t != 0).float().view(-1, *([1] * (len(x.shape) - 1)))
-        )  # no noise when t == 0
-        sample = mean_pred + nonzero_mask * sigma * noise
-        return {"sample": sample, "pred_xstart": out["pred_xstart"]}
-
-    def ddim_reverse_sample(
-        self,
-        model,
-        x,
-        t,
-        clip_denoised=True,
-        denoised_fn=None,
-        cond_fn=None,
-        model_kwargs=None,
-        eta=0.0,
-    ):
-        """
-        Sample x_{t+1} from the model using DDIM reverse ODE.
-        """
-        assert eta == 0.0, "Reverse ODE only for deterministic path"
-        out = self.p_mean_variance(
-            model,
-            x,
-            t,
-            clip_denoised=clip_denoised,
-            denoised_fn=denoised_fn,
-            model_kwargs=model_kwargs,
-        )
-        if cond_fn is not None:
-            out = self.condition_score(cond_fn, out, x, t, model_kwargs=model_kwargs)
-        # Usually our model outputs epsilon, but we re-derive it
-        # in case we used x_start or x_prev prediction.
-        eps = (
-            _extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x.shape) * x
-            - out["pred_xstart"]
-        ) / _extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x.shape)
-        alpha_bar_next = _extract_into_tensor(self.alphas_cumprod_next, t, x.shape)
-
-        # Equation 12. reversed
-        mean_pred = (
-            out["pred_xstart"] * th.sqrt(alpha_bar_next)
-            + th.sqrt(1 - alpha_bar_next) * eps
-        )
-
-        return {"sample": mean_pred, "pred_xstart": out["pred_xstart"]}
-
-    def ddim_sample_loop(
-        self,
-        model,
-        shape,
-        noise=None,
-        clip_denoised=True,
-        denoised_fn=None,
-        cond_fn=None,
-        model_kwargs=None,
-        device=None,
-        progress=False,
-        eta=0.0,
-    ):
-        """
-        Generate samples from the model using DDIM.
-        Same usage as p_sample_loop().
-        """
-        final = None
-        for sample in self.ddim_sample_loop_progressive(
-            model,
-            shape,
-            noise=noise,
-            clip_denoised=clip_denoised,
-            denoised_fn=denoised_fn,
-            cond_fn=cond_fn,
-            model_kwargs=model_kwargs,
-            device=device,
-            progress=progress,
-            eta=eta,
-        ):
-            final = sample
-        return final["sample"]
-
-    def ddim_sample_loop_progressive(
-        self,
-        model,
-        shape,
-        noise=None,
-        clip_denoised=True,
-        denoised_fn=None,
-        cond_fn=None,
-        model_kwargs=None,
-        device=None,
-        progress=False,
-        eta=0.0,
-    ):
-        """
-        Use DDIM to sample from the model and yield intermediate samples from
-        each timestep of DDIM.
-        Same usage as p_sample_loop_progressive().
-        """
-        if device is None:
-            device = next(model.parameters()).device
-        assert isinstance(shape, (tuple, list))
-        if noise is not None:
-            img = noise
-        else:
-            img = th.randn(*shape, device=device)
-        indices = list(range(self.num_timesteps))[::-1]
-
-        if progress:
-            # Lazy import so that we don't depend on tqdm.
-            from tqdm.auto import tqdm
-
-            indices = tqdm(indices)
-
-        for i in indices:
-            t = th.tensor([i] * shape[0], device=device)
-            with th.no_grad():
-                out = self.ddim_sample(
-                    model,
-                    img,
-                    t,
-                    clip_denoised=clip_denoised,
-                    denoised_fn=denoised_fn,
-                    cond_fn=cond_fn,
-                    model_kwargs=model_kwargs,
-                    eta=eta,
-                )
-                yield out
-                img = out["sample"]
-
-
-def _extract_into_tensor(arr, timesteps, broadcast_shape):
-    """
-    Extract values from a 1-D numpy array for a batch of indices.
-    :param arr: the 1-D numpy array.
-    :param timesteps: a tensor of indices into the array to extract.
-    :param broadcast_shape: a larger shape of K dimensions with the batch
-                            dimension equal to the length of timesteps.
-    :return: a tensor of shape [batch_size, 1, ...] where the shape has K dims.
-    """
-    res = arr.to(device=timesteps.device)[timesteps].float()
-    while len(res.shape) < len(broadcast_shape):
-        res = res[..., None]
-    return res + th.zeros(broadcast_shape, device=timesteps.device)
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/karlo/kakao/modules/diffusion/respace.py b/repositories/stable-diffusion-stability-ai/ldm/modules/karlo/kakao/modules/diffusion/respace.py
deleted file mode 100644
index 70c808f8b3ce947a8466026ca0d4ceccabda3745..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/ldm/modules/karlo/kakao/modules/diffusion/respace.py
+++ /dev/null
@@ -1,112 +0,0 @@
-# ------------------------------------------------------------------------------------
-# Adapted from Guided-Diffusion repo (https://github.com/openai/guided-diffusion)
-# ------------------------------------------------------------------------------------
-
-
-import torch as th
-
-from .gaussian_diffusion import GaussianDiffusion
-
-
-def space_timesteps(num_timesteps, section_counts):
-    """
-    Create a list of timesteps to use from an original diffusion process,
-    given the number of timesteps we want to take from equally-sized portions
-    of the original process.
-
-    For example, if there's 300 timesteps and the section counts are [10,15,20]
-    then the first 100 timesteps are strided to be 10 timesteps, the second 100
-    are strided to be 15 timesteps, and the final 100 are strided to be 20.
-
-    :param num_timesteps: the number of diffusion steps in the original
-                          process to divide up.
-    :param section_counts: either a list of numbers, or a string containing
-                           comma-separated numbers, indicating the step count
-                           per section. As a special case, use "ddimN" where N
-                           is a number of steps to use the striding from the
-                           DDIM paper.
-    :return: a set of diffusion steps from the original process to use.
-    """
-    if isinstance(section_counts, str):
-        if section_counts.startswith("ddim"):
-            desired_count = int(section_counts[len("ddim") :])
-            for i in range(1, num_timesteps):
-                if len(range(0, num_timesteps, i)) == desired_count:
-                    return set(range(0, num_timesteps, i))
-            raise ValueError(
-                f"cannot create exactly {num_timesteps} steps with an integer stride"
-            )
-        elif section_counts == "fast27":
-            steps = space_timesteps(num_timesteps, "10,10,3,2,2")
-            # Help reduce DDIM artifacts from noisiest timesteps.
-            steps.remove(num_timesteps - 1)
-            steps.add(num_timesteps - 3)
-            return steps
-        section_counts = [int(x) for x in section_counts.split(",")]
-    size_per = num_timesteps // len(section_counts)
-    extra = num_timesteps % len(section_counts)
-    start_idx = 0
-    all_steps = []
-    for i, section_count in enumerate(section_counts):
-        size = size_per + (1 if i < extra else 0)
-        if size < section_count:
-            raise ValueError(
-                f"cannot divide section of {size} steps into {section_count}"
-            )
-        if section_count <= 1:
-            frac_stride = 1
-        else:
-            frac_stride = (size - 1) / (section_count - 1)
-        cur_idx = 0.0
-        taken_steps = []
-        for _ in range(section_count):
-            taken_steps.append(start_idx + round(cur_idx))
-            cur_idx += frac_stride
-        all_steps += taken_steps
-        start_idx += size
-    return set(all_steps)
-
-
-class SpacedDiffusion(GaussianDiffusion):
-    """
-    A diffusion process which can skip steps in a base diffusion process.
-
-    :param use_timesteps: a collection (sequence or set) of timesteps from the
-                          original diffusion process to retain.
-    :param kwargs: the kwargs to create the base diffusion process.
-    """
-
-    def __init__(self, use_timesteps, **kwargs):
-        self.use_timesteps = set(use_timesteps)
-        self.original_num_steps = len(kwargs["betas"])
-
-        base_diffusion = GaussianDiffusion(**kwargs)  # pylint: disable=missing-kwoa
-        last_alpha_cumprod = 1.0
-        new_betas = []
-        timestep_map = []
-        for i, alpha_cumprod in enumerate(base_diffusion.alphas_cumprod):
-            if i in self.use_timesteps:
-                new_betas.append(1 - alpha_cumprod / last_alpha_cumprod)
-                last_alpha_cumprod = alpha_cumprod
-                timestep_map.append(i)
-        kwargs["betas"] = th.tensor(new_betas).numpy()
-        super().__init__(**kwargs)
-        self.register_buffer("timestep_map", th.tensor(timestep_map), persistent=False)
-
-    def p_mean_variance(self, model, *args, **kwargs):
-        return super().p_mean_variance(self._wrap_model(model), *args, **kwargs)
-
-    def condition_mean(self, cond_fn, *args, **kwargs):
-        return super().condition_mean(self._wrap_model(cond_fn), *args, **kwargs)
-
-    def condition_score(self, cond_fn, *args, **kwargs):
-        return super().condition_score(self._wrap_model(cond_fn), *args, **kwargs)
-
-    def _wrap_model(self, model):
-        def wrapped(x, ts, **kwargs):
-            ts_cpu = ts.detach().to("cpu")
-            return model(
-                x, self.timestep_map[ts_cpu].to(device=ts.device, dtype=ts.dtype), **kwargs
-            )
-
-        return wrapped
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/karlo/kakao/modules/nn.py b/repositories/stable-diffusion-stability-ai/ldm/modules/karlo/kakao/modules/nn.py
deleted file mode 100644
index 2eef3f5a06778b269f99168d2a2962df84422ad0..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/ldm/modules/karlo/kakao/modules/nn.py
+++ /dev/null
@@ -1,114 +0,0 @@
-# ------------------------------------------------------------------------------------
-# Adapted from Guided-Diffusion repo (https://github.com/openai/guided-diffusion)
-# ------------------------------------------------------------------------------------
-
-import math
-
-import torch as th
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-class GroupNorm32(nn.GroupNorm):
-    def __init__(self, num_groups, num_channels, swish, eps=1e-5):
-        super().__init__(num_groups=num_groups, num_channels=num_channels, eps=eps)
-        self.swish = swish
-
-    def forward(self, x):
-        y = super().forward(x.float()).to(x.dtype)
-        if self.swish == 1.0:
-            y = F.silu(y)
-        elif self.swish:
-            y = y * F.sigmoid(y * float(self.swish))
-        return y
-
-
-def conv_nd(dims, *args, **kwargs):
-    """
-    Create a 1D, 2D, or 3D convolution module.
-    """
-    if dims == 1:
-        return nn.Conv1d(*args, **kwargs)
-    elif dims == 2:
-        return nn.Conv2d(*args, **kwargs)
-    elif dims == 3:
-        return nn.Conv3d(*args, **kwargs)
-    raise ValueError(f"unsupported dimensions: {dims}")
-
-
-def linear(*args, **kwargs):
-    """
-    Create a linear module.
-    """
-    return nn.Linear(*args, **kwargs)
-
-
-def avg_pool_nd(dims, *args, **kwargs):
-    """
-    Create a 1D, 2D, or 3D average pooling module.
-    """
-    if dims == 1:
-        return nn.AvgPool1d(*args, **kwargs)
-    elif dims == 2:
-        return nn.AvgPool2d(*args, **kwargs)
-    elif dims == 3:
-        return nn.AvgPool3d(*args, **kwargs)
-    raise ValueError(f"unsupported dimensions: {dims}")
-
-
-def zero_module(module):
-    """
-    Zero out the parameters of a module and return it.
-    """
-    for p in module.parameters():
-        p.detach().zero_()
-    return module
-
-
-def scale_module(module, scale):
-    """
-    Scale the parameters of a module and return it.
-    """
-    for p in module.parameters():
-        p.detach().mul_(scale)
-    return module
-
-
-def normalization(channels, swish=0.0):
-    """
-    Make a standard normalization layer, with an optional swish activation.
-
-    :param channels: number of input channels.
-    :return: an nn.Module for normalization.
-    """
-    return GroupNorm32(num_channels=channels, num_groups=32, swish=swish)
-
-
-def timestep_embedding(timesteps, dim, max_period=10000):
-    """
-    Create sinusoidal timestep embeddings.
-
-    :param timesteps: a 1-D Tensor of N indices, one per batch element.
-                      These may be fractional.
-    :param dim: the dimension of the output.
-    :param max_period: controls the minimum frequency of the embeddings.
-    :return: an [N x dim] Tensor of positional embeddings.
-    """
-    half = dim // 2
-    freqs = th.exp(
-        -math.log(max_period)
-        * th.arange(start=0, end=half, dtype=th.float32, device=timesteps.device)
-        / half
-    )
-    args = timesteps[:, None].float() * freqs[None]
-    embedding = th.cat([th.cos(args), th.sin(args)], dim=-1)
-    if dim % 2:
-        embedding = th.cat([embedding, th.zeros_like(embedding[:, :1])], dim=-1)
-    return embedding
-
-
-def mean_flat(tensor):
-    """
-    Take the mean over all non-batch dimensions.
-    """
-    return tensor.mean(dim=list(range(1, len(tensor.shape))))
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/karlo/kakao/modules/resample.py b/repositories/stable-diffusion-stability-ai/ldm/modules/karlo/kakao/modules/resample.py
deleted file mode 100644
index 485421aa4066a34ec01bdba8a26bd113598461ac..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/ldm/modules/karlo/kakao/modules/resample.py
+++ /dev/null
@@ -1,68 +0,0 @@
-# ------------------------------------------------------------------------------------
-# Modified from Guided-Diffusion (https://github.com/openai/guided-diffusion)
-# ------------------------------------------------------------------------------------
-
-from abc import abstractmethod
-
-import torch as th
-
-
-def create_named_schedule_sampler(name, diffusion):
-    """
-    Create a ScheduleSampler from a library of pre-defined samplers.
-
-    :param name: the name of the sampler.
-    :param diffusion: the diffusion object to sample for.
-    """
-    if name == "uniform":
-        return UniformSampler(diffusion)
-    else:
-        raise NotImplementedError(f"unknown schedule sampler: {name}")
-
-
-class ScheduleSampler(th.nn.Module):
-    """
-    A distribution over timesteps in the diffusion process, intended to reduce
-    variance of the objective.
-
-    By default, samplers perform unbiased importance sampling, in which the
-    objective's mean is unchanged.
-    However, subclasses may override sample() to change how the resampled
-    terms are reweighted, allowing for actual changes in the objective.
-    """
-
-    @abstractmethod
-    def weights(self):
-        """
-        Get a numpy array of weights, one per diffusion step.
-
-        The weights needn't be normalized, but must be positive.
-        """
-
-    def sample(self, batch_size, device):
-        """
-        Importance-sample timesteps for a batch.
-
-        :param batch_size: the number of timesteps.
-        :param device: the torch device to save to.
-        :return: a tuple (timesteps, weights):
-                 - timesteps: a tensor of timestep indices.
-                 - weights: a tensor of weights to scale the resulting losses.
-        """
-        w = self.weights()
-        p = w / th.sum(w)
-        indices = p.multinomial(batch_size, replacement=True)
-        weights = 1 / (len(p) * p[indices])
-        return indices, weights
-
-
-class UniformSampler(ScheduleSampler):
-    def __init__(self, diffusion):
-        super(UniformSampler, self).__init__()
-        self.diffusion = diffusion
-        self.register_buffer(
-            "_weights", th.ones([diffusion.num_timesteps]), persistent=False
-        )
-
-    def weights(self):
-        return self._weights
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/karlo/kakao/modules/unet.py b/repositories/stable-diffusion-stability-ai/ldm/modules/karlo/kakao/modules/unet.py
deleted file mode 100644
index c99d0b791876add8b85354d05d61a6ae2c852e68..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/ldm/modules/karlo/kakao/modules/unet.py
+++ /dev/null
@@ -1,792 +0,0 @@
-# ------------------------------------------------------------------------------------
-# Modified from Guided-Diffusion (https://github.com/openai/guided-diffusion)
-# ------------------------------------------------------------------------------------
-
-import math
-from abc import abstractmethod
-
-import torch as th
-import torch.nn as nn
-import torch.nn.functional as F
-
-from .nn import (
-    avg_pool_nd,
-    conv_nd,
-    linear,
-    normalization,
-    timestep_embedding,
-    zero_module,
-)
-from .xf import LayerNorm
-
-
-class TimestepBlock(nn.Module):
-    """
-    Any module where forward() takes timestep embeddings as a second argument.
-    """
-
-    @abstractmethod
-    def forward(self, x, emb):
-        """
-        Apply the module to `x` given `emb` timestep embeddings.
-        """
-
-
-class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
-    """
-    A sequential module that passes timestep embeddings to the children that
-    support it as an extra input.
-    """
-
-    def forward(self, x, emb, encoder_out=None, mask=None):
-        for layer in self:
-            if isinstance(layer, TimestepBlock):
-                x = layer(x, emb)
-            elif isinstance(layer, AttentionBlock):
-                x = layer(x, encoder_out, mask=mask)
-            else:
-                x = layer(x)
-        return x
-
-
-class Upsample(nn.Module):
-    """
-    An upsampling layer with an optional convolution.
-
-    :param channels: channels in the inputs and outputs.
-    :param use_conv: a bool determining if a convolution is applied.
-    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
-                 upsampling occurs in the inner-two dimensions.
-    """
-
-    def __init__(self, channels, use_conv, dims=2, out_channels=None):
-        super().__init__()
-        self.channels = channels
-        self.out_channels = out_channels or channels
-        self.use_conv = use_conv
-        self.dims = dims
-        if use_conv:
-            self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=1)
-
-    def forward(self, x):
-        assert x.shape[1] == self.channels
-        if self.dims == 3:
-            x = F.interpolate(
-                x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest"
-            )
-        else:
-            x = F.interpolate(x, scale_factor=2, mode="nearest")
-        if self.use_conv:
-            x = self.conv(x)
-        return x
-
-
-class Downsample(nn.Module):
-    """
-    A downsampling layer with an optional convolution.
-
-    :param channels: channels in the inputs and outputs.
-    :param use_conv: a bool determining if a convolution is applied.
-    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
-                 downsampling occurs in the inner-two dimensions.
-    """
-
-    def __init__(self, channels, use_conv, dims=2, out_channels=None):
-        super().__init__()
-        self.channels = channels
-        self.out_channels = out_channels or channels
-        self.use_conv = use_conv
-        self.dims = dims
-        stride = 2 if dims != 3 else (1, 2, 2)
-        if use_conv:
-            self.op = conv_nd(
-                dims, self.channels, self.out_channels, 3, stride=stride, padding=1
-            )
-        else:
-            assert self.channels == self.out_channels
-            self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)
-
-    def forward(self, x):
-        assert x.shape[1] == self.channels
-        return self.op(x)
-
-
-class ResBlock(TimestepBlock):
-    """
-    A residual block that can optionally change the number of channels.
-
-    :param channels: the number of input channels.
-    :param emb_channels: the number of timestep embedding channels.
-    :param dropout: the rate of dropout.
-    :param out_channels: if specified, the number of out channels.
-    :param use_conv: if True and out_channels is specified, use a spatial
-        convolution instead of a smaller 1x1 convolution to change the
-        channels in the skip connection.
-    :param dims: determines if the signal is 1D, 2D, or 3D.
-    :param use_checkpoint: if True, use gradient checkpointing on this module.
-    :param up: if True, use this block for upsampling.
-    :param down: if True, use this block for downsampling.
-    """
-
-    def __init__(
-        self,
-        channels,
-        emb_channels,
-        dropout,
-        out_channels=None,
-        use_conv=False,
-        use_scale_shift_norm=False,
-        dims=2,
-        use_checkpoint=False,
-        up=False,
-        down=False,
-    ):
-        super().__init__()
-        self.channels = channels
-        self.emb_channels = emb_channels
-        self.dropout = dropout
-        self.out_channels = out_channels or channels
-        self.use_conv = use_conv
-        self.use_checkpoint = use_checkpoint
-        self.use_scale_shift_norm = use_scale_shift_norm
-
-        self.in_layers = nn.Sequential(
-            normalization(channels, swish=1.0),
-            nn.Identity(),
-            conv_nd(dims, channels, self.out_channels, 3, padding=1),
-        )
-
-        self.updown = up or down
-
-        if up:
-            self.h_upd = Upsample(channels, False, dims)
-            self.x_upd = Upsample(channels, False, dims)
-        elif down:
-            self.h_upd = Downsample(channels, False, dims)
-            self.x_upd = Downsample(channels, False, dims)
-        else:
-            self.h_upd = self.x_upd = nn.Identity()
-
-        self.emb_layers = nn.Sequential(
-            nn.SiLU(),
-            linear(
-                emb_channels,
-                2 * self.out_channels if use_scale_shift_norm else self.out_channels,
-            ),
-        )
-        self.out_layers = nn.Sequential(
-            normalization(
-                self.out_channels, swish=0.0 if use_scale_shift_norm else 1.0
-            ),
-            nn.SiLU() if use_scale_shift_norm else nn.Identity(),
-            nn.Dropout(p=dropout),
-            zero_module(
-                conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1)
-            ),
-        )
-
-        if self.out_channels == channels:
-            self.skip_connection = nn.Identity()
-        elif use_conv:
-            self.skip_connection = conv_nd(
-                dims, channels, self.out_channels, 3, padding=1
-            )
-        else:
-            self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)
-
-    def forward(self, x, emb):
-        """
-        Apply the block to a Tensor, conditioned on a timestep embedding.
-
-        :param x: an [N x C x ...] Tensor of features.
-        :param emb: an [N x emb_channels] Tensor of timestep embeddings.
-        :return: an [N x C x ...] Tensor of outputs.
-        """
-        if self.updown:
-            in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
-            h = in_rest(x)
-            h = self.h_upd(h)
-            x = self.x_upd(x)
-            h = in_conv(h)
-        else:
-            h = self.in_layers(x)
-        emb_out = self.emb_layers(emb)
-        while len(emb_out.shape) < len(h.shape):
-            emb_out = emb_out[..., None]
-        if self.use_scale_shift_norm:
-            out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
-            scale, shift = th.chunk(emb_out, 2, dim=1)
-            h = out_norm(h) * (1 + scale) + shift
-            h = out_rest(h)
-        else:
-            h = h + emb_out
-            h = self.out_layers(h)
-        return self.skip_connection(x) + h
-
-
-class ResBlockNoTimeEmbedding(nn.Module):
-    """
-    A residual block without time embedding
-
-    :param channels: the number of input channels.
-    :param emb_channels: the number of timestep embedding channels.
-    :param dropout: the rate of dropout.
-    :param out_channels: if specified, the number of out channels.
-    :param use_conv: if True and out_channels is specified, use a spatial
-        convolution instead of a smaller 1x1 convolution to change the
-        channels in the skip connection.
-    :param dims: determines if the signal is 1D, 2D, or 3D.
-    :param use_checkpoint: if True, use gradient checkpointing on this module.
-    :param up: if True, use this block for upsampling.
-    :param down: if True, use this block for downsampling.
-    """
-
-    def __init__(
-        self,
-        channels,
-        emb_channels,
-        dropout,
-        out_channels=None,
-        use_conv=False,
-        dims=2,
-        use_checkpoint=False,
-        up=False,
-        down=False,
-        **kwargs,
-    ):
-        super().__init__()
-        self.channels = channels
-        self.emb_channels = emb_channels
-        self.dropout = dropout
-        self.out_channels = out_channels or channels
-        self.use_conv = use_conv
-        self.use_checkpoint = use_checkpoint
-
-        self.in_layers = nn.Sequential(
-            normalization(channels, swish=1.0),
-            nn.Identity(),
-            conv_nd(dims, channels, self.out_channels, 3, padding=1),
-        )
-
-        self.updown = up or down
-
-        if up:
-            self.h_upd = Upsample(channels, False, dims)
-            self.x_upd = Upsample(channels, False, dims)
-        elif down:
-            self.h_upd = Downsample(channels, False, dims)
-            self.x_upd = Downsample(channels, False, dims)
-        else:
-            self.h_upd = self.x_upd = nn.Identity()
-
-        self.out_layers = nn.Sequential(
-            normalization(self.out_channels, swish=1.0),
-            nn.Dropout(p=dropout),
-            zero_module(
-                conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1)
-            ),
-        )
-
-        if self.out_channels == channels:
-            self.skip_connection = nn.Identity()
-        elif use_conv:
-            self.skip_connection = conv_nd(
-                dims, channels, self.out_channels, 3, padding=1
-            )
-        else:
-            self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)
-
-    def forward(self, x, emb=None):
-        """
-        Apply the block to a Tensor, NOT conditioned on a timestep embedding.
-
-        :param x: an [N x C x ...] Tensor of features.
-        :param emb: an [N x emb_channels] Tensor of timestep embeddings.
-        :return: an [N x C x ...] Tensor of outputs.
-        """
-        assert emb is None
-
-        if self.updown:
-            in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
-            h = in_rest(x)
-            h = self.h_upd(h)
-            x = self.x_upd(x)
-            h = in_conv(h)
-        else:
-            h = self.in_layers(x)
-        h = self.out_layers(h)
-        return self.skip_connection(x) + h
-
-
-class AttentionBlock(nn.Module):
-    """
-    An attention block that allows spatial positions to attend to each other.
-
-    Originally ported from here, but adapted to the N-d case.
-    https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
-    """
-
-    def __init__(
-        self,
-        channels,
-        num_heads=1,
-        num_head_channels=-1,
-        use_checkpoint=False,
-        encoder_channels=None,
-    ):
-        super().__init__()
-        self.channels = channels
-        if num_head_channels == -1:
-            self.num_heads = num_heads
-        else:
-            assert (
-                channels % num_head_channels == 0
-            ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
-            self.num_heads = channels // num_head_channels
-        self.use_checkpoint = use_checkpoint
-        self.norm = normalization(channels, swish=0.0)
-        self.qkv = conv_nd(1, channels, channels * 3, 1)
-        self.attention = QKVAttention(self.num_heads)
-
-        if encoder_channels is not None:
-            self.encoder_kv = conv_nd(1, encoder_channels, channels * 2, 1)
-        self.proj_out = zero_module(conv_nd(1, channels, channels, 1))
-
-    def forward(self, x, encoder_out=None, mask=None):
-        b, c, *spatial = x.shape
-        qkv = self.qkv(self.norm(x).view(b, c, -1))
-        if encoder_out is not None:
-            encoder_out = self.encoder_kv(encoder_out)
-            h = self.attention(qkv, encoder_out, mask=mask)
-        else:
-            h = self.attention(qkv)
-        h = self.proj_out(h)
-        return x + h.reshape(b, c, *spatial)
-
-
-class QKVAttention(nn.Module):
-    """
-    A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping
-    """
-
-    def __init__(self, n_heads):
-        super().__init__()
-        self.n_heads = n_heads
-
-    def forward(self, qkv, encoder_kv=None, mask=None):
-        """
-        Apply QKV attention.
-
-        :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs.
-        :return: an [N x (H * C) x T] tensor after attention.
-        """
-        bs, width, length = qkv.shape
-        assert width % (3 * self.n_heads) == 0
-        ch = width // (3 * self.n_heads)
-        q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1)
-        if encoder_kv is not None:
-            assert encoder_kv.shape[1] == self.n_heads * ch * 2
-            ek, ev = encoder_kv.reshape(bs * self.n_heads, ch * 2, -1).split(ch, dim=1)
-            k = th.cat([ek, k], dim=-1)
-            v = th.cat([ev, v], dim=-1)
-        scale = 1 / math.sqrt(math.sqrt(ch))
-        weight = th.einsum("bct,bcs->bts", q * scale, k * scale)
-        if mask is not None:
-            mask = F.pad(mask, (0, length), value=0.0)
-            mask = (
-                mask.unsqueeze(1)
-                .expand(-1, self.n_heads, -1)
-                .reshape(bs * self.n_heads, 1, -1)
-            )
-            weight = weight + mask
-        weight = th.softmax(weight, dim=-1)
-        a = th.einsum("bts,bcs->bct", weight, v)
-        return a.reshape(bs, -1, length)
-
-
-class UNetModel(nn.Module):
-    """
-    The full UNet model with attention and timestep embedding.
-
-    :param in_channels: channels in the input Tensor.
-    :param model_channels: base channel count for the model.
-    :param out_channels: channels in the output Tensor.
-    :param num_res_blocks: number of residual blocks per downsample.
-    :param attention_resolutions: a collection of downsample rates at which
-        attention will take place. May be a set, list, or tuple.
-        For example, if this contains 4, then at 4x downsampling, attention
-        will be used.
-    :param dropout: the dropout probability.
-    :param channel_mult: channel multiplier for each level of the UNet.
-    :param conv_resample: if True, use learned convolutions for upsampling and
-        downsampling.
-    :param dims: determines if the signal is 1D, 2D, or 3D.
-    :param clip_dim: dimension of clip feature.
-    :param num_classes: if specified (as an int), then this model will be
-        class-conditional with `num_classes` classes.
-    :param use_checkpoint: use gradient checkpointing to reduce memory usage.
-    :param num_heads: the number of attention heads in each attention layer.
-    :param num_heads_channels: if specified, ignore num_heads and instead use
-                               a fixed channel width per attention head.
-    :param num_heads_upsample: works with num_heads to set a different number
-                               of heads for upsampling. Deprecated.
-    :param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
-    :param resblock_updown: use residual blocks for up/downsampling.
-    :param encoder_channels: use to make the dimension of query and kv same in AttentionBlock.
-    :param use_time_embedding: use time embedding for condition.
-    """
-
-    def __init__(
-        self,
-        in_channels,
-        model_channels,
-        out_channels,
-        num_res_blocks,
-        attention_resolutions,
-        dropout=0,
-        channel_mult=(1, 2, 4, 8),
-        conv_resample=True,
-        dims=2,
-        clip_dim=None,
-        use_checkpoint=False,
-        num_heads=1,
-        num_head_channels=-1,
-        num_heads_upsample=-1,
-        use_scale_shift_norm=False,
-        use_middle_attention=True,
-        resblock_updown=False,
-        encoder_channels=None,
-        use_time_embedding=True,
-    ):
-        super().__init__()
-
-        if num_heads_upsample == -1:
-            num_heads_upsample = num_heads
-
-        self.in_channels = in_channels
-        self.model_channels = model_channels
-        self.out_channels = out_channels
-        self.num_res_blocks = num_res_blocks
-        self.attention_resolutions = attention_resolutions
-        self.dropout = dropout
-        self.channel_mult = channel_mult
-        self.conv_resample = conv_resample
-        self.clip_dim = clip_dim
-        self.use_checkpoint = use_checkpoint
-        self.num_heads = num_heads
-        self.num_head_channels = num_head_channels
-        self.num_heads_upsample = num_heads_upsample
-        self.use_middle_attention = use_middle_attention
-        self.use_time_embedding = use_time_embedding
-
-        if self.use_time_embedding:
-            time_embed_dim = model_channels * 4
-            self.time_embed = nn.Sequential(
-                linear(model_channels, time_embed_dim),
-                nn.SiLU(),
-                linear(time_embed_dim, time_embed_dim),
-            )
-
-            if self.clip_dim is not None:
-                self.clip_emb = nn.Linear(clip_dim, time_embed_dim)
-        else:
-            time_embed_dim = None
-
-        CustomResidualBlock = (
-            ResBlock if self.use_time_embedding else ResBlockNoTimeEmbedding
-        )
-        ch = input_ch = int(channel_mult[0] * model_channels)
-        self.input_blocks = nn.ModuleList(
-            [TimestepEmbedSequential(conv_nd(dims, in_channels, ch, 3, padding=1))]
-        )
-        self._feature_size = ch
-        input_block_chans = [ch]
-        ds = 1
-        for level, mult in enumerate(channel_mult):
-            for _ in range(num_res_blocks):
-                layers = [
-                    CustomResidualBlock(
-                        ch,
-                        time_embed_dim,
-                        dropout,
-                        out_channels=int(mult * model_channels),
-                        dims=dims,
-                        use_checkpoint=use_checkpoint,
-                        use_scale_shift_norm=use_scale_shift_norm,
-                    )
-                ]
-                ch = int(mult * model_channels)
-                if ds in attention_resolutions:
-                    layers.append(
-                        AttentionBlock(
-                            ch,
-                            use_checkpoint=use_checkpoint,
-                            num_heads=num_heads,
-                            num_head_channels=num_head_channels,
-                            encoder_channels=encoder_channels,
-                        )
-                    )
-                self.input_blocks.append(TimestepEmbedSequential(*layers))
-                self._feature_size += ch
-                input_block_chans.append(ch)
-            if level != len(channel_mult) - 1:
-                out_ch = ch
-                self.input_blocks.append(
-                    TimestepEmbedSequential(
-                        CustomResidualBlock(
-                            ch,
-                            time_embed_dim,
-                            dropout,
-                            out_channels=out_ch,
-                            dims=dims,
-                            use_checkpoint=use_checkpoint,
-                            use_scale_shift_norm=use_scale_shift_norm,
-                            down=True,
-                        )
-                        if resblock_updown
-                        else Downsample(
-                            ch, conv_resample, dims=dims, out_channels=out_ch
-                        )
-                    )
-                )
-                ch = out_ch
-                input_block_chans.append(ch)
-                ds *= 2
-                self._feature_size += ch
-
-        self.middle_block = TimestepEmbedSequential(
-            CustomResidualBlock(
-                ch,
-                time_embed_dim,
-                dropout,
-                dims=dims,
-                use_checkpoint=use_checkpoint,
-                use_scale_shift_norm=use_scale_shift_norm,
-            ),
-            *(
-                AttentionBlock(
-                    ch,
-                    use_checkpoint=use_checkpoint,
-                    num_heads=num_heads,
-                    num_head_channels=num_head_channels,
-                    encoder_channels=encoder_channels,
-                ),
-            )
-            if self.use_middle_attention
-            else tuple(),  # add AttentionBlock or not
-            CustomResidualBlock(
-                ch,
-                time_embed_dim,
-                dropout,
-                dims=dims,
-                use_checkpoint=use_checkpoint,
-                use_scale_shift_norm=use_scale_shift_norm,
-            ),
-        )
-        self._feature_size += ch
-
-        self.output_blocks = nn.ModuleList([])
-        for level, mult in list(enumerate(channel_mult))[::-1]:
-            for i in range(num_res_blocks + 1):
-                ich = input_block_chans.pop()
-                layers = [
-                    CustomResidualBlock(
-                        ch + ich,
-                        time_embed_dim,
-                        dropout,
-                        out_channels=int(model_channels * mult),
-                        dims=dims,
-                        use_checkpoint=use_checkpoint,
-                        use_scale_shift_norm=use_scale_shift_norm,
-                    )
-                ]
-                ch = int(model_channels * mult)
-                if ds in attention_resolutions:
-                    layers.append(
-                        AttentionBlock(
-                            ch,
-                            use_checkpoint=use_checkpoint,
-                            num_heads=num_heads_upsample,
-                            num_head_channels=num_head_channels,
-                            encoder_channels=encoder_channels,
-                        )
-                    )
-                if level and i == num_res_blocks:
-                    out_ch = ch
-                    layers.append(
-                        CustomResidualBlock(
-                            ch,
-                            time_embed_dim,
-                            dropout,
-                            out_channels=out_ch,
-                            dims=dims,
-                            use_checkpoint=use_checkpoint,
-                            use_scale_shift_norm=use_scale_shift_norm,
-                            up=True,
-                        )
-                        if resblock_updown
-                        else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch)
-                    )
-                    ds //= 2
-                self.output_blocks.append(TimestepEmbedSequential(*layers))
-                self._feature_size += ch
-
-        self.out = nn.Sequential(
-            normalization(ch, swish=1.0),
-            nn.Identity(),
-            zero_module(conv_nd(dims, input_ch, out_channels, 3, padding=1)),
-        )
-
-    def forward(self, x, timesteps, y=None):
-        """
-        Apply the model to an input batch.
-
-        :param x: an [N x C x ...] Tensor of inputs.
-        :param timesteps: a 1-D batch of timesteps.
-        :param y: an [N] Tensor of labels, if class-conditional.
-        :return: an [N x C x ...] Tensor of outputs.
-        """
-        assert (y is not None) == (
-            self.clip_dim is not None
-        ), "must specify y if and only if the model is clip-rep-conditional"
-
-        hs = []
-        if self.use_time_embedding:
-            emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
-            if self.clip_dim is not None:
-                emb = emb + self.clip_emb(y)
-        else:
-            emb = None
-
-        h = x
-        for module in self.input_blocks:
-            h = module(h, emb)
-            hs.append(h)
-        h = self.middle_block(h, emb)
-        for module in self.output_blocks:
-            h = th.cat([h, hs.pop()], dim=1)
-            h = module(h, emb)
-
-        return self.out(h)
-
-
-class SuperResUNetModel(UNetModel):
-    """
-    A UNetModel that performs super-resolution.
-
-    Expects an extra kwarg `low_res` to condition on a low-resolution image.
-    Assumes that the shape of low-resolution and the input should be the same.
-    """
-
-    def __init__(self, *args, **kwargs):
-        if "in_channels" in kwargs:
-            kwargs = dict(kwargs)
-            kwargs["in_channels"] = kwargs["in_channels"] * 2
-        else:
-            # Curse you, Python. Or really, just curse positional arguments :|.
-            args = list(args)
-            args[1] = args[1] * 2
-        super().__init__(*args, **kwargs)
-
-    def forward(self, x, timesteps, low_res=None, **kwargs):
-        _, _, new_height, new_width = x.shape
-        assert new_height == low_res.shape[2] and new_width == low_res.shape[3]
-
-        x = th.cat([x, low_res], dim=1)
-        return super().forward(x, timesteps, **kwargs)
-
-
-class PLMImUNet(UNetModel):
-    """
-    A UNetModel that conditions on text with a pretrained text encoder in CLIP.
-
-    :param text_ctx: number of text tokens to expect.
-    :param xf_width: width of the transformer.
-    :param clip_emb_mult: #extra tokens by projecting clip text feature.
-    :param clip_emb_type: type of condition (here, we fix clip image feature).
-    :param clip_emb_drop: dropout rato of clip image feature for cfg.
-    """
-
-    def __init__(
-        self,
-        text_ctx,
-        xf_width,
-        *args,
-        clip_emb_mult=None,
-        clip_emb_type="image",
-        clip_emb_drop=0.0,
-        **kwargs,
-    ):
-        self.text_ctx = text_ctx
-        self.xf_width = xf_width
-        self.clip_emb_mult = clip_emb_mult
-        self.clip_emb_type = clip_emb_type
-        self.clip_emb_drop = clip_emb_drop
-
-        if not xf_width:
-            super().__init__(*args, **kwargs, encoder_channels=None)
-        else:
-            super().__init__(*args, **kwargs, encoder_channels=xf_width)
-
-        # Project text encoded feat seq from pre-trained text encoder in CLIP
-        self.text_seq_proj = nn.Sequential(
-            nn.Linear(self.clip_dim, xf_width),
-            LayerNorm(xf_width),
-        )
-        # Project CLIP text feat
-        self.text_feat_proj = nn.Linear(self.clip_dim, self.model_channels * 4)
-
-        assert clip_emb_mult is not None
-        assert clip_emb_type == "image"
-        assert self.clip_dim is not None, "CLIP representation dim should be specified"
-
-        self.clip_tok_proj = nn.Linear(
-            self.clip_dim, self.xf_width * self.clip_emb_mult
-        )
-        if self.clip_emb_drop > 0:
-            self.cf_param = nn.Parameter(th.empty(self.clip_dim, dtype=th.float32))
-
-    def proc_clip_emb_drop(self, feat):
-        if self.clip_emb_drop > 0:
-            bsz, feat_dim = feat.shape
-            assert (
-                feat_dim == self.clip_dim
-            ), f"CLIP input dim: {feat_dim}, model CLIP dim: {self.clip_dim}"
-            drop_idx = th.rand((bsz,), device=feat.device) < self.clip_emb_drop
-            feat = th.where(
-                drop_idx[..., None], self.cf_param[None].type_as(feat), feat
-            )
-        return feat
-
-    def forward(
-        self, x, timesteps, txt_feat=None, txt_feat_seq=None, mask=None, y=None
-    ):
-        bsz = x.shape[0]
-        hs = []
-        emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
-        emb = emb + self.clip_emb(y)
-
-        xf_out = self.text_seq_proj(txt_feat_seq)
-        xf_out = xf_out.permute(0, 2, 1)
-        emb = emb + self.text_feat_proj(txt_feat)
-        xf_out = th.cat(
-            [
-                self.clip_tok_proj(y).reshape(bsz, -1, self.clip_emb_mult),
-                xf_out,
-            ],
-            dim=2,
-        )
-        mask = F.pad(mask, (self.clip_emb_mult, 0), value=True)
-        mask = th.where(mask, 0.0, float("-inf"))
-
-        h = x
-        for module in self.input_blocks:
-            h = module(h, emb, xf_out, mask=mask)
-            hs.append(h)
-        h = self.middle_block(h, emb, xf_out, mask=mask)
-        for module in self.output_blocks:
-            h = th.cat([h, hs.pop()], dim=1)
-            h = module(h, emb, xf_out, mask=mask)
-        h = self.out(h)
-
-        return h
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/karlo/kakao/modules/xf.py b/repositories/stable-diffusion-stability-ai/ldm/modules/karlo/kakao/modules/xf.py
deleted file mode 100644
index 66d7d4a2f3fd9fec420ac8272ae00b96c634a3a9..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/ldm/modules/karlo/kakao/modules/xf.py
+++ /dev/null
@@ -1,231 +0,0 @@
-# ------------------------------------------------------------------------------------
-# Adapted from the repos below:
-# (a) Guided-Diffusion (https://github.com/openai/guided-diffusion)
-# (b) CLIP ViT (https://github.com/openai/CLIP/)
-# ------------------------------------------------------------------------------------
-
-import math
-
-import torch as th
-import torch.nn as nn
-import torch.nn.functional as F
-
-from .nn import timestep_embedding
-
-
-def convert_module_to_f16(param):
-    """
-    Convert primitive modules to float16.
-    """
-    if isinstance(param, (nn.Linear, nn.Conv2d, nn.ConvTranspose2d)):
-        param.weight.data = param.weight.data.half()
-        if param.bias is not None:
-            param.bias.data = param.bias.data.half()
-
-
-class LayerNorm(nn.LayerNorm):
-    """
-    Implementation that supports fp16 inputs but fp32 gains/biases.
-    """
-
-    def forward(self, x: th.Tensor):
-        return super().forward(x.float()).to(x.dtype)
-
-
-class MultiheadAttention(nn.Module):
-    def __init__(self, n_ctx, width, heads):
-        super().__init__()
-        self.n_ctx = n_ctx
-        self.width = width
-        self.heads = heads
-        self.c_qkv = nn.Linear(width, width * 3)
-        self.c_proj = nn.Linear(width, width)
-        self.attention = QKVMultiheadAttention(heads, n_ctx)
-
-    def forward(self, x, mask=None):
-        x = self.c_qkv(x)
-        x = self.attention(x, mask=mask)
-        x = self.c_proj(x)
-        return x
-
-
-class MLP(nn.Module):
-    def __init__(self, width):
-        super().__init__()
-        self.width = width
-        self.c_fc = nn.Linear(width, width * 4)
-        self.c_proj = nn.Linear(width * 4, width)
-        self.gelu = nn.GELU()
-
-    def forward(self, x):
-        return self.c_proj(self.gelu(self.c_fc(x)))
-
-
-class QKVMultiheadAttention(nn.Module):
-    def __init__(self, n_heads: int, n_ctx: int):
-        super().__init__()
-        self.n_heads = n_heads
-        self.n_ctx = n_ctx
-
-    def forward(self, qkv, mask=None):
-        bs, n_ctx, width = qkv.shape
-        attn_ch = width // self.n_heads // 3
-        scale = 1 / math.sqrt(math.sqrt(attn_ch))
-        qkv = qkv.view(bs, n_ctx, self.n_heads, -1)
-        q, k, v = th.split(qkv, attn_ch, dim=-1)
-        weight = th.einsum("bthc,bshc->bhts", q * scale, k * scale)
-        wdtype = weight.dtype
-        if mask is not None:
-            weight = weight + mask[:, None, ...]
-        weight = th.softmax(weight, dim=-1).type(wdtype)
-        return th.einsum("bhts,bshc->bthc", weight, v).reshape(bs, n_ctx, -1)
-
-
-class ResidualAttentionBlock(nn.Module):
-    def __init__(
-        self,
-        n_ctx: int,
-        width: int,
-        heads: int,
-    ):
-        super().__init__()
-
-        self.attn = MultiheadAttention(
-            n_ctx,
-            width,
-            heads,
-        )
-        self.ln_1 = LayerNorm(width)
-        self.mlp = MLP(width)
-        self.ln_2 = LayerNorm(width)
-
-    def forward(self, x, mask=None):
-        x = x + self.attn(self.ln_1(x), mask=mask)
-        x = x + self.mlp(self.ln_2(x))
-        return x
-
-
-class Transformer(nn.Module):
-    def __init__(
-        self,
-        n_ctx: int,
-        width: int,
-        layers: int,
-        heads: int,
-    ):
-        super().__init__()
-        self.n_ctx = n_ctx
-        self.width = width
-        self.layers = layers
-        self.resblocks = nn.ModuleList(
-            [
-                ResidualAttentionBlock(
-                    n_ctx,
-                    width,
-                    heads,
-                )
-                for _ in range(layers)
-            ]
-        )
-
-    def forward(self, x, mask=None):
-        for block in self.resblocks:
-            x = block(x, mask=mask)
-        return x
-
-
-class PriorTransformer(nn.Module):
-    """
-    A Causal Transformer that conditions on CLIP text embedding, text.
-
-    :param text_ctx: number of text tokens to expect.
-    :param xf_width: width of the transformer.
-    :param xf_layers: depth of the transformer.
-    :param xf_heads: heads in the transformer.
-    :param xf_final_ln: use a LayerNorm after the output layer.
-    :param clip_dim: dimension of clip feature.
-    """
-
-    def __init__(
-        self,
-        text_ctx,
-        xf_width,
-        xf_layers,
-        xf_heads,
-        xf_final_ln,
-        clip_dim,
-    ):
-        super().__init__()
-
-        self.text_ctx = text_ctx
-        self.xf_width = xf_width
-        self.xf_layers = xf_layers
-        self.xf_heads = xf_heads
-        self.clip_dim = clip_dim
-        self.ext_len = 4
-
-        self.time_embed = nn.Sequential(
-            nn.Linear(xf_width, xf_width),
-            nn.SiLU(),
-            nn.Linear(xf_width, xf_width),
-        )
-        self.text_enc_proj = nn.Linear(clip_dim, xf_width)
-        self.text_emb_proj = nn.Linear(clip_dim, xf_width)
-        self.clip_img_proj = nn.Linear(clip_dim, xf_width)
-        self.out_proj = nn.Linear(xf_width, clip_dim)
-        self.transformer = Transformer(
-            text_ctx + self.ext_len,
-            xf_width,
-            xf_layers,
-            xf_heads,
-        )
-        if xf_final_ln:
-            self.final_ln = LayerNorm(xf_width)
-        else:
-            self.final_ln = None
-
-        self.positional_embedding = nn.Parameter(
-            th.empty(1, text_ctx + self.ext_len, xf_width)
-        )
-        self.prd_emb = nn.Parameter(th.randn((1, 1, xf_width)))
-
-        nn.init.normal_(self.prd_emb, std=0.01)
-        nn.init.normal_(self.positional_embedding, std=0.01)
-
-    def forward(
-        self,
-        x,
-        timesteps,
-        text_emb=None,
-        text_enc=None,
-        mask=None,
-        causal_mask=None,
-    ):
-        bsz = x.shape[0]
-        mask = F.pad(mask, (0, self.ext_len), value=True)
-
-        t_emb = self.time_embed(timestep_embedding(timesteps, self.xf_width))
-        text_enc = self.text_enc_proj(text_enc)
-        text_emb = self.text_emb_proj(text_emb)
-        x = self.clip_img_proj(x)
-
-        input_seq = [
-            text_enc,
-            text_emb[:, None, :],
-            t_emb[:, None, :],
-            x[:, None, :],
-            self.prd_emb.to(x.dtype).expand(bsz, -1, -1),
-        ]
-        input = th.cat(input_seq, dim=1)
-        input = input + self.positional_embedding.to(input.dtype)
-
-        mask = th.where(mask, 0.0, float("-inf"))
-        mask = (mask[:, None, :] + causal_mask).to(input.dtype)
-
-        out = self.transformer(input, mask=mask)
-        if self.final_ln is not None:
-            out = self.final_ln(out)
-
-        out = self.out_proj(out[:, -1])
-
-        return out
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/karlo/kakao/sampler.py b/repositories/stable-diffusion-stability-ai/ldm/modules/karlo/kakao/sampler.py
deleted file mode 100644
index b56bf2f20c280d735a2be10af03d7cd3e7bf85ac..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/ldm/modules/karlo/kakao/sampler.py
+++ /dev/null
@@ -1,272 +0,0 @@
-# ------------------------------------------------------------------------------------
-# Karlo-v1.0.alpha
-# Copyright (c) 2022 KakaoBrain. All Rights Reserved.
-
-# source: https://github.com/kakaobrain/karlo/blob/3c68a50a16d76b48a15c181d1c5a5e0879a90f85/karlo/sampler/t2i.py#L15
-# ------------------------------------------------------------------------------------
-
-from typing import Iterator
-
-import torch
-import torchvision.transforms.functional as TVF
-from torchvision.transforms import InterpolationMode
-
-from .template import BaseSampler, CKPT_PATH
-
-
-class T2ISampler(BaseSampler):
-    """
-    A sampler for text-to-image generation.
-    :param root_dir: directory for model checkpoints.
-    :param sampling_type: ["default", "fast"]
-    """
-
-    def __init__(
-            self,
-            root_dir: str,
-            sampling_type: str = "default",
-    ):
-        super().__init__(root_dir, sampling_type)
-
-    @classmethod
-    def from_pretrained(
-            cls,
-            root_dir: str,
-            clip_model_path: str,
-            clip_stat_path: str,
-            sampling_type: str = "default",
-    ):
-
-        model = cls(
-            root_dir=root_dir,
-            sampling_type=sampling_type,
-        )
-        model.load_clip(clip_model_path)
-        model.load_prior(
-            f"{CKPT_PATH['prior']}",
-            clip_stat_path=clip_stat_path,
-            prior_config="configs/karlo/prior_1B_vit_l.yaml"
-        )
-        model.load_decoder(f"{CKPT_PATH['decoder']}", decoder_config="configs/karlo/decoder_900M_vit_l.yaml")
-        model.load_sr_64_256(CKPT_PATH["sr_256"], sr_config="configs/karlo/improved_sr_64_256_1.4B.yaml")
-        return model
-
-    def preprocess(
-            self,
-            prompt: str,
-            bsz: int,
-    ):
-        """Setup prompts & cfg scales"""
-        prompts_batch = [prompt for _ in range(bsz)]
-
-        prior_cf_scales_batch = [self._prior_cf_scale] * len(prompts_batch)
-        prior_cf_scales_batch = torch.tensor(prior_cf_scales_batch, device="cuda")
-
-        decoder_cf_scales_batch = [self._decoder_cf_scale] * len(prompts_batch)
-        decoder_cf_scales_batch = torch.tensor(decoder_cf_scales_batch, device="cuda")
-
-        """ Get CLIP text feature """
-        clip_model = self._clip
-        tokenizer = self._tokenizer
-        max_txt_length = self._prior.model.text_ctx
-
-        tok, mask = tokenizer.padded_tokens_and_mask(prompts_batch, max_txt_length)
-        cf_token, cf_mask = tokenizer.padded_tokens_and_mask([""], max_txt_length)
-        if not (cf_token.shape == tok.shape):
-            cf_token = cf_token.expand(tok.shape[0], -1)
-            cf_mask = cf_mask.expand(tok.shape[0], -1)
-
-        tok = torch.cat([tok, cf_token], dim=0)
-        mask = torch.cat([mask, cf_mask], dim=0)
-
-        tok, mask = tok.to(device="cuda"), mask.to(device="cuda")
-        txt_feat, txt_feat_seq = clip_model.encode_text(tok)
-
-        return (
-            prompts_batch,
-            prior_cf_scales_batch,
-            decoder_cf_scales_batch,
-            txt_feat,
-            txt_feat_seq,
-            tok,
-            mask,
-        )
-
-    def __call__(
-            self,
-            prompt: str,
-            bsz: int,
-            progressive_mode=None,
-    ) -> Iterator[torch.Tensor]:
-        assert progressive_mode in ("loop", "stage", "final")
-        with torch.no_grad(), torch.cuda.amp.autocast():
-            (
-                prompts_batch,
-                prior_cf_scales_batch,
-                decoder_cf_scales_batch,
-                txt_feat,
-                txt_feat_seq,
-                tok,
-                mask,
-            ) = self.preprocess(
-                prompt,
-                bsz,
-            )
-
-            """ Transform CLIP text feature into image feature """
-            img_feat = self._prior(
-                txt_feat,
-                txt_feat_seq,
-                mask,
-                prior_cf_scales_batch,
-                timestep_respacing=self._prior_sm,
-            )
-
-            """ Generate 64x64px images """
-            images_64_outputs = self._decoder(
-                txt_feat,
-                txt_feat_seq,
-                tok,
-                mask,
-                img_feat,
-                cf_guidance_scales=decoder_cf_scales_batch,
-                timestep_respacing=self._decoder_sm,
-            )
-
-            images_64 = None
-            for k, out in enumerate(images_64_outputs):
-                images_64 = out
-                if progressive_mode == "loop":
-                    yield torch.clamp(out * 0.5 + 0.5, 0.0, 1.0)
-            if progressive_mode == "stage":
-                yield torch.clamp(out * 0.5 + 0.5, 0.0, 1.0)
-
-            images_64 = torch.clamp(images_64, -1, 1)
-
-            """ Upsample 64x64 to 256x256 """
-            images_256 = TVF.resize(
-                images_64,
-                [256, 256],
-                interpolation=InterpolationMode.BICUBIC,
-                antialias=True,
-            )
-            images_256_outputs = self._sr_64_256(
-                images_256, timestep_respacing=self._sr_sm
-            )
-
-            for k, out in enumerate(images_256_outputs):
-                images_256 = out
-                if progressive_mode == "loop":
-                    yield torch.clamp(out * 0.5 + 0.5, 0.0, 1.0)
-            if progressive_mode == "stage":
-                yield torch.clamp(out * 0.5 + 0.5, 0.0, 1.0)
-
-        yield torch.clamp(images_256 * 0.5 + 0.5, 0.0, 1.0)
-
-
-class PriorSampler(BaseSampler):
-    """
-    A sampler for text-to-image generation, but only the prior.
-    :param root_dir: directory for model checkpoints.
-    :param sampling_type: ["default", "fast"]
-    """
-
-    def __init__(
-            self,
-            root_dir: str,
-            sampling_type: str = "default",
-    ):
-        super().__init__(root_dir, sampling_type)
-
-    @classmethod
-    def from_pretrained(
-            cls,
-            root_dir: str,
-            clip_model_path: str,
-            clip_stat_path: str,
-            sampling_type: str = "default",
-    ):
-        model = cls(
-            root_dir=root_dir,
-            sampling_type=sampling_type,
-        )
-        model.load_clip(clip_model_path)
-        model.load_prior(
-            f"{CKPT_PATH['prior']}",
-            clip_stat_path=clip_stat_path,
-            prior_config="configs/karlo/prior_1B_vit_l.yaml"
-        )
-        return model
-
-    def preprocess(
-            self,
-            prompt: str,
-            bsz: int,
-    ):
-        """Setup prompts & cfg scales"""
-        prompts_batch = [prompt for _ in range(bsz)]
-
-        prior_cf_scales_batch = [self._prior_cf_scale] * len(prompts_batch)
-        prior_cf_scales_batch = torch.tensor(prior_cf_scales_batch, device="cuda")
-
-        decoder_cf_scales_batch = [self._decoder_cf_scale] * len(prompts_batch)
-        decoder_cf_scales_batch = torch.tensor(decoder_cf_scales_batch, device="cuda")
-
-        """ Get CLIP text feature """
-        clip_model = self._clip
-        tokenizer = self._tokenizer
-        max_txt_length = self._prior.model.text_ctx
-
-        tok, mask = tokenizer.padded_tokens_and_mask(prompts_batch, max_txt_length)
-        cf_token, cf_mask = tokenizer.padded_tokens_and_mask([""], max_txt_length)
-        if not (cf_token.shape == tok.shape):
-            cf_token = cf_token.expand(tok.shape[0], -1)
-            cf_mask = cf_mask.expand(tok.shape[0], -1)
-
-        tok = torch.cat([tok, cf_token], dim=0)
-        mask = torch.cat([mask, cf_mask], dim=0)
-
-        tok, mask = tok.to(device="cuda"), mask.to(device="cuda")
-        txt_feat, txt_feat_seq = clip_model.encode_text(tok)
-
-        return (
-            prompts_batch,
-            prior_cf_scales_batch,
-            decoder_cf_scales_batch,
-            txt_feat,
-            txt_feat_seq,
-            tok,
-            mask,
-        )
-
-    def __call__(
-            self,
-            prompt: str,
-            bsz: int,
-            progressive_mode=None,
-    ) -> Iterator[torch.Tensor]:
-        assert progressive_mode in ("loop", "stage", "final")
-        with torch.no_grad(), torch.cuda.amp.autocast():
-            (
-                prompts_batch,
-                prior_cf_scales_batch,
-                decoder_cf_scales_batch,
-                txt_feat,
-                txt_feat_seq,
-                tok,
-                mask,
-            ) = self.preprocess(
-                prompt,
-                bsz,
-            )
-
-            """ Transform CLIP text feature into image feature """
-            img_feat = self._prior(
-                txt_feat,
-                txt_feat_seq,
-                mask,
-                prior_cf_scales_batch,
-                timestep_respacing=self._prior_sm,
-            )
-
-            yield img_feat
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/karlo/kakao/template.py b/repositories/stable-diffusion-stability-ai/ldm/modules/karlo/kakao/template.py
deleted file mode 100644
index 949e80e67b6a8a7c7bbd2c7baad5ec8c8c7e2fdf..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/ldm/modules/karlo/kakao/template.py
+++ /dev/null
@@ -1,141 +0,0 @@
-# ------------------------------------------------------------------------------------
-# Karlo-v1.0.alpha
-# Copyright (c) 2022 KakaoBrain. All Rights Reserved.
-# ------------------------------------------------------------------------------------
-
-import os
-import logging
-import torch
-
-from omegaconf import OmegaConf
-
-from ldm.modules.karlo.kakao.models.clip import CustomizedCLIP, CustomizedTokenizer
-from ldm.modules.karlo.kakao.models.prior_model import PriorDiffusionModel
-from ldm.modules.karlo.kakao.models.decoder_model import Text2ImProgressiveModel
-from ldm.modules.karlo.kakao.models.sr_64_256 import ImprovedSupRes64to256ProgressiveModel
-
-
-SAMPLING_CONF = {
-    "default": {
-        "prior_sm": "25",
-        "prior_n_samples": 1,
-        "prior_cf_scale": 4.0,
-        "decoder_sm": "50",
-        "decoder_cf_scale": 8.0,
-        "sr_sm": "7",
-    },
-    "fast": {
-        "prior_sm": "25",
-        "prior_n_samples": 1,
-        "prior_cf_scale": 4.0,
-        "decoder_sm": "25",
-        "decoder_cf_scale": 8.0,
-        "sr_sm": "7",
-    },
-}
-
-CKPT_PATH = {
-    "prior": "prior-ckpt-step=01000000-of-01000000.ckpt",
-    "decoder": "decoder-ckpt-step=01000000-of-01000000.ckpt",
-    "sr_256": "improved-sr-ckpt-step=1.2M.ckpt",
-}
-
-
-class BaseSampler:
-    _PRIOR_CLASS = PriorDiffusionModel
-    _DECODER_CLASS = Text2ImProgressiveModel
-    _SR256_CLASS = ImprovedSupRes64to256ProgressiveModel
-
-    def __init__(
-        self,
-        root_dir: str,
-        sampling_type: str = "fast",
-    ):
-        self._root_dir = root_dir
-
-        sampling_type = SAMPLING_CONF[sampling_type]
-        self._prior_sm = sampling_type["prior_sm"]
-        self._prior_n_samples = sampling_type["prior_n_samples"]
-        self._prior_cf_scale = sampling_type["prior_cf_scale"]
-
-        assert self._prior_n_samples == 1
-
-        self._decoder_sm = sampling_type["decoder_sm"]
-        self._decoder_cf_scale = sampling_type["decoder_cf_scale"]
-
-        self._sr_sm = sampling_type["sr_sm"]
-
-    def __repr__(self):
-        line = ""
-        line += f"Prior, sampling method: {self._prior_sm}, cf_scale: {self._prior_cf_scale}\n"
-        line += f"Decoder, sampling method: {self._decoder_sm}, cf_scale: {self._decoder_cf_scale}\n"
-        line += f"SR(64->256), sampling method: {self._sr_sm}"
-
-        return line
-
-    def load_clip(self, clip_path: str):
-        clip = CustomizedCLIP.load_from_checkpoint(
-            os.path.join(self._root_dir, clip_path)
-        )
-        clip = torch.jit.script(clip)
-        clip.cuda()
-        clip.eval()
-
-        self._clip = clip
-        self._tokenizer = CustomizedTokenizer()
-
-    def load_prior(
-        self,
-        ckpt_path: str,
-        clip_stat_path: str,
-        prior_config: str = "configs/prior_1B_vit_l.yaml"
-    ):
-        logging.info(f"Loading prior: {ckpt_path}")
-
-        config = OmegaConf.load(prior_config)
-        clip_mean, clip_std = torch.load(
-            os.path.join(self._root_dir, clip_stat_path), map_location="cpu"
-        )
-
-        prior = self._PRIOR_CLASS.load_from_checkpoint(
-            config,
-            self._tokenizer,
-            clip_mean,
-            clip_std,
-            os.path.join(self._root_dir, ckpt_path),
-            strict=True,
-        )
-        prior.cuda()
-        prior.eval()
-        logging.info("done.")
-
-        self._prior = prior
-
-    def load_decoder(self, ckpt_path: str, decoder_config: str = "configs/decoder_900M_vit_l.yaml"):
-        logging.info(f"Loading decoder: {ckpt_path}")
-
-        config = OmegaConf.load(decoder_config)
-        decoder = self._DECODER_CLASS.load_from_checkpoint(
-            config,
-            self._tokenizer,
-            os.path.join(self._root_dir, ckpt_path),
-            strict=True,
-        )
-        decoder.cuda()
-        decoder.eval()
-        logging.info("done.")
-
-        self._decoder = decoder
-
-    def load_sr_64_256(self, ckpt_path: str, sr_config: str = "configs/improved_sr_64_256_1.4B.yaml"):
-        logging.info(f"Loading SR(64->256): {ckpt_path}")
-
-        config = OmegaConf.load(sr_config)
-        sr = self._SR256_CLASS.load_from_checkpoint(
-            config, os.path.join(self._root_dir, ckpt_path), strict=True
-        )
-        sr.cuda()
-        sr.eval()
-        logging.info("done.")
-
-        self._sr_64_256 = sr
\ No newline at end of file
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/midas/__init__.py b/repositories/stable-diffusion-stability-ai/ldm/modules/midas/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/midas/__pycache__/__init__.cpython-310.pyc b/repositories/stable-diffusion-stability-ai/ldm/modules/midas/__pycache__/__init__.cpython-310.pyc
deleted file mode 100644
index e7593698977be59f98e247aad4b713c1809332cd..0000000000000000000000000000000000000000
Binary files a/repositories/stable-diffusion-stability-ai/ldm/modules/midas/__pycache__/__init__.cpython-310.pyc and /dev/null differ
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/midas/__pycache__/api.cpython-310.pyc b/repositories/stable-diffusion-stability-ai/ldm/modules/midas/__pycache__/api.cpython-310.pyc
deleted file mode 100644
index 28dca929a3915f0d39ef62cc26ac792109895530..0000000000000000000000000000000000000000
Binary files a/repositories/stable-diffusion-stability-ai/ldm/modules/midas/__pycache__/api.cpython-310.pyc and /dev/null differ
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/midas/api.py b/repositories/stable-diffusion-stability-ai/ldm/modules/midas/api.py
deleted file mode 100644
index b58ebbffd942a2fc22264f0ab47e400c26b9f41c..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/ldm/modules/midas/api.py
+++ /dev/null
@@ -1,170 +0,0 @@
-# based on https://github.com/isl-org/MiDaS
-
-import cv2
-import torch
-import torch.nn as nn
-from torchvision.transforms import Compose
-
-from ldm.modules.midas.midas.dpt_depth import DPTDepthModel
-from ldm.modules.midas.midas.midas_net import MidasNet
-from ldm.modules.midas.midas.midas_net_custom import MidasNet_small
-from ldm.modules.midas.midas.transforms import Resize, NormalizeImage, PrepareForNet
-
-
-ISL_PATHS = {
-    "dpt_large": "midas_models/dpt_large-midas-2f21e586.pt",
-    "dpt_hybrid": "midas_models/dpt_hybrid-midas-501f0c75.pt",
-    "midas_v21": "",
-    "midas_v21_small": "",
-}
-
-
-def disabled_train(self, mode=True):
-    """Overwrite model.train with this function to make sure train/eval mode
-    does not change anymore."""
-    return self
-
-
-def load_midas_transform(model_type):
-    # https://github.com/isl-org/MiDaS/blob/master/run.py
-    # load transform only
-    if model_type == "dpt_large":  # DPT-Large
-        net_w, net_h = 384, 384
-        resize_mode = "minimal"
-        normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
-
-    elif model_type == "dpt_hybrid":  # DPT-Hybrid
-        net_w, net_h = 384, 384
-        resize_mode = "minimal"
-        normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
-
-    elif model_type == "midas_v21":
-        net_w, net_h = 384, 384
-        resize_mode = "upper_bound"
-        normalization = NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-
-    elif model_type == "midas_v21_small":
-        net_w, net_h = 256, 256
-        resize_mode = "upper_bound"
-        normalization = NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-
-    else:
-        assert False, f"model_type '{model_type}' not implemented, use: --model_type large"
-
-    transform = Compose(
-        [
-            Resize(
-                net_w,
-                net_h,
-                resize_target=None,
-                keep_aspect_ratio=True,
-                ensure_multiple_of=32,
-                resize_method=resize_mode,
-                image_interpolation_method=cv2.INTER_CUBIC,
-            ),
-            normalization,
-            PrepareForNet(),
-        ]
-    )
-
-    return transform
-
-
-def load_model(model_type):
-    # https://github.com/isl-org/MiDaS/blob/master/run.py
-    # load network
-    model_path = ISL_PATHS[model_type]
-    if model_type == "dpt_large":  # DPT-Large
-        model = DPTDepthModel(
-            path=model_path,
-            backbone="vitl16_384",
-            non_negative=True,
-        )
-        net_w, net_h = 384, 384
-        resize_mode = "minimal"
-        normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
-
-    elif model_type == "dpt_hybrid":  # DPT-Hybrid
-        model = DPTDepthModel(
-            path=model_path,
-            backbone="vitb_rn50_384",
-            non_negative=True,
-        )
-        net_w, net_h = 384, 384
-        resize_mode = "minimal"
-        normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
-
-    elif model_type == "midas_v21":
-        model = MidasNet(model_path, non_negative=True)
-        net_w, net_h = 384, 384
-        resize_mode = "upper_bound"
-        normalization = NormalizeImage(
-            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
-        )
-
-    elif model_type == "midas_v21_small":
-        model = MidasNet_small(model_path, features=64, backbone="efficientnet_lite3", exportable=True,
-                               non_negative=True, blocks={'expand': True})
-        net_w, net_h = 256, 256
-        resize_mode = "upper_bound"
-        normalization = NormalizeImage(
-            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
-        )
-
-    else:
-        print(f"model_type '{model_type}' not implemented, use: --model_type large")
-        assert False
-
-    transform = Compose(
-        [
-            Resize(
-                net_w,
-                net_h,
-                resize_target=None,
-                keep_aspect_ratio=True,
-                ensure_multiple_of=32,
-                resize_method=resize_mode,
-                image_interpolation_method=cv2.INTER_CUBIC,
-            ),
-            normalization,
-            PrepareForNet(),
-        ]
-    )
-
-    return model.eval(), transform
-
-
-class MiDaSInference(nn.Module):
-    MODEL_TYPES_TORCH_HUB = [
-        "DPT_Large",
-        "DPT_Hybrid",
-        "MiDaS_small"
-    ]
-    MODEL_TYPES_ISL = [
-        "dpt_large",
-        "dpt_hybrid",
-        "midas_v21",
-        "midas_v21_small",
-    ]
-
-    def __init__(self, model_type):
-        super().__init__()
-        assert (model_type in self.MODEL_TYPES_ISL)
-        model, _ = load_model(model_type)
-        self.model = model
-        self.model.train = disabled_train
-
-    def forward(self, x):
-        # x in 0..1 as produced by calling self.transform on a 0..1 float64 numpy array
-        # NOTE: we expect that the correct transform has been called during dataloading.
-        with torch.no_grad():
-            prediction = self.model(x)
-            prediction = torch.nn.functional.interpolate(
-                prediction.unsqueeze(1),
-                size=x.shape[2:],
-                mode="bicubic",
-                align_corners=False,
-            )
-        assert prediction.shape == (x.shape[0], 1, x.shape[2], x.shape[3])
-        return prediction
-
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/midas/midas/__init__.py b/repositories/stable-diffusion-stability-ai/ldm/modules/midas/midas/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/midas/midas/__pycache__/__init__.cpython-310.pyc b/repositories/stable-diffusion-stability-ai/ldm/modules/midas/midas/__pycache__/__init__.cpython-310.pyc
deleted file mode 100644
index b7081ebf6b9e71e36d4c1ca9bd0596435eeaa8d6..0000000000000000000000000000000000000000
Binary files a/repositories/stable-diffusion-stability-ai/ldm/modules/midas/midas/__pycache__/__init__.cpython-310.pyc and /dev/null differ
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/midas/midas/__pycache__/base_model.cpython-310.pyc b/repositories/stable-diffusion-stability-ai/ldm/modules/midas/midas/__pycache__/base_model.cpython-310.pyc
deleted file mode 100644
index 9b43616b20a41ef7f6a3e5c7935f9ef676539a99..0000000000000000000000000000000000000000
Binary files a/repositories/stable-diffusion-stability-ai/ldm/modules/midas/midas/__pycache__/base_model.cpython-310.pyc and /dev/null differ
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/midas/midas/__pycache__/blocks.cpython-310.pyc b/repositories/stable-diffusion-stability-ai/ldm/modules/midas/midas/__pycache__/blocks.cpython-310.pyc
deleted file mode 100644
index 5a926b9423da53f3669f6716e513ddee8c2d118c..0000000000000000000000000000000000000000
Binary files a/repositories/stable-diffusion-stability-ai/ldm/modules/midas/midas/__pycache__/blocks.cpython-310.pyc and /dev/null differ
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/midas/midas/__pycache__/dpt_depth.cpython-310.pyc b/repositories/stable-diffusion-stability-ai/ldm/modules/midas/midas/__pycache__/dpt_depth.cpython-310.pyc
deleted file mode 100644
index 2936d85f06f3bc50161c4f4d0dde7a1ec7c9bd87..0000000000000000000000000000000000000000
Binary files a/repositories/stable-diffusion-stability-ai/ldm/modules/midas/midas/__pycache__/dpt_depth.cpython-310.pyc and /dev/null differ
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/midas/midas/__pycache__/midas_net.cpython-310.pyc b/repositories/stable-diffusion-stability-ai/ldm/modules/midas/midas/__pycache__/midas_net.cpython-310.pyc
deleted file mode 100644
index 31b3c8f3c273889694e627d218d41d602c9d7494..0000000000000000000000000000000000000000
Binary files a/repositories/stable-diffusion-stability-ai/ldm/modules/midas/midas/__pycache__/midas_net.cpython-310.pyc and /dev/null differ
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/midas/midas/__pycache__/midas_net_custom.cpython-310.pyc b/repositories/stable-diffusion-stability-ai/ldm/modules/midas/midas/__pycache__/midas_net_custom.cpython-310.pyc
deleted file mode 100644
index 5b406ac9fb7df22717582cfbcf98f2b5d4ed22ef..0000000000000000000000000000000000000000
Binary files a/repositories/stable-diffusion-stability-ai/ldm/modules/midas/midas/__pycache__/midas_net_custom.cpython-310.pyc and /dev/null differ
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/midas/midas/__pycache__/transforms.cpython-310.pyc b/repositories/stable-diffusion-stability-ai/ldm/modules/midas/midas/__pycache__/transforms.cpython-310.pyc
deleted file mode 100644
index 378488281374d2d2b5e59028d8d38f89ff176068..0000000000000000000000000000000000000000
Binary files a/repositories/stable-diffusion-stability-ai/ldm/modules/midas/midas/__pycache__/transforms.cpython-310.pyc and /dev/null differ
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/midas/midas/__pycache__/vit.cpython-310.pyc b/repositories/stable-diffusion-stability-ai/ldm/modules/midas/midas/__pycache__/vit.cpython-310.pyc
deleted file mode 100644
index 886c55549450999c96a4ea964e32cb06f63d07b9..0000000000000000000000000000000000000000
Binary files a/repositories/stable-diffusion-stability-ai/ldm/modules/midas/midas/__pycache__/vit.cpython-310.pyc and /dev/null differ
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/midas/midas/base_model.py b/repositories/stable-diffusion-stability-ai/ldm/modules/midas/midas/base_model.py
deleted file mode 100644
index 5cf430239b47ec5ec07531263f26f5c24a2311cd..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/ldm/modules/midas/midas/base_model.py
+++ /dev/null
@@ -1,16 +0,0 @@
-import torch
-
-
-class BaseModel(torch.nn.Module):
-    def load(self, path):
-        """Load model from file.
-
-        Args:
-            path (str): file path
-        """
-        parameters = torch.load(path, map_location=torch.device('cpu'))
-
-        if "optimizer" in parameters:
-            parameters = parameters["model"]
-
-        self.load_state_dict(parameters)
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/midas/midas/blocks.py b/repositories/stable-diffusion-stability-ai/ldm/modules/midas/midas/blocks.py
deleted file mode 100644
index 2145d18fa98060a618536d9a64fe6589e9be4f78..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/ldm/modules/midas/midas/blocks.py
+++ /dev/null
@@ -1,342 +0,0 @@
-import torch
-import torch.nn as nn
-
-from .vit import (
-    _make_pretrained_vitb_rn50_384,
-    _make_pretrained_vitl16_384,
-    _make_pretrained_vitb16_384,
-    forward_vit,
-)
-
-def _make_encoder(backbone, features, use_pretrained, groups=1, expand=False, exportable=True, hooks=None, use_vit_only=False, use_readout="ignore",):
-    if backbone == "vitl16_384":
-        pretrained = _make_pretrained_vitl16_384(
-            use_pretrained, hooks=hooks, use_readout=use_readout
-        )
-        scratch = _make_scratch(
-            [256, 512, 1024, 1024], features, groups=groups, expand=expand
-        )  # ViT-L/16 - 85.0% Top1 (backbone)
-    elif backbone == "vitb_rn50_384":
-        pretrained = _make_pretrained_vitb_rn50_384(
-            use_pretrained,
-            hooks=hooks,
-            use_vit_only=use_vit_only,
-            use_readout=use_readout,
-        )
-        scratch = _make_scratch(
-            [256, 512, 768, 768], features, groups=groups, expand=expand
-        )  # ViT-H/16 - 85.0% Top1 (backbone)
-    elif backbone == "vitb16_384":
-        pretrained = _make_pretrained_vitb16_384(
-            use_pretrained, hooks=hooks, use_readout=use_readout
-        )
-        scratch = _make_scratch(
-            [96, 192, 384, 768], features, groups=groups, expand=expand
-        )  # ViT-B/16 - 84.6% Top1 (backbone)
-    elif backbone == "resnext101_wsl":
-        pretrained = _make_pretrained_resnext101_wsl(use_pretrained)
-        scratch = _make_scratch([256, 512, 1024, 2048], features, groups=groups, expand=expand)     # efficientnet_lite3  
-    elif backbone == "efficientnet_lite3":
-        pretrained = _make_pretrained_efficientnet_lite3(use_pretrained, exportable=exportable)
-        scratch = _make_scratch([32, 48, 136, 384], features, groups=groups, expand=expand)  # efficientnet_lite3     
-    else:
-        print(f"Backbone '{backbone}' not implemented")
-        assert False
-        
-    return pretrained, scratch
-
-
-def _make_scratch(in_shape, out_shape, groups=1, expand=False):
-    scratch = nn.Module()
-
-    out_shape1 = out_shape
-    out_shape2 = out_shape
-    out_shape3 = out_shape
-    out_shape4 = out_shape
-    if expand==True:
-        out_shape1 = out_shape
-        out_shape2 = out_shape*2
-        out_shape3 = out_shape*4
-        out_shape4 = out_shape*8
-
-    scratch.layer1_rn = nn.Conv2d(
-        in_shape[0], out_shape1, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
-    )
-    scratch.layer2_rn = nn.Conv2d(
-        in_shape[1], out_shape2, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
-    )
-    scratch.layer3_rn = nn.Conv2d(
-        in_shape[2], out_shape3, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
-    )
-    scratch.layer4_rn = nn.Conv2d(
-        in_shape[3], out_shape4, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
-    )
-
-    return scratch
-
-
-def _make_pretrained_efficientnet_lite3(use_pretrained, exportable=False):
-    efficientnet = torch.hub.load(
-        "rwightman/gen-efficientnet-pytorch",
-        "tf_efficientnet_lite3",
-        pretrained=use_pretrained,
-        exportable=exportable
-    )
-    return _make_efficientnet_backbone(efficientnet)
-
-
-def _make_efficientnet_backbone(effnet):
-    pretrained = nn.Module()
-
-    pretrained.layer1 = nn.Sequential(
-        effnet.conv_stem, effnet.bn1, effnet.act1, *effnet.blocks[0:2]
-    )
-    pretrained.layer2 = nn.Sequential(*effnet.blocks[2:3])
-    pretrained.layer3 = nn.Sequential(*effnet.blocks[3:5])
-    pretrained.layer4 = nn.Sequential(*effnet.blocks[5:9])
-
-    return pretrained
-    
-
-def _make_resnet_backbone(resnet):
-    pretrained = nn.Module()
-    pretrained.layer1 = nn.Sequential(
-        resnet.conv1, resnet.bn1, resnet.relu, resnet.maxpool, resnet.layer1
-    )
-
-    pretrained.layer2 = resnet.layer2
-    pretrained.layer3 = resnet.layer3
-    pretrained.layer4 = resnet.layer4
-
-    return pretrained
-
-
-def _make_pretrained_resnext101_wsl(use_pretrained):
-    resnet = torch.hub.load("facebookresearch/WSL-Images", "resnext101_32x8d_wsl")
-    return _make_resnet_backbone(resnet)
-
-
-
-class Interpolate(nn.Module):
-    """Interpolation module.
-    """
-
-    def __init__(self, scale_factor, mode, align_corners=False):
-        """Init.
-
-        Args:
-            scale_factor (float): scaling
-            mode (str): interpolation mode
-        """
-        super(Interpolate, self).__init__()
-
-        self.interp = nn.functional.interpolate
-        self.scale_factor = scale_factor
-        self.mode = mode
-        self.align_corners = align_corners
-
-    def forward(self, x):
-        """Forward pass.
-
-        Args:
-            x (tensor): input
-
-        Returns:
-            tensor: interpolated data
-        """
-
-        x = self.interp(
-            x, scale_factor=self.scale_factor, mode=self.mode, align_corners=self.align_corners
-        )
-
-        return x
-
-
-class ResidualConvUnit(nn.Module):
-    """Residual convolution module.
-    """
-
-    def __init__(self, features):
-        """Init.
-
-        Args:
-            features (int): number of features
-        """
-        super().__init__()
-
-        self.conv1 = nn.Conv2d(
-            features, features, kernel_size=3, stride=1, padding=1, bias=True
-        )
-
-        self.conv2 = nn.Conv2d(
-            features, features, kernel_size=3, stride=1, padding=1, bias=True
-        )
-
-        self.relu = nn.ReLU(inplace=True)
-
-    def forward(self, x):
-        """Forward pass.
-
-        Args:
-            x (tensor): input
-
-        Returns:
-            tensor: output
-        """
-        out = self.relu(x)
-        out = self.conv1(out)
-        out = self.relu(out)
-        out = self.conv2(out)
-
-        return out + x
-
-
-class FeatureFusionBlock(nn.Module):
-    """Feature fusion block.
-    """
-
-    def __init__(self, features):
-        """Init.
-
-        Args:
-            features (int): number of features
-        """
-        super(FeatureFusionBlock, self).__init__()
-
-        self.resConfUnit1 = ResidualConvUnit(features)
-        self.resConfUnit2 = ResidualConvUnit(features)
-
-    def forward(self, *xs):
-        """Forward pass.
-
-        Returns:
-            tensor: output
-        """
-        output = xs[0]
-
-        if len(xs) == 2:
-            output += self.resConfUnit1(xs[1])
-
-        output = self.resConfUnit2(output)
-
-        output = nn.functional.interpolate(
-            output, scale_factor=2, mode="bilinear", align_corners=True
-        )
-
-        return output
-
-
-
-
-class ResidualConvUnit_custom(nn.Module):
-    """Residual convolution module.
-    """
-
-    def __init__(self, features, activation, bn):
-        """Init.
-
-        Args:
-            features (int): number of features
-        """
-        super().__init__()
-
-        self.bn = bn
-
-        self.groups=1
-
-        self.conv1 = nn.Conv2d(
-            features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups
-        )
-        
-        self.conv2 = nn.Conv2d(
-            features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups
-        )
-
-        if self.bn==True:
-            self.bn1 = nn.BatchNorm2d(features)
-            self.bn2 = nn.BatchNorm2d(features)
-
-        self.activation = activation
-
-        self.skip_add = nn.quantized.FloatFunctional()
-
-    def forward(self, x):
-        """Forward pass.
-
-        Args:
-            x (tensor): input
-
-        Returns:
-            tensor: output
-        """
-        
-        out = self.activation(x)
-        out = self.conv1(out)
-        if self.bn==True:
-            out = self.bn1(out)
-       
-        out = self.activation(out)
-        out = self.conv2(out)
-        if self.bn==True:
-            out = self.bn2(out)
-
-        if self.groups > 1:
-            out = self.conv_merge(out)
-
-        return self.skip_add.add(out, x)
-
-        # return out + x
-
-
-class FeatureFusionBlock_custom(nn.Module):
-    """Feature fusion block.
-    """
-
-    def __init__(self, features, activation, deconv=False, bn=False, expand=False, align_corners=True):
-        """Init.
-
-        Args:
-            features (int): number of features
-        """
-        super(FeatureFusionBlock_custom, self).__init__()
-
-        self.deconv = deconv
-        self.align_corners = align_corners
-
-        self.groups=1
-
-        self.expand = expand
-        out_features = features
-        if self.expand==True:
-            out_features = features//2
-        
-        self.out_conv = nn.Conv2d(features, out_features, kernel_size=1, stride=1, padding=0, bias=True, groups=1)
-
-        self.resConfUnit1 = ResidualConvUnit_custom(features, activation, bn)
-        self.resConfUnit2 = ResidualConvUnit_custom(features, activation, bn)
-        
-        self.skip_add = nn.quantized.FloatFunctional()
-
-    def forward(self, *xs):
-        """Forward pass.
-
-        Returns:
-            tensor: output
-        """
-        output = xs[0]
-
-        if len(xs) == 2:
-            res = self.resConfUnit1(xs[1])
-            output = self.skip_add.add(output, res)
-            # output += res
-
-        output = self.resConfUnit2(output)
-
-        output = nn.functional.interpolate(
-            output, scale_factor=2, mode="bilinear", align_corners=self.align_corners
-        )
-
-        output = self.out_conv(output)
-
-        return output
-
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/midas/midas/dpt_depth.py b/repositories/stable-diffusion-stability-ai/ldm/modules/midas/midas/dpt_depth.py
deleted file mode 100644
index 4e9aab5d2767dffea39da5b3f30e2798688216f1..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/ldm/modules/midas/midas/dpt_depth.py
+++ /dev/null
@@ -1,109 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from .base_model import BaseModel
-from .blocks import (
-    FeatureFusionBlock,
-    FeatureFusionBlock_custom,
-    Interpolate,
-    _make_encoder,
-    forward_vit,
-)
-
-
-def _make_fusion_block(features, use_bn):
-    return FeatureFusionBlock_custom(
-        features,
-        nn.ReLU(False),
-        deconv=False,
-        bn=use_bn,
-        expand=False,
-        align_corners=True,
-    )
-
-
-class DPT(BaseModel):
-    def __init__(
-        self,
-        head,
-        features=256,
-        backbone="vitb_rn50_384",
-        readout="project",
-        channels_last=False,
-        use_bn=False,
-    ):
-
-        super(DPT, self).__init__()
-
-        self.channels_last = channels_last
-
-        hooks = {
-            "vitb_rn50_384": [0, 1, 8, 11],
-            "vitb16_384": [2, 5, 8, 11],
-            "vitl16_384": [5, 11, 17, 23],
-        }
-
-        # Instantiate backbone and reassemble blocks
-        self.pretrained, self.scratch = _make_encoder(
-            backbone,
-            features,
-            False, # Set to true of you want to train from scratch, uses ImageNet weights
-            groups=1,
-            expand=False,
-            exportable=False,
-            hooks=hooks[backbone],
-            use_readout=readout,
-        )
-
-        self.scratch.refinenet1 = _make_fusion_block(features, use_bn)
-        self.scratch.refinenet2 = _make_fusion_block(features, use_bn)
-        self.scratch.refinenet3 = _make_fusion_block(features, use_bn)
-        self.scratch.refinenet4 = _make_fusion_block(features, use_bn)
-
-        self.scratch.output_conv = head
-
-
-    def forward(self, x):
-        if self.channels_last == True:
-            x.contiguous(memory_format=torch.channels_last)
-
-        layer_1, layer_2, layer_3, layer_4 = forward_vit(self.pretrained, x)
-
-        layer_1_rn = self.scratch.layer1_rn(layer_1)
-        layer_2_rn = self.scratch.layer2_rn(layer_2)
-        layer_3_rn = self.scratch.layer3_rn(layer_3)
-        layer_4_rn = self.scratch.layer4_rn(layer_4)
-
-        path_4 = self.scratch.refinenet4(layer_4_rn)
-        path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
-        path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
-        path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
-
-        out = self.scratch.output_conv(path_1)
-
-        return out
-
-
-class DPTDepthModel(DPT):
-    def __init__(self, path=None, non_negative=True, **kwargs):
-        features = kwargs["features"] if "features" in kwargs else 256
-
-        head = nn.Sequential(
-            nn.Conv2d(features, features // 2, kernel_size=3, stride=1, padding=1),
-            Interpolate(scale_factor=2, mode="bilinear", align_corners=True),
-            nn.Conv2d(features // 2, 32, kernel_size=3, stride=1, padding=1),
-            nn.ReLU(True),
-            nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
-            nn.ReLU(True) if non_negative else nn.Identity(),
-            nn.Identity(),
-        )
-
-        super().__init__(head, **kwargs)
-
-        if path is not None:
-           self.load(path)
-
-    def forward(self, x):
-        return super().forward(x).squeeze(dim=1)
-
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/midas/midas/midas_net.py b/repositories/stable-diffusion-stability-ai/ldm/modules/midas/midas/midas_net.py
deleted file mode 100644
index 8a954977800b0a0f48807e80fa63041910e33c1f..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/ldm/modules/midas/midas/midas_net.py
+++ /dev/null
@@ -1,76 +0,0 @@
-"""MidashNet: Network for monocular depth estimation trained by mixing several datasets.
-This file contains code that is adapted from
-https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py
-"""
-import torch
-import torch.nn as nn
-
-from .base_model import BaseModel
-from .blocks import FeatureFusionBlock, Interpolate, _make_encoder
-
-
-class MidasNet(BaseModel):
-    """Network for monocular depth estimation.
-    """
-
-    def __init__(self, path=None, features=256, non_negative=True):
-        """Init.
-
-        Args:
-            path (str, optional): Path to saved model. Defaults to None.
-            features (int, optional): Number of features. Defaults to 256.
-            backbone (str, optional): Backbone network for encoder. Defaults to resnet50
-        """
-        print("Loading weights: ", path)
-
-        super(MidasNet, self).__init__()
-
-        use_pretrained = False if path is None else True
-
-        self.pretrained, self.scratch = _make_encoder(backbone="resnext101_wsl", features=features, use_pretrained=use_pretrained)
-
-        self.scratch.refinenet4 = FeatureFusionBlock(features)
-        self.scratch.refinenet3 = FeatureFusionBlock(features)
-        self.scratch.refinenet2 = FeatureFusionBlock(features)
-        self.scratch.refinenet1 = FeatureFusionBlock(features)
-
-        self.scratch.output_conv = nn.Sequential(
-            nn.Conv2d(features, 128, kernel_size=3, stride=1, padding=1),
-            Interpolate(scale_factor=2, mode="bilinear"),
-            nn.Conv2d(128, 32, kernel_size=3, stride=1, padding=1),
-            nn.ReLU(True),
-            nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
-            nn.ReLU(True) if non_negative else nn.Identity(),
-        )
-
-        if path:
-            self.load(path)
-
-    def forward(self, x):
-        """Forward pass.
-
-        Args:
-            x (tensor): input data (image)
-
-        Returns:
-            tensor: depth
-        """
-
-        layer_1 = self.pretrained.layer1(x)
-        layer_2 = self.pretrained.layer2(layer_1)
-        layer_3 = self.pretrained.layer3(layer_2)
-        layer_4 = self.pretrained.layer4(layer_3)
-
-        layer_1_rn = self.scratch.layer1_rn(layer_1)
-        layer_2_rn = self.scratch.layer2_rn(layer_2)
-        layer_3_rn = self.scratch.layer3_rn(layer_3)
-        layer_4_rn = self.scratch.layer4_rn(layer_4)
-
-        path_4 = self.scratch.refinenet4(layer_4_rn)
-        path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
-        path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
-        path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
-
-        out = self.scratch.output_conv(path_1)
-
-        return torch.squeeze(out, dim=1)
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/midas/midas/midas_net_custom.py b/repositories/stable-diffusion-stability-ai/ldm/modules/midas/midas/midas_net_custom.py
deleted file mode 100644
index 50e4acb5e53d5fabefe3dde16ab49c33c2b7797c..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/ldm/modules/midas/midas/midas_net_custom.py
+++ /dev/null
@@ -1,128 +0,0 @@
-"""MidashNet: Network for monocular depth estimation trained by mixing several datasets.
-This file contains code that is adapted from
-https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py
-"""
-import torch
-import torch.nn as nn
-
-from .base_model import BaseModel
-from .blocks import FeatureFusionBlock, FeatureFusionBlock_custom, Interpolate, _make_encoder
-
-
-class MidasNet_small(BaseModel):
-    """Network for monocular depth estimation.
-    """
-
-    def __init__(self, path=None, features=64, backbone="efficientnet_lite3", non_negative=True, exportable=True, channels_last=False, align_corners=True,
-        blocks={'expand': True}):
-        """Init.
-
-        Args:
-            path (str, optional): Path to saved model. Defaults to None.
-            features (int, optional): Number of features. Defaults to 256.
-            backbone (str, optional): Backbone network for encoder. Defaults to resnet50
-        """
-        print("Loading weights: ", path)
-
-        super(MidasNet_small, self).__init__()
-
-        use_pretrained = False if path else True
-                
-        self.channels_last = channels_last
-        self.blocks = blocks
-        self.backbone = backbone
-
-        self.groups = 1
-
-        features1=features
-        features2=features
-        features3=features
-        features4=features
-        self.expand = False
-        if "expand" in self.blocks and self.blocks['expand'] == True:
-            self.expand = True
-            features1=features
-            features2=features*2
-            features3=features*4
-            features4=features*8
-
-        self.pretrained, self.scratch = _make_encoder(self.backbone, features, use_pretrained, groups=self.groups, expand=self.expand, exportable=exportable)
-  
-        self.scratch.activation = nn.ReLU(False)    
-
-        self.scratch.refinenet4 = FeatureFusionBlock_custom(features4, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners)
-        self.scratch.refinenet3 = FeatureFusionBlock_custom(features3, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners)
-        self.scratch.refinenet2 = FeatureFusionBlock_custom(features2, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners)
-        self.scratch.refinenet1 = FeatureFusionBlock_custom(features1, self.scratch.activation, deconv=False, bn=False, align_corners=align_corners)
-
-        
-        self.scratch.output_conv = nn.Sequential(
-            nn.Conv2d(features, features//2, kernel_size=3, stride=1, padding=1, groups=self.groups),
-            Interpolate(scale_factor=2, mode="bilinear"),
-            nn.Conv2d(features//2, 32, kernel_size=3, stride=1, padding=1),
-            self.scratch.activation,
-            nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
-            nn.ReLU(True) if non_negative else nn.Identity(),
-            nn.Identity(),
-        )
-        
-        if path:
-            self.load(path)
-
-
-    def forward(self, x):
-        """Forward pass.
-
-        Args:
-            x (tensor): input data (image)
-
-        Returns:
-            tensor: depth
-        """
-        if self.channels_last==True:
-            print("self.channels_last = ", self.channels_last)
-            x.contiguous(memory_format=torch.channels_last)
-
-
-        layer_1 = self.pretrained.layer1(x)
-        layer_2 = self.pretrained.layer2(layer_1)
-        layer_3 = self.pretrained.layer3(layer_2)
-        layer_4 = self.pretrained.layer4(layer_3)
-        
-        layer_1_rn = self.scratch.layer1_rn(layer_1)
-        layer_2_rn = self.scratch.layer2_rn(layer_2)
-        layer_3_rn = self.scratch.layer3_rn(layer_3)
-        layer_4_rn = self.scratch.layer4_rn(layer_4)
-
-
-        path_4 = self.scratch.refinenet4(layer_4_rn)
-        path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
-        path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
-        path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
-        
-        out = self.scratch.output_conv(path_1)
-
-        return torch.squeeze(out, dim=1)
-
-
-
-def fuse_model(m):
-    prev_previous_type = nn.Identity()
-    prev_previous_name = ''
-    previous_type = nn.Identity()
-    previous_name = ''
-    for name, module in m.named_modules():
-        if prev_previous_type == nn.Conv2d and previous_type == nn.BatchNorm2d and type(module) == nn.ReLU:
-            # print("FUSED ", prev_previous_name, previous_name, name)
-            torch.quantization.fuse_modules(m, [prev_previous_name, previous_name, name], inplace=True)
-        elif prev_previous_type == nn.Conv2d and previous_type == nn.BatchNorm2d:
-            # print("FUSED ", prev_previous_name, previous_name)
-            torch.quantization.fuse_modules(m, [prev_previous_name, previous_name], inplace=True)
-        # elif previous_type == nn.Conv2d and type(module) == nn.ReLU:
-        #    print("FUSED ", previous_name, name)
-        #    torch.quantization.fuse_modules(m, [previous_name, name], inplace=True)
-
-        prev_previous_type = previous_type
-        prev_previous_name = previous_name
-        previous_type = type(module)
-        previous_name = name
\ No newline at end of file
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/midas/midas/transforms.py b/repositories/stable-diffusion-stability-ai/ldm/modules/midas/midas/transforms.py
deleted file mode 100644
index 350cbc11662633ad7f8968eb10be2e7de6e384e9..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/ldm/modules/midas/midas/transforms.py
+++ /dev/null
@@ -1,234 +0,0 @@
-import numpy as np
-import cv2
-import math
-
-
-def apply_min_size(sample, size, image_interpolation_method=cv2.INTER_AREA):
-    """Rezise the sample to ensure the given size. Keeps aspect ratio.
-
-    Args:
-        sample (dict): sample
-        size (tuple): image size
-
-    Returns:
-        tuple: new size
-    """
-    shape = list(sample["disparity"].shape)
-
-    if shape[0] >= size[0] and shape[1] >= size[1]:
-        return sample
-
-    scale = [0, 0]
-    scale[0] = size[0] / shape[0]
-    scale[1] = size[1] / shape[1]
-
-    scale = max(scale)
-
-    shape[0] = math.ceil(scale * shape[0])
-    shape[1] = math.ceil(scale * shape[1])
-
-    # resize
-    sample["image"] = cv2.resize(
-        sample["image"], tuple(shape[::-1]), interpolation=image_interpolation_method
-    )
-
-    sample["disparity"] = cv2.resize(
-        sample["disparity"], tuple(shape[::-1]), interpolation=cv2.INTER_NEAREST
-    )
-    sample["mask"] = cv2.resize(
-        sample["mask"].astype(np.float32),
-        tuple(shape[::-1]),
-        interpolation=cv2.INTER_NEAREST,
-    )
-    sample["mask"] = sample["mask"].astype(bool)
-
-    return tuple(shape)
-
-
-class Resize(object):
-    """Resize sample to given size (width, height).
-    """
-
-    def __init__(
-        self,
-        width,
-        height,
-        resize_target=True,
-        keep_aspect_ratio=False,
-        ensure_multiple_of=1,
-        resize_method="lower_bound",
-        image_interpolation_method=cv2.INTER_AREA,
-    ):
-        """Init.
-
-        Args:
-            width (int): desired output width
-            height (int): desired output height
-            resize_target (bool, optional):
-                True: Resize the full sample (image, mask, target).
-                False: Resize image only.
-                Defaults to True.
-            keep_aspect_ratio (bool, optional):
-                True: Keep the aspect ratio of the input sample.
-                Output sample might not have the given width and height, and
-                resize behaviour depends on the parameter 'resize_method'.
-                Defaults to False.
-            ensure_multiple_of (int, optional):
-                Output width and height is constrained to be multiple of this parameter.
-                Defaults to 1.
-            resize_method (str, optional):
-                "lower_bound": Output will be at least as large as the given size.
-                "upper_bound": Output will be at max as large as the given size. (Output size might be smaller than given size.)
-                "minimal": Scale as least as possible.  (Output size might be smaller than given size.)
-                Defaults to "lower_bound".
-        """
-        self.__width = width
-        self.__height = height
-
-        self.__resize_target = resize_target
-        self.__keep_aspect_ratio = keep_aspect_ratio
-        self.__multiple_of = ensure_multiple_of
-        self.__resize_method = resize_method
-        self.__image_interpolation_method = image_interpolation_method
-
-    def constrain_to_multiple_of(self, x, min_val=0, max_val=None):
-        y = (np.round(x / self.__multiple_of) * self.__multiple_of).astype(int)
-
-        if max_val is not None and y > max_val:
-            y = (np.floor(x / self.__multiple_of) * self.__multiple_of).astype(int)
-
-        if y < min_val:
-            y = (np.ceil(x / self.__multiple_of) * self.__multiple_of).astype(int)
-
-        return y
-
-    def get_size(self, width, height):
-        # determine new height and width
-        scale_height = self.__height / height
-        scale_width = self.__width / width
-
-        if self.__keep_aspect_ratio:
-            if self.__resize_method == "lower_bound":
-                # scale such that output size is lower bound
-                if scale_width > scale_height:
-                    # fit width
-                    scale_height = scale_width
-                else:
-                    # fit height
-                    scale_width = scale_height
-            elif self.__resize_method == "upper_bound":
-                # scale such that output size is upper bound
-                if scale_width < scale_height:
-                    # fit width
-                    scale_height = scale_width
-                else:
-                    # fit height
-                    scale_width = scale_height
-            elif self.__resize_method == "minimal":
-                # scale as least as possbile
-                if abs(1 - scale_width) < abs(1 - scale_height):
-                    # fit width
-                    scale_height = scale_width
-                else:
-                    # fit height
-                    scale_width = scale_height
-            else:
-                raise ValueError(
-                    f"resize_method {self.__resize_method} not implemented"
-                )
-
-        if self.__resize_method == "lower_bound":
-            new_height = self.constrain_to_multiple_of(
-                scale_height * height, min_val=self.__height
-            )
-            new_width = self.constrain_to_multiple_of(
-                scale_width * width, min_val=self.__width
-            )
-        elif self.__resize_method == "upper_bound":
-            new_height = self.constrain_to_multiple_of(
-                scale_height * height, max_val=self.__height
-            )
-            new_width = self.constrain_to_multiple_of(
-                scale_width * width, max_val=self.__width
-            )
-        elif self.__resize_method == "minimal":
-            new_height = self.constrain_to_multiple_of(scale_height * height)
-            new_width = self.constrain_to_multiple_of(scale_width * width)
-        else:
-            raise ValueError(f"resize_method {self.__resize_method} not implemented")
-
-        return (new_width, new_height)
-
-    def __call__(self, sample):
-        width, height = self.get_size(
-            sample["image"].shape[1], sample["image"].shape[0]
-        )
-
-        # resize sample
-        sample["image"] = cv2.resize(
-            sample["image"],
-            (width, height),
-            interpolation=self.__image_interpolation_method,
-        )
-
-        if self.__resize_target:
-            if "disparity" in sample:
-                sample["disparity"] = cv2.resize(
-                    sample["disparity"],
-                    (width, height),
-                    interpolation=cv2.INTER_NEAREST,
-                )
-
-            if "depth" in sample:
-                sample["depth"] = cv2.resize(
-                    sample["depth"], (width, height), interpolation=cv2.INTER_NEAREST
-                )
-
-            sample["mask"] = cv2.resize(
-                sample["mask"].astype(np.float32),
-                (width, height),
-                interpolation=cv2.INTER_NEAREST,
-            )
-            sample["mask"] = sample["mask"].astype(bool)
-
-        return sample
-
-
-class NormalizeImage(object):
-    """Normlize image by given mean and std.
-    """
-
-    def __init__(self, mean, std):
-        self.__mean = mean
-        self.__std = std
-
-    def __call__(self, sample):
-        sample["image"] = (sample["image"] - self.__mean) / self.__std
-
-        return sample
-
-
-class PrepareForNet(object):
-    """Prepare sample for usage as network input.
-    """
-
-    def __init__(self):
-        pass
-
-    def __call__(self, sample):
-        image = np.transpose(sample["image"], (2, 0, 1))
-        sample["image"] = np.ascontiguousarray(image).astype(np.float32)
-
-        if "mask" in sample:
-            sample["mask"] = sample["mask"].astype(np.float32)
-            sample["mask"] = np.ascontiguousarray(sample["mask"])
-
-        if "disparity" in sample:
-            disparity = sample["disparity"].astype(np.float32)
-            sample["disparity"] = np.ascontiguousarray(disparity)
-
-        if "depth" in sample:
-            depth = sample["depth"].astype(np.float32)
-            sample["depth"] = np.ascontiguousarray(depth)
-
-        return sample
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/midas/midas/vit.py b/repositories/stable-diffusion-stability-ai/ldm/modules/midas/midas/vit.py
deleted file mode 100644
index ea46b1be88b261b0dec04f3da0256f5f66f88a74..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/ldm/modules/midas/midas/vit.py
+++ /dev/null
@@ -1,491 +0,0 @@
-import torch
-import torch.nn as nn
-import timm
-import types
-import math
-import torch.nn.functional as F
-
-
-class Slice(nn.Module):
-    def __init__(self, start_index=1):
-        super(Slice, self).__init__()
-        self.start_index = start_index
-
-    def forward(self, x):
-        return x[:, self.start_index :]
-
-
-class AddReadout(nn.Module):
-    def __init__(self, start_index=1):
-        super(AddReadout, self).__init__()
-        self.start_index = start_index
-
-    def forward(self, x):
-        if self.start_index == 2:
-            readout = (x[:, 0] + x[:, 1]) / 2
-        else:
-            readout = x[:, 0]
-        return x[:, self.start_index :] + readout.unsqueeze(1)
-
-
-class ProjectReadout(nn.Module):
-    def __init__(self, in_features, start_index=1):
-        super(ProjectReadout, self).__init__()
-        self.start_index = start_index
-
-        self.project = nn.Sequential(nn.Linear(2 * in_features, in_features), nn.GELU())
-
-    def forward(self, x):
-        readout = x[:, 0].unsqueeze(1).expand_as(x[:, self.start_index :])
-        features = torch.cat((x[:, self.start_index :], readout), -1)
-
-        return self.project(features)
-
-
-class Transpose(nn.Module):
-    def __init__(self, dim0, dim1):
-        super(Transpose, self).__init__()
-        self.dim0 = dim0
-        self.dim1 = dim1
-
-    def forward(self, x):
-        x = x.transpose(self.dim0, self.dim1)
-        return x
-
-
-def forward_vit(pretrained, x):
-    b, c, h, w = x.shape
-
-    glob = pretrained.model.forward_flex(x)
-
-    layer_1 = pretrained.activations["1"]
-    layer_2 = pretrained.activations["2"]
-    layer_3 = pretrained.activations["3"]
-    layer_4 = pretrained.activations["4"]
-
-    layer_1 = pretrained.act_postprocess1[0:2](layer_1)
-    layer_2 = pretrained.act_postprocess2[0:2](layer_2)
-    layer_3 = pretrained.act_postprocess3[0:2](layer_3)
-    layer_4 = pretrained.act_postprocess4[0:2](layer_4)
-
-    unflatten = nn.Sequential(
-        nn.Unflatten(
-            2,
-            torch.Size(
-                [
-                    h // pretrained.model.patch_size[1],
-                    w // pretrained.model.patch_size[0],
-                ]
-            ),
-        )
-    )
-
-    if layer_1.ndim == 3:
-        layer_1 = unflatten(layer_1)
-    if layer_2.ndim == 3:
-        layer_2 = unflatten(layer_2)
-    if layer_3.ndim == 3:
-        layer_3 = unflatten(layer_3)
-    if layer_4.ndim == 3:
-        layer_4 = unflatten(layer_4)
-
-    layer_1 = pretrained.act_postprocess1[3 : len(pretrained.act_postprocess1)](layer_1)
-    layer_2 = pretrained.act_postprocess2[3 : len(pretrained.act_postprocess2)](layer_2)
-    layer_3 = pretrained.act_postprocess3[3 : len(pretrained.act_postprocess3)](layer_3)
-    layer_4 = pretrained.act_postprocess4[3 : len(pretrained.act_postprocess4)](layer_4)
-
-    return layer_1, layer_2, layer_3, layer_4
-
-
-def _resize_pos_embed(self, posemb, gs_h, gs_w):
-    posemb_tok, posemb_grid = (
-        posemb[:, : self.start_index],
-        posemb[0, self.start_index :],
-    )
-
-    gs_old = int(math.sqrt(len(posemb_grid)))
-
-    posemb_grid = posemb_grid.reshape(1, gs_old, gs_old, -1).permute(0, 3, 1, 2)
-    posemb_grid = F.interpolate(posemb_grid, size=(gs_h, gs_w), mode="bilinear")
-    posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_h * gs_w, -1)
-
-    posemb = torch.cat([posemb_tok, posemb_grid], dim=1)
-
-    return posemb
-
-
-def forward_flex(self, x):
-    b, c, h, w = x.shape
-
-    pos_embed = self._resize_pos_embed(
-        self.pos_embed, h // self.patch_size[1], w // self.patch_size[0]
-    )
-
-    B = x.shape[0]
-
-    if hasattr(self.patch_embed, "backbone"):
-        x = self.patch_embed.backbone(x)
-        if isinstance(x, (list, tuple)):
-            x = x[-1]  # last feature if backbone outputs list/tuple of features
-
-    x = self.patch_embed.proj(x).flatten(2).transpose(1, 2)
-
-    if getattr(self, "dist_token", None) is not None:
-        cls_tokens = self.cls_token.expand(
-            B, -1, -1
-        )  # stole cls_tokens impl from Phil Wang, thanks
-        dist_token = self.dist_token.expand(B, -1, -1)
-        x = torch.cat((cls_tokens, dist_token, x), dim=1)
-    else:
-        cls_tokens = self.cls_token.expand(
-            B, -1, -1
-        )  # stole cls_tokens impl from Phil Wang, thanks
-        x = torch.cat((cls_tokens, x), dim=1)
-
-    x = x + pos_embed
-    x = self.pos_drop(x)
-
-    for blk in self.blocks:
-        x = blk(x)
-
-    x = self.norm(x)
-
-    return x
-
-
-activations = {}
-
-
-def get_activation(name):
-    def hook(model, input, output):
-        activations[name] = output
-
-    return hook
-
-
-def get_readout_oper(vit_features, features, use_readout, start_index=1):
-    if use_readout == "ignore":
-        readout_oper = [Slice(start_index)] * len(features)
-    elif use_readout == "add":
-        readout_oper = [AddReadout(start_index)] * len(features)
-    elif use_readout == "project":
-        readout_oper = [
-            ProjectReadout(vit_features, start_index) for out_feat in features
-        ]
-    else:
-        assert (
-            False
-        ), "wrong operation for readout token, use_readout can be 'ignore', 'add', or 'project'"
-
-    return readout_oper
-
-
-def _make_vit_b16_backbone(
-    model,
-    features=[96, 192, 384, 768],
-    size=[384, 384],
-    hooks=[2, 5, 8, 11],
-    vit_features=768,
-    use_readout="ignore",
-    start_index=1,
-):
-    pretrained = nn.Module()
-
-    pretrained.model = model
-    pretrained.model.blocks[hooks[0]].register_forward_hook(get_activation("1"))
-    pretrained.model.blocks[hooks[1]].register_forward_hook(get_activation("2"))
-    pretrained.model.blocks[hooks[2]].register_forward_hook(get_activation("3"))
-    pretrained.model.blocks[hooks[3]].register_forward_hook(get_activation("4"))
-
-    pretrained.activations = activations
-
-    readout_oper = get_readout_oper(vit_features, features, use_readout, start_index)
-
-    # 32, 48, 136, 384
-    pretrained.act_postprocess1 = nn.Sequential(
-        readout_oper[0],
-        Transpose(1, 2),
-        nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
-        nn.Conv2d(
-            in_channels=vit_features,
-            out_channels=features[0],
-            kernel_size=1,
-            stride=1,
-            padding=0,
-        ),
-        nn.ConvTranspose2d(
-            in_channels=features[0],
-            out_channels=features[0],
-            kernel_size=4,
-            stride=4,
-            padding=0,
-            bias=True,
-            dilation=1,
-            groups=1,
-        ),
-    )
-
-    pretrained.act_postprocess2 = nn.Sequential(
-        readout_oper[1],
-        Transpose(1, 2),
-        nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
-        nn.Conv2d(
-            in_channels=vit_features,
-            out_channels=features[1],
-            kernel_size=1,
-            stride=1,
-            padding=0,
-        ),
-        nn.ConvTranspose2d(
-            in_channels=features[1],
-            out_channels=features[1],
-            kernel_size=2,
-            stride=2,
-            padding=0,
-            bias=True,
-            dilation=1,
-            groups=1,
-        ),
-    )
-
-    pretrained.act_postprocess3 = nn.Sequential(
-        readout_oper[2],
-        Transpose(1, 2),
-        nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
-        nn.Conv2d(
-            in_channels=vit_features,
-            out_channels=features[2],
-            kernel_size=1,
-            stride=1,
-            padding=0,
-        ),
-    )
-
-    pretrained.act_postprocess4 = nn.Sequential(
-        readout_oper[3],
-        Transpose(1, 2),
-        nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
-        nn.Conv2d(
-            in_channels=vit_features,
-            out_channels=features[3],
-            kernel_size=1,
-            stride=1,
-            padding=0,
-        ),
-        nn.Conv2d(
-            in_channels=features[3],
-            out_channels=features[3],
-            kernel_size=3,
-            stride=2,
-            padding=1,
-        ),
-    )
-
-    pretrained.model.start_index = start_index
-    pretrained.model.patch_size = [16, 16]
-
-    # We inject this function into the VisionTransformer instances so that
-    # we can use it with interpolated position embeddings without modifying the library source.
-    pretrained.model.forward_flex = types.MethodType(forward_flex, pretrained.model)
-    pretrained.model._resize_pos_embed = types.MethodType(
-        _resize_pos_embed, pretrained.model
-    )
-
-    return pretrained
-
-
-def _make_pretrained_vitl16_384(pretrained, use_readout="ignore", hooks=None):
-    model = timm.create_model("vit_large_patch16_384", pretrained=pretrained)
-
-    hooks = [5, 11, 17, 23] if hooks == None else hooks
-    return _make_vit_b16_backbone(
-        model,
-        features=[256, 512, 1024, 1024],
-        hooks=hooks,
-        vit_features=1024,
-        use_readout=use_readout,
-    )
-
-
-def _make_pretrained_vitb16_384(pretrained, use_readout="ignore", hooks=None):
-    model = timm.create_model("vit_base_patch16_384", pretrained=pretrained)
-
-    hooks = [2, 5, 8, 11] if hooks == None else hooks
-    return _make_vit_b16_backbone(
-        model, features=[96, 192, 384, 768], hooks=hooks, use_readout=use_readout
-    )
-
-
-def _make_pretrained_deitb16_384(pretrained, use_readout="ignore", hooks=None):
-    model = timm.create_model("vit_deit_base_patch16_384", pretrained=pretrained)
-
-    hooks = [2, 5, 8, 11] if hooks == None else hooks
-    return _make_vit_b16_backbone(
-        model, features=[96, 192, 384, 768], hooks=hooks, use_readout=use_readout
-    )
-
-
-def _make_pretrained_deitb16_distil_384(pretrained, use_readout="ignore", hooks=None):
-    model = timm.create_model(
-        "vit_deit_base_distilled_patch16_384", pretrained=pretrained
-    )
-
-    hooks = [2, 5, 8, 11] if hooks == None else hooks
-    return _make_vit_b16_backbone(
-        model,
-        features=[96, 192, 384, 768],
-        hooks=hooks,
-        use_readout=use_readout,
-        start_index=2,
-    )
-
-
-def _make_vit_b_rn50_backbone(
-    model,
-    features=[256, 512, 768, 768],
-    size=[384, 384],
-    hooks=[0, 1, 8, 11],
-    vit_features=768,
-    use_vit_only=False,
-    use_readout="ignore",
-    start_index=1,
-):
-    pretrained = nn.Module()
-
-    pretrained.model = model
-
-    if use_vit_only == True:
-        pretrained.model.blocks[hooks[0]].register_forward_hook(get_activation("1"))
-        pretrained.model.blocks[hooks[1]].register_forward_hook(get_activation("2"))
-    else:
-        pretrained.model.patch_embed.backbone.stages[0].register_forward_hook(
-            get_activation("1")
-        )
-        pretrained.model.patch_embed.backbone.stages[1].register_forward_hook(
-            get_activation("2")
-        )
-
-    pretrained.model.blocks[hooks[2]].register_forward_hook(get_activation("3"))
-    pretrained.model.blocks[hooks[3]].register_forward_hook(get_activation("4"))
-
-    pretrained.activations = activations
-
-    readout_oper = get_readout_oper(vit_features, features, use_readout, start_index)
-
-    if use_vit_only == True:
-        pretrained.act_postprocess1 = nn.Sequential(
-            readout_oper[0],
-            Transpose(1, 2),
-            nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
-            nn.Conv2d(
-                in_channels=vit_features,
-                out_channels=features[0],
-                kernel_size=1,
-                stride=1,
-                padding=0,
-            ),
-            nn.ConvTranspose2d(
-                in_channels=features[0],
-                out_channels=features[0],
-                kernel_size=4,
-                stride=4,
-                padding=0,
-                bias=True,
-                dilation=1,
-                groups=1,
-            ),
-        )
-
-        pretrained.act_postprocess2 = nn.Sequential(
-            readout_oper[1],
-            Transpose(1, 2),
-            nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
-            nn.Conv2d(
-                in_channels=vit_features,
-                out_channels=features[1],
-                kernel_size=1,
-                stride=1,
-                padding=0,
-            ),
-            nn.ConvTranspose2d(
-                in_channels=features[1],
-                out_channels=features[1],
-                kernel_size=2,
-                stride=2,
-                padding=0,
-                bias=True,
-                dilation=1,
-                groups=1,
-            ),
-        )
-    else:
-        pretrained.act_postprocess1 = nn.Sequential(
-            nn.Identity(), nn.Identity(), nn.Identity()
-        )
-        pretrained.act_postprocess2 = nn.Sequential(
-            nn.Identity(), nn.Identity(), nn.Identity()
-        )
-
-    pretrained.act_postprocess3 = nn.Sequential(
-        readout_oper[2],
-        Transpose(1, 2),
-        nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
-        nn.Conv2d(
-            in_channels=vit_features,
-            out_channels=features[2],
-            kernel_size=1,
-            stride=1,
-            padding=0,
-        ),
-    )
-
-    pretrained.act_postprocess4 = nn.Sequential(
-        readout_oper[3],
-        Transpose(1, 2),
-        nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
-        nn.Conv2d(
-            in_channels=vit_features,
-            out_channels=features[3],
-            kernel_size=1,
-            stride=1,
-            padding=0,
-        ),
-        nn.Conv2d(
-            in_channels=features[3],
-            out_channels=features[3],
-            kernel_size=3,
-            stride=2,
-            padding=1,
-        ),
-    )
-
-    pretrained.model.start_index = start_index
-    pretrained.model.patch_size = [16, 16]
-
-    # We inject this function into the VisionTransformer instances so that
-    # we can use it with interpolated position embeddings without modifying the library source.
-    pretrained.model.forward_flex = types.MethodType(forward_flex, pretrained.model)
-
-    # We inject this function into the VisionTransformer instances so that
-    # we can use it with interpolated position embeddings without modifying the library source.
-    pretrained.model._resize_pos_embed = types.MethodType(
-        _resize_pos_embed, pretrained.model
-    )
-
-    return pretrained
-
-
-def _make_pretrained_vitb_rn50_384(
-    pretrained, use_readout="ignore", hooks=None, use_vit_only=False
-):
-    model = timm.create_model("vit_base_resnet50_384", pretrained=pretrained)
-
-    hooks = [0, 1, 8, 11] if hooks == None else hooks
-    return _make_vit_b_rn50_backbone(
-        model,
-        features=[256, 512, 768, 768],
-        size=[384, 384],
-        hooks=hooks,
-        use_vit_only=use_vit_only,
-        use_readout=use_readout,
-    )
diff --git a/repositories/stable-diffusion-stability-ai/ldm/modules/midas/utils.py b/repositories/stable-diffusion-stability-ai/ldm/modules/midas/utils.py
deleted file mode 100644
index 9a9d3b5b66370fa98da9e067ba53ead848ea9a59..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/ldm/modules/midas/utils.py
+++ /dev/null
@@ -1,189 +0,0 @@
-"""Utils for monoDepth."""
-import sys
-import re
-import numpy as np
-import cv2
-import torch
-
-
-def read_pfm(path):
-    """Read pfm file.
-
-    Args:
-        path (str): path to file
-
-    Returns:
-        tuple: (data, scale)
-    """
-    with open(path, "rb") as file:
-
-        color = None
-        width = None
-        height = None
-        scale = None
-        endian = None
-
-        header = file.readline().rstrip()
-        if header.decode("ascii") == "PF":
-            color = True
-        elif header.decode("ascii") == "Pf":
-            color = False
-        else:
-            raise Exception("Not a PFM file: " + path)
-
-        dim_match = re.match(r"^(\d+)\s(\d+)\s$", file.readline().decode("ascii"))
-        if dim_match:
-            width, height = list(map(int, dim_match.groups()))
-        else:
-            raise Exception("Malformed PFM header.")
-
-        scale = float(file.readline().decode("ascii").rstrip())
-        if scale < 0:
-            # little-endian
-            endian = "<"
-            scale = -scale
-        else:
-            # big-endian
-            endian = ">"
-
-        data = np.fromfile(file, endian + "f")
-        shape = (height, width, 3) if color else (height, width)
-
-        data = np.reshape(data, shape)
-        data = np.flipud(data)
-
-        return data, scale
-
-
-def write_pfm(path, image, scale=1):
-    """Write pfm file.
-
-    Args:
-        path (str): pathto file
-        image (array): data
-        scale (int, optional): Scale. Defaults to 1.
-    """
-
-    with open(path, "wb") as file:
-        color = None
-
-        if image.dtype.name != "float32":
-            raise Exception("Image dtype must be float32.")
-
-        image = np.flipud(image)
-
-        if len(image.shape) == 3 and image.shape[2] == 3:  # color image
-            color = True
-        elif (
-            len(image.shape) == 2 or len(image.shape) == 3 and image.shape[2] == 1
-        ):  # greyscale
-            color = False
-        else:
-            raise Exception("Image must have H x W x 3, H x W x 1 or H x W dimensions.")
-
-        file.write("PF\n" if color else "Pf\n".encode())
-        file.write("%d %d\n".encode() % (image.shape[1], image.shape[0]))
-
-        endian = image.dtype.byteorder
-
-        if endian == "<" or endian == "=" and sys.byteorder == "little":
-            scale = -scale
-
-        file.write("%f\n".encode() % scale)
-
-        image.tofile(file)
-
-
-def read_image(path):
-    """Read image and output RGB image (0-1).
-
-    Args:
-        path (str): path to file
-
-    Returns:
-        array: RGB image (0-1)
-    """
-    img = cv2.imread(path)
-
-    if img.ndim == 2:
-        img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
-
-    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) / 255.0
-
-    return img
-
-
-def resize_image(img):
-    """Resize image and make it fit for network.
-
-    Args:
-        img (array): image
-
-    Returns:
-        tensor: data ready for network
-    """
-    height_orig = img.shape[0]
-    width_orig = img.shape[1]
-
-    if width_orig > height_orig:
-        scale = width_orig / 384
-    else:
-        scale = height_orig / 384
-
-    height = (np.ceil(height_orig / scale / 32) * 32).astype(int)
-    width = (np.ceil(width_orig / scale / 32) * 32).astype(int)
-
-    img_resized = cv2.resize(img, (width, height), interpolation=cv2.INTER_AREA)
-
-    img_resized = (
-        torch.from_numpy(np.transpose(img_resized, (2, 0, 1))).contiguous().float()
-    )
-    img_resized = img_resized.unsqueeze(0)
-
-    return img_resized
-
-
-def resize_depth(depth, width, height):
-    """Resize depth map and bring to CPU (numpy).
-
-    Args:
-        depth (tensor): depth
-        width (int): image width
-        height (int): image height
-
-    Returns:
-        array: processed depth
-    """
-    depth = torch.squeeze(depth[0, :, :, :]).to("cpu")
-
-    depth_resized = cv2.resize(
-        depth.numpy(), (width, height), interpolation=cv2.INTER_CUBIC
-    )
-
-    return depth_resized
-
-def write_depth(path, depth, bits=1):
-    """Write depth map to pfm and png file.
-
-    Args:
-        path (str): filepath without extension
-        depth (array): depth
-    """
-    write_pfm(path + ".pfm", depth.astype(np.float32))
-
-    depth_min = depth.min()
-    depth_max = depth.max()
-
-    max_val = (2**(8*bits))-1
-
-    if depth_max - depth_min > np.finfo("float").eps:
-        out = max_val * (depth - depth_min) / (depth_max - depth_min)
-    else:
-        out = np.zeros(depth.shape, dtype=depth.type)
-
-    if bits == 1:
-        cv2.imwrite(path + ".png", out.astype("uint8"))
-    elif bits == 2:
-        cv2.imwrite(path + ".png", out.astype("uint16"))
-
-    return
diff --git a/repositories/stable-diffusion-stability-ai/ldm/util.py b/repositories/stable-diffusion-stability-ai/ldm/util.py
deleted file mode 100644
index e1c98eab977fe3c5ad283fa8cfc8ea365726a841..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/ldm/util.py
+++ /dev/null
@@ -1,207 +0,0 @@
-import importlib
-
-import torch
-from torch import optim
-import numpy as np
-
-from inspect import isfunction
-from PIL import Image, ImageDraw, ImageFont
-
-
-def autocast(f):
-    def do_autocast(*args, **kwargs):
-        with torch.cuda.amp.autocast(enabled=True,
-                                     dtype=torch.get_autocast_gpu_dtype(),
-                                     cache_enabled=torch.is_autocast_cache_enabled()):
-            return f(*args, **kwargs)
-
-    return do_autocast
-
-
-def log_txt_as_img(wh, xc, size=10):
-    # wh a tuple of (width, height)
-    # xc a list of captions to plot
-    b = len(xc)
-    txts = list()
-    for bi in range(b):
-        txt = Image.new("RGB", wh, color="white")
-        draw = ImageDraw.Draw(txt)
-        font = ImageFont.truetype('data/DejaVuSans.ttf', size=size)
-        nc = int(40 * (wh[0] / 256))
-        lines = "\n".join(xc[bi][start:start + nc] for start in range(0, len(xc[bi]), nc))
-
-        try:
-            draw.text((0, 0), lines, fill="black", font=font)
-        except UnicodeEncodeError:
-            print("Cant encode string for logging. Skipping.")
-
-        txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0
-        txts.append(txt)
-    txts = np.stack(txts)
-    txts = torch.tensor(txts)
-    return txts
-
-
-def ismap(x):
-    if not isinstance(x, torch.Tensor):
-        return False
-    return (len(x.shape) == 4) and (x.shape[1] > 3)
-
-
-def isimage(x):
-    if not isinstance(x,torch.Tensor):
-        return False
-    return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1)
-
-
-def exists(x):
-    return x is not None
-
-
-def default(val, d):
-    if exists(val):
-        return val
-    return d() if isfunction(d) else d
-
-
-def mean_flat(tensor):
-    """
-    https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86
-    Take the mean over all non-batch dimensions.
-    """
-    return tensor.mean(dim=list(range(1, len(tensor.shape))))
-
-
-def count_params(model, verbose=False):
-    total_params = sum(p.numel() for p in model.parameters())
-    if verbose:
-        print(f"{model.__class__.__name__} has {total_params*1.e-6:.2f} M params.")
-    return total_params
-
-
-def instantiate_from_config(config):
-    if not "target" in config:
-        if config == '__is_first_stage__':
-            return None
-        elif config == "__is_unconditional__":
-            return None
-        raise KeyError("Expected key `target` to instantiate.")
-    return get_obj_from_str(config["target"])(**config.get("params", dict())).cuda()
-
-
-def get_obj_from_str(string, reload=False):
-    module, cls = string.rsplit(".", 1)
-    if reload:
-        module_imp = importlib.import_module(module)
-        importlib.reload(module_imp)
-    return getattr(importlib.import_module(module, package=None), cls)
-
-
-class AdamWwithEMAandWings(optim.Optimizer):
-    # credit to https://gist.github.com/crowsonkb/65f7265353f403714fce3b2595e0b298
-    def __init__(self, params, lr=1.e-3, betas=(0.9, 0.999), eps=1.e-8,  # TODO: check hyperparameters before using
-                 weight_decay=1.e-2, amsgrad=False, ema_decay=0.9999,   # ema decay to match previous code
-                 ema_power=1., param_names=()):
-        """AdamW that saves EMA versions of the parameters."""
-        if not 0.0 <= lr:
-            raise ValueError("Invalid learning rate: {}".format(lr))
-        if not 0.0 <= eps:
-            raise ValueError("Invalid epsilon value: {}".format(eps))
-        if not 0.0 <= betas[0] < 1.0:
-            raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0]))
-        if not 0.0 <= betas[1] < 1.0:
-            raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1]))
-        if not 0.0 <= weight_decay:
-            raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
-        if not 0.0 <= ema_decay <= 1.0:
-            raise ValueError("Invalid ema_decay value: {}".format(ema_decay))
-        defaults = dict(lr=lr, betas=betas, eps=eps,
-                        weight_decay=weight_decay, amsgrad=amsgrad, ema_decay=ema_decay,
-                        ema_power=ema_power, param_names=param_names)
-        super().__init__(params, defaults)
-
-    def __setstate__(self, state):
-        super().__setstate__(state)
-        for group in self.param_groups:
-            group.setdefault('amsgrad', False)
-
-    @torch.no_grad()
-    def step(self, closure=None):
-        """Performs a single optimization step.
-        Args:
-            closure (callable, optional): A closure that reevaluates the model
-                and returns the loss.
-        """
-        loss = None
-        if closure is not None:
-            with torch.enable_grad():
-                loss = closure()
-
-        for group in self.param_groups:
-            params_with_grad = []
-            grads = []
-            exp_avgs = []
-            exp_avg_sqs = []
-            ema_params_with_grad = []
-            state_sums = []
-            max_exp_avg_sqs = []
-            state_steps = []
-            amsgrad = group['amsgrad']
-            beta1, beta2 = group['betas']
-            ema_decay = group['ema_decay']
-            ema_power = group['ema_power']
-
-            for p in group['params']:
-                if p.grad is None:
-                    continue
-                params_with_grad.append(p)
-                if p.grad.is_sparse:
-                    raise RuntimeError('AdamW does not support sparse gradients')
-                grads.append(p.grad)
-
-                state = self.state[p]
-
-                # State initialization
-                if len(state) == 0:
-                    state['step'] = 0
-                    # Exponential moving average of gradient values
-                    state['exp_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format)
-                    # Exponential moving average of squared gradient values
-                    state['exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format)
-                    if amsgrad:
-                        # Maintains max of all exp. moving avg. of sq. grad. values
-                        state['max_exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format)
-                    # Exponential moving average of parameter values
-                    state['param_exp_avg'] = p.detach().float().clone()
-
-                exp_avgs.append(state['exp_avg'])
-                exp_avg_sqs.append(state['exp_avg_sq'])
-                ema_params_with_grad.append(state['param_exp_avg'])
-
-                if amsgrad:
-                    max_exp_avg_sqs.append(state['max_exp_avg_sq'])
-
-                # update the steps for each param group update
-                state['step'] += 1
-                # record the step after step update
-                state_steps.append(state['step'])
-
-            optim._functional.adamw(params_with_grad,
-                    grads,
-                    exp_avgs,
-                    exp_avg_sqs,
-                    max_exp_avg_sqs,
-                    state_steps,
-                    amsgrad=amsgrad,
-                    beta1=beta1,
-                    beta2=beta2,
-                    lr=group['lr'],
-                    weight_decay=group['weight_decay'],
-                    eps=group['eps'],
-                    maximize=False)
-
-            cur_ema_decay = min(ema_decay, 1 - state['step'] ** -ema_power)
-            for param, ema_param in zip(params_with_grad, ema_params_with_grad):
-                ema_param.mul_(cur_ema_decay).add_(param.float(), alpha=1 - cur_ema_decay)
-
-        return loss
\ No newline at end of file
diff --git a/repositories/stable-diffusion-stability-ai/modelcard.md b/repositories/stable-diffusion-stability-ai/modelcard.md
deleted file mode 100644
index 4b6190921e063f60e8089490d172e6ce8714a752..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/modelcard.md
+++ /dev/null
@@ -1,153 +0,0 @@
-# Stable Diffusion v2 Model Card
-This model card focuses on the models associated with the Stable Diffusion v2, available [here](https://github.com/Stability-AI/stablediffusion/).
-
-## Model Details
-- **Developed by:** Robin Rombach, Patrick Esser
-- **Model type:** Diffusion-based text-to-image generation model
-- **Language(s):** English
-- **License:** CreativeML Open RAIL++-M License
-- **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([OpenCLIP-ViT/H](https://github.com/mlfoundations/open_clip)).
-- **Resources for more information:** [GitHub Repository](https://github.com/Stability-AI/).
-- **Cite as:**
-
-      @InProceedings{Rombach_2022_CVPR,
-          author    = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
-          title     = {High-Resolution Image Synthesis With Latent Diffusion Models},
-          booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
-          month     = {June},
-          year      = {2022},
-          pages     = {10684-10695}
-      }
-
-# Uses
-
-## Direct Use 
-The model is intended for research purposes only. Possible research areas and tasks include
-
-- Safe deployment of models which have the potential to generate harmful content.
-- Probing and understanding the limitations and biases of generative models.
-- Generation of artworks and use in design and other artistic processes.
-- Applications in educational or creative tools.
-- Research on generative models.
-
-Excluded uses are described below.
-
- ### Misuse, Malicious Use, and Out-of-Scope Use
-_Note: This section is originally taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), was used for Stable Diffusion v1, but applies in the same way to Stable Diffusion v2_.
-
-The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
-
-#### Out-of-Scope Use
-The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.
-
-#### Misuse and Malicious Use
-Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:
-
-- Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc.
-- Intentionally promoting or propagating discriminatory content or harmful stereotypes.
-- Impersonating individuals without their consent.
-- Sexual content without consent of the people who might see it.
-- Mis- and disinformation
-- Representations of egregious violence and gore
-- Sharing of copyrighted or licensed material in violation of its terms of use.
-- Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use.
-
-## Limitations and Bias
-
-### Limitations
-
-- The model does not achieve perfect photorealism
-- The model cannot render legible text
-- The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere”
-- Faces and people in general may not be generated properly.
-- The model was trained mainly with English captions and will not work as well in other languages.
-- The autoencoding part of the model is lossy
-- The model was trained on a subset of the large-scale dataset
-  [LAION-5B](https://laion.ai/blog/laion-5b/), which contains adult, violent and sexual content. To partially mitigate this, we have filtered the dataset using LAION's NFSW detector (see Training section).
-
-### Bias
-While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. 
-Stable Diffusion vw was primarily trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/), 
-which consists of images that are limited to English descriptions. 
-Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for. 
-This affects the overall output of the model, as white and western cultures are often set as the default. Further, the 
-ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts.
-Stable Diffusion v2 mirrors and exacerbates biases to such a degree that viewer discretion must be advised irrespective of the input or its intent.
-
-
-## Training
-
-**Training Data**
-The model developers used the following dataset for training the model:
-
-- LAION-5B and subsets (details below). The training data is further filtered using LAION's NSFW detector. For more details, please refer to LAION-5B's [NeurIPS 2022](https://openreview.net/forum?id=M3Y74vmsMcY) paper and reviewer discussions on the topic.
-
-**Training Procedure**
-Stable Diffusion v2 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training, 
-
-- Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4
-- Text prompts are encoded through the OpenCLIP-ViT/H text-encoder.
-- The output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention.
-- The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet. We also use the so-called _v-objective_, see https://arxiv.org/abs/2202.00512.
-
-We currently provide the following checkpoints, for various versions:
-
-### Version 2.1
-
-- `512-base-ema.ckpt`: Fine-tuned on `512-base-ema.ckpt` 2.0 with 220k extra steps taken, with `punsafe=0.98` on the same dataset.
-- `768-v-ema.ckpt`: Resumed from `768-v-ema.ckpt` 2.0 with an additional 55k steps on the same dataset (`punsafe=0.1`), and then fine-tuned for another 155k extra steps with `punsafe=0.98`.
-
-**SD-unCLIP 2.1** is a finetuned version of Stable Diffusion 2.1, modified to accept (noisy) CLIP image embedding in addition to the text prompt, and can be used to create image variations ([Examples](https://github.com/Stability-AI/stablediffusion/blob/main/doc/UNCLIP.MD)) or can be chained with text-to-image CLIP priors. The amount of noise added to the image embedding can be specified via the `noise_level` (0 means no noise, 1000 full noise).
-
-If you plan on building applications on top of the model that the general public may use, you are responsible for adding the guardrails to minimize or prevent misuse of the application, especially for use-cases highlighted in the earlier section, Misuse, Malicious Use, and Out-of-Scope Use.
-
-A public demo of SD-unCLIP is already available at [clipdrop.co/stable-diffusion-reimagine](https://clipdrop.co/stable-diffusion-reimagine)
-
-### Version 2.0
-
-- `512-base-ema.ckpt`: 550k steps at resolution `256x256` on a subset of [LAION-5B](https://laion.ai/blog/laion-5b/) filtered for explicit pornographic material, using the [LAION-NSFW classifier](https://github.com/LAION-AI/CLIP-based-NSFW-Detector) with `punsafe=0.1` and an [aesthetic score](https://github.com/christophschuhmann/improved-aesthetic-predictor) >= `4.5`.
-  850k steps at resolution `512x512` on the same dataset with resolution `>= 512x512`.
-- `768-v-ema.ckpt`: Resumed from `512-base-ema.ckpt` and trained for 150k steps using a [v-objective](https://arxiv.org/abs/2202.00512) on the same dataset. Resumed for another 140k steps on a `768x768` subset of our dataset.
-- `512-depth-ema.ckpt`: Resumed from `512-base-ema.ckpt` and finetuned for 200k steps. Added an extra input channel to process the (relative) depth prediction produced by [MiDaS](https://github.com/isl-org/MiDaS) (`dpt_hybrid`) which is used as an additional conditioning.
-The additional input channels of the U-Net which process this extra information were zero-initialized.
-- `512-inpainting-ema.ckpt`: Resumed from `512-base-ema.ckpt` and trained for another 200k steps. Follows the mask-generation strategy presented in [LAMA](https://github.com/saic-mdal/lama) which, in combination with the latent VAE representations of the masked image, are used as an additional conditioning.
-The additional input channels of the U-Net which process this extra information were zero-initialized. The same strategy was used to train the [1.5-inpainting checkpoint](https://github.com/saic-mdal/lama).
-- `x4-upscaling-ema.ckpt`: Trained for 1.25M steps on a 10M subset of LAION containing images `>2048x2048`. The model was trained on crops of size `512x512` and is a text-guided [latent upscaling diffusion model](https://arxiv.org/abs/2112.10752).
-In addition to the textual input, it receives a `noise_level` as an input parameter, which can be used to add noise to the low-resolution input according to a [predefined diffusion schedule](configs/stable-diffusion/x4-upscaling.yaml). 
-
-- **Hardware:** 32 x 8 x A100 GPUs
-- **Optimizer:** AdamW
-- **Gradient Accumulations**: 1
-- **Batch:** 32 x 8 x 2 x 4 = 2048
-- **Learning rate:** warmup to 0.0001 for 10,000 steps and then kept constant
-
-## Evaluation Results 
-Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0,
-5.0, 6.0, 7.0, 8.0) and 50 steps DDIM sampling steps show the relative improvements of the checkpoints:
-
-![pareto](assets/model-variants.jpg) 
-
-Evaluated using 50 DDIM steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution.  Not optimized for FID scores.
-
-## Environmental Impact
-
-**Stable Diffusion v1** **Estimated Emissions**
-Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact.
-
-- **Hardware Type:** A100 PCIe 40GB
-- **Hours used:** 200000
-- **Cloud Provider:** AWS
-- **Compute Region:** US-east
-- **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 15000 kg CO2 eq.
-
-## Citation
-    @InProceedings{Rombach_2022_CVPR,
-        author    = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
-        title     = {High-Resolution Image Synthesis With Latent Diffusion Models},
-        booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
-        month     = {June},
-        year      = {2022},
-        pages     = {10684-10695}
-    }
-
-*This model card was written by: Robin Rombach, Patrick Esser and David Ha and is based on the [Stable Diffusion v1](https://github.com/CompVis/stable-diffusion/blob/main/Stable_Diffusion_v1_Model_Card.md) and [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).*
diff --git a/repositories/stable-diffusion-stability-ai/requirements.txt b/repositories/stable-diffusion-stability-ai/requirements.txt
deleted file mode 100644
index 23b4acb7847ce43c24219bb4579d48056901299c..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/requirements.txt
+++ /dev/null
@@ -1,19 +0,0 @@
-albumentations==0.4.3
-opencv-python
-pudb==2019.2
-imageio==2.9.0
-imageio-ffmpeg==0.4.2
-pytorch-lightning==1.4.2
-torchmetrics==0.6
-omegaconf==2.1.1
-test-tube>=0.7.5
-streamlit>=0.73.1
-einops==0.3.0
-transformers==4.19.2
-webdataset==0.2.5
-open-clip-torch==2.7.0
-gradio==3.13.2
-kornia==0.6
-invisible-watermark>=0.1.5
-streamlit-drawable-canvas==0.8.0
--e .
diff --git a/repositories/stable-diffusion-stability-ai/scripts/gradio/depth2img.py b/repositories/stable-diffusion-stability-ai/scripts/gradio/depth2img.py
deleted file mode 100644
index c791a4d0b2a510b3525658f4d852d14704ea9f1a..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/scripts/gradio/depth2img.py
+++ /dev/null
@@ -1,184 +0,0 @@
-import sys
-import torch
-import numpy as np
-import gradio as gr
-from PIL import Image
-from omegaconf import OmegaConf
-from einops import repeat, rearrange
-from pytorch_lightning import seed_everything
-from imwatermark import WatermarkEncoder
-
-from scripts.txt2img import put_watermark
-from ldm.util import instantiate_from_config
-from ldm.models.diffusion.ddim import DDIMSampler
-from ldm.data.util import AddMiDaS
-
-torch.set_grad_enabled(False)
-
-
-def initialize_model(config, ckpt):
-    config = OmegaConf.load(config)
-    model = instantiate_from_config(config.model)
-    model.load_state_dict(torch.load(ckpt)["state_dict"], strict=False)
-
-    device = torch.device(
-        "cuda") if torch.cuda.is_available() else torch.device("cpu")
-    model = model.to(device)
-    sampler = DDIMSampler(model)
-    return sampler
-
-
-def make_batch_sd(
-        image,
-        txt,
-        device,
-        num_samples=1,
-        model_type="dpt_hybrid"
-):
-    image = np.array(image.convert("RGB"))
-    image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
-    # sample['jpg'] is tensor hwc in [-1, 1] at this point
-    midas_trafo = AddMiDaS(model_type=model_type)
-    batch = {
-        "jpg": image,
-        "txt": num_samples * [txt],
-    }
-    batch = midas_trafo(batch)
-    batch["jpg"] = rearrange(batch["jpg"], 'h w c -> 1 c h w')
-    batch["jpg"] = repeat(batch["jpg"].to(device=device),
-                          "1 ... -> n ...", n=num_samples)
-    batch["midas_in"] = repeat(torch.from_numpy(batch["midas_in"][None, ...]).to(
-        device=device), "1 ... -> n ...", n=num_samples)
-    return batch
-
-
-def paint(sampler, image, prompt, t_enc, seed, scale, num_samples=1, callback=None,
-          do_full_sample=False):
-    device = torch.device(
-        "cuda") if torch.cuda.is_available() else torch.device("cpu")
-    model = sampler.model
-    seed_everything(seed)
-
-    print("Creating invisible watermark encoder (see https://github.com/ShieldMnt/invisible-watermark)...")
-    wm = "SDV2"
-    wm_encoder = WatermarkEncoder()
-    wm_encoder.set_watermark('bytes', wm.encode('utf-8'))
-
-    with torch.no_grad(),\
-            torch.autocast("cuda"):
-        batch = make_batch_sd(
-            image, txt=prompt, device=device, num_samples=num_samples)
-        z = model.get_first_stage_encoding(model.encode_first_stage(
-            batch[model.first_stage_key]))  # move to latent space
-        c = model.cond_stage_model.encode(batch["txt"])
-        c_cat = list()
-        for ck in model.concat_keys:
-            cc = batch[ck]
-            cc = model.depth_model(cc)
-            depth_min, depth_max = torch.amin(cc, dim=[1, 2, 3], keepdim=True), torch.amax(cc, dim=[1, 2, 3],
-                                                                                           keepdim=True)
-            display_depth = (cc - depth_min) / (depth_max - depth_min)
-            depth_image = Image.fromarray(
-                (display_depth[0, 0, ...].cpu().numpy() * 255.).astype(np.uint8))
-            cc = torch.nn.functional.interpolate(
-                cc,
-                size=z.shape[2:],
-                mode="bicubic",
-                align_corners=False,
-            )
-            depth_min, depth_max = torch.amin(cc, dim=[1, 2, 3], keepdim=True), torch.amax(cc, dim=[1, 2, 3],
-                                                                                           keepdim=True)
-            cc = 2. * (cc - depth_min) / (depth_max - depth_min) - 1.
-            c_cat.append(cc)
-        c_cat = torch.cat(c_cat, dim=1)
-        # cond
-        cond = {"c_concat": [c_cat], "c_crossattn": [c]}
-
-        # uncond cond
-        uc_cross = model.get_unconditional_conditioning(num_samples, "")
-        uc_full = {"c_concat": [c_cat], "c_crossattn": [uc_cross]}
-        if not do_full_sample:
-            # encode (scaled latent)
-            z_enc = sampler.stochastic_encode(
-                z, torch.tensor([t_enc] * num_samples).to(model.device))
-        else:
-            z_enc = torch.randn_like(z)
-        # decode it
-        samples = sampler.decode(z_enc, cond, t_enc, unconditional_guidance_scale=scale,
-                                 unconditional_conditioning=uc_full, callback=callback)
-        x_samples_ddim = model.decode_first_stage(samples)
-        result = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
-        result = result.cpu().numpy().transpose(0, 2, 3, 1) * 255
-    return [depth_image] + [put_watermark(Image.fromarray(img.astype(np.uint8)), wm_encoder) for img in result]
-
-
-def pad_image(input_image):
-    pad_w, pad_h = np.max(((2, 2), np.ceil(
-        np.array(input_image.size) / 64).astype(int)), axis=0) * 64 - input_image.size
-    im_padded = Image.fromarray(
-        np.pad(np.array(input_image), ((0, pad_h), (0, pad_w), (0, 0)), mode='edge'))
-    return im_padded
-
-
-def predict(input_image, prompt, steps, num_samples, scale, seed, eta, strength):
-    init_image = input_image.convert("RGB")
-    image = pad_image(init_image)  # resize to integer multiple of 32
-
-    sampler.make_schedule(steps, ddim_eta=eta, verbose=True)
-    assert 0. <= strength <= 1., 'can only work with strength in [0.0, 1.0]'
-    do_full_sample = strength == 1.
-    t_enc = min(int(strength * steps), steps-1)
-    result = paint(
-        sampler=sampler,
-        image=image,
-        prompt=prompt,
-        t_enc=t_enc,
-        seed=seed,
-        scale=scale,
-        num_samples=num_samples,
-        callback=None,
-        do_full_sample=do_full_sample
-    )
-    return result
-
-
-sampler = initialize_model(sys.argv[1], sys.argv[2])
-
-block = gr.Blocks().queue()
-with block:
-    with gr.Row():
-        gr.Markdown("## Stable Diffusion Depth2Img")
-
-    with gr.Row():
-        with gr.Column():
-            input_image = gr.Image(source='upload', type="pil")
-            prompt = gr.Textbox(label="Prompt")
-            run_button = gr.Button(label="Run")
-            with gr.Accordion("Advanced options", open=False):
-                num_samples = gr.Slider(
-                    label="Images", minimum=1, maximum=4, value=1, step=1)
-                ddim_steps = gr.Slider(label="Steps", minimum=1,
-                                       maximum=50, value=50, step=1)
-                scale = gr.Slider(
-                    label="Guidance Scale", minimum=0.1, maximum=30.0, value=9.0, step=0.1
-                )
-                strength = gr.Slider(
-                    label="Strength", minimum=0.0, maximum=1.0, value=0.9, step=0.01
-                )
-                seed = gr.Slider(
-                    label="Seed",
-                    minimum=0,
-                    maximum=2147483647,
-                    step=1,
-                    randomize=True,
-                )
-                eta = gr.Number(label="eta (DDIM)", value=0.0)
-        with gr.Column():
-            gallery = gr.Gallery(label="Generated images", show_label=False).style(
-                grid=[2], height="auto")
-
-    run_button.click(fn=predict, inputs=[
-                     input_image, prompt, ddim_steps, num_samples, scale, seed, eta, strength], outputs=[gallery])
-
-
-block.launch()
diff --git a/repositories/stable-diffusion-stability-ai/scripts/gradio/inpainting.py b/repositories/stable-diffusion-stability-ai/scripts/gradio/inpainting.py
deleted file mode 100644
index 09d44f3ddc528011d7421966915b93d0e2803ba5..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/scripts/gradio/inpainting.py
+++ /dev/null
@@ -1,195 +0,0 @@
-import sys
-import cv2
-import torch
-import numpy as np
-import gradio as gr
-from PIL import Image
-from omegaconf import OmegaConf
-from einops import repeat
-from imwatermark import WatermarkEncoder
-from pathlib import Path
-
-from ldm.models.diffusion.ddim import DDIMSampler
-from ldm.util import instantiate_from_config
-
-
-torch.set_grad_enabled(False)
-
-
-def put_watermark(img, wm_encoder=None):
-    if wm_encoder is not None:
-        img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
-        img = wm_encoder.encode(img, 'dwtDct')
-        img = Image.fromarray(img[:, :, ::-1])
-    return img
-
-
-def initialize_model(config, ckpt):
-    config = OmegaConf.load(config)
-    model = instantiate_from_config(config.model)
-
-    model.load_state_dict(torch.load(ckpt)["state_dict"], strict=False)
-
-    device = torch.device(
-        "cuda") if torch.cuda.is_available() else torch.device("cpu")
-    model = model.to(device)
-    sampler = DDIMSampler(model)
-
-    return sampler
-
-
-def make_batch_sd(
-        image,
-        mask,
-        txt,
-        device,
-        num_samples=1):
-    image = np.array(image.convert("RGB"))
-    image = image[None].transpose(0, 3, 1, 2)
-    image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
-
-    mask = np.array(mask.convert("L"))
-    mask = mask.astype(np.float32) / 255.0
-    mask = mask[None, None]
-    mask[mask < 0.5] = 0
-    mask[mask >= 0.5] = 1
-    mask = torch.from_numpy(mask)
-
-    masked_image = image * (mask < 0.5)
-
-    batch = {
-        "image": repeat(image.to(device=device), "1 ... -> n ...", n=num_samples),
-        "txt": num_samples * [txt],
-        "mask": repeat(mask.to(device=device), "1 ... -> n ...", n=num_samples),
-        "masked_image": repeat(masked_image.to(device=device), "1 ... -> n ...", n=num_samples),
-    }
-    return batch
-
-
-def inpaint(sampler, image, mask, prompt, seed, scale, ddim_steps, num_samples=1, w=512, h=512):
-    device = torch.device(
-        "cuda") if torch.cuda.is_available() else torch.device("cpu")
-    model = sampler.model
-
-    print("Creating invisible watermark encoder (see https://github.com/ShieldMnt/invisible-watermark)...")
-    wm = "SDV2"
-    wm_encoder = WatermarkEncoder()
-    wm_encoder.set_watermark('bytes', wm.encode('utf-8'))
-
-    prng = np.random.RandomState(seed)
-    start_code = prng.randn(num_samples, 4, h // 8, w // 8)
-    start_code = torch.from_numpy(start_code).to(
-        device=device, dtype=torch.float32)
-
-    with torch.no_grad(), \
-            torch.autocast("cuda"):
-        batch = make_batch_sd(image, mask, txt=prompt,
-                              device=device, num_samples=num_samples)
-
-        c = model.cond_stage_model.encode(batch["txt"])
-
-        c_cat = list()
-        for ck in model.concat_keys:
-            cc = batch[ck].float()
-            if ck != model.masked_image_key:
-                bchw = [num_samples, 4, h // 8, w // 8]
-                cc = torch.nn.functional.interpolate(cc, size=bchw[-2:])
-            else:
-                cc = model.get_first_stage_encoding(
-                    model.encode_first_stage(cc))
-            c_cat.append(cc)
-        c_cat = torch.cat(c_cat, dim=1)
-
-        # cond
-        cond = {"c_concat": [c_cat], "c_crossattn": [c]}
-
-        # uncond cond
-        uc_cross = model.get_unconditional_conditioning(num_samples, "")
-        uc_full = {"c_concat": [c_cat], "c_crossattn": [uc_cross]}
-
-        shape = [model.channels, h // 8, w // 8]
-        samples_cfg, intermediates = sampler.sample(
-            ddim_steps,
-            num_samples,
-            shape,
-            cond,
-            verbose=False,
-            eta=1.0,
-            unconditional_guidance_scale=scale,
-            unconditional_conditioning=uc_full,
-            x_T=start_code,
-        )
-        x_samples_ddim = model.decode_first_stage(samples_cfg)
-
-        result = torch.clamp((x_samples_ddim + 1.0) / 2.0,
-                             min=0.0, max=1.0)
-
-        result = result.cpu().numpy().transpose(0, 2, 3, 1) * 255
-    return [put_watermark(Image.fromarray(img.astype(np.uint8)), wm_encoder) for img in result]
-
-def pad_image(input_image):
-    pad_w, pad_h = np.max(((2, 2), np.ceil(
-        np.array(input_image.size) / 64).astype(int)), axis=0) * 64 - input_image.size
-    im_padded = Image.fromarray(
-        np.pad(np.array(input_image), ((0, pad_h), (0, pad_w), (0, 0)), mode='edge'))
-    return im_padded
-
-def predict(input_image, prompt, ddim_steps, num_samples, scale, seed):
-    init_image = input_image["image"].convert("RGB")
-    init_mask = input_image["mask"].convert("RGB")
-    image = pad_image(init_image) # resize to integer multiple of 32
-    mask = pad_image(init_mask) # resize to integer multiple of 32
-    width, height = image.size
-    print("Inpainting...", width, height)
-
-    result = inpaint(
-        sampler=sampler,
-        image=image,
-        mask=mask,
-        prompt=prompt,
-        seed=seed,
-        scale=scale,
-        ddim_steps=ddim_steps,
-        num_samples=num_samples,
-        h=height, w=width
-    )
-
-    return result
-
-
-sampler = initialize_model(sys.argv[1], sys.argv[2])
-
-block = gr.Blocks().queue()
-with block:
-    with gr.Row():
-        gr.Markdown("## Stable Diffusion Inpainting")
-
-    with gr.Row():
-        with gr.Column():
-            input_image = gr.Image(source='upload', tool='sketch', type="pil")
-            prompt = gr.Textbox(label="Prompt")
-            run_button = gr.Button(label="Run")
-            with gr.Accordion("Advanced options", open=False):
-                num_samples = gr.Slider(
-                    label="Images", minimum=1, maximum=4, value=4, step=1)
-                ddim_steps = gr.Slider(label="Steps", minimum=1,
-                                       maximum=50, value=45, step=1)
-                scale = gr.Slider(
-                    label="Guidance Scale", minimum=0.1, maximum=30.0, value=10, step=0.1
-                )
-                seed = gr.Slider(
-                    label="Seed",
-                    minimum=0,
-                    maximum=2147483647,
-                    step=1,
-                    randomize=True,
-                )
-        with gr.Column():
-            gallery = gr.Gallery(label="Generated images", show_label=False).style(
-                grid=[2], height="auto")
-
-    run_button.click(fn=predict, inputs=[
-                     input_image, prompt, ddim_steps, num_samples, scale, seed], outputs=[gallery])
-
-
-block.launch()
diff --git a/repositories/stable-diffusion-stability-ai/scripts/gradio/superresolution.py b/repositories/stable-diffusion-stability-ai/scripts/gradio/superresolution.py
deleted file mode 100644
index 3d08fbfae4f9639165e669f3c69c76763c5b32a8..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/scripts/gradio/superresolution.py
+++ /dev/null
@@ -1,197 +0,0 @@
-import sys
-import torch
-import numpy as np
-import gradio as gr
-from PIL import Image
-from omegaconf import OmegaConf
-from einops import repeat, rearrange
-from pytorch_lightning import seed_everything
-from imwatermark import WatermarkEncoder
-
-from scripts.txt2img import put_watermark
-from ldm.models.diffusion.ddim import DDIMSampler
-from ldm.models.diffusion.ddpm import LatentUpscaleDiffusion, LatentUpscaleFinetuneDiffusion
-from ldm.util import exists, instantiate_from_config
-
-
-torch.set_grad_enabled(False)
-
-
-def initialize_model(config, ckpt):
-    config = OmegaConf.load(config)
-    model = instantiate_from_config(config.model)
-    model.load_state_dict(torch.load(ckpt)["state_dict"], strict=False)
-
-    device = torch.device(
-        "cuda") if torch.cuda.is_available() else torch.device("cpu")
-    model = model.to(device)
-    sampler = DDIMSampler(model)
-    return sampler
-
-
-def make_batch_sd(
-        image,
-        txt,
-        device,
-        num_samples=1,
-):
-    image = np.array(image.convert("RGB"))
-    image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
-    batch = {
-        "lr": rearrange(image, 'h w c -> 1 c h w'),
-        "txt": num_samples * [txt],
-    }
-    batch["lr"] = repeat(batch["lr"].to(device=device),
-                         "1 ... -> n ...", n=num_samples)
-    return batch
-
-
-def make_noise_augmentation(model, batch, noise_level=None):
-    x_low = batch[model.low_scale_key]
-    x_low = x_low.to(memory_format=torch.contiguous_format).float()
-    x_aug, noise_level = model.low_scale_model(x_low, noise_level)
-    return x_aug, noise_level
-
-
-def paint(sampler, image, prompt, seed, scale, h, w, steps, num_samples=1, callback=None, eta=0., noise_level=None):
-    device = torch.device(
-        "cuda") if torch.cuda.is_available() else torch.device("cpu")
-    model = sampler.model
-    seed_everything(seed)
-    prng = np.random.RandomState(seed)
-    start_code = prng.randn(num_samples, model.channels, h, w)
-    start_code = torch.from_numpy(start_code).to(
-        device=device, dtype=torch.float32)
-
-    print("Creating invisible watermark encoder (see https://github.com/ShieldMnt/invisible-watermark)...")
-    wm = "SDV2"
-    wm_encoder = WatermarkEncoder()
-    wm_encoder.set_watermark('bytes', wm.encode('utf-8'))
-    with torch.no_grad(),\
-            torch.autocast("cuda"):
-        batch = make_batch_sd(
-            image, txt=prompt, device=device, num_samples=num_samples)
-        c = model.cond_stage_model.encode(batch["txt"])
-        c_cat = list()
-        if isinstance(model, LatentUpscaleFinetuneDiffusion):
-            for ck in model.concat_keys:
-                cc = batch[ck]
-                if exists(model.reshuffle_patch_size):
-                    assert isinstance(model.reshuffle_patch_size, int)
-                    cc = rearrange(cc, 'b c (p1 h) (p2 w) -> b (p1 p2 c) h w',
-                                   p1=model.reshuffle_patch_size, p2=model.reshuffle_patch_size)
-                c_cat.append(cc)
-            c_cat = torch.cat(c_cat, dim=1)
-            # cond
-            cond = {"c_concat": [c_cat], "c_crossattn": [c]}
-            # uncond cond
-            uc_cross = model.get_unconditional_conditioning(num_samples, "")
-            uc_full = {"c_concat": [c_cat], "c_crossattn": [uc_cross]}
-        elif isinstance(model, LatentUpscaleDiffusion):
-            x_augment, noise_level = make_noise_augmentation(
-                model, batch, noise_level)
-            cond = {"c_concat": [x_augment],
-                    "c_crossattn": [c], "c_adm": noise_level}
-            # uncond cond
-            uc_cross = model.get_unconditional_conditioning(num_samples, "")
-            uc_full = {"c_concat": [x_augment], "c_crossattn": [
-                uc_cross], "c_adm": noise_level}
-        else:
-            raise NotImplementedError()
-
-        shape = [model.channels, h, w]
-        samples, intermediates = sampler.sample(
-            steps,
-            num_samples,
-            shape,
-            cond,
-            verbose=False,
-            eta=eta,
-            unconditional_guidance_scale=scale,
-            unconditional_conditioning=uc_full,
-            x_T=start_code,
-            callback=callback
-        )
-    with torch.no_grad():
-        x_samples_ddim = model.decode_first_stage(samples)
-    result = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
-    result = result.cpu().numpy().transpose(0, 2, 3, 1) * 255
-    return [put_watermark(Image.fromarray(img.astype(np.uint8)), wm_encoder) for img in result]
-
-
-def pad_image(input_image):
-    pad_w, pad_h = np.max(((2, 2), np.ceil(
-        np.array(input_image.size) / 64).astype(int)), axis=0) * 64 - input_image.size
-    im_padded = Image.fromarray(
-        np.pad(np.array(input_image), ((0, pad_h), (0, pad_w), (0, 0)), mode='edge'))
-    return im_padded
-
-
-def predict(input_image, prompt, steps, num_samples, scale, seed, eta, noise_level):
-    init_image = input_image.convert("RGB")
-    image = pad_image(init_image)  # resize to integer multiple of 32
-    width, height = image.size
-
-    noise_level = torch.Tensor(
-        num_samples * [noise_level]).to(sampler.model.device).long()
-    sampler.make_schedule(steps, ddim_eta=eta, verbose=True)
-    result = paint(
-        sampler=sampler,
-        image=image,
-        prompt=prompt,
-        seed=seed,
-        scale=scale,
-        h=height, w=width, steps=steps,
-        num_samples=num_samples,
-        callback=None,
-        noise_level=noise_level
-    )
-    return result
-
-
-sampler = initialize_model(sys.argv[1], sys.argv[2])
-
-block = gr.Blocks().queue()
-with block:
-    with gr.Row():
-        gr.Markdown("## Stable Diffusion Upscaling")
-
-    with gr.Row():
-        with gr.Column():
-            input_image = gr.Image(source='upload', type="pil")
-            gr.Markdown(
-                "Tip: Add a description of the object that should be upscaled, e.g.: 'a professional photograph of a cat")
-            prompt = gr.Textbox(label="Prompt")
-            run_button = gr.Button(label="Run")
-            with gr.Accordion("Advanced options", open=False):
-                num_samples = gr.Slider(
-                    label="Number of Samples", minimum=1, maximum=4, value=1, step=1)
-                steps = gr.Slider(label="DDIM Steps", minimum=2,
-                                  maximum=200, value=75, step=1)
-                scale = gr.Slider(
-                    label="Scale", minimum=0.1, maximum=30.0, value=10, step=0.1
-                )
-                seed = gr.Slider(
-                    label="Seed",
-                    minimum=0,
-                    maximum=2147483647,
-                    step=1,
-                    randomize=True,
-                )
-                eta = gr.Number(label="eta (DDIM)",
-                                value=0.0, min=0.0, max=1.0)
-                noise_level = None
-                if isinstance(sampler.model, LatentUpscaleDiffusion):
-                    # TODO: make this work for all models
-                    noise_level = gr.Number(
-                        label="Noise Augmentation", min=0, max=350, value=20, step=1)
-
-        with gr.Column():
-            gallery = gr.Gallery(label="Generated images", show_label=False).style(
-                grid=[2], height="auto")
-
-    run_button.click(fn=predict, inputs=[
-                     input_image, prompt, steps, num_samples, scale, seed, eta, noise_level], outputs=[gallery])
-
-
-block.launch()
diff --git a/repositories/stable-diffusion-stability-ai/scripts/img2img.py b/repositories/stable-diffusion-stability-ai/scripts/img2img.py
deleted file mode 100644
index 9085ba9d37ea6402b9ee543e82f7d8c56a1c273a..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/scripts/img2img.py
+++ /dev/null
@@ -1,279 +0,0 @@
-"""make variations of input image"""
-
-import argparse, os
-import PIL
-import torch
-import numpy as np
-from omegaconf import OmegaConf
-from PIL import Image
-from tqdm import tqdm, trange
-from itertools import islice
-from einops import rearrange, repeat
-from torchvision.utils import make_grid
-from torch import autocast
-from contextlib import nullcontext
-from pytorch_lightning import seed_everything
-from imwatermark import WatermarkEncoder
-
-
-from scripts.txt2img import put_watermark
-from ldm.util import instantiate_from_config
-from ldm.models.diffusion.ddim import DDIMSampler
-
-
-def chunk(it, size):
-    it = iter(it)
-    return iter(lambda: tuple(islice(it, size)), ())
-
-
-def load_model_from_config(config, ckpt, verbose=False):
-    print(f"Loading model from {ckpt}")
-    pl_sd = torch.load(ckpt, map_location="cpu")
-    if "global_step" in pl_sd:
-        print(f"Global Step: {pl_sd['global_step']}")
-    sd = pl_sd["state_dict"]
-    model = instantiate_from_config(config.model)
-    m, u = model.load_state_dict(sd, strict=False)
-    if len(m) > 0 and verbose:
-        print("missing keys:")
-        print(m)
-    if len(u) > 0 and verbose:
-        print("unexpected keys:")
-        print(u)
-
-    model.cuda()
-    model.eval()
-    return model
-
-
-def load_img(path):
-    image = Image.open(path).convert("RGB")
-    w, h = image.size
-    print(f"loaded input image of size ({w}, {h}) from {path}")
-    w, h = map(lambda x: x - x % 64, (w, h))  # resize to integer multiple of 64
-    image = image.resize((w, h), resample=PIL.Image.LANCZOS)
-    image = np.array(image).astype(np.float32) / 255.0
-    image = image[None].transpose(0, 3, 1, 2)
-    image = torch.from_numpy(image)
-    return 2. * image - 1.
-
-
-def main():
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument(
-        "--prompt",
-        type=str,
-        nargs="?",
-        default="a painting of a virus monster playing guitar",
-        help="the prompt to render"
-    )
-
-    parser.add_argument(
-        "--init-img",
-        type=str,
-        nargs="?",
-        help="path to the input image"
-    )
-
-    parser.add_argument(
-        "--outdir",
-        type=str,
-        nargs="?",
-        help="dir to write results to",
-        default="outputs/img2img-samples"
-    )
-
-    parser.add_argument(
-        "--ddim_steps",
-        type=int,
-        default=50,
-        help="number of ddim sampling steps",
-    )
-
-    parser.add_argument(
-        "--fixed_code",
-        action='store_true',
-        help="if enabled, uses the same starting code across all samples ",
-    )
-
-    parser.add_argument(
-        "--ddim_eta",
-        type=float,
-        default=0.0,
-        help="ddim eta (eta=0.0 corresponds to deterministic sampling",
-    )
-    parser.add_argument(
-        "--n_iter",
-        type=int,
-        default=1,
-        help="sample this often",
-    )
-
-    parser.add_argument(
-        "--C",
-        type=int,
-        default=4,
-        help="latent channels",
-    )
-    parser.add_argument(
-        "--f",
-        type=int,
-        default=8,
-        help="downsampling factor, most often 8 or 16",
-    )
-
-    parser.add_argument(
-        "--n_samples",
-        type=int,
-        default=2,
-        help="how many samples to produce for each given prompt. A.k.a batch size",
-    )
-
-    parser.add_argument(
-        "--n_rows",
-        type=int,
-        default=0,
-        help="rows in the grid (default: n_samples)",
-    )
-
-    parser.add_argument(
-        "--scale",
-        type=float,
-        default=9.0,
-        help="unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))",
-    )
-
-    parser.add_argument(
-        "--strength",
-        type=float,
-        default=0.8,
-        help="strength for noising/unnoising. 1.0 corresponds to full destruction of information in init image",
-    )
-
-    parser.add_argument(
-        "--from-file",
-        type=str,
-        help="if specified, load prompts from this file",
-    )
-    parser.add_argument(
-        "--config",
-        type=str,
-        default="configs/stable-diffusion/v2-inference.yaml",
-        help="path to config which constructs model",
-    )
-    parser.add_argument(
-        "--ckpt",
-        type=str,
-        help="path to checkpoint of model",
-    )
-    parser.add_argument(
-        "--seed",
-        type=int,
-        default=42,
-        help="the seed (for reproducible sampling)",
-    )
-    parser.add_argument(
-        "--precision",
-        type=str,
-        help="evaluate at this precision",
-        choices=["full", "autocast"],
-        default="autocast"
-    )
-
-    opt = parser.parse_args()
-    seed_everything(opt.seed)
-
-    config = OmegaConf.load(f"{opt.config}")
-    model = load_model_from_config(config, f"{opt.ckpt}")
-
-    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
-    model = model.to(device)
-
-    sampler = DDIMSampler(model)
-
-    os.makedirs(opt.outdir, exist_ok=True)
-    outpath = opt.outdir
-
-    print("Creating invisible watermark encoder (see https://github.com/ShieldMnt/invisible-watermark)...")
-    wm = "SDV2"
-    wm_encoder = WatermarkEncoder()
-    wm_encoder.set_watermark('bytes', wm.encode('utf-8'))
-
-    batch_size = opt.n_samples
-    n_rows = opt.n_rows if opt.n_rows > 0 else batch_size
-    if not opt.from_file:
-        prompt = opt.prompt
-        assert prompt is not None
-        data = [batch_size * [prompt]]
-
-    else:
-        print(f"reading prompts from {opt.from_file}")
-        with open(opt.from_file, "r") as f:
-            data = f.read().splitlines()
-            data = list(chunk(data, batch_size))
-
-    sample_path = os.path.join(outpath, "samples")
-    os.makedirs(sample_path, exist_ok=True)
-    base_count = len(os.listdir(sample_path))
-    grid_count = len(os.listdir(outpath)) - 1
-
-    assert os.path.isfile(opt.init_img)
-    init_image = load_img(opt.init_img).to(device)
-    init_image = repeat(init_image, '1 ... -> b ...', b=batch_size)
-    init_latent = model.get_first_stage_encoding(model.encode_first_stage(init_image))  # move to latent space
-
-    sampler.make_schedule(ddim_num_steps=opt.ddim_steps, ddim_eta=opt.ddim_eta, verbose=False)
-
-    assert 0. <= opt.strength <= 1., 'can only work with strength in [0.0, 1.0]'
-    t_enc = int(opt.strength * opt.ddim_steps)
-    print(f"target t_enc is {t_enc} steps")
-
-    precision_scope = autocast if opt.precision == "autocast" else nullcontext
-    with torch.no_grad():
-        with precision_scope("cuda"):
-            with model.ema_scope():
-                all_samples = list()
-                for n in trange(opt.n_iter, desc="Sampling"):
-                    for prompts in tqdm(data, desc="data"):
-                        uc = None
-                        if opt.scale != 1.0:
-                            uc = model.get_learned_conditioning(batch_size * [""])
-                        if isinstance(prompts, tuple):
-                            prompts = list(prompts)
-                        c = model.get_learned_conditioning(prompts)
-
-                        # encode (scaled latent)
-                        z_enc = sampler.stochastic_encode(init_latent, torch.tensor([t_enc] * batch_size).to(device))
-                        # decode it
-                        samples = sampler.decode(z_enc, c, t_enc, unconditional_guidance_scale=opt.scale,
-                                                 unconditional_conditioning=uc, )
-
-                        x_samples = model.decode_first_stage(samples)
-                        x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0)
-
-                        for x_sample in x_samples:
-                            x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
-                            img = Image.fromarray(x_sample.astype(np.uint8))
-                            img = put_watermark(img, wm_encoder)
-                            img.save(os.path.join(sample_path, f"{base_count:05}.png"))
-                            base_count += 1
-                        all_samples.append(x_samples)
-
-                # additionally, save as grid
-                grid = torch.stack(all_samples, 0)
-                grid = rearrange(grid, 'n b c h w -> (n b) c h w')
-                grid = make_grid(grid, nrow=n_rows)
-
-                # to image
-                grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy()
-                grid = Image.fromarray(grid.astype(np.uint8))
-                grid = put_watermark(grid, wm_encoder)
-                grid.save(os.path.join(outpath, f'grid-{grid_count:04}.png'))
-                grid_count += 1
-
-    print(f"Your samples are ready and waiting for you here: \n{outpath} \nEnjoy.")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/repositories/stable-diffusion-stability-ai/scripts/streamlit/depth2img.py b/repositories/stable-diffusion-stability-ai/scripts/streamlit/depth2img.py
deleted file mode 100644
index 7f80223405a26ded02964513293b8b3316c34344..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/scripts/streamlit/depth2img.py
+++ /dev/null
@@ -1,157 +0,0 @@
-import sys
-import torch
-import numpy as np
-import streamlit as st
-from PIL import Image
-from omegaconf import OmegaConf
-from einops import repeat, rearrange
-from pytorch_lightning import seed_everything
-from imwatermark import WatermarkEncoder
-
-from scripts.txt2img import put_watermark
-from ldm.util import instantiate_from_config
-from ldm.models.diffusion.ddim import DDIMSampler
-from ldm.data.util import AddMiDaS
-
-torch.set_grad_enabled(False)
-
-
-@st.cache(allow_output_mutation=True)
-def initialize_model(config, ckpt):
-    config = OmegaConf.load(config)
-    model = instantiate_from_config(config.model)
-    model.load_state_dict(torch.load(ckpt)["state_dict"], strict=False)
-
-    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
-    model = model.to(device)
-    sampler = DDIMSampler(model)
-    return sampler
-
-
-def make_batch_sd(
-        image,
-        txt,
-        device,
-        num_samples=1,
-        model_type="dpt_hybrid"
-):
-    image = np.array(image.convert("RGB"))
-    image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
-    # sample['jpg'] is tensor hwc in [-1, 1] at this point
-    midas_trafo = AddMiDaS(model_type=model_type)
-    batch = {
-        "jpg": image,
-        "txt": num_samples * [txt],
-    }
-    batch = midas_trafo(batch)
-    batch["jpg"] = rearrange(batch["jpg"], 'h w c -> 1 c h w')
-    batch["jpg"] = repeat(batch["jpg"].to(device=device), "1 ... -> n ...", n=num_samples)
-    batch["midas_in"] = repeat(torch.from_numpy(batch["midas_in"][None, ...]).to(device=device), "1 ... -> n ...", n=num_samples)
-    return batch
-
-
-def paint(sampler, image, prompt, t_enc, seed, scale, num_samples=1, callback=None,
-          do_full_sample=False):
-    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
-    model = sampler.model
-    seed_everything(seed)
-
-    print("Creating invisible watermark encoder (see https://github.com/ShieldMnt/invisible-watermark)...")
-    wm = "SDV2"
-    wm_encoder = WatermarkEncoder()
-    wm_encoder.set_watermark('bytes', wm.encode('utf-8'))
-
-    with torch.no_grad(),\
-            torch.autocast("cuda"):
-        batch = make_batch_sd(image, txt=prompt, device=device, num_samples=num_samples)
-        z = model.get_first_stage_encoding(model.encode_first_stage(batch[model.first_stage_key]))  # move to latent space
-        c = model.cond_stage_model.encode(batch["txt"])
-        c_cat = list()
-        for ck in model.concat_keys:
-            cc = batch[ck]
-            cc = model.depth_model(cc)
-            depth_min, depth_max = torch.amin(cc, dim=[1, 2, 3], keepdim=True), torch.amax(cc, dim=[1, 2, 3],
-                                                                                           keepdim=True)
-            display_depth = (cc - depth_min) / (depth_max - depth_min)
-            st.image(Image.fromarray((display_depth[0, 0, ...].cpu().numpy() * 255.).astype(np.uint8)))
-            cc = torch.nn.functional.interpolate(
-                cc,
-                size=z.shape[2:],
-                mode="bicubic",
-                align_corners=False,
-            )
-            depth_min, depth_max = torch.amin(cc, dim=[1, 2, 3], keepdim=True), torch.amax(cc, dim=[1, 2, 3],
-                                                                                           keepdim=True)
-            cc = 2. * (cc - depth_min) / (depth_max - depth_min) - 1.
-            c_cat.append(cc)
-        c_cat = torch.cat(c_cat, dim=1)
-        # cond
-        cond = {"c_concat": [c_cat], "c_crossattn": [c]}
-
-        # uncond cond
-        uc_cross = model.get_unconditional_conditioning(num_samples, "")
-        uc_full = {"c_concat": [c_cat], "c_crossattn": [uc_cross]}
-        if not do_full_sample:
-            # encode (scaled latent)
-            z_enc = sampler.stochastic_encode(z, torch.tensor([t_enc] * num_samples).to(model.device))
-        else:
-            z_enc = torch.randn_like(z)
-        # decode it
-        samples = sampler.decode(z_enc, cond, t_enc, unconditional_guidance_scale=scale,
-                                 unconditional_conditioning=uc_full, callback=callback)
-        x_samples_ddim = model.decode_first_stage(samples)
-        result = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
-        result = result.cpu().numpy().transpose(0, 2, 3, 1) * 255
-    return [put_watermark(Image.fromarray(img.astype(np.uint8)), wm_encoder) for img in result]
-
-
-def run():
-    st.title("Stable Diffusion Depth2Img")
-    # run via streamlit run scripts/demo/depth2img.py <path-tp-config> <path-to-ckpt>
-    sampler = initialize_model(sys.argv[1], sys.argv[2])
-
-    image = st.file_uploader("Image", ["jpg", "png"])
-    if image:
-        image = Image.open(image)
-        w, h = image.size
-        st.text(f"loaded input image of size ({w}, {h})")
-        width, height = map(lambda x: x - x % 64, (w, h))  # resize to integer multiple of 64
-        image = image.resize((width, height))
-        st.text(f"resized input image to size ({width}, {height} (w, h))")
-        st.image(image)
-
-        prompt = st.text_input("Prompt")
-
-        seed = st.number_input("Seed", min_value=0, max_value=1000000, value=0)
-        num_samples = st.number_input("Number of Samples", min_value=1, max_value=64, value=1)
-        scale = st.slider("Scale", min_value=0.1, max_value=30.0, value=9.0, step=0.1)
-        steps = st.slider("DDIM Steps", min_value=0, max_value=50, value=50, step=1)
-        strength = st.slider("Strength", min_value=0., max_value=1., value=0.9)
-
-        t_progress = st.progress(0)
-        def t_callback(t):
-            t_progress.progress(min((t + 1) / t_enc, 1.))
-
-        assert 0. <= strength <= 1., 'can only work with strength in [0.0, 1.0]'
-        do_full_sample = strength == 1.
-        t_enc = min(int(strength * steps), steps-1)
-        sampler.make_schedule(steps, ddim_eta=0., verbose=True)
-        if st.button("Sample"):
-            result = paint(
-                sampler=sampler,
-                image=image,
-                prompt=prompt,
-                t_enc=t_enc,
-                seed=seed,
-                scale=scale,
-                num_samples=num_samples,
-                callback=t_callback,
-                do_full_sample=do_full_sample,
-            )
-            st.write("Result")
-            for image in result:
-                st.image(image, output_format='PNG')
-
-
-if __name__ == "__main__":
-    run()
diff --git a/repositories/stable-diffusion-stability-ai/scripts/streamlit/inpainting.py b/repositories/stable-diffusion-stability-ai/scripts/streamlit/inpainting.py
deleted file mode 100644
index c35772f063da8bdf0091e3931dbe12b1869dd11a..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/scripts/streamlit/inpainting.py
+++ /dev/null
@@ -1,195 +0,0 @@
-import sys
-import cv2
-import torch
-import numpy as np
-import streamlit as st
-from PIL import Image
-from omegaconf import OmegaConf
-from einops import repeat
-from streamlit_drawable_canvas import st_canvas
-from imwatermark import WatermarkEncoder
-
-from ldm.models.diffusion.ddim import DDIMSampler
-from ldm.util import instantiate_from_config
-
-
-torch.set_grad_enabled(False)
-
-
-def put_watermark(img, wm_encoder=None):
-    if wm_encoder is not None:
-        img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
-        img = wm_encoder.encode(img, 'dwtDct')
-        img = Image.fromarray(img[:, :, ::-1])
-    return img
-
-
-@st.cache(allow_output_mutation=True)
-def initialize_model(config, ckpt):
-    config = OmegaConf.load(config)
-    model = instantiate_from_config(config.model)
-
-    model.load_state_dict(torch.load(ckpt)["state_dict"], strict=False)
-
-    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
-    model = model.to(device)
-    sampler = DDIMSampler(model)
-
-    return sampler
-
-
-def make_batch_sd(
-        image,
-        mask,
-        txt,
-        device,
-        num_samples=1):
-    image = np.array(image.convert("RGB"))
-    image = image[None].transpose(0, 3, 1, 2)
-    image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
-
-    mask = np.array(mask.convert("L"))
-    mask = mask.astype(np.float32) / 255.0
-    mask = mask[None, None]
-    mask[mask < 0.5] = 0
-    mask[mask >= 0.5] = 1
-    mask = torch.from_numpy(mask)
-
-    masked_image = image * (mask < 0.5)
-
-    batch = {
-        "image": repeat(image.to(device=device), "1 ... -> n ...", n=num_samples),
-        "txt": num_samples * [txt],
-        "mask": repeat(mask.to(device=device), "1 ... -> n ...", n=num_samples),
-        "masked_image": repeat(masked_image.to(device=device), "1 ... -> n ...", n=num_samples),
-    }
-    return batch
-
-
-def inpaint(sampler, image, mask, prompt, seed, scale, ddim_steps, num_samples=1, w=512, h=512, eta=1.):
-    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
-    model = sampler.model
-
-    print("Creating invisible watermark encoder (see https://github.com/ShieldMnt/invisible-watermark)...")
-    wm = "SDV2"
-    wm_encoder = WatermarkEncoder()
-    wm_encoder.set_watermark('bytes', wm.encode('utf-8'))
-
-    prng = np.random.RandomState(seed)
-    start_code = prng.randn(num_samples, 4, h // 8, w // 8)
-    start_code = torch.from_numpy(start_code).to(device=device, dtype=torch.float32)
-
-    with torch.no_grad(), \
-            torch.autocast("cuda"):
-            batch = make_batch_sd(image, mask, txt=prompt, device=device, num_samples=num_samples)
-
-            c = model.cond_stage_model.encode(batch["txt"])
-
-            c_cat = list()
-            for ck in model.concat_keys:
-                cc = batch[ck].float()
-                if ck != model.masked_image_key:
-                    bchw = [num_samples, 4, h // 8, w // 8]
-                    cc = torch.nn.functional.interpolate(cc, size=bchw[-2:])
-                else:
-                    cc = model.get_first_stage_encoding(model.encode_first_stage(cc))
-                c_cat.append(cc)
-            c_cat = torch.cat(c_cat, dim=1)
-
-            # cond
-            cond = {"c_concat": [c_cat], "c_crossattn": [c]}
-
-            # uncond cond
-            uc_cross = model.get_unconditional_conditioning(num_samples, "")
-            uc_full = {"c_concat": [c_cat], "c_crossattn": [uc_cross]}
-
-            shape = [model.channels, h // 8, w // 8]
-            samples_cfg, intermediates = sampler.sample(
-                ddim_steps,
-                num_samples,
-                shape,
-                cond,
-                verbose=False,
-                eta=eta,
-                unconditional_guidance_scale=scale,
-                unconditional_conditioning=uc_full,
-                x_T=start_code,
-            )
-            x_samples_ddim = model.decode_first_stage(samples_cfg)
-
-            result = torch.clamp((x_samples_ddim + 1.0) / 2.0,
-                                 min=0.0, max=1.0)
-
-            result = result.cpu().numpy().transpose(0, 2, 3, 1) * 255
-    return [put_watermark(Image.fromarray(img.astype(np.uint8)), wm_encoder) for img in result]
-
-
-def run():
-    st.title("Stable Diffusion Inpainting")
-
-    sampler = initialize_model(sys.argv[1], sys.argv[2])
-
-    image = st.file_uploader("Image", ["jpg", "png"])
-    if image:
-        image = Image.open(image)
-        w, h = image.size
-        print(f"loaded input image of size ({w}, {h})")
-        width, height = map(lambda x: x - x % 64, (w, h))  # resize to integer multiple of 32
-        image = image.resize((width, height))
-
-        prompt = st.text_input("Prompt")
-
-        seed = st.number_input("Seed", min_value=0, max_value=1000000, value=0)
-        num_samples = st.number_input("Number of Samples", min_value=1, max_value=64, value=1)
-        scale = st.slider("Scale", min_value=0.1, max_value=30.0, value=10., step=0.1)
-        ddim_steps = st.slider("DDIM Steps", min_value=0, max_value=50, value=50, step=1)
-        eta = st.sidebar.number_input("eta (DDIM)", value=0., min_value=0., max_value=1.)
-
-        fill_color = "rgba(255, 255, 255, 0.0)"
-        stroke_width = st.number_input("Brush Size",
-                                       value=64,
-                                       min_value=1,
-                                       max_value=100)
-        stroke_color = "rgba(255, 255, 255, 1.0)"
-        bg_color = "rgba(0, 0, 0, 1.0)"
-        drawing_mode = "freedraw"
-
-        st.write("Canvas")
-        st.caption(
-            "Draw a mask to inpaint, then click the 'Send to Streamlit' button (bottom left, with an arrow on it).")
-        canvas_result = st_canvas(
-            fill_color=fill_color,
-            stroke_width=stroke_width,
-            stroke_color=stroke_color,
-            background_color=bg_color,
-            background_image=image,
-            update_streamlit=False,
-            height=height,
-            width=width,
-            drawing_mode=drawing_mode,
-            key="canvas",
-        )
-        if canvas_result:
-            mask = canvas_result.image_data
-            mask = mask[:, :, -1] > 0
-            if mask.sum() > 0:
-                mask = Image.fromarray(mask)
-
-                result = inpaint(
-                    sampler=sampler,
-                    image=image,
-                    mask=mask,
-                    prompt=prompt,
-                    seed=seed,
-                    scale=scale,
-                    ddim_steps=ddim_steps,
-                    num_samples=num_samples,
-                    h=height, w=width, eta=eta
-                )
-                st.write("Inpainted")
-                for image in result:
-                    st.image(image, output_format='PNG')
-
-
-if __name__ == "__main__":
-    run()
\ No newline at end of file
diff --git a/repositories/stable-diffusion-stability-ai/scripts/streamlit/stableunclip.py b/repositories/stable-diffusion-stability-ai/scripts/streamlit/stableunclip.py
deleted file mode 100644
index 122fa9a584b1961681883709bc6672bf90339cc5..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/scripts/streamlit/stableunclip.py
+++ /dev/null
@@ -1,416 +0,0 @@
-import importlib
-import streamlit as st
-import torch
-import cv2
-import numpy as np
-import PIL
-from omegaconf import OmegaConf
-from PIL import Image
-from tqdm import trange
-import io, os
-from torch import autocast
-from einops import rearrange, repeat
-from torchvision.utils import make_grid
-from pytorch_lightning import seed_everything
-from contextlib import nullcontext
-
-from ldm.models.diffusion.ddim import DDIMSampler
-from ldm.models.diffusion.plms import PLMSSampler
-from ldm.models.diffusion.dpm_solver import DPMSolverSampler
-
-torch.set_grad_enabled(False)
-
-PROMPTS_ROOT = "scripts/prompts/"
-SAVE_PATH = "outputs/demo/stable-unclip/"
-
-VERSION2SPECS = {
-    "Stable unCLIP-L": {"H": 768, "W": 768, "C": 4, "f": 8},
-    "Stable unOpenCLIP-H": {"H": 768, "W": 768, "C": 4, "f": 8},
-    "Full Karlo": {}
-}
-
-
-def get_obj_from_str(string, reload=False):
-    module, cls = string.rsplit(".", 1)
-    importlib.invalidate_caches()
-    if reload:
-        module_imp = importlib.import_module(module)
-        importlib.reload(module_imp)
-    return getattr(importlib.import_module(module, package=None), cls)
-
-
-def instantiate_from_config(config):
-    if not "target" in config:
-        raise KeyError("Expected key `target` to instantiate.")
-    return get_obj_from_str(config["target"])(**config.get("params", dict()))
-
-
-def get_interactive_image(key=None):
-    image = st.file_uploader("Input", type=["jpg", "JPEG", "png"], key=key)
-    if image is not None:
-        image = Image.open(image)
-        if not image.mode == "RGB":
-            image = image.convert("RGB")
-        return image
-
-
-def load_img(display=True, key=None):
-    image = get_interactive_image(key=key)
-    if display:
-        st.image(image)
-    w, h = image.size
-    print(f"loaded input image of size ({w}, {h})")
-    w, h = map(lambda x: x - x % 64, (w, h))
-    image = image.resize((w, h), resample=PIL.Image.LANCZOS)
-    image = np.array(image).astype(np.float32) / 255.0
-    image = image[None].transpose(0, 3, 1, 2)
-    image = torch.from_numpy(image)
-    return 2. * image - 1.
-
-
-def get_init_img(batch_size=1, key=None):
-    init_image = load_img(key=key).cuda()
-    init_image = repeat(init_image, '1 ... -> b ...', b=batch_size)
-    return init_image
-
-
-def sample(
-        model,
-        prompt,
-        n_runs=3,
-        n_samples=2,
-        H=512,
-        W=512,
-        C=4,
-        f=8,
-        scale=10.0,
-        ddim_steps=50,
-        ddim_eta=0.0,
-        callback=None,
-        skip_single_save=False,
-        save_grid=True,
-        ucg_schedule=None,
-        negative_prompt="",
-        adm_cond=None,
-        adm_uc=None,
-        use_full_precision=False,
-        only_adm_cond=False
-):
-    batch_size = n_samples
-    precision_scope = autocast if not use_full_precision else nullcontext
-    # decoderscope = autocast if not use_full_precision else nullcontext
-    if use_full_precision: st.warning(f"Running {model.__class__.__name__} at full precision.")
-    if isinstance(prompt, str):
-        prompt = [prompt]
-    prompts = batch_size * prompt
-
-    outputs = st.empty()
-
-    with precision_scope("cuda"):
-        with model.ema_scope():
-            all_samples = list()
-            for n in trange(n_runs, desc="Sampling"):
-                shape = [C, H // f, W // f]
-                if not only_adm_cond:
-                    uc = None
-                    if scale != 1.0:
-                        uc = model.get_learned_conditioning(batch_size * [negative_prompt])
-                    if isinstance(prompts, tuple):
-                        prompts = list(prompts)
-                    c = model.get_learned_conditioning(prompts)
-
-                if adm_cond is not None:
-                    if adm_cond.shape[0] == 1:
-                        adm_cond = repeat(adm_cond, '1 ... -> b ...', b=batch_size)
-                    if adm_uc is None:
-                        st.warning("Not guiding via c_adm")
-                        adm_uc = adm_cond
-                    else:
-                        if adm_uc.shape[0] == 1:
-                            adm_uc = repeat(adm_uc, '1 ... -> b ...', b=batch_size)
-                    if not only_adm_cond:
-                        c = {"c_crossattn": [c], "c_adm": adm_cond}
-                        uc = {"c_crossattn": [uc], "c_adm": adm_uc}
-                    else:
-                        c = adm_cond
-                        uc = adm_uc
-                samples_ddim, _ = sampler.sample(S=ddim_steps,
-                                                 conditioning=c,
-                                                 batch_size=batch_size,
-                                                 shape=shape,
-                                                 verbose=False,
-                                                 unconditional_guidance_scale=scale,
-                                                 unconditional_conditioning=uc,
-                                                 eta=ddim_eta,
-                                                 x_T=None,
-                                                 callback=callback,
-                                                 ucg_schedule=ucg_schedule
-                                                 )
-                x_samples = model.decode_first_stage(samples_ddim)
-                x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0)
-
-                if not skip_single_save:
-                    base_count = len(os.listdir(os.path.join(SAVE_PATH, "samples")))
-                    for x_sample in x_samples:
-                        x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
-                        Image.fromarray(x_sample.astype(np.uint8)).save(
-                            os.path.join(SAVE_PATH, "samples", f"{base_count:09}.png"))
-                        base_count += 1
-
-                all_samples.append(x_samples)
-
-                # get grid of all samples
-                grid = torch.stack(all_samples, 0)
-                grid = rearrange(grid, 'n b c h w -> (n h) (b w) c')
-                outputs.image(grid.cpu().numpy())
-
-            # additionally, save grid
-            grid = Image.fromarray((255. * grid.cpu().numpy()).astype(np.uint8))
-            if save_grid:
-                grid_count = len(os.listdir(SAVE_PATH)) - 1
-                grid.save(os.path.join(SAVE_PATH, f'grid-{grid_count:06}.png'))
-
-    return x_samples
-
-
-def make_oscillating_guidance_schedule(num_steps, max_weight=15., min_weight=1.):
-    schedule = list()
-    for i in range(num_steps):
-        if float(i / num_steps) < 0.1:
-            schedule.append(max_weight)
-        elif i % 2 == 0:
-            schedule.append(min_weight)
-        else:
-            schedule.append(max_weight)
-    print(f"OSCILLATING GUIDANCE SCHEDULE: \n {schedule}")
-    return schedule
-
-
-def torch2np(x):
-    x = ((x + 1.0) * 127.5).clamp(0, 255).to(dtype=torch.uint8)
-    x = x.permute(0, 2, 3, 1).detach().cpu().numpy()
-    return x
-
-
-@st.cache(allow_output_mutation=True, suppress_st_warning=True)
-def init(version="Stable unCLIP-L", load_karlo_prior=False):
-    state = dict()
-    if not "model" in state:
-        if version == "Stable unCLIP-L":
-            config = "configs/stable-diffusion/v2-1-stable-unclip-l-inference.yaml"
-            ckpt = "checkpoints/sd21-unclip-l.ckpt"
-
-        elif version == "Stable unOpenCLIP-H":
-            config = "configs/stable-diffusion/v2-1-stable-unclip-h-inference.yaml"
-            ckpt = "checkpoints/sd21-unclip-h.ckpt"
-
-        elif version == "Full Karlo":
-            from ldm.modules.karlo.kakao.sampler import T2ISampler
-            st.info("Loading full KARLO..")
-            karlo = T2ISampler.from_pretrained(
-                root_dir="checkpoints/karlo_models",
-                clip_model_path="ViT-L-14.pt",
-                clip_stat_path="ViT-L-14_stats.th",
-                sampling_type="default",
-            )
-            state["karlo_prior"] = karlo
-            state["msg"] = "loaded full Karlo"
-            return state
-        else:
-            raise ValueError(f"version {version} unknown!")
-
-        config = OmegaConf.load(config)
-        model, msg = load_model_from_config(config, ckpt, vae_sd=None)
-        state["msg"] = msg
-
-        if load_karlo_prior:
-            from ldm.modules.karlo.kakao.sampler import PriorSampler
-            st.info("Loading KARLO CLIP prior...")
-            karlo_prior = PriorSampler.from_pretrained(
-                root_dir="checkpoints/karlo_models",
-                clip_model_path="ViT-L-14.pt",
-                clip_stat_path="ViT-L-14_stats.th",
-                sampling_type="default",
-            )
-            state["karlo_prior"] = karlo_prior
-        state["model"] = model
-        state["ckpt"] = ckpt
-        state["config"] = config
-    return state
-
-
-def load_model_from_config(config, ckpt, verbose=False, vae_sd=None):
-    print(f"Loading model from {ckpt}")
-    pl_sd = torch.load(ckpt, map_location="cpu")
-    msg = None
-    if "global_step" in pl_sd:
-        msg = f"This is global step {pl_sd['global_step']}. "
-    if "model_ema.num_updates" in pl_sd["state_dict"]:
-        msg += f"And we got {pl_sd['state_dict']['model_ema.num_updates']} EMA updates."
-    global_step = pl_sd.get("global_step", "?")
-    sd = pl_sd["state_dict"]
-    if vae_sd is not None:
-        for k in sd.keys():
-            if "first_stage" in k:
-                sd[k] = vae_sd[k[len("first_stage_model."):]]
-
-    model = instantiate_from_config(config.model)
-    m, u = model.load_state_dict(sd, strict=False)
-    if len(m) > 0 and verbose:
-        print("missing keys:")
-        print(m)
-    if len(u) > 0 and verbose:
-        print("unexpected keys:")
-        print(u)
-
-    model.cuda()
-    model.eval()
-    print(f"Loaded global step {global_step}")
-    return model, msg
-
-
-if __name__ == "__main__":
-    st.title("Stable unCLIP")
-    mode = "txt2img"
-    version = st.selectbox("Model Version", list(VERSION2SPECS.keys()), 0)
-    use_karlo_prior = version in ["Stable unCLIP-L"] and st.checkbox("Use KARLO prior", False)
-    state = init(version=version, load_karlo_prior=use_karlo_prior)
-    prompt = st.text_input("Prompt", "a professional photograph")
-    negative_prompt = st.text_input("Negative Prompt", "")
-    scale = st.number_input("cfg-scale", value=10., min_value=-100., max_value=100.)
-    number_rows = st.number_input("num rows", value=2, min_value=1, max_value=10)
-    number_cols = st.number_input("num cols", value=2, min_value=1, max_value=10)
-    steps = st.sidebar.number_input("steps", value=20, min_value=1, max_value=1000)
-    eta = st.sidebar.number_input("eta (DDIM)", value=0., min_value=0., max_value=1.)
-    force_full_precision = st.sidebar.checkbox("Force FP32", False)  # TODO: check if/where things break.
-    if version != "Full Karlo":
-        H = st.sidebar.number_input("H", value=VERSION2SPECS[version]["H"], min_value=64, max_value=2048)
-        W = st.sidebar.number_input("W", value=VERSION2SPECS[version]["W"], min_value=64, max_value=2048)
-        C = VERSION2SPECS[version]["C"]
-        f = VERSION2SPECS[version]["f"]
-
-    SAVE_PATH = os.path.join(SAVE_PATH, version)
-    os.makedirs(os.path.join(SAVE_PATH, "samples"), exist_ok=True)
-
-    seed = st.sidebar.number_input("seed", value=42, min_value=0, max_value=int(1e9))
-    seed_everything(seed)
-
-    ucg_schedule = None
-    sampler = st.sidebar.selectbox("Sampler", ["DDIM", "DPM"], 0)
-    if version == "Full Karlo":
-        pass
-    else:
-        if sampler == "DPM":
-            sampler = DPMSolverSampler(state["model"])
-        elif sampler == "DDIM":
-            sampler = DDIMSampler(state["model"])
-        else:
-            raise ValueError(f"unknown sampler {sampler}!")
-
-    adm_cond, adm_uc = None, None
-    if use_karlo_prior:
-        # uses the prior
-        karlo_sampler = state["karlo_prior"]
-        noise_level = None
-        if state["model"].noise_augmentor is not None:
-            noise_level = st.number_input("Noise Augmentation for CLIP embeddings", min_value=0,
-                                          max_value=state["model"].noise_augmentor.max_noise_level - 1, value=0)
-        with torch.no_grad():
-            karlo_prediction = iter(
-                karlo_sampler(
-                    prompt=prompt,
-                    bsz=number_cols,
-                    progressive_mode="final",
-                )
-            ).__next__()
-            adm_cond = karlo_prediction
-            if noise_level is not None:
-                c_adm, noise_level_emb = state["model"].noise_augmentor(adm_cond, noise_level=repeat(
-                    torch.tensor([noise_level]).to(state["model"].device), '1 -> b', b=number_cols))
-                adm_cond = torch.cat((c_adm, noise_level_emb), 1)
-            adm_uc = torch.zeros_like(adm_cond)
-    elif version == "Full Karlo":
-        pass
-    else:
-        num_inputs = st.number_input("Number of Input Images", 1)
-
-
-        def make_conditionings_from_input(num=1, key=None):
-            init_img = get_init_img(batch_size=number_cols, key=key)
-            with torch.no_grad():
-                adm_cond = state["model"].embedder(init_img)
-                weight = st.slider(f"Weight for Input {num}", min_value=-10., max_value=10., value=1.)
-                if state["model"].noise_augmentor is not None:
-                    noise_level = st.number_input(f"Noise Augmentation for CLIP embedding of input #{num}", min_value=0,
-                                                  max_value=state["model"].noise_augmentor.max_noise_level - 1,
-                                                  value=0, )
-                    c_adm, noise_level_emb = state["model"].noise_augmentor(adm_cond, noise_level=repeat(
-                        torch.tensor([noise_level]).to(state["model"].device), '1 -> b', b=number_cols))
-                    adm_cond = torch.cat((c_adm, noise_level_emb), 1) * weight
-                adm_uc = torch.zeros_like(adm_cond)
-            return adm_cond, adm_uc, weight
-
-
-        adm_inputs = list()
-        weights = list()
-        for n in range(num_inputs):
-            adm_cond, adm_uc, w = make_conditionings_from_input(num=n + 1, key=n)
-            weights.append(w)
-            adm_inputs.append(adm_cond)
-        adm_cond = torch.stack(adm_inputs).sum(0) / sum(weights)
-        if num_inputs > 1:
-            if st.checkbox("Apply Noise to Embedding Mix", True):
-                noise_level = st.number_input(f"Noise Augmentation for averaged CLIP embeddings", min_value=0,
-                                              max_value=state["model"].noise_augmentor.max_noise_level - 1, value=50, )
-                c_adm, noise_level_emb = state["model"].noise_augmentor(
-                    adm_cond[:, :state["model"].noise_augmentor.time_embed.dim],
-                    noise_level=repeat(
-                        torch.tensor([noise_level]).to(state["model"].device), '1 -> b', b=number_cols))
-                adm_cond = torch.cat((c_adm, noise_level_emb), 1)
-
-    if st.button("Sample"):
-        print("running prompt:", prompt)
-        st.text("Sampling")
-        t_progress = st.progress(0)
-        result = st.empty()
-
-
-        def t_callback(t):
-            t_progress.progress(min((t + 1) / steps, 1.))
-
-
-        if version == "Full Karlo":
-            outputs = st.empty()
-            karlo_sampler = state["karlo_prior"]
-            all_samples = list()
-            with torch.no_grad():
-                for _ in range(number_rows):
-                    karlo_prediction = iter(
-                        karlo_sampler(
-                            prompt=prompt,
-                            bsz=number_cols,
-                            progressive_mode="final",
-                        )
-                    ).__next__()
-                    all_samples.append(karlo_prediction)
-            grid = torch.stack(all_samples, 0)
-            grid = rearrange(grid, 'n b c h w -> (n h) (b w) c')
-            outputs.image(grid.cpu().numpy())
-
-        else:
-            samples = sample(
-                state["model"],
-                prompt,
-                n_runs=number_rows,
-                n_samples=number_cols,
-                H=H, W=W, C=C, f=f,
-                scale=scale,
-                ddim_steps=steps,
-                ddim_eta=eta,
-                callback=t_callback,
-                ucg_schedule=ucg_schedule,
-                negative_prompt=negative_prompt,
-                adm_cond=adm_cond, adm_uc=adm_uc,
-                use_full_precision=force_full_precision,
-                only_adm_cond=False
-            )
diff --git a/repositories/stable-diffusion-stability-ai/scripts/streamlit/superresolution.py b/repositories/stable-diffusion-stability-ai/scripts/streamlit/superresolution.py
deleted file mode 100644
index c1172b02ea8141781118d4536ba28de0f24404a1..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/scripts/streamlit/superresolution.py
+++ /dev/null
@@ -1,170 +0,0 @@
-import sys
-import torch
-import numpy as np
-import streamlit as st
-from PIL import Image
-from omegaconf import OmegaConf
-from einops import repeat, rearrange
-from pytorch_lightning import seed_everything
-from imwatermark import WatermarkEncoder
-
-from scripts.txt2img import put_watermark
-from ldm.models.diffusion.ddim import DDIMSampler
-from ldm.models.diffusion.ddpm import LatentUpscaleDiffusion, LatentUpscaleFinetuneDiffusion
-from ldm.util import exists, instantiate_from_config
-
-
-torch.set_grad_enabled(False)
-
-
-@st.cache(allow_output_mutation=True)
-def initialize_model(config, ckpt):
-    config = OmegaConf.load(config)
-    model = instantiate_from_config(config.model)
-    model.load_state_dict(torch.load(ckpt)["state_dict"], strict=False)
-
-    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
-    model = model.to(device)
-    sampler = DDIMSampler(model)
-    return sampler
-
-
-def make_batch_sd(
-        image,
-        txt,
-        device,
-        num_samples=1,
-):
-    image = np.array(image.convert("RGB"))
-    image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
-    batch = {
-        "lr": rearrange(image, 'h w c -> 1 c h w'),
-        "txt": num_samples * [txt],
-    }
-    batch["lr"] = repeat(batch["lr"].to(device=device), "1 ... -> n ...", n=num_samples)
-    return batch
-
-
-def make_noise_augmentation(model, batch, noise_level=None):
-    x_low = batch[model.low_scale_key]
-    x_low = x_low.to(memory_format=torch.contiguous_format).float()
-    x_aug, noise_level = model.low_scale_model(x_low, noise_level)
-    return x_aug, noise_level
-
-
-def paint(sampler, image, prompt, seed, scale, h, w, steps, num_samples=1, callback=None, eta=0., noise_level=None):
-    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
-    model = sampler.model
-    seed_everything(seed)
-    prng = np.random.RandomState(seed)
-    start_code = prng.randn(num_samples, model.channels, h , w)
-    start_code = torch.from_numpy(start_code).to(device=device, dtype=torch.float32)
-
-    print("Creating invisible watermark encoder (see https://github.com/ShieldMnt/invisible-watermark)...")
-    wm = "SDV2"
-    wm_encoder = WatermarkEncoder()
-    wm_encoder.set_watermark('bytes', wm.encode('utf-8'))
-    with torch.no_grad(),\
-            torch.autocast("cuda"):
-        batch = make_batch_sd(image, txt=prompt, device=device, num_samples=num_samples)
-        c = model.cond_stage_model.encode(batch["txt"])
-        c_cat = list()
-        if isinstance(model, LatentUpscaleFinetuneDiffusion):
-            for ck in model.concat_keys:
-                cc = batch[ck]
-                if exists(model.reshuffle_patch_size):
-                    assert isinstance(model.reshuffle_patch_size, int)
-                    cc = rearrange(cc, 'b c (p1 h) (p2 w) -> b (p1 p2 c) h w',
-                                   p1=model.reshuffle_patch_size, p2=model.reshuffle_patch_size)
-                c_cat.append(cc)
-            c_cat = torch.cat(c_cat, dim=1)
-            # cond
-            cond = {"c_concat": [c_cat], "c_crossattn": [c]}
-            # uncond cond
-            uc_cross = model.get_unconditional_conditioning(num_samples, "")
-            uc_full = {"c_concat": [c_cat], "c_crossattn": [uc_cross]}
-        elif isinstance(model, LatentUpscaleDiffusion):
-            x_augment, noise_level = make_noise_augmentation(model, batch, noise_level)
-            cond = {"c_concat": [x_augment], "c_crossattn": [c], "c_adm": noise_level}
-            # uncond cond
-            uc_cross = model.get_unconditional_conditioning(num_samples, "")
-            uc_full = {"c_concat": [x_augment], "c_crossattn": [uc_cross], "c_adm": noise_level}
-        else:
-            raise NotImplementedError()
-
-        shape = [model.channels, h, w]
-        samples, intermediates = sampler.sample(
-            steps,
-            num_samples,
-            shape,
-            cond,
-            verbose=False,
-            eta=eta,
-            unconditional_guidance_scale=scale,
-            unconditional_conditioning=uc_full,
-            x_T=start_code,
-            callback=callback
-        )
-    with torch.no_grad():
-        x_samples_ddim = model.decode_first_stage(samples)
-    result = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
-    result = result.cpu().numpy().transpose(0, 2, 3, 1) * 255
-    st.text(f"upscaled image shape: {result.shape}")
-    return [put_watermark(Image.fromarray(img.astype(np.uint8)), wm_encoder) for img in result]
-
-
-def run():
-    st.title("Stable Diffusion Upscaling")
-    # run via streamlit run scripts/demo/depth2img.py <path-tp-config> <path-to-ckpt>
-    sampler = initialize_model(sys.argv[1], sys.argv[2])
-
-    image = st.file_uploader("Image", ["jpg", "png"])
-    if image:
-        image = Image.open(image)
-        w, h = image.size
-        st.text(f"loaded input image of size ({w}, {h})")
-        width, height = map(lambda x: x - x % 64, (w, h))  # resize to integer multiple of 64
-        image = image.resize((width, height))
-        st.text(f"resized input image to size ({width}, {height} (w, h))")
-        st.image(image)
-
-        st.write(f"\n Tip: Add a description of the object that should be upscaled, e.g.: 'a professional photograph of a cat'")
-        prompt = st.text_input("Prompt", "a high quality professional photograph")
-
-        seed = st.number_input("Seed", min_value=0, max_value=1000000, value=0)
-        num_samples = st.number_input("Number of Samples", min_value=1, max_value=64, value=1)
-        scale = st.slider("Scale", min_value=0.1, max_value=30.0, value=9.0, step=0.1)
-        steps = st.slider("DDIM Steps", min_value=2, max_value=250, value=50, step=1)
-        eta = st.sidebar.number_input("eta (DDIM)", value=0., min_value=0., max_value=1.)
-
-        noise_level = None
-        if isinstance(sampler.model, LatentUpscaleDiffusion):
-            # TODO: make this work for all models
-            noise_level = st.sidebar.number_input("Noise Augmentation", min_value=0, max_value=350, value=20)
-            noise_level = torch.Tensor(num_samples * [noise_level]).to(sampler.model.device).long()
-
-        t_progress = st.progress(0)
-        def t_callback(t):
-            t_progress.progress(min((t + 1) / steps, 1.))
-
-        sampler.make_schedule(steps, ddim_eta=eta, verbose=True)
-        if st.button("Sample"):
-            result = paint(
-                sampler=sampler,
-                image=image,
-                prompt=prompt,
-                seed=seed,
-                scale=scale,
-                h=height, w=width, steps=steps,
-                num_samples=num_samples,
-                callback=t_callback,
-                noise_level=noise_level,
-                eta=eta
-            )
-            st.write("Result")
-            for image in result:
-                st.image(image, output_format='PNG')
-
-
-if __name__ == "__main__":
-    run()
diff --git a/repositories/stable-diffusion-stability-ai/scripts/tests/test_watermark.py b/repositories/stable-diffusion-stability-ai/scripts/tests/test_watermark.py
deleted file mode 100644
index f93f8a6e70763c0e284157bc8225827520b2f5ef..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/scripts/tests/test_watermark.py
+++ /dev/null
@@ -1,18 +0,0 @@
-import cv2
-import fire
-from imwatermark import WatermarkDecoder
-
-
-def testit(img_path):
-    bgr = cv2.imread(img_path)
-    decoder = WatermarkDecoder('bytes', 136)
-    watermark = decoder.decode(bgr, 'dwtDct')
-    try:
-        dec = watermark.decode('utf-8')
-    except:
-        dec = "null"
-    print(dec)
-
-
-if __name__ == "__main__":
-    fire.Fire(testit)
\ No newline at end of file
diff --git a/repositories/stable-diffusion-stability-ai/scripts/txt2img.py b/repositories/stable-diffusion-stability-ai/scripts/txt2img.py
deleted file mode 100644
index 9d955e3dc7d562f0809fd3d89480dab2ee2d8ea0..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/scripts/txt2img.py
+++ /dev/null
@@ -1,388 +0,0 @@
-import argparse, os
-import cv2
-import torch
-import numpy as np
-from omegaconf import OmegaConf
-from PIL import Image
-from tqdm import tqdm, trange
-from itertools import islice
-from einops import rearrange
-from torchvision.utils import make_grid
-from pytorch_lightning import seed_everything
-from torch import autocast
-from contextlib import nullcontext
-from imwatermark import WatermarkEncoder
-
-from ldm.util import instantiate_from_config
-from ldm.models.diffusion.ddim import DDIMSampler
-from ldm.models.diffusion.plms import PLMSSampler
-from ldm.models.diffusion.dpm_solver import DPMSolverSampler
-
-torch.set_grad_enabled(False)
-
-def chunk(it, size):
-    it = iter(it)
-    return iter(lambda: tuple(islice(it, size)), ())
-
-
-def load_model_from_config(config, ckpt, device=torch.device("cuda"), verbose=False):
-    print(f"Loading model from {ckpt}")
-    pl_sd = torch.load(ckpt, map_location="cpu")
-    if "global_step" in pl_sd:
-        print(f"Global Step: {pl_sd['global_step']}")
-    sd = pl_sd["state_dict"]
-    model = instantiate_from_config(config.model)
-    m, u = model.load_state_dict(sd, strict=False)
-    if len(m) > 0 and verbose:
-        print("missing keys:")
-        print(m)
-    if len(u) > 0 and verbose:
-        print("unexpected keys:")
-        print(u)
-
-    if device == torch.device("cuda"):
-        model.cuda()
-    elif device == torch.device("cpu"):
-        model.cpu()
-        model.cond_stage_model.device = "cpu"
-    else:
-        raise ValueError(f"Incorrect device name. Received: {device}")
-    model.eval()
-    return model
-
-
-def parse_args():
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--prompt",
-        type=str,
-        nargs="?",
-        default="a professional photograph of an astronaut riding a triceratops",
-        help="the prompt to render"
-    )
-    parser.add_argument(
-        "--outdir",
-        type=str,
-        nargs="?",
-        help="dir to write results to",
-        default="outputs/txt2img-samples"
-    )
-    parser.add_argument(
-        "--steps",
-        type=int,
-        default=50,
-        help="number of ddim sampling steps",
-    )
-    parser.add_argument(
-        "--plms",
-        action='store_true',
-        help="use plms sampling",
-    )
-    parser.add_argument(
-        "--dpm",
-        action='store_true',
-        help="use DPM (2) sampler",
-    )
-    parser.add_argument(
-        "--fixed_code",
-        action='store_true',
-        help="if enabled, uses the same starting code across all samples ",
-    )
-    parser.add_argument(
-        "--ddim_eta",
-        type=float,
-        default=0.0,
-        help="ddim eta (eta=0.0 corresponds to deterministic sampling",
-    )
-    parser.add_argument(
-        "--n_iter",
-        type=int,
-        default=3,
-        help="sample this often",
-    )
-    parser.add_argument(
-        "--H",
-        type=int,
-        default=512,
-        help="image height, in pixel space",
-    )
-    parser.add_argument(
-        "--W",
-        type=int,
-        default=512,
-        help="image width, in pixel space",
-    )
-    parser.add_argument(
-        "--C",
-        type=int,
-        default=4,
-        help="latent channels",
-    )
-    parser.add_argument(
-        "--f",
-        type=int,
-        default=8,
-        help="downsampling factor, most often 8 or 16",
-    )
-    parser.add_argument(
-        "--n_samples",
-        type=int,
-        default=3,
-        help="how many samples to produce for each given prompt. A.k.a batch size",
-    )
-    parser.add_argument(
-        "--n_rows",
-        type=int,
-        default=0,
-        help="rows in the grid (default: n_samples)",
-    )
-    parser.add_argument(
-        "--scale",
-        type=float,
-        default=9.0,
-        help="unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))",
-    )
-    parser.add_argument(
-        "--from-file",
-        type=str,
-        help="if specified, load prompts from this file, separated by newlines",
-    )
-    parser.add_argument(
-        "--config",
-        type=str,
-        default="configs/stable-diffusion/v2-inference.yaml",
-        help="path to config which constructs model",
-    )
-    parser.add_argument(
-        "--ckpt",
-        type=str,
-        help="path to checkpoint of model",
-    )
-    parser.add_argument(
-        "--seed",
-        type=int,
-        default=42,
-        help="the seed (for reproducible sampling)",
-    )
-    parser.add_argument(
-        "--precision",
-        type=str,
-        help="evaluate at this precision",
-        choices=["full", "autocast"],
-        default="autocast"
-    )
-    parser.add_argument(
-        "--repeat",
-        type=int,
-        default=1,
-        help="repeat each prompt in file this often",
-    )
-    parser.add_argument(
-        "--device",
-        type=str,
-        help="Device on which Stable Diffusion will be run",
-        choices=["cpu", "cuda"],
-        default="cpu"
-    )
-    parser.add_argument(
-        "--torchscript",
-        action='store_true',
-        help="Use TorchScript",
-    )
-    parser.add_argument(
-        "--ipex",
-        action='store_true',
-        help="Use Intel® Extension for PyTorch*",
-    )
-    parser.add_argument(
-        "--bf16",
-        action='store_true',
-        help="Use bfloat16",
-    )
-    opt = parser.parse_args()
-    return opt
-
-
-def put_watermark(img, wm_encoder=None):
-    if wm_encoder is not None:
-        img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
-        img = wm_encoder.encode(img, 'dwtDct')
-        img = Image.fromarray(img[:, :, ::-1])
-    return img
-
-
-def main(opt):
-    seed_everything(opt.seed)
-
-    config = OmegaConf.load(f"{opt.config}")
-    device = torch.device("cuda") if opt.device == "cuda" else torch.device("cpu")
-    model = load_model_from_config(config, f"{opt.ckpt}", device)
-
-    if opt.plms:
-        sampler = PLMSSampler(model, device=device)
-    elif opt.dpm:
-        sampler = DPMSolverSampler(model, device=device)
-    else:
-        sampler = DDIMSampler(model, device=device)
-
-    os.makedirs(opt.outdir, exist_ok=True)
-    outpath = opt.outdir
-
-    print("Creating invisible watermark encoder (see https://github.com/ShieldMnt/invisible-watermark)...")
-    wm = "SDV2"
-    wm_encoder = WatermarkEncoder()
-    wm_encoder.set_watermark('bytes', wm.encode('utf-8'))
-
-    batch_size = opt.n_samples
-    n_rows = opt.n_rows if opt.n_rows > 0 else batch_size
-    if not opt.from_file:
-        prompt = opt.prompt
-        assert prompt is not None
-        data = [batch_size * [prompt]]
-
-    else:
-        print(f"reading prompts from {opt.from_file}")
-        with open(opt.from_file, "r") as f:
-            data = f.read().splitlines()
-            data = [p for p in data for i in range(opt.repeat)]
-            data = list(chunk(data, batch_size))
-
-    sample_path = os.path.join(outpath, "samples")
-    os.makedirs(sample_path, exist_ok=True)
-    sample_count = 0
-    base_count = len(os.listdir(sample_path))
-    grid_count = len(os.listdir(outpath)) - 1
-
-    start_code = None
-    if opt.fixed_code:
-        start_code = torch.randn([opt.n_samples, opt.C, opt.H // opt.f, opt.W // opt.f], device=device)
-
-    if opt.torchscript or opt.ipex:
-        transformer = model.cond_stage_model.model
-        unet = model.model.diffusion_model
-        decoder = model.first_stage_model.decoder
-        additional_context = torch.cpu.amp.autocast() if opt.bf16 else nullcontext()
-        shape = [opt.C, opt.H // opt.f, opt.W // opt.f]
-
-        if opt.bf16 and not opt.torchscript and not opt.ipex:
-            raise ValueError('Bfloat16 is supported only for torchscript+ipex')
-        if opt.bf16 and unet.dtype != torch.bfloat16:
-            raise ValueError("Use configs/stable-diffusion/intel/ configs with bf16 enabled if " +
-                             "you'd like to use bfloat16 with CPU.")
-        if unet.dtype == torch.float16 and device == torch.device("cpu"):
-            raise ValueError("Use configs/stable-diffusion/intel/ configs for your model if you'd like to run it on CPU.")
-
-        if opt.ipex:
-            import intel_extension_for_pytorch as ipex
-            bf16_dtype = torch.bfloat16 if opt.bf16 else None
-            transformer = transformer.to(memory_format=torch.channels_last)
-            transformer = ipex.optimize(transformer, level="O1", inplace=True)
-
-            unet = unet.to(memory_format=torch.channels_last)
-            unet = ipex.optimize(unet, level="O1", auto_kernel_selection=True, inplace=True, dtype=bf16_dtype)
-
-            decoder = decoder.to(memory_format=torch.channels_last)
-            decoder = ipex.optimize(decoder, level="O1", auto_kernel_selection=True, inplace=True, dtype=bf16_dtype)
-
-        if opt.torchscript:
-            with torch.no_grad(), additional_context:
-                # get UNET scripted
-                if unet.use_checkpoint:
-                    raise ValueError("Gradient checkpoint won't work with tracing. " +
-                    "Use configs/stable-diffusion/intel/ configs for your model or disable checkpoint in your config.")
-
-                img_in = torch.ones(2, 4, 96, 96, dtype=torch.float32)
-                t_in = torch.ones(2, dtype=torch.int64)
-                context = torch.ones(2, 77, 1024, dtype=torch.float32)
-                scripted_unet = torch.jit.trace(unet, (img_in, t_in, context))
-                scripted_unet = torch.jit.optimize_for_inference(scripted_unet)
-                print(type(scripted_unet))
-                model.model.scripted_diffusion_model = scripted_unet
-
-                # get Decoder for first stage model scripted
-                samples_ddim = torch.ones(1, 4, 96, 96, dtype=torch.float32)
-                scripted_decoder = torch.jit.trace(decoder, (samples_ddim))
-                scripted_decoder = torch.jit.optimize_for_inference(scripted_decoder)
-                print(type(scripted_decoder))
-                model.first_stage_model.decoder = scripted_decoder
-
-        prompts = data[0]
-        print("Running a forward pass to initialize optimizations")
-        uc = None
-        if opt.scale != 1.0:
-            uc = model.get_learned_conditioning(batch_size * [""])
-        if isinstance(prompts, tuple):
-            prompts = list(prompts)
-
-        with torch.no_grad(), additional_context:
-            for _ in range(3):
-                c = model.get_learned_conditioning(prompts)
-            samples_ddim, _ = sampler.sample(S=5,
-                                             conditioning=c,
-                                             batch_size=batch_size,
-                                             shape=shape,
-                                             verbose=False,
-                                             unconditional_guidance_scale=opt.scale,
-                                             unconditional_conditioning=uc,
-                                             eta=opt.ddim_eta,
-                                             x_T=start_code)
-            print("Running a forward pass for decoder")
-            for _ in range(3):
-                x_samples_ddim = model.decode_first_stage(samples_ddim)
-
-    precision_scope = autocast if opt.precision=="autocast" or opt.bf16 else nullcontext
-    with torch.no_grad(), \
-        precision_scope(opt.device), \
-        model.ema_scope():
-            all_samples = list()
-            for n in trange(opt.n_iter, desc="Sampling"):
-                for prompts in tqdm(data, desc="data"):
-                    uc = None
-                    if opt.scale != 1.0:
-                        uc = model.get_learned_conditioning(batch_size * [""])
-                    if isinstance(prompts, tuple):
-                        prompts = list(prompts)
-                    c = model.get_learned_conditioning(prompts)
-                    shape = [opt.C, opt.H // opt.f, opt.W // opt.f]
-                    samples, _ = sampler.sample(S=opt.steps,
-                                                     conditioning=c,
-                                                     batch_size=opt.n_samples,
-                                                     shape=shape,
-                                                     verbose=False,
-                                                     unconditional_guidance_scale=opt.scale,
-                                                     unconditional_conditioning=uc,
-                                                     eta=opt.ddim_eta,
-                                                     x_T=start_code)
-
-                    x_samples = model.decode_first_stage(samples)
-                    x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0)
-
-                    for x_sample in x_samples:
-                        x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
-                        img = Image.fromarray(x_sample.astype(np.uint8))
-                        img = put_watermark(img, wm_encoder)
-                        img.save(os.path.join(sample_path, f"{base_count:05}.png"))
-                        base_count += 1
-                        sample_count += 1
-
-                    all_samples.append(x_samples)
-
-            # additionally, save as grid
-            grid = torch.stack(all_samples, 0)
-            grid = rearrange(grid, 'n b c h w -> (n b) c h w')
-            grid = make_grid(grid, nrow=n_rows)
-
-            # to image
-            grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy()
-            grid = Image.fromarray(grid.astype(np.uint8))
-            grid = put_watermark(grid, wm_encoder)
-            grid.save(os.path.join(outpath, f'grid-{grid_count:04}.png'))
-            grid_count += 1
-
-    print(f"Your samples are ready and waiting for you here: \n{outpath} \n"
-          f" \nEnjoy.")
-
-
-if __name__ == "__main__":
-    opt = parse_args()
-    main(opt)
diff --git a/repositories/stable-diffusion-stability-ai/setup.py b/repositories/stable-diffusion-stability-ai/setup.py
deleted file mode 100644
index 00f5b4d874f0f19ece54fac2dd50b39774b86c5b..0000000000000000000000000000000000000000
--- a/repositories/stable-diffusion-stability-ai/setup.py
+++ /dev/null
@@ -1,13 +0,0 @@
-from setuptools import setup, find_packages
-
-setup(
-    name='stable-diffusion',
-    version='0.0.1',
-    description='',
-    packages=find_packages(),
-    install_requires=[
-        'torch',
-        'numpy',
-        'tqdm',
-    ],
-)
\ No newline at end of file
diff --git a/repositories/taming-transformers/License.txt b/repositories/taming-transformers/License.txt
deleted file mode 100644
index 57fb4153bafcd64b60377ba0ba2c79b7530efc1e..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/License.txt
+++ /dev/null
@@ -1,19 +0,0 @@
-Copyright (c) 2020 Patrick Esser and Robin Rombach and Björn Ommer
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
-EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
-MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
-IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
-DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
-OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE
-OR OTHER DEALINGS IN THE SOFTWARE./
diff --git a/repositories/taming-transformers/README.md b/repositories/taming-transformers/README.md
deleted file mode 100644
index d295fbf75703e6cd285330432785b8cdea072ba7..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/README.md
+++ /dev/null
@@ -1,410 +0,0 @@
-# Taming Transformers for High-Resolution Image Synthesis
-##### CVPR 2021 (Oral)
-![teaser](assets/mountain.jpeg)
-
-[**Taming Transformers for High-Resolution Image Synthesis**](https://compvis.github.io/taming-transformers/)<br/>
-[Patrick Esser](https://github.com/pesser)\*,
-[Robin Rombach](https://github.com/rromb)\*,
-[Björn Ommer](https://hci.iwr.uni-heidelberg.de/Staff/bommer)<br/>
-\* equal contribution
-
-**tl;dr** We combine the efficiancy of convolutional approaches with the expressivity of transformers by introducing a convolutional VQGAN, which learns a codebook of context-rich visual parts, whose composition is modeled with an autoregressive transformer.
-
-![teaser](assets/teaser.png)
-[arXiv](https://arxiv.org/abs/2012.09841) | [BibTeX](#bibtex) | [Project Page](https://compvis.github.io/taming-transformers/)
-
-
-### News
-#### 2022
-- More pretrained VQGANs (e.g. a f8-model with only 256 codebook entries) are available in our new work on [Latent Diffusion Models](https://github.com/CompVis/latent-diffusion).
-- Added scene synthesis models as proposed in the paper [High-Resolution Complex Scene Synthesis with Transformers](https://arxiv.org/abs/2105.06458), see [this section](#scene-image-synthesis).
-#### 2021
-- Thanks to [rom1504](https://github.com/rom1504) it is now easy to [train a VQGAN on your own datasets](#training-on-custom-data).
-- Included a bugfix for the quantizer. For backward compatibility it is
-  disabled by default (which corresponds to always training with `beta=1.0`).
-  Use `legacy=False` in the quantizer config to enable it.
-  Thanks [richcmwang](https://github.com/richcmwang) and [wcshin-git](https://github.com/wcshin-git)!
-- Our paper received an update: See https://arxiv.org/abs/2012.09841v3 and the corresponding changelog.
-- Added a pretrained, [1.4B transformer model](https://k00.fr/s511rwcv) trained for class-conditional ImageNet synthesis, which obtains state-of-the-art FID scores among autoregressive approaches and outperforms BigGAN.
-- Added pretrained, unconditional models on [FFHQ](https://k00.fr/yndvfu95) and [CelebA-HQ](https://k00.fr/2xkmielf).
-- Added accelerated sampling via caching of keys/values in the self-attention operation, used in `scripts/sample_fast.py`.
-- Added a checkpoint of a [VQGAN](https://heibox.uni-heidelberg.de/d/2e5662443a6b4307b470/) trained with f8 compression and Gumbel-Quantization. 
-  See also our updated [reconstruction notebook](https://colab.research.google.com/github/CompVis/taming-transformers/blob/master/scripts/reconstruction_usage.ipynb). 
-- We added a [colab notebook](https://colab.research.google.com/github/CompVis/taming-transformers/blob/master/scripts/reconstruction_usage.ipynb) which compares two VQGANs and OpenAI's [DALL-E](https://github.com/openai/DALL-E). See also [this section](#more-resources).
-- We now include an overview of pretrained models in [Tab.1](#overview-of-pretrained-models). We added models for [COCO](#coco) and [ADE20k](#ade20k).
-- The streamlit demo now supports image completions.
-- We now include a couple of examples from the D-RIN dataset so you can run the
-  [D-RIN demo](#d-rin) without preparing the dataset first.
-- You can now jump right into sampling with our [Colab quickstart notebook](https://colab.research.google.com/github/CompVis/taming-transformers/blob/master/scripts/taming-transformers.ipynb).
-
-## Requirements
-A suitable [conda](https://conda.io/) environment named `taming` can be created
-and activated with:
-
-```
-conda env create -f environment.yaml
-conda activate taming
-```
-## Overview of pretrained models
-The following table provides an overview of all models that are currently available. 
-FID scores were evaluated using [torch-fidelity](https://github.com/toshas/torch-fidelity).
-For reference, we also include a link to the recently released autoencoder of the [DALL-E](https://github.com/openai/DALL-E) model. 
-See the corresponding [colab
-notebook](https://colab.research.google.com/github/CompVis/taming-transformers/blob/master/scripts/reconstruction_usage.ipynb)
-for a comparison and discussion of reconstruction capabilities.
-
-| Dataset  | FID vs train | FID vs val | Link |  Samples (256x256) | Comments
-| ------------- | ------------- | ------------- |-------------  | -------------  |-------------  |
-| FFHQ (f=16) | 9.6 | -- | [ffhq_transformer](https://k00.fr/yndvfu95) |  [ffhq_samples](https://k00.fr/j626x093) |
-| CelebA-HQ (f=16) | 10.2 | -- | [celebahq_transformer](https://k00.fr/2xkmielf) | [celebahq_samples](https://k00.fr/j626x093) |
-| ADE20K (f=16) | -- | 35.5 | [ade20k_transformer](https://k00.fr/ot46cksa) | [ade20k_samples.zip](https://heibox.uni-heidelberg.de/f/70bb78cbaf844501b8fb/) [2k] | evaluated on val split (2k images)
-| COCO-Stuff (f=16) | -- | 20.4  | [coco_transformer](https://k00.fr/2zz6i2ce) | [coco_samples.zip](https://heibox.uni-heidelberg.de/f/a395a9be612f4a7a8054/) [5k] | evaluated on val split (5k images)
-| ImageNet (cIN) (f=16) | 15.98/15.78/6.59/5.88/5.20 | -- | [cin_transformer](https://k00.fr/s511rwcv) | [cin_samples](https://k00.fr/j626x093) | different decoding hyperparameters |  
-| |  | | || |
-| FacesHQ (f=16) | -- |  -- | [faceshq_transformer](https://k00.fr/qqfl2do8)
-| S-FLCKR (f=16) | -- | -- | [sflckr](https://heibox.uni-heidelberg.de/d/73487ab6e5314cb5adba/) 
-| D-RIN (f=16) | -- | -- | [drin_transformer](https://k00.fr/39jcugc5)
-| | |  | | || |
-| VQGAN ImageNet (f=16), 1024 |  10.54 | 7.94 | [vqgan_imagenet_f16_1024](https://heibox.uni-heidelberg.de/d/8088892a516d4e3baf92/) | [reconstructions](https://k00.fr/j626x093) | Reconstruction-FIDs.
-| VQGAN ImageNet (f=16), 16384 | 7.41 | 4.98 |[vqgan_imagenet_f16_16384](https://heibox.uni-heidelberg.de/d/a7530b09fed84f80a887/)  |  [reconstructions](https://k00.fr/j626x093) | Reconstruction-FIDs.
-| VQGAN OpenImages (f=8), 256 | -- | 1.49 |https://ommer-lab.com/files/latent-diffusion/vq-f8-n256.zip |  ---  | Reconstruction-FIDs. Available via [latent diffusion](https://github.com/CompVis/latent-diffusion).
-| VQGAN OpenImages (f=8), 16384 | -- | 1.14 |https://ommer-lab.com/files/latent-diffusion/vq-f8.zip  |  ---  | Reconstruction-FIDs. Available via [latent diffusion](https://github.com/CompVis/latent-diffusion)
-| VQGAN OpenImages (f=8), 8192, GumbelQuantization | 3.24 | 1.49 |[vqgan_gumbel_f8](https://heibox.uni-heidelberg.de/d/2e5662443a6b4307b470/)  |  ---  | Reconstruction-FIDs.
-| | |  | | || |
-| DALL-E dVAE (f=8), 8192, GumbelQuantization | 33.88 | 32.01 | https://github.com/openai/DALL-E | [reconstructions](https://k00.fr/j626x093) | Reconstruction-FIDs.
-
-
-## Running pretrained models
-
-The commands below will start a streamlit demo which supports sampling at
-different resolutions and image completions. To run a non-interactive version
-of the sampling process, replace `streamlit run scripts/sample_conditional.py --`
-by `python scripts/make_samples.py --outdir <path_to_write_samples_to>` and
-keep the remaining command line arguments. 
-
-To sample from unconditional or class-conditional models, 
-run `python scripts/sample_fast.py -r <path/to/config_and_checkpoint>`.
-We describe below how to use this script to sample from the ImageNet, FFHQ, and CelebA-HQ models, 
-respectively.
-
-### S-FLCKR
-![teaser](assets/sunset_and_ocean.jpg)
-
-You can also [run this model in a Colab
-notebook](https://colab.research.google.com/github/CompVis/taming-transformers/blob/master/scripts/taming-transformers.ipynb),
-which includes all necessary steps to start sampling.
-
-Download the
-[2020-11-09T13-31-51_sflckr](https://heibox.uni-heidelberg.de/d/73487ab6e5314cb5adba/)
-folder and place it into `logs`. Then, run
-```
-streamlit run scripts/sample_conditional.py -- -r logs/2020-11-09T13-31-51_sflckr/
-```
-
-### ImageNet
-![teaser](assets/imagenet.png)
-
-Download the [2021-04-03T19-39-50_cin_transformer](https://k00.fr/s511rwcv)
-folder and place it into logs.  Sampling from the class-conditional ImageNet
-model does not require any data preparation. To produce 50 samples for each of
-the 1000 classes of ImageNet, with k=600 for top-k sampling, p=0.92 for nucleus
-sampling and temperature t=1.0, run
-
-```
-python scripts/sample_fast.py -r logs/2021-04-03T19-39-50_cin_transformer/ -n 50 -k 600 -t 1.0 -p 0.92 --batch_size 25   
-```
-
-To restrict the model to certain classes, provide them via the `--classes` argument, separated by 
-commas. For example, to sample 50 *ostriches*, *border collies* and *whiskey jugs*, run
-
-```
-python scripts/sample_fast.py -r logs/2021-04-03T19-39-50_cin_transformer/ -n 50 -k 600 -t 1.0 -p 0.92 --batch_size 25 --classes 9,232,901   
-```
-We recommended to experiment with the autoregressive decoding parameters (top-k, top-p and temperature) for best results.  
-
-### FFHQ/CelebA-HQ
-
-Download the [2021-04-23T18-19-01_ffhq_transformer](https://k00.fr/yndvfu95) and 
-[2021-04-23T18-11-19_celebahq_transformer](https://k00.fr/2xkmielf) 
-folders and place them into logs. 
-Again, sampling from these unconditional models does not require any data preparation.
-To produce 50000 samples, with k=250 for top-k sampling,
-p=1.0 for nucleus sampling and temperature t=1.0, run
-
-```
-python scripts/sample_fast.py -r logs/2021-04-23T18-19-01_ffhq_transformer/   
-```
-for FFHQ and  
-
-```
-python scripts/sample_fast.py -r logs/2021-04-23T18-11-19_celebahq_transformer/   
-```
-to sample from the CelebA-HQ model.
-For both models it can be advantageous to vary the top-k/top-p parameters for sampling.
-
-### FacesHQ
-![teaser](assets/faceshq.jpg)
-
-Download [2020-11-13T21-41-45_faceshq_transformer](https://k00.fr/qqfl2do8) and
-place it into `logs`. Follow the data preparation steps for
-[CelebA-HQ](#celeba-hq) and [FFHQ](#ffhq). Run
-```
-streamlit run scripts/sample_conditional.py -- -r logs/2020-11-13T21-41-45_faceshq_transformer/
-```
-
-### D-RIN
-![teaser](assets/drin.jpg)
-
-Download [2020-11-20T12-54-32_drin_transformer](https://k00.fr/39jcugc5) and
-place it into `logs`. To run the demo on a couple of example depth maps
-included in the repository, run
-
-```
-streamlit run scripts/sample_conditional.py -- -r logs/2020-11-20T12-54-32_drin_transformer/ --ignore_base_data data="{target: main.DataModuleFromConfig, params: {batch_size: 1, validation: {target: taming.data.imagenet.DRINExamples}}}"
-```
-
-To run the demo on the complete validation set, first follow the data preparation steps for
-[ImageNet](#imagenet) and then run
-```
-streamlit run scripts/sample_conditional.py -- -r logs/2020-11-20T12-54-32_drin_transformer/
-```
-
-### COCO
-Download [2021-01-20T16-04-20_coco_transformer](https://k00.fr/2zz6i2ce) and
-place it into `logs`. To run the demo on a couple of example segmentation maps
-included in the repository, run
-
-```
-streamlit run scripts/sample_conditional.py -- -r logs/2021-01-20T16-04-20_coco_transformer/ --ignore_base_data data="{target: main.DataModuleFromConfig, params: {batch_size: 1, validation: {target: taming.data.coco.Examples}}}"
-```
-
-### ADE20k
-Download [2020-11-20T21-45-44_ade20k_transformer](https://k00.fr/ot46cksa) and
-place it into `logs`. To run the demo on a couple of example segmentation maps
-included in the repository, run
-
-```
-streamlit run scripts/sample_conditional.py -- -r logs/2020-11-20T21-45-44_ade20k_transformer/ --ignore_base_data data="{target: main.DataModuleFromConfig, params: {batch_size: 1, validation: {target: taming.data.ade20k.Examples}}}"
-```
-
-## Scene Image Synthesis
-![teaser](assets/scene_images_samples.svg)
-Scene image generation based on bounding box conditionals as done in our CVPR2021 AI4CC workshop paper [High-Resolution Complex Scene Synthesis with Transformers](https://arxiv.org/abs/2105.06458) (see talk on [workshop page](https://visual.cs.brown.edu/workshops/aicc2021/#awards)). Supporting the datasets COCO and Open Images.
-
-### Training
-Download first-stage models [COCO-8k-VQGAN](https://heibox.uni-heidelberg.de/f/78dea9589974474c97c1/) for COCO or [COCO/Open-Images-8k-VQGAN](https://heibox.uni-heidelberg.de/f/461d9a9f4fcf48ab84f4/) for Open Images.
-Change `ckpt_path` in `data/coco_scene_images_transformer.yaml` and `data/open_images_scene_images_transformer.yaml` to point to the downloaded first-stage models.
-Download the full COCO/OI datasets and adapt `data_path` in the same files, unless working with the 100 files provided for training and validation suits your needs already.
-
-Code can be run with
-`python main.py --base configs/coco_scene_images_transformer.yaml -t True --gpus 0,`
-or
-`python main.py --base configs/open_images_scene_images_transformer.yaml -t True --gpus 0,`
-
-### Sampling 
-Train a model as described above or download a pre-trained model:
- - [Open Images 1 billion parameter model](https://drive.google.com/file/d/1FEK-Z7hyWJBvFWQF50pzSK9y1W_CJEig/view?usp=sharing) available that trained 100 epochs. On 256x256 pixels, FID 41.48±0.21, SceneFID 14.60±0.15, Inception Score 18.47±0.27. The model was trained with 2d crops of images and is thus well-prepared for the task of generating high-resolution images, e.g. 512x512.
- - [Open Images distilled version of the above model with 125 million parameters](https://drive.google.com/file/d/1xf89g0mc78J3d8Bx5YhbK4tNRNlOoYaO) allows for sampling on smaller GPUs (4 GB is enough for sampling 256x256 px images). Model was trained for 60 epochs with 10% soft loss, 90% hard loss. On 256x256 pixels, FID 43.07±0.40, SceneFID 15.93±0.19, Inception Score 17.23±0.11.
- - [COCO 30 epochs](https://heibox.uni-heidelberg.de/f/0d0b2594e9074c7e9a33/)
- - [COCO 60 epochs](https://drive.google.com/file/d/1bInd49g2YulTJBjU32Awyt5qnzxxG5U9/) (find model statistics for both COCO versions in `assets/coco_scene_images_training.svg`)
-
-When downloading a pre-trained model, remember to change `ckpt_path` in `configs/*project.yaml` to point to your downloaded first-stage model (see ->Training).
-
-Scene image generation can be run with
-`python scripts/make_scene_samples.py --outdir=/some/outdir -r /path/to/pretrained/model --resolution=512,512`
-
-
-## Training on custom data
-
-Training on your own dataset can be beneficial to get better tokens and hence better images for your domain.
-Those are the steps to follow to make this work:
-1. install the repo with `conda env create -f environment.yaml`, `conda activate taming` and `pip install -e .`
-1. put your .jpg files in a folder `your_folder`
-2. create 2 text files a `xx_train.txt` and `xx_test.txt` that point to the files in your training and test set respectively (for example `find $(pwd)/your_folder -name "*.jpg" > train.txt`)
-3. adapt `configs/custom_vqgan.yaml` to point to these 2 files
-4. run `python main.py --base configs/custom_vqgan.yaml -t True --gpus 0,1` to
-   train on two GPUs. Use `--gpus 0,` (with a trailing comma) to train on a single GPU.
-
-## Data Preparation
-
-### ImageNet
-The code will try to download (through [Academic
-Torrents](http://academictorrents.com/)) and prepare ImageNet the first time it
-is used. However, since ImageNet is quite large, this requires a lot of disk
-space and time. If you already have ImageNet on your disk, you can speed things
-up by putting the data into
-`${XDG_CACHE}/autoencoders/data/ILSVRC2012_{split}/data/` (which defaults to
-`~/.cache/autoencoders/data/ILSVRC2012_{split}/data/`), where `{split}` is one
-of `train`/`validation`. It should have the following structure:
-
-```
-${XDG_CACHE}/autoencoders/data/ILSVRC2012_{split}/data/
-├── n01440764
-│   ├── n01440764_10026.JPEG
-│   ├── n01440764_10027.JPEG
-│   ├── ...
-├── n01443537
-│   ├── n01443537_10007.JPEG
-│   ├── n01443537_10014.JPEG
-│   ├── ...
-├── ...
-```
-
-If you haven't extracted the data, you can also place
-`ILSVRC2012_img_train.tar`/`ILSVRC2012_img_val.tar` (or symlinks to them) into
-`${XDG_CACHE}/autoencoders/data/ILSVRC2012_train/` /
-`${XDG_CACHE}/autoencoders/data/ILSVRC2012_validation/`, which will then be
-extracted into above structure without downloading it again.  Note that this
-will only happen if neither a folder
-`${XDG_CACHE}/autoencoders/data/ILSVRC2012_{split}/data/` nor a file
-`${XDG_CACHE}/autoencoders/data/ILSVRC2012_{split}/.ready` exist. Remove them
-if you want to force running the dataset preparation again.
-
-You will then need to prepare the depth data using
-[MiDaS](https://github.com/intel-isl/MiDaS). Create a symlink
-`data/imagenet_depth` pointing to a folder with two subfolders `train` and
-`val`, each mirroring the structure of the corresponding ImageNet folder
-described above and containing a `png` file for each of ImageNet's `JPEG`
-files. The `png` encodes `float32` depth values obtained from MiDaS as RGBA
-images. We provide the script `scripts/extract_depth.py` to generate this data.
-**Please note** that this script uses [MiDaS via PyTorch
-Hub](https://pytorch.org/hub/intelisl_midas_v2/). When we prepared the data,
-the hub provided the [MiDaS
-v2.0](https://github.com/intel-isl/MiDaS/releases/tag/v2) version, but now it
-provides a v2.1 version. We haven't tested our models with depth maps obtained
-via v2.1 and if you want to make sure that things work as expected, you must
-adjust the script to make sure it explicitly uses
-[v2.0](https://github.com/intel-isl/MiDaS/releases/tag/v2)!
-
-### CelebA-HQ
-Create a symlink `data/celebahq` pointing to a folder containing the `.npy`
-files of CelebA-HQ (instructions to obtain them can be found in the [PGGAN
-repository](https://github.com/tkarras/progressive_growing_of_gans)).
-
-### FFHQ
-Create a symlink `data/ffhq` pointing to the `images1024x1024` folder obtained
-from the [FFHQ repository](https://github.com/NVlabs/ffhq-dataset).
-
-### S-FLCKR
-Unfortunately, we are not allowed to distribute the images we collected for the
-S-FLCKR dataset and can therefore only give a description how it was produced.
-There are many resources on [collecting images from the
-web](https://github.com/adrianmrit/flickrdatasets) to get started.
-We collected sufficiently large images from [flickr](https://www.flickr.com)
-(see `data/flickr_tags.txt` for a full list of tags used to find images)
-and various [subreddits](https://www.reddit.com/r/sfwpornnetwork/wiki/network)
-(see `data/subreddits.txt` for all subreddits that were used).
-Overall, we collected 107625 images, and split them randomly into 96861
-training images and 10764 validation images. We then obtained segmentation
-masks for each image using [DeepLab v2](https://arxiv.org/abs/1606.00915)
-trained on [COCO-Stuff](https://arxiv.org/abs/1612.03716). We used a [PyTorch
-reimplementation](https://github.com/kazuto1011/deeplab-pytorch) and include an
-example script for this process in `scripts/extract_segmentation.py`.
-
-### COCO
-Create a symlink `data/coco` containing the images from the 2017 split in
-`train2017` and `val2017`, and their annotations in `annotations`. Files can be
-obtained from the [COCO webpage](https://cocodataset.org/). In addition, we use
-the [Stuff+thing PNG-style annotations on COCO 2017
-trainval](http://calvin.inf.ed.ac.uk/wp-content/uploads/data/cocostuffdataset/stuffthingmaps_trainval2017.zip)
-annotations from [COCO-Stuff](https://github.com/nightrome/cocostuff), which
-should be placed under `data/cocostuffthings`.
-
-### ADE20k
-Create a symlink `data/ade20k_root` containing the contents of
-[ADEChallengeData2016.zip](http://data.csail.mit.edu/places/ADEchallenge/ADEChallengeData2016.zip)
-from the [MIT Scene Parsing Benchmark](http://sceneparsing.csail.mit.edu/).
-
-## Training models
-
-### FacesHQ
-
-Train a VQGAN with
-```
-python main.py --base configs/faceshq_vqgan.yaml -t True --gpus 0,
-```
-
-Then, adjust the checkpoint path of the config key
-`model.params.first_stage_config.params.ckpt_path` in
-`configs/faceshq_transformer.yaml` (or download
-[2020-11-09T13-33-36_faceshq_vqgan](https://k00.fr/uxy5usa9) and place into `logs`, which
-corresponds to the preconfigured checkpoint path), then run
-```
-python main.py --base configs/faceshq_transformer.yaml -t True --gpus 0,
-```
-
-### D-RIN
-
-Train a VQGAN on ImageNet with
-```
-python main.py --base configs/imagenet_vqgan.yaml -t True --gpus 0,
-```
-
-or download a pretrained one from [2020-09-23T17-56-33_imagenet_vqgan](https://k00.fr/u0j2dtac)
-and place under `logs`. If you trained your own, adjust the path in the config
-key `model.params.first_stage_config.params.ckpt_path` of
-`configs/drin_transformer.yaml`.
-
-Train a VQGAN on Depth Maps of ImageNet with
-```
-python main.py --base configs/imagenetdepth_vqgan.yaml -t True --gpus 0,
-```
-
-or download a pretrained one from [2020-11-03T15-34-24_imagenetdepth_vqgan](https://k00.fr/55rlxs6i)
-and place under `logs`. If you trained your own, adjust the path in the config
-key `model.params.cond_stage_config.params.ckpt_path` of
-`configs/drin_transformer.yaml`.
-
-To train the transformer, run
-```
-python main.py --base configs/drin_transformer.yaml -t True --gpus 0,
-```
-
-## More Resources
-### Comparing Different First Stage Models
-The reconstruction and compression capabilities of different fist stage models can be analyzed in this [colab notebook](https://colab.research.google.com/github/CompVis/taming-transformers/blob/master/scripts/reconstruction_usage.ipynb). 
-In particular, the notebook compares two VQGANs with a downsampling factor of f=16 for each and codebook dimensionality of 1024 and 16384, 
-a VQGAN with f=8 and 8192 codebook entries and the discrete autoencoder of OpenAI's [DALL-E](https://github.com/openai/DALL-E) (which has f=8 and 8192 
-codebook entries).
-![firststages1](assets/first_stage_squirrels.png)
-![firststages2](assets/first_stage_mushrooms.png)
-
-### Other
-- A [video summary](https://www.youtube.com/watch?v=o7dqGcLDf0A&feature=emb_imp_woyt) by [Two Minute Papers](https://www.youtube.com/channel/UCbfYPyITQ-7l4upoX8nvctg).
-- A [video summary](https://www.youtube.com/watch?v=-wDSDtIAyWQ) by [Gradient Dude](https://www.youtube.com/c/GradientDude/about).
-- A [weights and biases report summarizing the paper](https://wandb.ai/ayush-thakur/taming-transformer/reports/-Overview-Taming-Transformers-for-High-Resolution-Image-Synthesis---Vmlldzo0NjEyMTY)
-by [ayulockin](https://github.com/ayulockin).
-- A [video summary](https://www.youtube.com/watch?v=JfUTd8fjtX8&feature=emb_imp_woyt) by [What's AI](https://www.youtube.com/channel/UCUzGQrN-lyyc0BWTYoJM_Sg).
-- Take a look at [ak9250's notebook](https://github.com/ak9250/taming-transformers/blob/master/tamingtransformerscolab.ipynb) if you want to run the streamlit demos on Colab.
-
-### Text-to-Image Optimization via CLIP
-VQGAN has been successfully used as an image generator guided by the [CLIP](https://github.com/openai/CLIP) model, both for pure image generation
-from scratch and image-to-image translation. We recommend the following notebooks/videos/resources:
-
- - [Advadnouns](https://twitter.com/advadnoun/status/1389316507134357506) Patreon and corresponding LatentVision notebooks: https://www.patreon.com/patronizeme
- - The [notebook]( https://colab.research.google.com/drive/1L8oL-vLJXVcRzCFbPwOoMkPKJ8-aYdPN) of [Rivers Have Wings](https://twitter.com/RiversHaveWings).
- - A [video](https://www.youtube.com/watch?v=90QDe6DQXF4&t=12s) explanation by [Dot CSV](https://www.youtube.com/channel/UCy5znSnfMsDwaLlROnZ7Qbg) (in Spanish, but English subtitles are available)
-
-![txt2img](assets/birddrawnbyachild.png)
-
-Text prompt: *'A bird drawn by a child'*
-
-## Shout-outs
-Thanks to everyone who makes their code and models available. In particular,
-
-- The architecture of our VQGAN is inspired by [Denoising Diffusion Probabilistic Models](https://github.com/hojonathanho/diffusion)
-- The very hackable transformer implementation [minGPT](https://github.com/karpathy/minGPT)
-- The good ol' [PatchGAN](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix) and [Learned Perceptual Similarity (LPIPS)](https://github.com/richzhang/PerceptualSimilarity)
-
-## BibTeX
-
-```
-@misc{esser2020taming,
-      title={Taming Transformers for High-Resolution Image Synthesis}, 
-      author={Patrick Esser and Robin Rombach and Björn Ommer},
-      year={2020},
-      eprint={2012.09841},
-      archivePrefix={arXiv},
-      primaryClass={cs.CV}
-}
-```
diff --git a/repositories/taming-transformers/assets/birddrawnbyachild.png b/repositories/taming-transformers/assets/birddrawnbyachild.png
deleted file mode 100644
index dec439d6f7dbf49e1d4aab94a4e5f32d52275f11..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/assets/birddrawnbyachild.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:165778bb85e86f8aaaed38eee4d33f62ab1ef237d890229cfa2e0685f5064127
-size 1607893
diff --git a/repositories/taming-transformers/assets/coco_scene_images_training.svg b/repositories/taming-transformers/assets/coco_scene_images_training.svg
deleted file mode 100644
index 6793487237678a6e8f32f7421cec24af547e571c..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/assets/coco_scene_images_training.svg
+++ /dev/null
@@ -1,2574 +0,0 @@
-<?xml version="1.0" encoding="UTF-8" standalone="no"?>
-<!-- Created with matplotlib (https://matplotlib.org/) -->
-
-<svg
-   height="362.97781pt"
-   version="1.1"
-   viewBox="0 0 1437.4859 362.97781"
-   width="1437.486pt"
-   id="svg1096"
-   sodipodi:docname="coco_scene_images_training.svg"
-   inkscape:version="1.1.1 (1:1.1+202109281949+c3084ef5ed)"
-   xmlns:inkscape="http://www.inkscape.org/namespaces/inkscape"
-   xmlns:sodipodi="http://sodipodi.sourceforge.net/DTD/sodipodi-0.dtd"
-   xmlns:xlink="http://www.w3.org/1999/xlink"
-   xmlns="http://www.w3.org/2000/svg"
-   xmlns:svg="http://www.w3.org/2000/svg"
-   xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
-   xmlns:cc="http://creativecommons.org/ns#"
-   xmlns:dc="http://purl.org/dc/elements/1.1/">
-  <sodipodi:namedview
-     id="namedview1098"
-     pagecolor="#ffffff"
-     bordercolor="#666666"
-     borderopacity="1.0"
-     inkscape:pageshadow="2"
-     inkscape:pageopacity="0.0"
-     inkscape:pagecheckerboard="0"
-     inkscape:document-units="pt"
-     showgrid="false"
-     fit-margin-top="0"
-     fit-margin-left="0"
-     fit-margin-right="0"
-     fit-margin-bottom="0"
-     inkscape:zoom="0.50026752"
-     inkscape:cx="894.5214"
-     inkscape:cy="198.89358"
-     inkscape:window-width="1848"
-     inkscape:window-height="1016"
-     inkscape:window-x="72"
-     inkscape:window-y="27"
-     inkscape:window-maximized="1"
-     inkscape:current-layer="svg1096" />
-  <metadata
-     id="metadata2">
-    <rdf:RDF>
-      <cc:Work>
-        <dc:type
-           rdf:resource="http://purl.org/dc/dcmitype/StillImage" />
-        <dc:date>2021-10-07T13:59:09.893851</dc:date>
-        <dc:format>image/svg+xml</dc:format>
-        <dc:creator>
-          <cc:Agent>
-            <dc:title>Matplotlib v3.3.3, https://matplotlib.org/</dc:title>
-          </cc:Agent>
-        </dc:creator>
-      </cc:Work>
-    </rdf:RDF>
-  </metadata>
-  <defs
-     id="defs6">
-    <style
-       type="text/css"
-       id="style4">*{stroke-linecap:butt;stroke-linejoin:round;}</style>
-    <defs
-       id="defs763">
-      <path
-         d="m 823.67934,-327.12787 v 6.93277 l 46.22212,137.87623 46.22212,69.03418 46.22212,23.923739 46.2231,14.917821 46.2221,17.951624 v -1.928158 0 l -46.2221,-18.018131 -46.2231,-14.878344 -46.22212,-23.851891 -46.22212,-71.19433 z"
-         id="m89ce144a55"
-         style="stroke:#55a868;stroke-opacity:0.2" />
-    </defs>
-    <defs
-       id="defs488">
-      <path
-         d="m 466.9032,-72.871147 v 16.379641 l 46.22212,-114.342964 46.22212,-69.913 46.22212,-47.62635 46.22312,6.68524 46.22212,-30.44176 v -14.99753 0 l -46.22212,33.92022 -46.22312,-3.90068 -46.22212,43.80921 -46.22212,70.3262 z"
-         id="m04b1e6744d"
-         style="stroke:#dd8452;stroke-opacity:0.2" />
-    </defs>
-    <defs
-       id="defs1009">
-      <path
-         d="m 1180.4555,-327.12787 v 3.64642 l 46.2221,157.71249 46.2221,56.82011 46.2221,24.224828 46.2232,18.115524 46.2221,10.116992 v -0.61253 0 l -46.2221,-10.321702 -46.2232,-18.755096 -46.2221,-24.966616 -46.2221,-57.44056 z"
-         id="m867c8604a3"
-         style="stroke:#c44e52;stroke-opacity:0.2" />
-    </defs>
-    <defs
-       id="defs282">
-      <path
-         d="M 52,44.1875 Q 55.375,50.25 60.0625,53.125 64.75,56 71.09375,56 q 8.546875,0 13.1875,-5.984375 4.640625,-5.96875 4.640625,-17 V 0 h -9.03125 v 32.71875 q 0,7.859375 -2.796875,11.65625 -2.78125,3.8125 -8.484375,3.8125 -6.984375,0 -11.046875,-4.640625 -4.046875,-4.625 -4.046875,-12.640625 V 0 h -9.03125 v 32.71875 q 0,7.90625 -2.78125,11.6875 -2.78125,3.78125 -8.59375,3.78125 -6.890625,0 -10.953125,-4.65625 -4.046875,-4.65625 -4.046875,-12.625 V 0 h -9.03125 v 54.6875 h 9.03125 v -8.5 q 3.078125,5.03125 7.375,7.421875 Q 29.78125,56 35.6875,56 41.65625,56 45.828125,52.96875 50,49.953125 52,44.1875 Z"
-         id="DejaVuSans-109" />
-      <path
-         d="M 18.3125,70.21875 V 54.6875 h 18.5 v -6.984375 h -18.5 v -29.6875 q 0,-6.6875 1.828125,-8.59375 1.828125,-1.90625 7.453125,-1.90625 H 36.8125 V 0 H 27.59375 Q 17.1875,0 13.234375,3.875 9.28125,7.765625 9.28125,18.015625 v 29.6875 H 2.6875 V 54.6875 h 6.59375 v 15.53125 z"
-         id="DejaVuSans-116" />
-      <path
-         d="m 41.109375,46.296875 q -1.515625,0.875 -3.296875,1.28125 Q 36.03125,48 33.890625,48 26.265625,48 22.1875,43.046875 18.109375,38.09375 18.109375,28.8125 V 0 h -9.03125 v 54.6875 h 9.03125 v -8.5 q 2.84375,4.984375 7.375,7.390625 Q 30.03125,56 36.53125,56 q 0.921875,0 2.046875,-0.125 1.125,-0.109375 2.484375,-0.359375 z"
-         id="DejaVuSans-114" />
-      <path
-         d="M 9.421875,54.6875 H 18.40625 V 0 H 9.421875 Z m 0,21.296875 H 18.40625 V 64.59375 H 9.421875 Z"
-         id="DejaVuSans-105" />
-      <path
-         id="DejaVuSans-32"
-         d="" />
-      <path
-         d="M 10.59375,45.40625 H 73.1875 V 37.203125 H 10.59375 Z m 0,-19.921875 H 73.1875 V 17.1875 H 10.59375 Z"
-         id="DejaVuSans-61" />
-      <path
-         d="M 25.390625,72.90625 H 33.6875 L 8.296875,-9.28125 H 0 Z"
-         id="DejaVuSans-47" />
-      <path
-         d="m 44.28125,53.078125 v -8.5 Q 40.484375,46.53125 36.375,47.5 q -4.09375,0.984375 -8.5,0.984375 -6.6875,0 -10.03125,-2.046875 Q 14.5,44.390625 14.5,40.28125 q 0,-3.125 2.390625,-4.90625 2.390625,-1.78125 9.625,-3.390625 l 3.078125,-0.6875 Q 39.15625,29.25 43.1875,25.515625 47.21875,21.78125 47.21875,15.09375 q 0,-7.625 -6.03125,-12.078125 -6.03125,-4.4375 -16.578125,-4.4375 -4.390625,0 -9.15625,0.859375 Q 10.6875,0.296875 5.421875,2 v 9.28125 q 4.984375,-2.59375 9.8125,-3.890625 4.828125,-1.28125 9.578125,-1.28125 6.34375,0 9.75,2.171875 3.421875,2.171875 3.421875,6.125 0,3.65625 -2.46875,5.609375 -2.453125,1.953125 -10.8125,3.765625 l -3.125,0.734375 q -8.34375,1.75 -12.0625,5.390625 -3.703125,3.640625 -3.703125,9.984375 0,7.71875 5.46875,11.90625 Q 16.75,56 26.8125,56 q 4.96875,0 9.359375,-0.734375 4.40625,-0.71875 8.109375,-2.1875 z"
-         id="DejaVuSans-115" />
-    </defs>
-    <defs
-       id="defs506">
-      <path
-         d="M 54.890625,33.015625 V 0 H 45.90625 v 32.71875 q 0,7.765625 -3.03125,11.609375 -3.03125,3.859375 -9.078125,3.859375 -7.28125,0 -11.484375,-4.640625 -4.203125,-4.625 -4.203125,-12.640625 V 0 h -9.03125 v 54.6875 h 9.03125 v -8.5 q 3.234375,4.9375 7.59375,7.375 4.375,2.4375 10.09375,2.4375 9.421875,0 14.25,-5.828125 4.84375,-5.828125 4.84375,-17.15625 z"
-         id="DejaVuSans-110" />
-      <path
-         d="m 50.984375,-16.609375 v -6.96875 h -51.96875 v 6.96875 z"
-         id="DejaVuSans-95" />
-    </defs>
-    <defs
-       id="defs781">
-      <path
-         d="M 37.109375,75.984375 V 68.5 h -8.59375 q -4.828125,0 -6.71875,-1.953125 -1.875,-1.953125 -1.875,-7.03125 V 54.6875 H 34.71875 V 47.703125 H 19.921875 V 0 h -9.03125 V 47.703125 H 2.296875 V 54.6875 h 8.59375 V 58.5 q 0,9.125 4.25,13.296875 4.25,4.1875 13.46875,4.1875 z"
-         id="DejaVuSans-102" />
-      <path
-         d="m 45.40625,46.390625 v 29.59375 h 8.984375 V 0 H 45.40625 v 8.203125 q -2.828125,-4.875 -7.15625,-7.25 -4.3125,-2.375 -10.375,-2.375 -9.90625,0 -16.140625,7.90625 -6.21875,7.921875 -6.21875,20.8125 0,12.890625 6.21875,20.796875 Q 17.96875,56 27.875,56 q 6.0625,0 10.375,-2.375 4.328125,-2.359375 7.15625,-7.234375 z M 14.796875,27.296875 q 0,-9.90625 4.078125,-15.546875 4.078125,-5.640625 11.203125,-5.640625 7.125,0 11.21875,5.640625 4.109375,5.640625 4.109375,15.546875 0,9.90625 -4.109375,15.546875 -4.09375,5.640625 -11.21875,5.640625 -7.125,0 -11.203125,-5.640625 -4.078125,-5.640625 -4.078125,-15.546875 z"
-         id="DejaVuSans-100" />
-    </defs>
-  </defs>
-  <path
-     d="M 0,362.97781 H 1437.486 V 0 H 0 Z"
-     style="fill:#ffffff"
-     id="path8" />
-  <path
-     d="M 49.807187,320.01812 H 359.95763 V 22.318125 H 49.807187 Z"
-     style="fill:#eaeaf2"
-     id="path11" />
-  <g
-     id="line2d_1">
-    <path
-       clip-path="url(#p33d8bb518f)"
-       d="M 59.283714,320.01812 V 22.318125"
-       style="fill:none;stroke:#ffffff;stroke-linecap:round"
-       id="path14" />
-  </g>
-  <g
-     id="text_1">
-    <!-- 0 -->
-    <g
-       style="fill:#262626"
-       transform="matrix(0.11,0,0,-0.11,55.784339,337.87641)"
-       id="g22">
-      <defs
-         id="defs18">
-        <path
-           d="m 31.78125,66.40625 q -7.609375,0 -11.453125,-7.5 Q 16.5,51.421875 16.5,36.375 q 0,-14.984375 3.828125,-22.484375 3.84375,-7.5 11.453125,-7.5 7.671875,0 11.5,7.5 3.84375,7.5 3.84375,22.484375 0,15.046875 -3.84375,22.53125 -3.828125,7.5 -11.5,7.5 z m 0,7.8125 q 12.265625,0 18.734375,-9.703125 6.46875,-9.6875 6.46875,-28.140625 0,-18.40625 -6.46875,-28.109375 -6.46875,-9.6875 -18.734375,-9.6875 -12.25,0 -18.71875,9.6875 Q 6.59375,17.96875 6.59375,36.375 q 0,18.453125 6.46875,28.140625 6.46875,9.703125 18.71875,9.703125 z"
-           id="DejaVuSans-48" />
-      </defs>
-      <use
-         xlink:href="#DejaVuSans-48"
-         id="use20"
-         x="0"
-         y="0"
-         width="100%"
-         height="100%" />
-    </g>
-  </g>
-  <g
-     id="line2d_2">
-    <path
-       clip-path="url(#p33d8bb518f)"
-       d="M 106.27604,320.01812 V 22.318125"
-       style="fill:none;stroke:#ffffff;stroke-linecap:round"
-       id="path26" />
-  </g>
-  <g
-     id="text_2">
-    <!-- 10 -->
-    <g
-       style="fill:#262626"
-       transform="matrix(0.11,0,0,-0.11,99.27729,337.87641)"
-       id="g36">
-      <defs
-         id="defs30">
-        <path
-           d="m 12.40625,8.296875 h 16.109375 v 55.625 L 10.984375,60.40625 v 8.984375 l 17.4375,3.515625 H 38.28125 V 8.296875 H 54.390625 V 0 H 12.40625 Z"
-           id="DejaVuSans-49" />
-      </defs>
-      <use
-         xlink:href="#DejaVuSans-49"
-         id="use32"
-         x="0"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="63.623047"
-         xlink:href="#DejaVuSans-48"
-         id="use34"
-         y="0"
-         width="100%"
-         height="100%" />
-    </g>
-  </g>
-  <g
-     id="line2d_3">
-    <path
-       clip-path="url(#p33d8bb518f)"
-       d="M 153.26837,320.01812 V 22.318125"
-       style="fill:none;stroke:#ffffff;stroke-linecap:round"
-       id="path40" />
-  </g>
-  <g
-     id="text_3">
-    <!-- 20 -->
-    <g
-       style="fill:#262626"
-       transform="matrix(0.11,0,0,-0.11,146.26962,337.87641)"
-       id="g50">
-      <defs
-         id="defs44">
-        <path
-           d="M 19.1875,8.296875 H 53.609375 V 0 H 7.328125 v 8.296875 q 5.609375,5.8125 15.296875,15.59375 9.703125,9.796875 12.1875,12.640625 4.734375,5.3125 6.609375,9 1.890625,3.6875 1.890625,7.25 0,5.8125 -4.078125,9.46875 -4.078125,3.671875 -10.625,3.671875 -4.640625,0 -9.796875,-1.609375 -5.140625,-1.609375 -11,-4.890625 v 9.96875 Q 13.765625,71.78125 18.9375,73 q 5.1875,1.21875 9.484375,1.21875 11.328125,0 18.0625,-5.671875 6.734375,-5.65625 6.734375,-15.125 0,-4.5 -1.6875,-8.53125 Q 49.859375,40.875 45.40625,35.40625 44.1875,33.984375 37.640625,27.21875 31.109375,20.453125 19.1875,8.296875 Z"
-           id="DejaVuSans-50" />
-      </defs>
-      <use
-         xlink:href="#DejaVuSans-50"
-         id="use46"
-         x="0"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="63.623047"
-         xlink:href="#DejaVuSans-48"
-         id="use48"
-         y="0"
-         width="100%"
-         height="100%" />
-    </g>
-  </g>
-  <g
-     id="line2d_4">
-    <path
-       clip-path="url(#p33d8bb518f)"
-       d="M 200.26069,320.01812 V 22.318125"
-       style="fill:none;stroke:#ffffff;stroke-linecap:round"
-       id="path54" />
-  </g>
-  <g
-     id="text_4">
-    <!-- 30 -->
-    <g
-       style="fill:#262626"
-       transform="matrix(0.11,0,0,-0.11,193.26194,337.87641)"
-       id="g64">
-      <defs
-         id="defs58">
-        <path
-           d="M 40.578125,39.3125 Q 47.65625,37.796875 51.625,33 q 3.984375,-4.78125 3.984375,-11.8125 0,-10.78125 -7.421875,-16.703125 -7.421875,-5.90625 -21.09375,-5.90625 -4.578125,0 -9.4375,0.90625 -4.859375,0.90625 -10.03125,2.71875 v 9.515625 q 4.09375,-2.390625 8.96875,-3.609375 4.890625,-1.21875 10.21875,-1.21875 9.265625,0 14.125,3.65625 4.859375,3.65625 4.859375,10.640625 0,6.453125 -4.515625,10.078125 -4.515625,3.640625 -12.5625,3.640625 h -8.5 v 8.109375 h 8.890625 q 7.265625,0 11.125,2.90625 3.859375,2.90625 3.859375,8.375 0,5.609375 -3.984375,8.609375 -3.96875,3.015625 -11.390625,3.015625 -4.0625,0 -8.703125,-0.890625 Q 15.375,64.15625 9.8125,62.3125 v 8.78125 q 5.625,1.5625 10.53125,2.34375 4.90625,0.78125 9.25,0.78125 11.234375,0 17.765625,-5.109375 6.546875,-5.09375 6.546875,-13.78125 0,-6.0625 -3.46875,-10.234375 -3.46875,-4.171875 -9.859375,-5.78125 z"
-           id="DejaVuSans-51" />
-      </defs>
-      <use
-         xlink:href="#DejaVuSans-51"
-         id="use60"
-         x="0"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="63.623047"
-         xlink:href="#DejaVuSans-48"
-         id="use62"
-         y="0"
-         width="100%"
-         height="100%" />
-    </g>
-  </g>
-  <g
-     id="line2d_5">
-    <path
-       clip-path="url(#p33d8bb518f)"
-       d="M 247.25302,320.01812 V 22.318125"
-       style="fill:none;stroke:#ffffff;stroke-linecap:round"
-       id="path68" />
-  </g>
-  <g
-     id="text_5">
-    <!-- 40 -->
-    <g
-       style="fill:#262626"
-       transform="matrix(0.11,0,0,-0.11,240.25427,337.87641)"
-       id="g78">
-      <defs
-         id="defs72">
-        <path
-           d="M 37.796875,64.3125 12.890625,25.390625 h 24.90625 z m -2.59375,8.59375 h 12.40625 V 25.390625 h 10.40625 V 17.1875 H 47.609375 V 0 h -9.8125 V 17.1875 H 4.890625 v 9.515625 z"
-           id="DejaVuSans-52" />
-      </defs>
-      <use
-         xlink:href="#DejaVuSans-52"
-         id="use74"
-         x="0"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="63.623047"
-         xlink:href="#DejaVuSans-48"
-         id="use76"
-         y="0"
-         width="100%"
-         height="100%" />
-    </g>
-  </g>
-  <g
-     id="line2d_6">
-    <path
-       clip-path="url(#p33d8bb518f)"
-       d="M 294.24534,320.01812 V 22.318125"
-       style="fill:none;stroke:#ffffff;stroke-linecap:round"
-       id="path82" />
-  </g>
-  <g
-     id="text_6">
-    <!-- 50 -->
-    <g
-       style="fill:#262626"
-       transform="matrix(0.11,0,0,-0.11,287.2466,337.87641)"
-       id="g92">
-      <defs
-         id="defs86">
-        <path
-           d="m 10.796875,72.90625 h 38.71875 v -8.3125 h -29.6875 V 46.734375 q 2.140625,0.734375 4.28125,1.09375 2.15625,0.359375 4.3125,0.359375 Q 40.625,48.1875 47.75,41.5 54.890625,34.8125 54.890625,23.390625 54.890625,11.625 47.5625,5.09375 40.234375,-1.421875 26.90625,-1.421875 q -4.59375,0 -9.359375,0.78125 -4.75,0.78125 -9.828125,2.34375 V 11.625 q 4.390625,-2.390625 9.078125,-3.5625 4.6875,-1.171875 9.90625,-1.171875 8.453125,0 13.375,4.4375 4.9375,4.4375 4.9375,12.0625 0,7.609375 -4.9375,12.046875 -4.921875,4.453125 -13.375,4.453125 -3.953125,0 -7.890625,-0.875 -3.921875,-0.875 -8.015625,-2.734375 z"
-           id="DejaVuSans-53" />
-      </defs>
-      <use
-         xlink:href="#DejaVuSans-53"
-         id="use88"
-         x="0"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="63.623047"
-         xlink:href="#DejaVuSans-48"
-         id="use90"
-         y="0"
-         width="100%"
-         height="100%" />
-    </g>
-  </g>
-  <g
-     id="line2d_7">
-    <path
-       clip-path="url(#p33d8bb518f)"
-       d="M 341.23767,320.01812 V 22.318125"
-       style="fill:none;stroke:#ffffff;stroke-linecap:round"
-       id="path96" />
-  </g>
-  <g
-     id="text_7">
-    <!-- 60 -->
-    <g
-       style="fill:#262626"
-       transform="matrix(0.11,0,0,-0.11,334.23892,337.87641)"
-       id="g106">
-      <defs
-         id="defs100">
-        <path
-           d="m 33.015625,40.375 q -6.640625,0 -10.53125,-4.546875 -3.875,-4.53125 -3.875,-12.4375 0,-7.859375 3.875,-12.4375 3.890625,-4.5625 10.53125,-4.5625 6.640625,0 10.515625,4.5625 3.875,4.578125 3.875,12.4375 0,7.90625 -3.875,12.4375 Q 39.65625,40.375 33.015625,40.375 Z M 52.59375,71.296875 V 62.3125 q -3.71875,1.75 -7.5,2.671875 -3.78125,0.9375 -7.5,0.9375 -9.765625,0 -14.921875,-6.59375 -5.140625,-6.59375 -5.875,-19.921875 2.875,4.25 7.21875,6.515625 4.359375,2.265625 9.578125,2.265625 10.984375,0 17.359375,-6.671875 6.375,-6.65625 6.375,-18.125 0,-11.234375 -6.640625,-18.03125 -6.640625,-6.78125 -17.671875,-6.78125 -12.65625,0 -19.34375,9.6875 -6.6875,9.703125 -6.6875,28.109375 0,17.28125 8.203125,27.5625 8.203125,10.28125 22.015625,10.28125 3.71875,0 7.5,-0.734375 3.78125,-0.734375 7.890625,-2.1875 z"
-           id="DejaVuSans-54" />
-      </defs>
-      <use
-         xlink:href="#DejaVuSans-54"
-         id="use102"
-         x="0"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="63.623047"
-         xlink:href="#DejaVuSans-48"
-         id="use104"
-         y="0"
-         width="100%"
-         height="100%" />
-    </g>
-  </g>
-  <g
-     style="fill:#262626"
-     transform="matrix(0.12,0,0,-0.12,186.60866,353.28219)"
-     id="g127">
-    <defs
-       id="defs115">
-      <path
-         d="m 56.203125,29.59375 v -4.390625 h -41.3125 q 0.59375,-9.28125 5.59375,-14.140625 5,-4.859375 13.9375,-4.859375 5.171875,0 10.03125,1.265625 4.859375,1.265625 9.65625,3.8125 v -8.5 q -4.84375,-2.046875 -9.921875,-3.125 -5.078125,-1.078125 -10.296875,-1.078125 -13.09375,0 -20.734375,7.609375 -7.640625,7.625 -7.640625,20.625 0,13.421875 7.25,21.296875 Q 20.015625,56 32.328125,56 q 11.03125,0 17.453125,-7.109375 6.421875,-7.09375 6.421875,-19.296875 z m -8.984375,2.640625 q -0.09375,7.359375 -4.125,11.75 -4.03125,4.40625 -10.671875,4.40625 -7.515625,0 -12.03125,-4.25 -4.515625,-4.25 -5.203125,-11.96875 z"
-         id="DejaVuSans-101" />
-      <path
-         d="m 18.109375,8.203125 v -29 H 9.078125 V 54.6875 h 9.03125 v -8.296875 q 2.84375,4.875 7.15625,7.234375 Q 29.59375,56 35.59375,56 q 9.96875,0 16.1875,-7.90625 6.234375,-7.90625 6.234375,-20.796875 0,-12.890625 -6.234375,-20.8125 -6.21875,-7.90625 -16.1875,-7.90625 -6,0 -10.328125,2.375 -4.3125,2.375 -7.15625,7.25 z M 48.6875,27.296875 q 0,9.90625 -4.078125,15.546875 -4.078125,5.640625 -11.203125,5.640625 -7.140625,0 -11.21875,-5.640625 -4.078125,-5.640625 -4.078125,-15.546875 0,-9.90625 4.078125,-15.546875 4.078125,-5.640625 11.21875,-5.640625 7.125,0 11.203125,5.640625 4.078125,5.640625 4.078125,15.546875 z"
-         id="DejaVuSans-112" />
-      <path
-         d="m 30.609375,48.390625 q -7.21875,0 -11.421875,-5.640625 -4.203125,-5.640625 -4.203125,-15.453125 0,-9.8125 4.171875,-15.453125 4.1875,-5.640625 11.453125,-5.640625 7.1875,0 11.375,5.65625 4.203125,5.671875 4.203125,15.4375 0,9.71875 -4.203125,15.40625 -4.1875,5.6875 -11.375,5.6875 z m 0,7.609375 q 11.71875,0 18.40625,-7.625 6.703125,-7.609375 6.703125,-21.078125 0,-13.421875 -6.703125,-21.078125 -6.6875,-7.640625 -18.40625,-7.640625 -11.765625,0 -18.4375,7.640625 -6.65625,7.65625 -6.65625,21.078125 0,13.46875 6.65625,21.078125 Q 18.84375,56 30.609375,56 Z"
-         id="DejaVuSans-111" />
-      <path
-         d="M 48.78125,52.59375 V 44.1875 q -3.8125,2.109375 -7.640625,3.15625 -3.828125,1.046875 -7.734375,1.046875 -8.75,0 -13.59375,-5.546875 -4.828125,-5.53125 -4.828125,-15.546875 0,-10.015625 4.828125,-15.5625 4.84375,-5.53125 13.59375,-5.53125 3.90625,0 7.734375,1.046875 3.828125,1.046875 7.640625,3.15625 v -8.3125 q -3.765625,-1.75 -7.796875,-2.625 -4.015625,-0.890625 -8.5625,-0.890625 -12.359375,0 -19.640625,7.765625 -7.265625,7.765625 -7.265625,20.953125 0,13.375 7.34375,21.03125 Q 20.21875,56 33.015625,56 q 4.140625,0 8.09375,-0.859375 3.953125,-0.84375 7.671875,-2.546875 z"
-         id="DejaVuSans-99" />
-      <path
-         d="M 54.890625,33.015625 V 0 H 45.90625 v 32.71875 q 0,7.765625 -3.03125,11.609375 -3.03125,3.859375 -9.078125,3.859375 -7.28125,0 -11.484375,-4.640625 -4.203125,-4.625 -4.203125,-12.640625 V 0 h -9.03125 v 75.984375 h 9.03125 V 46.1875 q 3.234375,4.9375 7.59375,7.375 4.375,2.4375 10.09375,2.4375 9.421875,0 14.25,-5.828125 4.84375,-5.828125 4.84375,-17.15625 z"
-         id="DejaVuSans-104" />
-    </defs>
-    <use
-       xlink:href="#DejaVuSans-101"
-       id="use117"
-       x="0"
-       y="0"
-       width="100%"
-       height="100%" />
-    <use
-       x="61.523438"
-       xlink:href="#DejaVuSans-112"
-       id="use119"
-       y="0"
-       width="100%"
-       height="100%" />
-    <use
-       x="125"
-       xlink:href="#DejaVuSans-111"
-       id="use121"
-       y="0"
-       width="100%"
-       height="100%" />
-    <use
-       x="186.18164"
-       xlink:href="#DejaVuSans-99"
-       id="use123"
-       y="0"
-       width="100%"
-       height="100%" />
-    <use
-       x="241.16211"
-       xlink:href="#DejaVuSans-104"
-       id="use125"
-       y="0"
-       width="100%"
-       height="100%" />
-  </g>
-  <g
-     id="line2d_8">
-    <path
-       clip-path="url(#p33d8bb518f)"
-       d="M 49.807187,297.68048 H 359.95763"
-       style="fill:none;stroke:#ffffff;stroke-linecap:round"
-       id="path131" />
-  </g>
-  <g
-     id="text_9">
-    <!-- 5.0 -->
-    <g
-       style="fill:#262626"
-       transform="matrix(0.11,0,0,-0.11,22.81375,301.85962)"
-       id="g143">
-      <defs
-         id="defs135">
-        <path
-           d="M 10.6875,12.40625 H 21 V 0 H 10.6875 Z"
-           id="DejaVuSans-46" />
-      </defs>
-      <use
-         xlink:href="#DejaVuSans-53"
-         id="use137"
-         x="0"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="63.623047"
-         xlink:href="#DejaVuSans-46"
-         id="use139"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="95.410156"
-         xlink:href="#DejaVuSans-48"
-         id="use141"
-         y="0"
-         width="100%"
-         height="100%" />
-    </g>
-  </g>
-  <g
-     id="line2d_9">
-    <path
-       clip-path="url(#p33d8bb518f)"
-       d="M 49.807187,261.98413 H 359.95763"
-       style="fill:none;stroke:#ffffff;stroke-linecap:round"
-       id="path147" />
-  </g>
-  <g
-     id="text_10">
-    <!-- 5.1 -->
-    <g
-       style="fill:#262626"
-       transform="matrix(0.11,0,0,-0.11,22.81375,266.16327)"
-       id="g156">
-      <use
-         xlink:href="#DejaVuSans-53"
-         id="use150"
-         x="0"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="63.623047"
-         xlink:href="#DejaVuSans-46"
-         id="use152"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="95.410156"
-         xlink:href="#DejaVuSans-49"
-         id="use154"
-         y="0"
-         width="100%"
-         height="100%" />
-    </g>
-  </g>
-  <g
-     id="line2d_10">
-    <path
-       clip-path="url(#p33d8bb518f)"
-       d="M 49.807187,226.28778 H 359.95763"
-       style="fill:none;stroke:#ffffff;stroke-linecap:round"
-       id="path160" />
-  </g>
-  <g
-     id="text_11">
-    <!-- 5.2 -->
-    <g
-       style="fill:#262626"
-       transform="matrix(0.11,0,0,-0.11,22.81375,230.46692)"
-       id="g169">
-      <use
-         xlink:href="#DejaVuSans-53"
-         id="use163"
-         x="0"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="63.623047"
-         xlink:href="#DejaVuSans-46"
-         id="use165"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="95.410156"
-         xlink:href="#DejaVuSans-50"
-         id="use167"
-         y="0"
-         width="100%"
-         height="100%" />
-    </g>
-  </g>
-  <g
-     id="line2d_11">
-    <path
-       clip-path="url(#p33d8bb518f)"
-       d="M 49.807187,190.59142 H 359.95763"
-       style="fill:none;stroke:#ffffff;stroke-linecap:round"
-       id="path173" />
-  </g>
-  <g
-     id="text_12">
-    <!-- 5.3 -->
-    <g
-       style="fill:#262626"
-       transform="matrix(0.11,0,0,-0.11,22.81375,194.77056)"
-       id="g182">
-      <use
-         xlink:href="#DejaVuSans-53"
-         id="use176"
-         x="0"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="63.623047"
-         xlink:href="#DejaVuSans-46"
-         id="use178"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="95.410156"
-         xlink:href="#DejaVuSans-51"
-         id="use180"
-         y="0"
-         width="100%"
-         height="100%" />
-    </g>
-  </g>
-  <g
-     id="line2d_12">
-    <path
-       clip-path="url(#p33d8bb518f)"
-       d="M 49.807187,154.89507 H 359.95763"
-       style="fill:none;stroke:#ffffff;stroke-linecap:round"
-       id="path186" />
-  </g>
-  <g
-     id="text_13">
-    <!-- 5.4 -->
-    <g
-       style="fill:#262626"
-       transform="matrix(0.11,0,0,-0.11,22.81375,159.07421)"
-       id="g195">
-      <use
-         xlink:href="#DejaVuSans-53"
-         id="use189"
-         x="0"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="63.623047"
-         xlink:href="#DejaVuSans-46"
-         id="use191"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="95.410156"
-         xlink:href="#DejaVuSans-52"
-         id="use193"
-         y="0"
-         width="100%"
-         height="100%" />
-    </g>
-  </g>
-  <g
-     id="line2d_13">
-    <path
-       clip-path="url(#p33d8bb518f)"
-       d="M 49.807187,119.19872 H 359.95763"
-       style="fill:none;stroke:#ffffff;stroke-linecap:round"
-       id="path199" />
-  </g>
-  <g
-     id="text_14">
-    <!-- 5.5 -->
-    <g
-       style="fill:#262626"
-       transform="matrix(0.11,0,0,-0.11,22.81375,123.37786)"
-       id="g208">
-      <use
-         xlink:href="#DejaVuSans-53"
-         id="use202"
-         x="0"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="63.623047"
-         xlink:href="#DejaVuSans-46"
-         id="use204"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="95.410156"
-         xlink:href="#DejaVuSans-53"
-         id="use206"
-         y="0"
-         width="100%"
-         height="100%" />
-    </g>
-  </g>
-  <g
-     id="line2d_14">
-    <path
-       clip-path="url(#p33d8bb518f)"
-       d="M 49.807187,83.502363 H 359.95763"
-       style="fill:none;stroke:#ffffff;stroke-linecap:round"
-       id="path212" />
-  </g>
-  <g
-     id="text_15">
-    <!-- 5.6 -->
-    <g
-       style="fill:#262626"
-       transform="matrix(0.11,0,0,-0.11,22.81375,87.681504)"
-       id="g221">
-      <use
-         xlink:href="#DejaVuSans-53"
-         id="use215"
-         x="0"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="63.623047"
-         xlink:href="#DejaVuSans-46"
-         id="use217"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="95.410156"
-         xlink:href="#DejaVuSans-54"
-         id="use219"
-         y="0"
-         width="100%"
-         height="100%" />
-    </g>
-  </g>
-  <g
-     id="line2d_15">
-    <path
-       clip-path="url(#p33d8bb518f)"
-       d="M 49.807187,47.80601 H 359.95763"
-       style="fill:none;stroke:#ffffff;stroke-linecap:round"
-       id="path225" />
-  </g>
-  <g
-     id="text_16">
-    <!-- 5.7 -->
-    <g
-       style="fill:#262626"
-       transform="matrix(0.11,0,0,-0.11,22.81375,51.985151)"
-       id="g237">
-      <defs
-         id="defs229">
-        <path
-           d="m 8.203125,72.90625 h 46.875 V 68.703125 L 28.609375,0 H 18.3125 L 43.21875,64.59375 H 8.203125 Z"
-           id="DejaVuSans-55" />
-      </defs>
-      <use
-         xlink:href="#DejaVuSans-53"
-         id="use231"
-         x="0"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="63.623047"
-         xlink:href="#DejaVuSans-46"
-         id="use233"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="95.410156"
-         xlink:href="#DejaVuSans-55"
-         id="use235"
-         y="0"
-         width="100%"
-         height="100%" />
-    </g>
-  </g>
-  <g
-     style="fill:#262626"
-     transform="matrix(0,-0.12,-0.12,0,16.318125,187.5575)"
-     id="g257">
-    <defs
-       id="defs245">
-      <path
-         d="M 2.984375,54.6875 H 12.5 L 29.59375,8.796875 46.6875,54.6875 h 9.515625 L 35.6875,0 H 23.484375 Z"
-         id="DejaVuSans-118" />
-      <path
-         d="m 34.28125,27.484375 q -10.890625,0 -15.09375,-2.484375 -4.203125,-2.484375 -4.203125,-8.5 0,-4.78125 3.15625,-7.59375 3.15625,-2.796875 8.5625,-2.796875 7.484375,0 12,5.296875 4.515625,5.296875 4.515625,14.078125 v 2 z m 17.921875,3.71875 V 0 H 43.21875 v 8.296875 q -3.078125,-4.96875 -7.671875,-7.34375 -4.59375,-2.375 -11.234375,-2.375 -8.390625,0 -13.359375,4.71875 Q 6,8.015625 6,15.921875 q 0,9.21875 6.171875,13.90625 6.1875,4.6875 18.4375,4.6875 H 43.21875 v 0.890625 q 0,6.203125 -4.078125,9.59375 -4.078125,3.390625 -11.453125,3.390625 -4.6875,0 -9.140625,-1.125 -4.4375,-1.125 -8.53125,-3.375 v 8.3125 q 4.921875,1.90625 9.5625,2.84375 Q 24.21875,56 28.609375,56 q 11.875,0 17.734375,-6.15625 5.859375,-6.140625 5.859375,-18.640625 z"
-         id="DejaVuSans-97" />
-      <path
-         d="M 9.421875,75.984375 H 18.40625 V 0 H 9.421875 Z"
-         id="DejaVuSans-108" />
-      <path
-         d="M 8.5,21.578125 V 54.6875 h 8.984375 V 21.921875 q 0,-7.765625 3.015625,-11.65625 3.03125,-3.875 9.09375,-3.875 7.265625,0 11.484375,4.640625 4.234375,4.640625 4.234375,12.65625 v 31 h 8.984375 V 0 H 45.3125 V 8.40625 Q 42.046875,3.421875 37.71875,1 33.40625,-1.421875 27.6875,-1.421875 18.265625,-1.421875 13.375,4.4375 8.5,10.296875 8.5,21.578125 Z M 31.109375,56 Z"
-         id="DejaVuSans-117" />
-    </defs>
-    <use
-       xlink:href="#DejaVuSans-118"
-       id="use247"
-       x="0"
-       y="0"
-       width="100%"
-       height="100%" />
-    <use
-       x="59.179688"
-       xlink:href="#DejaVuSans-97"
-       id="use249"
-       y="0"
-       width="100%"
-       height="100%" />
-    <use
-       x="120.45898"
-       xlink:href="#DejaVuSans-108"
-       id="use251"
-       y="0"
-       width="100%"
-       height="100%" />
-    <use
-       x="148.24219"
-       xlink:href="#DejaVuSans-117"
-       id="use253"
-       y="0"
-       width="100%"
-       height="100%" />
-    <use
-       x="211.62109"
-       xlink:href="#DejaVuSans-101"
-       id="use255"
-       y="0"
-       width="100%"
-       height="100%" />
-  </g>
-  <path
-     clip-path="url(#p33d8bb518f)"
-     d="m 63.904935,35.849943 4.622212,70.484717 4.622212,37.27556 4.622213,26.9259 4.622212,23.17967 4.622212,18.83923 4.622213,16.11683 4.622212,11.36533 4.622209,10.39647 4.62222,8.80378 4.62221,6.86845 4.62221,5.26436 4.62221,6.56208 4.62221,4.53499 4.62222,4.67883 4.62221,3.60716 4.62221,3.17789 4.62221,1.52171 4.62222,3.18912 4.62221,3.13227 4.62221,0.48119 4.62221,1.01039 4.62221,2.27439 4.62222,-10e-4 4.62221,-0.57005 4.62221,1.51712 4.62221,-2.51338 4.62222,-2.42009 4.62221,-3.04018 4.62221,-1.37329 4.62221,-4.74231 4.62221,-0.75711 4.62222,-4.57806 4.62221,-2.99287 4.62221,-1.57669 4.62221,-8.81212 4.62222,-1.62571 4.62221,-4.43644 4.62221,-4.00819 4.62221,-5.73858 4.62222,-1.44272 4.62221,-11.37622 4.62221,0.10247 4.62221,-3.82623 4.62222,-9.55323 4.62221,-1.48358 4.62221,-7.98489 4.62221,-5.8979 4.62221,-3.39507 4.62222,-5.52445 4.62221,-6.30284 4.62221,-4.8744 4.62221,-5.53381 4.62221,-9.06932 4.62222,-8.08735 4.62221,0.70945 4.62221,-10.70694 4.62221,-5.5864 4.62222,-5.58556 4.62221,-2.93618 4.62221,-3.76342 4.62221,-8.89808"
-     style="fill:none;stroke:#4c72b0;stroke-width:1.5;stroke-linecap:round"
-     id="path261" />
-  <path
-     d="M 49.807187,320.01812 V 22.318125"
-     style="fill:none;stroke:#ffffff;stroke-width:1.25;stroke-linecap:square;stroke-linejoin:miter"
-     id="path268" />
-  <path
-     d="M 49.807187,320.01812 H 359.95763"
-     style="fill:none;stroke:#ffffff;stroke-width:1.25;stroke-linecap:square;stroke-linejoin:miter"
-     id="path271" />
-  <use
-     x="322.02148"
-     xlink:href="#DejaVuSans-32"
-     id="use296"
-     y="0"
-     width="100%"
-     height="100%"
-     transform="matrix(0.12,0,0,-0.12,154.21428,16.318125)"
-     style="fill:#262626" />
-  <use
-     x="437.59766"
-     xlink:href="#DejaVuSans-32"
-     id="use300"
-     y="0"
-     width="100%"
-     height="100%"
-     transform="matrix(0.12,0,0,-0.12,154.21428,16.318125)"
-     style="fill:#262626" />
-  <path
-     d="M 406.58333,320.01812 H 716.73377 V 22.318125 H 406.58333 Z"
-     style="fill:#eaeaf2"
-     id="path322" />
-  <g
-     id="line2d_21">
-    <path
-       clip-path="url(#pc9aebdb348)"
-       d="M 416.05985,320.01812 V 22.318125"
-       style="fill:none;stroke:#ffffff;stroke-linecap:round"
-       id="path325" />
-  </g>
-  <g
-     id="text_19">
-    <!-- 0 -->
-    <g
-       style="fill:#262626"
-       transform="matrix(0.11,0,0,-0.11,412.56048,337.87641)"
-       id="g330">
-      <use
-         xlink:href="#DejaVuSans-48"
-         id="use328"
-         x="0"
-         y="0"
-         width="100%"
-         height="100%" />
-    </g>
-  </g>
-  <g
-     id="line2d_22">
-    <path
-       clip-path="url(#pc9aebdb348)"
-       d="M 463.05218,320.01812 V 22.318125"
-       style="fill:none;stroke:#ffffff;stroke-linecap:round"
-       id="path334" />
-  </g>
-  <g
-     id="text_20">
-    <!-- 10 -->
-    <g
-       style="fill:#262626"
-       transform="matrix(0.11,0,0,-0.11,456.05343,337.87641)"
-       id="g341">
-      <use
-         xlink:href="#DejaVuSans-49"
-         id="use337"
-         x="0"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="63.623047"
-         xlink:href="#DejaVuSans-48"
-         id="use339"
-         y="0"
-         width="100%"
-         height="100%" />
-    </g>
-  </g>
-  <g
-     id="line2d_23">
-    <path
-       clip-path="url(#pc9aebdb348)"
-       d="M 510.04451,320.01812 V 22.318125"
-       style="fill:none;stroke:#ffffff;stroke-linecap:round"
-       id="path345" />
-  </g>
-  <g
-     id="text_21">
-    <!-- 20 -->
-    <g
-       style="fill:#262626"
-       transform="matrix(0.11,0,0,-0.11,503.04575,337.87641)"
-       id="g352">
-      <use
-         xlink:href="#DejaVuSans-50"
-         id="use348"
-         x="0"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="63.623047"
-         xlink:href="#DejaVuSans-48"
-         id="use350"
-         y="0"
-         width="100%"
-         height="100%" />
-    </g>
-  </g>
-  <g
-     id="line2d_24">
-    <path
-       clip-path="url(#pc9aebdb348)"
-       d="M 557.03683,320.01812 V 22.318125"
-       style="fill:none;stroke:#ffffff;stroke-linecap:round"
-       id="path356" />
-  </g>
-  <g
-     id="text_22">
-    <!-- 30 -->
-    <g
-       style="fill:#262626"
-       transform="matrix(0.11,0,0,-0.11,550.03808,337.87641)"
-       id="g363">
-      <use
-         xlink:href="#DejaVuSans-51"
-         id="use359"
-         x="0"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="63.623047"
-         xlink:href="#DejaVuSans-48"
-         id="use361"
-         y="0"
-         width="100%"
-         height="100%" />
-    </g>
-  </g>
-  <g
-     id="line2d_25">
-    <path
-       clip-path="url(#pc9aebdb348)"
-       d="M 604.02916,320.01812 V 22.318125"
-       style="fill:none;stroke:#ffffff;stroke-linecap:round"
-       id="path367" />
-  </g>
-  <g
-     id="text_23">
-    <!-- 40 -->
-    <g
-       style="fill:#262626"
-       transform="matrix(0.11,0,0,-0.11,597.03041,337.87641)"
-       id="g374">
-      <use
-         xlink:href="#DejaVuSans-52"
-         id="use370"
-         x="0"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="63.623047"
-         xlink:href="#DejaVuSans-48"
-         id="use372"
-         y="0"
-         width="100%"
-         height="100%" />
-    </g>
-  </g>
-  <g
-     id="line2d_26">
-    <path
-       clip-path="url(#pc9aebdb348)"
-       d="M 651.02148,320.01812 V 22.318125"
-       style="fill:none;stroke:#ffffff;stroke-linecap:round"
-       id="path378" />
-  </g>
-  <g
-     id="text_24">
-    <!-- 50 -->
-    <g
-       style="fill:#262626"
-       transform="matrix(0.11,0,0,-0.11,644.02273,337.87641)"
-       id="g385">
-      <use
-         xlink:href="#DejaVuSans-53"
-         id="use381"
-         x="0"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="63.623047"
-         xlink:href="#DejaVuSans-48"
-         id="use383"
-         y="0"
-         width="100%"
-         height="100%" />
-    </g>
-  </g>
-  <g
-     id="line2d_27">
-    <path
-       clip-path="url(#pc9aebdb348)"
-       d="M 698.01381,320.01812 V 22.318125"
-       style="fill:none;stroke:#ffffff;stroke-linecap:round"
-       id="path389" />
-  </g>
-  <g
-     id="text_25">
-    <!-- 60 -->
-    <g
-       style="fill:#262626"
-       transform="matrix(0.11,0,0,-0.11,691.01506,337.87641)"
-       id="g396">
-      <use
-         xlink:href="#DejaVuSans-54"
-         id="use392"
-         x="0"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="63.623047"
-         xlink:href="#DejaVuSans-48"
-         id="use394"
-         y="0"
-         width="100%"
-         height="100%" />
-    </g>
-  </g>
-  <g
-     style="fill:#262626"
-     transform="matrix(0.12,0,0,-0.12,543.3848,353.28219)"
-     id="g410">
-    <use
-       xlink:href="#DejaVuSans-101"
-       id="use400"
-       x="0"
-       y="0"
-       width="100%"
-       height="100%" />
-    <use
-       x="61.523438"
-       xlink:href="#DejaVuSans-112"
-       id="use402"
-       y="0"
-       width="100%"
-       height="100%" />
-    <use
-       x="125"
-       xlink:href="#DejaVuSans-111"
-       id="use404"
-       y="0"
-       width="100%"
-       height="100%" />
-    <use
-       x="186.18164"
-       xlink:href="#DejaVuSans-99"
-       id="use406"
-       y="0"
-       width="100%"
-       height="100%" />
-    <use
-       x="241.16211"
-       xlink:href="#DejaVuSans-104"
-       id="use408"
-       y="0"
-       width="100%"
-       height="100%" />
-  </g>
-  <g
-     id="line2d_28">
-    <path
-       clip-path="url(#pc9aebdb348)"
-       d="M 406.58333,305.94571 H 716.73377"
-       style="fill:none;stroke:#ffffff;stroke-linecap:round"
-       id="path414" />
-  </g>
-  <g
-     id="text_27">
-    <!-- 16 -->
-    <g
-       style="fill:#262626"
-       transform="matrix(0.11,0,0,-0.11,383.08583,310.12485)"
-       id="g421">
-      <use
-         xlink:href="#DejaVuSans-49"
-         id="use417"
-         x="0"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="63.623047"
-         xlink:href="#DejaVuSans-54"
-         id="use419"
-         y="0"
-         width="100%"
-         height="100%" />
-    </g>
-  </g>
-  <g
-     id="line2d_29">
-    <path
-       clip-path="url(#pc9aebdb348)"
-       d="M 406.58333,256.40552 H 716.73377"
-       style="fill:none;stroke:#ffffff;stroke-linecap:round"
-       id="path425" />
-  </g>
-  <g
-     id="text_28">
-    <!-- 17 -->
-    <g
-       style="fill:#262626"
-       transform="matrix(0.11,0,0,-0.11,383.08583,260.58466)"
-       id="g432">
-      <use
-         xlink:href="#DejaVuSans-49"
-         id="use428"
-         x="0"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="63.623047"
-         xlink:href="#DejaVuSans-55"
-         id="use430"
-         y="0"
-         width="100%"
-         height="100%" />
-    </g>
-  </g>
-  <g
-     id="line2d_30">
-    <path
-       clip-path="url(#pc9aebdb348)"
-       d="M 406.58333,206.86533 H 716.73377"
-       style="fill:none;stroke:#ffffff;stroke-linecap:round"
-       id="path436" />
-  </g>
-  <g
-     id="text_29">
-    <!-- 18 -->
-    <g
-       style="fill:#262626"
-       transform="matrix(0.11,0,0,-0.11,383.08583,211.04447)"
-       id="g446">
-      <defs
-         id="defs440">
-        <path
-           d="m 31.78125,34.625 q -7.03125,0 -11.0625,-3.765625 -4.015625,-3.765625 -4.015625,-10.34375 0,-6.59375 4.015625,-10.359375 4.03125,-3.765625 11.0625,-3.765625 7.03125,0 11.078125,3.78125 4.0625,3.796875 4.0625,10.34375 0,6.578125 -4.03125,10.34375 Q 38.875,34.625 31.78125,34.625 Z m -9.859375,4.1875 Q 15.578125,40.375 12.03125,44.71875 8.5,49.078125 8.5,55.328125 q 0,8.734375 6.21875,13.8125 6.234375,5.078125 17.0625,5.078125 10.890625,0 17.09375,-5.078125 6.203125,-5.078125 6.203125,-13.8125 0,-6.25 -3.546875,-10.609375 Q 48,40.375 41.703125,38.8125 q 7.125,-1.65625 11.09375,-6.5 3.984375,-4.828125 3.984375,-11.796875 0,-10.609375 -6.46875,-16.28125 -6.46875,-5.65625 -18.53125,-5.65625 -12.046875,0 -18.53125,5.65625 -6.46875,5.671875 -6.46875,16.28125 0,6.96875 4,11.796875 4.015625,4.84375 11.140625,6.5 z M 18.3125,54.390625 q 0,-5.65625 3.53125,-8.828125 3.546875,-3.171875 9.9375,-3.171875 6.359375,0 9.9375,3.171875 3.59375,3.171875 3.59375,8.828125 0,5.671875 -3.59375,8.84375 -3.578125,3.171875 -9.9375,3.171875 -6.390625,0 -9.9375,-3.171875 Q 18.3125,60.0625 18.3125,54.390625 Z"
-           id="DejaVuSans-56" />
-      </defs>
-      <use
-         xlink:href="#DejaVuSans-49"
-         id="use442"
-         x="0"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="63.623047"
-         xlink:href="#DejaVuSans-56"
-         id="use444"
-         y="0"
-         width="100%"
-         height="100%" />
-    </g>
-  </g>
-  <g
-     id="line2d_31">
-    <path
-       clip-path="url(#pc9aebdb348)"
-       d="M 406.58333,157.32514 H 716.73377"
-       style="fill:none;stroke:#ffffff;stroke-linecap:round"
-       id="path450" />
-  </g>
-  <g
-     id="text_30">
-    <!-- 19 -->
-    <g
-       style="fill:#262626"
-       transform="matrix(0.11,0,0,-0.11,383.08583,161.50428)"
-       id="g460">
-      <defs
-         id="defs454">
-        <path
-           d="M 10.984375,1.515625 V 10.5 Q 14.703125,8.734375 18.5,7.8125 q 3.8125,-0.921875 7.484375,-0.921875 9.765625,0 14.90625,6.5625 5.15625,6.5625 5.890625,19.953125 -2.828125,-4.203125 -7.1875,-6.453125 -4.34375,-2.25 -9.609375,-2.25 -10.9375,0 -17.3125,6.609375 -6.375,6.625 -6.375,18.109375 0,11.21875 6.640625,18 6.640625,6.796875 17.671875,6.796875 12.65625,0 19.3125,-9.703125 6.671875,-9.6875 6.671875,-28.140625 0,-17.234375 -8.1875,-27.515625 -8.171875,-10.28125 -21.984375,-10.28125 -3.71875,0 -7.53125,0.734375 -3.796875,0.734375 -7.90625,2.203125 z m 19.625,30.90625 q 6.640625,0 10.515625,4.53125 3.890625,4.546875 3.890625,12.46875 0,7.859375 -3.890625,12.421875 -3.875,4.5625 -10.515625,4.5625 -6.640625,0 -10.515625,-4.5625 -3.875,-4.5625 -3.875,-12.421875 0,-7.921875 3.875,-12.46875 3.875,-4.53125 10.515625,-4.53125 z"
-           id="DejaVuSans-57" />
-      </defs>
-      <use
-         xlink:href="#DejaVuSans-49"
-         id="use456"
-         x="0"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="63.623047"
-         xlink:href="#DejaVuSans-57"
-         id="use458"
-         y="0"
-         width="100%"
-         height="100%" />
-    </g>
-  </g>
-  <g
-     id="line2d_32">
-    <path
-       clip-path="url(#pc9aebdb348)"
-       d="M 406.58333,107.78495 H 716.73377"
-       style="fill:none;stroke:#ffffff;stroke-linecap:round"
-       id="path464" />
-  </g>
-  <g
-     id="text_31">
-    <!-- 20 -->
-    <g
-       style="fill:#262626"
-       transform="matrix(0.11,0,0,-0.11,383.08583,111.96409)"
-       id="g471">
-      <use
-         xlink:href="#DejaVuSans-50"
-         id="use467"
-         x="0"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="63.623047"
-         xlink:href="#DejaVuSans-48"
-         id="use469"
-         y="0"
-         width="100%"
-         height="100%" />
-    </g>
-  </g>
-  <g
-     id="line2d_33">
-    <path
-       clip-path="url(#pc9aebdb348)"
-       d="M 406.58333,58.244763 H 716.73377"
-       style="fill:none;stroke:#ffffff;stroke-linecap:round"
-       id="path475" />
-  </g>
-  <g
-     id="text_32">
-    <!-- 21 -->
-    <g
-       style="fill:#262626"
-       transform="matrix(0.11,0,0,-0.11,383.08583,62.423903)"
-       id="g482">
-      <use
-         xlink:href="#DejaVuSans-50"
-         id="use478"
-         x="0"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="63.623047"
-         xlink:href="#DejaVuSans-49"
-         id="use480"
-         y="0"
-         width="100%"
-         height="100%" />
-    </g>
-  </g>
-  <use
-     style="fill:#dd8452;fill-opacity:0.2;stroke:#dd8452;stroke-opacity:0.2"
-     x="0"
-     xlink:href="#m04b1e6744d"
-     y="362.97781"
-     id="use490"
-     width="100%"
-     height="100%" />
-  <path
-     clip-path="url(#pc9aebdb348)"
-     d="m 466.9032,298.24152 46.22212,-112.55207 46.22212,-69.94726 46.22212,-45.493385 46.22312,4.861021 46.22212,-31.726114"
-     style="fill:none;stroke:#dd8452;stroke-width:1.5;stroke-linecap:round"
-     id="path495" />
-  <path
-     d="M 406.58333,320.01812 V 22.318125"
-     style="fill:none;stroke:#ffffff;stroke-width:1.25;stroke-linecap:square;stroke-linejoin:miter"
-     id="path498" />
-  <path
-     d="M 406.58333,320.01812 H 716.73377"
-     style="fill:none;stroke:#ffffff;stroke-width:1.25;stroke-linecap:square;stroke-linejoin:miter"
-     id="path501" />
-  <use
-     x="322.02148"
-     xlink:href="#DejaVuSans-32"
-     id="use520"
-     y="0"
-     width="100%"
-     height="100%"
-     transform="matrix(0.12,0,0,-0.12,480.36511,16.318125)"
-     style="fill:#262626" />
-  <use
-     x="437.59766"
-     xlink:href="#DejaVuSans-32"
-     id="use524"
-     y="0"
-     width="100%"
-     height="100%"
-     transform="matrix(0.12,0,0,-0.12,480.36511,16.318125)"
-     style="fill:#262626" />
-  <path
-     d="M 763.35946,320.01812 H 1073.5099 V 22.318125 H 763.35946 Z"
-     style="fill:#eaeaf2"
-     id="path564" />
-  <g
-     id="line2d_35">
-    <path
-       clip-path="url(#p802a2bf9b6)"
-       d="M 772.83599,320.01812 V 22.318125"
-       style="fill:none;stroke:#ffffff;stroke-linecap:round"
-       id="path567" />
-  </g>
-  <g
-     id="text_34">
-    <!-- 0 -->
-    <g
-       style="fill:#262626"
-       transform="matrix(0.11,0,0,-0.11,769.33662,337.87641)"
-       id="g572">
-      <use
-         xlink:href="#DejaVuSans-48"
-         id="use570"
-         x="0"
-         y="0"
-         width="100%"
-         height="100%" />
-    </g>
-  </g>
-  <g
-     id="line2d_36">
-    <path
-       clip-path="url(#p802a2bf9b6)"
-       d="M 819.82832,320.01812 V 22.318125"
-       style="fill:none;stroke:#ffffff;stroke-linecap:round"
-       id="path576" />
-  </g>
-  <g
-     id="text_35">
-    <!-- 10 -->
-    <g
-       style="fill:#262626"
-       transform="matrix(0.11,0,0,-0.11,812.82957,337.87641)"
-       id="g583">
-      <use
-         xlink:href="#DejaVuSans-49"
-         id="use579"
-         x="0"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="63.623047"
-         xlink:href="#DejaVuSans-48"
-         id="use581"
-         y="0"
-         width="100%"
-         height="100%" />
-    </g>
-  </g>
-  <g
-     id="line2d_37">
-    <path
-       clip-path="url(#p802a2bf9b6)"
-       d="M 866.82064,320.01812 V 22.318125"
-       style="fill:none;stroke:#ffffff;stroke-linecap:round"
-       id="path587" />
-  </g>
-  <g
-     id="text_36">
-    <!-- 20 -->
-    <g
-       style="fill:#262626"
-       transform="matrix(0.11,0,0,-0.11,859.82189,337.87641)"
-       id="g594">
-      <use
-         xlink:href="#DejaVuSans-50"
-         id="use590"
-         x="0"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="63.623047"
-         xlink:href="#DejaVuSans-48"
-         id="use592"
-         y="0"
-         width="100%"
-         height="100%" />
-    </g>
-  </g>
-  <g
-     id="line2d_38">
-    <path
-       clip-path="url(#p802a2bf9b6)"
-       d="M 913.81297,320.01812 V 22.318125"
-       style="fill:none;stroke:#ffffff;stroke-linecap:round"
-       id="path598" />
-  </g>
-  <g
-     id="text_37">
-    <!-- 30 -->
-    <g
-       style="fill:#262626"
-       transform="matrix(0.11,0,0,-0.11,906.81422,337.87641)"
-       id="g605">
-      <use
-         xlink:href="#DejaVuSans-51"
-         id="use601"
-         x="0"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="63.623047"
-         xlink:href="#DejaVuSans-48"
-         id="use603"
-         y="0"
-         width="100%"
-         height="100%" />
-    </g>
-  </g>
-  <g
-     id="line2d_39">
-    <path
-       clip-path="url(#p802a2bf9b6)"
-       d="M 960.8053,320.01812 V 22.318125"
-       style="fill:none;stroke:#ffffff;stroke-linecap:round"
-       id="path609" />
-  </g>
-  <g
-     id="text_38">
-    <!-- 40 -->
-    <g
-       style="fill:#262626"
-       transform="matrix(0.11,0,0,-0.11,953.80655,337.87641)"
-       id="g616">
-      <use
-         xlink:href="#DejaVuSans-52"
-         id="use612"
-         x="0"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="63.623047"
-         xlink:href="#DejaVuSans-48"
-         id="use614"
-         y="0"
-         width="100%"
-         height="100%" />
-    </g>
-  </g>
-  <g
-     id="line2d_40">
-    <path
-       clip-path="url(#p802a2bf9b6)"
-       d="M 1007.7976,320.01812 V 22.318125"
-       style="fill:none;stroke:#ffffff;stroke-linecap:round"
-       id="path620" />
-  </g>
-  <g
-     id="text_39">
-    <!-- 50 -->
-    <g
-       style="fill:#262626"
-       transform="matrix(0.11,0,0,-0.11,1000.7989,337.87641)"
-       id="g627">
-      <use
-         xlink:href="#DejaVuSans-53"
-         id="use623"
-         x="0"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="63.623047"
-         xlink:href="#DejaVuSans-48"
-         id="use625"
-         y="0"
-         width="100%"
-         height="100%" />
-    </g>
-  </g>
-  <g
-     id="line2d_41">
-    <path
-       clip-path="url(#p802a2bf9b6)"
-       d="M 1054.7899,320.01812 V 22.318125"
-       style="fill:none;stroke:#ffffff;stroke-linecap:round"
-       id="path631" />
-  </g>
-  <g
-     id="text_40">
-    <!-- 60 -->
-    <g
-       style="fill:#262626"
-       transform="matrix(0.11,0,0,-0.11,1047.7912,337.87641)"
-       id="g638">
-      <use
-         xlink:href="#DejaVuSans-54"
-         id="use634"
-         x="0"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="63.623047"
-         xlink:href="#DejaVuSans-48"
-         id="use636"
-         y="0"
-         width="100%"
-         height="100%" />
-    </g>
-  </g>
-  <g
-     style="fill:#262626"
-     transform="matrix(0.12,0,0,-0.12,900.16094,353.28219)"
-     id="g652">
-    <use
-       xlink:href="#DejaVuSans-101"
-       id="use642"
-       x="0"
-       y="0"
-       width="100%"
-       height="100%" />
-    <use
-       x="61.523438"
-       xlink:href="#DejaVuSans-112"
-       id="use644"
-       y="0"
-       width="100%"
-       height="100%" />
-    <use
-       x="125"
-       xlink:href="#DejaVuSans-111"
-       id="use646"
-       y="0"
-       width="100%"
-       height="100%" />
-    <use
-       x="186.18164"
-       xlink:href="#DejaVuSans-99"
-       id="use648"
-       y="0"
-       width="100%"
-       height="100%" />
-    <use
-       x="241.16211"
-       xlink:href="#DejaVuSans-104"
-       id="use650"
-       y="0"
-       width="100%"
-       height="100%" />
-  </g>
-  <g
-     id="line2d_42">
-    <path
-       clip-path="url(#p802a2bf9b6)"
-       d="M 763.35946,287.1221 H 1073.5099"
-       style="fill:none;stroke:#ffffff;stroke-linecap:round"
-       id="path656" />
-  </g>
-  <g
-     id="text_42">
-    <!-- 35.0 -->
-    <g
-       style="fill:#262626"
-       transform="matrix(0.11,0,0,-0.11,729.36728,291.30124)"
-       id="g667">
-      <use
-         xlink:href="#DejaVuSans-51"
-         id="use659"
-         x="0"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="63.623047"
-         xlink:href="#DejaVuSans-53"
-         id="use661"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="127.24609"
-         xlink:href="#DejaVuSans-46"
-         id="use663"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="159.0332"
-         xlink:href="#DejaVuSans-48"
-         id="use665"
-         y="0"
-         width="100%"
-         height="100%" />
-    </g>
-  </g>
-  <g
-     id="line2d_43">
-    <path
-       clip-path="url(#p802a2bf9b6)"
-       d="M 763.35946,249.05892 H 1073.5099"
-       style="fill:none;stroke:#ffffff;stroke-linecap:round"
-       id="path671" />
-  </g>
-  <g
-     id="text_43">
-    <!-- 37.5 -->
-    <g
-       style="fill:#262626"
-       transform="matrix(0.11,0,0,-0.11,729.36728,253.23807)"
-       id="g682">
-      <use
-         xlink:href="#DejaVuSans-51"
-         id="use674"
-         x="0"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="63.623047"
-         xlink:href="#DejaVuSans-55"
-         id="use676"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="127.24609"
-         xlink:href="#DejaVuSans-46"
-         id="use678"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="159.0332"
-         xlink:href="#DejaVuSans-53"
-         id="use680"
-         y="0"
-         width="100%"
-         height="100%" />
-    </g>
-  </g>
-  <g
-     id="line2d_44">
-    <path
-       clip-path="url(#p802a2bf9b6)"
-       d="M 763.35946,210.99575 H 1073.5099"
-       style="fill:none;stroke:#ffffff;stroke-linecap:round"
-       id="path686" />
-  </g>
-  <g
-     id="text_44">
-    <!-- 40.0 -->
-    <g
-       style="fill:#262626"
-       transform="matrix(0.11,0,0,-0.11,729.36728,215.17489)"
-       id="g697">
-      <use
-         xlink:href="#DejaVuSans-52"
-         id="use689"
-         x="0"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="63.623047"
-         xlink:href="#DejaVuSans-48"
-         id="use691"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="127.24609"
-         xlink:href="#DejaVuSans-46"
-         id="use693"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="159.0332"
-         xlink:href="#DejaVuSans-48"
-         id="use695"
-         y="0"
-         width="100%"
-         height="100%" />
-    </g>
-  </g>
-  <g
-     id="line2d_45">
-    <path
-       clip-path="url(#p802a2bf9b6)"
-       d="M 763.35946,172.93257 H 1073.5099"
-       style="fill:none;stroke:#ffffff;stroke-linecap:round"
-       id="path701" />
-  </g>
-  <g
-     id="text_45">
-    <!-- 42.5 -->
-    <g
-       style="fill:#262626"
-       transform="matrix(0.11,0,0,-0.11,729.36728,177.11171)"
-       id="g712">
-      <use
-         xlink:href="#DejaVuSans-52"
-         id="use704"
-         x="0"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="63.623047"
-         xlink:href="#DejaVuSans-50"
-         id="use706"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="127.24609"
-         xlink:href="#DejaVuSans-46"
-         id="use708"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="159.0332"
-         xlink:href="#DejaVuSans-53"
-         id="use710"
-         y="0"
-         width="100%"
-         height="100%" />
-    </g>
-  </g>
-  <g
-     id="line2d_46">
-    <path
-       clip-path="url(#p802a2bf9b6)"
-       d="M 763.35946,134.86939 H 1073.5099"
-       style="fill:none;stroke:#ffffff;stroke-linecap:round"
-       id="path716" />
-  </g>
-  <g
-     id="text_46">
-    <!-- 45.0 -->
-    <g
-       style="fill:#262626"
-       transform="matrix(0.11,0,0,-0.11,729.36728,139.04854)"
-       id="g727">
-      <use
-         xlink:href="#DejaVuSans-52"
-         id="use719"
-         x="0"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="63.623047"
-         xlink:href="#DejaVuSans-53"
-         id="use721"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="127.24609"
-         xlink:href="#DejaVuSans-46"
-         id="use723"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="159.0332"
-         xlink:href="#DejaVuSans-48"
-         id="use725"
-         y="0"
-         width="100%"
-         height="100%" />
-    </g>
-  </g>
-  <g
-     id="line2d_47">
-    <path
-       clip-path="url(#p802a2bf9b6)"
-       d="M 763.35946,96.806218 H 1073.5099"
-       style="fill:none;stroke:#ffffff;stroke-linecap:round"
-       id="path731" />
-  </g>
-  <g
-     id="text_47">
-    <!-- 47.5 -->
-    <g
-       style="fill:#262626"
-       transform="matrix(0.11,0,0,-0.11,729.36728,100.98536)"
-       id="g742">
-      <use
-         xlink:href="#DejaVuSans-52"
-         id="use734"
-         x="0"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="63.623047"
-         xlink:href="#DejaVuSans-55"
-         id="use736"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="127.24609"
-         xlink:href="#DejaVuSans-46"
-         id="use738"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="159.0332"
-         xlink:href="#DejaVuSans-53"
-         id="use740"
-         y="0"
-         width="100%"
-         height="100%" />
-    </g>
-  </g>
-  <g
-     id="line2d_48">
-    <path
-       clip-path="url(#p802a2bf9b6)"
-       d="M 763.35946,58.743042 H 1073.5099"
-       style="fill:none;stroke:#ffffff;stroke-linecap:round"
-       id="path746" />
-  </g>
-  <g
-     id="text_48">
-    <!-- 50.0 -->
-    <g
-       style="fill:#262626"
-       transform="matrix(0.11,0,0,-0.11,729.36728,62.922182)"
-       id="g757">
-      <use
-         xlink:href="#DejaVuSans-53"
-         id="use749"
-         x="0"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="63.623047"
-         xlink:href="#DejaVuSans-48"
-         id="use751"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="127.24609"
-         xlink:href="#DejaVuSans-46"
-         id="use753"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="159.0332"
-         xlink:href="#DejaVuSans-48"
-         id="use755"
-         y="0"
-         width="100%"
-         height="100%" />
-    </g>
-  </g>
-  <use
-     style="fill:#55a868;fill-opacity:0.2;stroke:#55a868;stroke-opacity:0.2"
-     x="0"
-     xlink:href="#m89ce144a55"
-     y="362.97781"
-     id="use765"
-     width="100%"
-     height="100%" />
-  <path
-     clip-path="url(#p802a2bf9b6)"
-     d="m 823.67934,39.29376 46.22212,139.28081 46.22212,70.10897 46.22212,23.95389 46.2231,14.91079 46.2221,17.98692"
-     style="fill:none;stroke:#55a868;stroke-width:1.5;stroke-linecap:round"
-     id="path770" />
-  <path
-     d="M 763.35946,320.01812 V 22.318125"
-     style="fill:none;stroke:#ffffff;stroke-width:1.25;stroke-linecap:square;stroke-linejoin:miter"
-     id="path773" />
-  <path
-     d="M 763.35946,320.01812 H 1073.5099"
-     style="fill:none;stroke:#ffffff;stroke-width:1.25;stroke-linecap:square;stroke-linejoin:miter"
-     id="path776" />
-  <use
-     x="322.02148"
-     xlink:href="#DejaVuSans-32"
-     id="use795"
-     y="0"
-     width="100%"
-     height="100%"
-     transform="matrix(0.12,0,0,-0.12,857.31531,16.318125)"
-     style="fill:#262626" />
-  <use
-     x="437.59766"
-     xlink:href="#DejaVuSans-32"
-     id="use799"
-     y="0"
-     width="100%"
-     height="100%"
-     transform="matrix(0.12,0,0,-0.12,857.31531,16.318125)"
-     style="fill:#262626" />
-  <path
-     d="M 1120.1356,320.01812 H 1430.286 V 22.318125 h -310.1504 z"
-     style="fill:#eaeaf2"
-     id="path827" />
-  <g
-     id="line2d_50">
-    <path
-       clip-path="url(#p3d5e40a9df)"
-       d="M 1129.6121,320.01812 V 22.318125"
-       style="fill:none;stroke:#ffffff;stroke-linecap:round"
-       id="path830" />
-  </g>
-  <g
-     id="text_50">
-    <!-- 0 -->
-    <g
-       style="fill:#262626"
-       transform="matrix(0.11,0,0,-0.11,1126.1128,337.87641)"
-       id="g835">
-      <use
-         xlink:href="#DejaVuSans-48"
-         id="use833"
-         x="0"
-         y="0"
-         width="100%"
-         height="100%" />
-    </g>
-  </g>
-  <g
-     id="line2d_51">
-    <path
-       clip-path="url(#p3d5e40a9df)"
-       d="M 1176.6045,320.01812 V 22.318125"
-       style="fill:none;stroke:#ffffff;stroke-linecap:round"
-       id="path839" />
-  </g>
-  <g
-     id="text_51">
-    <!-- 10 -->
-    <g
-       style="fill:#262626"
-       transform="matrix(0.11,0,0,-0.11,1169.6057,337.87641)"
-       id="g846">
-      <use
-         xlink:href="#DejaVuSans-49"
-         id="use842"
-         x="0"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="63.623047"
-         xlink:href="#DejaVuSans-48"
-         id="use844"
-         y="0"
-         width="100%"
-         height="100%" />
-    </g>
-  </g>
-  <g
-     id="line2d_52">
-    <path
-       clip-path="url(#p3d5e40a9df)"
-       d="M 1223.5968,320.01812 V 22.318125"
-       style="fill:none;stroke:#ffffff;stroke-linecap:round"
-       id="path850" />
-  </g>
-  <g
-     id="text_52">
-    <!-- 20 -->
-    <g
-       style="fill:#262626"
-       transform="matrix(0.11,0,0,-0.11,1216.598,337.87641)"
-       id="g857">
-      <use
-         xlink:href="#DejaVuSans-50"
-         id="use853"
-         x="0"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="63.623047"
-         xlink:href="#DejaVuSans-48"
-         id="use855"
-         y="0"
-         width="100%"
-         height="100%" />
-    </g>
-  </g>
-  <g
-     id="line2d_53">
-    <path
-       clip-path="url(#p3d5e40a9df)"
-       d="M 1270.5891,320.01812 V 22.318125"
-       style="fill:none;stroke:#ffffff;stroke-linecap:round"
-       id="path861" />
-  </g>
-  <g
-     id="text_53">
-    <!-- 30 -->
-    <g
-       style="fill:#262626"
-       transform="matrix(0.11,0,0,-0.11,1263.5904,337.87641)"
-       id="g868">
-      <use
-         xlink:href="#DejaVuSans-51"
-         id="use864"
-         x="0"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="63.623047"
-         xlink:href="#DejaVuSans-48"
-         id="use866"
-         y="0"
-         width="100%"
-         height="100%" />
-    </g>
-  </g>
-  <g
-     id="line2d_54">
-    <path
-       clip-path="url(#p3d5e40a9df)"
-       d="M 1317.5814,320.01812 V 22.318125"
-       style="fill:none;stroke:#ffffff;stroke-linecap:round"
-       id="path872" />
-  </g>
-  <g
-     id="text_54">
-    <!-- 40 -->
-    <g
-       style="fill:#262626"
-       transform="matrix(0.11,0,0,-0.11,1310.5827,337.87641)"
-       id="g879">
-      <use
-         xlink:href="#DejaVuSans-52"
-         id="use875"
-         x="0"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="63.623047"
-         xlink:href="#DejaVuSans-48"
-         id="use877"
-         y="0"
-         width="100%"
-         height="100%" />
-    </g>
-  </g>
-  <g
-     id="line2d_55">
-    <path
-       clip-path="url(#p3d5e40a9df)"
-       d="M 1364.5738,320.01812 V 22.318125"
-       style="fill:none;stroke:#ffffff;stroke-linecap:round"
-       id="path883" />
-  </g>
-  <g
-     id="text_55">
-    <!-- 50 -->
-    <g
-       style="fill:#262626"
-       transform="matrix(0.11,0,0,-0.11,1357.575,337.87641)"
-       id="g890">
-      <use
-         xlink:href="#DejaVuSans-53"
-         id="use886"
-         x="0"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="63.623047"
-         xlink:href="#DejaVuSans-48"
-         id="use888"
-         y="0"
-         width="100%"
-         height="100%" />
-    </g>
-  </g>
-  <g
-     id="line2d_56">
-    <path
-       clip-path="url(#p3d5e40a9df)"
-       d="M 1411.5661,320.01812 V 22.318125"
-       style="fill:none;stroke:#ffffff;stroke-linecap:round"
-       id="path894" />
-  </g>
-  <g
-     id="text_56">
-    <!-- 60 -->
-    <g
-       style="fill:#262626"
-       transform="matrix(0.11,0,0,-0.11,1404.5673,337.87641)"
-       id="g901">
-      <use
-         xlink:href="#DejaVuSans-54"
-         id="use897"
-         x="0"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="63.623047"
-         xlink:href="#DejaVuSans-48"
-         id="use899"
-         y="0"
-         width="100%"
-         height="100%" />
-    </g>
-  </g>
-  <g
-     style="fill:#262626"
-     transform="matrix(0.12,0,0,-0.12,1256.9371,353.28219)"
-     id="g915">
-    <use
-       xlink:href="#DejaVuSans-101"
-       id="use905"
-       x="0"
-       y="0"
-       width="100%"
-       height="100%" />
-    <use
-       x="61.523438"
-       xlink:href="#DejaVuSans-112"
-       id="use907"
-       y="0"
-       width="100%"
-       height="100%" />
-    <use
-       x="125"
-       xlink:href="#DejaVuSans-111"
-       id="use909"
-       y="0"
-       width="100%"
-       height="100%" />
-    <use
-       x="186.18164"
-       xlink:href="#DejaVuSans-99"
-       id="use911"
-       y="0"
-       width="100%"
-       height="100%" />
-    <use
-       x="241.16211"
-       xlink:href="#DejaVuSans-104"
-       id="use913"
-       y="0"
-       width="100%"
-       height="100%" />
-  </g>
-  <g
-     id="line2d_57">
-    <path
-       clip-path="url(#p3d5e40a9df)"
-       d="M 1120.1356,289.97586 H 1430.286"
-       style="fill:none;stroke:#ffffff;stroke-linecap:round"
-       id="path919" />
-  </g>
-  <g
-     id="text_58">
-    <!-- 10 -->
-    <g
-       style="fill:#262626"
-       transform="matrix(0.11,0,0,-0.11,1096.6381,294.155)"
-       id="g926">
-      <use
-         xlink:href="#DejaVuSans-49"
-         id="use922"
-         x="0"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="63.623047"
-         xlink:href="#DejaVuSans-48"
-         id="use924"
-         y="0"
-         width="100%"
-         height="100%" />
-    </g>
-  </g>
-  <g
-     id="line2d_58">
-    <path
-       clip-path="url(#p3d5e40a9df)"
-       d="M 1120.1356,254.57701 H 1430.286"
-       style="fill:none;stroke:#ffffff;stroke-linecap:round"
-       id="path930" />
-  </g>
-  <g
-     id="text_59">
-    <!-- 12 -->
-    <g
-       style="fill:#262626"
-       transform="matrix(0.11,0,0,-0.11,1096.6381,258.75615)"
-       id="g937">
-      <use
-         xlink:href="#DejaVuSans-49"
-         id="use933"
-         x="0"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="63.623047"
-         xlink:href="#DejaVuSans-50"
-         id="use935"
-         y="0"
-         width="100%"
-         height="100%" />
-    </g>
-  </g>
-  <g
-     id="line2d_59">
-    <path
-       clip-path="url(#p3d5e40a9df)"
-       d="M 1120.1356,219.17816 H 1430.286"
-       style="fill:none;stroke:#ffffff;stroke-linecap:round"
-       id="path941" />
-  </g>
-  <g
-     id="text_60">
-    <!-- 14 -->
-    <g
-       style="fill:#262626"
-       transform="matrix(0.11,0,0,-0.11,1096.6381,223.3573)"
-       id="g948">
-      <use
-         xlink:href="#DejaVuSans-49"
-         id="use944"
-         x="0"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="63.623047"
-         xlink:href="#DejaVuSans-52"
-         id="use946"
-         y="0"
-         width="100%"
-         height="100%" />
-    </g>
-  </g>
-  <g
-     id="line2d_60">
-    <path
-       clip-path="url(#p3d5e40a9df)"
-       d="M 1120.1356,183.77931 H 1430.286"
-       style="fill:none;stroke:#ffffff;stroke-linecap:round"
-       id="path952" />
-  </g>
-  <g
-     id="text_61">
-    <!-- 16 -->
-    <g
-       style="fill:#262626"
-       transform="matrix(0.11,0,0,-0.11,1096.6381,187.95845)"
-       id="g959">
-      <use
-         xlink:href="#DejaVuSans-49"
-         id="use955"
-         x="0"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="63.623047"
-         xlink:href="#DejaVuSans-54"
-         id="use957"
-         y="0"
-         width="100%"
-         height="100%" />
-    </g>
-  </g>
-  <g
-     id="line2d_61">
-    <path
-       clip-path="url(#p3d5e40a9df)"
-       d="M 1120.1356,148.38046 H 1430.286"
-       style="fill:none;stroke:#ffffff;stroke-linecap:round"
-       id="path963" />
-  </g>
-  <g
-     id="text_62">
-    <!-- 18 -->
-    <g
-       style="fill:#262626"
-       transform="matrix(0.11,0,0,-0.11,1096.6381,152.5596)"
-       id="g970">
-      <use
-         xlink:href="#DejaVuSans-49"
-         id="use966"
-         x="0"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="63.623047"
-         xlink:href="#DejaVuSans-56"
-         id="use968"
-         y="0"
-         width="100%"
-         height="100%" />
-    </g>
-  </g>
-  <g
-     id="line2d_62">
-    <path
-       clip-path="url(#p3d5e40a9df)"
-       d="M 1120.1356,112.98161 H 1430.286"
-       style="fill:none;stroke:#ffffff;stroke-linecap:round"
-       id="path974" />
-  </g>
-  <g
-     id="text_63">
-    <!-- 20 -->
-    <g
-       style="fill:#262626"
-       transform="matrix(0.11,0,0,-0.11,1096.6381,117.16075)"
-       id="g981">
-      <use
-         xlink:href="#DejaVuSans-50"
-         id="use977"
-         x="0"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="63.623047"
-         xlink:href="#DejaVuSans-48"
-         id="use979"
-         y="0"
-         width="100%"
-         height="100%" />
-    </g>
-  </g>
-  <g
-     id="line2d_63">
-    <path
-       clip-path="url(#p3d5e40a9df)"
-       d="M 1120.1356,77.582761 H 1430.286"
-       style="fill:none;stroke:#ffffff;stroke-linecap:round"
-       id="path985" />
-  </g>
-  <g
-     id="text_64">
-    <!-- 22 -->
-    <g
-       style="fill:#262626"
-       transform="matrix(0.11,0,0,-0.11,1096.6381,81.761901)"
-       id="g992">
-      <use
-         xlink:href="#DejaVuSans-50"
-         id="use988"
-         x="0"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="63.623047"
-         xlink:href="#DejaVuSans-50"
-         id="use990"
-         y="0"
-         width="100%"
-         height="100%" />
-    </g>
-  </g>
-  <g
-     id="line2d_64">
-    <path
-       clip-path="url(#p3d5e40a9df)"
-       d="M 1120.1356,42.183912 H 1430.286"
-       style="fill:none;stroke:#ffffff;stroke-linecap:round"
-       id="path996" />
-  </g>
-  <g
-     id="text_65">
-    <!-- 24 -->
-    <g
-       style="fill:#262626"
-       transform="matrix(0.11,0,0,-0.11,1096.6381,46.363052)"
-       id="g1003">
-      <use
-         xlink:href="#DejaVuSans-50"
-         id="use999"
-         x="0"
-         y="0"
-         width="100%"
-         height="100%" />
-      <use
-         x="63.623047"
-         xlink:href="#DejaVuSans-52"
-         id="use1001"
-         y="0"
-         width="100%"
-         height="100%" />
-    </g>
-  </g>
-  <use
-     style="fill:#c44e52;fill-opacity:0.2;stroke:#c44e52;stroke-opacity:0.2"
-     x="0"
-     xlink:href="#m867c8604a3"
-     y="362.97781"
-     id="use1011"
-     width="100%"
-     height="100%" />
-  <path
-     clip-path="url(#p3d5e40a9df)"
-     d="m 1180.4555,37.721093 46.2221,158.052587 46.2221,57.14264 46.2221,24.61652 46.2232,18.40299 46.2221,10.22851"
-     style="fill:none;stroke:#c44e52;stroke-width:1.5;stroke-linecap:round"
-     id="path1016" />
-  <path
-     d="M 1120.1356,320.01812 V 22.318125"
-     style="fill:none;stroke:#ffffff;stroke-width:1.25;stroke-linecap:square;stroke-linejoin:miter"
-     id="path1019" />
-  <path
-     d="M 1120.1356,320.01812 H 1430.286"
-     style="fill:none;stroke:#ffffff;stroke-width:1.25;stroke-linecap:square;stroke-linejoin:miter"
-     id="path1022" />
-  <use
-     x="322.02148"
-     xlink:href="#DejaVuSans-32"
-     id="use1037"
-     y="0"
-     width="100%"
-     height="100%"
-     transform="matrix(0.12,0,0,-0.12,1193.4805,16.318125)"
-     style="fill:#262626" />
-  <use
-     x="437.59766"
-     xlink:href="#DejaVuSans-32"
-     id="use1041"
-     y="0"
-     width="100%"
-     height="100%"
-     transform="matrix(0.12,0,0,-0.12,1193.4805,16.318125)"
-     style="fill:#262626" />
-  <defs
-     id="defs1094">
-    <clipPath
-       id="p33d8bb518f">
-      <rect
-         height="297.70001"
-         width="310.15045"
-         x="49.807186"
-         y="22.318125"
-         id="rect1082" />
-    </clipPath>
-    <clipPath
-       id="pc9aebdb348">
-      <rect
-         height="297.70001"
-         width="310.15045"
-         x="406.58331"
-         y="22.318125"
-         id="rect1085" />
-    </clipPath>
-    <clipPath
-       id="p802a2bf9b6">
-      <rect
-         height="297.70001"
-         width="310.15045"
-         x="763.35944"
-         y="22.318125"
-         id="rect1088" />
-    </clipPath>
-    <clipPath
-       id="p3d5e40a9df">
-      <rect
-         height="297.70001"
-         width="310.15045"
-         x="1120.1356"
-         y="22.318125"
-         id="rect1091" />
-    </clipPath>
-  </defs>
-  <text
-     xml:space="preserve"
-     style="font-size:12px;line-height:1.25;font-family:sans-serif;letter-spacing:0px;word-spacing:0px;stroke-width:0.75"
-     x="174.39751"
-     y="12.123849"
-     id="text5545"><tspan
-       sodipodi:role="line"
-       id="tspan5543"
-       style="stroke-width:0.75"
-       x="174.39751"
-       y="12.123849">NLL loss</tspan></text>
-  <text
-     xml:space="preserve"
-     style="font-size:12px;line-height:1.25;font-family:sans-serif;letter-spacing:0px;word-spacing:0px;stroke-width:0.75"
-     x="519.08502"
-     y="12.123849"
-     id="text5717"><tspan
-       sodipodi:role="line"
-       id="tspan5715"
-       style="stroke-width:0.75"
-       x="519.08502"
-       y="12.123849">Inception score</tspan></text>
-  <text
-     xml:space="preserve"
-     style="font-size:12px;line-height:1.25;font-family:sans-serif;letter-spacing:0px;word-spacing:0px;stroke-width:0.75"
-     x="914.9425"
-     y="12.123849"
-     id="text6933"><tspan
-       sodipodi:role="line"
-       id="tspan6931"
-       style="stroke-width:0.75"
-       x="914.9425"
-       y="12.123849">FID</tspan></text>
-  <text
-     xml:space="preserve"
-     style="font-size:12px;line-height:1.25;font-family:sans-serif;letter-spacing:0px;word-spacing:0px;stroke-width:0.75"
-     x="1257.3514"
-     y="12.123849"
-     id="text7819"><tspan
-       sodipodi:role="line"
-       id="tspan7817"
-       style="stroke-width:0.75"
-       x="1257.3514"
-       y="12.123849">SceneFID</tspan></text>
-</svg>
diff --git a/repositories/taming-transformers/assets/drin.jpg b/repositories/taming-transformers/assets/drin.jpg
deleted file mode 100644
index 77ca85c8a3a68102050064e89716436d125080ba..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/assets/drin.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/assets/faceshq.jpg b/repositories/taming-transformers/assets/faceshq.jpg
deleted file mode 100644
index 7a8177fb3f434323cf79041a372445d4bb3b9e45..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/assets/faceshq.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/assets/first_stage_mushrooms.png b/repositories/taming-transformers/assets/first_stage_mushrooms.png
deleted file mode 100644
index c3a521079c3bc996eb2226598bb4a67b141c04f8..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/assets/first_stage_mushrooms.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:425218621d5e01ea30c9e51fa0969ad36c22063a405dc6f6ccb6dd8db64000a0
-size 1353846
diff --git a/repositories/taming-transformers/assets/first_stage_squirrels.png b/repositories/taming-transformers/assets/first_stage_squirrels.png
deleted file mode 100644
index a1883fcee4aca3ee39d213587bcfa037caee3fd4..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/assets/first_stage_squirrels.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:b5f234ee1566d6c537339a7110a1a1df088d527812097c19ac61f01b335cd6ae
-size 1424657
diff --git a/repositories/taming-transformers/assets/imagenet.png b/repositories/taming-transformers/assets/imagenet.png
deleted file mode 100644
index fa831a2fd75344f96eefdba3e97de2d0dd56126f..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/assets/imagenet.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:2057d65399435ba17f265ad7ff421a9aabfb6051dec00bec5a37383dfccb2e54
-size 1029071
diff --git a/repositories/taming-transformers/assets/lake_in_the_mountains.png b/repositories/taming-transformers/assets/lake_in_the_mountains.png
deleted file mode 100644
index a66ea43c8f1146229eb18d8ebb9f67054438bc7c..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/assets/lake_in_the_mountains.png and /dev/null differ
diff --git a/repositories/taming-transformers/assets/mountain.jpeg b/repositories/taming-transformers/assets/mountain.jpeg
deleted file mode 100644
index 3df63a15199a91367067b587e0141c1c52ad778b..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/assets/mountain.jpeg and /dev/null differ
diff --git a/repositories/taming-transformers/assets/scene_images_samples.svg b/repositories/taming-transformers/assets/scene_images_samples.svg
deleted file mode 100644
index f646d3bdb60b3bb83f97edbed265b0ad8fb062ec..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/assets/scene_images_samples.svg
+++ /dev/null
@@ -1,453 +0,0 @@
-<?xml version="1.0" encoding="UTF-8" standalone="no"?>
-<svg
-   version="1.1"
-   viewBox="0 0 936.54071 273.42691"
-   fill="none"
-   stroke="none"
-   stroke-linecap="square"
-   stroke-miterlimit="10"
-   id="svg146"
-   sodipodi:docname="AI4CC.svg"
-   width="936.54071"
-   height="273.42691"
-   inkscape:version="1.1.1 (1:1.1+202109281949+c3084ef5ed)"
-   xmlns:inkscape="http://www.inkscape.org/namespaces/inkscape"
-   xmlns:sodipodi="http://sodipodi.sourceforge.net/DTD/sodipodi-0.dtd"
-   xmlns:xlink="http://www.w3.org/1999/xlink"
-   xmlns="http://www.w3.org/2000/svg"
-   xmlns:svg="http://www.w3.org/2000/svg">
-  <defs
-     id="defs150">
-    <rect
-       x="0.00045196852"
-       y="113.66694"
-       width="243.09966"
-       height="30.678291"
-       id="rect9909" />
-    <rect
-       x="0.00045196852"
-       y="113.66694"
-       width="243.09966"
-       height="30.678291"
-       id="rect11071" />
-  </defs>
-  <sodipodi:namedview
-     id="namedview148"
-     pagecolor="#ffffff"
-     bordercolor="#666666"
-     borderopacity="1.0"
-     inkscape:pageshadow="2"
-     inkscape:pageopacity="0.0"
-     inkscape:pagecheckerboard="0"
-     showgrid="false"
-     inkscape:zoom="0.99878833"
-     inkscape:cx="401.98708"
-     inkscape:cy="95.615855"
-     inkscape:window-width="1848"
-     inkscape:window-height="1016"
-     inkscape:window-x="72"
-     inkscape:window-y="27"
-     inkscape:window-maximized="1"
-     inkscape:current-layer="svg146" />
-  <clipPath
-     id="g108f8700c0f_0_0.0">
-    <path
-       d="M 0,0 H 960 V 540 H 0 Z"
-       clip-rule="nonzero"
-       id="path2" />
-  </clipPath>
-  <path
-     fill="#000000"
-     fill-opacity="0"
-     d="m 257.70697,15.719758 h 133.2615 V 144.57348 h -133.2615 z"
-     fill-rule="evenodd"
-     id="path18" />
-  <g
-     transform="matrix(0.26027638,0,0,0.25166745,257.70698,15.719758)"
-     id="g25">
-    <clipPath
-       id="g108f8700c0f_0_0.2">
-      <path
-         d="M 0,0 H 512 V 512 H 0 Z"
-         clip-rule="evenodd"
-         id="path20" />
-    </clipPath>
-    <image
-       clip-path="url(#g108f8700c0f_0_0.2)"
-       fill="#000000"
-       width="512"
-       height="512"
-       x="0"
-       y="0"
-       preserveAspectRatio="none"
-       xlink:href="data:image/jpeg;base64,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"
-       id="image23" />
-  </g>
-  <path
-     fill="#000000"
-     fill-opacity="0"
-     d="M 128.8528,144.5717 H 257.70627 V 273.42596 H 128.8528 Z"
-     fill-rule="evenodd"
-     id="path27" />
-  <g
-     transform="matrix(0.26027393,0,0,0.2516685,128.85279,144.5717)"
-     id="g34">
-    <clipPath
-       id="g108f8700c0f_0_0.3">
-      <path
-         d="M 0,0 H 495.06866 V 512 H 0 Z"
-         clip-rule="evenodd"
-         id="path29" />
-    </clipPath>
-    <image
-       clip-path="url(#g108f8700c0f_0_0.3)"
-       fill="#000000"
-       width="512"
-       height="512"
-       x="0"
-       y="0"
-       preserveAspectRatio="none"
-       xlink:href="data:image/jpeg;base64,/9j/4AAQSkZJRgABAgAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAARCAIAAgADASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD1bFFLjvSdKYB0PPSjrR2o/SgQuOOlKOaQentTgeM4oGKBj/Clxmm59aB9eKAHDjnrThSDqOKcB6UALjFHXNKM0f5xQAtKvSkx6daVelADgKdikBoOKADFKPfrTSefQUZ5zQBIKXtTAwo3+tAySg+351CZBTTN7dKAOE+LmofZ/CyW4P8Ax8TAH6AZr51uW7Z5r2z403ii106ESDcSzFB1xjrXh8z7n46VL3KexGPQZ/Gm8luBxTsZ5zipYY8lfWkJK5asrXzHG7OOwrstP09I1GBu49Ky9HtNw4XJHtXTwI8YwOo4JNYyZ20afUmhjCffzuz0FTAZGenPSkXI57GnYHXuKzOpIQkYwOtJ05Pf0ofleKjyV64xSHYk257+9PXn7x/KmJtzgdfeniPvnNA0PVfQYHbNA67hzg9KYQQfv8HjpT1AVevOaQ7Dwc0HHQn8qT1A60deBTGLg/8A66YT/EMZFBIYHJ59M008ADAx60EsUnOc9KcOo9DSKADgE5oIyRzmgB4xjjgehqNucnPIpTgZIPPcU0nIAB70CZmSoVnk9GHT1qBxlDHklSeM1bvT5c0cgGR6etR3ipHkD+9ke2On6EVcWctRakciloQSCDja2f51XjxJC8MpAYNtYe2Ov61OjlUw5LknI5+8MdPyzVWQgvkcsB2OeK0MbFXY8blD0HQ4xXpnhPVPtWmoJ8qyALjB5xXnFyGRo+6sN2a3fCGoGLVYsyFVfKkn+H8/akxNaHqasy4GAR7VOh7YI96hjIkIbeMHoB6VICGPC/L79aCCQMDjbn8aVTgknkZpi54ORx0pxJPWgB+QTgnFLye1MDduM+/JpRnnIJHtTEOGWx0Hrx0ozg5xkeuaQkrk5wehoAAXAJ/AUAOycZIozxkim7v/AK1L7d/QUALnHJ6d6M+lJ3x1o5HNADxwc9eKM5zTRn2+uKXPpzQBY7+wo2+hp+Mnv+NAXFbmIm3nNLgfjTsUUwE280bfT+dO/GjvQMTZQEGKdxRQIAMYpwFNpc96Bi46ZNLim8UueKQC8fjRmk7UmfyoAfk0hNNyKQk0AOJ9TxRu9KZnrTdx6UgJC3WmlyT1ppOaTpzQApbof0oLnaTTMnPShuAeRz6mmB4P8YLkSeJxGrZ8uMAjPTjpXmjDqe9db8QZPM8Z6kckjzDgmuTAz15qCmKqlyPStGwgDTgZzxVOGM546+lbGlwbpQx79PepkzWnG7Or062WCJQRjI6jvWl82MVTiOFG059+wqyjEDB/TisWehFEwI7jp70oHPX86YvbH51JnA5IpGgzr35prD8M04j/ACKCc8YqWMj6cAnPelUvjAOPcmnH6U0AZyD1GCKAJU5AB5qTJA68Co169efWlyB7/wCNIaFIxycnPWlL4Xk80gxjGNvOKaQfqaYDiQBnOR0NRSuwX2+tO2898elNbHOecnpSEyJZSTncQfQelTCQkd8DvULJzjo3Y1KmRjBGPSi4h4YnkjBJxxQTjIHpSAEgYHTvnpTM8kAcUAxl4heBT3ByKjmTeAXAKtxn3Ax/QVZKl8JjJPTmidDHbEqRnd+uBkfpVRZjNGOD94A42ngnt/n+tKYklQSxrtZeGB/PH0P9KixtcbCSynB9xU8ytCkhIKtkEAHj0I/rWqZyshkO+ERN8vP4inacUi1FFl37XwrFDgg54Pvj0PWq7MJIRIM5PBz2NSQSK80IJBO4HOOR9aGNHrWkSrNahfM3bSV3DoccdD05FagyMYI/KsbRN4tkMg5IwWHAJH69B178Vsqc4JP4g0kZMeMdCRmlB9evamgc5xmlXIwOnqaYhy/KccgZzz1NKrHDEk7vrTQWIzuxS9qBCjn+EAfSlycYz+NIWPGRzS4GPY0wAYI54FHGf8KTt05xSjOTxyPekAAdfTHHNKBkZ4I9hTT0xg89TmhRh/T0xQA8eppcDHB7Ugbml/HtzTAvcYpM0d+lJk5roMRfWlyabmk6dOtAD8igHFM98Uu4UwHcfjS560wtzikyMnFAD91Gab19qQtzxQA/dz0pQf5VEW5/lRv5FKwEpPT/ABpvU5z1pmfelJoGPyc+1IT703dQCDSAdnNIRk5/rRzR+tACY5HFH60vfp2oHWgBOo6VDMQkDuc4VSfyFT4OeaZJgod4+Ujn6UAfMPiu5/tPVp9TVeLiQsQP4eelc+AFY4NbevlItUuo4DmPzTjHsaxzExO4dO5rNGjRJHndgDg1vaSGIDHqOlYigoA2O9dBo4K/e/WokzaktToIHyMEYB64qwADxyarRL6E8e1WlOASOnesjtiPGVwAMjvzSk8dc1Hhiwyfl9qkC4I549aRYDknjmjJJIAP0pQuDnvUyD1/SkMhCscEdB1zS5DEcYqYrnkE4HqKZsIOSOKAAA4IyD705VGaPlPQc9qkXuB1FIBhXnqMGl2HPU07AwO+etP74BOKBkG0sSPQUgj4xipyijnvS4IUnHSkMqCPJJJ5FLtKj1FT7MgGhkyMUCK+0Zwckg0nTOT+FT7eOn41ERkn0+lAgQ4dT78CprohrdxwDnIJ7VHGNhBPG3oM0+Xa6vGFHK8nPJPrTRnJGE/+j3MdwiD5HBYEZzz0NNuMfMpbCA4DA8YzwakJba4dcEcMfbt/n6VW2mMkMVZWQkH1Oeh/CtkcktyMlod6sOc84PUVNYorXEckeMAjI7jn+VQoxVlVxuyMDPf0q3ZoiXaOoIDnbg89f602CPUdGEZ09MN8xIJyehwAf5VrD7oBAPFcx4VleUOGcMNuCcdx3rqtvTB+uO9SjOW4gyOec0vucUo3Dr+tAHqMUyRT9Dk9vWlbcFAI+Y+p4FNPy/d6+tKABnnB7d6YCgY7/lSnjn+dBIPU9+uKQA98mgQoyOMflSHI9x3FLllz056c9KTk/Ud8UAKM/h9KU7T2yPekwCO4oBPGBQA4HjjnHSlx8tKOe/4U3JPc8dKYF456Ck69aeRSbcDOK6TAYc0dKdikxntTATn1pOnFOI496Qg9hQA3PvQCQaXHFG3NAAaQnpzS45pMHr0oATrxmjNLj2o29zQAnQClz7CjHIpMelIYtAP4Udf/AK1LgHn+dIAz60ufT1puOfQUmTikMkBBFOzUXQ0c9e9AEhAP5d6q6i3ladcPuC7YmOT24qfd09ay/EE7Q6HduAOIznP0pPYpbnzVqa5ncnIJJOD35qioJcAjA9K070M78ndnncetZ5KmRjnp3rNGslqW4YUkkBY/QCtiwAQnbyO3FZFipeTOe1b1pHhgV5JqJG1NGpEpTvlqsqSq546c5psQwMFQccZziplh25PODjjNQdK0EUZIyoFSHB46d6kCcDBOAOARUarvypGDnAJBApMaY9AM+oxUoTaMg81AJAjqjfe96sK6kkKccdKRaY5cnGTg0/CMfX8Kap+XkD0pynDDH48UihSoHHQeuKMADgkmk3HJ3fnSq44Iz+NIdhQQRgEnIxyKQIqpt7A8ZNGcE4PH0o4Oe5oCw8Lk88DHamlcHg9e1ODDBPX8aQEEcevrQAzABOR+lAXkH36Yp/HIHWjnjjB70hDSo7cE9qhlUqOmRVno3r7U8R5T5uPbNAjObI+bt6VExz3wM8GrcibR65qq+ecDgCmDRn3A/fDnG4bSRVVSPM8tz8rZTP8AI/X5anvl2DJ+4fve3vWfMZI8sScq/X+v5c1rHY46mjsUyzozfM3J7+tbVpIt06JKdjuDgqcHPqPfNYrzGO4JZQY3XOceoqe2lDSKgbDA8EdRVMzTPSvCTutwQThymGUd2B5Pvxj867QDcByOehrkfCUfm2yTlssM/L6ZYnP05rrVJA3d/akkRJ6jsAcY6dgKTJA7H604N06ZHemkce/tTJAH/Z/M07HoCfwppPA9B1pct0A/KgQvQntnvRkkZXOAeSaMkKAB+FKu4KAo/wB6gAwSB79qQHA7ml60LnOe+OeaAEByMgflSjpmj5fXnPbpSYYZHXmgB2Vz7+1BOO31owVOcAHvk80hKgdcj1FAGtt68Ubam20bTXUYkGz3o2VMV70mM4xQBDs9aTyzVjYOtO20XCxW8vmjy/arG2kK07gVzHR5fbtVjApMUXAg8umlO2KsFRSEUXArlD9OKbtOas7O1JspAV9p70Fe1WPLGKQx0AQbSaTBqfy8UGPikMgx09KNvJ461MU7YpCvvikBERjkmquoWwvdPmt26SIVq6Rn3FV7qSOG0mkkbYioSzemBSexS3PmXVovs99NGRtIYrgnpg1ikENg9zW9rLrPeyOOdzE5rIbmQL71mjaS1NHTYjkYHBFdHbQfL14NZ+l2+EBAzmugtISHBK5Uis2zppx0JooSo/eAj0x3qTcoBJ49Klk2pFy3sAazbu4wgH8R6c1JqOubvyx97I7lT0qs2pMpwjBgffGKzZbsqfm/n0qjJeBGxk5A6etNENtHQLdxuR5jAY4G05//AFVajuO24N6EHrXFy6iP4Tz3BpsOryROCrEAds0chPt7PU7tbggkE5Pv3qUXXHNcva65G64k61cW+VxkfkTUtWN4VFJG6Jw2eRz2NKJd3AOAPSsMXWTxznvVmO63EL71LRqmayyjIA5PTnvTxKQoJOTWekz5ByuOvWnvKM5JwR+lIZdMgBPJB9KjNyDjtjg5rNe62c/e5qm98QSd2QOetOxDdjoFuhz1pklyBht34CucOsKFI3Dr3OMio28QRLwW78gGr5DF1UjpvtR6HduPpSx3TFuTgntmuS/4SEAhsjHQ881NDrwYgkbfqaOQSrROxJMijpu71UkUKD69ahtdRglRWUbdw7VK8yS7o1JLHvUuNjVSTRl6jlFU7cj3NZL3AbC9einP6f4VuXkWYQrHkDn0rEtykWoxO4BUSASJ6juaqLOequpHNbmS0MiniJgjAenUH8iKrQgNKhjbDFscfX/61X0WNLW7RmJP8JB68Y/nisrRi0uoooGQzDj+daS2OdHuHhNE/smFlJMhB3EgfNz2xXQ7uMn+VZ+hWwh02IAfIVyMcYrUxg4H86LGb3GcKx6/QmkBweOv0pzADnqKTPI6++KAGnG4kHr1xR8w5wfzpSTk+/SkwAc5/KkAoyRnOB7mjdyOenJz3pNvzYx7gUpzjBI+lADSQeQMUuQuONxI6UDOOmPxpRkg+uKBibsn7oNLwWU9h70DJUE8fSlVd3f86BCnCnGKXIA4xj0pAd+OO1A75H0NMDfxSYpc0me1dJkBAzSbQDS596UHNABijFH4Ue1ACYpDTqSgBnekJp2M0bfagQ00lO20bfyoAaBS4p23nmlxSGM28/pS7e9Ox60uKAG7e9Jtp+KQmgCJlAqMj86nNRP9OlIZEwxnivMPiP4kkWX+yLRiO87Dv7V6VdzpBbySyNtRFLFvQV4j4kJu7qW4RuXG71rOo7aHVhoczbZxvEsj4OcYqmUH2wCrAUp8gOM9TTYo/wDTB3BNQhzWp1unW5WBFHBIrZjIUdCBj04qppm3yY9xA47nGKs3Lr5RIbPpzWVzriV7u4XkDBIHGKxZmaZvvEe9T3cpUgDoetUJLjZyeR6ilcvYnFrEynfznuRStY2u3LLk+pNZdzfyBCFOAetZE+qsgxvLY9DVKLZlOpFbm3daRbspdflP1rDn09omwrhhVU6tOyMRu2g8kMTinR3zSqDuJ9jVqMkc7qQloAEkbHsR3q7b3rqfmPtxVcyhvvJmoweae4lpsdBb3DP05HpWlA7bT69q5/TmyxTcQa6W3tQQCB17E1nJHVSbZdty7Bd35YqSZggIPP1FX7eydkUnK8c80lxaBIm7Y71Fjexy97MY1Yjr65rCuLpuSfyBrV1WQIDlh6YrmpTubC9fetIo5asuiCScluen1pmWbkA5NSJCB8zZNW42CkAIAD6CrvY5uRspLFISOOvNShW6MCBWgkuBypx9KkMccwAQYPp1zS5ivYsjtpXhICyMARg4NalhfkPy7EZ4Pc1l+WUB3KRjtUkYbbhT3pPUqKaZ3qQfbLQSDaRjOQa5TULdY7liOT94EV1mg7m04xn5Se1c74hykhUEZ6Z+lRB6mtRXiZs8zRRg4GNp+vPFSeHIUXUoXfAjDhjnp16VCqGSVFc7lZSw57Zzir1msdvJGJF/c5+atDBJnutmUW2jEQAj2jAFWMdvbrmsjwzNHNosKxT+YEyu49foa1yowMDNMxaArxxznvSc5OSQR0ApTkKMdPWmn6dO4oEJjI5yB9KBtx057nFB6cA0vzdQBx60gGhQMnkntTv4e/T0oJA5xx3FBBOPQHOKAGqOntThuPSkIwcqM464oyGPyg8jnFAwBOOe1KOTgZxRgdByaVclhxjHUUCE+6xx0PSlK5Gacdp6jjsaQ8AcfiKANot/+ummTFRGQ9qYWru5Tn5icPmnh6qBwDmnb/fmjlDmLO/pSh/eq289KVXyeOtLlHcsg5pwBpi4x6U/NS0UG2lpM0UgFxRigUUAGBS4ozRnigAwKaxwKUtxUEj+9ADmcdqaX75qIsaTdRYVyTfnnJprNTc9MdKMk9qLDMDxhOsHhfUJG/557QPUmvDINQkMtvazHKlsA+1e2eOsHwndqV+8VGfTnrXz/cSbdSQ5wqsMHPvXPV1Z24d2iWLuAx3sqN0HHNEcKm5hZAAD2q9qah7lmGDu5zVCzVhdIOwyQPSs76Gko+8dIuVVQn3sdKjmkYLznAHSpMBgA3cU6WMsmAc8VDZ0JGLc3BK4DbvrWVPfJGGB610L6asgctEVVRy2awHsdlyGcBlHQmhEtMyp5p3XEkbLGenFRparPbPFghzyhHTPoa6YKr7kbqO9V/sMRfkAEc5HHpWkZ2M54bm1TOZtdH1G6u/s0NrLJK2cKo/M/StKWzhsI1tI2W6nzmaSPmNT/dU/xY7npnpnrXWB4IbUwxCNInGX+UEyf7x9PbpWZeyxKg2KvttGMUOpzaEQwrj1OXeR4SQ0Zxn1qIXQLHtWvLAJFJxkt+lZ7aW5JI5Ge9UmupE6clsXNPmzKpBx613thtdFZh27VwmmWMiygEfKa7GwkMaqpJzjoKym9dDpwyfU6aCY7GRCOR1PaodTlkS2HyIWz/COKjsVPmlt31Bq3qa7NOdmGQeAKlvQ7OVHl+qTmaZt3r0zWcvBPGfSrl5GXvHJNMjjDMGx0PWtVsebPcryTIgIkLAgZwKqPcAxmSKFyFOCzNxWpJZROS7Lu38Hnn8Kz20xw5RJV2E/xcVpFI55ufQiF1crDvwNh6AN/wDXq7ZalG5/eOUx3J6VYhga00ue2hhEt1Odry8FUTjhfcnOTVGPTb21Riqgbhg5XPHpTaiKLqLU6qPy7iENlXz0ZT1qWwtFaRw3XqDXIQTTWzkgbGJydp4P4dq6jQ9RS7kMbHEgHIPespRaOmnWUnaSszttIyoWLbk9jXP+JbcDU5FHpurc0h2W4VASCSMVmeKGDa6xxxs5yO9Zx3N6iujn7ZCt2FHJAwQa7ay020uNBkZY995ySOpHPAxXHacDJOZAMuWIH0q3Hq0ltrkVvbE5DbHbP3s1o3bUMLC8rHrnhSwOn6SgwB5wDkZ4GRW+CDyw4rP0kbdNgU9QgyKvj5+ByPSrOCp8bDYoLc/h/KohhRyOvSpcYBGePcUwgnr+XFIgblupJwfalyQhOOvenMxYgAkEDnNN5x/PFIBBj/ax26c0pxnoSPQmgZyB+tGDnnqOuaAA4ypByAKFJAIB+vFAPfbjNKcAYYg/TtQAAv2P5ijPORnnuT0pQTxkdfzpFXbx1HagB3XvRwMgDikAGf8A61OwQvpTAuGmnIBp3Tmjqa9E5BhyDS55oP5fWm0CHZOKeh5FRj0pc0mh3LavmpN1Ug+KkE3rUOJakWs0ue9VxKKXzR+FTyjuT0Z681D5lG8Z460WHcl3UFsGomcbM81Fv96LCuTs57GojzTN9BfHfNPlC4uKXApm7jrQG54osK48YoIFN3ep/CkLelKw7mN4tg8/wtqCKNzeXkfmK+eHthLII2GHJwOa+ldTKnSrwOQAYXz/AN8mvAFiCSGdl+YEleK56yszvwusWRahEyx2q55IAyD7Cn20RjuBuXn6VLON81rJyUZxkHsasOoW4Zz+NYdbHRJFpFGNoxn3qQKN2xRyfXoKig/eYwNp9R2q9EmeByPaoZrEGQCPA59Cax7uxLZZAGI7DvXSJCdpAIGe4HSkfTwoyq/Qf/XqS7KxxihY3y6g46g9qfi2IO4kE9CD0rcu9KMudpww6MR/nNYc+nXMRO6LK/3gad7jWhXkMAPyqSPdqrSIpGSoAqQwzN8vlP8Ah6VJFpV5O3EDAerVVyWVN4UYVV46HFWLTT5br5iCIweuK3bPwxjaZzvI6L2rVa2FsvyAfh2pOQlTb3MJ7RIUUIuCKfDHtkBxVuSPzHIJINSRwlSBjt1pXNowSLtlkOrYGCPWtnUoh9hjGd3mLzwazbOMgjI7VqTfPa4K429OaXQtnmesaW9reZKkxv0NZrQheCvFeoS2kN5AySoGUjn2rktU8PS2gaSHMkWc8dV+tXGXc5JU1c56J9owxz9af5iB93lx49xTjFjkjilEMZAJPeq5hci6ksd3j+CMZ/2ana7jcAvknPQDtVFo41PBJFSxx8g4yD+lO7Hojb0zRLDWoHW4j2vnClDgj3rD1nwpqGgTLdW7+ciHIdeCB7itzS7xbKTKOrHoUzW/LqUd4h3rww6elS5uISpQmvMzPC2pC/VZB97jcO4o8VKReIe7NtyR2xUvh3SEs9fke34gl5K9gaf4yiEd1bADgSZ6+1Tf3tBWajZmV4dhhGoxrM+xE5+tTahops/iEIApCyzrIo9M4P8APNY085glSVM8HBrt9OmTX9Z8O3QYG5VHWf32fdP4ittHE6KMeROXkenWsSrDGMdABU20qd2ce4pqjyxjFPOducVR4rd3cY+eeOaaATuwe9OOWwADjim49Dg0hAMY+bgjjOKT5Tyw59h1pwBwCV6U0Z8xgOPc0ANAYAdSe9KchM5yDRubac4x6ml2kAccexpAJjjn9BRweoJpT6YpVyR70AJgk/QfnRjnGQfalwGOf5CgrgcUwFUEZz0HakwRzmlAORk0pIxwOKALOcUbqQjNN5r0bHEPJz9KQikU4p+4HigYzn/9VLxk0Njik4zQAc/SjJxR2z29KTcD0NMA3d+lLvxTDj/9dJnmiwrkvmEdPwpRJ69ahz6GlGKVh3JzKWAXNM3YqPIGaBzRYLj9/wCApN2P8Kb+NJz+FFguO30u/pUZ4xSfjTsK5Lv6igyY6VCCeOKX+f1pWKuU9aJfRbxM4BiIz7V4jqLOZ1t1GxNvLdK9p12fyNHuDxyuwfjxXmWoadBdJvkHzAdRXFiH7yPSwS9xnP6cZGQwyLuUOCj+4qxcuUmYYGKv28KpAnAAjJAqhOD9pcH1rm6nZJaFuz5Iz0PWta2THLjPHBrMsyCoQ/Wt63CsAMA+tSyo7FmGLOMjk1aVAeMnHqajj456jHFTRknoAD2pWNEP+zo64PA9RUbWsQBCjI9xUhuT0IH0pxuZFX5Y1QHrTsOzKY0yBDkAAn2pxt40OG7dOKsea2AeB9QKrvcEZyc5NFhqJDLhBgeuAMVn3ODnjvyatT3KplSeapSOZSRkfSpsaJFXH7wcbs8cVP5K554P0pYlCg7shc4BFPMymTgYGKq1hO9y7ZRBmC98da1HtQtk7E5YDBx3qpYQB5FIJ+cYroJICkSAKdpGGyP0pJXFJ20OOjZlYYAxV9AkickZ9KqzQmGRwBypOaSKQ4yvU0WE0Zup+FoLkmS2zFIeqryp/DtXOSeEtS34jYH6Zru1vDEcsDnvmp4tYUSls9etVEzcWzkLDwFdz4N1dFP9lUrobTwBYwHexkmfuXORW8upiUDaQMVchvsqAAKohwZgT+FbUIfLhRSO6risS502S2JDKWUd69GiljIGcA1FdWMVyhB/OpaCOhxejxlLhT13ccdqzPGb/wCm2qMu35+fpiuqhsBa3hYfdzke1cf47l2ahbOcnJNJLUHrJGA9mbydLeFlZ2PygnGT6ZrsvhxYSLqjSuP9UjY9s4H9a4hZgs6PG+HDAg16/wCCGiuYrmeKPywNqn/aJGSf5fnW0TWs3CjJnWpTsjnkc004weoNIwCjKkcVR4ou32Of0phADYP1zT+QMbqacFSp5+tIBuT3OBRtPXd+lLuw2Dzkd6TdxgAAetAADuAORx60H5fmxx2YelIF9qkbKgrzzwBQBFyeT0J4Io47A/WnAEH8Kdlj7ikA1QzA5GQP0pWBVsYAPcijcwGN3FHJHXp1pgHA6k0fSj+tJk9CRQBY69ulJ7jr70p/WkxivSOECTRmk9PSgjtQMC3GKMk0hzmg0AG6jJ9aT3pMfnTELx0pPrS0UAJk/wD6qOvFL6dqNooATNC8cUuPU0ehoAdx07009KM0mfxpDEx+eaTOTjFKevWkHP0piDoOKQ8nk0uCR9aCM/jzQM5/xa7rpMZXO3zlz+RriZpQyMqnANeia7ZNf6PPDGMyDDpjuR/9bNecKuV459q8/Er3rnrYGScGjP8AtDL8m07Qc8VVlKyTs/8Ae6Ct17dDgqv3etcyZCb2VCD8rECuZHXPY0bb5WBJ7dhWxbS8jlcjt681jRZGOOKvpIdu0HbgZzSZUVobCTMWJyTnpU/nYAwR9DWQsx25znjt2oWYDqc5NBqkbK3G1RkEAc8Ux7xSCNxz6GstrzAK449S3Sqk+pqFIHX1zTNEjUlvn2njpWfcago6k57c1kXGqHGN2PxrMl1AkHB3YpMpzjE25NTC5LEDirWnyecd68g9ya5eOKSc7nzg9q6bSkFuiqTgVLITuae3bA2PrgVlw3I87npmt2SSI2sgA5HeuRD7ZmyfcUyl5naWF0IiCDx61tNrG+Ly2wF9+9cFb3wUhN+B61Ya7JJHmHcOnGR/OmrjcIvc6OcLcTOyY3AA49ay2XypDtB3dveqtpqDQzK5PHQ10EcMVyglIDKaZlJ8r1OPvtWAuPLYlXXqD2qv9vDHKHI7nNUPHtrHZ67E0GV86EM3PcHH+FYtleNu2yevWjle6JVVKVjt7TUg2OcemTW5b6gBhgx54yDXDwXBfaoPQ9+la0d66EqSuMYx2qkbaM7OC/EmDuyc+vStKO/QqU6fjXCQX4XG0jrWtb36hcjoKREqZvzfvHyME1w/jy2DSWshJ6nP5V11nciUkt07YrD8ZQrNaQNxw+OvtSRzPSaOIg0lp7+CCJw7SsMbuAPqa9x8K6d9g8PWqNtZpB5jMvQ56fpivJ/DnlyXd5H1ZdoQnsOc/wBK9f8ADsvmeHrM55RPLx6bSR/LFbRtuPH3VNLzNPauRkUjAY4GKXBx15pTyMe/WrPJITjoAaTH+RTnAyeaQkscZ4qRhhSDuxx75pm0KCVbPHIp+MDg/jQEAHvSAQEADkk+lACnqen1pTSEngBf/r0ANYBug/SjnGe2MUrHcMdDnvTVBDHPHHegB3y9en0pd2cYJwKQfTNLyT0pgIwyQV/SmkcVJkBhkY9KjIoAtFfagJ3qbaKXiu+5yWICv501hgVOTio2IPtRcLERpO4pX4b+VMyaoli/yo70deO1BoEJS5x/9aik/nTELnFBb86Q/rSfzoAXIxzSFsdaT3ppBxTAXcOvejcSaYcdKQdOtFguSEgj1FLg9qRRgj0qUMB7ipbKQgXjOc+1Hln1p5YZxS59SMUrjsNCYrhvFmgtZzHUrWP/AEZz+9VR/q2J6/Q13RYZ96U7HjZJFDq4KsrcgjuKyqQ51Zm1Ko6cro8ckvV2YY//AF6518NfzN0BOa7Txb4Yk0yRrm1jd7Jj1HJjPofb3rjkTfLnHzdM157g4uzPWVRTimi3G+0D1q7HKwHUD3qihxjPUU9JSCeR0qWaxehd3le+cc8GopLn5T1+pqItgAsDnFVZ5AqnLdfWkaJjbm/2IcP81ZE+oOxODk0l5cknDHms9iPvEc/WqSFKoSPcux9Wq3Z2rOwLZyTxUVpa7mDHjNbFrF86kZ+XpSIjrqy/awZkUgfKDyT61sJEsbB26nmq9u0QgkSRCN3Tnp7ise/mv7WI+XdiZlPAeMDj8DzU2b2NL2Ogv7kJaOsa/Njkn0rmPtQzlT071iXmvX8iMgKxsRyyjmstLu4UgEkj3NaRg+pm6yvZHYxXhVsdaspOz8AknqfeuVgvecsTmrLXwRSfm44NFhe2N2S62kc9K6zw7q6S2nlMNzZwD6V5XLeSzcKSoz69at2V5eWzbo7ho+egNPlF7S503jbbca4sakHyogpI9Sc4rkZoNjZAwPatdQ8uWJLOeSx7mq9wnBJHOfyo2KdinbzlTtc544PpV+G72qNzNnPbvWPLlWbnAp8U/AHfNARqNOx0ttd8jJxzxW1a3BJ+91HIrjre5+YZzgd629OnZ3JVhgHGD3pG/MzrLef5eMgZ5zUfiImTRzIf4GU5/T+tU458HaTz61t2kMeoPb2k43RyzRxuMdQWGaSV3YxqX3MHw/bLaxSOyYlkOeR2r0fwjLjTp4GIzHJuH0I/+t+tZvjDRLfSzYPajaGLIQPbH+NWfCIZZroHoEGR+NbW5XYms/aYfmOoBwTx9KRsc9QcUoIB4I96RmB5BwfT1pnlERABxg570gOD0pSTuOBx6mk4IyRSGLxzk4I4NJx0/HNGAR3NKD146UAN6nI70EY5pWP3SDximhjgggUgF6nr+BFNbnjIyO9Ayzc8D0oIw2QeOlACiQAAY5oJyCV5PtTd27j+HpT1woxngUAGOMZpM8dKduBJzjnt60hTHcY9M80wLhaml6jJ7/pSFq77HJcez5GCaYWz34ppOaYTTsJscTz19s03APFGaM9+9Mm4uaXqKbn8KM0AL6flSHrSZHSjPrxTEKfUduKSjNJzTAOP/wBdNbrilpGPGaBDGoH6UhPY/jQDzimA8HIx/OlB5wKjGDTi2OepqWikx4Y5zmn7sDnp7VGOnJp2wnGBSKE3kDOP1oQs7YUZ+lSJFhuTU4ZEXAGOe1Q5DUWNSAFSHwwIwVxxiuE8e6NYWcVpcWtpFBI7MHMa7d3A9OK70y54Fch4/fdp9pxx5jDP4VhW1idNDSaPMXPJHf2pCFzjFLOuCQBk1ErNnAHTrXCesi2rgY3cZGMmsy+bfkgfKBxV4SfLjPPpjrVK6AKHaD7mkWzDlYFsBfzot7cyvzk4P5U6VW3E8Z6VZtB5W7P1Oaoy6l9VRduz5cLzViFymCBwf0ql5xbJI4HU1IGxjIOKLGiuahu9ygHAx0JqC8OYfMU5bpxVXzG+5yB9anaZPsyg/wCs6YHajYprQ5S7UGQsTz7VUIGeK0btdjHcOp4NZ7cMTnitU7nDPRhGShJU81L5ru2GOQKhBK9+tOB+bj0osRdk8Z3N0wKuwcgA9MZ461Qi3M1alrGxbCrlsDg96GaQuX4ztVfmAJ681DcvtVjj6VbiTEeV+6CNynrVW6i37l5GMkkGoZ0LYxLhiDnGBUayZOAefWpblCnY/jVTnf2ppGDdmaULgkKVHPX3rc06cIxJOB6VzEMmCW7gce9benq0z7iDx09ql7nRCbaOot5wy+5PAroLB5WhaWEnzY8MpHqDxXG2kuZwM8fwk13fhy7jtIZTOqtvxtyOCKF8RrubbW2qeKIoYnlSCONt+6TPzH0rS0K1a1e6SQAMDsb6isp9TkRgY8ALjGDwBXRaVK11aPcsRiR9wC/QCt202c+Ico02uhaJBBA6EYpmCpII4xx7VI4IwF6daZ8wbB6UmeaGOBzn3FMb5sHAyOMGn4JP49xTWXG7OeP1pDG7iFx1PtRg/wB2lA2nOOD6dqRtnvmkAEE9PTJqNhnK45HUmnZyd2enemlQWzng0APGcHAwfrSYGz3pmwZ+9x9Kew+TYOooAQdBzg0c9+RTST3pRnsPzpgOUjdx+NGMd+KNxHpg9cUcYyaAJM00n3pCefSjNeicIE96b60poJNNAIT+dANH86SmIXPuPxo9qSg/WgBckn3o9f60go9xQID09KOO9Lg5P6UnU0AJ6U08fnTsHnmkI5/nQAw5x7U3ntSuQODxRkbecCmAE8e/pR2/pUM08UAUzypHuHBdgBWddeJtJtQTJexnb2T58/lmgaTZtoemelTPKqj5VwD3rz2b4naZFkRWlw7/AMIK7Rj1rLuPindbv3Nhbqvo8rEn+VZSkjWMWeqmTK8HjHemhhjGa8m/4WlqZORDbqpHH7s8D/vqq4+JOsLKuJVbvho1xjr6VmWew/M3IJ9xWH4utRL4ekkDDdAwf+lcnZfFQ5X7ZYgr/eiO1vyOc/pXU2muaZ4o024tba6UyyROPJddrjg4/pUzV1YqDtJM8tfluPyqHaVJ2noOakmYxylG4YHFROoK8dR0rgZ7EXdBjIyMY9ahn3MAAMDt709QeMDJ71JJ9zJwBUmhkPGPOBPTNK0i5CgED1pbojnGOemKpyOyjIGapGTdi/Gy5G44Aq15qhevX2rm5NQC8YJPbmmf2lKzcHA9M07MSq2OmPlhsGQe5pcoQQrbmz1Fc4t1K3OeO9P+1yg43cj0p8pXtGzVu7TfGc49qwbizkiIGc56Yq39ulIGTk/Wntel1O5Ru7HNNXRnJKRkENnkGpEBzjPWrLBevB4/rSKFGc59sVVzL2ZLaxEsARwa3oIjgEFSx7A9KwQzw5IIOOnFK2pyKCuzA9QaltmsWo7nQvII8AN+NVJ7lRw2Dn0rBk1In1x61GL5W5Y4I9aLClVXQ0rmMyjI6AHHOayygBFXLeUspbfwenPFQyrmUgULQzbvqOgQsQB0NdJo6bVc9QF/GsC0X5xnp9a6TTnXaxCgEAYz6Y5qJM6KJYWEfaBt5C+gxXX6Fol/rNtK9s8AjhIUrIxBJOTxgVzA4IOOD6V6h8PRjSrogYHmgc/T/wCvRTV5FVqjpx5olex8E3iy/wCnXsYj6lYcsT+JwB+tdhBbxWtukEShYo12qvtUucUhPfsK6LJHm1K06nxMhkPOAOtMJ6gnGOMYp5YMxK9aiY9CDnGfxNSyEBwAcZzimjJ6ngdqXlsfTmlAAIYZpFCkgcEmmHG4d6VskgDqaAccY5xQIY4P0B54pSpA6Z9aR8lj6U7kk80AMJAUYHLfpRnuOvelYfMPpimrkZz1zQAMobJC5OOeaN+V7/T0oLMeN2PSk2gBeTkd6AFA7Z/SlzxxzSYyad2Py0wHsnFRlSOo4xU5bGaYcE16FzjsRml2nGRTsfNjtUgxx+tO4WISMHpim4x25qVuvFNxzmncmw05pPY1KVHXmkKH/wDVRcLDOlHfNBBB56/Sm5piF4//AF0ucmm5IAFKOlAC9c/0pOTQQffNHO3/ABoA8n+I+t3UWvxWltPJGsESlgjkfOec/liuNk8Q6tkuuqXoz2Fy/wCvNT+Lb9NQ8T6lcwtvVpmCNnhlHAx+ArBcMVPIHNYSd2bx2LM9/c3b5nmeVx0Lndj86jM0jAAyvx23GoB9wc8k1IoCndnJPeoKLt3J5sisSRiNB1/2RVMu5OS5yfelmZnQeoAGR3FRhwCocZWgB73DqoAA/KmpcfMu8cDk4pZmCABMMgzioT0wEwT3oAtC6RjuY8+hqS1vXguFZZJFYH5WU4xis0qGUjpTRJJG2PlbjvSHc7a6YmQOTnPJJ71Ej7gSOPQUy3lW7sYWBy20cd896iMnlk4+7/KuSSPUpyurl4Bc574plxkwkdMHgDnNQiXdjkq3rS+ZlWVu/esmdCZmXAAHT61WMbycAnntV+4wTgMMjtUMQAmB5zTRjJalcaKJxtGAO59KqTeHJUY+RKd304rrbYguV6A9annt8kMo+YfrTTaH7JNHmrz3VlMYpAMqeQakhvTLIFIwScA9K6zVNEj1Ib2OyVBgOB+hrnZ/Dd5bv8gEg/2TzWys0cs6dSL01Q4Ag4PBp2B3NVpILgOWyYx2QDgfnSxC4ebyhJF/vPwKmzGpFraGHU0q/KeapSSXMblcRsQcZU1HvuZwdjKuOpzRZj57GjLKMckAe5qlLdRrnnJ9qiNrIw3SyK3tuqWK1VhhYiT7DNCSB8z2K3nFuAvfjNW7ax89QC25z94EdKvW+iytgsmwEcZHJrZh0+GzUAA5I6f4020hRpSe5iRWJtcqzcHoPSkcFcD+dad2p4d8ZZuw6Cq06IkW4cjOMg5qbjcbaCW6hSpIrZtJGHyjGfes0KUUFR1HVqu2wbzhn5SKzZvDQ1o2O0ZOSTXqPw8kxpVzGQeJdwJ78DP9K8stmLtk4wDmvXfBcKx+H0dQAXZmPv2/pTpfETiX7h0rNnAFQyEhtvQ07JzwOO+Kikzk55Pat2zz7Aeue4pMYwc5xQDngdR+tKrAZJxgdqkYcd2/SkPK4HpxkdKRfm5IpTkqfXNAAGJYntjigkjjODTQ2DleB6ml3Oew+ooARYy+QRyeaUrsB3Yxjgil3IEC557mkYgx4zyTxQAw8HI5+tIU3OAP4utOLEAfLn1oV8Md3DHkGgBCdmBjcaTPPQYPUZpzKNmQR7c0gUgAkdvWgBNoDEKeMcUpHHXml+VmAJ2sO9IN38ake46GmA4nH9KQseKQ5HPak5r0bHFcUsM04MM+tMPr+YppNFguSluopUXnjp2qJc5qdWGAP1pDQ8ICfWlJABB6UxmwvHpTCScZNIYjqGO4frR5QPf3penfpTsk4GKq5NhgRRxnmlKjr2p2COcUvQ//AFqVx2GkADgVznjDWrfStAugZlFzLGUiVW5yR14pnjzUm03w9vSWaN3mWMGJyjHqcZHI6V4vqWoSXZIN9dMmPuzS7/51EpWLULq5lSsM8de9QsSTyOKUkZ5kG3/d5pjbeQHU+nas2y7CltuAefcUK+eFx+IpmGweM/SkBPOAaQEm6TrtI4pC/IwO1NL+h/AUwyE5AHGOpoAlDHYSQD7Uze0jHH6dhU1pZTXZPljbGPvSN90fjWjDDpVgd87tetj7uNqA/wAz+lDY7GNvHIz+I7VWkfBIB6ntXSSa8sYMdraQQKDghYwf1IrMv4IL4tcWqrHKB+8iHRvdaQWNXQyZtHJB+ZCQKYZyHw3TvVjwlZy/2dLKxGG3YXPIxxVfVIVUFlzkHkGuea1OynL3CYXAOMHgVKbgAViw3I4UE++atfaCQcDI6Vm0dEKmhZkk3A8jmooCwc7v/wBdVjISx54Har9jCZSu1vmPTPelYd7mnbONoLge+RzWpAizZySO+FNZMSlck8YODk1bjmCsNp5HGQelFi1JovzWIfdsP1HWsmaC4hfLAsF6GtOO92x8EBc8nPNV5dSQ5BUKRwdw5pq6L511MqY+aMyxKSDzx1FRbYEXIt0Dem3tV2S4t2O7jPpUYaEnJyARwB2q7guVlNwkgMnkxgEkAbfWo10yEuAYl57Ba0EEIw3U571K94ikbUVSOp9aBOMSGLQ4Ac7AHPQAcVowWSgEgAY6gVWXUNw54A75qVLhSORk9vSpBWJGiWLGzJb3qJo1L5xyOSfWpgPlzu9+lIQTncMYGaljMrVIwYgQFAHSsNMhirr8u7gV0OoRthtx+6eg6VhwfPKAOcHJU0J6HPUXvGi8QdFCnGOmfwp6Eh8E7h1P1pjhWAXnk9PShcBhtzgdak0NewiLSIoP32AXPWvbtJt/sunW8ABVkjAOR3rzXwFpL3mom4cAxQjJzzz2r1PvxnJ61pTVtTlxErtRHM7KcKKQcgnjH1prEDAByaXAqznAqT3XPrTcLt2j8eKM84pwBHegAHA7fjQVOMevQUMM/QdDQo3EuTgnt6UANxgAUAkjbx75pXzgnqPUjFNUbjkA4xQADBHCceuaUHDZJHpRg5woz360MVX1yfWgBpOTnOfbFMCgZ55pwI6A8+1B2kHrnFADe49qUjJ6801PlTP86dnJ+7zQAm3ccnOOmfSpjkAc8AdM1GowRnp1ApzSbs/L/jTAd5eeD0pAnvTjIAP6VG7g8CvQOPQGx/So8YxjikJzilqiQA71ICev60wHHTpTtw6YoYD8etOTAOO9RFsCgNgcVI7loID3o4B9KrhyBgGlDnHrSsO5LuBpC2PSot3pzSEnGSaVh3PMvi1qa+ZYWCH51QzMPrwP5frXk00hJ2966/4j3puPF95h9ywhYVA7YHP65riHdiaye5rHYQuW6nmkL47ZNRkjk4/KkLEj0x3qRkgYr0OPcUCRl6N1qEE5JH3acG5H+cUCJPNyuMD/AHulXbC3inLS3DFYI+uOrH0FUY1LOF/iJq7dP5UKxRjaq/rSY0F9qks58pR5UKjCovAAqgGyxyTmoixIJJyaeORjNAXJd/QgfjihSY3DrnHt2qIAlx7dM1Mh5OccngUwOi8Oar9na4tjys6fL7ZIzS6uI9kexm3HIO4DGO39a5zzHtpUdM7lbNdDqMq3lpbXcZzHswRjofSsaiOmhK+hhKuyYh/u46jrVuJlkbC56cAd6rz8KGIxio4JSswKnH0qWropvlkXlj2zY2nnpXRaYiIq8jOCQRWWbuKSINjbJkbivX8KuafcBXXOce1ZM6ItM1poA6M20L7Zqk6sp+QYAHUVpqQ6k7l2+g61XkthISyNgA9KEzWxVEzsvTcB3HHWqtyjSAk5IHv3q2bchvlJGOvFPEMpHydB60yXFmA0ciE8HNNEkn3WYg5rYmtJ5CBt/EGoW025YEbMgc9qq5HKytFKhJ8wvn+HbTneJugIx3zThpNy44XGPQc1Yh0Z8/Me+eTS0GoyK0StINyKSg4zjOav28ToPnUge/arcGmMjAg/QDtVyO2XdgjIHWpbNIwfUhijbPzZ46CnvnkgjHfNTjCg457YqAlTuYHp2qWzSxl33ETY655JrPtosEuw2v61r3BGwgDAzms1mK8cY96DOSV7jG6EhicDg1Y063a4uESMFndgMe9VyC656Z/WvRfh5oIkeTUrmIbV+WHP971/L+dUlczlKyudv4f0pdJ0uO3KgSEBpMDvWmzgqQFGexo3Dv1pGPyk/pW3Q4W7u40bScgY9hSgksQQMjpimjJOM9KUEggYz6EUgHKDt54P86dj5SSDn2pxYgfLgelRBnkwTj6UCAkna3p2p56kDk96aeOhz+FIoO/IAx35oAUsGBXGD3JpNsePuAH2oK4PH5ml9sZoATGACGNNGXbc7cYwMDpSsrBjhuQOMUEbEA+9jmmA08Hj86aFBIJ5J7YqQMHJG1vpQCgBI4Gcc0WAYU2tkgk9/pQmRgtwD09qcWONvr3o24XGf0osIMgmlzmkGc9KeRx0pgRN60zPJp5GaaR3zXonFcbzRnuaMEUAdqYhRS5P4etJz9KKAHDp0/SkJ6cc0Gj+dILi59qXOOaaOPpSj2oAcCcmkYjaS3AHOTS/zrP12f7PoGoTZ27LaQgn12nFSykfO+t3pvdWvLknJmnd/wAyTWO+Ovep5TuJ5/GqzNzzXO2dCI88ED1pDnb1GPahjkcD8aaRj8aQCjqMU5Tg5x1pqBnbCgt9K0LbS2fmVyg7ADmgVh1pEFXzSMnnFOu4/wBwx5571IuAFjU8R5GcdaJz5kTLjnHFIoyOMZ6Ugzj2oXIPPPrSgc9aBEgAbv8ArTuF6Z6U0HjGMEc08YbOOD3NMB8h3R1d0C4MclzYvzBOnAP8LDkf4VnHJ7VJbOYZY5QeUbPv9KiSui6cuWSZrXMA8uRWYkLkAYrCwYpTj8a3rmTdGsygMj9Tisq5jLJ5qj8qyiduJjezQ+OfJDdwOK1ba48vbJKwCf3iOp9K51H2sCKuwXJDA5zjoabjc54Ts9Tr7W68wcZXacHIq2kmSAcgjrXN2V8ygKxJUHefetFdRDpk5P0FZONjvpzTRrB8Z+YHPenISBk7fp61m+eD1I+gNSRzhkJJIwM4pGyZprIMjip1kDHp09qzEnIAI4AOOtTpMG4zgj1osVoWxIqjBxuPrSswYALjjrVYygfxHA9+9J9pDDBbHuaB2RbDcnI6elNL7Qecj1qsZSqttOR7mo3lwgYtwR0pBsWZWwv3hiqkkhZSCOg6ioWnH3RkHHFV5ZyVJ3fL9eaLGbkMmkcjHGMZJFVHw2G5x69qe7ggE88U60t5r+4S3hQs7MFAHc0WMZSNDw3pU2saultGpZQcu2OFXvXudnaxWdpFbRDEca4FZfhXw4mgab5ZUefLgyH09q3CoIxW0Y21OKpU5mMLID0/Gk6nr+Qp6xjHyk04KcY9KdjO5GVGc9TSL1YsMHNSEnlRgk03YcZxz7mgBO/B69KOV96Ccn7v40nPOefakA5csemPWgrgAjp3xSLwOmAe5pzMFBGTn2pgNBOfu/SlOMZPGe2abhdvIyO5NKqZbJyAelAhB13Yxx3pPvdDUpRcHk/Q1EQwOPXvRYBpJAbbyf50qxgAHNOwEwAD9KeD0OOlADNp7DOKD7j9af3XapHPUUx1becdaYgHTlcClHI5OKVdyrycCl3Ajp+dMCI9ffvTSOvcU40hr0DjGEUU7HNGDTENx1FBHFOxilxz/WkAwj/GlGfwpSPypcfhRcY0D8qXpShR0pSPfikA0jPWuU+It0tr4JvjnBl2RD8Wz/IGut2ivOfjBcCPw/Z246zXO7j/AGVP/wAVUzehcdzxeRs9OKrknbzUzZUGn2tjJdPgAqndiK5zYqqrOQiAknsK1LXR2OHuGCj+4OtaMMNvZRnZ8z45Zu//ANaq8szsxHQdj60hpCmS3tR5cMYU+uOfzqLeX+bPy9c1BL8p+Y5J7UkcpC8YK5HGOlIY6PImlXb1ORUkJK70IyOvPUVXaQLeh1zyvIqcHbLuPQ9QaAKF5AI5SUztPNV+ox3rZkjSSDaTnB61kSIY3w1AmJk45BqReo6YPpUYOD9aUHsBTETcAHGMUwr1p4XI608BQGXqD/OgDS0VPtkVxbM2Ni71qCZDCSCMAdqZp921hdJMvQZDf7QPUVr3lguoWf2q1YOAPugHP0rnn7sj0qFqlLl6o5m5g2Euh+XuKiRwv0qwWZMqV4Hr2qCeA43ocj2rRM4pxaZZiuSV2nlTV+O9cIhRQpH8fXAHYCsFJPm9KsLKAd3UDp3pOI4TaOjguo1RcKVJ6A85PrVyO5RySOmB941yq3D8nJJxwTVuK7dAFPcZx6Vm4nTGudOJsuMspx6dql+04Urgc1zUl3KI/L3pGWbPHLGpBcSNFt3ZkOdx9RU2Zsqpvi8TnHXHFH2wgoWPQ8kVzouyqr9Pzqwl2N3Bxu7E0WL9qdAZ8EgOTxzgdage4U7VA5NZP24JGdud3SkkudwJUkADgUrCdQuS3DAqwIx3qsZy2cngGqpmJwSeTzTQSSQB37VVjJzuy6jFiMfxcD2r2jwB4L/s6zTVL1P9KmUNEhH3FI6/U1wfgnw99s1qzEy5RpAzKe6jk/yr6A61dOKephXk4+6UHidf4ajI9QR+FaWKaVBGPWtbHNczsgEjOM0E8cc+wq60Snt+NM8hduASMdKVh3Ku0fez+lLnPGDUzQtnIIPsaYY2UHINTYLjec9ajkTew24DHqfangDb7d6UAYFADCmwcZPsKNoJ6cmpG4+tIq7TkCgZEQdy/LxzUhXCjB596XaMg55oZeR25zQIYCCeT068UwDgnJ+vNSMi9eeKU9B6dhTAjxyMcA07aQSAOfamAfMTk/Slye7fkKQCj72M9ByRQ0ZYArgf1p0YGemDUvI4xVWAr4I6j60dvwp7gbiR+NN4x0osK5Hj2ppGeKd79/Sk6da7jkGkZNHfHanFcAHNJzigA9qPxo7Uo/8A10AIKAKXHFL396AEHWjHWilweDigBMHAzXmXxkU/2Tpbj7qzOD+Kj/CvQtS1Oy0m0a6vrhIIF4LOep9AOpP0rxzxv4vh8TvFbW8JWygYvGzj53OMZI7D2rOo9LGkNzh7a2DnfJnb2HrVx5iqBF4A7imMDtOTgDpUDv2XoTisWbIcxyMkk+xqCWZQMDqe9PYAqSTj+6PWoNncjJpANZmkOMZx3qYR7VGOnU05UVcEflSvkpjGPekBHJlZY3I4J2ipA4ZuQMVFPh4xjqpGKdyVB6CgCyFwpJboOar3EXmfeH7wenen72yVJ4I9O1PJVgcnk+nagZlEAHDdcd6Rc8DHTpVueAufcdCe9VRkdeh4oJHq5JyeKf1zzULdOM08N1A6jv60wHKCGw5+hq7Y389jLvhcqh4I6gj6VRLYxnrSAMgJGSnpUyjfcqE3B3RsXcEN6gntiA/8UfesrBjBByADyKfbybTvRyMenGKlZldsv1PesrOJ1Ooqmr3KFzZgnMbduhqqNycZNau0xnacsh+6e49qimt9wJTn2qkzCUOxWiuTE3IDcdD60pu3ZMZ/E01LVpnOwMcdQB0qJomByh3r7Cq0Iu0TpJ8wbOTVxJX2/L1rNjOKtxNyMnqKmSLhItI65Yv16fSl3gjjPXg1XyxBOeSOaAxwMDn0qbGnO9iyJMrwRweo+lOSVsHJDD3FV4wWIC5yetXIbF5CvYnpS0Rauxu4kgKOgwK09NtmkuEZweKWCy8sgtjitrTrfDE49+lRKR0U6d3dne+BU8vxDaDsEcn/AL5r1WvNfAwA13aQpK27N+qj+tekduDWtH4Tmxn8QdSHpSAkUZrY5QPSkI9KWkoAQgcUmM07qPajpxRYCJox3ApvlAjjgmp8ZNLiiwXKrQuCOcj+VARkUgjOferJHtzS4FFguU/KbaMLimlNrYIIGOKvY7UhUdxS5QuUGJzjGQOtNIyQccAVeKKeCtRm2XOQMUuUdyoAp+b8Kfjp698VObcY4Jphhcfxc0WC5Evy8AfjUikmkaJxzjmm7ivBz7cUWC4jAhiKaafww5NJ26UCIOc4FFB46jmgD867DnsB9KVIyeDxSqhyQcgCnu+BgH8aVwsRlNucnik6CgsT3pueaaExcjNGcd6bmjJ680wHZ6cGszXdfsvD+nPd3j+yRj70jeg/xpmva9Z+H9Pa6umyekcYPLt6D/GvC9d1y816/a6vJNxxhEH3UHoBUylYcY3HeIfEN94jvTPeSZAz5US/cjHoP8etZQxj/aNMHzEjHOcCh3wv6ZrBs3RHJIWJBNMc459ulDt3BBphJwSD9aQAG3dTTedxwelC59PxpQpwDwDSAkQ4wKe3zcDoB0pmcDjr6GlHPB7jrSGM2DDDoTxTEYFAPTipG656/So1CiYrnA6j3oES5JHIzilXqCOaYSVySefang5PPGP1oGSMflJzn6dqqzRZyVGBU69Tg/KadwV+nY0AZpj2t8w4PWm4IBwfxq80YPB6Z61C8YBIA4NAiunI5P0p8bnI3dqQowzgdP1oxzxQBKYQ3zxHB9qIZZIpAy5VuhHY+3vTUbbwDxUoKyLhhwfSk0NMkkeOcb412OPvIOh9x6fSo43DcA8+lIYNxyh5Hb1qF2kiO9lHv61DRopM1rOa3t4rkyqfOcKI2HVfU1koTb3jlRuUjJB70NOuC+TnsDUEYeScNk8jmkNs1G02O6USQ9SM7R3qp9jlTpk846dK0bWRmjRVyOeD3FbCeTdKRcJtlT7zJwTUc7RqqaZzIt3Ld+atQWRLk5+la50v5mMTqynke31pBaSocFPw9aXOaRpEVtaRHlguetaiRDGVAz71FBDtG7Bz245qVpduAOuahu5vGKQ5Yctgj39q2LVBtG3k1kwsZXIBKjHXNbdmm1QBjAFSbxO08DgDxBJgnP2U5/76WvRee1eY+DG2+KwM8fZ2Bx9R/n8a9OHFdVL4TzMX/EFPIozwBR1orU5hpyMY6UoPY9aXmgYPXjFABjArO1fV7fRUge6WTypX2bkXdt4zk1pDOevH0rD8Vab/AGnp9tCRJtW4QsydVXkE/rVRs3qIv6fqlnqcRe0uFk2nDDoV+oPIq9jpn/8AXXn2seFvs1u9zbS3F5vwshKhmzkfOWAyQMEYyetMtL7UB4HS3ZLqSNX8m4kGS6x8MxGceu2tfZp7MaR6GOpGaUD14+tedeG7zTdL1dp4J5/IlXymEkO18nBXhc56Yz710N/40s7K4ljW0ubkQuEleILtVj25YEn6CiVF3sgt2OkK8Uw469Kqz63p9paQXN5cLbpMoKCThuR6e1c/r9leeITbzackU1kg3LvcrvbPUDv+NTGnd66BFXep1Peg49eKyPDzTw2RtructIjlESRdrADsPUe9Sz6wY5gkdqzReZ5bSM4Xn2B5NDg72QmtTS5yaTGT0p2KDUWEN2etKFG4g80vQVEZYxcLFvHmspIXPJAxmiwyYohGCo/Gq72y87eBU4pWNKwGXtBAJPApXkAO0DPqaNo5P86btwa2MgLE/T0pn5U8rTSeetADMf5xSH9aceD1/KjBGc1SJG4x61n6xrFpommyXl2+EUfKo6u3ZRVjUb620yxlvLqQRwxrlif5D3rwvxR4kn8Rai0rkrbqT5MWfuj1+tEpWGo3K+v6/da/ftdXLYHREHRF9BWIxzkk4GaC2ScGmM5bAFYt3NUh4Jxzwe1Rycjgc96O3X6c9KZJn8B1xUjGDHZue1AGTg/mKaWGDjrQGJBJHagB3TJGSM9KeCOMZzTAMcDNOHHH6+tIBcnp70jEAHnPsaDz3xTcEsAOtABnGDxxSSN8ysPocUEE9sDvTWjJjI7nvSAdnIJH/wCupFPrjpUQbKDBz605cDOehphcmGRgEY9xQDn3APWkXJ4zwB60oxjFAxGIxk9M00r+FKR6jvSNyDzQA1huOeR7VFJFnOOw7VPggjvijAB+tICptIxkUqnJ4JwOtWCo5GOT0NNMWcYA6UCGhyoGD+FTeehUtKAQBkCoDGyk9xjpULAsrKAeeMmkyrldg0h3Ad+hqzb4LKCMds0KMxgE4bvSxREyDnJPas2zSK1N2zjTam7oOvPNa8dopYFSG44JrMso2kOFHIxz6it2NFeMAMUI71hI7YIclpxkjHcYoEDYJ3BveplDDruPGDilZwowFOe9QbJFF4flPp6+tQNDIw4BxV5kDAYzkcjioRvycdP5UyrEFudkhIXLdxXR2DBoMkgk1khQ4+UBW/i9609K+UFGB4pJlLQ6PwfP5fjIoRwYiv5/5FeqKRXlPhiFpfF7BMhlTf8AgMZr1GMnoOlddL4TzMV/EJ6KKK1OYM0daKWgApe1J0ozTELijaPSgUZoAx9V0b7VPaXVssfnWr7lVxgMD2JA/EVn+HvD11p9/d3V6kLPK+6Ns7mHXPb3rqQcUtWqkkrAcb4m0K8ur0XUMP2kFo8AkfIFOSuPQ8cj3rSdrqLMsWn3Lq43Y+0EFTjpt6flXQdaKftXawGTo8l3c6cv2+Bo50OGLDG7vkfnj8KqJpNyb2OSaGE7D/r2kLMRngKMAL710NIaSqNO6AZjA9qbu/OpDRipuIYOR7+tcddXV1/bC3izElZCqwkDG0HBAxnJxnn1NdoMVi60YNMgN3BZwvcFhtcxg4JPUnrWlN62Ki7Es+uW0EUUpSZo5RlWVeg9wSD+lLZa3a30/koJo3YEqJIyu7HXFZ7yC81GK1kiaPzo2mjkUgtGcYdRx7irSaMLe8iuluZZGQsSH5zkYxx0H4UOMUtR+6S9+OKQnnk0E80dT0pmA3+tIevJp2OaMZ4oAZ17Gorm4htLaS5nkWOKNSzux4UDvVg4Ck9ABzzXi3xF8aNql0+lWEv+gRH946n/AFzD/wBlB/Pr6UOVgsZfjTxlN4ivWjgZo9PjP7uP++f7xrlVkJB7D3pjckH0prcdDwecVkzRKw5pMkleoFCqW5PUUigcepNOHGcdM+tILgeMAnmonIZeSAaezDrzn3phxkqOvrQMiBB46e9OxgE9RTcYP0PNOGRn0NIBckA4GKcH4wBTOrEt6U+NcAkdD2oATtzThkcn86RuMfypA53dqBDwRjH8u9DZz9aaW2jGOP5Uws2N1AxduJOQdpGeKdnHfOaict17DkAUgZtuexoEThsAkHJ/nQWxznpwKjUEYzRgNyDQMeGJHNOB+nvTdgGMUEHHBAOOaAHN06njqaFPIyeh6+tNAxgY+tKec84oAXIPB6UEjvyaacg9OlMY4bOcn0oAkypznuOpphUYOMD3NOwCBx0pCDhSTz1ApDKzgK2M/e6E1dtiizRsxCqGGT6iq00W/B5yP0psB3MPY1nJGkJanXWpXOVAHHA9KvoCMZGSaxUuFD5X7oPOO1bFo+9ATk55ANczPQgy6j9skY4zUqrzjqPaoVB5IHNTRkjbj8c1JshCg2EDqDUDR5PHarJAJ64I6HNBXKnPB+tBRUSLLkkDNaFm22UZGFHaq4QBuxye1WIid/TpQhM3NDvUsfF9pI7Y8/8AdfXPA/WvVVkTrkfTNeFagxS5t7hQcocjJ7g5Feuafci50+K8ibMcqhs5+76j+ldVJ6WODFw1UjeDrjg0Bl7kZ+tYySGSTdk7V6Ad6upIN3U4I45rY5LF3cM9RRkdqiRtwyOvrmlXfkgMKQrEmRRkA0w7iVJY47804ntTAXeM0u7NRZ5xmnKccE9qBEm4YzS7hUeTmlB6c0wH5pQcdabk0UAOzntRntTc8cU0kZ64oEP/ABpKTGe4/A0AN6cUDFz3qC8tI76zkt5Bww6jsexqbBzR8w7U72AzDpxNxbzvInmQ9cJgEEYI/Hj8qus+B3NTld3JprQr7GhyvuFjIlljgTdI4A6de9ORhIAysCCMii9jVykTjyy5++rDPA9/y4pRiGHnOxBjJFaXJcR1IQOT+dRC5jYkBgSP5VznjnxONB0B/s0gF9OdkHqvq3Pp/M0XFYwviN4xFpBJothL+/cYndf4F/u/X1rxuQ5706WeUyNJKWcscs5OSSe5qMkMPlwfSobGkJyPcGm7gTjvmjB6CgcdunUVIxQQGOB+NG7PXjNB+bAVSF7jNMYc9OQKAEJ5z149abk49OaXHPI79aQ+uf8A69IBo+8cmnZA98ds0x+GHUDvSZOSQOaBkoIPGMDGadGcggcD0qIHAJ/SpEPHHGKAFDZ+uKOARx35pvuDzTh6dPegAxgHnmkweMnPFHqc8Z6UA4yfyBoATJA28fjUaDDFW6Cng9B69aRgRIDkfSgQoPAAPApVwvfjvTWxxgnNIMg4x+NMCYHHcUDjpzTEODnr70b8cDk45JoAexJPTr0NAG0YPemAnbgnOe1P34zk80ABOeM8d+aFPPp70wcilzwT2pAKCegAIpw5YAj8PSkHbAx75oznOPvfyoGOI2Arkjn86pzKbeYEdD1xVs5JBJpk0ZdflwSO9JoaZpaHGbo3GeQibip71p6RMZBuYkEEgDPGKztC1K306C6Myhi0R2gfxNngGsq1v5ImJRiBuLNk8H2rBwbudMaijY9EibIwTliOoqTBHOa5/Q7/AO0q7PkgY5z0zW+CcDcc56Vg1bQ7qc+ZDlP4Gl4Jz/8AqpDyc+nvThyAAKRqhCoXoMCnrkHNMwx79amRQp65Hr60AOnhE1uU6kjrjvWRp3iG80K5aDzHTJIBDHDfUdDWy24KcfhWPrum/abUzRjLLywH86uErMbScbM77QfGlvKY11Fth6CZV4z712MLxzgSRyCRWIw4PGK+cbO+ltX2M5ZTx1ruvCviK4sbhfLl3JjPlOeHH+NdSl3PPqUU9Ynsynj055p5IyeecVj6VrEGqRZhfbLn5oyORWg3ygBFYkHNUcjTTsyyoyOooU5J9RxTY1Kjls9+O9McNGAseASe9MkkHDZ7GgkHJ96jLHgAjJ6Yp2wht2RnGBntQA8HtTs+lRITjBzx1NSA5PtTEODcZzSg03FHOOBQAO5VT71GDwPWkkfL4HrjNJk5wADQBIHGMkUDHbiomUruJPBHQCkifJ24P1pDLOcAtuOcURy71560zdgYxTIBtkdSDjqDQgJg/Y07dntUWM5pRwOTTAoyxymPCsGkHG/ocelSWiSJCFkIdgTnnNLknAFId4/hIFFwEvr220+0kurt1jhiXczEdP8APpXzt4v8QS69rE15IzFCSsSH+FOw4rr/AIj+LI7u5bSLaQtBB/rSOjv/APW/nXlUkjM2fw4oQMcWBXJwT6mqzxkHcrH8Kk+ZTjHB60hOBkDp2NNkkYmBOGyOOtP9xTHAYnnH86jVjEcMflpAWM8YPFJwMYPNN3A8jkUvIHJ4+lACeueKQ/TgUEdwevagnjA4pANYYbIHFJnAB7Up5b5j2pSBkCgBOC3yj8KQ5Ckk8+x6UvPTj60DC9qBgh+UDtT+ig4OMU3uTzgClDE4yODQAu7OOOlIQN4xz680ZyB6UxjyAKBDzjrnp0FRyn5ckHOc0/GQMnHakYDawz7YoAaCSAT+YoLDnPboKSL7oHSlOASP1pgKD06Y70m49etIeR04+tGORgUgHq2Pal75xnio8ZY9c04EgZAAFADs4HrQCcdgO9NGcHnOe1P/AJjtmmAu7P19aCpwAo+tAbJ46GnA5XnOPSgYc54HHvUsY+QhuCP1pmcgYXpx1p54IxSaAo3C7DnHyn9KiXLcAj6Cr7jzAcgY96zT+5faeKTQzZsrwW+mTxrlZpHGST0Uf/XrodL1dJoYg5+Yj16VxZuA6f7VWbeYxtkcc5XHbNYyhc3hUaeh6NG276+tSAkdfwrE0W+NxEI5G2yYzyetbqgHAOMisGraHo05KSuKq5NSYwOvNGPTOQKcBkA4561JqgxkdMURAkknnsQe4pSTjk/hTQSrgge9Azk9e0trC7Msan7M5yD/AHT6VWt7wxgYzgHrXeXFql9asjjcpGMYrz3ULOTTb1kcfJng+tbQlfRnLUTjqj0fw/q51BIlEix6hH8qMThZx2VvRvQ9+h9a73SNd+0xiOfcrKcEMOVI4IOea8F0+8MEgIPy16Fput+fELk5a4iA80f894wOvu6jv1YDHJArZMxnFTVz1eGQfMS3y445qPe8kgHl4AI+bPbvXPabq8ZCgS77dgCrD09a6NMFTIJAUIGCOeParTOSUXFj3wHDE8jv/Sl3AtjIz6ZqBAHlwyFh6k1IYgoPlgjPXmgglz+VKCqj2qKPcEwSc96eGHQ0wHI4blTu+lOqAR+VIzIcg5z9f8KlSRZFDDvTERTZQFscGkBwcjtzVsLke1U7uSGCSJHkWNpDhA2fmPoKaQDnkDp3xntSFSgyg6dqjO5MOgLg9VANTgMWxtJGOuOlKwxQGJ4GAakJCsB2x1piBwSNrYHQ4/8Ar01kfcAFJ98Dj9aAJc4PtQcUzy5HY7lwO3zdak8o8ZIx6UCKxUeoz6Vxvj/xUug6YbS3lK6hcDC7Tyi929vQV02v6pbaJpNxf3L4SNeF7s3YCvm3W9ZudX1Oe9uZC8kh/IelAyndXHmk5P196pEkA4pX656+lM4P4+9NkgvI+bp9KGPGBxin9BjPAqF5AThelIBC3Tdx9KbuyTn9abk8cUmB1z2pAOUlGz1HepQd2CDxUIYYx78ZpoJjbGTtNAE5bJ460ZyCT+FCgse3tml2DjtQAAZwTSHOfanOxO1AenXFNx1yKAA8jqc+1IBgelHTp1zSkEg545oAB78inYXPOOOOtNBAxxS5G7jr60DBhwQaRB3Bxx2oJJXB70KNp/rQIdnjvxSHBJPGKRjkHuetIpKgUAMQbZGHYmnkEd+aZJlZFY9+OKc3TANMAHTBoPTGeaTJ784FLgjAPFIBuz5SMfWlGcf0NDNlenWl5HBH0NMBQcYGMYoz82evtTG6/wA6FoAlB59PrThkd85HIqAM2MEfpTi5A9KAJd2e34k1IACeMgY61X8wcDHWn7iAf6UDJmGFJByKrXVv5yZU/Ooz0qdXHAHB9DTlO0lc5z6UMDDXIPSrEdwFxu7VYvbUnMyLxj5sdqz8ZYe9SCNu1vn86J14cMAG/pXodmyzwxsT+PevNNPQRnzH+6nzc13Xh6582wjRvvDPNc1VHdhpW3N4ZzQcjuKMkDphu/NNByvqayO9DuOvUe9Cx5fk8Y4pUTO32FS4wDxkUiySMmNCDjj0NZOv6ct7blsDeOQcVpMSFwPypJDvQ5IGR3o2ZMo3VjzaMNFIY24IPFdPoV60TqTjcvrWNq8IjvCQCMnnFLYzEsBkAg11Rlc4lHldjurS7bTNQFtGWFrOPNthn7v95PwPb6V22ja6FxDM2IW6EfwmvOgp1LSWiHFzERJC2f4h2/HpWhpWofbbNXyVdfldT1DCtUiJJPRnrgVdgkUlwTkEHqKTzDv2sGHHGOa47R/EEtofJlYmInG09j6iustriK6XfC+SeSOhFOxySi0TFirnJHqB61IkiMOeKiCrvwBkjoaeCFB2jn3NBJOGB+6eDTlwoxjioEJ4OcVKG7UxEob0HFZ+pXVtFeWUM4bzHbMRB6Nnqavqc9K5zxZIkFzpczoX2y8ANjByOfeqjuNHSgc+9OAxSDlQR3GaXOOtIkXGBSHmnY/zikHX2osAm2l5pcYNBGRSGeGfFrxCbzWF0iCT9zagFwP4pCP6Dj868wc8nHarV5dSXl3LcSsWkkcuzHqST1NU24APUg0xETseuOlCf7XX1pCQTgjjOaGI6g8D9aQCOTnFRnrkHrS5OSaaTjnGB70AJz1H40meg96NxLcUoG44BxSANo6g5pCpZcnrSjKjPQgUZI4GKAGxSbTsft3qwHzyx4qrIhPzA8ikjffwetAFzGMkH8aRiB/vUwHH4deaeM9CMD0oAMkkdj60jYGe/rRjAzu+lJ6jvnmgBCcg4p4OTxx9aaCByQaU42jI9s0AO7YI+uKax9DS7s4HQjuaTA5BByRQMTdgEHoaQkjk+lDHA9fegFSM8+4JoASQbox2xzSphk5zk0o5BXI/Go4jhmB7HrQIkbOcY4xSZyxJ49qVmOAD196Tgjnr60wHDgYwMH2pG/X1pcccNSDjIPSgCPHHNJzzTmJx0o64BHPqKAEB9ePegHnpxSED8c0pBANIAAAbJzUgJUAD6nmo+wBxzShiAfpimBKGHt9e9S4DZxxx39aq5ABPU5qRXwOeKBlpG2phuQeCPWsy7tfstwuBmNvu1oYyAfbinSgXEZV8DjikwIbj5IUhHHOSfX0rb0bUBbxsq/6zGI1B4J7k+2KwV3B9knOB0q3EERBtG4g9Kxku51Ql2PRrfeygMc5HX1qdVCjjGc9ap6VJ5unwuwG4DBrQQHGTjHauZo9GD0FReBnhqUqDkDkjmgELkAZ9zTm6Dj5uvHekaohxhckc+1MbL457VIV5J556ihQMZHB9KY2cvrUO6VmA+Y9PrWGm+M/3SeldXr0AaDcBznOR2rkGf98dxraGxw1VZnU6NcujjnIYAcmppJ20rXPPx+5uB847Z9axdOLnG0gEHPJ7Vt3kQuUEhYlRlcVqmZXudA8itGrxnKNypq/pmsS28mzzCr/wtnqK5XQ7wlHspuSvK1dmyrYB5ByD7VstTOR6hp2sx3ICSkI57+tafH3uory2y1JmQK5w6+ldVpOuOuFlbdH3HpTcTGUOx1QODwKkVsnOKggkjuU8xG3D07ipQwU9CfwpGZYHOM1z/ipp0+xS2+0OjnLHGMcetbqsG9RUF00RcCeAyKOQxUEZ/HpTjuCZdgffDG3qgP6Vi+MYfN8Ps+DmKRXyD05xz+dWl1a3RljZXUdBwD+WCTVs3VtIhRpIyDxtYj+VNaO4J2dzzK0ku3MSW+pX1srBiQkzLGAATnIPt0xW3oPiTVRazhw12THvhD4JQBsFmOQSOffp9a61ra2kj8swRGPP3SgxUUenWUJUxW0aFM7SoxjPWtXVUlqjV1E1axn2Xir/AEk2+pW0sEm4ASeSVQc45yT+ddGTkcEGseXSbaVWUNKoYjJEhJIBzjnNaQbCAHqBWUrPYzbXQ+QiT2OPeo2YnIHanAZye3pSMihcjr3qWSRFjxjrTcHqR06VLwM46Co2kz8oFIYzoeaTg4zyc0ueeopM846YpABHOOlNAwM4pxz34HtRz0wKBBzjHakxlSe9HHOSaQkDAoAfn5QOv1qF0x8yj6mpQCVA/KlBwSCOvNIBkLhiBnBqfcTwRVSTAbKcY61LG4cAnrTAmwO2aDnbkY56UhJAA6Cl28dqAEBzx+dH3exxSMcYwefpSMxxxn60AB4JwRzRuI4/nSd+cexNOyOQSMnoaAEyB7/WmNy20DFKTzSMxwQaAJEHGSTzxmlIAkz2x+tMXdtGelDnIyex4oAkJx1596bhSwyeMUId469aQHBPr60wHAfPyOnShiQMtSZJXOOT3FABAAzwaAAcjg8UjAMeMDij5SSD69qOp4FADdpHGPrmk7cGpGJIqPac7RSAXJPbp2o6Hrz6UnPU0hwF+XrmgB3UYwABSBjjrn60jE84H40gyccc0wJkm2jHHFWt2SpBz3IrPLZboMVPHN0H8hSAmlTcPMT7y9R61Jp7qZMtyhpImC++exq7pEVsmpqZ2225znjoaia0ubUnrY29N1WO0cQyP8o4xjv9a6i3uIpkDxtuU+leeX13Zm4aKAgKGI8zPb2FamkXjWGG87dC+Bya5mup3U52djuOW6H8aQ8Hk5+lR27h1Dhsg9CKkPQ8c1mztjqMI9DzTcsRjHNSKh7jjH51LHCN2aCyncQCaFl67h0rg9SszZ3TLtJUfxV6eIRhu4rn9f0jz4DKn3171pB2Zz1oXRyWmzHcoHrgCuks2eRJoj97GR9RXNWmYbmSJuCVIzitW0u/s9zCHI3EbSM9On9K6EedLQmy0NylxHnKnP1rofMSaGOUAEMPm9qxmi2yyR44zxVnTJvLL2zElDypPetEwaLJJR9vbOQav2l6VYKTwe9ZzgxuUP3T0poLDkfd7n0rQTO207UpLdw8bHA6iux0/Uob5AAdsndc15XZXuMAnI/nW3aXhiZZAxwT29aTRMopnpPT2pfNCnBOM8ZrE0zWllCxXB69HrbOCAcZB9KRi1YjaxtJH8yS1hZyc7mjBOfXNNbTbVyDsZSP7sjD9AcVOOMDpTgc0CKh05QP3dxOo7AvuH60iWl2mB58bgeqEH+dXRxS9TRcCltuUJzErA9Nj/4gUn2iRQ262mGPQA5/Wr3IpeD1ouB8cbsDOOO+aaWzyeKYz5GBUZJJ68VLAcWJBIPHYGo8k45x60vPpSe+MnNABjHuaATyc5o29uh707bjp1oEIvI6cZoyc4/WnE4XgfNjFMzgc9aAFPC8dc9aAMk8cdqAex6UdSSDigAyeM9ugpzEknJGcdaYckcCjO3r39aAGYwDkcE5poHlSArzntUzKBzUMqFSGBoAnSQdOufWpRxjNUo5drle9WI5CCAeQTQA7G4ZwM96iBYDirBxgc++KicYbgc0AM39BinADOeo/lTBgnrij6cCgCTAwSDlaYM4OelChjxUgA7Z/GgBFPGDTuv0PtUfQkHmnjgD0oAbE212X8qcx468+1Mkysitng8cUvrigBy5FOx9fpSYx1xnGc0E7eg5NMAIwDjrQuSMn0oYnHAyaUcKBz7UAJxjBpB/nNBPOaXtzSAQqMcdaTbnjp60vfgUdRkGmBEBt6cilx/+qnnkevsKjIOSc9KQACOh4BPNMdvLkABGB6UMW7DP1FN5Jz+lAE0c5U5JzntmtC2lBGdx4HUelY+75gMc1bhk2DH8J60holv4NrrJHkKfvexr0H4e+GLC70573Uywkc/ukODlBxwOxznn0xXH29xDJaMsiK7DHBP15/CtnTdSvY0DRTbSFHA/h5AH9KznfodFNrdneX+npY3gMKFLZz8ij+E+lRdTzzXJzatcxXMkzXjyKmMrOd24g4Iz0HqPpXUWkqXMSTJyjLkVzTTR6VCfMrFpQMAf0p4HOMUzd6DpxToycnnk/lUnQTpuVTjGD3p7Qq8BRhkEdCKZGrHr+VWBkjHA9aCWjzbXdNaxvMxknvWPv/eIyjBDcivTNa0sXduzDl1GelecXaC3uXUEYPcCuilLocNen1R0KnKrIDkqw/EYpWG2Tercg8cVmWd2RAAegGCc+hzWivK4HUH9OtbmK1NVpFnhSUDJI5HoaYo5AHOagsH3loM4LdPrU/Yr/EO9WmQ9BpaSNtjjjsx71ftbw42E9vzFVXKzBQ55HykD+YqsGaKQgdulO4jp7XUnibax5HSur0bXCQFZsp6HtXnUcwZAw69xV+1vXgYMh9zVWuJpM9ehmjuEDxtkH9KlHFcVpGrkFZEbP94etdhbzpcQrIh4P6VFrGUo2J6WmgUtIkdRTfxp1Az4wYcccc0m316Gnfdo6jI4xSENONuCTSjGcdqTOPzpMlvagQ7Ix1zQT3z+tNA56UhycjpQAvUg5/Cg+oz9aBgL05NN5xgE4oAdwT26UcgD06gighQvHUmjIwB7c0ABPB5/GmgbuffvSr/OlJGDzknoKAEHTBoc8DB46Uh6YA/GkXhcHjnrQBC0YB3DtU6kHaR1pjAMDjpTYDgsDnjpQBbXDDr+FNOcEZPSkTKkZXpzSnGN2eT0oGQlcDGOh5penOacxyemDnmmYwSe1Ah4PTsafkEcZyfSowcjbzx604fL2xg0DHMoyxwMeopqnC9fwpG+71xTVHzDikA6bDJnuKchyv1pxAK4BHPc02MfIB2poQrE54PFAzwCaCeuOlJyOvTtTAd0JIP/ANelz6DmkxjBBxgUmD1zxjNIAGV+hoPOQDyaUHI7CgnoMd+tAw5DEEc9c0nK49aXkZ5FIAQRwaYgye/HrimEA5H5Cn7cEkGkAUdjmgBgAA9CBUbIQAc/lVgqzLkEY9KiIKj3pARhSTnp9acisFwM0q/e5/KgFlfrxRYZNZH96oPc8VtW84jVnPIjUn68ViKjPIDGrFs/wityHR9SuYhttSAc8scA/rUSLgQ390SoUcYwx9jXT+D9ReWxMcrkbHIXJ6Dr/WsZvCOoEF5J4FB/gySf5Yqa0sjpxZFkOScMw46VlNJo6KM3Cdz0OMmQYXp6inxIdpB6E1heHbl5mkQyFo1H3j1rpUjzycZrBqx6cJ3Q+MYGc9fWpgp9ulNCjqByKnVMr6DFIsidSy4I4xXmniaw+zXrt/CxyOK9QKkLg4rnfE2m/arNmB+ZeenWnF2ZnON1Y880+QkPGcbgc4/StlXYKCCCy9fesRSsU5yfnUYwKv2k28lf4u3vXWmebbldjWjIJWVGIBGRjvV+Vw8gYDAIyAP1/WsiCQgMgOCrVoI4ltQwHzxnI9x3FaJgxzZXgn3B9KccTICT83t61GCHH86RGMbnIwG7H1qiLD0baOmGB59xVqKXIVkPJ7etU2GRvAIx1oRiPbB/OmmI3bC9aCQFTx9eldlomrbJc5+Ruorz1Jh0xg9/etfTroqxUnkcgimx2uesxyq6B1OQe9P3n0rmdB1USERMflPH0NdIPWoMWrMfvHpShwelNzngjinds4oEfGnQZI/GkxuGO1PCjr6etDOBnHWkIaEHzZOKaQvHHHrTWJx1+goGMj2pAOz74570wgY980E5z6igD8KBAOTkn86MYwRyaCxIycc0c8BenrQAoJzwOaTAye5oGMdOc03JzjH1pgO5HX0pB93qMGjJ59DTlH3R2HagBvIx2HrSY5H16U4kbmzyabjOQTigAbAzg96jfIZWH5U8ggEDBJphzt6nOKQEw+b5h09akAyF6e1VbdxvKGpx6EYGaBg4ycjrTNxBGe3pUrLghs8eoqMjJI4BoAacgls9eKcHxgEU3tjGPXNIoO/FAiQ9DyMelNUnfgfrTmGBkfhmmgE/WgZYjG8denpUSDDMmD14p8eVU885pCv705z0yCKEDHc47Ec800cdRx3p5GRweaZnk8c0xBnIwOnvRgj39qQZIz6elKvXnr60AH07UvPApRwSTzQMbePyoATb79+nenbWwccmpEglmbEaMxzj5RWjFoF/OA3liNc8lzzRdDszJx1x94UhPHYHvXUweEScGa4xnnCitWz8OafEfni8x/8AaOf0pOaHys4RLaW4bbEjM3+yM1oQ+GtTnG4xCNexc16FHbxw4SK2jTJwCAFApWKxrlnU8chRUOfYpQOSt/BH8V3ege0Y/wAa07fw9pdqyloTOScYY81oG8ihUnYxb3Oc1VuNTeVSEUKM9Byam7ZaikXIrSCEBkiiiTuoUA1Z+3WUHPzBj2HaudklnlXc5O3uXNVZbuGEjdOGJ6LGc0nFsd0jT1HXCztFGnyA8E8E1mzSlmXEZbcByDwD6GqMt6C6mO23f7TnvSSatOV2EKqf3VGKOUXMdXp1hNp2p2Xm3Kb7pgqRr057GuyUfvMHjB5xXmOiXjX+o2omLiWKQMi9QR/iK9CTUIzc+U5IbO1mxxn/ABrKpE7cNP3TYRcnpjipAoHft0pIh8o3EdPzqXyxjHfPWszsuRtHxmq13F5kDKe4q8AT7YqORMqc0gPHdb097XUXx3Y9aqRyAYwcEd67XxfpvmQM6csDmuAhcBz6eldFN3Rw142dzoI5A/lSgjcRgrn/AD7Vq2jBSGUgo/H4GuctWMg8stgscqT61q2UrLlGyGGcD3rdGFywCILswdUPIPtU8gLZIOT1qHUELpHcKPmUfMRT4ZfNgB/iz+NXcRJCT0zn+Ej2qKX5ZOB7VL8yt8oOOoxTJsMobj86olkkU38I5Par8EwVlI5rGQ8/LxzV6NyV3DnpnFAI6uwujE+7JCntXd6PqC3kARj+9Tse49a8rs7lo/vYJC//AFq6DS9QME6SI5DLzlj1welDQpK56QCM4p2RVSyu4721WaM9eCPQ+lWugqTI+N2bIx0+tQFuSRQ+Mdab14xSEKDyOKXPXkU38cEUAc4xSAUdsfnS9QeaTJ2kn06UKuRj9aBCjPT86TOQee1OA465pOg4655FMBp7AdKOc5PNDn5sY5oHIAIoAd1wBxRuG78OtJ2I6460Dpx1oAAueMe+abkn8Kdk45FJjtnpQAA8Y6Me9ISuQB6c0Zwfm6U0gYUAfWgBrjByD0qZCJEB5zUPUHtRAxRitIC1khcVG/LZH4Cng4460hHYdqYyLnqe1KByOxPcUnIB9KUEgjH60hEjktgDqKaGw3IwfXNPfgDtUQOT14oGTdgSeaSQt8pI/GkXLjjPHSr0OlXFwF4Cg9z/AIUgsUyeMn86GOf89K6K08Nc/vpCVxnite30OxQ4aAEjks4zn8KfMNRZxMcEk5/dIzH2Ga0YPD19cDdsVP8AfOP0rri8FqoVI0DeiLiljvVDBVjyT0FS5j5THtPCLMoeafK552DpW3Z+GrCABjAJSTg+aeKl827zs2hHPfHamSSSY2eYxGMYXoKm7ZaSRdjhtLc+WnkqenyY5prTWucJvZvQCqUcH7vKxS9M8Lx+JppljtciaWJCnLBnAz+FFg0LpvAikmD5z05qF7qfYAqbCfTqay5tWslJZJHlYHqoP86hbXS4+S1XkYzK5P8AKiwXNgzTOmCHY/7TdKhkktrVczSLuwCQGyfyrAe7u5Rn7Q6DP3Yvlx+NMkkVTwWPH8ZyaLBc0X1a235iid+2WHFVbm/uLlAkaRRKvcdSKoPMM5HB9qjaYhTubAqkhXJmV5MebIzfVuMUmFVdqgVSa4A53DH0oa7RONw46nNAKxu6Zp8dwZJpW228K7pDnH4D3NWk0TR5xJdG/kWNW/1JQeYK5ZdTiAKCUYPUbutWIXkyJgx+Y5A9RSGmjaF7Y285is4GjB6zSHL/AIY6VoLcytGIZJ5WhQ5Vd2QT1yfWseeKO5iEsWN38Sg9KltWkRVQk9evoKVrlJs9N8PXf2zTULj514P4VtKeOB2rm/DkawWCxbw4BzuHeulRht4rmmrM9OlK8UOCZ5xz9ajkU7SKk3AqTnFRN8wIqbGqMrULYXMLx+o64rybV9OfTtRdWVvKLHa3SvaGhAQ/3uhNcx4n0oajZSIuPMTlCe1VD3XcitDnjZHnMcu4HAz9K1ILrzFRs/vFGCfUVgQuYrgo2VbOD7GrplUtleQTXYjyr2OtgcT27IeDjIJ7is22kNtcGMg4zjJqXSpvMiAZgGU8En7y9xUd7HtmLA5U9PaqRV9DSWQK3y9Oo+tOdR5TIDkg5B9qp20gkUZPIHBq0eF5PfGRTEU1yh461Ygk2nkjH1qJxhiwY4BwaRMK31qiTTSRkIHXrj3FXre4KH5T7g1mQOpVUYc4NTKzA7SM4H50Adh4d1s2V2pZj9nk4cHqPevQkkEiB1IIYZBzXjFrNgOM984xXf8AhDVvPh+xyv8AMnKZPakyJLqfLZ4680h6ZzinYPTqaME8AVJmMAyCB1pw65z+FKScd80Y46c0ACjdjj60FjxnmgEgHGeaTkcGgBD0Jzk0AtwB+dKM56YFHI56UANJ4peOtBBB/WkJIOcUAOAAHTrSsSFJyCSe1RliSOvtS4IJIJIoAd0Az+VNc4bp+PrThnPNKw68cdsigCI9MnmgHkYp34YpOQTxxQAEYwT1qKQ4kD4x6U8gkjrTZRuXP86AJlJIzUmcjnqRwar27/Lg1Y6jBH0oGMZc5P5VG3OCB3qU5GBTCOv8qQDmb90c/hUXQZ/KnpkKQelR4OT60AX9PlWO4V8E7ecV01rco0nmRRk5zx+HSuSt5NroSpJHoK1BcLszErr2G05pMpM6Zrm5dYzHF5SPjDsRyPxppiZ5G8y4j465m/wrEH2y5ULvlcKMAHnFRraydWVzzS5RtnVRxwmD5ri33P3Mgyo/Gm/atNtkJS4LSA8GJDz+Nc/Yq8VyrsgfB+4w4q3cGMSO6LhOwA6CnZWFc0Drke8lIZ5D6ucVXm1WaQP5cUMAYY+UZP8AhWb9oTByRhfeozewgnLZOPUcUaBcsy3V1OMT3MpXGMbyBj6VVKohDFRn6VC1/AG+Z88djVa41WFVKqC3vigC68o7YHHpTXYBdzOPpWJPrLsCqr+JFZ0l1K/V2oC50s2oRQqA0ijPvWZLrAViVYt9Bisjdk85pKdhXNFtXkYEBBj3NV2v7g/x4HtVbHelx2oFce00rnJdvzpmcjnmgDtRimAoPat611aH7GFlDLPGu1SvRvQ4rBA5FWAgIzjmkwuaUWuTWpJiHB65PWpB4mlJJkiDD2bFYzDk8fSo8EN3p2HzM9u8G6kl9pFuytglTuH90g4wfwwfxrt7QSCIlhkdQRXjXw41HyLiWyl4imbehPHzADOPw2/5NezWDkrjJwR3rkqRsz0sPO8BfK3kuXYH0HSlRcDBPNWGbJGMYPSmHgZyD61mddyvIWIOR+NZ17CJUI7d61mBxzjPeqMy4JFMpHj/AIp002OpNKoPlSHPToayYLj+E49RXp/ifSRf6fIEA3jkV5FJujfGCCp710UpXVjy8TT5J37nUaXdbQQO39eK1rnMsR+bdtPGK5TT7nbIrHOD1FdVG4aMOOp7CtjGL7kVpKI3B7MOlaQPylc855rK/wBVL04B6VoxHdH05HpVDGMdpYdc9aAo4xz3pzZJJH4UITjpzmmIsRNhenQ8e3FTnnkno2QMVXU4bIJwRViPB6j6ZpoByOEl9iMVsaNeNa6jBICeGGfpWIQcN6g9asxSFWVh+VMln//Z"
-       id="image32" />
-  </g>
-  <path
-     fill="#000000"
-     fill-opacity="0"
-     d="m 257.70752,144.57213 h 133.2615 v 128.85374 h -133.2615 z"
-     fill-rule="evenodd"
-     id="path36" />
-  <g
-     transform="matrix(0.26027638,0,0,0.25166745,257.70753,144.57212)"
-     id="g43">
-    <clipPath
-       id="g108f8700c0f_0_0.4">
-      <path
-         d="M 0,0 H 512 V 512 H 0 Z"
-         clip-rule="evenodd"
-         id="path38" />
-    </clipPath>
-    <image
-       clip-path="url(#g108f8700c0f_0_0.4)"
-       fill="#000000"
-       width="512"
-       height="512"
-       x="0"
-       y="0"
-       preserveAspectRatio="none"
-       xlink:href="data:image/jpeg;base64,/9j/4AAQSkZJRgABAgAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAARCAIAAgADASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwDxwZVSR2PFTxg7fm+v1qBTxjoBU6MY+VOeMZoJHJ8qbv0p5OdpB46007RjAxmgFc5AzjrigB+RtxjJ69aQEnJY/TFNIAHQkk5pW5U847YoADw2eQemMU5lYn5cEY4IpgJ4Gc980ZyT9OSKYAckYBAIOcmmhTjnp/OlDLgYHekJIbdjI9aQCDdvBOCM9B2oZgW4ODnoaVmBGcBTxkDvUR7449zSETEHGOp680gYlgWGMnGQKQAb9uMkDqaXyvlU857elMYnGSB6/lSNnOR64NHIXjg96BwqgnigRIoDDG4Bs5GaVZFQOrcZ7+lKFLLwoAzgMRioXiI/2j3FSIlR1Zvn3kDnnmhmDSM0ZG7dyOufemBZAm5TtBXrnrUQDJlSoznqRRYLFptrLw3PX3FKJ3GAHJOe3SoojEW8tlPzHG4HpT1gKyR+W3DHnIyOtTYViFlkjDM6sBuI5HekHGGVs+oIzVq8neaMCXauDkBBwfeqAIK/KCKaGWlmTBQjtk08SSbSIgcfriqibixVV5xzVpJZoihj2/LjPpUtCsXLSZJMAhm2D5t55x7U77TbwAuFQ7pDjaOdtVmnLlN8gjkVsB1Hb096Y8S+ZmKN3yOAfWpsgLxVLhd9v/rCTxjkD1qeO1lWMSO0cmOcAj2rOtArTbcE927fhVkXMUHmRuZAjOGADECpa7DK1xblxNEqCJUy43cbjWSSSw55PU10V7erdhEkxg4IJPJ/GsO9iWKVvKRiO5I4rSL7giMyZ4HJ7U19wTe3VqYDubJ6Uu47Cuc4OR7VdhhC2Tg5+oFWUeNZGQltjDhxUCyEhd44XkL2NPubnzmUIoQBQu0VL3EI42kgHdnPOagJK8E0uSBj9TTOo65ppDJYZGWX5QDx+VbEstwLcws5boWHTt0z+NYcLFJFZPvA5q5PMPKjdJeSeUPr3P41MlqDRPeRiOK3YwvGSuwPjgj1rOuYTA+dykHuDkVqaprE2qJAD+6Ea4ZFPyFv7wHY4rIYBhRHzEiFnJoLLt6/hSvjAAHPc1HitCg3YJpCxz7UGl2mgYoyRTlViDgEmgHpngVIC2MBsH+dAgiwpXILDvitC7iEbIYn3RBQBx1NVoXAxu/hI3DHatJ5o8KpKoYgZAxGSeOBUsRiksCQeD3qZSwiEi5ABxTXJncyucknmnL1AHKg5INMZFlkkLN0Ip3mbnBHFa/iXXBr9/bT/Y4LURWyQbIE2g7R944GM/yGB2rGZMdDxQBMt1LGG8ptuRz7ioxI8gO4k+55pvYelHIxk5pWESRuiqeMkDGDS7onbLEoTycdKhJIPXJ9KaTnqeKLBYkcBW+Q54pA5zilB4BA49aQZznJ+lMB24jlcZ7ginbhgAnGaaibjz8o9aNjL94Hnp70WAXODjvTWBz04py5B5NLtyMk0DLQw/HSpBwTt5471HG+CcjBxxxSq+49hn0qgJVIJ67fxzTsfMqrjA7+9RKQeQOacGfhcY5yaAJfmLHoSFx7UhJUDPcenWoy/wAh5wfahAWIOMmmBKCSACTRlSCoXtgZ61GQcnpn0pykimAAfMo5GOKCMAjJA6Gk3NnJIxntSsTjb/FnnHcYpARkljjOPrSDGQGBI+lGQRk880zzCOPekIe3yEkcg0sZPr37imkkggLz79qeY9qJuGDjcfWgY7JLH5l68YprsWGB65pD1yMZpSM4zx60AORm37ugxzznNBlOz5s89DTUAA44JNOOVYZ6KO1JiGsrBhlW9sfrQ77hjPPp6VI8wZVSMFFA5JPNRhlds7eTxg0gHrHI0ZCIzBcHb61CZZA+0MAM54pVkkSXco+bHb0ppZ5G4jAC+gpgDHJ9c9aktc+b8vGOpIzUKsSw4qYy4AQMNuOR2oAkeByh8vJYnhkOcD3qvvaIAOCGHGB61KsrwlZIuAev+FDKblmYAZJywxzUgRxM7NgDLMeAa07eR/KK7sMG9OQaziotpUOAVKggjvVmK+/fgsu5PQD+tRNPoItfakZigXdMEJB4ADZBz78VGiXUlwbyYqxLckrjP0FK1vBKS7b1fAOD0FMaNXyxuH5bhic/hUoCCRJfMYOE7fdP3asPaoYFDzNGWIB3Cs0tIrZIPJ6+tWZL0mJkLElsfKTnHrV2fQCHVLJrKeNVIZXQMGHvVHr25rSnv2mthbnJXIPzdvpWey4YnPFUvMYgZsjOSBxT0VXI7HNMDAfSm7iSOOlDQEskLE5D5GcY9KjaNoztccjofWpdxZcNwevHekuZ3nYMxy+OTjrSVwGShUKbGzxlj7+lNzkc80zk89TTl9TVDHbjQcMRjI9aYSCCcUDOAMY+tKwhJMgCozxyakOfrTSpPPQU0CG96m2qqDkl8/hQsJCZJUE9BTcP0CnI7etFxiOmH2HscZFX7SIQtDOwjZCSNrd8d6ZaXZt5YpJLZJFjJ+Vl6/Wq7OWPHAB4FLcRcUxO0x2j5j2/hqvMpOAORj1piSZOGwue9WovLERQoN3UfjRsBv8AhLwRe+LYr4afJGssFu0qRMfmlYY+Uc8ZJ6muTjQxyb24KnBGa6LQfEup+GJbm70ycI80DwPlcghhwR7g8j6Vgg+YpZz8zHJ+tMZv6zqOjara6dBbWzWl1BEI5Zm+4/A6gZOc559+lYRgZYCxIxnjnNOSNRJ+8OTjjNV2ncx+X/D6VKXYQrkeWoxg/wBKmCbYhIgDYwGU1WJL4J6gYp4c5PzdRVWGOKrJuI+U9hUXlMMnBNLuKt6j1qwJNuMjI9R2pbCIFOFJU4HpU5VVizj5sccdaicjzG2gY9Kbv3DrTGXrOY6ferJIiuyjO0jcD7EdxV/xFq1rrmsT3tpp8WnwuF/cxHgEKAccDqRnp/jWCHIFPGSN27HrQBKVUfe/SmkcYXsetIwdh7U9PunB+tAEx2FRgmh2B2hV28Y46n3oCDH3snNNDEFdo5HJzVASJhZFBB688U7dkA7iD65qLzdxJYHPt2pmeOmM0ATbujZ68fWnkmPr35GaZChJXaQMHHNLcSKzBfTgt3J9aLiFWUHIZjg9D6U4SfLwcjucVXySMYzT4wDGwJ6HHNMCXOcYGM0fK0m1iePSlVyTgsBgYHtTNwUM4OWz0xSGIQIyvGT3GKRSfNLDAHUAUvmqQN5/I45prc/dbB9fWgRYWcqwIHzE+nX3psqbUDuR8x6dyaiQLxnOR+lIwz95sknjNIB6kBgF5HqaVuU3bvm9BTAW3DFDYUfj0qhjzlFBPQ89KCzyMRg4x0pm9i4LcnHSnbRuB6jHSpYAOcAnp6UvlhhnLdfTtTHKgk859qVfMOAB34yKRJNJHt+ZDgkcn0pschQ8lvmXGRx/+ulWNy4Dn5SOQvf2qxJORGNyglCNhXjb0/PpRcZW8tFEm8gHHBB43elQsozkcj+VaGom1Z28lMMx3nYcrggcVnOhRcEEHFNMEKGIwMce9SleNysyn64qvu+7nGKmBGQFyVHrQxjR8wJ2nHrTlkMbDeDjNMwuAFLZz0p+0MAzKTnqMd6kRfSZVBRgzEnoG/wp5DvGDFkLuGSRjGaqxoI3RsZ6ZBrSSRHhaNSUXFZyVmMzWtXD+XHuEhPPeqcse2Qq6srA4bd2ratnYF3CEOFAPo3SoNQuVkIjaJihP3uMg/1qoy1sBlNhSDncuOajkiKxB9w2545pWR0YKQTUZJwVycCtLAxu7Ax2pFbg0mD74zQY2xkZ/KgB27DfJ1obJPA59O9N5wPUd6dnGee9KwmIoBOc49qVxsOG5B9DTSuWxzSnPGcnHrTsMaDyeRj2pyk859aQIQSQDR5bEnrmiwBwTx+tOmAX+LOacqI0MhLbWAG1cZ3HPT2qBsk9KSQWHpKpID52+3WlchcFW+aoQDx9atXtutrJHiSNi6B9iHOwHoCemfpnH1oe4WHPevNGFfr3IqNFMsoWINu9T0qNQA+R0qWN1G47tpHII60WsA6W1lWMzM64+vWrNlDB5ge5fK46Z5zj2qzptxHcWklrdW4njUb0cSJG6+2WHI56VQvUhE5+zmXYBwXwD+lLV6BbQhkkYEqGyM9KYxbk9CKlSDIDGU7+uO9aiQGawAZDI4YjAUkkHvVPQasZHm7kORnjg+lRKMnuT7CtYaZMHVvss308pv8ACp300x/6uCfLdQIX4/ShLyJbSMkwuY/MEbqPU4xUbR4/i5rpzBI1mqG2k9Nvkv0/Ksk6Xcl/+PacKDxmJv8AClHmd7oLmapwDwD9akRsd8H0q62nzImPImAPqh4/SolsJ0YFYpTzxhD/AIU7XGVH2jOc5FRk4A2nOfatia0aSFR9klDd8Rkc/lWe1rJGQHVlP+0MUosSaJ9KtDeX8UPmIhZuC54z6fjV/WdEOlBZVmE0RleJm27QHVjwPXjB9s47VRgtX2SEoxUDrtNRGC5kJ8qGZsnkBG/wpjafcYWx1xjtgUAAfMOjdBmrC6XfsozZ3WB6wt/hUy6VfMqqtjdmTPQRN0+mKLBYrZPIXNICSDjGKUE7c49hSeWcg8j1pgKVztwefp0qTauMNzjuKaTjAwfSkjUsTjOe9ITZKrE8HgY6ikKIMl2JPY+tDIyA5POc4I601pNxAOPwNJCE2rwQ2fanxyHpjoeoqJflJGDn0pUBLZwcd/aqGWM7iRt3AdexphBbKqB14NPKFlzgn2FPEZxgBgT/ADoGVzAcAjJY9AaXYQRkEH8qnILjaQwOOtRgMGwSTjuaEDDJHyYXGKYTjAPJPQ0/yDzyQD0Bp8dv5r4bKnvTsBGVJ5HymkfoCeuM1IxKOysTxkGonXAUkcY45oAciDI9TzT2Jy3zAZHUiljBVQG4V+MkVAzMRjGMcZpAAyqZGDzT0kdE4PvzTei54pdiFQwPHcE0WESiUlx/Du65704SAOAwBzkmoI0LP6U9yUAVcEk8etKwDHbLAKMBaeDkAseAelNHzAkj8qDGQo6hSeD607DI3QCQ+nalEu0nA4x3p7kEYG3mmBGzjFFgLtjeafCpF3p7TsehEu3H4YOa6DTIbDWLlLez0+2WZgSqyztzgZPb0zXLJC+/lSPTNdR4JQJ4vs+pJEn4/u2oVr2Gn0KDz6YGOLixGe22VsU5bjTDgfarHp2im/xqYeANTkY7rbb9ZF/oaF8A6pHMP9GcrkchwM0rlWDzdOALC7txxk4t5OPzJqItpkzjdcxHHChLVjkk1qnwRfpZTBbaVpWjVQqndnlSc4+n6VBp/gzV4dRtXm0+bylmRnIU/d3DP6UadhECz6FDKwuGXzQcODZdCOMcNVhV8MSxFoBavJgbY2tZAWPcZ30tz8PvEN/qNzPFppVJZndQ0qg4LEjrTo/h94i05TPLpxWJMMW81Djn2NOwX8yKceGra7nt3W1YwyNHu+zSgNg4yMSUeb4eMZijgsGDEceXOOe38eO5qa/+HPiG8vbi4g0iSaCWRnjdXGGUng+tQx/DLxXG4MejzKPZv8aTjZjTuuhAZfDEQJNrZyEdjHOM+33qE1Dw+oH/ABJdPwev+ubH4E/1q4fhr4tYKp0g9ckmaMfzapU+F/id15soIz/tXaf0JosP7jMfVdGSB5I9I07zFZQkbWrfMDnJzuOMYH1zUSeJdPXIPh/RuO7W7f41PrngXV9D0qS/vUhVEdU+SYMTuyOgHt+oos/ht4tvLWO4g01RDKocF5EBx24zmqsyboj/AOElsG/5l/Rh9LU//FUf2/p78nRNLUe1n/8AZVd/4Vp4uTj7LB/30lI3w68XIRm0tvxaOi0+wrx7lBtTsVt2mGlaZ5gKhYzZ5BznOTu4xj9apjW493GiaOP+3Y/41sv4A8UkYaxtvqbiMf1qNPh54mJ/49LP/wACl/xotISce5VtNahaXbNpekRjaSGGnrJyBwME9zxTpfEojIC6JoLD1OnKP61b/wCFeeIT1jsVP/XxUg+G2vuMF9OU+92P61PJK5SkrFFfGE0a/Jo+hL9NOjpB481JCNljpSY/u2KD+lXW+GmuAfNdaYvt9sX+tV7j4fapaxLJLe6ayswVRHOGJJOAOKfIwbQs/jXVVKmKWEAorHFugwSoJHT3qMeO9c8v5b6RH7bEQD/0Gtx/hJ4hHyzXOnRsoAOZwcY4po+EesE/8hTSx7ef/gKhRKZgr4/8S7udXu1HqGA/pTZPHXiZ+P7bvse0xH8q6IfCLVActq+kr/vTMP8A2Wg/Ce6GfM8S6Cn1uen6U+XyEcwfGniT+LWr4j/ru3+NMHjbxHj/AJDF7j3mNdQ/wqYA7vFvh5f+3qox8L7faC/jTQFyM/68f41SjIluK3Ofj8beI8ZGs3w9hO3+NSDx74oXga3ej/tqa6GP4aaUOG8c6Rz12Yb/ANmp7fDfQl5fxrZ8f3YAf/Z6fJMXNHuc4PiD4oB51u9/77ob4h+KT/zG77/v4RW63gTw1GPm8bQ/hbA/+z1C3hLwjG2W8Zlv92wJ/k5o5JBzw7mMfHniQjnWb1s9mlNIPH/imMnydau0TPA3A/0rd/4R/wACoMyeJr6Q+kVmV/mKjbSvAixOUv8AV52QgACNF3Zz0yO2Oc46jGeaXKHNEg0/x14omtruW41u8YQx5XDgYYnjPHSqMvjzxMzHGp3Yfswata1n8H2EU0Ji1RoZwPMDunODkdPemlPB73Mfl218rlgAryLsbJxzxnv1x+NOwKRkReNPE9zEXGsXhYH5tshH06U9fFniZSRLquoKOPvTv/jW9Zjw/aqzw6LuWTBKyTPwfwx/OrH9oaYGLDw/bkKM5kVXA7fxEms3JpmqV0cALKbbuaN8Yz04rQtdPmmgkPkO6EEAhejcY5rO+yXG3Gy4OOuQah8iQscLJx6g1pYyLzaVeKSGtpTjnhc4pq2NyjAi3l/74NUxZydSDk09LByQMPz6U+UXUklSQKoKN+KnioGQBuh96sy6fPbxpIwlCOCVOeuOKjELMuS0n4mkosLjRypA5wOCO1Ltz93jOM8U+OIrnDMMjHGKDCU6SN+VPlYXR2mlWHh1tLtpLq5gE7IfNV5CDnPpnjitOOHwlGuPMs/xYtXnCi43bUkGO3rU/wBlvSRzIcegNaXXYXoz0QS+EkA2vY5/65E1INU8LQgHzrX6C3Y/0rz5dK1FwNttdOPVY2OfyFOPh/VzyNLvj7i2c/0p8/kLlfc9Fj1vQ3yLdY5MDPyWn+IFI+vWC5CxupwDxbYxXG6JY6hp9273Gn3oRkK48h+uQfT2rbY3srkW2k3LMw2/PC+fwGK3gk43ZnJtOxyetKJNfv2TndO7A4xnJzVA/dwR0IFdLe6PqWraq9xaaZckPtDZiKhW2gMOcd80r+CNVVykn2aFyQcPcJkfgCTXPKDuzVSRmW7KkMe4QMqr9x3A5x156c4/Ks+5hT7wZQT2DBvw4ruLHwHpxRDqGqKsoUBkVHAz/vFCP0roIPBPhdVUnbOfR5W/oFpSjcpRbex5EsJ9QTjkUeV5bYZgR169K9wg8OeHLcfJplm31Ut/6Fmr0Wn6En3dMslx6QL/AIVDst2bRw9SWyPBGhYDO75gByKQRmViwDZHXFfRCHTY1wlrAgHQCJR/Snfa7JMMqxL7qoFQ6kTRYGs+h86+U0bfdOD2xWhZWZvUaMMV2qX+Zc9PSvem1S0AyzhiexFNOqWRHKrt642Ue0iaf2dXfQ+e5LN4iDtkAPP3eKlS33uuQ4HA3beK99/tGxY/NGPrilF/po/gZT/uihTh3B5biOx4TbWojJMrTLH2zFn+oq5aXT6dfw3lm6tJC2V3R4z7dTXtJvtMx90n6JTDe6aQR5cbegeIU70+5Ly/Er7J5M3i7xEijddXGMcMUXn9KjfxTrrgo97OG9CMH+VesNcaYRg2dsw9PIUj+VN8zSyvFnZ9MY8gcD8qfPDuL6hiP5DyY+JPEDtzql8pzztmZcfkeKV9c1oj5tTv2z63Ln+tesJJpqbnS0tY3xjiAAsPw7UPLYSHm2s3wOrQA/hzTUqf8xLwOJ/k/I8ge91q4t3aB7iTyupaQk89ByfrUEd3qCtDLcGdcttdWDAY6c17GGsU5TT7Lp1Fug/pUU2o6TDGfL0mWafusUMeB+gxWkIQqO0ZamdWjWormnCyPLn1LW4tQuraLUbsIpDRosh+VSAQAOwqN7zW9x826ve2MyNXpWmahplmiq/gZ55gAoldt7sAAAPunoAB+FaTeKWgUtB4GWIDoxtv67RVSorm1MYydjyE32rnbtubtumcSn/GrsQ8S3LKkE2oBW6srSH6fdrrZ/jLKh2W+mQRkdAm4VnTfGPV2ztto8e7N/jQqMe4nLyMufTvE1xoU1ncR3dw7Sqf3okyQACPvDsf5mnRQfEBYEhjlvEiRQFHnIuABwO1Vb/4oa9LcHatuqjtsz296gT4na+OsVmw94z/AI0mqadm2K83qki6bHx5wXvL0A/9Pv8AQNTW07xyTg3t2Se5vf8A7Kq7fE3WdufstiD7Rtn+dV2+JWtkn9xYj/tkf8aLUu7HefkXW0Xxoxz9suQPe9/+ypsugeK9o8y+kyx73bHNVh8StbHJhsvp5J/xpP8AhYmsOQxgsyR0Jjb/AOKotR7sX7zyLC+FfEpBzdD6m4c05vCOvn71yjAjvKxxVX/hY+toeIbMf9sj/jUg+JettjK2Y+kX/wBei1HzHefkSL4M1k8tLCfYs3+FRSeC9TLt+7R2HdCfXHHFOHxK1j+JbX/v1/8AXoHxO1nPyi1X0IjP9TRaj5i9/wAi/N4LvppZJEtAAzEhd2MfhUbeBdUBAWzUjHJ3ioZviB4thtI7yQPHaykrHMbXEbkdQrEYPQ1UHxR8Q55vFH/bFP8AChey63K95mmvgPVj/wAw4H6EVMnw/wBcP3dNA+rLWSPil4g6fbm+ojT/AApD8SfEp5/tWT8Ao/kKOal5itI3h8O/EBA26env+9Qf1p4+G3iN/wDlxQZ9Z04/Wucb4j+J2UqNWmA6npmoP+Fj+K1OF1m4/Q/0o5qfmHLI6xfhj4jBJaGAemZ//rVHc/DHxCSNkEZUD5iJxyfaubX4ieKyATrFySfQj/Co2+IPih+ur3OPTcB/Slen5haa7G3/AMKt8VMAfssWT2+0Lx+ZqaL4T+JWA3x2sf8AvTg/yzXOnxv4lcZOtXo+kxH8qibxbr7jMms37E+ty5/rUe50Hefkdgvwg11x819pifWZv6LVy0+DuooG8/V7LB5AjLHnn1HvXnLeKNZBONTux9Jm/wAavab4tuQty19qV02yFmjXzGYu+QAvPbnJ9gal26Fxb6o7qX4M3MpGdchVepxAx5/KpNY+HtpFpdrt15PttonzRGBiJzvJGHyQuEwAMdRz148vl8Q61cMz/a51QngKcAVF/bWtnIGp3w46C4cf1oumF2nsehjRrNABJqmw+hT/APVTxoVjIc/2upXPOUX+prhdMv7iW2l+0uWdG+UsexH/ANb9alN8CegyK82Sqc/KpM7Ulyc1j3EfDN7ZyYGvE3cHbMnI989aSP4Vo7sHMm7v5gi/+JrHi+K2jTEI8ur2xPZnEy/juOa1o/HOk3gjWPU4CQQQ8ymLB7Zr0nVtvH8DnVHXSS+8cfhND9oGyDTHC/Kwm87Ocf7Eijp7VIPg7ACw2WO0/wB17gY/NzU1zqOsWcIv4YraSGbJF1BKrK5/3h1pLbXfGV0gmtYI3jccMZlwfzWl7eCXb5Mbw0+6fo0VJPgrat92WJPoxP8A6EDUZ+CkYXCXkBP+3B/UMK2E1bxlk7rCEnqR9pT+oq5FrGvugMumo5HXbMmQfzo+sQ7i+ry7ficm3wSf+G6s8/8AXJ//AIqov+FNakjZXUbUAf7Dj+tdudY1ELiTRZD6kPz/AFqVNSmMe+XR75VXJ3bxtA/MU/bQfUf1Wf8AL+P/AATi4vhZrlrIJYtXwwHylLmVGH0Yc0+PwB44U5bxFMy986hNmusGp2csizLBIHHIJuxjI/4FitZL3UyA4sJiCMg+YmDVKpF7GbpOO6scIngPxiI2WXX5JWxwzTyH8Pvc/pVeXwD4vYAG5tie7JPLz/30xr0Q6lqeT/xL5PzX/GnC/wBRY82M657gA/1qlfyIdjyqfwD43Dt5csJTPAN1IcfnVSTwD4zdGS4tYJAejRXjof8A69expNeyH/VzL9VIp0r3sKF2DbR1I7Vpd+ROh45b+GfGWlwNFb6dMsZGPlMT4+hzkVHbWfijT9HfT/7LuSGyWkKSbmO7OTyR+QFexrLPMpImK/gKcqS7T5kgb0wMUnZq0kVCUoS5oOzPBzrtxpahdW0ySE54l3EL9MEf1qx/wmGnKAZLDbnoRMRn8xXe/Eqze78B6xCSTshE3/fDq/8A7Ka+ZG3dq56uHpN3UbfNnVHG10rOV/kj1/8A4TDSGTb5dzHznKuGx+pqe38W6VIwUXJH/XVf/ic/yrxtTKvRttBWRuS4P1Arn+rQWqb+9v8AM2jmVaPZ/I95iu4bpC9vLHKo67HBI/Dr+lRO+a8XsNXvtKmV0kJVTnBPH6cj6ivRtG8URamsaXBCvJxHMSMMf7r4+635Z4Pes6lNw16HrYLMoVWoVFZm6WwetJvpHXaxVgVxUZBAzuHsPWpsezp0Jd/T0pRJUG446UuaAJvMFLuqEEinAmncLEu6mOWHKcn0pM5pckUXCwqTB1yOPUelSqw5qAgbgwHzDvThxkEc0XuS4JBc6itjC0rKzbRkqo5x61XX4hpDGEiimRR2wP55ovdLgv8ADSSSrgFfkbHBrMbwfYNkia5/77H+FKKh9o83F08VOdqdrGvH8SreHMrpM5XrH03evODXQv4ts9R0xbm0YmN1JKkAOpAyVI74/WuDbwVZMeLq7H/A1/wqzYeF7fT7iKeG6uSY3VwrMNpKnI4x6k02qbi0mznp4fFKopVIpo6LUZHtryaCRE8yMgEryOenNZN1FBdQ+ZLbQsVB27owcVozLJdSu7N87MHOfUBR/wCyiq8sBW2ZR6YrilT5G5QbPXhTpOKU4q/U8s8SWaWWtzQogEfYAdOM/wDs1ZYyvbFdN4ujEniUoCAGjV8/VVrL+yQwyhJ3IAOCVG4/lmveactT4OTUZNGackjNO8vOPl4rWls7MJ1LH1ClQfzqtLZxAEKjAkUnCwudFHA4OAaEXtgVZGnpuIMnbtXQ+Hfh5q3iGUSQKbeyzj7TKpweRkL/AHjz9OOTQo3DmT2H6H8Otc8Q6S+oWVqGtxIUDM2CxHXA5OOfSq178L/FVrcJ/wAS52Qj7yZ/wFe/aTo+kaPor6ZLfQmB33lFIYjp36ZJyemOcYqhe2/gnTyJniiuJwc7icBfTn/9f4V1RhTta1/vIXN3PLp/hBe2XheXV7+9FuyQGUxPEvpwM5PJ9OtU/B/wuGvWP9o395NbWhJEQWMFpcdSM9ADxnnnI7V6fr9lpfjjURCLt47e2giNvIgBjc5bzAFznpsx/u+9QXPiOyt5F0y2TZa2iiKLaT91RjGfoMUpKEbXVilGVrplO+8Gadc+E7Xw41zctaWk7TRP8ocE5yM9MZYnp3rB/wCFTeHlBcXWo4ONv76M/qE5rduPFKG8ASCEQrgZEYyfU1owa1YXEkYlTJB3LlQcfhSVSh9pA6dXocO/wm0eW4WGHULpZm6RvLGWP4bRXY6P8INAt9C+xX0lxcytL5rTLsUggY2gkHjn8TzT/wCxLGC6uL1y95LcH9yzucKO/AI/p061ixXes26zLYWF1NbTFHTYWlUAZBwcnrkdz0FbTp0mk4bGcXNO0tzoW+E3g+Oxntkt5GnnQqk8rbmiOOGAUqODz79DWKnwQ8ORsfO1XUXOeisij+Rqo9/4leRpIvD2ogh+FKEcYHf/AD1qMv41mOV8N3gHq0gH8xWfsqfcvmkbP/CqPD6xNGt1fbG4x5kf5j5etV/+FPeGlOWnv/xmQf8AstY723xBLny9FkUdtzj/ABFM/s74jlwDp0i5x9x4j/7NRy01/wAMO7OgX4R+F+FM17+Nyn/xFWY/hL4VxxDcTe7XgH8lFcuNJ+IxQkWd0COwkT+jUNpPxIWNdlvdA9/nH9DUvk7fgPU7SH4Q+EfvPpowPW9c/wBcVa/4VT4PUZ/sdD9Jicfr+NeeGD4mW5D+RcgHII8wDHA55PP59qde3nxAvb6bMV1b2jMSsEcgbaM8KTnJ4796m0f6Qf1udqfAngpTtWzQY7AsD+eRVe58C+EZEKrZTlh0AuT/AFY15lf2HjjeAtrqsicABUkb+VUJtJ8YLgy6brGMdWt5QP1FDVP+kJc62PSx8PfDvVIL0R9wZ0x/6CT+tNPw/wDDcfJs3YdcSTvk/wDfJWvLTpniNkLfYdSIH3iIZDj9KqtZaygYz2V4oI/ihYcevSsmoF3kOONoBO4e5qNog4G0kZ9O1SKrZIK5z3NSRhi20KMU7smx0ngLxFd6Vra6a8pksLsEyW78oxAJPB7kDr1r2vxNK2jeG5pNJZYjaxKYTtDcAjg7gc8E9c+vWvnnTwINe0qZuizAN24zg/oa+itZiS58DTu27IsTj3YDv+VOMrNDtc8yXxjq96EMl5MpYAnY20fkOBWfdeJpYpJHkvXjtkAXe0h/eN3I/QYH93Peudub37BZNOVDBBgLnqeBXET3M13KZZpGdz3PYeg9BVzxdScr2SS8h+yjGPmz01PiLBDKAt1esv8AeRmH8yK7DSPiDa6java2+oP5rqy+XNwxyO2ev4E18/gngU9JWiYHLKQe1ZzqSl2+4IrlPZ7m1GTsRQx6nFdnBdT31volpPPNtlW3WQJMw3A4B6EdsivMvBOvLqrCwv5W8xMbJepK+h+nr6V30MT2mp6PsicBLyGN5GBww8zAxk+h9B0rqjVjJaaMzcOXrdHdjwfoY62Ct7u7sT+JOaYfDWijj+z0XgnCO4OAfY10OKx0snTXri4aRvLkRSqeYcbh329KxUm92U9NiEeFtOH+plv7dvWG+mX/ANmp9hqFxp+tRaHqEzXC3EbPZ3LgbmC/eR8dSODnuPetcDArlvF48rVfDF0pIddVjhyPRxz/AOg0opSdmO9jdEYhuZYx93OQPSpO1LOB9qJHcUuMilcEYniWzN54e1K2AyZrSaP80Yf1r5FUFsYHWvtCWMOoUjIPavkJrCWG4ljTrE5Q7h0IJFTNjSKZgYDqOfU0ot2xkN+QrXitk8sF43D9yWGD+FTIypwFTHuK53M2VJ7mCYnxztIzUQeWzl3p0PY9DXZQzQqgbKbj1GOlVtQt7W9gdNyB85UjqKj22tminSsrpl3w14xyEs9Qbch4WTqY+eh9R/jXaEAqCCGU8gg8EeteKOj20rLnBHBx3rsfCniXyJk0+8k/0eQ/upGP3G9D7VnOly+9Dbsetl2ZWapV3p0f+Z3GAO9JjHSnsMGmd+ayufR2FpwOaTilpAOBpc00UppDAnilycA03HOaUcd+KaYMep4p2fSmDnpTh70wuODc+9SBt3J61DnvT1OfagT1JgR6c+uaCm9WX1FNBqRe9S9iGjzXxLdQweJw9xCzr9mVFBxwQFGe/pWbLewTANHZwxuD94Fmz+BJH6Vc8coBq8DD+ONifwdl/wDZa5xCVzg8Y7V6NOpLkVux8HiKa9rL1f5mv9umI+WXy/8AcAX+VVTI2Pvk5PPNRh/lHGSRSxkeWM8c/rRzNmHIdR4P0RNXupJ5/wDj0t/vKDgu+Mhfp6n/ABr0O81O/nREjj8hUjC7In2jA7DA4xxxiuAm1qbQdGsrG1t7XzShZ3ePJbJzk4+uPwrnrjxJq07Nvu3XHQRAIP0rVT5TTkO51eTUEJW0jabcBmaWRVUE9RgncSPp9D0Jr2ukZmLy3Bnwcgt8o/LJrjP7RvZECvd3LL6ea2P51aS4dFLTSMc9CzEnNP6wNQSPUbvURp2kzSQvtYKFQe5AH+P5VxsF0ijLHLN9455Hr+NZt5fQx6TCyyBnJXK59jzj8qxEv5GkYnndWLlzSbN5u6iux3Ed/C20sFOTu2jjA9K0IJLYyRvvO4n16dq4S3vGL5BPmdye1asV1hggn6fe/wDrVErDTZ2guy1nPYeeVVxuicEgq3tXY6Z8TdAbTY4y/kvDCo8tFZgGxyvAOBkfrXkpuTuBGCVP3iSMVl293aAyLHp4Yq5Bc3BAb8NprXDtWcWZ19Wmj2af4m2jsTDHOUzjCr7++O2PyqP/AIWVA27/AEG4Zc/JllGBjp1NeOvqbqSsFvbBRjh97H9CufyqWG+vJSgC2/7w4IW0dtn/AI9zXTzxXQ5+Vs9Zb4iWrAEWD792DmYdKSXx9AQohsA7sCXBnGQc+3tXlA1C+N4YGuIbdtwUu8e1V5xk56D602bUbpJHiOpwyBTgPGQqN7jdzj60c/YOU9Wb4hGMlVsogATy7feH5DFOPxELFSLS2Vc84XOR+NeTfbpWVE+3hHZuydvwXkfjUdvdXd3I2/UTHnO3zJVTOM+p4/KlzDseuSfEC182dhBEqAFok3BS/IG3g5yAc59qrjx0rSuywMIsZjRHJIORwSeCOvOc5+teUpczOyCTUJsp1BGePwFOaeYOxbVdpZN6qYc4B5Azjr0PejnCx6qPGRZ4yJrpMt8xKKwUZ7YPPFFz45k8iM273KT7v3rMg2lcduD3xXkP2i6ZcjUpfM9AmP8A9VWbie6U/LfX5w2SJN3TPTg0nINT2Ea7qU1uskd7G6+XveJLqEPxk5wpDDjtj3x3qqviPUJziwuriaU8eSVLEH05GD+JBryj7bdyyw+bdOzSSgS7kbG04AJOOcHml+2XUMqtKFM4b5ZlG75s9+x5/nS0Hcbq97a3MsbW1uIsrmQYAy34VWgmjSZCykgHkCmNEGRt+Q3Y1BFG24DByOtQtAZqXskBht7iBdgWc49jgGvoYnzvAU+QPmtJR+YNfOMwxpir1bz1wPqDzX0hbIT4JKdc2hxj3BpXLSPl7xHcYigg9SWP4AVz4VuCPwrW8RKyaguR8u3ANV7OBCDI5wcZXPeoeiuV8UrCWuj398wWC3dvotWLvQNQsZPKuI2QgZ+YY/nXdeFtem02xhSZWmsZJ/ISWNBgSYzt5OTx7YrevNds5fEEFhqGjXQSUARzyAYYn0Xv+dZc8rnSqEHG9zlfg/ab/iXpbtPFJGnm7o25z+5f8OK9W1+8RNfsbWMQhRqUXKzZOPMDABQeMDjp2rC8H+Fra1+LFzPaObf7PEZ/LC/K25Ap9McyZ/Cuwl8ALN4h/tWS/P8Ax8ef5Yjxzu3Yzmuqi1a7OWpHlfKd1ngVXa3DXAmydwGAKnpQKE7EWExXMeMkLS+HMdtZgP6NXU4rG1u3+1ajo8ePliuTcMfQIp/qRVQaUtQa0LkhzMPoP5VJUfJcEjBPapKQDT0z6GvmLWIPI8V65bkAeXfzAfTca+nyPlIPpXzN48sZx4/1wQuUJuN/HfcoP9aiaui4uzMS4lVMk4wKw7meUvkOwUnjmtT+zL6QASF8dsr/APXqK70iSKNSxI9Mjis1G2427mT5r92P4ml3HHNS/Z42fHnDAB+6vpV3TdB1TVYpWsbK5nijI8x1T5Uz0y3QVS7Ikz8b1P8AOo1Yq209O3sfWusg8B6468WoB/2iT/6CCKkHw18RSHK2aH/toB/OnyS7Cuu5u+D9W/tTTDBM+bm3GDnqU6A/h0/Kugx271x+l+AvGOnXsd3aWKLMhwVMoIdehBx2xXoC6LqnlI09k6SFQWVDu2nuOK4qlJxlpsfU5ZmCqU/Z1HqvxRR20oGKuHTLsdYJf++DR/Z12P8Al2m/74NZ8rPVVWHcp4zRg1ZNldDrbyj/AIAaT7LMOsUn/fJo5WV7SPcgoxUpiYdUI+opNp9D+VLlY+YYAaOfWn7D6Gjb7UrMLobnmnAnPWkI9qUUmCaZIrGplOc1WzipFcVEmOyPN/G/zapbDv5Ug/8AIr1zZjdcLhh6ZFdP4ykSHWrWRgCpik4x38xjWT59rdRMYm2zIpYmQhQwHpnvXpU9YI+Dxi5cRNeZSAYHHPvSG3kZcLvx/sc1oxtbTwTSTpIkpCiMwY2g55JXHpngEc1oWHhma+EB03WIWaWfyfKYBHHPB2k5Ixzn8KuxhFXVw8R2N5aWekXczx3EVxZq6Sw5I6kFGJ/iUjBHaua8zzH4UgHGeK9EVZtFsbLT5JUuLKdYnZJmEoQuoYkIduDljgg+vPWsbU4kg1gwW0ljPbhi2UQrlV5PBPXGMAE5zweKc1bUtauxhLGpQYyCBzTrhSwbB78AdhXb6z4X0XT9UvLNL7UH8g7QRaqQz8fKDu568njoetc41jZoUDTuyugcGMq2O2CexGOmK5/axuXysS3t7TVbcwNmN4o8mQjpyB/Wqq+H76zlgMnlSrMu9QjZ455/L+ddV4etYkt72IBpYXZHXPDLgEfNjqPmzx05rXkfTbhtKjjkiuJrCN0mZDggknCkA9ADx+NcbryVSSWx7VLC050ISe5iaT4MvL66AlVYodhbdnr6U9fBlzDqfkIyukY3yOeAq/412+kXEanbGhAC7c+oFQatbSTfvFkltgJA5miXcA3bd3Ix2HTr65qNWTepc8NTWljzC7ttZl8+Sxspryyikx9ohiJAXg546dRUKLLCmG2Beu51Jyfx4r03TNW0iztte0i9vbW1YQyxoYyqH94uF8sdxgkn+7hR7156mmXMQAS7m9gGNelh+Zx5rHh4qMIT5YspfaAVOJWU7sgR/LgenAFLLLFtWR0mkYDaS2CTj3Jq3JZX3JNweDj5h/iaT+ztQByZQccj92DW9pHNoUxeIyFfIKrgr0HOfx9qQSwBHzHIW6gblXPr2NT+TeKQSwXjH+rX/Dmmf6QJC8eMkDO5E/wpajsJHdkrtW0Tr97JJx+eP0pWukfBNs4fAJJkGM46YxU32q6VCPs9u5B4LIT+HWljuLwMrJBbCQYORAMmquxWIVmQuW+ynzAv3jM3B/DFLFePFH5aKVyxO/zWJx+dXDdXysWNtGqY6tbRkn6kjmof7RkLN+7QAH5fLt4+n4AUNsVit53znfLOUYbSvHIP1NPjRJEYGJyRx88oX+lSHD8/Z5yxPDBsc/TmhEmz8ltPgnkPMACP++KQWIHMYhjVYQCQDvEhzwf932p8k7mRnaIkkYcA8ZPQ/XH8qstFL5YHk2yc9XZmI/kP0qOTz5XYG6h8wpkjB+ZRwPrSAR9U0acZQyx+2P8AP86iN3YKMpLvHoV2muXZnIA5Ipgz6Via3O3hs5r6G2a2geR7lsxJGpfKhirEkfdwR0PY5r6Os4SPD8cBUgiAKQf93FfLmleL/EOj2aWthqDRQJnamxTjJyeSM9ea2IviZ4wjXDapKVJx9xQP5UrPoO6SJPF+iLa6tcWM/EUoE0En9zKhsf8Aj2PwrC8OwxozJOqyBGIPcH/PNev+LfBdzr3gqz17colttPS4nR8jeBHlse4xx2PrXk+lWxt7tsD93LGsij0B5x74O4fhSn8NzWNudWOv0mz0pb5X8mNZc7kVRwDjGcV2Nlp9nqt7b3c9ogvYTsEjnJUA8YB6d64mytiXR7VVM6Hcu7ua9B8PDVb/AFYNPbOPLjG0rJ5gPsSfqTisoe8rnc5JK1jqdK00adeT3sVs7yXMaozeoQkD9K1FubknmzlH/Ah/UitORdij+6B+VRiQA4/SumOisjzJvmlcqfaLo4K2h/4E4GPyzThLfHgwQr9ZCf5CrW/NNJ9KdybEP+mNwZYUHshJ/U1HFZpHOZ2LSTkbfMc5IHoOwH0qxyeaQtjrQAhAzmjGTwKazqBnIHuTVKfWLS1B33Ck90T5j+lAy+wIU54ryrxD8PrfWPFl3qd1qdzbxThMRRRD5iqhc7ifb0rpNQ8e6Vak+bcRLjt5gLfkua5PUfiLaTlVt4LiYJnYSNoGfr2rCu6nI/Yq7N6MafN+9dkTxfDvQo+Ga8mHbfLjP/fIFV7/AEXSUtJoYLFJJQpEKyMSGbBwGOeB0GfesK88cavOjJEtvAh9AWb8TxXJ6lrGq3c7D7dIidxGdv8AKs6eGxb1m7f15G0sRgo6RV/l/merPoXg2ysmjs7eE3WB/pU6hVB77UyM/j69TVlte8LadKxstPtkJPCmQMD9RyAQc9K8KeGdnJkluGyeNzk/1qOSB8YXdn/eNdTo139pL5HPGvhYvWDfz/4B70/jqED9zb2aj02Cq83jy6x+6itR9E/+vXhiW0m3oxq/BA6jduwB3rnlhMS9fa/gjojjcGv+XH4s+g9E8W21xpqTX0sS3ByDGAAAATg8+1WpvFVmSiwyrtyd/TB9K+bpdfvrG4KwJAUU4G7PoPerVt42ugT51rAfo5X+hrOcMVHRJM6Kc8ul7zlKPlZOx9HRa5YXBH72AH6gVeWVHAZcEHoRXzva+ObRyEntZ42J6oBIP0wf0resfEwk+fT9RbI6qrlWH1B5rGWIq0takNPI7KWDwuIfLQra9noe2CQ9MD8qcvPYV5daeMtSjUK0okHbfzWtF4xvGgeVLbzPLHzhEZv5fifwrWnjKdT4R1spxFFNztbvc777Pv5KBvrTTp8LnP2SEt/1zFeYT/FZIVAaawjJHQyMzfiFBx+OKz5vjDtO0XMbehijkx9Odtbe1j1T+44fYz6Tj8mesvpFu3LWEH4RgfyqH/hHoZTn7DCoPsBXkLfF28mJEMkzntthz/7Mahf4i+JGG6G01Fh6pCV/lQpUW9ZW+TB/WoK6X3NP8LnsTeFrUjm0T8CBVWbwjbHlbdh9HX/GvHJPiD4pzn7Jqg+u4f1qs3jXxpLxDZ6ntPfL1Xs8NL/l6vxM/rWOh9iR63L4Xt4y3mW11j/YYf4GqEmk28UZxZ3AbHUsa80TxR48Bz9n1AfWQ1Yj8XeOFOWt77/v4T/Wh4bC9K8fx/yN45hjetGX4mP4u0fULq6tXjs7grtcE+Uxx82fT3rCPh++jTcbeUk/9M2H9K71fG/jYAL9jv2A6ARbqP8AhYHjBWAfS79sf9ObH+lXGNKKSVSL+Z59b2lSo5yptX8jjP7Iu1t+YpO3Hlk55qb+y9XYIsUAVWPLvDJ8vQZOBXo8HjTxeLRZpNDvijKGDLa8YIyOvsaI/HfiZ+V0PUTnoFss1XLG+sl9/wDwDNRdtEzhrjS9cFslvJdRXFrbhhC8KMAC2M43ANyByPr680hpHijUpjJDYifaxI8uMLycZ449B2r1vS/HWqpMTqHh3VpEZQQYLL73frwKra7rkGv37GfRdaS1EAh+zzxtEu4EkPlXzkZxnuK1UL6N6E8rvotTzi40/wAZmR5ZtAu3ldizMIyck9+DVZdF8UdT4c1Hnv8AY3IP6YNdhH4Oje3CQHU4pzjDmVWB/DNYHil7nwzNDA+pW8spU+ZAR5kg9CwBwOvfmpdCjFfELmqfymfHB4hsZHu3sJ7aFF/eM0JQAfTjFP0TUYLjUd0Yk2uSrZzgnr/T+dQadqMevOLK30VZNQY7klRQwGOuUxg/jnmrujaNP4Z1by9SCKt0PLjOQVU5yOfqAAQccmuKvRhrKJ6GExE4tRlsdnBd/Zo5H4REXIZzgZ/GuW/4WTrFrLILKZJIIwWEkkbbV6cgKRn8ePr1qn4v8WwXl7/Zem7Bp0Jw8qj5pz7E9EHbu3U9gOdhHmWAtmVUQ4LOc7nHXAGcAe+KqjhYx1luGJx8p+7DbuPvtV1jxbfrNeXLSLHkq78bQeT0+ldFb3dz9jEPmKzKPlck8/XrWLCY4UCKNqKOn9atC5A+YNk+9dkLRVjzJe87st3txeWrx7VDoR94IT/jVf8AtS7kddzSLtHJCNnOfoKtRXgUYJzGRyD0PrxVt9Q0eGNN9kzNjnbnnp0OD696druyYadTNN98xLzyY94c/wAzQt4CuPPkP0tl/rVv+2tHYsPsm1f4cRbv54pV1rSQDusXY9sQrj+dS423YX7FP7YQdpknP+7BGn+NC3obO6KZxjpJMxB/75xVo67YZxHphz3yiD+tIdeiBJTSUH1kXn8lNJWC77FcX0SkMNPtwcdQjN/6ETTvt85yVt5ueyHC/oKsf8JPPHgxWFsvsS5/likPirU36R2ac/8APFj/ADajQWpX+03RXBtZN2c5LH8qBJfSY8uyYt/sh2P86kPiXWCOLtE/65wIP5g1G/iXW2Xa+sXYHcLMUH6YovELMBY61P8A6vTLp/cQOB+ZFPj0XXQ2Dp4jLf8APSRR/NuKz59TuZwTJqE0h/25mbP5mqTMpz5k46dNwouFn1Ir+zWw1CWCIiWNCNpb+IYz2xXUeFfAeueLTG9lpCw2bE7r2UmOIdfuk5Lcgj5QcHrineBfDg8XeK7Sxn3/AGcKZbll6iNeo9skque26vp+FEsYVjXatuuFhijQKsaBeFAHQcGploy1qeUad8BLNWB1LWZpFxyltCEI/wCBMWz/AN8ivRdD8GeH/D0MSWGmwCSPpPKvmSk+u88jnnAwM9AK3VOQPWqt1I8c6EcgYOPzqdWyixPDHcW8kEyB45FKOpHDAjBFfM/iTQ5fD/iKTTski1ykTEffiYl1P6kfUV9LpIsgDK3XqDXCfErw3HqdjHqUUTvc2gOVjPzSRfxKP9odR9MUWunEqMrNM800sxsyME2tnBr17whp80KtPISqAYCkYJP+f88VhfD3S7DWvDrz3ViTIWISZ0I4BZRjPGflzxnk47V3mmoU02PzCDKy5kI/vd/1zWFOOl2dNaqrOCLFpOt3ZQ3AGBLGHx6ZGcVVu7BC3mxAr/eCd/eovD8obTIYCAGjiQ8HPBHX8w1a1bJ2d0cjRzcmpJGTxM+PRBVSfxHZ2wzPNBAPW4uI4v5tUfjnwPa+JdLuJraILqqpmJwcCQj+FgeDnpk98c189+T5Ux3QhXiVty7duWGTg/lXZTiqivFmM3y7nvE/jvR4UZjqtoQP+eJef/0AVz9/8T7WIslrFeXZ/vACJD+Z3fpXm0mA7BVOAcDANRmTaeeD6E4/nWnsUt2Z+1fQ6i+8f6zd5EMNtbL2LL5r/m3H6Vz95qmpX67bu/nlTrsLYXP+6OKgWOaX7kTEeuRirEWlXMrfMVQfnTUYroHPJ9TPKoO2ffrURmOdqjJ9K6aLw9b4BlZ5D3BOBVuC1gsmDQwxoB1YL0/GhvsScvHYXs4yy+Svq/XH0qxFpdvG48yQsO5P+AzXTBkm/wBXIRKc8Lxk/WpbWyVJkaaAOoPKOOtTcLGZFNplqiCLS7WaQfxyqXLfUEgfpUj3kt3E8EOi26oxBzHE2R/MCu1gtbJcBYYo89Nq4IqyYLaIH51B9CaTaK5WeaPGsbz+fpiRSRWxlXfGBv6qPx4JrJ1Bli0+M+WiFiT8v0HH5mtnxBO11q1/hv3BkWNR7IMH/wAeJrlNflYXKRFuAvIB7nn/AAqG0NXNLTvBX9oadDd/2jsaYFihtEfb8xHUnJ6VVvPh9eoSYbm1l9iPLJ/Dp+tdRoWoQ2+lQR4G8LyG6dc/1qSXU3acnYME+taezTWpPO7nmd74evrHi5tpYwTw38J/HoazGWe3lDpKwZTkEcEfj2r2231CNwEZQdwwwIyD/jWFr/geDUEe600xwOiM7oc7CAMk8dOAfb6daiVDTQqNTUX4fWd54tgdDIEe3dVlkZeNhz831GDxxn88HxO1f7Lcw+GtPdo7Cz/1nPMsmOSx7+n5+gqHTteuPh94ehjtEj/tK7cyvuXopAAVvUDGR6Fj71x2s6x/asvnSRN9pJJd92Qf61w0sNChKUo6NnqYnH1sTThCrK9v618zuPCPhfQJvD8Go6kJrua43EQKxjjjAYqM7SCxO3PXvXRxpo9iQLLR9PjYdGNursP+BMCf1rnfDEh/4RSw+kg/8iNV8cd/zr53GY+v7WUU9menhcHRdKLktzoU1S+KbkbYg7oCAPy4qtNql1N8rXEhUHj5jWWbmdkEZkbYBjbntSRkkE561xVcRUmrOTZ1QoUoO8YpP0Cex1a/leS0jV487Vy4HOO+T61y0OvXOm6jPY60DazRNg4yQOMjkZ7EfnXpvhvDwuncOf5V5N8Qog3jfUsnp5Y/KJBX0+FwFCphYS5dWjya2bYulXlDn91PY6SPX7ST/V6pB+NwB/WrkV6ZeY7pXH+w+f5V5QbZhjoR7GneSy9H21nPJ09pWNI57NfFG5655s4/ib8zTftcyn7zfma8m+03lqwMckykfxK+P5VYt/FGrWrZaZpV/uzHdn8ev61xVMlqR+GSfyOiOeQl8UWvnc9RbUrjGPOkA7gOcflTDqU4HEsmf981yWn+LLO7ZYrkrBKf4s/IfxPT8fzrbLjGc5BrzalGrRlaasejRrwrR5oO5abUJ88zSZ6/fNKNRnPBkYgerGorWa3u4Zkmiit2ik2rMZDmUbQThcYGM8uTgcAKTWZ4glax0dp7SUMxlWPO0ggEZzz+H510RwtarJQhr18l6mE8ZTppylp09fQi8R+L3sYPslhKftTffcf8sx7e/wDKuCw80heRmdmOSSckmtC10q5vJ933mc5JOT9Sa130BbGPznnhZ16whvnTuCR6cdemSBzX0GGwkcPDlj82fP4rFzxE7y26Iu+DL+z8N332i4uFj8xvKufvbxEf7oA5wygkZ5HA71keMfFs3ii9G2PybKInyYuMknGWY9ycfQfmTj3155/Crjuzd2qKytjdTqp4TOGNdLsnaPU5m21Z9Cxptg11mSQERL/49/8AWrQOTyQBjoMdqt6le2lvbW9jZKQQxM7ngdMKB/nvVRSrpwRjpTSsJlZoZHkG6XAzwAtKVe3G5WZgvJB9KlKkHkAkdDTpMBNx7frQIV5yirtbKn0+lTRXaCCRpF3eWN2MZJHfH8/wNZjvstmj/wCeZ2qfY0tlMn2lPNz5bHa4HoeD+lTIaJm1e2X7tsc+6ik/ttM/8eoz9R/hWe9uY5GRvvKSD9aQRHFRcbRoHX2wALfp0+f/AOtSN4gmONsCY/2jmqPl9+9KIuaLisiydaumH3I1/A/40DVL0jgIB7J/jUQjHAp+z5f50XCwHVL7OSUz/wBckP8AMUv2+7ZuWUE9xGg/kKAnQUvlFeo60XYA93csm12Drno43D9afFq93DjyUt1ZfukW8fH/AI7SCE+lVHUiZwPQUczCyPpj4ReHk03QZdVntvKu7qQxAsuCI14IwQGGXzkHrsU139/88LYJGYiPbnv+BA/M0zbDHpEYsAnkxxAQhMbcAcAY7cVMSbi1EkXzdHUE/eU84/p+FW3d3BaKxNbvvjQnglAcUy7TdtI7DkVWtbhVjQ84BEeD164q1LJtlB/h6UrWYzP2yIS8fzL3GaqXF4VAEuWBPykDqf8AH/PqK2JLeJ8uuVf+8jbSaq3FilwjCRBKDzhuMkcj9e9WpLqS0JY3ltFDCAqQoW8kgAKN+fl/A5/M1dtJ0l81FBXDtgHvgkE/TNULHSkW3zNEiO2C6J0yDkc+3rVDTbSBNWe8aIfbZFKPMzFto7gZJwMjoKlxWtir9zZtYBDdHy4wiopibCkZ5BXGeoAJ/Gr4ORTdwK5Bz9KUN3rMY6vG/iL4Wgs/Ef8AaUMe2G+UtIFAx5nRj+OQfqTXslZHiTSRrWhXNoADNtLQknGHA4/Dt+Na0Z8k7vYiceaNjwZdLtw3Klv94k1ZXTLcdIkH0FT24cMFmjaORSQ8bjBUjOQR2PFXCy5OAK9BnHYqR2aDACge+Kljt9rHI47VMp5xTy4A6ipbKQwKMccVXubdTGAXP0xT5HA7/jmqs1wGO0knHcc1FyrD4beGMFgSeByfWrCakqqsbE5zgEelZks8gUJEp571CsUyYZtxPWjQDpbV45pgQ/cfWoLtzp897ceeSqRkrkdW7CsmJ5U+dW29xg03WbmTy4IXJ3N+9bPoOn61lqi90YuSuAxz3Y+/U1ympTGW5dycqznbz2zXRX0hhs5pOhJCqP8AP4VzO3zbyNBzh8H65qXqylod5Zae0dpFKxG11yB3x0pZNozntS6VC5tAuTs44q61ljlhz712Jo5nuVIHwc+ldFp8rnTbgvtS3Vd80smdpXnC57dGJ/3cfxVjQWb3FwkUQ/eMwVR6knAH50fEXVV0yzTwxZPtXaoutv8AFs++3/AjgfhWVado2RpSjd3Z53qt++qanPfY3KzbYUfH3OxPv3rHlSSMMGU8dyOtW2mVyxJwo+7jsKguJMw4zxntXI2dB6L4VP8AxSth7B//AEY1aZrL8LjHhix+j4/77atPCjOT+Ar43FK9efqz6vD/AMGHogIwOuTU8ZGwfSqm/ByPWp1YkcDjpXO43NrnReE3BvbhP9r/ANlFeZ+Pv+R61MEd1/8AQFr0Hw3cJaXsskrhULksScBRtWvOfHlxFdeNtRlt3WSMsgDocgkIoPP1Br7fL/8AdafofJY7/eZepz5UgUHninbiRyOlOwj4ydpxXWcp0fh7Q9EvtOe61XUWhPmFBFE2CoAHLZB4OePp+Ws/gvwpcL/oviFQ+OFZ0Y/lxXM6dqdzDby6cL1IbeXJzK0mxT9FzycAZx+VUZIGW6aMyIWDY3q2VPvkdq0UYtCuza1L4bzAMbK5huRzja21+DySD/jVbQdK8R2eqR6VNZSCKTJDXB8tEABJIc8AfKfyOKRb67hitFSXZ9lYvGyHadxOc5ptxrl/NuVrhwuAdqtgZHHQcfxH6A4rGthqNSPLJG1DEVaM+aDC8ub2O6li80KgbAVGB/MjrXV6DoFl4m0yO3mvZFKsHkSNQG3fU/4VwTXbNIDISykc5PQiup8KX507V4pkYgE4ce1cTpuhDlg9DujWWIq81RanWaj4PfSbLZZ3MUMIGWaRQHPuW7n04AFcg0NrMssg2mDDBZPMAAVsqGfqxfndhuQB7V7SsserWRIVJl7oQMmvMPH2taBNpr2VvFCdSjZYwnlFfLjz8xB4BP8ADj0ZqdKtJvlNK1CEY8y6Hk6RGWTbjA/M4q9EDbY+6B2BPOaajJFkqq7mPY9TQI8ZeQ5Y9fYelbJWPPuMdfMZnY8ZqWyclV54Of51Tup8jyk796sWhwx4+Uc59BQnqBoSsEUMV3VEs+8lDwGFWgnnQnawAqg0RAYE4ZabEVp3BiC9yQT+AqCM4bnpUkhJcg9gP8f61GOtQxlyaSPzMswDMAW57kVXa4iHRvyqrcENJk9cD+VRcdqgZc+2Rjsc003Y7J+ZqoSaTP5UAW/tz9kX8qb9umBO0gZ9BVajNAE5vJz/AMtD+QppuZj1kb8KipKAJvtEh/jfH+9QXyOCfzqJadk0AfdSxxxwtDFwqcY9O9VYJ4bWwaOdgqhxFn13ttX9Tj2pYIpAGlc7ZHYtx+Q/QVT12283SJ5mwrxgOpHGSrBl/UCtbdCbmbZ3bzXu7d8rOWA7V0J8xTluQa4PRbj7OEhlDbQAA/UiutttQO0ICrgce9aSiSma8ZBXI4pwAJqrBOrnHQ+hq2mDWLVjRajwOMCsEJ5d3I/YzupP1JP+Fb4rMuoC4mKDlZAceuMGnBikTWzscjPAqd2PlsF6kYFVrYYL8d6L65+y2VxP/wA842f8gTStqBbtZlnt45Uk3o6Blb1BFT1j6RcK9nFDHhdi4T0x6VpxTeYWUja69QalqzGjj/HegNdQjVrcKJIEIm6DKDvn25/D6V5g9/ADxcwe+JB/jX0GQCCCMg9jXlXjjwmun3CXtnBt09ziQJnELH1HZT69B0rpoVL+4zKpC+qORN9AeRPHn2YVDJfQjrMv/fQ/xp76QhXdtyp6HsapTaREuSAR9CRXVyswuh76naEYaQdPXP8AKqz6lbLghztHTCmq8mlnJClvwNV30yQH70gI6YxUNMaaLw1q0T5mnl9B8rUja9p5cFpWye7j/E1lzaYxUbnyewP/ANas6bRpGBI24653VDv0LVjsbXVYJbhExmJjkyH5l/Jcn9Kq3k73VzLKVKpuwisMEKOn865lNPvY/LeZL37PnaZFO4qoA6DsMYxzVs28zJusteguABgW92phY/7OTxn8ce9ZSUupokug3XJlCwxK2VUbmNZvhuA3Wqwhv9pvyBNF/Hem2n8+zkUxKBK/8MeSQBnoSeMYPStLwfbEzzXPZF2j8ef6UQi+ZIU3aLZ3NtD9mtx93Gc8VJczJIF2/jVMPlevSiRliHmFiEAJbAzXU0cqbNjSJ47CG61aQKRaphCw6TMDtP8AwEBmI9MV5hcrc67Ld37IzG6cNGxPKxrkAfjyfyrW+IGqyWNlb6DEfLl5e72nrIfvj6AbUH0Ncpa63dwDyllAVQAoIBGMdK5m1KWp1Ri4rQbcaK4wqqwI65pLbQ5Hk23DmOLaTuHPIHFaB16+K8bPqFzUUWp3Ml1GuIWZsjDsACMc5pctO47yOs8PYXw9ZrkfKGBx/vtV8sGrK8PTb9I2HGY5XU4/3if61pEk9Oma+KxatXn6s+rwzvRh6IAh3deKmV8D8ajGce9PTnqOQaxbOhHQaBBa3sc0FxErKzc56kFSCP0rxcRgyHB4ycAema9C1i7uLLQJ5rdtoLiOTHowOP8A0GvPN4zxjPvX2GWzTwsPL/M+WzCLWJl/XQm8jcMhvl754xVcn5sAcCpRI+0j5QD6URxkHcGVhzkKCSP0rtbXQ40OWTGD/KrEroUSRSAehHfNVTJGoO5SvfJ7U6O3luADFE8g/wCmaFsflVKdlYTSZI8gbAB5+tQAH5ieOD1PtVoaWyAPJPDEhOAWkyQfdVyw/EUhht0bmZpT/wBMkyp/E4P6Um7giqQmwAnnPYdf88Vc08yedI0QZ0SPfLj+BQep9u341EUikDBY9hAB/eSc4yAcccnnOPaqV1CI0ZlOF6DtxWc1eNjWnJxkpI9c8NarHFpP9pQXskktm2blM5DRk/ex6Ac8elYPxTsbJlttatRsuJZfKmQAkSZUkN6A/Lj3z7VgeEbmW3tbu72+ZFbRFbqH/npA2c/kefpVa519ptHm0Zn+0wxzq9tO4G5YwDwSec/d6eh9q4owaloepVqwlS16ow13cGOIbu+TRMHGSY2OR2NTqDnBOAeOaZISo+VcMOoxXUeYZyxkkna2W68H8qtRCVSBsLBTwuzrSiXkMN2O4zyKnd54UEsLebEfXqtKwFiOaaIbnh8se1MuZQzq69GzVczmcgszfSgyhYnLdQpwPei4FbO7LHqTmmjnNKPuLjoBSDpUgQyLls1GUI5qZ/vc9KYTx0qRke00bT1qQk4AxTSv1FADcGk46VJ7U9bd3yQYx/vOB/M0AQEAUhqz9lK/eliH0bd/LNAhjz80v5LmgCt+FL2q/Jp6LaJOsxIckAFMdMe/vVf7M2MqVb8aLgfdO5CnLDFct4ru3sdNihB3fablVJPdB8xP4YFdRJGoUkIM+tcX4k8LXms3dnfLO8kFvC0ZtgcdTncOevAHrwK1p2vqTK9ijBAhxJHOuO+7oK2LVovlJkViOvlkH+tczb2cNq5Edup/3mz/ADrYtEkZwRa25HoUzXTJGSZ0kM0R+6Wz7ircVwijaXP1ArMjijZRvgC46Y7VZSKMDALj6OaxkjRM1YpFcfKwNJGMyTZ/vj/0EVUhXyzvZmx0wTU8Em65nX0cD/x1TWTViyOVfKmyOjYrN8R3trY6JdXN2WFqoHmYGTtLAH9M1sXY+QN3BrnvE9naarp7adeCQw3OQREwDcemeO9VBXaE9CpoGs6beWsV1p9yt1Zg+WzKCrIfRlPIPTtzmuo8zLph+/yOPusPQn1r5j1C5uPht48v7LRbqR7WIxh458ESqyK21wODjcRkYPpivVNA+J/h/UdP2vcLbzYy1tO4Ta2OSrnhvboaVTSRUNUenJdIzlOd4OCtSkBhzyDXn+neJ7abUVNzdzqGYYTysD0xk9vf8q7NL6ETeUJwWPRXGPwB/wAmoaK5WUdd0GDV4FjVEjliGY3xgH0U47fy/SvOZ7a3ileKa3XchKsAzAgjt1r18EOOmDXC+O9EZFbWYOI1H+lDPAH9/wDx/P1rpoVNeSRhUh1RwV/Y27MTCrxf8Cziueu7e/jOIWhYdzJzW0bqGUbo5Y5AehVs1DJPF3NdnKc5zM9tqIOA0RHqBVR4b8EEOoKnIIwa6h2RuQwz71A0a5zkVDihpnGXel30sxk+0TSueSWfn+dV/wCxNSkb52Yf7z5ruTGBkrg+1RYHOevpUOmmVztHNWfh2TzFF1cYTK5RcndzXWQxxWkeyBFRSOdvf8aqNIgNBuU2kE7QO5rSEEtiJSb3LFxKxYtbqkLk8hFwGP0/wq5Y3rpZXWpXUKhdPUPt5w83WMfTIyR9PWsnT9QeO9muEH7uOPYOOJGOAAfTJ7mr99eGCSx0w7Wlt915dZHDyj5tp9ckKgHoBUVnFWS3ZdGLbu9kef65JPe+ILtptzSoxjk3dd/8X5tmobfSL2fb5cfUAgk461Ua7eOd5AdzMxOW5yc9aJL+addjOcHsOlcTszoQ2bchIJ5zzzUAPPIp6Kz4XqO1alj4f1W+INpplzOpP3liJX8T0FSM3fBVzutrm2PVWDgexGD/ACH510xY5xisrQPh34tgvluxDaWgYFWSecYYH2QH6/hXYp4Sli51TXtMtUH3mjBJH/fRFeNisBUq1nKnqmexhsXCnSUamljFVyc8GpY25rb+w+DbYZuPF9m5HO0EH/0E5/Sg6j4EiU7NQtZj6J52T+a4rNZPVlu0jV5nRWyb/r1Od1sLJ4evI2bALRuMH0JH9a4NrOLgo8rHuEINevx3/hyUA22i6hcxsOGSNSrfnIM/lVy3tLK5YeR4MlbPeVIkx+YP8693BYWVCkqbdzx8ZWjWqua0PFfseO0/T+IdT+VOt7CWeXaIHUgZ+5J/jXvMGj3xBEOg2NoOxM5U/wDjgrQj0DUjjN5DEOpVUeUf+Ptx+Vdbh5nLc8DTR7wnalvPz0Khxn9auQ+EfEF6cQ6ZfTKOjeS5H5mve00FlO6TUL0r3RHES/8AkMKf1q6mgWTRjduwRkNK7Sn82JqeVLqFzwSHwBrsj4mhjg95JQT/AN8rlv0qxa+CJYkJ1S/trFR1MzsufpuAGfYkV73DZWEA2BTI/wDtDj8v/wBdePfH2QRR6DBGAgLzuQox2QD+tHQasYlyng3Q13XOoNqLE/6u2PJ/I4H4mvP9Z1GHUdRkntbUWltwI4Q5baMAdT1Jxn8az2fGaaMluawlJstIu2d3PCWEUjJvVkbacblI5B9jViNV2is1G2MCOnerSyEqMfmKVxk5baTlifrQJQdm7nCkc+5zVSQuTimCQrx3o5hWLbiPqv3qnjY243j54H6j0rOEvPcH0NTw3JjBQnKMOQad0BPJHEfmjYAn1qlOcnH+1zUpZW5HBqF2DSKP4hnNJgOHIpe49KReakCZPNICCRec+nalitnl+6rH2wa6XTFmW1EcGkzXjYLFo0Lbc9M4U1dY+IFAFv4eu4xnoYn/AMBQ4tb6DWuxyqaTeMMmJlHqQakXS5lOWjZvpXSrD4vZz5eiXSn+8LVj+pBpx0vxvK2fsd3GfVAE/lS5orqPlfY5+PSbhnGyycn0CM36Vov4Z1O5Jk/sqSNccn7P5Y/kBV9vDfjW4H74XpHo9w2B+uKYfAPiSQhntk5PVrhM/q1F4dxWkZjeGLhWCSyWcP8A10vIlz/49xTD4fUDc19YjnGBdI38ia1m+HniIEARQD0xcIR+hqSD4eeICxLiFQBxmT/Cq9wVpGauhWqoA+rWQXqArscZ+i0o0PTl+9rcAA9IXJH6VpS/D3XVbBezDH+9dY/mKWP4deIclQlq7Hul2uP5UcqC59WJOFXZKOg5JpYZN8AEZyAOG7N9K5rwt4m0nxWq3EdygvNoLWch2vEcc8H72P7wGPpVvTb2Sz1i40i7woaRpLWQdSDk4puI0ylqGgeXcvcwndETlkP8NXba1trcD5WZsdVHAroWQOMMByMVz+pHT7VH+23rRJGN5WORlIX1IU5x+laRqOSsyHGxMZwDhElP1Ap9tG+9nYYPYGsqz17SW2R6aLm4WQjLRQttXPQszAc+2SfatuBS3y5/OhsLFe5iVyC5aRs8KDxU1jKPtU2SDl+CD6AKf1U1ZlSKFNzEBvU1zVncg3c0e9sbmIYc98/1oS5osL2Z1F44EQ/31/nXEePX1uP7EdDlso5QX3m63cjjgY/HP4V0y70QO2JYsjJHUe9Yl74n0dnkjluUV4z5bZbac4BPX/e/MGlBO+mo5NW1PBfE+heIdQ1e61O8t0nnuSrMbQFl4UAY5J6CuYOk38LHfaTxtn+KMr/MV9DTeJdBVMgvcBCFYRNCSOvPL+31pbfxJoE+QluOOPnmYH8o1IqpUpt3SEqlPqzx7wd4im8NatDJqCytYElWx96EkffX0I/UZFfRulNDqemrIzwzQsN0cv3ldecfy/WuauLjRZlMUkFmzMpONzSHA/38Y6Vyl3cadYykWuo6pbLk/u4IYSg59C1ZPD127xidNPE0FHllP8P+CeoR/aobiM6fIRGGw8Mz5Qj1BwSMfXHtWsytd2ckF7bxMkitHIgbcrKRgjkDqK8Fl8bmxm2jX7wEdFntI1/9BNbej+O9Xv8A5bG6S8bIUIhAcnBPCsMngdunrU+yrx+KP4luWHn8M/vT/wCCb2q/CDRrmGT+zLq5sJesa5DxKc9wfm/Xj9K8q134d+MNGkP73zY+zQ5Kn8Rj9cV6Bc/EvV9Mn8m90u5BBxuaLAz6ZDVVuvi/MwZI9IjZo22ykvITgHBAwBz+NaQqVdjOdGktb/ceOzTa9YNtna4BH+3x+ppi+ItQX70k34gGvpC11Cz8U2Uc2mfZ5Rt/e22oLvZPzDZH0NY95pGktuW48M6C77iuFgRST7EOKarSkrxuxSwii7SaT89DwseI70gkyOR/urQviNyfmlmz7ECvXNQ8F6PLFuTwf5WTnfb3Dfy8wisSDwFpd+JPK0zV8RrvcLKvyjnnnPofXpVKtLqjJ4fs195wQ12IjBeYHvkhv6VpaDaT+J9Xi0+0nCNLuxJKpCgqhbB2g44Fdpb/AAv8PXMHnqNXeLJBMc8RAIxkcqBnn1FdBosmg+CrO4urPVr6Syt1bzLSadJUWQ4C7dvRjk8Z9Txgmn9Y7sPq0uxx1laP4e025GpbA8kimHC/vFYc9fQg9McFV98efavrE76pKYnA2kqxByG5/l+vA59NrxH4im1y9uL+c7VZtqIDkJnoPr3961tA8M6Jp1ss+vrF9ukZSttO5LjJ4GxThQSed2SPQVye1vLnk/Q6Z01CKpx+Zw+keHdV1yTZp9lNPjq4GEX6seB+JrvdN+E8kcanV7/YmdzRW6Zzx/fYcY9gR711+n2q6dbrFZbYVAAODtJwAMn3wKsu1zIjI0nmIeo38GvDrZu72grHpUstitZu5Fpmh+GdFAEGmxvIp++/zvn1yeB+FbD6oJJARHsQdh1xWN5qrwYwcccNQJkJxs/WuKeOlLeR3Qw0I7I5rxPYXE2qtFdXM86OoeMvIxGDzwM4Hp9RXPeI/D9ppd1DHaNI6yQrIfMUfKSOmR179hj3616BdwR3gRXJBQko3pnqPpWfqmixam8bPJ8yLsBA5Ir67CZ3hKtFe0dpdVb8j5zEZZiIVH7NXRwC6JunFu7bpTjCxfNk+nau10P4WWV5AXvZ5RIePLSRV2/Xg0ttp6aLJNeCdgTGyFsqpAPXBIOD2yOeSO9Ms/FdxZxtDYWVv5RYsT5ZLsT3ZgRuPvivQo1aOJi5ULNI46tKrQko1L3PS/DGjW3hizaynnDQIiiNs8nBJ/rWydY07JCznjsFH+NeVL4j1SUZfR5HI6ERP/8AXqzFrHiBmZYfD1ySBk5jYcflQ6Lb1ZPPboemrqNvIcI7MaeLgsv3gvuTXlz674lDbf7OjjPpI+3/ABpLbxRqH2lUvYrSVG4zHqSKVPphuM+xIo9iluw532PT3vIYx+8uU9CAapS6/p1qCgmLAdAO1c8Eju4vNgldkI5WRNrofQjkfiCQexqjPpzK/mt8y8Arn9f8+tXClB7siUp9DbuPGMG7bboS3qeK8o+MeqyapJpEjgDb53T3EddFND5d07IGxngYJrhviUWEunxuMMoc4+u3/CqxFOEaLa3JpSk5q5wzYIqc2si2YvGAELP5aknljjJwO4HGT7iqwNO3ZXFeRc7gB3nb0Hcir0UACnbPtz+dVIWVTyRn0NWhNtALRk/7QNNAyUWBc5M8hH1p39luTlXb26Uxbz+6WwO3elN5OfuqVHc9zTshAunBn2NKwbtmqUqPBIY5ByO9aazGVQScOPw/Oo5wl3FtJ2yJ0JpNKwGeHI4PSnx4LE85PWlihZnZWUq6nkGtGDRJ7mF3gkBdBlkPB/ChJtgzNV9smD61bUbsehNRjTbtyAE3k9ADzTrcuGMbA7l4xTSd7B0PUvh5rcFja31pNA7BmSQOiE4wCOcCutbxBaEnaJQPdGrB8M6Rb6LdyQRX8V6Z7SOdpY0KhCSw2fNyTjnt9K3XkXP3hn61wY7mhXdjvwqUqSYn9s2793/74NKNUhPADfitRNLj+L9ajacDGDk1xPFOO50LDpkp1mzUjeZAfaI/4UHW7LBwJj/2zqo0o44X2BppuD6KPpVPMKSWkX9//AF9Sbe/4f8ABLy65Z5IxN/3xTX8RWMbYYyBh22Gs7zyDnJ/OmmbJJJyTULMYfy/j/wB/UX/ADfh/wAEsyaxo0kpmNpvlPV/IOT+OKX+2bDbkQ3KjHARKredGcE9R0GKelzhzg/eOcVr/aVNL4PxJ/s9t/F+H/BPOr69vvDOuH7BeXW9MMj7sEA9Of8ACu/8LfE7UNVuQdZt5ZRbocTwxAsp6qST8p6d8dTyawNXRLSaTUG0OTUrYDm6YEqqgAYPBAwMdcVRg8a2kUZaDTJ1sVG2eFJ9q7j90gDjPB9MjivclJuLlGx5cUlJKWx7rpPxN0a/R45zJDcJnKsoAYZ4PXrjr2zVJb9NRS4Tzsn72CRlhjACivFj4s0a4uFMUVxA+MbpFGP0JqXU9bvrS1t7vRvP+1FwYp0Xci84PXj2rz1iKkZpNWO9YelKDlF3se8aNcYZUeDygA0abz0Oeg/q3cn2GNG31EvvFmsNyyHD+VMDivPPDV/qT6PZ77SSe5wFl89lVw3dsHkAnmvPfFUN1ouvzwyWz2007GQFnyGU8jGOPWuqhOE7q9n2ObEUZws7aHvsmqNLeG1e3Eb43MZJATj1wP8A61Z9vB9lucRoodZCTsH3gef5YrwbT9X1OCQrb6jPErjLlZmwceozjrTk+InimOQqupEEMR/q1PQD29669EjkSd9T6fSL9ydrnLAZA6V8/wCt+BPFd74p1iaCHybW4vJJFZ5wqlS3Bxn0xWr4K8eeKb7X4ba5mN5buSkimJAEzwrEqMj5sfnWGPEt9ftNeXz7i0sjMqgDb8x+UcZ4p4ekpTaYVG1G6IG+HN1Fxc6lab+4iYOf51Ys/B9xbPmLUL5eo/dyBBz16AU2LxZsUlUiQZ+UFcnHv0qb/hO2j/5aQg+yj/69dT5I6JIws3uy7D4QYnLXV2x77p2/xrUtfCcOCfNXPQ7nYnv/APXrmZviE23/AFxI9AMD8hVA+P7mRiIY8bhgknrUOfmWorsdTrngCPUIVWKe1hYHLOwbcfxCmotA8IT+Hrs3VjrrxTFSjNHCQNp9dxx+YqTSj4h1a2Se6vWs4W5VVXDsPX29vb610tlbLbRoC7zSKADLKcufcn1rxcZmmHotpPml5fqz08Pl9Wok2uVeYye0ubi3MdxfXFzuHPmYA6n2ya5ufTbjQ9TFzbPF9mupAJ/MgSTY3OGG4HGSQCfTntXZkgjkkn2qlqK2y2zi+kijgYFW84hVIPbmvnqmaVpzutPJf5ns0sLTpx5bX9ShNDqWmyxan5UME0bDGxgN56hWAULk9OPXBzwa0P7S0hdQi1OSJXt72IPHKV5jBwR9Dzj2wa5R7bSb6dAdQvtd8kDyrZ7otbpgcbmAwOnUkk4/iNbdiy2dksDyxBgzOQgIGWYsQvJwMk16NDHvD03y636PT1OavQWIlHn6dvwR0M3iq0L4jLNkZ+4cUth4gskupGZPLEoCucdRxWINQQHlx9aQ6lDn/XA/gf8ACo/tOo3dxX3gsDS6NlyXUILUIwhint0l3PFgHIIIyB6jg8ckDHfB4jXNK1Lxnq8dpYQPpvhy2b5JJFK7z/E4B+Ziegz9cjJrqjqsKupB6dWA61IutwrIGfLAds4ojmG6dk+9ypYa9rXsS2PhfTbO0tbay0wOlqd0cjx72DHGX3Y+8cDnjgAcDiluPD+nlWaawt+OS0Zi3D3HXmg+JLfYyrAVXHZ/58c1nP4ht8keWx/4EP8ACsJVKd7ueo1RnsolYSOBtddrrww96az5+lOmvbe+lRoRtlKkMp746H/vnj/gHvUToyjJIAPTmvIr03GWmqPRhLmWujFL0m4VCZgAc4/Oo2vYxnjP0rFQn2KLnm9s0jSgDANZ7X3Pyx1E19MfuoPqRWkKUh2Y/Vzv05xnnKn9RXMG88TX3iNdL0zVb6NpnCxRi9aNemTznA7/AMhW7N9ouonjL7Ny4BCjg+tcO2pX1veLMLmWK6hOBJGxUgj6V9Tkc17OdN73ueLm0XGUZnW3UGsabo6O97rkNyu83HnXBERZXC4TB5PfnHHPIrCuG1ezvY9UlMkd2ZyY5Hk3O7KclgCORnv0qvceJ9RuIZYjMIlm/wBctvGkKy/7wQKG/Gi48RSzwopWVJ1RY/MjmwMAg7sEE7iRkkEZ9K9tya2PKvB33Jl8S66Lh5or66E0pLMUdhknJJwCKibxPq6MwluJW7MHIYn67gafNr8d7OS4khMvmlyGyFLKoBGOTyuT3xxzUGpXlvc2Tsp/eyXCswIPOA+SCeSDlevJIqlVbexnKnFLR3FtvFGq29z5lu8McnOGEMat/wB9KoJ+nSt+DxlrOphLcXUkczH/AJZIgB9zkcfyriMqHVh1BqzbXc1lLHNBI8ciZ2SIcEZzn+ddEJWld7GMldWOv8/V7mBJrPWbl5XXcILlTA7rkgNEx4cEggAEMSD8uATXn+rXtxdSpHcs7PCCvznnrWxpupyaZeJcokc5VCgiuBvjIIwAynggZyB6gGtxvCuqeKvD9tefZQt7GQn2maZFEybTg5JznG0f/WzhYmpTcLOVvX8i6VKTd4xvY86pa15fDGrxTvEbC4O043CNtp9wSKmTwhrUhAFkwz0y6/415HtId1950ezn2f3GEB3Irsv+EPcRgwXW6Mkje6+h64FQW/gfVhIpYQKwIO1txH8sGu+0qwkSytomKicwrvXPyhwMcZ6E4Ofr7VlPEL7DVzeFB/8ALyJxtt4MVm3SagHHcRpz+p/pWxH4MsFj3NLMRjrvH9K3ZrRJfnCNFKOkqqB+fqKrG6Y5hGyaQHrGRj8R2rD2831NfYwXQ5HUNHt7eRFtZJSjtt+c/eOPwqn/AGFctcRqVTk9zgfpzXS3qPJfWzSbVCseMdMjFaYiVDgNgjpWnt5JGfsYtnLnQJMoZHXcihNwHUe9WYbVrVS8bOXkYABeMkDH8q23UvkE/jWjaafb2kS3Nw+ZnyqAdh7AUKvLuN0InM3VolpIIYWdJDHvZ1PQen6VzOqWy2OtrFG24NDbvux1Lxox/Vq7aa2VdWuGZ9wDKn1OK5XVAbvxWxc8q8MQ47KiKP5V10G3K3oc1VJI6o309hPZssZcPblSM+jHHP41YGsysMmFhWt4k0m3s7KyuI7ou6ylPK24A3KSTnv939e1c9057V4+e0l9ccmt7HsZTNSwqXZsvrrBxzE/5U4asD/CfxFZ+7FISK8b2cex6LNJdXiY42sPUnFPa/QE9Bj3FZEiq64I4PBqMQRddo/M1TpQeuo1E1zfR9dyH/gVN+3R/wB5P++qzPJj7KKTyI852ikqUQcTSN/GO6/99Ug1OAHDOo/Gs028ZOSqmnLDGvRV/Kr9nTt1J5Wc3pPjrWtKREhuMxqchWHA/rVO6uW1B9VufLjj8+VZjHGu1Vyx4A7D5qyVkt1Ub0dj6KcVd02USNdR4xvhOF9MMrfyFfWpJPQ+VbuXrLwxqWoWpuobU/Zgcec7BV64rpdP0jVdPshbJJNKSG2wogYdMnpnAx3zWt4d1C3i8IzQS3xt2RTtiMZcS5OQMDjr3PrVXwlc3t74gD3E00mF2gByuAe1dU6FOcUpq5jCvUhJuDsTvr3ijQ7PP/COTKIgPMmk3FGxxl8KMH8R1rpfBHjq41qxu9N8Q2kUscagxymAsJM53K2cjpx24rotWltHs7K2WRUP2kNIszEJkIxT5j1wwB+oHtVKSMs32mAJJFcAumzoOhI59DmueGDpQlzRR01MbWqR5Zslk8P+A9QaRhbWcLlcEo7Qdf8AZyB+lcRqnw6sLTWneC9k+wNEpAYqW3HgjOMY46+/tWzq0srA7bfOV4+boQR0/wDr1maZbTSx/PHG7buZGOccDgdj9a6VT1OZzujU8LXCQXD2umW5htoZR9o/eDL4P3m9eenX8K8k8RA23inWrZGcRxX86Ku88ASMBXslu4gkXfMM/wAIQAbvbivJ/iBCLbx7rCjo8/mj/gah/wD2aprLYcHcwvOIHGM0xpnPemIrSMFUEseAAOtallok95MIh97uB2HqTXNKSirs1jBydkVLG0n1C6SCFGeRzhVUZzXqXhzwTb6aUuL4rPcjkKeUj/xP6Dt2NP8ADmkWuj2+IBvuJOGfHJHoPQew/Wt1LvK4Rd+PQ4A/GvCxeOlU9ynpE9rC4ONP356v8jURRgtnpyzMQoA9cms6fxJp8MphtxNfTdNtuPlz6biP5ZqtNZLfMGv5mlTqIVO1fy7/AFNW4hFAgjgiSJB0CD+veuOFGKWqudU6km9ChLeeIL9dkZjsIT2T7/58kfpVeHw5F53nXMjzy92c7j+ZrZEgBq9aXdhEN1yrMwPAxkfzrroU4ylbYxq1JJXMtrZwiqkbBR90Y/lVV0ZThwR9a6w+IbUEqkMh9xgZ/DBqheyWd6hdY3jk6jA4NGKwNNxvCWpnRxM+a0loc4yVGU71ovYzgZ8psVCbWX/nm35V4sqVSO6PRU4vZlTAGKYWGeRU5QqeQakjSyK5mlkD91C4A9885+mB9RUxi27MpysUXIIxVVicnFaF79jU4tnZh6n/APUKzyfQ5NWlYcWMfcy/ITuHI+o/zj8acWlZQfM3L1GTTdx/GooseXtzgqSpH0PH6YrZX5AfxXJyMj5mFRZXu/6UwnGagdx1FJJllgvGASXJx/CByaeJFx8qv+JFUt/pSiVuhNaWdh6F/wA4gAYrh/EcDQ6tM4UhJMSA465HP65rqllPXtVHWbT7dbB0XdJEDhR1ZT1A9T3/ADruy2r7GuubRPQ4cxoe1oPl3Wpxm6jOauWxsVf/AEm3nmHpFOsf80atBLvTo2Ih0e3ZexuZZHcfijKD/wB819S1bc+XMLvxWxomjw6qs6z6rbWJjAMbXJwjH0JzkdOuD+FRXQluZN0dsFB6LFFtA/z9aW30yaU5+xx+xlcD9Cf6Vn7aEd3YuNKctk2Z7ROJ2iT94wOP3fzZ+mKlFjcHloyvrvIU/rWxHpE6r809rCp7ISf6ClOkW/Jlvd3rtUf4molj8PH7V/Q2jgMRL7P3mI1s0fzNImAc4Ukn+Vem+E71E8NQNJL5Yd3Yb2298Y5/3a5O3s9LhkeS4ucQxruIJG5zkjao/Dr2zU8mpxTyBre2jghVQqIAOg9SOp968zMK8MVT5Yp6a3PTwOHnh5tzau1tfU6651C1WRiLuOTnlYwWK/kKqJq9vPM0EbsJgMhHQqWHqAQM1h22pMkqKJW2DcCkEGSDgEDIHHUmibURJqdpG9vDbuTuHzlppCQQN2egwSfwFcry+j7DnTle1/Iv65VVf2dla9t9TfmvZ5JQ7SFyO7HrT45/NQSEBfYNyfw7VRUg9auIAFR0ZgT146VyYO/O/Q6MUlyoWWNmiyI4txPWTL1VMFwo2tOgQc7IomX+TVpFyF9QfTtVTISRjtBz3rvUmcDSMmSLdxIMZODyTg9jzVm2lkRSky/d6MR1FS3CIy/MyqMdDVRpZmXyzJwR8vyj5v161orshmtBbpOR5fzBuCM9DVmYx2NnJdSsWKApGCfXj9awNIlnhu2AfGM9T3q3qDyTzx27t5jINxQE7dx5yfYCqjHUUpXRQh8wqZpDljk/7zZBrm9ayfEV+0b/ADC4k2kcH5Wx+HSuruMKVVTuwuMgcD6VxdxJ5msXjjODLIcnvlya9DDP3mzhr7JFi0m1G71OA3NxLIUbJLsScCumMnFYunARqZT1IwKvCbHNeVmVT21a61toexl9L2NHXrqXN/GKQtyKr/aV9KUXCnjBJ+teYqbPQ5kTs3K5x1pSffmoN/I4/wA4p3PrRy6Fp6khPejNM/GjNTYY/NJmmZ96N455qkiWzzLJq/o741ONSD84ZOPdSKzxU9qxS5jZTghhj86+pPkr2OysZlKNbk4b7yZNdn4Aty16ZNoYlj+lefRrJF9jmZSFY7VPr2/nXVeG7+5sdQ/0dhvByoPQ12J3jY52rSPTfEsDS6M7+Q7tEwlRoh8ylTnpkcEAiuP0HVr6Ey29xHM1o7745DGSUzyDgjof6118HiKV7dWuNNkPuk20g1kvBBHqgeCBYbaflkkUs6se4OcYpR8wfkSSK13FvV7WQZ65wf58Gs5YiD87btrfdVTWlbymG3uI2VfvKwz64Htms+OWRoQefU/nWgikJpJdRWPHH8AFcd8QLe3Pi2WeeTb5tvC5yeuEC8f9811kR36iZNzBM7fl6/h+NVdasLe61SK6lhDSLGqru9icVy4usqVPnZ0Yak6s+RM5TS9IMq71jaGNhwWHzsP6CursbKK0j2xKAe5/+vShNgAXr7dq0YII0QK77QT8zdSK+YxWNnWfvaLsfRYfCwor3Vr3EWNNuGH1zn+VTi5jXq+P1ptxb26KohneT+8WGPyqoUXPU9eprgk9TqSui7/aHGACaT7e2fu5NTrod+qO8lq6ojFWL/Lgj+f1HBquwWPI3rx6UqlSrB2loOEYPYX7ZKf4ePpSefKec4qF7iNerj86pT61aQHBlBPovNZxlWn8Ny3GKWpqq8x6N+tSeZMv/LTGfRq4+78UKjYjhfHqzY/xrHn8YyIxwi/TzRmuqGDxk11+85pYjDx3aPR/tDr96ZvzpDqYj/5bTHHp/wDrrzNfHUoP3F+jMT/SrUHjeGQgXEBUH+JDn9Kv6jjIa6/eSsXhZaNr7jtZNVgByyMx9SxqrJrFtyPIJ+hP+NZSXcF7EJbeRXQ9wen19KgfINZqDekjqaja8djWOrwHP+jH/vqk/tiAD/jzBPuRWNkYyTSEgirVCPVEOxqtrzjiK3jT6jNVDqbyXEpeNRuIY7R7Y/8AZapYYnhTn2FNkil81SEOSp6/Uf8A166KdCnqrGdSpKKVi093uJwDUZuCR0qr5V0f+WY/Oni1uJEYMMegBqlSprqHtpPoPM47vTTcIDgvg+7Afzpg0+6OcbwPTzDSrpMoXBIBrZUqPWRlKvWvpEVblHPyujH0DZqUTSAZWNz+I/xqFdJnEiM9xM4Q5C44rQW0lwCE49azqKlH4XculUrST51YoPh2LNp8LOTyzqCT+tITMAQsMEYP91a1BZy55C4+tL9hL/ecfgMVDraW/wAy1SV7/wCRjOlw6hVlEZz8zqm4454xUMdpIkgVbu7lU8uHjKr7YJrfFkoxz+ZpxtRVRxXLFxsiJ4dSkpNvTzMf+zlkO5pJcntninrpNt/Em76mtb7ESfvmnLZnsxrL6w+kjT2MeqM+Kwt48FIlGOnFOHlyuFQh2PIAJP6VoGz+XBbrTXtDIgSSQsikYBAA46dBVRrJp88mKUOVrkiv6+RlLHbRk/6ZLHz0VlHTjvz2p1vBp63JniSWSfORI579PxrSFoqfdx+VSbGU5ABOOtE8XJrl5nbbcUaFNPm5Vf0GBjjofxq7aq4QbWzvzx6f5/rVXEhPLVat18tCwHfJqcI1zv0Jxa9xepI7vHkHrTVXfgkgZ7AZJoLCQEEc0/a6oM7QMetdqOFlSe3BPD/N+tQw/u3/AHqCSIkb17r/ALQ9x/8AW71ccqBzz7DmoJpIYk3OzZ9F61aZnIhCi3ui5YSI/wDGvaprS3XY0khYbjls9T6VWYSt5bxW+0A87z/hVtGlx+8jZJAM7XGQfcEVomZsdNEs06HYVUYC478155aKZZZJP4nbI/EmvTLshbaSZiPkUsMdgBk/yrgdHti8KSkgjJ4+n/666I1PZU5SMvZe1qxgXFGxFUDoKcMk1aEYxz1pwiUV40qnU93lXQqhD608A1Z2D+7Rs9VqOe5SRBGGEg+fg5wD9P8A61S7iDgHmpBCuQdvNKUzztok0yloR5JpRn1pxXjgUoX1FSO43B7ihc+lSYPQcYq3DYyvbCcr+7zjII6/0qlFvYiU0tzyXpUkLFZUYdQR1qQW/mKSp5HY9/pU8em3LYxH+o/xr6O58sd08MNtb3NhsTzEuiDKMbiqtwv0yuasacSt2+AAwAZaw7ZpI40Emd+3LZ9a2bKVWCSElXRhhsfpXZHYwlud5ZXKeVteMfOucgdDV9Y3mijYuhSIgjI5PJ7isW3kkS3RXHHbHTBHB/pV0SzjTiIgfNPQnsMdaOoilqNxcrdCNV3bgoxyeFqCPzBD877VCYLMMHHen3sAs5zMJ2JXOG654rOkufNt1gD/ALxvm2j075/KqEWLNE8hvLU9tpPXHqKJF3zksCCFHB+lXrCBUgO37oHJNZ8nmtqs/eMIm32OK8zNv92fqj0ct/3hfMeIxjGKUyfKFP8AD0pxVwOMZ9KhIJzXx7bufRoDJjpULy7SMdM04g1GYy2ecYBNUkUWbzW727YtPMzkgAnpnHFY91esB1JJ6AdTUrrknH865LXNcKytaWLDdj95MBz9F9Pr1rtw+GniJ3f3mNbEU8PH/Is6jqqW4IubgRt/zxj+Zz9fSuen8QyFiLW3RB2eT5z+XT+dUXgAJbrnk5o8gtwqZ+gr3qWEp013PErY2rVfZEU95c3JJlmZgTyBwPyHFQVYa2kBxt/DNRvDKn342X6g10HGRGjFLijHNMCzZahc2EyyQOVYdR2I9DXoOg6pbazBkrtmX78ef1FebD9Kt2F9Jp15HcQnBXgjP3hXJicLGtHs+51YbFzoS8ux6vJBCSuyFVwPzpBEB0jX8qNHu4tTtY5FBBPBB6g+n8q2/s1sg5icn/exXhujJaNnue1TSaMTyj6D8qjntiVRhjCsMjHrx/Wt7baDrbufq/8A9amTiP7M4S2Ctjhgx4PrTjTa1uvxFz9LGIIsU8RH2rVdLREieOLcXXJRpC2w5IwflX0zx61FJ5DL8tvsPs5qZR5dGyozvsUBGvc4+lBCqMA/pU/lew/OlCY/gzWbmzQqbgT2p4znOasGPAxtwfejyJH+5GzfRTS3Hci5I5NIE7A1bXT7t/u2s5+kZ/wpw0nUP+fK4/79mnyS7BzLuUfLJ70ojPatAaNqTEAWNz/36anDRNTzg2co+q4/nS9nPsx88e5n7HpNkmOBWg2kXyNtMPJ7B1P8jSf2Zf5A+zSf98k/yzSVKo+jHzw7oorEcgt096Gj54ar8+lXUSIWxuYn5TlcfXcAPypi6XdMcfuxn/poKbpTvawudNXuUPKYc0mD2rU/sm5XhmhB9GJP8hU8WlyEbTDaljnH+sPT05xQqUmS5pGL7VKAcH5sD6VspodxKQoeMHsM4/pVTUrCXTLmKKRlJdC3ytkdcf0/WunD0akZc7WhhXqwlHlT1M9RhirfWnSEu43tnHRAuAPwp0uFdD09xTWdmZsH5VJBPrXYcVhrnC8/kKpuByQqrnoTyanZsnr09ahxl9xOfQYrREMcqmJvMUbv7yE8MPSrEypCgeCV+AJFDHKkfT/PIpIgrRFGB46MKjsXDk2sxB6lCD2PUf59auJnIl1GZDod02GU+RJ9BlGGP5fnWHoOnrJoUE27DOzHH44/pWhqwkj8OXyE42x4PvyB/jVXQZHj0S1Ttgnr6kn+tTjW4Ya66v8AQrArmxGvRE4s8HkVJ9kA7Gp97selLiRv4TXi89z2LNEP2fHanfZzt7D61II5T/CacIJDkkKMc8tirjG+yBuxB5WDgkZ9uaaVQd+aseQP76Z9mpTbD1X8GqnCQcyKZ2r2yKbleuOKttEq5z29DUbIvajlY7oqnb2J+lSRsQAFZl5zgHj8qcY1XlhkemacZIQQFh2jv82c1abRMkmVV0uDIzHwOoAHSp4tIhxkq2c9BU6+IvDw6ecynkNLEVYf98sR+lTp4g0B2/100a46mPzMnPtivouWR8wnA5u8jjh1OWFAfkxw3UcCr2mx7jJHuyGHQ/Wo9cn0661YXGnzrKrxL5gERj2sOOh9gDn3pumyMl1kj5Twea7afwJnNP4mdhp6yfY2jkwdjAj8f/1VvwplIhtBGzkdO38qwdPyqyoSeQGXPfBFa8V0I7ZnkG4RgDG7nk/4fzpiRX1lIpoxGTtYqCFOBXJaakglmLH98WKAsedo54rpr65JjaRbaNY2HzSSNuP4VjBSNPS8jILwszH5fvLwTTQjZsH22xhdQCo4561HdbFZGXGSvODzn/OKm08rcQ542SD5SO1UZ1ZZ5N3qP/QRXmZv/uz9Uejln8dejGs/HFM3etNJpCxxXydrs+jWwjNTJCenSnAjdVe/nFrayTueBwPetIQu7LcUp8quzB8R6r9mgNrE2JZRyR/Av/1+lcaqBck/e7mrWpSmS8dnYlyfn571UAaRxGvPbPrX1OHoqjBR6nzVeu603LoKmXYLGu49q07bT1IEkrtIFYZAOFP9ada2qxfJ0P8AG3c+30q5IUjXyo/lizkJn/P51sjMHjWPC20YUdTsHapP7P09uLi/dX2BsRR+Z9QckdPbNVmnxEoyEjHAGapxM89ykSKod2CDe4QfixwAPcmq0JuSz+HIbpyLC9jkf+7Ivl5P1Nc9d2VxYzmG4haOQdVat9XdEeQA7UcRs6/Mu4g8ZHB6H8qs/aYb6H7Pep5kY4Vhy0f0P9KVr7BexyA9qevX3q7qWmSWLqQRJA/3ZB0P+B5qiOKQHUeDdUe11ZLV2OyX5V/3gPl/P7v417VG6X1rBm0jjYL8zAH5s8186Ru0U0csZKspBBHavozw9drfaZbXUf3ZY1f6EjJH4EkfhXJUpr2jfdHXSrSUEk9i5DpMUqgpaAnHJIY8/nUV/plnZxfaLuRLeMkKBjaCT27nPXp9a37aaVVwjYz7A/zrF8XfaWt7K5+yteLFdAyxBA+5CrD7vAOG2nn0qo06baTWhTrVO5ixxaNsaaC8W7hL+WH37fLbPClM5GRnn2GMVYtL3QJ8pbi2kcHBULIzA+4LVm3GjazrmoW6W2lWmmpGwly4WMybTwWCsxwMj8/pV22+HWrx3z3ceo2aSyE+aAGIT5cDHAycetdH1TDXv+hDxNe1v1LqXWkNbtPHBbvCucuIGwMdc56Y9+PemPq+jJp/21IreSIsUUx2+ctjOOU4P1wPer1v4FktrF7ODUY3jlXa5aPOPlC/KB9KltvA9vHp81l9tSaN5N7ExE7W6cYf+eafsKJHt6vcow6raNGsqR7VZtgKKiknA6AYJ69s9DV6KaOa3SUiXDQCfIA4UqSOpy3TnbnH4irlt4QtLRPLS5kZSwfawQ/MOhGRkU59B022iUNdTRLFGUH78KETHPbge9J0afQFVkYOo6tHp1zBAVVzMu4MJMg/MAMEZBzk4PTjnHZw1PfYx3UcEgV4hJ87Yxk4wf5Z9a1V8J6DcCOZ3kmULtRmuDt25zgHpjJq8vh/Ro7ZrfYohixuQyH5OpHQ5HUn8SaydCNtjWNez1MA3ypdiOX5YctucjkAY9+5PA645xVfV9RGnXP2cqsjBCzbGLAEEgDIBGSQPf1ArqH0DRCgeWKMhzkM0zfNnHQ578dKrPYeGo4vtDQ27oQMs+6QnIJAAOSTxwKPYRasH1h3dkcncXUpSWREBC7QMOCTkE9j7Y49farekSRySzCdPuMqoSW6kt2HXoBx0Jz06djbWukedJDFa2iyxgFkEa9CMg9OeKvpZKCN0KL9FHFZxwsIu+5UsVJrRWPPtTuQLpoQY2VInABK5JAVsg/mMciooL6KS1jaO3d33EShCxMY3ABsZweOxwM13kl4VMZWBxEzhDJnAB6fU88frUs0iRrvkZUiAO52bAH41p7GN72E8RPVJnE2kzmYmXR7kh0G0BWKKRvJG7GecKAcHr064bbjWQi3CaK4nUDbG0D45D5Jyef4OMj/AHeBXZyTJAAzMoXuScY96Ir9NxyAOhznjB6c/p+VT7GmugOtVe7Oc8LaXrVreX0msxNJFc7Sp3IAMFuqjoCCOB0rA8bCODWIYySF8s43EZPOexPrXoR1OCJyJ7mFV8zj95j5cD0PrmvJvHchu57KYuSW8w5B68JRWj+5sh0pt1NSjeMTDH5ZyX+UEGrEkgjiCAcDgD1NVNMG62V5TxEcjPc8VIzDLO5GM8Zrz7HWQsrKTubcSfSmbgGA6t6CkYvISTwD6VJbwh2wPxNWjMmyUgAEfDdyazLbcZd2CFXJ3emK0bmRolySGA4VB1JqoF+zWDIzbpGbDH3J/wAK0gZzDVJTN4c1JufuKeeoy4rRtLSKCxtkKD5YlH6CsvU42bw7fAAn5UBwP9sD+tSJPe7VRpowqgD5UP8AjSzBpQhFvuPAJuc5LyNoCBRnywadibyGnjhPkq20ybeM4ziskNMR89wx9ttTQ6hc2o/czMvOeg6+tebGpTbs7peXc9L2dW11a/qX45LgqWxhQMk46VWlldz3Oahn8Q6swO/U7rb6GZsfzrm7rx/LbzMi3NxKw4LLIT/OqVOVXSnd+v8Aw4pTjTV6ll/XobzxTO5IRmJ74pptrr/nlJ/3ya5Y/EnUd2VEvHTMp/wp4+JmosRvE3H/AE1z/SreBxG9vxM/rtHv+B0LpMnBGD6GovMdT1P0qpbfE53AS4aYrjGJPmX+tasGv6LrACyxRxux/wBZCdpH4dD9OPrUfV6sPjVjaNenP4Wn/XmVxMSMGg8nPapr7TZbRDcQP9otMj94owyem4dv5e9U1kGAc5FTJNGsbSvY5xPDOoPbiaONTHjIO8DNY08MsU8kTnDLjofbNe2yeCrUZ8livsFGKyLn4cQXEhkFyVJ6/ICK91VV1Z846b6I840aRkmkVuhwa6vSebk4APHINGs+EB4e8iUXPm+duXAj24xj3PrVfTZNtwCCAw6V6NF81NM46itOx3doA04ycBlOPbjn+VW4I7YnyZc7Zchsfw88H9aq2OyRYiSQ2DyK0xAPMhmiwMv6c9sj8KaJsc7ex3NqxjDKUjYookPoe1P0wh4GiY7Mk/uyPUetXtdso2v/ADJiSXHBHQfh+FFlpqIqurscZIBrREmVoM7WczWsxxGXwpIxtOcVo6lIjzqYyrKFwSpzyKyL23R7meIj5HlIwDyMNmludR0rTNlnLdFZ0RXMYjdiAVBByAeMV5ecQk8PaCvqj0MsklWvJ20JmNN69TWe3iLRSfm1BV9zE/8ARab/AG/ovbVISf8ArnL/APEV8usPW35H9x9B7en/ADL7zQAG/g8e9YHiW/H7u3UArFmST34Bx+n61sW1zFdgPbOJY2GQwBHfHcA1wmtXhnmZh/y1dj/wEdP6flXpZXRftm5rZHDmVX90oxe5jsWdy7HJblj61o2MO1AxHzv0z2qnHGHkUHgdSfatBZEeY+ZA6gdCD7enpXtnjFrcLZjGVDTnBjbqB6/pxTZAILcTSHljtTP6n8Kkgi84j5iAO55NWIdMn8Sav9mttq+U6RlD08vBy/0Uj8dw6nNMLkWh6Jd61PLsxDDEwE9y4+VPTHqT2A9eSAMjuLDw/oenX6WUNp/adzIgZbm6I8tyyMw2pyoUhDyQxBqp4pkhstCHhzQ5jHcpjzhjBljcYYg/XAb2bjjIpNGu7pND0OcKTeWUyWzg9SoJK5x0+R2/WriiWzetvC2mapZvNeaNZQIX8tVt4zG4I65ISM+nQYOeprjfFngGbw/ENR0udrmy6NFIwM0Y7ngAOPcAY9O9eweVBC8dvFEkESoZ5QqgfM2SxOO5GMnvmvONL1B7vxfc69qd2IrWFSkUbDdkHhVVf85/HNNxtsF00cNbzxXkTW8p/dSjGByQ3Y/Wubu7d7S5eF+qnGR0Nd3408ORaHfx32njGmX+WjCniNx95foCcj2I9K5zWUF3ptvfj/WI3lS+w/h/PDfkKlrqCfQxYjkgGvfvhlKk/he0UnLKXUj0wx/pivAIwN4Oa94+FsYHhyJl7u5x+J/wrnqL3kbQe56JtEYJCjOOpXNEpMkbLu2EjggdDTA785JoeQ7ORilsWjmbe707TvEL37as17eNuimUuSkSMynaiqCFxtHU9uuTWwniaylt7hS8y+YrbjEcmPjaCGwBnnPXjB5NU7m3gkc74kYH1Uc02KG3gQpFDEikglVQAcVnLEtPY3jRTWrL0WqWUNqtrbn5DNkNlVUAOGK9eeDjgHpzUI1+wiu9+WEQhKBEUsc7vYYHA45qELEq7RGgH+6KcGC5ZRgk84rN4mb6FexjfcmtvEVrbLtjtbqTcV3NFCuB8oH96sPWHtr67kuvJui5lWRFMBDcIF25GeDitYynHXrTfMJYHcQBnPPWlCvNy1Wg3TjFXi9R1nLYw6RbWc2GEcSq+bd8Z4JI6Y5q6dYsVEha4kBeQSApAePlC4+YYP196o+YAc7ufrSb0C4LcfWq9rUfb+vmJwhe7v8Ah/kXzrulPDFAZJ18oDAWI8Y6dj/+uqou9PMkXl/atsWzpbE7ihOOowOCRxz7iqiSoCS0pOGOCzdskgVKt7BGpAZTjJHPNWq6W7B0eqX9fgW7fVIILvzY7e9ICsEUW/C5AHGFGfck5NWZdfdpxO1pfSOoIRNqKBn269qxm1BHcn+EjGDihtQGTjGGXafpUPFxW1w+rt72FljmmAD5ij3ZDG1BkxvLhd2ex/lVmdYb1LhXMrtMroSlsNyo38JJbJxx/h0qlNqAaJoyU2tgnPscip9J1CGK7YyNxsOMA8c/4U6WLlUqRgnZd7CqUYwhKT1f9eQt80kqwxRltsSbP3gCnORg8Z/u1Qj0tsDe6k7QnXOAMew9BVjU9Xt3v5JFlXy+Np6ZGPeqi63ZqPmnjBz3cCuCtjK0ZyjF6X7HVToUnBNovLp7gkmdee/l/wD16y/EeiQzab5zySPPFkR4wAM4zkf8B/Wpj4ksFGPtCZ9jn+VUtU8R2c+nmOGQtIXGQFOAMHPP+etc6xWKnJRk3Yv2NKOsUrnMQt5NkFPXzD/SjJkPKsR7VAX82dwB8qn5atfdAZGGe4NdyVkjFu5NHGCmcFfYildXjXEfl7+oDNyaI5ndMF0X3INSxpGgJMTOf76/4UWE2UN8KM80sgVscKOWqsRutllf5Ixlwp6sTwCfw/nU009pHOYpCqsiiRwcnGc47Vnz3a3tzvCsE6Roew+ldFKLc0c9SSUWaLMp0K63HaG2cevzrUO/JBpblGOjXWMfuVSQqP8Aroo/rUHI2iufNviivI6cs2l8iyZABkmqd1eJEjOWCRqOWNNurmO3jMkrhUHUmuO1PU31GQqMrbj7q+p9TXJhcHKrq9jtxOLjRXmLqutTXzGGJisH/oVZaRJn5sfWlxtGP1ozj8a9+nCNNcsUfP1JyqS5pPUeY4OxOf51BJHtJ449akPPNG44IPI96pu5BXxxSq7xnKMQfapGTuBxTCKQI6vw34vubOZYbmTfG3y5YAgg9VYHqD710OorHbSLcW4ItZuRznY3dfceh9OvNeYkYNd74X1NLrTPIuR5iglGU/Tg/r+leZjKCgueK9UengsTLn5ZPoe3lWNHlkDB61lz+JtIiPzXkB+jj+lUJfG+lI3yytJ67VP9cVTTeyMk0VvHVo729jNuHlIzqy9+QMH9K8/t42S5bB6da9BvNbs/EWnSwQRyBojuywAByD05NcRZqBdSqXGQucY5617OEv7BXPNxFvaux0mlXO7Cn+AgjJrduJ5bC0c4yhwQev1rnIFEcSTLztJU/iOP1xXQ2N5Ff6cYHwXCfdPetWZobd3SX6wuAS2BkE896sxOkME07/LFDHz9AKoQwvbSBegUscEf1puvSsLC109Dhpvnlx/d4I/X+VMDDhlebdM+d7Nu/M5reufCug6hIl3d2IlnaNAziV1zhQB0YdgKz7WAeQSAAOmT6VsG+QKq5HAArz8zm0oJO252YGKk5XRkP4A8NsSfskvPQCdv8aavgPw4h4snJHrO/wDjW0L1fWnrOjd68V15fzfiekqUf5TOvrG3sLC5uYYwnlwkhR0+VeP5V43cMWeMH+FMA596928U2jQ+B9UuSOVgBH/AmVf/AGavB3Bb58Z4xXo4GMkpSluzixbV4xWyHQSKMg8lvlAIrRiUgJleCOo7VQhUOqqDtYdcd60Yl+Vdj9vSuw5i9EPItnm6qo7ep4FdjpCf8Ib4Hm1uS28+6ceZwPvIW2qM9QCck+uB6ZrnbS0N5JBbqCPMnjjLDsSf5dfyr0DxtqdroWgRS3Fu7QPKsAjjUHA2scYPBGBjFMRw/ie7jv8AQbHxDp2d8cm6Nm6hScFT68jH51reHZ0lvIruOMxtPKk7xk/dYf8A1mYe4C1Dp2q6Dr2lGwtsW8Uj+WI2iWMK554CkjBx+ea07aBdMZlkMZVRgyDkL8vH0q00rEs2dU1yPT/D19qUiyukzCJfL5IGAMdeypXHLpja3a27ac/7qTLrn5eOgJ9O9dDdwpdae9lDuCy7/MRXO3JUKDjOARzWbcavaeH9JFtp7JH5SBA7PkueM4/z1xVXe6ElfRmrqPh8f8Kwv9LmmilnsIftcIChMFGJYju3yuwJP+zXj9kRPp97bN1ZA6c/xAg/y3V33hXUoNd8WvYTQTCW9gnjSeWXJ2vG3BBHQgj9K8406QrOOuDlT+PFKV+VjW5nQZLAV7z8Kpd3htEIGY5JAD685/rXg6DZJ9DXtfwvu1h8PMWGcSv0/Cuao0mmbU9z0pn4+aqtzfQW8LNLIFVRnJrMm1V3J8pCfxrJv57i4iZHztbquK53UidCgyafxPpwbCz7vop/wqm/iq3XhFZvwrFbSizYCfkKV9FkhJ8544eM/vZVj4/4ERWDi5apG/MluzSfxYQDst/xLf8A1qqP4puiTtWNQegwf8ay7hLa3PzXcR4/5Zkyf+gA1l3F/EgbYk0h7ERgA/8AfRBqFSqPaI3Vgt2b0nie9JzvGfYCoH8S3jHmU/TNcvJqFwWO23TH+05/oKqSTX0jcJGn+7/9c1osLUe+hk8TTWx13/CRyjO5s/jSN4pRAC8q5+ori2t76TlpD+Bx/KmtAyf625hX/fkAqvqcOovrc+h17+MI8DAcnv8ALUDeMmyQsbfiB/8AXrlDLaJw15F/wEM38hUTX1khP7yR/dU/xIrRYWkuhDxNR9TrD4vuD92MA59Sf8KY3iXUHP8ArFX0AX/GuTOrQr9yF2+pA/xpp1t/+WcCD6tn/Cq+r01tEn28+51TavqUnW5f8OP5VBJeXUgxJcSEehYn+dcw2sXbdGVfov8AjmoWv7p+szfhx/Kn7GPZE+1l3OtS4OwAyZx2JqVLgY+/XEtNK2S0rt/vMTTQx60ewQe1Z3X2+FThriNfqwFaNjqFo9tfn7REzpb7kUOCSfMjHH4E/lXmZZvatjw8jTakByQFOR+n9aiWGiveuXCtJySO309HaFJD/Fya0Bg9FBqK3D7VRBhRVsx5H92sHudQxDEADjv0B5NXI4pZVBz5SA8DH86rwjyzw3FW1mbGEBIzyTSA4zV5ha+IrxIyHyqF8j7vy1JFdBUBjjAYj75/pWfr16I/GE8u35VCo4A6jaK1EgWHZNCQY2wwHYivQgvdRwSfvMv2UhNlqSuu9mtHIJ4xt+bP/jtZV3qMNrai4kkAB6D1Nb0JheyvzCuSbKfqOnyHivI5rma42iWR3x0DHpWGIwqryi29jpoYn2EWrassahqc+oS5ckRg/KtQgjAA6VCBk1KEYkKBzXTGKirI5JScndiFuaciNIwVQWJ6ADNaFrp0KATXrkRgj5QcZ/Gu7uLWHw7pDXEbQyCREKfZ1IHPPJ6sMfpn8NYwvuS2efnSdQVTmyuPU/ujwKqPG8blXUqw7EYNd9f3dxBoGmavHeND9tuJEIiBbYE254+X+9079zVjSA3iuyEGpJE0ok2+eU5VNp+b6gj8f1qnBdBXZ5wDk0106kV1Oq6BYOZn0WdpxAMyKcZYf3gPT8/6Vzjr8nPWo5QuVcVteHZjHLNGM/Ntb8s/41kFefxrpfDtgjQSXTMMltigdeBz+eR+VY1I80Wma03aSY0XpZeXOB2zTWmJ5zwD0rMEhHvQJnx369qSiTc9A8HTvMLqLbghQQfzqvuP9qSyBNpT70ft0NHgFt5vQ0iCRVVlQ9SOcn9VqfVsWHiGGYjasx2SL6HoT/n0ruoW9nY56vxG3YCORWjyNsi9D61CrS6e+SZCik7dickGnRKFdfL/ANYpzx0apLW+NrePHOmVb17j09qtEGtDcRXdv520hUBPPWob3/SLxrhgShjRU+gH+Jb8qsQpaSW0v2YqhkU8Z70NamWzgKuoKgqcf7xNVEGZ63GDtMe1R6VQkucOetb1vpilwWOSTzXlusa4bPWL6zVrhvs9xJFksOdrEf0rz8ww8q3LZ7Hdgq8aSldbnZi+x0yaeNQcdK83PiW5zwG/F/8ACrUfia3VQXtrkuOpFyMH/wAdryv7Ol3PSjj6d9UeheIfFMbeCdV02e4Vp5o40hXucSIx/QH8q8rjw0QGR1qK71WS6IGwKAT15Ppii1fMf49q9KjFwjys87EzjOd4GttWKNHWG3O4feDHzPxGcAc+naljvYx8pt2yD1DU5mglsbfyrZ0nDHzpWlBVx2wuMj06mqqSyfaNmz5Qeuea2MDt/BbJdeJLUBG+Xc2Cf9k//rrsX1rQvFE9x4eltJJpraJppY5cbflwDhlOQfmxXI/DrDeKogwx+6kII9lJ/pXY6b4Wi0zxXe61vjJuIWi8hATyWBySeh46DNVFXEzi4bHwvc6gkukebAbORXkt1IIPPXJzn09vSi98RR6Tq0kbPN9ouNyJHE5CsGO35wD0weM55qDUdOj0HVWsYUKpcSZuXUciJcKG/wC+iT+Wan022sk167vr+0aa8F9aSROP4Y0YtIMHg7gvP0q3F9BKS6kGu3+tNr+o6Xp9ixSK5eAXLg4+VipIA+lbQiVIreO4hgkmSNQzGMEbgoBIBHGcVq3+lzpqt3IBFOskryI8UgfeGJYHj60+PTJMhpkwM/hRa2rFvsWtL04PqkFwkyQtDwjCIZUcZ/QV4LbKPNJH/PQY/Ovc9Rv20zQdVumXYyQmKI93duhH4A14fEwt9ucZBDc98c1L6jM9x+9Yj+8cD8a9S0PWbfR9Ht7VIUyoO5s/fyScn35x+FeWkkNkdaebiYjmWTHpuNYTTexrF2PX/wDhMCATshUepyao3PjYbSGvI0HoAo/pmvKSWbqSfqabg1HsyudnfXXjOGQFWvJ5FP8ACXcj8ulZbeK7ZBiKKT8FArlOlBquRE3NyXxK7E7IPzb/AOtVV9dumzhY1H0rM9qKaVhFttUvG/5a4+iiojeXR/5byD6HH8qhwaXBpgK0kj/fkZvqc02nYoxgA8fSgBtHSnd6Np9KAGUtPEZPanCI9aAI8UuD61MID6GniA9MUgIACcCnCNj2qyID2XmrlpZSSMAnUcgUwMvZk4/Ouk8HW4bUJ2PRYsfmR/hR/YsvkiQRSFM8sF4H41ueFbeG1N68+MfKASenBJH6j8qippBlUvjRuCdok2xgZz6U4SsxAYc+1QT6hAARHGre5aq32iVvuukY9utcNmd1zR85IxyjE+5xWhBKrrliOegT+tY1uWdhjMg7kirYlWFZPlOVUk0IGcVr8ay+ILuRV/jPH95QdufzFaWisHtmtJDwPmjJ5x6inXFp9vzIhX7TETjsHBPIqxpdpcuWjW18kD7zPn9K7oyVjjlF8xb0yTfHqMfllStjc5OPSJq8mHP5V65br5Kamw7Wd1x/2yavJFq1sTLexJEvOcdK0baMZ3tVSFckAdzWnAEM8ayKTEnzOAQCR7EkCtEiCeLTp7mK7lu02xrDm3UNkls8cD6HOfWrl3qDP4DsYrl2M6TbMN1wN2OvoGFdhpUKSaKt1HGyJvOAyYI79K5XxJaubmF1hMkJY71x1zxWnLYVynaC61LRYtOaaN7a3mZ4HH8BfG4YxnnC/Stm6dNC8LXnk8TSgQK44OT1/QGksLCHT3tQZWRAeY0TcXJ9+1aWp28OHaZUa3GMxyA7eR145B9xVctkTzanD6RdtpUcV8NzEyFQB0GAP8f6d6l8RW0UV8ZbdQsNwglRR0XIzj8DWrfw2iIiW0VqojYFUkfJHADZB98dKztXllntbN5jEziPA8sYAHYUuWyBsxbO3N5dw26sqtI4QFzgDJxk+grvLHSo9M0xZhcxHYedkysQ2euOo6e/Y1w2n3JsdSguVjSRoZA4R13KSDnkdxXS3HiRryeSaa2Te77sZOPofauWpzc1lsbQceXXc5QORSrIwbimKCacvuO9Mk6DwnKY9YOGwWiYfXv/AErptY097uwD4Yy5zGxOeR2/KuT8NwvJrMRQEYDZ49q9ChlBgO8jPXHvXXR+EwqfEZnh/UTcRLFLxPHxg9SK3buD7VDvUHzF5G2sjVdHeORb2w4mUbj2DilstdM22OaNoZgBg9Aa1sQWraSWJnXYQQckNnn1rWsLgiCQLnylwQD/AA+1VVmSYiQMrEHBOBzVu02rFKS/zKMnA6e1UI2dNJlZiQeMcmvBPFAC+LdaA6C/nx/38avcrKYrOgJOG6e9eIeLMDxhrQHQX0w/8fNc+I6G1LYxeRz3paQUvOa5jUTJJq1ZthitVfepIH8uUNQI3Lf5rbcH+ZWxtx25pYUm8/eZMk9TtxVW1nSHzUcfK6YB9GGD/TH41biuFYAg9O1USd14KYWPiXTpZvKPmy+SdrZKhgQc/wDfVesG1hKb1uGaXoIkj7+7MQMV4tJ4htrqK3SKyjtZLchlePG5vXOFH+A545r1RPEUMttDcJCXjliWRMMFyxHzDp0DBh+FJuSXulR5b2kYviPQJbzXp5Qqm2kdWADAM2B0GR6k/nWfOLy2stS3aRawxxQpLbzpuaUt5iIwYhsEbXYjjsDU3iW7vr9IZFkS1tcsvlsC5kbt+X+elZ+m26QI3mIC0m5JMLyVIFbpSkrszbinZG1Y+KHi0uCYRJIHzHLncNsg69Dxkc/XNWf+EnsOWu7a4WMD5jCwY/kcVyjMLaCdY9skZyDs6HBOGA/zw1X759P0+20y9hfMcFobidy/zS3BYgJx0A+8AcEjqBmpcYtDjKSdjD8ba6bu3g0uNDEM5YH725sfe91XArze6YGd9v3QcL9BxWlf373lzJOxy5yAR6nr/n2rOaNm5xWbfRDWruVaaRVhom7fyo+zuTUFlfbmk2mrQtWIyRT/ALEenOfQigClijbngCtAWZPRT6VItk390/iKLgZm3PSl8o8fL+lbAsW42jBxzjvTxYEckDJ9qVwMXyT16CnrbMxxg1uJZknGz9KmWybH3fxoAwlsmI6Eg9MCpVsDwSpwenBGa3hpxHUZBHcU77GTliM8elGoGCtljPp609bQnjHA9q3RbbuAOfSn/Y2xnaM+h7mgLGF9ibrjini07Y4IrbNoQp49jkU4W3Ht6mkOxirZgDdj9KetpgDcO9bSQcgFflJ9O1Ktt6ggdjigLGStmT0AqUWxxx1rRNvggKT7+35VMLXgsMbj69aBlI3EwRVkbzEQYUP2rpvDiwzaXPL9njizMeBkg4VRnn3FYj2oKn+Ln0robB5rfQ7SOVUxtcptH8JkY8+vepqfDoXTfvakdxKyE/uYmH+zVYz25/1ltj3Bp0s8Rckhl9xSE2rLncxH+0a5LHRcfHPp46mZSe2c0STwlSgcM8pwvrgDJ4/D9ahWW3TPlwbj6msy68w30TrhWWMg4GMkk/4CqjG4SlYteXJFKCN2R0K1tWN3K0ZUANJ2A/mawUeeYhC/4KK3dPtmTamSO7mrIL1vYRpBqO4lmbT52/8AITZNeIRdOfSvboZRcS6i4+6NPuY056/un5rxBOFGemK66b9z7znqfE0WoPvjnmr8QtCkouo5pGIHlhHChT6nIOfpxWdAfnAra0ucRXsat9x2CscA8E89a0hsZs77QfENm+nGKdZIxkYMi9cDGeKlRtJ1LWrX7dfKLaAkxxAriSU8ISc9AcHv0rg72eS7mdYSyxDOAeMjOOam0cGCVVYB13jYxHQjnIrZe8rMluz0Ouv0tUkkMMm9UbcpB44rPmvRfxusilQQOc8nnOf1p11cRwJvkcKpPc9fao9IWGe8QysscDPku3ACk9farmlsZq5pPYaPBo8SCGV55HU7psN85B3Aex44PpXKeMpopNSEUMaIkEMMPyrjJVBkkev+FejeM7fTLLXYZLUxpY2FkksiKf8AWOxJUZ7kjH5GvItVuHmlMkhzJIxdj7k8/wCH4Vgn1NX2M6AD7QpJrWi8tTuyB6A+tZluFZj6jGKnkWQDA5+lc89WNFFRjtT0XcQBnk1dWyIHcj0NPWARnbjlu1FxnQeC3WHUphIq7XgKZA6HI/8Ar10ssRN0YUPr+dZvhKER6PeTPEn72VIkYjlSoJI9s71/Ktto/tsHnRKcjhxxlSPxrtoP3DCoveJLaaV4NrAbk6g9x60xLm1lQl8ZzjaDg020LD5eu3v6dKbd6erzGSI7Hzz71sQWkS2z+5hYMRx8/A/T6Vbt3WSPO3gdR61l2ttdxPksNn+0av5EMcki88HI6dqQXJ/DStcJ50jliPu57V5L44tjb+NtXXkbpzIf+BfN/WvVtAdIJNm/5AOD61xXjtkuvE1+qwxAfIN5X5vuL71hiNNWa0n0PPSKTGK02070yT7Cq0llLGTwcCuW6NipS4yeuBTyhU4YYNNxzxQBYilyAD25FWN5GDGMdQRnv/8Aqqj0qVJOzUxNGzaTkAZYbvfmu78IaupI0uc8Fi1tk9MnlP8AAev1rzSGQq45BUnGc4rSiuDGwZW46j/GkLY9ws7iN0eE7lJAKMMHOR/hzVG6swkr7FO3hgT+RNcJp3jK4tVUyASqO5O1vz59PSrd/wDEG6ul2W1tBAD/AB8sw/Hj+VNNop8rNm/t7TTleRrj/R1J3EjGScdPXJ3HHoa4HXNZluoUtImdLOP7isSSxx1+lNvdWkumDXEjTyDO0Ocqv4f5HtWesUt5JtVWYr+Sj+lU5NkehXsoTJNjHy4/WtEWfIwQM+taFrpX2dAG692xV2O1YDpx6jtUMpIxhYg/w5GeCBTvsZKgkfL2xW2toQm3bn3FPFvtXlfzFIqxhiyGQAvPHNPS1Gen04rX8kA4HX0xzTltyRwpJHYDpSAy0tCBn5eeCCAcj8elP+zYIyvf8DWrHZbwNx2888E0/wCy7S3PI9qYGUloQCR+lTrZAjPfqeK0EtxgEDJz1qRY2z93gEjIFAWKS2aqwVyAepIw34VILMKBtbBB4J9auqgCgkLkdPm608oQFOwqCSQAOP1zTApC3z93HTgHpSmyjPJHzZ/i5q2IyATgNkc809IssCWxxkUAUxbqDknBPJO0dKaLQBRg7iOdp6Ve2qTyeckZBNPRWOAI++QRweooCxT8j5snBK8c881GbJCdysxk9+au7CpC7fmHf0pCrbCwGCegJoGVjAARt+TPSnfZAowcHPbGc1OwVX3fOGB6ccVE0ohGHcZ6/Q+xpMENFo5ySGAHPT/OKalgxOfMXG7qG6VIRjMkb7iMZCPyO/IHP49PepI5JWKGR2JDE4bBJ+uf60h6EDWYLHLYAPGR71a+WGyih/u5wSOxJP8AM0plJYDeA2QGL4wevtimOBJFz0+vT9KU1oXHcpyqTnDfpVVolz85JPpUr5UkByT701sjlzXMbIFIjXjp6VQkuQ9w+zBKHB/IVfQl+q4HrisG4QDUJng+UlznJ79KqCIqM6OxZMCRhgVOb1nYxRFueHb+6PT61g2v227ZLWArubuO3qT7VsrFHbRiFH34OXc/xN3NLqNbGvanm6AwF+yTqB/2yavFF4WvcLPDSzDH/LtN/wCi2rxHafLDY4rrp/w16v8AQ56nxv5EsZIwR1rRtiZXUpjI5Iz9Ky0bb1qwjlDlTzVxdncg6Y2rtieP5opBuUenXr+Ib8j6V0XhO0S1ju5L+0WSVgpgEoyACOeeg/CuCiv5EH8ag/3KY8sbn77gDt5f/wBlV891Zgl2O11CLSILlbu6u1vJv4LYMCkf1A/GsiXUIz9riRmm89h86jAABzx+GB+FYXmQ4xmT6BQv65P8qmW88uJo0iRQw5c8sPYE9Pwpqp2Jce5r6hq89z+9vH4RQiRIcfdG0fljk/gPbm55DK+4+n5U6WUkjcc+3YVCoLHAHes223qPToOj3KAQD71MsrDB79qlSLcAoP4VKtn/AJzUWGaj2cmzaxzUI0xyBtA69Se1dGIG6FT9dpp/2ZQFG7P1qCrHLP8A2lbKEt7mRYd24xhzjPHOOnYfkK7ayupobO11KKRk89cOGXCM4GGx+PP41nvZb8qDkfSt2y1F9L0p7C/hW4WFVUoWBCqo2jAxjp3712YXVtGNbRIsqIrmHz2Ronxy6DIqlrK3cQt5Um/dTEhnixlSKt6Xquizgx20ps3bortuUH+Y/UVNfWqvA6m5gAPpzn6YGK6DI59rW7JVWnlxnOSx+b8K0rRGExtXmaRinz5HHPb8q55NQvtPuZ4ZXMhA/dTNzj/IrW0mSWEKVflizEHvwOaBG3ZjyLl41+Ubeo+tcj41iR/EUrJnJjjDfUKK7GKUTASYxIvt1Fc/4msJpdV+0RIGDxKSSe4GD/KssT8BpS+Kxx6wnoqkkntSeSCSDwMYyRWx/ZtzgkRjI68ig6VP5e/y9ueAC4rzzpsYLWcZzkZ59KgfTYuRt/EGujXSLiQnAXj1YU7+w7k8ho9vGSD0z26UXYcpyb6O3/LOQfQiq7aVdr92PcP9k13DeHrmPrJFjtyef0q2ljGVHmWltkKApXf+fDDNO7Hy+Z5wba6iPMEoHrsNIhnB+RHAHYA16b9kQgCKONTwNyhjkjr1Y0qWIdlDyCNjwZAm8r74zz9KpN9hOK7nnEUV6xwEbHoRWjb6TqE0gU5G76qP1r0CdXgumj85Z2UnMuDhye/PPf8AT8aaWkCjIAPY4HGf8im2yeVHNWvhfIBlkB9krXisBCqrHFjHYYq4wkYAb1AHGcdTUbxuNw3jPf5elGpSSRF5OCN6/U1KsaHAB/PPP6UGBw2JORxu4GMUohPUseg27RipsxjlA4y5JYdcHil22w5Q8qewP6e9CABipG4464/TNTADeMZHUcDHbNOwXRCGhyMRN8x55x/jR5SFvljOSBkBsZ/McVPGgX5uc9CM8gdKceAf4sY4Axzj8qBCb5N3lsFAOBnYAQB6ZGf8aj2DGRtBA6+v/wBepGAyTtJOOnFPMsYIUBwM9c9T6Y9KYEAUtySQM5GaekTKoO7Jxg/X+lKHLN82ckZJPIp4DquGTaOOR6/jSYDViyMqOOcev481Kiw7UJYbu6LycDv0x+p+lTpMig4jDBv4S52cHuP1pEAUHY23dw2zjd0OCP15pXHYiFnI0PmAPt5O7sRkDjPU1E8OxirEggYJwOfwq+by6kLBrqUJIMEFiQR2yCeagds5dgpYcdgP0xTCxW8ssexVuuBimkMQSMY7knNTj5VJXOAeOelIo+Yrhg2T70XCxWUsCqnpnPKf/Wp5kYKTvAwecjNSNvbBGcZ5yc4x/SoXfauSgBJ4OOtMQjKZT/Dgnp0qKaFXGdiqRnlhz+HpQ8zKuFDdeDnFJ5h3YLdT0OCefrRYLkRjQYdkxx2P3v8APNPBQR8sxYt0B/mKUsm1nLDpjLP/AIn/AD7VAJwVJK9CDgye36Hp/WgCXzJGYMEDANkoP88VKBIIMsAuf4c9KzhcRxM+NisTxl2UbfQ9j2Oany0kKm22OxG7IPB9euKmppEcNWQzscnaBUSlWK7mOfQU1rwcrNbsuOpVqdDFAwDRScnmuU6EXI4juVSPesG7gkh1S4jKnBkJXPcE8V0Vtdi2lzKEBHAdyMD657VHreq2V3eQmSaKeYq250dW4HQEg49fetIx93mM5y95IgjlisLYxQENNIP3sg/9BFET5AUcsT0qEvahcJEryehbpU9rG8jxrGoRc84rPdmmyOgssG/SBSCzQy7yRwMxsK8ejjCqGHTHIPQivYtH8ptZjiM6pMXVVj6lgeCT6YryFANoU9q6qb93lOWp8VyvJBzlBlfSoyGUnH5Vf2Y6d6Xy2PHNWQUhK2c7cY6Y6flTlcLywbHfFaMcIb5cgAc8j2qRrVhIAUGcnG3kfhjrTGZZmTOV3Zx0pBI+CFTJ962BbtjzFHJ70Lb45PU01cRkpaySn5iQD2q7HabCAPxJq2IWB4A5NSiMjAFFguMS22j5hyPSp/s+FByQcUq5GCw/KpVkcccbT6ChoLnRbpAMbSAD7ikKtgYA3E/jUoiyF+YnByM5qGTjbhWYZ+Y+n0rM0L+jyJBq8Es8PmwoSxViMZAOPyOPyrH8QazLLqxktLZfkbBfJC46Yz3rQ09oU1OFZTIIGYiRlOOCPx4q3r3hZXfek8j2/BUDHH19RXXQWjsYVWr6mRHbaNegSlJVlx8ywyDAP4jity1Fjb24Rp5pFHRRzj8ap2mkKJY8RwxqmATjBIrfFtCyrEsCPnpla6EmZHK30sd1eosUL26Z2lpASD+Iq7pyFpTEyfvB0denPNaXiLTTZ+Fb+dIMXKKpi8rgRHeM7seoPuBxnGa4/TNZ1nCJcW9sTjAkEmHPpnBqHJJ2KUW1c7qOMrb/ADMOTjcR7Vnat50VxF5gwGiUqO/v+oqrHfCeBg/2iIEfMYp1k2n/AD6VVmJZ1C3LXSqoCzPnkf0rKvL3bF0otO5Ny6EbzkdNvPPvS7/vASMm4AYHIYe/p0qtjkDDZxnB6Uu8tnaxwB1J71yHQXVlOcZ7fKGHb0pTJGnKszHpkqBj8s1U3yFMsABjAw3f1pVLiQEPznPemMkdgWzJIxP4Uu2Nl3su4A8Y6E1HGzDpnnjGfehFbOBt4OQCeDQBKoRW3soUk+ucfU1NsV5FVgAW5LdQPrVXy2IKkI4PQYNWrfKOhG7qOg6c9Af/AK1AhxtSSwwy7epIx+ef881G8W0gOeNvG0YyP6VbujGb1gkzsThjIwI3MeT3yeSevWoBuJLbWKqv3s7v/wBXNMRGeRxg55x15p6xsVBI+UnoB7fX39KB944ZmXjO3jj/AB5pwWUPkEc84ZsimIPnUM5Rt2fmbPINNIjdh6kgHgfnx0p2xwcH7pGCQP060jZXGPvDg5PJP9KQDDGu0hc5xx6Z/wD1UqqNxy5Jx0xj+tK/nKWdnypwMAYBoUEKu8EEjjPf9aGMQEghdwPJBOKlRiGA6Lxz1I/OmYJXGNm44z3pQGIYCH5scYpDHMWVm+Y8DkEd+aQKDu3OFfOCM5PA4pCHyI237c4HXIPp3qVN2zlSD15HQ/1pAUJxcwTxrCsckRBBAbBz68kYFWkXcwJ+X3IBx9M1LuYrhlPPP3ffv/nmmhT1Abg5AzxmkOwBtpAKnI9sYx3pVIUHG7qeN3QYpp+ZWZsDvyRgD05oD5UjGBuHAwM/jSAcXbeT5g6flQzlRhs89cZ/z3qJn2RsuFBAwc9aiAZx03EDgj0+v9aYD3nbAZX/ABzn+VRmZ1UJlmHvxTQOv8OW6jofxprBmjbODlecHGOaAEebk55LcbcD+vSo2li82JDJsLdDyQD77QcDv3+lJ5bA5Y5UHgEenv360pjzu6nqeT1NAixd6clvDk3tvJIDtMcZZs/7QOMEfjn2qjPFHEyeTOHBGDiMgD255/KnNEdxO3OT1x0qN94JZxklR07mqJZHJM6lipjyeo8sD+YqjLIuAEVVP+zxz0z+n6VZlfHG3PXLfpx/n0qsSWZlKcscAYFMVimGcnJkf7xIBwfSti3uStvEqN8oXGKzXs2O4gHbnHAxn86gkln01w0yFrdz26rUVIuS0KhLlZuvJb3AxJGu8evGazjEryP5MTJtJ+7mo4dbtlJ23EboegYYP8quRatY/aMpdRqJGG75h1965tVujpTT6mZrEFzc2OVDskbBnI9PeufKvbNlSSSO4rtpdVi+dLeUSIwKn5cAD096x5bRJVG7jFdFO/Lsc9RLm0Zl2l/NbSeYrZz1XtXQWWvWzEJI/lMD/F0/OsptJQsGWbbjnOAeaT+wJn+7Opz3Ze/4UpU1IIzcT0zwybObU1uAyGZjuDHnkHjB7Vzot4Y5Zl8qNts0gBIzxvI/pWHY6JPE4Juyhx1jzu+mcjFb0EfkqqhTgDuK0oU5Qbcne5NWalayIJNOtJMs0UeO4CgVG2iWMo3RxmIkDADE/jzV8guSFjx6YOSakjxkHac44I49q6LJmRjHw6gQmO4bf3yvFH9gvz+9UE8LkHj61t79xyQqqOig0qhuMjPpnpU8oXMRdAuHc/PDjuTkZ/Snp4bnwSWTHQAHJ/lW+obGF+8eDUqtJnjoKOUZhL4YnMvlGRVYfeyDgceuKmHhV1wZLlFUj5TtPzf5/pW6txMoXDHCfdBPC/SrZ1e4ljZXxkjlyTubnv2/SizHocu3hsE/LcHb6lf/AK9TL4XgHBuJM+y1tcqcjcpPK54wf60glbl3UKv+yOlFgMxUyqsJBn+7g8frURO1l+Yfe555P/1qQnGP9ZnPAGOBUMnzKVDDOc42d+wz26VlYu5Zt3hNwoOxQW244HU4rSkutR02LykjS5g/hyCWUenWsZHElzCZFJClT0PGMd/yrooLrLeRLEzL/Ccf4114d6M56urRQj1xY5EW4t7qOEN90KMEfjzW9b6tLOCmiaU8rdPOmG1f161FCY1uUWKPcufmbG0fn+laU2qiJPLt9kYX+L0rcgo3Vlr0trc/2zJbrZSxlSquMJ8wOBz7D26Vz8fh/RhnZFFIw/iwD+tXdWiXVLKd7yeY26Lv3yOQrbeemeeneoLWfTnijWK0dwQCp2lRg+lWl3AW30izhLNFAOOueaxVJMkgYbSsjAenU4/Su0hhSK3y1vIiHGNilj+lZ909pbzY+wR5b5gzp19eCPX+dc+Kj7l0aUb81jB7YYdemRTtvB4+6OctWuJIG3f6JGo9Sg4HPtxQbn5dscaKB24GfevPdzqsZIQMwKnKeqntUgtpcYMUhweDjj8avm7uFx5ZIU9SP15xUYuZpMMQCTwOAfxpajsRrp9y/wApikBHOAOtO+w3KqQ1vJ0ADA8Z/KnfaJT8yHOB829uSfxpwuN2A4Cpxn5Rlj7YpXkOyF/s25JG6J1yMAkgf5+tMjtZXJ2oSe53Dp6//Wp6SOzgbdgbopyT+Pv/AJxTS5zkO2cZYNjg807yFoW50klnaUxKoc58tGAxk+meOaheBwSNhcqMgZHH60L5ww3nOqk/d3HJ9yMj/GjDn5flfrtYEjGcepp3YWQfZZXUKQ4OMjGB/M0gs51ZlETNkY45x29eeaVt8u8hmA2/KXbliOvH+NKNq53HGflXAyfqRnj1ouxaDfImOQyONvAyePxyKaLdpCOFAz8pDLn19aV0TPysd7dWOQPbHelZreM/K5UkcbuM89cA0ahoBt9o+/GVJP8AGCR+FBgjSVUS4WUEAjZu6+mSB7UMoYFUJf5Thic849KFXGcjb8oLZz7fpzRqGhI8ayBdzKXJ4z09/WmeQmFJck5HTIA+pOP09KN24sqrJsz13Akj3GcUCMDIbdvIJ5U4H0o1DQkjWPPM0SHYSww20fTAPWlktokkXM2OM5VWyg+hwelIxPmP8sasQcqSevp16dxxSD92CwG5hkEqO3r7DrS1DQcY4NwZZm5x820ge/elYblLmOQhRyR0/Xv/AIU0zuzgP5uFPG1c/U5GKQzKH2+WWdfXjB/yKBh5cARTGHJUjA3AfUYHSjMgPmCKPG3GeQOnQfNml82TeiSfKMgrkkjHHqf8KZI2wlTJkgZAJ657DNICNwu5fkVW4JOS3Q89+lRPBvbKzOXIweoB9v1/SpkMf8KZfOQox6eveggbiHJwD0XBGOMmqERCJY5MxyN93gupAP0pHtlTawlyx4xsyDx3yKe6Bd2WOcc7sCmKhYkq/UcAE4H60xEZtDIpBVhnG3Ix+VM+zkOSTtfoQGJx7nrU/lfMWLbMryMbio9/el8ojKKwdck8Lj6ZJH+c00IheDAI+bAGRzjI/qKgkt2YHK7iFyf85q1sWTaodlw2TgDB6f55pcBmGyUlAerIFOffGfyoAoi2STJOOCOM46jjI9Kie0MYYKVVTwcGr+CuW8wBm6MFweMc9OKUoFiy2NyjhSSep4wfzpoRTjAt1WOMEknDMRwB6D1578VHKUlDKy8DrkZwP8KulYy7fwjsdvLH8D/nFVniZflwrEjGQOSM07CMyTTbB8j7NGQGwSB1PtimJpVjGw2wxhh82cfl1rTMYwG2KCD93HH0/wAmmmJ1AU9C2cY/+tTEU/sqA/KuF/hGMH60fZ8SBdvJAP8A9er2Dg424B6//rpxh7so6DJ9KLAVxDDGoA/eEcE4AGfQf406NSGwFPoPlyG9qsFDxltxzxjtQEdVABGD2B96OULkO3kbFwoGC2ck809iwBz1A6A/yqfC7QNmT6tn9Kd5K7z0RT0A55xV6iIhuU9PlI4PFKcMvyj5QcFioBP+FWFgjTo5BI5+XJ/nin+XGUA3cA524OKZJWKlSAAdx7mp0hLYJOF+vWiOIRk4UEtzyelWFO35cHrxjg1SENCkgjkn6VNHHlQpbGORnoRTQQVJGeOwOKlHy5Y9PQdqBjXiUtnOMH160jIN2enHOO4p5Izlc4I/vUgA28PyOp707CuMEZ35zzn+70FP2gKfmJOMMuetJhFAG7B7bmpMEtwjbuuQeKVhnNyTgNtaRRgZA5+aoRd7U5DMN2Rxwf8AGrj29uZC4fg+isT2+mD3pZVik+dY1Rt5JAyR+APQfiawNCjJeowZOWBGSAcAD0OavQeJlhjENzC0jBQFlCAkex59KzGtFVmkILDOckbQelQkQ7shASOrY6Hv/n+dXCbjsTKN9zqbfxLZvIokk2rkYJ4/nWvbz29wyyJGZE65AyDXnP2ZLyYxohkYdvQevsK7PQreCzuoA80xhyqsryZUAe3ApzxqprVG1DAyrPTRF3VNThEBPyMvKk9VX/69T6Yl7d2oaKObCKAGWLqeehPGePWtPXNDuNVsUWGWz8gMSEeTlu3HHHUfmPameGdSew0650O7GJrFt8fI5jbn8cE1H9oSSbcTsq5UoqLhO/R+Rs6VpvHmX0rr6ojZc/Vug+gz9awvFl8b+5gtYbeOK3tt4jVFPOSMkknk8DmtKLxJb4derdgazrxRdxNInUnIrz5ZhOpJcz0Nv7PhCDUVqc6o2nJjJ+Xv/WpxDIfuBCyrlyVB2j2/Knb180xyOIwoOQSV/l1olXgeXPG7nG5wT/XH+cV1qSaumefKLi7NEaowUBAfu5br8368Ujh2YIVVWz0Jz2HX3qVwx3MZoiw5z5mT9TgmogiFtpkiLHgASDj9c07okaUILEMCcEHnGef6f0pw2gctnLZ4Ht708REnmSE56YfkUot/MJHm24YEgjzBgfnSbCxDsclnMrdOmf8AOKlCuSrNglsYwvX6/nQ0BXOJIXHfEgPX09aekaqwAuoSCAcbm/LpSuh2ZCpkYbQg6cZOeB7/AOelKFYqSCrKG7nP6CpGjjcsxnhTqMZI/QZx3pwt1++LmHdknALDt9KdwsQgBWKtIUy2C20kHJ68elIdwDYIb/ZYmpCi/dV1U7cksCAx9sinfZ1SPcsy/KMgLnI6deKLiIUUiQLvVV3ZOBtz06E05UJ5VslgeoJI/XipVQSEqm1gMEl298H046etKiK7BQMsWxuJAz+bfpRcCNYlVVIfLDoDu+Y9SMYxj8aa5QEL5bhhglS3+A6UrkLJ+7WJmB7N0/yO9NDZI+XLbRtGcgD6np/9agNRV4GBECcnG5txzjqQab52RnJDAfc3ZPU9QP8A61OE0hQlZVRfQ7uePfmlM1yX2sdwz82M/l15oATzDLgCMs38WwkZA7ccCpCecASjkkHeOCenUf59qQytuLINhYANglc+xwe5/lTk80lnCR7TySVOOv60hkakMpZTjnA4BJ/EGpUhRsAyBjn7uxuB/j/hQ288xlmJzkKAB6Yp5idiz/LH8uQD6Zxjtn60AReSir85d22DaMA89D1NLtRc7ZULgKcBTkZH0x6fnT2hZAvzrGpO3kEc/l+tNWNTEgACuvoOQeeOOv1PpTENBkd8MzrHxgYPHPOO1L5bDkbC2MHP3l5//VUr/IGbA9dysMD8OpFLHB54BRlJ3dQwz0HvmgTIjkyEei9UB/x5P60hfLKBM7BjtGSdo+nNTrFIqZVZTyTvxxwM5Jx/kVH5TMx3KTgHJIyB6f5zTAjZiVyzgEN90dj/APq+tRqy+WPvZB4TGc/lzVhoTG/yhgx9R15+lIV3EdQo64+bA/xpANS6uYZjMsm4serkNz9Dmq0kpd8FgCR0LAA+9XDbKScHCnODxjP+PNR/Z1VcnAIAzyfTHTPT/PFUhMrg8nbtJBGSB1/WlCHhclMZOCPxzirAiRDuUsSDngDHagKy5YIMdOOcVSEyvgKACfwNR+XIGJxnPcHrVh+UGxOf7xPQ57cU1oRuLKWDkZJboPequSVyrcMOp6DHf86VbUN1IBLfgT9alVSNu522847ZpQrbSSAQOOR0oAiW2IKom0seDztC5+tBjEbEMOcdOtWSkiqQsjK47qeV+mOlQhJiQWYu+eSec+9MREseM4GfbNO8rpjkEcEipfLYfKFJPXNCAkYHGTjJqkIhEW08k9euOtKUZBnj8an2HaWIXHbBNKAPlLEjAz85xTEQFeeRn6inRxOeSME9T6VPgOBt5yf0zzzUgVo0bGzHOM849qAIRA20DPPpTypB46/XFS+WSowVAxk5GeaFjcg5BJPA9aBkSJxjPQ5Bx1pQrA54PHII5qcW8jfKQy/L1PerkOlXUrBEicNjIJGPxouFjNVGG3jnPSl8s7snqTxXQQ+FdQnGVj24P8R6/jWnB4HuWKtLKo7sQDx+lHOg5WciLdgVJQ8HII6fzp4gJiyQV75HSvQLTwakL7pphIoHClM/nW5FpFjBk/Z4cgc/IPrUOohqJ4LIFyuWJmzgkLjj/Gq7ECbZuG7BHGflOe/FbUiRhCViKnnJMhOfzqHyokOBEVUchgzZ+vNZ3LsZJjEgwSmd+Np9T9ccVUltSIv9Se+SeP6cds/hXQhLcsmISpA6szH/AApJEs5GZlgVSeCRnp7c9/epbHynHky2k3mxAo+MEg8n86cdflXIcgY9RXTNaaexCi3DgKOMEk/Tn/P6U1bSxIDR2qDBHAXJ/wDr1nOEJayRtTrVaekJWMCTxBuQLKkUoIHRzml/tiOa4ErJ5c4GDN5zbyvpya3Gt7VnMjRREjnGwHtkf4Zp6x20fG1ASMEbR+R446VmqUFsjaeMxFRcspXM63vI5GWWNyzepbNdRaal+7jKHK4+bNZEkNu6BQsapnJOwDn0pVijj+WNSPULnArmnhW37p0wx2nvIu6jdrd7RHgbTzjjNU1Q+XwPlA6npmligwoKREADJIU8/j2qQRANubc4V+hXOPwrro0/ZRscVar7WXNsI0cijJV8AZ5z9O/+eKQqQh4GwE/MpPTt9Kk8xBmNncYHG0A9ewz+vFKTBKzMCwIY8FhgAjitjEgIJ+XJYZGR0JHrUih2ViIzkcABu3XcakRot+NmwN2IJB6e+aDjexWXtzzjPPQf57UCHJGzHIHyZwCf4ice/wBacypwvzc4H3c49eR1/wAKasyoSCGZuMDP6ZHNNDozMT8o3EllfGP0/lSGL5KEIzKzMR1Qk568en/66aq7XVZf3YJyQPm2j+pxU8UkJRAnyuGA37sY59zx/wDWpisCAy+WCQRnIJH+P+elGgCL8mML/PI+hqZY3I3NhcfNjnA/zwKZHICVYOqhTlcsVAI785H60NIrtj5WcnBAbn0GMGgBrgswXc5XdyMkEnr/AFqMq53yBUU9wDgAfnz/AJNSE7pcSMFJOSQc5/M9fxpjKFfIIJAwTzSAFOEAYggEA9ck/Xt3qQGQK20bwuAe5A/Dr1xk4pIlwB+7ZhkthCRjPXsce9N6vz8hVuobjn26+tMVxwZgqsFJw3AUcYwePWkZy5O93ySSv+HJ/TFAmchVJZ0+8m8E+mePQ0uG4JzjcM9O/PegASRSytJubIGMcAAfU8U/MbnAUFhyGwPrTTHLvIABAHLFBx/h/wDWoELE8OXJJwO2cev40hksYV3VPOlwD025KjjoM4/WlZAhIySdpbaRjPJ4HB496a0e5SxRN2WAVSSFx6fn39KWNSjc7yoIDEMRkZBxz6jP50DCKPbtL/KSN5Vl55wcflTgWVt4k442ttyO+M8ccUNKWV85GByxbBIB/wD1flTV8sRhmdlPqcg89Mc0CARAuqnc3zHC7s49/Y0vlndjcsZfJChiQMDoPU06NopCI/LaM5znI9u59vahoU2KAQW285PQ+1MBRHMqRsR5ZcFBvGMkdev4UrQrGr7pAHQsuChBY/Ujgf4c4pscZBL8BQM/KcfzxnjtVgvEXMaIHLfdyMP0OMAEgfT6UxEZhNuwWXeOmSO4yORzj8ec01RhiGjUsfuNzx24H/6qQyRtKpULuXGc5AUYxzg/yod87lO0jZghg2Dz9T1oAQMdxIQtheQuVyM96VnDSYVsHONv3iTz14oB+7GEDYG0ZP3O3ekQoJNhMjEvjOBkj2FNAIAwcIZY89jnjHcds04qY15bCldpwSM/T9KULGA2eDgn5m+79Pzp6mQOuzJwvy7WPy5xyOaYiAq7DBXbgYG7qBn6VF5MgXATHY/Mcn8zV0RANuEhXLYA9frUrKGPMhYE9dp5x357UxFJI33EoG2gZ5P69al+zgxnLHJODxjP5Ve+yNHGJvL+UEKSCMA+mP5VJHb+bnbCVOeHORmi4WM7ynYEkrk+pxmkaDLggfN7mtiLSbjbt8twjEnPp+FaUPhxmYB5CvGS23P9afMhWOW+zY/g4BoFrIFL46dSD36f5Fd1B4XiRSXmLhhgbl/lVweFrFXUlZGbjI3cH9KOZC5Tzg2bZxsyxHQUC2LYKpkgdOuBXqq6TaQ8eSjEHbllzgntz9atrp8cau0UCpIeMAAZH1o5x8p5TBp000gWOCRmHXjOP6DpV5fDWoEBlg3g/wB1uma9OS3UKMBQpHIK4qYoRjj6ego52HKeff8ACIXBYKpG0jHLjP5AcfrVyz8GMvzXMnz7vlCt09un0rroBKoKyIWlfL8cAenfip0yQMYx3GOaXMx2Mi28Oafb/KIVbGM7h3+ta0duiggBenWlEeHOGzk5wRTyMMBtP1A4qbjBWXpwecCnkgYPc8dKjXerHcwx/DTmRnXBYg9cjtSAbKVKlWOSRwB1qK3kieAOqEFeOVKk/nzUsUOyIJJJ5hBzkjrzn86kOVyFH04oA8Ek3OpWXYp+6FPy+nPv9agk3MW3ZYMTzu7enTmrZj3Fd0isnQnGce3J61DMg3KjE7sZwFK/1/KmBAEaSQJGCW4yCTn8cCnLE6/L5ihBkkgYJ+v50Frd3UYO9MDH/wBbPakDoXUIu3JxgE+vGef85osMjZI12HcxJI/hIGfQc54oeSNw2wAsFLHDA8fT8P0qaTdMq72YZ4xkMT+J7VWYGUiIKpTphUGB78ClYBVYPKVYmRxySI849+nNPFvGFV3lmWNm5Yx4z7Dp9OKj2xqrIxYA5JXzSuT9O9EcTxyBXV4xxyAeBx24qWhpkgmgXbhHwTwXk3HHboOKAUjAVTKOcDcQeo/M9P0HtTsO21S4yCDgjAHvQYzHwkqbCvOSAfp/nrRYdwdY5Aru+5cZJIA7YyR+FLGYiBGFZcPtILHjPXPJx9aAFjZWVlyCCSq9vTjn605D5h+WZQxy2W6enGc59KdhNiBnWL75wRgbGY5/Dtx6UBi44dyVPGOhPHFPaJkbJwCVyWK8Z/GkwjkRsWRAcY2/TJAH8qYhSsoJIjyoXk8nHuaXy5CB+7YYx7c/lyMZ71FtKYG4lh0Gcc8/54pUScDbtGSQSxPA6df/AK9ADsMzEKr7SONrbyBmk8mNhtWNQBjJC4JODSFl3spUEkZ28nk9hT0jZmIZWjGcnyxnP4evXvQA2by/NyBHExHQHPA70MoC8S4DLjaFPPftTiArbSc8ZwDgnrwM02NzvQgFlUf6sOcYJGeg74pAORGYElduF+9y2P8ADmjYY9uFPUEt+Pbn60hGxwHTdkjI6nnjOOlKFk3sQgXPzBD90Y49qAJFjjLYCbeCoC8kfUYHak24UMp4ADBs5z04Ioyy8bREj4Rlz1/XOPepQ5JSIBskBVbsPUAkc9jTsBBKrNIxCkqFGdvUfUentxSq6KCfmAVuQBtAyCRjvmnSRtbzMph/eDHDDkZ/P8qjI3sPNZl6tk454z0waAHtMFGWdiANpKA/lmokubYESKp3K2Tx0H4Ak5pFhJbnByM4VsHt04IHFTg8gqclQcKq8rx+f457dqAI0czuWVM7vuqRyPbOKnErPwc/e/jBx27gZqBztZlTluc4Uflz/wDWp0UDBw6ydPm5Ix19j/KpaHcmVXVwuUQup+UMFGOvXPbHf0pySAENL5zLjd87E8cc5A4/HP4035Nqht7Hcp2bcZ7+ufWm5jdxvQSEttYFskn6A8npQA52ikmLQgKSv8Uu3oehzjijzkQxFWVct1BA9MnNQzXCNOAqMWxtZARtHp1/kKaY1kZXIcEHBB59cYpiLyhiGfazcEOww3tzj8ueuaGCgKUYZ2gllyeoxyB169vy64ghKhCmz7rDdgjgemeT/j+FXAHERVo5h5akIyrlQepOfXjHvx6UDIzKWIikdmVRwpwWwD7n9KasiqflB3A4YDAH+OeKZkICmzLr97K8g5+nWnxbTgGLJYbgGJUfj0/zmmIQbdm+PHlbuS7qT6cCkcgqPkVmYD5RgBv5/p60FxktGoC7u7H9MHr/AI0nnOo2xRAbudoB5+vIoAVzOilFV4zkgDb245OPbNClmdmeQ+Z1C7cKoH0P6Y/lSLNKFIjVRk5Axxn0680m9TliqBTnOCRnPtn8aAJUZZFyoxE3JOevI4xj/PFTq2H8raWIXGD/APq5FRmRpkBVQHCj5mB4x6Dr70kcrnb829W64Ofy9KYi0uGBDoqHdzlgRjHrnt+tPVcNlkBG7DYPuelNggkmkWN9w35KEHgnPoa6DTtPR9hDlzjhVXqPoP8A9dAGcLVvNhRj5iq+8xcke5HcnoP/ANVdRZW8cSb0LIGzxKT8o/Hn0qu0JtbmMRRvGrZZlUkHsM9fercMJDs+15Pl+ZT3GOo980AaUMSshKhdrD7x5z+NXI02AbwM8DIHfNVrVXijQAEoQODwR9KuqiOMEFgTwppACKOhj785FSAkEADI+lIGCnhATj7tOUsTlh26imAksatGVZcKGz8vr1pyAMq4zjPrzSui4BbDY5GcU4H5ASCuewpAMY7dox3HUU2dtrLucqScBR3qVsDbjPPaoypdyGChR0PXNMBjZXLb8/7IpzybYshG5HRRzTlTOTu6nt6UnAB2DJ6cnrSAjOTGWjJVuMKcVKqsFzu6DGSc0gBKgsQPoKBlcjkjHQmmA4DK4NSFsJkjio0PyAldtOOccd/ekBBcSR26GaTKqgyT6CnRzRzIrRMSjrkMD/jSRSTuG8yKMf3drk7v04p5ATO3aucFhQB4U4AQ43qecg9M1E6D+Lh88jHepyHZQC4KjOOoHvUYuI9g/e9W5Yj+nWmAxoWcgBDg9evP4VIqFFDiNlIY5JP8/wAKbLPaRSxku8hbO0JHnafXkgYHtmp1uIpFBLHO7JIPGPp6/jSuMrkbgyMRgkkYAUjjsR/k0pWTA2sCvQDHXHfA/rTwgzmMOzbSxTA7n0zSOGViSzMuQSpYDI6gUXCxGPLdtpjAfvhfce/pTVTEfCLuIwSzDj3/AKVKwmyWWE8c7ecL+lN5C5CgIQA38Pr7/WkA2MF2XbDk4GCC3J46AE01kRDkNtyeASOB7DH41MJAyB1dTuGMs2Mew/z3qEyMrMAibzz8g5P0/Kgdg2/IRtO4t/dzuY46nrTtrEuDETgdhwcev5043MCJjayBl28Hn8iTz/jS+ZZnG2VlAbPzrjuOmCcGmBGg2gud2RhiCvGOOvoDTts5LSby3Xcev+AA5pQjuSoJZdpOAwOefU9sY/KmiJ/NaNvlbJBXIxnnoaBBhDtBwQwH3fr1PNL1IQR5O3swwB/hSLnhlOB3YEgDntimh55WIfapOeGyAMc9/b8aAJA8fmbNwBBxwo9eqjPPfqaQQCQAmNj8nY9GJ9vyp25smQyDI+ZQMkkZ784/lSbopGXy92/jIKnHGPunPv6cUAIIvMYIrMwBGSAdw5HGMUrlyxLOYyuACGK9vQEc9qeSzATMHaIHkk9Ae2cc0zdFHh42BKHnywVz04z70AJGzswUyY5DDO48cdh/ninKCOVYksDkHJ3dew6EU9pWky2ZGUruyHH0PH9aZui8kbJFwR8yqD+vNADvNUr8+EKqFGATkfzzUJu55DkSypsIAPmYx9FzxSuWdohEGiQDksTgjGOwqREjiXJPJHJbjOen170WENW2+UPLJBJltxZSrEHOM8f1po2nLFAN2SpUnjp16k1YadjPvG5iAuWaQluOnbgY7c4x1qKREI3qzfOcAB+Sc+w4pgMU8hjv2kD7mAWHHbH60SFSWBKbsZJYnjn0yOtKQ5VQoJG3s/C/kOaI/L+8B82QApTbyOf89KAHwKjYjYOPmGCp5Ydxzn04p5Xai4bLbSCCePyz2zSCTIaQrv7H5wxPqevA+tKjQySIfOj6j7u7I9zkEDr+VIYIN758p2UZ+VCcY9s5x9c0oReFhA4bkdO314pw85RsjSVsDbguBnr147U4q4LsZNzH5iXbK54+n+RQBCYEjzk7VIP3SCTjPAODgc5//XRHDIrYRztAG7DY44496lkhMUiqRtL7docnDZ6d/wDPapZYmETK5wVGEAUjJJzwTxnP/wCukBAsqtKApAXphjwoz1HJyaa8kr7TNOWJzkNk4XPcmnBnY7ljUDcQDuznk9RxSSovn7HkIGeCFX/Ht/WgBySwNsjAZSScSNIFC8Y+g+uakAMo3NKfu7slh05GRk859aabITxPEJSoHQMec5P+fSlS3CIsCsNoPUk9MeufX2pgNwx+XB+XIKtznjrj/PSkbc3A4AG3oM/yznpVkhjEyIWxk7QpPXuf8+pqLywMPkByeirj8/agBm11K7hnnBJ7cUiBGXJUrjrxgdOuM1MsLB9r+YxB4I5z/wDWqWKNwrRsRuyeoANMRX2sZBggAKTtTr7mpYo2iUHeykHOM55x79BUgEYKCSQAHoitjjucf41LFATIQkYbHAIJPUY7diDQBb0y3808qCe6Ack8dAOprqrWHy2GQiOy8EsFAHY4+nFZmnaYZQC6gSbQCGHI9a2xpqrEy+ZjPRgOV56CkBEQm9plIkXdsLKuQvsMZ6VfhZGRdscqqR8uImAA/AVLaXsTFossSmEZycZPr+daETbgfmz7nmgDO88Rv5Rimz2aONuT19OBTzc5+ZopyQOAIm+b1zVx/K6MVBXuccUiuGGVO704oArJqAKn/RbpRngCE5pjar5bc2d6yHnHkn5f8auDAPAwD0J4p2AQSccjr1xRcCodXwcCxvtoxgfZzTH1rJDLY6huHIzAcfpV8Yzg8/5/SmuuWUgD60XCxnrrvm5ZNPvt6DkGHAGfxpW1CQHzDp95kDk+T1Hp97mrzKqFSR064FSK4JJwR7GgCkuqSOPl06+Xj/nkP8acNRmK5GnXQPcFV/nuq5kLk9QeuO1G8EA8AcE0XAote3QdSNNuSvf7vH0G6hL+6bB/sq4zjn5owR/49V0SAtx64pQwDdOD+lFwKDXl8JFxpchjzwWlQEH8zSG7vjLs/s07T6zLgVfLfMQRwOwFI5OwhByCCMjmgClJNf8ARdP+bsROtSLd3rIT/Z+GA4UzKN30NW4+Vzuzx3HNOO7sRnv70AeCFGaIDeyYzgbsjt/+uliyfmj8xSSCMygdu3QA07cCV8yVVZvlGOR278c/WmuWRXLLK2OCecDPHpyKYDydm5XVtx9Rg8defakbBYkI0g29mAHUZ9sUqrtCkSMuQABs4PTvtzimF3Yn5XcAbctkEDJ9vWgBDvZTtkdjyQitgDjoaVI7kgB512hc5ZiQPXpn3oKFIWcy7RuI3DJXPpwD/WkYOTgwuxOBwxXaT+HP9aBkLyPb5cuN5X5tmQD25qeEzYXzP3gwCnzg7SSPXg0RxExs+QQDu3EHcABjGSOR+vFLHuY+SDKyhQAC2ACOmc8d+tAhcgnZ829RuO5up49/ekN3LvBZ5Crc/KTkdu9OkQYVpZOW+TDKTk9u3HHr1xSIDFI5RmRiDuZ1U9+oOKBgZgrDnD4yPUgj29R3oWeNSMRGN8k9snp3FPC4cDy/Myg524HJ9cdaiUGQl28uJd33QMhBnHO3+v60gHtMrOrSJuYcYR8/gN35/jUjz2RjVFNwCMAghfx78fSq5UROxVpV45CMcHnA4FPaN1KERsMqBkAgdc9SODx/P1oGOCRsN8VxsZB93LZOPoMfrSiOSUO6uDyxyzDJOPryfaqa23yrvHlktjCHd1PsDT/Ik34EW5SAMMxGO34/nRYQPGeoKHJI7gk+lWgB8shZmQLztXG3pkA5x1ptvZMUw4TABOWVs/p/n9KQwoGLASn5QMIpwfTPH+cUDIWe2Uq0SKGXAJ+6en/1v1qWJlDgSqqLuBZtxZecdMEfSkEMO1WeRsOxxtXHJx3x1+tSBH5XyDKxQ8seM++MZ444P+FAhhW3RyE2s23IbOASewHTvT7hYprSOIRqsrEcFwOMcnGP19sU1IlkZ5CrbcfdReOw/iPP5/WnkkOBDI/Xg4BDCiwEUcAj2AYD8DAI+nP+f50KN5LHKgkkFV4Pr2/Kny7pPLG9uD8pReFP1x19/YUIrlyGV43A67sDGfw/yaokavnFCwfKgYAB5Hrim/LvOzBbG4gcD9Op9qlSPa+2N2GTuUBARx1PrQE3SIUL/PnBbGT9emKBlZlkWVi8hdhIcgjoB/WpIknRTIqfK3908/z560+RG3oEmLKx7KSSM856/wBB9aiC+WHXy2YYyDjoc454/rSAeI+duQhwAcMoySOBnPT2PSnhWkKiM7diAkBlG3t25xmrMFvM4b523KQSZdq9OmS38vQe1RrahJXMaSMxyARkqxGOmAf/ANdAxmy6j2JmNi3OSSec+vbFOG47gYkLFTtcjGB079PTtn608rOHt5UxARtx8/Jx3/PP5+1XZFRI4s3DFnfKsqsG7A5A+UjPGAeg/CkBQaMRqECsZ3wpJxgcZAz+fHameUThgwYq2d0bYxnv+n+FaN000xjLnjfuUtHtySBzjAHYf560fLQ7jGryFgcA8AkdsD+tAMYGk+T5/M6MSFP4j6f/AF6lRXMi+bO4Z8lU+6M+noB/jQ8lwDbELtUdpBkHnOW54/8A1Usk3l4BjDY2g/fbax549fX/ADmgB+QVyHUMQGztBHB5OT0OcdO1J5heRYy7KG5XryDznBOcnp701lU25dlZdysQzfLknk4HU/T1pY5JP9SDIoBGMsVHH5dKAFEW1d4j3qVwSAM+nIzUmSSCj/LnACYXceOOnFG75id8gbaBhUGOvX1oCbY2MkgT5icEDJP44oAQRTrIz+ZlU9X+UdsA571OsZVAzeXgY4IywB9w1QP+7kZljLbV+67dM9eQDn+uKVYZmSMoGAIz82QGJ/Uev4dqYF2GFTJtdmkl3ZyemAP7x7fj3rV022YsiMgK43x/Nk9s9eoqjDGWt8K7xhWBLZOAMjjHXsOnp2robOHYI8MVJxggDA9etAGraQRKyIM/J+WeeB7Ve2MFZuWVck55P6daijVdi7m+XoMD+dWFXKnbuLAnKtSASMuGHy5BHUf4fhUyAR8gtyeuM4PSo7Zcc5OQ2AOw+lSvsAOFbnn0zSABFl2O7PHTsDSLnYd+DKe/bp+lSDkZOFXHQUYw2QTnHINADFyww6fN3Ap/POG5x3pQMYJGc9qXBLZYgDtTASMED5iQcYJJ4pW3Hvj6UEYOQTjuDS8DHYkdqQETFm5PU/pSxk5GExxjNKwzICTlcfhTeQ3U8jkUASqwI64I4IpCeQvPHtwKMgnHYmhhmNwuQx/ioARFxLkOQCOh/nS5wwH5ECmCP5YzufcMc4GR9fWpSBs2jIPr60AV5nVbmPfOEznCH+I//WqZVY5yR14x1pW2K/mYGCMHpRHgR7NuQDxntQIRWBzhuDxSgEAliWz7U04GQFO4k9PWnhSQNxPTmgD/2Q=="
-       id="image41" />
-  </g>
-  <path
-     fill="#000000"
-     fill-opacity="0"
-     d="M 128.85481,15.720778 H 257.70828 V 144.57276 H 128.85481 Z"
-     fill-rule="evenodd"
-     id="path45" />
-  <g
-     transform="matrix(0.26027393,0,0,0.25166404,124.44803,15.720778)"
-     id="g52">
-    <clipPath
-       id="g108f8700c0f_0_0.5">
-      <path
-         d="M 16.931349,0 H 512.00001 V 512 H 16.931349 Z"
-         clip-rule="evenodd"
-         id="path47" />
-    </clipPath>
-    <image
-       clip-path="url(#g108f8700c0f_0_0.5)"
-       fill="#000000"
-       width="512"
-       height="512"
-       x="0"
-       y="0"
-       preserveAspectRatio="none"
-       xlink:href="data:image/jpeg;base64,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"
-       id="image50" />
-  </g>
-  <path
-     fill="#000000"
-     fill-opacity="0"
-     d="M 0,144.5717 H 128.85347 V 273.42596 H 0 Z"
-     fill-rule="evenodd"
-     id="path54" />
-  <g
-     transform="matrix(0.25166693,0,0,0.2516685,0,144.5717)"
-     id="g61">
-    <clipPath
-       id="g108f8700c0f_0_0.6">
-      <path
-         d="M 0,0 H 512 V 512 H 0 Z"
-         clip-rule="evenodd"
-         id="path56" />
-    </clipPath>
-    <image
-       clip-path="url(#g108f8700c0f_0_0.6)"
-       fill="#000000"
-       width="512"
-       height="512"
-       x="0"
-       y="0"
-       preserveAspectRatio="none"
-       xlink:href="data:image/jpeg;base64,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"
-       id="image59" />
-  </g>
-  <path
-     fill="#000000"
-     fill-opacity="0"
-     d="M 4.5170605e-4,15.719678 H 128.8539 V 144.57395 H 4.5170605e-4 Z"
-     fill-rule="evenodd"
-     id="path63" />
-  <g
-     transform="matrix(0.25166693,0,0,0.2516685,4.5170603e-4,15.719678)"
-     id="g70">
-    <clipPath
-       id="g108f8700c0f_0_0.7">
-      <path
-         d="M 0,0 H 512 V 512 H 0 Z"
-         clip-rule="evenodd"
-         id="path65" />
-    </clipPath>
-    <image
-       clip-path="url(#g108f8700c0f_0_0.7)"
-       fill="#000000"
-       width="512"
-       height="512"
-       x="0"
-       y="0"
-       preserveAspectRatio="none"
-       xlink:href="data:image/jpeg;base64,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"
-       id="image68" />
-  </g>
-  <path
-     fill="#000000"
-     fill-opacity="0"
-     d="M 678.8332,144.57182 H 807.68665 V 273.42691 H 678.8332 Z"
-     fill-rule="evenodd"
-     id="path72" />
-  <g
-     transform="matrix(0.25166693,0,0,0.25167008,678.83321,144.57181)"
-     id="g79">
-    <clipPath
-       id="g108f8700c0f_0_0.8">
-      <path
-         d="M 0,0 H 512 V 512 H 0 Z"
-         clip-rule="evenodd"
-         id="path74" />
-    </clipPath>
-    <image
-       clip-path="url(#g108f8700c0f_0_0.8)"
-       fill="#000000"
-       width="512"
-       height="512"
-       x="0"
-       y="0"
-       preserveAspectRatio="none"
-       xlink:href="data:image/jpeg;base64,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"
-       id="image77" />
-  </g>
-  <path
-     fill="#000000"
-     fill-opacity="0"
-     d="M 678.8337,15.719398 H 807.68715 V 144.57367 H 678.8337 Z"
-     fill-rule="evenodd"
-     id="path81" />
-  <g
-     transform="matrix(0.25166693,0,0,0.2516685,678.83367,15.719408)"
-     id="g88">
-    <clipPath
-       id="g108f8700c0f_0_0.9">
-      <path
-         d="M 0,0 H 512 V 512 H 0 Z"
-         clip-rule="evenodd"
-         id="path83" />
-    </clipPath>
-    <image
-       clip-path="url(#g108f8700c0f_0_0.9)"
-       fill="#000000"
-       width="512"
-       height="512"
-       x="0"
-       y="0"
-       preserveAspectRatio="none"
-       xlink:href="data:image/jpeg;base64,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"
-       id="image86" />
-  </g>
-  <path
-     fill="#000000"
-     fill-opacity="0"
-     d="M 807.68726,15.719678 H 936.54071 V 144.57395 H 807.68726 Z"
-     fill-rule="evenodd"
-     id="path90" />
-  <g
-     transform="matrix(0.25166693,0,0,0.2516685,807.68726,15.719678)"
-     id="g97">
-    <clipPath
-       id="g108f8700c0f_0_0.10">
-      <path
-         d="M 0,0 H 512 V 512 H 0 Z"
-         clip-rule="evenodd"
-         id="path92" />
-    </clipPath>
-    <image
-       clip-path="url(#g108f8700c0f_0_0.10)"
-       fill="#000000"
-       width="512"
-       height="512"
-       x="0"
-       y="0"
-       preserveAspectRatio="none"
-       xlink:href="data:image/jpeg;base64,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"
-       id="image95" />
-  </g>
-  <path
-     fill="#000000"
-     fill-opacity="0"
-     d="M 807.68726,144.5706 H 936.54071 V 273.42486 H 807.68726 Z"
-     fill-rule="evenodd"
-     id="path99" />
-  <g
-     transform="matrix(0.25166693,0,0,0.2516685,807.68726,144.5706)"
-     id="g106">
-    <clipPath
-       id="g108f8700c0f_0_0.11">
-      <path
-         d="M 0,0 H 512 V 512 H 0 Z"
-         clip-rule="evenodd"
-         id="path101" />
-    </clipPath>
-    <image
-       clip-path="url(#g108f8700c0f_0_0.11)"
-       fill="#000000"
-       width="512"
-       height="512"
-       x="0"
-       y="0"
-       preserveAspectRatio="none"
-       xlink:href="data:image/jpeg;base64,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"
-       id="image104" />
-  </g>
-  <path
-     fill="#000000"
-     fill-opacity="0"
-     d="M 524.2296,15.719618 H 653.08305 V 144.57389 H 524.2296 Z"
-     fill-rule="evenodd"
-     id="path108" />
-  <g
-     transform="matrix(0.25166693,0,0,0.2516685,524.22963,15.719618)"
-     id="g115">
-    <clipPath
-       id="g108f8700c0f_0_0.12">
-      <path
-         d="M 0,0 H 512 V 512 H 0 Z"
-         clip-rule="evenodd"
-         id="path110" />
-    </clipPath>
-    <image
-       clip-path="url(#g108f8700c0f_0_0.12)"
-       fill="#000000"
-       width="512"
-       height="512"
-       x="0"
-       y="0"
-       preserveAspectRatio="none"
-       xlink:href="data:image/jpeg;base64,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"
-       id="image113" />
-  </g>
-  <path
-     fill="#000000"
-     fill-opacity="0"
-     d="M 390.96954,18.438928 H 524.22967 V 151.69988 H 390.96954 Z"
-     fill-rule="evenodd"
-     id="path117" />
-  <g
-     transform="matrix(0.26027375,0,0,0.26027533,390.96953,18.438928)"
-     id="g124">
-    <clipPath
-       id="g108f8700c0f_0_0.13">
-      <path
-         d="M 0,0 H 512 V 512 H 0 Z"
-         clip-rule="evenodd"
-         id="path119" />
-    </clipPath>
-    <image
-       clip-path="url(#g108f8700c0f_0_0.13)"
-       fill="#000000"
-       width="512"
-       height="512"
-       x="0"
-       y="0"
-       preserveAspectRatio="none"
-       xlink:href="data:image/jpeg;base64,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"
-       id="image122" />
-  </g>
-  <path
-     fill="#000000"
-     fill-opacity="0"
-     d="M 524.2297,144.57156 H 653.08315 V 273.42584 H 524.2297 Z"
-     fill-rule="evenodd"
-     id="path126" />
-  <g
-     transform="matrix(0.25166693,0,0,0.2516685,524.22965,144.57156)"
-     id="g133">
-    <clipPath
-       id="g108f8700c0f_0_0.14">
-      <path
-         d="M 0,0 H 512 V 512 H 0 Z"
-         clip-rule="evenodd"
-         id="path128" />
-    </clipPath>
-    <image
-       clip-path="url(#g108f8700c0f_0_0.14)"
-       fill="#000000"
-       width="512"
-       height="512"
-       x="0"
-       y="0"
-       preserveAspectRatio="none"
-       xlink:href="data:image/jpeg;base64,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"
-       id="image131" />
-  </g>
-  <path
-     fill="#000000"
-     fill-opacity="0"
-     d="M 390.96854,144.57389 H 524.2287 V 277.83486 H 390.96854 Z"
-     fill-rule="evenodd"
-     id="path135" />
-  <g
-     transform="matrix(0.26027375,0,0,0.26027533,390.96853,144.5739)"
-     id="g142">
-    <clipPath
-       id="g108f8700c0f_0_0.15">
-      <path
-         d="M 0,0 H 512 V 512 H 0 Z"
-         clip-rule="evenodd"
-         id="path137" />
-    </clipPath>
-    <image
-       clip-path="url(#g108f8700c0f_0_0.15)"
-       fill="#000000"
-       width="512"
-       height="512"
-       x="0"
-       y="0"
-       preserveAspectRatio="none"
-       xlink:href="data:image/jpeg;base64,/9j/4AAQSkZJRgABAgAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAARCAIAAgADASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD13d6Y+tPIAGR+RppzjAH+NMzJwCq4HesCCYDPbnrS4PQgCo45M8ZJ/KnqG3luw7ZpjAgLznjufSkAIAdcMCOx6e9JIV5+bA96yL3WH2eXYiN24Blf7g+gHJNITaRsmaOOJ5JXVEUfMScCsyfXVLbbRBIQeZH4UD+ZrBubvaA13OZ5OmM/pgdKEju7tNyp5UIPAC9fw/rUuRDqPoWp2kuW8y5k3IOm4hR+Xp+tMVvOH7srtHAYg4x7epqyLCIRgP8AvGcDBY5IqxHthj2rwQcbm64pEWb3IobVVw3zFuxP+FWQpB+5tGeM06MhRgIx564pfN5xtOR3C1SLSAqVbbtz3zSfNkgAD6mkaUbsBTu9O5ojRsjce+SOooGOGEAyOe3oadz/AHQR3xT8hQBkAChpML82QvvxQAu35Sfl6ZA/rTFOcHKljzjHApGkQfN265JoBUNuGcn1NAx4Ax1/HPFL+A96aMF87XJ7ZxTtrAkkEg+/Q0AKUIOMHrRjaAT+QqIsi7izYwMktxisy510MTHp6id+AZGyI1+p/i+gpCbS3NkMArMxGFGTz0Hv6Vmz+IdNh4SY3DnhUhXeW+hHBrNFvdzlXvLvJPHYdfReg+p5q/a2tvCD5cYGcAseppk87exE19ql6QLa2S0j/vznc/8A3yOPzpv9lSTxlb69nuEb7yD92h/4CO1X1L5IXCrn5WY/0oMjZ+ZeD70XC76lWLTrO24jtYkz/scn/GrAReyqOfSl+QkMRg/7R604lQfvKB3HWkMZlegx6gA5oCDrwW7UokwCCytxwOmKQPu/hGD3A/xpgG4Z5XHsKMg8AAj3o4ByByeMA1INwGMCgBm05GOp6Z6Ck2NkcrjPYVIzYHPT0FAywPH0PpQAmFXgDnpzzSEqCQxA5xxTv3hbqcexpGEm/IA9ie1ADcAk4x+dKMHIwR696Fgbuc/jSrEQx2kH3NFgAKvqT+FKE/eZwCMdMdKfscHcSM0ixvyWdifUZosMTK7se/SnbRgkc/Wl2nPDHPvShcd6dgGbeMuce1LsAIIAYnrT9rgg5GPrS4JP3U+oosMbtJBGBSgfKCVIz0Bpw3EtuyfTB4FJs53Hr9KYCAYJHB+lHU+v4U7Z6E596UZxyKLARMBGpZuQKa6gjCkhsjnGcVKwOOCT7GoZQJo8Fscjnae1AhUCtkZyBxxT1B4JXHsaaZY8s3CkYGScfzqYDcVwv4hs0DsMKELnjPpRtY4GOTUoUZOc0bCAcH86LDK5VhLjaCh6cdDTgBwB+VSlDgELTSCck0WEMHJIx9aMdhTtnpS4OOlSAzadx44xQVzxt4pWUYGaaXbPBBz2IIpiEIIzwpA70EYOFUHnBJ6U4DI5Bz9ajaLc/LZAPFIBPLC4XqxPGBTcDbuzuxwBjGKk8kg7lbB/2jTiGwRwT9OlAWLGWQnLkjoDjmlV2Gd27H0pqqWG7ecZxwf8aBbyeX97gHjJzTuMe04VSS3HYCoZLwKpdlKRAfMznGPzpk7RQQNLcS4VRyT0+lc9cXEt7Lt6QjlY/T3PY1LYmyzdaqbhmTOIccIG5Ye5/pVMCS4J2MI4x/GRzj29KBBHGAWG5hyoznmtCC23EicdcYQdPx9SPSkRuQ2thFHN5u3eW5AbnHua092EK5JPfFRbmV1aMAtjG5hTxkDHlYbHbpQNKwjKzsWMhAPYCnJuzjfu59abK4XHHfoR1pUJYgeXkd88flQMeV2qSGU7uM07kqBuwMdBxSAZ6HaPRaUBR25plDYjgADjP8Qxyal3HhcEn1pmAO2Bnk5oLoDgsy5/M0CFMgDck575bpTd2XOWJYegzgVGCocgDkenb3NPMozjy3f6DqfegAUAyEscsDwBg4+tSLImNys5H97HFQhi7kIEUA4PXvSvuhiy4YbRgAAmi4EnmiLLS7cHp8w4PrVe91BbYL5nzMw+SJTlv/rD3qjfXc1mDuEIZ/8AVxjk4z95uOn/AOqoLLT5Lh/NueS/LBurfX0HtRuS3bREapd6wym4YCAdNvKn2UHqf9o/lWrHbR28IWCIRgDGAvJ/Gpwu1AQM4HY9BUbyMTtwVJGT83b+tISQwQYAITao9KcM9UOF/nS5UL1AAHHQ4/WlVQwwUXH0/wDr0DRGTuOFBzjnZ82KUq5GCxQH2A/rU+1Y/lXAX0HFMeRBndu44HHWmMhVSjkCJ3OfvDBz+NP2Op3MNozyAtNQzyDb5RUZ4Mjcj8FNSLbScBrhgueVQbQfzyaAsOKRPj5QxGDleKQzRICrlE7kllH9aeLSL+MM2ezMaPIiUYEUYB7YAzTGQrd2pU7XT6jLfypTOvGFnbP92JsfrVlcgcZP49KXyz3H8qAKyuzHOyXr3AHH/fVPLyL/AMspMevy/wDxVTkYPYHHfvQFYnOPyFAWGCQkldj4A46H+tRPM28qY2x2+YDP61KUONoyCfbiho3XBIyf5UwsN811XmNz6YZf8acJpCMeSd3cbxz/ADpyq/U8nsCKFUhT2Pv1oAasrNyYzjpgSCkaSXcFCDAHdu/5VLsPt/wGhlx6evWgYmZNv3B7fOM5/KlDSHjyyDjnJBH86E3ZyTnPbtTuSDnjnpTAaxkyQFbJGPvAf1oRpDxtI45OR/jUnI56n6UgLZ6cetAxuXP8Bxj2/wAaE8zPzDAx1A/+vUgyB/8AWpcED1oAPm7jn/epnJHKtn2pSWJGP5igZAJHB4zmgCOSRgcYOcZ5OMVEmUIJG49yHHNWCCcDoaaTz0YmlYQxkVyjEfOoyAzccjH41JlwFwRnvx/9emJgsflI5wf896kyARgHHc0xjw7Drg/geKQls8HryOP/AK9IGUEgZXPYml/A59hQAjO5X5Su715pgeQEBtp9SCRTuCSQp/Ogr0yPxpACs393Ppz/APWoORg44PUbv/rUpVsEjHtQASDkc98d6BDfmbPQA+uab5ZxgHp09qkwRyKXB6460ARlG5AdvyFRrG/cjI5GcmrHQZI56daQqeM7T6YoAYwkPHBHtnn9aizIOCg6nq/NTlSFPIzUZJH8JPvihjJ3fy+hB74BqOe8jghMsh2DOApznNPmmjhQtIQozyRxXP3MzXU27kkD5eScCouJuw64upLyQuzHyh91ew/xNVC7FvKhRj/sjHX3pDM0sptlI3gckDhfrWlbRLbqdqNtPLHBy1Bm9RLe1+znzXO5iMFvT2AqwpOOdwHYA803c74ygGOm7qKeFzkbMH1I4oGkBfjhTx6n/GlHQDLA5yelIuzA25Jz0UU4MFUswIPYY60xiEANv4ZzxnHQU7dgA9wPxpHOcdBjocUigcF2x3yAc0DHq2BkqR7ZNPAJPDHnqKYACx+9n0FPGAuSNv0oGJIoLAk5AHQ0ZAHyl+npSso46Z7HNRNKxIKgH0A7/rQIXLABSGA9AP60ihySXxyCMA9KMO0hIJRVX8TQwCJtYqN3OM/eH0oAPMiQERtuYcbgowPxxVPUb8wkqn72bGMn5ggPr7+gpt1eeW721uf3rAgsf4V45Ppj0FNsLTYd0hLOBwSOfqaEJvohlhY7XM05LyE5G7Jx9fU1oszggRrn19KkEYJXCsSPTpQ3yryoVfQUEpFd5JnIXaq+vPWkjgAO5uSeuR0qTADZU/hjFOKnJyCf60rDAoD909852igllUAD9OtKIwcA5HqMninBFVtozTsA1S+3nK5GTxn/APXSQQ/PvdjuPTPYU6dJMBFz83U5xgVMFOFAAH0FMdg3dcfnilyW7DGO1KFIPIB9cUHOM8daBibQDkhvxNIG5zg47HNOK5BXOVzShR6DPagYjYPY5+lJ8xAC9uuafg5xg0oBHNOwEJJGMkY9hUgJJwrA+uTQ57FcZ7mlCBeRwPTvQMeFyBzx7daGyBgZPPPFOAA9frSA89Dj60x2I25barDNLtJx0pSh6jrSqMDnJoEJ+PNNxzjNSYBJ4pOEBH9KAsNAOfUAU4A9xxTSp6gClGccE0DF7c8UuOBgU4AY96X6/wAqAsNHTJP60E54wKUikIyOlMAUfjRg8tt6U5V4HFSEHaeKAKrbiwx/PpQQ2eDxS9W4wDn1o+YngMfoaQhERlcsCD+HSlIYtTiVRSx3EDHGM5oVgwHXJ5+lAxoBJ56Uu0A8kk0/ap+v1oHstFgG7Tng8UENz0x6UrZB/lUbOyr0/SgB2f8AOaBx9PXNMDYHODn2pqklsA8Dt6UhWJucZJxQMnAFIGA6ZHuaeCD1zx3pjEIzzxTWJXGQfwp2VJz/ACpjHkNnk+9ILBuHYgH0puQQeTn0oBGDkZOeuKCo4PGRSCxiXd491c4U7Yc/JuPBPrVKRnlIhhJDt1C9/U+1KwMYwPmJ6L3+prStbdYh8+DJ/EfX2qbGV7jbe1S3BA2vn5mOM5NTCPcQ3PHQc8U9tzKAEwPwyacMOeExg4OTzmmFgUlgcE49c04MAvLAj3p2xT/DyeuBSBUUkccUxig4XrlcZ4pFRm44UnjpQ7KxKn7p9BxShwMALJgHjIoKGuMHBOAOMY4pqhs9AQO47U5trMWJwB0x2phUg5O8g80hD1cAkDPTn1JoWTjOOPzqNmPYAZqVYiBknr1FAxCzddoyf0FIQ2cZwvTr196kkYmLjknjd6VVuJEiAZxuJOFUck/QUASO+0gEHPXOflX6mqlzJPGxj3B5pTyVPAX156fzpT5sEZXakc0px8mDwDwRxngdz3NPtLYRKM7mbA3MTksfUmi1xNiWtqEwT9WOOpq6iZKnbx2oUE8gVMq5OG4HemCQbcAnnPTI6UyUFsZxn3p5GxSQxwD3qNypPJzmhgNGcDj3xTtpAODj6mhQAO+OxpVXLZB49xQAijnpz65p7bVUlugHOKEUjqeccU4IoGOvOaYDI0OQ7fePXPpUnHofrS8AY6U5QfSgdho5OCMegpCufUU5lJPBpFz6n60gFxnr/Kl2r0xTsUdCKYxqrzwPpT9ueuaCMdz9KUHPU8+4p3AhOTIQAOBUoUkgjpShRxwARTx1/wDr0JDIsH6ilweO1SY5P9KMDFMBu084OaTkcYp+0Z70AYzxz6UWGNxwSPSmAEnJqRuuOBmlCjr3oATbS49qUDnpSjrTAYQe5oxgH+tP+uc+9NxQA05AoXpzTsc9KAOelACDgf0prEgdTzTyM88UhTOODx+VAEJXDZ7075uTx+dKwAOeRSZB4Azk560hADkdaUcd+evWk2kdRznt2qXHA4FAxgHPNLyBnP604rxwKTbt69KAE2kjnHPrRjA5HbFPC4x1FIeM+nSmBGQ2enGfSm7SGzj6H1pzLyMYA7800/eHf+tIBwHPQAUqrtyBjFKACBn+dP2gDqc0AN27gOoH0p2wdMUDr0pxPt+lMCEwgDjj3NRGMp/FVs4z15x61GwUk8UmgObs4ArCV8Fn7+1XCy8AA4HYE8/WoxKnuW9T0p+5jzs/WszFD1bJ7qfbGKAy44bj3NNAOcKMD86GGB1JNAx29s9evanDAXdnI9ahLIuAAAT0OOTSO8YOcNv7UXAmLEjKgnPqRzSguRypC4556Go1lLLnYx4xzRmTn5RnsBQMlHy4YBN2M9On1ppcyOq/ePQ88CkWLIG9z9DU3C9ByOwNAxqIBgnJI9OMU5nBG0hhkcNimFgxDncVB446n3qGWTyo2YgseiRqeSfx60XAfLL5cZd2VQo/icDH1qrN58MbeYytJKP3aIdrLxnq3X3x06VWluAiq1wrG4U/LEACqHjH3uGb36CpNPtnT95cSSTTEDczkEjgfKMY/lRYVyaO2YSCaVzJcFdpY5OR6c9B/OtBEKgEjtzTUyR0z/On/KRt59ME0wRIpzyAfrjpSkgsc9v1poZNwHT/AGqJCuwjKigYjuAODkenWo1IDcDPp7UgJxwMkVLk4Ax244pCADvvJI5xmnKGbDMcjHShenTP5VIq8ZGPzpjGkAZB7daCcYA5PqR0pxUkHgY9qAScYHFADcHcBk9KkI98/WkHzN6np1pxHbHHamMQjP1pCPpSkDdnnNKASe9ACDPWn9uP1pFzj8fSnH07/WgY0844o2jPXFLigY9KAF4OKeQR0poGD7U4DNUhiY6ZzRgduKdjFLjvTAYM7sZpe3WlI4+7SYGQf50AAXjBIpSCCMin8cY7UMAwPJ+hoAj6npSge9IOP8Kdx0pjEwT6UuOO4pwGB/hQSOnNADCuMikAJ4zT8DjFJtAOcUAIF7dBR14p4Ao+tAELR88Hik2EYB6fWpSAe/5UYz3/ADosIj2jj+VSBaCBj6dqcBgDigYmODjFGPbn6U7FHTJJoAZg9KTbnrj8KU4P19aUAGgBm3IxgY+lAjwevFSD3PTtQeaQDCnoaD1xjinEDj2pCOf60gECkZ/xoCt0p4AxijgetMBgQnPrSEEDgdqkPFQ+fEQxDdDgkc0gMMbccE59BSlxtPz/AI4NJs6nBH4U/ao7HPpiszEYCxBAJakYtnG8lj7dKGO1vlXBxx70fMwDN07AA0AJsAA+fPqaRflcYVuTktilc4Bwfm7cHimK7EnncBSAsDJUHcRz608OoA5wfc4qJAzHhef72OlPChSWP3vfmgocTyTwQDnIpzbVXJ3DPQMOtIq7fmzz7gcVSur3y5GgQ75iPwUep9Pp3ouF7E8919n2kku7cIinr/8AW96y5ZSEMlxMPNPAIPCdPlUH+dRySKitJI+ZGGM87m9APQe1T2dpI8oeb74HyJ/c/oTQQ5XJLO2kdxNNlXxwn90enHBNaIAwvygikXGAo/OpQuMkk++adwSFUZGduO+VFPjOTkqfYmownP8AEw96mRSvoD2yaEUByGJAyOhpNu5snoOnHWh3KxEk9eB60kCHaM/e6ZamMk2Ajr+VII1Iwrke4HWjnJB5/A1ImQvTH1NFgGhcuPm6etPyAOVOPrTgWxncD7UhVmT7w59qdhiF92Bxj1pQOwPfvSHCrjP196QvgYwc+tS2JjwCG9RT8Z54qMMcdaXcMcn9aY0OHQY60LnByaNwOMDn60pHGf60CuKB3AP4UpGaQEYABB/GlODj+hplJiAdcZzSjOO1KCTS7eQe9MLiAU/GPb8abg5459qcBk9KYwHSlIHHWgZB56dqUZAyfzxTGMY5OBSjtxk0cg59acDz049aQgzweMfjScnjNO9Tjg+gpG6DngUxhjnoM0gHtz60q9s85NOwOucUwGgHHejB/wAadj6flRjtj9KAEApc+1KBmigBMe9NbOcZ96efTikIHtQBHtIxkZoPUD9KeQe1NyfX8hQAuCR14z6U/HGO9NIxnmnDtQAhPt+lNan859qb15x+lIY044pw7AfjQMkn1Hr2pw7+1MQ3H5+goIOOM04/jxTR83XmkAmMHpQOnX86djAzijHsKAG59qazEcngVIeBUcgJHrikIQPk/eFNwB1HToKNgxweT696Q8ZAHHTgUgMMswBI7dqNrPgFiOMnmlByRz09KXoCM5FQZIayEKP5HvTZQznfx7kY4/OnkEjLcDrnBNNAZ/mO4D054oAjVSi+n1HJpUwjFjnJ53dcfSkcsRtLbff1o8or9059RikBZiYNxkg56NSl03Ehifc8YqDyw+GbIPYHGPrVS7nYEwwyYOPmfP3fb60XHcnu7tgTBA2JCPmY9E+vvWTIwhIhjVpHPI7sT6k0iyux+zwK2cZLH+EerH1rRtbRbdSWO7cfmY9WP40zNu423s9rGSRtzHoew9hV1SQoG3kUi7umxse3FSqM5+XGPfrSGkG7+8mAfQU9WBAAVgB6ihVLMSCT9TUyqQTkdKEUNRvnwGyc8kjrUpZckHAPfn/61J0wdvFM3LGB8p3MemeTTGOC5YEZz3xUwTI4OD3JqNTIBtI+b0FKGKg73C/7pyPxNNDJFygz19cdaTdxllK89qq3d7BYL5l1cJErHau4/ePooHJ+mKpyahf3EW6wtPKjI/4+L4tGB77Mb2+ny/WqHY1iQW+Vue+BVe71Wzs38u4u4klI4QuNx+i9a5y7nsQzJqmt3N+wHMFu4ii6dMJ82P8Aeaozr9lYxvFpdlFbgjIYoMg85zjk/wDfWaahJlKDN06t5vzWdhezY4/49mQfm+0VG8mvSKBHpUEDEZ/0m9AP5KG/nXLXPirUGwBdEBudqADH55/nVJvEOpy7s3jMx+Q7ZDgfXmr9hIfsztjb624zcXtjbD1iMkg/XFBiVSRP4njVscgKFx+bVwRnvZiyyGRgR828E/qaYgl44yS2088LT9iHs0d49paS4x4oVuehMZwfzqxFp1gQFHiG3JAwcSpkn8G4/CvP8yx8jBY+/GefXH1qREujtLROEbHzHueuB6+tHsV3HyJdD0H+wL5whs9VSVM5c726e3JpP7L8QxK5EttuB+RTK+D9SQa4CS2KEh44pGyeqkfQD/H3py6hqFkQ0V9exqgHyxzsqj2weMU/ZdmHJHsegpa+IY4yzR2UrYHyBznPHsB6/lUEeuS20ixahYy2pJ4Y8o30P+Nc1a+M/ERhAinguOh3zw4I9htxu/zzV22+IZkhaDW9IWSM8NJb9D/wBj/7N2peymttQ5Y9DtIZY503xsCtSDrxXHWtzbJKs3hy/W4RuTp07bHx/wBMy2PyrpNN1S31RGaFiHjOJI3Xa8Z9GU8g1L8ybNaF4gNnPSnYOOTTcDPHPtTu2KQCYzkHmgZ7AU7oKTb3oGIOOtLxnH9KUAc0Yx1FMBvOQB09aUjilIxzg0gU9SP0oAQdqdShewpRnuKAG4oweKd070EDPv8AWmA3H0pD39qdgcZ60hwTSAb14zRnI6U4bSTgGgcdaAE46HuaefpSZH0oLYx6UAB6cD9aOTxQWBPFIMj1xSGLg57dKTmnD36/SkAU5Pf3oEMPJzj6CngYpMYPHH407kjtigBMZ9PwoI4HpSjHvikI5zn8KAGlcjtmkIyPQ+1OwB3NNIHXuaAZGQfWm4Iz1NPxx0zSkZH4UhHPFscE8+vWjC5ByQfQ0mARjGc+lBcqwXGT35xWZkB4LfpntTU3NjJPPQUhdiCD2NSqjL827JHUelIAClSQM8dcUpZlXO0knp7e9BfG4Bevc84NU7u6MACh8yyDCj+v0pgLc3AQiON/nb7zHkKOP19KzERZGNvCSABkuece59Sf0pkal1MON8jZJc8sx9c9sVq21vFDAiqnyoMA88+9KxN7iwwJAF2qcfrUgkI5Kn8RShV7dz0NSAAA5VSe/FMLAHPGQOnrxUkboRg8DPbpUOE3bdp+g/zxUihC+FTn2NIaLKMCQQc8+lPZTnHHTJ5qsrFJSuD7cdac7hVdmcgYzuamWiZpCq56+gFIFO3zGKqR2A6fnVXe6Dz5CUP8CdNo9TkdT+lZ97rWxkitY2uLplykRbaFH952zhV96Fq7IDUn1OC3haSRltoQcF3wM/jWfcX11NECkp0+1xzPOpMrD1VDwM+rf981zsupJayC4uplvdR/5Zvg+XCcH/Vp2/3jzWXd3d3eSsbuUyZJIiRgAvXlh6/rW8aTZrGHc1rjXbaymaTTrczXXR7y6+eRvoTgj6AD6Vj3WoXl2T9ouZJFYZxuwoP+7USoX2FATkfeJ4H0pyQEL5kbLvAPVAcnHf2rojCMS+UTGTGX3gkgJyFyfT1oEkGT5amU7upf5Rxz05OKmFqRkKOSBvZuSOehJ9+3amhI1OPl69x2pjsRNDASpdQGIGRyu735NPeQZGwqdo6KxJ6+maaYFJZscc/NjHbtSGGOOPKnceORxj8ulBIRylyyFnXDfxN+HepSuz5w5DMCWUtkE9B0qIuUHKlQ2MMq8++TUsCphjMY0GMfvGwR3Gf/AK1Nj2HqEAGCrY42kc4xx6d6bJc3oh8mVwIXIyCAM44GPQDHQYHFV5ry3hZUM0sm7/lnCBgfmcVWa+t9zBre48w4G4yL17dBx+dHJfUVy4s08ZC7SVPTGcN+B6H/ABqVZo7gNJ5IwuVDBcY9vb+tU1uonDOxuVZV6GTdn2GB/nFSNdqwRJIZvKGceZL+u08VLQXHNcvBG8F1Awjcrkf3T/ez1GKqtLKyq7szIw2oztuDAYx9D605ngyRHK2DnKyLzz6bSc/jUUrCSEIyxblPAUbRzjnJ/D8q1jowaGo3lkskjD0DLxn+h/Wuj0jxFPLeW7X3mLMgCLdxjdLtPO1x/GoOOvPXFcrukjlbzEICn5lx+HerLBUQPE5KH7pLZZSOvI/z0p1IRkJHsWj60mpEwsQlyozt5Adem4ZA4/lWr5bb9yjv/erxDTruS1nBMsiHkpJFjcHA+9nv9CcHmvTfC3iyLWs2VwwS/jXPHCzKP4l/qO1cU6biyWrHRMzA4xkCgPxn+VSZyelNzzUAIHHoaXctLkCgEe9MBM8Yz1NO4H4UE5PXigfSgBcjnrTSR1ApcUUAJgY70cZ707A5pNvNACBe/J/ClYZXBHFGPelxz1oAiVgwJU8U8Z6cUoH4EdOaTOKADrgEY5oxznPAoOM89ad06GgAOeDmjJJ4pfc0n4UDGkkn9aMHpTj64owCRxSENA/zilIHTk8UoUdxzSEHoOBQADIB4+lJgkdKdnjk9e1Lke/40ARsCQAOKbtbrn9akIB/+tTdh9/yoAYFPJ3UoTHPenFcc8H2oAO3JagDk97Akkn24pVJZs78Y9O9V2nJGAMgcc+tSIMqCGBNYmCJwcMCCpx3qQs3f5c9Gx0H0qGNHCqWHGc+uagubtbf5Cd0z/cAP6+wpXKC7uzbJtBLuwIVc9ff6VmKTICzndIe+OD7D0FGXeR5ZGy56t2HsK0bS2wwllHJHyA/wimQ3cktLMQZZwDIcbiRnGO3WrBijYhscDpxUnykBeDilKKPXPpmgdhgijHY89BinCFT0/wpwXJ4U+oqRVOcEr/WmOwxIgox1I9M1KUZjwMUEZ5DEkdqM4XlcHuaBjQdgIEagnqxqsZ/NbzMqsEZ+8edzf3V/wAagnujPefYod2xRmZ1zlQcfJ0xk/oKqajdJZIElhVskR29vFj5j2A4/X/61Guw0rkWo6iScRDzJJD+5V+/cseeg7/TFc9dz/Z4mxIZGcgvJ3kbsf8AADgCpr9mtoTLdSDz5Ryw74/hA7KMfifWsT7TPJIzhhE54GDyD359T+grrpwsjWMbEzowJaYNvYZIBA/H6c/jVhIsRguFY44Xbksff2qKIJjdIVAxyDye3buOakE+8E5DKTn5+T9B6Vsa2JfmdixYnIwxb7uMn8KbKwxt3lvZeR+Pr0qGRi4BaTnsAMAD2A/zmkcBBgKC56kc4+tFguKG3ksMMn3ic9/f86cqgjcpy3YNUGAMbjgNjIHQ0skwhBVjhsbvLByW49P8inYTZMf9WDhiP73OM98n+lMnuYINivJ6fJD68cE9fwzxVV7p5XKFvLjAyUUfNjsPc1SkKRL86q4xwvUkU1DuS5Fm8nmClPKEAJ6EFS3/AAJuDWdPcTK28pgseTL8+T+OR/ntU10jzW6zPuaHcBv3ZC+gGegqqsCMBsm+Yc7HGCfx6fnirSSERs7SDLsFz/cAAP4DFWIVTbh5kUqMqTKVx7YyQf0qNLGVtr+UUjPQyuFBH1Joa0jXkzQkKeQnzY+uBVCsWoFLyBd4kzkFBL0qxK8Yk4Eqq+SpwSuB3qhDHCjb/PnQgf8ALJdufbk1dt5VkmUqodkXaEYtkjHT5eBU9RpCriRCYW3jHPUd6aqrKgKpu45Gfm696uQGaSfbI8UsrZXDSKxGcD+Hg/8A1qiaMRyeWeCCT5ijO4ZxxRfULEDz+YBGzMEyNy79u79DzV5LKMRyG3n8xFwD5uADkfLgg9fbtz+Fe5sCsRmSRXBPzKDypqN7JUgWVLq2k3Y/do53j6gj+WaHYSZIbaQQ7rkFY5BlcrnPJGR9MHP41bfSr7SbeDUXmREbbJE8YKshx16ZAPbPUZrKWVwQSM7TwDyM12Gj659rk2XdrEZZWCSXHRXyejgDAz/e4Pes5totWZ1/hTxRHrsJtp/3d/CuXUjAkX++v9a6Pbk147eWkuj6lFNbyzwxoC0MpwzqwIBX0Pr6EfhXpXhrxFB4hsSwZVu4cCeNc4/3lz2P6dK5pxtqtjNqxsbeTyMU7AHrS4zx1oGBnFQAg604Clx0o5zQMTvScGlI9KKAE9s8UgyO9O6jpSYBxxQIAKDjNKPYUEZ/+tQA088ZpB1pxAoGaBiAfN0p+O2P0puORTj6ZoAQnaQO596T36inUUAJ+NGfej8KBQIMZNJ360uTg560hGeMUAGaTPqKAD6Uc5oAUEA4pCQOpJpcnJyKQDPbigBCCy9OD1oZhtxtAAHTFPxgHH60hzSGcaCFIK/Nz1xz+tOdl6hevAA7VDHMA5JjAZT82RyP8abcfIkknmKjkcbm2gfWsNzlFuL0wqY1VTIR8o3frWcEYBp5nUt1Zv8AD2pIbW5hjZpisk23ezKQQ5yemOgxyKtW0YvZPMjhIt1Py+YvMhx1+lVawE1lAZm3SKFjwPLXGOPWtMHZwv4mkVBgBR9TTwmT6k980hoTC5wVx7nJpfl4wTx0wacFzgkkD0zmnBMfdCkUFDdgVuGJBHTNO2sOcseO4pWU9lGexz0oQN3OR6DPNMoF4Xcw4/2qZKGkBiiZldh97H3R/e6EZ9BTyMkIOW7CpRCUUgEZ6sVXk0DSMySay0TT5AYzFaxKWZ25LH1PqST+Jrn1kZI31PU1KSyLhImPEKHonTgnHJ6k8dquzSrrOqs+1msLGTCkfdknHU4xyF/ma5vXr17y7wozHGcKgkwXb19Ppn+ta043dkaRVtSldTtd3DfagjbsbSSBgZ4zTBJuYPKxVVG0HcePzPX/ABqNSCPvlhyd5I4/z6VMLhIlAQkynhmXHy+wI/n7V2rRGiRLncCzHb83THA9gD0/wprsFjIV8MBkADrxxUa7sIUwAvLBgR9MZFOEbMPMJ+VvXo3TgdjRYpsVi7B2DEA8N/sj2z0Pao1dY0dy8cUajOWbBP09T9aHuEt1Ekx2x5IQZ5I+n1qp9rszCZJ0uGvFObdW2GALn+7+eRzk96qxFx1xdHc8cAXyz/y0YZY/T0qGIfee6maODOGkxndj0BI3H2+mcVnCdm5Y4x0x/Kh5GnMZlb5F+6CeB/hV7LQncu/aHa1X5SqOxd2LZZjxhc+gwPxNMfaVUOMyOcqc9M/0ppcR7WYARqcCLPJ564ow8Ei3M43CQZSIH7o9APTnr/hSQDroMjZJAWNRGqjGSccn2z1z9KgjjHlvO+dq9MHlien4cUsUVxc3IidfMl7KnTk5/AVdW1to4J5fMaeeIooER+Ri2eM98Ademcjty27IXUqztmZpJVV5W5O0BRn8P6VEHfhSOB2/pU0ahZCh8tSCRuIyF98/561fhUxxlXlVcsSnlNtbn0J5yfzoGZ8drcSPsWGQuOoCHpUotJAXJMMZX7waQZB9OtWxFJPBLET5UUJywdtmG/2geW568ZolBuUkMMELLF1fLBeeMgc7uvHpxQwGJDcL832Xdj+4dxB+oNaVvFK6jALfxtHtLHGfX8cVpaR4P1K8UGW2MA2ANJcEqWPsgP8AMCumsvAIjB+06pM3tAmwAenJNZSqpAchcabdRWEvmWixo7gtIflVM4GMHn8TXOm3ljcB0ZeeGbgH8elewr4F03GDcXhYcAmRcj/x2s6X4f3EbM9tqET5GNs0RXP1IJz+VKNddQsjzieymgRJMqwbqUYMAfTj/PNWNPuJ7WbzIXZG6ZU9R/WumufDmo6eS9xZSfZ0zvkt2BG3k9OTj6iqUaWtrLGIFgnZ23IdmQwzjkEnB+mKpTUh27F6J0uLB5PNkiYfekCYVSf4umAff86ybO6n0LV4r60dmkTiVAfldDjKnHX1B7de1bqyxllLorAAh4Zn3E46gHHTH4/XFZ82n7pNypFE7n5QGzgccHPX1FZrqmU46XPTdL1G21bT4r20ffFIPxU9wfQg1dx0rybw9rL+GtZBluGfTLjAuUCfLE54DqBn6N7euAK9ZXBAIIIIzx6VhJWdjLyFP15pOR3pe/SgfSoGNHelxSjrRn2pgJj/ACKP5UY9qXpQAlIeOhpcnvSEZ7frQAmDkZox+VLkDGaUd+KAE/hNA570v49KRTQAvHrQenWj3Ao5FIBMdx/KjnFKc9B6UDGPrTAMcdvxoyRxzQBQaBCEH1wKQnng/WlPToab0OcUDFxn/wCtTuf/ANdIOP8A9dLkegoAQ5xnJNJ26cU7FIQfWgDgrkRQSIsztktgHqW9gOtUHHnSrNKzr5ZzGm7aq/X1NLJuRmnmlEkz8dP0AxwP5+1XLKzLv5kuePupngfX1rFKxyX7E1s1zIoUvti2jlhlm/w/GrYDqwVSTkYOSKkDheerUo45xile4WEVGByWJqVSwH3SB7UmWOBtNPGSDwKCkNBJH3ufTNO24HLHHbmjAHUflTtoycEfSgYL0HPOO9SplBjBPYVGignPPXrUyhtxABwKaLRIuAAxUnPTArF8S6nJZ2iWdq5GoXx8uAddo/if2CjnPriteeaO3gkuLiTbGi7i3oBXLWEE19ez63efLcXChYEz/qoOCq/U/eP1+oFbal31sUdSePRNHhsrXC7l8semO7fU5/M1zC/OdwGUUdOeOav+I5WuL9vm4UeWpJ9Dz+tY7tuAAICH0GA3T8K66MeWPmzSKJWbzFZkGFXpn096esZCjIOSRy3BP+f61HHFgj922c5IK9frVuOJxsYkP0zg8r06VoajWVsEuTsB+Unv06Z6nv7ZptxKsFt5lzxu5RQfQfr9adceWXYeYBsPzY/h9M+tYVxdHUJTIFLEZCgcYA6t6Ae/+NWkZslvrtb3y5ZGOQOVAA2/5H8qqbAo3ZDJ2IPT6+hqbUYGExdZY5YUCossQwrAADOOxPU+5NVYyrk4ZVGOSxP9P5Va2FYlEQkIAbcT9xo+cnI4PpSPG8LtG6hmJwPY05zFHKfKeWSMHgnCFvqOR17c1KL663MUYRRtuVhCNvykglQfTgcdKABU2OzXSbnZEdEDcMDg8kdBj8c08ebc3DybGmmYHCImcD0Cjt6CkCRSRmZ3W3iQbY0GWYgD/POQOuB2pkMzEuioUWUAZzg8cg57H/69AjSjSDS3m+3Ri9aWANbiKQ4MhGVc9DhSANpA71Ddyv5iWygBuXaRVxvJGMjgccficmmafZi4nM8ymSNTtCKOv1P8K+9Ld/a5Xa9nGIM+XFLgrGcA/KnqODg/XJqUtdQ6aCRWsrMYI1VrhhjGc4HXA7ZP5DH5OvreUpCkrQRSKF3RlsMH/vk4OOT09ulRweZebfLDSM3yCIR9+3Q/5OK7zQPh6zosurvgHDC3jOOn94j+Q/8ArUTmo6sEc3oWh3usXLize4KrkSXDuTEp65HI3H9eeR3r0nQPCNrowMokluLk/fnmYsxz6en8/et+3s4reJY4o1WNRhVUcCrGMYY9q5J1XIBkceOo6+vepcDOAOnWl6DHX+lGOpHWsgF4I9/elJxTQO/NLnPGOO9ACHms+fQ9MnkaWWwt2lb7z+WAx/4EOa0SaMd6QGHJ4V0xh+6SWD/rnIc/+PZrOm8ITtCUS6jbAIjLKVIz2JGeM+1dbzS5qlOSC55de6Df2+9ruyZVUn5gBIhHHIIyR074rqvBepC401tOkkLT2Z2ruOS8X8JB7gfdz7DNdRgEEEcd6zTottBdC6s4I4ZwDkouA31xTc7rUNzQ5zR2pkUnmrkDBHBHoakxzUkiYoo/ClpjGmj8aCe1IBSAXHXmgk4pcUYpgNPbJo6cU6mkYOPagBMdqFowcHufY0qj5aADH1zR+FLz07UgJ6UgADvSgf5xQMjsfzoGSvTnvTAPrx70hyad0+tHXvQBGxJ/+vR14px65FJjuBQADJBpMZpcD0p3TtTAZz2HOKMNzn+dOPPOB+FHHPFIDz60tNzCSQ5ZjxnsK0+nQfN1+lRxqVGepJ4qTLDqMZrBnIh2RnkHNIGwSxB9OtKq4J2np1pXYKQPWkhirhjkEYxgVIFA7t+FMXJAx17AHrShTwDnPvTGPVehLcgcDvUi+2T9CaYvHQjHbPapIwyjoQO1BSHKGzwDipgSBngY64qMERgkj73asnVbua6uDpdk5Vsfv51H+rX0+p9KdyrpIrahL/wkN41pEf8AiW2z5nYdJnHIQH0HU/8A6qn1a/Nlp0s6k5UYBOOp4H+P4VZhto7K0ihhXaiAKoH+fWsHxdcyRW8EDOq7zvxnJwOB/M04+80i4I5IyiRiwBy38Tdz9aiVSQwKnOeQRkjjrSHKHpJ79qX5tm4/99J2/Gu5Ox0RRKofbuTdwMAv3bucZJznt9KmUYj80Z+VgNgHXpx6VEibmQnzDgjbjByfQDrmi5mkitJp0+dFzg9zgdcU1qU9DO1S5ECC2iyNx3OW47c4H5UWkdtpcFzO7RNdr+4NrLHuAB5yT0yCOQfT6gUIJZLi8hljJEoOQSRlGzycngfU8CpdauYZ7oQW0EdvbRAoiK4kJOcli4+8Se/TGMVql0Mb9Sl5rvJuc8EcAdB7055PMfCjCjrgAZP4U2LYgDTZEYPRTgt7CnyTtcbRtVIx91FGABVggVguV645xmtC7kisiltF5dzcIMySph0B6gIf9njJ9c445rPSF5pY7dEd5SwCIo5Zj91fqTWntt9KvJbUzGWJFTzU27GZiuWUHquDlT/KpY7GWzvM5lmcs5PJJrSks5rfTkuZIh5cj7Io3O13/wBrn+HPp7VDEhlu2u54EjSQl0RWCIOeh7gD8zjA9RYSJ7+/jWJprmfIWMKuEA67FXrj6flTuTYjeR1gyzxl5cBkD5BPY4HAHtVnS9A1DxBdeTAfMQECSU/cjA7Z/pXbaP8ADqFkEmpyt5mdwhhbAX2Ld+3TH1Nd3YafBYQR29rEscKLtCr2+pJyf5muedZbIGjH8OeDrDQVEke6a4PHnSdR7KOgrpVTqMYpQB179KeBwTmuZtvVgAH86XgkjH6UmB6U4d89KQCYIOOtHen7cUbfakAwfp9aP1FPx2xRQISgml4oxQAn4Ud8YpaPwpgJzRk88UE0E+3NAEbph/MjHzDqP7w9KkUhlDA5BpBn8KYoMcuAD5b9fQH19s0ASHPag5x70ueaGHHFAEZpVPHSgg9cc0o6dKADOKAe9NzS0ALz70mM0D9fWlAOPxoADnHXrSYpW6e/tRjj3oATnPGaTkc9adyQfX1zTDnjIzQA4HvzSZ9/wpR+VHOccmgABJ+ntSkccdfekOeOOO9HP/1qYB1pM8cUoGRS49qAG5Bo6A80vc5H0pMf/qoAUD3ozxQB+dL+NAHIKo6nPXAFPznAXOfWgYY9sU7GCeQR2xWByoDkjpz6HtTXJTHb3Heg55O7p0pgwX3dT6DtQBJGoGM5+tSLuzkY47k80gyFzt/PtQpOTkL70DRIhA6buOvHWnBjuJIOMcEdKZHuZQRnnqCaJ7iO2gkklZtgHBzxn0pNlIi1C9e2CwoS93P8sag54/vfQetNsLGOziIXksd7serN61HaQO8r3dwP38g4B/gX0rQ7enqTTEtdWRBkecqdrCMcjPeuD8W3Sz65IgXiFRHkjg8ZP6mu8tZ4BHI7SHKbmJ7bR3ryu8uTd3s1yM/vpGcbjnGTnFbUFqdFMgc4ypJxjqD3x/OpYzlcZAwuCf6imKdqk5xxzk09GPyDauc8Dgc10nQh43srlDjcQuNuR14Ht06+1VNRkTYtpnZtGTgYHp39+cVdMhABQ7CwHION/bGBWZfpbzXqwuQzAqAVfozAc5OM44JBIFVDfUc9tCnJaQQ2j7ZlaZiB5ca8AYzknqDkYIx+OME1Rjbt4LDqvrV+7cRXA+1Xi3CqMKYOCRntkDH5fzquY7aeQmGV4GGCrTgBXHrxnB6cc/XtWtzFoi8trgqpB80kIiZ9egA9c9/epVCQO0SiGTjHmu3yo3fA/HHfOM1A8hdjJGxBwUZ/XOenoCP5UoixGu3lj69vxqhrQs20zwXXmRsJLlG8yNtw2hwR8xz94jAI/Wo5mEkzzsxZ5GLsfUk0wR8BUPyjlj/dx2qRtzu9pEU2gjdKB1x2B9KCbk+n2k13exWtooluJ+mAcJ9T6AV7HoHhmz0WEGP97cMo8y4Yckdwo/hHt3rA+H+iR22ni/kUGW5AMXI3bOn4ZNd1FtJOeMYOM/X/AArjq1LuyBEiDAAxgHjmrCEYzjj1qENudTyD15qZRgY7HsKwAkA5zjn2FLjjgnNAGcdM04AZ6UxCYx0pQOKAMUtAAaM89aKKQhcjHNLxTaO/WgBeaXrSZP8Ak0ZPpQAYpcGkz60Z9qYBzRRmjr2oATNB5BpT2phPXigB2elA4PWhcFRS4zQAg60vTvxQO/rQeBx1+lABmjkZ5pc4pDz3pgJ/L3pB0pSOO9J9B+tIAbn3yaXmg89KTOOKADr1FGecUYzQRzQAhPOM0A470nI7UH070AOxnGaXp3JFGeOlNPrTAXtxmjn1pADil570AJwe4oOMYoxS0AHYcUhHvS5NJ+FAHLY+g55oZBnAB/OlIBXmmqAcnGTWBygBjjA69qeoOO4PvTlUAZ54pRnkD86BjTuwACAe5xSK5KnAYpnk4p5yMc59RmkZ2UHBJB9OwobGOVlKHbnn7zYHAqgCdQuvNPNvGcRAjhz/AHqJpTLKbWNiVXmVv7uei/jjpVuJAijaAB0AHakK92SgYxg1BqUiWum3NxIdqrGTkMQfzHT2qyM4yRxmm3EKywPFMA8bjBU8jFMq5lT3U9n4emuX2o62/KsTuyRjOTnvjivOSWYfKQTgZPcV3fiYfZ9FlUYzI6jLcnk5/UCuEBBIOGyegHHP+f510UVo2b0g24GCRyQMMOgP+etPZmXdiVUwhGN3Cj16ce9MYHYv+s44XAGPpT9rJF5jqwyN2FGen8vrW6N0LbuscWXR/LC7RtYKS349vasubU3sr+V7eGF50kJDOm7YcDJGe/qfyxWvZxrKQ7qHUDJLj+LjHI6EVzuovukmTcv72UuAgCgAE9v89BTpu9RxNZwapqXmMurw6ldvcTkLLJydg4z+dVsfNlhwO1AjJwFUhgPzqcq08TfIFMa/M44z9ff+fWt1ocz7jY1cRTbThAuXz6ZGMep5/me1SB3YlECogX5j1wPc9f8ADtVcEsFAxgd8dTV54W+SGVSBw3lKeQPQ+nuKGCQyBBd+cqlorZVJz/FJzwB/ntSq3kREL94Y2gdh/SrNywFqsSruaR9qqh4H19B7UmnRmXVLaJeW8xFcjBC/MB+P40r6Ngz2zSY0gsbe2jY+WkYVd3XGK0j8oypxx/8AWqtbAZzxktgBBwf89KsEg8hcnPU56fnXnsRNHkgdDwDjNTx5xUEeMA84P4mp15XjPToKQiQAZ6Z9ad3/AApo6Dg9Kf3I4/GmAAZpecfhQOKOmc9aYhBRSZo7Uhi5ozSZP4UhOT0oAd09qXNNzQR3zSAdn3ozTeBzmjJoAdk5pM+ppM9OeaPb+VMBeMdfzo6UmBgcUnOT1oEOXkGnU2MYzz3p2R3pgGPxP0oOeKCeRz+tNHBpAHQnI+lBP5U7GeeKUcdKYEZOSPSlHrkYpzdgRxTdoHSkAdR2ozilAwO9IeDxmmAn0pBk9+tKSeMDmgAdaADAHelHWkb/ADihRxQAvSjIPalBox9KAG+1GPanHgdaa2SPQ0AA5Bpe1NXgCncEUAGPekPSjj/9VLjikByeTuyRwOOakXtzTASDjHQ+lPySccViciFDYz2PuaTJGMZOPQdaQH5sAfpS5O48HGKTGGCcsc8HuDz7VXvLtoFVFyZ5TsjQd/f6DqTSz3MdrC8kxbaOmO57AeuagtbZjctd3HzTuMAdo1/uj+ZpoLktrbfZYlDNvc8s5HLn1NW1I6nH17UYAOTz7CnmNiRgEZ/ho1AcOFwRg+metRuZcnaACB1PP4VJyR1OB0Paowm1CB0zzQx3OV8VzSnTIlkgAVpxkkcAgH9f8K5BlUZAYggZ6jaPy7+tdd4zDrb2qlN6M7fKB7VyTqcEKQRgksx5+g9un8q6aXwnRS2EwU2DYyjHI2nn6U5MlQQFMv8Aec8Afl/OkVgUXIZF4Ax/9ekdSIyEYgMfmBzk47cVqtDoRYtxJslBcEDLYjY4Xgk5474/SuZt45ZLf97GN6gEgHbu5/PuPwrfczR6XdzAlAA2Vzjt+dYkMDNC9xcOUjXCuwOGfthR+HWnh9ZTZviXaEIkBw+IkkKxcMdx4U45pskzShYEY+RGxKAgDk4yfxxUbyGV8KvlpnIVegq1aQRmN7iYjyIiBtGMux6DBxketbnKx9oqW2LiUZI5ijI+83YkHt3o85zK20l2fOSwyc1DK73EokkwWHAx0GOBgduAKu2kbRRfaFJ81nKxgDOGG1izD0AOf/rUBrsiFkBmHzEqihWwerHk+nTj863fCUTXmv20E2FjTdInzYDMoyPc9fpx7A1zkjrHACrKc/MQO7dzXReA4xeeLIXErfuUdyGzgjAB9h1qajtFi9T2CFSqYzt4PA/lj9asgcYKYBHP6/41GhIQls5xk4PAqyFzkYXuMHNcG4hU4A74AqVOvfrzimKDgDOePX2/SpARgeue9Ah4PHPYetKDjr39qMcdfyoHbrnFMB2fX9aQnkL3NAxt46/TmlVcAdfxoAT8eaDxwRStyMgd+1RsCwPJ/CgCOe6ggwZZFX2JGarnWNPHW8hX/ebGPzqU6faNkm3jJJ5JHJqrJ4f0yQlvsyJ7ISKQF+KWOdd8bq6/3lOR+lPJ/KsZfDqxlfJuZAFOVDAHB9AwwQKelvqtvsRZYZF3DcZCTgd+ev55p2A1t3p0oz0pAM4/rS4JOOvpSABg84p2e5JxSEYHPFUbzV7SyDeaZCy4ysUTSEfUKDTAvkk9KbnAycVgR+M9EZ9j3UkD91uLeSL/ANCUVsW13a3sZkt7iOVO5jcN/KnYRYRt2cdDT/5VHD04PuKkpAJ3pce9GBRigBQKceBmmjoRzRj3pgIckUZ4/wAacOf/AK1H0oAZznrRyT1NOP07UZHoaQDMd89ulGRinYFGSP6CgBh5J/xp3AFAGfzowSetACjjuMUYpDwDzx7UuOBjpQAnOcdKSlPJpcd8UANxzS5GOc0HOKMZ/wD1UAA6f/Wpce9IMjHf1wadjjrzQBygz1yPrQDznGfwpg+ZcAnnrTgCEGOorE5ELyW5BzSGRY1YuwVFGWJOBgUuRt9PU+lYd2W1m7NtA+LOI/vGJwJG67c9dv8APFIdyzEWvrgXTAiFP+PdT/EMffI/lU8s0/CW0YLY+8/Cr/jUbSXO4RxpFyoCovBzxknJ6VdggCDLku/Ut/hTJb7FJtNuLnBn1K4x3WH92MenrSr4ftQD+9uWY/xPMWNaq9P8aXdjsfai4WGW1sltEIUZtqj+JiacSd3GB+FPViR0pTnb0oLON8aI7fY8NkfPgc9eOf8APrXKSbieVZQR90DHPrzXWeNijCzRk5IY7u+PT9f0rlHOJmLleOpJzkduP84rop/Cb0gALxgb8ZwPWmPhAdxkA4IKfxdamCjarbE9Sn+TUT7mc/KeerLzjrx7VqdKYl63/EknYIWyQu9uwyP8j6Vzkt19qWNGJO35UiT/AArpb1IX0K53eb93cARtyevPvxXMRARoWgC7sfNJx8o9v8aMJK/P6nTjY25PQds2A+adpTqnfrUksv2mRWRBFCgxHFn7v19SetQBMsMncSelXIlIy7EMqYLLkZ7cD1610nGMEe2Dz3BCbtuccE+g7VPM8zQrcNIoATyo4j1Vfm7duuc9yxPPJpgmjaVpjEFh+4sXUHHrknn/AB/Co0ia5n2l9qdZH6Bc/wBaLBexQmDZAwcelehfDDS54by5vZIZFWWIKjuhUHkHjpmuK1i7WSJLdFjhCyEqYAehzkEk5b8T0xXq3w186fwzGzb9jOTDuk3fKPl79OVbj3rGrL3bE2O2iUAA/qKmXnBB4A+tMjQbQFH4Ht71IoY4HPAz1rkAcAMEdfUE09RxgcepxTVHtxn0qRc88imIcOnP16UpODxSLxijrz3pgHcmlyMjn60gOAeagntIpzlzLn/YmdP5EUgJJCFU84Hr6VHLcJCm9yFX1Y4H51UfQrBnyftRI9bybH5b8VAnhfTonaS28y3kIwGUiTH4SBhTGrdR8XiPSJZhCuqWRlJ27RcJnd6Yz1rSE6nGce1cff8Ag288uUwyWt2GbIjeIR8emOVP6UzwtPd2WpPpF5bXUMbRmSESqWEeMZUP3HII69DzTa7G7pQceaLO3znkdKT+Wabwox6UKc1COew7HBPakJCruJwO5z0FPxlfWq85O+OMZ2sDk/Tt/n0ouArAy4ByIyOR3b8fSpFXao2gj2HSoXlhtIzLPNHEi9XdwqgfU1zuq/ETw7pTFVuZr6QMFMdmm/HvuJCn8Dn2o1Y9EdJc2sN3CUuIFkQ9VdQw/Wuam8KWVjeR3ulh7WdXGVjkO1hnkEc9s+n9aj0fxVqXiJnktdMSwtkfb5tw3ms49kBG0+5JH1ro4bYRsJGmllbGNzv/ACUYA/KjVCbTLUQxGB2HTNSc0i8jNOz2piEpc89KT2oz70ALx3oJwP8A61HH4U3OcYz9aAFJxzmgE4pOM4xzSjgnj8qAF7c0lBPtSgZoAOcDnj60dzxxRjHajt6igBvAPApccUdMc0ZHrx9aADtmkzjvS4IGQKQZPrigAH4UoOBSd+R2pcf5FAAPxob9KCTikOSep/CgBSvfHTuKOMZ5pOMClz70Acp35B47inccNnn0zSMMYHGD6VnatqIskWCIl7qb5Y0Hp/e9gBXPe5xXItQvGnmbTrSTEpG6Zx1RfT6n0qRWjtoYrcRFpl/diNe/9PeqVhAlpCrblZi2Xl3fNI57kn+Wa2LWBgd8oO89j/CPSqsA+1tzECZGDSHliDx9B7VYxk9OKT0AHAp4J5yMUDFHXpx7UY3DGB9aRQMnBz9acFx3pDHAHg/hxSc4z2pQP880dBjBpjOU8ZnMdoADwXIOcelckwUKQpKjnl+cflXa+L+NNjdgCBJjOenH/wBauKZGZyAQuV6BiSv+Oetb037pvSY0MSF2qmAoIG4fzpdkuG+ddxIxk8kduB178/SnqSUG7nnrnByKguZlt93l/PLjbtI3Ac84NarXQ6CeSMiykilIZGQnJbIGR1rkYIc/uC+zHKqTjd75/wA9a6dEaW02StIFKkbt3XP15rA0iCe5CQwKpjfA8xj6VODdnNHpY/SNP0FSERfNIAqHnIOQR/nirElufJWSceUCf3UTcdhkMM5wAc55zU93JaafKI7crdXKn5pD/q16/Ljv61nAtlpXJcseSec11pN6nntpaDpZDNIXJznG4nPGOB9B2+lTweU8LRySMrAHfhQcggYVe2eev60WsEss0awq3mSyBIlxklicD6/56V6doHw4s9NuVu7u8e8nByIygESk+xyTioqVEtCbJas890DwSfEF9iKSZbCJ8STOAOeCVXnr+le5WNhDYWkVtboBFGoRU9McD+QqzFEsS7UHygYAp4HHH0+hrlnJyIFUDGOOetLheOBkUoAHPtTgDUgIAPl4wcdaeP8A61AHGPwxmlx/OgQc7sHsKPSjH86WgBCOvakJxz39aD1FIecdKAA9uKduPpTe4GKX2oAUscY6Un+NH5UmRk4A9OaYwbDLj2xTcBQABilHrjA9qUjODSAchyeTQR8x9hkUi8HIoY/vkOODkGgRS1PTLHWLM21/bJPCTna2eD6gjkGseHwX4eszmPS4yMc72LAfga6SMnYpxxikKqcjjBpXYFKGJVKiNNkYGAAMDFXkUn7w5z0qJQA+DjgcVMuQMHrSQmOHHtS5OaTjpjml6HGOKYB75oOc8YpCRR/nrTAMDbjHNBUZ6Uu0fpSAAmgBcc5pT07UmMd+KQ5z3xQApHFHHajvRzmgAAOPel/Gm5x3pcn1oAM80h69aQ8k0tACE9qF4BwaQnnml5OP8KQDsH2pfbBzTfxNIW6DGaYC9+v19qORzmkOM5HHFAJz7+1ADsjnrRxjpxSAdetLz60AcdqF5HYw7mG6RjtjQdSa5ezDMJ9T1DlmfYqOPmb/AGVHYf5NKXfV9RW6QSzSbvkhOFWNfV/04610NrZGNhJM/mTkbS3ZfUAVlGNkcTSILCBvNFxO+1sYWIfdiHocEgn3rUWeIDG9evrSqoKheg9BTwFwcL096TY7DfPiZtodM9xkGnlkJADYz096eg6YOT707HbNBVhqjHsPSnAccmkBJPsKcOeP6UwEBHTHQcUhzke9OGeelIeRzj6UmBi+JIPN0lwSflYNx61whQAoJHaNSCFyOWGfTg/jzXour263WlXMTKWyhIB9RzXn8geNyGV1Kryu3p6ZFa09jWluRrggA7G543Ht61TlLCUtmNC3CjbyfcVdBSQL8o6BcAdSepqtLmS4QBF2owG4LyPYGtoPU6l7zSJYCUuYkYQ5HDbxnI9B6/8A1q5qORopp44JJPOd2RI4/lGMnv24rqbWQR3a7vICsMAKM4+g7cVzmrKtpq12keNzPkk8EAjdj9azwj/eyXkevj0nRg10f6FZ4o4RgSB5v4gv3V9vc/p9amt493DHdjou7gHPX0qFFQgE8nHrVxEWNFYOzPkHYo3ce/8AhXodDyki5ps0dpqtrdiMTtayrIFJwCVIP4dP617pZXMV5aQXcJYwzxrKm7g7WGR/OuA8KaDHqyyTakim3BAjtRyrHIbccdFHQL9c16NDGkCLGiBEUYVFGAB6YrhqyvLQVSSbsh4XBHPHXpRgHGB3Bpw6mjgc9+lZmQ4dKXPrSAYHSjtTAcDS0g9wfWl70AA6fjRR60nU/wCFAB7Unegn1o5oAB14pfwpPypc/WgBD70e3b2oOMUd+lMBRwOtBNHeg49s0gEBGce9MlxuRj2Yf4U7PemzH90xwenHFDAE4QAdhin9jxTUJPzAcZ7UpPH/ANekIZjc2epqYD/9WaiUd6eOuaQD+/Slwf8AJpARjNGc9KYAe3FGAcc4/Cj8KUDnrTATjGc9emaUDjrnFBpR0oAPyFB+8fpQRSY/GgBD16fSjPtSnkjjtQR2HSgBDR0//XS4pMHPWgQmOelHT/69LzjGeaMHGMjNAxvUdKdxjn9RSggjqaQkHvQAnHOBSYP0xSnaMUg+tACkg80HtilAwOtIe44/GgBMtxx+lKD1z19KASB+lGfakByFnaRWcKQwoEQdFUVZC85IoC45HNPAOMnis2zjQm3PY575qQAcY6mkBy3OaMDOSePrSKsPwAOxB9TSAccrk/Wggj049KCQMn0655pjFxznHUUvbOaaGLY45NLwR04z2oAXH0PtSMCABRxyeT9KAuByOaQEcoVkZTwGBFeaTW7QzvFsdiHIVQSN3H1r018kdRgDmuJ8UwPb3q3AhJikTblSVG4dj2JxzV09GODszJkYiMI+4hPlAIOSfQVVsoZJtQRW83ABJPHAPHf3qeSVTbGMltrHB3A5AHOOvf8ApVOKdx5yRxkK5HzY+6OePTnit4o7YPS6JyALmM+VMzrJy0qkMxPf+dZHiO0YXwufLYK6gFiOhHr+GP8AOK1I2KwLtlwVPyx9CpHWrWtQfadH+0LI3mIdw8zsv/1+TWSn7PELzPZUfbYK3Vfocsg3SR/MpAxkYxn1FT7Gi8y5uGREY4GGz74wMnp/kVVBCSoQeX59Mnvg1t6BZDUPElnamIbHlG7em7hfmOM8A4FejN2TPHTsj1TwVp76f4Ws45UKTSL5zL02lucfliujAwDntSIMgHr1px4PUYrzzIXHt260vqO30o/Khc85pCFHQUv+elIOg5FGe/FAxe/40uaQdBS5yOv50AB5HWjn/Joo/nQAZ6cUmeKOcDjoKP8APFAAKMc0c0DrTAPTigD3pevc5pN3TqaBi/h2pM4P9KTJPXoTijO7OKAsHDcEnFNIcqVB+p9qyZtXmsLgpfWjrCWws8R3KR2yOorRgv7a6iEkFxG+ePlYE0mKw+2ObSI5z8g5x7U7aScenWo7aQSxsI2yQzA+3JqxtCnI6UmIUDA460ZyelBx+FLx60wEJ9qO3pR3pec9KAAEY6c0uOOhpMetHP8A+qmAvt0pePU0h4NKDzQAdzR360uTk0hIzigBDnPNLnHWg9vWloATvSdc80ucduaQ/QfnQAnvxS570Dpij86ADII6mm+1Lj05o6Y4oAQcdiMUcfjSH68emaU85GfagBB7Upz79aaowAPT3p2cnp+dACD60ueKTHtS846UgOcC4PQ5p2AegoHHOR+NOBPesjlDBweec0pBx93t2oJByOn40hPTJzimMXkjGD700DBOTzSgD3P4UKevOAO1AAvHAp+eOh/KmrwOeM0pIx1z70AIME8nPsKUA/N83AozhfmzSYyTwKQCbTtyTzWTrtsLnTJR5JZoxuXDYOR1/TtWyQSuCfwFNccckfjT2BnlbSlnVQ7DGByuR+naqUEgYBRJJ8zHCAY3dO4/lW3r1sbPUZIsbs/NhmwCCCf8jHesEM0b+WuFyeAvJrqjtc66b0uXLaMsx2RKXLlSZBjae317Z/8Ar11dhFHd6L5M6CUqTEx6hhXNWbwtEWCQkFyjAsARhewxnBJz35FbGj6ncXGpxWNvB5qOQHO3AROu8ewz1/AdKwxEOdXjuj08PiY0tJbM4i5hKTy2r+Z5kTkKqjAGPfH0rtvhnaJLqVxdSCTzIIAiqexY8kemNuP+BVheJLGW18UXkTyyIxb1yjAgEY44zya6X4fCOPVZ1NwDK8HzIo4OCOTjGT0/WulycqaZxVUlJuOx6SnA9frTgTjORuPbFIgzxhT2+tOIwSAMAD0rnOcOgH86Og9aTPYjnHQ0gOfzzQMfztIGfqaB9KMZHQ596M4A460AOH07Uo60g6dqdzTATqKO4o4GO9Jn19KQB6/Sjj9KM8DoKM/gfrQMTt/9alzjrSdh/Wl/AUxAf8igkgY5z646UHoMYoH40DDgDHP40zr16Z7U49zzmmt7Z9uaBpASrAhhnIwcisibwzps9yJEWWEscuIZSof2P/1sVfmcwqzuVVB1JqaycTWyTKSRIoYEjFAnsSxRpFGsaKFRRgADpT6X3opEid6OtH4UYoABx3pePWjuKMYJ9KAF60ZNJil70wDkd+9KCO9NPUZPande9ABg+v4UE89Kd1ooAaexopT6UgoAMUZpaQ57UANOc+1GevJzSn1pCM0AISTikOQQefzpSePSm5B9D9aAD3zSAg0vSjkduKAAkdqAO9HTtQSD6UgFGT1oxj1pAcdKXJ7mhgYA69vwpeB/9ek4PHIz1pAuAeec1mcwvIPP6ClyD2/SlAFBJ6U7DAs208c03Bx+NOz0GPzowD64pMQDp3pR14HH0owD60vIwOaAEPTGKUAj059KUj079qXn05+tIBhyxxzjpxxTWXgc80/btwMe/WkZcjr1HHNMDl/FOlNPELyNXYRowZYyOnYkYyeffp2rgpYhHdkKwKqDnnkfT1r2GeGG5tXt5FJVlwR7+v581w2m+FZ76/ka6Bjgjbax2kbv9kZx2xzyK2py01Nqc0lZmVoujXGszmKBUVEwJJSPlT8fX27/AEFelaNo9potqY7dcu2PMlbq5H9PQdvzJuW0EdtbpBCmyNBhVHb86lAJ7flUSlfYcptnIeMPDrXMiaraxl5Ih++j/vIO4Hr+PTNYng8oPE9pLFucsjcDHQpn2zx/KvTkx3PLdqRbC0N0Lv7LCLnp5uwbzxg89elVGfu2NI1Hy8rLKDGMDj3PNPJ79vak4H5YpWI2+2O1IkYTliMmnA89T14pgJ7g8DvQMZH9KAJCwBIxSBjux29c0gJ5wRj2NA5xQMkB9c4pSQOc9qTr60Z59qADnik9eaXgdc9MUg9fyoAO39aTv/Sl6Z9fegnoO1ACdOeMYpR9B+VNJ6/TpS9F700AZ4GRQ7BASSFCjJJPSnHhc9QeKwPEd8INNlttrSyzLsKhsYBBySfTFFzajSdSSijWa8hjiaXzPkBALYz/AJ61lS+KtLEQaK5WTIBbac7AQTzgf7PTryK4K8WR7o3CARlsDyweAASAMewAqSxspGbyojkSOXw3bPJqbs9V5dGKvJnQW2v6hruopHZ2YSEnEhl5wvG7jj/OK7lQBwMAD9KxdE0dNNTewUzsmCQMYBwdo9eg5rbXkUXueVXcHK0NkL1x6Ue9BOKDjp3pmAnWim+1OHXmkAo6f40uO+KQYpT25/OmAh4zj+VLzyfakJAxzRwOnSgABz+dPzTF+n60/ntTADx3PSm5Jx0pxye3FAGc5FACbsDJpQeKTbg9BRjtQAcjvRmjtSfpQAH2NNP4ZpWAx/hTDk4z0pABBP1o7cY96Bj1pefWgBBz3570v40vIPY0h5PTj0oAaeuO4pMrnmnEDtSACgAGAP8A69Lx7U0D1xR+P6UgMTt1/Ojjrg/lRRg8dag5RcehzQATyf0pSTwKQL7frTGL+NBPGMflSYJPagnBxSEKPl/nSjJOeKTJ3Y9qcOTnvQMBk0v8XQ0Y470c4HvQAYHfOaTgr0NOOR6fSkw3ygE/nQOwgAyOR60W8RQEk5fJ5x054H5U4L8x6dO9PAI9jQNCgYPWpOi8ZPHamAZpwHv2pjFORtAzz6/SrCEYAHrjJqv+WQKnTgLkdevFUi4khPz4x2zTHClTzgdie1KeWIxjI6+1GATnHJP5UyxAcheBRzn7zdemBSYw2CBt7+xpR7Dpz04pDFxjODkdcGlU5HGeaaB0yF46ZpenHb69aYDwQelAPGf600E8Z7+ppc9en40AOz79qTJ6ZpB24oOPTt6UALSfielGTxx+dHrwMdxQAdaX09KRemMkfWnD8cUxgCDknP4VzusWFxPfwPbxo2QVfce3p7dunpXQnOeemeuaj/1g2soA6kkcUG1Gq6UuZHF3OiTIhKRqwyGZUbLAjj8sH8KrlW2ObYouCN7NxwCPlx2Hua7aezLqWRQGUfIF+UA9uKxxZRX7yRssayAFZHQAFPUHPX2yPTpSex6ix/ND3joY/mJy2ef0qb6dahgjEUaoCxVFCgsckgdOT1qbPHSkjxGB9CaSkOM80m7oPWgQ7vTsUwH5snp2FOBxyQCT7UAOGR/jSHJP9KAB170tMBOS3NNyCacR6daB+tADefyp4b34pOaMYPSgB27kUuRjimnr057U3nPemBICDQcZ602jODjvQAdM0HHvSMx7YzTN5wO+ewpXAcfb9aNvam7s4x+QpSGHJ5b0zQAuPU0hBwCM0nQZY8fyqN7iNFJd1AUZJJ4AoGkSZGfag9a5+88YafaRB0MtyT0jt0DMf6frUWneO9BvpRCbp7Oc8GG7jMRH4n5T+BoWo+V2vY6UtzRzigOpHBBBHXPam9D/AFpEXFx780mTil3c49PSjOaBmHjJoA555FA5p3vk1BzITB3dcCnYIGev0oIPHP60HJ4xTGNA4J5z3pRknrS+gIzSBcZBGM+9Agx+frT896QKAB1pw647fSkAgA9Pyp3bjBoGehFBA/8A1UDDqMkfXmmBc9evr6U89ehPHelUde2O1AxBg59fY0oBHPqOMCgA+g57ilOCCCRmmAqcEEAnHvT+p7ZpvYAjtngUZ+bv+FAD+/8AhU6khR/jUAByDjj3qZAD2xVIuJLyeOaaQSB81Oz82Ceg5oYAjO7nPrTNBvVckj09RTFQqeD9c08Lg8E/4Uo4HH60hjFYE/3T6E4peQR2z7UpAKgEYA6dqaFK7QGO0dRQAKWxyPpzQGwP8T1pSM9h17ik2gHjOe/+etACg+v59qAx5P5UHJHLHn0FIoUADcePX/61MY/PFKB/PvTSQCW6gHnANAIzwfpmgCQcHr+FL1PJ/KjO4j/CgjGcE+4piEIyMZFHbFGAB3oAyKADr9KAAGyPxoPt+lKg4ye9AXF7jNBPABpTz04oOT35qRCH8aQZB7UHOQKdgE+1ACfhSg+ooOeB/OjBoAUE549aXPGeaQZzzSZIzkDPrQAFh2/nTsjgc/jTQBjr+FJjHOeKYEmBx60ds9KYN2e9OzkEHmgBeCOxoBHIoC5HvRjnpTAT6Gmhzkjj8aULwcmkCDJ5pAIDknHPPWl2sDml2hFOBn0qG4uYbeJnnkWONcbmdtoHOBQBNuUA8fiBVO51CC1OJJArkHavUn8BWfJqVzO++AeRakZ8yVWLnp91T0H1qmY9PikMrXGJH+9IxIZvxzWM6qjsb06LluJc6pfXMm2DFtH3Zk3sfw6D9aYLaF23ySCSQHO92LEfTPT8KlB0t2/16ykH/nrn+tThNNYcBfbJP+Ncc6031OyFOEehB9njYElw271A5qpd6TZ3KFJraGQf7aA/lWiy2AZV+TeegDH/ABqCSQIcRsrA9RXPKTWtzaNmYtto8+jMZdFu5LY9TAzF4m+qnp+Famm+KWjYW+twrZyMcLODmFj/ALx+79DUcl4VODErD2NQT39vNCY5I1wwwQwyCO4Iq6eMnF66oc8NCqrWOzUr95TlfUGnk8Ajj6155puptosm2yXzLIH5oM/d90z0+ldrp+o22pWwuLWYOjdeOVPcEdjXpU6samqPMr4edB2lsUuvpRyAeRR1+nrThksc1R54DtzgUYBPTIzR908inZ4xigYmDuPHSlxxxz+FLjHOfwNKvXqaAE6gYpemOetKNwOSf1pDzQMPXBGKMAYJHI7Zpccd/wAaBycY4pCFVMZboKACRjqfQdaTkccfWpAOBkj5e1NIY3884oUAnjGPekxjGOuOcU88kAL2oARQQT8wpcHkjP40nQ989xS7sKM9+gBoAUcleD7fWp49vl53sd4yoAyceveoPKEwaPecsuOnQevOaXStJt9Is1t4Nxxyzt1Y/hx7fSqiaRRYRghEe4syL1PcevvTiOwHHcDtStGH4OQR0I7GmAupHmDAHRh0/wDrUyxxJxjvnvS9v4aB15HakxgEY47c0AJ1HrQfrS4AGefypvQdc/jQMUjuAT7Um0kcHHP5U7A64/KjHHB596AGN6ED3FJjpyc45p+Pr9PWmgkZ4bJ60DQKQF549jTFVtzZJAByoHOelSA4wM4z696asPmXgk3DCoeAT1zQMeko4DDa2OcmpVYNyGBFHkRYI8pfypuzyySnQ9RQJj+vT8KaeeKb5nPKkD6UpbJ4BNO4gOeQOpqQYC8UxVPBOM9PpT8Gk2ITkc4oJzjNLnr69qQE9O9IYhxnPcelOHAz0NIRnrz3pR0HFAhSOOvFITx+HWgnGT70E5P/ANagBfx7UmOnPNN49TS55C/pQA8Dj/GjHOM0wNzgcgUuTnvzTAXkEc0AZzz+QppYd/zo8xVH3uaAHZI6GlB9+KjeVUG5mVQO5OKrvqNkn3rqBT7yqP60BoW+ooHHO2sWbxboVtuDapbkqcYRt5z+FQXnipTau2m2k97OfuKsbBR9Tjii4Ghqus2ulWxmuGZnZgkcKjLyMf4VHeuVWW+1EC41BMXC/PFbhSUh6YP+03uenPrgRjSNSu7mK8v8zXZ3DIyqxKRghSQNuPYZYjkqCa2JorqKJ5YrcLhfmKkAnsOnes6l7aG1GKbvIwLg6nqEzPdTvb2mcJBbHBc57mtCDSbWNA09svTJMjlifrmnW/lW5BiiZp24BI6Vde2BwZZGZ3468flXDNnqaJEfl6am1PKt19MKKU2MDrmLaOOCOlWIdIs0yRAGP952LfzNRzQRWWZIF2KTlgv+eKxlHqQpK9kZ7RXVs3+qDL/eTHH4Gjzn24Tys9D+7AIrUguY7j5TjdTLiGJCxAHIznHWs2upqpa2aMp5mUYO33+UVTkJdizRpg+oq5JGjZOMj0FSR2MOA5HPUZrNO5tokYskUjMr2+1W7qejf4Gn2l3Jo9/HqC+ZEAw+0RgcSR5547kc49x+FX2hK3AW3+ZhwyHoR/T607UoJbmFfkRMckdTz2ranJxd0TPlkuWWxrXNlqtjlrfy76FQeGOyXP16H9DUFjqqXbiCWKS2uTn9xKpVvyPP5V1HvUFxZ293GY7iCOVPR0Dfzr2T5t009igmM5B/KnHORwePemtozwHNhcMqj/llMS6n6N94fmahWW4hCreWzQnnLqd0f13AcD/exRykuDRY9uKUsQvQZ9qZE6TKsiMGVhkMDwR/WnDBOOvPU1NiRcnkEfhQo96XqQD/APWpWxngc+lAxo596UZ607nHSk7kdPpTAUDPTH4UpHY/mBQo6YzkUDI5PUmgYdCAAcUAlTk4x9aGJ4IwSeB7U7Y23nsP73WgBg3BMt07AetOiiaRjtJ9ywztp8ELuTglV/vEckeg/wAavKgRVVR8tCRSiNWNY1CqOP507rzS88Uh78VRYdqTrwQCKCOOKX8KBkXllMbf9We3df8A61JkcZ5qb1pCAwwe3egCHceB2pefT8QKaQVcA9M9c8UoYjp/k0DFy3f060m7PH8qRSTk+lOBzj/P9aAEJycA9ucmmHjH3h36040z169MAigpCO2AccEdDUsL/vV5HTHFQnGdvPB6ClVtrqeMA8n0oGX8880mPTFGc96MUEBSdKcPrRQITGaQ9KU+mKPagBDilx6UdB2pDntikAv4UdTSZOKTHGM80AOAx0pO56HjpSgDH4U3vx+g60AJ0PI5PcUEkZPYdqGzjJ6dh71Wlm24UhmkPAVev19qYwuL+3toXmllCRoPmY8Cspdblvf+PC0luEI4lKmNPwY9fwBqxHajzFlvpFZ8/u1xhF9gD1PufWtANu4A7dsAUJAkYMtlrs7Am9tbVBwfKQu599zY/wDQaU6BLKuLnWNSlGPm2yiMf+Oit7ofu8kdcUzeMH5fpmixSijFj8LaYCS8cs5Ix+/mZ/64q1Fo2n2/EdlbjHTMYP8AOtEtjqMcfSgP2A7daQ7IhS3iT7kcak9lH+FO8vBAxxnuKkVmIOeuelNL/PhQPrjqaYxQvJyOPWlYKwwwBUdiM0mcKcnBz1qMOfciky0m9jJv4oNPmVo0I8w5244X1x6VnSXEv9p22MlRuOB9P/r1r6tGZLPzUwzRsGx3x3/Tn8KxTIVntZQqkebsY4weQQP1xXn4iNpaHdRbcdTfildkwRgEdap3ky7CrHnHTNKLhwGFZN2XlLDt71zSvbQunDW7KtjdGK+lt8klfmjyfvKe34Vttcb4AcHpzWIumQzxpMwKtjl/TP8A9eniaaGRoHO4+oH9KqUUoXZrK17mlFbo3zsW69PWq15GxbZBJIsf8WcZJ9Knsw7IC35GsfXNZTRVEkiTMHcIoiQMST9SAPqTRCneytuZyqW3ZuWEaxw4QKGxkjPNSSSK/DAZ6Gua03xRHd3L2UqTw3cbcxSbWGT6Om5T9Mg+3eug8vzVBx9KK1OVOXK0RCSkuZO51ODS85o6GgACvXPIFpcZyDyKKKAMyXQbLzGlt1a0mIwWtyFB+q4Kk+5Garpa6lartkWK7XP3ovkcD3VuD+B/CtzrR607iaTMVLmF5xAWKy9o5FKMfcA4OPepwvI9OnFX5raG4iMc8McsZ6rIgYH8DVI6SkYP2SeWE5J2sxkQn6NyB7KRQTyDQCWAHb1NLg5GOT3qFk1GAgNaJcLnBeBwD9SrYx9AWNIbxec+YhXPyOhRjjrgMBn6ilYhpos5xuJ6DoRSHDEEHIHPAqpbXkd4GNpHJJg4J8sj9WwK0Y7WQgGSTAxjYmefxoSY0mQ7S52qMnAyAOn19KsxWo48xt+Bjbjgf41OqKi7VUBewFOA/wAinYtIT09aX2peaTHvQUGefek60delFIAoxRR1pgHYetJSn86TvQAEAjBH51A0bIwxnaf0+tT5GORRweCMj0PegCvj0HP+yBUmcLg4+73qLYyzFD0P3T6+31pxGM4x06+ntQUIO3TOfSmn/INKWG0DB5GBgUzd0AxkngCgYhyoxxjPrimnk5IYDHalAJJPAAANNI+8M4OecntQBpKcqD7U7IqK3bdApJHTGRUpHegkTqaQ5xgd/el3A59aTHpQAhznn+XSl70HPJpAc8cUCHflSNk4peKYTk5pAGPmzTu/SkX196Xkf/rpgISATzxTc8E44pQcjpQSemaBlO/v4rOEPIHdmO2OJF3NIfRR3P8ASsS2k8QXp8021vp0bfKVlHmykdjwcD8c/St0xRvdiQqpdBsBPbOCf6VLxyO30popGSulXZwbjVrtnA/gCIPy2/1q2NOjBJeSeQf7czH9BirYOfqKXd2xxTKuVhaW6qVWJf8AeOSR+NSKAAB1HSnEemcduKF7n3oGHHH86QMT2IzSkfMeB09KFHJ5596QWD5v0qPIVtx6DqfSnFsZAySf1pkoVFJY5ZvvVLLhHmZBIzSTZziPPIP1qtqmrW2j2TXNy0ccScFnbGfYZ6/hUGqata6VbedcOPmO1EGS0jdgB/kD1FcUNL1fVdSj1LUdQmSZBmGK3cr5QPVc9PY8c45J4rKrWjHc7VTuihffEG61bJ0+3+z6aThpnYF8jnlcnAPT8a6ODUlnsIwV3sUEgYjr/wDXBGa5XV/CLabGbqzJaDGyZUQZ2dN3HUj8zW34HVL7S1mlkBZVKPg87gcZz3HGfxqsUqM6EatJ7aNGNF1IVHGfyOgt7pZdpHORxk1M0YYsSMgisC/Mem6pi8RlsrnBilH/ACxkHUEjoD1roLd8sFWTeq9xz+tebKNkmjq5tDJudFt5lnCOqGXjLAsU55KjsT/PB6jNS22ms9wso8zYi7V8xtx/M8n8cn3ro47aMjeyru7cU5o8FjjHvTbbjZmfMr6Ga5EKlBUc9lb39u0FzBFPG3VJFDD9avSRqxAxUajy2IzwTURbWwSSe5RsdGtbFWjtLeOLP3iF5/PqT0+mK01j2qUAFSKVRVPtih5AFx368VpK71bJWmiNmloor0zyxaXtSYozmgBaPwpKWmAUuaSg5pAGajmginhaKeNZIm+8jqGB/A1J9RRnPamBBHB9mjWO3CiNfup0x+P/AOuni5j3Kj/u5G6I5GT9Oxp5qrIPtEzRyxAwBcjK53Hv19KdwsXRxSYzj+lVFtnhH+jTsqj+CQmQfryPwP4VMkrAfvoyn+0Dkf4/pTAlx2FBBA60AggMCCD0IpetKwDaQU7GOlBpAJRRRQAHoKTtS+lGKACjPGKTFLigAILDmoNrFiv8XXA71OeBTSgYhiBleQfSgCswIOcH0+lMLHbjjr09atEbjhuCeh7GoJbfaPkH4nv9KLDIy2P7vI6ZqLcTztHsAM0hIAIZunqRz+dRvOUXaDnHIx0/XrSC5dspxv8ALJwDyPrV4cnpXP5aRsrKEB6MBljk9q0rO6MuIpQ4lAzuK43+4/rQOxe7ccUdhyKRSGODmjHv244piFx3zSfSgZ9R+NNBORzQIcQQeppMDv696XjJyfzpMCkAfSm55I5p545Iph+9mgBTgDPNITxngDvRmmyE7DjuMUxkcQyinu3P5mngkk4GPWjPHSkYnknPTtTKFJ/l9KbgEcjp2pu/OOP507I5HNMaExx0P4CkHBwM/hQzc49Pek6HPPIzkGkWh6gA/rxSE9cDB9TTd4xnsOKiuJNi4Bw7cZ9PepbsrjtfQbLMfM2pyR3PY+lZWsazb6Pa+bKHlmc7Y4UGXkb0A/LnoKrazrMWlWyhB5k7nbFEoyxP09KyNO0y5uL19R1GXzLuUYIHKxr/AHV9B9Ovf1rmnVSVz0KNFKN2JY6fdX10uoak4kucHYq8rCD1Vff1PX3rcFvtAA+7U0cYAAHSpgBkjtXA5czuxzlroR+WpTYVypHSuW8PWSaRr+q6bGdsBdbmNem0OAD+GRXXkADt0rk9YZrLxvpFwOI7uJ7WQjvzlR+Zqqc73h3QRXMbN/ZwXlnLDdIskTDBU9xTNFtzBaRRF2cIu3c7ZJAPFTsjOCucFuxFSwQhdoJNSm7WJdi+mAO59KdI4VcHJ9qjTrgHpTZ0K/MBmrvoZpaiEEjJwB7HNRvGChJYqT0NU7nVra2wsjNvJ4jQEuT9KbHrFtc4EcjF8ZEZQhiPoRQu9jRRbLCSN91xhlPPOdw9adjPO73FVTlSCwK5OQCelSxPuBJYe5NTzalOPY6iiiivXPGFFFFLTASlopKAFxS0lH4UAFGDS4ooAb0FQ7QsuQFz24qxzSEd6AGg5H680uT1Iz6fSgZ//XQTzigBioAxZflYjJI4z9fWlMxTIlAA/vg8fj6UvGDxTckjpkD0pgTcHBHIPTFJUAi2ZMJC9yMfKfw/w/WnrMCQsgMbH1PB+h/xwaAJKSl70Y7UgEJo70EYozQAD60dqKKEAYpM5paOc0AGNwwRxTDuTggsnrnkf/WqTtQO1AEElvFNgkcDkEdKoXuluED27MWH8OcflWmEMbbkGVPJT/CpFIIyOaLJgcukhhcEqV/vIP5jnirkquyGRGCMOd5H6DrV/UdNW9UMjmOZfuuP5Gs+3NxA6wTRFZAcI44Vj6Y5/wD10WKRrWk/2mDcQyupw6kdD+NTkZGP61jJK9tcrcKyiDOJQ3Vv/rgg/nW0AOCDkdcijyE9BB7ikwByBS8elJ0osITGPpRjFOPNJjFACdaQ46UuKQ5yaAG44wKac7lHbrTyeabxv46gYoQ0IRnGCaCOvT/ClI54/SjHX+tMoZ0wOc0nrkH6gU89KaM+hHNA0NPU8n3pp685/KpcYz1puBg9zn0pFpkTOsYZmI2Dk98VmX1x5FuGb/WyEAAfoPyP61fmO+dI+cDlscZHb9aw55vtWotJztQmJDn8zXNiZ8sDooRu79iJbCJ52nYbpT1c8kfTPT8KtxoABxgChQT06d6lHXkY4rzLvqdcmxV5IGOD3qQAc4xxUO0sQAcGpEBzjPehIiQ4jI5rmPGUbmwtriOTYYLhH345HOOPxNdQQTj9axPE8aS6DdByAqruJ9MEc1UNJodN6ov2kgvLZJ0BVZUDhWHK55wfpUqkMeDyax/DEV0DHMZibKSL/UuOQ+eo46EZ/T3rYjTYQpUYBwPYVrVjyTsjOTXM7FhcjHXNOlGUyT1HNJwDnHFDSKePUd6StYm+pVktUkUDCk+9L5QjIGAW6/LVktwAOtOQFunJ9MU0VzaFSWMSRkMnB7GsyXdECFwqZ555NXNTuTCgKrkDOcnH41StQtyDLJIM9No7UuXU2he1ztBS0UV6x4otFJS0AFLSUYoAWiiigApcUlFADsUh5opaAG4oxTqQ5oATGfY+9NxjvTzwOtNPSgBpOM+nvRjOcjPrx1oxgnPNLwD60ANUFf8AV4x/dPT8PSnKwbAOVbrtP+eaM4Jx2NIwV1ww4/r60BYf1NJk1DvkjJDjdH2YdR9fUe/6dzKMMAQcg9CDTsA6jNJS0gCjFBpBQAuKXNJijuKAHZxTSnzbl4bv6Glz7UopACtkAEYbuKjkjWZCjDK9xUhUNj1HemFtpAPDMcD0NO4zOmtmEe3hkXgFhnHp9frRpV0qn7E7EMMtFuOSyenvj+VaBbI64rPvLZ3O6N9rIdwIA4PqDj/9dDHuah5PNFVdPuzeQsJF2TxnbKnofX6HrVrHPSmSIaT8aCe1J+dIAzSde/FO600gdaAEPSkXgnOPb2oPHOaVOVHvTQxCpzgmjsOPyFLjtRjjrTGMPXPP1pFz60rc9egoAH69qRQu3vk5pHzjoenFKfQDp1pkrYUnn8aGNFGdykE0vzbsMFOOh7ViwRBI1AUKuKXxHrMOkWtrG7vmaXACAFmVRz1IHUr1z1/GstPEWkyxqTdmFv7ssbZHsdoI/WvOxdOpJrlV0enh6NT2fMk7M3o1yME8etPwSMY+lZK69pmFP29eewhfn/x2pH17TIwJDfAg8DCMefptzXMqFTsa+xq/yv7maik9SKevAPNc+/iuwR8bbpvQiMYP60q+LrJzlbe7HuUT/wCKrWOHqdhvCV39lm67HPHQD86yfESeboV8gBJMRwAO9Vj4oQqP9Cm6nq68iqF54leSKa3WxAP3QTPnI/KhYWtzJ2Ljgq7+ybvh6df7B08BQcW0YyPUDB/UVpgHvx3zXG+Cr/IewlGx4CzxrjgxnnA7naSRmuyifeuc5HTmnWTU2cDg4ScX0EZ9i8rmowCcHA+vpUsg64YUisNuO/tWaQCKCZNrL9M1McrHuAOfahQAfTNBbeCoOPerjsTe7MPVXZLdpI4JJyuPlXrUmnWpe2jmZSruuWXqB7VpMgxtOBSplEPTnkAd6hPU6FP3bI3KKKK9c8cUUUUUALRRRQAUUUUAFFFFABSikooAdRSUZoAD9KT+VLRzQA0joe9JTuv5UD3FADMgjNIOP/rmn496DnHSgBFJxz0x27VGYjE26EKCeq9mP9PrUhyGxjtS54OTye4oAbHIshIHDj7ynqKd3prRhzluGXow6jPvTVdgQsoxk8MOh/8Ar0wJTRSd+lKeDSsAA9aWj1zRkUAH40vPpSYooAU8UjAOu1unek6DrTqAKxZ1kEZOGP3SMfN+fcUuBIPmGcjGCOhqd0EqbW/D2NVQxjcxuCH6ggDke1A0Z91H9ivY763CgD5bhVA+dD3+q9ufWtpW3AMvII61VuYxPA8ZKlX4Oe9S2gC2cSgliqBT+HFAMlP61XLsbgKo+ULljjv2qd/lQt2HWoIgdu5h8zHcc0CJeuCRQTk9KTv+PcUEgH/61MY1zhT/AFp3TH+FRsfnUZPrUnJOOlAxeh/wpD7c+1NyQxGeg7Gl5OO9ACEZ4/nSAcjGaD7dhQBnHPbNAxwzz/jUFycYXJHGTj0qwOAeeKp3T8lQ31PoKTLieX+Pr+N9dt7URlmghyzsOPmJ/wDrVhBwoDDJHG/npWt42lL+LJicnyokVV6/Ltzn82NZyASRK6uQMY4AwcdRVR2sfZ4FctCC8h6zNuBIAZRjA7D3p/mpn+Igfzpqh1BAZiGHTPJpEjx99Yzk9SucVpY7tBxZpUAGMZDfeGBjp/T9acCyfw8Y4PpTvnY8vz2UDpS/eAIJGOp4NCKTH+fIBtIOSM49D7enSoHld2kAVtoywJOMtmpSMHJk4571CWJIPIAX5V449/5VTkNJFeXzmkjurScwXcR3QyqBwSOh9QQcEdCD+Xb+GfEUGpWrRMxhvIwTLayc7MnqD/EvYH88HgcXvCq6h+uedvOPaqjgGdJIndJo8lZUOCueOD/nNclampo83HZcq79pDSX5nqdxqUcIK7/3gzv3DhfeobHU7a7k3Qzq455Q5H5ivNtb1MSWVk8xa43SFZ43ldUbj0XGPUcnkCrPw+0y7sC6PM5iI3KhHyjpyPrnn6VjLD8lPmZ8vKbU/ZtanrMcm7nPOKe0gGT6jio7dQVHbjmnTlQuGGR6VyJ6CW5WklXOScCozcDdtzzj8qJWiPBQMe4xTItqgrjjsTUdToilY6migUV655AtFJS0ALSUUUALRSUtABRRSZ5xQAtFFFABRRRQAtFJS0AFFGaTPNABRjkcUdaKAGnAOcGl5J64NGOOcUvP+TQAdvXjtTeq4bkdPqKXPNGRj/61ADAShCt93sfSpO9IcEfMOPpTASjBWztJ4Pp7UASHNIPr2pcZNJ3PHFAC0opAcD0oHSgBaO1HFHQUALyAainhEoBHDr91qkJ7frSAkD+RoAgjbIKn5XB5GKfajYZF4zu3DHuP8c1BdK0biZBkfxgenrTEulSdN/3WGCw7Z5/pQMtXGWKx985Ye1A9KQZLF8/epR149KAQueegprZwT3pSMc1G5IbYvLN7dqAFjJdi3Ufw+mKk7n8qRQFUY6ClPcdfUCmMaev86XrjrnFIozyCevXFGef6UAJgDjHNO6jB/wDr0n8R5P40Z74HWgY4kbce1Z7ku+M9eeoq5MxWNuSPeqigbk6ZyKiRpFHHeNtBeaGLV7WPLxLtuR/eTsfwzz7VxtmEkVoWUryGypxkdx/9evbGRXVkIDK2QVI4PrmvLPEeiSaBqIMCH7FcE+S5P3T1KE/hx04HsaqErOzPeyrG/wDLib9P8jO+zlfvkMoB2sD7f/X/AEppwfl/nS+cxUJnDE/e6ZpCW3FWJBHatrn0Cb6ixggFmXk9fYVLtJTJI+9ngZ/CmoibRsx6Zz2/yKXAXIUHGccf5/zikHMOwoxnZjHTFViVEgDMm4DHzDHX6VMx+X5UZseo7UxmDoCQACAd3b6UApFd1Yr94qQOuOP89PzqlI+zeqyE5zyRV6QKMlh0OfY1VnyVIypHX6VnLRGiehDZ6S3iKW5skkAVEDudvRmPH8jXofh3S302zhhlIadV/eMP5flj8qy/AdhAmj/b1yZLmR959lYqP/Qa69VIJY8nvXnVcRJ3h0PkcXyzrymkXEGORTZSCnI6e1JExPBqRxlcHvWPNdHMlZmRNJ5LBjwCe1PinVkB4OamntPMjIBG7tnp9KyXjMJLeXIn94dqi7OqMVI7ulo6UZFeyeGFFFGaAFoozRmgAoozRmgAoozSZ96AFpaTNGRQAtFJketGaADNLSUUgFzRSZozQAUopM+9LxTAQ9qCeetLmk4oAKDS4H4UcAdKAE4xSMAykN09KXgUh46daAGxMeUY/MO/qPWpDzgGmSDGHXqp7dx3FPBDAMDkdqAEHpil+lJS0AGaD0zS8YzUFzM0KL5aeZIxwq7gPzz2ouBKD1pPr0quInnj2zzMVYcrEdv6jn9aje2ltZEe2mfYOHjkYuCOx5Of1oHYugYHPP8AWsg2UkOrwOGP2fkhR/C2On06/lWm8+wbjGccfdIP6VT1DUrS1t/OlkbCsMBYmZs/QDNAWL/ejvntWZpev6fq/mi2kmVojh1mgeMjP+8BWqR8vBB+lOwETsEQsefao4lb7zfePX2oyJpQy/dQ/L7n1qUEUgFJGMA/nTfX5SM/jS5y3HBpx54HQigYxQB+dKQeeOPelB4GccmkzweuCPWgY3qTz+VH8PTP4UA45OOueKQYB6ZPuaQWIZ3GQowc+oqNcmRMHKg8c052JmY9dvKiggBBgdBx7GkbbInz8pII/lVXULK31Kyms7mMSQyqN6+noR6EHkGrLjK4/GotpIHPWpaITad0eT3unXWi6mLG4w2QWhfHEidvoc9f/wBVMVVZcOvJzgkd/rXe+MLJbrw9c3IEYmtVMqO/t1XPoRn8celebWGqQahYpIuV/voeCpHFaQlbRn1WXYx4iPK/iRd5DYLg49aYwQ5+Yjdxx2pXZWQbSQ/vUJl3lckkDGRitLnqKDHOxVhuYEngY6HvUWZATll68e9O8zkgEFT0zTSwQfMcnPbpQWoEU0wGNwZivbPFViyfPkA+lWJ5Cigjg9xVPzA4ZeOT1702lysKiajodl4DuVPhyyjClRtZgT33Ozf1x+Fdivzr8p4964DwLexSWZsSw860kZHTPYsWUj2wR+Nd7FJtG3uTXhV9Js+Qeo7Y8YJHzD070odlGeoPpTvM3ZGKQsOn9KhE2HowKcH6UyVVZcN6dMVGX2ZGBmmNKGU8802xpM//2Q=="
-       id="image140" />
-  </g>
-  <text
-     xml:space="preserve"
-     id="text9907"
-     style="font-size:16px;line-height:1.25;font-family:sans-serif;letter-spacing:0px;word-spacing:0px;white-space:pre;shape-inside:url(#rect9909);fill:#000000"
-     transform="translate(-2.6246718e-7,-115.94727)"><tspan
-       x="0"
-       y="127.82227"
-       id="tspan12971">Open Images</tspan></text>
-  <text
-     xml:space="preserve"
-     id="text11069"
-     style="font-size:16px;line-height:1.25;font-family:sans-serif;letter-spacing:0px;word-spacing:0px;white-space:pre;shape-inside:url(#rect11071);fill:#000000"
-     transform="translate(679.09789,-115.94727)"><tspan
-       x="0"
-       y="127.82227"
-       id="tspan12973">COCO</tspan></text>
-</svg>
diff --git a/repositories/taming-transformers/assets/stormy.jpeg b/repositories/taming-transformers/assets/stormy.jpeg
deleted file mode 100644
index 335edab19ea17e31f318540390f4575c4ff27bf4..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/assets/stormy.jpeg and /dev/null differ
diff --git a/repositories/taming-transformers/assets/sunset_and_ocean.jpg b/repositories/taming-transformers/assets/sunset_and_ocean.jpg
deleted file mode 100644
index 5294e8f7b889b70d0dec79cd6775878754922838..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/assets/sunset_and_ocean.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/assets/teaser.png b/repositories/taming-transformers/assets/teaser.png
deleted file mode 100644
index c485153154e2aa0fd9f08773263ba13a4139868b..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/assets/teaser.png and /dev/null differ
diff --git a/repositories/taming-transformers/configs/coco_cond_stage.yaml b/repositories/taming-transformers/configs/coco_cond_stage.yaml
deleted file mode 100644
index 18a3dde147455c281a3687b1a0b42bbbc3fb2725..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/configs/coco_cond_stage.yaml
+++ /dev/null
@@ -1,49 +0,0 @@
-model:
-  base_learning_rate: 4.5e-06
-  target: taming.models.vqgan.VQSegmentationModel
-  params:
-    embed_dim: 256
-    n_embed: 1024
-    image_key: "segmentation"
-    n_labels: 183
-    ddconfig:
-      double_z: false
-      z_channels: 256
-      resolution: 256
-      in_channels: 183
-      out_ch: 183
-      ch: 128
-      ch_mult:
-      - 1
-      - 1
-      - 2
-      - 2
-      - 4
-      num_res_blocks: 2
-      attn_resolutions:
-      - 16
-      dropout: 0.0
-
-    lossconfig:
-      target: taming.modules.losses.segmentation.BCELossWithQuant
-      params:
-        codebook_weight: 1.0
-
-data:
-  target: main.DataModuleFromConfig
-  params:
-    batch_size: 12
-    train:
-      target: taming.data.coco.CocoImagesAndCaptionsTrain
-      params:
-        size: 296
-        crop_size: 256
-        onehot_segmentation: true
-        use_stuffthing: true
-    validation:
-      target: taming.data.coco.CocoImagesAndCaptionsValidation
-      params:
-        size: 256
-        crop_size: 256
-        onehot_segmentation: true
-        use_stuffthing: true
diff --git a/repositories/taming-transformers/configs/coco_scene_images_transformer.yaml b/repositories/taming-transformers/configs/coco_scene_images_transformer.yaml
deleted file mode 100644
index a03078de708182cc175f139078f8455ca3ec8a09..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/configs/coco_scene_images_transformer.yaml
+++ /dev/null
@@ -1,80 +0,0 @@
-model:
-  base_learning_rate: 4.5e-06
-  target: taming.models.cond_transformer.Net2NetTransformer
-  params:
-    cond_stage_key: objects_bbox
-    transformer_config:
-      target: taming.modules.transformer.mingpt.GPT
-      params:
-        vocab_size: 8192
-        block_size: 348  # = 256 + 92 = dim(vqgan_latent_space,16x16) + dim(conditional_builder.embedding_dim)
-        n_layer: 40
-        n_head: 16
-        n_embd: 1408
-        embd_pdrop: 0.1
-        resid_pdrop: 0.1
-        attn_pdrop: 0.1
-    first_stage_config:
-      target: taming.models.vqgan.VQModel
-      params:
-        ckpt_path: /path/to/coco_epoch117.ckpt  # https://heibox.uni-heidelberg.de/f/78dea9589974474c97c1/
-        embed_dim: 256
-        n_embed: 8192
-        ddconfig:
-          double_z: false
-          z_channels: 256
-          resolution: 256
-          in_channels: 3
-          out_ch: 3
-          ch: 128
-          ch_mult:
-          - 1
-          - 1
-          - 2
-          - 2
-          - 4
-          num_res_blocks: 2
-          attn_resolutions:
-          - 16
-          dropout: 0.0
-        lossconfig:
-          target: taming.modules.losses.DummyLoss
-    cond_stage_config:
-      target: taming.models.dummy_cond_stage.DummyCondStage
-      params:
-        conditional_key: objects_bbox
-
-data:
-  target: main.DataModuleFromConfig
-  params:
-    batch_size: 6
-    train:
-      target: taming.data.annotated_objects_coco.AnnotatedObjectsCoco
-      params:
-        data_path: data/coco_annotations_100  # substitute with path to full dataset
-        split: train
-        keys: [image, objects_bbox, file_name, annotations]
-        no_tokens: 8192
-        target_image_size: 256
-        min_object_area: 0.00001
-        min_objects_per_image: 2
-        max_objects_per_image: 30
-        crop_method: random-1d
-        random_flip: true
-        use_group_parameter: true
-        encode_crop: true
-    validation:
-      target: taming.data.annotated_objects_coco.AnnotatedObjectsCoco
-      params:
-        data_path: data/coco_annotations_100  # substitute with path to full dataset
-        split: validation
-        keys: [image, objects_bbox, file_name, annotations]
-        no_tokens: 8192
-        target_image_size: 256
-        min_object_area: 0.00001
-        min_objects_per_image: 2
-        max_objects_per_image: 30
-        crop_method: center
-        random_flip: false
-        use_group_parameter: true
-        encode_crop: true
diff --git a/repositories/taming-transformers/configs/custom_vqgan.yaml b/repositories/taming-transformers/configs/custom_vqgan.yaml
deleted file mode 100644
index 908687f38325dfe49430692d668733a8e1598375..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/configs/custom_vqgan.yaml
+++ /dev/null
@@ -1,43 +0,0 @@
-model:
-  base_learning_rate: 4.5e-6
-  target: taming.models.vqgan.VQModel
-  params:
-    embed_dim: 256
-    n_embed: 1024
-    ddconfig:
-      double_z: False
-      z_channels: 256
-      resolution: 256
-      in_channels: 3
-      out_ch: 3
-      ch: 128
-      ch_mult: [ 1,1,2,2,4]  # num_down = len(ch_mult)-1
-      num_res_blocks: 2
-      attn_resolutions: [16]
-      dropout: 0.0
-
-    lossconfig:
-      target: taming.modules.losses.vqperceptual.VQLPIPSWithDiscriminator
-      params:
-        disc_conditional: False
-        disc_in_channels: 3
-        disc_start: 10000
-        disc_weight: 0.8
-        codebook_weight: 1.0
-
-data:
-  target: main.DataModuleFromConfig
-  params:
-    batch_size: 5
-    num_workers: 8
-    train:
-      target: taming.data.custom.CustomTrain
-      params:
-        training_images_list_file: some/training.txt
-        size: 256
-    validation:
-      target: taming.data.custom.CustomTest
-      params:
-        test_images_list_file: some/test.txt
-        size: 256
-
diff --git a/repositories/taming-transformers/configs/drin_transformer.yaml b/repositories/taming-transformers/configs/drin_transformer.yaml
deleted file mode 100644
index bead4567d2dcc3d0f1a7b8eec823df4b427cab07..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/configs/drin_transformer.yaml
+++ /dev/null
@@ -1,77 +0,0 @@
-model:
-  base_learning_rate: 4.5e-06
-  target: taming.models.cond_transformer.Net2NetTransformer
-  params:
-    cond_stage_key: depth
-    transformer_config:
-      target: taming.modules.transformer.mingpt.GPT
-      params:
-        vocab_size: 1024
-        block_size: 512
-        n_layer: 24
-        n_head: 16
-        n_embd: 1024
-    first_stage_config:
-      target: taming.models.vqgan.VQModel
-      params:
-        ckpt_path: logs/2020-09-23T17-56-33_imagenet_vqgan/checkpoints/last.ckpt
-        embed_dim: 256
-        n_embed: 1024
-        ddconfig:
-          double_z: false
-          z_channels: 256
-          resolution: 256
-          in_channels: 3
-          out_ch: 3
-          ch: 128
-          ch_mult:
-          - 1
-          - 1
-          - 2
-          - 2
-          - 4
-          num_res_blocks: 2
-          attn_resolutions:
-          - 16
-          dropout: 0.0
-        lossconfig:
-          target: taming.modules.losses.DummyLoss
-    cond_stage_config:
-      target: taming.models.vqgan.VQModel
-      params:
-        ckpt_path: logs/2020-11-03T15-34-24_imagenetdepth_vqgan/checkpoints/last.ckpt
-        embed_dim: 256
-        n_embed: 1024
-        ddconfig:
-          double_z: false
-          z_channels: 256
-          resolution: 256
-          in_channels: 1
-          out_ch: 1
-          ch: 128
-          ch_mult:
-          - 1
-          - 1
-          - 2
-          - 2
-          - 4
-          num_res_blocks: 2
-          attn_resolutions:
-          - 16
-          dropout: 0.0
-        lossconfig:
-          target: taming.modules.losses.DummyLoss
-
-data:
-  target: main.DataModuleFromConfig
-  params:
-    batch_size: 2
-    num_workers: 8
-    train:
-      target: taming.data.imagenet.RINTrainWithDepth
-      params:
-        size: 256
-    validation:
-      target: taming.data.imagenet.RINValidationWithDepth
-      params:
-        size: 256
diff --git a/repositories/taming-transformers/configs/faceshq_transformer.yaml b/repositories/taming-transformers/configs/faceshq_transformer.yaml
deleted file mode 100644
index b93391f9c9c41d63d28dd38dbf83615552642db3..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/configs/faceshq_transformer.yaml
+++ /dev/null
@@ -1,61 +0,0 @@
-model:
-  base_learning_rate: 4.5e-06
-  target: taming.models.cond_transformer.Net2NetTransformer
-  params:
-    cond_stage_key: coord
-    transformer_config:
-      target: taming.modules.transformer.mingpt.GPT
-      params:
-        vocab_size: 1024
-        block_size: 512
-        n_layer: 24
-        n_head: 16
-        n_embd: 1024
-    first_stage_config:
-      target: taming.models.vqgan.VQModel
-      params:
-        ckpt_path: logs/2020-11-09T13-33-36_faceshq_vqgan/checkpoints/last.ckpt
-        embed_dim: 256
-        n_embed: 1024
-        ddconfig:
-          double_z: false
-          z_channels: 256
-          resolution: 256
-          in_channels: 3
-          out_ch: 3
-          ch: 128
-          ch_mult:
-          - 1
-          - 1
-          - 2
-          - 2
-          - 4
-          num_res_blocks: 2
-          attn_resolutions:
-          - 16
-          dropout: 0.0
-        lossconfig:
-          target: taming.modules.losses.DummyLoss
-    cond_stage_config:
-      target: taming.modules.misc.coord.CoordStage
-      params:
-        n_embed: 1024
-        down_factor: 16
-
-data:
-  target: main.DataModuleFromConfig
-  params:
-    batch_size: 2
-    num_workers: 8
-    train:
-      target: taming.data.faceshq.FacesHQTrain
-      params:
-        size: 256
-        crop_size: 256
-        coord: True
-    validation:
-      target: taming.data.faceshq.FacesHQValidation
-      params:
-        size: 256
-        crop_size: 256
-        coord: True
diff --git a/repositories/taming-transformers/configs/faceshq_vqgan.yaml b/repositories/taming-transformers/configs/faceshq_vqgan.yaml
deleted file mode 100644
index 3960f784551bfd9caddb5b084fc592c6eed6483b..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/configs/faceshq_vqgan.yaml
+++ /dev/null
@@ -1,42 +0,0 @@
-model:
-  base_learning_rate: 4.5e-6
-  target: taming.models.vqgan.VQModel
-  params:
-    embed_dim: 256
-    n_embed: 1024
-    ddconfig:
-      double_z: False
-      z_channels: 256
-      resolution: 256
-      in_channels: 3
-      out_ch: 3
-      ch: 128
-      ch_mult: [ 1,1,2,2,4]  # num_down = len(ch_mult)-1
-      num_res_blocks: 2
-      attn_resolutions: [16]
-      dropout: 0.0
-
-    lossconfig:
-      target: taming.modules.losses.vqperceptual.VQLPIPSWithDiscriminator
-      params:
-        disc_conditional: False
-        disc_in_channels: 3
-        disc_start: 30001
-        disc_weight: 0.8
-        codebook_weight: 1.0
-
-data:
-  target: main.DataModuleFromConfig
-  params:
-    batch_size: 3
-    num_workers: 8
-    train:
-      target: taming.data.faceshq.FacesHQTrain
-      params:
-        size: 256
-        crop_size: 256
-    validation:
-      target: taming.data.faceshq.FacesHQValidation
-      params:
-        size: 256
-        crop_size: 256
diff --git a/repositories/taming-transformers/configs/imagenet_vqgan.yaml b/repositories/taming-transformers/configs/imagenet_vqgan.yaml
deleted file mode 100644
index f6dc21ff6de9a26474fa18a0d496a4a0b9bb0837..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/configs/imagenet_vqgan.yaml
+++ /dev/null
@@ -1,42 +0,0 @@
-model:
-  base_learning_rate: 4.5e-6
-  target: taming.models.vqgan.VQModel
-  params:
-    embed_dim: 256
-    n_embed: 1024
-    ddconfig:
-      double_z: False
-      z_channels: 256
-      resolution: 256
-      in_channels: 3
-      out_ch: 3
-      ch: 128
-      ch_mult: [ 1,1,2,2,4]  # num_down = len(ch_mult)-1
-      num_res_blocks: 2
-      attn_resolutions: [16]
-      dropout: 0.0
-
-    lossconfig:
-      target: taming.modules.losses.vqperceptual.VQLPIPSWithDiscriminator
-      params:
-        disc_conditional: False
-        disc_in_channels: 3
-        disc_start: 250001
-        disc_weight: 0.8
-        codebook_weight: 1.0
-
-data:
-  target: main.DataModuleFromConfig
-  params:
-    batch_size: 12
-    num_workers: 24
-    train:
-      target: taming.data.imagenet.ImageNetTrain
-      params:
-        config:
-          size: 256
-    validation:
-      target: taming.data.imagenet.ImageNetValidation
-      params:
-        config:
-          size: 256
diff --git a/repositories/taming-transformers/configs/imagenetdepth_vqgan.yaml b/repositories/taming-transformers/configs/imagenetdepth_vqgan.yaml
deleted file mode 100644
index 88d2f34f1c0661e350899cf4229cdf60697baf0d..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/configs/imagenetdepth_vqgan.yaml
+++ /dev/null
@@ -1,41 +0,0 @@
-model:
-  base_learning_rate: 4.5e-6
-  target: taming.models.vqgan.VQModel
-  params:
-    embed_dim: 256
-    n_embed: 1024
-    image_key: depth
-    ddconfig:
-      double_z: False
-      z_channels: 256
-      resolution: 256
-      in_channels: 1
-      out_ch: 1
-      ch: 128
-      ch_mult: [ 1,1,2,2,4]  # num_down = len(ch_mult)-1
-      num_res_blocks: 2
-      attn_resolutions: [16]
-      dropout: 0.0
-
-    lossconfig:
-      target: taming.modules.losses.vqperceptual.VQLPIPSWithDiscriminator
-      params:
-        disc_conditional: False
-        disc_in_channels: 1
-        disc_start: 50001
-        disc_weight: 0.75
-        codebook_weight: 1.0
-
-data:
-  target: main.DataModuleFromConfig
-  params:
-    batch_size: 3
-    num_workers: 8
-    train:
-      target: taming.data.imagenet.ImageNetTrainWithDepth
-      params:
-        size: 256
-    validation:
-      target: taming.data.imagenet.ImageNetValidationWithDepth
-      params:
-        size: 256
diff --git a/repositories/taming-transformers/configs/open_images_scene_images_transformer.yaml b/repositories/taming-transformers/configs/open_images_scene_images_transformer.yaml
deleted file mode 100644
index f4e41e0d67d3e3eb17509862e063e4d626b06d4b..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/configs/open_images_scene_images_transformer.yaml
+++ /dev/null
@@ -1,86 +0,0 @@
-model:
-  base_learning_rate: 4.5e-06
-  target: taming.models.cond_transformer.Net2NetTransformer
-  params:
-    cond_stage_key: objects_bbox
-    transformer_config:
-      target: taming.modules.transformer.mingpt.GPT
-      params:
-        vocab_size: 8192
-        block_size: 348  # = 256 + 92 = dim(vqgan_latent_space,16x16) + dim(conditional_builder.embedding_dim)
-        n_layer: 36
-        n_head: 16
-        n_embd: 1536
-        embd_pdrop: 0.1
-        resid_pdrop: 0.1
-        attn_pdrop: 0.1
-    first_stage_config:
-      target: taming.models.vqgan.VQModel
-      params:
-        ckpt_path: /path/to/coco_oi_epoch12.ckpt  # https://heibox.uni-heidelberg.de/f/461d9a9f4fcf48ab84f4/
-        embed_dim: 256
-        n_embed: 8192
-        ddconfig:
-          double_z: false
-          z_channels: 256
-          resolution: 256
-          in_channels: 3
-          out_ch: 3
-          ch: 128
-          ch_mult:
-          - 1
-          - 1
-          - 2
-          - 2
-          - 4
-          num_res_blocks: 2
-          attn_resolutions:
-          - 16
-          dropout: 0.0
-        lossconfig:
-          target: taming.modules.losses.DummyLoss
-    cond_stage_config:
-      target: taming.models.dummy_cond_stage.DummyCondStage
-      params:
-        conditional_key: objects_bbox
-
-data:
-  target: main.DataModuleFromConfig
-  params:
-    batch_size: 6
-    train:
-      target: taming.data.annotated_objects_open_images.AnnotatedObjectsOpenImages
-      params:
-        data_path: data/open_images_annotations_100  # substitute with path to full dataset
-        split: train
-        keys: [image, objects_bbox, file_name, annotations]
-        no_tokens: 8192
-        target_image_size: 256
-        category_allow_list_target: taming.data.open_images_helper.top_300_classes_plus_coco_compatibility
-        category_mapping_target: taming.data.open_images_helper.open_images_unify_categories_for_coco
-        min_object_area: 0.0001
-        min_objects_per_image: 2
-        max_objects_per_image: 30
-        crop_method: random-2d
-        random_flip: true
-        use_group_parameter: true
-        use_additional_parameters: true
-        encode_crop: true
-    validation:
-      target: taming.data.annotated_objects_open_images.AnnotatedObjectsOpenImages
-      params:
-        data_path: data/open_images_annotations_100  # substitute with path to full dataset
-        split: validation
-        keys: [image, objects_bbox, file_name, annotations]
-        no_tokens: 8192
-        target_image_size: 256
-        category_allow_list_target: taming.data.open_images_helper.top_300_classes_plus_coco_compatibility
-        category_mapping_target: taming.data.open_images_helper.open_images_unify_categories_for_coco
-        min_object_area: 0.0001
-        min_objects_per_image: 2
-        max_objects_per_image: 30
-        crop_method: center
-        random_flip: false
-        use_group_parameter: true
-        use_additional_parameters: true
-        encode_crop: true
diff --git a/repositories/taming-transformers/configs/sflckr_cond_stage.yaml b/repositories/taming-transformers/configs/sflckr_cond_stage.yaml
deleted file mode 100644
index d48b50a700c4b44098ce4f3c752d5a4d7158f8a9..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/configs/sflckr_cond_stage.yaml
+++ /dev/null
@@ -1,43 +0,0 @@
-model:
-  base_learning_rate: 4.5e-06
-  target: taming.models.vqgan.VQSegmentationModel
-  params:
-    embed_dim: 256
-    n_embed: 1024
-    image_key: "segmentation"
-    n_labels: 182
-    ddconfig:
-      double_z: false
-      z_channels: 256
-      resolution: 256
-      in_channels: 182
-      out_ch: 182
-      ch: 128
-      ch_mult:
-      - 1
-      - 1
-      - 2
-      - 2
-      - 4
-      num_res_blocks: 2
-      attn_resolutions:
-      - 16
-      dropout: 0.0
-
-    lossconfig:
-      target: taming.modules.losses.segmentation.BCELossWithQuant
-      params:
-        codebook_weight: 1.0
-
-data:
-  target: cutlit.DataModuleFromConfig
-  params:
-    batch_size: 12
-    train:
-      target: taming.data.sflckr.Examples # adjust
-      params:
-        size: 256
-    validation:
-      target: taming.data.sflckr.Examples # adjust
-      params:
-        size: 256
diff --git a/repositories/taming-transformers/data/ade20k_examples.txt b/repositories/taming-transformers/data/ade20k_examples.txt
deleted file mode 100644
index 0b5a07823cb09fea08ac676eb6da6e09fc222b83..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/data/ade20k_examples.txt
+++ /dev/null
@@ -1,30 +0,0 @@
-ADE_val_00000636.jpg
-ADE_val_00000126.jpg
-ADE_val_00001412.jpg
-ADE_val_00001845.jpg
-ADE_val_00001200.jpg
-ADE_val_00001578.jpg
-ADE_val_00000880.jpg
-ADE_val_00000875.jpg
-ADE_val_00000123.jpg
-ADE_val_00001209.jpg
-ADE_val_00000203.jpg
-ADE_val_00001851.jpg
-ADE_val_00001583.jpg
-ADE_val_00000287.jpg
-ADE_val_00001947.jpg
-ADE_val_00000262.jpg
-ADE_val_00000603.jpg
-ADE_val_00000125.jpg
-ADE_val_00001698.jpg
-ADE_val_00001966.jpg
-ADE_val_00000532.jpg
-ADE_val_00001177.jpg
-ADE_val_00000734.jpg
-ADE_val_00001498.jpg
-ADE_val_00001766.jpg
-ADE_val_00000303.jpg
-ADE_val_00000509.jpg
-ADE_val_00000573.jpg
-ADE_val_00000289.jpg
-ADE_val_00001388.jpg
diff --git a/repositories/taming-transformers/data/ade20k_images/ADE_val_00000123.jpg b/repositories/taming-transformers/data/ade20k_images/ADE_val_00000123.jpg
deleted file mode 100644
index 774407647dfac63260246595bca7561f7e675aec..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/ade20k_images/ADE_val_00000123.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/ade20k_images/ADE_val_00000125.jpg b/repositories/taming-transformers/data/ade20k_images/ADE_val_00000125.jpg
deleted file mode 100644
index 71c7cf3de0e0d7a42192782a5e65df1098954c27..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/ade20k_images/ADE_val_00000125.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/ade20k_images/ADE_val_00000126.jpg b/repositories/taming-transformers/data/ade20k_images/ADE_val_00000126.jpg
deleted file mode 100644
index 9df65d17ce65c1b0f6eebdb2652bb565f06eedbb..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/ade20k_images/ADE_val_00000126.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/ade20k_images/ADE_val_00000203.jpg b/repositories/taming-transformers/data/ade20k_images/ADE_val_00000203.jpg
deleted file mode 100644
index ae6c97025be2af4f159fed427cdb499cb6e51635..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/ade20k_images/ADE_val_00000203.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/ade20k_images/ADE_val_00000262.jpg b/repositories/taming-transformers/data/ade20k_images/ADE_val_00000262.jpg
deleted file mode 100644
index cf572bef07474f2ba4f20e9799b0899ef777ebc1..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/ade20k_images/ADE_val_00000262.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/ade20k_images/ADE_val_00000287.jpg b/repositories/taming-transformers/data/ade20k_images/ADE_val_00000287.jpg
deleted file mode 100644
index d7ea848ce142e3740ccabd5c679abe96d78ff0cd..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/ade20k_images/ADE_val_00000287.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/ade20k_images/ADE_val_00000289.jpg b/repositories/taming-transformers/data/ade20k_images/ADE_val_00000289.jpg
deleted file mode 100644
index f74858ffbffac9d0d1ee28f7d7cb94f62fd16cd1..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/ade20k_images/ADE_val_00000289.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/ade20k_images/ADE_val_00000303.jpg b/repositories/taming-transformers/data/ade20k_images/ADE_val_00000303.jpg
deleted file mode 100644
index a37aa933d234cd05ed7086239123b5f1f99d85d3..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/ade20k_images/ADE_val_00000303.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/ade20k_images/ADE_val_00000509.jpg b/repositories/taming-transformers/data/ade20k_images/ADE_val_00000509.jpg
deleted file mode 100644
index 6a3e5ab26a32e692395ee0117ce36e0081615901..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/ade20k_images/ADE_val_00000509.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/ade20k_images/ADE_val_00000532.jpg b/repositories/taming-transformers/data/ade20k_images/ADE_val_00000532.jpg
deleted file mode 100644
index 54c13f1c4d22d1ad358390590492b389a9076392..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/ade20k_images/ADE_val_00000532.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/ade20k_images/ADE_val_00000573.jpg b/repositories/taming-transformers/data/ade20k_images/ADE_val_00000573.jpg
deleted file mode 100644
index 7e45edd22c1e42fb2d0aaac268d23884e6b7a3dc..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/ade20k_images/ADE_val_00000573.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/ade20k_images/ADE_val_00000603.jpg b/repositories/taming-transformers/data/ade20k_images/ADE_val_00000603.jpg
deleted file mode 100644
index 96d7b4fe874f23c303d8eb29b0f39ead9bac586d..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/ade20k_images/ADE_val_00000603.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/ade20k_images/ADE_val_00000636.jpg b/repositories/taming-transformers/data/ade20k_images/ADE_val_00000636.jpg
deleted file mode 100644
index 39aec80ed48787d2d410bf4282e90e0e9079dca7..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/ade20k_images/ADE_val_00000636.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/ade20k_images/ADE_val_00000734.jpg b/repositories/taming-transformers/data/ade20k_images/ADE_val_00000734.jpg
deleted file mode 100644
index a70ba1c5c9639434e079c3b1bac61c868490d938..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/ade20k_images/ADE_val_00000734.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/ade20k_images/ADE_val_00000875.jpg b/repositories/taming-transformers/data/ade20k_images/ADE_val_00000875.jpg
deleted file mode 100644
index 8d43f5234a50dcd4935c098a30730736ef72cae6..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/ade20k_images/ADE_val_00000875.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/ade20k_images/ADE_val_00000880.jpg b/repositories/taming-transformers/data/ade20k_images/ADE_val_00000880.jpg
deleted file mode 100644
index c08c6203a41ab64c9a55e3828d4461ca543e468d..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/ade20k_images/ADE_val_00000880.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/ade20k_images/ADE_val_00001177.jpg b/repositories/taming-transformers/data/ade20k_images/ADE_val_00001177.jpg
deleted file mode 100644
index 422f079910d559e7792d9b4c460d356d77dcce37..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/ade20k_images/ADE_val_00001177.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/ade20k_images/ADE_val_00001200.jpg b/repositories/taming-transformers/data/ade20k_images/ADE_val_00001200.jpg
deleted file mode 100644
index 910d9277a95eec22b73cf66cebd0d7099e5f0210..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/ade20k_images/ADE_val_00001200.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/ade20k_images/ADE_val_00001209.jpg b/repositories/taming-transformers/data/ade20k_images/ADE_val_00001209.jpg
deleted file mode 100644
index c87f719f5afa0aae1a69ea8e996fbd6230f343d6..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/ade20k_images/ADE_val_00001209.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/ade20k_images/ADE_val_00001388.jpg b/repositories/taming-transformers/data/ade20k_images/ADE_val_00001388.jpg
deleted file mode 100644
index 0de8775327d18140254b8973fb951fe35e888721..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/ade20k_images/ADE_val_00001388.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/ade20k_images/ADE_val_00001412.jpg b/repositories/taming-transformers/data/ade20k_images/ADE_val_00001412.jpg
deleted file mode 100644
index 3c569ddd8e45bb60519fa7eb13365657157d97d1..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/ade20k_images/ADE_val_00001412.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/ade20k_images/ADE_val_00001498.jpg b/repositories/taming-transformers/data/ade20k_images/ADE_val_00001498.jpg
deleted file mode 100644
index f661b40c3c0fc3a0655c9caceeccb42d5bedc19e..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/ade20k_images/ADE_val_00001498.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/ade20k_images/ADE_val_00001578.jpg b/repositories/taming-transformers/data/ade20k_images/ADE_val_00001578.jpg
deleted file mode 100644
index 00621febe8fc4bbd6e9d4d86fcfa315f294593e1..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/ade20k_images/ADE_val_00001578.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/ade20k_images/ADE_val_00001583.jpg b/repositories/taming-transformers/data/ade20k_images/ADE_val_00001583.jpg
deleted file mode 100644
index 1a7946f312c05343d70b7eea723c272d6078595d..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/ade20k_images/ADE_val_00001583.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/ade20k_images/ADE_val_00001698.jpg b/repositories/taming-transformers/data/ade20k_images/ADE_val_00001698.jpg
deleted file mode 100644
index 859c515a9a5e5936ce6338a4d6c4d196365ee998..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/ade20k_images/ADE_val_00001698.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/ade20k_images/ADE_val_00001766.jpg b/repositories/taming-transformers/data/ade20k_images/ADE_val_00001766.jpg
deleted file mode 100644
index 1690dee906ad750084327062eafb8e3a9d25a690..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/ade20k_images/ADE_val_00001766.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/ade20k_images/ADE_val_00001845.jpg b/repositories/taming-transformers/data/ade20k_images/ADE_val_00001845.jpg
deleted file mode 100644
index 39494fdede0e8678fae5b7fe4e6385b4cff9507c..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/ade20k_images/ADE_val_00001845.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/ade20k_images/ADE_val_00001851.jpg b/repositories/taming-transformers/data/ade20k_images/ADE_val_00001851.jpg
deleted file mode 100644
index d38c1050deeefd858e6d65a3cdb857165493e2f4..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/ade20k_images/ADE_val_00001851.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/ade20k_images/ADE_val_00001947.jpg b/repositories/taming-transformers/data/ade20k_images/ADE_val_00001947.jpg
deleted file mode 100644
index 9776d4cae49779fbe90996d01a4fb111f0f00e60..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/ade20k_images/ADE_val_00001947.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/ade20k_images/ADE_val_00001966.jpg b/repositories/taming-transformers/data/ade20k_images/ADE_val_00001966.jpg
deleted file mode 100644
index 494260bc0220ba01e70f5d54d569f78911664b0e..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/ade20k_images/ADE_val_00001966.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00000123.png b/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00000123.png
deleted file mode 100644
index f951e5f3311603f3fa6c76ca5188de5e024138de..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00000123.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00000125.png b/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00000125.png
deleted file mode 100644
index 91e49e228ac93aac1314e21b118c084f7a923084..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00000125.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00000126.png b/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00000126.png
deleted file mode 100644
index f78143fcc1774678d1a138133ff257a46c1e68d6..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00000126.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00000203.png b/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00000203.png
deleted file mode 100644
index 8251aa7db06872d0f9299cfd13aa98cceed2cd09..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00000203.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00000262.png b/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00000262.png
deleted file mode 100644
index 1e63cc80466bd5d014b328d9b2a486b4bc26b677..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00000262.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00000287.png b/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00000287.png
deleted file mode 100644
index 4f23834a64636a9e8434e50ec8631811887f6b67..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00000287.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00000289.png b/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00000289.png
deleted file mode 100644
index 1413d5a0f757d45d3e4d14a2fd9c222c374ba1f3..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00000289.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00000303.png b/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00000303.png
deleted file mode 100644
index 24cc97329f11a700469c66f73926df18626e916a..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00000303.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00000509.png b/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00000509.png
deleted file mode 100644
index 0aa65c57f15337597cae42f8360bf4fc4b987ead..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00000509.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00000532.png b/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00000532.png
deleted file mode 100644
index 53a7866ce50cfbe7ee30bb8f418ea93f1ea6cae0..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00000532.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00000573.png b/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00000573.png
deleted file mode 100644
index 9a2b394c02b9e4bc862e73ecc96e8b7743bead40..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00000573.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00000603.png b/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00000603.png
deleted file mode 100644
index d6aad36b4d328df004e7b50dbe00be919f1f9f9a..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00000603.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00000636.png b/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00000636.png
deleted file mode 100644
index d037b4bb9b03ca03214853850efa42020f75b122..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00000636.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00000734.png b/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00000734.png
deleted file mode 100644
index 6019e31c2280e92e1164ad87eb0d41193ebf4c96..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00000734.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00000875.png b/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00000875.png
deleted file mode 100644
index e81ac5bacf0fb5be81dcd150dbda8f197ea69da1..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00000875.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00000880.png b/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00000880.png
deleted file mode 100644
index 5b056ea99f81ea9dde14ad32d3b63eb773e7cd00..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00000880.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00001177.png b/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00001177.png
deleted file mode 100644
index 37154e5087b3b5d403780012787ad9a0aa937b33..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00001177.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00001200.png b/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00001200.png
deleted file mode 100644
index d71561e9ca9419e83eaa9e2fe2f8fa2da0a7082b..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00001200.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00001209.png b/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00001209.png
deleted file mode 100644
index d348cfc6e58489d0d509670478dcb192f9972b44..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00001209.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00001388.png b/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00001388.png
deleted file mode 100644
index 5b78c30bc146d21835cee2d0ce1a42008603347a..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00001388.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00001412.png b/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00001412.png
deleted file mode 100644
index 09e2b9a044eb833377764ab3a19edf89a818d6f6..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00001412.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00001498.png b/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00001498.png
deleted file mode 100644
index af5c7d9f5ab3c19840d157ad5633b908c8e6e506..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00001498.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00001578.png b/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00001578.png
deleted file mode 100644
index cc4d9934dcabf52dca6459fc1850373b30f095a9..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00001578.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00001583.png b/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00001583.png
deleted file mode 100644
index d5a5575bc48c70f99f266fcc2dbc1822c93933a7..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00001583.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00001698.png b/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00001698.png
deleted file mode 100644
index 859c06146e8b87753076290216ea537392cbdcd0..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00001698.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00001766.png b/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00001766.png
deleted file mode 100644
index c94eae43649a9be98904fa8ca1beba65fc239bff..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00001766.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00001845.png b/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00001845.png
deleted file mode 100644
index 8cc120bd910c46d067c91e10df6a3b03b35bd02e..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00001845.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00001851.png b/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00001851.png
deleted file mode 100644
index 052a2ddbe2153d91e74948aa5ff89a01f96b8a4d..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00001851.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00001947.png b/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00001947.png
deleted file mode 100644
index 975a93e70715f58b1427d71124651ffd7e272593..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00001947.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00001966.png b/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00001966.png
deleted file mode 100644
index bb4f76305ff9e6c2841dd12bf34468f7ec52d9ce..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/ade20k_segmentations/ADE_val_00001966.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/celebahqtrain.txt b/repositories/taming-transformers/data/celebahqtrain.txt
deleted file mode 100644
index d508ef4bf54e12232f24542021e5d31af22f4d2c..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/data/celebahqtrain.txt
+++ /dev/null
@@ -1,25000 +0,0 @@
-imgHQ20126.npy
-imgHQ29348.npy
-imgHQ26284.npy
-imgHQ26144.npy
-imgHQ07926.npy
-imgHQ22701.npy
-imgHQ01851.npy
-imgHQ09737.npy
-imgHQ28459.npy
-imgHQ12762.npy
-imgHQ00347.npy
-imgHQ23563.npy
-imgHQ25347.npy
-imgHQ09323.npy
-imgHQ16876.npy
-imgHQ22235.npy
-imgHQ19549.npy
-imgHQ12230.npy
-imgHQ26515.npy
-imgHQ27551.npy
-imgHQ09451.npy
-imgHQ24495.npy
-imgHQ09204.npy
-imgHQ01126.npy
-imgHQ19642.npy
-imgHQ26392.npy
-imgHQ07815.npy
-imgHQ27527.npy
-imgHQ22812.npy
-imgHQ21918.npy
-imgHQ08976.npy
-imgHQ18276.npy
-imgHQ19676.npy
-imgHQ09096.npy
-imgHQ05013.npy
-imgHQ29569.npy
-imgHQ28004.npy
-imgHQ02419.npy
-imgHQ25129.npy
-imgHQ28115.npy
-imgHQ29846.npy
-imgHQ10939.npy
-imgHQ02503.npy
-imgHQ28509.npy
-imgHQ18493.npy
-imgHQ01130.npy
-imgHQ28731.npy
-imgHQ00705.npy
-imgHQ24196.npy
-imgHQ03434.npy
-imgHQ26750.npy
-imgHQ12691.npy
-imgHQ25022.npy
-imgHQ01066.npy
-imgHQ07896.npy
-imgHQ27404.npy
-imgHQ26604.npy
-imgHQ01069.npy
-imgHQ14486.npy
-imgHQ22704.npy
-imgHQ10461.npy
-imgHQ20113.npy
-imgHQ13444.npy
-imgHQ07080.npy
-imgHQ01026.npy
-imgHQ19293.npy
-imgHQ02648.npy
-imgHQ08469.npy
-imgHQ16298.npy
-imgHQ18679.npy
-imgHQ23792.npy
-imgHQ05529.npy
-imgHQ20808.npy
-imgHQ26940.npy
-imgHQ14302.npy
-imgHQ13461.npy
-imgHQ25779.npy
-imgHQ22209.npy
-imgHQ01588.npy
-imgHQ29947.npy
-imgHQ10755.npy
-imgHQ08852.npy
-imgHQ25689.npy
-imgHQ05461.npy
-imgHQ21976.npy
-imgHQ15614.npy
-imgHQ05502.npy
-imgHQ02458.npy
-imgHQ01201.npy
-imgHQ12905.npy
-imgHQ00974.npy
-imgHQ14359.npy
-imgHQ14327.npy
-imgHQ29008.npy
-imgHQ22501.npy
-imgHQ21095.npy
-imgHQ14783.npy
-imgHQ15985.npy
-imgHQ01565.npy
-imgHQ06875.npy
-imgHQ00121.npy
-imgHQ04194.npy
-imgHQ06316.npy
-imgHQ00049.npy
-imgHQ05097.npy
-imgHQ15479.npy
-imgHQ10945.npy
-imgHQ02999.npy
-imgHQ23062.npy
-imgHQ16148.npy
-imgHQ02792.npy
-imgHQ09059.npy
-imgHQ25528.npy
-imgHQ23650.npy
-imgHQ18597.npy
-imgHQ04474.npy
-imgHQ07850.npy
-imgHQ27369.npy
-imgHQ16764.npy
-imgHQ17677.npy
-imgHQ04863.npy
-imgHQ26762.npy
-imgHQ25088.npy
-imgHQ06880.npy
-imgHQ05025.npy
-imgHQ19864.npy
-imgHQ01076.npy
-imgHQ26227.npy
-imgHQ06051.npy
-imgHQ10905.npy
-imgHQ00815.npy
-imgHQ04326.npy
-imgHQ18229.npy
-imgHQ12076.npy
-imgHQ04896.npy
-imgHQ17237.npy
-imgHQ29566.npy
-imgHQ09033.npy
-imgHQ17835.npy
-imgHQ28863.npy
-imgHQ22565.npy
-imgHQ03210.npy
-imgHQ28730.npy
-imgHQ13503.npy
-imgHQ19961.npy
-imgHQ18252.npy
-imgHQ04138.npy
-imgHQ05392.npy
-imgHQ24562.npy
-imgHQ14036.npy
-imgHQ24186.npy
-imgHQ03630.npy
-imgHQ20193.npy
-imgHQ02171.npy
-imgHQ10407.npy
-imgHQ10944.npy
-imgHQ16827.npy
-imgHQ00322.npy
-imgHQ08846.npy
-imgHQ27565.npy
-imgHQ26158.npy
-imgHQ14152.npy
-imgHQ08369.npy
-imgHQ00648.npy
-imgHQ06477.npy
-imgHQ17400.npy
-imgHQ09080.npy
-imgHQ18966.npy
-imgHQ23680.npy
-imgHQ02133.npy
-imgHQ26954.npy
-imgHQ17683.npy
-imgHQ17651.npy
-imgHQ13020.npy
-imgHQ23228.npy
-imgHQ28539.npy
-imgHQ05101.npy
-imgHQ10334.npy
-imgHQ24700.npy
-imgHQ01018.npy
-imgHQ08699.npy
-imgHQ05885.npy
-imgHQ20599.npy
-imgHQ00079.npy
-imgHQ15951.npy
-imgHQ19212.npy
-imgHQ19466.npy
-imgHQ25039.npy
-imgHQ24387.npy
-imgHQ11327.npy
-imgHQ01371.npy
-imgHQ09728.npy
-imgHQ28962.npy
-imgHQ07871.npy
-imgHQ19452.npy
-imgHQ14582.npy
-imgHQ06064.npy
-imgHQ24401.npy
-imgHQ16053.npy
-imgHQ16919.npy
-imgHQ14357.npy
-imgHQ17172.npy
-imgHQ15879.npy
-imgHQ06458.npy
-imgHQ04970.npy
-imgHQ04968.npy
-imgHQ09641.npy
-imgHQ28311.npy
-imgHQ24424.npy
-imgHQ16634.npy
-imgHQ15861.npy
-imgHQ06183.npy
-imgHQ00330.npy
-imgHQ15396.npy
-imgHQ10258.npy
-imgHQ19274.npy
-imgHQ17674.npy
-imgHQ13871.npy
-imgHQ18495.npy
-imgHQ01641.npy
-imgHQ28748.npy
-imgHQ14943.npy
-imgHQ22667.npy
-imgHQ11644.npy
-imgHQ04588.npy
-imgHQ22173.npy
-imgHQ25226.npy
-imgHQ26026.npy
-imgHQ09521.npy
-imgHQ06358.npy
-imgHQ26615.npy
-imgHQ11961.npy
-imgHQ15624.npy
-imgHQ01993.npy
-imgHQ22035.npy
-imgHQ09189.npy
-imgHQ25357.npy
-imgHQ09292.npy
-imgHQ13911.npy
-imgHQ02936.npy
-imgHQ22409.npy
-imgHQ06578.npy
-imgHQ17242.npy
-imgHQ12373.npy
-imgHQ27348.npy
-imgHQ07910.npy
-imgHQ10707.npy
-imgHQ05477.npy
-imgHQ21214.npy
-imgHQ08923.npy
-imgHQ17288.npy
-imgHQ21127.npy
-imgHQ28056.npy
-imgHQ13001.npy
-imgHQ02166.npy
-imgHQ19278.npy
-imgHQ20684.npy
-imgHQ07883.npy
-imgHQ08945.npy
-imgHQ20158.npy
-imgHQ27478.npy
-imgHQ14777.npy
-imgHQ07232.npy
-imgHQ04059.npy
-imgHQ20316.npy
-imgHQ01852.npy
-imgHQ08892.npy
-imgHQ19584.npy
-imgHQ18645.npy
-imgHQ21333.npy
-imgHQ18347.npy
-imgHQ22542.npy
-imgHQ13487.npy
-imgHQ28691.npy
-imgHQ14691.npy
-imgHQ02104.npy
-imgHQ12820.npy
-imgHQ00508.npy
-imgHQ29036.npy
-imgHQ00589.npy
-imgHQ10754.npy
-imgHQ17313.npy
-imgHQ27349.npy
-imgHQ01044.npy
-imgHQ26520.npy
-imgHQ15821.npy
-imgHQ19453.npy
-imgHQ04163.npy
-imgHQ27402.npy
-imgHQ25110.npy
-imgHQ16374.npy
-imgHQ10977.npy
-imgHQ29635.npy
-imgHQ13781.npy
-imgHQ03021.npy
-imgHQ25100.npy
-imgHQ21805.npy
-imgHQ05824.npy
-imgHQ06861.npy
-imgHQ21735.npy
-imgHQ27159.npy
-imgHQ23838.npy
-imgHQ21773.npy
-imgHQ11572.npy
-imgHQ21016.npy
-imgHQ29655.npy
-imgHQ14529.npy
-imgHQ13408.npy
-imgHQ00948.npy
-imgHQ23840.npy
-imgHQ02080.npy
-imgHQ03996.npy
-imgHQ19983.npy
-imgHQ10783.npy
-imgHQ07311.npy
-imgHQ22377.npy
-imgHQ16877.npy
-imgHQ07298.npy
-imgHQ25313.npy
-imgHQ03681.npy
-imgHQ05349.npy
-imgHQ01352.npy
-imgHQ02764.npy
-imgHQ09353.npy
-imgHQ13638.npy
-imgHQ19687.npy
-imgHQ17948.npy
-imgHQ20443.npy
-imgHQ21370.npy
-imgHQ05371.npy
-imgHQ23284.npy
-imgHQ18360.npy
-imgHQ21866.npy
-imgHQ04348.npy
-imgHQ15893.npy
-imgHQ25317.npy
-imgHQ06608.npy
-imgHQ11397.npy
-imgHQ25539.npy
-imgHQ18033.npy
-imgHQ01329.npy
-imgHQ19921.npy
-imgHQ26199.npy
-imgHQ16366.npy
-imgHQ14046.npy
-imgHQ18594.npy
-imgHQ07387.npy
-imgHQ25040.npy
-imgHQ11037.npy
-imgHQ01320.npy
-imgHQ01727.npy
-imgHQ11500.npy
-imgHQ06646.npy
-imgHQ05709.npy
-imgHQ08902.npy
-imgHQ04508.npy
-imgHQ10422.npy
-imgHQ08332.npy
-imgHQ05154.npy
-imgHQ06168.npy
-imgHQ28673.npy
-imgHQ00001.npy
-imgHQ22212.npy
-imgHQ08584.npy
-imgHQ07275.npy
-imgHQ18992.npy
-imgHQ08524.npy
-imgHQ02244.npy
-imgHQ15811.npy
-imgHQ19716.npy
-imgHQ12922.npy
-imgHQ12395.npy
-imgHQ03029.npy
-imgHQ20893.npy
-imgHQ17748.npy
-imgHQ04376.npy
-imgHQ09896.npy
-imgHQ22818.npy
-imgHQ22396.npy
-imgHQ17138.npy
-imgHQ26807.npy
-imgHQ03802.npy
-imgHQ19389.npy
-imgHQ05790.npy
-imgHQ23313.npy
-imgHQ12417.npy
-imgHQ24295.npy
-imgHQ26266.npy
-imgHQ10142.npy
-imgHQ07356.npy
-imgHQ15168.npy
-imgHQ18492.npy
-imgHQ05619.npy
-imgHQ22210.npy
-imgHQ29075.npy
-imgHQ24430.npy
-imgHQ27519.npy
-imgHQ13973.npy
-imgHQ10443.npy
-imgHQ12042.npy
-imgHQ23342.npy
-imgHQ14659.npy
-imgHQ27090.npy
-imgHQ24472.npy
-imgHQ19063.npy
-imgHQ28085.npy
-imgHQ02032.npy
-imgHQ25678.npy
-imgHQ10948.npy
-imgHQ21365.npy
-imgHQ14354.npy
-imgHQ17143.npy
-imgHQ28235.npy
-imgHQ08521.npy
-imgHQ23739.npy
-imgHQ17033.npy
-imgHQ04487.npy
-imgHQ07888.npy
-imgHQ09775.npy
-imgHQ13545.npy
-imgHQ13876.npy
-imgHQ24726.npy
-imgHQ18288.npy
-imgHQ21644.npy
-imgHQ14746.npy
-imgHQ27580.npy
-imgHQ16562.npy
-imgHQ05440.npy
-imgHQ23148.npy
-imgHQ07368.npy
-imgHQ00382.npy
-imgHQ13018.npy
-imgHQ19245.npy
-imgHQ27294.npy
-imgHQ05942.npy
-imgHQ16020.npy
-imgHQ01828.npy
-imgHQ28973.npy
-imgHQ02481.npy
-imgHQ03395.npy
-imgHQ20506.npy
-imgHQ19735.npy
-imgHQ10245.npy
-imgHQ21659.npy
-imgHQ21887.npy
-imgHQ10643.npy
-imgHQ19417.npy
-imgHQ07491.npy
-imgHQ02197.npy
-imgHQ15549.npy
-imgHQ10577.npy
-imgHQ05613.npy
-imgHQ26876.npy
-imgHQ12048.npy
-imgHQ21369.npy
-imgHQ09570.npy
-imgHQ20537.npy
-imgHQ15211.npy
-imgHQ29347.npy
-imgHQ23059.npy
-imgHQ19591.npy
-imgHQ17325.npy
-imgHQ23560.npy
-imgHQ20787.npy
-imgHQ13567.npy
-imgHQ00495.npy
-imgHQ16302.npy
-imgHQ09335.npy
-imgHQ04560.npy
-imgHQ20456.npy
-imgHQ07927.npy
-imgHQ00601.npy
-imgHQ13536.npy
-imgHQ22747.npy
-imgHQ11047.npy
-imgHQ06918.npy
-imgHQ27606.npy
-imgHQ13121.npy
-imgHQ00253.npy
-imgHQ25151.npy
-imgHQ10549.npy
-imgHQ09010.npy
-imgHQ19811.npy
-imgHQ27271.npy
-imgHQ27258.npy
-imgHQ03313.npy
-imgHQ19294.npy
-imgHQ05295.npy
-imgHQ21540.npy
-imgHQ11382.npy
-imgHQ12924.npy
-imgHQ20289.npy
-imgHQ19728.npy
-imgHQ27025.npy
-imgHQ17718.npy
-imgHQ01453.npy
-imgHQ07510.npy
-imgHQ27550.npy
-imgHQ09667.npy
-imgHQ16244.npy
-imgHQ08236.npy
-imgHQ20568.npy
-imgHQ00759.npy
-imgHQ09560.npy
-imgHQ00927.npy
-imgHQ15959.npy
-imgHQ04391.npy
-imgHQ29757.npy
-imgHQ00464.npy
-imgHQ01137.npy
-imgHQ07929.npy
-imgHQ16861.npy
-imgHQ26635.npy
-imgHQ24988.npy
-imgHQ07317.npy
-imgHQ14457.npy
-imgHQ15465.npy
-imgHQ10041.npy
-imgHQ14630.npy
-imgHQ04032.npy
-imgHQ11058.npy
-imgHQ02034.npy
-imgHQ04431.npy
-imgHQ11894.npy
-imgHQ10237.npy
-imgHQ21742.npy
-imgHQ07410.npy
-imgHQ21940.npy
-imgHQ23605.npy
-imgHQ28286.npy
-imgHQ12067.npy
-imgHQ27567.npy
-imgHQ08401.npy
-imgHQ05441.npy
-imgHQ16494.npy
-imgHQ08460.npy
-imgHQ28415.npy
-imgHQ25447.npy
-imgHQ14714.npy
-imgHQ16215.npy
-imgHQ29932.npy
-imgHQ15651.npy
-imgHQ29151.npy
-imgHQ17599.npy
-imgHQ07620.npy
-imgHQ07119.npy
-imgHQ19161.npy
-imgHQ00975.npy
-imgHQ01583.npy
-imgHQ08347.npy
-imgHQ14548.npy
-imgHQ26938.npy
-imgHQ19035.npy
-imgHQ13727.npy
-imgHQ14234.npy
-imgHQ15844.npy
-imgHQ28086.npy
-imgHQ22779.npy
-imgHQ10695.npy
-imgHQ27760.npy
-imgHQ23819.npy
-imgHQ04284.npy
-imgHQ05391.npy
-imgHQ26721.npy
-imgHQ17200.npy
-imgHQ27022.npy
-imgHQ07983.npy
-imgHQ06787.npy
-imgHQ18828.npy
-imgHQ29977.npy
-imgHQ17220.npy
-imgHQ00710.npy
-imgHQ18251.npy
-imgHQ22180.npy
-imgHQ05730.npy
-imgHQ09118.npy
-imgHQ21520.npy
-imgHQ15738.npy
-imgHQ01801.npy
-imgHQ05493.npy
-imgHQ05786.npy
-imgHQ14694.npy
-imgHQ16753.npy
-imgHQ15716.npy
-imgHQ22000.npy
-imgHQ14042.npy
-imgHQ25773.npy
-imgHQ29217.npy
-imgHQ03645.npy
-imgHQ12838.npy
-imgHQ28535.npy
-imgHQ25808.npy
-imgHQ28701.npy
-imgHQ11569.npy
-imgHQ24133.npy
-imgHQ09796.npy
-imgHQ08431.npy
-imgHQ14245.npy
-imgHQ26421.npy
-imgHQ25343.npy
-imgHQ18894.npy
-imgHQ23834.npy
-imgHQ09065.npy
-imgHQ24540.npy
-imgHQ18576.npy
-imgHQ06386.npy
-imgHQ02875.npy
-imgHQ11934.npy
-imgHQ20990.npy
-imgHQ29190.npy
-imgHQ10069.npy
-imgHQ00914.npy
-imgHQ03077.npy
-imgHQ01897.npy
-imgHQ01450.npy
-imgHQ11042.npy
-imgHQ29878.npy
-imgHQ19940.npy
-imgHQ28208.npy
-imgHQ24649.npy
-imgHQ17653.npy
-imgHQ08876.npy
-imgHQ18717.npy
-imgHQ12512.npy
-imgHQ15527.npy
-imgHQ08564.npy
-imgHQ28491.npy
-imgHQ29103.npy
-imgHQ23045.npy
-imgHQ00337.npy
-imgHQ16986.npy
-imgHQ17885.npy
-imgHQ29792.npy
-imgHQ16356.npy
-imgHQ23705.npy
-imgHQ13834.npy
-imgHQ19924.npy
-imgHQ21082.npy
-imgHQ00150.npy
-imgHQ13768.npy
-imgHQ05232.npy
-imgHQ13227.npy
-imgHQ19776.npy
-imgHQ21004.npy
-imgHQ27894.npy
-imgHQ29553.npy
-imgHQ02963.npy
-imgHQ07818.npy
-imgHQ02805.npy
-imgHQ29727.npy
-imgHQ24006.npy
-imgHQ14494.npy
-imgHQ16555.npy
-imgHQ12439.npy
-imgHQ00762.npy
-imgHQ01001.npy
-imgHQ14364.npy
-imgHQ29544.npy
-imgHQ08594.npy
-imgHQ14479.npy
-imgHQ10879.npy
-imgHQ18116.npy
-imgHQ15571.npy
-imgHQ12855.npy
-imgHQ16745.npy
-imgHQ22340.npy
-imgHQ22764.npy
-imgHQ01757.npy
-imgHQ04797.npy
-imgHQ12056.npy
-imgHQ18367.npy
-imgHQ16386.npy
-imgHQ25654.npy
-imgHQ06733.npy
-imgHQ15634.npy
-imgHQ00113.npy
-imgHQ14270.npy
-imgHQ08033.npy
-imgHQ11015.npy
-imgHQ11890.npy
-imgHQ13339.npy
-imgHQ24357.npy
-imgHQ23759.npy
-imgHQ23333.npy
-imgHQ02172.npy
-imgHQ29555.npy
-imgHQ09678.npy
-imgHQ19529.npy
-imgHQ10270.npy
-imgHQ06361.npy
-imgHQ12535.npy
-imgHQ27562.npy
-imgHQ02910.npy
-imgHQ23982.npy
-imgHQ09781.npy
-imgHQ26229.npy
-imgHQ19376.npy
-imgHQ27481.npy
-imgHQ02597.npy
-imgHQ12748.npy
-imgHQ19970.npy
-imgHQ06756.npy
-imgHQ02699.npy
-imgHQ19873.npy
-imgHQ02924.npy
-imgHQ09052.npy
-imgHQ26516.npy
-imgHQ26942.npy
-imgHQ18962.npy
-imgHQ19418.npy
-imgHQ29853.npy
-imgHQ17086.npy
-imgHQ16203.npy
-imgHQ25012.npy
-imgHQ14622.npy
-imgHQ12093.npy
-imgHQ18314.npy
-imgHQ07565.npy
-imgHQ02035.npy
-imgHQ13694.npy
-imgHQ25223.npy
-imgHQ27997.npy
-imgHQ26829.npy
-imgHQ03976.npy
-imgHQ24841.npy
-imgHQ02338.npy
-imgHQ07804.npy
-imgHQ23197.npy
-imgHQ05505.npy
-imgHQ13430.npy
-imgHQ10894.npy
-imgHQ11888.npy
-imgHQ04317.npy
-imgHQ11371.npy
-imgHQ01285.npy
-imgHQ10168.npy
-imgHQ09104.npy
-imgHQ26539.npy
-imgHQ14011.npy
-imgHQ12209.npy
-imgHQ12122.npy
-imgHQ03744.npy
-imgHQ10048.npy
-imgHQ22736.npy
-imgHQ24588.npy
-imgHQ00812.npy
-imgHQ04257.npy
-imgHQ18943.npy
-imgHQ22257.npy
-imgHQ20429.npy
-imgHQ10678.npy
-imgHQ10829.npy
-imgHQ29277.npy
-imgHQ18134.npy
-imgHQ10616.npy
-imgHQ28068.npy
-imgHQ14528.npy
-imgHQ00897.npy
-imgHQ11523.npy
-imgHQ10787.npy
-imgHQ08787.npy
-imgHQ06497.npy
-imgHQ11104.npy
-imgHQ15198.npy
-imgHQ29872.npy
-imgHQ13728.npy
-imgHQ20152.npy
-imgHQ05501.npy
-imgHQ29536.npy
-imgHQ13154.npy
-imgHQ20546.npy
-imgHQ09237.npy
-imgHQ04334.npy
-imgHQ29283.npy
-imgHQ24033.npy
-imgHQ19422.npy
-imgHQ25549.npy
-imgHQ23914.npy
-imgHQ10617.npy
-imgHQ08578.npy
-imgHQ04624.npy
-imgHQ27959.npy
-imgHQ27916.npy
-imgHQ12914.npy
-imgHQ07236.npy
-imgHQ00146.npy
-imgHQ28001.npy
-imgHQ12341.npy
-imgHQ10437.npy
-imgHQ12061.npy
-imgHQ17522.npy
-imgHQ14271.npy
-imgHQ01901.npy
-imgHQ29273.npy
-imgHQ22333.npy
-imgHQ22182.npy
-imgHQ10993.npy
-imgHQ18931.npy
-imgHQ16848.npy
-imgHQ15230.npy
-imgHQ26354.npy
-imgHQ11819.npy
-imgHQ12149.npy
-imgHQ28076.npy
-imgHQ18405.npy
-imgHQ15315.npy
-imgHQ08073.npy
-imgHQ00269.npy
-imgHQ13110.npy
-imgHQ20000.npy
-imgHQ26518.npy
-imgHQ10878.npy
-imgHQ10604.npy
-imgHQ00352.npy
-imgHQ18083.npy
-imgHQ23655.npy
-imgHQ07028.npy
-imgHQ00653.npy
-imgHQ13165.npy
-imgHQ15277.npy
-imgHQ01748.npy
-imgHQ06107.npy
-imgHQ09897.npy
-imgHQ10717.npy
-imgHQ07441.npy
-imgHQ24170.npy
-imgHQ07494.npy
-imgHQ02345.npy
-imgHQ24890.npy
-imgHQ13899.npy
-imgHQ26541.npy
-imgHQ26417.npy
-imgHQ27432.npy
-imgHQ21993.npy
-imgHQ07839.npy
-imgHQ24019.npy
-imgHQ21794.npy
-imgHQ29123.npy
-imgHQ07335.npy
-imgHQ24250.npy
-imgHQ22615.npy
-imgHQ01857.npy
-imgHQ19348.npy
-imgHQ26255.npy
-imgHQ06408.npy
-imgHQ06383.npy
-imgHQ17957.npy
-imgHQ13277.npy
-imgHQ12266.npy
-imgHQ05219.npy
-imgHQ11178.npy
-imgHQ13885.npy
-imgHQ27547.npy
-imgHQ02357.npy
-imgHQ16856.npy
-imgHQ16650.npy
-imgHQ20558.npy
-imgHQ05753.npy
-imgHQ20815.npy
-imgHQ26378.npy
-imgHQ10074.npy
-imgHQ00457.npy
-imgHQ17368.npy
-imgHQ19058.npy
-imgHQ17178.npy
-imgHQ05079.npy
-imgHQ03033.npy
-imgHQ23037.npy
-imgHQ19299.npy
-imgHQ22372.npy
-imgHQ11622.npy
-imgHQ24156.npy
-imgHQ07808.npy
-imgHQ00951.npy
-imgHQ07376.npy
-imgHQ02703.npy
-imgHQ20604.npy
-imgHQ08761.npy
-imgHQ29795.npy
-imgHQ18403.npy
-imgHQ16536.npy
-imgHQ23215.npy
-imgHQ27274.npy
-imgHQ16361.npy
-imgHQ10007.npy
-imgHQ17386.npy
-imgHQ08331.npy
-imgHQ06148.npy
-imgHQ28663.npy
-imgHQ18889.npy
-imgHQ01873.npy
-imgHQ18518.npy
-imgHQ21601.npy
-imgHQ28127.npy
-imgHQ07509.npy
-imgHQ21126.npy
-imgHQ27610.npy
-imgHQ26372.npy
-imgHQ01039.npy
-imgHQ20653.npy
-imgHQ29525.npy
-imgHQ10937.npy
-imgHQ21983.npy
-imgHQ29451.npy
-imgHQ19583.npy
-imgHQ10807.npy
-imgHQ16112.npy
-imgHQ26459.npy
-imgHQ03016.npy
-imgHQ24259.npy
-imgHQ14315.npy
-imgHQ12028.npy
-imgHQ18391.npy
-imgHQ18336.npy
-imgHQ27449.npy
-imgHQ20942.npy
-imgHQ05165.npy
-imgHQ06947.npy
-imgHQ16853.npy
-imgHQ09255.npy
-imgHQ01229.npy
-imgHQ17378.npy
-imgHQ27033.npy
-imgHQ29579.npy
-imgHQ27731.npy
-imgHQ19358.npy
-imgHQ10274.npy
-imgHQ06341.npy
-imgHQ24936.npy
-imgHQ27837.npy
-imgHQ14833.npy
-imgHQ23822.npy
-imgHQ24017.npy
-imgHQ22395.npy
-imgHQ10281.npy
-imgHQ12660.npy
-imgHQ12157.npy
-imgHQ18690.npy
-imgHQ07433.npy
-imgHQ14996.npy
-imgHQ23470.npy
-imgHQ20409.npy
-imgHQ10498.npy
-imgHQ27750.npy
-imgHQ01043.npy
-imgHQ10724.npy
-imgHQ11004.npy
-imgHQ11158.npy
-imgHQ07858.npy
-imgHQ11785.npy
-imgHQ02958.npy
-imgHQ28790.npy
-imgHQ24214.npy
-imgHQ29330.npy
-imgHQ05385.npy
-imgHQ24626.npy
-imgHQ25725.npy
-imgHQ08886.npy
-imgHQ11182.npy
-imgHQ15054.npy
-imgHQ03773.npy
-imgHQ01253.npy
-imgHQ17016.npy
-imgHQ28071.npy
-imgHQ18387.npy
-imgHQ26525.npy
-imgHQ26163.npy
-imgHQ15573.npy
-imgHQ25567.npy
-imgHQ25325.npy
-imgHQ17537.npy
-imgHQ20467.npy
-imgHQ20603.npy
-imgHQ21116.npy
-imgHQ02381.npy
-imgHQ15516.npy
-imgHQ18744.npy
-imgHQ21323.npy
-imgHQ20420.npy
-imgHQ11576.npy
-imgHQ07822.npy
-imgHQ15257.npy
-imgHQ13725.npy
-imgHQ17670.npy
-imgHQ10917.npy
-imgHQ12105.npy
-imgHQ26489.npy
-imgHQ11846.npy
-imgHQ29234.npy
-imgHQ09388.npy
-imgHQ15285.npy
-imgHQ03853.npy
-imgHQ15206.npy
-imgHQ23068.npy
-imgHQ01487.npy
-imgHQ12063.npy
-imgHQ21092.npy
-imgHQ03230.npy
-imgHQ16938.npy
-imgHQ09812.npy
-imgHQ21279.npy
-imgHQ08982.npy
-imgHQ12637.npy
-imgHQ04434.npy
-imgHQ24204.npy
-imgHQ27953.npy
-imgHQ20164.npy
-imgHQ07035.npy
-imgHQ06513.npy
-imgHQ14491.npy
-imgHQ24501.npy
-imgHQ14970.npy
-imgHQ05482.npy
-imgHQ07488.npy
-imgHQ05859.npy
-imgHQ04747.npy
-imgHQ24084.npy
-imgHQ07455.npy
-imgHQ24396.npy
-imgHQ23330.npy
-imgHQ05050.npy
-imgHQ02790.npy
-imgHQ23372.npy
-imgHQ23153.npy
-imgHQ13776.npy
-imgHQ04620.npy
-imgHQ16570.npy
-imgHQ08493.npy
-imgHQ14499.npy
-imgHQ09472.npy
-imgHQ12156.npy
-imgHQ24245.npy
-imgHQ13413.npy
-imgHQ19714.npy
-imgHQ13225.npy
-imgHQ09268.npy
-imgHQ11881.npy
-imgHQ15175.npy
-imgHQ07597.npy
-imgHQ17541.npy
-imgHQ29380.npy
-imgHQ27906.npy
-imgHQ15664.npy
-imgHQ10110.npy
-imgHQ13577.npy
-imgHQ02082.npy
-imgHQ17589.npy
-imgHQ15665.npy
-imgHQ23026.npy
-imgHQ22243.npy
-imgHQ13806.npy
-imgHQ29186.npy
-imgHQ11308.npy
-imgHQ14114.npy
-imgHQ24732.npy
-imgHQ18289.npy
-imgHQ16641.npy
-imgHQ08900.npy
-imgHQ10599.npy
-imgHQ12223.npy
-imgHQ29268.npy
-imgHQ05522.npy
-imgHQ08267.npy
-imgHQ14521.npy
-imgHQ27359.npy
-imgHQ15584.npy
-imgHQ28760.npy
-imgHQ23335.npy
-imgHQ05586.npy
-imgHQ08990.npy
-imgHQ16787.npy
-imgHQ09749.npy
-imgHQ07405.npy
-imgHQ00023.npy
-imgHQ29290.npy
-imgHQ17365.npy
-imgHQ11039.npy
-imgHQ04018.npy
-imgHQ14665.npy
-imgHQ04042.npy
-imgHQ21929.npy
-imgHQ11882.npy
-imgHQ28275.npy
-imgHQ21832.npy
-imgHQ06782.npy
-imgHQ18812.npy
-imgHQ23620.npy
-imgHQ24664.npy
-imgHQ29540.npy
-imgHQ23953.npy
-imgHQ08434.npy
-imgHQ22141.npy
-imgHQ20891.npy
-imgHQ08095.npy
-imgHQ02847.npy
-imgHQ10017.npy
-imgHQ07371.npy
-imgHQ12771.npy
-imgHQ01336.npy
-imgHQ20366.npy
-imgHQ02914.npy
-imgHQ07111.npy
-imgHQ26960.npy
-imgHQ14300.npy
-imgHQ28006.npy
-imgHQ13296.npy
-imgHQ12530.npy
-imgHQ00522.npy
-imgHQ03149.npy
-imgHQ28484.npy
-imgHQ28273.npy
-imgHQ01818.npy
-imgHQ11546.npy
-imgHQ20027.npy
-imgHQ10692.npy
-imgHQ07833.npy
-imgHQ25096.npy
-imgHQ22022.npy
-imgHQ14063.npy
-imgHQ29601.npy
-imgHQ01360.npy
-imgHQ15482.npy
-imgHQ20450.npy
-imgHQ18043.npy
-imgHQ23469.npy
-imgHQ15637.npy
-imgHQ18829.npy
-imgHQ09157.npy
-imgHQ07689.npy
-imgHQ11213.npy
-imgHQ19587.npy
-imgHQ28206.npy
-imgHQ09995.npy
-imgHQ13415.npy
-imgHQ18309.npy
-imgHQ15524.npy
-imgHQ29129.npy
-imgHQ11165.npy
-imgHQ09379.npy
-imgHQ05333.npy
-imgHQ08362.npy
-imgHQ24346.npy
-imgHQ08682.npy
-imgHQ03714.npy
-imgHQ14966.npy
-imgHQ26843.npy
-imgHQ28233.npy
-imgHQ27076.npy
-imgHQ07686.npy
-imgHQ21791.npy
-imgHQ23615.npy
-imgHQ06042.npy
-imgHQ27242.npy
-imgHQ09355.npy
-imgHQ05655.npy
-imgHQ29914.npy
-imgHQ02038.npy
-imgHQ11551.npy
-imgHQ16358.npy
-imgHQ01111.npy
-imgHQ18346.npy
-imgHQ08673.npy
-imgHQ00640.npy
-imgHQ17710.npy
-imgHQ18462.npy
-imgHQ12787.npy
-imgHQ26300.npy
-imgHQ08557.npy
-imgHQ10830.npy
-imgHQ03888.npy
-imgHQ02662.npy
-imgHQ29015.npy
-imgHQ26767.npy
-imgHQ26507.npy
-imgHQ15199.npy
-imgHQ11529.npy
-imgHQ23958.npy
-imgHQ29506.npy
-imgHQ10904.npy
-imgHQ08138.npy
-imgHQ24363.npy
-imgHQ29177.npy
-imgHQ07594.npy
-imgHQ00471.npy
-imgHQ15060.npy
-imgHQ03004.npy
-imgHQ27329.npy
-imgHQ21627.npy
-imgHQ21630.npy
-imgHQ17765.npy
-imgHQ07396.npy
-imgHQ12998.npy
-imgHQ09076.npy
-imgHQ19515.npy
-imgHQ13438.npy
-imgHQ01891.npy
-imgHQ18217.npy
-imgHQ28215.npy
-imgHQ11782.npy
-imgHQ08076.npy
-imgHQ09550.npy
-imgHQ13156.npy
-imgHQ15183.npy
-imgHQ08883.npy
-imgHQ04951.npy
-imgHQ17474.npy
-imgHQ11071.npy
-imgHQ27770.npy
-imgHQ25141.npy
-imgHQ03042.npy
-imgHQ28683.npy
-imgHQ13797.npy
-imgHQ24087.npy
-imgHQ13187.npy
-imgHQ10457.npy
-imgHQ29236.npy
-imgHQ27413.npy
-imgHQ12041.npy
-imgHQ04004.npy
-imgHQ10589.npy
-imgHQ24674.npy
-imgHQ01608.npy
-imgHQ10550.npy
-imgHQ00582.npy
-imgHQ09926.npy
-imgHQ20390.npy
-imgHQ03005.npy
-imgHQ13793.npy
-imgHQ14138.npy
-imgHQ13301.npy
-imgHQ18514.npy
-imgHQ03550.npy
-imgHQ04964.npy
-imgHQ07519.npy
-imgHQ21546.npy
-imgHQ27801.npy
-imgHQ29027.npy
-imgHQ16417.npy
-imgHQ26100.npy
-imgHQ28807.npy
-imgHQ18090.npy
-imgHQ29523.npy
-imgHQ05075.npy
-imgHQ24591.npy
-imgHQ24036.npy
-imgHQ16935.npy
-imgHQ21002.npy
-imgHQ19833.npy
-imgHQ14505.npy
-imgHQ07501.npy
-imgHQ08679.npy
-imgHQ23310.npy
-imgHQ18135.npy
-imgHQ14874.npy
-imgHQ02147.npy
-imgHQ07942.npy
-imgHQ15518.npy
-imgHQ27397.npy
-imgHQ26050.npy
-imgHQ23744.npy
-imgHQ13651.npy
-imgHQ13634.npy
-imgHQ27552.npy
-imgHQ01704.npy
-imgHQ06685.npy
-imgHQ00195.npy
-imgHQ27026.npy
-imgHQ10359.npy
-imgHQ18873.npy
-imgHQ24166.npy
-imgHQ18547.npy
-imgHQ07221.npy
-imgHQ19146.npy
-imgHQ25060.npy
-imgHQ16435.npy
-imgHQ16348.npy
-imgHQ23453.npy
-imgHQ05133.npy
-imgHQ25716.npy
-imgHQ26964.npy
-imgHQ18994.npy
-imgHQ24450.npy
-imgHQ06592.npy
-imgHQ01997.npy
-imgHQ17784.npy
-imgHQ19241.npy
-imgHQ13130.npy
-imgHQ03869.npy
-imgHQ27709.npy
-imgHQ06396.npy
-imgHQ05626.npy
-imgHQ12850.npy
-imgHQ01792.npy
-imgHQ07022.npy
-imgHQ28474.npy
-imgHQ17536.npy
-imgHQ18160.npy
-imgHQ00040.npy
-imgHQ29237.npy
-imgHQ19727.npy
-imgHQ24796.npy
-imgHQ29377.npy
-imgHQ06104.npy
-imgHQ24967.npy
-imgHQ06245.npy
-imgHQ17146.npy
-imgHQ09165.npy
-imgHQ24451.npy
-imgHQ25419.npy
-imgHQ01946.npy
-imgHQ14000.npy
-imgHQ27474.npy
-imgHQ25651.npy
-imgHQ04331.npy
-imgHQ01407.npy
-imgHQ19853.npy
-imgHQ29516.npy
-imgHQ12086.npy
-imgHQ28685.npy
-imgHQ13884.npy
-imgHQ00547.npy
-imgHQ22324.npy
-imgHQ18978.npy
-imgHQ28989.npy
-imgHQ17907.npy
-imgHQ04743.npy
-imgHQ14586.npy
-imgHQ26424.npy
-imgHQ03723.npy
-imgHQ21093.npy
-imgHQ10144.npy
-imgHQ29838.npy
-imgHQ04023.npy
-imgHQ03658.npy
-imgHQ08847.npy
-imgHQ11337.npy
-imgHQ19941.npy
-imgHQ22848.npy
-imgHQ18845.npy
-imgHQ24497.npy
-imgHQ13514.npy
-imgHQ18581.npy
-imgHQ02298.npy
-imgHQ07143.npy
-imgHQ13902.npy
-imgHQ18030.npy
-imgHQ08899.npy
-imgHQ18564.npy
-imgHQ22392.npy
-imgHQ14266.npy
-imgHQ16410.npy
-imgHQ01938.npy
-imgHQ13266.npy
-imgHQ20717.npy
-imgHQ10455.npy
-imgHQ11802.npy
-imgHQ08335.npy
-imgHQ21719.npy
-imgHQ01620.npy
-imgHQ22773.npy
-imgHQ17922.npy
-imgHQ06927.npy
-imgHQ09544.npy
-imgHQ12912.npy
-imgHQ17572.npy
-imgHQ16773.npy
-imgHQ05450.npy
-imgHQ27043.npy
-imgHQ16402.npy
-imgHQ06041.npy
-imgHQ29477.npy
-imgHQ25032.npy
-imgHQ06701.npy
-imgHQ13635.npy
-imgHQ14352.npy
-imgHQ07382.npy
-imgHQ16199.npy
-imgHQ20278.npy
-imgHQ13039.npy
-imgHQ24272.npy
-imgHQ27465.npy
-imgHQ00570.npy
-imgHQ28619.npy
-imgHQ29500.npy
-imgHQ05530.npy
-imgHQ05436.npy
-imgHQ14476.npy
-imgHQ00793.npy
-imgHQ00053.npy
-imgHQ02467.npy
-imgHQ13896.npy
-imgHQ13613.npy
-imgHQ14945.npy
-imgHQ21415.npy
-imgHQ25397.npy
-imgHQ15987.npy
-imgHQ18708.npy
-imgHQ08376.npy
-imgHQ05221.npy
-imgHQ05383.npy
-imgHQ09535.npy
-imgHQ25685.npy
-imgHQ23916.npy
-imgHQ14292.npy
-imgHQ00675.npy
-imgHQ01445.npy
-imgHQ29384.npy
-imgHQ10475.npy
-imgHQ02949.npy
-imgHQ27323.npy
-imgHQ08271.npy
-imgHQ01888.npy
-imgHQ07462.npy
-imgHQ16351.npy
-imgHQ18270.npy
-imgHQ05428.npy
-imgHQ01781.npy
-imgHQ05659.npy
-imgHQ23711.npy
-imgHQ20972.npy
-imgHQ12667.npy
-imgHQ21689.npy
-imgHQ09087.npy
-imgHQ11242.npy
-imgHQ17900.npy
-imgHQ18264.npy
-imgHQ02497.npy
-imgHQ24750.npy
-imgHQ06732.npy
-imgHQ27576.npy
-imgHQ24998.npy
-imgHQ26947.npy
-imgHQ09111.npy
-imgHQ12153.npy
-imgHQ15559.npy
-imgHQ13622.npy
-imgHQ04280.npy
-imgHQ15989.npy
-imgHQ15521.npy
-imgHQ09889.npy
-imgHQ07032.npy
-imgHQ04924.npy
-imgHQ07018.npy
-imgHQ19660.npy
-imgHQ04989.npy
-imgHQ22803.npy
-imgHQ23259.npy
-imgHQ22166.npy
-imgHQ19930.npy
-imgHQ02024.npy
-imgHQ21474.npy
-imgHQ26499.npy
-imgHQ05303.npy
-imgHQ09768.npy
-imgHQ12204.npy
-imgHQ29928.npy
-imgHQ23337.npy
-imgHQ14387.npy
-imgHQ09971.npy
-imgHQ03124.npy
-imgHQ14080.npy
-imgHQ20552.npy
-imgHQ13112.npy
-imgHQ17190.npy
-imgHQ15778.npy
-imgHQ26027.npy
-imgHQ25557.npy
-imgHQ17678.npy
-imgHQ02979.npy
-imgHQ03602.npy
-imgHQ19072.npy
-imgHQ01510.npy
-imgHQ15763.npy
-imgHQ05176.npy
-imgHQ18305.npy
-imgHQ22398.npy
-imgHQ27589.npy
-imgHQ05958.npy
-imgHQ16476.npy
-imgHQ24225.npy
-imgHQ16635.npy
-imgHQ18707.npy
-imgHQ16552.npy
-imgHQ25670.npy
-imgHQ26913.npy
-imgHQ07997.npy
-imgHQ04679.npy
-imgHQ18086.npy
-imgHQ03058.npy
-imgHQ18424.npy
-imgHQ02640.npy
-imgHQ14989.npy
-imgHQ08941.npy
-imgHQ25754.npy
-imgHQ15574.npy
-imgHQ21462.npy
-imgHQ21536.npy
-imgHQ07660.npy
-imgHQ25703.npy
-imgHQ04539.npy
-imgHQ28690.npy
-imgHQ15308.npy
-imgHQ00769.npy
-imgHQ10854.npy
-imgHQ00378.npy
-imgHQ26311.npy
-imgHQ29274.npy
-imgHQ03811.npy
-imgHQ16340.npy
-imgHQ07547.npy
-imgHQ07706.npy
-imgHQ21935.npy
-imgHQ26905.npy
-imgHQ14368.npy
-imgHQ14794.npy
-imgHQ15601.npy
-imgHQ25806.npy
-imgHQ00822.npy
-imgHQ08495.npy
-imgHQ26408.npy
-imgHQ17514.npy
-imgHQ17650.npy
-imgHQ13842.npy
-imgHQ14598.npy
-imgHQ23351.npy
-imgHQ00420.npy
-imgHQ21439.npy
-imgHQ21100.npy
-imgHQ05089.npy
-imgHQ14648.npy
-imgHQ09367.npy
-imgHQ03628.npy
-imgHQ13594.npy
-imgHQ21404.npy
-imgHQ22556.npy
-imgHQ25578.npy
-imgHQ19409.npy
-imgHQ12374.npy
-imgHQ02541.npy
-imgHQ18190.npy
-imgHQ12731.npy
-imgHQ14134.npy
-imgHQ28743.npy
-imgHQ28799.npy
-imgHQ19707.npy
-imgHQ24316.npy
-imgHQ29463.npy
-imgHQ16726.npy
-imgHQ07732.npy
-imgHQ03620.npy
-imgHQ11231.npy
-imgHQ11403.npy
-imgHQ08174.npy
-imgHQ04200.npy
-imgHQ00413.npy
-imgHQ21146.npy
-imgHQ06665.npy
-imgHQ10390.npy
-imgHQ15502.npy
-imgHQ05550.npy
-imgHQ05511.npy
-imgHQ18815.npy
-imgHQ03345.npy
-imgHQ14719.npy
-imgHQ15293.npy
-imgHQ06409.npy
-imgHQ17635.npy
-imgHQ29406.npy
-imgHQ09357.npy
-imgHQ29606.npy
-imgHQ01232.npy
-imgHQ29152.npy
-imgHQ09320.npy
-imgHQ17032.npy
-imgHQ26623.npy
-imgHQ15539.npy
-imgHQ03404.npy
-imgHQ05354.npy
-imgHQ19965.npy
-imgHQ16499.npy
-imgHQ15550.npy
-imgHQ17233.npy
-imgHQ16583.npy
-imgHQ25479.npy
-imgHQ06736.npy
-imgHQ24575.npy
-imgHQ18301.npy
-imgHQ13661.npy
-imgHQ13026.npy
-imgHQ25147.npy
-imgHQ05751.npy
-imgHQ23678.npy
-imgHQ28532.npy
-imgHQ10299.npy
-imgHQ09558.npy
-imgHQ07163.npy
-imgHQ21425.npy
-imgHQ15859.npy
-imgHQ26697.npy
-imgHQ22784.npy
-imgHQ17501.npy
-imgHQ21996.npy
-imgHQ12037.npy
-imgHQ21241.npy
-imgHQ01098.npy
-imgHQ02387.npy
-imgHQ05041.npy
-imgHQ11000.npy
-imgHQ16027.npy
-imgHQ23261.npy
-imgHQ03236.npy
-imgHQ16462.npy
-imgHQ22999.npy
-imgHQ24710.npy
-imgHQ24628.npy
-imgHQ18428.npy
-imgHQ00241.npy
-imgHQ11394.npy
-imgHQ04517.npy
-imgHQ08630.npy
-imgHQ21236.npy
-imgHQ29721.npy
-imgHQ03975.npy
-imgHQ08628.npy
-imgHQ06818.npy
-imgHQ11424.npy
-imgHQ27310.npy
-imgHQ08826.npy
-imgHQ14800.npy
-imgHQ06964.npy
-imgHQ18784.npy
-imgHQ00192.npy
-imgHQ18486.npy
-imgHQ08987.npy
-imgHQ01188.npy
-imgHQ10866.npy
-imgHQ17201.npy
-imgHQ05520.npy
-imgHQ24860.npy
-imgHQ14865.npy
-imgHQ26042.npy
-imgHQ04876.npy
-imgHQ10317.npy
-imgHQ11776.npy
-imgHQ05160.npy
-imgHQ21058.npy
-imgHQ25938.npy
-imgHQ29134.npy
-imgHQ18304.npy
-imgHQ23217.npy
-imgHQ23166.npy
-imgHQ22851.npy
-imgHQ09850.npy
-imgHQ21339.npy
-imgHQ20502.npy
-imgHQ18712.npy
-imgHQ15449.npy
-imgHQ04203.npy
-imgHQ09629.npy
-imgHQ14088.npy
-imgHQ24781.npy
-imgHQ15466.npy
-imgHQ02193.npy
-imgHQ06494.npy
-imgHQ17713.npy
-imgHQ06633.npy
-imgHQ25737.npy
-imgHQ18435.npy
-imgHQ15093.npy
-imgHQ12829.npy
-imgHQ19778.npy
-imgHQ12989.npy
-imgHQ07755.npy
-imgHQ01485.npy
-imgHQ07101.npy
-imgHQ26394.npy
-imgHQ29784.npy
-imgHQ26386.npy
-imgHQ00539.npy
-imgHQ07372.npy
-imgHQ02956.npy
-imgHQ14264.npy
-imgHQ22023.npy
-imgHQ03267.npy
-imgHQ14706.npy
-imgHQ25465.npy
-imgHQ23950.npy
-imgHQ24631.npy
-imgHQ25907.npy
-imgHQ23095.npy
-imgHQ04299.npy
-imgHQ21178.npy
-imgHQ06557.npy
-imgHQ24586.npy
-imgHQ08629.npy
-imgHQ24651.npy
-imgHQ09673.npy
-imgHQ14060.npy
-imgHQ07247.npy
-imgHQ14695.npy
-imgHQ08905.npy
-imgHQ04155.npy
-imgHQ20549.npy
-imgHQ21625.npy
-imgHQ19553.npy
-imgHQ23559.npy
-imgHQ00058.npy
-imgHQ18985.npy
-imgHQ24756.npy
-imgHQ05746.npy
-imgHQ17995.npy
-imgHQ12399.npy
-imgHQ00745.npy
-imgHQ06577.npy
-imgHQ16110.npy
-imgHQ23477.npy
-imgHQ25984.npy
-imgHQ27160.npy
-imgHQ17387.npy
-imgHQ03466.npy
-imgHQ29492.npy
-imgHQ22620.npy
-imgHQ26047.npy
-imgHQ00127.npy
-imgHQ19290.npy
-imgHQ07190.npy
-imgHQ04593.npy
-imgHQ17414.npy
-imgHQ26310.npy
-imgHQ01533.npy
-imgHQ16097.npy
-imgHQ19915.npy
-imgHQ01586.npy
-imgHQ25248.npy
-imgHQ16905.npy
-imgHQ18028.npy
-imgHQ25276.npy
-imgHQ10709.npy
-imgHQ09215.npy
-imgHQ21170.npy
-imgHQ19037.npy
-imgHQ22628.npy
-imgHQ12411.npy
-imgHQ25181.npy
-imgHQ28981.npy
-imgHQ18922.npy
-imgHQ01978.npy
-imgHQ23460.npy
-imgHQ05559.npy
-imgHQ25607.npy
-imgHQ22984.npy
-imgHQ13794.npy
-imgHQ06257.npy
-imgHQ07618.npy
-imgHQ15887.npy
-imgHQ03960.npy
-imgHQ27020.npy
-imgHQ27925.npy
-imgHQ07714.npy
-imgHQ11698.npy
-imgHQ13918.npy
-imgHQ18687.npy
-imgHQ27165.npy
-imgHQ20356.npy
-imgHQ29950.npy
-imgHQ03476.npy
-imgHQ24771.npy
-imgHQ28792.npy
-imgHQ29589.npy
-imgHQ22321.npy
-imgHQ25123.npy
-imgHQ02437.npy
-imgHQ03203.npy
-imgHQ01590.npy
-imgHQ04511.npy
-imgHQ21461.npy
-imgHQ14525.npy
-imgHQ07173.npy
-imgHQ29199.npy
-imgHQ18688.npy
-imgHQ15484.npy
-imgHQ05297.npy
-imgHQ25500.npy
-imgHQ08172.npy
-imgHQ29272.npy
-imgHQ11246.npy
-imgHQ23086.npy
-imgHQ17601.npy
-imgHQ07586.npy
-imgHQ06067.npy
-imgHQ25424.npy
-imgHQ10513.npy
-imgHQ07693.npy
-imgHQ06987.npy
-imgHQ12934.npy
-imgHQ14212.npy
-imgHQ05925.npy
-imgHQ21431.npy
-imgHQ20030.npy
-imgHQ10393.npy
-imgHQ09318.npy
-imgHQ09086.npy
-imgHQ19311.npy
-imgHQ27308.npy
-imgHQ05583.npy
-imgHQ14366.npy
-imgHQ01427.npy
-imgHQ00583.npy
-imgHQ25143.npy
-imgHQ11113.npy
-imgHQ06363.npy
-imgHQ17355.npy
-imgHQ03972.npy
-imgHQ10272.npy
-imgHQ03771.npy
-imgHQ22802.npy
-imgHQ11492.npy
-imgHQ13074.npy
-imgHQ00318.npy
-imgHQ08277.npy
-imgHQ24158.npy
-imgHQ18422.npy
-imgHQ23625.npy
-imgHQ27636.npy
-imgHQ24182.npy
-imgHQ25733.npy
-imgHQ18990.npy
-imgHQ03632.npy
-imgHQ24576.npy
-imgHQ11693.npy
-imgHQ13282.npy
-imgHQ00372.npy
-imgHQ03730.npy
-imgHQ15843.npy
-imgHQ10182.npy
-imgHQ23004.npy
-imgHQ29892.npy
-imgHQ03465.npy
-imgHQ16186.npy
-imgHQ17015.npy
-imgHQ15742.npy
-imgHQ04631.npy
-imgHQ11354.npy
-imgHQ03409.npy
-imgHQ13989.npy
-imgHQ13959.npy
-imgHQ06050.npy
-imgHQ11794.npy
-imgHQ24804.npy
-imgHQ16682.npy
-imgHQ14219.npy
-imgHQ10991.npy
-imgHQ29212.npy
-imgHQ10641.npy
-imgHQ23273.npy
-imgHQ20092.npy
-imgHQ09387.npy
-imgHQ17632.npy
-imgHQ28650.npy
-imgHQ24904.npy
-imgHQ29891.npy
-imgHQ07595.npy
-imgHQ29571.npy
-imgHQ23968.npy
-imgHQ04817.npy
-imgHQ28543.npy
-imgHQ05091.npy
-imgHQ03867.npy
-imgHQ02576.npy
-imgHQ19432.npy
-imgHQ27347.npy
-imgHQ02781.npy
-imgHQ16488.npy
-imgHQ20432.npy
-imgHQ17219.npy
-imgHQ06436.npy
-imgHQ07844.npy
-imgHQ17129.npy
-imgHQ02952.npy
-imgHQ25340.npy
-imgHQ26012.npy
-imgHQ19350.npy
-imgHQ04080.npy
-imgHQ28593.npy
-imgHQ09187.npy
-imgHQ25891.npy
-imgHQ11160.npy
-imgHQ05772.npy
-imgHQ24228.npy
-imgHQ10326.npy
-imgHQ00743.npy
-imgHQ23773.npy
-imgHQ21890.npy
-imgHQ26552.npy
-imgHQ11132.npy
-imgHQ19114.npy
-imgHQ15956.npy
-imgHQ01876.npy
-imgHQ26694.npy
-imgHQ17669.npy
-imgHQ00051.npy
-imgHQ29435.npy
-imgHQ26817.npy
-imgHQ05521.npy
-imgHQ19248.npy
-imgHQ05571.npy
-imgHQ05667.npy
-imgHQ01931.npy
-imgHQ00555.npy
-imgHQ26600.npy
-imgHQ08855.npy
-imgHQ14112.npy
-imgHQ17159.npy
-imgHQ27555.npy
-imgHQ25950.npy
-imgHQ18383.npy
-imgHQ23263.npy
-imgHQ16443.npy
-imgHQ22264.npy
-imgHQ26364.npy
-imgHQ25314.npy
-imgHQ28003.npy
-imgHQ24546.npy
-imgHQ03048.npy
-imgHQ17839.npy
-imgHQ27949.npy
-imgHQ18034.npy
-imgHQ19969.npy
-imgHQ24367.npy
-imgHQ04162.npy
-imgHQ04848.npy
-imgHQ07106.npy
-imgHQ26296.npy
-imgHQ18247.npy
-imgHQ19491.npy
-imgHQ16922.npy
-imgHQ24157.npy
-imgHQ13246.npy
-imgHQ05763.npy
-imgHQ03462.npy
-imgHQ20139.npy
-imgHQ05241.npy
-imgHQ09819.npy
-imgHQ01282.npy
-imgHQ27305.npy
-imgHQ00385.npy
-imgHQ14904.npy
-imgHQ29316.npy
-imgHQ04760.npy
-imgHQ08764.npy
-imgHQ11775.npy
-imgHQ27250.npy
-imgHQ16434.npy
-imgHQ16825.npy
-imgHQ29738.npy
-imgHQ11063.npy
-imgHQ28891.npy
-imgHQ08814.npy
-imgHQ13624.npy
-imgHQ03778.npy
-imgHQ16957.npy
-imgHQ06939.npy
-imgHQ24144.npy
-imgHQ02633.npy
-imgHQ27313.npy
-imgHQ08050.npy
-imgHQ03474.npy
-imgHQ09141.npy
-imgHQ07503.npy
-imgHQ16079.npy
-imgHQ21648.npy
-imgHQ05957.npy
-imgHQ17369.npy
-imgHQ17358.npy
-imgHQ12761.npy
-imgHQ17213.npy
-imgHQ00599.npy
-imgHQ17826.npy
-imgHQ27377.npy
-imgHQ25158.npy
-imgHQ13217.npy
-imgHQ00987.npy
-imgHQ13309.npy
-imgHQ27827.npy
-imgHQ00414.npy
-imgHQ22907.npy
-imgHQ24654.npy
-imgHQ18532.npy
-imgHQ23269.npy
-imgHQ06568.npy
-imgHQ00030.npy
-imgHQ26856.npy
-imgHQ11665.npy
-imgHQ29244.npy
-imgHQ02063.npy
-imgHQ02850.npy
-imgHQ29543.npy
-imgHQ27754.npy
-imgHQ05970.npy
-imgHQ04102.npy
-imgHQ03956.npy
-imgHQ12596.npy
-imgHQ28022.npy
-imgHQ17937.npy
-imgHQ21957.npy
-imgHQ25034.npy
-imgHQ24931.npy
-imgHQ03344.npy
-imgHQ24043.npy
-imgHQ11376.npy
-imgHQ27942.npy
-imgHQ05795.npy
-imgHQ21038.npy
-imgHQ00924.npy
-imgHQ29844.npy
-imgHQ07966.npy
-imgHQ08920.npy
-imgHQ20273.npy
-imgHQ05434.npy
-imgHQ06627.npy
-imgHQ15646.npy
-imgHQ18869.npy
-imgHQ05670.npy
-imgHQ05812.npy
-imgHQ01775.npy
-imgHQ08526.npy
-imgHQ02030.npy
-imgHQ15486.npy
-imgHQ21826.npy
-imgHQ26331.npy
-imgHQ28950.npy
-imgHQ12408.npy
-imgHQ19701.npy
-imgHQ18725.npy
-imgHQ28511.npy
-imgHQ13780.npy
-imgHQ12798.npy
-imgHQ11701.npy
-imgHQ05743.npy
-imgHQ14778.npy
-imgHQ17701.npy
-imgHQ18448.npy
-imgHQ01309.npy
-imgHQ10255.npy
-imgHQ29120.npy
-imgHQ05293.npy
-imgHQ27167.npy
-imgHQ20412.npy
-imgHQ13525.npy
-imgHQ23356.npy
-imgHQ11634.npy
-imgHQ22138.npy
-imgHQ21359.npy
-imgHQ06850.npy
-imgHQ09399.npy
-imgHQ13581.npy
-imgHQ04749.npy
-imgHQ05646.npy
-imgHQ05519.npy
-imgHQ11584.npy
-imgHQ03600.npy
-imgHQ20298.npy
-imgHQ06123.npy
-imgHQ23393.npy
-imgHQ15188.npy
-imgHQ26704.npy
-imgHQ25483.npy
-imgHQ05278.npy
-imgHQ17742.npy
-imgHQ21213.npy
-imgHQ03604.npy
-imgHQ09765.npy
-imgHQ25763.npy
-imgHQ23750.npy
-imgHQ11870.npy
-imgHQ01616.npy
-imgHQ08166.npy
-imgHQ11463.npy
-imgHQ05453.npy
-imgHQ03283.npy
-imgHQ09075.npy
-imgHQ17418.npy
-imgHQ02710.npy
-imgHQ04622.npy
-imgHQ05755.npy
-imgHQ10514.npy
-imgHQ20595.npy
-imgHQ09791.npy
-imgHQ02740.npy
-imgHQ10765.npy
-imgHQ06984.npy
-imgHQ27345.npy
-imgHQ15545.npy
-imgHQ16188.npy
-imgHQ18895.npy
-imgHQ21222.npy
-imgHQ11694.npy
-imgHQ27157.npy
-imgHQ05175.npy
-imgHQ00340.npy
-imgHQ22555.npy
-imgHQ08198.npy
-imgHQ03914.npy
-imgHQ23528.npy
-imgHQ20673.npy
-imgHQ15242.npy
-imgHQ18316.npy
-imgHQ07087.npy
-imgHQ20275.npy
-imgHQ03285.npy
-imgHQ12198.npy
-imgHQ04017.npy
-imgHQ29115.npy
-imgHQ24850.npy
-imgHQ17870.npy
-imgHQ09148.npy
-imgHQ11284.npy
-imgHQ08226.npy
-imgHQ27514.npy
-imgHQ03595.npy
-imgHQ25662.npy
-imgHQ05118.npy
-imgHQ11726.npy
-imgHQ14636.npy
-imgHQ26230.npy
-imgHQ06135.npy
-imgHQ26755.npy
-imgHQ17943.npy
-imgHQ25055.npy
-imgHQ23664.npy
-imgHQ12458.npy
-imgHQ23719.npy
-imgHQ22452.npy
-imgHQ10369.npy
-imgHQ10835.npy
-imgHQ19895.npy
-imgHQ20303.npy
-imgHQ09982.npy
-imgHQ03676.npy
-imgHQ20341.npy
-imgHQ10226.npy
-imgHQ15415.npy
-imgHQ29030.npy
-imgHQ18071.npy
-imgHQ16685.npy
-imgHQ27618.npy
-imgHQ21275.npy
-imgHQ03978.npy
-imgHQ10460.npy
-imgHQ01049.npy
-imgHQ02153.npy
-imgHQ11674.npy
-imgHQ03028.npy
-imgHQ24643.npy
-imgHQ21729.npy
-imgHQ18461.npy
-imgHQ11667.npy
-imgHQ17279.npy
-imgHQ20707.npy
-imgHQ22569.npy
-imgHQ18484.npy
-imgHQ01314.npy
-imgHQ08689.npy
-imgHQ25089.npy
-imgHQ13576.npy
-imgHQ06227.npy
-imgHQ00851.npy
-imgHQ24066.npy
-imgHQ11458.npy
-imgHQ14858.npy
-imgHQ00767.npy
-imgHQ00878.npy
-imgHQ08488.npy
-imgHQ10815.npy
-imgHQ21943.npy
-imgHQ25233.npy
-imgHQ11749.npy
-imgHQ07721.npy
-imgHQ19705.npy
-imgHQ27696.npy
-imgHQ13693.npy
-imgHQ21226.npy
-imgHQ08459.npy
-imgHQ25620.npy
-imgHQ20295.npy
-imgHQ09581.npy
-imgHQ19596.npy
-imgHQ13712.npy
-imgHQ16329.npy
-imgHQ14438.npy
-imgHQ10452.npy
-imgHQ25173.npy
-imgHQ01746.npy
-imgHQ11892.npy
-imgHQ09020.npy
-imgHQ15014.npy
-imgHQ07623.npy
-imgHQ04199.npy
-imgHQ19932.npy
-imgHQ03116.npy
-imgHQ16173.npy
-imgHQ07638.npy
-imgHQ00417.npy
-imgHQ12713.npy
-imgHQ13779.npy
-imgHQ29077.npy
-imgHQ20763.npy
-imgHQ29167.npy
-imgHQ15091.npy
-imgHQ16075.npy
-imgHQ17927.npy
-imgHQ24494.npy
-imgHQ24831.npy
-imgHQ21668.npy
-imgHQ02293.npy
-imgHQ17696.npy
-imgHQ07790.npy
-imgHQ01305.npy
-imgHQ00321.npy
-imgHQ01823.npy
-imgHQ05387.npy
-imgHQ14417.npy
-imgHQ15107.npy
-imgHQ27462.npy
-imgHQ26285.npy
-imgHQ27180.npy
-imgHQ10109.npy
-imgHQ21724.npy
-imgHQ25608.npy
-imgHQ12764.npy
-imgHQ08618.npy
-imgHQ06793.npy
-imgHQ23778.npy
-imgHQ05208.npy
-imgHQ02005.npy
-imgHQ28772.npy
-imgHQ25322.npy
-imgHQ19249.npy
-imgHQ02342.npy
-imgHQ24809.npy
-imgHQ03790.npy
-imgHQ16214.npy
-imgHQ11186.npy
-imgHQ05489.npy
-imgHQ12312.npy
-imgHQ26678.npy
-imgHQ04801.npy
-imgHQ19526.npy
-imgHQ21581.npy
-imgHQ08722.npy
-imgHQ01847.npy
-imgHQ19334.npy
-imgHQ13802.npy
-imgHQ17915.npy
-imgHQ23248.npy
-imgHQ10750.npy
-imgHQ28686.npy
-imgHQ05642.npy
-imgHQ10318.npy
-imgHQ10389.npy
-imgHQ26573.npy
-imgHQ04746.npy
-imgHQ24252.npy
-imgHQ26991.npy
-imgHQ24210.npy
-imgHQ23764.npy
-imgHQ27205.npy
-imgHQ00838.npy
-imgHQ23490.npy
-imgHQ23129.npy
-imgHQ12340.npy
-imgHQ06845.npy
-imgHQ13499.npy
-imgHQ09098.npy
-imgHQ22095.npy
-imgHQ13659.npy
-imgHQ17719.npy
-imgHQ14130.npy
-imgHQ17731.npy
-imgHQ11575.npy
-imgHQ06194.npy
-imgHQ00479.npy
-imgHQ24797.npy
-imgHQ26138.npy
-imgHQ15692.npy
-imgHQ27896.npy
-imgHQ25167.npy
-imgHQ13967.npy
-imgHQ28642.npy
-imgHQ20067.npy
-imgHQ11230.npy
-imgHQ07408.npy
-imgHQ28860.npy
-imgHQ05983.npy
-imgHQ16995.npy
-imgHQ29383.npy
-imgHQ06781.npy
-imgHQ08456.npy
-imgHQ10715.npy
-imgHQ13897.npy
-imgHQ00445.npy
-imgHQ14150.npy
-imgHQ17581.npy
-imgHQ27436.npy
-imgHQ04289.npy
-imgHQ28341.npy
-imgHQ07914.npy
-imgHQ03934.npy
-imgHQ13228.npy
-imgHQ16438.npy
-imgHQ07229.npy
-imgHQ21349.npy
-imgHQ03011.npy
-imgHQ27248.npy
-imgHQ22168.npy
-imgHQ24113.npy
-imgHQ11482.npy
-imgHQ29062.npy
-imgHQ19955.npy
-imgHQ27064.npy
-imgHQ29225.npy
-imgHQ24453.npy
-imgHQ22743.npy
-imgHQ00205.npy
-imgHQ15072.npy
-imgHQ17857.npy
-imgHQ18218.npy
-imgHQ17662.npy
-imgHQ25473.npy
-imgHQ17180.npy
-imgHQ17340.npy
-imgHQ13829.npy
-imgHQ01639.npy
-imgHQ09085.npy
-imgHQ14744.npy
-imgHQ26597.npy
-imgHQ03661.npy
-imgHQ13337.npy
-imgHQ07152.npy
-imgHQ09724.npy
-imgHQ11629.npy
-imgHQ15626.npy
-imgHQ02959.npy
-imgHQ18239.npy
-imgHQ19302.npy
-imgHQ19617.npy
-imgHQ21759.npy
-imgHQ22468.npy
-imgHQ09302.npy
-imgHQ26132.npy
-imgHQ06878.npy
-imgHQ23967.npy
-imgHQ06692.npy
-imgHQ26239.npy
-imgHQ25921.npy
-imgHQ00188.npy
-imgHQ20455.npy
-imgHQ19522.npy
-imgHQ08730.npy
-imgHQ11043.npy
-imgHQ06507.npy
-imgHQ28477.npy
-imgHQ20560.npy
-imgHQ06246.npy
-imgHQ13976.npy
-imgHQ28126.npy
-imgHQ01804.npy
-imgHQ02146.npy
-imgHQ11711.npy
-imgHQ19243.npy
-imgHQ00422.npy
-imgHQ28712.npy
-imgHQ20666.npy
-imgHQ15587.npy
-imgHQ15391.npy
-imgHQ14472.npy
-imgHQ19182.npy
-imgHQ18132.npy
-imgHQ13405.npy
-imgHQ23529.npy
-imgHQ01006.npy
-imgHQ00898.npy
-imgHQ07344.npy
-imgHQ14866.npy
-imgHQ24075.npy
-imgHQ29279.npy
-imgHQ11951.npy
-imgHQ10688.npy
-imgHQ01969.npy
-imgHQ27840.npy
-imgHQ27004.npy
-imgHQ12897.npy
-imgHQ19497.npy
-imgHQ07646.npy
-imgHQ00281.npy
-imgHQ12774.npy
-imgHQ09539.npy
-imgHQ18898.npy
-imgHQ29165.npy
-imgHQ08291.npy
-imgHQ08251.npy
-imgHQ11085.npy
-imgHQ07784.npy
-imgHQ26425.npy
-imgHQ29393.npy
-imgHQ06525.npy
-imgHQ05923.npy
-imgHQ12785.npy
-imgHQ24478.npy
-imgHQ09576.npy
-imgHQ18358.npy
-imgHQ07516.npy
-imgHQ07829.npy
-imgHQ15061.npy
-imgHQ08101.npy
-imgHQ00313.npy
-imgHQ19229.npy
-imgHQ16564.npy
-imgHQ10300.npy
-imgHQ09445.npy
-imgHQ19756.npy
-imgHQ04788.npy
-imgHQ07946.npy
-imgHQ02623.npy
-imgHQ10592.npy
-imgHQ02477.npy
-imgHQ26696.npy
-imgHQ03470.npy
-imgHQ23324.npy
-imgHQ26146.npy
-imgHQ16503.npy
-imgHQ02393.npy
-imgHQ17087.npy
-imgHQ17417.npy
-imgHQ12941.npy
-imgHQ15223.npy
-imgHQ15395.npy
-imgHQ20514.npy
-imgHQ00323.npy
-imgHQ01122.npy
-imgHQ27515.npy
-imgHQ13126.npy
-imgHQ00300.npy
-imgHQ12858.npy
-imgHQ02201.npy
-imgHQ25890.npy
-imgHQ17485.npy
-imgHQ08116.npy
-imgHQ08472.npy
-imgHQ11549.npy
-imgHQ15701.npy
-imgHQ07876.npy
-imgHQ04342.npy
-imgHQ29142.npy
-imgHQ21192.npy
-imgHQ16574.npy
-imgHQ10832.npy
-imgHQ05226.npy
-imgHQ23949.npy
-imgHQ16301.npy
-imgHQ10404.npy
-imgHQ09031.npy
-imgHQ15753.npy
-imgHQ05301.npy
-imgHQ03607.npy
-imgHQ17415.npy
-imgHQ24413.npy
-imgHQ10642.npy
-imgHQ09549.npy
-imgHQ04585.npy
-imgHQ02185.npy
-imgHQ22740.npy
-imgHQ28704.npy
-imgHQ19684.npy
-imgHQ29592.npy
-imgHQ10025.npy
-imgHQ06242.npy
-imgHQ19478.npy
-imgHQ02455.npy
-imgHQ08842.npy
-imgHQ09278.npy
-imgHQ20926.npy
-imgHQ11647.npy
-imgHQ22543.npy
-imgHQ26222.npy
-imgHQ05756.npy
-imgHQ14814.npy
-imgHQ22488.npy
-imgHQ20711.npy
-imgHQ01815.npy
-imgHQ02438.npy
-imgHQ17860.npy
-imgHQ28892.npy
-imgHQ27740.npy
-imgHQ23048.npy
-imgHQ11723.npy
-imgHQ19002.npy
-imgHQ15382.npy
-imgHQ05270.npy
-imgHQ19688.npy
-imgHQ11369.npy
-imgHQ15907.npy
-imgHQ14693.npy
-imgHQ17919.npy
-imgHQ00348.npy
-imgHQ21657.npy
-imgHQ14100.npy
-imgHQ23471.npy
-imgHQ17808.npy
-imgHQ21550.npy
-imgHQ16052.npy
-imgHQ14444.npy
-imgHQ29222.npy
-imgHQ10955.npy
-imgHQ06840.npy
-imgHQ28083.npy
-imgHQ05029.npy
-imgHQ22330.npy
-imgHQ17101.npy
-imgHQ17727.npy
-imgHQ10145.npy
-imgHQ16573.npy
-imgHQ08082.npy
-imgHQ16328.npy
-imgHQ03851.npy
-imgHQ20804.npy
-imgHQ16448.npy
-imgHQ06900.npy
-imgHQ08962.npy
-imgHQ05045.npy
-imgHQ18330.npy
-imgHQ00483.npy
-imgHQ12247.npy
-imgHQ01408.npy
-imgHQ01090.npy
-imgHQ12686.npy
-imgHQ03324.npy
-imgHQ13900.npy
-imgHQ23758.npy
-imgHQ15220.npy
-imgHQ26863.npy
-imgHQ22284.npy
-imgHQ28300.npy
-imgHQ19091.npy
-imgHQ11275.npy
-imgHQ29252.npy
-imgHQ15399.npy
-imgHQ16719.npy
-imgHQ29471.npy
-imgHQ20615.npy
-imgHQ00925.npy
-imgHQ15928.npy
-imgHQ13886.npy
-imgHQ23203.npy
-imgHQ15883.npy
-imgHQ10684.npy
-imgHQ23669.npy
-imgHQ14641.npy
-imgHQ11651.npy
-imgHQ18976.npy
-imgHQ25644.npy
-imgHQ13504.npy
-imgHQ13558.npy
-imgHQ21988.npy
-imgHQ09311.npy
-imgHQ20168.npy
-imgHQ20589.npy
-imgHQ03765.npy
-imgHQ02798.npy
-imgHQ21848.npy
-imgHQ29198.npy
-imgHQ23141.npy
-imgHQ04099.npy
-imgHQ00406.npy
-imgHQ14788.npy
-imgHQ14277.npy
-imgHQ11277.npy
-imgHQ22694.npy
-imgHQ06202.npy
-imgHQ16308.npy
-imgHQ26760.npy
-imgHQ15695.npy
-imgHQ11094.npy
-imgHQ01030.npy
-imgHQ12786.npy
-imgHQ15433.npy
-imgHQ06039.npy
-imgHQ17290.npy
-imgHQ11276.npy
-imgHQ10345.npy
-imgHQ03060.npy
-imgHQ23286.npy
-imgHQ14631.npy
-imgHQ20637.npy
-imgHQ17351.npy
-imgHQ09612.npy
-imgHQ08254.npy
-imgHQ21380.npy
-imgHQ22967.npy
-imgHQ11590.npy
-imgHQ23339.npy
-imgHQ05732.npy
-imgHQ25318.npy
-imgHQ17072.npy
-imgHQ20626.npy
-imgHQ15531.npy
-imgHQ12363.npy
-imgHQ12049.npy
-imgHQ00057.npy
-imgHQ04057.npy
-imgHQ02830.npy
-imgHQ19509.npy
-imgHQ29017.npy
-imgHQ16850.npy
-imgHQ22977.npy
-imgHQ22073.npy
-imgHQ05437.npy
-imgHQ29112.npy
-imgHQ01924.npy
-imgHQ16800.npy
-imgHQ14520.npy
-imgHQ11024.npy
-imgHQ26671.npy
-imgHQ29466.npy
-imgHQ15024.npy
-imgHQ14662.npy
-imgHQ16860.npy
-imgHQ10319.npy
-imgHQ20320.npy
-imgHQ05229.npy
-imgHQ27493.npy
-imgHQ22646.npy
-imgHQ09845.npy
-imgHQ22050.npy
-imgHQ21874.npy
-imgHQ18131.npy
-imgHQ03468.npy
-imgHQ27483.npy
-imgHQ22405.npy
-imgHQ07128.npy
-imgHQ16546.npy
-imgHQ04243.npy
-imgHQ09383.npy
-imgHQ11530.npy
-imgHQ10430.npy
-imgHQ15099.npy
-imgHQ27445.npy
-imgHQ10752.npy
-imgHQ23519.npy
-imgHQ05531.npy
-imgHQ21256.npy
-imgHQ19579.npy
-imgHQ26277.npy
-imgHQ09494.npy
-imgHQ13456.npy
-imgHQ23511.npy
-imgHQ09186.npy
-imgHQ20838.npy
-imgHQ13527.npy
-imgHQ04857.npy
-imgHQ27800.npy
-imgHQ18458.npy
-imgHQ10304.npy
-imgHQ28033.npy
-imgHQ19742.npy
-imgHQ18671.npy
-imgHQ26340.npy
-imgHQ23630.npy
-imgHQ16508.npy
-imgHQ02001.npy
-imgHQ13706.npy
-imgHQ10014.npy
-imgHQ21757.npy
-imgHQ22860.npy
-imgHQ18552.npy
-imgHQ04455.npy
-imgHQ20170.npy
-imgHQ13958.npy
-imgHQ15623.npy
-imgHQ17634.npy
-imgHQ27161.npy
-imgHQ07935.npy
-imgHQ23272.npy
-imgHQ16626.npy
-imgHQ15268.npy
-imgHQ21181.npy
-imgHQ23562.npy
-imgHQ15529.npy
-imgHQ21493.npy
-imgHQ10595.npy
-imgHQ14522.npy
-imgHQ08182.npy
-imgHQ07444.npy
-imgHQ28165.npy
-imgHQ17492.npy
-imgHQ03202.npy
-imgHQ24640.npy
-imgHQ25169.npy
-imgHQ14635.npy
-imgHQ03635.npy
-imgHQ20099.npy
-imgHQ10725.npy
-imgHQ22496.npy
-imgHQ00702.npy
-imgHQ21067.npy
-imgHQ14473.npy
-imgHQ15431.npy
-imgHQ29145.npy
-imgHQ06451.npy
-imgHQ20865.npy
-imgHQ03896.npy
-imgHQ06949.npy
-imgHQ25859.npy
-imgHQ16345.npy
-imgHQ28306.npy
-imgHQ19179.npy
-imgHQ10397.npy
-imgHQ14495.npy
-imgHQ21690.npy
-imgHQ16419.npy
-imgHQ26646.npy
-imgHQ14565.npy
-imgHQ18296.npy
-imgHQ01443.npy
-imgHQ01904.npy
-imgHQ19169.npy
-imgHQ12610.npy
-imgHQ15596.npy
-imgHQ13016.npy
-imgHQ13676.npy
-imgHQ29974.npy
-imgHQ25777.npy
-imgHQ15770.npy
-imgHQ11359.npy
-imgHQ01251.npy
-imgHQ25924.npy
-imgHQ00944.npy
-imgHQ20495.npy
-imgHQ01731.npy
-imgHQ20263.npy
-imgHQ19093.npy
-imgHQ29254.npy
-imgHQ16706.npy
-imgHQ23924.npy
-imgHQ16018.npy
-imgHQ05414.npy
-imgHQ12742.npy
-imgHQ06606.npy
-imgHQ25532.npy
-imgHQ02973.npy
-imgHQ14316.npy
-imgHQ09950.npy
-imgHQ03358.npy
-imgHQ17813.npy
-imgHQ25764.npy
-imgHQ27084.npy
-imgHQ17111.npy
-imgHQ01475.npy
-imgHQ14593.npy
-imgHQ11091.npy
-imgHQ12433.npy
-imgHQ24238.npy
-imgHQ01099.npy
-imgHQ05886.npy
-imgHQ06881.npy
-imgHQ22048.npy
-imgHQ18077.npy
-imgHQ22953.npy
-imgHQ18612.npy
-imgHQ28062.npy
-imgHQ14759.npy
-imgHQ14376.npy
-imgHQ05832.npy
-imgHQ05044.npy
-imgHQ28572.npy
-imgHQ10713.npy
-imgHQ03814.npy
-imgHQ09880.npy
-imgHQ27869.npy
-imgHQ03886.npy
-imgHQ07837.npy
-imgHQ12955.npy
-imgHQ16694.npy
-imgHQ07131.npy
-imgHQ12756.npy
-imgHQ07971.npy
-imgHQ20694.npy
-imgHQ10063.npy
-imgHQ16956.npy
-imgHQ27373.npy
-imgHQ25135.npy
-imgHQ28662.npy
-imgHQ17043.npy
-imgHQ05042.npy
-imgHQ10308.npy
-imgHQ12356.npy
-imgHQ19851.npy
-imgHQ16984.npy
-imgHQ25930.npy
-imgHQ10657.npy
-imgHQ18542.npy
-imgHQ07999.npy
-imgHQ25131.npy
-imgHQ06397.npy
-imgHQ08992.npy
-imgHQ12542.npy
-imgHQ05615.npy
-imgHQ16481.npy
-imgHQ00643.npy
-imgHQ07788.npy
-imgHQ26764.npy
-imgHQ22451.npy
-imgHQ20949.npy
-imgHQ01295.npy
-imgHQ10749.npy
-imgHQ03013.npy
-imgHQ17597.npy
-imgHQ11752.npy
-imgHQ01396.npy
-imgHQ10089.npy
-imgHQ05460.npy
-imgHQ14182.npy
-imgHQ13941.npy
-imgHQ27839.npy
-imgHQ01970.npy
-imgHQ16191.npy
-imgHQ10803.npy
-imgHQ28016.npy
-imgHQ14770.npy
-imgHQ26759.npy
-imgHQ25656.npy
-imgHQ28519.npy
-imgHQ14197.npy
-imgHQ16977.npy
-imgHQ17120.npy
-imgHQ28178.npy
-imgHQ25524.npy
-imgHQ04693.npy
-imgHQ17584.npy
-imgHQ13566.npy
-imgHQ06936.npy
-imgHQ15676.npy
-imgHQ08349.npy
-imgHQ15934.npy
-imgHQ29867.npy
-imgHQ15215.npy
-imgHQ00813.npy
-imgHQ11172.npy
-imgHQ09218.npy
-imgHQ26309.npy
-imgHQ05247.npy
-imgHQ03605.npy
-imgHQ22069.npy
-imgHQ12894.npy
-imgHQ10216.npy
-imgHQ06981.npy
-imgHQ19071.npy
-imgHQ25095.npy
-imgHQ18758.npy
-imgHQ18340.npy
-imgHQ23157.npy
-imgHQ17604.npy
-imgHQ21836.npy
-imgHQ07109.npy
-imgHQ16057.npy
-imgHQ01713.npy
-imgHQ12971.npy
-imgHQ09406.npy
-imgHQ07772.npy
-imgHQ29843.npy
-imgHQ04223.npy
-imgHQ26502.npy
-imgHQ00421.npy
-imgHQ01095.npy
-imgHQ16381.npy
-imgHQ16029.npy
-imgHQ05906.npy
-imgHQ07134.npy
-imgHQ06643.npy
-imgHQ12861.npy
-imgHQ16806.npy
-imgHQ24380.npy
-imgHQ14925.npy
-imgHQ16072.npy
-imgHQ25546.npy
-imgHQ19683.npy
-imgHQ01783.npy
-imgHQ29038.npy
-imgHQ26060.npy
-imgHQ15358.npy
-imgHQ15015.npy
-imgHQ15966.npy
-imgHQ04864.npy
-imgHQ01192.npy
-imgHQ19992.npy
-imgHQ22925.npy
-imgHQ29949.npy
-imgHQ00876.npy
-imgHQ20446.npy
-imgHQ12377.npy
-imgHQ18834.npy
-imgHQ19663.npy
-imgHQ13257.npy
-imgHQ18277.npy
-imgHQ28142.npy
-imgHQ15796.npy
-imgHQ11333.npy
-imgHQ26556.npy
-imgHQ28271.npy
-imgHQ09366.npy
-imgHQ24976.npy
-imgHQ07533.npy
-imgHQ08519.npy
-imgHQ16009.npy
-imgHQ02248.npy
-imgHQ22099.npy
-imgHQ08949.npy
-imgHQ04550.npy
-imgHQ16924.npy
-imgHQ12792.npy
-imgHQ08860.npy
-imgHQ03490.npy
-imgHQ15656.npy
-imgHQ22930.npy
-imgHQ11522.npy
-imgHQ17510.npy
-imgHQ29107.npy
-imgHQ04179.npy
-imgHQ09329.npy
-imgHQ20939.npy
-imgHQ24185.npy
-imgHQ11876.npy
-imgHQ17544.npy
-imgHQ02461.npy
-imgHQ09631.npy
-imgHQ21046.npy
-imgHQ00683.npy
-imgHQ10413.npy
-imgHQ06699.npy
-imgHQ19546.npy
-imgHQ20920.npy
-imgHQ18397.npy
-imgHQ15700.npy
-imgHQ10973.npy
-imgHQ22125.npy
-imgHQ26506.npy
-imgHQ26397.npy
-imgHQ09408.npy
-imgHQ26971.npy
-imgHQ18851.npy
-imgHQ13611.npy
-imgHQ05808.npy
-imgHQ21344.npy
-imgHQ04603.npy
-imgHQ19995.npy
-imgHQ03155.npy
-imgHQ24378.npy
-imgHQ11106.npy
-imgHQ06967.npy
-imgHQ11729.npy
-imgHQ14514.npy
-imgHQ04870.npy
-imgHQ23354.npy
-imgHQ22053.npy
-imgHQ00117.npy
-imgHQ00110.npy
-imgHQ23108.npy
-imgHQ29281.npy
-imgHQ24614.npy
-imgHQ05435.npy
-imgHQ08137.npy
-imgHQ00684.npy
-imgHQ07139.npy
-imgHQ04703.npy
-imgHQ22026.npy
-imgHQ05086.npy
-imgHQ29903.npy
-imgHQ07145.npy
-imgHQ28019.npy
-imgHQ22783.npy
-imgHQ09689.npy
-imgHQ24854.npy
-imgHQ01344.npy
-imgHQ15677.npy
-imgHQ08919.npy
-imgHQ22792.npy
-imgHQ04877.npy
-imgHQ16289.npy
-imgHQ11867.npy
-imgHQ11211.npy
-imgHQ27496.npy
-imgHQ14268.npy
-imgHQ03132.npy
-imgHQ05503.npy
-imgHQ11784.npy
-imgHQ10640.npy
-imgHQ24211.npy
-imgHQ21824.npy
-imgHQ05463.npy
-imgHQ24924.npy
-imgHQ07052.npy
-imgHQ01943.npy
-imgHQ29144.npy
-imgHQ00226.npy
-imgHQ28499.npy
-imgHQ18172.npy
-imgHQ01920.npy
-imgHQ28816.npy
-imgHQ28288.npy
-imgHQ12812.npy
-imgHQ01803.npy
-imgHQ14156.npy
-imgHQ06035.npy
-imgHQ18053.npy
-imgHQ06259.npy
-imgHQ09223.npy
-imgHQ29510.npy
-imgHQ05504.npy
-imgHQ10103.npy
-imgHQ10131.npy
-imgHQ04227.npy
-imgHQ10757.npy
-imgHQ06440.npy
-imgHQ06764.npy
-imgHQ12630.npy
-imgHQ07097.npy
-imgHQ04072.npy
-imgHQ02413.npy
-imgHQ24653.npy
-imgHQ15684.npy
-imgHQ18523.npy
-imgHQ02142.npy
-imgHQ13472.npy
-imgHQ02663.npy
-imgHQ18933.npy
-imgHQ14834.npy
-imgHQ29680.npy
-imgHQ10518.npy
-imgHQ26137.npy
-imgHQ16678.npy
-imgHQ08841.npy
-imgHQ14753.npy
-imgHQ11468.npy
-imgHQ28265.npy
-imgHQ03854.npy
-imgHQ04735.npy
-imgHQ05189.npy
-imgHQ24811.npy
-imgHQ07976.npy
-imgHQ07951.npy
-imgHQ29362.npy
-imgHQ27669.npy
-imgHQ22244.npy
-imgHQ21419.npy
-imgHQ25595.npy
-imgHQ08403.npy
-imgHQ07374.npy
-imgHQ24473.npy
-imgHQ19197.npy
-imgHQ24108.npy
-imgHQ18694.npy
-imgHQ13146.npy
-imgHQ19225.npy
-imgHQ27841.npy
-imgHQ25049.npy
-imgHQ27694.npy
-imgHQ21416.npy
-imgHQ13143.npy
-imgHQ20826.npy
-imgHQ29221.npy
-imgHQ16671.npy
-imgHQ16963.npy
-imgHQ20608.npy
-imgHQ24153.npy
-imgHQ12559.npy
-imgHQ16015.npy
-imgHQ25342.npy
-imgHQ09706.npy
-imgHQ14240.npy
-imgHQ24433.npy
-imgHQ11939.npy
-imgHQ27181.npy
-imgHQ17991.npy
-imgHQ26901.npy
-imgHQ23780.npy
-imgHQ21022.npy
-imgHQ29208.npy
-imgHQ29686.npy
-imgHQ27427.npy
-imgHQ25293.npy
-imgHQ19640.npy
-imgHQ07157.npy
-imgHQ15336.npy
-imgHQ29131.npy
-imgHQ03068.npy
-imgHQ20371.npy
-imgHQ13894.npy
-imgHQ21934.npy
-imgHQ28223.npy
-imgHQ19043.npy
-imgHQ12023.npy
-imgHQ09109.npy
-imgHQ24216.npy
-imgHQ06059.npy
-imgHQ22671.npy
-imgHQ17665.npy
-imgHQ12457.npy
-imgHQ28589.npy
-imgHQ06314.npy
-imgHQ11307.npy
-imgHQ06814.npy
-imgHQ13549.npy
-imgHQ03777.npy
-imgHQ06177.npy
-imgHQ27973.npy
-imgHQ01308.npy
-imgHQ04977.npy
-imgHQ03163.npy
-imgHQ22412.npy
-imgHQ06366.npy
-imgHQ03650.npy
-imgHQ16486.npy
-imgHQ11405.npy
-imgHQ10441.npy
-imgHQ23888.npy
-imgHQ00456.npy
-imgHQ29973.npy
-imgHQ21298.npy
-imgHQ15881.npy
-imgHQ11465.npy
-imgHQ12446.npy
-imgHQ13117.npy
-imgHQ25136.npy
-imgHQ08435.npy
-imgHQ01742.npy
-imgHQ13186.npy
-imgHQ06581.npy
-imgHQ16047.npy
-imgHQ18189.npy
-imgHQ02671.npy
-imgHQ03508.npy
-imgHQ18824.npy
-imgHQ00211.npy
-imgHQ14987.npy
-imgHQ04494.npy
-imgHQ28657.npy
-imgHQ06010.npy
-imgHQ17238.npy
-imgHQ25025.npy
-imgHQ09992.npy
-imgHQ03497.npy
-imgHQ11714.npy
-imgHQ06078.npy
-imgHQ14571.npy
-imgHQ19712.npy
-imgHQ09447.npy
-imgHQ12701.npy
-imgHQ21291.npy
-imgHQ21891.npy
-imgHQ21670.npy
-imgHQ27983.npy
-imgHQ22903.npy
-imgHQ20308.npy
-imgHQ21124.npy
-imgHQ15457.npy
-imgHQ10509.npy
-imgHQ21113.npy
-imgHQ00728.npy
-imgHQ27988.npy
-imgHQ03708.npy
-imgHQ29202.npy
-imgHQ27403.npy
-imgHQ27503.npy
-imgHQ00371.npy
-imgHQ13954.npy
-imgHQ17306.npy
-imgHQ23118.npy
-imgHQ28431.npy
-imgHQ22471.npy
-imgHQ23991.npy
-imgHQ07031.npy
-imgHQ14082.npy
-imgHQ18253.npy
-imgHQ29830.npy
-imgHQ17506.npy
-imgHQ03518.npy
-imgHQ22597.npy
-imgHQ05060.npy
-imgHQ10154.npy
-imgHQ02840.npy
-imgHQ08285.npy
-imgHQ01668.npy
-imgHQ00771.npy
-imgHQ03035.npy
-imgHQ00505.npy
-imgHQ06215.npy
-imgHQ15023.npy
-imgHQ07123.npy
-imgHQ03188.npy
-imgHQ26768.npy
-imgHQ17448.npy
-imgHQ01169.npy
-imgHQ26470.npy
-imgHQ25949.npy
-imgHQ29615.npy
-imgHQ24276.npy
-imgHQ23441.npy
-imgHQ26464.npy
-imgHQ21660.npy
-imgHQ24184.npy
-imgHQ21402.npy
-imgHQ28817.npy
-imgHQ19560.npy
-imgHQ20186.npy
-imgHQ06992.npy
-imgHQ29434.npy
-imgHQ24091.npy
-imgHQ27410.npy
-imgHQ08640.npy
-imgHQ21840.npy
-imgHQ11770.npy
-imgHQ09338.npy
-imgHQ09173.npy
-imgHQ29531.npy
-imgHQ02989.npy
-imgHQ23411.npy
-imgHQ00746.npy
-imgHQ13402.npy
-imgHQ08620.npy
-imgHQ19237.npy
-imgHQ20780.npy
-imgHQ23295.npy
-imgHQ06446.npy
-imgHQ14343.npy
-imgHQ20211.npy
-imgHQ00784.npy
-imgHQ07274.npy
-imgHQ06471.npy
-imgHQ07793.npy
-imgHQ12264.npy
-imgHQ29981.npy
-imgHQ29162.npy
-imgHQ18465.npy
-imgHQ05085.npy
-imgHQ12360.npy
-imgHQ15791.npy
-imgHQ02990.npy
-imgHQ13007.npy
-imgHQ15839.npy
-imgHQ08581.npy
-imgHQ22121.npy
-imgHQ04255.npy
-imgHQ28991.npy
-imgHQ05191.npy
-imgHQ29373.npy
-imgHQ02971.npy
-imgHQ28614.npy
-imgHQ23189.npy
-imgHQ05432.npy
-imgHQ24018.npy
-imgHQ11056.npy
-imgHQ13557.npy
-imgHQ29125.npy
-imgHQ19822.npy
-imgHQ22933.npy
-imgHQ21613.npy
-imgHQ10960.npy
-imgHQ11141.npy
-imgHQ24616.npy
-imgHQ12724.npy
-imgHQ03757.npy
-imgHQ09518.npy
-imgHQ27629.npy
-imgHQ05855.npy
-imgHQ26994.npy
-imgHQ00135.npy
-imgHQ05239.npy
-imgHQ22113.npy
-imgHQ07224.npy
-imgHQ14843.npy
-imgHQ20041.npy
-imgHQ16019.npy
-imgHQ08447.npy
-imgHQ10483.npy
-imgHQ07256.npy
-imgHQ26970.npy
-imgHQ19577.npy
-imgHQ18578.npy
-imgHQ10963.npy
-imgHQ09163.npy
-imgHQ16396.npy
-imgHQ15836.npy
-imgHQ00210.npy
-imgHQ25888.npy
-imgHQ28929.npy
-imgHQ07026.npy
-imgHQ26023.npy
-imgHQ13263.npy
-imgHQ22363.npy
-imgHQ18044.npy
-imgHQ07341.npy
-imgHQ13448.npy
-imgHQ29095.npy
-imgHQ00973.npy
-imgHQ28221.npy
-imgHQ26503.npy
-imgHQ11105.npy
-imgHQ15237.npy
-imgHQ21475.npy
-imgHQ00580.npy
-imgHQ01351.npy
-imgHQ28340.npy
-imgHQ02194.npy
-imgHQ08881.npy
-imgHQ29824.npy
-imgHQ18489.npy
-imgHQ22541.npy
-imgHQ02150.npy
-imgHQ22883.npy
-imgHQ23699.npy
-imgHQ20692.npy
-imgHQ09283.npy
-imgHQ11898.npy
-imgHQ04477.npy
-imgHQ12173.npy
-imgHQ14121.npy
-imgHQ13913.npy
-imgHQ14175.npy
-imgHQ14456.npy
-imgHQ11509.npy
-imgHQ11034.npy
-imgHQ18756.npy
-imgHQ21553.npy
-imgHQ08582.npy
-imgHQ04702.npy
-imgHQ13071.npy
-imgHQ09312.npy
-imgHQ26812.npy
-imgHQ22539.npy
-imgHQ00130.npy
-imgHQ27339.npy
-imgHQ04218.npy
-imgHQ12642.npy
-imgHQ20845.npy
-imgHQ12217.npy
-imgHQ27038.npy
-imgHQ27132.npy
-imgHQ00498.npy
-imgHQ06062.npy
-imgHQ03783.npy
-imgHQ15085.npy
-imgHQ11645.npy
-imgHQ10348.npy
-imgHQ07323.npy
-imgHQ23147.npy
-imgHQ09710.npy
-imgHQ29189.npy
-imgHQ06189.npy
-imgHQ05685.npy
-imgHQ11623.npy
-imgHQ12598.npy
-imgHQ07129.npy
-imgHQ03737.npy
-imgHQ15233.npy
-imgHQ25947.npy
-imgHQ18045.npy
-imgHQ18726.npy
-imgHQ20630.npy
-imgHQ21479.npy
-imgHQ27101.npy
-imgHQ27560.npy
-imgHQ16266.npy
-imgHQ06464.npy
-imgHQ22410.npy
-imgHQ28599.npy
-imgHQ27482.npy
-imgHQ03239.npy
-imgHQ02002.npy
-imgHQ22387.npy
-imgHQ08491.npy
-imgHQ27661.npy
-imgHQ06252.npy
-imgHQ21395.npy
-imgHQ29073.npy
-imgHQ15475.npy
-imgHQ11851.npy
-imgHQ01052.npy
-imgHQ04395.npy
-imgHQ02164.npy
-imgHQ20117.npy
-imgHQ27814.npy
-imgHQ23609.npy
-imgHQ08247.npy
-imgHQ09865.npy
-imgHQ14192.npy
-imgHQ16566.npy
-imgHQ19907.npy
-imgHQ06018.npy
-imgHQ13929.npy
-imgHQ14829.npy
-imgHQ10672.npy
-imgHQ01692.npy
-imgHQ18487.npy
-imgHQ14220.npy
-imgHQ15425.npy
-imgHQ20402.npy
-imgHQ26745.npy
-imgHQ22519.npy
-imgHQ26032.npy
-imgHQ09592.npy
-imgHQ02219.npy
-imgHQ05184.npy
-imgHQ08352.npy
-imgHQ17845.npy
-imgHQ19540.npy
-imgHQ25928.npy
-imgHQ23188.npy
-imgHQ11387.npy
-imgHQ12152.npy
-imgHQ23340.npy
-imgHQ04256.npy
-imgHQ19192.npy
-imgHQ05995.npy
-imgHQ26748.npy
-imgHQ26044.npy
-imgHQ22132.npy
-imgHQ21765.npy
-imgHQ24945.npy
-imgHQ01464.npy
-imgHQ21074.npy
-imgHQ23505.npy
-imgHQ05948.npy
-imgHQ05779.npy
-imgHQ21254.npy
-imgHQ13522.npy
-imgHQ00955.npy
-imgHQ18027.npy
-imgHQ27419.npy
-imgHQ29056.npy
-imgHQ09761.npy
-imgHQ14792.npy
-imgHQ26937.npy
-imgHQ27954.npy
-imgHQ24483.npy
-imgHQ06747.npy
-imgHQ13394.npy
-imgHQ15929.npy
-imgHQ11484.npy
-imgHQ09092.npy
-imgHQ01168.npy
-imgHQ03456.npy
-imgHQ06406.npy
-imgHQ24618.npy
-imgHQ19670.npy
-imgHQ09669.npy
-imgHQ29365.npy
-imgHQ10043.npy
-imgHQ19651.npy
-imgHQ22454.npy
-imgHQ10134.npy
-imgHQ10704.npy
-imgHQ14379.npy
-imgHQ22614.npy
-imgHQ20898.npy
-imgHQ24580.npy
-imgHQ13697.npy
-imgHQ00392.npy
-imgHQ29702.npy
-imgHQ21464.npy
-imgHQ21974.npy
-imgHQ11617.npy
-imgHQ12675.npy
-imgHQ14248.npy
-imgHQ24241.npy
-imgHQ12123.npy
-imgHQ17855.npy
-imgHQ25611.npy
-imgHQ00907.npy
-imgHQ17986.npy
-imgHQ27140.npy
-imgHQ14038.npy
-imgHQ20293.npy
-imgHQ06474.npy
-imgHQ00666.npy
-imgHQ24361.npy
-imgHQ24150.npy
-imgHQ15173.npy
-imgHQ16194.npy
-imgHQ05347.npy
-imgHQ28842.npy
-imgHQ11090.npy
-imgHQ02282.npy
-imgHQ17639.npy
-imgHQ22757.npy
-imgHQ24232.npy
-imgHQ13479.npy
-imgHQ14555.npy
-imgHQ22111.npy
-imgHQ24093.npy
-imgHQ06305.npy
-imgHQ28506.npy
-imgHQ09450.npy
-imgHQ28563.npy
-imgHQ01164.npy
-imgHQ13247.npy
-imgHQ17090.npy
-imgHQ05040.npy
-imgHQ28162.npy
-imgHQ10307.npy
-imgHQ02589.npy
-imgHQ03908.npy
-imgHQ05228.npy
-imgHQ10124.npy
-imgHQ16192.npy
-imgHQ16041.npy
-imgHQ09707.npy
-imgHQ21596.npy
-imgHQ27945.npy
-imgHQ25273.npy
-imgHQ22913.npy
-imgHQ07748.npy
-imgHQ27764.npy
-imgHQ17846.npy
-imgHQ16280.npy
-imgHQ13127.npy
-imgHQ18480.npy
-imgHQ17831.npy
-imgHQ09291.npy
-imgHQ02831.npy
-imgHQ21641.npy
-imgHQ10930.npy
-imgHQ09330.npy
-imgHQ17007.npy
-imgHQ00869.npy
-imgHQ15165.npy
-imgHQ27232.npy
-imgHQ04806.npy
-imgHQ16418.npy
-imgHQ13924.npy
-imgHQ23266.npy
-imgHQ15497.npy
-imgHQ03569.npy
-imgHQ16300.npy
-imgHQ17404.npy
-imgHQ06855.npy
-imgHQ18467.npy
-imgHQ00105.npy
-imgHQ05468.npy
-imgHQ09156.npy
-imgHQ05212.npy
-imgHQ03973.npy
-imgHQ22445.npy
-imgHQ18618.npy
-imgHQ28937.npy
-imgHQ21149.npy
-imgHQ22861.npy
-imgHQ06137.npy
-imgHQ09037.npy
-imgHQ27846.npy
-imgHQ13699.npy
-imgHQ19394.npy
-imgHQ20005.npy
-imgHQ24469.npy
-imgHQ08471.npy
-imgHQ15611.npy
-imgHQ21123.npy
-imgHQ01789.npy
-imgHQ00625.npy
-imgHQ10296.npy
-imgHQ18528.npy
-imgHQ02151.npy
-imgHQ18872.npy
-imgHQ08833.npy
-imgHQ17884.npy
-imgHQ11292.npy
-imgHQ22595.npy
-imgHQ18665.npy
-imgHQ02642.npy
-imgHQ14526.npy
-imgHQ16428.npy
-imgHQ13087.npy
-imgHQ20661.npy
-imgHQ15528.npy
-imgHQ22046.npy
-imgHQ17282.npy
-imgHQ15630.npy
-imgHQ29403.npy
-imgHQ20974.npy
-imgHQ04440.npy
-imgHQ17232.npy
-imgHQ11314.npy
-imgHQ12546.npy
-imgHQ25428.npy
-imgHQ16367.npy
-imgHQ02459.npy
-imgHQ04370.npy
-imgHQ07294.npy
-imgHQ05726.npy
-imgHQ08748.npy
-imgHQ19700.npy
-imgHQ11570.npy
-imgHQ20905.npy
-imgHQ07573.npy
-imgHQ08775.npy
-imgHQ24895.npy
-imgHQ13324.npy
-imgHQ09869.npy
-imgHQ03504.npy
-imgHQ26669.npy
-imgHQ15376.npy
-imgHQ13280.npy
-imgHQ17715.npy
-imgHQ27460.npy
-imgHQ08296.npy
-imgHQ04815.npy
-imgHQ10687.npy
-imgHQ13050.npy
-imgHQ24913.npy
-imgHQ04899.npy
-imgHQ11972.npy
-imgHQ14516.npy
-imgHQ22670.npy
-imgHQ08400.npy
-imgHQ19126.npy
-imgHQ05972.npy
-imgHQ12777.npy
-imgHQ01880.npy
-imgHQ15932.npy
-imgHQ16686.npy
-imgHQ01557.npy
-imgHQ11336.npy
-imgHQ19804.npy
-imgHQ08753.npy
-imgHQ28318.npy
-imgHQ03215.npy
-imgHQ06863.npy
-imgHQ07809.npy
-imgHQ04704.npy
-imgHQ17094.npy
-imgHQ05678.npy
-imgHQ11864.npy
-imgHQ01649.npy
-imgHQ22639.npy
-imgHQ13752.npy
-imgHQ03177.npy
-imgHQ02962.npy
-imgHQ02311.npy
-imgHQ02778.npy
-imgHQ16981.npy
-imgHQ29185.npy
-imgHQ20384.npy
-imgHQ10520.npy
-imgHQ12036.npy
-imgHQ01811.npy
-imgHQ11443.npy
-imgHQ05185.npy
-imgHQ05869.npy
-imgHQ29148.npy
-imgHQ08065.npy
-imgHQ17334.npy
-imgHQ14165.npy
-imgHQ03167.npy
-imgHQ03417.npy
-imgHQ29276.npy
-imgHQ13687.npy
-imgHQ28858.npy
-imgHQ24335.npy
-imgHQ25346.npy
-imgHQ01277.npy
-imgHQ11849.npy
-imgHQ09793.npy
-imgHQ28879.npy
-imgHQ28872.npy
-imgHQ14380.npy
-imgHQ27461.npy
-imgHQ05707.npy
-imgHQ27637.npy
-imgHQ11491.npy
-imgHQ14961.npy
-imgHQ26787.npy
-imgHQ08665.npy
-imgHQ10693.npy
-imgHQ10293.npy
-imgHQ09986.npy
-imgHQ00889.npy
-imgHQ14230.npy
-imgHQ20988.npy
-imgHQ02019.npy
-imgHQ11092.npy
-imgHQ06233.npy
-imgHQ04215.npy
-imgHQ24130.npy
-imgHQ12958.npy
-imgHQ28112.npy
-imgHQ15279.npy
-imgHQ29860.npy
-imgHQ23131.npy
-imgHQ21382.npy
-imgHQ23091.npy
-imgHQ15067.npy
-imgHQ01391.npy
-imgHQ26967.npy
-imgHQ25677.npy
-imgHQ26951.npy
-imgHQ04089.npy
-imgHQ17906.npy
-imgHQ25255.npy
-imgHQ18013.npy
-imgHQ13720.npy
-imgHQ26593.npy
-imgHQ19485.npy
-imgHQ13341.npy
-imgHQ27998.npy
-imgHQ17564.npy
-imgHQ02173.npy
-imgHQ18560.npy
-imgHQ29804.npy
-imgHQ02786.npy
-imgHQ03590.npy
-imgHQ19185.npy
-imgHQ13762.npy
-imgHQ12314.npy
-imgHQ25732.npy
-imgHQ13830.npy
-imgHQ16221.npy
-imgHQ22846.npy
-imgHQ24045.npy
-imgHQ18566.npy
-imgHQ16738.npy
-imgHQ18421.npy
-imgHQ17598.npy
-imgHQ11850.npy
-imgHQ00875.npy
-imgHQ28424.npy
-imgHQ26399.npy
-imgHQ24112.npy
-imgHQ13903.npy
-imgHQ02086.npy
-imgHQ16845.npy
-imgHQ21948.npy
-imgHQ26622.npy
-imgHQ08276.npy
-imgHQ00379.npy
-imgHQ14674.npy
-imgHQ19698.npy
-imgHQ17144.npy
-imgHQ04219.npy
-imgHQ05480.npy
-imgHQ01281.npy
-imgHQ08107.npy
-imgHQ28824.npy
-imgHQ09123.npy
-imgHQ07504.npy
-imgHQ28079.npy
-imgHQ11993.npy
-imgHQ28479.npy
-imgHQ24675.npy
-imgHQ02507.npy
-imgHQ20781.npy
-imgHQ28560.npy
-imgHQ22721.npy
-imgHQ22868.npy
-imgHQ03003.npy
-imgHQ12040.npy
-imgHQ05163.npy
-imgHQ15213.npy
-imgHQ01077.npy
-imgHQ20579.npy
-imgHQ28125.npy
-imgHQ23717.npy
-imgHQ10596.npy
-imgHQ14027.npy
-imgHQ10823.npy
-imgHQ25771.npy
-imgHQ04698.npy
-imgHQ26350.npy
-imgHQ03589.npy
-imgHQ00761.npy
-imgHQ14173.npy
-imgHQ23891.npy
-imgHQ25326.npy
-imgHQ06090.npy
-imgHQ25194.npy
-imgHQ06985.npy
-imgHQ28927.npy
-imgHQ04731.npy
-imgHQ11995.npy
-imgHQ08057.npy
-imgHQ24792.npy
-imgHQ03416.npy
-imgHQ17987.npy
-imgHQ09647.npy
-imgHQ14093.npy
-imgHQ05211.npy
-imgHQ26685.npy
-imgHQ25618.npy
-imgHQ24195.npy
-imgHQ28137.npy
-imgHQ16297.npy
-imgHQ11848.npy
-imgHQ02998.npy
-imgHQ29158.npy
-imgHQ09871.npy
-imgHQ08556.npy
-imgHQ28570.npy
-imgHQ09718.npy
-imgHQ29794.npy
-imgHQ21816.npy
-imgHQ25090.npy
-imgHQ14039.npy
-imgHQ21784.npy
-imgHQ22193.npy
-imgHQ17403.npy
-imgHQ05243.npy
-imgHQ10453.npy
-imgHQ10637.npy
-imgHQ01041.npy
-imgHQ11006.npy
-imgHQ09934.npy
-imgHQ23558.npy
-imgHQ24403.npy
-imgHQ18892.npy
-imgHQ20919.npy
-imgHQ04650.npy
-imgHQ15609.npy
-imgHQ15772.npy
-imgHQ23363.npy
-imgHQ08340.npy
-imgHQ03764.npy
-imgHQ24529.npy
-imgHQ22973.npy
-imgHQ13942.npy
-imgHQ07280.npy
-imgHQ12769.npy
-imgHQ03447.npy
-imgHQ01725.npy
-imgHQ20201.npy
-imgHQ16515.npy
-imgHQ20575.npy
-imgHQ11005.npy
-imgHQ18735.npy
-imgHQ25954.npy
-imgHQ11511.npy
-imgHQ18842.npy
-imgHQ24023.npy
-imgHQ15151.npy
-imgHQ03025.npy
-imgHQ17660.npy
-imgHQ01055.npy
-imgHQ06025.npy
-imgHQ14569.npy
-imgHQ21942.npy
-imgHQ24521.npy
-imgHQ29421.npy
-imgHQ19754.npy
-imgHQ26491.npy
-imgHQ05156.npy
-imgHQ06832.npy
-imgHQ06642.npy
-imgHQ25561.npy
-imgHQ08275.npy
-imgHQ00359.npy
-imgHQ04113.npy
-imgHQ01092.npy
-imgHQ05924.npy
-imgHQ04726.npy
-imgHQ14817.npy
-imgHQ18912.npy
-imgHQ22762.npy
-imgHQ24545.npy
-imgHQ10107.npy
-imgHQ00367.npy
-imgHQ29407.npy
-imgHQ22550.npy
-imgHQ04308.npy
-imgHQ28825.npy
-imgHQ08677.npy
-imgHQ28411.npy
-imgHQ06304.npy
-imgHQ13232.npy
-imgHQ03339.npy
-imgHQ01996.npy
-imgHQ27689.npy
-imgHQ01255.npy
-imgHQ03801.npy
-imgHQ25735.npy
-imgHQ07487.npy
-imgHQ16149.npy
-imgHQ08398.npy
-imgHQ22936.npy
-imgHQ06134.npy
-imgHQ19960.npy
-imgHQ00410.npy
-imgHQ03316.npy
-imgHQ15598.npy
-imgHQ01498.npy
-imgHQ28698.npy
-imgHQ26674.npy
-imgHQ10635.npy
-imgHQ21545.npy
-imgHQ04786.npy
-imgHQ23698.npy
-imgHQ20559.npy
-imgHQ03108.npy
-imgHQ09058.npy
-imgHQ07237.npy
-imgHQ15903.npy
-imgHQ09401.npy
-imgHQ17423.npy
-imgHQ19266.npy
-imgHQ21797.npy
-imgHQ09016.npy
-imgHQ20664.npy
-imgHQ18162.npy
-imgHQ14856.npy
-imgHQ03736.npy
-imgHQ18661.npy
-imgHQ28080.npy
-imgHQ20811.npy
-imgHQ25522.npy
-imgHQ02921.npy
-imgHQ14581.npy
-imgHQ00699.npy
-imgHQ09561.npy
-imgHQ01176.npy
-imgHQ10521.npy
-imgHQ04345.npy
-imgHQ12618.npy
-imgHQ19132.npy
-imgHQ09959.npy
-imgHQ06006.npy
-imgHQ13027.npy
-imgHQ17797.npy
-imgHQ00054.npy
-imgHQ05038.npy
-imgHQ18246.npy
-imgHQ07258.npy
-imgHQ14071.npy
-imgHQ00841.npy
-imgHQ10613.npy
-imgHQ25695.npy
-imgHQ15686.npy
-imgHQ11670.npy
-imgHQ16094.npy
-imgHQ03842.npy
-imgHQ17036.npy
-imgHQ08520.npy
-imgHQ09128.npy
-imgHQ27102.npy
-imgHQ25161.npy
-imgHQ12475.npy
-imgHQ06997.npy
-imgHQ19425.npy
-imgHQ10068.npy
-imgHQ15974.npy
-imgHQ07112.npy
-imgHQ12647.npy
-imgHQ15760.npy
-imgHQ11073.npy
-imgHQ04733.npy
-imgHQ15388.npy
-imgHQ27891.npy
-imgHQ04689.npy
-imgHQ08088.npy
-imgHQ24729.npy
-imgHQ23067.npy
-imgHQ13785.npy
-imgHQ00046.npy
-imgHQ15447.npy
-imgHQ02688.npy
-imgHQ02327.npy
-imgHQ11269.npy
-imgHQ13425.npy
-imgHQ11289.npy
-imgHQ26792.npy
-imgHQ27371.npy
-imgHQ23032.npy
-imgHQ09375.npy
-imgHQ26836.npy
-imgHQ20221.npy
-imgHQ12949.npy
-imgHQ06022.npy
-imgHQ03192.npy
-imgHQ15265.npy
-imgHQ29079.npy
-imgHQ18924.npy
-imgHQ21972.npy
-imgHQ19893.npy
-imgHQ18685.npy
-imgHQ26225.npy
-imgHQ14578.npy
-imgHQ06751.npy
-imgHQ16152.npy
-imgHQ18023.npy
-imgHQ07261.npy
-imgHQ08106.npy
-imgHQ03007.npy
-imgHQ19669.npy
-imgHQ28765.npy
-imgHQ20505.npy
-imgHQ29372.npy
-imgHQ19051.npy
-imgHQ18350.npy
-imgHQ08648.npy
-imgHQ11087.npy
-imgHQ11193.npy
-imgHQ08805.npy
-imgHQ04403.npy
-imgHQ22220.npy
-imgHQ03080.npy
-imgHQ01380.npy
-imgHQ03272.npy
-imgHQ24154.npy
-imgHQ17019.npy
-imgHQ25404.npy
-imgHQ17121.npy
-imgHQ22367.npy
-imgHQ00957.npy
-imgHQ06777.npy
-imgHQ29730.npy
-imgHQ07985.npy
-imgHQ27748.npy
-imgHQ25591.npy
-imgHQ21770.npy
-imgHQ10598.npy
-imgHQ21633.npy
-imgHQ14257.npy
-imgHQ12021.npy
-imgHQ09760.npy
-imgHQ07514.npy
-imgHQ05580.npy
-imgHQ08758.npy
-imgHQ27226.npy
-imgHQ18438.npy
-imgHQ22797.npy
-imgHQ15195.npy
-imgHQ15730.npy
-imgHQ17041.npy
-imgHQ13096.npy
-imgHQ20796.npy
-imgHQ28147.npy
-imgHQ02728.npy
-imgHQ14178.npy
-imgHQ03264.npy
-imgHQ24349.npy
-imgHQ08927.npy
-imgHQ19327.npy
-imgHQ19928.npy
-imgHQ10490.npy
-imgHQ02083.npy
-imgHQ22030.npy
-imgHQ20720.npy
-imgHQ03690.npy
-imgHQ16892.npy
-imgHQ18747.npy
-imgHQ19809.npy
-imgHQ16966.npy
-imgHQ02047.npy
-imgHQ19069.npy
-imgHQ09611.npy
-imgHQ22172.npy
-imgHQ11053.npy
-imgHQ25460.npy
-imgHQ00543.npy
-imgHQ29227.npy
-imgHQ10473.npy
-imgHQ17065.npy
-imgHQ24386.npy
-imgHQ17779.npy
-imgHQ02256.npy
-imgHQ14209.npy
-imgHQ23596.npy
-imgHQ00744.npy
-imgHQ09273.npy
-imgHQ15561.npy
-imgHQ10982.npy
-imgHQ11679.npy
-imgHQ09415.npy
-imgHQ11539.npy
-imgHQ19643.npy
-imgHQ25026.npy
-imgHQ28495.npy
-imgHQ05975.npy
-imgHQ23074.npy
-imgHQ26231.npy
-imgHQ04388.npy
-imgHQ10385.npy
-imgHQ07762.npy
-imgHQ11615.npy
-imgHQ12001.npy
-imgHQ08872.npy
-imgHQ29009.npy
-imgHQ06143.npy
-imgHQ12422.npy
-imgHQ15519.npy
-imgHQ10999.npy
-imgHQ11797.npy
-imgHQ05960.npy
-imgHQ27614.npy
-imgHQ19758.npy
-imgHQ16265.npy
-imgHQ11653.npy
-imgHQ23317.npy
-imgHQ21476.npy
-imgHQ18281.npy
-imgHQ07398.npy
-imgHQ28440.npy
-imgHQ23000.npy
-imgHQ00459.npy
-imgHQ05526.npy
-imgHQ06938.npy
-imgHQ12754.npy
-imgHQ15071.npy
-imgHQ13714.npy
-imgHQ19180.npy
-imgHQ02782.npy
-imgHQ24885.npy
-imgHQ00773.npy
-imgHQ05941.npy
-imgHQ21486.npy
-imgHQ10768.npy
-imgHQ28654.npy
-imgHQ05757.npy
-imgHQ09582.npy
-imgHQ08894.npy
-imgHQ23209.npy
-imgHQ28007.npy
-imgHQ09658.npy
-imgHQ29294.npy
-imgHQ07188.npy
-imgHQ24851.npy
-imgHQ26838.npy
-imgHQ11452.npy
-imgHQ03893.npy
-imgHQ20899.npy
-imgHQ15616.npy
-imgHQ06797.npy
-imgHQ20157.npy
-imgHQ00098.npy
-imgHQ21473.npy
-imgHQ06757.npy
-imgHQ16872.npy
-imgHQ02014.npy
-imgHQ04953.npy
-imgHQ16547.npy
-imgHQ28971.npy
-imgHQ06849.npy
-imgHQ07552.npy
-imgHQ29659.npy
-imgHQ06595.npy
-imgHQ28193.npy
-imgHQ05672.npy
-imgHQ16321.npy
-imgHQ20622.npy
-imgHQ24728.npy
-imgHQ25278.npy
-imgHQ21577.npy
-imgHQ11255.npy
-imgHQ01266.npy
-imgHQ26653.npy
-imgHQ00168.npy
-imgHQ14634.npy
-imgHQ22271.npy
-imgHQ15916.npy
-imgHQ16870.npy
-imgHQ29402.npy
-imgHQ04207.npy
-imgHQ16334.npy
-imgHQ28350.npy
-imgHQ18298.npy
-imgHQ06444.npy
-imgHQ01210.npy
-imgHQ11156.npy
-imgHQ29291.npy
-imgHQ07908.npy
-imgHQ01315.npy
-imgHQ01286.npy
-imgHQ15272.npy
-imgHQ10968.npy
-imgHQ27542.npy
-imgHQ08685.npy
-imgHQ03072.npy
-imgHQ15098.npy
-imgHQ28210.npy
-imgHQ27612.npy
-imgHQ11553.npy
-imgHQ19516.npy
-imgHQ26028.npy
-imgHQ16128.npy
-imgHQ00072.npy
-imgHQ27416.npy
-imgHQ14115.npy
-imgHQ02580.npy
-imgHQ01196.npy
-imgHQ00795.npy
-imgHQ19403.npy
-imgHQ00678.npy
-imgHQ12296.npy
-imgHQ19308.npy
-imgHQ00107.npy
-imgHQ26640.npy
-imgHQ03126.npy
-imgHQ08325.npy
-imgHQ08716.npy
-imgHQ20832.npy
-imgHQ29014.npy
-imgHQ26757.npy
-imgHQ28389.npy
-imgHQ28400.npy
-imgHQ19505.npy
-imgHQ25874.npy
-imgHQ28376.npy
-imgHQ28623.npy
-imgHQ12281.npy
-imgHQ08625.npy
-imgHQ22649.npy
-imgHQ06046.npy
-imgHQ15020.npy
-imgHQ23214.npy
-imgHQ23971.npy
-imgHQ25078.npy
-imgHQ27844.npy
-imgHQ02849.npy
-imgHQ17827.npy
-imgHQ10537.npy
-imgHQ22076.npy
-imgHQ28768.npy
-imgHQ00181.npy
-imgHQ18477.npy
-imgHQ10504.npy
-imgHQ22761.npy
-imgHQ16159.npy
-imgHQ20150.npy
-imgHQ17811.npy
-imgHQ22991.npy
-imgHQ18011.npy
-imgHQ06874.npy
-imgHQ26535.npy
-imgHQ27124.npy
-imgHQ11152.npy
-imgHQ26585.npy
-imgHQ22442.npy
-imgHQ21634.npy
-imgHQ24085.npy
-imgHQ07644.npy
-imgHQ05794.npy
-imgHQ10898.npy
-imgHQ12074.npy
-imgHQ15088.npy
-imgHQ16553.npy
-imgHQ03113.npy
-imgHQ18008.npy
-imgHQ11518.npy
-imgHQ03453.npy
-imgHQ12835.npy
-imgHQ05104.npy
-imgHQ18394.npy
-imgHQ12976.npy
-imgHQ22224.npy
-imgHQ12436.npy
-imgHQ17186.npy
-imgHQ28942.npy
-imgHQ21930.npy
-imgHQ02520.npy
-imgHQ24317.npy
-imgHQ05439.npy
-imgHQ00094.npy
-imgHQ15936.npy
-imgHQ25855.npy
-imgHQ02396.npy
-imgHQ25094.npy
-imgHQ14291.npy
-imgHQ12801.npy
-imgHQ14504.npy
-imgHQ11804.npy
-imgHQ18599.npy
-imgHQ11192.npy
-imgHQ10180.npy
-imgHQ10266.npy
-imgHQ08859.npy
-imgHQ10114.npy
-imgHQ26040.npy
-imgHQ00956.npy
-imgHQ28977.npy
-imgHQ05378.npy
-imgHQ02349.npy
-imgHQ01532.npy
-imgHQ10600.npy
-imgHQ16384.npy
-imgHQ26693.npy
-imgHQ02103.npy
-imgHQ23865.npy
-imgHQ04355.npy
-imgHQ14515.npy
-imgHQ12303.npy
-imgHQ04132.npy
-imgHQ25431.npy
-imgHQ05513.npy
-imgHQ25818.npy
-imgHQ14592.npy
-imgHQ19597.npy
-imgHQ06888.npy
-imgHQ16288.npy
-imgHQ24337.npy
-imgHQ14218.npy
-imgHQ14168.npy
-imgHQ14311.npy
-imgHQ13880.npy
-imgHQ28060.npy
-imgHQ28559.npy
-imgHQ11136.npy
-imgHQ01184.npy
-imgHQ01548.npy
-imgHQ07670.npy
-imgHQ27601.npy
-imgHQ23401.npy
-imgHQ16120.npy
-imgHQ29927.npy
-imgHQ16755.npy
-imgHQ13344.npy
-imgHQ24549.npy
-imgHQ27630.npy
-imgHQ28302.npy
-imgHQ04073.npy
-imgHQ03268.npy
-imgHQ03629.npy
-imgHQ23370.npy
-imgHQ08548.npy
-imgHQ19673.npy
-imgHQ14724.npy
-imgHQ15439.npy
-imgHQ03697.npy
-imgHQ14749.npy
-imgHQ13836.npy
-imgHQ28324.npy
-imgHQ10638.npy
-imgHQ22215.npy
-imgHQ20069.npy
-imgHQ12733.npy
-imgHQ07369.npy
-imgHQ01057.npy
-imgHQ03262.npy
-imgHQ25102.npy
-imgHQ05343.npy
-imgHQ24454.npy
-imgHQ03522.npy
-imgHQ01981.npy
-imgHQ27408.npy
-imgHQ12488.npy
-imgHQ22151.npy
-imgHQ14123.npy
-imgHQ08554.npy
-imgHQ23303.npy
-imgHQ11417.npy
-imgHQ27946.npy
-imgHQ26549.npy
-imgHQ18243.npy
-imgHQ07959.npy
-imgHQ25157.npy
-imgHQ04312.npy
-imgHQ28419.npy
-imgHQ26447.npy
-imgHQ20612.npy
-imgHQ05342.npy
-imgHQ06505.npy
-imgHQ29779.npy
-imgHQ03298.npy
-imgHQ18149.npy
-imgHQ12268.npy
-imgHQ22133.npy
-imgHQ28140.npy
-imgHQ24189.npy
-imgHQ02101.npy
-imgHQ24271.npy
-imgHQ14099.npy
-imgHQ06657.npy
-imgHQ23661.npy
-imgHQ24152.npy
-imgHQ02766.npy
-imgHQ19015.npy
-imgHQ15331.npy
-imgHQ02160.npy
-imgHQ17124.npy
-imgHQ13734.npy
-imgHQ08502.npy
-imgHQ26302.npy
-imgHQ04127.npy
-imgHQ11829.npy
-imgHQ16851.npy
-imgHQ26166.npy
-imgHQ15087.npy
-imgHQ23077.npy
-imgHQ23775.npy
-imgHQ28475.npy
-imgHQ15833.npy
-imgHQ20121.npy
-imgHQ29814.npy
-imgHQ22171.npy
-imgHQ26107.npy
-imgHQ17684.npy
-imgHQ20387.npy
-imgHQ01449.npy
-imgHQ07383.npy
-imgHQ07167.npy
-imgHQ06989.npy
-imgHQ09360.npy
-imgHQ14464.npy
-imgHQ19464.npy
-imgHQ17050.npy
-imgHQ04315.npy
-imgHQ10961.npy
-imgHQ03195.npy
-imgHQ27228.npy
-imgHQ25265.npy
-imgHQ24334.npy
-imgHQ19405.npy
-imgHQ12684.npy
-imgHQ03402.npy
-imgHQ03274.npy
-imgHQ27454.npy
-imgHQ08896.npy
-imgHQ21080.npy
-imgHQ29232.npy
-imgHQ04322.npy
-imgHQ24319.npy
-imgHQ26051.npy
-imgHQ11852.npy
-imgHQ09125.npy
-imgHQ26117.npy
-imgHQ18956.npy
-imgHQ26139.npy
-imgHQ09632.npy
-imgHQ05691.npy
-imgHQ23016.npy
-imgHQ08146.npy
-imgHQ23667.npy
-imgHQ24488.npy
-imgHQ17438.npy
-imgHQ12191.npy
-imgHQ12859.npy
-imgHQ19506.npy
-imgHQ10095.npy
-imgHQ12192.npy
-imgHQ09028.npy
-imgHQ22714.npy
-imgHQ16647.npy
-imgHQ00288.npy
-imgHQ05407.npy
-imgHQ09656.npy
-imgHQ26289.npy
-imgHQ24400.npy
-imgHQ13923.npy
-imgHQ26380.npy
-imgHQ15800.npy
-imgHQ01223.npy
-imgHQ03318.npy
-imgHQ01526.npy
-imgHQ26986.npy
-imgHQ20260.npy
-imgHQ29629.npy
-imgHQ12497.npy
-imgHQ28913.npy
-imgHQ01844.npy
-imgHQ28413.npy
-imgHQ18478.npy
-imgHQ17100.npy
-imgHQ23735.npy
-imgHQ28327.npy
-imgHQ15901.npy
-imgHQ19460.npy
-imgHQ09108.npy
-imgHQ00069.npy
-imgHQ24207.npy
-imgHQ12593.npy
-imgHQ18250.npy
-imgHQ28409.npy
-imgHQ18063.npy
-imgHQ10288.npy
-imgHQ23757.npy
-imgHQ19275.npy
-imgHQ05536.npy
-imgHQ18183.npy
-imgHQ17118.npy
-imgHQ03706.npy
-imgHQ20847.npy
-imgHQ16951.npy
-imgHQ09296.npy
-imgHQ17179.npy
-imgHQ20542.npy
-imgHQ12854.npy
-imgHQ21138.npy
-imgHQ11198.npy
-imgHQ03392.npy
-imgHQ29948.npy
-imgHQ01303.npy
-imgHQ18778.npy
-imgHQ06533.npy
-imgHQ08589.npy
-imgHQ09444.npy
-imgHQ27105.npy
-imgHQ15017.npy
-imgHQ28709.npy
-imgHQ19124.npy
-imgHQ04447.npy
-imgHQ02974.npy
-imgHQ01610.npy
-imgHQ23136.npy
-imgHQ20025.npy
-imgHQ07126.npy
-imgHQ23079.npy
-imgHQ02565.npy
-imgHQ25328.npy
-imgHQ21939.npy
-imgHQ02926.npy
-imgHQ10320.npy
-imgHQ07476.npy
-imgHQ27798.npy
-imgHQ27939.npy
-imgHQ26112.npy
-imgHQ15269.npy
-imgHQ19220.npy
-imgHQ21534.npy
-imgHQ26925.npy
-imgHQ04365.npy
-imgHQ12159.npy
-imgHQ25495.npy
-imgHQ00362.npy
-imgHQ13572.npy
-imgHQ04063.npy
-imgHQ05394.npy
-imgHQ06011.npy
-imgHQ12491.npy
-imgHQ23456.npy
-imgHQ11768.npy
-imgHQ06159.npy
-imgHQ10254.npy
-imgHQ16646.npy
-imgHQ06932.npy
-imgHQ03782.npy
-imgHQ13352.npy
-imgHQ02577.npy
-imgHQ16474.npy
-imgHQ18417.npy
-imgHQ28054.npy
-imgHQ11917.npy
-imgHQ22728.npy
-imgHQ07629.npy
-imgHQ27060.npy
-imgHQ28520.npy
-imgHQ27184.npy
-imgHQ25787.npy
-imgHQ14272.npy
-imgHQ21508.npy
-imgHQ03965.npy
-imgHQ25535.npy
-imgHQ18766.npy
-imgHQ28434.npy
-imgHQ16974.npy
-imgHQ11483.npy
-imgHQ06576.npy
-imgHQ20925.npy
-imgHQ27932.npy
-imgHQ19078.npy
-imgHQ08132.npy
-imgHQ18139.npy
-imgHQ09729.npy
-imgHQ16224.npy
-imgHQ29626.npy
-imgHQ08282.npy
-imgHQ09993.npy
-imgHQ20276.npy
-imgHQ10298.npy
-imgHQ17003.npy
-imgHQ08688.npy
-imgHQ08428.npy
-imgHQ29902.npy
-imgHQ17264.npy
-imgHQ05380.npy
-imgHQ05404.npy
-imgHQ12920.npy
-imgHQ00928.npy
-imgHQ12839.npy
-imgHQ28055.npy
-imgHQ08888.npy
-imgHQ00131.npy
-imgHQ26783.npy
-imgHQ10609.npy
-imgHQ08832.npy
-imgHQ09943.npy
-imgHQ09279.npy
-imgHQ13221.npy
-imgHQ25016.npy
-imgHQ12930.npy
-imgHQ09233.npy
-imgHQ29777.npy
-imgHQ00578.npy
-imgHQ28591.npy
-imgHQ17445.npy
-imgHQ29342.npy
-imgHQ03228.npy
-imgHQ26181.npy
-imgHQ26619.npy
-imgHQ19746.npy
-imgHQ18625.npy
-imgHQ14583.npy
-imgHQ13223.npy
-imgHQ08129.npy
-imgHQ29650.npy
-imgHQ04464.npy
-imgHQ04737.npy
-imgHQ27543.npy
-imgHQ28761.npy
-imgHQ11236.npy
-imgHQ09465.npy
-imgHQ29138.npy
-imgHQ08363.npy
-imgHQ20324.npy
-imgHQ09821.npy
-imgHQ28850.npy
-imgHQ02403.npy
-imgHQ08058.npy
-imgHQ28822.npy
-imgHQ05839.npy
-imgHQ09664.npy
-imgHQ02953.npy
-imgHQ04239.npy
-imgHQ11422.npy
-imgHQ27441.npy
-imgHQ21122.npy
-imgHQ13904.npy
-imgHQ29760.npy
-imgHQ20928.npy
-imgHQ06287.npy
-imgHQ05986.npy
-imgHQ02493.npy
-imgHQ08440.npy
-imgHQ06091.npy
-imgHQ15568.npy
-imgHQ24027.npy
-imgHQ06493.npy
-imgHQ15139.npy
-imgHQ18798.npy
-imgHQ20197.npy
-imgHQ13835.npy
-imgHQ17619.npy
-imgHQ12725.npy
-imgHQ03865.npy
-imgHQ06080.npy
-imgHQ27927.npy
-imgHQ19082.npy
-imgHQ10080.npy
-imgHQ03883.npy
-imgHQ28845.npy
-imgHQ14709.npy
-imgHQ27238.npy
-imgHQ25766.npy
-imgHQ25249.npy
-imgHQ15668.npy
-imgHQ26210.npy
-imgHQ21583.npy
-imgHQ15689.npy
-imgHQ26598.npy
-imgHQ29333.npy
-imgHQ26487.npy
-imgHQ03921.npy
-imgHQ17310.npy
-imgHQ08937.npy
-imgHQ28245.npy
-imgHQ02922.npy
-imgHQ27418.npy
-imgHQ18062.npy
-imgHQ21277.npy
-imgHQ24578.npy
-imgHQ20641.npy
-imgHQ20302.npy
-imgHQ12112.npy
-imgHQ28494.npy
-imgHQ25237.npy
-imgHQ15284.npy
-imgHQ06748.npy
-imgHQ00657.npy
-imgHQ29429.npy
-imgHQ11126.npy
-imgHQ06249.npy
-imgHQ27456.npy
-imgHQ28069.npy
-imgHQ05167.npy
-imgHQ17253.npy
-imgHQ28432.npy
-imgHQ05054.npy
-imgHQ21060.npy
-imgHQ08784.npy
-imgHQ22447.npy
-imgHQ09625.npy
-imgHQ17886.npy
-imgHQ24590.npy
-imgHQ07203.npy
-imgHQ17836.npy
-imgHQ01973.npy
-imgHQ04596.npy
-imgHQ06917.npy
-imgHQ05457.npy
-imgHQ02141.npy
-imgHQ26770.npy
-imgHQ15685.npy
-imgHQ20473.npy
-imgHQ27725.npy
-imgHQ18573.npy
-imgHQ29114.npy
-imgHQ08189.npy
-imgHQ21304.npy
-imgHQ26299.npy
-imgHQ27885.npy
-imgHQ00242.npy
-imgHQ24394.npy
-imgHQ09258.npy
-imgHQ12836.npy
-imgHQ01509.npy
-imgHQ26202.npy
-imgHQ23876.npy
-imgHQ19815.npy
-imgHQ19494.npy
-imgHQ08241.npy
-imgHQ13518.npy
-imgHQ04908.npy
-imgHQ26009.npy
-imgHQ04192.npy
-imgHQ00293.npy
-imgHQ25392.npy
-imgHQ12012.npy
-imgHQ21542.npy
-imgHQ20987.npy
-imgHQ28303.npy
-imgHQ02375.npy
-imgHQ15954.npy
-imgHQ26338.npy
-imgHQ20231.npy
-imgHQ21010.npy
-imgHQ28648.npy
-imgHQ03009.npy
-imgHQ11373.npy
-imgHQ10132.npy
-imgHQ26785.npy
-imgHQ05700.npy
-imgHQ27154.npy
-imgHQ18917.npy
-imgHQ13955.npy
-imgHQ28257.npy
-imgHQ23361.npy
-imgHQ16908.npy
-imgHQ23808.npy
-imgHQ18606.npy
-imgHQ10618.npy
-imgHQ04209.npy
-imgHQ06492.npy
-imgHQ25498.npy
-imgHQ04328.npy
-imgHQ13378.npy
-imgHQ12726.npy
-imgHQ18534.npy
-imgHQ23875.npy
-imgHQ04791.npy
-imgHQ29003.npy
-imgHQ28197.npy
-imgHQ28609.npy
-imgHQ24922.npy
-imgHQ19770.npy
-imgHQ28744.npy
-imgHQ23790.npy
-imgHQ26778.npy
-imgHQ20746.npy
-imgHQ19938.npy
-imgHQ20335.npy
-imgHQ09047.npy
-imgHQ01903.npy
-imgHQ16708.npy
-imgHQ00276.npy
-imgHQ03353.npy
-imgHQ01429.npy
-imgHQ08917.npy
-imgHQ12643.npy
-imgHQ26077.npy
-imgHQ07034.npy
-imgHQ11137.npy
-imgHQ19430.npy
-imgHQ26159.npy
-imgHQ12091.npy
-imgHQ21007.npy
-imgHQ20136.npy
-imgHQ04178.npy
-imgHQ03587.npy
-imgHQ06169.npy
-imgHQ18408.npy
-imgHQ29936.npy
-imgHQ07653.npy
-imgHQ02934.npy
-imgHQ08653.npy
-imgHQ08098.npy
-imgHQ13278.npy
-imgHQ07860.npy
-imgHQ09936.npy
-imgHQ14844.npy
-imgHQ28880.npy
-imgHQ12674.npy
-imgHQ13428.npy
-imgHQ07879.npy
-imgHQ06968.npy
-imgHQ15437.npy
-imgHQ29978.npy
-imgHQ10893.npy
-imgHQ03346.npy
-imgHQ27307.npy
-imgHQ20363.npy
-imgHQ17375.npy
-imgHQ29595.npy
-imgHQ22032.npy
-imgHQ27480.npy
-imgHQ17655.npy
-imgHQ26445.npy
-imgHQ28966.npy
-imgHQ26879.npy
-imgHQ28644.npy
-imgHQ18323.npy
-imgHQ27742.npy
-imgHQ19152.npy
-imgHQ08957.npy
-imgHQ02088.npy
-imgHQ13061.npy
-imgHQ03817.npy
-imgHQ23746.npy
-imgHQ17363.npy
-imgHQ01733.npy
-imgHQ11625.npy
-imgHQ28841.npy
-imgHQ02015.npy
-imgHQ07083.npy
-imgHQ21688.npy
-imgHQ00979.npy
-imgHQ28179.npy
-imgHQ28944.npy
-imgHQ24351.npy
-imgHQ04405.npy
-imgHQ19948.npy
-imgHQ00858.npy
-imgHQ18944.npy
-imgHQ10153.npy
-imgHQ19737.npy
-imgHQ24662.npy
-imgHQ23345.npy
-imgHQ17807.npy
-imgHQ22570.npy
-imgHQ25933.npy
-imgHQ29742.npy
-imgHQ04613.npy
-imgHQ26126.npy
-imgHQ26450.npy
-imgHQ20938.npy
-imgHQ14485.npy
-imgHQ29382.npy
-imgHQ27056.npy
-imgHQ22164.npy
-imgHQ00749.npy
-imgHQ20106.npy
-imgHQ00676.npy
-imgHQ17682.npy
-imgHQ01750.npy
-imgHQ15227.npy
-imgHQ05447.npy
-imgHQ25965.npy
-imgHQ28696.npy
-imgHQ10055.npy
-imgHQ20350.npy
-imgHQ05625.npy
-imgHQ21593.npy
-imgHQ27170.npy
-imgHQ04082.npy
-imgHQ29157.npy
-imgHQ20621.npy
-imgHQ12221.npy
-imgHQ17617.npy
-imgHQ29050.npy
-imgHQ09334.npy
-imgHQ26283.npy
-imgHQ10685.npy
-imgHQ12317.npy
-imgHQ26936.npy
-imgHQ20681.npy
-imgHQ24762.npy
-imgHQ12659.npy
-imgHQ09964.npy
-imgHQ26702.npy
-imgHQ11562.npy
-imgHQ25804.npy
-imgHQ19857.npy
-imgHQ11155.npy
-imgHQ07635.npy
-imgHQ06391.npy
-imgHQ15055.npy
-imgHQ28909.npy
-imgHQ26477.npy
-imgHQ02789.npy
-imgHQ16100.npy
-imgHQ10207.npy
-imgHQ27987.npy
-imgHQ06197.npy
-imgHQ28887.npy
-imgHQ26979.npy
-imgHQ29083.npy
-imgHQ28814.npy
-imgHQ14312.npy
-imgHQ25213.npy
-imgHQ04242.npy
-imgHQ26412.npy
-imgHQ21117.npy
-imgHQ07355.npy
-imgHQ28243.npy
-imgHQ23012.npy
-imgHQ05612.npy
-imgHQ06831.npy
-imgHQ18589.npy
-imgHQ21238.npy
-imgHQ24059.npy
-imgHQ05359.npy
-imgHQ24693.npy
-imgHQ21568.npy
-imgHQ11060.npy
-imgHQ12255.npy
-imgHQ02299.npy
-imgHQ00859.npy
-imgHQ25309.npy
-imgHQ09407.npy
-imgHQ02204.npy
-imgHQ08575.npy
-imgHQ16857.npy
-imgHQ11298.npy
-imgHQ09428.npy
-imgHQ26995.npy
-imgHQ10311.npy
-imgHQ01900.npy
-imgHQ29312.npy
-imgHQ20267.npy
-imgHQ18642.npy
-imgHQ00515.npy
-imgHQ02427.npy
-imgHQ04278.npy
-imgHQ02388.npy
-imgHQ18393.npy
-imgHQ06251.npy
-imgHQ28053.npy
-imgHQ00967.npy
-imgHQ12966.npy
-imgHQ13276.npy
-imgHQ07267.npy
-imgHQ17397.npy
-imgHQ00899.npy
-imgHQ28040.npy
-imgHQ14799.npy
-imgHQ03838.npy
-imgHQ14913.npy
-imgHQ27448.npy
-imgHQ24935.npy
-imgHQ04840.npy
-imgHQ15034.npy
-imgHQ13387.npy
-imgHQ04423.npy
-imgHQ26448.npy
-imgHQ29873.npy
-imgHQ23287.npy
-imgHQ05478.npy
-imgHQ23427.npy
-imgHQ08514.npy
-imgHQ28561.npy
-imgHQ07603.npy
-imgHQ22942.npy
-imgHQ07986.npy
-imgHQ19120.npy
-imgHQ23279.npy
-imgHQ20102.npy
-imgHQ00718.npy
-imgHQ21069.npy
-imgHQ26186.npy
-imgHQ06224.npy
-imgHQ07283.npy
-imgHQ21417.npy
-imgHQ04236.npy
-imgHQ08944.npy
-imgHQ11692.npy
-imgHQ28734.npy
-imgHQ06146.npy
-imgHQ23194.npy
-imgHQ04357.npy
-imgHQ10776.npy
-imgHQ24937.npy
-imgHQ10210.npy
-imgHQ01910.npy
-imgHQ00446.npy
-imgHQ07178.npy
-imgHQ21713.npy
-imgHQ05563.npy
-imgHQ08522.npy
-imgHQ03825.npy
-imgHQ20958.npy
-imgHQ10406.npy
-imgHQ06028.npy
-imgHQ18609.npy
-imgHQ29140.npy
-imgHQ05523.npy
-imgHQ10850.npy
-imgHQ06631.npy
-imgHQ20373.npy
-imgHQ11742.npy
-imgHQ26275.npy
-imgHQ04742.npy
-imgHQ22644.npy
-imgHQ25281.npy
-imgHQ11395.npy
-imgHQ21778.npy
-imgHQ00915.npy
-imgHQ11258.npy
-imgHQ09435.npy
-imgHQ16304.npy
-imgHQ07572.npy
-imgHQ17142.npy
-imgHQ21412.npy
-imgHQ05360.npy
-imgHQ02615.npy
-imgHQ10324.npy
-imgHQ17479.npy
-imgHQ19142.npy
-imgHQ01655.npy
-imgHQ19825.npy
-imgHQ20461.npy
-imgHQ00477.npy
-imgHQ16921.npy
-imgHQ27330.npy
-imgHQ23291.npy
-imgHQ02383.npy
-imgHQ05290.npy
-imgHQ01256.npy
-imgHQ20171.npy
-imgHQ26045.npy
-imgHQ16743.npy
-imgHQ24939.npy
-imgHQ13980.npy
-imgHQ06994.npy
-imgHQ16390.npy
-imgHQ20328.npy
-imgHQ23035.npy
-imgHQ08559.npy
-imgHQ15025.npy
-imgHQ04336.npy
-imgHQ00149.npy
-imgHQ19258.npy
-imgHQ14567.npy
-imgHQ23387.npy
-imgHQ21611.npy
-imgHQ24879.npy
-imgHQ28157.npy
-imgHQ29452.npy
-imgHQ28397.npy
-imgHQ16930.npy
-imgHQ24722.npy
-imgHQ21334.npy
-imgHQ25009.npy
-imgHQ18575.npy
-imgHQ09191.npy
-imgHQ08312.npy
-imgHQ29944.npy
-imgHQ10773.npy
-imgHQ22686.npy
-imgHQ24802.npy
-imgHQ14862.npy
-imgHQ05861.npy
-imgHQ14898.npy
-imgHQ04191.npy
-imgHQ24714.npy
-imgHQ02129.npy
-imgHQ21040.npy
-imgHQ27139.npy
-imgHQ27452.npy
-imgHQ25297.npy
-imgHQ15525.npy
-imgHQ20019.npy
-imgHQ19075.npy
-imgHQ01716.npy
-imgHQ11317.npy
-imgHQ28592.npy
-imgHQ05898.npy
-imgHQ15915.npy
-imgHQ27546.npy
-imgHQ29861.npy
-imgHQ18568.npy
-imgHQ14041.npy
-imgHQ29996.npy
-imgHQ05973.npy
-imgHQ16314.npy
-imgHQ21136.npy
-imgHQ06833.npy
-imgHQ13932.npy
-imgHQ15764.npy
-imgHQ08642.npy
-imgHQ00831.npy
-imgHQ11282.npy
-imgHQ15490.npy
-imgHQ06373.npy
-imgHQ06755.npy
-imgHQ12631.npy
-imgHQ04890.npy
-imgHQ12766.npy
-imgHQ03948.npy
-imgHQ05931.npy
-imgHQ26708.npy
-imgHQ19034.npy
-imgHQ05788.npy
-imgHQ22105.npy
-imgHQ23264.npy
-imgHQ04515.npy
-imgHQ18290.npy
-imgHQ16460.npy
-imgHQ08523.npy
-imgHQ20380.npy
-imgHQ07723.npy
-imgHQ17712.npy
-imgHQ25885.npy
-imgHQ18865.npy
-imgHQ18138.npy
-imgHQ08410.npy
-imgHQ18716.npy
-imgHQ08299.npy
-imgHQ19578.npy
-imgHQ22421.npy
-imgHQ07133.npy
-imgHQ27151.npy
-imgHQ18384.npy
-imgHQ08988.npy
-imgHQ25565.npy
-imgHQ14775.npy
-imgHQ27917.npy
-imgHQ08533.npy
-imgHQ10578.npy
-imgHQ12749.npy
-imgHQ18271.npy
-imgHQ00906.npy
-imgHQ06084.npy
-imgHQ15309.npy
-imgHQ21802.npy
-imgHQ27006.npy
-imgHQ03727.npy
-imgHQ21041.npy
-imgHQ00623.npy
-imgHQ09254.npy
-imgHQ27319.npy
-imgHQ12521.npy
-imgHQ02772.npy
-imgHQ18680.npy
-imgHQ09200.npy
-imgHQ22227.npy
-imgHQ00798.npy
-imgHQ17207.npy
-imgHQ15824.npy
-imgHQ01240.npy
-imgHQ04346.npy
-imgHQ14971.npy
-imgHQ20045.npy
-imgHQ20730.npy
-imgHQ18530.npy
-imgHQ01458.npy
-imgHQ19686.npy
-imgHQ11702.npy
-imgHQ22631.npy
-imgHQ03678.npy
-imgHQ18646.npy
-imgHQ17985.npy
-imgHQ03342.npy
-imgHQ01178.npy
-imgHQ28725.npy
-imgHQ29021.npy
-imgHQ22867.npy
-imgHQ22790.npy
-imgHQ01301.npy
-imgHQ05300.npy
-imgHQ13169.npy
-imgHQ13132.npy
-imgHQ03539.npy
-imgHQ00308.npy
-imgHQ01579.npy
-imgHQ29888.npy
-imgHQ14959.npy
-imgHQ20009.npy
-imgHQ09690.npy
-imgHQ13469.npy
-imgHQ13507.npy
-imgHQ10602.npy
-imgHQ18652.npy
-imgHQ07654.npy
-imgHQ04153.npy
-imgHQ19881.npy
-imgHQ09308.npy
-imgHQ16078.npy
-imgHQ17399.npy
-imgHQ29102.npy
-imgHQ00734.npy
-imgHQ25993.npy
-imgHQ24099.npy
-imgHQ14180.npy
-imgHQ17702.npy
-imgHQ19374.npy
-imgHQ27372.npy
-imgHQ11161.npy
-imgHQ25291.npy
-imgHQ10101.npy
-imgHQ10118.npy
-imgHQ09190.npy
-imgHQ18857.npy
-imgHQ07857.npy
-imgHQ07302.npy
-imgHQ26782.npy
-imgHQ13758.npy
-imgHQ08605.npy
-imgHQ29436.npy
-imgHQ02026.npy
-imgHQ22169.npy
-imgHQ11537.npy
-imgHQ06196.npy
-imgHQ26214.npy
-imgHQ18670.npy
-imgHQ18505.npy
-imgHQ02192.npy
-imgHQ04500.npy
-imgHQ15044.npy
-imgHQ12335.npy
-imgHQ04363.npy
-imgHQ17853.npy
-imgHQ29412.npy
-imgHQ06205.npy
-imgHQ07710.npy
-imgHQ15740.npy
-imgHQ02309.npy
-imgHQ07016.npy
-imgHQ10921.npy
-imgHQ26658.npy
-imgHQ11873.npy
-imgHQ24594.npy
-imgHQ19166.npy
-imgHQ16269.npy
-imgHQ23262.npy
-imgHQ22424.npy
-imgHQ21640.npy
-imgHQ09431.npy
-imgHQ16663.npy
-imgHQ23030.npy
-imgHQ09779.npy
-imgHQ14870.npy
-imgHQ22513.npy
-imgHQ21950.npy
-imgHQ27538.npy
-imgHQ07144.npy
-imgHQ17174.npy
-imgHQ00215.npy
-imgHQ02596.npy
-imgHQ07428.npy
-imgHQ24685.npy
-imgHQ10052.npy
-imgHQ08044.npy
-imgHQ11315.npy
-imgHQ08797.npy
-imgHQ24340.npy
-imgHQ23697.npy
-imgHQ11844.npy
-imgHQ28671.npy
-imgHQ14139.npy
-imgHQ16506.npy
-imgHQ07015.npy
-imgHQ03646.npy
-imgHQ20368.npy
-imgHQ20799.npy
-imgHQ12909.npy
-imgHQ26234.npy
-imgHQ17881.npy
-imgHQ12203.npy
-imgHQ04614.npy
-imgHQ19397.npy
-imgHQ02970.npy
-imgHQ16060.npy
-imgHQ15515.npy
-imgHQ28264.npy
-imgHQ21591.npy
-imgHQ17049.npy
-imgHQ14532.npy
-imgHQ04537.npy
-imgHQ17371.npy
-imgHQ08844.npy
-imgHQ17312.npy
-imgHQ18345.npy
-imgHQ05664.npy
-imgHQ18144.npy
-imgHQ05375.npy
-imgHQ23886.npy
-imgHQ27174.npy
-imgHQ27175.npy
-imgHQ24014.npy
-imgHQ19456.npy
-imgHQ22093.npy
-imgHQ09817.npy
-imgHQ22756.npy
-imgHQ10840.npy
-imgHQ18022.npy
-imgHQ26111.npy
-imgHQ29818.npy
-imgHQ26527.npy
-imgHQ05617.npy
-imgHQ15334.npy
-imgHQ06901.npy
-imgHQ11274.npy
-imgHQ28192.npy
-imgHQ00081.npy
-imgHQ14002.npy
-imgHQ25407.npy
-imgHQ24637.npy
-imgHQ01180.npy
-imgHQ07750.npy
-imgHQ26254.npy
-imgHQ12780.npy
-imgHQ28833.npy
-imgHQ21308.npy
-imgHQ12928.npy
-imgHQ18434.npy
-imgHQ07845.npy
-imgHQ02601.npy
-imgHQ19800.npy
-imgHQ26621.npy
-imgHQ22161.npy
-imgHQ09001.npy
-imgHQ13891.npy
-imgHQ17350.npy
-imgHQ25978.npy
-imgHQ14279.npy
-imgHQ15947.npy
-imgHQ26485.npy
-imgHQ14164.npy
-imgHQ04893.npy
-imgHQ07305.npy
-imgHQ23374.npy
-imgHQ21908.npy
-imgHQ17593.npy
-imgHQ12293.npy
-imgHQ24242.npy
-imgHQ15693.npy
-imgHQ28476.npy
-imgHQ17775.npy
-imgHQ02364.npy
-imgHQ23531.npy
-imgHQ04658.npy
-imgHQ28131.npy
-imgHQ23491.npy
-imgHQ29454.npy
-imgHQ22957.npy
-imgHQ07333.npy
-imgHQ13574.npy
-imgHQ11764.npy
-imgHQ23720.npy
-imgHQ23159.npy
-imgHQ16167.npy
-imgHQ15621.npy
-imgHQ22985.npy
-imgHQ09704.npy
-imgHQ29832.npy
-imgHQ15930.npy
-imgHQ17096.npy
-imgHQ15296.npy
-imgHQ09835.npy
-imgHQ05263.npy
-imgHQ02334.npy
-imgHQ15657.npy
-imgHQ15006.npy
-imgHQ04071.npy
-imgHQ02093.npy
-imgHQ27050.npy
-imgHQ27355.npy
-imgHQ29462.npy
-imgHQ00663.npy
-imgHQ22817.npy
-imgHQ08553.npy
-imgHQ23920.npy
-imgHQ23696.npy
-imgHQ03545.npy
-imgHQ26850.npy
-imgHQ17128.npy
-imgHQ05077.npy
-imgHQ25497.npy
-imgHQ09171.npy
-imgHQ03165.npy
-imgHQ01260.npy
-imgHQ15483.npy
-imgHQ05723.npy
-imgHQ12802.npy
-imgHQ22529.npy
-imgHQ15806.npy
-imgHQ27214.npy
-imgHQ01872.npy
-imgHQ12354.npy
-imgHQ11050.npy
-imgHQ29133.npy
-imgHQ27607.npy
-imgHQ26974.npy
-imgHQ03322.npy
-imgHQ19191.npy
-imgHQ28090.npy
-imgHQ02813.npy
-imgHQ09606.npy
-imgHQ27536.npy
-imgHQ16655.npy
-imgHQ24633.npy
-imgHQ21852.npy
-imgHQ01455.npy
-imgHQ18409.npy
-imgHQ16157.npy
-imgHQ28995.npy
-imgHQ16282.npy
-imgHQ00172.npy
-imgHQ14884.npy
-imgHQ01093.npy
-imgHQ06175.npy
-imgHQ24429.npy
-imgHQ16403.npy
-imgHQ29845.npy
-imgHQ16816.npy
-imgHQ02868.npy
-imgHQ13488.npy
-imgHQ29793.npy
-imgHQ04368.npy
-imgHQ11650.npy
-imgHQ11188.npy
-imgHQ25270.npy
-imgHQ04884.npy
-imgHQ24364.npy
-imgHQ11550.npy
-imgHQ07402.npy
-imgHQ25534.npy
-imgHQ20924.npy
-imgHQ12333.npy
-imgHQ20635.npy
-imgHQ28659.npy
-imgHQ09904.npy
-imgHQ08998.npy
-imgHQ10113.npy
-imgHQ05195.npy
-imgHQ11430.npy
-imgHQ25675.npy
-imgHQ20710.npy
-imgHQ21310.npy
-imgHQ14480.npy
-imgHQ01218.npy
-imgHQ14587.npy
-imgHQ17046.npy
-imgHQ11758.npy
-imgHQ11962.npy
-imgHQ20394.npy
-imgHQ15380.npy
-imgHQ11374.npy
-imgHQ06390.npy
-imgHQ23369.npy
-imgHQ06364.npy
-imgHQ07718.npy
-imgHQ17304.npy
-imgHQ25785.npy
-imgHQ20284.npy
-imgHQ08599.npy
-imgHQ17592.npy
-imgHQ19906.npy
-imgHQ02713.npy
-imgHQ18993.npy
-imgHQ27398.npy
-imgHQ22994.npy
-imgHQ13627.npy
-imgHQ28525.npy
-imgHQ21039.npy
-imgHQ10418.npy
-imgHQ05852.npy
-imgHQ10150.npy
-imgHQ14382.npy
-imgHQ09639.npy
-imgHQ22892.npy
-imgHQ20983.npy
-imgHQ03120.npy
-imgHQ19524.npy
-imgHQ12059.npy
-imgHQ11943.npy
-imgHQ18451.npy
-imgHQ27680.npy
-imgHQ13889.npy
-imgHQ12330.npy
-imgHQ16150.npy
-imgHQ18753.npy
-imgHQ11149.npy
-imgHQ26025.npy
-imgHQ25556.npy
-imgHQ07098.npy
-imgHQ13241.npy
-imgHQ11967.npy
-imgHQ11938.npy
-imgHQ05710.npy
-imgHQ16420.npy
-imgHQ14221.npy
-imgHQ14237.npy
-imgHQ07336.npy
-imgHQ14163.npy
-imgHQ27266.npy
-imgHQ14463.npy
-imgHQ12503.npy
-imgHQ23087.npy
-imgHQ24329.npy
-imgHQ25250.npy
-imgHQ24516.npy
-imgHQ17263.npy
-imgHQ11597.npy
-imgHQ13178.npy
-imgHQ21962.npy
-imgHQ23172.npy
-imgHQ09980.npy
-imgHQ00244.npy
-imgHQ01598.npy
-imgHQ14106.npy
-imgHQ20690.npy
-imgHQ22310.npy
-imgHQ10558.npy
-imgHQ23912.npy
-imgHQ21523.npy
-imgHQ03469.npy
-imgHQ22672.npy
-imgHQ04352.npy
-imgHQ14442.npy
-imgHQ21317.npy
-imgHQ26438.npy
-imgHQ21747.npy
-imgHQ18006.npy
-imgHQ29834.npy
-imgHQ10480.npy
-imgHQ28333.npy
-imgHQ12913.npy
-imgHQ10000.npy
-imgHQ06109.npy
-imgHQ25819.npy
-imgHQ12126.npy
-imgHQ07249.npy
-imgHQ16679.npy
-imgHQ01340.npy
-imgHQ17066.npy
-imgHQ00295.npy
-imgHQ26076.npy
-imgHQ09392.npy
-imgHQ18567.npy
-imgHQ11187.npy
-imgHQ04532.npy
-imgHQ27337.npy
-imgHQ03937.npy
-imgHQ11438.npy
-imgHQ19634.npy
-imgHQ16055.npy
-imgHQ13442.npy
-imgHQ15640.npy
-imgHQ02272.npy
-imgHQ18042.npy
-imgHQ07202.npy
-imgHQ25140.npy
-imgHQ17776.npy
-imgHQ03471.npy
-imgHQ00283.npy
-imgHQ16145.npy
-imgHQ16132.npy
-imgHQ19251.npy
-imgHQ14851.npy
-imgHQ05012.npy
-imgHQ25706.npy
-imgHQ13965.npy
-imgHQ14020.npy
-imgHQ07470.npy
-imgHQ27922.npy
-imgHQ12796.npy
-imgHQ16441.npy
-imgHQ29521.npy
-imgHQ27379.npy
-imgHQ20190.npy
-imgHQ12715.npy
-imgHQ15089.npy
-imgHQ14991.npy
-imgHQ11552.npy
-imgHQ26749.npy
-imgHQ05854.npy
-imgHQ10978.npy
-imgHQ04141.npy
-imgHQ21812.npy
-imgHQ11194.npy
-imgHQ18502.npy
-imgHQ12218.npy
-imgHQ22049.npy
-imgHQ23600.npy
-imgHQ18675.npy
-imgHQ17234.npy
-imgHQ24322.npy
-imgHQ13451.npy
-imgHQ05690.npy
-imgHQ28546.npy
-imgHQ16785.npy
-imgHQ25104.npy
-imgHQ05231.npy
-imgHQ04408.npy
-imgHQ19532.npy
-imgHQ17518.npy
-imgHQ27195.npy
-imgHQ25841.npy
-imgHQ29029.npy
-imgHQ24641.npy
-imgHQ02070.npy
-imgHQ04233.npy
-imgHQ16530.npy
-imgHQ24559.npy
-imgHQ26881.npy
-imgHQ21620.npy
-imgHQ16947.npy
-imgHQ10808.npy
-imgHQ02040.npy
-imgHQ18315.npy
-imgHQ28108.npy
-imgHQ16915.npy
-imgHQ26558.npy
-imgHQ10847.npy
-imgHQ19392.npy
-imgHQ26864.npy
-imgHQ23881.npy
-imgHQ01603.npy
-imgHQ24807.npy
-imgHQ09483.npy
-imgHQ10469.npy
-imgHQ26548.npy
-imgHQ17778.npy
-imgHQ28410.npy
-imgHQ22329.npy
-imgHQ01921.npy
-imgHQ08424.npy
-imgHQ19520.npy
-imgHQ02882.npy
-imgHQ14261.npy
-imgHQ19203.npy
-imgHQ03336.npy
-imgHQ09574.npy
-imgHQ22939.npy
-imgHQ24861.npy
-imgHQ24636.npy
-imgHQ10719.npy
-imgHQ00919.npy
-imgHQ06121.npy
-imgHQ16131.npy
-imgHQ24648.npy
-imgHQ03487.npy
-imgHQ23436.npy
-imgHQ10297.npy
-imgHQ07391.npy
-imgHQ24031.npy
-imgHQ05289.npy
-imgHQ18241.npy
-imgHQ23691.npy
-imgHQ17854.npy
-imgHQ20153.npy
-imgHQ11538.npy
-imgHQ08327.npy
-imgHQ13302.npy
-imgHQ14654.npy
-imgHQ23782.npy
-imgHQ02318.npy
-imgHQ05721.npy
-imgHQ09910.npy
-imgHQ17034.npy
-imgHQ22782.npy
-imgHQ23862.npy
-imgHQ14273.npy
-imgHQ05277.npy
-imgHQ01987.npy
-imgHQ25069.npy
-imgHQ00985.npy
-imgHQ12782.npy
-imgHQ02939.npy
-imgHQ03281.npy
-imgHQ09373.npy
-imgHQ14920.npy
-imgHQ10881.npy
-imgHQ20726.npy
-imgHQ26740.npy
-imgHQ06131.npy
-imgHQ03352.npy
-imgHQ19650.npy
-imgHQ05834.npy
-imgHQ06749.npy
-imgHQ12969.npy
-imgHQ20353.npy
-imgHQ27433.npy
-imgHQ22893.npy
-imgHQ13722.npy
-imgHQ05579.npy
-imgHQ25339.npy
-imgHQ07207.npy
-imgHQ26336.npy
-imgHQ00934.npy
-imgHQ11733.npy
-imgHQ08646.npy
-imgHQ11716.npy
-imgHQ20515.npy
-imgHQ25259.npy
-imgHQ06126.npy
-imgHQ01945.npy
-imgHQ16256.npy
-imgHQ01436.npy
-imgHQ23536.npy
-imgHQ01397.npy
-imgHQ14053.npy
-imgHQ25019.npy
-imgHQ02524.npy
-imgHQ02057.npy
-imgHQ06862.npy
-imgHQ28191.npy
-imgHQ13423.npy
-imgHQ06367.npy
-imgHQ15160.npy
-imgHQ11459.npy
-imgHQ13384.npy
-imgHQ16327.npy
-imgHQ17600.npy
-imgHQ16736.npy
-imgHQ06190.npy
-imgHQ21001.npy
-imgHQ28552.npy
-imgHQ18791.npy
-imgHQ29578.npy
-imgHQ29299.npy
-imgHQ16544.npy
-imgHQ29243.npy
-imgHQ04151.npy
-imgHQ29852.npy
-imgHQ08960.npy
-imgHQ21110.npy
-imgHQ07243.npy
-imgHQ20178.npy
-imgHQ17552.npy
-imgHQ13988.npy
-imgHQ00200.npy
-imgHQ24404.npy
-imgHQ10910.npy
-imgHQ06368.npy
-imgHQ08481.npy
-imgHQ11620.npy
-imgHQ06421.npy
-imgHQ10078.npy
-imgHQ08278.npy
-imgHQ15965.npy
-imgHQ16858.npy
-imgHQ05796.npy
-imgHQ03567.npy
-imgHQ03895.npy
-imgHQ17031.npy
-imgHQ16370.npy
-imgHQ16352.npy
-imgHQ20200.npy
-imgHQ06343.npy
-imgHQ13851.npy
-imgHQ24471.npy
-imgHQ05711.npy
-imgHQ11261.npy
-imgHQ02988.npy
-imgHQ24277.npy
-imgHQ15035.npy
-imgHQ10305.npy
-imgHQ25395.npy
-imgHQ12815.npy
-imgHQ26243.npy
-imgHQ07932.npy
-imgHQ04235.npy
-imgHQ25520.npy
-imgHQ04866.npy
-imgHQ12811.npy
-imgHQ17706.npy
-imgHQ21631.npy
-imgHQ18888.npy
-imgHQ03452.npy
-imgHQ07712.npy
-imgHQ00284.npy
-imgHQ10925.npy
-imgHQ25943.npy
-imgHQ21744.npy
-imgHQ06718.npy
-imgHQ20728.npy
-imgHQ16936.npy
-imgHQ03871.npy
-imgHQ14267.npy
-imgHQ14170.npy
-imgHQ03159.npy
-imgHQ16709.npy
-imgHQ05410.npy
-imgHQ03425.npy
-imgHQ29085.npy
-imgHQ05555.npy
-imgHQ09528.npy
-imgHQ18370.npy
-imgHQ04180.npy
-imgHQ17471.npy
-imgHQ13893.npy
-imgHQ24776.npy
-imgHQ11365.npy
-imgHQ09475.npy
-imgHQ10316.npy
-imgHQ12595.npy
-imgHQ20076.npy
-imgHQ20991.npy
-imgHQ21658.npy
-imgHQ24985.npy
-imgHQ25238.npy
-imgHQ19563.npy
-imgHQ29835.npy
-imgHQ23347.npy
-imgHQ09773.npy
-imgHQ24579.npy
-imgHQ13072.npy
-imgHQ24005.npy
-imgHQ02752.npy
-imgHQ09284.npy
-imgHQ07766.npy
-imgHQ21661.npy
-imgHQ20545.npy
-imgHQ27539.npy
-imgHQ28890.npy
-imgHQ28586.npy
-imgHQ06113.npy
-imgHQ13173.npy
-imgHQ22929.npy
-imgHQ28795.npy
-imgHQ20523.npy
-imgHQ08322.npy
-imgHQ23925.npy
-imgHQ02449.npy
-imgHQ14604.npy
-imgHQ23434.npy
-imgHQ22001.npy
-imgHQ05806.npy
-imgHQ12175.npy
-imgHQ03583.npy
-imgHQ14198.npy
-imgHQ27388.npy
-imgHQ26651.npy
-imgHQ29143.npy
-imgHQ01035.npy
-imgHQ23289.npy
-imgHQ15478.npy
-imgHQ14649.npy
-imgHQ27031.npy
-imgHQ17497.npy
-imgHQ17024.npy
-imgHQ07292.npy
-imgHQ06558.npy
-imgHQ27218.npy
-imgHQ24381.npy
-imgHQ20344.npy
-imgHQ14191.npy
-imgHQ08281.npy
-imgHQ02681.npy
-imgHQ23392.npy
-imgHQ10583.npy
-imgHQ28198.npy
-imgHQ00857.npy
-imgHQ07933.npy
-imgHQ05397.npy
-imgHQ06088.npy
-imgHQ11168.npy
-imgHQ01666.npy
-imgHQ06761.npy
-imgHQ29890.npy
-imgHQ16668.npy
-imgHQ10621.npy
-imgHQ01221.npy
-imgHQ16011.npy
-imgHQ00918.npy
-imgHQ12663.npy
-imgHQ00709.npy
-imgHQ02264.npy
-imgHQ08040.npy
-imgHQ08918.npy
-imgHQ10355.npy
-imgHQ19067.npy
-imgHQ06119.npy
-imgHQ13922.npy
-imgHQ17421.npy
-imgHQ10408.npy
-imgHQ23837.npy
-imgHQ26735.npy
-imgHQ04126.npy
-imgHQ28151.npy
-imgHQ29913.npy
-imgHQ17729.npy
-imgHQ23323.npy
-imgHQ26179.npy
-imgHQ22342.npy
-imgHQ14224.npy
-imgHQ15734.npy
-imgHQ17681.npy
-imgHQ09824.npy
-imgHQ14430.npy
-imgHQ14014.npy
-imgHQ09578.npy
-imgHQ07116.npy
-imgHQ27870.npy
-imgHQ29182.npy
-imgHQ05610.npy
-imgHQ07447.npy
-imgHQ21428.npy
-imgHQ01381.npy
-imgHQ24402.npy
-imgHQ26890.npy
-imgHQ19706.npy
-imgHQ06920.npy
-imgHQ25124.npy
-imgHQ01318.npy
-imgHQ02656.npy
-imgHQ29859.npy
-imgHQ11118.npy
-imgHQ26852.npy
-imgHQ20463.npy
-imgHQ28565.npy
-imgHQ16166.npy
-imgHQ21849.npy
-imgHQ06047.npy
-imgHQ22385.npy
-imgHQ18629.npy
-imgHQ17296.npy
-imgHQ27531.npy
-imgHQ05915.npy
-imgHQ23954.npy
-imgHQ23763.npy
-imgHQ09805.npy
-imgHQ22509.npy
-imgHQ12164.npy
-imgHQ15771.npy
-imgHQ20516.npy
-imgHQ21302.npy
-imgHQ17580.npy
-imgHQ26313.npy
-imgHQ06952.npy
-imgHQ16607.npy
-imgHQ25242.npy
-imgHQ00938.npy
-imgHQ20022.npy
-imgHQ03651.npy
-imgHQ29210.npy
-imgHQ01813.npy
-imgHQ12935.npy
-imgHQ07360.npy
-imgHQ17838.npy
-imgHQ17069.npy
-imgHQ19621.npy
-imgHQ05398.npy
-imgHQ29678.npy
-imgHQ08953.npy
-imgHQ11566.npy
-imgHQ11901.npy
-imgHQ24365.npy
-imgHQ22247.npy
-imgHQ04505.npy
-imgHQ07002.npy
-imgHQ20100.npy
-imgHQ14072.npy
-imgHQ10037.npy
-imgHQ11688.npy
-imgHQ26581.npy
-imgHQ17198.npy
-imgHQ12931.npy
-imgHQ19710.npy
-imgHQ07322.npy
-imgHQ29426.npy
-imgHQ06617.npy
-imgHQ29684.npy
-imgHQ05787.npy
-imgHQ00425.npy
-imgHQ12927.npy
-imgHQ25084.npy
-imgHQ24383.npy
-imgHQ19004.npy
-imgHQ21235.npy
-imgHQ10175.npy
-imgHQ20425.npy
-imgHQ02460.npy
-imgHQ12646.npy
-imgHQ03209.npy
-imgHQ08786.npy
-imgHQ24815.npy
-imgHQ20343.npy
-imgHQ06101.npy
-imgHQ12182.npy
-imgHQ15412.npy
-imgHQ04045.npy
-imgHQ22884.npy
-imgHQ22739.npy
-imgHQ22444.npy
-imgHQ04367.npy
-imgHQ05141.npy
-imgHQ09281.npy
-imgHQ18124.npy
-imgHQ04904.npy
-imgHQ22793.npy
-imgHQ12658.npy
-imgHQ08926.npy
-imgHQ01084.npy
-imgHQ02049.npy
-imgHQ08353.npy
-imgHQ09776.npy
-imgHQ08778.npy
-imgHQ08387.npy
-imgHQ06830.npy
-imgHQ03343.npy
-imgHQ02099.npy
-imgHQ23902.npy
-imgHQ07961.npy
-imgHQ26274.npy
-imgHQ27065.npy
-imgHQ28470.npy
-imgHQ08948.npy
-imgHQ15643.npy
-imgHQ28514.npy
-imgHQ05940.npy
-imgHQ21212.npy
-imgHQ06521.npy
-imgHQ13506.npy
-imgHQ22086.npy
-imgHQ23509.npy
-imgHQ28361.npy
-imgHQ00893.npy
-imgHQ06052.npy
-imgHQ26177.npy
-imgHQ28451.npy
-imgHQ02098.npy
-imgHQ19273.npy
-imgHQ03489.npy
-imgHQ07925.npy
-imgHQ27169.npy
-imgHQ15276.npy
-imgHQ25848.npy
-imgHQ16970.npy
-imgHQ20647.npy
-imgHQ13521.npy
-imgHQ00664.npy
-imgHQ05142.npy
-imgHQ11671.npy
-imgHQ05759.npy
-imgHQ16690.npy
-imgHQ11185.npy
-imgHQ23730.npy
-imgHQ11212.npy
-imgHQ23658.npy
-imgHQ14111.npy
-imgHQ05225.npy
-imgHQ16775.npy
-imgHQ10794.npy
-imgHQ22109.npy
-imgHQ21153.npy
-imgHQ26238.npy
-imgHQ05180.npy
-imgHQ19165.npy
-imgHQ19449.npy
-imgHQ02629.npy
-imgHQ08616.npy
-imgHQ07673.npy
-imgHQ21779.npy
-imgHQ06432.npy
-imgHQ27765.npy
-imgHQ24175.npy
-imgHQ01078.npy
-imgHQ21867.npy
-imgHQ25459.npy
-imgHQ03394.npy
-imgHQ05993.npy
-imgHQ07916.npy
-imgHQ08893.npy
-imgHQ10900.npy
-imgHQ08201.npy
-imgHQ12740.npy
-imgHQ10468.npy
-imgHQ07342.npy
-imgHQ21699.npy
-imgHQ19235.npy
-imgHQ26903.npy
-imgHQ20757.npy
-imgHQ09490.npy
-imgHQ28426.npy
-imgHQ29302.npy
-imgHQ01383.npy
-imgHQ11045.npy
-imgHQ09449.npy
-imgHQ06734.npy
-imgHQ12594.npy
-imgHQ10152.npy
-imgHQ13643.npy
-imgHQ24738.npy
-imgHQ08032.npy
-imgHQ06518.npy
-imgHQ19371.npy
-imgHQ28550.npy
-imgHQ12814.npy
-imgHQ04635.npy
-imgHQ00360.npy
-imgHQ08687.npy
-imgHQ07675.npy
-imgHQ25429.npy
-imgHQ03521.npy
-imgHQ22871.npy
-imgHQ24743.npy
-imgHQ16849.npy
-imgHQ06799.npy
-imgHQ02439.npy
-imgHQ19310.npy
-imgHQ25416.npy
-imgHQ14048.npy
-imgHQ12426.npy
-imgHQ02398.npy
-imgHQ26677.npy
-imgHQ02336.npy
-imgHQ27938.npy
-imgHQ02081.npy
-imgHQ00574.npy
-imgHQ23713.npy
-imgHQ23499.npy
-imgHQ16629.npy
-imgHQ13079.npy
-imgHQ08393.npy
-imgHQ02606.npy
-imgHQ22460.npy
-imgHQ23814.npy
-imgHQ17942.npy
-imgHQ14263.npy
-imgHQ25205.npy
-imgHQ26649.npy
-imgHQ08006.npy
-imgHQ14861.npy
-imgHQ26429.npy
-imgHQ27990.npy
-imgHQ14761.npy
-imgHQ25676.npy
-imgHQ01642.npy
-imgHQ25436.npy
-imgHQ01673.npy
-imgHQ08540.npy
-imgHQ13584.npy
-imgHQ16385.npy
-imgHQ14887.npy
-imgHQ09759.npy
-imgHQ09903.npy
-imgHQ28782.npy
-imgHQ12081.npy
-imgHQ06813.npy
-imgHQ06867.npy
-imgHQ16030.npy
-imgHQ29849.npy
-imgHQ29559.npy
-imgHQ18233.npy
-imgHQ17834.npy
-imgHQ24537.npy
-imgHQ04843.npy
-imgHQ26583.npy
-imgHQ19743.npy
-imgHQ12311.npy
-imgHQ05552.npy
-imgHQ22060.npy
-imgHQ20248.npy
-imgHQ23714.npy
-imgHQ08326.npy
-imgHQ28212.npy
-imgHQ11263.npy
-imgHQ05930.npy
-imgHQ23799.npy
-imgHQ23874.npy
-imgHQ16805.npy
-imgHQ15523.npy
-imgHQ29823.npy
-imgHQ27717.npy
-imgHQ25870.npy
-imgHQ26092.npy
-imgHQ14432.npy
-imgHQ05840.npy
-imgHQ07583.npy
-imgHQ21206.npy
-imgHQ14133.npy
-imgHQ06804.npy
-imgHQ10816.npy
-imgHQ20517.npy
-imgHQ13332.npy
-imgHQ18718.npy
-imgHQ18957.npy
-imgHQ10634.npy
-imgHQ21248.npy
-imgHQ06029.npy
-imgHQ17917.npy
-imgHQ09348.npy
-imgHQ07033.npy
-imgHQ07251.npy
-imgHQ22390.npy
-imgHQ17591.npy
-imgHQ12054.npy
-imgHQ11886.npy
-imgHQ09499.npy
-imgHQ18000.npy
-imgHQ15618.npy
-imgHQ20165.npy
-imgHQ06868.npy
-imgHQ16565.npy
-imgHQ20426.npy
-imgHQ18426.npy
-imgHQ16483.npy
-imgHQ29267.npy
-imgHQ28008.npy
-imgHQ09485.npy
-imgHQ04213.npy
-imgHQ06439.npy
-imgHQ17268.npy
-imgHQ00302.npy
-imgHQ06128.npy
-imgHQ01096.npy
-imgHQ02829.npy
-imgHQ03712.npy
-imgHQ24193.npy
-imgHQ27506.npy
-imgHQ08160.npy
-imgHQ28615.npy
-imgHQ11153.npy
-imgHQ10070.npy
-imgHQ26726.npy
-imgHQ25623.npy
-imgHQ28907.npy
-imgHQ15662.npy
-imgHQ14776.npy
-imgHQ06187.npy
-imgHQ10864.npy
-imgHQ15697.npy
-imgHQ29255.npy
-imgHQ07545.npy
-imgHQ02490.npy
-imgHQ18061.npy
-imgHQ28571.npy
-imgHQ16446.npy
-imgHQ23583.npy
-imgHQ03166.npy
-imgHQ10482.npy
-imgHQ09564.npy
-imgHQ03356.npy
-imgHQ27269.npy
-imgHQ29747.npy
-imgHQ00557.npy
-imgHQ11100.npy
-imgHQ15541.npy
-imgHQ01663.npy
-imgHQ14501.npy
-imgHQ07124.npy
-imgHQ12890.npy
-imgHQ11999.npy
-imgHQ07617.npy
-imgHQ02652.npy
-imgHQ07691.npy
-imgHQ14864.npy
-imgHQ10938.npy
-imgHQ04611.npy
-imgHQ18474.npy
-imgHQ13066.npy
-imgHQ01346.npy
-imgHQ13052.npy
-imgHQ00890.npy
-imgHQ00375.npy
-imgHQ26385.npy
-imgHQ01512.npy
-imgHQ25294.npy
-imgHQ15114.npy
-imgHQ19909.npy
-imgHQ29877.npy
-imgHQ02572.npy
-imgHQ20026.npy
-imgHQ22625.npy
-imgHQ22524.npy
-imgHQ01612.npy
-imgHQ06020.npy
-imgHQ08361.npy
-imgHQ09694.npy
-imgHQ28628.npy
-imgHQ09049.npy
-imgHQ10767.npy
-imgHQ15628.npy
-imgHQ03366.npy
-imgHQ03208.npy
-imgHQ27545.npy
-imgHQ15310.npy
-imgHQ16784.npy
-imgHQ28979.npy
-imgHQ08306.npy
-imgHQ08103.npy
-imgHQ04805.npy
-imgHQ01672.npy
-imgHQ10970.npy
-imgHQ24622.npy
-imgHQ19888.npy
-imgHQ11150.npy
-imgHQ12258.npy
-imgHQ28796.npy
-imgHQ07792.npy
-imgHQ27972.npy
-imgHQ10756.npy
-imgHQ01739.npy
-imgHQ13934.npy
-imgHQ23547.npy
-imgHQ25781.npy
-imgHQ07058.npy
-imgHQ19369.npy
-imgHQ00853.npy
-imgHQ12891.npy
-imgHQ27475.npy
-imgHQ01288.npy
-imgHQ05798.npy
-imgHQ08613.npy
-imgHQ10706.npy
-imgHQ17449.npy
-imgHQ17566.npy
-imgHQ26519.npy
-imgHQ24647.npy
-imgHQ25708.npy
-imgHQ11842.npy
-imgHQ21065.npy
-imgHQ24681.npy
-imgHQ23101.npy
-imgHQ06092.npy
-imgHQ14789.npy
-imgHQ03640.npy
-imgHQ04874.npy
-imgHQ09646.npy
-imgHQ08158.npy
-imgHQ17208.npy
-imgHQ11467.npy
-imgHQ04294.npy
-imgHQ04800.npy
-imgHQ25334.npy
-imgHQ26587.npy
-imgHQ06234.npy
-imgHQ03535.npy
-imgHQ02817.npy
-imgHQ12388.npy
-imgHQ29355.npy
-imgHQ26948.npy
-imgHQ16455.npy
-imgHQ08229.npy
-imgHQ04426.npy
-imgHQ02380.npy
-imgHQ08728.npy
-imgHQ17469.npy
-imgHQ08185.npy
-imgHQ28467.npy
-imgHQ05052.npy
-imgHQ18699.npy
-imgHQ08433.npy
-imgHQ22837.npy
-imgHQ04587.npy
-imgHQ02379.npy
-imgHQ12499.npy
-imgHQ28465.npy
-imgHQ29327.npy
-imgHQ10500.npy
-imgHQ07062.npy
-imgHQ10438.npy
-imgHQ10710.npy
-imgHQ18225.npy
-imgHQ17503.npy
-imgHQ22414.npy
-imgHQ19370.npy
-imgHQ11001.npy
-imgHQ20724.npy
-imgHQ26191.npy
-imgHQ26983.npy
-imgHQ27261.npy
-imgHQ23381.npy
-imgHQ15774.npy
-imgHQ05469.npy
-imgHQ16247.npy
-imgHQ02444.npy
-imgHQ05474.npy
-imgHQ19147.npy
-imgHQ00624.npy
-imgHQ14319.npy
-imgHQ08179.npy
-imgHQ24136.npy
-imgHQ23234.npy
-imgHQ22748.npy
-imgHQ18109.npy
-imgHQ07439.npy
-imgHQ18822.npy
-imgHQ29576.npy
-imgHQ10705.npy
-imgHQ11228.npy
-imgHQ00832.npy
-imgHQ27361.npy
-imgHQ25568.npy
-imgHQ23873.npy
-imgHQ13710.npy
-imgHQ20577.npy
-imgHQ26481.npy
-imgHQ07218.npy
-imgHQ20562.npy
-imgHQ10006.npy
-imgHQ16065.npy
-imgHQ13470.npy
-imgHQ09604.npy
-imgHQ23293.npy
-imgHQ19303.npy
-imgHQ07847.npy
-imgHQ02356.npy
-imgHQ25887.npy
-imgHQ13846.npy
-imgHQ22666.npy
-imgHQ13784.npy
-imgHQ18156.npy
-imgHQ25105.npy
-imgHQ21466.npy
-imgHQ06108.npy
-imgHQ14755.npy
-imgHQ27517.npy
-imgHQ26497.npy
-imgHQ28005.npy
-imgHQ20765.npy
-imgHQ21511.npy
-imgHQ08637.npy
-imgHQ28910.npy
-imgHQ07445.npy
-imgHQ07239.npy
-imgHQ03145.npy
-imgHQ10380.npy
-imgHQ12627.npy
-imgHQ25833.npy
-imgHQ10924.npy
-imgHQ28433.npy
-imgHQ11493.npy
-imgHQ00481.npy
-imgHQ07507.npy
-imgHQ08219.npy
-imgHQ22969.npy
-imgHQ18856.npy
-imgHQ11375.npy
-imgHQ10497.npy
-imgHQ25642.npy
-imgHQ03396.npy
-imgHQ12827.npy
-imgHQ19011.npy
-imgHQ10338.npy
-imgHQ05770.npy
-imgHQ04246.npy
-imgHQ17664.npy
-imgHQ25229.npy
-imgHQ10940.npy
-imgHQ11777.npy
-imgHQ16695.npy
-imgHQ11217.npy
-imgHQ08419.npy
-imgHQ01284.npy
-imgHQ13639.npy
-imgHQ24353.npy
-imgHQ22898.npy
-imgHQ10932.npy
-imgHQ03917.npy
-imgHQ26498.npy
-imgHQ25193.npy
-imgHQ05377.npy
-imgHQ24169.npy
-imgHQ06248.npy
-imgHQ04356.npy
-imgHQ11740.npy
-imgHQ01519.npy
-imgHQ26200.npy
-imgHQ14169.npy
-imgHQ10922.npy
-imgHQ04671.npy
-imgHQ17216.npy
-imgHQ04888.npy
-imgHQ10526.npy
-imgHQ11442.npy
-imgHQ26490.npy
-imgHQ10149.npy
-imgHQ01361.npy
-imgHQ02495.npy
-imgHQ01829.npy
-imgHQ22326.npy
-imgHQ12791.npy
-imgHQ20386.npy
-imgHQ19875.npy
-imgHQ22610.npy
-imgHQ09809.npy
-imgHQ25056.npy
-imgHQ25228.npy
-imgHQ08223.npy
-imgHQ08717.npy
-imgHQ16095.npy
-imgHQ13733.npy
-imgHQ14330.npy
-imgHQ17562.npy
-imgHQ08070.npy
-imgHQ14425.npy
-imgHQ14309.npy
-imgHQ29431.npy
-imgHQ19677.npy
-imgHQ15960.npy
-imgHQ22651.npy
-imgHQ17393.npy
-imgHQ04872.npy
-imgHQ25598.npy
-imgHQ18664.npy
-imgHQ06381.npy
-imgHQ00369.npy
-imgHQ08940.npy
-imgHQ21271.npy
-imgHQ01211.npy
-imgHQ28050.npy
-imgHQ01614.npy
-imgHQ28030.npy
-imgHQ14007.npy
-imgHQ00078.npy
-imgHQ00349.npy
-imgHQ23654.npy
-imgHQ02965.npy
-imgHQ05269.npy
-imgHQ04161.npy
-imgHQ26099.npy
-imgHQ07195.npy
-imgHQ11944.npy
-imgHQ29223.npy
-imgHQ19931.npy
-imgHQ27828.npy
-imgHQ08829.npy
-imgHQ03798.npy
-imgHQ10195.npy
-imgHQ27513.npy
-imgHQ29590.npy
-imgHQ25112.npy
-imgHQ17851.npy
-imgHQ03566.npy
-imgHQ03663.npy
-imgHQ29338.npy
-imgHQ18987.npy
-imgHQ06572.npy
-imgHQ18113.npy
-imgHQ26934.npy
-imgHQ01827.npy
-imgHQ02274.npy
-imgHQ08426.npy
-imgHQ19055.npy
-imgHQ12383.npy
-imgHQ21469.npy
-imgHQ11642.npy
-imgHQ27252.npy
-imgHQ19438.npy
-imgHQ08819.npy
-imgHQ25673.npy
-imgHQ27007.npy
-imgHQ23825.npy
-imgHQ06155.npy
-imgHQ06958.npy
-imgHQ29798.npy
-imgHQ06333.npy
-imgHQ19672.npy
-imgHQ20892.npy
-imgHQ07885.npy
-imgHQ16080.npy
-imgHQ03679.npy
-imgHQ18114.npy
-imgHQ21478.npy
-imgHQ01460.npy
-imgHQ29483.npy
-imgHQ09806.npy
-imgHQ13895.npy
-imgHQ04415.npy
-imgHQ06601.npy
-imgHQ13086.npy
-imgHQ04034.npy
-imgHQ11583.npy
-imgHQ11573.npy
-imgHQ20436.npy
-imgHQ09069.npy
-imgHQ24737.npy
-imgHQ29934.npy
-imgHQ22885.npy
-imgHQ07118.npy
-imgHQ15142.npy
-imgHQ16457.npy
-imgHQ13607.npy
-imgHQ12721.npy
-imgHQ07562.npy
-imgHQ05835.npy
-imgHQ18989.npy
-imgHQ07987.npy
-imgHQ23127.npy
-imgHQ28667.npy
-imgHQ13604.npy
-imgHQ20313.npy
-imgHQ21920.npy
-imgHQ20511.npy
-imgHQ25682.npy
-imgHQ12978.npy
-imgHQ24128.npy
-imgHQ23527.npy
-imgHQ15078.npy
-imgHQ08212.npy
-imgHQ14299.npy
-imgHQ04676.npy
-imgHQ22951.npy
-imgHQ12109.npy
-imgHQ19849.npy
-imgHQ23070.npy
-imgHQ16742.npy
-imgHQ03939.npy
-imgHQ15912.npy
-imgHQ24704.npy
-imgHQ26414.npy
-imgHQ18291.npy
-imgHQ21869.npy
-imgHQ13879.npy
-imgHQ23770.npy
-imgHQ24734.npy
-imgHQ13684.npy
-imgHQ20054.npy
-imgHQ17585.npy
-imgHQ22024.npy
-imgHQ28912.npy
-imgHQ04007.npy
-imgHQ12308.npy
-imgHQ16869.npy
-imgHQ02624.npy
-imgHQ22419.npy
-imgHQ28042.npy
-imgHQ12142.npy
-imgHQ14921.npy
-imgHQ18539.npy
-imgHQ07598.npy
-imgHQ04175.npy
-imgHQ06695.npy
-imgHQ07796.npy
-imgHQ22431.npy
-imgHQ17656.npy
-imgHQ14428.npy
-imgHQ12189.npy
-imgHQ26626.npy
-imgHQ19711.npy
-imgHQ02410.npy
-imgHQ12775.npy
-imgHQ29288.npy
-imgHQ00442.npy
-imgHQ18848.npy
-imgHQ12760.npy
-imgHQ22283.npy
-imgHQ28028.npy
-imgHQ09731.npy
-imgHQ22960.npy
-imgHQ28276.npy
-imgHQ19989.npy
-imgHQ02809.npy
-imgHQ26122.npy
-imgHQ17744.npy
-imgHQ03234.npy
-imgHQ23486.npy
-imgHQ29205.npy
-imgHQ24523.npy
-imgHQ00717.npy
-imgHQ08091.npy
-imgHQ01774.npy
-imgHQ25743.npy
-imgHQ12522.npy
-imgHQ03517.npy
-imgHQ13398.npy
-imgHQ09610.npy
-imgHQ17424.npy
-imgHQ15666.npy
-imgHQ20645.npy
-imgHQ15767.npy
-imgHQ27304.npy
-imgHQ22794.npy
-imgHQ27216.npy
-imgHQ19981.npy
-imgHQ28813.npy
-imgHQ07400.npy
-imgHQ05706.npy
-imgHQ01990.npy
-imgHQ14287.npy
-imgHQ12540.npy
-imgHQ05019.npy
-imgHQ12904.npy
-imgHQ11524.npy
-imgHQ19866.npy
-imgHQ28027.npy
-imgHQ21063.npy
-imgHQ26927.npy
-imgHQ22525.npy
-imgHQ13433.npy
-imgHQ11067.npy
-imgHQ05395.npy
-imgHQ18627.npy
-imgHQ19681.npy
-imgHQ18983.npy
-imgHQ09025.npy
-imgHQ15946.npy
-imgHQ21347.npy
-imgHQ09478.npy
-imgHQ00794.npy
-imgHQ10831.npy
-imgHQ27362.npy
-imgHQ00208.npy
-imgHQ27163.npy
-imgHQ04273.npy
-imgHQ00374.npy
-imgHQ23109.npy
-imgHQ14406.npy
-imgHQ02609.npy
-imgHQ19922.npy
-imgHQ02570.npy
-imgHQ15511.npy
-imgHQ28800.npy
-imgHQ17495.npy
-imgHQ24744.npy
-imgHQ03870.npy
-imgHQ18036.npy
-imgHQ02288.npy
-imgHQ10330.npy
-imgHQ02867.npy
-imgHQ09638.npy
-imgHQ02836.npy
-imgHQ27670.npy
-imgHQ00513.npy
-imgHQ24071.npy
-imgHQ23927.npy
-imgHQ20943.npy
-imgHQ28573.npy
-imgHQ23357.npy
-imgHQ22455.npy
-imgHQ11531.npy
-imgHQ06943.npy
-imgHQ08169.npy
-imgHQ18773.npy
-imgHQ14836.npy
-imgHQ25640.npy
-imgHQ10215.npy
-imgHQ15190.npy
-imgHQ23093.npy
-imgHQ28187.npy
-imgHQ12688.npy
-imgHQ28128.npy
-imgHQ22470.npy
-imgHQ03531.npy
-imgHQ12031.npy
-imgHQ11219.npy
-imgHQ16305.npy
-imgHQ15238.npy
-imgHQ29444.npy
-imgHQ17394.npy
-imgHQ24755.npy
-imgHQ16066.npy
-imgHQ19739.npy
-imgHQ07745.npy
-imgHQ06503.npy
-imgHQ04665.npy
-imgHQ02136.npy
-imgHQ29071.npy
-imgHQ03325.npy
-imgHQ02372.npy
-imgHQ01939.npy
-imgHQ29242.npy
-imgHQ22031.npy
-imgHQ04037.npy
-imgHQ04821.npy
-imgHQ16393.npy
-imgHQ27431.npy
-imgHQ21639.npy
-imgHQ18674.npy
-imgHQ26984.npy
-imgHQ05899.npy
-imgHQ21604.npy
-imgHQ05569.npy
-imgHQ20230.npy
-imgHQ24941.npy
-imgHQ15282.npy
-imgHQ25175.npy
-imgHQ23582.npy
-imgHQ06476.npy
-imgHQ09106.npy
-imgHQ03721.npy
-imgHQ28124.npy
-imgHQ29971.npy
-imgHQ09784.npy
-imgHQ07330.npy
-imgHQ14215.npy
-imgHQ09184.npy
-imgHQ24774.npy
-imgHQ26707.npy
-imgHQ02754.npy
-imgHQ06468.npy
-imgHQ04084.npy
-imgHQ14262.npy
-imgHQ25351.npy
-imgHQ17690.npy
-imgHQ23126.npy
-imgHQ02008.npy
-imgHQ01718.npy
-imgHQ19226.npy
-imgHQ24875.npy
-imgHQ07393.npy
-imgHQ00415.npy
-imgHQ10314.npy
-imgHQ16666.npy
-imgHQ18039.npy
-imgHQ18749.npy
-imgHQ01640.npy
-imgHQ13799.npy
-imgHQ14679.npy
-imgHQ27588.npy
-imgHQ07655.npy
-imgHQ28145.npy
-imgHQ06488.npy
-imgHQ21326.npy
-imgHQ20070.npy
-imgHQ28444.npy
-imgHQ25323.npy
-imgHQ23311.npy
-imgHQ18388.npy
-imgHQ17311.npy
-imgHQ19021.npy
-imgHQ15090.npy
-imgHQ18460.npy
-imgHQ29847.npy
-imgHQ21125.npy
-imgHQ05344.npy
-imgHQ02478.npy
-imgHQ15557.npy
-imgHQ11210.npy
-imgHQ01529.npy
-imgHQ02169.npy
-imgHQ21597.npy
-imgHQ00218.npy
-imgHQ17801.npy
-imgHQ02742.npy
-imgHQ24052.npy
-imgHQ00039.npy
-imgHQ19083.npy
-imgHQ07761.npy
-imgHQ11567.npy
-imgHQ08555.npy
-imgHQ02468.npy
-imgHQ28513.npy
-imgHQ09649.npy
-imgHQ05220.npy
-imgHQ08357.npy
-imgHQ05265.npy
-imgHQ17070.npy
-imgHQ27272.npy
-imgHQ28168.npy
-imgHQ10427.npy
-imgHQ02689.npy
-imgHQ09565.npy
-imgHQ04958.npy
-imgHQ23394.npy
-imgHQ06334.npy
-imgHQ00025.npy
-imgHQ25692.npy
-imgHQ26449.npy
-imgHQ16670.npy
-imgHQ29897.npy
-imgHQ09015.npy
-imgHQ21907.npy
-imgHQ17820.npy
-imgHQ19125.npy
-imgHQ14584.npy
-imgHQ03647.npy
-imgHQ28439.npy
-imgHQ18525.npy
-imgHQ17224.npy
-imgHQ20634.npy
-imgHQ01357.npy
-imgHQ26149.npy
-imgHQ24060.npy
-imgHQ12636.npy
-imgHQ16817.npy
-imgHQ00655.npy
-imgHQ03562.npy
-imgHQ15274.npy
-imgHQ05813.npy
-imgHQ29495.npy
-imgHQ24457.npy
-imgHQ29135.npy
-imgHQ14575.npy
-imgHQ22702.npy
-imgHQ19679.npy
-imgHQ14492.npy
-imgHQ05874.npy
-imgHQ29909.npy
-imgHQ01767.npy
-imgHQ12533.npy
-imgHQ00598.npy
-imgHQ08420.npy
-imgHQ03827.npy
-imgHQ14050.npy
-imgHQ15902.npy
-imgHQ24981.npy
-imgHQ07549.npy
-imgHQ04543.npy
-imgHQ02787.npy
-imgHQ10933.npy
-imgHQ24608.npy
-imgHQ06897.npy
-imgHQ24746.npy
-imgHQ12729.npy
-imgHQ10603.npy
-imgHQ00629.npy
-imgHQ17328.npy
-imgHQ18257.npy
-imgHQ25679.npy
-imgHQ12548.npy
-imgHQ05677.npy
-imgHQ29538.npy
-imgHQ14141.npy
-imgHQ28111.npy
-imgHQ07746.npy
-imgHQ14481.npy
-imgHQ27762.npy
-imgHQ09577.npy
-imgHQ29670.npy
-imgHQ14911.npy
-imgHQ20179.npy
-imgHQ25915.npy
-imgHQ05997.npy
-imgHQ28342.npy
-imgHQ19899.npy
-imgHQ06972.npy
-imgHQ13778.npy
-imgHQ09400.npy
-imgHQ20016.npy
-imgHQ01648.npy
-imgHQ17116.npy
-imgHQ06283.npy
-imgHQ06213.npy
-imgHQ15688.npy
-imgHQ04044.npy
-imgHQ25718.npy
-imgHQ24601.npy
-imgHQ16463.npy
-imgHQ29517.npy
-imgHQ15851.npy
-imgHQ22712.npy
-imgHQ02319.npy
-imgHQ25387.npy
-imgHQ25406.npy
-imgHQ13996.npy
-imgHQ21807.npy
-imgHQ07579.npy
-imgHQ01341.npy
-imgHQ05799.npy
-imgHQ01846.npy
-imgHQ02278.npy
-imgHQ20556.npy
-imgHQ14462.npy
-imgHQ01520.npy
-imgHQ09963.npy
-imgHQ06193.npy
-imgHQ04482.npy
-imgHQ13562.npy
-imgHQ18771.npy
-imgHQ17618.npy
-imgHQ22408.npy
-imgHQ26687.npy
-imgHQ16158.npy
-imgHQ03664.npy
-imgHQ08052.npy
-imgHQ28801.npy
-imgHQ03692.npy
-imgHQ21285.npy
-imgHQ07783.npy
-imgHQ15115.npy
-imgHQ09389.npy
-imgHQ27866.npy
-imgHQ17017.npy
-imgHQ28601.npy
-imgHQ29024.npy
-imgHQ24233.npy
-imgHQ20361.npy
-imgHQ25590.npy
-imgHQ05438.npy
-imgHQ20768.npy
-imgHQ02820.npy
-imgHQ16404.npy
-imgHQ29580.npy
-imgHQ02810.npy
-imgHQ09094.npy
-imgHQ19267.npy
-imgHQ00019.npy
-imgHQ12134.npy
-imgHQ21368.npy
-imgHQ16778.npy
-imgHQ10791.npy
-imgHQ21330.npy
-imgHQ00076.npy
-imgHQ07191.npy
-imgHQ01363.npy
-imgHQ09042.npy
-imgHQ06603.npy
-imgHQ24291.npy
-imgHQ05729.npy
-imgHQ28358.npy
-imgHQ09319.npy
-imgHQ19360.npy
-imgHQ13005.npy
-imgHQ13883.npy
-imgHQ12019.npy
-imgHQ18830.npy
-imgHQ02932.npy
-imgHQ13180.npy
-imgHQ00814.npy
-imgHQ27879.npy
-imgHQ21788.npy
-imgHQ01374.npy
-imgHQ25728.npy
-imgHQ27391.npy
-imgHQ09160.npy
-imgHQ06453.npy
-imgHQ20704.npy
-imgHQ29880.npy
-imgHQ15030.npy
-imgHQ26791.npy
-imgHQ03032.npy
-imgHQ29542.npy
-imgHQ04211.npy
-imgHQ28163.npy
-imgHQ15398.npy
-imgHQ11489.npy
-imgHQ00662.npy
-imgHQ21783.npy
-imgHQ24056.npy
-imgHQ00306.npy
-imgHQ22341.npy
-imgHQ25527.npy
-imgHQ25948.npy
-imgHQ24857.npy
-imgHQ13290.npy
-imgHQ06340.npy
-imgHQ09081.npy
-imgHQ14295.npy
-imgHQ00510.npy
-imgHQ06154.npy
-imgHQ03168.npy
-imgHQ06133.npy
-imgHQ08840.npy
-imgHQ07454.npy
-imgHQ17832.npy
-imgHQ00558.npy
-imgHQ20963.npy
-imgHQ00124.npy
-imgHQ27318.npy
-imgHQ22948.npy
-imgHQ25944.npy
-imgHQ21284.npy
-imgHQ00523.npy
-imgHQ05235.npy
-imgHQ09715.npy
-imgHQ08966.npy
-imgHQ25923.npy
-imgHQ18977.npy
-imgHQ29427.npy
-imgHQ06834.npy
-imgHQ27886.npy
-imgHQ09905.npy
-imgHQ01535.npy
-imgHQ06802.npy
-imgHQ25065.npy
-imgHQ16826.npy
-imgHQ26213.npy
-imgHQ13608.npy
-imgHQ07767.npy
-imgHQ14762.npy
-imgHQ25815.npy
-imgHQ16623.npy
-imgHQ24436.npy
-imgHQ15855.npy
-imgHQ18940.npy
-imgHQ17852.npy
-imgHQ05151.npy
-imgHQ22126.npy
-imgHQ26944.npy
-imgHQ28703.npy
-imgHQ14791.npy
-imgHQ11095.npy
-imgHQ26733.npy
-imgHQ05307.npy
-imgHQ27677.npy
-imgHQ04416.npy
-imgHQ01272.npy
-imgHQ05760.npy
-imgHQ21857.npy
-imgHQ14468.npy
-imgHQ08725.npy
-imgHQ29091.npy
-imgHQ08767.npy
-imgHQ05645.npy
-imgHQ13389.npy
-imgHQ18764.npy
-imgHQ19457.npy
-imgHQ21384.npy
-imgHQ24995.npy
-imgHQ11544.npy
-imgHQ21086.npy
-imgHQ06398.npy
-imgHQ14095.npy
-imgHQ05717.npy
-imgHQ04485.npy
-imgHQ15659.npy
-imgHQ26681.npy
-imgHQ11123.npy
-imgHQ27487.npy
-imgHQ12102.npy
-imgHQ07729.npy
-imgHQ26874.npy
-imgHQ03543.npy
-imgHQ10660.npy
-imgHQ10964.npy
-imgHQ03199.npy
-imgHQ21978.npy
-imgHQ25307.npy
-imgHQ00161.npy
-imgHQ20127.npy
-imgHQ11656.npy
-imgHQ12277.npy
-imgHQ01634.npy
-imgHQ16218.npy
-imgHQ19276.npy
-imgHQ26921.npy
-imgHQ12046.npy
-imgHQ03121.npy
-imgHQ16818.npy
-imgHQ25396.npy
-imgHQ29240.npy
-imgHQ29972.npy
-imgHQ25722.npy
-imgHQ13807.npy
-imgHQ29063.npy
-imgHQ26577.npy
-imgHQ28664.npy
-imgHQ25031.npy
-imgHQ13172.npy
-imgHQ18025.npy
-imgHQ00424.npy
-imgHQ14564.npy
-imgHQ12161.npy
-imgHQ29271.npy
-imgHQ01051.npy
-imgHQ05687.npy
-imgHQ29300.npy
-imgHQ25241.npy
-imgHQ12810.npy
-imgHQ02845.npy
-imgHQ21345.npy
-imgHQ10795.npy
-imgHQ28902.npy
-imgHQ13355.npy
-imgHQ13563.npy
-imgHQ06839.npy
-imgHQ20984.npy
-imgHQ12233.npy
-imgHQ11048.npy
-imgHQ14768.npy
-imgHQ24496.npy
-imgHQ09207.npy
-imgHQ12566.npy
-imgHQ01259.npy
-imgHQ06676.npy
-imgHQ06715.npy
-imgHQ05061.npy
-imgHQ13877.npy
-imgHQ24646.npy
-imgHQ11273.npy
-imgHQ05703.npy
-imgHQ04701.npy
-imgHQ06150.npy
-imgHQ27823.npy
-imgHQ09263.npy
-imgHQ05254.npy
-imgHQ25164.npy
-imgHQ03219.npy
-imgHQ19022.npy
-imgHQ27961.npy
-imgHQ05916.npy
-imgHQ14458.npy
-imgHQ13696.npy
-imgHQ21717.npy
-imgHQ27008.npy
-imgHQ14717.npy
-imgHQ02433.npy
-imgHQ16068.npy
-imgHQ06388.npy
-imgHQ06527.npy
-imgHQ09705.npy
-imgHQ00714.npy
-imgHQ04359.npy
-imgHQ18294.npy
-imgHQ01715.npy
-imgHQ18641.npy
-imgHQ16567.npy
-imgHQ16182.npy
-imgHQ17011.npy
-imgHQ07948.npy
-imgHQ10030.npy
-imgHQ05825.npy
-imgHQ10718.npy
-imgHQ06000.npy
-imgHQ29857.npy
-imgHQ19919.npy
-imgHQ29034.npy
-imgHQ26841.npy
-imgHQ15940.npy
-imgHQ27986.npy
-imgHQ07140.npy
-imgHQ24458.npy
-imgHQ20816.npy
-imgHQ01597.npy
-imgHQ01720.npy
-imgHQ06680.npy
-imgHQ11791.npy
-imgHQ27808.npy
-imgHQ16941.npy
-imgHQ07363.npy
-imgHQ15757.npy
-imgHQ16154.npy
-imgHQ28417.npy
-imgHQ12528.npy
-imgHQ20004.npy
-imgHQ11682.npy
-imgHQ15311.npy
-imgHQ29211.npy
-imgHQ10653.npy
-imgHQ25980.npy
-imgHQ24910.npy
-imgHQ11856.npy
-imgHQ07774.npy
-imgHQ00259.npy
-imgHQ23110.npy
-imgHQ21217.npy
-imgHQ16509.npy
-imgHQ08810.npy
-imgHQ25748.npy
-imgHQ13166.npy
-imgHQ23312.npy
-imgHQ01073.npy
-imgHQ29773.npy
-imgHQ10432.npy
-imgHQ16383.npy
-imgHQ01337.npy
-imgHQ18903.npy
-imgHQ16294.npy
-imgHQ10538.npy
-imgHQ29061.npy
-imgHQ21030.npy
-imgHQ12661.npy
-imgHQ27156.npy
-imgHQ03445.npy
-imgHQ01992.npy
-imgHQ12386.npy
-imgHQ00627.npy
-imgHQ27524.npy
-imgHQ20611.npy
-imgHQ14278.npy
-imgHQ27633.npy
-imgHQ25035.npy
-imgHQ12492.npy
-imgHQ22941.npy
-imgHQ25369.npy
-imgHQ26642.npy
-imgHQ14333.npy
-imgHQ17620.npy
-imgHQ18512.npy
-imgHQ09315.npy
-imgHQ20703.npy
-imgHQ19338.npy
-imgHQ23521.npy
-imgHQ20279.npy
-imgHQ11814.npy
-imgHQ01687.npy
-imgHQ22108.npy
-imgHQ25478.npy
-imgHQ10782.npy
-imgHQ13103.npy
-imgHQ27675.npy
-imgHQ26956.npy
-imgHQ07115.npy
-imgHQ21538.npy
-imgHQ04374.npy
-imgHQ22995.npy
-imgHQ12004.npy
-imgHQ17648.npy
-imgHQ12394.npy
-imgHQ16044.npy
-imgHQ16799.npy
-imgHQ13350.npy
-imgHQ08318.npy
-imgHQ20680.npy
-imgHQ01651.npy
-imgHQ15159.npy
-imgHQ03433.npy
-imgHQ04602.npy
-imgHQ25047.npy
-imgHQ03816.npy
-imgHQ06423.npy
-imgHQ10731.npy
-imgHQ08381.npy
-imgHQ21026.npy
-imgHQ22250.npy
-imgHQ23548.npy
-imgHQ04078.npy
-imgHQ09079.npy
-imgHQ27152.npy
-imgHQ21078.npy
-imgHQ23939.npy
-imgHQ20886.npy
-imgHQ26369.npy
-imgHQ00381.npy
-imgHQ18986.npy
-imgHQ09598.npy
-imgHQ11778.npy
-imgHQ22804.npy
-imgHQ09179.npy
-imgHQ09208.npy
-imgHQ02412.npy
-imgHQ19760.npy
-imgHQ24125.npy
-imgHQ07285.npy
-imgHQ25277.npy
-imgHQ16466.npy
-imgHQ26127.npy
-imgHQ03477.npy
-imgHQ11813.npy
-imgHQ14049.npy
-imgHQ17483.npy
-imgHQ10631.npy
-imgHQ19092.npy
-imgHQ17500.npy
-imgHQ00502.npy
-imgHQ03082.npy
-imgHQ21728.npy
-imgHQ23391.npy
-imgHQ09828.npy
-imgHQ16412.npy
-imgHQ15080.npy
-imgHQ02685.npy
-imgHQ12910.npy
-imgHQ20249.npy
-imgHQ16176.npy
-imgHQ17115.npy
-imgHQ11028.npy
-imgHQ21006.npy
-imgHQ04656.npy
-imgHQ06093.npy
-imgHQ08897.npy
-imgHQ29275.npy
-imgHQ07542.npy
-imgHQ18592.npy
-imgHQ21161.npy
-imgHQ16962.npy
-imgHQ03046.npy
-imgHQ03187.npy
-imgHQ26877.npy
-imgHQ11810.npy
-imgHQ00443.npy
-imgHQ14193.npy
-imgHQ11464.npy
-imgHQ00219.npy
-imgHQ01123.npy
-imgHQ27622.npy
-imgHQ06313.npy
-imgHQ16888.npy
-imgHQ13319.npy
-imgHQ03621.npy
-imgHQ06866.npy
-imgHQ25671.npy
-imgHQ04926.npy
-imgHQ07955.npy
-imgHQ05067.npy
-imgHQ12648.npy
-imgHQ24443.npy
-imgHQ29127.npy
-imgHQ13194.npy
-imgHQ04577.npy
-imgHQ25831.npy
-imgHQ00430.npy
-imgHQ06579.npy
-imgHQ11166.npy
-imgHQ20895.npy
-imgHQ22162.npy
-imgHQ20291.npy
-imgHQ06765.npy
-imgHQ13195.npy
-imgHQ02434.npy
-imgHQ05034.npy
-imgHQ26569.npy
-imgHQ10972.npy
-imgHQ14431.npy
-imgHQ27224.npy
-imgHQ22552.npy
-imgHQ09507.npy
-imgHQ10283.npy
-imgHQ12478.npy
-imgHQ25378.npy
-imgHQ24621.npy
-imgHQ18841.npy
-imgHQ24432.npy
-imgHQ26713.npy
-imgHQ18925.npy
-imgHQ20184.npy
-imgHQ28980.npy
-imgHQ27791.npy
-imgHQ25468.npy
-imgHQ10076.npy
-imgHQ12140.npy
-imgHQ23567.npy
-imgHQ20480.npy
-imgHQ12501.npy
-imgHQ21586.npy
-imgHQ20907.npy
-imgHQ18901.npy
-imgHQ19133.npy
-imgHQ20272.npy
-imgHQ02028.npy
-imgHQ02676.npy
-imgHQ17606.npy
-imgHQ29895.npy
-imgHQ12229.npy
-imgHQ02846.npy
-imgHQ22774.npy
-imgHQ15718.npy
-imgHQ25172.npy
-imgHQ18355.npy
-imgHQ02885.npy
-imgHQ08983.npy
-imgHQ14160.npy
-imgHQ08069.npy
-imgHQ06058.npy
-imgHQ24944.npy
-imgHQ00963.npy
-imgHQ14332.npy
-imgHQ20056.npy
-imgHQ10199.npy
-imgHQ25191.npy
-imgHQ25698.npy
-imgHQ17028.npy
-imgHQ01038.npy
-imgHQ14785.npy
-imgHQ04659.npy
-imgHQ29851.npy
-imgHQ18727.npy
-imgHQ06005.npy
-imgHQ21913.npy
-imgHQ01766.npy
-imgHQ21844.npy
-imgHQ22891.npy
-imgHQ05928.npy
-imgHQ07911.npy
-imgHQ23766.npy
-imgHQ18803.npy
-imgHQ17679.npy
-imgHQ07669.npy
-imgHQ24445.npy
-imgHQ27634.npy
-imgHQ02519.npy
-imgHQ15570.npy
-imgHQ25085.npy
-imgHQ02004.npy
-imgHQ14539.npy
-imgHQ13708.npy
-imgHQ10219.npy
-imgHQ07266.npy
-imgHQ03580.npy
-imgHQ14677.npy
-imgHQ24699.npy
-imgHQ11488.npy
-imgHQ26550.npy
-imgHQ13927.npy
-imgHQ29815.npy
-imgHQ11728.npy
-imgHQ16397.npy
-imgHQ10620.npy
-imgHQ20247.npy
-imgHQ07414.npy
-imgHQ17971.npy
-imgHQ11441.npy
-imgHQ08666.npy
-imgHQ26845.npy
-imgHQ23977.npy
-imgHQ20043.npy
-imgHQ06418.npy
-imgHQ00885.npy
-imgHQ21549.npy
-imgHQ29090.npy
-imgHQ19667.npy
-imgHQ20367.npy
-imgHQ21363.npy
-imgHQ11807.npy
-imgHQ20494.npy
-imgHQ17057.npy
-imgHQ07546.npy
-imgHQ24321.npy
-imgHQ02313.npy
-imgHQ21860.npy
-imgHQ00770.npy
-imgHQ27718.npy
-imgHQ08714.npy
-imgHQ16423.npy
-imgHQ07937.npy
-imgHQ24461.npy
-imgHQ03928.npy
-imgHQ03847.npy
-imgHQ27676.npy
-imgHQ01307.npy
-imgHQ11400.npy
-imgHQ20292.npy
-imgHQ07456.npy
-imgHQ24131.npy
-imgHQ12027.npy
-imgHQ22801.npy
-imgHQ12849.npy
-imgHQ29310.npy
-imgHQ13606.npy
-imgHQ29122.npy
-imgHQ25308.npy
-imgHQ13465.npy
-imgHQ15945.npy
-imgHQ21526.npy
-imgHQ25413.npy
-imgHQ08793.npy
-imgHQ20411.npy
-imgHQ16937.npy
-imgHQ01042.npy
-imgHQ13058.npy
-imgHQ03486.npy
-imgHQ02717.npy
-imgHQ15405.npy
-imgHQ19990.npy
-imgHQ28092.npy
-imgHQ02631.npy
-imgHQ17109.npy
-imgHQ03958.npy
-imgHQ17613.npy
-imgHQ28762.npy
-imgHQ15731.npy
-imgHQ02905.npy
-imgHQ18893.npy
-imgHQ11635.npy
-imgHQ18266.npy
-imgHQ11975.npy
-imgHQ16379.npy
-imgHQ16953.npy
-imgHQ07502.npy
-imgHQ12432.npy
-imgHQ19068.npy
-imgHQ02255.npy
-imgHQ17444.npy
-imgHQ03713.npy
-imgHQ05005.npy
-imgHQ23895.npy
-imgHQ25981.npy
-imgHQ27082.npy
-imgHQ22054.npy
-imgHQ09818.npy
-imgHQ15592.npy
-imgHQ05256.npy
-imgHQ19325.npy
-imgHQ17787.npy
-imgHQ20024.npy
-imgHQ24139.npy
-imgHQ21144.npy
-imgHQ06735.npy
-imgHQ10481.npy
-imgHQ25198.npy
-imgHQ02704.npy
-imgHQ05113.npy
-imgHQ12321.npy
-imgHQ01287.npy
-imgHQ24635.npy
-imgHQ00380.npy
-imgHQ09512.npy
-imgHQ23747.npy
-imgHQ26496.npy
-imgHQ28764.npy
-imgHQ02486.npy
-imgHQ02374.npy
-imgHQ24047.npy
-imgHQ17231.npy
-imgHQ26235.npy
-imgHQ19309.npy
-imgHQ05140.npy
-imgHQ24673.npy
-imgHQ24570.npy
-imgHQ11508.npy
-imgHQ19365.npy
-imgHQ03532.npy
-imgHQ05827.npy
-imgHQ13615.npy
-imgHQ02056.npy
-imgHQ15459.npy
-imgHQ21200.npy
-imgHQ23006.npy
-imgHQ17966.npy
-imgHQ26244.npy
-imgHQ25628.npy
-imgHQ18431.npy
-imgHQ21777.npy
-imgHQ13375.npy
-imgHQ17903.npy
-imgHQ08898.npy
-imgHQ24828.npy
-imgHQ06684.npy
-imgHQ23349.npy
-imgHQ18677.npy
-imgHQ12650.npy
-imgHQ25266.npy
-imgHQ03010.npy
-imgHQ20842.npy
-imgHQ18836.npy
-imgHQ23543.npy
-imgHQ14146.npy
-imgHQ13859.npy
-imgHQ04867.npy
-imgHQ27941.npy
-imgHQ23439.npy
-imgHQ17020.npy
-imgHQ24817.npy
-imgHQ04985.npy
-imgHQ13133.npy
-imgHQ25256.npy
-imgHQ26049.npy
-imgHQ07698.npy
-imgHQ17004.npy
-imgHQ18731.npy
-imgHQ17252.npy
-imgHQ27148.npy
-imgHQ20227.npy
-imgHQ25530.npy
-imgHQ04998.npy
-imgHQ05443.npy
-imgHQ25750.npy
-imgHQ11199.npy
-imgHQ24735.npy
-imgHQ07293.npy
-imgHQ00752.npy
-imgHQ28390.npy
-imgHQ21027.npy
-imgHQ19777.npy
-imgHQ08601.npy
-imgHQ10559.npy
-imgHQ27120.npy
-imgHQ15416.npy
-imgHQ15809.npy
-imgHQ16200.npy
-imgHQ12739.npy
-imgHQ02691.npy
-imgHQ15768.npy
-imgHQ10667.npy
-imgHQ21336.npy
-imgHQ29395.npy
-imgHQ06003.npy
-imgHQ27136.npy
-imgHQ02564.npy
-imgHQ27926.npy
-imgHQ22849.npy
-imgHQ29309.npy
-imgHQ20778.npy
-imgHQ18410.npy
-imgHQ21559.npy
-imgHQ12065.npy
-imgHQ26002.npy
-imgHQ18363.npy
-imgHQ27387.npy
-imgHQ28049.npy
-imgHQ29922.npy
-imgHQ16750.npy
-imgHQ22413.npy
-imgHQ21061.npy
-imgHQ29156.npy
-imgHQ13368.npy
-imgHQ20998.npy
-imgHQ02612.npy
-imgHQ27583.npy
-imgHQ00809.npy
-imgHQ19863.npy
-imgHQ25379.npy
-imgHQ11895.npy
-imgHQ29023.npy
-imgHQ08293.npy
-imgHQ14275.npy
-imgHQ29962.npy
-imgHQ09628.npy
-imgHQ24477.npy
-imgHQ10797.npy
-imgHQ07465.npy
-imgHQ11377.npy
-imgHQ28152.npy
-imgHQ16571.npy
-imgHQ18453.npy
-imgHQ15761.npy
-imgHQ19151.npy
-imgHQ18877.npy
-imgHQ11026.npy
-imgHQ19744.npy
-imgHQ02759.npy
-imgHQ29750.npy
-imgHQ16307.npy
-imgHQ09162.npy
-imgHQ06158.npy
-imgHQ25752.npy
-imgHQ01161.npy
-imgHQ16153.npy
-imgHQ09219.npy
-imgHQ18572.npy
-imgHQ01296.npy
-imgHQ27787.npy
-imgHQ14167.npy
-imgHQ20355.npy
-imgHQ15304.npy
-imgHQ03599.npy
-imgHQ27188.npy
-imgHQ03349.npy
-imgHQ20073.npy
-imgHQ07832.npy
-imgHQ09425.npy
-imgHQ21786.npy
-imgHQ04448.npy
-imgHQ04638.npy
-imgHQ11641.npy
-imgHQ11774.npy
-imgHQ00667.npy
-imgHQ01024.npy
-imgHQ24903.npy
-imgHQ07982.npy
-imgHQ25829.npy
-imgHQ20454.npy
-imgHQ17609.npy
-imgHQ04745.npy
-imgHQ24564.npy
-imgHQ19979.npy
-imgHQ19279.npy
-imgHQ10396.npy
-imgHQ01747.npy
-imgHQ21795.npy
-imgHQ21432.npy
-imgHQ09538.npy
-imgHQ23684.npy
-imgHQ27584.npy
-imgHQ29070.npy
-imgHQ21675.npy
-imgHQ26367.npy
-imgHQ12412.npy
-imgHQ02359.npy
-imgHQ08475.npy
-imgHQ16248.npy
-imgHQ02745.npy
-imgHQ06239.npy
-imgHQ22510.npy
-imgHQ20697.npy
-imgHQ11880.npy
-imgHQ04541.npy
-imgHQ20145.npy
-imgHQ21272.npy
-imgHQ00520.npy
-imgHQ25985.npy
-imgHQ25258.npy
-imgHQ14293.npy
-imgHQ01327.npy
-imgHQ05850.npy
-imgHQ13289.npy
-imgHQ17913.npy
-imgHQ09840.npy
-imgHQ02837.npy
-imgHQ17928.npy
-imgHQ13709.npy
-imgHQ10517.npy
-imgHQ06940.npy
-imgHQ21167.npy
-imgHQ16395.npy
-imgHQ11977.npy
-imgHQ10574.npy
-imgHQ19869.npy
-imgHQ01695.npy
-imgHQ00942.npy
-imgHQ23573.npy
-imgHQ09772.npy
-imgHQ15124.npy
-imgHQ00193.npy
-imgHQ22426.npy
-imgHQ03636.npy
-imgHQ25036.npy
-imgHQ15458.npy
-imgHQ10656.npy
-imgHQ11054.npy
-imgHQ10766.npy
-imgHQ14673.npy
-imgHQ27193.npy
-imgHQ17229.npy
-imgHQ10671.npy
-imgHQ02899.npy
-imgHQ24065.npy
-imgHQ07007.npy
-imgHQ00595.npy
-imgHQ05171.npy
-imgHQ26216.npy
-imgHQ23388.npy
-imgHQ23433.npy
-imgHQ06254.npy
-imgHQ12778.npy
-imgHQ16714.npy
-imgHQ16344.npy
-imgHQ18352.npy
-imgHQ05376.npy
-imgHQ16602.npy
-imgHQ03439.npy
-imgHQ07558.npy
-imgHQ18185.npy
-imgHQ03582.npy
-imgHQ26929.npy
-imgHQ25021.npy
-imgHQ20616.npy
-imgHQ07834.npy
-imgHQ18923.npy
-imgHQ09166.npy
-imgHQ03762.npy
-imgHQ22866.npy
-imgHQ03818.npy
-imgHQ09178.npy
-imgHQ19329.npy
-imgHQ26382.npy
-imgHQ11772.npy
-imgHQ21966.npy
-imgHQ17478.npy
-imgHQ25852.npy
-imgHQ26205.npy
-imgHQ17887.npy
-imgHQ23405.npy
-imgHQ01679.npy
-imgHQ18516.npy
-imgHQ14243.npy
-imgHQ25399.npy
-imgHQ01446.npy
-imgHQ25803.npy
-imgHQ04029.npy
-imgHQ20748.npy
-imgHQ12743.npy
-imgHQ03293.npy
-imgHQ26665.npy
-imgHQ23653.npy
-imgHQ17025.npy
-imgHQ04106.npy
-imgHQ15993.npy
-imgHQ27757.npy
-imgHQ05444.npy
-imgHQ11577.npy
-imgHQ00197.npy
-imgHQ26062.npy
-imgHQ16923.npy
-imgHQ20413.npy
-imgHQ08253.npy
-imgHQ18810.npy
-imgHQ15389.npy
-imgHQ26574.npy
-imgHQ26834.npy
-imgHQ16251.npy
-imgHQ26387.npy
-imgHQ06120.npy
-imgHQ27994.npy
-imgHQ28665.npy
-imgHQ04068.npy
-imgHQ12963.npy
-imgHQ22304.npy
-imgHQ28167.npy
-imgHQ21091.npy
-imgHQ08946.npy
-imgHQ13440.npy
-imgHQ15146.npy
-imgHQ15789.npy
-imgHQ16934.npy
-imgHQ07168.npy
-imgHQ09575.npy
-imgHQ05731.npy
-imgHQ14734.npy
-imgHQ28713.npy
-imgHQ16179.npy
-imgHQ05237.npy
-imgHQ16939.npy
-imgHQ04086.npy
-imgHQ11388.npy
-imgHQ19943.npy
-imgHQ21667.npy
-imgHQ22691.npy
-imgHQ03224.npy
-imgHQ26847.npy
-imgHQ02684.npy
-imgHQ16759.npy
-imgHQ20306.npy
-imgHQ24162.npy
-imgHQ24948.npy
-imgHQ21237.npy
-imgHQ10789.npy
-imgHQ08622.npy
-imgHQ22723.npy
-imgHQ17463.npy
-imgHQ18930.npy
-imgHQ15617.npy
-imgHQ08667.npy
-imgHQ10172.npy
-imgHQ24282.npy
-imgHQ28492.npy
-imgHQ23633.npy
-imgHQ05934.npy
-imgHQ11035.npy
-imgHQ17582.npy
-imgHQ03660.npy
-imgHQ25674.npy
-imgHQ18855.npy
-imgHQ00964.npy
-imgHQ11860.npy
-imgHQ27131.npy
-imgHQ26233.npy
-imgHQ06032.npy
-imgHQ24062.npy
-imgHQ22820.npy
-imgHQ28405.npy
-imgHQ07438.npy
-imgHQ02504.npy
-imgHQ01898.npy
-imgHQ23210.npy
-imgHQ01139.npy
-imgHQ07956.npy
-imgHQ28521.npy
-imgHQ17654.npy
-imgHQ14577.npy
-imgHQ16946.npy
-imgHQ22485.npy
-imgHQ02682.npy
-imgHQ10257.npy
-imgHQ11361.npy
-imgHQ21405.npy
-imgHQ07161.npy
-imgHQ29786.npy
-imgHQ29110.npy
-imgHQ13038.npy
-imgHQ18240.npy
-imgHQ09021.npy
-imgHQ15687.npy
-imgHQ16490.npy
-imgHQ10994.npy
-imgHQ13462.npy
-imgHQ02315.npy
-imgHQ02777.npy
-imgHQ02488.npy
-imgHQ18678.npy
-imgHQ25579.npy
-imgHQ17898.npy
-imgHQ25901.npy
-imgHQ00712.npy
-imgHQ05047.npy
-imgHQ01233.npy
-imgHQ17429.npy
-imgHQ11462.npy
-imgHQ08014.npy
-imgHQ03894.npy
-imgHQ05335.npy
-imgHQ07012.npy
-imgHQ06848.npy
-imgHQ00524.npy
-imgHQ03754.npy
-imgHQ17697.npy
-imgHQ00868.npy
-imgHQ01497.npy
-imgHQ00224.npy
-imgHQ29762.npy
-imgHQ00828.npy
-imgHQ08932.npy
-imgHQ04567.npy
-imgHQ19424.npy
-imgHQ19850.npy
-imgHQ19724.npy
-imgHQ27264.npy
-imgHQ06906.npy
-imgHQ03328.npy
-imgHQ16161.npy
-imgHQ27393.npy
-imgHQ01793.npy
-imgHQ02996.npy
-imgHQ06033.npy
-imgHQ04931.npy
-imgHQ12467.npy
-imgHQ01547.npy
-imgHQ10996.npy
-imgHQ24701.npy
-imgHQ22469.npy
-imgHQ18799.npy
-imgHQ25900.npy
-imgHQ14003.npy
-imgHQ20980.npy
-imgHQ07390.npy
-imgHQ10384.npy
-imgHQ09586.npy
-imgHQ12734.npy
-imgHQ26738.npy
-imgHQ08861.npy
-imgHQ29508.npy
-imgHQ20573.npy
-imgHQ29356.npy
-imgHQ16250.npy
-imgHQ17412.npy
-imgHQ17611.npy
-imgHQ27662.npy
-imgHQ10952.npy
-imgHQ28398.npy
-imgHQ09716.npy
-imgHQ12808.npy
-imgHQ20259.npy
-imgHQ06790.npy
-imgHQ16259.npy
-imgHQ13205.npy
-imgHQ12921.npy
-imgHQ27950.npy
-imgHQ22042.npy
-imgHQ07657.npy
-imgHQ19028.npy
-imgHQ00026.npy
-imgHQ18371.npy
-imgHQ26383.npy
-imgHQ00576.npy
-imgHQ22787.npy
-imgHQ10263.npy
-imgHQ29476.npy
-imgHQ10394.npy
-imgHQ12887.npy
-imgHQ25525.npy
-imgHQ14972.npy
-imgHQ28368.npy
-imgHQ01417.npy
-imgHQ24657.npy
-imgHQ07590.npy
-imgHQ00325.npy
-imgHQ21151.npy
-imgHQ16638.npy
-imgHQ11554.npy
-imgHQ14493.npy
-imgHQ04955.npy
-imgHQ26370.npy
-imgHQ27058.npy
-imgHQ11449.npy
-imgHQ03863.npy
-imgHQ04709.npy
-imgHQ28606.npy
-imgHQ21162.npy
-imgHQ29775.npy
-imgHQ28373.npy
-imgHQ23581.npy
-imgHQ18196.npy
-imgHQ09479.npy
-imgHQ10435.npy
-imgHQ17661.npy
-imgHQ10758.npy
-imgHQ28399.npy
-imgHQ22221.npy
-imgHQ22153.npy
-imgHQ07851.npy
-imgHQ15429.npy
-imgHQ25762.npy
-imgHQ09093.npy
-imgHQ04282.npy
-imgHQ26475.npy
-imgHQ02911.npy
-imgHQ26859.npy
-imgHQ06303.npy
-imgHQ21245.npy
-imgHQ02758.npy
-imgHQ01630.npy
-imgHQ22303.npy
-imgHQ19806.npy
-imgHQ28025.npy
-imgHQ01245.npy
-imgHQ03406.npy
-imgHQ17383.npy
-imgHQ09922.npy
-imgHQ07265.npy
-imgHQ21565.npy
-imgHQ12452.npy
-imgHQ05944.npy
-imgHQ16881.npy
-imgHQ05742.npy
-imgHQ06206.npy
-imgHQ22656.npy
-imgHQ25114.npy
-imgHQ11588.npy
-imgHQ09620.npy
-imgHQ23708.npy
-imgHQ29828.npy
-imgHQ10478.npy
-imgHQ05177.npy
-imgHQ06353.npy
-imgHQ18817.npy
-imgHQ29917.npy
-imgHQ15150.npy
-imgHQ23868.npy
-imgHQ23879.npy
-imgHQ19996.npy
-imgHQ05809.npy
-imgHQ22516.npy
-imgHQ09763.npy
-imgHQ19335.npy
-imgHQ00805.npy
-imgHQ26612.npy
-imgHQ14941.npy
-imgHQ00873.npy
-imgHQ24199.npy
-imgHQ11743.npy
-imgHQ24711.npy
-imgHQ12944.npy
-imgHQ15374.npy
-imgHQ15723.npy
-imgHQ25599.npy
-imgHQ22678.npy
-imgHQ27895.npy
-imgHQ18038.npy
-imgHQ12455.npy
-imgHQ17867.npy
-imgHQ10057.npy
-imgHQ00720.npy
-imgHQ12736.npy
-imgHQ08890.npy
-imgHQ03670.npy
-imgHQ10414.npy
-imgHQ13726.npy
-imgHQ08474.npy
-imgHQ12025.npy
-imgHQ27743.npy
-imgHQ11206.npy
-imgHQ11839.npy
-imgHQ20941.npy
-imgHQ08614.npy
-imgHQ09633.npy
-imgHQ07989.npy
-imgHQ02561.npy
-imgHQ02539.npy
-imgHQ28408.npy
-imgHQ14136.npy
-imgHQ29741.npy
-imgHQ15421.npy
-imgHQ09361.npy
-imgHQ12514.npy
-imgHQ09434.npy
-imgHQ22523.npy
-imgHQ08813.npy
-imgHQ20759.npy
-imgHQ18081.npy
-imgHQ09347.npy
-imgHQ04551.npy
-imgHQ20532.npy
-imgHQ03822.npy
-imgHQ13809.npy
-imgHQ15056.npy
-imgHQ13740.npy
-imgHQ00182.npy
-imgHQ26570.npy
-imgHQ08053.npy
-imgHQ09046.npy
-imgHQ17054.npy
-imgHQ29940.npy
-imgHQ02079.npy
-imgHQ17333.npy
-imgHQ11420.npy
-imgHQ17871.npy
-imgHQ27086.npy
-imgHQ12857.npy
-imgHQ14727.npy
-imgHQ21606.npy
-imgHQ24180.npy
-imgHQ07461.npy
-imgHQ23265.npy
-imgHQ22148.npy
-imgHQ05801.npy
-imgHQ17732.npy
-imgHQ29910.npy
-imgHQ05713.npy
-imgHQ15660.npy
-imgHQ22730.npy
-imgHQ21315.npy
-imgHQ17132.npy
-imgHQ18122.npy
-imgHQ22474.npy
-imgHQ16376.npy
-imgHQ24177.npy
-imgHQ03780.npy
-imgHQ12077.npy
-imgHQ17663.npy
-imgHQ04109.npy
-imgHQ03117.npy
-imgHQ01083.npy
-imgHQ07707.npy
-imgHQ21255.npy
-imgHQ02887.npy
-imgHQ24892.npy
-imgHQ13999.npy
-imgHQ24323.npy
-imgHQ10949.npy
-imgHQ16151.npy
-imgHQ23752.npy
-imgHQ03556.npy
-imgHQ23642.npy
-imgHQ29386.npy
-imgHQ21681.npy
-imgHQ12490.npy
-imgHQ13196.npy
-imgHQ26436.npy
-imgHQ09524.npy
-imgHQ05111.npy
-imgHQ02061.npy
-imgHQ06776.npy
-imgHQ14647.npy
-imgHQ07068.npy
-imgHQ03090.npy
-imgHQ27236.npy
-imgHQ26114.npy
-imgHQ04264.npy
-imgHQ01741.npy
-imgHQ24148.npy
-imgHQ15829.npy
-imgHQ26314.npy
-imgHQ09642.npy
-imgHQ29119.npy
-imgHQ23553.npy
-imgHQ21718.npy
-imgHQ22961.npy
-imgHQ22417.npy
-imgHQ22441.npy
-imgHQ19482.npy
-imgHQ04708.npy
-imgHQ06746.npy
-imgHQ08348.npy
-imgHQ01421.npy
-imgHQ29912.npy
-imgHQ06937.npy
-imgHQ27665.npy
-imgHQ19031.npy
-imgHQ26253.npy
-imgHQ00052.npy
-imgHQ17850.npy
-imgHQ02763.npy
-imgHQ04340.npy
-imgHQ05109.npy
-imgHQ16590.npy
-imgHQ15343.npy
-imgHQ27138.npy
-imgHQ11978.npy
-imgHQ18203.npy
-imgHQ16525.npy
-imgHQ20918.npy
-imgHQ02245.npy
-imgHQ25097.npy
-imgHQ25558.npy
-imgHQ23594.npy
-imgHQ25512.npy
-imgHQ14910.npy
-imgHQ21229.npy
-imgHQ06638.npy
-imgHQ02607.npy
-imgHQ13791.npy
-imgHQ06562.npy
-imgHQ14639.npy
-imgHQ12287.npy
-imgHQ06543.npy
-imgHQ09441.npy
-imgHQ03338.npy
-imgHQ05386.npy
-imgHQ19543.npy
-imgHQ26926.npy
-imgHQ04556.npy
-imgHQ01658.npy
-imgHQ00402.npy
-imgHQ19399.npy
-imgHQ09436.npy
-imgHQ07110.npy
-imgHQ14965.npy
-imgHQ29528.npy
-imgHQ29567.npy
-imgHQ04512.npy
-imgHQ27595.npy
-imgHQ13097.npy
-imgHQ00638.npy
-imgHQ14073.npy
-imgHQ23502.npy
-imgHQ00620.npy
-imgHQ16268.npy
-imgHQ11480.npy
-imgHQ16093.npy
-imgHQ28516.npy
-imgHQ25772.npy
-imgHQ05849.npy
-imgHQ16217.npy
-imgHQ02424.npy
-imgHQ20297.npy
-imgHQ28284.npy
-imgHQ23231.npy
-imgHQ20163.npy
-imgHQ02258.npy
-imgHQ25103.npy
-imgHQ15221.npy
-imgHQ16927.npy
-imgHQ10178.npy
-imgHQ25284.npy
-imgHQ20881.npy
-imgHQ04159.npy
-imgHQ12952.npy
-imgHQ18772.npy
-imgHQ22972.npy
-imgHQ12744.npy
-imgHQ04183.npy
-imgHQ28848.npy
-imgHQ19848.npy
-imgHQ24013.npy
-imgHQ23003.npy
-imgHQ13975.npy
-imgHQ27773.npy
-imgHQ00707.npy
-imgHQ13219.npy
-imgHQ00399.npy
-imgHQ01254.npy
-imgHQ13534.npy
-imgHQ12239.npy
-imgHQ25551.npy
-imgHQ03359.npy
-imgHQ13754.npy
-imgHQ22881.npy
-imgHQ19255.npy
-imgHQ04856.npy
-imgHQ21019.npy
-imgHQ01433.npy
-imgHQ19690.npy
-imgHQ02739.npy
-imgHQ19158.npy
-imgHQ22534.npy
-imgHQ01874.npy
-imgHQ19486.npy
-imgHQ08686.npy
-imgHQ15052.npy
-imgHQ22795.npy
-imgHQ17766.npy
-imgHQ27384.npy
-imgHQ29239.npy
-imgHQ25516.npy
-imgHQ13377.npy
-imgHQ25486.npy
-imgHQ03085.npy
-imgHQ15582.npy
-imgHQ16842.npy
-imgHQ15755.npy
-imgHQ11709.npy
-imgHQ06384.npy
-imgHQ03227.npy
-imgHQ07968.npy
-imgHQ25502.npy
-imgHQ00104.npy
-imgHQ23911.npy
-imgHQ29673.npy
-imgHQ14729.npy
-imgHQ07081.npy
-imgHQ02347.npy
-imgHQ21817.npy
-imgHQ10303.npy
-imgHQ08668.npy
-imgHQ08414.npy
-imgHQ27332.npy
-imgHQ28680.npy
-imgHQ28668.npy
-imgHQ04013.npy
-imgHQ02620.npy
-imgHQ12413.npy
-imgHQ04647.npy
-imgHQ27842.npy
-imgHQ27171.npy
-imgHQ20767.npy
-imgHQ21391.npy
-imgHQ20952.npy
-imgHQ24039.npy
-imgHQ19925.npy
-imgHQ03377.npy
-imgHQ25155.npy
-imgHQ27299.npy
-imgHQ18551.npy
-imgHQ29296.npy
-imgHQ03509.npy
-imgHQ22869.npy
-imgHQ15471.npy
-imgHQ05862.npy
-imgHQ15451.npy
-imgHQ03018.npy
-imgHQ18195.npy
-imgHQ02418.npy
-imgHQ00913.npy
-imgHQ22767.npy
-imgHQ24048.npy
-imgHQ06979.npy
-imgHQ29216.npy
-imgHQ22648.npy
-imgHQ24003.npy
-imgHQ04734.npy
-imgHQ26393.npy
-imgHQ15794.npy
-imgHQ25545.npy
-imgHQ23598.npy
-imgHQ07241.npy
-imgHQ24226.npy
-imgHQ06344.npy
-imgHQ01636.npy
-imgHQ03027.npy
-imgHQ02382.npy
-imgHQ07061.npy
-imgHQ18847.npy
-imgHQ09617.npy
-imgHQ00028.npy
-imgHQ25268.npy
-imgHQ11610.npy
-imgHQ22598.npy
-imgHQ27911.npy
-imgHQ25044.npy
-imgHQ24882.npy
-imgHQ03337.npy
-imgHQ17825.npy
-imgHQ00536.npy
-imgHQ05581.npy
-imgHQ23404.npy
-imgHQ26206.npy
-imgHQ08740.npy
-imgHQ02716.npy
-imgHQ03652.npy
-imgHQ22680.npy
-imgHQ25011.npy
-imgHQ17504.npy
-imgHQ04389.npy
-imgHQ09036.npy
-imgHQ22406.npy
-imgHQ13358.npy
-imgHQ24827.npy
-imgHQ21708.npy
-imgHQ23863.npy
-imgHQ02918.npy
-imgHQ24824.npy
-imgHQ06674.npy
-imgHQ05608.npy
-imgHQ02033.npy
-imgHQ04492.npy
-imgHQ19301.npy
-imgHQ21843.npy
-imgHQ05224.npy
-imgHQ28325.npy
-imgHQ06509.npy
-imgHQ10572.npy
-imgHQ15543.npy
-imgHQ11214.npy
-imgHQ28756.npy
-imgHQ03498.npy
-imgHQ07019.npy
-imgHQ09999.npy
-imgHQ11437.npy
-imgHQ08634.npy
-imgHQ09831.npy
-imgHQ03133.npy
-imgHQ18321.npy
-imgHQ19463.npy
-imgHQ04019.npy
-imgHQ04413.npy
-imgHQ09282.npy
-imgHQ09853.npy
-imgHQ00926.npy
-imgHQ27597.npy
-imgHQ01613.npy
-imgHQ09954.npy
-imgHQ08155.npy
-imgHQ08997.npy
-imgHQ20470.npy
-imgHQ08659.npy
-imgHQ00043.npy
-imgHQ26684.npy
-imgHQ09170.npy
-imgHQ18861.npy
-imgHQ19713.npy
-imgHQ09609.npy
-imgHQ23693.npy
-imgHQ07468.npy
-imgHQ15011.npy
-imgHQ21947.npy
-imgHQ21558.npy
-imgHQ10506.npy
-imgHQ12352.npy
-imgHQ02303.npy
-imgHQ07560.npy
-imgHQ10424.npy
-imgHQ03809.npy
-imgHQ00631.npy
-imgHQ11793.npy
-imgHQ10593.npy
-imgHQ23704.npy
-imgHQ27851.npy
-imgHQ08658.npy
-imgHQ28869.npy
-imgHQ22043.npy
-imgHQ12448.npy
-imgHQ04910.npy
-imgHQ19791.npy
-imgHQ05783.npy
-imgHQ26439.npy
-imgHQ01877.npy
-imgHQ29667.npy
-imgHQ22450.npy
-imgHQ15092.npy
-imgHQ12644.npy
-imgHQ10860.npy
-imgHQ21961.npy
-imgHQ05984.npy
-imgHQ18811.npy
-imgHQ05173.npy
-imgHQ05551.npy
-imgHQ12833.npy
-imgHQ23898.npy
-imgHQ01392.npy
-imgHQ20031.npy
-imgHQ26806.npy
-imgHQ13597.npy
-imgHQ13313.npy
-imgHQ14531.npy
-imgHQ09595.npy
-imgHQ28270.npy
-imgHQ04100.npy
-imgHQ06293.npy
-imgHQ01244.npy
-imgHQ07304.npy
-imgHQ07684.npy
-imgHQ25941.npy
-imgHQ11773.npy
-imgHQ24054.npy
-imgHQ16284.npy
-imgHQ11799.npy
-imgHQ21311.npy
-imgHQ24315.npy
-imgHQ23416.npy
-imgHQ09666.npy
-imgHQ02811.npy
-imgHQ29351.npy
-imgHQ11840.npy
-imgHQ26920.npy
-imgHQ06655.npy
-imgHQ13198.npy
-imgHQ08047.npy
-imgHQ05540.npy
-imgHQ25315.npy
-imgHQ06663.npy
-imgHQ28594.npy
-imgHQ15868.npy
-imgHQ03429.npy
-imgHQ25826.npy
-imgHQ02316.npy
-imgHQ05773.npy
-imgHQ08569.npy
-imgHQ17512.npy
-imgHQ11984.npy
-imgHQ29387.npy
-imgHQ07943.npy
-imgHQ07902.npy
-imgHQ27217.npy
-imgHQ03745.npy
-imgHQ07771.npy
-imgHQ26815.npy
-imgHQ11032.npy
-imgHQ01853.npy
-imgHQ10639.npy
-imgHQ16600.npy
-imgHQ09925.npy
-imgHQ21876.npy
-imgHQ27593.npy
-imgHQ09477.npy
-imgHQ00301.npy
-imgHQ19558.npy
-imgHQ28781.npy
-imgHQ18868.npy
-imgHQ25650.npy
-imgHQ17571.npy
-imgHQ20547.npy
-imgHQ12645.npy
-imgHQ15825.npy
-imgHQ18866.npy
-imgHQ08915.npy
-imgHQ01243.npy
-imgHQ12043.npy
-imgHQ28545.npy
-imgHQ01398.npy
-imgHQ23461.npy
-imgHQ25615.npy
-imgHQ26715.npy
-imgHQ21487.npy
-imgHQ24374.npy
-imgHQ20055.npy
-imgHQ01678.npy
-imgHQ18711.npy
-imgHQ06461.npy
-imgHQ27759.npy
-imgHQ07781.npy
-imgHQ13419.npy
-imgHQ09767.npy
-imgHQ15885.npy
-imgHQ14412.npy
-imgHQ12693.npy
-imgHQ28779.npy
-imgHQ17973.npy
-imgHQ21823.npy
-imgHQ19999.npy
-imgHQ12342.npy
-imgHQ13621.npy
-imgHQ11135.npy
-imgHQ03581.npy
-imgHQ22386.npy
-imgHQ05268.npy
-imgHQ28894.npy
-imgHQ25118.npy
-imgHQ05334.npy
-imgHQ03502.npy
-imgHQ25207.npy
-imgHQ21488.npy
-imgHQ27128.npy
-imgHQ18026.npy
-imgHQ04077.npy
-imgHQ28155.npy
-imgHQ24212.npy
-imgHQ24544.npy
-imgHQ18402.npy
-imgHQ10056.npy
-imgHQ16105.npy
-imgHQ17861.npy
-imgHQ15605.npy
-imgHQ27358.npy
-imgHQ03347.npy
-imgHQ01140.npy
-imgHQ08909.npy
-imgHQ07601.npy
-imgHQ19401.npy
-imgHQ27789.npy
-imgHQ19759.npy
-imgHQ17468.npy
-imgHQ19135.npy
-imgHQ21924.npy
-imgHQ23230.npy
-imgHQ03478.npy
-imgHQ24789.npy
-imgHQ17175.npy
-imgHQ27821.npy
-imgHQ08551.npy
-imgHQ06752.npy
-imgHQ28946.npy
-imgHQ07671.npy
-imgHQ08823.npy
-imgHQ17553.npy
-imgHQ10224.npy
-imgHQ12169.npy
-imgHQ11429.npy
-imgHQ20670.npy
-imgHQ24512.npy
-imgHQ11041.npy
-imgHQ02669.npy
-imgHQ07326.npy
-imgHQ15148.npy
-imgHQ22145.npy
-imgHQ27336.npy
-imgHQ23943.npy
-imgHQ04545.npy
-imgHQ12200.npy
-imgHQ03576.npy
-imgHQ14329.npy
-imgHQ06614.npy
-imgHQ29551.npy
-imgHQ15378.npy
-imgHQ27937.npy
-imgHQ29737.npy
-imgHQ20837.npy
-imgHQ00386.npy
-imgHQ03526.npy
-imgHQ29865.npy
-imgHQ02348.npy
-imgHQ29132.npy
-imgHQ27122.npy
-imgHQ00376.npy
-imgHQ15509.npy
-imgHQ10356.npy
-imgHQ22337.npy
-imgHQ01697.npy
-imgHQ01300.npy
-imgHQ22517.npy
-imgHQ02594.npy
-imgHQ19552.npy
-imgHQ15262.npy
-imgHQ28462.npy
-imgHQ00602.npy
-imgHQ06089.npy
-imgHQ01758.npy
-imgHQ20258.npy
-imgHQ04919.npy
-imgHQ05781.npy
-imgHQ28035.npy
-imgHQ02190.npy
-imgHQ04978.npy
-imgHQ17568.npy
-imgHQ12211.npy
-imgHQ09738.npy
-imgHQ08566.npy
-imgHQ18790.npy
-imgHQ11558.npy
-imgHQ17372.npy
-imgHQ11787.npy
-imgHQ27609.npy
-imgHQ25324.npy
-imgHQ06738.npy
-imgHQ07309.npy
-imgHQ12118.npy
-imgHQ10405.npy
-imgHQ12678.npy
-imgHQ27491.npy
-imgHQ18916.npy
-imgHQ06803.npy
-imgHQ11766.npy
-imgHQ10370.npy
-imgHQ27295.npy
-imgHQ23524.npy
-imgHQ29181.npy
-imgHQ02110.npy
-imgHQ03423.npy
-imgHQ19167.npy
-imgHQ11905.npy
-imgHQ20824.npy
-imgHQ24029.npy
-imgHQ00719.npy
-imgHQ21505.npy
-imgHQ26451.npy
-imgHQ11677.npy
-imgHQ24989.npy
-imgHQ08412.npy
-imgHQ27774.npy
-imgHQ00419.npy
-imgHQ15953.npy
-imgHQ20017.npy
-imgHQ20596.npy
-imgHQ26006.npy
-imgHQ07259.npy
-imgHQ17918.npy
-imgHQ05172.npy
-imgHQ10859.npy
-imgHQ12278.npy
-imgHQ16746.npy
-imgHQ19029.npy
-imgHQ28679.npy
-imgHQ17810.npy
-imgHQ05296.npy
-imgHQ29096.npy
-imgHQ29360.npy
-imgHQ05258.npy
-imgHQ04390.npy
-imgHQ04983.npy
-imgHQ27824.npy
-imgHQ07877.npy
-imgHQ28176.npy
-imgHQ16424.npy
-imgHQ15404.npy
-imgHQ22435.npy
-imgHQ09342.npy
-imgHQ00713.npy
-imgHQ09402.npy
-imgHQ22875.npy
-imgHQ11795.npy
-imgHQ23044.npy
-imgHQ21306.npy
-imgHQ03314.npy
-imgHQ24360.npy
-imgHQ11455.npy
-imgHQ13065.npy
-imgHQ06720.npy
-imgHQ25382.npy
-imgHQ11963.npy
-imgHQ08465.npy
-imgHQ22150.npy
-imgHQ04325.npy
-imgHQ21155.npy
-imgHQ15921.npy
-imgHQ08078.npy
-imgHQ09362.npy
-imgHQ20503.npy
-imgHQ20675.npy
-imgHQ04793.npy
-imgHQ03023.npy
-imgHQ18074.npy
-imgHQ10919.npy
-imgHQ00758.npy
-imgHQ01599.npy
-imgHQ27014.npy
-imgHQ15880.npy
-imgHQ27206.npy
-imgHQ10734.npy
-imgHQ23276.npy
-imgHQ18212.npy
-imgHQ10576.npy
-imgHQ29609.npy
-imgHQ21115.npy
-imgHQ25632.npy
-imgHQ20063.npy
-imgHQ19734.npy
-imgHQ04574.npy
-imgHQ15652.npy
-imgHQ09132.npy
-imgHQ08449.npy
-imgHQ19443.npy
-imgHQ15033.npy
-imgHQ15174.npy
-imgHQ23078.npy
-imgHQ00291.npy
-imgHQ04244.npy
-imgHQ17189.npy
-imgHQ09742.npy
-imgHQ09468.npy
-imgHQ16632.npy
-imgHQ10801.npy
-imgHQ17555.npy
-imgHQ28928.npy
-imgHQ21882.npy
-imgHQ08100.npy
-imgHQ11906.npy
-imgHQ23637.npy
-imgHQ09531.npy
-imgHQ00506.npy
-imgHQ11445.npy
-imgHQ20975.npy
-imgHQ28172.npy
-imgHQ18200.npy
-imgHQ05846.npy
-imgHQ04604.npy
-imgHQ13648.npy
-imgHQ28747.npy
-imgHQ28931.npy
-imgHQ22938.npy
-imgHQ19876.npy
-imgHQ21021.npy
-imgHQ14877.npy
-imgHQ04201.npy
-imgHQ21522.npy
-imgHQ17343.npy
-imgHQ03779.npy
-imgHQ00204.npy
-imgHQ15873.npy
-imgHQ15599.npy
-imgHQ21071.npy
-imgHQ18638.npy
-imgHQ16387.npy
-imgHQ21503.npy
-imgHQ18322.npy
-imgHQ09060.npy
-imgHQ04812.npy
-imgHQ11506.npy
-imgHQ11877.npy
-imgHQ18491.npy
-imgHQ13993.npy
-imgHQ28864.npy
-imgHQ01328.npy
-imgHQ00401.npy
-imgHQ23365.npy
-imgHQ02886.npy
-imgHQ19128.npy
-imgHQ01228.npy
-imgHQ24926.npy
-imgHQ22110.npy
-imgHQ10779.npy
-imgHQ24620.npy
-imgHQ17970.npy
-imgHQ25897.npy
-imgHQ05487.npy
-imgHQ23450.npy
-imgHQ21830.npy
-imgHQ03501.npy
-imgHQ13473.npy
-imgHQ05541.npy
-imgHQ09082.npy
-imgHQ13804.npy
-imgHQ06879.npy
-imgHQ20296.npy
-imgHQ11399.npy
-imgHQ10242.npy
-imgHQ09979.npy
-imgHQ17837.npy
-imgHQ18328.npy
-imgHQ02993.npy
-imgHQ12045.npy
-imgHQ02509.npy
-imgHQ14619.npy
-imgHQ10610.npy
-imgHQ15846.npy
-imgHQ10908.npy
-imgHQ25253.npy
-imgHQ21617.npy
-imgHQ04594.npy
-imgHQ12609.npy
-imgHQ26022.npy
-imgHQ20569.npy
-imgHQ10421.npy
-imgHQ08803.npy
-imgHQ08596.npy
-imgHQ06023.npy
-imgHQ11640.npy
-imgHQ15104.npy
-imgHQ21296.npy
-imgHQ05126.npy
-imgHQ19808.npy
-imgHQ04883.npy
-imgHQ29258.npy
-imgHQ24269.npy
-imgHQ25741.npy
-imgHQ22203.npy
-imgHQ16637.npy
-imgHQ01788.npy
-imgHQ17608.npy
-imgHQ19025.npy
-imgHQ07556.npy
-imgHQ25664.npy
-imgHQ03065.npy
-imgHQ16652.npy
-imgHQ28794.npy
-imgHQ14999.npy
-imgHQ00566.npy
-imgHQ29308.npy
-imgHQ24522.npy
-imgHQ25384.npy
-imgHQ07367.npy
-imgHQ26796.npy
-imgHQ15856.npy
-imgHQ17405.npy
-imgHQ18706.npy
-imgHQ06095.npy
-imgHQ26440.npy
-imgHQ04820.npy
-imgHQ03303.npy
-imgHQ16098.npy
-imgHQ04898.npy
-imgHQ23281.npy
-imgHQ21598.npy
-imgHQ04552.npy
-imgHQ09130.npy
-imgHQ03718.npy
-imgHQ06536.npy
-imgHQ19176.npy
-imgHQ01864.npy
-imgHQ20110.npy
-imgHQ28074.npy
-imgHQ15750.npy
-imgHQ05863.npy
-imgHQ06686.npy
-imgHQ11707.npy
-imgHQ20061.npy
-imgHQ18852.npy
-imgHQ22439.npy
-imgHQ21137.npy
-imgHQ09757.npy
-imgHQ12378.npy
-imgHQ03220.npy
-imgHQ29612.npy
-imgHQ18201.npy
-imgHQ13505.npy
-imgHQ20884.npy
-imgHQ04173.npy
-imgHQ25745.npy
-imgHQ14597.npy
-imgHQ19163.npy
-imgHQ27888.npy
-imgHQ19732.npy
-imgHQ15140.npy
-imgHQ17302.npy
-imgHQ00818.npy
-imgHQ16538.npy
-imgHQ11476.npy
-imgHQ15187.npy
-imgHQ24766.npy
-imgHQ21043.npy
-imgHQ24012.npy
-imgHQ01587.npy
-imgHQ06038.npy
-imgHQ09122.npy
-imgHQ04979.npy
-imgHQ09232.npy
-imgHQ18590.npy
-imgHQ20358.npy
-imgHQ24002.npy
-imgHQ26837.npy
-imgHQ27511.npy
-imgHQ04713.npy
-imgHQ18881.npy
-imgHQ18356.npy
-imgHQ15984.npy
-imgHQ20741.npy
-imgHQ21377.npy
-imgHQ09377.npy
-imgHQ02734.npy
-imgHQ09994.npy
-imgHQ20874.npy
-imgHQ20329.npy
-imgHQ02202.npy
-imgHQ25058.npy
-imgHQ07198.npy
-imgHQ12763.npy
-imgHQ05433.npy
-imgHQ14936.npy
-imgHQ03134.npy
-imgHQ06712.npy
-imgHQ08719.npy
-imgHQ01932.npy
-imgHQ16971.npy
-imgHQ27383.npy
-imgHQ28360.npy
-imgHQ15461.npy
-imgHQ15747.npy
-imgHQ27965.npy
-imgHQ20060.npy
-imgHQ18429.npy
-imgHQ12882.npy
-imgHQ28186.npy
-imgHQ10251.npy
-imgHQ11125.npy
-imgHQ03390.npy
-imgHQ11454.npy
-imgHQ18616.npy
-imgHQ26633.npy
-imgHQ24964.npy
-imgHQ03785.npy
-imgHQ10573.npy
-imgHQ02545.npy
-imgHQ04605.npy
-imgHQ22654.npy
-imgHQ24556.npy
-imgHQ16891.npy
-imgHQ05657.npy
-imgHQ15784.npy
-imgHQ08265.npy
-imgHQ21351.npy
-imgHQ10476.npy
-imgHQ16689.npy
-imgHQ08056.npy
-imgHQ17936.npy
-imgHQ13163.npy
-imgHQ19066.npy
-imgHQ20656.npy
-imgHQ19953.npy
-imgHQ08368.npy
-imgHQ06974.npy
-imgHQ06417.npy
-imgHQ15491.npy
-imgHQ14498.npy
-imgHQ23083.npy
-imgHQ11385.npy
-imgHQ12135.npy
-imgHQ27619.npy
-imgHQ26914.npy
-imgHQ08399.npy
-imgHQ08319.npy
-imgHQ15629.npy
-imgHQ03156.npy
-imgHQ20265.npy
-imgHQ00152.npy
-imgHQ23195.npy
-imgHQ29339.npy
-imgHQ09933.npy
-imgHQ06071.npy
-imgHQ00015.npy
-imgHQ20572.npy
-imgHQ19963.npy
-imgHQ02051.npy
-imgHQ06275.npy
-imgHQ04362.npy
-imgHQ11328.npy
-imgHQ08317.npy
-imgHQ07492.npy
-imgHQ24870.npy
-imgHQ27049.npy
-imgHQ25210.npy
-imgHQ10164.npy
-imgHQ23186.npy
-imgHQ18613.npy
-imgHQ07272.npy
-imgHQ26416.npy
-imgHQ13207.npy
-imgHQ20337.npy
-imgHQ04653.npy
-imgHQ23301.npy
-imgHQ26291.npy
-imgHQ17456.npy
-imgHQ00236.npy
-imgHQ07395.npy
-imgHQ10116.npy
-imgHQ28310.npy
-imgHQ12263.npy
-imgHQ28533.npy
-imgHQ07008.npy
-imgHQ13520.npy
-imgHQ02397.npy
-imgHQ21451.npy
-imgHQ05977.npy
-imgHQ05179.npy
-imgHQ06186.npy
-imgHQ16597.npy
-imgHQ20415.npy
-imgHQ18586.npy
-imgHQ04481.npy
-imgHQ29425.npy
-imgHQ08741.npy
-imgHQ07299.npy
-imgHQ07295.npy
-imgHQ05327.npy
-imgHQ10367.npy
-imgHQ24503.npy
-imgHQ19145.npy
-imgHQ24147.npy
-imgHQ18052.npy
-imgHQ24930.npy
-imgHQ22989.npy
-imgHQ17195.npy
-imgHQ25899.npy
-imgHQ06905.npy
-imgHQ18826.npy
-imgHQ14162.npy
-imgHQ01136.npy
-imgHQ23379.npy
-imgHQ11826.npy
-imgHQ17055.npy
-imgHQ19559.npy
-imgHQ05088.npy
-imgHQ07897.npy
-imgHQ23104.npy
-imgHQ12670.npy
-imgHQ29692.npy
-imgHQ14119.npy
-imgHQ03122.npy
-imgHQ19159.npy
-imgHQ08657.npy
-imgHQ27525.npy
-imgHQ27061.npy
-imgHQ05546.npy
-imgHQ10126.npy
-imgHQ27199.npy
-imgHQ29248.npy
-imgHQ20607.npy
-imgHQ18389.npy
-imgHQ05115.npy
-imgHQ24482.npy
-imgHQ10463.npy
-imgHQ28319.npy
-imgHQ20486.npy
-imgHQ24920.npy
-imgHQ25219.npy
-imgHQ10551.npy
-imgHQ21005.npy
-imgHQ19694.npy
-imgHQ07042.npy
-imgHQ21160.npy
-imgHQ20955.npy
-imgHQ08067.npy
-imgHQ16673.npy
-imgHQ00003.npy
-imgHQ28317.npy
-imgHQ00685.npy
-imgHQ14434.npy
-imgHQ26281.npy
-imgHQ01646.npy
-imgHQ27213.npy
-imgHQ00921.npy
-imgHQ08358.npy
-imgHQ15667.npy
-imgHQ17061.npy
-imgHQ27000.npy
-imgHQ27088.npy
-imgHQ17909.npy
-imgHQ16209.npy
-imgHQ28723.npy
-imgHQ22079.npy
-imgHQ00968.npy
-imgHQ19189.npy
-imgHQ08908.npy
-imgHQ22518.npy
-imgHQ12750.npy
-imgHQ05413.npy
-imgHQ24563.npy
-imgHQ10568.npy
-imgHQ16721.npy
-imgHQ27068.npy
-imgHQ09746.npy
-imgHQ09751.npy
-imgHQ20793.npy
-imgHQ26293.npy
-imgHQ07977.npy
-imgHQ13346.npy
-imgHQ13275.npy
-imgHQ07812.npy
-imgHQ29535.npy
-imgHQ07903.npy
-imgHQ21582.npy
-imgHQ25997.npy
-imgHQ05722.npy
-imgHQ05308.npy
-imgHQ17816.npy
-imgHQ00512.npy
-imgHQ28238.npy
-imgHQ08409.npy
-imgHQ25982.npy
-imgHQ29081.npy
-imgHQ02436.npy
-imgHQ11416.npy
-imgHQ12886.npy
-imgHQ21098.npy
-imgHQ13336.npy
-imgHQ02333.npy
-imgHQ29497.npy
-imgHQ14423.npy
-imgHQ18041.npy
-imgHQ04320.npy
-imgHQ10125.npy
-imgHQ21517.npy
-imgHQ09193.npy
-imgHQ15354.npy
-imgHQ10959.npy
-imgHQ14129.npy
-imgHQ13493.npy
-imgHQ29593.npy
-imgHQ09890.npy
-imgHQ18057.npy
-imgHQ01236.npy
-imgHQ00470.npy
-imgHQ14669.npy
-imgHQ18659.npy
-imgHQ27430.npy
-imgHQ03899.npy
-imgHQ24435.npy
-imgHQ02036.npy
-imgHQ20020.npy
-imgHQ04075.npy
-imgHQ27374.npy
-imgHQ07269.npy
-imgHQ13145.npy
-imgHQ23901.npy
-imgHQ02234.npy
-imgHQ01173.npy
-imgHQ13042.npy
-imgHQ19012.npy
-imgHQ08119.npy
-imgHQ27981.npy
-imgHQ17123.npy
-imgHQ06772.npy
-imgHQ14098.npy
-imgHQ28218.npy
-imgHQ23116.npy
-imgHQ26629.npy
-imgHQ28835.npy
-imgHQ29789.npy
-imgHQ09480.npy
-imgHQ12361.npy
-imgHQ26501.npy
-imgHQ13860.npy
-imgHQ03564.npy
-imgHQ08811.npy
-imgHQ01345.npy
-imgHQ29549.npy
-imgHQ22780.npy
-imgHQ05739.npy
-imgHQ26923.npy
-imgHQ10856.npy
-imgHQ27028.npy
-imgHQ22855.npy
-imgHQ26072.npy
-imgHQ14615.npy
-imgHQ18082.npy
-imgHQ27010.npy
-imgHQ03953.npy
-imgHQ17137.npy
-imgHQ01849.npy
-imgHQ25909.npy
-imgHQ15171.npy
-imgHQ27684.npy
-imgHQ24615.npy
-imgHQ08506.npy
-imgHQ01751.npy
-imgHQ22261.npy
-imgHQ08536.npy
-imgHQ26247.npy
-imgHQ22979.npy
-imgHQ21808.npy
-imgHQ11486.npy
-imgHQ01321.npy
-imgHQ26427.npy
-imgHQ28102.npy
-imgHQ11574.npy
-imgHQ07919.npy
-imgHQ20631.npy
-imgHQ04046.npy
-imgHQ10728.npy
-imgHQ29441.npy
-imgHQ20735.npy
-imgHQ15997.npy
-imgHQ26992.npy
-imgHQ07076.npy
-imgHQ08086.npy
-imgHQ20472.npy
-imgHQ14566.npy
-imgHQ23496.npy
-imgHQ17594.npy
-imgHQ16193.npy
-imgHQ29359.npy
-imgHQ13033.npy
-imgHQ26377.npy
-imgHQ15576.npy
-imgHQ19341.npy
-imgHQ05547.npy
-imgHQ21047.npy
-imgHQ24035.npy
-imgHQ23643.npy
-imgHQ00169.npy
-imgHQ03341.npy
-imgHQ09293.npy
-imgHQ09887.npy
-imgHQ27425.npy
-imgHQ09153.npy
-imgHQ19628.npy
-imgHQ24418.npy
-imgHQ06031.npy
-imgHQ05069.npy
-imgHQ13914.npy
-imgHQ09226.npy
-imgHQ11259.npy
-imgHQ22935.npy
-imgHQ27836.npy
-imgHQ24437.npy
-imgHQ10626.npy
-imgHQ09602.npy
-imgHQ13625.npy
-imgHQ26732.npy
-imgHQ16165.npy
-imgHQ05198.npy
-imgHQ06647.npy
-imgHQ00129.npy
-imgHQ02591.npy
-imgHQ11753.npy
-imgHQ01762.npy
-imgHQ06264.npy
-imgHQ09594.npy
-imgHQ05245.npy
-imgHQ20860.npy
-imgHQ16734.npy
-imgHQ13206.npy
-imgHQ21525.npy
-imgHQ01257.npy
-imgHQ05623.npy
-imgHQ02065.npy
-imgHQ17908.npy
-imgHQ25243.npy
-imgHQ14422.npy
-imgHQ02276.npy
-imgHQ24371.npy
-imgHQ21309.npy
-imgHQ23029.npy
-imgHQ14124.npy
-imgHQ23223.npy
-imgHQ15485.npy
-imgHQ22727.npy
-imgHQ20805.npy
-imgHQ26781.npy
-imgHQ01342.npy
-imgHQ15288.npy
-imgHQ13948.npy
-imgHQ27288.npy
-imgHQ24740.npy
-imgHQ28906.npy
-imgHQ28677.npy
-imgHQ13281.npy
-imgHQ22167.npy
-imgHQ16144.npy
-imgHQ10522.npy
-imgHQ21403.npy
-imgHQ08912.npy
-imgHQ27352.npy
-imgHQ21755.npy
-imgHQ12437.npy
-imgHQ27346.npy
-imgHQ27807.npy
-imgHQ12088.npy
-imgHQ02673.npy
-imgHQ09556.npy
-imgHQ01556.npy
-imgHQ07370.npy
-imgHQ00632.npy
-imgHQ04319.npy
-imgHQ16400.npy
-imgHQ22808.npy
-imgHQ03211.npy
-imgHQ18141.npy
-imgHQ11762.npy
-imgHQ23826.npy
-imgHQ06724.npy
-imgHQ05331.npy
-imgHQ19542.npy
-imgHQ12047.npy
-imgHQ04578.npy
-imgHQ19678.npy
-imgHQ17507.npy
-imgHQ09149.npy
-imgHQ02023.npy
-imgHQ06919.npy
-imgHQ10387.npy
-imgHQ22834.npy
-imgHQ08191.npy
-imgHQ27756.npy
-imgHQ05274.npy
-imgHQ03275.npy
-imgHQ07339.npy
-imgHQ11543.npy
-imgHQ24377.npy
-imgHQ20309.npy
-imgHQ02074.npy
-imgHQ25398.npy
-imgHQ03769.npy
-imgHQ03144.npy
-imgHQ23909.npy
-imgHQ22040.npy
-imgHQ23455.npy
-imgHQ18133.npy
-imgHQ12994.npy
-imgHQ26586.npy
-imgHQ13327.npy
-imgHQ14687.npy
-imgHQ23407.npy
-imgHQ22139.npy
-imgHQ15857.npy
-imgHQ07426.npy
-imgHQ24203.npy
-imgHQ14733.npy
-imgHQ05768.npy
-imgHQ05369.npy
-imgHQ09068.npy
-imgHQ29654.npy
-imgHQ29266.npy
-imgHQ04398.npy
-imgHQ04221.npy
-imgHQ19469.npy
-imgHQ18770.npy
-imgHQ12050.npy
-imgHQ13786.npy
-imgHQ29307.npy
-imgHQ18331.npy
-imgHQ26943.npy
-imgHQ22956.npy
-imgHQ10379.npy
-imgHQ24745.npy
-imgHQ20437.npy
-imgHQ10929.npy
-imgHQ13700.npy
-imgHQ25521.npy
-imgHQ22185.npy
-imgHQ28488.npy
-imgHQ11124.npy
-imgHQ29472.npy
-imgHQ00688.npy
-imgHQ17644.npy
-imgHQ22707.npy
-imgHQ10001.npy
-imgHQ19784.npy
-imgHQ09688.npy
-imgHQ18763.npy
-imgHQ12295.npy
-imgHQ09044.npy
-imgHQ01437.npy
-imgHQ27796.npy
-imgHQ04880.npy
-imgHQ13747.npy
-imgHQ18112.npy
-imgHQ09849.npy
-imgHQ05791.npy
-imgHQ12121.npy
-imgHQ06816.npy
-imgHQ15360.npy
-imgHQ10622.npy
-imgHQ17420.npy
-imgHQ19904.npy
-imgHQ01555.npy
-imgHQ12344.npy
-imgHQ29400.npy
-imgHQ07884.npy
-imgHQ04592.npy
-imgHQ23629.npy
-imgHQ12581.npy
-imgHQ00700.npy
-imgHQ12370.npy
-imgHQ28403.npy
-imgHQ12235.npy
-imgHQ14517.npy
-imgHQ18934.npy
-imgHQ06404.npy
-imgHQ27012.npy
-imgHQ05818.npy
-imgHQ04935.npy
-imgHQ09753.npy
-imgHQ20932.npy
-imgHQ08935.npy
-imgHQ14512.npy
-imgHQ13906.npy
-imgHQ05217.npy
-imgHQ22876.npy
-imgHQ15167.npy
-imgHQ26831.npy
-imgHQ17953.npy
-imgHQ05843.npy
-imgHQ16270.npy
-imgHQ00189.npy
-imgHQ01688.npy
-imgHQ11933.npy
-imgHQ22751.npy
-imgHQ27660.npy
-imgHQ03909.npy
-imgHQ26474.npy
-imgHQ26529.npy
-imgHQ02464.npy
-imgHQ19730.npy
-imgHQ13964.npy
-imgHQ04024.npy
-imgHQ22545.npy
-imgHQ18361.npy
-imgHQ27504.npy
-imgHQ27785.npy
-imgHQ04496.npy
-imgHQ23449.npy
-imgHQ03881.npy
-imgHQ27598.npy
-imgHQ26341.npy
-imgHQ01952.npy
-imgHQ25653.npy
-imgHQ09196.npy
-imgHQ11129.npy
-imgHQ18504.npy
-imgHQ17240.npy
-imgHQ16973.npy
-imgHQ23610.npy
-imgHQ28308.npy
-imgHQ15724.npy
-imgHQ10531.npy
-imgHQ10466.npy
-imgHQ26980.npy
-imgHQ03221.npy
-imgHQ27210.npy
-imgHQ02638.npy
-imgHQ19074.npy
-imgHQ05048.npy
-imgHQ02127.npy
-imgHQ26513.npy
-imgHQ20050.npy
-imgHQ16320.npy
-imgHQ21366.npy
-imgHQ24862.npy
-imgHQ09419.npy
-imgHQ24619.npy
-imgHQ07205.npy
-imgHQ14128.npy
-imgHQ03301.npy
-imgHQ24077.npy
-imgHQ13805.npy
-imgHQ28372.npy
-imgHQ27164.npy
-imgHQ20416.npy
-imgHQ02363.npy
-imgHQ00024.npy
-imgHQ04160.npy
-imgHQ03312.npy
-imgHQ20518.npy
-imgHQ16498.npy
-imgHQ13908.npy
-imgHQ14089.npy
-imgHQ06260.npy
-imgHQ01843.npy
-imgHQ26784.npy
-imgHQ01837.npy
-imgHQ01637.npy
-imgHQ19819.npy
-imgHQ00035.npy
-imgHQ18909.npy
-imgHQ04108.npy
-imgHQ18624.npy
-imgHQ28808.npy
-imgHQ07709.npy
-imgHQ04672.npy
-imgHQ29841.npy
-imgHQ10911.npy
-imgHQ15507.npy
-imgHQ22536.npy
-imgHQ28924.npy
-imgHQ26106.npy
-imgHQ28328.npy
-imgHQ28496.npy
-imgHQ16350.npy
-imgHQ22633.npy
-imgHQ29989.npy
-imgHQ10736.npy
-imgHQ20855.npy
-imgHQ22378.npy
-imgHQ02630.npy
-imgHQ06982.npy
-imgHQ20166.npy
-imgHQ25017.npy
-imgHQ13089.npy
-imgHQ21698.npy
-imgHQ19769.npy
-imgHQ23018.npy
-imgHQ06195.npy
-imgHQ00823.npy
-imgHQ14697.npy
-imgHQ25183.npy
-imgHQ16681.npy
-imgHQ03143.npy
-imgHQ20521.npy
-imgHQ10417.npy
-imgHQ14025.npy
-imgHQ27290.npy
-imgHQ18635.npy
-imgHQ27960.npy
-imgHQ14545.npy
-imgHQ01132.npy
-imgHQ21169.npy
-imgHQ12248.npy
-imgHQ28501.npy
-imgHQ26468.npy
-imgHQ00405.npy
-imgHQ13909.npy
-imgHQ29420.npy
-imgHQ27375.npy
-imgHQ02196.npy
-imgHQ23250.npy
-imgHQ20090.npy
-imgHQ25331.npy
-imgHQ25279.npy
-imgHQ00320.npy
-imgHQ14933.npy
-imgHQ23663.npy
-imgHQ27715.npy
-imgHQ01842.npy
-imgHQ23871.npy
-imgHQ06582.npy
-imgHQ20488.npy
-imgHQ15495.npy
-imgHQ25463.npy
-imgHQ09469.npy
-imgHQ29192.npy
-imgHQ08656.npy
-imgHQ25001.npy
-imgHQ27792.npy
-imgHQ24634.npy
-imgHQ17314.npy
-imgHQ20125.npy
-imgHQ26673.npy
-imgHQ04457.npy
-imgHQ25393.npy
-imgHQ00170.npy
-imgHQ05702.npy
-imgHQ16129.npy
-imgHQ13399.npy
-imgHQ27772.npy
-imgHQ05598.npy
-imgHQ24106.npy
-imgHQ25051.npy
-imgHQ07014.npy
-imgHQ03103.npy
-imgHQ08270.npy
-imgHQ01909.npy
-imgHQ10836.npy
-imgHQ18623.npy
-imgHQ02159.npy
-imgHQ24279.npy
-imgHQ27820.npy
-imgHQ10129.npy
-imgHQ26910.npy
-imgHQ28105.npy
-imgHQ18963.npy
-imgHQ05353.npy
-imgHQ10484.npy
-imgHQ01779.npy
-imgHQ29455.npy
-imgHQ04401.npy
-imgHQ25807.npy
-imgHQ04036.npy
-imgHQ07716.npy
-imgHQ14253.npy
-imgHQ02930.npy
-imgHQ04158.npy
-imgHQ13869.npy
-imgHQ03657.npy
-imgHQ18776.npy
-imgHQ29532.npy
-imgHQ26053.npy
-imgHQ21754.npy
-imgHQ23143.npy
-imgHQ25657.npy
-imgHQ25246.npy
-imgHQ18593.npy
-imgHQ27366.npy
-imgHQ08809.npy
-imgHQ09024.npy
-imgHQ04718.npy
-imgHQ11987.npy
-imgHQ25211.npy
-imgHQ18620.npy
-imgHQ17822.npy
-imgHQ24163.npy
-imgHQ25195.npy
-imgHQ22528.npy
-imgHQ22759.npy
-imgHQ01423.npy
-imgHQ09084.npy
-imgHQ13586.npy
-imgHQ15727.npy
-imgHQ04565.npy
-imgHQ00571.npy
-imgHQ24782.npy
-imgHQ19323.npy
-imgHQ03110.npy
-imgHQ27046.npy
-imgHQ21998.npy
-imgHQ15801.npy
-imgHQ28266.npy
-imgHQ21785.npy
-imgHQ05358.npy
-imgHQ22223.npy
-imgHQ03974.npy
-imgHQ26616.npy
-imgHQ21752.npy
-imgHQ01119.npy
-imgHQ11869.npy
-imgHQ19753.npy
-imgHQ03017.npy
-imgHQ09484.npy
-imgHQ21101.npy
-imgHQ11578.npy
-imgHQ14345.npy
-imgHQ07429.npy
-imgHQ21916.npy
-imgHQ04055.npy
-imgHQ02931.npy
-imgHQ29822.npy
-imgHQ05696.npy
-imgHQ00701.npy
-imgHQ25839.npy
-imgHQ11708.npy
-imgHQ00234.npy
-imgHQ14725.npy
-imgHQ24550.npy
-imgHQ08676.npy
-imgHQ17879.npy
-imgHQ23211.npy
-imgHQ23833.npy
-imgHQ04844.npy
-imgHQ10885.npy
-imgHQ22476.npy
-imgHQ29481.npy
-imgHQ13450.npy
-imgHQ10462.npy
-imgHQ04459.npy
-imgHQ23238.npy
-imgHQ02909.npy
-imgHQ23432.npy
-imgHQ00207.npy
-imgHQ10882.npy
-imgHQ21371.npy
-imgHQ04750.npy
-imgHQ11817.npy
-imgHQ03568.npy
-imgHQ05382.npy
-imgHQ20583.npy
-imgHQ00447.npy
-imgHQ06286.npy
-imgHQ13815.npy
-imgHQ11607.npy
-imgHQ24839.npy
-imgHQ07588.npy
-imgHQ09063.npy
-imgHQ03748.npy
-imgHQ23947.npy
-imgHQ01214.npy
-imgHQ28164.npy
-imgHQ26456.npy
-imgHQ08150.npy
-imgHQ00801.npy
-imgHQ03069.npy
-imgHQ28402.npy
-imgHQ24176.npy
-imgHQ00856.npy
-imgHQ12746.npy
-imgHQ17114.npy
-imgHQ17211.npy
-imgHQ09542.npy
-imgHQ09726.npy
-imgHQ00393.npy
-imgHQ01264.npy
-imgHQ12549.npy
-imgHQ00920.npy
-imgHQ03233.npy
-imgHQ02873.npy
-imgHQ01930.npy
-imgHQ26242.npy
-imgHQ12225.npy
-imgHQ01905.npy
-imgHQ07740.npy
-imgHQ28236.npy
-imgHQ11378.npy
-imgHQ21482.npy
-imgHQ18808.npy
-imgHQ18886.npy
-imgHQ07125.npy
-imgHQ14645.npy
-imgHQ02680.npy
-imgHQ04486.npy
-imgHQ25240.npy
-imgHQ15217.npy
-imgHQ22582.npy
-imgHQ15877.npy
-imgHQ26664.npy
-imgHQ16228.npy
-imgHQ14355.npy
-imgHQ29494.npy
-imgHQ03997.npy
-imgHQ20053.npy
-imgHQ27118.npy
-imgHQ14365.npy
-imgHQ14177.npy
-imgHQ13623.npy
-imgHQ10364.npy
-imgHQ13500.npy
-imgHQ10841.npy
-imgHQ23260.npy
-imgHQ06528.npy
-imgHQ25592.npy
-imgHQ18569.npy
-imgHQ07085.npy
-imgHQ21042.npy
-imgHQ13646.npy
-imgHQ18760.npy
-imgHQ27059.npy
-imgHQ27117.npy
-imgHQ05889.npy
-imgHQ17286.npy
-imgHQ08528.npy
-imgHQ18820.npy
-imgHQ09603.npy
-imgHQ28639.npy
-imgHQ15775.npy
-imgHQ22438.npy
-imgHQ05499.npy
-imgHQ04026.npy
-imgHQ23922.npy
-imgHQ28899.npy
-imgHQ13985.npy
-imgHQ16036.npy
-imgHQ03758.npy
-imgHQ04873.npy
-imgHQ18154.npy
-imgHQ18166.npy
-imgHQ18260.npy
-imgHQ02626.npy
-imgHQ28568.npy
-imgHQ16691.npy
-imgHQ05017.npy
-imgHQ12936.npy
-imgHQ08588.npy
-imgHQ16335.npy
-imgHQ19574.npy
-imgHQ07331.npy
-imgHQ25121.npy
-imgHQ27146.npy
-imgHQ02263.npy
-imgHQ03966.npy
-imgHQ09977.npy
-imgHQ21652.npy
-imgHQ05828.npy
-imgHQ03634.npy
-imgHQ01177.npy
-imgHQ10799.npy
-imgHQ00507.npy
-imgHQ19256.npy
-imgHQ00652.npy
-imgHQ10495.npy
-imgHQ27646.npy
-imgHQ22349.npy
-imgHQ11904.npy
-imgHQ02661.npy
-imgHQ14818.npy
-imgHQ28448.npy
-imgHQ29247.npy
-imgHQ06602.npy
-imgHQ23341.npy
-imgHQ15638.npy
-imgHQ07807.npy
-imgHQ15898.npy
-imgHQ02236.npy
-imgHQ16700.npy
-imgHQ19656.npy
-imgHQ14075.npy
-imgHQ16092.npy
-imgHQ17526.npy
-imgHQ25373.npy
-imgHQ07169.npy
-imgHQ29282.npy
-imgHQ12017.npy
-imgHQ06208.npy
-imgHQ04641.npy
-imgHQ15444.npy
-imgHQ16569.npy
-imgHQ28783.npy
-imgHQ02579.npy
-imgHQ28884.npy
-imgHQ15469.npy
-imgHQ29548.npy
-imgHQ24519.npy
-imgHQ13867.npy
-imgHQ22874.npy
-imgHQ21560.npy
-imgHQ26478.npy
-imgHQ12937.npy
-imgHQ10444.npy
-imgHQ22842.npy
-imgHQ09339.npy
-imgHQ02091.npy
-imgHQ24431.npy
-imgHQ24790.npy
-imgHQ12402.npy
-imgHQ16122.npy
-imgHQ22554.npy
-imgHQ28374.npy
-imgHQ24947.npy
-imgHQ10571.npy
-imgHQ12979.npy
-imgHQ28855.npy
-imgHQ22143.npy
-imgHQ01941.npy
-imgHQ18219.npy
-imgHQ00255.npy
-imgHQ08821.npy
-imgHQ21899.npy
-imgHQ22813.npy
-imgHQ26724.npy
-imgHQ28575.npy
-imgHQ02806.npy
-imgHQ16696.npy
-imgHQ02635.npy
-imgHQ07029.npy
-imgHQ24957.npy
-imgHQ05252.npy
-imgHQ11900.npy
-imgHQ22983.npy
-imgHQ07680.npy
-imgHQ19821.npy
-imgHQ11453.npy
-imgHQ25827.npy
-imgHQ06342.npy
-imgHQ14181.npy
-imgHQ24905.npy
-imgHQ05080.npy
-imgHQ28542.npy
-imgHQ13901.npy
-imgHQ21878.npy
-imgHQ20923.npy
-imgHQ12832.npy
-imgHQ21489.npy
-imgHQ06211.npy
-imgHQ02731.npy
-imgHQ11167.npy
-imgHQ16815.npy
-imgHQ01778.npy
-imgHQ17135.npy
-imgHQ23716.npy
-imgHQ07936.npy
-imgHQ24067.npy
-imgHQ16090.npy
-imgHQ29752.npy
-imgHQ13431.npy
-imgHQ10141.npy
-imgHQ03383.npy
-imgHQ23304.npy
-imgHQ09112.npy
-imgHQ24260.npy
-imgHQ07616.npy
-imgHQ14081.npy
-imgHQ01776.npy
-imgHQ22959.npy
-imgHQ25013.npy
-imgHQ21491.npy
-imgHQ18334.npy
-imgHQ14254.npy
-imgHQ17045.npy
-imgHQ09813.npy
-imgHQ27850.npy
-imgHQ16333.npy
-imgHQ29329.npy
-imgHQ05023.npy
-imgHQ25092.npy
-imgHQ17236.npy
-imgHQ28116.npy
-imgHQ12806.npy
-imgHQ14061.npy
-imgHQ09802.npy
-imgHQ27668.npy
-imgHQ05454.npy
-imgHQ09786.npy
-imgHQ11356.npy
-imgHQ25417.npy
-imgHQ04921.npy
-imgHQ02284.npy
-imgHQ06991.npy
-imgHQ03819.npy
-imgHQ16620.npy
-imgHQ15711.npy
-imgHQ23418.npy
-imgHQ24652.npy
-imgHQ23859.npy
-imgHQ15453.npy
-imgHQ24917.npy
-imgHQ18127.npy
-imgHQ21218.npy
-imgHQ24911.npy
-imgHQ10073.npy
-imgHQ22058.npy
-imgHQ14062.npy
-imgHQ17818.npy
-imgHQ16022.npy
-imgHQ05157.npy
-imgHQ21760.npy
-imgHQ06977.npy
-imgHQ17323.npy
-imgHQ00686.npy
-imgHQ26286.npy
-imgHQ14435.npy
-imgHQ23402.npy
-imgHQ28886.npy
-imgHQ00334.npy
-imgHQ17243.npy
-imgHQ00833.npy
-imgHQ08395.npy
-imgHQ16873.npy
-imgHQ18087.npy
-imgHQ17726.npy
-imgHQ18300.npy
-imgHQ16548.npy
-imgHQ10159.npy
-imgHQ29623.npy
-imgHQ08483.npy
-imgHQ23191.npy
-imgHQ07208.npy
-imgHQ13315.npy
-imgHQ26046.npy
-imgHQ17051.npy
-imgHQ22581.npy
-imgHQ28821.npy
-imgHQ09077.npy
-imgHQ17998.npy
-imgHQ25153.npy
-imgHQ02268.npy
-imgHQ13715.npy
-imgHQ06623.npy
-imgHQ19789.npy
-imgHQ15976.npy
-imgHQ19094.npy
-imgHQ15653.npy
-imgHQ04855.npy
-imgHQ26327.npy
-imgHQ22199.npy
-imgHQ15270.npy
-imgHQ11556.npy
-imgHQ09327.npy
-imgHQ17192.npy
-imgHQ25515.npy
-imgHQ27194.npy
-imgHQ11760.npy
-imgHQ00222.npy
-imgHQ06611.npy
-imgHQ11479.npy
-imgHQ06151.npy
-imgHQ18167.npy
-imgHQ06083.npy
-imgHQ17150.npy
-imgHQ01227.npy
-imgHQ26198.npy
-imgHQ23005.npy
-imgHQ09409.npy
-imgHQ14883.npy
-imgHQ07030.npy
-imgHQ12983.npy
-imgHQ09074.npy
-imgHQ28441.npy
-imgHQ01807.npy
-imgHQ23774.npy
-imgHQ03378.npy
-imgHQ11116.npy
-imgHQ10827.npy
-imgHQ04966.npy
-imgHQ24489.npy
-imgHQ28478.npy
-imgHQ18745.npy
-imgHQ28969.npy
-imgHQ02457.npy
-imgHQ12114.npy
-imgHQ21066.npy
-imgHQ20142.npy
-imgHQ16316.npy
-imgHQ15347.npy
-imgHQ21335.npy
-imgHQ29968.npy
-imgHQ06876.npy
-imgHQ04226.npy
-imgHQ23944.npy
-imgHQ10523.npy
-imgHQ10218.npy
-imgHQ04369.npy
-imgHQ18506.npy
-imgHQ20398.npy
-imgHQ04906.npy
-imgHQ22829.npy
-imgHQ11411.npy
-imgHQ19046.npy
-imgHQ21318.npy
-imgHQ14704.npy
-imgHQ07831.npy
-imgHQ23270.npy
-imgHQ26428.npy
-imgHQ21131.npy
-imgHQ29100.npy
-imgHQ29318.npy
-imgHQ29862.npy
-imgHQ26922.npy
-imgHQ28745.npy
-imgHQ00341.npy
-imgHQ26589.npy
-imgHQ23860.npy
-imgHQ28156.npy
-imgHQ24246.npy
-imgHQ23513.npy
-imgHQ17962.npy
-imgHQ17849.npy
-imgHQ29028.npy
-imgHQ26699.npy
-imgHQ17422.npy
-imgHQ02972.npy
-imgHQ23586.npy
-imgHQ19715.npy
-imgHQ05159.npy
-imgHQ14398.npy
-imgHQ29704.npy
-imgHQ02721.npy
-imgHQ18377.npy
-imgHQ01962.npy
-imgHQ25411.npy
-imgHQ00887.npy
-imgHQ23182.npy
-imgHQ21188.npy
-imgHQ25581.npy
-imgHQ18715.npy
-imgHQ09789.npy
-imgHQ11627.npy
-imgHQ27591.npy
-imgHQ17486.npy
-imgHQ02502.npy
-imgHQ18610.npy
-imgHQ20311.npy
-imgHQ00272.npy
-imgHQ25109.npy
-imgHQ16543.npy
-imgHQ27790.npy
-imgHQ17815.npy
-imgHQ01275.npy
-imgHQ23054.npy
-imgHQ14344.npy
-imgHQ29315.npy
-imgHQ29197.npy
-imgHQ28718.npy
-imgHQ27643.npy
-imgHQ23344.npy
-imgHQ15872.npy
-imgHQ18001.npy
-imgHQ23762.npy
-imgHQ20226.npy
-imgHQ04861.npy
-imgHQ18111.npy
-imgHQ24723.npy
-imgHQ05688.npy
-imgHQ28702.npy
-imgHQ25914.npy
-imgHQ04311.npy
-imgHQ12620.npy
-imgHQ09221.npy
-imgHQ04615.npy
-imgHQ02344.npy
-imgHQ24493.npy
-imgHQ27472.npy
-imgHQ25477.npy
-imgHQ26875.npy
-imgHQ11593.npy
-imgHQ29746.npy
-imgHQ03829.npy
-imgHQ23163.npy
-imgHQ24120.npy
-imgHQ29729.npy
-imgHQ25937.npy
-imgHQ03926.npy
-imgHQ06662.npy
-imgHQ17125.npy
-imgHQ04645.npy
-imgHQ07924.npy
-imgHQ20116.npy
-imgHQ08025.npy
-imgHQ22036.npy
-imgHQ11498.npy
-imgHQ18683.npy
-imgHQ03609.npy
-imgHQ12421.npy
-imgHQ03719.npy
-imgHQ23649.npy
-imgHQ13631.npy
-imgHQ26522.npy
-imgHQ01029.npy
-imgHQ15081.npy
-imgHQ16409.npy
-imgHQ24280.npy
-imgHQ00437.npy
-imgHQ13192.npy
-imgHQ15401.npy
-imgHQ03400.npy
-imgHQ11912.npy
-imgHQ28770.npy
-imgHQ04143.npy
-imgHQ17085.npy
-imgHQ14090.npy
-imgHQ14880.npy
-imgHQ11563.npy
-imgHQ00366.npy
-imgHQ20989.npy
-imgHQ26559.npy
-imgHQ05669.npy
-imgHQ26628.npy
-imgHQ20587.npy
-imgHQ16788.npy
-imgHQ10612.npy
-imgHQ22280.npy
-imgHQ13105.npy
-imgHQ14831.npy
-imgHQ12732.npy
-imgHQ10804.npy
-imgHQ05693.npy
-imgHQ28616.npy
-imgHQ01537.npy
-imgHQ22601.npy
-imgHQ24138.npy
-imgHQ01420.npy
-imgHQ22116.npy
-imgHQ15222.npy
-imgHQ13642.npy
-imgHQ00821.npy
-imgHQ24952.npy
-imgHQ26030.npy
-imgHQ04633.npy
-imgHQ02346.npy
-imgHQ04579.npy
-imgHQ13571.npy
-imgHQ14573.npy
-imgHQ10862.npy
-imgHQ25603.npy
-imgHQ22005.npy
-imgHQ18299.npy
-imgHQ23385.npy
-imgHQ18816.npy
-imgHQ10012.npy
-imgHQ15920.npy
-imgHQ17460.npy
-imgHQ07452.npy
-imgHQ05110.npy
-imgHQ01908.npy
-imgHQ09249.npy
-imgHQ25865.npy
-imgHQ12592.npy
-imgHQ04292.npy
-imgHQ25894.npy
-imgHQ04214.npy
-imgHQ21649.npy
-imgHQ22609.npy
-imgHQ10539.npy
-imgHQ20351.npy
-imgHQ17929.npy
-imgHQ28530.npy
-imgHQ21973.npy
-imgHQ03290.npy
-imgHQ14786.npy
-imgHQ03588.npy
-imgHQ17809.npy
-imgHQ17140.npy
-imgHQ00695.npy
-imgHQ26769.npy
-imgHQ08662.npy
-imgHQ00538.npy
-imgHQ01193.npy
-imgHQ12096.npy
-imgHQ20588.npy
-imgHQ00747.npy
-imgHQ26125.npy
-imgHQ14256.npy
-imgHQ24160.npy
-imgHQ07165.npy
-imgHQ02654.npy
-imgHQ21789.npy
-imgHQ22616.npy
-imgHQ00274.npy
-imgHQ06670.npy
-imgHQ13881.npy
-imgHQ04288.npy
-imgHQ12552.npy
-imgHQ23718.npy
-imgHQ22934.npy
-imgHQ05194.npy
-imgHQ24645.npy
-imgHQ08991.npy
-imgHQ27573.npy
-imgHQ28057.npy
-imgHQ29241.npy
-imgHQ01885.npy
-imgHQ20959.npy
-imgHQ22078.npy
-imgHQ05481.npy
-imgHQ05988.npy
-imgHQ05699.npy
-imgHQ07536.npy
-imgHQ27853.npy
-imgHQ15045.npy
-imgHQ14259.npy
-imgHQ06613.npy
-imgHQ04009.npy
-imgHQ12210.npy
-imgHQ02399.npy
-imgHQ13153.npy
-imgHQ29037.npy
-imgHQ25573.npy
-imgHQ28087.npy
-imgHQ01395.npy
-imgHQ04835.npy
-imgHQ29057.npy
-imgHQ15362.npy
-imgHQ14810.npy
-imgHQ19813.npy
-imgHQ06330.npy
-imgHQ23476.npy
-imgHQ25810.npy
-imgHQ20086.npy
-imgHQ13616.npy
-imgHQ02378.npy
-imgHQ27720.npy
-imgHQ16713.npy
-imgHQ28726.npy
-imgHQ10238.npy
-imgHQ01131.npy
-imgHQ28093.npy
-imgHQ24548.npy
-imgHQ26754.npy
-imgHQ11953.npy
-imgHQ09858.npy
-imgHQ20761.npy
-imgHQ10969.npy
-imgHQ13938.npy
-imgHQ11347.npy
-imgHQ25832.npy
-imgHQ19316.npy
-imgHQ05525.npy
-imgHQ18228.npy
-imgHQ07625.npy
-imgHQ04761.npy
-imgHQ20462.npy
-imgHQ05871.npy
-imgHQ09298.npy
-imgHQ25700.npy
-imgHQ27816.npy
-imgHQ22532.npy
-imgHQ05593.npy
-imgHQ20088.npy
-imgHQ25895.npy
-imgHQ25600.npy
-imgHQ18125.npy
-imgHQ26854.npy
-imgHQ07490.npy
-imgHQ14646.npy
-imgHQ11021.npy
-imgHQ07288.npy
-imgHQ26862.npy
-imgHQ12889.npy
-imgHQ22888.npy
-imgHQ05822.npy
-imgHQ29780.npy
-imgHQ06661.npy
-imgHQ18951.npy
-imgHQ22268.npy
-imgHQ28953.npy
-imgHQ29648.npy
-imgHQ01964.npy
-imgHQ12208.npy
-imgHQ09552.npy
-imgHQ08436.npy
-imgHQ16475.npy
-imgHQ27249.npy
-imgHQ08745.npy
-imgHQ05260.npy
-imgHQ12752.npy
-imgHQ02698.npy
-imgHQ01524.npy
-imgHQ00829.npy
-imgHQ00780.npy
-imgHQ06425.npy
-imgHQ11841.npy
-imgHQ19442.npy
-imgHQ18522.npy
-imgHQ09677.npy
-imgHQ08663.npy
-imgHQ09214.npy
-imgHQ02337.npy
-imgHQ26008.npy
-imgHQ20550.npy
-imgHQ06076.npy
-imgHQ01949.npy
-imgHQ00901.npy
-imgHQ12011.npy
-imgHQ16212.npy
-imgHQ07539.npy
-imgHQ29229.npy
-imgHQ19318.npy
-imgHQ21951.npy
-imgHQ29371.npy
-imgHQ19080.npy
-imgHQ14909.npy
-imgHQ02260.npy
-imgHQ08421.npy
-imgHQ29584.npy
-imgHQ25939.npy
-imgHQ10162.npy
-imgHQ13152.npy
-imgHQ17817.npy
-imgHQ27251.npy
-imgHQ14211.npy
-imgHQ17880.npy
-imgHQ12195.npy
-imgHQ29607.npy
-imgHQ21072.npy
-imgHQ17038.npy
-imgHQ27335.npy
-imgHQ00005.npy
-imgHQ25966.npy
-imgHQ11404.npy
-imgHQ28464.npy
-imgHQ18186.npy
-imgHQ27884.npy
-imgHQ16281.npy
-imgHQ13853.npy
-imgHQ18580.npy
-imgHQ26603.npy
-imgHQ21289.npy
-imgHQ05374.npy
-imgHQ29764.npy
-imgHQ05758.npy
-imgHQ18446.npy
-imgHQ29414.npy
-imgHQ22758.npy
-imgHQ22135.npy
-imgHQ07915.npy
-imgHQ00257.npy
-imgHQ04632.npy
-imgHQ28692.npy
-imgHQ17194.npy
-imgHQ09050.npy
-imgHQ00529.npy
-imgHQ05336.npy
-imgHQ01172.npy
-imgHQ29099.npy
-imgHQ07066.npy
-imgHQ15552.npy
-imgHQ04444.npy
-imgHQ06302.npy
-imgHQ02675.npy
-imgHQ17077.npy
-imgHQ08800.npy
-imgHQ10474.npy
-imgHQ06170.npy
-imgHQ06448.npy
-imgHQ20686.npy
-imgHQ05008.npy
-imgHQ04439.npy
-imgHQ06941.npy
-imgHQ04134.npy
-imgHQ02432.npy
-imgHQ17170.npy
-imgHQ00673.npy
-imgHQ19570.npy
-imgHQ27868.npy
-imgHQ21847.npy
-imgHQ06783.npy
-imgHQ10659.npy
-imgHQ02916.npy
-imgHQ23420.npy
-imgHQ25903.npy
-imgHQ16653.npy
-imgHQ17171.npy
-imgHQ12981.npy
-imgHQ06539.npy
-imgHQ25731.npy
-imgHQ27459.npy
-imgHQ17390.npy
-imgHQ10260.npy
-imgHQ18805.npy
-imgHQ05475.npy
-imgHQ14353.npy
-imgHQ15294.npy
-imgHQ06411.npy
-imgHQ14946.npy
-imgHQ13326.npy
-imgHQ08290.npy
-imgHQ02009.npy
-imgHQ17454.npy
-imgHQ05389.npy
-imgHQ21664.npy
-imgHQ21456.npy
-imgHQ14947.npy
-imgHQ06596.npy
-imgHQ11914.npy
-imgHQ29669.npy
-imgHQ07724.npy
-imgHQ05273.npy
-imgHQ16844.npy
-imgHQ11003.npy
-imgHQ23788.npy
-imgHQ06843.npy
-imgHQ08718.npy
-imgHQ08652.npy
-imgHQ27451.npy
-imgHQ09456.npy
-imgHQ09969.npy
-imgHQ15566.npy
-imgHQ12502.npy
-imgHQ02938.npy
-imgHQ11969.npy
-imgHQ13742.npy
-imgHQ00261.npy
-imgHQ29745.npy
-imgHQ19157.npy
-imgHQ27605.npy
-imgHQ10759.npy
-imgHQ26256.npy
-imgHQ26401.npy
-imgHQ16252.npy
-imgHQ25154.npy
-imgHQ14640.npy
-imgHQ22089.npy
-imgHQ07694.npy
-imgHQ05607.npy
-imgHQ08059.npy
-imgHQ03857.npy
-imgHQ28315.npy
-imgHQ08849.npy
-imgHQ12261.npy
-imgHQ27054.npy
-imgHQ10869.npy
-imgHQ25179.npy
-imgHQ04157.npy
-imgHQ05188.npy
-imgHQ03305.npy
-imgHQ23676.npy
-imgHQ03218.npy
-imgHQ02085.npy
-imgHQ00101.npy
-imgHQ24356.npy
-imgHQ07082.npy
-imgHQ05426.npy
-imgHQ08834.npy
-imgHQ00262.npy
-imgHQ20238.npy
-imgHQ06297.npy
-imgHQ11721.npy
-imgHQ06560.npy
-imgHQ18636.npy
-imgHQ17105.npy
-imgHQ24716.npy
-imgHQ11329.npy
-imgHQ15892.npy
-imgHQ15533.npy
-imgHQ24514.npy
-imgHQ02877.npy
-imgHQ00178.npy
-imgHQ10751.npy
-imgHQ24983.npy
-imgHQ03014.npy
-imgHQ21376.npy
-imgHQ02223.npy
-imgHQ15955.npy
-imgHQ22587.npy
-imgHQ18051.npy
-imgHQ25977.npy
-imgHQ28960.npy
-imgHQ09917.npy
-imgHQ18273.npy
-imgHQ19005.npy
-imgHQ08196.npy
-imgHQ13253.npy
-imgHQ12110.npy
-imgHQ20078.npy
-imgHQ07388.npy
-imgHQ12601.npy
-imgHQ04563.npy
-imgHQ21390.npy
-imgHQ16452.npy
-imgHQ15302.npy
-imgHQ18274.npy
-imgHQ25945.npy
-imgHQ09250.npy
-imgHQ20264.npy
-imgHQ04005.npy
-imgHQ03053.npy
-imgHQ14078.npy
-imgHQ14841.npy
-imgHQ02368.npy
-imgHQ04083.npy
-imgHQ04409.npy
-imgHQ13014.npy
-imgHQ10158.npy
-imgHQ02795.npy
-imgHQ24644.npy
-imgHQ12113.npy
-imgHQ02076.npy
-imgHQ09608.npy
-imgHQ01045.npy
-imgHQ14690.npy
-imgHQ00837.npy
-imgHQ10851.npy
-imgHQ27962.npy
-imgHQ19626.npy
-imgHQ04523.npy
-imgHQ12323.npy
-imgHQ16768.npy
-imgHQ13177.npy
-imgHQ08994.npy
-imgHQ07127.npy
-imgHQ13239.npy
-imgHQ25691.npy
-imgHQ14740.npy
-imgHQ02984.npy
-imgHQ07401.npy
-imgHQ18676.npy
-imgHQ08545.npy
-imgHQ21748.npy
-imgHQ17472.npy
-imgHQ16595.npy
-imgHQ08423.npy
-imgHQ14952.npy
-imgHQ28101.npy
-imgHQ03872.npy
-imgHQ19173.npy
-imgHQ09553.npy
-imgHQ19355.npy
-imgHQ08118.npy
-imgHQ08418.npy
-imgHQ17381.npy
-imgHQ28930.npy
-imgHQ09497.npy
-imgHQ18689.npy
-imgHQ20970.npy
-imgHQ18639.npy
-imgHQ27620.npy
-imgHQ03541.npy
-imgHQ18017.npy
-imgHQ24889.npy
-imgHQ08768.npy
-imgHQ25770.npy
-imgHQ04918.npy
-imgHQ14223.npy
-imgHQ20339.npy
-imgHQ14552.npy
-imgHQ14985.npy
-imgHQ01826.npy
-imgHQ14390.npy
-imgHQ14204.npy
-imgHQ17332.npy
-imgHQ22255.npy
-imgHQ01561.npy
-imgHQ23024.npy
-imgHQ22007.npy
-imgHQ00608.npy
-imgHQ00213.npy
-imgHQ13432.npy
-imgHQ24577.npy
-imgHQ03606.npy
-imgHQ19867.npy
-imgHQ14228.npy
-imgHQ13798.npy
-imgHQ27505.npy
-imgHQ24846.npy
-imgHQ11595.npy
-imgHQ28549.npy
-imgHQ26526.npy
-imgHQ05804.npy
-imgHQ18179.npy
-imgHQ20242.npy
-imgHQ02530.npy
-imgHQ27768.npy
-imgHQ24899.npy
-imgHQ22327.npy
-imgHQ07934.npy
-imgHQ04795.npy
-imgHQ16893.npy
-imgHQ05769.npy
-imgHQ26993.npy
-imgHQ13122.npy
-imgHQ19096.npy
-imgHQ00697.npy
-imgHQ29527.npy
-imgHQ07279.npy
-imgHQ00965.npy
-imgHQ18605.npy
-imgHQ16593.npy
-imgHQ02884.npy
-imgHQ15409.npy
-imgHQ10249.npy
-imgHQ23738.npy
-imgHQ22170.npy
-imgHQ20091.npy
-imgHQ17040.npy
-imgHQ04900.npy
-imgHQ05094.npy
-imgHQ18840.npy
-imgHQ12366.npy
-imgHQ28347.npy
-imgHQ24492.npy
-imgHQ29640.npy
-imgHQ26419.npy
-imgHQ10193.npy
-imgHQ24987.npy
-imgHQ12728.npy
-imgHQ18929.npy
-imgHQ08729.npy
-imgHQ02853.npy
-imgHQ15417.npy
-imgHQ23096.npy
-imgHQ22425.npy
-imgHQ07179.npy
-imgHQ16473.npy
-imgHQ07273.npy
-imgHQ12271.npy
-imgHQ03563.npy
-imgHQ13284.npy
-imgHQ28997.npy
-imgHQ07365.npy
-imgHQ10295.npy
-imgHQ08446.npy
-imgHQ20458.npy
-imgHQ11414.npy
-imgHQ08097.npy
-imgHQ08288.npy
-imgHQ09892.npy
-imgHQ04442.npy
-imgHQ03440.npy
-imgHQ27225.npy
-imgHQ07347.npy
-imgHQ24240.npy
-imgHQ27182.npy
-imgHQ19207.npy
-imgHQ25127.npy
-imgHQ01662.npy
-imgHQ24736.npy
-imgHQ14624.npy
-imgHQ05724.npy
-imgHQ19801.npy
-imgHQ15095.npy
-imgHQ25441.npy
-imgHQ07974.npy
-imgHQ16195.npy
-imgHQ06682.npy
-imgHQ02154.npy
-imgHQ24064.npy
-imgHQ20829.npy
-imgHQ15193.npy
-imgHQ14982.npy
-imgHQ11115.npy
-imgHQ19633.npy
-imgHQ16606.npy
-imgHQ17230.npy
-imgHQ04149.npy
-imgHQ17151.npy
-imgHQ13482.npy
-imgHQ21901.npy
-imgHQ18261.npy
-imgHQ21134.npy
-imgHQ11171.npy
-imgHQ23843.npy
-imgHQ15845.npy
-imgHQ09356.npy
-imgHQ29215.npy
-imgHQ09808.npy
-imgHQ29839.npy
-imgHQ28267.npy
-imgHQ22401.npy
-imgHQ23501.npy
-imgHQ24868.npy
-imgHQ06407.npy
-imgHQ25920.npy
-imgHQ01991.npy
-imgHQ06318.npy
-imgHQ19314.npy
-imgHQ00319.npy
-imgHQ14821.npy
-imgHQ07006.npy
-imgHQ13429.npy
-imgHQ12520.npy
-imgHQ20431.npy
-imgHQ14074.npy
-imgHQ24803.npy
-imgHQ07486.npy
-imgHQ27523.npy
-imgHQ23965.npy
-imgHQ21295.npy
-imgHQ05240.npy
-imgHQ02603.npy
-imgHQ10365.npy
-imgHQ15379.npy
-imgHQ11853.npy
-imgHQ23047.npy
-imgHQ21527.npy
-imgHQ24609.npy
-imgHQ11499.npy
-imgHQ00490.npy
-imgHQ11133.npy
-imgHQ12347.npy
-imgHQ05124.npy
-imgHQ20474.npy
-imgHQ18439.npy
-imgHQ28582.npy
-imgHQ27700.npy
-imgHQ09566.npy
-imgHQ20688.npy
-imgHQ12284.npy
-imgHQ01607.npy
-imgHQ22345.npy
-imgHQ20315.npy
-imgHQ22295.npy
-imgHQ17530.npy
-imgHQ12185.npy
-imgHQ16432.npy
-imgHQ27641.npy
-imgHQ18338.npy
-imgHQ00783.npy
-imgHQ10818.npy
-imgHQ25425.npy
-imgHQ07310.npy
-imgHQ15803.npy
-imgHQ05816.npy
-imgHQ15553.npy
-imgHQ24908.npy
-imgHQ26119.npy
-imgHQ08531.npy
-imgHQ24178.npy
-imgHQ25645.npy
-imgHQ10039.npy
-imgHQ25968.npy
-imgHQ04116.npy
-imgHQ23795.npy
-imgHQ29378.npy
-imgHQ14283.npy
-imgHQ05174.npy
-imgHQ23142.npy
-imgHQ24971.npy
-imgHQ21975.npy
-imgHQ17247.npy
-imgHQ23504.npy
-imgHQ17452.npy
-imgHQ02889.npy
-imgHQ03991.npy
-imgHQ22665.npy
-imgHQ24733.npy
-imgHQ20718.npy
-imgHQ10209.npy
-imgHQ01736.npy
-imgHQ00724.npy
-imgHQ12357.npy
-imgHQ06944.npy
-imgHQ26865.npy
-imgHQ16730.npy
-imgHQ23767.npy
-imgHQ05337.npy
-imgHQ01971.npy
-imgHQ06339.npy
-imgHQ20581.npy
-imgHQ20438.npy
-imgHQ03887.npy
-imgHQ29203.npy
-imgHQ13764.npy
-imgHQ12864.npy
-imgHQ04666.npy
-imgHQ04205.npy
-imgHQ28595.npy
-imgHQ12083.npy
-imgHQ29641.npy
-imgHQ05018.npy
-imgHQ02554.npy
-imgHQ23288.npy
-imgHQ21164.npy
-imgHQ04928.npy
-imgHQ24336.npy
-imgHQ10601.npy
-imgHQ21120.npy
-imgHQ28805.npy
-imgHQ01462.npy
-imgHQ21968.npy
-imgHQ29881.npy
-imgHQ16069.npy
-imgHQ21411.npy
-imgHQ28248.npy
-imgHQ04056.npy
-imgHQ20104.npy
-imgHQ07726.npy
-imgHQ20038.npy
-imgHQ18570.npy
-imgHQ02900.npy
-imgHQ20218.npy
-imgHQ09103.npy
-imgHQ20526.npy
-imgHQ14790.npy
-imgHQ10273.npy
-imgHQ09591.npy
-imgHQ27975.npy
-imgHQ21423.npy
-imgHQ13128.npy
-imgHQ09571.npy
-imgHQ09762.npy
-imgHQ07663.npy
-imgHQ18529.npy
-imgHQ15591.npy
-imgHQ01701.npy
-imgHQ09708.npy
-imgHQ24393.npy
-imgHQ27289.npy
-imgHQ21135.npy
-imgHQ17348.npy
-imgHQ07867.npy
-imgHQ22106.npy
-imgHQ21340.npy
-imgHQ05933.npy
-imgHQ10501.npy
-imgHQ03633.npy
-imgHQ11974.npy
-imgHQ19195.npy
-imgHQ29418.npy
-imgHQ22422.npy
-imgHQ02515.npy
-imgHQ25180.npy
-imgHQ00106.npy
-imgHQ15430.npy
-imgHQ07385.npy
-imgHQ14600.npy
-imgHQ09369.npy
-imgHQ29513.npy
-imgHQ04354.npy
-imgHQ05491.npy
-imgHQ17182.npy
-imgHQ23510.npy
-imgHQ29959.npy
-imgHQ19550.npy
-imgHQ26343.npy
-imgHQ27219.npy
-imgHQ14551.npy
-imgHQ29871.npy
-imgHQ14415.npy
-imgHQ17496.npy
-imgHQ08822.npy
-imgHQ18442.npy
-imgHQ10780.npy
-imgHQ00525.npy
-imgHQ21555.npy
-imgHQ21936.npy
-imgHQ09675.npy
-imgHQ02549.npy
-imgHQ16180.npy
-imgHQ01182.npy
-imgHQ05805.npy
-imgHQ24763.npy
-imgHQ07797.npy
-imgHQ22514.npy
-imgHQ05784.npy
-imgHQ06635.npy
-imgHQ21049.npy
-imgHQ01086.npy
-imgHQ03095.npy
-imgHQ19726.npy
-imgHQ11007.npy
-imgHQ00147.npy
-imgHQ27970.npy
-imgHQ03201.npy
-imgHQ04722.npy
-imgHQ29364.npy
-imgHQ13248.npy
-imgHQ26886.npy
-imgHQ24793.npy
-imgHQ25302.npy
-imgHQ03717.npy
-imgHQ01174.npy
-imgHQ24069.npy
-imgHQ04079.npy
-imgHQ04350.npy
-imgHQ23587.npy
-imgHQ21089.npy
-imgHQ05543.npy
-imgHQ25239.npy
-imgHQ08611.npy
-imgHQ20383.npy
-imgHQ02588.npy
-imgHQ22457.npy
-imgHQ19838.npy
-imgHQ15278.npy
-imgHQ20212.npy
-imgHQ17188.npy
-imgHQ04499.npy
-imgHQ28339.npy
-imgHQ29349.npy
-imgHQ06459.npy
-imgHQ25071.npy
-imgHQ15373.npy
-imgHQ25828.npy
-imgHQ07264.npy
-imgHQ04557.npy
-imgHQ20177.npy
-imgHQ01299.npy
-imgHQ27298.npy
-imgHQ29059.npy
-imgHQ17692.npy
-imgHQ19367.npy
-imgHQ04295.npy
-imgHQ18197.npy
-imgHQ21029.npy
-imgHQ07717.npy
-imgHQ13055.npy
-imgHQ25646.npy
-imgHQ05856.npy
-imgHQ06455.npy
-imgHQ03631.npy
-imgHQ21539.npy
-imgHQ22591.npy
-imgHQ14157.npy
-imgHQ12202.npy
-imgHQ07235.npy
-imgHQ29826.npy
-imgHQ13943.npy
-imgHQ29663.npy
-imgHQ19719.npy
-imgHQ02761.npy
-imgHQ07416.npy
-imgHQ00264.npy
-imgHQ27758.npy
-imgHQ05187.npy
-imgHQ18767.npy
-imgHQ27405.npy
-imgHQ26810.npy
-imgHQ23428.npy
-imgHQ03684.npy
-imgHQ10206.npy
-imgHQ00280.npy
-imgHQ18130.npy
-imgHQ24898.npy
-imgHQ28161.npy
-imgHQ04148.npy
-imgHQ14375.npy
-imgHQ07513.npy
-imgHQ24810.npy
-imgHQ13930.npy
-imgHQ29088.npy
-imgHQ27256.npy
-imgHQ10663.npy
-imgHQ06329.npy
-imgHQ19951.npy
-imgHQ27963.npy
-imgHQ24438.npy
-imgHQ01919.npy
-imgHQ05125.npy
-imgHQ10670.npy
-imgHQ20294.npy
-imgHQ13925.npy
-imgHQ08048.npy
-imgHQ12536.npy
-imgHQ12250.npy
-imgHQ07181.npy
-imgHQ09054.npy
-imgHQ27908.npy
-imgHQ01109.npy
-imgHQ17309.npy
-imgHQ27417.npy
-imgHQ05629.npy
-imgHQ16233.npy
-imgHQ26294.npy
-imgHQ26187.npy
-imgHQ19242.npy
-imgHQ10294.npy
-imgHQ24008.npy
-imgHQ24392.npy
-imgHQ18096.npy
-imgHQ11547.npy
-imgHQ28021.npy
-imgHQ07817.npy
-imgHQ26505.npy
-imgHQ24690.npy
-imgHQ04912.npy
-imgHQ17627.npy
-imgHQ10575.npy
-imgHQ11428.npy
-imgHQ00090.npy
-imgHQ21584.npy
-imgHQ15909.npy
-imgHQ00511.npy
-imgHQ05059.npy
-imgHQ29565.npy
-imgHQ08301.npy
-imgHQ15715.npy
-imgHQ18571.npy
-imgHQ14385.npy
-imgHQ01333.npy
-imgHQ25709.npy
-imgHQ12441.npy
-imgHQ10186.npy
-imgHQ18224.npy
-imgHQ28819.npy
-imgHQ07485.npy
-imgHQ19439.npy
-imgHQ04822.npy
-imgHQ20404.npy
-imgHQ28031.npy
-imgHQ02265.npy
-imgHQ07017.npy
-imgHQ23171.npy
-imgHQ21725.npy
-imgHQ11761.npy
-imgHQ23358.npy
-imgHQ14019.npy
-imgHQ13858.npy
-imgHQ17384.npy
-imgHQ11250.npy
-imgHQ12071.npy
-imgHQ09513.npy
-imgHQ10151.npy
-imgHQ23165.npy
-imgHQ10123.npy
-imgHQ23321.npy
-imgHQ13137.npy
-imgHQ06859.npy
-imgHQ28344.npy
-imgHQ06479.npy
-imgHQ13852.npy
-imgHQ12095.npy
-imgHQ03147.npy
-imgHQ13766.npy
-imgHQ10552.npy
-imgHQ29717.npy
-imgHQ15330.npy
-imgHQ15327.npy
-imgHQ25439.npy
-imgHQ22062.npy
-imgHQ15850.npy
-imgHQ24117.npy
-imgHQ09529.npy
-imgHQ00830.npy
-imgHQ12483.npy
-imgHQ03135.npy
-imgHQ17999.npy
-imgHQ17621.npy
-imgHQ25415.npy
-imgHQ04599.npy
-imgHQ27262.npy
-imgHQ12418.npy
-imgHQ17771.npy
-imgHQ15847.npy
-imgHQ28351.npy
-imgHQ05851.npy
-imgHQ07553.npy
-imgHQ29336.npy
-imgHQ19204.npy
-imgHQ13703.npy
-imgHQ10555.npy
-imgHQ17964.npy
-imgHQ20878.npy
-imgHQ19968.npy
-imgHQ13857.npy
-imgHQ20947.npy
-imgHQ22465.npy
-imgHQ10146.npy
-imgHQ22690.npy
-imgHQ24109.npy
-imgHQ19594.npy
-imgHQ05747.npy
-imgHQ07760.npy
-imgHQ03244.npy
-imgHQ24285.npy
-imgHQ06942.npy
-imgHQ21044.npy
-imgHQ07189.npy
-imgHQ05789.npy
-imgHQ03079.npy
-imgHQ20935.npy
-imgHQ09797.npy
-imgHQ18070.npy
-imgHQ11713.npy
-imgHQ05001.npy
-imgHQ21009.npy
-imgHQ11280.npy
-imgHQ12866.npy
-imgHQ27865.npy
-imgHQ08606.npy
-imgHQ20733.npy
-imgHQ16063.npy
-imgHQ15918.npy
-imgHQ17225.npy
-imgHQ29350.npy
-imgHQ17277.npy
-imgHQ16371.npy
-imgHQ11389.npy
-imgHQ17221.npy
-imgHQ18181.npy
-imgHQ13181.npy
-imgHQ25345.npy
-imgHQ20285.npy
-imgHQ21018.npy
-imgHQ00000.npy
-imgHQ13073.npy
-imgHQ06630.npy
-imgHQ10454.npy
-imgHQ11659.npy
-imgHQ15590.npy
-imgHQ06873.npy
-imgHQ09743.npy
-imgHQ23508.npy
-imgHQ05914.npy
-imgHQ01618.npy
-imgHQ07021.npy
-imgHQ20755.npy
-imgHQ01700.npy
-imgHQ11355.npy
-imgHQ23995.npy
-imgHQ17892.npy
-imgHQ28096.npy
-imgHQ27744.npy
-imgHQ22785.npy
-imgHQ07142.npy
-imgHQ09722.npy
-imgHQ27145.npy
-imgHQ02578.npy
-imgHQ09838.npy
-imgHQ23597.npy
-imgHQ06956.npy
-imgHQ02891.npy
-imgHQ21856.npy
-imgHQ15473.npy
-imgHQ29136.npy
-imgHQ21102.npy
-imgHQ24218.npy
-imgHQ26853.npy
-imgHQ08168.npy
-imgHQ14518.npy
-imgHQ09730.npy
-imgHQ20174.npy
-imgHQ20749.npy
-imgHQ00983.npy
-imgHQ00071.npy
-imgHQ08092.npy
-imgHQ19385.npy
-imgHQ25163.npy
-imgHQ14805.npy
-imgHQ09266.npy
-imgHQ16033.npy
-imgHQ08955.npy
-imgHQ25856.npy
-imgHQ24689.npy
-imgHQ25976.npy
-imgHQ07318.npy
-imgHQ18237.npy
-imgHQ03480.npy
-imgHQ04208.npy
-imgHQ15158.npy
-imgHQ16142.npy
-imgHQ03329.npy
-imgHQ24669.npy
-imgHQ02105.npy
-imgHQ19691.npy
-imgHQ02140.npy
-imgHQ29428.npy
-imgHQ07538.npy
-imgHQ24535.npy
-imgHQ04590.npy
-imgHQ13849.npy
-imgHQ19877.npy
-imgHQ11678.npy
-imgHQ13495.npy
-imgHQ15748.npy
-imgHQ27019.npy
-imgHQ14052.npy
-imgHQ15513.npy
-imgHQ17388.npy
-imgHQ15013.npy
-imgHQ17941.npy
-imgHQ16141.npy
-imgHQ02480.npy
-imgHQ21427.npy
-imgHQ09931.npy
-imgHQ22165.npy
-imgHQ00677.npy
-imgHQ20169.npy
-imgHQ27497.npy
-imgHQ04720.npy
-imgHQ22411.npy
-imgHQ05027.npy
-imgHQ11765.npy
-imgHQ11636.npy
-imgHQ07587.npy
-imgHQ20403.npy
-imgHQ10399.npy
-imgHQ25149.npy
-imgHQ19608.npy
-imgHQ10236.npy
-imgHQ28843.npy
-imgHQ10253.npy
-imgHQ24656.npy
-imgHQ21842.npy
-imgHQ19518.npy
-imgHQ26069.npy
-imgHQ10223.npy
-imgHQ07720.npy
-imgHQ27532.npy
-imgHQ15424.npy
-imgHQ18899.npy
-imgHQ06216.npy
-imgHQ15028.npy
-imgHQ29987.npy
-imgHQ00426.npy
-imgHQ04683.npy
-imgHQ13960.npy
-imgHQ29020.npy
-imgHQ06428.npy
-imgHQ28577.npy
-imgHQ28480.npy
-imgHQ29807.npy
-imgHQ14969.npy
-imgHQ14705.npy
-imgHQ13519.npy
-imgHQ24813.npy
-imgHQ00999.npy
-imgHQ05771.npy
-imgHQ15838.npy
-imgHQ18721.npy
-imgHQ10087.npy
-imgHQ00871.npy
-imgHQ02534.npy
-imgHQ20676.npy
-imgHQ22336.npy
-imgHQ12409.npy
-imgHQ10401.npy
-imgHQ05207.npy
-imgHQ23072.npy
-imgHQ02469.npy
-imgHQ00777.npy
-imgHQ17398.npy
-imgHQ00788.npy
-imgHQ14682.npy
-imgHQ26887.npy
-imgHQ01737.npy
-imgHQ06886.npy
-imgHQ22198.npy
-imgHQ15737.npy
-imgHQ11029.npy
-imgHQ16513.npy
-imgHQ13575.npy
-imgHQ08996.npy
-imgHQ18089.npy
-imgHQ06308.npy
-imgHQ28012.npy
-imgHQ04146.npy
-imgHQ04323.npy
-imgHQ03087.npy
-imgHQ03688.npy
-imgHQ29055.npy
-imgHQ15202.npy
-imgHQ24118.npy
-imgHQ05735.npy
-imgHQ26162.npy
-imgHQ27092.npy
-imgHQ18846.npy
-imgHQ06061.npy
-imgHQ02655.npy
-imgHQ22844.npy
-imgHQ02391.npy
-imgHQ13945.npy
-imgHQ29734.npy
-imgHQ28103.npy
-imgHQ01248.npy
-imgHQ23481.npy
-imgHQ29837.npy
-imgHQ11755.npy
-imgHQ06253.npy
-imgHQ16897.npy
-imgHQ02859.npy
-imgHQ15371.npy
-imgHQ26840.npy
-imgHQ07749.npy
-imgHQ28387.npy
-imgHQ20496.npy
-imgHQ11744.npy
-imgHQ15275.npy
-imgHQ23526.npy
-imgHQ29408.npy
-imgHQ28338.npy
-imgHQ06324.npy
-imgHQ03795.npy
-imgHQ29176.npy
-imgHQ12032.npy
-imgHQ02668.npy
-imgHQ02832.npy
-imgHQ01822.npy
-imgHQ07889.npy
-imgHQ19636.npy
-imgHQ04601.npy
-imgHQ11683.npy
-imgHQ08580.npy
-imgHQ05000.npy
-imgHQ23113.npy
-imgHQ21085.npy
-imgHQ07027.npy
-imgHQ01965.npy
-imgHQ19041.npy
-imgHQ08683.npy
-imgHQ00338.npy
-imgHQ04454.npy
-imgHQ01553.npy
-imgHQ23185.npy
-imgHQ28566.npy
-imgHQ19565.npy
-imgHQ29025.npy
-imgHQ19625.npy
-imgHQ15428.npy
-imgHQ12906.npy
-imgHQ16468.npy
-imgHQ15002.npy
-imgHQ07789.npy
-imgHQ00217.npy
-imgHQ07656.npy
-imgHQ28750.npy
-imgHQ07805.npy
-imgHQ00542.npy
-imgHQ13317.npy
-imgHQ15869.npy
-imgHQ19211.npy
-imgHQ07568.npy
-imgHQ28554.npy
-imgHQ11357.npy
-imgHQ07505.npy
-imgHQ13021.npy
-imgHQ06469.npy
-imgHQ16982.npy
-imgHQ19178.npy
-imgHQ21781.npy
-imgHQ27794.npy
-imgHQ20617.npy
-imgHQ17099.npy
-imgHQ21820.npy
-imgHQ02540.npy
-imgHQ25609.npy
-imgHQ03193.npy
-imgHQ11162.npy
-imgHQ15823.npy
-imgHQ24808.npy
-imgHQ27852.npy
-imgHQ04107.npy
-imgHQ11672.npy
-imgHQ24721.npy
-imgHQ11783.npy
-imgHQ26303.npy
-imgHQ07480.npy
-imgHQ22912.npy
-imgHQ20421.npy
-imgHQ25868.npy
-imgHQ09443.npy
-imgHQ29048.npy
-imgHQ10679.npy
-imgHQ15335.npy
-imgHQ04808.npy
-imgHQ09572.npy
-imgHQ16846.npy
-imgHQ17534.npy
-imgHQ16560.npy
-imgHQ28089.npy
-imgHQ10036.npy
-imgHQ02726.npy
-imgHQ07768.npy
-imgHQ28711.npy
-imgHQ12336.npy
-imgHQ22115.npy
-imgHQ23485.npy
-imgHQ12940.npy
-imgHQ22859.npy
-imgHQ26080.npy
-imgHQ05287.npy
-imgHQ01356.npy
-imgHQ28556.npy
-imgHQ06347.npy
-imgHQ09430.npy
-imgHQ27303.npy
-imgHQ23844.npy
-imgHQ24611.npy
-imgHQ09686.npy
-imgHQ11815.npy
-imgHQ13578.npy
-imgHQ00250.npy
-imgHQ14908.npy
-imgHQ27325.npy
-imgHQ12003.npy
-imgHQ27697.npy
-imgHQ29594.npy
-imgHQ18526.npy
-imgHQ22163.npy
-imgHQ09563.npy
-imgHQ05431.npy
-imgHQ00735.npy
-imgHQ17037.npy
-imgHQ00980.npy
-imgHQ06671.npy
-imgHQ07895.npy
-imgHQ05271.npy
-imgHQ06773.npy
-imgHQ19582.npy
-imgHQ17336.npy
-imgHQ00027.npy
-imgHQ08696.npy
-imgHQ06780.npy
-imgHQ11478.npy
-imgHQ18447.npy
-imgHQ11098.npy
-imgHQ16230.npy
-imgHQ14537.npy
-imgHQ16735.npy
-imgHQ19652.npy
-imgHQ06806.npy
-imgHQ20269.npy
-imgHQ22356.npy
-imgHQ14251.npy
-imgHQ01525.npy
-imgHQ01430.npy
-imgHQ26085.npy
-imgHQ08139.npy
-imgHQ15170.npy
-imgHQ18827.npy
-imgHQ21745.npy
-imgHQ25138.npy
-imgHQ13619.npy
-imgHQ17823.npy
-imgHQ09120.npy
-imgHQ29174.npy
-imgHQ16833.npy
-imgHQ01338.npy
-imgHQ08054.npy
-imgHQ29328.npy
-imgHQ18538.npy
-imgHQ01947.npy
-imgHQ24925.npy
-imgHQ24283.npy
-imgHQ04570.npy
-imgHQ19913.npy
-imgHQ19175.npy
-imgHQ02447.npy
-imgHQ05350.npy
-imgHQ28219.npy
-imgHQ08973.npy
-imgHQ29608.npy
-imgHQ00260.npy
-imgHQ01273.npy
-imgHQ23298.npy
-imgHQ26204.npy
-imgHQ21749.npy
-imgHQ10953.npy
-imgHQ02735.npy
-imgHQ28222.npy
-imgHQ06094.npy
-imgHQ15079.npy
-imgHQ19116.npy
-imgHQ12682.npy
-imgHQ04881.npy
-imgHQ09540.npy
-imgHQ21023.npy
-imgHQ07450.npy
-imgHQ27872.npy
-imgHQ08380.npy
-imgHQ23539.npy
-imgHQ18608.npy
-imgHQ01149.npy
-imgHQ25091.npy
-imgHQ04377.npy
-imgHQ11110.npy
-imgHQ03924.npy
-imgHQ16715.npy
-imgHQ13365.npy
-imgHQ19272.npy
-imgHQ02658.npy
-imgHQ12401.npy
-imgHQ02358.npy
-imgHQ25563.npy
-imgHQ17156.npy
-imgHQ12946.npy
-imgHQ24369.npy
-imgHQ15252.npy
-imgHQ02097.npy
-imgHQ18700.npy
-imgHQ26517.npy
-imgHQ16236.npy
-imgHQ19354.npy
-imgHQ06890.npy
-imgHQ15826.npy
-imgHQ01696.npy
-imgHQ10727.npy
-imgHQ05962.npy
-imgHQ04347.npy
-imgHQ19708.npy
-imgHQ28174.npy
-imgHQ05627.npy
-imgHQ08929.npy
-imgHQ18960.npy
-imgHQ00884.npy
-imgHQ12139.npy
-imgHQ03686.npy
-imgHQ15134.npy
-imgHQ02719.npy
-imgHQ13801.npy
-imgHQ20737.npy
-imgHQ29251.npy
-imgHQ23545.npy
-imgHQ17005.npy
-imgHQ04925.npy
-imgHQ19595.npy
-imgHQ11338.npy
-imgHQ03585.npy
-imgHQ21152.npy
-imgHQ12625.npy
-imgHQ14603.npy
-imgHQ15366.npy
-imgHQ09909.npy
-imgHQ25007.npy
-imgHQ13200.npy
-imgHQ00674.npy
-imgHQ28454.npy
-imgHQ23013.npy
-imgHQ06759.npy
-imgHQ27644.npy
-imgHQ08351.npy
-imgHQ02404.npy
-imgHQ00591.npy
-imgHQ17924.npy
-imgHQ03702.npy
-imgHQ27826.npy
-imgHQ21980.npy
-imgHQ09452.npy
-imgHQ08496.npy
-imgHQ17876.npy
-imgHQ16220.npy
-imgHQ03558.npy
-imgHQ10564.npy
-imgHQ23451.npy
-imgHQ23088.npy
-imgHQ15472.npy
-imgHQ28199.npy
-imgHQ26418.npy
-imgHQ22497.npy
-imgHQ25261.npy
-imgHQ20753.npy
-imgHQ09188.npy
-imgHQ01231.npy
-imgHQ05857.npy
-imgHQ27652.npy
-imgHQ15556.npy
-imgHQ26108.npy
-imgHQ20906.npy
-imgHQ22922.npy
-imgHQ22281.npy
-imgHQ12561.npy
-imgHQ20395.npy
-imgHQ28624.npy
-imgHQ11221.npy
-imgHQ06456.npy
-imgHQ04489.npy
-imgHQ02619.npy
-imgHQ18395.npy
-imgHQ10388.npy
-imgHQ18510.npy
-imgHQ23115.npy
-imgHQ08780.npy
-imgHQ18312.npy
-imgHQ08828.npy
-imgHQ24739.npy
-imgHQ14447.npy
-imgHQ07440.npy
-imgHQ06220.npy
-imgHQ22658.npy
-imgHQ11052.npy
-imgHQ29966.npy
-imgHQ07798.npy
-imgHQ00606.npy
-imgHQ11923.npy
-imgHQ06345.npy
-imgHQ15322.npy
-imgHQ21989.npy
-imgHQ07901.npy
-imgHQ07580.npy
-imgHQ23942.npy
-imgHQ21426.npy
-imgHQ12334.npy
-imgHQ27810.npy
-imgHQ13855.npy
-imgHQ03368.npy
-imgHQ22119.npy
-imgHQ08486.npy
-imgHQ28217.npy
-imgHQ20133.npy
-imgHQ23885.npy
-imgHQ05315.npy
-imgHQ23325.npy
-imgHQ09881.npy
-imgHQ24161.npy
-imgHQ00428.npy
-imgHQ20722.npy
-imgHQ10777.npy
-imgHQ00976.npy
-imgHQ02241.npy
-imgHQ04553.npy
-imgHQ05689.npy
-imgHQ01784.npy
-imgHQ19284.npy
-imgHQ08255.npy
-imgHQ04117.npy
-imgHQ09814.npy
-imgHQ21932.npy
-imgHQ19826.npy
-imgHQ18513.npy
-imgHQ07162.npy
-imgHQ28529.npy
-imgHQ01148.npy
-imgHQ08959.npy
-imgHQ01459.npy
-imgHQ29286.npy
-imgHQ28847.npy
-imgHQ03943.npy
-imgHQ27081.npy
-imgHQ01707.npy
-imgHQ21468.npy
-imgHQ12166.npy
-imgHQ29980.npy
-imgHQ24275.npy
-imgHQ07120.npy
-imgHQ18918.npy
-imgHQ01448.npy
-imgHQ11179.npy
-imgHQ26189.npy
-imgHQ18091.npy
-imgHQ02472.npy
-imgHQ12120.npy
-imgHQ20539.npy
-imgHQ17800.npy
-imgHQ27328.npy
-imgHQ11710.npy
-imgHQ24812.npy
-imgHQ03294.npy
-imgHQ15512.npy
-imgHQ22003.npy
-imgHQ20758.npy
-imgHQ04901.npy
-imgHQ09151.npy
-imgHQ24205.npy
-imgHQ08933.npy
-imgHQ17044.npy
-imgHQ08476.npy
-imgHQ22218.npy
-imgHQ29515.npy
-imgHQ12879.npy
-imgHQ04269.npy
-imgHQ28935.npy
-imgHQ14685.npy
-imgHQ14837.npy
-imgHQ27874.npy
-imgHQ16613.npy
-imgHQ07418.npy
-imgHQ20513.npy
-imgHQ28471.npy
-imgHQ00009.npy
-imgHQ23611.npy
-imgHQ08018.npy
-imgHQ03734.npy
-imgHQ06321.npy
-imgHQ23823.npy
-imgHQ05451.npy
-imgHQ28574.npy
-imgHQ14166.npy
-imgHQ21655.npy
-imgHQ13736.npy
-imgHQ13304.npy
-imgHQ19606.npy
-imgHQ08413.npy
-imgHQ04724.npy
-imgHQ25232.npy
-imgHQ05366.npy
-imgHQ00597.npy
-imgHQ09790.npy
-imgHQ22293.npy
-imgHQ02647.npy
-imgHQ07495.npy
-imgHQ29858.npy
-imgHQ07132.npy
-imgHQ00164.npy
-imgHQ29803.npy
-imgHQ27784.npy
-imgHQ19852.npy
-imgHQ00531.npy
-imgHQ04305.npy
-imgHQ16976.npy
-imgHQ01481.npy
-imgHQ18155.npy
-imgHQ26900.npy
-imgHQ17806.npy
-imgHQ23514.npy
-imgHQ23887.npy
-imgHQ01950.npy
-imgHQ17009.npy
-imgHQ19264.npy
-imgHQ18019.npy
-imgHQ20770.npy
-imgHQ04186.npy
-imgHQ23710.npy
-imgHQ21440.npy
-imgHQ26232.npy
-imgHQ28846.npy
-imgHQ29816.npy
-imgHQ20828.npy
-imgHQ28758.npy
-imgHQ11685.npy
-imgHQ12369.npy
-imgHQ23828.npy
-imgHQ21433.npy
-imgHQ29141.npy
-imgHQ20544.npy
-imgHQ13716.npy
-imgHQ23769.npy
-imgHQ20679.npy
-imgHQ24072.npy
-imgHQ09900.npy
-imgHQ16741.npy
-imgHQ01821.npy
-imgHQ12573.npy
-imgHQ16163.npy
-imgHQ01271.npy
-imgHQ02084.npy
-imgHQ11391.npy
-imgHQ08217.npy
-imgHQ05161.npy
-imgHQ06785.npy
-imgHQ04660.npy
-imgHQ16983.npy
-imgHQ24816.npy
-imgHQ07855.npy
-imgHQ16918.npy
-imgHQ05844.npy
-imgHQ01899.npy
-imgHQ03074.npy
-imgHQ01079.npy
-imgHQ21895.npy
-imgHQ28915.npy
-imgHQ05068.npy
-imgHQ08383.npy
-imgHQ05384.npy
-imgHQ11440.npy
-imgHQ26833.npy
-imgHQ06412.npy
-imgHQ27070.npy
-imgHQ20464.npy
-imgHQ08081.npy
-imgHQ06828.npy
-imgHQ03512.npy
-imgHQ03261.npy
-imgHQ29430.npy
-imgHQ20743.npy
-imgHQ22558.npy
-imgHQ24752.npy
-imgHQ15503.npy
-imgHQ14394.npy
-imgHQ01799.npy
-imgHQ11533.npy
-imgHQ00387.npy
-imgHQ15890.npy
-imgHQ11358.npy
-imgHQ05351.npy
-imgHQ24057.npy
-imgHQ19538.npy
-imgHQ12569.npy
-imgHQ05449.npy
-imgHQ29712.npy
-imgHQ16657.npy
-imgHQ15164.npy
-imgHQ19103.npy
-imgHQ23056.npy
-imgHQ15162.npy
-imgHQ20628.npy
-imgHQ18310.npy
-imgHQ28367.npy
-imgHQ13995.npy
-imgHQ14120.npy
-imgHQ09886.npy
-imgHQ19885.npy
-imgHQ20318.npy
-imgHQ18191.npy
-imgHQ19198.npy
-imgHQ24668.npy
-imgHQ00290.npy
-imgHQ29445.npy
-imgHQ21579.npy
-imgHQ16324.npy
-imgHQ17362.npy
-imgHQ08443.npy
-imgHQ19477.npy
-imgHQ04428.npy
-imgHQ07227.npy
-imgHQ01913.npy
-imgHQ05442.npy
-imgHQ25199.npy
-imgHQ19320.npy
-imgHQ00863.npy
-imgHQ26090.npy
-imgHQ18238.npy
-imgHQ13915.npy
-imgHQ09142.npy
-imgHQ03137.npy
-imgHQ23734.npy
-imgHQ05682.npy
-imgHQ20930.npy
-imgHQ02872.npy
-imgHQ09662.npy
-imgHQ21220.npy
-imgHQ04074.npy
-imgHQ14955.npy
-imgHQ17473.npy
-imgHQ16267.npy
-imgHQ05266.npy
-imgHQ16175.npy
-imgHQ26261.npy
-imgHQ17545.npy
-imgHQ23328.npy
-imgHQ07233.npy
-imgHQ19590.npy
-imgHQ20322.npy
-imgHQ27829.npy
-imgHQ15822.npy
-imgHQ12072.npy
-imgHQ13467.npy
-imgHQ13636.npy
-imgHQ15063.npy
-imgHQ07962.npy
-imgHQ00006.npy
-imgHQ04277.npy
-imgHQ00154.npy
-imgHQ19437.npy
-imgHQ09891.npy
-imgHQ29619.npy
-imgHQ14208.npy
-imgHQ03741.npy
-imgHQ24894.npy
-imgHQ15299.npy
-imgHQ24105.npy
-imgHQ15298.npy
-imgHQ16902.npy
-imgHQ03703.npy
-imgHQ26212.npy
-imgHQ24331.npy
-imgHQ17764.npy
-imgHQ13862.npy
-imgHQ16979.npy
-imgHQ10676.npy
-imgHQ19372.npy
-imgHQ23359.npy
-imgHQ13258.npy
-imgHQ09210.npy
-imgHQ01080.npy
-imgHQ19761.npy
-imgHQ00553.npy
-imgHQ19659.npy
-imgHQ25510.npy
-imgHQ21068.npy
-imgHQ29206.npy
-imgHQ09003.npy
-imgHQ01595.npy
-imgHQ21999.npy
-imgHQ25880.npy
-imgHQ18129.npy
-imgHQ12605.npy
-imgHQ23682.npy
-imgHQ10891.npy
-imgHQ10941.npy
-imgHQ28143.npy
-imgHQ23033.npy
-imgHQ22059.npy
-imgHQ16534.npy
-imgHQ16648.npy
-imgHQ22190.npy
-imgHQ15026.npy
-imgHQ25886.npy
-imgHQ10339.npy
-imgHQ27572.npy
-imgHQ04956.npy
-imgHQ08603.npy
-imgHQ22278.npy
-imgHQ20058.npy
-imgHQ20071.npy
-imgHQ02558.npy
-imgHQ16645.npy
-imgHQ04341.npy
-imgHQ13314.npy
-imgHQ23993.npy
-imgHQ20821.npy
-imgHQ20910.npy
-imgHQ04694.npy
-imgHQ06355.npy
-imgHQ11223.npy
-imgHQ19797.npy
-imgHQ27611.npy
-imgHQ24422.npy
-imgHQ27692.npy
-imgHQ13551.npy
-imgHQ03807.npy
-imgHQ01457.npy
-imgHQ06066.npy
-imgHQ13107.npy
-imgHQ22251.npy
-imgHQ00134.npy
-imgHQ14373.npy
-imgHQ13202.npy
-imgHQ25778.npy
-imgHQ09102.npy
-imgHQ10234.npy
-imgHQ16539.npy
-imgHQ04115.npy
-imgHQ12236.npy
-imgHQ00808.npy
-imgHQ05561.npy
-imgHQ23046.npy
-imgHQ22213.npy
-imgHQ12570.npy
-imgHQ19108.npy
-imgHQ29658.npy
-imgHQ22241.npy
-imgHQ17750.npy
-imgHQ05965.npy
-imgHQ03505.npy
-imgHQ10796.npy
-imgHQ03787.npy
-imgHQ27664.npy
-imgHQ00804.npy
-imgHQ06811.npy
-imgHQ05891.npy
-imgHQ14718.npy
-imgHQ18117.npy
-imgHQ23488.npy
-imgHQ03557.npy
-imgHQ16967.npy
-imgHQ20305.npy
-imgHQ28253.npy
-imgHQ16994.npy
-imgHQ24921.npy
-imgHQ00467.npy
-imgHQ27450.npy
-imgHQ06079.npy
-imgHQ22928.npy
-imgHQ19292.npy
-imgHQ24574.npy
-imgHQ18349.npy
-imgHQ29449.npy
-imgHQ08516.npy
-imgHQ05417.npy
-imgHQ11306.npy
-imgHQ18920.npy
-imgHQ24835.npy
-imgHQ16906.npy
-imgHQ10492.npy
-imgHQ17895.npy
-imgHQ23883.npy
-imgHQ13447.npy
-imgHQ09236.npy
-imgHQ15176.npy
-imgHQ17841.npy
-imgHQ22642.npy
-imgHQ18202.npy
-imgHQ07599.npy
-imgHQ23094.npy
-imgHQ01422.npy
-imgHQ20148.npy
-imgHQ23973.npy
-imgHQ21201.npy
-imgHQ22914.npy
-imgHQ05393.npy
-imgHQ26873.npy
-imgHQ01729.npy
-imgHQ07497.npy
-imgHQ24004.npy
-imgHQ22841.npy
-imgHQ11827.npy
-imgHQ07563.npy
-imgHQ06348.npy
-imgHQ01726.npy
-imgHQ15586.npy
-imgHQ08176.npy
-imgHQ15982.npy
-imgHQ13477.npy
-imgHQ05066.npy
-imgHQ26442.npy
-imgHQ03276.npy
-imgHQ25872.npy
-imgHQ28052.npy
-imgHQ12932.npy
-imgHQ02784.npy
-imgHQ09820.npy
-imgHQ24513.npy
-imgHQ00982.npy
-imgHQ27257.npy
-imgHQ03984.npy
-imgHQ05883.npy
-imgHQ16085.npy
-imgHQ13053.npy
-imgHQ18584.npy
-imgHQ04120.npy
-imgHQ25913.npy
-imgHQ07727.npy
-imgHQ05078.npy
-imgHQ06884.npy
-imgHQ14629.npy
-imgHQ05974.npy
-imgHQ28678.npy
-imgHQ28013.npy
-imgHQ20576.npy
-imgHQ22214.npy
-imgHQ03128.npy
-imgHQ13810.npy
-imgHQ00289.npy
-imgHQ29696.npy
-imgHQ24206.npy
-imgHQ23561.npy
-imgHQ03980.npy
-imgHQ25024.npy
-imgHQ28722.npy
-imgHQ27370.npy
-imgHQ22696.npy
-imgHQ03084.npy
-imgHQ06258.npy
-imgHQ14938.npy
-imgHQ04976.npy
-imgHQ14021.npy
-imgHQ23408.npy
-imgHQ00895.npy
-imgHQ28749.npy
-imgHQ01425.npy
-imgHQ09061.npy
-imgHQ21894.npy
-imgHQ24777.npy
-imgHQ20921.npy
-imgHQ15489.npy
-imgHQ23492.npy
-imgHQ20933.npy
-imgHQ05340.npy
-imgHQ03575.npy
-imgHQ10127.npy
-imgHQ12698.npy
-imgHQ18681.npy
-imgHQ19862.npy
-imgHQ08796.npy
-imgHQ19407.npy
-imgHQ20068.npy
-imgHQ20567.npy
-imgHQ03180.npy
-imgHQ10225.npy
-imgHQ22697.npy
-imgHQ25457.npy
-imgHQ22816.npy
-imgHQ18825.npy
-imgHQ18632.npy
-imgHQ12234.npy
-imgHQ16184.npy
-imgHQ19118.npy
-imgHQ15214.npy
-imgHQ06055.npy
-imgHQ15163.npy
-imgHQ21216.npy
-imgHQ08757.npy
-imgHQ02143.npy
-imgHQ02756.npy
-imgHQ21290.npy
-imgHQ27734.npy
-imgHQ29718.npy
-imgHQ25908.npy
-imgHQ13411.npy
-imgHQ01061.npy
-imgHQ25214.npy
-imgHQ28746.npy
-imgHQ14757.npy
-imgHQ03950.npy
-imgHQ17746.npy
-imgHQ26332.npy
-imgHQ29679.npy
-imgHQ20843.npy
-imgHQ06218.npy
-imgHQ00645.npy
-imgHQ11102.npy
-imgHQ03711.npy
-imgHQ28018.npy
-imgHQ23516.npy
-imgHQ18014.npy
-imgHQ08583.npy
-imgHQ02470.npy
-imgHQ10111.npy
-imgHQ09212.npy
-imgHQ14372.npy
-imgHQ07177.npy
-imgHQ17933.npy
-imgHQ10918.npy
-imgHQ23353.npy
-imgHQ27334.npy
-imgHQ04195.npy
-imgHQ16521.npy
-imgHQ06400.npy
-imgHQ24505.npy
-imgHQ18789.npy
-imgHQ02997.npy
-imgHQ22809.npy
-imgHQ25391.npy
-imgHQ02295.npy
-imgHQ10202.npy
-imgHQ26647.npy
-imgHQ14893.npy
-imgHQ25742.npy
-imgHQ08755.npy
-imgHQ29614.npy
-imgHQ14871.npy
-imgHQ16852.npy
-imgHQ15690.npy
-imgHQ02670.npy
-imgHQ11730.npy
-imgHQ20729.npy
-imgHQ14203.npy
-imgHQ12292.npy
-imgHQ26608.npy
-imgHQ27202.npy
-imgHQ20334.npy
-imgHQ00533.npy
-imgHQ23419.npy
-imgHQ28452.npy
-imgHQ18126.npy
-imgHQ02975.npy
-imgHQ26500.npy
-imgHQ20074.npy
-imgHQ19749.npy
-imgHQ15672.npy
-imgHQ04994.npy
-imgHQ14626.npy
-imgHQ28715.npy
-imgHQ25877.npy
-imgHQ13321.npy
-imgHQ05250.npy
-imgHQ04939.npy
-imgHQ03877.npy
-imgHQ13102.npy
-imgHQ04527.npy
-imgHQ11763.npy
-imgHQ08592.npy
-imgHQ29676.npy
-imgHQ04463.npy
-imgHQ12662.npy
-imgHQ22239.npy
-imgHQ15923.npy
-imgHQ07245.npy
-imgHQ23034.npy
-imgHQ28538.npy
-imgHQ03555.npy
-imgHQ07226.npy
-imgHQ25209.npy
-imgHQ22014.npy
-imgHQ25776.npy
-imgHQ14459.npy
-imgHQ24104.npy
-imgHQ21642.npy
-imgHQ29361.npy
-imgHQ24281.npy
-imgHQ04643.npy
-imgHQ15432.npy
-imgHQ25427.npy
-imgHQ23076.npy
-imgHQ22561.npy
-imgHQ01403.npy
-imgHQ19614.npy
-imgHQ22466.npy
-imgHQ21422.npy
-imgHQ16740.npy
-imgHQ28504.npy
-imgHQ11181.npy
-imgHQ25033.npy
-imgHQ04625.npy
-imgHQ25217.npy
-imgHQ20554.npy
-imgHQ29783.npy
-imgHQ13002.npy
-imgHQ12486.npy
-imgHQ24603.npy
-imgHQ27645.npy
-imgHQ15532.npy
-imgHQ03332.npy
-imgHQ14625.npy
-imgHQ10428.npy
-imgHQ15871.npy
-imgHQ26043.npy
-imgHQ19566.npy
-imgHQ11296.npy
-imgHQ24068.npy
-imgHQ20950.npy
-imgHQ26217.npy
-imgHQ07315.npy
-imgHQ09624.npy
-imgHQ01927.npy
-imgHQ24767.npy
-imgHQ07605.npy
-imgHQ27763.npy
-imgHQ17575.npy
-imgHQ29326.npy
-imgHQ04981.npy
-imgHQ13048.npy
-imgHQ15113.npy
-imgHQ25523.npy
-imgHQ03259.npy
-imgHQ11139.npy
-imgHQ24542.npy
-imgHQ18647.npy
-imgHQ16960.npy
-imgHQ00453.npy
-imgHQ22428.npy
-imgHQ12953.npy
-imgHQ28120.npy
-imgHQ28867.npy
-imgHQ16862.npy
-imgHQ03075.npy
-imgHQ23681.npy
-imgHQ03516.npy
-imgHQ13603.npy
-imgHQ16295.npy
-imgHQ01147.npy
-imgHQ18839.npy
-imgHQ23132.npy
-imgHQ09734.npy
-imgHQ29001.npy
-imgHQ26433.npy
-imgHQ29547.npy
-imgHQ17346.npy
-imgHQ06068.npy
-imgHQ03026.npy
-imgHQ17946.npy
-imgHQ20693.npy
-imgHQ04396.npy
-imgHQ09105.npy
-imgHQ26917.npy
-imgHQ14562.npy
-imgHQ23466.npy
-imgHQ16864.npy
-imgHQ07174.npy
-imgHQ12272.npy
-imgHQ18975.npy
-imgHQ17603.npy
-imgHQ14255.npy
-imgHQ18056.npy
-imgHQ01261.npy
-imgHQ28070.npy
-imgHQ18998.npy
-imgHQ02305.npy
-imgHQ11163.npy
-imgHQ08843.npy
-imgHQ21682.npy
-imgHQ26776.npy
-imgHQ15102.npy
-imgHQ20744.npy
-imgHQ03257.npy
-imgHQ01071.npy
-imgHQ24692.npy
-imgHQ14546.npy
-imgHQ07212.npy
-imgHQ27508.npy
-imgHQ09580.npy
-imgHQ06294.npy
-imgHQ01734.npy
-imgHQ09830.npy
-imgHQ20033.npy
-imgHQ06140.npy
-imgHQ20491.npy
-imgHQ09744.npy
-imgHQ16777.npy
-imgHQ24049.npy
-imgHQ12591.npy
-imgHQ28916.npy
-imgHQ10761.npy
-imgHQ11121.npy
-imgHQ01432.npy
-imgHQ17119.npy
-imgHQ17110.npy
-imgHQ23216.npy
-imgHQ12310.npy
-imgHQ28225.npy
-imgHQ27568.npy
-imgHQ17521.npy
-imgHQ18550.npy
-imgHQ17027.npy
-imgHQ10411.npy
-imgHQ28385.npy
-imgHQ13404.npy
-imgHQ21917.npy
-imgHQ12628.npy
-imgHQ12695.npy
-imgHQ18464.npy
-imgHQ06851.npy
-imgHQ14953.npy
-imgHQ04451.npy
-imgHQ05103.npy
-imgHQ16985.npy
-imgHQ09927.npy
-imgHQ23125.npy
-imgHQ19184.npy
-imgHQ29424.npy
-imgHQ27663.npy
-imgHQ09180.npy
-imgHQ08126.npy
-imgHQ00879.npy
-imgHQ12990.npy
-imgHQ18099.npy
-imgHQ09403.npy
-imgHQ23268.npy
-imgHQ07442.npy
-imgHQ10560.npy
-imgHQ27777.npy
-imgHQ01362.npy
-imgHQ28097.npy
-imgHQ13421.npy
-imgHQ19766.npy
-imgHQ08539.npy
-imgHQ20310.npy
-imgHQ05262.npy
-imgHQ20887.npy
-imgHQ06315.npy
-imgHQ29432.npy
-imgHQ05202.npy
-imgHQ00548.npy
-imgHQ17792.npy
-imgHQ15245.npy
-imgHQ09404.npy
-imgHQ13605.npy
-imgHQ28213.npy
-imgHQ17799.npy
-imgHQ26756.npy
-imgHQ28379.npy
-imgHQ20487.npy
-imgHQ01226.npy
-imgHQ17437.npy
-imgHQ19239.npy
-imgHQ16126.npy
-imgHQ21418.npy
-imgHQ16437.npy
-imgHQ04385.npy
-imgHQ20481.npy
-imgHQ12970.npy
-imgHQ27360.npy
-imgHQ12145.npy
-imgHQ06864.npy
-imgHQ21193.npy
-imgHQ07550.npy
-imgHQ00435.npy
-imgHQ12538.npy
-imgHQ04996.npy
-imgHQ16210.npy
-imgHQ06484.npy
-imgHQ05146.npy
-imgHQ06763.npy
-imgHQ17196.npy
-imgHQ07500.npy
-imgHQ08792.npy
-imgHQ02343.npy
-imgHQ29687.npy
-imgHQ14901.npy
-imgHQ14143.npy
-imgHQ01494.npy
-imgHQ06070.npy
-imgHQ29194.npy
-imgHQ27813.npy
-imgHQ02881.npy
-imgHQ00429.npy
-imgHQ15392.npy
-imgHQ23613.npy
-imgHQ28084.npy
-imgHQ12253.npy
-imgHQ24840.npy
-imgHQ14296.npy
-imgHQ09055.npy
-imgHQ20392.npy
-imgHQ12066.npy
-imgHQ23334.npy
-imgHQ02249.npy
-imgHQ24526.npy
-imgHQ04573.npy
-imgHQ27095.npy
-imgHQ04250.npy
-imgHQ16162.npy
-imgHQ01477.npy
-imgHQ06136.npy
-imgHQ12505.npy
-imgHQ24780.npy
-imgHQ04030.npy
-imgHQ14216.npy
-imgHQ05967.npy
-imgHQ21764.npy
-imgHQ09973.npy
-imgHQ17502.npy
-imgHQ22472.npy
-imgHQ25287.npy
-imgHQ15303.npy
-imgHQ19277.npy
-imgHQ23267.npy
-imgHQ14808.npy
-imgHQ02114.npy
-imgHQ07862.npy
-imgHQ05694.npy
-imgHQ23831.npy
-imgHQ26226.npy
-imgHQ02296.npy
-imgHQ08972.npy
-imgHQ16824.npy
-imgHQ01566.npy
-imgHQ22481.npy
-imgHQ23119.npy
-imgHQ21595.npy
-imgHQ10541.npy
-imgHQ12813.npy
-imgHQ24479.npy
-imgHQ20098.npy
-imgHQ04247.npy
-imgHQ22187.npy
-imgHQ26725.npy
-imgHQ01926.npy
-imgHQ03049.npy
-imgHQ02994.npy
-imgHQ00157.npy
-imgHQ26357.npy
-imgHQ05092.npy
-imgHQ11924.npy
-imgHQ01685.npy
-imgHQ01730.npy
-imgHQ13334.npy
-imgHQ28660.npy
-imgHQ24569.npy
-imgHQ16577.npy
-imgHQ04091.npy
-imgHQ12604.npy
-imgHQ25086.npy
-imgHQ21472.npy
-imgHQ00358.npy
-imgHQ08022.npy
-imgHQ28061.npy
-imgHQ29440.npy
-imgHQ05476.npy
-imgHQ06556.npy
-imgHQ15891.npy
-imgHQ12343.npy
-imgHQ09101.npy
-imgHQ14384.npy
-imgHQ24388.npy
-imgHQ09022.npy
-imgHQ22504.npy
-imgHQ03277.npy
-imgHQ04540.npy
-imgHQ01923.npy
-imgHQ29121.npy
-imgHQ23050.npy
-imgHQ20097.npy
-imgHQ14206.npy
-imgHQ18376.npy
-imgHQ28437.npy
-imgHQ13652.npy
-imgHQ27873.npy
-imgHQ15121.npy
-imgHQ13660.npy
-imgHQ23677.npy
-imgHQ15049.npy
-imgHQ15204.npy
-imgHQ03191.npy
-imgHQ22197.npy
-imgHQ09432.npy
-imgHQ02351.npy
-imgHQ20965.npy
-imgHQ03548.npy
-imgHQ06721.npy
-imgHQ19232.npy
-imgHQ16702.npy
-imgHQ22256.npy
-imgHQ09464.npy
-imgHQ06382.npy
-imgHQ08503.npy
-imgHQ10553.npy
-imgHQ15414.npy
-imgHQ21322.npy
-imgHQ12249.npy
-imgHQ10176.npy
-imgHQ00356.npy
-imgHQ15460.npy
-imgHQ29929.npy
-imgHQ04262.npy
-imgHQ16349.npy
-imgHQ05114.npy
-imgHQ01279.npy
-imgHQ09023.npy
-imgHQ06359.npy
-imgHQ02634.npy
-imgHQ10902.npy
-imgHQ28751.npy
-imgHQ17127.npy
-imgHQ00797.npy
-imgHQ24083.npy
-imgHQ03380.npy
-imgHQ21247.npy
-imgHQ13583.npy
-imgHQ23952.npy
-imgHQ10280.npy
-imgHQ27738.npy
-imgHQ05719.npy
-imgHQ21650.npy
-imgHQ14069.npy
-imgHQ03473.npy
-imgHQ12896.npy
-imgHQ29246.npy
-imgHQ05209.npy
-imgHQ24000.npy
-imgHQ06085.npy
-imgHQ04780.npy
-imgHQ25484.npy
-imgHQ00084.npy
-imgHQ01848.npy
-imgHQ20271.npy
-imgHQ26488.npy
-imgHQ24912.npy
-imgHQ27053.npy
-imgHQ16325.npy
-imgHQ16016.npy
-imgHQ04950.npy
-imgHQ09583.npy
-imgHQ16025.npy
-imgHQ23224.npy
-imgHQ14010.npy
-imgHQ29811.npy
-imgHQ28014.npy
-imgHQ00986.npy
-imgHQ07979.npy
-imgHQ29739.npy
-imgHQ16319.npy
-imgHQ02737.npy
-imgHQ26171.npy
-imgHQ06454.npy
-imgHQ08411.npy
-imgHQ22294.npy
-imgHQ18497.npy
-imgHQ02484.npy
-imgHQ27857.npy
-imgHQ10445.npy
-imgHQ25844.npy
-imgHQ06326.npy
-imgHQ14605.npy
-imgHQ27047.npy
-imgHQ11291.npy
-imgHQ02738.npy
-imgHQ16699.npy
-imgHQ29699.npy
-imgHQ22652.npy
-imgHQ29976.npy
-imgHQ24524.npy
-imgHQ20188.npy
-imgHQ05715.npy
-imgHQ19285.npy
-imgHQ18780.npy
-imgHQ00221.npy
-imgHQ15619.npy
-imgHQ20349.npy
-imgHQ22222.npy
-imgHQ18161.npy
-imgHQ17060.npy
-imgHQ00384.npy
-imgHQ21810.npy
-imgHQ02636.npy
-imgHQ27703.npy
-imgHQ17244.npy
-imgHQ25585.npy
-imgHQ24191.npy
-imgHQ05430.npy
-imgHQ08123.npy
-imgHQ07863.npy
-imgHQ03430.npy
-imgHQ20638.npy
-imgHQ25857.npy
-imgHQ26771.npy
-imgHQ24254.npy
-imgHQ17569.npy
-imgHQ23017.npy
-imgHQ05087.npy
-imgHQ20985.npy
-imgHQ14735.npy
-imgHQ02523.npy
-imgHQ21463.npy
-imgHQ03815.npy
-imgHQ29924.npy
-imgHQ03253.npy
-imgHQ04130.npy
-imgHQ06414.npy
-imgHQ08931.npy
-imgHQ15769.npy
-imgHQ07996.npy
-imgHQ04361.npy
-imgHQ09135.npy
-imgHQ28710.npy
-imgHQ03335.npy
-imgHQ12241.npy
-imgHQ08188.npy
-imgHQ24143.npy
-imgHQ20999.npy
-imgHQ00077.npy
-imgHQ10569.npy
-imgHQ11599.npy
-imgHQ21343.npy
-imgHQ20203.npy
-imgHQ07075.npy
-imgHQ20052.npy
-imgHQ14144.npy
-imgHQ05166.npy
-imgHQ08102.npy
-imgHQ03677.npy
-imgHQ28856.npy
-imgHQ03207.npy
-imgHQ18692.npy
-imgHQ20417.npy
-imgHQ01536.npy
-imgHQ21084.npy
-imgHQ26249.npy
-imgHQ17896.npy
-imgHQ14400.npy
-imgHQ18205.npy
-imgHQ18823.npy
-imgHQ24224.npy
-imgHQ28380.npy
-imgHQ10108.npy
-imgHQ12565.npy
-imgHQ23979.npy
-imgHQ03643.npy
-imgHQ26939.npy
-imgHQ18496.npy
-imgHQ07904.npy
-imgHQ05472.npy
-imgHQ14403.npy
-imgHQ20662.npy
-imgHQ03365.npy
-imgHQ10471.npy
-imgHQ18208.npy
-imgHQ01082.npy
-imgHQ01773.npy
-imgHQ29097.npy
-imgHQ20708.npy
-imgHQ06243.npy
-imgHQ22174.npy
-imgHQ11600.npy
-imgHQ13761.npy
-imgHQ09547.npy
-imgHQ05364.npy
-imgHQ29690.npy
-imgHQ11668.npy
-imgHQ04744.npy
-imgHQ21408.npy
-imgHQ01686.npy
-imgHQ23218.npy
-imgHQ05317.npy
-imgHQ22305.npy
-imgHQ00144.npy
-imgHQ00939.npy
-imgHQ10962.npy
-imgHQ07242.npy
-imgHQ06995.npy
-imgHQ06648.npy
-imgHQ29842.npy
-imgHQ17872.npy
-imgHQ08167.npy
-imgHQ29153.npy
-imgHQ16631.npy
-imgHQ06331.npy
-imgHQ01929.npy
-imgHQ20192.npy
-imgHQ04933.npy
-imgHQ07250.npy
-imgHQ29896.npy
-imgHQ07886.npy
-imgHQ23297.npy
-imgHQ09661.npy
-imgHQ25305.npy
-imgHQ18198.npy
-imgHQ20639.npy
-imgHQ02022.npy
-imgHQ15577.npy
-imgHQ20112.npy
-imgHQ06129.npy
-imgHQ26353.npy
-imgHQ25045.npy
-imgHQ22708.npy
-imgHQ10557.npy
-imgHQ00916.npy
-imgHQ18819.npy
-imgHQ03897.npy
-imgHQ08457.npy
-imgHQ24235.npy
-imgHQ20447.npy
-imgHQ13158.npy
-imgHQ21570.npy
-imgHQ06694.npy
-imgHQ01015.npy
-imgHQ16838.npy
-imgHQ25306.npy
-imgHQ17587.npy
-imgHQ02537.npy
-imgHQ08875.npy
-imgHQ13011.npy
-imgHQ28510.npy
-imgHQ00726.npy
-imgHQ04819.npy
-imgHQ25349.npy
-imgHQ10658.npy
-imgHQ08558.npy
-imgHQ13972.npy
-imgHQ28119.npy
-imgHQ03424.npy
-imgHQ04811.npy
-imgHQ00143.npy
-imgHQ28292.npy
-imgHQ20519.npy
-imgHQ07602.npy
-imgHQ26695.npy
-imgHQ29931.npy
-imgHQ12526.npy
-imgHQ19803.npy
-imgHQ05959.npy
-imgHQ08698.npy
-imgHQ28836.npy
-imgHQ02512.npy
-imgHQ24963.npy
-imgHQ20883.npy
-imgHQ13340.npy
-imgHQ25571.npy
-imgHQ02011.npy
-imgHQ09879.npy
-imgHQ10340.npy
-imgHQ22260.npy
-imgHQ06301.npy
-imgHQ05952.npy
-imgHQ05920.npy
-imgHQ12244.npy
-imgHQ26089.npy
-imgHQ28412.npy
-imgHQ09559.npy
-imgHQ08543.npy
-imgHQ29627.npy
-imgHQ09224.npy
-imgHQ13540.npy
-imgHQ18603.npy
-imgHQ16692.npy
-imgHQ16202.npy
-imgHQ18668.npy
-imgHQ03506.npy
-imgHQ16264.npy
-imgHQ28780.npy
-imgHQ20105.npy
-imgHQ18498.npy
-imgHQ20868.npy
-imgHQ03105.npy
-imgHQ24589.npy
-imgHQ26712.npy
-imgHQ09752.npy
-imgHQ17709.npy
-imgHQ03654.npy
-imgHQ26395.npy
-imgHQ04204.npy
-imgHQ08963.npy
-imgHQ14101.npy
-imgHQ16408.npy
-imgHQ12018.npy
-imgHQ15766.npy
-imgHQ27027.npy
-imgHQ03369.npy
-imgHQ24968.npy
-imgHQ23366.npy
-imgHQ26720.npy
-imgHQ01157.npy
-imgHQ26743.npy
-imgHQ07869.npy
-imgHQ15197.npy
-imgHQ07325.npy
-imgHQ21262.npy
-imgHQ25244.npy
-imgHQ14847.npy
-imgHQ28849.npy
-imgHQ17205.npy
-imgHQ04422.npy
-imgHQ25050.npy
-imgHQ01100.npy
-imgHQ00621.npy
-imgHQ17992.npy
-imgHQ26075.npy
-imgHQ18640.npy
-imgHQ09508.npy
-imgHQ15971.npy
-imgHQ18719.npy
-imgHQ24707.npy
-imgHQ12101.npy
-imgHQ17969.npy
-imgHQ11996.npy
-imgHQ01156.npy
-imgHQ13953.npy
-imgHQ14405.npy
-imgHQ02983.npy
-imgHQ15615.npy
-imgHQ23446.npy
-imgHQ13382.npy
-imgHQ25178.npy
-imgHQ08485.npy
-imgHQ11592.npy
-imgHQ06027.npy
-imgHQ07648.npy
-imgHQ21253.npy
-imgHQ06001.npy
-imgHQ05203.npy
-imgHQ01845.npy
-imgHQ24765.npy
-imgHQ10059.npy
-imgHQ08454.npy
-imgHQ10811.npy
-imgHQ06604.npy
-imgHQ19153.npy
-imgHQ21656.npy
-imgHQ05314.npy
-imgHQ02246.npy
-imgHQ23152.npy
-imgHQ06652.npy
-imgHQ15234.npy
-imgHQ24977.npy
-imgHQ26690.npy
-imgHQ16831.npy
-imgHQ12487.npy
-imgHQ14912.npy
-imgHQ21465.npy
-imgHQ24506.npy
-imgHQ04324.npy
-imgHQ29470.npy
-imgHQ02415.npy
-imgHQ01521.npy
-imgHQ18209.npy
-imgHQ28122.npy
-imgHQ26270.npy
-imgHQ22527.npy
-imgHQ29043.npy
-imgHQ02270.npy
-imgHQ14318.npy
-imgHQ06204.npy
-imgHQ07758.npy
-imgHQ03094.npy
-imgHQ11444.npy
-imgHQ02604.npy
-imgHQ05455.npy
-imgHQ07406.npy
-imgHQ23520.npy
-imgHQ25374.npy
-imgHQ01691.npy
-imgHQ17551.npy
-imgHQ29925.npy
-imgHQ19113.npy
-imgHQ04070.npy
-imgHQ11936.npy
-imgHQ05651.npy
-imgHQ25765.npy
-imgHQ21732.npy
-imgHQ17267.npy
-imgHQ26871.npy
-imgHQ03770.npy
-imgHQ25350.npy
-imgHQ07703.npy
-imgHQ18177.npy
-imgHQ27386.npy
-imgHQ15137.npy
-imgHQ26000.npy
-imgHQ02694.npy
-imgHQ14824.npy
-imgHQ20809.npy
-imgHQ27197.npy
-imgHQ13718.npy
-imgHQ16470.npy
-imgHQ15383.npy
-imgHQ29126.npy
-imgHQ15680.npy
-imgHQ29825.npy
-imgHQ15246.npy
-imgHQ22063.npy
-imgHQ23156.npy
-imgHQ23816.npy
-imgHQ27495.npy
-imgHQ28818.npy
-imgHQ11013.npy
-imgHQ15783.npy
-imgHQ16082.npy
-imgHQ05003.npy
-imgHQ20235.npy
-imgHQ25235.npy
-imgHQ22142.npy
-imgHQ20674.npy
-imgHQ16916.npy
-imgHQ14410.npy
-imgHQ11505.npy
-imgHQ06589.npy
-imgHQ25388.npy
-imgHQ28811.npy
-imgHQ19291.npy
-imgHQ01895.npy
-imgHQ21500.npy
-imgHQ07160.npy
-imgHQ19503.npy
-imgHQ12874.npy
-imgHQ23417.npy
-imgHQ17676.npy
-imgHQ29875.npy
-imgHQ15897.npy
-imgHQ16497.npy
-imgHQ21767.npy
-imgHQ18704.npy
-imgHQ10181.npy
-imgHQ03258.npy
-imgHQ04364.npy
-imgHQ19281.npy
-imgHQ09007.npy
-imgHQ28150.npy
-imgHQ15267.npy
-imgHQ23651.npy
-imgHQ08037.npy
-imgHQ19856.npy
-imgHQ07688.npy
-imgHQ27711.npy
-imgHQ02667.npy
-imgHQ15681.npy
-imgHQ24773.npy
-imgHQ12477.npy
-imgHQ28897.npy
-imgHQ01121.npy
-imgHQ22314.npy
-imgHQ09932.npy
-imgHQ17487.npy
-imgHQ02229.npy
-imgHQ06719.npy
-imgHQ19752.npy
-imgHQ11874.npy
-imgHQ25959.npy
-imgHQ20410.npy
-imgHQ01348.npy
-imgHQ16782.npy
-imgHQ21679.npy
-imgHQ24122.npy
-imgHQ15726.npy
-imgHQ02901.npy
-imgHQ11229.npy
-imgHQ23244.npy
-imgHQ02807.npy
-imgHQ17700.npy
-imgHQ20535.npy
-imgHQ08573.npy
-imgHQ07328.npy
-imgHQ23241.npy
-imgHQ18102.npy
-imgHQ20483.npy
-imgHQ03302.npy
-imgHQ24592.npy
-imgHQ01552.npy
-imgHQ04490.npy
-imgHQ10525.npy
-imgHQ13320.npy
-imgHQ16336.npy
-imgHQ15281.npy
-imgHQ24007.npy
-imgHQ26563.npy
-imgHQ11916.npy
-imgHQ09100.npy
-imgHQ03464.npy
-imgHQ05998.npy
-imgHQ27066.npy
-imgHQ18703.npy
-imgHQ08328.npy
-imgHQ18667.npy
-imgHQ29498.npy
-imgHQ13069.npy
-imgHQ25918.npy
-imgHQ19757.npy
-imgHQ12148.npy
-imgHQ28078.npy
-imgHQ12147.npy
-imgHQ22734.npy
-imgHQ22207.npy
-imgHQ11801.npy
-imgHQ26406.npy
-imgHQ12390.npy
-imgHQ03554.npy
-imgHQ24650.npy
-imgHQ19193.npy
-imgHQ22325.npy
-imgHQ17804.npy
-imgHQ00223.npy
-imgHQ00786.npy
-imgHQ26999.npy
-imgHQ08473.npy
-imgHQ02126.npy
-imgHQ06111.npy
-imgHQ13032.npy
-imgHQ06378.npy
-imgHQ18548.npy
-imgHQ02645.npy
-imgHQ18777.npy
-imgHQ05108.npy
-imgHQ11589.npy
-imgHQ22799.npy
-imgHQ26560.npy
-imgHQ04047.npy
-imgHQ11477.npy
-imgHQ15300.npy
-imgHQ01031.npy
-imgHQ09287.npy
-imgHQ24956.npy
-imgHQ17614.npy
-imgHQ00996.npy
-imgHQ03109.npy
-imgHQ15128.npy
-imgHQ17274.npy
-imgHQ23701.npy
-imgHQ00958.npy
-imgHQ06336.npy
-imgHQ22853.npy
-imgHQ20836.npy
-imgHQ25518.npy
-imgHQ05193.npy
-imgHQ12461.npy
-imgHQ20658.npy
-imgHQ17717.npy
-imgHQ22810.npy
-imgHQ23380.npy
-imgHQ08061.npy
-imgHQ04300.npy
-imgHQ19533.npy
-imgHQ17158.npy
-imgHQ02593.npy
-imgHQ29478.npy
-imgHQ29320.npy
-imgHQ01195.npy
-imgHQ01717.npy
-imgHQ03749.npy
-imgHQ22057.npy
-imgHQ12073.npy
-imgHQ14848.npy
-imgHQ24791.npy
-imgHQ12531.npy
-imgHQ00541.npy
-imgHQ03536.npy
-imgHQ12420.npy
-imgHQ26141.npy
-imgHQ13100.npy
-imgHQ05785.npy
-imgHQ08308.npy
-imgHQ11410.npy
-imgHQ17226.npy
-imgHQ11732.npy
-imgHQ17533.npy
-imgHQ01711.npy
-imgHQ08567.npy
-imgHQ26547.npy
-imgHQ14488.npy
-imgHQ12612.npy
-imgHQ04304.npy
-imgHQ06823.npy
-imgHQ14371.npy
-imgHQ06877.npy
-imgHQ11490.npy
-imgHQ05639.npy
-imgHQ18874.npy
-imgHQ03834.npy
-imgHQ01740.npy
-imgHQ19977.npy
-imgHQ07150.npy
-imgHQ28949.npy
-imgHQ28675.npy
-imgHQ17402.npy
-imgHQ02862.npy
-imgHQ04190.npy
-imgHQ17257.npy
-imgHQ10863.npy
-imgHQ28100.npy
-imgHQ27535.npy
-imgHQ03043.npy
-imgHQ16102.npy
-imgHQ21497.npy
-imgHQ16190.npy
-imgHQ14878.npy
-imgHQ10213.npy
-imgHQ23618.npy
-imgHQ13933.npy
-imgHQ06610.npy
-imgHQ23192.npy
-imgHQ09774.npy
-imgHQ22905.npy
-imgHQ01444.npy
-imgHQ23884.npy
-imgHQ27815.npy
-imgHQ26282.npy
-imgHQ07047.npy
-imgHQ24420.npy
-imgHQ12982.npy
-imgHQ08258.npy
-imgHQ20648.npy
-imgHQ27958.npy
-imgHQ03173.npy
-imgHQ28354.npy
-imgHQ24215.npy
-imgHQ09877.npy
-imgHQ00782.npy
-imgHQ22864.npy
-imgHQ24932.npy
-imgHQ26918.npy
-imgHQ09659.npy
-imgHQ27648.npy
-imgHQ01181.npy
-imgHQ26898.npy
-imgHQ04167.npy
-imgHQ17294.npy
-imgHQ03733.npy
-imgHQ13729.npy
-imgHQ02951.npy
-imgHQ01916.npy
-imgHQ20660.npy
-imgHQ19554.npy
-imgHQ00062.npy
-imgHQ25330.npy
-imgHQ01003.npy
-imgHQ28965.npy
-imgHQ19629.npy
-imgHQ05761.npy
-imgHQ13695.npy
-imgHQ16747.npy
-imgHQ23786.npy
-imgHQ18485.npy
-imgHQ28269.npy
-imgHQ21694.npy
-imgHQ18031.npy
-imgHQ26968.npy
-imgHQ28138.npy
-imgHQ09780.npy
-imgHQ08907.npy
-imgHQ16718.npy
-imgHQ02672.npy
-imgHQ21260.npy
-imgHQ15445.npy
-imgHQ01560.npy
-imgHQ13113.npy
-imgHQ16491.npy
-imgHQ20775.npy
-imgHQ01516.npy
-imgHQ18724.npy
-imgHQ00390.npy
-imgHQ26113.npy
-imgHQ23257.npy
-imgHQ08194.npy
-imgHQ17270.npy
-imgHQ04394.npy
-imgHQ13480.npy
-imgHQ09294.npy
-imgHQ07506.npy
-imgHQ24880.npy
-imgHQ07381.npy
-imgHQ15157.npy
-imgHQ14806.npy
-imgHQ17926.npy
-imgHQ05534.npy
-imgHQ07791.npy
-imgHQ01102.npy
-imgHQ21087.npy
-imgHQ02157.npy
-imgHQ08702.npy
-imgHQ03923.npy
-imgHQ04067.npy
-imgHQ26718.npy
-imgHQ12884.npy
-imgHQ00176.npy
-imgHQ01674.npy
-imgHQ01366.npy
-imgHQ02117.npy
-imgHQ11612.npy
-imgHQ01882.npy
-imgHQ20048.npy
-imgHQ15083.npy
-imgHQ05218.npy
-imgHQ06996.npy
-imgHQ23221.npy
-imgHQ26276.npy
-imgHQ02479.npy
-imgHQ29731.npy
-imgHQ07678.npy
-imgHQ12119.npy
-imgHQ24992.npy
-imgHQ24376.npy
-imgHQ11204.npy
-imgHQ19349.npy
-imgHQ05564.npy
-imgHQ02463.npy
-imgHQ01819.npy
-imgHQ23213.npy
-imgHQ10212.npy
-imgHQ28490.npy
-imgHQ24753.npy
-imgHQ10459.npy
-imgHQ17540.npy
-imgHQ08489.npy
-imgHQ00654.npy
-imgHQ17557.npy
-imgHQ13325.npy
-imgHQ05494.npy
-imgHQ00775.npy
-imgHQ20252.npy
-imgHQ15645.npy
-imgHQ27380.npy
-imgHQ02289.npy
-imgHQ17330.npy
-imgHQ27468.npy
-imgHQ14317.npy
-imgHQ04332.npy
-imgHQ12445.npy
-imgHQ05155.npy
-imgHQ09002.npy
-imgHQ16516.npy
-imgHQ06959.npy
-imgHQ15908.npy
-imgHQ11183.npy
-imgHQ04778.npy
-imgHQ28541.npy
-imgHQ12219.npy
-imgHQ06395.npy
-imgHQ00299.npy
-imgHQ18080.npy
-imgHQ05982.npy
-imgHQ04504.npy
-imgHQ06696.npy
-imgHQ11822.npy
-imgHQ23889.npy
-imgHQ23632.npy
-imgHQ24772.npy
-imgHQ06666.npy
-imgHQ02291.npy
-imgHQ08430.npy
-imgHQ10027.npy
-imgHQ25282.npy
-imgHQ05936.npy
-imgHQ13395.npy
-imgHQ13864.npy
-imgHQ26209.npy
-imgHQ21502.npy
-imgHQ14064.npy
-imgHQ27221.npy
-imgHQ00191.npy
-imgHQ13013.npy
-imgHQ29488.npy
-imgHQ00344.npy
-imgHQ29916.npy
-imgHQ22836.npy
-imgHQ22044.npy
-imgHQ08311.npy
-imgHQ12996.npy
-imgHQ15470.npy
-imgHQ08133.npy
-imgHQ15604.npy
-imgHQ14779.npy
-imgHQ08669.npy
-imgHQ24605.npy
-imgHQ07206.npy
-imgHQ05620.npy
-imgHQ25501.npy
-imgHQ10950.npy
-imgHQ00377.npy
-imgHQ21111.npy
-imgHQ07421.npy
-imgHQ05589.npy
-imgHQ22520.npy
-imgHQ04343.npy
-imgHQ01534.npy
-imgHQ14932.npy
-imgHQ19882.npy
-imgHQ01967.npy
-imgHQ09588.npy
-imgHQ22085.npy
-imgHQ17556.npy
-imgHQ29054.npy
-imgHQ25531.npy
-imgHQ17091.npy
-imgHQ26657.npy
-imgHQ03538.npy
-imgHQ23114.npy
-imgHQ02641.npy
-imgHQ11628.npy
-imgHQ23797.npy
-imgHQ27876.npy
-imgHQ17967.npy
-imgHQ29631.npy
-imgHQ24844.npy
-imgHQ03859.npy
-imgHQ09654.npy
-imgHQ24197.npy
-imgHQ03288.npy
-imgHQ14957.npy
-imgHQ16814.npy
-imgHQ23081.npy
-imgHQ11806.npy
-imgHQ02720.npy
-imgHQ01732.npy
-imgHQ21955.npy
-imgHQ23850.npy
-imgHQ15219.npy
-imgHQ00855.npy
-imgHQ18423.npy
-imgHQ14596.npy
-imgHQ24441.npy
-imgHQ16310.npy
-imgHQ22319.npy
-imgHQ11811.npy
-imgHQ10434.npy
-imgHQ09286.npy
-imgHQ19210.npy
-imgHQ19014.npy
-imgHQ19998.npy
-imgHQ29801.npy
-imgHQ08320.npy
-imgHQ28637.npy
-imgHQ00902.npy
-imgHQ07024.npy
-imgHQ27822.npy
-imgHQ02471.npy
-imgHQ13595.npy
-imgHQ16480.npy
-imgHQ09460.npy
-imgHQ17625.npy
-imgHQ26410.npy
-imgHQ05718.npy
-imgHQ28865.npy
-imgHQ29287.npy
-imgHQ27751.npy
-imgHQ17447.npy
-imgHQ04769.npy
-imgHQ07146.npy
-imgHQ16364.npy
-imgHQ05641.npy
-imgHQ28177.npy
-imgHQ28700.npy
-imgHQ18339.npy
-imgHQ29790.npy
-imgHQ25638.npy
-imgHQ14568.npy
-imgHQ17413.npy
-imgHQ24167.npy
-imgHQ03299.npy
-imgHQ24787.npy
-imgHQ20240.npy
-imgHQ22738.npy
-imgHQ02513.npy
-imgHQ00959.npy
-imgHQ23621.npy
-imgHQ19624.npy
-imgHQ04929.npy
-imgHQ21495.npy
-imgHQ08390.npy
-imgHQ12251.npy
-imgHQ08670.npy
-imgHQ25749.npy
-imgHQ01491.npy
-imgHQ08295.npy
-imgHQ06980.npy
-imgHQ29708.npy
-imgHQ14030.npy
-imgHQ00011.npy
-imgHQ00368.npy
-imgHQ02794.npy
-imgHQ04052.npy
-imgHQ00723.npy
-imgHQ01953.npy
-imgHQ06729.npy
-imgHQ23690.npy
-imgHQ27749.npy
-imgHQ19445.npy
-imgHQ09862.npy
-imgHQ24236.npy
-imgHQ16871.npy
-imgHQ23893.npy
-imgHQ21276.npy
-imgHQ07197.npy
-imgHQ04987.npy
-imgHQ14260.npy
-imgHQ09129.npy
-imgHQ13753.npy
-imgHQ11384.npy
-imgHQ13512.npy
-imgHQ08776.npy
-imgHQ10903.npy
-imgHQ02361.npy
-imgHQ22179.npy
-imgHQ17052.npy
-imgHQ07307.npy
-imgHQ00891.npy
-imgHQ22811.npy
-imgHQ02838.npy
-imgHQ21524.npy
-imgHQ10372.npy
-imgHQ06376.npy
-imgHQ01158.npy
-imgHQ03705.npy
-imgHQ01316.npy
-imgHQ01375.npy
-imgHQ21267.npy
-imgHQ04462.npy
-imgHQ01274.npy
-imgHQ28160.npy
-imgHQ09277.npy
-imgHQ01056.npy
-imgHQ06117.npy
-imgHQ12680.npy
-imgHQ07425.npy
-imgHQ27469.npy
-imgHQ11663.npy
-imgHQ23607.npy
-imgHQ00177.npy
-imgHQ26431.npy
-imgHQ22176.npy
-imgHQ00848.npy
-imgHQ13650.npy
-imgHQ00231.npy
-imgHQ13303.npy
-imgHQ25074.npy
-imgHQ20548.npy
-imgHQ17258.npy
-imgHQ14461.npy
-imgHQ07773.npy
-imgHQ27354.npy
-imgHQ02753.npy
-imgHQ02149.npy
-imgHQ08980.npy
-imgHQ22980.npy
-imgHQ07419.npy
-imgHQ15594.npy
-imgHQ19615.npy
-imgHQ06954.npy
-imgHQ05153.npy
-imgHQ20766.npy
-imgHQ24573.npy
-imgHQ20362.npy
-imgHQ25843.npy
-imgHQ27512.npy
-imgHQ12999.npy
-imgHQ04674.npy
-imgHQ13668.npy
-imgHQ06009.npy
-imgHQ01128.npy
-imgHQ28727.npy
-imgHQ25699.npy
-imgHQ26972.npy
-imgHQ14563.npy
-imgHQ20325.npy
-imgHQ17166.npy
-imgHQ00537.npy
-imgHQ03728.npy
-imgHQ17877.npy
-imgHQ01975.npy
-imgHQ18210.npy
-imgHQ23154.npy
-imgHQ18503.npy
-imgHQ20531.npy
-imgHQ25454.npy
-imgHQ13410.npy
-imgHQ21610.npy
-imgHQ23375.npy
-imgHQ09947.npy
-imgHQ18455.npy
-imgHQ04225.npy
-imgHQ00122.npy
-imgHQ19428.npy
-imgHQ10349.npy
-imgHQ11681.npy
-imgHQ28874.npy
-imgHQ26482.npy
-imgHQ24459.npy
-imgHQ28803.npy
-imgHQ20051.npy
-imgHQ07567.npy
-imgHQ03675.npy
-imgHQ09035.npy
-imgHQ01574.npy
-imgHQ09053.npy
-imgHQ19156.npy
-imgHQ27992.npy
-imgHQ23106.npy
-imgHQ00013.npy
-imgHQ09587.npy
-imgHQ03190.npy
-imgHQ09882.npy
-imgHQ00363.npy
-imgHQ01063.npy
-imgHQ14771.npy
-imgHQ18430.npy
-imgHQ04006.npy
-imgHQ16439.npy
-imgHQ28425.npy
-imgHQ22921.npy
-imgHQ06019.npy
-imgHQ20879.npy
-imgHQ23957.npy
-imgHQ00941.npy
-imgHQ25739.npy
-imgHQ17777.npy
-imgHQ10269.npy
-imgHQ19356.npy
-imgHQ10788.npy
-imgHQ15248.npy
-imgHQ05549.npy
-imgHQ02215.npy
-imgHQ23726.npy
-imgHQ15572.npy
-imgHQ23789.npy
-imgHQ25932.npy
-imgHQ28998.npy
-imgHQ22066.npy
-imgHQ23656.npy
-imgHQ29259.npy
-imgHQ00637.npy
-imgHQ23222.npy
-imgHQ03083.npy
-imgHQ29526.npy
-imgHQ23123.npy
-imgHQ25998.npy
-imgHQ05676.npy
-imgHQ22715.npy
-imgHQ23278.npy
-imgHQ03150.npy
-imgHQ26024.npy
-imgHQ21938.npy
-imgHQ23749.npy
-imgHQ19794.npy
-imgHQ20507.npy
-imgHQ11151.npy
-imgHQ21448.npy
-imgHQ12553.npy
-imgHQ05049.npy
-imgHQ06677.npy
-imgHQ08230.npy
-imgHQ19062.npy
-imgHQ21059.npy
-imgHQ00866.npy
-imgHQ26930.npy
-imgHQ19535.npy
-imgHQ00668.npy
-imgHQ21454.npy
-imgHQ12220.npy
-imgHQ12847.npy
-imgHQ24253.npy
-imgHQ07043.npy
-imgHQ21587.npy
-imgHQ28468.npy
-imgHQ13366.npy
-imgHQ01976.npy
-imgHQ09295.npy
-imgHQ19802.npy
-imgHQ21571.npy
-imgHQ18964.npy
-imgHQ12617.npy
-imgHQ24187.npy
-imgHQ18739.npy
-imgHQ09398.npy
-imgHQ18406.npy
-imgHQ17590.npy
-imgHQ11303.npy
-imgHQ13297.npy
-imgHQ06910.npy
-imgHQ05517.npy
-imgHQ27396.npy
-imgHQ23503.npy
-imgHQ19148.npy
-imgHQ21879.npy
-imgHQ06504.npy
-imgHQ13946.npy
-imgHQ13329.npy
-imgHQ17082.npy
-imgHQ26551.npy
-imgHQ07138.npy
-imgHQ03616.npy
-imgHQ14956.npy
-imgHQ10928.npy
-imgHQ01134.npy
-imgHQ03270.npy
-imgHQ15119.npy
-imgHQ10477.npy
-imgHQ02301.npy
-imgHQ07039.npy
-imgHQ05081.npy
-imgHQ11956.npy
-imgHQ18752.npy
-imgHQ20613.npy
-imgHQ14370.npy
-imgHQ18317.npy
-imgHQ27447.npy
-imgHQ01635.npy
-imgHQ10040.npy
-imgHQ13360.npy
-imgHQ00355.npy
-imgHQ07708.npy
-imgHQ09895.npy
-imgHQ21969.npy
-imgHQ08438.npy
-imgHQ22596.npy
-imgHQ12611.npy
-imgHQ10819.npy
-imgHQ21231.npy
-imgHQ05636.npy
-imgHQ28130.npy
-imgHQ08510.npy
-imgHQ09438.npy
-imgHQ01456.npy
-imgHQ24697.npy
-imgHQ04216.npy
-imgHQ12681.npy
-imgHQ12201.npy
-imgHQ00559.npy
-imgHQ04154.npy
-imgHQ24679.npy
-imgHQ10247.npy
-imgHQ22216.npy
-imgHQ13548.npy
-imgHQ15040.npy
-imgHQ26054.npy
-imgHQ20478.npy
-imgHQ10035.npy
-imgHQ15065.npy
-imgHQ01334.npy
-imgHQ00802.npy
-imgHQ24485.npy
-imgHQ04387.npy
-imgHQ02548.npy
-imgHQ10540.npy
-imgHQ08089.npy
-imgHQ06147.npy
-imgHQ09533.npy
-imgHQ29139.npy
-imgHQ27904.npy
-imgHQ16836.npy
-imgHQ10716.npy
-imgHQ15263.npy
-imgHQ04476.npy
-imgHQ15975.npy
-imgHQ19772.npy
-imgHQ04809.npy
-imgHQ03813.npy
-imgHQ04909.npy
-imgHQ11072.npy
-imgHQ19598.npy
-imgHQ04634.npy
-imgHQ05600.npy
-imgHQ27137.npy
-imgHQ26533.npy
-imgHQ08602.npy
-imgHQ21605.npy
-imgHQ00704.npy
-imgHQ24801.npy
-imgHQ06963.npy
-imgHQ08608.npy
-imgHQ00835.npy
-imgHQ27003.npy
-imgHQ26700.npy
-imgHQ07312.npy
-imgHQ12842.npy
-imgHQ22302.npy
-imgHQ29689.npy
-imgHQ00045.npy
-imgHQ20381.npy
-imgHQ12186.npy
-imgHQ20008.npy
-imgHQ22703.npy
-imgHQ19247.npy
-imgHQ02854.npy
-imgHQ06008.npy
-imgHQ12364.npy
-imgHQ26969.npy
-imgHQ24324.npy
-imgHQ22742.npy
-imgHQ13749.npy
-imgHQ26304.npy
-imgHQ21133.npy
-imgHQ16477.npy
-imgHQ13593.npy
-imgHQ00670.npy
-imgHQ27900.npy
-imgHQ03127.npy
-imgHQ22674.npy
-imgHQ09370.npy
-imgHQ03808.npy
-imgHQ09685.npy
-imgHQ03912.npy
-imgHQ09220.npy
-imgHQ05946.npy
-imgHQ12313.npy
-imgHQ19119.npy
-imgHQ00748.npy
-imgHQ15632.npy
-imgHQ02184.npy
-imgHQ17760.npy
-imgHQ18710.npy
-imgHQ09713.npy
-imgHQ17407.npy
-imgHQ00395.npy
-imgHQ10524.npy
-imgHQ08971.npy
-imgHQ09416.npy
-imgHQ04664.npy
-imgHQ09332.npy
-imgHQ00227.npy
-imgHQ14609.npy
-imgHQ01508.npy
-imgHQ10579.npy
-imgHQ10344.npy
-imgHQ22766.npy
-imgHQ04265.npy
-imgHQ11673.npy
-imgHQ04699.npy
-imgHQ12841.npy
-imgHQ10627.npy
-imgHQ09299.npy
-imgHQ18543.npy
-imgHQ25456.npy
-imgHQ00017.npy
-imgHQ11203.npy
-imgHQ06192.npy
-imgHQ12672.npy
-imgHQ06290.npy
-imgHQ25893.npy
-imgHQ00977.npy
-imgHQ14131.npy
-imgHQ16353.npy
-imgHQ14610.npy
-imgHQ25602.npy
-imgHQ15722.npy
-imgHQ00740.npy
-imgHQ12519.npy
-imgHQ22624.npy
-imgHQ19832.npy
-imgHQ09837.npy
-imgHQ01405.npy
-imgHQ03830.npy
-imgHQ27683.npy
-imgHQ01831.npy
-imgHQ09919.npy
-imgHQ09424.npy
-imgHQ26848.npy
-imgHQ23818.npy
-imgHQ10726.npy
-imgHQ23574.npy
-imgHQ02559.npy
-imgHQ23866.npy
-imgHQ08626.npy
-imgHQ27133.npy
-imgHQ05727.npy
-imgHQ13875.npy
-imgHQ25023.npy
-imgHQ20929.npy
-imgHQ03000.npy
-imgHQ12322.npy
-imgHQ03034.npy
-imgHQ11719.npy
-imgHQ15691.npy
-imgHQ13997.npy
-imgHQ19240.npy
-imgHQ20864.npy
-imgHQ24366.npy
-imgHQ07872.npy
-imgHQ10649.npy
-imgHQ11319.npy
-imgHQ08587.npy
-imgHQ04386.npy
-imgHQ11227.npy
-imgHQ09864.npy
-imgHQ14668.npy
-imgHQ07581.npy
-imgHQ03774.npy
-imgHQ28343.npy
-imgHQ26643.npy
-imgHQ06496.npy
-imgHQ24384.npy
-imgHQ12087.npy
-imgHQ17148.npy
-imgHQ03196.npy
-imgHQ22574.npy
-imgHQ14907.npy
-imgHQ05244.npy
-imgHQ08248.npy
-imgHQ26036.npy
-imgHQ13022.npy
-imgHQ23706.npy
-imgHQ20304.npy
-imgHQ13531.npy
-imgHQ10570.npy
-imgHQ03064.npy
-imgHQ15874.npy
-imgHQ25435.npy
-imgHQ27635.npy
-imgHQ02815.npy
-imgHQ21858.npy
-imgHQ06948.npy
-imgHQ27964.npy
-imgHQ23346.npy
-imgHQ26492.npy
-imgHQ26753.npy
-imgHQ21031.npy
-imgHQ12965.npy
-imgHQ03592.npy
-imgHQ08649.npy
-imgHQ00404.npy
-imgHQ09498.npy
-imgHQ08462.npy
-imgHQ13041.npy
-imgHQ15712.npy
-imgHQ02053.npy
-imgHQ10449.npy
-imgHQ18490.npy
-imgHQ10927.npy
-imgHQ22013.npy
-imgHQ26990.npy
-imgHQ26059.npy
-imgHQ01605.npy
-imgHQ28617.npy
-imgHQ24257.npy
-imgHQ08877.npy
-imgHQ01564.npy
-imgHQ10106.npy
-imgHQ15226.npy
-imgHQ25400.npy
-imgHQ11044.npy
-imgHQ25046.npy
-imgHQ19270.npy
-imgHQ13441.npy
-imgHQ29761.npy
-imgHQ06639.npy
-imgHQ06580.npy
-imgHQ13070.npy
-imgHQ19861.npy
-imgHQ15663.npy
-imgHQ06807.npy
-imgHQ22217.npy
-imgHQ27126.npy
-imgHQ13492.npy
-imgHQ04639.npy
-imgHQ03200.npy
-imgHQ09154.npy
-imgHQ12624.npy
-imgHQ26523.npy
-imgHQ17489.npy
-imgHQ29446.npy
-imgHQ25474.npy
-imgHQ15555.npy
-imgHQ29744.npy
-imgHQ14408.npy
-imgHQ06714.npy
-imgHQ20216.npy
-imgHQ15593.npy
-imgHQ16101.npy
-imgHQ21449.npy
-imgHQ20262.npy
-imgHQ24749.npy
-imgHQ15126.npy
-imgHQ27919.npy
-imgHQ12107.npy
-imgHQ04252.npy
-imgHQ06672.npy
-imgHQ15580.npy
-imgHQ20173.npy
-imgHQ17251.npy
-imgHQ20785.npy
-imgHQ17863.npy
-imgHQ01070.npy
-imgHQ11497.npy
-imgHQ15084.npy
-imgHQ29756.npy
-imgHQ17607.npy
-imgHQ07238.npy
-imgHQ25450.npy
-imgHQ17844.npy
-imgHQ25146.npy
-imgHQ11561.npy
-imgHQ08157.npy
-imgHQ21191.npy
-imgHQ08684.npy
-imgHQ00681.npy
-imgHQ19411.npy
-imgHQ14769.npy
-imgHQ12779.npy
-imgHQ12524.npy
-imgHQ03444.npy
-imgHQ28558.npy
-imgHQ04803.npy
-imgHQ16879.npy
-imgHQ24585.npy
-imgHQ05105.npy
-imgHQ19820.npy
-imgHQ10136.npy
-imgHQ08763.npy
-imgHQ10587.npy
-imgHQ18164.npy
-imgHQ17515.npy
-imgHQ12803.npy
-imgHQ04644.npy
-imgHQ18900.npy
-imgHQ05388.npy
-imgHQ17321.npy
-imgHQ00461.npy
-imgHQ14807.npy
-imgHQ24053.npy
-imgHQ14876.npy
-imgHQ20981.npy
-imgHQ23872.npy
-imgHQ14803.npy
-imgHQ19111.npy
-imgHQ21195.npy
-imgHQ14854.npy
-imgHQ19828.npy
-imgHQ28508.npy
-imgHQ22463.npy
-imgHQ24949.npy
-imgHQ17202.npy
-imgHQ17053.npy
-imgHQ11868.npy
-imgHQ01476.npy
-imgHQ19600.npy
-imgHQ05251.npy
-imgHQ02940.npy
-imgHQ00115.npy
-imgHQ12679.npy
-imgHQ04303.npy
-imgHQ24504.npy
-imgHQ18919.npy
-imgHQ07511.npy
-imgHQ18078.npy
-imgHQ25159.npy
-imgHQ24434.npy
-imgHQ22537.npy
-imgHQ09949.npy
-imgHQ01163.npy
-imgHQ05918.npy
-imgHQ23604.npy
-imgHQ18656.npy
-imgHQ24607.npy
-imgHQ22872.npy
-imgHQ07730.npy
-imgHQ15866.npy
-imgHQ29253.npy
-imgHQ06598.npy
-imgHQ15232.npy
-imgHQ09265.npy
-imgHQ02042.npy
-imgHQ22206.npy
-imgHQ10814.npy
-imgHQ29331.npy
-imgHQ21157.npy
-imgHQ25139.npy
-imgHQ19964.npy
-imgHQ10020.npy
-imgHQ26347.npy
-imgHQ27880.npy
-imgHQ23601.npy
-imgHQ09785.npy
-imgHQ04491.npy
-imgHQ24511.npy
-imgHQ17063.npy
-imgHQ25119.npy
-imgHQ29035.npy
-imgHQ25719.npy
-imgHQ03503.npy
-imgHQ16587.npy
-imgHQ01934.npy
-imgHQ11502.npy
-imgHQ02330.npy
-imgHQ03707.npy
-imgHQ28153.npy
-imgHQ07998.npy
-imgHQ16993.npy
-imgHQ28114.npy
-imgHQ08512.npy
-imgHQ22140.npy
-imgHQ13787.npy
-imgHQ04453.npy
-imgHQ26035.npy
-imgHQ10825.npy
-imgHQ12397.npy
-imgHQ00630.npy
-imgHQ19593.npy
-imgHQ19897.npy
-imgHQ19090.npy
-imgHQ14926.npy
-imgHQ23556.npy
-imgHQ11427.npy
-imgHQ17482.npy
-imgHQ11973.npy
-imgHQ22506.npy
-imgHQ20669.npy
-imgHQ25594.npy
-imgHQ08234.npy
-imgHQ02744.npy
-imgHQ05396.npy
-imgHQ12616.npy
-imgHQ12704.npy
-imgHQ02366.npy
-imgHQ13738.npy
-imgHQ03696.npy
-imgHQ04646.npy
-imgHQ01194.npy
-imgHQ25586.npy
-imgHQ03362.npy
-imgHQ13008.npy
-imgHQ07023.npy
-imgHQ12332.npy
-imgHQ29324.npy
-imgHQ16229.npy
-imgHQ28363.npy
-imgHQ25380.npy
-imgHQ15870.npy
-imgHQ14712.npy
-imgHQ18544.npy
-imgHQ26237.npy
-imgHQ25080.npy
-imgHQ24502.npy
-imgHQ04231.npy
-imgHQ22887.npy
-imgHQ05649.npy
-imgHQ03415.npy
-imgHQ24916.npy
-imgHQ24311.npy
-imgHQ13979.npy
-imgHQ06591.npy
-imgHQ29965.npy
-imgHQ04687.npy
-imgHQ20434.npy
-imgHQ08156.npy
-imgHQ22288.npy
-imgHQ29997.npy
-imgHQ27333.npy
-imgHQ01209.npy
-imgHQ07821.npy
-imgHQ01522.npy
-imgHQ04818.npy
-imgHQ07291.npy
-imgHQ21862.npy
-imgHQ24464.npy
-imgHQ25292.npy
-imgHQ24980.npy
-imgHQ24354.npy
-imgHQ02125.npy
-imgHQ20128.npy
-imgHQ20649.npy
-imgHQ15410.npy
-imgHQ14205.npy
-imgHQ07271.npy
-imgHQ18318.npy
-imgHQ29002.npy
-imgHQ05357.npy
-imgHQ12393.npy
-imgHQ15138.npy
-imgHQ14092.npy
-imgHQ06681.npy
-imgHQ26819.npy
-imgHQ15361.npy
-imgHQ04561.npy
-imgHQ02804.npy
-imgHQ23599.npy
-imgHQ03920.npy
-imgHQ18092.npy
-imgHQ19262.npy
-imgHQ09099.npy
-imgHQ01523.npy
-imgHQ25482.npy
-imgHQ27835.npy
-imgHQ09457.npy
-imgHQ09750.npy
-imgHQ25018.npy
-imgHQ12460.npy
-imgHQ26889.npy
-imgHQ29482.npy
-imgHQ18472.npy
-imgHQ16125.npy
-imgHQ05683.npy
-imgHQ08452.npy
-imgHQ21020.npy
-imgHQ28173.npy
-imgHQ28831.npy
-imgHQ14852.npy
-imgHQ10764.npy
-imgHQ11955.npy
-imgHQ27471.npy
-imgHQ02370.npy
-imgHQ29419.npy
-imgHQ05020.npy
-imgHQ24838.npy
-imgHQ07759.npy
-imgHQ06605.npy
-imgHQ20492.npy
-imgHQ18622.npy
-imgHQ27040.npy
-imgHQ15696.npy
-imgHQ21986.npy
-imgHQ15192.npy
-imgHQ24754.npy
-imgHQ25633.npy
-imgHQ21186.npy
-imgHQ01714.npy
-imgHQ03496.npy
-imgHQ13471.npy
-imgHQ04222.npy
-imgHQ15751.npy
-imgHQ26088.npy
-imgHQ04327.npy
-imgHQ15968.npy
-imgHQ18902.npy
-imgHQ26537.npy
-imgHQ16416.npy
-imgHQ14608.npy
-imgHQ12668.npy
-imgHQ23897.npy
-imgHQ11585.npy
-imgHQ21990.npy
-imgHQ00166.npy
-imgHQ23899.npy
-imgHQ09385.npy
-imgHQ04165.npy
-imgHQ27198.npy
-imgHQ24507.npy
-imgHQ03993.npy
-imgHQ10947.npy
-imgHQ08041.npy
-imgHQ03488.npy
-imgHQ11928.npy
-imgHQ10899.npy
-imgHQ06433.npy
-imgHQ03112.npy
-imgHQ06824.npy
-imgHQ01000.npy
-imgHQ23751.npy
-imgHQ07685.npy
-imgHQ18697.npy
-imgHQ26215.npy
-imgHQ09056.npy
-imgHQ00031.npy
-imgHQ26211.npy
-imgHQ21413.npy
-imgHQ15359.npy
-imgHQ13077.npy
-imgHQ07064.npy
-imgHQ24407.npy
-imgHQ21057.npy
-imgHQ02736.npy
-imgHQ19971.npy
-imgHQ24671.npy
-imgHQ23940.npy
-imgHQ02788.npy
-imgHQ21437.npy
-imgHQ23733.npy
-imgHQ10104.npy
-imgHQ16656.npy
-imgHQ17848.npy
-imgHQ03451.npy
-imgHQ10133.npy
-imgHQ07204.npy
-imgHQ08148.npy
-imgHQ24070.npy
-imgHQ00472.npy
-imgHQ13437.npy
-imgHQ09532.npy
-imgHQ01424.npy
-imgHQ07682.npy
-imgHQ20357.npy
-imgHQ12689.npy
-imgHQ23435.npy
-imgHQ26961.npy
-imgHQ17951.npy
-imgHQ07838.npy
-imgHQ24970.npy
-imgHQ05484.npy
-imgHQ18663.npy
-imgHQ06485.npy
-imgHQ20912.npy
-imgHQ02451.npy
-imgHQ22352.npy
-imgHQ10044.npy
-imgHQ18407.npy
-imgHQ11164.npy
-imgHQ04259.npy
-imgHQ29514.npy
-imgHQ10486.npy
-imgHQ16948.npy
-imgHQ24849.npy
-imgHQ26788.npy
-imgHQ15406.npy
-imgHQ28612.npy
-imgHQ19017.npy
-imgHQ15979.npy
-imgHQ07524.npy
-imgHQ18947.npy
-imgHQ05611.npy
-imgHQ02031.npy
-imgHQ27882.npy
-imgHQ17803.npy
-imgHQ14602.npy
-imgHQ22662.npy
-imgHQ07776.npy
-imgHQ14903.npy
-imgHQ27287.npy
-imgHQ17152.npy
-imgHQ14338.npy
-imgHQ07000.npy
-imgHQ22645.npy
-imgHQ09939.npy
-imgHQ18148.npy
-imgHQ14033.npy
-imgHQ01580.npy
-imgHQ18969.npy
-imgHQ01841.npy
-imgHQ04053.npy
-imgHQ25156.npy
-imgHQ05527.npy
-imgHQ14196.npy
-imgHQ07479.npy
-imgHQ17068.npy
-imgHQ22286.npy
-imgHQ18541.npy
-imgHQ12555.npy
-imgHQ22245.npy
-imgHQ17980.npy
-imgHQ04606.npy
-imgHQ05679.npy
-imgHQ22641.npy
-imgHQ23355.npy
-imgHQ25649.npy
-imgHQ15387.npy
-imgHQ25445.npy
-imgHQ05058.npy
-imgHQ18999.npy
-imgHQ00083.npy
-imgHQ08815.npy
-imgHQ25423.npy
-imgHQ16586.npy
-imgHQ27603.npy
-imgHQ11075.npy
-imgHQ12805.npy
-imgHQ18230.npy
-imgHQ19489.npy
-imgHQ22047.npy
-imgHQ18072.npy
-imgHQ19517.npy
-imgHQ23060.npy
-imgHQ12943.npy
-imgHQ26388.npy
-imgHQ13870.npy
-imgHQ29776.npy
-imgHQ25272.npy
-imgHQ12865.npy
-imgHQ08870.npy
-imgHQ23855.npy
-imgHQ07964.npy
-imgHQ03821.npy
-imgHQ25540.npy
-imgHQ06241.npy
-imgHQ14487.npy
-imgHQ12918.npy
-imgHQ14383.npy
-imgHQ08837.npy
-imgHQ22547.npy
-imgHQ07219.npy
-imgHQ10623.npy
-imgHQ25593.npy
-imgHQ25224.npy
-imgHQ27934.npy
-imgHQ19824.npy
-imgHQ26620.npy
-imgHQ02456.npy
-imgHQ19105.npy
-imgHQ13907.npy
-imgHQ06073.npy
-imgHQ15131.npy
-imgHQ23145.npy
-imgHQ23495.npy
-imgHQ27222.npy
-imgHQ23566.npy
-imgHQ27952.npy
-imgHQ25015.npy
-imgHQ21564.npy
-imgHQ09698.npy
-imgHQ19328.npy
-imgHQ13949.npy
-imgHQ23913.npy
-imgHQ01242.npy
-imgHQ26786.npy
-imgHQ06975.npy
-imgHQ25300.npy
-imgHQ21099.npy
-imgHQ20823.npy
-imgHQ11959.npy
-imgHQ11691.npy
-imgHQ08136.npy
-imgHQ12837.npy
-imgHQ24217.npy
-imgHQ04612.npy
-imgHQ05403.npy
-imgHQ24764.npy
-imgHQ26102.npy
-imgHQ25286.npy
-imgHQ16059.npy
-imgHQ00811.npy
-imgHQ08205.npy
-imgHQ02605.npy
-imgHQ11835.npy
-imgHQ14978.npy
-imgHQ29149.npy
-imgHQ15179.npy
-imgHQ23173.npy
-imgHQ28522.npy
-imgHQ19010.npy
-imgHQ11735.npy
-imgHQ00929.npy
-imgHQ00545.npy
-imgHQ13176.npy
-imgHQ01091.npy
-imgHQ01110.npy
-imgHQ10662.npy
-imgHQ03665.npy
-imgHQ27253.npy
-imgHQ05905.npy
-imgHQ01369.npy
-imgHQ25784.npy
-imgHQ16581.npy
-imgHQ15931.npy
-imgHQ16765.npy
-imgHQ03102.npy
-imgHQ19312.npy
-imgHQ25062.npy
-imgHQ04522.npy
-imgHQ29671.npy
-imgHQ15588.npy
-imgHQ11451.npy
-imgHQ12770.npy
-imgHQ23237.npy
-imgHQ20667.npy
-imgHQ02712.npy
-imgHQ18037.npy
-imgHQ13763.npy
-imgHQ10429.npy
-imgHQ19799.npy
-imgHQ16283.npy
-imgHQ26021.npy
-imgHQ03831.npy
-imgHQ16932.npy
-imgHQ28330.npy
-imgHQ28844.npy
-imgHQ01467.npy
-imgHQ17762.npy
-imgHQ03852.npy
-imgHQ27901.npy
-imgHQ07754.npy
-imgHQ06034.npy
-imgHQ18015.npy
-imgHQ15096.npy
-imgHQ12414.npy
-imgHQ08655.npy
-imgHQ11503.npy
-imgHQ23657.npy
-imgHQ21964.npy
-imgHQ07677.npy
-imgHQ09898.npy
-imgHQ15606.npy
-imgHQ19333.npy
-imgHQ03793.npy
-imgHQ05024.npy
-imgHQ25030.npy
-imgHQ00317.npy
-imgHQ05585.npy
-imgHQ28038.npy
-imgHQ20866.npy
-imgHQ28301.npy
-imgHQ15375.npy
-imgHQ03695.npy
-imgHQ11370.npy
-imgHQ28921.npy
-imgHQ20885.npy
-imgHQ06762.npy
-imgHQ09413.npy
-imgHQ01752.npy
-imgHQ23830.npy
-imgHQ26071.npy
-imgHQ09764.npy
-imgHQ19704.npy
-imgHQ22382.npy
-imgHQ13243.npy
-imgHQ14087.npy
-imgHQ08365.npy
-imgHQ09405.npy
-imgHQ17013.npy
-imgHQ26867.npy
-imgHQ02115.npy
-imgHQ03291.npy
-imgHQ05802.npy
-imgHQ24845.npy
-imgHQ07446.npy
-imgHQ26676.npy
-imgHQ27674.npy
-imgHQ06149.npy
-imgHQ27803.npy
-imgHQ18223.npy
-imgHQ13845.npy
-imgHQ28524.npy
-imgHQ10588.npy
-imgHQ09354.npy
-imgHQ05686.npy
-imgHQ00692.npy
-imgHQ13968.npy
-imgHQ16517.npy
-imgHQ08617.npy
-imgHQ02161.npy
-imgHQ08170.npy
-imgHQ04765.npy
-imgHQ07614.npy
-imgHQ12064.npy
-imgHQ20854.npy
-imgHQ27761.npy
-imgHQ11982.npy
-imgHQ21704.npy
-imgHQ25359.npy
-imgHQ07569.npy
-imgHQ21787.npy
-imgHQ07095.npy
-imgHQ27458.npy
-imgHQ17910.npy
-imgHQ22475.npy
-imgHQ02665.npy
-imgHQ06322.npy
-imgHQ07172.npy
-imgHQ24425.npy
-imgHQ04974.npy
-imgHQ24688.npy
-imgHQ20833.npy
-imgHQ21965.npy
-imgHQ08812.npy
-imgHQ22613.npy
-imgHQ11903.npy
-imgHQ19764.npy
-imgHQ04686.npy
-imgHQ11891.npy
-imgHQ07040.npy
-imgHQ02176.npy
-imgHQ03653.npy
-imgHQ01769.npy
-imgHQ27682.npy
-imgHQ00790.npy
-imgHQ28585.npy
-imgHQ12587.npy
-imgHQ03693.npy
-imgHQ16929.npy
-imgHQ00590.npy
-imgHQ06645.npy
-imgHQ19592.npy
-imgHQ28988.npy
-imgHQ14542.npy
-imgHQ27035.npy
-imgHQ00494.npy
-imgHQ27338.npy
-imgHQ05716.npy
-imgHQ21822.npy
-imgHQ20909.npy
-imgHQ04959.npy
-imgHQ03902.npy
-imgHQ27626.npy
-imgHQ28356.npy
-imgHQ06730.npy
-imgHQ23457.npy
-imgHQ22993.npy
-imgHQ29694.npy
-imgHQ04061.npy
-imgHQ26454.npy
-imgHQ29998.npy
-imgHQ25134.npy
-imgHQ19459.npy
-imgHQ06280.npy
-imgHQ23280.npy
-imgHQ20354.npy
-imgHQ23984.npy
-imgHQ11748.npy
-imgHQ07843.npy
-imgHQ21297.npy
-imgHQ04773.npy
-imgHQ18401.npy
-imgHQ01454.npy
-imgHQ17749.npy
-imgHQ29087.npy
-imgHQ05888.npy
-imgHQ26083.npy
-imgHQ21614.npy
-imgHQ21609.npy
-imgHQ05877.npy
-imgHQ20709.npy
-imgHQ07362.npy
-imgHQ03426.npy
-imgHQ19336.npy
-imgHQ18804.npy
-imgHQ25936.npy
-imgHQ07046.npy
-imgHQ10682.npy
-imgHQ09626.npy
-imgHQ10913.npy
-imgHQ14116.npy
-imgHQ19541.npy
-imgHQ26866.npy
-imgHQ25285.npy
-imgHQ10871.npy
-imgHQ04889.npy
-imgHQ10988.npy
-imgHQ01593.npy
-imgHQ09365.npy
-imgHQ04021.npy
-imgHQ04094.npy
-imgHQ16665.npy
-imgHQ01689.npy
-imgHQ26607.npy
-imgHQ17525.npy
-imgHQ05227.npy
-imgHQ22718.npy
-imgHQ19498.npy
-imgHQ19476.npy
-imgHQ25066.npy
-imgHQ14451.npy
-imgHQ16258.npy
-imgHQ12498.npy
-imgHQ15203.npy
-imgHQ27440.npy
-imgHQ18972.npy
-imgHQ22684.npy
-imgHQ26727.npy
-imgHQ04629.npy
-imgHQ26452.npy
-imgHQ28922.npy
-imgHQ08869.npy
-imgHQ14917.npy
-imgHQ12318.npy
-imgHQ21324.npy
-imgHQ03901.npy
-imgHQ01504.npy
-imgHQ13723.npy
-imgHQ14541.npy
-imgHQ28588.npy
-imgHQ18481.npy
-imgHQ11184.npy
-imgHQ24629.npy
-imgHQ03151.npy
-imgHQ25152.npy
-imgHQ27267.npy
-imgHQ06012.npy
-imgHQ03450.npy
-imgHQ25738.npy
-imgHQ14524.npy
-imgHQ16331.npy
-imgHQ03455.npy
-imgHQ06103.npy
-imgHQ20047.npy
-imgHQ06413.npy
-imgHQ28017.npy
-imgHQ10946.npy
-imgHQ11920.npy
-imgHQ11412.npy
-imgHQ11695.npy
-imgHQ02903.npy
-imgHQ25974.npy
-imgHQ04961.npy
-imgHQ08366.npy
-imgHQ15236.npy
-imgHQ01027.npy
-imgHQ27746.npy
-imgHQ24683.npy
-imgHQ10122.npy
-imgHQ10769.npy
-imgHQ26078.npy
-imgHQ16907.npy
-imgHQ24783.npy
-imgHQ13155.npy
-imgHQ23063.npy
-imgHQ03427.npy
-imgHQ29520.npy
-imgHQ04470.npy
-imgHQ28883.npy
-imgHQ06255.npy
-imgHQ10472.npy
-imgHQ21743.npy
-imgHQ10456.npy
-imgHQ25491.npy
-imgHQ28463.npy
-imgHQ28578.npy
-imgHQ04281.npy
-imgHQ23685.npy
-imgHQ26130.npy
-imgHQ22157.npy
-imgHQ20066.npy
-imgHQ06538.npy
-imgHQ24975.npy
-imgHQ10986.npy
-imgHQ02320.npy
-imgHQ24338.npy
-imgHQ10983.npy
-imgHQ06160.npy
-imgHQ21621.npy
-imgHQ05961.npy
-imgHQ09140.npy
-imgHQ24080.npy
-imgHQ08177.npy
-imgHQ05372.npy
-imgHQ07626.npy
-imgHQ25464.npy
-imgHQ29376.npy
-imgHQ15832.npy
-imgHQ20469.npy
-imgHQ18860.npy
-imgHQ27863.npy
-imgHQ05754.npy
-imgHQ13362.npy
-imgHQ20010.npy
-imgHQ06561.npy
-imgHQ11613.npy
-imgHQ26805.npy
-imgHQ27520.npy
-imgHQ15048.npy
-imgHQ22205.npy
-imgHQ09227.npy
-imgHQ20632.npy
-imgHQ17721.npy
-imgHQ13224.npy
-imgHQ03683.npy
-imgHQ05744.npy
-imgHQ07882.npy
-imgHQ08600.npy
-imgHQ20162.npy
-imgHQ14018.npy
-imgHQ19064.npy
-imgHQ20756.npy
-imgHQ29146.npy
-imgHQ18765.npy
-imgHQ18946.npy
-imgHQ15418.npy
-imgHQ15441.npy
-imgHQ19199.npy
-imgHQ04382.npy
-imgHQ29433.npy
-imgHQ19860.npy
-imgHQ26203.npy
-imgHQ18617.npy
-imgHQ29484.npy
-imgHQ14988.npy
-imgHQ27021.npy
-imgHQ27327.npy
-imgHQ19007.npy
-imgHQ10516.npy
-imgHQ00633.npy
-imgHQ06498.npy
-imgHQ11393.npy
-imgHQ09134.npy
-imgHQ25824.npy
-imgHQ14324.npy
-imgHQ10839.npy
-imgHQ06282.npy
-imgHQ21361.npy
-imgHQ07458.npy
-imgHQ22564.npy
-imgHQ25401.npy
-imgHQ16005.npy
-imgHQ28793.npy
-imgHQ13256.npy
-imgHQ27509.npy
-imgHQ15958.npy
-imgHQ18469.npy
-imgHQ01940.npy
-imgHQ16146.npy
-imgHQ27245.npy
-imgHQ10083.npy
-imgHQ20451.npy
-imgHQ10289.npy
-imgHQ22112.npy
-imgHQ04418.npy
-imgHQ19112.npy
-imgHQ04862.npy
-imgHQ14995.npy
-imgHQ19221.npy
-imgHQ10608.npy
-imgHQ25433.npy
-imgHQ29558.npy
-imgHQ10091.npy
-imgHQ20222.npy
-imgHQ04598.npy
-imgHQ07349.npy
-imgHQ21919.npy
-imgHQ06817.npy
-imgHQ25227.npy
-imgHQ03549.npy
-imgHQ27100.npy
-imgHQ29325.npy
-imgHQ12716.npy
-imgHQ05663.npy
-imgHQ01955.npy
-imgHQ10042.npy
-imgHQ04393.npy
-imgHQ11779.npy
-imgHQ25969.npy
-imgHQ10645.npy
-imgHQ19510.npy
-imgHQ12730.npy
-imgHQ26373.npy
-imgHQ03618.npy
-imgHQ11495.npy
-imgHQ15790.npy
-imgHQ10395.npy
-imgHQ14763.npy
-imgHQ25125.npy
-imgHQ14873.npy
-imgHQ12697.npy
-imgHQ13974.npy
-imgHQ18669.npy
-imgHQ01325.npy
-imgHQ16791.npy
-imgHQ21671.npy
-imgHQ21886.npy
-imgHQ20520.npy
-imgHQ11011.npy
-imgHQ00162.npy
-imgHQ08422.npy
-imgHQ04446.npy
-imgHQ19363.npy
-imgHQ22381.npy
-imgHQ06306.npy
-imgHQ19224.npy
-imgHQ04286.npy
-imgHQ10315.npy
-imgHQ13240.npy
-imgHQ02632.npy
-imgHQ05216.npy
-imgHQ08261.npy
-imgHQ24300.npy
-imgHQ11922.npy
-imgHQ27976.npy
-imgHQ05011.npy
-imgHQ00271.npy
-imgHQ20241.npy
-imgHQ05200.npy
-imgHQ20084.npy
-imgHQ25660.npy
-imgHQ10464.npy
-imgHQ00180.npy
-imgHQ22700.npy
-imgHQ00351.npy
-imgHQ24103.npy
-imgHQ13481.npy
-imgHQ24990.npy
-imgHQ02092.npy
-imgHQ11239.npy
-imgHQ05880.npy
-imgHQ01657.npy
-imgHQ23530.npy
-imgHQ17254.npy
-imgHQ12051.npy
-imgHQ16169.npy
-imgHQ11981.npy
-imgHQ25597.npy
-imgHQ13347.npy
-imgHQ15305.npy
-imgHQ28258.npy
-imgHQ05884.npy
-imgHQ05562.npy
-imgHQ19775.npy
-imgHQ16717.npy
-imgHQ13004.npy
-imgHQ28305.npy
-imgHQ11208.npy
-imgHQ03472.npy
-imgHQ05592.npy
-imgHQ03527.npy
-imgHQ06100.npy
-imgHQ11609.npy
-imgHQ04316.npy
-imgHQ02331.npy
-imgHQ15725.npy
-imgHQ27282.npy
-imgHQ17769.npy
-imgHQ04756.npy
-imgHQ12867.npy
-imgHQ13236.npy
-imgHQ08334.npy
-imgHQ09345.npy
-imgHQ03092.npy
-imgHQ28636.npy
-imgHQ14741.npy
-imgHQ27426.npy
-imgHQ03300.npy
-imgHQ20108.npy
-imgHQ15754.npy
-imgHQ06007.npy
-imgHQ21775.npy
-imgHQ24727.npy
-imgHQ00622.npy
-imgHQ12529.npy
-imgHQ16575.npy
-imgHQ20625.npy
-imgHQ27015.npy
-imgHQ12163.npy
-imgHQ08315.npy
-imgHQ27142.npy
-imgHQ06069.npy
-imgHQ02800.npy
-imgHQ05752.npy
-imgHQ08207.npy
-imgHQ14236.npy
-imgHQ07158.npy
-imgHQ14360.npy
-imgHQ19019.npy
-imgHQ05026.npy
-imgHQ02027.npy
-imgHQ28656.npy
-imgHQ25956.npy
-imgHQ02966.npy
-imgHQ22669.npy
-imgHQ19030.npy
-imgHQ23133.npy
-imgHQ01759.npy
-imgHQ17212.npy
-imgHQ07909.npy
-imgHQ17955.npy
-imgHQ18075.npy
-imgHQ00600.npy
-imgHQ19257.npy
-imgHQ00305.npy
-imgHQ15649.npy
-imgHQ16711.npy
-imgHQ01032.npy
-imgHQ09014.npy
-imgHQ19109.npy
-imgHQ00004.npy
-imgHQ19741.npy
-imgHQ11144.npy
-imgHQ09945.npy
-imgHQ03114.npy
-imgHQ13580.npy
-imgHQ15041.npy
-imgHQ17981.npy
-imgHQ28720.npy
-imgHQ22034.npy
-imgHQ12974.npy
-imgHQ28456.npy
-imgHQ13446.npy
-imgHQ10436.npy
-imgHQ04616.npy
-imgHQ25902.npy
-imgHQ29810.npy
-imgHQ06511.npy
-imgHQ01258.npy
-imgHQ10912.npy
-imgHQ02621.npy
-imgHQ21739.npy
-imgHQ14930.npy
-imgHQ28810.npy
-imgHQ10689.npy
-imgHQ05701.npy
-imgHQ15448.npy
-imgHQ03413.npy
-imgHQ05775.npy
-imgHQ02517.npy
-imgHQ12294.npy
-imgHQ10139.npy
-imgHQ21034.npy
-imgHQ07453.npy
-imgHQ10833.npy
-imgHQ27534.npy
-imgHQ03185.npy
-imgHQ19377.npy
-imgHQ05483.npy
-imgHQ06659.npy
-imgHQ23360.npy
-imgHQ21612.npy
-imgHQ09908.npy
-imgHQ07624.npy
-imgHQ03212.npy
-imgHQ16604.npy
-imgHQ26915.npy
-imgHQ27083.npy
-imgHQ26540.npy
-imgHQ21373.npy
-imgHQ19818.npy
-imgHQ20079.npy
-imgHQ21398.npy
-imgHQ04202.npy
-imgHQ10049.npy
-imgHQ19978.npy
-imgHQ14346.npy
-imgHQ05362.npy
-imgHQ02895.npy
-imgHQ28838.npy
-imgHQ01059.npy
-imgHQ08030.npy
-imgHQ27107.npy
-imgHQ19343.npy
-imgHQ01585.npy
-imgHQ08681.npy
-imgHQ09940.npy
-imgHQ04657.npy
-imgHQ14118.npy
-imgHQ15865.npy
-imgHQ22719.npy
-imgHQ14016.npy
-imgHQ29797.npy
-imgHQ15047.npy
-imgHQ09119.npy
-imgHQ15352.npy
-imgHQ00103.npy
-imgHQ29799.npy
-imgHQ03364.npy
-imgHQ03280.npy
-imgHQ21592.npy
-imgHQ24759.npy
-imgHQ05594.npy
-imgHQ02723.npy
-imgHQ18284.npy
-imgHQ19572.npy
-imgHQ06437.npy
-imgHQ19288.npy
-imgHQ20476.npy
-imgHQ14977.npy
-imgHQ26825.npy
-imgHQ26415.npy
-imgHQ10243.npy
-imgHQ02878.npy
-imgHQ02025.npy
-imgHQ14015.npy
-imgHQ18020.npy
-imgHQ23389.npy
-imgHQ28852.npy
-imgHQ22070.npy
-imgHQ07466.npy
-imgHQ21622.npy
-imgHQ12260.npy
-imgHQ14242.npy
-imgHQ15320.npy
-imgHQ00585.npy
-imgHQ19779.npy
-imgHQ25160.npy
-imgHQ29644.npy
-imgHQ13062.npy
-imgHQ13383.npy
-imgHQ29098.npy
-imgHQ02696.npy
-imgHQ24547.npy
-imgHQ29084.npy
-imgHQ03938.npy
-imgHQ01995.npy
-imgHQ16911.npy
-imgHQ26888.npy
-imgHQ23178.npy
-imgHQ25630.npy
-imgHQ22088.npy
-imgHQ15963.npy
-imgHQ20651.npy
-imgHQ11913.npy
-imgHQ06542.npy
-imgHQ19880.npy
-imgHQ09525.npy
-imgHQ16823.npy
-imgHQ09336.npy
-imgHQ07424.npy
-imgHQ21014.npy
-imgHQ15031.npy
-imgHQ13822.npy
-imgHQ25589.npy
-imgHQ06722.npy
-imgHQ19362.npy
-imgHQ07159.npy
-imgHQ20510.npy
-imgHQ12299.npy
-imgHQ25862.npy
-imgHQ14159.npy
-imgHQ04618.npy
-imgHQ08231.npy
-imgHQ12222.npy
-imgHQ28613.npy
-imgHQ17865.npy
-imgHQ05720.npy
-imgHQ08868.npy
-imgHQ16260.npy
-imgHQ20968.npy
-imgHQ02614.npy
-imgHQ28861.npy
-imgHQ09634.npy
-imgHQ11705.npy
-imgHQ13409.npy
-imgHQ07813.npy
-imgHQ27437.npy
-imgHQ28185.npy
-imgHQ23640.npy
-imgHQ11402.npy
-imgHQ25791.npy
-imgHQ14760.npy
-imgHQ14812.npy
-imgHQ27732.npy
-imgHQ20155.npy
-imgHQ26004.npy
-imgHQ20914.npy
-imgHQ26530.npy
-imgHQ23732.npy
-imgHQ02893.npy
-imgHQ16285.npy
-imgHQ04945.npy
-imgHQ22857.npy
-imgHQ05010.npy
-imgHQ13059.npy
-imgHQ24583.npy
-imgHQ04251.npy
-imgHQ04973.npy
-imgHQ16725.npy
-imgHQ03746.npy
-imgHQ08397.npy
-imgHQ24557.npy
-imgHQ23168.npy
-imgHQ15776.npy
-imgHQ27706.npy
-imgHQ19117.npy
-imgHQ28348.npy
-imgHQ14313.npy
-imgHQ23990.npy
-imgHQ02367.npy
-imgHQ26154.npy
-imgHQ05137.npy
-imgHQ23619.npy
-imgHQ07084.npy
-imgHQ12329.npy
-imgHQ15143.npy
-imgHQ29868.npy
-imgHQ05807.npy
-imgHQ07564.npy
-imgHQ17638.npy
-imgHQ03720.npy
-imgHQ00698.npy
-imgHQ10998.npy
-imgHQ19121.npy
-imgHQ13211.npy
-imgHQ03788.npy
-imgHQ17891.npy
-imgHQ00994.npy
-imgHQ27502.npy
-imgHQ24305.npy
-imgHQ15535.npy
-imgHQ21776.npy
-imgHQ15972.npy
-imgHQ08754.npy
-imgHQ09907.npy
-imgHQ22611.npy
-imgHQ22459.npy
-imgHQ14549.npy
-imgHQ07954.npy
-imgHQ15546.npy
-imgHQ02067.npy
-imgHQ22584.npy
-imgHQ20407.npy
-imgHQ08373.npy
-imgHQ21861.npy
-imgHQ15551.npy
-imgHQ27394.npy
-imgHQ13609.npy
-imgHQ15834.npy
-imgHQ21409.npy
-imgHQ22486.npy
-imgHQ25759.npy
-imgHQ22071.npy
-imgHQ16895.npy
-imgHQ26625.npy
-imgHQ11224.npy
-imgHQ01141.npy
-imgHQ11582.npy
-imgHQ06667.npy
-imgHQ26722.npy
-imgHQ12694.npy
-imgHQ05947.npy
-imgHQ04031.npy
-imgHQ04860.npy
-imgHQ14331.npy
-imgHQ24372.npy
-imgHQ02883.npy
-imgHQ26120.npy
-imgHQ22636.npy
-imgHQ13759.npy
-imgHQ12765.npy
-imgHQ18890.npy
-imgHQ26152.npy
-imgHQ18585.npy
-imgHQ18265.npy
-imgHQ20687.npy
-imgHQ17956.npy
-imgHQ22175.npy
-imgHQ02279.npy
-imgHQ26965.npy
-imgHQ21814.npy
-imgHQ18297.npy
-imgHQ06726.npy
-imgHQ22511.npy
-imgHQ12873.npy
-imgHQ16793.npy
-imgHQ20120.npy
-imgHQ01298.npy
-imgHQ06788.npy
-imgHQ24288.npy
-imgHQ22512.npy
-imgHQ08978.npy
-imgHQ12215.npy
-imgHQ29457.npy
-imgHQ16980.npy
-imgHQ08240.npy
-imgHQ20606.npy
-imgHQ03529.npy
-imgHQ22560.npy
-imgHQ12265.npy
-imgHQ17331.npy
-imgHQ29507.npy
-imgHQ06644.npy
-imgHQ22433.npy
-imgHQ01378.npy
-imgHQ29184.npy
-imgHQ17356.npy
-imgHQ06887.npy
-imgHQ06520.npy
-imgHQ06508.npy
-imgHQ15807.npy
-imgHQ04417.npy
-imgHQ11805.npy
-imgHQ13825.npy
-imgHQ10285.npy
-imgHQ01925.npy
-imgHQ14361.npy
-imgHQ04969.npy
-imgHQ11985.npy
-imgHQ07306.npy
-imgHQ21672.npy
-imgHQ17260.npy
-imgHQ19168.npy
-imgHQ10112.npy
-imgHQ10105.npy
-imgHQ11418.npy
-imgHQ04456.npy
-imgHQ06424.npy
-imgHQ21790.npy
-imgHQ21096.npy
-imgHQ11241.npy
-imgHQ02980.npy
-imgHQ22144.npy
-imgHQ11245.npy
-imgHQ03892.npy
-imgHQ28914.npy
-imgHQ00133.npy
-imgHQ13705.npy
-imgHQ14657.npy
-imgHQ23852.npy
-imgHQ06231.npy
-imgHQ18859.npy
-imgHQ03052.npy
-imgHQ08742.npy
-imgHQ02925.npy
-imgHQ21669.npy
-imgHQ02942.npy
-imgHQ28117.npy
-imgHQ11339.npy
-imgHQ17389.npy
-imgHQ27883.npy
-imgHQ06223.npy
-imgHQ11843.npy
-imgHQ17239.npy
-imgHQ22908.npy
-imgHQ20566.npy
-imgHQ22882.npy
-imgHQ15991.npy
-imgHQ12391.npy
-imgHQ06271.npy
-imgHQ12819.npy
-imgHQ27235.npy
-imgHQ00952.npy
-imgHQ11862.npy
-imgHQ23025.npy
-imgHQ13573.npy
-imgHQ01782.npy
-imgHQ16232.npy
-imgHQ28526.npy
-imgHQ01937.npy
-imgHQ21859.npy
-imgHQ10690.npy
-imgHQ06965.npy
-imgHQ25336.npy
-imgHQ25420.npy
-imgHQ24639.npy
-imgHQ27903.npy
-imgHQ08705.npy
-imgHQ06700.npy
-imgHQ19709.npy
-imgHQ19514.npy
-imgHQ00672.npy
-imgHQ00056.npy
-imgHQ22344.npy
-imgHQ18385.npy
-imgHQ28241.npy
-imgHQ26316.npy
-imgHQ20129.npy
-imgHQ09337.npy
-imgHQ13850.npy
-imgHQ24568.npy
-imgHQ21997.npy
-imgHQ06182.npy
-imgHQ25686.npy
-imgHQ18794.npy
-imgHQ19209.npy
-imgHQ16796.npy
-imgHQ18308.npy
-imgHQ28107.npy
-imgHQ16086.npy
-imgHQ08939.npy
-imgHQ24800.npy
-imgHQ20813.npy
-imgHQ08104.npy
-imgHQ25794.npy
-imgHQ12154.npy
-imgHQ28297.npy
-imgHQ19889.npy
-imgHQ00936.npy
-imgHQ05793.npy
-imgHQ28682.npy
-imgHQ25757.npy
-imgHQ03022.npy
-imgHQ21250.npy
-imgHQ18908.npy
-imgHQ23117.npy
-imgHQ23970.npy
-imgHQ08266.npy
-imgHQ03320.npy
-imgHQ27989.npy
-imgHQ14889.npy
-imgHQ16050.npy
-imgHQ29284.npy
-imgHQ21233.npy
-imgHQ20062.npy
-imgHQ12179.npy
-imgHQ26457.npy
-imgHQ15435.npy
-imgHQ16223.npy
-imgHQ06564.npy
-imgHQ17316.npy
-imgHQ08463.npy
-imgHQ00616.npy
-imgHQ25003.npy
-imgHQ25858.npy
-imgHQ16589.npy
-imgHQ21109.npy
-imgHQ13550.npy
-imgHQ09078.npy
-imgHQ16226.npy
-imgHQ08500.npy
-imgHQ28945.npy
-imgHQ25550.npy
-imgHQ27617.npy
-imgHQ17988.npy
-imgHQ28290.npy
-imgHQ20732.npy
-imgHQ22018.npy
-imgHQ05971.npy
-imgHQ16913.npy
-imgHQ20771.npy
-imgHQ25236.npy
-imgHQ05325.npy
-imgHQ25836.npy
-imgHQ16187.npy
-imgHQ04999.npy
-imgHQ05630.npy
-imgHQ19562.npy
-imgHQ09700.npy
-imgHQ04649.npy
-imgHQ21240.npy
-imgHQ08274.npy
-imgHQ07672.npy
-imgHQ11594.npy
-imgHQ11669.npy
-imgHQ03355.npy
-imgHQ11780.npy
-imgHQ29933.npy
-imgHQ23756.npy
-imgHQ11062.npy
-imgHQ18073.npy
-imgHQ23331.npy
-imgHQ17187.npy
-imgHQ02852.npy
-imgHQ08384.npy
-imgHQ23929.npy
-imgHQ15977.npy
-imgHQ25693.npy
-imgHQ29166.npy
-imgHQ09740.npy
-imgHQ09423.npy
-imgHQ17337.npy
-imgHQ26453.npy
-imgHQ26998.npy
-imgHQ15943.npy
-imgHQ15184.npy
-imgHQ22724.npy
-imgHQ04069.npy
-imgHQ11957.npy
-imgHQ14614.npy
-imgHQ26679.npy
-imgHQ04967.npy
-imgHQ17961.npy
-imgHQ12158.npy
-imgHQ19525.npy
-imgHQ09782.npy
-imgHQ03308.npy
-imgHQ12558.npy
-imgHQ29306.npy
-imgHQ08027.npy
-imgHQ20159.npy
-imgHQ28009.npy
-imgHQ00354.npy
-imgHQ10128.npy
-imgHQ10633.npy
-imgHQ26631.npy
-imgHQ12872.npy
-imgHQ18419.npy
-imgHQ09328.npy
-imgHQ29893.npy
-imgHQ23880.npy
-imgHQ26351.npy
-imgHQ24965.npy
-imgHQ13771.npy
-imgHQ04427.npy
-imgHQ01311.npy
-imgHQ22901.npy
-imgHQ18501.npy
-imgHQ07213.npy
-imgHQ24165.npy
-imgHQ06442.npy
-imgHQ10826.npy
-imgHQ11759.npy
-imgHQ07631.npy
-imgHQ10075.npy
-imgHQ29937.npy
-imgHQ23820.npy
-imgHQ03828.npy
-imgHQ04637.npy
-imgHQ00229.npy
-imgHQ18862.npy
-imgHQ06472.npy
-imgHQ05486.npy
-imgHQ04420.npy
-imgHQ15194.npy
-imgHQ18533.npy
-imgHQ19793.npy
-imgHQ00644.npy
-imgHQ07662.npy
-imgHQ18327.npy
-imgHQ29874.npy
-imgHQ21746.npy
-imgHQ14739.npy
-imgHQ24328.npy
-imgHQ24358.npy
-imgHQ09727.npy
-imgHQ06812.npy
-imgHQ09697.npy
-imgHQ11699.npy
-imgHQ01820.npy
-imgHQ26168.npy
-imgHQ01200.npy
-imgHQ20460.npy
-imgHQ07102.npy
-imgHQ27604.npy
-imgHQ02377.npy
-imgHQ18100.npy
-imgHQ28146.npy
-imgHQ29876.npy
-imgHQ22706.npy
-imgHQ20299.npy
-imgHQ15905.npy
-imgHQ06230.npy
-imgHQ03131.npy
-imgHQ03323.npy
-imgHQ13226.npy
-imgHQ02796.npy
-imgHQ29986.npy
-imgHQ24202.npy
-imgHQ16058.npy
-imgHQ28298.npy
-imgHQ22334.npy
-imgHQ05820.npy
-imgHQ25208.npy
-imgHQ08210.npy
-imgHQ03229.npy
-imgHQ20280.npy
-imgHQ28646.npy
-imgHQ23027.npy
-imgHQ29068.npy
-imgHQ02659.npy
-imgHQ27316.npy
-imgHQ11271.npy
-imgHQ00689.npy
-imgHQ08415.npy
-imgHQ24587.npy
-imgHQ21183.npy
-imgHQ19725.npy
-imgHQ02095.npy
-imgHQ21159.npy
-imgHQ21841.npy
-imgHQ19008.npy
-imgHQ08624.npy
-imgHQ23472.npy
-imgHQ01359.npy
-imgHQ01928.npy
-imgHQ02189.npy
-imgHQ23639.npy
-imgHQ15578.npy
-imgHQ05261.npy
-imgHQ03076.npy
-imgHQ01966.npy
-imgHQ22429.npy
-imgHQ20137.npy
-imgHQ16943.npy
-imgHQ06429.npy
-imgHQ13878.npy
-imgHQ07854.npy
-imgHQ08131.npy
-imgHQ21872.npy
-imgHQ07180.npy
-imgHQ09630.npy
-imgHQ09537.npy
-imgHQ04832.npy
-imgHQ09138.npy
-imgHQ19585.npy
-imgHQ10022.npy
-imgHQ10654.npy
-imgHQ11309.npy
-imgHQ23015.npy
-imgHQ26315.npy
-imgHQ04140.npy
-imgHQ27710.npy
-imgHQ06502.npy
-imgHQ26465.npy
-imgHQ21269.npy
-imgHQ24741.npy
-imgHQ23396.npy
-imgHQ12945.npy
-imgHQ13670.npy
-imgHQ12351.npy
-imgHQ08297.npy
-imgHQ17203.npy
-imgHQ10161.npy
-imgHQ15480.npy
-imgHQ22604.npy
-imgHQ08467.npy
-imgHQ26400.npy
-imgHQ28895.npy
-imgHQ15520.npy
-imgHQ02119.npy
-imgHQ16904.npy
-imgHQ24107.npy
-imgHQ22343.npy
-imgHQ21263.npy
-imgHQ13630.npy
-imgHQ00881.npy
-imgHQ01155.npy
-imgHQ25053.npy
-imgHQ24826.npy
-imgHQ24599.npy
-imgHQ07950.npy
-imgHQ07359.npy
-imgHQ08850.npy
-imgHQ12973.npy
-imgHQ12480.npy
-imgHQ29985.npy
-imgHQ16954.npy
-imgHQ29289.npy
-imgHQ16739.npy
-imgHQ13935.npy
-imgHQ07473.npy
-imgHQ29213.npy
-imgHQ02904.npy
-imgHQ16106.npy
-imgHQ12098.npy
-imgHQ02420.npy
-imgHQ02373.npy
-imgHQ18730.npy
-imgHQ05096.npy
-imgHQ18984.npy
-imgHQ12193.npy
-imgHQ16820.npy
-imgHQ12550.npy
-imgHQ21721.npy
-imgHQ09573.npy
-imgHQ08610.npy
-imgHQ13905.npy
-imgHQ08024.npy
-imgHQ07725.npy
-imgHQ21288.npy
-imgHQ03297.npy
-imgHQ06624.npy
-imgHQ16972.npy
-imgHQ26544.npy
-imgHQ01944.npy
-imgHQ13131.npy
-imgHQ28661.npy
-imgHQ13203.npy
-imgHQ12852.npy
-imgHQ00573.npy
-imgHQ04529.npy
-imgHQ04184.npy
-imgHQ19246.npy
-imgHQ05106.npy
-imgHQ23969.npy
-imgHQ01756.npy
-imgHQ13537.npy
-imgHQ07105.npy
-imgHQ25882.npy
-imgHQ11983.npy
-imgHQ26031.npy
-imgHQ22287.npy
-imgHQ16035.npy
-imgHQ00142.npy
-imgHQ21312.npy
-imgHQ22725.npy
-imgHQ08173.npy
-imgHQ09644.npy
-imgHQ15498.npy
-imgHQ24097.npy
-imgHQ23700.npy
-imgHQ05704.npy
-imgHQ15610.npy
-imgHQ09213.npy
-imgHQ13057.npy
-imgHQ11083.npy
-imgHQ22368.npy
-imgHQ07607.npy
-imgHQ10439.npy
-imgHQ10277.npy
-imgHQ00394.npy
-imgHQ23617.npy
-imgHQ12585.npy
-imgHQ03805.npy
-imgHQ16624.npy
-imgHQ22272.npy
-imgHQ19127.npy
-imgHQ25648.npy
-imgHQ21446.npy
-imgHQ15381.npy
-imgHQ27739.npy
-imgHQ21771.npy
-imgHQ10505.npy
-imgHQ08237.npy
-imgHQ11120.npy
-imgHQ14722.npy
-imgHQ00256.npy
-imgHQ09489.npy
-imgHQ17637.npy
-imgHQ08542.npy
-imgHQ12800.npy
-imgHQ15062.npy
-imgHQ24009.npy
-imgHQ03038.npy
-imgHQ26578.npy
-imgHQ04954.npy
-imgHQ05182.npy
-imgHQ15990.npy
-imgHQ25041.npy
-imgHQ23878.npy
-imgHQ16554.npy
-imgHQ02793.npy
-imgHQ28956.npy
-imgHQ29137.npy
-imgHQ02923.npy
-imgHQ14335.npy
-imgHQ13047.npy
-imgHQ06758.npy
-imgHQ10032.npy
-imgHQ11631.npy
-imgHQ02664.npy
-imgHQ20114.npy
-imgHQ11363.npy
-imgHQ01393.npy
-imgHQ10561.npy
-imgHQ04533.npy
-imgHQ10931.npy
-imgHQ25409.npy
-imgHQ14079.npy
-imgHQ04002.npy
-imgHQ23679.npy
-imgHQ12155.npy
-imgHQ01089.npy
-imgHQ10046.npy
-imgHQ17275.npy
-imgHQ01125.npy
-imgHQ20644.npy
-imgHQ23424.npy
-imgHQ11068.npy
-imgHQ27845.npy
-imgHQ16401.npy
-imgHQ18746.npy
-imgHQ00521.npy
-imgHQ00827.npy
-imgHQ06181.npy
-imgHQ29836.npy
-imgHQ01203.npy
-imgHQ28904.npy
-imgHQ03994.npy
-imgHQ15853.npy
-imgHQ14684.npy
-imgHQ02222.npy
-imgHQ09709.npy
-imgHQ00309.npy
-imgHQ07254.npy
-imgHQ00750.npy
-imgHQ12795.npy
-imgHQ19136.npy
-imgHQ08710.npy
-imgHQ25487.npy
-imgHQ11466.npy
-imgHQ18936.npy
-imgHQ22634.npy
-imgHQ00950.npy
-imgHQ09231.npy
-imgHQ18650.npy
-imgHQ07780.npy
-imgHQ25327.npy
-imgHQ13486.npy
-imgHQ02516.npy
-imgHQ24602.npy
-imgHQ13739.npy
-imgHQ03340.npy
-imgHQ19748.npy
-imgHQ23861.npy
-imgHQ00961.npy
-imgHQ18365.npy
-imgHQ10702.npy
-imgHQ08825.npy
-imgHQ08333.npy
-imgHQ05098.npy
-imgHQ18582.npy
-imgHQ28798.npy
-imgHQ10382.npy
-imgHQ22178.npy
-imgHQ13811.npy
-imgHQ02390.npy
-imgHQ25889.npy
-imgHQ29480.npy
-imgHQ03799.npy
-imgHQ09346.npy
-imgHQ01777.npy
-imgHQ20214.npy
-imgHQ29945.npy
-imgHQ09829.npy
-imgHQ21286.npy
-imgHQ15894.npy
-imgHQ02355.npy
-imgHQ21278.npy
-imgHQ00905.npy
-imgHQ09209.npy
-imgHQ23937.npy
-imgHQ21142.npy
-imgHQ12916.npy
-imgHQ02839.npy
-imgHQ22404.npy
-imgHQ23140.npy
-imgHQ07346.npy
-imgHQ11281.npy
-imgHQ28500.npy
-imgHQ18720.npy
-imgHQ02981.npy
-imgHQ21357.npy
-imgHQ19408.npy
-imgHQ12305.npy
-imgHQ17994.npy
-imgHQ08286.npy
-imgHQ10861.npy
-imgHQ02907.npy
-imgHQ21094.npy
-imgHQ20894.npy
-imgHQ12755.npy
-imgHQ10700.npy
-imgHQ00460.npy
-imgHQ11781.npy
-imgHQ05714.npy
-imgHQ26305.npy
-imgHQ08609.npy
-imgHQ13213.npy
-imgHQ27766.npy
-imgHQ03648.npy
-imgHQ24686.npy
-imgHQ17780.npy
-imgHQ29530.npy
-imgHQ00766.npy
-imgHQ25605.npy
-imgHQ26641.npy
-imgHQ21722.npy
-imgHQ00239.npy
-imgHQ02701.npy
-imgHQ18069.npy
-imgHQ03296.npy
-imgHQ26065.npy
-imgHQ22807.npy
-imgHQ27187.npy
-imgHQ15569.npy
-imgHQ18286.npy
-imgHQ00864.npy
-imgHQ16388.npy
-imgHQ10163.npy
-imgHQ26661.npy
-imgHQ13081.npy
-imgHQ05606.npy
-imgHQ05545.npy
-imgHQ10200.npy
-imgHQ12170.npy
-imgHQ15069.npy
-imgHQ05009.npy
-imgHQ20817.npy
-imgHQ28447.npy
-imgHQ28503.npy
-imgHQ10327.npy
-imgHQ19095.npy
-imgHQ12442.npy
-imgHQ14997.npy
-imgHQ07477.npy
-imgHQ01017.npy
-imgHQ00018.npy
-imgHQ24953.npy
-imgHQ27858.npy
-imgHQ03458.npy
-imgHQ10381.npy
-imgHQ09380.npy
-imgHQ01145.npy
-imgHQ05632.npy
-imgHQ14882.npy
-imgHQ28640.npy
-imgHQ05401.npy
-imgHQ27322.npy
-imgHQ16622.npy
-imgHQ07403.npy
-imgHQ28133.npy
-imgHQ21626.npy
-imgHQ22890.npy
-imgHQ07308.npy
-imgHQ02226.npy
-imgHQ03250.npy
-imgHQ04663.npy
-imgHQ18449.npy
-imgHQ21637.npy
-imgHQ26579.npy
-imgHQ09290.npy
-imgHQ05879.npy
-imgHQ28976.npy
-imgHQ18157.npy
-imgHQ10868.npy
-imgHQ17505.npy
-imgHQ20281.npy
-imgHQ02050.npy
-imgHQ00528.npy
-imgHQ02191.npy
-imgHQ22870.npy
-imgHQ14350.npy
-imgHQ04351.npy
-imgHQ00285.npy
-imgHQ28321.npy
-imgHQ09914.npy
-imgHQ13017.npy
-imgHQ24278.npy
-imgHQ14747.npy
-imgHQ28384.npy
-imgHQ16502.npy
-imgHQ09253.npy
-imgHQ24055.npy
-imgHQ12482.npy
-imgHQ04378.npy
-imgHQ24822.npy
-imgHQ11581.npy
-imgHQ12382.npy
-imgHQ13679.npy
-imgHQ03689.npy
-imgHQ06737.npy
-imgHQ16748.npy
-imgHQ13944.npy
-imgHQ27613.npy
-imgHQ19599.npy
-imgHQ21821.npy
-imgHQ05737.npy
-imgHQ29417.npy
-imgHQ27208.npy
-imgHQ06199.npy
-imgHQ20369.npy
-imgHQ10284.npy
-imgHQ08163.npy
-imgHQ27111.npy
-imgHQ28882.npy
-imgHQ26342.npy
-imgHQ06514.npy
-imgHQ11948.npy
-imgHQ14902.npy
-imgHQ10542.npy
-imgHQ11731.npy
-imgHQ20484.npy
-imgHQ20812.npy
-imgHQ02783.npy
-imgHQ19016.npy
-imgHQ01007.npy
-imgHQ29485.npy
-imgHQ24326.npy
-imgHQ08180.npy
-imgHQ20444.npy
-imgHQ18187.npy
-imgHQ11789.npy
-imgHQ26912.npy
-imgHQ19200.npy
-imgHQ09256.npy
-imgHQ11089.npy
-imgHQ14186.npy
-imgHQ16375.npy
-imgHQ03310.npy
-imgHQ27590.npy
-imgHQ05309.npy
-imgHQ00641.npy
-imgHQ21314.npy
-imgHQ15462.npy
-imgHQ25621.npy
-imgHQ18353.npy
-imgHQ16500.npy
-imgHQ29942.npy
-imgHQ12022.npy
-imgHQ00560.npy
-imgHQ26746.npy
-imgHQ25508.npy
-imgHQ00565.npy
-imgHQ29399.npy
-imgHQ29187.npy
-imgHQ18973.npy
-imgHQ19733.npy
-imgHQ14132.npy
-imgHQ26816.npy
-imgHQ12823.npy
-imgHQ05506.npy
-imgHQ22576.npy
-imgHQ03668.npy
-imgHQ22515.npy
-imgHQ27656.npy
-imgHQ14839.npy
-imgHQ25767.npy
-imgHQ05083.npy
-imgHQ21354.npy
-imgHQ20993.npy
-imgHQ29550.npy
-imgHQ03740.npy
-imgHQ17925.npy
-imgHQ26472.npy
-imgHQ12026.npy
-imgHQ09861.npy
-imgHQ16716.npy
-imgHQ05037.npy
-imgHQ12355.npy
-imgHQ12472.npy
-imgHQ17441.npy
-imgHQ16108.npy
-imgHQ26196.npy
-imgHQ04419.npy
-imgHQ22234.npy
-imgHQ11820.npy
-imgHQ03408.npy
-imgHQ26736.npy
-imgHQ16493.npy
-imgHQ04283.npy
-imgHQ22307.npy
-imgHQ16949.npy
-imgHQ27687.npy
-imgHQ29583.npy
-imgHQ20792.npy
-imgHQ05881.npy
-imgHQ19081.npy
-imgHQ28205.npy
-imgHQ21837.npy
-imgHQ25613.npy
-imgHQ21406.npy
-imgHQ09990.npy
-imgHQ28141.npy
-imgHQ07991.npy
-imgHQ22117.npy
-imgHQ02286.npy
-imgHQ14322.npy
-imgHQ04833.npy
-imgHQ23731.npy
-imgHQ00254.npy
-imgHQ25926.npy
-imgHQ00067.npy
-imgHQ02967.npy
-imgHQ16240.npy
-imgHQ03015.npy
-imgHQ08853.npy
-imgHQ26638.npy
-imgHQ09747.npy
-imgHQ28857.npy
-imgHQ22265.npy
-imgHQ18292.npy
-imgHQ07738.npy
-imgHQ04460.npy
-imgHQ25898.npy
-imgHQ13318.npy
-imgHQ03020.npy
-imgHQ16028.npy
-imgHQ00725.npy
-imgHQ20442.npy
-imgHQ03515.npy
-imgHQ10884.npy
-imgHQ10488.npy
-imgHQ17191.npy
-imgHQ17805.npy
-imgHQ20089.npy
-imgHQ22926.npy
-imgHQ12781.npy
-imgHQ14059.npy
-imgHQ14782.npy
-imgHQ18263.npy
-imgHQ25805.npy
-imgHQ21441.npy
-imgHQ29755.npy
-imgHQ13035.npy
-imgHQ23985.npy
-imgHQ28158.npy
-imgHQ24829.npy
-imgHQ08607.npy
-imgHQ08405.npy
-imgHQ02835.npy
-imgHQ24725.npy
-imgHQ07117.npy
-imgHQ13892.npy
-imgHQ03403.npy
-imgHQ26140.npy
-imgHQ03139.npy
-imgHQ17206.npy
-imgHQ24873.npy
-imgHQ05022.npy
-imgHQ00998.npy
-imgHQ21710.npy
-imgHQ00592.npy
-imgHQ01652.npy
-imgHQ01942.npy
-imgHQ22998.npy
-imgHQ18362.npy
-imgHQ01754.npy
-imgHQ08914.npy
-imgHQ05848.npy
-imgHQ29617.npy
-imgHQ25448.npy
-imgHQ06247.npy
-imgHQ04088.npy
-imgHQ00068.npy
-imgHQ08723.npy
-imgHQ23049.npy
-imgHQ28278.npy
-imgHQ06212.npy
-imgHQ16549.npy
-imgHQ04421.npy
-imgHQ14949.npy
-imgHQ01960.npy
-imgHQ04886.npy
-imgHQ22230.npy
-imgHQ24918.npy
-imgHQ12489.npy
-imgHQ06599.npy
-imgHQ01143.npy
-imgHQ20541.npy
-imgHQ19993.npy
-imgHQ22267.npy
-imgHQ26512.npy
-imgHQ07070.npy
-imgHQ02216.npy
-imgHQ08807.npy
-imgHQ23039.npy
-imgHQ15650.npy
-imgHQ16545.npy
-imgHQ26723.npy
-imgHQ22277.npy
-imgHQ27549.npy
-imgHQ00556.npy
-imgHQ07641.npy
-imgHQ16155.npy
-imgHQ28323.npy
-imgHQ19172.npy
-imgHQ15925.npy
-imgHQ21941.npy
-imgHQ19306.npy
-imgHQ25574.npy
-imgHQ22682.npy
-imgHQ16925.npy
-imgHQ01911.npy
-imgHQ13629.npy
-imgHQ14290.npy
-imgHQ19721.npy
-imgHQ18216.npy
-imgHQ15607.npy
-imgHQ01812.npy
-imgHQ27653.npy
-imgHQ06240.npy
-imgHQ10031.npy
-imgHQ24821.npy
-imgHQ28249.npy
-imgHQ15105.npy
-imgHQ04651.npy
-imgHQ24855.npy
-imgHQ19131.npy
-imgHQ27052.npy
-imgHQ22566.npy
-imgHQ03265.npy
-imgHQ11689.npy
-imgHQ05815.npy
-imgHQ29503.npy
-imgHQ24274.npy
-imgHQ25552.npy
-imgHQ07327.npy
-imgHQ09462.npy
-imgHQ10762.npy
-imgHQ18262.npy
-imgHQ20714.npy
-imgHQ29086.npy
-imgHQ02307.npy
-imgHQ19441.npy
-imgHQ26557.npy
-imgHQ08513.npy
-imgHQ13485.npy
-imgHQ09041.npy
-imgHQ21234.npy
-imgHQ09810.npy
-imgHQ07824.npy
-imgHQ10528.npy
-imgHQ10611.npy
-imgHQ22540.npy
-imgHQ27096.npy
-imgHQ02505.npy
-imgHQ28469.npy
-imgHQ21484.npy
-imgHQ29231.npy
-imgHQ07764.npy
-imgHQ25694.npy
-imgHQ17944.npy
-imgHQ15728.npy
-imgHQ12563.npy
-imgHQ15487.npy
-imgHQ15155.npy
-imgHQ07751.npy
-imgHQ26116.npy
-imgHQ01426.npy
-imgHQ17308.npy
-imgHQ14931.npy
-imgHQ00327.npy
-imgHQ04240.npy
-imgHQ04230.npy
-imgHQ01862.npy
-imgHQ17197.npy
-imgHQ16519.npy
-imgHQ17890.npy
-imgHQ14284.npy
-imgHQ12509.npy
-imgHQ02856.npy
-imgHQ22800.npy
-imgHQ21496.npy
-imgHQ25606.npy
-imgHQ27943.npy
-imgHQ09579.npy
-imgHQ28693.npy
-imgHQ03311.npy
-imgHQ09868.npy
-imgHQ27321.npy
-imgHQ15563.npy
-imgHQ11439.npy
-imgHQ28109.npy
-imgHQ07874.npy
-imgHQ18849.npy
-imgHQ29632.npy
-imgHQ10366.npy
-imgHQ23938.npy
-imgHQ01222.npy
-imgHQ04802.npy
-imgHQ27309.npy
-imgHQ07960.npy
-imgHQ11660.npy
-imgHQ25986.npy
-imgHQ19695.npy
-imgHQ15464.npy
-imgHQ21108.npy
-imgHQ09410.npy
-imgHQ08906.npy
-imgHQ16988.npy
-imgHQ15969.npy
-imgHQ21519.npy
-imgHQ09623.npy
-imgHQ03315.npy
-imgHQ00365.npy
-imgHQ05553.npy
-imgHQ12844.npy
-imgHQ06912.npy
-imgHQ12915.npy
-imgHQ10834.npy
-imgHQ06960.npy
-imgHQ28839.npy
-imgHQ19655.npy
-imgHQ00465.npy
-imgHQ24659.npy
-imgHQ03837.npy
-imgHQ12466.npy
-imgHQ14976.npy
-imgHQ07645.npy
-imgHQ15835.npy
-imgHQ27510.npy
-imgHQ19616.npy
-imgHQ16088.npy
-imgHQ10870.npy
-imgHQ25311.npy
-imgHQ00486.npy
-imgHQ29724.npy
-imgHQ18169.npy
-imgHQ13494.npy
-imgHQ17703.npy
-imgHQ21342.npy
-imgHQ00462.npy
-imgHQ22647.npy
-imgHQ02302.npy
-imgHQ08019.npy
-imgHQ27594.npy
-imgHQ12078.npy
-imgHQ05573.npy
-imgHQ10062.npy
-imgHQ05158.npy
-imgHQ00526.npy
-imgHQ11513.npy
-imgHQ11703.npy
-imgHQ04865.npy
-imgHQ06235.npy
-imgHQ15008.npy
-imgHQ08943.npy
-imgHQ21758.npy
-imgHQ23098.npy
-imgHQ29113.npy
-imgHQ18511.npy
-imgHQ26645.npy
-imgHQ10002.npy
-imgHQ13951.npy
-imgHQ24778.npy
-imgHQ12240.npy
-imgHQ08790.npy
-imgHQ01631.npy
-imgHQ19944.npy
-imgHQ11918.npy
-imgHQ24598.npy
-imgHQ17338.npy
-imgHQ20597.npy
-imgHQ20788.npy
-imgHQ03955.npy
-imgHQ25846.npy
-imgHQ16958.npy
-imgHQ20683.npy
-imgHQ08773.npy
-imgHQ01385.npy
-imgHQ13637.npy
-imgHQ13743.npy
-imgHQ21054.npy
-imgHQ08372.npy
-imgHQ19317.npy
-imgHQ04960.npy
-imgHQ10990.npy
-imgHQ22583.npy
-imgHQ20370.npy
-imgHQ05692.npy
-imgHQ25363.npy
-imgHQ13839.npy
-imgHQ13170.npy
-imgHQ04258.npy
-imgHQ20585.npy
-imgHQ01451.npy
-imgHQ23253.npy
-imgHQ20849.npy
-imgHQ19937.npy
-imgHQ03940.npy
-imgHQ16776.npy
-imgHQ23336.npy
-imgHQ07230.npy
-imgHQ28968.npy
-imgHQ18378.npy
-imgHQ17640.npy
-imgHQ27265.npy
-imgHQ09376.npy
-imgHQ29787.npy
-imgHQ18258.npy
-imgHQ29245.npy
-imgHQ26987.npy
-imgHQ00854.npy
-imgHQ26773.npy
-imgHQ03941.npy
-imgHQ21963.npy
-imgHQ02010.npy
-imgHQ07571.npy
-imgHQ18508.npy
-imgHQ28118.npy
-imgHQ01912.npy
-imgHQ12722.npy
-imgHQ13626.npy
-imgHQ11734.npy
-imgHQ00969.npy
-imgHQ18754.npy
-imgHQ26611.npy
-imgHQ20185.npy
-imgHQ20327.npy
-imgHQ23754.npy
-imgHQ14506.npy
-imgHQ02132.npy
-imgHQ27406.npy
-imgHQ00569.npy
-imgHQ15477.npy
-imgHQ18867.npy
-imgHQ29919.npy
-imgHQ24448.npy
-imgHQ02138.npy
-imgHQ14853.npy
-imgHQ13010.npy
-imgHQ04826.npy
-imgHQ29545.npy
-imgHQ22769.npy
-imgHQ03070.npy
-imgHQ01202.npy
-imgHQ00904.npy
-imgHQ27153.npy
-imgHQ00408.npy
-imgHQ02819.npy
-imgHQ12586.npy
-imgHQ15153.npy
-imgHQ19023.npy
-imgHQ25295.npy
-imgHQ27412.npy
-imgHQ23907.npy
-imgHQ28666.npy
-imgHQ04185.npy
-imgHQ23061.npy
-imgHQ29453.npy
-imgHQ05728.npy
-imgHQ21815.npy
-imgHQ00760.npy
-imgHQ11792.npy
-imgHQ07394.npy
-imgHQ23606.npy
-imgHQ07072.npy
-imgHQ00731.npy
-imgHQ07939.npy
-imgHQ16996.npy
-imgHQ23300.npy
-imgHQ16789.npy
-imgHQ07965.npy
-imgHQ00245.npy
-imgHQ10770.npy
-imgHQ12544.npy
-imgHQ25680.npy
-imgHQ15709.npy
-imgHQ22065.npy
-imgHQ23292.npy
-imgHQ06909.npy
-imgHQ14276.npy
-imgHQ21619.npy
-imgHQ18614.npy
-imgHQ05324.npy
-imgHQ29067.npy
-imgHQ25774.npy
-imgHQ22477.npy
-imgHQ26073.npy
-imgHQ12044.npy
-imgHQ03906.npy
-imgHQ27685.npy
-imgHQ27211.npy
-imgHQ03751.npy
-imgHQ10442.npy
-imgHQ11878.npy
-imgHQ26891.npy
-imgHQ03680.npy
-imgHQ26463.npy
-imgHQ10121.npy
-imgHQ02112.npy
-imgHQ29588.npy
-imgHQ19099.npy
-imgHQ15683.npy
-imgHQ25884.npy
-imgHQ23252.npy
-imgHQ06210.npy
-imgHQ07701.npy
-imgHQ29638.npy
-imgHQ22369.npy
-imgHQ26801.npy
-imgHQ04066.npy
-imgHQ22856.npy
-imgHQ29960.npy
-imgHQ14443.npy
-imgHQ02514.npy
-imgHQ08970.npy
-imgHQ08969.npy
-imgHQ29450.npy
-imgHQ00755.npy
-imgHQ15258.npy
-imgHQ27540.npy
-imgHQ20346.npy
-imgHQ04353.npy
-imgHQ03610.npy
-imgHQ29840.npy
-imgHQ16447.npy
-imgHQ03091.npy
-imgHQ05666.npy
-imgHQ23638.npy
-imgHQ24663.npy
-imgHQ27283.npy
-imgHQ23443.npy
-imgHQ10562.npy
-imgHQ28947.npy
-imgHQ28763.npy
-imgHQ06976.npy
-imgHQ09088.npy
-imgHQ23729.npy
-imgHQ18591.npy
-imgHQ16771.npy
-imgHQ10045.npy
-imgHQ26802.npy
-imgHQ15178.npy
-imgHQ03861.npy
-imgHQ28366.npy
-imgHQ12020.npy
-imgHQ21714.npy
-imgHQ29938.npy
-imgHQ04690.npy
-imgHQ19238.npy
-imgHQ25823.npy
-imgHQ15682.npy
-imgHQ28349.npy
-imgHQ27996.npy
-imgHQ03289.npy
-imgHQ00335.npy
-imgHQ10222.npy
-imgHQ29502.npy
-imgHQ18418.npy
-imgHQ04710.npy
-imgHQ11808.npy
-imgHQ25466.npy
-imgHQ22312.npy
-imgHQ03967.npy
-imgHQ24907.npy
-imgHQ04630.npy
-imgHQ23981.npy
-imgHQ05733.npy
-imgHQ21393.npy
-imgHQ24415.npy
-imgHQ20769.npy
-imgHQ10499.npy
-imgHQ13524.npy
-imgHQ22618.npy
-imgHQ19101.npy
-imgHQ28548.npy
-imgHQ27097.npy
-imgHQ06931.npy
-imgHQ05878.npy
-imgHQ18227.npy
-imgHQ04997.npy
-imgHQ18738.npy
-imgHQ09057.npy
-imgHQ20624.npy
-imgHQ20422.npy
-imgHQ26835.npy
-imgHQ00789.npy
-imgHQ28450.npy
-imgHQ23350.npy
-imgHQ29649.npy
-imgHQ07498.npy
-imgHQ23305.npy
-imgHQ23090.npy
-imgHQ11343.npy
-imgHQ08904.npy
-imgHQ10677.npy
-imgHQ13351.npy
-imgHQ23776.npy
-imgHQ07231.npy
-imgHQ20147.npy
-imgHQ09393.npy
-imgHQ21509.npy
-imgHQ09470.npy
-imgHQ00448.npy
-imgHQ00075.npy
-imgHQ03930.npy
-imgHQ11234.npy
-imgHQ02267.npy
-imgHQ25446.npy
-imgHQ26495.npy
-imgHQ07864.npy
-imgHQ02582.npy
-imgHQ12206.npy
-imgHQ25625.npy
-imgHQ28742.npy
-imgHQ27306.npy
-imgHQ01150.npy
-imgHQ17067.npy
-imgHQ13868.npy
-imgHQ25610.npy
-imgHQ27920.npy
-imgHQ00651.npy
-imgHQ27444.npy
-imgHQ25029.npy
-imgHQ01999.npy
-imgHQ22627.npy
-imgHQ23900.npy
-imgHQ24219.npy
-imgHQ22263.npy
-imgHQ17758.npy
-imgHQ24871.npy
-imgHQ17657.npy
-imgHQ08968.npy
-imgHQ29437.npy
-imgHQ19368.npy
-imgHQ09911.npy
-imgHQ08339.npy
-imgHQ25554.npy
-imgHQ04763.npy
-imgHQ20797.npy
-imgHQ15235.npy
-imgHQ21352.npy
-imgHQ04714.npy
-imgHQ19218.npy
-imgHQ12297.npy
-imgHQ05064.npy
-imgHQ22825.npy
-imgHQ00642.npy
-imgHQ06060.npy
-imgHQ20533.npy
-imgHQ08838.npy
-imgHQ18748.npy
-imgHQ01022.npy
-imgHQ01085.npy
-imgHQ14728.npy
-imgHQ08140.npy
-imgHQ07856.npy
-imgHQ10985.npy
-imgHQ11725.npy
-imgHQ05535.npy
-imgHQ25960.npy
-imgHQ01515.npy
-imgHQ05131.npy
-imgHQ04678.npy
-imgHQ08801.npy
-imgHQ01025.npy
-imgHQ13826.npy
-imgHQ18691.npy
-imgHQ04011.npy
-imgHQ10548.npy
-imgHQ12307.npy
-imgHQ22298.npy
-imgHQ19952.npy
-imgHQ13969.npy
-imgHQ24355.npy
-imgHQ28812.npy
-imgHQ03375.npy
-imgHQ07548.npy
-imgHQ27389.npy
-imgHQ06399.npy
-imgHQ25354.npy
-imgHQ18559.npy
-imgHQ02251.npy
-imgHQ16628.npy
-imgHQ13335.npy
-imgHQ26766.npy
-imgHQ00776.npy
-imgHQ04170.npy
-imgHQ20082.npy
-imgHQ13539.npy
-imgHQ17519.npy
-imgHQ28877.npy
-imgHQ25186.npy
-imgHQ22373.npy
-imgHQ08303.npy
-imgHQ20540.npy
-imgHQ17610.npy
-imgHQ08477.npy
-imgHQ03579.npy
-imgHQ06036.npy
-imgHQ09915.npy
-imgHQ28687.npy
-imgHQ13049.npy
-imgHQ19194.npy
-imgHQ10491.npy
-imgHQ20208.npy
-imgHQ17543.npy
-imgHQ24567.npy
-imgHQ19440.npy
-imgHQ04758.npy
-imgHQ28885.npy
-imgHQ12588.npy
-imgHQ27716.npy
-imgHQ28967.npy
-imgHQ11248.npy
-imgHQ09071.npy
-imgHQ29645.npy
-imgHQ06541.npy
-imgHQ11302.npy
-imgHQ27220.npy
-imgHQ27913.npy
-imgHQ05508.npy
-imgHQ04640.npy
-imgHQ12024.npy
-imgHQ07990.npy
-imgHQ05355.npy
-imgHQ25726.npy
-imgHQ00617.npy
-imgHQ08377.npy
-imgHQ11941.npy
-imgHQ13713.npy
-imgHQ06024.npy
-imgHQ04923.npy
-imgHQ17228.npy
-imgHQ11954.npy
-imgHQ19304.npy
-imgHQ01795.npy
-imgHQ10198.npy
-imgHQ09185.npy
-imgHQ00190.npy
-imgHQ29600.npy
-imgHQ27856.npy
-imgHQ15385.npy
-imgHQ06441.npy
-imgHQ07981.npy
-imgHQ15788.npy
-imgHQ10098.npy
-imgHQ08734.npy
-imgHQ07640.npy
-imgHQ26201.npy
-imgHQ01914.npy
-imgHQ07037.npy
-imgHQ22737.npy
-imgHQ11883.npy
-imgHQ27296.npy
-imgHQ22025.npy
-imgHQ20096.npy
-imgHQ09386.npy
-imgHQ06649.npy
-imgHQ16926.npy
-imgHQ01606.npy
-imgHQ08808.npy
-imgHQ19140.npy
-imgHQ23829.npy
-imgHQ12444.npy
-imgHQ20187.npy
-imgHQ25203.npy
-imgHQ26536.npy
-imgHQ26346.npy
-imgHQ06731.npy
-imgHQ05887.npy
-imgHQ23989.npy
-imgHQ01020.npy
-imgHQ23976.npy
-imgHQ19326.npy
-imgHQ10138.npy
-imgHQ04121.npy
-imgHQ16783.npy
-imgHQ28316.npy
-imgHQ24874.npy
-imgHQ11360.npy
-imgHQ09619.npy
-imgHQ04502.npy
-imgHQ25577.npy
-imgHQ10240.npy
-imgHQ27923.npy
-imgHQ13570.npy
-imgHQ05423.npy
-imgHQ20890.npy
-imgHQ19836.npy
-imgHQ08406.npy
-imgHQ10467.npy
-imgHQ16450.npy
-imgHQ07928.npy
-imgHQ22402.npy
-imgHQ00584.npy
-imgHQ11010.npy
-imgHQ29438.npy
-imgHQ29260.npy
-imgHQ13483.npy
-imgHQ29855.npy
-imgHQ10624.npy
-imgHQ12579.npy
-imgHQ29570.npy
-imgHQ28630.npy
-imgHQ27420.npy
-imgHQ00171.npy
-imgHQ16840.npy
-imgHQ19903.npy
-imgHQ22440.npy
-imgHQ23429.npy
-imgHQ18911.npy
-imgHQ15749.npy
-imgHQ02325.npy
-imgHQ05422.npy
-imgHQ21299.npy
-imgHQ09394.npy
-imgHQ26883.npy
-imgHQ07186.npy
-imgHQ10208.npy
-imgHQ11267.npy
-imgHQ25289.npy
-imgHQ12621.npy
-imgHQ24984.npy
-imgHQ15070.npy
-imgHQ28194.npy
-imgHQ28553.npy
-imgHQ21325.npy
-imgHQ22619.npy
-imgHQ10923.npy
-imgHQ22158.npy
-imgHQ16661.npy
-imgHQ04562.npy
-imgHQ16313.npy
-imgHQ22500.npy
-imgHQ13412.npy
-imgHQ02843.npy
-imgHQ13381.npy
-imgHQ14109.npy
-imgHQ29039.npy
-imgHQ04920.npy
-imgHQ20594.npy
-imgHQ14591.npy
-imgHQ18146.npy
-imgHQ16197.npy
-imgHQ22878.npy
-imgHQ13392.npy
-imgHQ15353.npy
-imgHQ02058.npy
-imgHQ06634.npy
-imgHQ08004.npy
-imgHQ28868.npy
-imgHQ21838.npy
-imgHQ15210.npy
-imgHQ28629.npy
-imgHQ08153.npy
-imgHQ04436.npy
-imgHQ21104.npy
-imgHQ06771.npy
-imgHQ17549.npy
-imgHQ19263.npy
-imgHQ25688.npy
-imgHQ26747.npy
-imgHQ17102.npy
-imgHQ05912.npy
-imgHQ29473.npy
-imgHQ17079.npy
-imgHQ22530.npy
-imgHQ29064.npy
-imgHQ12273.npy
-imgHQ14244.npy
-imgHQ25335.npy
-imgHQ16453.npy
-imgHQ10425.npy
-imgHQ14558.npy
-imgHQ03260.npy
-imgHQ00546.npy
-imgHQ18818.npy
-imgHQ26236.npy
-imgHQ28996.npy
-imgHQ27300.npy
-imgHQ11727.npy
-imgHQ20424.npy
-imgHQ13610.npy
-imgHQ23908.npy
-imgHQ14519.npy
-imgHQ12683.npy
-imgHQ16168.npy
-imgHQ01602.npy
-imgHQ08565.npy
-imgHQ24881.npy
-imgHQ09168.npy
-imgHQ13003.npy
-imgHQ11065.npy
-imgHQ21696.npy
-imgHQ07338.npy
-imgHQ23440.npy
-imgHQ28774.npy
-imgHQ08220.npy
-imgHQ24582.npy
-imgHQ19653.npy
-imgHQ22695.npy
-imgHQ25925.npy
-imgHQ17136.npy
-imgHQ00160.npy
-imgHQ16659.npy
-imgHQ08818.npy
-imgHQ25629.npy
-imgHQ08802.npy
-imgHQ13183.npy
-imgHQ21197.npy
-imgHQ00836.npy
-imgHQ06145.npy
-imgHQ28820.npy
-imgHQ07301.npy
-imgHQ16442.npy
-imgHQ22763.npy
-imgHQ19137.npy
-imgHQ18961.npy
-imgHQ23329.npy
-imgHQ03461.npy
-imgHQ23447.npy
-imgHQ22290.npy
-imgHQ15642.npy
-imgHQ13982.npy
-imgHQ17161.npy
-imgHQ00297.npy
-imgHQ24001.npy
-imgHQ14126.npy
-imgHQ00803.npy
-imgHQ19887.npy
-imgHQ06021.npy
-imgHQ01517.npy
-imgHQ28699.npy
-imgHQ04449.npy
-imgHQ19410.npy
-imgHQ20862.npy
-imgHQ24876.npy
-imgHQ11020.npy
-imgHQ29474.npy
-imgHQ22456.npy
-imgHQ24032.npy
-imgHQ01207.npy
-imgHQ14815.npy
-imgHQ13569.npy
-imgHQ13209.npy
-imgHQ09181.npy
-imgHQ11368.npy
-imgHQ15369.npy
-imgHQ17098.npy
-imgHQ18343.npy
-imgHQ14998.npy
-imgHQ05135.npy
-imgHQ02060.npy
-imgHQ04754.npy
-imgHQ29610.npy
-imgHQ24428.npy
-imgHQ12584.npy
-imgHQ11624.npy
-imgHQ05122.npy
-imgHQ00485.npy
-imgHQ02269.npy
-imgHQ03655.npy
-imgHQ08715.npy
-imgHQ07364.npy
-imgHQ12183.npy
-imgHQ11937.npy
-imgHQ24102.npy
-imgHQ28461.npy
-imgHQ22354.npy
-imgHQ04972.npy
-imgHQ25503.npy
-imgHQ28676.npy
-imgHQ10325.npy
-imgHQ21956.npy
-imgHQ28647.npy
-imgHQ19680.npy
-imgHQ23282.npy
-imgHQ09417.npy
-imgHQ24487.npy
-imgHQ12365.npy
-imgHQ28322.npy
-imgHQ01280.npy
-imgHQ23183.npy
-imgHQ27239.npy
-imgHQ14611.npy
-imgHQ26039.npy
-imgHQ18968.npy
-imgHQ12807.npy
-imgHQ28458.npy
-imgHQ03537.npy
-imgHQ28881.npy
-imgHQ17821.npy
-imgHQ16591.npy
-imgHQ07621.npy
-imgHQ12270.npy
-imgHQ28853.npy
-imgHQ08999.npy
-imgHQ13135.npy
-imgHQ13345.npy
-imgHQ00275.npy
-imgHQ26240.npy
-imgHQ00796.npy
-imgHQ10342.npy
-imgHQ08077.npy
-imgHQ28029.npy
-imgHQ25076.npy
-imgHQ01400.npy
-imgHQ06524.npy
-imgHQ14051.npy
-imgHQ06438.npy
-imgHQ20578.npy
-imgHQ23532.npy
-imgHQ01186.npy
-imgHQ19720.npy
-imgHQ24303.npy
-imgHQ08848.npy
-imgHQ13361.npy
-imgHQ05402.npy
-imgHQ10028.npy
-imgHQ24695.npy
-imgHQ24292.npy
-imgHQ06537.npy
-imgHQ12562.npy
-imgHQ13554.npy
-imgHQ11331.npy
-imgHQ25120.npy
-imgHQ19480.npy
-imgHQ01859.npy
-imgHQ04913.npy
-imgHQ15346.npy
-imgHQ01653.npy
-imgHQ05095.npy
-imgHQ15051.npy
-imgHQ09147.npy
-imgHQ20423.npy
-imgHQ06626.npy
-imgHQ21121.npy
-imgHQ26566.npy
-imgHQ17335.npy
-imgHQ29012.npy
-imgHQ20740.npy
-imgHQ16271.npy
-imgHQ02946.npy
-imgHQ10033.npy
-imgHQ03971.npy
-imgHQ23666.npy
-imgHQ23858.npy
-imgHQ06546.npy
-imgHQ25736.npy
-imgHQ24050.npy
-imgHQ22233.npy
-imgHQ13453.npy
-imgHQ01225.npy
-imgHQ02842.npy
-imgHQ22940.npy
-imgHQ00034.npy
-imgHQ01330.npy
-imgHQ20600.npy
-imgHQ14058.npy
-imgHQ03500.npy
-imgHQ07736.npy
-imgHQ10082.npy
-imgHQ15003.npy
-imgHQ10822.npy
-imgHQ06753.npy
-imgHQ13803.npy
-imgHQ04335.npy
-imgHQ03932.npy
-imgHQ02695.npy
-imgHQ14550.npy
-imgHQ23996.npy
-imgHQ24237.npy
-imgHQ15797.npy
-imgHQ04982.npy
-imgHQ14246.npy
-imgHQ10246.npy
-imgHQ17563.npy
-imgHQ23675.npy
-imgHQ27260.npy
-imgHQ08010.npy
-imgHQ22006.npy
-imgHQ00791.npy
-imgHQ17993.npy
-imgHQ27293.npy
-imgHQ15743.npy
-imgHQ00032.npy
-imgHQ21850.npy
-imgHQ02870.npy
-imgHQ10451.npy
-imgHQ11140.npy
-imgHQ00532.npy
-imgHQ23102.npy
-imgHQ07340.npy
-imgHQ02407.npy
-imgHQ17977.npy
-imgHQ26953.npy
-imgHQ20677.npy
-imgHQ11470.npy
-imgHQ23975.npy
-imgHQ13588.npy
-imgHQ10664.npy
-imgHQ13197.npy
-imgHQ08879.npy
-imgHQ17073.npy
-imgHQ11057.npy
-imgHQ17959.npy
-imgHQ19859.npy
-imgHQ12286.npy
-imgHQ14707.npy
-imgHQ17728.npy
-imgHQ02416.npy
-imgHQ12479.npy
-imgHQ15043.npy
-imgHQ06660.npy
-imgHQ29946.npy
-imgHQ27887.npy
-imgHQ22970.npy
-imgHQ14174.npy
-imgHQ27331.npy
-imgHQ02613.npy
-imgHQ29765.npy
-imgHQ26262.npy
-imgHQ01839.npy
-imgHQ14594.npy
-imgHQ24301.npy
-imgHQ19765.npy
-imgHQ00409.npy
-imgHQ11313.npy
-imgHQ14894.npy
-imgHQ06523.npy
-imgHQ22733.npy
-imgHQ07610.npy
-imgHQ15286.npy
-imgHQ12719.npy
-imgHQ26916.npy
-imgHQ24327.npy
-imgHQ20081.npy
-imgHQ09663.npy
-imgHQ27062.npy
-imgHQ06173.npy
-imgHQ13273.npy
-imgHQ15313.npy
-imgHQ17276.npy
-imgHQ12084.npy
-imgHQ16990.npy
-imgHQ14502.npy
-imgHQ14973.npy
-imgHQ25727.npy
-imgHQ27788.npy
-imgHQ19982.npy
-imgHQ04410.npy
-imgHQ29442.npy
-imgHQ28597.npy
-imgHQ01220.npy
-imgHQ06198.npy
-imgHQ26105.npy
-imgHQ00900.npy
-imgHQ15999.npy
-imgHQ05911.npy
-imgHQ11101.npy
-imgHQ08774.npy
-imgHQ00751.npy
-imgHQ03243.npy
-imgHQ27878.npy
-imgHQ27909.npy
-imgHQ21846.npy
-imgHQ07742.npy
-imgHQ25635.npy
-imgHQ10584.npy
-imgHQ07187.npy
-imgHQ27400.npy
-imgHQ01291.npy
-imgHQ09929.npy
-imgHQ00646.npy
-imgHQ28091.npy
-imgHQ03919.npy
-imgHQ22947.npy
-imgHQ03176.npy
-imgHQ09107.npy
-imgHQ13363.npy
-imgHQ28277.npy
-imgHQ13863.npy
-imgHQ13449.npy
-imgHQ19013.npy
-imgHQ03866.npy
-imgHQ00988.npy
-imgHQ26775.npy
-imgHQ21434.npy
-imgHQ26165.npy
-imgHQ15729.npy
-imgHQ13568.npy
-imgHQ21327.npy
-imgHQ19345.npy
-imgHQ28993.npy
-imgHQ08000.npy
-imgHQ07930.npy
-imgHQ09615.npy
-imgHQ23158.npy
-imgHQ16219.npy
-imgHQ16749.npy
-imgHQ25054.npy
-imgHQ14944.npy
-imgHQ09217.npy
-imgHQ12718.npy
-imgHQ12577.npy
-imgHQ20738.npy
-imgHQ03998.npy
-imgHQ04298.npy
-imgHQ19261.npy
-imgHQ20640.npy
-imgHQ24121.npy
-imgHQ12822.npy
-imgHQ18870.npy
-imgHQ08213.npy
-imgHQ10895.npy
-imgHQ12804.npy
-imgHQ11618.npy
-imgHQ22028.npy
-imgHQ14297.npy
-imgHQ29501.npy
-imgHQ29581.npy
-imgHQ09243.npy
-imgHQ17162.npy
-imgHQ19470.npy
-imgHQ26288.npy
-imgHQ22992.npy
-imgHQ02821.npy
-imgHQ15154.npy
-imgHQ22195.npy
-imgHQ26443.npy
-imgHQ17287.npy
-imgHQ24419.npy
-imgHQ12615.npy
-imgHQ16639.npy
-imgHQ13259.npy
-imgHQ10378.npy
-imgHQ20044.npy
-imgHQ05365.npy
-imgHQ25861.npy
-imgHQ26271.npy
-imgHQ10533.npy
-imgHQ26553.npy
-imgHQ25967.npy
-imgHQ29957.npy
-imgHQ14906.npy
-imgHQ20852.npy
-imgHQ08225.npy
-imgHQ10256.npy
-imgHQ28562.npy
-imgHQ19155.npy
-imgHQ09288.npy
-imgHQ26730.npy
-imgHQ29802.npy
-imgHQ07799.npy
-imgHQ13692.npy
-imgHQ26016.npy
-imgHQ17078.npy
-imgHQ23483.npy
-imgHQ23777.npy
-imgHQ06426.npy
-imgHQ15949.npy
-imgHQ14433.npy
-imgHQ10848.npy
-imgHQ14897.npy
-imgHQ16136.npy
-imgHQ29352.npy
-imgHQ24901.npy
-imgHQ11738.npy
-imgHQ20901.npy
-imgHQ09991.npy
-imgHQ13460.npy
-imgHQ06725.npy
-imgHQ04752.npy
-imgHQ10837.npy
-imgHQ20931.npy
-imgHQ18863.npy
-imgHQ21055.npy
-imgHQ24286.npy
-imgHQ28507.npy
-imgHQ09371.npy
-imgHQ25108.npy
-imgHQ12977.npy
-imgHQ04668.npy
-imgHQ26799.npy
-imgHQ00509.npy
-imgHQ19149.npy
-imgHQ11643.npy
-imgHQ25912.npy
-imgHQ28738.npy
-imgHQ26855.npy
-imgHQ09411.npy
-imgHQ16579.npy
-imgHQ14840.npy
-imgHQ02491.npy
-imgHQ27556.npy
-imgHQ24914.npy
-imgHQ20119.npy
-imgHQ14784.npy
-imgHQ23207.npy
-imgHQ09017.npy
-imgHQ16461.npy
-imgHQ29563.npy
-imgHQ29770.npy
-imgHQ17227.npy
-imgHQ02775.npy
-imgHQ05705.npy
-imgHQ23715.npy
-imgHQ21665.npy
-imgHQ07278.npy
-imgHQ20846.npy
-imgHQ06450.npy
-imgHQ07217.npy
-imgHQ22416.npy
-imgHQ07253.npy
-imgHQ28123.npy
-imgHQ12623.npy
-imgHQ14830.npy
-imgHQ13843.npy
-imgHQ14356.npy
-imgHQ13250.npy
-imgHQ20782.npy
-imgHQ28834.npy
-imgHQ02332.npy
-imgHQ03970.npy
-imgHQ02802.npy
-imgHQ03614.npy
-imgHQ28279.npy
-imgHQ17659.npy
-imgHQ18953.npy
-imgHQ22729.npy
-imgHQ07770.npy
-imgHQ02869.npy
-imgHQ01570.npy
-imgHQ14942.npy
-imgHQ29524.npy
-imgHQ09131.npy
-imgHQ26803.npy
-imgHQ15427.npy
-imgHQ16794.npy
-imgHQ27037.npy
-imgHQ08243.npy
-imgHQ21615.npy
-imgHQ08127.npy
-imgHQ29599.npy
-imgHQ04275.npy
-imgHQ04548.npy
-imgHQ29460.npy
-imgHQ13357.npy
-imgHQ04220.npy
-imgHQ27726.npy
-imgHQ27701.npy
-imgHQ07918.npy
-imgHQ28189.npy
-imgHQ05420.npy
-imgHQ02583.npy
-imgHQ00845.npy
-imgHQ15224.npy
-imgHQ16944.npy
-imgHQ04845.npy
-imgHQ26996.npy
-imgHQ15759.npy
-imgHQ17931.npy
-imgHQ27173.npy
-imgHQ22129.npy
-imgHQ16426.npy
-imgHQ29305.npy
-imgHQ20584.npy
-imgHQ09509.npy
-imgHQ20389.npy
-imgHQ08023.npy
-imgHQ00097.npy
-imgHQ01065.npy
-imgHQ13242.npy
-imgHQ29204.npy
-imgHQ15493.npy
-imgHQ24676.npy
-imgHQ13765.npy
-imgHQ15283.npy
-imgHQ29295.npy
-imgHQ18939.npy
-imgHQ21829.npy
-imgHQ00661.npy
-imgHQ28999.npy
-imgHQ12517.npy
-imgHQ26978.npy
-imgHQ29154.npy
-imgHQ22924.npy
-imgHQ13644.npy
-imgHQ28335.npy
-imgHQ03597.npy
-imgHQ05143.npy
-imgHQ03627.npy
-imgHQ09794.npy
-imgHQ24348.npy
-imgHQ18332.npy
-imgHQ19396.npy
-imgHQ07103.npy
-imgHQ14094.npy
-imgHQ02751.npy
-imgHQ15039.npy
-imgHQ21851.npy
-imgHQ25099.npy
-imgHQ23290.npy
-imgHQ25231.npy
-imgHQ28392.npy
-imgHQ13599.npy
-imgHQ26096.npy
-imgHQ04798.npy
-imgHQ14127.npy
-imgHQ16422.npy
-imgHQ17430.npy
-imgHQ07995.npy
-imgHQ08164.npy
-imgHQ29698.npy
-imgHQ25911.npy
-imgHQ25612.npy
-imgHQ22699.npy
-imgHQ25332.npy
-imgHQ03384.npy
-imgHQ00499.npy
-imgHQ03198.npy
-imgHQ07687.npy
-imgHQ01368.npy
-imgHQ29561.npy
-imgHQ19973.npy
-imgHQ20435.npy
-imgHQ08701.npy
-imgHQ17477.npy
-imgHQ02780.npy
-imgHQ10433.npy
-imgHQ02610.npy
-imgHQ18341.npy
-imgHQ13704.npy
-imgHQ25185.npy
-imgHQ24866.npy
-imgHQ11494.npy
-imgHQ00418.npy
-imgHQ01469.npy
-imgHQ14859.npy
-imgHQ22966.npy
-imgHQ06914.npy
-imgHQ29831.npy
-imgHQ25849.npy
-imgHQ01205.npy
-imgHQ14005.npy
-imgHQ01572.npy
-imgHQ17921.npy
-imgHQ05339.npy
-imgHQ00389.npy
-imgHQ17740.npy
-imgHQ11540.npy
-imgHQ15022.npy
-imgHQ21677.npy
-imgHQ06689.npy
-imgHQ21410.npy
-imgHQ24820.npy
-imgHQ12252.npy
-imgHQ02465.npy
-imgHQ03282.npy
-imgHQ16013.npy
-imgHQ25548.npy
-imgHQ22906.npy
-imgHQ18283.npy
-imgHQ02017.npy
-imgHQ05949.npy
-imgHQ28312.npy
-imgHQ23105.npy
-imgHQ11964.npy
-imgHQ11525.npy
-imgHQ06392.npy
-imgHQ05647.npy
-imgHQ25624.npy
-imgHQ19252.npy
-imgHQ15678.npy
-imgHQ28580.npy
-imgHQ21528.npy
-imgHQ05332.npy
-imgHQ19298.npy
-imgHQ10606.npy
-imgHQ11238.npy
-imgHQ02774.npy
-imgHQ16147.npy
-imgHQ25863.npy
-imgHQ01048.npy
-imgHQ28204.npy
-imgHQ05329.npy
-imgHQ04725.npy
-imgHQ03152.npy
-imgHQ07947.npy
-imgHQ29398.npy
-imgHQ28034.npy
-imgHQ15317.npy
-imgHQ18797.npy
-imgHQ13101.npy
-imgHQ22754.npy
-imgHQ18060.npy
-imgHQ21594.npy
-imgHQ22858.npy
-imgHQ04397.npy
-imgHQ19219.npy
-imgHQ10544.npy
-imgHQ17303.npy
-imgHQ00324.npy
-imgHQ17567.npy
-imgHQ12162.npy
-imgHQ05328.npy
-imgHQ01656.npy
-imgHQ27429.npy
-imgHQ22258.npy
-imgHQ17517.npy
-imgHQ27284.npy
-imgHQ15068.npy
-imgHQ25187.npy
-imgHQ25422.npy
-imgHQ29715.npy
-imgHQ11088.npy
-imgHQ07779.npy
-imgHQ03540.npy
-imgHQ21588.npy
-imgHQ03530.npy
-imgHQ10004.npy
-imgHQ12513.npy
-imgHQ19630.npy
-imgHQ05695.npy
-imgHQ09719.npy
-imgHQ08756.npy
-imgHQ00665.npy
-imgHQ00842.npy
-imgHQ23320.npy
-imgHQ17705.npy
-imgHQ19530.npy
-imgHQ15456.npy
-imgHQ28481.npy
-imgHQ19685.npy
-imgHQ03382.npy
-imgHQ03925.npy
-imgHQ09826.npy
-imgHQ01154.npy
-imgHQ12985.npy
-imgHQ04748.npy
-imgHQ01507.npy
-imgHQ00883.npy
-imgHQ28658.npy
-imgHQ26813.npy
-imgHQ23728.npy
-imgHQ27087.npy
-imgHQ04782.npy
-imgHQ22592.npy
-imgHQ21654.npy
-imgHQ14556.npy
-imgHQ08252.npy
-imgHQ20002.npy
-imgHQ00475.npy
-imgHQ08598.npy
-imgHQ19984.npy
-imgHQ10335.npy
-imgHQ20338.npy
-imgHQ05913.npy
-imgHQ07268.npy
-imgHQ03057.npy
-imgHQ19330.npy
-imgHQ08470.npy
-imgHQ20551.npy
-imgHQ13685.npy
-imgHQ08375.npy
-imgHQ12925.npy
-imgHQ09946.npy
-imgHQ21481.npy
-imgHQ19555.npy
-imgHQ18374.npy
-imgHQ19781.npy
-imgHQ16183.npy
-imgHQ08108.npy
-imgHQ28515.npy
-imgHQ12282.npy
-imgHQ00671.npy
-imgHQ29634.npy
-imgHQ02599.npy
-imgHQ06371.npy
-imgHQ14826.npy
-imgHQ22478.npy
-imgHQ21731.npy
-imgHQ02801.npy
-imgHQ26020.npy
-imgHQ05082.npy
-imgHQ08344.npy
-imgHQ03715.npy
-imgHQ27297.npy
-imgHQ12707.npy
-imgHQ26307.npy
-imgHQ29233.npy
-imgHQ15864.npy
-imgHQ05866.npy
-imgHQ06531.npy
-imgHQ00931.npy
-imgHQ16693.npy
-imgHQ15161.npy
-imgHQ25274.npy
-imgHQ19289.npy
-imgHQ09182.npy
-imgHQ25797.npy
-imgHQ25917.npy
-imgHQ15241.npy
-imgHQ26931.npy
-imgHQ04789.npy
-imgHQ24713.npy
-imgHQ17419.npy
-imgHQ19098.npy
-imgHQ05591.npy
-imgHQ19736.npy
-imgHQ03735.npy
-imgHQ03551.npy
-imgHQ17529.npy
-imgHQ08612.npy
-imgHQ10828.npy
-imgHQ13538.npy
-imgHQ25377.npy
-imgHQ03432.npy
-imgHQ15941.npy
-imgHQ07389.npy
-imgHQ04869.npy
-imgHQ12033.npy
-imgHQ15831.npy
-imgHQ25220.npy
-imgHQ22963.npy
-imgHQ02987.npy
-imgHQ16171.npy
-imgHQ16594.npy
-imgHQ02587.npy
-imgHQ02214.npy
-imgHQ17023.npy
-imgHQ15884.npy
-imgHQ16014.npy
-imgHQ05466.npy
-imgHQ28067.npy
-imgHQ17131.npy
-imgHQ28547.npy
-imgHQ19980.npy
-imgHQ04847.npy
-imgHQ23007.npy
-imgHQ23011.npy
-imgHQ20028.npy
-imgHQ26595.npy
-imgHQ15862.npy
-imgHQ23727.npy
-imgHQ16322.npy
-imgHQ06844.npy
-imgHQ21283.npy
-imgHQ06250.npy
-imgHQ23555.npy
-imgHQ19351.npy
-imgHQ26634.npy
-imgHQ12302.npy
-imgHQ13458.npy
-imgHQ14534.npy
-imgHQ16737.npy
-imgHQ21991.npy
-imgHQ25760.npy
-imgHQ11059.npy
-imgHQ01198.npy
-imgHQ03874.npy
-imgHQ25983.npy
-imgHQ09358.npy
-imgHQ21994.npy
-imgHQ15506.npy
-imgHQ24474.npy
-imgHQ14047.npy
-imgHQ10567.npy
-imgHQ10507.npy
-imgHQ15492.npy
-imgHQ19388.npy
-imgHQ00416.npy
-imgHQ08321.npy
-imgHQ15344.npy
-imgHQ11461.npy
-imgHQ20734.npy
-imgHQ17649.npy
-imgHQ10352.npy
-imgHQ04237.npy
-imgHQ27231.npy
-imgHQ10597.npy
-imgHQ17646.npy
-imgHQ14465.npy
-imgHQ04497.npy
-imgHQ16803.npy
-imgHQ11809.npy
-imgHQ05643.npy
-imgHQ13542.npy
-imgHQ07483.npy
-imgHQ21215.npy
-imgHQ29422.npy
-imgHQ21889.npy
-imgHQ15149.npy
-imgHQ11148.npy
-imgHQ07611.npy
-imgHQ27931.npy
-imgHQ10547.npy
-imgHQ07630.npy
-imgHQ08294.npy
-imgHQ27166.npy
-imgHQ04991.npy
-imgHQ22600.npy
-imgHQ01116.npy
-imgHQ16354.npy
-imgHQ17006.npy
-imgHQ02986.npy
-imgHQ17261.npy
-imgHQ22687.npy
-imgHQ17794.npy
-imgHQ13832.npy
-imgHQ20268.npy
-imgHQ28159.npy
-imgHQ21740.npy
-imgHQ01780.npy
-imgHQ01013.npy
-imgHQ03547.npy
-imgHQ03024.npy
-imgHQ20196.npy
-imgHQ03968.npy
-imgHQ09502.npy
-imgHQ00185.npy
-imgHQ07920.npy
-imgHQ06053.npy
-imgHQ27544.npy
-imgHQ22091.npy
-imgHQ29370.npy
-imgHQ23075.npy
-imgHQ03739.npy
-imgHQ19668.npy
-imgHQ26374.npy
-imgHQ15345.npy
-imgHQ15125.npy
-imgHQ24041.npy
-imgHQ10722.npy
-imgHQ25825.npy
-imgHQ21946.npy
-imgHQ19473.npy
-imgHQ07100.npy
-imgHQ22775.npy
-imgHQ01810.npy
-imgHQ01479.npy
-imgHQ03890.npy
-imgHQ25616.npy
-imgHQ24134.npy
-imgHQ14449.npy
-imgHQ21015.npy
-imgHQ04135.npy
-imgHQ28104.npy
-imgHQ05302.npy
-imgHQ01806.npy
-imgHQ16414.npy
-imgHQ26379.npy
-imgHQ21926.npy
-imgHQ13476.npy
-imgHQ05917.npy
-imgHQ11746.npy
-imgHQ01376.npy
-imgHQ14034.npy
-imgHQ15814.npy
-imgHQ25697.npy
-imgHQ02919.npy
-imgHQ02016.npy
-imgHQ01875.npy
-imgHQ09313.npy
-imgHQ08298.npy
-imgHQ16609.npy
-imgHQ06365.npy
-imgHQ19139.npy
-imgHQ20217.npy
-imgHQ29264.npy
-imgHQ29105.npy
-imgHQ10084.npy
-imgHQ00096.npy
-imgHQ13665.npy
-imgHQ18743.npy
-imgHQ16411.npy
-imgHQ14958.npy
-imgHQ03306.npy
-imgHQ08001.npy
-imgHQ01187.npy
-imgHQ00070.npy
-imgHQ03217.npy
-imgHQ09251.npy
-imgHQ02692.npy
-imgHQ14585.npy
-imgHQ09091.npy
-imgHQ25290.npy
-imgHQ11532.npy
-imgHQ27183.npy
-imgHQ19918.npy
-imgHQ26280.npy
-imgHQ17160.npy
-imgHQ24691.npy
-imgHQ20807.npy
-imgHQ16421.npy
-imgHQ13245.npy
-imgHQ20046.npy
-imgHQ06405.npy
-imgHQ08660.npy
-imgHQ11366.npy
-imgHQ27130.npy
-imgHQ27023.npy
-imgHQ23170.npy
-imgHQ20229.npy
-imgHQ21199.npy
-imgHQ18955.npy
-imgHQ10010.npy
-imgHQ15042.npy
-imgHQ28769.npy
-imgHQ01575.npy
-imgHQ07681.npy
-imgHQ19612.npy
-imgHQ03756.npy
-imgHQ27285.npy
-imgHQ12745.npy
-imgHQ19475.npy
-imgHQ05074.npy
-imgHQ11863.npy
-imgHQ13978.npy
-imgHQ04583.npy
-imgHQ01833.npy
-imgHQ27185.npy
-imgHQ03699.npy
-imgHQ24172.npy
-imgHQ26705.npy
-imgHQ12462.npy
-imgHQ08020.npy
-imgHQ27831.npy
-imgHQ01755.npy
-imgHQ01246.npy
-imgHQ09653.npy
-imgHQ20206.npy
-imgHQ12708.npy
-imgHQ14149.npy
-imgHQ03571.npy
-imgHQ24517.npy
-imgHQ03709.npy
-imgHQ24126.npy
-imgHQ05015.npy
-imgHQ08242.npy
-imgHQ07499.npy
-imgHQ15101.npy
-imgHQ26430.npy
-imgHQ07375.npy
-imgHQ13707.npy
-imgHQ02494.npy
-imgHQ11081.npy
-imgHQ18751.npy
-imgHQ26188.npy
-imgHQ12425.npy
-imgHQ11341.npy
-imgHQ17425.npy
-imgHQ20347.npy
-imgHQ27892.npy
-imgHQ04128.npy
-imgHQ20705.npy
-imgHQ07071.npy
-imgHQ18982.npy
-imgHQ03399.npy
-imgHQ06056.npy
-imgHQ09723.npy
-imgHQ17301.npy
-imgHQ04016.npy
-imgHQ23761.npy
-imgHQ02240.npy
-imgHQ05007.npy
-imgHQ28986.npy
-imgHQ29743.npy
-imgHQ15673.npy
-imgHQ19845.npy
-imgHQ19001.npy
-imgHQ24051.npy
-imgHQ02122.npy
-imgHQ16999.npy
-imgHQ10100.npy
-imgHQ18630.npy
-imgHQ11235.npy
-imgHQ25352.npy
-imgHQ14022.npy
-imgHQ00765.npy
-imgHQ08572.npy
-imgHQ20668.npy
-imgHQ26790.npy
-imgHQ15597.npy
-imgHQ21780.npy
-imgHQ29894.npy
-imgHQ27639.npy
-imgHQ24173.npy
-imgHQ01596.npy
-imgHQ23236.npy
-imgHQ11142.npy
-imgHQ20776.npy
-imgHQ22366.npy
-imgHQ22098.npy
-imgHQ22988.npy
-imgHQ24463.npy
-imgHQ10018.npy
-imgHQ19817.npy
-imgHQ05624.npy
-imgHQ03419.npy
-imgHQ01530.npy
-imgHQ19398.npy
-imgHQ14813.npy
-imgHQ03954.npy
-imgHQ09005.npy
-imgHQ01866.npy
-imgHQ06256.npy
-imgHQ01064.npy
-imgHQ18741.npy
-imgHQ17169.npy
-imgHQ23107.npy
-imgHQ00020.npy
-imgHQ02522.npy
-imgHQ05638.npy
-imgHQ27108.npy
-imgHQ17542.npy
-imgHQ17214.npy
-imgHQ28246.npy
-imgHQ13141.npy
-imgHQ04766.npy
-imgHQ02814.npy
-imgHQ20922.npy
-imgHQ04263.npy
-imgHQ23674.npy
-imgHQ17675.npy
-imgHQ17830.npy
-imgHQ16830.npy
-imgHQ01894.npy
-imgHQ08770.npy
-imgHQ09276.npy
-imgHQ19042.npy
-imgHQ02308.npy
-imgHQ04498.npy
-imgHQ08039.npy
-imgHQ08735.npy
-imgHQ22082.npy
-imgHQ27045.npy
-imgHQ14188.npy
-imgHQ10744.npy
-imgHQ08125.npy
-imgHQ12349.npy
-imgHQ11859.npy
-imgHQ25842.npy
-imgHQ23695.npy
-imgHQ01760.npy
-imgHQ07522.npy
-imgHQ16002.npy
-imgHQ22562.npy
-imgHQ18399.npy
-imgHQ26048.npy
-imgHQ23554.npy
-imgHQ20064.npy
-imgHQ03374.npy
-imgHQ19052.npy
-imgHQ04759.npy
-imgHQ07093.npy
-imgHQ09247.npy
-imgHQ24486.npy
-imgHQ27600.npy
-imgHQ04012.npy
-imgHQ00892.npy
-imgHQ27204.npy
-imgHQ20863.npy
-imgHQ02406.npy
-imgHQ04338.npy
-imgHQ27747.npy
-imgHQ16512.npy
-imgHQ29930.npy
-imgHQ01505.npy
-imgHQ24444.npy
-imgHQ14029.npy
-imgHQ03463.npy
-imgHQ12664.npy
-imgHQ14535.npy
-imgHQ06827.npy
-imgHQ21828.npy
-imgHQ11448.npy
-imgHQ23202.npy
-imgHQ16942.npy
-imgHQ04727.npy
-imgHQ13212.npy
-imgHQ08744.npy
-imgHQ12590.npy
-imgHQ11616.npy
-imgHQ25176.npy
-imgHQ29013.npy
-imgHQ02116.npy
-imgHQ21051.npy
-imgHQ07628.npy
-imgHQ27881.npy
-imgHQ25073.npy
-imgHQ23588.npy
-imgHQ29392.npy
-imgHQ15496.npy
-imgHQ29183.npy
-imgHQ28362.npy
-imgHQ03443.npy
-imgHQ21831.npy
-imgHQ06586.npy
-imgHQ24265.npy
-imgHQ12825.npy
-imgHQ27918.npy
-imgHQ02553.npy
-imgHQ27353.npy
-imgHQ25364.npy
-imgHQ14125.npy
-imgHQ17010.npy
-imgHQ05138.npy
-imgHQ15765.npy
-imgHQ02445.npy
-imgHQ14397.npy
-imgHQ22248.npy
-imgHQ02770.npy
-imgHQ11093.npy
-imgHQ01365.npy
-imgHQ25582.npy
-imgHQ15350.npy
-imgHQ21877.npy
-imgHQ10061.npy
-imgHQ08444.npy
-imgHQ28059.npy
-imgHQ29774.npy
-imgHQ17975.npy
-imgHQ25440.npy
-imgHQ09115.npy
-imgHQ28331.npy
-imgHQ19297.npy
-imgHQ06703.npy
-imgHQ04854.npy
-imgHQ11940.npy
-imgHQ24698.npy
-imgHQ04993.npy
-imgHQ26701.npy
-imgHQ04060.npy
-imgHQ05867.npy
-imgHQ07262.npy
-imgHQ08492.npy
-imgHQ21863.npy
-imgHQ08075.npy
-imgHQ01062.npy
-imgHQ11690.npy
-imgHQ07282.npy
-imgHQ24596.npy
-imgHQ04189.npy
-imgHQ01690.npy
-imgHQ23397.npy
-imgHQ08035.npy
-imgHQ25462.npy
-imgHQ16398.npy
-imgHQ18858.npy
-imgHQ11771.npy
-imgHQ05749.npy
-imgHQ18445.npy
-imgHQ14951.npy
-imgHQ25262.npy
-imgHQ22492.npy
-imgHQ01278.npy
-imgHQ02941.npy
-imgHQ28737.npy
-imgHQ15501.npy
-imgHQ03485.npy
-imgHQ01115.npy
-imgHQ25509.npy
-imgHQ08272.npy
-imgHQ05213.npy
-imgHQ12160.npy
-imgHQ03826.npy
-imgHQ08746.npy
-imgHQ19504.npy
-imgHQ27340.npy
-imgHQ07543.npy
-imgHQ25860.npy
-imgHQ06895.npy
-imgHQ01297.npy
-imgHQ26405.npy
-imgHQ27186.npy
-imgHQ24446.npy
-imgHQ08538.npy
-imgHQ28787.npy
-imgHQ16680.npy
-imgHQ14054.npy
-imgHQ20961.npy
-imgHQ18035.npy
-imgHQ11942.npy
-imgHQ23614.npy
-imgHQ18348.npy
-imgHQ10843.npy
-imgHQ01660.npy
-imgHQ21909.npy
-imgHQ10805.npy
-imgHQ16772.npy
-imgHQ25758.npy
-imgHQ03006.npy
-imgHQ06510.npy
-imgHQ24730.npy
-imgHQ19902.npy
-imgHQ29961.npy
-imgHQ14227.npy
-imgHQ19426.npy
-imgHQ13285.npy
-imgHQ08090.npy
-imgHQ07044.npy
-imgHQ01250.npy
-imgHQ28618.npy
-imgHQ17920.npy
-imgHQ25145.npy
-imgHQ02643.npy
-imgHQ06141.npy
-imgHQ03284.npy
-imgHQ02003.npy
-imgHQ04995.npy
-imgHQ13748.npy
-imgHQ27596.npy
-imgHQ29923.npy
-imgHQ14798.npy
-imgHQ10204.npy
-imgHQ23994.npy
-imgHQ17130.npy
-imgHQ12665.npy
-imgHQ17982.npy
-imgHQ12353.npy
-imgHQ16007.npy
-imgHQ00779.npy
-imgHQ14233.npy
-imgHQ17426.npy
-imgHQ27344.npy
-imgHQ22746.npy
-imgHQ12919.npy
-imgHQ21052.npy
-imgHQ04022.npy
-imgHQ29214.npy
-imgHQ07827.npy
-imgHQ29005.npy
-imgHQ14068.npy
-imgHQ17439.npy
-imgHQ13115.npy
-imgHQ05416.npy
-imgHQ25994.npy
-imgHQ06983.npy
-imgHQ15494.npy
-imgHQ26037.npy
-imgHQ02408.npy
-imgHQ08958.npy
-imgHQ01710.npy
-imgHQ22128.npy
-imgHQ22830.npy
-imgHQ26360.npy
-imgHQ27244.npy
-imgHQ11580.npy
-imgHQ21032.npy
-imgHQ18255.npy
-imgHQ20314.npy
-imgHQ07816.npy
-imgHQ20574.npy
-imgHQ00937.npy
-imgHQ17122.npy
-imgHQ29854.npy
-imgHQ13168.npy
-imgHQ19076.npy
-imgHQ17002.npy
-imgHQ11401.npy
-imgHQ21898.npy
-imgHQ01645.npy
-imgHQ21073.npy
-imgHQ20789.npy
-imgHQ27055.npy
-imgHQ21884.npy
-imgHQ22204.npy
-imgHQ11838.npy
-imgHQ16237.npy
-imgHQ25580.npy
-imgHQ05842.npy
-imgHQ17916.npy
-imgHQ15962.npy
-imgHQ20365.npy
-imgHQ07527.npy
-imgHQ07036.npy
-imgHQ17000.npy
-imgHQ17739.npy
-imgHQ11326.npy
-imgHQ01382.npy
-imgHQ18067.npy
-imgHQ06162.npy
-imgHQ27984.npy
-imgHQ26571.npy
-imgHQ09374.npy
-imgHQ15935.npy
-imgHQ07795.npy
-imgHQ24368.npy
-imgHQ07154.npy
-imgHQ17117.npy
-imgHQ10440.npy
-imgHQ29975.npy
-imgHQ06281.npy
-imgHQ23785.npy
-imgHQ17968.npy
-imgHQ17950.npy
-imgHQ01191.npy
-imgHQ02440.npy
-imgHQ12406.npy
-imgHQ02799.npy
-imgHQ24092.npy
-imgHQ03511.npy
-imgHQ22986.npy
-imgHQ20880.npy
-imgHQ02400.npy
-imgHQ09305.npy
-imgHQ15321.npy
-imgHQ00434.npy
-imgHQ24900.npy
-imgHQ08499.npy
-imgHQ28200.npy
-imgHQ04473.npy
-imgHQ03019.npy
-imgHQ02812.npy
-imgHQ27311.npy
-imgHQ07475.npy
-imgHQ22320.npy
-imgHQ12690.npy
-imgHQ07377.npy
-imgHQ09960.npy
-imgHQ29616.npy
-imgHQ10535.npy
-imgHQ22580.npy
-imgHQ29379.npy
-imgHQ08099.npy
-imgHQ16866.npy
-imgHQ04169.npy
-imgHQ14147.npy
-imgHQ23251.npy
-imgHQ23608.npy
-imgHQ22094.npy
-imgHQ01543.npy
-imgHQ19855.npy
-imgHQ18996.npy
-imgHQ19416.npy
-imgHQ07668.npy
-imgHQ15732.npy
-imgHQ10892.npy
-imgHQ22383.npy
-imgHQ06393.npy
-imgHQ13750.npy
-imgHQ02107.npy
-imgHQ06588.npy
-imgHQ25271.npy
-imgHQ09551.npy
-imgHQ17513.npy
-imgHQ10375.npy
-imgHQ28785.npy
-imgHQ02722.npy
-imgHQ07878.npy
-imgHQ19773.npy
-imgHQ20509.npy
-imgHQ27811.npy
-imgHQ11831.npy
-imgHQ06926.npy
-imgHQ15000.npy
-imgHQ08724.npy
-imgHQ24919.npy
-imgHQ13109.npy
-imgHQ06338.npy
-imgHQ08072.npy
-imgHQ26941.npy
-imgHQ05196.npy
-imgHQ20940.npy
-imgHQ08355.npy
-imgHQ24665.npy
-imgHQ01353.npy
-imgHQ04488.npy
-imgHQ16723.npy
-imgHQ15889.npy
-imgHQ04228.npy
-imgHQ27301.npy
-imgHQ17698.npy
-imgHQ23256.npy
-imgHQ27695.npy
-imgHQ02657.npy
-imgHQ15530.npy
-imgHQ22833.npy
-imgHQ16786.npy
-imgHQ14145.npy
-imgHQ12300.npy
-imgHQ15201.npy
-imgHQ27072.npy
-imgHQ03055.npy
-imgHQ13645.npy
-imgHQ25538.npy
-imgHQ02321.npy
-imgHQ10519.npy
-imgHQ11036.npy
-imgHQ04936.npy
-imgHQ19337.npy
-imgHQ22954.npy
-imgHQ13056.npy
-imgHQ10323.npy
-imgHQ29344.npy
-imgHQ26317.npy
-imgHQ14835.npy
-imgHQ06274.npy
-imgHQ18158.npy
-imgHQ03781.npy
-imgHQ07769.npy
-imgHQ15876.npy
-imgHQ08619.npy
-imgHQ14536.npy
-imgHQ06702.npy
-imgHQ11857.npy
-imgHQ06760.npy
-imgHQ27484.npy
-imgHQ25979.npy
-imgHQ09803.npy
-imgHQ28082.npy
-imgHQ15635.npy
-imgHQ26246.npy
-imgHQ25796.npy
-imgHQ04129.npy
-imgHQ15705.npy
-imgHQ00940.npy
-imgHQ14696.npy
-imgHQ28195.npy
-imgHQ29163.npy
-imgHQ10301.npy
-imgHQ13564.npy
-imgHQ05865.npy
-imgHQ01406.npy
-imgHQ01786.npy
-imgHQ02000.npy
-imgHQ24140.npy
-imgHQ14772.npy
-imgHQ17912.npy
-imgHQ14750.npy
-imgHQ23422.npy
-imgHQ13054.npy
-imgHQ24375.npy
-imgHQ15922.npy
-imgHQ18556.npy
-imgHQ04041.npy
-imgHQ13721.npy
-imgHQ02535.npy
-imgHQ23847.npy
-imgHQ02352.npy
-imgHQ09987.npy
-imgHQ15802.npy
-imgHQ14031.npy
-imgHQ08942.npy
-imgHQ03662.npy
-imgHQ19059.npy
-imgHQ09004.npy
-imgHQ02235.npy
-imgHQ24757.npy
-imgHQ25934.npy
-imgHQ29448.npy
-imgHQ17380.npy
-imgHQ19461.npy
-imgHQ22068.npy
-imgHQ08235.npy
-imgHQ07757.npy
-imgHQ22770.npy
-imgHQ20953.npy
-imgHQ17480.npy
-imgHQ06265.npy
-imgHQ12582.npy
-imgHQ19545.npy
-imgHQ25851.npy
-imgHQ03786.npy
-imgHQ02569.npy
-imgHQ20727.npy
-imgHQ24129.npy
-imgHQ27477.npy
-imgHQ00966.npy
-imgHQ15005.npy
-imgHQ20840.npy
-imgHQ19265.npy
-imgHQ09519.npy
-imgHQ10739.npy
-imgHQ20385.npy
-imgHQ27277.npy
-imgHQ07060.npy
-imgHQ04229.npy
-imgHQ01867.npy
-imgHQ22949.npy
-imgHQ04076.npy
-imgHQ15658.npy
-imgHQ17695.npy
-imgHQ02944.npy
-imgHQ22839.npy
-imgHQ00112.npy
-imgHQ11014.npy
-imgHQ10875.npy
-imgHQ19244.npy
-imgHQ06679.npy
-imgHQ27464.npy
-imgHQ27699.npy
-imgHQ23712.npy
-imgHQ05267.npy
-imgHQ09913.npy
-imgHQ18040.npy
-imgHQ02144.npy
-imgHQ20374.npy
-imgHQ24959.npy
-imgHQ21741.npy
-imgHQ26034.npy
-imgHQ25116.npy
-imgHQ16205.npy
-imgHQ08921.npy
-imgHQ10681.npy
-imgHQ03386.npy
-imgHQ23464.npy
-imgHQ18120.npy
-imgHQ06048.npy
-imgHQ05595.npy
-imgHQ09672.npy
-imgHQ08817.npy
-imgHQ24630.npy
-imgHQ09271.npy
-imgHQ09827.npy
-imgHQ01514.npy
-imgHQ29597.npy
-imgHQ13688.npy
-imgHQ10858.npy
-imgHQ18807.npy
-imgHQ28154.npy
-imgHQ03251.npy
-imgHQ14780.npy
-imgHQ25572.npy
-imgHQ16263.npy
-imgHQ27390.npy
-imgHQ25442.npy
-imgHQ03598.npy
-imgHQ09242.npy
-imgHQ04965.npy
-imgHQ28815.npy
-imgHQ15355.npy
-imgHQ23023.npy
-imgHQ05897.npy
-imgHQ00021.npy
-imgHQ07952.npy
-imgHQ07649.npy
-imgHQ13772.npy
-imgHQ25707.npy
-imgHQ05424.npy
-imgHQ04942.npy
-imgHQ12387.npy
-imgHQ14560.npy
-imgHQ26355.npy
-imgHQ19958.npy
-imgHQ19588.npy
-imgHQ22693.npy
-imgHQ21337.npy
-imgHQ24296.npy
-imgHQ07457.npy
-imgHQ13279.npy
-imgHQ02128.npy
-imgHQ12968.npy
-imgHQ05488.npy
-imgHQ26546.npy
-imgHQ21693.npy
-imgHQ27854.npy
-imgHQ00535.npy
-imgHQ12817.npy
-imgHQ03241.npy
-imgHQ20348.npy
-imgHQ14736.npy
-imgHQ21118.npy
-imgHQ18837.npy
-imgHQ19829.npy
-imgHQ02261.npy
-imgHQ00248.npy
-imgHQ00441.npy
-imgHQ21364.npy
-imgHQ01954.npy
-imgHQ04662.npy
-imgHQ03484.npy
-imgHQ03059.npy
-imgHQ10412.npy
-imgHQ19631.npy
-imgHQ06172.npy
-imgHQ25634.npy
-imgHQ02335.npy
-imgHQ20610.npy
-imgHQ14367.npy
-imgHQ23856.npy
-imgHQ07025.npy
-imgHQ20591.npy
-imgHQ06669.npy
-imgHQ03063.npy
-imgHQ22229.npy
-imgHQ24996.npy
-imgHQ09458.npy
-imgHQ02897.npy
-imgHQ13424.npy
-imgHQ08989.npy
-imgHQ12557.npy
-imgHQ17586.npy
-imgHQ15442.npy
-imgHQ00041.npy
-imgHQ10005.npy
-imgHQ01531.npy
-imgHQ24666.npy
-imgHQ06074.npy
-imgHQ00706.npy
-imgHQ21750.npy
-imgHQ26308.npy
-imgHQ23811.npy
-imgHQ24672.npy
-imgHQ20331.npy
-imgHQ00167.npy
-imgHQ17021.npy
-imgHQ27233.npy
-imgHQ10846.npy
-imgHQ19609.npy
-imgHQ10651.npy
-imgHQ15613.npy
-imgHQ08359.npy
-imgHQ12226.npy
-imgHQ15581.npy
-imgHQ18390.npy
-imgHQ10647.npy
-imgHQ02915.npy
-imgHQ12144.npy
-imgHQ13674.npy
-imgHQ01213.npy
-imgHQ18871.npy
-imgHQ23184.npy
-imgHQ29772.npy
-imgHQ10738.npy
-imgHQ07866.npy
-imgHQ19567.npy
-imgHQ22720.npy
-imgHQ02955.npy
-imgHQ11885.npy
-imgHQ09923.npy
-imgHQ19483.npy
-imgHQ22495.npy
-imgHQ08110.npy
-imgHQ21551.npy
-imgHQ03051.npy
-imgHQ08901.npy
-imgHQ17210.npy
-imgHQ09878.npy
-imgHQ25714.npy
-imgHQ22379.npy
-imgHQ18368.npy
-imgHQ20747.npy
-imgHQ14184.npy
-imgHQ09874.npy
-imgHQ25701.npy
-imgHQ02394.npy
-imgHQ22394.npy
-imgHQ15073.npy
-imgHQ12738.npy
-imgHQ29230.npy
-imgHQ12100.npy
-imgHQ06004.npy
-imgHQ28357.npy
-imgHQ25192.npy
-imgHQ07622.npy
-imgHQ10786.npy
-imgHQ19032.npy
-imgHQ21225.npy
-imgHQ13023.npy
-imgHQ24307.npy
-imgHQ27114.npy
-imgHQ14310.npy
-imgHQ09942.npy
-imgHQ14057.npy
-imgHQ28888.npy
-imgHQ07155.npy
-imgHQ16612.npy
-imgHQ26252.npy
-imgHQ20111.npy
-imgHQ27530.npy
-imgHQ16391.npy
-imgHQ29018.npy
-imgHQ08209.npy
-imgHQ29954.npy
-imgHQ20889.npy
-imgHQ23130.npy
-imgHQ10536.npy
-imgHQ26010.npy
-imgHQ26435.npy
-imgHQ03162.npy
-imgHQ18137.npy
-imgHQ13215.npy
-imgHQ15522.npy
-imgHQ25542.npy
-imgHQ22301.npy
-imgHQ04799.npy
-imgHQ20512.npy
-imgHQ12951.npy
-imgHQ03904.npy
-imgHQ14511.npy
-imgHQ05893.npy
-imgHQ03761.npy
-imgHQ10013.npy
-imgHQ14363.npy
-imgHQ20006.npy
-imgHQ27190.npy
-imgHQ17672.npy
-imgHQ11510.npy
-imgHQ13287.npy
-imgHQ07544.npy
-imgHQ00085.npy
-imgHQ11138.npy
-imgHQ07314.npy
-imgHQ19671.npy
-imgHQ10331.npy
-imgHQ22201.npy
-imgHQ25517.npy
-imgHQ28304.npy
-imgHQ20618.npy
-imgHQ27818.npy
-imgHQ16801.npy
-imgHQ22911.npy
-imgHQ03763.npy
-imgHQ24806.npy
-imgHQ12626.npy
-imgHQ16061.npy
-imgHQ18757.npy
-imgHQ09998.npy
-imgHQ14274.npy
-imgHQ13445.npy
-imgHQ28974.npy
-imgHQ26894.npy
-imgHQ14968.npy
-imgHQ12468.npy
-imgHQ22423.npy
-imgHQ27825.npy
-imgHQ20160.npy
-imgHQ16909.npy
-imgHQ13364.npy
-imgHQ11023.npy
-imgHQ25560.npy
-imgHQ04465.npy
-imgHQ14795.npy
-imgHQ10361.npy
-imgHQ29736.npy
-imgHQ18702.npy
-imgHQ08547.npy
-imgHQ19458.npy
-imgHQ14199.npy
-imgHQ09239.npy
-imgHQ13274.npy
-imgHQ24525.npy
-imgHQ14924.npy
-imgHQ02808.npy
-imgHQ17958.npy
-imgHQ06171.npy
-imgHQ13796.npy
-imgHQ23442.npy
-imgHQ28603.npy
-imgHQ04514.npy
-imgHQ09978.npy
-imgHQ26777.npy
-imgHQ11481.npy
-imgHQ22226.npy
-imgHQ04518.npy
-imgHQ14314.npy
-imgHQ17145.npy
-imgHQ00579.npy
-imgHQ02218.npy
-imgHQ29369.npy
-imgHQ15402.npy
-imgHQ07554.npy
-imgHQ11320.npy
-imgHQ29833.npy
-imgHQ08636.npy
-imgHQ04581.npy
-imgHQ20571.npy
-imgHQ25506.npy
-imgHQ28789.npy
-imgHQ25619.npy
-imgHQ09777.npy
-imgHQ07559.npy
-imgHQ27439.npy
-imgHQ14671.npy
-imgHQ10721.npy
-imgHQ08147.npy
-imgHQ24761.npy
-imgHQ10666.npy
-imgHQ13612.npy
-imgHQ10771.npy
-imgHQ11008.npy
-imgHQ19260.npy
-imgHQ13457.npy
-imgHQ27907.npy
-imgHQ25405.npy
-imgHQ29574.npy
-imgHQ14305.npy
-imgHQ05479.npy
-imgHQ17491.npy
-imgHQ09026.npy
-imgHQ13452.npy
-imgHQ01578.npy
-imgHQ08404.npy
-imgHQ15372.npy
-imgHQ22798.npy
-imgHQ20962.npy
-imgHQ16156.npy
-imgHQ09652.npy
-imgHQ11684.npy
-imgHQ23364.npy
-imgHQ06377.npy
-imgHQ28048.npy
-imgHQ19603.npy
-imgHQ16003.npy
-imgHQ10530.npy
-imgHQ08501.npy
-imgHQ15403.npy
-imgHQ14086.npy
-imgHQ23386.npy
-imgHQ03792.npy
-imgHQ09902.npy
-imgHQ20825.npy
-imgHQ21977.npy
-imgHQ02499.npy
-imgHQ27667.npy
-imgHQ04521.npy
-imgHQ21392.npy
-imgHQ19547.npy
-imgHQ11896.npy
-imgHQ16760.npy
-imgHQ13926.npy
-imgHQ19039.npy
-imgHQ05764.npy
-imgHQ24724.npy
-imgHQ13940.npy
-imgHQ01902.npy
-imgHQ25929.npy
-imgHQ28957.npy
-imgHQ03528.npy
-imgHQ19003.npy
-imgHQ06658.npy
-imgHQ07988.npy
-imgHQ27753.npy
-imgHQ16234.npy
-imgHQ19884.npy
-imgHQ00540.npy
-imgHQ25991.npy
-imgHQ21154.npy
-imgHQ09496.npy
-imgHQ27129.npy
-imgHQ01680.npy
-imgHQ19920.npy
-imgHQ23149.npy
-imgHQ11364.npy
-imgHQ15973.npy
-imgHQ13675.npy
-imgHQ24145.npy
-imgHQ23930.npy
-imgHQ19974.npy
-imgHQ05201.npy
-imgHQ08954.npy
-imgHQ27378.npy
-imgHQ17097.npy
-imgHQ06929.npy
-imgHQ20936.npy
-imgHQ07873.npy
-imgHQ24934.npy
-imgHQ21897.npy
-imgHQ03875.npy
-imgHQ05510.npy
-imgHQ19890.npy
-imgHQ02290.npy
-imgHQ08816.npy
-imgHQ14460.npy
-imgHQ18021.npy
-imgHQ07949.npy
-imgHQ11830.npy
-imgHQ16360.npy
-imgHQ04621.npy
-imgHQ00036.npy
-imgHQ16551.npy
-imgHQ22149.npy
-imgHQ03959.npy
-imgHQ19627.npy
-imgHQ18935.npy
-imgHQ17770.npy
-imgHQ28978.npy
-imgHQ18235.npy
-imgHQ28184.npy
-imgHQ10563.npy
-imgHQ01834.npy
-imgHQ28406.npy
-imgHQ16535.npy
-imgHQ29116.npy
-imgHQ11256.npy
-imgHQ19933.npy
-imgHQ27893.npy
-imgHQ21804.npy
-imgHQ02075.npy
-imgHQ26086.npy
-imgHQ25583.npy
-imgHQ03659.npy
-imgHQ16311.npy
-imgHQ12551.npy
-imgHQ14544.npy
-imgHQ14590.npy
-imgHQ05152.npy
-imgHQ27571.npy
-imgHQ22276.npy
-imgHQ19020.npy
-imgHQ26897.npy
-imgHQ07413.npy
-imgHQ15565.npy
-imgHQ28688.npy
-imgHQ27442.npy
-imgHQ16790.npy
-imgHQ02858.npy
-imgHQ23552.npy
-imgHQ21854.npy
-imgHQ25430.npy
-imgHQ11367.npy
-imgHQ06795.npy
-imgHQ12262.npy
-imgHQ05492.npy
-imgHQ14026.npy
-imgHQ25269.npy
-imgHQ27991.npy
-imgHQ12991.npy
-imgHQ28291.npy
-imgHQ22254.npy
-imgHQ26123.npy
-imgHQ17547.npy
-imgHQ01313.npy
-imgHQ07289.npy
-imgHQ15446.npy
-imgHQ08379.npy
-imgHQ20199.npy
-imgHQ04607.npy
-imgHQ03235.npy
-imgHQ18937.npy
-imgHQ11244.npy
-imgHQ16139.npy
-imgHQ20049.npy
-imgHQ28990.npy
-imgHQ24490.npy
-imgHQ20093.npy
-imgHQ28094.npy
-imgHQ01404.npy
-imgHQ02300.npy
-imgHQ23092.npy
-imgHQ24788.npy
-imgHQ26789.npy
-imgHQ04944.npy
-imgHQ17249.npy
-imgHQ07166.npy
-imgHQ02861.npy
-imgHQ22918.npy
-imgHQ21163.npy
-imgHQ18942.npy
-imgHQ25361.npy
-imgHQ19500.npy
-imgHQ05197.npy
-imgHQ26328.npy
-imgHQ24116.npy
-imgHQ07512.npy
-imgHQ00066.npy
-imgHQ04510.npy
-imgHQ08591.npy
-imgHQ11154.npy
-imgHQ01390.npy
-imgHQ24833.npy
-imgHQ06822.npy
-imgHQ29675.npy
-imgHQ16040.npy
-imgHQ07351.npy
-imgHQ23935.npy
-imgHQ14885.npy
-imgHQ21875.npy
-imgHQ04008.npy
-imgHQ02135.npy
-imgHQ06473.npy
-imgHQ00225.npy
-imgHQ05190.npy
-imgHQ16917.npy
-imgHQ15186.npy
-imgHQ13138.npy
-imgHQ26824.npy
-imgHQ10661.npy
-imgHQ12824.npy
-imgHQ23988.npy
-imgHQ11879.npy
-imgHQ09073.npy
-imgHQ12184.npy
-imgHQ24475.npy
-imgHQ06379.npy
-imgHQ01980.npy
-imgHQ23568.npy
-imgHQ00722.npy
-imgHQ13015.npy
-imgHQ20312.npy
-imgHQ18079.npy
-imgHQ27013.npy
-imgHQ21628.npy
-imgHQ09048.npy
-imgHQ27554.npy
-imgHQ28684.npy
-imgHQ03062.npy
-imgHQ25443.npy
-imgHQ25204.npy
-imgHQ26352.npy
-imgHQ22231.npy
-imgHQ19942.npy
-imgHQ19373.npy
-imgHQ18295.npy
-imgHQ19521.npy
-imgHQ23019.npy
-imgHQ19070.npy
-imgHQ11111.npy
-imgHQ10120.npy
-imgHQ02054.npy
-imgHQ25189.npy
-imgHQ05030.npy
-imgHQ15735.npy
-imgHQ07260.npy
-imgHQ09679.npy
-imgHQ21806.npy
-imgHQ06993.npy
-imgHQ04558.npy
-imgHQ20911.npy
-imgHQ20085.npy
-imgHQ22252.npy
-imgHQ08975.npy
-imgHQ11884.npy
-imgHQ09505.npy
-imgHQ10009.npy
-imgHQ15713.npy
-imgHQ18761.npy
-imgHQ14055.npy
-imgHQ06710.npy
-imgHQ19065.npy
-imgHQ01230.npy
-imgHQ15820.npy
-imgHQ20636.npy
-imgHQ20764.npy
-imgHQ16961.npy
-imgHQ25082.npy
-imgHQ12288.npy
-imgHQ10171.npy
-imgHQ01058.npy
-imgHQ17357.npy
-imgHQ09257.npy
-imgHQ08711.npy
-imgHQ08360.npy
-imgHQ06427.npy
-imgHQ22436.npy
-imgHQ05903.npy
-imgHQ05330.npy
-imgHQ00785.npy
-imgHQ02714.npy
-imgHQ11952.npy
-imgHQ03385.npy
-imgHQ00636.npy
-imgHQ05766.npy
-imgHQ26682.npy
-imgHQ17359.npy
-imgHQ18267.npy
-imgHQ18755.npy
-imgHQ20256.npy
-imgHQ27201.npy
-imgHQ21130.npy
-imgHQ09873.npy
-imgHQ14623.npy
-imgHQ18180.npy
-imgHQ19790.npy
-imgHQ24382.npy
-imgHQ08113.npy
-imgHQ24819.npy
-imgHQ09161.npy
-imgHQ06142.npy
-imgHQ01735.npy
-imgHQ25631.npy
-imgHQ21303.npy
-imgHQ22253.npy
-imgHQ14107.npy
-imgHQ21305.npy
-imgHQ06819.npy
-imgHQ16878.npy
-imgHQ02254.npy
-imgHQ26151.npy
-imgHQ18864.npy
-imgHQ02510.npy
-imgHQ20831.npy
-imgHQ10431.npy
-imgHQ05310.npy
-imgHQ10556.npy
-imgHQ21529.npy
-imgHQ10184.npy
-imgHQ06369.npy
-imgHQ08121.npy
-imgHQ04507.npy
-imgHQ11897.npy
-imgHQ05853.npy
-imgHQ01722.npy
-imgHQ16568.npy
-imgHQ12567.npy
-imgHQ18259.npy
-imgHQ21720.npy
-imgHQ06478.npy
-imgHQ19911.npy
-imgHQ13649.npy
-imgHQ29479.npy
-imgHQ29735.npy
-imgHQ15326.npy
-imgHQ28289.npy
-imgHQ20497.npy
-imgHQ22946.npy
-imgHQ29256.npy
-imgHQ28992.npy
-imgHQ03732.npy
-imgHQ15117.npy
-imgHQ12933.npy
-imgHQ29958.npy
-imgHQ03169.npy
-imgHQ12696.npy
-imgHQ03107.npy
-imgHQ27723.npy
-imgHQ10189.npy
-imgHQ09378.npy
-imgHQ26135.npy
-imgHQ00954.npy
-imgHQ16261.npy
-imgHQ14655.npy
-imgHQ18615.npy
-imgHQ03242.npy
-imgHQ16045.npy
-imgHQ12454.npy
-imgHQ05170.npy
-imgHQ06269.npy
-imgHQ21490.npy
-imgHQ08759.npy
-imgHQ06587.npy
-imgHQ19206.npy
-imgHQ03189.npy
-imgHQ27094.npy
-imgHQ28620.npy
-imgHQ24468.npy
-imgHQ08080.npy
-imgHQ09381.npy
-imgHQ08721.npy
-imgHQ00577.npy
-imgHQ16697.npy
-imgHQ14664.npy
-imgHQ10732.npy
-imgHQ04626.npy
-imgHQ05408.npy
-imgHQ14439.npy
-imgHQ29019.npy
-imgHQ03056.npy
-imgHQ19513.npy
-imgHQ17869.npy
-imgHQ07430.npy
-imgHQ26175.npy
-imgHQ17579.npy
-imgHQ23326.npy
-imgHQ14774.npy
-imgHQ08269.npy
-imgHQ04249.npy
-imgHQ08623.npy
-imgHQ27255.npy
-imgHQ17256.npy
-imgHQ26666.npy
-imgHQ08343.npy
-imgHQ22931.npy
-imgHQ16038.npy
-imgHQ26321.npy
-imgHQ26170.npy
-imgHQ12404.npy
-imgHQ02044.npy
-imgHQ17756.npy
-imgHQ07652.npy
-imgHQ16855.npy
-imgHQ07922.npy
-imgHQ14678.npy
-imgHQ11965.npy
-imgHQ12560.npy
-imgHQ24266.npy
-imgHQ20323.npy
-imgHQ12895.npy
-imgHQ02317.npy
-imgHQ29456.npy
-imgHQ29598.npy
-imgHQ01016.npy
-imgHQ13234.npy
-imgHQ29337.npy
-imgHQ17367.npy
-imgHQ28386.npy
-imgHQ12007.npy
-imgHQ25639.npy
-imgHQ26729.npy
-imgHQ05890.npy
-imgHQ24243.npy
-imgHQ08951.npy
-imgHQ22752.npy
-imgHQ16875.npy
-imgHQ27625.npy
-imgHQ17812.npy
-imgHQ24449.npy
-imgHQ02425.npy
-imgHQ03269.npy
-imgHQ11798.npy
-imgHQ19123.npy
-imgHQ07846.npy
-imgHQ19448.npy
-imgHQ26872.npy
-imgHQ29969.npy
-imgHQ26184.npy
-imgHQ27229.npy
-imgHQ18468.npy
-imgHQ10586.npy
-imgHQ19914.npy
-imgHQ18795.npy
-imgHQ19571.npy
-imgHQ16950.npy
-imgHQ18115.npy
-imgHQ11380.npy
-imgHQ29491.npy
-imgHQ03292.npy
-imgHQ22259.npy
-imgHQ17081.npy
-imgHQ10877.npy
-imgHQ08537.npy
-imgHQ13977.npy
-imgHQ04875.npy
-imgHQ12315.npy
-imgHQ16928.npy
-imgHQ10292.npy
-imgHQ13229.npy
-imgHQ16051.npy
-imgHQ15699.npy
-imgHQ23377.npy
-imgHQ04549.npy
-imgHQ10503.npy
-imgHQ11019.npy
-imgHQ27735.npy
-imgHQ25999.npy
-imgHQ15010.npy
-imgHQ26809.npy
-imgHQ21180.npy
-imgHQ18175.npy
-imgHQ02170.npy
-imgHQ21544.npy
-imgHQ19342.npy
-imgHQ28136.npy
-imgHQ07130.npy
-imgHQ25212.npy
-imgHQ28404.npy
-imgHQ23707.npy
-imgHQ11960.npy
-imgHQ26223.npy
-imgHQ22588.npy
-imgHQ09066.npy
-imgHQ21576.npy
-imgHQ17291.npy
-imgHQ28418.npy
-imgHQ17435.npy
-imgHQ27385.npy
-imgHQ02822.npy
-imgHQ01268.npy
-imgHQ21761.npy
-imgHQ09454.npy
-imgHQ16471.npy
-imgHQ17339.npy
-imgHQ07300.npy
-imgHQ20803.npy
-imgHQ11654.npy
-imgHQ12506.npy
-imgHQ15964.npy
-imgHQ05698.npy
-imgHQ17773.npy
-imgHQ11022.npy
-imgHQ26966.npy
-imgHQ08479.npy
-imgHQ01189.npy
-imgHQ05653.npy
-imgHQ08154.npy
-imgHQ07225.npy
-imgHQ11989.npy
-imgHQ01918.npy
-imgHQ08382.npy
-imgHQ03642.npy
-imgHQ05055.npy
-imgHQ25064.npy
-imgHQ21204.npy
-imgHQ03479.npy
-imgHQ22159.npy
-imgHQ25931.npy
-imgHQ18234.npy
-imgHQ07900.npy
-imgHQ18709.npy
-imgHQ03520.npy
-imgHQ27192.npy
-imgHQ28717.npy
-imgHQ11353.npy
-imgHQ05576.npy
-imgHQ16084.npy
-imgHQ11446.npy
-imgHQ11325.npy
-imgHQ09557.npy
-imgHQ29656.npy
-imgHQ00615.npy
-imgHQ24491.npy
-imgHQ20077.npy
-imgHQ14543.npy
-imgHQ23245.npy
-imgHQ06770.npy
-imgHQ14210.npy
-imgHQ06970.npy
-imgHQ08364.npy
-imgHQ10714.npy
-imgHQ12214.npy
-imgHQ20274.npy
-imgHQ06110.npy
-imgHQ20300.npy
-imgHQ12254.npy
-imgHQ12720.npy
-imgHQ10416.npy
-imgHQ09018.npy
-imgHQ03334.npy
-imgHQ18883.npy
-imgHQ26521.npy
-imgHQ05826.npy
-imgHQ14446.npy
-imgHQ05955.npy
-imgHQ27782.npy
-imgHQ11119.npy
-imgHQ06801.npy
-imgHQ19250.npy
-imgHQ29768.npy
-imgHQ18364.npy
-imgHQ00754.npy
-imgHQ24933.npy
-imgHQ08354.npy
-imgHQ12717.npy
-imgHQ16898.npy
-imgHQ08145.npy
-imgHQ22602.npy
-imgHQ16621.npy
-imgHQ09756.npy
-imgHQ22916.npy
-imgHQ12090.npy
-imgHQ14489.npy
-imgHQ04946.npy
-imgHQ00252.npy
-imgHQ23008.npy
-imgHQ06857.npy
-imgHQ28230.npy
-imgHQ04234.npy
-imgHQ23794.npy
-imgHQ16339.npy
-imgHQ20333.npy
-imgHQ14110.npy
-imgHQ05567.npy
-imgHQ28873.npy
-imgHQ23316.npy
-imgHQ07384.npy
-imgHQ17576.npy
-imgHQ21321.npy
-imgHQ21455.npy
-imgHQ20399.npy
-imgHQ22462.npy
-imgHQ06122.npy
-imgHQ02310.npy
-imgHQ20021.npy
-imgHQ19236.npy
-imgHQ13295.npy
-imgHQ07633.npy
-imgHQ16642.npy
-imgHQ09856.npy
-imgHQ19870.npy
-imgHQ19645.npy
-imgHQ19774.npy
-imgHQ26150.npy
-imgHQ27781.npy
-imgHQ07921.npy
-imgHQ02118.npy
-imgHQ04293.npy
-imgHQ17868.npy
-imgHQ21143.npy
-imgHQ01094.npy
-imgHQ02350.npy
-imgHQ13590.npy
-imgHQ27147.npy
-imgHQ17173.npy
-imgHQ04597.npy
-imgHQ26263.npy
-imgHQ13783.npy
-imgHQ02954.npy
-imgHQ14618.npy
-imgHQ24466.npy
-imgHQ28502.npy
-imgHQ28732.npy
-imgHQ12908.npy
-imgHQ04922.npy
-imgHQ00411.npy
-imgHQ06957.npy
-imgHQ16049.npy
-imgHQ16795.npy
-imgHQ02052.npy
-imgHQ26644.npy
-imgHQ06836.npy
-imgHQ29469.npy
-imgHQ16599.npy
-imgHQ25712.npy
-imgHQ03499.npy
-imgHQ24325.npy
-imgHQ26164.npy
-imgHQ03922.npy
-imgHQ16116.npy
-imgHQ17035.npy
-imgHQ28472.npy
-imgHQ29082.npy
-imgHQ11344.npy
-imgHQ06526.npy
-imgHQ08218.npy
-imgHQ25362.npy
-imgHQ10306.npy
-imgHQ20827.npy
-imgHQ16099.npy
-imgHQ29257.npy
-imgHQ23465.npy
-imgHQ04445.npy
-imgHQ14135.npy
-imgHQ29952.npy
-imgHQ05658.npy
-imgHQ24304.npy
-imgHQ12876.npy
-imgHQ26637.npy
-imgHQ11107.npy
-imgHQ07923.npy
-imgHQ12671.npy
-imgHQ21512.npy
-imgHQ08068.npy
-imgHQ18800.npy
-imgHQ09801.npy
-imgHQ10838.npy
-imgHQ00037.npy
-imgHQ02960.npy
-imgHQ02224.npy
-imgHQ10926.npy
-imgHQ14395.npy
-imgHQ13189.npy
-imgHQ25871.npy
-imgHQ20915.npy
-imgHQ05996.npy
-imgHQ03513.npy
-imgHQ12082.npy
-imgHQ26528.npy
-imgHQ12227.npy
-imgHQ03050.npy
-imgHQ28313.npy
-imgHQ25942.npy
-imgHQ03977.npy
-imgHQ22895.npy
-imgHQ01545.npy
-imgHQ03846.npy
-imgHQ14651.npy
-imgHQ00092.npy
-imgHQ20508.npy
-imgHQ29022.npy
-imgHQ04493.npy
-imgHQ15390.npy
-imgHQ25174.npy
-imgHQ02741.npy
-imgHQ09593.npy
-imgHQ28975.npy
-imgHQ27356.npy
-imgHQ26349.npy
-imgHQ23494.npy
-imgHQ21139.npy
-imgHQ24095.npy
-imgHQ11858.npy
-imgHQ28721.npy
-imgHQ02442.npy
-imgHQ13149.npy
-imgHQ13094.npy
-imgHQ20800.npy
-imgHQ12216.npy
-imgHQ24026.npy
-imgHQ03999.npy
-imgHQ09534.npy
-imgHQ08346.npy
-imgHQ27341.npy
-imgHQ21268.npy
-imgHQ27812.npy
-imgHQ27921.npy
-imgHQ09150.npy
-imgHQ10792.npy
-imgHQ06475.npy
-imgHQ14416.npy
-imgHQ09097.npy
-imgHQ22965.npy
-imgHQ24481.npy
-imgHQ27368.npy
-imgHQ20181.npy
-imgHQ18563.npy
-imgHQ21300.npy
-imgHQ00650.npy
-imgHQ02294.npy
-imgHQ17979.npy
-imgHQ28064.npy
-imgHQ19539.npy
-imgHQ27708.npy
-imgHQ04684.npy
-imgHQ17686.npy
-imgHQ12079.npy
-imgHQ18222.npy
-imgHQ06535.npy
-imgHQ18838.npy
-imgHQ01824.npy
-imgHQ10235.npy
-imgHQ27557.npy
-imgHQ05587.npy
-imgHQ24440.npy
-imgHQ02426.npy
-imgHQ27809.npy
-imgHQ06179.npy
-imgHQ29150.npy
-imgHQ02055.npy
-imgHQ24308.npy
-imgHQ28982.npy
-imgHQ11652.npy
-imgHQ23869.npy
-imgHQ28329.npy
-imgHQ03158.npy
-imgHQ25165.npy
-imgHQ17089.npy
-imgHQ07378.npy
-imgHQ04241.npy
-imgHQ21244.npy
-imgHQ04049.npy
-imgHQ05537.npy
-imgHQ05255.npy
-imgHQ19865.npy
-imgHQ24267.npy
-imgHQ22225.npy
-imgHQ12099.npy
-imgHQ09974.npy
-imgHQ14823.npy
-imgHQ05926.npy
-imgHQ04905.npy
-imgHQ04984.npy
-imgHQ24399.npy
-imgHQ27273.npy
-imgHQ25800.npy
-imgHQ00361.npy
-imgHQ03089.npy
-imgHQ29991.npy
-imgHQ16854.npy
-imgHQ20790.npy
-imgHQ16683.npy
-imgHQ04691.npy
-imgHQ29297.npy
-imgHQ02950.npy
-imgHQ09832.npy
-imgHQ07627.npy
-imgHQ01738.npy
-imgHQ06903.npy
-imgHQ18311.npy
-imgHQ10244.npy
-imgHQ05819.npy
-imgHQ02560.npy
-imgHQ27507.npy
-imgHQ21307.npy
-imgHQ27476.npy
-imgHQ28073.npy
-imgHQ11559.npy
-imgHQ15900.npy
-imgHQ08882.npy
-imgHQ27982.npy
-imgHQ01104.npy
-imgHQ10812.npy
-imgHQ05004.npy
-imgHQ28802.npy
-imgHQ16389.npy
-imgHQ23219.npy
-imgHQ05652.npy
-imgHQ16235.npy
-imgHQ24703.npy
-imgHQ22020.npy
-imgHQ26728.npy
-imgHQ04111.npy
-imgHQ28905.npy
-imgHQ07148.npy
-imgHQ28002.npy
-imgHQ21589.npy
-imgHQ04224.npy
-imgHQ08967.npy
-imgHQ24194.npy
-imgHQ07004.npy
-imgHQ24929.npy
-imgHQ04907.npy
-imgHQ14105.npy
-imgHQ22136.npy
-imgHQ17450.npy
-imgHQ06871.npy
-imgHQ01538.npy
-imgHQ08871.npy
-imgHQ09736.npy
-imgHQ00240.npy
-imgHQ23813.npy
-imgHQ04139.npy
-imgHQ22827.npy
-imgHQ19254.npy
-imgHQ10723.npy
-imgHQ13916.npy
-imgHQ28579.npy
-imgHQ16662.npy
-imgHQ06229.npy
-imgHQ08096.npy
-imgHQ20534.npy
-imgHQ21483.npy
-imgHQ14816.npy
-imgHQ16115.npy
-imgHQ00772.npy
-imgHQ21548.npy
-imgHQ06716.npy
-imgHQ04534.npy
-imgHQ02162.npy
-imgHQ08183.npy
-imgHQ08455.npy
-imgHQ22976.npy
-imgHQ26772.npy
-imgHQ25667.npy
-imgHQ00423.npy
-imgHQ27291.npy
-imgHQ02581.npy
-imgHQ13216.npy
-imgHQ14414.npy
-imgHQ07787.npy
-imgHQ28026.npy
-imgHQ14811.npy
-imgHQ11833.npy
-imgHQ14869.npy
-imgHQ20633.npy
-imgHQ11012.npy
-imgHQ09885.npy
-imgHQ19771.npy
-imgHQ06629.npy
-imgHQ08323.npy
-imgHQ20896.npy
-imgHQ04595.npy
-imgHQ10629.npy
-imgHQ13373.npy
-imgHQ20007.npy
-imgHQ23478.npy
-imgHQ22448.npy
-imgHQ07636.npy
-imgHQ08889.npy
-imgHQ10190.npy
-imgHQ01171.npy
-imgHQ15924.npy
-imgHQ00126.npy
-imgHQ23071.npy
-imgHQ03248.npy
-imgHQ07777.npy
-imgHQ12938.npy
-imgHQ26193.npy
-imgHQ07332.npy
-imgHQ00125.npy
-imgHQ06808.npy
-imgHQ15812.npy
-imgHQ21769.npy
-imgHQ08260.npy
-imgHQ09888.npy
-imgHQ08337.npy
-imgHQ21979.npy
-imgHQ22896.npy
-imgHQ23668.npy
-imgHQ06328.npy
-imgHQ12493.npy
-imgHQ21535.npy
-imgHQ28878.npy
-imgHQ26260.npy
-imgHQ04087.npy
-imgHQ22900.npy
-imgHQ13970.npy
-imgHQ28214.npy
-imgHQ29301.npy
-imgHQ13936.npy
-imgHQ12571.npy
-imgHQ22932.npy
-imgHQ16752.npy
-imgHQ15301.npy
-imgHQ13333.npy
-imgHQ20342.npy
-imgHQ11602.npy
-imgHQ28294.npy
-imgHQ07702.npy
-imgHQ19188.npy
-imgHQ13585.npy
-imgHQ29375.npy
-imgHQ17377.npy
-imgHQ05381.npy
-imgHQ19141.npy
-imgHQ05829.npy
-imgHQ09854.npy
-imgHQ16070.npy
-imgHQ02124.npy
-imgHQ20960.npy
-imgHQ07493.npy
-imgHQ19471.npy
-imgHQ13812.npy
-imgHQ27968.npy
-imgHQ15950.npy
-imgHQ03644.npy
-imgHQ23903.npy
-imgHQ05622.npy
-imgHQ23926.npy
-imgHQ02369.npy
-imgHQ08535.npy
-imgHQ00357.npy
-imgHQ10748.npy
-imgHQ17248.npy
-imgHQ16239.npy
-imgHQ20742.npy
-imgHQ22975.npy
-imgHQ29092.npy
-imgHQ12619.npy
-imgHQ13046.npy
-imgHQ26063.npy
-imgHQ09192.npy
-imgHQ26868.npy
-imgHQ26134.npy
-imgHQ05901.npy
-imgHQ18880.npy
-imgHQ07489.npy
-imgHQ22617.npy
-imgHQ20428.npy
-imgHQ09748.npy
-imgHQ09928.npy
-imgHQ08862.npy
-imgHQ16619.npy
-imgHQ14644.npy
-imgHQ06628.npy
-imgHQ19605.npy
-imgHQ11330.npy
-imgHQ22090.npy
-imgHQ25816.npy
-imgHQ04892.npy
-imgHQ13270.npy
-imgHQ20034.npy
-imgHQ10888.npy
-imgHQ06856.npy
-imgHQ16952.npy
-imgHQ10646.npy
-imgHQ27862.npy
-imgHQ29357.npy
-imgHQ16625.npy
-imgHQ00153.npy
-imgHQ07578.npy
-imgHQ19472.npy
-imgHQ29820.npy
-imgHQ17859.npy
-imgHQ08727.npy
-imgHQ16119.npy
-imgHQ15191.npy
-imgHQ17193.npy
-imgHQ20858.npy
-imgHQ09289.npy
-imgHQ09340.npy
-imgHQ24076.npy
-imgHQ00581.npy
-imgHQ10989.npy
-imgHQ23246.npy
-imgHQ20175.npy
-imgHQ22296.npy
-imgHQ14580.npy
-imgHQ15848.npy
-imgHQ24805.npy
-imgHQ17945.npy
-imgHQ09657.npy
-imgHQ28407.npy
-imgHQ18762.npy
-imgHQ29771.npy
-imgHQ29908.npy
-imgHQ22621.npy
-imgHQ27521.npy
-imgHQ19115.npy
-imgHQ09461.npy
-imgHQ01379.npy
-imgHQ15717.npy
-imgHQ12705.npy
-imgHQ19402.npy
-imgHQ28263.npy
-imgHQ02769.npy
-imgHQ28065.npy
-imgHQ11268.npy
-imgHQ02252.npy
-imgHQ20802.npy
-imgHQ10196.npy
-imgHQ27071.npy
-imgHQ28827.npy
-imgHQ03836.npy
-imgHQ23832.npy
-imgHQ09448.npy
-imgHQ03619.npy
-imgHQ08345.npy
-imgHQ22663.npy
-imgHQ15348.npy
-imgHQ14004.npy
-imgHQ03929.npy
-imgHQ11988.npy
-imgHQ26946.npy
-imgHQ26356.npy
-imgHQ01684.npy
-imgHQ02711.npy
-imgHQ15986.npy
-imgHQ05178.npy
-imgHQ20251.npy
-imgHQ17774.npy
-imgHQ05006.npy
-imgHQ22996.npy
-imgHQ06678.npy
-imgHQ24342.npy
-imgHQ06184.npy
-imgHQ12962.npy
-imgHQ00265.npy
-imgHQ24409.npy
-imgHQ25006.npy
-imgHQ16511.npy
-imgHQ04885.npy
-imgHQ25005.npy
-imgHQ04400.npy
-imgHQ11568.npy
-imgHQ14012.npy
-imgHQ28377.npy
-imgHQ17176.npy
-imgHQ12298.npy
-imgHQ01743.npy
-imgHQ19196.npy
-imgHQ04432.npy
-imgHQ09139.npy
-imgHQ19610.npy
-imgHQ00388.npy
-imgHQ18470.npy
-imgHQ01388.npy
-imgHQ06637.npy
-imgHQ01699.npy
-imgHQ19842.npy
-imgHQ09137.npy
-imgHQ06687.npy
-imgHQ20650.npy
-imgHQ12232.npy
-imgHQ10400.npy
-imgHQ01972.npy
-imgHQ11381.npy
-imgHQ29677.npy
-imgHQ10821.npy
-imgHQ23309.npy
-imgHQ29591.npy
-imgHQ03044.npy
-imgHQ06463.npy
-imgHQ27776.npy
-imgHQ24270.npy
-imgHQ09823.npy
-imgHQ17498.npy
-imgHQ02179.npy
-imgHQ02730.npy
-imgHQ08222.npy
-imgHQ27871.npy
-imgHQ03475.npy
-imgHQ07753.npy
-imgHQ03319.npy
-imgHQ24465.npy
-imgHQ00730.npy
-imgHQ05406.npy
-imgHQ22607.npy
-imgHQ29651.npy
-imgHQ18535.npy
-imgHQ23474.npy
-imgHQ09660.npy
-imgHQ06530.npy
-imgHQ01886.npy
-imgHQ29800.npy
-imgHQ14915.npy
-imgHQ25569.npy
-imgHQ24720.npy
-imgHQ28251.npy
-imgHQ14500.npy
-imgHQ04137.npy
-imgHQ05609.npy
-imgHQ19223.npy
-imgHQ00487.npy
-imgHQ05943.npy
-imgHQ28039.npy
-imgHQ29633.npy
-imgHQ13208.npy
-imgHQ01435.npy
-imgHQ14282.npy
-imgHQ22964.npy
-imgHQ13669.npy
-imgHQ14703.npy
-imgHQ27119.npy
-imgHQ17042.npy
-imgHQ01237.npy
-imgHQ16343.npy
-imgHQ18546.npy
-imgHQ04285.npy
-imgHQ18351.npy
-imgHQ00203.npy
-imgHQ24994.npy
-imgHQ16496.npy
-imgHQ14407.npy
-imgHQ17107.npy
-imgHQ13099.npy
-imgHQ21566.npy
-imgHQ02072.npy
-imgHQ10019.npy
-imgHQ23249.npy
-imgHQ25144.npy
-imgHQ11976.npy
-imgHQ29026.npy
-imgHQ07907.npy
-imgHQ13106.npy
-imgHQ22494.npy
-imgHQ08178.npy
-imgHQ21835.npy
-imgHQ03388.npy
-imgHQ25553.npy
-imgHQ11218.npy
-imgHQ19134.npy
-imgHQ22551.npy
-imgHQ15904.npy
-imgHQ06214.npy
-imgHQ29596.npy
-imgHQ11718.npy
-imgHQ05390.npy
-imgHQ28220.npy
-imgHQ13406.npy
-imgHQ16291.npy
-imgHQ19213.npy
-imgHQ08115.npy
-imgHQ11528.npy
-imgHQ06885.npy
-imgHQ00332.npy
-imgHQ06778.npy
-imgHQ00014.npy
-imgHQ20699.npy
-imgHQ29196.npy
-imgHQ23239.npy
-imgHQ25596.npy
-imgHQ15328.npy
-imgHQ18059.npy
-imgHQ15108.npy
-imgHQ06847.npy
-imgHQ29486.npy
-imgHQ14455.npy
-imgHQ15122.npy
-imgHQ18379.npy
-imgHQ21992.npy
-imgHQ24480.npy
-imgHQ03226.npy
-imgHQ01217.npy
-imgHQ26744.npy
-imgHQ12710.npy
-imgHQ25962.npy
-imgHQ00463.npy
-imgHQ20453.npy
-imgHQ24902.npy
-imgHQ19991.npy
-imgHQ27795.npy
-imgHQ12992.npy
-imgHQ09783.npy
-imgHQ01644.npy
-imgHQ26851.npy
-imgHQ23058.npy
-imgHQ29827.npy
-imgHQ07752.npy
-imgHQ24509.npy
-imgHQ14187.npy
-imgHQ04291.npy
-imgHQ03918.npy
-imgHQ16720.npy
-imgHQ23139.npy
-imgHQ17527.npy
-imgHQ25280.npy
-imgHQ12181.npy
-imgHQ08639.npy
-imgHQ22446.npy
-imgHQ16222.npy
-imgHQ27516.npy
-imgHQ18193.npy
-imgHQ18325.npy
-imgHQ11288.npy
-imgHQ12634.npy
-imgHQ13497.npy
-imgHQ17689.npy
-imgHQ09852.npy
-imgHQ16763.npy
-imgHQ28940.npy
-imgHQ02148.npy
-imgHQ29389.npy
-imgHQ12606.npy
-imgHQ09916.npy
-imgHQ16012.npy
-imgHQ23809.npy
-imgHQ06319.npy
-imgHQ19511.npy
-imgHQ21355.npy
-imgHQ10024.npy
-imgHQ07570.npy
-imgHQ12880.npy
-imgHQ05774.npy
-imgHQ18651.npy
-imgHQ24853.npy
-imgHQ09787.npy
-imgHQ20072.npy
-imgHQ23373.npy
-imgHQ28918.npy
-imgHQ12006.npy
-imgHQ06774.npy
-imgHQ15029.npy
-imgHQ12727.npy
-imgHQ22643.npy
-imgHQ13683.npy
-imgHQ20934.npy
-imgHQ27745.npy
-imgHQ26007.npy
-imgHQ04648.npy
-imgHQ14474.npy
-imgHQ25299.npy
-imgHQ06225.npy
-imgHQ14340.npy
-imgHQ07994.npy
-imgHQ22388.npy
-imgHQ03935.npy
-imgHQ09421.npy
-imgHQ01412.npy
-imgHQ14441.npy
-imgHQ25418.npy
-imgHQ23623.npy
-imgHQ11812.npy
-imgHQ27933.npy
-imgHQ18174.npy
-imgHQ00991.npy
-imgHQ26514.npy
-imgHQ24660.npy
-imgHQ03351.npy
-imgHQ23458.npy
-imgHQ24786.npy
-imgHQ00174.npy
-imgHQ01493.npy
-imgHQ04554.npy
-imgHQ09957.npy
-imgHQ29261.npy
-imgHQ18272.npy
-imgHQ03223.npy
-imgHQ29397.npy
-imgHQ29541.npy
-imgHQ23635.npy
-imgHQ13359.npy
-imgHQ13831.npy
-imgHQ12092.npy
-imgHQ12494.npy
-imgHQ08442.npy
-imgHQ04988.npy
-imgHQ04466.npy
-imgHQ25601.npy
-imgHQ09899.npy
-imgHQ25171.npy
-imgHQ26763.npy
-imgHQ14643.npy
-imgHQ09684.npy
-imgHQ27805.npy
-imgHQ04020.npy
-imgHQ07354.npy
-imgHQ05169.npy
-imgHQ02077.npy
-imgHQ27563.npy
-imgHQ06549.npy
-imgHQ29193.npy
-imgHQ10673.npy
-imgHQ01241.npy
-imgHQ23009.npy
-imgHQ01014.npy
-imgHQ08985.npy
-imgHQ15474.npy
-imgHQ23232.npy
-imgHQ08672.npy
-imgHQ01675.npy
-imgHQ23760.npy
-imgHQ08726.npy
-imgHQ10696.npy
-imgHQ19976.npy
-imgHQ17410.npy
-imgHQ03454.npy
-imgHQ26675.npy
-imgHQ11487.npy
-imgHQ16081.npy
-imgHQ26115.npy
-imgHQ22675.npy
-imgHQ17862.npy
-imgHQ06569.npy
-imgHQ18147.npy
-imgHQ04520.npy
-imgHQ23721.npy
-imgHQ14304.npy
-imgHQ10987.npy
-imgHQ20712.npy
-imgHQ02365.npy
-imgHQ29681.npy
-imgHQ21223.npy
-imgHQ09937.npy
-imgHQ03525.npy
-imgHQ18450.npy
-imgHQ05662.npy
-imgHQ18101.npy
-imgHQ05633.npy
-imgHQ00732.npy
-imgHQ04210.npy
-imgHQ11925.npy
-imgHQ26228.npy
-imgHQ14391.npy
-imgHQ23399.npy
-imgHQ16347.npy
-imgHQ00816.npy
-imgHQ00141.npy
-imgHQ00819.npy
-imgHQ28446.npy
-imgHQ04792.npy
-imgHQ23523.npy
-imgHQ09437.npy
-imgHQ24784.npy
-imgHQ28870.npy
-imgHQ08279.npy
-imgHQ08831.npy
-imgHQ14104.npy
-imgHQ15888.npy
-imgHQ23931.npy
-imgHQ03565.npy
-imgHQ25499.npy
-imgHQ19586.npy
-imgHQ16667.npy
-imgHQ25002.npy
-imgHQ03101.npy
-imgHQ08874.npy
-imgHQ09962.npy
-imgHQ00992.npy
-imgHQ29164.npy
-imgHQ25375.npy
-imgHQ11215.npy
-imgHQ19936.npy
-imgHQ22315.npy
-imgHQ26268.npy
-imgHQ04260.npy
-imgHQ01170.npy
-imgHQ05512.npy
-imgHQ26147.npy
-imgHQ20130.npy
-imgHQ18192.npy
-imgHQ18058.npy
-imgHQ09766.npy
-imgHQ07585.npy
-imgHQ04871.npy
-imgHQ01830.npy
-imgHQ14154.npy
-imgHQ00872.npy
-imgHQ24414.npy
-imgHQ09822.npy
-imgHQ10760.npy
-imgHQ25668.npy
-imgHQ27428.npy
-imgHQ21150.npy
-imgHQ15510.npy
-imgHQ28166.npy
-imgHQ17798.npy
-imgHQ02568.npy
-imgHQ07575.npy
-imgHQ20036.npy
-imgHQ14672.npy
-imgHQ13268.npy
-imgHQ10191.npy
-imgHQ10058.npy
-imgHQ01994.npy
-imgHQ04700.npy
-imgHQ08635.npy
-imgHQ17262.npy
-imgHQ12459.npy
-imgHQ04064.npy
-imgHQ23052.npy
-imgHQ05597.npy
-imgHQ26977.npy
-imgHQ17250.npy
-imgHQ22852.npy
-imgHQ29447.npy
-imgHQ03495.npy
-imgHQ09285.npy
-imgHQ08130.npy
-imgHQ16896.npy
-imgHQ27276.npy
-imgHQ20135.npy
-imgHQ29710.npy
-imgHQ11066.npy
-imgHQ14850.npy
-imgHQ28632.npy
-imgHQ07038.npy
-imgHQ27488.npy
-imgHQ01584.npy
-imgHQ26001.npy
-imgHQ07467.npy
-imgHQ19957.npy
-imgHQ09643.npy
-imgHQ01488.npy
-imgHQ11426.npy
-imgHQ05748.npy
-imgHQ27489.npy
-imgHQ06935.npy
-imgHQ12629.npy
-imgHQ02211.npy
-imgHQ13390.npy
-imgHQ08494.npy
-imgHQ06768.npy
-imgHQ00059.npy
-imgHQ25000.npy
-imgHQ13541.npy
-imgHQ00311.npy
-imgHQ20029.npy
-imgHQ03357.npy
-imgHQ21185.npy
-imgHQ08093.npy
-imgHQ04458.npy
-imgHQ16701.npy
-imgHQ23647.npy
-imgHQ17012.npy
-imgHQ02200.npy
-imgHQ22308.npy
-imgHQ13306.npy
-imgHQ08244.npy
-imgHQ12500.npy
-imgHQ12967.npy
-imgHQ12510.npy
-imgHQ06908.npy
-imgHQ02068.npy
-imgHQ06081.npy
-imgHQ01892.npy
-imgHQ20564.npy
-imgHQ27608.npy
-imgHQ14091.npy
-imgHQ13354.npy
-imgHQ25663.npy
-imgHQ26458.npy
-imgHQ04513.npy
-imgHQ18958.npy
-imgHQ09872.npy
-imgHQ13025.npy
-imgHQ24670.npy
-imgHQ06375.npy
-imgHQ08324.npy
-imgHQ07409.npy
-imgHQ06127.npy
-imgHQ23585.npy
-imgHQ00870.npy
-imgHQ05582.npy
-imgHQ04681.npy
-imgHQ06176.npy
-imgHQ27116.npy
-imgHQ22579.npy
-imgHQ15560.npy
-imgHQ23793.npy
-imgHQ18921.npy
-imgHQ06865.npy
-imgHQ11076.npy
-imgHQ26396.npy
-imgHQ17029.npy
-imgHQ04781.npy
-imgHQ10644.npy
-imgHQ03969.npy
-imgHQ16241.npy
-imgHQ27834.npy
-imgHQ25402.npy
-imgHQ28043.npy
-imgHQ17271.npy
-imgHQ06419.npy
-imgHQ27302.npy
-imgHQ13861.npy
-imgHQ07972.npy
-imgHQ12856.npy
-imgHQ03326.npy
-imgHQ06869.npy
-imgHQ14857.npy
-imgHQ20255.npy
-imgHQ04050.npy
-imgHQ25866.npy
-imgHQ20601.npy
-imgHQ15059.npy
-imgHQ21348.npy
-imgHQ01265.npy
-imgHQ18577.npy
-imgHQ01647.npy
-imgHQ14974.npy
-imgHQ28669.npy
-imgHQ13755.npy
-imgHQ09985.npy
-imgHQ29988.npy
-imgHQ08021.npy
-imgHQ29366.npy
-imgHQ15536.npy
-imgHQ19268.npy
-imgHQ06794.npy
-imgHQ05976.npy
-imgHQ12541.npy
-imgHQ05236.npy
-imgHQ13386.npy
-imgHQ28809.npy
-imgHQ08259.npy
-imgHQ26257.npy
-imgHQ23362.npy
-imgHQ10554.npy
-imgHQ29983.npy
-imgHQ11932.npy
-imgHQ06323.npy
-imgHQ10809.npy
-imgHQ00048.npy
-imgHQ26038.npy
-imgHQ26104.npy
-imgHQ09501.npy
-imgHQ00050.npy
-imgHQ01339.npy
-imgHQ17299.npy
-imgHQ04152.npy
-imgHQ08450.npy
-imgHQ18950.npy
-imgHQ28252.npy
-imgHQ16274.npy
-imgHQ05908.npy
-imgHQ05421.npy
-imgHQ17874.npy
-imgHQ02602.npy
-imgHQ00682.npy
-imgHQ20167.npy
-imgHQ15537.npy
-imgHQ10410.npy
-imgHQ12537.npy
-imgHQ14658.npy
-imgHQ06114.npy
-imgHQ11630.npy
-imgHQ22489.npy
-imgHQ05405.npy
-imgHQ02982.npy
-imgHQ03348.npy
-imgHQ02259.npy
-imgHQ24600.npy
-imgHQ10302.npy
-imgHQ20501.npy
-imgHQ20700.npy
-imgHQ21050.npy
-imgHQ26691.npy
-imgHQ25793.npy
-imgHQ08268.npy
-imgHQ14937.npy
-imgHQ14895.npy
-imgHQ02511.npy
-imgHQ24460.npy
-imgHQ02089.npy
-imgHQ22971.npy
-imgHQ22850.npy
-imgHQ01129.npy
-imgHQ24595.npy
-imgHQ16368.npy
-imgHQ01933.npy
-imgHQ26161.npy
-imgHQ17615.npy
-imgHQ21424.npy
-imgHQ12783.npy
-imgHQ29345.npy
-imgHQ28581.npy
-imgHQ13064.npy
-imgHQ04503.npy
-imgHQ17509.npy
-imgHQ16710.npy
-imgHQ06188.npy
-imgHQ05932.npy
-imgHQ26182.npy
-imgHQ03350.npy
-imgHQ01389.npy
-imgHQ20405.npy
-imgHQ11190.npy
-imgHQ07692.npy
-imgHQ09159.npy
-imgHQ01771.npy
-imgHQ01709.npy
-imgHQ14612.npy
-imgHQ11362.npy
-imgHQ06565.npy
-imgHQ13950.npy
-imgHQ13019.npy
-imgHQ01683.npy
-imgHQ21239.npy
-imgHQ29340.npy
-imgHQ29763.npy
-imgHQ21995.npy
-imgHQ06164.npy
-imgHQ29713.npy
-imgHQ15671.npy
-imgHQ13119.npy
-imgHQ05496.npy
-imgHQ05485.npy
-imgHQ01501.npy
-imgHQ19967.npy
-imgHQ20772.npy
-imgHQ08645.npy
-imgHQ29072.npy
-imgHQ01411.npy
-imgHQ13931.npy
-imgHQ08458.npy
-imgHQ22376.npy
-imgHQ00489.npy
-imgHQ08709.npy
-imgHQ13819.npy
-imgHQ15106.npy
-imgHQ26156.npy
-imgHQ15499.npy
-imgHQ08074.npy
-imgHQ13990.npy
-imgHQ25864.npy
-imgHQ05283.npy
-imgHQ29887.npy
-imgHQ14553.npy
-imgHQ09645.npy
-imgHQ13672.npy
-imgHQ01097.npy
-imgHQ00618.npy
-imgHQ05654.npy
-imgHQ10403.npy
-imgHQ25845.npy
-imgHQ29016.npy
-imgHQ09473.npy
-imgHQ29490.npy
-imgHQ25038.npy
-imgHQ21774.npy
-imgHQ00922.npy
-imgHQ07992.npy
-imgHQ14752.npy
-imgHQ15998.npy
-imgHQ10896.npy
-imgHQ29529.npy
-imgHQ24314.npy
-imgHQ16444.npy
-imgHQ29749.npy
-imgHQ15018.npy
-imgHQ23564.npy
-imgHQ19434.npy
-imgHQ10943.npy
-imgHQ25564.npy
-imgHQ01838.npy
-imgHQ18221.npy
-imgHQ01072.npy
-imgHQ01629.npy
-imgHQ15636.npy
-imgHQ17071.npy
-imgHQ24960.npy
-imgHQ04538.npy
-imgHQ22051.npy
-imgHQ15408.npy
-imgHQ14084.npy
-imgHQ05446.npy
-imgHQ01440.npy
-imgHQ21803.npy
-imgHQ19501.npy
-imgHQ08197.npy
-imgHQ13746.npy
-imgHQ15127.npy
-imgHQ09510.npy
-imgHQ10376.npy
-imgHQ11222.npy
-imgHQ23181.npy
-imgHQ25547.npy
-imgHQ13372.npy
-imgHQ14900.npy
-imgHQ09893.npy
-imgHQ04096.npy
-imgHQ22152.npy
-imgHQ28183.npy
-imgHQ16598.npy
-imgHQ18094.npy
-imgHQ26197.npy
-imgHQ28149.npy
-imgHQ15798.npy
-imgHQ00179.npy
-imgHQ17458.npy
-imgHQ07735.npy
-imgHQ17904.npy
-imgHQ15129.npy
-imgHQ04794.npy
-imgHQ13656.npy
-imgHQ15927.npy
-imgHQ08427.npy
-imgHQ12481.npy
-imgHQ04823.npy
-imgHQ08071.npy
-imgHQ03148.npy
-imgHQ12451.npy
-imgHQ17440.npy
-imgHQ28804.npy
-imgHQ13800.npy
-imgHQ01772.npy
-imgHQ20716.npy
-imgHQ07337.npy
-imgHQ29808.npy
-imgHQ12484.npy
-imgHQ00364.npy
-imgHQ09855.npy
-imgHQ25070.npy
-imgHQ10565.npy
-imgHQ14023.npy
-imgHQ02864.npy
-imgHQ15316.npy
-imgHQ14201.npy
-imgHQ12259.npy
-imgHQ15130.npy
-imgHQ03494.npy
-imgHQ01558.npy
-imgHQ07277.npy
-imgHQ21372.npy
-imgHQ23174.npy
-imgHQ09637.npy
-imgHQ03849.npy
-imgHQ20982.npy
-imgHQ01802.npy
-imgHQ03225.npy
-imgHQ12450.npy
-imgHQ24718.npy
-imgHQ28371.npy
-imgHQ28741.npy
-imgHQ23378.npy
-imgHQ06272.npy
-imgHQ02906.npy
-imgHQ07423.npy
-imgHQ02271.npy
-imgHQ23755.npy
-imgHQ24661.npy
-imgHQ10053.npy
-imgHQ15840.npy
-imgHQ17453.npy
-imgHQ18085.npy
-imgHQ18600.npy
-imgHQ07460.npy
-imgHQ12276.npy
-imgHQ18285.npy
-imgHQ04092.npy
-imgHQ08461.npy
-imgHQ11080.npy
-imgHQ00694.npy
-imgHQ08737.npy
-imgHQ26220.npy
-imgHQ24183.npy
-imgHQ16196.npy
-imgHQ08350.npy
-imgHQ27351.npy
-imgHQ00214.npy
-imgHQ21971.npy
-imgHQ11475.npy
-imgHQ19945.npy
-imgHQ08863.npy
-imgHQ23112.npy
-imgHQ10873.npy
-imgHQ23198.npy
-imgHQ16989.npy
-imgHQ22100.npy
-imgHQ25329.npy
-imgHQ18012.npy
-imgHQ12960.npy
-imgHQ19666.npy
-imgHQ21984.npy
-imgHQ17470.npy
-imgHQ05518.npy
-imgHQ29709.npy
-imgHQ14171.npy
-imgHQ03460.npy
-imgHQ04033.npy
-imgHQ03483.npy
-imgHQ06385.npy
-imgHQ14307.npy
-imgHQ17824.npy
-imgHQ24044.npy
-imgHQ22358.npy
-imgHQ04568.npy
-imgHQ24655.npy
-imgHQ03685.npy
-imgHQ08062.npy
-imgHQ08884.npy
-imgHQ04506.npy
-imgHQ02340.npy
-imgHQ18809.npy
-imgHQ05644.npy
-imgHQ06953.npy
-imgHQ05448.npy
-imgHQ13533.npy
-imgHQ27280.npy
-imgHQ20665.npy
-imgHQ29758.npy
-imgHQ16945.npy
-imgHQ16245.npy
-imgHQ17442.npy
-imgHQ17939.npy
-imgHQ18831.npy
-imgHQ08203.npy
-imgHQ21313.npy
-imgHQ21282.npy
-imgHQ00454.npy
-imgHQ20752.npy
-imgHQ19383.npy
-imgHQ21386.npy
-imgHQ19322.npy
-imgHQ29748.npy
-imgHQ26751.npy
-imgHQ01624.npy
-imgHQ02175.npy
-imgHQ27802.npy
-imgHQ18740.npy
-imgHQ12821.npy
-imgHQ13422.npy
-imgHQ05532.npy
-imgHQ03086.npy
-imgHQ21715.npy
-imgHQ29821.npy
-imgHQ15782.npy
-imgHQ13331.npy
-imgHQ18891.npy
-imgHQ09238.npy
-imgHQ20946.npy
-imgHQ13856.npy
-imgHQ23904.npy
-imgHQ28755.npy
-imgHQ24082.npy
-imgHQ17524.npy
-imgHQ09429.npy
-imgHQ03672.npy
-imgHQ15185.npy
-imgHQ14298.npy
-imgHQ06553.npy
-imgHQ25444.npy
-imgHQ25077.npy
-imgHQ11889.npy
-imgHQ05275.npy
-imgHQ06030.npy
-imgHQ22269.npy
-imgHQ14822.npy
-imgHQ18728.npy
-imgHQ03624.npy
-imgHQ08922.npy
-imgHQ08910.npy
-imgHQ05990.npy
-imgHQ18634.npy
-imgHQ29111.npy
-imgHQ02362.npy
-imgHQ09039.npy
-imgHQ05345.npy
-imgHQ13912.npy
-imgHQ03876.npy
-imgHQ06116.npy
-imgHQ08186.npy
-imgHQ02600.npy
-imgHQ14035.npy
-imgHQ06835.npy
-imgHQ14008.npy
-imgHQ19536.npy
-imgHQ25905.npy
-imgHQ19519.npy
-imgHQ09495.npy
-imgHQ12767.npy
-imgHQ15120.npy
-imgHQ14484.npy
-imgHQ27912.npy
-imgHQ01542.npy
-imgHQ01643.npy
-imgHQ14715.npy
-imgHQ02666.npy
-imgHQ06357.npy
-imgHQ17209.npy
-imgHQ22683.npy
-imgHQ24500.npy
-imgHQ27729.npy
-imgHQ20791.npy
-imgHQ02826.npy
-imgHQ03030.npy
-imgHQ03542.npy
-imgHQ22879.npy
-imgHQ03240.npy
-imgHQ03171.npy
-imgHQ05904.npy
-imgHQ02238.npy
-imgHQ02500.npy
-imgHQ00102.npy
-imgHQ20876.npy
-imgHQ06545.npy
-imgHQ29900.npy
-imgHQ11496.npy
-imgHQ08527.npy
-imgHQ07246.npy
-imgHQ25059.npy
-imgHQ28893.npy
-imgHQ10214.npy
-imgHQ24760.npy
-imgHQ02137.npy
-imgHQ17899.npy
-imgHQ00880.npy
-imgHQ25636.npy
-imgHQ09676.npy
-imgHQ17139.npy
-imgHQ11741.npy
-imgHQ03700.npy
-imgHQ07183.npy
-imgHQ02748.npy
-imgHQ22189.npy
-imgHQ00436.npy
-imgHQ15595.npy
-imgHQ13701.npy
-imgHQ12212.npy
-imgHQ18204.npy
-imgHQ14627.npy
-imgHQ26532.npy
-imgHQ16810.npy
-imgHQ18759.npy
-imgHQ19307.npy
-imgHQ17755.npy
-imgHQ15745.npy
-imgHQ23403.npy
-imgHQ04814.npy
-imgHQ10262.npy
-imgHQ27490.npy
-imgHQ05205.npy
-imgHQ19036.npy
-imgHQ02531.npy
-imgHQ28823.npy
-imgHQ05032.npy
-imgHQ15314.npy
-imgHQ16330.npy
-imgHQ01798.npy
-imgHQ11472.npy
-imgHQ28024.npy
-imgHQ10785.npy
-imgHQ11514.npy
-imgHQ21763.npy
-imgHQ29385.npy
-imgHQ24717.npy
-imgHQ14738.npy
-imgHQ03249.npy
-imgHQ26826.npy
-imgHQ08713.npy
-imgHQ00550.npy
-imgHQ02571.npy
-imgHQ17605.npy
-imgHQ22538.npy
-imgHQ16189.npy
-imgHQ21507.npy
-imgHQ20691.npy
-imgHQ25107.npy
-imgHQ08367.npy
-imgHQ20317.npy
-imgHQ23057.npy
-imgHQ05872.npy
-imgHQ02078.npy
-imgHQ15377.npy
-imgHQ26860.npy
-imgHQ03982.npy
-imgHQ00693.npy
-imgHQ14935.npy
-imgHQ12583.npy
-imgHQ01483.npy
-imgHQ04917.npy
-imgHQ13795.npy
-imgHQ26933.npy
-imgHQ02937.npy
-imgHQ11724.npy
-imgHQ09228.npy
-imgHQ13528.npy
-imgHQ00839.npy
-imgHQ20976.npy
-imgHQ26368.npy
-imgHQ05490.npy
-imgHQ10092.npy
-imgHQ25850.npy
-imgHQ20151.npy
-imgHQ05631.npy
-imgHQ04757.npy
-imgHQ23124.npy
-imgHQ02584.npy
-imgHQ09471.npy
-imgHQ22122.npy
-imgHQ14599.npy
-imgHQ10094.npy
-imgHQ20739.npy
-imgHQ15413.npy
-imgHQ15863.npy
-imgHQ16124.npy
-imgHQ22546.npy
-imgHQ02616.npy
-imgHQ28757.npy
-imgHQ02395.npy
-imgHQ17511.npy
-imgHQ05363.npy
-imgHQ22603.npy
-imgHQ28708.npy
-imgHQ00527.npy
-imgHQ07092.npy
-imgHQ17391.npy
-imgHQ02525.npy
-imgHQ10282.npy
-imgHQ25838.npy
-imgHQ07944.npy
-imgHQ26344.npy
-imgHQ22374.npy
-imgHQ12578.npy
-imgHQ26689.npy
-imgHQ07823.npy
-imgHQ02879.npy
-imgHQ20822.npy
-imgHQ10271.npy
-imgHQ01546.npy
-imgHQ28487.npy
-imgHQ17167.npy
-imgHQ03594.npy
-imgHQ02421.npy
-imgHQ02518.npy
-imgHQ19795.npy
-imgHQ15967.npy
-imgHQ22364.npy
-imgHQ07881.npy
-imgHQ18775.npy
-imgHQ05127.npy
-imgHQ15913.npy
-imgHQ01183.npy
-imgHQ24715.npy
-imgHQ15704.npy
-imgHQ20913.npy
-imgHQ05222.npy
-imgHQ07099.npy
-imgHQ16992.npy
-imgHQ10368.npy
-imgHQ01326.npy
-imgHQ24886.npy
-imgHQ08586.npy
-imgHQ13254.npy
-imgHQ26279.npy
-imgHQ14708.npy
-imgHQ29106.npy
-imgHQ26610.npy
-imgHQ06740.npy
-imgHQ05246.npy
-imgHQ13653.npy
-imgHQ09455.npy
-imgHQ04642.npy
-imgHQ06872.npy
-imgHQ28000.npy
-imgHQ21008.npy
-imgHQ11704.npy
-imgHQ29643.npy
-imgHQ11657.npy
-imgHQ22346.npy
-imgHQ20619.npy
-imgHQ02743.npy
-imgHQ04524.npy
-imgHQ01124.npy
-imgHQ23781.npy
-imgHQ20330.npy
-imgHQ11423.npy
-imgHQ23803.npy
-imgHQ15792.npy
-imgHQ16415.npy
-imgHQ29224.npy
-imgHQ14868.npy
-imgHQ11225.npy
-imgHQ05919.npy
-imgHQ22399.npy
-imgHQ05603.npy
-imgHQ21316.npy
-imgHQ21819.npy
-imgHQ16406.npy
-imgHQ24982.npy
-imgHQ24390.npy
-imgHQ00091.npy
-imgHQ12057.npy
-imgHQ18524.npy
-imgHQ22768.npy
-imgHQ13546.npy
-imgHQ23765.npy
-imgHQ24174.npy
-imgHQ15631.npy
-imgHQ09938.npy
-imgHQ03840.npy
-imgHQ19171.npy
-imgHQ19507.npy
-imgHQ16064.npy
-imgHQ28605.npy
-imgHQ25916.npy
-imgHQ27500.npy
-imgHQ09996.npy
-imgHQ08079.npy
-imgHQ06916.npy
-imgHQ12428.npy
-imgHQ05028.npy
-imgHQ07009.npy
-imgHQ18163.npy
-imgHQ27930.npy
-imgHQ17699.npy
-imgHQ29967.npy
-imgHQ16089.npy
-imgHQ29611.npy
-imgHQ21768.npy
-imgHQ00860.npy
-imgHQ22567.npy
-imgHQ27592.npy
-imgHQ02546.npy
-imgHQ24264.npy
-imgHQ26091.npy
-imgHQ12030.npy
-imgHQ12597.npy
-imgHQ28175.npy
-imgHQ12430.npy
-imgHQ21915.npy
-imgHQ07953.npy
-imgHQ24412.npy
-imgHQ24942.npy
-imgHQ12709.npy
-imgHQ21521.npy
-imgHQ08504.npy
-imgHQ16608.npy
-imgHQ22711.npy
-imgHQ16529.npy
-imgHQ04692.npy
-imgHQ07969.npy
-imgHQ07898.npy
-imgHQ02158.npy
-imgHQ12622.npy
-imgHQ11159.npy
-imgHQ16518.npy
-imgHQ21733.npy
-imgHQ04267.npy
-imgHQ27804.npy
-imgHQ28631.npy
-imgHQ18182.npy
-imgHQ27779.npy
-imgHQ19841.npy
-imgHQ26565.npy
-imgHQ00588.npy
-imgHQ01033.npy
-imgHQ26251.npy
-imgHQ13434.npy
-imgHQ07056.npy
-imgHQ29829.npy
-imgHQ20902.npy
-imgHQ18910.npy
-imgHQ20144.npy
-imgHQ24389.npy
-imgHQ18802.npy
-imgHQ10203.npy
-imgHQ02557.npy
-imgHQ23327.npy
-imgHQ05379.npy
-imgHQ16644.npy
-imgHQ00432.npy
-imgHQ15001.npy
-imgHQ04589.npy
-imgHQ15603.npy
-imgHQ25434.npy
-imgHQ01840.npy
-imgHQ18897.npy
-imgHQ03724.npy
-imgHQ19934.npy
-imgHQ16802.npy
-imgHQ19750.npy
-imgHQ05604.npy
-imgHQ25513.npy
-imgHQ02209.npy
-imgHQ09280.npy
-imgHQ11887.npy
-imgHQ02213.npy
-imgHQ17554.npy
-imgHQ20557.npy
-imgHQ00932.npy
-imgHQ14508.npy
-imgHQ04371.npy
-imgHQ25298.npy
-imgHQ25182.npy
-imgHQ19481.npy
-imgHQ13349.npy
-imgHQ05650.npy
-imgHQ02917.npy
-imgHQ24896.npy
-imgHQ09836.npy
-imgHQ10793.npy
-imgHQ27254.npy
-imgHQ12948.npy
-imgHQ16874.npy
-imgHQ27875.npy
-imgHQ02232.npy
-imgHQ16792.npy
-imgHQ15450.npy
-imgHQ21762.npy
-imgHQ28674.npy
-imgHQ16143.npy
-imgHQ10479.npy
-imgHQ06185.npy
-imgHQ29401.npy
-imgHQ02851.npy
-imgHQ15323.npy
-imgHQ15882.npy
-imgHQ05312.npy
-imgHQ26259.npy
-imgHQ23986.npy
-imgHQ14561.npy
-imgHQ05100.npy
-imgHQ04838.npy
-imgHQ24768.npy
-imgHQ12862.npy
-imgHQ18412.npy
-imgHQ19201.npy
-imgHQ11415.npy
-imgHQ01484.npy
-imgHQ04824.npy
-imgHQ28626.npy
-imgHQ18932.npy
-imgHQ07420.npy
-imgHQ26798.npy
-imgHQ14117.npy
-imgHQ05673.npy
-imgHQ24742.npy
-imgHQ29995.npy
-imgHQ12231.npy
-imgHQ00237.npy
-imgHQ10844.npy
-imgHQ23933.npy
-imgHQ17720.npy
-imgHQ02928.npy
-imgHQ03459.npy
-imgHQ13443.npy
-imgHQ27651.npy
-imgHQ10625.npy
-imgHQ05939.npy
-imgHQ02476.npy
-imgHQ27647.npy
-imgHQ07801.npy
-imgHQ28598.npy
-imgHQ16204.npy
-imgHQ01661.npy
-imgHQ21818.npy
-imgHQ05264.npy
-imgHQ28643.npy
-imgHQ11535.npy
-imgHQ17693.npy
-imgHQ29464.npy
-imgHQ10915.npy
-imgHQ00659.npy
-imgHQ21921.npy
-imgHQ01500.npy
-imgHQ15386.npy
-imgHQ24192.npy
-imgHQ22480.npy
-imgHQ25130.npy
-imgHQ17344.npy
-imgHQ27935.npy
-imgHQ11990.npy
-imgHQ14720.npy
-imgHQ16510.npy
-imgHQ21140.npy
-imgHQ01985.npy
-imgHQ11504.npy
-imgHQ15698.npy
-imgHQ23111.npy
-imgHQ25514.npy
-imgHQ12058.npy
-imgHQ01355.npy
-imgHQ03900.npy
-imgHQ01549.npy
-imgHQ06688.npy
-imgHQ26157.npy
-imgHQ29926.npy
-imgHQ13459.npy
-imgHQ01054.npy
-imgHQ27223.npy
-imgHQ14984.npy
-imgHQ24115.npy
-imgHQ11033.npy
-imgHQ03307.npy
-imgHQ19045.npy
-imgHQ03835.npy
-imgHQ18736.npy
-imgHQ05409.npy
-imgHQ13872.npy
-imgHQ04767.npy
-imgHQ26441.npy
-imgHQ17772.npy
-imgHQ02453.npy
-imgHQ13199.npy
-imgHQ00099.npy
-imgHQ22502.npy
-imgHQ17490.npy
-imgHQ22521.npy
-imgHQ20397.npy
-imgHQ00534.npy
-imgHQ02231.npy
-imgHQ25820.npy
-imgHQ21800.npy
-imgHQ08336.npy
-imgHQ24859.npy
-imgHQ02871.npy
-imgHQ10333.npy
-imgHQ28355.npy
-imgHQ11757.npy
-imgHQ15644.npy
-imgHQ01889.npy
-imgHQ10712.npy
-imgHQ26554.npy
-imgHQ07600.npy
-imgHQ21792.npy
-imgHQ10447.npy
-imgHQ25014.npy
-imgHQ05873.npy
-imgHQ25063.npy
-imgHQ21000.npy
-imgHQ26219.npy
-imgHQ14849.npy
-imgHQ09300.npy
-imgHQ28564.npy
-imgHQ03041.npy
-imgHQ25496.npy
-imgHQ27957.npy
-imgHQ09703.npy
-imgHQ18065.npy
-imgHQ07422.npy
-imgHQ19214.npy
-imgHQ06798.npy
-imgHQ07690.npy
-imgHQ00220.npy
-imgHQ13162.npy
-imgHQ16378.npy
-imgHQ00433.npy
-imgHQ04916.npy
-imgHQ06792.npy
-imgHQ24010.npy
-imgHQ02168.npy
-imgHQ28926.npy
-imgHQ17735.npy
-imgHQ29178.npy
-imgHQ26981.npy
-imgHQ01671.npy
-imgHQ05539.npy
-imgHQ20122.npy
-imgHQ07700.npy
-imgHQ16732.npy
-imgHQ26097.npy
-imgHQ26958.npy
-imgHQ29439.npy
-imgHQ25792.npy
-imgHQ23836.npy
-imgHQ21112.npy
-imgHQ24141.npy
-imgHQ06902.npy
-imgHQ26857.npy
-imgHQ20465.npy
-imgHQ04654.npy
-imgHQ04680.npy
-imgHQ29487.npy
-imgHQ18153.npy
-imgHQ13587.npy
-imgHQ28653.npy
-imgHQ22275.npy
-imgHQ00397.npy
-imgHQ25494.npy
-imgHQ00491.npy
-imgHQ23694.npy
-imgHQ01796.npy
-imgHQ12143.npy
-imgHQ11432.npy
-imgHQ11515.npy
-imgHQ25883.npy
-imgHQ13956.npy
-imgHQ26818.npy
-imgHQ14045.npy
-imgHQ29703.npy
-imgHQ10016.npy
-imgHQ16332.npy
-imgHQ09795.npy
-imgHQ14028.npy
-imgHQ00010.npy
-imgHQ28517.npy
-imgHQ17840.npy
-imgHQ26524.npy
-imgHQ14963.npy
-imgHQ28939.npy
-imgHQ27951.npy
-imgHQ12655.npy
-imgHQ07859.npy
-imgHQ13468.npy
-imgHQ06299.npy
-imgHQ20835.npy
-imgHQ11300.npy
-imgHQ29628.npy
-imgHQ22350.npy
-imgHQ05570.npy
-imgHQ10155.npy
-imgHQ13391.npy
-imgHQ09230.npy
-imgHQ26118.npy
-imgHQ18734.npy
-imgHQ26828.npy
-imgHQ11025.npy
-imgHQ16672.npy
-imgHQ29782.npy
-imgHQ11979.npy
-imgHQ21674.npy
-imgHQ06105.npy
-imgHQ20266.npy
-imgHQ14137.npy
-imgHQ02411.npy
-imgHQ21599.npy
-imgHQ06057.npy
-imgHQ27501.npy
-imgHQ03573.npy
-imgHQ26680.npy
-imgHQ17409.npy
-imgHQ16296.npy
-imgHQ07436.npy
-imgHQ05991.npy
-imgHQ04852.npy
-imgHQ04934.npy
-imgHQ04948.npy
-imgHQ20954.npy
-imgHQ19901.npy
-imgHQ15169.npy
-imgHQ22568.npy
-imgHQ17889.npy
-imgHQ01884.npy
-imgHQ04145.npy
-imgHQ04705.npy
-imgHQ22899.npy
-imgHQ20586.npy
-imgHQ09240.npy
-imgHQ24731.npy
-imgHQ13827.npy
-imgHQ27479.npy
-imgHQ23636.npy
-imgHQ07443.npy
-imgHQ29630.npy
-imgHQ07003.npy
-imgHQ21684.npy
-imgHQ06332.npy
-imgHQ19499.npy
-imgHQ27783.npy
-imgHQ19060.npy
-imgHQ05838.npy
-imgHQ19607.npy
-imgHQ19886.npy
-imgHQ27093.npy
-imgHQ02482.npy
-imgHQ10966.npy
-imgHQ09884.npy
-imgHQ25412.npy
-imgHQ14328.npy
-imgHQ20075.npy
-imgHQ21603.npy
-imgHQ20032.npy
-imgHQ02165.npy
-imgHQ06317.npy
-imgHQ21709.npy
-imgHQ07800.npy
-imgHQ21190.npy
-imgHQ08703.npy
-imgHQ21870.npy
-imgHQ17103.npy
-imgHQ08304.npy
-imgHQ16484.npy
-imgHQ08749.npy
-imgHQ22196.npy
-imgHQ29173.npy
-imgHQ25658.npy
-imgHQ21429.npy
-imgHQ01573.npy
-imgHQ08378.npy
-imgHQ05065.npy
-imgHQ27158.npy
-imgHQ18482.npy
-imgHQ07121.npy
-imgHQ25659.npy
-imgHQ20570.npy
-imgHQ12002.npy
-imgHQ02392.npy
-imgHQ18769.npy
-imgHQ13530.npy
-imgHQ25117.npy
-imgHQ25971.npy
-imgHQ10183.npy
-imgHQ28959.npy
-imgHQ06002.npy
-imgHQ09453.npy
-imgHQ07049.npy
-imgHQ01167.npy
-imgHQ10607.npy
-imgHQ00603.npy
-imgHQ22332.npy
-imgHQ03601.npy
-imgHQ29915.npy
-imgHQ10221.npy
-imgHQ28058.npy
-imgHQ16279.npy
-imgHQ24094.npy
-imgHQ13735.npy
-imgHQ06548.npy
-imgHQ29512.npy
-imgHQ29568.npy
-imgHQ12902.npy
-imgHQ11220.npy
-imgHQ25576.npy
-imgHQ12511.npy
-imgHQ09548.npy
-imgHQ22009.npy
-imgHQ14040.npy
-imgHQ29218.npy
-imgHQ26079.npy
-imgHQ22380.npy
-imgHQ17319.npy
-imgHQ17289.npy
-imgHQ23227.npy
-imgHQ21756.npy
-imgHQ24034.npy
-imgHQ20057.npy
-imgHQ05071.npy
-imgHQ07196.npy
-imgHQ18047.npy
-imgHQ17873.npy
-imgHQ03691.npy
-imgHQ06723.npy
-imgHQ23791.npy
-imgHQ20234.npy
-imgHQ02212.npy
-imgHQ24124.npy
-imgHQ00586.npy
-imgHQ29363.npy
-imgHQ21928.npy
-imgHQ12826.npy
-imgHQ19468.npy
-imgHQ08593.npy
-imgHQ19620.npy
-imgHQ16342.npy
-imgHQ21379.npy
-imgHQ16043.npy
-imgHQ23482.npy
-imgHQ24678.npy
-imgHQ11994.npy
-imgHQ12379.npy
-imgHQ24395.npy
-imgHQ19359.npy
-imgHQ29519.npy
-imgHQ15452.npy
-imgHQ27737.npy
-imgHQ20725.npy
-imgHQ04164.npy
-imgHQ08532.npy
-imgHQ11560.npy
-imgHQ19467.npy
-imgHQ08934.npy
-imgHQ27947.npy
-imgHQ23557.npy
-imgHQ14589.npy
-imgHQ29920.npy
-imgHQ27985.npy
-imgHQ05565.npy
-imgHQ14225.npy
-imgHQ12700.npy
-imgHQ08671.npy
-imgHQ07819.npy
-imgHQ11283.npy
-imgHQ08162.npy
-imgHQ18905.npy
-imgHQ01693.npy
-imgHQ28901.npy
-imgHQ03246.npy
-imgHQ10420.npy
-imgHQ14321.npy
-imgHQ23489.npy
-imgHQ20439.npy
-imgHQ02199.npy
-imgHQ04755.npy
-imgHQ27582.npy
-imgHQ13690.npy
-imgHQ04591.npy
-imgHQ03731.npy
-imgHQ04582.npy
-imgHQ17628.npy
-imgHQ19722.npy
-imgHQ13307.npy
-imgHQ13160.npy
-imgHQ22805.npy
-imgHQ25061.npy
-imgHQ05557.npy
-imgHQ09590.npy
-imgHQ25746.npy
-imgHQ28876.npy
-imgHQ16703.npy
-imgHQ11127.npy
-imgHQ06691.npy
-imgHQ21531.npy
-imgHQ19170.npy
-imgHQ25622.npy
-imgHQ01968.npy
-imgHQ09500.npy
-imgHQ10371.npy
-imgHQ19085.npy
-imgHQ02732.npy
-imgHQ15100.npy
-imgHQ23038.npy
-imgHQ27575.npy
-imgHQ04110.npy
-imgHQ08743.npy
-imgHQ17952.npy
-imgHQ09683.npy
-imgHQ28281.npy
-imgHQ11756.npy
-imgHQ14252.npy
-imgHQ07893.npy
-imgHQ10211.npy
-imgHQ17093.npy
-imgHQ08851.npy
-imgHQ07737.npy
-imgHQ02622.npy
-imgHQ16303.npy
-imgHQ03411.npy
-imgHQ04914.npy
-imgHQ14828.npy
-imgHQ23948.npy
-imgHQ24416.npy
-imgHQ08468.npy
-imgHQ22822.npy
-imgHQ29759.npy
-imgHQ06962.npy
-imgHQ25303.npy
-imgHQ09833.npy
-imgHQ13686.npy
-imgHQ25451.npy
-imgHQ17320.npy
-imgHQ24719.npy
-imgHQ29788.npy
-imgHQ00658.npy
-imgHQ08885.npy
-imgHQ11680.npy
-imgHQ22096.npy
-imgHQ24852.npy
-imgHQ17265.npy
-imgHQ08836.npy
-imgHQ20430.npy
-imgHQ16137.npy
-imgHQ25371.npy
-imgHQ12809.npy
-imgHQ03612.npy
-imgHQ10920.npy
-imgHQ21025.npy
-imgHQ16054.npy
-imgHQ20253.npy
-imgHQ15172.npy
-imgHQ22238.npy
-imgHQ21070.npy
-imgHQ24498.npy
-imgHQ20904.npy
-imgHQ26273.npy
-imgHQ29219.npy
-imgHQ11074.npy
-imgHQ12816.npy
-imgHQ09321.npy
-imgHQ11077.npy
-imgHQ08122.npy
-imgHQ28987.npy
-imgHQ05288.npy
-imgHQ18110.npy
-imgHQ06466.npy
-imgHQ14409.npy
-imgHQ02749.npy
-imgHQ10775.npy
-imgHQ01936.npy
-imgHQ10065.npy
-imgHQ07731.npy
-imgHQ22958.npy
-imgHQ23896.npy
-imgHQ05305.npy
-imgHQ05817.npy
-imgHQ26324.npy
-imgHQ08257.npy
-imgHQ23395.npy
-imgHQ06769.npy
-imgHQ09754.npy
-imgHQ09350.npy
-imgHQ27719.npy
-imgHQ12005.npy
-imgHQ23041.npy
-imgHQ04785.npy
-imgHQ10185.npy
-imgHQ05134.npy
-imgHQ20035.npy
-imgHQ07059.npy
-imgHQ02528.npy
-imgHQ05121.npy
-imgHQ00022.npy
-imgHQ27176.npy
-imgHQ26709.npy
-imgHQ07778.npy
-imgHQ13403.npy
-imgHQ22824.npy
-imgHQ17300.npy
-imgHQ03864.npy
-imgHQ03983.npy
-imgHQ28840.npy
-imgHQ13037.npy
-imgHQ24439.npy
-imgHQ04528.npy
-imgHQ08827.npy
-imgHQ23459.npy
-imgHQ10261.npy
-imgHQ09526.npy
-imgHQ12714.npy
-imgHQ19429.npy
-imgHQ02071.npy
-imgHQ20609.npy
-imgHQ26297.npy
-imgHQ11097.npy
-imgHQ23406.npy
-imgHQ19366.npy
-imgHQ01235.npy
-imgHQ00626.npy
-imgHQ05234.npy
-imgHQ05680.npy
-imgHQ12392.npy
-imgHQ17528.npy
-imgHQ03304.npy
-imgHQ28534.npy
-imgHQ27679.npy
-imgHQ22840.npy
-imgHQ29964.npy
-imgHQ22075.npy
-imgHQ26101.npy
-imgHQ00912.npy
-imgHQ23589.npy
-imgHQ17373.npy
-imgHQ03523.npy
-imgHQ25957.npy
-imgHQ03073.npy
-imgHQ02803.npy
-imgHQ12435.npy
-imgHQ19718.npy
-imgHQ28314.npy
-imgHQ18324.npy
-imgHQ08856.npy
-imgHQ27522.npy
-imgHQ23662.npy
-imgHQ20499.npy
-imgHQ10321.npy
-imgHQ19573.npy
-imgHQ04768.npy
-imgHQ19693.npy
-imgHQ12424.npy
-imgHQ00273.npy
-imgHQ26087.npy
-imgHQ10515.npy
-imgHQ16520.npy
-imgHQ27898.npy
-imgHQ28958.npy
-imgHQ06325.npy
-imgHQ12495.npy
-imgHQ29200.npy
-imgHQ01625.npy
-imgHQ10086.npy
-imgHQ25687.npy
-imgHQ19950.npy
-imgHQ10784.npy
-imgHQ13682.npy
-imgHQ13789.npy
-imgHQ16835.npy
-imgHQ20745.npy
-imgHQ15324.npy
-imgHQ24946.npy
-imgHQ04310.npy
-imgHQ05238.npy
-imgHQ08508.npy
-imgHQ05927.npy
-imgHQ22323.npy
-imgHQ18519.npy
-imgHQ22017.npy
-imgHQ07782.npy
-imgHQ22755.npy
-imgHQ19623.npy
-imgHQ11512.npy
-imgHQ06167.npy
-imgHQ08370.npy
-imgHQ00303.npy
-imgHQ22192.npy
-imgHQ06551.npy
-imgHQ14250.npy
-imgHQ13201.npy
-imgHQ09172.npy
-imgHQ02715.npy
-imgHQ09426.npy
-imgHQ02039.npy
-imgHQ13040.npy
-imgHQ24284.npy
-imgHQ05276.npy
-imgHQ06826.npy
-imgHQ02727.npy
-imgHQ03979.npy
-imgHQ10386.npy
-imgHQ18657.npy
-imgHQ07540.npy
-imgHQ07659.npy
-imgHQ04404.npy
-imgHQ22573.npy
-imgHQ07743.npy
-imgHQ10680.npy
-imgHQ06445.npy
-imgHQ20798.npy
-imgHQ17064.npy
-imgHQ00978.npy
-imgHQ27548.npy
-imgHQ09842.npy
-imgHQ26924.npy
-imgHQ27411.npy
-imgHQ14731.npy
-imgHQ13983.npy
-imgHQ14628.npy
-imgHQ28307.npy
-imgHQ00482.npy
-imgHQ07209.npy
-imgHQ16541.npy
-imgHQ05168.npy
-imgHQ22606.npy
-imgHQ20834.npy
-imgHQ03913.npy
-imgHQ10228.npy
-imgHQ04849.npy
-imgHQ15542.npy
-imgHQ05671.npy
-imgHQ12772.npy
-imgHQ05062.npy
-imgHQ29292.npy
-imgHQ12124.npy
-imgHQ19908.npy
-imgHQ10652.npy
-imgHQ17433.npy
-imgHQ28908.npy
-imgHQ15579.npy
-imgHQ27966.npy
-imgHQ13136.npy
-imgHQ15944.npy
-imgHQ06430.npy
-imgHQ09202.npy
-imgHQ08674.npy
-imgHQ06124.npy
-imgHQ21331.npy
-imgHQ29411.npy
-imgHQ11861.npy
-imgHQ28963.npy
-imgHQ02709.npy
-imgHQ20215.npy
-imgHQ15455.npy
-imgHQ20844.npy
-imgHQ02100.npy
-imgHQ04407.npy
-imgHQ11147.npy
-imgHQ06632.npy
-imgHQ18106.npy
-imgHQ22741.npy
-imgHQ00984.npy
-imgHQ25704.npy
-imgHQ14151.npy
-imgHQ06951.npy
-imgHQ13820.npy
-imgHQ11316.npy
-imgHQ27115.npy
-imgHQ29010.npy
-imgHQ04245.npy
-imgHQ02324.npy
-imgHQ24842.npy
-imgHQ27067.npy
-imgHQ16431.npy
-imgHQ17707.npy
-imgHQ26269.npy
-imgHQ03129.npy
-imgHQ07650.npy
-imgHQ06422.npy
-imgHQ21580.npy
-imgHQ24220.npy
-imgHQ18143.npy
-imgHQ06673.npy
-imgHQ19259.npy
-imgHQ23162.npy
-imgHQ07135.npy
-imgHQ20482.npy
-imgHQ13874.npy
-imgHQ15434.npy
-imgHQ15240.npy
-imgHQ24299.npy
-imgHQ06153.npy
-imgHQ11971.npy
-imgHQ16758.npy
-imgHQ20213.npy
-imgHQ19462.npy
-imgHQ17972.npy
-imgHQ09169.npy
-imgHQ12111.npy
-imgHQ05858.npy
-imgHQ14510.npy
-imgHQ07836.npy
-imgHQ01350.npy
-imgHQ13272.npy
-imgHQ29044.npy
-imgHQ15810.npy
-imgHQ03172.npy
-imgHQ23815.npy
-imgHQ22242.npy
-imgHQ06156.npy
-imgHQ03586.npy
-imgHQ24345.npy
-imgHQ25740.npy
-imgHQ01087.npy
-imgHQ06480.npy
-imgHQ29728.npy
-imgHQ08561.npy
-imgHQ02423.npy
-imgHQ11143.npy
-imgHQ11601.npy
-imgHQ18392.npy
-imgHQ18997.npy
-imgHQ23644.npy
-imgHQ07350.npy
-imgHQ10353.npy
-imgHQ11754.npy
-imgHQ13204.npy
-imgHQ15860.npy
-imgHQ00756.npy
-imgHQ16677.npy
-imgHQ21575.npy
-imgHQ27558.npy
-imgHQ04333.npy
-imgHQ23296.npy
-imgHQ16617.npy
-imgHQ29939.npy
-imgHQ12676.npy
-imgHQ19840.npy
-imgHQ01600.npy
-imgHQ06573.npy
-imgHQ19282.npy
-imgHQ25170.npy
-imgHQ14085.npy
-imgHQ12845.npy
-imgHQ13427.npy
-imgHQ06014.npy
-imgHQ04098.npy
-imgHQ22873.npy
-imgHQ13579.npy
-imgHQ08046.npy
-imgHQ18792.npy
-imgHQ26575.npy
-imgHQ17349.npy
-imgHQ14801.npy
-imgHQ25989.npy
-imgHQ05326.npy
-imgHQ01665.npy
-imgHQ08439.npy
-imgHQ00530.npy
-imgHQ26511.npy
-imgHQ05298.npy
-imgHQ11907.npy
-imgHQ29809.npy
-imgHQ27621.npy
-imgHQ01040.npy
-imgHQ03843.npy
-imgHQ20627.npy
-imgHQ23066.npy
-imgHQ19916.npy
-imgHQ09246.npy
-imgHQ21602.npy
-imgHQ09585.npy
-imgHQ10310.npy
-imgHQ26562.npy
-imgHQ05253.npy
-imgHQ22266.npy
-imgHQ07658.npy
-imgHQ20408.npy
-imgHQ04103.npy
-imgHQ27778.npy
-imgHQ14396.npy
-imgHQ00086.npy
-imgHQ01364.npy
-imgHQ22937.npy
-imgHQ07399.npy
-imgHQ27098.npy
-imgHQ28590.npy
-imgHQ28332.npy
-imgHQ19130.npy
-imgHQ24751.npy
-imgHQ19568.npy
-imgHQ07608.npy
-imgHQ12926.npy
-imgHQ19319.npy
-imgHQ12429.npy
-imgHQ17747.npy
-imgHQ13921.npy
-imgHQ29220.npy
-imgHQ08084.npy
-imgHQ03146.npy
-imgHQ15500.npy
-imgHQ22120.npy
-imgHQ15878.npy
-imgHQ03962.npy
-imgHQ21945.npy
-imgHQ28015.npy
-imgHQ11078.npy
-imgHQ17754.npy
-imgHQ27658.npy
-imgHQ15082.npy
-imgHQ14554.npy
-imgHQ11419.npy
-imgHQ22731.npy
-imgHQ20094.npy
-imgHQ15504.npy
-imgHQ15736.npy
-imgHQ26752.npy
-imgHQ15981.npy
-imgHQ19446.npy
-imgHQ22791.npy
-imgHQ04938.npy
-imgHQ09984.npy
-imgHQ07065.npy
-imgHQ17184.npy
-imgHQ19419.npy
-imgHQ03422.npy
-imgHQ02611.npy
-imgHQ10711.npy
-imgHQ20290.npy
-imgHQ10276.npy
-imgHQ26295.npy
-imgHQ24223.npy
-imgHQ10790.npy
-imgHQ23343.npy
-imgHQ21922.npy
-imgHQ12167.npy
-imgHQ26160.npy
-imgHQ21356.npy
-imgHQ17535.npy
-imgHQ18832.npy
-imgHQ14285.npy
-imgHQ11855.npy
-imgHQ14796.npy
-imgHQ21156.npy
-imgHQ17984.npy
-imgHQ17298.npy
-imgHQ15739.npy
-imgHQ17596.npy
-imgHQ00230.npy
-imgHQ28934.npy
-imgHQ29124.npy
-imgHQ24955.npy
-imgHQ09481.npy
-imgHQ09274.npy
-imgHQ25935.npy
-imgHQ16774.npy
-imgHQ17829.npy
-imgHQ16355.npy
-imgHQ28900.npy
-imgHQ08012.npy
-imgHQ23845.npy
-imgHQ01698.npy
-imgHQ19006.npy
-imgHQ01664.npy
-imgHQ04569.npy
-imgHQ25386.npy
-imgHQ20851.npy
-imgHQ10487.npy
-imgHQ01322.npy
-imgHQ04337.npy
-imgHQ18928.npy
-imgHQ18145.npy
-imgHQ08202.npy
-imgHQ27890.npy
-imgHQ27104.npy
-imgHQ08546.npy
-imgHQ26404.npy
-imgHQ23961.npy
-imgHQ22498.npy
-imgHQ04608.npy
-imgHQ04015.npy
-imgHQ20393.npy
-imgHQ10493.npy
-imgHQ04986.npy
-imgHQ09857.npy
-imgHQ06139.npy
-imgHQ21172.npy
-imgHQ28961.npy
-imgHQ07963.npy
-imgHQ12603.npy
-imgHQ00945.npy
-imgHQ16931.npy
-imgHQ28651.npy
-imgHQ27292.npy
-imgHQ12129.npy
-imgHQ15132.npy
-imgHQ00736.npy
-imgHQ07794.npy
-imgHQ21727.npy
-imgHQ12405.npy
-imgHQ28716.npy
-imgHQ15053.npy
-imgHQ22274.npy
-imgHQ23741.npy
-imgHQ29963.npy
-imgHQ10956.npy
-imgHQ29956.npy
-imgHQ19382.npy
-imgHQ19488.npy
-imgHQ13662.npy
-imgHQ26588.npy
-imgHQ03140.npy
-imgHQ15622.npy
-imgHQ01983.npy
-imgHQ09245.npy
-imgHQ14113.npy
-imgHQ18796.npy
-imgHQ22923.npy
-imgHQ07343.npy
-imgHQ00567.npy
-imgHQ07841.npy
-imgHQ14680.npy
-imgHQ04652.npy
-imgHQ17704.npy
-imgHQ17932.npy
-imgHQ09175.npy
-imgHQ10701.npy
-imgHQ22865.npy
-imgHQ16495.npy
-imgHQ28493.npy
-imgHQ21893.npy
-imgHQ09555.npy
-imgHQ20372.npy
-imgHQ04697.npy
-imgHQ22955.npy
-imgHQ23537.npy
-imgHQ00118.npy
-imgHQ23534.npy
-imgHQ13666.npy
-imgHQ03414.npy
-imgHQ28337.npy
-imgHQ01791.npy
-imgHQ17133.npy
-imgHQ03578.npy
-imgHQ05210.npy
-imgHQ29078.npy
-imgHQ06594.npy
-imgHQ10392.npy
-imgHQ24632.npy
-imgHQ23254.npy
-imgHQ23923.npy
-imgHQ17315.npy
-imgHQ17734.npy
-imgHQ24534.npy
-imgHQ17745.npy
-imgHQ22950.npy
-imgHQ23515.npy
-imgHQ26376.npy
-imgHQ09205.npy
-imgHQ22819.npy
-imgHQ24343.npy
-imgHQ04804.npy
-imgHQ01234.npy
-imgHQ04217.npy
-imgHQ07975.npy
-imgHQ29089.npy
-imgHQ03331.npy
-imgHQ08865.npy
-imgHQ02508.npy
-imgHQ15255.npy
-imgHQ20243.npy
-imgHQ14742.npy
-imgHQ05780.npy
-imgHQ00910.npy
-imgHQ14721.npy
-imgHQ03760.npy
-imgHQ02690.npy
-imgHQ26245.npy
-imgHQ07434.npy
-imgHQ01860.npy
-imgHQ02646.npy
-imgHQ21647.npy
-imgHQ09613.npy
-imgHQ27537.npy
-imgHQ11173.npy
-imgHQ15983.npy
-imgHQ05538.npy
-imgHQ20180.npy
-imgHQ11108.npy
-imgHQ11686.npy
-imgHQ03181.npy
-imgHQ00619.npy
-imgHQ21888.npy
-imgHQ28414.npy
-imgHQ10897.npy
-imgHQ03905.npy
-imgHQ20115.npy
-imgHQ29935.npy
-imgHQ01667.npy
-imgHQ12723.npy
-imgHQ17833.npy
-imgHQ22177.npy
-imgHQ09303.npy
-imgHQ17763.npy
-imgHQ23383.npy
-imgHQ09527.npy
-imgHQ21252.npy
-imgHQ08867.npy
-imgHQ28436.npy
-imgHQ07281.npy
-imgHQ05635.npy
-imgHQ25296.npy
-imgHQ01088.npy
-imgHQ26361.npy
-imgHQ14301.npy
-imgHQ01794.npy
-imgHQ14559.npy
-imgHQ18658.npy
-imgHQ00427.npy
-imgHQ17878.npy
-imgHQ16754.npy
-imgHQ26015.npy
-imgHQ01878.npy
-imgHQ10735.npy
-imgHQ08165.npy
-imgHQ05964.npy
-imgHQ08772.npy
-imgHQ01166.npy
-imgHQ06860.npy
-imgHQ21855.npy
-imgHQ27940.npy
-imgHQ02041.npy
-imgHQ01101.npy
-imgHQ06519.npy
-imgHQ24024.npy
-imgHQ04695.npy
-imgHQ13592.npy
-imgHQ11621.npy
-imgHQ16185.npy
-imgHQ00847.npy
-imgHQ26804.npy
-imgHQ24667.npy
-imgHQ02430.npy
-imgHQ29796.npy
-imgHQ10976.npy
-imgHQ12180.npy
-imgHQ14743.npy
-imgHQ27867.npy
-imgHQ25148.npy
-imgHQ10166.npy
-imgHQ12130.npy
-imgHQ28948.npy
-imgHQ28923.npy
-imgHQ26808.npy
-imgHQ29045.npy
-imgHQ25519.npy
-imgHQ05548.npy
-imgHQ02064.npy
-imgHQ25783.npy
-imgHQ00480.npy
-imgHQ01785.npy
-imgHQ05063.npy
-imgHQ14478.npy
-imgHQ28523.npy
-imgHQ25555.npy
-imgHQ19646.npy
-imgHQ21129.npy
-imgHQ19122.npy
-imgHQ24273.npy
-imgHQ12016.npy
-imgHQ06913.npy
-imgHQ28293.npy
-imgHQ11324.npy
-imgHQ04772.npy
-imgHQ14437.npy
-imgHQ07107.npy
-imgHQ05811.npy
-imgHQ06337.npy
-imgHQ13051.npy
-imgHQ27518.npy
-imgHQ16592.npy
-imgHQ07248.npy
-imgHQ12987.npy
-imgHQ10802.npy
-imgHQ00042.npy
-imgHQ17353.npy
-imgHQ20598.npy
-imgHQ04544.npy
-imgHQ27457.npy
-imgHQ13535.npy
-imgHQ23467.npy
-imgHQ02725.npy
-imgHQ08789.npy
-imgHQ21338.npy
-imgHQ22493.npy
-imgHQ22123.npy
-imgHQ24208.npy
-imgHQ10890.npy
-imgHQ27579.npy
-imgHQ02281.npy
-imgHQ03194.npy
-imgHQ10067.npy
-imgHQ24758.npy
-imgHQ08199.npy
-imgHQ20736.npy
-imgHQ17416.npy
-imgHQ19871.npy
-imgHQ15517.npy
-imgHQ08175.npy
-imgHQ13439.npy
-imgHQ06889.npy
-imgHQ21261.npy
-imgHQ02466.npy
-imgHQ15858.npy
-imgHQ06600.npy
-imgHQ27733.npy
-imgHQ23193.npy
-imgHQ11262.npy
-imgHQ02920.npy
-imgHQ12128.npy
-imgHQ01238.npy
-imgHQ20391.npy
-imgHQ05616.npy
-imgHQ09422.npy
-imgHQ28420.npy
-imgHQ16807.npy
-imgHQ24878.npy
-imgHQ05830.npy
-imgHQ12338.npy
-imgHQ24999.npy
-imgHQ18782.npy
-imgHQ11322.npy
-imgHQ19100.npy
-imgHQ23423.npy
-imgHQ24770.npy
-imgHQ03560.npy
-imgHQ19527.npy
-imgHQ24411.npy
-imgHQ28925.npy
-imgHQ12398.npy
-imgHQ27980.npy
-imgHQ12269.npy
-imgHQ04014.npy
-imgHQ08371.npy
-imgHQ26423.npy
-imgHQ06846.npy
-imgHQ28512.npy
-imgHQ10115.npy
-imgHQ25910.npy
-imgHQ06664.npy
-imgHQ15147.npy
-imgHQ02475.npy
-imgHQ06552.npy
-imgHQ17112.npy
-imgHQ21552.npy
-imgHQ28121.npy
-imgHQ14172.npy
-imgHQ26432.npy
-imgHQ21711.npy
-imgHQ27134.npy
-imgHQ22297.npy
-imgHQ26652.npy
-imgHQ28383.npy
-imgHQ09317.npy
-imgHQ29767.npy
-imgHQ15970.npy
-imgHQ11392.npy
-imgHQ27435.npy
-imgHQ03266.npy
-imgHQ18359.npy
-imgHQ11252.npy
-imgHQ17629.npy
-imgHQ07352.npy
-imgHQ23134.npy
-imgHQ21077.npy
-imgHQ21494.npy
-imgHQ25566.npy
-imgHQ23371.npy
-imgHQ14934.npy
-imgHQ21396.npy
-imgHQ23959.npy
-imgHQ09343.npy
-imgHQ18307.npy
-imgHQ03561.npy
-imgHQ05199.npy
-imgHQ15575.npy
-imgHQ26371.npy
-imgHQ16023.npy
-imgHQ28259.npy
-imgHQ22453.npy
-imgHQ13491.npy
-imgHQ08262.npy
-imgHQ16306.npy
-imgHQ11436.npy
-imgHQ24536.npy
-imgHQ14793.npy
-imgHQ28320.npy
-imgHQ07079.npy
-imgHQ22788.npy
-imgHQ21905.npy
-imgHQ21624.npy
-imgHQ05792.npy
-imgHQ27109.npy
-imgHQ06540.npy
-imgHQ06295.npy
-imgHQ26446.npy
-imgHQ03482.npy
-imgHQ01490.npy
-imgHQ26870.npy
-imgHQ05614.npy
-imgHQ23894.npy
-imgHQ22605.npy
-imgHQ16380.npy
-imgHQ19217.npy
-imgHQ12324.npy
-imgHQ27657.npy
-imgHQ16262.npy
-imgHQ18170.npy
-imgHQ17366.npy
-imgHQ13454.npy
-imgHQ10157.npy
-imgHQ25414.npy
-imgHQ23410.npy
-imgHQ18558.npy
-imgHQ22081.npy
-imgHQ24606.npy
-imgHQ24423.npy
-imgHQ09517.npy
-imgHQ29341.npy
-imgHQ02073.npy
-imgHQ11114.npy
-imgHQ21636.npy
-imgHQ18404.npy
-imgHQ23507.npy
-imgHQ27320.npy
-imgHQ02627.npy
-imgHQ01988.npy
-imgHQ08482.npy
-imgHQ01012.npy
-imgHQ27112.npy
-imgHQ18545.npy
-imgHQ12476.npy
-imgHQ29573.npy
-imgHQ07828.npy
-imgHQ08785.npy
-imgHQ25975.npy
-imgHQ06675.npy
-imgHQ03963.npy
-imgHQ21128.npy
-imgHQ02448.npy
-imgHQ21543.npy
-imgHQ15180.npy
-imgHQ00304.npy
-imgHQ27241.npy
-imgHQ23974.npy
-imgHQ17326.npy
-imgHQ02585.npy
-imgHQ14388.npy
-imgHQ01324.npy
-imgHQ11632.npy
-imgHQ09584.npy
-imgHQ12860.npy
-imgHQ24030.npy
-imgHQ05458.npy
-imgHQ14992.npy
-imgHQ26842.npy
-imgHQ22147.npy
-imgHQ24872.npy
-imgHQ29321.npy
-imgHQ07785.npy
-imgHQ10426.npy
-imgHQ28596.npy
-imgHQ28171.npy
-imgHQ25042.npy
-imgHQ03373.npy
-imgHQ12607.npy
-imgHQ01865.npy
-imgHQ29585.npy
-imgHQ12172.npy
-imgHQ12151.npy
-imgHQ04932.npy
-imgHQ15937.npy
-imgHQ14765.npy
-imgHQ26659.npy
-imgHQ11202.npy
-imgHQ11304.npy
-imgHQ08680.npy
-imgHQ09043.npy
-imgHQ11176.npy
-imgHQ21515.npy
-imgHQ03231.npy
-imgHQ07210.npy
-imgHQ04188.npy
-imgHQ21937.npy
-imgHQ23851.npy
-imgHQ11596.npy
-imgHQ10489.npy
-imgHQ12319.npy
-imgHQ14426.npy
-imgHQ12469.npy
-imgHQ01212.npy
-imgHQ13175.npy
-imgHQ23602.npy
-imgHQ06278.npy
-imgHQ24709.npy
-imgHQ07958.npy
-imgHQ18032.npy
-imgHQ22389.npy
-imgHQ23964.npy
-imgHQ13544.npy
-imgHQ06709.npy
-imgHQ07192.npy
-imgHQ07676.npy
-imgHQ00691.npy
-imgHQ29249.npy
-imgHQ05292.npy
-imgHQ05323.npy
-imgHQ16616.npy
-imgHQ07199.npy
-imgHQ11871.npy
-imgHQ19834.npy
-imgHQ22348.npy
-imgHQ01836.npy
-imgHQ00908.npy
-imgHQ01749.npy
-imgHQ21629.npy
-imgHQ10729.npy
-imgHQ19883.npy
-imgHQ28346.npy
-imgHQ16083.npy
-imgHQ06907.npy
-imgHQ00778.npy
-imgHQ07481.npy
-imgHQ03671.npy
-imgHQ07899.npy
-imgHQ17788.npy
-imgHQ08013.npy
-imgHQ27367.npy
-imgHQ21189.npy
-imgHQ02498.npy
-imgHQ17385.npy
-imgHQ21541.npy
-imgHQ21912.npy
-imgHQ15830.npy
-imgHQ12463.npy
-imgHQ21547.npy
-imgHQ10512.npy
-imgHQ28600.npy
-imgHQ06784.npy
-imgHQ09778.npy
-imgHQ09636.npy
-imgHQ17360.npy
-imgHQ21319.npy
-imgHQ26976.npy
-imgHQ11247.npy
-imgHQ01473.npy
-imgHQ07697.npy
-imgHQ27263.npy
-imgHQ13185.npy
-imgHQ06017.npy
-imgHQ11201.npy
-imgHQ06924.npy
-imgHQ02225.npy
-imgHQ20873.npy
-imgHQ12863.npy
-imgHQ17630.npy
-imgHQ04739.npy
-imgHQ22886.npy
-imgHQ27286.npy
-imgHQ08200.npy
-imgHQ07013.npy
-imgHQ18140.npy
-imgHQ20888.npy
-imgHQ25717.npy
-imgHQ08083.npy
-imgHQ25533.npy
-imgHQ09167.npy
-imgHQ24016.npy
-imgHQ19492.npy
-imgHQ24863.npy
-imgHQ12132.npy
-imgHQ08085.npy
-imgHQ27240.npy
-imgHQ01387.npy
-imgHQ03559.npy
-imgHQ27615.npy
-imgHQ11079.npy
-imgHQ09841.npy
-imgHQ13171.npy
-imgHQ24954.npy
-imgHQ25964.npy
-imgHQ29674.npy
-imgHQ08643.npy
-imgHQ29943.npy
-imgHQ11712.npy
-imgHQ02995.npy
-imgHQ22659.npy
-imgHQ17475.npy
-imgHQ11017.npy
-imgHQ26306.npy
-imgHQ00108.npy
-imgHQ26153.npy
-imgHQ01004.npy
-imgHQ03753.npy
-imgHQ00843.npy
-imgHQ20663.npy
-imgHQ08143.npy
-imgHQ10995.npy
-imgHQ28429.npy
-imgHQ16046.npy
-imgHQ16407.npy
-imgHQ04097.npy
-imgHQ20493.npy
-imgHQ11929.npy
-imgHQ15256.npy
-imgHQ11751.npy
-imgHQ10312.npy
-imgHQ26103.npy
-imgHQ09325.npy
-imgHQ27967.npy
-imgHQ08562.npy
-imgHQ24244.npy
-imgHQ07074.npy
-imgHQ08184.npy
-imgHQ27832.npy
-imgHQ06452.npy
-imgHQ13193.npy
-imgHQ17134.npy
-imgHQ18055.npy
-imgHQ15021.npy
-imgHQ00614.npy
-imgHQ19551.npy
-imgHQ28453.npy
-imgHQ09523.npy
-imgHQ19305.npy
-imgHQ23910.npy
-imgHQ09382.npy
-imgHQ11146.npy
-imgHQ12613.npy
-imgHQ06744.npy
-imgHQ14148.npy
-imgHQ14103.npy
-imgHQ28608.npy
-imgHQ21944.npy
-imgHQ12237.npy
-imgHQ22623.npy
-imgHQ13601.npy
-imgHQ01465.npy
-imgHQ08408.npy
-imgHQ05145.npy
-imgHQ04478.npy
-imgHQ23709.npy
-imgHQ06435.npy
-imgHQ24973.npy
-imgHQ05814.npy
-imgHQ28724.npy
-imgHQ09158.npy
-imgHQ23980.npy
-imgHQ27376.npy
-imgHQ00896.npy
-imgHQ10957.npy
-imgHQ12759.npy
-imgHQ27649.npy
-imgHQ06354.npy
-imgHQ17058.npy
-imgHQ01302.npy
-imgHQ28970.npy
-imgHQ24058.npy
-imgHQ02649.npy
-imgHQ08641.npy
-imgHQ03546.npy
-imgHQ01887.npy
-imgHQ06810.npy
-imgHQ21871.npy
-imgHQ28903.npy
-imgHQ24155.npy
-imgHQ06292.npy
-imgHQ18583.npy
-imgHQ03903.npy
-imgHQ17030.npy
-imgHQ12923.npy
-imgHQ08015.npy
-imgHQ11474.npy
-imgHQ00138.npy
-imgHQ25234.npy
-imgHQ22027.npy
-imgHQ23905.npy
-imgHQ06099.npy
-imgHQ07432.npy
-imgHQ02257.npy
-imgHQ26839.npy
-imgHQ20378.npy
-imgHQ19038.npy
-imgHQ19202.npy
-imgHQ05280.npy
-imgHQ04172.npy
-imgHQ18595.npy
-imgHQ27672.npy
-imgHQ26997.npy
-imgHQ21083.npy
-imgHQ05777.npy
-imgHQ10619.npy
-imgHQ08864.npy
-imgHQ02542.npy
-imgHQ08049.npy
-imgHQ14288.npy
-imgHQ08330.npy
-imgHQ16779.npy
-imgHQ02767.npy
-imgHQ24909.npy
-imgHQ24447.npy
-imgHQ16276.npy
-imgHQ07733.npy
-imgHQ20528.npy
-imgHQ02563.npy
-imgHQ21320.npy
-imgHQ14637.npy
-imgHQ22160.npy
-imgHQ04775.npy
-imgHQ04177.npy
-imgHQ11633.npy
-imgHQ05291.npy
-imgHQ06267.npy
-imgHQ04829.npy
-imgHQ16436.npy
-imgHQ01399.npy
-imgHQ12368.npy
-imgHQ06380.npy
-imgHQ27162.npy
-imgHQ10842.npy
-imgHQ24858.npy
-imgHQ17392.npy
-imgHQ25004.npy
-imgHQ24190.npy
-imgHQ10350.npy
-imgHQ03123.npy
-imgHQ12757.npy
-imgHQ04858.npy
-imgHQ13565.npy
-imgHQ03125.npy
-imgHQ03524.npy
-imgHQ29511.npy
-imgHQ08529.npy
-imgHQ00763.npy
-imgHQ12309.npy
-imgHQ09876.npy
-imgHQ06387.npy
-imgHQ06697.npy
-imgHQ20529.npy
-imgHQ02833.npy
-imgHQ24350.npy
-imgHQ06102.npy
-imgHQ27364.npy
-imgHQ18607.npy
-imgHQ27655.npy
-imgHQ07147.npy
-imgHQ26174.npy
-imgHQ08466.npy
-imgHQ19412.npy
-imgHQ01269.npy
-imgHQ25267.npy
-imgHQ22661.npy
-imgHQ16769.npy
-imgHQ06296.npy
-imgHQ04375.npy
-imgHQ09113.npy
-imgHQ09304.npy
-imgHQ29505.npy
-imgHQ02733.npy
-imgHQ02283.npy
-imgHQ21457.npy
-imgHQ15567.npy
-imgHQ08055.npy
-imgHQ22338.npy
-imgHQ01267.npy
-imgHQ29041.npy
-imgHQ16292.npy
-imgHQ00593.npy
-imgHQ12245.npy
-imgHQ04736.npy
-imgHQ07887.npy
-imgHQ19216.npy
-imgHQ07917.npy
-imgHQ01106.npy
-imgHQ25200.npy
-imgHQ19479.npy
-imgHQ10566.npy
-imgHQ21264.npy
-imgHQ13095.npy
-imgHQ14323.npy
-imgHQ08484.npy
-imgHQ19420.npy
-imgHQ27993.npy
-imgHQ01496.npy
-imgHQ00888.npy
-imgHQ02536.npy
-imgHQ08574.npy
-imgHQ18536.npy
-imgHQ21736.npy
-imgHQ27438.npy
-imgHQ08117.npy
-imgHQ03797.npy
-imgHQ16076.npy
-imgHQ08664.npy
-imgHQ16487.npy
-imgHQ08750.npy
-imgHQ01540.npy
-imgHQ07063.npy
-imgHQ22137.npy
-imgHQ02637.npy
-imgHQ19061.npy
-imgHQ16032.npy
-imgHQ01989.npy
-imgHQ16177.npy
-imgHQ14440.npy
-imgHQ02262.npy
-imgHQ26462.npy
-imgHQ07214.npy
-imgHQ06998.npy
-imgHQ14663.npy
-imgHQ14235.npy
-imgHQ24566.npy
-imgHQ15046.npy
-imgHQ02590.npy
-imgHQ08550.npy
-imgHQ17154.npy
-imgHQ25197.npy
-imgHQ21443.npy
-imgHQ23740.npy
-imgHQ11626.npy
-imgHQ14377.npy
-imgHQ04831.npy
-imgHQ18159.npy
-imgHQ29226.npy
-imgHQ24096.npy
-imgHQ18507.npy
-imgHQ15795.npy
-imgHQ00947.npy
-imgHQ11722.npy
-imgHQ06743.npy
-imgHQ10287.npy
-imgHQ22202.npy
-imgHQ15805.npy
-imgHQ12014.npy
-imgHQ01060.npy
-imgHQ05102.npy
-imgHQ04878.npy
-imgHQ04584.npy
-imgHQ19053.npy
-imgHQ03278.npy
-imgHQ15896.npy
-imgHQ09771.npy
-imgHQ07164.npy
-imgHQ26734.npy
-imgHQ17108.npy
-imgHQ05361.npy
-imgHQ21959.npy
-imgHQ26064.npy
-imgHQ09491.npy
-imgHQ11174.npy
-imgHQ14369.npy
-imgHQ19738.npy
-imgHQ02908.npy
-imgHQ06728.npy
-imgHQ02888.npy
-imgHQ18779.npy
-imgHQ18733.npy
-imgHQ14436.npy
-imgHQ16821.npy
-imgHQ24565.npy
-imgHQ29522.npy
-imgHQ15123.npy
-imgHQ16968.npy
-imgHQ18686.npy
-imgHQ02700.npy
-imgHQ23689.npy
-imgHQ05090.npy
-imgHQ25196.npy
-imgHQ22370.npy
-imgHQ09127.npy
-imgHQ17914.npy
-imgHQ11655.npy
-imgHQ02414.npy
-imgHQ20900.npy
-imgHQ07316.npy
-imgHQ19400.npy
-imgHQ25837.npy
-imgHQ03858.npy
-imgHQ16459.npy
-imgHQ19512.npy
-imgHQ16887.npy
-imgHQ06739.npy
-imgHQ24627.npy
-imgHQ15075.npy
-imgHQ03387.npy
-imgHQ07517.npy
-imgHQ02978.npy
-imgHQ17342.npy
-imgHQ28428.npy
-imgHQ29732.npy
-imgHQ25365.npy
-imgHQ20754.npy
-imgHQ07200.npy
-imgHQ23128.npy
-imgHQ05797.npy
-imgHQ05907.npy
-imgHQ27057.npy
-imgHQ01185.npy
-imgHQ24352.npy
-imgHQ06178.npy
-imgHQ17266.npy
-imgHQ01219.npy
-imgHQ18927.npy
-imgHQ29374.npy
-imgHQ15758.npy
-imgHQ06228.npy
-imgHQ27485.npy
-imgHQ16206.npy
-imgHQ10849.npy
-imgHQ17595.npy
-imgHQ01654.npy
-imgHQ23448.npy
-imgHQ00163.npy
-imgHQ12751.npy
-imgHQ29992.npy
-imgHQ24830.npy
-imgHQ21793.npy
-imgHQ28170.npy
-imgHQ09702.npy
-imgHQ06894.npy
-imgHQ09859.npy
-imgHQ07357.npy
-imgHQ14667.npy
-imgHQ03570.npy
-imgHQ28375.npy
-imgHQ17395.npy
-imgHQ16198.npy
-imgHQ12797.npy
-imgHQ10865.npy
-imgHQ25106.npy
-imgHQ23444.npy
-imgHQ28791.npy
-imgHQ18588.npy
-imgHQ06555.npy
-imgHQ24258.npy
-imgHQ14692.npy
-imgHQ23787.npy
-imgHQ01511.npy
-imgHQ17725.npy
-imgHQ27434.npy
-imgHQ01611.npy
-imgHQ26041.npy
-imgHQ19692.npy
-imgHQ12507.npy
-imgHQ06045.npy
-imgHQ29653.npy
-imgHQ15995.npy
-imgHQ18320.npy
-imgHQ26272.npy
-imgHQ21177.npy
-imgHQ21839.npy
-imgHQ23398.npy
-imgHQ15244.npy
-imgHQ00198.npy
-imgHQ29539.npy
-imgHQ23421.npy
-imgHQ20996.npy
-imgHQ28983.npy
-imgHQ28401.npy
-imgHQ14732.npy
-imgHQ17546.npy
-imgHQ24555.npy
-imgHQ02043.npy
-imgHQ04366.npy
-imgHQ01165.npy
-imgHQ20623.npy
-imgHQ05823.npy
-imgHQ12735.npy
-imgHQ24028.npy
-imgHQ29459.npy
-imgHQ19000.npy
-imgHQ23055.npy
-imgHQ26194.npy
-imgHQ22038.npy
-imgHQ12997.npy
-imgHQ19972.npy
-imgHQ08517.npy
-imgHQ19283.npy
-imgHQ06097.npy
-imgHQ07865.npy
-imgHQ05734.npy
-imgHQ22679.npy
-imgHQ10038.npy
-imgHQ05968.npy
-imgHQ06745.npy
-imgHQ18887.npy
-imgHQ08341.npy
-imgHQ14748.npy
-imgHQ03213.npy
-imgHQ08762.npy
-imgHQ04038.npy
-imgHQ24966.npy
-imgHQ21280.npy
-imgHQ19788.npy
-imgHQ11473.npy
-imgHQ29323.npy
-imgHQ19947.npy
-imgHQ24261.npy
-imgHQ16317.npy
-imgHQ29533.npy
-imgHQ01506.npy
-imgHQ21227.npy
-imgHQ11946.npy
-imgHQ16603.npy
-imgHQ22943.npy
-imgHQ01835.npy
-imgHQ25008.npy
-imgHQ26698.npy
-imgHQ24476.npy
-imgHQ21228.npy
-imgHQ05183.npy
-imgHQ10674.npy
-imgHQ17843.npy
-imgHQ17147.npy
-imgHQ00403.npy
-imgHQ07114.npy
-imgHQ02506.npy
-imgHQ22688.npy
-imgHQ20857.npy
-imgHQ02562.npy
-imgHQ05073.npy
-imgHQ25892.npy
-imgHQ04025.npy
-imgHQ22052.npy
-imgHQ00137.npy
-imgHQ23687.npy
-imgHQ09390.npy
-imgHQ11145.npy
-imgHQ08448.npy
-imgHQ22676.npy
-imgHQ21914.npy
-imgHQ20477.npy
-imgHQ21445.npy
-imgHQ15251.npy
-imgHQ04887.npy
-imgHQ29982.npy
-imgHQ07849.npy
-imgHQ02538.npy
-imgHQ27212.npy
-imgHQ23277.npy
-imgHQ26348.npy
-imgHQ23928.npy
-imgHQ00562.npy
-imgHQ07508.npy
-imgHQ00279.npy
-imgHQ29582.npy
-imgHQ25437.npy
-imgHQ06575.npy
-imgHQ25348.npy
-imgHQ05399.npy
-imgHQ26739.npy
-imgHQ02773.npy
-imgHQ11260.npy
-imgHQ17789.npy
-imgHQ04054.npy
-imgHQ02785.npy
-imgHQ05938.npy
-imgHQ07747.npy
-imgHQ22877.npy
-imgHQ00206.npy
-imgHQ05056.npy
-imgHQ02131.npy
-imgHQ21105.npy
-imgHQ26878.npy
-imgHQ01468.npy
-imgHQ11226.npy
-imgHQ12257.npy
-imgHQ27752.npy
-imgHQ16559.npy
-imgHQ01502.npy
-imgHQ11130.npy
-imgHQ04779.npy
-imgHQ08788.npy
-imgHQ01372.npy
-imgHQ14681.npy
-imgHQ00047.npy
-imgHQ15422.npy
-imgHQ09344.npy
-imgHQ11335.npy
-imgHQ24869.npy
-imgHQ07530.npy
-imgHQ08029.npy
-imgHQ29990.npy
-imgHQ10529.npy
-imgHQ03617.npy
-imgHQ29159.npy
-imgHQ04043.npy
-imgHQ03572.npy
-imgHQ09126.npy
-imgHQ18878.npy
-imgHQ09680.npy
-imgHQ25455.npy
-imgHQ01276.npy
-imgHQ20875.npy
-imgHQ19994.npy
-imgHQ01495.npy
-imgHQ21232.npy
-imgHQ11293.npy
-imgHQ01153.npy
-imgHQ03405.npy
-imgHQ29060.npy
-imgHQ07802.npy
-imgHQ03791.npy
-imgHQ25875.npy
-imgHQ23571.npy
-imgHQ02828.npy
-imgHQ15109.npy
-imgHQ27799.npy
-imgHQ12485.npy
-imgHQ26057.npy
-imgHQ17297.npy
-imgHQ06825.npy
-imgHQ14588.npy
-imgHQ04516.npy
-imgHQ16004.npy
-imgHQ01998.npy
-imgHQ12929.npy
-imgHQ03376.npy
-imgHQ25301.npy
-imgHQ18396.npy
-imgHQ11196.npy
-imgHQ17901.npy
-imgHQ09514.npy
-imgHQ05800.npy
-imgHQ08109.npy
-imgHQ06898.npy
-imgHQ28364.npy
-imgHQ18579.npy
-imgHQ08736.npy
-imgHQ21184.npy
-imgHQ28777.npy
-imgHQ02957.npy
-imgHQ04536.npy
-imgHQ16584.npy
-imgHQ18254.npy
-imgHQ19814.npy
-imgHQ25922.npy
-imgHQ18561.npy
-imgHQ08394.npy
-imgHQ28255.npy
-imgHQ13237.npy
-imgHQ07184.npy
-imgHQ04095.npy
-imgHQ26173.npy
-imgHQ25367.npy
-imgHQ27843.npy
-imgHQ18275.npy
-imgHQ01577.npy
-imgHQ10743.npy
-imgHQ09146.npy
-imgHQ24306.npy
-imgHQ07967.npy
-imgHQ04349.npy
-imgHQ26056.npy
-imgHQ13222.npy
-imgHQ17947.npy
-imgHQ22974.npy
-imgHQ01492.npy
-imgHQ11697.npy
-imgHQ00552.npy
-imgHQ20973.npy
-imgHQ22630.npy
-imgHQ03381.npy
-imgHQ07679.npy
-imgHQ02275.npy
-imgHQ07870.npy
-imgHQ25481.npy
-imgHQ12641.npy
-imgHQ07240.npy
-imgHQ00554.npy
-imgHQ27018.npy
-imgHQ03130.npy
-imgHQ00156.npy
-imgHQ20194.npy
-imgHQ06356.npy
-imgHQ19639.npy
-imgHQ09674.npy
-imgHQ05708.npy
-imgHQ18178.npy
-imgHQ06896.npy
-imgHQ01289.npy
-imgHQ23672.npy
-imgHQ15514.npy
-imgHQ20471.npy
-imgHQ25537.npy
-imgHQ12070.npy
-imgHQ13264.npy
-imgHQ19962.npy
-imgHQ13388.npy
-imgHQ17978.npy
-imgHQ16610.npy
-imgHQ18436.npy
-imgHQ03855.npy
-imgHQ18537.npy
-imgHQ23542.npy
-imgHQ01332.npy
-imgHQ25940.npy
-imgHQ02435.npy
-imgHQ27243.npy
-imgHQ05634.npy
-imgHQ08031.npy
-imgHQ18214.npy
-imgHQ04559.npy
-imgHQ23307.npy
-imgHQ13300.npy
-imgHQ27585.npy
-imgHQ01814.npy
-imgHQ00995.npy
-imgHQ03119.npy
-imgHQ29410.npy
-imgHQ16273.npy
-imgHQ12753.npy
-imgHQ20689.npy
-imgHQ27032.npy
-imgHQ22191.npy
-imgHQ04810.npy
-imgHQ09605.npy
-imgHQ27741.npy
-imgHQ01869.npy
-imgHQ02443.npy
-imgHQ02243.npy
-imgHQ22650.npy
-imgHQ14024.npy
-imgHQ07842.npy
-imgHQ27001.npy
-imgHQ10909.npy
-imgHQ25529.npy
-imgHQ18517.npy
-imgHQ10820.npy
-imgHQ04187.npy
-imgHQ25087.npy
-imgHQ10450.npy
-imgHQ09439.npy
-imgHQ12799.npy
-imgHQ21766.npy
-imgHQ24694.npy
-imgHQ04212.npy
-imgHQ04526.npy
-imgHQ05144.npy
-imgHQ05994.npy
-imgHQ18293.npy
-imgHQ23626.npy
-imgHQ12556.npy
-imgHQ11720.npy
-imgHQ13917.npy
-imgHQ10916.npy
-imgHQ29691.npy
-imgHQ10358.npy
-imgHQ00314.npy
-imgHQ16373.npy
-imgHQ27407.npy
-imgHQ16894.npy
-imgHQ25372.npy
-imgHQ19835.npy
-imgHQ15116.npy
-imgHQ01559.npy
-imgHQ18232.npy
-imgHQ11919.npy
-imgHQ05233.npy
-imgHQ24865.npy
-imgHQ19269.npy
-imgHQ03947.npy
-imgHQ05674.npy
-imgHQ26365.npy
-imgHQ01854.npy
-imgHQ26706.npy
-imgHQ22612.npy
-imgHQ26602.npy
-imgHQ27110.npy
-imgHQ13760.npy
-imgHQ11611.npy
-imgHQ04344.npy
-imgHQ08760.npy
-imgHQ11947.npy
-imgHQ22282.npy
-imgHQ12471.npy
-imgHQ19404.npy
-imgHQ10510.npy
-imgHQ10383.npy
-imgHQ08615.npy
-imgHQ22772.npy
-imgHQ20917.npy
-imgHQ13251.npy
-imgHQ26411.npy
-imgHQ01935.npy
-imgHQ10545.npy
-imgHQ23592.npy
-imgHQ29572.npy
-imgHQ23454.npy
-imgHQ02386.npy
-imgHQ24687.npy
-imgHQ12881.npy
-imgHQ26493.npy
-imgHQ22653.npy
-imgHQ19040.npy
-imgHQ27075.npy
-imgHQ05712.npy
-imgHQ15440.npy
-imgHQ03845.npy
-imgHQ03175.npy
-imgHQ19208.npy
-imgHQ24799.npy
-imgHQ28381.npy
-imgHQ16181.npy
-imgHQ00990.npy
-imgHQ22553.npy
-imgHQ25111.npy
-imgHQ24625.npy
-imgHQ01513.npy
-imgHQ26731.npy
-imgHQ02018.npy
-imgHQ00484.npy
-imgHQ17245.npy
-imgHQ04435.npy
-imgHQ26082.npy
-imgHQ29109.npy
-imgHQ26667.npy
-imgHQ03833.npy
-imgHQ04309.npy
-imgHQ05876.npy
-imgHQ08314.npy
-imgHQ12213.npy
-imgHQ00852.npy
-imgHQ11342.npy
-imgHQ25115.npy
-imgHQ18971.npy
-imgHQ05935.npy
-imgHQ02037.npy
-imgHQ06707.npy
-imgHQ19723.npy
-imgHQ06559.npy
-imgHQ29171.npy
-imgHQ22311.npy
-imgHQ16278.npy
-imgHQ20377.npy
-imgHQ05841.npy
-imgHQ04774.npy
-imgHQ10197.npy
-imgHQ22400.npy
-imgHQ23001.npy
-imgHQ18499.npy
-imgHQ04171.npy
-imgHQ29293.npy
-imgHQ27392.npy
-imgHQ01868.npy
-imgHQ21900.npy
-imgHQ10648.npy
-imgHQ00711.npy
-imgHQ20657.npy
-imgHQ24979.npy
-imgHQ20652.npy
-imgHQ13517.npy
-imgHQ12692.npy
-imgHQ18705.npy
-imgHQ18440.npy
-imgHQ21230.npy
-imgHQ10230.npy
-imgHQ03931.npy
-imgHQ20457.npy
-imgHQ09515.npy
-imgHQ14056.npy
-imgHQ29467.npy
-imgHQ13080.npy
-imgHQ08961.npy
-imgHQ20176.npy
-imgHQ27017.npy
-imgHQ17322.npy
-imgHQ18415.npy
-imgHQ10737.npy
-imgHQ13024.npy
-imgHQ22246.npy
-imgHQ27924.npy
-imgHQ11847.npy
-imgHQ17626.npy
-imgHQ01677.npy
-imgHQ28268.npy
-imgHQ27155.npy
-imgHQ10527.npy
-imgHQ02550.npy
-imgHQ23771.npy
-imgHQ07891.npy
-imgHQ22705.npy
-imgHQ05418.npy
-imgHQ09391.npy
-imgHQ01409.npy
-imgHQ25896.npy
-imgHQ25485.npy
-imgHQ12116.npy
-imgHQ02768.npy
-imgHQ28964.npy
-imgHQ09270.npy
-imgHQ11406.npy
-imgHQ09235.npy
-imgHQ22186.npy
-imgHQ09459.npy
-imgHQ13435.npy
-imgHQ08845.npy
-imgHQ25122.npy
-imgHQ00492.npy
-imgHQ10363.npy
-imgHQ05467.npy
-imgHQ11408.npy
-imgHQ16464.npy
-imgHQ06118.npy
-imgHQ04297.npy
-imgHQ06482.npy
-imgHQ25263.npy
-imgHQ18981.npy
-imgHQ14804.npy
-imgHQ13416.npy
-imgHQ29011.npy
-imgHQ11169.npy
-imgHQ24484.npy
-imgHQ11715.npy
-imgHQ19222.npy
-imgHQ12911.npy
-imgHQ08273.npy
-imgHQ23987.npy
-imgHQ29697.npy
-imgHQ08208.npy
-imgHQ07861.npy
-imgHQ26797.npy
-imgHQ21514.npy
-imgHQ26765.npy
-imgHQ13937.npy
-imgHQ02360.npy
-imgHQ07108.npy
-imgHQ23431.npy
-imgHQ03823.npy
-imgHQ27473.npy
-imgHQ24114.npy
-imgHQ19912.npy
-imgHQ12008.npy
-imgHQ26952.npy
-imgHQ24127.npy
-imgHQ21158.npy
-imgHQ15443.npy
-imgHQ07973.npy
-imgHQ09396.npy
-imgHQ23748.npy
-imgHQ11564.npy
-imgHQ13380.npy
-imgHQ20850.npy
-imgHQ23002.npy
-imgHQ19827.npy
-imgHQ19054.npy
-imgHQ13657.npy
-imgHQ05989.npy
-imgHQ28518.npy
-imgHQ05833.npy
-imgHQ26318.npy
-imgHQ26390.npy
-imgHQ26614.npy
-imgHQ22505.npy
-imgHQ20254.npy
-imgHQ06607.npy
-imgHQ05427.npy
-imgHQ05987.npy
-imgHQ03154.npy
-imgHQ27144.npy
-imgHQ00911.npy
-imgHQ29065.npy
-imgHQ19959.npy
-imgHQ17645.npy
-imgHQ11265.npy
-imgHQ05909.npy
-imgHQ03716.npy
-imgHQ23743.npy
-imgHQ14158.npy
-imgHQ28081.npy
-imgHQ17401.npy
-imgHQ01571.npy
-imgHQ19107.npy
-imgHQ10135.npy
-imgHQ16767.npy
-imgHQ14595.npy
-imgHQ10217.npy
-imgHQ10419.npy
-imgHQ17382.npy
-imgHQ10746.npy
-imgHQ10774.npy
-imgHQ13233.npy
-imgHQ26128.npy
-imgHQ01019.npy
-imgHQ05524.npy
-imgHQ13420.npy
-imgHQ05242.npy
-imgHQ15799.npy
-imgHQ04868.npy
-imgHQ12869.npy
-imgHQ14490.npy
-imgHQ12453.npy
-imgHQ25221.npy
-imgHQ12103.npy
-imgHQ11587.npy
-imgHQ28672.npy
-imgHQ19537.npy
-imgHQ15708.npy
-imgHQ07606.npy
-imgHQ20040.npy
-imgHQ28282.npy
-imgHQ12868.npy
-imgHQ16563.npy
-imgHQ19780.npy
-imgHQ00093.npy
-imgHQ28232.npy
-imgHQ03111.npy
-imgHQ05599.npy
-imgHQ02120.npy
-imgHQ02145.npy
-imgHQ29094.npy
-imgHQ12568.npy
-imgHQ04330.npy
-imgHQ26334.npy
-imgHQ13490.npy
-imgHQ17126.npy
-imgHQ12197.npy
-imgHQ00594.npy
-imgHQ13157.npy
-imgHQ10742.npy
-imgHQ15118.npy
-imgHQ17255.npy
-imgHQ01879.npy
-imgHQ16006.npy
-imgHQ28649.npy
-imgHQ15225.npy
-imgHQ23177.npy
-imgHQ14967.npy
-imgHQ03987.npy
-imgHQ25320.npy
-imgHQ08694.npy
-imgHQ22608.npy
-imgHQ19892.npy
-imgHQ18554.npy
-imgHQ07806.npy
-imgHQ18302.npy
-imgHQ23612.npy
-imgHQ00844.npy
-imgHQ09961.npy
-imgHQ28260.npy
-imgHQ22503.npy
-imgHQ13552.npy
-imgHQ00258.npy
-imgHQ26780.npy
-imgHQ14875.npy
-imgHQ08700.npy
-imgHQ24795.npy
-imgHQ15867.npy
-imgHQ04841.npy
-imgHQ26366.npy
-imgHQ18876.npy
-imgHQ03330.npy
-imgHQ28229.npy
-imgHQ06988.npy
-imgHQ23097.npy
-imgHQ01961.npy
-imgHQ01431.npy
-imgHQ26058.npy
-imgHQ09851.npy
-imgHQ28497.npy
-imgHQ03533.npy
-imgHQ29586.npy
-imgHQ28551.npy
-imgHQ22657.npy
-imgHQ10698.npy
-imgHQ01503.npy
-imgHQ10096.npy
-imgHQ21567.npy
-imgHQ10279.npy
-imgHQ29647.npy
-imgHQ11249.npy
-imgHQ27559.npy
-imgHQ14616.npy
-imgHQ11332.npy
-imgHQ24543.npy
-imgHQ06529.npy
-imgHQ22118.npy
-imgHQ25669.npy
-imgHQ13075.npy
-imgHQ04101.npy
-imgHQ09363.npy
-imgHQ10683.npy
-imgHQ07613.npy
-imgHQ25068.npy
-imgHQ02866.npy
-imgHQ20087.npy
-imgHQ12828.npy
-imgHQ23810.npy
-imgHQ10511.npy
-imgHQ03884.npy
-imgHQ12972.npy
-imgHQ18372.npy
-imgHQ11557.npy
-imgHQ21833.npy
-imgHQ25316.npy
-imgHQ20522.npy
-imgHQ18788.npy
-imgHQ23603.npy
-imgHQ25536.npy
-imgHQ24362.npy
-imgHQ17858.npy
-imgHQ05765.npy
-imgHQ16458.npy
-imgHQ00656.npy
-imgHQ25587.npy
-imgHQ21691.npy
-imgHQ23240.npy
-imgHQ24499.npy
-imgHQ07551.npy
-imgHQ15329.npy
-imgHQ24938.npy
-imgHQ15654.npy
-imgHQ09599.npy
-imgHQ28046.npy
-imgHQ05661.npy
-imgHQ22745.npy
-imgHQ17047.npy
-imgHQ07472.npy
-imgHQ15152.npy
-imgHQ02890.npy
-imgHQ06221.npy
-imgHQ17791.npy
-imgHQ13293.npy
-imgHQ02273.npy
-imgHQ25368.npy
-imgHQ06870.npy
-imgHQ25782.npy
-imgHQ00339.npy
-imgHQ12554.npy
-imgHQ03995.npy
-imgHQ28740.npy
-imgHQ18333.npy
-imgHQ03393.npy
-imgHQ26389.npy
-imgHQ18427.npy
-imgHQ21211.npy
-imgHQ24530.npy
-imgHQ08192.npy
-imgHQ27944.npy
-imgHQ13330.npy
-imgHQ17842.npy
-imgHQ24962.npy
-imgHQ17652.npy
-imgHQ23946.npy
-imgHQ05495.npy
-imgHQ21024.npy
-imgHQ14229.npy
-imgHQ26345.npy
-imgHQ06217.npy
-imgHQ02933.npy
-imgHQ22847.npy
-imgHQ19271.npy
-imgHQ04770.npy
-imgHQ13741.npy
-imgHQ25230.npy
-imgHQ28694.npy
-imgHQ17548.npy
-imgHQ10857.npy
-imgHQ21638.npy
-imgHQ05120.npy
-imgHQ19332.npy
-imgHQ02651.npy
-imgHQ06261.npy
-imgHQ27681.npy
-imgHQ29322.npy
-imgHQ09011.npy
-imgHQ15804.npy
-imgHQ20250.npy
-imgHQ19837.npy
-imgHQ00123.npy
-imgHQ12474.npy
-imgHQ07011.npy
-imgHQ04740.npy
-imgHQ09562.npy
-imgHQ22188.npy
-imgHQ20530.npy
-imgHQ24642.npy
-imgHQ01544.npy
-imgHQ19844.npy
-imgHQ04566.npy
-imgHQ09968.npy
-imgHQ21003.npy
-imgHQ22092.npy
-imgHQ06796.npy
-imgHQ14964.npy
-imgHQ03008.npy
-imgHQ09967.npy
-imgHQ10097.npy
-imgHQ16505.npy
-imgHQ26323.npy
-imgHQ18231.npy
-imgHQ26710.npy
-imgHQ29147.npy
-imgHQ17520.npy
-imgHQ07632.npy
-imgHQ16207.npy
-imgHQ06550.npy
-imgHQ09488.npy
-imgHQ27179.npy
-imgHQ27080.npy
-imgHQ28295.npy
-imgHQ13596.npy
-imgHQ26902.npy
-imgHQ25093.npy
-imgHQ06200.npy
-imgHQ00639.npy
-imgHQ24552.npy
-imgHQ05618.npy
-imgHQ24551.npy
-imgHQ16290.npy
-imgHQ18306.npy
-imgHQ17222.npy
-imgHQ03182.npy
-imgHQ21772.npy
-imgHQ20138.npy
-imgHQ19421.npy
-imgHQ11541.npy
-imgHQ02825.npy
-imgHQ03099.npy
-imgHQ06309.npy
-imgHQ12224.npy
-imgHQ20853.npy
-imgHQ23497.npy
-imgHQ17284.npy
-imgHQ01917.npy
-imgHQ14557.npy
-imgHQ13866.npy
-imgHQ22843.npy
-imgHQ12706.npy
-imgHQ09601.npy
-imgHQ03067.npy
-imgHQ01768.npy
-imgHQ00753.npy
-imgHQ29278.npy
-imgHQ09948.npy
-imgHQ26534.npy
-imgHQ26479.npy
-imgHQ04790.npy
-imgHQ19611.npy
-imgHQ10980.npy
-imgHQ13547.npy
-imgHQ22397.npy
-imgHQ07557.npy
-imgHQ22997.npy
-imgHQ19444.npy
-imgHQ06650.npy
-imgHQ24972.npy
-imgHQ24256.npy
-imgHQ22821.npy
-imgHQ18337.npy
-imgHQ05272.npy
-imgHQ28584.npy
-imgHQ10730.npy
-imgHQ15287.npy
-imgHQ25027.npy
-imgHQ29621.npy
-imgHQ29714.npy
-imgHQ27486.npy
-imgHQ11157.npy
-imgHQ00607.npy
-imgHQ16017.npy
-imgHQ18926.npy
-imgHQ23522.npy
-imgHQ03448.npy
-imgHQ21573.npy
-imgHQ23688.npy
-imgHQ16578.npy
-imgHQ19321.npy
-imgHQ20301.npy
-imgHQ19689.npy
-imgHQ02139.npy
-imgHQ25715.npy
-imgHQ19783.npy
-imgHQ27819.npy
-imgHQ22300.npy
-imgHQ20080.npy
-imgHQ15540.npy
-imgHQ22384.npy
-imgHQ07704.npy
-imgHQ00628.npy
-imgHQ23921.npy
-imgHQ03810.npy
-imgHQ19576.npy
-imgHQ16640.npy
-imgHQ14140.npy
-imgHQ07215.npy
-imgHQ07880.npy
-imgHQ13308.npy
-imgHQ06166.npy
-imgHQ02314.npy
-imgHQ02880.npy
-imgHQ22944.npy
-imgHQ18781.npy
-imgHQ20966.npy
-imgHQ00064.npy
-imgHQ01373.npy
-imgHQ21036.npy
-imgHQ03942.npy
-imgHQ12956.npy
-imgHQ22750.npy
-imgHQ26561.npy
-imgHQ23882.npy
-imgHQ27769.npy
-imgHQ00186.npy
-imgHQ21358.npy
-imgHQ21904.npy
-imgHQ17183.npy
-imgHQ23570.npy
-imgHQ24040.npy
-imgHQ05894.npy
-imgHQ28098.npy
-imgHQ27016.npy
-imgHQ03431.npy
-imgHQ23103.npy
-imgHQ16449.npy
-imgHQ04266.npy
-imgHQ03832.npy
-imgHQ19154.npy
-imgHQ22744.npy
-imgHQ02483.npy
-imgHQ13681.npy
-imgHQ07220.npy
-imgHQ21442.npy
-imgHQ29637.npy
-imgHQ12907.npy
-imgHQ21079.npy
-imgHQ25835.npy
-imgHQ17508.npy
-imgHQ27861.npy
-imgHQ00233.npy
-imgHQ12950.npy
-imgHQ09798.npy
-imgHQ04035.npy
-imgHQ19088.npy
-imgHQ01712.npy
-imgHQ04329.npy
-imgHQ15216.npy
-imgHQ26208.npy
-imgHQ08544.npy
-imgHQ13817.npy
-imgHQ09668.npy
-imgHQ15906.npy
-imgHQ02353.npy
-imgHQ04051.npy
-imgHQ20332.npy
-imgHQ04144.npy
-imgHQ26932.npy
-imgHQ06791.npy
-imgHQ12038.npy
-imgHQ06389.npy
-imgHQ15548.npy
-imgHQ14570.npy
-imgHQ22045.npy
-imgHQ27494.npy
-imgHQ02292.npy
-imgHQ16955.npy
-imgHQ04314.npy
-imgHQ14325.npy
-imgHQ25811.npy
-imgHQ03363.npy
-imgHQ17708.npy
-imgHQ02527.npy
-imgHQ05950.npy
-imgHQ20204.npy
-imgHQ29562.npy
-imgHQ04274.npy
-imgHQ13182.npy
-imgHQ16809.npy
-imgHQ21293.npy
-imgHQ22021.npy
-imgHQ28345.npy
-imgHQ15086.npy
-imgHQ20156.npy
-imgHQ19186.npy
-imgHQ19575.npy
-imgHQ20702.npy
-imgHQ19987.npy
-imgHQ00249.npy
-imgHQ22056.npy
-imgHQ12711.npy
-imgHQ02287.npy
-imgHQ04105.npy
-imgHQ22371.npy
-imgHQ24584.npy
-imgHQ06955.npy
-imgHQ18107.npy
-imgHQ28859.npy
-imgHQ23517.npy
-imgHQ14533.npy
-imgHQ14097.npy
-imgHQ29999.npy
-imgHQ14756.npy
-imgHQ17974.npy
-imgHQ19144.npy
-imgHQ27599.npy
-imgHQ26320.npy
-imgHQ07077.npy
-imgHQ25166.npy
-imgHQ03961.npy
-imgHQ27401.npy
-imgHQ06969.npy
-imgHQ20877.npy
-imgHQ17795.npy
-imgHQ15141.npy
-imgHQ16722.npy
-imgHQ01005.npy
-imgHQ24456.npy
-imgHQ19985.npy
-imgHQ09264.npy
-imgHQ01053.npy
-imgHQ17164.npy
-imgHQ18698.npy
-imgHQ10264.npy
-imgHQ26908.npy
-imgHQ02221.npy
-imgHQ11340.npy
-imgHQ21902.npy
-imgHQ21374.npy
-imgHQ01632.npy
-imgHQ07151.npy
-imgHQ10047.npy
-imgHQ19768.npy
-imgHQ16780.npy
-imgHQ22915.npy
-imgHQ14547.npy
-imgHQ07137.npy
-imgHQ00158.npy
-imgHQ21088.npy
-imgHQ03141.npy
-imgHQ27422.npy
-imgHQ17559.npy
-imgHQ21673.npy
-imgHQ00329.npy
-imgHQ16537.npy
-imgHQ26654.npy
-imgHQ18631.npy
-imgHQ07041.npy
-imgHQ02242.npy
-imgHQ24974.npy
-imgHQ21219.npy
-imgHQ17459.npy
-imgHQ18696.npy
-imgHQ13060.npy
-imgHQ18604.npy
-imgHQ14102.npy
-imgHQ22114.npy
-imgHQ13854.npy
-imgHQ09427.npy
-imgHQ16837.npy
-imgHQ14650.npy
-imgHQ20143.npy
-imgHQ15394.npy
-imgHQ20527.npy
-imgHQ07905.npy
-imgHQ29711.npy
-imgHQ05740.npy
-imgHQ09420.npy
-imgHQ13818.npy
-imgHQ03593.npy
-imgHQ26880.npy
-imgHQ27029.npy
-imgHQ14453.npy
-imgHQ24011.npy
-imgHQ22306.npy
-imgHQ06532.npy
-imgHQ25672.npy
-imgHQ00613.npy
-imgHQ21458.npy
-imgHQ18076.npy
-imgHQ09607.npy
-imgHQ09970.npy
-imgHQ12741.npy
-imgHQ02985.npy
-imgHQ11287.npy
-imgHQ13847.npy
-imgHQ05875.npy
-imgHQ29155.npy
-imgHQ14607.npy
-imgHQ08112.npy
-imgHQ06815.npy
-imgHQ23772.npy
-imgHQ15111.npy
-imgHQ06063.npy
-imgHQ22544.npy
-imgHQ08385.npy
-imgHQ06460.npy
-imgHQ25488.npy
-imgHQ05762.npy
-imgHQ14286.npy
-imgHQ05590.npy
-imgHQ00266.npy
-imgHQ00292.npy
-imgHQ14452.npy
-imgHQ28638.npy
-imgHQ03157.npy
-imgHQ19315.npy
-imgHQ13091.npy
-imgHQ01215.npy
-imgHQ04399.npy
-imgHQ13400.npy
-imgHQ26542.npy
-imgHQ23841.npy
-imgHQ06717.npy
-imgHQ21294.npy
-imgHQ12196.npy
-imgHQ05896.npy
-imgHQ00549.npy
-imgHQ22393.npy
-imgHQ15534.npy
-imgHQ18456.npy
-imgHQ10188.npy
-imgHQ11911.npy
-imgHQ03946.npy
-imgHQ15886.npy
-imgHQ21480.npy
-imgHQ13356.npy
-imgHQ26169.npy
-imgHQ23998.npy
-imgHQ13191.npy
-imgHQ24151.npy
-imgHQ19049.npy
-imgHQ04675.npy
-imgHQ21680.npy
-imgHQ15367.npy
-imgHQ22968.npy
-imgHQ00452.npy
-imgHQ24370.npy
-imgHQ19077.npy
-imgHQ10543.npy
-imgHQ13098.npy
-imgHQ19346.npy
-imgHQ03638.npy
-imgHQ18729.npy
-imgHQ19654.npy
-imgHQ28256.npy
-imgHQ09261.npy
-imgHQ22418.npy
-imgHQ19057.npy
-imgHQ22557.npy
-imgHQ21738.npy
-imgHQ00696.npy
-imgHQ18914.npy
-imgHQ04122.npy
-imgHQ29683.npy
-imgHQ24100.npy
-imgHQ25705.npy
-imgHQ12878.npy
-imgHQ02816.npy
-imgHQ01021.npy
-imgHQ07276.npy
-imgHQ25344.npy
-imgHQ16550.npy
-imgHQ18104.npy
-imgHQ25812.npy
-imgHQ22990.npy
-imgHQ03911.npy
-imgHQ01478.npy
-imgHQ08518.npy
-imgHQ28369.npy
-imgHQ22632.npy
-imgHQ27024.npy
-imgHQ09966.npy
-imgHQ14202.npy
-imgHQ15992.npy
-imgHQ00609.npy
-imgHQ29285.npy
-imgHQ06276.npy
-imgHQ21700.npy
-imgHQ23906.npy
-imgHQ07674.npy
-imgHQ11565.npy
-imgHQ19106.npy
-imgHQ22200.npy
-imgHQ09955.npy
-imgHQ19812.npy
-imgHQ19110.npy
-imgHQ28837.npy
-imgHQ20620.npy
-imgHQ27467.npy
-imgHQ14044.npy
-imgHQ17741.npy
-imgHQ19589.npy
-imgHQ28427.npy
-imgHQ13984.npy
-imgHQ08211.npy
-imgHQ09095.npy
-imgHQ00634.npy
-imgHQ11970.npy
-imgHQ19531.npy
-imgHQ10446.npy
-imgHQ09541.npy
-imgHQ04093.npy
-imgHQ02834.npy
-imgHQ22531.npy
-imgHQ20400.npy
-imgHQ23201.npy
-imgHQ10470.npy
-imgHQ18024.npy
-imgHQ01270.npy
-imgHQ02573.npy
-imgHQ16804.npy
-imgHQ07683.npy
-imgHQ00002.npy
-imgHQ14349.npy
-imgHQ00768.npy
-imgHQ13466.npy
-imgHQ00882.npy
-imgHQ15714.npy
-imgHQ07321.npy
-imgHQ18662.npy
-imgHQ22786.npy
-imgHQ14751.npy
-imgHQ26779.npy
-imgHQ00551.npy
-imgHQ03916.npy
-imgHQ13717.npy
-imgHQ21518.npy
-imgHQ11517.npy
-imgHQ06971.npy
-imgHQ09506.npy
-imgHQ03371.npy
-imgHQ05681.npy
-imgHQ02111.npy
-imgHQ12331.npy
-imgHQ03097.npy
-imgHQ18509.npy
-imgHQ23798.npy
-imgHQ07411.npy
-imgHQ27350.npy
-imgHQ01367.npy
-imgHQ17560.npy
-imgHQ01068.npy
-imgHQ26662.npy
-imgHQ22655.npy
-imgHQ23274.npy
-imgHQ15007.npy
-imgHQ27034.npy
-imgHQ02677.npy
-imgHQ12901.npy
-imgHQ13006.npy
-imgHQ05341.npy
-imgHQ17464.npy
-imgHQ29314.npy
-imgHQ28569.npy
-imgHQ27528.npy
-imgHQ01413.npy
-imgHQ10362.npy
-imgHQ03412.npy
-imgHQ26407.npy
-imgHQ20695.npy
-imgHQ10668.npy
-imgHQ29262.npy
-imgHQ29901.npy
-imgHQ12843.npy
-imgHQ21388.npy
-imgHQ15112.npy
-imgHQ01518.npy
-imgHQ23997.npy
-imgHQ04975.npy
-imgHQ24234.npy
-imgHQ25963.npy
-imgHQ29817.npy
-imgHQ20418.npy
-imgHQ07175.npy
-imgHQ16091.npy
-imgHQ04915.npy
-imgHQ28829.npy
-imgHQ19949.npy
-imgHQ18226.npy
-imgHQ20773.npy
-imgHQ05945.npy
-imgHQ20671.npy
-imgHQ04842.npy
-imgHQ21931.npy
-imgHQ00741.npy
-imgHQ25987.npy
-imgHQ08752.npy
-imgHQ18400.npy
-imgHQ04380.npy
-imgHQ09520.npy
-imgHQ24961.npy
-imgHQ00140.npy
-imgHQ19026.npy
-imgHQ11131.npy
-imgHQ16485.npy
-imgHQ13556.npy
-imgHQ27678.npy
-imgHQ15259.npy
-imgHQ18420.npy
-imgHQ28296.npy
-imgHQ19986.npy
-imgHQ20219.npy
-imgHQ27654.npy
-imgHQ09843.npy
-imgHQ00612.npy
-imgHQ01721.npy
-imgHQ11321.npy
-imgHQ08781.npy
-imgHQ09758.npy
-imgHQ01120.npy
-imgHQ01050.npy
-imgHQ05860.npy
-imgHQ15351.npy
-imgHQ08313.npy
-imgHQ00065.npy
-imgHQ23084.npy
-imgHQ08798.npy
-imgHQ28697.npy
-imgHQ27455.npy
-imgHQ12285.npy
-imgHQ01107.npy
-imgHQ02429.npy
-imgHQ14892.npy
-imgHQ26592.npy
-imgHQ19143.npy
-imgHQ03271.npy
-imgHQ18814.npy
-imgHQ03752.npy
-imgHQ11824.npy
-imgHQ06767.npy
-imgHQ24310.npy
-imgHQ28786.npy
-imgHQ10997.npy
-imgHQ08632.npy
-imgHQ00212.npy
-imgHQ26329.npy
-imgHQ29848.npy
-imgHQ27736.npy
-imgHQ27767.npy
-imgHQ00159.npy
-imgHQ22072.npy
-imgHQ20605.npy
-imgHQ01870.npy
-imgHQ18142.npy
-imgHQ12649.npy
-imgHQ22184.npy
-imgHQ21498.npy
-imgHQ07090.npy
-imgHQ04254.npy
-imgHQ11949.npy
-imgHQ00493.npy
-imgHQ06946.npy
-imgHQ11536.npy
-imgHQ10615.npy
-imgHQ24209.npy
-imgHQ08289.npy
-imgHQ15050.npy
-imgHQ21651.npy
-imgHQ22771.npy
-imgHQ06491.npy
-imgHQ16392.npy
-imgHQ29552.npy
-imgHQ16882.npy
-imgHQ28148.npy
-imgHQ01825.npy
-imgHQ23919.npy
-imgHQ22559.npy
-imgHQ06706.npy
-imgHQ26973.npy
-imgHQ17493.npy
-imgHQ15181.npy
-imgHQ13947.npy
-imgHQ04894.npy
-imgHQ28889.npy
-imgHQ13844.npy
-imgHQ23595.npy
-imgHQ11117.npy
-imgHQ17965.npy
-imgHQ16660.npy
-imgHQ25312.npy
-imgHQ08417.npy
-imgHQ26668.npy
-imgHQ04480.npy
-imgHQ16489.npy
-imgHQ20307.npy
-imgHQ20107.npy
-imgHQ02247.npy
-imgHQ00867.npy
-imgHQ02045.npy
-imgHQ02729.npy
-imgHQ02227.npy
-imgHQ08161.npy
-imgHQ22778.npy
-imgHQ05868.npy
-imgHQ25562.npy
-imgHQ04895.npy
-imgHQ16087.npy
-imgHQ15721.npy
-imgHQ10465.npy
-imgHQ08880.npy
-imgHQ26896.npy
-imgHQ28135.npy
-imgHQ23051.npy
-imgHQ04114.npy
-imgHQ19024.npy
-imgHQ04937.npy
-imgHQ22835.npy
-imgHQ03820.npy
-imgHQ00500.npy
-imgHQ05136.npy
-imgHQ06841.npy
-imgHQ05043.npy
-imgHQ16135.npy
-imgHQ14200.npy
-imgHQ27005.npy
-imgHQ01179.npy
-imgHQ22487.npy
-imgHQ21653.npy
-imgHQ04555.npy
-imgHQ13655.npy
-imgHQ03698.npy
-imgHQ22339.npy
-imgHQ15562.npy
-imgHQ27977.npy
-imgHQ22978.npy
-imgHQ21328.npy
-imgHQ10585.npy
-imgHQ02706.npy
-imgHQ24230.npy
-imgHQ19823.npy
-imgHQ14183.npy
-imgHQ06741.npy
-imgHQ26074.npy
-imgHQ28188.npy
-imgHQ04706.npy
-imgHQ01377.npy
-imgHQ03036.npy
-imgHQ03360.npy
-imgHQ02776.npy
-imgHQ03577.npy
-imgHQ19205.npy
-imgHQ06360.npy
-imgHQ02454.npy
-imgHQ10889.npy
-imgHQ27969.npy
-imgHQ18242.npy
-imgHQ18335.npy
-imgHQ20452.npy
-imgHQ16997.npy
-imgHQ00807.npy
-imgHQ05206.npy
-imgHQ11542.npy
-imgHQ11603.npy
-imgHQ13992.npy
-imgHQ22904.npy
-imgHQ26567.npy
-imgHQ19490.npy
-imgHQ08590.npy
-imgHQ09006.npy
-imgHQ04147.npy
-imgHQ26094.npy
-imgHQ21701.npy
-imgHQ08402.npy
-imgHQ09013.npy
-imgHQ26985.npy
-imgHQ11175.npy
-imgHQ00610.npy
-imgHQ20971.npy
-imgHQ11297.npy
-imgHQ23014.npy
-imgHQ23199.npy
-imgHQ22328.npy
-imgHQ21868.npy
-imgHQ17327.npy
-imgHQ20189.npy
-imgHQ13151.npy
-imgHQ11450.npy
-imgHQ13828.npy
-imgHQ01621.npy
-imgHQ08938.npy
-imgHQ21663.npy
-imgHQ18329.npy
-imgHQ11796.npy
-imgHQ26717.npy
-imgHQ25988.npy
-imgHQ11736.npy
-imgHQ13370.npy
-imgHQ09145.npy
-imgHQ00061.npy
-imgHQ25222.npy
-imgHQ04834.npy
-imgHQ13816.npy
-imgHQ28938.npy
-imgHQ16130.npy
-imgHQ23137.npy
-imgHQ23591.npy
-imgHQ09883.npy
-imgHQ00817.npy
-imgHQ27499.npy
-imgHQ05051.npy
-imgHQ14825.npy
-imgHQ01447.npy
-imgHQ01808.npy
-imgHQ26555.npy
-imgHQ24455.npy
-imgHQ16216.npy
-imgHQ21827.npy
-imgHQ23812.npy
-imgHQ16914.npy
-imgHQ27864.npy
-imgHQ26018.npy
-imgHQ06512.npy
-imgHQ05870.npy
-imgHQ24248.npy
-imgHQ16369.npy
-imgHQ24222.npy
-imgHQ20182.npy
-imgHQ16556.npy
-imgHQ27278.npy
-imgHQ17875.npy
-imgHQ01386.npy
-imgHQ17292.npy
-imgHQ20870.npy
-imgHQ04816.npy
-imgHQ27914.npy
-imgHQ01347.npy
-imgHQ24426.npy
-imgHQ06268.npy
-imgHQ18602.npy
-imgHQ04807.npy
-imgHQ01850.npy
-imgHQ13174.npy
-imgHQ20654.npy
-imgHQ15342.npy
-imgHQ12013.npy
-imgHQ22889.npy
-imgHQ05578.npy
-imgHQ25355.npy
-imgHQ26375.npy
-imgHQ29468.npy
-imgHQ19447.npy
-imgHQ00455.npy
-imgHQ20731.npy
-imgHQ13614.npy
-imgHQ16889.npy
-imgHQ27838.npy
-imgHQ13338.npy
-imgHQ09530.npy
-imgHQ21011.npy
-imgHQ26398.npy
-imgHQ16249.npy
-imgHQ07713.npy
-imgHQ11837.npy
-imgHQ17092.npy
-imgHQ29864.npy
-imgHQ03197.npy
-imgHQ20223.npy
-imgHQ15253.npy
-imgHQ08627.npy
-imgHQ12194.npy
-imgHQ28309.npy
-imgHQ22507.npy
-imgHQ19604.npy
-imgHQ03584.npy
-imgHQ20565.npy
-imgHQ21205.npy
-imgHQ02183.npy
-imgHQ10093.npy
-imgHQ13343.npy
-imgHQ01948.npy
-imgHQ26066.npy
-imgHQ26473.npy
-imgHQ04198.npy
-imgHQ19033.npy
-imgHQ15793.npy
-imgHQ20678.npy
-imgHQ16572.npy
-imgHQ25310.npy
-imgHQ11836.npy
-imgHQ09000.npy
-imgHQ17106.npy
-imgHQ22420.npy
-imgHQ17667.npy
-imgHQ06704.npy
-imgHQ25584.npy
-imgHQ00346.npy
-imgHQ01527.npy
-imgHQ22862.npy
-imgHQ03789.npy
-imgHQ10309.npy
-imgHQ20555.npy
-imgHQ25801.npy
-imgHQ05123.npy
-imgHQ29766.npy
-imgHQ25188.npy
-imgHQ21477.npy
-imgHQ00561.npy
-imgHQ26599.npy
-imgHQ02618.npy
-imgHQ02824.npy
-imgHQ03178.npy
-imgHQ07244.npy
-imgHQ28625.npy
-imgHQ21438.npy
-imgHQ22585.npy
-imgHQ07848.npy
-imgHQ12788.npy
-imgHQ29556.npy
-imgHQ20723.npy
-imgHQ16425.npy
-imgHQ14450.npy
-imgHQ27030.npy
-imgHQ06574.npy
-imgHQ23164.npy
-imgHQ19767.npy
-imgHQ07984.npy
-imgHQ05248.npy
-imgHQ26663.npy
-imgHQ09203.npy
-imgHQ14402.npy
-imgHQ04441.npy
-imgHQ19423.npy
-imgHQ07993.npy
-imgHQ00865.npy
-imgHQ28567.npy
-imgHQ19187.npy
-imgHQ17427.npy
-imgHQ15707.npy
-imgHQ23645.npy
-imgHQ28830.npy
-imgHQ08644.npy
-imgHQ25744.npy
-imgHQ20149.npy
-imgHQ00449.npy
-imgHQ18151.npy
-imgHQ16429.npy
-imgHQ20916.npy
-imgHQ16705.npy
-imgHQ25802.npy
-imgHQ19997.npy
-imgHQ02007.npy
-imgHQ24410.npy
-imgHQ01551.npy
-imgHQ04661.npy
-imgHQ14867.npy
-imgHQ21375.npy
-imgHQ22526.npy
-imgHQ05516.npy
-imgHQ29080.npy
-imgHQ19805.npy
-imgHQ12900.npy
-imgHQ04383.npy
-imgHQ17583.npy
-imgHQ06990.npy
-imgHQ28045.npy
-imgHQ13029.npy
-imgHQ16026.npy
-imgHQ00960.npy
-imgHQ04729.npy
-imgHQ21175.npy
-imgHQ17642.npy
-imgHQ10697.npy
-imgHQ12504.npy
-imgHQ00820.npy
-imgHQ10409.npy
-imgHQ15027.npy
-imgHQ25790.npy
-imgHQ08135.npy
-imgHQ23576.npy
-imgHQ24088.npy
-imgHQ04402.npy
-imgHQ01023.npy
-imgHQ19665.npy
-imgHQ11998.npy
-imgHQ08464.npy
-imgHQ12470.npy
-imgHQ18904.npy
-imgHQ28075.npy
-imgHQ21702.npy
-imgHQ05999.npy
-imgHQ07386.npy
-imgHQ22434.npy
-imgHQ19384.npy
-imgHQ23426.npy
-imgHQ24078.npy
-imgHQ16890.npy
-imgHQ16174.npy
-imgHQ10170.npy
-imgHQ22317.npy
-imgHQ16255.npy
-imgHQ08771.npy
-imgHQ29993.npy
-imgHQ09267.npy
-imgHQ13083.npy
-imgHQ23487.npy
-imgHQ15196.npy
-imgHQ26434.npy
-imgHQ12599.npy
-imgHQ04547.npy
-imgHQ10286.npy
-imgHQ18211.npy
-imgHQ09536.npy
-imgHQ11548.npy
-imgHQ12527.npy
-imgHQ28224.npy
-imgHQ16912.npy
-imgHQ07053.npy
-imgHQ25408.npy
-imgHQ13680.npy
-imgHQ16712.npy
-imgHQ11845.npy
-imgHQ22776.npy
-imgHQ07171.npy
-imgHQ16365.npy
-imgHQ17666.npy
-imgHQ00310.npy
-imgHQ14217.npy
-imgHQ23692.npy
-imgHQ25449.npy
-imgHQ15952.npy
-imgHQ14975.npy
-imgHQ25637.npy
-imgHQ20336.npy
-imgHQ25795.npy
-imgHQ26363.npy
-imgHQ21796.npy
-imgHQ18774.npy
-imgHQ28460.npy
-imgHQ28202.npy
-imgHQ25666.npy
-imgHQ10130.npy
-imgHQ18644.npy
-imgHQ18885.npy
-imgHQ26692.npy
-imgHQ13316.npy
-imgHQ29726.npy
-imgHQ05411.npy
-imgHQ16077.npy
-imgHQ02155.npy
-imgHQ18279.npy
-imgHQ27259.npy
-imgHQ23430.npy
-imgHQ07529.npy
-imgHQ05139.npy
-imgHQ08930.npy
-imgHQ16707.npy
-imgHQ13367.npy
-imgHQ01604.npy
-imgHQ25410.npy
-imgHQ24539.npy
-imgHQ16225.npy
-imgHQ24385.npy
-imgHQ14929.npy
-imgHQ27995.npy
-imgHQ23229.npy
-imgHQ18437.npy
-imgHQ10174.npy
-imgHQ28239.npy
-imgHQ28382.npy
-imgHQ17104.npy
-imgHQ03759.npy
-imgHQ04882.npy
-imgHQ19160.npy
-imgHQ22732.npy
-imgHQ18949.npy
-imgHQ13730.npy
-imgHQ25319.npy
-imgHQ18278.npy
-imgHQ10220.npy
-imgHQ22482.npy
-imgHQ26110.npy
-imgHQ24168.npy
-imgHQ04372.npy
-imgHQ02474.npy
-imgHQ23099.npy
-imgHQ11122.npy
-imgHQ01419.npy
-imgHQ04471.npy
-imgHQ24079.npy
-imgHQ13142.npy
-imgHQ20449.npy
-imgHQ13553.npy
-imgHQ29899.npy
-imgHQ20364.npy
-imgHQ16104.npy
-imgHQ09597.npy
-imgHQ18693.npy
-imgHQ05776.npy
-imgHQ06567.npy
-imgHQ11816.npy
-imgHQ28485.npy
-imgHQ17434.npy
-imgHQ17954.npy
-imgHQ07073.npy
-imgHQ08310.npy
-imgHQ11893.npy
-imgHQ04623.npy
-imgHQ21453.npy
-imgHQ17494.npy
-imgHQ26402.npy
-imgHQ10291.npy
-imgHQ17153.npy
-imgHQ09019.npy
-imgHQ11009.npy
-imgHQ04564.npy
-imgHQ28287.npy
-imgHQ17411.npy
-imgHQ25113.npy
-imgHQ29161.npy
-imgHQ27078.npy
-imgHQ01009.npy
-imgHQ01753.npy
-imgHQ23753.npy
-imgHQ26703.npy
-imgHQ27533.npy
-imgHQ28262.npy
-imgHQ10347.npy
-imgHQ21048.npy
-imgHQ21666.npy
-imgHQ16611.npy
-imgHQ03118.npy
-imgHQ04571.npy
-imgHQ28767.npy
-imgHQ11520.npy
-imgHQ01984.npy
-imgHQ16522.npy
-imgHQ03492.npy
-imgHQ17080.npy
-imgHQ04414.npy
-imgHQ06082.npy
-imgHQ16048.npy
-imgHQ18188.npy
-imgHQ26133.npy
-imgHQ07582.npy
-imgHQ02544.npy
-imgHQ00396.npy
-imgHQ01319.npy
-imgHQ16832.npy
-imgHQ13092.npy
-imgHQ09516.npy
-imgHQ14891.npy
-imgHQ11516.npy
-imgHQ12464.npy
-imgHQ13260.npy
-imgHQ19923.npy
-imgHQ02532.npy
-imgHQ07348.npy
-imgHQ06933.npy
-imgHQ14424.npy
-imgHQ06279.npy
-imgHQ04090.npy
-imgHQ18965.npy
-imgHQ18380.npy
-imgHQ22154.npy
-imgHQ24227.npy
-imgHQ27786.npy
-imgHQ16724.npy
-imgHQ23779.npy
-imgHQ21692.npy
-imgHQ24856.npy
-imgHQ28486.npy
-imgHQ08895.npy
-imgHQ07361.npy
-imgHQ01283.npy
-imgHQ25822.npy
-imgHQ25665.npy
-imgHQ09627.npy
-imgHQ24775.npy
-imgHQ24638.npy
-imgHQ27902.npy
-imgHQ24785.npy
-imgHQ28933.npy
-imgHQ07566.npy
-imgHQ11606.npy
-imgHQ08215.npy
-imgHQ21037.npy
-imgHQ05821.npy
-imgHQ21467.npy
-imgHQ00810.npy
-imgHQ01855.npy
-imgHQ25341.npy
-imgHQ20246.npy
-imgHQ20956.npy
-imgHQ19786.npy
-imgHQ04837.npy
-imgHQ23169.npy
-imgHQ17218.npy
-imgHQ00183.npy
-imgHQ15208.npy
-imgHQ07835.npy
-imgHQ02339.npy
-imgHQ08227.npy
-imgHQ06434.npy
-imgHQ18425.npy
-imgHQ27123.npy
-imgHQ19177.npy
-imgHQ26911.npy
-imgHQ07531.npy
-imgHQ26055.npy
-imgHQ05605.npy
-imgHQ11854.npy
-imgHQ19581.npy
-imgHQ23736.npy
-imgHQ28352.npy
-imgHQ07222.npy
-imgHQ25775.npy
-imgHQ15978.npy
-imgHQ04392.npy
-imgHQ17680.npy
-imgHQ03915.npy
-imgHQ18088.npy
-imgHQ17781.npy
-imgHQ29660.npy
-imgHQ01861.npy
-imgHQ25215.npy
-imgHQ15136.npy
-imgHQ15780.npy
-imgHQ29701.npy
-imgHQ09825.npy
-imgHQ16883.npy
-imgHQ18413.npy
-imgHQ12687.npy
-imgHQ16863.npy
-imgHQ18398.npy
-imgHQ04682.npy
-imgHQ12702.npy
-imgHQ16113.npy
-imgHQ18123.npy
-imgHQ14334.npy
-imgHQ21119.npy
-imgHQ17949.npy
-imgHQ14683.npy
-imgHQ01401.npy
-imgHQ23839.npy
-imgHQ25724.npy
-imgHQ15289.npy
-imgHQ26820.npy
-imgHQ14418.npy
-imgHQ22012.npy
-imgHQ21678.npy
-imgHQ06487.npy
-imgHQ04707.npy
-imgHQ09144.npy
-imgHQ13271.npy
-imgHQ13865.npy
-imgHQ11958.npy
-imgHQ10402.npy
-imgHQ23243.npy
-imgHQ03766.npy
-imgHQ28077.npy
-imgHQ21949.npy
-imgHQ02326.npy
-imgHQ09072.npy
-imgHQ00294.npy
-imgHQ06515.npy
-imgHQ06693.npy
-imgHQ21572.npy
-imgHQ15229.npy
-imgHQ10630.npy
-imgHQ01204.npy
-imgHQ24098.npy
-imgHQ11109.npy
-imgHQ06416.npy
-imgHQ02913.npy
-imgHQ15239.npy
-imgHQ25394.npy
-imgHQ29006.npy
-imgHQ10201.npy
-imgHQ12410.npy
-imgHQ21510.npy
-imgHQ28254.npy
-imgHQ15419.npy
-imgHQ22638.npy
-imgHQ01482.npy
-imgHQ09951.npy
-imgHQ11676.npy
-imgHQ07474.npy
-imgHQ21346.npy
-imgHQ18471.npy
-imgHQ15231.npy
-imgHQ22010.npy
-imgHQ21257.npy
-imgHQ12168.npy
-imgHQ11233.npy
-imgHQ23196.npy
-imgHQ14990.npy
-imgHQ25206.npy
-imgHQ09983.npy
-imgHQ08873.npy
-imgHQ17539.npy
-imgHQ08585.npy
-imgHQ10914.npy
-imgHQ20538.npy
-imgHQ00846.npy
-imgHQ09482.npy
-imgHQ00286.npy
-imgHQ24188.npy
-imgHQ06566.npy
-imgHQ05981.npy
-imgHQ26081.npy
-imgHQ22698.npy
-imgHQ07642.npy
-imgHQ18655.npy
-imgHQ29622.npy
-imgHQ28037.npy
-imgHQ26486.npy
-imgHQ29313.npy
-imgHQ10011.npy
-imgHQ03174.npy
-imgHQ08374.npy
-imgHQ29049.npy
-imgHQ12776.npy
-imgHQ25162.npy
-imgHQ04048.npy
-imgHQ23314.npy
-imgHQ08706.npy
-imgHQ03591.npy
-imgHQ20321.npy
-imgHQ22831.npy
-imgHQ23082.npy
-imgHQ23368.npy
-imgHQ29416.npy
-imgHQ12516.npy
-imgHQ01974.npy
-imgHQ23725.npy
-imgHQ14872.npy
-imgHQ14538.npy
-imgHQ15612.npy
-imgHQ02526.npy
-imgHQ15205.npy
-imgHQ08697.npy
-imgHQ00943.npy
-imgHQ04027.npy
-imgHQ12957.npy
-imgHQ14621.npy
-imgHQ29693.npy
-imgHQ03098.npy
-imgHQ19847.npy
-imgHQ15032.npy
-imgHQ28576.npy
-imgHQ28421.npy
-imgHQ03214.npy
-imgHQ08525.npy
-imgHQ14037.npy
-imgHQ03012.npy
-imgHQ16582.npy
-imgHQ04990.npy
-imgHQ03204.npy
-imgHQ14401.npy
-imgHQ24553.npy
-imgHQ00729.npy
-imgHQ17612.npy
-imgHQ24332.npy
-imgHQ25604.npy
-imgHQ22473.npy
-imgHQ07122.npy
-imgHQ23641.npy
-imgHQ24073.npy
-imgHQ16293.npy
-imgHQ10259.npy
-imgHQ07615.npy
-imgHQ27561.npy
-imgHQ12947.npy
-imgHQ22668.npy
-imgHQ14358.npy
-imgHQ22219.npy
-imgHQ06372.npy
-imgHQ29317.npy
-imgHQ22577.npy
-imgHQ02608.npy
-imgHQ27470.npy
-imgHQ29706.npy
-imgHQ05093.npy
-imgHQ24289.npy
-imgHQ23235.npy
-imgHQ00350.npy
-imgHQ18357.npy
-imgHQ11675.npy
-imgHQ24883.npy
-imgHQ25101.npy
-imgHQ05628.npy
-imgHQ29685.npy
-imgHQ10071.npy
-imgHQ09800.npy
-imgHQ19487.npy
-imgHQ02574.npy
-imgHQ28023.npy
-imgHQ28627.npy
-imgHQ19450.npy
-imgHQ01703.npy
-imgHQ00501.npy
-imgHQ27125.npy
-imgHQ05980.npy
-imgHQ28941.npy
-imgHQ02533.npy
-imgHQ25952.npy
-imgHQ28095.npy
-imgHQ16978.npy
-imgHQ14675.npy
-imgHQ11064.npy
-imgHQ09368.npy
-imgHQ16688.npy
-imgHQ17157.npy
-imgHQ20582.npy
-imgHQ14711.npy
-imgHQ07417.npy
-imgHQ29496.npy
-imgHQ07938.npy
-imgHQ26467.npy
-imgHQ28274.npy
-imgHQ12207.npy
-imgHQ24037.npy
-imgHQ14642.npy
-imgHQ02121.npy
-imgHQ22131.npy
-imgHQ03179.npy
-imgHQ17488.npy
-imgHQ06207.npy
-imgHQ13702.npy
-imgHQ01470.npy
-imgHQ24421.npy
-imgHQ29509.npy
-imgHQ22535.npy
-imgHQ02250.npy
-imgHQ20401.npy
-imgHQ24928.npy
-imgHQ02297.npy
-imgHQ24061.npy
-imgHQ11790.npy
-imgHQ15370.npy
-imgHQ27897.npy
-imgHQ16528.npy
-imgHQ18054.npy
-imgHQ18844.npy
-imgHQ05509.npy
-imgHQ04181.npy
-imgHQ01817.npy
-imgHQ05584.npy
-imgHQ05810.npy
-imgHQ11526.npy
-imgHQ28854.npy
-imgHQ00333.npy
-imgHQ14781.npy
-imgHQ17790.npy
-imgHQ09045.npy
-imgHQ12818.npy
-imgHQ21881.npy
-imgHQ26510.npy
-imgHQ22461.npy
-imgHQ17743.npy
-imgHQ29235.npy
-imgHQ18813.npy
-imgHQ24038.npy
-imgHQ20414.npy
-imgHQ26301.npy
-imgHQ22449.npy
-imgHQ22726.npy
-imgHQ02208.npy
-imgHQ12085.npy
-imgHQ06044.npy
-imgHQ18018.npy
-imgHQ22578.npy
-imgHQ16601.npy
-imgHQ16669.npy
-imgHQ15837.npy
-imgHQ03309.npy
-imgHQ28237.npy
-imgHQ23028.npy
-imgHQ12115.npy
-imgHQ23951.npy
-imgHQ01832.npy
-imgHQ15135.npy
-imgHQ22011.npy
-imgHQ24239.npy
-imgHQ25946.npy
-imgHQ09222.npy
-imgHQ27077.npy
-imgHQ19215.npy
-imgHQ15988.npy
-imgHQ09695.npy
-imgHQ07086.npy
-imgHQ26811.npy
-imgHQ24843.npy
-imgHQ19894.npy
-imgHQ28733.npy
-imgHQ09133.npy
-imgHQ04197.npy
-imgHQ29753.npy
-imgHQ05992.npy
-imgHQ15332.npy
-imgHQ03804.npy
-imgHQ22437.npy
-imgHQ11299.npy
-imgHQ08577.npy
-imgHQ11739.npy
-imgHQ03878.npy
-imgHQ11350.npy
-imgHQ02174.npy
-imgHQ15247.npy
-imgHQ12177.npy
-imgHQ06923.npy
-imgHQ09935.npy
-imgHQ15627.npy
-imgHQ15145.npy
-imgHQ11608.npy
-imgHQ10391.npy
-imgHQ15808.npy
-imgHQ16440.npy
-imgHQ16829.npy
-imgHQ16213.npy
-imgHQ18003.npy
-imgHQ10173.npy
-imgHQ25876.npy
-imgHQ02485.npy
-imgHQ27666.npy
-imgHQ07765.npy
-imgHQ06346.npy
-imgHQ05782.npy
-imgHQ00298.npy
-imgHQ09815.npy
-imgHQ20042.npy
-imgHQ11991.npy
-imgHQ27215.npy
-imgHQ13464.npy
-imgHQ02096.npy
-imgHQ07664.npy
-imgHQ00800.npy
-imgHQ04472.npy
-imgHQ19935.npy
-imgHQ18326.npy
-imgHQ04287.npy
-imgHQ26795.npy
-imgHQ13647.npy
-imgHQ19910.npy
-imgHQ20986.npy
-imgHQ16557.npy
-imgHQ13118.npy
-imgHQ09241.npy
-imgHQ05046.npy
-imgHQ28247.npy
-imgHQ06612.npy
-imgHQ08552.npy
-imgHQ07739.npy
-imgHQ28088.npy
-imgHQ09589.npy
-imgHQ02976.npy
-imgHQ19380.npy
-imgHQ17484.npy
-imgHQ21683.npy
-imgHQ03457.npy
-imgHQ05320.npy
-imgHQ04903.npy
-imgHQ01452.npy
-imgHQ22919.npy
-imgHQ23010.npy
-imgHQ21883.npy
-imgHQ20969.npy
-imgHQ18248.npy
-imgHQ11290.npy
-imgHQ20433.npy
-imgHQ11902.npy
-imgHQ15306.npy
-imgHQ18732.npy
-imgHQ04112.npy
-imgHQ29869.npy
-imgHQ06420.npy
-imgHQ05750.npy
-imgHQ04104.npy
-imgHQ15819.npy
-imgHQ08765.npy
-imgHQ25869.npy
-imgHQ25769.npy
-imgHQ12133.npy
-imgHQ22331.npy
-imgHQ10580.npy
-imgHQ07875.npy
-imgHQ16766.npy
-imgHQ09384.npy
-imgHQ27421.npy
-imgHQ12137.npy
-imgHQ04609.npy
-imgHQ14979.npy
-imgHQ05902.npy
-imgHQ19900.npy
-imgHQ10448.npy
-imgHQ06711.npy
-imgHQ13986.npy
-imgHQ20059.npy
-imgHQ23412.npy
-imgHQ02280.npy
-imgHQ19331.npy
-imgHQ09008.npy
-imgHQ18048.npy
-imgHQ10275.npy
-imgHQ28621.npy
-imgHQ21686.npy
-imgHQ18280.npy
-imgHQ23242.npy
-imgHQ19097.npy
-imgHQ24313.npy
-imgHQ12274.npy
-imgHQ17602.npy
-imgHQ09009.npy
-imgHQ11086.npy
-imgHQ23936.npy
-imgHQ02462.npy
-imgHQ13265.npy
-imgHQ09124.npy
-imgHQ20018.npy
-imgHQ25723.npy
-imgHQ28438.npy
-imgHQ26330.npy
-imgHQ14258.npy
-imgHQ21825.npy
-imgHQ04655.npy
-imgHQ08120.npy
-imgHQ04610.npy
-imgHQ26988.npy
-imgHQ27541.npy
-imgHQ28182.npy
-imgHQ15243.npy
-imgHQ12138.npy
-imgHQ21561.npy
-imgHQ20239.npy
-imgHQ26584.npy
-imgHQ12339.npy
-imgHQ04851.npy
-imgHQ26221.npy
-imgHQ20701.npy
-imgHQ01463.npy
-imgHQ11351.npy
-imgHQ04947.npy
-imgHQ21035.npy
-imgHQ16242.npy
-imgHQ17022.npy
-imgHQ05368.npy
-imgHQ07303.npy
-imgHQ24391.npy
-imgHQ06362.npy
-imgHQ27577.npy
-imgHQ04859.npy
-imgHQ21265.npy
-imgHQ09699.npy
-imgHQ16394.npy
-imgHQ18479.npy
-imgHQ12980.npy
-imgHQ23722.npy
-imgHQ10268.npy
-imgHQ13269.npy
-imgHQ28645.npy
-imgHQ17574.npy
-imgHQ26195.npy
-imgHQ22962.npy
-imgHQ11128.npy
-imgHQ23593.npy
-imgHQ08233.npy
-imgHQ19435.npy
-imgHQ13981.npy
-imgHQ27628.npy
-imgHQ14374.npy
-imgHQ24969.npy
-imgHQ17443.npy
-imgHQ14389.npy
-imgHQ12326.npy
-imgHQ21896.npy
-imgHQ06237.npy
-imgHQ15012.npy
-imgHQ03772.npy
-imgHQ14797.npy
-imgHQ07284.npy
-imgHQ02860.npy
-imgHQ04716.npy
-imgHQ18714.npy
-imgHQ29207.npy
-imgHQ16558.npy
-imgHQ20202.npy
-imgHQ21703.npy
-imgHQ26904.npy
-imgHQ06522.npy
-imgHQ05036.npy
-imgHQ09198.npy
-imgHQ13963.npy
-imgHQ17714.npy
-imgHQ13719.npy
-imgHQ12400.npy
-imgHQ13184.npy
-imgHQ23348.npy
-imgHQ01615.npy
-imgHQ11927.npy
-imgHQ22130.npy
-imgHQ07193.npy
-imgHQ09648.npy
-imgHQ01591.npy
-imgHQ21585.npy
-imgHQ08799.npy
-imgHQ19966.npy
-imgHQ26180.npy
-imgHQ02874.npy
-imgHQ07104.npy
-imgHQ01146.npy
-imgHQ12372.npy
-imgHQ06209.npy
-imgHQ21530.npy
-imgHQ01896.npy
-imgHQ05281.npy
-imgHQ15828.npy
-imgHQ04358.npy
-imgHQ21360.npy
-imgHQ04897.npy
-imgHQ06327.npy
-imgHQ26928.npy
-imgHQ12015.npy
-imgHQ08028.npy
-imgHQ05465.npy
-imgHQ17641.npy
-imgHQ03449.npy
-imgHQ17751.npy
-imgHQ01728.npy
-imgHQ20132.npy
-imgHQ24684.npy
-imgHQ19493.npy
-imgHQ24993.npy
-imgHQ17084.npy
-imgHQ25734.npy
-imgHQ23737.npy
-imgHQ24251.npy
-imgHQ23425.npy
-imgHQ14981.npy
-imgHQ06516.npy
-imgHQ02575.npy
-imgHQ19661.npy
-imgHQ03729.npy
-imgHQ22660.npy
-imgHQ17730.npy
-imgHQ07153.npy
-imgHQ02405.npy
-imgHQ26590.npy
-imgHQ00806.npy
-imgHQ29791.npy
-imgHQ25834.npy
-imgHQ11980.npy
-imgHQ15454.npy
-imgHQ13560.npy
-imgHQ01550.npy
-imgHQ26714.npy
-imgHQ24021.npy
-imgHQ04688.npy
-imgHQ16000.npy
-imgHQ15364.npy
-imgHQ27279.npy
-imgHQ19796.npy
-imgHQ23065.npy
-imgHQ02385.npy
-imgHQ07176.npy
-imgHQ22084.npy
-imgHQ27638.npy
-imgHQ22285.npy
-imgHQ21132.npy
-imgHQ17361.npy
-imgHQ13898.npy
-imgHQ18557.npy
-imgHQ13374.npy
-imgHQ25072.npy
-imgHQ12283.npy
-imgHQ01787.npy
-imgHQ07296.npy
-imgHQ20237.npy
-imgHQ13379.npy
-imgHQ06311.npy
-imgHQ00277.npy
-imgHQ01809.npy
-imgHQ20706.npy
-imgHQ29195.npy
-imgHQ19843.npy
-imgHQ07201.npy
-imgHQ26543.npy
-imgHQ03964.npy
-imgHQ00451.npy
-imgHQ04124.npy
-imgHQ23187.npy
-imgHQ10591.npy
-imgHQ28020.npy
-imgHQ18722.npy
-imgHQ02322.npy
-imgHQ21274.npy
diff --git a/repositories/taming-transformers/data/celebahqvalidation.txt b/repositories/taming-transformers/data/celebahqvalidation.txt
deleted file mode 100644
index 6f58ba090a787b5dadc7aca6ca1e5975fcd8c4c0..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/data/celebahqvalidation.txt
+++ /dev/null
@@ -1,5000 +0,0 @@
-imgHQ05953.npy
-imgHQ17723.npy
-imgHQ04424.npy
-imgHQ24462.npy
-imgHQ21251.npy
-imgHQ17523.npy
-imgHQ22232.npy
-imgHQ19414.npy
-imgHQ20814.npy
-imgHQ29033.npy
-imgHQ26483.npy
-imgHQ03031.npy
-imgHQ17177.npy
-imgHQ10165.npy
-imgHQ07535.npy
-imgHQ15994.npy
-imgHQ28875.npy
-imgHQ19703.npy
-imgHQ01890.npy
-imgHQ21028.npy
-imgHQ03910.npy
-imgHQ29104.npy
-imgHQ12465.npy
-imgHQ11648.npy
-imgHQ08105.npy
-imgHQ16246.npy
-imgHQ15762.npy
-imgHQ03839.npy
-imgHQ17673.npy
-imgHQ27623.npy
-imgHQ21706.npy
-imgHQ03205.npy
-imgHQ13555.npy
-imgHQ28932.npy
-imgHQ08806.npy
-imgHQ18108.npy
-imgHQ22760.npy
-imgHQ09333.npy
-imgHQ00478.npy
-imgHQ23578.npy
-imgHQ08795.npy
-imgHQ29662.npy
-imgHQ02328.npy
-imgHQ01138.npy
-imgHQ18915.npy
-imgHQ19622.npy
-imgHQ05299.npy
-imgHQ00874.npy
-imgHQ01037.npy
-imgHQ22102.npy
-imgHQ13559.npy
-imgHQ16627.npy
-imgHQ13104.npy
-imgHQ26248.npy
-imgHQ08830.npy
-imgHQ08149.npy
-imgHQ23892.npy
-imgHQ04509.npy
-imgHQ04677.npy
-imgHQ22981.npy
-imgHQ23540.npy
-imgHQ09769.npy
-imgHQ19474.npy
-imgHQ05978.npy
-imgHQ21554.npy
-imgHQ07223.npy
-imgHQ18457.npy
-imgHQ02771.npy
-imgHQ29335.npy
-imgHQ26504.npy
-imgHQ14606.npy
-imgHQ02446.npy
-imgHQ10880.npy
-imgHQ06298.npy
-imgHQ25333.npy
-imgHQ21075.npy
-imgHQ03743.npy
-imgHQ11968.npy
-imgHQ21064.npy
-imgHQ03514.npy
-imgHQ13598.npy
-imgHQ18199.npy
-imgHQ23796.npy
-imgHQ27830.npy
-imgHQ18562.npy
-imgHQ07525.npy
-imgHQ06277.npy
-imgHQ21114.npy
-imgHQ06481.npy
-imgHQ29381.npy
-imgHQ10066.npy
-imgHQ26963.npy
-imgHQ00074.npy
-imgHQ03889.npy
-imgHQ05348.npy
-imgHQ23934.npy
-imgHQ28072.npy
-imgHQ24101.npy
-imgHQ22443.npy
-imgHQ00082.npy
-imgHQ27971.npy
-imgHQ20209.npy
-imgHQ29856.npy
-imgHQ16456.npy
-imgHQ08911.npy
-imgHQ14386.npy
-imgHQ21811.npy
-imgHQ10029.npy
-imgHQ22127.npy
-imgHQ02156.npy
-imgHQ04732.npy
-imgHQ08947.npy
-imgHQ23308.npy
-imgHQ17624.npy
-imgHQ28180.npy
-imgHQ15467.npy
-imgHQ20154.npy
-imgHQ00412.npy
-imgHQ08239.npy
-imgHQ28207.npy
-imgHQ01312.npy
-imgHQ20872.npy
-imgHQ08338.npy
-imgHQ06547.npy
-imgHQ06401.npy
-imgHQ16073.npy
-imgHQ13120.npy
-imgHQ02818.npy
-imgHQ26422.npy
-imgHQ27041.npy
-imgHQ20994.npy
-imgHQ05223.npy
-imgHQ06266.npy
-imgHQ05648.npy
-imgHQ21381.npy
-imgHQ15679.npy
-imgHQ26858.npy
-imgHQ07141.npy
-imgHQ17235.npy
-imgHQ12141.npy
-imgHQ10336.npy
-imgHQ08675.npy
-imgHQ19390.npy
-imgHQ00175.npy
-imgHQ25321.npy
-imgHQ11383.npy
-imgHQ03401.npy
-imgHQ12328.npy
-imgHQ18896.npy
-imgHQ18596.npy
-imgHQ21499.npy
-imgHQ25383.npy
-imgHQ07091.npy
-imgHQ19138.npy
-imgHQ08638.npy
-imgHQ07830.npy
-imgHQ06144.npy
-imgHQ25904.npy
-imgHQ26476.npy
-imgHQ15200.npy
-imgHQ06263.npy
-imgHQ05316.npy
-imgHQ20490.npy
-imgHQ04062.npy
-imgHQ01638.npy
-imgHQ00894.npy
-imgHQ16561.npy
-imgHQ11915.npy
-imgHQ15349.npy
-imgHQ03891.npy
-imgHQ29785.npy
-imgHQ07156.npy
-imgHQ05321.npy
-imgHQ14214.npy
-imgHQ20001.npy
-imgHQ02797.npy
-imgHQ06641.npy
-imgHQ20795.npy
-imgHQ09206.npy
-imgHQ08389.npy
-imgHQ24706.npy
-imgHQ20379.npy
-imgHQ24520.npy
-imgHQ00128.npy
-imgHQ29058.npy
-imgHQ25081.npy
-imgHQ03710.npy
-imgHQ24249.npy
-imgHQ10747.npy
-imgHQ11286.npy
-imgHQ15641.npy
-imgHQ01770.npy
-imgHQ00861.npy
-imgHQ18952.npy
-imgHQ28528.npy
-imgHQ03879.npy
-imgHQ20360.npy
-imgHQ15338.npy
-imgHQ09721.npy
-imgHQ23493.npy
-imgHQ07786.npy
-imgHQ26437.npy
-imgHQ03002.npy
-imgHQ29280.npy
-imgHQ06490.npy
-imgHQ08221.npy
-imgHQ15917.npy
-imgHQ13678.npy
-imgHQ13085.npy
-imgHQ18801.npy
-imgHQ16382.npy
-imgHQ02402.npy
-imgHQ16761.npy
-imgHQ19696.npy
-imgHQ10192.npy
-imgHQ28735.npy
-imgHQ26420.npy
-imgHQ26955.npy
-imgHQ16118.npy
-imgHQ14983.npy
-imgHQ03510.npy
-imgHQ25492.npy
-imgHQ02134.npy
-imgHQ14601.npy
-imgHQ23702.npy
-imgHQ18786.npy
-imgHQ10883.npy
-imgHQ15633.npy
-imgHQ13737.npy
-imgHQ16111.npy
-imgHQ13286.npy
-imgHQ26613.npy
-imgHQ29951.npy
-imgHQ19674.npy
-imgHQ20561.npy
-imgHQ17364.npy
-imgHQ26061.npy
-imgHQ21865.npy
-imgHQ19956.npy
-imgHQ28280.npy
-imgHQ15773.npy
-imgHQ21933.npy
-imgHQ07592.npy
-imgHQ28483.npy
-imgHQ27578.npy
-imgHQ01670.npy
-imgHQ03988.npy
-imgHQ09912.npy
-imgHQ08509.npy
-imgHQ08597.npy
-imgHQ06882.npy
-imgHQ07051.npy
-imgHQ11865.npy
-imgHQ02013.npy
-imgHQ15066.npy
-imgHQ15291.npy
-imgHQ21249.npy
-imgHQ01633.npy
-imgHQ15815.npy
-imgHQ00196.npy
-imgHQ10669.npy
-imgHQ20719.npy
-imgHQ22019.npy
-imgHQ25511.npy
-imgHQ00398.npy
-imgHQ22087.npy
-imgHQ00328.npy
-imgHQ11591.npy
-imgHQ09847.npy
-imgHQ29921.npy
-imgHQ25472.npy
-imgHQ08305.npy
-imgHQ11966.npy
-imgHQ07576.npy
-imgHQ17893.npy
-imgHQ01486.npy
-imgHQ12136.npy
-imgHQ19751.npy
-imgHQ01669.npy
-imgHQ16001.npy
-imgHQ27227.npy
-imgHQ10374.npy
-imgHQ18785.npy
-imgHQ00431.npy
-imgHQ04339.npy
-imgHQ19234.npy
-imgHQ24290.npy
-imgHQ26844.npy
-imgHQ26609.npy
-imgHQ21880.npy
-imgHQ22249.npy
-imgHQ17658.npy
-imgHQ25919.npy
-imgHQ28129.npy
-imgHQ24119.npy
-imgHQ18995.npy
-imgHQ03880.npy
-imgHQ29722.npy
-imgHQ12608.npy
-imgHQ14713.npy
-imgHQ29391.npy
-imgHQ02187.npy
-imgHQ13513.npy
-imgHQ23180.npy
-imgHQ15639.npy
-imgHQ07913.npy
-imgHQ27910.npy
-imgHQ26885.npy
-imgHQ16651.npy
-imgHQ09930.npy
-imgHQ17577.npy
-imgHQ25470.npy
-imgHQ24818.npy
-imgHQ19745.npy
-imgHQ00469.npy
-imgHQ01310.npy
-imgHQ14916.npy
-imgHQ18483.npy
-imgHQ00132.npy
-imgHQ14122.npy
-imgHQ00466.npy
-imgHQ12367.npy
-imgHQ26949.npy
-imgHQ18375.npy
-imgHQ10720.npy
-imgHQ14341.npy
-imgHQ08766.npy
-imgHQ26335.npy
-imgHQ12316.npy
-imgHQ20856.npy
-imgHQ06163.npy
-imgHQ26033.npy
-imgHQ16532.npy
-imgHQ13190.npy
-imgHQ15911.npy
-imgHQ23848.npy
-imgHQ27673.npy
-imgHQ26950.npy
-imgHQ17671.npy
-imgHQ18619.npy
-imgHQ29668.npy
-imgHQ28622.npy
-imgHQ27624.npy
-imgHQ03669.npy
-imgHQ06470.npy
-imgHQ07637.npy
-imgHQ07591.npy
-imgHQ28943.npy
-imgHQ22097.npy
-imgHQ10605.npy
-imgHQ05725.npy
-imgHQ02048.npy
-imgHQ14176.npy
-imgHQ08066.npy
-imgHQ18194.npy
-imgHQ27492.npy
-imgHQ06621.npy
-imgHQ15297.npy
-imgHQ10971.npy
-imgHQ23801.npy
-imgHQ04301.npy
-imgHQ24848.npy
-imgHQ04065.npy
-imgHQ14467.npy
-imgHQ28555.npy
-imgHQ28455.npy
-imgHQ14281.npy
-imgHQ23206.npy
-imgHQ18648.npy
-imgHQ02354.npy
-imgHQ19874.npy
-imgHQ22952.npy
-imgHQ00111.npy
-imgHQ17246.npy
-imgHQ02167.npy
-imgHQ04531.npy
-imgHQ29353.npy
-imgHQ09867.npy
-imgHQ11348.npy
-imgHQ23338.npy
-imgHQ12060.npy
-imgHQ04696.npy
-imgHQ21287.npy
-imgHQ04373.npy
-imgHQ00287.npy
-imgHQ19433.npy
-imgHQ27326.npy
-imgHQ00391.npy
-imgHQ13063.npy
-imgHQ28826.npy
-imgHQ26601.npy
-imgHQ00962.npy
-imgHQ27113.npy
-imgHQ08480.npy
-imgHQ25809.npy
-imgHQ27275.npy
-imgHQ02927.npy
-imgHQ16885.npy
-imgHQ26093.npy
-imgHQ07353.npy
-imgHQ22347.npy
-imgHQ21798.npy
-imgHQ00989.npy
-imgHQ01472.npy
-imgHQ14351.npy
-imgHQ03742.npy
-imgHQ17451.npy
-imgHQ29819.npy
-imgHQ23463.npy
-imgHQ26716.npy
-imgHQ04475.npy
-imgHQ11237.npy
-imgHQ03844.npy
-imgHQ26460.npy
-imgHQ11485.npy
-imgHQ24623.npy
-imgHQ15325.npy
-imgHQ25881.npy
-imgHQ06593.npy
-imgHQ08777.npy
-imgHQ04003.npy
-imgHQ24708.npy
-imgHQ06615.npy
-imgHQ01601.npy
-imgHQ01907.npy
-imgHQ06829.npy
-imgHQ20818.npy
-imgHQ06775.npy
-imgHQ01415.npy
-imgHQ24020.npy
-imgHQ26962.npy
-imgHQ18121.npy
-imgHQ14153.npy
-imgHQ07612.npy
-imgHQ12371.npy
-imgHQ24330.npy
-imgHQ09090.npy
-imgHQ10887.npy
-imgHQ12986.npy
-imgHQ11232.npy
-imgHQ20563.npy
-imgHQ13294.npy
-imgHQ02823.npy
-imgHQ12150.npy
-imgHQ29250.npy
-imgHQ10855.npy
-imgHQ07604.npy
-imgHQ21430.npy
-imgHQ22375.npy
-imgHQ06654.npy
-imgHQ28911.npy
-imgHQ10332.npy
-imgHQ19353.npy
-imgHQ07094.npy
-imgHQ29716.npy
-imgHQ22586.npy
-imgHQ21574.npy
-imgHQ05456.npy
-imgHQ29733.npy
-imgHQ14342.npy
-imgHQ06622.npy
-imgHQ06571.npy
-imgHQ23255.npy
-imgHQ18475.npy
-imgHQ08857.npy
-imgHQ07463.npy
-imgHQ17905.npy
-imgHQ16987.npy
-imgHQ23498.npy
-imgHQ25730.npy
-imgHQ06043.npy
-imgHQ01199.npy
-imgHQ14303.npy
-imgHQ08342.npy
-imgHQ01659.npy
-imgHQ04627.npy
-imgHQ09732.npy
-imgHQ10582.npy
-imgHQ29032.npy
-imgHQ07523.npy
-imgHQ04738.npy
-imgHQ28216.npy
-imgHQ11825.npy
-imgHQ15720.npy
-imgHQ05319.npy
-imgHQ26639.npy
-imgHQ03856.npy
-imgHQ14527.npy
-imgHQ08820.npy
-imgHQ02898.npy
-imgHQ00232.npy
-imgHQ12789.npy
-imgHQ28394.npy
-imgHQ28557.npy
-imgHQ04952.npy
-imgHQ24408.npy
-imgHQ27999.npy
-imgHQ22322.npy
-imgHQ23121.npy
-imgHQ17273.npy
-imgHQ13757.npy
-imgHQ15249.npy
-imgHQ28985.npy
-imgHQ06191.npy
-imgHQ28655.npy
-imgHQ23590.npy
-imgHQ23652.npy
-imgHQ08141.npy
-imgHQ28226.npy
-imgHQ28443.npy
-imgHQ10079.npy
-imgHQ20951.npy
-imgHQ05112.npy
-imgHQ01370.npy
-imgHQ08151.npy
-imgHQ19755.npy
-imgHQ20427.npy
-imgHQ08316.npy
-imgHQ26509.npy
-imgHQ20830.npy
-imgHQ07404.npy
-imgHQ09901.npy
-imgHQ21501.npy
-imgHQ26148.npy
-imgHQ26572.npy
-imgHQ17352.npy
-imgHQ10026.npy
-imgHQ20841.npy
-imgHQ26632.npy
-imgHQ06291.npy
-imgHQ19287.npy
-imgHQ08034.npy
-imgHQ21207.npy
-imgHQ08224.npy
-imgHQ19954.npy
-imgHQ19647.npy
-imgHQ01609.npy
-imgHQ05864.npy
-imgHQ11421.npy
-imgHQ10060.npy
-imgHQ29624.npy
-imgHQ02876.npy
-imgHQ06945.npy
-imgHQ15177.npy
-imgHQ01160.npy
-imgHQ17428.npy
-imgHQ06742.npy
-imgHQ28391.npy
-imgHQ04150.npy
-imgHQ10981.npy
-imgHQ13838.npy
-imgHQ18520.npy
-imgHQ09875.npy
-imgHQ19648.npy
-imgHQ25370.npy
-imgHQ23506.npy
-imgHQ07136.npy
-imgHQ23080.npy
-imgHQ23915.npy
-imgHQ29688.npy
-imgHQ11082.npy
-imgHQ10650.npy
-imgHQ02935.npy
-imgHQ28227.npy
-imgHQ16211.npy
-imgHQ28972.npy
-imgHQ22897.npy
-imgHQ22033.npy
-imgHQ09958.npy
-imgHQ05002.npy
-imgHQ29882.npy
-imgHQ02762.npy
-imgHQ05039.npy
-imgHQ25817.npy
-imgHQ24398.npy
-imgHQ16178.npy
-imgHQ12359.npy
-imgHQ12848.npy
-imgHQ22183.npy
-imgHQ16819.npy
-imgHQ12094.npy
-imgHQ29805.npy
-imgHQ16433.npy
-imgHQ23445.npy
-imgHQ19675.npy
-imgHQ16901.npy
-imgHQ04673.npy
-imgHQ11456.npy
-imgHQ03949.npy
-imgHQ13770.npy
-imgHQ11658.npy
-imgHQ10741.npy
-imgHQ18452.npy
-imgHQ00145.npy
-imgHQ17318.npy
-imgHQ22589.npy
-imgHQ00834.npy
-imgHQ05533.npy
-imgHQ08974.npy
-imgHQ03986.npy
-imgHQ18463.npy
-imgHQ07471.npy
-imgHQ14247.npy
-imgHQ19717.npy
-imgHQ01081.npy
-imgHQ18411.npy
-imgHQ08181.npy
-imgHQ08595.npy
-imgHQ29101.npy
-imgHQ27314.npy
-imgHQ19087.npy
-imgHQ22664.npy
-imgHQ21013.npy
-imgHQ10015.npy
-imgHQ11910.npy
-imgHQ07255.npy
-imgHQ21987.npy
-imgHQ21056.npy
-imgHQ14155.npy
-imgHQ02708.npy
-imgHQ27833.npy
-imgHQ05966.npy
-imgHQ05072.npy
-imgHQ27707.npy
-imgHQ13426.npy
-imgHQ12666.npy
-imgHQ09920.npy
-imgHQ27168.npy
-imgHQ00727.npy
-imgHQ26192.npy
-imgHQ12673.npy
-imgHQ11945.npy
-imgHQ23533.npy
-imgHQ15426.npy
-imgHQ08064.npy
-imgHQ05035.npy
-imgHQ23020.npy
-imgHQ12127.npy
-imgHQ28113.npy
-imgHQ23247.npy
-imgHQ16277.npy
-imgHQ17888.npy
-imgHQ29879.npy
-imgHQ29642.npy
-imgHQ13188.npy
-imgHQ00012.npy
-imgHQ11696.npy
-imgHQ02639.npy
-imgHQ11571.npy
-imgHQ21399.npy
-imgHQ13640.npy
-imgHQ03263.npy
-imgHQ10628.npy
-imgHQ28936.npy
-imgHQ03100.npy
-imgHQ19792.npy
-imgHQ04437.npy
-imgHQ06713.npy
-imgHQ06486.npy
-imgHQ10975.npy
-imgHQ21646.npy
-imgHQ07584.npy
-imgHQ19104.npy
-imgHQ15719.npy
-imgHQ07182.npy
-imgHQ08979.npy
-imgHQ09442.npy
-imgHQ17573.npy
-imgHQ15827.npy
-imgHQ13084.npy
-imgHQ05560.npy
-imgHQ03992.npy
-imgHQ02029.npy
-imgHQ18574.npy
-imgHQ28285.npy
-imgHQ04136.npy
-imgHQ22814.npy
-imgHQ15468.npy
-imgHQ26067.npy
-imgHQ03951.npy
-imgHQ18098.npy
-imgHQ16377.npy
-imgHQ08534.npy
-imgHQ03796.npy
-imgHQ18821.npy
-imgHQ14326.npy
-imgHQ04040.npy
-imgHQ26660.npy
-imgHQ25927.npy
-imgHQ04827.npy
-imgHQ25953.npy
-imgHQ17768.npy
-imgHQ13731.npy
-imgHQ10763.npy
-imgHQ17499.npy
-imgHQ02066.npy
-imgHQ05459.npy
-imgHQ16031.npy
-imgHQ08925.npy
-imgHQ12380.npy
-imgHQ16920.npy
-imgHQ09143.npy
-imgHQ16975.npy
-imgHQ26333.npy
-imgHQ16865.npy
-imgHQ26136.npy
-imgHQ01883.npy
-imgHQ24891.npy
-imgHQ14195.npy
-imgHQ04313.npy
-imgHQ24297.npy
-imgHQ02943.npy
-imgHQ28866.npy
-imgHQ02496.npy
-imgHQ18366.npy
-imgHQ10415.npy
-imgHQ16405.npy
-imgHQ23285.npy
-imgHQ08560.npy
-imgHQ15781.npy
-imgHQ17406.npy
-imgHQ12473.npy
-imgHQ29518.npy
-imgHQ09467.npy
-imgHQ16969.npy
-imgHQ20195.npy
-imgHQ20997.npy
-imgHQ19662.npy
-imgHQ19495.npy
-imgHQ22002.npy
-imgHQ14827.npy
-imgHQ18637.npy
-imgHQ23390.npy
-imgHQ14513.npy
-imgHQ11207.npy
-imgHQ02750.npy
-imgHQ17636.npy
-imgHQ04461.npy
-imgHQ24712.npy
-imgHQ01761.npy
-imgHQ23384.npy
-imgHQ11031.npy
-imgHQ23225.npy
-imgHQ09696.npy
-imgHQ19534.npy
-imgHQ04542.npy
-imgHQ28994.npy
-imgHQ18672.npy
-imgHQ00824.npy
-imgHQ08720.npy
-imgHQ13667.npy
-imgHQ21270.npy
-imgHQ24298.npy
-imgHQ00247.npy
-imgHQ29228.npy
-imgHQ02087.npy
-imgHQ09543.npy
-imgHQ08570.npy
-imgHQ09989.npy
-imgHQ16933.npy
-imgHQ07596.npy
-imgHQ17616.npy
-imgHQ26250.npy
-imgHQ19339.npy
-imgHQ25254.npy
-imgHQ09816.npy
-imgHQ03081.npy
-imgHQ13821.npy
-imgHQ25970.npy
-imgHQ20978.npy
-imgHQ11614.npy
-imgHQ19174.npy
-imgHQ10343.npy
-imgHQ06625.npy
-imgHQ13238.npy
-imgHQ21485.npy
-imgHQ21460.npy
-imgHQ18565.npy
-imgHQ04010.npy
-imgHQ24986.npy
-imgHQ11637.npy
-imgHQ07574.npy
-imgHQ11251.npy
-imgHQ06786.npy
-imgHQ14632.npy
-imgHQ27079.npy
-imgHQ01800.npy
-imgHQ02501.npy
-imgHQ17783.npy
-imgHQ16728.npy
-imgHQ02747.npy
-imgHQ23220.npy
-imgHQ08009.npy
-imgHQ06986.npy
-imgHQ16991.npy
-imgHQ00187.npy
-imgHQ21533.npy
-imgHQ00296.npy
-imgHQ05415.npy
-imgHQ19740.npy
-imgHQ09395.npy
-imgHQ00444.npy
-imgHQ02841.npy
-imgHQ06961.npy
-imgHQ18531.npy
-imgHQ16834.npy
-imgHQ22101.npy
-imgHQ19649.npy
-imgHQ12518.npy
-imgHQ06978.npy
-imgHQ14613.npy
-imgHQ10088.npy
-imgHQ20023.npy
-imgHQ29000.npy
-imgHQ28641.npy
-imgHQ15182.npy
-imgHQ20287.npy
-imgHQ20191.npy
-imgHQ02492.npy
-imgHQ00970.npy
-imgHQ10810.npy
-imgHQ21435.npy
-imgHQ08497.npy
-imgHQ11769.npy
-imgHQ03850.npy
-imgHQ26648.npy
-imgHQ18168.npy
-imgHQ11899.npy
-imgHQ22240.npy
-imgHQ02130.npy
-imgHQ25426.npy
-imgHQ11997.npy
-imgHQ26455.npy
-imgHQ07263.npy
-imgHQ25201.npy
-imgHQ16514.npy
-imgHQ07234.npy
-imgHQ27955.npy
-imgHQ16467.npy
-imgHQ11049.npy
-imgHQ15318.npy
-imgHQ09064.npy
-imgHQ23036.npy
-imgHQ28250.npy
-imgHQ10508.npy
-imgHQ04836.npy
-imgHQ27775.npy
-imgHQ24682.npy
-imgHQ27659.npy
-imgHQ27209.npy
-imgHQ06517.npy
-imgHQ06236.npy
-imgHQ13920.npy
-imgHQ26241.npy
-imgHQ08695.npy
-imgHQ16751.npy
-imgHQ18991.npy
-imgHQ26224.npy
-imgHQ24867.npy
-imgHQ16227.npy
-imgHQ20524.npy
-imgHQ08903.npy
-imgHQ12870.npy
-imgHQ08511.npy
-imgHQ08839.npy
-imgHQ27526.npy
-imgHQ03722.npy
-imgHQ22146.npy
-imgHQ15895.npy
-imgHQ17668.npy
-imgHQ01438.npy
-imgHQ29040.npy
-imgHQ29605.npy
-imgHQ09197.npy
-imgHQ13671.npy
-imgHQ27569.npy
-imgHQ06636.npy
-imgHQ05129.npy
-imgHQ01108.npy
-imgHQ09569.npy
-imgHQ24293.npy
-imgHQ00497.npy
-imgHQ26068.npy
-imgHQ16062.npy
-imgHQ07459.npy
-imgHQ18515.npy
-imgHQ16008.npy
-imgHQ01239.npy
-imgHQ09921.npy
-imgHQ02489.npy
-imgHQ25755.npy
-imgHQ21053.npy
-imgHQ23302.npy
-imgHQ13231.npy
-imgHQ27974.npy
-imgHQ17588.npy
-imgHQ14661.npy
-imgHQ28044.npy
-imgHQ24312.npy
-imgHQ29886.npy
-imgHQ12199.npy
-imgHQ05282.npy
-imgHQ26975.npy
-imgHQ12846.npy
-imgHQ15250.npy
-imgHQ19846.npy
-imgHQ13417.npy
-imgHQ18938.npy
-imgHQ26909.npy
-imgHQ11821.npy
-imgHQ26178.npy
-imgHQ10954.npy
-imgHQ21407.npy
-imgHQ13124.npy
-imgHQ07520.npy
-imgHQ15368.npy
-imgHQ14445.npy
-imgHQ01528.npy
-imgHQ07089.npy
-imgHQ03367.npy
-imgHQ19878.npy
-imgHQ08016.npy
-imgHQ17722.npy
-imgHQ18441.npy
-imgHQ17455.npy
-imgHQ29870.npy
-imgHQ08738.npy
-imgHQ07534.npy
-imgHQ23565.npy
-imgHQ11294.npy
-imgHQ27978.npy
-imgHQ07696.npy
-imgHQ26003.npy
-imgHQ24171.npy
-imgHQ17786.npy
-imgHQ11240.npy
-imgHQ25840.npy
-imgHQ27399.npy
-imgHQ15779.npy
-imgHQ28196.npy
-imgHQ14362.npy
-imgHQ28784.npy
-imgHQ26827.npy
-imgHQ18695.npy
-imgHQ20672.npy
-imgHQ06086.npy
-imgHQ16134.npy
-imgHQ20109.npy
-imgHQ21378.npy
-imgHQ19431.npy
-imgHQ13484.npy
-imgHQ06842.npy
-imgHQ13393.npy
-imgHQ24359.npy
-imgHQ12403.npy
-imgHQ18150.npy
-imgHQ01113.npy
-imgHQ25142.npy
-imgHQ03238.npy
-imgHQ18236.npy
-imgHQ29269.npy
-imgHQ11002.npy
-imgHQ26084.npy
-imgHQ15016.npy
-imgHQ00544.npy
-imgHQ11279.npy
-imgHQ20140.npy
-imgHQ05445.npy
-imgHQ20448.npy
-imgHQ27103.npy
-imgHQ15228.npy
-imgHQ27363.npy
-imgHQ15583.npy
-imgHQ00877.npy
-imgHQ08152.npy
-imgHQ16326.npy
-imgHQ26821.npy
-imgHQ28051.npy
-imgHQ20977.npy
-imgHQ14652.npy
-imgHQ07744.npy
-imgHQ10137.npy
-imgHQ09136.npy
-imgHQ23089.npy
-imgHQ23258.npy
-imgHQ22927.npy
-imgHQ03047.npy
-imgHQ16160.npy
-imgHQ09397.npy
-imgHQ21203.npy
-imgHQ28788.npy
-imgHQ07482.npy
-imgHQ13509.npy
-imgHQ25780.npy
-imgHQ07890.npy
-imgHQ03945.npy
-imgHQ17181.npy
-imgHQ12959.npy
-imgHQ02177.npy
-imgHQ17538.npy
-imgHQ22029.npy
-imgHQ13543.npy
-imgHQ04711.npy
-imgHQ06351.npy
-imgHQ07695.npy
-imgHQ22262.npy
-imgHQ06495.npy
-imgHQ10935.npy
-imgHQ11872.npy
-imgHQ00953.npy
-imgHQ17737.npy
-imgHQ28047.npy
-imgHQ13966.npy
-imgHQ28695.npy
-imgHQ14819.npy
-imgHQ20590.npy
-imgHQ21853.npy
-imgHQ18967.npy
-imgHQ04166.npy
-imgHQ10534.npy
-imgHQ19548.npy
-imgHQ10229.npy
-imgHQ09032.npy
-imgHQ26627.npy
-imgHQ27690.npy
-imgHQ17633.npy
-imgHQ15292.npy
-imgHQ13288.npy
-imgHQ04261.npy
-imgHQ19898.npy
-imgHQ05249.npy
-imgHQ17793.npy
-imgHQ15057.npy
-imgHQ15852.npy
-imgHQ11435.npy
-imgHQ19637.npy
-imgHQ29238.npy
-imgHQ07211.npy
-imgHQ09359.npy
-imgHQ11038.npy
-imgHQ27587.npy
-imgHQ29358.npy
-imgHQ13463.npy
-imgHQ22004.npy
-imgHQ04685.npy
-imgHQ01623.npy
-imgHQ12358.npy
-imgHQ17347.npy
-imgHQ28896.npy
-imgHQ25132.npy
-imgHQ21537.npy
-imgHQ29554.npy
-imgHQ16675.npy
-imgHQ15307.npy
-imgHQ12545.npy
-imgHQ12108.npy
-imgHQ24268.npy
-imgHQ22548.npy
-imgHQ04764.npy
-imgHQ20232.npy
-imgHQ21707.npy
-imgHQ08437.npy
-imgHQ16580.npy
-imgHQ04085.npy
-imgHQ04891.npy
-imgHQ12174.npy
-imgHQ23849.npy
-imgHQ11639.npy
-imgHQ15144.npy
-imgHQ19387.npy
-imgHQ17532.npy
-imgHQ15356.npy
-imgHQ26325.npy
-imgHQ06651.npy
-imgHQ14142.npy
-imgHQ13888.npy
-imgHQ16731.npy
-imgHQ05937.npy
-imgHQ09952.npy
-imgHQ24887.npy
-imgHQ28334.npy
-imgHQ02452.npy
-imgHQ15600.npy
-imgHQ10845.npy
-imgHQ01790.npy
-imgHQ07478.npy
-imgHQ15463.npy
-imgHQ18093.npy
-imgHQ06800.npy
-imgHQ08647.npy
-imgHQ03327.npy
-imgHQ24624.npy
-imgHQ16658.npy
-imgHQ00886.npy
-imgHQ28228.npy
-imgHQ02163.npy
-imgHQ16231.npy
-imgHQ09463.npy
-imgHQ09988.npy
-imgHQ10346.npy
-imgHQ28211.npy
-imgHQ20172.npy
-imgHQ15207.npy
-imgHQ04468.npy
-imgHQ17168.npy
-imgHQ09906.npy
-imgHQ20655.npy
-imgHQ17623.npy
-imgHQ05892.npy
-imgHQ20014.npy
-imgHQ25575.npy
-imgHQ21450.npy
-imgHQ16654.npy
-imgHQ20659.npy
-imgHQ07313.npy
-imgHQ05956.npy
-imgHQ06415.npy
-imgHQ11407.npy
-imgHQ05021.npy
-imgHQ08003.npy
-imgHQ12995.npy
-imgHQ06285.npy
-imgHQ20783.npy
-imgHQ28011.npy
-imgHQ03694.npy
-imgHQ08690.npy
-imgHQ28396.npy
-imgHQ27847.npy
-imgHQ12903.npy
-imgHQ17516.npy
-imgHQ10140.npy
-imgHQ29270.npy
-imgHQ08060.npy
-imgHQ20468.npy
-imgHQ14656.npy
-imgHQ17026.npy
-imgHQ15209.npy
-imgHQ05767.npy
-imgHQ14320.npy
-imgHQ07435.npy
-imgHQ26545.npy
-imgHQ04168.npy
-imgHQ24888.npy
-imgHQ26605.npy
-imgHQ27498.npy
-imgHQ12640.npy
-imgHQ10817.npy
-imgHQ03421.npy
-imgHQ26670.npy
-imgHQ25878.npy
-imgHQ13632.npy
-imgHQ22823.npy
-imgHQ28604.npy
-imgHQ10167.npy
-imgHQ14572.npy
-imgHQ17481.npy
-imgHQ10745.npy
-imgHQ22134.npy
-imgHQ16630.npy
-imgHQ00268.npy
-imgHQ27150.npy
-imgHQ01765.npy
-imgHQ01036.npy
-imgHQ23627.npy
-imgHQ12010.npy
-imgHQ00331.npy
-imgHQ29682.npy
-imgHQ08977.npy
-imgHQ27149.npy
-imgHQ00109.npy
-imgHQ07868.npy
-imgHQ15661.npy
-imgHQ01619.npy
-imgHQ18684.npy
-imgHQ15156.npy
-imgHQ01805.npy
-imgHQ17467.npy
-imgHQ22838.npy
-imgHQ22080.npy
-imgHQ20376.npy
-imgHQ00850.npy
-imgHQ01354.npy
-imgHQ25543.npy
-imgHQ21716.npy
-imgHQ13161.npy
-imgHQ09635.npy
-imgHQ16479.npy
-imgHQ28719.npy
-imgHQ15608.npy
-imgHQ07464.npy
-imgHQ09259.npy
-imgHQ12381.npy
-imgHQ02473.npy
-imgHQ14905.npy
-imgHQ09307.npy
-imgHQ15436.npy
-imgHQ29754.npy
-imgHQ20161.npy
-imgHQ03885.npy
-imgHQ02431.npy
-imgHQ10008.npy
-imgHQ08747.npy
-imgHQ12125.npy
-imgHQ19381.npy
-imgHQ25075.npy
-imgHQ14413.npy
-imgHQ29396.npy
-imgHQ22016.npy
-imgHQ02210.npy
-imgHQ22427.npy
-imgHQ27632.npy
-imgHQ29665.npy
-imgHQ01541.npy
-imgHQ12187.npy
-imgHQ13292.npy
-imgHQ06161.npy
-imgHQ18287.npy
-imgHQ20141.npy
-imgHQ04469.npy
-imgHQ01466.npy
-imgHQ24571.npy
-imgHQ02285.npy
-imgHQ25617.npy
-imgHQ17062.npy
-imgHQ10590.npy
-imgHQ14700.npy
-imgHQ28681.npy
-imgHQ15669.npy
-imgHQ03989.npy
-imgHQ26469.npy
-imgHQ14161.npy
-imgHQ13756.npy
-imgHQ06026.npy
-imgHQ20245.npy
-imgHQ18473.npy
-imgHQ04433.npy
-imgHQ14096.npy
-imgHQ23648.npy
-imgHQ18245.npy
-imgHQ12508.npy
-imgHQ24142.npy
-imgHQ13882.npy
-imgHQ10876.npy
-imgHQ13255.npy
-imgHQ14006.npy
-imgHQ20810.npy
-imgHQ06583.npy
-imgHQ08661.npy
-imgHQ23932.npy
-imgHQ13401.npy
-imgHQ00055.npy
-imgHQ03544.npy
-imgHQ21387.npy
-imgHQ06853.npy
-imgHQ14421.npy
-imgHQ07541.npy
-imgHQ00201.npy
-imgHQ14726.npy
-imgHQ09492.npy
-imgHQ22735.npy
-imgHQ16309.npy
-imgHQ12532.npy
-imgHQ11519.npy
-imgHQ17018.npy
-imgHQ01208.npy
-imgHQ20553.npy
-imgHQ18136.npy
-imgHQ09997.npy
-imgHQ11431.npy
-imgHQ01745.npy
-imgHQ23646.npy
-imgHQ00270.npy
-imgHQ21590.npy
-imgHQ25505.npy
-imgHQ24747.npy
-imgHQ28954.npy
-imgHQ24373.npy
-imgHQ00400.npy
-imgHQ05845.npy
-imgHQ16643.npy
-imgHQ08005.npy
-imgHQ26814.npy
-imgHQ02006.npy
-imgHQ25684.npy
-imgHQ19729.npy
-imgHQ25647.npy
-imgHQ27073.npy
-imgHQ00516.npy
-imgHQ04467.npy
-imgHQ28283.npy
-imgHQ12438.npy
-imgHQ23817.npy
-imgHQ16859.npy
-imgHQ13043.npy
-imgHQ15319.npy
-imgHQ01144.npy
-imgHQ03936.npy
-imgHQ16039.npy
-imgHQ19831.npy
-imgHQ00468.npy
-imgHQ14226.npy
-imgHQ08195.npy
-imgHQ26906.npy
-imgHQ28416.npy
-imgHQ03868.npy
-imgHQ04902.npy
-imgHQ11534.npy
-imgHQ29666.npy
-imgHQ16208.npy
-imgHQ01959.npy
-imgHQ11875.npy
-imgHQ13732.npy
-imgHQ07515.npy
-imgHQ21470.npy
-imgHQ23233.npy
-imgHQ04438.npy
-imgHQ14427.npy
-imgHQ28449.npy
-imgHQ06590.npy
-imgHQ19544.npy
-imgHQ22894.npy
-imgHQ15547.npy
-imgHQ26013.npy
-imgHQ17295.npy
-imgHQ02947.npy
-imgHQ09687.npy
-imgHQ22902.npy
-imgHQ03841.npy
-imgHQ19190.npy
-imgHQ04525.npy
-imgHQ13663.npy
-imgHQ01881.npy
-imgHQ11016.npy
-imgHQ03104.npy
-imgHQ28729.npy
-imgHQ29172.npy
-imgHQ12840.npy
-imgHQ18128.npy
-imgHQ15271.npy
-imgHQ18974.npy
-imgHQ08530.npy
-imgHQ20236.npy
-imgHQ09848.npy
-imgHQ11312.npy
-imgHQ02551.npy
-imgHQ29046.npy
-imgHQ08159.npy
-imgHQ22863.npy
-imgHQ23807.npy
-imgHQ15038.npy
-imgHQ26899.npy
-imgHQ23827.npy
-imgHQ11460.npy
-imgHQ08678.npy
-imgHQ10248.npy
-imgHQ13711.npy
-imgHQ25133.npy
-imgHQ17990.npy
-imgHQ02929.npy
-imgHQ27553.npy
-imgHQ20233.npy
-imgHQ19658.npy
-imgHQ04306.npy
-imgHQ01568.npy
-imgHQ06403.npy
-imgHQ06077.npy
-imgHQ21734.npy
-imgHQ10594.npy
-imgHQ13673.npy
-imgHQ29906.npy
-imgHQ19086.npy
-imgHQ27574.npy
-imgHQ14497.npy
-imgHQ16698.npy
-imgHQ08216.npy
-imgHQ19879.npy
-imgHQ03767.npy
-imgHQ01719.npy
-imgHQ24135.npy
-imgHQ04411.npy
-imgHQ00572.npy
-imgHQ29618.npy
-imgHQ29587.npy
-imgHQ29918.npy
-imgHQ21179.npy
-imgHQ25389.npy
-imgHQ06300.npy
-imgHQ13068.npy
-imgHQ24923.npy
-imgHQ29332.npy
-imgHQ20459.npy
-imgHQ21492.npy
-imgHQ27727.npy
-imgHQ04118.npy
-imgHQ25955.npy
-imgHQ22360.npy
-imgHQ29984.npy
-imgHQ22237.npy
-imgHQ21444.npy
-imgHQ18835.npy
-imgHQ09487.npy
-imgHQ02102.npy
-imgHQ18913.npy
-imgHQ25729.npy
-imgHQ21563.npy
-imgHQ07852.npy
-imgHQ09616.npy
-imgHQ11301.npy
-imgHQ01067.npy
-imgHQ07185.npy
-imgHQ12652.npy
-imgHQ14767.npy
-imgHQ06891.npy
-imgHQ09671.npy
-imgHQ01816.npy
-imgHQ09062.npy
-imgHQ14879.npy
-imgHQ03247.npy
-imgHQ28862.npy
-imgHQ02945.npy
-imgHQ13698.npy
-imgHQ20261.npy
-imgHQ10241.npy
-imgHQ01127.npy
-imgHQ01763.npy
-imgHQ28010.npy
-imgHQ19702.npy
-imgHQ06132.npy
-imgHQ03398.npy
-imgHQ07287.npy
-imgHQ14699.npy
-imgHQ00184.npy
-imgHQ14918.npy
-imgHQ06244.npy
-imgHQ09183.npy
-imgHQ23414.npy
-imgHQ08965.npy
-imgHQ12304.npy
-imgHQ16469.npy
-imgHQ15710.npy
-imgHQ02181.npy
-imgHQ28106.npy
-imgHQ03803.npy
-imgHQ16253.npy
-imgHQ27042.npy
-imgHQ25028.npy
-imgHQ12888.npy
-imgHQ08576.npy
-imgHQ12898.npy
-imgHQ01304.npy
-imgHQ17113.npy
-imgHQ26017.npy
-imgHQ28917.npy
-imgHQ04058.npy
-imgHQ19484.npy
-imgHQ14419.npy
-imgHQ12320.npy
-imgHQ02529.npy
-imgHQ16841.npy
-imgHQ12534.npy
-imgHQ08232.npy
-imgHQ12117.npy
-imgHQ21910.npy
-imgHQ21187.npy
-imgHQ25710.npy
-imgHQ12651.npy
-imgHQ24527.npy
-imgHQ03493.npy
-imgHQ06467.npy
-imgHQ26650.npy
-imgHQ08478.npy
-imgHQ27357.npy
-imgHQ29475.npy
-imgHQ14523.npy
-imgHQ28365.npy
-imgHQ22156.npy
-imgHQ25366.npy
-imgHQ22637.npy
-imgHQ21208.npy
-imgHQ09414.npy
-imgHQ14185.npy
-imgHQ18215.npy
-imgHQ01105.npy
-imgHQ02059.npy
-imgHQ16880.npy
-imgHQ04484.npy
-imgHQ22335.npy
-imgHQ11084.npy
-imgHQ16959.npy
-imgHQ08451.npy
-imgHQ11469.npy
-imgHQ14347.npy
-imgHQ29343.npy
-imgHQ23535.npy
-imgHQ00787.npy
-imgHQ17048.npy
-imgHQ21165.npy
-imgHQ10177.npy
-imgHQ03407.npy
-imgHQ24893.npy
-imgHQ02678.npy
-imgHQ00228.npy
-imgHQ04600.npy
-imgHQ22211.npy
-imgHQ03882.npy
-imgHQ14660.npy
-imgHQ11311.npy
-imgHQ18373.npy
-imgHQ18105.npy
-imgHQ20220.npy
-imgHQ27011.npy
-imgHQ26531.npy
-imgHQ14507.npy
-imgHQ19393.npy
-imgHQ11747.npy
-imgHQ05425.npy
-imgHQ01418.npy
-imgHQ29907.npy
-imgHQ28898.npy
-imgHQ29955.npy
-imgHQ13114.npy
-imgHQ18945.npy
-imgHQ18184.npy
-imgHQ18806.npy
-imgHQ01617.npy
-imgHQ11935.npy
-imgHQ02046.npy
-imgHQ13376.npy
-imgHQ12984.npy
-imgHQ10496.npy
-imgHQ06899.npy
-imgHQ27602.npy
-imgHQ23703.npy
-imgHQ12993.npy
-imgHQ21012.npy
-imgHQ14448.npy
-imgHQ27178.npy
-imgHQ19230.npy
-imgHQ09195.npy
-imgHQ00680.npy
-imgHQ07561.npy
-imgHQ07705.npy
-imgHQ22635.npy
-imgHQ14954.npy
-imgHQ20819.npy
-imgHQ26508.npy
-imgHQ28752.npy
-imgHQ11664.npy
-imgHQ14863.npy
-imgHQ25458.npy
-imgHQ24132.npy
-imgHQ05528.npy
-imgHQ25037.npy
-imgHQ26494.npy
-imgHQ07715.npy
-imgHQ03295.npy
-imgHQ02598.npy
-imgHQ22362.npy
-imgHQ12075.npy
-imgHQ17738.npy
-imgHQ00687.npy
-imgHQ06563.npy
-imgHQ09614.npy
-imgHQ21952.npy
-imgHQ22351.npy
-imgHQ27566.npy
-imgHQ00519.npy
-imgHQ12000.npy
-imgHQ18666.npy
-imgHQ14716.npy
-imgHQ25786.npy
-imgHQ03812.npy
-imgHQ05745.npy
-imgHQ26830.npy
-imgHQ14179.npy
-imgHQ06838.npy
-imgHQ14337.npy
-imgHQ25288.npy
-imgHQ07945.npy
-imgHQ11030.npy
-imgHQ25713.npy
-imgHQ03093.npy
-imgHQ04039.npy
-imgHQ27899.npy
-imgHQ05279.npy
-imgHQ10351.npy
-imgHQ09670.npy
-imgHQ18068.npy
-imgHQ06858.npy
-imgHQ10373.npy
-imgHQ11257.npy
-imgHQ01290.npy
-imgHQ25813.npy
-imgHQ17558.npy
-imgHQ21953.npy
-imgHQ10322.npy
-imgHQ19436.npy
-imgHQ27423.npy
-imgHQ18750.npy
-imgHQ25768.npy
-imgHQ03637.npy
-imgHQ24309.npy
-imgHQ23622.npy
-imgHQ14676.npy
-imgHQ24877.npy
-imgHQ26339.npy
-imgHQ23634.npy
-imgHQ28445.npy
-imgHQ20882.npy
-imgHQ24341.npy
-imgHQ29093.npy
-imgHQ25150.npy
-imgHQ07320.npy
-imgHQ11706.npy
-imgHQ07397.npy
-imgHQ28110.npy
-imgHQ03674.npy
-imgHQ12669.npy
-imgHQ21301.npy
-imgHQ07940.npy
-imgHQ08487.npy
-imgHQ07980.npy
-imgHQ22103.npy
-imgHQ20210.npy
-imgHQ09844.npy
-imgHQ02277.npy
-imgHQ11950.npy
-imgHQ23462.npy
-imgHQ08794.npy
-imgHQ18103.npy
-imgHQ24515.npy
-imgHQ17733.npy
-imgHQ14083.npy
-imgHQ16605.npy
-imgHQ27141.npy
-imgHQ01627.npy
-imgHQ06570.npy
-imgHQ15476.npy
-imgHQ26207.npy
-imgHQ12176.npy
-imgHQ12407.npy
-imgHQ11396.npy
-imgHQ18598.npy
-imgHQ01114.npy
-imgHQ08190.npy
-imgHQ20498.npy
-imgHQ10267.npy
-imgHQ08142.npy
-imgHQ01871.npy
-imgHQ13928.npy
-imgHQ11349.npy
-imgHQ12892.npy
-imgHQ05637.npy
-imgHQ12306.npy
-imgHQ17039.npy
-imgHQ19102.npy
-imgHQ08751.npy
-imgHQ02341.npy
-imgHQ27877.npy
-imgHQ20103.npy
-imgHQ18046.npy
-imgHQ24748.npy
-imgHQ12784.npy
-imgHQ04721.npy
-imgHQ14986.npy
-imgHQ03170.npy
-imgHQ24531.npy
-imgHQ15939.npy
-imgHQ15058.npy
-imgHQ11334.npy
-imgHQ14842.npy
-imgHQ05831.npy
-imgHQ14043.npy
-imgHQ20388.npy
-imgHQ23580.npy
-imgHQ09234.npy
-imgHQ06072.npy
-imgHQ03656.npy
-imgHQ17457.npy
-imgHQ10081.npy
-imgHQ27688.npy
-imgHQ07055.npy
-imgHQ24159.npy
-imgHQ25541.npy
-imgHQ16037.npy
-imgHQ05668.npy
-imgHQ09834.npy
-imgHQ19364.npy
-imgHQ09733.npy
-imgHQ01922.npy
-imgHQ02552.npy
-imgHQ05429.npy
-imgHQ20536.npy
-imgHQ16636.npy
-imgHQ07820.npy
-imgHQ08498.npy
-imgHQ22361.npy
-imgHQ13677.npy
-imgHQ16664.npy
-imgHQ17083.npy
-imgHQ15813.npy
-imgHQ19228.npy
-imgHQ26121.npy
-imgHQ16445.npy
-imgHQ02755.npy
-imgHQ10148.npy
-imgHQ24015.npy
-imgHQ14232.npy
-imgHQ16357.npy
-imgHQ26131.npy
-imgHQ27721.npy
-imgHQ03775.npy
-imgHQ23631.npy
-imgHQ20762.npy
-imgHQ13961.npy
-imgHQ28132.npy
-imgHQ06921.npy
-imgHQ05322.npy
-imgHQ16585.npy
-imgHQ21062.npy
-imgHQ04941.npy
-imgHQ12576.npy
-imgHQ05031.npy
-imgHQ16465.npy
-imgHQ02487.npy
-imgHQ24263.npy
-imgHQ10337.npy
-imgHQ27466.npy
-imgHQ16998.npy
-imgHQ21224.npy
-imgHQ26358.npy
-imgHQ20015.npy
-imgHQ14469.npy
-imgHQ10708.npy
-imgHQ06928.npy
-imgHQ26576.npy
-imgHQ23167.npy
-imgHQ18948.npy
-imgHQ23857.npy
-imgHQ12362.npy
-imgHQ25873.npy
-imgHQ10753.npy
-imgHQ21676.npy
-imgHQ24181.npy
-imgHQ25020.npy
-imgHQ04825.npy
-imgHQ27044.npy
-imgHQ02217.npy
-imgHQ25614.npy
-imgHQ19632.npy
-imgHQ12871.npy
-imgHQ17283.npy
-imgHQ06656.npy
-imgHQ21107.npy
-imgHQ17736.npy
-imgHQ13139.npy
-imgHQ10907.npy
-imgHQ08866.npy
-imgHQ11295.npy
-imgHQ01394.npy
-imgHQ22549.npy
-imgHQ22309.npy
-imgHQ00935.npy
-imgHQ16762.npy
-imgHQ26617.npy
-imgHQ26774.npy
-imgHQ20525.npy
-imgHQ08416.npy
-imgHQ22685.npy
-imgHQ14509.npy
-imgHQ18002.npy
-imgHQ08309.npy
-imgHQ09174.npy
-imgHQ24610.npy
-imgHQ08283.npy
-imgHQ27780.npy
-imgHQ06837.npy
-imgHQ19810.npy
-imgHQ19641.npy
-imgHQ06705.npy
-imgHQ25476.npy
-imgHQ04751.npy
-imgHQ12768.npy
-imgHQ11433.npy
-imgHQ24470.npy
-imgHQ02827.npy
-imgHQ28272.npy
-imgHQ21562.npy
-imgHQ00243.npy
-imgHQ12415.npy
-imgHQ08981.npy
-imgHQ20012.npy
-imgHQ10034.npy
-imgHQ00635.npy
-imgHQ19027.npy
-imgHQ22499.npy
-imgHQ16588.npy
-imgHQ21685.npy
-imgHQ02791.npy
-imgHQ22594.npy
-imgHQ21737.npy
-imgHQ02329.npy
-imgHQ05099.npy
-imgHQ26391.npy
-imgHQ07050.npy
-imgHQ09651.npy
-imgHQ14670.npy
-imgHQ25360.npy
-imgHQ25570.npy
-imgHQ21421.npy
-imgHQ18954.npy
-imgHQ06501.npy
-imgHQ05356.npy
-imgHQ12656.npy
-imgHQ12035.npy
-imgHQ22273.npy
-imgHQ23992.npy
-imgHQ04575.npy
-imgHQ02969.npy
-imgHQ03952.npy
-imgHQ08936.npy
-imgHQ28806.npy
-imgHQ27324.npy
-imgHQ29970.npy
-imgHQ26155.npy
-imgHQ23208.npy
-imgHQ16940.npy
-imgHQ12954.npy
-imgHQ03985.npy
-imgHQ18354.npy
-imgHQ07005.npy
-imgHQ18626.npy
-imgHQ11409.npy
-imgHQ11992.npy
-imgHQ15438.npy
-imgHQ29751.npy
-imgHQ23802.npy
-imgHQ00596.npy
-imgHQ19048.npy
-imgHQ14576.npy
-imgHQ23546.npy
-imgHQ00679.npy
-imgHQ11788.npy
-imgHQ03164.npy
-imgHQ26794.npy
-imgHQ17376.npy
-imgHQ18587.npy
-imgHQ25906.npy
-imgHQ13910.npy
-imgHQ09944.npy
-imgHQ12104.npy
-imgHQ21892.npy
-imgHQ27905.npy
-imgHQ26484.npy
-imgHQ28036.npy
-imgHQ17782.npy
-imgHQ24149.npy
-imgHQ29564.npy
-imgHQ29700.npy
-imgHQ19150.npy
-imgHQ14773.npy
-imgHQ17476.npy
-imgHQ18628.npy
-imgHQ21332.npy
-imgHQ00342.npy
-imgHQ29813.npy
-imgHQ07001.npy
-imgHQ17631.npy
-imgHQ28754.npy
-imgHQ11737.npy
-imgHQ02062.npy
-imgHQ13939.npy
-imgHQ23660.npy
-imgHQ04193.npy
-imgHQ23413.npy
-imgHQ26183.npy
-imgHQ02592.npy
-imgHQ18906.npy
-imgHQ13159.npy
-imgHQ29657.npy
-imgHQ02304.npy
-imgHQ01439.npy
-imgHQ19569.npy
-imgHQ29863.npy
-imgHQ23978.npy
-imgHQ17828.npy
-imgHQ19896.npy
-imgHQ23204.npy
-imgHQ11197.npy
-imgHQ12069.npy
-imgHQ28587.npy
-imgHQ14077.npy
-imgHQ26982.npy
-imgHQ10906.npy
-imgHQ03273.npy
-imgHQ22458.npy
-imgHQ18173.npy
-imgHQ09870.npy
-imgHQ25098.npy
-imgHQ03596.npy
-imgHQ09621.npy
-imgHQ24593.npy
-imgHQ07194.npy
-imgHQ17285.npy
-imgHQ07811.npy
-imgHQ04777.npy
-imgHQ10205.npy
-imgHQ01112.npy
-imgHQ14764.npy
-imgHQ04963.npy
-imgHQ16847.npy
-imgHQ27859.npy
-imgHQ21097.npy
-imgHQ01028.npy
-imgHQ16238.npy
-imgHQ07931.npy
-imgHQ12539.npy
-imgHQ23226.npy
-imgHQ16507.npy
-imgHQ19762.npy
-imgHQ26098.npy
-imgHQ26822.npy
-imgHQ27036.npy
-imgHQ22467.npy
-imgHQ22318.npy
-imgHQ24814.npy
-imgHQ11909.npy
-imgHQ08187.npy
-imgHQ17001.npy
-imgHQ29723.npy
-imgHQ03666.npy
-imgHQ04174.npy
-imgHQ21506.npy
-imgHQ04628.npy
-imgHQ24063.npy
-imgHQ03001.npy
-imgHQ12600.npy
-imgHQ13305.npy
-imgHQ29443.npy
-imgHQ27443.npy
-imgHQ17691.npy
-imgHQ12097.npy
-imgHQ25126.npy
-imgHQ00757.npy
-imgHQ18555.npy
-imgHQ07978.npy
-imgHQ20039.npy
-imgHQ03768.npy
-imgHQ04957.npy
-imgHQ13792.npy
-imgHQ21967.npy
-imgHQ22590.npy
-imgHQ03142.npy
-imgHQ13031.npy
-imgHQ16299.npy
-imgHQ10143.npy
-imgHQ24613.npy
-imgHQ15508.npy
-imgHQ06130.npy
-imgHQ18653.npy
-imgHQ12449.npy
-imgHQ06320.npy
-imgHQ25696.npy
-imgHQ29504.npy
-imgHQ23853.npy
-imgHQ24991.npy
-imgHQ17014.npy
-imgHQ06203.npy
-imgHQ26124.npy
-imgHQ18010.npy
-imgHQ20903.npy
-imgHQ17864.npy
-imgHQ09711.npy
-imgHQ06054.npy
-imgHQ16770.npy
-imgHQ08952.npy
-imgHQ06465.npy
-imgHQ18443.npy
-imgHQ09117.npy
-imgHQ12205.npy
-imgHQ20183.npy
-imgHQ20839.npy
-imgHQ12875.npy
-imgHQ17716.npy
-imgHQ21645.npy
-imgHQ06335.npy
-imgHQ29885.npy
-imgHQ07912.npy
-imgHQ21246.npy
-imgHQ25879.npy
-imgHQ28209.npy
-imgHQ00246.npy
-imgHQ12885.npy
-imgHQ19787.npy
-imgHQ29117.npy
-imgHQ01190.npy
-imgHQ20375.npy
-imgHQ12496.npy
-imgHQ09504.npy
-imgHQ01744.npy
-imgHQ07228.npy
-imgHQ20146.npy
-imgHQ09770.npy
-imgHQ08654.npy
-imgHQ18768.npy
-imgHQ27650.npy
-imgHQ22880.npy
-imgHQ15670.npy
-imgHQ00660.npy
-imgHQ27414.npy
-imgHQ21196.npy
-imgHQ08094.npy
-imgHQ15009.npy
-imgHQ23479.npy
-imgHQ09474.npy
-imgHQ06037.npy
-imgHQ17059.npy
-imgHQ02441.npy
-imgHQ00007.npy
-imgHQ13658.npy
-imgHQ16338.npy
-imgHQ29201.npy
-imgHQ03755.npy
-imgHQ25853.npy
-imgHQ06307.npy
-imgHQ03608.npy
-imgHQ14108.npy
-imgHQ09040.npy
-imgHQ05572.npy
-imgHQ00315.npy
-imgHQ03441.npy
-imgHQ16272.npy
-imgHQ03491.npy
-imgHQ18084.npy
-imgHQ17374.npy
-imgHQ04307.npy
-imgHQ23319.npy
-imgHQ13418.npy
-imgHQ04828.npy
-imgHQ25996.npy
-imgHQ22270.npy
-imgHQ12703.npy
-imgHQ11818.npy
-imgHQ07469.npy
-imgHQ28766.npy
-imgHQ27203.npy
-imgHQ07734.npy
-imgHQ17938.npy
-imgHQ12089.npy
-imgHQ29941.npy
-imgHQ18979.npy
-imgHQ19763.npy
-imgHQ02896.npy
-imgHQ01622.npy
-imgHQ18007.npy
-imgHQ09476.npy
-imgHQ18494.npy
-imgHQ00080.npy
-imgHQ20685.npy
-imgHQ25043.npy
-imgHQ15948.npy
-imgHQ02894.npy
-imgHQ27693.npy
-imgHQ23525.npy
-imgHQ07521.npy
-imgHQ11921.npy
-imgHQ13436.npy
-imgHQ14809.npy
-imgHQ17531.npy
-imgHQ27705.npy
-imgHQ19286.npy
-imgHQ03216.npy
-imgHQ21174.npy
-imgHQ16684.npy
-imgHQ28689.npy
-imgHQ29884.npy
-imgHQ05778.npy
-imgHQ26011.npy
-imgHQ00139.npy
-imgHQ04930.npy
-imgHQ13125.npy
-imgHQ13369.npy
-imgHQ07667.npy
-imgHQ23572.npy
-imgHQ23205.npy
-imgHQ20037.npy
-imgHQ11069.npy
-imgHQ29180.npy
-imgHQ01249.npy
-imgHQ15875.npy
-imgHQ06352.npy
-imgHQ00930.npy
-imgHQ28670.npy
-imgHQ01764.npy
-imgHQ13510.npy
-imgHQ17215.npy
-imgHQ13034.npy
-imgHQ04268.npy
-imgHQ13489.npy
-imgHQ09012.npy
-imgHQ20083.npy
-imgHQ24287.npy
-imgHQ28032.npy
-imgHQ09116.npy
-imgHQ02239.npy
-imgHQ12589.npy
-imgHQ20288.npy
-imgHQ28602.npy
-imgHQ00089.npy
-imgHQ07437.npy
-imgHQ06883.npy
-imgHQ00564.npy
-imgHQ29047.npy
-imgHQ04576.npy
-imgHQ19854.npy
-imgHQ26172.npy
-imgHQ20479.npy
-imgHQ14475.npy
-imgHQ03990.npy
-imgHQ10117.npy
-imgHQ23783.npy
-imgHQ18313.npy
-imgHQ17088.npy
-imgHQ14758.npy
-imgHQ25461.npy
-imgHQ18682.npy
-imgHQ09811.npy
-imgHQ29575.npy
-imgHQ05130.npy
-imgHQ07448.npy
-imgHQ24677.npy
-imgHQ04232.npy
-imgHQ02855.npy
-imgHQ05070.npy
-imgHQ18660.npy
-imgHQ16533.npy
-imgHQ21607.npy
-imgHQ01589.npy
-imgHQ04728.npy
-imgHQ29346.npy
-imgHQ03615.npy
-imgHQ21148.npy
-imgHQ25469.npy
-imgHQ28955.npy
-imgHQ05464.npy
-imgHQ16341.npy
-imgHQ15393.npy
-imgHQ17307.npy
-imgHQ10665.npy
-imgHQ07496.npy
-imgHQ29160.npy
-imgHQ20859.npy
-imgHQ10119.npy
-imgHQ16811.npy
-imgHQ00238.npy
-imgHQ11666.npy
-imgHQ22357.npy
-imgHQ25137.npy
-imgHQ26630.npy
-imgHQ19340.npy
-imgHQ27929.npy
-imgHQ28505.npy
-imgHQ10090.npy
-imgHQ02968.npy
-imgHQ01592.npy
-imgHQ13244.npy
-imgHQ13848.npy
-imgHQ02422.npy
-imgHQ12275.npy
-imgHQ07449.npy
-imgHQ29423.npy
-imgHQ16472.npy
-imgHQ06457.npy
-imgHQ03481.npy
-imgHQ06449.npy
-imgHQ18466.npy
-imgHQ16056.npy
-imgHQ19618.npy
-imgHQ25490.npy
-imgHQ14411.npy
-imgHQ01554.npy
-imgHQ25190.npy
-imgHQ29191.npy
-imgHQ12699.npy
-imgHQ26711.npy
-imgHQ26095.npy
-imgHQ07639.npy
-imgHQ02306.npy
-imgHQ17578.npy
-imgHQ13009.npy
-imgHQ14888.npy
-imgHQ21420.npy
-imgHQ12942.npy
-imgHQ25475.npy
-imgHQ27246.npy
-imgHQ26882.npy
-imgHQ27315.npy
-imgHQ09301.npy
-imgHQ20134.npy
-imgHQ08292.npy
-imgHQ14477.npy
-imgHQ22987.npy
-imgHQ24837.npy
-imgHQ11434.npy
-imgHQ01705.npy
-imgHQ03898.npy
-imgHQ12638.npy
-imgHQ11866.npy
-imgHQ09956.npy
-imgHQ25751.npy
-imgHQ02253.npy
-imgHQ11243.npy
-imgHQ21106.npy
-imgHQ16478.npy
-imgHQ23376.npy
-imgHQ21447.npy
-imgHQ19073.npy
-imgHQ27135.npy
-imgHQ16828.npy
-imgHQ19089.npy
-imgHQ27806.npy
-imgHQ21401.npy
-imgHQ21809.npy
-imgHQ05738.npy
-imgHQ08651.npy
-imgHQ12243.npy
-imgHQ08993.npy
-imgHQ26444.npy
-imgHQ23352.npy
-imgHQ26413.npy
-imgHQ00503.npy
-imgHQ13283.npy
-imgHQ28466.npy
-imgHQ12635.npy
-imgHQ02266.npy
-imgHQ10691.npy
-imgHQ08391.npy
-imgHQ02617.npy
-imgHQ05882.npy
-imgHQ11323.npy
-imgHQ02113.npy
-imgHQ07853.npy
-imgHQ23918.npy
-imgHQ02090.npy
-imgHQ17996.npy
-imgHQ14820.npy
-imgHQ29298.npy
-imgHQ05352.npy
-imgHQ21166.npy
-imgHQ10232.npy
-imgHQ10800.npy
-imgHQ16359.npy
-imgHQ06040.npy
-imgHQ22077.npy
-imgHQ20964.npy
-imgHQ14503.npy
-imgHQ28531.npy
-imgHQ20750.npy
-imgHQ21436.npy
-imgHQ28378.npy
-imgHQ23452.npy
-imgHQ17076.npy
-imgHQ01628.npy
-imgHQ20945.npy
-imgHQ00440.npy
-imgHQ24604.npy
-imgHQ22064.npy
-imgHQ09860.npy
-imgHQ20602.npy
-imgHQ03361.npy
-imgHQ15004.npy
-imgHQ04787.npy
-imgHQ03613.npy
-imgHQ08733.npy
-imgHQ20992.npy
-imgHQ21600.npy
-imgHQ09372.npy
-imgHQ28359.npy
-imgHQ13620.npy
-imgHQ16323.npy
-imgHQ26580.npy
-imgHQ05192.npy
-imgHQ07555.npy
-imgHQ11070.npy
-imgHQ17963.npy
-imgHQ01856.npy
-imgHQ15703.npy
-imgHQ00151.npy
-imgHQ04360.npy
-imgHQ19697.npy
-imgHQ03794.npy
-imgHQ03321.npy
-imgHQ11170.npy
-imgHQ06506.npy
-imgHQ09225.npy
-imgHQ11649.npy
-imgHQ19047.npy
-imgHQ08956.npy
-imgHQ26624.npy
-imgHQ09692.npy
-imgHQ11209.npy
-imgHQ03534.npy
-imgHQ19451.npy
-imgHQ23053.npy
-imgHQ07763.npy
-imgHQ07526.npy
-imgHQ02237.npy
-imgHQ24950.npy
-imgHQ19379.npy
-imgHQ25641.npy
-imgHQ08011.npy
-imgHQ16243.npy
-imgHQ02779.npy
-imgHQ28706.npy
-imgHQ28739.npy
-imgHQ17550.npy
-imgHQ17217.npy
-imgHQ14392.npy
-imgHQ17847.npy
-imgHQ02746.npy
-imgHQ12654.npy
-imgHQ10984.npy
-imgHQ23541.npy
-imgHQ25216.npy
-imgHQ00518.npy
-imgHQ13498.npy
-imgHQ05304.npy
-imgHQ08693.npy
-imgHQ05132.npy
-imgHQ19613.npy
-imgHQ02220.npy
-imgHQ12325.npy
-imgHQ19253.npy
-imgHQ16523.npy
-imgHQ03245.npy
-imgHQ11986.npy
-imgHQ26618.npy
-imgHQ18500.npy
-imgHQ16910.npy
-imgHQ10655.npy
-imgHQ09596.npy
-imgHQ05929.npy
-imgHQ28776.npy
-imgHQ03625.npy
-imgHQ11823.npy
-imgHQ24884.npy
-imgHQ15694.npy
-imgHQ29850.npy
-imgHQ24823.npy
-imgHQ12456.npy
-imgHQ01118.npy
-imgHQ08650.npy
-imgHQ05568.npy
-imgHQ04156.npy
-imgHQ23021.npy
-imgHQ16257.npy
-imgHQ22313.npy
-imgHQ04443.npy
-imgHQ29720.npy
-imgHQ20345.npy
-imgHQ09941.npy
-imgHQ19164.npy
-imgHQ20382.npy
-imgHQ09714.npy
-imgHQ29128.npy
-imgHQ24541.npy
-imgHQ25438.npy
-imgHQ22593.npy
-imgHQ26426.npy
-imgHQ10942.npy
-imgHQ00849.npy
-imgHQ11605.npy
-imgHQ26596.npy
-imgHQ09310.npy
-imgHQ13617.npy
-imgHQ26319.npy
-imgHQ14832.npy
-imgHQ12419.npy
-imgHQ29461.npy
-imgHQ05601.npy
-imgHQ28498.npy
-imgHQ23438.npy
-imgHQ25655.npy
-imgHQ29604.npy
-imgHQ25432.npy
-imgHQ05922.npy
-imgHQ04028.npy
-imgHQ21923.npy
-imgHQ27237.npy
-imgHQ09316.npy
-imgHQ00316.npy
-imgHQ25799.npy
-imgHQ03623.npy
-imgHQ28633.npy
-imgHQ03286.npy
-imgHQ07252.npy
-imgHQ08964.npy
-imgHQ14922.npy
-imgHQ00488.npy
-imgHQ12301.npy
-imgHQ06554.npy
-imgHQ22083.npy
-imgHQ27713.npy
-imgHQ19657.npy
-imgHQ08507.npy
-imgHQ22490.npy
-imgHQ22479.npy
-imgHQ23473.npy
-imgHQ00353.npy
-imgHQ18459.npy
-imgHQ14994.npy
-imgHQ29812.npy
-imgHQ26861.npy
-imgHQ06310.npy
-imgHQ05497.npy
-imgHQ02323.npy
-imgHQ07048.npy
-imgHQ18381.npy
-imgHQ00474.npy
-imgHQ03037.npy
-imgHQ07825.npy
-imgHQ10169.npy
-imgHQ12572.npy
-imgHQ23659.npy
-imgHQ19731.npy
-imgHQ17565.npy
-imgHQ16615.npy
-imgHQ05370.npy
-imgHQ12280.npy
-imgHQ16633.npy
-imgHQ01563.npy
-imgHQ18066.npy
-imgHQ08924.npy
-imgHQ24864.npy
-imgHQ23484.npy
-imgHQ18050.npy
-imgHQ00263.npy
-imgHQ11379.npy
-imgHQ05462.npy
-imgHQ24397.npy
-imgHQ12447.npy
-imgHQ18382.npy
-imgHQ22124.npy
-imgHQ09701.npy
-imgHQ01133.npy
-imgHQ10360.npy
-imgHQ27343.npy
-imgHQ10958.npy
-imgHQ25756.npy
-imgHQ10806.npy
-imgHQ00312.npy
-imgHQ29625.npy
-imgHQ04617.npy
-imgHQ29557.npy
-imgHQ12396.npy
-imgHQ05373.npy
-imgHQ22629.npy
-imgHQ00764.npy
-imgHQ13261.npy
-imgHQ09272.npy
-imgHQ26594.npy
-imgHQ22710.npy
-imgHQ20013.npy
-imgHQ18206.npy
-imgHQ00439.npy
-imgHQ01977.npy
-imgHQ00116.npy
-imgHQ16010.npy
-imgHQ27564.npy
-imgHQ24906.npy
-imgHQ20095.npy
-imgHQ13751.npy
-imgHQ09924.npy
-imgHQ26892.npy
-imgHQ10179.npy
-imgHQ25067.npy
-imgHQ23160.npy
-imgHQ28203.npy
-imgHQ13036.npy
-imgHQ09260.npy
-imgHQ07634.npy
-imgHQ02389.npy
-imgHQ02094.npy
-imgHQ23069.npy
-imgHQ01197.npy
-imgHQ23161.npy
-imgHQ12423.npy
-imgHQ03317.npy
-imgHQ03389.npy
-imgHQ25854.npy
-imgHQ10867.npy
-imgHQ29066.npy
-imgHQ03420.npy
-imgHQ14653.npy
-imgHQ16140.npy
-imgHQ22826.npy
-imgHQ07379.npy
-imgHQ14633.npy
-imgHQ21705.npy
-imgHQ05313.npy
-imgHQ19502.npy
-imgHQ12346.npy
-imgHQ15357.npy
-imgHQ16312.npy
-imgHQ10636.npy
-imgHQ23963.npy
-imgHQ08887.npy
-imgHQ05979.npy
-imgHQ28299.npy
-imgHQ04971.npy
-imgHQ07286.npy
-imgHQ21954.npy
-imgHQ08407.npy
-imgHQ11545.npy
-imgHQ23742.npy
-imgHQ21578.npy
-imgHQ29889.npy
-imgHQ08228.npy
-imgHQ17185.npy
-imgHQ03957.npy
-imgHQ08238.npy
-imgHQ20340.npy
-imgHQ09622.npy
-imgHQ17281.npy
-imgHQ06950.npy
-imgHQ16362.npy
-imgHQ02992.npy
-imgHQ27848.npy
-imgHQ17883.npy
-imgHQ27196.npy
-imgHQ00575.npy
-imgHQ20897.npy
-imgHQ23437.npy
-imgHQ20774.npy
-imgHQ26337.npy
-imgHQ17341.npy
-imgHQ18171.npy
-imgHQ21389.npy
-imgHQ16138.npy
-imgHQ29979.npy
-imgHQ03726.npy
-imgHQ10772.npy
-imgHQ29646.npy
-imgHQ25264.npy
-imgHQ25992.npy
-imgHQ23073.npy
-imgHQ06226.npy
-imgHQ27627.npy
-imgHQ10699.npy
-imgHQ08307.npy
-imgHQ11750.npy
-imgHQ24943.npy
-imgHQ06312.npy
-imgHQ08913.npy
-imgHQ04980.npy
-imgHQ09252.npy
-imgHQ07484.npy
-imgHQ29725.npy
-imgHQ01979.npy
-imgHQ01682.npy
-imgHQ26945.npy
-imgHQ20786.npy
-imgHQ16822.npy
-imgHQ09655.npy
-imgHQ08916.npy
-imgHQ06238.npy
-imgHQ21726.npy
-imgHQ20489.npy
-imgHQ09965.npy
-imgHQ19406.npy
-imgHQ13291.npy
-imgHQ13298.npy
-imgHQ29303.npy
-imgHQ01034.npy
-imgHQ28775.npy
-imgHQ13385.npy
-imgHQ06892.npy
-imgHQ01461.npy
-imgHQ05473.npy
-imgHQ12523.npy
-imgHQ06852.npy
-imgHQ01723.npy
-imgHQ23890.npy
-imgHQ20270.npy
-imgHQ12574.npy
-imgHQ24560.npy
-imgHQ14579.npy
-imgHQ05338.npy
-imgHQ00716.npy
-imgHQ16127.npy
-imgHQ07810.npy
-imgHQ27724.npy
-imgHQ29053.npy
-imgHQ06115.npy
-imgHQ05697.npy
-imgHQ16903.npy
-imgHQ13744.npy
-imgHQ25493.npy
-imgHQ29304.npy
-imgHQ13516.npy
-imgHQ26287.npy
-imgHQ12440.npy
-imgHQ20011.npy
-imgHQ22291.npy
-imgHQ12564.npy
-imgHQ20820.npy
-imgHQ06789.npy
-imgHQ23575.npy
-imgHQ25690.npy
-imgHQ03860.npy
-imgHQ23138.npy
-imgHQ18527.npy
-imgHQ22107.npy
-imgHQ12883.npy
-imgHQ07149.npy
-imgHQ11216.npy
-imgHQ04501.npy
-imgHQ13813.npy
-imgHQ27247.npy
-imgHQ23299.npy
-imgHQ21643.npy
-imgHQ12345.npy
-imgHQ14280.npy
-imgHQ04992.npy
-imgHQ15817.npy
-imgHQ13600.npy
-imgHQ02757.npy
-imgHQ24510.npy
-imgHQ10458.npy
-imgHQ23100.npy
-imgHQ06013.npy
-imgHQ17757.npy
-imgHQ09975.npy
-imgHQ00173.npy
-imgHQ21281.npy
-imgHQ21459.npy
-imgHQ28634.npy
-imgHQ01159.npy
-imgHQ23804.npy
-imgHQ02977.npy
-imgHQ00282.npy
-imgHQ08051.npy
-imgHQ00568.npy
-imgHQ14919.npy
-imgHQ06934.npy
-imgHQ13082.npy
-imgHQ05656.npy
-imgHQ03161.npy
-imgHQ10194.npy
-imgHQ06805.npy
-imgHQ24769.npy
-imgHQ23322.npy
-imgHQ10147.npy
-imgHQ07407.npy
-imgHQ02650.npy
-imgHQ09725.npy
-imgHQ10085.npy
-imgHQ13230.npy
-imgHQ28707.npy
-imgHQ23870.npy
-imgHQ15787.npy
-imgHQ00993.npy
-imgHQ19375.npy
-imgHQ13150.npy
-imgHQ06201.npy
-imgHQ22074.npy
-imgHQ06232.npy
-imgHQ01142.npy
-imgHQ08356.npy
-imgHQ15996.npy
-imgHQ05412.npy
-imgHQ27424.npy
-imgHQ29404.npy
-imgHQ11717.npy
-imgHQ24200.npy
-imgHQ03944.npy
-imgHQ23315.npy
-imgHQ18783.npy
-imgHQ01582.npy
-imgHQ26466.npy
-imgHQ24847.npy
-imgHQ21414.npy
-imgHQ10252.npy
-imgHQ04321.npy
-imgHQ29994.npy
-imgHQ11745.npy
-imgHQ29031.npy
-imgHQ04238.npy
-imgHQ10003.npy
-imgHQ24320.npy
-imgHQ22626.npy
-imgHQ17561.npy
-imgHQ22316.npy
-imgHQ13147.npy
-imgHQ10329.npy
-imgHQ21985.npy
-imgHQ25661.npy
-imgHQ19361.npy
-imgHQ26461.npy
-imgHQ24089.npy
-imgHQ15423.npy
-imgHQ22796.npy
-imgHQ26290.npy
-imgHQ20871.npy
-imgHQ22945.npy
-imgHQ02178.npy
-imgHQ07170.npy
-imgHQ23671.npy
-imgHQ25951.npy
-imgHQ25683.npy
-imgHQ13774.npy
-imgHQ16540.npy
-imgHQ15339.npy
-imgHQ22622.npy
-imgHQ29537.npy
-imgHQ11272.npy
-imgHQ09894.npy
-imgHQ12758.npy
-imgHQ17056.npy
-imgHQ09799.npy
-imgHQ05660.npy
-imgHQ01489.npy
-imgHQ24558.npy
-imgHQ08445.npy
-imgHQ06180.npy
-imgHQ08392.npy
-imgHQ24958.npy
-imgHQ10992.npy
-imgHQ29602.npy
-imgHQ14339.npy
-imgHQ10813.npy
-imgHQ29613.npy
-imgHQ18540.npy
-imgHQ19044.npy
-imgHQ09211.npy
-imgHQ20908.npy
-imgHQ03138.npy
-imgHQ21076.npy
-imgHQ00033.npy
-imgHQ13076.npy
-imgHQ08824.npy
-imgHQ19415.npy
-imgHQ06500.npy
-imgHQ00202.npy
-imgHQ00647.npy
-imgHQ01982.npy
-imgHQ12653.npy
-imgHQ10102.npy
-imgHQ06049.npy
-imgHQ15260.npy
-imgHQ07345.npy
-imgHQ13148.npy
-imgHQ28759.npy
-imgHQ06015.npy
-imgHQ02586.npy
-imgHQ22208.npy
-imgHQ23579.npy
-imgHQ12853.npy
-imgHQ22037.npy
-imgHQ06402.npy
-imgHQ28832.npy
-imgHQ25052.npy
-imgHQ23544.npy
-imgHQ02521.npy
-imgHQ01358.npy
-imgHQ21182.npy
-imgHQ24146.npy
-imgHQ03626.npy
-imgHQ14420.npy
-imgHQ16286.npy
-imgHQ05910.npy
-imgHQ14980.npy
-imgHQ04318.npy
-imgHQ23146.npy
-imgHQ19556.npy
-imgHQ02595.npy
-imgHQ13837.npy
-imgHQ03354.npy
-imgHQ27849.npy
-imgHQ29265.npy
-imgHQ01152.npy
-imgHQ02848.npy
-imgHQ21513.npy
-imgHQ23271.npy
-imgHQ09070.npy
-imgHQ12747.npy
-imgHQ23956.npy
-imgHQ01694.npy
-imgHQ22299.npy
-imgHQ21243.npy
-imgHQ11390.npy
-imgHQ23821.npy
-imgHQ28041.npy
-imgHQ02206.npy
-imgHQ15218.npy
-imgHQ12146.npy
-imgHQ08732.npy
-imgHQ12939.npy
-imgHQ00972.npy
-imgHQ24074.npy
-imgHQ29636.npy
-imgHQ22430.npy
-imgHQ21970.npy
-imgHQ28952.npy
-imgHQ23175.npy
-imgHQ00119.npy
-imgHQ09418.npy
-imgHQ20682.npy
-imgHQ07431.npy
-imgHQ29705.npy
-imgHQ20643.npy
-imgHQ04879.npy
-imgHQ16024.npy
-imgHQ04962.npy
-imgHQ13824.npy
-imgHQ00971.npy
-imgHQ26298.npy
-imgHQ05084.npy
-imgHQ27581.npy
-imgHQ13767.npy
-imgHQ00917.npy
-imgHQ09511.npy
-imgHQ18414.npy
-imgHQ05675.npy
-imgHQ23805.npy
-imgHQ21198.npy
-imgHQ23042.npy
-imgHQ05259.npy
-imgHQ15602.npy
-imgHQ08204.npy
-imgHQ13775.npy
-imgHQ06489.npy
-imgHQ11318.npy
-imgHQ03066.npy
-imgHQ00825.npy
-imgHQ17819.npy
-imgHQ14213.npy
-imgHQ09567.npy
-imgHQ08284.npy
-imgHQ09433.npy
-imgHQ20967.npy
-imgHQ06820.npy
-imgHQ06597.npy
-imgHQ00165.npy
-imgHQ22832.npy
-imgHQ13455.npy
-imgHQ06698.npy
-imgHQ09546.npy
-imgHQ27956.npy
-imgHQ00458.npy
-imgHQ23400.npy
-imgHQ29898.npy
-imgHQ10341.npy
-imgHQ24832.npy
-imgHQ05985.npy
-imgHQ01650.npy
-imgHQ03442.npy
-imgHQ29409.npy
-imgHQ13262.npy
-imgHQ04762.npy
-imgHQ06690.npy
-imgHQ13414.npy
-imgHQ19638.npy
-imgHQ14239.npy
-imgHQ21504.npy
-imgHQ03115.npy
-imgHQ27463.npy
-imgHQ18988.npy
-imgHQ09201.npy
-imgHQ28422.npy
-imgHQ07297.npy
-imgHQ05400.npy
-imgHQ00563.npy
-imgHQ07957.npy
-imgHQ00774.npy
-imgHQ28240.npy
-imgHQ05346.npy
-imgHQ02683.npy
-imgHQ00504.npy
-imgHQ18701.npy
-imgHQ17446.npy
-imgHQ02203.npy
-imgHQ14306.npy
-imgHQ13214.npy
-imgHQ14471.npy
-imgHQ02644.npy
-imgHQ00715.npy
-imgHQ05285.npy
-imgHQ23724.npy
-imgHQ09164.npy
-imgHQ04176.npy
-imgHQ13823.npy
-imgHQ07589.npy
-imgHQ06065.npy
-imgHQ02948.npy
-imgHQ25761.npy
-imgHQ20101.npy
-imgHQ18884.npy
-imgHQ11800.npy
-imgHQ10239.npy
-imgHQ20283.npy
-imgHQ06165.npy
-imgHQ26935.npy
-imgHQ21341.npy
-imgHQ01567.npy
-imgHQ13111.npy
-imgHQ28388.npy
-imgHQ13582.npy
-imgHQ07661.npy
-imgHQ02228.npy
-imgHQ08549.npy
-imgHQ14638.npy
-imgHQ28066.npy
-imgHQ06157.npy
-imgHQ22982.npy
-imgHQ09839.npy
-imgHQ21960.npy
-imgHQ03256.npy
-imgHQ12384.npy
-imgHQ14231.npy
-imgHQ09349.npy
-imgHQ29175.npy
-imgHQ20642.npy
-imgHQ17997.npy
-imgHQ00044.npy
-imgHQ00370.npy
-imgHQ06534.npy
-imgHQ13502.npy
-imgHQ26919.npy
-imgHQ10965.npy
-imgHQ02964.npy
-imgHQ18723.npy
-imgHQ00669.npy
-imgHQ08579.npy
-imgHQ05311.npy
-imgHQ23475.npy
-imgHQ29334.npy
-imgHQ27312.npy
-imgHQ16524.npy
-imgHQ09412.npy
-imgHQ01103.npy
-imgHQ00903.npy
-imgHQ17408.npy
-imgHQ08256.npy
-imgHQ15702.npy
-imgHQ07518.npy
-imgHQ02428.npy
-imgHQ15938.npy
-imgHQ20948.npy
-imgHQ03437.npy
-imgHQ21471.npy
-imgHQ07412.npy
-imgHQ22910.npy
-imgHQ23972.npy
-imgHQ13474.npy
-imgHQ22508.npy
-imgHQ04182.npy
-imgHQ28714.npy
-imgHQ03467.npy
-imgHQ05558.npy
-imgHQ22415.npy
-imgHQ06349.npy
-imgHQ10250.npy
-imgHQ16275.npy
-imgHQ06640.npy
-imgHQ15818.npy
-imgHQ14723.npy
-imgHQ12575.npy
-imgHQ09693.npy
-imgHQ19858.npy
-imgHQ22041.npy
-imgHQ16843.npy
-imgHQ22789.npy
-imgHQ02686.npy
-imgHQ12009.npy
-imgHQ27712.npy
-imgHQ24561.npy
-imgHQ19129.npy
-imgHQ08441.npy
-imgHQ13140.npy
-imgHQ02106.npy
-imgHQ13144.npy
-imgHQ26895.npy
-imgHQ16757.npy
-imgHQ02230.npy
-imgHQ23306.npy
-imgHQ04479.npy
-imgHQ16114.npy
-imgHQ11134.npy
-imgHQ15926.npy
-imgHQ25973.npy
-imgHQ26957.npy
-imgHQ17761.npy
-imgHQ16531.npy
-imgHQ25421.npy
-imgHQ27063.npy
-imgHQ04131.npy
-imgHQ19162.npy
-imgHQ22008.npy
-imgHQ22753.npy
-imgHQ17095.npy
-imgHQ13220.npy
-imgHQ05452.npy
-imgHQ23670.npy
-imgHQ29661.npy
-imgHQ13691.npy
-imgHQ26761.npy
-imgHQ00997.npy
-imgHQ24164.npy
-imgHQ03933.npy
-imgHQ23135.npy
-imgHQ05230.npy
-imgHQ09486.npy
-imgHQ21171.npy
-imgHQ03862.npy
-imgHQ13962.npy
-imgHQ16103.npy
-imgHQ21958.npy
-imgHQ19313.npy
-imgHQ01162.npy
-imgHQ24825.npy
-imgHQ12389.npy
-imgHQ13353.npy
-imgHQ28430.npy
-imgHQ06922.npy
-imgHQ19050.npy
-imgHQ06854.npy
-imgHQ03806.npy
-imgHQ04081.npy
-imgHQ19347.npy
-imgHQ04425.npy
-imgHQ27928.npy
-imgHQ23842.npy
-imgHQ06016.npy
-imgHQ22749.npy
-imgHQ11908.npy
-imgHQ09229.npy
-imgHQ05542.npy
-imgHQ26109.npy
-imgHQ10187.npy
-imgHQ07078.npy
-imgHQ27889.npy
-imgHQ22717.npy
-imgHQ06616.npy
-imgHQ13407.npy
-imgHQ28261.npy
-imgHQ10328.npy
-imgHQ14496.npy
-imgHQ18633.npy
-imgHQ13618.npy
-imgHQ09029.npy
-imgHQ25958.npy
-imgHQ15933.npy
-imgHQ11096.npy
-imgHQ13210.npy
-imgHQ15407.npy
-imgHQ08633.npy
-imgHQ11253.npy
-imgHQ13561.npy
-imgHQ27069.npy
-imgHQ04384.npy
-imgHQ15785.npy
-imgHQ01008.npy
-imgHQ22722.npy
-imgHQ09918.npy
-imgHQ08490.npy
-imgHQ12639.npy
-imgHQ19561.npy
-imgHQ12029.npy
-imgHQ12385.npy
-imgHQ18875.npy
-imgHQ08111.npy
-imgHQ27002.npy
-imgHQ06925.npy
-imgHQ03088.npy
-imgHQ21210.npy
-imgHQ08128.npy
-imgHQ12525.npy
-imgHQ11398.npy
-imgHQ21801.npy
-imgHQ02693.npy
-imgHQ03160.npy
-imgHQ13312.npy
-imgHQ13971.npy
-imgHQ00708.npy
-imgHQ01539.npy
-imgHQ06653.npy
-imgHQ00473.npy
-imgHQ03237.npy
-imgHQ00136.npy
-imgHQ12106.npy
-imgHQ17204.npy
-imgHQ00336.npy
-imgHQ17259.npy
-imgHQ26793.npy
-imgHQ27085.npy
-imgHQ08134.npy
-imgHQ04796.npy
-imgHQ21557.npy
-imgHQ04813.npy
-imgHQ01986.npy
-imgHQ06152.npy
-imgHQ05215.npy
-imgHQ15290.npy
-imgHQ01011.npy
-imgHQ07894.npy
-imgHQ24528.npy
-imgHQ24554.npy
-imgHQ22681.npy
-imgHQ01349.npy
-imgHQ07840.npy
-imgHQ11200.npy
-imgHQ24798.npy
-imgHQ08396.npy
-imgHQ02182.npy
-imgHQ05076.npy
-imgHQ02152.npy
-imgHQ14194.npy
-imgHQ09034.npy
-imgHQ06273.npy
-imgHQ17647.npy
-imgHQ29489.npy
-imgHQ14308.npy
-imgHQ10934.npy
-imgHQ08692.npy
-imgHQ05419.npy
-imgHQ13496.npy
-imgHQ26322.npy
-imgHQ23877.npy
-imgHQ02371.npy
-imgHQ07906.npy
-imgHQ21782.npy
-imgHQ08264.npy
-imgHQ15910.npy
-imgHQ04783.npy
-imgHQ20326.npy
-imgHQ11346.npy
-imgHQ21873.npy
-imgHQ21753.npy
-imgHQ25048.npy
-imgHQ00100.npy
-imgHQ26959.npy
-imgHQ12242.npy
-imgHQ07941.npy
-imgHQ23577.npy
-imgHQ28244.npy
-imgHQ08246.npy
-imgHQ29707.npy
-imgHQ01581.npy
-imgHQ22491.npy
-imgHQ15133.npy
-imgHQ29354.npy
-imgHQ09331.npy
-imgHQ15273.npy
-imgHQ23962.npy
-imgHQ21176.npy
-imgHQ08124.npy
-imgHQ14404.npy
-imgHQ14265.npy
-imgHQ26656.npy
-imgHQ16287.npy
-imgHQ17465.npy
-imgHQ28544.npy
-imgHQ28395.npy
-imgHQ18386.npy
-imgHQ27234.npy
-imgHQ04753.npy
-imgHQ21623.npy
-imgHQ24897.npy
-imgHQ26005.npy
-imgHQ12291.npy
-imgHQ24213.npy
-imgHQ26359.npy
-imgHQ08835.npy
-imgHQ07719.npy
-imgHQ26893.npy
-imgHQ00073.npy
-imgHQ26655.npy
-imgHQ07666.npy
-imgHQ08038.npy
-imgHQ16576.npy
-imgHQ07334.npy
-imgHQ19233.npy
-imgHQ08691.npy
-imgHQ23551.npy
-imgHQ23955.npy
-imgHQ02625.npy
-imgHQ20593.npy
-imgHQ05577.npy
-imgHQ16430.npy
-imgHQ22403.npy
-imgHQ15816.npy
-imgHQ14540.npy
-imgHQ16337.npy
-imgHQ24333.npy
-imgHQ07380.npy
-imgHQ29004.npy
-imgHQ14249.npy
-imgHQ05186.npy
-imgHQ18907.npy
-imgHQ08515.npy
-imgHQ25627.npy
-imgHQ25721.npy
-imgHQ02450.npy
-imgHQ24978.npy
-imgHQ27586.npy
-imgHQ19872.npy
-imgHQ15341.npy
-imgHQ09364.npy
-imgHQ11205.npy
-imgHQ17280.npy
-imgHQ00909.npy
-imgHQ03574.npy
-imgHQ13164.npy
-imgHQ16676.npy
-imgHQ25202.npy
-imgHQ09262.npy
-imgHQ20957.npy
-imgHQ09682.npy
-imgHQ18444.npy
-imgHQ05574.npy
-imgHQ07722.npy
-imgHQ04271.npy
-imgHQ00933.npy
-imgHQ29051.npy
-imgHQ21329.npy
-imgHQ01708.npy
-imgHQ13745.npy
-imgHQ18095.npy
-imgHQ01306.npy
-imgHQ08505.npy
-imgHQ26800.npy
-imgHQ14070.npy
-imgHQ15648.npy
-imgHQ26480.npy
-imgHQ28984.npy
-imgHQ16501.npy
-imgHQ21712.npy
-imgHQ12376.npy
-imgHQ24452.npy
-imgHQ04272.npy
-imgHQ11055.npy
-imgHQ16733.npy
-imgHQ19528.npy
-imgHQ03184.npy
-imgHQ29806.npy
-imgHQ15919.npy
-imgHQ07593.npy
-imgHQ24339.npy
-imgHQ24344.npy
-imgHQ01893.npy
-imgHQ27686.npy
-imgHQ24779.npy
-imgHQ16618.npy
-imgHQ16527.npy
-imgHQ01010.npy
-imgHQ27979.npy
-imgHQ23040.npy
-imgHQ13299.npy
-imgHQ24042.npy
-imgHQ20580.npy
-imgHQ19868.npy
-imgHQ04719.npy
-imgHQ24247.npy
-imgHQ14399.npy
-imgHQ06284.npy
-imgHQ17622.npy
-imgHQ10398.npy
-imgHQ17278.npy
-imgHQ10502.npy
-imgHQ25252.npy
-imgHQ14802.npy
-imgHQ24927.npy
-imgHQ07415.npy
-imgHQ21845.npy
-imgHQ01343.npy
-imgHQ21266.npy
-imgHQ03279.npy
-imgHQ27936.npy
-imgHQ10485.npy
-imgHQ26737.npy
-imgHQ26265.npy
-imgHQ15400.npy
-imgHQ20396.npy
-imgHQ02188.npy
-imgHQ15554.npy
-imgHQ21397.npy
-imgHQ09326.npy
-imgHQ29007.npy
-imgHQ13511.npy
-imgHQ27207.npy
-imgHQ12830.npy
-imgHQ16798.npy
-imgHQ25504.npy
-imgHQ16744.npy
-imgHQ19391.npy
-imgHQ00438.npy
-imgHQ26869.npy
-imgHQ15420.npy
-imgHQ21516.npy
-imgHQ08621.npy
-imgHQ16363.npy
-imgHQ03039.npy
-imgHQ14482.npy
-imgHQ08042.npy
-imgHQ01293.npy
-imgHQ23538.npy
-imgHQ18654.npy
-imgHQ23800.npy
-imgHQ04430.npy
-imgHQ20475.npy
-imgHQ14001.npy
-imgHQ25247.npy
-imgHQ12877.npy
-imgHQ25489.npy
-imgHQ23941.npy
-imgHQ12893.npy
-imgHQ12375.npy
-imgHQ23212.npy
-imgHQ14737.npy
-imgHQ02707.npy
-imgHQ13218.npy
-imgHQ24658.npy
-imgHQ00199.npy
-imgHQ00345.npy
-imgHQ00946.npy
-imgHQ14698.npy
-imgHQ01402.npy
-imgHQ01428.npy
-imgHQ02628.npy
-imgHQ17269.npy
-imgHQ23176.npy
-imgHQ28583.npy
-imgHQ04667.npy
-imgHQ13724.npy
-imgHQ15854.npy
-imgHQ11177.npy
-imgHQ18029.npy
-imgHQ08144.npy
-imgHQ27317.npy
-imgHQ11195.npy
-imgHQ21292.npy
-imgHQ20131.npy
-imgHQ07319.npy
-imgHQ17894.npy
-imgHQ15752.npy
-imgHQ00604.npy
-imgHQ28457.npy
-imgHQ18118.npy
-imgHQ09863.npy
-imgHQ21194.npy
-imgHQ11930.npy
-imgHQ27704.npy
-imgHQ24680.npy
-imgHQ28536.npy
-imgHQ24025.npy
-imgHQ22067.npy
-imgHQ23415.npy
-imgHQ17930.npy
-imgHQ22391.npy
-imgHQ07451.npy
-imgHQ00649.npy
-imgHQ18005.npy
-imgHQ00114.npy
-imgHQ18097.npy
-imgHQ03553.npy
-imgHQ07643.npy
-imgHQ17753.npy
-imgHQ25337.npy
-imgHQ02198.npy
-imgHQ07803.npy
-imgHQ07358.npy
-imgHQ24229.npy
-imgHQ06930.npy
-imgHQ11270.npy
-imgHQ22483.npy
-imgHQ14294.npy
-imgHQ01294.npy
-imgHQ28736.npy
-imgHQ03641.npy
-imgHQ13090.npy
-imgHQ02857.npy
-imgHQ23846.npy
-imgHQ14378.npy
-imgHQ11598.npy
-imgHQ11803.npy
-imgHQ05900.npy
-imgHQ16318.npy
-imgHQ13952.npy
-imgHQ02702.npy
-imgHQ00690.npy
-imgHQ21687.npy
-imgHQ08858.npy
-imgHQ06410.npy
-imgHQ23550.npy
-imgHQ12712.npy
-imgHQ22432.npy
-imgHQ23784.npy
-imgHQ13532.npy
-imgHQ05306.npy
-imgHQ08063.npy
-imgHQ20760.npy
-imgHQ24705.npy
-imgHQ20124.npy
-imgHQ09351.npy
-imgHQ11638.npy
-imgHQ11931.npy
-imgHQ08432.npy
-imgHQ01963.npy
-imgHQ24597.npy
-imgHQ00383.npy
-imgHQ19564.npy
-imgHQ08087.npy
-imgHQ29695.npy
-imgHQ13628.npy
-imgHQ09735.npy
-imgHQ08214.npy
-imgHQ29672.npy
-imgHQ17329.npy
-imgHQ14013.npy
-imgHQ27106.npy
-imgHQ28201.npy
-imgHQ03519.npy
-imgHQ09741.npy
-imgHQ07020.npy
-imgHQ17911.npy
-imgHQ13311.npy
-imgHQ28920.npy
-imgHQ21903.npy
-imgHQ20995.npy
-imgHQ18454.npy
-imgHQ10979.npy
-imgHQ24572.npy
-imgHQ00981.npy
-imgHQ15558.npy
-imgHQ24405.npy
-imgHQ25257.npy
-imgHQ29904.npy
-imgHQ02660.npy
-imgHQ11507.npy
-imgHQ17923.npy
-imgHQ04717.npy
-imgHQ13841.npy
-imgHQ22563.npy
-imgHQ26176.npy
-imgHQ05640.npy
-imgHQ28728.npy
-imgHQ20224.npy
-imgHQ18244.npy
-imgHQ03927.npy
-imgHQ10778.npy
-imgHQ03054.npy
-imgHQ09110.npy
-imgHQ08707.npy
-imgHQ25245.npy
-imgHQ01206.npy
-imgHQ29052.npy
-imgHQ12614.npy
-imgHQ13987.npy
-imgHQ26258.npy
-imgHQ25526.npy
-imgHQ00923.npy
-imgHQ14067.npy
-imgHQ15786.npy
-imgHQ14688.npy
-imgHQ02556.npy
-imgHQ13501.npy
-imgHQ13012.npy
-imgHQ17141.npy
-imgHQ19917.npy
-imgHQ21147.npy
-imgHQ25788.npy
-imgHQ11700.npy
-imgHQ17796.npy
-imgHQ06966.npy
-imgHQ23960.npy
-imgHQ16042.npy
-imgHQ26019.npy
-imgHQ12171.npy
-imgHQ09739.npy
-imgHQ12238.npy
-imgHQ27172.npy
-imgHQ13833.npy
-imgHQ29168.npy
-imgHQ21045.npy
-imgHQ19946.npy
-imgHQ16868.npy
-imgHQ11662.npy
-imgHQ27771.npy
-imgHQ06462.npy
-imgHQ05588.npy
-imgHQ01569.npy
-imgHQ05318.npy
-imgHQ09807.npy
-imgHQ05016.npy
-imgHQ13602.npy
-imgHQ06727.npy
-imgHQ22289.npy
-imgHQ19557.npy
-imgHQ13123.npy
-imgHQ14899.npy
-imgHQ01224.npy
-imgHQ16117.npy
-imgHQ19782.npy
-imgHQ05596.npy
-imgHQ25559.npy
-imgHQ19084.npy
-imgHQ20445.npy
-imgHQ13777.npy
-imgHQ04196.npy
-imgHQ04572.npy
-imgHQ20937.npy
-imgHQ08563.npy
-imgHQ23945.npy
-imgHQ18432.npy
-imgHQ27793.npy
-imgHQ24302.npy
-imgHQ15340.npy
-imgHQ12602.npy
-imgHQ20225.npy
-imgHQ20504.npy
-imgHQ09755.npy
-imgHQ09720.npy
-imgHQ00373.npy
-imgHQ09121.npy
-imgHQ19927.npy
-imgHQ02108.npy
-imgHQ09067.npy
-imgHQ03704.npy
-imgHQ27268.npy
-imgHQ12188.npy
-imgHQ09522.npy
-imgHQ01335.npy
-imgHQ06288.npy
-imgHQ24221.npy
-imgHQ10781.npy
-imgHQ00799.npy
-imgHQ09155.npy
-imgHQ29405.npy
-imgHQ12348.npy
-imgHQ21367.npy
-imgHQ06098.npy
-imgHQ04123.npy
-imgHQ18344.npy
-imgHQ08731.npy
-imgHQ01676.npy
-imgHQ06585.npy
-imgHQ14838.npy
-imgHQ27570.npy
-imgHQ28423.npy
-imgHQ12053.npy
-imgHQ25304.npy
-imgHQ25867.npy
-imgHQ07711.npy
-imgHQ05803.npy
-imgHQ26129.npy
-imgHQ15074.npy
-imgHQ17223.npy
-imgHQ29130.npy
-imgHQ15849.npy
-imgHQ07577.npy
-imgHQ16254.npy
-imgHQ04001.npy
-imgHQ19988.npy
-imgHQ25588.npy
-imgHQ21556.npy
-imgHQ01724.npy
-imgHQ26403.npy
-imgHQ09269.npy
-imgHQ28393.npy
-imgHQ14454.npy
-imgHQ09275.npy
-imgHQ09981.npy
-imgHQ25507.npy
-imgHQ17436.npy
-imgHQ11413.npy
-imgHQ00587.npy
-imgHQ15841.npy
-imgHQ05895.npy
-imgHQ06106.npy
-imgHQ14962.npy
-imgHQ04830.npy
-imgHQ07088.npy
-imgHQ24581.npy
-imgHQ25079.npy
-imgHQ16201.npy
-imgHQ04940.npy
-imgHQ07532.npy
-imgHQ11501.npy
-imgHQ20257.npy
-imgHQ27728.npy
-imgHQ13589.npy
-imgHQ09194.npy
-imgHQ25814.npy
-imgHQ27342.npy
-imgHQ08007.npy
-imgHQ02760.npy
-imgHQ02312.npy
-imgHQ06754.npy
-imgHQ15544.npy
-imgHQ21353.npy
-imgHQ22917.npy
-imgHQ04586.npy
-imgHQ13129.npy
-imgHQ26591.npy
-imgHQ16413.npy
-imgHQ11386.npy
-imgHQ29415.npy
-imgHQ16781.npy
-imgHQ01562.npy
-imgHQ00611.npy
-imgHQ03784.npy
-imgHQ00733.npy
-imgHQ22236.npy
-imgHQ28919.npy
-imgHQ02233.npy
-imgHQ21385.npy
-imgHQ12039.npy
-imgHQ27722.npy
-imgHQ29620.npy
-imgHQ22359.npy
-imgHQ04771.npy
-imgHQ19378.npy
-imgHQ19644.npy
-imgHQ06289.npy
-imgHQ17643.npy
-imgHQ28190.npy
-imgHQ02207.npy
-imgHQ04279.npy
-imgHQ07290.npy
-imgHQ08043.npy
-imgHQ04712.npy
-imgHQ06112.npy
-imgHQ02567.npy
-imgHQ12515.npy
-imgHQ15942.npy
-imgHQ28771.npy
-imgHQ02912.npy
-imgHQ13529.npy
-imgHQ26823.npy
-imgHQ06138.npy
-imgHQ16399.npy
-imgHQ08783.npy
-imgHQ02844.npy
-imgHQ13994.npy
-imgHQ14701.npy
-imgHQ09640.npy
-imgHQ12964.npy
-imgHQ12131.npy
-imgHQ10233.npy
-imgHQ16071.npy
-imgHQ17902.npy
-imgHQ04730.npy
-imgHQ19798.npy
-imgHQ03738.npy
-imgHQ18643.npy
-imgHQ19465.npy
-imgHQ04846.npy
-imgHQ17866.npy
-imgHQ08984.npy
-imgHQ29108.npy
-imgHQ12256.npy
-imgHQ05367.npy
-imgHQ18970.npy
-imgHQ13475.npy
-imgHQ03391.npy
-imgHQ11103.npy
-imgHQ17466.npy
-imgHQ03981.npy
-imgHQ15647.npy
-imgHQ01384.npy
-imgHQ05150.npy
-imgHQ22575.npy
-imgHQ26218.npy
-imgHQ04715.npy
-imgHQ25990.npy
-imgHQ27051.npy
-imgHQ23367.npy
-imgHQ26719.npy
-imgHQ15266.npy
-imgHQ21400.npy
-imgHQ23294.npy
-imgHQ12899.npy
-imgHQ17155.npy
-imgHQ17897.npy
-imgHQ20751.npy
-imgHQ23179.npy
-imgHQ02401.npy
-imgHQ13957.npy
-imgHQ27714.npy
-imgHQ01263.npy
-imgHQ20944.npy
-imgHQ17976.npy
-imgHQ04142.npy
-imgHQ29493.npy
-imgHQ13515.npy
-imgHQ23867.npy
-imgHQ03649.npy
-imgHQ17785.npy
-imgHQ06973.npy
-imgHQ04379.npy
-imgHQ08891.npy
-imgHQ16074.npy
-imgHQ27270.npy
-imgHQ21616.npy
-imgHQ14923.npy
-imgHQ11285.npy
-imgHQ19664.npy
-imgHQ02409.npy
-imgHQ01046.npy
-imgHQ01957.npy
-imgHQ28610.npy
-imgHQ24997.npy
-imgHQ28527.npy
-imgHQ20244.npy
-imgHQ06431.npy
-imgHQ28063.npy
-imgHQ28353.npy
-imgHQ17163.npy
-imgHQ20867.npy
-imgHQ22828.npy
-imgHQ01863.npy
-imgHQ20614.npy
-imgHQ26688.npy
-imgHQ24836.npy
-imgHQ21723.npy
-imgHQ03372.npy
-imgHQ25128.npy
-imgHQ23569.npy
-imgHQ05514.npy
-imgHQ18207.npy
-imgHQ01002.npy
-imgHQ18793.npy
-imgHQ23745.npy
-imgHQ01441.npy
-imgHQ16315.npy
-imgHQ02205.npy
-imgHQ00063.npy
-imgHQ05554.npy
-imgHQ24090.npy
-imgHQ06708.npy
-imgHQ06779.npy
-imgHQ25798.npy
-imgHQ24915.npy
-imgHQ19816.npy
-imgHQ23200.npy
-imgHQ09466.npy
-imgHQ08245.npy
-imgHQ19619.npy
-imgHQ14429.npy
-imgHQ05119.npy
-imgHQ21927.npy
-imgHQ01906.npy
-imgHQ05621.npy
-imgHQ23512.npy
-imgHQ16542.npy
-imgHQ20205.npy
-imgHQ02718.npy
-imgHQ07067.npy
-imgHQ02892.npy
-imgHQ03333.npy
-imgHQ16123.npy
-imgHQ16107.npy
-imgHQ00949.npy
-imgHQ29169.npy
-imgHQ18742.npy
-imgHQ16504.npy
-imgHQ24201.npy
-imgHQ02195.npy
-imgHQ08878.npy
-imgHQ29069.npy
-imgHQ25057.npy
-imgHQ20485.npy
-imgHQ21145.npy
-imgHQ09545.npy
-imgHQ02679.npy
-imgHQ14689.npy
-imgHQ28482.npy
-imgHQ09030.npy
-imgHQ04839.npy
-imgHQ02020.npy
-imgHQ00737.npy
-imgHQ11352.npy
-imgHQ12988.npy
-imgHQ08114.npy
-imgHQ22365.npy
-imgHQ12975.npy
-imgHQ25995.npy
-imgHQ08263.npy
-imgHQ13249.npy
-imgHQ24696.npy
-imgHQ05566.npy
-imgHQ06075.npy
-imgHQ12052.npy
-imgHQ03071.npy
-imgHQ16346.npy
-imgHQ20696.npy
-imgHQ29388.npy
-imgHQ20419.npy
-imgHQ13508.npy
-imgHQ14710.npy
-imgHQ19975.npy
-imgHQ20286.npy
-imgHQ14032.npy
-imgHQ29368.npy
-imgHQ21452.npy
-imgHQ02687.npy
-imgHQ21017.npy
-imgHQ26636.npy
-imgHQ06999.npy
-imgHQ12246.npy
-imgHQ13108.npy
-imgHQ03603.npy
-imgHQ23382.npy
-imgHQ11687.npy
-imgHQ18833.npy
-imgHQ18064.npy
-imgHQ15280.npy
-imgHQ25681.npy
-imgHQ02653.npy
-imgHQ13323.npy
-imgHQ11834.npy
-imgHQ05556.npy
-imgHQ18369.npy
-imgHQ09216.npy
-imgHQ08302.npy
-imgHQ06374.npy
-imgHQ26758.npy
-imgHQ03552.npy
-imgHQ22104.npy
-imgHQ12178.npy
-imgHQ18303.npy
-imgHQ29778.npy
-imgHQ07054.npy
-imgHQ27860.npy
-imgHQ25711.npy
-imgHQ10798.npy
-imgHQ25847.npy
-imgHQ17960.npy
-imgHQ09176.npy
-imgHQ20277.npy
-imgHQ19056.npy
-imgHQ26564.npy
-imgHQ17305.npy
-imgHQ09792.npy
-imgHQ15019.npy
-imgHQ05204.npy
-imgHQ17802.npy
-imgHQ20848.npy
-imgHQ13342.npy
-imgHQ16727.npy
-imgHQ24794.npy
-imgHQ15744.npy
-imgHQ20441.npy
-imgHQ10974.npy
-imgHQ21242.npy
-imgHQ06443.npy
-imgHQ27143.npy
-imgHQ14066.npy
-imgHQ20927.npy
-imgHQ03078.npy
-imgHQ16900.npy
-imgHQ13773.npy
-imgHQ21209.npy
-imgHQ20806.npy
-imgHQ23122.npy
-imgHQ17317.npy
-imgHQ19939.npy
-imgHQ12337.npy
-imgHQ21697.npy
-imgHQ11579.npy
-imgHQ08631.npy
-imgHQ09322.npy
-imgHQ10313.npy
-imgHQ18009.npy
-imgHQ28242.npy
-imgHQ19231.npy
-imgHQ01416.npy
-imgHQ08568.npy
-imgHQ25184.npy
-imgHQ01075.npy
-imgHQ27631.npy
-imgHQ00703.npy
-imgHQ11828.npy
-imgHQ22673.npy
-imgHQ14189.npy
-imgHQ24046.npy
-imgHQ21362.npy
-imgHQ12793.npy
-imgHQ04406.npy
-imgHQ13030.npy
-imgHQ09493.npy
-imgHQ04276.npy
-imgHQ16812.npy
-imgHQ16649.npy
-imgHQ10050.npy
-imgHQ27702.npy
-imgHQ05128.npy
-imgHQ18843.npy
-imgHQ29534.npy
-imgHQ01247.npy
-imgHQ21090.npy
-imgHQ06222.npy
-imgHQ21695.npy
-imgHQ00721.npy
-imgHQ21730.npy
-imgHQ01480.npy
-imgHQ22777.npy
-imgHQ12580.npy
-imgHQ03725.npy
-imgHQ01216.npy
-imgHQ17075.npy
-imgHQ13689.npy
-imgHQ15488.npy
-imgHQ07045.npy
-imgHQ01681.npy
-imgHQ16109.npy
-imgHQ23686.npy
-imgHQ26070.npy
-imgHQ19427.npy
-imgHQ05836.npy
-imgHQ28134.npy
-imgHQ18256.npy
-imgHQ06683.npy
-imgHQ26538.npy
-imgHQ12685.npy
-imgHQ14238.npy
-imgHQ08026.npy
-imgHQ16813.npy
-imgHQ00148.npy
-imgHQ09554.npy
-imgHQ21103.npy
-imgHQ04450.npy
-imgHQ15166.npy
-imgHQ06809.npy
-imgHQ02863.npy
-imgHQ24940.npy
-imgHQ01471.npy
-imgHQ19181.npy
-imgHQ25390.npy
-imgHQ22677.npy
-imgHQ20713.npy
-imgHQ15397.npy
-imgHQ20543.npy
-imgHQ22806.npy
-imgHQ06219.npy
-imgHQ09297.npy
-imgHQ15064.npy
-imgHQ00155.npy
-imgHQ19357.npy
-imgHQ03446.npy
-imgHQ07096.npy
-imgHQ24179.npy
-imgHQ15094.npy
-imgHQ25260.npy
-imgHQ03435.npy
-imgHQ04452.npy
-imgHQ16729.npy
-imgHQ09712.npy
-imgHQ07373.npy
-imgHQ12267.npy
-imgHQ09976.npy
-imgHQ12547.npy
-imgHQ16526.npy
-imgHQ23835.npy
-imgHQ15295.npy
-imgHQ24086.npy
-imgHQ18152.npy
-imgHQ04519.npy
-imgHQ14065.npy
-imgHQ05116.npy
-imgHQ12062.npy
-imgHQ15674.npy
-imgHQ28144.npy
-imgHQ07270.npy
-imgHQ16133.npy
-imgHQ28489.npy
-imgHQ05684.npy
-imgHQ09745.npy
-imgHQ24110.npy
-imgHQ13371.npy
-imgHQ23824.npy
-imgHQ15526.npy
-imgHQ27415.npy
-imgHQ10494.npy
-imgHQ23500.npy
-imgHQ11555.npy
-imgHQ19352.npy
-imgHQ26312.npy
-imgHQ23806.npy
-imgHQ29546.npy
-imgHQ02902.npy
-imgHQ14617.npy
-imgHQ24518.npy
-imgHQ19905.npy
-imgHQ04253.npy
-imgHQ27099.npy
-imgHQ17149.npy
-imgHQ20118.npy
-imgHQ08541.npy
-imgHQ29883.npy
-imgHQ08804.npy
-imgHQ26471.npy
-imgHQ13873.npy
-imgHQ19601.npy
-imgHQ12961.npy
-imgHQ01626.npy
-imgHQ28540.npy
-imgHQ17272.npy
-imgHQ04125.npy
-imgHQ26190.npy
-imgHQ10874.npy
-imgHQ08045.npy
-imgHQ08300.npy
-imgHQ18549.npy
-imgHQ12794.npy
-imgHQ17008.npy
-imgHQ29866.npy
-imgHQ14530.npy
-imgHQ08002.npy
-imgHQ16687.npy
-imgHQ23616.npy
-imgHQ21394.npy
-imgHQ21635.npy
-imgHQ05164.npy
-imgHQ01442.npy
-imgHQ05736.npy
-imgHQ11926.npy
-imgHQ24255.npy
-imgHQ27089.npy
-imgHQ20715.npy
-imgHQ22765.npy
-imgHQ18213.npy
-imgHQ04927.npy
-imgHQ16096.npy
-imgHQ22061.npy
-imgHQ11018.npy
-imgHQ25467.npy
-imgHQ19018.npy
-imgHQ27691.npy
-imgHQ21258.npy
-imgHQ11305.npy
-imgHQ23917.npy
-imgHQ08712.npy
-imgHQ13591.npy
-imgHQ11447.npy
-imgHQ18016.npy
-imgHQ11786.npy
-imgHQ14574.npy
-imgHQ25789.npy
-imgHQ24467.npy
-imgHQ13167.npy
-imgHQ14950.npy
-imgHQ09244.npy
-imgHQ07665.npy
-imgHQ03287.npy
-imgHQ01175.npy
-imgHQ19602.npy
-imgHQ20646.npy
-imgHQ18959.npy
-imgHQ13991.npy
-imgHQ19280.npy
-imgHQ27616.npy
-imgHQ04302.npy
-imgHQ26143.npy
-imgHQ05471.npy
-imgHQ15733.npy
-imgHQ29367.npy
-imgHQ26907.npy
-imgHQ12431.npy
-imgHQ23723.npy
-imgHQ20721.npy
-imgHQ27529.npy
-imgHQ22572.npy
-imgHQ17724.npy
-imgHQ16899.npy
-imgHQ10886.npy
-imgHQ26384.npy
-imgHQ04495.npy
-imgHQ05286.npy
-imgHQ13179.npy
-imgHQ07427.npy
-imgHQ06893.npy
-imgHQ18269.npy
-imgHQ11051.npy
-imgHQ16021.npy
-imgHQ07647.npy
-imgHQ29458.npy
-imgHQ27453.npy
-imgHQ11027.npy
-imgHQ02069.npy
-imgHQ09051.npy
-imgHQ11040.npy
-imgHQ17694.npy
-imgHQ17074.npy
-imgHQ24612.npy
-imgHQ27948.npy
-imgHQ29179.npy
-imgHQ01117.npy
-imgHQ23999.npy
-imgHQ27446.npy
-imgHQ09199.npy
-imgHQ03800.npy
-imgHQ05107.npy
-imgHQ17379.npy
-imgHQ25251.npy
-imgHQ13088.npy
-imgHQ03040.npy
-imgHQ17199.npy
-imgHQ05507.npy
-imgHQ02123.npy
-imgHQ21168.npy
-imgHQ06904.npy
-imgHQ18476.npy
-imgHQ10546.npy
-imgHQ06096.npy
-imgHQ02555.npy
-imgHQ11586.npy
-imgHQ25961.npy
-imgHQ09600.npy
-imgHQ00450.npy
-imgHQ00517.npy
-imgHQ03397.npy
-imgHQ05602.npy
-imgHQ08995.npy
-imgHQ18787.npy
-imgHQ05214.npy
-imgHQ17354.npy
-imgHQ22353.npy
-imgHQ03639.npy
-imgHQ25177.npy
-imgHQ12416.npy
-imgHQ10951.npy
-imgHQ09177.npy
-imgHQ22713.npy
-imgHQ15961.npy
-imgHQ16067.npy
-imgHQ20406.npy
-imgHQ27381.npy
-imgHQ23064.npy
-imgHQ01702.npy
-imgHQ18176.npy
-imgHQ16170.npy
-imgHQ00251.npy
-imgHQ26362.npy
-imgHQ26014.npy
-imgHQ05294.npy
-imgHQ18713.npy
-imgHQ01958.npy
-imgHQ16492.npy
-imgHQ27191.npy
-imgHQ13808.npy
-imgHQ16797.npy
-imgHQ00862.npy
-imgHQ06270.npy
-imgHQ01414.npy
-imgHQ04853.npy
-imgHQ04741.npy
-imgHQ08017.npy
-imgHQ03436.npy
-imgHQ20794.npy
-imgHQ10532.npy
-imgHQ02109.npy
-imgHQ21081.npy
-imgHQ08008.npy
-imgHQ10901.npy
-imgHQ13788.npy
-imgHQ23768.npy
-imgHQ03667.npy
-imgHQ18416.npy
-imgHQ28370.npy
-imgHQ11521.npy
-imgHQ14269.npy
-imgHQ12034.npy
-imgHQ14855.npy
-imgHQ22407.npy
-imgHQ03153.npy
-imgHQ12190.npy
-imgHQ12055.npy
-imgHQ01951.npy
-imgHQ22571.npy
-imgHQ29076.npy
-imgHQ25972.npy
-imgHQ27230.npy
-imgHQ08779.npy
-imgHQ25403.npy
-imgHQ08782.npy
-imgHQ28607.npy
-imgHQ07392.npy
-imgHQ00029.npy
-imgHQ00343.npy
-imgHQ17345.npy
-imgHQ18119.npy
-imgHQ13267.npy
-imgHQ02865.npy
-imgHQ11425.npy
-imgHQ13890.npy
-imgHQ23283.npy
-imgHQ28442.npy
-imgHQ17396.npy
-imgHQ09038.npy
-imgHQ23673.npy
-imgHQ09788.npy
-imgHQ08950.npy
-imgHQ17293.npy
-imgHQ13116.npy
-imgHQ09324.npy
-imgHQ29740.npy
-imgHQ02674.npy
-imgHQ11264.npy
-imgHQ00038.npy
-imgHQ16451.npy
-imgHQ16884.npy
-imgHQ29188.npy
-imgHQ12834.npy
-imgHQ17462.npy
-imgHQ01151.npy
-imgHQ18049.npy
-imgHQ01434.npy
-imgHQ03410.npy
-imgHQ08791.npy
-imgHQ24318.npy
-imgHQ16034.npy
-imgHQ21799.npy
-imgHQ28797.npy
-imgHQ25376.npy
-imgHQ25385.npy
-imgHQ14666.npy
-imgHQ27409.npy
-imgHQ02724.npy
-imgHQ12543.npy
-imgHQ22640.npy
-imgHQ07756.npy
-imgHQ27091.npy
-imgHQ16674.npy
-imgHQ24022.npy
-imgHQ22015.npy
-imgHQ06447.npy
-imgHQ27642.npy
-imgHQ28326.npy
-imgHQ10231.npy
-imgHQ09866.npy
-imgHQ16964.npy
-imgHQ09446.npy
-imgHQ24508.npy
-imgHQ14948.npy
-imgHQ24702.npy
-imgHQ13641.npy
-imgHQ24262.npy
-imgHQ00088.npy
-imgHQ14241.npy
-imgHQ18165.npy
-imgHQ26846.npy
-imgHQ09314.npy
-imgHQ20979.npy
-imgHQ24417.npy
-imgHQ10581.npy
-imgHQ12080.npy
-imgHQ14914.npy
-imgHQ00826.npy
-imgHQ10824.npy
-imgHQ01047.npy
-imgHQ14466.npy
-imgHQ19496.npy
-imgHQ25083.npy
-imgHQ13328.npy
-imgHQ21864.npy
-imgHQ08453.npy
-imgHQ05014.npy
-imgHQ18268.npy
-imgHQ29911.npy
-imgHQ07329.npy
-imgHQ14686.npy
-imgHQ04580.npy
-imgHQ19891.npy
-imgHQ24231.npy
-imgHQ04546.npy
-imgHQ00605.npy
-imgHQ19296.npy
-imgHQ00008.npy
-imgHQ17989.npy
-imgHQ07528.npy
-imgHQ06174.npy
-imgHQ09953.npy
-imgHQ10357.npy
-imgHQ14745.npy
-imgHQ27797.npy
-imgHQ09717.npy
-imgHQ14190.npy
-imgHQ17711.npy
-imgHQ03254.npy
-imgHQ24532.npy
-imgHQ13396.npy
-imgHQ15261.npy
-imgHQ22854.npy
-imgHQ10064.npy
-imgHQ00738.npy
-imgHQ26849.npy
-imgHQ04535.npy
-imgHQ26672.npy
-imgHQ09083.npy
-imgHQ06370.npy
-imgHQ00194.npy
-imgHQ21662.npy
-imgHQ18342.npy
-imgHQ09114.npy
-imgHQ00209.npy
-imgHQ13998.npy
-imgHQ10967.npy
-imgHQ22709.npy
-imgHQ03701.npy
-imgHQ13067.npy
-imgHQ29170.npy
-imgHQ13348.npy
-imgHQ09846.npy
-imgHQ09665.npy
-imgHQ04943.npy
-imgHQ13028.npy
-imgHQ18980.npy
-imgHQ12165.npy
-imgHQ21532.npy
-imgHQ15980.npy
-imgHQ08769.npy
-imgHQ25652.npy
-imgHQ24427.npy
-imgHQ05181.npy
-imgHQ14846.npy
-imgHQ01331.npy
-imgHQ21911.npy
-imgHQ26167.npy
-imgHQ05544.npy
-imgHQ12068.npy
-imgHQ18853.npy
-imgHQ02417.npy
-imgHQ04949.npy
-imgHQ27365.npy
-imgHQ09503.npy
-imgHQ27189.npy
-imgHQ18249.npy
-imgHQ19295.npy
-imgHQ07216.npy
-imgHQ05284.npy
-imgHQ18879.npy
-imgHQ11646.npy
-imgHQ07651.npy
-imgHQ18882.npy
-imgHQ17940.npy
-imgHQ25168.npy
-imgHQ21751.npy
-imgHQ04911.npy
-imgHQ00060.npy
-imgHQ11345.npy
-imgHQ05033.npy
-imgHQ21273.npy
-imgHQ21221.npy
-imgHQ09568.npy
-imgHQ22599.npy
-imgHQ15097.npy
-imgHQ24617.npy
-imgHQ10377.npy
-imgHQ05515.npy
-imgHQ09681.npy
-imgHQ19395.npy
-imgHQ24123.npy
-imgHQ28181.npy
-imgHQ27200.npy
-imgHQ26142.npy
-imgHQ29577.npy
-imgHQ05575.npy
-imgHQ28828.npy
-imgHQ07537.npy
-imgHQ03379.npy
-imgHQ09309.npy
-imgHQ05257.npy
-imgHQ15254.npy
-imgHQ08386.npy
-imgHQ03255.npy
-imgHQ28139.npy
-imgHQ23854.npy
-imgHQ12289.npy
-imgHQ15505.npy
-imgHQ25480.npy
-imgHQ15899.npy
-imgHQ13664.npy
-imgHQ17759.npy
-imgHQ04296.npy
-imgHQ01706.npy
-imgHQ17983.npy
-imgHQ18850.npy
-imgHQ03747.npy
-imgHQ04133.npy
-imgHQ20003.npy
-imgHQ23120.npy
-imgHQ19079.npy
-imgHQ13044.npy
-imgHQ08287.npy
-imgHQ14845.npy
-imgHQ19807.npy
-imgHQ28231.npy
-imgHQ04619.npy
-imgHQ02547.npy
-imgHQ17685.npy
-imgHQ17934.npy
-imgHQ07057.npy
-imgHQ12773.npy
-imgHQ20779.npy
-imgHQ19839.npy
-imgHQ05847.npy
-imgHQ17570.npy
-imgHQ28611.npy
-imgHQ29499.npy
-imgHQ02376.npy
-imgHQ23683.npy
-imgHQ29603.npy
-imgHQ13322.npy
-imgHQ20198.npy
-imgHQ04412.npy
-imgHQ25643.npy
-imgHQ20352.npy
-imgHQ24951.npy
-imgHQ08206.npy
-imgHQ08329.npy
-imgHQ08249.npy
-imgHQ26267.npy
-imgHQ28635.npy
-imgHQ13045.npy
-imgHQ08425.npy
-imgHQ19454.npy
-imgHQ29953.npy
-imgHQ00739.npy
-imgHQ13654.npy
-imgHQ26683.npy
-imgHQ22279.npy
-imgHQ11310.npy
-imgHQ00781.npy
-imgHQ03611.npy
-imgHQ02021.npy
-imgHQ20466.npy
-imgHQ23275.npy
-imgHQ05162.npy
-imgHQ11372.npy
-imgHQ06544.npy
-imgHQ29074.npy
-imgHQ16596.npy
-imgHQ09341.npy
-imgHQ28336.npy
-imgHQ24406.npy
-imgHQ06668.npy
-imgHQ17814.npy
-imgHQ15706.npy
-imgHQ28652.npy
-imgHQ22689.npy
-imgHQ11112.npy
-imgHQ28099.npy
-imgHQ10051.npy
-imgHQ12443.npy
-imgHQ01317.npy
-imgHQ06584.npy
-imgHQ12290.npy
-imgHQ15212.npy
-imgHQ23864.npy
-imgHQ07728.npy
-imgHQ14207.npy
-imgHQ02012.npy
-imgHQ05921.npy
-imgHQ14483.npy
-imgHQ16704.npy
-imgHQ15337.npy
-imgHQ00407.npy
-imgHQ15746.npy
-imgHQ12790.npy
-imgHQ20861.npy
-imgHQ23155.npy
-imgHQ18737.npy
-imgHQ04483.npy
-imgHQ22845.npy
-imgHQ08604.npy
-imgHQ21885.npy
-imgHQ27382.npy
-imgHQ19300.npy
-imgHQ16482.npy
-imgHQ28473.npy
-imgHQ27127.npy
-imgHQ27039.npy
-imgHQ21813.npy
-imgHQ17324.npy
-imgHQ25830.npy
-imgHQ15620.npy
-imgHQ23031.npy
-imgHQ16867.npy
-imgHQ25338.npy
-imgHQ17461.npy
-imgHQ03183.npy
-imgHQ12434.npy
-imgHQ03252.npy
-imgHQ05969.npy
-imgHQ03061.npy
-imgHQ17935.npy
-imgHQ11767.npy
-imgHQ05951.npy
-imgHQ07113.npy
-imgHQ00742.npy
-imgHQ21906.npy
-imgHQ00496.npy
-imgHQ13782.npy
-imgHQ11527.npy
-imgHQ20123.npy
-imgHQ15312.npy
-imgHQ28851.npy
-imgHQ13769.npy
-imgHQ04206.npy
-imgHQ13633.npy
-imgHQ23665.npy
-imgHQ05963.npy
-imgHQ20500.npy
-imgHQ24379.npy
-imgHQ03750.npy
-imgHQ10054.npy
-imgHQ27915.npy
-imgHQ11191.npy
-imgHQ13814.npy
-imgHQ17165.npy
-imgHQ08171.npy
-imgHQ18004.npy
-imgHQ29560.npy
-imgHQ13093.npy
-imgHQ05498.npy
-imgHQ22909.npy
-imgHQ26409.npy
-imgHQ01576.npy
-imgHQ20592.npy
-imgHQ23480.npy
-imgHQ08193.npy
-imgHQ28773.npy
-imgHQ26832.npy
-imgHQ23983.npy
-imgHQ21925.npy
-imgHQ28234.npy
-imgHQ18319.npy
-imgHQ05741.npy
-imgHQ26278.npy
-imgHQ22181.npy
-imgHQ10072.npy
-imgHQ03428.npy
-imgHQ06821.npy
-imgHQ15538.npy
-imgHQ02566.npy
-imgHQ00095.npy
-imgHQ03682.npy
-imgHQ18601.npy
-imgHQ14393.npy
-imgHQ10872.npy
-imgHQ28778.npy
-imgHQ12350.npy
-imgHQ27730.npy
-imgHQ18621.npy
-imgHQ07257.npy
-imgHQ19699.npy
-imgHQ20784.npy
-imgHQ10354.npy
-imgHQ05470.npy
-imgHQ01956.npy
-imgHQ26989.npy
-imgHQ01915.npy
-imgHQ23190.npy
-imgHQ16614.npy
-imgHQ27074.npy
-imgHQ07069.npy
-imgHQ11457.npy
-imgHQ07609.npy
-imgHQ13919.npy
-imgHQ18553.npy
-imgHQ24347.npy
-imgHQ04670.npy
-imgHQ16454.npy
-imgHQ14381.npy
-imgHQ29639.npy
-imgHQ06766.npy
-imgHQ28435.npy
-imgHQ11278.npy
-imgHQ00087.npy
-imgHQ23468.npy
-imgHQ25544.npy
-imgHQ22228.npy
-imgHQ19785.npy
-imgHQ04850.npy
-imgHQ07699.npy
-imgHQ14009.npy
-imgHQ03873.npy
-imgHQ29719.npy
-imgHQ01858.npy
-imgHQ17882.npy
-imgHQ10632.npy
-imgHQ23332.npy
-imgHQ29042.npy
-imgHQ10099.npy
-imgHQ21033.npy
-imgHQ25453.npy
-imgHQ08388.npy
-imgHQ12228.npy
-imgHQ28753.npy
-imgHQ13526.npy
-imgHQ23409.npy
-imgHQ08036.npy
-imgHQ16839.npy
-imgHQ10265.npy
-imgHQ00840.npy
-imgHQ14993.npy
-imgHQ09352.npy
-imgHQ14939.npy
-imgHQ18521.npy
-imgHQ16808.npy
-imgHQ27009.npy
-imgHQ14754.npy
-imgHQ23518.npy
-imgHQ00016.npy
-imgHQ02705.npy
-imgHQ25283.npy
-imgHQ00278.npy
-imgHQ14348.npy
-imgHQ01499.npy
-imgHQ21202.npy
-imgHQ28169.npy
-imgHQ04119.npy
-imgHQ00792.npy
-imgHQ04248.npy
-imgHQ15036.npy
-imgHQ23549.npy
-imgHQ24538.npy
-imgHQ10023.npy
-imgHQ12279.npy
-imgHQ19929.npy
-imgHQ15077.npy
-imgHQ19635.npy
-imgHQ00120.npy
-imgHQ26185.npy
-imgHQ13000.npy
-imgHQ07814.npy
-imgHQ03673.npy
-imgHQ09804.npy
-imgHQ20440.npy
-imgHQ23150.npy
-imgHQ08280.npy
-imgHQ26326.npy
-imgHQ10852.npy
-imgHQ15110.npy
-imgHQ11471.npy
-imgHQ15103.npy
-imgHQ13887.npy
-imgHQ00307.npy
-imgHQ23144.npy
-imgHQ20207.npy
-imgHQ29390.npy
-imgHQ14730.npy
-imgHQ09248.npy
-imgHQ00235.npy
-imgHQ19227.npy
-imgHQ18649.npy
-imgHQ10694.npy
-imgHQ09089.npy
-imgHQ18611.npy
-imgHQ22464.npy
-imgHQ23085.npy
-imgHQ25358.npy
-imgHQ24834.npy
-imgHQ23151.npy
-imgHQ19455.npy
-imgHQ01323.npy
-imgHQ06609.npy
-imgHQ26029.npy
-imgHQ16756.npy
-imgHQ22692.npy
-imgHQ01135.npy
-imgHQ03824.npy
-imgHQ29465.npy
-imgHQ06915.npy
-imgHQ06499.npy
-imgHQ13078.npy
-imgHQ07775.npy
-imgHQ26145.npy
-imgHQ29118.npy
-imgHQ07826.npy
-imgHQ11254.npy
-imgHQ15655.npy
-imgHQ14620.npy
-imgHQ10227.npy
-imgHQ19830.npy
-imgHQ21618.npy
-imgHQ10077.npy
-imgHQ10675.npy
-imgHQ05837.npy
-imgHQ02765.npy
-imgHQ09691.npy
-imgHQ15756.npy
-imgHQ29263.npy
-imgHQ05665.npy
-imgHQ10740.npy
-imgHQ29209.npy
-imgHQ22292.npy
-imgHQ04429.npy
-imgHQ20319.npy
-imgHQ03907.npy
-imgHQ14336.npy
-imgHQ26884.npy
-imgHQ12677.npy
-imgHQ21981.npy
-imgHQ04784.npy
-imgHQ08704.npy
-imgHQ23318.npy
-imgHQ27048.npy
-imgHQ04723.npy
-imgHQ17432.npy
-imgHQ08854.npy
-imgHQ05149.npy
-imgHQ25452.npy
-imgHQ29781.npy
-imgHQ00216.npy
-imgHQ19009.npy
-imgHQ03507.npy
-imgHQ05147.npy
-imgHQ24442.npy
-imgHQ10290.npy
-imgHQ26606.npy
-imgHQ20698.npy
-imgHQ10160.npy
-imgHQ14470.npy
-imgHQ10853.npy
-imgHQ03136.npy
-imgHQ01410.npy
-imgHQ22355.npy
-imgHQ12327.npy
-imgHQ27817.npy
-imgHQ06620.npy
-imgHQ14886.npy
-imgHQ25275.npy
-imgHQ19747.npy
-imgHQ15625.npy
-imgHQ21608.npy
-imgHQ14896.npy
-imgHQ13397.npy
-imgHQ01797.npy
-imgHQ16427.npy
-imgHQ24533.npy
-imgHQ24198.npy
-imgHQ17687.npy
-imgHQ08739.npy
-imgHQ26686.npy
-imgHQ06483.npy
-imgHQ25702.npy
-imgHQ13235.npy
-imgHQ02543.npy
-imgHQ04636.npy
-imgHQ15589.npy
-imgHQ15957.npy
-imgHQ23966.npy
-imgHQ24111.npy
-imgHQ11061.npy
-imgHQ27640.npy
-imgHQ23043.npy
-imgHQ17431.npy
-imgHQ03045.npy
-imgHQ06911.npy
-imgHQ13790.npy
-imgHQ25010.npy
-imgHQ14927.npy
-imgHQ05053.npy
-imgHQ06619.npy
-imgHQ03186.npy
-imgHQ20359.npy
-imgHQ26292.npy
-imgHQ21569.npy
-imgHQ15481.npy
-imgHQ03687.npy
-imgHQ15914.npy
-imgHQ25471.npy
-imgHQ03206.npy
-imgHQ07970.npy
-imgHQ27395.npy
-imgHQ08928.npy
-imgHQ19926.npy
-imgHQ26582.npy
-imgHQ21350.npy
-imgHQ07366.npy
-imgHQ25353.npy
-imgHQ18433.npy
-imgHQ15384.npy
-imgHQ14940.npy
-imgHQ21259.npy
-imgHQ06125.npy
-imgHQ20228.npy
-imgHQ11661.npy
-imgHQ17370.npy
-imgHQ16164.npy
-imgHQ20801.npy
-imgHQ04290.npy
-imgHQ03096.npy
-imgHQ23624.npy
-imgHQ09306.npy
-imgHQ26381.npy
-imgHQ14289.npy
-imgHQ20065.npy
-imgHQ25381.npy
-imgHQ11619.npy
-imgHQ10021.npy
-imgHQ03232.npy
-imgHQ15564.npy
-imgHQ13134.npy
-imgHQ11604.npy
-imgHQ19324.npy
-imgHQ06750.npy
-imgHQ16172.npy
-imgHQ02697.npy
-imgHQ15411.npy
-imgHQ25356.npy
-imgHQ18673.npy
-imgHQ14787.npy
-imgHQ11266.npy
-imgHQ14928.npy
-imgHQ13840.npy
-imgHQ19508.npy
-imgHQ15842.npy
-imgHQ03776.npy
-imgHQ07619.npy
-imgHQ15741.npy
-imgHQ22815.npy
-imgHQ10614.npy
-imgHQ25720.npy
-imgHQ16886.npy
-imgHQ27121.npy
-imgHQ02991.npy
-imgHQ05057.npy
-imgHQ28951.npy
-imgHQ19344.npy
-imgHQ10733.npy
-imgHQ03370.npy
-imgHQ01474.npy
-imgHQ14890.npy
-imgHQ15675.npy
-imgHQ12737.npy
-imgHQ09972.npy
-imgHQ27755.npy
-imgHQ15365.npy
-imgHQ18854.npy
-imgHQ14222.npy
-imgHQ20777.npy
-imgHQ03622.npy
-imgHQ29905.npy
-imgHQ20629.npy
-imgHQ02961.npy
-imgHQ11046.npy
-imgHQ27698.npy
-imgHQ12851.npy
-imgHQ05148.npy
-imgHQ26741.npy
-imgHQ24294.npy
-imgHQ00326.npy
-imgHQ22522.npy
-imgHQ14017.npy
-imgHQ04776.npy
-imgHQ07010.npy
-imgHQ22484.npy
-imgHQ29413.npy
-imgHQ09152.npy
-imgHQ19580.npy
-imgHQ08250.npy
-imgHQ16965.npy
-imgHQ12632.npy
-imgHQ07741.npy
-imgHQ01292.npy
-imgHQ29769.npy
-imgHQ19413.npy
-imgHQ11180.npy
-imgHQ09027.npy
-imgHQ10703.npy
-imgHQ22716.npy
-imgHQ13523.npy
-imgHQ01252.npy
-imgHQ21141.npy
-imgHQ25218.npy
-imgHQ22055.npy
-imgHQ08986.npy
-imgHQ09440.npy
-imgHQ25753.npy
-imgHQ03848.npy
-imgHQ06350.npy
-imgHQ03418.npy
-imgHQ22533.npy
-imgHQ12633.npy
-imgHQ00514.npy
-imgHQ19386.npy
-imgHQ04000.npy
-imgHQ17767.npy
-imgHQ01594.npy
-imgHQ22781.npy
-imgHQ21383.npy
-imgHQ22920.npy
-imgHQ17752.npy
-imgHQ29311.npy
-imgHQ10686.npy
-imgHQ10423.npy
-imgHQ26264.npy
-imgHQ04381.npy
-imgHQ18941.npy
-imgHQ06618.npy
-imgHQ20282.npy
-imgHQ07324.npy
-imgHQ18488.npy
-imgHQ15037.npy
-imgHQ29319.npy
-imgHQ21982.npy
-imgHQ09618.npy
-imgHQ22194.npy
-imgHQ18220.npy
-imgHQ04270.npy
-imgHQ03438.npy
-imgHQ16121.npy
-imgHQ27855.npy
-imgHQ05954.npy
-imgHQ12831.npy
-imgHQ27671.npy
-imgHQ27177.npy
-imgHQ11189.npy
-imgHQ01262.npy
-imgHQ23628.npy
-imgHQ08571.npy
-imgHQ24137.npy
-imgHQ10936.npy
-imgHQ27281.npy
-imgHQ00476.npy
-imgHQ08429.npy
-imgHQ15363.npy
-imgHQ23584.npy
-imgHQ21632.npy
-imgHQ15264.npy
-imgHQ29652.npy
-imgHQ11832.npy
-imgHQ19523.npy
-imgHQ15076.npy
-imgHQ21834.npy
-imgHQ28537.npy
-imgHQ02180.npy
-imgHQ25225.npy
-imgHQ15189.npy
-imgHQ25626.npy
-imgHQ03222.npy
-imgHQ26742.npy
-imgHQ11099.npy
-imgHQ10156.npy
-imgHQ05500.npy
-imgHQ18282.npy
-imgHQ06262.npy
-imgHQ09650.npy
-imgHQ14076.npy
-imgHQ21173.npy
-imgHQ19682.npy
-imgHQ05117.npy
-imgHQ00267.npy
-imgHQ15585.npy
-imgHQ29394.npy
-imgHQ02186.npy
-imgHQ22039.npy
-imgHQ17241.npy
-imgHQ04669.npy
-imgHQ13310.npy
-imgHQ03106.npy
-imgHQ14881.npy
-imgHQ24081.npy
-imgHQ04530.npy
-imgHQ12427.npy
-imgHQ23022.npy
-imgHQ29664.npy
-imgHQ20869.npy
-imgHQ26052.npy
-imgHQ08708.npy
-imgHQ12917.npy
-imgHQ13252.npy
-imgHQ22155.npy
-imgHQ02384.npy
-imgHQ25821.npy
-imgHQ15333.npy
-imgHQ10278.npy
-imgHQ13478.npy
-imgHQ25747.npy
-imgHQ15777.npy
-imgHQ17688.npy
-imgHQ14960.npy
-imgHQ06087.npy
-imgHQ06394.npy
-imgHQ16372.npy
-imgHQ28705.npy
-imgHQ14702.npy
-imgHQ07892.npy
-imgHQ26568.npy
-imgHQ28871.npy
-imgHQ17856.npy
-imgHQ12657.npy
-imgHQ14766.npy
-imgHQ19183.npy
-imgHQ14860.npy
-imgHQ01074.npy
diff --git a/repositories/taming-transformers/data/coco_annotations_100/annotations/instances_train2017.json b/repositories/taming-transformers/data/coco_annotations_100/annotations/instances_train2017.json
deleted file mode 100644
index 2b73f89679f80e8f0b3c0483ddcebcf6f5675115..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/data/coco_annotations_100/annotations/instances_train2017.json
+++ /dev/null
@@ -1 +0,0 @@
-{"info": {"description": "COCO 2017 Dataset", "url": "http://cocodataset.org", "version": "1.0", "year": 2017, "contributor": "COCO Consortium", "date_created": "2017/09/01"}, "licenses": [{"url": "http://creativecommons.org/licenses/by-nc-sa/2.0/", "id": 1, "name": "Attribution-NonCommercial-ShareAlike License"}, {"url": "http://creativecommons.org/licenses/by-nc/2.0/", "id": 2, "name": "Attribution-NonCommercial License"}, {"url": "http://creativecommons.org/licenses/by-nc-nd/2.0/", "id": 3, "name": "Attribution-NonCommercial-NoDerivs License"}, {"url": "http://creativecommons.org/licenses/by/2.0/", "id": 4, "name": "Attribution License"}, {"url": "http://creativecommons.org/licenses/by-sa/2.0/", "id": 5, "name": "Attribution-ShareAlike License"}, {"url": "http://creativecommons.org/licenses/by-nd/2.0/", "id": 6, "name": "Attribution-NoDerivs License"}, {"url": "http://flickr.com/commons/usage/", "id": 7, "name": "No known copyright restrictions"}, {"url": "http://www.usa.gov/copyright.shtml", "id": 8, "name": "United States Government Work"}], "images": [{"license": 4, "file_name": "000000010442.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010442.jpg", "height": 426, "width": 640, "date_captured": "2013-11-19 21:00:09", "flickr_url": "http://farm4.staticflickr.com/3188/2662753853_11d673a05d_z.jpg", "id": 10442}, {"license": 1, "file_name": "000000010142.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010142.jpg", "height": 480, "width": 640, "date_captured": "2013-11-19 22:51:01", "flickr_url": "http://farm6.staticflickr.com/5171/5408281064_c3f8d3e67b_z.jpg", "id": 10142}, {"license": 1, "file_name": "000000010393.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010393.jpg", "height": 480, "width": 640, "date_captured": "2013-11-15 00:40:34", "flickr_url": "http://farm3.staticflickr.com/2743/4192626071_8f54f4578e_z.jpg", "id": 10393}, {"license": 6, "file_name": "000000010077.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010077.jpg", "height": 259, "width": 640, "date_captured": "2013-11-18 21:04:46", "flickr_url": "http://farm3.staticflickr.com/2112/2273706470_b115ccda6b_z.jpg", "id": 10077}, {"license": 3, "file_name": "000000010123.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010123.jpg", "height": 427, "width": 640, "date_captured": "2013-11-21 05:12:31", "flickr_url": "http://farm5.staticflickr.com/4085/4948947491_1c37f153fc_z.jpg", "id": 10123}, {"license": 4, "file_name": "000000010432.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010432.jpg", "height": 480, "width": 640, "date_captured": "2013-11-19 19:52:48", "flickr_url": "http://farm6.staticflickr.com/5457/9690393462_ba33edf573_z.jpg", "id": 10432}, {"license": 1, "file_name": "000000010192.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010192.jpg", "height": 497, "width": 640, "date_captured": "2013-11-14 20:22:48", "flickr_url": "http://farm9.staticflickr.com/8196/8125509122_e8348e8d89_z.jpg", "id": 10192}, {"license": 3, "file_name": "000000010039.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010039.jpg", "height": 640, "width": 428, "date_captured": "2013-11-15 14:11:21", "flickr_url": "http://farm5.staticflickr.com/4098/4751788411_7b6a63cd92_z.jpg", "id": 10039}, {"license": 2, "file_name": "000000010023.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010023.jpg", "height": 427, "width": 640, "date_captured": "2013-11-20 09:15:13", "flickr_url": "http://farm4.staticflickr.com/3397/3201363151_ea35e217e3_z.jpg", "id": 10023}, {"license": 3, "file_name": "000000010428.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010428.jpg", "height": 640, "width": 427, "date_captured": "2013-11-14 23:33:26", "flickr_url": "http://farm3.staticflickr.com/2685/4078908984_668deecc62_z.jpg", "id": 10428}, {"license": 1, "file_name": "000000010400.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010400.jpg", "height": 640, "width": 427, "date_captured": "2013-11-24 15:06:26", "flickr_url": "http://farm9.staticflickr.com/8323/8134674673_d9489c39b9_z.jpg", "id": 10400}, {"license": 5, "file_name": "000000010249.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010249.jpg", "height": 640, "width": 426, "date_captured": "2013-11-16 21:44:34", "flickr_url": "http://farm9.staticflickr.com/8335/8402579163_8363ba0c7e_z.jpg", "id": 10249}, {"license": 1, "file_name": "000000010290.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010290.jpg", "height": 640, "width": 482, "date_captured": "2013-11-15 04:16:50", "flickr_url": "http://farm6.staticflickr.com/5048/5238196330_aeeaf4d917_z.jpg", "id": 10290}, {"license": 2, "file_name": "000000010205.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010205.jpg", "height": 426, "width": 640, "date_captured": "2013-11-21 22:57:22", "flickr_url": "http://farm2.staticflickr.com/1316/1243676572_54ae68d563_z.jpg", "id": 10205}, {"license": 2, "file_name": "000000010217.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010217.jpg", "height": 480, "width": 640, "date_captured": "2013-11-24 23:26:35", "flickr_url": "http://farm8.staticflickr.com/7284/8742930563_207f40cd15_z.jpg", "id": 10217}, {"license": 5, "file_name": "000000010263.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010263.jpg", "height": 375, "width": 500, "date_captured": "2013-11-22 01:41:21", "flickr_url": "http://farm3.staticflickr.com/2602/3909329409_085a7c9f41_z.jpg", "id": 10263}, {"license": 2, "file_name": "000000010040.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010040.jpg", "height": 480, "width": 640, "date_captured": "2013-11-16 21:57:07", "flickr_url": "http://farm9.staticflickr.com/8213/8264511585_0a5e6212c2_z.jpg", "id": 10040}, {"license": 4, "file_name": "000000010324.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010324.jpg", "height": 640, "width": 425, "date_captured": "2013-11-18 15:34:49", "flickr_url": "http://farm2.staticflickr.com/1059/5150417990_0d7762864e_z.jpg", "id": 10324}, {"license": 3, "file_name": "000000010008.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010008.jpg", "height": 427, "width": 640, "date_captured": "2013-11-20 20:26:10", "flickr_url": "http://farm7.staticflickr.com/6076/6088064448_2690929cbc_z.jpg", "id": 10008}, {"license": 2, "file_name": "000000010149.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010149.jpg", "height": 484, "width": 640, "date_captured": "2013-11-14 21:39:00", "flickr_url": "http://farm8.staticflickr.com/7235/7169336131_521711d854_z.jpg", "id": 10149}, {"license": 1, "file_name": "000000010463.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010463.jpg", "height": 446, "width": 640, "date_captured": "2013-11-15 12:19:52", "flickr_url": "http://farm8.staticflickr.com/7250/6910961220_961bf73cae_z.jpg", "id": 10463}, {"license": 3, "file_name": "000000010138.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010138.jpg", "height": 480, "width": 640, "date_captured": "2013-11-20 21:12:31", "flickr_url": "http://farm9.staticflickr.com/8098/8506152881_ecf43c76c3_z.jpg", "id": 10138}, {"license": 4, "file_name": "000000010014.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010014.jpg", "height": 426, "width": 640, "date_captured": "2013-11-16 15:41:53", "flickr_url": "http://farm9.staticflickr.com/8185/8145478875_28543f87be_z.jpg", "id": 10014}, {"license": 1, "file_name": "000000010219.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010219.jpg", "height": 480, "width": 640, "date_captured": "2013-11-18 12:18:01", "flickr_url": "http://farm1.staticflickr.com/116/293545180_9be1f625c9_z.jpg", "id": 10219}, {"license": 4, "file_name": "000000010114.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010114.jpg", "height": 640, "width": 480, "date_captured": "2013-11-14 18:59:23", "flickr_url": "http://farm9.staticflickr.com/8509/8528792276_824cb10b59_z.jpg", "id": 10114}, {"license": 3, "file_name": "000000010211.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010211.jpg", "height": 424, "width": 640, "date_captured": "2013-11-17 00:23:46", "flickr_url": "http://farm4.staticflickr.com/3032/2751379693_252d3c8e44_z.jpg", "id": 10211}, {"license": 2, "file_name": "000000010216.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010216.jpg", "height": 480, "width": 640, "date_captured": "2013-11-21 03:03:18", "flickr_url": "http://farm9.staticflickr.com/8436/7926050860_db828d8494_z.jpg", "id": 10216}, {"license": 1, "file_name": "000000010115.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010115.jpg", "height": 640, "width": 427, "date_captured": "2013-11-15 03:15:50", "flickr_url": "http://farm8.staticflickr.com/7200/6854804253_f7fa6e98b8_z.jpg", "id": 10115}, {"license": 4, "file_name": "000000010440.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010440.jpg", "height": 427, "width": 640, "date_captured": "2013-11-16 05:51:18", "flickr_url": "http://farm3.staticflickr.com/2865/9443592476_3c3ce98973_z.jpg", "id": 10440}, {"license": 1, "file_name": "000000010256.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010256.jpg", "height": 360, "width": 640, "date_captured": "2013-11-16 22:33:29", "flickr_url": "http://farm9.staticflickr.com/8480/8214519939_ee9d1486b6_z.jpg", "id": 10256}, {"license": 2, "file_name": "000000010056.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010056.jpg", "height": 482, "width": 640, "date_captured": "2013-11-17 00:42:05", "flickr_url": "http://farm9.staticflickr.com/8472/8138318480_cf2cd6fa5a_z.jpg", "id": 10056}, {"license": 2, "file_name": "000000010107.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010107.jpg", "height": 640, "width": 480, "date_captured": "2013-11-18 14:17:26", "flickr_url": "http://farm5.staticflickr.com/4039/4471901818_ac8353d2e5_z.jpg", "id": 10107}, {"license": 3, "file_name": "000000010449.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010449.jpg", "height": 480, "width": 640, "date_captured": "2013-11-18 02:08:04", "flickr_url": "http://farm4.staticflickr.com/3732/9431336544_e6345159b7_z.jpg", "id": 10449}, {"license": 3, "file_name": "000000010012.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010012.jpg", "height": 480, "width": 640, "date_captured": "2013-11-19 21:21:03", "flickr_url": "http://farm2.staticflickr.com/1039/1374745468_a4cb23032f_z.jpg", "id": 10012}, {"license": 1, "file_name": "000000010395.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010395.jpg", "height": 476, "width": 500, "date_captured": "2013-11-18 11:37:47", "flickr_url": "http://farm5.staticflickr.com/4005/4468891744_00bb759e17_z.jpg", "id": 10395}, {"license": 3, "file_name": "000000010104.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010104.jpg", "height": 457, "width": 640, "date_captured": "2013-11-15 21:47:19", "flickr_url": "http://farm7.staticflickr.com/6237/6282305534_1b1fb1f8a1_z.jpg", "id": 10104}, {"license": 1, "file_name": "000000010386.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010386.jpg", "height": 480, "width": 640, "date_captured": "2013-11-18 01:56:20", "flickr_url": "http://farm4.staticflickr.com/3279/2982566887_93e5d316a9_z.jpg", "id": 10386}, {"license": 1, "file_name": "000000010046.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010046.jpg", "height": 480, "width": 640, "date_captured": "2013-11-20 11:50:10", "flickr_url": "http://farm8.staticflickr.com/7363/9675286251_78248282b6_z.jpg", "id": 10046}, {"license": 1, "file_name": "000000010430.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010430.jpg", "height": 480, "width": 640, "date_captured": "2013-11-17 07:44:21", "flickr_url": "http://farm4.staticflickr.com/3678/9477571775_99039714f4_z.jpg", "id": 10430}, {"license": 1, "file_name": "000000010166.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010166.jpg", "height": 351, "width": 470, "date_captured": "2013-11-19 21:19:23", "flickr_url": "http://farm4.staticflickr.com/3248/3015319163_b0a61a7c77_z.jpg", "id": 10166}, {"license": 2, "file_name": "000000010275.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010275.jpg", "height": 427, "width": 640, "date_captured": "2013-11-18 09:32:30", "flickr_url": "http://farm9.staticflickr.com/8177/8029583325_02172d3a6f_z.jpg", "id": 10275}, {"license": 3, "file_name": "000000010321.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010321.jpg", "height": 640, "width": 480, "date_captured": "2013-11-19 03:39:00", "flickr_url": "http://farm9.staticflickr.com/8330/8374378941_80fda27ffa_z.jpg", "id": 10321}, {"license": 2, "file_name": "000000010176.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010176.jpg", "height": 480, "width": 640, "date_captured": "2013-11-22 00:19:33", "flickr_url": "http://farm1.staticflickr.com/164/335653813_61b9a128e2_z.jpg", "id": 10176}, {"license": 1, "file_name": "000000010037.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010037.jpg", "height": 426, "width": 640, "date_captured": "2013-11-19 18:06:00", "flickr_url": "http://farm3.staticflickr.com/2602/3741919994_d9d0343f82_z.jpg", "id": 10037}, {"license": 4, "file_name": "000000010313.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010313.jpg", "height": 640, "width": 427, "date_captured": "2013-11-20 07:30:18", "flickr_url": "http://farm3.staticflickr.com/2595/3691544165_5f8f5d92de_z.jpg", "id": 10313}, {"license": 3, "file_name": "000000010421.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010421.jpg", "height": 480, "width": 640, "date_captured": "2013-11-20 11:47:35", "flickr_url": "http://farm4.staticflickr.com/3286/2704773968_ed600e2d11_z.jpg", "id": 10421}, {"license": 2, "file_name": "000000010161.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010161.jpg", "height": 427, "width": 640, "date_captured": "2013-11-25 13:54:08", "flickr_url": "http://farm9.staticflickr.com/8385/8583133395_109ca97781_z.jpg", "id": 10161}, {"license": 4, "file_name": "000000010405.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010405.jpg", "height": 453, "width": 640, "date_captured": "2013-11-18 01:11:58", "flickr_url": "http://farm6.staticflickr.com/5068/5694693358_890d562b38_z.jpg", "id": 10405}, {"license": 4, "file_name": "000000010444.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010444.jpg", "height": 500, "width": 375, "date_captured": "2013-11-20 16:02:25", "flickr_url": "http://farm3.staticflickr.com/2205/2342560721_8a9545fd1a_z.jpg", "id": 10444}, {"license": 3, "file_name": "000000010084.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010084.jpg", "height": 424, "width": 640, "date_captured": "2013-11-17 07:31:31", "flickr_url": "http://farm8.staticflickr.com/7428/9977327403_ec9211b17d_z.jpg", "id": 10084}, {"license": 3, "file_name": "000000010179.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010179.jpg", "height": 502, "width": 640, "date_captured": "2013-11-23 01:23:34", "flickr_url": "http://farm3.staticflickr.com/2753/5765793056_50a8694618_z.jpg", "id": 10179}, {"license": 4, "file_name": "000000010125.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010125.jpg", "height": 436, "width": 640, "date_captured": "2013-11-24 00:31:28", "flickr_url": "http://farm9.staticflickr.com/8051/8100436361_e70cea2ba0_z.jpg", "id": 10125}, {"license": 2, "file_name": "000000010082.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010082.jpg", "height": 425, "width": 640, "date_captured": "2013-11-24 02:46:01", "flickr_url": "http://farm6.staticflickr.com/5103/5660629949_220d6cb304_z.jpg", "id": 10082}, {"license": 3, "file_name": "000000010318.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010318.jpg", "height": 400, "width": 640, "date_captured": "2013-11-16 21:21:19", "flickr_url": "http://farm4.staticflickr.com/3730/9164843685_66a5f3834b_z.jpg", "id": 10318}, {"license": 3, "file_name": "000000010130.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010130.jpg", "height": 427, "width": 640, "date_captured": "2013-11-24 09:51:10", "flickr_url": "http://farm8.staticflickr.com/7055/6802904874_5313b371f7_z.jpg", "id": 10130}, {"license": 1, "file_name": "000000010403.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010403.jpg", "height": 416, "width": 640, "date_captured": "2013-11-19 18:10:23", "flickr_url": "http://farm5.staticflickr.com/4120/5416067648_a01a1d2872_z.jpg", "id": 10403}, {"license": 6, "file_name": "000000010041.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010041.jpg", "height": 480, "width": 640, "date_captured": "2013-11-19 21:28:20", "flickr_url": "http://farm8.staticflickr.com/7157/6791989693_84a9ec0060_z.jpg", "id": 10041}, {"license": 3, "file_name": "000000010343.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010343.jpg", "height": 427, "width": 640, "date_captured": "2013-11-17 08:20:12", "flickr_url": "http://farm4.staticflickr.com/3768/9317101988_c25e7800c3_z.jpg", "id": 10343}, {"license": 3, "file_name": "000000010388.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010388.jpg", "height": 426, "width": 640, "date_captured": "2013-11-14 21:36:08", "flickr_url": "http://farm8.staticflickr.com/7376/9612632350_fe5f9d2e02_z.jpg", "id": 10388}, {"license": 2, "file_name": "000000010414.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010414.jpg", "height": 420, "width": 500, "date_captured": "2013-11-19 20:53:38", "flickr_url": "http://farm1.staticflickr.com/68/187106012_c60041a5fa_z.jpg", "id": 10414}, {"license": 2, "file_name": "000000010265.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010265.jpg", "height": 427, "width": 640, "date_captured": "2013-11-23 04:52:56", "flickr_url": "http://farm2.staticflickr.com/1153/1340250930_630cae707b_z.jpg", "id": 10265}, {"license": 3, "file_name": "000000010222.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010222.jpg", "height": 333, "width": 500, "date_captured": "2013-11-16 17:51:33", "flickr_url": "http://farm1.staticflickr.com/145/406189235_49af59f7aa_z.jpg", "id": 10222}, {"license": 1, "file_name": "000000010241.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010241.jpg", "height": 480, "width": 640, "date_captured": "2013-11-16 16:15:07", "flickr_url": "http://farm5.staticflickr.com/4034/4618195712_406d349d8c_z.jpg", "id": 10241}, {"license": 4, "file_name": "000000010239.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010239.jpg", "height": 360, "width": 640, "date_captured": "2013-11-16 22:41:38", "flickr_url": "http://farm9.staticflickr.com/8527/8640248164_346015db59_z.jpg", "id": 10239}, {"license": 6, "file_name": "000000010369.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010369.jpg", "height": 424, "width": 640, "date_captured": "2013-11-18 16:30:42", "flickr_url": "http://farm6.staticflickr.com/5210/5279959659_617eb40ec2_z.jpg", "id": 10369}, {"license": 3, "file_name": "000000010136.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010136.jpg", "height": 640, "width": 316, "date_captured": "2013-11-24 12:57:39", "flickr_url": "http://farm1.staticflickr.com/48/143700223_df3abd2533_z.jpg", "id": 10136}, {"license": 3, "file_name": "000000010445.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010445.jpg", "height": 349, "width": 500, "date_captured": "2013-11-19 17:56:31", "flickr_url": "http://farm2.staticflickr.com/1133/973971082_0524b5a07c_z.jpg", "id": 10445}, {"license": 3, "file_name": "000000010015.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010015.jpg", "height": 633, "width": 640, "date_captured": "2013-11-19 18:15:10", "flickr_url": "http://farm4.staticflickr.com/3422/3393927411_26ab4342e9_z.jpg", "id": 10015}, {"license": 1, "file_name": "000000010434.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010434.jpg", "height": 426, "width": 640, "date_captured": "2013-11-15 11:54:59", "flickr_url": "http://farm1.staticflickr.com/8/7845077_9cbe3bd89d_z.jpg", "id": 10434}, {"license": 2, "file_name": "000000010097.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010097.jpg", "height": 360, "width": 640, "date_captured": "2013-11-14 20:26:22", "flickr_url": "http://farm5.staticflickr.com/4018/4255629097_9a832c6dae_z.jpg", "id": 10097}, {"license": 2, "file_name": "000000010058.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010058.jpg", "height": 426, "width": 640, "date_captured": "2013-11-17 19:16:50", "flickr_url": "http://farm9.staticflickr.com/8349/8197861619_93df634c02_z.jpg", "id": 10058}, {"license": 3, "file_name": "000000010108.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010108.jpg", "height": 453, "width": 640, "date_captured": "2013-11-17 02:20:45", "flickr_url": "http://farm4.staticflickr.com/3360/3444049112_fc8b6acf6e_z.jpg", "id": 10108}, {"license": 3, "file_name": "000000010232.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010232.jpg", "height": 427, "width": 640, "date_captured": "2013-11-17 15:38:00", "flickr_url": "http://farm4.staticflickr.com/3675/9453111678_c2cb8874d9_z.jpg", "id": 10232}, {"license": 4, "file_name": "000000010069.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010069.jpg", "height": 640, "width": 427, "date_captured": "2013-11-16 16:09:44", "flickr_url": "http://farm8.staticflickr.com/7229/7167720922_420895b285_z.jpg", "id": 10069}, {"license": 3, "file_name": "000000010024.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010024.jpg", "height": 640, "width": 480, "date_captured": "2013-11-19 20:21:14", "flickr_url": "http://farm9.staticflickr.com/8528/8463731510_53f79a717b_z.jpg", "id": 10024}, {"license": 3, "file_name": "000000010327.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010327.jpg", "height": 480, "width": 640, "date_captured": "2013-11-18 14:53:56", "flickr_url": "http://farm5.staticflickr.com/4025/4461943910_f88106188f_z.jpg", "id": 10327}, {"license": 1, "file_name": "000000010337.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010337.jpg", "height": 640, "width": 480, "date_captured": "2013-11-15 06:08:56", "flickr_url": "http://farm8.staticflickr.com/7064/6801977878_94d6095195_z.jpg", "id": 10337}, {"license": 1, "file_name": "000000010248.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010248.jpg", "height": 427, "width": 640, "date_captured": "2013-11-18 17:00:29", "flickr_url": "http://farm7.staticflickr.com/6013/6015945247_2d03377c4c_z.jpg", "id": 10248}, {"license": 2, "file_name": "000000010276.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010276.jpg", "height": 426, "width": 640, "date_captured": "2013-11-18 19:59:57", "flickr_url": "http://farm4.staticflickr.com/3746/9895771396_45554292ba_z.jpg", "id": 10276}, {"license": 6, "file_name": "000000010196.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010196.jpg", "height": 443, "width": 640, "date_captured": "2013-11-19 19:58:23", "flickr_url": "http://farm9.staticflickr.com/8505/8405340354_90e8d1757d_z.jpg", "id": 10196}, {"license": 2, "file_name": "000000010145.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010145.jpg", "height": 427, "width": 640, "date_captured": "2013-11-22 00:21:49", "flickr_url": "http://farm5.staticflickr.com/4118/4740387609_ce9bb3f14a_z.jpg", "id": 10145}, {"license": 1, "file_name": "000000010407.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010407.jpg", "height": 480, "width": 640, "date_captured": "2013-11-20 07:19:18", "flickr_url": "http://farm3.staticflickr.com/2037/2071235362_a8f850624b_z.jpg", "id": 10407}, {"license": 3, "file_name": "000000010243.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010243.jpg", "height": 500, "width": 375, "date_captured": "2013-11-20 13:39:40", "flickr_url": "http://farm3.staticflickr.com/2212/2416407264_ca4e59c12e_z.jpg", "id": 10243}, {"license": 4, "file_name": "000000010230.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010230.jpg", "height": 480, "width": 640, "date_captured": "2013-11-19 19:40:06", "flickr_url": "http://farm5.staticflickr.com/4094/4788218715_e64dbe9bb7_z.jpg", "id": 10230}, {"license": 6, "file_name": "000000010245.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010245.jpg", "height": 427, "width": 640, "date_captured": "2013-11-21 01:16:08", "flickr_url": "http://farm8.staticflickr.com/7253/7459060240_5e24c744f6_z.jpg", "id": 10245}, {"license": 1, "file_name": "000000010319.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010319.jpg", "height": 427, "width": 640, "date_captured": "2013-11-22 22:31:20", "flickr_url": "http://farm3.staticflickr.com/2521/4128276778_bb74ec1296_z.jpg", "id": 10319}, {"license": 4, "file_name": "000000010229.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010229.jpg", "height": 480, "width": 640, "date_captured": "2013-11-17 20:53:03", "flickr_url": "http://farm6.staticflickr.com/5312/6915913274_7d268c51fd_z.jpg", "id": 10229}, {"license": 4, "file_name": "000000010073.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010073.jpg", "height": 469, "width": 640, "date_captured": "2013-11-21 05:37:17", "flickr_url": "http://farm5.staticflickr.com/4038/4663314869_680cc1b4c9_z.jpg", "id": 10073}, {"license": 1, "file_name": "000000010342.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010342.jpg", "height": 640, "width": 478, "date_captured": "2013-11-16 14:34:19", "flickr_url": "http://farm9.staticflickr.com/8436/7984397533_7a232d7e44_z.jpg", "id": 10342}, {"license": 3, "file_name": "000000010244.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010244.jpg", "height": 640, "width": 475, "date_captured": "2013-11-16 16:56:12", "flickr_url": "http://farm9.staticflickr.com/8453/8055076139_cd811120a4_z.jpg", "id": 10244}, {"license": 5, "file_name": "000000010005.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010005.jpg", "height": 400, "width": 500, "date_captured": "2013-11-18 11:09:03", "flickr_url": "http://farm3.staticflickr.com/2558/4197396435_45d477dc72_z.jpg", "id": 10005}, {"license": 5, "file_name": "000000010083.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010083.jpg", "height": 480, "width": 640, "date_captured": "2013-11-22 00:56:30", "flickr_url": "http://farm3.staticflickr.com/2033/2033435444_1b91e9988c_z.jpg", "id": 10083}, {"license": 5, "file_name": "000000010420.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010420.jpg", "height": 375, "width": 500, "date_captured": "2013-11-17 06:42:55", "flickr_url": "http://farm2.staticflickr.com/1213/1165637087_cf1cc6a168_z.jpg", "id": 10420}, {"license": 2, "file_name": "000000010281.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010281.jpg", "height": 427, "width": 640, "date_captured": "2013-11-22 14:50:06", "flickr_url": "http://farm9.staticflickr.com/8337/8244811136_bd49523ae1_z.jpg", "id": 10281}, {"license": 3, "file_name": "000000010175.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010175.jpg", "height": 480, "width": 640, "date_captured": "2013-11-14 20:17:42", "flickr_url": "http://farm9.staticflickr.com/8103/8549598429_22f161d92e_z.jpg", "id": 10175}, {"license": 5, "file_name": "000000010303.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010303.jpg", "height": 468, "width": 640, "date_captured": "2013-11-19 21:21:59", "flickr_url": "http://farm3.staticflickr.com/2509/4198054407_db61df57cc_z.jpg", "id": 10303}, {"license": 6, "file_name": "000000010346.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010346.jpg", "height": 480, "width": 640, "date_captured": "2013-11-21 20:02:54", "flickr_url": "http://farm4.staticflickr.com/3498/3456906266_7257a4a061_z.jpg", "id": 10346}, {"license": 4, "file_name": "000000010309.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010309.jpg", "height": 500, "width": 333, "date_captured": "2013-11-19 21:48:30", "flickr_url": "http://farm1.staticflickr.com/71/191429048_ad837c4f93_z.jpg", "id": 10309}, {"license": 5, "file_name": "000000010094.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010094.jpg", "height": 500, "width": 500, "date_captured": "2013-11-22 20:54:17", "flickr_url": "http://farm3.staticflickr.com/2289/2461849747_8a952aac05_z.jpg", "id": 10094}, {"license": 3, "file_name": "000000010358.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010358.jpg", "height": 640, "width": 478, "date_captured": "2013-11-18 15:08:37", "flickr_url": "http://farm7.staticflickr.com/6188/6135058772_4d7a2c78c7_z.jpg", "id": 10358}], "annotations": [{"segmentation": [[411.45, 288.12, 377.89, 293.54, 374.64, 196.09, 388.72, 184.18, 384.38, 165.77, 406.04, 164.69, 407.12, 185.26, 422.28, 193.92, 424.45, 289.21]], "area": 5224.604, "iscrowd": 0, "image_id": 10149, "bbox": [374.64, 164.69, 49.81, 128.85], "category_id": 44, "id": 81885}, {"segmentation": [[495.45, 170.7, 491.29, 189.44, 484.0, 197.76, 471.51, 212.34, 472.55, 233.15, 474.63, 286.24, 536.04, 292.48, 541.25, 284.15, 540.21, 227.95, 539.17, 204.01, 522.51, 189.44, 517.31, 177.99, 517.31, 170.7, 513.14, 167.58]], "area": 6900.9613500000005, "iscrowd": 0, "image_id": 10149, "bbox": [471.51, 167.58, 69.74, 124.9], "category_id": 44, "id": 87365}, {"segmentation": [[555.66, 161.87, 559.1, 181.1, 553.6, 202.38, 549.48, 223.67, 547.42, 246.33, 550.17, 261.44, 552.23, 282.04, 554.98, 288.91, 574.89, 287.54, 588.62, 286.85, 591.37, 281.36, 591.37, 212.69, 588.62, 197.58, 581.07, 182.47, 578.32, 165.99, 573.52, 158.44, 564.59, 157.75]], "area": 4584.169500000003, "iscrowd": 0, "image_id": 10149, "bbox": [547.42, 157.75, 43.95, 131.16], "category_id": 44, "id": 90634}, {"segmentation": [[602.27, 163.7, 591.8, 199.75, 591.8, 228.25, 594.13, 284.65, 599.94, 290.46, 605.18, 252.67, 606.34, 213.13, 605.76, 169.52, 605.76, 153.24, 600.52, 152.66, 602.27, 163.12]], "area": 1422.607900000004, "iscrowd": 0, "image_id": 10149, "bbox": [591.8, 152.66, 14.54, 137.8], "category_id": 44, "id": 91251}, {"segmentation": [[37.78, 483.24, 38.74, 516.35, 43.3, 517.07, 49.54, 517.79, 54.1, 519.71, 54.58, 524.27, 54.1, 533.15, 54.1, 534.59, 57.46, 530.03, 57.46, 489.47, 56.5, 488.27, 51.46, 486.6, 50.98, 482.52, 49.3, 480.12, 48.34, 478.44, 45.7, 477.0, 50.26, 472.44, 56.5, 473.4, 50.02, 468.36, 43.78, 465.96, 41.38, 466.44, 38.74, 468.36, 41.14, 470.76, 41.14, 476.28, 41.14, 479.4, 38.26, 482.52]], "area": 833.6604000000004, "iscrowd": 0, "image_id": 10290, "bbox": [37.78, 465.96, 19.68, 68.63], "category_id": 44, "id": 92202}, {"segmentation": [[88.39, 472.46, 92.51, 447.79, 108.95, 417.99, 138.76, 391.26, 165.48, 380.99, 195.29, 376.87, 232.29, 376.87, 240.51, 364.54, 244.63, 341.93, 217.9, 321.37, 211.73, 313.15, 203.51, 316.23, 190.15, 330.62, 189.12, 330.62, 189.12, 341.93, 213.79, 360.43, 231.26, 363.51, 227.15, 374.82, 187.07, 360.43, 174.73, 345.01, 172.68, 317.26, 164.45, 310.06, 160.34, 327.54, 160.34, 330.62, 156.23, 338.84, 144.93, 347.07, 139.79, 343.98, 148.01, 331.65, 155.2, 302.87, 152.12, 278.2, 162.4, 276.15, 171.65, 276.15, 176.79, 285.4, 180.9, 299.79, 195.29, 305.95, 199.4, 296.7, 188.09, 276.15, 201.46, 268.95, 211.73, 278.2, 214.82, 286.42, 217.9, 300.81, 226.12, 305.95, 242.57, 302.87, 265.18, 290.54, 281.63, 302.87, 274.43, 331.65, 278.54, 334.73, 345.35, 335.76, 342.27, 348.09, 287.79, 352.21, 280.6, 357.34, 294.99, 380.99, 324.8, 403.6, 374.13, 456.02, 386.47, 476.57, 388.52, 478.63, 89.42, 478.63, 89.42, 478.63], [452.25, 355.29, 456.36, 361.46, 448.14, 392.29, 473.83, 417.99, 515.97, 446.77, 528.31, 459.1, 515.97, 477.6, 571.48, 479.66, 640.0, 478.63, 639.31, 388.18, 609.51, 375.85, 561.2, 370.71, 512.89, 380.99, 499.53, 390.24, 470.75, 343.98, 473.83, 335.76, 470.75, 333.7, 451.22, 351.18], [435.8, 425.18, 452.25, 436.49, 440.94, 480.0, 422.44, 480.0, 437.86, 430.32]], "area": 49315.70765, "iscrowd": 0, "image_id": 10393, "bbox": [88.39, 268.95, 551.61, 211.05], "category_id": 2, "id": 129872}, {"segmentation": [[23.93, 448.38, 9.79, 378.77, 4.35, 357.02, 7.61, 355.93, 4.35, 309.16, 8.7, 259.13, 3.26, 147.1, 20.67, 139.49, 38.07, 128.61, 53.29, 115.56, 65.26, 103.6, 79.4, 76.41, 106.59, 47.04, 127.25, 41.6, 150.09, 49.22, 166.41, 62.27, 174.02, 85.11, 168.58, 114.47, 160.97, 134.05, 156.62, 155.8, 151.18, 161.24, 147.92, 163.42, 145.74, 164.51, 152.27, 202.57, 171.85, 258.04, 199.04, 299.37, 239.28, 336.35, 250.16, 347.23, 254.51, 371.16, 255.6, 387.47, 247.98, 387.47, 226.23, 367.89, 209.91, 343.97, 203.39, 323.3, 160.97, 299.37, 137.04, 280.88, 145.74, 383.12, 164.23, 396.17, 158.8, 404.87, 153.36, 423.36, 155.53, 454.91, 154.44, 477.75, 60.91, 478.83, 39.16, 472.31, 34.8, 471.22, 26.1, 434.24, 15.23, 400.52, 7.61, 364.63]], "area": 60447.9789, "iscrowd": 0, "image_id": 10149, "bbox": [3.26, 41.6, 252.34, 437.23], "category_id": 1, "id": 191566}, {"segmentation": [[248.96, 301.31, 271.98, 279.19, 277.85, 275.58, 294.1, 271.06, 299.52, 265.65, 299.52, 261.58, 353.69, 207.41, 362.71, 216.89, 372.64, 221.41, 379.87, 222.76, 382.12, 223.21, 385.28, 226.82, 393.41, 231.79, 398.37, 237.66, 399.28, 240.37, 406.95, 245.78, 407.85, 246.23, 410.56, 252.55, 414.17, 251.2, 415.53, 257.52, 416.43, 266.1, 415.08, 269.71, 411.01, 272.87, 403.34, 275.58, 396.57, 279.19, 390.7, 282.8, 383.48, 290.02, 379.87, 293.18, 375.8, 297.69, 370.39, 301.76, 355.94, 317.1, 350.53, 325.68, 345.56, 334.71, 342.4, 344.19, 341.5, 346.9, 344.66, 352.31, 363.62, 368.56, 379.87, 382.56, 388.44, 432.66, 390.7, 455.68, 389.8, 478.25, 424.56, 476.45, 429.07, 415.06, 469.24, 453.88, 476.01, 464.71, 468.34, 477.35, 518.45, 475.55, 528.38, 457.04, 451.64, 389.78, 451.19, 359.08, 460.67, 342.38, 521.15, 308.08, 536.5, 274.67, 537.41, 265.65, 533.34, 243.98, 533.34, 235.4, 501.29, 197.94, 508.97, 177.62, 507.16, 146.48, 457.51, 110.36, 442.61, 103.59, 419.59, 89.6, 385.28, 74.25, 351.43, 86.89, 341.5, 77.41, 329.76, 77.41, 329.31, 81.02, 325.25, 69.29, 321.19, 65.68, 317.57, 51.68, 314.41, 44.01, 307.64, 36.79, 295.46, 32.27, 287.33, 31.37, 278.75, 30.92, 268.37, 34.53, 259.34, 42.2, 253.48, 47.62, 249.41, 55.74, 248.06, 64.32, 250.77, 70.19, 255.28, 73.8, 257.09, 85.09, 265.66, 95.47, 271.53, 102.24, 274.24, 111.72, 283.27, 122.55, 286.43, 125.26, 289.59, 130.68, 291.39, 135.19, 300.87, 141.51, 303.13, 144.22, 303.58, 147.38, 303.13, 150.54, 290.94, 173.56, 285.07, 184.39, 265.66, 199.74, 257.09, 215.09, 255.28, 224.12, 250.32, 234.5, 245.8, 242.62, 242.64, 249.39, 238.58, 257.07, 236.32, 258.87, 232.26, 264.74, 228.65, 270.16, 225.04, 272.42, 212.4, 285.06, 207.43, 291.37, 204.27, 299.05, 206.08, 300.4, 220.52, 300.4, 222.33, 300.4, 227.75, 303.56, 230.45, 305.82, 227.29, 307.63, 224.59, 308.53, 223.23, 309.43, 222.33, 310.78, 235.42, 318.01, 239.48, 309.43, 241.74, 307.17]], "area": 65796.14955, "iscrowd": 0, "image_id": 10393, "bbox": [204.27, 30.92, 333.14, 447.33], "category_id": 1, "id": 207348}, {"segmentation": [[263.8, 396.69, 265.04, 408.54, 265.67, 417.88, 267.53, 422.87, 278.75, 422.87, 291.84, 422.87, 301.19, 418.51, 299.32, 407.29, 301.19, 395.45, 300.56, 387.97, 302.43, 385.48, 306.17, 382.98, 311.16, 381.11, 315.52, 379.24, 314.9, 374.88, 318.01, 369.9, 321.13, 361.17, 317.39, 352.45, 314.9, 351.2, 309.91, 341.85, 305.55, 338.74, 300.56, 337.49, 295.58, 335.0, 289.97, 335.62, 286.85, 335.0, 279.38, 329.39, 273.77, 329.39, 268.78, 331.88, 261.3, 335.62, 256.32, 338.11, 249.46, 339.98, 247.59, 342.47, 246.35, 353.07, 251.33, 353.07, 256.32, 355.56, 256.94, 359.3, 260.06, 364.91, 258.19, 369.27, 266.29, 377.37, 262.55, 383.61, 266.29, 385.48, 266.91, 387.97, 266.91, 392.33, 264.42, 396.07]], "area": 4257.51855, "iscrowd": 0, "image_id": 10428, "bbox": [246.35, 329.39, 74.78, 93.48], "category_id": 64, "id": 291506}, {"segmentation": [[0.0, 276.54, 88.77, 286.56, 90.2, 633.05, 0.0, 631.61]], "area": 31385.451349999996, "iscrowd": 0, "image_id": 10114, "bbox": [0.0, 276.54, 90.2, 356.51], "category_id": 82, "id": 332730}, {"segmentation": [[565.68, 223.65, 579.27, 218.83, 592.01, 219.97, 600.78, 225.06, 607.86, 234.12, 608.99, 244.88, 604.75, 254.79, 595.12, 264.7, 582.1, 272.91, 571.62, 273.19, 560.02, 268.95, 554.92, 264.13, 551.81, 258.19, 550.96, 248.28, 550.96, 238.09, 553.79, 231.29, 557.75, 226.2]], "area": 2450.2187000000004, "iscrowd": 0, "image_id": 10192, "bbox": [550.96, 218.83, 58.03, 54.36], "category_id": 85, "id": 334198}, {"segmentation": [[262.66, 510.94, 260.07, 500.41, 266.09, 493.39, 275.12, 492.64, 285.4, 496.65, 301.45, 506.18, 310.47, 513.95, 314.74, 518.97, 320.5, 523.48, 330.78, 539.03, 338.31, 551.56, 341.06, 568.87, 339.31, 572.88, 324.26, 577.39, 309.72, 569.87, 287.91, 551.82, 276.12, 538.53, 266.09, 525.24, 262.58, 518.22]], "area": 3597.4312999999993, "iscrowd": 0, "image_id": 10039, "bbox": [260.07, 492.64, 80.99, 84.75], "category_id": 85, "id": 334903}, {"segmentation": [[198.34, 77.48, 190.3, 74.6, 182.26, 64.28, 178.93, 55.48, 179.08, 43.65, 181.96, 38.34, 184.39, 33.48, 187.27, 30.52, 189.27, 28.92, 195.58, 27.61, 199.58, 27.61, 202.58, 29.12, 206.59, 31.72, 209.29, 34.72, 211.59, 37.22, 214.3, 42.33, 215.7, 45.93, 216.1, 52.84, 215.8, 57.14, 213.89, 63.35, 212.19, 66.95, 210.69, 69.55, 208.49, 71.56, 205.39, 73.56, 203.99, 74.96]], "area": 1406.5342999999998, "iscrowd": 0, "image_id": 10192, "bbox": [178.93, 27.61, 37.17, 49.87], "category_id": 85, "id": 340757}, {"segmentation": [[461.26, 331.71, 524.92, 335.06, 534.97, 340.64, 538.32, 348.46, 538.32, 352.93, 529.39, 415.47, 526.04, 440.04, 491.42, 433.34, 494.77, 400.95, 497.0, 380.85, 494.77, 374.15, 474.66, 371.91, 463.49, 362.98, 464.61, 351.81, 460.14, 339.52]], "area": 5156.796500000002, "iscrowd": 0, "image_id": 10192, "bbox": [460.14, 331.71, 78.18, 108.33], "category_id": 62, "id": 381435}, {"segmentation": [[547.37, 443.83, 550.73, 424.8, 550.73, 408.01, 548.49, 397.94, 547.37, 383.38, 547.37, 378.91, 550.73, 373.31, 573.12, 366.59, 599.98, 367.71, 621.25, 369.95, 636.92, 371.07, 640.0, 378.91, 640.0, 391.22, 640.0, 412.49, 640.0, 422.56, 640.0, 428.16, 638.04, 432.64, 629.09, 434.88, 612.3, 438.23, 610.06, 446.07, 606.7, 452.78, 586.55, 451.67, 568.64, 449.43, 558.57, 444.95, 550.73, 441.59]], "area": 6870.079099999999, "iscrowd": 0, "image_id": 10192, "bbox": [547.37, 366.59, 92.63, 86.19], "category_id": 62, "id": 384254}, {"segmentation": [[300.65, 121.53, 320.66, 109.44, 328.73, 107.91, 339.98, 109.86, 345.13, 107.22, 351.38, 105.55, 371.39, 108.75, 386.68, 120.42, 400.3, 140.29, 404.75, 162.95, 404.19, 167.26, 402.94, 191.86, 402.11, 214.93, 401.0, 219.93, 428.65, 231.61, 443.11, 236.19, 451.45, 240.5, 454.5, 249.4, 457.01, 259.26, 456.17, 272.33, 456.45, 289.56, 459.37, 296.23, 460.34, 309.44, 463.26, 317.5, 461.73, 326.95, 463.82, 337.51, 464.09, 341.96, 460.2, 343.63, 461.18, 357.94, 473.27, 372.26, 473.27, 376.29, 471.6, 378.93, 481.61, 393.38, 490.5, 410.89, 495.37, 421.04, 495.23, 423.68, 492.31, 428.27, 491.89, 435.35, 424.35, 424.65, 416.98, 425.07, 402.8, 423.54, 360.0, 416.31, 356.94, 416.59, 355.55, 408.25, 355.97, 405.33, 352.07, 398.94, 332.34, 393.1, 286.89, 388.38, 264.38, 389.63, 254.93, 392.27, 248.95, 394.35, 246.73, 393.94, 244.64, 373.09, 242.42, 353.91, 244.64, 325.14, 247.84, 295.12, 247.42, 268.85, 245.75, 253.43, 240.19, 248.7, 231.99, 238.14, 229.35, 229.1, 228.52, 220.9, 228.94, 216.32, 230.05, 214.79, 228.52, 209.51, 229.08, 204.5, 232.55, 202.98, 237.83, 202.42, 238.25, 197.69, 241.45, 197.0, 251.73, 198.67, 256.04, 199.36, 254.65, 196.17, 252.15, 192.13, 252.15, 190.47, 254.09, 190.47, 259.79, 192.41, 261.88, 192.69, 262.15, 190.05, 263.82, 187.13, 265.91, 185.46, 267.02, 184.21, 268.96, 185.46, 271.05, 192.69, 274.11, 204.09, 274.52, 211.73, 276.75, 220.35, 276.75, 235.36, 274.66, 254.4, 274.66, 265.52, 272.72, 277.19, 272.99, 294.15, 274.11, 305.54, 275.36, 299.71, 273.97, 287.2, 274.38, 278.58, 277.72, 273.3, 279.8, 270.38, 279.67, 261.49, 282.17, 254.54, 285.5, 247.31, 295.79, 241.47, 306.49, 238.42, 313.3, 236.89, 308.3, 227.58, 302.32, 216.73, 295.93, 199.5, 294.4, 191.58, 288.28, 184.49, 283.83, 176.29, 280.22, 166.15, 278.97, 159.34, 282.03, 143.07, 292.87, 130.57]], "area": 53402.56380000003, "iscrowd": 0, "image_id": 10192, "bbox": [228.52, 105.55, 266.85, 329.8], "category_id": 1, "id": 446799}, {"segmentation": [[410.92, 83.19, 427.96, 75.17, 436.98, 75.17, 450.01, 85.19, 458.03, 101.23, 467.05, 134.3, 466.04, 143.32, 476.07, 150.34, 476.07, 166.37, 486.09, 171.38, 493.11, 190.43, 502.13, 229.51, 506.13, 259.58, 500.12, 276.62, 496.11, 278.62, 483.08, 280.63, 462.04, 308.69, 465.04, 331.74, 460.03, 342.77, 381.86, 336.76, 366.82, 322.72, 360.81, 302.68, 347.78, 289.65, 364.82, 263.59, 351.79, 249.56, 360.81, 172.39, 381.86, 156.35, 386.87, 144.32, 380.85, 134.3, 380.85, 122.27, 390.88, 115.26, 394.89, 98.22]], "area": 28784.099999999995, "iscrowd": 0, "image_id": 10463, "bbox": [347.78, 75.17, 158.35, 267.6], "category_id": 1, "id": 503497}, {"segmentation": [[181.82, 219.48, 174.78, 175.29, 168.76, 169.26, 210.95, 154.19, 355.59, 252.63, 360.62, 261.67, 354.59, 275.74, 350.57, 285.78, 355.59, 296.83, 358.61, 306.88, 367.65, 328.98, 379.7, 336.01, 408.83, 344.04, 431.94, 347.06, 447.0, 347.06, 462.07, 346.05, 466.09, 362.12, 470.11, 374.18, 482.16, 378.2, 496.23, 371.16, 512.3, 363.13, 516.32, 360.11, 545.45, 378.2, 539.42, 387.24, 530.38, 394.27, 525.36, 437.46, 518.32, 439.47, 515.31, 438.47, 514.31, 398.29, 471.11, 427.42, 466.09, 439.47, 447.0, 440.48, 446.0, 423.4, 262.18, 251.63, 268.2, 305.87, 259.16, 307.88, 254.14, 290.8, 252.13, 245.6, 180.81, 182.32, 186.84, 215.47]], "area": 21156.32775, "iscrowd": 0, "image_id": 10463, "bbox": [168.76, 154.19, 376.69, 286.29], "category_id": 15, "id": 574554}, {"segmentation": [[473.12, 147.66, 474.13, 165.74, 482.16, 165.74, 489.19, 176.79, 489.19, 186.84, 490.2, 187.84, 516.32, 202.91, 511.29, 250.12, 518.32, 253.14, 525.36, 248.11, 525.36, 211.95, 639.87, 279.25, 637.86, 224.0]], "area": 7383.804549999999, "iscrowd": 0, "image_id": 10463, "bbox": [473.12, 147.66, 166.75, 131.59], "category_id": 15, "id": 575438}, {"segmentation": [[164.32, 479.28, 172.97, 493.69, 184.5, 496.58, 201.8, 496.58, 213.33, 486.49, 214.77, 472.07, 201.8, 464.86, 197.48, 479.28, 177.3, 474.95, 177.3, 469.19, 167.21, 473.51, 167.21, 477.84]], "area": 982.9235000000003, "iscrowd": 0, "image_id": 10428, "bbox": [164.32, 464.86, 50.45, 31.72], "category_id": 51, "id": 712505}, {"segmentation": [[221.92, 426.81, 168.95, 436.83, 173.24, 474.05, 178.97, 519.87, 229.08, 522.73, 230.51, 488.37, 236.24, 448.28]], "area": 5191.05035, "iscrowd": 0, "image_id": 10115, "bbox": [168.95, 426.81, 67.29, 95.92], "category_id": 70, "id": 1093932}, {"segmentation": [[368.02, 601.42, 322.65, 600.39, 311.31, 575.64, 306.15, 556.05, 318.52, 547.8, 303.06, 532.33, 295.84, 503.46, 292.75, 500.37, 281.4, 482.84, 286.56, 464.27, 276.25, 399.31, 277.28, 397.25, 381.43, 395.19, 389.67, 414.78, 387.61, 432.31, 378.33, 465.31, 361.83, 481.8, 378.33, 491.09, 387.61, 516.86, 381.43, 535.42, 378.33, 542.64, 364.93, 546.77]], "area": 16832.950249999998, "iscrowd": 0, "image_id": 10290, "bbox": [276.25, 395.19, 113.42, 206.23], "category_id": 70, "id": 1094202}, {"segmentation": [[214.29, 572.4, 155.33, 632.81, 126.56, 632.81, 92.04, 632.81, 86.29, 430.02, 79.1, 376.81, 99.24, 368.18, 211.42, 395.51]], "area": 29809.95354999999, "iscrowd": 0, "image_id": 10114, "bbox": [79.1, 368.18, 135.19, 264.63], "category_id": 79, "id": 1127226}, {"segmentation": [[125.97, 542.33, 88.34, 584.21, 81.98, 592.16, 311.5, 628.73, 321.57, 524.84, 317.86, 520.07, 165.73, 507.35, 168.38, 521.13, 173.68, 526.96, 172.09, 539.15, 156.72, 558.76, 137.63, 565.66, 129.68, 554.52, 130.21, 542.86, 124.38, 540.21]], "area": 18555.994549999996, "iscrowd": 0, "image_id": 10428, "bbox": [81.98, 507.35, 239.59, 121.38], "category_id": 81, "id": 1134617}, {"segmentation": [[49.01, 629.19, 172.97, 630.63, 177.3, 518.2, 60.54, 516.76, 47.57, 627.75]], "area": 13632.745200000003, "iscrowd": 0, "image_id": 10290, "bbox": [47.57, 516.76, 129.73, 113.87], "category_id": 81, "id": 1134747}, {"segmentation": [[318.47, 349.74, 307.73, 358.0, 427.5, 368.74, 428.33, 358.83]], "area": 1100.4525500000007, "iscrowd": 0, "image_id": 10114, "bbox": [307.73, 349.74, 120.6, 19.0], "category_id": 81, "id": 1135189}, {"segmentation": [[548.94, 207.47, 544.32, 207.2, 534.0, 207.74, 525.85, 207.74, 522.59, 207.74, 514.99, 209.64, 514.45, 209.91, 515.26, 213.44, 516.08, 215.61, 514.72, 220.5, 513.36, 221.86, 512.27, 226.75, 510.37, 229.19, 510.1, 230.28, 509.01, 233.54, 507.39, 238.7, 506.84, 245.22, 507.93, 252.28, 516.89, 276.45, 547.31, 275.91, 552.47, 262.6, 548.94, 250.65, 548.94, 243.59, 550.02, 237.61, 553.55, 234.08, 555.45, 232.45, 553.28, 226.75, 547.85, 223.76, 547.04, 222.13, 546.76, 214.46]], "area": 2630.4818500000006, "iscrowd": 0, "image_id": 10192, "bbox": [506.84, 207.2, 48.61, 69.25], "category_id": 86, "id": 1154039}, {"segmentation": [[479.28, 211.12, 482.77, 210.07, 488.34, 210.77, 490.78, 211.12, 495.66, 211.12, 498.8, 207.28, 496.36, 215.65, 496.01, 232.38, 508.21, 239.0, 508.91, 260.61, 499.15, 274.9, 471.27, 276.3, 457.67, 258.17, 457.67, 245.63, 462.55, 237.96, 467.43, 234.12, 464.64, 221.23, 466.73, 212.86, 469.17, 209.38, 470.92, 208.33, 475.1, 207.28, 478.24, 210.42]], "area": 2592.1464499999997, "iscrowd": 0, "image_id": 10192, "bbox": [457.67, 207.28, 51.24, 69.02], "category_id": 86, "id": 1154210}, {"segmentation": [[365.78, 95.19, 416.77, 126.98, 428.76, 156.37, 504.35, 194.77, 504.95, 171.37, 512.75, 147.98, 457.56, 107.18, 438.36, 101.18, 386.17, 71.79, 356.78, 87.39, 359.18, 92.79]], "area": 6931.902250000001, "iscrowd": 0, "image_id": 10393, "bbox": [356.78, 71.79, 155.97, 122.98], "category_id": 27, "id": 1169587}, {"segmentation": [[474.06, 175.64, 485.96, 249.7, 502.49, 280.78, 521.66, 315.82, 522.99, 331.69, 513.07, 358.14, 505.13, 372.69, 487.94, 375.33, 470.75, 373.35, 463.48, 347.56, 465.46, 316.48, 458.85, 283.42, 457.52, 259.62, 460.83, 217.96, 461.49, 188.2, 462.15, 169.03, 467.44, 167.04, 473.39, 167.04]], "area": 7389.887850000001, "iscrowd": 0, "image_id": 10463, "bbox": [457.52, 167.04, 65.47, 208.29], "category_id": 31, "id": 1176483}, {"segmentation": [[0.0, 163.45, 0.0, 163.45, 11.94, 164.61, 12.46, 171.73, 14.53, 177.16, 13.88, 180.78, 11.04, 182.72, 0.0, 183.24]], "area": 245.53195000000005, "iscrowd": 0, "image_id": 10393, "bbox": [0.0, 163.45, 14.53, 19.79], "category_id": 3, "id": 1337570}, {"segmentation": [[25.95, 183.24, 101.62, 181.08, 135.14, 180.0, 135.14, 163.78, 127.57, 157.3, 120.0, 142.16, 25.95, 140.0]], "area": 4218.4301000000005, "iscrowd": 0, "image_id": 10393, "bbox": [25.95, 140.0, 109.19, 43.24], "category_id": 8, "id": 1373987}, {"segmentation": [[167.37, 177.86, 139.8, 177.24, 140.11, 166.09, 143.21, 158.65, 168.3, 156.8, 174.8, 157.42, 178.52, 162.37, 182.24, 162.37, 184.72, 166.71, 184.72, 170.43, 182.55, 175.07, 166.75, 177.24]], "area": 801.1055999999998, "iscrowd": 0, "image_id": 10393, "bbox": [139.8, 156.8, 44.92, 21.06], "category_id": 8, "id": 1374168}, {"segmentation": [[131.13, 322.95, 132.21, 317.89, 133.66, 315.36, 134.2, 311.03, 134.38, 309.04, 138.9, 307.6, 142.87, 309.04, 142.87, 312.47, 148.1, 322.4, 146.12, 329.99, 146.12, 335.77, 146.66, 338.84, 146.12, 340.82, 140.88, 340.64, 139.44, 340.64, 138.72, 339.2, 139.26, 331.61, 138.35, 327.46, 135.28, 325.83, 131.49, 325.65]], "area": 332.86639999999966, "iscrowd": 0, "image_id": 10114, "bbox": [131.13, 307.6, 16.97, 33.22], "category_id": 44, "id": 1490093}, {"segmentation": [[127.15, 364.68, 130.42, 366.72, 133.7, 365.08, 133.08, 362.02, 133.08, 358.13, 140.24, 357.11, 140.65, 345.04, 139.22, 339.92, 138.61, 332.97, 138.4, 328.47, 135.33, 325.19, 128.58, 326.22, 129.4, 334.81, 124.69, 340.13, 126.33, 355.47, 127.76, 364.68]], "area": 453.16710000000023, "iscrowd": 0, "image_id": 10114, "bbox": [124.69, 325.19, 15.96, 41.53], "category_id": 44, "id": 1491594}, {"segmentation": [[123.49, 325.61, 123.49, 312.53, 129.11, 311.68, 128.97, 326.88, 129.11, 333.07, 127.0, 337.85, 125.17, 334.62, 124.19, 327.44, 123.21, 326.04]], "area": 120.98685000000033, "iscrowd": 0, "image_id": 10114, "bbox": [123.21, 311.68, 5.9, 26.17], "category_id": 44, "id": 1492171}, {"segmentation": [[278.33, 296.46, 253.13, 267.67, 250.33, 265.27, 248.33, 266.47, 272.73, 297.26]], "area": 154.0279999999994, "iscrowd": 0, "image_id": 10149, "bbox": [248.33, 265.27, 30.0, 31.99], "category_id": 49, "id": 1522417}, {"segmentation": [[199.63, 483.58, 200.0, 448.54, 208.85, 435.26, 225.08, 431.57, 248.32, 428.99, 257.54, 429.36, 268.98, 432.31, 288.16, 441.16, 294.06, 450.75, 296.28, 468.09, 295.17, 484.0]], "area": 4719.046599999999, "iscrowd": 0, "image_id": 10149, "bbox": [199.63, 428.99, 96.65, 55.01], "category_id": 51, "id": 1536016}, {"segmentation": [[204.4, 347.84, 199.64, 347.98, 198.52, 345.74, 196.42, 344.9, 193.62, 345.6, 190.96, 347.98, 186.62, 347.42, 185.92, 341.68, 185.92, 340.0, 186.34, 339.02, 191.8, 337.34, 197.96, 337.34, 206.79, 337.34, 214.07, 338.6, 216.87, 339.58, 217.57, 340.0, 216.45, 345.32, 215.33, 348.96]], "area": 296.14900000000034, "iscrowd": 0, "image_id": 10114, "bbox": [185.92, 337.34, 31.65, 11.62], "category_id": 51, "id": 1539812}, {"segmentation": [[325.57, 405.95, 324.07, 423.75, 320.88, 423.57, 309.08, 413.07, 309.83, 409.89, 313.2, 407.64, 317.51, 405.95, 321.45, 405.02]], "area": 202.2828000000002, "iscrowd": 0, "image_id": 10115, "bbox": [309.08, 405.02, 16.49, 18.73], "category_id": 81, "id": 1646121}, {"segmentation": [[535.68, 159.5, 548.65, 154.63, 554.81, 146.85, 561.3, 142.95, 578.49, 141.01, 601.52, 139.71, 615.14, 146.2, 619.36, 150.09, 628.76, 151.06, 632.33, 155.28, 536.65, 159.17]], "area": 1084.1826000000005, "iscrowd": 0, "image_id": 10393, "bbox": [535.68, 139.71, 96.65, 19.79], "category_id": 3, "id": 1771663}, {"segmentation": [[425.26, 290.42, 422.3, 200.78, 430.45, 192.63, 437.11, 187.45, 438.6, 180.04, 437.86, 166.71, 461.08, 166.33, 460.7, 173.94, 459.94, 178.13, 458.42, 180.42, 459.56, 186.89, 466.03, 191.84, 473.27, 197.93, 475.55, 204.79, 472.13, 212.4, 474.03, 277.52, 475.17, 292.75, 425.29, 291.99]], "area": 5527.4650999999985, "iscrowd": 0, "image_id": 10149, "bbox": [422.3, 166.33, 53.25, 126.42], "category_id": 44, "id": 1865257}, {"segmentation": [[192.45, 236.99, 199.4, 241.71, 202.13, 247.67, 202.63, 251.64, 200.64, 262.07, 200.64, 268.52, 197.42, 269.52, 194.93, 262.56, 186.24, 260.08, 183.02, 256.6, 184.01, 252.13, 182.02, 246.67, 183.26, 238.97, 185.25, 236.74, 190.46, 236.0, 193.44, 236.99]], "area": 454.67104999999947, "iscrowd": 0, "image_id": 10149, "bbox": [182.02, 236.0, 20.61, 33.52], "category_id": 50, "id": 1895680}, {"segmentation": [[229.14, 134.23, 229.14, 164.75, 233.27, 179.69, 234.54, 188.9, 230.09, 195.26, 224.37, 199.39, 223.74, 206.7, 226.28, 213.06, 231.05, 217.51, 240.26, 217.19, 244.4, 214.01, 249.16, 205.75, 248.53, 198.12, 243.12, 190.49, 238.67, 185.73, 234.86, 129.78, 232.64, 119.61, 229.14, 118.02, 226.91, 119.3, 226.6, 124.06, 226.91, 130.74]], "area": 1010.7432500000004, "iscrowd": 0, "image_id": 10149, "bbox": [223.74, 118.02, 25.42, 99.49], "category_id": 50, "id": 1896038}, {"segmentation": [[261.44, 187.52, 264.09, 194.83, 258.45, 205.46, 254.13, 206.79, 248.81, 205.46, 243.83, 190.18, 246.15, 187.19, 253.46, 185.2, 255.79, 127.39, 254.79, 122.74, 256.12, 106.47, 261.77, 107.79, 260.11, 176.56]], "area": 792.6437999999996, "iscrowd": 0, "image_id": 10149, "bbox": [243.83, 106.47, 20.26, 100.32], "category_id": 50, "id": 1896410}, {"segmentation": [[213.15, 265.86, 216.88, 266.95, 219.07, 269.58, 218.63, 273.97, 217.1, 278.57, 214.91, 280.76, 211.84, 282.74, 206.8, 282.74, 205.7, 281.2, 204.82, 277.69, 205.26, 273.09, 206.14, 268.93, 207.67, 267.17, 209.65, 266.08, 213.59, 265.64]], "area": 191.3573500000002, "iscrowd": 0, "image_id": 10149, "bbox": [204.82, 265.64, 14.25, 17.1], "category_id": 50, "id": 1896507}, {"segmentation": [[216.13, 348.09, 216.96, 349.34, 223.82, 349.96, 231.09, 348.92, 236.49, 347.67, 238.57, 344.76, 238.78, 339.78, 230.88, 331.26, 224.23, 330.43, 220.7, 330.22, 218.83, 330.63, 216.13, 333.75, 212.81, 335.62, 212.81, 338.11, 217.58, 339.36, 217.38, 342.06, 216.55, 348.5]], "area": 373.27934999999997, "iscrowd": 0, "image_id": 10114, "bbox": [212.81, 330.22, 25.97, 19.74], "category_id": 51, "id": 1902891}, {"segmentation": [[374.75, 85.5, 383.09, 68.82, 373.82, 60.48, 372.9, 52.14, 360.85, 41.95, 357.61, 27.59, 359.92, 16.47, 360.85, 16.0, 362.24, 15.54, 369.65, 12.76, 384.94, 12.76, 396.06, 7.66, 404.87, 10.44, 407.65, 14.15, 412.74, 22.49, 412.74, 23.88, 431.28, 22.95, 440.08, 24.81, 442.4, 28.98, 444.71, 46.58, 439.62, 60.95, 433.13, 64.65, 421.08, 61.87, 418.77, 56.78, 412.74, 66.04, 412.74, 79.48, 413.67, 86.89, 409.04, 90.14, 399.31, 90.6, 394.21, 92.45]], "area": 4545.517799999999, "iscrowd": 0, "image_id": 10149, "bbox": [357.61, 7.66, 87.1, 84.79], "category_id": 64, "id": 1954200}, {"segmentation": [[640.0, 452.45, 486.23, 432.3, 342.73, 495.81, 640.0, 497.0]], "area": 12950.418099999999, "iscrowd": 0, "image_id": 10192, "bbox": [342.73, 432.3, 297.27, 64.7], "category_id": 67, "id": 1959512}, {"segmentation": [[476.35, 433.75, 366.62, 481.51, 357.58, 444.07, 365.33, 413.09, 416.96, 429.87, 458.27, 418.25]], "area": 4096.8555000000015, "iscrowd": 0, "image_id": 10192, "bbox": [357.58, 413.09, 118.77, 68.42], "category_id": 67, "id": 1959910}, {"segmentation": [[64.0, 423.19, 1.17, 434.92, 1.17, 454.19, 0.0, 497.0, 344.66, 495.66, 368.54, 483.09, 357.23, 445.39, 365.61, 418.17, 357.23, 416.07, 354.71, 401.41, 340.05, 395.97, 321.62, 391.78, 305.28, 388.43, 288.95, 387.17, 270.94, 390.1, 260.04, 392.61, 246.22, 393.87, 244.13, 393.03, 223.18, 395.97]], "area": 32284.978699999996, "iscrowd": 0, "image_id": 10192, "bbox": [0.0, 387.17, 368.54, 109.83], "category_id": 67, "id": 1962651}, {"segmentation": [[173.57, 461.6, 173.57, 448.46, 177.72, 441.54, 182.56, 439.46, 183.25, 429.09, 185.32, 427.01, 185.32, 416.64, 193.62, 418.71, 199.16, 420.1, 197.08, 423.56, 195.01, 423.56, 194.32, 425.63, 195.01, 431.16, 196.39, 440.16, 205.38, 449.15, 204.0, 470.59, 204.69, 478.2, 187.4, 466.44, 180.48, 461.6]], "area": 1072.58015, "iscrowd": 0, "image_id": 10428, "bbox": [173.57, 416.64, 31.81, 61.56], "category_id": 44, "id": 2094835}, {"segmentation": [[549.64, 321.54, 565.15, 324.8, 575.34, 328.88, 576.98, 340.71, 574.12, 388.44, 573.3, 401.5, 533.33, 400.27, 536.18, 379.06, 530.47, 369.68, 529.25, 328.07, 542.3, 322.77]], "area": 3339.3894999999998, "iscrowd": 0, "image_id": 10149, "bbox": [529.25, 321.54, 47.73, 79.96], "category_id": 47, "id": 2100171}, {"segmentation": [[473.3, 399.75, 510.85, 399.75, 513.38, 330.44, 503.63, 324.66, 488.11, 322.86, 474.39, 326.83, 467.17, 330.08, 470.42, 398.67]], "area": 3221.5601999999994, "iscrowd": 0, "image_id": 10149, "bbox": [467.17, 322.86, 46.21, 76.89], "category_id": 47, "id": 2100778}, {"segmentation": [[411.08, 319.82, 410.61, 354.22, 415.79, 397.09, 451.13, 398.04, 455.37, 324.54, 453.95, 317.47, 412.02, 317.94]], "area": 3292.3652499999985, "iscrowd": 0, "image_id": 10149, "bbox": [410.61, 317.47, 44.76, 80.57], "category_id": 47, "id": 2100798}, {"segmentation": [[259.65, 265.54, 275.7, 287.46, 283.52, 292.54, 288.61, 290.19, 280.0, 281.59, 263.96, 260.85, 261.22, 260.85, 259.26, 263.19]], "area": 251.60530000000043, "iscrowd": 0, "image_id": 10149, "bbox": [259.26, 260.85, 29.35, 31.69], "category_id": 49, "id": 2104794}, {"segmentation": [[270.53, 389.61, 263.96, 401.59, 265.5, 413.18, 267.43, 420.14, 296.41, 421.3, 297.19, 410.86, 292.55, 393.47, 289.07, 391.54]], "area": 876.2192, "iscrowd": 0, "image_id": 10428, "bbox": [263.96, 389.61, 33.23, 31.69], "category_id": 86, "id": 2145731}, {"segmentation": [[412.88, 56.62, 415.85, 88.74, 411.09, 90.52, 399.2, 90.52, 393.85, 90.52, 384.93, 70.3, 386.12, 53.65, 388.5, 50.08, 408.71, 40.57]], "area": 1172.7528499999996, "iscrowd": 0, "image_id": 10149, "bbox": [384.93, 40.57, 30.92, 49.95], "category_id": 86, "id": 2146666}, {"segmentation": [[166.87, 385.21, 194.22, 387.62, 217.76, 389.43, 227.01, 395.06, 230.43, 399.29, 213.33, 400.69, 174.91, 399.49, 156.41, 398.88, 166.87, 394.86, 167.07, 390.44], [95.86, 376.96, 101.49, 376.35, 109.54, 388.22, 103.7, 389.03, 98.67, 388.63, 95.86, 385.81, 95.25, 376.76]], "area": 861.2610499999998, "iscrowd": 0, "image_id": 10149, "bbox": [95.25, 376.35, 135.18, 24.34], "category_id": 49, "id": 2230908}, {"segmentation": [[165.63, 384.82, 168.1, 390.02, 166.62, 392.0, 156.96, 398.69, 165.87, 400.18, 222.09, 400.67, 230.51, 399.19, 226.05, 395.72, 218.38, 391.01, 209.46, 388.29, 166.37, 384.82], [151.26, 393.0, 151.51, 386.56, 153.24, 385.57, 154.73, 389.78, 151.51, 394.23], [101.98, 375.66, 96.78, 376.4, 95.54, 378.88, 95.29, 384.57, 97.52, 387.79, 103.46, 389.78, 106.19, 388.29, 112.38, 387.79, 107.68, 384.57, 102.72, 377.89]], "area": 939.0742000000004, "iscrowd": 0, "image_id": 10149, "bbox": [95.29, 375.66, 135.22, 25.01], "category_id": 49, "id": 2230919}, {"segmentation": [[261.6, 252.03, 268.9, 255.96, 272.83, 262.13, 294.15, 284.58, 289.38, 286.54, 285.45, 285.7, 262.16, 260.45, 259.08, 255.12]], "area": 301.3193999999992, "iscrowd": 0, "image_id": 10149, "bbox": [259.08, 252.03, 35.07, 34.51], "category_id": 49, "id": 2230933}, {"segmentation": [[180.34, 281.37, 174.19, 303.92, 171.12, 319.29, 176.24, 344.9, 215.18, 368.47, 243.87, 363.35, 269.49, 326.46, 283.83, 333.63, 284.86, 324.41, 313.55, 341.83, 402.69, 325.43, 422.16, 331.58, 432.41, 325.43, 446.75, 317.24, 453.93, 302.89, 461.1, 293.67, 478.52, 285.47, 451.88, 261.9, 441.63, 257.81, 418.06, 266.0, 387.32, 279.32, 374.0, 257.81, 350.43, 255.76, 334.04, 317.24, 306.37, 322.36, 299.2, 313.14, 318.67, 285.47, 325.84, 256.78, 313.55, 251.66, 301.25, 232.19, 292.03, 215.79, 293.05, 203.5, 269.49, 204.52, 256.17, 195.3, 248.99, 230.14, 291.0, 246.53, 301.25, 268.05, 301.25, 281.37, 228.5, 288.55]], "area": 19373.624399999997, "iscrowd": 0, "image_id": 10104, "bbox": [171.12, 195.3, 307.4, 173.17], "category_id": 4, "id": 150470}, {"segmentation": [[328.57, 280.82, 328.57, 318.07, 328.57, 331.44, 347.68, 344.81, 599.84, 334.3, 605.57, 309.47, 598.88, 298.96, 595.06, 280.82, 596.97, 271.26, 600.79, 265.53, 603.66, 257.89, 607.48, 219.69, 619.9, 211.09, 628.49, 176.7, 624.67, 170.02, 616.08, 170.02, 589.33, 49.67, 353.41, 46.8, 339.08, 50.62, 332.39, 51.58, 330.48, 75.46, 329.53, 79.28, 315.2, 62.09, 312.34, 59.22, 309.47, 85.01, 305.65, 89.78, 298.01, 107.93, 298.01, 133.72, 331.44, 140.41, 328.57, 159.51, 326.66, 170.02, 364.87, 158.56, 383.97, 150.91, 392.57, 139.45, 403.08, 139.45, 400.21, 148.05, 400.21, 148.05, 408.81, 157.6, 408.81, 166.2, 401.17, 174.79, 389.7, 174.79, 383.02, 184.35, 374.42, 199.63, 328.57, 234.01, 329.53, 309.47]], "area": 78748.37814999999, "iscrowd": 0, "image_id": 10014, "bbox": [298.01, 46.8, 330.48, 298.01], "category_id": 6, "id": 165509}, {"segmentation": [[144.73, 147.18, 132.25, 144.43, 129.5, 135.19, 117.76, 129.7, 110.52, 139.19, 103.53, 143.93, 101.53, 143.69, 91.29, 134.2, 82.8, 140.19, 76.56, 142.19, 66.32, 139.94, 62.83, 118.46, 60.58, 130.7, 55.08, 137.94, 50.84, 141.44, 41.85, 143.19, 40.6, 148.18, 34.11, 152.18, 26.87, 157.67, 19.87, 159.92, 22.12, 160.67, 12.13, 160.67, 0.0, 165.41, 0.4, 169.41, 115.77, 166.66, 134.25, 161.17, 145.48, 148.43], [131.81, 174.16, 129.7, 180.49, 28.34, 183.66, 24.12, 182.08, 31.51, 176.8, 128.64, 174.68]], "area": 3885.831749999999, "iscrowd": 0, "image_id": 10104, "bbox": [0.0, 118.46, 145.48, 65.2], "category_id": 9, "id": 180981}, {"segmentation": [[327.55, 153.42, 326.63, 167.23, 326.63, 176.43, 336.75, 171.83, 343.19, 166.31, 356.99, 158.94, 375.4, 158.02, 385.52, 147.9, 391.96, 139.62, 401.16, 135.94, 403.92, 140.54, 400.24, 144.22, 395.64, 147.9, 398.4, 151.58, 403.0, 152.5, 407.6, 162.63, 400.24, 168.15, 395.64, 174.59, 387.36, 174.59, 385.52, 174.59, 376.32, 181.03, 368.95, 193.91, 356.99, 208.63, 326.63, 232.55, 324.79, 235.31, 324.79, 286.84, 306.39, 308.92, 295.35, 345.72, 290.75, 388.97, 283.39, 404.61, 285.23, 420.25, 247.5, 420.25, 239.22, 352.16, 239.22, 319.96, 239.22, 287.76, 238.3, 258.31, 249.34, 241.75, 261.3, 203.11, 276.03, 172.75, 284.31, 160.79, 292.59, 146.98, 298.11, 137.78, 306.39, 130.42, 316.51, 130.42, 322.95, 131.34, 329.39, 140.54]], "area": 20437.850900000012, "iscrowd": 0, "image_id": 10014, "bbox": [238.3, 130.42, 169.3, 289.83], "category_id": 1, "id": 188926}, {"segmentation": [[124.97, 241.82, 115.85, 223.58, 105.82, 216.28, 112.2, 212.63, 113.11, 198.95, 124.97, 185.27, 140.48, 171.58, 152.34, 167.93, 160.55, 167.93, 170.58, 173.41, 176.97, 187.09, 180.62, 199.86, 181.53, 201.69, 190.65, 217.19, 189.74, 230.88, 185.18, 237.26, 207.98, 243.65, 218.02, 246.38, 235.35, 249.12, 240.82, 261.89, 237.17, 276.49, 237.17, 301.12, 237.17, 318.45, 239.91, 333.96, 234.44, 339.43, 240.82, 364.06, 242.65, 421.53, 133.18, 419.7, 99.43, 417.88, 104.9, 364.06, 90.31, 408.76, 81.19, 415.14, 70.24, 408.76, 63.85, 393.25, 63.85, 358.58, 64.77, 303.85, 83.01, 269.19, 105.82, 257.33]], "area": 33470.63339999999, "iscrowd": 0, "image_id": 10014, "bbox": [63.85, 167.93, 178.8, 253.6], "category_id": 1, "id": 189863}, {"segmentation": [[247.22, 157.92, 241.24, 159.12, 237.66, 161.51, 232.87, 162.71, 228.09, 168.69, 226.89, 169.88, 220.91, 169.28, 218.52, 169.28, 215.53, 171.08, 211.35, 172.87, 204.17, 172.87, 201.78, 172.87, 192.81, 194.4, 192.81, 202.77, 196.4, 209.35, 206.56, 218.32, 208.36, 225.49, 211.35, 232.07, 211.95, 237.45, 217.33, 242.23, 225.1, 244.03, 230.48, 245.82, 231.68, 251.8, 226.89, 262.56, 230.48, 265.55, 239.45, 267.35, 254.4, 270.34, 246.63, 273.92, 232.87, 273.92, 230.48, 273.92, 228.09, 273.33, 222.71, 272.13, 216.13, 275.72, 208.36, 282.29, 206.56, 284.09, 210.15, 294.25, 215.53, 299.64, 217.93, 305.02, 220.91, 311.0, 224.5, 316.38, 226.3, 317.57, 229.88, 318.77, 231.08, 319.96, 235.27, 317.57, 235.86, 312.19, 236.46, 309.8, 237.06, 306.81, 232.28, 300.23, 231.68, 296.65, 231.68, 290.67, 231.68, 289.47, 271.74, 287.08, 281.9, 286.48, 289.68, 283.49, 296.85, 279.9, 301.04, 273.33, 301.64, 268.54, 301.64, 261.96, 298.65, 257.18, 284.3, 247.02, 273.53, 241.04, 263.97, 237.45, 256.19, 230.27, 253.2, 227.28, 249.02, 224.89, 249.62, 214.73, 250.21, 211.74, 250.81, 207.55, 250.81, 204.56, 253.8, 194.4, 255.0, 192.01, 265.76, 199.78, 271.74, 203.96, 275.93, 205.16, 284.89, 203.37, 295.06, 199.78, 299.24, 199.18, 303.43, 195.59, 304.63, 194.4, 305.82, 193.2, 311.2, 192.01, 315.39, 190.81, 317.78, 187.82, 321.97, 184.83, 322.56, 180.65, 320.77, 178.85, 324.36, 178.25, 326.75, 176.46, 324.96, 174.67, 319.57, 174.07, 314.19, 174.07, 310.61, 174.07, 307.02, 176.46, 301.04, 177.66, 293.86, 178.25, 283.7, 178.25, 278.32, 178.25, 274.73, 178.25, 272.34, 174.07, 270.54, 172.27, 266.36, 169.28, 262.77, 166.29, 257.39, 161.51, 251.41, 157.33], [241.24, 129.82, 250.81, 131.61, 257.39, 133.41, 262.77, 134.01, 267.55, 135.2, 271.74, 138.79, 272.34, 135.8, 272.34, 133.41, 268.75, 129.22, 266.36, 125.63, 263.37, 125.04, 256.19, 123.24, 251.41, 122.65, 247.82, 123.84, 243.04, 128.62]], "area": 9512.658049999996, "iscrowd": 0, "image_id": 10104, "bbox": [192.81, 122.65, 133.94, 197.31], "category_id": 1, "id": 190109}, {"segmentation": [[260.44, 204.49, 254.4, 191.28, 247.61, 188.08, 244.78, 179.96, 241.95, 176.0, 240.25, 170.34, 236.1, 167.89, 229.69, 168.46, 226.67, 175.25, 226.1, 179.21, 229.12, 185.62, 224.78, 188.08, 218.18, 195.24, 208.18, 216.0, 208.37, 222.6, 212.71, 226.0, 221.95, 231.09, 221.01, 244.86, 228.74, 254.48, 240.44, 263.54, 239.5, 253.16, 248.93, 245.43, 252.89, 235.24, 253.83, 222.41, 255.91, 209.96, 258.17, 206.56]], "area": 2742.208, "iscrowd": 0, "image_id": 10014, "bbox": [208.18, 167.89, 52.26, 95.65], "category_id": 1, "id": 204203}, {"segmentation": [[271.61, 176.6, 249.89, 156.12, 226.3, 164.81, 225.06, 170.4, 201.48, 171.64, 191.54, 197.09, 205.2, 216.95, 205.82, 235.57, 207.68, 244.26, 197.13, 246.74, 184.72, 236.81, 171.68, 217.57, 154.92, 213.22, 147.48, 213.22, 149.34, 226.88, 138.17, 229.36, 125.13, 233.71, 112.1, 225.64, 109.62, 215.09, 104.65, 204.53, 105.27, 200.19, 109.62, 192.74, 111.48, 185.91, 121.41, 187.15, 126.99, 179.71, 134.44, 161.71, 139.41, 153.02, 160.51, 131.91, 193.41, 118.88, 202.72, 127.57, 213.89, 125.71, 256.72, 127.57, 279.68, 139.98, 289.61, 146.81, 298.3, 154.88, 301.41, 163.57, 279.68, 179.71]], "area": 11033.530300000002, "iscrowd": 0, "image_id": 10104, "bbox": [104.65, 118.88, 196.76, 127.86], "category_id": 1, "id": 225283}, {"segmentation": [[290.63, 176.32, 298.85, 157.84, 329.66, 142.43, 366.63, 123.95, 391.27, 117.79, 408.73, 117.79, 434.41, 131.14, 457.0, 144.49, 476.51, 158.87, 492.94, 170.16, 513.48, 180.43, 529.91, 187.62, 519.64, 198.92, 503.21, 195.84, 459.05, 182.49, 429.27, 182.49, 410.79, 185.57, 403.6, 189.67, 391.27, 168.11, 372.79, 158.87, 346.09, 160.92, 324.52, 170.16, 295.77, 175.3], [295.77, 159.89, 330.68, 141.41, 330.68, 132.16, 313.22, 141.41, 297.82, 150.65]], "area": 8452.299299999999, "iscrowd": 0, "image_id": 10104, "bbox": [290.63, 117.79, 239.28, 81.13], "category_id": 1, "id": 227693}, {"segmentation": [[329.37, 141.53, 329.37, 140.5, 330.4, 139.47, 329.37, 137.41, 329.37, 136.38, 329.37, 130.2, 329.37, 127.12, 329.37, 125.06, 330.4, 121.97, 337.6, 112.71, 338.63, 112.71, 344.81, 111.68, 348.93, 111.68, 355.1, 111.68, 360.25, 117.85, 364.36, 125.06, 364.36, 126.09, 363.34, 128.15, 353.04, 130.2, 344.81, 132.26, 334.52, 138.44, 331.43, 140.5], [383.92, 165.2, 392.16, 174.46, 392.16, 175.49, 393.18, 182.7, 396.27, 184.76, 397.3, 192.99, 397.3, 194.02, 397.3, 197.11, 397.3, 201.22, 395.24, 208.43, 397.3, 215.63, 395.24, 227.99, 395.24, 232.1, 398.33, 238.28, 393.18, 244.45, 390.1, 248.57, 376.72, 249.6, 366.42, 249.6, 357.16, 247.54, 355.1, 245.48, 348.93, 245.48, 345.84, 248.57, 346.87, 261.95, 345.84, 277.39, 334.52, 314.44, 325.25, 319.59, 309.81, 321.65, 304.67, 321.65, 289.23, 317.53, 288.2, 316.5, 294.37, 310.33, 303.64, 308.27, 317.02, 301.06, 323.19, 293.86, 327.31, 276.36, 327.31, 259.89, 327.31, 250.63, 327.31, 234.16, 327.31, 233.13, 313.93, 232.1, 306.73, 231.07, 298.49, 226.96, 314.96, 219.75, 317.02, 219.75, 317.02, 219.75, 313.93, 219.75, 311.87, 218.72, 303.64, 214.6, 301.58, 203.28, 307.75, 196.08, 317.02, 192.99, 320.11, 191.96, 322.16, 186.81, 322.16, 185.78, 329.37, 175.49, 330.4, 173.43, 332.46, 168.29, 333.49, 167.26, 341.72, 166.23, 349.95, 162.11, 365.39, 162.11]], "area": 8872.4605, "iscrowd": 0, "image_id": 10104, "bbox": [288.2, 111.68, 110.13, 209.97], "category_id": 1, "id": 237196}, {"segmentation": [[363.34, 258.86, 396.27, 250.63, 419.95, 242.4, 416.86, 238.28, 385.98, 237.25, 405.54, 221.81, 467.29, 217.69, 500.23, 222.84, 538.31, 258.86, 535.23, 270.19, 565.07, 300.03, 583.6, 323.71, 561.99, 323.71, 552.72, 313.42, 530.08, 302.09, 517.73, 290.77, 495.08, 278.42, 473.47, 269.16, 424.06, 260.92, 395.24, 275.33, 376.72, 287.68, 367.45, 276.36, 366.42, 267.1]], "area": 8580.723049999999, "iscrowd": 0, "image_id": 10104, "bbox": [363.34, 217.69, 220.26, 106.02], "category_id": 1, "id": 237443}, {"segmentation": [[153.17, 326.65, 162.88, 268.4, 163.96, 246.83, 140.22, 121.71, 163.96, 103.37, 166.11, 68.85, 193.08, 65.62, 193.08, 110.92, 199.55, 202.61, 186.61, 248.99, 181.21, 309.39, 179.06, 323.42, 202.79, 344.99, 187.69, 347.15, 186.61, 360.09, 172.58, 362.25]], "area": 10062.564550000003, "iscrowd": 0, "image_id": 10040, "bbox": [140.22, 65.62, 62.57, 296.63], "category_id": 1, "id": 443546}, {"segmentation": [[226.73, 628.69, 311.39, 630.12, 318.57, 564.11, 322.87, 562.68, 330.04, 630.12, 404.66, 632.99, 413.27, 625.82, 400.36, 552.63, 371.66, 489.49, 334.35, 424.92, 324.3, 368.96, 353.0, 370.39, 357.31, 354.61, 363.05, 333.08, 365.92, 305.82, 340.09, 275.68, 312.83, 229.76, 311.39, 209.67, 305.65, 191.02, 289.87, 170.93, 272.65, 160.88, 233.9, 160.88, 228.16, 162.32, 203.77, 173.8, 206.64, 162.32, 219.55, 117.84, 218.12, 104.92, 202.33, 92.01, 185.11, 89.14, 177.94, 80.53, 175.07, 80.53, 170.76, 86.27, 167.89, 92.01, 170.76, 107.79, 172.2, 122.14, 170.76, 149.41, 160.72, 162.32, 143.5, 182.41, 111.93, 244.11, 124.84, 298.64, 139.19, 335.95, 142.06, 401.96, 152.11, 499.54, 196.59, 532.54, 205.2, 533.98, 223.86, 548.33, 231.03, 561.24, 231.03, 577.03, 233.9, 597.12]], "area": 94542.05124999995, "iscrowd": 0, "image_id": 10249, "bbox": [111.93, 80.53, 301.34, 552.46], "category_id": 1, "id": 468383}, {"segmentation": [[208.92, 104.02, 218.76, 98.77, 216.14, 82.36, 219.42, 72.52, 225.33, 68.58, 237.8, 70.55, 246.99, 77.77, 245.67, 84.99, 248.96, 88.27, 246.33, 93.52, 245.02, 96.14, 243.05, 100.74, 239.11, 105.33, 237.14, 107.96, 231.89, 106.64, 231.89, 108.61, 242.39, 117.8, 247.64, 126.99, 246.33, 144.71, 243.05, 158.49, 244.36, 175.56, 249.61, 193.28, 247.64, 196.96, 251.58, 212.05, 251.58, 230.43, 250.27, 259.97, 248.3, 277.03, 247.64, 282.28, 249.61, 286.22, 245.02, 290.16, 246.33, 300.0, 252.24, 309.85, 263.39, 311.81, 267.33, 317.72, 267.33, 321.66, 250.92, 321.66, 240.42, 319.03, 232.55, 319.03, 228.61, 316.41, 230.58, 307.22, 232.55, 295.41, 226.64, 275.06, 225.33, 259.97, 221.39, 243.56, 219.42, 241.59, 214.83, 261.28, 213.51, 270.47, 215.48, 276.37, 212.86, 286.87, 211.55, 291.47, 216.8, 300.0, 216.8, 305.25, 218.11, 308.53, 225.98, 315.75, 227.95, 318.38, 225.33, 322.97, 212.2, 322.32, 200.39, 320.35, 200.39, 313.78, 200.39, 302.63, 200.39, 294.09, 195.14, 291.47, 198.42, 265.87, 200.39, 242.9, 204.98, 219.27, 207.61, 181.21, 209.58, 139.2, 208.26, 128.04, 207.61, 122.79, 201.7, 118.2, 200.39, 113.61, 206.95, 103.76]], "area": 9761.904000000002, "iscrowd": 0, "image_id": 10040, "bbox": [195.14, 68.58, 72.19, 254.39], "category_id": 1, "id": 557625}, {"segmentation": [[2.88, 345.17, 139.51, 316.4, 159.64, 385.44, 148.13, 457.35, 181.21, 517.75, 197.03, 519.19, 0.0, 582.47]], "area": 35454.96620000001, "iscrowd": 0, "image_id": 10249, "bbox": [0.0, 316.4, 197.03, 266.07], "category_id": 15, "id": 573228}, {"segmentation": [[613.45, 363.77, 595.16, 364.84, 582.24, 337.94, 563.95, 333.63, 559.64, 367.0, 538.12, 363.77, 533.81, 343.32, 521.97, 339.01, 515.52, 311.03, 499.37, 312.11, 496.14, 286.28, 526.28, 213.09, 611.3, 217.4, 611.3, 242.15, 618.83, 264.75, 607.0, 287.35, 611.3, 355.16]], "area": 13752.665599999998, "iscrowd": 0, "image_id": 10040, "bbox": [496.14, 213.09, 122.69, 153.91], "category_id": 15, "id": 574806}, {"segmentation": [[2.88, 119.13, 25.89, 145.02, 28.46, 158.84, 45.28, 172.05, 62.69, 190.66, 71.1, 206.28, 74.1, 220.69, 72.9, 236.9, 69.3, 249.51, 68.7, 263.93, 70.5, 297.55, 70.5, 324.58, 65.09, 353.4, 55.48, 369.62, 48.28, 376.82, 39.87, 387.03, 29.06, 390.03, 26.66, 379.82, 26.66, 364.21, 26.06, 349.2, 18.85, 369.62, 17.05, 364.81, 22.46, 349.2, 20.65, 335.99, 15.25, 324.58, 18.25, 310.16, 24.26, 292.15, 30.86, 274.73, 29.06, 270.53, 24.86, 269.93, 18.85, 274.13, 13.45, 278.94, 8.64, 281.34, 9.85, 286.74, 6.84, 292.15, 1.44, 300.56]], "area": 12683.455949999998, "iscrowd": 0, "image_id": 10249, "bbox": [1.44, 119.13, 72.66, 270.9], "category_id": 1, "id": 1210047}, {"segmentation": [[67.97, 403.98, 43.08, 414.51, 36.38, 407.81, 52.65, 391.54, 21.06, 334.1, 16.27, 317.82, 3.83, 281.45, 0.0, 192.42, 18.19, 183.8, 12.44, 149.34, 26.8, 141.68, 43.08, 142.64, 68.93, 158.91, 58.4, 180.93, 91.9, 204.86, 93.82, 220.18, 103.39, 240.28, 103.39, 258.47, 71.8, 286.23, 63.18, 359.95, 67.97, 402.07]], "area": 15610.024899999999, "iscrowd": 0, "image_id": 10014, "bbox": [0.0, 141.68, 103.39, 272.83], "category_id": 1, "id": 1211482}, {"segmentation": [[130.46, 181.94, 125.25, 173.12, 128.85, 170.72, 130.86, 167.12, 130.06, 163.11, 122.84, 159.5, 116.43, 157.1, 106.02, 157.1, 100.01, 161.51, 98.41, 167.12, 100.41, 174.33, 102.81, 177.53, 102.41, 183.54, 96.0, 190.75, 91.6, 195.96, 90.79, 196.76, 90.39, 201.57, 94.4, 219.2, 96.8, 233.22, 103.21, 245.64, 110.83, 251.65, 114.03, 251.65, 114.83, 251.65, 124.05, 247.24, 126.05, 244.84, 125.25, 240.43, 125.25, 240.43, 120.04, 218.39, 116.83, 215.99, 115.63, 210.78, 115.63, 205.17, 116.03, 199.16, 118.04, 195.56]], "area": 2362.5342499999997, "iscrowd": 0, "image_id": 10014, "bbox": [90.39, 157.1, 40.47, 94.55], "category_id": 1, "id": 1243346}, {"segmentation": [[257.51, 200.08, 253.69, 191.46, 244.11, 188.59, 244.11, 179.02, 243.16, 173.27, 237.41, 168.49, 238.37, 161.78, 245.07, 156.04, 250.81, 155.08, 255.6, 162.74, 255.6, 172.31, 257.51, 180.93, 262.3, 194.33]], "area": 549.3787999999997, "iscrowd": 0, "image_id": 10014, "bbox": [237.41, 155.08, 24.89, 45.0], "category_id": 1, "id": 1261907}, {"segmentation": [[193.57, 240.27, 189.49, 221.63, 191.82, 204.74, 186.0, 191.35, 184.83, 179.7, 195.32, 165.72, 199.98, 156.98, 212.21, 159.31, 215.71, 163.97, 213.38, 179.11, 216.29, 197.75, 206.39, 218.14, 222.7, 233.28, 220.95, 244.35, 196.48, 243.19]], "area": 2066.8635999999997, "iscrowd": 0, "image_id": 10014, "bbox": [184.83, 156.98, 37.87, 87.37], "category_id": 1, "id": 1296527}, {"segmentation": [[267.75, 178.82, 266.22, 173.9, 265.14, 172.21, 260.38, 170.37, 259.0, 168.68, 259.15, 166.07, 260.07, 163.61, 261.61, 162.38, 263.61, 162.69, 265.45, 164.69, 268.21, 169.6, 268.98, 171.29, 268.67, 180.66]], "area": 79.33435000000014, "iscrowd": 0, "image_id": 10014, "bbox": [259.0, 162.38, 9.98, 18.28], "category_id": 1, "id": 1304616}, {"segmentation": [[13.9, 150.18, 12.22, 149.15, 9.04, 148.59, 5.03, 149.06, 3.16, 150.56, 2.04, 153.45, 1.85, 155.04, 2.13, 156.82, 3.53, 159.15, 3.44, 160.37, 0.0, 163.64, 0.82, 165.32, 3.16, 166.91, 9.03, 168.91, 9.49, 172.3, 9.95, 175.07, 7.49, 176.14, 9.34, 176.14, 14.57, 179.99, 14.73, 184.3, 15.5, 185.38, 15.96, 185.23, 18.42, 182.61, 17.65, 171.99, 16.88, 165.68, 15.65, 160.29, 15.65, 158.14, 15.03, 154.13, 14.88, 149.98, 13.65, 148.75]], "area": 360.76669999999984, "iscrowd": 0, "image_id": 10014, "bbox": [0.0, 148.59, 18.42, 36.79], "category_id": 1, "id": 1311650}, {"segmentation": [[192.4, 75.91, 204.43, 80.54, 208.13, 94.42, 205.36, 112.94, 200.73, 114.79, 208.13, 121.27, 209.06, 138.86, 205.36, 157.37, 196.1, 116.64, 188.69, 108.31, 191.47, 96.27, 189.62, 82.39, 194.25, 81.46, 195.17, 77.76]], "area": 846.5882, "iscrowd": 0, "image_id": 10040, "bbox": [188.69, 75.91, 20.37, 81.46], "category_id": 1, "id": 1739783}, {"segmentation": [[191.33, 181.69, 184.4, 183.42, 185.37, 189.58, 188.83, 197.66, 192.29, 188.61]], "area": 76.22715, "iscrowd": 0, "image_id": 10014, "bbox": [184.4, 181.69, 7.89, 15.97], "category_id": 27, "id": 1826471}, {"segmentation": [[237.66, 162.32, 237.66, 162.32, 230.67, 159.14, 224.95, 162.64, 222.72, 167.4, 223.36, 172.8, 216.05, 177.25, 215.42, 189.33, 217.32, 195.36, 228.44, 185.83, 225.27, 176.62, 230.03, 167.72, 236.71, 167.4]], "area": 308.0061000000003, "iscrowd": 0, "image_id": 10014, "bbox": [215.42, 159.14, 22.24, 36.22], "category_id": 1, "id": 2007571}, {"segmentation": [[444.75, 174.98, 442.79, 168.27, 441.95, 160.16, 443.91, 151.21, 451.18, 139.46, 459.57, 132.75, 469.08, 129.39, 470.76, 129.39, 472.72, 134.42, 474.4, 140.86, 473.28, 145.61, 467.41, 148.69, 460.69, 151.77, 457.06, 157.08, 452.58, 167.99, 450.06, 173.86], [471.56, 128.24, 469.5, 124.53, 466.93, 122.46, 466.82, 118.44, 467.65, 116.07, 468.06, 112.98, 467.96, 108.65, 467.96, 104.63, 467.96, 102.57, 471.56, 100.4, 475.38, 100.3, 482.49, 100.61, 487.13, 100.71, 489.3, 101.74, 489.91, 105.04, 489.91, 106.79, 480.33, 107.62, 478.47, 108.75, 476.0, 111.12, 474.35, 112.16, 473.42, 114.73, 472.6, 117.93, 472.6, 123.19]], "area": 957.1310000000007, "iscrowd": 0, "image_id": 10040, "bbox": [441.95, 100.3, 47.96, 74.68], "category_id": 1, "id": 2026582}, {"segmentation": [[608.49, 234.45, 622.21, 235.43, 639.86, 236.17, 640.0, 205.29, 625.4, 199.41, 623.68, 198.68, 620.25, 205.78, 618.54, 210.2, 616.58, 211.67, 616.33, 217.06, 612.9, 220.49, 608.74, 222.2]], "area": 874.87125, "iscrowd": 0, "image_id": 10014, "bbox": [608.49, 198.68, 31.51, 37.49], "category_id": 3, "id": 2037311}, {"segmentation": [[472.26, 145.38, 457.27, 156.87, 451.28, 171.85, 446.29, 186.33, 439.3, 200.81, 437.8, 208.3, 449.28, 217.79, 456.78, 206.81, 464.27, 195.82, 469.76, 197.82, 464.76, 208.3, 464.76, 235.77, 464.76, 251.75, 465.26, 268.73, 465.76, 284.21, 468.76, 302.68, 465.76, 324.66, 465.76, 333.15, 456.28, 336.64, 456.28, 344.63, 459.77, 352.62, 461.77, 354.12, 470.76, 354.12, 478.25, 346.63, 480.74, 335.64, 481.74, 337.64, 483.24, 350.12, 474.75, 364.11, 488.73, 369.6, 497.72, 370.6, 503.72, 366.6, 502.72, 353.62, 502.72, 343.63, 505.71, 332.15, 505.71, 310.67, 499.22, 280.21, 507.21, 266.73, 512.7, 251.75, 515.2, 240.26, 516.7, 227.28, 519.2, 221.29, 527.19, 216.79, 534.68, 208.8, 535.68, 198.32, 527.19, 165.86, 515.7, 154.87, 504.22, 147.38, 499.22, 142.39, 500.72, 132.9, 500.72, 128.4, 498.72, 129.9, 499.22, 123.41, 499.22, 119.42, 499.22, 112.42, 494.23, 108.93, 478.25, 110.43, 473.75, 114.42, 471.26, 121.41, 471.26, 130.4, 472.75, 139.89, 466.76, 148.88]], "area": 12529.036600000007, "iscrowd": 0, "image_id": 10040, "bbox": [437.8, 108.93, 97.88, 261.67], "category_id": 1, "id": 2156056}, {"segmentation": [[98.98, 30.09, 107.17, 30.48, 124.34, 36.72, 132.92, 39.84, 139.16, 43.74, 135.26, 59.74, 144.23, 71.44, 131.75, 70.27, 134.87, 76.9, 128.24, 88.99, 129.8, 92.11, 118.88, 95.24, 119.66, 103.43, 129.02, 106.94, 140.33, 124.88, 150.08, 144.0, 153.21, 165.06, 149.69, 172.48, 151.64, 195.49, 160.62, 218.51, 161.4, 235.28, 164.52, 248.94, 160.23, 267.66, 148.13, 270.39, 146.57, 289.9, 137.99, 325.79, 129.8, 356.21, 120.05, 371.43, 113.8, 386.64, 116.54, 394.44, 116.54, 406.93, 114.19, 422.92, 132.92, 436.19, 150.47, 438.14, 152.03, 443.6, 143.84, 452.57, 132.92, 454.13, 117.32, 454.13, 109.51, 449.06, 105.61, 454.91, 83.77, 447.5, 79.48, 443.6, 80.65, 406.93, 70.5, 382.35, 75.57, 343.73, 81.82, 287.56, 77.53, 262.98, 68.55, 231.77, 67.38, 210.71, 65.43, 186.13, 61.92, 161.16, 59.58, 124.49, 67.77, 114.74, 77.53, 104.21, 92.74, 90.16, 93.52, 76.9, 88.84, 66.76, 81.04, 64.81, 81.04, 57.01, 90.4, 48.03, 93.52, 40.23, 93.91, 31.65]], "area": 27117.02940000002, "iscrowd": 0, "image_id": 10040, "bbox": [59.58, 30.09, 104.94, 424.82], "category_id": 1, "id": 2157581}, {"segmentation": [[579.23, 399.33, 577.62, 393.51, 579.23, 379.28, 588.29, 371.19, 596.7, 361.81, 609.0, 362.46, 611.58, 358.57, 624.2, 360.51, 623.88, 365.37, 634.23, 365.37, 639.08, 369.9, 640.0, 372.81, 640.0, 401.92, 631.64, 402.57, 628.08, 407.42, 626.14, 406.13, 624.85, 401.92, 587.0, 396.42, 585.38, 399.98]], "area": 2148.780000000001, "iscrowd": 0, "image_id": 10056, "bbox": [577.62, 358.57, 62.38, 48.85], "category_id": 3, "id": 136050}, {"segmentation": [[517.66, 417.16, 553.33, 405.27, 557.65, 301.52, 337.18, 235.6, 316.65, 230.19, 162.11, 220.47, 161.03, 262.61, 152.38, 338.26, 137.25, 379.33, 132.93, 408.51, 184.8, 422.56, 274.5, 443.09, 311.25, 451.74, 370.69, 444.17, 377.17, 408.51, 384.74, 389.06, 398.78, 386.9, 403.11, 420.4, 402.03, 444.17, 452.82, 434.45, 466.87, 432.29, 501.45, 423.64, 503.61, 398.78, 510.1, 382.57, 517.66, 413.91]], "area": 72983.1538, "iscrowd": 0, "image_id": 10056, "bbox": [132.93, 220.47, 424.72, 231.27], "category_id": 6, "id": 166623}, {"segmentation": [[147.31, 352.29, 89.9, 345.79, 74.74, 351.21, 66.07, 350.13, 57.41, 345.79, 46.58, 343.63, 34.66, 354.46, 20.58, 352.29, 14.08, 344.71, 0.0, 350.13, 1.08, 339.3, 2.17, 272.14, 0.0, 188.74, 161.39, 225.57, 160.31, 241.81, 160.31, 254.81, 157.06, 271.06, 155.97, 285.14, 155.97, 300.3, 153.81, 317.63, 150.56, 341.46]], "area": 22039.122900000002, "iscrowd": 0, "image_id": 10056, "bbox": [0.0, 188.74, 161.39, 165.72], "category_id": 6, "id": 168007}, {"segmentation": [[105.66, 236.51, 112.43, 238.33, 113.58, 239.81, 113.25, 241.13, 112.43, 242.12, 109.79, 243.6, 109.13, 243.77, 107.48, 244.43, 109.13, 243.77, 107.64, 244.92, 108.14, 248.06, 108.14, 248.72, 108.47, 249.71, 107.15, 249.71, 105.5, 249.21, 104.84, 249.05, 104.84, 246.74, 104.34, 248.39, 104.18, 248.39, 103.36, 248.55, 101.87, 248.06, 99.89, 247.4, 98.9, 247.07, 100.22, 243.11, 100.39, 240.14, 100.06, 238.16, 100.06, 235.52, 100.39, 232.06, 101.71, 231.73, 104.67, 232.39, 106.16, 233.05, 105.83, 236.18]], "area": 148.98265000000004, "iscrowd": 0, "image_id": 10056, "bbox": [98.9, 231.73, 14.68, 17.98], "category_id": 1, "id": 211642}, {"segmentation": [[120.15, 302.11, 121.26, 300.3, 121.61, 298.63, 121.26, 296.06, 120.78, 294.8, 119.59, 294.66, 117.37, 294.52, 115.77, 294.52, 115.77, 296.75, 115.49, 299.54, 115.56, 301.34, 115.21, 302.46, 114.23, 304.41, 114.23, 304.41, 114.23, 304.48, 114.23, 306.84, 114.58, 308.03, 114.86, 309.77, 115.0, 310.95, 116.46, 311.37, 118.69, 311.78, 121.89, 311.65, 122.59, 311.3, 122.59, 308.79, 122.45, 307.33, 122.31, 305.73, 121.75, 304.82, 121.06, 303.57, 120.22, 303.29, 119.94, 302.74]], "area": 110.90524999999995, "iscrowd": 0, "image_id": 10056, "bbox": [114.23, 294.52, 8.36, 17.26], "category_id": 1, "id": 216891}, {"segmentation": [[492.94, 324.93, 495.12, 320.58, 499.78, 319.33, 501.65, 321.2, 502.58, 324.93, 503.2, 326.8, 504.14, 329.29, 506.0, 331.78, 508.18, 333.95, 509.74, 338.93, 510.67, 343.28, 511.29, 345.15, 511.29, 347.33, 507.87, 347.95, 491.38, 347.33, 490.76, 341.42, 488.58, 336.13, 490.14, 330.84, 491.38, 327.42]], "area": 436.0946500000001, "iscrowd": 0, "image_id": 10056, "bbox": [488.58, 319.33, 22.71, 28.62], "category_id": 1, "id": 256879}, {"segmentation": [[76.77, 293.36, 75.77, 293.03, 75.59, 292.46, 76.09, 291.28, 76.91, 290.21, 77.81, 289.78, 78.13, 289.53, 77.99, 289.21, 78.17, 288.6, 78.27, 287.95, 78.99, 287.56, 79.6, 287.38, 80.06, 287.38, 80.53, 287.49, 80.85, 287.95, 80.85, 289.1, 80.39, 289.78, 81.06, 290.17, 81.71, 291.24, 81.6, 290.42, 82.28, 290.85, 82.53, 291.67, 82.5, 292.96, 82.28, 293.46, 81.64, 293.61, 81.46, 293.61, 81.42, 294.93, 81.03, 295.47, 80.49, 296.07, 79.63, 296.25, 78.42, 296.25, 76.99, 296.07, 76.63, 295.54, 76.63, 294.68, 76.56, 293.75]], "area": 39.79460000000004, "iscrowd": 0, "image_id": 10056, "bbox": [75.59, 287.38, 6.94, 8.87], "category_id": 1, "id": 257592}, {"segmentation": [[534.28, 216.12, 535.0, 211.78, 535.36, 204.56, 534.28, 199.33, 532.83, 195.9, 532.65, 188.14, 537.35, 182.0, 540.42, 181.64, 545.29, 181.64, 548.0, 185.07, 549.44, 188.68, 549.62, 194.82, 548.54, 200.23, 550.89, 200.59, 549.44, 202.94, 548.54, 205.29, 548.54, 206.19, 548.0, 208.72, 548.0, 212.87, 548.18, 216.3, 544.39, 216.3, 542.76, 219.19, 538.07, 219.01, 538.25, 214.67, 538.97, 212.33, 539.15, 208.17, 537.35, 204.92, 537.35, 209.98, 536.99, 214.13, 535.72, 215.94, 534.82, 216.48]], "area": 493.42755000000017, "iscrowd": 0, "image_id": 10211, "bbox": [532.65, 181.64, 18.24, 37.55], "category_id": 24, "id": 588133}, {"segmentation": [[196.65, 186.38, 195.08, 182.27, 193.32, 186.18, 189.6, 185.6, 188.82, 174.24, 195.28, 167.78, 199.0, 166.61, 204.28, 167.78, 207.02, 170.52, 207.81, 177.18, 207.61, 186.97, 200.96, 187.16, 197.43, 186.77]], "area": 321.0947500000001, "iscrowd": 0, "image_id": 10211, "bbox": [188.82, 166.61, 18.99, 20.55], "category_id": 24, "id": 588435}, {"segmentation": [[208.18, 180.96, 214.86, 170.46, 230.14, 168.55, 230.14, 177.14, 228.23, 189.56, 208.18, 181.92, 208.18, 181.92]], "area": 310.5062999999993, "iscrowd": 0, "image_id": 10211, "bbox": [208.18, 168.55, 21.96, 21.01], "category_id": 24, "id": 591194}, {"segmentation": [[182.73, 413.03, 202.08, 413.77, 205.06, 413.03, 196.13, 403.35, 202.08, 348.29, 205.8, 325.23, 213.99, 313.32, 219.94, 337.88, 222.91, 401.12, 227.38, 406.33, 225.89, 417.49, 245.24, 416.0, 234.82, 401.12, 233.33, 343.83, 237.8, 307.37, 246.72, 283.56, 269.05, 251.56, 272.02, 250.08, 280.21, 253.05, 283.93, 249.33, 297.32, 244.87, 302.53, 244.12, 304.76, 250.82, 301.04, 323.74, 304.02, 353.5, 299.55, 397.4, 302.53, 404.1, 299.55, 412.28, 319.64, 416.75, 315.18, 394.42, 315.92, 386.98, 312.2, 377.31, 313.69, 336.39, 318.9, 321.51, 318.9, 293.98, 322.62, 272.4, 323.36, 311.83, 325.6, 340.11, 327.08, 392.19, 329.32, 403.35, 333.78, 411.54, 328.57, 420.47, 350.89, 423.44, 340.48, 397.4, 338.99, 329.69, 341.22, 305.88, 341.22, 277.61, 348.66, 253.8, 353.13, 232.96, 362.8, 214.36, 362.8, 197.25, 358.33, 183.85, 361.31, 170.46, 391.82, 160.04, 432.74, 143.67, 461.76, 125.07, 466.97, 139.95, 472.18, 154.83, 477.39, 154.83, 480.36, 146.65, 480.36, 128.05, 491.52, 113.17, 493.75, 107.96, 504.92, 99.03, 493.75, 95.31, 493.75, 81.92, 490.03, 81.92, 483.34, 90.1, 477.39, 90.1, 478.87, 78.2, 475.9, 78.2, 466.97, 90.84, 459.53, 90.84, 450.6, 81.17, 451.34, 96.8, 427.53, 110.93, 395.54, 114.65, 346.43, 120.61, 329.32, 116.89, 320.39, 119.12, 302.53, 134.0, 266.07, 157.81, 235.56, 168.97, 210.27, 200.97, 207.29, 224.03, 203.57, 264.21, 190.18, 288.02, 181.25, 313.32, 181.25, 358.71, 187.2, 364.66, 183.48, 392.94, 185.71, 401.86, 185.71, 408.56]], "area": 33446.479349999994, "iscrowd": 0, "image_id": 10211, "bbox": [181.25, 78.2, 323.67, 345.24], "category_id": 25, "id": 598283}, {"segmentation": [[0.95, 401.56, 7.64, 403.47, 6.68, 411.11, 20.05, 411.11, 20.05, 406.33, 26.74, 406.33, 34.38, 408.24, 30.56, 375.77, 34.38, 353.81, 34.38, 335.67, 40.11, 323.25, 37.24, 310.84, 50.61, 260.23, 57.3, 238.26, 48.7, 207.7, 46.79, 184.78, 58.25, 163.77, 77.35, 128.44, 85.95, 101.7, 97.41, 91.2, 111.73, 95.97, 127.96, 103.61, 130.83, 107.43, 134.65, 116.98, 142.29, 108.39, 140.38, 97.88, 133.69, 90.24, 125.1, 74.96, 112.68, 56.82, 104.09, 51.09, 93.59, 55.86, 80.22, 53.95, 79.26, 65.41, 82.13, 71.14, 72.58, 84.51, 61.12, 99.79, 55.39, 109.34, 42.97, 123.67, 28.65, 136.08, 20.05, 145.63, 12.41, 159.0, 0.0, 182.87, 0.95, 272.64, 3.82, 279.32, 3.82, 290.78, 3.82, 312.75, 0.0, 339.49, 0.95, 348.08, 0.95, 371.95, 0.95, 387.23]], "area": 15042.883049999999, "iscrowd": 0, "image_id": 10211, "bbox": [0.0, 51.09, 142.29, 360.02], "category_id": 25, "id": 599408}, {"segmentation": [[506.58, 208.19, 505.99, 208.23, 503.43, 211.01, 501.08, 211.86, 499.59, 209.73, 497.88, 208.66, 496.6, 210.79, 495.96, 216.12, 492.13, 218.89, 489.14, 221.45, 488.29, 222.31, 484.88, 222.31, 483.17, 224.22, 483.17, 226.14, 481.47, 228.7, 482.32, 229.98, 484.67, 229.77, 485.73, 230.83, 487.22, 231.05, 489.57, 229.55, 493.62, 230.62, 501.08, 230.62, 502.57, 232.33, 509.61, 250.23, 518.14, 267.08, 526.24, 276.24, 529.01, 280.29, 529.44, 283.71, 526.45, 289.46, 526.03, 294.58, 527.09, 300.12, 531.36, 308.22, 531.78, 312.91, 530.93, 320.59, 531.36, 329.76, 530.08, 335.51, 528.37, 339.35, 527.94, 345.11, 529.22, 349.58, 529.22, 355.55, 527.94, 364.93, 527.94, 369.84, 526.67, 376.02, 525.39, 381.14, 527.09, 383.91, 531.78, 383.7, 532.42, 381.14, 532.0, 374.31, 531.78, 367.49, 533.49, 355.34, 533.91, 354.06, 535.41, 358.54, 535.62, 368.13, 535.41, 374.53, 533.7, 378.15, 534.34, 384.76, 539.46, 383.48, 541.59, 378.37, 540.52, 371.97, 540.1, 360.24, 539.67, 354.91, 541.16, 351.72, 542.44, 342.33, 542.44, 335.09, 543.08, 326.77, 543.08, 322.51, 544.79, 312.49, 552.04, 308.86, 558.01, 311.63, 566.53, 312.06, 569.09, 310.36, 570.37, 312.91, 573.78, 320.16, 578.47, 328.26, 582.52, 339.14, 585.08, 348.94, 585.72, 354.49, 586.36, 366.21, 585.51, 371.12, 584.65, 381.35, 584.23, 386.68, 582.52, 389.66, 589.56, 388.17, 592.12, 386.89, 594.46, 385.4, 594.68, 380.28, 592.97, 374.74, 592.54, 367.49, 592.76, 357.05, 593.82, 350.22, 596.59, 342.97, 597.02, 339.35, 593.4, 333.59, 590.62, 326.98, 590.62, 324.0, 591.26, 315.9, 593.4, 307.37, 595.74, 296.28, 600.01, 294.15, 604.48, 296.5, 606.4, 300.97, 608.32, 309.93, 607.68, 316.11, 608.53, 320.38, 611.3, 326.13, 611.52, 328.05, 611.3, 336.37, 612.16, 340.2, 613.86, 342.76, 615.36, 342.33, 617.7, 336.58, 616.63, 333.38, 618.34, 334.66, 618.34, 336.79, 618.98, 331.89, 617.06, 328.05, 615.14, 326.13, 610.66, 320.8, 610.24, 315.9, 610.66, 306.52, 609.81, 299.06, 605.97, 293.3, 600.86, 288.61, 593.82, 286.05, 589.98, 282.64, 584.02, 277.52, 568.24, 273.47, 562.06, 270.06, 555.66, 264.73, 548.2, 260.04, 544.57, 257.7, 540.31, 255.35, 534.34, 251.3, 529.86, 245.33, 526.45, 240.64, 519.42, 229.77, 515.15, 224.01, 514.51, 224.01, 517.5, 219.32, 519.63, 216.12, 518.14, 213.56, 515.15, 213.78, 512.81, 214.2, 510.46, 216.34, 508.76, 217.61, 507.69, 214.42, 511.53, 209.73, 510.89, 208.45, 508.97, 209.73, 507.69, 208.66, 506.84, 208.45]], "area": 6104.840349999997, "iscrowd": 0, "image_id": 10211, "bbox": [481.47, 208.19, 137.51, 181.47], "category_id": 25, "id": 600144}, {"segmentation": [[591.22, 367.67, 580.69, 378.73, 577.79, 380.31, 575.42, 378.21, 576.47, 369.25, 577.53, 364.25, 581.48, 360.3, 589.12, 350.55, 613.87, 352.13, 616.77, 358.19, 610.98, 359.51, 608.61, 362.93]], "area": 562.4163500000008, "iscrowd": 0, "image_id": 10056, "bbox": [575.42, 350.55, 41.35, 29.76], "category_id": 3, "id": 1350975}, {"segmentation": [[245.2, 306.16, 237.35, 300.98, 237.01, 292.29, 236.85, 285.78, 234.68, 283.1, 225.49, 281.26, 221.48, 288.78, 221.98, 294.13, 217.13, 304.65, 213.46, 313.34, 213.79, 318.02, 219.97, 327.54, 223.98, 334.06, 229.66, 328.04, 227.16, 324.2, 229.83, 318.85, 237.52, 316.01]], "area": 855.3172000000003, "iscrowd": 0, "image_id": 10056, "bbox": [213.46, 281.26, 31.74, 52.8], "category_id": 1, "id": 1688972}, {"segmentation": [[46.12, 292.07, 46.12, 289.09, 45.98, 286.92, 46.66, 284.62, 48.15, 283.0, 50.72, 282.32, 52.89, 283.0, 54.24, 284.22, 53.57, 286.38, 52.21, 287.46, 52.21, 289.22, 52.21, 290.04, 50.86, 290.71, 50.86, 292.34, 49.37, 292.47, 45.85, 292.2]], "area": 59.632950000000044, "iscrowd": 0, "image_id": 10056, "bbox": [45.85, 282.32, 8.39, 10.15], "category_id": 1, "id": 1726843}, {"segmentation": [[62.6, 293.72, 63.23, 292.12, 65.46, 291.4, 65.19, 290.69, 64.92, 288.81, 65.55, 286.85, 67.6, 284.97, 69.84, 284.88, 71.44, 286.31, 71.53, 289.52, 70.55, 289.97, 69.3, 289.97, 69.84, 291.58, 72.34, 292.56, 73.05, 293.99, 73.05, 295.06]], "area": 60.47485000000014, "iscrowd": 0, "image_id": 10056, "bbox": [62.6, 284.88, 10.45, 10.18], "category_id": 1, "id": 1733772}, {"segmentation": [[136.61, 255.14, 135.55, 249.87, 137.66, 249.16, 138.01, 246.63, 139.42, 244.94, 142.02, 244.87, 143.01, 246.42, 142.17, 250.78, 145.47, 254.3, 145.47, 256.41]], "area": 69.52549999999991, "iscrowd": 0, "image_id": 10056, "bbox": [135.55, 244.87, 9.92, 11.54], "category_id": 1, "id": 1746835}, {"segmentation": [[463.74, 314.78, 434.48, 319.21, 420.3, 307.68, 414.98, 281.97, 414.09, 252.71, 397.24, 242.96, 386.6, 219.9, 391.03, 197.73, 403.45, 189.75, 411.43, 182.66, 411.43, 160.49, 391.03, 147.19, 381.28, 137.44, 383.05, 125.02, 400.79, 112.61, 407.88, 108.18, 409.66, 89.56, 419.41, 55.86, 427.39, 47.88, 495.67, 51.43, 500.1, 89.56, 488.57, 135.67, 490.34, 161.38, 485.02, 193.3, 485.02, 215.47, 485.02, 242.96, 478.82, 289.06, 469.95, 315.67]], "area": 22016.922049999997, "iscrowd": 0, "image_id": 10256, "bbox": [381.28, 47.88, 118.82, 271.33], "category_id": 11, "id": 1808675}, {"segmentation": [[169.37, 195.23, 191.35, 183.6, 218.51, 182.3, 234.02, 183.6, 256.0, 191.35, 276.69, 200.4, 293.49, 202.99, 315.47, 206.87, 333.58, 219.8, 332.28, 232.73, 343.92, 239.19, 363.31, 245.66, 380.12, 257.29, 395.64, 277.98, 391.76, 293.49, 387.88, 299.96, 402.1, 307.72, 416.32, 315.47, 430.55, 329.7, 435.72, 352.97, 447.35, 373.66, 465.45, 407.27, 473.21, 430.55, 477.09, 483.56, 475.8, 515.88, 458.99, 535.27, 451.23, 543.03, 468.04, 616.73, 475.8, 632.24, 447.35, 632.24, 438.3, 630.95, 429.25, 629.66, 424.08, 575.35, 382.71, 576.65, 334.87, 580.53, 301.25, 583.11, 281.86, 599.92, 254.71, 618.02, 219.8, 628.36, 201.7, 632.24, 147.39, 632.24, 139.64, 627.07, 156.44, 611.56, 217.21, 601.21, 246.95, 583.11, 253.41, 576.65, 227.56, 572.77, 221.09, 568.89, 226.26, 554.67, 252.12, 532.69, 256.0, 524.93, 230.14, 541.74, 210.75, 537.86, 188.77, 533.98, 162.91, 530.1, 153.86, 533.98, 143.52, 533.98, 131.88, 527.52, 124.12, 522.34, 115.07, 513.29, 121.54, 501.66, 137.05, 504.24, 155.15, 508.12, 190.06, 513.29, 201.7, 506.83, 187.47, 502.95, 195.23, 492.61, 213.33, 484.85, 227.56, 464.16, 232.73, 439.6, 237.9, 415.03, 239.19, 381.41, 231.43, 359.43, 215.92, 351.68, 191.35, 349.09, 178.42, 337.45, 173.25, 333.58, 159.03, 318.06, 155.15, 311.6, 165.49, 303.84, 159.03, 297.37, 142.22, 292.2, 131.88, 275.39, 130.59, 270.22, 137.05, 245.66, 140.93, 227.56, 155.15, 214.63, 165.49, 197.82]], "area": 98498.61659999996, "iscrowd": 0, "image_id": 10107, "bbox": [115.07, 182.3, 362.02, 449.94], "category_id": 18, "id": 6268}, {"segmentation": [[146.42, 98.75, 199.76, 90.99, 233.7, 106.51, 251.15, 110.38, 289.94, 100.69, 348.12, 101.66, 353.94, 102.63, 352.97, 89.05, 367.52, 46.38, 396.61, 21.17, 436.36, 15.35, 453.82, 24.08, 460.61, 30.87, 421.82, 49.29, 405.33, 71.6, 399.52, 95.84, 388.85, 113.29, 381.09, 122.99, 396.61, 150.14, 423.76, 170.51, 451.88, 182.14, 457.7, 172.44, 510.06, 164.69, 529.45, 170.51, 504.24, 184.08, 473.21, 197.66, 433.45, 232.57, 360.73, 211.23, 288.97, 270.38, 298.67, 302.38, 370.42, 405.17, 346.18, 424.57, 327.76, 405.17, 305.45, 365.41, 256.97, 322.75, 219.15, 316.93, 164.85, 322.75, 145.45, 335.35, 140.61, 385.78, 118.3, 385.78, 107.64, 337.29, 120.24, 277.17, 106.67, 234.51, 109.58, 195.72, 89.21, 149.17]], "area": 69146.47120000001, "iscrowd": 0, "image_id": 10219, "bbox": [89.21, 15.35, 440.24, 409.22], "category_id": 18, "id": 12916}, {"segmentation": [[372.86, 372.54, 377.25, 358.27, 379.07, 352.78, 378.34, 343.27, 368.1, 332.3, 374.32, 323.52, 388.95, 326.08, 391.51, 328.64, 389.68, 337.78, 391.88, 346.93, 397.0, 355.71, 400.29, 369.61, 394.44, 387.54, 392.24, 404.73, 390.05, 406.56, 373.95, 374.37]], "area": 1365.0866500000009, "iscrowd": 0, "image_id": 10386, "bbox": [368.1, 323.52, 32.19, 83.04], "category_id": 32, "id": 295226}, {"segmentation": [[2.14, 163.33, 179.7, 140.87, 192.54, 102.36, 204.31, 75.62, 275.97, 78.83, 300.58, 78.83, 338.01, 68.13, 358.34, 114.13, 497.39, 88.46, 496.32, 466.05, 111.24, 468.19, 2.14, 463.91, 1.07, 164.4]], "area": 175404.22400000002, "iscrowd": 0, "image_id": 10395, "bbox": [1.07, 68.13, 496.32, 400.06], "category_id": 65, "id": 314491}, {"segmentation": [[250.25, 471.37, 244.85, 455.19, 251.33, 440.09, 258.88, 406.65, 261.03, 374.29, 270.74, 333.3, 334.38, 307.42, 344.09, 285.84, 332.22, 276.13, 318.2, 244.85, 310.65, 223.28, 322.52, 192.0, 364.58, 174.74, 416.36, 184.45, 424.99, 210.34, 419.6, 222.2, 426.07, 243.78, 419.6, 270.74, 405.57, 281.53, 402.34, 296.63, 423.91, 308.49, 456.27, 324.67, 473.53, 333.3, 475.69, 386.16, 488.63, 443.33, 492.94, 459.51, 487.55, 474.61, 251.33, 470.29]], "area": 46656.33625000001, "iscrowd": 0, "image_id": 10386, "bbox": [244.85, 174.74, 248.09, 299.87], "category_id": 1, "id": 499365}, {"segmentation": [[88.15, 322.04, 88.46, 315.84, 88.46, 306.52, 88.46, 299.38, 88.15, 293.79, 84.74, 293.79, 81.63, 290.38, 81.63, 285.41, 81.94, 277.96, 82.25, 274.86, 85.36, 265.23, 89.39, 260.57, 95.6, 257.47, 99.95, 256.85, 106.78, 257.47, 115.78, 258.71, 118.89, 259.33, 124.16, 260.88, 127.58, 267.09, 129.44, 273.92, 129.13, 280.75, 128.2, 287.58, 126.96, 296.28, 124.47, 306.52, 123.54, 309.94, 123.54, 314.9, 120.75, 317.39, 121.06, 321.42, 117.33, 322.67, 114.85, 322.67, 113.92, 318.63, 112.99, 313.66, 111.12, 310.56, 110.5, 315.84, 110.5, 321.11, 112.06, 323.6, 103.98, 323.91, 101.81, 323.91, 101.81, 319.87, 102.12, 309.01, 102.43, 304.35, 98.71, 299.69, 98.4, 313.04, 98.4, 322.04, 98.4, 324.53]], "area": 2501.76605, "iscrowd": 0, "image_id": 10449, "bbox": [81.63, 256.85, 47.81, 67.68], "category_id": 22, "id": 581256}, {"segmentation": [[184.81, 319.47, 184.09, 298.83, 174.84, 263.25, 178.4, 241.9, 196.19, 234.07, 204.73, 232.65, 212.56, 236.21, 218.25, 231.94, 228.93, 234.78, 237.47, 242.61, 245.3, 246.17, 246.23, 265.6, 248.06, 276.94, 256.48, 287.19, 247.7, 282.06, 242.94, 304.38, 246.23, 316.45, 234.89, 320.48, 229.77, 317.18, 228.31, 283.16, 226.48, 282.06, 224.28, 289.02, 218.43, 289.02, 212.21, 317.92, 214.04, 323.04]], "area": 5058.261750000001, "iscrowd": 0, "image_id": 10449, "bbox": [174.84, 231.94, 81.64, 91.1], "category_id": 22, "id": 583251}, {"segmentation": [[574.65, 274.75, 569.52, 275.32, 566.67, 273.61, 558.12, 273.32, 557.55, 271.04, 555.55, 269.62, 549.57, 269.62, 546.43, 269.05, 542.44, 267.91, 537.88, 269.33, 535.03, 270.19, 529.33, 266.2, 525.34, 261.92, 520.78, 259.07, 513.94, 259.35, 509.09, 260.49, 505.96, 265.34, 508.24, 268.19, 508.52, 269.62, 506.53, 272.47, 505.39, 273.89, 504.82, 267.05, 503.11, 256.22, 502.54, 254.79, 502.54, 251.37, 495.98, 244.82, 488.85, 239.4, 479.73, 233.42, 481.16, 229.14, 482.58, 222.01, 487.14, 212.04, 492.56, 207.48, 499.69, 206.62, 507.38, 207.19, 511.09, 210.33, 517.07, 211.75, 525.62, 208.05, 532.47, 207.19, 538.74, 207.48, 543.87, 209.47, 547.57, 210.9, 554.13, 209.19, 562.39, 207.19, 568.38, 206.34, 574.37, 210.04, 581.21, 214.32, 586.91, 220.3, 593.18, 228.57, 594.89, 243.68, 587.48, 253.65, 586.62, 256.79, 584.63, 260.49, 583.2, 263.35, 579.78, 266.48, 577.5, 270.47]], "area": 5717.9408, "iscrowd": 0, "image_id": 10449, "bbox": [479.73, 206.34, 115.16, 68.98], "category_id": 22, "id": 583543}, {"segmentation": [[336.2, 427.46, 317.26, 422.8, 294.29, 415.35, 275.35, 409.14, 256.41, 403.86, 240.27, 400.14, 229.4, 398.58, 225.05, 398.58, 221.95, 399.51, 222.26, 409.14, 227.85, 417.21, 232.2, 420.01, 246.48, 425.59, 252.06, 429.63, 255.17, 434.91, 275.35, 442.05, 298.32, 449.81, 336.51, 458.19, 368.18, 464.4, 387.11, 466.26, 390.53, 464.71, 399.84, 464.71, 411.95, 465.95, 416.61, 465.33, 421.58, 462.85, 423.75, 456.64, 425.3, 450.74, 424.99, 450.12, 420.33, 450.43, 407.92, 450.43, 393.01, 447.95, 367.25, 442.36, 353.9, 439.56, 341.48, 435.84, 324.71, 432.42, 322.23, 432.11, 326.58, 431.8, 334.65, 429.94]], "area": 4969.974099999998, "iscrowd": 0, "image_id": 10219, "bbox": [221.95, 398.58, 203.35, 67.68], "category_id": 34, "id": 603145}, {"segmentation": [[229.86, 79.61, 254.56, 71.85, 306.78, 76.79, 318.07, 67.62, 335.01, 66.2, 353.36, 81.73, 357.59, 97.96, 358.3, 110.66, 359.0, 141.01, 346.3, 188.29, 320.89, 229.93, 313.84, 236.99, 311.01, 245.46, 317.36, 256.04, 337.12, 266.63, 352.65, 275.1, 362.53, 275.8, 387.23, 268.04, 409.81, 267.33, 426.04, 272.98, 428.16, 292.03, 426.75, 318.14, 420.4, 332.26, 377.35, 351.31, 349.12, 352.02, 327.95, 359.08, 316.66, 381.66, 343.48, 378.13, 371.0, 385.19, 378.06, 393.66, 384.41, 413.42, 371.7, 426.12, 349.12, 431.76, 266.55, 445.88, 178.34, 445.17, 105.65, 438.12, 62.6, 436.7, 64.01, 423.3, 66.13, 414.12, 69.66, 404.95, 83.77, 395.07, 138.11, 392.24, 140.94, 361.19, 138.82, 325.2, 85.18, 306.15, 49.19, 275.8, 54.13, 235.58, 74.6, 217.23, 107.77, 228.52, 163.52, 255.34, 187.51, 244.75, 185.4, 236.99, 172.69, 186.88, 179.04, 126.9, 205.86, 90.9]], "area": 84587.43910000003, "iscrowd": 0, "image_id": 10395, "bbox": [49.19, 66.2, 378.97, 379.68], "category_id": 88, "id": 1161234}, {"segmentation": [[186.75, 309.01, 191.93, 305.55, 194.82, 302.95, 197.41, 300.36, 200.87, 295.75, 207.21, 292.58, 211.24, 289.99, 219.31, 288.26, 225.65, 288.84, 228.24, 290.28, 231.41, 291.43, 236.88, 291.43, 240.05, 292.29, 244.95, 292.87, 247.83, 294.02, 250.71, 295.75, 244.95, 300.94, 242.93, 301.51, 237.75, 303.24, 236.88, 306.12, 237.46, 309.29, 236.02, 312.46, 232.85, 317.94, 231.99, 321.97, 231.99, 323.41, 227.66, 327.45, 222.48, 328.31, 216.14, 329.18, 208.07, 328.6, 204.32, 328.02, 202.88, 322.26, 202.6, 320.53, 195.39, 319.38, 194.24, 319.09, 192.22, 317.36, 188.77, 314.77, 185.02, 313.04, 183.87, 312.18]], "area": 1582.0875000000005, "iscrowd": 0, "image_id": 10324, "bbox": [183.87, 288.26, 66.84, 40.92], "category_id": 18, "id": 5515}, {"segmentation": [[241.26, 106.39, 240.05, 88.39, 237.42, 85.96, 236.61, 79.69, 234.99, 77.67, 234.79, 75.24, 232.36, 75.04, 230.34, 75.64, 230.14, 77.67, 228.11, 81.91, 227.91, 85.96, 225.08, 87.78, 223.06, 90.21, 222.04, 103.76, 222.65, 105.58]], "area": 425.87115000000006, "iscrowd": 0, "image_id": 10324, "bbox": [222.04, 75.04, 19.22, 31.35], "category_id": 44, "id": 79728}, {"segmentation": [[356.69, 325.86, 356.69, 349.38, 351.59, 357.88, 352.44, 381.12, 367.75, 380.84, 365.2, 370.07, 370.3, 364.68, 372.28, 350.8, 376.82, 346.26, 374.83, 321.89, 359.25, 322.18], [352.44, 389.34, 350.74, 401.81, 374.83, 401.24, 374.26, 387.36]], "area": 1340.3254499999998, "iscrowd": 0, "image_id": 10324, "bbox": [350.74, 321.89, 26.08, 79.92], "category_id": 44, "id": 81503}, {"segmentation": [[318.06, 368.19, 323.56, 358.56, 326.32, 348.92, 327.69, 343.42, 329.07, 337.91, 327.69, 322.77, 329.07, 317.27, 337.33, 310.39, 340.08, 311.76, 341.46, 314.52, 345.59, 318.65, 346.96, 326.9, 344.21, 335.16, 344.21, 347.55, 344.21, 355.81, 344.21, 361.31, 342.83, 373.7, 340.08, 376.45, 337.33, 375.08, 333.2, 376.45, 329.07, 376.45, 326.32, 375.08, 315.31, 373.7]], "area": 1189.6992999999989, "iscrowd": 0, "image_id": 10324, "bbox": [315.31, 310.39, 31.65, 66.06], "category_id": 44, "id": 92315}, {"segmentation": [[170.41, 179.11, 195.19, 179.89, 195.71, 165.55, 188.67, 157.98, 189.45, 140.77, 187.88, 138.17, 185.28, 137.64, 180.58, 137.38, 177.71, 138.43, 176.93, 154.33, 171.46, 161.11, 171.2, 166.59]], "area": 739.8487499999998, "iscrowd": 0, "image_id": 10324, "bbox": [170.41, 137.38, 25.3, 42.51], "category_id": 44, "id": 92530}, {"segmentation": [[322.13, 101.64, 324.22, 110.0, 321.43, 118.7, 323.87, 130.19, 338.49, 118.0, 336.75, 109.65, 333.27, 97.46], [340.93, 123.92, 341.28, 131.93, 331.88, 139.59, 327.0, 134.71]], "area": 451.9323999999996, "iscrowd": 0, "image_id": 10324, "bbox": [321.43, 97.46, 19.85, 42.13], "category_id": 44, "id": 93741}, {"segmentation": [[292.72, 67.08, 292.41, 61.86, 294.25, 56.63, 294.87, 51.71, 297.02, 50.18, 297.64, 55.4, 300.4, 57.25]], "area": 57.0981499999997, "iscrowd": 0, "image_id": 10324, "bbox": [292.41, 50.18, 7.99, 16.9], "category_id": 44, "id": 97168}, {"segmentation": [[308.94, 41.46, 310.95, 41.2, 311.49, 45.75, 308.81, 48.3, 308.94, 41.73], [309.74, 53.98, 304.86, 58.66, 305.27, 66.58, 314.0, 56.42, 312.78, 51.14, 309.74, 54.39]], "area": 75.33564999999984, "iscrowd": 0, "image_id": 10324, "bbox": [304.86, 41.2, 9.14, 25.38], "category_id": 44, "id": 97550}, {"segmentation": [[115.06, 7.55, 112.18, 505.17, 133.75, 542.56, 296.27, 556.94, 414.2, 559.82, 396.94, 10.43, 348.04, 7.55, 302.02, 0.36]], "area": 159474.27025000003, "iscrowd": 0, "image_id": 10324, "bbox": [112.18, 0.36, 302.02, 559.46], "category_id": 82, "id": 330631}, {"segmentation": [[335.46, 382.74, 327.91, 398.92, 320.36, 400.0, 320.36, 412.94, 349.48, 424.81, 350.56, 407.55, 359.19, 400.0, 360.27, 366.56, 374.29, 342.83, 380.76, 357.93, 378.61, 382.74, 377.53, 404.31, 382.92, 421.57, 401.26, 422.65, 405.57, 397.84, 405.57, 355.78, 404.49, 325.57, 408.81, 296.45, 405.57, 267.33, 399.1, 216.63, 394.79, 211.24, 372.13, 195.06, 373.21, 168.09, 352.72, 173.48, 340.85, 187.51, 348.4, 212.31, 347.33, 241.44, 331.15, 242.52, 325.75, 269.48, 339.78, 273.8, 354.88, 275.96, 354.88, 287.82, 345.17, 314.79, 343.01, 335.28, 337.62, 362.25]], "area": 13476.947949999998, "iscrowd": 0, "image_id": 10142, "bbox": [320.36, 168.09, 88.45, 256.72], "category_id": 1, "id": 469874}, {"segmentation": [[501.97, 248.91, 490.14, 253.5, 479.63, 261.39, 474.38, 280.44, 468.46, 297.52, 468.46, 307.37, 472.41, 314.6, 478.32, 314.6, 481.6, 311.97, 482.92, 310.0, 490.14, 310.0, 495.4, 317.88, 498.03, 355.33, 508.54, 407.88, 517.08, 426.0, 522.84, 392.18, 524.15, 378.38, 528.75, 344.22, 531.38, 323.2, 535.32, 311.38, 537.29, 312.69, 535.32, 321.23, 535.32, 324.52, 535.98, 327.14, 543.2, 325.17, 548.46, 316.63, 543.2, 269.33, 535.32, 257.51, 528.09, 247.66, 525.47, 247.0, 524.81, 229.26, 516.27, 222.04, 504.44, 222.04, 505.76, 232.55]], "area": 7545.963900000004, "iscrowd": 0, "image_id": 10442, "bbox": [468.46, 222.04, 80.0, 203.96], "category_id": 1, "id": 470210}, {"segmentation": [[266.61, 247.9, 267.57, 239.47, 264.92, 236.82, 264.92, 229.83, 264.92, 227.91, 264.68, 225.98, 266.13, 223.57, 269.26, 222.13, 271.18, 222.61, 272.15, 224.78, 272.63, 226.7, 272.87, 230.08, 273.59, 233.69, 274.31, 240.91, 274.31, 245.25, 273.35, 249.1, 270.22, 250.79]], "area": 203.00394999999966, "iscrowd": 0, "image_id": 10142, "bbox": [264.68, 222.13, 9.63, 28.66], "category_id": 1, "id": 475005}, {"segmentation": [[465.34, 289.28, 464.38, 269.13, 457.66, 258.57, 455.74, 247.06, 453.82, 235.55, 460.54, 218.28, 469.18, 211.56, 472.05, 204.84, 475.89, 189.49, 478.77, 180.86, 494.12, 181.82, 496.04, 192.37, 497.0, 201.97, 502.76, 208.68, 508.51, 214.44, 505.64, 222.11, 503.72, 229.79, 503.72, 237.47, 503.72, 243.22, 496.04, 251.86, 483.57, 263.37, 474.93, 275.84, 473.01, 288.32, 469.18, 296.95], [467.26, 324.78, 468.22, 343.01, 471.09, 352.6, 473.01, 353.56, 477.81, 355.48, 484.53, 356.44, 494.12, 355.48, 496.04, 346.84, 496.04, 329.57, 493.16, 322.86, 487.41, 326.7, 482.61, 333.41, 477.81, 329.57, 475.89, 322.86]], "area": 4055.0317000000005, "iscrowd": 0, "image_id": 10442, "bbox": [453.82, 180.86, 54.69, 175.58], "category_id": 1, "id": 483257}, {"segmentation": [[293.89, 168.49, 320.7, 186.67, 336.97, 172.31, 347.5, 167.53, 354.2, 157.96, 365.69, 157.96, 362.82, 171.36, 363.78, 175.19, 380.05, 184.76, 379.09, 242.2, 378.13, 249.86, 370.48, 245.07, 362.82, 245.07, 368.56, 232.62, 368.56, 205.82, 335.06, 314.95, 321.65, 311.12, 328.36, 195.29, 317.82, 197.2, 299.64, 177.1, 291.98, 176.14, 291.98, 169.44]], "area": 5565.394749999999, "iscrowd": 0, "image_id": 10442, "bbox": [291.98, 157.96, 88.07, 156.99], "category_id": 1, "id": 561954}, {"segmentation": [[220.49, 261.91, 224.41, 207.66, 227.02, 187.39, 218.53, 169.09, 228.33, 140.33, 244.02, 134.45, 241.4, 120.07, 244.67, 114.84, 264.94, 123.33, 259.06, 140.98, 271.47, 152.75, 283.24, 210.28, 268.21, 226.62, 268.86, 203.08, 271.47, 195.89, 262.98, 178.9, 266.24, 224.66, 264.94, 273.03, 265.59, 281.53, 251.86, 276.3, 245.98, 218.12, 240.75, 234.46, 234.22, 274.99, 222.45, 272.37]], "area": 6333.2877499999995, "iscrowd": 0, "image_id": 10442, "bbox": [218.53, 114.84, 64.71, 166.69], "category_id": 1, "id": 570788}, {"segmentation": [[81.37, 242.2, 79.46, 214.44, 83.29, 195.29, 83.29, 186.67, 67.97, 182.84, 72.76, 168.49, 74.67, 134.98, 80.41, 121.58, 90.94, 116.79, 96.69, 97.64, 104.35, 92.86, 119.66, 97.64, 112.0, 117.75, 123.49, 130.19, 133.07, 168.49, 134.02, 194.33, 123.49, 189.55, 117.75, 238.37, 122.53, 250.81, 104.35, 248.9, 106.26, 195.29, 92.86, 223.05, 93.82, 243.16, 89.99, 248.9]], "area": 6188.757300000001, "iscrowd": 0, "image_id": 10442, "bbox": [67.97, 92.86, 66.05, 157.95], "category_id": 1, "id": 571062}, {"segmentation": [[363.49, 231.51, 363.49, 280.55, 365.42, 329.6, 347.15, 322.87, 351.95, 291.13, 351.95, 257.48, 351.95, 244.97], [384.65, 194.97, 384.65, 183.43, 390.42, 168.04, 397.15, 173.81, 397.15, 187.28, 388.5, 208.43], [311.57, 212.28, 300.99, 237.28, 292.33, 247.86, 300.99, 260.36, 311.57, 260.36, 321.18, 251.71, 321.18, 231.51, 327.91, 219.01, 331.76, 209.39, 331.76, 196.89, 318.3, 203.62]], "area": 2654.906800000003, "iscrowd": 0, "image_id": 10442, "bbox": [292.33, 168.04, 104.82, 161.56], "category_id": 1, "id": 571102}, {"segmentation": [[81.41, 19.12, 77.28, 15.0, 77.01, 0.0, 113.02, 1.25, 110.55, 8.68, 112.2, 11.7, 110.27, 16.1, 110.0, 19.95, 108.35, 30.67, 107.52, 40.02, 105.87, 47.72, 106.15, 57.61, 105.87, 60.36, 96.25, 59.26, 94.88, 59.26, 99.55, 55.96, 98.45, 51.01, 94.6, 27.37, 92.68, 44.69, 92.68, 54.04, 91.85, 58.44, 78.38, 58.44, 78.11, 57.89, 83.33, 53.21, 83.61, 46.62]], "area": 1506.72295, "iscrowd": 0, "image_id": 10442, "bbox": [77.01, 0.0, 36.01, 60.36], "category_id": 1, "id": 571191}, {"segmentation": [[243.87, 178.68, 250.85, 187.41, 254.34, 179.26, 248.52, 160.06, 218.26, 166.46, 211.86, 169.37, 176.35, 168.2, 161.22, 178.68, 164.13, 206.04, 163.55, 189.16, 165.88, 184.5, 170.53, 197.31, 169.37, 204.29, 171.7, 222.33, 179.26, 220.59, 176.93, 206.04, 184.5, 194.4, 210.69, 194.98, 214.18, 223.5, 220.59, 221.75, 224.08, 194.4, 239.21, 175.19]], "area": 2625.4631500000014, "iscrowd": 0, "image_id": 10077, "bbox": [161.22, 160.06, 93.12, 63.44], "category_id": 24, "id": 588352}, {"segmentation": [[292.24, 375.97, 296.29, 387.68, 367.45, 455.24, 374.21, 454.79, 368.35, 448.03, 342.68, 420.56, 324.21, 412.45, 321.96, 406.15, 321.96, 401.19, 293.59, 375.07], [389.52, 425.97, 395.82, 452.09, 402.13, 455.24, 402.58, 455.24, 398.98, 421.46]], "area": 934.7678000000005, "iscrowd": 0, "image_id": 10142, "bbox": [292.24, 375.07, 110.34, 80.17], "category_id": 35, "id": 606831}, {"segmentation": [[271.87, 204.8, 267.18, 209.49, 267.18, 217.2, 265.26, 220.78, 270.22, 227.4, 280.96, 216.93, 282.34, 213.89, 282.34, 211.14, 283.44, 204.53, 279.03, 203.98]], "area": 267.2549999999997, "iscrowd": 0, "image_id": 10442, "bbox": [265.26, 203.98, 18.18, 23.42], "category_id": 40, "id": 635697}, {"segmentation": [[363.18, 244.67, 368.86, 235.98, 373.72, 236.48, 378.9, 236.14, 380.91, 243.84, 382.08, 248.35, 380.57, 247.69, 379.07, 245.51, 377.73, 250.86, 374.55, 256.22, 370.2, 258.39, 363.18, 255.05]], "area": 297.1658000000003, "iscrowd": 0, "image_id": 10442, "bbox": [363.18, 235.98, 18.9, 22.41], "category_id": 40, "id": 635718}, {"segmentation": [[478.72, 330.71, 480.4, 329.8, 479.48, 326.43, 479.79, 324.6, 481.77, 323.53, 486.21, 323.83, 488.65, 325.36, 490.33, 325.21, 489.72, 323.53, 484.53, 315.88, 485.29, 313.44, 484.68, 311.45, 483.3, 310.23, 482.23, 309.16, 480.86, 306.71, 479.79, 306.56, 478.41, 306.25, 475.66, 306.71, 473.36, 306.56, 471.84, 306.25, 470.92, 306.71, 470.0, 307.93, 469.85, 309.77, 469.69, 311.3, 469.85, 313.13, 470.46, 316.19, 470.46, 318.48, 472.6, 321.85, 474.43, 325.06, 475.5, 327.2, 476.88, 329.19, 478.87, 331.33]], "area": 283.3042500000002, "iscrowd": 0, "image_id": 10442, "bbox": [469.69, 306.25, 20.64, 25.08], "category_id": 40, "id": 636393}, {"segmentation": [[132.98, 195.36, 123.32, 188.73, 121.38, 181.83, 123.59, 176.86, 130.22, 173.54, 135.19, 174.92, 135.74, 189.56, 136.3, 192.32]], "area": 227.1367500000002, "iscrowd": 0, "image_id": 10442, "bbox": [121.38, 173.54, 14.92, 21.82], "category_id": 40, "id": 636462}, {"segmentation": [[209.06, 185.12, 215.3, 185.12, 219.6, 182.32, 220.25, 175.43, 219.6, 170.05, 213.36, 170.27, 204.32, 171.56, 203.25, 177.58, 205.62, 184.04, 207.34, 185.12]], "area": 219.87634999999986, "iscrowd": 0, "image_id": 10442, "bbox": [203.25, 170.05, 17.0, 15.07], "category_id": 40, "id": 636675}, {"segmentation": [[524.88, 201.3, 529.76, 199.08, 535.98, 199.3, 540.87, 202.63, 548.64, 207.3, 551.3, 216.18, 540.87, 221.51, 534.2, 231.95, 531.32, 235.06, 528.87, 235.06, 527.99, 229.51, 525.99, 215.07, 523.99, 205.74, 523.99, 201.3]], "area": 579.4633499999993, "iscrowd": 0, "image_id": 10012, "bbox": [523.99, 199.08, 27.31, 35.98], "category_id": 40, "id": 637133}, {"segmentation": [[302.11, 240.93, 307.15, 237.75, 310.86, 236.16, 313.52, 237.22, 314.58, 238.28, 314.84, 242.26, 312.72, 245.71, 311.92, 248.89, 311.39, 253.14, 310.07, 254.99, 305.82, 257.12, 297.86, 260.04, 295.74, 258.18, 294.15, 256.85, 291.23, 253.67, 289.64, 251.55, 290.96, 248.36, 293.35, 246.5, 297.33, 244.38, 300.51, 243.32]], "area": 345.34390000000025, "iscrowd": 0, "image_id": 10442, "bbox": [289.64, 236.16, 25.2, 23.88], "category_id": 40, "id": 637715}, {"segmentation": [[13.11, 240.5, 0.0, 239.04, 0.44, 157.17, 0.44, 144.55, 97.82, 147.87, 90.67, 243.06]], "area": 8968.320249999999, "iscrowd": 0, "image_id": 10324, "bbox": [0.0, 144.55, 97.82, 98.51], "category_id": 79, "id": 1124122}, {"segmentation": [[299.0, 168.39, 298.65, 157.43, 286.98, 150.0, 287.33, 147.17, 284.5, 139.39, 284.5, 137.27, 285.57, 133.38, 301.48, 125.95, 308.9, 123.12, 312.09, 121.0, 319.16, 121.0, 324.82, 123.83, 326.23, 130.9, 327.65, 135.5, 327.29, 140.1, 325.52, 146.11, 325.52, 146.11, 340.73, 152.83, 343.21, 158.49, 346.04, 164.85, 344.27, 166.97, 339.67, 167.68, 336.13, 168.03, 334.72, 170.86, 334.37, 175.11, 325.88, 181.83, 322.34, 182.53, 318.81, 182.53, 313.5, 178.64, 308.55, 176.17, 304.66, 171.22, 303.95, 167.68, 301.83, 167.68, 300.42, 168.03], [301.83, 182.89, 303.25, 193.5, 299.71, 204.1, 299.36, 216.48, 299.36, 226.03, 300.06, 236.64, 299.71, 239.82, 299.71, 239.82, 299.71, 239.82, 301.96, 235.67, 303.4, 234.23, 306.53, 233.06, 307.18, 232.41, 313.82, 217.29, 312.91, 214.16, 317.73, 204.26, 322.69, 195.92, 309.87, 189.66, 303.06, 181.64], [296.15, 257.79, 294.81, 269.0, 294.81, 276.03, 295.31, 280.54, 290.12, 284.56, 289.62, 285.89, 291.8, 288.91, 296.48, 290.24, 301.67, 289.57, 303.34, 285.89, 304.34, 282.05, 307.19, 277.53, 308.19, 272.18, 310.53, 254.95, 306.18, 256.96, 302.5, 259.3, 299.99, 259.3], [318.17, 229.75, 321.2, 249.31, 319.27, 257.3, 319.0, 266.4, 319.55, 277.97, 318.72, 285.41, 319.82, 288.72, 322.3, 289.54, 325.06, 266.12, 323.96, 258.68, 326.44, 245.18, 326.99, 240.22, 326.99, 237.47, 326.44, 235.81, 325.33, 233.06, 325.33, 221.21]], "area": 3595.855900000001, "iscrowd": 0, "image_id": 10442, "bbox": [284.5, 121.0, 61.54, 169.24], "category_id": 1, "id": 1236284}, {"segmentation": [[528.66, 339.17, 535.38, 378.51, 531.54, 395.78, 549.77, 398.66, 552.65, 381.39, 552.65, 357.4, 544.01, 339.17, 544.01, 329.57, 535.38, 311.34, 529.62, 325.74], [497.0, 336.29, 497.96, 369.87, 488.36, 379.47, 501.8, 397.7], [543.02, 266.95, 541.99, 253.07, 537.88, 243.81, 530.68, 240.73, 523.48, 236.1, 519.88, 242.27, 529.14, 254.61, 537.36, 263.35, 540.96, 269.0], [524.51, 230.96, 525.02, 223.76, 525.02, 218.62, 522.45, 215.02, 514.23, 214.5, 505.49, 215.53, 500.86, 220.16, 503.94, 226.33]], "area": 2188.9351500000007, "iscrowd": 0, "image_id": 10442, "bbox": [488.36, 214.5, 64.29, 184.16], "category_id": 1, "id": 1240065}, {"segmentation": [[461.53, 76.48, 460.98, 72.08, 458.23, 70.71, 454.65, 70.71, 455.48, 65.21, 458.5, 64.93, 460.7, 62.18, 462.9, 57.79, 465.1, 53.66, 466.75, 50.09, 466.2, 44.86, 468.4, 39.09, 471.15, 34.97, 478.02, 35.79, 480.77, 42.12, 479.95, 47.34, 481.87, 51.19, 482.15, 55.59, 482.42, 61.08, 482.69, 66.03, 482.69, 69.33, 483.52, 74.56, 481.6, 75.38, 474.72, 75.66, 468.67, 75.38, 467.57, 75.11, 465.65, 75.11]], "area": 733.3720500000001, "iscrowd": 0, "image_id": 10442, "bbox": [454.65, 34.97, 28.87, 41.51], "category_id": 1, "id": 1267952}, {"segmentation": [[502.51, 250.4, 494.52, 253.36, 490.23, 249.51, 488.89, 245.51, 488.75, 242.7, 488.01, 239.3, 490.23, 235.75, 493.19, 233.68, 495.26, 234.12, 498.66, 238.12, 499.55, 240.63, 504.28, 240.78, 506.95, 242.56, 506.65, 246.7, 504.28, 250.1]], "area": 234.51474999999982, "iscrowd": 0, "image_id": 10442, "bbox": [488.01, 233.68, 18.94, 19.68], "category_id": 40, "id": 1470592}, {"segmentation": [[201.43, 72.98, 210.28, 72.98, 209.92, 77.13, 218.77, 92.12, 222.38, 101.33, 221.84, 105.84, 187.89, 104.04, 188.98, 93.93, 198.0, 82.19, 201.25, 76.77]], "area": 727.7048500000003, "iscrowd": 0, "image_id": 10324, "bbox": [187.89, 72.98, 34.49, 32.86], "category_id": 44, "id": 1485308}, {"segmentation": [[331.58, 381.34, 331.58, 365.51, 343.04, 350.68, 342.7, 329.79, 346.75, 326.08, 357.19, 327.09, 357.86, 348.66, 348.09, 363.35, 347.08, 381.89], [333.27, 389.3, 335.96, 400.08, 348.77, 399.41, 347.76, 388.63, 332.93, 388.96]], "area": 968.3620500000002, "iscrowd": 0, "image_id": 10324, "bbox": [331.58, 326.08, 26.28, 74.0], "category_id": 44, "id": 1488432}, {"segmentation": [[289.51, 157.38, 289.86, 151.49, 292.28, 148.2, 294.02, 146.81, 294.71, 145.25, 294.54, 126.55, 298.35, 125.51, 299.21, 126.55, 299.91, 130.53, 300.08, 142.31, 300.42, 143.52, 301.12, 148.02, 301.29, 150.1]], "area": 182.36385000000013, "iscrowd": 0, "image_id": 10324, "bbox": [289.51, 125.51, 11.78, 31.87], "category_id": 44, "id": 1489416}, {"segmentation": [[217.89, 171.33, 239.46, 164.85, 250.25, 183.19, 266.43, 190.74, 302.02, 241.44, 303.1, 256.54, 295.55, 264.09, 271.82, 272.72, 266.43, 274.88, 238.38, 271.64, 237.3, 258.7, 223.28, 240.36, 203.87, 223.1, 187.69, 209.08]], "area": 7022.1494999999995, "iscrowd": 0, "image_id": 10432, "bbox": [187.69, 164.85, 115.41, 110.03], "category_id": 52, "id": 1543037}, {"segmentation": [[252.49, 78.4, 244.82, 86.92, 243.33, 90.75, 239.5, 101.4, 238.86, 114.39, 245.46, 130.79, 262.29, 146.77, 260.58, 155.29, 252.92, 157.42, 234.39, 153.16, 224.16, 140.8, 225.87, 131.86, 223.1, 97.78, 223.1, 92.88, 225.44, 88.2, 231.83, 83.3, 247.59, 74.14, 251.0, 72.01]], "area": 1618.9473000000005, "iscrowd": 0, "image_id": 10432, "bbox": [223.1, 72.01, 39.19, 85.41], "category_id": 52, "id": 1543213}, {"segmentation": [[338.06, 239.44, 334.5, 243.71, 326.67, 246.56, 323.12, 245.85, 319.56, 250.83, 312.44, 257.23, 302.48, 256.52, 300.34, 244.42, 286.82, 225.21, 266.19, 193.9, 270.45, 182.51, 278.28, 171.12, 281.84, 162.58, 288.96, 155.47, 304.61, 146.93, 314.58, 146.93, 314.58, 165.43, 317.42, 181.8, 330.94, 202.43, 345.18, 222.36]], "area": 4723.9360000000015, "iscrowd": 0, "image_id": 10432, "bbox": [266.19, 146.93, 78.99, 110.3], "category_id": 52, "id": 1545333}, {"segmentation": [[424.3, 364.87, 426.46, 363.79, 441.59, 375.68, 463.22, 424.33, 463.22, 424.33, 487.0, 476.22, 490.24, 479.46, 409.16, 480.0, 395.11, 460.0, 394.03, 441.63, 391.86, 422.17, 391.86, 417.84, 391.86, 398.38, 408.08, 368.11, 418.89, 361.63]], "area": 7509.557899999997, "iscrowd": 0, "image_id": 10432, "bbox": [391.86, 361.63, 98.38, 118.37], "category_id": 52, "id": 1545539}, {"segmentation": [[356.65, 269.53, 344.12, 272.12, 332.02, 282.49, 321.22, 287.24, 313.87, 300.63, 313.44, 326.12, 327.27, 360.69, 345.41, 388.77, 361.4, 397.84, 369.17, 392.66, 370.04, 389.2, 376.09, 391.79, 379.54, 389.2, 381.27, 377.97, 388.18, 378.83, 391.64, 374.51, 386.89, 358.53, 378.25, 341.25, 367.88, 316.62, 360.97, 296.31, 360.1, 284.22]], "area": 5917.327400000001, "iscrowd": 0, "image_id": 10432, "bbox": [313.44, 269.53, 78.2, 128.31], "category_id": 52, "id": 1546568}, {"segmentation": [[406.87, 252.31, 364.09, 263.29, 357.74, 265.61, 361.78, 293.35, 390.11, 367.34, 398.2, 364.45, 401.67, 353.47, 415.54, 363.29, 419.01, 362.72, 430.57, 355.78, 429.99, 354.62, 439.24, 360.4, 447.91, 358.67, 455.42, 348.27, 455.42, 348.27, 456.58, 345.95, 456.0, 336.13, 455.42, 335.55, 469.87, 335.55, 469.3, 334.97, 438.08, 236.7, 438.08, 236.7, 436.35, 232.66, 425.94, 237.28, 423.63, 239.02, 413.8, 250.0]], "area": 9439.170650000002, "iscrowd": 0, "image_id": 10432, "bbox": [357.74, 232.66, 112.13, 134.68], "category_id": 52, "id": 1546710}, {"segmentation": [[449.83, 239.68, 452.69, 233.56, 455.14, 231.72, 459.21, 226.42, 464.31, 221.32, 467.57, 220.3, 470.63, 220.3, 472.47, 221.12, 475.53, 220.71, 476.55, 220.3, 479.2, 218.88, 482.87, 217.65, 484.29, 217.65, 486.74, 217.65, 487.97, 218.27, 489.6, 219.28, 490.82, 221.73, 490.82, 227.44, 487.76, 236.41, 492.45, 242.73, 496.12, 255.58, 501.42, 268.22, 504.28, 274.34, 513.25, 288.82, 520.39, 298.81, 524.46, 305.33, 525.48, 315.94, 522.02, 318.59, 516.31, 315.94, 514.68, 320.83, 510.19, 321.24, 508.76, 321.65, 498.77, 316.55, 489.6, 308.39, 487.56, 299.83, 480.83, 289.84, 471.65, 276.99, 467.98, 267.81, 460.03, 249.46, 453.3, 242.33, 450.24, 240.08]], "area": 3333.2997999999993, "iscrowd": 0, "image_id": 10432, "bbox": [449.83, 217.65, 75.65, 104.0], "category_id": 52, "id": 1547671}, {"segmentation": [[234.09, 316.88, 229.8, 326.55, 214.77, 331.92, 206.17, 350.17, 206.17, 368.43, 211.54, 387.75, 226.58, 426.41, 238.39, 444.67, 249.13, 446.82, 254.5, 443.59, 253.42, 423.19, 246.98, 403.86, 237.32, 373.8, 235.17, 348.02, 235.17, 335.14, 243.76, 320.1, 235.17, 320.1]], "area": 3270.470749999999, "iscrowd": 0, "image_id": 10432, "bbox": [206.17, 316.88, 48.33, 129.94], "category_id": 52, "id": 1547838}, {"segmentation": [[156.4, 293.39, 141.3, 284.76, 131.6, 284.76, 127.28, 284.76, 117.57, 255.64, 128.36, 198.47, 135.91, 189.84, 144.54, 189.84, 157.48, 168.27, 175.82, 169.35, 185.53, 186.61, 190.92, 188.76, 190.92, 203.87, 190.92, 212.49, 228.67, 244.85, 237.3, 259.96, 237.3, 271.82, 218.97, 271.82, 206.02, 278.29, 190.92, 277.21, 171.51, 288.0, 171.51, 288.0]], "area": 9079.806449999998, "iscrowd": 0, "image_id": 10432, "bbox": [117.57, 168.27, 119.73, 125.12], "category_id": 52, "id": 1547931}, {"segmentation": [[297.71, 288.01, 289.88, 298.26, 280.84, 295.85, 268.19, 315.13, 273.01, 344.05, 297.11, 398.89, 315.79, 403.71, 321.22, 391.66, 293.5, 328.39, 295.3, 304.28, 304.34, 291.03, 298.32, 287.41]], "area": 2715.3953000000006, "iscrowd": 0, "image_id": 10432, "bbox": [268.19, 287.41, 53.03, 116.3], "category_id": 52, "id": 1548110}, {"segmentation": [[444.36, 227.46, 438.06, 235.68, 428.19, 252.39, 425.45, 262.26, 421.89, 278.7, 428.47, 297.34, 444.36, 323.1, 457.79, 338.17, 467.65, 340.36, 470.4, 338.72, 474.51, 334.06, 483.82, 332.41, 489.03, 328.3, 484.65, 323.1, 484.37, 316.25, 489.58, 312.14, 489.85, 304.19, 484.92, 292.95, 483.0, 290.76, 469.57, 271.85, 469.57, 269.93, 461.08, 248.37, 450.66, 240.15, 447.1, 230.55, 447.38, 225.89]], "area": 4404.489949999999, "iscrowd": 0, "image_id": 10432, "bbox": [421.89, 225.89, 67.96, 114.47], "category_id": 52, "id": 1548192}, {"segmentation": [[498.34, 374.29, 482.16, 373.21, 481.08, 366.74, 484.31, 357.03, 484.31, 331.15, 477.84, 304.18, 472.45, 272.9, 470.29, 263.19, 450.88, 257.8, 454.11, 207.1, 457.35, 190.92, 454.11, 175.82, 453.03, 149.93, 462.74, 133.75, 483.24, 121.89, 486.47, 117.57, 477.84, 108.94, 470.29, 98.16, 472.45, 77.66, 488.63, 76.58, 500.49, 78.74, 505.89, 100.31, 510.2, 117.57, 544.72, 129.44, 549.03, 154.25, 551.19, 199.55, 549.03, 220.04, 544.72, 235.15, 526.38, 242.7, 541.48, 336.54, 549.03, 364.58, 544.72, 377.53, 528.54, 373.21, 524.22, 341.93, 498.34, 252.4, 511.28, 372.13]], "area": 19197.105450000003, "iscrowd": 0, "image_id": 10012, "bbox": [450.88, 76.58, 100.31, 300.95], "category_id": 1, "id": 1730659}, {"segmentation": [[494.27, 303.89, 485.72, 311.97, 485.72, 315.06, 487.62, 319.33, 492.37, 326.22, 492.61, 321.94, 494.03, 316.96, 494.75, 312.68, 494.98, 306.51, 497.12, 303.89, 496.88, 302.47, 493.8, 304.37]], "area": 121.09799999999966, "iscrowd": 0, "image_id": 10442, "bbox": [485.72, 302.47, 11.4, 23.75], "category_id": 40, "id": 1858847}, {"segmentation": [[234.91, 182.62, 215.34, 180.71, 217.25, 142.52, 221.07, 141.08, 221.07, 136.79, 225.37, 134.4, 230.62, 133.92, 233.48, 135.83, 235.87, 151.59, 235.39, 181.66]], "area": 855.6226999999998, "iscrowd": 0, "image_id": 10324, "bbox": [215.34, 133.92, 20.53, 48.7], "category_id": 44, "id": 1868706}, {"segmentation": [[156.49, 90.09, 158.43, 100.25, 160.23, 105.04, 163.07, 109.52, 168.45, 119.84, 170.24, 123.43, 175.03, 134.79, 178.76, 143.91, 183.7, 153.48, 186.09, 159.9, 186.09, 165.43, 183.4, 168.42, 180.41, 170.97, 172.63, 169.77, 162.21, 157.88, 162.91, 163.82, 162.91, 167.44, 158.72, 168.48, 157.09, 170.7, 155.46, 171.63, 150.56, 171.75, 146.72, 169.18, 142.53, 164.29, 141.25, 164.64, 139.73, 167.9, 135.19, 168.48, 127.85, 167.78, 120.86, 161.03, 117.02, 155.79, 113.52, 149.26, 113.06, 145.07, 108.89, 141.25, 102.29, 135.58, 97.14, 129.24, 93.58, 121.85, 91.2, 114.59, 90.28, 104.83, 91.89, 98.59, 95.28, 91.8, 100.53, 86.25, 104.74, 83.58, 110.4, 82.45, 114.3, 82.45, 118.42, 83.06, 127.26, 79.05, 132.61, 75.45, 142.68, 74.12, 146.79, 75.04, 150.09, 76.89, 153.27, 77.92, 155.95, 78.23, 157.28, 81.93, 156.67, 83.27, 156.67, 90.05]], "area": 6036.474, "iscrowd": 0, "image_id": 10432, "bbox": [90.28, 74.12, 95.81, 97.63], "category_id": 52, "id": 1904361}, {"segmentation": [[192.9, 63.17, 196.76, 44.39, 184.72, 32.35, 188.57, 25.61, 207.35, 30.9, 209.28, 21.27, 217.95, 24.64, 220.36, 33.31, 226.14, 38.61, 227.58, 55.95, 216.98, 63.17, 207.35, 63.65]], "area": 1175.3052500000001, "iscrowd": 0, "image_id": 10442, "bbox": [184.72, 21.27, 42.86, 42.38], "category_id": 1, "id": 2000401}, {"segmentation": [[364.5, 376.15, 373.06, 354.15, 381.61, 341.93, 396.28, 342.54, 406.06, 354.76, 404.22, 366.37, 393.22, 376.15, 391.39, 377.98, 375.5, 379.2], [374.89, 402.42, 376.11, 387.76, 381.61, 389.59, 383.45, 398.76]], "area": 1159.7842999999998, "iscrowd": 0, "image_id": 10324, "bbox": [364.5, 341.93, 41.56, 60.49], "category_id": 44, "id": 2093222}, {"segmentation": [[533.15, 205.61, 512.64, 215.45, 499.51, 231.04, 509.36, 259.76, 529.87, 279.45, 548.74, 279.45, 570.07, 285.19, 587.3, 272.06, 561.87, 246.63, 542.18, 220.38, 547.1, 203.97]], "area": 3787.6509499999984, "iscrowd": 0, "image_id": 10432, "bbox": [499.51, 203.97, 87.79, 81.22], "category_id": 52, "id": 2110149}, {"segmentation": [[248.27, 411.01, 248.27, 405.19, 244.74, 399.17, 237.88, 396.88, 229.78, 398.13, 226.24, 401.66, 226.03, 408.52, 232.06, 413.92, 248.68, 413.51]], "area": 315.93870000000004, "iscrowd": 0, "image_id": 10324, "bbox": [226.03, 396.88, 22.65, 17.04], "category_id": 53, "id": 2111233}, {"segmentation": [[179.25, 106.22, 179.25, 119.5, 179.25, 123.93, 171.51, 131.67, 164.87, 148.27, 163.76, 165.97, 163.76, 172.61, 169.29, 183.68, 171.51, 188.1, 172.61, 196.96, 174.83, 204.7, 174.83, 245.64, 174.83, 255.6, 175.93, 262.24, 184.78, 267.77, 188.1, 267.77, 189.21, 265.56, 188.1, 257.81, 187.0, 231.26, 189.21, 210.23, 191.42, 203.59, 193.64, 200.28, 198.06, 214.66, 196.96, 221.3, 193.64, 240.11, 194.74, 246.75, 194.74, 255.6, 199.17, 263.35, 204.7, 267.77, 205.81, 264.45, 206.91, 255.6, 206.91, 254.49, 209.13, 240.11, 210.23, 225.72, 212.45, 215.77, 209.13, 204.7, 206.91, 185.89, 204.7, 165.97, 204.7, 150.48, 209.13, 159.34, 213.55, 170.4, 217.98, 162.65, 206.91, 130.57, 201.38, 128.35, 198.06, 125.03, 196.96, 102.9, 190.32, 101.8, 183.68, 101.8]], "area": 5216.454249999997, "iscrowd": 0, "image_id": 10442, "bbox": [163.76, 101.8, 54.22, 165.97], "category_id": 1, "id": 2152847}, {"segmentation": [[150.48, 0.0, 146.06, 23.24, 156.02, 8.85, 156.02, 61.96, 152.7, 77.45, 159.34, 70.82, 165.97, 32.09, 167.08, 77.45, 173.72, 64.18, 180.36, 29.88, 190.32, 12.17, 191.42, 1.11, 154.91, 0.0]], "area": 1750.1656999999998, "iscrowd": 0, "image_id": 10442, "bbox": [146.06, 0.0, 45.36, 77.45], "category_id": 1, "id": 2154531}, {"segmentation": [[143.67, 119.45, 149.84, 205.95, 160.14, 205.26, 198.58, 187.41, 193.09, 109.16, 173.87, 107.79, 144.35, 119.45]], "area": 4334.445350000001, "iscrowd": 0, "image_id": 10008, "bbox": [143.67, 107.79, 54.91, 98.16], "category_id": 72, "id": 32211}, {"segmentation": [[234.6, 249.32, 253.33, 248.83, 257.95, 235.45, 260.14, 226.21, 254.06, 220.37, 265.49, 207.72, 270.35, 200.67, 277.24, 193.91, 296.53, 191.7, 333.73, 191.98, 353.85, 193.91, 354.95, 203.83, 359.08, 216.23, 362.94, 220.36, 361.01, 225.87, 364.04, 230.83, 363.49, 238.27, 376.18, 253.89, 385.55, 276.36, 386.17, 291.35, 390.54, 306.96, 391.48, 330.06, 388.36, 339.42, 385.16, 371.64, 377.9, 401.34, 338.97, 402.66, 250.53, 405.96, 179.26, 400.02, 154.18, 392.1, 156.16, 355.14, 177.94, 316.87]], "area": 37032.70650000001, "iscrowd": 0, "image_id": 10008, "bbox": [154.18, 191.7, 237.3, 214.26], "category_id": 65, "id": 315245}, {"segmentation": [[213.83, 187.33, 208.82, 178.97, 202.97, 171.04, 198.79, 166.44, 198.79, 164.35, 198.38, 162.27, 204.22, 162.27, 205.48, 158.92, 220.51, 151.41, 242.65, 139.29, 252.67, 132.61, 266.04, 127.6, 281.91, 111.73, 286.92, 107.13, 279.82, 97.53, 282.74, 84.16, 291.93, 72.05, 291.93, 64.53, 298.2, 54.92, 306.55, 44.9, 312.4, 49.08, 331.61, 71.21, 339.13, 86.67, 339.55, 90.01, 353.75, 92.1, 380.06, 105.46, 391.34, 121.33, 401.36, 129.27, 398.02, 140.55, 414.31, 176.47, 427.68, 211.55, 433.52, 221.16, 431.85, 224.08, 431.43, 225.75, 426.42, 225.75, 428.93, 229.93, 426.0, 232.85, 422.25, 227.42, 424.75, 239.53, 423.08, 241.62, 421.41, 236.61, 415.56, 220.74, 411.8, 203.61, 393.01, 163.1, 383.82, 151.82, 378.81, 146.39, 383.82, 164.35, 379.64, 173.13, 373.8, 174.8, 369.2, 189.41, 365.86, 202.78, 361.27, 206.54, 357.92, 203.2, 345.39, 204.03, 337.04, 206.54, 334.54, 209.46, 331.19, 209.46, 328.69, 222.41, 330.36, 233.69, 334.12, 244.55, 331.19, 258.33, 322.0, 268.35, 309.06, 270.86, 305.3, 262.92, 314.9, 239.95, 311.15, 225.75, 306.13, 207.37, 301.12, 196.93, 289.01, 199.86, 278.99, 229.51, 272.72, 251.23, 273.97, 264.18, 270.21, 271.28, 251.84, 267.52, 234.71, 265.01, 225.94, 256.24, 228.86, 252.9, 249.75, 245.8, 256.43, 242.46, 262.7, 241.62, 263.53, 191.5, 270.21, 172.71, 278.15, 163.94, 303.63, 155.58, 317.83, 150.15, 313.65, 141.38, 306.97, 131.78, 301.96, 131.78, 292.77, 133.45, 278.15, 135.95, 262.28, 145.14, 239.72, 156.84, 221.76, 162.27, 217.17, 166.02, 215.92, 173.54, 218.42, 181.06, 218.84, 184.4]], "area": 18230.852450000002, "iscrowd": 0, "image_id": 10023, "bbox": [198.38, 44.9, 235.14, 226.38], "category_id": 1, "id": 439238}, {"segmentation": [[242.77, 276.35, 321.45, 329.13, 323.37, 349.28, 312.81, 352.16, 231.25, 310.89, 221.66, 306.1, 220.7, 287.87, 224.53, 277.31, 231.25, 273.47, 243.73, 279.23]], "area": 3400.38795, "iscrowd": 0, "image_id": 10023, "bbox": [220.7, 273.47, 102.67, 78.69], "category_id": 41, "id": 642104}, {"segmentation": [[391.67, 233.54, 390.56, 422.25, 639.95, 418.13, 635.51, 227.36, 404.82, 229.58]], "area": 47243.47455, "iscrowd": 0, "image_id": 10008, "bbox": [390.56, 227.36, 249.39, 194.89], "category_id": 65, "id": 1957130}, {"segmentation": [[196.22, 59.17, 199.19, 74.28, 203.63, 87.33, 191.77, 82.58, 190.89, 80.81, 192.07, 74.58, 191.48, 59.76]], "area": 188.5965500000001, "iscrowd": 0, "image_id": 10138, "bbox": [190.89, 59.17, 12.74, 28.16], "category_id": 44, "id": 79208}, {"segmentation": [[206.29, 56.77, 207.05, 74.3, 202.85, 79.64, 202.47, 92.99, 202.47, 105.57, 221.92, 104.81, 220.77, 78.12, 214.67, 69.35, 213.15, 59.43, 212.77, 55.24, 210.48, 54.1, 207.05, 55.24]], "area": 709.77385, "iscrowd": 0, "image_id": 10138, "bbox": [202.47, 54.1, 19.45, 51.47], "category_id": 44, "id": 85615}, {"segmentation": [[495.13, 342.7, 519.35, 345.26, 545.03, 340.13, 545.4, 334.62, 555.67, 325.45, 565.95, 325.82, 567.42, 322.51, 576.22, 322.51, 580.26, 322.51, 578.42, 335.72, 586.5, 335.36, 583.56, 333.16, 577.92, 250.75, 583.57, 251.69, 583.57, 259.22, 616.53, 258.28, 614.65, 252.16, 604.76, 248.39, 586.39, 246.98, 587.81, 241.33, 581.68, 237.09, 573.68, 234.73, 567.08, 238.03, 572.26, 253.1, 572.26, 260.16, 565.67, 266.76, 559.08, 268.64, 549.66, 262.99, 545.42, 257.34, 529.88, 264.87, 525.64, 274.29, 520.93, 287.48, 511.04, 287.48, 492.67, 291.72, 495.03, 311.03, 495.97, 340.23], [487.49, 339.75, 461.12, 332.69, 463.48, 322.8, 477.13, 310.08, 478.55, 306.79, 483.73, 314.79, 488.44, 338.34], [459.24, 313.85, 443.7, 290.31, 446.99, 272.41, 455.47, 274.76, 462.06, 259.69, 466.3, 248.86, 466.3, 239.91, 466.3, 230.97, 471.48, 225.79, 479.02, 224.84, 500.21, 224.37, 488.44, 240.86, 475.72, 256.4, 463.01, 271.47, 461.59, 278.06, 448.88, 288.42, 454.53, 297.84, 463.95, 298.78, 468.66, 306.79], [395.66, 282.77, 410.26, 281.83, 412.14, 291.25, 417.32, 299.25, 426.74, 312.44, 429.57, 323.27, 431.45, 333.63, 422.98, 330.34, 411.2, 311.97, 401.31, 297.84]], "area": 8588.112249999995, "iscrowd": 0, "image_id": 10205, "bbox": [395.66, 224.37, 220.87, 120.89], "category_id": 63, "id": 115206}, {"segmentation": [[199.55, 237.3, 201.71, 207.1, 222.2, 166.11, 252.4, 149.93, 264.27, 142.38, 259.96, 116.49, 257.8, 90.61, 265.35, 62.56, 297.71, 57.17, 322.52, 63.64, 343.01, 77.66, 350.56, 94.92, 350.56, 121.89, 349.48, 140.22, 350.56, 145.62, 381.84, 155.33, 401.26, 171.51, 414.2, 196.31, 424.99, 245.93, 424.99, 257.8, 418.52, 272.9, 405.57, 283.69, 265.35, 268.58, 253.48, 252.4, 240.54, 242.7, 198.47, 245.93, 201.71, 232.99]], "area": 31544.90015, "iscrowd": 0, "image_id": 10138, "bbox": [198.47, 57.17, 226.52, 226.52], "category_id": 1, "id": 228945}, {"segmentation": [[319.53, 114.35, 321.17, 115.07, 321.56, 115.59, 321.95, 116.28, 322.09, 117.86, 322.09, 118.55, 321.99, 119.23, 321.86, 119.53, 321.53, 119.96, 321.04, 120.25, 320.41, 120.35, 319.56, 120.38, 319.13, 120.38, 318.31, 120.28, 317.72, 120.05, 317.1, 119.59, 316.48, 119.07, 316.02, 118.48, 315.72, 117.33, 315.76, 116.05, 316.28, 115.07, 317.13, 114.45, 317.63, 114.25, 318.35, 114.19, 319.46, 114.22, 319.69, 114.41]], "area": 32.30234999999993, "iscrowd": 0, "image_id": 10216, "bbox": [315.72, 114.19, 6.37, 6.19], "category_id": 37, "id": 304745}, {"segmentation": [[139.51, 348.4, 149.49, 344.27, 158.0, 342.56, 167.97, 350.35, 168.95, 363.24, 164.81, 370.54, 158.24, 377.35, 143.89, 380.75, 131.97, 373.46, 129.05, 361.05, 131.97, 354.0, 135.62, 348.16]], "area": 1119.6623500000003, "iscrowd": 0, "image_id": 10123, "bbox": [129.05, 342.56, 39.9, 38.19], "category_id": 37, "id": 306033}, {"segmentation": [[411.89, 2.7, 558.92, 4.86, 541.62, 152.97, 461.62, 150.81, 457.3, 263.24, 448.65, 272.97, 442.16, 281.62, 442.16, 288.11, 417.3, 283.78, 421.62, 269.73, 421.62, 252.43, 424.86, 236.22, 425.95, 227.57, 402.16, 174.59, 408.65, 1.62]], "area": 27047.282799999994, "iscrowd": 0, "image_id": 10138, "bbox": [402.16, 1.62, 156.76, 286.49], "category_id": 82, "id": 330862}, {"segmentation": [[201.12, 113.03, 197.08, 116.13, 194.91, 120.48, 193.04, 126.38, 193.04, 130.72, 194.6, 135.07, 196.46, 139.11, 199.56, 140.04, 203.91, 139.11, 206.7, 135.38, 209.81, 131.66, 209.81, 125.45, 209.5, 120.17, 207.32, 114.27, 205.15, 113.34]], "area": 353.15585, "iscrowd": 0, "image_id": 10216, "bbox": [193.04, 113.03, 16.77, 27.01], "category_id": 85, "id": 336279}, {"segmentation": [[324.86, 70.46, 329.57, 74.34, 337.94, 74.34, 343.67, 71.89, 345.1, 69.43, 346.12, 70.46, 348.37, 116.23, 346.32, 115.83, 343.46, 92.94, 334.67, 94.78, 335.29, 121.34, 332.63, 121.55, 331.2, 100.29, 312.19, 97.23, 310.76, 119.5, 308.72, 118.28, 308.31, 84.76, 307.08, 83.74, 308.72, 81.9, 319.55, 77.61, 320.78, 68.0, 322.82, 68.21, 322.82, 70.05]], "area": 1060.3012500000002, "iscrowd": 0, "image_id": 10216, "bbox": [307.08, 68.0, 41.29, 53.55], "category_id": 62, "id": 376131}, {"segmentation": [[278.92, 118.85, 280.12, 80.69, 284.09, 77.9, 286.88, 76.31, 291.65, 74.32, 303.97, 74.32, 312.72, 73.93, 305.96, 85.85, 303.57, 117.26, 279.32, 116.07]], "area": 1085.7662999999995, "iscrowd": 0, "image_id": 10216, "bbox": [278.92, 73.93, 33.8, 44.92], "category_id": 62, "id": 378965}, {"segmentation": [[326.98, 319.61, 334.7, 290.46, 332.6, 288.35, 332.6, 283.79, 322.06, 254.98, 323.11, 247.96, 325.22, 244.8, 334.0, 244.45, 339.62, 253.58, 346.65, 269.03, 348.75, 282.03, 349.11, 289.06, 348.4, 290.46, 347.0, 308.73, 344.89, 320.32, 343.13, 321.02, 344.19, 289.76, 341.03, 294.32, 334.0, 316.1, 332.6, 315.05, 340.32, 289.41, 336.46, 289.76, 329.44, 318.56]], "area": 987.1263999999981, "iscrowd": 0, "image_id": 10205, "bbox": [322.06, 244.45, 27.05, 76.57], "category_id": 62, "id": 385197}, {"segmentation": [[235.92, 238.84, 257.23, 238.84, 261.34, 239.81, 261.1, 247.8, 259.4, 248.29, 259.89, 263.78, 269.57, 265.96, 271.27, 270.08, 270.78, 287.99, 272.0, 319.47, 266.67, 318.74, 264.01, 306.64, 259.65, 306.4, 258.68, 317.53, 255.29, 315.35, 254.8, 303.49, 241.73, 303.01, 242.7, 320.44, 238.1, 320.2]], "area": 2167.9228999999987, "iscrowd": 0, "image_id": 10205, "bbox": [235.92, 238.84, 36.08, 81.6], "category_id": 62, "id": 385929}, {"segmentation": [[371.02, 332.17, 382.87, 308.46, 340.93, 240.09, 340.93, 225.5, 373.76, 202.71, 395.64, 189.94, 371.93, 127.94, 298.99, 140.71, 262.53, 142.53, 253.41, 136.15, 255.24, 120.65, 274.38, 126.12, 340.93, 111.54, 338.21, 95.13, 354.61, 80.54, 339.12, 64.13, 340.02, 35.86, 371.02, 30.39, 381.96, 36.77, 391.08, 51.36, 426.64, 51.36, 444.87, 90.56, 464.93, 148.92, 476.78, 201.8, 461.29, 251.94, 454.9, 273.82, 445.79, 285.67, 467.66, 346.75, 484.08, 377.75, 490.46, 380.49, 469.49, 416.04, 440.31, 419.69, 450.34, 387.78, 440.31, 341.29, 423.9, 302.09, 410.22, 294.79, 414.79, 252.85, 412.96, 241.91, 396.55, 247.38, 386.52, 247.38, 402.93, 303.91, 420.26, 335.82, 392.91, 344.03, 361.0, 351.32, 359.17, 341.29, 365.56, 324.88]], "area": 31105.181600000007, "iscrowd": 0, "image_id": 10205, "bbox": [253.41, 30.39, 237.05, 389.3], "category_id": 1, "id": 440791}, {"segmentation": [[452.41, 290.27, 469.63, 260.26, 497.19, 229.74, 500.14, 220.89, 511.46, 219.9, 525.73, 229.25, 529.67, 244.02, 542.96, 259.27, 531.15, 278.96, 512.94, 278.46, 511.95, 274.03, 509.0, 271.57, 510.97, 278.96, 517.37, 289.78, 508.51, 289.29, 502.6, 288.8, 500.14, 283.38, 501.13, 265.18, 496.21, 269.11, 492.76, 279.94, 492.76, 291.26, 494.24, 321.28, 497.19, 338.01, 494.73, 346.87, 491.29, 347.85, 485.38, 346.37, 483.41, 311.43, 479.47, 305.04, 476.52, 302.58, 476.52, 309.96, 474.55, 312.91, 462.74, 323.74, 457.82, 317.34, 465.7, 307.5, 467.17, 303.07]], "area": 4643.441100000001, "iscrowd": 0, "image_id": 10205, "bbox": [452.41, 219.9, 90.55, 127.95], "category_id": 1, "id": 442715}, {"segmentation": [[292.15, 193.15, 295.86, 194.74, 290.03, 200.58, 283.66, 211.71, 279.95, 219.13, 273.59, 227.09, 268.82, 228.68, 262.99, 223.38, 262.99, 219.13, 264.58, 211.71, 268.29, 208.53, 274.65, 206.94, 283.49, 200.47]], "area": 420.53524999999996, "iscrowd": 0, "image_id": 10216, "bbox": [262.99, 193.15, 32.87, 35.53], "category_id": 43, "id": 656517}, {"segmentation": [[198.63, 222.62, 206.3, 237.97, 182.31, 278.27, 171.76, 319.53, 170.8, 378.06, 158.33, 416.44, 127.62, 420.28, 102.67, 410.69, 84.44, 384.78, 79.64, 335.84, 106.51, 290.74, 137.22, 282.11, 163.12, 271.55, 184.23, 248.52]], "area": 11796.60735, "iscrowd": 0, "image_id": 10123, "bbox": [79.64, 222.62, 126.66, 197.66], "category_id": 43, "id": 659921}, {"segmentation": [[181.99, 283.03, 160.92, 274.71, 138.74, 281.92, 137.63, 294.12, 145.39, 302.43, 150.38, 324.06, 171.45, 323.5, 178.1, 317.96, 177.55, 304.1, 177.0, 291.9, 180.88, 286.35]], "area": 1566.9302500000006, "iscrowd": 0, "image_id": 10138, "bbox": [137.63, 274.71, 44.36, 49.35], "category_id": 47, "id": 669563}, {"segmentation": [[185.64, 91.93, 190.97, 115.47, 201.86, 115.25, 202.52, 89.93, 194.08, 89.71, 185.42, 90.6]], "area": 357.95600000000053, "iscrowd": 0, "image_id": 10138, "bbox": [185.42, 89.71, 17.1, 25.76], "category_id": 47, "id": 681104}, {"segmentation": [[227.75, 266.65, 231.75, 261.54, 238.86, 260.21, 239.97, 260.65, 235.31, 264.87, 232.42, 266.42]], "area": 36.615549999999985, "iscrowd": 0, "image_id": 10138, "bbox": [227.75, 260.21, 12.22, 6.44], "category_id": 50, "id": 707337}, {"segmentation": [[222.55, 244.69, 244.77, 247.07, 250.32, 251.44, 252.7, 254.21, 253.49, 266.11, 246.75, 282.38, 230.88, 290.31, 212.64, 289.91, 199.55, 283.17, 192.8, 266.11, 192.8, 258.97, 195.98, 251.44, 209.86, 246.68, 219.78, 245.49]], "area": 2246.305500000001, "iscrowd": 0, "image_id": 10138, "bbox": [192.8, 244.69, 60.69, 45.62], "category_id": 51, "id": 709546}, {"segmentation": [[354.29, 379.81, 378.74, 372.59, 396.53, 354.25, 393.75, 333.68, 391.53, 317.57, 355.96, 299.23, 329.28, 296.45, 296.5, 302.56, 273.71, 319.24, 268.15, 343.69, 267.04, 357.03, 289.27, 377.03, 332.62, 384.81]], "area": 8889.649099999999, "iscrowd": 0, "image_id": 10138, "bbox": [267.04, 296.45, 129.49, 88.36], "category_id": 61, "id": 1089029}, {"segmentation": [[249.75, 131.31, 246.29, 127.67, 242.11, 122.58, 237.2, 121.13, 238.3, 117.86, 240.3, 116.22, 245.2, 117.49, 267.02, 127.85, 263.56, 130.58, 259.38, 129.67, 249.57, 131.49]], "area": 202.9367499999999, "iscrowd": 0, "image_id": 10205, "bbox": [237.2, 116.22, 29.82, 15.27], "category_id": 75, "id": 1108240}, {"segmentation": [[5.73, 141.31, 44.82, 128.72, 103.57, 131.34, 67.63, 142.88, 6.0, 141.31]], "area": 806.2030999999996, "iscrowd": 0, "image_id": 10138, "bbox": [5.73, 128.72, 97.84, 14.16], "category_id": 81, "id": 1133095}, {"segmentation": [[544.07, 214.04, 555.73, 211.54, 562.4, 212.79, 566.57, 219.46, 570.32, 227.79, 569.9, 234.46, 565.73, 235.29, 564.9, 237.38, 574.07, 254.88, 567.4, 264.04, 559.07, 268.21, 549.48, 260.29, 547.4, 259.04, 542.4, 257.38, 534.07, 248.21, 529.9, 242.79, 529.07, 237.38, 540.73, 241.96, 544.48, 242.38, 545.32, 241.13, 541.57, 231.54, 540.32, 227.79, 540.73, 222.79, 544.07, 214.88]], "area": 1465.3540999999996, "iscrowd": 0, "image_id": 10205, "bbox": [529.07, 211.54, 45.0, 56.67], "category_id": 1, "id": 1217885}, {"segmentation": [[293.11, 322.28, 287.95, 321.35, 285.6, 318.06, 284.19, 310.08, 284.66, 288.95, 288.42, 279.56, 288.89, 260.78, 298.75, 244.35, 302.5, 235.89, 303.91, 222.75, 305.32, 220.87, 292.17, 224.63, 287.48, 225.1, 286.07, 219.46, 294.99, 218.05, 302.5, 214.77, 298.75, 203.97, 296.4, 200.21, 295.93, 196.46, 292.17, 195.99, 289.36, 192.23, 293.58, 187.07, 299.69, 190.82, 303.91, 199.27, 309.55, 206.78, 312.83, 214.3, 317.53, 202.09, 323.63, 199.27, 329.74, 206.78, 325.04, 217.11, 332.55, 226.5, 334.43, 240.12, 341.94, 259.37, 335.37, 261.25, 331.14, 252.8, 325.51, 236.83, 321.75, 239.65, 316.59, 259.84, 313.77, 280.03, 310.96, 282.85, 305.79, 309.14, 306.26, 326.04, 301.57, 325.1, 296.87, 314.77, 298.28, 308.2, 302.03, 289.89, 303.44, 266.88, 302.03, 265.94, 296.87, 278.15, 290.3, 292.7, 291.24, 307.26, 293.11, 314.77]], "area": 2969.8928999999994, "iscrowd": 0, "image_id": 10216, "bbox": [284.19, 187.07, 57.75, 138.97], "category_id": 1, "id": 1246165}, {"segmentation": [[482.16, 96.0, 505.89, 96.0, 514.52, 96.0, 518.83, 94.92, 511.28, 77.66, 506.97, 74.43, 503.73, 70.11, 496.18, 64.72, 487.55, 72.27, 478.92, 78.74, 480.0, 89.53]], "area": 844.1293, "iscrowd": 0, "image_id": 10216, "bbox": [478.92, 64.72, 39.91, 31.28], "category_id": 1, "id": 1264732}, {"segmentation": [[155.42, 107.45, 152.75, 93.41, 152.41, 76.03, 156.42, 65.0, 161.6, 60.65, 160.77, 58.98, 159.26, 56.14, 158.76, 52.63, 158.76, 50.79, 159.26, 50.79, 160.1, 47.79, 161.27, 46.45, 163.44, 45.45, 165.11, 45.61, 167.45, 46.78, 170.96, 50.13, 171.47, 54.97, 170.96, 59.15, 170.46, 60.65, 170.13, 62.66, 170.13, 63.5, 172.47, 63.83, 173.47, 69.85, 173.64, 73.69, 171.8, 81.88, 174.14, 95.92, 174.47, 103.27, 174.14, 107.12, 173.64, 113.47, 173.64, 116.14, 172.13, 125.17, 173.3, 143.72, 178.82, 147.9, 183.67, 150.07, 184.5, 151.91, 180.16, 153.41, 170.13, 153.41, 166.79, 152.07, 166.79, 149.4, 168.12, 146.39, 168.46, 143.38, 167.96, 137.53, 166.28, 129.51, 165.28, 125.84, 163.44, 123.83, 161.77, 127.17, 159.26, 130.35, 157.09, 148.4, 158.43, 149.74, 160.94, 150.24, 161.6, 153.58, 164.45, 157.92, 164.61, 159.6, 161.77, 160.43, 156.42, 159.26, 153.08, 156.92, 153.25, 148.73, 151.74, 139.37, 151.74, 132.69, 152.08, 129.01, 153.25, 126.0, 153.42, 120.65, 154.58, 113.47, 155.92, 107.62]], "area": 1856.6204499999994, "iscrowd": 0, "image_id": 10216, "bbox": [151.74, 45.45, 32.76, 114.98], "category_id": 1, "id": 1272700}, {"segmentation": [[510.6, 71.45, 514.82, 64.8, 519.47, 60.15, 524.79, 58.38, 527.67, 56.6, 525.45, 52.39, 527.22, 49.29, 529.0, 47.96, 534.09, 44.42, 540.52, 47.08, 542.96, 49.29, 542.96, 53.28, 541.18, 58.16, 539.63, 60.37, 538.53, 60.37, 538.75, 62.81, 541.85, 74.11, 540.96, 75.66]], "area": 570.1823499999995, "iscrowd": 0, "image_id": 10216, "bbox": [510.6, 44.42, 32.36, 31.24], "category_id": 1, "id": 1278515}, {"segmentation": [[557.59, 42.55, 569.7, 39.44, 577.46, 45.96, 574.67, 57.14, 576.22, 68.63, 569.39, 72.66, 559.46, 68.63, 553.25, 68.31, 550.76, 63.97, 549.21, 58.07, 546.73, 50.62, 553.25, 52.79, 554.8, 58.69, 560.08, 56.83, 562.56, 53.1, 562.56, 52.48, 559.15, 46.58]], "area": 595.4245000000001, "iscrowd": 0, "image_id": 10216, "bbox": [546.73, 39.44, 30.73, 33.22], "category_id": 1, "id": 1280415}, {"segmentation": [[525.3, 312.81, 538.25, 337.62, 539.33, 360.27, 572.76, 364.58, 587.87, 317.12, 588.94, 313.89, 578.16, 303.1, 564.13, 299.87, 547.96, 297.71, 526.38, 303.1, 524.22, 306.34]], "area": 2998.657399999999, "iscrowd": 0, "image_id": 10138, "bbox": [524.22, 297.71, 64.72, 66.87], "category_id": 47, "id": 1502676}, {"segmentation": [[640.0, 366.13, 630.14, 352.21, 630.91, 341.38, 634.0, 333.65, 640.0, 328.24]], "area": 245.79270000000042, "iscrowd": 0, "image_id": 10138, "bbox": [630.14, 328.24, 9.86, 37.89], "category_id": 51, "id": 1535236}, {"segmentation": [[464.78, 149.82, 618.68, 149.05, 620.22, 159.87, 607.08, 301.4, 501.9, 294.44, 502.67, 275.88, 487.2, 272.79, 479.47, 262.73, 459.36, 261.19, 463.23, 178.44]], "area": 21386.958700000003, "iscrowd": 0, "image_id": 10138, "bbox": [459.36, 149.05, 160.86, 152.35], "category_id": 62, "id": 1578778}, {"segmentation": [[357.78, 55.61, 359.06, 50.27, 362.69, 49.63, 371.23, 49.41, 377.21, 50.48, 378.71, 51.55, 379.13, 54.54, 379.13, 56.67, 378.92, 57.31, 378.49, 57.95]], "area": 143.20760000000007, "iscrowd": 0, "image_id": 10216, "bbox": [357.78, 49.41, 21.35, 8.54], "category_id": 62, "id": 1582582}, {"segmentation": [[636.14, 284.76, 637.62, 294.4, 631.68, 302.56, 607.95, 302.56, 608.69, 275.85, 612.4, 273.63, 623.52, 244.7, 617.59, 235.05, 619.07, 217.25, 625.01, 197.22, 622.78, 175.7, 616.11, 151.96, 593.11, 148.99, 563.43, 137.12, 568.63, 116.35, 603.49, 97.06, 610.17, 72.58, 627.23, 84.45, 639.1, 100.03, 639.1, 206.12]], "area": 6827.851650000001, "iscrowd": 0, "image_id": 10138, "bbox": [563.43, 72.58, 75.67, 229.98], "category_id": 64, "id": 1605547}, {"segmentation": [[639.64, 418.52, 640.0, 480.0, 76.58, 474.61, 0.0, 473.53, 1.08, 368.9, 57.17, 333.3, 73.35, 332.22, 125.12, 288.0, 159.64, 263.19, 192.46, 263.85, 193.13, 256.78, 200.54, 251.39, 219.05, 246.01, 238.91, 246.01, 251.03, 250.05, 254.4, 254.09, 253.39, 267.55, 259.78, 267.89, 262.81, 270.92, 441.9, 288.09, 439.21, 278.32, 446.28, 271.25, 460.41, 269.23, 462.77, 263.51, 469.84, 262.5, 480.27, 264.52, 483.98, 271.59, 494.41, 272.94, 502.49, 281.35, 498.45, 293.81, 537.16, 296.17, 605.16, 302.9, 632.77, 305.59, 640.0, 327.51, 640.0, 398.37]], "area": 120117.90779999997, "iscrowd": 0, "image_id": 10138, "bbox": [0.0, 246.01, 640.0, 233.99], "category_id": 67, "id": 1621290}, {"segmentation": [[230.5, 161.42, 231.52, 123.57, 231.01, 120.51, 233.57, 117.44, 248.4, 110.28, 256.58, 106.19, 259.65, 111.81, 261.69, 117.95, 262.72, 135.85, 265.78, 144.03, 264.76, 146.08, 238.17, 155.28, 237.66, 155.79, 236.12, 156.3], [343.51, 118.97, 361.92, 102.1, 343.51, 102.1, 344.03, 112.84]], "area": 1428.3696000000002, "iscrowd": 0, "image_id": 10138, "bbox": [230.5, 102.1, 131.42, 59.32], "category_id": 79, "id": 1642180}, {"segmentation": [[209.45, 108.01, 238.09, 99.32, 241.16, 122.84, 211.5, 133.58]], "area": 740.3571500000004, "iscrowd": 0, "image_id": 10216, "bbox": [209.45, 99.32, 31.71, 34.26], "category_id": 85, "id": 1664530}, {"segmentation": [[540.74, 35.34, 548.54, 36.67, 552.03, 40.82, 549.87, 46.8, 558.67, 48.62, 558.0, 52.11, 558.34, 58.25, 556.68, 59.58, 553.19, 52.44, 552.19, 51.45, 546.22, 51.61, 552.03, 68.05, 541.24, 68.54, 538.58, 60.58, 540.57, 52.77, 540.57, 52.77, 539.74, 47.96, 538.75, 44.64, 535.93, 40.82, 536.26, 35.18]], "area": 412.42964999999936, "iscrowd": 0, "image_id": 10216, "bbox": [535.93, 35.18, 22.74, 33.36], "category_id": 1, "id": 1676412}, {"segmentation": [[461.47, 34.69, 468.8, 24.92, 478.16, 24.92, 478.77, 20.86, 482.02, 19.23, 486.91, 20.04, 490.57, 25.74, 492.2, 28.59, 492.2, 32.05, 490.97, 37.54, 491.58, 45.47, 487.72, 55.24, 487.72, 59.72, 485.48, 64.39, 483.65, 68.06, 472.46, 67.85, 472.87, 62.16, 466.76, 58.49, 464.93, 58.09, 464.93, 49.54, 461.88, 48.93, 464.93, 64.39, 460.46, 67.04, 457.81, 64.6, 458.02, 60.73, 458.02, 51.58, 458.42, 48.12, 459.64, 43.44, 462.29, 37.34]], "area": 1205.2337500000003, "iscrowd": 0, "image_id": 10216, "bbox": [457.81, 19.23, 34.39, 48.83], "category_id": 1, "id": 1682278}, {"segmentation": [[439.66, 30.17, 435.57, 35.08, 434.55, 36.31, 459.28, 37.94, 460.92, 34.26, 463.37, 31.2, 439.25, 28.74], [462.35, 17.71, 463.98, 22.41, 462.75, 26.29, 462.75, 27.52, 441.71, 25.47, 440.68, 25.06, 441.3, 20.77, 442.73, 17.71, 444.97, 15.66, 447.22, 14.44, 446.81, 11.99, 446.2, 9.12, 445.59, 5.45, 445.59, 3.4, 447.22, 1.97, 450.29, 1.15, 455.19, 1.15, 456.42, 4.02, 457.65, 9.33, 457.65, 13.01, 457.65, 14.23, 460.1, 15.87, 461.32, 16.28, 463.37, 18.32, 463.98, 20.98, 464.19, 22.0]], "area": 562.8165999999999, "iscrowd": 0, "image_id": 10216, "bbox": [434.55, 1.15, 29.64, 36.79], "category_id": 1, "id": 1682466}, {"segmentation": [[438.59, 36.54, 425.2, 38.7, 423.04, 36.98, 413.54, 35.25, 416.56, 25.31, 439.89, 26.18], [441.19, 26.61, 416.56, 23.58, 415.7, 19.69, 419.58, 11.92, 423.9, 10.19, 424.34, 1.98, 428.66, 0.0, 430.82, 0.68, 433.41, 4.57, 433.84, 9.76, 440.75, 11.92, 442.48, 20.56]], "area": 714.7219500000001, "iscrowd": 0, "image_id": 10216, "bbox": [413.54, 0.0, 28.94, 38.7], "category_id": 1, "id": 1685931}, {"segmentation": [[403.23, 4.48, 400.76, 2.57, 397.75, 2.29, 393.09, 7.77, 393.91, 11.34, 391.99, 14.08, 393.09, 20.38, 395.55, 21.75, 402.95, 23.12, 414.46, 22.02, 413.91, 16.82, 410.08, 13.8, 408.43, 12.16, 408.43, 8.05, 405.97, 4.21], [415.01, 26.41, 395.83, 25.04, 395.83, 25.04, 394.46, 31.62, 403.5, 34.9, 413.37, 36.0, 414.46, 36.0]], "area": 497.57030000000015, "iscrowd": 0, "image_id": 10216, "bbox": [391.99, 2.29, 23.02, 33.71], "category_id": 1, "id": 1687015}, {"segmentation": [[393.8, 21.71, 391.33, 16.78, 394.07, 12.12, 394.07, 7.19, 391.33, 5.54, 386.95, 3.62, 385.85, 0.34, 372.97, 0.61, 373.52, 3.35, 366.67, 6.09, 360.64, 9.38, 359.27, 10.75, 358.45, 13.22, 359.82, 14.04, 365.3, 14.04, 367.76, 17.05, 368.86, 20.07, 372.42, 22.53, 394.35, 23.08], [395.99, 25.82, 394.9, 31.3, 389.41, 33.49, 387.22, 33.49, 386.67, 28.01, 384.48, 25.0, 374.89, 23.35, 370.23, 28.84, 368.59, 22.53, 378.73, 22.53, 397.09, 24.73]], "area": 680.9992999999996, "iscrowd": 0, "image_id": 10216, "bbox": [358.45, 0.34, 38.64, 33.15], "category_id": 1, "id": 1688117}, {"segmentation": [[221.38, 13.73, 216.29, 22.02, 214.6, 28.62, 214.6, 31.26, 220.06, 29.18, 225.34, 27.87, 230.24, 26.55, 232.5, 24.85, 235.71, 25.6, 250.97, 21.83, 251.54, 18.63, 251.16, 17.12, 250.79, 16.37, 249.47, 15.61, 248.52, 15.61, 246.64, 15.61, 244.57, 15.99, 242.12, 15.8, 237.4, 14.67, 236.08, 13.54, 236.84, 11.9, 236.27, 9.26, 236.27, 6.62, 236.27, 4.74, 235.9, 2.29, 233.82, 1.72, 231.94, 1.15, 230.05, 1.15, 228.92, 1.34, 227.41, 1.72, 224.96, 3.98, 224.4, 7.0, 224.4, 7.94, 225.53, 8.69, 225.53, 9.45, 224.4, 10.58, 223.27, 12.09, 222.7, 12.84]], "area": 527.3456, "iscrowd": 0, "image_id": 10216, "bbox": [214.6, 1.15, 36.94, 30.11], "category_id": 1, "id": 1694497}, {"segmentation": [[380.19, 58.24, 381.11, 53.9, 384.01, 51.93, 386.9, 51.27, 397.04, 51.53, 400.85, 52.71, 401.64, 54.16, 400.85, 60.61]], "area": 156.9059, "iscrowd": 0, "image_id": 10216, "bbox": [380.19, 51.27, 21.45, 9.34], "category_id": 62, "id": 1933821}, {"segmentation": [[42.25, 61.51, 38.87, 55.51, 37.37, 49.88, 36.25, 45.95, 37.93, 43.7, 41.87, 42.01, 46.37, 41.82, 50.87, 42.38, 53.5, 43.51, 55.93, 48.57, 58.18, 51.38, 60.25, 53.82, 61.0, 55.88]], "area": 321.49435000000005, "iscrowd": 0, "image_id": 10216, "bbox": [36.25, 41.82, 24.75, 19.69], "category_id": 62, "id": 1933979}, {"segmentation": [[345.76, 353.79, 350.08, 361.0, 362.57, 362.44, 385.14, 359.07, 390.91, 364.36, 397.15, 376.84, 400.51, 391.25, 404.35, 426.79, 388.99, 427.0, 384.66, 379.25, 370.26, 378.28, 363.05, 379.73, 358.73, 382.13, 357.77, 383.09, 355.37, 386.93, 354.89, 400.38, 353.93, 427.0, 341.44, 425.35, 346.24, 401.82, 342.88, 388.37, 342.4, 379.73, 340.96, 357.15, 345.28, 356.19], [279.48, 381.65, 284.77, 390.77, 298.7, 426.79, 291.97, 425.83, 286.69, 405.66, 283.81, 426.31, 268.44, 424.87, 276.6, 386.93, 257.39, 391.25, 254.99, 402.3, 254.51, 424.87, 240.58, 424.87, 251.63, 386.45]], "area": 3336.0703499999995, "iscrowd": 0, "image_id": 10123, "bbox": [240.58, 353.79, 163.77, 73.21], "category_id": 62, "id": 1934128}, {"segmentation": [[26.43, 64.93, 22.5, 59.93, 20.35, 59.57, 18.03, 54.21, 17.85, 51.36, 18.39, 49.93, 23.93, 47.78, 29.86, 46.79, 31.26, 46.49, 32.95, 47.09, 37.64, 52.98, 39.44, 58.27, 41.31, 61.24, 31.92, 62.28, 30.18, 65.06, 28.1, 65.06]], "area": 280.57000000000005, "iscrowd": 0, "image_id": 10216, "bbox": [17.85, 46.49, 23.46, 18.57], "category_id": 62, "id": 1938384}, {"segmentation": [[339.91, 253.4, 340.12, 248.79, 342.85, 246.06, 347.04, 244.81, 353.12, 257.17, 353.12, 257.17, 366.12, 280.86, 368.63, 290.72, 366.75, 291.35, 369.05, 294.28, 368.84, 303.3, 367.58, 301.41, 368.21, 319.86, 368.21, 326.15, 365.07, 326.15, 363.81, 291.56, 360.67, 291.56, 361.5, 314.2, 359.41, 314.2, 359.62, 317.76, 356.89, 317.76, 353.75, 299.73, 345.57, 323.21, 343.48, 321.74, 353.12, 291.76, 349.14, 290.3, 349.14, 288.2, 349.97, 286.94, 349.97, 284.43, 347.46, 269.12]], "area": 980.7689999999991, "iscrowd": 0, "image_id": 10205, "bbox": [339.91, 244.81, 29.14, 81.34], "category_id": 62, "id": 1938765}, {"segmentation": [[16.26, 33.97, 16.26, 26.61, 17.73, 22.52, 18.55, 21.71, 24.43, 20.73, 34.08, 23.34, 37.02, 28.08, 37.02, 29.23, 18.55, 36.75]], "area": 223.05610000000001, "iscrowd": 0, "image_id": 10216, "bbox": [16.26, 20.73, 20.76, 16.02], "category_id": 62, "id": 1944774}, {"segmentation": [[13.01, 25.35, 17.64, 35.6, 17.32, 37.0, 17.21, 38.08, 8.91, 42.93, 5.56, 43.58, 2.0, 41.74, 0.0, 37.86, 0.0, 33.87, 0.0, 26.32, 10.85, 24.38]], "area": 258.45559999999995, "iscrowd": 0, "image_id": 10216, "bbox": [0.0, 24.38, 17.64, 19.2], "category_id": 62, "id": 1945472}, {"segmentation": [[445.14, 50.82, 446.23, 40.22, 444.67, 38.67, 430.64, 37.89, 426.9, 39.6, 425.19, 46.77, 425.34, 48.48, 419.27, 49.89, 417.24, 53.0, 424.56, 55.19, 425.97, 57.84, 424.72, 62.67, 425.81, 62.2, 429.08, 57.21, 430.64, 56.43, 440.93, 55.97, 441.08, 53.63, 442.64, 52.07, 444.04, 51.29]], "area": 386.167, "iscrowd": 0, "image_id": 10216, "bbox": [417.24, 37.89, 28.99, 24.78], "category_id": 62, "id": 1945926}, {"segmentation": [[402.61, 60.42, 406.84, 54.33, 416.67, 53.82, 424.29, 55.17, 425.64, 56.53, 424.63, 61.95]], "area": 142.22320000000005, "iscrowd": 0, "image_id": 10216, "bbox": [402.61, 53.82, 23.03, 8.13], "category_id": 62, "id": 1945953}, {"segmentation": [[586.14, 70.43, 588.04, 67.1, 587.33, 67.1, 585.19, 68.53, 581.63, 69.95, 579.96, 70.9, 578.54, 72.33, 574.27, 71.38, 572.84, 70.43, 573.08, 68.53, 576.64, 66.63, 581.86, 62.83, 582.58, 59.03, 582.81, 53.33, 583.53, 50.24, 584.71, 49.06, 587.33, 48.11, 588.51, 48.34, 587.09, 47.16, 586.14, 45.26, 586.61, 42.88, 586.38, 40.03, 587.56, 38.13, 591.36, 36.95, 593.26, 36.95, 594.92, 38.61, 596.11, 39.08, 598.72, 40.74, 598.01, 42.41, 597.54, 43.59, 596.59, 43.59, 597.07, 46.04, 596.83, 46.65, 596.71, 47.01, 597.81, 47.01, 598.54, 47.62, 602.69, 47.87, 603.91, 48.11, 606.35, 49.82, 606.47, 52.38, 606.47, 53.97, 603.91, 55.31, 596.34, 59.22, 595.12, 60.32, 594.88, 62.15, 594.63, 63.12, 593.66, 63.73, 592.19, 65.08, 586.46, 69.96], [605.37, 58.12, 605.37, 72.52, 599.76, 72.89, 597.56, 75.21, 596.22, 77.28, 594.88, 79.85, 593.66, 79.72, 593.78, 72.77, 588.41, 72.52, 605.01, 57.14]], "area": 652.1805000000011, "iscrowd": 0, "image_id": 10216, "bbox": [572.84, 36.95, 33.63, 42.9], "category_id": 1, "id": 2021899}, {"segmentation": [[220.68, 99.48, 220.21, 83.66, 219.5, 75.39, 218.79, 71.85, 218.55, 68.07, 217.84, 64.76, 217.14, 62.16, 218.08, 59.56, 220.44, 58.85, 222.8, 62.4, 224.93, 67.12, 225.17, 70.19, 225.64, 80.11, 221.62, 101.14]], "area": 188.99809999999965, "iscrowd": 0, "image_id": 10138, "bbox": [217.14, 58.85, 8.5, 42.29], "category_id": 44, "id": 2094307}, {"segmentation": [[69.38, 121.92, 62.85, 86.09, 58.09, 81.73, 58.09, 78.76, 55.92, 76.98, 52.35, 78.17, 51.96, 79.36, 51.96, 81.73, 52.55, 82.52, 51.76, 84.9, 50.57, 87.27, 51.36, 93.21, 54.73, 120.33, 55.92, 124.89, 68.59, 122.12]], "area": 562.893, "iscrowd": 0, "image_id": 10138, "bbox": [50.57, 76.98, 18.81, 47.91], "category_id": 44, "id": 2096136}, {"segmentation": [[338.55, 251.75, 340.06, 249.94, 341.58, 248.12, 344.6, 245.7, 346.11, 245.1, 345.51, 244.79, 343.69, 244.19, 341.88, 244.19, 339.15, 245.1, 337.34, 246.01, 336.43, 246.61, 336.13, 247.22]], "area": 32.802750000000046, "iscrowd": 0, "image_id": 10205, "bbox": [336.13, 244.19, 9.98, 7.56], "category_id": 62, "id": 2124447}, {"segmentation": [[215.7, 95.28, 234.59, 92.14, 249.28, 98.96, 261.35, 110.5, 272.36, 122.56, 281.28, 123.09, 268.69, 140.4, 293.35, 182.37, 299.65, 189.19, 310.14, 195.49, 332.17, 216.47, 358.4, 233.79, 373.09, 239.03, 384.11, 241.13, 400.37, 242.18, 415.59, 242.7, 418.21, 251.1, 396.7, 252.67, 395.13, 254.25, 414.01, 274.71, 422.93, 285.72, 419.26, 289.4, 413.49, 291.5, 401.95, 282.05, 404.05, 294.64, 398.8, 287.82, 386.21, 268.94, 385.68, 284.68, 380.96, 283.63, 379.39, 271.03, 369.95, 258.97, 357.88, 255.82, 335.84, 247.43, 298.07, 228.01, 314.33, 264.74, 326.4, 282.05, 320.11, 287.82, 325.88, 301.46, 348.44, 348.16, 355.26, 357.07, 341.09, 357.6, 345.29, 391.18, 344.76, 402.72, 343.19, 427.0, 298.07, 425.8, 280.76, 383.31, 251.38, 388.03, 238.26, 427.0, 186.32, 426.85, 184.23, 395.9, 181.08, 387.5, 182.65, 367.57, 178.98, 338.19, 173.73, 315.1, 183.18, 275.76, 191.05, 262.12, 196.29, 249.0, 206.26, 237.98, 207.31, 221.19, 191.05, 222.77, 185.8, 239.03, 183.7, 241.66, 177.41, 226.97, 168.49, 217.52, 163.77, 211.59, 152.75, 209.49, 150.65, 204.24, 139.11, 177.48, 129.66, 157.55, 107.11, 169.62, 96.09, 166.47, 110.78, 161.22, 115.5, 148.63, 118.65, 139.71, 118.12, 97.22, 111.83, 86.72, 95.04, 56.29, 93.46, 37.41, 100.81, 21.37, 114.45, 10.36, 121.27, 5.64, 132.81, 3.01, 141.73, 4.59, 151.17, 10.36, 159.04, 15.6, 167.96, 23.47, 174.78, 34.49, 178.46, 44.98, 178.46, 54.43, 175.83, 60.72, 187.37, 88.0, 204.16, 92.2, 212.56, 94.82]], "area": 57424.77520000001, "iscrowd": 0, "image_id": 10123, "bbox": [93.46, 3.01, 329.47, 423.99], "category_id": 1, "id": 2152925}, {"segmentation": [[623.09, 315.0, 623.41, 338.2, 613.88, 338.52, 614.52, 317.55, 608.16, 317.23, 613.24, 339.15, 609.11, 342.33, 601.17, 341.06, 595.13, 314.69, 594.18, 311.19, 590.68, 310.24, 588.78, 312.46, 586.55, 313.73, 586.55, 336.61, 581.79, 334.7, 581.15, 329.3, 583.38, 319.45, 579.24, 263.53, 578.61, 252.09, 582.1, 249.86, 582.74, 251.45, 583.38, 260.03, 611.97, 258.12, 613.56, 250.18, 619.28, 251.45, 622.78, 304.2]], "area": 2978.710299999998, "iscrowd": 0, "image_id": 10205, "bbox": [578.61, 249.86, 44.8, 92.47], "category_id": 62, "id": 2191957}, {"segmentation": [[2.15, 452.08, 30.2, 435.06, 55.74, 445.15, 74.65, 463.75, 68.98, 470.68, 56.05, 480.0, 0.0, 480.0]], "area": 2421.199550000001, "iscrowd": 0, "image_id": 10138, "bbox": [0.0, 435.06, 74.65, 44.94], "category_id": 77, "id": 2195897}, {"segmentation": [[132.3, 76.26, 132.43, 59.68, 134.93, 60.21, 135.19, 62.44, 134.4, 75.6]], "area": 39.33569999999988, "iscrowd": 0, "image_id": 10138, "bbox": [132.3, 59.68, 2.89, 16.58], "category_id": 49, "id": 2230916}, {"segmentation": [[132.28, 59.68, 135.27, 60.55, 135.14, 67.28, 134.4, 73.56, 132.22, 73.69, 131.78, 72.82, 132.03, 61.3, 132.09, 59.74]], "area": 41.66949999999996, "iscrowd": 0, "image_id": 10138, "bbox": [131.78, 59.68, 3.49, 14.01], "category_id": 49, "id": 2230939}, {"segmentation": [[138.12, 59.24, 141.43, 59.09, 140.98, 71.74, 140.25, 73.28, 138.26, 72.99, 138.41, 67.84]], "area": 39.93589999999998, "iscrowd": 0, "image_id": 10138, "bbox": [138.12, 59.09, 3.31, 14.19], "category_id": 49, "id": 2230945}, {"segmentation": [[143.22, 72.48, 146.12, 72.87, 144.41, 86.03, 141.78, 85.64, 142.96, 72.87]], "area": 38.43164999999994, "iscrowd": 0, "image_id": 10138, "bbox": [141.78, 72.48, 4.34, 13.55], "category_id": 49, "id": 2230959}, {"segmentation": [[263.43, 216.57, 266.02, 207.74, 269.82, 203.02, 275.61, 198.76, 281.39, 197.24, 289.61, 197.09, 296.31, 200.59, 300.88, 206.37, 302.25, 211.55, 301.48, 218.1, 299.81, 224.34, 295.7, 227.53, 290.07, 232.25, 285.2, 233.32, 280.17, 234.38, 262.21, 217.03]], "area": 1050.1988000000001, "iscrowd": 0, "image_id": 10400, "bbox": [262.21, 197.09, 40.04, 37.29], "category_id": 85, "id": 334462}, {"segmentation": [[211.89, 281.62, 264.86, 391.89, 264.86, 391.89, 317.84, 401.62, 408.65, 315.14, 503.78, 201.62, 486.49, 194.05, 425.95, 201.62, 361.08, 185.41, 305.95, 172.43, 272.43, 170.27, 243.24, 170.27, 259.46, 181.08, 260.54, 184.32, 264.86, 207.03, 264.86, 223.24, 249.73, 241.62, 247.57, 243.78, 234.59, 260.0, 232.43, 263.24, 214.05, 278.38]], "area": 37887.92384999998, "iscrowd": 0, "image_id": 10217, "bbox": [211.89, 170.27, 291.89, 231.35], "category_id": 59, "id": 1570928}, {"segmentation": [[445.56, 84.97, 460.63, 53.76, 476.77, 20.39, 496.14, 17.17, 542.42, 23.62, 587.62, 35.46, 636.05, 86.04, 640.0, 110.8, 639.28, 187.21, 610.22, 200.13, 482.15, 168.91, 443.41, 126.94]], "area": 27252.161699999997, "iscrowd": 0, "image_id": 10217, "bbox": [443.41, 17.17, 196.59, 182.96], "category_id": 74, "id": 1629792}, {"segmentation": {"counts": [121, 12, 347, 2, 2, 8, 3, 1, 1, 1, 5, 2, 3, 1, 1, 1, 2, 2, 3, 5, 1, 7, 1, 5, 1, 4, 1, 3, 1, 4, 1, 5, 1, 12, 1, 2, 1, 1, 2, 1, 14, 30, 11, 11, 1, 7, 1, 6, 3, 2, 12, 8, 8, 38, 1, 1, 1, 4, 2, 2, 1, 1, 1, 2, 1, 85, 128, 2, 1, 10, 2, 42, 1, 41, 1, 1, 7, 38, 7, 11, 1, 25, 5, 9, 8, 140, 128, 14, 1, 87, 3, 42, 6, 37, 4, 10, 8, 140, 128, 14, 1, 133, 5, 38, 2, 11, 8, 140, 128, 14, 1, 133, 4, 53, 7, 140, 128, 149, 3, 54, 6, 140, 128, 206, 6, 140, 99, 22, 7, 206, 6, 140, 95, 29, 4, 206, 7, 139, 70, 57, 1, 206, 7, 139, 63, 65, 1, 72, 6, 127, 7, 138, 60, 69, 1, 73, 5, 127, 8, 137, 57, 146, 5, 127, 8, 136, 57, 147, 5, 126, 10, 135, 55, 149, 5, 125, 11, 134, 53, 152, 6, 124, 12, 133, 50, 155, 6, 123, 13, 132, 50, 156, 7, 121, 15, 131, 48, 157, 8, 119, 18, 129, 48, 157, 10, 114, 23, 127, 49, 155, 12, 113, 25, 125, 49, 158, 11, 112, 26, 123, 49, 160, 10, 112, 28, 120, 50, 161, 7, 114, 29, 118, 50, 283, 31, 115, 50, 283, 35, 111, 49, 285, 35, 109, 47, 288, 40, 12, 1, 91, 45, 291, 41, 7, 5, 89, 46, 291, 56, 65, 66, 293, 58, 59, 69, 294, 59, 56, 71, 293, 61, 53, 71, 295, 64, 44, 76, 295, 67, 40, 76, 191, 2, 103, 69, 37, 77, 188, 6, 102, 179, 190, 8, 102, 177, 191, 10, 102, 176, 190, 12, 102, 174, 191, 12, 104, 172, 191, 12, 105, 172, 190, 13, 105, 171, 189, 14, 106, 170, 188, 16, 106, 170, 189, 14, 107, 169, 193, 10, 108, 169, 194, 9, 108, 169, 195, 7, 109, 169, 196, 6, 109, 169, 196, 6, 109, 169, 196, 5, 110, 169, 196, 4, 111, 169, 196, 4, 111, 169, 196, 3, 112, 169, 196, 3, 112, 169, 196, 2, 112, 171, 196, 1, 112, 171, 309, 172, 308, 173, 307, 174, 306, 175, 304, 177, 303, 179, 14, 3, 283, 183, 8, 7, 282, 199, 280, 202, 17, 2, 259, 205, 7, 13, 254, 227, 253, 228, 251, 230, 249, 232, 247, 235, 13, 2, 229, 239, 7, 8, 225, 261, 53, 1, 163, 263, 53, 4, 159, 265, 52, 6, 156, 267, 51, 11, 151, 268, 38, 1, 10, 1, 162, 269, 211, 270, 210, 272, 208, 275, 205, 279, 201, 279, 201, 279, 201, 279, 201, 279, 201, 279, 111, 5, 85, 279, 106, 11, 13, 10, 61, 279, 105, 14, 7, 19, 55, 280, 100, 20, 4, 24, 52, 281, 96, 53, 50, 281, 94, 57, 48, 282, 93, 59, 45, 284, 92, 61, 43, 285, 92, 62, 40, 287, 92, 62, 39, 288, 92, 62, 37, 290, 93, 61, 36, 291, 93, 62, 33, 293, 93, 62, 32, 294, 93, 62, 30, 297, 91, 62, 29, 301, 12, 1, 75, 63, 27, 315, 75, 64, 25, 316, 75, 65, 23, 317, 75, 66, 20, 319, 75, 67, 18, 320, 75, 68, 15, 322, 75, 69, 11, 325, 74, 71, 6, 329, 73, 407, 71, 409, 68, 412, 67, 414, 66, 271, 8, 135, 65, 269, 14, 131, 66, 268, 16, 130, 66, 266, 20, 128, 66, 265, 22, 127, 66, 265, 22, 127, 67, 263, 24, 126, 67, 262, 26, 125, 68, 261, 26, 124, 69, 260, 28, 123, 69, 260, 28, 123, 69, 260, 28, 123, 69, 260, 28, 124, 68, 260, 28, 124, 68, 260, 28, 125, 67, 260, 28, 124, 68, 260, 28, 123, 70, 259, 28, 123, 70, 259, 28, 123, 71, 258, 28, 123, 72, 257, 28, 123, 73, 256, 28, 123, 73, 256, 28, 123, 12, 2, 60, 255, 28, 123, 12, 3, 60, 254, 28, 123, 11, 5, 59, 254, 28, 124, 9, 3, 62, 254, 28, 126, 5, 3, 64, 254, 28, 133, 65, 254, 28, 129, 70, 253, 28, 125, 74, 253, 28, 124, 75, 253, 28, 123, 76, 252, 29, 123, 76, 252, 29, 123, 75, 252, 30, 123, 74, 253, 30, 123, 72, 255, 30, 123, 68, 259, 30, 124, 68, 258, 30, 124, 69, 257, 30, 124, 70, 13, 14, 228, 30, 125, 71, 9, 21, 224, 30, 125, 71, 7, 26, 221, 29, 126, 71, 5, 30, 219, 29, 127, 70, 3, 33, 218, 29, 126, 71, 2, 36, 216, 28, 127, 71, 1, 39, 213, 29, 127, 113, 211, 29, 127, 115, 209, 31, 125, 117, 207, 32, 4, 8, 112, 118, 205, 48, 110, 119, 202, 50, 108, 122, 200, 52, 106, 125, 196, 54, 105, 127, 193, 55, 104, 130, 191, 57, 102, 133, 187, 59, 101, 135, 185, 61, 99, 138, 181, 63, 98, 140, 179, 63, 98, 143, 176, 66, 95, 145, 172, 69, 94, 147, 169, 72, 92, 149, 165, 75, 91, 151, 162, 77, 90, 153, 159, 79, 89, 155, 155, 82, 88, 157, 152, 84, 87, 157, 151, 86, 87, 157, 149, 88, 87, 157, 148, 90, 84, 159, 146, 92, 83, 159, 145, 95, 81, 160, 144, 96, 80, 161, 142, 99, 78, 162, 141, 103, 74, 162, 141, 106, 71, 163, 139, 108, 69, 165, 138, 110, 67, 166, 137, 111, 66, 167, 136, 111, 66, 168, 135, 112, 65, 169, 134, 113, 64, 170, 133, 113, 64, 170, 133, 114, 63, 171, 132, 114, 63, 172, 131, 114, 63, 172, 131, 114, 63, 173, 130, 114, 63, 173, 130, 46, 23, 45, 63, 173, 130, 44, 29, 41, 63, 174, 129, 42, 34, 38, 63, 174, 129, 40, 40, 34, 63, 175, 128, 37, 46, 31, 63, 175, 128, 35, 51, 28, 64, 177, 124, 34, 56, 24, 65, 176, 125, 32, 61, 21, 65, 174, 126, 31, 65, 18, 66, 173, 127, 31, 68, 14, 67, 172, 127, 32, 70, 11, 68, 171, 128, 31, 74, 7, 69, 170, 128, 32, 76, 6, 67, 171, 128, 32, 79, 3, 67, 71, 1, 99, 127, 32, 83, 1, 66, 71, 4, 96, 127, 32, 150, 71, 8, 91, 127, 32, 151, 71, 12, 9, 17, 61, 127, 32, 151, 71, 15, 4, 32, 15, 5, 28, 127, 32, 151, 71, 73, 25, 128, 31, 152, 70, 75, 24, 127, 32, 152, 70, 76, 23, 127, 32, 152, 71, 76, 21, 128, 31, 153, 72, 76, 20, 128, 31, 153, 73, 77, 18, 128, 28, 156, 73, 78, 17, 128, 26, 158, 73, 79, 15, 129, 24, 160, 73, 80, 14, 129, 22, 162, 73, 81, 13, 129, 24, 160, 73, 81, 12, 130, 25, 13, 24, 122, 73, 81, 12, 130, 25, 11, 38, 110, 73, 81, 12, 130, 25, 10, 45, 104, 73, 39, 3, 40, 10, 131, 25, 8, 51, 100, 73, 28, 17, 37, 11, 130, 25, 6, 56, 97, 73, 25, 22, 35, 13, 128, 25, 4, 61, 94, 72, 23, 28, 32, 15, 126, 25, 3, 65, 91, 71, 21, 33, 31, 15, 125, 25, 1, 70, 88, 69, 21, 38, 28, 17, 123, 99, 85, 63, 24, 43, 26, 19, 121, 102, 82, 65, 10, 1, 10, 46, 24, 20, 120, 105, 79, 67, 7, 1, 10, 48, 24, 21, 118, 108, 76, 69, 4, 1, 10, 50, 23, 23, 116, 111, 73, 70, 2, 2, 9, 52, 22, 25, 113, 115, 70, 71, 1, 1, 9, 54, 21, 26, 110, 120, 67, 73, 8, 56, 20, 27, 108, 124, 64, 77, 2, 58, 21, 27, 105, 129, 61, 138, 20, 27, 104, 133, 23, 2, 33, 139, 19, 27, 103, 147, 9, 5, 31, 140, 17, 29, 101, 165, 28, 141, 16, 29, 100, 167, 27, 142, 15, 30, 98, 170, 25, 143, 14, 30, 97, 172, 24, 144, 12, 32, 95, 174, 23, 145, 11, 32, 94, 175, 23, 146, 8, 35, 92, 177, 22, 147, 2, 1, 1, 38, 92, 178, 21, 190, 89, 180, 21, 190, 88, 182, 21, 190, 86, 183, 21, 190, 86, 183, 21, 191, 84, 184, 21, 189, 85, 185, 20, 187, 88, 39, 6, 140, 20, 184, 90, 39, 13, 134, 20, 182, 92, 37, 21, 128, 20, 181, 92, 37, 28, 122, 20, 179, 94, 35, 36, 116, 20, 178, 95, 34, 40, 113, 20, 176, 96, 33, 44, 110, 21, 175, 97, 32, 47, 42, 1, 65, 21, 173, 99, 30, 52, 39, 11, 54, 22, 172, 99, 30, 55, 37, 11, 53, 23, 170, 101, 28, 59, 35, 11, 52, 25, 166, 103, 28, 62, 33, 11, 51, 27, 155, 1, 7, 105, 26, 67, 30, 11, 50, 29, 154, 2, 4, 107, 27, 68, 27, 12, 48, 32, 153, 3, 2, 108, 27, 70, 25, 12, 32, 6, 7, 37, 151, 113, 27, 73, 21, 14, 31, 54, 147, 113, 27, 75, 18, 15, 31, 55, 146, 113, 28, 76, 15, 17, 30, 55, 146, 113, 28, 78, 12, 19, 29, 55, 146, 113, 28, 81, 8, 21, 27, 57, 144, 114, 29, 82, 4, 24, 26, 58, 142, 115, 29, 111, 24, 61, 140, 115, 27, 114, 22, 60, 141, 117, 25, 116, 20, 60, 142, 117, 24, 118, 18, 60, 142, 119, 21, 29, 8, 84, 16, 60, 143, 120, 19, 17, 23, 84, 12, 61, 143, 122, 17, 13, 31, 84, 6, 63, 144, 123, 14, 13, 35, 150, 144, 125, 12, 13, 38, 148, 143, 127, 11, 11, 42, 145, 144, 127, 12, 9, 46, 141, 144, 128, 13, 6, 50, 139, 144, 128, 13, 5, 53, 136, 144, 127, 16, 2, 58, 133, 144, 124, 19, 1, 61, 131, 143, 124, 84, 128, 144, 122, 88, 126, 144, 121, 92, 123, 144, 119, 96, 121, 144, 118, 99, 119, 144, 117, 103, 36, 7, 73, 144, 116, 106, 34, 38, 42, 144, 116, 108, 32, 41, 39, 144, 115, 111, 30, 42, 38, 144, 114, 40, 27, 48, 28, 43, 36, 153, 4, 8, 93, 38, 32, 47, 26, 44, 35, 153, 2, 16, 86, 37, 36, 47, 24, 44, 35, 177, 80, 35, 41, 47, 19, 47, 34, 180, 77, 34, 44, 47, 14, 51, 33, 182, 75, 32, 49, 46, 10, 53, 32, 184, 73, 31, 53, 46, 6, 56, 30, 187, 71, 29, 58, 46, 1, 58, 30, 188, 70, 28, 61, 103, 29, 191, 67, 29, 64, 100, 29, 192, 66, 29, 66, 98, 29, 194, 63, 29, 70, 95, 29, 195, 61, 30, 72, 93, 29, 197, 59, 29, 76, 90, 29, 198, 57, 30, 77, 89, 29, 200, 55, 29, 80, 87, 29, 201, 53, 30, 81, 86, 29, 203, 51, 29, 84, 83, 30, 204, 49, 30, 86, 81, 30, 206, 47, 29, 88, 79, 31, 207, 46, 28, 91, 76, 32, 209, 44, 27, 93, 74, 33, 210, 43, 27, 95, 71, 34, 211, 42, 26, 97, 69, 35, 213, 39, 26, 101, 10, 2, 6, 2, 44, 37, 214, 24, 2, 12, 25, 133, 30, 40, 216, 19, 6, 11, 24, 204, 217, 16, 9, 10, 23, 205, 218, 12, 13, 9, 22, 206, 220, 8, 15, 9, 22, 206, 221, 5, 18, 8, 21, 207, 245, 7, 20, 208, 245, 7, 20, 208, 246, 6, 20, 208, 246, 6, 20, 208, 246, 6, 20, 208, 247, 5, 19, 209, 247, 5, 19, 209, 247, 5, 19, 209, 247, 5, 19, 209, 247, 6, 18, 209, 247, 6, 17, 210, 247, 7, 16, 210, 247, 8, 17, 208, 247, 9, 10, 2, 8, 204, 247, 10, 9, 9, 5, 200, 247, 11, 7, 17, 2, 196, 247, 13, 5, 23, 1, 191, 247, 16, 2, 215, 247, 233, 247, 233, 246, 234, 147, 1, 98, 234, 148, 2, 95, 235, 148, 3, 94, 235, 148, 5, 91, 236, 148, 8, 88, 236, 148, 8, 88, 236, 148, 7, 89, 236, 148, 7, 89, 236, 148, 7, 89, 236, 148, 6, 90, 236, 148, 6, 90, 236, 148, 6, 90, 236, 148, 6, 90, 236, 148, 6, 90, 236, 148, 6, 90, 236, 148, 6, 90, 236, 148, 6, 90, 236, 148, 6, 89, 237, 148, 6, 89, 237, 148, 6, 88, 238, 148, 6, 88, 238, 148, 6, 87, 239, 148, 6, 87, 239, 148, 6, 86, 240, 148, 7, 85, 240, 148, 7, 84, 241, 148, 8, 82, 242, 147, 9, 81, 243, 147, 10, 79, 244, 147, 10, 79, 244, 146, 12, 77, 245, 146, 12, 76, 246, 145, 14, 75, 246, 145, 15, 74, 246, 144, 17, 73, 246, 144, 18, 72, 246, 143, 20, 71, 246, 143, 21, 70, 246, 142, 22, 69, 247, 141, 24, 68, 248, 139, 25, 68, 249, 137, 27, 66, 251, 135, 29, 65, 252, 135, 29, 63, 255, 133, 30, 62, 256, 133, 30, 60, 259, 131, 32, 58, 261, 129, 33, 56, 263, 129, 33, 55, 265, 127, 33, 54, 267, 126, 33, 54, 269, 124, 34, 53, 271, 122, 34, 53, 274, 119, 35, 52, 276, 117, 35, 52, 277, 116, 36, 51, 278, 115, 36, 51, 279, 114, 36, 51, 280, 113, 37, 50, 281, 112, 37, 49, 284, 110, 38, 48, 285, 109, 38, 47, 288, 107, 39, 46, 290, 105, 39, 45, 294, 21, 9, 72, 40, 44, 298, 13, 13, 72, 40, 43, 325, 73, 40, 42, 325, 73, 41, 40, 325, 74, 42, 38, 325, 75, 43, 36, 326, 75, 44, 34, 326, 76, 45, 32, 141, 11, 174, 77, 47, 28, 143, 13, 172, 77, 48, 26, 145, 13, 170, 78, 50, 22, 147, 14, 169, 78, 53, 16, 150, 15, 168, 78, 57, 8, 153, 17, 166, 79, 218, 19, 164, 79, 218, 22, 161, 79, 218, 24, 159, 79, 9, 12, 197, 26, 156, 80, 6, 18, 193, 29, 154, 80, 4, 22, 191, 32, 150, 81, 2, 26, 188, 35, 148, 119, 176, 39, 145, 123, 172, 42, 143, 125, 169, 46, 140, 127, 165, 50, 137, 130, 162, 53, 135, 131, 159, 57, 133, 132, 157, 61, 130, 133, 146, 73, 128, 134, 146, 74, 126, 135, 145, 76, 124, 136, 144, 79, 121, 137, 143, 81, 119, 138, 143, 82, 117, 139, 142, 84, 115, 139, 142, 87, 112, 140, 141, 89, 110, 141, 140, 91, 108, 141, 141, 92, 106, 142, 140, 94, 104, 142, 139, 97, 102, 142, 138, 101, 99, 143, 137, 103, 97, 143, 135, 107, 95, 143, 134, 110, 93, 143, 133, 113, 91, 143, 132, 117, 88, 143, 132, 119, 86, 143, 132, 121, 84, 143, 132, 123, 82, 143, 132, 125, 80, 143, 132, 127, 79, 142, 131, 131, 76, 142, 131, 133, 74, 142, 131, 135, 73, 141, 131, 137, 71, 141, 130, 140, 69, 141, 129, 144, 66, 141, 129, 146, 64, 141, 128, 148, 63, 143, 126, 149, 62, 145, 124, 150, 61, 146, 122, 151, 61, 147, 121, 151, 61, 148, 120, 151, 62, 148, 119, 151, 62, 149, 118, 151, 63, 149, 116, 152, 63, 149, 116, 152, 64, 149, 115, 152, 64, 150, 113, 153, 65, 149, 113, 153, 65, 150, 112, 153, 66, 149, 112, 153, 67, 148, 112, 153, 68, 148, 111, 153, 69, 147, 111, 153, 70, 146, 111, 153, 71, 145, 110, 154, 72, 144, 109, 155, 73, 143, 109, 155, 73, 143, 108, 156, 74, 142, 108, 156, 74, 142, 107, 157, 75, 141, 107, 157, 76, 139, 108, 157, 77, 138, 107, 158, 77, 137, 108, 158, 78, 136, 108, 158, 78, 135, 109, 158, 79, 134, 108, 159, 79, 133, 108, 160, 80, 132, 108, 160, 80, 131, 108, 161, 80, 131, 107, 162, 81, 129, 108, 162, 81, 129, 107, 163, 82, 128, 106, 164, 82, 127, 106, 165, 83, 126, 106, 165, 83, 125, 106, 166, 83, 125, 106, 166, 83, 124, 107, 166, 83, 124, 106, 167, 83, 123, 106, 168, 83, 123, 106, 168, 83, 122, 106, 169, 83, 120, 108, 169, 83, 120, 106, 171, 83, 119, 106, 172, 83, 119, 105, 173, 83, 118, 105, 174, 83, 50, 1, 67, 104, 175, 83, 50, 2, 66, 103, 176, 83, 50, 2, 66, 102, 177, 84, 48, 3, 66, 101, 178, 84, 48, 4, 65, 100, 179, 85, 46, 5, 65, 99, 180, 85, 46, 6, 64, 98, 181, 86, 44, 7, 64, 97, 182, 86, 44, 8, 63, 96, 183, 87, 42, 9, 63, 95, 184, 87, 42, 10, 62, 95, 184, 88, 40, 11, 62, 94, 185, 89, 38, 13, 61, 93, 186, 90, 36, 15, 60, 93, 186, 91, 34, 16, 60, 92, 187, 92, 32, 18, 59, 92, 187, 94, 28, 20, 59, 91, 188, 95, 26, 21, 59, 91, 188, 97, 22, 24, 58, 90, 189, 100, 16, 27, 58, 90, 189, 104, 8, 32, 57, 8, 12, 69, 143, 1, 46, 144, 56, 6, 21, 63, 143, 3, 44, 144, 56, 4, 26, 59, 144, 6, 41, 145, 55, 2, 31, 55, 145, 9, 38, 145, 92, 51, 145, 12, 33, 148, 94, 47, 146, 13, 31, 149, 97, 44, 146, 15, 27, 152, 99, 40, 147, 18, 21, 155, 101, 37, 147, 23, 13, 159, 103, 36, 146, 195, 105, 33, 146, 196, 107, 30, 147, 196, 108, 29, 147, 196, 109, 27, 147, 197, 110, 26, 147, 197, 111, 24, 148, 198, 111, 22, 148, 199, 112, 21, 147, 201, 112, 19, 147, 202, 113, 17, 148, 203, 113, 16, 147, 204, 113, 15, 146, 207, 113, 13, 146, 208, 114, 12, 144, 211, 113, 11, 142, 215, 113, 10, 141, 217, 112, 9, 142, 218, 112, 6, 143, 220, 111, 5, 143, 222, 111, 3, 143, 225, 109, 2, 143, 227, 108, 1, 145, 228, 252, 231, 249, 235, 245, 242, 238, 242, 238, 242, 238, 242, 238, 242, 238, 242, 238, 242, 238, 242, 238, 242, 237, 243, 237, 243, 237, 243, 236, 244, 235, 245, 234, 247, 233, 247, 232, 249, 229, 251, 228, 253, 225, 255, 222, 259, 219, 261, 217, 264, 213, 268, 208, 273, 199, 282, 198, 283, 197, 285, 195, 286, 26, 2, 166, 288, 22, 5, 165, 291, 16, 9, 164, 295, 8, 15, 162, 319, 161, 321, 159, 322, 158, 322, 158, 323, 157, 323, 157, 324, 156, 324, 156, 325, 154, 327, 153, 328, 151, 329, 151, 100], "size": [480, 640]}, "area": 161254, "iscrowd": 1, "image_id": 10432, "bbox": [0, 0, 639, 479], "category_id": 52, "id": 905200010432}, {"segmentation": {"counts": [6, 8, 54, 4, 16, 4, 393, 10, 46, 3, 1, 9, 8, 11, 391, 11, 46, 33, 388, 13, 46, 33, 387, 14, 46, 33, 386, 15, 46, 33, 386, 16, 46, 32, 386, 17, 45, 32, 386, 18, 44, 32, 386, 19, 42, 33, 387, 19, 40, 34, 387, 19, 39, 34, 388, 19, 38, 34, 390, 18, 37, 36, 390, 19, 35, 36, 391, 19, 34, 36, 391, 19, 34, 36, 391, 19, 34, 36, 392, 19, 33, 35, 390, 4, 1, 17, 33, 34, 390, 23, 33, 34, 389, 24, 33, 33, 390, 24, 32, 34, 390, 23, 33, 33, 391, 19, 37, 33, 391, 19, 37, 32, 392, 21, 35, 31, 393, 22, 35, 30, 393, 23, 35, 29, 393, 24, 36, 11, 1, 14, 394, 24, 39, 8, 4, 10, 396, 23, 40, 6, 6, 9, 396, 23, 41, 4, 7, 9, 396, 28, 47, 9, 396, 29, 46, 9, 396, 30, 46, 7, 397, 31, 46, 5, 398, 31, 449, 31, 449, 31, 449, 31, 449, 31, 449, 32, 448, 32, 448, 36, 444, 37, 443, 39, 441, 40, 440, 41, 439, 41, 439, 41, 439, 41, 439, 41, 439, 41, 439, 41, 439, 41, 440, 40, 440, 40, 439, 42, 21, 7, 410, 42, 18, 12, 409, 41, 13, 18, 407, 42, 8, 23, 407, 42, 4, 28, 406, 43, 1, 30, 406, 74, 405, 75, 405, 75, 406, 74, 99, 5, 1, 6, 296, 73, 92, 20, 295, 73, 81, 32, 294, 73, 79, 36, 292, 72, 74, 43, 291, 72, 73, 45, 290, 72, 66, 53, 289, 71, 62, 59, 288, 70, 61, 62, 287, 16, 2, 52, 60, 64, 286, 16, 1, 32, 1, 19, 60, 65, 286, 47, 3, 18, 58, 68, 287, 45, 4, 8, 1, 8, 50, 6, 2, 70, 285, 46, 4, 8, 3, 4, 51, 79, 285, 46, 4, 8, 56, 81, 286, 29, 1, 15, 4, 8, 54, 84, 285, 29, 1, 15, 4, 8, 53, 86, 284, 46, 3, 9, 51, 87, 284, 47, 3, 9, 49, 88, 284, 47, 2, 11, 47, 89, 284, 47, 2, 11, 47, 89, 284, 47, 2, 11, 47, 89, 284, 60, 47, 74, 2, 13, 284, 61, 46, 72, 4, 13, 284, 61, 46, 62, 14, 13, 284, 62, 45, 60, 17, 11, 285, 15, 1, 46, 44, 55, 24, 9, 286, 62, 44, 53, 28, 5, 288, 62, 44, 67, 307, 62, 43, 73, 302, 62, 43, 75, 300, 62, 43, 76, 299, 29, 1, 32, 43, 77, 298, 27, 4, 31, 43, 78, 298, 26, 4, 31, 38, 84, 296, 27, 5, 30, 37, 85, 296, 27, 5, 30, 36, 86, 297, 26, 5, 30, 35, 87, 297, 26, 6, 29, 34, 89, 295, 26, 7, 29, 33, 90, 296, 25, 7, 28, 34, 90, 296, 26, 7, 27, 34, 90, 296, 25, 10, 24, 35, 91, 295, 25, 11, 23, 35, 45, 2, 4, 1, 39, 295, 25, 10, 24, 35, 43, 14, 34, 295, 25, 10, 23, 36, 39, 18, 34, 295, 25, 11, 22, 36, 37, 20, 34, 295, 25, 11, 22, 36, 31, 26, 34, 293, 27, 12, 20, 38, 29, 28, 33, 292, 27, 12, 20, 40, 26, 31, 31, 292, 28, 12, 13, 49, 23, 34, 14, 1, 14, 292, 28, 13, 12, 50, 21, 37, 6, 1, 2, 3, 15, 292, 28, 13, 12, 50, 20, 50, 14, 293, 28, 13, 13, 50, 17, 52, 14, 293, 28, 13, 13, 50, 15, 54, 13, 294, 27, 14, 13, 51, 10, 58, 12, 295, 28, 13, 13, 52, 8, 59, 12, 295, 28, 14, 12, 54, 4, 61, 12, 295, 28, 14, 13, 118, 12, 295, 28, 15, 12, 118, 12, 295, 27, 15, 13, 118, 12, 295, 27, 15, 13, 118, 12, 295, 27, 15, 13, 118, 12, 295, 27, 16, 12, 119, 10, 296, 27, 16, 13, 119, 8, 297, 27, 16, 13, 121, 4, 299, 26, 17, 13, 424, 26, 17, 13, 425, 25, 17, 13, 427, 23, 18, 13, 426, 23, 18, 13, 426, 23, 18, 13, 427, 22, 18, 13, 427, 22, 18, 13, 427, 22, 19, 12, 427, 22, 19, 13, 427, 21, 19, 13, 426, 21, 20, 13, 426, 21, 7, 6, 7, 13, 426, 21, 5, 28, 77, 4, 1, 6, 338, 21, 4, 30, 60, 3, 1, 24, 336, 22, 2, 32, 44, 1, 3, 44, 331, 57, 32, 62, 328, 58, 16, 3, 1, 78, 323, 60, 3, 4, 1, 90, 322, 130, 11, 21, 318, 75, 20, 32, 21, 15, 317, 72, 28, 18, 40, 9, 313, 69, 37, 6, 47, 11, 310, 67, 92, 14, 307, 65, 95, 15, 306, 63, 77, 9, 10, 18, 304, 45, 1, 3, 4, 8, 81, 6, 10, 21, 302, 43, 2, 1, 9, 4, 84, 5, 10, 22, 300, 43, 14, 2, 88, 1, 10, 26, 296, 43, 106, 3, 6, 28, 294, 43, 109, 1, 5, 31, 291, 43, 111, 1, 2, 34, 289, 42, 115, 37, 287, 40, 119, 36, 285, 39, 123, 37, 280, 33, 1, 4, 129, 34, 280, 32, 135, 37, 276, 32, 3, 5, 131, 35, 274, 18, 3, 11, 2, 8, 130, 38, 269, 19, 4, 10, 1, 10, 133, 35, 266, 21, 4, 22, 134, 37, 261, 22, 4, 22, 138, 34, 260, 22, 5, 21, 139, 37, 256, 22, 7, 4, 2, 13, 143, 35, 254, 22, 13, 13, 144, 38, 250, 22, 14, 12, 148, 35, 249, 22, 13, 13, 150, 37, 245, 22, 13, 13, 153, 35, 244, 23, 12, 13, 155, 37, 240, 23, 12, 13, 159, 36, 237, 23, 12, 13, 161, 37, 234, 24, 11, 13, 164, 37, 231, 24, 11, 12, 13, 6, 148, 37, 229, 24, 11, 12, 12, 8, 150, 37, 226, 24, 11, 13, 10, 10, 151, 37, 224, 24, 11, 12, 10, 12, 154, 37, 220, 24, 11, 12, 10, 12, 156, 37, 218, 31, 4, 12, 9, 14, 158, 37, 215, 47, 9, 15, 160, 36, 214, 46, 9, 16, 161, 37, 213, 44, 10, 15, 165, 35, 211, 44, 10, 16, 166, 37, 207, 44, 11, 16, 169, 34, 206, 43, 13, 16, 169, 37, 202, 44, 13, 15, 173, 35, 200, 44, 13, 16, 173, 38, 196, 44, 14, 15, 177, 35, 195, 43, 16, 16, 177, 37, 191, 43, 17, 15, 181, 34, 190, 43, 17, 16, 181, 37, 186, 43, 18, 15, 185, 35, 184, 43, 18, 16, 185, 38, 180, 43, 19, 16, 188, 35, 179, 43, 20, 16, 189, 34, 177, 43, 22, 15, 192, 32, 177, 43, 21, 16, 193, 30, 177, 43, 22, 16, 196, 26, 177, 43, 23, 16, 197, 24, 176, 44, 24, 15, 200, 21, 176, 43, 25, 16, 201, 19, 176, 43, 26, 16, 203, 16, 177, 15, 3, 24, 27, 16, 205, 13, 177, 15, 8, 2, 4, 13, 28, 15, 205, 13, 176, 15, 14, 14, 28, 16, 204, 13, 176, 15, 16, 12, 29, 15, 204, 13, 176, 15, 15, 13, 30, 16, 202, 12, 177, 15, 15, 13, 31, 15, 202, 12, 177, 14, 16, 13, 31, 16, 200, 13, 178, 12, 17, 13, 32, 15, 200, 13, 177, 12, 18, 13, 32, 16, 199, 13, 177, 11, 19, 12, 34, 16, 199, 12, 177, 10, 20, 12, 35, 16, 197, 12, 178, 2, 5, 1, 22, 12, 36, 15, 197, 12, 179, 1, 27, 13, 36, 16, 196, 13, 177, 1, 28, 13, 37, 16, 195, 13, 206, 13, 38, 16, 194, 12, 207, 13, 39, 15, 194, 12, 207, 13, 39, 16, 192, 13, 207, 12, 41, 16, 191, 13, 206, 13, 42, 16, 190, 13, 206, 13, 43, 15, 190, 13, 206, 13, 43, 16, 189, 13, 178, 1, 27, 13, 44, 15, 189, 12, 179, 1, 27, 13, 45, 16, 186, 13, 179, 13, 14, 13, 47, 15, 186, 13, 179, 13, 14, 13, 47, 16, 185, 13, 179, 14, 13, 13, 48, 15, 186, 12, 178, 15, 14, 12, 48, 16, 184, 13, 177, 16, 13, 13, 50, 15, 183, 12, 178, 16, 13, 12, 51, 16, 182, 13, 177, 16, 12, 13, 52, 15, 182, 13, 177, 16, 12, 13, 52, 16, 181, 13, 177, 16, 12, 13, 53, 16, 180, 12, 178, 16, 12, 13, 54, 16, 179, 12, 178, 16, 12, 13, 55, 15, 178, 13, 178, 16, 12, 12, 56, 16, 177, 13, 178, 16, 11, 13, 57, 16, 176, 13, 178, 16, 11, 13, 58, 16, 175, 13, 178, 17, 11, 12, 59, 15, 175, 13, 178, 21, 6, 13, 59, 16, 174, 12, 179, 21, 6, 12, 61, 15, 174, 12, 179, 21, 6, 12, 62, 16, 171, 13, 180, 20, 5, 13, 63, 15, 172, 12, 181, 19, 5, 13, 63, 16, 171, 12, 183, 4, 2, 10, 6, 13, 64, 15, 170, 13, 188, 11, 6, 13, 64, 16, 169, 13, 183, 15, 7, 13, 66, 15, 168, 13, 182, 16, 7, 12, 67, 16, 167, 13, 181, 17, 7, 12, 68, 16, 166, 13, 180, 18, 7, 12, 68, 17, 165, 12, 181, 18, 7, 12, 69, 17, 164, 12, 181, 18, 7, 12, 71, 16, 162, 13, 181, 19, 6, 12, 71, 17, 161, 13, 181, 20, 6, 10, 72, 19, 159, 13, 180, 21, 7, 8, 74, 18, 159, 13, 180, 21, 9, 4, 77, 18, 158, 12, 181, 21, 92, 17, 157, 12, 181, 21, 92, 19, 155, 13, 180, 21, 93, 18, 155, 13, 180, 21, 94, 18, 154, 12, 181, 20, 97, 18, 152, 12, 181, 19, 98, 19, 150, 13, 181, 17, 101, 18, 150, 13, 181, 14, 105, 18, 149, 13, 181, 14, 107, 18, 147, 13, 181, 14, 107, 19, 146, 13, 182, 13, 108, 18, 146, 12, 184, 11, 110, 18, 144, 13, 183, 11, 113, 18, 142, 13, 182, 10, 115, 19, 141, 13, 182, 10, 116, 19, 140, 13, 182, 11, 117, 17, 140, 13, 182, 11, 118, 18, 138, 4, 191, 11, 119, 18, 137, 1, 194, 11, 119, 19, 331, 11, 121, 18, 330, 11, 122, 18, 329, 11, 123, 18, 328, 10, 125, 18, 139, 5, 183, 10, 126, 18, 135, 8, 183, 10, 127, 18, 132, 10, 183, 10, 128, 18, 129, 12, 183, 10, 129, 18, 127, 13, 183, 11, 129, 18, 126, 12, 184, 11, 130, 18, 125, 12, 184, 11, 131, 18, 123, 13, 184, 11, 132, 18, 122, 13, 184, 11, 134, 17, 122, 12, 184, 11, 134, 18, 120, 12, 185, 10, 136, 18, 119, 8, 189, 10, 137, 18, 315, 10, 139, 17, 314, 10, 139, 18, 313, 10, 140, 18, 312, 9, 142, 18, 311, 9, 144, 17, 310, 9, 144, 18, 122, 2, 185, 9, 145, 18, 116, 7, 185, 9, 38, 7, 101, 18, 110, 12, 185, 9, 37, 9, 102, 18, 107, 13, 185, 9, 36, 11, 101, 19, 106, 12, 186, 9, 23, 10, 2, 13, 101, 18, 106, 12, 186, 9, 17, 17, 1, 13, 102, 18, 105, 13, 185, 9, 15, 33, 104, 18, 103, 12, 186, 9, 14, 34, 104, 19, 102, 12, 186, 9, 13, 35, 105, 18, 101, 13, 186, 9, 12, 36, 106, 18, 100, 13, 186, 9, 12, 36, 108, 18, 98, 13, 186, 9, 12, 37, 107, 19, 97, 13, 186, 9, 12, 38, 107, 18, 97, 13, 186, 9, 12, 38, 109, 17, 96, 12, 187, 9, 12, 38, 110, 18, 93, 13, 187, 9, 12, 38, 110, 19, 92, 13, 187, 9, 12, 39, 110, 18, 92, 13, 187, 9, 12, 39, 112, 18, 90, 13, 187, 9, 12, 39, 113, 18, 89, 13, 187, 9, 13, 38, 113, 19, 88, 12, 188, 9, 14, 37, 114, 18, 88, 13, 187, 9, 16, 35, 116, 18, 86, 13, 187, 9, 19, 32, 117, 18, 85, 13, 187, 9, 20, 31, 117, 19, 84, 12, 188, 9, 22, 29, 119, 17, 84, 12, 188, 9, 25, 26, 120, 18, 81, 13, 188, 9, 26, 25, 121, 18, 80, 13, 188, 9, 29, 22, 122, 18, 79, 13, 188, 9, 31, 21, 122, 17, 79, 13, 188, 9, 33, 19, 123, 18, 77, 13, 188, 9, 33, 19, 124, 18, 76, 12, 189, 9, 33, 19, 125, 18, 74, 13, 189, 9, 33, 19, 126, 17, 74, 13, 189, 9, 33, 19, 127, 18, 72, 13, 189, 9, 33, 19, 128, 18, 72, 12, 189, 9, 33, 18, 130, 18, 70, 13, 189, 9, 33, 17, 132, 18, 69, 12, 190, 9, 33, 16, 134, 18, 68, 13, 189, 9, 32, 16, 136, 18, 67, 13, 189, 9, 26, 20, 139, 18, 66, 13, 189, 9, 25, 20, 141, 18, 65, 12, 190, 9, 21, 23, 143, 18, 64, 12, 190, 9, 20, 23, 145, 18, 62, 13, 190, 9, 19, 23, 147, 18, 61, 13, 190, 9, 16, 27, 147, 18, 60, 13, 190, 9, 15, 29, 147, 19, 58, 13, 190, 9, 10, 34, 148, 18, 58, 13, 190, 9, 6, 38, 149, 18, 57, 12, 191, 9, 5, 39, 150, 18, 56, 12, 191, 9, 5, 39, 151, 19, 53, 13, 191, 9, 6, 38, 152, 19, 53, 12, 191, 9, 6, 38, 153, 18, 53, 12, 191, 9, 6, 37, 156, 17, 51, 13, 191, 9, 6, 36, 158, 18, 49, 13, 191, 8, 7, 36, 158, 19, 48, 13, 191, 8, 7, 36, 159, 18, 48, 13, 191, 7, 8, 36, 161, 18, 46, 13, 191, 6, 11, 34, 162, 18, 45, 12, 192, 6, 12, 33, 162, 19, 43, 13, 192, 6, 15, 2, 2, 26, 163, 18, 43, 13, 192, 5, 16, 2, 5, 23, 165, 18, 41, 13, 192, 5, 23, 23, 166, 18, 40, 13, 192, 4, 23, 24, 166, 19, 40, 12, 192, 4, 25, 21, 168, 18, 39, 12, 193, 1, 28, 20, 171, 18, 37, 12, 193, 1, 28, 19, 173, 18, 36, 13, 192, 1, 29, 16, 175, 19, 35, 13, 192, 1, 31, 14, 177, 17, 35, 12, 193, 1, 32, 13, 178, 18, 33, 12, 193, 1, 33, 11, 180, 18, 31, 13, 193, 1, 35, 7, 183, 18, 30, 13, 193, 1, 226, 17, 30, 13, 193, 1, 227, 18, 28, 13, 193, 1, 228, 18, 27, 13, 193, 1, 229, 18, 26, 12, 194, 1, 230, 18, 24, 13, 194, 1, 231, 18, 23, 13, 194, 4, 229, 18, 22, 13, 194, 5, 229, 18, 21, 13, 194, 5, 230, 18, 20, 13, 194, 5, 231, 18, 19, 12, 195, 6, 231, 18, 18, 13, 194, 6, 232, 18, 17, 13, 194, 7, 232, 17, 17, 13, 194, 7, 233, 16, 17, 12, 195, 6, 235, 15, 17, 12, 195, 5, 237, 15, 15, 13, 195, 4, 240, 13, 15, 13, 195, 3, 241, 13, 15, 13, 195, 3, 241, 13, 15, 13, 195, 3, 241, 13, 15, 13, 195, 3, 242, 12, 15, 12, 196, 4, 241, 13, 13, 13, 196, 5, 240, 13, 13, 13, 196, 5, 240, 13, 13, 13, 196, 5, 240, 13, 14, 12, 196, 5, 240, 13, 13, 13, 197, 5, 240, 13, 12, 13, 196, 8, 238, 13, 12, 13, 196, 13, 233, 13, 12, 13, 196, 14, 232, 13, 12, 13, 196, 15, 231, 13, 12, 12, 197, 16, 230, 13, 11, 13, 197, 17, 230, 13, 10, 13, 197, 17, 230, 13, 10, 13, 197, 17, 230, 13, 10, 13, 197, 17, 230, 13, 10, 13, 197, 17, 230, 13, 10, 12, 198, 16, 232, 12, 10, 12, 198, 14, 234, 13, 9, 13, 197, 12, 236, 13, 9, 13, 197, 12, 236, 13, 9, 12, 199, 10, 237, 13, 9, 12, 200, 8, 238, 13, 8, 13, 202, 4, 241, 13, 7, 13, 447, 13, 7, 13, 447, 13, 7, 13, 447, 13, 7, 13, 447, 13, 7, 12, 449, 12, 6, 13, 449, 13, 5, 13, 449, 13, 5, 13, 449, 13, 5, 13, 449, 13, 5, 13, 201, 8, 240, 13, 5, 12, 201, 10, 240, 13, 4, 13, 200, 10, 240, 13, 4, 13, 199, 11, 240, 13, 4, 13, 199, 12, 239, 13, 4, 12, 200, 12, 239, 13, 4, 12, 200, 12, 240, 12, 3, 13, 200, 14, 238, 13, 2, 13, 200, 18, 234, 13, 2, 13, 200, 18, 234, 13, 2, 13, 201, 16, 235, 13, 2, 13, 202, 14, 236, 13, 2, 12, 204, 1, 4, 8, 237, 12, 1, 13, 454, 26, 454, 26, 454, 13, 1, 12, 454, 26, 454, 26, 455, 25, 455, 25, 455, 25, 455, 24, 205, 1, 250, 24, 205, 4, 248, 23, 205, 12, 240, 23, 205, 13, 239, 23, 206, 12, 239, 23, 207, 10, 240, 23, 208, 8, 241, 22, 211, 4, 14, 1, 229, 21, 217, 14, 228, 21, 216, 15, 228, 21, 215, 16, 228, 21, 209, 22, 228, 21, 208, 21, 231, 20, 207, 21, 232, 20, 207, 21, 232, 20, 207, 21, 232, 20, 207, 21, 232, 19, 208, 21, 232, 19, 208, 21, 233, 18, 208, 21, 233, 18, 209, 20, 233, 18, 211, 18, 233, 18, 213, 16, 233, 17, 214, 12, 238, 16, 214, 12, 238, 17, 213, 11, 239, 17, 214, 10, 239, 16, 217, 8, 239, 16, 214, 11, 239, 16, 211, 14, 240, 15, 208, 17, 240, 15, 207, 19, 239, 15, 206, 21, 239, 14, 206, 23, 237, 13, 207, 24, 237, 12, 207, 26, 235, 12, 207, 28, 233, 12, 208, 29, 231, 12, 208, 29, 232, 10, 209, 29, 233, 8, 210, 29, 235, 4, 212, 29, 451, 29, 451, 29, 451, 29, 451, 36, 443, 38, 4, 6, 432, 39, 2, 10, 14, 4, 411, 39, 1, 12, 1, 17, 410, 71, 409, 73, 406, 74, 406, 74, 406, 74, 406, 22, 2, 50, 406, 22, 3, 46, 1, 2, 406, 22, 4, 44, 2, 1, 407, 22, 6, 4, 1, 35, 413, 21, 13, 30, 417, 20, 7, 28, 424, 21, 6, 29, 423, 22, 5, 30, 423, 22, 5, 30, 423, 23, 3, 31, 423, 24, 1, 32, 423, 24, 1, 32, 423, 56, 424, 46, 4, 4, 427, 35, 1, 4, 439, 35, 445, 35, 445, 34, 446, 32, 448, 29, 451, 29, 451, 28, 453, 20, 1, 4, 456, 19, 461, 19, 462, 18, 464, 15, 466, 14, 468, 11, 474, 4, 1931, 6, 473, 8, 471, 10, 468, 12, 462, 18, 446, 6, 9, 19, 445, 8, 7, 21, 443, 10, 6, 22, 442, 10, 6, 22, 442, 10, 5, 23, 442, 10, 5, 23, 442, 10, 5, 23, 442, 10, 5, 23, 442, 10, 5, 23, 443, 8, 6, 23, 445, 4, 8, 23, 458, 22, 460, 20, 462, 19, 462, 19, 463, 18, 462, 18, 462, 18, 462, 18, 462, 18, 463, 16, 466, 14, 467, 13, 467, 13, 467, 11, 469, 11, 467, 13, 466, 14, 465, 15, 465, 15, 455, 6, 4, 15, 454, 26, 453, 27, 453, 26, 454, 26, 454, 26, 454, 26, 454, 27, 453, 27, 453, 27, 454, 26, 456, 24, 458, 22, 460, 20, 464, 15, 465, 15, 465, 16, 465, 16, 465, 16, 440, 6, 18, 16, 440, 8, 16, 17, 2, 1, 436, 9, 15, 20, 436, 10, 14, 8, 2, 10, 436, 11, 14, 7, 2, 10, 436, 11, 14, 19, 436, 11, 14, 18, 437, 11, 14, 16, 439, 11, 14, 16, 439, 11, 14, 16, 439, 11, 13, 16, 440, 11, 13, 16, 440, 10, 14, 8, 1, 6, 441, 10, 14, 8, 2, 4, 442, 10, 14, 8, 448, 10, 15, 6, 45, 4, 400, 11, 15, 4, 43, 8, 399, 11, 61, 9, 399, 11, 60, 10, 399, 11, 59, 11, 399, 11, 57, 13, 399, 11, 54, 16, 399, 11, 53, 17, 399, 11, 53, 17, 399, 11, 53, 18, 398, 11, 53, 18, 398, 12, 45, 4, 3, 18, 73, 5, 320, 12, 44, 6, 2, 18, 72, 7, 319, 12, 43, 8, 1, 22, 58, 4, 4, 10, 318, 12, 37, 4, 2, 8, 1, 23, 56, 6, 2, 11, 318, 12, 23, 6, 7, 6, 1, 8, 2, 22, 49, 26, 318, 12, 21, 11, 3, 16, 5, 19, 48, 27, 318, 12, 20, 13, 2, 16, 8, 16, 36, 4, 1, 34, 318, 12, 16, 18, 1, 16, 8, 16, 28, 46, 319, 11, 4, 8, 3, 37, 9, 14, 28, 46, 320, 11, 2, 11, 1, 38, 10, 11, 25, 40, 2, 6, 323, 11, 1, 51, 10, 11, 24, 40, 4, 4, 324, 54, 1, 8, 10, 11, 23, 38, 335, 54, 1, 8, 11, 10, 22, 38, 336, 53, 3, 6, 13, 8, 22, 32, 343, 52, 5, 4, 14, 8, 21, 23, 1, 8, 344, 50, 25, 8, 21, 19, 357, 50, 25, 7, 22, 10, 366], "size": [480, 640]}, "area": 37028, "iscrowd": 1, "image_id": 10216, "bbox": [0, 0, 639, 305], "category_id": 1, "id": 900100010216}, {"segmentation": {"counts": [3024, 9, 415, 13, 411, 17, 407, 20, 374, 7, 25, 21, 371, 10, 8, 6, 9, 25, 368, 11, 5, 9, 8, 26, 366, 13, 4, 10, 6, 29, 364, 13, 3, 14, 3, 29, 363, 14, 3, 16, 1, 33, 359, 14, 2, 52, 358, 14, 2, 55, 356, 13, 2, 56, 355, 13, 2, 57, 354, 73, 353, 74, 352, 75, 351, 75, 351, 75, 351, 75, 352, 74, 352, 74, 351, 75, 351, 74, 352, 73, 353, 71, 355, 72, 354, 73, 353, 73, 352, 75, 351, 76, 350, 76, 351, 75, 351, 75, 350, 12, 1, 63, 350, 13, 1, 62, 350, 13, 2, 61, 351, 13, 2, 60, 351, 13, 1, 60, 352, 12, 2, 59, 353, 12, 2, 57, 355, 12, 3, 39, 4, 11, 357, 12, 1, 42, 372, 55, 371, 55, 370, 24, 1, 31, 370, 25, 2, 29, 370, 25, 5, 26, 370, 24, 9, 23, 371, 23, 12, 20, 371, 23, 14, 17, 371, 23, 17, 14, 372, 22, 25, 5, 374, 19, 407, 19, 408, 17, 409, 17, 408, 19, 407, 19, 407, 19, 407, 19, 407, 19, 408, 18, 407, 19, 408, 18, 407, 19, 407, 19, 407, 19, 407, 19, 407, 19, 407, 19, 422, 4, 423, 3, 424, 2, 425, 1, 58405, 3, 422, 5, 421, 6, 419, 8, 417, 9, 417, 9, 416, 10, 415, 11, 415, 11, 388, 12, 14, 12, 386, 40, 386, 40, 385, 41, 383, 43, 383, 42, 383, 42, 384, 42, 382, 43, 381, 45, 381, 44, 381, 45, 381, 44, 381, 45, 381, 45, 381, 45, 382, 44, 382, 49, 378, 49, 378, 49, 375, 52, 372, 54, 371, 55, 371, 55, 370, 56, 370, 56, 370, 56, 370, 56, 370, 55, 372, 53, 374, 52, 375, 51, 376, 6, 1, 5, 2, 4, 1, 13, 3, 14, 397, 11, 5, 12, 400, 8, 8, 8, 404, 4, 31518, 6, 418, 9, 417, 16, 409, 20, 406, 26, 399, 42, 384, 43, 383, 44, 383, 44, 382, 44, 383, 43, 384, 42, 371, 6, 9, 40, 369, 10, 8, 39, 369, 11, 7, 39, 368, 13, 6, 38, 369, 14, 5, 37, 369, 16, 3, 36, 371, 49, 377, 55, 371, 56, 370, 57, 369, 58, 368, 59, 367, 59, 368, 58, 368, 58, 369, 57, 369, 57, 370, 56, 370, 55, 372, 53, 374, 52, 375, 50, 378, 48, 379, 47, 381, 44, 385, 40, 387, 37, 390, 34, 394, 32, 394, 30, 397, 27, 400, 25, 401, 24, 403, 22, 405, 20, 408, 10, 2, 4, 46431, 6, 10, 6, 402, 13, 3, 9, 401, 26, 399, 28, 398, 28, 397, 29, 396, 30, 394, 32, 394, 32, 393, 33, 393, 32, 393, 32, 394, 32, 393, 33, 393, 33, 380, 7, 6, 33, 378, 48, 378, 48, 377, 49, 377, 49, 376, 50, 376, 50, 376, 50, 377, 49, 377, 49, 378, 47, 380, 46, 381, 45, 381, 45, 381, 45, 381, 45, 381, 45, 382, 44, 382, 44, 383, 42, 383, 42, 384, 41, 385, 39, 388, 35, 391, 33, 390, 36, 388, 40, 386, 41, 384, 43, 1, 7, 375, 52, 373, 54, 372, 55, 371, 55, 371, 55, 370, 56, 370, 56, 370, 56, 370, 56, 371, 55, 371, 55, 372, 53, 374, 51, 377, 4, 6, 37, 387, 38, 386, 38, 388, 37, 388, 38, 388, 37, 388, 37, 389, 36, 390, 35, 392, 22, 1, 9, 394, 21, 4, 4, 398, 19, 408, 13, 415, 4, 1, 4, 33173], "size": [426, 640]}, "area": 10376, "iscrowd": 1, "image_id": 10442, "bbox": [7, 0, 555, 87], "category_id": 1, "id": 900100010442}, {"segmentation": [[107.35, 420.13, 91.87, 393.29, 97.03, 322.06, 107.35, 279.74, 140.39, 225.03, 167.23, 207.48, 210.58, 194.06, 238.45, 187.87, 270.45, 198.19, 293.16, 225.03, 298.32, 268.39, 293.16, 333.42, 264.26, 408.77, 239.48, 455.23, 203.35, 466.58, 131.1, 465.55, 109.42, 422.19, 101.16, 407.74]], "area": 44717.0985, "iscrowd": 0, "image_id": 10046, "bbox": [91.87, 187.87, 206.45, 278.71], "category_id": 58, "id": 765}, {"segmentation": [[533.2, 274.79, 525.52, 250.05, 501.63, 232.98, 494.81, 204.83, 501.63, 169.85, 526.37, 147.67, 555.38, 142.55, 591.21, 163.88, 621.07, 189.47, 630.45, 192.03, 631.31, 204.83, 625.34, 215.92, 615.95, 226.16, 623.63, 234.69, 629.6, 244.07, 619.36, 247.49, 612.54, 247.49, 618.51, 252.61, 615.95, 263.7, 598.89, 264.55, 588.65, 272.23, 580.12, 264.55, 574.15, 267.11, 571.59, 270.52, 582.68, 287.58, 601.45, 295.26, 614.25, 306.35, 623.63, 309.76, 625.34, 322.56, 610.83, 321.71, 601.45, 321.71, 592.92, 313.18, 585.24, 304.65, 563.06, 295.26, 542.58, 285.02, 534.91, 280.76, 532.35, 272.23]], "area": 14629.467750000005, "iscrowd": 0, "image_id": 10369, "bbox": [494.81, 142.55, 136.5, 180.01], "category_id": 18, "id": 4791}, {"segmentation": [[185.46, 0.92, 244.12, 0.92, 239.81, 19.04, 249.3, 24.21, 270.86, 23.35, 281.22, 19.04, 288.98, 25.07, 312.27, 2.65, 384.73, 1.78, 383.87, 22.49, 416.65, 48.36, 411.47, 50.95, 376.11, 44.05, 381.28, 56.13, 373.52, 75.97, 364.89, 87.18, 384.73, 119.96, 389.04, 138.08, 389.04, 217.44, 408.88, 255.39, 405.43, 270.92, 389.91, 288.17, 365.75, 309.74, 335.56, 314.05, 334.7, 334.76, 319.17, 358.91, 282.08, 378.75, 271.73, 383.93, 258.79, 399.45, 270.86, 422.74, 208.76, 422.74, 202.72, 420.16, 207.89, 408.94, 206.17, 386.51, 144.92, 372.71, 143.2, 383.06, 156.14, 389.1, 164.76, 395.14, 163.9, 412.39, 161.31, 419.29, 155.27, 421.88, 105.24, 421.88, 95.75, 406.35, 100.06, 386.51, 79.36, 299.39, 76.77, 267.47, 78.5, 256.26, 84.54, 251.08, 73.32, 242.46, 56.93, 265.75, 47.44, 270.92, 31.05, 270.06, 27.6, 255.39, 25.02, 235.55, 35.37, 207.95, 46.58, 201.05, 55.21, 165.68, 51.76, 151.88, 64.7, 144.12, 70.74, 124.28, 79.36, 118.24, 76.77, 99.26, 81.09, 92.36, 111.28, 81.14, 125.08, 56.13, 124.22, 49.23, 156.14, 19.9, 166.49, 17.31, 181.15, 17.31, 186.33, 2.65]], "area": 114934.57880000002, "iscrowd": 0, "image_id": 10248, "bbox": [25.02, 0.92, 391.63, 421.82], "category_id": 18, "id": 9359}, {"segmentation": [[384.58, 192.9, 379.72, 205.07, 365.11, 198.58, 361.87, 212.37, 359.43, 222.92, 355.38, 236.71, 352.13, 211.56, 337.53, 213.18, 331.85, 227.79, 329.41, 239.96, 323.73, 236.71, 327.79, 213.18, 319.68, 239.15, 317.24, 229.41, 318.86, 216.43, 317.24, 204.26, 314.81, 196.15, 324.54, 190.47, 329.41, 196.15, 339.96, 186.41, 347.26, 186.41, 355.38, 187.22, 368.36, 191.28, 376.47, 198.58, 381.34, 188.84]], "area": 1690.5639500000004, "iscrowd": 0, "image_id": 10005, "bbox": [314.81, 186.41, 69.77, 53.55], "category_id": 18, "id": 13017}, {"segmentation": [[537.02, 309.82, 533.58, 302.07, 530.13, 295.19, 528.41, 291.75, 526.69, 285.72, 533.58, 279.7, 537.88, 277.12, 550.79, 277.12, 557.67, 277.98, 568.86, 279.7, 580.05, 273.67, 586.93, 271.09, 597.26, 267.65, 609.31, 280.56, 611.03, 286.58, 614.47, 294.33, 614.47, 295.19, 617.92, 307.24, 617.05, 323.59, 613.61, 329.61, 616.19, 344.24, 612.75, 347.68, 605.01, 336.5, 604.15, 332.19, 604.15, 327.89, 600.7, 322.73, 598.98, 329.61, 583.49, 345.96, 577.47, 348.55, 578.33, 355.43, 577.47, 364.9, 562.84, 364.9, 562.84, 359.73, 555.95, 356.29, 553.37, 350.27, 553.37, 343.38, 557.67, 335.64, 558.53, 328.75, 549.93, 327.89, 545.62, 327.03, 532.72, 318.42, 531.85, 305.52]], "area": 5467.03105, "iscrowd": 0, "image_id": 10276, "bbox": [526.69, 267.65, 91.23, 97.25], "category_id": 18, "id": 15870}, {"segmentation": [[288.54, 208.77, 297.45, 206.65, 300.0, 204.95, 300.85, 212.17, 301.27, 219.38, 304.24, 223.62, 304.24, 228.71, 304.24, 229.98, 306.79, 228.71, 310.6, 232.53, 311.88, 227.44, 310.6, 223.2, 310.18, 218.95, 312.73, 213.86, 313.57, 212.17, 314.42, 216.83, 317.39, 217.26, 319.09, 214.29, 319.09, 207.07, 317.39, 203.26, 316.97, 195.62, 311.45, 190.53, 299.15, 186.71, 283.88, 186.71, 276.66, 183.74, 271.15, 186.29, 268.18, 186.71, 269.88, 196.47, 272.42, 207.07, 271.57, 213.86, 269.45, 217.26, 277.51, 217.68, 278.79, 210.47, 283.45, 209.2, 281.33, 213.86, 277.51, 219.38, 282.18, 219.8, 286.42, 213.01, 288.54, 209.62]], "area": 1344.3865499999997, "iscrowd": 0, "image_id": 10005, "bbox": [268.18, 183.74, 50.91, 48.79], "category_id": 18, "id": 17044}, {"segmentation": [[365.95, 188.29, 374.81, 188.78, 380.71, 187.3, 390.06, 181.89, 396.45, 180.42, 391.04, 197.64, 389.07, 204.03, 389.07, 216.82, 384.65, 224.7, 374.81, 237.49, 372.35, 251.76, 365.95, 265.53, 362.01, 286.2, 363.0, 307.84, 363.49, 313.75, 360.54, 314.73, 358.08, 315.22, 354.14, 309.32, 352.67, 302.92, 353.16, 284.23, 353.16, 271.93, 352.67, 269.96, 349.22, 265.04, 342.83, 274.88, 347.75, 304.89, 347.75, 314.24, 344.3, 316.7, 342.83, 311.29, 340.86, 282.75, 339.38, 275.86, 334.46, 273.9, 331.02, 273.9, 327.57, 284.23, 327.08, 291.12, 324.62, 293.08, 329.05, 298.5, 337.91, 302.92, 333.48, 310.8, 331.51, 310.3, 329.05, 307.35, 322.16, 301.45, 317.24, 297.02, 313.8, 288.16, 314.29, 280.78, 312.81, 275.86, 311.34, 273.4, 309.86, 275.86, 308.88, 287.18, 308.39, 296.04, 306.91, 301.94, 305.93, 311.29, 304.94, 313.75, 303.47, 312.27, 302.48, 299.48, 302.48, 282.75, 302.48, 270.94, 297.07, 268.98, 294.12, 266.02, 291.66, 262.58, 286.74, 252.74, 283.79, 241.42, 284.77, 233.06, 291.17, 230.6, 299.53, 227.16, 315.27, 221.25, 328.07, 214.86, 341.35, 215.84, 347.25, 209.94, 350.7, 204.03, 355.62, 201.08, 356.11, 198.62, 354.14, 195.67, 354.14, 190.26, 354.14, 187.3, 356.6, 182.88, 359.55, 181.89, 363.49, 181.89, 368.41, 184.84]], "area": 7066.845449999999, "iscrowd": 0, "image_id": 10276, "bbox": [283.79, 180.42, 112.66, 136.28], "category_id": 18, "id": 17744}, {"segmentation": [[280.97, 266.21, 287.24, 263.26, 285.39, 245.2, 282.45, 244.09, 280.6, 226.77, 287.24, 220.87, 293.13, 223.82, 300.87, 219.4, 303.45, 210.92, 306.77, 203.18, 309.35, 199.86, 304.56, 194.7, 300.51, 193.23, 299.4, 185.12, 277.65, 173.33, 274.34, 175.17, 273.6, 181.07, 263.28, 182.91, 262.91, 187.33, 264.02, 190.65, 261.44, 195.07, 257.38, 198.02, 249.27, 204.29]], "area": 2554.6601999999975, "iscrowd": 0, "image_id": 10145, "bbox": [249.27, 173.33, 60.08, 92.88], "category_id": 64, "id": 21815}, {"segmentation": [[122.86, 128.4, 194.37, 135.45, 193.36, 219.04, 119.84, 241.19, 120.85, 154.59, 121.86, 128.4]], "area": 7191.936650000001, "iscrowd": 0, "image_id": 10145, "bbox": [119.84, 128.4, 74.53, 112.79], "category_id": 72, "id": 32610}, {"segmentation": [[220.24, 242.96, 223.23, 205.26, 225.02, 192.7, 222.63, 102.94, 222.03, 98.16, 220.24, 95.76, 79.02, 96.36, 73.04, 102.94, 79.02, 221.42, 79.02, 277.67, 92.19, 277.67, 98.77, 282.45, 121.51, 278.86, 181.94, 265.1, 205.28, 256.13, 210.66, 252.54, 211.86, 248.35, 220.24, 243.56]], "area": 25377.6892, "iscrowd": 0, "image_id": 10346, "bbox": [73.04, 95.76, 151.98, 186.69], "category_id": 72, "id": 33123}, {"segmentation": [[118.1, 64.31, 368.72, 5.71, 378.75, 9.56, 371.03, 169.46, 156.66, 252.74, 147.41, 245.03]], "area": 43929.302899999995, "iscrowd": 0, "image_id": 10083, "bbox": [118.1, 5.71, 260.65, 247.03], "category_id": 72, "id": 33563}, {"segmentation": [[2.42, 266.37, 22.52, 255.91, 32.98, 243.05, 47.46, 252.7, 53.89, 263.96, 44.24, 272.0, 35.39, 269.59, 32.17, 272.8, 31.37, 284.07, 31.37, 287.28, 35.39, 291.3, 35.39, 293.72, 21.72, 293.72, 10.46, 295.33, 12.07, 299.35, 1.61, 298.54]], "area": 1557.7888000000003, "iscrowd": 0, "image_id": 10346, "bbox": [1.61, 243.05, 52.28, 56.3], "category_id": 17, "id": 50970}, {"segmentation": [[111.05, 612.0, 111.05, 605.86, 111.05, 598.19, 112.58, 594.36, 112.58, 589.75, 110.28, 582.08, 107.98, 575.18, 110.28, 565.97, 111.05, 561.36, 109.52, 549.86, 107.21, 535.28, 106.45, 529.14, 101.84, 530.68, 100.31, 532.21, 96.47, 534.51, 93.4, 532.98, 90.33, 522.24, 91.87, 506.89, 92.64, 485.41, 92.64, 478.5, 98.01, 459.32, 95.71, 443.98, 91.1, 410.99, 95.71, 387.2, 104.14, 368.79, 121.79, 357.28, 127.16, 354.21, 141.74, 351.14, 154.02, 348.84, 163.22, 349.61, 169.36, 353.44, 172.43, 364.19, 172.43, 368.79, 172.43, 381.83, 173.2, 390.27, 167.06, 403.31, 162.45, 414.06, 157.08, 435.54, 157.08, 438.61, 156.32, 452.42, 157.85, 464.69, 157.85, 470.83, 160.15, 477.74, 160.92, 480.81, 161.69, 487.71, 161.69, 487.71, 164.76, 490.78, 160.92, 490.01, 157.08, 494.62, 154.02, 501.52, 151.71, 509.96, 150.95, 509.19, 147.11, 523.0, 144.81, 532.98, 144.81, 536.81, 143.27, 549.86, 143.27, 552.16, 141.74, 562.13, 146.34, 585.92, 154.02, 605.86, 154.02, 609.7, 142.51, 612.77, 135.6, 609.7, 131.77, 619.67, 120.26, 618.91, 112.58, 615.07], [180.1, 486.18, 173.2, 490.01, 168.59, 490.78, 171.66, 496.15, 174.73, 496.92, 181.64, 498.45, 193.14, 499.22, 195.45, 499.22, 200.05, 501.52, 206.19, 504.59, 206.95, 505.36, 215.39, 510.73, 216.93, 526.07, 218.46, 541.42, 213.86, 564.43, 213.09, 578.24, 214.63, 584.38, 215.39, 588.22, 213.86, 602.8, 209.26, 611.24, 204.65, 622.74, 206.19, 630.42, 206.19, 632.72, 209.26, 638.7, 215.39, 639.47, 221.53, 639.47, 224.6, 639.47, 224.6, 633.33, 224.6, 629.5, 224.6, 621.06, 227.67, 595.74, 233.81, 585.0, 237.64, 568.12, 233.81, 558.14, 238.41, 538.2, 243.01, 520.55, 243.78, 517.48, 252.99, 513.64, 260.66, 510.57, 261.43, 527.45, 258.36, 540.5, 257.59, 549.7, 256.82, 562.75, 255.29, 569.65, 253.76, 594.2, 252.99, 598.81, 249.15, 608.01, 249.15, 612.62, 252.22, 621.06, 252.99, 627.19, 252.22, 636.4, 249.92, 640.0, 268.33, 640.0, 271.4, 639.47, 273.7, 637.94, 273.7, 633.33, 270.63, 621.06, 269.1, 611.85, 269.1, 608.01, 269.87, 596.5, 276.01, 578.09, 278.31, 561.98, 278.31, 550.47, 278.31, 546.63, 285.98, 526.69, 289.05, 519.78, 294.42, 507.51, 296.72, 502.9, 298.26, 498.3, 305.93, 481.42, 308.23, 475.28, 313.6, 464.54, 316.67, 456.1, 316.67, 451.5, 329.71, 442.29, 341.22, 436.92, 351.96, 433.08, 356.57, 431.55, 365.77, 436.92, 369.61, 449.2, 378.82, 457.64, 384.19, 459.94, 388.02, 461.47, 394.16, 466.84, 395.69, 469.14, 399.53, 481.42, 404.13, 487.56, 408.74, 496.0, 411.04, 501.37, 432.52, 503.67, 437.13, 500.6, 443.26, 496.0, 445.56, 490.63, 448.63, 483.72, 450.17, 475.28, 449.4, 472.21, 445.56, 459.94, 443.26, 446.13, 440.19, 437.69, 437.89, 430.78, 434.82, 420.81, 433.29, 416.97, 438.66, 406.23, 433.29, 395.49, 430.22, 392.42, 427.15, 384.75, 430.99, 377.08, 421.78, 364.8, 415.64, 361.73, 412.57, 355.59, 412.57, 348.69, 410.27, 342.55, 407.2, 341.78, 403.37, 347.92, 398.76, 357.13, 398.0, 357.13, 391.09, 357.13, 383.42, 357.13, 377.28, 354.06, 368.07, 348.69, 362.7, 348.69, 311.3, 347.15, 270.63, 349.46, 244.55, 347.92, 227.67, 345.62, 230.74, 351.76, 228.44, 364.8, 227.67, 371.7, 223.07, 380.91, 220.0, 387.82, 206.19, 390.89, 200.05, 397.02, 194.68, 406.23, 193.91, 412.37, 187.77, 425.41, 187.01, 430.01, 187.01, 434.62, 181.64, 447.66, 178.57, 459.17, 178.57, 461.47, 179.33, 466.84, 181.64, 479.12, 184.7, 486.02, 179.33, 489.09]], "area": 52294.53945, "iscrowd": 0, "image_id": 10321, "bbox": [90.33, 341.78, 359.84, 298.22], "category_id": 19, "id": 60142}, {"segmentation": [[608.51, 12.7, 613.44, 12.7, 613.76, 17.16, 616.15, 20.98, 616.31, 26.07, 618.86, 36.58, 619.09, 46.04, 619.07, 54.85, 615.4, 60.36, 615.4, 63.51, 602.81, 64.82, 604.64, 60.1, 605.43, 46.72, 604.38, 34.66, 605.69, 20.75, 608.58, 16.82]], "area": 610.8264500000004, "iscrowd": 0, "image_id": 10175, "bbox": [602.81, 12.7, 16.28, 52.12], "category_id": 44, "id": 81031}, {"segmentation": [[306.45, 271.49, 345.77, 269.61, 370.72, 283.97, 376.96, 272.73, 411.91, 271.49, 463.08, 283.34, 451.22, 320.16, 453.72, 351.36, 454.34, 384.43, 448.72, 431.75, 433.12, 442.98, 436.24, 461.7, 434.37, 473.56, 365.73, 442.98, 404.42, 416.18, 411.28, 398.71, 410.04, 386.89, 401.92, 385.64, 397.56, 377.53, 396.31, 358.81, 377.59, 363.84, 348.88, 363.84, 333.91, 363.84, 325.8, 365.09, 323.3, 351.36, 318.93, 343.87, 305.83, 346.37, 297.09, 366.33, 298.97, 322.03, 314.57, 296.45, 308.33, 275.85, 305.21, 270.86], [513.62, 347.61, 526.72, 390.67, 539.83, 449.33, 518.61, 480.0, 470.56, 478.65, 482.42, 436.85, 482.42, 405.65, 508.63, 347.61], [564.16, 293.95, 640.0, 305.81, 640.0, 420.0, 596.61, 420.0, 568.53, 421.25]], "area": 32755.140199999994, "iscrowd": 0, "image_id": 10176, "bbox": [297.09, 269.61, 342.91, 210.39], "category_id": 63, "id": 98179}, {"segmentation": [[1.02, 336.31, 39.32, 342.3, 51.28, 330.32, 40.51, 315.97, 41.7, 299.22, 59.66, 296.82, 70.43, 298.02, 69.23, 325.55, 77.61, 331.52, 135.06, 605.59, 239.18, 509.85, 275.09, 513.45, 287.06, 525.41, 266.71, 542.17, 271.5, 631.92, 1.02, 627.14, 1.02, 338.7]], "area": 43908.68619999999, "iscrowd": 0, "image_id": 10342, "bbox": [1.02, 296.82, 286.04, 335.1], "category_id": 62, "id": 104895}, {"segmentation": [[476.39, 480.71, 443.38, 460.57, 432.67, 464.0, 427.52, 470.0, 425.81, 477.71, 426.67, 486.29, 428.38, 498.29, 427.52, 516.29, 425.38, 524.0, 427.1, 541.15, 432.67, 562.58, 442.1, 553.15, 445.1, 553.58, 446.81, 554.86, 440.38, 593.87, 432.24, 596.01, 429.24, 598.15, 427.52, 604.58, 427.1, 610.15, 420.67, 621.73, 427.52, 624.73, 426.24, 638.44, 476.39, 639.73, 477.24, 517.15, 477.24, 479.86]], "area": 7880.197950000003, "iscrowd": 0, "image_id": 10342, "bbox": [420.67, 460.57, 56.57, 179.16], "category_id": 62, "id": 106535}, {"segmentation": [[468.15, 309.14, 469.97, 287.19, 467.23, 280.79, 471.8, 267.99, 532.15, 269.82, 549.52, 227.76, 564.15, 227.76, 592.5, 249.71, 640.0, 267.99, 639.13, 315.54, 592.5, 313.71, 582.44, 302.74, 568.72, 304.57, 514.78, 335.65, 491.92, 321.94, 470.89, 312.79, 469.97, 308.22]], "area": 10532.29455, "iscrowd": 0, "image_id": 10145, "bbox": [467.23, 227.76, 172.77, 107.89], "category_id": 63, "id": 117617}, {"segmentation": [[558.45, 392.26, 448.0, 364.39, 478.97, 326.19, 374.71, 299.35, 361.29, 299.35, 344.77, 308.65, 347.87, 261.16, 352.0, 243.61, 362.32, 222.97, 401.55, 219.87, 406.71, 176.52, 545.03, 182.71, 638.97, 194.06, 637.94, 261.16, 635.87, 333.42, 604.9, 321.03]], "area": 43707.9344, "iscrowd": 0, "image_id": 10346, "bbox": [344.77, 176.52, 294.2, 215.74], "category_id": 63, "id": 117898}, {"segmentation": [[58.86, 70.35, 35.28, 83.95, 30.75, 99.38, 86.07, 130.21, 143.21, 109.35, 145.02, 139.28, 236.62, 135.65, 233.9, 112.98, 264.74, 122.05, 279.25, 143.81, 349.99, 117.51, 346.36, 96.65, 330.04, 83.05, 396.25, 80.33, 408.04, 60.38, 412.58, 22.29, 421.65, 64.91, 400.78, 103.9, 405.31, 224.53, 392.62, 230.89, 389.9, 230.89, 381.74, 220.0, 384.45, 114.79, 270.18, 155.6, 267.46, 183.72, 262.93, 199.14, 379.92, 219.09, 381.74, 229.07, 328.23, 228.16, 272.0, 228.16, 249.32, 243.58, 239.35, 299.81, 236.62, 174.65, 236.62, 310.7, 217.58, 305.25, 211.23, 171.03, 193.09, 160.14, 110.56, 148.35, 112.37, 186.44, 90.61, 174.65, 87.88, 142.91, 25.3, 103.01, 21.68, 82.15, 46.16, 75.79]], "area": 20731.587799999983, "iscrowd": 0, "image_id": 10369, "bbox": [21.68, 22.29, 399.97, 288.41], "category_id": 67, "id": 118496}, {"segmentation": [[567.99, 189.85, 584.77, 187.01, 593.28, 192.95, 594.83, 198.11, 592.25, 201.72, 581.41, 205.34, 563.34, 204.56, 564.89, 196.56, 567.47, 189.85]], "area": 435.77680000000004, "iscrowd": 0, "image_id": 10318, "bbox": [563.34, 187.01, 31.49, 18.33], "category_id": 3, "id": 143548}, {"segmentation": [[601.16, 194.21, 603.57, 186.98, 619.95, 185.05, 624.76, 187.46, 627.65, 188.91, 627.65, 190.83, 629.58, 194.21, 629.1, 199.99, 625.24, 199.99, 623.32, 199.5, 621.87, 201.91, 618.5, 202.88, 617.06, 202.88, 612.72, 199.5, 604.53, 200.47, 604.05, 202.88, 603.09, 202.88, 600.68, 200.47, 600.68, 194.21]], "area": 393.1824500000003, "iscrowd": 0, "image_id": 10318, "bbox": [600.68, 185.05, 28.9, 17.83], "category_id": 3, "id": 143652}, {"segmentation": [[537.3, 205.05, 537.1, 201.63, 536.9, 199.42, 537.1, 196.2, 540.32, 194.39, 542.13, 192.38, 543.14, 190.97, 546.76, 188.76, 552.39, 188.76, 559.43, 189.57, 566.47, 194.19, 564.86, 197.21, 564.26, 202.84, 561.24, 205.25, 551.99, 208.87, 543.74, 206.86]], "area": 444.0049500000004, "iscrowd": 0, "image_id": 10318, "bbox": [536.9, 188.76, 29.57, 20.11], "category_id": 3, "id": 146668}, {"segmentation": [[395.94, 210.41, 395.94, 200.25, 401.02, 196.19, 413.2, 193.15, 423.35, 195.18, 428.43, 200.25, 427.41, 211.42, 416.24, 211.42, 402.03, 213.45, 396.95, 211.42, 396.95, 208.38], [473.1, 189.09, 481.22, 189.09, 488.32, 190.1, 492.39, 193.15, 491.37, 205.33, 482.23, 206.35, 471.07, 193.15, 472.08, 190.1, 476.14, 191.12], [460.91, 209.39, 459.9, 201.27, 463.96, 192.13, 474.11, 211.42, 461.93, 209.39]], "area": 927.6084999999998, "iscrowd": 0, "image_id": 10318, "bbox": [395.94, 189.09, 96.45, 24.36], "category_id": 3, "id": 147502}, {"segmentation": [[640.0, 200.25, 635.59, 200.39, 635.24, 201.58, 634.33, 201.72, 633.07, 201.37, 632.65, 200.25, 632.3, 195.06, 632.86, 191.77, 635.66, 186.16, 639.72, 184.13, 639.65, 200.18]], "area": 98.9961000000003, "iscrowd": 0, "image_id": 10318, "bbox": [632.3, 184.13, 7.7, 17.59], "category_id": 3, "id": 147599}, {"segmentation": [[378.51, 87.91, 387.73, 87.91, 370.61, 156.42, 386.42, 160.37, 393.0, 153.78, 395.64, 153.78, 394.32, 164.32, 252.03, 163.0, 330.29, 149.48], [155.6, 172.38, 220.79, 174.99, 386.81, 178.46, 381.6, 186.29, 373.77, 189.76, 332.92, 192.37, 287.72, 205.41, 244.26, 208.89, 239.91, 212.36, 247.74, 216.71, 247.74, 222.79, 242.52, 226.27, 233.83, 226.27, 231.22, 221.06, 232.96, 216.71, 237.31, 212.36, 237.31, 209.76, 228.61, 211.5, 222.53, 218.45, 175.59, 223.66, 175.59, 217.58, 174.72, 210.63, 158.21, 213.23, 156.47, 212.36], [64.72, 205.68, 72.62, 209.63, 72.62, 218.85, 79.21, 220.17, 79.21, 212.27, 85.8, 214.9, 89.75, 218.85, 96.34, 218.85, 95.02, 212.27, 124.01, 209.63, 124.01, 199.09, 102.93, 200.41, 101.61, 208.31, 79.21, 210.95, 77.89, 197.77]], "area": 10345.872749999999, "iscrowd": 0, "image_id": 10342, "bbox": [64.72, 87.91, 330.92, 138.36], "category_id": 5, "id": 158410}, {"segmentation": [[416.52, 308.04, 416.78, 308.04, 456.79, 308.04, 456.79, 238.6, 401.04, 222.86, 399.49, 216.4, 394.07, 217.18, 381.16, 218.21, 357.15, 217.44, 355.35, 218.47, 357.67, 223.37, 356.12, 277.32, 367.99, 283.78, 370.06, 288.94, 373.16, 290.75, 377.8, 288.42, 398.45, 297.97, 400.26, 304.68, 404.65, 304.68, 405.68, 303.91]], "area": 7450.6056, "iscrowd": 0, "image_id": 10318, "bbox": [355.35, 216.4, 101.44, 91.64], "category_id": 6, "id": 167481}, {"segmentation": [[76.76, 173.39, 207.26, 181.06, 437.56, 206.97, 527.75, 217.52, 552.7, 222.32, 564.22, 223.28, 569.01, 234.8, 573.81, 252.07, 569.01, 267.42, 502.8, 259.74, 379.02, 229.04, 117.07, 200.25, 69.09, 198.33, 71.97, 173.39]], "area": 16100.235900000003, "iscrowd": 0, "image_id": 10343, "bbox": [69.09, 173.39, 504.72, 94.03], "category_id": 7, "id": 173062}, {"segmentation": [[251.86, 240.09, 251.86, 208.56, 252.76, 195.95, 288.34, 193.25, 295.1, 190.1, 304.56, 190.1, 306.36, 234.68, 285.64, 236.94], [312.67, 193.7, 328.43, 196.85, 325.73, 233.33, 314.02, 234.68], [338.34, 200.46, 344.19, 200.0, 345.09, 232.43, 336.99, 232.43], [371.22, 205.41, 379.77, 203.16, 387.43, 202.71, 392.83, 206.31, 396.44, 245.94, 395.09, 249.55, 369.41, 251.35, 368.96, 204.96]], "area": 4301.0380000000005, "iscrowd": 0, "image_id": 10430, "bbox": [251.86, 190.1, 144.58, 61.25], "category_id": 7, "id": 174583}, {"segmentation": [[600.76, 338.86, 595.02, 350.34, 591.2, 350.34, 585.46, 370.61, 575.51, 375.97, 573.98, 377.5, 455.38, 390.51, 414.06, 325.85, 412.53, 317.43, 406.03, 307.49, 398.0, 290.65, 401.82, 285.3, 419.04, 279.94, 464.56, 283.77, 501.67, 294.86, 541.08, 310.55, 602.67, 338.86]], "area": 14129.428699999999, "iscrowd": 0, "image_id": 10388, "bbox": [398.0, 279.94, 204.67, 110.57], "category_id": 9, "id": 176584}, {"segmentation": [[275.87, 405.16, 271.8, 385.81, 313.54, 371.56, 356.29, 369.53, 364.44, 398.03, 303.36, 414.32]], "area": 2907.6278999999995, "iscrowd": 0, "image_id": 10405, "bbox": [271.8, 369.53, 92.64, 44.79], "category_id": 9, "id": 176652}, {"segmentation": [[107.73, 404.29, 115.34, 386.22, 118.19, 368.15, 132.46, 356.74, 146.72, 352.93, 158.14, 354.83, 161.94, 357.69, 163.84, 358.64, 171.45, 364.34, 173.35, 365.29, 175.25, 367.2, 176.2, 369.1, 177.16, 371.0, 178.11, 372.9, 179.06, 374.8, 180.01, 377.66, 180.01, 379.56, 180.01, 382.41, 180.01, 385.27, 180.01, 390.97, 180.01, 393.82, 179.06, 397.63, 179.06, 399.53, 178.11, 406.19, 179.06, 409.04, 180.96, 409.99, 182.86, 411.89, 183.81, 414.75, 183.81, 416.65, 183.81, 418.55, 183.81, 423.31, 180.96, 425.21, 179.06, 428.06, 178.11, 429.01, 177.16, 431.86, 187.62, 433.77, 198.08, 446.13, 211.39, 469.9, 209.49, 471.81, 220.9, 488.92, 265.6, 488.92, 278.91, 492.73, 291.2, 495.22, 300.31, 498.12, 302.79, 499.36, 298.24, 502.67, 301.55, 511.78, 290.37, 522.96, 284.16, 515.5, 268.85, 505.16, 215.86, 510.95, 208.82, 508.47, 220.0, 526.68, 207.58, 539.51, 165.77, 575.11, 162.05, 579.25, 145.9, 579.67, 127.28, 568.08, 113.62, 505.57, 117.76, 487.77, 108.65, 480.73, 101.61, 446.38, 99.13, 434.78, 103.68, 428.99, 111.96, 424.85, 115.69, 424.85, 111.55, 419.47, 114.03, 413.67, 108.65, 412.43, 109.06, 404.57], [63.11, 428.16, 70.57, 462.11, 55.66, 436.44, 58.98, 430.65]], "area": 19614.002749999992, "iscrowd": 0, "image_id": 10342, "bbox": [55.66, 352.93, 247.13, 226.74], "category_id": 1, "id": 186490}, {"segmentation": [[100.23, 216.54, 106.03, 192.05, 113.12, 179.17, 124.08, 169.5, 120.86, 160.48, 115.7, 147.59, 112.48, 142.43, 109.9, 130.83, 104.75, 120.52, 98.95, 106.34, 104.75, 101.83, 113.12, 95.38, 129.24, 86.36, 145.99, 85.07, 149.21, 83.14, 167.9, 97.96, 175.64, 106.34, 177.57, 108.92, 179.5, 119.87, 180.15, 134.7, 174.99, 149.52, 176.93, 161.76, 195.62, 164.34, 214.95, 176.59, 225.26, 195.28, 230.42, 206.88, 233.64, 215.9, 229.77, 230.08, 227.84, 238.46, 231.71, 281.64, 222.04, 309.35, 211.08, 324.82, 196.26, 342.86, 188.53, 357.04, 182.73, 365.42, 175.64, 366.71, 161.46, 375.09, 158.24, 369.93, 164.68, 357.68, 151.15, 355.11, 155.01, 353.17, 171.13, 343.51, 185.3, 331.26, 191.75, 323.53, 191.1, 316.44, 189.82, 307.42, 194.97, 293.24, 196.26, 269.39, 195.62, 250.7, 190.46, 233.3, 192.39, 219.77, 191.1, 210.74, 179.5, 208.81, 164.68, 208.81, 149.86, 209.46, 133.1, 210.74, 115.06, 215.26, 104.1, 216.54], [200.13, 359.62, 196.91, 374.44, 196.26, 389.91, 196.91, 398.93, 197.55, 400.0, 171.13, 400.0, 147.93, 399.58, 107.97, 400.0, 95.08, 398.93, 95.72, 393.13, 97.66, 383.46, 98.3, 375.09, 99.59, 368.0, 112.48, 367.35, 128.59, 366.06, 144.06, 363.48, 162.1, 362.84, 153.08, 377.66, 157.59, 380.24, 165.33, 374.44, 176.93, 370.57, 191.1, 368.0]], "area": 18281.4437, "iscrowd": 0, "image_id": 10005, "bbox": [95.08, 83.14, 138.56, 316.86], "category_id": 1, "id": 205677}, {"segmentation": [[405.22, 226.13, 406.06, 220.06, 405.75, 218.6, 399.26, 218.7, 399.26, 217.76, 401.56, 216.82, 403.44, 217.23, 404.81, 215.14, 404.18, 213.88, 404.6, 212.42, 404.7, 212.0, 406.69, 211.9, 407.84, 211.9, 407.95, 212.73, 409.2, 214.3, 411.3, 217.65, 410.67, 224.25, 408.37, 227.91, 408.26, 228.96, 407.95, 229.9, 406.69, 230.11]], "area": 91.3957999999999, "iscrowd": 0, "image_id": 10342, "bbox": [399.26, 211.9, 12.04, 18.21], "category_id": 1, "id": 210086}, {"segmentation": [[325.28, 226.55, 326.08, 228.36, 325.68, 233.19, 324.27, 239.62, 325.28, 242.64, 326.08, 245.45, 325.88, 247.67, 323.87, 243.24, 323.87, 245.25, 322.26, 246.86, 320.65, 245.66, 321.86, 242.84, 319.04, 239.02, 319.44, 237.21, 319.64, 226.95, 320.05, 226.35, 319.04, 224.13, 321.25, 221.92, 322.86, 222.12, 324.67, 225.34, 325.88, 226.15]], "area": 124.35724999999984, "iscrowd": 0, "image_id": 10318, "bbox": [319.04, 221.92, 7.04, 25.75], "category_id": 1, "id": 215428}, {"segmentation": [[310.02, 249.25, 309.01, 244.02, 309.82, 237.38, 310.02, 234.16, 310.02, 231.55, 309.21, 229.33, 308.01, 227.52, 306.2, 227.12, 305.99, 230.94, 305.99, 233.96, 305.79, 235.77, 305.39, 237.98, 305.79, 239.99, 305.79, 242.0, 306.4, 244.82, 307.0, 246.63, 306.6, 247.44, 304.99, 248.04, 305.19, 248.64, 308.01, 249.65, 309.01, 249.65, 309.62, 249.45]], "area": 78.69084999999956, "iscrowd": 0, "image_id": 10318, "bbox": [304.99, 227.12, 5.03, 22.53], "category_id": 1, "id": 215669}, {"segmentation": [[230.9, 166.7, 236.83, 180.34, 240.39, 190.42, 238.61, 195.76, 238.02, 199.31, 257.59, 202.28, 254.03, 191.01, 266.48, 192.79, 271.82, 188.64, 273.01, 185.67, 280.12, 185.08, 288.43, 185.67, 290.8, 186.86, 290.8, 163.14, 284.28, 145.94], [242.17, 157.8, 247.51, 144.16, 264.71, 118.66, 268.86, 111.54, 274.79, 113.32, 273.6, 100.28, 285.46, 94.94, 289.02, 96.72, 292.58, 106.8, 290.21, 117.47, 271.23, 123.41, 267.67, 129.93, 252.25, 147.72, 253.44, 150.68, 247.51, 160.17]], "area": 2828.0351000000005, "iscrowd": 0, "image_id": 10005, "bbox": [230.9, 94.94, 61.68, 107.34], "category_id": 1, "id": 215759}, {"segmentation": [[317.81, 198.31, 317.58, 191.34, 314.56, 184.61, 309.68, 183.68, 307.36, 184.14, 304.57, 188.79, 302.95, 199.47, 296.67, 204.12, 294.12, 216.2, 294.12, 227.34, 295.74, 236.17, 299.23, 249.41, 299.69, 261.25, 301.55, 282.85, 302.71, 292.61, 303.87, 297.95, 308.75, 298.88, 314.56, 296.32, 312.0, 270.31, 313.16, 250.8, 318.97, 263.58, 323.38, 280.53, 330.58, 294.47, 331.05, 296.56, 332.91, 297.72, 344.52, 297.02, 344.29, 295.86, 336.16, 287.5, 329.19, 263.34, 326.4, 246.62, 331.98, 245.92, 328.26, 214.57, 327.1, 208.3, 324.78, 200.63, 321.99, 198.31]], "area": 2959.1717, "iscrowd": 0, "image_id": 10388, "bbox": [294.12, 183.68, 50.4, 115.2], "category_id": 1, "id": 219814}, {"segmentation": [[299.94, 251.61, 299.94, 238.64, 298.84, 237.66, 297.62, 236.44, 297.74, 235.34, 298.47, 234.11, 299.33, 232.28, 299.45, 231.18, 299.45, 230.32, 299.45, 228.97, 301.29, 227.38, 302.15, 227.38, 302.76, 228.36, 302.76, 229.83, 303.0, 230.57, 303.13, 230.93, 304.35, 231.67, 304.96, 231.79, 305.08, 233.14, 305.21, 233.87, 305.08, 236.93, 303.61, 245.5, 302.64, 248.56, 302.76, 250.15, 304.23, 250.88, 302.88, 251.86]], "area": 107.80479999999993, "iscrowd": 0, "image_id": 10318, "bbox": [297.62, 227.38, 7.59, 24.48], "category_id": 1, "id": 224274}, {"segmentation": [[327.36, 224.66, 328.8, 224.25, 330.34, 226.3, 330.45, 226.82, 330.45, 227.95, 331.37, 229.19, 332.3, 230.11, 331.37, 229.91, 331.17, 232.58, 331.17, 234.64, 331.17, 235.16, 330.65, 235.88, 330.96, 238.35, 330.45, 239.99, 329.11, 240.72, 329.11, 241.44, 328.7, 240.51, 327.67, 239.27, 326.95, 238.24, 327.77, 234.85, 326.54, 233.0, 326.12, 231.45, 326.12, 230.32, 326.64, 228.88, 327.05, 227.44, 327.57, 226.72, 326.84, 225.48, 327.26, 224.86]], "area": 60.32805000000001, "iscrowd": 0, "image_id": 10318, "bbox": [326.12, 224.25, 6.18, 17.19], "category_id": 1, "id": 224825}, {"segmentation": [[525.78, 153.49, 531.97, 141.12, 538.93, 139.57, 555.94, 139.57, 574.5, 139.57, 591.52, 138.8, 637.14, 137.25, 638.69, 140.34, 639.46, 301.2, 639.46, 306.62, 613.17, 312.8, 576.82, 307.39, 565.99, 304.3, 565.99, 327.5, 551.3, 325.18, 550.53, 310.48, 551.3, 277.23, 551.3, 267.18, 548.98, 255.57, 547.43, 239.33, 542.02, 230.05, 537.38, 220.77, 531.19, 202.21, 525.78, 192.16, 548.98, 189.84, 555.94, 184.42, 560.58, 181.33, 562.9, 172.82, 562.9, 165.09, 559.03, 159.68, 552.85, 154.26, 543.57, 153.49, 529.65, 155.04]], "area": 16094.31430000001, "iscrowd": 0, "image_id": 10229, "bbox": [525.78, 137.25, 113.68, 190.25], "category_id": 3, "id": 242854}, {"segmentation": [[77.66, 403.42, 70.11, 368.9, 66.88, 332.22, 63.64, 310.65, 55.01, 279.37, 56.09, 242.7, 62.56, 212.49, 81.98, 188.76, 96.0, 173.66, 127.28, 156.4, 147.78, 148.85, 155.33, 141.3, 163.96, 116.49, 176.9, 97.08, 187.69, 83.06, 214.65, 75.51, 279.37, 75.51, 333.3, 73.35, 388.31, 73.35, 431.46, 75.51, 470.29, 87.37, 491.87, 115.42, 503.73, 152.09, 512.36, 169.35, 516.67, 165.03, 528.54, 156.4, 555.51, 155.33, 559.82, 172.58, 550.11, 184.45, 541.48, 189.84, 530.7, 198.47, 550.11, 244.85, 550.11, 273.98, 554.43, 328.99, 547.96, 376.45, 528.54, 391.55, 505.89, 378.61, 501.57, 367.82, 492.94, 352.72, 478.92, 341.93, 471.37, 373.21, 459.51, 404.49, 418.52, 412.04, 402.34, 361.35, 381.84, 352.72, 299.87, 361.35, 261.03, 375.37, 226.52, 382.92, 210.34, 373.21, 202.79, 362.43, 181.21, 359.19, 166.11, 357.03, 144.54, 357.03, 136.99, 395.87, 131.6, 404.49, 98.16, 409.89]], "area": 131353.89925000005, "iscrowd": 0, "image_id": 10229, "bbox": [55.01, 73.35, 504.81, 338.69], "category_id": 3, "id": 243118}, {"segmentation": [[167.88, 155.17, 175.8, 157.16, 186.21, 158.15, 193.14, 155.92, 213.46, 147.99, 229.81, 128.67, 243.43, 111.82, 252.1, 112.32, 242.44, 157.41, 240.21, 159.14, 164.16, 158.64, 164.9, 155.42, 168.12, 155.42]], "area": 1234.877049999999, "iscrowd": 0, "image_id": 10342, "bbox": [164.16, 111.82, 87.94, 47.32], "category_id": 5, "id": 247024}, {"segmentation": [[156.71, 147.72, 170.02, 147.28, 197.07, 148.61, 214.8, 146.4, 195.29, 156.59, 177.55, 156.15, 158.49, 156.15, 153.61, 153.04, 153.17, 149.05, 158.93, 148.61], [245.84, 148.17, 267.57, 147.28, 262.25, 156.15, 243.18, 156.59, 243.18, 149.5], [281.76, 148.17, 297.72, 143.29, 319.45, 120.68, 326.1, 120.68, 318.56, 147.72, 309.25, 150.83, 296.84, 153.93, 276.88, 156.15, 278.21, 147.72, 287.08, 146.4]], "area": 1269.0786500000004, "iscrowd": 0, "image_id": 10342, "bbox": [153.17, 120.68, 172.93, 35.91], "category_id": 5, "id": 247182}, {"segmentation": [[76.33, 240.14, 78.38, 261.21, 78.38, 275.34, 96.12, 275.34, 111.02, 275.34, 134.92, 275.08, 156.51, 274.06, 154.19, 264.55, 147.51, 256.84, 138.0, 238.59, 133.38, 237.31, 118.73, 237.31, 93.03, 238.59, 77.36, 239.11], [162.93, 256.33, 175.27, 247.33, 176.29, 264.81, 170.64, 267.12, 163.96, 273.54, 162.93, 265.83], [160.1, 235.25, 176.81, 235.77, 175.27, 239.11, 162.42, 247.07], [147.0, 237.05, 152.91, 235.77, 157.02, 247.07, 155.99, 252.21, 152.39, 245.53]], "area": 2998.824849999999, "iscrowd": 0, "image_id": 10318, "bbox": [76.33, 235.25, 100.48, 40.09], "category_id": 6, "id": 248358}, {"segmentation": [[534.02, 105.72, 541.51, 105.72, 542.24, 102.66, 542.24, 102.09, 540.06, 101.93, 538.37, 101.13, 538.05, 98.71, 537.89, 95.41, 537.41, 92.91, 537.41, 92.18, 536.44, 91.94, 535.31, 93.15, 535.15, 95.0, 535.31, 97.66, 535.63, 99.19, 535.47, 100.97, 535.8, 101.77, 535.39, 102.42, 532.01, 102.74, 532.25, 104.19, 533.86, 105.48]], "area": 58.58784999999995, "iscrowd": 0, "image_id": 10318, "bbox": [532.01, 91.94, 10.23, 13.78], "category_id": 9, "id": 249962}, {"segmentation": [[81.57, 135.4, 75.52, 129.73, 72.11, 124.81, 76.28, 97.57, 85.36, 87.35, 96.71, 79.41, 95.19, 70.71, 100.49, 73.35, 101.25, 75.25, 118.65, 71.46, 139.84, 84.33, 151.95, 101.35, 142.87, 107.03, 135.68, 105.14, 130.76, 102.49, 102.0, 114.22, 100.49, 129.35, 97.09, 131.62]], "area": 2646.522100000001, "iscrowd": 0, "image_id": 10244, "bbox": [72.11, 70.71, 79.84, 64.69], "category_id": 28, "id": 280395}, {"segmentation": [[300.39, 120.27, 323.44, 126.7, 350.24, 148.68, 352.39, 150.29, 334.16, 157.79, 319.69, 162.08, 310.04, 170.12, 302.0, 184.6, 305.75, 188.88, 319.69, 198.53, 297.71, 204.97, 299.85, 120.27], [272.52, 121.34, 246.25, 116.52, 194.79, 138.5, 180.85, 144.93, 177.1, 156.72, 161.55, 187.81, 170.67, 187.28, 181.39, 195.85, 183.53, 196.92, 193.72, 185.67, 206.05, 180.31, 213.01, 181.38, 224.27, 186.2, 226.95, 196.39, 226.42, 204.43, 225.88, 207.65, 227.49, 210.33, 237.14, 212.47, 272.52, 214.61, 273.59, 121.34]], "area": 9003.237149999999, "iscrowd": 0, "image_id": 10275, "bbox": [161.55, 116.52, 190.84, 98.09], "category_id": 28, "id": 285274}, {"segmentation": [[354.07, 152.63, 387.66, 139.2, 446.19, 139.2, 466.34, 161.27, 471.14, 191.01, 418.36, 218.84, 378.06, 235.15, 337.76, 213.08, 318.57, 210.21, 320.49, 197.73, 308.98, 182.38, 303.22, 175.66, 308.02, 158.39]], "area": 10999.8843, "iscrowd": 0, "image_id": 10275, "bbox": [303.22, 139.2, 167.92, 95.95], "category_id": 28, "id": 286967}, {"segmentation": [[222.37, 91.74, 235.19, 88.49, 240.88, 92.15, 242.31, 96.22, 250.85, 98.86, 253.7, 107.82, 254.51, 113.72, 252.68, 122.06, 248.0, 126.53, 241.29, 130.4, 230.3, 131.42, 219.52, 128.16, 216.06, 121.65, 213.21, 116.77, 212.4, 109.65, 215.86, 103.75, 220.74, 95.41]], "area": 1332.1228499999997, "iscrowd": 0, "image_id": 10230, "bbox": [212.4, 88.49, 42.11, 42.93], "category_id": 37, "id": 302549}, {"segmentation": [[243.84, 137.08, 247.68, 134.2, 248.76, 134.68, 251.4, 136.72, 248.28, 142.36, 243.96, 146.19, 241.68, 145.95, 239.76, 143.91, 240.12, 141.16, 241.08, 139.0, 243.96, 136.84]], "area": 79.0734, "iscrowd": 0, "image_id": 10265, "bbox": [239.76, 134.2, 11.64, 11.99], "category_id": 37, "id": 305510}, {"segmentation": [[301.35, 60.91, 235.7, 92.13, 220.63, 126.57, 232.47, 212.66, 278.74, 263.25, 384.22, 308.45, 493.99, 334.28, 619.91, 350.42, 640.0, 319.21, 639.28, 31.86, 638.21, 11.41, 640.0, 6.03, 561.79, 1.72, 430.49, 20.02, 372.38, 20.02]], "area": 115708.0046, "iscrowd": 0, "image_id": 10421, "bbox": [220.63, 1.72, 419.37, 348.7], "category_id": 54, "id": 309442}, {"segmentation": [[6.46, 306.73, 21.52, 312.11, 55.96, 330.19, 117.31, 297.9, 137.76, 276.38, 157.13, 280.68, 233.54, 235.48, 236.77, 227.95, 232.47, 209.65, 223.86, 170.91, 219.55, 130.01, 227.09, 98.8, 263.68, 78.35, 301.35, 58.98, 299.19, 53.6, 291.66, 41.76, 240.0, 21.31, 195.87, 4.09, 149.6, 0.0, 31.21, 0.86, 3.23, 10.55, 0.0, 10.55]], "area": 68200.36965000002, "iscrowd": 0, "image_id": 10421, "bbox": [0.0, 0.0, 301.35, 330.19], "category_id": 54, "id": 312027}, {"segmentation": [[270.92, 142.18, 272.71, 126.08, 279.86, 114.16, 294.76, 105.22, 314.44, 107.6, 330.53, 123.7, 335.3, 136.81, 336.49, 155.89, 331.13, 172.58, 322.78, 184.51, 309.07, 191.66, 288.21, 188.08, 275.09, 173.78, 270.92, 158.87, 269.72, 139.2]], "area": 4504.112900000001, "iscrowd": 0, "image_id": 10125, "bbox": [269.72, 105.22, 66.77, 86.44], "category_id": 85, "id": 337009}, {"segmentation": [[385.79, 295.65, 384.84, 265.01, 381.01, 242.04, 383.88, 218.11, 386.75, 199.92, 403.02, 191.3, 414.51, 174.07, 434.62, 159.71, 449.93, 156.84, 493.97, 153.97, 541.83, 157.8, 558.11, 158.75, 586.83, 187.47, 600.23, 205.66, 610.76, 202.79, 617.46, 209.49, 615.55, 215.23, 608.84, 219.06, 620.33, 230.55, 628.95, 243.0, 630.86, 274.59, 627.03, 296.6, 625.12, 310.96, 608.84, 318.62, 600.23, 312.88, 593.53, 300.43, 493.97, 300.43, 472.91, 302.35, 452.8, 293.73, 445.15, 311.92, 439.4, 315.75, 435.57, 307.13, 428.87, 291.82, 433.66, 285.12, 408.77, 281.29, 409.73, 294.69, 403.02, 303.31, 392.49, 302.35, 382.92, 293.73, 379.09, 286.07]], "area": 31385.54095000001, "iscrowd": 0, "image_id": 10434, "bbox": [379.09, 153.97, 251.77, 164.65], "category_id": 3, "id": 347590}, {"segmentation": [[369.91, 142.75, 368.55, 125.99, 375.8, 106.07, 407.05, 103.8, 416.1, 122.82, 416.56, 132.33, 417.01, 143.2, 407.95, 142.29, 399.8, 135.05, 388.48, 138.67, 388.03, 145.01, 377.16, 149.09, 368.1, 146.37]], "area": 1627.8997999999997, "iscrowd": 0, "image_id": 10434, "bbox": [368.1, 103.8, 48.91, 45.29], "category_id": 3, "id": 349558}, {"segmentation": [[524.96, 136.64, 524.41, 120.15, 520.29, 119.32, 514.79, 123.44, 501.04, 123.44, 498.29, 128.12, 498.02, 134.17, 493.62, 135.82, 492.25, 138.56, 494.72, 141.04, 505.72, 142.14, 513.14, 142.96, 516.71, 145.71, 520.56, 152.31, 526.34, 151.49, 526.06, 137.19], [538.16, 116.3, 559.6, 113.0, 570.05, 112.72, 568.4, 126.19, 568.95, 131.97, 564.55, 128.12, 552.18, 127.84, 543.11, 131.97, 536.23, 138.29]], "area": 1150.5815999999998, "iscrowd": 0, "image_id": 10434, "bbox": [492.25, 112.72, 77.8, 39.59], "category_id": 3, "id": 355721}, {"segmentation": [[2.13, 120.52, 51.99, 119.68, 81.56, 118.83, 66.35, 135.73, 55.37, 152.63, 48.61, 163.62, 47.76, 172.91, 42.69, 180.52, 36.78, 181.36, 29.17, 177.14, 29.17, 171.22, 16.5, 172.07, 12.27, 177.14, 8.04, 180.52, 3.82, 181.36, 1.28, 174.6, 2.13, 119.68], [69.73, 162.77, 81.56, 154.32, 90.01, 150.94, 94.24, 153.48, 97.62, 161.08, 96.77, 165.31, 95.08, 167.84, 91.7, 172.07, 90.01, 177.98, 86.63, 180.52, 81.56, 180.52, 75.65, 177.98, 70.58, 172.91, 70.58, 172.91, 66.35, 170.38]], "area": 3888.86325, "iscrowd": 0, "image_id": 10434, "bbox": [1.28, 118.83, 96.34, 62.53], "category_id": 3, "id": 361274}, {"segmentation": [[317.84, 39.19, 471.73, 4.67, 475.0, 211.78, 362.43, 175.82, 345.17, 183.01, 323.6, 165.75, 320.72, 154.25, 296.27, 145.62]], "area": 27078.976950000007, "iscrowd": 0, "image_id": 10244, "bbox": [296.27, 4.67, 178.73, 207.11], "category_id": 6, "id": 365876}, {"segmentation": [[382.53, 14.67, 323.76, 52.71, 184.6, 153.87, 158.45, 174.79, 155.83, 227.75, 156.56, 258.47, 155.84, 259.67, 155.84, 267.62, 169.26, 275.64, 169.88, 293.58, 180.09, 296.05, 181.01, 294.51, 197.71, 299.45, 210.7, 301.31, 215.65, 298.84, 229.57, 300.69, 229.57, 303.78, 232.97, 303.78, 235.76, 298.53, 242.87, 299.15, 254.31, 301.0, 262.05, 296.05, 270.71, 296.05, 280.91, 289.87, 281.84, 284.3, 284.01, 283.68, 290.5, 277.5, 291.74, 280.28, 303.18, 270.38, 302.87, 265.74, 307.51, 258.94, 313.7, 249.97, 313.7, 243.78, 320.81, 236.67, 353.6, 203.58, 402.77, 152.23, 442.98, 113.57, 464.01, 91.3, 466.18, 94.71, 468.03, 96.56, 478.86, 91.92, 494.63, 82.95, 499.27, 72.75, 503.91, 68.73, 512.88, 62.54, 527.11, 55.74, 570.72, 21.41, 578.14, 20.79, 581.23, 16.15, 594.53, 9.65, 594.84, 5.01, 601.78, 0.63, 402.13, 1.58]], "area": 56417.027949999974, "iscrowd": 0, "image_id": 10084, "bbox": [155.83, 0.63, 445.95, 303.15], "category_id": 7, "id": 366905}, {"segmentation": [[148.32, 370.12, 152.18, 366.55, 160.79, 383.78, 179.2, 500.0, 134.67, 500.0, 131.4, 496.6, 118.93, 492.15, 108.54, 481.76, 104.08, 470.77, 103.19, 463.35, 110.91, 444.94, 123.68, 428.91, 144.46, 408.72, 147.43, 396.54, 147.14, 381.99], [79.36, 491.28, 76.09, 499.0, 23.54, 500.0, 37.49, 493.35, 65.4, 489.2]], "area": 5940.250749999997, "iscrowd": 0, "image_id": 10243, "bbox": [23.54, 366.55, 155.66, 133.45], "category_id": 62, "id": 382355}, {"segmentation": [[379.17, 102.38, 359.24, 84.67, 359.24, 3.47, 558.54, 4.95, 637.51, 19.72, 639.72, 427.0, 2.73, 427.0, 2.0, 0.53, 53.66, 3.47, 89.83, 33.0, 83.92, 59.57, 85.4, 66.22, 72.85, 83.93, 82.44, 134.13, 89.09, 165.12, 105.33, 168.81, 64.73, 207.2, 59.57, 256.65, 67.69, 289.86, 89.09, 320.86, 149.61, 358.51, 271.41, 395.41, 361.45, 396.15, 426.41, 368.84, 517.2, 334.15, 562.22, 287.65, 619.79, 364.42, 632.34, 346.7, 637.51, 343.01, 579.93, 255.91, 579.2, 201.29, 545.25, 158.48, 518.68, 142.24, 534.91, 126.01, 526.05, 112.72, 549.68, 78.03, 534.17, 68.44, 512.76, 68.44, 469.96, 92.05, 453.72, 80.98, 441.17, 89.09, 447.81, 100.91, 382.86, 103.86], [71.37, 0.08, 94.26, 35.51, 103.11, 26.66, 120.83, 27.4, 124.52, 22.22, 140.02, 19.27, 151.83, 25.92, 157.0, 28.13, 171.02, 14.84, 180.62, 1.55, 73.59, 2.29]], "area": 110712.74180000006, "iscrowd": 0, "image_id": 10161, "bbox": [2.0, 0.08, 637.72, 426.92], "category_id": 67, "id": 392660}, {"segmentation": [[161.8, 433.62, 197.39, 453.03, 278.29, 458.43, 340.85, 453.03, 366.74, 435.78, 400.18, 421.75, 412.04, 406.65, 409.89, 388.31, 399.1, 363.51, 385.08, 357.03, 335.46, 345.17, 262.11, 348.4, 184.45, 364.58, 158.56, 384.0, 142.38, 413.12, 148.85, 422.83]], "area": 23518.753249999998, "iscrowd": 0, "image_id": 10176, "bbox": [142.38, 345.17, 269.66, 113.26], "category_id": 67, "id": 393164}, {"segmentation": [[474.21, 351.74, 512.42, 315.49, 638.81, 328.22, 637.83, 431.1, 549.65, 429.14, 494.79, 426.2, 471.27, 419.34, 470.29, 389.95, 474.21, 350.76, 478.13, 345.86]], "area": 17225.573699999997, "iscrowd": 0, "image_id": 10125, "bbox": [470.29, 315.49, 168.52, 115.61], "category_id": 8, "id": 397146}, {"segmentation": [[142.38, 358.11, 132.67, 398.02, 125.12, 408.81, 75.51, 409.89, 63.64, 366.74, 66.88, 316.04, 59.33, 268.58, 58.25, 197.39, 87.37, 183.37, 99.24, 159.64, 110.02, 153.17, 143.46, 146.7, 165.03, 101.39, 184.45, 79.82, 293.39, 73.35, 435.78, 77.66, 474.61, 90.61, 492.94, 107.87, 492.94, 122.97, 510.2, 168.27, 526.38, 153.17, 560.9, 153.17, 565.21, 175.82, 528.54, 189.84, 538.25, 216.81, 550.11, 241.62, 558.74, 299.87, 550.11, 364.58, 542.56, 389.39, 497.26, 387.24, 495.1, 344.09, 480.0, 344.09, 469.21, 414.2, 412.04, 414.2, 405.57, 360.27, 364.58, 359.19, 361.35, 368.9, 344.09, 368.9, 344.09, 360.27, 294.47, 355.96, 293.39, 364.58, 284.76, 366.74, 263.19, 369.98, 261.03, 381.84, 220.04, 386.16, 208.18, 357.03, 193.08, 352.72, 202.79, 369.98, 176.9, 360.27, 179.06, 354.88, 147.78, 352.72]], "area": 134246.28490000003, "iscrowd": 0, "image_id": 10229, "bbox": [58.25, 73.35, 506.96, 340.85], "category_id": 8, "id": 397350}, {"segmentation": [[531.66, 144.16, 640.0, 140.93, 638.21, 307.75, 596.23, 307.75, 569.33, 300.22, 560.72, 324.97, 557.49, 286.22, 543.5, 218.42, 531.66, 193.67, 561.79, 176.45, 563.95, 154.92, 553.18, 150.62, 523.05, 152.77]], "area": 14706.711400000002, "iscrowd": 0, "image_id": 10229, "bbox": [523.05, 140.93, 116.95, 184.04], "category_id": 8, "id": 399754}, {"segmentation": [[266.85, 354.62, 276.38, 164.96, 284.0, 149.72, 301.15, 137.33, 312.59, 125.89, 505.1, 118.27, 537.5, 136.37, 578.48, 178.31, 599.45, 174.49, 615.65, 218.33, 619.46, 247.88, 620.42, 298.39, 624.23, 285.05, 621.37, 370.82, 599.45, 370.82, 579.44, 374.63, 569.91, 390.83, 553.7, 389.88, 530.83, 362.24, 410.75, 367.0, 407.89, 403.22, 371.68, 405.13, 334.51, 394.64, 330.7, 379.39, 328.79, 355.57]], "area": 82795.81754999999, "iscrowd": 0, "image_id": 10058, "bbox": [266.85, 118.27, 357.38, 286.86], "category_id": 8, "id": 400533}, {"segmentation": [[203.82, 27.16, 208.78, 274.68, 282.86, 273.18, 288.85, 122.03, 270.89, 102.57, 276.13, 72.64, 263.41, 61.41, 268.64, 30.73, 257.42, 22.5, 246.94, 21.75, 227.49, 28.49]], "area": 18377.789800000006, "iscrowd": 0, "image_id": 10222, "bbox": [203.82, 21.75, 85.03, 252.93], "category_id": 10, "id": 403063}, {"segmentation": [[555.53, 275.17, 551.31, 57.35, 461.82, 39.63, 440.72, 105.48, 359.67, 131.65, 379.23, 336.16, 555.85, 328.58]], "area": 47000.18014999998, "iscrowd": 0, "image_id": 10241, "bbox": [359.67, 39.63, 196.18, 296.53], "category_id": 10, "id": 406094}, {"segmentation": [[181.62, 132.68, 200.45, 132.68, 202.43, 132.68, 215.32, 136.65, 238.12, 137.64, 250.02, 113.85, 259.93, 102.94, 265.88, 102.94, 283.73, 111.86, 284.72, 126.73, 273.81, 145.57, 263.9, 151.52, 261.92, 156.48, 269.85, 162.42, 273.81, 202.08, 263.9, 212.98, 258.94, 220.92, 266.87, 277.42, 264.89, 298.24, 264.89, 328.98, 254.98, 340.87, 244.07, 346.82, 239.12, 346.82, 238.12, 344.84, 239.12, 335.92, 243.08, 321.04, 244.07, 311.13, 244.07, 296.26, 243.08, 293.29, 242.09, 293.29, 236.14, 296.26, 232.18, 301.22, 231.18, 315.1, 231.18, 328.98, 232.18, 335.92, 218.3, 336.91, 205.41, 336.91, 208.38, 327.98, 219.29, 316.09, 220.28, 279.41, 220.28, 222.9, 215.32, 168.37, 213.34, 154.49, 185.58, 142.6, 171.7, 143.59]], "area": 11080.774350000007, "iscrowd": 0, "image_id": 10358, "bbox": [171.7, 102.94, 113.02, 243.88], "category_id": 1, "id": 429293}, {"segmentation": [[170.05, 423.99, 176.8, 408.22, 176.8, 371.06, 181.31, 317.0, 165.54, 301.24, 162.16, 292.23, 155.41, 291.1, 152.03, 289.98, 150.9, 298.99, 146.4, 304.62, 144.14, 313.63, 121.62, 319.26, 114.86, 310.25, 108.11, 289.98, 106.98, 271.96, 106.98, 271.96, 56.31, 262.95, 55.18, 268.58, 43.92, 282.09, 38.29, 292.23, 33.78, 298.99, 23.65, 291.1, 23.65, 278.72, 27.03, 266.33, 24.77, 257.32, 25.9, 243.81, 30.41, 238.18, 50.68, 241.55, 78.83, 249.44, 97.97, 246.06, 128.38, 244.93, 139.64, 203.27, 156.53, 189.75, 182.43, 163.85, 195.95, 157.09, 213.96, 153.72, 220.72, 153.72, 220.72, 137.95, 227.48, 126.69, 238.74, 119.93, 253.38, 118.81, 263.51, 124.44, 277.03, 131.19, 279.28, 143.58, 271.4, 145.83, 271.4, 145.83, 269.14, 164.98, 269.14, 175.11, 269.14, 181.87, 270.27, 196.51, 266.89, 216.78, 266.89, 228.04, 252.25, 230.29, 248.87, 230.29, 242.12, 232.55, 237.61, 238.18, 224.1, 246.06, 209.46, 256.19, 199.32, 262.95, 216.22, 301.24, 216.22, 323.76, 208.33, 358.67, 198.2, 395.83, 198.2, 409.35, 216.22, 419.48, 224.1, 425.11, 226.35, 434.12, 218.47, 437.5, 189.19, 437.5, 172.3, 436.37]], "area": 24826.732949999994, "iscrowd": 0, "image_id": 10309, "bbox": [23.65, 118.81, 255.63, 318.69], "category_id": 1, "id": 438428}, {"segmentation": [[189.03, 494.57, 171.84, 432.26, 182.59, 364.58, 191.19, 343.09, 143.91, 319.45, 117.05, 281.84, 126.72, 259.28, 175.07, 203.42, 225.56, 178.7, 211.6, 143.26, 211.6, 114.24, 226.63, 92.75, 252.42, 98.13, 276.05, 114.24, 273.91, 167.96, 325.48, 193.75, 335.16, 212.01, 308.3, 313.0, 319.04, 323.75, 310.44, 352.75, 327.63, 429.04, 262.09, 460.19, 270.69, 478.46, 334.07, 455.89, 342.67, 493.49, 193.33, 491.35]], "area": 52552.47525000001, "iscrowd": 0, "image_id": 10179, "bbox": [117.05, 92.75, 225.62, 401.82], "category_id": 1, "id": 440556}, {"segmentation": [[76.4, 299.14, 59.25, 299.14, 57.01, 281.98, 51.79, 279.74, 62.97, 251.4, 60.74, 247.67, 83.11, 214.1, 96.54, 179.79, 95.79, 154.43, 92.06, 150.7, 77.89, 161.15, 71.93, 162.64, 69.69, 166.37, 59.25, 214.1, 47.31, 218.58, 39.11, 209.63, 36.87, 199.93, 51.79, 152.2, 74.91, 122.36, 95.79, 107.44, 104.74, 102.22, 114.44, 82.83, 134.58, 90.29, 141.29, 97.74, 181.57, 105.2, 203.95, 117.14, 211.41, 130.56, 227.07, 136.53, 211.41, 151.45, 201.71, 137.28, 191.27, 136.53, 174.86, 135.04, 165.16, 132.06, 167.4, 163.38, 175.61, 182.78, 196.49, 220.82, 184.56, 261.1, 205.44, 299.88, 162.92, 293.92, 165.16, 279.0, 165.91, 274.52, 149.5, 261.1, 164.42, 229.02, 161.43, 219.33, 132.34, 206.65, 108.47, 223.06, 104.74, 227.53, 95.79, 235.74, 82.37, 261.84, 86.1, 272.28, 80.88, 278.25, 82.37, 287.95]], "area": 18237.7949, "iscrowd": 0, "image_id": 10434, "bbox": [36.87, 82.83, 190.2, 217.05], "category_id": 1, "id": 451116}, {"segmentation": [[541.32, 157.23, 547.81, 150.12, 558.62, 146.72, 554.61, 138.38, 557.08, 135.6, 556.15, 122.92, 555.53, 117.05, 553.68, 113.03, 556.77, 112.42, 558.62, 109.02, 561.71, 99.44, 567.9, 93.56, 573.15, 90.16, 585.2, 91.4, 591.38, 93.87, 594.78, 100.98, 596.33, 107.78, 593.55, 117.98, 591.69, 122.0, 587.68, 127.87, 588.6, 130.03, 588.6, 137.14, 597.26, 143.63, 606.84, 149.5, 614.56, 156.61, 619.82, 164.34, 622.6, 168.67, 620.45, 186.63, 619.83, 192.81, 621.06, 197.75, 619.21, 200.84, 618.59, 205.17, 617.05, 212.59, 615.5, 228.35, 613.65, 234.53, 612.41, 244.42, 610.56, 255.24, 609.32, 273.17, 610.56, 280.27, 608.39, 289.86, 604.37, 295.11, 599.74, 296.96, 596.65, 302.22, 594.17, 300.98, 597.57, 290.47, 591.7, 301.29, 589.54, 299.75, 567.59, 295.73, 567.59, 285.84, 568.83, 281.51, 566.36, 270.38, 577.79, 267.91, 583.67, 264.2, 585.52, 257.09, 583.36, 238.86, 577.18, 220.32, 571.61, 202.08, 565.43, 187.25, 564.2, 177.36, 559.87, 172.1, 550.29, 162.83, 546.27, 160.36]], "area": 8640.924449999995, "iscrowd": 0, "image_id": 10176, "bbox": [541.32, 90.16, 81.28, 212.06], "category_id": 1, "id": 451458}, {"segmentation": [[5.62, 185.05, 30.34, 160.33, 8.99, 149.09, 5.62, 137.86, 3.37, 114.26, 4.49, 86.17, 16.85, 51.34, 29.21, 42.35, 79.78, 62.58, 80.9, 64.83, 86.52, 87.3, 92.13, 106.4, 97.75, 128.87, 98.88, 141.23, 102.25, 154.71, 103.37, 158.08, 141.57, 172.69, 151.69, 173.81, 203.37, 177.18, 241.57, 191.79, 255.06, 198.53, 248.31, 215.39, 183.15, 217.63, 176.4, 217.63, 135.96, 222.13, 131.46, 222.13, 140.45, 261.45, 141.57, 279.43, 146.07, 328.87, 139.33, 352.47, 142.7, 400.78, 130.34, 431.12, 104.49, 467.07, 102.25, 487.3, 89.89, 488.42, 75.28, 433.36, 75.28, 425.5, 50.56, 350.22, 40.45, 299.66, 39.33, 286.17, 22.47, 249.09, 11.24, 261.45, 7.87, 278.31]], "area": 42897.21920000001, "iscrowd": 0, "image_id": 10243, "bbox": [3.37, 42.35, 251.69, 446.07], "category_id": 1, "id": 456997}, {"segmentation": [[445.48, 426.97, 441.17, 435.6, 434.7, 462.56, 434.7, 471.19, 477.84, 473.35, 483.24, 436.67, 484.31, 404.31, 509.12, 346.07, 518.83, 360.09, 530.7, 405.39, 537.17, 447.46, 571.69, 423.73, 572.76, 404.31, 571.69, 378.43, 567.37, 300.76, 567.37, 272.72, 585.71, 264.09, 578.16, 218.79, 564.13, 177.8, 552.27, 162.7, 518.83, 142.2, 509.12, 135.73, 512.36, 109.84, 504.81, 90.43, 488.63, 77.48, 468.13, 79.64, 453.03, 83.96, 447.64, 94.74, 448.72, 115.24, 449.8, 130.34, 449.8, 141.12, 458.43, 158.38, 463.82, 159.46, 459.51, 170.25, 458.43, 184.27, 465.98, 226.34, 465.98, 251.15, 473.53, 272.72, 464.9, 278.11, 463.82, 284.58, 455.19, 314.79, 453.03, 329.89, 453.03, 356.85]], "area": 35054.99769999999, "iscrowd": 0, "image_id": 10176, "bbox": [434.7, 77.48, 151.01, 395.87], "category_id": 1, "id": 459617}, {"segmentation": [[618.91, 419.32, 588.2, 373.27, 596.84, 361.75, 577.65, 349.28, 565.18, 334.88, 547.9, 299.38, 532.55, 284.99, 508.56, 285.95, 494.17, 293.62, 488.41, 310.89, 488.41, 325.29, 487.45, 332.0, 488.41, 343.52, 479.78, 350.24, 471.14, 359.83, 487.45, 386.7, 479.78, 391.5, 491.29, 421.24, 612.19, 416.44]], "area": 12366.98715, "iscrowd": 0, "image_id": 10130, "bbox": [471.14, 284.99, 147.77, 136.25], "category_id": 1, "id": 465262}, {"segmentation": [[345.47, 194.54, 375.66, 174.41, 361.13, 169.94, 355.54, 149.82, 352.18, 130.81, 348.83, 114.04, 333.18, 112.92, 336.53, 100.62, 351.06, 93.92, 355.54, 89.44, 365.6, 76.03, 377.9, 73.79, 394.67, 79.38, 414.79, 91.68, 419.27, 119.63, 413.67, 130.81, 417.03, 145.35, 424.86, 155.41, 443.86, 161.0, 461.75, 163.23, 480.76, 167.71, 490.82, 172.18, 508.71, 192.3, 518.77, 209.07, 528.83, 223.61, 540.01, 235.91, 548.96, 248.2, 557.9, 261.62, 550.08, 269.45, 551.19, 294.04, 550.08, 313.05, 555.67, 333.18, 560.14, 339.88, 562.37, 348.83, 566.85, 360.01, 557.9, 368.95, 541.13, 377.9, 527.71, 375.66, 521.01, 368.95, 510.94, 355.54, 517.65, 333.18, 522.12, 317.52, 522.12, 304.11, 518.77, 294.04, 510.94, 294.04, 502.0, 296.28, 494.17, 302.99, 497.53, 322.0, 506.47, 343.24, 518.77, 384.61, 518.77, 417.03, 516.53, 429.33, 513.18, 436.04, 513.18, 442.74, 510.94, 480.76, 510.94, 487.47, 504.24, 493.06, 386.84, 496.41, 385.72, 456.16, 383.49, 444.98, 382.37, 436.04, 382.37, 427.09, 373.43, 449.45, 354.42, 456.16, 337.65, 450.57, 334.29, 433.8, 337.65, 423.74, 338.77, 396.9, 344.36, 382.37, 338.77, 356.65, 334.29, 332.06, 334.29, 324.23, 319.76, 319.76, 310.82, 315.29, 307.46, 306.34, 334.29, 210.19, 345.47, 193.42]], "area": 67431.156, "iscrowd": 0, "image_id": 10179, "bbox": [307.46, 73.79, 259.39, 422.62], "category_id": 1, "id": 469960}, {"segmentation": [[288.82, 125.7, 258.12, 140.09, 257.16, 149.69, 298.42, 170.8, 298.42, 170.8, 299.38, 155.45, 300.34, 132.42, 302.26, 127.62]], "area": 1234.843399999999, "iscrowd": 0, "image_id": 10245, "bbox": [257.16, 125.7, 45.1, 45.1], "category_id": 1, "id": 473149}, {"segmentation": [[244.85, 480.0, 244.85, 480.0, 245.93, 480.0, 245.93, 477.84, 395.87, 480.0, 393.71, 448.72, 409.89, 410.97, 423.91, 413.12, 405.57, 381.84, 394.79, 358.11, 386.16, 293.39, 382.92, 257.8, 389.39, 230.83, 360.27, 186.61, 336.54, 174.74, 335.46, 166.11, 343.01, 134.83, 343.01, 115.42, 336.54, 83.06, 306.34, 74.43, 279.37, 88.45, 278.29, 120.81, 276.13, 128.36, 280.45, 141.3, 279.37, 161.8, 261.03, 159.64, 257.8, 153.17, 258.88, 129.44, 255.64, 111.1, 247.01, 126.2, 231.91, 133.75, 214.65, 119.73, 210.34, 115.42, 206.02, 130.52, 208.18, 149.93, 216.81, 157.48, 216.81, 185.53, 215.73, 211.42, 230.83, 239.46, 242.7, 298.79, 235.15, 419.6], [213.57, 104.63, 220.04, 98.16, 224.36, 90.61, 217.89, 86.29, 209.26, 88.45, 208.18, 94.92, 209.26, 103.55], [247.01, 96.0, 254.56, 102.47, 254.56, 87.37, 249.17, 85.21, 242.7, 88.45, 243.78, 94.92]], "area": 56148.40265000001, "iscrowd": 0, "image_id": 10230, "bbox": [206.02, 74.43, 217.89, 405.57], "category_id": 1, "id": 473553}, {"segmentation": [[403.23, 330.52, 364.48, 280.3, 320.0, 224.33, 318.57, 194.2, 320.0, 146.85, 294.17, 115.28, 269.78, 89.45, 249.69, 29.18, 198.03, 6.22, 162.15, 30.61, 144.93, 70.79, 143.5, 138.24, 117.67, 154.02, 113.36, 230.07, 134.89, 304.69, 165.02, 364.96, 139.19, 395.1, 136.32, 425.23, 113.36, 498.42, 101.88, 616.08, 93.27, 637.61, 93.27, 640.0, 139.19, 640.0, 140.63, 621.82, 140.63, 597.43, 177.94, 517.07, 196.59, 438.15, 212.38, 442.45, 228.16, 442.45, 232.47, 445.32, 337.22, 537.16, 358.74, 560.12, 351.57, 603.17, 330.04, 626.13, 334.35, 636.17, 363.05, 636.17, 388.88, 626.13, 416.14, 598.86, 408.97, 558.68, 406.1, 545.77, 386.01, 535.72, 341.52, 479.76, 305.65, 428.1, 279.82, 413.75, 297.04, 402.27, 308.52, 408.01, 374.53, 380.75, 384.57, 360.66, 396.05, 352.05, 403.23, 333.39]], "area": 102389.06404999999, "iscrowd": 0, "image_id": 10069, "bbox": [93.27, 6.22, 322.87, 633.78], "category_id": 1, "id": 473954}, {"segmentation": [[404.46, 229.87, 399.84, 257.06, 399.33, 265.78, 400.87, 272.97, 400.87, 281.69, 401.9, 284.25, 403.44, 284.77, 407.54, 288.87, 419.34, 286.31, 424.47, 283.74, 425.5, 279.64, 425.5, 267.32, 426.01, 260.14, 428.06, 255.01, 428.58, 247.31, 432.68, 233.97, 432.68, 222.69, 423.96, 189.34, 418.83, 188.31, 413.7, 189.85, 411.13, 192.42, 410.11, 200.11, 410.11, 203.7, 407.03, 209.35, 399.33, 214.99, 400.87, 225.76]], "area": 2448.8273500000005, "iscrowd": 0, "image_id": 10281, "bbox": [399.33, 188.31, 33.35, 100.56], "category_id": 1, "id": 474848}, {"segmentation": [[125.02, 5.29, 120.21, 50.49, 102.9, 134.16, 100.98, 187.05, 100.98, 217.83, 117.33, 218.79, 120.21, 219.75, 127.91, 299.57, 137.52, 337.08, 140.41, 371.7, 161.57, 414.98, 169.26, 418.83, 236.58, 421.71, 230.81, 389.97, 226.0, 334.19, 222.16, 289.96, 214.46, 282.26, 209.65, 259.18, 204.84, 246.68, 200.04, 231.29, 226.0, 228.41, 253.89, 219.75, 266.39, 235.14, 272.16, 244.76, 271.2, 245.72, 254.85, 249.56, 251.97, 267.84, 254.85, 281.3, 252.93, 297.65, 252.93, 349.58, 253.89, 377.47, 265.43, 418.83, 280.82, 418.83, 440.46, 412.09, 436.62, 328.42, 428.92, 263.03, 419.31, 237.06, 404.88, 235.14, 401.03, 234.18, 399.11, 217.83, 392.38, 205.33, 398.15, 202.44, 426.04, 200.52, 457.77, 199.56, 460.66, 199.56, 460.66, 208.21, 476.05, 174.55, 467.39, 134.16, 451.04, 97.61, 433.73, 69.72, 424.11, 41.83, 425.08, 5.29, 425.08, 2.4]], "area": 124324.93295, "iscrowd": 0, "image_id": 10232, "bbox": [100.98, 2.4, 375.07, 419.31], "category_id": 1, "id": 475960}, {"segmentation": [[139.33, 80.56, 146.07, 119.88, 157.3, 131.12, 188.76, 132.24, 211.24, 122.13, 228.09, 115.39, 252.81, 114.26, 273.03, 113.14, 289.89, 108.65, 296.63, 90.67, 292.13, 71.57, 292.13, 56.96, 310.11, 42.35, 298.88, 36.74, 339.33, 26.62, 360.67, 28.87, 371.91, 45.72, 382.02, 58.08, 396.63, 62.58, 401.12, 68.2, 395.51, 79.43, 383.15, 85.05, 376.4, 88.42, 379.78, 101.9, 379.78, 116.51, 377.53, 126.62, 428.09, 161.45, 478.65, 241.23, 483.15, 253.59, 430.34, 307.52, 408.99, 317.63, 388.76, 318.76, 383.15, 305.27, 383.15, 288.42, 387.64, 285.05, 376.4, 273.81, 422.47, 267.07, 434.83, 256.96, 438.2, 251.34, 433.71, 244.6, 429.21, 243.48, 413.48, 231.12, 396.63, 212.02, 382.02, 214.26, 389.89, 228.87, 385.39, 247.97, 379.78, 247.97, 388.76, 260.33, 383.15, 270.44, 375.28, 270.44, 375.28, 277.18, 378.65, 288.42, 378.65, 296.29, 382.02, 310.89, 386.52, 337.86, 378.65, 389.54, 356.18, 461.45, 344.94, 489.54, 339.33, 491.79, 267.42, 492.92, 264.04, 473.81, 253.93, 436.74, 250.56, 414.26, 250.56, 391.79, 247.19, 376.06, 244.94, 358.08, 244.94, 332.24, 251.69, 309.77, 251.69, 294.04, 248.31, 278.31, 242.7, 234.49, 232.58, 210.89, 215.73, 195.16, 196.63, 189.54, 174.16, 188.42, 155.06, 187.3, 138.2, 180.56, 120.22, 163.7, 119.1, 141.23, 116.85, 126.62, 115.73, 109.77, 117.98, 92.92], [114.61, 69.32, 114.61, 74.94, 113.48, 83.93, 113.48, 91.79, 106.74, 98.53, 97.75, 99.66, 101.12, 86.17, 105.62, 73.81, 113.48, 62.58, 120.22, 58.08, 119.1, 64.83]], "area": 75171.61760000003, "iscrowd": 0, "image_id": 10094, "bbox": [97.75, 26.62, 385.4, 466.3], "category_id": 1, "id": 477016}, {"segmentation": [[49.66, 417.36, 47.75, 365.79, 61.12, 344.78, 53.48, 316.12, 51.57, 247.36, 41.07, 202.47, 23.88, 181.46, 8.6, 170.96, 14.33, 148.03, 17.19, 133.71, 0.96, 122.25, 2.87, 417.36]], "area": 11868.79725, "iscrowd": 0, "image_id": 10082, "bbox": [0.96, 122.25, 60.16, 295.11], "category_id": 1, "id": 478535}, {"segmentation": [[270.59, 208.22, 271.55, 178.48, 260.04, 166.0, 268.67, 143.93, 256.2, 138.18, 246.6, 123.78, 251.4, 88.28, 265.8, 75.8, 293.62, 71.01, 309.93, 87.32, 330.09, 133.38, 345.44, 176.56, 359.83, 210.14]], "area": 9733.725249999998, "iscrowd": 0, "image_id": 10130, "bbox": [246.6, 71.01, 113.23, 139.13], "category_id": 1, "id": 479245}, {"segmentation": [[512.92, 196.57, 512.92, 201.06, 515.27, 204.05, 520.83, 205.55, 525.11, 204.48, 532.8, 203.62, 537.07, 201.7, 541.99, 199.78, 544.98, 199.14, 547.55, 197.21, 547.55, 194.01, 546.91, 189.73, 546.69, 186.31, 545.62, 181.61, 540.92, 175.2, 531.73, 174.56, 529.38, 174.77, 522.75, 177.98, 519.55, 179.9, 516.13, 186.95, 514.42, 190.8, 514.42, 191.01]], "area": 819.9342500000007, "iscrowd": 0, "image_id": 10434, "bbox": [512.92, 174.56, 34.63, 30.99], "category_id": 1, "id": 480341}, {"segmentation": [[164.12, 311.88, 165.5, 284.36, 167.56, 272.66, 173.07, 257.53, 174.44, 245.15, 173.75, 228.63, 177.88, 210.75, 187.51, 199.05, 199.21, 183.92, 211.59, 184.6, 217.78, 185.98, 223.98, 190.8, 230.17, 214.87, 228.79, 225.88, 232.92, 227.26, 241.86, 251.34, 246.68, 253.4, 254.25, 234.83, 270.07, 219.69, 270.76, 268.54, 267.32, 282.3, 255.62, 280.23, 245.99, 279.54, 245.99, 282.3, 254.94, 300.87, 257.0, 310.5]], "area": 9399.22, "iscrowd": 0, "image_id": 10275, "bbox": [164.12, 183.92, 106.64, 127.96], "category_id": 1, "id": 480882}, {"segmentation": [[464.54, 171.15, 469.56, 172.27, 466.21, 178.96, 474.57, 188.44, 469.0, 203.49, 461.75, 207.4, 451.15, 214.09, 429.4, 219.67, 426.06, 220.78, 424.94, 230.26, 454.5, 223.57, 468.44, 223.01, 466.77, 230.26, 475.69, 245.32, 482.38, 272.64, 487.4, 282.12, 494.65, 286.03, 499.67, 313.35, 506.36, 316.14, 505.24, 284.35, 501.9, 253.68, 508.59, 203.49, 506.36, 180.07, 496.32, 175.61, 496.88, 163.9, 487.96, 153.31, 476.8, 153.31, 463.98, 162.23], [471.82, 326.22, 470.95, 336.65, 470.95, 339.25, 464.87, 344.47, 457.48, 347.51, 457.48, 350.98, 468.34, 351.85, 473.99, 351.85, 475.73, 351.85, 478.33, 355.33, 486.16, 356.2, 493.11, 356.2, 500.06, 355.33, 502.67, 353.16, 507.01, 351.85, 507.45, 342.73, 508.32, 332.74, 508.75, 324.91]], "area": 5933.446499999998, "iscrowd": 0, "image_id": 10239, "bbox": [424.94, 153.31, 83.81, 202.89], "category_id": 1, "id": 484235}, {"segmentation": [[189.84, 316.04, 199.55, 338.7, 212.49, 354.88, 231.91, 369.98, 248.09, 363.51, 303.1, 351.64, 346.25, 349.48, 422.83, 368.9, 421.75, 395.87, 484.31, 430.38, 565.21, 472.45, 595.42, 473.53, 600.81, 432.54, 610.52, 390.47, 605.12, 377.53, 631.01, 365.66, 640.0, 256.72, 637.48, 237.3, 609.44, 201.71, 594.34, 157.48, 578.16, 118.65, 564.13, 81.98, 531.78, 35.6, 498.34, 15.1, 467.06, 2.16, 361.35, 1.08, 323.6, 1.08, 302.02, 6.47, 166.11, 7.55, 175.82, 35.6, 188.76, 71.19, 221.12, 93.84, 230.83, 108.94, 238.38, 131.6, 239.46, 142.38, 239.46, 159.64, 237.3, 184.45, 239.46, 199.55, 253.48, 187.69, 254.56, 173.66, 266.43, 184.45, 276.13, 184.45, 289.08, 170.43, 292.31, 159.64, 314.97, 175.82, 339.78, 170.43, 344.09, 190.92, 371.06, 207.1, 379.69, 223.28, 387.24, 241.62, 423.91, 302.02, 367.82, 299.87, 264.27, 288.0, 226.52, 289.08, 204.94, 299.87, 188.76, 314.97]], "area": 132502.2999, "iscrowd": 0, "image_id": 10175, "bbox": [166.11, 1.08, 473.89, 472.45], "category_id": 1, "id": 489633}, {"segmentation": [[101.56, 306.97, 123.73, 247.39, 133.43, 203.05, 141.74, 183.65, 133.43, 179.49, 127.89, 168.4, 5.94, 164.25, 0.0, 125.45, 14.26, 135.15, 29.5, 142.08, 100.17, 129.6, 141.74, 135.15, 181.93, 130.99, 211.03, 137.92, 230.43, 136.53, 230.43, 115.75, 242.9, 99.12, 276.16, 92.19, 295.55, 114.36, 299.71, 132.38, 305.25, 137.92, 359.3, 104.66, 380.08, 101.89, 405.02, 119.9, 380.08, 136.53, 348.21, 136.53, 290.01, 176.72, 262.3, 185.03, 251.21, 198.89, 242.9, 204.43, 209.64, 276.49, 285.85, 300.04, 294.17, 305.59, 294.17, 326.37, 278.93, 373.49, 266.46, 433.07, 269.23, 445.54, 299.71, 460.79, 308.03, 473.26, 292.78, 487.11, 198.56, 489.88, 193.01, 503.74, 156.99, 507.9, 132.04, 494.04, 134.81, 478.8, 144.51, 469.1, 144.51, 458.01, 169.46, 416.44, 181.93, 395.66, 118.19, 395.66, 94.63, 381.8, 94.63, 334.69]], "area": 60930.67275, "iscrowd": 0, "image_id": 10313, "bbox": [0.0, 92.19, 405.02, 415.71], "category_id": 1, "id": 489888}, {"segmentation": [[233.9, 463.5, 236.77, 430.49, 236.77, 384.57, 232.47, 347.26, 219.55, 314.26, 193.72, 335.78, 208.07, 279.82, 218.12, 261.17, 235.34, 239.64, 241.08, 208.07, 268.34, 223.86, 281.26, 246.82, 304.22, 274.08, 324.3, 314.26, 302.78, 324.3, 281.26, 324.3, 282.69, 341.52, 275.52, 390.31, 275.52, 433.36, 278.39, 466.37, 265.47, 492.2, 256.86, 485.02, 229.6, 479.28]], "area": 15634.278399999997, "iscrowd": 0, "image_id": 10024, "bbox": [193.72, 208.07, 130.58, 284.13], "category_id": 1, "id": 490664}, {"segmentation": [[337.31, 311.29, 333.24, 315.15, 324.88, 314.93, 308.17, 314.72, 307.1, 311.29, 308.39, 309.79, 321.45, 302.29, 322.95, 286.87, 322.95, 268.87, 323.6, 261.59, 333.24, 249.81, 344.38, 241.45, 331.95, 237.38, 328.74, 236.74, 329.59, 246.81, 324.02, 259.02, 317.81, 271.87, 315.67, 275.73, 315.24, 273.16, 313.96, 273.59, 312.46, 275.09, 311.81, 278.73, 309.46, 280.23, 308.6, 272.09, 316.1, 255.59, 319.74, 243.81, 316.74, 227.31, 309.03, 226.24, 305.17, 223.24, 304.1, 219.6, 301.32, 215.53, 299.82, 210.82, 305.6, 201.39, 315.24, 201.39, 322.95, 207.6, 337.52, 206.75, 357.87, 212.32, 362.8, 217.03, 368.37, 224.53, 379.94, 235.03, 379.29, 239.74, 392.36, 249.16, 392.58, 255.38, 397.07, 261.16, 409.5, 257.3, 424.5, 260.52, 436.71, 256.66, 442.92, 256.45, 445.49, 263.95, 450.84, 265.87, 451.49, 274.23, 453.2, 280.01, 449.56, 278.73, 439.49, 272.51, 420.21, 267.8, 392.79, 273.8, 373.94, 262.45, 364.94, 257.73, 352.3, 265.87, 344.38, 263.73, 333.24, 267.37, 335.81, 279.58, 332.81, 291.37, 333.02, 302.72]], "area": 5189.584999999999, "iscrowd": 0, "image_id": 10265, "bbox": [299.82, 201.39, 153.38, 113.76], "category_id": 1, "id": 493301}, {"segmentation": [[111.08, 314.14, 117.38, 292.08, 106.35, 260.57, 103.2, 219.6, 111.08, 201.48, 103.2, 175.48, 94.54, 146.34, 97.69, 132.16, 101.63, 118.76, 118.17, 114.82, 120.53, 108.52, 118.96, 88.04, 125.26, 75.43, 136.29, 69.13, 148.11, 72.28, 149.68, 83.31, 149.68, 101.43, 149.68, 110.88, 148.11, 120.34, 159.93, 136.09, 176.47, 178.64, 197.74, 210.94, 219.8, 232.99, 211.92, 250.33, 205.62, 250.33, 184.35, 221.97, 154.41, 198.33, 158.35, 225.9, 175.68, 261.36, 167.8, 297.6, 159.93, 313.35, 118.17, 340.14, 102.42, 335.41, 106.35, 325.96]], "area": 15389.807000000003, "iscrowd": 0, "image_id": 10445, "bbox": [94.54, 69.13, 125.26, 271.01], "category_id": 1, "id": 494678}, {"segmentation": [[152.71, 120.68, 146.37, 138.78, 141.39, 152.81, 127.82, 155.53, 123.75, 147.83, 128.72, 127.92, 132.8, 104.39, 139.13, 79.5, 139.58, 65.48, 146.82, 54.62, 155.87, 43.3, 167.64, 46.02, 171.71, 57.78, 178.5, 65.48, 179.86, 79.5, 185.74, 95.79, 190.27, 109.82, 197.05, 125.12, 199.77, 141.41, 203.84, 151.36, 193.43, 155.89, 186.65, 150.01, 184.38, 142.31, 182.12, 129.64, 173.07, 110.64, 171.71, 121.5, 167.64, 126.93, 159.04, 126.47, 153.61, 122.4]], "area": 4517.318650000001, "iscrowd": 0, "image_id": 10265, "bbox": [123.75, 43.3, 80.09, 112.59], "category_id": 1, "id": 495760}, {"segmentation": [[318.66, 256.63, 318.66, 235.59, 312.85, 231.99, 319.77, 209.3, 321.7, 206.81, 320.04, 195.73, 327.24, 197.12, 330.84, 210.13, 333.88, 222.58, 333.05, 233.66, 330.28, 256.63, 327.52, 255.25, 327.24, 253.86, 326.96, 248.33, 323.36, 238.64, 322.81, 242.51, 320.32, 249.43]], "area": 716.7143499999991, "iscrowd": 0, "image_id": 10281, "bbox": [312.85, 195.73, 21.03, 60.9], "category_id": 1, "id": 496819}, {"segmentation": [[116.78, 419.26, 115.82, 362.78, 100.51, 340.77, 98.59, 299.61, 96.68, 244.09, 93.81, 206.76, 89.02, 199.1, 88.06, 157.94, 114.86, 144.54, 124.44, 130.18, 117.74, 121.57, 111.04, 110.08, 110.08, 83.28, 127.31, 71.79, 155.07, 74.66, 158.9, 93.81, 154.11, 121.57, 161.77, 134.01, 194.31, 143.58, 206.76, 156.98, 214.41, 192.4, 213.46, 213.46, 226.86, 279.5, 217.29, 299.61, 211.54, 317.79, 218.24, 331.19, 224.94, 334.07, 236.43, 420.21]], "area": 36071.7022, "iscrowd": 0, "image_id": 10082, "bbox": [88.06, 71.79, 148.37, 348.42], "category_id": 1, "id": 510678}, {"segmentation": [[108.11, 470.72, 79.95, 470.72, 67.57, 459.46, 60.81, 441.44, 60.81, 423.42, 63.06, 405.41, 55.18, 364.86, 54.05, 319.82, 59.68, 299.55, 68.69, 293.92, 83.33, 286.04, 94.59, 282.66, 99.1, 275.9, 101.35, 268.02, 90.09, 264.64, 88.96, 252.25, 92.34, 233.11, 100.23, 227.48, 111.49, 226.35, 125.0, 225.23, 132.88, 228.6, 137.39, 233.11, 144.14, 233.11, 154.28, 239.86, 146.4, 250.0, 145.27, 253.38, 145.27, 256.76, 145.27, 264.64, 144.14, 275.9, 137.39, 284.91, 134.01, 287.16, 137.39, 297.3, 143.02, 305.18, 144.14, 314.19, 145.27, 325.45, 147.52, 334.46, 148.65, 343.47, 147.52, 356.98, 147.52, 367.12, 153.15, 372.75, 153.15, 389.64, 152.03, 420.05, 152.03, 426.8, 147.52, 429.05, 128.38, 432.43, 120.5, 433.56, 114.86, 440.32, 112.61, 454.95, 111.49, 461.71]], "area": 17834.80885, "iscrowd": 0, "image_id": 10094, "bbox": [54.05, 225.23, 100.23, 245.49], "category_id": 1, "id": 512541}, {"segmentation": [[460.49, 259.51, 464.64, 233.1, 467.65, 216.69, 470.86, 204.05, 475.39, 191.6, 495.2, 157.08, 499.35, 153.12, 513.68, 117.28, 527.64, 101.81, 520.66, 95.96, 509.72, 80.68, 508.02, 57.86, 520.47, 45.79, 536.32, 40.88, 555.56, 24.85, 573.86, 23.72, 605.36, 28.43, 634.41, 42.39, 625.92, 69.93, 620.07, 82.57, 616.11, 92.95, 636.29, 105.96, 638.75, 117.09, 640.0, 116.15, 639.31, 119.17, 639.5, 121.05, 639.5, 151.42, 639.88, 156.14, 639.31, 159.53, 639.31, 196.69, 639.12, 234.04, 638.37, 271.77, 637.8, 308.74, 637.99, 346.85, 637.99, 384.2, 637.8, 420.04, 638.18, 420.41, 638.75, 422.3, 565.74, 424.75, 535.19, 340.81, 533.68, 334.96, 529.53, 323.46, 526.89, 310.44, 526.51, 307.61, 526.7, 214.24, 520.28, 173.68, 504.25, 196.13, 489.73, 224.05, 480.48, 235.17, 477.09, 241.4, 485.95, 266.3, 489.73, 272.52, 480.86, 285.54, 476.33, 287.62, 470.86, 282.71, 466.71, 279.13, 464.45, 274.98, 460.68, 262.72, 459.92, 258.19]], "area": 46133.73725, "iscrowd": 0, "image_id": 10082, "bbox": [459.92, 23.72, 180.08, 401.03], "category_id": 1, "id": 513597}, {"segmentation": [[248.13, 191.51, 198.28, 180.68, 193.95, 163.34, 182.03, 135.17, 178.78, 114.58, 192.87, 116.75, 195.03, 130.84, 203.7, 149.26, 204.79, 163.34, 222.12, 163.34, 264.38, 162.26, 277.38, 154.67, 269.8, 133.0, 270.88, 108.08, 291.47, 99.41, 301.22, 100.5, 312.05, 114.58, 322.89, 127.58, 322.89, 135.17, 315.3, 140.59, 304.47, 153.59, 317.47, 162.26, 318.56, 173.09, 318.56, 241.35, 312.05, 271.69, 326.14, 298.78, 317.47, 337.79, 295.8, 347.54, 280.63, 357.29, 248.13, 364.88, 248.13, 306.37, 244.88, 252.19, 216.7, 251.11, 214.54, 235.94, 222.12, 230.52, 236.21, 232.69, 241.63, 232.69]], "area": 18701.789650000006, "iscrowd": 0, "image_id": 10176, "bbox": [178.78, 99.41, 147.36, 265.47], "category_id": 1, "id": 517372}, {"segmentation": [[388.71, 286.0, 387.65, 283.33, 387.65, 279.6, 391.38, 275.87, 392.44, 274.27, 390.85, 264.67, 388.18, 262.54, 381.25, 260.94, 379.12, 260.41, 370.59, 268.94, 370.05, 273.2, 370.59, 278.0, 372.19, 285.46, 380.72, 289.19, 386.58, 289.19]], "area": 478.05169999999976, "iscrowd": 0, "image_id": 10041, "bbox": [370.05, 260.41, 22.39, 28.78], "category_id": 1, "id": 517982}, {"segmentation": [[269.81, 264.37, 267.83, 266.72, 264.04, 268.34, 262.6, 266.17, 265.85, 264.37, 264.4, 254.63, 268.73, 250.48, 268.01, 249.94, 263.68, 251.57, 262.6, 250.48, 262.6, 247.78, 264.4, 247.06, 266.93, 246.7, 268.37, 240.21, 268.73, 238.4, 268.19, 234.8, 269.27, 231.91, 272.88, 232.09, 275.22, 235.34, 276.49, 241.65, 277.75, 247.42, 277.03, 253.37, 276.31, 253.91, 279.91, 265.63, 280.63, 267.98, 278.47, 269.96, 273.6, 268.88, 274.32, 265.45, 271.26, 255.35, 269.09, 257.52]], "area": 357.5469999999998, "iscrowd": 0, "image_id": 10041, "bbox": [262.6, 231.91, 18.03, 38.05], "category_id": 1, "id": 524873}, {"segmentation": [[517.75, 287.08, 512.36, 295.71, 511.28, 300.03, 520.99, 305.42, 520.99, 302.18, 519.91, 295.71, 520.99, 290.32]], "area": 105.34870000000001, "iscrowd": 0, "image_id": 10041, "bbox": [511.28, 287.08, 9.71, 18.34], "category_id": 1, "id": 539146}, {"segmentation": [[339.28, 107.36, 338.39, 101.58, 337.51, 94.93, 336.62, 90.49, 334.84, 86.93, 332.62, 85.16, 331.29, 79.39, 331.29, 71.4, 333.07, 66.07, 334.84, 59.86, 337.95, 56.75, 339.73, 54.97, 344.17, 53.2, 349.05, 55.42, 350.82, 57.19, 360.59, 61.19, 361.92, 67.4, 363.25, 72.73, 365.03, 79.39, 364.59, 82.5, 360.59, 88.71, 360.15, 97.15, 360.15, 104.25, 360.59, 111.35, 356.6, 110.91, 356.15, 114.9, 356.15, 116.23, 354.38, 118.9, 351.27, 119.79, 345.94, 120.23, 341.5, 120.23, 337.95, 118.01, 341.5, 115.79, 342.83, 112.24, 339.73, 110.02, 338.39, 103.8]], "area": 1577.0200999999993, "iscrowd": 0, "image_id": 10015, "bbox": [331.29, 53.2, 33.74, 67.03], "category_id": 1, "id": 543665}, {"segmentation": [[433.66, 119.73, 439.43, 109.97, 444.76, 92.65, 449.64, 82.0, 452.75, 77.12, 458.96, 67.35, 466.95, 69.57, 467.84, 75.34, 475.39, 81.11, 475.39, 94.43, 476.72, 103.75, 470.95, 111.74, 469.17, 130.83, 462.07, 131.27, 464.73, 110.41, 456.3, 131.27, 450.97, 136.6, 439.87, 128.61]], "area": 1763.46995, "iscrowd": 0, "image_id": 10015, "bbox": [433.66, 67.35, 43.06, 69.25], "category_id": 1, "id": 547297}, {"segmentation": [[278.99, 156.32, 279.22, 148.22, 279.22, 142.9, 279.91, 136.89, 283.84, 135.5, 290.09, 135.73, 293.56, 139.66, 294.48, 145.45, 293.33, 148.68, 292.17, 154.47, 289.16, 157.47, 285.23, 159.79, 282.69, 185.23, 288.47, 209.51, 286.85, 215.99, 283.15, 233.8, 285.23, 237.26, 288.01, 240.73, 288.24, 244.67, 283.84, 243.74, 280.84, 240.5, 277.14, 236.34, 280.84, 221.07, 282.22, 211.13, 280.61, 209.05, 277.83, 209.74, 271.59, 218.53, 265.57, 228.94, 264.88, 232.41, 266.73, 236.57, 271.35, 240.97, 269.04, 243.51, 264.65, 243.51, 263.49, 239.11, 257.94, 234.72, 262.33, 223.85, 271.82, 205.58, 268.35, 186.15, 268.35, 174.59, 272.74, 161.87]], "area": 1468.7433500000006, "iscrowd": 0, "image_id": 10097, "bbox": [257.94, 135.5, 36.54, 109.17], "category_id": 1, "id": 553770}, {"segmentation": [[323.37, 245.64, 349.28, 300.34, 355.99, 319.53, 362.71, 365.59, 361.75, 389.58, 333.92, 384.78, 311.85, 408.77, 307.06, 412.61, 301.3, 420.28, 396.29, 417.4, 408.77, 389.58, 421.24, 357.91, 430.84, 345.44, 443.31, 338.72, 451.95, 330.09, 454.83, 327.21, 473.06, 316.65, 508.56, 282.11, 526.79, 274.43, 540.23, 282.11, 535.43, 291.7, 518.16, 304.18, 512.4, 312.81, 498.97, 326.25, 473.06, 335.84, 465.38, 344.48, 460.58, 353.11, 458.67, 362.71, 466.34, 357.91, 490.33, 394.38, 496.09, 410.69, 496.09, 414.53, 498.01, 421.24, 553.66, 419.32, 526.79, 360.79, 522.96, 331.04, 519.12, 317.61, 542.15, 294.58, 545.98, 269.63, 525.83, 261.96, 506.64, 279.23, 497.05, 288.82, 481.69, 288.82, 479.78, 280.19, 479.78, 267.71, 464.42, 217.82, 458.67, 195.75, 452.91, 188.07, 432.76, 174.64, 399.17, 164.08, 383.82, 171.76, 374.22, 168.88, 372.31, 139.13, 354.07, 125.7, 333.92, 122.82, 322.41, 122.82, 294.58, 127.62, 279.23, 131.46, 271.55, 157.37, 276.35, 166.96, 257.16, 192.87, 291.7, 204.38, 300.34, 206.3, 306.1, 204.38, 323.37, 219.74, 330.09, 223.58]], "area": 40378.679449999996, "iscrowd": 0, "image_id": 10319, "bbox": [257.16, 122.82, 296.5, 298.42], "category_id": 1, "id": 555865}, {"segmentation": [[596.59, 184.11, 599.22, 184.11, 605.15, 183.13, 605.81, 179.17, 605.81, 173.91, 603.5, 168.64, 603.17, 167.65, 601.53, 167.32, 600.21, 169.63, 597.57, 170.61, 595.93, 173.91, 595.27, 178.19, 595.27, 182.14, 595.27, 182.8, 596.26, 183.78]], "area": 138.78214999999966, "iscrowd": 0, "image_id": 10230, "bbox": [595.27, 167.32, 10.54, 16.79], "category_id": 1, "id": 557247}, {"segmentation": [[488.48, 426.12, 640.0, 426.78, 638.58, 136.08, 571.26, 146.66, 569.33, 163.97, 563.56, 182.24, 557.79, 197.63, 552.02, 208.21, 559.72, 214.94, 560.68, 232.25, 563.56, 238.02, 551.06, 254.37, 527.02, 300.53, 500.09, 330.35, 474.12, 351.51, 471.24, 378.43]], "area": 32442.68595000003, "iscrowd": 0, "image_id": 10275, "bbox": [471.24, 136.08, 168.76, 290.7], "category_id": 1, "id": 558741}, {"segmentation": [[575.76, 170.1, 577.24, 169.69, 578.59, 171.18, 577.78, 173.21, 578.59, 174.56, 580.62, 175.91, 580.62, 178.34, 579.68, 180.64, 579.54, 182.13, 579.68, 184.15, 580.49, 185.64, 579.95, 186.18, 574.14, 185.64, 573.32, 185.37, 573.73, 182.67, 574.0, 180.64, 574.0, 179.69, 572.92, 178.21, 571.97, 176.18, 572.38, 175.1, 574.0, 176.18, 574.95, 176.32, 575.62, 175.37, 575.76, 174.02, 574.68, 173.07, 574.68, 171.86, 574.95, 171.59, 575.08, 170.5, 575.89, 170.23]], "area": 86.52359999999979, "iscrowd": 0, "image_id": 10230, "bbox": [571.97, 169.69, 8.65, 16.49], "category_id": 1, "id": 562518}, {"segmentation": [[299.79, 199.37, 303.09, 201.39, 302.84, 204.94, 303.6, 207.23, 306.13, 208.49, 307.91, 213.82, 309.68, 219.14, 310.44, 223.2, 309.43, 227.0, 308.16, 223.2, 306.64, 220.16, 304.86, 225.99, 299.79, 233.09, 301.82, 240.19, 302.58, 242.47, 301.31, 240.7, 298.52, 237.4, 298.52, 239.94, 298.27, 244.25, 296.75, 241.46, 297.26, 235.37, 297.0, 229.79, 295.74, 226.5, 293.2, 221.68, 292.69, 214.07, 295.23, 209.76, 297.0, 206.97, 295.99, 204.94, 296.24, 200.89, 298.02, 199.11]], "area": 378.77840000000015, "iscrowd": 0, "image_id": 10281, "bbox": [292.69, 199.11, 17.75, 45.14], "category_id": 1, "id": 562916}, {"segmentation": [[385.89, 71.24, 376.18, 78.53, 379.42, 87.42, 410.97, 93.9, 411.78, 100.37, 404.49, 106.84, 410.16, 116.55, 455.46, 77.72, 447.37, 66.39, 446.56, 53.45, 440.09, 33.22, 418.25, 37.27, 412.58, 19.47, 462.74, 7.33, 334.92, 1.67, 354.34, 28.37, 349.48, 43.74, 383.46, 65.58, 383.46, 65.58]], "area": 7153.5055999999995, "iscrowd": 0, "image_id": 10097, "bbox": [334.92, 1.67, 127.82, 114.88], "category_id": 1, "id": 568316}, {"segmentation": [[102.17, 202.27, 100.08, 201.02, 99.66, 200.18, 98.82, 196.41, 98.82, 194.32, 102.59, 189.71, 104.26, 188.88, 108.45, 190.55, 110.12, 191.81, 112.63, 198.09, 114.31, 203.95, 116.82, 209.81, 118.08, 214.83, 119.75, 218.18, 120.17, 224.88, 119.75, 227.39, 117.66, 229.06, 113.05, 233.25, 111.38, 235.76, 108.45, 238.27, 107.19, 237.85, 105.94, 236.18, 105.52, 232.41, 106.36, 224.04, 107.61, 218.6, 107.19, 213.57, 104.26, 208.55, 104.26, 206.88, 102.59, 203.11, 102.59, 203.11], [87.94, 240.36, 87.52, 244.97, 88.36, 251.25, 88.36, 257.53, 88.36, 264.64, 87.94, 269.67, 86.68, 275.53, 87.1, 281.81, 87.52, 286.83, 87.52, 289.34, 89.61, 290.18, 91.7, 289.76, 91.7, 283.48, 90.87, 276.78, 90.87, 272.18, 92.12, 265.9, 92.54, 259.2, 94.63, 255.43, 95.47, 254.18, 100.49, 254.18, 102.17, 259.2, 102.59, 265.06, 102.59, 270.5, 102.59, 275.11, 102.59, 280.13, 103.84, 285.16, 103.84, 285.57, 105.52, 288.09, 106.36, 288.09, 106.77, 281.81, 106.36, 273.85, 105.94, 268.41, 106.36, 262.97, 108.45, 259.2, 105.94, 256.69, 102.59, 253.76, 98.82, 250.41, 94.22, 246.23, 92.12, 242.88, 90.45, 241.62, 89.19, 240.78, 88.77, 240.36]], "area": 924.13265, "iscrowd": 0, "image_id": 10281, "bbox": [86.68, 188.88, 33.49, 101.3], "category_id": 1, "id": 568876}, {"segmentation": [[4.31, 262.95, 117.93, 218.37, 165.39, 363.63, 133.75, 435.54, 4.31, 493.06], [346.61, 152.21, 381.12, 145.02, 427.0, 293.15, 422.83, 366.5, 375.37, 372.25, 399.82, 343.49, 404.13, 324.79, 343.73, 255.76, 312.09, 212.61, 333.66, 152.21]], "area": 44159.2518, "iscrowd": 0, "image_id": 10069, "bbox": [4.31, 145.02, 422.69, 348.04], "category_id": 15, "id": 575240}, {"segmentation": [[100.14, 313.5, 112.24, 281.48, 118.64, 260.84, 125.05, 240.2, 130.03, 218.14, 137.15, 203.2, 144.26, 191.81, 155.65, 185.41, 173.44, 183.98, 181.27, 183.98, 195.5, 188.25, 204.04, 193.24, 211.16, 195.37, 218.27, 186.83, 222.54, 186.83, 228.24, 186.83, 230.37, 180.43, 227.53, 173.31, 228.24, 167.62, 232.51, 160.5, 236.78, 155.52, 223.26, 124.92, 246.74, 142.71, 255.99, 122.07, 268.8, 146.27, 294.42, 125.63, 295.84, 137.73, 284.46, 155.52, 282.32, 169.04, 286.59, 181.14, 288.02, 191.1, 285.17, 204.62, 284.46, 218.86, 280.19, 228.82, 273.07, 232.38, 275.92, 240.92, 274.49, 251.59, 273.07, 262.27, 272.36, 275.08, 269.51, 290.02, 265.24, 293.58, 263.82, 307.81, 258.13, 316.35, 250.3, 327.74, 249.59, 342.68, 247.45, 353.36, 246.03, 362.61, 242.47, 370.44, 242.47, 384.67, 248.87, 403.17, 248.16, 411.0, 240.34, 411.71, 233.93, 415.27, 233.93, 413.13, 231.8, 413.13, 233.22, 407.44, 233.22, 401.04, 231.8, 401.75, 227.53, 397.48, 229.66, 387.51, 231.08, 378.26, 231.08, 365.45, 231.8, 349.09, 232.51, 332.72, 232.51, 321.33, 233.22, 310.66, 233.22, 306.39, 229.66, 305.68, 222.54, 305.68, 217.56, 305.68, 209.73, 312.79, 216.85, 317.06, 216.85, 331.29, 220.41, 339.83, 220.41, 356.91, 217.56, 365.45, 210.45, 369.72, 209.02, 381.11, 209.02, 390.36, 209.02, 399.61, 209.02, 409.58, 208.31, 414.56, 199.06, 415.27, 194.79, 414.56, 194.79, 410.29, 197.64, 402.46, 197.64, 396.05, 200.48, 381.82, 201.91, 372.57, 202.62, 364.03, 202.62, 351.93, 201.19, 344.1, 200.48, 329.87, 199.06, 319.91, 195.5, 315.64, 188.39, 320.62, 184.83, 327.03, 181.27, 336.99, 175.58, 351.22, 174.15, 369.01, 174.15, 373.99, 175.58, 383.25, 178.42, 393.92, 177.71, 400.32, 167.75, 400.32, 163.48, 398.9, 160.63, 389.65, 164.19, 381.11, 164.19, 369.01, 164.19, 359.76, 161.34, 350.51, 159.21, 345.53, 156.36, 348.37, 154.94, 354.78, 154.94, 370.44, 154.94, 376.84, 154.94, 388.23, 153.51, 398.19, 147.11, 398.19, 141.42, 396.77, 139.28, 388.94, 144.97, 369.72, 144.97, 359.76, 145.69, 346.95, 144.26, 324.89, 142.13, 317.06, 135.72, 306.39, 134.3, 290.73, 132.88, 286.46, 127.9, 292.87, 125.05, 300.69, 117.93, 308.52, 110.1, 319.2, 104.41, 326.31, 99.43, 329.16, 97.29, 329.16, 95.87, 326.31, 95.87, 322.04, 98.72, 314.93]], "area": 27745.74895, "iscrowd": 0, "image_id": 10327, "bbox": [95.87, 122.07, 199.97, 293.2], "category_id": 24, "id": 589704}, {"segmentation": [[524.66, 369.13, 362.62, 437.13, 271.96, 476.79, 261.77, 466.59, 261.77, 456.39, 265.16, 454.12, 522.4, 358.94]], "area": 4615.889149999999, "iscrowd": 0, "image_id": 10179, "bbox": [261.77, 358.94, 262.89, 117.85], "category_id": 39, "id": 629190}, {"segmentation": [[531.1, 287.0, 471.34, 338.08, 391.34, 421.94, 391.34, 416.16, 410.61, 386.28, 417.36, 359.29, 432.78, 343.87, 443.39, 334.23, 472.3, 317.84, 521.46, 271.57]], "area": 3206.021099999997, "iscrowd": 0, "image_id": 10319, "bbox": [391.34, 271.57, 139.76, 150.37], "category_id": 39, "id": 629220}, {"segmentation": [[14.74, 285.71, 29.48, 292.52, 32.88, 270.98, 150.79, 52.15, 165.53, 29.48, 159.86, 15.87, 154.2, 15.87, 145.12, 15.87, 79.37, 142.86, 24.94, 272.11, 13.61, 276.64, 23.81, 285.71]], "area": 5165.0255499999985, "iscrowd": 0, "image_id": 10094, "bbox": [13.61, 15.87, 151.92, 276.65], "category_id": 39, "id": 630078}, {"segmentation": [[404.4, 377.88, 412.73, 392.19, 418.71, 399.66, 420.42, 406.07, 420.42, 410.34, 426.82, 415.68, 428.96, 423.36, 429.6, 430.84, 427.68, 459.24, 425.12, 467.14, 420.63, 474.4, 415.72, 480.0, 402.05, 478.03, 396.29, 476.96, 395.22, 471.62, 391.17, 439.81, 388.82, 427.63, 383.48, 408.63, 382.84, 400.3, 378.99, 393.04, 374.94, 389.84, 376.22, 387.28, 379.42, 383.22, 384.12, 383.86, 387.11, 379.59, 391.59, 380.02, 394.8, 379.59, 401.41, 378.74]], "area": 3461.586549999999, "iscrowd": 0, "image_id": 10230, "bbox": [374.94, 377.88, 54.66, 102.12], "category_id": 40, "id": 632755}, {"segmentation": [[119.33, 259.57, 134.81, 267.07, 138.56, 280.21, 150.29, 291.0, 150.76, 297.57, 143.25, 302.73, 142.31, 307.89, 130.59, 317.74, 122.14, 319.15, 110.41, 300.85, 107.13, 271.77]], "area": 1641.5885500000006, "iscrowd": 0, "image_id": 10309, "bbox": [107.13, 259.57, 43.63, 59.58], "category_id": 40, "id": 633523}, {"segmentation": [[188.05, 371.31, 147.76, 317.58, 131.45, 294.55, 127.61, 269.61, 135.28, 262.89, 148.72, 265.77, 156.39, 276.32, 159.27, 285.92, 166.95, 295.51, 175.58, 302.23, 192.85, 319.5, 211.08, 345.41, 214.92, 360.76, 203.41, 374.19, 192.85, 373.23]], "area": 3835.3549000000003, "iscrowd": 0, "image_id": 10434, "bbox": [127.61, 262.89, 87.31, 111.3], "category_id": 41, "id": 640137}, {"segmentation": [[447.15, 247.16, 427.28, 234.83, 415.64, 216.34, 376.59, 169.07, 349.19, 135.51, 329.33, 103.31, 319.74, 99.89, 323.16, 114.27, 338.23, 138.25, 332.75, 147.84, 327.27, 147.84, 327.27, 154.69, 343.03, 157.43, 347.82, 153.32, 403.31, 215.65, 395.77, 223.87, 392.35, 232.78, 406.05, 236.89, 414.27, 231.41, 422.49, 233.46, 441.67, 247.85]], "area": 2244.4401999999986, "iscrowd": 0, "image_id": 10097, "bbox": [319.74, 99.89, 127.41, 147.96], "category_id": 41, "id": 640727}, {"segmentation": [[206.1, 489.98, 214.1, 491.07, 226.47, 491.8, 249.75, 495.8, 277.03, 500.16, 289.76, 500.89, 300.67, 504.53, 309.77, 504.53, 314.5, 505.26, 314.5, 507.8, 301.04, 510.71, 280.67, 515.08, 276.66, 519.08, 275.94, 521.62, 283.94, 524.17, 287.21, 520.9, 295.22, 520.17, 297.76, 524.53, 297.76, 530.35, 295.58, 535.08, 287.21, 536.9, 282.48, 531.45, 261.75, 533.63, 258.84, 533.63, 257.75, 539.45, 247.93, 540.18, 245.75, 537.99, 245.38, 531.08, 251.93, 528.54, 253.38, 525.99, 253.75, 523.08, 252.66, 520.17, 221.74, 511.8, 216.65, 512.17, 217.01, 514.71, 214.1, 517.62, 207.55, 519.81, 203.55, 516.17, 190.82, 517.62, 182.45, 520.17, 178.82, 520.17, 178.45, 523.81, 171.18, 526.72, 167.54, 524.9, 164.63, 521.62, 163.54, 517.62, 163.9, 514.35, 168.27, 511.8, 174.81, 510.71, 179.18, 513.26, 186.45, 507.07, 184.64, 505.26, 170.09, 503.8, 159.9, 503.07, 159.54, 501.62, 166.08, 499.8, 174.45, 499.8, 189.36, 501.62, 199.55, 499.07, 203.19, 495.8, 204.28, 492.89]], "area": 3365.407199999999, "iscrowd": 0, "image_id": 10313, "bbox": [159.54, 489.98, 154.96, 50.2], "category_id": 41, "id": 642768}, {"segmentation": [[196.85, 175.44, 218.03, 154.26, 243.91, 142.5, 268.22, 132.3, 304.3, 119.75, 346.65, 107.99, 349.0, 121.32, 354.49, 138.58, 366.25, 170.73, 374.88, 185.63, 370.96, 195.04, 369.39, 210.73, 330.96, 215.43, 272.93, 220.14, 254.89, 217.79, 223.52, 214.65, 215.67, 213.87, 213.32, 215.43, 210.18, 209.94, 202.34, 204.45], [366.25, 105.64, 421.15, 94.66, 456.44, 91.52, 473.7, 91.52, 499.58, 91.52, 499.58, 173.87, 491.74, 179.36, 416.45, 201.32, 389.0, 206.02, 392.13, 197.4, 385.08, 185.63, 380.37, 154.26, 371.74, 133.87, 364.69, 119.75]], "area": 25820.562449999998, "iscrowd": 0, "image_id": 10445, "bbox": [196.85, 91.52, 302.73, 128.62], "category_id": 42, "id": 648995}, {"segmentation": [[167.62, 136.07, 249.3, 140.71, 220.08, 154.66, 203.48, 169.27, 200.82, 179.23, 200.82, 217.08, 175.59, 186.54, 160.98, 143.37], [11.56, 185.87, 99.22, 157.32, 109.84, 192.51, 103.87, 206.46, 103.87, 219.74, 103.87, 237.67, 103.87, 247.63, 1.6, 265.56, 1.6, 199.82]], "area": 11350.24745, "iscrowd": 0, "image_id": 10445, "bbox": [1.6, 136.07, 247.7, 129.49], "category_id": 42, "id": 650370}, {"segmentation": [[232.66, 213.26, 303.87, 228.91, 328.91, 228.91, 310.91, 232.82, 328.13, 242.99, 333.6, 247.69, 337.91, 252.38, 337.91, 258.25, 337.91, 259.82, 338.69, 267.25, 357.47, 265.69, 353.56, 269.21, 339.08, 270.38, 337.13, 282.51, 325.0, 297.38, 310.13, 309.51, 330.47, 311.46, 320.69, 313.81, 302.69, 316.16, 192.36, 356.85, 189.23, 350.59, 204.88, 324.77, 224.44, 301.29, 229.14, 264.91], [145.02, 212.48, 189.23, 211.69, 188.45, 240.26, 191.97, 269.6, 191.97, 289.95, 191.97, 296.21, 189.23, 302.07, 182.97, 307.16, 190.79, 319.68, 187.66, 327.51, 182.58, 331.03, 170.06, 337.29, 150.89, 351.37, 153.63, 354.89, 167.71, 352.16, 165.75, 361.94, 82.81, 366.24, 2.6, 363.89, 0.64, 271.17, 0.25, 232.43, 48.38, 221.87, 126.63, 211.69]], "area": 38347.61120000001, "iscrowd": 0, "image_id": 10005, "bbox": [0.25, 211.69, 357.22, 154.55], "category_id": 42, "id": 651079}, {"segmentation": [[267.86, 124.68, 293.26, 117.86, 295.74, 111.67, 296.98, 119.72, 308.13, 117.24, 314.95, 121.58, 300.69, 135.21, 277.15, 150.08, 260.42, 151.94, 254.22, 156.28, 239.97, 163.09, 213.95, 168.67, 210.85, 170.53, 227.58, 146.98, 249.89, 132.73, 256.7, 129.02, 245.55, 147.6, 246.79, 152.56, 246.17, 154.42, 246.79, 157.52, 249.89, 154.42, 252.36, 147.6, 264.14, 130.26]], "area": 1864.1982000000003, "iscrowd": 0, "image_id": 10005, "bbox": [210.85, 111.67, 104.1, 58.86], "category_id": 42, "id": 653611}, {"segmentation": [[313.53, 277.59, 316.54, 275.92, 316.54, 272.57, 313.03, 271.9, 312.36, 274.25, 311.86, 276.25], [308.34, 274.75, 296.63, 279.43, 288.77, 281.94, 287.77, 282.94, 282.41, 284.78, 277.73, 286.12, 273.21, 287.8, 268.7, 289.3, 263.68, 291.98, 261.0, 293.99, 260.83, 297.5, 263.85, 299.0, 271.21, 296.5, 276.89, 294.66, 285.93, 289.97, 289.44, 288.3, 303.33, 281.27, 308.34, 278.93, 310.02, 278.26, 308.85, 275.42, 308.51, 274.75]], "area": 327.01220000000023, "iscrowd": 0, "image_id": 10265, "bbox": [260.83, 271.9, 55.71, 27.1], "category_id": 43, "id": 657811}, {"segmentation": [[302.59, 348.59, 307.53, 345.98, 315.17, 345.61, 320.56, 346.28, 323.26, 348.04, 322.32, 368.96, 319.54, 375.46, 317.87, 373.52, 313.17, 376.68, 308.88, 375.6, 305.87, 371.5, 304.79, 368.53, 303.71, 368.26, 303.27, 365.77, 304.81, 366.12, 304.0, 363.68, 303.05, 355.78, 301.28, 356.8, 300.07, 359.5, 300.6, 362.21, 298.05, 361.25, 297.37, 357.2, 299.39, 353.43, 302.8, 353.04]], "area": 562.2471999999997, "iscrowd": 0, "image_id": 10176, "bbox": [297.37, 345.61, 25.89, 31.07], "category_id": 47, "id": 672620}, {"segmentation": [[267.86, 401.94, 267.86, 365.35, 272.16, 362.77, 284.21, 359.76, 297.12, 360.19, 304.01, 364.49, 304.01, 380.85, 303.15, 403.66, 288.09, 405.81, 276.46, 404.52]], "area": 1549.9942499999993, "iscrowd": 0, "image_id": 10176, "bbox": [267.86, 359.76, 36.15, 46.05], "category_id": 47, "id": 673100}, {"segmentation": [[95.96, 170.8, 76.76, 89.24, 75.8, 82.52, 90.2, 65.25, 125.7, 59.49, 161.2, 54.69, 181.36, 55.65, 192.87, 65.25, 193.83, 122.82, 153.53, 136.26, 111.31, 165.04]], "area": 9534.2515, "iscrowd": 0, "image_id": 10161, "bbox": [75.8, 54.69, 118.03, 116.11], "category_id": 47, "id": 682193}, {"segmentation": [[630.25, 460.57, 608.12, 441.6, 577.56, 440.54, 502.73, 415.25, 491.13, 422.63, 467.95, 418.41, 448.98, 407.87, 431.06, 399.44, 422.63, 402.6, 411.03, 412.09, 392.06, 416.3, 377.31, 415.25, 418.41, 442.65, 442.65, 447.92, 462.68, 442.65, 464.78, 452.14, 501.67, 446.87, 522.75, 448.98, 525.91, 462.68, 556.48, 462.68, 613.39, 461.62]], "area": 6711.333399999998, "iscrowd": 0, "image_id": 10073, "bbox": [377.31, 399.44, 252.94, 63.24], "category_id": 48, "id": 687353}, {"segmentation": [[0.0, 401.63, 29.43, 371.62, 65.26, 346.62, 87.76, 332.46, 97.35, 321.62, 119.85, 287.45, 98.6, 328.29, 126.93, 285.79, 104.43, 328.71, 134.43, 282.45, 108.18, 328.29, 138.18, 286.2, 113.6, 336.62, 100.68, 344.54, 86.93, 345.79, 61.51, 354.96, 44.43, 366.62, 0.68, 413.71]], "area": 1731.0175499999993, "iscrowd": 0, "image_id": 10037, "bbox": [0.0, 282.45, 138.18, 131.26], "category_id": 48, "id": 688032}, {"segmentation": [[172.89, 304.0, 194.36, 257.82, 244.83, 195.54, 258.79, 172.99, 259.87, 188.02, 265.23, 190.17, 259.87, 203.06, 213.69, 280.37, 181.48, 310.44, 173.96, 311.51]], "area": 3146.568699999999, "iscrowd": 0, "image_id": 10175, "bbox": [172.89, 172.99, 92.34, 138.52], "category_id": 49, "id": 695357}, {"segmentation": [[224.66, 206.06, 237.05, 201.93, 242.84, 208.13, 247.79, 204.0, 252.34, 192.02, 258.95, 178.79, 260.19, 170.53, 253.99, 170.12, 248.62, 173.42, 244.49, 177.14, 238.29, 184.99, 237.05, 189.54, 233.75, 194.08, 223.83, 197.39]], "area": 613.3241, "iscrowd": 0, "image_id": 10243, "bbox": [223.83, 170.12, 36.36, 38.01], "category_id": 50, "id": 705953}, {"segmentation": [[464.64, 93.46, 507.59, 69.87, 539.28, 66.7, 550.9, 79.02, 546.67, 91.34, 529.42, 105.43, 505.48, 118.45, 485.76, 123.38]], "area": 2893.47385, "iscrowd": 0, "image_id": 10161, "bbox": [464.64, 66.7, 86.26, 56.68], "category_id": 50, "id": 706795}, {"segmentation": [[273.09, 319.8, 275.92, 283.08, 290.04, 253.88, 317.35, 226.57, 350.31, 212.45, 384.22, 212.45, 408.7, 217.16, 434.13, 230.34, 457.67, 246.35, 484.04, 295.32, 484.04, 334.87, 468.97, 378.19, 448.25, 399.85, 421.88, 414.91, 409.64, 419.62, 361.61, 419.62, 341.84, 417.74, 308.88, 400.79, 288.16, 368.77, 277.8, 349.0]], "area": 35125.34070000001, "iscrowd": 0, "image_id": 10414, "bbox": [273.09, 212.45, 210.95, 207.17], "category_id": 51, "id": 711288}, {"segmentation": [[4.23, 401.92, 83.45, 377.63, 114.08, 349.11, 172.18, 306.86, 188.02, 273.06, 195.42, 235.03, 204.92, 224.47, 201.75, 191.72, 193.3, 175.88, 183.8, 156.86, 151.05, 142.07, 111.97, 128.34, 83.45, 123.06, 52.82, 120.95, 26.41, 122.0, 8.45, 124.12, 1.06, 124.12, 5.28, 399.81]], "area": 42774.42295, "iscrowd": 0, "image_id": 10073, "bbox": [1.06, 120.95, 203.86, 280.97], "category_id": 51, "id": 711574}, {"segmentation": [[243.51, 216.13, 260.49, 250.11, 269.93, 294.47, 264.27, 340.72, 239.73, 381.3, 187.82, 412.45, 119.87, 414.34, 50.02, 365.26, 36.81, 315.24, 49.08, 246.34, 74.56, 213.3, 126.47, 186.88, 184.04, 182.16, 229.35, 206.7]], "area": 42428.327000000005, "iscrowd": 0, "image_id": 10414, "bbox": [36.81, 182.16, 233.12, 232.18], "category_id": 51, "id": 712204}, {"segmentation": [[568.64, 420.26, 532.26, 389.62, 509.29, 358.99, 494.93, 316.87, 488.22, 283.36, 488.22, 262.3, 494.93, 242.2, 511.2, 218.27, 538.0, 201.03, 577.25, 188.59, 640.0, 189.55, 640.0, 393.45, 640.0, 418.34]], "area": 29549.62155, "iscrowd": 0, "image_id": 10037, "bbox": [488.22, 188.59, 151.78, 231.67], "category_id": 51, "id": 713175}, {"segmentation": [[0.0, 361.56, 24.12, 354.98, 49.53, 349.8, 72.59, 346.98, 92.82, 348.86, 102.23, 353.09, 106.47, 361.56, 103.17, 371.92, 99.88, 373.33, 97.53, 400.62, 91.41, 421.33, 84.35, 437.33, 63.18, 457.56, 36.35, 470.26, 16.12, 476.38, 0.59, 477.32]], "area": 10553.405899999998, "iscrowd": 0, "image_id": 10175, "bbox": [0.0, 346.98, 106.47, 130.34], "category_id": 51, "id": 714821}, {"segmentation": [[90.2, 178.18, 146.81, 141.72, 223.58, 111.97, 309.93, 101.42, 387.66, 101.42, 455.79, 115.81, 505.68, 133.08, 560.38, 171.47, 582.45, 224.24, 571.89, 264.54, 535.43, 319.24, 413.57, 375.85, 386.7, 392.16, 326.25, 400.8, 294.58, 400.8, 243.73, 388.32, 189.99, 369.13, 116.11, 340.35, 71.01, 298.13, 55.65, 251.11, 71.01, 197.37]], "area": 118552.32175000003, "iscrowd": 0, "image_id": 10161, "bbox": [55.65, 101.42, 526.8, 299.38], "category_id": 51, "id": 715642}, {"segmentation": [[337.93, 362.82, 341.76, 332.18, 323.57, 332.18, 278.58, 342.71, 214.44, 394.41, 205.82, 426.0, 278.58, 424.09, 289.11, 381.01, 296.76, 363.78, 329.31, 361.86], [422.17, 358.03, 413.56, 376.22, 427.91, 382.92, 446.1, 397.28, 457.59, 425.04, 479.61, 426.0, 456.63, 382.92, 432.7, 363.78]], "area": 7325.5190999999995, "iscrowd": 0, "image_id": 10037, "bbox": [205.82, 332.18, 273.79, 93.82], "category_id": 51, "id": 716143}, {"segmentation": [[215.9, 83.79, 222.14, 63.74, 240.85, 38.8, 260.0, 25.43, 283.16, 16.08, 301.87, 9.84, 329.94, 7.61, 355.77, 12.51, 381.61, 20.98, 401.66, 32.56, 421.26, 49.04, 437.29, 72.21, 444.42, 88.69, 446.65, 106.51, 441.3, 134.12, 434.17, 149.27, 425.71, 160.4, 415.46, 182.68, 408.34, 198.71, 395.86, 214.75, 388.74, 212.97, 369.14, 212.97, 353.99, 212.97, 339.29, 216.53, 325.04, 223.21, 317.91, 228.11, 307.22, 236.13, 305.88, 237.91, 289.85, 233.01, 266.68, 222.77, 241.29, 203.17, 227.48, 182.68, 222.14, 167.09, 219.47, 152.83, 218.13, 136.8, 217.68, 121.65, 214.57, 99.38, 216.35, 83.79]], "area": 40444.323, "iscrowd": 0, "image_id": 10414, "bbox": [214.57, 7.61, 232.08, 230.3], "category_id": 51, "id": 717212}, {"segmentation": [[80.04, 183.63, 108.3, 184.57, 128.07, 182.69, 147.85, 182.69, 166.68, 177.04, 183.63, 168.57, 199.64, 152.56, 217.53, 126.19, 219.42, 109.24, 223.18, 75.34, 221.3, 39.55, 158.21, 10.36, 112.06, 6.59, 91.35, 9.42, 75.34, 15.07, 52.74, 28.25, 36.73, 44.26, 16.01, 80.04, 14.13, 103.59, 17.89, 118.65, 19.78, 133.72, 23.54, 145.02, 38.61, 166.68, 48.97, 173.27, 61.21, 178.92, 71.57, 183.63, 82.87, 186.46]], "area": 30321.100100000003, "iscrowd": 0, "image_id": 10414, "bbox": [14.13, 6.59, 209.05, 179.87], "category_id": 51, "id": 717404}, {"segmentation": [[112.56, 113.54, 37.2, 103.76, 11.75, 98.86, 1.96, 126.27, 0.98, 176.19, 76.35, 180.1, 103.76, 185.0, 134.1, 174.23, 121.37, 144.87, 124.31, 141.93]], "area": 8753.221249999997, "iscrowd": 0, "image_id": 10403, "bbox": [0.98, 98.86, 133.12, 86.14], "category_id": 52, "id": 1044227}, {"segmentation": [[24.17, 223.98, 54.33, 205.64, 131.22, 172.52, 156.07, 193.22, 183.86, 200.32, 153.11, 209.78, 72.67, 242.9, 43.1, 257.69, 22.4, 257.69, 16.48, 254.73, 14.12, 250.59, 12.93, 244.68, 17.66, 233.44]], "area": 6005.9652000000015, "iscrowd": 0, "image_id": 10403, "bbox": [12.93, 172.52, 170.93, 85.17], "category_id": 52, "id": 1044732}, {"segmentation": [[179.49, 307.23, 189.4, 329.25, 201.52, 342.47, 254.37, 356.78, 352.38, 367.79, 437.17, 334.76, 475.71, 300.62, 514.25, 289.61, 546.18, 276.4, 549.49, 312.73, 527.46, 357.88, 480.11, 398.63, 414.04, 432.76, 322.64, 456.99, 236.75, 444.88, 188.3, 428.36, 155.27, 394.22, 147.56, 372.2, 150.86, 335.86, 155.27, 314.94, 145.36, 308.33, 137.65, 292.91, 177.29, 295.12, 186.1, 311.63]], "area": 35745.71755, "iscrowd": 0, "image_id": 10303, "bbox": [137.65, 276.4, 411.84, 180.59], "category_id": 52, "id": 1045120}, {"segmentation": [[106.69, 112.56, 99.84, 112.56, 70.48, 107.67, 51.88, 103.76, 39.15, 102.78, 33.28, 102.78, 26.43, 98.86, 22.51, 97.88, 25.45, 91.03, 26.43, 90.05, 37.2, 69.5, 46.98, 64.6, 55.79, 61.67, 66.56, 61.67, 76.35, 60.69, 85.16, 60.69, 92.99, 60.69, 108.65, 62.64, 117.46, 65.58, 133.12, 70.48, 142.91, 75.37, 157.59, 82.22, 174.23, 90.05, 177.17, 91.03, 193.81, 99.84, 209.47, 109.63, 219.26, 114.52, 207.51, 112.56, 191.85, 105.71, 181.08, 102.78, 166.4, 93.97, 151.72, 91.03, 138.01, 91.03, 125.29, 93.97, 115.5, 98.86, 113.54, 105.71, 112.56, 112.56, 109.63, 114.52]], "area": 5072.884500000001, "iscrowd": 0, "image_id": 10403, "bbox": [22.51, 60.69, 196.75, 53.83], "category_id": 52, "id": 1045315}, {"segmentation": [[254.49, 171.97, 248.62, 170.02, 244.71, 160.23, 238.83, 138.69, 231.0, 122.05, 220.24, 115.2, 201.64, 109.33, 186.96, 108.35, 173.25, 103.46, 161.51, 99.54, 148.78, 92.69, 133.12, 92.69, 121.37, 96.6, 111.59, 110.31, 115.5, 121.08, 129.2, 135.76, 129.2, 148.48, 123.33, 163.16, 139.97, 177.85, 151.72, 184.7, 158.57, 191.55, 166.4, 197.42, 185.98, 200.36, 202.62, 201.34, 216.32, 203.3, 222.19, 206.23, 223.17, 211.13, 220.24, 216.02, 156.61, 261.05, 156.61, 261.05, 154.65, 265.94, 154.65, 271.81, 157.59, 278.67, 160.53, 286.5, 162.48, 290.41, 170.32, 294.33, 181.08, 294.33, 190.87, 294.33, 203.6, 294.33, 216.32, 290.41, 230.02, 280.62, 241.77, 268.88, 245.68, 268.88, 235.9, 282.58, 228.07, 292.37, 220.24, 301.18, 217.3, 311.95, 220.24, 319.78, 227.09, 326.63, 240.79, 330.54, 256.45, 330.54, 268.2, 323.69, 275.05, 309.99, 278.96, 294.33, 278.96, 279.64, 284.84, 265.94, 290.71, 247.34, 301.48, 227.77, 290.71, 211.13]], "area": 22627.262500000008, "iscrowd": 0, "image_id": 10403, "bbox": [111.59, 92.69, 189.89, 237.85], "category_id": 52, "id": 1046246}, {"segmentation": [[1.69, 198.64, 17.98, 187.25, 50.54, 187.25, 78.22, 191.32, 104.27, 207.6, 121.36, 236.9, 127.06, 267.84, 123.81, 299.59, 108.34, 328.9, 155.56, 336.22, 183.23, 352.5, 200.33, 379.37, 209.28, 399.72, 207.66, 423.33, 203.59, 443.0, 34.26, 443.0, 34.26, 414.37, 44.84, 378.55, 50.54, 368.79, 29.37, 359.83, 3.32, 349.25]], "area": 34408.68220000001, "iscrowd": 0, "image_id": 10196, "bbox": [1.69, 187.25, 207.59, 255.75], "category_id": 53, "id": 1048678}, {"segmentation": [[348.0, 377.1, 334.0, 363.1, 339.0, 340.1, 345.0, 320.1, 361.0, 305.1, 374.0, 293.1, 405.0, 303.1, 429.0, 329.1, 427.0, 359.1, 412.0, 380.1, 376.0, 382.1, 361.0, 382.1]], "area": 6343.5, "iscrowd": 0, "image_id": 10037, "bbox": [334.0, 293.1, 95.0, 89.0], "category_id": 55, "id": 1050734}, {"segmentation": [[409.8, 177.51, 439.89, 162.98, 456.49, 156.76, 471.01, 153.65, 503.17, 167.13, 524.96, 183.73, 528.07, 210.71, 528.07, 237.68, 516.66, 259.47, 504.21, 266.73, 478.27, 278.14, 455.45, 287.48, 429.51, 291.63, 409.8, 283.33, 389.05, 263.62, 380.75, 242.87, 383.86, 216.93, 394.24, 194.11]], "area": 15222.65395, "iscrowd": 0, "image_id": 10196, "bbox": [380.75, 153.65, 147.32, 137.98], "category_id": 55, "id": 1052035}, {"segmentation": [[638.5, 340.8, 621.46, 347.82, 598.4, 322.76, 592.39, 291.68, 599.41, 268.63, 615.44, 253.6, 636.49, 244.57]], "area": 3461.3243500000012, "iscrowd": 0, "image_id": 10037, "bbox": [592.39, 244.57, 46.11, 103.25], "category_id": 55, "id": 1052832}, {"segmentation": [[524.3, 196.03, 527.43, 150.17, 551.4, 128.28, 586.84, 126.19, 625.41, 140.79, 640.0, 160.59, 636.88, 255.45, 617.07, 264.83, 588.93, 254.4, 553.49, 248.15, 535.77, 228.34, 525.35, 191.86]], "area": 12892.222500000005, "iscrowd": 0, "image_id": 10196, "bbox": [524.3, 126.19, 115.7, 138.64], "category_id": 55, "id": 1052869}, {"segmentation": [[528.67, 28.73, 529.72, 56.94, 537.03, 97.69, 547.48, 112.32, 574.65, 133.21, 619.57, 132.17, 639.42, 124.85, 640.0, 4.7, 568.38, 1.57, 540.17, 16.19]], "area": 12858.581, "iscrowd": 0, "image_id": 10196, "bbox": [528.67, 1.57, 111.33, 131.64], "category_id": 55, "id": 1053238}, {"segmentation": [[384.28, 382.77, 370.07, 392.92, 362.97, 406.79, 359.58, 414.91, 356.54, 423.37, 356.88, 425.06, 462.44, 426.0, 458.04, 414.91, 449.59, 397.99, 435.37, 385.14, 418.46, 374.99, 417.44, 374.99, 416.43, 374.65, 412.7, 380.06, 412.7, 381.08, 384.62, 382.09]], "area": 3752.981750000001, "iscrowd": 0, "image_id": 10037, "bbox": [356.54, 374.65, 105.9, 51.35], "category_id": 55, "id": 1053346}, {"segmentation": [[333.32, 360.23, 305.88, 362.6, 285.07, 382.94, 278.92, 399.97, 278.92, 413.68, 280.81, 426.0, 354.6, 424.56, 362.17, 406.12, 369.74, 388.61, 359.81, 381.52, 345.62, 377.26, 331.9, 362.6]], "area": 4475.59625, "iscrowd": 0, "image_id": 10037, "bbox": [278.92, 360.23, 90.82, 65.77], "category_id": 55, "id": 1053417}, {"segmentation": [[533.68, 373.23, 519.09, 366.98, 500.33, 356.55, 486.78, 338.83, 480.52, 304.44, 480.52, 284.63, 485.74, 270.04, 506.58, 258.57, 538.9, 248.15, 581.63, 255.45, 606.65, 258.57, 611.86, 265.87, 629.58, 281.5, 634.79, 310.69, 627.5, 349.26, 594.14, 371.15, 574.34, 379.49, 544.11, 376.36, 533.68, 369.06]], "area": 15983.7849, "iscrowd": 0, "image_id": 10196, "bbox": [480.52, 248.15, 154.27, 131.34], "category_id": 55, "id": 1053752}, {"segmentation": [[561.32, 360.85, 574.35, 360.85, 600.41, 342.8, 597.4, 310.73, 586.38, 290.68, 564.32, 277.65, 531.25, 283.67, 505.19, 303.71, 505.19, 308.72, 526.24, 335.79, 552.3, 358.84, 563.32, 363.85]], "area": 5461.2543000000005, "iscrowd": 0, "image_id": 10037, "bbox": [505.19, 277.65, 95.22, 86.2], "category_id": 55, "id": 1054003}, {"segmentation": [[46.31, 63.25, 60.99, 75.67, 74.54, 65.51, 76.8, 63.25, 89.22, 58.73, 98.26, 70.02, 103.91, 75.67, 123.11, 98.26, 136.66, 79.06, 138.92, 77.93, 141.18, 77.93, 153.6, 85.84, 161.51, 92.61, 181.84, 89.22, 185.22, 88.09, 185.22, 106.16, 186.35, 115.2, 208.94, 115.2, 219.11, 120.85, 230.4, 128.75, 233.79, 136.66, 246.21, 146.82, 243.95, 166.02, 257.51, 154.73, 266.54, 136.66, 275.58, 136.66, 282.35, 143.44, 283.48, 153.6, 283.48, 163.76, 295.91, 173.93, 303.81, 180.71, 303.81, 198.78, 303.81, 228.14, 304.94, 242.82, 306.07, 257.51, 311.72, 273.32, 304.94, 302.68, 301.55, 328.66, 297.04, 365.93, 289.13, 376.09, 288.0, 398.68, 290.26, 412.24, 300.42, 422.4, 307.2, 425.79, 313.98, 426.92, 336.56, 457.41, 334.31, 470.96, 326.4, 477.74, 326.4, 477.74, 325.27, 480.0, 4.52, 478.87, 11.29, 111.81, 15.81, 105.04, 23.72, 105.04, 30.49, 97.13, 33.88, 96.0, 35.01, 56.47, 42.92, 49.69]], "area": 111590.77340000002, "iscrowd": 0, "image_id": 10407, "bbox": [4.52, 49.69, 332.04, 430.31], "category_id": 56, "id": 1056237}, {"segmentation": [[294.06, 100.6, 304.27, 93.02, 312.83, 86.44, 321.06, 84.46, 330.94, 89.4, 335.22, 101.58, 341.14, 114.09, 350.36, 127.92, 356.95, 137.14, 363.2, 145.37, 350.36, 145.37, 339.82, 138.13, 325.34, 126.93, 309.53, 126.28, 295.38, 114.42]], "area": 1888.5172000000011, "iscrowd": 0, "image_id": 10166, "bbox": [294.06, 84.46, 69.14, 60.91], "category_id": 56, "id": 1059204}, {"segmentation": [[387.34, 122.23, 361.74, 107.36, 355.96, 89.2, 366.69, 76.81, 382.38, 73.5, 398.08, 67.72, 421.2, 89.2, 432.76, 106.54, 422.03, 121.4, 405.51, 122.23]], "area": 2839.2364, "iscrowd": 0, "image_id": 10166, "bbox": [355.96, 67.72, 76.8, 54.51], "category_id": 56, "id": 1059232}, {"segmentation": [[159.67, 171.69, 185.74, 177.71, 209.31, 168.69, 209.81, 160.66, 197.78, 136.6, 184.24, 133.59, 167.69, 136.09, 156.66, 132.59, 142.12, 130.58, 140.62, 148.13, 149.14, 162.67, 162.18, 174.2]], "area": 2352.54315, "iscrowd": 0, "image_id": 10166, "bbox": [140.62, 130.58, 69.19, 47.13], "category_id": 56, "id": 1059625}, {"segmentation": [[168.89, 72.71, 179.77, 68.43, 188.72, 73.1, 193.0, 81.27, 197.66, 85.15, 201.55, 78.54, 206.6, 69.99, 215.94, 69.99, 225.27, 69.99, 231.49, 74.65, 224.1, 98.37, 210.1, 111.59, 203.11, 116.65, 179.77, 112.76, 177.05, 104.98, 177.05, 94.87, 165.78, 94.1, 161.89, 82.04]], "area": 2176.6643000000004, "iscrowd": 0, "image_id": 10166, "bbox": [161.89, 68.43, 69.6, 48.22], "category_id": 56, "id": 1060098}, {"segmentation": [[200.69, 217.21, 180.04, 213.08, 175.09, 198.21, 175.09, 187.48, 168.48, 178.39, 160.22, 178.39, 147.01, 185.82, 131.32, 197.39, 128.01, 211.43, 131.32, 225.47, 142.05, 226.29, 151.96, 220.51, 165.18, 221.34, 184.17, 227.94, 198.21, 225.47, 199.86, 219.68]], "area": 2046.8189500000005, "iscrowd": 0, "image_id": 10166, "bbox": [128.01, 178.39, 72.68, 49.55], "category_id": 56, "id": 1060138}, {"segmentation": [[106.02, 99.95, 98.51, 97.27, 91.0, 98.34, 82.43, 99.95, 82.69, 85.2, 89.4, 70.46, 95.02, 58.4, 105.75, 53.84, 116.74, 48.75, 123.98, 56.79, 133.36, 54.11, 141.67, 63.22, 148.37, 68.58, 150.51, 75.55, 140.06, 76.63, 140.33, 87.35, 131.75, 92.71, 120.22, 93.51, 108.43, 98.34]], "area": 2253.9273000000003, "iscrowd": 0, "image_id": 10166, "bbox": [82.43, 48.75, 68.08, 51.2], "category_id": 56, "id": 1060510}, {"segmentation": [[306.54, 427.71, 366.97, 444.08, 426.77, 459.18, 429.29, 451.63, 427.4, 403.79, 409.15, 377.36, 402.85, 354.69, 401.59, 321.96, 394.67, 306.86, 382.08, 280.42, 379.56, 272.87, 361.31, 266.57, 344.31, 259.65, 316.62, 249.58, 304.03, 247.06, 305.92, 253.35, 309.69, 261.54, 312.21, 266.57, 304.66, 285.45, 300.88, 298.04, 298.99, 313.78, 293.96, 327.63, 287.66, 345.25, 285.14, 361.62, 291.44, 391.2, 294.59, 407.57, 298.99, 417.01, 301.51, 425.82, 302.77, 426.45]], "area": 20059.09555, "iscrowd": 0, "image_id": 10407, "bbox": [285.14, 247.06, 144.15, 212.12], "category_id": 57, "id": 1061762}, {"segmentation": [[521.79, 300.42, 635.86, 369.32, 640.0, 472.09, 522.92, 472.09, 465.32, 370.45, 477.74, 307.2]], "area": 22006.0726, "iscrowd": 0, "image_id": 10407, "bbox": [465.32, 300.42, 174.68, 171.67], "category_id": 57, "id": 1064657}, {"segmentation": [[459.24, 427.38, 441.2, 394.3, 431.64, 364.24, 431.09, 345.37, 443.94, 337.99, 468.81, 345.65, 464.71, 369.98, 479.47, 396.76, 483.3, 401.41, 501.07, 444.33, 505.17, 455.53, 510.09, 475.49, 457.06, 476.58, 469.63, 443.78]], "area": 4956.79845, "iscrowd": 0, "image_id": 10407, "bbox": [431.09, 337.99, 79.0, 138.59], "category_id": 57, "id": 1065139}, {"segmentation": [[11.83, 344.69, 2.37, 332.07, 0.79, 126.2, 14.2, 116.74, 29.97, 104.12, 41.02, 86.76, 71.78, 64.68, 126.99, 29.18, 143.56, 22.87, 151.44, 12.62, 169.58, 0.0, 456.69, 1.58, 467.74, 8.68, 470.0, 32.34, 470.0, 109.64, 437.76, 155.39, 399.9, 178.26, 373.09, 188.51, 354.16, 194.82, 341.53, 203.5, 317.87, 222.43, 308.41, 240.57, 309.98, 258.71, 287.11, 280.01, 246.09, 312.35, 202.71, 324.97, 184.57, 333.65, 138.82, 343.9, 134.88, 344.69, 55.21, 347.06]], "area": 119209.93014999999, "iscrowd": 0, "image_id": 10166, "bbox": [0.79, 0.0, 469.21, 347.06], "category_id": 59, "id": 1074907}, {"segmentation": [[539.61, 60.07, 516.43, 70.61, 508.0, 83.26, 506.94, 94.85, 510.1, 110.66, 513.27, 128.58, 514.32, 147.55, 514.32, 177.06, 516.43, 200.25, 509.05, 225.54, 497.46, 246.62, 484.81, 276.13, 480.59, 292.99, 473.22, 311.96, 465.84, 327.77, 450.03, 344.64, 418.41, 354.12, 412.09, 358.34, 377.31, 358.34, 366.77, 346.74, 354.12, 334.1, 286.67, 345.69, 264.54, 353.07, 242.4, 341.47, 221.33, 327.77, 185.49, 311.96, 173.9, 299.32, 173.9, 292.99, 187.6, 263.48, 203.41, 240.3, 207.62, 216.06, 207.62, 202.36, 186.55, 169.68, 169.68, 158.09, 138.07, 143.33, 107.5, 134.9, 87.48, 129.63, 55.86, 127.53, 31.62, 123.31, 26.35, 120.15, 63.24, 86.42, 79.04, 69.56, 96.96, 61.13, 100.12, 60.07, 119.09, 32.67, 129.63, 36.89, 130.69, 43.21, 167.58, 31.62, 193.92, 26.35, 225.54, 21.08, 260.32, 18.97, 292.99, 17.92, 336.2, 16.86, 367.82, 16.86, 426.84, 21.08, 455.3, 23.19, 481.65, 25.29, 509.05, 34.78, 532.24, 48.48]], "area": 113511.33254999998, "iscrowd": 0, "image_id": 10073, "bbox": [26.35, 16.86, 513.26, 341.48], "category_id": 59, "id": 1077450}, {"segmentation": [[448.02, 137.85, 466.21, 180.93, 470.04, 207.73, 453.76, 246.03, 426.0, 274.75, 358.99, 290.06, 293.89, 284.32, 255.6, 269.96, 232.62, 242.2, 224.01, 207.73, 224.97, 179.02, 242.2, 154.13, 269.96, 131.15, 300.59, 116.79, 316.87, 110.09, 346.54, 105.3, 367.6, 103.39, 383.88, 105.3, 409.73, 114.88, 432.7, 125.41, 437.49, 128.28]], "area": 35966.54044999999, "iscrowd": 0, "image_id": 10037, "bbox": [224.01, 103.39, 246.03, 186.67], "category_id": 60, "id": 1078956}, {"segmentation": [[0.0, 435.65, 38.2, 467.42, 74.16, 485.39, 146.07, 487.64, 214.61, 470.79, 246.07, 446.07, 271.91, 412.36, 288.76, 364.04, 288.76, 329.21, 271.91, 296.63, 226.97, 261.8, 185.39, 241.57, 144.94, 233.71, 69.66, 250.56, 5.62, 293.26, 0.0, 303.37]], "area": 59049.35005000001, "iscrowd": 0, "image_id": 10444, "bbox": [0.0, 233.71, 288.76, 253.93], "category_id": 60, "id": 1079276}, {"segmentation": [[0.96, 150.16, 73.88, 0.48, 635.16, 2.4, 638.04, 419.76, 1.92, 420.72]], "area": 260688.60319999998, "iscrowd": 0, "image_id": 10037, "bbox": [0.96, 0.48, 637.08, 420.24], "category_id": 67, "id": 1091124}, {"segmentation": [[118.28, 534.5, 95.86, 544.01, 84.08, 545.53, 74.58, 545.53, 69.64, 541.72, 71.54, 513.6, 67.36, 509.04, 59.76, 504.86, 47.59, 493.46, 44.17, 486.62, 41.13, 483.2, 43.41, 474.08, 49.87, 466.09, 66.22, 458.11, 102.7, 450.89, 118.66, 455.83, 122.46, 459.25, 131.97, 460.01, 132.73, 468.37, 125.5, 470.28, 115.24, 482.82, 111.82, 486.62, 115.62, 485.86, 121.32, 482.82, 127.4, 485.48, 133.49, 496.5, 136.15, 509.04, 131.21, 513.98, 124.74, 515.88, 121.32, 512.46, 119.04, 510.56, 116.76, 521.96, 115.62, 526.52]], "area": 5833.763949999999, "iscrowd": 0, "image_id": 10337, "bbox": [41.13, 450.89, 95.02, 94.64], "category_id": 70, "id": 1095547}, {"segmentation": [[485.13, 240.44, 475.27, 236.68, 468.23, 223.53, 466.35, 216.49, 465.88, 210.86, 469.64, 205.69, 475.27, 202.4, 479.5, 203.34, 482.32, 201.94, 488.42, 202.87, 491.71, 206.63, 501.1, 208.04, 507.2, 213.67, 513.3, 221.19, 518.47, 228.7, 520.35, 235.74, 520.81, 241.84, 520.81, 246.07, 516.12, 247.48, 507.2, 248.89, 495.93, 247.01, 489.36, 243.72]], "area": 1749.2476999999997, "iscrowd": 0, "image_id": 10083, "bbox": [465.88, 201.94, 54.93, 46.95], "category_id": 74, "id": 1107099}, {"segmentation": [[185.48, 121.6, 187.17, 121.99, 188.2, 127.68, 180.44, 130.52, 180.18, 124.06, 183.29, 123.8, 185.23, 123.54, 185.23, 121.99]], "area": 44.70694999999999, "iscrowd": 0, "image_id": 10176, "bbox": [180.18, 121.6, 8.02, 8.92], "category_id": 75, "id": 1113756}, {"segmentation": [[471.97, 274.82, 459.02, 275.44, 458.71, 270.2, 469.5, 268.04, 476.28, 270.81, 471.35, 274.82]], "area": 91.59094999999988, "iscrowd": 0, "image_id": 10176, "bbox": [458.71, 268.04, 17.57, 7.4], "category_id": 75, "id": 1114099}, {"segmentation": [[203.87, 379.69, 143.46, 303.1, 148.85, 294.47, 222.2, 257.8, 327.91, 207.1, 363.51, 204.94, 387.24, 192.0, 468.13, 240.54, 468.13, 244.85, 419.6, 270.74, 410.97, 280.45, 374.29, 300.94, 295.55, 344.09, 248.09, 369.98, 224.36, 377.53]], "area": 29166.849700000002, "iscrowd": 0, "image_id": 10083, "bbox": [143.46, 192.0, 324.67, 187.69], "category_id": 76, "id": 1115246}, {"segmentation": [[252.91, 380.99, 461.7, 262.6, 514.44, 303.5, 301.35, 439.1, 259.37, 389.6]], "area": 16888.100650000008, "iscrowd": 0, "image_id": 10083, "bbox": [252.91, 262.6, 261.53, 176.5], "category_id": 76, "id": 1116701}, {"segmentation": [[243.19, 227.5, 263.49, 186.9, 322.43, 179.7, 323.08, 191.49, 368.27, 192.8, 368.92, 203.93, 381.36, 232.74, 385.95, 236.02, 423.93, 304.77, 334.21, 299.54, 246.46, 283.82]], "area": 15536.312549999999, "iscrowd": 0, "image_id": 10175, "bbox": [243.19, 179.7, 180.74, 125.07], "category_id": 79, "id": 1123456}, {"segmentation": [[14.02, 268.58, 126.2, 249.17, 182.29, 294.47, 3.24, 334.38]], "area": 8760.860550000003, "iscrowd": 0, "image_id": 10175, "bbox": [3.24, 249.17, 179.05, 85.21], "category_id": 81, "id": 1129473}, {"segmentation": [[445.69, 69.11, 449.06, 68.97, 448.92, 92.12, 447.01, 92.41, 444.96, 91.82]], "area": 84.93395000000046, "iscrowd": 0, "image_id": 10346, "bbox": [444.96, 68.97, 4.1, 23.44], "category_id": 84, "id": 1138841}, {"segmentation": [[193.27, 383.83, 214.96, 388.06, 208.61, 475.33, 188.51, 473.22, 192.22, 430.9, 191.16, 430.9]], "area": 1823.1149500000013, "iscrowd": 0, "image_id": 10136, "bbox": [188.51, 383.83, 26.45, 91.5], "category_id": 84, "id": 1142593}, {"segmentation": [[38.52, 366.63, 277.52, 375.87, 276.2, 279.48, 37.2, 263.63]], "area": 23810.545599999998, "iscrowd": 0, "image_id": 10136, "bbox": [37.2, 263.63, 240.32, 112.24], "category_id": 84, "id": 1142653}, {"segmentation": [[449.97, 69.99, 453.36, 69.82, 451.67, 91.87, 449.29, 91.7, 449.29, 69.31]], "area": 70.7363999999999, "iscrowd": 0, "image_id": 10346, "bbox": [449.29, 69.31, 4.07, 22.56], "category_id": 84, "id": 1144073}, {"segmentation": [[246.23, 392.92, 239.0, 475.67, 265.51, 479.69, 274.35, 387.29, 247.84, 386.49]], "area": 2471.516600000001, "iscrowd": 0, "image_id": 10136, "bbox": [239.0, 386.49, 35.35, 93.2], "category_id": 84, "id": 1144196}, {"segmentation": [[458.63, 70.08, 453.8, 70.56, 452.59, 91.8, 457.42, 91.32]], "area": 102.00840000000022, "iscrowd": 0, "image_id": 10346, "bbox": [452.59, 70.08, 6.04, 21.72], "category_id": 84, "id": 1145687}, {"segmentation": [[435.51, 421.02, 306.57, 416.25, 287.47, 389.5, 281.74, 251.02, 270.28, 282.54, 276.01, 312.14, 259.78, 306.41, 247.36, 334.11, 227.3, 331.25, 214.89, 321.69, 217.75, 289.22, 238.76, 231.92, 248.31, 205.18, 255.96, 165.07, 274.1, 127.82, 252.13, 102.99, 252.13, 71.47, 237.81, 47.59, 244.49, 25.63, 258.82, 10.35, 283.65, 0.0, 293.2, 3.66, 317.08, 0.0, 384.89, 0.8, 387.75, 6.53, 406.85, 0.0, 441.24, 3.66, 452.7, 37.09, 429.78, 59.05, 428.82, 98.21, 411.63, 123.04, 407.81, 124.0, 418.31, 157.43, 425.96, 191.81, 437.42, 218.55, 452.7, 252.93, 453.65, 265.35, 456.52, 293.04, 454.61, 315.01, 439.33, 323.6, 449.83, 380.91, 444.1, 395.23, 441.24, 417.2]], "area": 75660.35605, "iscrowd": 0, "image_id": 10082, "bbox": [214.89, 0.0, 241.63, 421.02], "category_id": 88, "id": 1159433}, {"segmentation": [[117.57, 422.83, 78.74, 421.75, 46.38, 428.22, 23.73, 462.74, 11.87, 469.21, 145.62, 473.53, 149.93, 441.17, 139.15, 428.22, 131.6, 424.99]], "area": 5395.958049999997, "iscrowd": 0, "image_id": 10176, "bbox": [11.87, 421.75, 138.06, 51.78], "category_id": 27, "id": 1166330}, {"segmentation": [[299.58, 296.25, 346.67, 306.25, 377.51, 301.67, 382.92, 267.91, 380.42, 257.91, 373.34, 246.66, 380.42, 239.58, 357.09, 236.24, 339.58, 234.16, 330.0, 232.91, 331.67, 224.99, 331.67, 216.24, 327.08, 209.15, 325.83, 214.57, 322.5, 223.32, 319.58, 229.57, 312.91, 234.58, 305.83, 239.16, 300.83, 248.33, 296.66, 252.91, 296.24, 253.33, 296.24, 293.33]], "area": 5612.925149999999, "iscrowd": 0, "image_id": 10275, "bbox": [296.24, 209.15, 86.68, 97.1], "category_id": 31, "id": 1173081}, {"segmentation": [[142.12, 145.51, 143.55, 155.48, 143.55, 159.18, 143.55, 159.18, 143.83, 171.42, 140.98, 170.85, 143.83, 175.69, 156.07, 173.13, 156.93, 172.28, 154.93, 157.19, 154.93, 157.19, 153.23, 154.91, 151.8, 154.34, 152.66, 148.65, 148.67, 145.23, 146.11, 141.53]], "area": 339.3100499999999, "iscrowd": 0, "image_id": 10244, "bbox": [140.98, 141.53, 15.95, 34.16], "category_id": 31, "id": 1173590}, {"segmentation": [[469.03, 170.13, 456.61, 204.52, 457.57, 217.89, 456.61, 222.67, 453.75, 226.49, 454.7, 267.56, 469.99, 422.32, 561.69, 420.41, 546.41, 381.24, 553.09, 376.46, 553.09, 354.49, 540.68, 334.43, 526.35, 311.51, 520.62, 293.36, 508.2, 279.98, 514.88, 266.61, 515.84, 255.15, 508.2, 238.91, 510.11, 236.04, 504.38, 224.58, 496.73, 215.98, 482.4, 194.01]], "area": 15645.8498, "iscrowd": 0, "image_id": 10232, "bbox": [453.75, 170.13, 107.94, 252.19], "category_id": 31, "id": 1176820}, {"segmentation": [[168.43, 179.01, 170.97, 184.08, 170.97, 188.36, 171.44, 192.01, 171.44, 193.75, 165.9, 192.96, 164.15, 185.82, 163.68, 182.81, 163.36, 179.64, 163.52, 176.95, 163.52, 175.2, 165.1, 174.41, 166.05, 174.57, 167.8, 177.58, 168.59, 179.17]], "area": 105.8502999999999, "iscrowd": 0, "image_id": 10230, "bbox": [163.36, 174.41, 8.08, 19.34], "category_id": 1, "id": 1189565}, {"segmentation": [[216.46, 230.98, 216.24, 226.47, 220.32, 222.39, 220.75, 220.25, 220.32, 216.81, 220.32, 215.52, 217.96, 209.51, 214.74, 205.65, 211.73, 204.57, 212.38, 197.06, 209.37, 193.19, 204.0, 195.98, 202.29, 199.21, 201.64, 203.71, 201.0, 207.15, 196.7, 225.4, 196.92, 227.98, 200.78, 236.56, 204.86, 240.0, 206.36, 240.64, 206.79, 238.5, 205.72, 234.42, 207.22, 233.34, 210.44, 233.56, 212.59, 233.34, 215.17, 233.13]], "area": 681.8614500000002, "iscrowd": 0, "image_id": 10281, "bbox": [196.7, 193.19, 24.05, 47.45], "category_id": 1, "id": 1189576}, {"segmentation": [[530.03, 208.26, 530.03, 199.99, 529.51, 198.54, 529.2, 197.09, 529.2, 196.06, 529.2, 194.92, 529.4, 193.48, 529.82, 192.86, 530.54, 192.75, 531.06, 192.13, 531.58, 190.68, 532.3, 190.58, 533.13, 190.58, 534.06, 191.72, 534.06, 192.65, 534.68, 192.75, 535.19, 193.06, 535.3, 194.2, 535.5, 200.61, 534.16, 203.09, 534.16, 204.02, 534.26, 205.57, 534.37, 206.61, 534.78, 207.43, 535.3, 207.95, 535.3, 208.88, 534.47, 209.4, 533.44, 209.61, 533.23, 209.09, 533.23, 207.85, 533.13, 203.3, 532.92, 202.57, 532.2, 202.78, 531.89, 203.82, 532.09, 206.09, 532.09, 207.54, 532.09, 208.47, 532.09, 209.09, 531.47, 209.29, 531.06, 209.4, 530.65, 209.4, 530.03, 208.98]], "area": 84.71504999999986, "iscrowd": 0, "image_id": 10318, "bbox": [529.2, 190.58, 6.3, 19.03], "category_id": 1, "id": 1207029}, {"segmentation": [[404.75, 233.95, 404.75, 235.6, 402.27, 232.84, 402.27, 231.47, 400.35, 230.36, 396.76, 228.44, 393.18, 226.51, 392.63, 224.58, 394.28, 222.1, 395.39, 220.44, 399.24, 220.72, 401.45, 222.37, 401.17, 222.1, 400.35, 220.17, 399.79, 216.31, 401.72, 213.0, 401.72, 211.9, 399.79, 211.08, 398.14, 210.8, 396.49, 209.42, 397.04, 204.46, 399.24, 201.43, 406.41, 201.43, 409.71, 205.29, 409.71, 206.12]], "area": 303.3419499999994, "iscrowd": 0, "image_id": 10281, "bbox": [392.63, 201.43, 17.08, 34.17], "category_id": 1, "id": 1212442}, {"segmentation": [[150.78, 176.96, 151.76, 174.74, 153.81, 175.62, 155.32, 177.32, 154.16, 184.52, 154.52, 188.35, 153.01, 190.93, 153.19, 193.24, 149.63, 192.79, 150.43, 189.5, 152.65, 186.57, 152.03, 182.21, 150.16, 182.12, 149.81, 177.14]], "area": 59.492049999999935, "iscrowd": 0, "image_id": 10230, "bbox": [149.63, 174.74, 5.69, 18.5], "category_id": 1, "id": 1214085}, {"segmentation": [[608.36, 186.07, 600.27, 148.85, 606.74, 135.91, 622.11, 126.2, 629.39, 133.48, 620.49, 148.04, 624.54, 152.9, 635.87, 154.52, 640.0, 160.18, 629.39, 164.22, 615.64, 163.42, 625.35, 179.6]], "area": 1140.4444999999998, "iscrowd": 0, "image_id": 10097, "bbox": [600.27, 126.2, 39.73, 59.87], "category_id": 1, "id": 1216711}, {"segmentation": [[108.76, 326.1, 113.66, 319.16, 114.48, 312.21, 114.89, 309.35, 121.02, 304.86, 125.1, 302.82, 124.69, 294.24, 126.33, 279.12, 132.86, 266.46, 138.58, 260.74, 143.07, 258.29, 148.38, 259.11, 155.33, 265.64, 162.27, 270.54, 169.22, 288.52, 167.58, 293.83, 163.91, 314.66, 162.68, 319.97, 154.51, 324.06, 152.06, 331.0, 146.75, 331.0, 146.34, 324.06, 148.38, 316.7, 148.79, 312.62, 150.43, 302.82, 136.54, 308.53, 129.19, 321.2, 124.69, 325.28, 115.3, 331.41, 109.17, 331.82]], "area": 2395.900299999999, "iscrowd": 0, "image_id": 10015, "bbox": [108.76, 258.29, 60.46, 73.53], "category_id": 1, "id": 1224547}, {"segmentation": [[32.62, 127.44, 39.71, 126.35, 44.61, 133.7, 44.34, 139.43, 50.06, 144.88, 52.79, 163.68, 61.51, 185.75, 62.6, 201.55, 54.97, 184.11, 49.79, 175.39, 55.51, 197.47, 57.15, 202.1, 51.15, 202.1, 43.79, 179.75, 34.53, 178.66, 28.81, 170.22, 27.99, 155.78, 29.9, 142.7, 32.08, 139.15, 32.08, 127.71]], "area": 1287.0788000000005, "iscrowd": 0, "image_id": 10069, "bbox": [27.99, 126.35, 34.61, 75.75], "category_id": 1, "id": 1240549}, {"segmentation": [[109.85, 190.22, 109.85, 186.75, 110.48, 183.37, 111.1, 181.41, 110.66, 178.21, 112.7, 177.76, 113.77, 177.5, 113.59, 180.79, 115.19, 181.77, 114.04, 184.26, 114.21, 190.75, 112.88, 191.11, 112.52, 186.57, 111.81, 187.9, 110.92, 191.2]], "area": 44.698150000000076, "iscrowd": 0, "image_id": 10230, "bbox": [109.85, 177.5, 5.34, 13.7], "category_id": 1, "id": 1241479}, {"segmentation": [[318.55, 262.91, 318.16, 231.53, 326.2, 224.26, 329.64, 217.75, 334.62, 207.04, 344.19, 208.57, 346.1, 217.37, 356.81, 224.64, 359.11, 240.71, 362.31, 258.81, 361.38, 269.03, 356.04, 269.03, 354.18, 266.94, 351.85, 280.18, 347.53, 296.19, 347.03, 302.08, 349.0, 310.92, 349.0, 320.75, 344.58, 318.78, 339.67, 322.71, 327.39, 316.32, 331.32, 310.43, 330.83, 295.7, 326.9, 270.16, 323.46, 289.8, 318.55, 285.87, 317.08, 262.79]], "area": 3291.9686000000006, "iscrowd": 0, "image_id": 10239, "bbox": [317.08, 207.04, 45.23, 115.67], "category_id": 1, "id": 1242558}, {"segmentation": [[149.28, 471.27, 160.4, 412.51, 168.34, 395.04, 168.34, 364.86, 179.45, 331.51, 163.57, 337.87, 155.63, 307.69, 155.63, 280.69, 165.16, 267.99, 184.22, 258.46, 196.92, 256.87, 195.33, 240.99, 206.45, 217.17, 220.74, 214.0, 239.8, 221.94, 228.68, 267.99, 244.57, 282.28, 255.68, 325.16, 266.8, 337.87, 254.09, 360.1, 239.8, 353.75, 230.27, 348.98, 206.45, 352.16, 195.33, 388.68, 176.28, 423.62, 163.57, 455.38, 182.63, 483.97, 184.22, 496.67, 168.34, 498.26, 144.52, 471.27]], "area": 13039.704799999998, "iscrowd": 0, "image_id": 10321, "bbox": [144.52, 214.0, 122.28, 284.26], "category_id": 1, "id": 1256012}, {"segmentation": [[337.15, 218.06, 339.62, 216.6, 341.64, 216.71, 341.64, 217.72, 341.3, 220.3, 337.94, 220.98, 336.59, 220.53, 336.03, 218.62, 336.14, 218.06]], "area": 18.08885000000007, "iscrowd": 0, "image_id": 10281, "bbox": [336.03, 216.6, 5.61, 4.38], "category_id": 1, "id": 1260936}, {"segmentation": [[430.81, 253.88, 430.95, 249.55, 432.54, 248.11, 432.54, 246.24, 434.7, 243.64, 437.3, 243.5, 439.03, 247.25, 440.9, 250.27, 441.63, 251.57, 441.63, 252.15, 437.44, 252.44], [430.95, 258.35, 431.53, 254.89, 435.57, 253.59, 440.76, 254.46, 440.76, 257.05, 440.47, 257.48]], "area": 105.08180000000007, "iscrowd": 0, "image_id": 10318, "bbox": [430.81, 243.5, 10.82, 14.85], "category_id": 1, "id": 1265505}, {"segmentation": [[0.0, 260.35, 12.3, 259.2, 20.73, 260.35, 33.77, 270.7, 38.76, 275.69, 47.19, 283.36, 48.34, 288.34, 44.51, 294.09, 44.51, 309.05, 44.51, 324.0, 45.28, 330.14, 41.44, 337.04, 36.07, 335.12, 38.76, 324.39, 36.07, 310.2, 33.77, 303.3, 31.47, 298.7, 27.25, 297.93, 25.34, 300.61, 28.4, 310.97, 27.25, 319.79, 26.87, 325.15, 24.57, 329.37, 16.13, 332.06, 11.53, 333.59, 11.53, 324.0, 11.92, 317.1, 12.68, 315.18, 7.31, 313.65, 4.25, 307.51, 3.1, 304.45, 0.41, 305.98, 4.63, 326.69, 10.38, 333.97, 7.7, 336.66, 0.41, 341.64]], "area": 2579.0673500000003, "iscrowd": 0, "image_id": 10281, "bbox": [0.0, 259.2, 48.34, 82.44], "category_id": 1, "id": 1280124}, {"segmentation": [[130.09, 228.72, 131.97, 227.15, 135.74, 227.78, 136.36, 230.6, 135.11, 232.79, 135.74, 236.56, 138.25, 238.75, 138.56, 240.32, 138.56, 241.26, 136.99, 241.26, 135.42, 241.26, 132.6, 242.2, 130.41, 241.89, 128.84, 241.57, 126.96, 241.26, 126.02, 240.32, 124.13, 238.75, 126.02, 235.3, 129.15, 234.36, 130.09, 230.91, 131.03, 229.03]], "area": 121.89719999999991, "iscrowd": 0, "image_id": 10281, "bbox": [124.13, 227.15, 14.43, 15.05], "category_id": 1, "id": 1283845}, {"segmentation": [[332.91, 349.37, 334.91, 331.57, 345.19, 324.3, 347.7, 311.26, 356.98, 311.51, 358.23, 316.53, 354.72, 324.3, 361.49, 331.07, 363.25, 345.11, 361.24, 355.89, 332.91, 350.13]], "area": 884.5516999999998, "iscrowd": 0, "image_id": 10321, "bbox": [332.91, 311.26, 30.34, 44.63], "category_id": 1, "id": 1287800}, {"segmentation": [[113.6, 272.32, 114.11, 259.95, 113.6, 256.67, 112.34, 251.62, 111.84, 247.83, 111.58, 244.55, 111.08, 236.98, 110.57, 233.44, 109.56, 227.63, 109.82, 224.1, 111.84, 220.56, 113.35, 216.27, 116.63, 211.98, 122.19, 216.27, 122.19, 220.56, 123.95, 220.31, 126.98, 219.3, 127.74, 221.83, 123.2, 227.89, 123.95, 233.95, 122.19, 248.34, 120.42, 255.91, 120.42, 260.71, 119.92, 266.77, 120.67, 270.3, 122.94, 272.57, 119.66, 272.83]], "area": 605.09915, "iscrowd": 0, "image_id": 10239, "bbox": [109.56, 211.98, 18.18, 60.85], "category_id": 1, "id": 1294191}, {"segmentation": [[327.78, 308.82, 327.78, 306.08, 329.73, 303.93, 333.25, 304.71, 333.05, 308.43, 331.68, 310.97, 328.75, 313.7, 327.19, 315.85, 324.84, 324.06, 323.67, 331.09, 321.72, 333.82, 320.35, 322.1, 321.13, 316.24, 323.28, 312.33, 325.24, 310.38]], "area": 137.2716999999999, "iscrowd": 0, "image_id": 10321, "bbox": [320.35, 303.93, 12.9, 29.89], "category_id": 1, "id": 1294505}, {"segmentation": [[597.29, 224.81, 596.55, 223.02, 596.18, 220.06, 596.18, 217.71, 596.67, 216.41, 597.29, 215.98, 597.35, 214.68, 598.16, 213.75, 599.15, 214.19, 599.39, 214.68, 599.39, 214.8, 599.46, 216.6, 599.58, 216.72, 600.32, 217.34, 600.57, 217.89, 600.57, 218.7, 600.75, 220.98, 600.75, 222.84, 599.95, 224.88, 599.64, 225.68, 599.46, 225.8, 599.21, 225.87]], "area": 40.53215000000066, "iscrowd": 0, "image_id": 10281, "bbox": [596.18, 213.75, 4.57, 12.12], "category_id": 1, "id": 1298951}, {"segmentation": [[368.26, 307.51, 369.71, 307.47, 371.11, 307.07, 371.2, 304.93, 369.45, 303.57, 367.48, 304.22, 367.39, 305.54, 367.87, 306.33]], "area": 11.576249999999987, "iscrowd": 0, "image_id": 10321, "bbox": [367.39, 303.57, 3.81, 3.94], "category_id": 1, "id": 1306570}, {"segmentation": [[363.53, 345.1, 362.95, 339.24, 363.92, 336.31, 361.97, 335.92, 360.99, 331.23, 361.19, 329.47, 362.56, 330.06, 363.92, 330.65, 364.9, 328.3, 367.64, 325.17, 366.46, 323.42, 365.88, 322.05, 367.24, 318.92, 369.98, 318.34, 373.11, 321.07, 374.47, 324.78, 373.11, 325.96, 376.43, 329.86, 378.38, 335.92, 379.16, 344.32, 377.01, 346.08, 376.82, 353.11, 372.13, 350.38, 367.05, 346.08, 363.53, 346.08]], "area": 373.8783000000003, "iscrowd": 0, "image_id": 10321, "bbox": [360.99, 318.34, 18.17, 34.77], "category_id": 1, "id": 1319412}, {"segmentation": [[292.51, 331.14, 291.66, 322.6, 292.23, 313.2, 299.06, 313.2, 300.77, 306.94, 305.89, 306.37, 307.03, 311.49, 306.46, 315.76, 307.32, 320.6, 307.32, 323.17, 306.75, 328.29, 305.04, 336.26, 305.89, 345.94, 305.89, 348.79, 298.78, 349.93, 297.35, 349.64, 295.08, 343.09, 293.94, 335.69]], "area": 508.1385499999998, "iscrowd": 0, "image_id": 10321, "bbox": [291.66, 306.37, 15.66, 43.56], "category_id": 1, "id": 1325000}, {"segmentation": [[269.98, 346.19, 270.42, 339.11, 267.32, 335.57, 266.88, 326.72, 268.87, 323.62, 263.78, 322.29, 262.45, 320.3, 263.56, 319.41, 269.09, 319.19, 269.31, 318.09, 270.86, 315.87, 271.53, 312.33, 274.4, 310.56, 277.5, 312.11, 277.94, 314.54, 280.82, 316.98, 279.94, 320.74, 277.28, 321.85, 277.28, 324.95, 275.29, 330.04, 278.17, 340.66, 279.49, 345.31, 279.94, 348.19, 277.06, 348.63, 274.85, 339.55, 274.18, 344.2, 273.3, 349.07, 270.64, 351.06, 269.75, 348.41]], "area": 338.7781500000001, "iscrowd": 0, "image_id": 10321, "bbox": [262.45, 310.56, 18.37, 40.5], "category_id": 1, "id": 1325448}, {"segmentation": [[454.38, 341.75, 455.02, 324.37, 457.28, 323.41, 457.28, 316.65, 459.53, 313.43, 465.32, 318.58, 465.64, 324.37, 467.9, 326.63, 470.47, 335.32, 467.25, 335.64, 468.22, 365.57, 470.79, 368.14, 468.22, 370.07, 464.36, 368.46, 463.39, 347.22, 461.78, 345.94, 462.1, 366.85, 456.95, 369.11, 457.28, 351.41, 454.7, 342.72]], "area": 570.7439500000014, "iscrowd": 0, "image_id": 10321, "bbox": [454.38, 313.43, 16.41, 56.64], "category_id": 1, "id": 1325905}, {"segmentation": [[61.25, 364.69, 54.64, 365.93, 55.47, 358.5, 55.88, 341.15, 54.64, 332.06, 56.29, 328.35, 55.47, 323.8, 53.4, 321.74, 54.64, 309.76, 59.6, 307.28, 59.6, 300.26, 65.38, 297.78, 67.86, 301.91, 67.86, 304.8, 71.16, 308.93, 65.38, 314.3, 64.55, 318.85, 71.99, 322.98, 72.81, 331.24, 71.99, 346.1, 72.81, 356.02, 76.53, 356.02, 76.53, 358.08, 75.29, 360.56, 68.68, 364.28, 67.86, 360.97, 65.38, 343.63, 62.9, 344.87, 61.66, 363.04]], "area": 899.84315, "iscrowd": 0, "image_id": 10321, "bbox": [53.4, 297.78, 23.13, 68.15], "category_id": 1, "id": 1327430}, {"segmentation": [[252.72, 314.6, 254.71, 312.79, 254.54, 310.14, 255.7, 306.0, 260.66, 304.02, 263.14, 307.33, 263.14, 311.13, 261.99, 313.78, 261.99, 315.93, 263.14, 318.91, 262.15, 320.56, 261.49, 321.88, 262.15, 328.83, 261.82, 331.31, 257.35, 327.01, 255.7, 325.36]], "area": 180.52394999999996, "iscrowd": 0, "image_id": 10321, "bbox": [252.72, 304.02, 10.42, 27.29], "category_id": 1, "id": 1329455}, {"segmentation": [[278.77, 322.04, 280.48, 320.64, 280.98, 318.23, 280.08, 316.52, 280.28, 314.71, 281.68, 313.21, 285.3, 313.31, 288.11, 314.41, 288.31, 316.42, 287.51, 320.24, 288.11, 322.45, 288.92, 324.15, 289.92, 326.16, 289.32, 330.78, 288.41, 339.72, 286.51, 336.1, 283.79, 331.18, 282.29, 326.36, 279.88, 324.45, 279.17, 323.35, 278.37, 322.65]], "area": 158.06115000000014, "iscrowd": 0, "image_id": 10321, "bbox": [278.37, 313.21, 11.55, 26.51], "category_id": 1, "id": 1329877}, {"segmentation": [[592.79, 206.34, 594.2, 214.79, 600.18, 219.37, 606.17, 220.42, 610.4, 219.01, 618.85, 211.62, 622.72, 204.58, 621.31, 198.59, 597.01, 205.28], [635.87, 159.37, 640.0, 159.37, 640.0, 175.55, 638.29, 175.55, 635.06, 161.8]], "area": 463.3245500000009, "iscrowd": 0, "image_id": 10097, "bbox": [592.79, 159.37, 47.21, 61.05], "category_id": 2, "id": 1334389}, {"segmentation": [[82.25, 118.89, 71.43, 118.05, 70.78, 116.46, 73.3, 112.45, 75.54, 109.28, 86.35, 110.03, 86.35, 113.57, 82.34, 119.07]], "area": 113.0777999999999, "iscrowd": 0, "image_id": 10434, "bbox": [70.78, 109.28, 15.57, 9.79], "category_id": 3, "id": 1339072}, {"segmentation": [[460.53, 199.96, 460.31, 206.54, 447.72, 209.6, 441.26, 211.53, 434.57, 211.41, 427.88, 209.94, 427.88, 205.52, 427.88, 201.55, 427.88, 199.17, 434.23, 194.07, 451.92, 193.61, 458.95, 197.92, 460.42, 198.15]], "area": 481.9141999999998, "iscrowd": 0, "image_id": 10318, "bbox": [427.88, 193.61, 32.65, 17.92], "category_id": 3, "id": 1344065}, {"segmentation": [[639.98, 208.91, 632.2, 207.46, 618.79, 207.75, 616.05, 209.2, 611.43, 210.93, 606.24, 211.94, 604.8, 213.38, 604.65, 217.85, 608.11, 218.71, 609.56, 219.29, 610.42, 223.04, 610.85, 226.21, 611.43, 227.22, 611.72, 227.22, 613.16, 226.21, 613.45, 224.91, 627.87, 225.92, 630.32, 226.21, 633.35, 226.36, 636.38, 227.08, 638.83, 228.09, 639.98, 229.38, 640.0, 227.94]], "area": 565.3374000000007, "iscrowd": 0, "image_id": 10318, "bbox": [604.65, 207.46, 35.35, 21.92], "category_id": 3, "id": 1346177}, {"segmentation": [[405.91, 202.93, 408.08, 163.47, 467.48, 161.31, 476.15, 150.9, 495.65, 158.71, 496.09, 161.31, 543.34, 173.88, 544.64, 275.32, 542.91, 310.87, 513.43, 315.21, 498.69, 265.79, 512.56, 204.23], [561.4, 205.1, 566.37, 213.9, 573.26, 221.93, 564.84, 251.02, 564.08, 250.63, 561.02, 210.07]], "area": 10037.861700000001, "iscrowd": 0, "image_id": 10239, "bbox": [405.91, 150.9, 167.35, 164.31], "category_id": 6, "id": 1366308}, {"segmentation": [[302.25, 220.17, 301.97, 222.9, 299.79, 223.72, 297.07, 224.53, 293.26, 223.99, 292.98, 222.9, 292.98, 221.81, 293.26, 220.99, 290.8, 220.17, 291.35, 219.36, 291.89, 218.54, 291.35, 218.27, 289.44, 219.36, 288.35, 219.08, 286.72, 218.54, 286.72, 217.72, 288.08, 216.36, 289.17, 216.09, 289.71, 213.63, 289.71, 211.45, 289.17, 207.37, 288.9, 206.28, 288.9, 206.0, 291.62, 205.73, 292.98, 205.46, 295.16, 204.91, 298.98, 204.37, 303.06, 204.1, 305.79, 204.1, 309.6, 204.1, 311.24, 205.73, 313.69, 207.09, 313.69, 204.1, 316.96, 201.92, 319.14, 200.28, 321.32, 199.46, 325.95, 199.46, 329.77, 199.46, 332.77, 199.74, 335.22, 201.1, 337.94, 205.19, 339.85, 210.09, 338.22, 215.27, 337.94, 216.9, 337.94, 218.81, 337.4, 219.08, 318.05, 220.72, 316.42, 222.35, 316.69, 223.72, 316.69, 226.17, 316.42, 227.53, 312.6, 228.89, 310.42, 225.35, 310.15, 224.26, 309.6, 223.17, 303.34, 220.99]], "area": 953.5763000000004, "iscrowd": 0, "image_id": 10342, "bbox": [286.72, 199.46, 53.13, 29.43], "category_id": 8, "id": 1370191}, {"segmentation": [[407.91, 207.89, 408.72, 165.11, 466.84, 160.26, 462.8, 166.72, 469.26, 169.95, 469.26, 174.79, 473.3, 186.09, 472.49, 200.62, 466.84, 207.08, 447.47, 215.15, 436.97, 210.31, 424.06, 210.31, 420.83, 200.62, 412.76, 203.85], [493.48, 161.88, 541.91, 174.79, 542.71, 270.04, 540.29, 313.63, 534.64, 304.75, 529.8, 304.75, 527.38, 314.43, 509.62, 315.24, 505.58, 275.69, 505.58, 274.88, 502.35, 239.37, 499.13, 180.44, 491.05, 178.02], [561.28, 207.08, 569.35, 213.54, 570.16, 245.82, 566.12, 249.86, 560.47, 243.4]], "area": 8881.1214, "iscrowd": 0, "image_id": 10239, "bbox": [407.91, 160.26, 162.25, 154.98], "category_id": 8, "id": 1370910}, {"segmentation": [[153.58, 210.98, 148.73, 210.8, 149.57, 208.56, 154.7, 208.84, 154.14, 210.52]], "area": 11.495099999999981, "iscrowd": 0, "image_id": 10058, "bbox": [148.73, 208.56, 5.97, 2.42], "category_id": 9, "id": 1375447}, {"segmentation": [[81.62, 76.51, 70.1, 76.01, 70.93, 57.48, 80.61, 57.82]], "area": 197.22820000000007, "iscrowd": 0, "image_id": 10434, "bbox": [70.1, 57.48, 11.52, 19.03], "category_id": 10, "id": 1384480}, {"segmentation": [[119.09, 51.98, 119.63, 50.18, 126.55, 50.54, 125.56, 68.78, 125.02, 69.05, 119.27, 69.41, 119.18, 67.25]], "area": 129.79064999999989, "iscrowd": 0, "image_id": 10434, "bbox": [119.09, 50.18, 7.46, 19.23], "category_id": 10, "id": 1384691}, {"segmentation": [[170.75, 97.16, 176.99, 96.9, 176.86, 105.48, 171.01, 105.74, 170.75, 97.42]], "area": 51.91680000000012, "iscrowd": 0, "image_id": 10434, "bbox": [170.75, 96.9, 6.24, 8.84], "category_id": 10, "id": 1386730}, {"segmentation": [[166.63, 96.18, 159.56, 96.75, 159.7, 100.14, 165.64, 103.25, 166.91, 101.97]], "area": 38.41675, "iscrowd": 0, "image_id": 10434, "bbox": [159.56, 96.18, 7.35, 7.07], "category_id": 10, "id": 1386805}, {"segmentation": [[391.39, 27.47, 393.94, 25.44, 400.56, 24.42, 405.65, 16.53, 407.68, 11.43, 412.52, 7.99, 421.69, 0.0, 423.98, 1.12, 421.94, 11.56, 419.14, 15.63, 417.36, 18.43, 416.85, 23.52, 411.76, 26.82, 412.01, 30.89, 414.81, 33.69, 417.87, 35.22, 417.61, 38.78, 412.78, 39.04, 411.5, 38.02, 409.72, 34.45, 405.39, 36.49, 402.08, 35.47, 398.01, 32.42, 394.96, 29.62, 391.9, 28.34, 391.14, 27.84]], "area": 486.99205000000063, "iscrowd": 0, "image_id": 10445, "bbox": [391.14, 0.0, 32.84, 39.04], "category_id": 16, "id": 1399705}, {"segmentation": [[352.95, 268.68, 355.74, 285.87, 355.28, 292.38, 356.21, 297.72, 359.69, 299.35, 371.31, 290.06, 365.73, 275.19, 363.41, 271.24, 361.32, 269.84, 357.6, 268.45, 354.35, 267.98]], "area": 353.45510000000087, "iscrowd": 0, "image_id": 10239, "bbox": [352.95, 267.98, 18.36, 31.37], "category_id": 31, "id": 1432731}, {"segmentation": [[379.46, 135.05, 370.0, 144.5, 385.76, 178.52, 382.61, 222.63, 351.73, 221.37, 345.43, 203.73, 351.73, 152.69, 315.81, 140.72, 324.64, 136.94, 370.63, 137.57]], "area": 3008.1870999999983, "iscrowd": 0, "image_id": 10082, "bbox": [315.81, 135.05, 69.95, 87.58], "category_id": 32, "id": 1442505}, {"segmentation": [[345.72, 120.74, 340.22, 122.28, 335.82, 121.62, 331.87, 119.86, 328.13, 118.76, 325.27, 120.3, 326.81, 122.72, 331.87, 124.92, 340.66, 124.7, 345.06, 123.16], [366.83, 117.66, 355.39, 117.66, 356.49, 120.3, 367.05, 120.08, 367.71, 118.54]], "area": 95.50090000000006, "iscrowd": 0, "image_id": 10015, "bbox": [325.27, 117.66, 42.44, 7.26], "category_id": 35, "id": 1450756}, {"segmentation": [[454.53, 140.67, 450.41, 140.5, 429.34, 120.28, 432.26, 118.4, 450.93, 137.76], [455.21, 132.96, 462.92, 136.9, 463.26, 135.19, 456.07, 131.42]], "area": 100.04635000000016, "iscrowd": 0, "image_id": 10015, "bbox": [429.34, 118.4, 33.92, 22.27], "category_id": 35, "id": 1453020}, {"segmentation": [[152.86, 329.05, 147.53, 337.85, 142.45, 347.84, 140.08, 353.85, 138.22, 355.2, 134.92, 354.86, 131.37, 351.9, 137.46, 344.71, 145.67, 331.08, 146.6, 330.41, 149.31, 330.41], [111.65, 334.05, 107.67, 339.88, 100.56, 350.55, 98.7, 353.93, 95.4, 353.85, 91.68, 350.63, 93.46, 346.82, 105.39, 333.45, 106.57, 331.68, 107.42, 331.68]], "area": 335.13825000000054, "iscrowd": 0, "image_id": 10015, "bbox": [91.68, 329.05, 61.18, 26.15], "category_id": 35, "id": 1454651}, {"segmentation": [[329.15, 124.21, 322.91, 124.5, 321.48, 123.92, 318.6, 124.78, 318.89, 126.7, 319.17, 126.99, 319.46, 127.28, 330.4, 126.32, 333.95, 124.11], [356.2, 116.68, 362.04, 114.74, 362.89, 114.31, 365.43, 114.99, 365.51, 115.5, 365.34, 116.01, 357.05, 118.38]], "area": 50.46624999999989, "iscrowd": 0, "image_id": 10015, "bbox": [318.6, 114.31, 46.91, 12.97], "category_id": 35, "id": 1454974}, {"segmentation": [[611.84, 160.72, 630.14, 144.86, 631.36, 127.78, 622.82, 116.19, 616.11, 109.48, 610.01, 108.26, 603.3, 105.82, 597.81, 105.82, 590.49, 111.92, 591.71, 111.92, 604.52, 112.53, 619.16, 118.02, 624.04, 127.17, 624.04, 144.86, 611.84, 157.06]], "area": 421.58930000000197, "iscrowd": 0, "image_id": 10281, "bbox": [590.49, 105.82, 40.87, 54.9], "category_id": 38, "id": 1466660}, {"segmentation": [[570.9, 147.72, 573.42, 147.22, 574.92, 147.47, 576.93, 149.23, 577.31, 152.24, 577.06, 155.26, 576.81, 157.64, 579.07, 159.65, 580.2, 157.77, 581.7, 155.38, 581.96, 150.74, 580.7, 146.97, 578.31, 143.83, 572.91, 142.32, 570.78, 143.08, 569.27, 144.08, 568.02, 145.21, 567.51, 147.72, 569.4, 148.98]], "area": 102.07080000000099, "iscrowd": 0, "image_id": 10281, "bbox": [567.51, 142.32, 14.45, 17.33], "category_id": 38, "id": 1468369}, {"segmentation": [[142.31, 23.02, 152.54, 24.86, 152.54, 27.74, 152.54, 38.63, 151.89, 40.2, 149.53, 42.04, 144.8, 42.04, 141.4, 40.59, 140.35, 33.77, 139.95, 26.04]], "area": 210.18714999999995, "iscrowd": 0, "image_id": 10281, "bbox": [139.95, 23.02, 12.59, 19.02], "category_id": 38, "id": 1468450}, {"segmentation": [[68.31, 105.33, 50.73, 117.21, 47.4, 124.81, 41.7, 145.25, 41.7, 180.89, 70.69, 220.34, 87.8, 238.4, 95.88, 249.8, 101.1, 257.41, 103.48, 259.78, 112.03, 256.93, 118.69, 251.23, 121.54, 249.8, 132.47, 257.88, 138.17, 258.36, 148.63, 261.21, 160.04, 262.63, 162.89, 263.11, 176.67, 276.89, 183.8, 278.32, 204.23, 268.34, 211.36, 265.01, 217.07, 262.63, 227.05, 258.36, 211.36, 254.56, 193.78, 255.98, 176.19, 255.98, 168.59, 252.18, 164.79, 243.15, 151.48, 238.4, 144.83, 238.4, 139.12, 238.87, 132.47, 238.87, 129.62, 238.87, 120.11, 236.5, 115.36, 234.59, 108.23, 239.82, 104.43, 233.64, 107.76, 232.22, 107.76, 220.34, 106.33, 213.21, 99.68, 202.75, 96.35, 198.48, 91.6, 193.72, 83.04, 189.45, 64.98, 188.02, 67.84, 174.71, 69.26, 168.53, 68.79, 160.46, 60.23, 148.1, 64.98, 128.14, 69.74, 119.58, 74.97, 114.36, 77.82, 111.5, 72.59, 106.75]], "area": 5806.002300000002, "iscrowd": 0, "image_id": 10281, "bbox": [41.7, 105.33, 185.35, 172.99], "category_id": 38, "id": 1469313}, {"segmentation": [[521.87, 353.58, 496.69, 340.62, 479.29, 344.7, 441.51, 338.03, 390.04, 336.18, 378.56, 345.07, 371.53, 343.95, 359.68, 337.29, 341.53, 336.18, 315.24, 348.4, 308.95, 348.77, 294.5, 336.55, 236.0, 336.18, 188.23, 341.73, 172.31, 348.03, 168.23, 338.03, 183.41, 329.51, 206.0, 309.89, 232.66, 293.96, 251.55, 312.85, 285.99, 298.41, 303.02, 325.07, 354.49, 319.89, 392.63, 325.07, 426.33, 275.82, 448.55, 302.85, 475.21, 290.63, 500.76, 315.44, 509.65, 329.88, 522.98, 332.48, 528.17, 346.55, 526.68, 350.62]], "area": 10490.944599999999, "iscrowd": 0, "image_id": 10281, "bbox": [168.23, 275.82, 359.94, 77.76], "category_id": 38, "id": 1469830}, {"segmentation": [[583.33, 278.29, 599.83, 283.92, 612.3, 302.03, 625.58, 323.76, 617.94, 328.59, 580.51, 322.96, 556.77, 331.0, 540.27, 330.2, 540.27, 324.57, 507.27, 326.17, 555.16, 316.52, 570.05, 305.65, 576.89, 296.8, 580.92, 280.3]], "area": 2275.8699500000002, "iscrowd": 0, "image_id": 10281, "bbox": [507.27, 278.29, 118.31, 52.71], "category_id": 38, "id": 1469921}, {"segmentation": [[436.54, 270.08, 451.21, 262.91, 454.15, 269.76, 460.67, 294.87, 472.41, 289.98, 482.19, 294.54, 492.96, 281.82, 501.11, 290.96, 507.3, 306.93, 519.7, 308.24, 517.41, 288.02, 514.8, 278.24, 482.85, 276.28, 471.11, 281.82, 462.63, 264.54, 451.21, 248.89, 446.65, 252.15]], "area": 1340.1867500000008, "iscrowd": 0, "image_id": 10281, "bbox": [436.54, 248.89, 83.16, 59.35], "category_id": 38, "id": 1469970}, {"segmentation": [[365.52, 305.25, 342.86, 304.26, 331.13, 303.86, 320.79, 300.48, 317.21, 299.89, 302.31, 304.86, 290.38, 308.63, 283.42, 304.86, 279.65, 299.69, 289.19, 284.38, 308.67, 268.28, 341.47, 249.6, 351.4, 243.83, 352.2, 242.44, 360.15, 266.29, 367.7, 280.41, 372.87, 275.44, 392.35, 262.12, 397.32, 261.52, 399.31, 265.1, 403.09, 280.61, 407.26, 291.34, 396.33, 300.09, 386.98, 302.27]], "area": 4563.353949999999, "iscrowd": 0, "image_id": 10281, "bbox": [279.65, 242.44, 127.61, 66.19], "category_id": 38, "id": 1470035}, {"segmentation": [[226.51, 119.49, 228.51, 127.48, 226.7, 128.02, 226.7, 130.2, 226.7, 130.74, 227.06, 134.73, 227.6, 136.91, 228.51, 141.63, 228.69, 142.18, 229.96, 149.8, 229.96, 153.43, 228.69, 157.96, 226.88, 160.14, 225.79, 162.14, 225.06, 164.68, 224.52, 167.04, 225.61, 180.83, 225.79, 184.46, 237.22, 189.18, 237.4, 157.96, 238.13, 153.43, 238.31, 149.25, 238.31, 147.44, 240.13, 144.35, 235.77, 125.66, 235.04, 125.3, 233.41, 117.31, 232.5, 117.31, 229.78, 116.04, 226.88, 117.86, 226.15, 120.22], [231.41, 211.96, 235.73, 230.47, 241.28, 229.03, 241.08, 223.68, 240.05, 218.13, 239.64, 212.99, 237.79, 209.91, 234.91, 210.94, 231.41, 211.76]], "area": 847.6174500000003, "iscrowd": 0, "image_id": 10243, "bbox": [224.52, 116.04, 16.76, 114.43], "category_id": 44, "id": 1483932}, {"segmentation": [[4.62, 220.98, 33.77, 261.47, 41.06, 265.52, 59.68, 293.86, 15.96, 361.88, 0.57, 342.45, 0.0, 218.55]], "area": 4738.451349999999, "iscrowd": 0, "image_id": 10046, "bbox": [0.0, 218.55, 59.68, 143.33], "category_id": 44, "id": 1487988}, {"segmentation": [[261.06, 113.81, 261.34, 105.59, 275.86, 55.44, 279.42, 57.08, 280.52, 59.82, 272.02, 96.27, 272.02, 96.54, 271.75, 97.37, 267.64, 109.97]], "area": 387.5058499999995, "iscrowd": 0, "image_id": 10046, "bbox": [261.06, 55.44, 19.46, 58.37], "category_id": 50, "id": 1530578}, {"segmentation": [[267.77, 155.72, 251.32, 138.22, 237.48, 123.6, 222.86, 105.46, 217.64, 90.05, 218.94, 89.27, 227.82, 98.93, 246.1, 119.82, 255.76, 136.01, 261.76, 142.79, 272.21, 150.76]], "area": 537.1475499999998, "iscrowd": 0, "image_id": 10046, "bbox": [217.64, 89.27, 54.57, 66.45], "category_id": 50, "id": 1531235}, {"segmentation": [[250.79, 124.79, 277.17, 113.53, 303.2, 106.85, 328.87, 105.44, 332.39, 114.94, 339.42, 159.96, 314.8, 209.9, 301.79, 214.12, 273.65, 193.72, 231.8, 171.92, 230.04, 134.99, 243.05, 129.36, 269.78, 157.14, 273.65, 155.03]], "area": 7925.686750000002, "iscrowd": 0, "image_id": 10046, "bbox": [230.04, 105.44, 109.38, 108.68], "category_id": 51, "id": 1540184}, {"segmentation": [[369.56, 110.78, 364.18, 99.0, 363.17, 95.63, 365.52, 86.88, 373.27, 78.8, 391.78, 67.35, 395.48, 65.33, 406.59, 82.5, 414.67, 76.11, 410.3, 61.63, 419.39, 56.58, 441.94, 50.52, 467.86, 44.13, 488.39, 43.45, 498.49, 45.81, 500.18, 53.55, 498.16, 71.06, 499.17, 82.17, 476.28, 74.42, 462.47, 71.73, 439.58, 74.76, 422.08, 79.14, 403.9, 84.86, 384.04, 89.24, 377.64, 99.67]], "area": 3189.1501500000004, "iscrowd": 0, "image_id": 10046, "bbox": [363.17, 43.45, 137.01, 67.33], "category_id": 51, "id": 1540335}, {"segmentation": [[306.14, 94.62, 316.12, 102.94, 336.08, 110.15, 341.07, 127.34, 353.82, 135.1, 368.8, 112.37, 363.25, 92.96, 377.11, 76.32, 396.52, 61.35, 387.65, 51.93, 363.81, 54.14, 341.07, 58.58, 320.56, 73.0, 341.07, 84.64, 312.79, 81.87, 303.92, 89.63]], "area": 3479.3437500000005, "iscrowd": 0, "image_id": 10046, "bbox": [303.92, 51.93, 92.6, 83.17], "category_id": 51, "id": 1540502}, {"segmentation": [[86.29, 254.56, 76.58, 241.62, 61.48, 234.07, 55.01, 222.2, 78.74, 200.63, 96.0, 190.92, 104.63, 186.61, 120.81, 175.82, 159.64, 171.51, 180.13, 166.11, 177.98, 177.98, 177.98, 189.84, 144.54, 215.73, 111.1, 240.54]], "area": 5238.639949999999, "iscrowd": 0, "image_id": 10046, "bbox": [55.01, 166.11, 125.12, 88.45], "category_id": 51, "id": 1540993}, {"segmentation": [[250.51, 123.62, 289.24, 107.87, 308.27, 103.93, 301.05, 79.65, 282.67, 79.65, 248.55, 92.77, 248.55, 92.77, 224.92, 103.93, 219.67, 115.09, 203.92, 126.25, 224.92, 132.81, 247.89, 121.65]], "area": 2594.32115, "iscrowd": 0, "image_id": 10046, "bbox": [203.92, 79.65, 104.35, 53.16], "category_id": 51, "id": 1541105}, {"segmentation": [[291.62, 56.01, 317.94, 51.45, 324.96, 58.12, 322.15, 69.35, 317.24, 70.75, 306.71, 65.13, 292.32, 58.12], [279.34, 64.78, 284.95, 62.68, 305.66, 74.96, 312.33, 80.57, 307.06, 84.44, 300.04, 81.98, 296.53, 78.47, 284.95, 78.82, 274.07, 79.52], [264.6, 71.8, 247.76, 78.47, 244.95, 86.19, 244.6, 90.4, 259.34, 89.0, 264.6, 84.79, 272.32, 65.13]], "area": 1059.1706, "iscrowd": 0, "image_id": 10046, "bbox": [244.6, 51.45, 80.36, 38.95], "category_id": 51, "id": 1541218}, {"segmentation": [[378.82, 203.13, 364.17, 158.73, 378.36, 145.45, 381.57, 135.84, 380.65, 106.09, 414.53, 93.73, 426.89, 99.68, 438.79, 114.33, 444.28, 116.16, 439.71, 95.56, 453.44, 83.65, 476.33, 72.67, 496.93, 85.03, 494.64, 115.7, 512.03, 121.65, 516.15, 99.22, 543.16, 95.1, 568.34, 116.16, 564.22, 138.59, 574.75, 151.41, 586.65, 163.77, 590.77, 177.04, 588.94, 190.77, 568.8, 202.22, 580.24, 230.14, 545.45, 256.69, 535.38, 266.76, 490.52, 275.46, 453.9, 270.88, 424.6, 256.69, 420.02, 242.96, 408.58, 239.76, 379.74, 216.41, 376.99, 200.84]], "area": 31860.3224, "iscrowd": 0, "image_id": 10303, "bbox": [364.17, 72.67, 226.6, 202.79], "category_id": 52, "id": 1543566}, {"segmentation": [[416.93, 409.46, 416.93, 409.46, 424.41, 390.76, 439.37, 375.8, 457.13, 367.39, 490.79, 365.52, 514.16, 376.74, 524.44, 392.63, 535.66, 410.39, 535.66, 410.39]], "area": 3876.4890499999988, "iscrowd": 0, "image_id": 10403, "bbox": [416.93, 365.52, 118.73, 44.87], "category_id": 55, "id": 1556639}, {"segmentation": [[581.26, 406.75, 583.47, 391.71, 584.8, 375.35, 592.31, 354.57, 599.83, 340.86, 605.58, 330.25, 615.75, 322.29, 630.34, 309.46, 639.19, 305.93, 640.0, 416.0, 625.04, 405.87, 596.29, 404.54, 582.59, 405.87]], "area": 4269.166750000004, "iscrowd": 0, "image_id": 10403, "bbox": [581.26, 305.93, 58.74, 110.07], "category_id": 55, "id": 1558516}, {"segmentation": [[282.32, 187.9, 259.88, 175.75, 253.34, 164.53, 247.73, 144.9, 247.73, 123.4, 255.21, 97.22, 271.1, 73.85, 292.6, 65.44, 314.1, 58.89, 329.06, 55.16, 340.28, 57.02, 352.43, 61.7, 379.54, 80.4, 385.15, 99.09, 387.02, 129.01, 383.28, 130.88, 367.39, 123.4, 331.87, 124.33, 305.69, 136.49, 287.93, 147.7, 287.93, 175.75]], "area": 10059.791499999998, "iscrowd": 0, "image_id": 10403, "bbox": [247.73, 55.16, 139.29, 132.74], "category_id": 55, "id": 1558638}, {"segmentation": [[361.78, 230.9, 318.78, 229.03, 300.08, 233.71, 287.93, 247.73, 279.51, 262.69, 279.51, 294.47, 291.67, 315.04, 334.67, 261.75]], "area": 3495.946350000002, "iscrowd": 0, "image_id": 10403, "bbox": [279.51, 229.03, 82.27, 86.01], "category_id": 55, "id": 1558700}, {"segmentation": [[92.11, 343.17, 106.58, 338.35, 122.08, 337.31, 143.1, 336.97, 163.08, 354.2, 167.91, 359.37, 175.83, 378.66, 175.14, 402.09, 171.01, 413.12, 167.91, 415.18, 66.27, 416.0, 60.75, 400.37, 64.2, 378.66, 70.4, 363.16, 73.16, 359.37, 83.49, 347.65, 90.38, 343.86]], "area": 7657.026199999999, "iscrowd": 0, "image_id": 10403, "bbox": [60.75, 336.97, 115.08, 79.03], "category_id": 55, "id": 1558874}, {"segmentation": [[41.5, 212.52, 49.46, 192.62, 59.19, 179.35, 79.09, 176.7, 109.16, 182.89, 109.16, 182.89]], "area": 1005.4959000000003, "iscrowd": 0, "image_id": 10403, "bbox": [41.5, 176.7, 67.66, 35.82], "category_id": 55, "id": 1558908}, {"segmentation": [[263.21, 368.75, 261.83, 356.69, 265.27, 344.98, 274.58, 336.36, 283.19, 331.88, 290.08, 329.47, 293.53, 323.96, 293.18, 317.76, 290.43, 313.28, 288.01, 308.45, 273.2, 312.93, 262.17, 322.92, 253.21, 333.26, 249.77, 339.12, 247.7, 347.04, 247.01, 359.45]], "area": 1168.2665999999992, "iscrowd": 0, "image_id": 10403, "bbox": [247.01, 308.45, 46.52, 60.3], "category_id": 55, "id": 1559164}, {"segmentation": [[319.32, 55.71, 316.79, 51.49, 312.14, 52.76, 310.87, 54.66, 310.24, 57.61, 313.41, 62.26, 314.46, 63.32, 310.66, 67.75, 308.76, 65.43, 304.12, 65.01, 301.37, 65.85, 297.99, 63.74, 293.98, 60.78, 296.3, 57.4, 298.84, 53.81, 298.84, 51.7, 295.88, 51.7, 292.71, 53.18, 288.7, 53.39, 289.12, 44.52, 294.61, 34.38, 301.37, 29.53, 305.17, 28.68, 305.81, 28.68, 306.86, 25.51, 309.82, 24.46, 311.93, 26.36, 313.83, 26.36, 315.73, 27.63, 318.27, 34.6, 318.48, 36.28, 316.79, 39.66, 318.9, 43.68, 320.17, 44.94, 326.08, 45.58]], "area": 875.8981499999999, "iscrowd": 0, "image_id": 10414, "bbox": [288.7, 24.46, 37.38, 43.29], "category_id": 56, "id": 1560923}, {"segmentation": [[327.14, 187.65, 319.74, 170.8, 323.03, 160.94, 326.72, 151.09, 337.41, 146.57, 345.21, 137.53, 353.43, 131.37, 365.34, 134.65, 365.34, 143.28, 359.59, 153.14, 353.43, 157.66, 358.77, 173.27, 360.41, 188.06, 356.3, 189.29, 344.39, 189.29]], "area": 1727.1499999999992, "iscrowd": 0, "image_id": 10414, "bbox": [319.74, 131.37, 45.6, 57.92], "category_id": 56, "id": 1561570}, {"segmentation": [[303.68, 183.91, 304.6, 182.99, 306.89, 179.09, 309.19, 177.26, 311.02, 177.03, 313.55, 175.19, 313.32, 173.58, 312.17, 171.06, 309.88, 168.31, 307.81, 167.85, 307.58, 168.08, 305.29, 167.39, 302.53, 166.01, 300.7, 166.01, 296.57, 166.24, 291.75, 168.31, 290.14, 170.83, 288.3, 172.9, 287.84, 174.04, 288.53, 180.93, 295.42, 183.91, 300.01, 185.52, 302.76, 185.29, 303.68, 185.06]], "area": 347.4833500000003, "iscrowd": 0, "image_id": 10414, "bbox": [287.84, 166.01, 25.71, 19.51], "category_id": 56, "id": 1562132}, {"segmentation": [[401.92, 111.38, 407.8, 107.6, 413.27, 106.55, 415.79, 106.97, 417.68, 112.22, 423.57, 109.91, 426.51, 108.86, 431.98, 109.49, 433.87, 112.43, 433.87, 117.69, 432.19, 125.68, 431.77, 127.36, 428.83, 129.88, 419.37, 134.3, 416.21, 133.88, 420.63, 129.88, 420.63, 129.88, 420.42, 128.41, 415.58, 127.57, 412.64, 127.15, 410.11, 129.25, 409.06, 128.41, 406.54, 125.05, 401.49, 123.58, 398.76, 120.42, 398.34, 119.16, 400.44, 113.9]], "area": 637.5105, "iscrowd": 0, "image_id": 10414, "bbox": [398.34, 106.55, 35.53, 27.75], "category_id": 57, "id": 1564863}, {"segmentation": [[252.13, 336.27, 257.54, 325.64, 265.65, 319.33, 272.32, 317.35, 284.39, 316.99, 292.51, 317.53, 297.73, 319.33, 303.14, 320.77, 308.55, 324.74, 310.71, 328.7, 309.81, 331.41, 306.57, 335.55, 305.84, 340.42, 304.4, 344.56, 300.26, 346.37, 294.13, 348.89, 287.64, 351.95, 285.11, 352.5, 280.25, 349.25, 274.84, 348.71, 270.69, 348.89, 266.19, 349.25, 263.48, 349.61, 262.22, 349.97, 261.32, 350.33, 259.88, 349.97, 258.62, 349.61, 257.36, 347.09, 256.63, 343.48, 255.91, 340.78, 254.65, 338.62, 253.75, 337.35, 253.39, 336.99]], "area": 1539.3582999999996, "iscrowd": 0, "image_id": 10243, "bbox": [252.13, 316.99, 58.58, 35.51], "category_id": 60, "id": 1573412}, {"segmentation": [[245.74, 345.6, 251.26, 341.02, 251.4, 335.23, 249.64, 333.08, 249.64, 330.78, 247.09, 327.15, 243.18, 326.2, 234.15, 325.93, 229.71, 329.3, 226.47, 332.13, 224.18, 335.5, 224.18, 338.87, 223.78, 342.64, 226.2, 346.14, 227.82, 347.49, 231.59, 347.76, 239.0, 346.28, 245.6, 345.74]], "area": 476.1917499999997, "iscrowd": 0, "image_id": 10243, "bbox": [223.78, 325.93, 27.62, 21.83], "category_id": 60, "id": 1573790}, {"segmentation": [[288.64, 315.69, 288.64, 311.88, 292.34, 307.35, 298.54, 305.92, 303.19, 305.68, 306.64, 306.03, 309.03, 306.39, 312.96, 309.13, 314.27, 312.47, 314.75, 317.72, 314.75, 318.91, 313.91, 322.13, 310.93, 323.68, 303.42, 322.36, 298.66, 319.38, 291.98, 317.12]], "area": 343.8032499999999, "iscrowd": 0, "image_id": 10243, "bbox": [288.64, 305.68, 26.11, 18.0], "category_id": 60, "id": 1574716}, {"segmentation": [[359.94, 298.09, 366.91, 304.07, 369.3, 313.43, 363.52, 322.79, 348.39, 325.18, 338.63, 331.35, 327.07, 330.56, 319.11, 327.97, 314.33, 321.39, 314.33, 312.03, 324.88, 302.47, 334.84, 298.29, 342.01, 297.89]], "area": 1393.2164500000013, "iscrowd": 0, "image_id": 10243, "bbox": [314.33, 297.89, 54.97, 33.46], "category_id": 60, "id": 1574880}, {"segmentation": [[334.14, 393.17, 347.74, 387.48, 356.89, 377.59, 356.89, 363.99, 350.7, 356.33, 330.92, 349.41, 317.57, 346.69, 306.69, 347.67, 293.83, 356.82, 284.19, 366.47, 282.95, 379.82, 290.12, 387.98, 304.47, 392.92, 305.21, 397.13, 308.92, 397.87, 314.36, 394.16, 321.53, 394.9, 326.22, 397.37, 330.92, 397.37, 333.64, 395.89]], "area": 2804.1184999999996, "iscrowd": 0, "image_id": 10243, "bbox": [282.95, 346.69, 73.94, 51.18], "category_id": 60, "id": 1575260}, {"segmentation": [[205.05, 294.87, 191.69, 288.63, 188.12, 279.13, 188.12, 276.46, 194.66, 271.71, 196.73, 272.0, 203.56, 269.93, 210.1, 269.03, 216.33, 269.33, 221.67, 271.41, 226.72, 272.6, 229.99, 274.68, 232.07, 279.72, 232.07, 282.69, 219.0, 284.18, 213.96, 287.15, 209.2, 290.41]], "area": 710.4103000000001, "iscrowd": 0, "image_id": 10243, "bbox": [188.12, 269.03, 43.95, 25.84], "category_id": 60, "id": 1575314}, {"segmentation": [[253.7, 266.2, 250.09, 264.93, 248.81, 262.38, 248.81, 261.11, 249.45, 261.11, 250.09, 253.88, 256.67, 248.15, 268.14, 244.54, 278.76, 244.54, 285.99, 246.24, 289.17, 250.49, 289.6, 253.88, 288.32, 256.86, 287.9, 258.13, 284.71, 257.71, 282.8, 259.41, 281.31, 262.59, 277.49, 264.93, 268.57, 267.48, 261.56, 268.75, 255.4, 267.48]], "area": 724.9205000000005, "iscrowd": 0, "image_id": 10243, "bbox": [248.81, 244.54, 40.79, 24.21], "category_id": 60, "id": 1575399}, {"segmentation": [[328.16, 295.98, 312.58, 295.98, 304.53, 292.46, 303.02, 289.19, 302.27, 286.18, 301.26, 281.15, 310.06, 271.34, 318.86, 268.58, 326.4, 266.06, 335.2, 268.58, 344.51, 269.83, 348.78, 273.61, 351.55, 278.13, 351.55, 284.67, 349.28, 287.69, 346.27, 289.95, 340.48, 292.71]], "area": 1148.042800000001, "iscrowd": 0, "image_id": 10243, "bbox": [301.26, 266.06, 50.29, 29.92], "category_id": 60, "id": 1575922}, {"segmentation": [[285.34, 308.1, 291.75, 306.27, 296.48, 303.07, 298.77, 297.88, 298.92, 289.64, 298.31, 285.21, 292.66, 282.0, 285.34, 281.24, 271.75, 281.7, 261.53, 283.38, 256.8, 285.06, 249.93, 289.02, 246.26, 294.21, 246.72, 298.95, 248.55, 303.83, 249.47, 305.51, 246.42, 307.19, 248.71, 308.87, 255.42, 310.39, 266.26, 311.0, 264.58, 312.68, 264.88, 313.29, 271.45, 313.9, 276.64, 313.29, 279.38, 312.07, 281.22, 311.46, 284.57, 310.85]], "area": 1357.62875, "iscrowd": 0, "image_id": 10243, "bbox": [246.26, 281.24, 52.66, 32.66], "category_id": 60, "id": 1576115}, {"segmentation": [[375.0, 280.56, 367.67, 281.65, 357.21, 281.83, 351.08, 281.65, 349.82, 281.65, 350.18, 276.78, 347.3, 273.53, 344.05, 269.93, 339.37, 269.39, 340.09, 265.6, 342.97, 260.38, 351.44, 257.49, 358.83, 256.59, 370.37, 257.13, 375.0, 258.39]], "area": 726.1996999999997, "iscrowd": 0, "image_id": 10243, "bbox": [339.37, 256.59, 35.63, 25.24], "category_id": 60, "id": 1576143}, {"segmentation": [[307.49, 261.58, 307.17, 257.39, 308.14, 254.81, 312.98, 251.58, 316.53, 249.97, 320.72, 248.35, 322.01, 248.35, 327.17, 248.03, 333.63, 247.71, 338.79, 248.03, 343.95, 249.32, 349.76, 253.52, 350.41, 255.77, 351.05, 257.06, 349.44, 258.36, 345.89, 259.32, 340.08, 262.23, 338.47, 267.71, 337.18, 268.36, 328.14, 268.36, 322.66, 266.74, 318.78, 265.13, 313.94, 270.29, 312.98, 269.65, 309.75, 262.23]], "area": 671.1234000000001, "iscrowd": 0, "image_id": 10243, "bbox": [307.17, 247.71, 43.88, 22.58], "category_id": 60, "id": 1576198}, {"segmentation": [[246.44, 273.29, 243.53, 278.45, 245.15, 280.71, 253.54, 286.52, 258.05, 284.9, 263.54, 278.77, 265.15, 275.87, 260.96, 273.61, 249.67, 272.97]], "area": 193.16459999999927, "iscrowd": 0, "image_id": 10243, "bbox": [243.53, 272.97, 21.62, 13.55], "category_id": 60, "id": 1576218}, {"segmentation": [[355.85, 0.0, 526.32, 0.0, 627.25, 0.0, 616.65, 34.81, 592.9, 103.09, 574.67, 125.56, 560.67, 124.71, 556.43, 110.29, 556.43, 92.91, 553.46, 67.04, 542.86, 57.71, 547.53, 95.45, 536.08, 93.76, 513.6, 97.57, 505.12, 116.23, 497.06, 121.74, 479.25, 127.68, 391.05, 98.42, 369.0, 66.19, 351.18, 61.53, 312.17, 58.14, 323.62, 0.0]], "area": 28942.88735, "iscrowd": 0, "image_id": 10403, "bbox": [312.17, 0.0, 315.08, 127.68], "category_id": 62, "id": 1583560}, {"segmentation": [[154.51, 75.8, 148.39, 49.79, 154.89, 47.11, 163.69, 47.87, 167.13, 53.61, 168.66, 64.71, 184.73, 67.77, 184.73, 69.68, 171.72, 69.68, 172.1, 75.42]], "area": 530.8789000000004, "iscrowd": 0, "image_id": 10082, "bbox": [148.39, 47.11, 36.34, 28.69], "category_id": 62, "id": 1594617}, {"segmentation": [[190.7, 70.94, 191.29, 65.43, 188.73, 57.37, 187.35, 49.7, 187.35, 45.96, 190.11, 44.19, 193.45, 43.41, 196.6, 43.21, 198.56, 43.41, 201.51, 44.0, 203.28, 44.19, 204.66, 49.5, 206.23, 57.17, 205.64, 67.59, 205.25, 69.36]], "area": 427.65205, "iscrowd": 0, "image_id": 10082, "bbox": [187.35, 43.21, 18.88, 27.73], "category_id": 62, "id": 1594766}, {"segmentation": [[208.76, 40.08, 211.48, 39.0, 214.01, 38.46, 217.99, 38.27, 222.33, 38.27, 223.24, 38.64, 224.32, 57.09, 224.32, 57.82, 225.05, 58.18, 227.22, 58.18, 230.84, 58.9, 232.83, 60.17, 232.83, 61.07, 232.28, 61.62, 233.91, 64.87, 234.09, 67.95, 232.28, 68.85, 230.29, 65.42, 228.67, 64.33, 228.67, 66.32, 228.48, 68.31, 227.4, 68.67, 225.05, 64.69, 224.5, 65.05, 224.5, 67.41, 223.78, 68.49, 222.51, 66.68, 219.8, 64.33, 214.37, 64.69, 212.02, 64.51, 210.03, 62.34, 208.4, 58.18, 208.22, 54.92, 208.22, 49.13, 208.22, 43.52, 207.49, 41.35, 208.04, 40.63]], "area": 478.71460000000013, "iscrowd": 0, "image_id": 10082, "bbox": [207.49, 38.27, 26.6, 30.58], "category_id": 62, "id": 1596540}, {"segmentation": [[152.12, 73.24, 149.52, 56.07, 134.95, 57.37, 136.24, 70.32]], "area": 227.71305000000004, "iscrowd": 0, "image_id": 10082, "bbox": [134.95, 56.07, 17.17, 17.17], "category_id": 62, "id": 1597397}, {"segmentation": [[319.53, 370.39, 329.13, 375.18, 327.21, 418.36, 323.37, 419.32, 591.08, 414.53, 609.31, 360.79, 607.4, 335.84, 597.8, 316.65, 582.45, 304.18, 571.89, 301.3, 537.35, 324.33, 519.12, 333.92, 438.51, 301.3, 428.92, 303.22, 417.4, 319.53, 418.36, 332.96, 418.36, 337.76, 382.86, 350.24, 379.02, 353.11, 355.03, 337.76, 342.56, 335.84, 328.17, 339.68, 318.57, 350.24, 318.57, 362.71]], "area": 25682.540049999996, "iscrowd": 0, "image_id": 10145, "bbox": [318.57, 301.3, 290.74, 118.02], "category_id": 62, "id": 1598926}, {"segmentation": [[318.14, 382.86, 324.41, 382.01, 325.55, 379.15, 329.54, 380.58, 339.51, 372.31, 337.23, 368.89, 334.67, 366.9, 330.39, 366.61, 315.85, 378.01, 316.71, 383.15]], "area": 205.66459999999992, "iscrowd": 0, "image_id": 10176, "bbox": [315.85, 366.61, 23.66, 16.54], "category_id": 75, "id": 1632982}, {"segmentation": [[226.59, 431.05, 298.65, 436.0, 297.3, 426.55, 296.4, 424.74, 231.09, 417.99, 224.34, 425.64, 225.69, 431.05]], "area": 874.1210999999997, "iscrowd": 0, "image_id": 10176, "bbox": [224.34, 417.99, 74.31, 18.01], "category_id": 75, "id": 1633143}, {"segmentation": [[374.14, 279.06, 397.42, 278.7, 397.9, 275.95, 397.3, 275.0, 373.06, 275.59, 373.06, 277.98]], "area": 87.80820000000018, "iscrowd": 0, "image_id": 10145, "bbox": [373.06, 275.0, 24.84, 4.06], "category_id": 75, "id": 1633279}, {"segmentation": [[267.9, 375.63, 259.19, 376.17, 245.49, 378.48, 244.6, 377.68, 241.39, 374.83, 243.53, 371.01, 267.37, 368.78]], "area": 174.09700000000072, "iscrowd": 0, "image_id": 10176, "bbox": [241.39, 368.78, 26.51, 9.7], "category_id": 75, "id": 1633877}, {"segmentation": [[413.41, 263.24, 430.81, 265.08, 433.32, 266.76, 433.82, 277.63, 431.98, 279.81, 410.74, 277.97, 407.23, 276.13, 405.22, 271.61, 410.57, 265.59, 411.07, 264.58]], "area": 372.3500000000003, "iscrowd": 0, "image_id": 10145, "bbox": [405.22, 263.24, 28.6, 16.57], "category_id": 76, "id": 1634235}, {"segmentation": [[190.87, 159.95, 213.36, 170.45, 214.4, 163.41, 194.56, 155.11, 192.37, 155.22, 190.76, 154.65, 190.29, 154.42, 189.49, 153.72, 189.37, 153.72, 188.45, 153.61, 185.45, 153.96, 184.76, 153.96, 180.49, 154.19, 179.22, 154.88, 179.34, 155.69, 186.95, 155.69, 189.72, 155.69, 191.22, 155.92, 192.6, 156.72, 192.6, 156.95, 192.6, 157.88, 191.45, 159.03]], "area": 171.92539999999957, "iscrowd": 0, "image_id": 10130, "bbox": [179.22, 153.61, 35.18, 16.84], "category_id": 77, "id": 1636873}, {"segmentation": [[423.52, 115.37, 575.22, 41.4, 582.73, 44.08, 584.34, 45.69, 620.25, 113.23, 622.4, 123.95, 619.72, 128.77, 614.89, 133.6, 608.46, 135.2, 465.87, 202.21, 457.83, 197.92, 452.47, 192.56, 434.24, 145.93, 428.35, 131.99, 419.23, 123.95]], "area": 17712.0472, "iscrowd": 0, "image_id": 10130, "bbox": [419.23, 41.4, 203.17, 160.81], "category_id": 77, "id": 1637273}, {"segmentation": [[21.5, 219.0, 100.5, 218.5, 106.5, 225.0, 104.5, 392.5, 82.5, 398.5, 16.0, 399.5, 8.0, 388.5, 10.0, 224.0]], "area": 17232.5, "iscrowd": 0, "image_id": 10130, "bbox": [8.0, 218.5, 98.5, 181.0], "category_id": 77, "id": 1639664}, {"segmentation": [[298.97, 74.62, 298.28, 61.39, 304.72, 60.58, 308.63, 61.62, 308.98, 78.99, 305.07, 79.34]], "area": 169.7854500000001, "iscrowd": 0, "image_id": 10130, "bbox": [298.28, 60.58, 10.7, 18.76], "category_id": 77, "id": 1639926}, {"segmentation": [[309.53, 78.96, 309.53, 62.63, 311.72, 59.98, 315.29, 59.86, 320.81, 59.75, 321.38, 61.25, 321.38, 78.62, 320.35, 79.31]], "area": 224.79370000000037, "iscrowd": 0, "image_id": 10130, "bbox": [309.53, 59.75, 11.85, 19.56], "category_id": 77, "id": 1640051}, {"segmentation": [[536.7, 267.45, 545.46, 267.21, 546.9, 268.77, 546.9, 285.33, 536.58, 285.93, 535.62, 284.49, 535.5, 269.01, 536.46, 266.85]], "area": 205.6679999999997, "iscrowd": 0, "image_id": 10130, "bbox": [535.5, 266.85, 11.4, 19.08], "category_id": 77, "id": 1640094}, {"segmentation": [[558.81, 284.91, 559.61, 267.31, 562.95, 265.58, 568.47, 265.58, 570.31, 267.65, 570.43, 284.91, 564.79, 284.1]], "area": 205.40699999999978, "iscrowd": 0, "image_id": 10130, "bbox": [558.81, 265.58, 11.62, 19.33], "category_id": 77, "id": 1640113}, {"segmentation": [[136.26, 219.98, 231.25, 221.9, 228.37, 396.53, 221.66, 399.41, 135.3, 397.49, 136.26, 361.03]], "area": 16619.879700000005, "iscrowd": 0, "image_id": 10130, "bbox": [135.3, 219.98, 95.95, 179.43], "category_id": 77, "id": 1640127}, {"segmentation": [[288.31, 71.32, 288.12, 62.78, 290.85, 61.5, 294.12, 61.5, 296.67, 61.5, 297.03, 73.68, 288.67, 71.5]], "area": 93.39489999999992, "iscrowd": 0, "image_id": 10130, "bbox": [288.12, 61.5, 8.91, 12.18], "category_id": 77, "id": 1640138}, {"segmentation": [[0.0, 53.22, 65.71, 41.92, 65.71, 34.5, 57.96, 0.0, 0.85, 0.0]], "area": 2969.5187, "iscrowd": 0, "image_id": 10243, "bbox": [0.0, 0.0, 65.71, 53.22], "category_id": 78, "id": 1640488}, {"segmentation": [[436.27, 89.33, 436.68, 68.69, 444.04, 68.49, 445.26, 70.33, 443.42, 91.79, 435.45, 91.79]], "area": 184.5608000000003, "iscrowd": 0, "image_id": 10346, "bbox": [435.45, 68.49, 9.81, 23.3], "category_id": 84, "id": 1653596}, {"segmentation": [[51.76, 182.56, 71.57, 139.46, 77.98, 116.74, 78.56, 110.33, 80.89, 99.26, 82.64, 87.61, 92.54, 75.96, 92.54, 71.3, 104.19, 53.24, 114.09, 42.17, 122.25, 32.27, 129.82, 33.43, 133.32, 38.09, 142.05, 35.76, 153.71, 35.18, 163.61, 38.09, 174.09, 46.25, 174.68, 66.06, 174.09, 72.46, 177.59, 78.87, 171.18, 85.28, 164.19, 95.18, 158.37, 105.67, 158.95, 112.08, 163.61, 130.72, 164.19, 138.29, 168.27, 143.53, 177.59, 144.12, 184.0, 141.2, 185.75, 137.13, 189.82, 138.29, 193.9, 141.2, 201.47, 141.79, 210.79, 141.79, 216.04, 149.94, 209.05, 152.27, 198.56, 154.6, 188.66, 161.59, 180.5, 164.5, 168.85, 170.33, 165.94, 165.67, 142.64, 163.92, 128.07, 163.92, 110.6, 169.75, 105.94, 177.9, 89.04, 179.07, 76.23, 181.4, 59.33, 182.56]], "area": 12441.07505, "iscrowd": 0, "image_id": 10130, "bbox": [51.76, 32.27, 164.28, 150.29], "category_id": 1, "id": 1685208}, {"segmentation": [[416.1, 236.96, 413.93, 236.96, 411.77, 236.96, 411.91, 235.52, 412.49, 234.22, 412.06, 230.61, 411.91, 229.6, 412.78, 228.16, 415.23, 228.45, 416.67, 231.33, 418.26, 234.22, 417.54, 236.52]], "area": 41.93810000000009, "iscrowd": 0, "image_id": 10318, "bbox": [411.77, 228.16, 6.49, 8.8], "category_id": 1, "id": 1695063}, {"segmentation": [[387.12, 237.26, 385.92, 235.53, 386.32, 233.8, 387.52, 232.47, 388.98, 232.47, 389.51, 235.39, 389.38, 236.33, 389.38, 237.12]], "area": 13.368099999999982, "iscrowd": 0, "image_id": 10318, "bbox": [385.92, 232.47, 3.59, 4.79], "category_id": 1, "id": 1695507}, {"segmentation": [[393.77, 240.18, 393.77, 238.67, 395.45, 238.25, 396.21, 238.25, 396.21, 239.42, 396.04, 240.01], [394.11, 241.94, 396.96, 241.86, 395.62, 243.62, 395.7, 244.38, 395.7, 245.39, 394.69, 245.64, 393.94, 244.97]], "area": 10.982549999999947, "iscrowd": 0, "image_id": 10318, "bbox": [393.77, 238.25, 3.19, 7.39], "category_id": 1, "id": 1697617}, {"segmentation": [[514.95, 190.69, 517.11, 184.21, 517.98, 181.62, 521.43, 181.18, 524.02, 187.23, 525.32, 189.83]], "area": 60.95819999999968, "iscrowd": 0, "image_id": 10230, "bbox": [514.95, 181.18, 10.37, 9.51], "category_id": 1, "id": 1700352}, {"segmentation": [[551.97, 184.46, 551.03, 182.57, 550.71, 180.78, 550.39, 178.79, 549.97, 177.84, 549.87, 176.06, 550.08, 174.48, 550.18, 172.8, 550.81, 171.33, 552.81, 170.28, 554.07, 170.28, 553.97, 169.44, 553.76, 168.07, 554.6, 167.23, 555.96, 166.81, 557.54, 166.92, 558.17, 167.65, 557.85, 168.91, 557.33, 170.28, 557.43, 171.22, 557.64, 173.43, 558.38, 175.53, 558.48, 177.32, 558.48, 178.68, 558.9, 180.47, 558.9, 182.36, 558.8, 184.88]], "area": 123.69389999999981, "iscrowd": 0, "image_id": 10230, "bbox": [549.87, 166.81, 9.03, 18.07], "category_id": 1, "id": 1703245}, {"segmentation": [[399.57, 247.34, 399.17, 238.67, 399.17, 235.45, 401.79, 235.25, 404.81, 239.48, 404.81, 243.51, 405.22, 247.54, 403.61, 250.36, 400.58, 248.15]], "area": 69.35205000000016, "iscrowd": 0, "image_id": 10318, "bbox": [399.17, 235.25, 6.05, 15.11], "category_id": 1, "id": 1703852}, {"segmentation": [[582.18, 182.99, 581.89, 176.9, 581.4, 175.45, 579.47, 173.81, 578.89, 172.45, 579.37, 171.2, 580.24, 170.52, 581.02, 170.13, 580.92, 168.68, 580.92, 167.52, 581.6, 167.14, 582.95, 167.04, 583.72, 167.43, 584.21, 168.68, 585.08, 170.04, 588.36, 172.55, 588.85, 173.61, 587.49, 174.68, 586.24, 175.26, 585.56, 178.25, 585.56, 180.38, 585.46, 181.93, 584.98, 183.09]], "area": 78.42550000000037, "iscrowd": 0, "image_id": 10230, "bbox": [578.89, 167.04, 9.96, 16.05], "category_id": 1, "id": 1704484}, {"segmentation": [[196.43, 226.83, 199.11, 216.45, 201.28, 207.59, 199.78, 204.58, 196.1, 202.9, 196.27, 198.39, 196.27, 195.88, 195.1, 193.03, 191.08, 191.86, 188.4, 193.54, 187.57, 196.88, 188.91, 202.74, 186.9, 207.76, 186.73, 212.1, 185.89, 214.11, 183.89, 220.97, 183.89, 222.14, 186.23, 224.15, 187.9, 224.15, 189.24, 218.63, 191.08, 210.43, 191.75, 208.42, 193.09, 208.93, 193.76, 213.61, 192.75, 219.63, 194.93, 221.64, 194.93, 226.32, 196.77, 229.34]], "area": 308.34675000000044, "iscrowd": 0, "image_id": 10281, "bbox": [183.89, 191.86, 17.39, 37.48], "category_id": 1, "id": 1711506}, {"segmentation": [[307.21, 349.9, 328.1, 296.73, 347.1, 281.53, 357.23, 280.27, 352.01, 273.31, 387.61, 232.79, 397.11, 239.75, 397.11, 239.75, 411.04, 237.85, 425.6, 245.45, 430.66, 262.54, 424.33, 280.27, 424.33, 280.27, 419.59, 287.44, 418.63, 289.76, 404.71, 304.96, 404.71, 304.96, 407.87, 343.57, 413.57, 349.27, 413.57, 349.27, 414.2, 341.04, 424.96, 344.84, 424.33, 365.1, 399.64, 370.16, 385.71, 370.16, 377.48, 371.43, 378.75, 377.13, 306.58, 394.85, 318.61, 368.26, 314.18, 361.3, 304.68, 360.03, 306.58, 351.8]], "area": 11986.421250000005, "iscrowd": 0, "image_id": 10130, "bbox": [304.68, 232.79, 125.98, 162.06], "category_id": 1, "id": 1713472}, {"segmentation": [[78.71, 200.97, 26.05, 124.71, 32.86, 98.84, 59.19, 78.87, 107.75, 51.18, 125.91, 35.3, 111.84, 104.29, 105.49, 135.61, 100.04, 208.23]], "area": 8576.342250000002, "iscrowd": 0, "image_id": 10232, "bbox": [26.05, 35.3, 99.86, 172.93], "category_id": 1, "id": 1717125}, {"segmentation": [[41.9, 172.14, 43.63, 172.67, 43.71, 174.78, 45.29, 175.15, 46.65, 176.81, 44.99, 177.94, 45.22, 180.28, 45.37, 184.72, 44.84, 188.56, 45.22, 189.99, 43.71, 189.99, 41.37, 189.46, 40.85, 185.55, 40.7, 181.18, 40.7, 178.77, 38.81, 178.47, 39.42, 175.68, 40.25, 174.85, 40.92, 174.32, 40.92, 172.37, 41.83, 172.22]], "area": 79.07260000000004, "iscrowd": 0, "image_id": 10230, "bbox": [38.81, 172.14, 7.84, 17.85], "category_id": 1, "id": 1722632}, {"segmentation": [[127.57, 6.33, 123.36, 40.06, 59.04, 70.64, 32.68, 90.67, 21.09, 94.89, 1.05, 87.51, 1.05, 48.5, 2.11, 2.11, 124.41, 2.11], [87.51, 216.14, 81.18, 241.44, 80.13, 265.69, 79.07, 281.5, 73.8, 293.1, 68.53, 309.97, 70.64, 319.46, 76.97, 327.89, 85.4, 338.44, 97.0, 342.65, 107.54, 352.14, 117.03, 415.4, 117.03, 421.73, 160.26, 417.51, 141.28, 372.18, 140.22, 330.0, 117.03, 218.24, 95.94, 206.65]], "area": 17710.756699999998, "iscrowd": 0, "image_id": 10232, "bbox": [1.05, 2.11, 159.21, 419.62], "category_id": 1, "id": 1741584}, {"segmentation": [[142.74, 314.45, 157.25, 280.6, 188.42, 272.54, 205.08, 237.07, 240.54, 266.62, 227.11, 285.43, 246.99, 307.47, 235.17, 340.25, 245.38, 381.09, 224.96, 391.27, 206.69, 435.87, 193.25, 451.99, 181.43, 433.18, 155.64, 429.42, 147.58, 412.76, 156.71, 384.28, 149.73, 354.24, 156.71, 331.68]], "area": 14060.934849999998, "iscrowd": 0, "image_id": 10094, "bbox": [142.74, 237.07, 104.25, 214.92], "category_id": 1, "id": 1741894}, {"segmentation": [[517.1, 227.51, 517.51, 231.41, 517.2, 233.88, 514.43, 234.08, 514.74, 230.59, 514.23, 228.23, 514.84, 226.9, 514.84, 225.15, 515.56, 224.84, 516.18, 225.25, 516.9, 226.59]], "area": 22.72984999999996, "iscrowd": 0, "image_id": 10281, "bbox": [514.23, 224.84, 3.28, 9.24], "category_id": 1, "id": 1744146}, {"segmentation": [[258.33, 135.42, 269.64, 122.41, 282.66, 92.42, 293.41, 74.89, 312.64, 62.44, 322.26, 60.74, 320.56, 58.48, 316.03, 48.86, 320.56, 38.11, 329.05, 27.93, 336.97, 22.84, 354.5, 23.87, 364.12, 38.02, 366.38, 47.63, 368.08, 54.42, 369.21, 56.12, 362.42, 61.78, 355.64, 66.3, 355.64, 70.26, 362.42, 75.35, 364.69, 84.97, 363.56, 101.38, 365.82, 113.82, 379.96, 159.97, 383.92, 180.34, 387.88, 193.92, 390.15, 198.44, 391.28, 200.71, 382.79, 212.02, 377.7, 212.02, 370.91, 212.02, 366.95, 212.02, 369.21, 204.67, 374.87, 184.86, 353.94, 135.08, 348.85, 108.49, 300.76, 122.07, 300.19, 122.07, 295.1, 107.92, 287.18, 124.9, 286.05, 126.59, 258.9, 135.65], [348.59, 215.94, 343.72, 243.52, 348.59, 261.37, 338.04, 270.29, 335.61, 291.38, 319.79, 294.22, 319.79, 288.14, 315.32, 294.22, 296.26, 301.93, 295.04, 298.28, 290.58, 297.87, 286.12, 298.28, 307.21, 266.64, 300.72, 266.24, 309.65, 233.79, 300.72, 219.99, 349.4, 212.69, 348.99, 217.56]], "area": 10674.347200000002, "iscrowd": 0, "image_id": 10445, "bbox": [258.33, 22.84, 132.95, 279.09], "category_id": 1, "id": 1750386}, {"segmentation": [[389.16, 87.15, 382.5, 120.44, 375.85, 131.93, 376.45, 143.43, 376.45, 152.51, 390.37, 167.04, 382.5, 164.62, 369.79, 152.51, 371.61, 142.22, 363.14, 142.22, 352.24, 144.04, 351.03, 156.14, 371.0, 165.22, 181.56, 161.59, 172.49, 160.99, 180.35, 157.96, 158.57, 147.67, 154.33, 114.99, 165.22, 106.51, 170.07, 124.67, 177.33, 113.17, 180.96, 116.2, 183.38, 125.88, 175.51, 138.59, 173.09, 145.86, 175.51, 151.3, 186.41, 153.12, 185.8, 131.93, 199.12, 129.51, 196.7, 151.91, 212.43, 149.49, 233.01, 137.38, 237.85, 117.41, 246.93, 118.01, 249.96, 134.96, 259.64, 130.72, 301.4, 133.75, 320.77, 130.72, 333.48, 130.72, 336.51, 108.33, 346.79, 102.88, 350.43, 108.94, 349.82, 133.75, 371.0, 131.93, 372.21, 120.44, 371.0, 107.73, 371.0, 101.07, 366.77, 92.59, 372.21, 92.59, 374.03, 101.07, 376.45, 111.96, 383.11, 99.25, 383.11, 86.54, 387.95, 85.94]], "area": 7013.835699999999, "iscrowd": 0, "image_id": 10229, "bbox": [154.33, 85.94, 236.04, 81.1], "category_id": 2, "id": 1764862}, {"segmentation": [[125.49, 142.56, 166.35, 56.68, 290.33, 59.45, 283.4, 249.91, 284.09, 297.7, 179.51, 307.4, 181.59, 309.47, 162.2, 298.39, 161.51, 295.62, 151.12, 294.93, 152.6, 274.83, 148.32, 271.02, 141.17, 269.11, 136.41, 274.35, 131.17, 275.78, 125.93, 275.78, 125.45, 190.52, 124.02, 141.45]], "area": 36776.659799999994, "iscrowd": 0, "image_id": 10239, "bbox": [124.02, 56.68, 166.31, 252.79], "category_id": 6, "id": 1794665}, {"segmentation": [[118.43, 99.17, 123.51, 93.64, 177.0, 94.97, 167.93, 100.49]], "area": 293.88424999999984, "iscrowd": 0, "image_id": 10318, "bbox": [118.43, 93.64, 58.57, 6.85], "category_id": 9, "id": 1802355}, {"segmentation": [[32.16, 102.5, 36.19, 94.19, 43.09, 94.71, 45.5, 99.73, 49.47, 99.83, 49.78, 102.03, 34.21, 102.65]], "area": 93.96210000000013, "iscrowd": 0, "image_id": 10318, "bbox": [32.16, 94.19, 17.62, 8.46], "category_id": 9, "id": 1802682}, {"segmentation": [[52.03, 99.13, 53.08, 93.69, 56.22, 92.86, 59.46, 92.65, 62.38, 93.27, 63.43, 95.57, 63.32, 97.77, 63.53, 98.92, 65.0, 99.23, 65.41, 99.65, 65.41, 101.11, 64.89, 103.31, 55.17, 104.36, 53.18, 104.25, 52.66, 102.68]], "area": 125.71050000000001, "iscrowd": 0, "image_id": 10318, "bbox": [52.03, 92.65, 13.38, 11.71], "category_id": 9, "id": 1803143}, {"segmentation": [[79.77, 92.59, 90.55, 94.2, 93.32, 94.78, 93.32, 96.68, 83.12, 99.3, 76.56, 93.76]], "area": 65.10764999999994, "iscrowd": 0, "image_id": 10318, "bbox": [76.56, 92.59, 16.76, 6.71], "category_id": 9, "id": 1803353}, {"segmentation": [[13.37, 100.1, 19.53, 98.63, 23.59, 97.89, 32.33, 98.26, 30.98, 102.81, 11.4, 102.44, 12.14, 100.35, 14.23, 99.61]], "area": 78.80505000000002, "iscrowd": 0, "image_id": 10318, "bbox": [11.4, 97.89, 20.93, 4.92], "category_id": 9, "id": 1803380}, {"segmentation": [[287.91, 144.0, 285.82, 165.48, 290.89, 167.27, 293.57, 179.21, 297.75, 168.76, 302.53, 157.43, 307.6, 154.14, 303.12, 147.88, 304.02, 143.7, 308.79, 141.02, 305.51, 138.93, 304.02, 133.26]], "area": 519.5937499999998, "iscrowd": 0, "image_id": 10005, "bbox": [285.82, 133.26, 22.97, 45.95], "category_id": 27, "id": 1825629}, {"segmentation": [[471.36, 409.25, 456.95, 258.42, 455.84, 237.35, 456.67, 226.91, 463.33, 213.6, 431.16, 201.4, 397.89, 202.51, 400.11, 229.13, 418.96, 230.24, 428.95, 261.29, 446.69, 427.0, 473.31, 425.44], [254.82, 220.26, 271.45, 245.77, 251.49, 252.42, 251.49, 276.82, 241.51, 280.15, 261.47, 427.0, 238.18, 426.55, 219.33, 281.26, 212.67, 251.31, 201.58, 238.0, 201.58, 232.46, 208.24, 229.13, 257.04, 218.04]], "area": 13035.366000000002, "iscrowd": 0, "image_id": 10232, "bbox": [201.58, 201.4, 271.73, 225.6], "category_id": 33, "id": 1843412}, {"segmentation": [[141.43, 122.0, 155.01, 122.29, 164.27, 123.44, 171.78, 126.33, 177.27, 129.51, 179.87, 134.43, 179.87, 139.34, 175.83, 141.94, 174.96, 143.1, 171.49, 145.12, 162.24, 146.57, 152.41, 145.7, 144.03, 143.1, 137.96, 140.21, 133.62, 137.03, 131.02, 135.0, 129.58, 128.93, 133.05, 124.31, 137.67, 122.58]], "area": 943.7643999999996, "iscrowd": 0, "image_id": 10358, "bbox": [129.58, 122.0, 50.29, 24.57], "category_id": 34, "id": 1844853}, {"segmentation": [[205.46, 236.54, 208.32, 234.63, 214.06, 233.68, 215.33, 230.49, 220.75, 225.08, 222.66, 219.35, 227.12, 215.2, 234.44, 211.38, 240.17, 208.2, 249.41, 207.56, 255.14, 202.78, 260.88, 193.55, 265.97, 188.13, 265.97, 180.81, 266.29, 176.03, 270.43, 170.3, 274.25, 169.34, 276.48, 173.16, 273.62, 183.99, 272.03, 193.23, 271.39, 200.87, 270.11, 206.29, 265.97, 210.43, 263.11, 216.16, 260.56, 218.07, 256.74, 219.03, 251.64, 216.8, 249.41, 214.89, 241.77, 218.07, 236.35, 221.26, 230.62, 223.8, 226.16, 227.31, 228.71, 230.81, 225.52, 237.18, 222.02, 241.0, 222.02, 244.51, 214.38, 250.88, 211.51, 252.79, 207.05, 243.87, 207.37, 239.09]], "area": 1161.6313499999992, "iscrowd": 0, "image_id": 10281, "bbox": [205.46, 169.34, 71.02, 83.45], "category_id": 38, "id": 1855596}, {"segmentation": [[437.29, 144.28, 439.63, 143.53, 442.29, 143.53, 443.43, 143.97, 446.4, 145.55, 449.06, 151.25, 448.55, 154.41, 447.04, 156.44, 445.45, 158.91, 443.74, 159.6, 443.43, 158.53, 445.9, 155.3, 446.53, 153.02, 446.91, 151.0, 446.91, 150.68, 445.64, 147.96, 443.74, 146.44, 438.81, 145.99, 436.53, 147.01, 433.11, 148.78, 432.98, 147.39, 433.68, 146.06]], "area": 57.8832499999999, "iscrowd": 0, "image_id": 10281, "bbox": [432.98, 143.53, 16.08, 16.07], "category_id": 38, "id": 1855911}, {"segmentation": [[285.51, 177.71, 284.0, 173.79, 284.0, 171.83, 284.61, 170.33, 286.26, 169.72, 288.67, 170.48, 290.33, 171.68, 289.13, 172.13, 286.41, 171.53, 285.51, 172.13, 285.66, 174.24, 285.66, 175.3]], "area": 15.357250000000063, "iscrowd": 0, "image_id": 10281, "bbox": [284.0, 169.72, 6.33, 7.99], "category_id": 38, "id": 1856943}, {"segmentation": [[245.95, 174.82, 247.86, 173.8, 249.52, 174.05, 251.69, 175.58, 251.56, 177.62, 250.8, 180.43, 249.65, 181.07, 249.01, 179.41, 249.65, 178.13, 249.78, 176.99, 249.52, 176.22, 248.63, 175.97, 246.84, 175.97]], "area": 17.47104999999998, "iscrowd": 0, "image_id": 10281, "bbox": [245.95, 173.8, 5.74, 7.27], "category_id": 38, "id": 1856966}, {"segmentation": [[271.73, 6.65, 280.6, 106.47, 280.6, 106.47, 359.35, 99.82, 359.35, 1.11, 272.84, 2.22, 272.84, 2.22]], "area": 8475.393949999998, "iscrowd": 0, "image_id": 10161, "bbox": [271.73, 1.11, 87.62, 105.36], "category_id": 47, "id": 1884682}, {"segmentation": [[291.84, 58.58, 338.38, 83.7, 324.59, 85.67, 295.29, 69.91, 278.05, 55.63, 281.0, 53.41, 292.82, 58.34]], "area": 454.36664999999937, "iscrowd": 0, "image_id": 10046, "bbox": [278.05, 53.41, 60.33, 32.26], "category_id": 50, "id": 1895370}, {"segmentation": [[76.96, 153.13, 112.42, 199.31, 123.14, 211.68, 133.04, 211.68, 136.34, 203.43, 127.27, 195.18, 82.73, 141.58, 76.14, 144.88, 80.26, 153.13]], "area": 1089.5754499999996, "iscrowd": 0, "image_id": 10046, "bbox": [76.14, 141.58, 60.2, 70.1], "category_id": 50, "id": 1896193}, {"segmentation": [[88.41, 135.7, 90.93, 133.18, 94.71, 132.23, 96.6, 135.7, 96.29, 140.11, 103.22, 146.73, 109.53, 149.57, 112.68, 150.51, 127.81, 161.55, 131.59, 163.12, 128.76, 165.01, 125.92, 167.85, 125.6, 171.63, 99.76, 154.3, 89.35, 149.57, 86.2, 145.15, 85.89, 140.74, 86.52, 136.33]], "area": 521.52215, "iscrowd": 0, "image_id": 10046, "bbox": [85.89, 132.23, 45.7, 39.4], "category_id": 50, "id": 1896393}, {"segmentation": [[80.62, 151.73, 97.25, 149.15, 102.11, 126.05, 121.56, 116.32, 136.15, 110.25, 150.73, 83.5, 178.69, 79.86, 194.49, 99.31, 199.36, 116.32, 220.02, 112.68, 239.47, 113.89, 256.49, 118.76, 261.35, 110.25, 286.88, 122.4, 301.46, 166.16, 295.74, 183.5, 288.09, 217.22, 275.94, 226.94, 265.0, 236.67, 257.7, 251.25, 234.61, 264.63, 207.86, 276.78, 189.63, 276.78, 137.36, 282.86, 128.85, 268.27, 94.82, 252.47, 88.74, 234.24, 77.8, 224.51, 81.44, 216.0, 76.58, 201.42, 73.64, 185.14, 71.32, 167.58]], "area": 32320.78125000001, "iscrowd": 0, "image_id": 10303, "bbox": [71.32, 79.86, 230.14, 203.0], "category_id": 52, "id": 1904301}, {"segmentation": [[595.24, 286.73, 599.7, 278.77, 593.97, 273.67, 583.46, 269.53, 562.75, 268.89, 554.47, 270.48, 542.36, 276.86, 539.49, 281.0, 548.41, 281.95, 572.31, 280.04, 583.78, 284.5, 589.19, 289.6]], "area": 661.317850000001, "iscrowd": 0, "image_id": 10037, "bbox": [539.49, 268.89, 60.21, 20.71], "category_id": 53, "id": 1906457}, {"segmentation": [[369.33, 140.1, 391.71, 120.21, 411.61, 109.02, 420.31, 86.63, 480.0, 80.41, 548.39, 96.58, 594.4, 123.94, 629.22, 169.95, 637.93, 189.84, 639.17, 311.71, 605.6, 327.88, 537.2, 334.09, 509.84, 334.09, 478.76, 353.99, 445.18, 342.8, 379.27, 298.03, 346.94, 227.15, 345.7, 181.14, 358.13, 152.54, 360.62, 146.32]], "area": 61925.627299999986, "iscrowd": 0, "image_id": 10046, "bbox": [345.7, 80.41, 293.47, 273.58], "category_id": 54, "id": 1909962}, {"segmentation": [[606.66, 391.6, 618.12, 362.06, 633.11, 349.27, 640.0, 350.59, 638.4, 390.72, 628.26, 386.31, 622.53, 386.31, 607.54, 392.48]], "area": 867.2648000000003, "iscrowd": 0, "image_id": 10037, "bbox": [606.66, 349.27, 33.34, 43.21], "category_id": 55, "id": 1912484}, {"segmentation": [[380.03, 137.5, 372.82, 144.7, 367.63, 148.16, 365.61, 150.47, 361.58, 153.93, 358.41, 156.52, 360.43, 159.7, 355.52, 160.85, 355.81, 163.73, 361.0, 170.36, 361.58, 175.26, 361.29, 179.59, 363.88, 185.06, 370.51, 184.78, 379.45, 183.33, 393.0, 176.7, 397.61, 171.8, 397.9, 166.33, 400.5, 162.87, 401.36, 162.0, 404.24, 159.7, 405.11, 157.97, 405.11, 155.08, 403.09, 154.51, 400.5, 149.32, 394.44, 153.06, 394.44, 153.35, 392.42, 153.93, 391.56, 151.34, 390.69, 147.88, 386.95, 144.42, 392.14, 132.02]], "area": 1459.87455, "iscrowd": 0, "image_id": 10414, "bbox": [355.52, 132.02, 49.59, 53.04], "category_id": 56, "id": 1914782}, {"segmentation": [[256.05, 51.08, 242.62, 51.78, 229.9, 46.13, 217.88, 44.72, 212.94, 41.89, 213.16, 31.9, 202.9, 31.9, 217.36, 20.71, 236.94, 15.11, 263.98, 22.57, 273.77, 30.03, 277.97, 35.16, 268.64, 37.96, 258.85, 38.89, 256.06, 42.16]], "area": 1588.4836, "iscrowd": 0, "image_id": 10166, "bbox": [202.9, 15.11, 75.07, 36.67], "category_id": 56, "id": 1914998}, {"segmentation": [[53.82, 118.84, 50.27, 124.52, 34.64, 128.78, 16.89, 131.62, 16.17, 125.23, 34.64, 121.68, 46.01, 111.02, 54.53, 101.08, 68.03, 105.34, 78.69, 117.41, 70.16, 121.68]], "area": 712.5349000000001, "iscrowd": 0, "image_id": 10166, "bbox": [16.17, 101.08, 62.52, 30.54], "category_id": 56, "id": 1915729}, {"segmentation": [[362.64, 63.76, 355.0, 68.12, 348.45, 67.03, 344.09, 59.39, 341.91, 50.67, 344.09, 44.12, 353.91, 45.21, 365.91, 47.39, 370.27, 58.3, 361.55, 63.76]], "area": 476.70645000000025, "iscrowd": 0, "image_id": 10414, "bbox": [341.91, 44.12, 28.36, 24.0], "category_id": 56, "id": 1915786}, {"segmentation": [[263.56, 66.18, 258.81, 63.66, 247.55, 68.4, 240.88, 77.3, 237.33, 83.67, 238.36, 88.56, 243.25, 89.15, 246.81, 82.33, 252.29, 76.55, 260.89, 69.29, 263.7, 67.07]], "area": 249.63624999999965, "iscrowd": 0, "image_id": 10414, "bbox": [237.33, 63.66, 26.37, 25.49], "category_id": 56, "id": 1915961}, {"segmentation": [[251.52, 155.22, 246.88, 148.0, 244.81, 143.35, 247.39, 140.26, 245.84, 134.58, 247.39, 130.45, 250.49, 131.48, 257.71, 127.87, 261.84, 130.45, 263.39, 133.55, 264.42, 136.64, 266.49, 138.19, 271.65, 137.16, 275.26, 137.16, 276.29, 139.74, 278.87, 141.8, 281.45, 157.29, 287.13, 176.38, 282.48, 179.47, 268.55, 172.77, 263.39, 170.19]], "area": 1194.9491999999998, "iscrowd": 0, "image_id": 10414, "bbox": [244.81, 127.87, 42.32, 51.6], "category_id": 56, "id": 1916052}, {"segmentation": [[230.18, 114.45, 235.18, 108.2, 239.24, 101.32, 249.24, 98.82, 255.8, 99.45, 262.99, 103.2, 258.93, 106.32, 256.43, 105.39, 249.24, 104.76, 238.62, 111.95, 238.62, 119.76, 237.68, 123.82]], "area": 242.67460000000005, "iscrowd": 0, "image_id": 10414, "bbox": [230.18, 98.82, 32.81, 25.0], "category_id": 56, "id": 1916243}, {"segmentation": [[380.24, 132.52, 376.62, 118.82, 388.41, 110.65, 390.89, 129.66]], "area": 200.32384999999994, "iscrowd": 0, "image_id": 10414, "bbox": [376.62, 110.65, 14.27, 21.87], "category_id": 57, "id": 1917223}, {"segmentation": [[289.4, 68.22, 268.31, 70.81, 264.98, 65.26, 265.35, 54.9, 290.88, 53.79]], "area": 373.2578499999995, "iscrowd": 0, "image_id": 10414, "bbox": [264.98, 53.79, 25.9, 17.02], "category_id": 57, "id": 1917941}, {"segmentation": [[219.74, 309.4, 207.84, 307.17, 205.23, 299.73, 207.09, 294.52, 216.39, 285.96, 231.27, 282.24, 238.34, 281.87, 243.55, 282.24, 246.52, 283.73, 248.01, 288.57, 248.38, 292.29, 238.34, 303.82, 231.64, 309.03, 223.09, 309.03]], "area": 866.9764999999998, "iscrowd": 0, "image_id": 10243, "bbox": [205.23, 281.87, 43.15, 27.53], "category_id": 61, "id": 1927386}, {"segmentation": [[173.88, 269.9, 173.88, 239.82, 210.64, 236.0, 206.83, 249.37, 191.07, 268.47, 177.23, 269.42]], "area": 885.197250000001, "iscrowd": 0, "image_id": 10094, "bbox": [173.88, 236.0, 36.76, 33.9], "category_id": 62, "id": 1931233}, {"segmentation": [[597.19, 331.64, 498.7, 462.55, 499.95, 473.77, 639.58, 473.77, 637.09, 330.39]], "area": 13419.140850000002, "iscrowd": 0, "image_id": 10346, "bbox": [498.7, 330.39, 140.88, 143.38], "category_id": 63, "id": 1951790}, {"segmentation": [[638.18, 460.71, 639.4, 1.22, 0.0, 2.43, 1.22, 461.92]], "area": 293237.3282, "iscrowd": 0, "image_id": 10303, "bbox": [0.0, 1.22, 639.4, 460.7], "category_id": 67, "id": 1964868}, {"segmentation": [[375.0, 150.65, 359.74, 150.65, 364.94, 115.58, 372.73, 94.81, 375.0, 76.62, 355.84, 72.73, 324.68, 58.44, 310.39, 48.05, 319.48, 36.36, 312.99, 22.08, 274.03, 20.78, 258.44, 29.87, 263.64, 46.75, 248.05, 55.84, 216.88, 67.53, 164.94, 67.53, 149.35, 88.31, 146.75, 96.1, 170.13, 111.69, 181.82, 138.96, 181.82, 144.16, 0.0, 136.36, 0.0, 493.51, 372.73, 490.91]], "area": 149232.75710000002, "iscrowd": 0, "image_id": 10444, "bbox": [0.0, 20.78, 375.0, 472.73], "category_id": 67, "id": 1965041}, {"segmentation": [[393.09, 344.53, 400.66, 355.25, 409.9, 356.72, 412.21, 359.03, 416.21, 357.98, 406.54, 342.85, 396.45, 341.38, 393.3, 344.11]], "area": 206.27259999999976, "iscrowd": 0, "image_id": 10130, "bbox": [393.09, 341.38, 23.12, 17.65], "category_id": 77, "id": 1976101}, {"segmentation": [[2.49, 166.69, 2.49, 191.63, 61.09, 246.48, 132.16, 226.54, 113.45, 157.96]], "area": 8036.809199999998, "iscrowd": 0, "image_id": 10175, "bbox": [2.49, 157.96, 129.67, 88.52], "category_id": 78, "id": 1979000}, {"segmentation": [[367.43, 426.17, 360.11, 429.5, 364.77, 433.5, 368.1, 438.16, 369.43, 438.16, 370.09, 432.83, 367.43, 427.51], [318.83, 441.49, 324.82, 445.48, 324.82, 438.82, 322.16, 439.49], [358.11, 458.8, 350.79, 462.13, 360.77, 465.46, 360.11, 460.13], [459.31, 474.78, 458.65, 468.12, 444.0, 465.46, 445.33, 456.8, 430.02, 449.48, 408.71, 442.15, 393.4, 439.49, 385.41, 439.49, 386.74, 432.83, 437.34, 437.49, 465.97, 448.15, 478.62, 457.47, 466.64, 468.79], [445.33, 422.18, 445.33, 424.18, 439.34, 427.51, 446.66, 434.16, 448.66, 438.16, 451.99, 436.16, 464.64, 433.5, 466.64, 430.17, 467.97, 422.84, 479.96, 422.84, 475.96, 418.85, 423.36, 412.19, 421.36, 414.85, 424.03, 421.51, 425.36, 421.51, 428.02, 416.19]], "area": 1960.4197000000004, "iscrowd": 0, "image_id": 10337, "bbox": [318.83, 412.19, 161.13, 62.59], "category_id": 81, "id": 1982802}, {"segmentation": [[446.55, 457.01, 425.48, 466.71, 422.95, 477.66, 412.84, 479.77, 357.64, 465.44, 359.74, 448.17, 386.29, 441.0, 396.4, 440.16, 443.18, 456.59]], "area": 2121.9383499999994, "iscrowd": 0, "image_id": 10337, "bbox": [357.64, 440.16, 88.91, 39.61], "category_id": 81, "id": 1982960}, {"segmentation": [[123.9, 476.02, 120.62, 474.71, 117.99, 464.2, 120.62, 430.38, 122.26, 411.34, 121.61, 388.03, 125.55, 387.7, 126.53, 387.7, 127.19, 387.7, 127.51, 390.0, 124.56, 449.76, 125.22, 466.83]], "area": 457.08020000000005, "iscrowd": 0, "image_id": 10136, "bbox": [117.99, 387.7, 9.52, 88.32], "category_id": 84, "id": 1984879}, {"segmentation": [[53.31, 112.74, 69.91, 112.74, 61.09, 169.8, 46.56, 170.32, 51.23, 114.81]], "area": 943.0586500000002, "iscrowd": 0, "image_id": 10136, "bbox": [46.56, 112.74, 23.35, 57.58], "category_id": 84, "id": 1986276}, {"segmentation": [[71.67, 116.91, 78.03, 117.78, 73.12, 167.79, 65.02, 169.81, 64.16, 169.52]], "area": 392.40740000000017, "iscrowd": 0, "image_id": 10136, "bbox": [64.16, 116.91, 13.87, 52.9], "category_id": 84, "id": 1987460}, {"segmentation": [[511.67, 47.76, 528.29, 50.41, 530.94, 52.13, 532.23, 54.35, 532.3, 56.71, 532.45, 58.79, 528.29, 59.58, 519.7, 59.87, 514.32, 57.07, 511.17, 53.56, 510.38, 51.34, 510.38, 48.26]], "area": 194.67339999999982, "iscrowd": 0, "image_id": 10245, "bbox": [510.38, 47.76, 22.07, 12.11], "category_id": 84, "id": 1988360}, {"segmentation": [[127.97, 170.58, 133.19, 116.67, 140.72, 117.83, 133.19, 172.61]], "area": 356.61750000000006, "iscrowd": 0, "image_id": 10136, "bbox": [127.97, 116.67, 12.75, 55.94], "category_id": 84, "id": 1988473}, {"segmentation": [[206.52, 474.58, 214.87, 386.89, 243.41, 386.89, 237.14, 476.67]], "area": 2632.420249999999, "iscrowd": 0, "image_id": 10136, "bbox": [206.52, 386.89, 36.89, 89.78], "category_id": 84, "id": 1989007}, {"segmentation": [[503.12, 16.09, 504.92, 53.8, 511.02, 54.77, 507.83, 15.81, 503.26, 15.81]], "area": 206.97969999999893, "iscrowd": 0, "image_id": 10245, "bbox": [503.12, 15.81, 7.9, 38.96], "category_id": 84, "id": 1989107}, {"segmentation": [[120.63, 125.15, 114.73, 165.32, 113.37, 172.82, 120.18, 172.36, 126.08, 132.64, 126.31, 125.15]], "area": 307.0600500000001, "iscrowd": 0, "image_id": 10136, "bbox": [113.37, 125.15, 12.94, 47.67], "category_id": 84, "id": 1989129}, {"segmentation": [[146.8, 173.52, 142.71, 174.46, 139.56, 174.15, 137.99, 171.0, 137.67, 158.4, 141.13, 117.14, 141.13, 115.29, 148.06, 114.34, 148.38, 124.42, 148.69, 137.65, 149.32, 154.03, 148.38, 165.68]], "area": 559.5045000000002, "iscrowd": 0, "image_id": 10136, "bbox": [137.67, 114.34, 11.65, 60.12], "category_id": 84, "id": 1989387}, {"segmentation": [[510.0, 19.19, 510.0, 44.17, 529.99, 48.67, 525.99, 25.19]], "area": 425.3977000000002, "iscrowd": 0, "image_id": 10245, "bbox": [510.0, 19.19, 19.99, 29.48], "category_id": 84, "id": 1989542}, {"segmentation": [[52.3, 384.71, 55.57, 471.81, 65.36, 471.81, 66.45, 384.71, 56.65, 384.71]], "area": 1042.5870000000004, "iscrowd": 0, "image_id": 10136, "bbox": [52.3, 384.71, 14.15, 87.1], "category_id": 84, "id": 1989709}, {"segmentation": [[117.25, 189.61, 108.11, 259.22, 114.73, 259.53, 123.86, 192.76, 117.87, 191.5]], "area": 461.6104499999998, "iscrowd": 0, "image_id": 10136, "bbox": [108.11, 189.61, 15.75, 69.92], "category_id": 84, "id": 1989894}, {"segmentation": [[253.08, 202.59, 260.41, 202.59, 267.14, 223.37, 263.47, 226.42, 250.02, 227.03, 245.75, 215.42, 246.36, 209.31]], "area": 387.5798999999998, "iscrowd": 0, "image_id": 10136, "bbox": [245.75, 202.59, 21.39, 24.44], "category_id": 85, "id": 1991743}, {"segmentation": [[29.59, 190.04, 29.59, 182.63, 28.79, 179.59, 28.92, 174.96, 27.34, 172.18, 24.69, 174.56, 24.56, 177.73, 25.62, 182.36, 23.23, 186.73, 23.63, 190.57, 26.28, 190.3, 26.81, 186.33, 28.53, 191.1]], "area": 77.88664999999999, "iscrowd": 0, "image_id": 10230, "bbox": [23.23, 172.18, 6.36, 18.92], "category_id": 1, "id": 2021204}, {"segmentation": [[306.3, 307.73, 309.83, 304.2, 311.08, 302.75, 314.19, 302.75, 314.19, 304.2, 314.4, 306.69, 313.16, 308.56, 312.32, 310.02, 312.53, 312.1, 320.22, 322.9, 320.01, 323.73, 307.55, 324.15]], "area": 168.37839999999974, "iscrowd": 0, "image_id": 10321, "bbox": [306.3, 302.75, 13.92, 21.4], "category_id": 1, "id": 2023865}, {"segmentation": [[377.22, 239.15, 378.22, 234.63, 378.66, 231.66, 378.22, 230.0, 375.57, 230.22, 374.58, 232.98, 372.92, 236.29, 372.26, 237.94]], "area": 36.02630000000029, "iscrowd": 0, "image_id": 10318, "bbox": [372.26, 230.0, 6.4, 9.15], "category_id": 1, "id": 2024156}, {"segmentation": [[573.02, 93.98, 572.83, 92.38, 573.02, 91.45, 573.36, 89.73, 573.84, 89.88, 574.29, 90.89, 574.62, 92.19, 575.22, 93.35, 575.52, 93.95]], "area": 6.607399999999988, "iscrowd": 0, "image_id": 10318, "bbox": [572.83, 89.73, 2.69, 4.25], "category_id": 1, "id": 2033641}, {"segmentation": [[5.55, 2.22, 640.0, 2.22, 638.84, 421.45, 0.0, 419.24, 1.11, 0.0]], "area": 267132.84280000004, "iscrowd": 0, "image_id": 10275, "bbox": [0.0, 0.0, 640.0, 421.45], "category_id": 7, "id": 2050978}, {"segmentation": [[381.25, 220.08, 381.25, 233.29, 379.73, 236.59, 375.66, 236.08, 374.65, 245.98, 381.76, 249.54, 392.43, 251.82, 400.3, 250.81, 405.89, 249.03, 408.43, 249.03, 410.71, 250.81, 415.03, 250.81, 418.08, 246.24, 419.86, 233.54, 408.17, 232.78, 404.11, 219.32, 382.78, 218.05, 381.76, 219.06]], "area": 1076.7957000000001, "iscrowd": 0, "image_id": 10342, "bbox": [374.65, 218.05, 45.21, 33.77], "category_id": 8, "id": 2051669}, {"segmentation": [[0.0, 208.43, 15.06, 212.88, 23.96, 242.32, 37.99, 244.37, 40.05, 267.3, 27.73, 268.67, 22.59, 265.59, 16.09, 272.1, 9.93, 276.54, 7.53, 276.89, 4.79, 272.44, 0.0, 270.73]], "area": 1690.2626000000002, "iscrowd": 0, "image_id": 10342, "bbox": [0.0, 208.43, 40.05, 68.46], "category_id": 8, "id": 2053653}, {"segmentation": [[216.49, 212.08, 215.48, 213.35, 228.71, 213.6, 231.13, 211.57, 229.35, 211.82, 216.49, 212.08]], "area": 21.39369999999989, "iscrowd": 0, "image_id": 10058, "bbox": [215.48, 211.57, 15.65, 2.03], "category_id": 9, "id": 2054682}, {"segmentation": [[230.51, 95.69, 222.31, 96.22, 224.82, 99.92, 222.7, 102.57, 225.88, 106.14, 231.97, 106.8, 230.91, 95.82]], "area": 79.24590000000006, "iscrowd": 0, "image_id": 10434, "bbox": [222.31, 95.69, 9.66, 11.11], "category_id": 10, "id": 2057256}, {"segmentation": [[431.45, 339.29, 432.49, 341.98, 432.19, 345.4, 420.42, 345.4, 420.42, 341.68, 420.72, 338.4, 424.3, 337.95, 428.47, 339.0, 430.85, 339.44]], "area": 79.12274999999968, "iscrowd": 0, "image_id": 10321, "bbox": [420.42, 337.95, 12.07, 7.45], "category_id": 31, "id": 2077559}, {"segmentation": [[107.26, 367.41, 115.26, 367.41, 123.98, 368.28, 125.73, 369.59, 131.98, 370.6, 136.49, 374.82, 153.51, 375.55, 159.33, 378.17, 160.78, 374.97, 163.11, 374.24, 165.29, 374.24, 168.49, 375.26, 175.61, 381.22, 178.38, 386.89, 175.47, 395.91, 171.25, 400.42, 160.93, 401.15, 169.51, 413.65, 178.09, 422.67, 179.4, 427.0, 115.99, 427.0, 81.66, 427.0, 45.01, 427.0, 22.76, 427.0, 26.54, 423.54, 30.47, 419.18, 41.67, 396.49, 44.72, 392.27, 41.96, 392.27, 37.45, 391.69, 34.4, 389.22, 34.25, 385.44, 22.76, 379.62, 15.93, 377.15, 9.67, 374.24, 0.0, 372.64, 0.0, 350.39, 0.0, 333.23, 5.02, 335.85, 12.87, 332.21, 20.87, 328.28, 38.18, 332.5, 40.07, 335.12, 54.16, 333.87, 65.18, 331.01, 71.58, 328.8, 78.63, 324.39, 86.57, 321.97, 89.65, 321.53, 94.94, 329.46, 94.5, 333.65, 102.26, 337.54, 104.01, 345.97, 110.26, 352.37, 111.86, 358.19, 112.3, 361.24, 107.94, 364.3]], "area": 12154.781850000001, "iscrowd": 0, "image_id": 10281, "bbox": [0.0, 321.53, 179.4, 105.47], "category_id": 38, "id": 2090089}, {"segmentation": [[366.2, 438.5, 373.42, 418.86, 383.59, 420.44, 385.84, 441.21, 371.62, 444.37, 370.49, 440.76]], "area": 350.3185999999991, "iscrowd": 0, "image_id": 10337, "bbox": [366.2, 418.86, 19.64, 25.51], "category_id": 44, "id": 2095989}, {"segmentation": [[301.18, 111.16, 321.3, 116.46, 347.77, 132.34, 357.3, 141.87, 374.24, 134.46, 390.12, 119.64, 401.77, 101.64, 409.18, 89.99, 411.3, 75.16, 412.35, 56.11, 406.0, 38.11, 398.59, 20.11, 385.88, 12.69, 364.71, 3.17, 356.24, 1.05, 292.71, 1.05, 274.71, 20.11, 263.06, 35.99, 253.53, 47.64, 250.36, 57.17, 256.71, 77.28, 269.41, 91.05, 275.77, 89.99, 277.88, 89.99, 285.3, 86.81, 292.71, 87.87, 296.94, 89.99, 301.18, 93.16, 299.06, 100.58, 300.12, 109.05]], "area": 15784.109099999998, "iscrowd": 0, "image_id": 10196, "bbox": [250.36, 1.05, 161.99, 140.82], "category_id": 53, "id": 2112002}, {"segmentation": [[240.23, 364.1, 231.64, 363.96, 229.32, 363.14, 227.55, 361.5, 227.14, 359.46, 226.46, 356.32, 226.32, 354.68, 226.86, 351.41, 227.96, 349.77, 230.0, 348.96, 233.41, 347.59, 242.28, 346.5, 245.96, 346.91, 251.69, 348.0, 254.14, 350.18, 255.1, 352.09, 255.1, 354.55, 255.1, 356.32, 255.1, 358.37, 249.78, 360.0, 248.55, 361.5, 245.28, 363.55, 240.64, 363.96]], "area": 415.7546, "iscrowd": 0, "image_id": 10243, "bbox": [226.32, 346.5, 28.78, 17.6], "category_id": 60, "id": 2118252}, {"segmentation": [[172.58, 84.23, 172.79, 72.72, 185.55, 72.31, 185.97, 83.18]], "area": 146.3859000000001, "iscrowd": 0, "image_id": 10082, "bbox": [172.58, 72.31, 13.39, 11.92], "category_id": 62, "id": 2121088}, {"segmentation": [[433.54, 236.39, 431.74, 230.62, 432.1, 223.04, 434.26, 223.04, 463.13, 221.6, 462.77, 229.89, 461.33, 232.42, 461.69, 233.14, 486.58, 224.84, 489.47, 223.76, 493.08, 208.24, 500.66, 207.16, 516.53, 223.4, 514.01, 224.84, 517.98, 223.76, 523.75, 226.29, 524.47, 228.45, 520.5, 268.14, 465.3, 268.14, 466.74, 268.14, 469.26, 293.04, 444.73, 295.57, 437.51, 284.02, 434.26, 273.19, 434.99, 265.62, 420.91, 262.73, 409.01, 263.09, 408.65, 264.89, 406.48, 265.98, 379.78, 264.89, 384.83, 245.41, 381.94, 236.03, 436.07, 237.11]], "area": 6383.886499999998, "iscrowd": 0, "image_id": 10145, "bbox": [379.78, 207.16, 144.69, 88.41], "category_id": 62, "id": 2121741}, {"segmentation": [[551.43, 285.13, 558.7, 285.52, 559.27, 266.76, 549.13, 267.34, 548.37, 281.5, 548.56, 284.56, 550.66, 285.52]], "area": 187.32630000000046, "iscrowd": 0, "image_id": 10130, "bbox": [548.37, 266.76, 10.9, 18.76], "category_id": 77, "id": 2137248}, {"segmentation": [[534.07, 264.55, 523.25, 263.77, 522.47, 243.28, 533.69, 243.47]], "area": 228.7694000000008, "iscrowd": 0, "image_id": 10130, "bbox": [522.47, 243.28, 11.6, 21.27], "category_id": 77, "id": 2137411}, {"segmentation": [[310.67, 395.39, 308.11, 391.78, 305.7, 391.02, 305.7, 389.97, 309.01, 390.12, 315.19, 386.96, 320.16, 386.96, 323.93, 391.63]], "area": 85.7347, "iscrowd": 0, "image_id": 10130, "bbox": [305.7, 386.96, 18.23, 8.43], "category_id": 77, "id": 2138309}, {"segmentation": [[126.92, 188.47, 120.25, 247.99, 124.54, 247.99, 131.68, 188.0, 128.35, 187.04]], "area": 271.8360500000004, "iscrowd": 0, "image_id": 10136, "bbox": [120.25, 187.04, 11.43, 60.95], "category_id": 84, "id": 2141654}, {"segmentation": [[450.92, 24.84, 449.83, 44.79, 449.52, 51.8, 449.52, 55.39, 449.05, 56.16, 457.1, 57.25, 459.27, 23.99, 451.01, 23.13, 451.01, 25.52]], "area": 268.6148000000003, "iscrowd": 0, "image_id": 10346, "bbox": [449.05, 23.13, 10.22, 34.12], "category_id": 84, "id": 2144340}, {"segmentation": [[492.19, 176.43, 497.4, 94.4, 476.56, 81.38, 467.45, 59.24, 446.61, 54.04, 438.8, 83.98, 449.22, 115.23, 440.1, 142.58, 463.54, 166.02]], "area": 4898.673499999997, "iscrowd": 0, "image_id": 10094, "bbox": [438.8, 54.04, 58.6, 122.39], "category_id": 1, "id": 2156668}, {"segmentation": [[379.79, 312.6, 382.71, 308.23, 383.54, 304.48, 387.29, 303.85, 387.5, 306.56, 387.29, 309.48, 389.38, 310.94, 390.63, 311.56, 390.83, 313.85, 390.63, 317.81, 391.04, 320.94, 390.21, 324.48, 389.79, 325.73, 387.71, 348.85, 383.33, 349.27, 382.92, 333.44, 385.42, 326.77, 382.71, 324.69, 380.63, 320.1, 379.17, 316.77, 380.21, 312.19]], "area": 303.46995, "iscrowd": 0, "image_id": 10321, "bbox": [379.17, 303.85, 11.87, 45.42], "category_id": 1, "id": 2157700}, {"segmentation": [[125.61, 227.26, 131.8, 233.65, 138.58, 232.17, 136.91, 228.04, 133.77, 224.6, 132.98, 221.16, 133.18, 218.9, 138.39, 218.21, 138.39, 214.48, 138.39, 199.73, 138.19, 194.82, 138.88, 180.08, 140.55, 174.67, 141.14, 172.21, 143.01, 170.05, 143.79, 157.27, 143.79, 155.01, 142.61, 144.49, 146.45, 141.05, 147.92, 137.61, 148.22, 135.16, 146.74, 122.58, 144.09, 117.46, 143.79, 115.99, 141.34, 110.29, 135.44, 106.85, 132.98, 105.18, 132.0, 104.88, 112.63, 110.58, 101.23, 115.7, 100.5, 130.4, 100.5, 132.69, 100.31, 142.06, 105.28, 148.37, 106.23, 150.86, 105.28, 175.53, 109.1, 177.44, 109.1, 186.62, 107.19, 194.26, 104.7, 201.91, 107.57, 205.74, 108.15, 206.12, 109.1, 210.52, 109.1, 215.3, 110.06, 217.59, 111.4, 220.08, 112.35, 220.46, 114.26, 220.08, 114.84, 216.25, 116.18, 209.94, 115.41, 205.74, 119.04, 206.12, 119.62, 203.82, 119.62, 200.96, 119.62, 199.04, 121.53, 194.84, 122.87, 192.93, 124.78, 175.14, 125.35, 193.31, 126.31, 216.25, 126.69, 218.55], [110.53, 107.0, 113.03, 103.0, 115.11, 102.17, 115.94, 99.93, 112.19, 97.84, 112.65, 93.34, 112.65, 88.13, 115.25, 84.01, 116.35, 81.27, 120.74, 79.9, 124.85, 79.63, 129.65, 82.64, 132.12, 84.01, 132.8, 89.5, 131.84, 93.88, 129.79, 98.0, 129.1, 99.37, 129.65, 100.05]], "area": 4355.59415, "iscrowd": 0, "image_id": 10244, "bbox": [100.31, 79.63, 47.91, 154.02], "category_id": 1, "id": 2159840}, {"segmentation": [[383.91, 177.87, 382.89, 178.21, 382.89, 179.05, 383.4, 180.41, 382.38, 180.75, 382.04, 182.1, 381.2, 182.78, 381.37, 183.29, 381.87, 184.3, 381.87, 187.01, 381.87, 187.69, 382.21, 189.72, 381.87, 193.96, 381.37, 194.3, 381.37, 196.16, 383.06, 195.82, 384.41, 193.62, 384.58, 192.09, 384.92, 194.13, 385.09, 195.82, 386.95, 195.99, 387.29, 194.8, 386.28, 192.26, 386.11, 190.74, 386.62, 189.38, 386.79, 188.37, 387.12, 186.0, 386.28, 185.66, 386.62, 184.64, 386.95, 182.95, 385.94, 181.42, 385.6, 181.25, 385.6, 180.07, 385.6, 179.22, 385.6, 177.53, 384.24, 178.04]], "area": 76.4423000000002, "iscrowd": 0, "image_id": 10230, "bbox": [381.2, 177.53, 6.09, 18.63], "category_id": 1, "id": 2160088}, {"segmentation": [[345.88, 141.75, 351.41, 135.43, 354.57, 135.43, 354.83, 135.17, 354.83, 132.01, 355.89, 130.16, 357.2, 130.16, 360.63, 131.48, 360.63, 134.91, 362.21, 135.96, 360.89, 138.86, 361.94, 141.49, 360.36, 144.39, 359.31, 146.23, 358.26, 150.18, 360.1, 155.71, 358.26, 156.76, 355.89, 156.24, 355.1, 154.13, 354.57, 153.08, 351.41, 153.08, 351.67, 154.92, 350.62, 161.77, 348.51, 164.14, 347.99, 160.19, 346.67, 151.5, 348.51, 146.49, 345.09, 142.54]], "area": 299.12694999999997, "iscrowd": 0, "image_id": 10005, "bbox": [345.09, 130.16, 17.12, 33.98], "category_id": 1, "id": 2160616}, {"segmentation": [[158.96, 422.04, 131.41, 410.23, 101.88, 352.17, 149.12, 326.58, 150.1, 307.88, 128.45, 250.8, 95.98, 245.88, 82.2, 229.15, 57.6, 229.15, 45.79, 216.36, 46.77, 204.55, 23.15, 175.03, 0.0, 165.19, 0.52, 427.0, 163.88, 427.0]], "area": 27574.92169999999, "iscrowd": 0, "image_id": 10275, "bbox": [0.0, 165.19, 163.88, 261.81], "category_id": 1, "id": 2165549}, {"segmentation": [[149.48, 86.04, 141.56, 82.27, 135.19, 81.75, 133.25, 81.75, 131.95, 50.32, 131.43, 46.56, 130.78, 49.94, 131.04, 73.44, 131.82, 81.49, 129.22, 82.14, 126.49, 83.31, 123.9, 83.83, 123.51, 85.13, 127.27, 85.91, 128.57, 84.22, 129.35, 82.92, 129.87, 82.79, 129.74, 83.96, 130.13, 86.69, 140.0, 86.3, 139.74, 84.22, 140.65, 83.44, 141.43, 83.44, 142.34, 84.35, 143.12, 86.04, 149.35, 86.43]], "area": 125.61184999999983, "iscrowd": 0, "image_id": 10318, "bbox": [123.51, 46.56, 25.97, 40.13], "category_id": 9, "id": 2172388}, {"segmentation": [[42.97, 94.38, 50.18, 93.74, 51.63, 93.74, 52.59, 95.34, 52.27, 98.87, 51.47, 102.39, 49.7, 101.11, 45.53, 99.67]], "area": 53.831400000000066, "iscrowd": 0, "image_id": 10318, "bbox": [42.97, 93.74, 9.62, 8.65], "category_id": 9, "id": 2172466}, {"segmentation": [[86.77, 76.98, 96.31, 76.79, 96.31, 75.72, 93.88, 74.26, 92.81, 70.17, 92.32, 61.51, 89.4, 61.99, 89.98, 72.6, 86.77, 75.42]], "area": 62.87454999999996, "iscrowd": 0, "image_id": 10318, "bbox": [86.77, 61.51, 9.54, 15.47], "category_id": 9, "id": 2172507}, {"segmentation": [[240.46, 480.65, 254.15, 507.35, 259.97, 521.04, 267.5, 518.3, 255.18, 489.89, 250.04, 477.23, 239.77, 479.62], [234.91, 470.48, 225.93, 456.57, 225.93, 438.6, 230.28, 446.43, 231.44, 455.12, 232.3, 459.47, 234.62, 460.92, 235.49, 469.61], [264.76, 467.0, 266.21, 486.71, 268.24, 487.87, 274.33, 487.87, 272.59, 467.58, 265.92, 467.0]], "area": 674.0252499999993, "iscrowd": 0, "image_id": 10024, "bbox": [225.93, 438.6, 48.4, 82.44], "category_id": 35, "id": 2179292}, {"segmentation": [[250.46, 359.06, 240.73, 368.38, 237.9, 373.25, 237.9, 376.49, 246.41, 385.0, 260.59, 386.62, 269.51, 382.57, 276.0, 375.27, 278.43, 363.92, 273.56, 360.28, 268.7, 356.63, 256.54, 357.84, 254.11, 357.84]], "area": 883.0281500000003, "iscrowd": 0, "image_id": 10243, "bbox": [237.9, 356.63, 40.53, 29.99], "category_id": 61, "id": 2189469}, {"segmentation": [[56.28, 91.79, 44.24, 99.12, 29.59, 102.26, 34.3, 89.18, 33.25, 68.77, 36.91, 62.48, 36.91, 49.92, 43.19, 57.77, 54.71, 57.77, 56.8, 52.02, 60.47, 60.91, 64.13, 60.91, 62.04, 92.32, 55.23, 89.7], [73.55, 63.53, 99.2, 68.24, 109.67, 59.34, 125.89, 68.77, 135.84, 59.87, 153.63, 65.62, 172.48, 76.62, 193.93, 84.99, 196.03, 120.06, 196.55, 186.53, 180.33, 199.09, 177.71, 193.86, 183.47, 176.59, 183.47, 163.5, 171.95, 178.68, 173.0, 188.62, 167.24, 193.34, 164.1, 177.63, 176.14, 157.74, 160.44, 127.91, 148.4, 127.39, 109.67, 127.39, 112.28, 159.84, 114.38, 188.1, 104.43, 181.82, 106.53, 160.36, 96.58, 133.14, 98.68, 168.74, 100.77, 180.77, 91.35, 179.73, 82.97, 136.28, 72.51, 112.21]], "area": 11109.5226, "iscrowd": 0, "image_id": 10276, "bbox": [29.59, 49.92, 166.96, 149.17], "category_id": 19, "id": 2191492}, {"segmentation": [[103.98, 1.24, 362.84, 54.33, 393.81, 41.06, 428.11, 47.7, 461.29, 47.7, 481.2, 46.59, 486.74, 67.61, 512.18, 63.18, 537.62, 54.33, 561.96, 34.42, 602.89, 30.0, 619.48, 32.21, 640.0, 56.55, 640.0, 418.28, 0.0, 422.7, 0.0, 2.34]], "area": 248914.49725, "iscrowd": 0, "image_id": 10245, "bbox": [0.0, 1.24, 640.0, 421.46], "category_id": 65, "id": 2194040}, {"segmentation": [[249.8, 263.06, 252.67, 264.85, 267.38, 266.65, 267.03, 262.34, 251.23, 263.06], [380.46, 286.39, 390.87, 287.83, 391.23, 285.31, 380.1, 284.95]], "area": 68.64890000000011, "iscrowd": 0, "image_id": 10041, "bbox": [249.8, 262.34, 141.43, 25.49], "category_id": 35, "id": 2201817}, {"segmentation": [[409.47, 94.88, 409.6, 92.95, 408.98, 91.64, 410.09, 90.75, 409.74, 89.85, 411.05, 89.71, 411.33, 90.4, 411.33, 90.75, 412.36, 91.44, 411.81, 93.44, 411.53, 95.02]], "area": 11.56084999999992, "iscrowd": 0, "image_id": 10318, "bbox": [408.98, 89.71, 3.38, 5.31], "category_id": 1, "id": 2206997}, {"segmentation": [[356.44, 159.21, 343.37, 201.98, 351.68, 251.88, 415.84, 332.67, 468.12, 352.87, 478.81, 349.31, 489.5, 339.8, 513.27, 327.92, 583.37, 340.99, 626.14, 324.36, 640.0, 299.41, 640.0, 180.59, 591.68, 130.69, 561.98, 105.74, 515.64, 86.73, 471.68, 80.79, 458.61, 85.54, 449.11, 91.49, 425.35, 91.49, 417.03, 93.86, 422.97, 109.31, 402.77, 120.0, 381.39, 128.32, 368.32, 134.26, 357.62, 165.15]], "area": 61644.439500000015, "iscrowd": 0, "image_id": 10046, "bbox": [343.37, 80.79, 296.63, 272.08], "category_id": 58, "id": 2219898}, {"segmentation": [[285.4, 391.53, 274.13, 396.36, 270.5, 408.44, 280.57, 420.12, 301.51, 421.32, 309.56, 409.65, 308.75, 400.39, 300.7, 393.94]], "area": 891.7685500000002, "iscrowd": 0, "image_id": 10243, "bbox": [270.5, 391.53, 39.06, 29.79], "category_id": 61, "id": 2222255}, {"segmentation": [[527.84, 286.31, 503.7, 268.47, 498.46, 251.68, 505.8, 233.84, 491.11, 224.39, 491.11, 218.1, 520.49, 175.07, 521.54, 228.59, 525.74, 244.33, 519.44, 264.27]], "area": 1951.76175, "iscrowd": 0, "image_id": 10082, "bbox": [491.11, 175.07, 36.73, 111.24], "category_id": 64, "id": 2225436}, {"segmentation": [[285.46, 245.31, 286.87, 239.85, 296.24, 236.72, 300.45, 235.01, 304.35, 235.63, 305.6, 236.1, 307.63, 235.32, 308.72, 235.01, 309.66, 234.85, 313.41, 234.38, 318.09, 235.48, 325.43, 239.85, 325.27, 243.44, 324.49, 245.31, 322.93, 247.5, 319.96, 248.9, 315.13, 249.84, 312.0, 250.31, 309.19, 250.31, 304.35, 249.21, 301.08, 248.59, 298.11, 248.28, 294.99, 249.21, 292.96, 249.84, 289.05, 249.84, 287.81, 249.53, 286.56, 247.65]], "area": 493.21300000000036, "iscrowd": 0, "image_id": 10243, "bbox": [285.46, 234.38, 39.97, 15.93], "category_id": 61, "id": 2231215}, {"segmentation": [[305.67, 394.69, 289.54, 387.78, 281.64, 377.9, 286.57, 361.44, 298.76, 351.89, 310.61, 347.28, 328.05, 348.27, 345.83, 354.85, 353.73, 360.12, 357.35, 370.0, 356.37, 379.22, 346.49, 390.41, 332.66, 395.68, 325.75, 397.32, 323.77, 395.02, 314.56, 394.03, 309.95, 396.66, 304.02, 396.34]], "area": 2844.5682500000016, "iscrowd": 0, "image_id": 10243, "bbox": [281.64, 347.28, 75.71, 50.04], "category_id": 61, "id": 2231255}, {"segmentation": {"counts": [18304, 52, 28, 12, 547, 68, 5, 21, 545, 96, 543, 98, 541, 99, 541, 99, 541, 99, 541, 99, 406, 9, 126, 99, 392, 6, 6, 12, 125, 99, 391, 26, 124, 99, 386, 32, 123, 99, 371, 47, 123, 98, 362, 57, 122, 98, 357, 63, 121, 98, 350, 71, 120, 98, 350, 73, 119, 98, 348, 75, 119, 98, 347, 76, 118, 99, 346, 77, 117, 100, 345, 79, 116, 100, 345, 79, 116, 100, 345, 79, 116, 100, 345, 79, 116, 100, 345, 79, 116, 99, 346, 79, 116, 10, 19, 69, 347, 79, 117, 9, 45, 11, 1, 17, 8, 4, 349, 79, 118, 8, 435, 79, 120, 6, 435, 79, 122, 4, 436, 78, 563, 77, 564, 76, 564, 76, 564, 76, 213, 1, 349, 77, 213, 2, 348, 77, 213, 3, 347, 77, 213, 4, 346, 77, 125, 1, 36, 55, 346, 77, 124, 93, 346, 77, 123, 95, 345, 77, 123, 96, 345, 76, 122, 97, 345, 76, 121, 98, 345, 76, 120, 99, 330, 2, 13, 77, 119, 100, 320, 12, 12, 77, 119, 101, 309, 23, 11, 77, 119, 102, 298, 34, 10, 77, 119, 102, 288, 44, 11, 76, 119, 103, 275, 1, 1, 54, 12, 75, 119, 104, 273, 57, 12, 75, 119, 104, 272, 58, 11, 77, 118, 104, 271, 59, 11, 78, 117, 104, 271, 59, 11, 79, 116, 104, 271, 59, 11, 80, 115, 104, 271, 59, 11, 80, 116, 103, 271, 59, 11, 80, 116, 102, 272, 59, 11, 80, 117, 100, 273, 59, 11, 80, 117, 99, 275, 58, 12, 79, 118, 95, 277, 60, 12, 78, 119, 93, 278, 60, 12, 77, 121, 92, 278, 60, 13, 75, 123, 92, 276, 61, 11, 77, 122, 94, 275, 61, 10, 78, 122, 94, 274, 62, 9, 79, 122, 94, 274, 62, 8, 80, 122, 94, 274, 63, 7, 81, 121, 94, 274, 64, 6, 81, 121, 94, 274, 64, 6, 81, 121, 94, 274, 64, 6, 81, 122, 93, 274, 64, 6, 81, 123, 92, 274, 64, 6, 81, 123, 92, 274, 64, 7, 80, 120, 95, 273, 65, 7, 80, 119, 96, 273, 65, 7, 80, 118, 97, 273, 65, 7, 79, 118, 98, 273, 65, 8, 77, 119, 99, 272, 65, 9, 76, 119, 99, 272, 65, 11, 73, 120, 99, 271, 66, 13, 64, 127, 99, 270, 72, 8, 56, 135, 99, 270, 73, 7, 46, 1, 2, 142, 99, 270, 74, 7, 40, 151, 99, 269, 75, 6, 23, 168, 99, 269, 61, 3, 11, 6, 19, 172, 99, 269, 55, 9, 11, 7, 16, 174, 99, 269, 48, 16, 11, 8, 8, 51, 1, 129, 99, 270, 40, 23, 11, 59, 9, 129, 99, 271, 33, 29, 11, 52, 16, 129, 99, 273, 24, 36, 11, 45, 23, 130, 70, 17, 10, 274, 17, 43, 10, 8, 1, 29, 31, 131, 56, 34, 5, 276, 12, 41, 15, 8, 3, 21, 28, 6, 4, 133, 41, 329, 11, 35, 18, 11, 3, 14, 26, 15, 4, 134, 2, 15, 12, 342, 9, 28, 23, 13, 3, 7, 24, 24, 5, 134, 1, 399, 31, 12, 25, 33, 6, 222, 1, 281, 6, 16, 38, 12, 16, 39, 10, 186, 21, 7, 9, 279, 8, 9, 44, 12, 7, 39, 20, 166, 57, 277, 62, 50, 28, 145, 78, 276, 63, 41, 37, 132, 91, 276, 45, 11, 7, 33, 45, 131, 92, 276, 35, 22, 7, 8, 1, 15, 53, 130, 93, 276, 25, 32, 8, 7, 1, 6, 62, 127, 96, 276, 14, 44, 8, 6, 69, 126, 97, 276, 4, 54, 9, 5, 68, 126, 98, 276, 2, 50, 15, 5, 67, 126, 100, 275, 2, 43, 22, 5, 65, 127, 101, 275, 3, 35, 29, 5, 57, 134, 102, 275, 3, 27, 37, 5, 53, 138, 102, 276, 2, 20, 17, 17, 10, 5, 45, 146, 102, 277, 1, 13, 12, 31, 8, 5, 37, 154, 102, 284, 7, 44, 7, 6, 34, 156, 102, 335, 6, 8, 31, 158, 102, 335, 6, 10, 3, 3, 15, 166, 101, 336, 6, 16, 13, 169, 99, 337, 6, 16, 13, 169, 100, 335, 7, 16, 13, 169, 100, 335, 7, 16, 12, 170, 100, 334, 7, 17, 12, 170, 100, 329, 11, 18, 13, 169, 100, 328, 12, 18, 13, 169, 100, 329, 10, 19, 13, 170, 99, 330, 8, 20, 13, 170, 99, 332, 3, 23, 12, 171, 99, 358, 12, 171, 99, 282, 6, 70, 13, 170, 98, 282, 8, 69, 13, 170, 98, 281, 10, 68, 13, 170, 98, 280, 12, 67, 13, 170, 98, 280, 12, 67, 12, 171, 98, 280, 12, 68, 11, 171, 97, 281, 12, 67, 13, 171, 74, 1, 20, 282, 12, 67, 13, 171, 69, 13, 3, 1, 6, 285, 12, 67, 13, 171, 62, 315, 12, 67, 13, 172, 52, 325, 10, 68, 13, 172, 48, 330, 8, 70, 11, 173, 48, 332, 4, 71, 13, 173, 47, 407, 13, 174, 46, 407, 13, 175, 45, 407, 13, 177, 44, 406, 13, 177, 44, 407, 11, 178, 44, 406, 13, 177, 44, 406, 13, 177, 44, 406, 13, 177, 44, 406, 13, 177, 44, 406, 13, 178, 42, 408, 11, 179, 42, 408, 11, 179, 42, 407, 13, 178, 42, 407, 13, 179, 41, 407, 13, 180, 40, 407, 13, 182, 38, 407, 13, 185, 35, 408, 11, 186, 35, 407, 13, 185, 35, 407, 13, 185, 35, 407, 13, 185, 35, 407, 13, 185, 35, 407, 13, 185, 34, 409, 11, 187, 32, 409, 13, 187, 29, 411, 13, 189, 14, 424, 13, 627, 13, 627, 13, 628, 11, 628, 13, 627, 13, 627, 13, 627, 13, 627, 13, 628, 12, 628, 12, 627, 13, 627, 13, 627, 13, 627, 13, 628, 12, 628, 12, 627, 13, 627, 13, 627, 13, 627, 13, 627, 13, 628, 11, 629, 12, 627, 13, 627, 13, 627, 13, 627, 13, 628, 12, 628, 12, 627, 13, 627, 13, 627, 13, 627, 13, 628, 12, 628, 12, 627, 13, 627, 13, 627, 13, 627, 13, 628, 12, 628, 12, 628, 12, 627, 13, 627, 13, 627, 13, 628, 12, 628, 12, 628, 12, 627, 13, 627, 13, 627, 13, 628, 12, 628, 12, 628, 12, 627, 13, 627, 13, 627, 13, 628, 12, 628, 12, 628, 12, 628, 12, 627, 13, 627, 13, 628, 12, 628, 12, 628, 12, 628, 12, 627, 13, 627, 13, 628, 12, 628, 12, 628, 12, 628, 12, 628, 12, 627, 13, 627, 13, 628, 12, 628, 12, 628, 12, 628, 12, 627, 13, 627, 13, 628, 12, 628, 12, 628, 12, 628, 12, 46, 6, 576, 12, 45, 8, 575, 12, 44, 10, 574, 12, 43, 12, 573, 12, 43, 12, 573, 12, 43, 12, 544, 40, 44, 12, 3, 6, 521, 6, 5, 42, 12, 19, 14, 22, 519, 54, 11, 57, 517, 56, 9, 59, 515, 58, 7, 60, 514, 59, 7, 60, 513, 61, 6, 60, 513, 62, 5, 60, 513, 62, 5, 60, 513, 62, 5, 61, 512, 62, 5, 62, 511, 62, 5, 62, 511, 62, 5, 62, 511, 62, 5, 62, 511, 62, 5, 62, 511, 62, 5, 62, 511, 62, 5, 62, 511, 62, 6, 61, 511, 62, 6, 60, 512, 62, 7, 59, 513, 61, 7, 59, 513, 60, 1, 3, 4, 59, 513, 127, 513, 126, 514, 125, 515, 125, 515, 125, 515, 125, 515, 125, 516, 124, 516, 19, 1, 103, 365], "size": [640, 316]}, "area": 30530, "iscrowd": 1, "image_id": 10136, "bbox": [28, 109, 287, 373], "category_id": 84, "id": 908400010136}, {"segmentation": {"counts": [175851, 7, 471, 15, 464, 31, 448, 33, 447, 34, 445, 36, 444, 37, 443, 37, 443, 37, 443, 38, 442, 38, 442, 38, 443, 36, 445, 35, 446, 34, 447, 32, 451, 10, 6, 4, 4, 4, 454, 4, 1, 1, 15, 4, 454, 4, 17, 4, 453, 6, 17, 5, 451, 11, 14, 5, 449, 16, 1, 8, 1, 6, 448, 33, 446, 35, 445, 35, 445, 35, 445, 36, 444, 36, 444, 36, 444, 35, 446, 34, 447, 33, 447, 32, 448, 31, 449, 30, 450, 20, 1, 6, 453, 16, 464, 17, 463, 17, 463, 17, 463, 16, 464, 16, 464, 16, 464, 17, 463, 17, 463, 17, 463, 16, 464, 16, 463, 17, 463, 17, 463, 17, 463, 17, 463, 18, 462, 18, 462, 17, 463, 17, 464, 16, 464, 16, 464, 16, 464, 16, 463, 18, 462, 18, 463, 17, 463, 17, 463, 17, 463, 16, 464, 16, 464, 17, 463, 17, 463, 17, 463, 17, 463, 17, 463, 17, 463, 17, 463, 17, 463, 17, 464, 16, 463, 17, 463, 18, 462, 18, 462, 18, 462, 17, 463, 17, 463, 17, 464, 16, 464, 16, 463, 18, 462, 19, 461, 19, 461, 19, 461, 20, 460, 20, 461, 19, 461, 19, 460, 20, 460, 19, 461, 19, 461, 20, 460, 20, 460, 20, 460, 21, 459, 21, 459, 21, 459, 20, 460, 20, 460, 20, 460, 19, 461, 19, 462, 19, 460, 20, 460, 20, 460, 21, 459, 21, 459, 20, 460, 20, 460, 20, 461, 19, 461, 19, 462, 18, 462, 19, 461, 19, 460, 31, 449, 32, 448, 33, 447, 34, 446, 35, 445, 35, 445, 35, 445, 36, 444, 36, 444, 36, 444, 35, 445, 35, 445, 35, 445, 34, 446, 33, 448, 31, 448, 20, 2, 3, 1, 3, 451, 16, 464, 17, 463, 17, 463, 17, 463, 16, 464, 16, 465, 15, 464, 16, 464, 16, 463, 18, 462, 18, 462, 18, 462, 17, 463, 14, 466, 11, 469, 11, 470, 10, 471, 9, 472, 9, 472, 9, 474, 3, 1915, 21, 457, 24, 455, 26, 453, 28, 452, 29, 450, 30, 450, 30, 450, 31, 449, 31, 449, 31, 449, 30, 450, 30, 451, 29, 452, 27, 454, 25, 456, 23, 460, 6, 4, 3, 1, 3, 45885], "size": [480, 640]}, "area": 4029, "iscrowd": 1, "image_id": 10230, "bbox": [366, 166, 178, 41], "category_id": 1, "id": 900100010230}, {"segmentation": {"counts": [110406, 6, 420, 8, 418, 10, 416, 12, 415, 12, 415, 12, 415, 12, 415, 13, 414, 14, 413, 14, 414, 13, 415, 12, 415, 12, 415, 12, 415, 12, 416, 10, 418, 8, 421, 4, 89637, 6, 420, 8, 418, 10, 416, 12, 415, 12, 415, 12, 415, 12, 415, 12, 415, 12, 415, 12, 416, 10, 418, 8, 421, 4, 31166, 6, 420, 8, 418, 10, 416, 12, 415, 12, 415, 12, 415, 12, 415, 12, 415, 12, 415, 12, 416, 10, 418, 8, 421, 4, 14940, 11, 415, 13, 413, 15, 411, 17, 410, 17, 410, 17, 410, 17, 410, 17, 410, 17, 410, 17, 411, 15, 413, 13, 416, 4, 1, 4, 4473], "size": [427, 640]}, "area": 650, "iscrowd": 1, "image_id": 10281, "bbox": [258, 211, 371, 39], "category_id": 1, "id": 900100010281}, {"segmentation": {"counts": [28882, 4, 395, 6, 393, 8, 392, 8, 392, 9, 391, 9, 391, 8, 392, 8, 393, 6, 395, 4, 16397, 6, 393, 8, 391, 10, 389, 12, 388, 12, 388, 12, 388, 12, 387, 13, 387, 13, 387, 13, 387, 12, 388, 12, 388, 12, 388, 12, 389, 10, 391, 10, 389, 12, 388, 12, 388, 12, 388, 12, 388, 12, 388, 12, 388, 12, 389, 10, 391, 8, 394, 4, 32801, 4, 395, 6, 393, 8, 392, 8, 392, 8, 392, 8, 392, 8, 393, 6, 395, 4, 4793, 6, 393, 8, 391, 10, 389, 12, 388, 12, 388, 12, 388, 12, 388, 12, 144, 8, 236, 12, 139, 14, 235, 12, 138, 16, 235, 10, 138, 18, 235, 8, 138, 19, 237, 6, 138, 19, 236, 8, 137, 19, 235, 10, 136, 19, 234, 12, 135, 19, 234, 12, 135, 19, 234, 12, 135, 18, 235, 12, 136, 16, 236, 12, 137, 13, 238, 12, 139, 9, 240, 12, 388, 11, 389, 10, 390, 8, 393, 6, 395, 4, 396, 4, 395, 6, 393, 8, 392, 8, 392, 8, 392, 8, 392, 8, 393, 6, 395, 4, 18799, 6, 393, 8, 391, 10, 389, 12, 388, 12, 388, 12, 388, 12, 388, 12, 388, 12, 388, 12, 389, 10, 391, 10, 389, 12, 388, 12, 388, 12, 388, 12, 388, 12, 388, 12, 388, 12, 389, 10, 391, 8, 394, 4, 1594, 6, 393, 8, 391, 10, 129, 6, 254, 12, 127, 8, 253, 12, 126, 10, 252, 12, 125, 12, 251, 12, 125, 12, 251, 12, 125, 12, 251, 12, 125, 12, 251, 12, 125, 12, 252, 10, 126, 12, 253, 8, 127, 12, 255, 4, 130, 10, 391, 8, 394, 4, 1608, 3, 397, 4, 245, 6, 145, 5, 243, 8, 145, 5, 241, 10, 140, 9, 240, 12, 136, 12, 240, 12, 136, 12, 240, 12, 136, 12, 240, 12, 136, 13, 239, 12, 136, 14, 238, 13, 126, 6, 4, 13, 238, 14, 124, 8, 4, 12, 239, 14, 122, 10, 3, 12, 240, 13, 121, 12, 5, 9, 241, 12, 121, 12, 5, 9, 241, 12, 121, 12, 5, 9, 241, 12, 121, 12, 2, 12, 241, 12, 121, 12, 2, 12, 241, 12, 121, 12, 2, 12, 242, 10, 122, 12, 2, 12, 243, 8, 124, 10, 3, 4, 2, 3, 2, 1, 245, 4, 127, 8, 4, 4, 2, 1, 4, 1, 378, 4, 6, 4, 2, 1, 2, 3, 389, 10, 391, 8, 394, 4, 645, 6, 393, 8, 391, 10, 389, 12, 153, 1, 234, 12, 143, 6, 2, 4, 233, 12, 143, 12, 233, 12, 143, 12, 233, 12, 143, 12, 233, 12, 143, 12, 233, 5, 5, 2, 143, 12, 233, 5, 5, 2, 143, 13, 232, 7, 1, 4, 143, 14, 231, 12, 143, 14, 231, 12, 144, 13, 231, 12, 145, 12, 231, 12, 145, 12, 231, 12, 145, 12, 232, 10, 145, 13, 232, 10, 145, 13, 231, 12, 144, 13, 231, 12, 144, 13, 231, 12, 144, 13, 231, 12, 143, 14, 231, 12, 142, 15, 231, 12, 142, 15, 231, 12, 142, 15, 232, 10, 143, 15, 232, 10, 143, 15, 231, 12, 142, 15, 231, 12, 142, 15, 230, 13, 143, 10, 233, 14, 144, 8, 233, 15, 146, 3, 235, 16, 384, 16, 384, 15, 385, 14, 186, 6, 194, 12, 187, 8, 193, 12, 186, 10, 192, 12, 185, 12, 192, 10, 186, 12, 193, 8, 154, 6, 27, 12, 195, 4, 155, 8, 26, 12, 199, 6, 148, 10, 25, 12, 198, 8, 146, 12, 24, 12, 197, 10, 145, 12, 24, 12, 196, 12, 144, 12, 25, 10, 197, 12, 144, 12, 26, 8, 198, 12, 144, 12, 28, 4, 200, 12, 144, 12, 232, 12, 144, 12, 232, 12, 145, 10, 233, 12, 146, 8, 235, 10, 149, 4, 236, 10, 389, 9, 390, 10, 389, 12, 388, 12, 388, 12, 388, 12, 388, 12, 388, 12, 388, 12, 389, 10, 391, 8, 394, 4, 795, 4, 395, 6, 393, 8, 391, 10, 389, 12, 388, 12, 388, 12, 388, 12, 388, 12, 387, 13, 387, 13, 387, 12, 388, 11, 389, 9, 392, 6, 395, 4, 2, 4, 394, 7, 392, 9, 390, 10, 389, 12, 388, 12, 388, 12, 388, 12, 388, 12, 388, 12, 388, 12, 389, 11, 388, 13, 387, 13, 387, 13, 387, 13, 387, 13, 387, 12, 388, 12, 389, 10, 391, 8, 394, 4, 388, 6, 393, 8, 391, 10, 389, 12, 388, 12, 388, 12, 388, 12, 388, 12, 388, 12, 388, 12, 389, 10, 391, 8, 394, 6, 393, 8, 391, 10, 389, 12, 388, 12, 388, 12, 388, 12, 388, 16, 384, 17, 383, 18, 383, 18, 383, 17, 385, 15, 388, 12, 385, 15, 384, 16, 383, 17, 382, 19, 381, 19, 381, 19, 381, 19, 381, 19, 381, 19, 381, 19, 380, 19, 380, 19, 380, 19, 380, 20, 380, 20, 380, 20, 380, 12, 1, 6, 381, 12, 2, 4, 380, 14, 385, 15, 384, 15, 385, 14, 386, 12, 388, 8, 392, 8, 393, 6, 395, 4, 1605, 6, 393, 8, 391, 10, 389, 12, 388, 12, 388, 18, 382, 19, 381, 20, 380, 21, 379, 21, 380, 20, 381, 11, 2, 6, 383, 4, 1, 3, 3, 6, 388, 5, 1, 6, 377, 4, 7, 13, 375, 6, 7, 12, 374, 8, 5, 13, 374, 8, 5, 13, 374, 8, 5, 13, 374, 8, 5, 13, 374, 8, 5, 13, 375, 6, 6, 12, 377, 4, 8, 10, 391, 7, 392, 6, 393, 8, 391, 10, 389, 12, 388, 12, 372, 6, 10, 12, 371, 8, 9, 12, 370, 10, 8, 12, 369, 12, 7, 12, 369, 12, 7, 12, 369, 12, 8, 10, 370, 12, 9, 8, 371, 12, 11, 4, 373, 12, 388, 12, 389, 10, 391, 8, 394, 4, 3611, 7, 392, 9, 390, 11, 388, 13, 387, 13, 387, 13, 387, 13, 387, 13, 387, 13, 387, 13, 388, 11, 390, 9, 393, 6, 6307], "size": [400, 640]}, "area": 5451, "iscrowd": 1, "image_id": 10318, "bbox": [72, 67, 552, 222], "category_id": 1, "id": 900100010318}, {"segmentation": {"counts": [927, 67, 573, 67, 573, 70, 569, 72, 567, 74, 566, 75, 564, 77, 563, 77, 563, 78, 561, 80, 560, 80, 560, 81, 559, 81, 559, 82, 558, 82, 558, 82, 558, 83, 557, 83, 557, 83, 557, 83, 557, 83, 557, 83, 557, 83, 557, 83, 557, 83, 557, 83, 558, 82, 558, 82, 558, 82, 558, 82, 558, 82, 558, 81, 559, 81, 559, 82, 558, 83, 557, 83, 557, 83, 558, 82, 558, 82, 559, 81, 559, 81, 560, 79, 561, 78, 563, 76, 564, 76, 565, 75, 566, 74, 567, 73, 568, 72, 569, 71, 569, 70, 569, 70, 570, 68, 571, 25, 7, 16, 591, 20, 619, 17, 622, 16, 624, 16, 624, 15, 625, 13, 627, 12, 628, 12, 628, 11, 630, 10, 631, 8, 19, 3, 612, 7, 15, 6, 614, 7, 12, 8, 612, 11, 8, 9, 611, 13, 6, 11, 610, 16, 3, 11, 610, 17, 1, 13, 609, 32, 608, 37, 603, 38, 602, 39, 602, 41, 600, 42, 13, 9, 576, 47, 7, 11, 574, 49, 5, 13, 573, 50, 3, 15, 571, 52, 2, 15, 570, 70, 570, 70, 569, 71, 569, 71, 569, 71, 568, 71, 569, 71, 569, 70, 570, 69, 571, 68, 572, 68, 572, 67, 573, 67, 573, 68, 572, 68, 572, 68, 572, 68, 572, 68, 572, 68, 572, 70, 571, 70, 570, 71, 570, 71, 569, 71, 570, 70, 570, 70, 570, 70, 570, 70, 570, 70, 570, 70, 570, 70, 570, 70, 570, 69, 572, 68, 573, 67, 573, 66, 574, 66, 574, 65, 575, 64, 576, 63, 577, 63, 577, 63, 577, 63, 577, 63, 577, 63, 577, 62, 578, 62, 578, 62, 578, 62, 579, 61, 579, 61, 579, 61, 578, 62, 578, 62, 578, 62, 578, 62, 578, 61, 579, 61, 579, 61, 579, 61, 579, 60, 580, 60, 580, 59, 581, 59, 581, 59, 581, 58, 582, 58, 582, 58, 582, 58, 583, 57, 583, 57, 583, 56, 584, 55, 585, 54, 601, 41, 602, 39, 605, 36, 608, 33, 611, 29, 615, 25, 618, 22, 622, 18, 624, 16, 624, 1, 2, 13, 628, 11, 630, 9, 633, 5, 52432, 2, 637, 7, 632, 11, 628, 16, 623, 16, 624, 11, 628, 9, 630, 10, 630, 10, 629, 10, 630, 10, 25, 1, 604, 10, 26, 1, 602, 12, 8, 2, 5, 7, 2, 3, 601, 14, 4, 7, 3, 12, 600, 26, 3, 12, 8, 1, 590, 26, 3, 13, 6, 2, 590, 26, 3, 14, 3, 4, 590, 26, 4, 3, 3, 14, 590, 26, 4, 1, 12, 7, 590, 26, 18, 6, 590, 24, 21, 5, 590, 22, 618, 19, 621, 18, 622, 17, 623, 17, 30, 3, 590, 18, 17, 4, 601, 18, 14, 11, 598, 20, 7, 19, 594, 21, 8, 19, 593, 21, 4, 1, 3, 17, 594, 18, 12, 16, 595, 17, 13, 16, 594, 17, 15, 15, 594, 16, 18, 12, 593, 17, 20, 10, 593, 17, 22, 9, 591, 18, 24, 7, 591, 19, 3, 5, 16, 6, 591, 29, 9, 12, 590, 50, 590, 50, 589, 22, 10, 19, 589, 19, 19, 13, 589, 19, 23, 9, 589, 19, 29, 3, 589, 19, 621, 19, 622, 18, 622, 18, 622, 14, 626, 11, 629, 11, 630, 10, 630, 10, 630, 10, 631, 9, 25, 12, 594, 10, 9, 2, 8, 16, 596, 8, 18, 18, 597, 6, 19, 17, 598, 5, 20, 16, 600, 2, 22, 16, 601, 1, 21, 17, 601, 1, 6, 4, 11, 18, 599, 2, 5, 7, 9, 19, 598, 15, 8, 19, 597, 17, 7, 20, 595, 20, 5, 21, 594, 21, 4, 21, 593, 24, 2, 22, 591, 26, 1, 22, 590, 22, 10, 18, 589, 20, 19, 13, 588, 18, 20, 14, 588, 17, 18, 18, 587, 16, 13, 25, 586, 15, 11, 28, 586, 15, 7, 32, 586, 11, 9, 34, 587, 9, 9, 35, 588, 7, 9, 36, 588, 8, 7, 37, 588, 8, 6, 37, 589, 8, 4, 38, 590, 43, 597, 35, 605, 34, 607, 33, 606, 33, 607, 32, 608, 31, 609, 31, 609, 30, 610, 29, 611, 29, 611, 28, 612, 24, 616, 18, 622, 15, 625, 15, 625, 15, 625, 15, 625, 15, 626, 14, 626, 14, 626, 14, 12, 1, 613, 14, 10, 4, 612, 14, 8, 7, 611, 18, 2, 10, 610, 31, 609, 32, 607, 34, 606, 33, 607, 34, 6, 7, 3, 2, 588, 34, 16, 4, 586, 32, 18, 5, 585, 25, 1, 5, 19, 6, 584, 23, 5, 1, 21, 7, 583, 8, 2, 12, 28, 8, 582, 7, 4, 11, 29, 8, 581, 7, 4, 11, 30, 8, 580, 8, 3, 12, 30, 7, 580, 24, 30, 7, 579, 24, 30, 8, 578, 27, 2, 2, 24, 7, 578, 32, 24, 7, 578, 32, 23, 7, 578, 34, 15, 13, 578, 37, 11, 15, 576, 46, 2, 16, 576, 17, 5, 42, 576, 14, 10, 40, 575, 14, 14, 37, 574, 12, 18, 9, 3, 24, 573, 11, 21, 5, 19, 11, 573, 11, 22, 1, 22, 11, 573, 11, 45, 11, 573, 10, 45, 12, 573, 16, 36, 15, 573, 17, 24, 26, 573, 18, 14, 35, 574, 26, 1, 39, 575, 65, 575, 64, 576, 64, 577, 62, 578, 62, 579, 60, 580, 61, 578, 62, 578, 63, 576, 64, 576, 64, 576, 65, 574, 66, 574, 66, 574, 66, 574, 66, 574, 66, 574, 66, 574, 67, 573, 66, 574, 66, 574, 66, 574, 66, 574, 66, 574, 66, 574, 66, 574, 65, 576, 71, 569, 72, 569, 72, 568, 73, 567, 73, 566, 74, 566, 74, 566, 74, 565, 75, 565, 75, 565, 75, 564, 75, 565, 77, 563, 78, 562, 79, 561, 80, 560, 81, 559, 82, 558, 83, 557, 83, 557, 84, 556, 85, 555, 85, 555, 86, 555, 85, 555, 85, 555, 86, 554, 86, 554, 86, 553, 88, 552, 88, 552, 88, 551, 89, 551, 89, 551, 89, 551, 89, 551, 32, 19, 39, 550, 31, 24, 35, 550, 31, 27, 16, 3, 13, 550, 23, 53, 15, 549, 22, 54, 15, 549, 21, 54, 16, 549, 22, 53, 16, 549, 23, 31, 14, 6, 17, 549, 24, 30, 37, 549, 25, 43, 23, 549, 26, 50, 15, 549, 32, 45, 14, 549, 33, 44, 14, 549, 34, 9, 23, 11, 14, 549, 38, 5, 31, 3, 14, 549, 41, 2, 32, 1, 15, 549, 91, 549, 91, 549, 91, 549, 91, 550, 90, 550, 90, 551, 88, 552, 88, 553, 86, 258], "size": [640, 480]}, "area": 24824, "iscrowd": 1, "image_id": 10321, "bbox": [1, 283, 478, 100], "category_id": 1, "id": 900100010321}, {"segmentation": {"counts": [45100, 3, 421, 9, 417, 11, 415, 13, 413, 15, 412, 16, 411, 16, 410, 17, 410, 17, 410, 17, 410, 17, 410, 18, 408, 19, 408, 20, 406, 21, 406, 21, 406, 21, 406, 21, 406, 21, 406, 21, 407, 20, 407, 20, 407, 20, 406, 21, 406, 20, 408, 18, 409, 18, 410, 15, 412, 15, 413, 14, 413, 13, 414, 12, 415, 12, 368, 7, 41, 9, 369, 10, 41, 5, 369, 13, 414, 14, 412, 15, 412, 16, 410, 17, 410, 17, 410, 17, 410, 17, 410, 17, 410, 17, 410, 17, 410, 17, 410, 17, 410, 17, 410, 17, 410, 17, 410, 17, 410, 18, 409, 18, 409, 19, 408, 19, 408, 19, 408, 19, 408, 19, 409, 18, 409, 18, 410, 16, 412, 14, 414, 13, 415, 10, 419, 6, 24404, 7, 419, 10, 415, 13, 414, 14, 412, 14, 413, 14, 412, 15, 412, 16, 411, 17, 410, 17, 410, 17, 409, 18, 409, 19, 407, 20, 407, 20, 407, 20, 407, 20, 407, 20, 406, 21, 406, 22, 404, 23, 404, 23, 404, 23, 404, 23, 376, 7, 21, 23, 375, 10, 18, 24, 373, 13, 17, 25, 372, 14, 15, 26, 371, 17, 13, 26, 371, 18, 12, 26, 370, 20, 11, 26, 370, 20, 11, 26, 370, 21, 10, 26, 370, 21, 9, 27, 370, 21, 8, 29, 369, 21, 8, 29, 369, 21, 8, 29, 369, 21, 7, 31, 368, 21, 7, 31, 367, 21, 8, 30, 368, 20, 9, 30, 368, 20, 9, 31, 367, 19, 10, 31, 367, 19, 10, 32, 366, 18, 10, 33, 366, 18, 10, 33, 365, 19, 10, 33, 365, 20, 9, 33, 365, 20, 9, 33, 364, 21, 9, 16, 1, 16, 362, 23, 9, 16, 1, 16, 362, 22, 9, 17, 1, 15, 362, 24, 8, 16, 2, 15, 362, 24, 8, 17, 1, 13, 363, 25, 8, 17, 1, 11, 365, 25, 8, 17, 1, 9, 367, 25, 8, 16, 3, 7, 368, 25, 8, 16, 3, 5, 370, 25, 7, 17, 3, 4, 371, 24, 8, 16, 4, 2, 372, 24, 8, 17, 378, 24, 8, 20, 375, 23, 9, 18, 377, 49, 378, 48, 379, 48, 365, 5, 1, 55, 357, 69, 357, 69, 356, 70, 357, 69, 357, 70, 357, 69, 357, 69, 358, 68, 359, 67, 360, 66, 361, 66, 362, 64, 363, 56, 1, 6, 364, 55, 2, 5, 365, 61, 366, 19, 3, 2, 1, 36, 366, 17, 9, 34, 367, 17, 9, 33, 368, 17, 10, 32, 368, 16, 12, 30, 370, 16, 12, 29, 370, 16, 14, 26, 371, 16, 15, 25, 370, 17, 17, 22, 371, 17, 21, 17, 372, 17, 22, 16, 373, 16, 23, 13, 375, 16, 25, 9, 377, 16, 411, 17, 410, 17, 410, 17, 410, 17, 410, 17, 410, 17, 411, 16, 411, 16, 411, 16, 411, 16, 411, 17, 410, 17, 410, 16, 411, 16, 411, 16, 412, 16, 410, 17, 410, 17, 410, 17, 410, 17, 410, 17, 411, 16, 411, 16, 411, 16, 411, 17, 410, 17, 410, 17, 410, 17, 410, 16, 17, 1, 393, 16, 412, 16, 13, 4, 394, 16, 13, 7, 390, 17, 12, 11, 387, 17, 10, 16, 384, 17, 10, 19, 382, 16, 9, 23, 379, 16, 9, 26, 376, 16, 8, 30, 373, 16, 8, 32, 371, 17, 7, 34, 369, 17, 7, 35, 368, 17, 7, 35, 368, 17, 8, 34, 368, 17, 8, 34, 368, 16, 10, 33, 369, 16, 10, 32, 369, 16, 10, 31, 370, 16, 10, 31, 369, 17, 10, 31, 370, 16, 10, 31, 370, 16, 10, 31, 370, 16, 5, 36, 370, 16, 4, 37, 371, 15, 2, 38, 371, 17, 1, 37, 372, 56, 371, 56, 371, 56, 371, 56, 372, 54, 373, 54, 373, 54, 373, 53, 374, 53, 374, 52, 375, 17, 1, 34, 375, 16, 3, 32, 376, 16, 5, 29, 378, 16, 7, 26, 378, 16, 8, 24, 378, 17, 9, 23, 378, 17, 10, 21, 379, 17, 12, 5, 1, 13, 379, 17, 18, 13, 380, 16, 18, 13, 380, 16, 18, 14, 379, 16, 17, 17, 377, 17, 16, 17, 377, 17, 16, 17, 377, 17, 16, 17, 377, 17, 16, 17, 377, 17, 16, 17, 377, 16, 17, 16, 379, 16, 15, 16, 380, 16, 15, 17, 379, 16, 15, 17, 378, 17, 15, 17, 378, 17, 15, 17, 378, 17, 15, 16, 380, 16, 14, 17, 380, 16, 14, 16, 381, 16, 14, 16, 381, 17, 13, 16, 381, 17, 12, 17, 381, 17, 13, 16, 381, 17, 13, 16, 381, 17, 12, 17, 381, 17, 12, 17, 382, 16, 12, 16, 383, 16, 11, 17, 383, 16, 11, 17, 383, 17, 8, 19, 383, 22, 3, 19, 383, 44, 383, 43, 384, 43, 384, 43, 385, 42, 1, 7, 376, 53, 374, 54, 373, 55, 372, 55, 372, 56, 372, 55, 372, 55, 373, 49, 2, 3, 374, 46, 3, 4, 375, 43, 5, 4, 376, 41, 5, 5, 378, 37, 7, 4, 379, 33, 1, 1, 8, 4, 381, 32, 10, 4, 382, 30, 10, 3, 385, 27, 12, 1, 389, 23, 405, 22, 405, 21, 407, 19, 409, 18, 411, 16, 412, 17, 410, 18, 404, 25, 401, 27, 398, 30, 397, 32, 394, 34, 393, 35, 391, 38, 389, 39, 388, 41, 386, 43, 384, 45, 383, 46, 381, 46, 382, 46, 382, 46, 382, 47, 382, 46, 387, 40, 387, 41, 387, 17, 2, 22, 387, 41, 386, 41, 386, 41, 386, 40, 387, 22, 1, 17, 388, 38, 17, 1, 371, 22, 1, 15, 16, 3, 371, 21, 2, 13, 18, 3, 370, 21, 3, 12, 17, 5, 369, 21, 4, 11, 16, 7, 368, 21, 6, 9, 15, 9, 367, 21, 6, 9, 14, 11, 367, 20, 4, 11, 12, 14, 366, 21, 2, 12, 11, 16, 366, 34, 10, 16, 354, 47, 10, 16, 353, 48, 9, 16, 352, 50, 6, 18, 353, 50, 5, 18, 353, 51, 6, 17, 353, 52, 7, 16, 351, 53, 8, 15, 351, 53, 9, 14, 351, 53, 10, 13, 351, 53, 11, 12, 351, 53, 12, 11, 352, 52, 13, 10, 352, 52, 16, 6, 354, 51, 18, 4, 355, 50, 378, 49, 380, 47, 381, 46, 380, 47, 380, 47, 380, 47, 380, 48, 379, 48, 380, 47, 380, 47, 381, 46, 382, 49, 379, 52, 376, 58, 370, 57, 371, 56, 372, 55, 373, 53, 373, 54, 373, 54, 372, 54, 372, 54, 372, 55, 371, 56, 368, 59, 367, 60, 366, 60, 3, 12, 351, 77, 349, 79, 345, 83, 343, 84, 342, 85, 342, 85, 339, 88, 338, 89, 336, 92, 335, 27, 1, 64, 334, 27, 1, 65, 334, 94, 332, 95, 332, 95, 332, 81, 1, 13, 332, 81, 1, 13, 332, 81, 1, 13, 333, 79, 1, 13, 334, 93, 335, 92, 336, 91, 337, 90, 339, 87, 346, 81, 348, 78, 352, 75, 351, 76, 351, 76, 349, 78, 348, 79, 348, 78, 348, 79, 348, 79, 348, 79, 348, 78, 350, 13, 1, 63, 350, 12, 2, 62, 352, 10, 2, 63, 353, 8, 3, 64, 354, 4, 4, 65, 362, 64, 363, 62, 365, 61, 365, 61, 365, 60, 367, 59, 366, 57, 369, 54, 371, 52, 375, 48, 378, 48, 379, 47, 379, 48, 379, 41, 2, 5, 379, 40, 4, 5, 378, 40, 5, 4, 378, 40, 5, 4, 378, 39, 6, 5, 377, 39, 6, 5, 377, 38, 7, 5, 378, 37, 7, 6, 377, 37, 8, 5, 377, 37, 8, 5, 377, 43, 3, 5, 376, 45, 2, 4, 376, 46, 1, 4, 376, 52, 374, 53, 374, 54, 372, 56, 371, 58, 369, 59, 368, 60, 368, 61, 365, 63, 364, 65, 361, 67, 360, 68, 359, 68, 359, 68, 357, 70, 357, 71, 355, 73, 354, 73, 353, 75, 352, 75, 352, 75, 352, 75, 352, 75, 353, 74, 353, 74, 354, 74, 354, 75, 353, 75, 354, 74, 353, 76, 351, 77, 350, 78, 350, 77, 350, 78, 350, 17, 1, 59, 351, 16, 1, 59, 352, 13, 3, 59, 354, 9, 5, 59, 369, 58, 369, 58, 370, 20, 2, 34, 372, 17, 10, 1, 2, 24, 119], "size": [427, 640]}, "area": 23862, "iscrowd": 1, "image_id": 10281, "bbox": [105, 207, 534, 102], "category_id": 38, "id": 903800010281}, {"segmentation": {"counts": [29637, 12, 412, 18, 407, 22, 403, 26, 399, 30, 396, 32, 394, 34, 392, 36, 390, 38, 388, 40, 386, 42, 385, 42, 384, 44, 382, 46, 381, 46, 380, 48, 379, 48, 379, 48, 378, 50, 377, 50, 377, 50, 377, 50, 377, 50, 377, 50, 377, 50, 377, 51, 376, 51, 376, 52, 375, 52, 375, 52, 375, 53, 374, 53, 374, 53, 374, 53, 374, 53, 374, 53, 375, 52, 375, 52, 375, 52, 375, 52, 375, 52, 375, 52, 376, 51, 376, 51, 377, 50, 377, 50, 378, 48, 379, 48, 380, 46, 381, 46, 382, 44, 383, 44, 384, 42, 385, 42, 386, 40, 388, 38, 390, 36, 392, 34, 394, 32, 397, 28, 400, 26, 403, 22, 408, 16, 415, 8, 32784, 10, 414, 16, 409, 20, 405, 24, 402, 26, 400, 28, 398, 31, 395, 34, 392, 37, 390, 38, 20, 10, 358, 40, 16, 16, 354, 42, 13, 20, 352, 43, 10, 24, 350, 44, 8, 26, 348, 45, 7, 28, 347, 46, 5, 30, 346, 47, 3, 32, 345, 47, 2, 34, 344, 47, 2, 34, 344, 84, 343, 85, 342, 85, 342, 85, 342, 86, 341, 86, 341, 86, 341, 86, 341, 86, 342, 85, 342, 85, 343, 84, 343, 84, 344, 83, 345, 82, 345, 82, 346, 81, 347, 39, 2, 38, 349, 38, 2, 38, 351, 35, 4, 36, 353, 33, 5, 36, 356, 29, 7, 34, 359, 26, 9, 33, 361, 22, 11, 32, 363, 20, 13, 30, 319, 12, 36, 14, 17, 28, 317, 18, 36, 8, 21, 26, 316, 22, 65, 22, 316, 26, 64, 20, 316, 28, 66, 14, 318, 30, 68, 8, 320, 32, 394, 34, 392, 36, 391, 36, 390, 38, 388, 40, 387, 40, 387, 40, 386, 42, 385, 42, 385, 42, 385, 42, 385, 42, 385, 42, 385, 42, 385, 42, 385, 42, 385, 42, 385, 42, 385, 42, 385, 42, 386, 41, 386, 41, 387, 40, 387, 40, 387, 41, 386, 41, 386, 41, 387, 40, 387, 40, 387, 25, 9, 6, 387, 24, 10, 6, 387, 24, 11, 5, 387, 24, 11, 5, 387, 24, 11, 5, 387, 24, 11, 5, 387, 24, 12, 4, 387, 24, 12, 4, 387, 35, 1, 4, 387, 40, 387, 24, 13, 3, 387, 24, 14, 2, 387, 24, 15, 1, 387, 23, 16, 1, 387, 23, 404, 23, 404, 23, 404, 23, 404, 24, 403, 24, 403, 40, 12, 10, 365, 25, 24, 16, 362, 23, 24, 20, 360, 22, 23, 24, 358, 22, 22, 27, 356, 22, 21, 29, 355, 22, 20, 31, 354, 22, 20, 32, 353, 22, 20, 33, 352, 22, 20, 34, 351, 22, 20, 34, 351, 22, 20, 35, 350, 23, 18, 37, 349, 78, 349, 78, 349, 79, 348, 79, 349, 78, 349, 78, 348, 79, 348, 79, 347, 80, 346, 82, 345, 82, 327, 10, 8, 82, 324, 16, 4, 83, 322, 20, 2, 83, 320, 64, 1, 42, 319, 65, 1, 42, 318, 66, 2, 41, 317, 67, 2, 41, 316, 68, 3, 40, 315, 69, 3, 40, 315, 69, 3, 40, 314, 70, 3, 40, 313, 71, 3, 40, 313, 71, 3, 41, 253, 15, 44, 71, 3, 41, 250, 21, 40, 72, 3, 41, 248, 25, 38, 71, 4, 42, 245, 29, 36, 71, 4, 42, 243, 33, 34, 70, 5, 42, 242, 35, 33, 70, 5, 43, 240, 38, 31, 69, 6, 43, 239, 40, 30, 69, 6, 43, 238, 42, 29, 68, 7, 44, 236, 44, 28, 68, 7, 44, 235, 46, 27, 67, 9, 43, 235, 47, 26, 67, 9, 44, 233, 49, 25, 66, 11, 43, 232, 51, 24, 66, 11, 43, 232, 51, 24, 65, 12, 43, 231, 53, 23, 65, 12, 44, 230, 54, 22, 65, 12, 44, 230, 54, 22, 66, 12, 43, 229, 56, 21, 66, 12, 43, 229, 56, 21, 66, 13, 42, 229, 56, 21, 66, 13, 42, 229, 57, 20, 66, 14, 41, 229, 57, 20, 66, 13, 42, 229, 57, 20, 66, 13, 42, 229, 57, 20, 66, 13, 42, 229, 57, 20, 66, 13, 42, 229, 57, 20, 66, 13, 42, 229, 57, 20, 66, 13, 42, 229, 57, 20, 66, 13, 42, 229, 57, 20, 66, 13, 42, 229, 57, 20, 66, 13, 42, 229, 57, 20, 66, 13, 42, 229, 57, 21, 65, 13, 42, 230, 56, 21, 65, 13, 42, 230, 56, 22, 64, 13, 41, 232, 55, 22, 64, 14, 40, 232, 54, 24, 63, 14, 39, 234, 53, 25, 62, 15, 38, 234, 52, 26, 62, 15, 37, 236, 51, 27, 60, 17, 36, 236, 50, 29, 59, 18, 34, 238, 49, 30, 57, 19, 33, 240, 47, 32, 56, 20, 31, 242, 46, 33, 54, 22, 29, 244, 44, 34, 54, 23, 26, 247, 42, 36, 52, 26, 23, 250, 39, 38, 51, 27, 20, 253, 37, 40, 49, 31, 14, 258, 34, 43, 46, 35, 8, 264, 29, 46, 44, 310, 26, 50, 40, 313, 22, 55, 36, 317, 16, 62, 30, 323, 8, 67, 28, 402, 22, 408, 16, 7310, 6, 420, 8, 418, 12, 414, 15, 412, 17, 410, 18, 408, 18, 29, 4, 376, 17, 22, 19, 370, 16, 21, 21, 369, 15, 21, 26, 366, 14, 20, 29, 365, 12, 21, 33, 362, 11, 23, 32, 363, 8, 27, 30, 363, 7, 29, 31, 361, 5, 32, 30, 362, 3, 35, 31, 398, 30, 400, 28, 401, 27, 403, 25, 405, 22, 407, 20, 410, 17, 412, 15, 415, 12, 417, 10, 420, 7, 423, 4, 425, 2, 1202, 1, 701, 10, 141, 1, 272, 16, 137, 1, 271, 20, 135, 1, 269, 24, 133, 1, 268, 26, 131, 1, 268, 28, 130, 1, 267, 30, 396, 32, 228, 1, 165, 34, 228, 6, 159, 34, 228, 7, 157, 36, 228, 7, 155, 38, 227, 8, 154, 38, 228, 8, 153, 38, 228, 8, 152, 40, 228, 7, 152, 40, 227, 8, 152, 40, 227, 8, 152, 40, 226, 9, 152, 40, 226, 9, 152, 40, 225, 9, 153, 40, 225, 8, 154, 40, 225, 6, 156, 40, 387, 40, 387, 40, 387, 40, 387, 40, 388, 38, 389, 38, 390, 36, 391, 36, 392, 34, 394, 33, 394, 32, 396, 30, 398, 28, 400, 26, 403, 22, 406, 20, 410, 14, 416, 8, 972, 5, 420, 6, 420, 7, 419, 7, 419, 8, 418, 8, 418, 9, 418, 8, 419, 8, 419, 7, 420, 7, 420, 6, 421, 6, 421, 5, 422, 5, 422, 4, 181, 10, 232, 4, 178, 16, 229, 3, 177, 20, 227, 3, 175, 24, 225, 2, 175, 26, 225, 1, 174, 28, 398, 30, 396, 32, 394, 37, 390, 40, 386, 43, 204, 7, 172, 46, 201, 7, 173, 47, 199, 8, 173, 48, 197, 8, 173, 4, 14, 32, 196, 8, 173, 4, 20, 27, 195, 7, 174, 4, 20, 28, 194, 7, 174, 4, 20, 28, 194, 6, 175, 4, 21, 28, 193, 6, 175, 4, 21, 29, 192, 5, 176, 4, 21, 29, 192, 5, 176, 4, 21, 29, 192, 4, 177, 4, 21, 30, 191, 4, 177, 4, 21, 30, 191, 3, 178, 4, 21, 30, 191, 3, 178, 21, 4, 30, 191, 3, 178, 55, 191, 2, 179, 28, 18, 9, 192, 1, 179, 28, 18, 9, 372, 28, 18, 9, 372, 28, 18, 9, 372, 28, 18, 9, 371, 29, 18, 9, 371, 29, 18, 9, 371, 29, 18, 9, 371, 29, 18, 9, 371, 29, 18, 9, 157, 7, 207, 29, 18, 9, 156, 9, 206, 56, 155, 11, 205, 56, 154, 14, 203, 31, 15, 21, 143, 15, 202, 29, 18, 23, 140, 16, 201, 29, 18, 25, 138, 19, 197, 30, 18, 27, 136, 22, 194, 30, 18, 29, 134, 23, 193, 30, 18, 30, 133, 28, 188, 30, 18, 31, 133, 28, 187, 29, 19, 32, 133, 27, 187, 29, 19, 33, 132, 26, 188, 29, 19, 34, 132, 25, 188, 29, 9, 5, 4, 36, 132, 23, 189, 30, 17, 36, 133, 22, 189, 29, 18, 37, 132, 21, 189, 30, 18, 38, 132, 20, 189, 29, 19, 38, 133, 18, 189, 30, 19, 38, 134, 17, 188, 31, 19, 39, 134, 15, 189, 31, 19, 40, 133, 15, 189, 31, 19, 41, 132, 14, 189, 32, 19, 41, 133, 13, 189, 33, 18, 42, 133, 11, 190, 34, 17, 43, 133, 10, 190, 94, 133, 9, 190, 95, 134, 8, 190, 96, 134, 6, 191, 96, 135, 5, 109, 1, 81, 96, 135, 5, 191, 96, 136, 5, 107, 1, 82, 96, 136, 5, 107, 1, 82, 97, 136, 4, 107, 1, 82, 98, 136, 4, 105, 2, 82, 98, 136, 4, 105, 2, 82, 98, 137, 3, 104, 2, 83, 99, 136, 4, 103, 1, 84, 99, 137, 4, 101, 2, 84, 99, 139, 3, 100, 1, 85, 99, 139, 5, 97, 1, 86, 99, 139, 7, 182, 99, 140, 8, 180, 99, 140, 10, 178, 99, 141, 11, 177, 98, 142, 12, 175, 98, 143, 13, 174, 97, 143, 14, 173, 97, 144, 15, 172, 96, 144, 16, 172, 95, 145, 15, 172, 95, 147, 15, 171, 94, 147, 16, 171, 92, 148, 17, 171, 91, 149, 17, 172, 89, 150, 16, 172, 89, 151, 16, 170, 89, 155, 14, 168, 90, 155, 14, 167, 90, 156, 15, 165, 91, 156, 15, 165, 90, 158, 14, 164, 91, 159, 13, 163, 91, 160, 13, 163, 91, 161, 12, 163, 91, 161, 12, 162, 93, 160, 12, 162, 94, 160, 10, 163, 94, 161, 8, 164, 94, 163, 4, 166, 95, 332, 95, 332, 95, 332, 95, 332, 95, 332, 95, 213, 1, 10, 2, 106, 95, 213, 6, 3, 4, 106, 95, 213, 13, 106, 95, 213, 13, 107, 94, 213, 12, 108, 40, 2, 52, 214, 10, 110, 38, 3, 52, 215, 8, 111, 37, 5, 51, 217, 4, 114, 35, 6, 51, 336, 33, 8, 50, 336, 32, 10, 48, 338, 30, 11, 48, 339, 28, 13, 46, 341, 26, 15, 45, 343, 22, 18, 43, 345, 20, 21, 41, 348, 14, 25, 39, 352, 8, 31, 35, 394, 32, 396, 30, 104], "size": [427, 640]}, "area": 37201, "iscrowd": 1, "image_id": 10130, "bbox": [69, 3, 570, 406], "category_id": 77, "id": 907700010130}], "categories": [{"supercategory": "person", "id": 1, "name": "person"}, {"supercategory": "vehicle", "id": 2, "name": "bicycle"}, {"supercategory": "vehicle", "id": 3, "name": "car"}, {"supercategory": "vehicle", "id": 4, "name": "motorcycle"}, {"supercategory": "vehicle", "id": 5, "name": "airplane"}, {"supercategory": "vehicle", "id": 6, "name": "bus"}, {"supercategory": "vehicle", "id": 7, "name": "train"}, {"supercategory": "vehicle", "id": 8, "name": "truck"}, {"supercategory": "vehicle", "id": 9, "name": "boat"}, {"supercategory": "outdoor", "id": 10, "name": "traffic light"}, {"supercategory": "outdoor", "id": 11, "name": "fire hydrant"}, {"supercategory": "outdoor", "id": 13, "name": "stop sign"}, {"supercategory": "outdoor", "id": 14, "name": "parking meter"}, {"supercategory": "outdoor", "id": 15, "name": "bench"}, {"supercategory": "animal", "id": 16, "name": "bird"}, {"supercategory": "animal", "id": 17, "name": "cat"}, {"supercategory": "animal", "id": 18, "name": "dog"}, {"supercategory": "animal", "id": 19, "name": "horse"}, {"supercategory": "animal", "id": 20, "name": "sheep"}, {"supercategory": "animal", "id": 21, "name": "cow"}, {"supercategory": "animal", "id": 22, "name": "elephant"}, {"supercategory": "animal", "id": 23, "name": "bear"}, {"supercategory": "animal", "id": 24, "name": "zebra"}, {"supercategory": "animal", "id": 25, "name": "giraffe"}, {"supercategory": "accessory", "id": 27, "name": "backpack"}, {"supercategory": "accessory", "id": 28, "name": "umbrella"}, {"supercategory": "accessory", "id": 31, "name": "handbag"}, {"supercategory": "accessory", "id": 32, "name": "tie"}, {"supercategory": "accessory", "id": 33, "name": "suitcase"}, {"supercategory": "sports", "id": 34, "name": "frisbee"}, {"supercategory": "sports", "id": 35, "name": "skis"}, {"supercategory": "sports", "id": 36, "name": "snowboard"}, {"supercategory": "sports", "id": 37, "name": "sports ball"}, {"supercategory": "sports", "id": 38, "name": "kite"}, {"supercategory": "sports", "id": 39, "name": "baseball bat"}, {"supercategory": "sports", "id": 40, "name": "baseball glove"}, {"supercategory": "sports", "id": 41, "name": "skateboard"}, {"supercategory": "sports", "id": 42, "name": "surfboard"}, {"supercategory": "sports", "id": 43, "name": "tennis racket"}, {"supercategory": "kitchen", "id": 44, "name": "bottle"}, {"supercategory": "kitchen", "id": 46, "name": "wine glass"}, {"supercategory": "kitchen", "id": 47, "name": "cup"}, {"supercategory": "kitchen", "id": 48, "name": "fork"}, {"supercategory": "kitchen", "id": 49, "name": "knife"}, {"supercategory": "kitchen", "id": 50, "name": "spoon"}, {"supercategory": "kitchen", "id": 51, "name": "bowl"}, {"supercategory": "food", "id": 52, "name": "banana"}, {"supercategory": "food", "id": 53, "name": "apple"}, {"supercategory": "food", "id": 54, "name": "sandwich"}, {"supercategory": "food", "id": 55, "name": "orange"}, {"supercategory": "food", "id": 56, "name": "broccoli"}, {"supercategory": "food", "id": 57, "name": "carrot"}, {"supercategory": "food", "id": 58, "name": "hot dog"}, {"supercategory": "food", "id": 59, "name": "pizza"}, {"supercategory": "food", "id": 60, "name": "donut"}, {"supercategory": "food", "id": 61, "name": "cake"}, {"supercategory": "furniture", "id": 62, "name": "chair"}, {"supercategory": "furniture", "id": 63, "name": "couch"}, {"supercategory": "furniture", "id": 64, "name": "potted plant"}, {"supercategory": "furniture", "id": 65, "name": "bed"}, {"supercategory": "furniture", "id": 67, "name": "dining table"}, {"supercategory": "furniture", "id": 70, "name": "toilet"}, {"supercategory": "electronic", "id": 72, "name": "tv"}, {"supercategory": "electronic", "id": 73, "name": "laptop"}, {"supercategory": "electronic", "id": 74, "name": "mouse"}, {"supercategory": "electronic", "id": 75, "name": "remote"}, {"supercategory": "electronic", "id": 76, "name": "keyboard"}, {"supercategory": "electronic", "id": 77, "name": "cell phone"}, {"supercategory": "appliance", "id": 78, "name": "microwave"}, {"supercategory": "appliance", "id": 79, "name": "oven"}, {"supercategory": "appliance", "id": 80, "name": "toaster"}, {"supercategory": "appliance", "id": 81, "name": "sink"}, {"supercategory": "appliance", "id": 82, "name": "refrigerator"}, {"supercategory": "indoor", "id": 84, "name": "book"}, {"supercategory": "indoor", "id": 85, "name": "clock"}, {"supercategory": "indoor", "id": 86, "name": "vase"}, {"supercategory": "indoor", "id": 87, "name": "scissors"}, {"supercategory": "indoor", "id": 88, "name": "teddy bear"}, {"supercategory": "indoor", "id": 89, "name": "hair drier"}, {"supercategory": "indoor", "id": 90, "name": "toothbrush"}]}
\ No newline at end of file
diff --git a/repositories/taming-transformers/data/coco_annotations_100/annotations/instances_val2017.json b/repositories/taming-transformers/data/coco_annotations_100/annotations/instances_val2017.json
deleted file mode 100644
index edfed4d9628c23f03468f85c84b0af3272dee176..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/data/coco_annotations_100/annotations/instances_val2017.json
+++ /dev/null
@@ -1 +0,0 @@
-{"info": {"description": "COCO 2017 Dataset", "url": "http://cocodataset.org", "version": "1.0", "year": 2017, "contributor": "COCO Consortium", "date_created": "2017/09/01"}, "licenses": [{"url": "http://creativecommons.org/licenses/by-nc-sa/2.0/", "id": 1, "name": "Attribution-NonCommercial-ShareAlike License"}, {"url": "http://creativecommons.org/licenses/by-nc/2.0/", "id": 2, "name": "Attribution-NonCommercial License"}, {"url": "http://creativecommons.org/licenses/by-nc-nd/2.0/", "id": 3, "name": "Attribution-NonCommercial-NoDerivs License"}, {"url": "http://creativecommons.org/licenses/by/2.0/", "id": 4, "name": "Attribution License"}, {"url": "http://creativecommons.org/licenses/by-sa/2.0/", "id": 5, "name": "Attribution-ShareAlike License"}, {"url": "http://creativecommons.org/licenses/by-nd/2.0/", "id": 6, "name": "Attribution-NoDerivs License"}, {"url": "http://flickr.com/commons/usage/", "id": 7, "name": "No known copyright restrictions"}, {"url": "http://www.usa.gov/copyright.shtml", "id": 8, "name": "United States Government Work"}], "images": [{"license": 1, "file_name": "000000017627.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000017627.jpg", "height": 480, "width": 640, "date_captured": "2013-11-16 15:13:53", "flickr_url": "http://farm5.staticflickr.com/4145/4977243989_c2efb1b911_z.jpg", "id": 17627}, {"license": 3, "file_name": "000000016228.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000016228.jpg", "height": 440, "width": 640, "date_captured": "2013-11-19 00:09:53", "flickr_url": "http://farm4.staticflickr.com/3737/10031812195_372ae7538f_z.jpg", "id": 16228}, {"license": 2, "file_name": "000000011813.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000011813.jpg", "height": 500, "width": 333, "date_captured": "2013-11-24 04:49:37", "flickr_url": "http://farm4.staticflickr.com/3545/3411897599_c3ae826bbb_z.jpg", "id": 11813}, {"license": 3, "file_name": "000000015079.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000015079.jpg", "height": 424, "width": 640, "date_captured": "2013-11-20 14:57:40", "flickr_url": "http://farm8.staticflickr.com/7064/6970424809_69d949181f_z.jpg", "id": 15079}, {"license": 3, "file_name": "000000011197.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000011197.jpg", "height": 427, "width": 640, "date_captured": "2013-11-17 03:59:36", "flickr_url": "http://farm8.staticflickr.com/7002/6732385791_cd9fcb9572_z.jpg", "id": 11197}, {"license": 3, "file_name": "000000016010.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000016010.jpg", "height": 471, "width": 640, "date_captured": "2013-11-18 21:20:54", "flickr_url": "http://farm2.staticflickr.com/1134/950664990_8070cd6e3a_z.jpg", "id": 16010}, {"license": 3, "file_name": "000000018150.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000018150.jpg", "height": 480, "width": 640, "date_captured": "2013-11-21 03:12:35", "flickr_url": "http://farm8.staticflickr.com/7155/6476497205_c725257677_z.jpg", "id": 18150}, {"license": 3, "file_name": "000000011760.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000011760.jpg", "height": 425, "width": 640, "date_captured": "2013-11-18 18:15:02", "flickr_url": "http://farm3.staticflickr.com/2477/5856512283_9abe8e859f_z.jpg", "id": 11760}, {"license": 3, "file_name": "000000015278.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000015278.jpg", "height": 480, "width": 640, "date_captured": "2013-11-19 23:14:33", "flickr_url": "http://farm4.staticflickr.com/3633/3291104715_bdec9818d3_z.jpg", "id": 15278}, {"license": 6, "file_name": "000000011051.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000011051.jpg", "height": 536, "width": 640, "date_captured": "2013-11-18 05:42:28", "flickr_url": "http://farm4.staticflickr.com/3155/3281278459_15063a4662_z.jpg", "id": 11051}, {"license": 3, "file_name": "000000016958.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000016958.jpg", "height": 428, "width": 640, "date_captured": "2013-11-21 20:49:33", "flickr_url": "http://farm8.staticflickr.com/7108/7435713066_78b44aae33_z.jpg", "id": 16958}, {"license": 2, "file_name": "000000010977.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000010977.jpg", "height": 375, "width": 500, "date_captured": "2013-11-15 02:16:38", "flickr_url": "http://farm9.staticflickr.com/8060/8241415983_02d80c2fca_z.jpg", "id": 10977}, {"license": 1, "file_name": "000000017436.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000017436.jpg", "height": 640, "width": 481, "date_captured": "2013-11-15 14:11:51", "flickr_url": "http://farm4.staticflickr.com/3265/2922590857_cc7c32cfaa_z.jpg", "id": 17436}, {"license": 4, "file_name": "000000013659.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000013659.jpg", "height": 480, "width": 640, "date_captured": "2013-11-17 06:35:46", "flickr_url": "http://farm7.staticflickr.com/6144/5987177856_785814a990_z.jpg", "id": 13659}, {"license": 1, "file_name": "000000020059.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000020059.jpg", "height": 427, "width": 640, "date_captured": "2013-11-18 21:44:47", "flickr_url": "http://farm1.staticflickr.com/177/393601030_4451879068_z.jpg", "id": 20059}, {"license": 3, "file_name": "000000012576.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000012576.jpg", "height": 640, "width": 480, "date_captured": "2013-11-21 01:24:50", "flickr_url": "http://farm3.staticflickr.com/2813/9012011605_cbb7999e2b_z.jpg", "id": 12576}, {"license": 4, "file_name": "000000014439.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000014439.jpg", "height": 404, "width": 640, "date_captured": "2013-11-22 17:25:30", "flickr_url": "http://farm4.staticflickr.com/3544/3506151605_2a3530b2f9_z.jpg", "id": 14439}, {"license": 3, "file_name": "000000018575.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000018575.jpg", "height": 480, "width": 640, "date_captured": "2013-11-20 01:42:34", "flickr_url": "http://farm4.staticflickr.com/3252/2464830020_ecf3bcf1c3_z.jpg", "id": 18575}, {"license": 2, "file_name": "000000013546.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000013546.jpg", "height": 425, "width": 640, "date_captured": "2013-11-20 06:39:19", "flickr_url": "http://farm3.staticflickr.com/2689/4091707765_2f3bf944a7_z.jpg", "id": 13546}, {"license": 3, "file_name": "000000013923.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000013923.jpg", "height": 427, "width": 640, "date_captured": "2013-11-21 21:17:47", "flickr_url": "http://farm9.staticflickr.com/8550/8973469217_ddac383dc5_z.jpg", "id": 13923}, {"license": 1, "file_name": "000000017379.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000017379.jpg", "height": 640, "width": 478, "date_captured": "2013-11-15 01:16:56", "flickr_url": "http://farm6.staticflickr.com/5123/5316824279_9f0a0584c7_z.jpg", "id": 17379}, {"license": 1, "file_name": "000000010363.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000010363.jpg", "height": 361, "width": 640, "date_captured": "2013-11-15 07:08:52", "flickr_url": "http://farm4.staticflickr.com/3027/2477308902_443e5baf08_z.jpg", "id": 10363}, {"license": 2, "file_name": "000000017178.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000017178.jpg", "height": 427, "width": 640, "date_captured": "2013-11-15 08:30:55", "flickr_url": "http://farm4.staticflickr.com/3354/3626704406_be9d80e909_z.jpg", "id": 17178}, {"license": 1, "file_name": "000000015338.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000015338.jpg", "height": 424, "width": 640, "date_captured": "2013-11-16 17:43:06", "flickr_url": "http://farm9.staticflickr.com/8526/8519097250_f635a04651_z.jpg", "id": 15338}, {"license": 3, "file_name": "000000011122.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000011122.jpg", "height": 480, "width": 640, "date_captured": "2013-11-17 09:24:02", "flickr_url": "http://farm4.staticflickr.com/3278/3105013044_2f108fed95_z.jpg", "id": 11122}, {"license": 5, "file_name": "000000010707.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000010707.jpg", "height": 480, "width": 640, "date_captured": "2013-11-21 23:35:53", "flickr_url": "http://farm1.staticflickr.com/147/434840964_a9c5bc3b58_z.jpg", "id": 10707}, {"license": 4, "file_name": "000000012120.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000012120.jpg", "height": 428, "width": 640, "date_captured": "2013-11-23 03:42:48", "flickr_url": "http://farm4.staticflickr.com/3492/3179857331_fdd8bbd30f_z.jpg", "id": 12120}, {"license": 1, "file_name": "000000018193.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000018193.jpg", "height": 480, "width": 640, "date_captured": "2013-11-20 13:24:37", "flickr_url": "http://farm1.staticflickr.com/242/516677812_f03351fffd_z.jpg", "id": 18193}, {"license": 3, "file_name": "000000019109.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000019109.jpg", "height": 428, "width": 640, "date_captured": "2013-11-17 02:08:36", "flickr_url": "http://farm8.staticflickr.com/7223/7017616831_24cd0b6131_z.jpg", "id": 19109}, {"license": 4, "file_name": "000000013291.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000013291.jpg", "height": 335, "width": 500, "date_captured": "2013-11-18 16:17:44", "flickr_url": "http://farm4.staticflickr.com/3394/3491087208_8efdfeb6d4_z.jpg", "id": 13291}, {"license": 1, "file_name": "000000017714.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000017714.jpg", "height": 480, "width": 640, "date_captured": "2013-11-24 14:14:00", "flickr_url": "http://farm7.staticflickr.com/6133/5943387737_ffd52a7f27_z.jpg", "id": 17714}, {"license": 4, "file_name": "000000012062.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000012062.jpg", "height": 427, "width": 640, "date_captured": "2013-11-16 20:16:30", "flickr_url": "http://farm4.staticflickr.com/3775/9086068242_2064357878_z.jpg", "id": 12062}, {"license": 4, "file_name": "000000015517.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000015517.jpg", "height": 480, "width": 640, "date_captured": "2013-11-16 23:41:40", "flickr_url": "http://farm9.staticflickr.com/8184/8407007011_9456edeb6d_z.jpg", "id": 15517}, {"license": 1, "file_name": "000000010764.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000010764.jpg", "height": 424, "width": 640, "date_captured": "2013-11-19 19:23:45", "flickr_url": "http://farm3.staticflickr.com/2714/5810596234_b5090d75f6_z.jpg", "id": 10764}, {"license": 4, "file_name": "000000015272.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000015272.jpg", "height": 640, "width": 424, "date_captured": "2013-11-16 12:18:43", "flickr_url": "http://farm9.staticflickr.com/8386/8584364941_15ab4eb0e4_z.jpg", "id": 15272}, {"license": 3, "file_name": "000000010583.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000010583.jpg", "height": 612, "width": 612, "date_captured": "2013-11-20 14:18:50", "flickr_url": "http://farm9.staticflickr.com/8497/8288720209_c13ea818f8_z.jpg", "id": 10583}, {"license": 4, "file_name": "000000017029.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000017029.jpg", "height": 640, "width": 640, "date_captured": "2013-11-18 14:33:10", "flickr_url": "http://farm8.staticflickr.com/7304/8746020648_f1e2075b86_z.jpg", "id": 17029}, {"license": 2, "file_name": "000000012748.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000012748.jpg", "height": 640, "width": 480, "date_captured": "2013-11-19 01:51:12", "flickr_url": "http://farm8.staticflickr.com/7295/9090127695_6dc690f776_z.jpg", "id": 12748}, {"license": 1, "file_name": "000000015597.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000015597.jpg", "height": 640, "width": 433, "date_captured": "2013-11-20 01:36:59", "flickr_url": "http://farm7.staticflickr.com/6128/5960989557_660cd5f173_z.jpg", "id": 15597}, {"license": 4, "file_name": "000000013348.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000013348.jpg", "height": 427, "width": 640, "date_captured": "2013-11-16 14:52:33", "flickr_url": "http://farm9.staticflickr.com/8286/7733450942_0da3e941b4_z.jpg", "id": 13348}, {"license": 3, "file_name": "000000011699.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000011699.jpg", "height": 640, "width": 480, "date_captured": "2013-11-18 10:35:30", "flickr_url": "http://farm7.staticflickr.com/6010/5946492599_6248b4620e_z.jpg", "id": 11699}, {"license": 1, "file_name": "000000010995.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000010995.jpg", "height": 480, "width": 640, "date_captured": "2013-11-20 21:22:47", "flickr_url": "http://farm4.staticflickr.com/3438/3815505319_1c077a82b6_z.jpg", "id": 10995}, {"license": 1, "file_name": "000000015497.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000015497.jpg", "height": 480, "width": 640, "date_captured": "2013-11-18 00:19:02", "flickr_url": "http://farm3.staticflickr.com/2197/2269556612_b210991e62_z.jpg", "id": 15497}, {"license": 2, "file_name": "000000017115.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000017115.jpg", "height": 640, "width": 443, "date_captured": "2013-11-18 18:09:41", "flickr_url": "http://farm6.staticflickr.com/5189/5672755536_fd276c33d6_z.jpg", "id": 17115}, {"license": 6, "file_name": "000000019742.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000019742.jpg", "height": 374, "width": 500, "date_captured": "2013-11-24 03:31:54", "flickr_url": "http://farm1.staticflickr.com/70/194348221_493851f79b_z.jpg", "id": 19742}, {"license": 4, "file_name": "000000011149.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000011149.jpg", "height": 375, "width": 500, "date_captured": "2013-11-15 04:15:40", "flickr_url": "http://farm4.staticflickr.com/3625/3323418866_ea5017538d_z.jpg", "id": 11149}, {"license": 2, "file_name": "000000017207.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000017207.jpg", "height": 480, "width": 640, "date_captured": "2013-11-15 10:53:00", "flickr_url": "http://farm6.staticflickr.com/5131/5537426848_a4508fe44f_z.jpg", "id": 17207}, {"license": 6, "file_name": "000000018737.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000018737.jpg", "height": 427, "width": 640, "date_captured": "2013-11-15 18:31:53", "flickr_url": "http://farm5.staticflickr.com/4026/4511471485_c1791c0767_z.jpg", "id": 18737}, {"license": 1, "file_name": "000000015751.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000015751.jpg", "height": 427, "width": 640, "date_captured": "2013-11-17 05:52:45", "flickr_url": "http://farm8.staticflickr.com/7351/9419515091_a211b75ce5_z.jpg", "id": 15751}, {"license": 1, "file_name": "000000014888.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000014888.jpg", "height": 480, "width": 640, "date_captured": "2013-11-17 18:50:25", "flickr_url": "http://farm9.staticflickr.com/8380/8634330041_a47c2cdaeb_z.jpg", "id": 14888}, {"license": 3, "file_name": "000000020333.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000020333.jpg", "height": 640, "width": 427, "date_captured": "2013-11-18 04:03:40", "flickr_url": "http://farm5.staticflickr.com/4027/4697218785_7f0414cdf8_z.jpg", "id": 20333}, {"license": 4, "file_name": "000000012280.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000012280.jpg", "height": 640, "width": 480, "date_captured": "2013-11-18 07:29:51", "flickr_url": "http://farm9.staticflickr.com/8516/8544258219_95e49e415e_z.jpg", "id": 12280}, {"license": 5, "file_name": "000000010092.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000010092.jpg", "height": 426, "width": 640, "date_captured": "2013-11-21 00:20:22", "flickr_url": "http://farm9.staticflickr.com/8276/8710590452_08a7a8f59c_z.jpg", "id": 10092}, {"license": 1, "file_name": "000000019221.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000019221.jpg", "height": 478, "width": 640, "date_captured": "2013-11-19 22:51:01", "flickr_url": "http://farm7.staticflickr.com/6156/6237638989_9d2c007615_z.jpg", "id": 19221}, {"license": 1, "file_name": "000000018770.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000018770.jpg", "height": 428, "width": 640, "date_captured": "2013-11-18 02:44:01", "flickr_url": "http://farm9.staticflickr.com/8020/7499130232_34bd20c007_z.jpg", "id": 18770}, {"license": 3, "file_name": "000000012639.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000012639.jpg", "height": 640, "width": 480, "date_captured": "2013-11-22 21:09:08", "flickr_url": "http://farm5.staticflickr.com/4102/4795012771_81c0b6b502_z.jpg", "id": 12639}, {"license": 3, "file_name": "000000012670.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000012670.jpg", "height": 428, "width": 640, "date_captured": "2013-11-24 01:39:03", "flickr_url": "http://farm5.staticflickr.com/4144/5072551441_fd302a2c44_z.jpg", "id": 12670}, {"license": 3, "file_name": "000000014038.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000014038.jpg", "height": 427, "width": 640, "date_captured": "2013-11-24 11:18:00", "flickr_url": "http://farm9.staticflickr.com/8507/8561635383_4bc0c679dd_z.jpg", "id": 14038}, {"license": 4, "file_name": "000000020247.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000020247.jpg", "height": 457, "width": 640, "date_captured": "2013-11-18 14:33:42", "flickr_url": "http://farm8.staticflickr.com/7027/6473127891_50c1ca2c03_z.jpg", "id": 20247}, {"license": 3, "file_name": "000000015956.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000015956.jpg", "height": 480, "width": 640, "date_captured": "2013-11-18 23:12:04", "flickr_url": "http://farm5.staticflickr.com/4057/5078510649_c131c12862_z.jpg", "id": 15956}, {"license": 3, "file_name": "000000011615.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000011615.jpg", "height": 640, "width": 480, "date_captured": "2013-11-17 07:15:52", "flickr_url": "http://farm1.staticflickr.com/47/151953288_f6b6a50f78_z.jpg", "id": 11615}, {"license": 3, "file_name": "000000014380.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000014380.jpg", "height": 427, "width": 640, "date_captured": "2013-11-17 09:54:28", "flickr_url": "http://farm6.staticflickr.com/5463/8750190611_325720f243_z.jpg", "id": 14380}, {"license": 1, "file_name": "000000018519.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000018519.jpg", "height": 640, "width": 515, "date_captured": "2013-11-20 01:22:59", "flickr_url": "http://farm8.staticflickr.com/7199/6892325386_145a03efa0_z.jpg", "id": 18519}, {"license": 4, "file_name": "000000019924.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000019924.jpg", "height": 500, "width": 458, "date_captured": "2013-11-18 01:01:49", "flickr_url": "http://farm4.staticflickr.com/3407/3642510664_1c89fbc90a_z.jpg", "id": 19924}, {"license": 3, "file_name": "000000016598.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000016598.jpg", "height": 640, "width": 478, "date_captured": "2013-11-18 03:39:49", "flickr_url": "http://farm6.staticflickr.com/5091/5391571396_41823477da_z.jpg", "id": 16598}, {"license": 5, "file_name": "000000013774.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000013774.jpg", "height": 480, "width": 640, "date_captured": "2013-11-18 18:22:53", "flickr_url": "http://farm8.staticflickr.com/7167/6837122127_de8b1f8d77_z.jpg", "id": 13774}, {"license": 5, "file_name": "000000018491.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000018491.jpg", "height": 427, "width": 640, "date_captured": "2013-11-19 17:54:54", "flickr_url": "http://farm3.staticflickr.com/2134/5814907667_496ae24446_z.jpg", "id": 18491}, {"license": 4, "file_name": "000000012667.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000012667.jpg", "height": 480, "width": 640, "date_captured": "2013-11-15 15:06:56", "flickr_url": "http://farm1.staticflickr.com/197/499050773_995cd1e9bb_z.jpg", "id": 12667}, {"license": 3, "file_name": "000000013201.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000013201.jpg", "height": 640, "width": 427, "date_captured": "2013-11-19 21:57:52", "flickr_url": "http://farm9.staticflickr.com/8391/8577059857_69c575151f_z.jpg", "id": 13201}, {"license": 1, "file_name": "000000017959.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000017959.jpg", "height": 427, "width": 640, "date_captured": "2013-11-22 18:46:36", "flickr_url": "http://farm4.staticflickr.com/3031/2389030002_e3a04076c3_z.jpg", "id": 17959}, {"license": 1, "file_name": "000000017899.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000017899.jpg", "height": 640, "width": 480, "date_captured": "2013-11-14 15:45:58", "flickr_url": "http://farm8.staticflickr.com/7031/6746562893_cbf91d331c_z.jpg", "id": 17899}, {"license": 4, "file_name": "000000015746.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000015746.jpg", "height": 640, "width": 427, "date_captured": "2013-11-16 21:36:18", "flickr_url": "http://farm4.staticflickr.com/3643/3517907686_509f46055f_z.jpg", "id": 15746}, {"license": 4, "file_name": "000000014473.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000014473.jpg", "height": 427, "width": 640, "date_captured": "2013-11-17 11:06:09", "flickr_url": "http://farm9.staticflickr.com/8401/8607706408_e00725a54d_z.jpg", "id": 14473}, {"license": 1, "file_name": "000000014831.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000014831.jpg", "height": 640, "width": 480, "date_captured": "2013-11-17 19:58:56", "flickr_url": "http://farm9.staticflickr.com/8113/8656986875_366dec25c9_z.jpg", "id": 14831}, {"license": 1, "file_name": "000000016502.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000016502.jpg", "height": 500, "width": 375, "date_captured": "2013-11-16 18:53:28", "flickr_url": "http://farm1.staticflickr.com/153/388880244_55dd5edd8e_z.jpg", "id": 16502}, {"license": 3, "file_name": "000000015254.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000015254.jpg", "height": 427, "width": 640, "date_captured": "2013-11-20 01:29:32", "flickr_url": "http://farm1.staticflickr.com/140/385285808_120242fcc0_z.jpg", "id": 15254}, {"license": 1, "file_name": "000000015660.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000015660.jpg", "height": 348, "width": 640, "date_captured": "2013-11-22 08:41:16", "flickr_url": "http://farm4.staticflickr.com/3356/3414127711_6772851b0d_z.jpg", "id": 15660}, {"license": 5, "file_name": "000000011511.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000011511.jpg", "height": 464, "width": 640, "date_captured": "2013-11-16 13:42:03", "flickr_url": "http://farm8.staticflickr.com/7124/7556029168_519acfdf09_z.jpg", "id": 11511}, {"license": 3, "file_name": "000000017905.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000017905.jpg", "height": 640, "width": 480, "date_captured": "2013-11-16 18:01:33", "flickr_url": "http://farm1.staticflickr.com/44/173771776_53b9c22bb6_z.jpg", "id": 17905}, {"license": 1, "file_name": "000000013597.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000013597.jpg", "height": 425, "width": 640, "date_captured": "2013-11-19 20:18:39", "flickr_url": "http://farm5.staticflickr.com/4040/4474734743_bc5e8973c7_z.jpg", "id": 13597}, {"license": 3, "file_name": "000000020107.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000020107.jpg", "height": 500, "width": 333, "date_captured": "2013-11-16 23:28:30", "flickr_url": "http://farm2.staticflickr.com/1311/968078473_4a7d696ec6_z.jpg", "id": 20107}, {"license": 1, "file_name": "000000018833.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000018833.jpg", "height": 426, "width": 640, "date_captured": "2013-11-17 16:35:54", "flickr_url": "http://farm1.staticflickr.com/92/217798870_1d648607b4_z.jpg", "id": 18833}, {"license": 1, "file_name": "000000016439.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000016439.jpg", "height": 480, "width": 640, "date_captured": "2013-11-22 19:14:13", "flickr_url": "http://farm1.staticflickr.com/52/171945415_f37fb5d1b2_z.jpg", "id": 16439}, {"license": 3, "file_name": "000000016451.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000016451.jpg", "height": 612, "width": 612, "date_captured": "2013-11-20 20:12:37", "flickr_url": "http://farm8.staticflickr.com/7460/9050893513_1fb9508000_z.jpg", "id": 16451}, {"license": 5, "file_name": "000000019432.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000019432.jpg", "height": 480, "width": 640, "date_captured": "2013-11-21 04:39:45", "flickr_url": "http://farm6.staticflickr.com/5131/5533564723_6f1633b85e_z.jpg", "id": 19432}, {"license": 3, "file_name": "000000019786.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000019786.jpg", "height": 375, "width": 500, "date_captured": "2013-11-22 00:20:10", "flickr_url": "http://farm1.staticflickr.com/115/317277248_05f5ac031e_z.jpg", "id": 19786}, {"license": 1, "file_name": "000000017031.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000017031.jpg", "height": 334, "width": 500, "date_captured": "2013-11-16 22:09:32", "flickr_url": "http://farm4.staticflickr.com/3431/3811840964_b913f6f140_z.jpg", "id": 17031}, {"license": 5, "file_name": "000000018837.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000018837.jpg", "height": 480, "width": 640, "date_captured": "2013-11-17 18:58:56", "flickr_url": "http://farm9.staticflickr.com/8219/8372455764_124e052754_z.jpg", "id": 18837}, {"license": 3, "file_name": "000000014007.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000014007.jpg", "height": 426, "width": 640, "date_captured": "2013-11-24 11:03:51", "flickr_url": "http://farm8.staticflickr.com/7161/6701691801_dc2cee1aa9_z.jpg", "id": 14007}, {"license": 1, "file_name": "000000018380.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000018380.jpg", "height": 426, "width": 640, "date_captured": "2013-11-25 14:13:45", "flickr_url": "http://farm6.staticflickr.com/5474/9701242334_0797348359_z.jpg", "id": 18380}, {"license": 3, "file_name": "000000016249.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000016249.jpg", "height": 365, "width": 500, "date_captured": "2013-11-14 12:26:50", "flickr_url": "http://farm4.staticflickr.com/3168/2931904036_1c5828e037_z.jpg", "id": 16249}, {"license": 4, "file_name": "000000013004.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000013004.jpg", "height": 500, "width": 375, "date_captured": "2013-11-19 21:30:33", "flickr_url": "http://farm5.staticflickr.com/4049/4208826429_3432119060_z.jpg", "id": 13004}, {"license": 4, "file_name": "000000019402.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000019402.jpg", "height": 480, "width": 640, "date_captured": "2013-11-21 04:31:37", "flickr_url": "http://farm5.staticflickr.com/4135/5435897535_a9b3e0a053_z.jpg", "id": 19402}, {"license": 3, "file_name": "000000013177.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000013177.jpg", "height": 427, "width": 640, "date_captured": "2013-11-15 20:03:10", "flickr_url": "http://farm8.staticflickr.com/7127/7607618094_78c23b097e_z.jpg", "id": 13177}, {"license": 3, "file_name": "000000014226.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000014226.jpg", "height": 480, "width": 640, "date_captured": "2013-11-17 04:22:03", "flickr_url": "http://farm1.staticflickr.com/6/6710769_d74169f5b7_z.jpg", "id": 14226}, {"license": 2, "file_name": "000000015440.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000015440.jpg", "height": 640, "width": 404, "date_captured": "2013-11-17 09:50:00", "flickr_url": "http://farm4.staticflickr.com/3261/2870016347_39ffc0ca45_z.jpg", "id": 15440}, {"license": 4, "file_name": "000000017182.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000017182.jpg", "height": 428, "width": 640, "date_captured": "2013-11-19 22:30:23", "flickr_url": "http://farm4.staticflickr.com/3020/2906486794_80400b009e_z.jpg", "id": 17182}, {"license": 6, "file_name": "000000019042.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000019042.jpg", "height": 371, "width": 640, "date_captured": "2013-11-17 01:03:41", "flickr_url": "http://farm8.staticflickr.com/7289/9652593344_4469e9a6f1_z.jpg", "id": 19042}, {"license": 2, "file_name": "000000013729.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000013729.jpg", "height": 480, "width": 640, "date_captured": "2013-11-21 20:10:40", "flickr_url": "http://farm2.staticflickr.com/1009/559099100_98414a0e66_z.jpg", "id": 13729}, {"license": 2, "file_name": "000000015335.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000015335.jpg", "height": 480, "width": 640, "date_captured": "2013-11-25 14:00:10", "flickr_url": "http://farm6.staticflickr.com/5533/10257288534_c916fafd78_z.jpg", "id": 15335}], "annotations": [{"segmentation": [[284.76, 320.72, 267.51, 322.16, 247.37, 322.16, 211.42, 322.16, 149.57, 320.72, 145.26, 287.64, 152.45, 214.29, 310.65, 220.04, 307.78, 271.82, 309.21, 304.9, 284.76, 322.16]], "area": 16606.205650000004, "iscrowd": 0, "image_id": 17379, "bbox": [145.26, 214.29, 165.39, 107.87], "category_id": 72, "id": 30246}, {"segmentation": [[213.53, 622.48, 211.99, 617.19, 209.57, 613.44, 210.45, 607.5, 211.33, 603.09, 209.35, 601.33, 209.79, 597.14, 205.82, 593.84, 204.72, 591.85, 204.94, 587.23, 206.04, 584.8, 206.92, 584.58, 208.47, 588.11, 208.69, 589.87, 210.01, 590.75, 213.09, 592.73, 214.41, 591.63, 219.26, 593.39, 217.72, 595.38, 217.72, 596.92, 218.16, 598.9, 217.94, 602.43, 216.4, 603.97, 215.74, 606.61, 214.19, 610.14, 213.53, 612.78, 213.53, 614.99, 213.75, 617.63, 214.64, 620.5, 214.64, 622.48, 214.19, 624.02]], "area": 198.93215000000026, "iscrowd": 0, "image_id": 17899, "bbox": [204.72, 584.58, 14.54, 39.44], "category_id": 16, "id": 36197}, {"segmentation": [[422.38, 178.56, 403.0, 207.63, 390.88, 225.4, 373.11, 224.59, 379.57, 209.25, 368.27, 211.67, 367.46, 197.94, 388.46, 188.25, 350.5, 189.87, 340.81, 239.94, 323.85, 239.94, 327.08, 206.83, 314.97, 243.98, 296.39, 241.55, 294.78, 234.28, 307.7, 222.17, 297.2, 191.48, 282.66, 174.52, 258.43, 172.91, 245.51, 164.83, 247.94, 133.33, 250.36, 108.3, 258.43, 110.72, 267.32, 126.87, 276.2, 127.68, 285.09, 113.95, 294.78, 121.22, 344.04, 116.37, 385.23, 108.3, 419.96, 114.76, 440.15, 142.22, 442.57, 164.02, 456.3, 172.1, 464.37, 162.41, 471.64, 173.71, 453.07, 191.48]], "area": 16892.101950000004, "iscrowd": 0, "image_id": 10363, "bbox": [245.51, 108.3, 226.13, 135.68], "category_id": 17, "id": 47902}, {"segmentation": [[407.74, 174.13, 396.15, 178.93, 391.48, 180.67, 388.16, 177.73, 381.76, 180.52, 377.77, 180.52, 378.17, 189.72, 374.97, 194.11, 375.77, 200.11, 374.97, 206.9, 376.57, 216.9, 378.97, 224.09, 379.76, 233.69, 380.96, 240.08, 380.96, 246.88, 380.56, 252.87, 377.37, 255.27, 378.97, 258.87, 384.16, 260.07, 386.56, 259.27, 384.16, 254.87, 385.36, 247.67, 384.16, 234.08, 383.36, 220.89, 387.76, 218.5, 392.16, 220.89, 392.16, 235.28, 394.95, 240.88, 394.95, 247.67, 395.35, 251.67, 394.55, 254.87, 393.35, 258.47, 400.55, 259.27, 399.75, 252.87, 398.95, 248.87, 397.35, 240.88, 397.75, 236.88, 398.55, 234.48, 398.55, 232.89, 396.95, 231.69, 396.95, 226.09, 397.75, 222.49, 404.15, 220.49, 407.23, 231.41, 410.14, 238.88, 410.54, 244.48, 407.74, 254.07, 404.15, 260.47, 410.54, 263.66, 413.74, 263.66, 412.94, 258.07, 414.14, 256.87, 414.54, 251.67, 416.14, 230.49, 414.26, 218.73, 414.56, 220.27, 420.4, 231.38, 420.53, 245.28, 420.53, 252.87, 421.33, 262.06, 420.53, 267.26, 427.33, 263.26, 426.13, 255.67, 424.53, 249.67, 426.13, 247.28, 432.53, 247.28, 433.33, 246.08, 432.93, 240.08, 428.93, 221.69, 428.13, 207.7, 430.37, 188.95, 423.73, 179.33, 407.89, 173.6]], "area": 3310.1459500000014, "iscrowd": 0, "image_id": 17178, "bbox": [374.97, 173.6, 58.36, 93.66], "category_id": 19, "id": 54220}, {"segmentation": [[331.7, 188.39, 339.95, 180.15, 351.86, 181.06, 360.11, 160.9, 364.69, 166.4, 369.27, 168.23, 375.69, 162.74, 375.69, 177.4, 377.52, 195.72, 375.69, 210.38, 368.36, 208.55, 364.69, 222.3, 364.69, 246.12, 370.19, 260.78, 363.77, 260.78, 363.77, 250.7, 360.11, 226.88, 357.36, 218.63, 349.11, 220.46, 350.03, 238.79, 350.03, 254.37, 350.03, 256.2, 345.45, 239.71, 339.95, 242.46, 339.95, 254.37, 338.12, 255.28, 335.37, 241.54, 334.45, 221.38, 328.04, 206.72, 328.95, 192.06]], "area": 2554.3836499999984, "iscrowd": 0, "image_id": 17178, "bbox": [328.04, 160.9, 49.48, 99.88], "category_id": 19, "id": 57131}, {"segmentation": [[273.94, 201.32, 269.43, 201.49, 263.89, 202.01, 262.16, 202.01, 260.77, 202.19, 259.21, 202.71, 257.31, 206.17, 256.79, 210.33, 256.62, 211.89, 256.27, 215.35, 256.96, 216.39, 259.21, 217.08, 260.25, 217.26, 260.77, 214.66, 261.12, 213.97, 263.54, 211.71, 264.76, 210.67, 265.28, 209.29, 265.8, 208.42, 268.92, 208.94, 270.82, 209.98, 273.42, 212.41, 277.06, 214.83, 278.1, 216.04, 279.66, 219.68, 281.39, 222.28, 282.25, 224.36, 282.25, 227.82, 283.47, 233.37, 283.64, 233.37, 284.85, 229.56, 284.85, 224.53, 284.85, 219.51, 285.03, 217.43, 288.32, 217.26, 289.88, 217.08, 292.13, 217.43, 294.03, 220.72, 293.86, 224.36, 293.69, 227.13, 293.69, 230.94, 294.9, 233.54, 295.25, 234.41, 297.15, 233.89, 297.5, 230.42, 297.33, 227.65, 296.81, 224.36, 297.15, 221.41, 297.33, 220.55, 298.89, 218.82, 300.27, 218.64, 302.18, 220.9, 302.52, 224.19, 302.35, 227.82, 301.31, 230.6, 300.96, 232.68, 300.96, 234.06, 303.22, 233.37, 304.95, 230.6, 305.64, 229.21, 306.16, 227.82, 306.85, 224.36, 307.2, 221.93, 306.85, 218.82, 307.89, 216.22, 309.11, 217.78, 308.41, 224.88, 307.55, 228.69, 311.18, 227.65, 313.26, 224.36, 313.61, 222.63, 313.96, 216.04, 312.74, 211.54, 312.05, 209.81, 310.49, 206.34, 309.28, 204.61, 309.11, 204.26, 305.29, 203.92, 302.0, 204.09, 295.25, 204.96, 294.03, 204.78, 291.78, 204.26, 287.8, 203.4, 286.76, 202.88, 283.12, 201.84, 279.83, 201.32, 276.36, 201.67, 273.94, 201.49]], "area": 921.3810500000001, "iscrowd": 0, "image_id": 17178, "bbox": [256.27, 201.32, 57.69, 33.09], "category_id": 19, "id": 58138}, {"segmentation": [[458.11, 190.94, 459.91, 179.86, 465.9, 177.17, 472.18, 175.37, 472.48, 179.26, 484.46, 178.96, 511.7, 183.15, 503.62, 194.83, 499.08, 198.86, 495.17, 204.63, 480.14, 198.86, 477.26, 196.18, 474.58, 196.8, 472.93, 198.45, 476.84, 201.54, 476.64, 211.42, 473.14, 208.95, 471.49, 198.24, 465.93, 192.48, 458.72, 192.27]], "area": 943.4261, "iscrowd": 0, "image_id": 17178, "bbox": [458.11, 175.37, 53.59, 36.05], "category_id": 19, "id": 58916}, {"segmentation": [[150.7, 28.8, 154.92, 28.8, 154.92, 31.96, 157.38, 33.72, 159.84, 44.62, 159.84, 65.71, 148.24, 66.41, 147.18, 46.37, 147.89, 36.18, 149.29, 32.66, 151.05, 31.26]], "area": 404.1564499999999, "iscrowd": 0, "image_id": 10363, "bbox": [147.18, 28.8, 12.66, 37.61], "category_id": 44, "id": 80785}, {"segmentation": [[4.12, 374.39, 59.06, 353.79, 103.0, 393.61, 23.35, 440.31, 8.24, 418.33], [86.52, 338.68, 113.99, 318.08, 119.48, 320.82, 131.85, 375.76]], "area": 6017.466, "iscrowd": 0, "image_id": 17899, "bbox": [4.12, 318.08, 127.73, 122.23], "category_id": 62, "id": 106130}, {"segmentation": [[125.46, 310.93, 140.08, 363.5, 144.43, 359.54, 161.43, 354.41, 170.91, 352.83, 167.75, 327.53, 169.73, 321.21, 145.62, 291.96, 125.06, 307.37], [179.21, 261.92, 182.77, 256.38, 193.84, 299.47, 178.03, 264.29]], "area": 2208.5101000000013, "iscrowd": 0, "image_id": 17899, "bbox": [125.06, 256.38, 68.78, 107.12], "category_id": 62, "id": 108544}, {"segmentation": [[83.22, 632.06, 1.51, 627.52, 1.51, 509.5, 0.0, 453.52, 102.88, 389.98, 140.71, 370.31, 164.92, 344.59, 178.53, 344.59, 190.64, 379.39, 204.26, 373.33, 208.79, 365.77, 223.92, 373.33, 240.57, 365.77, 211.82, 324.92, 217.87, 303.74, 264.78, 279.53, 338.91, 294.66, 369.17, 294.66, 437.26, 305.25, 447.85, 317.35, 447.85, 337.02, 480.0, 352.15, 478.11, 632.06, 266.29, 629.03, 51.44, 627.52]], "area": 136955.75629999995, "iscrowd": 0, "image_id": 17899, "bbox": [0.0, 279.53, 480.0, 352.53], "category_id": 67, "id": 119334}, {"segmentation": [[222.16, 121.37, 204.15, 131.43, 198.32, 148.39, 198.32, 171.17, 205.21, 201.89, 210.51, 210.37, 211.03, 222.56, 214.74, 222.56, 220.57, 211.96, 246.53, 222.03, 267.19, 205.6, 276.73, 216.2, 294.21, 211.96, 298.45, 193.42, 345.6, 158.98, 345.6, 166.93, 340.3, 174.35, 330.24, 198.72, 324.41, 224.68, 332.35, 227.32, 350.9, 262.29, 372.62, 271.83, 403.35, 272.36, 422.95, 242.16, 416.59, 203.48, 404.93, 176.47, 389.57, 161.63, 395.93, 156.33, 402.82, 113.42, 371.56, 116.6, 342.42, 112.36, 357.78, 106.0, 345.6, 103.36, 320.17, 103.36, 296.86, 98.06, 294.21, 99.65, 293.15, 107.06, 320.17, 107.06, 333.94, 110.24, 338.71, 112.36, 339.77, 136.73, 275.14, 119.25, 268.25, 108.65, 282.56, 107.59, 286.26, 100.18, 283.09, 99.12, 275.14, 98.06, 272.49, 98.06, 268.78, 94.88, 266.13, 92.76, 258.19, 91.17, 252.36, 95.41, 256.6, 102.83, 261.36, 104.94, 263.48, 106.53, 266.66, 108.12, 266.66, 113.42, 264.01, 118.19, 260.83, 118.72, 252.89, 113.95, 249.18, 111.3, 241.76, 109.71, 238.05, 108.65, 228.52, 108.12, 224.81, 108.12, 216.33, 110.24, 216.33, 120.31]], "area": 23082.81575, "iscrowd": 0, "image_id": 11149, "bbox": [198.32, 91.17, 224.63, 181.19], "category_id": 2, "id": 126643}, {"segmentation": [[492.36, 117.42, 637.6, 82.4, 638.63, 267.81, 638.63, 283.26, 623.18, 279.14, 584.03, 290.47, 572.7, 313.13, 543.86, 318.28, 523.26, 271.93, 482.06, 268.84, 489.27, 118.45]], "area": 28786.120799999997, "iscrowd": 0, "image_id": 17207, "bbox": [482.06, 82.4, 156.57, 235.88], "category_id": 3, "id": 135232}, {"segmentation": [[246.1, 135.08, 246.1, 169.24, 254.64, 170.8, 265.51, 173.12, 281.81, 176.23, 281.81, 194.09, 281.81, 194.09, 301.22, 196.42, 304.33, 211.17, 305.1, 219.71, 305.1, 226.69, 295.79, 232.13, 295.79, 232.13, 295.79, 241.44, 308.21, 245.32, 319.08, 243.0, 341.59, 242.22, 350.91, 197.97, 352.46, 190.98, 384.29, 187.1, 375.75, 197.19, 367.99, 199.52, 366.43, 208.84, 366.43, 208.84, 374.97, 211.17, 374.97, 225.14, 379.63, 228.25, 395.94, 223.59, 421.55, 180.11, 437.08, 187.1, 437.08, 187.1, 457.27, 187.1, 467.36, 175.45, 467.36, 175.45, 465.81, 164.58, 453.38, 155.27, 439.41, 155.27, 435.53, 138.19, 426.99, 122.66, 408.36, 112.57, 397.49, 110.24, 475.12, 66.77, 541.11, 32.61, 640.0, 27.17, 639.71, 359.45, 410.69, 361.0, 93.94, 361.0, 90.83, 311.31, 107.14, 251.54, 111.79, 249.21, 137.41, 190.98, 173.12, 164.58, 205.73, 144.4, 244.55, 135.08]], "area": 121140.34724999999, "iscrowd": 0, "image_id": 10363, "bbox": [90.83, 27.17, 549.17, 333.83], "category_id": 3, "id": 138210}, {"segmentation": [[11.73, 207.04, 27.22, 203.95, 45.19, 205.8, 45.5, 210.45, 45.5, 216.65, 44.26, 224.08, 44.26, 232.75, 45.8, 238.64, 46.73, 241.74, 44.88, 242.67, 41.78, 238.64, 35.27, 238.33, 21.64, 241.74, 15.14, 239.88, 14.52, 235.54, 20.09, 233.99, 25.98, 233.37, 24.74, 228.73, 19.17, 221.29, 10.8, 212.31, 9.56, 208.28]], "area": 969.1859499999997, "iscrowd": 0, "image_id": 17207, "bbox": [9.56, 203.95, 37.17, 38.72], "category_id": 3, "id": 139920}, {"segmentation": [[443.35, 337.08, 479.89, 338.04, 523.17, 406.32, 526.06, 427.0, 640.0, 427.0, 640.0, 108.19, 627.04, 112.04, 608.76, 122.62, 595.3, 135.12, 586.64, 144.74, 552.98, 151.47, 528.94, 163.97, 507.78, 184.17, 499.13, 201.48, 492.4, 204.36, 474.12, 201.48, 469.32, 193.78, 450.08, 189.94, 441.43, 204.36, 443.35, 211.1, 468.35, 215.9, 476.05, 214.94, 458.74, 223.6, 445.27, 228.41, 437.58, 249.56, 437.58, 255.33, 433.73, 292.84, 435.66, 332.27]], "area": 47071.29030000001, "iscrowd": 0, "image_id": 17178, "bbox": [433.73, 108.19, 206.27, 318.81], "category_id": 3, "id": 140522}, {"segmentation": [[0.0, 133.87, 0.0, 265.63, 14.36, 280.83, 21.96, 279.14, 26.18, 269.85, 30.41, 261.4, 38.85, 263.09, 50.68, 263.09, 54.05, 271.54, 55.74, 286.74, 61.66, 308.7, 68.41, 323.06, 72.64, 333.19, 83.61, 345.86, 96.28, 350.93, 120.78, 349.24, 130.91, 339.95, 135.14, 324.75, 136.82, 310.39, 136.82, 295.19, 135.98, 280.83, 132.6, 259.71, 126.69, 247.89, 119.93, 235.22, 113.18, 223.4, 108.95, 212.42, 103.89, 208.19, 99.66, 202.28, 95.44, 192.99, 92.91, 182.01, 90.37, 171.88, 89.53, 163.43, 89.53, 154.98, 90.37, 151.6, 108.11, 138.94, 103.04, 130.49, 95.44, 130.49, 90.37, 132.18, 88.68, 133.02, 81.93, 133.02, 77.7, 132.18, 79.39, 126.27, 81.08, 122.04, 85.3, 119.51, 88.68, 111.91, 88.68, 103.46, 92.91, 93.33, 97.13, 89.95, 103.04, 84.88, 111.49, 76.44, 111.49, 69.68, 106.42, 68.83, 90.37, 68.83, 86.15, 68.83, 85.3, 75.59, 86.15, 77.28, 84.46, 102.62, 75.17, 105.15, 71.79, 106.84, 67.57, 110.22, 61.66, 106.84, 53.21, 97.55, 47.3, 97.55, 39.7, 103.46, 36.32, 106.0, 36.32, 110.22, 33.78, 115.29, 29.56, 118.67, 27.03, 121.2, 21.11, 121.2, 16.05, 115.29, 8.45, 109.38, 0.84, 104.31]], "area": 22842.389549999996, "iscrowd": 0, "image_id": 11149, "bbox": [0.0, 68.83, 136.82, 282.1], "category_id": 4, "id": 151281}, {"segmentation": [[56.09, 107.87, 60.4, 117.57, 40.99, 234.07, 58.25, 296.63, 50.7, 302.02, 49.62, 325.75, 46.38, 348.4, 52.85, 364.58, 62.56, 372.13, 101.39, 372.13, 121.89, 372.13, 124.04, 345.17, 460.58, 373.21, 480.0, 271.82, 489.71, 163.96, 487.55, 28.04, 482.16, 3.24, 485.39, 1.08, 88.45, 1.08, 65.8, 87.37, 48.54, 80.9, 56.09, 107.87]], "area": 153500.98140000002, "iscrowd": 0, "image_id": 17207, "bbox": [40.99, 1.08, 448.72, 372.13], "category_id": 6, "id": 162985}, {"segmentation": [[21.11, 120.35, 30.41, 103.46, 34.63, 95.02, 39.7, 99.24, 58.28, 99.24, 67.57, 103.46, 75.17, 103.46, 73.48, 97.55, 51.52, 76.44, 13.51, 51.94, 22.8, 40.12, 22.8, 23.23, 19.43, 13.09, 17.74, 7.18, 10.14, 5.49, 3.38, 3.8, 0.0, 46.03, 1.69, 108.53, 1.69, 108.53, 19.43, 121.2]], "area": 3560.2923000000005, "iscrowd": 0, "image_id": 11149, "bbox": [0.0, 3.8, 75.17, 117.4], "category_id": 1, "id": 197935}, {"segmentation": [[417.13, 288.9, 436.52, 268.68, 439.04, 228.23, 434.83, 143.12, 445.79, 111.1, 445.79, 98.46, 458.43, 79.92, 450.0, 57.16, 453.37, 34.41, 471.07, 1.54, 498.88, 1.54, 500.0, 245.08, 488.76, 260.25, 486.24, 281.32, 485.39, 310.81, 474.44, 315.03, 467.7, 315.87, 460.96, 313.34, 471.91, 233.29, 470.22, 222.33, 469.38, 222.33, 460.11, 259.41, 453.37, 293.12, 423.88, 297.33]], "area": 15959.356599999996, "iscrowd": 0, "image_id": 11149, "bbox": [417.13, 1.54, 82.87, 314.33], "category_id": 1, "id": 199247}, {"segmentation": [[87.73, 384.0, 44.58, 356.67, 1.44, 335.1, 1.44, 135.19, 40.27, 116.49, 40.27, 56.09, 96.36, 14.38, 132.31, 11.51, 172.58, 34.52, 181.21, 58.97, 202.79, 87.73, 171.15, 129.44, 152.45, 159.64, 195.6, 300.58, 222.92, 336.54, 244.49, 365.3, 217.17, 372.49, 201.35, 376.81, 172.58, 362.43, 166.83, 327.91, 146.7, 286.2, 135.19, 267.51, 125.12, 280.45, 113.62, 302.02, 102.11, 317.84, 92.04, 322.16, 84.85, 326.47, 110.74, 346.61, 123.69, 358.11, 123.69, 373.93, 122.25, 386.88, 100.67, 391.19, 96.36, 391.19]], "area": 54516.36135000001, "iscrowd": 0, "image_id": 17899, "bbox": [1.44, 11.51, 243.05, 379.68], "category_id": 1, "id": 209675}, {"segmentation": [[94.17, 98.47, 95.01, 90.91, 98.37, 89.22, 109.3, 89.22, 113.51, 94.27, 114.35, 97.63, 114.35, 106.88, 116.03, 117.81, 118.55, 140.51, 131.17, 112.77, 131.17, 91.75, 125.28, 82.5, 119.39, 76.61, 108.46, 71.57, 108.46, 69.89, 110.99, 63.16, 116.03, 60.64, 125.28, 58.95, 143.78, 72.41, 153.87, 82.5, 146.3, 97.63, 148.82, 124.54, 176.57, 131.26, 193.39, 153.97, 199.27, 212.82, 161.43, 230.48, 134.53, 228.8, 110.99, 206.1, 103.42, 178.35, 97.53, 175.83, 88.28, 171.62, 91.65, 148.08, 105.1, 146.4, 110.99, 133.79, 108.46, 127.06, 111.83, 107.72, 98.37, 97.63]], "area": 10372.88825, "iscrowd": 0, "image_id": 11149, "bbox": [88.28, 58.95, 110.99, 171.53], "category_id": 2, "id": 240899}, {"segmentation": [[469.47, 192.7, 414.74, 224.96, 393.43, 205.37, 391.12, 169.66, 362.32, 158.14, 358.29, 123.0, 358.29, 117.24, 356.56, 106.29, 366.93, 101.11, 381.33, 101.11, 400.34, 95.93, 409.56, 91.89, 413.59, 91.89, 415.89, 103.99, 411.86, 120.12, 415.32, 134.52, 438.94, 139.13, 469.47, 144.31, 478.11, 144.31, 478.11, 160.44, 471.19, 195.01]], "area": 8831.1007, "iscrowd": 0, "image_id": 17899, "bbox": [356.56, 91.89, 121.55, 133.07], "category_id": 63, "id": 389786}, {"segmentation": [[76.55, 146.73, 52.9, 164.62, 42.67, 197.22, 50.34, 225.98, 63.76, 232.37, 119.36, 233.65, 138.54, 248.98, 137.9, 266.24, 137.26, 275.83, 133.42, 284.13, 123.2, 296.28, 119.36, 310.98, 125.12, 342.29, 157.71, 349.96, 160.91, 345.49, 151.96, 330.79, 158.99, 316.73, 190.3, 280.94, 189.03, 268.8, 183.91, 256.65, 171.13, 253.46, 162.18, 259.21, 148.12, 250.9, 129.59, 237.48, 130.23, 232.37, 159.63, 216.39, 173.69, 198.5, 179.44, 184.44, 174.97, 180.6, 163.46, 183.16, 156.43, 189.55, 121.92, 197.22, 112.97, 195.94, 102.11, 171.65, 103.39, 171.01, 109.14, 165.26, 119.36, 143.53, 114.25, 121.8, 94.44, 116.69, 80.38, 126.28, 75.91, 143.53], [65.04, 239.4, 89.33, 236.85, 116.81, 241.32, 124.48, 249.63, 123.84, 259.22, 103.39, 262.41, 93.8, 267.52, 84.21, 267.52, 74.63, 263.05, 66.32, 256.02, 62.49, 242.6]], "area": 13752.52955, "iscrowd": 0, "image_id": 16249, "bbox": [42.67, 116.69, 147.63, 233.27], "category_id": 1, "id": 447686}, {"segmentation": [[286.26, 161.78, 287.15, 138.83, 279.2, 134.41, 280.08, 126.91, 275.23, 117.64, 278.32, 105.72, 276.99, 100.86, 280.97, 92.03, 288.47, 74.38, 293.33, 69.52, 303.92, 66.87, 311.87, 62.9, 311.43, 54.51, 314.52, 43.03, 322.46, 38.18, 333.94, 39.94, 338.8, 47.89, 337.47, 56.28, 331.29, 68.2, 329.97, 72.17, 342.77, 78.79, 346.75, 100.41, 346.29, 112.93, 343.21, 117.2, 333.52, 112.95, 330.97, 123.66, 330.09, 131.05, 332.77, 134.1, 332.62, 138.39, 329.53, 143.24, 326.88, 148.1, 322.46, 153.84, 314.96, 160.02, 312.31, 164.43]], "area": 5683.427350000001, "iscrowd": 0, "image_id": 16249, "bbox": [275.23, 38.18, 71.52, 126.25], "category_id": 1, "id": 452702}, {"segmentation": [[129.57, 490.94, 126.37, 495.73, 126.37, 502.12, 125.84, 504.25, 123.18, 505.31, 118.39, 509.04, 113.59, 510.1, 112.0, 512.77, 110.93, 522.88, 113.06, 538.86, 113.06, 563.35, 115.19, 573.47, 117.85, 575.6, 126.91, 573.47, 130.63, 572.94, 139.69, 624.05, 145.54, 629.91, 147.14, 635.23, 155.66, 635.23, 158.85, 627.25, 152.46, 619.26, 158.32, 619.26, 165.78, 619.79, 177.49, 616.07, 166.84, 608.61, 166.31, 604.88, 166.31, 596.9, 156.19, 568.14, 150.87, 550.57, 157.79, 552.17, 166.31, 552.7, 165.78, 541.52, 160.98, 531.4, 160.45, 525.01, 151.12, 513.71, 144.98, 512.83, 144.98, 504.94, 144.1, 494.41, 139.72, 490.9, 132.7, 490.03]], "area": 4899.2145, "iscrowd": 0, "image_id": 17436, "bbox": [110.93, 490.03, 66.56, 145.2], "category_id": 1, "id": 469589}, {"segmentation": [[316.07, 158.03, 328.9, 142.79, 334.52, 130.76, 337.73, 121.93, 350.56, 113.91, 360.19, 99.47, 367.41, 98.67, 377.84, 111.51, 374.63, 126.75, 374.63, 131.56, 374.63, 144.4, 378.64, 150.01, 382.65, 160.44, 385.05, 162.85, 389.07, 179.69, 390.67, 182.9, 394.68, 192.53, 405.91, 202.96, 414.74, 198.95, 417.95, 198.14, 433.19, 204.56, 441.21, 217.4, 442.01, 223.81, 443.62, 234.24, 442.81, 239.86, 442.01, 247.88, 447.63, 267.13, 447.63, 267.13, 448.43, 264.73, 454.04, 275.96, 437.2, 281.57, 427.57, 279.16, 414.74, 275.96, 389.87, 277.56, 378.64, 274.35, 369.01, 266.33, 365.8, 259.91, 371.42, 254.3, 376.23, 243.07, 379.44, 242.26, 383.45, 240.66, 385.86, 237.45, 385.86, 237.45, 387.46, 231.03, 389.87, 223.01, 383.45, 219.8, 377.03, 219.0, 374.63, 219.0, 360.19, 216.59, 347.35, 210.98, 338.53, 202.15, 336.12, 191.73, 332.91, 184.51, 316.07, 177.29, 314.46, 171.67, 314.46, 162.85, 314.46, 161.24, 315.26, 168.46, 320.88, 156.43, 322.48, 152.42]], "area": 10542.287450000005, "iscrowd": 0, "image_id": 16249, "bbox": [314.46, 98.67, 139.58, 182.9], "category_id": 1, "id": 558256}, {"segmentation": [[463.13, 245.42, 486.43, 221.25, 491.26, 215.97, 476.76, 206.74, 467.53, 203.23, 437.2, 198.39, 431.05, 192.24, 422.25, 171.14, 424.45, 152.68, 430.17, 141.25, 433.68, 131.14, 446.87, 120.15, 466.21, 101.25, 474.56, 103.45, 481.15, 109.16, 478.96, 117.96, 471.92, 139.93, 456.98, 139.93, 463.13, 142.57, 469.73, 164.55, 488.19, 181.25, 495.22, 184.77, 500.0, 184.77, 499.62, 225.64, 489.95, 237.07, 482.47, 243.67, 492.58, 253.34, 463.13, 259.05, 483.93, 248.58]], "area": 5333.7381, "iscrowd": 0, "image_id": 16249, "bbox": [422.25, 101.25, 77.75, 157.8], "category_id": 1, "id": 561321}, {"segmentation": [[230.06, 199.99, 222.9, 283.28, 225.43, 283.28, 230.06, 288.75, 230.9, 298.43, 230.9, 300.53, 225.85, 304.32, 225.85, 306.84, 232.58, 310.21, 241.83, 312.73, 244.78, 311.89, 247.72, 308.95, 251.09, 306.84, 254.46, 305.58, 257.82, 303.48, 264.13, 300.95, 271.28, 299.69, 274.65, 298.43, 276.33, 297.17, 276.75, 294.64, 267.5, 263.09, 260.77, 259.72, 273.39, 251.31, 277.17, 247.94, 282.64, 241.21, 286.01, 236.16, 281.38, 218.08, 275.91, 210.08, 267.92, 205.45, 259.08, 202.93, 246.04, 201.67, 231.74, 202.09]], "area": 5022.396499999998, "iscrowd": 0, "image_id": 16249, "bbox": [222.9, 199.99, 63.11, 112.74], "category_id": 15, "id": 573041}, {"segmentation": [[22.31, 538.8, 113.25, 534.17, 115.57, 558.84, 111.71, 568.85, 124.04, 575.79, 129.44, 585.81, 60.85, 587.35, 60.08, 630.51, 50.06, 628.2, 45.43, 591.21, 23.85, 588.12, 22.31, 583.5, 24.62, 565.0, 25.39, 539.57], [164.12, 537.26, 171.06, 536.48, 172.6, 570.4, 177.99, 580.42, 177.22, 585.81, 176.45, 589.66, 163.35, 589.66, 154.1, 567.31, 157.18, 558.06, 155.64, 551.9, 164.12, 551.13, 164.89, 541.11, 164.12, 538.03]], "area": 6032.239250000002, "iscrowd": 0, "image_id": 17436, "bbox": [22.31, 534.17, 155.68, 96.34], "category_id": 15, "id": 574923}, {"segmentation": [[442.18, 489.59, 458.73, 492.2, 468.31, 493.95, 480.0, 504.4, 479.64, 529.66, 458.73, 593.26, 454.38, 603.71, 444.79, 607.2, 429.11, 606.33, 413.43, 593.26, 415.17, 513.11, 416.91, 498.3, 426.5, 492.2, 443.05, 490.46]], "area": 6274.411050000001, "iscrowd": 0, "image_id": 17899, "bbox": [413.43, 489.59, 66.57, 117.61], "category_id": 47, "id": 672382}, {"segmentation": [[442.25, 99.11, 428.22, 101.27, 410.97, 101.27, 398.02, 96.95, 390.47, 105.58, 357.03, 170.3, 339.78, 256.59, 322.52, 290.03, 308.49, 286.8, 298.79, 280.32, 295.55, 277.09, 283.84, 228.69, 300.03, 145.86, 332.4, 84.92, 354.3, 61.12, 383.82, 48.74, 411.43, 69.69, 414.28, 78.26, 447.61, 91.59]], "area": 14720.852149999997, "iscrowd": 0, "image_id": 12667, "bbox": [283.84, 48.74, 163.77, 241.29], "category_id": 52, "id": 1043443}, {"segmentation": [[295.98, 255.75, 272.55, 255.75, 254.07, 259.36, 244.16, 264.76, 240.55, 278.73, 245.96, 295.4, 253.62, 304.41, 257.23, 304.41, 254.52, 327.84, 261.73, 336.41, 271.19, 339.56, 300.48, 333.25, 296.43, 257.1]], "area": 3897.0741500000004, "iscrowd": 0, "image_id": 10977, "bbox": [240.55, 255.75, 59.93, 83.81], "category_id": 70, "id": 1095365}, {"segmentation": [[415.69, 216.04, 343.54, 224.72, 416.23, 253.47, 418.4, 213.86]], "area": 1403.9370999999996, "iscrowd": 0, "image_id": 10977, "bbox": [343.54, 213.86, 74.86, 39.61], "category_id": 81, "id": 1132016}, {"segmentation": [[411.33, 570.97, 394.07, 562.34, 379.69, 549.39, 379.69, 533.57, 392.63, 526.38, 398.38, 523.51, 409.89, 523.51, 424.27, 523.51, 437.21, 523.51, 440.09, 523.51, 455.91, 522.07, 465.98, 523.51, 474.61, 526.38, 476.04, 529.26, 477.48, 530.7, 478.0, 540.76, 478.0, 549.39, 477.48, 559.46, 477.48, 569.53, 473.17, 569.53, 467.42, 570.97, 453.03, 572.4, 441.53, 573.84, 432.9, 575.28, 428.58, 575.28, 419.96, 572.4, 407.01, 568.09]], "area": 4353.856100000001, "iscrowd": 0, "image_id": 17379, "bbox": [379.69, 522.07, 98.31, 53.21], "category_id": 81, "id": 1134446}, {"segmentation": [[476.94, 103.73, 483.62, 99.0, 495.86, 102.89, 500.0, 105.67, 500.0, 111.52, 500.0, 127.09, 499.19, 144.9, 470.82, 143.23, 468.87, 140.45, 474.71, 137.11, 476.38, 126.81, 479.16, 117.91, 480.28, 110.96, 479.44, 108.46]], "area": 987.6813, "iscrowd": 0, "image_id": 16249, "bbox": [468.87, 99.0, 31.13, 45.9], "category_id": 1, "id": 1269423}, {"segmentation": [[229.7, 483.09, 242.07, 480.88, 245.38, 490.16, 250.24, 491.7, 258.63, 494.35, 255.54, 500.76, 252.67, 504.95, 244.94, 506.72, 240.3, 497.66, 235.89, 493.69, 229.7, 491.48]], "area": 364.3076500000003, "iscrowd": 0, "image_id": 17436, "bbox": [229.7, 480.88, 28.93, 25.84], "category_id": 15, "id": 1392353}, {"segmentation": [[48.86, 205.26, 42.89, 205.26, 34.92, 200.78, 29.44, 203.77, 29.44, 210.24, 37.91, 216.22, 49.36, 256.05, 54.84, 279.46, 62.8, 300.37, 48.86, 308.84, 43.38, 332.24, 49.36, 337.72, 50.85, 336.72, 56.33, 334.23, 55.83, 326.76, 55.83, 319.3, 64.3, 310.83, 70.27, 308.84, 75.25, 308.84, 67.78, 331.25, 71.77, 335.73, 76.25, 334.73, 81.73, 310.33, 90.19, 310.33, 92.68, 310.83, 93.18, 324.77, 98.16, 329.75, 100.15, 328.76, 100.15, 323.78, 100.15, 312.82, 113.1, 318.8, 118.08, 332.24, 118.57, 343.2, 120.57, 349.67, 126.54, 347.68, 125.55, 339.71, 124.05, 331.25, 120.57, 318.8, 116.58, 310.33, 110.11, 304.85, 117.58, 296.89, 129.03, 286.93, 134.01, 279.46, 134.51, 272.99, 133.51, 268.5, 130.53, 267.01, 131.52, 257.05, 130.53, 248.59, 123.55, 241.12, 118.08, 236.14, 106.13, 231.65, 93.68, 232.15, 75.25, 233.65, 65.79, 234.14, 57.82, 231.65, 57.82, 227.67, 54.34, 212.73, 54.34, 210.74, 49.36, 205.76], [141.8, 232.37, 149.66, 242.35, 150.57, 246.28, 149.36, 249.3, 147.85, 249.3, 145.13, 244.16, 143.01, 239.63, 139.38, 236.0], [165.2, 306.09, 168.53, 318.63, 169.04, 322.73, 169.3, 326.57, 169.3, 328.62, 169.3, 330.41, 172.88, 332.2, 172.37, 334.5, 168.02, 335.53, 163.92, 336.04, 154.71, 334.25, 154.71, 331.94, 158.55, 330.66, 159.83, 330.66, 160.08, 327.85, 160.08, 323.24, 159.31, 317.61, 158.8, 313.26, 160.34, 309.67, 163.15, 306.6]], "area": 7504.523850000002, "iscrowd": 0, "image_id": 16249, "bbox": [29.44, 200.78, 143.44, 148.89], "category_id": 15, "id": 1394910}, {"segmentation": [[433.52, 240.66, 438.11, 261.28, 445.9, 261.74, 443.15, 247.08, 436.73, 235.16, 440.4, 234.7, 446.81, 247.08, 448.65, 259.45, 455.06, 258.99, 454.14, 246.62, 450.02, 232.41, 460.56, 228.75, 470.64, 213.63, 470.18, 201.71, 464.68, 187.05, 453.69, 181.09, 428.94, 179.72, 425.28, 157.72, 376.25, 153.6, 379.0, 164.6, 391.83, 180.63, 394.12, 195.76, 430.32, 201.25, 434.9, 216.37]], "area": 3923.8549499999995, "iscrowd": 0, "image_id": 16249, "bbox": [376.25, 153.6, 94.39, 108.14], "category_id": 15, "id": 1396783}, {"segmentation": [[328.91, 197.88, 339.57, 240.53, 342.85, 243.81, 332.19, 252.83, 324.81, 267.6, 334.65, 277.44, 337.11, 278.26, 347.78, 279.08, 355.98, 276.62, 369.92, 272.52, 379.76, 273.34, 374.84, 279.08, 383.04, 281.54, 387.97, 279.08, 387.97, 265.96, 383.87, 253.65, 387.97, 237.25, 399.45, 232.33, 399.45, 221.67, 392.89, 219.21, 387.97, 204.44, 383.87, 197.88, 378.12, 194.6, 363.36, 192.96, 347.78, 194.6, 339.57, 193.78, 331.37, 182.29, 328.91, 187.22, 332.19, 199.52]], "area": 4722.786300000001, "iscrowd": 0, "image_id": 16249, "bbox": [324.81, 182.29, 74.64, 99.25], "category_id": 15, "id": 1397318}, {"segmentation": [[275.44, 106.14, 275.79, 106.14, 295.07, 114.35, 295.07, 131.85, 285.79, 135.77, 279.72, 132.92, 275.08, 118.28], [298.64, 67.23, 304.0, 68.65, 300.43, 84.01, 295.43, 89.36, 298.28, 69.73]], "area": 527.3554000000003, "iscrowd": 0, "image_id": 16249, "bbox": [275.08, 67.23, 28.92, 68.54], "category_id": 27, "id": 1416870}, {"segmentation": [[122.71, 372.37, 146.02, 368.45, 148.95, 370.21, 150.13, 369.23, 169.32, 366.69, 173.43, 369.82, 172.84, 370.02, 149.15, 374.33, 142.88, 377.07, 125.46, 379.61, 124.87, 373.54]], "area": 281.81834999999967, "iscrowd": 0, "image_id": 17899, "bbox": [122.71, 366.69, 50.72, 12.92], "category_id": 50, "id": 1526874}, {"segmentation": [[349.66, 379.22, 343.89, 380.87, 327.4, 371.81, 326.99, 365.21, 329.87, 360.27, 350.48, 355.32, 353.36, 358.21, 354.19, 359.86, 352.13, 364.8, 351.3, 370.16, 350.07, 377.58]], "area": 471.87925000000064, "iscrowd": 0, "image_id": 17899, "bbox": [326.99, 355.32, 27.2, 25.55], "category_id": 61, "id": 1576783}, {"segmentation": [[428.52, 183.18, 427.01, 176.56, 430.92, 172.04, 434.84, 180.17, 440.56, 181.07, 448.38, 181.37, 456.2, 183.18, 461.92, 185.29, 467.04, 191.61, 469.45, 200.63, 471.55, 206.65, 474.26, 207.56, 472.76, 209.06, 467.34, 212.37, 464.93, 217.49, 464.03, 220.8, 460.72, 226.22, 459.21, 228.62, 463.73, 235.85, 468.54, 241.56, 467.34, 247.88, 468.84, 251.5, 471.25, 252.7, 471.25, 255.41, 464.93, 255.71, 459.21, 254.81, 458.01, 247.88, 456.51, 242.77, 451.99, 233.74, 450.19, 229.83, 447.78, 229.83, 445.97, 228.32, 451.39, 215.38, 449.28, 203.64, 444.47, 192.81, 441.16, 189.5, 433.03, 185.59, 427.92, 184.08]], "area": 1302.8786500000008, "iscrowd": 0, "image_id": 16249, "bbox": [427.01, 172.04, 47.25, 83.67], "category_id": 62, "id": 1601358}, {"segmentation": [[31.6, 277.34, 26.69, 271.93, 30.13, 271.44, 31.6, 250.79, 31.6, 245.39, 24.72, 242.93, 13.91, 237.52, 11.94, 233.1, 17.84, 232.6, 21.28, 232.6, 24.72, 232.6, 22.26, 224.74, 14.4, 219.82, 9.97, 213.43, 4.57, 209.99, 11.45, 210.48, 10.96, 202.12, 5.55, 191.8, 0.63, 192.29, 2.11, 207.53, 3.58, 238.5, 0.0, 279.31, 3.09, 302.41, 13.41, 301.43, 15.38, 297.0, 19.31, 290.61, 24.72, 280.29]], "area": 2044.2795500000004, "iscrowd": 0, "image_id": 17207, "bbox": [0.0, 191.8, 31.6, 110.61], "category_id": 1, "id": 1735330}, {"segmentation": [[6.41, 249.03, 14.47, 250.27, 17.57, 252.75, 23.62, 275.38, 24.55, 284.38, 19.12, 289.65, 17.88, 291.35, 18.35, 294.3, 14.47, 295.85, 6.56, 294.76, 6.87, 300.19, 4.39, 302.36, 0.0, 302.67, 0.0, 299.88, 0.0, 265.93, 0.0, 249.34, 0.0, 243.29, 0.0, 239.88, 0.0, 236.31, 0.0, 234.61, 4.55, 238.79, 7.18, 247.17]], "area": 1070.0957999999998, "iscrowd": 0, "image_id": 17207, "bbox": [0.0, 234.61, 24.55, 68.06], "category_id": 4, "id": 1789894}, {"segmentation": [[184.21, 639.92, 190.43, 626.95, 183.69, 607.23, 157.23, 594.78, 128.18, 590.63, 96.01, 593.74, 66.44, 602.05, 20.27, 619.68, 4.71, 637.84, 4.19, 640.0]], "area": 6815.9781, "iscrowd": 0, "image_id": 17379, "bbox": [4.19, 590.63, 186.24, 49.37], "category_id": 81, "id": 1982096}, {"segmentation": [[263.13, 271.81, 260.95, 291.43, 260.29, 312.14, 253.32, 313.23, 228.69, 313.23, 225.63, 299.06, 225.63, 291.87, 224.76, 284.68, 224.54, 267.67, 225.63, 264.84, 226.51, 263.1, 219.97, 260.92, 216.04, 254.38, 218.0, 250.45, 220.4, 248.27, 224.76, 240.42, 228.9, 235.19, 233.26, 233.23, 242.42, 233.23, 248.52, 234.54, 253.1, 243.04, 249.61, 256.99, 248.52, 259.61, 263.13, 267.02]], "area": 2645.4526500000015, "iscrowd": 0, "image_id": 17379, "bbox": [216.04, 233.23, 47.09, 80.0], "category_id": 1, "id": 2008412}, {"segmentation": [[167.2, 279.69, 164.05, 289.13, 164.05, 304.85, 164.84, 314.29, 197.08, 314.29, 197.86, 284.41, 190.79, 274.97, 189.21, 265.54, 191.57, 255.32, 190.79, 248.24, 178.21, 242.73, 171.92, 243.52, 164.84, 249.02, 159.33, 254.53, 157.76, 265.54]], "area": 2145.587850000001, "iscrowd": 0, "image_id": 17379, "bbox": [157.76, 242.73, 40.1, 71.56], "category_id": 1, "id": 2012579}, {"segmentation": [[497.95, 0.0, 492.55, 18.88, 494.71, 34.54, 507.39, 52.9, 544.65, 34.0, 567.87, 33.19, 574.08, 25.36, 574.08, 25.36, 571.65, 17.53, 585.15, 1.87, 593.79, 1.6, 581.64, 29.68, 635.36, 28.87, 635.9, 1.06, 559.23, 1.87, 558.96, 0.52, 538.17, 0.52]], "area": 4492.532800000001, "iscrowd": 0, "image_id": 10363, "bbox": [492.55, 0.0, 143.35, 52.9], "category_id": 2, "id": 2168366}, {"segmentation": [[419.27, 499.13, 429.09, 491.27, 444.3, 489.31, 456.57, 491.76, 467.13, 494.71, 480.0, 457.41, 480.0, 450.78, 474.0, 437.28, 458.54, 432.38, 440.87, 427.96, 417.55, 437.04, 419.27, 463.54, 419.76, 473.85]], "area": 3366.6167, "iscrowd": 0, "image_id": 17899, "bbox": [417.55, 427.96, 62.45, 71.17], "category_id": 47, "id": 2183804}, {"segmentation": [[249.4, 429.37, 257.79, 399.78, 281.46, 385.97, 294.78, 380.54, 303.65, 388.93, 302.67, 405.2, 283.93, 430.35, 273.57, 445.64, 261.24, 443.67]], "area": 2111.6567999999997, "iscrowd": 0, "image_id": 17899, "bbox": [249.4, 380.54, 54.25, 65.1], "category_id": 58, "id": 2220573}, {"segmentation": [[178.53, 169.79, 193.25, 132.06, 242.95, 87.88, 277.92, 74.08, 346.02, 70.4, 412.28, 70.4, 475.77, 75.0, 508.9, 80.52, 549.39, 109.97, 565.96, 132.06, 565.96, 173.47, 554.92, 228.68, 551.23, 238.81, 559.52, 257.21, 540.19, 257.21, 486.82, 244.33, 469.33, 238.81, 458.29, 235.13, 387.43, 242.49, 338.66, 231.45, 333.13, 243.41, 297.24, 248.01, 280.68, 259.05, 269.64, 242.49, 248.47, 228.68, 233.75, 238.81, 233.75, 261.81, 228.22, 276.54, 205.22, 279.3, 197.86, 261.81, 194.17, 248.01, 184.97, 236.97, 176.69, 216.72, 175.77, 202.92, 168.41, 206.6, 160.13, 199.24, 130.68, 208.44, 131.6, 203.84, 138.96, 193.71, 161.97, 179.91, 179.45, 166.11]], "area": 62556.14115000001, "iscrowd": 0, "image_id": 12062, "bbox": [130.68, 70.4, 435.28, 208.9], "category_id": 20, "id": 64311}, {"segmentation": [[0.0, 257.18, 44.08, 230.55, 59.69, 218.61, 74.38, 198.41, 92.75, 184.64, 123.05, 174.53, 140.5, 183.72, 140.5, 196.57, 130.4, 209.43, 130.4, 213.1, 156.11, 202.08, 163.45, 211.27, 171.72, 208.51, 183.66, 230.55, 191.0, 243.41, 197.43, 254.42, 200.18, 274.63, 203.86, 281.05, 225.9, 278.3, 231.41, 263.61, 231.41, 248.91, 236.92, 236.98, 247.02, 232.39, 261.71, 236.98, 270.89, 249.83, 270.89, 260.85, 250.69, 264.53, 238.75, 278.3, 236.92, 287.48, 213.04, 290.24, 200.18, 281.05, 183.66, 279.22, 164.37, 277.38, 143.25, 280.14, 137.74, 280.14, 134.07, 270.04, 124.89, 269.12, 125.8, 281.05, 104.68, 292.99, 92.75, 304.01, 92.75, 320.54, 95.5, 342.58, 95.5, 357.27, 83.56, 366.45, 78.05, 381.15, 70.71, 400.43, 67.95, 421.55, 0.92, 422.47]], "area": 27289.100100000007, "iscrowd": 0, "image_id": 12062, "bbox": [0.0, 174.53, 270.89, 247.94], "category_id": 20, "id": 64787}, {"segmentation": [[330.58, 5.74, 322.32, 25.02, 292.93, 51.65, 273.65, 85.63, 286.5, 90.22, 272.73, 136.13, 269.97, 166.44, 288.34, 194.9, 300.28, 198.58, 318.64, 186.64, 338.85, 164.6, 358.13, 138.89, 359.05, 126.03, 361.8, 99.4, 373.74, 79.2, 381.09, 67.26, 378.33, 51.65, 376.49, 47.98, 374.66, 34.21, 379.25, 20.43, 357.21, 14.92, 366.39, 2.98, 366.39, 2.98]], "area": 13623.237050000003, "iscrowd": 0, "image_id": 13177, "bbox": [269.97, 2.98, 111.12, 195.6], "category_id": 2, "id": 128386}, {"segmentation": [[142.49, 220.17, 171.89, 221.63, 177.3, 222.21, 179.2, 218.7, 175.99, 216.95, 173.94, 212.41, 179.2, 211.97, 179.2, 209.78, 150.83, 208.61, 142.34, 212.85, 140.15, 215.34, 140.59, 220.02], [180.67, 222.21, 189.74, 222.21, 190.32, 219.0, 189.3, 217.97, 184.62, 213.14, 181.25, 210.07, 179.64, 209.78]], "area": 494.9808999999998, "iscrowd": 0, "image_id": 15338, "bbox": [140.15, 208.61, 50.17, 13.6], "category_id": 3, "id": 133117}, {"segmentation": [[273.25, 300.22, 270.58, 293.11, 266.72, 291.03, 264.65, 283.32, 265.54, 278.58, 266.13, 266.13, 277.4, 239.75, 279.47, 235.89, 280.36, 235.3, 348.24, 235.6, 365.73, 255.46, 372.55, 263.46, 373.74, 264.35, 375.22, 271.17, 375.22, 278.28, 373.15, 290.44, 367.22, 292.51, 366.33, 285.7, 350.62, 290.74, 350.02, 296.07, 346.47, 299.92, 343.21, 300.52, 340.54, 296.37, 285.4, 295.18, 284.51, 300.81, 276.51, 302.59]], "area": 5876.319200000001, "iscrowd": 0, "image_id": 17627, "bbox": [264.65, 235.3, 110.57, 67.29], "category_id": 3, "id": 135748}, {"segmentation": [[30.14, 233.78, 37.55, 317.35, 70.08, 314.88, 77.9, 314.06, 79.96, 320.65, 84.9, 326.0, 89.43, 326.0, 89.84, 327.65, 102.19, 324.76, 109.6, 323.53, 113.3, 313.65, 119.89, 308.3, 141.71, 305.0, 139.65, 299.24, 147.47, 295.95, 145.83, 287.3, 143.36, 284.42, 131.01, 284.42, 123.6, 284.01, 118.24, 270.83, 98.89, 264.25, 79.96, 263.84, 72.55, 259.72, 71.31, 254.78, 66.78, 253.13, 65.55, 254.37, 56.49, 244.9, 44.96, 236.66, 40.02, 233.37, 31.38, 233.37]], "area": 6009.330949999997, "iscrowd": 0, "image_id": 17627, "bbox": [30.14, 233.37, 117.33, 94.28], "category_id": 3, "id": 136849}, {"segmentation": [[29.45, 218.02, 33.1, 233.54, 52.27, 247.24, 60.49, 256.37, 66.88, 257.28, 73.28, 260.02, 79.67, 264.59, 93.37, 264.59, 100.67, 266.41, 103.41, 265.5, 105.24, 264.59, 91.05, 220.78, 81.63, 215.79, 47.26, 214.68, 27.3, 214.68]], "area": 2534.161049999999, "iscrowd": 0, "image_id": 17627, "bbox": [27.3, 214.68, 77.94, 51.73], "category_id": 3, "id": 136903}, {"segmentation": [[269.19, 247.35, 252.6, 239.45, 207.19, 237.08, 171.65, 257.61, 168.49, 271.83, 178.76, 286.44, 183.5, 292.36, 193.37, 293.15, 196.92, 286.44, 231.28, 292.36, 242.73, 297.89, 251.42, 293.15, 257.34, 286.44, 264.45, 284.86, 266.42, 259.98, 270.37, 246.16]], "area": 4554.6753499999995, "iscrowd": 0, "image_id": 17627, "bbox": [168.49, 237.08, 101.88, 60.81], "category_id": 3, "id": 141360}, {"segmentation": [[421.18, 237.15, 413.84, 249.25, 412.11, 276.9, 413.84, 284.24, 422.91, 283.81, 424.21, 278.2, 434.58, 276.47, 436.3, 281.65, 443.22, 281.65, 446.67, 276.47, 458.34, 276.9, 459.2, 284.68, 466.98, 285.54, 469.57, 277.33, 483.83, 276.04, 481.67, 261.35, 480.37, 254.87, 468.71, 235.86, 421.18, 234.99, 419.89, 237.58]], "area": 2852.0705000000003, "iscrowd": 0, "image_id": 17627, "bbox": [412.11, 234.99, 71.72, 50.55], "category_id": 3, "id": 141859}, {"segmentation": [[0.0, 187.2, 28.0, 190.21, 30.46, 194.59, 32.65, 202.53, 32.37, 214.57, 1.18, 214.29]], "area": 791.4543999999997, "iscrowd": 0, "image_id": 15338, "bbox": [0.0, 187.2, 32.65, 27.37], "category_id": 3, "id": 142585}, {"segmentation": [[544.74, 284.16, 486.75, 285.23, 480.31, 274.49, 479.23, 265.9, 481.38, 250.87, 485.68, 246.57, 495.34, 237.98, 541.52, 234.76, 545.81, 235.84, 562.99, 253.02, 569.44, 260.53, 569.44, 282.01, 543.66, 284.16]], "area": 3848.700550000003, "iscrowd": 0, "image_id": 17627, "bbox": [479.23, 234.76, 90.21, 50.47], "category_id": 3, "id": 143876}, {"segmentation": [[0.0, 28.79, 149.69, 14.39, 179.44, 19.19, 205.34, 42.22, 205.34, 74.84, 173.68, 107.47, 194.79, 150.65, 240.85, 214.94, 237.97, 254.28, 189.03, 327.21, 169.84, 368.47, 129.54, 403.01, 82.52, 419.32, 59.49, 422.2, 35.5, 421.24, 5.76, 415.49, 0.0, 403.01, 1.92, 29.75]], "area": 77920.8604, "iscrowd": 0, "image_id": 13177, "bbox": [0.0, 14.39, 240.85, 407.81], "category_id": 4, "id": 150329}, {"segmentation": [[308.02, 206.3, 361.75, 213.02, 376.14, 235.09, 368.47, 244.69, 355.99, 252.36, 357.91, 272.51, 396.29, 257.16, 412.61, 271.55, 432.76, 277.31, 456.75, 253.32, 437.56, 251.4, 439.47, 243.73, 446.19, 236.05, 435.64, 236.05, 434.68, 230.29, 447.15, 224.53, 451.95, 233.17, 463.46, 211.1, 454.83, 184.23, 470.18, 168.88, 492.25, 159.29, 522.0, 177.52, 535.43, 188.07, 537.35, 178.48, 553.66, 172.72, 552.7, 181.36, 564.22, 187.11, 568.05, 199.59, 556.54, 218.78, 555.58, 245.64, 567.09, 260.04, 557.5, 267.71, 573.81, 297.46, 592.04, 332.0, 591.08, 362.71, 581.49, 375.18, 552.7, 355.99, 542.15, 338.72, 500.89, 363.67, 430.84, 370.39, 430.84, 370.39, 417.4, 377.1, 393.42, 394.38, 377.1, 407.81, 346.4, 405.89, 318.57, 384.78, 284.03, 379.02, 278.27, 376.14, 277.31, 367.51, 291.7, 364.63, 289.78, 334.88, 277.31, 332.96, 282.11, 316.65, 297.46, 320.49, 298.42, 308.02, 310.89, 304.18, 302.26, 284.99, 264.84, 286.91, 255.24, 249.48, 254.28, 225.49, 277.31, 222.62, 284.99, 230.29, 317.61, 219.74, 313.77, 210.14]], "area": 47586.101599999995, "iscrowd": 0, "image_id": 18737, "bbox": [254.28, 159.29, 337.76, 248.52], "category_id": 4, "id": 151923}, {"segmentation": [[0.0, 211.1, 89.24, 213.02, 74.84, 142.01, 86.36, 142.01, 135.3, 205.34, 272.51, 203.42, 268.67, 192.87, 274.43, 192.87, 285.95, 202.47, 486.49, 201.51, 547.9, 216.86, 559.42, 228.37, 566.13, 237.01, 549.82, 251.4, 523.91, 261.0, 458.67, 262.92, 446.19, 265.8, 425.08, 261.96, 403.01, 263.88, 379.98, 261.96, 377.1, 268.67, 351.2, 268.67, 341.6, 264.84, 331.04, 284.99, 288.82, 285.95, 280.19, 272.51, 268.67, 269.63, 260.04, 261.96, 240.85, 270.59, 236.05, 263.88, 237.97, 259.08, 189.03, 256.2, 181.36, 263.88, 172.72, 268.67, 164.08, 257.16, 144.89, 258.12, 135.3, 263.88, 129.54, 262.92, 136.26, 253.32, 142.97, 251.4, 96.91, 234.13, 68.13, 228.37, 64.29, 224.53, 2.88, 215.9]], "area": 30298.91215, "iscrowd": 0, "image_id": 13348, "bbox": [0.0, 142.01, 566.13, 143.94], "category_id": 5, "id": 161024}, {"segmentation": [[2.16, 473.53, 60.4, 472.45, 59.33, 454.11, 48.54, 441.17, 20.49, 69.03, 127.28, 88.45, 144.54, 94.92, 142.38, 122.97, 127.28, 119.73, 118.65, 127.28, 126.2, 153.17, 126.2, 179.06, 129.44, 203.87, 139.15, 218.97, 147.78, 194.16, 148.85, 167.19, 146.7, 160.72, 156.4, 167.19, 154.25, 149.93, 151.01, 144.54, 152.09, 115.42, 142.38, 51.78, 15.1, 22.65, 12.94, 2.16, 1.08, 1.08, 2.16, 470.29]], "area": 22851.46634999999, "iscrowd": 0, "image_id": 17627, "bbox": [1.08, 1.08, 155.32, 472.45], "category_id": 6, "id": 165041}, {"segmentation": [[452.62, 171.72, 517.52, 172.62, 536.0, 196.05, 530.59, 267.27, 520.22, 271.77, 444.95, 271.32]], "area": 8207.99295, "iscrowd": 0, "image_id": 15338, "bbox": [444.95, 171.72, 91.05, 100.05], "category_id": 6, "id": 165112}, {"segmentation": [[271.39, 203.14, 295.84, 202.54, 296.39, 201.17, 322.99, 201.17, 333.69, 204.46, 342.46, 210.22, 348.77, 218.72, 348.22, 221.19, 344.66, 221.19, 345.48, 235.72, 347.12, 236.82, 347.67, 246.69, 285.15, 246.97, 264.58, 247.51, 267.87, 205.83], [327.1, 251.9, 327.1, 248.61, 346.58, 248.34, 347.95, 252.45, 326.56, 252.18], [323.81, 251.35, 324.09, 248.34, 285.97, 248.61, 284.87, 255.47, 290.9, 253.27, 296.39, 253.82, 299.68, 251.63, 318.05, 251.9], [282.13, 249.71, 281.85, 260.4, 265.95, 259.31, 265.67, 249.98]], "area": 3881.2101000000002, "iscrowd": 0, "image_id": 15338, "bbox": [264.58, 201.17, 84.19, 59.23], "category_id": 6, "id": 168151}, {"segmentation": [[303.77, 303.77, 363.95, 280.85, 382.1, 254.1, 384.97, 196.78, 419.36, 186.28, 353.45, 127.05, 379.24, 61.14, 433.69, 62.09, 466.17, 103.17, 542.59, 150.93, 594.17, 213.98, 616.14, 302.82, 576.98, 362.04, 550.23, 365.86, 530.17, 404.07, 504.38, 412.67, 426.04, 371.6, 347.71, 353.45, 308.55, 332.43, 301.86, 313.32]], "area": 62508.03035000002, "iscrowd": 0, "image_id": 13177, "bbox": [301.86, 61.14, 314.28, 351.53], "category_id": 1, "id": 183777}, {"segmentation": [[350.29, 290.33, 349.82, 281.93, 349.82, 272.83, 351.22, 273.3, 351.46, 270.26, 353.79, 272.36, 356.12, 273.53, 355.66, 278.66, 355.19, 287.06, 355.66, 290.33, 352.39, 291.03, 351.92, 283.33, 351.46, 290.8, 350.29, 290.8]], "area": 100.89410000000015, "iscrowd": 0, "image_id": 13348, "bbox": [349.82, 270.26, 6.3, 20.77], "category_id": 1, "id": 191150}, {"segmentation": [[158.68, 293.56, 154.79, 280.6, 152.63, 276.71, 152.2, 269.36, 150.9, 262.02, 150.04, 252.52, 151.33, 244.74, 150.9, 238.69, 150.9, 235.67, 157.38, 233.94, 153.93, 225.3, 157.81, 224.0, 167.32, 232.64, 166.89, 238.26, 164.73, 243.01, 166.46, 252.52, 169.05, 265.04, 167.32, 272.82, 164.73, 283.19, 166.02, 295.72, 158.25, 293.56]], "area": 910.7307500000002, "iscrowd": 0, "image_id": 17627, "bbox": [150.04, 224.0, 19.01, 71.72], "category_id": 1, "id": 196731}, {"segmentation": [[271.28, 248.13, 263.22, 243.43, 259.18, 240.74, 259.85, 237.6, 262.32, 234.46, 263.22, 232.67, 263.22, 232.67, 263.22, 232.22, 263.22, 230.43, 264.56, 228.19, 267.25, 228.19, 268.15, 229.31, 268.82, 233.12, 267.92, 234.46, 266.8, 235.58, 265.91, 240.29, 273.3, 245.67, 272.4, 249.25]], "area": 114.68830000000003, "iscrowd": 0, "image_id": 17627, "bbox": [259.18, 228.19, 14.12, 21.06], "category_id": 1, "id": 209837}, {"segmentation": [[345.61, 235.81, 345.61, 234.18, 351.34, 233.36, 351.34, 231.11, 345.21, 229.68, 344.39, 227.64, 357.06, 227.84, 361.96, 228.45, 366.46, 233.56, 370.34, 242.35, 371.57, 242.96, 373.2, 244.39, 371.98, 246.85, 371.16, 252.36, 366.25, 254.2, 355.42, 243.58, 350.11, 237.04, 346.64, 235.61]], "area": 380.7914999999999, "iscrowd": 0, "image_id": 17627, "bbox": [344.39, 227.64, 28.81, 26.56], "category_id": 3, "id": 244760}, {"segmentation": [[25.73, 289.09, 26.23, 287.47, 26.23, 286.15, 24.92, 285.34, 23.19, 284.33, 22.99, 282.91, 23.09, 282.0, 22.69, 280.99, 21.77, 280.38, 20.56, 280.78, 20.36, 282.81, 20.25, 284.23, 18.33, 284.84, 17.62, 286.46, 17.11, 288.49, 17.72, 291.02, 18.53, 293.35, 20.36, 292.44, 20.96, 290.92, 23.9, 290.01, 25.02, 289.3]], "area": 63.09925000000013, "iscrowd": 0, "image_id": 13348, "bbox": [17.11, 280.38, 9.12, 12.97], "category_id": 1, "id": 257469}, {"segmentation": [[125.39, 272.39, 125.24, 271.68, 125.63, 271.12, 126.19, 270.57, 127.45, 270.81, 128.32, 271.52, 127.77, 272.79, 129.11, 274.13, 129.03, 275.08, 128.24, 276.58, 127.85, 278.8, 127.69, 281.49, 127.93, 285.53, 127.61, 286.95, 127.53, 288.22, 128.32, 288.85, 127.69, 289.8, 125.87, 289.8, 125.0, 289.17, 125.08, 287.19, 125.08, 285.13, 124.68, 282.84, 123.73, 281.89, 123.34, 280.7, 123.89, 278.8, 124.05, 276.5, 123.57, 276.74, 122.7, 277.14, 121.36, 277.61, 119.78, 278.64, 119.14, 278.96, 117.8, 278.96, 117.4, 277.77, 117.32, 277.14, 118.43, 276.35, 119.7, 276.35, 120.88, 275.55, 122.62, 273.97, 124.21, 273.42]], "area": 84.49289999999978, "iscrowd": 0, "image_id": 13348, "bbox": [117.32, 270.57, 11.79, 19.23], "category_id": 1, "id": 261461}, {"segmentation": [[41.95, 119.97, 42.47, 52.44, 49.08, 52.44, 49.87, 43.67, 73.68, 41.63, 87.29, 38.91, 109.74, 44.35, 124.03, 46.39, 153.28, 47.75, 168.25, 43.67, 188.66, 49.79, 228.79, 52.52, 247.84, 47.75, 262.81, 54.56, 270.29, 55.24, 273.69, 63.4, 276.42, 87.89, 275.06, 129.39, 275.06, 134.45, 274.08, 132.5, 271.58, 136.78, 49.37, 124.39, 49.37, 121.09]], "area": 19343.462300000003, "iscrowd": 0, "image_id": 15272, "bbox": [41.95, 38.91, 234.47, 97.87], "category_id": 10, "id": 407642}, {"segmentation": [[233.87, 279.96, 233.42, 225.2, 232.52, 136.33, 233.42, 133.19, 278.3, 134.98, 277.85, 139.02, 288.18, 144.41, 292.67, 160.12, 289.08, 173.58, 281.89, 183.45, 285.49, 191.09, 292.22, 205.0, 291.32, 215.32, 282.79, 229.24, 279.65, 231.93, 283.24, 235.97, 292.22, 247.19, 292.67, 268.29, 280.55, 280.4, 262.59, 279.96, 236.11, 280.85]], "area": 8021.1976, "iscrowd": 0, "image_id": 17905, "bbox": [232.52, 133.19, 60.15, 147.66], "category_id": 10, "id": 408512}, {"segmentation": [[168.1, 528.63, 163.74, 428.29, 158.75, 426.42, 150.03, 426.42, 143.79, 419.57, 150.65, 411.47, 159.38, 414.58, 164.36, 408.35, 161.24, 390.28, 152.52, 385.29, 135.69, 385.91, 132.58, 378.44, 126.35, 389.03, 120.74, 381.55, 108.27, 382.8, 98.92, 370.33, 92.07, 374.7, 88.95, 364.72, 71.5, 364.1, 72.13, 349.77, 87.71, 347.27, 90.2, 339.8, 99.55, 339.8, 100.17, 334.19, 105.16, 327.33, 120.11, 326.09, 121.36, 323.59, 127.59, 326.71, 130.08, 321.1, 137.56, 319.85, 151.9, 317.98, 148.78, 295.55, 136.94, 283.71, 130.08, 270.0, 124.48, 263.76, 123.85, 254.42, 126.97, 250.05, 130.71, 235.72, 144.42, 219.52, 151.9, 214.53, 152.52, 209.54, 164.98, 205.18, 178.07, 204.56, 181.81, 205.81, 194.28, 199.57, 204.25, 197.7, 209.86, 201.44, 221.07, 202.07, 235.41, 199.57, 247.87, 202.07, 247.87, 207.68, 264.7, 220.76, 274.67, 236.34, 277.79, 235.72, 285.89, 245.69, 279.66, 254.42, 275.92, 271.87, 268.44, 280.59, 265.94, 288.07, 267.81, 298.66, 267.19, 307.39, 284.64, 308.64, 284.02, 315.49, 287.76, 316.74, 293.37, 308.01, 298.98, 308.01, 313.93, 313.0, 318.29, 319.23, 320.79, 327.33, 325.77, 321.72, 331.38, 324.22, 333.25, 327.96, 333.25, 328.58, 350.08, 329.82, 349.46, 342.91, 336.99, 345.41, 335.12, 349.14, 330.76, 356.0, 324.53, 355.38, 322.03, 349.77, 320.16, 356.0, 317.05, 368.46, 308.95, 368.46, 306.45, 370.33, 298.98, 373.45, 292.12, 367.84, 291.5, 364.1, 289.63, 370.33, 288.38, 373.45, 273.42, 382.17, 263.45, 386.54, 271.55, 446.99, 269.06, 470.67, 272.8, 537.35, 267.19, 569.14, 257.22, 574.75, 257.84, 570.39, 261.58, 550.44, 260.96, 551.69, 254.73, 566.02, 246.63, 570.39, 237.9, 571.01, 231.67, 575.37, 233.54, 563.53, 237.9, 554.18, 226.68, 556.67, 222.32, 539.22, 223.57, 519.9, 215.46, 529.25, 215.46, 531.75, 219.83, 538.6, 212.35, 551.69, 212.35, 552.31, 211.1, 549.82, 203.62, 550.44, 201.75, 555.43, 199.26, 556.67, 197.39, 528.63, 188.67, 537.35, 184.3, 551.07, 181.19, 556.05]], "area": 48257.932500000046, "iscrowd": 0, "image_id": 15746, "bbox": [71.5, 197.7, 278.58, 377.67], "category_id": 11, "id": 417096}, {"segmentation": [[122.64, 55.18, 123.08, 68.71, 123.51, 80.93, 123.51, 87.47, 123.95, 95.33, 125.69, 100.57, 126.13, 107.11, 123.08, 114.09, 121.77, 122.82, 121.77, 132.42, 121.77, 141.15, 126.57, 151.62, 130.06, 183.91, 124.39, 187.84, 113.48, 190.02, 108.24, 187.4, 106.93, 185.22, 99.08, 185.22, 96.02, 182.16, 97.33, 179.11, 99.51, 177.8, 103.88, 173.87, 105.19, 162.96, 103.44, 145.95, 101.26, 131.11, 99.51, 123.69, 97.33, 112.35, 96.9, 104.49, 96.9, 101.0, 91.22, 92.71, 90.35, 84.42, 90.35, 75.69, 96.02, 68.71, 99.51, 65.66, 95.59, 60.42, 92.1, 53.0, 93.4, 42.97, 96.46, 38.6, 106.06, 38.6, 113.04, 41.22, 114.79, 44.28, 115.66, 47.77, 118.28, 49.95, 121.77, 53.44]], "area": 3858.6890499999995, "iscrowd": 0, "image_id": 11511, "bbox": [90.35, 38.6, 39.71, 151.42], "category_id": 1, "id": 463291}, {"segmentation": [[162.68, 88.5, 161.77, 76.69, 163.13, 68.98, 164.04, 62.17, 163.13, 57.63, 161.31, 53.09, 160.41, 49.46, 157.23, 41.29, 163.13, 33.58, 169.03, 32.22, 174.48, 35.39, 184.01, 45.83, 189.91, 54.46, 189.91, 65.8, 188.55, 90.31, 187.64, 100.3, 185.37, 102.56, 181.74, 119.36, 186.28, 142.96, 187.18, 166.56, 183.55, 170.64, 175.38, 171.55, 173.57, 168.37, 173.11, 163.84, 174.02, 161.11, 174.93, 149.77, 173.11, 137.51, 171.75, 130.25, 170.85, 122.53, 168.12, 109.37, 164.94, 94.4, 164.04, 88.5], [152.24, 158.84, 149.97, 162.47, 141.34, 166.56, 139.53, 169.28, 141.8, 172.46, 147.24, 172.46, 154.05, 172.46, 154.51, 172.01, 152.24, 159.3]], "area": 2691.584550000001, "iscrowd": 0, "image_id": 11511, "bbox": [139.53, 32.22, 50.38, 140.24], "category_id": 1, "id": 470792}, {"segmentation": [[83.54, 181.4, 78.04, 148.38, 80.79, 124.3, 78.73, 110.55, 75.29, 97.47, 77.35, 90.6, 71.16, 71.33, 64.97, 63.08, 60.15, 50.01, 43.64, 44.5, 35.39, 54.82, 42.27, 75.46, 36.08, 86.47, 38.83, 100.91, 44.33, 121.55, 49.83, 137.37, 58.78, 156.64, 64.28, 169.02, 63.59, 184.15, 71.16, 190.34, 78.73, 190.34, 85.61, 189.66]], "area": 4081.7877500000004, "iscrowd": 0, "image_id": 11511, "bbox": [35.39, 44.5, 50.22, 145.84], "category_id": 1, "id": 494713}, {"segmentation": [[214.72, 227.02, 56.56, 269.97, 37.71, 289.87, 56.56, 342.24, 89.03, 359.0, 147.68, 366.33, 189.58, 415.56, 226.24, 411.37, 244.05, 393.56, 256.61, 376.8, 259.76, 347.48, 261.85, 321.29, 296.42, 305.58, 315.27, 285.68, 283.85, 269.97, 246.14, 258.45, 225.19, 232.26]], "area": 29588.63449999999, "iscrowd": 0, "image_id": 11511, "bbox": [37.71, 227.02, 277.56, 188.54], "category_id": 15, "id": 572604}, {"segmentation": [[290.91, 348.26, 287.78, 373.29, 291.96, 401.44, 286.74, 429.59, 398.31, 433.76, 393.1, 350.35]], "area": 8744.866899999999, "iscrowd": 0, "image_id": 11511, "bbox": [286.74, 348.26, 111.57, 85.5], "category_id": 31, "id": 1181355}, {"segmentation": [[513.92, 337.99, 516.39, 328.61, 510.96, 312.34, 508.5, 293.1, 513.43, 283.73, 513.92, 275.84, 504.55, 265.48, 491.23, 260.55, 475.94, 264.99, 467.56, 271.89, 456.71, 275.35, 454.24, 280.77, 448.82, 301.49, 465.09, 343.41, 464.11, 346.86, 453.75, 350.81, 454.74, 356.24, 485.81, 364.13, 509.48, 350.32]], "area": 5175.7271, "iscrowd": 0, "image_id": 11511, "bbox": [448.82, 260.55, 67.57, 103.58], "category_id": 31, "id": 1181635}, {"segmentation": [[73.3, 282.35, 74.74, 283.52, 75.78, 283.52, 76.95, 281.31, 76.56, 277.01, 77.48, 272.97, 76.69, 269.84, 75.0, 270.1, 72.39, 273.36, 67.83, 272.84, 67.7, 271.92, 67.05, 270.36, 66.27, 270.23, 65.35, 271.79, 59.36, 271.79, 57.79, 273.36, 54.93, 274.14, 53.23, 275.31, 54.14, 277.27, 54.4, 278.31, 53.23, 278.44, 52.32, 278.96, 53.75, 282.09, 55.84, 282.48, 55.97, 283.78, 57.92, 284.18, 57.27, 283.92, 61.18, 284.57, 62.35, 282.22]], "area": 249.27475000000007, "iscrowd": 0, "image_id": 13348, "bbox": [52.32, 269.84, 25.16, 14.73], "category_id": 8, "id": 1198098}, {"segmentation": [[89.49, 256.9, 114.18, 255.4, 115.04, 263.13, 89.49, 263.13, 86.69, 264.2, 86.69, 273.22, 81.97, 271.07, 81.33, 267.85, 86.48, 262.49, 88.41, 260.98, 87.77, 255.61]], "area": 224.67705000000007, "iscrowd": 0, "image_id": 13348, "bbox": [81.33, 255.4, 33.71, 17.82], "category_id": 8, "id": 1198133}, {"segmentation": [[129.54, 241.54, 110.05, 242.18, 100.09, 249.17, 102.63, 258.92, 103.48, 266.12, 111.11, 268.03, 110.9, 258.7, 107.08, 258.28, 108.14, 252.98, 114.5, 252.56, 115.35, 255.53, 115.35, 258.49, 114.71, 259.34, 114.5, 268.45, 118.31, 270.78, 120.85, 273.11, 121.49, 276.71, 129.12, 276.71]], "area": 690.1310499999998, "iscrowd": 0, "image_id": 17627, "bbox": [100.09, 241.54, 29.45, 35.17], "category_id": 3, "id": 1345556}, {"segmentation": [[500.27, 100.53, 511.66, 100.53, 514.83, 94.2, 521.16, 94.84, 525.59, 99.27, 529.39, 96.74, 529.39, 70.15, 524.32, 60.65, 525.59, 55.59, 530.65, 53.06, 528.75, 50.53, 515.46, 51.79, 509.13, 56.86, 500.9, 61.92, 499.63, 64.45, 499.63, 79.01, 500.27, 100.53], [488.87, 79.64, 492.04, 87.87, 489.51, 89.77, 486.34, 86.61, 483.81, 84.71, 488.24, 84.08]], "area": 1267.7626000000005, "iscrowd": 0, "image_id": 11511, "bbox": [483.81, 50.53, 46.84, 50.0], "category_id": 4, "id": 1360514}, {"segmentation": [[0.96, 246.84, 7.68, 243.97, 13.43, 246.84, 23.99, 243.01, 29.75, 242.05, 37.42, 242.05, 47.98, 242.05, 53.73, 242.05, 54.69, 237.25, 54.69, 233.41, 53.73, 231.49, 45.1, 231.49, 31.67, 229.57, 20.15, 228.61, 4.8, 230.53, 0.0, 230.53]], "area": 720.6528500000001, "iscrowd": 0, "image_id": 13348, "bbox": [0.0, 228.61, 54.69, 18.23], "category_id": 5, "id": 1364096}, {"segmentation": [[92.46, 190.41, 90.14, 221.65, 145.67, 216.44, 140.47, 186.94, 97.66, 185.79], [76.84, 193.31, 86.09, 190.41, 88.99, 216.44, 68.74, 217.02, 67.58, 213.55]], "area": 2070.4994, "iscrowd": 0, "image_id": 15338, "bbox": [67.58, 185.79, 78.09, 35.86], "category_id": 8, "id": 1371612}, {"segmentation": [[16.05, 303.61, 16.23, 299.08, 17.86, 296.91, 18.76, 292.93, 23.47, 289.31, 39.4, 291.66, 40.48, 301.8, 40.3, 305.24, 29.26, 305.96, 22.74, 306.5, 22.56, 308.31, 19.67, 308.13, 18.04, 304.51, 16.59, 304.51]], "area": 351.72779999999943, "iscrowd": 0, "image_id": 13348, "bbox": [16.05, 289.31, 24.43, 19.0], "category_id": 8, "id": 1372048}, {"segmentation": [[76.39, 273.08, 75.78, 281.2, 79.03, 281.41, 79.54, 283.03, 79.94, 284.25, 82.18, 284.86, 84.61, 284.86, 86.14, 284.86, 86.95, 282.83, 90.2, 281.81, 90.71, 283.03, 91.52, 284.35, 93.86, 284.86, 96.7, 285.27, 97.41, 283.64, 100.86, 283.64, 102.79, 283.44, 103.5, 282.22, 103.2, 280.09, 102.79, 278.97, 99.85, 279.17, 98.22, 277.65, 97.21, 276.84, 96.19, 274.1, 95.38, 272.47, 92.84, 272.27, 90.1, 272.37, 88.68, 271.86, 87.86, 273.08, 87.86, 273.28]], "area": 256.6577500000002, "iscrowd": 0, "image_id": 13348, "bbox": [75.78, 271.86, 27.72, 13.41], "category_id": 8, "id": 1373235}, {"segmentation": [[640.0, 314.73, 632.34, 302.26, 579.57, 303.22, 520.08, 292.66, 454.83, 316.65, 399.17, 350.24, 357.91, 394.38, 344.48, 418.36, 634.26, 417.4, 640.0, 350.24]], "area": 27975.93394999999, "iscrowd": 0, "image_id": 12062, "bbox": [344.48, 292.66, 295.52, 125.7], "category_id": 20, "id": 1405357}, {"segmentation": [[41.14, 108.7, 45.89, 125.46, 48.89, 136.08, 46.51, 144.34, 40.76, 149.09, 30.76, 146.96, 25.38, 137.46, 24.01, 121.83, 27.38, 109.58, 27.76, 106.08, 28.13, 103.95, 29.63, 95.45]], "area": 857.6344500000001, "iscrowd": 0, "image_id": 11511, "bbox": [24.01, 95.45, 24.88, 53.64], "category_id": 31, "id": 1434557}, {"segmentation": [[173.15, 240.08, 174.26, 246.37, 175.03, 248.91, 174.04, 250.67, 175.91, 250.78, 176.13, 248.91, 176.69, 248.91, 177.79, 251.56, 178.23, 251.56, 178.67, 247.14, 179.56, 245.04, 180.11, 243.28, 180.0, 242.18, 178.34, 241.07, 177.9, 240.3, 174.04, 240.3, 173.15, 240.08]], "area": 50.52239999999993, "iscrowd": 0, "image_id": 13348, "bbox": [173.15, 240.08, 6.96, 11.48], "category_id": 1, "id": 1681190}, {"segmentation": [[638.98, 242.95, 619.58, 242.61, 609.71, 243.29, 603.58, 252.14, 598.47, 255.54, 598.81, 270.86, 600.18, 273.92, 605.62, 274.26, 607.32, 275.62, 612.09, 276.3, 618.21, 273.92, 636.93, 271.88, 636.93, 269.84, 639.32, 268.48, 640.0, 244.31]], "area": 1175.4748500000003, "iscrowd": 0, "image_id": 17627, "bbox": [598.47, 242.61, 41.53, 33.69], "category_id": 3, "id": 1786777}, {"segmentation": [[568.69, 276.96, 574.52, 277.76, 575.05, 272.19, 577.7, 272.45, 579.03, 277.49, 582.74, 278.55, 586.72, 276.17, 586.99, 270.86, 585.92, 266.35, 584.6, 263.97, 583.8, 260.52, 582.74, 257.07, 580.89, 254.69, 581.95, 254.69, 582.48, 250.44, 576.38, 250.71, 568.42, 241.96, 551.98, 241.96, 569.48, 259.19]], "area": 574.3420499999985, "iscrowd": 0, "image_id": 17627, "bbox": [551.98, 241.96, 35.01, 36.59], "category_id": 3, "id": 1786928}, {"segmentation": [[460.41, 235.66, 463.52, 231.81, 493.44, 231.36, 527.22, 233.59, 528.7, 235.81, 495.96, 238.18, 481.74, 250.48, 480.7, 255.81, 469.0, 235.66, 460.7, 235.36]], "area": 538.6895499999997, "iscrowd": 0, "image_id": 17627, "bbox": [460.41, 231.36, 68.29, 24.45], "category_id": 3, "id": 1787363}, {"segmentation": [[431.63, 640.0, 432.6, 582.29, 446.23, 576.13, 460.84, 578.4, 469.6, 583.27, 468.63, 639.74, 432.28, 640.0]], "area": 2259.0351500000006, "iscrowd": 0, "image_id": 17905, "bbox": [431.63, 576.13, 37.97, 63.87], "category_id": 11, "id": 1808732}, {"segmentation": [[78.71, 252.96, 80.29, 250.49, 81.41, 248.13, 86.69, 241.17, 88.71, 240.04, 92.42, 237.91, 95.23, 240.49, 97.25, 242.18, 100.96, 241.39, 112.2, 244.2, 115.23, 243.53, 127.7, 245.44, 148.6, 252.4, 156.35, 252.4, 163.32, 254.31, 163.88, 252.85, 164.78, 251.05, 167.47, 251.17, 173.54, 253.75, 181.86, 263.19, 183.65, 272.4, 181.29, 281.28, 180.4, 286.56, 179.16, 295.77, 177.25, 300.04, 176.69, 301.61, 172.53, 312.06, 171.52, 314.09, 170.73, 314.87, 168.04, 314.65, 167.7, 313.52, 169.5, 310.15, 170.4, 306.11, 169.72, 302.06, 169.27, 300.94, 166.69, 300.6, 166.01, 302.4, 167.7, 305.55, 166.8, 311.05, 163.88, 312.96, 161.63, 311.61, 159.83, 307.23, 159.05, 305.66, 155.9, 301.5, 150.06, 299.03, 132.98, 297.01, 129.05, 294.2, 128.6, 300.83, 128.6, 305.55, 128.6, 308.36, 127.14, 310.72, 124.67, 311.28, 120.62, 309.82, 120.96, 307.34, 121.52, 303.3, 121.52, 301.17, 120.17, 297.35, 119.05, 289.03, 116.24, 288.24, 108.71, 285.55, 106.13, 284.31, 98.49, 283.08, 94.33, 281.28, 93.54, 278.24, 94.56, 274.87, 95.68, 268.81, 95.79, 265.89, 95.68, 261.62, 94.11, 260.94, 92.76, 260.27, 88.38, 261.84, 83.77, 266.33, 82.31, 267.23, 81.3, 267.23, 76.47, 264.2, 75.23, 261.73, 76.13, 258.36, 76.69, 257.12]], "area": 4621.808499999997, "iscrowd": 0, "image_id": 16502, "bbox": [75.23, 237.91, 108.42, 76.96], "category_id": 20, "id": 1817553}, {"segmentation": [[90.05, 84.23, 90.3, 89.08, 90.17, 92.53, 91.32, 97.12, 94.13, 102.48, 95.66, 104.26, 98.34, 102.09, 101.53, 96.61, 99.87, 90.61, 98.59, 85.0, 95.4, 78.75, 91.83, 82.32, 91.32, 82.7]], "area": 186.3826000000002, "iscrowd": 0, "image_id": 11511, "bbox": [90.05, 78.75, 11.48, 25.51], "category_id": 31, "id": 1834488}, {"segmentation": [[173.1, 252.88, 173.75, 244.84, 174.84, 242.01, 174.84, 237.01, 177.67, 236.14, 181.8, 235.27, 182.88, 238.97, 182.67, 242.23, 180.93, 244.62, 180.27, 245.71, 180.06, 248.1, 179.19, 251.36, 178.75, 253.75, 177.67, 256.57, 175.93, 258.75, 174.62, 262.66, 174.41, 264.62, 174.19, 265.27, 173.1, 262.88, 172.88, 260.27, 173.32, 257.01, 173.32, 252.88]], "area": 156.28030000000012, "iscrowd": 0, "image_id": 17627, "bbox": [172.88, 235.27, 10.0, 30.0], "category_id": 1, "id": 2006197}, {"segmentation": [[191.58, 248.77, 190.28, 245.52, 187.25, 244.44, 187.03, 241.63, 190.49, 236.66, 193.09, 231.47, 195.47, 228.22, 198.5, 226.28, 201.31, 227.79, 201.09, 232.76, 200.23, 236.66, 203.04, 239.9]], "area": 190.49135000000007, "iscrowd": 0, "image_id": 17627, "bbox": [187.03, 226.28, 16.01, 22.49], "category_id": 1, "id": 2011679}, {"segmentation": [[126.95, 251.27, 128.48, 250.69, 129.07, 252.45, 128.83, 253.62, 128.36, 254.91, 127.89, 255.62, 127.07, 256.32, 126.95, 255.03, 126.95, 253.97, 126.6, 253.62, 125.43, 253.27, 124.96, 252.8, 125.66, 252.21, 126.48, 252.21, 126.95, 252.21, 127.42, 251.74, 127.42, 251.51]], "area": 10.264200000000002, "iscrowd": 0, "image_id": 13348, "bbox": [124.96, 250.69, 4.11, 5.63], "category_id": 1, "id": 2015225}, {"segmentation": [[151.15, 246.42, 152.3, 245.07, 153.64, 244.69, 156.52, 245.55, 160.07, 251.02, 158.63, 251.79, 153.07, 252.08, 149.13, 255.24, 148.94, 254.67, 150.48, 252.65, 150.96, 250.54, 150.96, 246.51]], "area": 54.30999999999999, "iscrowd": 0, "image_id": 13348, "bbox": [148.94, 244.69, 11.13, 10.55], "category_id": 1, "id": 2017079}, {"segmentation": [[637.25, 58.61, 625.79, 62.75, 636.94, 62.11, 636.94, 60.52], [620.06, 65.62, 611.46, 69.44, 610.5, 102.88, 621.01, 101.29, 623.24, 66.89, 620.37, 65.62], [609.86, 71.67, 605.4, 78.67, 603.81, 82.18, 603.17, 85.36, 601.58, 90.78, 601.58, 94.28, 601.58, 97.47, 606.36, 101.29, 608.91, 98.74, 610.5, 73.9], [625.79, 65.62, 636.62, 65.62, 633.75, 102.24, 623.24, 101.29], [639.48, 67.53, 636.3, 103.52, 639.8, 103.2, 638.85, 74.85], [620.06, 104.47, 611.14, 103.84, 608.91, 114.67, 610.18, 116.89, 611.77, 121.04, 614.96, 124.54, 618.46, 125.18, 619.74, 127.72, 621.01, 106.07], [622.92, 105.43, 633.75, 106.07, 633.75, 131.23, 622.92, 129.95, 623.56, 106.38], [640.0, 106.07, 636.94, 106.07, 635.66, 131.86, 639.17, 131.86, 638.21, 107.34], [604.45, 103.2, 607.32, 110.21, 608.59, 103.2, 604.13, 103.2]], "area": 1546.975700000001, "iscrowd": 0, "image_id": 12062, "bbox": [601.58, 58.61, 38.42, 73.25], "category_id": 37, "id": 2088406}, {"segmentation": [[190.53, 455.67, 185.46, 432.52, 186.91, 433.96, 186.18, 427.45, 186.18, 419.49, 186.18, 415.87, 189.08, 421.66, 189.08, 425.28, 189.08, 429.62, 186.91, 432.52, 189.08, 433.96, 193.42, 430.35, 197.76, 423.11, 195.59, 414.43, 192.7, 410.81, 191.25, 408.64, 189.8, 407.19, 189.08, 402.13, 189.08, 395.61, 188.35, 386.21, 188.35, 383.31, 186.91, 363.78, 184.74, 339.9, 192.7, 334.11, 184.01, 297.21, 178.22, 291.42, 153.62, 281.29, 145.66, 279.84, 144.22, 267.54, 150.01, 258.13, 150.01, 250.18, 150.01, 242.22, 147.83, 234.98, 144.22, 229.91, 140.6, 229.19, 134.81, 229.19, 126.85, 229.19, 122.51, 231.36, 120.34, 234.26, 116.0, 240.05, 115.27, 248.0, 113.83, 254.52, 115.27, 261.03, 117.44, 265.37, 120.34, 268.99, 121.06, 282.01, 94.29, 295.04, 89.95, 303.72, 85.61, 322.53, 81.27, 337.73, 90.67, 342.79, 88.5, 363.78, 88.5, 382.59, 88.5, 405.74, 89.95, 420.22, 88.5, 431.79, 92.84, 443.37, 102.97, 447.71, 101.53, 441.2, 105.14, 437.58, 105.87, 430.35, 102.25, 417.32, 99.36, 413.7, 105.87, 374.63, 110.21, 382.59, 108.76, 393.44, 107.31, 405.02, 107.31, 417.32, 108.04, 433.96, 109.49, 449.16, 113.1, 463.63, 111.66, 478.1, 113.83, 481.0, 114.55, 491.85, 114.55, 520.07, 114.55, 533.09, 119.62, 563.48, 118.89, 570.72, 115.27, 577.23, 108.04, 586.64, 111.66, 593.87, 126.13, 591.7, 133.36, 585.91, 136.26, 576.51, 134.81, 571.44, 132.64, 556.25, 136.26, 531.65, 136.26, 512.11, 134.81, 490.4, 141.32, 481.0, 139.88, 468.69, 145.66, 433.24, 152.18, 474.48, 155.79, 473.04, 157.24, 488.23, 160.86, 496.19, 163.75, 512.83, 166.65, 534.54, 172.44, 552.63, 172.44, 564.93, 169.54, 576.51, 189.08, 591.7, 200.66, 588.81, 199.21, 583.74, 191.25, 574.34, 187.63, 571.44, 186.18, 534.54, 184.74, 507.04, 181.12, 488.95, 178.95, 475.21, 178.95, 475.21, 177.5, 473.04, 191.97, 467.25, 191.97, 467.25, 189.8, 453.5]], "area": 23736.03525000002, "iscrowd": 0, "image_id": 17905, "bbox": [81.27, 229.19, 119.39, 364.68], "category_id": 1, "id": 2157397}, {"segmentation": [[21.65, 212.33, 46.49, 214.29, 62.82, 211.02, 54.33, 203.18, 43.64, 184.58, 38.53, 170.35, 45.83, 147.35, 51.67, 140.41, 33.42, 86.38, 5.08, 51.73, 1.27, 133.09, 25.42, 180.13, 27.97, 204.28]], "area": 4610.6455, "iscrowd": 0, "image_id": 11511, "bbox": [1.27, 51.73, 61.55, 162.56], "category_id": 1, "id": 2165380}, {"segmentation": [[164.58, 280.64, 210.99, 268.51, 237.4, 254.23, 239.55, 253.51, 255.25, 273.5, 262.39, 276.36, 277.39, 311.34, 280.24, 304.2, 285.95, 312.77, 284.53, 381.31, 295.95, 399.16, 295.95, 319.2, 298.8, 302.78, 298.09, 277.07, 295.23, 264.94, 293.09, 249.23, 265.96, 241.37, 238.83, 241.37, 209.56, 231.38, 220.98, 239.95, 163.87, 249.23, 167.44, 256.37, 152.44, 262.79, 155.3, 281.36]], "area": 5652.242499999999, "iscrowd": 0, "image_id": 15751, "bbox": [152.44, 231.38, 146.36, 167.78], "category_id": 16, "id": 36909}, {"segmentation": [[107.01, 368.05, 107.5, 356.26, 112.9, 348.9, 117.32, 344.97, 120.76, 333.18, 126.65, 326.31, 136.47, 323.36, 143.84, 325.33, 147.28, 322.87, 143.84, 331.22, 142.37, 345.95, 145.31, 358.23, 152.19, 368.54, 153.17, 370.5, 135.98, 370.5, 121.74, 371.48]], "area": 1405.1597500000003, "iscrowd": 0, "image_id": 15751, "bbox": [107.01, 322.87, 46.16, 48.61], "category_id": 16, "id": 38274}, {"segmentation": [[321.53, 205.29, 325.74, 190.93, 323.98, 179.02, 329.24, 171.31, 322.93, 167.46, 321.18, 165.01, 319.78, 168.51, 319.78, 171.66, 320.83, 173.41, 321.18, 174.12, 318.73, 174.12, 315.23, 173.06, 313.82, 173.06, 311.72, 174.47, 309.97, 176.22, 308.92, 176.92, 308.57, 178.32, 308.22, 181.12, 308.22, 186.03, 308.57, 189.53, 311.37, 193.73, 314.18, 197.23, 316.98, 198.64, 318.03, 203.54, 319.78, 204.24, 320.83, 204.94]], "area": 450.39435, "iscrowd": 0, "image_id": 19042, "bbox": [308.22, 165.01, 21.02, 40.28], "category_id": 16, "id": 40014}, {"segmentation": [[69.77, 355.19, 76.33, 359.39, 97.87, 367.0, 119.66, 372.25, 159.83, 369.1, 170.86, 368.58, 168.76, 375.4, 166.66, 376.98, 146.44, 383.02, 117.03, 394.05, 105.48, 397.2, 86.31, 395.89, 72.92, 389.58, 61.89, 385.38, 51.13, 380.39, 44.04, 374.09, 43.51, 368.05, 44.83, 358.07, 49.29, 350.98, 56.38, 347.05, 66.88, 348.1]], "area": 2996.3760999999986, "iscrowd": 0, "image_id": 15751, "bbox": [43.51, 347.05, 127.35, 50.15], "category_id": 16, "id": 40780}, {"segmentation": [[191.46, 178.06, 220.18, 178.06, 270.92, 132.11, 285.28, 111.05, 291.98, 46.91, 304.42, 49.78, 336.97, 107.22, 346.54, 118.71, 369.52, 127.32, 381.01, 145.51, 389.62, 149.34, 408.77, 142.64, 436.53, 112.96, 469.08, 106.26, 507.37, 92.86, 466.21, 225.92, 447.06, 269.0, 446.1, 268.04, 431.74, 298.68, 422.17, 313.04, 409.73, 302.51, 399.2, 266.13, 379.09, 287.19, 356.12, 289.11, 377.18, 316.87, 336.01, 283.36, 354.2, 340.8, 319.74, 293.89, 308.25, 370.48, 307.29, 275.7, 244.11, 326.44, 302.51, 268.04, 274.75, 256.56, 263.26, 259.43, 234.54, 229.75, 210.61, 226.88, 186.67, 177.1]], "area": 42418.22265, "iscrowd": 0, "image_id": 18833, "bbox": [186.67, 46.91, 320.7, 323.57], "category_id": 17, "id": 51982}, {"segmentation": [[592.45, 416.49, 620.56, 335.27, 621.61, 270.72, 621.61, 237.4, 624.73, 194.71, 613.28, 148.89, 624.73, 77.05, 635.14, 57.27, 639.31, 4.16, 0.0, 3.12, 1.04, 244.69, 63.51, 226.98, 91.63, 218.66, 142.65, 222.82, 217.61, 193.67, 239.48, 183.25, 263.43, 186.38, 266.55, 194.71, 301.95, 198.87, 320.69, 195.75, 322.78, 203.04, 356.1, 210.33, 387.33, 202.0, 396.7, 222.82, 434.19, 220.74, 470.63, 220.74, 501.87, 187.42, 520.61, 170.76, 516.44, 216.57, 518.52, 264.47, 535.18, 316.53, 554.97, 385.25, 554.97, 424.82, 562.26, 441.48, 591.41, 437.31]], "area": 146899.25910000002, "iscrowd": 0, "image_id": 14888, "bbox": [0.0, 3.12, 639.31, 438.36], "category_id": 21, "id": 70360}, {"segmentation": [[364.64, 383.79, 395.54, 380.7, 417.17, 385.85, 425.41, 374.52, 416.14, 348.77, 422.32, 318.9, 440.86, 290.06, 449.1, 291.09, 463.52, 299.33, 476.91, 325.08, 480.0, 330.23, 488.24, 333.32, 476.91, 374.52, 468.67, 399.24, 467.64, 431.18, 448.07, 449.72, 409.96, 456.93, 423.35, 428.09, 417.17, 417.79, 401.72, 421.91, 388.33, 436.33, 378.03, 442.51]], "area": 10332.542850000002, "iscrowd": 0, "image_id": 13659, "bbox": [364.64, 290.06, 123.6, 166.87], "category_id": 62, "id": 103597}, {"segmentation": [[305.75, 157.23, 314.04, 183.86, 317.1, 205.26, 313.17, 209.62, 324.08, 208.75, 372.99, 212.68, 381.28, 219.23, 381.72, 256.35, 374.3, 263.77, 316.22, 262.89, 317.1, 268.57, 377.79, 269.01, 384.78, 258.97, 384.34, 215.74, 377.79, 210.93, 390.45, 207.0, 388.27, 247.61, 385.65, 248.49, 387.4, 252.85, 398.75, 204.38, 378.66, 200.02, 371.24, 211.81, 358.14, 210.06, 360.33, 202.2, 361.64, 197.4, 334.13, 195.22, 333.69, 176.88, 328.45, 146.75, 316.22, 145.44, 306.18, 158.1]], "area": 2528.2628999999984, "iscrowd": 0, "image_id": 13659, "bbox": [305.75, 145.44, 93.0, 123.57], "category_id": 62, "id": 105100}, {"segmentation": [[482.27, 192.3, 486.07, 221.75, 486.07, 283.49, 484.17, 318.64, 514.56, 321.48, 563.96, 322.43, 560.16, 296.79, 569.66, 186.6, 563.96, 173.3, 490.82, 175.2, 484.17, 200.85]], "area": 11571.237649999995, "iscrowd": 0, "image_id": 13659, "bbox": [482.27, 173.3, 87.39, 149.13], "category_id": 62, "id": 105395}, {"segmentation": [[190.35, 133.67, 194.95, 155.54, 196.68, 156.69, 209.91, 157.84, 208.19, 167.04, 209.34, 172.8, 213.36, 194.67, 219.69, 191.21, 218.54, 182.01, 236.38, 181.43, 247.89, 181.43, 257.1, 176.83, 258.25, 182.01, 266.3, 183.16, 264.0, 176.25, 277.81, 168.77, 275.51, 153.23, 266.88, 144.03, 254.79, 142.88, 249.04, 149.78, 250.77, 175.68, 247.31, 176.25, 243.29, 172.8, 243.86, 150.93, 238.68, 132.52, 223.15, 125.61, 213.94, 124.46, 188.62, 130.22]], "area": 3250.8677000000002, "iscrowd": 0, "image_id": 13659, "bbox": [188.62, 124.46, 89.19, 70.21], "category_id": 62, "id": 107179}, {"segmentation": [[380.9, 439.74, 405.68, 416.0, 418.06, 420.13, 422.19, 428.39, 412.9, 445.94, 402.58, 473.81, 378.84, 473.81, 388.13, 458.32, 381.94, 456.26, 377.81, 442.84]], "area": 1518.9017999999994, "iscrowd": 0, "image_id": 13659, "bbox": [377.81, 416.0, 44.38, 57.81], "category_id": 62, "id": 110683}, {"segmentation": [[384.06, 252.16, 384.06, 252.16, 384.06, 251.69, 400.09, 181.42, 397.26, 173.87, 397.26, 172.93, 400.09, 165.39, 406.7, 159.73, 407.64, 159.26, 425.56, 154.07, 440.18, 156.43, 450.55, 159.26, 465.17, 153.6, 471.77, 147.94, 477.43, 144.64, 480.73, 150.3, 480.73, 150.77, 482.15, 155.01, 482.62, 155.95, 490.64, 155.01, 494.41, 155.01, 498.18, 155.01, 512.33, 155.01, 528.36, 157.37, 543.92, 158.78, 557.13, 161.14, 568.45, 169.63, 569.86, 171.52, 573.63, 176.23, 565.15, 183.78, 532.14, 187.08, 516.1, 185.66, 490.64, 185.66, 479.32, 188.49, 475.07, 206.41, 479.79, 235.18, 480.73, 257.34, 481.2, 270.08, 475.55, 278.09, 473.19, 278.09, 471.77, 278.57, 458.57, 282.34, 446.37, 282.36, 447.78, 269.15, 443.54, 262.08, 442.59, 261.14, 414.77, 258.78, 396.85, 259.72, 391.19, 257.36]], "area": 12086.843950000002, "iscrowd": 0, "image_id": 13659, "bbox": [384.06, 144.64, 189.57, 137.72], "category_id": 67, "id": 119383}, {"segmentation": [[621.18, 218.92, 605.36, 352.19, 617.79, 347.67, 624.56, 282.16, 626.82, 229.08, 640.0, 238.12, 639.25, 216.66, 622.31, 217.79]], "area": 1523.5983999999999, "iscrowd": 0, "image_id": 13659, "bbox": [605.36, 216.66, 34.64, 135.53], "category_id": 67, "id": 123229}, {"segmentation": [[570.34, 167.58, 571.44, 168.5, 576.77, 165.66, 577.69, 166.57, 575.21, 169.7, 580.17, 170.16, 581.73, 170.8, 580.91, 172.91, 580.81, 176.86, 579.07, 183.75, 578.7, 185.86, 573.65, 179.62, 572.0, 176.58, 568.23, 173.74, 565.66, 175.3, 565.57, 170.34, 566.21, 165.38, 565.93, 162.44, 568.96, 163.73, 569.88, 165.1, 569.15, 167.22], [561.89, 173.46, 558.68, 175.39, 553.99, 180.44, 554.82, 185.31, 557.85, 190.09, 562.26, 174.56], [588.9, 169.33, 592.02, 169.33, 591.19, 167.49, 589.63, 167.31, 586.6, 171.17, 585.31, 180.9, 584.49, 185.22, 585.22, 188.25, 590.37, 191.19, 595.88, 191.19, 599.46, 188.62, 601.94, 182.56, 600.38, 178.06, 597.81, 175.3, 593.58, 174.01, 588.99, 174.38, 586.88, 176.58, 588.99, 169.42]], "area": 492.7924500000005, "iscrowd": 0, "image_id": 11197, "bbox": [553.99, 162.44, 47.95, 28.75], "category_id": 2, "id": 127482}, {"segmentation": [[410.97, 185.58, 430.48, 186.9, 433.64, 190.85, 439.45, 191.38, 446.83, 191.12, 448.15, 188.48, 471.62, 189.27, 473.2, 194.02, 480.85, 193.75, 484.01, 190.06, 484.54, 184.0, 486.12, 178.46, 484.01, 172.92, 482.43, 170.02, 474.78, 166.07, 466.87, 156.84, 433.38, 153.67, 423.89, 161.58, 419.14, 164.22, 416.24, 167.38, 410.44, 166.86, 404.9, 169.23, 400.15, 173.71, 401.21, 180.83, 400.42, 185.84, 407.54, 186.63, 409.91, 184.53]], "area": 2349.6031000000003, "iscrowd": 0, "image_id": 11197, "bbox": [400.15, 153.67, 85.97, 40.35], "category_id": 3, "id": 132902}, {"segmentation": [[76.64, 237.53, 87.92, 237.53, 88.33, 232.82, 86.07, 228.72, 78.08, 228.1, 76.03, 231.59, 75.41, 237.33]], "area": 104.71550000000003, "iscrowd": 0, "image_id": 14380, "bbox": [75.41, 228.1, 12.92, 9.43], "category_id": 3, "id": 134914}, {"segmentation": [[173.07, 165.77, 173.07, 161.39, 173.07, 159.0, 172.67, 154.63, 175.86, 151.44, 172.27, 150.65, 172.67, 149.45, 174.26, 149.05, 176.25, 149.05, 178.64, 146.67, 179.84, 145.07, 185.41, 143.48, 196.95, 143.88, 197.75, 143.88, 197.75, 164.58, 182.62, 164.58, 181.43, 167.76, 176.65, 166.96, 173.47, 166.57], [206.9, 150.65, 208.09, 159.4, 208.89, 165.37, 212.47, 158.21, 210.88, 155.02, 208.09, 149.05]], "area": 544.3247000000002, "iscrowd": 0, "image_id": 11197, "bbox": [172.27, 143.48, 40.2, 24.28], "category_id": 3, "id": 135824}, {"segmentation": [[344.99, 10.63, 353.27, 1.95, 369.05, 1.17, 388.76, 1.17, 391.53, 7.87, 363.92, 11.02, 346.57, 11.81]], "area": 364.2751999999999, "iscrowd": 0, "image_id": 19042, "bbox": [344.99, 1.17, 46.54, 10.64], "category_id": 3, "id": 143831}, {"segmentation": [[95.43, 367.75, 95.43, 372.57, 93.5, 378.36, 90.61, 382.21, 83.86, 386.07, 76.15, 388.96, 72.3, 395.71, 56.87, 386.07, 51.09, 377.39, 52.05, 372.57, 43.38, 376.43, 40.49, 378.36, 41.45, 388.0, 38.56, 388.96, 33.74, 383.18, 27.95, 383.18, 21.21, 382.21, 17.35, 371.61, 16.39, 366.79, 25.06, 363.9, 26.99, 358.11, 25.06, 351.36, 24.1, 346.55, 27.95, 338.83, 27.95, 328.23, 26.03, 317.63, 20.24, 311.84, 23.14, 306.06, 29.88, 307.02, 35.67, 309.91, 43.38, 311.84, 44.34, 302.2, 49.16, 298.35, 40.49, 293.53, 29.88, 296.42, 26.99, 294.49, 28.92, 286.78, 36.63, 284.85, 44.34, 284.85, 47.23, 281.96, 45.31, 275.21, 39.52, 270.39, 37.59, 267.5, 33.74, 259.79, 38.56, 255.93, 43.38, 255.93, 45.31, 263.64, 47.23, 265.57, 51.09, 272.32, 52.05, 280.03, 56.87, 281.0, 61.69, 279.07, 63.62, 278.1, 65.55, 276.18, 67.48, 281.96, 71.33, 281.96, 73.26, 287.74, 73.26, 289.67, 74.23, 296.42, 71.33, 300.27, 74.23, 306.06, 74.23, 310.88, 81.94, 326.3, 84.83, 327.27, 81.94, 338.83, 81.94, 341.73, 90.61, 349.44, 92.54, 352.33, 95.43, 355.22, 91.58, 358.11, 93.5, 362.93]], "area": 5968.806500000001, "iscrowd": 0, "image_id": 19109, "bbox": [16.39, 255.93, 79.04, 139.78], "category_id": 4, "id": 148051}, {"segmentation": [[93.16, 259.28, 107.76, 254.02, 121.77, 253.44, 129.36, 263.36, 132.28, 270.95, 145.71, 277.96, 147.46, 282.63, 142.21, 296.65, 143.37, 301.9, 153.88, 317.08, 146.29, 326.43, 146.29, 335.77, 155.64, 346.28, 155.64, 359.71, 150.38, 374.31, 136.37, 384.23, 128.78, 385.4, 121.77, 384.82, 114.76, 381.9, 113.01, 377.23, 110.09, 372.55, 108.34, 365.55, 102.5, 352.7, 92.57, 351.53, 82.65, 339.27, 84.4, 330.51, 78.56, 318.25, 74.47, 307.74, 72.14, 301.9, 72.48, 299.88, 91.13, 303.06, 93.86, 298.97, 91.59, 292.14, 92.04, 286.23, 75.66, 287.14, 73.39, 285.32, 71.11, 283.95, 71.57, 279.86, 74.3, 278.04, 77.03, 277.13, 87.04, 277.58, 92.04, 277.58, 93.86, 276.22, 94.32, 273.95, 90.22, 271.22, 82.94, 269.4, 82.03, 263.48, 86.13, 259.39, 92.5, 260.3, 94.32, 263.03]], "area": 7202.677, "iscrowd": 0, "image_id": 19109, "bbox": [71.11, 253.44, 84.53, 131.96], "category_id": 4, "id": 152529}, {"segmentation": [[155.83, 317.49, 151.76, 310.18, 142.83, 300.43, 142.83, 291.49, 146.08, 289.87, 146.89, 283.37, 150.95, 281.74, 149.33, 271.99, 149.33, 267.93, 171.27, 267.93, 172.08, 265.49, 178.58, 257.36, 181.83, 258.18, 185.08, 258.18, 188.33, 258.18, 187.52, 263.05, 183.46, 266.3, 173.7, 267.93, 183.46, 297.18, 187.52, 300.43, 187.52, 306.12, 191.58, 318.31, 191.58, 323.18, 188.33, 328.87, 183.46, 332.93, 188.33, 346.75, 189.96, 352.43, 188.33, 357.31, 185.89, 358.94, 186.71, 371.94, 179.39, 376.0, 175.33, 371.94, 170.45, 365.44, 167.2, 358.94, 164.77, 350.81, 161.52, 339.43, 156.64, 340.25, 146.89, 333.75]], "area": 3320.82665, "iscrowd": 0, "image_id": 19109, "bbox": [142.83, 257.36, 48.75, 118.64], "category_id": 4, "id": 154233}, {"segmentation": [[186.17, 339.33, 196.73, 358.52, 211.12, 351.8, 216.88, 347.01, 231.27, 347.97, 231.27, 365.24, 246.63, 363.32, 243.75, 356.6, 264.86, 345.09, 277.34, 331.65, 282.13, 353.72, 294.61, 366.2, 317.64, 355.64, 320.52, 343.17, 312.84, 327.81, 320.52, 324.93, 296.53, 288.47, 284.05, 271.19, 244.71, 275.99, 259.1, 278.87, 262.94, 283.67, 255.26, 297.1, 269.66, 319.18, 265.82, 331.65, 261.02, 328.77, 245.67, 312.46, 231.27, 295.19, 216.88, 295.19, 206.32, 279.83, 193.85, 281.75, 188.09, 299.02, 189.05, 308.62, 191.93, 320.14]], "area": 7753.239449999999, "iscrowd": 0, "image_id": 19109, "bbox": [186.17, 271.19, 134.35, 95.01], "category_id": 4, "id": 155152}, {"segmentation": [[326.49, 275.69, 324.4, 281.55, 319.8, 285.32, 316.87, 285.32, 312.68, 289.51, 320.21, 297.04, 329.01, 310.44, 338.22, 321.32, 342.82, 330.95, 342.4, 341.0, 338.64, 342.72, 353.29, 354.44, 372.55, 342.3, 372.13, 325.14, 371.71, 322.2, 363.75, 313.43, 360.82, 312.18, 364.59, 282.06, 357.47, 281.22, 337.8, 251.54, 335.29, 251.54, 329.42, 251.54, 320.63, 257.82, 322.73, 262.43, 322.73, 268.71, 318.96, 268.71, 313.1, 269.54, 333.19, 276.24]], "area": 3149.922050000001, "iscrowd": 0, "image_id": 19109, "bbox": [312.68, 251.54, 59.87, 102.9], "category_id": 4, "id": 155355}, {"segmentation": [[362.1, 282.31, 361.39, 269.9, 366.71, 267.42, 364.23, 263.52, 363.52, 258.56, 365.29, 256.78, 369.19, 260.33, 370.61, 265.65, 375.93, 272.74, 376.99, 278.05, 378.76, 281.95, 386.56, 294.0, 392.59, 306.06, 395.42, 311.73, 394.36, 313.86, 392.23, 314.21, 399.32, 318.46, 404.64, 326.97, 404.29, 339.73, 398.61, 345.41, 390.11, 348.6, 385.85, 343.63, 386.21, 335.13, 384.79, 330.52, 381.6, 325.91, 378.05, 317.4, 374.51, 314.21, 369.9, 300.39, 366.36, 296.13, 363.17, 286.92, 364.58, 281.24]], "area": 1508.619950000001, "iscrowd": 0, "image_id": 19109, "bbox": [361.39, 256.78, 43.25, 91.82], "category_id": 4, "id": 155874}, {"segmentation": [[189.14, 180.64, 520.62, 218.85, 556.91, 224.58, 560.74, 228.4, 593.21, 266.61, 591.3, 279.98, 576.98, 282.85, 573.15, 289.54, 573.15, 295.27, 563.6, 301.95, 545.45, 301.95, 528.26, 308.64, 338.16, 275.21, 321.92, 269.47, 302.82, 271.39, 183.41, 251.32], [0.96, 154.84, 175.77, 179.68, 172.9, 239.86, 171.95, 246.55, 42.99, 232.22, 38.21, 226.49, 28.66, 223.62, 25.79, 229.35, 20.06, 232.22, 5.73, 226.49, 0.96, 224.58, 0.0, 155.8]], "area": 43199.8505, "iscrowd": 0, "image_id": 14473, "bbox": [0.0, 154.84, 593.21, 153.8], "category_id": 7, "id": 169920}, {"segmentation": [[565.18, 312.88, 202.47, 260.1, 193.83, 259.14, 186.15, 262.02, 186.15, 282.17, 206.3, 286.01, 419.32, 344.54, 506.64, 360.85, 567.09, 362.77, 566.13, 311.92], [633.3, 323.43, 640.0, 339.74, 639.06, 383.88, 601.64, 383.88, 577.65, 378.13, 578.61, 315.76]], "area": 20587.109299999996, "iscrowd": 0, "image_id": 14380, "bbox": [186.15, 259.14, 453.85, 124.74], "category_id": 7, "id": 173493}, {"segmentation": [[202.47, 218.9, 167.13, 244.97, 171.76, 276.83, 185.09, 309.85, 183.93, 316.22, 189.72, 320.28, 209.42, 331.29, 227.38, 339.4, 236.07, 332.45, 241.28, 332.45, 251.71, 336.5, 226.22, 374.74, 220.43, 396.17, 235.49, 404.28, 229.7, 415.29, 216.95, 417.61, 207.1, 415.29, 200.73, 402.55, 183.93, 412.97, 176.98, 410.08, 161.91, 412.97, 154.38, 422.24, 152.06, 433.83, 161.91, 444.26, 181.03, 474.38, 191.46, 479.02, 379.74, 479.02, 375.11, 429.77, 360.63, 361.99, 342.67, 328.97, 325.87, 316.8, 300.38, 306.95, 294.58, 300.0, 292.84, 295.37, 284.15, 296.53, 269.09, 282.04, 275.46, 273.35, 277.2, 266.98, 271.41, 259.45, 267.93, 237.43, 256.92, 223.53, 215.79, 220.63, 212.32, 217.16]], "area": 36192.03204999999, "iscrowd": 0, "image_id": 13659, "bbox": [152.06, 217.16, 227.68, 261.86], "category_id": 1, "id": 199407}, {"segmentation": [[421.75, 133.75, 424.99, 106.79, 408.81, 103.55, 406.65, 80.9, 413.12, 72.27, 420.67, 59.33, 428.22, 46.38, 431.46, 38.83, 439.01, 33.44, 442.25, 25.89, 450.88, 18.34, 450.88, 10.79, 457.35, 9.71, 460.58, 19.42, 467.06, 33.44, 472.45, 51.78, 488.63, 75.51, 491.87, 85.21, 484.31, 99.24, 475.69, 126.2, 475.69, 142.38, 474.61, 147.78, 468.13, 151.01, 440.09, 146.7, 427.15, 135.91]], "area": 7141.66215, "iscrowd": 0, "image_id": 13659, "bbox": [406.65, 9.71, 85.22, 141.3], "category_id": 1, "id": 201643}, {"segmentation": [[413.83, 129.27, 432.28, 139.65, 440.94, 146.58, 453.62, 147.73, 459.39, 151.19, 461.7, 154.65, 461.7, 158.69, 454.2, 159.84, 436.9, 155.8, 399.41, 137.92, 393.64, 150.61, 391.34, 157.53, 383.26, 169.64, 397.68, 179.45, 407.48, 183.49, 417.29, 185.79, 419.02, 188.1, 421.9, 192.71, 416.14, 261.35, 397.68, 263.65, 387.88, 265.38, 386.15, 259.62, 387.88, 248.08, 397.1, 209.44, 376.92, 198.48, 374.61, 207.71, 372.88, 214.05, 363.08, 214.05, 360.19, 211.17, 359.62, 206.56, 360.77, 197.33, 343.47, 198.48, 336.55, 195.6, 333.66, 193.29, 331.36, 189.25, 332.51, 180.03, 333.66, 169.64, 349.81, 133.89, 350.39, 125.24, 353.85, 116.01, 361.92, 112.55, 367.69, 111.97, 374.61, 111.97, 380.38, 114.28, 375.76, 110.82, 374.61, 106.2, 377.49, 101.59, 382.11, 98.13, 387.88, 96.4, 393.64, 95.82, 398.26, 94.09, 403.45, 94.09, 406.91, 95.82, 408.64, 98.71, 413.25, 108.51, 414.98, 115.43, 410.37, 126.39, 416.14, 130.43]], "area": 8584.56095, "iscrowd": 0, "image_id": 13659, "bbox": [331.36, 94.09, 130.34, 171.29], "category_id": 1, "id": 212964}, {"segmentation": [[547.08, 86.75, 557.13, 75.64, 568.77, 75.64, 583.05, 82.52, 601.03, 86.75, 628.54, 88.87, 638.06, 95.74, 638.59, 195.18, 626.95, 213.7, 617.96, 218.99, 626.95, 228.51, 625.37, 239.62, 594.16, 238.56, 605.8, 164.51, 596.8, 164.51, 585.17, 176.67, 576.17, 177.2, 567.71, 180.37, 565.07, 173.5, 568.77, 162.92, 577.23, 145.46, 577.76, 140.17, 549.73, 156.04, 520.64, 156.57, 519.05, 151.81, 520.11, 146.52, 530.16, 146.52, 546.02, 144.41, 570.36, 128.54, 572.47, 122.19, 570.36, 115.84, 561.36, 112.67, 553.96, 108.97, 551.31, 103.15, 548.67, 93.1]], "area": 9316.189800000004, "iscrowd": 0, "image_id": 13659, "bbox": [519.05, 75.64, 119.54, 163.98], "category_id": 1, "id": 213292}, {"segmentation": [[324.03, 289.23, 323.69, 275.87, 323.19, 275.54, 322.36, 274.37, 322.19, 274.21, 322.19, 274.04, 322.69, 273.54, 326.03, 271.53, 327.37, 271.2, 330.37, 274.04, 331.21, 277.54, 331.21, 279.38, 333.04, 285.39, 333.38, 286.39, 333.38, 288.23, 333.38, 298.58, 333.71, 300.41, 335.55, 308.26, 334.54, 313.6, 331.71, 313.1, 330.87, 312.1, 330.7, 309.09, 330.2, 307.92, 328.7, 304.58, 328.53, 300.75, 328.37, 298.24, 328.87, 296.57, 327.2, 298.07, 327.2, 298.58, 327.53, 304.08, 326.53, 304.58, 326.87, 306.75, 325.86, 312.1, 321.36, 311.26, 320.69, 311.09, 320.19, 309.59, 322.53, 304.42, 322.53, 303.58, 322.19, 300.91, 322.02, 298.41, 323.36, 293.07, 322.36, 289.06, 323.19, 288.56, 323.53, 288.06]], "area": 380.4250500000004, "iscrowd": 0, "image_id": 14473, "bbox": [320.19, 271.2, 15.36, 42.4], "category_id": 1, "id": 233526}, {"segmentation": [[277.52, 289.44, 280.82, 282.08, 283.1, 278.02, 283.61, 271.69, 288.93, 269.4, 292.73, 271.43, 292.73, 274.73, 294.0, 278.28, 296.03, 279.55, 297.55, 286.39, 295.52, 296.03, 293.75, 297.04, 292.48, 307.69, 284.87, 305.16, 285.13, 297.55, 283.61, 293.75, 282.84, 289.94, 280.82, 293.49, 277.01, 291.46, 277.27, 287.66]], "area": 447.80070000000023, "iscrowd": 0, "image_id": 14473, "bbox": [277.01, 269.4, 20.54, 38.29], "category_id": 1, "id": 235867}, {"segmentation": [[201.34, 266.15, 203.08, 258.58, 209.49, 260.91, 212.4, 266.15, 218.23, 277.8, 220.56, 289.45, 217.07, 295.28, 212.99, 294.69, 207.16, 292.95, 204.25, 285.37, 204.83, 277.8, 204.83, 277.22, 203.67, 276.64, 203.08, 274.89, 203.67, 273.14, 202.5, 269.06, 202.5, 266.73]], "area": 428.71489999999983, "iscrowd": 0, "image_id": 14473, "bbox": [201.34, 258.58, 19.22, 36.7], "category_id": 1, "id": 236039}, {"segmentation": [[157.01, 281.56, 159.33, 274.6, 159.33, 271.7, 162.81, 263.58, 166.87, 258.36, 169.19, 253.72, 172.67, 254.3, 172.67, 262.42, 169.19, 275.76, 169.19, 282.14, 165.13, 287.36, 164.55, 281.56, 165.13, 276.34, 162.81, 274.02, 161.07, 280.4, 159.33, 285.62, 155.85, 279.82]], "area": 253.14099999999945, "iscrowd": 0, "image_id": 14473, "bbox": [155.85, 253.72, 16.82, 33.64], "category_id": 1, "id": 237050}, {"segmentation": [[125.65, 276.81, 125.33, 267.05, 122.4, 262.49, 121.1, 258.91, 119.8, 255.98, 120.77, 252.4, 121.1, 249.15, 121.1, 246.87, 123.38, 245.57, 129.23, 246.22, 128.58, 246.55, 131.84, 249.47, 133.79, 252.4, 134.44, 257.28, 135.42, 261.52, 136.07, 266.4, 136.39, 274.21, 137.37, 278.11, 137.37, 280.72, 134.12, 280.06, 133.14, 278.11, 131.19, 267.05, 130.54, 276.81, 129.89, 278.76, 129.56, 279.09, 125.65, 278.44]], "area": 376.57574999999997, "iscrowd": 0, "image_id": 14473, "bbox": [119.8, 245.57, 17.57, 35.15], "category_id": 1, "id": 238488}, {"segmentation": [[78.11, 270.1, 80.19, 264.58, 80.19, 263.5, 77.84, 259.88, 80.92, 252.82, 84.9, 246.85, 86.71, 247.03, 89.06, 245.76, 92.14, 245.94, 93.41, 247.57, 93.59, 250.28, 94.13, 252.27, 94.49, 255.17, 91.96, 260.42, 93.04, 268.38, 91.41, 271.82, 89.79, 273.09, 89.42, 273.09, 88.16, 271.28, 88.16, 268.02, 87.25, 264.4, 85.8, 263.5, 83.45, 267.3, 82.0, 271.64]], "area": 291.97255, "iscrowd": 0, "image_id": 14473, "bbox": [77.84, 245.76, 16.65, 27.33], "category_id": 1, "id": 239121}, {"segmentation": [[29.69, 259.48, 29.69, 247.09, 27.33, 245.52, 25.96, 240.21, 26.35, 233.91, 26.94, 232.14, 28.91, 230.96, 29.1, 230.17, 29.89, 225.85, 31.27, 225.26, 34.22, 225.85, 34.81, 228.8, 34.81, 230.37, 37.56, 231.75, 39.14, 236.27, 39.14, 240.8, 38.35, 243.55, 36.77, 245.32, 37.76, 258.3, 39.14, 261.05, 35.2, 260.66, 34.02, 259.48, 33.43, 248.86, 33.04, 249.45, 32.84, 259.48]], "area": 307.9714500000003, "iscrowd": 0, "image_id": 14473, "bbox": [25.96, 225.26, 13.18, 35.79], "category_id": 1, "id": 239422}, {"segmentation": [[230.54, 9.22, 233.74, 8.21, 236.55, 8.76, 239.36, 8.99, 241.08, 9.22, 242.56, 9.61, 242.87, 9.93, 243.5, 11.18, 243.65, 11.57, 238.97, 11.96, 231.55, 12.19, 228.82, 12.03, 228.98, 10.55], [221.09, 11.88, 222.34, 10.79, 223.2, 10.94, 223.2, 12.35, 223.2, 12.89, 222.03, 13.05, 221.09, 12.74], [225.39, 11.18, 226.01, 9.54, 227.03, 8.68, 227.65, 8.52, 227.88, 9.69, 227.26, 12.43, 227.18, 12.89, 226.4, 13.05, 225.0, 12.5], [243.58, 9.38, 244.82, 9.15, 246.46, 9.54, 246.54, 11.1, 246.54, 11.88, 245.84, 11.49, 244.2, 9.38]], "area": 58.205500000000036, "iscrowd": 0, "image_id": 19042, "bbox": [221.09, 8.21, 25.45, 4.84], "category_id": 3, "id": 243269}, {"segmentation": [[465.7, 334.68, 471.76, 338.42, 481.56, 337.25, 487.4, 333.28, 490.67, 321.61, 487.86, 315.78, 484.36, 312.28, 480.86, 310.41, 480.86, 307.85, 472.93, 300.61, 473.16, 295.25, 471.53, 287.08, 465.7, 282.65, 455.9, 267.01, 451.7, 254.12, 447.03, 253.65, 450.76, 262.75, 445.63, 266.72, 437.23, 262.29, 433.73, 252.95, 430.93, 255.29, 436.29, 266.02, 444.23, 271.15, 441.19, 276.52, 454.5, 301.49, 460.1, 311.06, 460.8, 315.02, 462.2, 320.16, 463.6, 324.82, 465.7, 328.56, 466.16, 329.02]], "area": 1670.7341499999998, "iscrowd": 0, "image_id": 19109, "bbox": [430.93, 252.95, 59.74, 85.47], "category_id": 4, "id": 245859}, {"segmentation": [[423.46, 249.1, 435.08, 263.12, 437.89, 271.54, 440.69, 275.15, 456.32, 308.0, 461.53, 322.43, 461.53, 330.44, 455.92, 344.06, 449.51, 344.06, 440.69, 343.26, 433.08, 344.46, 427.07, 342.46, 421.86, 339.66, 417.05, 336.85, 408.24, 335.65, 406.23, 335.65, 405.03, 333.25, 405.03, 328.44, 405.03, 323.23, 399.42, 318.42, 395.42, 317.22, 395.02, 316.02, 395.42, 314.81, 395.82, 312.01, 395.82, 310.01, 392.21, 300.39, 382.99, 287.17, 378.59, 281.96, 391.41, 285.56, 394.21, 285.56, 401.83, 282.76, 402.23, 282.36, 405.83, 279.95, 408.64, 278.75, 412.25, 275.15, 411.04, 273.94, 407.44, 272.74, 406.64, 272.74, 405.83, 271.54, 406.23, 270.34, 411.04, 269.13, 413.45, 265.13, 407.04, 259.52, 406.23, 256.71, 407.04, 255.11, 411.84, 254.31, 412.65, 253.91, 415.05, 251.9, 417.45, 249.9, 419.46, 249.1]], "area": 4308.7966000000015, "iscrowd": 0, "image_id": 19109, "bbox": [378.59, 249.1, 82.94, 95.36], "category_id": 4, "id": 246641}, {"segmentation": [[312.92, 249.41, 309.74, 243.04, 314.74, 239.4, 317.47, 212.56, 318.38, 208.01, 319.29, 199.37, 319.75, 172.07, 323.84, 137.95, 328.85, 129.77, 337.94, 125.67, 339.31, 123.85, 340.22, 116.57, 348.41, 112.02, 356.14, 115.21, 362.96, 118.39, 358.87, 122.94, 355.23, 132.95, 349.32, 132.95, 348.86, 137.04, 353.41, 150.69, 353.41, 155.24, 357.96, 180.26, 358.87, 189.81, 351.59, 193.91, 351.59, 187.54, 345.68, 179.35, 342.95, 204.37, 340.22, 227.12, 338.4, 242.58, 348.86, 251.23, 342.95, 253.05, 337.03, 252.14, 328.39, 249.41, 322.02, 247.59]], "area": 3796.8146999999994, "iscrowd": 0, "image_id": 11197, "bbox": [309.74, 112.02, 53.22, 141.03], "category_id": 1, "id": 252476}, {"segmentation": [[18.44, 184.05, 21.57, 170.78, 20.79, 155.41, 17.4, 141.09, 21.57, 128.33, 24.43, 119.21, 29.64, 112.97, 36.41, 113.75, 39.01, 116.09, 40.58, 120.52, 39.8, 126.77, 35.63, 128.33, 38.75, 134.84, 41.36, 141.35, 42.14, 155.15, 41.1, 161.92, 44.48, 173.38, 42.14, 176.24, 43.18, 186.4, 19.22, 184.84]], "area": 1453.6855499999997, "iscrowd": 0, "image_id": 11197, "bbox": [17.4, 112.97, 27.08, 73.43], "category_id": 1, "id": 255782}, {"segmentation": [[584.1, 184.99, 583.02, 188.23, 583.5, 190.27, 583.02, 191.58, 576.9, 191.7, 574.86, 190.63, 578.34, 188.35, 579.06, 184.75, 579.78, 178.39, 577.86, 175.99, 575.82, 172.75, 581.82, 173.11, 581.82, 171.91, 574.5, 170.59, 574.62, 168.56, 575.7, 167.36, 577.38, 167.0, 577.62, 163.52, 571.14, 169.64, 568.87, 168.8, 567.91, 167.24, 570.79, 164.6, 571.14, 162.92, 569.35, 162.2, 569.11, 159.44, 574.02, 156.44, 575.34, 153.2, 573.42, 151.76, 573.18, 149.0, 573.66, 147.21, 575.34, 146.97, 576.66, 146.97, 578.22, 148.53, 579.42, 149.96, 580.74, 151.16, 581.22, 151.4, 582.3, 152.72, 584.58, 155.0, 584.94, 157.28, 588.42, 164.48, 590.7, 166.28, 590.7, 166.76, 588.54, 170.59, 586.98, 172.63]], "area": 444.30720000000065, "iscrowd": 0, "image_id": 11197, "bbox": [567.91, 146.97, 22.79, 44.73], "category_id": 1, "id": 256875}, {"segmentation": [[194.58, 115.77, 229.32, 156.11, 223.72, 185.25, 190.1, 223.35, 147.52, 217.75, 118.38, 183.01, 116.14, 147.15, 150.88, 115.77]], "area": 9075.6204, "iscrowd": 0, "image_id": 15440, "bbox": [116.14, 115.77, 113.18, 107.58], "category_id": 13, "id": 269186}, {"segmentation": [[252.18, 219.95, 252.18, 219.95, 276.71, 244.48, 276.71, 244.48, 306.62, 242.69, 329.95, 219.95, 329.95, 185.86, 329.95, 185.86, 308.41, 164.32, 308.41, 164.32, 308.41, 164.32, 272.52, 162.53, 250.39, 191.24, 253.38, 220.55]], "area": 5178.6560500000005, "iscrowd": 0, "image_id": 11122, "bbox": [250.39, 162.53, 79.56, 81.95], "category_id": 13, "id": 270172}, {"segmentation": [[1.44, 261.75, 296.27, 232.99, 480.0, 270.38, 480.0, 638.56, 4.31, 638.56]], "area": 185874.36909999998, "iscrowd": 0, "image_id": 14831, "bbox": [1.44, 232.99, 478.56, 405.57], "category_id": 17, "id": 273446}, {"segmentation": [[1.43, 0.2, 480.0, 0.2, 480.0, 269.38, 339.33, 239.31, 274.9, 235.01, 184.7, 246.47, 1.43, 259.35, 0.0, 260.79]], "area": 119214.07190000001, "iscrowd": 0, "image_id": 14831, "bbox": [0.0, 0.2, 480.0, 269.18], "category_id": 65, "id": 316818}, {"segmentation": [[59.33, 437.6, 90.61, 410.64, 114.34, 399.85, 135.91, 414.95, 167.19, 461.33, 173.66, 459.18, 188.76, 458.1, 222.2, 467.81, 226.52, 472.12, 210.34, 472.12, 87.37, 469.96, 64.72, 439.76]], "area": 5439.402099999999, "iscrowd": 0, "image_id": 14226, "bbox": [59.33, 399.85, 167.19, 72.27], "category_id": 62, "id": 377650}, {"segmentation": [[394.79, 30.95, 433.62, 16.93, 568.45, 22.32, 593.26, 50.37, 545.8, 271.49, 535.01, 216.48, 523.15, 176.57, 532.85, 145.29, 496.18, 119.4, 442.25, 95.67, 433.62, 79.49, 428.22, 66.55, 395.87, 35.27]], "area": 20048.027450000005, "iscrowd": 0, "image_id": 14226, "bbox": [394.79, 16.93, 198.47, 254.56], "category_id": 62, "id": 380478}, {"segmentation": [[66.88, 135.91, 49.62, 204.94, 35.6, 339.78, 24.81, 395.87, 58.25, 458.43, 307.42, 449.8, 331.15, 473.53, 365.66, 470.29, 393.71, 459.51, 379.69, 419.6, 377.53, 392.63, 405.57, 345.17, 443.33, 323.6, 441.17, 269.66, 462.74, 288.0, 461.66, 344.09, 450.88, 378.61, 475.69, 381.84, 482.16, 431.46, 519.91, 437.93, 560.9, 428.22, 584.63, 384.0, 584.63, 339.78, 581.39, 312.81, 588.94, 279.37, 606.2, 255.64, 601.89, 163.96, 455.19, 158.56, 405.57, 100.31, 339.78, 112.18, 305.26, 102.47, 273.98, 74.43, 119.73, 92.76, 92.76, 124.04]], "area": 172087.161, "iscrowd": 0, "image_id": 18837, "bbox": [24.81, 74.43, 581.39, 399.1], "category_id": 8, "id": 396804}, {"segmentation": [[179.85, 68.75, 187.37, 70.04, 187.8, 87.43, 179.64, 87.21]], "area": 140.4489500000003, "iscrowd": 0, "image_id": 11197, "bbox": [179.64, 68.75, 8.16, 18.68], "category_id": 10, "id": 404283}, {"segmentation": [[253.34, 24.64, 258.03, 24.64, 263.01, 24.64, 263.28, 21.88, 267.15, 19.67, 267.15, 46.46, 269.36, 50.6, 284.82, 50.6, 284.82, 57.51, 260.24, 58.33, 250.85, 56.95, 248.37, 52.81, 249.2, 29.06, 251.41, 27.13]], "area": 740.5159999999994, "iscrowd": 0, "image_id": 11197, "bbox": [248.37, 19.67, 36.45, 38.66], "category_id": 10, "id": 406334}, {"segmentation": [[220.58, 75.43, 220.89, 90.48, 229.99, 89.86, 227.17, 73.24, 219.95, 73.86]], "area": 132.28740000000005, "iscrowd": 0, "image_id": 11197, "bbox": [219.95, 73.24, 10.04, 17.24], "category_id": 10, "id": 407305}, {"segmentation": [[261.21, 490.66, 270.18, 476.08, 292.6, 457.03, 317.26, 247.38, 328.48, 228.33, 333.0, 215.99, 333.0, 158.82, 310.54, 152.09, 287.0, 113.98, 283.63, 97.16, 243.27, 87.07, 191.7, 103.89, 177.13, 122.94, 160.31, 149.85, 142.38, 162.18, 135.65, 190.21, 141.26, 227.2, 126.68, 249.63, 121.08, 281.02, 125.56, 305.68, 145.74, 322.5, 153.59, 340.43, 149.1, 470.48, 171.52, 483.93, 207.4, 492.9, 258.97, 494.02]], "area": 63020.718700000005, "iscrowd": 0, "image_id": 20107, "bbox": [121.08, 87.07, 211.92, 406.95], "category_id": 11, "id": 417811}, {"segmentation": [[372.97, 30.81, 410.81, 36.22, 434.59, 65.41, 436.76, 78.38, 438.92, 83.78, 441.08, 95.68, 440.0, 104.32, 500.54, 119.46, 535.14, 155.14, 531.89, 181.08, 530.81, 180.0, 541.62, 289.19, 541.62, 315.14, 448.65, 370.27, 441.08, 370.27, 434.59, 363.78, 430.27, 355.14, 428.11, 340.0, 422.7, 331.35, 407.57, 327.03, 387.03, 328.11, 380.54, 323.78, 385.95, 310.81, 404.32, 315.14, 418.38, 308.65, 411.89, 304.32, 379.46, 311.89, 378.38, 300.0, 382.7, 295.68, 404.32, 294.59, 415.14, 294.59, 417.3, 291.35, 417.3, 288.11, 409.73, 282.7, 397.84, 287.03, 393.51, 284.86, 380.54, 289.19, 383.78, 278.38, 391.35, 272.97, 397.84, 271.89, 401.08, 249.19, 354.59, 252.43, 287.57, 245.95, 251.89, 241.62, 235.68, 237.3, 228.11, 238.38, 252.97, 253.51, 251.89, 265.41, 249.73, 275.14, 246.49, 278.38, 247.57, 284.86, 245.41, 294.59, 230.27, 295.68, 220.54, 291.35, 214.05, 276.22, 208.65, 248.11, 208.65, 238.38, 244.32, 228.65, 263.78, 232.97, 274.59, 229.73, 286.49, 214.59, 292.97, 202.7, 314.59, 190.81, 315.68, 165.95, 320.0, 130.27, 321.08, 124.86, 325.41, 111.89, 318.92, 83.78, 332.97, 51.35, 347.03, 38.38, 369.73, 31.89]], "area": 55030.58024999999, "iscrowd": 0, "image_id": 14226, "bbox": [208.65, 30.81, 332.97, 339.46], "category_id": 1, "id": 476415}, {"segmentation": [[340.54, 50.27, 352.43, 36.22, 360.0, 22.16, 365.41, 14.59, 378.38, 15.68, 379.46, 29.73, 378.38, 43.78, 383.78, 48.11, 391.35, 53.51, 394.59, 65.41, 394.59, 76.22, 398.92, 79.46, 398.92, 85.95, 401.08, 89.19, 429.19, 117.3, 438.92, 124.86, 451.89, 122.7, 455.14, 129.19, 455.14, 144.32, 467.03, 158.38, 447.57, 158.38, 433.51, 138.92, 422.7, 132.43, 411.89, 121.62, 403.24, 105.41, 384.86, 100.0, 362.16, 102.16, 357.84, 114.05, 345.95, 109.73, 336.22, 93.51, 327.57, 76.22]], "area": 5614.067099999995, "iscrowd": 0, "image_id": 18837, "bbox": [327.57, 14.59, 139.46, 143.79], "category_id": 1, "id": 498085}, {"segmentation": [[337.74, 51.73, 336.45, 47.43, 342.04, 45.71, 337.31, 40.55, 331.72, 35.82, 329.57, 33.24, 329.57, 27.22, 325.27, 25.06, 313.66, 21.62, 308.93, 23.77, 309.36, 29.8, 299.89, 28.94, 296.88, 32.38, 299.89, 42.27, 304.19, 46.57, 306.78, 47.86, 309.36, 53.45, 312.37, 58.18, 316.67, 55.17, 316.67, 57.75, 309.79, 64.2, 305.05, 74.96, 300.75, 83.13, 297.74, 87.43, 295.59, 91.73, 294.73, 95.17, 294.3, 99.04, 299.89, 101.19, 305.48, 106.78, 307.64, 112.38, 309.79, 118.4, 326.13, 120.55, 339.46, 123.13, 341.61, 113.24, 342.04, 105.49, 342.47, 104.63, 341.61, 102.05, 336.45, 93.88, 331.72, 86.14, 328.28, 74.96, 330.43, 65.49, 334.73, 56.89, 336.45, 51.73]], "area": 2987.8379, "iscrowd": 0, "image_id": 18837, "bbox": [294.3, 21.62, 48.17, 101.51], "category_id": 1, "id": 500347}, {"segmentation": [[437.15, 332.59, 404.81, 336.94, 395.4, 366.63, 391.29, 378.69, 383.33, 395.35, 371.98, 407.9, 375.85, 424.79, 392.02, 453.03, 392.26, 462.68, 380.91, 476.68, 412.05, 469.44, 416.88, 466.79, 419.53, 480.0, 435.94, 479.82, 430.88, 444.34, 429.19, 432.03, 427.74, 411.28, 427.74, 405.72, 438.36, 401.38, 448.25, 360.35, 444.87, 327.04, 445.36, 331.39, 449.46, 310.87, 454.53, 306.53, 452.11, 299.53, 449.46, 286.74, 443.18, 286.74, 443.18, 319.32, 442.94, 330.42]], "area": 7237.5148500000005, "iscrowd": 0, "image_id": 18837, "bbox": [371.98, 286.74, 82.55, 193.26], "category_id": 1, "id": 518437}, {"segmentation": [[17.48, 262.56, 30.8, 262.77, 32.84, 262.15, 34.69, 254.16, 32.02, 249.66, 24.65, 245.77, 26.9, 243.1, 27.52, 239.62, 26.9, 235.32, 17.07, 235.11]], "area": 355.6931499999998, "iscrowd": 0, "image_id": 18837, "bbox": [17.07, 235.11, 17.62, 27.66], "category_id": 1, "id": 555506}, {"segmentation": [[224.7, 236.56, 397.64, 262.23, 396.74, 266.29, 400.8, 272.14, 387.29, 277.1, 383.23, 282.95, 385.93, 288.81, 405.75, 287.45, 413.41, 287.9, 416.11, 294.21, 382.78, 296.91, 376.93, 297.81, 376.93, 304.57, 379.18, 307.27, 391.34, 307.72, 411.16, 307.72, 414.76, 311.32, 408.45, 312.68, 390.89, 313.58, 382.78, 313.13, 380.53, 317.18, 381.88, 323.03, 396.29, 326.64, 418.81, 325.74, 420.16, 333.84, 424.22, 337.9, 429.17, 340.6, 432.32, 348.26, 433.68, 351.86, 434.58, 359.07, 426.02, 369.87, 257.58, 335.19, 255.77, 327.99, 238.21, 288.35, 247.67, 283.85, 249.02, 279.35, 249.02, 277.1, 253.52, 268.09, 252.62, 264.48, 250.37, 259.98, 250.37, 254.13, 247.67, 252.77, 235.96, 251.87, 235.51, 246.92, 231.9, 243.77, 224.7, 242.42, 221.54, 239.71, 223.35, 237.46]], "area": 15654.174449999991, "iscrowd": 0, "image_id": 14226, "bbox": [221.54, 236.56, 213.04, 133.31], "category_id": 73, "id": 1099277}, {"segmentation": [[56.09, 385.08, 86.29, 348.4, 130.52, 402.34, 199.55, 412.04, 181.21, 417.44, 171.51, 408.81, 158.56, 424.99, 161.8, 440.09, 172.58, 460.58, 114.34, 458.43, 88.45, 444.4, 89.53, 437.93, 53.93, 382.92]], "area": 6723.906750000004, "iscrowd": 0, "image_id": 13659, "bbox": [53.93, 348.4, 145.62, 112.18], "category_id": 73, "id": 1100204}, {"segmentation": [[189.84, 336.36, 127.28, 325.57, 97.08, 274.88, 119.73, 253.3, 155.33, 291.06, 199.55, 313.71]], "area": 3885.3440500000006, "iscrowd": 0, "image_id": 13659, "bbox": [97.08, 253.3, 102.47, 83.06], "category_id": 73, "id": 1103792}, {"segmentation": [[303.05, 129.01, 302.78, 125.03, 302.78, 121.53, 302.78, 119.63, 308.78, 119.19, 312.1, 119.35, 312.43, 119.84, 312.43, 120.55, 312.81, 121.1, 313.2, 121.81, 313.63, 122.3, 313.74, 122.3, 313.74, 122.35, 314.29, 122.95, 314.18, 123.88, 312.87, 123.94, 312.81, 124.15, 312.87, 125.41, 312.87, 125.95, 313.2, 126.5, 313.2, 127.64, 312.98, 128.19, 312.92, 128.63, 311.72, 128.63, 310.47, 128.68, 311.29, 129.17, 312.38, 129.61, 310.0, 131.03]], "area": 107.98460000000024, "iscrowd": 0, "image_id": 11197, "bbox": [302.78, 119.19, 11.51, 11.84], "category_id": 1, "id": 1237643}, {"segmentation": [[7.14, 238.42, 17.65, 233.16, 15.4, 228.66, 16.15, 223.4, 19.15, 219.64, 26.67, 219.64, 27.42, 223.4, 28.17, 228.66, 25.16, 235.42, 25.91, 239.17, 24.77, 247.77, 25.48, 261.62, 10.91, 260.91, 11.27, 254.16, 10.2, 245.28, 4.52, 243.15, 4.16, 241.02]], "area": 578.1933500000005, "iscrowd": 0, "image_id": 19109, "bbox": [4.16, 219.64, 24.01, 41.98], "category_id": 1, "id": 1252919}, {"segmentation": [[71.7, 282.24, 52.44, 279.73, 44.49, 283.08, 45.75, 260.47, 47.42, 255.45, 46.58, 248.33, 52.44, 240.38, 50.35, 228.24, 54.96, 224.05, 64.59, 225.31, 68.35, 228.65, 71.7, 239.12, 71.7, 242.05, 76.31, 252.1, 77.15, 272.19, 72.54, 280.57]], "area": 1453.2044000000003, "iscrowd": 0, "image_id": 19109, "bbox": [44.49, 224.05, 32.66, 59.03], "category_id": 1, "id": 1292605}, {"segmentation": [[348.93, 254.19, 350.1, 248.34, 351.27, 246.0, 353.37, 243.43, 355.01, 239.46, 355.01, 237.35, 355.94, 234.31, 356.88, 231.5, 357.81, 230.33, 361.09, 229.4, 363.66, 229.63, 367.87, 234.08, 368.34, 236.41, 368.81, 238.52, 368.1, 242.26, 368.57, 245.3, 370.68, 248.81, 369.74, 258.63, 369.97, 268.92, 367.87, 266.58, 367.4, 261.91, 367.4, 257.7, 366.23, 256.06, 363.19, 258.87, 360.85, 257.93, 356.64, 256.53, 351.73, 256.53, 347.29, 256.76]], "area": 461.8997500000001, "iscrowd": 0, "image_id": 19109, "bbox": [347.29, 229.4, 23.39, 39.52], "category_id": 1, "id": 1316008}, {"segmentation": [[168.93, 329.47, 193.52, 322.33, 232.0, 327.09, 235.17, 325.5, 243.9, 326.3, 244.69, 328.28, 284.75, 333.04, 289.51, 329.07, 314.5, 332.25, 318.07, 335.82, 319.66, 337.01, 329.18, 338.59, 345.05, 341.77, 344.65, 352.48, 346.63, 368.34, 347.82, 376.67, 348.22, 390.16, 346.24, 393.73, 335.53, 400.87, 331.56, 403.25, 315.3, 405.23, 311.73, 406.82, 309.74, 406.82, 304.19, 401.27, 247.07, 390.16, 222.87, 385.8, 216.92, 386.59, 213.35, 390.56, 202.25, 386.19, 201.06, 381.43, 191.54, 379.45, 173.69, 374.69, 162.58, 370.33, 165.75, 363.98, 166.15, 343.75, 168.53, 329.87]], "area": 11153.758949999998, "iscrowd": 0, "image_id": 15517, "bbox": [162.58, 322.33, 185.64, 84.49], "category_id": 6, "id": 1364776}, {"segmentation": [[0.67, 311.49, 0.27, 309.9, 20.15, 306.32, 84.15, 311.09, 88.53, 309.5, 98.07, 310.3, 103.63, 310.69, 104.43, 312.28, 106.81, 313.88, 125.7, 316.61, 127.87, 352.09, 115.08, 357.39, 102.29, 358.84, 93.36, 360.29, 83.7, 360.29, 75.98, 362.95, 68.98, 358.36, 65.84, 357.39, 27.95, 348.95, 27.95, 344.84, 27.23, 341.95, 22.4, 338.09, 21.19, 336.88, 21.67, 332.05, 18.54, 329.64, 14.43, 329.16, 7.19, 322.64, 2.37, 320.47, 0.0, 318.54]], "area": 5137.878850000002, "iscrowd": 0, "image_id": 15517, "bbox": [0.0, 306.32, 127.87, 56.63], "category_id": 6, "id": 1365665}, {"segmentation": [[260.49, 276.27, 298.15, 276.27, 315.71, 275.71, 317.68, 289.06, 319.23, 297.49, 319.09, 301.14, 314.87, 302.55, 282.97, 300.3, 242.22, 298.33, 230.7, 296.64, 230.56, 295.24, 231.4, 279.36, 235.06, 278.52]], "area": 2018.6980000000017, "iscrowd": 0, "image_id": 15517, "bbox": [230.56, 275.71, 88.67, 26.84], "category_id": 6, "id": 1365903}, {"segmentation": [[378.75, 308.16, 489.02, 314.05, 489.33, 314.05, 483.76, 293.3, 378.13, 292.37, 374.72, 308.16, 379.68, 308.78]], "area": 1998.5035000000007, "iscrowd": 0, "image_id": 15517, "bbox": [374.72, 292.37, 114.61, 21.68], "category_id": 6, "id": 1365946}, {"segmentation": [[362.48, 276.85, 416.21, 277.05, 414.9, 276.3, 430.1, 275.55, 429.54, 277.42, 445.67, 277.8, 447.92, 279.67, 447.17, 284.55, 432.91, 284.55, 432.54, 287.93, 411.9, 286.62, 408.9, 285.68, 406.46, 286.05, 404.39, 286.8, 393.13, 286.99, 390.32, 284.55, 376.62, 284.55, 376.62, 286.05, 375.12, 286.05, 374.56, 287.18, 367.62, 288.12, 363.3, 288.3, 361.24, 285.49]], "area": 796.1568499999997, "iscrowd": 0, "image_id": 15517, "bbox": [361.24, 275.55, 86.68, 12.75], "category_id": 6, "id": 1366003}, {"segmentation": [[435.5, 283.85, 481.68, 285.56, 481.96, 283.28, 492.79, 283.85, 494.79, 285.56, 500.77, 285.84, 507.61, 285.84, 511.89, 285.84, 515.88, 288.12, 515.88, 310.64, 511.89, 310.93, 506.76, 310.93, 504.48, 311.78, 501.91, 312.35, 498.21, 312.64, 495.64, 310.93, 491.94, 310.36, 488.8, 308.65, 487.09, 295.53, 482.53, 293.54, 466.0, 292.97, 459.73, 292.97, 456.88, 288.69, 452.89, 287.55, 449.18, 287.55, 446.62, 288.41, 441.77, 288.69, 433.5, 287.84, 433.22, 284.13]], "area": 1068.522, "iscrowd": 0, "image_id": 15517, "bbox": [433.22, 283.28, 82.66, 29.36], "category_id": 6, "id": 1366086}, {"segmentation": [[418.97, 289.04, 392.19, 287.74, 389.6, 285.15, 378.79, 285.15, 375.77, 288.61, 367.56, 289.04, 363.67, 288.61, 361.08, 285.58, 344.23, 285.15, 340.78, 287.74, 333.0, 289.04, 326.52, 289.04, 322.2, 297.68, 322.2, 300.27, 334.73, 304.59, 373.18, 306.32, 373.18, 298.54, 373.18, 292.5, 381.39, 292.06, 395.64, 291.63, 411.63, 289.9]], "area": 1033.8563499999989, "iscrowd": 0, "image_id": 15517, "bbox": [322.2, 285.15, 96.77, 21.17], "category_id": 6, "id": 1366254}, {"segmentation": [[89.0, 105.26, 89.28, 96.6, 89.28, 95.39, 94.86, 95.67, 95.05, 105.16, 89.38, 105.53]], "area": 56.959599999999966, "iscrowd": 0, "image_id": 11197, "bbox": [89.0, 95.39, 6.05, 10.14], "category_id": 10, "id": 1381133}, {"segmentation": [[49.94, 109.27, 46.12, 109.56, 43.99, 108.99, 43.43, 107.43, 44.13, 105.16, 44.84, 103.89, 44.84, 103.18, 42.72, 102.19, 43.57, 99.78, 44.98, 99.07, 43.43, 97.37, 42.86, 97.09, 42.43, 94.26, 46.97, 94.26, 50.37, 94.82]], "area": 97.67039999999994, "iscrowd": 0, "image_id": 11197, "bbox": [42.43, 94.26, 7.94, 15.3], "category_id": 10, "id": 1382049}, {"segmentation": [[7.27, 157.1, 12.22, 145.79, 12.6, 143.08, 15.03, 140.84, 17.55, 142.8, 18.02, 145.23, 21.29, 147.1, 21.48, 150.37, 21.48, 152.24, 24.19, 153.55, 24.28, 156.82, 24.19, 161.77, 23.44, 162.99, 23.16, 164.39, 20.92, 171.4, 16.8, 174.29, 12.69, 173.64, 10.26, 168.22, 10.17, 165.79, 5.96, 165.6, 4.56, 163.27, 5.12, 161.58]], "area": 400.6877999999999, "iscrowd": 0, "image_id": 11197, "bbox": [4.56, 140.84, 19.72, 33.45], "category_id": 27, "id": 1422630}, {"segmentation": [[115.44, 367.25, 109.22, 372.36, 106.78, 370.58, 91.9, 352.37, 90.12, 346.6, 89.45, 335.94, 89.45, 329.05, 93.45, 322.17, 102.34, 321.5, 111.44, 321.06, 118.77, 323.28, 118.77, 330.61, 118.55, 337.05, 116.99, 347.71, 116.99, 356.15, 116.99, 363.92]], "area": 1161.5609999999997, "iscrowd": 0, "image_id": 13659, "bbox": [89.45, 321.06, 29.32, 51.3], "category_id": 47, "id": 1507735}, {"segmentation": [[75.51, 474.61, 0.0, 474.61, 0.0, 357.03, 72.27, 398.02, 83.06, 408.81, 87.37, 415.28, 62.56, 440.09, 74.43, 473.53]], "area": 7101.842100000003, "iscrowd": 0, "image_id": 14226, "bbox": [0.0, 357.03, 87.37, 117.58], "category_id": 62, "id": 1579697}, {"segmentation": [[600.59, 225.25, 609.8, 155.19, 603.66, 131.16, 595.99, 105.08, 595.99, 80.02, 599.06, 62.63, 618.49, 36.55, 640.0, 27.86, 639.97, 142.92, 640.0, 231.39, 628.72, 224.74, 621.05, 220.14, 614.4, 218.6]], "area": 6957.147500000005, "iscrowd": 0, "image_id": 14226, "bbox": [595.99, 27.86, 44.01, 203.53], "category_id": 62, "id": 1582509}, {"segmentation": [[239.46, 295.55, 257.8, 332.22, 257.8, 332.22, 429.3, 366.74, 527.46, 319.28, 636.4, 351.64, 639.64, 470.29, 445.48, 470.29, 154.25, 352.72, 225.44, 296.63, 225.44, 296.63]], "area": 46650.441000000006, "iscrowd": 0, "image_id": 14226, "bbox": [154.25, 295.55, 485.39, 174.74], "category_id": 67, "id": 1619249}, {"segmentation": [[231.1, 79.09, 194.92, 79.7, 196.15, 115.88, 233.55, 115.06, 234.98, 78.88]], "area": 1398.5539, "iscrowd": 0, "image_id": 13659, "bbox": [194.92, 78.88, 40.06, 37.0], "category_id": 72, "id": 1625798}, {"segmentation": [[286.07, 230.44, 290.04, 225.06, 291.91, 223.19, 291.91, 220.62, 291.21, 217.11, 291.45, 214.77, 293.32, 212.67, 297.76, 212.9, 301.74, 221.08, 300.33, 226.0, 302.44, 228.57, 304.54, 241.2, 300.8, 259.91, 298.7, 266.92, 298.7, 269.73, 291.91, 268.09, 288.87, 268.56, 289.11, 259.91, 287.7, 257.57, 288.64, 250.79, 286.53, 237.46]], "area": 729.4708500000006, "iscrowd": 0, "image_id": 19109, "bbox": [286.07, 212.67, 18.47, 57.06], "category_id": 1, "id": 1680320}, {"segmentation": [[305.89, 193.68, 313.93, 204.19, 321.65, 216.86, 325.36, 228.92, 328.14, 237.26, 326.6, 240.04, 322.27, 240.04, 306.82, 240.97, 304.66, 239.12, 305.89, 198.63]], "area": 672.3748999999993, "iscrowd": 0, "image_id": 18837, "bbox": [304.66, 193.68, 23.48, 47.29], "category_id": 1, "id": 1682653}, {"segmentation": [[448.65, 265.68, 449.17, 262.28, 445.64, 254.84, 447.34, 253.4, 446.95, 244.25, 451.52, 241.9, 456.1, 245.43, 455.31, 252.88, 453.74, 254.58, 450.87, 253.14, 448.0, 252.09, 447.6, 256.54, 452.31, 261.37, 451.52, 263.33, 449.82, 265.68, 447.87, 266.07]], "area": 112.45500000000025, "iscrowd": 0, "image_id": 19109, "bbox": [445.64, 241.9, 10.46, 24.17], "category_id": 1, "id": 1691370}, {"segmentation": [[554.91, 244.23, 560.7, 239.34, 562.04, 237.56, 562.63, 235.63, 562.93, 231.92, 563.37, 230.29, 565.45, 228.36, 569.45, 227.77, 571.83, 230.14, 571.97, 233.55, 572.27, 237.41, 571.68, 238.45, 577.46, 242.16, 578.35, 246.76, 581.47, 252.84, 581.77, 254.03, 581.77, 254.47, 578.95, 251.8, 577.17, 250.76, 574.65, 250.32, 569.75, 251.65, 569.16, 254.77, 569.75, 256.1, 571.83, 259.07, 572.57, 259.66, 575.83, 259.66, 575.68, 261.29, 574.65, 263.07, 569.6, 262.78, 568.41, 260.85, 567.82, 258.33, 564.41, 258.03, 563.07, 258.18, 562.93, 259.51, 562.93, 259.96, 555.36, 255.95, 552.84, 252.54, 552.39, 245.12, 555.06, 244.83]], "area": 509.1810999999998, "iscrowd": 0, "image_id": 19109, "bbox": [552.39, 227.77, 29.38, 35.3], "category_id": 1, "id": 1692596}, {"segmentation": [[540.75, 237.85, 542.73, 235.08, 545.3, 233.69, 546.89, 233.29, 548.67, 233.29, 552.63, 234.68, 554.22, 235.67, 554.61, 236.86, 554.61, 239.04, 554.61, 240.42, 554.02, 242.2, 554.22, 243.39, 554.22, 243.79, 552.63, 244.58, 551.05, 246.36, 549.46, 246.76, 548.87, 246.56, 548.28, 247.35]], "area": 130.22425000000015, "iscrowd": 0, "image_id": 19109, "bbox": [540.75, 233.29, 13.86, 14.06], "category_id": 1, "id": 1699564}, {"segmentation": [[598.9, 271.39, 599.38, 265.98, 599.99, 264.18, 599.99, 261.41, 599.5, 260.69, 597.22, 260.33, 596.5, 259.61, 596.74, 257.81, 597.34, 251.79, 598.54, 243.62, 599.38, 239.41, 599.87, 235.08, 601.19, 231.84, 598.66, 231.12, 597.1, 229.55, 596.74, 226.19, 596.74, 224.74, 596.74, 223.3, 598.3, 221.86, 600.11, 221.74, 602.15, 222.34, 603.71, 223.06, 604.19, 224.5, 604.67, 226.07, 606.6, 227.63, 606.84, 229.07, 610.32, 232.44, 611.41, 234.6, 610.93, 238.09, 609.96, 238.69, 604.67, 239.29, 601.07, 240.61, 600.47, 243.14, 600.95, 260.57, 604.19, 262.01, 606.96, 262.25, 604.31, 263.34, 603.47, 263.94, 603.11, 265.62, 602.99, 267.66, 602.75, 270.91, 602.63, 273.8]], "area": 274.67395000000084, "iscrowd": 0, "image_id": 19109, "bbox": [596.5, 221.74, 14.91, 52.06], "category_id": 1, "id": 1704009}, {"segmentation": [[25.4, 261.91, 25.96, 242.3, 27.61, 233.73, 32.31, 224.9, 35.9, 223.52, 41.15, 224.62, 45.01, 227.93, 45.29, 230.97, 45.29, 236.5, 47.78, 245.34, 47.78, 247.54, 46.95, 250.03, 46.12, 258.32, 44.74, 258.59, 39.21, 256.94, 37.56, 258.87, 35.9, 261.63, 35.35, 263.01, 35.35, 264.67, 30.1, 263.29, 26.23, 262.46]], "area": 691.7104499999999, "iscrowd": 0, "image_id": 19109, "bbox": [25.4, 223.52, 22.38, 41.15], "category_id": 1, "id": 1705615}, {"segmentation": [[366.56, 230.1, 369.6, 227.31, 375.69, 228.07, 379.49, 235.68, 382.79, 235.42, 390.14, 244.3, 386.84, 250.13, 388.87, 266.36, 382.03, 274.22, 380.76, 281.58, 377.46, 271.69, 370.87, 269.15, 370.61, 250.13, 368.84, 244.04, 368.33, 236.18, 368.33, 236.18]], "area": 724.9064999999999, "iscrowd": 0, "image_id": 19109, "bbox": [366.56, 227.31, 23.58, 54.27], "category_id": 1, "id": 1726831}, {"segmentation": [[157.76, 232.42, 115.32, 232.73, 118.11, 214.45, 126.78, 206.71, 125.54, 187.5, 124.62, 181.62, 129.88, 173.87, 141.96, 172.01, 150.64, 175.73, 157.14, 186.57, 160.86, 205.78, 163.95, 210.43, 157.76, 231.18]], "area": 2118.3906999999995, "iscrowd": 0, "image_id": 18837, "bbox": [115.32, 172.01, 48.63, 60.72], "category_id": 1, "id": 1728910}, {"segmentation": [[8.34, 246.33, 12.48, 258.29, 9.26, 267.49, 18.0, 295.09, 17.08, 329.13, 16.62, 372.78, 2.36, 371.86, 0.0, 289.98, 0.0, 245.37, 0.06, 236.63, 2.82, 235.25]], "area": 1887.4875000000002, "iscrowd": 0, "image_id": 19109, "bbox": [0.0, 235.25, 18.0, 137.53], "category_id": 1, "id": 1757640}, {"segmentation": [[475.24, 250.58, 476.96, 246.99, 481.7, 247.14, 482.42, 250.87]], "area": 21.92360000000008, "iscrowd": 0, "image_id": 19109, "bbox": [475.24, 246.99, 7.18, 3.88], "category_id": 1, "id": 1758059}, {"segmentation": [[602.12, 254.64, 611.21, 256.59, 624.18, 281.24, 624.5, 301.67, 614.45, 294.21, 606.02, 289.35, 592.39, 289.67, 591.1, 278.32, 599.2, 265.67, 599.53, 255.94]], "area": 911.3174999999992, "iscrowd": 0, "image_id": 18837, "bbox": [591.1, 254.64, 33.4, 47.03], "category_id": 3, "id": 1770129}, {"segmentation": [[582.36, 309.67, 577.88, 305.88, 575.12, 298.64, 572.37, 290.71, 568.57, 284.16, 565.82, 280.03, 560.65, 271.75, 559.27, 269.0, 565.82, 263.14, 567.2, 261.41, 568.92, 256.93, 568.92, 252.11, 569.26, 250.73, 575.47, 250.04, 583.74, 255.55, 596.84, 278.3, 604.07, 290.37, 608.9, 297.26, 611.66, 304.5, 612.35, 311.04, 612.0, 317.59, 611.66, 318.97, 606.83, 316.21, 600.28, 313.11, 596.15, 312.08, 596.49, 323.45, 594.77, 325.18, 590.29, 325.18, 587.19, 325.18, 585.12, 322.07, 583.74, 317.94]], "area": 1958.5072499999997, "iscrowd": 0, "image_id": 19109, "bbox": [559.27, 250.04, 53.08, 75.14], "category_id": 4, "id": 1789837}, {"segmentation": [[592.04, 269.01, 603.21, 274.04, 612.43, 278.51, 612.43, 288.0, 610.19, 290.52, 613.27, 295.54, 619.97, 299.73, 620.25, 308.11, 614.66, 313.42, 613.27, 314.53, 610.19, 300.01, 600.7, 286.05, 594.56, 272.92]], "area": 385.6857499999986, "iscrowd": 0, "image_id": 19109, "bbox": [592.04, 269.01, 28.21, 45.52], "category_id": 4, "id": 1790834}, {"segmentation": [[632.67, 294.79, 637.8, 297.17, 639.45, 295.52, 639.08, 281.95, 629.0, 271.14, 624.42, 264.17, 617.82, 260.87, 601.32, 264.9, 600.4, 269.85, 615.8, 281.59]], "area": 678.7150000000017, "iscrowd": 0, "image_id": 19109, "bbox": [600.4, 260.87, 39.05, 36.3], "category_id": 4, "id": 1791159}, {"segmentation": [[324.69, 312.44, 334.91, 318.05, 336.23, 320.69, 338.54, 326.29, 340.52, 331.57, 340.52, 337.84, 339.53, 342.12, 336.56, 344.43, 333.26, 346.74, 329.31, 349.38, 322.38, 349.71, 318.42, 349.05, 316.77, 345.09, 316.11, 339.81, 315.79, 335.86, 315.79, 332.56, 315.13, 331.57, 313.81, 327.28, 318.09, 325.3, 319.41, 323.98, 320.4, 319.37, 319.08, 316.4, 315.79, 311.12, 312.49, 309.14, 308.2, 305.19, 303.58, 301.89, 296.33, 296.61, 289.07, 284.74, 283.79, 270.89, 290.06, 266.93, 286.43, 258.35, 285.11, 252.42, 290.06, 250.77, 291.71, 253.08, 291.71, 256.04, 291.71, 259.67, 293.69, 266.6, 297.32, 269.57, 300.28, 271.55, 303.91, 273.85, 304.24, 279.13, 304.9, 281.77, 306.88, 283.09, 310.84, 286.72, 310.84, 287.71, 312.82, 290.67, 316.77, 294.3, 320.07, 298.92, 322.05, 301.56, 324.03, 305.52, 325.02, 307.49, 326.01, 309.14, 326.01, 309.8]], "area": 1589.229399999998, "iscrowd": 0, "image_id": 19109, "bbox": [283.79, 250.77, 56.73, 98.94], "category_id": 4, "id": 1791194}, {"segmentation": [[238.53, 184.3, 238.53, 184.3, 261.63, 189.91, 262.25, 250.78, 257.26, 250.78, 255.39, 260.76, 197.33, 259.2, 192.34, 256.4, 194.21, 218.63, 226.36, 183.67, 238.84, 182.74, 238.84, 182.74]], "area": 4511.170749999998, "iscrowd": 0, "image_id": 15440, "bbox": [192.34, 182.74, 69.91, 78.02], "category_id": 6, "id": 1794201}, {"segmentation": [[142.33, 253.24, 120.77, 253.54, 114.19, 243.96, 113.59, 192.16, 118.68, 191.86, 144.73, 217.31]], "area": 1488.5892499999998, "iscrowd": 0, "image_id": 15440, "bbox": [113.59, 191.86, 31.14, 61.68], "category_id": 6, "id": 1794431}, {"segmentation": [[116.14, 280.01, 116.32, 267.24, 128.04, 265.84, 128.04, 265.49, 133.11, 264.79, 134.86, 265.84, 135.39, 265.84, 136.44, 264.79, 142.56, 264.79, 142.73, 265.14, 143.61, 265.14, 144.13, 264.09, 149.38, 263.74, 151.66, 265.31, 159.36, 264.09, 165.13, 263.04, 169.68, 264.79, 171.25, 267.76, 172.13, 268.81, 171.25, 269.69, 170.03, 269.34, 170.03, 274.41, 161.28, 277.56, 146.76, 279.14, 145.88, 281.59, 142.91, 281.94, 142.56, 282.64, 135.04, 282.46, 126.29, 282.11]], "area": 811.9457999999998, "iscrowd": 0, "image_id": 15517, "bbox": [116.14, 263.04, 55.99, 19.6], "category_id": 6, "id": 1794468}, {"segmentation": [[403.06, 276.34, 402.54, 263.45, 338.57, 263.45, 338.57, 280.47, 351.47, 279.95, 355.08, 282.53, 358.18, 282.53, 361.27, 280.47, 362.3, 276.86]], "area": 943.6624500000008, "iscrowd": 0, "image_id": 15517, "bbox": [338.57, 263.45, 64.49, 19.08], "category_id": 6, "id": 1794789}, {"segmentation": [[273.56, 262.83, 273.71, 276.16, 336.23, 275.12, 336.52, 271.57, 337.56, 269.94, 337.56, 269.64, 336.23, 268.76, 336.23, 265.06, 273.88, 263.0]], "area": 729.8530000000011, "iscrowd": 0, "image_id": 15517, "bbox": [273.56, 262.83, 64.0, 13.33], "category_id": 6, "id": 1794891}, {"segmentation": [[519.42, 302.18, 519.42, 290.61, 516.37, 285.73, 502.97, 285.73, 493.83, 285.13, 489.56, 282.08, 492.0, 276.6, 492.61, 272.94, 527.95, 275.38, 535.26, 281.47, 538.3, 288.78, 536.48, 302.79, 518.81, 305.23]], "area": 812.3689500000012, "iscrowd": 0, "image_id": 15517, "bbox": [489.56, 272.94, 48.74, 32.29], "category_id": 6, "id": 1794936}, {"segmentation": [[232.0, 297.54, 190.2, 292.36, 190.57, 286.44, 173.56, 283.86, 163.57, 293.1, 145.82, 288.66, 147.66, 276.83, 182.06, 275.72, 236.8, 274.98, 236.8, 277.94, 230.15, 279.79]], "area": 1340.9209000000003, "iscrowd": 0, "image_id": 15517, "bbox": [145.82, 274.98, 90.98, 22.56], "category_id": 6, "id": 1795152}, {"segmentation": [[312.15, 194.95, 308.63, 199.12, 304.72, 198.6, 299.12, 193.65, 290.39, 190.39, 289.61, 165.63, 332.61, 170.71, 333.26, 176.83, 350.6, 178.66, 355.16, 190.13, 355.42, 199.77, 352.68, 199.25, 350.34, 202.9, 344.99, 203.29, 341.73, 200.29, 339.65, 197.82]], "area": 1642.8896500000003, "iscrowd": 0, "image_id": 11615, "bbox": [289.61, 165.63, 65.81, 37.66], "category_id": 8, "id": 1799592}, {"segmentation": [[222.88, 410.38, 226.87, 398.83, 234.43, 393.06, 240.2, 386.39, 245.98, 381.06, 256.64, 389.5, 262.86, 397.94, 261.52, 408.61, 261.52, 415.71, 269.96, 427.0, 245.09, 425.49, 234.43, 422.82, 225.1, 411.71]], "area": 1281.5749999999998, "iscrowd": 0, "image_id": 15751, "bbox": [222.88, 381.06, 47.08, 45.94], "category_id": 16, "id": 1814016}, {"segmentation": [[131.0, 328.79, 160.49, 251.58, 186.52, 185.65, 185.65, 162.23, 173.51, 160.49, 163.1, 169.17, 125.79, 184.78, 80.68, 170.04, 86.75, 161.36, 117.98, 144.01, 169.17, 128.39, 188.25, 110.18, 194.33, 94.56, 188.25, 69.4, 191.72, 44.24, 212.55, 49.45, 217.75, 73.74, 241.17, 58.99, 235.1, 40.77, 253.32, 34.7, 268.07, 60.73, 270.67, 74.61, 275.88, 83.28, 275.88, 90.22, 296.7, 117.98, 302.77, 124.92, 303.64, 133.6, 304.5, 143.14, 317.52, 143.14, 340.94, 152.69, 360.03, 170.9, 360.03, 177.84, 340.94, 185.65, 307.97, 175.24, 288.89, 164.83, 281.95, 161.36, 289.76, 179.58, 297.56, 195.19, 294.09, 202.14, 287.15, 220.35, 269.8, 223.82, 253.32, 229.03, 236.84, 235.1, 233.37, 243.78, 217.75, 294.09, 204.74, 334.0]], "area": 30882.77995, "iscrowd": 0, "image_id": 17031, "bbox": [80.68, 34.7, 279.35, 299.3], "category_id": 25, "id": 1823362}, {"segmentation": [[371.32, 254.35, 373.28, 254.95, 372.98, 257.51, 375.99, 257.51, 376.59, 255.25, 381.11, 256.0, 379.61, 264.29, 370.12, 263.54]], "area": 77.43375000000034, "iscrowd": 0, "image_id": 19109, "bbox": [370.12, 254.35, 10.99, 9.94], "category_id": 31, "id": 1838054}, {"segmentation": [[314.1, 294.94, 320.01, 294.7, 323.08, 290.69, 325.68, 290.45, 326.86, 292.1, 324.73, 295.41, 332.05, 298.24, 331.58, 300.13, 329.93, 301.55, 334.18, 305.8, 334.42, 308.4, 330.17, 312.18, 327.09, 310.05, 320.95, 310.05, 314.34, 303.44, 311.98, 298.48, 313.63, 294.23]], "area": 276.5560500000005, "iscrowd": 0, "image_id": 14226, "bbox": [311.98, 290.45, 22.44, 21.73], "category_id": 53, "id": 1906808}, {"segmentation": [[310.5, 220.77, 326.47, 217.81, 331.79, 220.77, 319.96, 254.47, 314.05, 281.67, 313.46, 305.91, 307.55, 310.64, 302.23, 302.95, 304.0, 276.94, 291.58, 273.39, 282.71, 268.66, 282.12, 259.79, 288.63, 252.1]], "area": 1981.2406999999996, "iscrowd": 0, "image_id": 13659, "bbox": [282.12, 217.81, 49.67, 92.83], "category_id": 62, "id": 1934026}, {"segmentation": [[567.8, 229.98, 575.32, 233.56, 582.12, 261.12, 586.06, 281.89, 587.49, 284.75, 589.64, 279.03, 587.49, 263.99, 582.84, 247.52, 578.9, 232.48, 591.07, 232.48, 593.58, 223.89, 601.45, 204.2, 607.54, 173.05, 610.76, 164.81, 603.24, 160.52, 595.37, 165.53, 589.64, 172.33, 582.12, 175.55, 574.24, 178.78, 572.81, 178.78, 572.09, 184.15, 572.81, 190.59, 572.45, 191.31, 573.53, 193.81, 575.32, 194.53, 577.11, 195.6, 577.82, 200.26, 578.9, 207.78, 578.9, 213.86, 573.17, 216.12, 567.8, 221.84, 567.44, 228.65]], "area": 1927.1629000000012, "iscrowd": 0, "image_id": 13659, "bbox": [567.44, 160.52, 43.32, 124.23], "category_id": 62, "id": 1941594}, {"segmentation": [[492.69, 78.86, 494.08, 75.24, 498.55, 76.35, 500.5, 78.86, 504.41, 79.7, 508.87, 81.1, 512.78, 83.61, 515.57, 88.07, 517.52, 92.53, 518.63, 96.16, 517.24, 101.74, 515.84, 108.16, 515.84, 114.01, 517.24, 119.59, 517.8, 125.73, 517.52, 132.98, 517.24, 137.45, 516.68, 142.75, 515.57, 149.44, 515.84, 153.07, 515.57, 154.46, 500.5, 152.23, 499.66, 148.05, 499.39, 141.35, 496.6, 135.22, 492.41, 132.15, 488.51, 127.96, 485.99, 123.5, 484.6, 119.59, 482.93, 113.18, 480.14, 109.83]], "area": 1996.0132, "iscrowd": 0, "image_id": 13659, "bbox": [480.14, 75.24, 38.49, 79.22], "category_id": 1, "id": 2008612}, {"segmentation": [[81.82, 227.99, 80.11, 200.55, 78.39, 182.84, 75.54, 177.7, 71.53, 174.84, 58.96, 184.55, 56.11, 195.41, 57.25, 210.84, 55.53, 223.41, 69.82, 230.27, 73.25, 232.56]], "area": 1149.92285, "iscrowd": 0, "image_id": 19109, "bbox": [55.53, 174.84, 26.29, 57.72], "category_id": 1, "id": 2025750}, {"segmentation": [[474.84, 304.99, 472.79, 300.03, 472.79, 287.74, 469.51, 282.41, 457.22, 269.3, 457.22, 265.2, 460.91, 258.65, 472.79, 261.51, 480.58, 261.51, 485.08, 254.55, 496.15, 255.78, 496.96, 251.68, 503.93, 260.28, 513.35, 280.77, 514.58, 295.52, 513.35, 299.62, 526.06, 315.19, 527.28, 323.38, 526.47, 329.12, 518.27, 334.45, 512.94, 336.08, 504.75, 336.9, 501.88, 335.27, 496.96, 332.81, 494.1, 327.89, 489.18, 320.51, 489.18, 315.6, 488.77, 313.96, 482.62, 310.68]], "area": 3163.526349999999, "iscrowd": 0, "image_id": 19109, "bbox": [457.22, 251.68, 70.06, 85.22], "category_id": 4, "id": 2048655}, {"segmentation": [[251.53, 270.25, 186.01, 270.61, 182.43, 272.76, 242.94, 273.48, 242.58, 278.13, 254.03, 276.7]], "area": 228.70974999999976, "iscrowd": 0, "image_id": 15517, "bbox": [182.43, 270.25, 71.6, 7.88], "category_id": 6, "id": 2050429}, {"segmentation": [[558.97, 107.31, 558.56, 116.6, 571.73, 116.73, 571.73, 106.89, 559.25, 106.89]], "area": 126.6013000000003, "iscrowd": 0, "image_id": 11197, "bbox": [558.56, 106.89, 13.17, 9.84], "category_id": 10, "id": 2058468}, {"segmentation": [[578.63, 152.53, 581.36, 152.35, 584.1, 153.08, 585.47, 153.72, 589.12, 154.45, 592.04, 156.0, 593.95, 157.82, 595.23, 161.29, 593.86, 166.03, 590.85, 166.86, 589.03, 165.12, 587.11, 161.02, 584.65, 156.46, 583.01, 154.17, 581.73, 153.17, 578.72, 153.08]], "area": 92.62910000000045, "iscrowd": 0, "image_id": 11197, "bbox": [578.63, 152.35, 16.6, 14.51], "category_id": 27, "id": 2073987}, {"segmentation": [[300.58, 190.41, 304.62, 207.9, 302.47, 222.96, 303.54, 229.42, 304.62, 236.68, 307.58, 239.64, 308.65, 245.56, 314.3, 239.64, 316.99, 214.08, 320.22, 182.07, 319.68, 170.24, 319.68, 161.63, 319.41, 141.19, 316.45, 130.43, 309.73, 128.81, 307.85, 157.86, 307.85, 174.54, 306.77, 180.19, 300.58, 183.42]], "area": 1424.9449000000006, "iscrowd": 0, "image_id": 11197, "bbox": [300.58, 128.81, 19.64, 116.75], "category_id": 1, "id": 2157164}, {"segmentation": [[237.42, 305.81, 251.61, 343.23, 258.06, 369.03, 264.52, 392.26, 256.77, 412.9, 255.48, 430.97, 256.77, 452.9, 256.77, 465.81, 263.23, 481.29, 267.1, 486.45, 276.13, 489.03, 280.0, 499.35, 283.87, 507.1, 280.0, 510.97, 277.42, 514.84, 273.55, 516.13, 267.1, 518.71, 260.65, 521.29, 261.94, 526.45, 265.81, 534.19, 281.29, 541.94, 286.45, 536.77, 287.74, 531.61, 295.48, 521.29, 304.52, 509.68, 312.26, 510.97, 326.45, 507.1, 335.48, 494.19, 335.48, 486.45, 339.35, 473.55, 350.97, 464.52, 350.97, 450.32, 349.68, 437.42, 347.1, 428.39, 350.97, 423.23, 362.58, 425.81, 366.45, 425.81, 370.32, 421.94, 369.03, 410.32, 362.58, 405.16, 353.55, 397.42, 352.26, 393.55, 345.81, 369.03, 344.52, 358.71, 341.94, 343.23, 338.06, 325.16, 331.61, 308.39, 331.61, 287.74, 332.9, 278.71, 335.48, 268.39, 338.06, 252.9, 340.65, 230.97, 341.94, 221.94, 347.1, 218.06, 349.68, 209.03, 350.97, 206.45, 350.97, 196.13, 361.29, 192.26, 378.06, 200.0, 380.65, 201.29, 387.1, 206.45, 389.68, 206.45, 389.68, 206.45, 390.97, 206.45, 401.29, 190.97, 405.16, 184.52, 405.16, 184.52, 405.16, 184.52, 405.16, 175.48, 405.16, 175.48, 402.58, 172.9, 398.71, 166.45, 393.55, 160.0, 384.52, 156.13, 381.94, 154.84, 369.03, 138.06, 367.74, 132.9, 361.29, 113.55, 361.29, 113.55, 339.35, 107.1, 325.16, 105.81, 320.0, 108.39, 313.55, 122.58, 312.26, 130.32, 300.65, 139.35, 291.61, 141.94, 281.29, 145.81, 278.71, 150.97, 289.03, 174.19, 289.03, 174.19, 291.61, 167.74, 290.32, 176.77, 281.29, 183.23, 278.71, 185.81, 268.39, 190.97, 252.9, 200.0, 243.87, 211.61, 242.58, 212.9, 237.42, 221.94, 232.26, 233.55, 229.68, 243.87, 225.81, 245.16, 194.84, 245.16, 193.55, 245.16, 181.94, 242.58, 176.77, 242.58, 174.19, 242.58, 170.32, 237.42, 161.29, 232.26, 154.84, 237.42, 154.84, 243.87, 154.84, 251.61, 158.71, 256.77, 162.58, 263.23, 162.58, 264.52, 175.48, 270.97, 187.1, 278.71, 194.84, 282.58, 205.16, 289.03, 218.06, 294.19, 234.84, 301.94]], "area": 40990.855599999995, "iscrowd": 0, "image_id": 17029, "bbox": [154.84, 105.81, 250.32, 436.13], "category_id": 18, "id": 12032}, {"segmentation": [[276.13, 29.12, 332.22, 52.85, 365.66, 78.74, 381.84, 93.84, 420.67, 99.24, 468.13, 80.9, 468.13, 118.65, 464.9, 144.54, 484.31, 173.66, 468.13, 199.55, 443.33, 227.6, 439.01, 243.78, 426.07, 285.84, 399.1, 318.2, 416.36, 415.28, 423.91, 432.54, 413.12, 454.11, 385.08, 439.01, 374.29, 431.46, 346.25, 443.33, 330.07, 434.7, 321.44, 407.73, 305.26, 373.21, 276.13, 326.83, 268.58, 286.92, 251.33, 254.56, 229.75, 239.46, 239.46, 266.43, 221.12, 281.53, 196.31, 286.92, 156.4, 307.42, 83.06, 304.18, 32.36, 254.56, 35.6, 184.45, 50.7, 114.34, 96.0, 73.35, 146.7, 30.2, 215.73, 20.49, 284.76, 29.12]], "area": 113647.79000000001, "iscrowd": 0, "image_id": 15497, "bbox": [32.36, 20.49, 451.95, 433.62], "category_id": 17, "id": 51186}, {"segmentation": [[155.06, 305.62, 184.27, 348.31, 201.12, 359.55, 228.09, 369.66, 264.04, 313.48, 270.79, 294.38, 282.02, 306.74, 276.4, 352.81, 237.08, 389.89, 247.19, 500.0, 194.38, 497.75, 215.73, 455.06, 195.51, 383.15, 153.93, 342.7, 142.7, 319.1]], "area": 8496.704749999995, "iscrowd": 0, "image_id": 19924, "bbox": [142.7, 294.38, 139.32, 205.62], "category_id": 32, "id": 295058}, {"segmentation": [[182.65, 297.71, 211.42, 314.97, 238.74, 283.33, 241.62, 316.4, 230.11, 336.54, 235.87, 424.27, 228.67, 510.56, 182.65, 494.74, 201.35, 444.4, 201.35, 384.0, 220.04, 342.29, 215.73, 327.91, 179.78, 309.21, 178.34, 300.58, 182.65, 296.27]], "area": 6695.2979, "iscrowd": 0, "image_id": 20333, "bbox": [178.34, 283.33, 63.28, 227.23], "category_id": 32, "id": 295083}, {"segmentation": [[242.61, 301.14, 262.6, 301.91, 277.98, 312.68, 279.52, 348.82, 300.28, 394.19, 327.96, 450.33, 341.04, 518.49, 341.04, 552.05, 345.65, 637.9, 267.98, 640.0, 263.37, 554.08, 256.45, 457.71, 244.91, 405.95, 239.53, 386.72, 242.61, 347.01, 224.15, 321.63]], "area": 22546.875699999997, "iscrowd": 0, "image_id": 16598, "bbox": [224.15, 301.14, 121.5, 338.86], "category_id": 32, "id": 295130}, {"segmentation": [[347.84, 175.94, 352.46, 193.76, 361.04, 215.54, 348.5, 235.34, 335.96, 257.78, 318.14, 286.82, 306.26, 311.9, 293.06, 338.96, 276.56, 377.24, 268.64, 344.24, 286.46, 291.34, 318.14, 217.15, 325.4, 194.71, 331.34, 181.51]], "area": 5500.258500000002, "iscrowd": 0, "image_id": 18770, "bbox": [268.64, 175.94, 92.4, 201.3], "category_id": 32, "id": 295246}, {"segmentation": [[208.64, 206.36, 201.45, 202.76, 195.45, 207.56, 201.45, 219.55, 207.45, 227.95, 195.45, 307.09, 244.62, 424.6, 256.61, 359.85, 224.23, 229.14, 215.84, 200.37, 202.65, 197.97]], "area": 6649.593500000003, "iscrowd": 0, "image_id": 11051, "bbox": [195.45, 197.97, 61.16, 226.63], "category_id": 32, "id": 298197}, {"segmentation": [[404.36, 150.52, 373.38, 150.52, 347.36, 151.34, 341.17, 156.3, 340.75, 188.51, 342.41, 267.4, 353.14, 264.51, 356.45, 254.6, 372.56, 248.4, 412.62, 250.05, 412.62, 247.16, 396.1, 245.1, 379.16, 242.62, 370.49, 236.84, 368.43, 228.99, 373.38, 221.97, 395.27, 218.25, 412.62, 220.73, 413.03, 192.23, 413.86, 159.19, 408.9, 152.17, 404.45, 150.67]], "area": 6270.100049999997, "iscrowd": 0, "image_id": 16598, "bbox": [340.75, 150.52, 73.11, 116.88], "category_id": 77, "id": 325853}, {"segmentation": [[378.25, 28.76, 404.13, 25.89, 440.09, 31.64, 497.62, 58.97, 522.07, 58.97, 547.96, 57.53, 555.15, 80.54, 563.78, 106.43, 569.53, 112.18, 565.21, 138.07, 543.64, 148.13, 501.93, 149.57, 480.36, 149.57, 444.4, 178.34, 414.2, 172.58, 409.89, 158.2, 378.25, 151.01, 396.94, 128.0, 417.08, 89.17, 376.81, 77.66, 339.42, 73.35, 379.69, 30.2], [113.62, 87.73, 80.54, 96.36, 64.72, 104.99, 60.4, 123.69, 56.09, 138.07, 56.09, 148.13, 76.22, 159.64, 97.8, 162.52, 110.74, 172.58, 143.82, 168.27, 165.39, 168.27, 171.15, 151.01]], "area": 26342.933849999998, "iscrowd": 0, "image_id": 17029, "bbox": [56.09, 25.89, 513.44, 152.45], "category_id": 3, "id": 359663}, {"segmentation": [[156.28, 250.48, 154.09, 249.02, 157.01, 245.36, 160.3, 241.34, 172.0, 243.53, 173.09, 249.38, 177.84, 253.4, 175.65, 255.59, 168.71, 256.32, 170.9, 261.07, 168.34, 263.63, 179.3, 273.13, 181.5, 288.84, 182.23, 308.94, 180.76, 312.23, 176.75, 314.42, 177.48, 318.81, 176.38, 319.9, 178.57, 322.82, 178.94, 326.84, 193.19, 363.02, 183.69, 369.59, 182.23, 366.31, 180.76, 353.52, 167.61, 350.59, 170.17, 368.5, 155.55, 369.96, 146.42, 367.77, 146.42, 365.94, 155.92, 364.84, 161.4, 360.09, 159.57, 352.06, 157.01, 350.59, 145.69, 349.13, 143.13, 346.94, 152.26, 274.23, 158.11, 265.82, 152.99, 262.17, 152.99, 256.69, 156.28, 252.3, 156.28, 250.84]], "area": 3545.2962500000003, "iscrowd": 0, "image_id": 12280, "bbox": [143.13, 241.34, 50.06, 128.62], "category_id": 1, "id": 426225}, {"segmentation": [[8.67, 625.91, 24.56, 514.67, 20.23, 406.32, 28.89, 323.97, 95.35, 267.63, 111.24, 191.06, 138.69, 102.93, 173.36, 23.48, 257.16, 17.7, 290.38, 42.26, 294.72, 89.93, 313.5, 157.83, 319.28, 253.18, 314.94, 305.19, 348.17, 282.08, 385.73, 227.18, 440.63, 228.62, 459.41, 295.08, 466.64, 362.98, 478.0, 404.88, 478.0, 543.57, 401.63, 520.45, 397.29, 611.47, 405.96, 624.47]], "area": 197548.36384999997, "iscrowd": 0, "image_id": 16598, "bbox": [8.67, 17.7, 469.33, 608.21], "category_id": 1, "id": 430275}, {"segmentation": [[86.49, 530.45, 87.93, 554.95, 77.84, 552.07, 76.4, 533.33, 70.63, 547.75, 60.54, 543.42, 51.89, 517.48, 83.6, 481.44, 92.25, 386.31, 82.16, 363.24, 103.78, 347.39, 118.2, 285.41, 128.29, 266.67, 142.7, 256.58, 131.17, 236.4, 121.08, 229.19, 100.9, 210.45, 98.02, 196.04, 110.99, 161.44, 121.08, 129.73, 142.7, 113.87, 164.32, 106.67, 198.92, 110.99, 232.07, 128.29, 243.6, 144.14, 265.23, 168.65, 265.23, 183.06, 255.14, 194.59, 256.58, 197.48, 253.69, 204.68, 253.69, 219.1, 255.14, 227.75, 276.76, 239.28, 292.61, 246.49, 320.0, 262.34, 330.09, 266.67, 347.39, 275.32, 366.13, 327.21, 367.57, 345.95, 367.57, 364.68, 357.48, 376.22, 343.06, 381.98, 348.83, 407.93, 351.71, 428.11, 344.5, 439.64, 338.74, 445.41, 338.74, 459.82, 340.18, 480.0, 341.62, 491.53, 340.18, 500.18, 341.62, 513.15, 347.39, 527.57, 332.97, 546.31, 307.03, 565.05, 269.55, 566.49, 252.25, 550.63, 258.02, 534.77, 272.43, 520.36, 269.55, 510.27, 273.87, 495.86, 272.43, 477.12, 262.34, 471.35, 247.93, 455.5, 233.51, 458.38, 233.51, 461.26, 229.19, 510.27, 201.8, 495.86, 184.5, 497.3, 172.97, 495.86, 170.09, 491.53, 149.91, 480.0, 138.38, 467.03, 138.38, 449.73, 131.17, 441.08, 131.17, 436.76, 119.64, 472.79, 125.41, 495.86, 134.05, 510.27, 122.52, 531.89, 116.76, 526.13, 119.64, 513.15, 116.76, 507.39, 105.23, 517.48, 105.23, 527.57, 113.87, 534.77, 109.55, 541.98, 99.46, 541.98, 90.81, 534.77]], "area": 86087.77294999996, "iscrowd": 0, "image_id": 20333, "bbox": [51.89, 106.67, 315.68, 459.82], "category_id": 1, "id": 446896}, {"segmentation": [[288.29, 631.35, 282.52, 539.1, 278.2, 482.88, 273.87, 448.29, 269.55, 428.11, 242.16, 420.9, 226.31, 418.02, 223.42, 405.05, 224.86, 381.98, 227.75, 356.04, 229.19, 340.18, 221.98, 330.09, 220.54, 325.77, 220.54, 281.08, 220.54, 255.14, 217.66, 245.05, 213.33, 227.75, 213.33, 210.45, 213.33, 196.04, 213.33, 188.83, 211.89, 181.62, 203.24, 200.36, 194.59, 211.89, 187.39, 188.83, 191.71, 180.18, 203.24, 161.44, 207.57, 154.23, 211.89, 136.94, 219.1, 116.76, 233.51, 103.78, 252.25, 99.46, 275.32, 99.46, 292.61, 115.32, 304.14, 128.29, 311.35, 144.14, 315.68, 162.88, 322.88, 170.09, 332.97, 178.74, 337.3, 178.74, 351.71, 183.06, 369.01, 196.04, 390.63, 224.86, 413.69, 256.58, 428.11, 276.76, 432.43, 292.61, 419.46, 309.91, 403.6, 322.88, 389.19, 328.65, 377.66, 334.41, 366.13, 344.5, 354.59, 354.59, 344.5, 363.24, 337.3, 366.13, 337.3, 366.13, 322.88, 389.19, 321.44, 413.69, 318.56, 436.76, 312.79, 495.86, 307.03, 531.89, 302.7, 553.51, 309.91, 572.25, 315.68, 586.67, 321.44, 601.08, 325.77, 616.94, 325.77, 632.79]], "area": 53233.094850000016, "iscrowd": 0, "image_id": 11699, "bbox": [187.39, 99.46, 245.04, 533.33], "category_id": 1, "id": 509752}, {"segmentation": [[60.54, 418.74, 118.2, 401.44, 149.91, 404.32, 168.65, 418.74, 197.48, 408.65, 209.01, 359.64, 223.42, 307.75, 221.98, 261.62, 206.13, 225.59, 193.15, 206.85, 209.01, 159.28, 216.22, 131.89, 216.22, 113.15, 197.48, 87.21, 174.41, 82.88, 147.03, 94.41, 138.38, 114.59, 125.41, 133.33, 126.85, 153.51, 126.85, 170.81, 115.32, 176.58, 87.93, 176.58, 69.19, 241.44, 66.31, 293.33, 66.31, 320.72, 64.86, 363.96, 61.98, 395.68]], "area": 39898.78195, "iscrowd": 0, "image_id": 11699, "bbox": [60.54, 82.88, 162.88, 335.86], "category_id": 1, "id": 516637}, {"segmentation": [[27.17, 268.06, 29.29, 267.53, 26.11, 263.29, 22.4, 254.28, 26.11, 248.45, 30.35, 243.15, 35.65, 240.5, 46.78, 243.68, 54.2, 249.51, 55.79, 255.87, 57.38, 261.7, 52.08, 272.3, 53.14, 275.48, 56.85, 280.25, 49.43, 280.78, 33.0, 280.78, 28.23, 283.96, 24.52, 297.22, 18.16, 308.35, 10.21, 301.46, 8.09, 296.69, 5.44, 290.85, 10.74, 283.43, 19.22, 273.89]], "area": 1579.1349999999993, "iscrowd": 0, "image_id": 11699, "bbox": [5.44, 240.5, 51.94, 67.85], "category_id": 1, "id": 517883}, {"segmentation": [[173.91, 421.0, 187.43, 407.47, 206.75, 383.32, 216.42, 360.13, 226.08, 339.84, 231.87, 313.75, 244.43, 273.18, 260.86, 238.4, 284.05, 197.82, 297.57, 174.63, 322.69, 147.58, 327.52, 139.85, 324.62, 133.09, 294.67, 117.63, 259.89, 101.2, 249.26, 84.78, 241.53, 52.9, 236.7, 20.05, 236.7, 7.49, 260.86, 1.69, 329.45, 0.72, 370.03, 5.56, 404.81, 12.32, 438.63, 6.52, 467.61, 4.59, 503.36, 0.72, 527.51, 16.18, 544.9, 31.64, 565.19, 52.9, 577.75, 67.39, 592.24, 88.64, 618.33, 106.03, 638.62, 112.8, 640.0, 135.02, 640.0, 151.44, 640.0, 170.77, 640.0, 191.05, 640.0, 217.14, 638.62, 242.26, 638.62, 273.18, 638.62, 306.02, 640.0, 331.14, 640.0, 381.38, 640.0, 397.81, 640.0, 412.3, 640.0, 420.03, 640.0, 422.93]], "area": 154390.84090000004, "iscrowd": 0, "image_id": 18770, "bbox": [173.91, 0.72, 466.09, 422.21], "category_id": 1, "id": 527264}, {"segmentation": [[626.34, 529.98, 623.93, 510.71, 634.77, 502.27, 632.36, 483.0, 625.13, 458.91, 619.11, 450.48, 611.88, 444.46, 609.47, 439.64, 622.72, 367.37, 626.34, 334.85, 617.91, 296.31, 579.36, 239.69, 578.16, 224.04, 590.2, 181.88, 599.84, 160.2, 597.43, 140.93, 601.04, 125.27, 587.79, 108.4, 586.59, 98.77, 574.54, 86.72, 564.91, 86.72, 550.45, 77.09, 538.41, 78.29, 532.39, 80.7, 527.57, 89.13, 519.14, 80.7, 505.89, 85.52, 502.27, 96.36, 502.27, 99.97, 490.23, 107.2, 483.0, 112.02, 472.16, 134.9, 483.0, 173.45, 484.21, 190.31, 493.84, 207.17, 498.66, 250.53, 483.0, 255.35, 467.34, 258.97, 416.76, 364.96, 362.55, 289.08, 358.94, 283.06, 361.35, 251.74, 369.78, 239.69, 350.51, 205.97, 331.24, 201.15, 320.4, 211.99, 317.99, 222.83, 309.56, 219.22, 309.56, 239.69, 317.99, 245.72, 317.99, 250.53, 317.99, 274.62, 302.33, 255.35, 291.49, 245.72, 273.42, 245.72, 263.78, 244.51, 258.97, 252.94, 258.97, 258.97, 256.56, 263.78, 260.17, 271.01, 249.33, 272.22, 252.94, 280.65, 267.4, 289.08, 279.44, 308.35, 303.53, 321.6, 354.12, 383.03, 372.19, 403.51, 426.39, 438.44, 445.66, 449.28, 444.46, 457.71, 433.62, 505.89, 430.0, 527.57, 431.21, 536.0, 623.93, 533.59]], "area": 84787.13445, "iscrowd": 0, "image_id": 11051, "bbox": [249.33, 77.09, 385.44, 458.91], "category_id": 1, "id": 530192}, {"segmentation": [[7.23, 526.36, 14.45, 240.9, 104.79, 193.92, 106.0, 160.2, 130.09, 108.4, 134.9, 50.59, 150.56, 15.66, 205.97, 2.41, 279.44, 53.0, 267.4, 134.9, 240.9, 190.31, 319.19, 219.22, 319.19, 269.81, 287.87, 245.72, 266.19, 251.74, 252.94, 272.22, 286.67, 316.78, 315.58, 337.26, 367.37, 396.28, 393.87, 527.57]], "area": 130170.74170000001, "iscrowd": 0, "image_id": 11051, "bbox": [7.23, 2.41, 386.64, 525.16], "category_id": 1, "id": 539627}, {"segmentation": [[377.57, 425.79, 362.09, 386.59, 341.46, 337.08, 340.43, 307.16, 333.21, 279.31, 333.21, 244.23, 333.21, 221.54, 345.59, 196.78, 377.57, 186.46, 398.2, 176.15, 438.43, 182.34, 458.03, 193.68, 468.35, 214.32, 480.73, 232.88, 490.01, 258.67, 496.2, 283.43, 501.36, 325.73, 506.52, 368.02, 510.64, 403.1, 496.2, 419.6, 478.66, 415.48, 468.35, 402.07, 462.16, 422.7, 452.87, 435.08, 427.08, 420.64, 426.05, 396.91, 423.99, 383.5, 410.58, 386.59, 417.8, 410.32, 423.99, 430.95, 408.51, 439.2]], "area": 35139.281200000005, "iscrowd": 0, "image_id": 20247, "bbox": [333.21, 176.15, 177.43, 263.05], "category_id": 23, "id": 587181}, {"segmentation": [[8.22, 36.66, 11.3, 15.09, 30.81, 16.12, 59.56, 25.36, 85.24, 29.47, 86.27, 50.01, 83.18, 59.25, 72.91, 100.33, 55.46, 137.3, 44.16, 190.7, 39.02, 231.78, 48.27, 253.35, 39.02, 264.64, 1.03, 258.48, 1.03, 27.41]], "area": 14133.482649999998, "iscrowd": 0, "image_id": 20247, "bbox": [1.03, 15.09, 85.24, 249.55], "category_id": 23, "id": 588042}, {"segmentation": [[342.29, 122.25, 325.03, 115.06, 319.28, 104.99, 326.47, 86.29, 348.04, 77.66, 381.12, 77.66, 405.57, 87.73, 414.2, 102.11, 401.26, 112.18, 385.44, 119.37, 368.18, 120.81, 355.24, 120.81, 343.73, 119.37]], "area": 3224.9854499999997, "iscrowd": 0, "image_id": 17029, "bbox": [319.28, 77.66, 94.92, 44.59], "category_id": 34, "id": 603297}, {"segmentation": [[224.17, 361.92, 219.18, 354.6, 213.52, 348.28, 205.54, 342.29, 200.21, 339.62, 191.89, 337.96, 183.24, 336.63, 170.27, 339.29, 164.94, 342.29, 156.62, 347.94, 151.97, 353.93, 148.97, 360.59, 147.64, 368.57, 147.64, 376.23, 148.97, 383.88, 150.63, 388.87, 152.96, 395.52, 157.62, 405.17, 161.61, 411.16, 167.27, 420.15, 172.6, 428.47, 178.25, 436.45, 183.58, 442.44, 188.9, 448.76, 193.56, 453.42, 198.22, 456.08, 208.86, 455.42, 215.19, 454.09, 219.18, 452.76, 227.83, 449.76, 232.49, 446.77, 239.14, 443.11, 245.13, 439.45, 245.13, 434.46, 246.13, 428.47, 244.47, 416.15, 241.14, 403.51, 236.81, 389.87, 232.82, 378.89, 228.5, 369.24, 224.17, 361.92]], "area": 8247.795649999996, "iscrowd": 0, "image_id": 15497, "bbox": [147.64, 336.63, 98.49, 119.45], "category_id": 74, "id": 1106517}, {"segmentation": [[358.11, 478.92, 323.6, 483.24, 316.4, 470.29, 317.84, 445.84, 323.6, 422.83, 323.6, 419.96, 323.6, 405.57, 317.84, 394.07, 299.15, 381.12, 290.52, 372.49, 287.64, 343.73, 287.64, 309.21, 260.31, 235.87, 257.44, 225.8, 258.88, 218.61, 287.64, 256.0, 312.09, 327.91, 317.84, 332.22, 323.6, 348.04, 332.22, 375.37, 335.1, 382.56, 345.17, 365.3, 355.24, 372.49, 362.43, 388.31, 362.43, 418.52, 366.74, 442.97, 366.74, 467.42]], "area": 8476.824750000003, "iscrowd": 0, "image_id": 11699, "bbox": [257.44, 218.61, 109.3, 264.63], "category_id": 31, "id": 1180493}, {"segmentation": [[2.88, 500.49, 17.26, 417.08, 99.24, 412.76, 106.43, 407.01, 119.37, 407.01, 128.0, 399.82, 138.07, 407.01, 140.94, 404.13, 158.2, 422.83, 163.96, 414.2, 176.9, 421.39, 169.71, 408.45, 194.16, 407.01, 260.31, 422.83, 279.01, 629.93, 1.44, 632.81, 1.44, 503.37]], "area": 57762.926649999994, "iscrowd": 0, "image_id": 11699, "bbox": [1.44, 399.82, 277.57, 232.99], "category_id": 33, "id": 1183379}, {"segmentation": [[176.63, 323.52, 188.3, 313.97, 201.04, 320.12, 224.19, 362.8, 204.44, 372.78, 196.58, 371.5, 183.63, 347.72, 174.29, 329.68]], "area": 1581.3299499999994, "iscrowd": 0, "image_id": 12280, "bbox": [174.29, 313.97, 49.9, 58.81], "category_id": 33, "id": 1185876}, {"segmentation": [[8.63, 396.94, 28.76, 404.13, 54.65, 389.75, 67.6, 326.47, 76.22, 264.63, 79.1, 222.92, 67.6, 224.36, 57.53, 248.81, 8.63, 243.06], [103.55, 208.54, 113.62, 172.58, 128.0, 184.09, 119.37, 220.04]], "area": 9896.783449999999, "iscrowd": 0, "image_id": 11699, "bbox": [8.63, 172.58, 119.37, 231.55], "category_id": 31, "id": 1436493}, {"segmentation": [[1.44, 592.64, 1.44, 595.51, 21.57, 630.03, 208.54, 624.28, 264.63, 543.74, 220.04, 506.34, 132.31, 494.84, 126.56, 543.74, 66.16, 562.43, 51.78, 536.55, 61.84, 506.34, 15.82, 526.48, 2.88, 537.98]], "area": 24508.585249999996, "iscrowd": 0, "image_id": 20333, "bbox": [1.44, 494.84, 263.19, 135.19], "category_id": 51, "id": 1538251}, {"segmentation": [[2.1, 126.71, 53.85, 139.27, 51.63, 150.36, 56.81, 155.54, 50.89, 191.03, 0.62, 187.33, 2.84, 128.92]], "area": 2926.4472000000005, "iscrowd": 0, "image_id": 12280, "bbox": [0.62, 126.71, 56.19, 64.32], "category_id": 72, "id": 1624294}, {"segmentation": [[339.75, 250.26, 346.39, 253.11, 353.98, 285.37, 350.19, 288.21, 348.29, 300.55, 348.29, 300.55, 353.98, 306.24, 341.65, 312.88, 343.54, 300.55, 340.7, 253.11]], "area": 467.69994999999994, "iscrowd": 0, "image_id": 12280, "bbox": [339.75, 250.26, 14.23, 62.62], "category_id": 1, "id": 1705693}, {"segmentation": [[14.61, 494.38, 14.61, 405.62, 40.45, 353.93, 141.57, 317.98, 42.7, 267.42, 41.57, 164.04, 70.79, 123.6, 129.21, 100.0, 133.71, 84.27, 183.15, 33.71, 247.19, 29.21, 288.76, 44.94, 305.62, 78.65, 384.27, 95.51, 428.09, 132.58, 412.36, 193.26, 347.19, 247.19, 353.93, 307.87, 401.12, 330.34, 441.57, 442.7, 449.44, 491.01]], "area": 149804.31675, "iscrowd": 0, "image_id": 19924, "bbox": [14.61, 29.21, 434.83, 465.17], "category_id": 1, "id": 1705814}, {"segmentation": [[1.13, 44.59, 9.47, 44.59, 12.49, 48.29, 13.65, 49.92, 14.8, 53.86, 15.04, 57.33, 16.43, 55.71, 16.89, 55.25, 19.21, 55.01, 20.6, 55.71, 21.06, 55.94, 21.53, 56.4, 21.76, 56.64, 21.76, 57.56, 19.67, 58.49, 16.43, 58.49, 16.43, 60.34, 18.05, 60.81, 18.98, 61.27, 19.9, 62.43, 20.83, 66.6, 19.9, 69.38, 18.98, 72.16, 18.05, 77.26, 17.12, 79.35, 17.12, 82.13, 16.89, 84.45, 14.11, 85.37, 13.88, 82.82, 13.18, 87.69, 12.02, 88.39, 6.93, 88.85, 5.77, 86.76, 4.61, 82.82, 0.0, 81.9]], "area": 689.3531500000001, "iscrowd": 0, "image_id": 17029, "bbox": [0.0, 44.59, 21.76, 44.26], "category_id": 3, "id": 1770576}, {"segmentation": [[308.77, 286.83, 310.93, 284.24, 312.51, 281.37, 314.38, 280.65, 316.82, 281.22, 318.26, 295.46, 313.95, 298.19, 309.49, 296.32, 307.47, 292.44]], "area": 133.42794999999973, "iscrowd": 0, "image_id": 12280, "bbox": [307.47, 280.65, 10.79, 17.54], "category_id": 1, "id": 2022239}, {"segmentation": [[429.86, 249.39, 485.35, 409.31, 512.54, 478.93, 640.0, 478.93, 636.56, 75.32, 637.65, 5.69, 48.01, 1.34, 0.14, 1.34, 1.23, 268.97, 0.14, 479.02, 198.14, 480.0, 229.69, 479.02, 223.16, 457.26, 199.23, 458.35, 167.68, 423.54, 147.01, 375.67, 151.36, 348.47, 168.76, 335.42, 199.23, 338.68, 225.34, 361.53, 244.92, 409.4, 251.45, 431.16, 234.04, 452.91, 242.74, 480.0, 303.66, 474.67, 321.07, 463.79, 342.83, 454.0, 351.53, 450.74, 343.92, 444.21, 331.95, 437.68, 294.96, 344.12, 278.64, 282.11, 268.85, 270.15, 264.5, 265.79, 250.36, 260.35, 238.39, 283.2, 212.28, 278.85, 182.91, 297.34, 137.22, 288.64, 85.57, 269.06, 56.2, 210.31, 56.2, 120.01, 109.51, 61.27, 167.17, 34.07, 265.08, 24.28, 317.3, 31.89, 347.76, 52.56, 369.52, 86.29, 408.68, 104.78, 430.44, 93.9, 468.52, 76.5, 482.66, 86.29, 465.25, 133.07, 479.39, 153.74, 484.83, 166.79, 489.19, 173.32, 433.7, 248.39]], "area": 171088.63380000004, "iscrowd": 0, "image_id": 15497, "bbox": [0.14, 1.34, 639.86, 478.66], "category_id": 63, "id": 2224611}, {"segmentation": [[387.24, 261.25, 380.64, 278.21, 372.16, 292.35, 381.58, 312.13, 390.06, 327.21, 395.72, 342.28, 376.87, 340.4, 372.16, 330.98, 362.74, 333.8, 358.03, 325.32, 356.15, 316.84, 357.09, 301.77, 358.97, 288.58, 356.15, 280.1, 359.91, 262.2, 355.2, 253.72, 348.61, 240.53, 343.9, 231.1, 340.13, 221.68, 344.84, 211.32, 354.26, 200.95, 373.1, 185.88, 421.16, 193.42, 442.83, 185.88, 463.55, 172.69, 486.17, 167.04, 497.47, 164.21, 495.59, 156.67, 498.42, 152.9, 508.78, 163.27, 510.66, 157.61, 514.43, 159.5, 518.2, 170.8, 528.57, 183.05, 534.22, 201.9, 537.99, 225.45, 532.33, 234.87, 517.26, 233.93, 513.49, 222.62, 507.84, 216.97, 497.47, 212.26, 490.88, 227.34, 488.05, 232.99, 483.34, 247.12, 479.57, 266.91, 472.98, 290.46, 473.92, 313.07, 475.8, 328.15, 483.34, 339.46, 487.11, 350.76, 486.17, 352.65, 463.55, 350.76, 460.73, 340.4, 460.73, 323.44, 458.84, 293.29, 459.79, 277.27, 456.02, 275.39, 447.54, 273.5, 437.17, 281.04, 432.46, 306.48, 419.27, 331.92, 411.73, 342.28, 408.91, 343.22, 399.49, 335.69, 410.79, 322.5, 422.1, 301.77, 422.1, 281.04, 420.21, 269.73]], "area": 18147.233050000006, "iscrowd": 0, "image_id": 16228, "bbox": [340.13, 152.9, 197.86, 199.75], "category_id": 19, "id": 54058}, {"segmentation": [[229.45, 223.79, 228.25, 251.27, 229.32, 259.17, 227.82, 267.93, 230.17, 279.03, 230.17, 281.38, 231.1, 286.4, 230.1, 289.56, 225.61, 290.39, 224.44, 292.06, 226.27, 297.38, 225.28, 301.21, 224.61, 302.37, 222.95, 303.2, 209.47, 300.87, 214.79, 294.05, 215.13, 292.72, 220.78, 286.4, 222.39, 276.97, 221.83, 266.61, 219.57, 259.26, 219.57, 251.91, 212.22, 224.58, 205.43, 220.99, 202.79, 220.62, 198.08, 227.03, 185.45, 240.78, 181.31, 251.72, 184.13, 258.69, 188.09, 269.62, 193.93, 275.46, 192.61, 279.42, 195.63, 280.18, 197.89, 279.99, 203.55, 285.83, 189.79, 290.35, 184.51, 292.62, 183.19, 287.53, 177.35, 284.32, 176.22, 277.54, 174.9, 275.65, 175.65, 272.83, 174.71, 266.79, 169.87, 250.59, 173.73, 241.44, 180.07, 230.89, 183.58, 216.82, 180.77, 205.22, 176.55, 196.42, 176.55, 186.57, 177.25, 180.24, 177.25, 178.84, 172.24, 168.06, 171.6, 161.65, 165.2, 161.01, 160.5, 161.22, 157.3, 161.65, 153.45, 161.86, 149.4, 164.85, 148.76, 167.84, 149.4, 170.19, 149.82, 173.4, 148.12, 175.53, 146.19, 175.74, 144.91, 176.38, 143.84, 178.31, 143.2, 179.37, 140.64, 179.16, 136.58, 176.38, 132.1, 171.47, 131.89, 164.43, 132.31, 137.31, 132.53, 125.57, 130.39, 120.44, 130.82, 117.88, 132.1, 115.53, 134.24, 113.82, 135.3, 112.97, 129.54, 102.93, 133.17, 105.49, 137.23, 109.76, 139.15, 107.63, 139.57, 104.64, 139.57, 104.21, 139.36, 95.67, 142.35, 86.49, 143.42, 85.42, 147.69, 84.78, 150.46, 85.0, 154.31, 86.49, 156.44, 88.84, 157.08, 92.26, 157.3, 95.89, 157.08, 99.52, 156.87, 101.65, 156.44, 104.0, 156.44, 106.99, 156.44, 109.34, 157.72, 111.05, 158.36, 113.18, 169.25, 115.74, 179.08, 120.87, 193.81, 130.9, 207.26, 143.72, 213.45, 147.77, 230.32, 150.98, 244.82, 160.1, 258.77, 163.69, 285.06, 159.7, 302.2, 159.7, 314.15, 161.3, 324.51, 159.3, 334.47, 159.7, 341.64, 163.29, 345.23, 166.08, 347.22, 170.86, 348.42, 180.82, 352.8, 197.95, 354.0, 212.3, 357.58, 224.65, 359.18, 233.42, 359.58, 239.79, 353.2, 238.2, 350.01, 235.41, 347.62, 233.02, 343.64, 227.84, 335.27, 210.31, 330.49, 199.15, 328.5, 203.93, 325.71, 221.46, 333.68, 232.62, 341.64, 234.21, 344.83, 238.2, 356.39, 274.06, 357.58, 278.44, 365.55, 287.6, 359.97, 297.17, 342.84, 277.64, 340.45, 268.08, 334.87, 244.17, 326.5, 235.01, 316.14, 227.04, 311.36, 223.45, 308.57, 221.06, 306.64, 214.56, 302.1, 217.68, 300.41, 219.62, 301.45, 228.56, 303.78, 237.12, 307.16, 243.86, 301.19, 266.84, 298.69, 275.33, 298.69, 281.48, 297.03, 284.65, 291.2, 286.81, 292.87, 291.97, 287.88, 294.63, 276.56, 293.63, 279.89, 289.8, 285.55, 284.48, 290.54, 278.99, 294.2, 265.35, 294.89, 243.23, 290.32, 236.2, 286.45, 227.76, 282.58, 216.85, 272.38, 221.78, 254.45, 225.3, 241.78, 224.24, 231.58, 221.78]], "area": 18372.4854, "iscrowd": 0, "image_id": 15956, "bbox": [129.54, 84.78, 236.01, 218.42], "category_id": 19, "id": 54081}, {"segmentation": [[219.74, 518.68, 241.72, 482.98, 238.97, 436.28, 168.93, 298.94, 166.18, 248.13, 135.97, 202.8, 122.23, 162.98, 137.34, 143.75, 144.21, 90.19, 126.35, 127.27, 103.0, 139.63, 85.15, 135.51, 92.02, 105.29, 75.54, 76.45, 76.91, 50.36, 57.68, 80.57, 37.08, 123.15, 4.12, 136.88, 1.37, 417.05, 39.83, 419.8, 120.86, 496.71, 135.97, 511.82, 145.58, 539.28, 175.79, 555.77, 178.54, 550.27, 208.76, 539.28]], "area": 66767.92135, "iscrowd": 0, "image_id": 12748, "bbox": [1.37, 50.36, 240.35, 505.41], "category_id": 19, "id": 55490}, {"segmentation": [[256.0, 450.16, 258.88, 421.39, 276.13, 412.76, 358.11, 379.69, 348.04, 398.38, 345.17, 432.9, 346.61, 458.79, 345.17, 470.29, 381.12, 496.18, 396.94, 481.8, 405.57, 481.8, 402.7, 500.49, 412.76, 499.06, 419.96, 516.31, 455.91, 494.74, 480.0, 458.79, 480.0, 430.02, 463.1, 405.57, 457.35, 381.12, 457.35, 362.43, 454.47, 349.48, 480.0, 366.74, 480.0, 329.35, 458.79, 313.53, 448.72, 300.58, 431.46, 291.96, 415.64, 287.64, 434.34, 267.51, 441.53, 248.81, 434.34, 215.73, 434.34, 211.42, 421.39, 191.28, 407.01, 188.4, 368.18, 192.72, 327.91, 225.8, 325.03, 260.31, 332.22, 279.01, 330.79, 299.15, 346.61, 313.53, 349.48, 316.4, 336.54, 337.98, 323.6, 355.24, 287.64, 369.62, 266.07, 384.0, 251.69, 396.94, 235.87, 402.7, 222.92, 404.13, 217.17, 425.71, 225.8, 445.84, 243.06, 455.91, 256.0, 445.84], [373.93, 572.4, 395.51, 566.65, 425.71, 552.27, 430.02, 549.39, 437.21, 585.35, 447.28, 608.36, 465.98, 631.37, 470.29, 631.37, 431.46, 632.81, 412.76, 616.99, 396.94, 599.73, 382.56, 585.35]], "area": 43323.950949999984, "iscrowd": 0, "image_id": 12748, "bbox": [217.17, 188.4, 262.83, 444.41], "category_id": 1, "id": 208131}, {"segmentation": [[222.42, 399.78, 235.34, 320.86, 284.13, 293.6, 261.17, 273.51, 233.9, 223.28, 220.99, 203.19, 223.86, 183.1, 255.43, 151.53, 278.39, 138.62, 299.91, 141.49, 330.04, 181.67, 337.22, 204.63, 321.43, 244.81, 335.78, 306.51, 345.83, 326.6, 312.83, 362.48], [235.34, 457.18, 233.9, 510.28, 258.3, 540.41, 266.91, 541.85, 252.56, 622.21, 255.43, 630.82, 444.84, 632.25, 373.09, 570.55, 427.62, 547.59, 449.15, 612.16, 469.24, 632.25, 462.06, 540.41, 480.0, 507.41, 480.0, 465.79, 427.62, 517.45, 404.66, 494.49, 401.79, 480.14, 377.4, 497.36, 345.83, 468.66, 351.57, 384.0, 255.43, 424.18, 256.86, 454.31], [450.58, 349.56, 462.06, 409.83, 477.85, 427.05, 480.0, 369.65], [420.45, 287.86, 431.93, 272.07, 446.28, 270.64, 472.11, 317.99, 469.24, 323.73]], "area": 58405.78259999999, "iscrowd": 0, "image_id": 12748, "bbox": [220.99, 138.62, 259.01, 493.63], "category_id": 1, "id": 215760}, {"segmentation": [[283.42, 287.44, 285.95, 281.67, 285.68, 279.26, 284.47, 281.47, 283.73, 281.07, 283.39, 279.53, 281.65, 281.14, 280.51, 279.79, 286.95, 272.54, 288.5, 265.63, 291.05, 265.02, 294.54, 264.75, 293.67, 262.86, 294.74, 260.77, 295.82, 259.23, 296.29, 260.77, 295.28, 262.92, 295.55, 263.59, 298.77, 262.32, 300.58, 262.32, 302.13, 263.46, 303.81, 264.2, 305.55, 264.87, 305.95, 261.45, 307.5, 262.25, 308.1, 264.33, 306.83, 266.95, 305.69, 268.76, 303.6, 268.5, 303.07, 268.63, 301.25, 274.69, 297.16, 280.8, 294.54, 284.63, 294.41, 288.32, 294.94, 289.67, 293.2, 289.46, 291.72, 288.25, 293.06, 283.29, 294.81, 279.93, 292.59, 279.32, 291.25, 281.54, 290.04, 286.51, 288.3, 288.59, 287.29, 289.46, 286.55, 288.05, 289.44, 276.71, 288.56, 276.71, 286.28, 284.16, 285.27, 288.12, 283.8, 290.2, 282.59, 289.73, 283.66, 286.91]], "area": 343.10035000000005, "iscrowd": 0, "image_id": 16010, "bbox": [280.51, 259.23, 27.59, 30.97], "category_id": 21, "id": 278208}, {"segmentation": [[500.25, 147.28, 493.25, 141.73, 493.01, 133.52, 497.6, 127.97, 503.87, 126.28, 511.12, 128.45, 514.74, 137.62, 513.77, 143.66, 509.18, 146.07, 505.56, 146.79]], "area": 354.5723499999999, "iscrowd": 0, "image_id": 15956, "bbox": [493.01, 126.28, 21.73, 21.0], "category_id": 85, "id": 336552}, {"segmentation": [[43.24, 99.43, 42.6, 93.69, 44.3, 88.59, 47.06, 85.4, 49.83, 84.12, 55.99, 83.27, 57.27, 83.27, 63.43, 84.33, 67.26, 93.26, 67.89, 100.28, 65.56, 103.68, 62.15, 105.38, 56.42, 106.87, 52.38, 107.08, 49.61, 105.8, 47.49, 105.17, 46.21, 104.32, 44.94, 102.4]], "area": 482.2635000000001, "iscrowd": 0, "image_id": 15956, "bbox": [42.6, 83.27, 25.29, 23.81], "category_id": 85, "id": 340211}, {"segmentation": [[194.31, 294.95, 184.71, 258.0, 186.85, 254.8, 193.95, 243.79, 209.23, 235.62, 233.74, 233.84, 250.09, 233.84, 239.78, 235.97, 242.63, 265.82, 241.56, 287.49, 194.66, 293.89]], "area": 2835.5094499999996, "iscrowd": 0, "image_id": 17182, "bbox": [184.71, 233.84, 65.38, 61.11], "category_id": 62, "id": 377970}, {"segmentation": [[297.36, 185.24, 319.19, 184.49, 330.48, 176.96, 328.22, 154.38, 336.51, 142.33, 344.03, 138.57, 361.35, 140.83, 364.36, 155.13, 362.1, 169.43, 375.65, 181.48, 389.96, 204.81, 397.48, 220.62, 412.54, 234.17, 420.07, 255.25, 429.1, 271.81, 447.92, 271.81, 466.74, 265.79, 483.3, 260.52, 494.6, 258.26, 497.61, 260.52, 499.11, 296.66, 492.34, 300.42, 486.31, 286.87, 459.97, 294.4, 431.36, 297.41, 389.2, 264.29, 368.88, 252.99, 353.07, 249.23, 350.06, 256.76, 355.33, 270.31, 351.56, 285.36, 338.01, 285.36, 323.71, 289.88, 322.2, 274.83, 328.98, 264.29, 320.7, 248.48, 320.7, 219.87, 328.98, 207.83, 314.67, 208.58, 298.87, 207.07, 288.33, 195.03]], "area": 11459.433249999996, "iscrowd": 0, "image_id": 13291, "bbox": [288.33, 138.57, 210.78, 161.85], "category_id": 1, "id": 442908}, {"segmentation": [[235.68, 241.29, 260.6, 186.79, 272.78, 165.34, 294.81, 139.25, 309.88, 144.47, 313.36, 134.61, 304.67, 125.92, 306.78, 108.42, 318.58, 84.81, 338.47, 80.77, 360.53, 82.94, 383.83, 99.72, 389.11, 117.12, 390.35, 126.75, 379.17, 126.44, 376.99, 136.07, 385.38, 141.35, 372.95, 170.87, 370.69, 178.55, 367.98, 187.58, 354.43, 179.0, 357.59, 192.55, 355.34, 210.62, 391.47, 232.75, 427.16, 247.66, 446.77, 224.18, 464.17, 217.01, 480.04, 212.92, 483.62, 219.57, 470.82, 241.58, 464.17, 265.12, 458.54, 279.45, 432.44, 270.75, 421.18, 267.17, 410.44, 285.08, 404.3, 314.25, 383.83, 338.81, 330.6, 371.05, 349.03, 371.56, 355.68, 382.31, 348.0, 392.54, 338.28, 395.61, 269.71, 395.1, 257.94, 382.82, 263.05, 356.21, 236.44, 345.97, 224.16, 321.41, 221.09, 305.55, 218.53, 295.31, 221.6, 289.17, 221.6, 266.65, 227.74, 255.91, 235.93, 242.09]], "area": 41177.207800000004, "iscrowd": 0, "image_id": 10764, "bbox": [218.53, 80.77, 265.09, 314.84], "category_id": 1, "id": 471070}, {"segmentation": [[248.19, 194.15, 248.84, 189.38, 247.76, 185.7, 249.28, 178.98, 254.04, 171.83, 254.69, 169.23, 250.36, 164.9, 246.24, 163.82, 239.74, 157.97, 239.96, 151.68, 238.44, 148.0, 238.23, 144.75, 239.53, 141.07, 244.08, 138.47, 246.03, 138.03, 249.06, 135.43, 256.64, 137.38, 261.19, 140.85, 265.53, 148.65, 265.96, 154.07, 268.78, 154.07, 271.38, 152.33, 276.36, 153.85, 283.08, 156.67, 286.54, 158.83, 294.13, 164.47, 296.73, 173.13, 296.73, 182.23, 295.86, 182.67, 290.01, 183.53, 288.71, 187.22, 288.71, 188.95, 293.91, 186.35, 290.23, 190.25, 292.18, 192.42, 294.78, 203.25, 297.81, 205.85, 302.14, 208.88, 313.63, 209.53, 319.04, 208.67, 311.24, 216.9, 306.91, 223.62, 303.44, 226.0, 302.36, 228.17, 301.28, 239.65, 304.09, 246.59, 304.96, 251.14, 296.73, 257.64, 298.89, 261.75, 298.46, 266.74, 293.91, 273.89, 285.46, 280.39, 281.13, 282.55, 278.09, 280.39, 275.71, 277.57, 276.36, 273.45, 284.16, 266.74, 288.06, 258.5, 284.81, 260.24, 276.58, 259.8, 271.38, 255.9, 271.38, 252.87, 272.68, 252.65, 283.94, 249.4, 283.73, 247.67, 273.11, 240.3, 269.21, 238.79, 255.99, 235.54, 254.26, 228.39, 263.79, 216.47, 265.96, 207.15, 261.19, 204.77, 260.33, 210.4, 255.34, 213.43, 248.41, 215.6, 246.03, 213.22, 247.98, 208.67, 249.71, 201.3]], "area": 5149.0828999999985, "iscrowd": 0, "image_id": 13291, "bbox": [238.23, 135.43, 80.81, 147.12], "category_id": 1, "id": 475509}, {"segmentation": [[245.98, 468.33, 306.13, 359.84, 356.62, 339.43, 441.48, 350.18, 486.59, 359.84, 523.11, 357.69, 528.49, 360.92, 538.15, 380.25, 552.12, 399.59, 547.82, 414.62, 545.67, 421.07, 517.74, 461.89, 498.41, 474.78, 244.91, 474.78]], "area": 31987.406849999992, "iscrowd": 0, "image_id": 19221, "bbox": [244.91, 339.43, 307.21, 135.35], "category_id": 1, "id": 502731}, {"segmentation": [[227.0, 210.05, 240.1, 191.72, 261.93, 199.57, 270.66, 209.18, 273.28, 223.15, 290.74, 237.99, 310.82, 243.23, 318.68, 250.22, 318.68, 254.58, 319.55, 261.57, 303.84, 255.45, 294.23, 252.83, 282.88, 267.68, 286.38, 272.92, 294.23, 292.13, 278.52, 264.19, 275.9, 259.82, 267.17, 264.19, 269.79, 279.9, 266.29, 291.25, 250.58, 320.94, 228.75, 327.05, 214.78, 334.91, 211.29, 338.4, 229.62, 354.12, 223.51, 365.47, 213.03, 384.68, 229.62, 399.52, 232.24, 413.49, 209.54, 437.84, 185.09, 457.05, 184.22, 460.54, 180.73, 470.15, 171.12, 470.15, 172.0, 478.0, 172.0, 493.72, 180.73, 500.7, 165.88, 510.31, 160.64, 505.07, 159.77, 499.83, 123.1, 470.15, 123.97, 448.32, 130.09, 437.84, 138.82, 435.22, 129.21, 423.87, 126.59, 417.76, 137.94, 410.77, 150.17, 399.42, 153.66, 390.69, 117.86, 377.59, 103.89, 359.26, 97.78, 324.33, 103.89, 294.65, 128.34, 269.33, 147.55, 261.47, 160.64, 251.86, 166.76, 237.89, 158.9, 194.38, 142.31, 169.94, 130.96, 157.71, 123.97, 137.63, 108.26, 127.15, 95.16, 114.93, 93.41, 100.09, 108.26, 86.99, 136.2, 128.03, 148.42, 144.62, 170.25, 159.46, 175.49, 175.18, 181.6, 189.15, 199.94, 200.5, 209.54, 206.61, 219.14, 206.61, 229.62, 205.73]], "area": 34807.5968, "iscrowd": 0, "image_id": 13201, "bbox": [93.41, 86.99, 226.14, 423.32], "category_id": 1, "id": 508301}, {"segmentation": [[285.49, 328.97, 283.13, 346.77, 281.46, 365.58, 274.07, 362.89, 276.75, 358.53, 276.42, 324.27, 275.07, 320.91, 272.72, 330.31, 270.37, 329.64, 271.04, 307.81, 273.73, 296.05, 282.13, 294.04, 282.13, 290.01, 281.12, 286.31, 282.46, 283.63, 284.48, 282.28, 287.5, 281.95, 290.52, 284.97, 288.84, 293.03, 288.84, 294.04, 295.23, 296.05, 300.6, 313.52, 301.61, 326.28, 301.61, 329.98, 298.25, 331.66, 295.56, 329.31, 297.24, 324.27, 294.55, 312.18, 293.21, 325.95, 295.23, 345.09, 294.55, 357.18, 293.21, 368.27, 287.84, 366.59, 289.18, 355.84, 287.84, 341.4, 286.49, 331.99]], "area": 1495.2492499999992, "iscrowd": 0, "image_id": 13774, "bbox": [270.37, 281.95, 31.24, 86.32], "category_id": 1, "id": 509934}, {"segmentation": [[81.3, 93.35, 80.55, 88.83, 89.58, 67.0, 97.11, 66.25, 108.4, 67.0, 116.69, 76.03, 116.69, 92.6, 112.92, 106.15, 124.97, 111.42, 142.28, 123.46, 148.3, 145.29, 148.3, 167.88, 143.03, 179.17, 160.35, 197.24, 159.6, 214.55, 159.6, 224.34, 158.09, 234.12, 155.83, 246.92, 156.58, 255.96, 160.35, 266.49, 166.37, 280.04, 147.55, 275.53, 141.53, 267.25, 131.74, 255.2, 137.01, 248.43, 143.03, 229.61, 143.79, 216.06, 123.46, 205.52, 115.93, 203.26, 106.9, 211.54, 98.62, 233.37, 94.85, 236.38, 88.08, 246.92, 92.6, 264.99, 94.1, 280.04, 86.57, 293.6, 78.29, 298.87, 62.48, 298.11, 62.48, 293.6, 60.22, 253.7, 57.21, 230.36, 67.0, 161.1, 61.73, 152.07, 43.66, 135.51, 41.4, 132.49, 44.42, 118.94, 48.18, 113.67, 60.98, 94.85, 73.78, 84.31, 79.8, 87.33]], "area": 15137.624950000005, "iscrowd": 0, "image_id": 13291, "bbox": [41.4, 66.25, 124.97, 232.62], "category_id": 1, "id": 547120}, {"segmentation": [[527.12, 260.42, 529.55, 257.61, 538.55, 251.8, 540.23, 241.69, 540.79, 233.07, 623.78, 235.5, 620.22, 247.49, 620.22, 251.8, 619.47, 263.04, 616.85, 268.85, 614.42, 282.15, 608.05, 284.02, 605.05, 284.21, 600.74, 292.27, 596.99, 291.7, 599.62, 281.78, 555.97, 278.4, 560.28, 283.46, 551.66, 278.78, 546.23, 279.15, 541.54, 286.27, 538.55, 285.9, 543.23, 270.54, 540.23, 262.85]], "area": 3807.737349999998, "iscrowd": 0, "image_id": 16228, "bbox": [527.12, 233.07, 96.66, 59.2], "category_id": 15, "id": 575192}, {"segmentation": [[399.47, 267.04, 399.47, 267.04, 425.5, 266.77, 437.3, 272.14, 440.52, 284.48, 445.08, 302.46, 445.08, 302.46, 447.5, 309.97, 446.69, 312.38, 443.47, 306.48, 436.23, 290.92, 436.23, 290.92, 436.23, 296.28, 436.23, 296.28, 439.45, 303.26, 439.45, 319.63, 432.74, 320.16, 432.74, 320.16, 430.86, 325.26, 425.88, 323.28, 428.59, 319.47, 428.59, 319.47, 430.49, 313.76, 430.49, 313.76, 430.87, 308.68, 430.87, 308.68, 421.86, 292.56, 421.86, 292.56, 421.86, 292.56, 413.74, 293.58, 413.74, 293.58, 402.82, 295.35, 402.57, 295.35, 401.43, 296.88, 401.43, 296.88, 403.33, 314.9, 401.17, 315.4, 401.17, 315.4, 400.41, 318.32, 394.45, 319.21, 398.76, 312.87, 398.76, 312.87, 398.38, 306.65, 398.38, 306.65, 397.62, 302.21, 395.84, 294.72, 393.69, 294.08, 391.4, 297.0, 391.27, 318.83, 390.39, 322.64, 390.51, 325.05, 384.67, 324.54, 384.93, 304.62, 383.79, 298.4, 382.26, 294.97, 379.09, 295.08, 376.02, 299.92, 374.94, 305.66, 375.13, 310.11, 372.76, 317.82, 372.17, 323.16, 370.19, 323.36, 370.19, 324.84, 368.61, 324.35, 365.64, 321.48, 363.76, 318.12, 363.96, 312.28, 361.78, 307.24, 361.49, 303.19, 361.49, 303.19, 360.96, 300.34, 358.93, 300.53, 358.57, 296.1, 365.58, 286.69, 365.21, 278.02, 365.39, 277.83, 374.8, 268.42, 383.66, 266.58, 394.73, 266.58, 400.08, 267.5]], "area": 2996.3666000000003, "iscrowd": 0, "image_id": 16010, "bbox": [358.57, 266.58, 88.93, 58.68], "category_id": 24, "id": 589000}, {"segmentation": [[365.51, 409.93, 365.51, 443.16, 365.51, 466.28, 365.51, 480.72, 372.73, 495.17, 372.73, 515.4, 372.73, 545.73, 372.73, 580.41, 372.73, 583.3, 404.51, 602.08, 418.96, 602.08, 417.52, 576.07, 410.29, 539.95, 405.96, 509.62, 408.85, 459.05, 416.07, 431.6, 418.96, 408.49, 430.52, 357.92, 431.96, 331.92, 440.63, 279.91, 423.3, 213.45, 408.85, 199.01, 403.07, 197.56, 416.07, 190.34, 427.63, 177.34, 434.85, 164.33, 431.96, 144.11, 410.29, 141.22, 387.18, 158.56, 381.4, 158.56, 374.18, 158.56, 369.84, 158.56, 355.4, 155.67, 323.61, 122.44, 296.16, 136.88, 300.5, 177.34, 252.82, 238.01, 249.93, 269.8, 242.71, 308.8, 251.38, 349.26, 265.82, 372.37, 278.83, 385.37, 283.16, 404.15, 287.49, 467.72, 297.61, 531.29, 294.72, 567.4, 294.72, 589.07, 294.72, 590.52, 316.39, 590.52, 340.95, 581.85, 340.95, 547.18, 340.95, 511.06, 343.84, 480.72, 346.73, 447.49, 345.28, 435.94, 332.28, 409.93, 359.73, 399.82]], "area": 60087.8676, "iscrowd": 0, "image_id": 17115, "bbox": [242.71, 122.44, 197.92, 479.64], "category_id": 24, "id": 589088}, {"segmentation": [[525.28, 87.87, 538.65, 106.97, 566.35, 99.33, 571.12, 125.11, 583.54, 146.12, 605.51, 198.65, 603.6, 229.21, 566.35, 274.1, 560.62, 323.76, 557.75, 355.28, 562.53, 396.35, 547.25, 409.72, 540.56, 396.35, 528.15, 386.8, 524.33, 400.17, 496.63, 418.31, 486.12, 407.81, 491.85, 386.8, 496.63, 343.82, 489.94, 325.67, 493.76, 297.98, 483.26, 264.55, 456.52, 233.99, 453.65, 189.1, 475.62, 149.94, 482.3, 147.08, 458.43, 127.02, 459.38, 108.88, 475.62, 97.42, 490.9, 113.65, 498.54, 117.47, 507.13, 102.19, 510.96, 91.69]], "area": 30324.790750000004, "iscrowd": 0, "image_id": 11760, "bbox": [453.65, 87.87, 151.86, 330.44], "category_id": 24, "id": 589666}, {"segmentation": [[268.9, 136.0, 279.88, 130.93, 292.56, 136.85, 301.85, 127.55, 314.53, 108.96, 329.73, 100.52, 346.63, 105.59, 348.32, 136.0, 366.07, 130.09, 376.21, 138.54, 368.6, 155.44, 385.5, 176.56, 388.03, 226.41, 371.14, 247.53, 364.38, 274.57, 357.62, 315.97, 351.7, 361.6, 355.08, 396.24, 344.1, 394.55, 319.59, 393.71, 311.15, 395.4, 309.46, 362.44, 308.61, 321.89, 306.08, 299.07, 304.39, 281.33, 290.02, 237.39, 284.11, 215.43, 280.73, 191.77, 280.73, 168.95, 284.95, 159.66, 269.74, 151.21, 272.28, 137.69, 284.95, 134.31]], "area": 20181.378099999998, "iscrowd": 0, "image_id": 11760, "bbox": [268.9, 100.52, 119.13, 295.72], "category_id": 24, "id": 590311}, {"segmentation": [[61.89, 485.34, 64.31, 515.65, 59.46, 522.92, 59.46, 532.62, 59.46, 552.02, 64.31, 567.78, 59.46, 595.66, 90.99, 583.54, 83.71, 552.02, 86.14, 504.74, 86.14, 491.4, 89.77, 457.46, 87.35, 423.51, 84.92, 393.2, 97.05, 379.87, 99.47, 384.71, 92.2, 387.14, 104.32, 416.24, 104.32, 453.82, 109.17, 481.7, 107.96, 507.16, 115.23, 531.41, 112.81, 561.72, 120.08, 585.96, 128.57, 592.03, 130.99, 559.29, 126.14, 539.89, 123.72, 507.16, 123.72, 467.15, 132.21, 433.21, 143.12, 497.46, 143.12, 542.32, 150.39, 559.29, 166.15, 553.23, 161.3, 493.83, 158.88, 465.94, 161.3, 430.78, 167.36, 399.26, 173.43, 379.87, 194.03, 327.91, 197.67, 280.62, 177.06, 227.91, 158.88, 189.12, 158.88, 185.48, 173.43, 176.99, 192.82, 156.38, 207.37, 129.71, 180.7, 129.71, 158.88, 141.84, 149.18, 144.26, 138.27, 140.62, 134.63, 132.14, 128.57, 117.59, 116.45, 96.98, 101.9, 98.19, 97.05, 104.25, 83.71, 111.53, 81.29, 113.95, 75.23, 124.86, 69.16, 118.8, 52.19, 112.74, 48.55, 149.11, 54.62, 164.87, 59.46, 170.93, 47.34, 173.36, 38.86, 185.48, 35.22, 190.33, 36.43, 189.12, 27.94, 200.03, 25.52, 209.73, 23.09, 217.0, 17.03, 237.61, 12.18, 252.16, 10.97, 263.07, 7.33, 266.71, 2.48, 271.56, 7.33, 291.48, 14.61, 349.68, 21.88, 372.71, 30.37, 371.5, 41.28, 394.53, 44.92, 404.23, 50.98, 419.99, 58.25, 435.75, 59.46, 447.88, 60.68, 463.64, 61.89, 469.7, 61.89, 478.19, 64.31, 483.03, 64.31, 483.03]], "area": 56492.645699999994, "iscrowd": 0, "image_id": 17115, "bbox": [2.48, 96.98, 204.89, 498.68], "category_id": 24, "id": 590562}, {"segmentation": [[76.23, 362.11, 63.85, 299.22, 46.69, 221.08, 35.26, 184.87, 50.5, 144.84, 70.52, 121.97, 96.24, 113.4, 126.74, 127.69, 129.6, 101.96, 149.61, 119.11, 159.14, 99.1, 176.29, 107.68, 179.15, 134.36, 202.97, 110.54, 215.36, 117.21, 209.64, 142.94, 185.82, 154.37, 181.05, 218.22, 175.34, 251.57, 160.09, 269.67, 150.56, 316.37, 146.75, 387.84, 123.88, 385.93, 127.69, 337.33, 117.21, 280.16, 113.4, 268.72, 96.24, 291.59, 100.06, 344.96, 92.43, 379.26, 74.33, 376.4]], "area": 27830.795599999998, "iscrowd": 0, "image_id": 11760, "bbox": [35.26, 99.1, 180.1, 288.74], "category_id": 24, "id": 590625}, {"segmentation": [[114.52, 250.7, 114.52, 272.99, 116.79, 304.38, 123.16, 306.65, 120.89, 278.9, 122.25, 266.16, 125.44, 280.27, 125.44, 302.56, 131.35, 307.11, 129.53, 288.91, 130.9, 277.54, 129.53, 267.07, 130.9, 252.52, 138.18, 258.89, 140.45, 266.16, 139.54, 278.9, 148.18, 301.65, 156.83, 306.2, 155.01, 295.28, 155.01, 288.91, 161.38, 270.26, 165.93, 265.25, 165.93, 254.79, 175.93, 250.24, 175.48, 261.16, 168.65, 287.09, 174.57, 292.55, 179.12, 291.64, 175.48, 284.36, 181.39, 262.98, 187.76, 250.24, 188.67, 264.34, 194.58, 275.72, 201.86, 286.64, 207.32, 274.35, 205.5, 267.98, 209.14, 252.06, 209.14, 249.33, 217.79, 239.78, 215.51, 239.78, 206.41, 241.6, 196.4, 217.94, 188.67, 202.93, 175.48, 200.2, 156.83, 200.2, 130.44, 200.2, 124.07, 202.48, 109.97, 217.03, 109.52, 236.59]], "area": 6907.305150000001, "iscrowd": 0, "image_id": 20059, "bbox": [109.52, 200.2, 108.27, 106.91], "category_id": 24, "id": 591261}, {"segmentation": [[466.85, 246.73, 474.14, 255.39, 485.54, 287.3, 487.37, 290.04, 488.73, 293.69, 491.93, 296.88, 503.32, 289.13, 503.32, 276.36, 505.6, 262.23, 506.52, 254.93, 506.52, 238.52, 499.22, 235.33, 449.52, 196.12, 434.02, 193.38, 406.21, 193.38, 398.46, 193.38, 367.91, 185.17, 355.6, 192.92, 348.3, 207.51, 346.93, 221.65, 345.11, 233.05, 342.38, 241.25, 341.46, 270.89, 348.3, 286.39, 355.6, 290.95, 357.88, 294.6, 361.07, 294.14, 362.44, 283.2, 358.79, 271.8, 358.79, 264.05, 362.44, 255.84, 368.82, 249.01, 373.84, 242.17, 378.39, 246.27, 406.21, 249.01, 423.99, 250.83, 418.06, 266.79, 416.69, 280.92, 415.33, 294.14, 415.33, 300.53, 429.46, 293.23, 433.11, 266.33, 434.02, 264.05, 435.84, 261.32, 443.14, 261.77, 444.51, 277.27, 447.24, 286.39, 451.35, 291.86, 455.45, 294.14, 460.92, 298.7, 462.74, 292.32, 460.46, 286.39, 455.45, 274.99, 455.45, 252.65, 465.94, 249.92]], "area": 10740.670599999998, "iscrowd": 0, "image_id": 20059, "bbox": [341.46, 185.17, 165.06, 115.36], "category_id": 24, "id": 592307}, {"segmentation": [[236.31, 219.32, 237.35, 213.84, 243.36, 209.66, 248.84, 209.66, 253.02, 214.88, 253.02, 221.41, 248.06, 226.37, 243.1, 227.42, 236.83, 225.07, 235.78, 220.89]], "area": 237.83700000000005, "iscrowd": 0, "image_id": 13774, "bbox": [235.78, 209.66, 17.24, 17.76], "category_id": 34, "id": 602311}, {"segmentation": [[106.65, 155.46, 113.52, 180.37, 122.32, 202.48, 119.1, 204.41, 117.17, 199.91, 112.23, 181.23, 105.36, 155.25]], "area": 103.96229999999969, "iscrowd": 0, "image_id": 18491, "bbox": [105.36, 155.25, 16.96, 49.16], "category_id": 39, "id": 629357}, {"segmentation": [[313.57, 217.13, 306.97, 225.2, 300.67, 235.76, 298.03, 242.06, 300.08, 244.85, 305.36, 245.29, 311.96, 235.32, 314.75, 231.36, 318.85, 229.3, 319.0, 218.74, 316.21, 216.69]], "area": 285.53729999999996, "iscrowd": 0, "image_id": 18491, "bbox": [298.03, 216.69, 20.97, 28.6], "category_id": 40, "id": 634537}, {"segmentation": [[400.29, 238.18, 412.62, 242.92, 434.43, 241.02, 439.18, 229.64, 441.07, 221.1, 451.51, 223.0, 459.1, 218.26, 471.43, 210.67, 480.91, 212.57, 483.76, 224.9, 474.27, 240.07, 466.68, 250.51, 459.1, 268.53, 457.2, 278.02, 445.82, 278.02, 438.23, 278.02, 429.69, 268.53, 420.21, 267.58, 415.46, 257.15, 406.93, 253.35, 393.65, 253.35, 388.9, 251.46, 396.49, 239.13]], "area": 2867.833599999999, "iscrowd": 0, "image_id": 10764, "bbox": [388.9, 210.67, 94.86, 67.35], "category_id": 40, "id": 635135}, {"segmentation": [[323.24, 142.51, 331.53, 142.51, 331.68, 146.21, 329.46, 153.31, 328.42, 154.94, 327.54, 157.6, 323.98, 158.34, 320.58, 154.79, 318.95, 151.39, 319.99, 148.57, 322.5, 146.8, 322.8, 143.69]], "area": 138.23775000000003, "iscrowd": 0, "image_id": 18491, "bbox": [318.95, 142.51, 12.73, 15.83], "category_id": 40, "id": 635185}, {"segmentation": [[107.9, 492.04, 113.18, 485.71, 117.93, 483.07, 121.62, 474.63, 129.54, 457.21, 124.79, 455.1, 122.15, 452.99, 126.37, 447.71, 131.12, 442.96, 134.81, 442.96, 139.56, 431.88, 145.37, 427.13, 153.28, 427.13, 159.09, 429.77, 166.48, 435.05, 170.7, 440.85, 174.92, 448.24, 175.98, 458.8, 167.0, 485.71, 152.23, 519.48, 146.42, 516.84, 142.73, 523.18, 137.98, 521.06, 134.29, 511.57, 125.32, 502.07, 118.98, 497.32, 111.6, 496.79]], "area": 3725.12715, "iscrowd": 0, "image_id": 13201, "bbox": [107.9, 427.13, 68.08, 96.05], "category_id": 41, "id": 638543}, {"segmentation": [[177.65, 165.88, 234.12, 148.24, 309.41, 147.06, 321.18, 145.88, 343.53, 134.12, 320.0, 90.59, 244.71, 72.94, 156.47, 85.88, 117.65, 103.53, 104.71, 114.12, 74.12, 147.06, 48.24, 201.18, 44.71, 231.76, 55.29, 272.94, 76.47, 290.59, 97.65, 284.71, 116.47, 241.18, 158.82, 184.71]], "area": 26679.727, "iscrowd": 0, "image_id": 13004, "bbox": [44.71, 72.94, 298.82, 217.65], "category_id": 52, "id": 1043946}, {"segmentation": [[280.05, 355.41, 267.68, 376.78, 285.68, 384.65, 290.17, 402.64, 291.3, 419.52, 262.06, 442.01, 219.32, 448.76, 191.2, 446.51, 178.83, 434.14, 167.58, 428.51, 147.34, 400.4, 121.47, 389.15, 111.35, 377.9, 97.85, 354.28, 92.23, 319.42, 85.48, 283.43, 87.73, 255.31, 101.22, 233.94, 119.22, 210.32, 142.84, 178.83, 169.83, 157.46, 214.82, 134.96, 239.56, 128.22, 227.19, 140.59, 213.69, 158.58, 219.32, 172.08, 238.44, 200.2, 264.31, 227.19, 283.43, 254.18, 307.04, 269.93, 330.66, 269.93, 338.54, 263.18, 338.54, 228.32, 316.04, 203.57, 287.92, 178.83, 296.92, 152.96, 301.42, 133.84, 326.16, 136.09, 348.66, 145.09, 363.28, 154.08, 377.9, 164.21, 381.28, 175.45, 374.53, 187.83, 366.65, 205.82, 354.28, 208.07, 346.41, 220.44, 349.78, 235.06, 355.41, 256.43, 373.4, 260.93, 395.9, 273.3, 424.01, 278.93, 448.76, 282.3, 442.01, 300.3, 435.26, 323.92, 428.51, 339.66, 427.39, 352.03, 399.27, 338.54, 373.4, 335.16, 347.53, 345.28, 328.41, 356.53, 311.54, 347.53, 298.05, 350.91]], "area": 62490.61550000001, "iscrowd": 0, "image_id": 19221, "bbox": [85.48, 128.22, 363.28, 320.54], "category_id": 56, "id": 1056513}, {"segmentation": [[123.94, 113.35, 152.11, 141.52, 178.03, 135.89, 197.18, 121.24, 218.59, 114.48, 216.34, 76.17, 241.13, 50.25, 274.93, 40.11, 290.7, 52.51, 340.28, 67.15, 403.38, 87.44, 452.96, 55.89, 496.9, 81.8, 507.04, 107.72, 552.11, 94.2, 583.66, 89.69, 602.82, 89.69, 597.18, 103.21, 619.72, 132.51, 640.0, 116.73, 640.0, 259.83, 640.0, 351.1, 638.87, 405.18, 602.82, 400.68, 573.52, 392.79, 538.59, 381.52, 505.92, 369.13, 500.28, 382.65, 486.76, 419.83, 438.31, 444.62, 401.13, 454.76, 323.38, 440.11, 307.61, 472.79, 287.32, 464.9, 297.46, 435.61, 271.55, 414.2, 250.14, 410.82, 214.08, 422.08, 193.8, 414.2, 160.0, 427.72, 150.99, 446.87, 110.42, 450.25, 47.32, 458.14, 24.79, 425.46, 58.59, 389.41, 52.96, 347.72, 65.35, 328.56, 52.96, 288.0, 33.8, 354.48, 1.13, 355.61, 0.0, 189.97, 7.89, 195.61, 7.89, 164.06, 21.41, 160.68, 43.94, 168.56, 66.48, 157.3, 65.35, 134.76, 74.37, 132.51, 86.76, 113.35, 119.44, 114.48]], "area": 205946.95595000006, "iscrowd": 0, "image_id": 15278, "bbox": [0.0, 40.11, 640.0, 432.68], "category_id": 56, "id": 1057752}, {"segmentation": [[79.27, 126.07, 69.72, 137.53, 76.4, 189.1, 483.26, 415.45, 498.54, 410.67, 520.51, 420.22, 526.24, 407.81, 518.6, 401.12, 519.55, 380.11, 531.01, 385.84, 524.33, 358.15, 308.48, 29.61, 243.54, 42.98, 170.0, 68.76, 104.1, 107.92]], "area": 82770.7217, "iscrowd": 0, "image_id": 13597, "bbox": [69.72, 29.61, 461.29, 390.61], "category_id": 61, "id": 1086039}, {"segmentation": [[30.41, 10.14, 375.0, 6.76, 375.0, 476.35, 3.38, 484.23, 1.13, 14.64]], "area": 175772.03385, "iscrowd": 0, "image_id": 13004, "bbox": [1.13, 6.76, 373.87, 477.47], "category_id": 67, "id": 1091866}, {"segmentation": [[240.5, 234.44, 265.52, 230.8, 297.83, 236.26, 299.19, 300.86, 286.45, 300.86, 251.88, 298.59, 242.09, 306.07]], "area": 3853.217499999999, "iscrowd": 0, "image_id": 17182, "bbox": [240.5, 230.8, 58.69, 75.27], "category_id": 84, "id": 1139750}, {"segmentation": [[300.64, 301.67, 299.16, 238.38, 275.92, 230.97, 288.28, 229.48, 308.06, 235.42, 311.02, 297.22, 302.12, 300.18]], "area": 725.5337999999992, "iscrowd": 0, "image_id": 17182, "bbox": [275.92, 229.48, 35.1, 72.19], "category_id": 84, "id": 1145467}, {"segmentation": [[321.66, 235.95, 322.04, 265.58, 322.04, 296.91, 315.39, 298.05, 309.89, 296.91, 309.32, 246.97, 307.42, 235.19, 292.8, 229.5, 295.26, 226.84, 301.53, 227.98, 316.91, 232.15]], "area": 891.7369000000022, "iscrowd": 0, "image_id": 17182, "bbox": [292.8, 226.84, 29.24, 71.21], "category_id": 84, "id": 1147579}, {"segmentation": [[333.97, 295.27, 322.27, 297.22, 321.78, 235.78, 305.45, 229.2, 307.15, 226.76, 334.46, 233.1, 333.97, 295.76]], "area": 855.8296000000017, "iscrowd": 0, "image_id": 17182, "bbox": [305.45, 226.76, 29.01, 70.46], "category_id": 84, "id": 1148280}, {"segmentation": [[346.0, 232.83, 314.81, 228.57, 335.37, 236.37, 334.3, 294.86, 344.58, 293.09]], "area": 695.3083499999989, "iscrowd": 0, "image_id": 17182, "bbox": [314.81, 228.57, 31.19, 66.29], "category_id": 84, "id": 1149094}, {"segmentation": [[356.3, 231.15, 318.46, 228.11, 346.63, 234.19, 345.53, 293.29, 355.19, 291.36]], "area": 646.0348500000011, "iscrowd": 0, "image_id": 17182, "bbox": [318.46, 228.11, 37.84, 65.18], "category_id": 84, "id": 1149374}, {"segmentation": [[220.55, 295.05, 224.11, 300.13, 228.68, 302.16, 233.76, 299.62, 231.73, 291.49, 234.27, 274.71, 236.3, 264.04, 232.75, 256.42, 226.65, 252.35, 222.58, 248.28, 220.55, 242.69, 223.6, 238.63, 238.34, 237.61, 242.4, 236.59, 243.42, 229.99, 241.9, 217.79, 235.29, 211.18, 233.76, 207.62, 237.32, 194.91, 234.78, 191.36, 227.16, 189.32, 212.42, 193.9, 211.91, 201.52, 206.82, 209.15, 199.2, 226.43, 196.15, 243.71, 200.72, 252.86, 207.33, 264.55, 212.92, 267.09, 214.96, 280.81, 218.01, 290.98]], "area": 2937.9041, "iscrowd": 0, "image_id": 16228, "bbox": [196.15, 189.32, 47.27, 112.84], "category_id": 1, "id": 1206679}, {"segmentation": [[319.31, 178.53, 330.17, 179.05, 334.31, 179.74, 335.17, 219.56, 335.0, 220.77, 324.31, 225.94, 323.97, 224.56, 324.14, 220.08, 321.38, 221.28, 319.83, 223.52, 319.31, 226.11, 317.93, 223.6, 316.55, 226.01, 315.18, 227.05, 314.31, 224.46, 315.87, 221.88, 320.17, 218.43, 323.28, 216.7, 324.83, 210.33, 326.04, 204.29, 326.04, 199.98, 326.04, 191.36, 321.9, 189.64, 321.04, 188.78, 320.86, 184.64, 321.21, 182.74, 321.04, 181.37, 319.14, 180.5, 318.28, 179.64, 318.97, 178.95, 318.97, 178.95], [337.59, 187.4, 342.93, 193.09, 346.38, 202.57, 346.55, 210.5, 347.76, 214.29, 351.03, 217.39, 338.1, 230.32, 337.24, 220.5, 335.69, 187.23], [334.14, 224.12, 328.1, 227.56, 331.38, 231.53, 333.79, 233.43, 336.55, 231.53, 336.21, 228.25, 335.86, 223.43, 333.62, 223.6]], "area": 919.7349499999996, "iscrowd": 0, "image_id": 16228, "bbox": [314.31, 178.53, 36.72, 54.9], "category_id": 1, "id": 1218312}, {"segmentation": [[347.98, 280.81, 354.39, 282.41, 361.2, 278.41, 366.01, 275.2, 368.81, 281.61, 363.2, 292.83, 358.4, 296.84, 353.99, 302.85, 349.18, 305.25, 344.77, 302.44, 341.97, 303.25, 331.55, 302.44, 322.34, 302.04, 319.53, 299.64, 321.94, 295.23, 315.53, 285.22, 309.12, 278.81, 307.11, 273.6, 300.7, 274.4, 287.48, 277.2, 277.86, 281.21, 265.44, 280.81, 259.03, 275.2, 250.22, 267.99, 244.61, 240.34, 243.01, 235.13, 245.41, 221.91, 254.63, 207.09, 260.64, 203.08, 261.84, 199.08, 261.84, 196.27, 265.84, 189.06, 269.45, 184.65, 282.27, 186.25, 287.48, 191.06, 288.28, 199.08, 293.09, 204.28, 290.69, 207.49, 286.28, 205.49, 279.47, 219.11, 285.88, 223.52, 290.28, 229.93, 292.29, 233.93, 288.68, 237.14, 283.87, 235.54, 284.28, 242.35, 286.68, 249.56, 293.09, 250.76, 299.9, 252.76, 305.51, 254.37, 311.92, 253.56, 315.53, 254.77, 322.34, 263.98, 322.74, 265.18, 333.15, 274.0, 339.97, 278.81, 350.38, 282.41]], "area": 5264.408100000001, "iscrowd": 0, "image_id": 18491, "bbox": [243.01, 184.65, 125.8, 120.6], "category_id": 1, "id": 1235753}, {"segmentation": [[90.23, 206.77, 92.55, 215.4, 92.55, 225.83, 96.03, 230.08, 95.13, 237.42, 100.79, 250.95, 104.01, 254.55, 105.17, 254.94, 107.49, 255.07, 109.04, 255.2, 109.04, 256.87, 106.85, 259.58, 101.82, 261.38, 98.73, 261.38, 94.61, 253.78, 87.66, 243.09, 84.82, 247.86, 83.28, 255.71, 84.44, 258.81, 88.3, 259.84, 89.46, 261.77, 80.7, 263.06, 77.48, 262.15, 77.87, 254.94, 77.87, 248.89, 78.64, 234.85, 78.38, 231.5, 76.84, 227.12, 78.25, 224.03, 77.22, 209.09, 78.64, 206.64, 80.7, 203.81, 82.12, 202.77, 82.5, 199.3, 86.11, 197.24, 90.49, 196.72, 92.55, 197.88, 92.55, 203.42]], "area": 986.9378500000001, "iscrowd": 0, "image_id": 16228, "bbox": [76.84, 196.72, 32.2, 66.34], "category_id": 1, "id": 1242637}, {"segmentation": [[359.83, 292.66, 351.2, 275.39, 343.52, 260.04, 343.52, 246.6, 348.32, 239.89, 351.2, 227.41, 341.6, 222.62, 333.92, 221.66, 322.41, 231.25, 315.69, 242.77, 306.1, 250.44, 301.3, 244.69, 301.3, 233.17, 307.06, 225.49, 318.57, 215.9, 323.37, 213.02, 330.09, 207.26, 330.09, 199.59, 336.8, 186.15, 345.44, 189.99, 355.03, 197.67, 356.95, 203.42, 366.55, 208.22, 374.22, 213.98, 389.58, 224.53, 390.54, 231.25, 406.85, 242.77, 404.93, 256.2, 394.38, 260.04, 386.7, 267.71, 390.54, 273.47, 393.42, 277.31, 405.89, 283.07, 412.61, 288.82, 422.2, 300.34, 422.2, 304.18, 410.69, 307.06, 382.86, 312.81, 377.1, 308.02, 375.18, 292.66]], "area": 5928.673350000003, "iscrowd": 0, "image_id": 18491, "bbox": [301.3, 186.15, 120.9, 126.66], "category_id": 1, "id": 1247297}, {"segmentation": [[343.03, 183.99, 354.43, 151.3, 378.0, 207.55, 390.92, 206.03, 375.72, 146.74, 378.76, 95.04, 364.32, 94.28, 359.76, 69.96, 342.27, 82.88, 343.03, 96.56, 332.39, 113.29, 331.63, 147.5, 330.11, 180.19, 344.55, 188.55]], "area": 4403.250250000003, "iscrowd": 0, "image_id": 18491, "bbox": [330.11, 69.96, 60.81, 137.59], "category_id": 1, "id": 1250410}, {"segmentation": [[328.32, 156.97, 331.5, 148.84, 329.73, 141.41, 329.02, 126.91, 324.07, 115.59, 316.29, 94.02, 305.68, 89.07, 303.21, 86.95, 304.97, 84.12, 305.68, 72.8, 294.36, 68.91, 288.71, 72.45, 288.0, 82.0, 293.66, 94.37, 282.34, 97.2, 281.28, 121.25, 274.91, 132.92, 275.62, 138.23, 284.11, 136.81, 285.52, 135.4, 283.05, 130.8, 289.77, 121.96, 291.53, 134.34, 285.88, 160.51, 285.17, 173.24, 286.94, 190.92, 289.41, 203.67, 285.17, 211.81, 298.25, 216.41, 296.84, 204.38, 300.73, 188.47, 302.14, 175.03, 303.21, 164.77, 304.62, 154.87, 307.8, 166.54, 311.69, 185.64, 313.82, 189.88, 311.34, 191.3, 314.52, 190.24, 315.58, 202.26, 309.57, 207.21, 316.29, 201.91, 317.71, 206.86, 313.11, 211.46, 314.52, 215.7, 330.79, 212.52, 328.32, 205.44, 326.9, 185.99, 321.24, 173.61, 319.83, 153.1, 321.95, 144.69, 315.23, 130.54, 318.06, 125.94, 315.94, 119.93, 321.6, 124.53, 323.72, 141.86, 321.6, 148.93, 322.66, 157.42, 325.84, 158.13]], "area": 4446.742150000001, "iscrowd": 0, "image_id": 18491, "bbox": [274.91, 68.91, 56.59, 147.5], "category_id": 1, "id": 1266544}, {"segmentation": [[195.83, 150.11, 208.25, 145.34, 209.2, 94.71, 199.65, 88.02, 192.96, 77.51, 183.41, 77.51, 181.5, 91.84, 177.68, 105.21, 177.68, 132.92, 180.54, 152.02, 193.92, 150.11]], "area": 1959.6433999999997, "iscrowd": 0, "image_id": 18491, "bbox": [177.68, 77.51, 31.52, 74.51], "category_id": 1, "id": 1269631}, {"segmentation": [[76.05, 214.29, 83.77, 217.47, 88.76, 213.39, 84.22, 205.22, 83.77, 196.59, 89.21, 177.07, 92.85, 162.55, 101.02, 175.71, 104.19, 189.33, 106.46, 203.4, 106.01, 206.58, 120.53, 213.84, 121.44, 211.57, 122.8, 209.76, 113.27, 200.68, 111.91, 197.5, 109.64, 173.44, 106.92, 153.93, 107.37, 136.68, 104.19, 118.52, 94.66, 113.53, 99.2, 106.27, 95.57, 98.1, 88.76, 95.37, 85.58, 95.37, 82.86, 108.99, 81.04, 114.89, 78.32, 119.43, 69.7, 139.4, 71.51, 153.93, 74.69, 157.1, 78.77, 157.1, 81.95, 163.0, 76.96, 193.42, 76.05, 205.8, 75.14, 213.06]], "area": 2735.70945, "iscrowd": 0, "image_id": 18491, "bbox": [69.7, 95.37, 53.1, 122.1], "category_id": 1, "id": 1277912}, {"segmentation": [[65.21, 119.75, 60.06, 111.38, 61.77, 104.08, 61.56, 99.78, 60.06, 95.06, 58.77, 89.69, 59.41, 80.25, 62.63, 75.74, 67.14, 71.01, 66.93, 65.22, 71.65, 61.57, 79.16, 63.28, 80.02, 72.09, 87.32, 76.38, 89.47, 87.76, 88.83, 90.34, 95.05, 97.42, 89.9, 96.13, 86.46, 95.92, 83.89, 100.21, 81.1, 106.01, 82.17, 109.02, 80.24, 114.81, 76.59, 117.18, 79.16, 108.16, 77.45, 104.08, 74.23, 103.65, 71.22, 107.3, 70.36, 110.52, 69.5, 115.24, 68.21, 117.61, 67.14, 118.25]], "area": 1137.3147999999997, "iscrowd": 0, "image_id": 18491, "bbox": [58.77, 61.57, 36.28, 58.18], "category_id": 1, "id": 1280740}, {"segmentation": [[122.79, 118.53, 125.71, 85.54, 133.23, 74.68, 138.66, 50.04, 141.16, 44.61, 150.35, 26.65, 157.04, 35.83, 158.71, 49.2, 164.14, 51.71, 159.96, 126.47, 153.69, 126.47, 151.61, 122.71, 151.61, 115.61, 147.01, 111.01, 146.18, 85.54, 139.08, 84.7, 133.23, 92.64, 132.81, 125.22, 122.79, 126.05]], "area": 2216.2866499999996, "iscrowd": 0, "image_id": 18491, "bbox": [122.79, 26.65, 41.35, 99.82], "category_id": 1, "id": 1287702}, {"segmentation": [[34.51, 146.57, 34.08, 139.27, 37.51, 135.41, 39.88, 134.55, 41.16, 129.18, 42.24, 123.38, 42.45, 120.38, 43.31, 116.08, 44.81, 109.86, 44.81, 104.49, 43.95, 99.98, 35.15, 94.18, 35.37, 86.45, 34.29, 82.59, 29.35, 81.09, 24.63, 83.45, 23.56, 85.81, 20.98, 90.53, 16.9, 98.91, 13.25, 109.86, 13.04, 120.38, 15.18, 129.61, 19.05, 136.91, 23.34, 146.14, 27.85, 147.65, 33.65, 147.0]], "area": 1449.07155, "iscrowd": 0, "image_id": 18491, "bbox": [13.04, 81.09, 31.77, 66.56], "category_id": 1, "id": 1290047}, {"segmentation": [[119.25, 255.0, 117.94, 249.25, 120.29, 241.68, 122.12, 232.01, 120.03, 221.04, 119.51, 218.95, 118.46, 216.33, 117.42, 212.15, 120.81, 204.57, 121.08, 203.27, 119.77, 199.61, 120.29, 196.74, 123.17, 195.69, 127.87, 198.04, 130.22, 199.61, 127.61, 206.66, 127.35, 213.72, 128.91, 221.3, 126.82, 228.35, 126.56, 234.88, 124.47, 244.03, 121.08, 252.65]], "area": 383.46764999999976, "iscrowd": 0, "image_id": 16228, "bbox": [117.42, 195.69, 12.8, 59.31], "category_id": 1, "id": 1291466}, {"segmentation": [[141.05, 219.12, 134.53, 218.3, 133.31, 193.67, 134.94, 190.61, 136.97, 188.98, 140.64, 190.0, 142.06, 201.61, 139.82, 204.46, 141.25, 211.18, 142.88, 213.41, 144.51, 216.67, 143.08, 218.91], [133.11, 200.79, 128.42, 204.46, 127.0, 213.62, 129.44, 220.74, 126.79, 228.28, 125.98, 238.46, 121.5, 250.87, 121.3, 256.78, 124.96, 257.39, 131.88, 257.19, 134.73, 256.57, 131.68, 254.54, 129.24, 252.5, 129.85, 249.86, 132.7, 244.56, 132.5, 213.82, 132.7, 206.29]], "area": 567.2201499999994, "iscrowd": 0, "image_id": 16228, "bbox": [121.3, 188.98, 23.21, 68.41], "category_id": 1, "id": 1294336}, {"segmentation": [[5.27, 92.66, 6.49, 97.29, 10.39, 100.95, 11.85, 110.21, 12.34, 115.57, 10.39, 125.08, 10.14, 133.61, 7.71, 139.95, 5.51, 145.8, 5.51, 145.8, 6.98, 147.51, 0.39, 147.75, 0.39, 83.64, 3.08, 82.91, 4.78, 85.59, 5.51, 87.29]], "area": 550.3268999999999, "iscrowd": 0, "image_id": 18491, "bbox": [0.39, 82.91, 11.95, 64.84], "category_id": 1, "id": 1307857}, {"segmentation": [[39.77, 233.8, 40.52, 240.08, 42.28, 244.61, 40.77, 257.67, 40.52, 262.69, 49.06, 261.69, 49.31, 252.39, 49.56, 251.89, 52.83, 250.38, 53.58, 244.1, 50.32, 236.07, 50.32, 229.79, 51.07, 226.77, 48.31, 220.99, 46.8, 213.96, 48.06, 208.68, 48.81, 203.91, 46.3, 200.9, 38.51, 203.16, 38.26, 211.7, 38.26, 223.0]], "area": 607.4024000000001, "iscrowd": 0, "image_id": 16228, "bbox": [38.26, 200.9, 15.32, 61.79], "category_id": 1, "id": 1311594}, {"segmentation": [[159.23, 219.16, 159.63, 216.1, 159.84, 212.03, 159.23, 210.61, 156.38, 210.61, 156.17, 208.77, 157.6, 207.35, 158.41, 205.72, 158.41, 204.29, 158.82, 201.65, 159.43, 199.82, 161.06, 198.59, 163.71, 197.98, 165.74, 202.67, 166.76, 219.16, 159.63, 219.77]], "area": 151.30675000000025, "iscrowd": 0, "image_id": 16228, "bbox": [156.17, 197.98, 10.59, 21.79], "category_id": 1, "id": 1321605}, {"segmentation": [[149.44, 143.43, 210.85, 141.07, 223.94, 141.29, 223.94, 144.29, 192.6, 146.22, 148.58, 146.65], [142.35, 144.29, 104.57, 144.51, 105.64, 147.73, 139.78, 148.59, 144.93, 148.37, 144.29, 145.79, 143.86, 145.58], [68.07, 146.05, 2.46, 147.53, 0.0, 150.99, 0.49, 152.96, 69.55, 151.97, 71.52, 150.99], [79.91, 146.55, 74.48, 146.55, 74.48, 149.01, 80.4, 149.51]], "area": 845.11925, "iscrowd": 0, "image_id": 18491, "bbox": [0.0, 141.07, 223.94, 11.89], "category_id": 15, "id": 1394556}, {"segmentation": [[408.63, 170.83, 406.8, 193.48, 414.11, 191.65, 415.2, 170.1, 427.26, 163.16, 440.41, 167.55, 461.22, 159.87, 471.09, 163.89, 477.3, 150.01, 470.36, 144.9, 417.4, 129.92, 406.07, 130.66, 376.12, 138.69, 349.82, 154.76, 349.46, 168.64, 356.4, 170.83, 367.72, 166.81, 382.7, 169.01, 396.58, 164.62, 407.17, 166.45]], "area": 3626.221100000001, "iscrowd": 0, "image_id": 16228, "bbox": [349.46, 129.92, 127.84, 63.56], "category_id": 28, "id": 1425221}, {"segmentation": [[591.69, 169.62, 601.49, 164.59, 610.03, 162.08, 615.31, 161.33, 626.36, 165.6, 632.64, 168.36, 633.39, 170.12, 632.14, 172.13, 616.06, 173.38, 604.25, 174.39, 599.23, 174.89, 594.46, 173.89, 591.94, 172.38]], "area": 364.80684999999886, "iscrowd": 0, "image_id": 16228, "bbox": [591.69, 161.33, 41.7, 13.56], "category_id": 28, "id": 1425987}, {"segmentation": [[226.04, 214.83, 224.69, 214.46, 223.08, 216.19, 223.82, 217.8, 223.7, 221.51, 225.67, 228.06, 226.29, 231.15, 225.8, 238.69, 226.66, 241.65, 228.76, 243.88, 228.52, 230.53, 228.76, 225.71, 227.03, 220.03, 225.8, 216.69, 226.54, 215.21]], "area": 80.16185000000007, "iscrowd": 0, "image_id": 16228, "bbox": [223.08, 214.46, 5.68, 29.42], "category_id": 32, "id": 1441803}, {"segmentation": [[428.1, 280.5, 428.38, 272.49, 432.24, 265.86, 438.32, 265.03, 439.7, 264.76, 443.02, 261.72, 445.78, 261.44, 444.12, 265.59, 444.12, 265.59, 446.05, 264.76, 450.75, 264.76, 454.62, 267.8, 455.72, 270.56, 455.72, 270.84, 455.72, 274.15, 455.44, 276.08, 455.44, 276.08, 455.44, 276.36, 455.17, 276.91]], "area": 359.12915000000055, "iscrowd": 0, "image_id": 17182, "bbox": [428.1, 261.44, 27.62, 19.06], "category_id": 53, "id": 1550078}, {"segmentation": [[414.49, 1.91, 507.13, 36.29, 590.22, 100.28, 612.19, 136.57, 639.89, 184.33, 639.89, 0.0], [2.87, 148.03, 33.43, 96.46, 113.65, 42.98, 169.04, 18.15, 217.75, 2.87, 0.96, 2.87], [593.09, 416.4, 621.74, 383.93, 631.29, 361.97, 639.89, 347.64, 639.89, 419.27, 596.91, 419.27], [0.0, 381.07, 13.37, 399.21, 23.88, 412.58, 30.56, 420.22, 1.91, 417.36]], "area": 27365.62059999999, "iscrowd": 0, "image_id": 13597, "bbox": [0.0, 0.0, 639.89, 420.22], "category_id": 67, "id": 1617660}, {"segmentation": [[71.21, 260.41, 72.35, 256.72, 71.5, 251.89, 70.08, 247.63, 68.94, 239.96, 67.81, 234.28, 67.81, 229.46, 68.37, 226.05, 67.52, 221.51, 66.67, 217.53, 69.23, 214.98, 69.51, 212.7, 68.66, 209.86, 69.79, 207.31, 73.49, 205.61, 77.18, 207.88, 77.18, 210.15, 76.61, 212.99, 76.89, 216.4, 77.46, 221.79, 77.18, 227.75, 76.61, 230.59, 76.61, 234.0, 77.18, 236.84, 78.88, 245.07, 80.58, 251.61, 78.31, 255.86, 76.04, 258.99, 75.47, 260.12]], "area": 454.97255, "iscrowd": 0, "image_id": 16228, "bbox": [66.67, 205.61, 13.91, 54.8], "category_id": 1, "id": 1676785}, {"segmentation": [[18.7, 223.18, 18.7, 228.54, 17.04, 230.94, 19.9, 237.41, 22.03, 243.69, 27.75, 252.74, 28.95, 254.41, 29.23, 255.88, 30.71, 259.49, 27.66, 261.43, 25.26, 261.06, 19.25, 251.45, 15.84, 258.19, 15.84, 261.61, 19.81, 262.91, 12.6, 263.28, 8.26, 259.3, 14.17, 252.19, 11.13, 247.57, 9.74, 241.38, 10.39, 238.06, 8.72, 234.55, 11.4, 228.91, 11.59, 225.4, 14.36, 222.72, 17.22, 222.44, 17.31, 222.44]], "area": 418.1907999999998, "iscrowd": 0, "image_id": 16228, "bbox": [8.26, 222.44, 22.45, 40.84], "category_id": 1, "id": 1703142}, {"segmentation": [[264.48, 216.5, 264.48, 212.42, 265.59, 209.83, 267.63, 207.79, 267.91, 207.42, 268.37, 207.24, 268.65, 209.09, 269.29, 214.37, 269.66, 217.52, 266.7, 218.26], [270.41, 206.13, 270.78, 202.33, 271.05, 198.17, 271.98, 196.87, 273.65, 195.67, 275.5, 196.22, 277.16, 198.35, 277.81, 200.67, 278.46, 202.52, 277.81, 204.09, 279.29, 206.78, 281.7, 209.0, 283.09, 211.59, 283.09, 214.18, 281.05, 217.52, 280.87, 217.79, 280.87, 218.91, 279.29, 219.46, 274.48, 219.18, 274.02, 219.18, 271.24, 219.28, 270.59, 218.53]], "area": 254.53865000000008, "iscrowd": 0, "image_id": 16228, "bbox": [264.48, 195.67, 18.61, 23.79], "category_id": 1, "id": 1704592}, {"segmentation": [[530.65, 200.27, 540.31, 199.7, 540.59, 191.46, 547.97, 193.17, 547.97, 193.17, 541.16, 185.5, 537.18, 180.39, 538.6, 177.83, 538.6, 177.83, 536.9, 170.45, 531.22, 175.56, 531.22, 175.56, 533.21, 178.12, 528.67, 186.35, 531.79, 191.18]], "area": 254.42455000000007, "iscrowd": 0, "image_id": 16228, "bbox": [528.67, 170.45, 19.3, 29.82], "category_id": 1, "id": 1705724}, {"segmentation": [[409.89, 280.45, 407.73, 316.04, 403.42, 336.54, 365.66, 336.54, 368.9, 326.83, 384.0, 325.75, 394.79, 276.13, 395.87, 262.11, 386.16, 254.56, 394.79, 235.15, 399.1, 224.36, 399.1, 193.08, 405.57, 180.13, 396.94, 155.33, 401.26, 138.07, 427.15, 142.38, 434.7, 163.96, 431.46, 172.58, 437.93, 196.31, 428.22, 240.54, 432.54, 280.45, 446.56, 334.38, 412.04, 332.22, 414.2, 325.75, 424.99, 323.6, 427.15, 297.71, 412.04, 280.45]], "area": 7072.564200000001, "iscrowd": 0, "image_id": 15956, "bbox": [365.66, 138.07, 80.9, 198.47], "category_id": 1, "id": 1719503}, {"segmentation": [[91.51, 211.77, 93.26, 210.15, 92.18, 207.04, 92.72, 204.21, 94.21, 202.99, 96.91, 202.72, 99.48, 203.13, 99.62, 205.96, 99.08, 209.07, 97.99, 210.29, 98.8, 213.8, 99.75, 216.23, 99.75, 220.02, 99.75, 221.23, 101.37, 222.58, 101.24, 224.75, 100.02, 225.15, 98.94, 224.88, 98.13, 227.85, 97.99, 231.23, 98.67, 232.85, 100.16, 232.45, 102.32, 245.69, 105.56, 248.8, 105.43, 252.04, 104.08, 253.13, 102.86, 251.37, 101.37, 247.31, 100.02, 245.29, 97.05, 243.94, 95.29, 237.18, 95.97, 231.91, 93.8, 227.18, 93.13, 225.56, 91.78, 215.56, 91.64, 212.45], [87.72, 243.8, 85.29, 252.45, 85.02, 255.83, 87.32, 255.96, 89.35, 256.64, 92.18, 257.18, 93.26, 256.5, 91.51, 254.88, 90.16, 254.48, 88.67, 253.8, 90.29, 250.15, 91.37, 247.04, 90.56, 245.42, 88.8, 243.8]], "area": 325.05034999999987, "iscrowd": 0, "image_id": 16228, "bbox": [85.02, 202.72, 20.54, 54.46], "category_id": 1, "id": 1739202}, {"segmentation": [[207.0, 319.27, 123.55, 280.26, 133.3, 262.92, 130.05, 254.25, 126.8, 175.13, 119.21, 169.71, 138.72, 161.04, 139.8, 151.29, 222.17, 135.03, 295.86, 136.12, 299.11, 150.21, 346.8, 156.71, 335.96, 163.21, 339.21, 295.43, 290.44, 312.77, 290.44, 300.85, 259.01, 303.01, 237.34, 314.94]], "area": 33878.41649999999, "iscrowd": 0, "image_id": 16228, "bbox": [119.21, 135.03, 227.59, 184.24], "category_id": 7, "id": 1795625}, {"segmentation": [[332.13, 266.04, 335.22, 265.2, 336.65, 265.08, 337.12, 263.3, 339.26, 262.59, 341.88, 262.35, 344.85, 263.18, 346.51, 263.78, 348.65, 266.15, 350.08, 267.22, 351.03, 267.22, 351.03, 266.39, 351.03, 265.2, 351.62, 263.78, 352.22, 263.3, 355.19, 263.78, 356.02, 264.73, 356.26, 266.51, 355.78, 267.82, 355.31, 268.53, 355.78, 271.98, 355.78, 275.9, 354.83, 277.44, 353.05, 277.44, 351.98, 277.33, 350.67, 277.09, 350.08, 276.61, 349.25, 276.14, 348.65, 275.66, 348.53, 275.42, 347.46, 275.42, 346.04, 274.83, 345.8, 274.24, 345.56, 274.71, 343.42, 275.9, 344.85, 276.97, 344.85, 278.99, 343.3, 285.29, 341.28, 286.83, 341.05, 284.81, 341.88, 282.2, 342.47, 279.94, 342.71, 278.39, 342.47, 276.85, 341.16, 279.46, 340.93, 281.25, 339.14, 283.62, 337.6, 283.62, 338.67, 281.25, 339.14, 279.35, 339.62, 277.8, 339.98, 276.26, 336.29, 276.49, 334.87, 274.71, 333.44, 269.96, 332.25, 267.7]], "area": 307.4943499999999, "iscrowd": 0, "image_id": 16010, "bbox": [332.13, 262.35, 24.13, 24.48], "category_id": 21, "id": 1820374}, {"segmentation": [[307.58, 285.56, 305.92, 283.07, 305.42, 276.27, 307.08, 273.95, 307.91, 270.31, 309.57, 266.33, 314.37, 264.67, 318.52, 264.17, 322.0, 263.68, 325.81, 263.18, 328.79, 263.68, 330.62, 265.33, 332.44, 269.64, 335.59, 277.93, 338.57, 276.77, 338.9, 278.43, 337.08, 280.09, 337.58, 283.73, 336.58, 287.71, 334.1, 290.2, 330.95, 288.54, 330.45, 287.05, 330.12, 286.72, 330.34, 283.62, 327.5, 282.07, 325.69, 280.13, 324.66, 277.55, 322.98, 287.62, 322.59, 289.56, 321.43, 288.53, 321.3, 286.46, 322.33, 283.62, 323.11, 280.26, 323.62, 277.94, 323.36, 276.77, 321.43, 277.94, 318.2, 277.94, 315.74, 277.94, 315.62, 277.16, 313.16, 281.68, 314.71, 286.98, 316.0, 289.82, 314.84, 289.82, 311.87, 284.52, 310.71, 281.42, 312.13, 278.71, 313.16, 277.16, 311.22, 276.65, 310.19, 282.97, 310.19, 288.92, 309.16, 290.08, 308.64, 288.53, 309.16, 279.62, 309.16, 274.32, 308.9, 274.19, 308.25, 274.71]], "area": 507.7857499999995, "iscrowd": 0, "image_id": 16010, "bbox": [305.42, 263.18, 33.48, 27.02], "category_id": 21, "id": 1820429}, {"segmentation": [[618.39, 161.03, 633.02, 153.29, 633.39, 164.78, 630.61, 165.51, 625.65, 164.78, 618.15, 162.12]], "area": 107.22555000000007, "iscrowd": 0, "image_id": 16228, "bbox": [618.15, 153.29, 15.24, 12.22], "category_id": 28, "id": 1830129}, {"segmentation": [[99.47, 155.72, 105.28, 152.75, 113.5, 151.95, 124.34, 153.55, 132.1, 160.28, 134.04, 163.82, 133.24, 167.47, 135.98, 172.04, 134.15, 182.42, 128.22, 189.15, 117.61, 194.4, 108.59, 194.29, 99.47, 190.41, 92.85, 183.67, 92.16, 179.0, 90.11, 170.44, 90.45, 166.67, 93.19, 162.0]], "area": 1515.3847000000003, "iscrowd": 0, "image_id": 13291, "bbox": [90.11, 151.95, 45.87, 42.45], "category_id": 34, "id": 1844890}, {"segmentation": [[250.6, 211.44, 250.6, 211.44, 246.25, 187.95, 227.1, 185.34, 217.53, 199.26, 217.53, 215.79, 222.75, 218.4, 233.19, 216.66, 237.55, 221.01]], "area": 896.86995, "iscrowd": 0, "image_id": 13291, "bbox": [217.53, 185.34, 33.07, 35.67], "category_id": 34, "id": 1844909}, {"segmentation": [[212.31, 229.71, 215.79, 213.18, 214.92, 201.0, 203.61, 195.78, 190.56, 199.26, 182.73, 210.57, 184.47, 226.23, 191.43, 229.71, 200.13, 232.32]], "area": 954.8293500000001, "iscrowd": 0, "image_id": 13291, "bbox": [182.73, 195.78, 33.06, 36.54], "category_id": 34, "id": 1844920}, {"segmentation": [[256.4, 174.3, 259.6, 170.76, 262.68, 167.45, 265.53, 166.43, 277.4, 165.63, 287.78, 169.62, 294.63, 180.8, 293.71, 181.94, 289.95, 184.45, 286.87, 187.42, 292.12, 188.79, 293.37, 188.33, 293.49, 196.32, 285.73, 203.62, 278.2, 206.13, 270.89, 206.48, 257.54, 200.2, 257.43, 197.8, 258.46, 196.44, 259.48, 193.93, 258.34, 191.76, 254.46, 188.56, 252.41, 184.0, 255.49, 176.7]], "area": 1283.6699499999993, "iscrowd": 0, "image_id": 13291, "bbox": [252.41, 165.63, 42.22, 40.85], "category_id": 34, "id": 1844923}, {"segmentation": [[16.2, 205.04, 15.69, 215.76, 14.41, 221.62, 10.33, 225.19, 9.82, 231.57, 9.06, 242.54, 9.57, 246.11, 7.02, 253.6, 6.51, 200.55, 9.57, 198.51, 10.08, 194.68, 15.69, 194.94, 16.96, 197.74, 18.75, 199.27, 17.22, 201.83, 16.71, 204.89]], "area": 330.39009999999996, "iscrowd": 0, "image_id": 16228, "bbox": [6.51, 194.68, 12.24, 58.92], "category_id": 1, "id": 2018451}, {"segmentation": [[421.16, 266.88, 439.5, 265.02, 444.17, 270.61, 446.34, 276.21, 446.34, 286.16, 446.66, 305.13, 443.24, 292.69, 441.99, 281.81, 437.95, 270.3, 432.35, 269.99]], "area": 197.07139999999964, "iscrowd": 0, "image_id": 16010, "bbox": [421.16, 265.02, 25.5, 40.11], "category_id": 24, "id": 2070488}, {"segmentation": [[225.36, 136.61, 219.27, 142.7, 214.92, 149.66, 213.18, 154.01, 210.57, 156.62, 195.78, 157.49, 170.55, 174.03, 167.94, 177.51, 175.77, 206.22, 164.45, 213.18, 160.97, 214.05, 159.23, 225.36, 159.23, 229.71, 160.97, 232.32, 170.55, 242.77, 176.64, 246.25, 182.73, 251.47, 176.64, 254.95, 174.03, 263.65, 174.9, 267.13, 195.78, 257.56, 197.52, 256.69, 189.69, 243.64, 183.6, 235.81, 187.95, 234.06, 192.3, 241.03, 198.39, 241.03, 201.87, 241.03, 211.44, 239.29, 220.14, 230.58, 225.36, 227.97, 230.58, 228.84, 221.01, 249.73, 217.53, 252.34, 232.32, 261.04, 243.64, 260.17, 238.42, 251.47, 234.94, 249.73, 233.19, 242.77, 241.03, 227.1, 241.9, 222.75, 233.19, 220.14, 225.36, 220.14, 218.4, 213.18, 217.53, 203.61, 219.27, 196.65, 225.36, 187.08, 238.42, 183.6, 244.51, 187.95, 244.51, 187.95, 241.9, 185.34, 237.55, 180.12, 237.55, 172.29, 237.55, 161.84, 237.55, 153.14, 237.55, 141.83, 234.94, 138.35, 230.58, 137.48, 227.1, 137.48]], "area": 5920.345849999999, "iscrowd": 0, "image_id": 13291, "bbox": [159.23, 136.61, 85.28, 130.52], "category_id": 1, "id": 2155598}, {"segmentation": [[528.49, 136.83, 546.54, 143.48, 562.69, 140.63, 562.69, 140.63, 576.94, 128.28, 586.43, 117.83, 586.43, 113.08, 587.38, 109.28, 592.13, 110.23, 600.68, 97.88, 606.38, 87.44, 606.38, 87.44, 603.53, 67.49, 591.18, 54.19, 574.09, 52.29, 566.49, 53.24, 556.04, 62.74, 547.49, 72.24, 537.99, 73.19, 531.34, 77.94, 537.04, 88.39, 537.04, 100.73, 531.34, 113.08, 531.34, 113.08, 528.49, 130.18, 527.54, 136.83]], "area": 4814.376850000001, "iscrowd": 0, "image_id": 18575, "bbox": [527.54, 52.29, 78.84, 91.19], "category_id": 44, "id": 91656}, {"segmentation": [[10.12, 12.47, 221.45, 15.85, 224.82, 1.23, 359.72, 14.72, 483.37, 16.97, 522.72, 3.48, 567.68, 5.73, 640.0, 5.73, 638.5, 472.24, 7.87, 466.62]], "area": 288776.07965, "iscrowd": 0, "image_id": 18575, "bbox": [7.87, 1.23, 632.13, 471.01], "category_id": 67, "id": 121076}, {"segmentation": [[152.16, 149.23, 156.16, 153.54, 162.31, 154.77, 167.85, 156.0, 173.39, 156.62, 180.16, 156.62, 180.16, 154.15, 185.7, 156.0, 218.94, 153.23, 223.55, 155.38, 225.71, 151.69, 229.09, 159.08, 265.71, 156.92, 264.79, 150.15, 273.1, 147.69, 284.79, 148.31, 285.4, 145.23, 265.71, 132.31, 244.48, 124.0, 209.4, 123.07, 184.47, 128.92, 163.54, 138.77, 152.77, 143.08, 152.16, 147.69]], "area": 3273.6866499999996, "iscrowd": 0, "image_id": 16451, "bbox": [152.16, 123.07, 133.24, 36.01], "category_id": 28, "id": 282644}, {"segmentation": [[180.13, 122.97, 154.25, 139.15, 116.49, 158.56, 98.16, 182.29, 87.37, 196.31, 43.15, 208.18, 29.12, 222.2, 28.04, 234.07, 37.75, 280.45, 48.54, 335.46, 56.09, 366.74, 75.51, 403.42, 89.53, 429.3, 111.1, 456.27, 119.73, 470.29, 194.16, 467.06, 145.62, 430.38, 115.42, 407.73, 87.37, 357.03, 99.24, 326.83, 114.34, 288.0, 113.26, 266.43, 116.49, 243.78, 129.44, 220.04, 138.07, 201.71, 142.38, 192.0, 163.96, 169.35, 177.98, 158.56, 187.69, 125.12], [346.25, 220.04, 396.94, 229.75, 394.79, 250.25, 381.84, 275.06, 368.9, 334.38, 364.58, 334.38, 364.58, 273.98, 344.09, 264.27]], "area": 23100.089600000003, "iscrowd": 0, "image_id": 18193, "bbox": [28.04, 122.97, 368.9, 347.32], "category_id": 62, "id": 375844}, {"segmentation": [[180.11, 193.23, 203.49, 194.95, 209.33, 206.98, 206.93, 232.08, 203.32, 241.19, 199.53, 227.09, 186.12, 230.53, 178.73, 235.35, 174.95, 222.45, 175.29, 195.81]], "area": 1203.375250000001, "iscrowd": 0, "image_id": 16451, "bbox": [174.95, 193.23, 34.38, 47.96], "category_id": 62, "id": 377860}, {"segmentation": [[223.93, 230.79, 225.37, 243.79, 244.15, 251.02, 260.05, 243.79, 280.27, 238.01, 293.27, 238.01, 304.83, 243.79, 304.83, 265.46, 309.16, 281.35, 319.28, 294.36, 336.61, 305.91, 339.5, 316.03, 338.06, 327.58, 346.73, 330.47, 351.06, 330.47, 355.4, 342.03, 338.06, 355.03, 326.5, 362.26, 314.94, 375.26, 300.5, 388.26, 293.27, 396.93, 278.83, 399.82, 274.49, 391.15, 239.82, 378.15, 235.49, 373.81, 257.16, 340.59, 257.16, 337.7, 257.16, 323.25, 257.16, 323.25, 252.82, 317.47, 239.82, 330.47, 228.26, 339.14, 199.37, 353.59, 170.47, 355.03, 141.58, 356.48, 131.47, 359.37, 124.24, 359.37, 124.24, 342.03, 135.8, 339.14, 151.69, 340.59, 180.59, 333.36, 200.81, 326.14, 216.7, 313.14, 223.93, 304.47, 235.49, 291.47, 232.6, 284.24, 216.7, 277.02, 206.59, 268.35, 184.92, 255.35, 192.14, 236.57, 193.59, 229.35, 213.81, 226.46, 222.48, 225.01, 225.37, 232.23]], "area": 16779.1999, "iscrowd": 0, "image_id": 18519, "bbox": [124.24, 225.01, 231.16, 174.81], "category_id": 1, "id": 423290}, {"segmentation": [[531.97, 235.61, 526.24, 239.43, 521.46, 243.25, 511.91, 246.11, 513.82, 258.53, 555.84, 250.89, 558.71, 248.98, 559.66, 244.2, 541.52, 184.99, 545.34, 177.35, 560.62, 164.93, 576.85, 145.83, 570.17, 124.82, 537.7, 65.61, 536.74, 59.88, 531.01, 50.33, 525.28, 49.37, 510.96, 46.51, 509.04, 48.42, 488.99, 23.58, 479.44, 19.76, 477.53, 8.3, 470.84, 4.48, 468.93, 8.3, 468.93, 13.08, 473.71, 23.58, 503.31, 52.24, 504.27, 53.19, 501.4, 68.47, 508.09, 78.02, 516.69, 92.35, 518.6, 105.72, 521.46, 108.58, 509.04, 118.14, 502.36, 116.23, 497.58, 117.18, 515.73, 128.64, 525.28, 121.0, 530.06, 118.14, 531.01, 119.09, 531.97, 121.96, 530.06, 137.24, 501.4, 142.01, 492.81, 148.7, 493.76, 163.02, 510.0, 184.99, 512.87, 192.63, 512.87, 192.63, 517.64, 194.54, 515.73, 198.36, 507.13, 198.36, 501.4, 203.14, 499.49, 213.64, 507.13, 213.64, 510.0, 213.64, 515.73, 210.78, 521.46, 206.96, 522.42, 205.05, 523.37, 202.18, 518.6, 186.9, 518.6, 182.12, 520.51, 180.21, 527.19, 191.67, 528.15, 202.18, 535.79, 221.28, 536.74, 227.01, 536.74, 231.79, 535.79, 233.7]], "area": 8977.680499999997, "iscrowd": 0, "image_id": 13546, "bbox": [468.93, 4.48, 107.92, 254.05], "category_id": 1, "id": 479061}, {"segmentation": [[459.08, 97.45, 462.22, 84.86, 459.08, 75.23, 460.45, 64.02, 456.32, 63.82, 455.93, 62.25, 457.31, 58.32, 464.98, 51.63, 464.78, 48.49, 465.96, 48.09, 470.29, 44.55, 474.61, 46.52, 472.84, 53.01, 474.81, 62.64, 470.88, 68.54, 469.89, 85.06, 465.37, 96.47, 467.34, 101.78, 457.7, 100.4]], "area": 570.1451999999997, "iscrowd": 0, "image_id": 13546, "bbox": [455.93, 44.55, 18.88, 57.23], "category_id": 1, "id": 480899}, {"segmentation": [[524.01, 163.14, 522.65, 157.41, 532.47, 160.41, 532.74, 165.6, 532.74, 172.69, 530.02, 180.06, 527.01, 179.51, 518.56, 169.69, 516.92, 165.32, 525.1, 163.96]], "area": 215.9518000000007, "iscrowd": 0, "image_id": 16451, "bbox": [516.92, 157.41, 15.82, 22.65], "category_id": 1, "id": 557373}, {"segmentation": [[107.88, 375.34, 124.12, 365.97, 185.95, 328.5, 197.2, 319.75, 195.32, 331.62, 192.2, 338.49, 180.96, 342.86, 195.32, 345.98, 191.57, 353.48, 180.33, 359.1, 171.59, 358.48, 174.71, 351.61, 169.71, 352.23, 147.23, 369.09, 135.98, 373.47, 143.48, 377.84, 139.73, 381.59, 134.11, 382.21, 130.99, 382.21, 126.61, 384.09, 120.37, 379.71, 119.74, 378.46, 109.13, 379.71, 104.13, 380.34]], "area": 1544.6851499999987, "iscrowd": 0, "image_id": 15597, "bbox": [104.13, 319.75, 93.07, 64.34], "category_id": 41, "id": 639627}, {"segmentation": [[510.23, 212.53, 525.35, 208.95, 533.05, 231.22, 534.15, 237.81, 523.43, 245.79, 518.2, 245.24, 515.73, 240.29, 510.23, 215.0], [520.95, 254.86, 532.77, 254.86, 541.84, 252.66, 547.34, 252.11, 550.09, 252.93, 548.17, 256.51, 536.35, 258.71, 523.98, 258.43, 519.3, 257.61, 518.48, 254.58]], "area": 713.107399999998, "iscrowd": 0, "image_id": 13546, "bbox": [510.23, 208.95, 39.86, 49.76], "category_id": 41, "id": 644648}, {"segmentation": [[246.37, 329.25, 264.74, 351.79, 280.6, 364.32, 298.97, 371.0, 321.52, 363.48, 329.87, 345.95, 322.35, 325.91, 308.99, 305.87, 283.11, 295.85, 264.74, 292.51, 246.37, 298.36, 239.69, 309.21, 243.87, 315.89]], "area": 4650.782999999999, "iscrowd": 0, "image_id": 16451, "bbox": [239.69, 292.51, 90.18, 78.49], "category_id": 42, "id": 648278}, {"segmentation": [[623.02, 384.94, 576.05, 379.99, 520.42, 198.28, 513.0, 174.79, 489.52, 161.19, 462.32, 45.0, 452.43, 9.15, 491.99, 37.58, 627.97, 371.34]], "area": 15594.414550000012, "iscrowd": 0, "image_id": 15079, "bbox": [452.43, 9.15, 175.54, 375.79], "category_id": 49, "id": 694531}, {"segmentation": [[611.54, 234.66, 542.55, 263.75, 534.24, 254.61, 510.13, 261.26, 481.04, 271.23, 475.22, 250.45, 475.22, 247.13, 496.84, 237.15, 514.29, 232.17, 524.26, 220.53, 545.88, 213.88, 561.67, 211.39, 612.0, 191.44]], "area": 5149.121449999999, "iscrowd": 0, "image_id": 10583, "bbox": [475.22, 191.44, 136.78, 79.79], "category_id": 49, "id": 697623}, {"segmentation": [[524.48, 43.07, 526.06, 37.99, 549.14, 27.41, 568.37, 65.88, 565.49, 110.12, 552.98, 129.35, 623.19, 391.9, 615.5, 398.63, 601.07, 391.9, 545.29, 145.7]], "area": 7438.18450000001, "iscrowd": 0, "image_id": 15254, "bbox": [524.48, 27.41, 98.71, 371.22], "category_id": 50, "id": 701534}, {"segmentation": [[391.5, 173.74, 381.9, 186.22, 380.94, 199.65, 382.86, 333.99, 386.7, 363.73, 404.93, 384.84, 467.3, 390.6, 573.81, 382.92, 592.04, 356.06, 552.7, 183.34, 540.23, 173.74, 460.58, 163.19, 391.5, 171.82]], "area": 41047.939, "iscrowd": 0, "image_id": 15254, "bbox": [380.94, 163.19, 211.1, 227.41], "category_id": 51, "id": 714498}, {"segmentation": [[65.25, 166.96, 358.87, 168.88, 372.31, 185.19, 376.14, 279.23, 379.02, 361.75, 374.22, 378.06, 351.2, 383.82, 242.77, 380.94, 9.6, 375.18, 1.92, 366.55, 1.92, 351.2, 40.3, 181.36, 50.86, 169.84, 64.29, 166.0]], "area": 75021.1945, "iscrowd": 0, "image_id": 15254, "bbox": [1.92, 166.0, 377.1, 217.82], "category_id": 51, "id": 1038814}, {"segmentation": [[341.6, 165.04, 81.56, 166.0, 71.01, 154.49, 63.33, 134.34, 90.2, 31.67, 111.31, 15.35, 155.45, 10.56, 215.9, 8.64, 276.35, 9.6, 333.92, 9.6, 363.67, 23.03, 365.59, 75.8, 370.39, 126.66, 367.51, 158.33, 353.11, 167.92, 334.88, 166.96, 322.41, 166.0]], "area": 44504.08415000002, "iscrowd": 0, "image_id": 15254, "bbox": [63.33, 8.64, 307.06, 159.28], "category_id": 51, "id": 1039006}, {"segmentation": [[397.86, 33.16, 400.88, 165.78, 441.07, 158.74, 418.96, 21.1, 402.89, 34.16]], "area": 4220.173699999998, "iscrowd": 0, "image_id": 15254, "bbox": [397.86, 21.1, 43.21, 144.68], "category_id": 57, "id": 1061708}, {"segmentation": [[479.12, 28.67, 502.18, 48.71, 521.22, 159.97, 503.18, 155.97, 473.11, 27.66]], "area": 2562.5592000000006, "iscrowd": 0, "image_id": 15254, "bbox": [473.11, 27.66, 48.11, 132.31], "category_id": 57, "id": 1063967}, {"segmentation": [[404.84, 11.58, 378.66, 19.64, 383.7, 68.98, 381.68, 91.14, 379.67, 127.4, 387.72, 149.55, 395.78, 155.59, 403.84, 155.59, 397.79, 33.74, 406.86, 26.69]], "area": 2694.4661499999984, "iscrowd": 0, "image_id": 15254, "bbox": [378.66, 11.58, 28.2, 144.01], "category_id": 57, "id": 1064137}, {"segmentation": [[279.46, 164.06, 286.9, 167.25, 289.56, 172.04, 287.43, 184.8, 278.92, 195.97, 271.48, 196.5, 260.31, 190.65, 256.59, 187.46, 253.93, 180.01, 251.8, 173.63, 265.63, 172.57, 270.42, 172.57, 278.39, 172.57, 278.39, 171.51, 270.95, 160.34]], "area": 765.5433000000003, "iscrowd": 0, "image_id": 18193, "bbox": [251.8, 160.34, 37.76, 36.16], "category_id": 58, "id": 1067128}, {"segmentation": [[502.61, 398.37, 513.37, 449.85, 524.12, 452.15, 556.39, 452.92, 562.54, 442.17, 565.61, 429.87, 567.92, 415.28, 569.45, 392.23, 569.45, 390.69]], "area": 3329.4693999999986, "iscrowd": 0, "image_id": 16451, "bbox": [502.61, 390.69, 66.84, 62.23], "category_id": 31, "id": 1181954}, {"segmentation": [[565.08, 455.65, 571.52, 459.95, 596.22, 464.24, 606.96, 459.95, 612.0, 413.77, 611.26, 390.14, 597.3, 380.47, 580.11, 381.55, 565.08, 378.32, 548.97, 377.25, 535.01, 383.69, 541.45, 391.21, 562.93, 392.29, 570.45, 396.58, 570.45, 409.47, 570.45, 428.8, 567.23, 438.47, 562.93, 448.13]], "area": 3738.074199999998, "iscrowd": 0, "image_id": 16451, "bbox": [535.01, 377.25, 76.99, 86.99], "category_id": 31, "id": 1182620}, {"segmentation": [[110.02, 413.12, 85.21, 358.11, 118.65, 238.38, 122.97, 217.89, 177.98, 154.25, 193.08, 142.38, 183.37, 115.42, 186.61, 66.88, 212.49, 45.3, 265.35, 46.38, 293.39, 79.82, 291.24, 169.35, 303.1, 193.08, 318.2, 189.84, 332.22, 197.39, 344.09, 226.52, 339.78, 307.42, 298.79, 314.97, 300.94, 325.75, 387.24, 340.85, 334.38, 358.11, 319.28, 376.45, 323.6, 409.89, 345.17, 430.38, 420.67, 437.93, 443.33, 429.3, 472.45, 404.49, 481.08, 434.7, 461.66, 472.45, 199.55, 472.45, 143.46, 435.78, 142.38, 424.99, 113.26, 415.28]], "area": 82944.30595, "iscrowd": 0, "image_id": 18193, "bbox": [85.21, 45.3, 395.87, 427.15], "category_id": 1, "id": 1207285}, {"segmentation": [[208.73, 115.35, 296.58, 115.07, 299.04, 120.0, 298.22, 122.19, 285.9, 123.01, 285.63, 137.24, 282.89, 137.79, 280.16, 136.69, 278.52, 122.19, 228.16, 123.56, 225.7, 142.17, 218.59, 142.44, 216.94, 135.87, 217.76, 124.1, 210.1, 124.65, 205.18, 122.74]], "area": 986.5886000000002, "iscrowd": 0, "image_id": 13546, "bbox": [205.18, 115.07, 93.86, 27.37], "category_id": 15, "id": 1395158}, {"segmentation": [[102.78, 116.41, 176.28, 115.16, 176.59, 121.08, 176.28, 123.26, 162.26, 124.81, 161.33, 144.75, 151.05, 144.12, 152.92, 126.99, 102.78, 127.62]], "area": 969.7507500000003, "iscrowd": 0, "image_id": 13546, "bbox": [102.78, 115.16, 73.81, 29.59], "category_id": 15, "id": 1395366}, {"segmentation": [[174.54, 168.45, 211.63, 167.94, 211.63, 163.37, 195.88, 159.3, 185.21, 156.25, 173.53, 161.33, 170.99, 165.4]], "area": 336.8364999999994, "iscrowd": 0, "image_id": 16451, "bbox": [170.99, 156.25, 40.64, 12.2], "category_id": 28, "id": 1427561}, {"segmentation": [[465.04, 101.25, 458.53, 101.08, 456.3, 100.92, 458.61, 99.6, 465.87, 99.35, 467.27, 100.34, 466.61, 101.91]], "area": 16.46515, "iscrowd": 0, "image_id": 13546, "bbox": [456.3, 99.35, 10.97, 2.56], "category_id": 41, "id": 1474947}, {"segmentation": [[0.01, 139.13, 7.42, 135.14, 20.82, 134.85, 15.98, 149.96, 7.71, 189.87, 0.58, 198.13]], "area": 748.6730000000002, "iscrowd": 0, "image_id": 18575, "bbox": [0.01, 134.85, 20.81, 63.28], "category_id": 49, "id": 1524383}, {"segmentation": [[376.59, 15.95, 381.13, 13.22, 402.92, 9.14, 406.55, 19.12, 418.81, 15.04, 426.07, 28.2, 441.95, 32.74, 449.22, 27.75, 469.19, 30.47, 472.37, 29.11, 479.17, 29.11, 498.24, 44.99, 514.12, 115.8, 524.11, 160.74, 502.78, 160.74, 399.29, 159.38, 386.13, 152.11, 379.32, 141.22, 379.32, 73.59, 376.14, 57.7]], "area": 17563.99925, "iscrowd": 0, "image_id": 15254, "bbox": [376.14, 9.14, 147.97, 151.6], "category_id": 57, "id": 1563326}, {"segmentation": [[232.49, 195.35, 264.92, 196.39, 266.47, 211.18, 262.84, 220.26, 269.85, 224.41, 272.96, 222.59, 276.33, 217.66, 277.11, 216.88, 278.41, 221.81, 277.63, 223.11, 272.96, 228.04, 275.29, 251.12, 276.85, 257.35, 268.81, 257.61, 267.25, 256.05, 261.29, 248.53, 247.8, 230.89, 243.13, 230.37, 248.06, 212.47, 260.77, 204.69, 258.95, 201.06, 243.91, 208.06, 228.86, 200.02]], "area": 1278.7054500000006, "iscrowd": 0, "image_id": 16451, "bbox": [228.86, 195.35, 49.55, 62.26], "category_id": 62, "id": 1579921}, {"segmentation": [[437.73, 17.29, 612.0, 106.48, 612.0, 599.1, 610.63, 612.0, 0.0, 612.0, 0.0, 250.56, 65.87, 197.05, 39.79, 180.58, 27.44, 172.35, 9.61, 63.94, 100.17, 62.57, 189.36, 37.87, 215.43, 14.55, 238.76, 2.2, 388.33, 0.0]], "area": 346540.82444999996, "iscrowd": 0, "image_id": 10583, "bbox": [0.0, 0.0, 612.0, 612.0], "category_id": 67, "id": 1619221}, {"segmentation": [[313.33, 460.84, 335.95, 462.89, 367.31, 456.21, 372.97, 452.61, 389.42, 443.36, 420.78, 440.79, 425.41, 438.73, 421.81, 433.59, 425.41, 431.53, 425.92, 420.74, 415.13, 420.74, 399.7, 423.31, 385.31, 427.42, 380.17, 431.02, 374.51, 436.67, 377.6, 439.76, 363.72, 445.93, 359.6, 442.33, 339.55, 443.36, 320.53, 445.41, 311.28, 453.64, 312.82, 459.81]], "area": 1896.0828, "iscrowd": 0, "image_id": 15597, "bbox": [311.28, 420.74, 114.64, 42.15], "category_id": 2, "id": 1765348}, {"segmentation": [[503.19, 543.39, 523.84, 548.41, 565.68, 550.08, 568.47, 550.08, 580.75, 491.5, 580.75, 487.6, 572.94, 465.84, 531.09, 455.79, 526.63, 454.68, 522.72, 453.56, 518.82, 454.68, 513.24, 456.91, 508.21, 460.26, 505.42, 461.37, 507.1, 470.3, 503.75, 471.97, 495.94, 503.78, 499.29, 535.02, 500.96, 541.16, 503.19, 543.39]], "area": 6704.131800000002, "iscrowd": 0, "image_id": 16451, "bbox": [495.94, 453.56, 84.81, 96.52], "category_id": 31, "id": 1837105}, {"segmentation": [[426.95, 483.06, 451.86, 478.22, 474.69, 445.01, 471.93, 400.72, 451.17, 365.44, 436.64, 358.52, 420.72, 350.91, 397.2, 353.67, 384.05, 363.36, 375.06, 382.73, 374.37, 427.02, 381.98, 451.93, 409.65, 480.99, 431.1, 483.75]], "area": 10443.619800000002, "iscrowd": 0, "image_id": 16451, "bbox": [374.37, 350.91, 100.32, 132.84], "category_id": 42, "id": 1862621}, {"segmentation": [[504.48, 73.76, 479.71, 87.91, 467.92, 105.02, 459.66, 125.07, 464.97, 142.76, 472.63, 157.5, 492.1, 165.17, 510.97, 159.86, 528.66, 135.68, 533.97, 105.02, 537.51, 90.86, 532.79, 80.25, 520.4, 74.35, 503.89, 71.99]], "area": 5052.268299999999, "iscrowd": 0, "image_id": 18575, "bbox": [459.66, 71.99, 77.85, 93.18], "category_id": 44, "id": 1865579}, {"segmentation": [[476.26, 91.01, 468.78, 66.08, 480.0, 37.4, 486.23, 29.92, 499.95, 9.97, 508.68, 2.49, 538.6, 4.99, 553.56, 26.18, 532.36, 74.81, 508.68, 71.06, 481.25, 87.27]], "area": 4538.105649999999, "iscrowd": 0, "image_id": 18575, "bbox": [468.78, 2.49, 84.78, 88.52], "category_id": 44, "id": 1869111}, {"segmentation": [[368.36, 411.95, 419.07, 455.85, 452.37, 469.47, 515.2, 470.23, 572.72, 442.98, 583.32, 432.38, 603.75, 426.33, 622.68, 420.27, 637.06, 390.0, 631.0, 380.16, 631.76, 361.99, 625.7, 350.64, 624.19, 351.4, 616.62, 290.09, 615.11, 271.92, 598.46, 253.76, 575.75, 224.99, 574.23, 202.29, 562.88, 192.45, 513.68, 196.99, 511.41, 185.64, 498.54, 184.88, 493.25, 190.93, 474.32, 186.39, 454.64, 172.77, 437.99, 162.93, 434.21, 174.28, 428.15, 194.72, 425.88, 198.5, 407.72, 206.83, 383.5, 224.24, 355.49, 264.35, 355.49, 266.62, 330.51, 264.35, 323.7, 268.14, 332.78, 284.79, 339.6, 286.3, 344.14, 292.36, 331.27, 299.17, 323.7, 322.63, 317.65, 346.86, 328.24, 371.83, 341.11, 391.51, 372.14, 411.95]], "area": 68002.80745000002, "iscrowd": 0, "image_id": 18575, "bbox": [317.65, 162.93, 319.41, 307.3], "category_id": 51, "id": 1903575}, {"segmentation": [[1.97, 293.31, 17.6, 321.28, 43.92, 336.08, 62.84, 341.02, 74.36, 341.02, 101.5, 340.2, 117.13, 331.97, 116.31, 360.76, 124.53, 379.68, 141.8, 407.64, 167.3, 416.69, 201.85, 425.74, 251.2, 396.13, 263.54, 368.98, 268.48, 332.79, 262.72, 295.26, 259.43, 294.44, 245.45, 296.91, 223.24, 270.58, 203.5, 259.07, 196.92, 260.71, 201.03, 283.75, 204.32, 295.26, 198.56, 301.02, 197.74, 296.08, 182.93, 268.12, 179.64, 268.94, 182.11, 285.39, 171.42, 287.86, 162.37, 283.75, 144.27, 222.92, 122.06, 203.18, 106.43, 192.49, 76.82, 191.67, 59.55, 191.67, 34.87, 196.6, 21.71, 205.65, 10.2, 222.1, 1.97, 236.08]], "area": 36377.26175000001, "iscrowd": 0, "image_id": 18575, "bbox": [1.97, 191.67, 266.51, 234.07], "category_id": 54, "id": 1908510}, {"segmentation": [[197.13, 351.31, 233.48, 345.81, 351.31, 278.63, 351.31, 274.22, 384.35, 263.21, 383.25, 241.18, 371.14, 233.48, 379.95, 223.56, 377.75, 197.13, 337.0, 165.19, 298.45, 145.37, 262.11, 125.55, 210.35, 113.43, 183.92, 103.52, 139.86, 112.33, 90.31, 120.04, 60.57, 136.56, 60.57, 159.69, 71.58, 188.32, 58.37, 200.44, 55.06, 223.56, 62.77, 259.91, 87.0, 298.45, 127.75, 332.59, 153.08, 332.59, 179.51, 352.42]], "area": 58874.72895, "iscrowd": 0, "image_id": 15079, "bbox": [55.06, 103.52, 329.29, 248.9], "category_id": 54, "id": 1909873}, {"segmentation": [[206.59, 108.29, 203.36, 96.3, 204.29, 83.86, 223.65, 65.88, 269.75, 17.93, 284.51, 10.09, 326.0, 8.25, 372.57, 13.32, 403.92, 25.3, 430.2, 37.29, 444.95, 47.43, 456.94, 64.49, 457.86, 67.26, 461.55, 82.47, 476.76, 131.8, 470.31, 162.23, 461.08, 180.21, 438.49, 216.17, 405.3, 223.09, 393.77, 222.17, 377.64, 200.96, 337.53, 162.23, 308.94, 143.33, 248.55, 122.12]], "area": 35861.601400000014, "iscrowd": 0, "image_id": 15079, "bbox": [203.36, 8.25, 273.4, 214.84], "category_id": 54, "id": 1910679}, {"segmentation": [[539.5, 341.23, 543.93, 328.59, 549.93, 316.9, 554.67, 310.59, 559.41, 307.43, 578.37, 306.48, 612.0, 310.27, 612.0, 343.44, 605.54, 383.89, 576.15, 383.25, 533.19, 379.78, 530.66, 369.67, 532.24, 357.66]], "area": 5286.277399999999, "iscrowd": 0, "image_id": 16451, "bbox": [530.66, 306.48, 81.34, 77.41], "category_id": 62, "id": 1932578}, {"segmentation": [[326.63, 134.39, 328.07, 121.42, 315.58, 117.58, 319.42, 110.37, 341.04, 113.73, 366.51, 112.77, 397.26, 111.33, 393.42, 117.1, 386.69, 118.06, 385.73, 127.67, 381.89, 130.55, 377.56, 125.26, 379.48, 119.5, 334.32, 124.3, 333.36, 134.87]], "area": 816.1281500000002, "iscrowd": 0, "image_id": 13546, "bbox": [315.58, 110.37, 81.68, 24.5], "category_id": 15, "id": 2061131}, {"segmentation": [[415.43, 118.1, 413.9, 115.22, 413.73, 111.33, 418.31, 110.31, 490.83, 107.77, 491.34, 110.14, 491.17, 112.34, 489.31, 114.21, 486.42, 115.39, 475.41, 116.07, 474.22, 116.92, 473.72, 130.13, 468.97, 130.47, 467.79, 129.46, 467.45, 117.43, 433.05, 117.93, 431.69, 122.0, 432.03, 132.34, 427.63, 133.69, 423.73, 129.8, 423.39, 118.61, 417.8, 119.12]], "area": 832.2201000000006, "iscrowd": 0, "image_id": 13546, "bbox": [413.73, 107.77, 77.61, 25.92], "category_id": 15, "id": 2062913}, {"segmentation": [[495.25, 510.78, 502.1, 486.59, 507.12, 470.61, 507.12, 465.13, 509.86, 460.57, 515.79, 455.54, 519.45, 451.89, 525.84, 450.98, 539.08, 458.74, 550.03, 460.57, 566.01, 463.3, 571.03, 463.76, 576.96, 471.07, 579.7, 483.39, 578.79, 498.45, 575.14, 512.61, 575.14, 512.61]], "area": 3979.3375999999994, "iscrowd": 0, "image_id": 16451, "bbox": [495.25, 450.98, 84.45, 61.63], "category_id": 27, "id": 2071818}, {"segmentation": [[8.7, 203.24, 42.41, 191.62, 52.87, 191.62, 72.05, 196.56, 91.22, 200.04, 103.43, 200.92, 102.56, 197.14, 91.8, 196.56, 93.84, 190.45, 99.36, 186.68, 106.33, 184.93, 110.98, 178.54, 124.35, 177.38, 131.03, 179.7, 132.48, 188.42, 138.59, 191.33, 141.49, 198.01, 143.24, 211.38, 132.77, 213.41, 129.58, 213.41, 138.3, 221.55, 140.62, 225.9, 149.92, 240.14, 161.83, 262.52, 176.94, 277.63, 182.17, 283.44, 191.47, 301.16, 203.67, 314.82, 201.06, 324.12, 195.25, 319.47, 183.92, 308.43, 173.45, 292.74, 158.64, 279.37, 160.67, 293.61, 166.77, 307.56, 176.36, 322.96, 185.66, 320.92, 192.34, 321.79, 183.62, 330.8, 164.16, 339.23, 149.63, 316.85, 151.37, 309.01, 144.4, 295.93, 141.49, 279.08, 131.03, 275.59, 111.56, 271.23, 106.91, 276.17, 104.59, 297.09, 105.46, 313.95, 109.53, 332.54, 110.69, 340.68, 113.6, 343.59, 118.54, 344.17, 119.99, 346.2, 121.15, 365.96, 111.85, 375.55, 99.07, 375.84, 98.49, 371.48, 103.43, 365.38, 98.49, 344.46, 90.06, 323.25, 82.22, 294.77, 79.02, 270.65, 82.8, 241.89, 88.03, 234.04, 87.74, 221.26, 79.89, 212.54, 67.69, 209.05, 52.0, 203.24, 44.44, 202.08, 37.76, 205.27, 32.82, 207.31, 20.32, 209.34, 24.68, 218.93, 17.42, 215.15, 19.74, 223.87, 16.55, 224.16, 9.28, 211.09]], "area": 10613.482650000002, "iscrowd": 0, "image_id": 15597, "bbox": [8.7, 177.38, 194.97, 198.46], "category_id": 1, "id": 2159220}, {"segmentation": [[363.13, 0.0, 359.23, 30.84, 346.57, 53.24, 327.09, 74.67, 322.22, 75.64, 292.03, 79.54, 278.4, 76.61, 259.89, 62.01, 242.36, 41.55, 234.57, 27.92, 231.65, 1.62]], "area": 8008.663750000001, "iscrowd": 0, "image_id": 18575, "bbox": [231.65, 0.0, 131.48, 79.54], "category_id": 46, "id": 2183095}, {"segmentation": [[221.88, 129.87, 233.63, 151.02, 252.42, 169.81, 295.88, 196.82, 335.81, 205.04, 398.06, 209.74, 436.82, 202.69, 440.34, 180.38, 427.42, 149.84, 403.93, 129.87, 383.96, 107.56, 374.57, 92.29, 351.08, 77.02, 315.84, 81.72, 288.83, 64.1, 251.24, 66.45, 241.85, 81.72, 233.63, 95.81, 227.75, 100.51, 233.63, 107.56, 220.71, 122.83]], "area": 21564.3236, "iscrowd": 0, "image_id": 10583, "bbox": [220.71, 64.1, 219.63, 145.64], "category_id": 54, "id": 2219494}, {"segmentation": [[94.97, 340.17, 110.04, 308.51, 170.33, 246.71, 179.38, 230.13, 171.84, 195.46, 155.26, 186.41, 174.86, 180.38, 174.86, 162.3, 206.51, 139.68, 221.59, 135.16, 254.75, 162.3, 289.42, 187.92, 322.58, 203.0, 394.94, 207.52, 426.59, 207.52, 450.71, 203.0, 464.28, 209.02, 462.77, 230.13, 479.35, 269.32, 471.81, 358.26, 471.81, 397.45, 443.17, 433.63, 396.44, 453.22, 342.18, 472.82, 280.37, 456.24, 274.34, 424.58, 232.14, 420.06, 132.65, 389.91, 107.02, 368.81, 90.44, 344.69]], "area": 83762.15575, "iscrowd": 0, "image_id": 10583, "bbox": [90.44, 135.16, 388.91, 337.66], "category_id": 54, "id": 2219694}, {"segmentation": [[294.54, 190.25, 295.75, 185.14, 293.32, 181.74, 291.86, 179.31, 289.19, 181.74, 290.89, 176.63, 291.13, 176.39, 289.92, 172.74, 292.35, 171.04, 293.32, 167.39, 293.32, 164.71, 295.27, 161.55, 294.16, 157.88, 292.65, 156.04, 293.82, 153.7, 294.16, 150.35, 294.83, 149.35, 294.16, 144.5, 297.34, 143.49, 299.34, 144.33, 299.85, 142.99, 302.19, 140.48, 304.36, 141.48, 303.86, 145.0, 303.86, 147.0, 305.7, 149.35, 305.37, 151.35, 303.53, 152.86, 302.69, 154.7, 301.85, 157.21, 301.52, 158.71, 302.36, 159.89, 304.03, 158.88, 304.36, 158.21, 306.2, 157.71, 307.37, 159.22, 307.88, 159.22, 310.39, 158.55, 312.73, 159.72, 313.06, 162.9, 311.39, 164.07, 312.73, 165.57, 314.74, 166.58, 314.23, 168.75, 308.71, 168.92, 307.21, 169.59, 306.87, 171.93, 309.05, 173.77, 307.71, 177.45, 307.04, 178.46, 304.36, 181.13, 303.19, 182.3, 304.2, 184.64, 303.53, 184.81, 307.2, 189.47, 306.63, 193.64, 306.06, 197.05, 303.6, 198.57, 297.72, 200.65, 295.45, 200.27, 294.5, 198.0, 295.45, 195.35, 295.64, 194.21, 294.69, 193.07, 292.61, 192.5, 291.85, 190.8, 292.61, 190.04, 293.55, 189.85, 294.12, 189.28]], "area": 750.6299500000002, "iscrowd": 0, "image_id": 13923, "bbox": [289.19, 140.48, 25.55, 60.17], "category_id": 64, "id": 25639}, {"segmentation": [[265.95, 199.8, 270.84, 200.52, 276.47, 200.16, 277.55, 197.62, 279.01, 192.36, 278.1, 190.18, 274.47, 188.37, 272.84, 185.65, 272.84, 180.75, 273.02, 176.76, 274.29, 170.95, 274.29, 168.05, 272.48, 165.88, 272.48, 160.98, 273.02, 155.9, 274.29, 154.27, 272.11, 152.63, 268.67, 160.43, 265.95, 163.34, 263.59, 160.8, 262.86, 162.97, 261.95, 170.41, 262.14, 174.58, 260.68, 175.85, 263.22, 179.06, 264.31, 181.24, 264.31, 184.32, 266.67, 184.32, 267.94, 184.86, 267.4, 188.49, 262.14, 188.67, 262.14, 190.67, 261.05, 192.12, 261.59, 195.75, 263.59, 198.29, 266.85, 199.74]], "area": 467.7418500000002, "iscrowd": 0, "image_id": 13923, "bbox": [260.68, 152.63, 18.33, 47.89], "category_id": 64, "id": 27164}, {"segmentation": [[401.89, 159.68, 400.41, 147.35, 341.21, 143.4, 339.24, 197.66, 397.94, 211.47, 406.82, 205.06, 405.83, 181.88, 389.06, 180.4, 386.59, 166.09, 397.45, 165.11, 396.95, 159.68]], "area": 3536.8818499999998, "iscrowd": 0, "image_id": 13923, "bbox": [339.24, 143.4, 67.58, 68.07], "category_id": 72, "id": 28853}, {"segmentation": [[446.93, 124.66, 480.0, 121.21, 480.0, 41.59, 462.42, 41.59, 420.24, 42.45, 418.09, 101.85, 425.41, 105.72, 426.27, 112.61, 433.59, 113.04, 443.06, 111.74, 446.07, 114.76, 443.49, 122.51]], "area": 4610.519150000002, "iscrowd": 0, "image_id": 12576, "bbox": [418.09, 41.59, 61.91, 83.07], "category_id": 72, "id": 29816}, {"segmentation": [[214.26, 390.13, 219.37, 391.31, 225.54, 391.08, 228.04, 390.01, 229.11, 387.99, 228.04, 359.37, 227.32, 356.05, 224.83, 352.61, 223.88, 351.77, 222.81, 331.35, 221.27, 330.4, 219.25, 330.28, 217.35, 330.64, 215.69, 331.71, 215.81, 333.37, 215.09, 350.94, 214.15, 351.89, 214.15, 353.67, 212.13, 355.93, 213.78, 389.77]], "area": 757.57145, "iscrowd": 0, "image_id": 10707, "bbox": [212.13, 330.28, 16.98, 61.03], "category_id": 44, "id": 84825}, {"segmentation": [[276.95, 260.8, 274.71, 254.51, 276.95, 250.01, 282.35, 242.83, 281.45, 239.23, 281.9, 234.29, 280.55, 232.49, 283.69, 230.69, 289.09, 230.24, 292.23, 230.24, 295.38, 233.39, 294.03, 240.58, 299.42, 246.87, 303.91, 256.75, 307.96, 273.83, 308.41, 287.31, 294.93, 284.61, 281.9, 272.48, 276.95, 259.45, 276.95, 259.45]], "area": 1157.656850000001, "iscrowd": 0, "image_id": 18150, "bbox": [274.71, 230.24, 33.7, 57.07], "category_id": 44, "id": 91276}, {"segmentation": [[524.41, 242.45, 527.11, 241.91, 528.55, 242.99, 528.73, 248.03, 529.27, 253.43, 527.29, 253.79, 528.73, 260.99, 530.17, 261.71, 529.09, 272.86, 527.83, 283.3, 518.11, 282.94, 518.47, 260.09, 522.43, 254.87, 523.51, 242.45, 525.31, 241.91]], "area": 359.65620000000024, "iscrowd": 0, "image_id": 13729, "bbox": [518.11, 241.91, 12.06, 41.39], "category_id": 44, "id": 91322}, {"segmentation": [[244.79, 302.3, 313.81, 285.74, 315.65, 296.78, 329.45, 290.34, 335.89, 279.3, 329.45, 278.38, 335.89, 263.65, 335.89, 237.89, 331.29, 232.37, 306.45, 236.97, 289.88, 240.65, 264.11, 242.49, 260.43, 244.33, 259.51, 267.34, 259.51, 278.38, 242.95, 279.3, 229.14, 280.22, 215.34, 286.66, 213.5, 293.1, 214.42, 333.59, 216.26, 342.8, 250.31, 370.4, 265.03, 365.8, 253.07, 352.92, 245.71, 332.67, 242.03, 305.07, 244.79, 304.15], [265.95, 361.2, 266.87, 367.64, 315.65, 350.16, 325.77, 344.64, 304.61, 331.75, 295.4, 332.67, 314.73, 347.4, 266.87, 361.2]], "area": 7257.563049999998, "iscrowd": 0, "image_id": 13923, "bbox": [213.5, 232.37, 122.39, 138.03], "category_id": 62, "id": 108331}, {"segmentation": [[285.74, 402.45, 276.96, 393.67, 262.59, 386.48, 265.78, 377.7, 253.0, 347.36, 246.62, 340.17, 244.23, 304.24, 312.89, 286.67, 327.27, 329.78, 332.86, 338.57, 324.07, 347.36, 308.9, 355.34, 307.3, 372.9, 313.69, 384.88, 330.46, 399.26, 337.65, 404.84, 311.3, 410.43, 291.33, 402.45, 283.35, 400.06]], "area": 7032.8252999999995, "iscrowd": 0, "image_id": 13923, "bbox": [244.23, 286.67, 93.42, 123.76], "category_id": 62, "id": 108430}, {"segmentation": [[494.95, 363.33, 512.52, 341.77, 527.69, 313.01, 555.64, 330.58, 570.81, 355.34, 543.66, 405.64, 532.48, 413.63, 538.87, 419.22, 514.92, 422.41, 444.65, 419.22, 482.18, 408.04, 502.94, 390.47, 500.54, 375.3, 495.75, 364.13]], "area": 5709.348249999997, "iscrowd": 0, "image_id": 13923, "bbox": [444.65, 313.01, 126.16, 109.4], "category_id": 62, "id": 108630}, {"segmentation": [[474.03, 244.16, 480.49, 246.01, 491.56, 260.77, 491.56, 259.84, 492.48, 282.9, 486.95, 305.03, 485.1, 328.09, 482.33, 338.23, 475.88, 334.54, 436.22, 324.4, 408.56, 333.62, 406.71, 324.4, 410.4, 308.72, 406.71, 292.12, 411.32, 282.9, 413.17, 278.29, 420.54, 277.37, 429.77, 281.98, 434.38, 285.67, 442.68, 283.82, 450.06, 272.75, 462.05, 262.61]], "area": 4938.046050000001, "iscrowd": 0, "image_id": 13923, "bbox": [406.71, 244.16, 85.77, 94.07], "category_id": 62, "id": 109275}, {"segmentation": [[142.55, 60.54, 101.24, 79.17, 97.19, 72.69, 92.33, 72.69, 104.48, 105.9, 107.72, 115.62, 111.77, 131.01, 111.77, 154.5, 106.1, 173.93, 93.95, 186.08, 89.9, 192.56, 72.08, 192.56, 71.27, 204.71, 73.7, 224.96, 85.85, 275.18, 87.47, 283.27, 112.58, 287.32, 145.79, 247.64, 154.7, 248.45, 153.08, 159.35, 152.27, 111.57, 149.03, 58.92, 142.55, 60.54], [123.92, 288.13, 157.13, 293.8, 157.13, 254.93, 150.65, 253.31, 118.25, 285.7], [156.32, 64.59, 166.85, 72.69, 172.52, 96.99, 176.57, 101.85, 186.29, 101.85, 195.19, 165.83, 198.43, 200.66, 214.63, 235.49, 218.68, 253.31, 218.68, 273.56, 213.82, 288.13, 164.42, 244.4], [161.99, 251.69, 162.8, 297.04, 213.82, 305.14, 214.63, 296.23, 164.42, 250.88], [289.96, 159.35, 306.97, 162.59, 322.35, 234.68, 324.78, 273.56, 322.35, 305.95, 320.73, 317.29, 320.73, 318.1, 298.06, 312.43, 294.01, 239.54, 290.77, 195.8, 294.01, 169.88]], "area": 25186.966450000004, "iscrowd": 0, "image_id": 19786, "bbox": [71.27, 58.92, 253.51, 259.18], "category_id": 63, "id": 111323}, {"segmentation": [[540.34, 477.27, 532.58, 433.42, 533.93, 419.93, 544.73, 400.03, 566.65, 344.72, 574.07, 315.04, 576.43, 293.11, 575.42, 289.4, 577.11, 281.65, 580.48, 258.04, 582.5, 247.92, 640.0, 261.41, 640.0, 480.0]], "area": 18650.655, "iscrowd": 0, "image_id": 10707, "bbox": [532.58, 247.92, 107.42, 232.08], "category_id": 63, "id": 113866}, {"segmentation": [[165.65, 264.57, 94.79, 311.51, 95.71, 326.23, 91.11, 327.15, 89.27, 310.59, 56.14, 310.59, 53.37, 286.66, 43.25, 274.7, 34.05, 273.78, 34.97, 256.29, 23.01, 249.85, 14.72, 251.69, 15.64, 238.81, 19.33, 228.68, 76.38, 215.8, 115.03, 202.92, 118.71, 201.08, 132.52, 207.52, 146.32, 205.68, 164.73, 213.96, 171.17, 216.72, 176.69, 235.13, 179.45, 244.33, 175.77, 253.53, 161.05, 266.41]], "area": 11637.646799999997, "iscrowd": 0, "image_id": 13923, "bbox": [14.72, 201.08, 164.73, 126.07], "category_id": 63, "id": 114321}, {"segmentation": [[264.91, 182.17, 264.91, 184.06, 266.17, 229.9, 267.42, 238.07, 270.56, 248.12, 274.96, 229.9, 297.57, 190.34, 296.31, 201.02, 303.85, 195.99, 303.85, 185.94, 309.5, 168.99, 312.01, 159.57, 303.22, 128.16, 302.59, 102.41, 301.34, 91.11, 310.13, 93.62, 286.26, 82.32, 264.91, 84.83, 256.74, 87.34, 240.42, 87.97, 233.51, 94.25, 222.83, 98.65, 214.04, 106.81, 210.9, 128.16, 221.57, 137.58, 227.86, 147.63, 241.04, 163.96, 258.63, 166.47]], "area": 8557.35595, "iscrowd": 0, "image_id": 18150, "bbox": [210.9, 82.32, 101.11, 165.8], "category_id": 63, "id": 115127}, {"segmentation": [[1.03, 238.56, 186.04, 201.56, 203.51, 205.67, 205.57, 210.81, 176.79, 229.31, 171.65, 248.84, 137.73, 255.0, 126.42, 242.67, 78.12, 232.39, 68.87, 251.92, 68.87, 292.01, 81.2, 310.51, 99.7, 312.56, 106.9, 318.73, 153.15, 323.87, 160.34, 339.29, 142.87, 330.04, 119.23, 329.01, 103.81, 334.15, 91.48, 347.51, 92.51, 364.98, 88.39, 395.82, 82.23, 405.07, 81.2, 420.48, 55.5, 427.68, 51.39, 414.32, 17.47, 383.48, 2.06, 300.23, 0.0, 241.64], [196.32, 322.84, 206.6, 314.62, 225.1, 316.67, 235.37, 313.59, 236.4, 339.29, 223.04, 343.4, 224.07, 332.09, 215.85, 333.12, 212.76, 349.56, 199.4, 353.68, 196.32, 327.98]], "area": 18782.9575, "iscrowd": 0, "image_id": 10707, "bbox": [0.0, 201.56, 236.4, 226.12], "category_id": 63, "id": 116290}, {"segmentation": [[111.81, 472.17, 100.52, 390.85, 230.4, 351.32, 308.33, 406.66, 308.33, 474.43, 290.26, 474.43, 283.48, 425.86, 172.8, 458.62, 175.06, 474.43, 141.18, 472.17, 141.18, 458.62, 120.85, 425.86, 128.75, 474.43]], "area": 16145.946999999996, "iscrowd": 0, "image_id": 10707, "bbox": [100.52, 351.32, 207.81, 123.11], "category_id": 67, "id": 120550}, {"segmentation": [[173.39, 2.42, 166.13, 79.03, 176.61, 102.42, 188.71, 107.26, 199.19, 211.29, 208.87, 232.26, 219.35, 244.35, 220.16, 370.16, 271.77, 370.16, 275.81, 356.45, 303.23, 312.1, 295.16, 237.9, 290.32, 204.84, 291.94, 187.9, 294.35, 162.9, 288.71, 147.58, 273.39, 137.1, 258.06, 118.55, 253.23, 108.06, 254.84, 97.58, 261.29, 79.03, 262.1, 59.68, 261.29, 39.52, 271.77, 22.58, 271.77, 16.94, 266.94, 0.81, 172.58, 1.61]], "area": 31031.758850000013, "iscrowd": 0, "image_id": 19786, "bbox": [166.13, 0.81, 137.1, 369.35], "category_id": 1, "id": 187695}, {"segmentation": [[325.02, 445.69, 344.39, 430.62, 346.55, 416.63, 345.47, 375.73, 348.7, 345.6, 361.61, 331.61, 368.07, 316.54, 372.38, 296.09, 384.22, 269.18, 387.44, 251.96, 397.13, 233.67, 396.05, 220.75, 396.05, 207.84, 376.68, 196.0, 367.0, 191.7, 370.22, 183.09, 371.3, 175.55, 372.38, 169.09, 365.92, 155.1, 358.39, 149.72, 347.62, 147.57, 341.17, 147.57, 334.71, 147.57, 322.87, 155.1, 317.49, 162.64, 316.41, 175.55, 317.49, 194.92, 313.18, 198.15, 303.5, 198.15, 285.2, 204.61, 280.9, 214.3, 262.6, 236.9, 251.84, 249.81, 246.46, 263.8, 255.07, 275.64, 259.37, 282.1, 260.45, 287.48, 258.3, 293.94, 252.91, 298.24, 245.38, 299.32, 228.16, 301.47, 232.47, 325.15, 241.08, 334.83, 252.91, 350.98, 260.45, 363.89, 284.13, 385.42, 295.96, 396.18, 299.19, 413.4, 315.34, 413.4, 316.41, 398.33, 280.9, 353.13, 269.06, 324.07, 264.75, 304.7, 265.83, 295.01, 270.13, 288.56, 287.35, 281.02, 299.19, 284.25, 301.35, 291.78, 319.64, 304.7, 319.64, 307.93, 316.41, 316.54, 316.41, 324.07, 316.41, 329.45, 317.49, 346.67, 317.49, 374.65, 321.79, 397.26, 321.79, 401.56, 318.57, 409.09, 314.26, 417.7, 303.5, 445.69, 303.5, 445.69, 316.41, 457.52, 337.94, 436.0, 337.94, 436.0]], "area": 22882.21915000001, "iscrowd": 0, "image_id": 10707, "bbox": [228.16, 147.57, 168.97, 309.95], "category_id": 1, "id": 193096}, {"segmentation": [[302.53, 368.26, 316.85, 322.75, 318.54, 266.29, 310.96, 176.97, 307.58, 145.79, 289.89, 141.57, 265.45, 120.51, 250.28, 91.85, 268.82, 33.71, 287.36, 20.22, 298.31, 16.01, 365.73, 53.09, 364.89, 78.37, 399.44, 101.97, 464.33, 130.62, 479.49, 144.1, 474.44, 369.94, 422.19, 371.63, 435.67, 315.17, 417.98, 323.6, 397.75, 341.29, 356.46, 353.93, 355.62, 367.42, 300.84, 370.79]], "area": 49923.1806, "iscrowd": 0, "image_id": 19786, "bbox": [250.28, 16.01, 229.21, 355.62], "category_id": 1, "id": 196714}, {"segmentation": [[430.28, 187.53, 420.06, 187.84, 411.69, 191.24, 403.02, 198.37, 396.82, 209.83, 398.37, 224.7, 399.92, 230.27, 401.78, 233.99, 401.78, 237.71, 399.3, 241.12, 392.49, 245.76, 389.7, 251.34, 386.6, 261.87, 382.89, 272.71, 377.62, 285.41, 358.28, 322.28, 354.05, 344.82, 348.89, 351.86, 347.01, 366.41, 354.05, 381.91, 360.16, 386.6, 367.67, 391.3, 373.77, 390.83, 385.51, 413.34, 372.37, 429.3, 364.85, 433.53, 359.69, 434.0, 354.99, 439.63, 356.87, 451.37, 367.67, 452.31, 390.21, 449.96, 408.05, 450.88, 413.21, 405.8, 413.21, 405.8, 410.87, 376.22, 417.91, 371.06, 427.3, 374.82, 431.05, 374.82, 443.29, 361.27, 465.98, 360.27, 469.21, 348.4, 465.98, 316.04, 472.45, 253.48, 472.45, 253.48, 471.37, 235.15, 465.98, 222.2, 461.66, 216.81, 453.03, 209.26, 445.48, 212.49, 433.62, 212.49, 431.9, 207.37, 430.96, 194.22, 430.96, 187.65]], "area": 18923.138400000003, "iscrowd": 0, "image_id": 10707, "bbox": [347.01, 187.53, 125.44, 264.78], "category_id": 1, "id": 221149}, {"segmentation": [[293.39, 307.42, 298.79, 339.78, 268.58, 346.25, 286.92, 354.88, 269.66, 358.11, 265.35, 368.9, 288.0, 373.21, 316.04, 363.51, 308.49, 337.62, 318.2, 284.76, 336.54, 221.12, 330.07, 159.64, 322.52, 120.81, 305.26, 94.92, 305.26, 77.66, 285.84, 65.8, 266.43, 73.35, 252.4, 80.9, 262.11, 90.61, 265.35, 111.1, 279.37, 118.65, 270.74, 138.07, 268.58, 196.31, 277.21, 226.52, 277.21, 296.63]], "area": 13876.206750000001, "iscrowd": 0, "image_id": 13729, "bbox": [252.4, 65.8, 84.14, 307.41], "category_id": 1, "id": 222432}, {"segmentation": [[475.69, 34.52, 489.71, 8.63, 511.28, 2.16, 540.4, 5.39, 550.11, 32.36, 536.09, 76.58, 560.9, 94.92, 594.34, 111.1, 600.81, 146.7, 585.71, 213.57, 574.92, 283.69, 569.53, 298.79, 570.61, 335.46, 544.72, 388.31, 528.54, 435.78, 536.09, 472.45, 470.29, 473.53, 474.61, 439.01, 476.76, 396.94, 476.76, 364.58, 476.76, 313.89, 474.61, 265.35, 475.69, 229.75, 480.0, 207.1, 474.61, 180.13, 458.43, 200.63, 440.09, 210.34, 434.7, 211.42, 422.83, 142.38, 423.91, 91.69, 426.07, 75.51, 437.93, 53.93, 445.48, 105.71, 439.01, 122.97, 451.96, 176.9, 464.9, 143.46, 471.37, 114.34, 474.61, 99.24, 467.06, 72.27, 473.53, 45.3, 475.69, 35.6]], "area": 45726.941900000005, "iscrowd": 0, "image_id": 10707, "bbox": [422.83, 2.16, 177.98, 471.37], "category_id": 1, "id": 233159}, {"segmentation": [[397.27, 150.52, 407.3, 132.0, 411.16, 124.29, 408.08, 112.71, 395.73, 107.31, 388.79, 86.48, 391.87, 61.01, 413.48, 54.07, 432.77, 56.38, 439.71, 68.73, 440.49, 84.16, 435.08, 99.59, 459.78, 130.46, 470.58, 179.84, 465.18, 200.68, 459.05, 215.54, 466.34, 221.92, 464.52, 242.88, 455.41, 273.87, 443.56, 288.45, 438.09, 321.25, 460.88, 342.21, 476.37, 372.29, 491.86, 386.87, 475.46, 426.05, 466.34, 432.43, 451.76, 429.7, 462.7, 410.56, 464.52, 394.16, 459.05, 377.75, 419.87, 342.21, 413.49, 380.49, 412.58, 395.98, 377.04, 395.07, 360.63, 392.33, 358.81, 382.31, 386.15, 373.2, 396.18, 362.26, 393.44, 330.37, 389.8, 320.34, 385.24, 312.14, 395.26, 268.4, 387.06, 257.46, 388.88, 246.53, 399.82, 240.15, 400.73, 215.54, 397.09, 166.33, 386.15, 166.33, 371.57, 147.2, 375.22, 140.82]], "area": 21120.806099999983, "iscrowd": 0, "image_id": 13729, "bbox": [358.81, 54.07, 133.05, 378.36], "category_id": 1, "id": 258665}, {"segmentation": [[540.4, 285.32, 540.89, 274.67, 540.89, 264.01, 543.31, 259.17, 544.76, 252.39, 546.7, 246.09, 550.57, 244.64, 551.54, 258.2, 550.09, 284.35, 547.67, 290.65, 540.89, 288.22]], "area": 373.89345000000077, "iscrowd": 0, "image_id": 13729, "bbox": [540.4, 244.64, 11.14, 46.01], "category_id": 44, "id": 288369}, {"segmentation": [[113.66, 289.41, 174.79, 242.61, 209.18, 196.76, 226.37, 189.12, 288.46, 187.21, 439.37, 188.17, 431.73, 332.39, 412.63, 376.33, 95.52, 374.42, 93.61, 312.34, 114.62, 285.59]], "area": 55302.32280000001, "iscrowd": 0, "image_id": 10092, "bbox": [93.61, 187.21, 345.76, 189.12], "category_id": 65, "id": 315160}, {"segmentation": [[132.08, 411.38, 191.14, 368.43, 277.05, 329.77, 373.69, 279.3, 428.46, 244.94, 461.74, 212.72, 588.46, 230.98, 640.0, 243.86, 640.0, 339.43, 635.7, 416.75, 636.78, 473.66, 525.1, 474.73, 429.53, 468.29, 328.59, 471.51, 252.35, 474.73, 197.58, 472.59, 135.3, 471.51, 107.38, 469.37, 132.08, 403.86]], "area": 95589.10889999998, "iscrowd": 0, "image_id": 10995, "bbox": [107.38, 212.72, 532.62, 262.01], "category_id": 65, "id": 315527}, {"segmentation": [[570.26, 279.99, 576.2, 274.05, 581.63, 243.4, 590.04, 236.97, 617.73, 234.99, 628.11, 247.85, 620.69, 298.77, 617.73, 340.31, 609.32, 340.8, 609.82, 309.65, 597.46, 311.63, 585.59, 359.59, 579.66, 357.61, 586.08, 312.62, 568.29, 304.71, 565.81, 279.99, 572.24, 277.02]], "area": 4339.788350000003, "iscrowd": 0, "image_id": 16958, "bbox": [565.81, 234.99, 62.3, 124.6], "category_id": 62, "id": 376025}, {"segmentation": [[344.09, 163.63, 352.38, 59.92, 385.57, 58.26, 416.27, 63.23, 404.66, 166.95, 399.68, 151.18, 352.38, 151.18, 349.07, 166.12]], "area": 5872.678250000003, "iscrowd": 0, "image_id": 19432, "bbox": [344.09, 58.26, 72.18, 108.69], "category_id": 62, "id": 378536}, {"segmentation": [[194.49, 19.09, 222.33, 15.99, 238.57, 17.54, 233.93, 57.75, 224.65, 56.21, 190.62, 53.11, 197.58, 36.1, 196.03, 22.18]], "area": 1569.4438999999998, "iscrowd": 0, "image_id": 19432, "bbox": [190.62, 15.99, 47.95, 41.76], "category_id": 62, "id": 382669}, {"segmentation": [[317.14, 229.91, 348.95, 241.47, 348.95, 281.0, 377.87, 294.49, 327.75, 304.13, 325.82, 359.08, 308.47, 351.36, 319.07, 298.35, 309.43, 287.74, 309.43, 276.18, 309.43, 227.01]], "area": 3605.6617499999984, "iscrowd": 0, "image_id": 16958, "bbox": [308.47, 227.01, 69.4, 132.07], "category_id": 62, "id": 382759}, {"segmentation": [[540.4, 59.0, 587.87, 57.92, 596.49, 73.02, 584.63, 173.33, 580.31, 170.1, 577.08, 157.15, 531.78, 156.07, 529.62, 170.1, 522.07, 170.1, 532.85, 65.47, 532.85, 61.15]], "area": 6399.3884999999955, "iscrowd": 0, "image_id": 19432, "bbox": [522.07, 57.92, 74.42, 115.41], "category_id": 62, "id": 383277}, {"segmentation": [[284.32, 151.89, 287.57, 143.24, 291.89, 124.86, 292.97, 103.24, 292.97, 79.46, 292.97, 63.24, 292.97, 41.62, 295.14, 26.49, 302.7, 16.76, 315.68, 16.76, 341.62, 15.68, 351.35, 23.24, 354.59, 53.51, 348.11, 108.65, 344.86, 128.11, 343.78, 149.73, 342.7, 165.95, 336.22, 162.7, 336.22, 146.49, 320.0, 148.65, 299.46, 147.57, 285.41, 156.22, 282.16, 161.62]], "area": 7672.035949999999, "iscrowd": 0, "image_id": 19432, "bbox": [282.16, 15.68, 72.43, 150.27], "category_id": 62, "id": 384172}, {"segmentation": [[364.87, 20.4, 372.62, 14.51, 412.57, 15.75, 416.6, 18.85, 419.7, 19.16, 415.05, 64.07, 402.97, 60.98, 392.13, 58.81, 356.82, 60.05, 361.46, 31.24]], "area": 2528.3536, "iscrowd": 0, "image_id": 19432, "bbox": [356.82, 14.51, 62.88, 49.56], "category_id": 62, "id": 385840}, {"segmentation": [[420.54, 57.3, 422.7, 33.51, 423.78, 16.22, 434.59, 14.05, 464.86, 14.05, 476.76, 18.38, 480.0, 27.03, 478.92, 52.97, 475.68, 61.62, 455.14, 59.46]], "area": 2511.9208500000004, "iscrowd": 0, "image_id": 19432, "bbox": [420.54, 14.05, 59.46, 47.57], "category_id": 62, "id": 386016}, {"segmentation": [[479.08, 61.7, 479.08, 61.7, 484.06, 25.41, 489.04, 15.44, 536.01, 17.58, 540.28, 28.96, 536.01, 66.68, 526.76, 60.99, 484.77, 59.56, 479.08, 63.12]], "area": 2481.1789999999996, "iscrowd": 0, "image_id": 19432, "bbox": [479.08, 15.44, 61.2, 51.24], "category_id": 62, "id": 387128}, {"segmentation": [[417.16, 60.41, 411.74, 116.75, 417.16, 167.67, 464.83, 167.67, 467.0, 60.41]], "area": 5520.135899999997, "iscrowd": 0, "image_id": 19432, "bbox": [411.74, 60.41, 55.26, 107.26], "category_id": 62, "id": 387701}, {"segmentation": [[224.96, 161.01, 237.0, 58.1, 251.78, 58.64, 282.43, 58.64, 292.84, 63.02, 286.82, 165.39, 278.61, 166.49, 281.89, 147.87, 234.81, 148.97, 229.89, 167.03]], "area": 5495.6161999999995, "iscrowd": 0, "image_id": 19432, "bbox": [224.96, 58.1, 67.88, 108.93], "category_id": 62, "id": 388156}, {"segmentation": [[172.62, 79.18, 173.33, 69.26, 176.16, 60.76, 183.25, 57.22, 196.71, 57.22, 210.17, 56.51, 222.92, 58.64, 232.84, 64.3, 230.71, 77.77, 229.3, 91.93, 225.76, 94.77, 225.76, 108.23, 225.76, 123.82, 224.34, 135.15, 225.05, 141.53, 226.46, 145.78, 226.46, 152.15, 225.05, 155.7, 222.92, 157.82, 220.09, 162.78, 217.25, 166.32, 216.55, 162.78, 211.59, 161.36, 200.96, 160.66, 190.33, 157.82, 184.66, 157.11, 180.41, 153.57, 181.12, 159.95, 179.0, 162.07, 177.58, 159.95, 172.62, 150.03, 172.62, 147.19, 169.08, 141.53, 166.24, 134.44, 166.24, 126.65, 169.08, 107.52, 169.08, 94.77, 172.62, 84.85]], "area": 5778.258949999999, "iscrowd": 0, "image_id": 19432, "bbox": [166.24, 56.51, 66.6, 109.81], "category_id": 62, "id": 388869}, {"segmentation": [[310.63, 376.51, 317.36, 388.05, 336.6, 402.48, 347.18, 408.25, 365.45, 416.9, 402.0, 421.71, 433.73, 422.67, 465.47, 422.67, 491.43, 411.13, 500.09, 392.86, 500.09, 377.47, 500.09, 357.28, 491.43, 349.58, 477.97, 340.93, 460.66, 336.12, 449.12, 335.16, 435.66, 331.31, 423.15, 331.31, 402.96, 326.5, 387.57, 324.58, 363.53, 326.5, 363.53, 327.46, 351.99, 332.27, 338.52, 336.12, 328.91, 339.97, 318.33, 345.74, 309.67, 358.24, 307.75, 363.05, 310.63, 371.7, 310.63, 373.63, 310.63, 377.47]], "area": 15030.829199999998, "iscrowd": 0, "image_id": 13923, "bbox": [307.75, 324.58, 192.34, 98.09], "category_id": 67, "id": 390122}, {"segmentation": [[57.43, 110.25, 62.84, 98.33, 63.93, 80.99, 81.26, 65.82, 91.02, 49.57, 104.02, 34.4, 122.44, 26.82, 144.11, 35.49, 147.36, 49.57, 147.36, 71.24, 149.53, 92.91, 154.94, 113.5, 204.79, 205.6, 209.12, 216.43, 184.2, 219.68, 172.28, 180.68, 157.11, 153.59, 146.28, 136.25, 141.94, 127.58, 128.94, 175.26, 146.28, 225.1, 154.94, 246.77, 161.44, 277.11, 150.61, 303.12, 144.11, 328.04, 138.69, 349.71, 134.36, 368.13, 134.36, 381.13, 136.52, 386.55, 149.53, 395.21, 165.78, 406.05, 172.28, 409.3, 157.11, 412.55, 141.94, 411.47, 127.86, 402.8, 117.02, 400.63, 114.85, 411.47, 102.93, 416.88, 117.02, 429.89, 112.69, 430.97, 100.77, 429.89, 63.93, 427.72, 55.26, 416.88, 62.84, 393.05, 92.1, 308.53, 89.93, 286.86, 70.43, 244.6, 65.01, 218.6, 57.43, 175.26, 55.26, 160.09, 65.01, 111.33, 62.84, 102.66]], "area": 30702.014250000004, "iscrowd": 0, "image_id": 19432, "bbox": [55.26, 26.82, 153.86, 404.15], "category_id": 1, "id": 428692}, {"segmentation": [[360.0, 239.46, 323.24, 180.0, 303.78, 165.95, 260.54, 127.03, 285.41, 70.81, 315.68, 3.78, 477.84, 0.54, 534.05, 62.16, 567.57, 70.81, 593.51, 127.03, 582.7, 169.19, 556.76, 209.19, 541.62, 227.57, 619.46, 257.84, 637.84, 281.62, 636.76, 443.78, 636.76, 475.14, 431.35, 475.14, 364.32, 475.14, 367.57, 465.41, 403.24, 418.92, 404.32, 378.92, 392.43, 323.78, 395.68, 287.03, 367.57, 248.11, 353.51, 232.97]], "area": 117542.00074999998, "iscrowd": 0, "image_id": 19402, "bbox": [260.54, 0.54, 377.3, 474.6], "category_id": 1, "id": 452266}, {"segmentation": [[425.77, 201.2, 429.06, 188.96, 423.41, 180.96, 417.77, 172.02, 407.42, 172.49, 401.77, 167.79, 396.59, 160.73, 387.18, 156.02, 386.71, 142.37, 382.95, 135.32, 373.53, 137.2, 371.18, 133.43, 374.95, 128.73, 382.48, 127.32, 389.06, 122.14, 389.06, 109.43, 398.47, 101.44, 405.53, 96.73, 406.94, 89.2, 414.94, 79.79, 427.18, 70.85, 446.0, 60.97, 448.83, 56.73, 454.0, 43.08, 469.06, 37.91, 478.0, 42.14, 489.76, 34.61, 504.35, 32.73, 506.7, 28.03, 510.47, 27.09, 525.06, 28.03, 539.64, 37.91, 557.06, 47.32, 557.53, 47.79, 566.0, 52.03, 579.17, 61.91, 582.94, 67.55, 585.76, 65.2, 591.41, 59.08, 597.05, 63.79, 597.52, 69.44, 596.11, 81.67, 606.94, 92.49, 606.94, 97.67, 606.94, 102.38, 603.64, 116.02, 608.35, 122.61, 616.82, 129.67, 605.99, 142.85, 605.52, 143.32, 610.23, 155.08, 613.52, 167.31, 616.35, 179.55, 613.99, 181.9, 604.58, 187.55, 603.17, 195.08, 594.23, 201.2, 589.52, 201.67, 587.17, 203.08, 587.17, 205.9, 601.76, 226.61, 622.46, 248.25, 636.11, 250.61, 638.93, 253.9, 639.88, 295.78, 640.0, 389.42, 639.41, 473.65, 617.76, 475.07, 605.52, 475.07, 600.35, 473.18, 597.52, 471.77, 585.29, 476.48, 571.64, 474.13, 560.82, 467.54, 544.82, 464.71, 542.94, 464.24, 537.76, 464.71, 529.29, 465.18, 518.0, 480.0, 406.94, 479.77, 407.42, 477.42, 410.71, 477.42, 417.77, 476.95, 420.59, 476.95, 420.12, 475.07, 425.3, 475.54, 433.77, 474.13, 441.77, 477.89, 448.83, 478.36, 452.12, 471.3, 452.12, 470.36, 452.59, 464.71, 452.59, 462.36, 456.82, 457.66, 456.82, 452.48, 457.77, 446.36, 454.47, 443.54, 434.71, 434.6, 424.83, 427.07, 422.47, 415.77, 421.06, 395.54, 425.77, 378.13, 424.83, 379.07, 408.83, 371.07, 384.36, 357.42, 371.65, 351.31, 365.06, 345.19, 360.83, 336.25, 356.12, 327.78, 358.95, 324.01, 361.3, 319.31, 366.95, 315.07, 371.65, 315.07, 374.0, 315.07, 391.89, 313.66, 406.0, 315.54, 415.41, 323.54, 423.89, 322.6, 425.77, 321.66, 428.12, 318.84, 432.36, 315.07, 434.71, 313.19, 436.59, 310.84, 444.59, 312.72, 447.88, 312.25, 446.0, 302.37, 452.12, 293.9, 472.35, 283.07, 481.29, 271.31, 485.06, 256.72, 487.88, 251.08, 468.59, 253.9, 455.41, 248.25, 454.47, 242.14, 453.53, 240.72, 446.94, 237.9, 444.59, 235.55, 444.59, 229.43, 439.41, 223.78, 439.88, 215.78, 434.24, 212.49, 430.94, 213.43, 425.3, 211.55]], "area": 86553.94925, "iscrowd": 0, "image_id": 18150, "bbox": [356.12, 27.09, 283.88, 452.91], "category_id": 1, "id": 521385}, {"segmentation": [[7.55, 121.89, 44.22, 104.63, 86.29, 80.9, 98.16, 35.6, 112.18, 16.18, 156.4, 2.16, 204.94, 34.52, 222.2, 78.74, 212.49, 122.97, 230.83, 157.48, 276.13, 253.48, 298.79, 273.98, 321.44, 286.92, 326.83, 250.25, 377.53, 226.52, 412.04, 223.28, 389.39, 259.96, 388.31, 302.02, 344.09, 333.3, 294.47, 371.06, 232.99, 387.24, 188.76, 366.74, 94.92, 432.54, 31.28, 432.54, 11.87, 428.22, 3.24, 127.28]], "area": 96617.92659999996, "iscrowd": 0, "image_id": 18150, "bbox": [3.24, 2.16, 408.8, 430.38], "category_id": 1, "id": 532200}, {"segmentation": [[150.92, 248.11, 149.02, 242.4, 161.39, 245.26, 155.68, 252.88, 151.87, 243.35], [139.49, 258.59, 145.21, 255.73, 149.02, 267.16, 139.49, 288.1, 144.25, 349.04, 132.83, 372.84, 123.31, 379.51, 99.5, 375.7, 88.08, 353.8, 94.74, 334.76, 94.74, 325.24, 116.64, 299.53, 126.16, 296.67, 133.78, 285.25]], "area": 3830.6329500000006, "iscrowd": 0, "image_id": 19432, "bbox": [88.08, 242.4, 73.31, 137.11], "category_id": 43, "id": 655150}, {"segmentation": [[163.34, 344.39, 140.27, 215.2, 151.04, 206.74, 176.41, 200.59, 197.18, 202.13, 214.86, 209.05, 217.17, 209.82, 213.32, 296.71, 214.09, 335.93, 209.48, 345.93, 165.65, 345.93]], "area": 8977.492099999998, "iscrowd": 0, "image_id": 12576, "bbox": [140.27, 200.59, 76.9, 145.34], "category_id": 47, "id": 678023}, {"segmentation": [[366.96, 355.72, 365.21, 231.14, 364.34, 219.82, 383.51, 214.59, 406.16, 209.36, 434.91, 212.85, 455.82, 219.82, 453.2, 229.4, 438.39, 323.49, 426.2, 370.53, 407.03, 377.5, 388.74, 377.5, 370.44, 366.17, 368.7, 360.95]], "area": 12199.483799999998, "iscrowd": 0, "image_id": 12576, "bbox": [364.34, 209.36, 91.48, 168.14], "category_id": 47, "id": 678923}, {"segmentation": [[147.81, 414.02, 134.56, 415.67, 126.51, 386.5, 121.92, 367.11, 122.74, 362.02, 127.84, 358.45, 137.03, 357.43, 146.21, 359.98, 148.76, 362.53, 148.76, 372.23, 147.23, 384.47, 147.23, 384.47]], "area": 1162.5563, "iscrowd": 0, "image_id": 10707, "bbox": [121.92, 357.43, 26.84, 58.24], "category_id": 47, "id": 682380}, {"segmentation": [[369.49, 311.12, 351.72, 312.0, 317.07, 297.2, 317.67, 287.43, 330.99, 292.46, 347.87, 297.79, 367.41, 299.27], [480.0, 304.78, 461.25, 305.26, 440.76, 304.29, 442.23, 296.97, 480.0, 298.43]], "area": 881.472649999999, "iscrowd": 0, "image_id": 12576, "bbox": [317.07, 287.43, 162.93, 24.57], "category_id": 48, "id": 686666}, {"segmentation": [[480.0, 493.49, 439.18, 456.56, 392.85, 408.98, 396.61, 403.35, 417.27, 415.24, 449.82, 439.65, 479.87, 464.7]], "area": 1894.063649999999, "iscrowd": 0, "image_id": 12576, "bbox": [392.85, 403.35, 87.15, 90.14], "category_id": 49, "id": 693155}, {"segmentation": [[0.83, 640.0, 446.95, 640.0, 449.93, 608.76, 392.43, 497.73, 370.62, 461.04, 301.22, 414.45, 235.79, 384.71, 133.68, 375.79, 46.43, 392.64, 0.0, 408.5]], "area": 98073.03180000001, "iscrowd": 0, "image_id": 12576, "bbox": [0.0, 375.79, 449.93, 264.21], "category_id": 59, "id": 1073128}, {"segmentation": [[320.36, 300.61, 317.12, 279.04, 317.12, 265.02, 324.67, 249.92, 351.64, 241.29, 378.61, 230.5, 401.26, 227.27, 409.89, 227.27, 412.04, 230.5, 402.34, 244.52, 384.0, 265.02, 355.96, 282.28, 340.85, 288.75, 336.54, 293.06]], "area": 3391.5886999999984, "iscrowd": 0, "image_id": 18150, "bbox": [317.12, 227.27, 94.92, 73.34], "category_id": 59, "id": 1076324}, {"segmentation": [[444.1, 90.17, 448.31, 124.72, 466.01, 131.46, 483.71, 130.62, 482.02, 86.8]], "area": 1499.873349999999, "iscrowd": 0, "image_id": 19786, "bbox": [444.1, 86.8, 39.61, 44.66], "category_id": 73, "id": 1101948}, {"segmentation": [[380.44, 155.15, 378.09, 150.88, 373.61, 147.04, 378.09, 141.7, 385.78, 148.96, 381.72, 149.6, 381.3, 151.95, 386.85, 153.01, 391.97, 157.5, 396.88, 160.27, 396.67, 162.41, 392.4, 166.47, 390.05, 165.4, 390.05, 161.13, 387.92, 159.21, 384.5, 160.49, 382.58, 158.99, 384.29, 157.71, 381.72, 155.58, 380.66, 155.15], [257.42, 270.82, 266.18, 271.25, 266.18, 280.22, 259.99, 279.79, 259.56, 273.17, 257.85, 271.04], [256.78, 278.94, 255.08, 273.6, 250.59, 269.97, 251.45, 274.03, 252.73, 278.51, 257.21, 279.37], [248.43, 280.03, 247.63, 276.04, 244.84, 275.64, 246.04, 279.63, 248.03, 278.43], [237.67, 276.84, 231.69, 272.06, 232.09, 276.84, 237.27, 276.84]], "area": 270.0995000000001, "iscrowd": 0, "image_id": 13729, "bbox": [231.69, 141.7, 165.19, 138.52], "category_id": 75, "id": 1109128}, {"segmentation": [[433.6, 65.92, 434.38, 60.94, 441.2, 57.53, 436.75, 52.81, 439.89, 46.77, 447.5, 48.87, 443.83, 67.23], [430.75, 95.91, 429.06, 102.12, 433.57, 106.07, 431.5, 112.27, 438.84, 113.78, 439.78, 104.94, 443.16, 103.62, 440.91, 96.1]], "area": 326.82705, "iscrowd": 0, "image_id": 10707, "bbox": [429.06, 46.77, 18.44, 67.01], "category_id": 75, "id": 1109589}, {"segmentation": [[445.31, 238.53, 427.04, 223.14, 470.32, 212.56, 497.25, 226.02, 446.27, 242.37]], "area": 1053.42085, "iscrowd": 0, "image_id": 16958, "bbox": [427.04, 212.56, 70.21, 29.81], "category_id": 84, "id": 1144744}, {"segmentation": [[422.83, 312.79, 445.0, 321.56, 452.14, 324.94, 445.13, 328.32, 406.41, 339.1, 401.9, 339.1, 375.72, 325.82, 351.28, 314.67, 349.53, 312.04, 353.29, 309.15, 382.11, 302.39, 391.63, 299.88, 404.16, 304.89, 417.31, 310.03]], "area": 2081.5555500000014, "iscrowd": 0, "image_id": 16958, "bbox": [349.53, 299.88, 102.61, 39.22], "category_id": 84, "id": 1145311}, {"segmentation": [[439.5, 269.92, 438.14, 266.75, 438.14, 261.3, 439.5, 254.49, 439.5, 245.87, 440.41, 240.87, 442.68, 237.7, 443.59, 237.7, 446.31, 237.7, 450.39, 238.15, 452.21, 241.78, 450.39, 247.68, 449.94, 253.58, 449.94, 257.67, 449.94, 264.48, 449.94, 267.65, 441.77, 271.28, 440.86, 271.28]], "area": 353.8353999999995, "iscrowd": 0, "image_id": 13923, "bbox": [438.14, 237.7, 14.07, 33.58], "category_id": 86, "id": 1152315}, {"segmentation": [[385.3, 335.03, 381.51, 360.43, 382.46, 366.3, 387.96, 368.01, 400.65, 367.06, 401.79, 366.11, 400.47, 334.08, 391.75, 331.81]], "area": 612.6138000000011, "iscrowd": 0, "image_id": 13923, "bbox": [381.51, 331.81, 20.28, 36.2], "category_id": 86, "id": 1153517}, {"segmentation": [[281.4, 90.85, 286.01, 97.12, 286.84, 102.99, 279.73, 104.24, 278.47, 108.85, 279.31, 115.55, 279.31, 119.73, 279.31, 124.34, 282.66, 129.78, 284.75, 132.29, 285.17, 133.97, 285.17, 138.57, 296.89, 140.66, 304.43, 140.25, 303.59, 132.29, 301.5, 126.01, 304.85, 118.89, 306.1, 112.61, 304.85, 105.92, 303.59, 101.31, 304.43, 96.29, 298.57, 87.91, 292.71, 87.5, 286.84, 88.75, 279.73, 88.75]], "area": 1124.7253999999998, "iscrowd": 0, "image_id": 16958, "bbox": [278.47, 87.5, 27.63, 53.16], "category_id": 86, "id": 1154080}, {"segmentation": [[23.1, 75.6, 32.5, 84.01, 40.41, 92.41, 28.05, 91.42, 26.56, 96.37, 24.09, 108.24, 29.04, 113.18, 27.55, 122.57, 33.98, 129.0, 35.46, 135.43, 34.47, 136.42, 52.27, 138.39, 57.71, 137.9, 56.23, 129.5, 59.2, 122.57, 62.16, 117.63, 65.13, 113.67, 63.15, 107.25, 62.16, 99.83, 61.17, 94.89, 56.23, 91.42, 50.3, 91.42, 52.27, 86.48, 58.21, 81.54, 62.66, 80.55, 62.16, 77.09, 57.22, 77.58, 45.85, 73.63, 42.88, 73.63, 39.91, 74.12, 33.49, 75.6, 25.57, 76.59]], "area": 1858.8759999999997, "iscrowd": 0, "image_id": 16958, "bbox": [23.1, 73.63, 42.03, 64.76], "category_id": 86, "id": 1156571}, {"segmentation": [[116.62, 138.36, 94.87, 137.86, 96.35, 134.9, 101.29, 130.45, 102.28, 126.0, 99.32, 116.11, 93.38, 107.21, 93.38, 99.79, 96.84, 95.84, 93.38, 87.43, 94.87, 77.05, 95.86, 66.17, 94.87, 53.81, 95.86, 43.92, 107.23, 45.41, 108.71, 63.21, 112.67, 83.48, 110.19, 90.4, 116.62, 97.82, 112.67, 109.19, 109.7, 116.11, 108.22, 126.49, 113.16, 131.93, 117.61, 134.9]], "area": 1395.50215, "iscrowd": 0, "image_id": 16958, "bbox": [93.38, 43.92, 24.23, 94.44], "category_id": 86, "id": 1156677}, {"segmentation": [[220.82, 135.77, 210.39, 125.76, 210.17, 117.88, 211.24, 111.06, 212.73, 107.65, 231.47, 105.1, 243.61, 110.63, 249.57, 124.05, 250.64, 133.64, 251.49, 149.82, 251.07, 155.57, 235.94, 159.83, 229.98, 160.69, 228.49, 149.61, 225.51, 143.22]], "area": 1571.6321000000005, "iscrowd": 0, "image_id": 18150, "bbox": [210.17, 105.1, 41.32, 55.59], "category_id": 27, "id": 1420782}, {"segmentation": [[165.5, 368.35, 171.39, 371.79, 171.89, 374.25, 174.84, 408.66, 181.72, 403.75, 180.74, 372.29, 175.33, 364.42, 173.85, 350.65, 165.0, 348.69, 165.0, 363.93, 162.55, 366.88, 166.97, 368.35]], "area": 507.0550500000008, "iscrowd": 0, "image_id": 10707, "bbox": [162.55, 348.69, 19.17, 59.97], "category_id": 44, "id": 1484033}, {"segmentation": [[427.23, 89.48, 431.99, 99.95, 431.64, 101.25, 429.74, 103.07, 427.49, 105.31, 425.06, 105.75, 422.04, 108.43, 420.22, 107.48, 418.83, 102.37, 418.58, 99.52, 421.26, 92.77, 422.21, 89.48, 423.42, 76.42, 426.36, 76.16, 426.54, 87.15]], "area": 215.77320000000057, "iscrowd": 0, "image_id": 13923, "bbox": [418.58, 76.16, 13.41, 32.27], "category_id": 44, "id": 1488664}, {"segmentation": [[421.97, 80.47, 421.46, 90.15, 419.16, 97.03, 418.91, 102.37, 418.91, 109.51, 415.09, 109.76, 413.31, 100.08, 417.13, 93.21, 418.91, 84.55, 418.4, 79.96, 421.97, 78.64]], "area": 120.71215000000049, "iscrowd": 0, "image_id": 13923, "bbox": [413.31, 78.64, 8.66, 31.12], "category_id": 44, "id": 1490683}, {"segmentation": [[277.85, 191.96, 276.09, 170.4, 276.09, 154.6, 274.59, 145.32, 272.33, 137.55, 272.33, 129.78, 272.33, 126.27, 273.08, 122.5, 277.35, 121.5, 281.11, 120.25, 284.87, 119.5, 294.4, 118.99, 300.66, 118.99, 305.43, 119.24, 308.44, 120.5, 311.2, 122.25, 306.68, 156.86, 304.68, 162.37, 301.92, 170.65, 299.41, 187.45, 297.66, 191.21, 289.63, 194.22, 281.86, 192.46]], "area": 2209.591200000002, "iscrowd": 0, "image_id": 12576, "bbox": [272.33, 118.99, 38.87, 75.23], "category_id": 47, "id": 1506295}, {"segmentation": [[132.07, 359.07, 127.8, 358.85, 126.09, 349.88, 130.79, 344.97, 136.34, 343.69, 143.6, 343.26, 148.94, 343.9, 151.5, 347.11, 150.22, 370.81, 149.37, 371.88, 148.73, 362.48, 145.1, 359.92, 138.48, 358.0, 133.57, 358.21]], "area": 375.7898500000002, "iscrowd": 0, "image_id": 10707, "bbox": [126.09, 343.26, 25.41, 28.62], "category_id": 47, "id": 1506809}, {"segmentation": [[415.65, 168.99, 403.98, 171.11, 395.5, 171.11, 382.25, 168.46, 381.72, 117.05, 410.35, 112.81, 426.25, 115.99]], "area": 2161.795799999999, "iscrowd": 0, "image_id": 12576, "bbox": [381.72, 112.81, 44.53, 58.3], "category_id": 47, "id": 1509406}, {"segmentation": [[2.88, 323.6, 89.17, 319.28, 110.74, 326.47, 143.82, 326.47, 107.87, 337.98, 86.29, 332.22]], "area": 1024.8209000000027, "iscrowd": 0, "image_id": 12576, "bbox": [2.88, 319.28, 140.94, 18.7], "category_id": 48, "id": 1518224}, {"segmentation": [[591.3, 34.48, 601.81, 50.72, 616.14, 69.83, 604.68, 106.13, 583.66, 123.32, 578.89, 120.45, 570.29, 105.17, 566.47, 80.33, 567.42, 57.41, 576.98, 40.21]], "area": 2947.335499999998, "iscrowd": 0, "image_id": 13923, "bbox": [566.47, 34.48, 49.67, 88.84], "category_id": 51, "id": 1536045}, {"segmentation": [[640.0, 128.72, 633.89, 128.1, 623.88, 132.48, 614.18, 150.0, 610.43, 184.42, 614.18, 210.39, 625.76, 227.59, 636.08, 227.59, 640.0, 227.59]], "area": 2431.2010500000047, "iscrowd": 0, "image_id": 13923, "bbox": [610.43, 128.1, 29.57, 99.49], "category_id": 51, "id": 1539705}, {"segmentation": [[536.57, 122.55, 543.23, 117.14, 548.65, 120.89, 556.98, 127.55, 560.32, 133.39, 561.15, 159.64, 555.32, 172.14, 549.48, 180.48, 544.48, 185.06, 536.98, 185.06, 532.81, 177.56, 531.15, 167.14, 531.98, 143.39, 537.4, 122.14]], "area": 1558.4840500000016, "iscrowd": 0, "image_id": 13923, "bbox": [531.15, 117.14, 30.0, 67.92], "category_id": 51, "id": 1540774}, {"segmentation": [[288.92, 480.0, 288.41, 475.58, 281.26, 466.39, 272.59, 459.76, 256.77, 461.8, 222.59, 477.11]], "area": 780.9657500000004, "iscrowd": 0, "image_id": 18150, "bbox": [222.59, 459.76, 66.33, 20.24], "category_id": 59, "id": 1570250}, {"segmentation": [[306.75, 207.74, 316.3, 203.07, 323.94, 200.31, 330.53, 198.4, 337.32, 196.7, 340.93, 196.49, 341.35, 196.49, 349.21, 195.43, 366.41, 196.7, 373.84, 190.76, 379.99, 191.18, 387.21, 193.52, 393.58, 194.37, 398.47, 192.88, 406.53, 192.24, 411.42, 195.43, 416.51, 195.64, 420.55, 195.22, 424.16, 198.83, 425.43, 199.25, 428.4, 196.07, 430.74, 195.43, 434.98, 199.89, 447.3, 201.37, 459.19, 201.16, 470.44, 203.07, 476.6, 206.47, 480.0, 207.74, 479.57, 234.28, 454.52, 232.16, 455.37, 220.27, 448.78, 217.3, 435.2, 212.41, 422.46, 210.72, 392.95, 211.35, 378.51, 215.17, 371.08, 216.87, 367.04, 219.63, 364.92, 226.64, 364.71, 227.91, 341.78, 226.43, 311.42, 223.24, 306.32, 216.24, 306.11, 213.26, 306.53, 206.89]], "area": 3897.9527999999996, "iscrowd": 0, "image_id": 12576, "bbox": [306.11, 190.76, 173.89, 43.52], "category_id": 59, "id": 1571077}, {"segmentation": [[231.63, 202.02, 233.51, 228.37, 248.57, 212.68, 251.71, 197.0, 237.9, 190.1, 220.33, 181.94, 196.49, 176.92, 173.91, 173.16, 156.34, 176.29, 134.38, 181.31, 101.12, 193.86, 87.32, 205.78, 122.46, 205.15]], "area": 3650.014600000002, "iscrowd": 0, "image_id": 12576, "bbox": [87.32, 173.16, 164.39, 55.21], "category_id": 59, "id": 1571233}, {"segmentation": [[538.25, 63.64, 542.56, 19.42, 560.9, 18.34, 593.26, 18.34, 596.49, 29.12, 594.34, 47.46, 595.42, 65.8]], "area": 2525.8217000000013, "iscrowd": 0, "image_id": 19432, "bbox": [538.25, 18.34, 58.24, 47.46], "category_id": 62, "id": 1593544}, {"segmentation": [[590.44, 390.68, 639.32, 393.48, 639.72, 419.92, 639.32, 424.73, 594.85, 426.0, 589.24, 425.53, 589.24, 412.31, 579.62, 395.08, 580.83, 389.47]], "area": 1806.1752000000004, "iscrowd": 0, "image_id": 10092, "bbox": [579.62, 389.47, 60.1, 36.53], "category_id": 62, "id": 1598158}, {"segmentation": [[546.22, 275.9, 546.22, 275.9, 561.34, 268.34, 603.18, 263.69, 639.79, 265.44, 639.21, 342.73, 609.57, 341.57, 610.15, 367.72, 608.99, 390.97, 590.39, 392.13, 594.46, 328.78, 594.46, 328.78, 574.7, 311.93, 568.31, 424.68, 553.2, 425.26, 555.52, 411.31, 554.36, 299.14]], "area": 8324.60595, "iscrowd": 0, "image_id": 10092, "bbox": [546.22, 263.69, 93.57, 161.57], "category_id": 67, "id": 1613191}, {"segmentation": [[92.04, 161.17, 4.31, 220.14, 1.44, 628.59, 477.48, 624.28, 480.0, 176.99, 451.6, 181.31, 438.65, 149.67, 437.21, 141.04, 424.27, 113.71, 350.92, 100.77, 339.42, 112.28, 332.22, 120.9, 332.22, 141.04, 323.6, 135.29, 313.53, 126.66, 289.08, 115.15, 264.63, 132.41, 264.63, 159.74, 238.74, 155.42]], "area": 226460.51669999998, "iscrowd": 0, "image_id": 12576, "bbox": [1.44, 100.77, 478.56, 527.82], "category_id": 67, "id": 1620693}, {"segmentation": [[199.09, 260.48, 196.9, 265.97, 154.1, 274.38, 124.1, 257.55, 170.19, 249.14, 201.29, 259.75]], "area": 1101.245200000001, "iscrowd": 0, "image_id": 10707, "bbox": [124.1, 249.14, 77.19, 25.24], "category_id": 73, "id": 1628363}, {"segmentation": [[233.93, 389.5, 250.62, 363.86, 265.13, 366.04, 252.55, 392.16, 244.09, 395.07, 239.73, 395.07, 237.56, 393.37, 236.1, 392.41]], "area": 516.80155, "iscrowd": 0, "image_id": 10707, "bbox": [233.93, 363.86, 31.2, 31.21], "category_id": 75, "id": 1630006}, {"segmentation": [[257.93, 266.48, 253.11, 265.52, 246.59, 262.14, 249.0, 261.17, 260.35, 261.17, 262.28, 263.83]], "area": 54.222799999999644, "iscrowd": 0, "image_id": 13729, "bbox": [246.59, 261.17, 15.69, 5.31], "category_id": 75, "id": 1630509}, {"segmentation": [[193.97, 119.78, 217.79, 102.57, 225.73, 89.34, 219.11, 50.96, 231.02, 31.11, 254.85, 15.23, 281.31, 36.4, 276.02, 82.72, 274.7, 109.19, 281.31, 134.33, 285.28, 150.21, 281.31, 150.21, 257.49, 151.54, 213.82, 133.01, 199.26, 125.07]], "area": 7332.252549999997, "iscrowd": 0, "image_id": 12576, "bbox": [193.97, 15.23, 91.31, 136.31], "category_id": 1, "id": 1725035}, {"segmentation": [[101.61, 342.07, 101.14, 304.04, 99.26, 280.56, 100.2, 266.48, 97.85, 239.25, 97.85, 227.98, 96.44, 185.8, 95.51, 168.89, 98.32, 162.32, 102.08, 157.63, 109.12, 152.93, 115.69, 152.93, 109.59, 146.83, 104.43, 138.84, 101.14, 134.62, 101.14, 120.09, 99.73, 106.47, 100.2, 101.31, 116.16, 91.45, 126.49, 89.57, 135.88, 90.98, 143.4, 94.74, 147.15, 98.02, 154.19, 114.92, 155.6, 123.85, 157.01, 140.75, 156.54, 144.03, 161.71, 150.14, 181.35, 161.23, 192.62, 180.02, 205.3, 206.78, 220.32, 221.33, 243.8, 259.83, 261.17, 273.92, 257.41, 280.02, 250.84, 283.78, 242.86, 279.55, 236.75, 275.33, 235.82, 278.61, 224.55, 259.83, 196.85, 229.31, 181.35, 203.02, 177.6, 208.66, 184.17, 241.05, 192.15, 265.0, 194.03, 290.82, 191.21, 334.48, 187.46, 340.49, 188.4, 350.82, 197.79, 367.72, 201.07, 395.42, 203.89, 421.72, 207.18, 431.57, 199.19, 433.92, 181.35, 422.65, 175.72, 416.55, 179.0, 413.26, 186.05, 414.67, 175.25, 387.44, 167.27, 361.62, 161.63, 347.53, 157.88, 347.06, 139.1, 350.82, 133.46, 357.39, 128.77, 378.52, 132.99, 400.04, 127.36, 427.75, 115.15, 460.61, 111.4, 475.17, 89.33, 474.23, 82.76, 468.12, 77.59, 462.02, 77.59, 460.14, 78.53, 456.39, 94.96, 449.34, 98.25, 439.48, 99.66, 398.64, 100.6, 362.48, 100.13, 347.46]], "area": 28375.149850000005, "iscrowd": 0, "image_id": 13729, "bbox": [77.59, 89.57, 183.58, 385.6], "category_id": 1, "id": 1729676}, {"segmentation": [[217.89, 224.36, 207.1, 213.57, 197.39, 195.24, 189.84, 174.74, 179.06, 161.8, 166.11, 152.09, 156.4, 142.38, 159.64, 124.04, 161.8, 118.65, 154.25, 107.87, 153.17, 90.61, 157.48, 83.06, 169.35, 79.82, 179.06, 86.29, 196.31, 96.0, 200.63, 103.55, 187.69, 107.87, 194.16, 118.65, 208.18, 138.07, 218.97, 173.66, 228.67, 208.18, 227.6, 226.52], [183.37, 207.1, 220.04, 257.8, 213.57, 314.97, 216.81, 335.46, 209.26, 339.78, 197.39, 343.01, 195.24, 338.7, 201.71, 322.52, 199.55, 290.16, 199.55, 279.37, 190.92, 262.11, 184.45, 237.3, 177.98, 221.12, 174.74, 201.71]], "area": 7349.56295, "iscrowd": 0, "image_id": 13729, "bbox": [153.17, 79.82, 75.5, 263.19], "category_id": 1, "id": 1730702}, {"segmentation": [[0.0, 124.04, 47.46, 84.13, 133.75, 52.85, 225.44, 96.0, 323.6, 176.9, 392.63, 288.0, 394.79, 339.78, 403.42, 412.04, 366.74, 472.45, 336.54, 480.0, 0.0, 478.92, 2.16, 122.97]], "area": 140541.77395000003, "iscrowd": 0, "image_id": 19402, "bbox": [0.0, 52.85, 403.42, 427.15], "category_id": 1, "id": 1734330}, {"segmentation": [[196.54, 139.18, 194.02, 138.97, 192.77, 132.89, 187.74, 129.12, 185.22, 123.25, 185.01, 107.95, 186.48, 105.01, 188.78, 104.38, 191.09, 106.06, 192.77, 112.98, 193.4, 115.07, 205.55, 124.93, 212.68, 139.6, 213.31, 141.28, 210.59, 141.07, 205.55, 136.46, 202.83, 133.94, 199.27, 133.94, 197.8, 136.25, 197.17, 139.39]], "area": 436.96835000000016, "iscrowd": 0, "image_id": 10092, "bbox": [185.01, 104.38, 28.3, 36.9], "category_id": 16, "id": 1814184}, {"segmentation": [[101.27, 428.88, 88.81, 394.61, 92.97, 361.9, 89.33, 349.95, 100.75, 332.82, 124.64, 323.47, 153.2, 329.7, 163.58, 346.32, 161.5, 366.57, 150.6, 370.2, 151.64, 344.24, 138.14, 342.17, 129.31, 344.76, 126.2, 352.03, 126.2, 358.78, 119.97, 365.01, 124.12, 379.03, 103.87, 385.78, 106.47, 425.76, 106.47, 436.14, 106.99, 444.45, 102.31, 446.01, 100.75, 441.34, 77.91, 450.16, 74.27, 443.41]], "area": 3391.0124499999993, "iscrowd": 0, "image_id": 10707, "bbox": [74.27, 323.47, 89.31, 126.69], "category_id": 27, "id": 1825577}, {"segmentation": [[321.67, 288.93, 320.67, 287.29, 320.12, 282.56, 320.12, 279.83, 321.49, 275.83, 323.22, 273.55, 326.49, 271.64, 329.95, 271.0, 334.95, 271.46, 338.5, 273.1, 340.23, 276.01, 342.23, 280.29, 342.05, 284.29, 340.69, 287.66, 336.86, 289.2, 333.22, 290.11, 329.77, 291.21, 322.94, 290.2]], "area": 359.60995, "iscrowd": 0, "image_id": 19432, "bbox": [320.12, 271.0, 22.11, 20.21], "category_id": 37, "id": 1853933}, {"segmentation": [[476.06, 441.83, 446.04, 433.87, 431.95, 413.65, 422.14, 389.14, 419.19, 380.0, 425.8, 385.04, 441.23, 415.28, 424.22, 375.91, 428.32, 374.02, 449.11, 416.22, 451.0, 414.96, 430.84, 376.54, 456.98, 413.07, 458.87, 412.76, 434.93, 370.55, 442.81, 378.74, 472.41, 417.17, 480.0, 429.77, 479.97, 434.49, 480.0, 438.9]], "area": 1719.0022999999992, "iscrowd": 0, "image_id": 12576, "bbox": [419.19, 370.55, 60.81, 71.28], "category_id": 48, "id": 1888354}, {"segmentation": [[21.19, 386.49, 82.29, 304.21, 130.91, 331.64, 211.95, 340.36, 228.16, 347.84, 236.88, 372.78, 234.39, 390.23, 164.57, 421.4, 145.87, 448.83, 114.7, 443.84, 71.06, 466.29, 24.94, 468.78, 6.23, 472.52, 3.74, 398.96]], "area": 23284.81415, "iscrowd": 0, "image_id": 19402, "bbox": [3.74, 304.21, 233.14, 168.31], "category_id": 59, "id": 1924077}, {"segmentation": [[541.85, 378.49, 543.94, 319.87, 538.19, 304.17, 561.22, 304.17, 561.22, 321.97, 560.17, 340.81, 557.03, 383.2, 556.51, 402.57, 551.27, 406.76, 549.7, 403.09, 551.27, 368.55, 548.13, 363.84, 547.09, 382.16], [572.98, 379.41, 593.8, 380.75, 591.12, 371.34, 572.98, 372.02], [573.65, 356.57, 595.15, 357.24, 595.15, 351.86, 575.67, 349.18], [575.67, 339.77, 596.49, 341.79, 594.47, 327.68, 574.32, 310.89, 574.32, 340.44], [617.03, 266.55, 621.06, 224.23, 631.13, 204.74, 640.0, 204.74, 639.87, 267.89, 631.13, 266.55, 623.07, 263.86]], "area": 3310.2827499999967, "iscrowd": 0, "image_id": 10092, "bbox": [538.19, 204.74, 101.81, 202.02], "category_id": 62, "id": 1938423}, {"segmentation": [[444.3, 315.25, 442.2, 308.94, 440.09, 303.16, 451.14, 301.06, 460.6, 298.43, 463.76, 291.59, 464.81, 287.91, 464.81, 282.65, 464.81, 280.02, 477.43, 272.66, 497.94, 244.79, 506.88, 242.16, 515.29, 240.58, 528.44, 240.58, 543.69, 243.21, 555.78, 246.37, 564.2, 250.05, 567.35, 253.2, 568.93, 255.83, 564.72, 297.37, 564.72, 304.21, 563.15, 309.99, 558.94, 322.62, 555.26, 333.66, 551.58, 340.49, 544.74, 345.75, 538.43, 351.01, 537.38, 352.06, 538.43, 355.74, 535.28, 364.68, 525.28, 372.57, 525.81, 376.25, 508.46, 379.93, 511.09, 383.09, 516.87, 385.72, 527.91, 388.87, 534.75, 392.56, 540.01, 396.76, 543.69, 403.6, 545.79, 411.49, 546.32, 415.69, 537.38, 413.59, 533.7, 406.23, 518.97, 399.92, 507.93, 396.24, 498.99, 393.08, 500.04, 394.13, 501.09, 414.12, 502.67, 419.9, 502.15, 423.06, 503.72, 427.79, 493.21, 426.74, 492.15, 420.43, 491.63, 412.54, 490.05, 403.6, 489.53, 394.13, 489.0, 389.4, 487.42, 388.35, 484.27, 388.87, 477.96, 392.56, 466.91, 395.18, 460.08, 397.29, 452.72, 398.34, 452.72, 393.61, 453.77, 388.87, 453.24, 388.35, 454.29, 385.19, 463.23, 383.62, 470.07, 379.93, 474.8, 374.68, 475.33, 373.62, 472.7, 372.05, 461.66, 365.74, 455.87, 363.11, 454.82, 361.0, 454.82, 358.9, 459.03, 356.8, 466.39, 353.12, 473.22, 348.38, 476.38, 339.44, 473.22, 334.18, 469.54, 328.93, 462.18, 323.14, 456.4, 320.51, 449.56, 316.83], [444.3, 388.87, 442.72, 393.61, 437.99, 405.7, 443.25, 407.81, 443.78, 404.65, 443.78, 399.92, 445.35, 393.61]], "area": 13529.487949999999, "iscrowd": 0, "image_id": 16958, "bbox": [437.99, 240.58, 130.94, 187.21], "category_id": 62, "id": 1939880}, {"segmentation": [[447.82, 270.69, 441.13, 269.73, 436.99, 261.77, 436.04, 254.44, 441.77, 251.58, 441.13, 239.47, 439.54, 235.01, 436.04, 231.51, 428.71, 222.27, 429.35, 221.64, 433.81, 221.64, 437.31, 219.41, 443.36, 226.1, 440.18, 213.99, 435.72, 207.62, 434.76, 202.21, 442.09, 201.89, 444.95, 206.35, 457.06, 208.58, 456.1, 213.67, 449.73, 214.31, 457.69, 219.73, 457.38, 223.87, 451.96, 222.91, 451.32, 227.69, 451.64, 236.92, 447.82, 238.52, 448.29, 252.04, 450.59, 257.77, 449.77, 264.32]], "area": 856.5159999999997, "iscrowd": 0, "image_id": 13923, "bbox": [428.71, 201.89, 28.98, 68.8], "category_id": 64, "id": 1954318}, {"segmentation": [[386.65, 334.87, 383.46, 337.1, 381.55, 343.79, 381.55, 350.48, 381.87, 353.98, 380.28, 359.71, 382.83, 366.4, 387.92, 368.31, 392.38, 368.95, 393.65, 368.0, 397.16, 365.77, 400.98, 363.85, 400.66, 357.48, 399.39, 352.71, 398.75, 346.97, 400.03, 341.56, 400.03, 337.1, 398.75, 332.0, 395.57, 325.32, 397.8, 321.49, 400.66, 323.72, 407.99, 324.04, 407.35, 321.49, 412.13, 321.18, 415.31, 320.86, 419.14, 317.35, 416.59, 312.26, 413.4, 308.75, 411.49, 303.34, 405.12, 295.06, 400.66, 286.46, 391.11, 282.64, 383.46, 280.41, 373.59, 282.32, 366.9, 286.78, 363.4, 289.32, 360.53, 291.55, 357.98, 296.33, 361.8, 302.7, 361.17, 308.75, 361.17, 311.62, 361.8, 315.44, 373.91, 314.81, 373.91, 320.86, 375.82, 323.4, 376.14, 328.5, 384.1, 328.5, 389.83, 328.18, 392.7, 329.77, 388.56, 335.19]], "area": 2564.863350000001, "iscrowd": 0, "image_id": 13923, "bbox": [357.98, 280.41, 61.16, 88.54], "category_id": 64, "id": 1954498}, {"segmentation": [[640.0, 369.73, 628.69, 368.51, 610.44, 368.51, 575.15, 386.76, 543.1, 416.37, 478.21, 467.48, 478.21, 478.84, 640.0, 478.43]], "area": 11664.891299999997, "iscrowd": 0, "image_id": 13729, "bbox": [478.21, 368.51, 161.79, 110.33], "category_id": 67, "id": 1966637}, {"segmentation": [[289.44, 316.05, 368.04, 309.5, 368.36, 316.05, 340.53, 318.67, 290.75, 320.96, 288.13, 316.7], [442.7, 302.62, 480.0, 303.93, 480.0, 311.13, 439.75, 315.72, 441.39, 307.53]], "area": 881.6853500000002, "iscrowd": 0, "image_id": 12576, "bbox": [288.13, 302.62, 191.87, 18.34], "category_id": 49, "id": 2104032}, {"segmentation": [[270.4, 427.0, 254.99, 415.65, 245.92, 397.51, 240.48, 379.38, 240.48, 369.41, 249.55, 378.48, 283.09, 397.51, 310.29, 412.93, 332.04, 421.08, 339.3, 420.18, 339.3, 427.0, 271.31, 425.62]], "area": 2115.54865, "iscrowd": 0, "image_id": 13923, "bbox": [240.48, 369.41, 98.82, 57.59], "category_id": 62, "id": 2123120}, {"segmentation": [[207.71, 361.75, 193.15, 369.34, 194.38, 375.8, 201.56, 380.11, 213.35, 374.88, 212.74, 362.27]], "area": 254.56780000000032, "iscrowd": 0, "image_id": 10707, "bbox": [193.15, 361.75, 20.2, 18.36], "category_id": 75, "id": 2135228}, {"segmentation": [[305.24, 367.86, 295.79, 355.27, 304.34, 345.38, 310.78, 350.77]], "area": 170.51259999999968, "iscrowd": 0, "image_id": 19786, "bbox": [295.79, 345.38, 14.99, 22.48], "category_id": 75, "id": 2135614}, {"segmentation": [[261.31, 91.94, 254.29, 83.51, 257.7, 76.09, 262.31, 73.28, 263.52, 82.91]], "area": 98.12794999999994, "iscrowd": 0, "image_id": 19786, "bbox": [254.29, 73.28, 9.23, 18.66], "category_id": 75, "id": 2136418}, {"segmentation": [[74.29, 56.66, 77.35, 34.96, 79.14, 23.98, 82.72, 16.06, 88.85, 13.25, 91.91, 12.48, 126.64, 13.25, 131.75, 16.57, 133.54, 19.12, 133.54, 27.3, 132.26, 30.36, 128.69, 28.83, 125.11, 25.25, 123.83, 24.74, 118.98, 26.79, 116.43, 27.3, 113.87, 27.3, 110.81, 30.11, 109.53, 30.62, 108.0, 31.64, 106.21, 34.19, 101.87, 37.77, 98.55, 41.34, 96.51, 42.36, 93.44, 43.13, 91.14, 45.17, 86.29, 46.96, 83.48, 47.98, 81.18, 51.56, 78.89, 54.62, 77.35, 55.9]], "area": 1275.47215, "iscrowd": 0, "image_id": 19432, "bbox": [74.29, 12.48, 59.25, 44.18], "category_id": 62, "id": 2192372}, {"segmentation": [[615.41, 281.19, 615.99, 275.88, 616.1, 271.15, 618.87, 270.92, 622.45, 271.15, 624.07, 271.61, 625.34, 272.07, 625.57, 272.3, 625.11, 279.92, 624.41, 283.27, 622.22, 283.04, 620.26, 282.92, 618.53, 283.16, 616.57, 282.35]], "area": 109.40540000000038, "iscrowd": 0, "image_id": 13729, "bbox": [615.41, 270.92, 10.16, 12.35], "category_id": 47, "id": 2214578}, {"segmentation": [[329.14, 241.75, 331.37, 231.59, 332.36, 224.65, 334.34, 218.21, 336.82, 206.81, 345.49, 208.54, 401.5, 217.71, 401.25, 232.83, 398.03, 240.01, 392.58, 249.93, 391.34, 256.62, 396.79, 262.07, 390.84, 285.61, 378.7, 292.05, 373.5, 328.48, 373.25, 339.88, 327.9, 327.49, 312.54, 322.04, 312.54, 316.34, 320.71, 275.94, 325.17, 263.06]], "area": 7926.755249999999, "iscrowd": 0, "image_id": 13729, "bbox": [312.54, 206.81, 88.96, 133.07], "category_id": 63, "id": 2231408}, {"segmentation": [[389.28, 202.3, 389.65, 201.46, 390.27, 201.09, 391.06, 200.83, 391.48, 200.78, 392.37, 200.67, 393.11, 200.67, 393.63, 200.67, 394.0, 200.99, 394.1, 201.41, 394.37, 202.14, 394.16, 203.03, 394.05, 203.56, 393.79, 204.03, 393.11, 204.19, 391.9, 204.29, 391.53, 204.34, 390.64, 204.34, 390.33, 204.29, 389.7, 204.13, 389.33, 203.77, 389.12, 203.14]], "area": 16.039750000000094, "iscrowd": 0, "image_id": 12120, "bbox": [389.12, 200.67, 5.25, 3.67], "category_id": 37, "id": 305373}, {"segmentation": [[76.25, 133.97, 76.07, 123.82, 76.59, 122.27, 78.14, 122.27, 83.47, 123.31, 91.21, 122.79, 95.34, 122.62, 96.03, 123.13, 95.85, 129.33, 96.2, 135.52, 96.89, 138.44, 99.98, 137.58, 99.64, 142.05, 97.75, 142.74, 94.99, 144.81, 94.82, 146.7, 94.99, 153.75, 94.48, 153.06, 94.31, 139.13, 89.15, 139.3, 81.92, 138.27, 77.97, 135.52, 76.42, 134.14]], "area": 336.7431500000001, "iscrowd": 0, "image_id": 14439, "bbox": [76.07, 122.27, 23.91, 31.48], "category_id": 62, "id": 372865}, {"segmentation": [[375.33, 33.24, 375.02, 24.93, 375.02, 20.57, 376.74, 19.25, 380.8, 19.45, 384.14, 20.06, 385.97, 20.47, 385.46, 28.88, 386.07, 33.84, 378.57, 33.24]], "area": 145.84575000000024, "iscrowd": 0, "image_id": 12120, "bbox": [375.02, 19.25, 11.05, 14.59], "category_id": 62, "id": 373319}, {"segmentation": [[118.23, 153.48, 117.49, 147.68, 117.0, 144.48, 116.5, 142.13, 116.13, 137.45, 116.01, 135.6, 115.52, 133.75, 114.28, 131.53, 112.93, 131.03, 109.47, 131.16, 101.71, 131.28, 99.12, 131.28, 98.5, 134.24, 98.5, 136.34, 103.43, 135.23, 104.91, 135.84, 103.56, 140.65, 101.46, 142.75, 100.97, 143.98, 101.71, 144.72, 107.75, 145.09, 111.82, 145.22, 114.9, 144.48, 115.64, 144.48, 116.26, 152.37, 116.5, 155.08, 117.98, 154.71, 118.35, 153.23], [99.24, 143.74, 98.01, 155.7, 99.86, 155.45, 100.23, 151.26, 100.35, 145.83, 102.32, 153.97, 102.32, 155.58, 102.94, 154.71, 103.06, 152.74, 108.86, 152.74, 112.93, 152.74, 112.93, 155.82, 113.91, 155.21, 114.04, 149.66, 114.04, 147.07, 114.41, 145.96, 103.8, 146.33, 101.95, 146.2, 101.21, 145.09, 101.09, 142.87, 99.61, 142.75, 99.36, 143.37]], "area": 325.07545000000005, "iscrowd": 0, "image_id": 14439, "bbox": [98.01, 131.03, 20.34, 24.79], "category_id": 62, "id": 375587}, {"segmentation": [[362.47, 20.74, 361.45, 29.79, 360.14, 30.52, 359.85, 33.73, 371.31, 33.88, 371.52, 28.91, 371.31, 21.91, 368.39, 18.84, 362.55, 19.28]], "area": 141.66634999999977, "iscrowd": 0, "image_id": 12120, "bbox": [359.85, 18.84, 11.67, 15.04], "category_id": 62, "id": 380029}, {"segmentation": [[349.56, 7.55, 355.39, 8.26, 356.74, 8.98, 358.98, 12.57, 360.41, 13.73, 360.59, 18.75, 357.81, 19.83, 356.02, 16.87, 354.14, 12.3, 350.37, 10.95]], "area": 61.62440000000016, "iscrowd": 0, "image_id": 12120, "bbox": [349.56, 7.55, 11.03, 12.28], "category_id": 62, "id": 385411}, {"segmentation": [[15.1, 340.31, 14.91, 336.49, 18.73, 334.86, 15.82, 339.76], [21.64, 336.13, 24.55, 337.95, 24.55, 343.22, 24.55, 346.85, 12.73, 369.03, 11.28, 371.58, 9.1, 369.03, 9.1, 361.4, 9.64, 355.22, 10.37, 350.49, 11.28, 348.13, 12.55, 346.49, 15.28, 344.67], [27.15, 347.75, 13.7, 371.93, 8.97, 381.39, 8.24, 400.47, 5.7, 404.29, 3.33, 405.2, 1.7, 405.2, 5.33, 408.29, 13.7, 406.11, 15.15, 404.66, 16.24, 400.66, 18.06, 396.47, 18.24, 391.38, 18.97, 390.11, 18.97, 401.02, 18.6, 403.56, 21.69, 404.84, 23.88, 404.11, 25.15, 402.29, 26.6, 371.75, 27.69, 366.11, 28.78, 364.48]], "area": 1007.6924500000001, "iscrowd": 0, "image_id": 17959, "bbox": [1.7, 334.86, 27.08, 73.43], "category_id": 1, "id": 449302}, {"segmentation": [[130.17, 401.45, 129.41, 380.01, 127.99, 379.77, 127.64, 365.4, 123.78, 362.53, 122.96, 346.2, 123.67, 344.67, 126.41, 345.28, 127.63, 341.32, 129.86, 340.41, 134.74, 342.34, 135.65, 345.28, 140.22, 346.7, 142.86, 347.11, 144.08, 349.85, 142.25, 355.64, 137.98, 371.21, 132.74, 392.67, 131.1, 400.85], [143.23, 392.9, 143.23, 396.32, 142.16, 398.2, 142.56, 401.88, 145.71, 402.18, 145.71, 402.99, 137.68, 403.91, 138.6, 402.18, 138.6, 402.18, 138.6, 399.24, 137.79, 398.32, 142.66, 393.34]], "area": 653.1867500000004, "iscrowd": 0, "image_id": 17959, "bbox": [122.96, 340.41, 22.75, 63.5], "category_id": 1, "id": 478072}, {"segmentation": [[198.63, 264.32, 193.16, 250.25, 196.29, 244.77, 202.54, 232.26, 205.67, 224.44, 205.67, 221.31, 205.67, 217.4, 208.02, 211.15, 208.8, 204.89, 208.02, 197.85, 207.24, 193.16, 188.47, 189.25, 186.9, 192.38, 188.47, 196.29, 189.25, 199.42, 182.99, 207.24, 165.01, 222.09, 159.53, 226.79, 155.62, 233.82, 154.06, 245.56, 155.62, 252.59, 157.97, 253.38, 160.31, 247.12, 169.7, 229.13, 173.61, 226.0, 175.17, 227.57, 175.17, 243.99, 175.17, 258.85, 179.08, 260.41, 192.38, 258.85]], "area": 2014.4765500000005, "iscrowd": 0, "image_id": 15660, "bbox": [154.06, 189.25, 54.74, 75.07], "category_id": 1, "id": 478703}, {"segmentation": [[81.98, 556.14, 64.72, 544.64, 58.97, 544.64, 48.9, 548.95, 38.83, 543.2, 24.45, 523.07, 21.57, 511.56, 25.89, 507.25, 28.76, 501.49, 21.57, 479.92, 21.57, 466.98, 21.57, 439.65, 35.96, 397.94, 47.46, 382.12, 51.78, 364.86, 53.21, 361.99, 46.02, 347.61, 46.02, 328.91, 46.02, 318.84, 51.78, 311.65, 71.91, 310.21, 92.04, 310.21, 103.55, 311.65, 115.06, 320.28, 116.49, 349.04, 116.49, 364.86, 120.81, 364.86, 133.75, 361.99, 133.75, 361.99, 169.71, 347.61, 182.65, 357.67, 169.71, 374.93, 172.58, 395.07, 174.02, 403.7, 151.01, 393.63, 139.51, 380.68, 122.25, 379.25, 96.36, 385.0, 112.18, 420.95, 104.99, 438.21, 96.36, 456.91, 172.58, 462.66, 178.34, 491.43, 109.3, 520.19, 94.92, 527.38, 104.99, 550.39]], "area": 22642.328600000008, "iscrowd": 0, "image_id": 12639, "bbox": [21.57, 310.21, 161.08, 245.93], "category_id": 1, "id": 507402}, {"segmentation": [[23.58, 238.16, 24.54, 224.01, 29.05, 220.14, 29.05, 211.13, 31.62, 203.73, 38.38, 201.8, 47.71, 205.34, 45.14, 209.52, 47.07, 212.74, 45.14, 216.93, 45.78, 219.82, 51.9, 228.51, 51.9, 232.69, 59.3, 240.42, 80.54, 239.77, 85.37, 234.95, 93.09, 226.26, 97.27, 225.61, 106.28, 243.64, 89.87, 255.54, 68.95, 253.93, 48.36, 253.29, 39.67, 250.72, 35.81, 245.57, 27.44, 239.13]], "area": 1910.9380999999998, "iscrowd": 0, "image_id": 12639, "bbox": [23.58, 201.8, 82.7, 53.74], "category_id": 1, "id": 543629}, {"segmentation": [[84.01, 231.05, 77.25, 227.19, 76.61, 220.43, 75.64, 214.0, 69.53, 206.92, 67.27, 202.41, 70.49, 192.11, 77.57, 189.22, 77.25, 186.64, 73.71, 182.78, 73.71, 177.63, 75.96, 174.41, 79.5, 173.45, 83.69, 172.81, 87.23, 178.92, 84.65, 189.22, 90.44, 191.15, 96.24, 197.26, 97.2, 205.31, 97.52, 207.56, 100.74, 211.1, 100.74, 215.28, 94.31, 218.18, 91.73, 225.58, 88.83, 224.94, 84.33, 227.51]], "area": 1045.0987, "iscrowd": 0, "image_id": 12639, "bbox": [67.27, 172.81, 33.47, 58.24], "category_id": 1, "id": 564675}, {"segmentation": [[86.84, 187.88, 88.05, 190.3, 91.85, 190.64, 96.35, 188.39, 100.84, 189.95, 102.05, 192.54, 102.05, 197.04, 101.36, 201.18, 99.8, 203.26, 101.01, 205.51, 104.64, 207.06, 108.1, 208.27, 109.66, 209.13, 109.14, 204.99, 109.66, 200.67, 109.83, 196.0, 109.48, 193.41, 111.38, 190.47, 112.42, 187.53, 113.46, 187.53, 113.11, 185.8, 113.98, 183.21, 113.63, 180.44, 110.52, 178.71, 107.41, 177.68, 103.78, 178.54, 101.7, 180.44, 100.49, 182.0, 100.49, 184.59, 98.77, 185.28, 96.0, 186.32, 93.24, 187.18, 90.99, 187.53, 88.91, 187.53, 86.84, 187.36]], "area": 325.1160999999999, "iscrowd": 0, "image_id": 12639, "bbox": [86.84, 177.68, 27.14, 31.45], "category_id": 1, "id": 568141}, {"segmentation": [[144.79, 229.87, 143.89, 229.52, 143.71, 228.44, 143.36, 225.94, 141.92, 223.97, 140.49, 222.72, 135.84, 218.6, 135.3, 216.99, 135.3, 215.74, 132.62, 213.95, 131.72, 212.69, 130.29, 210.19, 129.58, 209.11, 127.07, 208.22, 127.07, 209.11, 125.82, 208.93, 123.85, 208.4, 122.42, 206.97, 122.24, 204.82, 121.88, 202.67, 122.42, 201.24, 122.95, 199.09, 123.31, 198.38, 122.95, 196.05, 122.6, 194.26, 122.77, 192.29, 124.39, 190.14, 125.64, 189.78, 128.5, 189.78, 130.83, 190.68, 131.9, 192.29, 132.62, 194.44, 132.26, 195.87, 132.26, 197.66, 131.9, 199.27, 131.9, 199.27, 132.98, 200.52, 134.41, 200.52, 136.2, 200.52, 137.99, 202.31, 138.34, 204.64, 141.39, 206.61, 143.0, 209.65, 145.15, 212.69, 146.04, 215.56, 148.19, 219.14, 151.95, 222.18, 153.02, 223.79, 153.56, 226.65, 153.38, 228.44, 150.52, 229.16, 148.73, 229.87, 146.58, 230.23]], "area": 476.2708999999998, "iscrowd": 0, "image_id": 12639, "bbox": [121.88, 189.78, 31.68, 40.45], "category_id": 1, "id": 569493}, {"segmentation": [[172.58, 189.4, 176.9, 179.34, 178.34, 163.52, 186.97, 164.95, 189.84, 179.34, 198.47, 198.03, 201.35, 223.92, 192.72, 228.23, 169.71, 231.11, 169.71, 187.97]], "area": 1503.5066499999996, "iscrowd": 0, "image_id": 12639, "bbox": [169.71, 163.52, 31.64, 67.59], "category_id": 1, "id": 570997}, {"segmentation": [[227.87, 85.34, 163.42, 237.86, 288.7, 340.45, 284.16, 307.77, 288.7, 275.99, 282.35, 246.94, 299.6, 226.06, 347.71, 217.89, 398.55, 184.3, 432.14, 191.56, 477.54, 190.65, 523.84, 185.2, 546.53, 192.47, 589.2, 191.56, 620.98, 187.02, 621.89, 171.59, 583.76, 177.94, 528.38, 177.03, 496.6, 179.76, 475.72, 179.76, 432.14, 167.05, 385.84, 172.49, 376.76, 173.4, 368.59, 183.39, 316.84, 179.76, 311.4, 173.4, 293.24, 178.85, 275.08, 168.86, 256.93, 128.01, 242.4, 111.67, 243.31, 95.33, 240.58, 88.06]], "area": 24565.449300000007, "iscrowd": 0, "image_id": 14439, "bbox": [163.42, 85.34, 458.47, 255.11], "category_id": 38, "id": 622491}, {"segmentation": [[237.12, 249.41, 242.67, 256.03, 242.31, 258.9, 238.2, 262.12, 234.26, 261.94, 231.57, 259.61, 230.5, 256.03, 231.22, 253.17, 233.9, 251.74], [250.54, 197.15, 247.5, 216.3, 243.92, 226.86, 241.42, 237.06, 235.87, 237.78, 236.41, 226.86, 234.79, 215.94, 237.12, 196.97, 241.24, 192.5, 244.1, 192.68, 248.4, 194.47]], "area": 570.7298999999998, "iscrowd": 0, "image_id": 12639, "bbox": [230.5, 192.5, 20.04, 69.62], "category_id": 39, "id": 627314}, {"segmentation": [[237.73, 315.16, 185.34, 319.85, 153.28, 334.71, 162.66, 342.53, 203.33, 340.96, 257.29, 329.23, 307.33, 315.16, 320.63, 291.69, 272.14, 301.86, 245.56, 306.55, 223.66, 314.37]], "area": 3341.5383999999985, "iscrowd": 0, "image_id": 15660, "bbox": [153.28, 291.69, 167.35, 50.84], "category_id": 42, "id": 651146}, {"segmentation": [[103.32, 140.38, 111.45, 136.94, 114.58, 135.69, 118.02, 132.87, 120.84, 131.31, 123.03, 131.31, 124.91, 131.31, 125.53, 131.31, 126.47, 133.18, 127.72, 135.38, 127.1, 138.19, 124.91, 140.38, 120.53, 140.69, 116.14, 140.69, 115.21, 140.07, 111.76, 139.76, 108.95, 139.76, 105.82, 140.69, 104.88, 141.95]], "area": 124.56849999999991, "iscrowd": 0, "image_id": 12120, "bbox": [103.32, 131.31, 24.4, 10.64], "category_id": 43, "id": 658529}, {"segmentation": [[218.97, 167.94, 369.98, 167.94, 361.35, 263.94, 359.19, 282.28, 350.56, 288.75, 349.48, 296.3, 358.11, 300.61, 307.42, 354.55, 135.91, 331.9, 134.83, 320.03, 186.61, 288.75, 198.47, 282.28, 209.26, 285.51, 212.49, 283.36, 211.42, 274.73, 201.71, 273.65, 215.73, 165.78]], "area": 29331.165500000003, "iscrowd": 0, "image_id": 16439, "bbox": [134.83, 165.78, 235.15, 188.77], "category_id": 73, "id": 1101993}, {"segmentation": [[201.52, 82.2, 238.85, 72.38, 279.87, 73.96, 308.44, 78.16, 319.37, 85.73, 338.7, 124.39, 338.7, 199.19, 307.6, 273.15, 289.11, 284.07, 295.0, 304.24, 287.43, 347.11, 224.4, 345.42, 219.36, 316.85, 219.36, 297.52, 219.36, 293.32, 174.81, 205.07, 172.29, 124.39, 187.42, 91.61]], "area": 34394.36045, "iscrowd": 0, "image_id": 19742, "bbox": [172.29, 72.38, 166.41, 274.73], "category_id": 86, "id": 1151441}, {"segmentation": [[385.59, 237.14, 380.77, 248.7, 378.84, 259.31, 380.77, 268.95, 382.69, 292.08, 375.95, 305.58, 363.41, 318.11, 361.49, 333.53, 356.67, 343.17, 349.92, 345.1, 342.21, 348.95, 336.42, 339.32, 340.28, 319.07, 334.5, 301.72, 325.82, 276.66, 314.25, 267.98, 301.72, 263.16, 315.22, 257.38, 327.75, 259.31, 332.57, 260.27, 333.53, 244.85, 339.32, 235.21, 335.46, 224.6, 340.28, 216.89, 352.88, 212.37, 364.01, 218.15, 367.94, 223.05, 382.99, 226.31, 391.15, 237.06]], "area": 5564.19405, "iscrowd": 0, "image_id": 12670, "bbox": [301.72, 212.37, 89.43, 136.58], "category_id": 88, "id": 1161855}, {"segmentation": [[324.83, 249.93, 324.83, 241.46, 326.38, 235.29, 327.15, 232.6, 330.23, 233.37, 331.0, 237.6, 329.84, 241.84, 329.84, 248.78, 327.15, 252.24, 327.15, 253.4, 325.22, 255.32, 325.6, 250.32]], "area": 90.85039999999984, "iscrowd": 0, "image_id": 12670, "bbox": [324.83, 232.6, 6.17, 22.72], "category_id": 27, "id": 1169165}, {"segmentation": [[89.32, 160.29, 93.98, 169.18, 93.98, 171.22, 98.79, 171.65, 103.3, 169.61, 104.61, 164.08, 100.24, 160.15, 94.27, 156.36, 88.45, 157.09, 86.84, 159.27]], "area": 156.5958999999999, "iscrowd": 0, "image_id": 14439, "bbox": [86.84, 156.36, 17.77, 15.29], "category_id": 27, "id": 1170759}, {"segmentation": [[307.31, 109.79, 303.22, 98.87, 302.31, 92.04, 302.76, 84.76, 304.13, 78.39, 306.4, 75.21, 312.32, 73.84, 316.87, 73.84, 319.6, 75.66, 323.69, 78.85, 325.06, 79.76, 325.06, 83.4, 325.06, 83.85, 319.6, 82.03, 314.14, 79.3, 311.86, 79.76, 307.77, 88.4, 307.31, 97.05]], "area": 243.00409999999988, "iscrowd": 0, "image_id": 12670, "bbox": [302.31, 73.84, 22.75, 35.95], "category_id": 27, "id": 1171715}, {"segmentation": [[39.43, 309.7, 53.86, 311.62, 58.67, 309.7, 61.56, 302.97, 50.98, 292.39, 53.86, 277.96, 59.63, 271.23, 50.01, 264.49, 38.47, 255.84, 34.62, 253.91, 41.36, 245.26, 36.55, 235.64, 27.89, 235.64, 22.12, 241.41, 16.35, 250.07, 6.73, 249.11, 7.69, 265.46, 3.85, 287.58, 9.62, 293.35, 14.43, 302.0, 15.39, 310.66, 25.01, 306.81, 23.08, 301.04, 33.66, 292.39, 44.24, 288.54, 34.62, 306.81]], "area": 2649.9406999999997, "iscrowd": 0, "image_id": 12120, "bbox": [3.85, 235.64, 57.71, 75.98], "category_id": 1, "id": 1203494}, {"segmentation": [[26.88, 329.59, 28.02, 329.87, 29.57, 330.44, 30.99, 331.57, 31.42, 332.84, 30.71, 334.83, 32.41, 336.24, 26.03, 345.73, 24.47, 344.6, 24.47, 342.9, 24.47, 341.34, 24.47, 340.35, 22.49, 336.81, 21.92, 335.82, 21.78, 334.83, 23.62, 331.85, 25.18, 330.72], [33.26, 337.94, 38.64, 345.17, 38.64, 347.15, 33.68, 362.45, 28.87, 385.96, 25.75, 402.25, 24.62, 403.53, 23.91, 401.12, 24.47, 384.69, 24.76, 381.15, 25.47, 376.33, 27.31, 366.98, 26.88, 366.56, 27.31, 365.57, 28.16, 363.72, 27.87, 356.64, 26.88, 351.68, 26.74, 350.41, 26.88, 348.28, 26.6, 346.44, 32.55, 337.52]], "area": 474.89009999999996, "iscrowd": 0, "image_id": 17959, "bbox": [21.78, 329.59, 16.86, 73.94], "category_id": 1, "id": 1207016}, {"segmentation": [[461.2, 179.76, 413.08, 166.14, 416.71, 124.38, 423.07, 122.56, 423.07, 99.87, 435.78, 90.79, 440.31, 89.88, 453.93, 96.23, 454.84, 120.75, 447.58, 122.56, 470.27, 139.81, 486.62, 124.38, 495.69, 119.84, 507.5, 118.93, 494.79, 140.72, 474.81, 151.61, 468.46, 153.43, 462.1, 177.03], [455.75, 193.38, 421.25, 193.38, 432.14, 203.36, 433.96, 224.24, 415.8, 280.53, 439.41, 288.7, 444.85, 286.89, 439.41, 274.18, 444.85, 248.76, 450.3, 239.68, 455.75, 282.35, 486.62, 277.81, 470.27, 245.12, 470.27, 228.78, 461.2, 192.47]], "area": 7641.4994000000015, "iscrowd": 0, "image_id": 14439, "bbox": [413.08, 89.88, 94.42, 198.82], "category_id": 1, "id": 1213473}, {"segmentation": [[119.32, 174.69, 114.7, 170.07, 114.35, 162.25, 113.28, 152.31, 112.57, 148.4, 111.15, 144.49, 110.44, 142.71, 109.73, 138.81, 109.37, 135.61, 110.08, 134.19, 111.86, 134.19, 112.57, 135.96, 113.28, 136.32, 113.64, 131.7, 113.28, 128.5, 112.92, 122.46, 112.92, 119.98, 111.86, 118.55, 110.79, 116.07, 108.66, 113.23, 105.82, 112.51, 103.69, 115.36, 103.33, 117.84, 102.98, 120.33, 101.56, 122.46, 96.58, 124.24, 90.54, 132.77, 96.58, 136.32, 93.74, 140.23, 93.38, 142.71, 94.45, 148.75, 96.23, 151.6, 95.16, 155.86, 91.61, 163.68, 90.9, 167.58, 90.54, 172.2, 89.83, 173.62, 90.19, 175.76, 92.32, 177.18, 94.81, 178.24, 95.16, 178.24, 94.81, 175.05, 93.74, 172.56, 93.74, 170.07, 95.16, 167.94, 98.0, 163.32, 99.07, 161.54, 99.42, 156.22, 99.42, 154.08, 100.84, 148.04, 102.62, 148.04, 108.66, 151.95, 108.66, 156.57, 108.66, 163.32, 109.37, 164.74, 110.08, 170.43, 111.15, 173.62, 113.28, 175.4, 117.54, 176.47, 118.25, 176.47, 117.9, 175.4]], "area": 820.40285, "iscrowd": 0, "image_id": 12120, "bbox": [89.83, 112.51, 29.49, 65.73], "category_id": 1, "id": 1213619}, {"segmentation": [[317.12, 136.22, 314.23, 143.42, 311.35, 157.84, 312.79, 169.37, 311.35, 193.87, 312.79, 209.73, 320.0, 216.94, 320.0, 195.32, 322.88, 186.67, 325.77, 190.99, 332.97, 186.67, 328.65, 170.81, 328.65, 157.84, 328.65, 143.42, 321.44, 143.42, 318.56, 134.77]], "area": 1018.3404499999979, "iscrowd": 0, "image_id": 12639, "bbox": [311.35, 134.77, 21.62, 82.17], "category_id": 1, "id": 1215138}, {"segmentation": [[279.88, 177.93, 276.04, 144.27, 276.04, 126.0, 269.3, 106.76, 263.53, 92.33, 268.34, 75.98, 281.81, 71.17, 276.04, 58.67, 283.73, 46.17, 304.89, 46.17, 308.74, 65.4, 315.47, 73.1, 336.63, 86.56, 340.48, 100.99, 329.9, 109.64, 321.24, 127.92, 315.47, 142.35, 316.43, 168.31, 316.43, 201.98, 310.66, 234.68, 299.12, 242.37, 289.5, 231.79, 288.54, 217.37, 285.65, 208.71, 285.65, 199.09]], "area": 7830.536100000001, "iscrowd": 0, "image_id": 12670, "bbox": [263.53, 46.17, 76.95, 196.2], "category_id": 1, "id": 1231358}, {"segmentation": [[350.07, 220.41, 342.64, 218.76, 341.81, 211.32, 331.07, 205.95, 327.35, 191.5, 333.14, 187.78, 336.85, 184.07, 335.2, 177.46, 340.16, 170.85, 347.59, 176.63, 347.18, 179.11, 356.68, 192.74, 355.85, 196.46, 348.83, 193.15, 350.07, 199.76, 355.85, 208.85, 359.57, 214.22, 357.09, 215.45, 349.66, 220.0, 349.24, 220.0]], "area": 830.1729499999997, "iscrowd": 0, "image_id": 12639, "bbox": [327.35, 170.85, 32.22, 49.56], "category_id": 1, "id": 1242094}, {"segmentation": [[505.67, 150.36, 514.31, 144.44, 514.31, 124.88, 528.87, 105.31, 549.8, 103.04, 564.81, 126.7, 564.36, 145.35, 563.9, 152.18, 572.09, 165.82, 573.45, 196.76, 548.89, 194.49, 539.79, 202.22, 537.51, 202.22, 548.43, 180.38, 540.7, 174.92, 527.5, 194.94, 523.86, 202.22, 519.31, 196.31, 519.31, 195.85, 518.86, 176.29, 518.86, 174.92, 507.03, 152.63]], "area": 4379.644600000002, "iscrowd": 0, "image_id": 12670, "bbox": [505.67, 103.04, 67.78, 99.18], "category_id": 1, "id": 1281554}, {"segmentation": [[580.54, 120.24, 584.67, 112.54, 584.67, 108.69, 584.39, 106.77, 586.59, 102.37, 583.57, 93.02, 578.34, 91.37, 579.44, 86.42, 575.59, 84.77, 572.57, 85.6, 572.02, 90.27, 572.02, 92.2, 570.37, 93.85, 569.54, 96.05, 568.44, 100.17, 570.09, 101.0, 572.84, 96.87, 577.24, 99.62, 575.04, 105.12, 575.32, 106.77, 574.22, 111.17, 574.77, 111.44, 577.24, 111.44, 577.79, 115.02, 577.79, 117.49, 578.89, 119.42], [570.09, 100.72, 571.19, 102.65, 571.48, 105.8, 571.9, 106.59, 572.69, 107.0, 573.17, 107.83, 573.51, 104.25, 573.89, 101.44, 573.72, 100.85, 572.41, 100.92]], "area": 321.9169500000001, "iscrowd": 0, "image_id": 12120, "bbox": [568.44, 84.77, 18.15, 35.47], "category_id": 1, "id": 1287176}, {"segmentation": [[439.54, 422.23, 432.81, 375.1, 443.39, 373.18, 439.54, 304.89, 423.19, 299.12, 389.53, 289.5, 355.87, 320.28, 365.48, 331.82, 358.75, 344.32, 351.06, 344.32, 346.25, 379.91, 334.71, 419.34, 337.59, 422.23]], "area": 10978.319500000003, "iscrowd": 0, "image_id": 12670, "bbox": [334.71, 289.5, 108.68, 132.73], "category_id": 1, "id": 1289729}, {"segmentation": [[386.64, 286.62, 384.72, 252.95, 383.76, 227.95, 407.8, 219.29, 431.85, 238.53, 435.69, 245.26, 453.01, 271.23, 462.62, 278.92, 481.86, 310.66, 489.56, 325.09, 494.36, 338.55, 492.44, 356.83, 478.01, 364.52, 482.82, 377.99, 478.98, 383.76, 481.86, 387.6, 478.01, 401.07, 478.01, 402.03, 478.01, 406.84, 478.01, 413.57, 467.43, 414.53, 440.5, 400.11, 436.66, 390.49, 452.04, 349.13, 443.39, 319.32, 442.43, 310.66]], "area": 9101.397749999996, "iscrowd": 0, "image_id": 12670, "bbox": [383.76, 219.29, 110.6, 195.24], "category_id": 1, "id": 1290414}, {"segmentation": [[541.58, 360.03, 540.59, 351.32, 539.6, 351.2, 540.37, 347.89, 541.47, 346.02, 543.13, 345.8, 543.46, 349.11, 544.01, 351.54, 545.11, 356.06, 545.33, 360.26, 542.8, 360.59]], "area": 50.99424999999972, "iscrowd": 0, "image_id": 17959, "bbox": [539.6, 345.8, 5.73, 14.79], "category_id": 1, "id": 1292393}, {"segmentation": [[238.61, 344.24, 242.98, 337.99, 259.21, 332.68, 265.46, 329.56, 269.51, 318.32, 272.01, 307.08, 275.76, 299.28, 275.13, 288.04, 278.26, 277.74, 283.88, 272.43, 294.49, 263.69, 310.72, 262.44, 323.21, 272.43, 331.64, 290.54, 334.45, 305.52, 340.07, 322.69, 336.95, 338.3, 350.02, 347.86, 350.03, 368.36, 343.15, 393.26, 339.29, 410.23, 336.28, 418.39, 340.36, 422.47, 352.6, 423.33, 356.25, 422.04, 363.34, 423.76, 363.98, 426.55, 362.69, 428.0, 276.16, 428.0, 275.3, 425.04, 278.31, 421.18, 281.53, 419.03, 282.17, 413.02, 282.6, 400.78, 281.74, 388.54, 282.82, 379.95, 274.87, 369.43, 265.85, 358.48, 254.69, 350.32, 242.23, 344.52]], "area": 10949.096749999995, "iscrowd": 0, "image_id": 12670, "bbox": [238.61, 262.44, 125.37, 165.56], "category_id": 1, "id": 1292520}, {"segmentation": [[568.47, 100.24, 572.12, 102.68, 571.39, 108.77, 571.39, 113.89, 570.9, 116.33, 566.76, 114.38, 561.4, 112.67, 555.55, 111.45, 556.76, 104.38, 559.93, 101.21, 560.18, 98.04, 561.4, 93.65, 563.83, 90.73, 567.25, 91.46, 569.69, 94.39, 568.95, 97.31, 568.47, 99.75]], "area": 262.514200000001, "iscrowd": 0, "image_id": 12120, "bbox": [555.55, 90.73, 16.57, 25.6], "category_id": 1, "id": 1295280}, {"segmentation": [[138.19, 403.05, 144.91, 378.1, 159.3, 355.07, 167.94, 333.96, 166.98, 312.84, 164.1, 295.57, 166.98, 278.3, 179.45, 266.78, 196.73, 262.94, 210.16, 266.78, 222.64, 286.93, 225.52, 309.96, 232.23, 328.2, 242.79, 343.55, 259.1, 353.15, 268.7, 362.74, 275.42, 373.3, 282.13, 387.7, 283.09, 410.73, 276.38, 422.24, 134.35, 419.36, 141.07, 402.09]], "area": 14381.618300000002, "iscrowd": 0, "image_id": 12670, "bbox": [134.35, 262.94, 148.74, 159.3], "category_id": 1, "id": 1296661}, {"segmentation": [[58.55, 283.69, 66.2, 259.14, 71.83, 248.67, 78.67, 240.62, 91.55, 234.18, 100.41, 230.96, 116.51, 230.96, 123.75, 232.17, 149.51, 298.98, 154.34, 309.45, 153.54, 324.74, 154.34, 331.59, 154.34, 340.84, 147.9, 349.7, 144.68, 379.48, 139.45, 391.96, 137.04, 413.29, 135.83, 428.0, 60.56, 427.38, 57.74, 417.32, 56.13, 406.85, 54.52, 400.81, 55.33, 397.59, 55.73, 391.96, 56.13, 389.95, 55.33, 386.73, 55.33, 386.73, 40.84, 362.17, 42.05, 358.15, 44.46, 356.54, 58.55, 355.73, 62.57, 342.85, 64.99, 334.8, 68.21, 335.21, 68.61, 334.8, 69.01, 326.76, 63.78, 319.51, 60.96, 315.08, 60.96, 313.88, 63.78, 309.85, 63.78, 303.41]], "area": 16034.45445, "iscrowd": 0, "image_id": 12670, "bbox": [40.84, 230.96, 113.5, 197.04], "category_id": 1, "id": 1299579}, {"segmentation": [[537.06, 112.84, 537.06, 106.49, 532.99, 102.42, 537.57, 95.56, 541.12, 93.78, 541.38, 89.46, 543.67, 86.41, 549.0, 86.41, 551.8, 90.48, 550.02, 94.8, 554.85, 98.36, 559.42, 101.15, 557.14, 103.95, 551.8, 101.41, 551.8, 106.24, 555.36, 109.03, 554.34, 114.11, 541.38, 117.16, 537.57, 113.35]], "area": 452.98854999999975, "iscrowd": 0, "image_id": 12120, "bbox": [532.99, 86.41, 26.43, 30.75], "category_id": 1, "id": 1302229}, {"segmentation": [[57.84, 422.7, 53.98, 400.53, 55.91, 391.85, 42.41, 368.72, 41.45, 356.18, 54.95, 352.33, 60.73, 352.33, 64.59, 351.36, 65.55, 338.83, 70.37, 334.98, 69.41, 324.37, 63.62, 319.55, 66.51, 308.95, 64.59, 288.71, 56.87, 279.07, 44.34, 269.43, 25.06, 266.54, 13.5, 270.39, 1.93, 277.14, 0.0, 422.7]], "area": 8687.98255, "iscrowd": 0, "image_id": 12670, "bbox": [0.0, 266.54, 70.37, 156.16], "category_id": 1, "id": 1305161}, {"segmentation": [[108.39, 230.47, 104.63, 225.45, 105.05, 205.4, 104.63, 190.37, 106.72, 185.35, 122.17, 173.66, 122.59, 161.96, 127.18, 152.77, 129.69, 150.27, 129.27, 144.42, 128.44, 138.15, 128.86, 130.63, 137.21, 123.95, 144.31, 118.52, 155.17, 122.7, 164.36, 133.14, 165.2, 146.09, 174.39, 153.61, 170.63, 166.56, 173.97, 169.06, 173.55, 171.99, 181.9, 174.91, 183.58, 174.49, 188.17, 177.83, 192.77, 200.81, 196.11, 225.39, 191.51, 236.67, 185.66, 239.17, 182.74, 237.92, 183.99, 245.44, 172.72, 246.63, 161.85, 246.63, 155.59, 257.07, 153.92, 265.0, 153.5, 283.8, 153.08, 290.48, 149.32, 298.42, 130.53, 247.04, 122.17, 230.33]], "area": 8663.100849999997, "iscrowd": 0, "image_id": 12670, "bbox": [104.63, 118.52, 91.48, 179.9], "category_id": 1, "id": 1310729}, {"segmentation": [[566.39, 358.45, 566.59, 356.73, 567.4, 354.19, 567.0, 353.07, 566.79, 352.36, 565.47, 352.05, 565.47, 350.02, 566.18, 348.7, 566.69, 347.69, 566.59, 346.77, 566.49, 345.25, 568.01, 345.15, 568.62, 345.35, 568.82, 346.37, 569.33, 347.89, 569.33, 349.41, 569.84, 350.53, 570.45, 351.65, 570.96, 352.76, 570.55, 352.87, 570.25, 352.87, 570.75, 354.59, 571.77, 356.22, 572.58, 356.73, 572.48, 357.23, 572.07, 357.23, 570.35, 357.33, 569.74, 355.71, 568.93, 354.39, 568.42, 355.71, 568.32, 356.83, 568.72, 357.94, 568.72, 358.45, 568.11, 358.55, 567.2, 358.45]], "area": 44.05905000000021, "iscrowd": 0, "image_id": 17959, "bbox": [565.47, 345.15, 7.11, 13.4], "category_id": 1, "id": 1311792}, {"segmentation": [[242.56, 339.24, 230.7, 314.52, 224.27, 297.21, 220.32, 256.17, 231.69, 228.49, 228.23, 206.73, 235.64, 200.3, 231.19, 181.52, 228.72, 168.66, 238.61, 156.3, 258.88, 159.76, 269.76, 165.7, 275.2, 178.06, 288.05, 202.78, 294.97, 234.42, 292.5, 261.12, 280.14, 277.43, 265.8, 328.85]], "area": 9283.1788, "iscrowd": 0, "image_id": 12670, "bbox": [220.32, 156.3, 74.65, 182.94], "category_id": 1, "id": 1313606}, {"segmentation": [[576.24, 357.01, 573.84, 356.8, 573.32, 355.39, 572.17, 354.55, 571.59, 352.73, 572.22, 351.42, 573.53, 350.17, 575.77, 349.65, 577.81, 349.49, 576.66, 351.73, 577.02, 352.99, 575.67, 354.29, 575.51, 354.66, 576.08, 355.7, 576.81, 356.59]], "area": 28.21764999999978, "iscrowd": 0, "image_id": 17959, "bbox": [571.59, 349.49, 6.22, 7.52], "category_id": 1, "id": 1313720}, {"segmentation": [[522.0, 100.23, 513.47, 99.68, 512.37, 96.11, 514.85, 87.58, 518.7, 84.83, 519.52, 78.51, 525.57, 76.58, 527.77, 81.26, 527.5, 85.38, 533.82, 90.33, 537.4, 94.46, 536.02, 98.85, 534.65, 99.95, 533.0, 98.3, 533.0, 96.66, 531.35, 96.11, 531.07, 99.4, 529.15, 99.4, 526.95, 95.28, 523.1, 97.2]], "area": 331.00515000000007, "iscrowd": 0, "image_id": 12120, "bbox": [512.37, 76.58, 25.03, 23.65], "category_id": 1, "id": 1316481}, {"segmentation": [[460.16, 97.43, 453.47, 99.52, 452.63, 105.38, 448.44, 104.96, 447.6, 98.68, 453.47, 94.92, 451.79, 91.57, 453.05, 86.54, 456.81, 84.45, 456.4, 79.84, 461.0, 77.33, 463.09, 77.75, 463.51, 83.61, 468.54, 88.22, 466.44, 99.1, 461.42, 97.43]], "area": 282.6418499999998, "iscrowd": 0, "image_id": 12120, "bbox": [447.6, 77.33, 20.94, 28.05], "category_id": 1, "id": 1316505}, {"segmentation": [[502.92, 125.43, 482.8, 120.75, 483.04, 114.67, 485.38, 112.33, 484.44, 106.48, 490.52, 99.47, 492.63, 92.69, 496.13, 93.15, 498.94, 97.6, 499.64, 100.4, 503.38, 103.44, 504.55, 109.29, 503.85, 113.27, 504.32, 120.75]], "area": 483.83000000000015, "iscrowd": 0, "image_id": 12120, "bbox": [482.8, 92.69, 21.75, 32.74], "category_id": 1, "id": 1318073}, {"segmentation": [[384.06, 228.16, 393.18, 225.12, 393.18, 221.53, 390.14, 217.66, 389.59, 215.18, 388.76, 212.14, 387.65, 209.65, 386.27, 206.34, 384.89, 204.13, 384.89, 201.09, 384.61, 197.5, 382.68, 195.02, 381.58, 191.98, 380.19, 189.77, 378.81, 187.83, 377.71, 185.9, 374.67, 183.14, 372.46, 181.21, 369.42, 179.0, 368.04, 176.79, 364.73, 175.41, 362.24, 173.47, 357.82, 172.64, 354.23, 172.64, 350.09, 172.92, 346.77, 174.3, 343.46, 175.13, 340.97, 175.96, 337.11, 177.89, 332.41, 180.93, 331.31, 183.41, 329.1, 189.22, 328.27, 194.74, 328.27, 197.5, 328.27, 201.37, 326.06, 202.47, 325.78, 203.85, 326.61, 204.68, 326.89, 206.62, 326.33, 208.0, 325.51, 209.65, 325.23, 211.59, 325.23, 212.97, 324.95, 214.9, 323.57, 215.45, 323.57, 216.28, 323.57, 217.39, 323.85, 218.49, 322.47, 219.6, 321.64, 220.43, 321.92, 221.53, 322.47, 222.91, 322.19, 224.02, 321.36, 224.85, 320.81, 225.67, 319.98, 227.06, 319.43, 228.44, 318.88, 229.26, 319.43, 229.82, 320.26, 229.82, 322.74, 229.26, 323.3, 228.99, 323.85, 229.26, 324.13, 230.37, 324.68, 232.03, 324.4, 233.41, 322.74, 235.34, 322.19, 236.45, 322.19, 237.27, 320.53, 238.93, 319.98, 239.76, 318.88, 240.31, 319.15, 241.97, 318.88, 244.73, 318.33, 245.84, 317.77, 246.67, 317.5, 247.77, 316.94, 250.53, 316.39, 252.19, 315.56, 254.68, 315.29, 256.33, 315.29, 256.61, 316.67, 256.89, 318.6, 257.44, 319.98, 257.16, 321.64, 256.89, 323.02, 256.61, 325.78, 256.33, 326.89, 256.89, 329.37, 257.99, 331.58, 259.65, 331.86, 259.65, 329.93, 257.44, 329.65, 255.78, 329.93, 253.85, 329.93, 252.74, 329.93, 251.64, 330.75, 250.26, 331.03, 249.43, 331.58, 248.88, 332.14, 248.05, 332.96, 246.67, 333.24, 245.01, 333.24, 243.63, 333.52, 242.8, 334.62, 241.14, 335.73, 238.93, 336.0, 238.1, 337.11, 235.89, 337.38, 235.34, 338.49, 235.07, 339.87, 234.79, 340.15, 234.51, 339.59, 233.68, 338.49, 232.58, 337.66, 231.47, 336.55, 230.09, 336.0, 228.71, 336.0, 227.61, 335.73, 226.5, 335.73, 224.29, 335.73, 223.74, 335.73, 223.19, 336.28, 221.53, 336.55, 220.43, 336.83, 219.32, 337.66, 218.49, 338.21, 217.66, 339.59, 216.84, 340.15, 216.01, 341.25, 215.18, 342.91, 214.63, 343.74, 214.07, 344.56, 214.07, 346.22, 213.52, 347.05, 213.24, 347.88, 213.24, 348.98, 213.24, 349.81, 212.97, 350.36, 212.97, 351.19, 212.69, 352.3, 212.69, 354.23, 212.69, 355.06, 212.69, 356.44, 212.69, 357.82, 213.8, 359.2, 214.63, 360.31, 215.73, 360.86, 216.28, 362.52, 217.66, 362.79, 218.49, 363.07, 219.6, 363.07, 220.98, 363.07, 221.53, 363.07, 223.46, 363.07, 223.19, 364.17, 223.19, 365.0, 222.91, 365.83, 222.91, 366.66, 222.91, 367.49, 222.91, 368.32, 222.91, 369.42, 222.91, 370.53, 222.91, 371.36, 223.19, 372.46, 224.02, 374.12, 224.29, 374.39, 224.29, 374.67, 224.57, 375.5, 224.85, 376.6, 225.4, 377.43, 225.95, 377.99, 226.5, 379.09, 227.06, 379.92, 227.06, 381.3, 227.88, 381.85, 227.88, 383.51, 227.88, 383.79, 227.88]], "area": 2982.624900000001, "iscrowd": 0, "image_id": 12670, "bbox": [315.29, 172.64, 77.89, 87.01], "category_id": 1, "id": 1319466}, {"segmentation": [[593.2, 358.22, 592.81, 356.91, 592.94, 350.92, 593.59, 349.09, 593.46, 348.57, 593.33, 346.75, 592.29, 346.49, 592.29, 344.92, 593.2, 344.4, 593.2, 342.44, 594.64, 341.53, 595.33, 342.85, 595.4, 343.83, 596.31, 344.25, 597.08, 345.3, 597.64, 346.56, 597.92, 348.02, 597.85, 349.49, 598.33, 350.4, 598.13, 353.54, 598.33, 355.15, 598.47, 357.87, 597.78, 358.71, 596.17, 357.66, 595.54, 357.94, 594.98, 358.5, 593.79, 358.5]], "area": 75.22300000000003, "iscrowd": 0, "image_id": 17959, "bbox": [592.29, 341.53, 6.18, 17.18], "category_id": 1, "id": 1321325}, {"segmentation": [[471.28, 290.46, 449.16, 265.46, 429.92, 234.68, 419.34, 219.29, 433.77, 207.75, 422.23, 185.63, 430.89, 169.28, 444.35, 162.54, 469.36, 170.24, 474.17, 195.24, 468.4, 221.21, 462.62, 239.49, 471.28, 268.34, 477.05, 293.35]], "area": 4137.523649999998, "iscrowd": 0, "image_id": 12670, "bbox": [419.34, 162.54, 57.71, 130.81], "category_id": 1, "id": 1323932}, {"segmentation": [[535.03, 113.58, 535.78, 105.33, 533.62, 102.33, 530.44, 100.46, 528.76, 99.95, 528.76, 97.51, 527.96, 96.48, 525.97, 96.31, 524.95, 96.37, 523.87, 97.22, 522.9, 98.19, 522.85, 98.99, 522.9, 100.63, 522.33, 101.94, 522.45, 102.63, 520.06, 103.88, 518.92, 107.17, 519.09, 113.03, 519.26, 114.51, 521.37, 115.31, 526.03, 114.91, 533.71, 114.85, 534.62, 113.26]], "area": 237.06269999999998, "iscrowd": 0, "image_id": 12120, "bbox": [518.92, 96.31, 16.86, 19.0], "category_id": 1, "id": 1324175}, {"segmentation": [[450.17, 351.18, 451.71, 349.36, 452.97, 348.59, 453.74, 345.37, 457.1, 343.83, 457.73, 345.79, 458.01, 348.24, 458.15, 348.73, 458.22, 350.34, 458.36, 351.18]], "area": 35.253050000000044, "iscrowd": 0, "image_id": 17959, "bbox": [450.17, 343.83, 8.19, 7.35], "category_id": 1, "id": 1324179}, {"segmentation": [[556.37, 384.69, 559.13, 375.03, 557.06, 368.83, 557.75, 359.86, 559.82, 351.59, 561.89, 344.0, 575.68, 336.42, 593.61, 341.24, 599.82, 352.97, 603.27, 360.55, 611.54, 369.52, 618.44, 386.07, 616.37, 394.34, 613.61, 396.41, 623.95, 404.69, 626.02, 413.65, 626.71, 419.17, 629.47, 427.44, 605.34, 428.0, 606.02, 392.96, 610.85, 383.31, 608.09, 374.34, 603.27, 363.31, 590.85, 350.9, 577.75, 350.9, 567.41, 355.04, 562.58, 364.0, 562.58, 376.41, 562.58, 383.31, 556.37, 385.38]], "area": 1485.1226, "iscrowd": 0, "image_id": 12670, "bbox": [556.37, 336.42, 73.1, 91.58], "category_id": 27, "id": 1416967}, {"segmentation": [[249.42, 401.25, 251.79, 403.25, 254.7, 401.07, 259.42, 405.43, 260.51, 409.07, 262.51, 415.25, 264.15, 419.07, 257.79, 421.07, 247.24, 418.34, 242.7, 418.16, 240.52, 412.89, 249.06, 400.89]], "area": 305.6836499999998, "iscrowd": 0, "image_id": 17959, "bbox": [240.52, 400.89, 23.63, 20.18], "category_id": 27, "id": 1418162}, {"segmentation": [[1.54, 263.21, 10.01, 240.63, 0.0, 225.26, 3.74, 221.5, 15.97, 231.22, 20.99, 240.94, 27.89, 237.49, 36.98, 225.26, 44.82, 217.11, 38.86, 240.0, 42.63, 250.35, 51.41, 263.21, 52.35, 273.24, 51.72, 274.5, 51.72, 276.38, 29.45, 269.48, 4.05, 272.3, 0.29, 276.07]], "area": 1677.5977500000008, "iscrowd": 0, "image_id": 12670, "bbox": [0.0, 217.11, 52.35, 59.27], "category_id": 27, "id": 1422343}, {"segmentation": [[549.78, 66.5, 561.04, 71.71, 564.57, 81.62, 562.89, 94.06, 561.21, 112.04, 561.38, 115.91, 567.76, 116.58, 569.95, 116.07, 576.5, 112.21, 578.86, 103.81, 575.66, 90.53, 572.97, 80.61, 570.62, 77.25, 570.29, 75.4, 568.27, 76.92, 563.9, 71.87, 558.18, 67.17, 552.47, 64.65, 550.79, 64.65]], "area": 566.7530999999992, "iscrowd": 0, "image_id": 12670, "bbox": [549.78, 64.65, 29.08, 51.93], "category_id": 27, "id": 1422414}, {"segmentation": [[227.84, 52.0, 235.81, 49.41, 238.6, 52.8, 240.98, 63.15, 240.59, 69.32, 238.79, 71.12, 234.61, 72.71, 231.83, 70.12, 229.24, 62.36, 228.24, 55.59]], "area": 218.73834999999968, "iscrowd": 0, "image_id": 15660, "bbox": [227.84, 49.41, 13.14, 23.3], "category_id": 38, "id": 1464301}, {"segmentation": [[303.76, 173.34, 242.58, 141.91, 270.08, 121.7, 300.95, 159.87, 308.81, 164.36, 320.59, 166.6, 344.17, 174.46, 356.52, 183.44, 375.04, 192.98, 413.76, 209.82, 426.67, 214.31, 459.23, 229.47, 424.43, 221.61, 378.97, 202.52, 352.02, 190.74, 331.26, 181.2, 309.93, 172.22]], "area": 2456.223600000001, "iscrowd": 0, "image_id": 17959, "bbox": [242.58, 121.7, 216.65, 107.77], "category_id": 38, "id": 1465879}, {"segmentation": [[578.71, 310.45, 561.63, 301.41, 564.64, 296.39, 548.57, 284.33, 548.57, 296.39, 534.5, 290.36, 519.43, 267.25, 505.37, 268.26, 508.38, 279.31, 496.32, 280.31, 481.25, 271.27, 470.2, 258.21, 451.11, 247.16, 446.09, 238.12, 465.18, 214.0, 518.43, 201.95, 538.52, 220.03, 534.5, 229.07, 550.58, 238.12, 554.6, 238.12, 567.66, 260.22, 599.81, 280.31, 584.74, 307.44]], "area": 7668.203399999998, "iscrowd": 0, "image_id": 17959, "bbox": [446.09, 201.95, 153.72, 108.5], "category_id": 38, "id": 1467191}, {"segmentation": [[406.91, 267.25, 393.84, 252.18, 382.79, 254.19, 355.67, 237.11, 335.57, 229.07, 332.56, 244.14, 321.51, 248.16, 304.43, 229.07, 292.37, 228.07, 271.27, 215.01, 278.3, 188.88, 303.42, 176.83, 337.58, 183.86, 343.61, 191.9, 345.62, 196.92, 353.66, 194.91, 383.8, 208.98, 416.95, 222.04, 419.97, 224.05, 412.93, 259.21]], "area": 6586.9691, "iscrowd": 0, "image_id": 17959, "bbox": [271.27, 176.83, 148.7, 90.42], "category_id": 38, "id": 1469129}, {"segmentation": [[192.35, 292.56, 225.58, 281.48, 240.69, 282.48, 264.86, 272.41, 275.94, 309.68, 290.04, 325.79, 297.09, 349.96, 321.26, 351.97, 324.28, 369.09, 317.23, 377.15, 294.07, 394.27, 285.0, 383.19, 272.92, 376.14, 272.92, 393.26, 234.65, 398.3, 247.74, 365.06, 236.66, 345.93, 227.6, 358.02, 223.57, 336.87, 217.53, 331.83, 199.4, 312.7, 191.34, 303.63]], "area": 8236.113599999993, "iscrowd": 0, "image_id": 17959, "bbox": [191.34, 272.41, 132.94, 125.89], "category_id": 38, "id": 1469183}, {"segmentation": [[164.13, 160.82, 193.19, 141.29, 215.11, 185.59, 213.68, 191.79, 236.54, 230.85, 255.12, 250.86, 287.52, 281.34, 317.05, 300.88, 323.72, 309.45, 331.34, 331.84, 330.87, 344.7, 324.2, 340.41, 318.48, 308.5, 288.94, 284.68, 260.84, 261.34, 236.07, 235.14, 210.34, 214.18, 205.58, 201.31, 199.86, 206.08]], "area": 2665.4564000000023, "iscrowd": 0, "image_id": 17959, "bbox": [164.13, 141.29, 167.21, 203.41], "category_id": 38, "id": 1469432}, {"segmentation": [[23.11, 407.91, 51.24, 302.42, 79.37, 224.05, 95.45, 203.96, 118.56, 248.16, 172.81, 266.25, 159.75, 301.41, 114.54, 252.18, 96.45, 223.04, 95.45, 266.25, 84.4, 296.39, 80.38, 326.53, 69.32, 349.64, 63.3, 377.77, 42.2, 396.86, 25.12, 408.92]], "area": 6913.322649999998, "iscrowd": 0, "image_id": 17959, "bbox": [23.11, 203.96, 149.7, 204.96], "category_id": 38, "id": 1469484}, {"segmentation": [[640.0, 277.3, 606.84, 273.28, 589.76, 249.17, 564.64, 233.09, 547.56, 218.02, 518.43, 198.93, 497.33, 184.87, 448.1, 165.78, 428.0, 146.69, 417.96, 134.63, 386.81, 111.52, 360.69, 95.45, 349.64, 126.59, 385.81, 129.61, 414.94, 152.72, 474.22, 178.84, 556.61, 228.07, 573.69, 256.2, 602.82, 269.26, 610.86, 277.3, 608.85, 274.28]], "area": 2788.935549999991, "iscrowd": 0, "image_id": 17959, "bbox": [349.64, 95.45, 290.36, 181.85], "category_id": 38, "id": 1469614}, {"segmentation": [[325.73, 248.01, 312.1, 294.69, 285.86, 277.32, 311.07, 233.7, 320.96, 249.03], [309.03, 302.19, 299.49, 323.32, 291.99, 331.49, 285.52, 319.57, 280.41, 314.8, 272.23, 302.19, 280.41, 284.47, 282.45, 279.7]], "area": 2170.817199999999, "iscrowd": 0, "image_id": 17959, "bbox": [272.23, 233.7, 53.5, 97.79], "category_id": 38, "id": 1469712}, {"segmentation": [[359.33, 240.58, 380.65, 253.12, 380.03, 266.92, 377.52, 295.45, 372.81, 319.28, 370.93, 329.63, 365.6, 337.16, 366.23, 363.81, 365.6, 374.16, 358.71, 378.55, 350.87, 384.19, 342.09, 390.46, 334.56, 398.61, 332.68, 402.06, 331.43, 402.69, 335.82, 367.57, 340.83, 329.94, 348.04, 280.09, 353.37, 261.28]], "area": 4085.2117499999986, "iscrowd": 0, "image_id": 17959, "bbox": [331.43, 240.58, 49.22, 162.11], "category_id": 38, "id": 1469717}, {"segmentation": [[195.23, 385.29, 211.35, 385.29, 215.26, 384.8, 222.1, 386.26, 221.12, 388.46, 231.38, 390.42, 217.95, 393.1, 203.04, 393.59, 192.29, 394.57, 194.0, 384.8], [186.68, 386.26, 192.54, 384.55, 189.85, 396.04, 186.43, 394.33, 185.7, 393.59, 176.41, 392.86, 174.7, 392.86, 174.95, 390.91, 182.52, 391.64, 182.77, 387.73]], "area": 337.9363999999995, "iscrowd": 0, "image_id": 17959, "bbox": [174.7, 384.55, 56.68, 11.49], "category_id": 38, "id": 1469806}, {"segmentation": [[132.93, 365.0, 140.55, 353.95, 151.98, 352.05, 167.97, 350.52, 175.97, 349.0, 184.73, 352.81, 184.73, 359.28, 175.97, 365.38, 168.73, 375.66, 169.11, 384.42, 171.4, 388.23, 178.25, 393.18, 177.11, 399.27, 159.97, 394.7, 150.07, 387.46, 141.31, 384.42, 136.74, 380.23, 133.7, 376.8, 132.55, 369.18]], "area": 1584.0033999999996, "iscrowd": 0, "image_id": 12639, "bbox": [132.55, 349.0, 52.18, 50.27], "category_id": 40, "id": 1470364}, {"segmentation": [[492.25, 237.08, 493.35, 236.86, 493.9, 237.03, 494.4, 237.3, 494.4, 238.4, 494.23, 238.95, 493.63, 239.23, 492.69, 239.34, 492.2, 239.28, 491.92, 239.12, 491.7, 238.73, 491.87, 238.24, 492.2, 237.74, 492.42, 237.08], [495.28, 235.1, 495.17, 232.95, 495.0, 231.69, 495.0, 231.63, 495.44, 230.75, 495.99, 230.2, 496.93, 229.49, 498.53, 228.77, 500.12, 228.11, 501.33, 226.95, 502.38, 225.19, 502.66, 223.43, 502.05, 222.44, 500.78, 221.34, 499.74, 220.68, 497.81, 220.35, 496.71, 220.4, 495.88, 220.68, 494.95, 221.06, 493.57, 221.45, 492.31, 222.0, 492.25, 221.94, 490.38, 222.27, 489.17, 223.26, 488.34, 224.09, 488.18, 224.75, 487.85, 226.13, 488.29, 227.28, 488.67, 228.49, 489.28, 229.32, 489.83, 229.65, 490.38, 229.87, 491.59, 230.7, 492.31, 231.3, 492.25, 232.79, 492.2, 234.44, 492.2, 235.16]], "area": 128.9662999999999, "iscrowd": 0, "image_id": 12120, "bbox": [487.85, 220.35, 14.81, 18.99], "category_id": 43, "id": 1481890}, {"segmentation": [[417.36, 267.67, 423.01, 263.27, 431.18, 262.02, 439.97, 262.02, 446.88, 262.02, 453.78, 265.16, 455.67, 277.72, 455.67, 302.83, 448.13, 311.63, 439.34, 325.44, 439.97, 331.72, 441.23, 334.86, 450.02, 339.26, 454.41, 347.42, 453.16, 348.68, 436.2, 353.07, 423.01, 353.07, 417.99, 350.56, 421.13, 340.51, 430.55, 328.58, 426.15, 321.67, 416.11, 313.51, 412.34, 303.46, 412.34, 290.28]], "area": 2882.545150000001, "iscrowd": 0, "image_id": 16439, "bbox": [412.34, 262.02, 43.33, 91.05], "category_id": 46, "id": 1497036}, {"segmentation": [[311.73, 10.06, 312.1, 21.21, 310.7, 20.46, 309.86, 19.24, 307.61, 18.68, 304.7, 18.49, 304.7, 15.59, 301.61, 14.18, 299.74, 14.93, 300.02, 7.72, 311.45, 8.28]], "area": 112.83430000000028, "iscrowd": 0, "image_id": 12120, "bbox": [299.74, 7.72, 12.36, 13.49], "category_id": 62, "id": 1586452}, {"segmentation": [[387.75, 22.11, 381.91, 18.55, 377.84, 19.57, 375.81, 18.8, 375.81, 14.99, 376.06, 10.42, 380.38, 9.15, 385.46, 8.64, 387.24, 10.92, 387.5, 17.79]], "area": 118.48469999999995, "iscrowd": 0, "image_id": 12120, "bbox": [375.81, 8.64, 11.94, 13.47], "category_id": 62, "id": 1597360}, {"segmentation": [[133.2, 27.02, 148.4, 27.02, 148.56, 36.08, 131.66, 36.54, 131.66, 27.48]], "area": 155.92200000000005, "iscrowd": 0, "image_id": 12120, "bbox": [131.66, 27.02, 16.9, 9.52], "category_id": 62, "id": 1599619}, {"segmentation": [[472.25, 123.95, 480.43, 125.02, 480.64, 127.61, 479.35, 155.15, 472.25, 154.29, 473.33, 149.56, 475.26, 146.54, 476.34, 143.1, 477.41, 136.64, 478.28, 129.11, 474.4, 126.96]], "area": 131.0995500000004, "iscrowd": 0, "image_id": 12120, "bbox": [472.25, 123.95, 8.39, 31.2], "category_id": 62, "id": 1600157}, {"segmentation": [[285.81, 167.01, 283.46, 152.9, 283.98, 135.66, 292.08, 166.23, 294.69, 168.06, 298.61, 166.75, 298.09, 159.7, 296.52, 158.91, 295.48, 151.07, 293.39, 144.28, 293.65, 137.23, 296.26, 136.44, 296.26, 125.73, 297.83, 122.59, 294.69, 112.4, 293.36, 103.72, 289.08, 100.24, 284.0, 97.3, 283.73, 93.02, 281.33, 90.35, 277.85, 89.82, 274.11, 91.69, 272.77, 92.76, 271.97, 95.16, 273.04, 97.3, 274.37, 99.44, 271.97, 102.38, 269.83, 104.79, 268.64, 116.2, 269.15, 122.32, 273.06, 128.62, 273.92, 130.16, 272.89, 132.03, 275.28, 137.14, 276.39, 155.0, 276.95, 162.26, 277.51, 164.49, 272.49, 166.72, 273.6, 168.95, 278.63, 167.28, 285.33, 167.28]], "area": 1481.6397999999997, "iscrowd": 0, "image_id": 14439, "bbox": [268.64, 89.82, 29.97, 79.13], "category_id": 1, "id": 1681269}, {"segmentation": [[279.52, 89.03, 279.72, 82.53, 284.39, 79.88, 290.28, 81.1, 291.7, 85.37, 294.95, 93.09, 298.0, 103.25, 295.97, 112.59, 294.75, 108.32, 292.52, 103.04, 284.59, 97.76, 282.56, 90.45]], "area": 271.4347000000001, "iscrowd": 0, "image_id": 14439, "bbox": [279.52, 79.88, 18.48, 32.71], "category_id": 1, "id": 1684258}, {"segmentation": [[383.02, 112.91, 377.86, 113.77, 375.72, 113.77, 376.15, 106.04, 374.43, 103.04, 367.13, 100.89, 366.7, 96.59, 365.84, 88.43, 367.56, 81.99, 366.7, 79.42, 363.69, 77.27, 363.69, 73.4, 365.41, 67.39, 364.12, 62.24, 366.7, 58.8, 367.56, 57.51, 374.0, 59.66, 374.0, 66.53, 379.58, 69.11, 380.87, 78.99, 380.44, 81.99, 378.72, 98.31, 380.87, 105.61, 382.16, 112.91]], "area": 623.4129500000006, "iscrowd": 0, "image_id": 14439, "bbox": [363.69, 57.51, 19.33, 56.26], "category_id": 1, "id": 1695019}, {"segmentation": [[416.61, 292.96, 420.4, 219.9, 413.76, 215.15, 410.91, 162.01, 427.04, 151.58, 427.99, 131.65, 434.63, 125.01, 445.07, 129.75, 445.07, 147.78, 460.25, 162.96, 467.84, 185.74, 461.2, 185.74, 463.1, 224.64, 455.51, 224.64, 458.36, 253.11, 445.07, 278.72, 445.07, 278.72, 451.71, 292.96, 433.69, 292.96, 435.58, 244.57, 431.79, 257.85, 427.04, 278.72, 431.79, 292.96]], "area": 6073.2352, "iscrowd": 0, "image_id": 12639, "bbox": [410.91, 125.01, 56.93, 167.95], "category_id": 1, "id": 1710025}, {"segmentation": [[436.14, 361.33, 433.88, 361.47, 433.03, 356.37, 432.46, 353.82, 431.19, 353.82, 432.46, 351.27, 433.17, 349.57, 434.87, 349.43, 435.15, 351.7, 436.57, 356.94]], "area": 36.056650000000076, "iscrowd": 0, "image_id": 17959, "bbox": [431.19, 349.43, 5.38, 12.04], "category_id": 1, "id": 1711987}, {"segmentation": [[339.29, 79.74, 335.11, 79.22, 334.59, 69.05, 333.03, 67.75, 333.29, 59.93, 328.34, 50.54, 330.68, 45.85, 333.29, 44.81, 334.07, 40.11, 336.68, 38.55, 339.81, 41.16, 340.07, 45.07, 342.94, 45.85, 347.11, 49.5, 342.15, 57.84, 339.55, 78.7]], "area": 366.50915000000003, "iscrowd": 0, "image_id": 14439, "bbox": [328.34, 38.55, 18.77, 41.19], "category_id": 1, "id": 1720080}, {"segmentation": [[564.54, 353.92, 565.21, 348.4, 565.55, 346.56, 564.38, 345.72, 563.37, 341.87, 562.54, 341.21, 561.37, 341.37, 560.86, 346.22, 560.2, 347.23, 560.2, 351.41, 560.36, 353.25, 564.21, 353.25]], "area": 47.31054999999961, "iscrowd": 0, "image_id": 17959, "bbox": [560.2, 341.21, 5.35, 12.71], "category_id": 1, "id": 1725965}, {"segmentation": [[463.58, 351.15, 464.3, 349.59, 464.3, 348.03, 464.42, 346.83, 466.09, 346.59, 466.45, 346.59, 467.05, 347.91, 467.77, 350.07, 468.01, 351.03], [463.46, 354.15, 463.7, 358.11, 463.82, 359.55, 464.78, 360.99, 465.97, 360.75, 467.77, 359.91, 468.01, 358.95, 467.65, 356.67, 467.53, 353.19, 463.7, 353.79]], "area": 42.29759999999989, "iscrowd": 0, "image_id": 17959, "bbox": [463.46, 346.59, 4.55, 14.4], "category_id": 1, "id": 1727181}, {"segmentation": [[385.01, 44.81, 385.01, 42.96, 385.92, 40.69, 388.76, 40.08, 390.59, 41.3, 390.79, 44.35, 390.39, 45.98, 391.81, 48.21, 394.04, 50.65, 394.45, 53.09, 396.08, 63.24, 394.45, 63.45, 393.23, 59.99, 392.83, 56.13, 391.81, 55.93, 391.0, 59.99, 392.22, 79.49, 392.01, 82.95, 389.37, 82.95, 388.97, 80.51, 388.76, 69.74, 387.34, 67.71, 385.51, 66.9, 383.28, 68.52, 383.89, 82.13, 380.84, 79.29, 381.65, 65.27, 382.87, 56.34, 380.23, 65.07, 378.2, 62.43, 378.61, 59.79, 380.64, 53.49, 382.47, 49.63, 383.68, 47.6, 384.9, 46.18, 385.92, 46.38]], "area": 367.1871000000001, "iscrowd": 0, "image_id": 14439, "bbox": [378.2, 40.08, 17.88, 42.87], "category_id": 1, "id": 1729788}, {"segmentation": [[558.52, 176.32, 540.34, 177.32, 517.62, 177.32, 516.61, 175.81, 511.06, 170.76, 510.55, 163.19, 509.54, 155.61, 508.03, 151.07, 505.5, 142.49, 509.54, 140.47, 519.64, 142.49, 525.7, 146.53, 527.44, 143.42, 529.46, 140.79, 529.86, 140.19, 529.86, 138.57, 526.02, 135.74, 524.81, 131.49, 523.8, 125.22, 525.18, 117.15, 529.47, 114.39, 532.84, 114.39, 539.58, 114.08, 546.74, 120.26, 547.92, 128.52, 545.96, 138.75, 545.96, 139.54, 551.07, 141.9, 551.46, 148.98, 553.04, 160.39, 554.61, 167.07, 555.79, 174.15], [560.12, 192.25, 562.48, 207.59, 566.41, 221.76, 578.61, 233.95, 590.41, 245.36, 597.1, 244.97, 597.88, 249.29, 594.34, 269.75, 587.26, 270.93, 587.26, 270.93, 583.33, 271.72, 582.54, 265.42, 582.94, 260.7, 577.43, 263.06, 575.85, 260.31, 551.46, 228.84, 545.17, 213.1, 542.41, 213.1, 538.87, 222.94, 534.55, 231.59, 532.58, 250.87, 532.19, 257.16, 532.19, 261.49, 533.76, 266.21, 534.94, 269.36, 534.94, 270.15, 514.88, 265.82, 514.48, 261.1, 516.06, 255.2, 519.2, 240.64, 517.24, 234.35, 516.84, 224.12, 520.78, 212.31, 520.78, 206.41, 520.78, 197.76, 520.78, 188.71, 523.92, 189.1]], "area": 5222.28025, "iscrowd": 0, "image_id": 14439, "bbox": [505.5, 114.08, 92.38, 157.64], "category_id": 1, "id": 1733380}, {"segmentation": [[640.0, 124.81, 637.98, 123.36, 638.24, 120.73, 637.58, 117.58, 636.27, 116.13, 634.03, 115.48, 632.46, 114.82, 633.51, 113.9, 634.95, 113.11, 635.22, 112.06, 635.22, 110.35, 634.82, 108.77, 634.82, 107.33, 634.69, 105.88, 635.35, 104.96, 637.06, 104.04, 638.64, 103.91, 639.29, 104.43, 640.0, 105.35]], "area": 79.85544999999946, "iscrowd": 0, "image_id": 12120, "bbox": [632.46, 103.91, 7.54, 20.9], "category_id": 1, "id": 1740684}, {"segmentation": [[290.31, 205.38, 288.13, 202.65, 287.77, 198.83, 285.4, 195.93, 283.59, 193.93, 279.04, 193.56, 276.5, 196.83, 275.95, 200.83, 275.59, 204.1, 279.41, 203.38, 284.68, 203.92, 289.04, 205.01]], "area": 106.37539999999967, "iscrowd": 0, "image_id": 12639, "bbox": [275.59, 193.56, 14.72, 11.82], "category_id": 1, "id": 1742643}, {"segmentation": [[581.15, 176.44, 605.82, 176.86, 607.07, 168.08, 608.74, 160.55, 610.83, 146.34, 613.76, 146.34, 609.16, 130.03, 603.31, 127.52, 604.14, 120.83, 603.31, 115.4, 597.87, 112.47, 593.27, 115.81, 590.76, 122.09, 591.6, 129.61, 585.75, 139.65, 585.75, 146.34, 586.58, 150.1, 582.82, 160.55, 583.66, 164.73, 585.33, 163.48, 584.49, 171.84, 582.4, 176.02], [579.06, 188.57, 579.06, 192.33, 578.64, 197.35, 589.93, 198.6, 590.76, 190.66, 593.27, 188.15], [594.95, 188.15, 592.44, 192.75, 594.53, 196.51, 602.05, 196.93, 605.4, 191.49, 604.56, 186.9]], "area": 1524.5267499999989, "iscrowd": 0, "image_id": 14439, "bbox": [578.64, 112.47, 35.12, 86.13], "category_id": 1, "id": 1749825}, {"segmentation": [[53.38, 153.89, 47.3, 149.55, 46.87, 146.95, 42.53, 146.95, 41.22, 149.55, 38.18, 149.55, 32.1, 145.21, 26.89, 139.56, 25.16, 132.62, 26.46, 124.8, 28.63, 120.46, 33.84, 117.85, 38.62, 116.99, 44.26, 117.42, 46.87, 124.37, 49.91, 127.41, 52.95, 133.92, 54.68, 138.26, 58.16, 143.04, 63.37, 146.08, 65.54, 150.42, 59.46, 154.76]], "area": 840.4303499999999, "iscrowd": 0, "image_id": 14439, "bbox": [25.16, 116.99, 40.38, 37.77], "category_id": 1, "id": 1751086}, {"segmentation": [[187.53, 60.92, 187.8, 53.01, 188.85, 50.9, 190.96, 49.58, 192.02, 44.57, 195.18, 45.89, 195.97, 50.37, 200.71, 53.01, 197.02, 61.18]], "area": 138.24575000000004, "iscrowd": 0, "image_id": 14439, "bbox": [187.53, 44.57, 13.18, 16.61], "category_id": 1, "id": 1754508}, {"segmentation": [[170.36, 60.39, 169.82, 57.81, 169.82, 56.46, 170.09, 54.83, 170.61, 52.29, 171.49, 51.55, 172.36, 51.03, 172.01, 47.75, 173.26, 47.27, 174.86, 47.82, 176.61, 49.01, 176.61, 51.1, 175.49, 51.86, 175.98, 52.84, 177.65, 54.09, 178.21, 56.74, 178.14, 60.08, 177.44, 61.41, 174.52, 61.41, 171.73, 61.27, 170.54, 61.06, 170.27, 60.5]], "area": 88.33755, "iscrowd": 0, "image_id": 14439, "bbox": [169.82, 47.27, 8.39, 14.14], "category_id": 1, "id": 1755416}, {"segmentation": [[175.25, 92.49, 173.87, 88.76, 174.66, 84.63, 176.62, 82.07, 183.7, 83.45, 186.06, 88.36, 187.04, 93.87, 192.54, 99.17, 195.1, 103.89, 196.47, 111.55, 197.06, 117.06, 195.1, 120.79, 193.53, 121.77, 191.76, 124.72, 193.53, 138.09, 195.69, 148.5, 198.44, 153.81, 198.83, 156.17, 194.12, 166.98, 191.76, 166.78, 191.76, 164.82, 186.65, 143.0, 184.49, 138.28, 181.93, 134.75, 179.96, 140.64, 179.96, 146.73, 179.57, 153.22, 180.55, 159.51, 182.32, 160.1, 182.52, 162.06, 180.75, 164.23, 178.2, 166.39, 175.64, 165.21, 174.27, 161.28, 173.48, 155.38, 172.1, 138.87, 172.1, 130.23, 172.3, 124.13, 171.91, 121.77, 171.51, 123.74, 168.76, 122.56, 167.58, 119.02, 164.83, 112.93, 165.81, 105.46, 168.17, 98.98, 171.51, 95.44]], "area": 1622.7973000000002, "iscrowd": 0, "image_id": 14439, "bbox": [164.83, 82.07, 34.0, 84.91], "category_id": 1, "id": 1755623}, {"segmentation": [[435.0, 246.18, 441.86, 239.32, 445.29, 237.04, 448.14, 235.32, 450.43, 235.32, 451.0, 235.32, 453.29, 234.18, 451.0, 230.18, 446.43, 231.32, 436.14, 232.47, 433.29, 235.32, 433.29, 240.47], [457.29, 276.47, 463.0, 266.18, 462.43, 249.04, 464.72, 246.75, 465.86, 259.33, 461.29, 277.61]], "area": 219.4885500000003, "iscrowd": 0, "image_id": 12670, "bbox": [433.29, 230.18, 32.57, 47.43], "category_id": 27, "id": 1825310}, {"segmentation": [[118.79, 167.73, 108.85, 169.52, 103.68, 168.92, 104.28, 164.35, 100.1, 160.17, 102.49, 157.59, 105.27, 154.8, 112.83, 156.2, 113.02, 157.19, 118.79, 160.17, 119.59, 163.75]], "area": 197.40875000000005, "iscrowd": 0, "image_id": 14439, "bbox": [100.1, 154.8, 19.49, 14.72], "category_id": 27, "id": 1826473}, {"segmentation": [[64.42, 157.03, 69.92, 156.07, 72.53, 155.38, 75.15, 155.24, 79.14, 155.93, 85.88, 158.82, 92.9, 166.94, 92.76, 171.89, 88.22, 172.72, 75.7, 173.54, 60.7, 172.31, 57.54, 166.94, 58.09, 162.95, 58.64, 160.89, 59.05, 159.65, 61.25, 158.27]], "area": 509.22274999999985, "iscrowd": 0, "image_id": 14439, "bbox": [57.54, 155.24, 35.36, 18.3], "category_id": 27, "id": 1826731}, {"segmentation": [[65.88, 153.79, 85.42, 151.09, 82.11, 138.46, 77.91, 132.45, 72.2, 134.25, 70.09, 138.16, 64.08, 144.47]], "area": 300.87115, "iscrowd": 0, "image_id": 14439, "bbox": [64.08, 132.45, 21.34, 21.34], "category_id": 27, "id": 1827291}, {"segmentation": [[408.93, 350.53, 408.48, 348.57, 409.23, 347.37, 412.09, 347.67, 413.14, 349.47, 412.24, 350.07, 413.14, 350.68, 412.24, 353.83, 411.94, 356.84, 411.79, 358.49, 413.29, 360.89, 413.29, 362.1, 409.38, 362.85, 406.98, 360.59, 407.13, 357.44, 407.28, 354.88, 407.88, 352.78]], "area": 70.2078000000001, "iscrowd": 0, "image_id": 17959, "bbox": [406.98, 347.37, 6.31, 15.48], "category_id": 1, "id": 2009873}, {"segmentation": [[167.89, 193.93, 161.26, 197.72, 161.58, 204.66, 160.0, 208.45, 158.11, 212.24, 154.32, 214.77, 160.95, 217.93, 160.95, 225.19, 159.05, 228.35, 160.0, 230.24, 163.79, 230.87, 165.68, 231.19, 170.74, 228.98, 169.16, 224.87, 166.95, 220.77, 169.79, 219.82, 170.11, 194.56]], "area": 338.75715000000025, "iscrowd": 0, "image_id": 12639, "bbox": [154.32, 193.93, 16.42, 37.26], "category_id": 1, "id": 2014077}, {"segmentation": [[111.07, 93.68, 105.56, 89.26, 109.97, 88.16, 112.73, 82.64, 114.39, 74.64, 113.56, 70.78, 112.18, 66.92, 111.35, 65.81, 106.66, 71.33, 103.9, 68.85, 110.52, 60.57, 111.9, 55.33, 113.56, 54.23, 110.8, 51.47, 112.73, 45.68, 114.39, 45.12, 116.87, 46.23, 117.7, 50.37, 117.7, 51.19, 122.11, 53.95, 125.97, 64.16, 128.18, 70.78, 126.52, 73.54, 122.11, 66.64, 122.11, 69.12, 121.01, 76.3, 122.11, 79.06, 123.49, 86.78, 124.87, 91.47, 121.56, 92.3, 118.25, 92.02, 120.45, 89.81, 121.01, 88.71, 117.42, 79.06, 115.21, 85.12, 112.73, 90.09, 112.45, 93.12]], "area": 486.7893, "iscrowd": 0, "image_id": 14439, "bbox": [103.9, 45.12, 24.28, 48.56], "category_id": 1, "id": 2016432}, {"segmentation": [[605.78, 355.78, 605.88, 354.13, 604.59, 353.03, 604.59, 352.11, 605.42, 351.84, 605.78, 350.19, 606.88, 350.46, 607.25, 351.56, 609.45, 351.84, 608.9, 353.4, 607.89, 353.95, 607.89, 356.15]], "area": 15.385749999999973, "iscrowd": 0, "image_id": 17959, "bbox": [604.59, 350.19, 4.86, 5.96], "category_id": 1, "id": 2025196}, {"segmentation": [[351.64, 131.21, 361.56, 116.81, 361.56, 108.49, 357.72, 98.88, 355.16, 94.72, 358.68, 85.44, 360.92, 78.72, 362.2, 74.24, 367.96, 73.6, 378.84, 73.92, 383.01, 77.76, 384.29, 86.4, 384.29, 98.56, 389.41, 109.77, 393.25, 119.69, 392.93, 136.97, 393.25, 144.02, 394.85, 156.82, 396.45, 168.98, 384.61, 167.06, 386.21, 148.18, 385.89, 142.1, 383.97, 133.13, 381.73, 127.37, 377.88, 126.09, 370.84, 126.09, 366.68, 126.09, 363.48, 128.65, 357.4, 136.01, 355.48, 140.17, 351.32, 140.81, 349.4, 139.21]], "area": 1955.6273000000012, "iscrowd": 0, "image_id": 12670, "bbox": [349.4, 73.6, 47.05, 95.38], "category_id": 1, "id": 2026694}, {"segmentation": [[508.82, 99.45, 509.4, 95.95, 509.59, 94.2, 510.18, 91.29, 509.01, 85.46, 506.48, 84.49, 507.07, 80.02, 507.07, 77.49, 504.73, 75.94, 500.65, 77.3, 499.88, 82.74, 499.88, 84.49, 496.96, 87.21, 496.57, 93.43, 498.91, 97.51, 500.65, 101.39, 501.24, 102.37, 503.57, 99.64]], "area": 234.4547500000002, "iscrowd": 0, "image_id": 12120, "bbox": [496.57, 75.94, 13.61, 26.43], "category_id": 1, "id": 2030099}, {"segmentation": [[512.36, 138.93, 513.36, 132.98, 518.57, 131.74, 518.32, 125.78, 520.06, 124.05, 525.02, 125.29, 524.77, 131.49, 528.49, 133.97, 531.22, 138.69, 531.96, 142.16]], "area": 187.85259999999954, "iscrowd": 0, "image_id": 12120, "bbox": [512.36, 124.05, 19.6, 18.11], "category_id": 1, "id": 2031548}, {"segmentation": [[134.39, 193.17, 157.14, 254.14, 164.39, 245.92, 174.56, 245.43, 189.56, 248.34, 192.46, 263.34, 191.01, 269.63, 177.46, 272.53, 169.72, 278.34, 165.36, 287.05, 163.91, 296.24, 161.98, 303.02, 155.68, 304.95, 151.33, 301.56, 152.78, 267.21, 152.78, 258.5, 144.56, 245.92, 132.46, 202.37]], "area": 1806.0620999999983, "iscrowd": 0, "image_id": 12670, "bbox": [132.46, 193.17, 60.0, 111.78], "category_id": 31, "id": 2078052}, {"segmentation": [[162.75, 379.36, 182.76, 410.67, 190.12, 427.78, 201.24, 427.44, 192.68, 407.59, 175.41, 381.42, 171.47, 374.74, 164.11, 370.64, 162.92, 364.82, 160.86, 364.82, 154.53, 367.39, 162.92, 379.53]], "area": 687.3572000000007, "iscrowd": 0, "image_id": 12670, "bbox": [154.53, 364.82, 46.71, 62.96], "category_id": 31, "id": 2078792}, {"segmentation": [[588.13, 180.91, 580.79, 198.47, 579.83, 207.73, 587.49, 224.33, 589.73, 224.33, 596.11, 195.91, 600.26, 190.49, 590.05, 187.29], [608.56, 175.8, 621.65, 163.03, 633.15, 137.81, 633.78, 131.74, 638.25, 132.7, 633.78, 148.98, 623.57, 168.46, 612.4, 178.67]], "area": 676.2805500000015, "iscrowd": 0, "image_id": 12670, "bbox": [579.83, 131.74, 58.42, 92.59], "category_id": 31, "id": 2079349}, {"segmentation": [[532.03, 76.34, 534.14, 92.98, 530.13, 96.77, 522.97, 101.4, 521.92, 111.93, 528.24, 106.67, 533.29, 103.09, 542.35, 103.09, 538.56, 92.13, 535.82, 76.13]], "area": 241.7636500000001, "iscrowd": 0, "image_id": 12670, "bbox": [521.92, 76.13, 20.43, 35.8], "category_id": 31, "id": 2079406}, {"segmentation": [[312.08, 71.92, 313.14, 61.18, 316.72, 45.6, 321.98, 39.7, 325.14, 41.59, 324.72, 45.17, 320.93, 46.65, 319.25, 56.97, 316.93, 74.45]], "area": 172.19735000000006, "iscrowd": 0, "image_id": 12670, "bbox": [312.08, 39.7, 13.06, 34.75], "category_id": 31, "id": 2079438}, {"segmentation": [[88.67, 46.29, 98.39, 36.57, 103.49, 34.43, 110.72, 36.09, 118.54, 42.26, 117.83, 43.32, 117.36, 43.68, 110.13, 40.83, 102.31, 40.12, 96.5, 42.26, 93.3, 50.79, 94.96, 58.26, 101.48, 67.03, 101.24, 69.64, 98.99, 69.28, 90.22, 59.44, 87.96, 50.91]], "area": 244.36524999999997, "iscrowd": 0, "image_id": 15660, "bbox": [87.96, 34.43, 30.58, 35.21], "category_id": 38, "id": 2089043}, {"segmentation": [[485.07, 34.07, 478.29, 29.34, 474.51, 28.71, 469.0, 31.55, 464.74, 36.27, 465.69, 42.73, 467.1, 44.31, 467.89, 45.1, 466.47, 46.36, 462.85, 39.74, 463.95, 34.54, 465.84, 31.23, 469.63, 27.92, 472.93, 26.66, 479.24, 26.97, 483.65, 31.07]], "area": 73.13659999999989, "iscrowd": 0, "image_id": 15660, "bbox": [462.85, 26.66, 22.22, 19.7], "category_id": 38, "id": 2089894}, {"segmentation": [[456.65, 120.31, 456.56, 115.57, 457.46, 115.53, 458.19, 116.25, 459.4, 115.71, 459.59, 112.91, 457.82, 112.05, 454.94, 111.29, 454.35, 112.55, 452.0, 115.26, 452.41, 117.61, 455.03, 120.77, 456.11, 120.68]], "area": 41.09249999999979, "iscrowd": 0, "image_id": 15660, "bbox": [452.0, 111.29, 7.59, 9.48], "category_id": 38, "id": 2090079}, {"segmentation": [[134.21, 157.31, 140.76, 161.47, 139.17, 171.18, 130.65, 163.55, 129.95, 160.28, 130.65, 158.4, 132.53, 157.11, 133.72, 157.01]], "area": 87.71535000000002, "iscrowd": 0, "image_id": 12670, "bbox": [129.95, 157.01, 10.81, 14.17], "category_id": 77, "id": 2137256}, {"segmentation": [[504.53, 223.71, 503.18, 235.86, 498.45, 238.56, 491.7, 231.14, 489.0, 241.26, 484.28, 241.94, 482.26, 247.34, 493.73, 249.36, 501.15, 250.71, 498.45, 260.16, 493.05, 266.23, 497.1, 268.26, 490.35, 294.58, 482.93, 313.48, 489.68, 318.2, 497.1, 301.33, 505.2, 281.08, 510.6, 275.01, 517.35, 279.06, 534.9, 312.8, 537.6, 320.9, 541.65, 324.28, 547.05, 318.88, 543.67, 310.78, 529.5, 283.78, 525.45, 263.53, 527.47, 247.34, 531.52, 239.24, 534.9, 230.46, 537.6, 225.74, 532.87, 220.34, 524.1, 208.19, 518.02, 210.22, 516.0, 221.69, 507.23, 222.36]], "area": 2817.1480000000006, "iscrowd": 0, "image_id": 12120, "bbox": [482.26, 208.19, 64.79, 116.09], "category_id": 1, "id": 2152647}, {"segmentation": [[100.31, 227.36, 100.31, 224.45, 100.31, 219.26, 100.52, 216.35, 105.3, 212.81, 107.17, 209.9, 107.38, 208.66, 106.96, 203.46, 106.96, 200.97, 108.42, 197.02, 113.27, 193.73, 120.34, 195.6, 121.79, 200.79, 123.04, 206.2, 123.46, 209.73, 128.23, 211.18, 128.86, 213.68, 130.1, 217.21, 134.47, 220.95, 138.62, 224.69, 136.34, 230.51, 137.59, 232.38, 143.4, 230.51, 146.52, 231.55, 146.31, 235.29, 146.52, 239.44, 142.36, 243.81, 139.46, 246.09, 138.42, 246.51, 133.85, 247.96, 127.82, 247.55, 119.92, 247.34, 109.53, 245.68, 105.38, 245.68, 102.88, 243.6, 102.68, 238.82, 100.18, 234.46, 98.94, 231.34]], "area": 1565.9484000000007, "iscrowd": 0, "image_id": 12639, "bbox": [98.94, 193.73, 47.58, 54.23], "category_id": 1, "id": 2159540}, {"segmentation": [[295.64, 243.79, 286.18, 266.4, 290.39, 271.66, 305.63, 259.56, 298.8, 252.72]], "area": 233.39200000000037, "iscrowd": 0, "image_id": 12670, "bbox": [286.18, 243.79, 19.45, 27.87], "category_id": 27, "id": 2175880}, {"segmentation": [[601.82, 112.59, 601.99, 114.13, 604.38, 115.16, 605.24, 123.89, 608.67, 123.89, 609.35, 122.52, 609.35, 113.62, 608.32, 111.9, 605.41, 110.53, 601.64, 112.59]], "area": 64.69290000000022, "iscrowd": 0, "image_id": 15660, "bbox": [601.64, 110.53, 7.71, 13.36], "category_id": 38, "id": 2180609}, {"segmentation": [[285.4, 18.66, 285.19, 8.34, 296.35, 7.08, 297.62, 20.56, 296.99, 22.67, 294.67, 19.51, 286.03, 18.24]], "area": 136.48975000000027, "iscrowd": 0, "image_id": 12120, "bbox": [285.19, 7.08, 12.43, 15.59], "category_id": 62, "id": 2192396}, {"segmentation": [[307.94, 262.82, 301.47, 296.08, 295.93, 302.55, 301.47, 366.3, 306.09, 376.46, 317.18, 422.65, 305.17, 459.61, 300.55, 473.46, 317.18, 501.18, 295.93, 500.26, 270.98, 491.02, 279.3, 469.77, 291.31, 430.97, 271.91, 385.7, 250.66, 421.73, 240.5, 449.44, 233.1, 464.23, 222.94, 481.78, 230.33, 515.96, 210.93, 518.73, 199.85, 505.8, 205.39, 482.7, 207.24, 456.83, 228.49, 407.87, 227.56, 378.31, 237.72, 327.31, 234.03, 289.43, 219.25, 303.29, 198.92, 301.44, 199.85, 289.43, 219.25, 259.86, 232.18, 237.69, 242.34, 236.95, 244.19, 241.57, 245.11, 252.66, 254.35, 250.81, 247.89, 228.64, 253.43, 213.86, 271.91, 204.62, 284.84, 204.62, 295.93, 212.01, 303.32, 224.02, 316.25, 226.79, 309.78, 235.1, 298.7, 250.81, 299.62, 260.05]], "area": 19730.2787, "iscrowd": 0, "image_id": 12639, "bbox": [198.92, 204.62, 118.26, 314.11], "category_id": 1, "id": 2204643}, {"segmentation": [[421.65, 427.54, 416.65, 411.05, 408.65, 402.55, 404.15, 396.55, 397.65, 395.55, 393.65, 394.05, 389.15, 394.55, 383.16, 401.05, 377.16, 409.05, 372.16, 413.55, 364.66, 424.55, 360.66, 426.04, 361.66, 416.55, 369.16, 406.05, 373.66, 404.55, 384.16, 391.05, 392.65, 389.05, 399.15, 388.55, 407.15, 391.05, 411.65, 396.55, 419.15, 405.05, 426.65, 424.05, 427.65, 427.04]], "area": 577.4524999999998, "iscrowd": 0, "image_id": 12670, "bbox": [360.66, 388.55, 66.99, 38.99], "category_id": 31, "id": 2210874}, {"segmentation": [[503.98, 256.15, 499.08, 254.81, 493.95, 251.92, 495.07, 242.79, 488.39, 240.34, 485.27, 241.67, 484.16, 247.69, 469.68, 239.22, 469.9, 188.0, 478.36, 187.33, 501.75, 192.9, 504.42, 196.02]], "area": 1935.829100000001, "iscrowd": 0, "image_id": 14038, "bbox": [469.68, 187.33, 34.74, 68.82], "category_id": 72, "id": 33179}, {"segmentation": [[354.85, 298.42, 356.67, 283.83, 341.16, 266.49, 340.25, 248.25, 353.94, 230.92, 357.58, 219.06, 355.76, 209.94, 362.15, 197.17, 362.15, 185.31, 364.88, 177.1, 368.53, 174.36, 375.83, 157.94, 377.65, 153.38, 386.78, 166.15, 389.51, 178.92, 402.28, 182.57, 411.4, 178.01, 414.14, 183.48, 409.58, 192.61, 415.97, 206.29, 414.14, 217.24, 407.76, 223.62, 405.93, 229.09, 413.23, 243.69, 416.88, 251.9, 415.05, 259.2, 414.14, 264.67, 403.19, 259.2, 394.07, 274.7, 358.5, 296.6]], "area": 6369.582550000001, "iscrowd": 0, "image_id": 14007, "bbox": [340.25, 153.38, 76.63, 145.04], "category_id": 17, "id": 48706}, {"segmentation": [[76.85, 128.56, 76.03, 122.19, 79.93, 117.87, 80.55, 105.54, 80.55, 103.28, 84.24, 103.69, 87.74, 104.31, 88.15, 117.67, 90.0, 121.57, 91.85, 123.83, 91.02, 126.5]], "area": 236.76289999999995, "iscrowd": 0, "image_id": 14038, "bbox": [76.03, 103.28, 15.82, 25.28], "category_id": 44, "id": 82834}, {"segmentation": [[46.39, 121.53, 50.05, 118.22, 50.22, 115.09, 48.48, 114.22, 48.48, 112.66, 49.18, 111.79, 48.66, 106.22, 52.83, 106.22, 53.01, 113.0, 53.7, 114.57, 53.01, 118.22, 53.01, 118.92, 56.49, 121.35, 56.83, 123.44, 55.79, 124.49, 47.09, 123.09, 46.74, 122.22, 47.44, 121.01]], "area": 89.52940000000002, "iscrowd": 0, "image_id": 14038, "bbox": [46.39, 106.22, 10.44, 18.27], "category_id": 44, "id": 90888}, {"segmentation": [[2.06, 147.61, 5.16, 143.48, 34.06, 144.52, 45.42, 169.29, 2.06, 227.1], [179.61, 161.03, 260.13, 171.35, 269.42, 200.26, 258.06, 217.81, 270.45, 237.42, 271.48, 241.55, 248.77, 267.35, 222.97, 288.0, 192.0, 207.48, 179.61, 174.45], [358.19, 176.52, 438.71, 164.13, 547.1, 171.35, 473.81, 209.55, 434.58, 244.65, 419.1, 317.94, 410.84, 315.87, 369.55, 260.13, 349.94, 263.23, 353.03, 250.84]], "area": 22449.340150000004, "iscrowd": 0, "image_id": 15335, "bbox": [2.06, 143.48, 545.04, 174.46], "category_id": 63, "id": 98619}, {"segmentation": [[537.0, 421.1, 525.0, 412.1, 528.0, 422.1, 505.0, 420.1, 512.0, 373.1, 500.0, 368.1, 503.0, 357.1, 531.0, 353.1, 537.0, 319.1, 560.0, 307.1, 581.0, 303.1, 602.0, 311.1, 614.0, 324.1, 617.0, 336.1, 620.0, 346.1, 630.0, 346.1, 639.0, 343.1, 640.0, 420.1, 536.0, 420.1]], "area": 12500.5, "iscrowd": 0, "image_id": 14038, "bbox": [500.0, 303.1, 140.0, 119.0], "category_id": 67, "id": 118573}, {"segmentation": [[126.79, 268.3, 134.72, 98.49, 417.74, 84.91, 527.55, 81.51, 542.26, 101.89, 553.58, 263.77, 569.43, 326.04, 566.04, 374.72, 568.3, 443.77, 559.25, 469.81, 463.02, 472.08, 281.89, 469.81, 117.74, 460.75, 117.74, 410.94, 97.36, 340.75, 120.0, 279.62, 129.06, 267.17]], "area": 165524.46034999998, "iscrowd": 0, "image_id": 17714, "bbox": [97.36, 81.51, 472.07, 390.57], "category_id": 67, "id": 123020}, {"segmentation": [[305.09, 246.31, 303.03, 187.4, 315.36, 187.4, 329.06, 193.57, 340.02, 203.84, 344.13, 213.43, 348.24, 224.39, 356.45, 244.94, 356.45, 245.62, 344.81, 246.31, 344.81, 257.95, 344.81, 259.32, 337.28, 260.0, 332.48, 255.21, 331.11, 250.42, 324.26, 250.42, 306.46, 247.68]], "area": 2552.801500000002, "iscrowd": 0, "image_id": 14038, "bbox": [303.03, 187.4, 53.42, 72.6], "category_id": 65, "id": 319540}, {"segmentation": [[580.91, 423.42, 588.35, 407.59, 590.5, 406.87, 592.06, 406.87, 612.09, 412.03, 610.66, 417.9, 605.62, 427.0, 592.06, 426.78, 580.67, 426.42, 580.91, 423.3]], "area": 465.28155, "iscrowd": 0, "image_id": 14038, "bbox": [580.67, 406.87, 31.42, 20.13], "category_id": 77, "id": 321540}, {"segmentation": [[352.29, 304.66, 427.91, 255.84, 541.83, 297.96, 586.83, 416.67, 306.34, 422.41, 300.59, 333.38]], "area": 34540.20885000002, "iscrowd": 0, "image_id": 14007, "bbox": [300.59, 255.84, 286.24, 166.57], "category_id": 82, "id": 330699}, {"segmentation": [[18.27, 153.39, 59.63, 153.39, 67.32, 172.63, 75.01, 179.36, 91.36, 180.32, 105.79, 179.36, 114.44, 176.47, 122.14, 172.63, 126.95, 171.67, 129.83, 231.29, 148.1, 414.02, 136.56, 419.79, 72.13, 421.71, 57.7, 416.9, 43.28, 357.28, 41.35, 345.74, 33.66, 290.92, 25.97, 293.8]], "area": 25975.8725, "iscrowd": 0, "image_id": 14038, "bbox": [18.27, 153.39, 129.83, 268.32], "category_id": 82, "id": 331203}, {"segmentation": [[5.62, 336.43, 53.06, 332.73, 59.74, 333.47, 67.89, 340.14, 81.97, 356.45, 85.68, 373.5, 94.58, 388.32, 84.2, 390.55, 79.75, 376.46, 1.91, 387.58, 0.96, 376.9]], "area": 3695.744649999997, "iscrowd": 0, "image_id": 11813, "bbox": [0.96, 332.73, 93.62, 57.82], "category_id": 62, "id": 384799}, {"segmentation": [[123.89, 420.29, 209.66, 249.7, 257.32, 171.55, 284.0, 127.71, 352.62, 121.99, 513.68, 385.98, 536.55, 426.0, 123.89, 424.53]], "area": 73967.79544999999, "iscrowd": 0, "image_id": 18380, "bbox": [123.89, 121.99, 412.66, 304.01], "category_id": 67, "id": 393944}, {"segmentation": [[228.43, 140.41, 202.88, 135.07, 199.45, 149.18, 212.79, 158.71, 226.14, 164.43, 228.43, 177.4, 236.06, 189.22, 246.35, 196.08, 259.7, 201.8, 273.05, 202.19, 287.54, 199.52, 300.13, 189.6, 308.9, 173.2, 308.9, 158.71, 305.46, 143.84, 296.69, 130.87, 287.92, 124.01, 278.01, 119.43, 268.09, 118.29, 257.79, 118.29, 247.5, 120.19, 238.34, 127.06, 230.72, 133.54]], "area": 6167.2139, "iscrowd": 0, "image_id": 17714, "bbox": [199.45, 118.29, 109.45, 83.9], "category_id": 47, "id": 682310}, {"segmentation": [[512.12, 444.55, 512.12, 442.12, 496.35, 433.23, 477.07, 420.55, 476.66, 419.2, 482.19, 422.17, 497.43, 430.26, 514.68, 439.56, 515.22, 438.89, 514.41, 437.27, 502.14, 430.26, 479.76, 416.37, 479.09, 415.43, 480.84, 414.89, 509.56, 431.47, 516.17, 435.25, 517.25, 434.84, 516.98, 433.36, 513.34, 430.8, 503.76, 425.41, 494.19, 419.47, 487.45, 415.02, 484.62, 413.27, 484.75, 412.06, 490.82, 414.08, 504.57, 422.17, 515.63, 428.78, 519.4, 430.53, 520.35, 429.72, 519.4, 427.97, 516.44, 426.62, 493.78, 412.87, 491.22, 410.44, 481.25, 403.16, 471.94, 396.42, 465.2, 396.15, 461.16, 397.63, 459.54, 397.36, 443.49, 384.42, 438.37, 381.32, 425.02, 370.53, 413.96, 362.44, 404.53, 354.08, 387.54, 342.48, 368.8, 330.35, 367.99, 331.02, 368.39, 336.15, 374.86, 347.34, 392.39, 360.55, 407.76, 370.12, 432.98, 383.74, 441.87, 389.27, 449.69, 395.34, 455.9, 405.85, 458.86, 412.87, 465.47, 418.39, 477.34, 425.14, 488.39, 432.28, 501.07, 438.75]], "area": 1927.435099999998, "iscrowd": 0, "image_id": 17714, "bbox": [367.99, 330.35, 152.36, 114.2], "category_id": 48, "id": 688294}, {"segmentation": [[321.78, 434.97, 305.26, 429.62, 293.25, 424.63, 294.44, 422.25, 321.78, 431.41, 321.78, 428.91, 293.73, 419.52, 294.56, 418.09, 321.9, 424.39, 321.66, 421.54, 296.34, 414.53, 295.75, 413.34, 323.92, 419.4, 323.44, 416.67, 297.29, 410.01, 298.01, 408.23, 332.6, 415.36, 363.15, 423.09, 416.4, 431.65, 466.93, 434.14, 479.05, 435.21, 483.33, 439.85, 482.14, 448.29, 479.29, 451.62, 472.99, 452.33, 430.19, 440.2, 393.82, 433.55, 365.29, 431.53, 333.79, 435.09]], "area": 2086.9935500000015, "iscrowd": 0, "image_id": 17714, "bbox": [293.25, 408.23, 190.08, 44.1], "category_id": 48, "id": 689203}, {"segmentation": [[430.87, 383.76, 438.47, 378.36, 443.98, 376.38, 458.51, 362.95, 466.99, 354.03, 471.83, 349.52, 475.14, 354.47, 468.75, 362.18, 456.97, 371.32, 449.81, 377.92, 446.73, 384.2, 443.43, 390.26, 436.82, 392.9, 432.08, 391.36, 430.43, 385.85]], "area": 408.4520000000003, "iscrowd": 0, "image_id": 18380, "bbox": [430.43, 349.52, 44.71, 43.38], "category_id": 48, "id": 689218}, {"segmentation": [[203.0, 426.0, 217.16, 425.87, 216.16, 407.38, 213.66, 402.72, 204.0, 402.72, 203.67, 415.71]], "area": 294.70059999999984, "iscrowd": 0, "image_id": 18380, "bbox": [203.0, 402.72, 14.16, 23.28], "category_id": 48, "id": 691095}, {"segmentation": [[247.99, 222.45, 257.77, 228.3, 261.71, 231.43, 262.82, 235.26, 266.95, 237.78, 272.9, 235.06, 268.16, 234.96, 264.83, 232.74, 263.12, 230.02, 249.91, 219.73, 247.59, 220.13]], "area": 71.72710000000009, "iscrowd": 0, "image_id": 18380, "bbox": [247.59, 219.73, 25.31, 18.05], "category_id": 48, "id": 691625}, {"segmentation": [[486.38, 429.54, 479.9, 436.02, 399.54, 410.96, 374.92, 405.35, 302.34, 384.18, 301.04, 379.43, 302.34, 375.54, 317.46, 372.51, 339.92, 373.81, 355.48, 377.7, 373.19, 388.07, 387.88, 394.11, 411.64, 402.76, 419.42, 406.21, 433.67, 413.99, 448.8, 420.47, 462.62, 423.06, 472.56, 424.36, 478.17, 425.65]], "area": 2427.6952500000007, "iscrowd": 0, "image_id": 17714, "bbox": [301.04, 372.51, 185.34, 63.51], "category_id": 49, "id": 695922}, {"segmentation": [[171.86, 354.01, 198.79, 353.04, 211.65, 352.55, 215.29, 353.53, 244.17, 354.5, 243.44, 349.4, 237.62, 348.91, 233.98, 349.16, 224.51, 349.64, 213.11, 349.64, 199.03, 349.4, 181.32, 349.4, 176.22, 348.67, 172.34, 348.67, 170.16, 349.64, 169.67, 352.55, 170.16, 353.77, 172.1, 353.28]], "area": 310.9677000000006, "iscrowd": 0, "image_id": 18380, "bbox": [169.67, 348.67, 74.5, 5.83], "category_id": 49, "id": 697699}, {"segmentation": [[494.25, 357.47, 499.17, 360.47, 497.46, 362.18, 473.9, 380.18, 471.11, 382.32, 470.04, 383.82, 468.97, 386.18, 449.69, 403.53, 445.41, 396.46, 463.61, 381.68, 468.97, 378.04, 484.18, 366.04, 493.17, 357.9]], "area": 390.7774499999987, "iscrowd": 0, "image_id": 18380, "bbox": [445.41, 357.47, 53.76, 46.06], "category_id": 49, "id": 700005}, {"segmentation": [[424.98, 354.74, 424.74, 358.47, 432.43, 363.82, 442.2, 364.05, 447.56, 358.47, 451.51, 355.67, 467.58, 354.51, 471.07, 349.85, 475.03, 352.41, 481.78, 350.55, 478.98, 346.59, 449.65, 352.65, 445.23, 354.98, 440.11, 356.84, 429.87, 354.28, 427.07, 353.35, 424.51, 354.98]], "area": 304.4475000000013, "iscrowd": 0, "image_id": 18380, "bbox": [424.51, 346.59, 57.27, 17.46], "category_id": 50, "id": 703294}, {"segmentation": [[250.25, 219.66, 261.04, 228.63, 264.42, 231.1, 271.06, 234.61, 271.06, 237.08, 267.42, 237.73, 263.51, 237.21, 262.34, 236.17, 262.47, 234.09, 261.3, 231.36, 249.34, 222.52, 247.78, 221.22, 247.78, 220.05, 248.17, 219.4]], "area": 87.68955000000028, "iscrowd": 0, "image_id": 18380, "bbox": [247.78, 219.4, 23.28, 18.33], "category_id": 50, "id": 703843}, {"segmentation": [[394.77, 359.01, 409.94, 373.09, 414.82, 383.92, 416.08, 389.52, 412.11, 402.16, 402.36, 416.43, 394.59, 424.19, 387.19, 426.0, 359.38, 426.0, 352.7, 426.0, 348.91, 423.83, 341.51, 419.32, 333.38, 411.19, 340.06, 407.94, 339.7, 405.23, 334.11, 401.8, 325.62, 401.98, 320.56, 392.59, 318.76, 385.73, 318.76, 373.81, 327.6, 361.72, 335.37, 355.4, 339.52, 350.7, 346.93, 348.53, 354.15, 347.27, 359.02, 348.9, 368.23, 350.88, 384.84, 354.31]], "area": 5753.44605, "iscrowd": 0, "image_id": 18380, "bbox": [318.76, 347.27, 97.32, 78.73], "category_id": 51, "id": 717021}, {"segmentation": [[257.89, 258.94, 291.32, 242.71, 332.39, 260.85, 334.3, 278.05, 307.56, 290.46, 300.87, 296.19, 277.0, 289.51, 263.62, 275.18]], "area": 2621.348499999999, "iscrowd": 0, "image_id": 18380, "bbox": [257.89, 242.71, 76.41, 53.48], "category_id": 51, "id": 717977}, {"segmentation": [[328.64, 157.5, 324.69, 156.78, 318.35, 156.18, 305.43, 156.18, 297.77, 158.45, 295.14, 160.97, 294.78, 164.08, 298.49, 165.99, 306.62, 170.66, 308.54, 174.85, 317.63, 175.44, 316.92, 171.73, 318.23, 169.94, 320.74, 170.9, 322.78, 172.21, 326.73, 171.85, 330.8, 174.25, 333.67, 177.96, 341.09, 174.85, 343.24, 169.46, 343.36, 165.39, 341.45, 161.32, 339.53, 163.0, 337.02, 164.91]], "area": 671.2061499999999, "iscrowd": 0, "image_id": 18380, "bbox": [294.78, 156.18, 48.58, 21.78], "category_id": 51, "id": 1039990}, {"segmentation": [[443.57, 425.41, 452.69, 415.03, 468.46, 406.32, 485.88, 406.74, 501.64, 412.96, 518.65, 421.26, 523.21, 426.0, 497.49, 426.0, 466.38, 426.0, 450.2, 426.0]], "area": 1048.1505000000006, "iscrowd": 0, "image_id": 18380, "bbox": [443.57, 406.32, 79.64, 19.68], "category_id": 51, "id": 1041015}, {"segmentation": [[311.6, 192.53, 313.1, 208.38, 315.89, 214.8, 329.81, 216.52, 350.59, 215.23, 351.66, 214.37, 352.52, 208.16, 353.16, 204.95, 349.73, 195.74, 347.59, 191.88, 346.52, 190.81, 331.31, 192.1, 319.32, 191.88, 315.03, 192.31]], "area": 917.8527000000001, "iscrowd": 0, "image_id": 18380, "bbox": [311.6, 190.81, 41.56, 25.71], "category_id": 51, "id": 1041025}, {"segmentation": [[304.2, 226.07, 309.14, 233.13, 321.85, 239.83, 344.07, 238.77, 356.07, 234.89, 359.6, 228.19, 354.66, 221.84, 349.72, 218.66, 342.66, 219.02, 338.43, 216.19, 331.73, 218.66, 327.84, 218.31, 323.26, 215.84, 321.49, 216.55, 319.02, 218.31, 316.2, 219.37, 312.67, 219.72, 306.32, 223.25, 305.97, 224.31]], "area": 955.3017500000005, "iscrowd": 0, "image_id": 18380, "bbox": [304.2, 215.84, 55.4, 23.99], "category_id": 51, "id": 1041183}, {"segmentation": [[235.15, 353.47, 250.25, 359.94, 266.43, 362.1, 292.31, 363.18, 306.34, 364.25, 325.75, 364.25, 334.38, 341.6, 325.75, 321.11, 313.89, 297.38, 305.26, 279.04, 293.39, 270.41, 280.45, 260.7, 262.11, 252.07, 242.7, 248.84, 218.97, 246.68, 187.69, 248.84, 162.88, 259.63, 148.85, 287.67, 143.46, 307.09, 140.22, 331.9, 134.83, 347.0, 132.67, 363.18, 132.67, 379.36, 145.62, 394.46, 155.33, 407.4, 163.96, 422.5, 177.98, 439.76, 196.31, 448.39, 217.89, 454.86, 242.7, 459.18, 264.27, 458.1, 277.21, 446.23, 283.69, 434.37, 270.74, 425.74, 262.11, 413.87, 252.4, 400.93, 249.17, 386.91, 241.62, 378.28, 238.38, 370.73, 238.38, 370.73]], "area": 28204.1199, "iscrowd": 0, "image_id": 17714, "bbox": [132.67, 246.68, 201.71, 212.5], "category_id": 59, "id": 1072602}, {"segmentation": [[5.41, 457.57, 37.61, 453.55, 41.06, 451.82, 79.01, 390.3, 168.71, 386.85, 137.09, 451.82, 131.91, 457.0, 120.99, 458.72, 40.48, 463.9, 34.73, 462.75, 3.11, 466.2, 0.23, 467.35, 0.23, 459.3]], "area": 7008.352999999999, "iscrowd": 0, "image_id": 11813, "bbox": [0.23, 386.85, 168.48, 80.5], "category_id": 73, "id": 1102694}, {"segmentation": [[508.26, 275.22, 517.13, 283.15, 543.03, 284.09, 543.96, 281.75, 544.2, 280.59, 544.2, 277.55, 522.73, 277.55, 515.5, 270.55, 508.5, 270.55, 507.8, 274.05, 508.96, 275.45]], "area": 253.86084999999926, "iscrowd": 0, "image_id": 14038, "bbox": [507.8, 270.55, 36.4, 13.54], "category_id": 84, "id": 1138773}, {"segmentation": [[503.52, 292.57, 513.61, 303.52, 544.74, 303.09, 544.95, 304.81, 513.82, 306.52, 504.8, 297.29, 501.8, 293.86, 502.66, 291.71]], "area": 109.23585000000037, "iscrowd": 0, "image_id": 14038, "bbox": [501.8, 291.71, 43.15, 14.81], "category_id": 84, "id": 1138926}, {"segmentation": [[514.79, 300.39, 543.21, 300.26, 544.12, 300.13, 544.12, 295.83, 514.4, 295.31, 502.67, 285.8, 502.28, 289.97, 514.27, 300.0]], "area": 196.55980000000022, "iscrowd": 0, "image_id": 14038, "bbox": [502.28, 285.8, 41.84, 14.59], "category_id": 84, "id": 1141001}, {"segmentation": [[518.83, 239.89, 522.05, 242.74, 543.47, 242.55, 543.47, 246.15, 522.24, 245.96, 519.21, 243.11]], "area": 81.96849999999985, "iscrowd": 0, "image_id": 14038, "bbox": [518.83, 239.89, 24.64, 6.26], "category_id": 84, "id": 1142874}, {"segmentation": [[510.08, 200.93, 520.81, 204.93, 549.9, 205.48, 550.08, 211.11, 520.27, 209.11, 507.54, 205.3, 509.18, 201.3, 509.18, 201.3]], "area": 202.3378000000003, "iscrowd": 0, "image_id": 14038, "bbox": [507.54, 200.93, 42.54, 10.18], "category_id": 84, "id": 1148460}, {"segmentation": [[514.09, 291.36, 504.46, 282.76, 505.23, 275.28, 508.24, 274.93, 516.84, 283.02, 539.46, 284.22, 539.2, 290.24, 539.28, 291.27]], "area": 296.45120000000037, "iscrowd": 0, "image_id": 14038, "bbox": [504.46, 274.93, 35.0, 16.43], "category_id": 84, "id": 1148880}, {"segmentation": [[620.99, 19.37, 605.92, 32.29, 590.85, 51.66, 583.32, 78.57, 566.1, 153.9, 555.34, 171.12, 534.89, 189.42, 470.31, 223.86, 444.48, 272.29, 434.8, 311.03, 419.73, 347.62, 398.21, 389.6, 390.67, 424.04, 374.53, 461.7, 373.45, 462.78, 365.92, 473.54, 416.5, 472.47, 438.03, 463.86, 447.71, 441.26, 450.94, 415.43, 457.4, 393.9, 469.24, 369.15, 481.08, 343.32, 495.07, 330.4, 509.06, 335.78, 516.59, 360.54, 513.36, 393.9, 524.13, 416.5, 570.4, 432.65, 602.69, 421.88, 627.44, 421.88, 640.0, 419.73, 640.0, 407.89, 639.28, 15.07, 622.06, 23.68]], "area": 56257.02764999998, "iscrowd": 0, "image_id": 15335, "bbox": [365.92, 15.07, 274.08, 458.47], "category_id": 1, "id": 1216332}, {"segmentation": [[296.02, 114.23, 302.88, 95.69, 317.98, 88.83, 317.98, 73.04, 322.1, 64.12, 333.08, 70.99, 335.83, 86.09, 346.81, 91.58, 359.16, 114.23, 357.1, 123.84, 343.38, 123.15, 326.9, 130.01, 317.29, 129.33, 314.55, 121.09, 296.02, 118.34]], "area": 2233.3998, "iscrowd": 0, "image_id": 18380, "bbox": [296.02, 64.12, 63.14, 65.89], "category_id": 1, "id": 1225902}, {"segmentation": [[256.72, 454.11, 208.18, 399.1, 193.08, 384.0, 180.13, 362.43, 173.66, 346.25, 192.0, 311.73, 220.04, 279.37, 229.75, 269.66, 248.09, 264.27, 265.35, 259.96, 277.21, 252.4, 259.96, 215.73, 267.51, 181.21, 278.29, 148.85, 300.94, 139.15, 327.91, 145.62, 347.33, 157.48, 354.88, 170.43, 361.35, 187.69, 355.96, 223.28, 347.33, 254.56, 354.88, 267.51, 367.82, 275.06, 376.45, 279.37, 379.69, 305.26, 378.61, 334.38, 381.84, 376.45, 375.37, 407.73, 371.06, 434.7, 367.82, 462.74, 365.66, 472.45, 255.64, 474.61]], "area": 44578.0246, "iscrowd": 0, "image_id": 15335, "bbox": [173.66, 139.15, 208.18, 335.46], "category_id": 1, "id": 1230490}, {"segmentation": [[263.38, 78.51, 266.11, 64.86, 275.67, 55.3, 279.76, 46.43, 296.14, 47.11, 307.74, 58.03, 307.74, 73.05, 305.01, 71.68, 307.74, 77.83, 313.2, 82.6, 314.57, 88.06, 313.88, 97.62, 311.84, 105.81, 311.15, 112.63, 318.66, 109.9, 327.53, 120.14, 334.36, 134.47, 337.09, 148.12, 322.76, 140.61, 312.52, 138.56, 300.24, 138.56, 276.35, 150.85, 273.62, 153.58, 237.45, 151.53, 240.86, 126.96, 247.69, 116.72, 261.34, 108.54, 259.29, 98.3, 259.97, 88.74, 263.38, 79.87]], "area": 5927.082549999999, "iscrowd": 0, "image_id": 15335, "bbox": [237.45, 46.43, 99.64, 107.15], "category_id": 1, "id": 1248228}, {"segmentation": [[5.74, 228.5, 11.49, 192.13, 29.68, 171.06, 62.22, 168.19, 97.64, 186.38, 108.18, 214.14, 100.52, 247.65, 76.58, 275.41, 82.33, 285.94, 100.52, 340.51, 112.96, 384.54, 129.24, 412.31, 121.58, 418.05, 84.24, 419.01, 0.96, 419.96, 0.96, 264.88]], "area": 23863.4946, "iscrowd": 0, "image_id": 18380, "bbox": [0.96, 168.19, 128.28, 251.77], "category_id": 1, "id": 1255873}, {"segmentation": [[69.22, 275.06, 67.47, 276.81, 81.45, 283.22, 88.44, 296.03, 105.92, 365.93, 128.63, 409.61, 144.94, 386.9, 156.59, 368.26, 157.17, 362.43, 172.9, 332.73, 169.99, 323.99, 175.81, 313.5, 175.81, 301.86, 172.32, 289.04, 176.4, 278.56, 176.4, 272.15, 171.15, 256.42, 173.48, 252.93, 173.48, 248.85, 175.81, 241.28, 173.48, 227.3, 160.67, 205.75, 161.25, 193.03, 156.59, 187.2, 156.59, 177.88, 148.44, 162.74, 137.37, 156.91, 130.96, 151.67, 121.06, 152.84, 98.34, 165.65, 92.52, 178.47, 98.34, 184.29, 104.17, 195.94, 107.66, 202.93, 108.25, 212.25, 104.75, 222.74, 103.0, 233.22, 94.27, 247.78, 91.94, 251.28, 83.2, 261.18, 77.96, 272.25, 73.3, 273.41]], "area": 16176.037199999995, "iscrowd": 0, "image_id": 18380, "bbox": [67.47, 151.67, 108.93, 257.94], "category_id": 1, "id": 1258473}, {"segmentation": [[209.73, 407.03, 174.05, 348.65, 188.11, 321.62, 206.49, 301.08, 211.89, 281.62, 214.05, 238.38, 206.49, 214.59, 185.95, 198.38, 171.89, 176.76, 160.0, 133.51, 149.19, 95.68, 131.89, 78.38, 104.86, 71.89, 82.16, 71.89, 78.92, 71.89, 56.22, 83.78, 44.32, 102.16, 36.76, 129.19, 36.76, 134.59, 42.16, 163.78, 44.32, 177.84, 22.7, 192.97, 2.16, 204.86, 1.08, 448.11, 23.78, 434.05, 36.76, 429.73, 41.08, 411.35, 44.32, 391.89, 44.32, 390.81, 43.24, 385.41, 68.11, 374.59, 87.57, 370.27, 120.0, 369.19, 137.3, 377.84, 149.19, 389.73, 184.86, 405.95]], "area": 53323.984500000006, "iscrowd": 0, "image_id": 15335, "bbox": [1.08, 71.89, 212.97, 376.22], "category_id": 1, "id": 1277736}, {"segmentation": [[487.27, 340.8, 512.16, 341.76, 539.92, 301.55, 527.47, 278.58, 542.79, 253.69, 545.66, 219.22, 554.28, 209.65, 544.71, 179.97, 554.28, 151.25, 585.87, 133.07, 612.67, 133.07, 624.16, 156.04, 633.73, 201.03, 640.0, 223.05, 640.0, 414.51, 639.48, 421.21, 640.0, 426.0, 552.36, 426.0, 538.96, 426.0, 519.82, 399.2, 488.22, 342.71]], "area": 30090.851249999996, "iscrowd": 0, "image_id": 18380, "bbox": [487.27, 133.07, 152.73, 292.93], "category_id": 1, "id": 1279796}, {"segmentation": [[512.23, 83.52, 515.21, 70.95, 516.28, 50.72, 515.43, 35.38, 525.22, 37.51, 533.11, 39.85, 545.25, 45.39, 554.83, 65.84, 558.24, 95.87, 549.93, 113.12, 543.54, 127.82, 540.56, 163.6, 528.42, 163.81, 515.64, 164.66, 512.87, 164.02, 509.89, 156.57, 508.83, 147.41, 511.81, 112.69]], "area": 4577.374699999999, "iscrowd": 0, "image_id": 15335, "bbox": [508.83, 35.38, 49.41, 129.28], "category_id": 1, "id": 1282318}, {"segmentation": [[458.81, 199.8, 463.28, 187.12, 500.58, 184.88, 509.53, 202.79, 514.0, 235.6, 515.5, 250.52, 534.14, 283.34, 532.65, 312.43, 512.51, 343.76, 486.41, 341.52, 463.28, 308.7, 455.82, 293.78, 454.33, 276.63, 461.05, 250.52, 457.32, 220.69, 459.55, 202.79]], "area": 9113.590549999997, "iscrowd": 0, "image_id": 18380, "bbox": [454.33, 184.88, 79.81, 158.88], "category_id": 1, "id": 1282440}, {"segmentation": [[395.53, 197.82, 396.79, 186.48, 395.53, 170.1, 398.68, 153.71, 402.46, 139.22, 424.52, 137.33, 429.56, 144.89, 427.67, 163.8, 427.04, 171.36, 439.01, 177.66, 447.83, 195.3, 456.66, 213.58, 454.77, 233.11, 449.72, 247.61, 442.79, 250.76, 437.12, 250.76, 436.49, 247.61, 436.49, 247.61, 433.97, 250.76, 425.78, 232.48, 414.44, 217.36, 404.98, 205.39, 398.05, 197.82]], "area": 4010.0322, "iscrowd": 0, "image_id": 18380, "bbox": [395.53, 137.33, 61.13, 113.43], "category_id": 1, "id": 1299617}, {"segmentation": [[236.84, 211.36, 228.5, 175.52, 223.09, 171.77, 226.84, 158.86, 224.34, 146.36, 221.42, 137.6, 213.5, 133.85, 198.5, 134.27, 188.92, 140.1, 188.92, 166.36, 190.17, 178.02, 187.67, 186.36, 191.83, 206.78, 195.17, 213.44, 192.67, 231.78, 194.75, 238.86, 217.67, 226.78, 222.25, 219.69, 227.67, 220.53, 235.17, 216.36]], "area": 3630.373600000001, "iscrowd": 0, "image_id": 18380, "bbox": [187.67, 133.85, 49.17, 105.01], "category_id": 1, "id": 1309723}, {"segmentation": [[228.46, 173.8, 224.86, 165.38, 224.86, 158.57, 224.86, 152.16, 222.86, 144.15, 221.65, 140.55, 220.45, 136.14, 223.26, 125.72, 232.87, 115.71, 238.48, 110.1, 239.28, 107.69, 237.68, 102.49, 236.88, 99.28, 236.88, 96.88, 236.88, 94.07, 238.48, 87.26, 246.09, 79.65, 254.1, 78.45, 260.51, 82.86, 263.72, 88.46, 264.92, 98.48, 264.12, 104.49, 259.71, 112.9, 259.31, 116.51, 262.12, 123.72, 257.71, 126.52, 252.5, 134.14, 252.5, 139.34, 246.49, 149.76, 243.29, 157.77, 242.49, 162.18]], "area": 2273.3313000000003, "iscrowd": 0, "image_id": 18380, "bbox": [220.45, 78.45, 44.47, 95.35], "category_id": 1, "id": 1310638}, {"segmentation": [[458.36, 187.11, 459.69, 177.1, 458.02, 173.26, 457.19, 171.59, 459.19, 167.25, 459.19, 153.11, 457.19, 144.6, 454.02, 136.59, 453.18, 128.51, 451.18, 123.0, 448.84, 118.49, 441.83, 109.98, 438.33, 104.97, 437.33, 102.85, 436.99, 98.34, 434.99, 97.18, 434.49, 95.17, 434.49, 93.34, 437.33, 88.0, 437.33, 85.83, 436.33, 84.32, 437.49, 77.53, 437.16, 73.69, 436.16, 71.85, 433.49, 69.02, 430.65, 67.18, 427.98, 66.01, 424.81, 64.84, 420.8, 63.68, 417.13, 63.84, 414.13, 64.34, 413.96, 64.34, 413.63, 64.84, 413.13, 65.51, 412.96, 66.51, 410.62, 68.35, 408.95, 70.19, 407.95, 72.02, 406.95, 74.69, 405.78, 77.03, 405.28, 79.2, 404.95, 81.7, 405.12, 84.04, 404.62, 85.87, 403.61, 88.21, 403.28, 90.38, 402.78, 92.72, 402.78, 94.22, 404.11, 99.85, 405.62, 101.68, 406.95, 104.52, 408.45, 106.69, 408.95, 109.19, 409.46, 108.53, 409.62, 109.86, 408.62, 111.53, 407.45, 112.7, 406.62, 114.03, 405.45, 115.87, 403.95, 117.54, 402.78, 119.37, 402.28, 119.71, 399.78, 120.88, 398.77, 121.38, 397.94, 122.54, 397.27, 124.21, 396.44, 127.38, 392.26, 143.86, 392.26, 151.87, 391.76, 156.88, 389.76, 161.29, 387.26, 164.29, 384.75, 167.97, 381.58, 170.47, 379.91, 172.14, 383.42, 178.81, 384.56, 180.11, 386.96, 177.71, 387.38, 177.0, 386.96, 182.65, 386.96, 185.34, 389.22, 190.71, 390.92, 193.39, 391.91, 193.82, 393.6, 192.12, 393.88, 190.0, 392.37, 183.54, 391.35, 181.77, 393.63, 174.94, 396.16, 169.62, 396.67, 163.55, 397.43, 157.23, 398.94, 151.41, 401.47, 147.61, 405.52, 140.78, 415.64, 138.0, 417.92, 133.95, 423.99, 133.45, 428.04, 140.78, 432.08, 141.54, 432.34, 144.07, 433.85, 148.12, 434.87, 151.91, 434.36, 155.96, 432.08, 158.49, 431.83, 159.76, 433.6, 162.79, 434.36, 163.3, 436.89, 164.56, 440.43, 166.08, 440.68, 167.85, 444.98, 169.62, 448.02, 171.39, 446.0, 175.44, 444.23, 180.5, 444.98, 187.33, 448.53, 196.44, 449.79, 199.47, 451.56, 196.94, 455.1, 190.11, 458.14, 186.83]], "area": 4303.59345, "iscrowd": 0, "image_id": 18380, "bbox": [379.91, 63.68, 79.78, 135.79], "category_id": 1, "id": 1317070}, {"segmentation": [[557.44, 87.5, 554.15, 65.9, 560.25, 53.69, 567.3, 44.3, 571.99, 44.3, 572.93, 34.44, 581.38, 24.58, 584.67, 23.64, 590.77, 26.46, 585.14, 44.3, 574.81, 49.94, 564.48, 71.53, 558.85, 86.56]], "area": 696.98475, "iscrowd": 0, "image_id": 15335, "bbox": [554.15, 23.64, 36.62, 63.86], "category_id": 1, "id": 1318954}, {"segmentation": [[378.48, 168.94, 383.29, 160.39, 380.08, 157.71, 377.95, 151.83, 377.95, 142.2, 380.08, 135.25, 383.29, 130.44, 383.83, 128.83, 383.83, 127.23, 374.74, 119.21, 370.46, 105.84, 372.06, 100.49, 377.41, 88.73, 379.02, 83.91, 381.69, 78.03, 388.64, 76.96, 398.27, 82.31, 401.48, 88.73, 403.62, 95.14, 407.36, 107.44, 406.29, 114.4, 396.13, 127.23, 393.99, 139.0, 392.38, 158.25, 391.32, 160.92, 384.36, 168.94]], "area": 1856.7338000000007, "iscrowd": 0, "image_id": 18380, "bbox": [370.46, 76.96, 36.9, 91.98], "category_id": 1, "id": 1321626}, {"segmentation": [[238.37, 215.39, 227.84, 175.19, 248.9, 154.13, 252.73, 135.94, 267.09, 121.58, 278.58, 139.77, 276.66, 166.57, 265.17, 197.2, 241.24, 205.82, 239.33, 214.44]], "area": 2496.4767500000003, "iscrowd": 0, "image_id": 18380, "bbox": [227.84, 121.58, 50.74, 93.81], "category_id": 1, "id": 1329602}, {"segmentation": [[226.29, 116.44, 232.12, 90.19, 233.37, 76.02, 238.37, 59.77, 254.21, 52.69, 255.87, 44.77, 260.04, 36.44, 272.12, 31.44, 276.71, 42.27, 277.12, 51.44, 273.79, 58.94, 269.21, 64.77, 265.04, 64.36, 264.21, 68.11, 264.62, 78.52, 265.46, 90.19, 275.87, 90.19, 280.87, 92.27, 277.96, 95.61, 273.37, 100.19, 267.12, 100.19, 265.46, 93.11, 260.87, 78.11, 249.62, 75.19, 245.45, 80.19, 240.04, 86.02, 237.12, 91.02, 238.37, 101.44, 238.37, 107.69, 230.45, 117.28, 227.95, 118.11]], "area": 1566.5604500000002, "iscrowd": 0, "image_id": 18380, "bbox": [226.29, 31.44, 54.58, 86.67], "category_id": 1, "id": 1330599}, {"segmentation": [[243.82, 312.61, 238.47, 304.87, 239.26, 302.49, 236.68, 297.73, 243.43, 292.17, 245.61, 295.74, 249.38, 297.53, 257.91, 308.44, 258.71, 310.83, 255.13, 312.21, 243.82, 312.41]], "area": 251.38490000000021, "iscrowd": 0, "image_id": 14038, "bbox": [236.68, 292.17, 22.03, 20.44], "category_id": 44, "id": 1491536}, {"segmentation": [[407.52, 331.59, 392.97, 327.62, 389.66, 320.34, 402.23, 316.38, 402.89, 294.55, 391.65, 278.02, 395.61, 258.84, 407.52, 257.52, 422.07, 266.78, 415.45, 292.57, 405.53, 297.2, 406.19, 317.7, 408.84, 324.97, 409.5, 331.59, 405.53, 332.25, 402.89, 332.25]], "area": 1094.0496999999996, "iscrowd": 0, "image_id": 18380, "bbox": [389.66, 257.52, 32.41, 74.73], "category_id": 46, "id": 1497149}, {"segmentation": [[238.29, 326.99, 248.75, 367.67, 256.88, 372.32, 261.53, 372.32, 265.6, 371.16, 271.41, 360.69, 274.9, 323.5, 269.09, 319.43, 259.79, 317.11, 253.98, 317.69, 236.54, 325.24]], "area": 1497.9717500000006, "iscrowd": 0, "image_id": 18380, "bbox": [236.54, 317.11, 38.36, 55.21], "category_id": 47, "id": 1511357}, {"segmentation": [[411.32, 306.52, 423.8, 304.44, 436.27, 309.01, 442.09, 313.59, 442.09, 318.99, 440.84, 323.15, 432.11, 353.5, 426.29, 354.33, 423.8, 358.49, 409.66, 355.16, 406.75, 319.41, 405.92, 312.75, 410.08, 306.93]], "area": 1473.2216499999995, "iscrowd": 0, "image_id": 18380, "bbox": [405.92, 304.44, 36.17, 54.05], "category_id": 47, "id": 1512624}, {"segmentation": [[239.65, 319.56, 236.48, 320.46, 235.12, 310.05, 235.12, 306.42, 239.2, 297.82, 251.42, 295.1, 265.01, 296.92, 269.54, 303.25, 269.54, 311.41, 268.18, 314.12, 267.73, 314.58, 264.1, 315.48, 256.86, 315.93, 251.88, 316.39, 243.27, 321.37, 241.46, 321.37]], "area": 683.4626999999999, "iscrowd": 0, "image_id": 18380, "bbox": [235.12, 295.1, 34.42, 26.27], "category_id": 47, "id": 1514444}, {"segmentation": [[364.53, 234.42, 364.53, 232.2, 367.01, 230.41, 369.57, 229.81, 373.76, 229.64, 377.51, 229.64, 382.3, 230.32, 384.52, 231.52, 386.74, 232.63, 387.34, 233.99, 386.82, 236.73, 385.8, 239.29, 384.6, 246.89, 384.18, 249.45, 379.82, 244.84, 374.61, 243.9, 370.34, 243.9, 367.09, 243.9, 365.22, 243.99]], "area": 311.31845000000044, "iscrowd": 0, "image_id": 18380, "bbox": [364.53, 229.64, 22.81, 19.81], "category_id": 47, "id": 1515310}, {"segmentation": [[307.27, 207.14, 302.05, 205.21, 296.55, 204.11, 293.8, 202.74, 292.15, 186.79, 301.5, 183.77, 309.47, 187.07, 307.82, 206.59]], "area": 311.0243500000004, "iscrowd": 0, "image_id": 18380, "bbox": [292.15, 183.77, 17.32, 23.37], "category_id": 47, "id": 1515344}, {"segmentation": [[281.14, 219.5, 284.16, 237.92, 285.26, 242.04, 293.78, 242.32, 301.48, 241.77, 303.4, 233.8, 304.23, 228.57, 304.5, 224.18, 304.5, 221.43, 303.4, 218.68, 302.03, 217.03, 300.93, 216.48, 294.06, 215.1, 287.46, 215.38]], "area": 535.11985, "iscrowd": 0, "image_id": 18380, "bbox": [281.14, 215.1, 23.36, 27.22], "category_id": 47, "id": 1515900}, {"segmentation": [[350.02, 176.91, 351.02, 195.98, 353.03, 196.98, 355.87, 195.31, 363.23, 192.47, 364.74, 176.91, 362.06, 175.24, 356.71, 173.56, 351.36, 174.4]], "area": 273.2422000000004, "iscrowd": 0, "image_id": 18380, "bbox": [350.02, 173.56, 14.72, 23.42], "category_id": 47, "id": 1515940}, {"segmentation": [[344.32, 165.63, 345.7, 174.26, 345.7, 182.54, 348.47, 184.61, 349.85, 184.04, 350.19, 176.56, 352.84, 173.68, 357.21, 172.19, 357.9, 165.63, 356.63, 164.02, 352.03, 163.56, 348.24, 163.21, 345.47, 164.36, 344.09, 165.05]], "area": 170.0991499999999, "iscrowd": 0, "image_id": 18380, "bbox": [344.09, 163.21, 13.81, 21.4], "category_id": 47, "id": 1516054}, {"segmentation": [[357.59, 247.94, 360.62, 279.35, 366.4, 282.38, 373.57, 282.66, 377.15, 281.0, 382.94, 250.42, 382.94, 250.42, 381.56, 246.56, 381.56, 246.56, 373.57, 244.08, 365.85, 243.53, 365.85, 243.53, 359.52, 246.29]], "area": 782.9148999999999, "iscrowd": 0, "image_id": 18380, "bbox": [357.59, 243.53, 25.35, 39.13], "category_id": 47, "id": 1516080}, {"segmentation": [[292.54, 184.65, 292.16, 176.14, 290.27, 169.15, 293.3, 167.45, 296.32, 166.88, 300.48, 166.5, 301.81, 166.69, 303.7, 167.82, 305.02, 168.58, 305.4, 169.53, 305.02, 171.42, 304.45, 173.5, 303.51, 183.9, 300.48, 183.33, 298.78, 184.27, 296.7, 185.22]], "area": 216.15659999999963, "iscrowd": 0, "image_id": 18380, "bbox": [290.27, 166.5, 15.13, 18.72], "category_id": 47, "id": 1516094}, {"segmentation": [[356.89, 214.32, 363.94, 211.11, 372.69, 211.33, 377.82, 212.39, 382.73, 215.6, 387.0, 217.95, 388.92, 218.8, 423.51, 215.17, 442.09, 211.97, 453.41, 205.77, 456.61, 212.18, 457.04, 216.88, 455.97, 219.87, 454.26, 223.71, 449.56, 222.43, 387.64, 225.85, 378.46, 234.17, 369.06, 238.23, 360.31, 236.95, 360.09, 234.39, 359.24, 229.26, 358.17, 223.28]], "area": 1292.5555000000002, "iscrowd": 0, "image_id": 17714, "bbox": [356.89, 205.77, 100.15, 32.46], "category_id": 50, "id": 1530842}, {"segmentation": [[21.85, 438.48, 33.38, 434.0, 52.38, 430.37, 68.82, 429.52, 83.77, 430.37, 93.59, 436.56, 98.93, 444.89, 98.93, 463.68, 89.75, 474.78, 85.48, 480.0, 0.28, 480.0, 0.92, 451.72, 19.5, 439.13]], "area": 4289.2145, "iscrowd": 0, "image_id": 15335, "bbox": [0.28, 429.52, 98.65, 50.48], "category_id": 51, "id": 1538947}, {"segmentation": [[449.51, 292.14, 456.94, 281.6, 461.28, 274.17, 472.43, 275.41, 478.94, 279.13, 475.53, 295.23, 471.19, 305.77, 464.07, 302.05, 449.51, 294.0]], "area": 580.1357999999994, "iscrowd": 0, "image_id": 17714, "bbox": [449.51, 274.17, 29.43, 31.6], "category_id": 52, "id": 1541515}, {"segmentation": [[439.76, 318.91, 434.15, 314.32, 428.54, 312.28, 424.96, 310.24, 425.47, 297.99, 426.49, 295.44, 432.11, 291.36, 437.21, 290.85, 444.86, 293.4, 447.92, 293.91, 451.5, 295.44, 452.52, 296.46, 453.03, 296.97, 451.5, 301.57, 448.43, 306.16, 446.9, 309.22, 445.37, 312.28, 444.35, 314.83, 442.31, 319.42]], "area": 532.9654999999999, "iscrowd": 0, "image_id": 17714, "bbox": [424.96, 290.85, 28.07, 28.57], "category_id": 52, "id": 1542205}, {"segmentation": [[456.77, 299.28, 452.57, 301.95, 444.56, 314.16, 443.8, 318.35, 457.53, 327.5, 465.54, 331.32, 469.73, 332.46, 473.93, 332.84, 482.32, 324.84, 486.89, 322.55, 483.84, 311.11, 475.07, 306.91, 465.54, 303.48, 457.91, 298.9, 457.91, 298.9]], "area": 903.9799999999993, "iscrowd": 0, "image_id": 17714, "bbox": [443.8, 298.9, 43.09, 33.94], "category_id": 52, "id": 1544267}, {"segmentation": [[489.5, 309.56, 500.31, 303.26, 506.62, 302.36, 511.12, 308.21, 512.02, 319.02, 508.42, 333.43, 501.21, 343.34, 485.45, 337.94, 483.2, 330.28, 487.7, 316.77, 487.7, 313.61]], "area": 838.6250500000001, "iscrowd": 0, "image_id": 17714, "bbox": [483.2, 302.36, 28.82, 40.98], "category_id": 52, "id": 1544966}, {"segmentation": [[515.49, 323.16, 512.86, 324.86, 510.6, 328.81, 507.59, 337.46, 506.09, 344.23, 507.59, 351.18, 529.78, 363.03, 532.04, 361.72, 535.43, 349.3, 534.86, 341.59, 534.11, 335.76]], "area": 779.6114999999998, "iscrowd": 0, "image_id": 17714, "bbox": [506.09, 323.16, 29.34, 39.87], "category_id": 52, "id": 1545032}, {"segmentation": [[222.67, 285.41, 210.57, 295.78, 198.04, 299.67, 188.97, 300.96, 185.08, 300.1, 179.89, 295.35, 179.46, 291.46, 180.33, 288.0, 187.67, 282.39, 195.02, 278.5, 201.06, 276.34, 209.7, 273.75, 216.62, 274.18, 225.26, 278.93, 224.39, 284.11, 222.23, 286.71]], "area": 802.5652500000001, "iscrowd": 0, "image_id": 17714, "bbox": [179.46, 273.75, 45.8, 27.21], "category_id": 52, "id": 1545967}, {"segmentation": [[153.3, 332.29, 169.1, 333.53, 184.28, 323.62, 185.21, 309.99, 179.94, 304.72, 165.38, 306.58, 150.82, 320.21, 147.42, 324.24]], "area": 743.9523000000002, "iscrowd": 0, "image_id": 17714, "bbox": [147.42, 304.72, 37.79, 28.81], "category_id": 52, "id": 1546781}, {"segmentation": [[473.52, 303.58, 476.18, 301.41, 484.63, 303.58, 488.49, 310.58, 486.32, 315.89, 485.11, 317.34, 484.14, 312.03, 482.94, 310.1, 478.83, 307.2]], "area": 85.47070000000002, "iscrowd": 0, "image_id": 17714, "bbox": [473.52, 301.41, 14.97, 15.93], "category_id": 52, "id": 1546978}, {"segmentation": [[283.26, 271.0, 286.88, 269.93, 287.74, 265.03, 285.6, 262.89, 283.9, 261.83, 281.13, 261.83, 278.14, 264.81, 278.78, 268.65, 280.06, 270.78]], "area": 68.42420000000023, "iscrowd": 0, "image_id": 18380, "bbox": [278.14, 261.83, 9.6, 9.17], "category_id": 57, "id": 1564982}, {"segmentation": [[308.9, 258.03, 308.35, 254.77, 309.8, 253.5, 312.34, 253.86, 313.25, 254.59, 313.25, 256.95, 309.99, 257.67]], "area": 16.719399999999933, "iscrowd": 0, "image_id": 18380, "bbox": [308.35, 253.5, 4.9, 4.53], "category_id": 57, "id": 1565695}, {"segmentation": [[230.37, 244.04, 203.15, 242.78, 196.82, 226.32, 182.25, 226.32, 174.02, 227.59, 170.86, 232.02, 170.23, 233.92, 169.59, 228.85, 169.59, 226.95, 168.96, 225.69, 171.49, 221.25, 168.96, 216.82, 190.48, 214.92, 198.71, 218.09, 207.58, 218.72, 215.17, 215.56, 219.61, 225.05, 233.53, 244.04]], "area": 943.4883999999998, "iscrowd": 0, "image_id": 14038, "bbox": [168.96, 214.92, 64.57, 29.12], "category_id": 63, "id": 1604128}, {"segmentation": [[554.4, 82.87, 554.4, 67.03, 550.65, 51.61, 550.24, 38.28, 534.4, 62.45, 532.73, 61.61, 530.65, 66.61, 525.23, 66.61, 522.73, 82.03, 519.4, 82.03, 511.9, 87.87, 508.15, 90.78, 498.56, 111.2, 496.48, 116.2, 506.48, 117.04, 505.64, 124.96, 515.65, 131.62, 521.06, 131.62, 520.23, 121.2, 516.48, 105.37, 516.06, 95.78, 531.07, 93.7, 527.31, 116.62, 533.98, 121.2, 536.48, 104.95, 536.9, 87.03, 548.57, 82.03, 556.07, 83.7, 556.07, 79.11]], "area": 1831.63675, "iscrowd": 0, "image_id": 14038, "bbox": [496.48, 38.28, 59.59, 93.34], "category_id": 64, "id": 1605902}, {"segmentation": [[506.41, 210.05, 515.96, 216.77, 551.32, 218.19, 551.32, 221.37, 513.48, 222.43, 498.27, 213.94]], "area": 261.4922500000004, "iscrowd": 0, "image_id": 14038, "bbox": [498.27, 210.05, 53.05, 12.38], "category_id": 84, "id": 1651488}, {"segmentation": [[516.09, 89.24, 513.69, 107.04, 513.69, 110.29, 513.97, 112.69, 515.81, 117.5, 516.52, 118.35, 523.16, 118.06, 524.85, 98.85, 524.29, 94.32, 524.57, 88.81, 520.9, 88.39, 517.36, 88.81]], "area": 279.0357499999989, "iscrowd": 0, "image_id": 18380, "bbox": [513.69, 88.39, 11.16, 29.96], "category_id": 86, "id": 1668246}, {"segmentation": [[561.65, 162.93, 548.86, 162.93, 541.45, 162.25, 544.48, 125.22, 557.95, 95.6, 563.33, 78.43, 572.42, 56.89, 579.15, 51.5, 586.22, 42.08, 591.61, 24.24, 599.35, 21.21, 608.1, 21.88, 616.52, 22.89, 589.93, 55.21, 575.45, 109.07, 573.43, 120.17]], "area": 2982.312849999994, "iscrowd": 0, "image_id": 15335, "bbox": [541.45, 21.21, 75.07, 141.72], "category_id": 1, "id": 1694331}, {"segmentation": [[400.45, 53.65, 382.13, 70.08, 379.79, 79.0, 378.38, 84.16, 376.97, 88.86, 383.07, 107.64, 385.89, 111.4, 385.89, 116.09, 385.42, 123.13, 383.07, 124.54, 373.21, 125.48, 364.29, 128.3, 356.31, 130.18, 352.09, 135.34, 348.33, 142.38, 345.51, 148.49, 343.63, 152.24, 344.57, 156.47, 350.68, 157.88, 511.25, 165.39, 506.55, 146.61, 500.45, 137.22, 493.41, 131.58, 487.77, 127.83, 479.79, 125.01, 468.99, 124.54, 458.66, 123.6, 445.99, 119.85, 440.35, 115.15, 449.27, 95.43, 449.74, 73.84, 444.11, 65.85, 438.48, 63.04, 430.96, 59.75, 422.51, 57.4, 416.41, 55.99, 411.24, 54.59]], "area": 9835.369400000001, "iscrowd": 0, "image_id": 15335, "bbox": [343.63, 53.65, 167.62, 111.74], "category_id": 1, "id": 1705756}, {"segmentation": [[168.32, 151.37, 165.88, 141.01, 161.61, 133.09, 160.39, 93.49, 176.24, 72.16, 192.08, 66.68, 218.88, 67.29, 234.11, 80.08, 237.77, 91.05, 232.29, 97.75, 233.51, 108.11, 236.55, 112.37, 234.11, 116.64, 234.11, 125.78, 234.72, 131.26, 233.51, 139.79, 231.07, 143.45, 238.38, 150.15, 174.41, 151.37]], "area": 5697.13675, "iscrowd": 0, "image_id": 15335, "bbox": [160.39, 66.68, 77.99, 84.69], "category_id": 1, "id": 1753431}, {"segmentation": [[317.22, 330.57, 313.51, 326.37, 303.36, 322.9, 293.95, 324.14, 284.79, 331.07, 285.04, 337.26, 285.78, 345.43, 285.04, 356.82, 287.27, 363.5, 290.98, 368.95, 297.42, 372.91, 298.41, 373.9, 298.16, 389.5, 299.15, 396.18, 292.47, 399.65, 286.03, 405.1, 286.28, 412.03, 289.0, 417.23, 293.21, 420.45, 298.41, 421.93, 306.33, 421.19, 309.8, 420.94, 315.0, 417.23, 316.73, 413.27, 317.97, 410.3, 313.02, 408.07, 312.27, 404.6, 312.52, 399.9, 315.99, 399.9, 315.0, 398.17, 308.06, 394.45, 304.85, 394.2, 305.59, 373.41, 311.53, 367.96, 316.23, 363.26, 320.2, 346.17, 317.72, 331.32]], "area": 2227.0983499999998, "iscrowd": 0, "image_id": 18380, "bbox": [284.79, 322.9, 35.41, 99.03], "category_id": 46, "id": 1874376}, {"segmentation": [[331.12, 147.76, 342.05, 147.35, 343.29, 150.94, 342.6, 156.06, 341.63, 159.93, 339.28, 162.98, 337.34, 165.19, 337.48, 176.12, 340.28, 179.48, 341.92, 180.13, 338.65, 180.95, 338.16, 180.3, 336.85, 179.64, 334.89, 179.32, 334.57, 179.32, 335.55, 178.01, 335.87, 174.91, 335.55, 167.88, 335.22, 164.94, 333.91, 162.66, 331.14, 157.76, 330.81, 152.53, 331.79, 147.95], [347.98, 124.27, 350.09, 124.12, 355.95, 124.12, 356.55, 127.42, 355.65, 131.93, 353.99, 135.09, 352.19, 138.1, 352.64, 148.02, 351.29, 148.17, 350.99, 137.8, 349.34, 135.09, 347.53, 131.93, 347.08, 128.03, 347.38, 124.12, 349.03, 123.97], [334.45, 122.31, 343.02, 122.16, 343.47, 127.12, 342.42, 129.53, 340.77, 133.14, 339.87, 134.79, 339.41, 140.2, 339.57, 142.0, 337.76, 142.15, 337.61, 135.69, 337.76, 133.59, 336.26, 132.54, 334.0, 128.93, 333.4, 125.32, 334.0, 123.22, 334.61, 122.31]], "area": 425.16774999999996, "iscrowd": 0, "image_id": 18380, "bbox": [330.81, 122.16, 25.74, 58.79], "category_id": 46, "id": 1875540}, {"segmentation": [[264.43, 296.4, 264.43, 279.27, 255.87, 269.9, 252.2, 257.25, 253.42, 247.87, 257.09, 243.39, 270.96, 242.98, 276.67, 244.61, 278.71, 258.07, 276.26, 269.08, 272.18, 276.01, 268.51, 278.05, 267.69, 296.4, 273.4, 297.22, 277.48, 302.52, 277.89, 307.01, 269.73, 310.68, 268.92, 304.15, 263.62, 299.67]], "area": 925.0581499999994, "iscrowd": 0, "image_id": 18380, "bbox": [252.2, 242.98, 26.51, 67.7], "category_id": 46, "id": 1875981}, {"segmentation": [[640.0, 425.34, 634.94, 424.12, 623.96, 422.9, 613.38, 424.12, 608.1, 425.74, 604.03, 430.62, 600.78, 437.54, 599.96, 480.0, 640.0, 479.83]], "area": 2162.5128, "iscrowd": 0, "image_id": 15335, "bbox": [599.96, 422.9, 40.04, 57.1], "category_id": 47, "id": 1879878}, {"segmentation": [[307.0, 156.11, 305.53, 148.27, 306.02, 145.5, 309.78, 145.17, 315.5, 145.17, 318.27, 146.31, 317.13, 148.11, 316.8, 149.74, 317.29, 154.64, 316.64, 155.95, 312.07, 155.62]], "area": 116.7201500000004, "iscrowd": 0, "image_id": 18380, "bbox": [305.53, 145.17, 12.74, 10.94], "category_id": 47, "id": 1880331}, {"segmentation": [[376.52, 253.09, 344.1, 236.88, 335.38, 216.94, 314.18, 205.71, 319.17, 193.25, 340.36, 194.49, 359.06, 179.53, 387.74, 177.04, 407.69, 195.74, 412.68, 210.7, 410.18, 234.39, 396.47, 253.09]], "area": 4901.9827, "iscrowd": 0, "image_id": 17714, "bbox": [314.18, 177.04, 98.5, 76.05], "category_id": 47, "id": 1882059}, {"segmentation": [[223.39, 253.89, 227.51, 253.89, 226.93, 256.0, 225.88, 256.86, 220.42, 256.76, 220.14, 255.14, 220.14, 253.7, 220.33, 253.22], [231.34, 254.75, 236.89, 255.81, 236.22, 256.38, 231.81, 256.67, 230.47, 255.81, 229.61, 255.33]], "area": 28.893200000000142, "iscrowd": 0, "image_id": 18380, "bbox": [220.14, 253.22, 16.75, 3.64], "category_id": 49, "id": 1891275}, {"segmentation": [[379.23, 220.44, 403.31, 224.24, 419.48, 226.14, 418.52, 228.99, 390.32, 225.19]], "area": 98.14925000000011, "iscrowd": 0, "image_id": 18380, "bbox": [379.23, 220.44, 40.25, 8.55], "category_id": 49, "id": 1891995}, {"segmentation": [[265.04, 324.06, 259.52, 330.68, 262.83, 353.85, 271.66, 363.78, 279.39, 353.85, 282.7, 329.58, 278.28, 318.54, 269.46, 318.54]], "area": 736.8537999999998, "iscrowd": 0, "image_id": 17714, "bbox": [259.52, 318.54, 23.18, 45.24], "category_id": 52, "id": 1905012}, {"segmentation": [[454.76, 134.74, 464.39, 128.47, 472.58, 129.92, 472.58, 131.36, 472.58, 150.63, 472.1, 181.45, 461.02, 186.75, 459.58, 172.78, 458.61, 170.86, 457.65, 142.92]], "area": 771.4241000000003, "iscrowd": 0, "image_id": 18380, "bbox": [454.76, 128.47, 17.82, 58.28], "category_id": 62, "id": 1937487}, {"segmentation": [[563.51, 286.83, 575.78, 287.65, 576.59, 302.37, 570.46, 302.77, 563.92, 303.18, 562.7, 286.83], [583.13, 302.37, 598.67, 308.5, 605.21, 314.22, 621.56, 315.45, 620.33, 310.95, 620.33, 309.72, 628.92, 306.86, 632.19, 312.58, 639.95, 313.4, 639.95, 299.91, 585.59, 301.96]], "area": 733.107450000001, "iscrowd": 0, "image_id": 14038, "bbox": [562.7, 286.83, 77.25, 28.62], "category_id": 62, "id": 1941759}, {"segmentation": [[26.58, 318.6, 47.35, 323.29, 46.01, 315.92, 9.16, 306.54, 2.47, 310.56, 3.81, 318.6, 21.89, 318.6]], "area": 380.79115000000024, "iscrowd": 0, "image_id": 15335, "bbox": [2.47, 306.54, 44.88, 16.75], "category_id": 77, "id": 1976132}, {"segmentation": [[505.39, 283.67, 519.38, 291.17, 542.36, 291.17, 542.86, 294.66, 514.88, 296.16, 500.9, 284.67]], "area": 191.10030000000046, "iscrowd": 0, "image_id": 14038, "bbox": [500.9, 283.67, 41.96, 12.49], "category_id": 84, "id": 1987215}, {"segmentation": [[519.51, 245.7, 539.38, 245.89, 540.34, 248.59, 521.05, 249.56, 518.93, 246.86]], "area": 66.72250000000052, "iscrowd": 0, "image_id": 14038, "bbox": [518.93, 245.7, 21.41, 3.86], "category_id": 84, "id": 1987615}, {"segmentation": [[550.35, 171.17, 549.52, 172.55, 521.79, 173.38, 508.76, 171.86, 511.26, 168.12, 524.29, 170.19]], "area": 111.2164000000003, "iscrowd": 0, "image_id": 14038, "bbox": [508.76, 168.12, 41.59, 5.26], "category_id": 84, "id": 1989043}, {"segmentation": [[506.23, 175.6, 506.52, 171.79, 522.94, 174.72, 553.71, 173.54, 553.12, 177.94, 522.35, 178.23]], "area": 182.03309999999993, "iscrowd": 0, "image_id": 14038, "bbox": [506.23, 171.79, 47.48, 6.44], "category_id": 84, "id": 1989219}, {"segmentation": [[362.44, 128.22, 364.36, 119.75, 366.28, 115.14, 370.13, 108.79, 373.98, 105.13, 377.63, 102.44, 383.4, 104.55, 385.71, 110.13, 386.67, 112.06, 386.67, 119.75, 386.48, 124.18, 383.4, 125.72, 363.21, 130.14]], "area": 453.39979999999986, "iscrowd": 0, "image_id": 15335, "bbox": [362.44, 102.44, 24.23, 27.7], "category_id": 1, "id": 2025828}, {"segmentation": [[565.97, 49.29, 559.87, 48.21, 561.3, 36.36, 559.87, 29.54, 556.28, 25.95, 553.05, 21.64, 543.71, 25.59, 540.12, 35.29, 535.82, 41.39, 543.71, 49.64, 551.97, 60.77, 554.84, 68.31, 554.84, 68.67]], "area": 701.5897999999987, "iscrowd": 0, "image_id": 15335, "bbox": [535.82, 21.64, 30.15, 47.03], "category_id": 1, "id": 2028889}, {"segmentation": [[140.16, 143.42, 146.78, 113.62, 157.82, 102.58, 174.37, 102.58, 182.1, 112.51, 186.51, 129.07, 188.72, 147.83, 194.24, 172.11, 194.24, 194.18, 189.82, 198.6, 194.24, 226.19, 199.76, 233.91, 227.35, 217.36, 233.97, 227.29, 215.21, 243.85, 197.55, 252.68, 201.96, 279.16, 186.51, 303.44, 176.58, 295.72, 175.48, 263.71, 172.17, 244.95, 176.58, 219.57, 164.44, 196.39, 153.4, 166.59, 143.47, 154.45]], "area": 6514.457450000002, "iscrowd": 0, "image_id": 18380, "bbox": [140.16, 102.58, 93.81, 200.86], "category_id": 1, "id": 2030411}, {"segmentation": [[349.82, 276.07, 354.39, 285.44, 357.2, 291.53, 358.14, 297.39, 359.54, 300.08, 363.06, 303.48, 363.88, 305.0, 359.9, 304.53, 359.66, 302.07, 358.26, 302.77, 358.02, 304.65, 356.62, 304.18, 354.98, 302.19, 356.5, 299.73, 355.68, 292.12, 352.52, 286.73, 349.47, 283.33, 346.43, 276.07, 346.9, 274.08, 349.12, 274.67]], "area": 93.23754999999967, "iscrowd": 0, "image_id": 18380, "bbox": [346.43, 274.08, 17.45, 30.92], "category_id": 50, "id": 2105467}, {"segmentation": [[283.51, 255.99, 285.93, 251.96, 289.16, 249.8, 294.0, 249.26, 296.69, 249.53, 298.57, 250.88, 295.07, 254.11, 291.58, 255.18]], "area": 61.06944999999999, "iscrowd": 0, "image_id": 18380, "bbox": [283.51, 249.26, 15.06, 6.73], "category_id": 57, "id": 2115749}, {"segmentation": [[160.48, 286.89, 234.09, 286.17, 232.29, 324.78, 162.29, 323.7, 161.93, 289.42]], "area": 2683.3680499999987, "iscrowd": 0, "image_id": 14038, "bbox": [160.48, 286.17, 73.61, 38.61], "category_id": 78, "id": 2138715}, {"segmentation": [[282.83, 365.14, 279.33, 363.98, 274.32, 360.48, 272.34, 353.96, 271.29, 339.28, 274.09, 328.56, 274.21, 327.74, 276.3, 325.29, 278.87, 323.66, 281.78, 323.31, 283.88, 322.96, 286.44, 322.73, 290.28, 322.96, 292.03, 324.13, 289.12, 325.64, 285.39, 330.07, 284.93, 333.92, 284.46, 342.89, 284.69, 353.26, 286.91, 361.3, 289.47, 363.74, 287.95, 365.96, 285.74, 366.19, 286.21, 374.93, 290.52, 374.35, 293.2, 375.51, 294.94, 377.26, 296.11, 378.89, 298.21, 382.97, 295.41, 387.16, 292.15, 389.96, 288.54, 391.94, 286.56, 392.05, 287.49, 385.53, 284.34, 381.34, 282.01, 378.07, 278.75, 376.44, 280.85, 375.51, 282.71, 374.93]], "area": 731.8126000000001, "iscrowd": 0, "image_id": 18380, "bbox": [271.29, 322.73, 26.92, 69.32], "category_id": 46, "id": 2182899}, {"segmentation": [[301.99, 185.09, 295.05, 186.32, 293.32, 187.07, 292.33, 187.56, 294.56, 200.68, 294.56, 203.66, 298.52, 204.65, 303.97, 205.39, 305.7, 205.64, 307.43, 205.64, 308.18, 191.03, 308.18, 189.54, 308.92, 186.57, 308.42, 186.32, 304.96, 185.83, 302.73, 185.58]], "area": 272.3815500000001, "iscrowd": 0, "image_id": 18380, "bbox": [292.33, 185.09, 16.59, 20.55], "category_id": 46, "id": 2183182}, {"segmentation": [[538.62, 323.51, 539.83, 321.09, 530.16, 312.46, 510.99, 310.73, 511.68, 315.57, 511.68, 317.98, 508.75, 320.4, 504.77, 323.16, 503.22, 327.14, 504.77, 328.35, 503.39, 330.76, 502.87, 332.84, 511.86, 345.1, 533.79, 345.44, 534.31, 335.94, 535.86, 327.14, 538.8, 322.99]], "area": 938.0186499999996, "iscrowd": 0, "image_id": 14038, "bbox": [502.87, 310.73, 36.96, 34.71], "category_id": 84, "id": 2197005}, {"segmentation": [[242.4, 398.12, 243.13, 386.36, 249.57, 378.08, 261.15, 374.04, 278.06, 374.04, 286.7, 381.21, 287.8, 389.85, 285.05, 399.22, 284.13, 411.36, 281.92, 426.0, 250.12, 425.88, 245.15, 402.17]], "area": 1971.7296499999998, "iscrowd": 0, "image_id": 18380, "bbox": [242.4, 374.04, 45.4, 51.96], "category_id": 47, "id": 2214844}, {"segmentation": {"counts": [5943, 2, 437, 2, 435, 4, 427, 13, 422, 18, 422, 18, 7, 4, 411, 31, 409, 33, 407, 36, 405, 35, 406, 34, 408, 32, 410, 38, 401, 40, 394, 48, 391, 51, 388, 53, 6, 1, 379, 57, 2, 3, 378, 62, 378, 62, 378, 62, 378, 62, 378, 62, 378, 62, 379, 60, 381, 14, 6, 38, 408, 16, 6, 8, 3475, 1, 434, 8, 427, 13, 427, 13, 427, 13, 427, 13, 427, 13, 427, 13, 427, 13, 427, 12, 428, 12, 429, 10, 431, 8, 434, 4, 16739, 1, 433, 8, 12, 2, 412, 3, 4, 7, 13, 4, 409, 4, 2, 13, 9, 5, 407, 25, 6, 3, 406, 28, 5, 2, 386, 48, 6, 1, 384, 50, 4, 2, 383, 57, 378, 63, 376, 64, 375, 37, 2, 26, 374, 39, 2, 24, 375, 64, 376, 62, 378, 56, 384, 56, 384, 56, 384, 56, 385, 55, 386, 19, 4, 30, 389, 4, 1, 10, 9, 26, 397, 6, 14, 21, 425, 11, 434, 1, 8330, 8, 429, 13, 425, 16, 5, 1, 417, 19, 2, 3, 415, 25, 414, 26, 414, 26, 414, 26, 414, 26, 414, 26, 414, 25, 415, 24, 417, 21, 420, 9, 433, 5, 6156, 18, 420, 21, 417, 24, 415, 26, 413, 27, 412, 28, 412, 28, 412, 28, 412, 28, 412, 28, 412, 27, 413, 26, 415, 23, 418, 23, 419, 21, 420, 20, 420, 20, 420, 20, 420, 20, 420, 20, 420, 19, 421, 18, 422, 19, 420, 21, 418, 22, 418, 22, 418, 22, 417, 23, 416, 24, 415, 25, 415, 25, 415, 25, 415, 25, 415, 25, 415, 25, 415, 23, 418, 20, 421, 17, 425, 12, 116652, 6, 433, 8, 431, 10, 429, 12, 426, 14, 425, 15, 424, 16, 421, 40, 397, 44, 395, 46, 393, 48, 377, 63, 376, 64, 374, 66, 370, 70, 360, 8, 1, 71, 359, 81, 358, 81, 358, 81, 359, 79, 360, 78, 362, 78, 362, 78, 362, 78, 362, 78, 362, 78, 362, 78, 362, 77, 364, 75, 366, 74, 368, 73, 368, 73, 369, 71, 372, 69, 371, 70, 370, 70, 370, 70, 370, 70, 370, 70, 370, 70, 370, 70, 370, 57, 1, 12, 370, 57, 1, 12, 371, 56, 1, 12, 372, 67, 374, 66, 373, 67, 373, 67, 373, 67, 373, 67, 373, 67, 373, 66, 374, 65, 375, 64, 376, 63, 377, 63, 378, 61, 379, 60, 380, 58, 382, 48, 392, 47, 393, 45, 396, 43, 397, 41, 399, 37, 404, 31, 410, 30, 412, 27, 416, 23, 418, 21, 420, 18, 423, 10, 432, 6, 378, 6, 433, 8, 431, 10, 429, 12, 423, 17, 421, 19, 420, 25, 414, 27, 412, 29, 410, 31, 409, 31, 409, 31, 409, 31, 409, 31, 409, 31, 409, 31, 409, 31, 409, 31, 410, 30, 411, 29, 411, 28, 412, 28, 412, 27, 414, 25, 416, 23, 419, 20, 429, 9, 5271, 6, 433, 8, 431, 10, 429, 12, 428, 13, 427, 14, 426, 15, 424, 17, 422, 18, 421, 19, 421, 19, 421, 19, 421, 19, 421, 19, 420, 20, 419, 21, 419, 20, 420, 19, 421, 19, 421, 18, 422, 18, 422, 18, 422, 17, 423, 16, 425, 17, 424, 17, 425, 16, 425, 16, 423, 17, 422, 18, 421, 19, 421, 19, 420, 20, 420, 20, 420, 20, 420, 20, 420, 20, 420, 20, 420, 19, 421, 18, 422, 17, 423, 15, 425, 14, 426, 13, 428, 11, 430, 9, 433, 5, 4657], "size": [440, 640]}, "area": 9419, "iscrowd": 1, "image_id": 16228, "bbox": [13, 166, 616, 127], "category_id": 1, "id": 900100016228}, {"segmentation": {"counts": [62771, 6, 397, 8, 355, 12, 28, 10, 352, 33, 1, 19, 346, 58, 344, 60, 342, 63, 340, 63, 341, 63, 338, 69, 333, 20, 2, 54, 328, 13, 11, 54, 324, 13, 15, 57, 317, 12, 21, 56, 315, 11, 24, 54, 313, 12, 26, 54, 310, 12, 28, 54, 310, 12, 27, 55, 309, 12, 55, 28, 309, 6, 1, 4, 66, 18, 309, 2, 7, 1, 71, 14, 306, 3, 83, 11, 305, 5, 83, 11, 305, 5, 84, 8, 306, 6, 83, 9, 306, 6, 58, 15, 9, 10, 306, 6, 55, 22, 4, 11, 306, 7, 51, 25, 3, 12, 306, 7, 53, 38, 306, 8, 53, 37, 307, 9, 53, 34, 308, 11, 53, 32, 310, 14, 51, 27, 316, 11, 395, 10, 1241, 2, 377, 1, 22, 5, 398, 4, 399, 4, 384, 4, 11, 4, 385, 12, 1, 3, 390, 14, 392, 10, 42792, 6, 397, 8, 395, 10, 393, 12, 392, 12, 392, 12, 392, 13, 391, 12, 392, 12, 392, 12, 393, 10, 395, 8, 398, 4, 12901, 6, 397, 8, 395, 11, 8, 13, 352, 8, 11, 15, 3, 16, 349, 37, 1, 18, 348, 57, 346, 58, 346, 58, 346, 59, 345, 58, 346, 58, 346, 58, 347, 57, 347, 58, 348, 56, 352, 26, 5, 21, 354, 13, 4, 3, 10, 20, 354, 11, 19, 9, 2, 9, 355, 9, 22, 4, 370, 7, 397, 7, 398, 7, 398, 6, 400, 4, 404, 1, 35505, 25, 377, 29, 375, 29, 374, 31, 371, 33, 371, 33, 370, 34, 370, 34, 370, 34, 370, 33, 371, 33, 371, 31, 373, 29, 376, 25, 379, 24, 382, 20, 385, 19, 387, 27, 381, 25, 378, 26, 378, 27, 377, 27, 377, 27, 377, 27, 377, 27, 378, 26, 378, 25, 381, 23, 388, 14, 21397, 6, 396, 10, 394, 10, 393, 12, 392, 12, 392, 12, 392, 12, 392, 12, 392, 12, 392, 12, 392, 12, 392, 12, 392, 12, 392, 12, 392, 12, 392, 12, 392, 12, 392, 12, 392, 12, 392, 12, 392, 12, 392, 12, 392, 12, 392, 13, 392, 12, 392, 12, 392, 12, 392, 12, 392, 12, 392, 12, 392, 12, 392, 12, 392, 12, 392, 12, 392, 12, 392, 12, 392, 12, 392, 12, 392, 13, 391, 12, 392, 12, 392, 12, 392, 13, 391, 13, 391, 13, 391, 13, 392, 12, 392, 12, 392, 12, 392, 12, 392, 12, 392, 12, 392, 12, 392, 12, 392, 12, 392, 12, 392, 12, 392, 12, 393, 10, 394, 10, 396, 6, 15319], "size": [404, 640]}, "area": 5646, "iscrowd": 1, "image_id": 14439, "bbox": [155, 7, 447, 170], "category_id": 1, "id": 900100014439}, {"segmentation": {"counts": [346, 6, 390, 12, 8, 9, 1, 10, 386, 42, 379, 3, 1, 45, 374, 54, 371, 57, 369, 59, 367, 60, 360, 68, 357, 70, 356, 63, 362, 61, 361, 65, 359, 67, 358, 68, 358, 60, 2, 6, 354, 65, 3, 4, 352, 67, 2, 5, 351, 69, 1, 5, 18, 1, 332, 74, 18, 3, 331, 74, 17, 5, 324, 79, 17, 5, 324, 79, 1, 1, 15, 6, 323, 79, 17, 6, 324, 76, 1, 2, 16, 6, 320, 81, 17, 7, 320, 81, 20, 4, 320, 75, 27, 1, 323, 75, 26, 2, 319, 78, 27, 1, 317, 75, 32, 2, 317, 75, 32, 1, 317, 75, 33, 1, 317, 75, 345, 79, 37, 1, 23, 2, 283, 75, 42, 4, 18, 5, 282, 75, 43, 5, 14, 8, 281, 74, 45, 6, 10, 11, 275, 78, 47, 8, 5, 14, 272, 75, 53, 27, 271, 75, 54, 27, 269, 75, 57, 26, 264, 78, 59, 25, 262, 74, 66, 26, 259, 75, 68, 25, 258, 75, 69, 26, 254, 1, 1, 74, 71, 26, 249, 78, 74, 26, 247, 75, 79, 26, 246, 75, 80, 27, 244, 74, 82, 27, 237, 79, 84, 26, 236, 75, 90, 26, 234, 75, 92, 26, 233, 75, 93, 26, 227, 79, 95, 27, 224, 74, 102, 27, 222, 75, 103, 26, 222, 75, 104, 26, 217, 78, 106, 26, 214, 77, 110, 26, 212, 75, 115, 25, 210, 75, 117, 24, 210, 75, 118, 24, 204, 78, 120, 26, 200, 75, 126, 26, 199, 75, 127, 26, 198, 75, 128, 26, 197, 73, 131, 27, 195, 68, 137, 27, 195, 67, 138, 26, 195, 66, 140, 26, 195, 64, 142, 26, 194, 58, 149, 26, 194, 57, 151, 24, 195, 56, 152, 24, 195, 54, 153, 26, 194, 51, 156, 26, 194, 46, 161, 26, 194, 45, 162, 26, 194, 43, 164, 27, 193, 41, 166, 27, 194, 33, 173, 26, 195, 32, 174, 26, 196, 30, 175, 26, 197, 27, 177, 26, 197, 20, 185, 25, 199, 17, 186, 24, 201, 15, 187, 24, 203, 11, 188, 26, 205, 5, 191, 26, 401, 26, 401, 26, 401, 27, 400, 27, 400, 26, 401, 26, 401, 26, 401, 26, 402, 24, 403, 24, 402, 26, 401, 26, 401, 26, 401, 26, 401, 27, 400, 27, 400, 26, 401, 26, 401, 26, 401, 26, 402, 25, 402, 24, 403, 24, 402, 26, 401, 26, 401, 26, 401, 26, 401, 27, 400, 27, 400, 26, 401, 26, 401, 26, 401, 26, 402, 5, 18, 1, 403, 5, 421, 7, 420, 5, 422, 3, 424, 2, 425, 2, 424, 3, 424, 4, 423, 4, 423, 5, 423, 5, 422, 7, 420, 7, 420, 8, 418, 9, 418, 9, 417, 10, 14, 2, 401, 10, 10, 7, 400, 11, 6, 10, 400, 13, 1, 13, 400, 27, 400, 27, 400, 27, 400, 26, 402, 25, 401, 26, 401, 25, 401, 26, 401, 26, 401, 26, 400, 27, 400, 27, 400, 27, 400, 27, 400, 27, 401, 26, 401, 26, 401, 25, 401, 26, 401, 25, 401, 27, 400, 28, 399, 28, 399, 28, 399, 28, 399, 28, 399, 28, 399, 28, 399, 28, 400, 26, 400, 27, 399, 28, 399, 28, 399, 28, 398, 29, 398, 29, 398, 29, 398, 29, 398, 29, 398, 28, 400, 27, 400, 26, 401, 26, 400, 28, 399, 29, 397, 31, 396, 31, 396, 31, 396, 31, 396, 31, 396, 31, 396, 31, 396, 31, 396, 31, 396, 32, 395, 33, 393, 34, 393, 34, 393, 34, 392, 35, 392, 35, 392, 35, 392, 35, 392, 35, 392, 35, 393, 34, 393, 34, 393, 34, 392, 35, 392, 35, 391, 36, 391, 36, 391, 36, 391, 36, 391, 36, 391, 36, 391, 36, 391, 36, 391, 36, 391, 36, 391, 36, 390, 37, 390, 37, 389, 38, 389, 38, 389, 38, 389, 38, 389, 38, 389, 38, 389, 38, 390, 37, 390, 36, 390, 37, 390, 25, 1, 10, 390, 26, 2, 8, 391, 26, 4, 4, 393, 26, 401, 26, 401, 27, 400, 27, 400, 26, 401, 27, 400, 27, 400, 27, 400, 27, 400, 27, 400, 27, 400, 27, 401, 26, 401, 25, 402, 25, 403, 25, 402, 25, 402, 26, 400, 27, 400, 27, 400, 27, 400, 27, 400, 27, 400, 27, 401, 26, 401, 26, 401, 25, 403, 25, 402, 25, 402, 26, 401, 26, 401, 26, 400, 27, 400, 27, 400, 27, 401, 26, 401, 26, 401, 26, 401, 26, 402, 25, 402, 25, 402, 26, 401, 26, 401, 26, 401, 27, 400, 27, 400, 27, 400, 27, 400, 26, 401, 26, 401, 26, 402, 25, 402, 25, 403, 25, 401, 26, 401, 26, 401, 27, 400, 27, 400, 27, 400, 27, 400, 27, 400, 26, 401, 26, 402, 25, 402, 25, 403, 25, 402, 25, 401, 27, 400, 27, 400, 27, 400, 27, 400, 27, 400, 31, 396, 33, 394, 34, 394, 34, 393, 35, 393, 34, 393, 35, 392, 36, 390, 38, 389, 38, 389, 39, 388, 39, 388, 39, 388, 40, 387, 40, 388, 39, 388, 39, 389, 38, 389, 38, 389, 38, 389, 38, 389, 38, 388, 39, 388, 38, 389, 37, 390, 27, 3, 5, 393, 26, 401, 26, 401, 26, 402, 25, 402, 25, 402, 26, 401, 26, 401, 26, 401, 26, 401, 27, 400, 27, 400, 27, 400, 27, 400, 28, 399, 28, 399, 28, 399, 28, 399, 29, 398, 29, 398, 29, 398, 28, 399, 28, 399, 28, 399, 28, 400, 27, 400, 27, 400, 27, 399, 28, 399, 29, 398, 29, 398, 29, 398, 29, 398, 29, 398, 29, 398, 29, 398, 29, 399, 28, 399, 28, 399, 28, 399, 29, 397, 30, 397, 29, 398, 29, 398, 29, 398, 29, 398, 29, 398, 28, 399, 28, 400, 28, 399, 28, 399, 28, 399, 28, 399, 29, 397, 30, 397, 30, 397, 29, 398, 29, 398, 29, 398, 29, 398, 29, 399, 27, 400, 27, 400, 28, 399, 28, 399, 28, 399, 28, 398, 30, 397, 30, 397, 29, 398, 18, 9, 2, 398, 13, 15, 1, 398, 13, 15, 1, 399, 12, 15, 1, 399, 12, 9, 2, 4, 1, 399, 15, 1, 9, 2, 1, 400, 27, 399, 29, 398, 29, 398, 28, 398, 29, 398, 29, 398, 29, 398, 29, 398, 28, 400, 27, 400, 27, 400, 28, 400, 27, 399, 28, 399, 28, 399, 28, 399, 28, 398, 29, 398, 18, 1, 10, 398, 15, 7, 6, 399, 15, 12, 1, 400, 15, 412, 19, 7, 1, 400, 27, 401, 26, 401, 27, 399, 28, 399, 28, 399, 28, 399, 29, 398, 29, 398, 29, 398, 29, 398, 29, 398, 29, 398, 29, 399, 27, 400, 14, 1, 11, 401, 13, 2, 10, 401, 13, 3, 10, 401, 10, 6, 10, 401, 9, 7, 11, 399, 10, 7, 12, 397, 10, 8, 13, 395, 16, 3, 14, 394, 34, 393, 34, 392, 35, 392, 35, 392, 35, 392, 19, 1, 2, 6, 7, 392, 15, 5, 2, 7, 6, 392, 15, 5, 2, 7, 5, 393, 16, 4, 2, 7, 4, 394, 19, 1, 11, 397, 30, 398, 29, 399, 28, 399, 28, 400, 27, 400, 26, 401, 26, 400, 27, 400, 27, 400, 27, 400, 28, 399, 28, 399, 28, 399, 28, 399, 28, 399, 28, 399, 28, 400, 27, 400, 26, 401, 26, 400, 27, 400, 27, 400, 28, 399, 28, 399, 28, 399, 28, 399, 28, 399, 28, 399, 28, 399, 28, 400, 27, 400, 26, 400, 27, 400, 27, 400, 28, 399, 28, 399, 28, 399, 28, 399, 29, 398, 29, 398, 28, 399, 28, 399, 28, 399, 28, 399, 27, 400, 27, 400, 28, 399, 28, 399, 28, 399, 29, 399, 28, 399, 28, 399, 28, 398, 29, 398, 28, 399, 28, 399, 28, 399, 27, 400, 28, 399, 28, 399, 29, 398, 29, 398, 29, 399, 28, 398, 29, 398, 29, 398, 29, 398, 28, 399, 28, 399, 28, 399, 28, 399, 15, 2, 11, 399, 12, 8, 9, 398, 11, 15, 3, 399, 10, 15, 3, 399, 16, 9, 3, 399, 20, 5, 3, 398, 29, 398, 29, 398, 29, 398, 29, 398, 28, 399, 28, 399, 28, 399, 29, 398, 29, 398, 29, 399, 28, 399, 28, 399, 28, 398, 29, 398, 29, 398, 10, 9, 10, 398, 7, 12, 10, 398, 7, 12, 9, 399, 10, 9, 9, 399, 12, 4, 13, 398, 15, 3, 11, 398, 11, 8, 2, 3, 5, 398, 11, 13, 5, 398, 12, 8, 10, 397, 16, 6, 8, 396, 21, 3, 6, 397, 18, 3, 2, 1, 7, 396, 17, 5, 9, 396, 17, 6, 7, 397, 16, 7, 7, 397, 16, 4, 2, 1, 7, 397, 16, 2, 1, 1, 10, 397, 29, 398, 29, 398, 30, 397, 30, 396, 31, 396, 31, 395, 33, 394, 33, 394, 32, 395, 32, 395, 32, 395, 32, 395, 32, 394, 32, 395, 32, 395, 14, 2, 4, 7, 6, 394, 11, 17, 5, 394, 12, 16, 5, 394, 14, 13, 6, 394, 15, 13, 6, 393, 19, 9, 6, 393, 33, 394, 33, 394, 33, 394, 33, 394, 32, 395, 32, 395, 21, 2, 10, 394, 20, 5, 8, 394, 20, 5, 8, 394, 21, 2, 10, 394, 21, 2, 11, 393, 34, 393, 33, 394, 33, 394, 33, 394, 33, 394, 33, 394, 32, 395, 32, 395, 33, 394, 33, 394, 33, 394, 33, 394, 34, 393, 34, 393, 33, 394, 33, 394, 33, 394, 33, 394, 32, 395, 32, 395, 33, 394, 33, 394, 33, 394, 33, 394, 34, 393, 34, 394, 32, 396, 31, 398, 29, 401, 26, 63], "size": [427, 640]}, "area": 23866, "iscrowd": 1, "image_id": 17959, "bbox": [0, 132, 639, 239], "category_id": 1, "id": 900100017959}, {"segmentation": {"counts": [112572, 10, 413, 16, 409, 19, 404, 21, 405, 23, 403, 24, 402, 26, 400, 27, 399, 29, 397, 30, 395, 33, 392, 36, 381, 1, 7, 38, 380, 1, 6, 41, 377, 3, 4, 42, 377, 3, 3, 44, 376, 3, 2, 45, 376, 50, 376, 50, 376, 50, 376, 50, 376, 50, 376, 50, 376, 50, 376, 50, 377, 49, 377, 48, 379, 47, 380, 45, 382, 43, 384, 41, 386, 39, 389, 13, 2, 21, 393, 7, 12, 12, 414, 9, 25496, 17, 406, 23, 402, 25, 399, 29, 1, 8, 385, 44, 381, 46, 378, 50, 375, 52, 373, 54, 372, 54, 370, 57, 366, 60, 356, 71, 352, 73, 352, 70, 354, 70, 355, 69, 357, 67, 358, 65, 360, 64, 362, 61, 364, 59, 367, 57, 369, 55, 370, 54, 372, 54, 372, 54, 372, 54, 372, 54, 372, 54, 372, 54, 372, 54, 372, 54, 372, 55, 371, 55, 371, 56, 370, 56, 370, 57, 369, 58, 368, 58, 368, 59, 368, 59, 367, 61, 366, 62, 365, 64, 363, 64, 363, 59, 368, 52, 1, 2, 373, 45, 384, 40, 402, 21, 405, 20, 407, 18, 408, 17, 410, 15, 412, 13, 414, 10, 417, 9, 418, 8, 420, 5, 424, 2, 20127, 16, 407, 21, 403, 23, 402, 24, 400, 26, 399, 27, 375, 51, 372, 54, 371, 55, 368, 58, 366, 60, 360, 66, 357, 69, 356, 70, 354, 72, 353, 73, 352, 74, 351, 74, 351, 75, 350, 76, 350, 76, 349, 77, 349, 77, 348, 78, 348, 78, 348, 78, 348, 78, 348, 78, 348, 78, 348, 78, 348, 78, 348, 78, 348, 78, 348, 78, 348, 77, 349, 77, 349, 78, 348, 80, 346, 82, 344, 84, 343, 85, 341, 87, 340, 88, 339, 89, 338, 90, 337, 94, 333, 100, 327, 106, 322, 112, 317, 116, 315, 121, 307, 125, 303, 125, 304, 123, 315, 113, 314, 114, 313, 115, 312, 115, 312, 116, 311, 117, 310, 118, 309, 118, 309, 117, 310, 116, 311, 115, 312, 114, 313, 113, 314, 112, 316, 110, 318, 107, 320, 105, 323, 101, 326, 99, 328, 96, 332, 92, 335, 90, 338, 86, 342, 82, 346, 77, 352, 62, 368, 46, 392, 25, 404, 21, 406, 19, 409, 16, 413, 12, 418, 7, 423, 2, 37275], "size": [426, 640]}, "area": 13422, "iscrowd": 1, "image_id": 18380, "bbox": [264, 24, 288, 244], "category_id": 1, "id": 900100018380}, {"segmentation": {"counts": [489, 22, 24, 10, 5, 5, 414, 22, 23, 22, 27, 6, 380, 22, 23, 22, 25, 10, 378, 22, 22, 23, 24, 12, 377, 22, 22, 23, 24, 12, 378, 21, 22, 23, 20, 17, 378, 20, 22, 23, 18, 19, 378, 19, 23, 23, 17, 20, 378, 19, 23, 23, 17, 20, 378, 19, 9, 6, 1, 6, 1, 24, 15, 21, 378, 19, 7, 16, 1, 23, 15, 21, 378, 24, 1, 41, 15, 21, 379, 65, 15, 20, 381, 64, 15, 19, 28, 6, 349, 63, 15, 18, 27, 10, 346, 64, 15, 17, 27, 12, 345, 64, 15, 17, 27, 13, 344, 64, 15, 17, 26, 14, 344, 64, 15, 17, 26, 16, 342, 63, 17, 16, 26, 17, 341, 63, 17, 16, 26, 17, 341, 41, 1, 21, 17, 18, 24, 18, 3, 11, 327, 39, 2, 21, 17, 20, 22, 34, 326, 22, 3, 4, 3, 4, 4, 21, 17, 21, 21, 35, 325, 21, 19, 21, 17, 24, 19, 34, 325, 19, 21, 21, 18, 25, 16, 36, 324, 18, 22, 21, 18, 26, 15, 36, 323, 19, 22, 21, 18, 29, 11, 37, 323, 19, 22, 21, 18, 31, 6, 40, 322, 20, 22, 21, 18, 33, 2, 42, 322, 20, 22, 21, 18, 77, 322, 20, 22, 21, 18, 77, 322, 20, 4, 6, 13, 19, 19, 76, 322, 21, 2, 15, 2, 23, 19, 75, 323, 21, 1, 41, 19, 74, 324, 21, 1, 41, 19, 72, 326, 63, 2, 85, 330, 150, 330, 150, 330, 150, 330, 150, 330, 150, 330, 149, 331, 21, 1, 128, 330, 21, 2, 127, 330, 21, 3, 126, 330, 21, 5, 4, 1, 119, 330, 21, 12, 4, 4, 4, 1, 103, 331, 21, 25, 103, 331, 21, 25, 102, 332, 21, 25, 27, 2, 10, 1, 61, 333, 21, 26, 23, 17, 60, 333, 21, 26, 21, 13, 66, 333, 21, 26, 20, 12, 27, 3, 38, 333, 21, 25, 21, 11, 26, 7, 36, 333, 21, 25, 20, 12, 24, 13, 32, 333, 21, 25, 20, 11, 25, 16, 29, 333, 22, 23, 21, 11, 24, 19, 27, 333, 35, 10, 21, 11, 24, 21, 25, 333, 37, 8, 20, 12, 24, 21, 25, 332, 39, 7, 19, 13, 23, 23, 23, 333, 41, 5, 19, 13, 22, 24, 22, 334, 42, 4, 19, 13, 21, 26, 20, 335, 65, 14, 12, 2, 4, 29, 10, 2, 5, 337, 65, 15, 10, 37, 8, 345, 65, 16, 8, 40, 4, 347, 66, 17, 4, 393, 66, 414, 66, 414, 66, 414, 66, 414, 66, 415, 23, 1, 41, 415, 21, 10, 33, 417, 20, 12, 7, 5, 18, 419, 18, 25, 17, 421, 16, 25, 16, 425, 13, 26, 12, 432, 5, 24004, 4, 477, 5, 475, 6, 475, 5, 462, 1, 10, 8, 460, 20, 460, 20, 459, 21, 458, 22, 457, 23, 457, 23, 456, 23, 457, 23, 15, 7, 435, 23, 13, 11, 433, 23, 12, 13, 432, 22, 13, 13, 432, 22, 12, 15, 431, 22, 12, 15, 431, 22, 12, 16, 5, 7, 418, 22, 12, 16, 3, 11, 415, 23, 12, 16, 2, 13, 414, 22, 13, 16, 2, 13, 413, 23, 13, 16, 1, 15, 25, 13, 374, 23, 14, 15, 1, 15, 22, 18, 372, 23, 15, 14, 1, 15, 15, 26, 371, 23, 15, 30, 13, 29, 370, 23, 15, 73, 369, 23, 15, 73, 369, 23, 15, 74, 368, 23, 15, 74, 368, 23, 15, 74, 368, 23, 15, 74, 368, 23, 15, 74, 368, 23, 15, 74, 368, 23, 15, 74, 367, 24, 15, 73, 367, 25, 15, 72, 368, 25, 15, 59, 4, 8, 369, 25, 15, 51, 14, 4, 371, 25, 15, 48, 392, 25, 16, 26, 1, 10, 2, 6, 394, 25, 16, 24, 415, 25, 16, 23, 416, 25, 16, 21, 418, 25, 16, 14, 1, 4, 420, 25, 16, 14, 425, 25, 16, 14, 425, 25, 16, 12, 427, 25, 14, 13, 428, 25, 13, 13, 429, 25, 13, 13, 429, 25, 6, 19, 431, 24, 2, 23, 431, 48, 433, 47, 434, 46, 434, 46, 435, 45, 436, 44, 437, 43, 439, 41, 444, 36, 444, 31, 1, 4, 444, 28, 4, 4, 444, 26, 6, 4, 444, 23, 458, 14, 2, 1, 464, 11, 476, 2, 19214, 12, 463, 17, 461, 19, 458, 22, 448, 6, 3, 23, 447, 33, 446, 34, 441, 39, 440, 40, 439, 41, 438, 42, 437, 43, 436, 43, 436, 33, 102, 1, 343, 32, 104, 2, 341, 30, 107, 2, 341, 29, 108, 2, 341, 27, 110, 3, 340, 25, 112, 3, 340, 25, 112, 3, 340, 25, 112, 4, 339, 24, 113, 5, 338, 24, 113, 5, 338, 24, 113, 5, 338, 24, 113, 5, 338, 24, 113, 6, 338, 23, 113, 7, 337, 23, 113, 7, 337, 23, 113, 8, 336, 23, 113, 8, 336, 23, 113, 8, 336, 23, 113, 9, 335, 23, 113, 9, 335, 23, 113, 10, 334, 23, 113, 10, 334, 23, 113, 10, 334, 23, 113, 11, 333, 23, 113, 12, 332, 23, 113, 12, 332, 22, 114, 12, 332, 22, 114, 13, 331, 22, 114, 13, 331, 22, 114, 13, 331, 22, 114, 10, 334, 22, 115, 3, 340, 22, 458, 22, 458, 22, 458, 22, 458, 23, 457, 23, 127, 3, 327, 23, 119, 12, 326, 23, 119, 12, 326, 23, 118, 13, 326, 23, 118, 14, 325, 23, 118, 14, 326, 21, 120, 14, 326, 19, 122, 13, 326, 13, 128, 13, 326, 11, 130, 14, 326, 9, 131, 15, 326, 6, 134, 14, 328, 3, 136, 13, 468, 13, 467, 13, 467, 14, 466, 14, 467, 14, 466, 14, 467, 13, 467, 13, 467, 14, 467, 14, 466, 14, 467, 13, 468, 13, 467, 13, 467, 14, 466, 14, 467, 14, 467, 13, 467, 13, 467, 14, 466, 15, 466, 14, 467, 13, 468, 12, 468, 13, 467, 14, 466, 14, 467, 14, 466, 14, 467, 13, 467, 13, 467, 14, 467, 14, 466, 14, 467, 13, 467, 13, 468, 13, 467, 14, 466, 14, 467, 14, 467, 13, 467, 13, 467, 14, 466, 15, 466, 14, 467, 13, 467, 13, 468, 13, 467, 14, 466, 14, 467, 14, 466, 14, 467, 13, 467, 13, 467, 14, 467, 14, 466, 14, 467, 13, 467, 13, 468, 13, 467, 14, 466, 14, 467, 14, 467, 13, 467, 13, 467, 14, 466, 15, 466, 14, 467, 13, 467, 13, 467, 14, 467, 14, 466, 14, 467, 14, 466, 14, 467, 13, 467, 13, 467, 14, 467, 14, 466, 14, 467, 13, 467, 13, 468, 13, 467, 14, 466, 14, 467, 14, 467, 13, 467, 13, 467, 14, 466, 15, 466, 14, 467, 13, 467, 13, 467, 14, 467, 14, 466, 14, 467, 14, 467, 13, 467, 13, 467, 13, 467, 14, 467, 14, 466, 14, 467, 13, 467, 13, 467, 14, 467, 14, 466, 14, 467, 14, 467, 13, 467, 13, 467, 14, 466, 15, 466, 14, 467, 13, 467, 13, 467, 14, 467, 14, 466, 14, 467, 14, 467, 13, 467, 13, 467, 13, 467, 14, 467, 14, 466, 14, 467, 13, 467, 13, 467, 14, 467, 14, 466, 14, 467, 14, 467, 13, 467, 13, 467, 14, 466, 15, 466, 14, 467, 13, 467, 13, 467, 14, 467, 14, 466, 14, 467, 14, 467, 13, 6, 6, 455, 13, 5, 8, 454, 13, 4, 10, 453, 14, 2, 12, 453, 27, 453, 27, 454, 26, 454, 26, 454, 26, 455, 25, 455, 24, 457, 22, 459, 20, 460, 20, 459, 19, 460, 19, 460, 20, 459, 20, 458, 22, 457, 23, 457, 24, 455, 25, 453, 28, 355, 47, 49, 29, 325, 25, 3, 50, 47, 30, 324, 27, 2, 51, 46, 30, 323, 82, 43, 33, 321, 83, 42, 35, 304, 99, 41, 36, 303, 100, 41, 22, 1, 13, 302, 101, 39, 24, 1, 13, 301, 102, 37, 25, 2, 14, 299, 103, 37, 25, 3, 14, 298, 65, 3, 34, 37, 25, 4, 14, 297, 61, 10, 31, 37, 24, 7, 14, 294, 61, 19, 22, 37, 26, 8, 13, 293, 59, 24, 20, 36, 26, 9, 13, 294, 31, 11, 15, 26, 19, 36, 26, 9, 14, 292, 31, 12, 13, 28, 19, 35, 26, 10, 15, 291, 29, 14, 12, 28, 20, 33, 26, 13, 14, 290, 22, 1, 4, 17, 12, 28, 20, 32, 27, 14, 13, 290, 21, 23, 12, 28, 20, 31, 27, 15, 13, 289, 22, 23, 12, 28, 20, 31, 27, 15, 14, 288, 22, 23, 12, 27, 21, 29, 28, 17, 14, 287, 22, 23, 12, 27, 21, 28, 27, 19, 14, 287, 22, 23, 12, 27, 21, 27, 28, 20, 13, 287, 22, 23, 12, 27, 21, 26, 29, 21, 13, 286, 22, 23, 12, 27, 21, 25, 29, 22, 13, 286, 22, 23, 12, 27, 21, 23, 30, 23, 13, 286, 22, 23, 12, 27, 21, 22, 29, 25, 14, 285, 22, 23, 12, 26, 22, 22, 29, 26, 14, 284, 22, 23, 12, 26, 22, 21, 30, 26, 14, 284, 22, 23, 12, 26, 22, 19, 31, 28, 13, 284, 22, 23, 12, 27, 21, 18, 31, 29, 13, 284, 22, 23, 12, 26, 22, 17, 31, 30, 14, 284, 21, 23, 12, 26, 23, 16, 30, 32, 14, 283, 21, 23, 12, 26, 23, 14, 32, 32, 14, 283, 21, 23, 12, 26, 23, 13, 32, 34, 14, 282, 21, 23, 12, 26, 23, 12, 32, 36, 13, 282, 21, 23, 18, 20, 23, 11, 32, 37, 13, 283, 20, 23, 19, 19, 23, 9, 33, 38, 14, 282, 20, 23, 20, 18, 24, 7, 34, 38, 15, 281, 20, 23, 21, 17, 25, 5, 34, 40, 14, 281, 20, 23, 21, 17, 25, 5, 33, 42, 13, 280, 22, 22, 22, 16, 12, 1, 12, 3, 34, 43, 13, 280, 22, 22, 22, 16, 25, 2, 34, 44, 14, 279, 22, 22, 23, 15, 25, 1, 35, 45, 14, 278, 22, 22, 23, 15, 60, 46, 14, 278, 22, 22, 23, 15, 42, 1, 16, 48, 13, 278, 22, 22, 23, 15, 13, 1, 27, 1, 16, 50, 13, 277, 22, 22, 24, 14, 13, 1, 27, 1, 16, 50, 13, 277, 22, 22, 25, 14, 12, 1, 25, 2, 16, 51, 13, 277, 21, 23, 25, 14, 37, 2, 16, 52, 14, 276, 20, 24, 25, 15, 12, 1, 22, 2, 16, 54, 14, 275, 20, 24, 25, 15, 34, 2, 16, 55, 14, 275, 20, 24, 26, 12, 35, 3, 16, 56, 13, 275, 20, 24, 30, 6, 35, 4, 16, 57, 13, 275, 20, 16, 79, 3, 16, 58, 14, 274, 20, 9, 85, 3, 16, 60, 14, 273, 113, 3, 16, 61, 14, 273, 101, 9, 1, 5, 16, 62, 14, 272, 91, 25, 15, 64, 13, 272, 81, 33, 16, 65, 13, 272, 71, 42, 16, 66, 14, 272, 61, 50, 16, 67, 15, 271, 51, 60, 16, 68, 14, 272, 40, 70, 15, 70, 13, 272, 31, 77, 16, 71, 13, 273, 20, 78, 2, 6, 16, 72, 14, 273, 9, 83, 5, 7, 16, 73, 15, 364, 5, 7, 16, 74, 14, 364, 4, 8, 15, 76, 13, 364, 3, 7, 16, 78, 13, 363, 1, 8, 16, 79, 13, 372, 15, 80, 14, 370, 16, 80, 14, 370, 15, 82, 14, 367, 16, 83, 14, 366, 16, 85, 13, 366, 16, 85, 13, 365, 16, 86, 14, 364, 15, 88, 14, 361, 16, 89, 14, 360, 16, 91, 14, 359, 16, 92, 13, 358, 16, 93, 13, 358, 15, 94, 14, 357, 14, 95, 15, 356, 13, 97, 14, 356, 13, 98, 13, 356, 12, 99, 13, 356, 12, 99, 14, 355, 10, 101, 15, 354, 9, 103, 14, 354, 9, 104, 13, 354, 8, 106, 13, 353, 8, 106, 13, 353, 6, 108, 14, 352, 5, 109, 14, 352, 5, 110, 14, 351, 4, 111, 14, 351, 4, 112, 13, 351, 2, 114, 13, 351, 1, 115, 14, 350, 1, 116, 14, 349, 1, 116, 14, 467, 13, 468, 13, 467, 13, 467, 14, 466, 14, 467, 14, 467, 13, 467, 13, 467, 14, 466, 15, 466, 14, 467, 13, 468, 13, 467, 13, 467, 14, 466, 14, 356, 3, 108, 14, 346, 12, 108, 14, 338, 20, 109, 13, 329, 29, 109, 13, 321, 37, 108, 15, 311, 46, 106, 18, 302, 54, 102, 22, 293, 62, 101, 24, 248, 1, 36, 70, 97, 28, 248, 3, 32, 71, 96, 30, 224, 17, 7, 5, 30, 70, 92, 35, 202, 8, 6, 39, 5, 2, 21, 68, 93, 36, 201, 62, 20, 66, 90, 40, 201, 64, 19, 37, 2, 25, 89, 1, 1, 40, 201, 65, 19, 32, 8, 21, 89, 43, 203, 66, 18, 30, 12, 17, 87, 42, 208, 66, 18, 30, 17, 12, 85, 42, 210, 66, 18, 15, 1, 14, 17, 12, 80, 43, 213, 67, 18, 15, 2, 13, 17, 12, 79, 42, 215, 29, 5, 33, 18, 15, 2, 13, 17, 12, 74, 42, 220, 29, 6, 32, 18, 15, 2, 13, 18, 12, 71, 43, 221, 29, 11, 27, 18, 15, 2, 12, 18, 13, 67, 42, 226, 28, 12, 26, 19, 15, 2, 12, 18, 13, 65, 42, 228, 27, 13, 26, 19, 15, 1, 13, 18, 13, 60, 43, 232, 25, 15, 26, 19, 15, 1, 13, 18, 13, 59, 42, 233, 24, 17, 26, 19, 15, 1, 13, 19, 12, 54, 43, 237, 24, 17, 26, 19, 16, 1, 12, 19, 12, 50, 42, 242, 24, 17, 26, 19, 29, 19, 12, 48, 43, 243, 24, 17, 26, 19, 28, 20, 12, 44, 42, 248, 24, 17, 26, 19, 29, 19, 12, 42, 42, 250, 24, 17, 26, 19, 29, 19, 12, 37, 43, 254, 24, 17, 26, 19, 29, 19, 12, 36, 42, 256, 24, 17, 26, 19, 28, 20, 12, 31, 43, 260, 24, 17, 26, 19, 28, 20, 12, 30, 42, 262, 24, 17, 26, 19, 27, 21, 12, 25, 42, 266, 25, 17, 26, 19, 28, 20, 12, 23, 42, 268, 25, 17, 26, 19, 28, 20, 12, 19, 42, 272, 25, 17, 26, 19, 28, 19, 13, 15, 41, 1, 1, 275, 25, 17, 26, 19, 28, 18, 14, 12, 43, 278, 25, 17, 26, 19, 27, 19, 13, 9, 42, 283, 25, 17, 26, 19, 27, 19, 13, 7, 43, 284, 26, 16, 26, 19, 27, 19, 13, 3, 42, 289, 27, 15, 26, 19, 27, 19, 13, 1, 42, 291, 28, 14, 26, 19, 32, 14, 52, 295, 28, 14, 26, 19, 33, 13, 50, 297, 28, 14, 26, 19, 34, 12, 46, 301, 28, 14, 26, 19, 35, 11, 44, 303, 28, 12, 28, 19, 35, 11, 39, 309, 27, 11, 29, 19, 35, 11, 37, 312, 26, 10, 30, 19, 35, 11, 33, 316, 25, 10, 31, 19, 35, 11, 28, 321, 24, 11, 31, 19, 35, 11, 27, 323, 21, 13, 31, 19, 35, 11, 22, 329, 17, 16, 31, 19, 34, 12, 21, 331, 16, 16, 31, 19, 33, 13, 16, 317], "size": [480, 640]}, "area": 33435, "iscrowd": 1, "image_id": 19432, "bbox": [1, 9, 638, 288], "category_id": 62, "id": 906200019432}, {"segmentation": {"counts": [212508, 4, 385, 8, 29, 6, 382, 12, 27, 6, 380, 15, 25, 7, 379, 17, 25, 6, 379, 18, 24, 6, 378, 20, 26, 1, 341, 5, 33, 33, 355, 6, 33, 34, 353, 7, 33, 32, 355, 7, 33, 32, 18, 3, 334, 1, 1, 6, 31, 31, 21, 3, 335, 6, 31, 28, 25, 3, 335, 6, 30, 28, 26, 3, 3, 3, 328, 7, 21, 36, 27, 3, 3, 3, 327, 7, 18, 39, 28, 3, 3, 2, 301, 12, 14, 8, 8, 48, 28, 4, 3, 1, 298, 18, 12, 7, 8, 48, 29, 4, 301, 20, 10, 7, 8, 48, 30, 3, 3, 1, 297, 22, 8, 7, 1, 1, 6, 49, 29, 3, 3, 2, 291, 1, 4, 24, 7, 8, 6, 50, 28, 3, 3, 2, 291, 1, 4, 26, 5, 8, 6, 50, 28, 3, 3, 3, 291, 1, 3, 27, 4, 8, 6, 18, 1, 32, 27, 3, 3, 4, 285, 1, 4, 1, 3, 27, 5, 7, 6, 18, 3, 2, 3, 26, 26, 3, 3, 4, 284, 2, 4, 1, 4, 26, 5, 7, 6, 19, 3, 1, 4, 26, 25, 3, 3, 5, 238, 8, 36, 3, 4, 1, 4, 26, 5, 8, 5, 20, 7, 27, 24, 3, 3, 5, 223, 8, 5, 12, 33, 4, 4, 1, 4, 26, 5, 8, 5, 20, 7, 28, 23, 3, 3, 5, 221, 12, 1, 15, 32, 5, 3, 1, 4, 26, 5, 8, 5, 20, 7, 28, 23, 3, 2, 6, 219, 31, 30, 6, 3, 1, 4, 27, 4, 8, 5, 20, 7, 28, 23, 3, 2, 6, 218, 33, 28, 7, 3, 1, 4, 27, 4, 8, 5, 20, 7, 28, 23, 3, 2, 7, 217, 34, 27, 7, 3, 1, 4, 27, 4, 8, 5, 20, 7, 28, 23, 3, 2, 7, 216, 36, 25, 8, 3, 1, 4, 27, 4, 8, 5, 20, 7, 28, 23, 3, 2, 7, 215, 38, 24, 8, 3, 1, 4, 27, 4, 8, 4, 21, 7, 28, 23, 3, 2, 7, 215, 38, 24, 8, 3, 1, 4, 27, 4, 8, 4, 21, 7, 28, 23, 3, 2, 7, 215, 39, 22, 9, 3, 1, 4, 27, 4, 8, 4, 21, 7, 28, 23, 3, 2, 7, 215, 39, 22, 9, 3, 1, 4, 27, 4, 8, 4, 21, 7, 28, 23, 3, 2, 7, 214, 40, 22, 9, 3, 1, 4, 27, 4, 8, 4, 21, 6, 29, 23, 3, 2, 7, 214, 40, 22, 9, 3, 1, 4, 27, 5, 7, 4, 21, 6, 29, 23, 3, 2, 7, 214, 40, 22, 9, 2, 2, 4, 27, 5, 7, 4, 21, 6, 29, 23, 3, 2, 7, 215, 39, 22, 9, 2, 2, 4, 27, 5, 7, 4, 21, 6, 29, 6, 1, 16, 3, 2, 7, 215, 39, 22, 9, 2, 2, 4, 27, 5, 7, 4, 21, 6, 29, 6, 1, 10, 1, 5, 3, 2, 7, 215, 39, 22, 9, 2, 2, 4, 27, 5, 7, 4, 21, 6, 29, 6, 1, 10, 1, 5, 3, 2, 7, 215, 39, 22, 9, 2, 2, 4, 27, 5, 7, 4, 21, 6, 29, 6, 1, 10, 1, 5, 3, 2, 7, 214, 40, 22, 9, 2, 2, 4, 27, 5, 7, 4, 21, 4, 1, 1, 29, 6, 8, 3, 1, 5, 3, 2, 6, 215, 40, 22, 9, 2, 2, 4, 27, 5, 7, 4, 21, 4, 31, 7, 7, 3, 1, 5, 3, 2, 6, 216, 39, 22, 9, 2, 1, 5, 27, 5, 7, 4, 21, 4, 31, 7, 7, 3, 1, 5, 3, 1, 6, 216, 40, 22, 9, 2, 1, 5, 27, 5, 7, 4, 21, 4, 31, 7, 11, 5, 3, 1, 6, 216, 40, 22, 9, 2, 1, 5, 27, 5, 7, 4, 56, 4, 14, 5, 3, 1, 5, 217, 40, 22, 9, 2, 1, 5, 27, 5, 7, 4, 88, 217, 40, 22, 10, 1, 1, 5, 27, 5, 7, 4, 87, 218, 40, 23, 9, 1, 1, 4, 28, 5, 8, 3, 87, 218, 40, 23, 9, 1, 1, 4, 28, 5, 8, 3, 86, 219, 40, 24, 8, 1, 1, 4, 28, 6, 7, 3, 85, 220, 39, 25, 10, 4, 31, 3, 7, 3, 84, 222, 38, 26, 9, 4, 41, 3, 83, 223, 37, 27, 9, 4, 126, 224, 36, 29, 8, 4, 124, 226, 35, 30, 135, 228, 33, 32, 132, 230, 32, 34, 100, 4, 24, 234, 30, 36, 97, 10, 16, 239, 28, 38, 94, 268, 25, 41, 90, 272, 22, 45, 84, 279, 17, 48, 71, 297, 8, 53, 61, 368, 55, 373, 48, 381, 45, 385, 40, 391, 33, 402, 8, 5, 8, 30527], "size": [427, 640]}, "area": 9151, "iscrowd": 1, "image_id": 14038, "bbox": [497, 100, 71, 212], "category_id": 84, "id": 908400014038}, {"segmentation": {"counts": [92732, 6, 421, 8, 419, 10, 417, 12, 416, 12, 415, 13, 414, 14, 413, 16, 411, 18, 409, 20, 407, 21, 407, 21, 407, 21, 406, 22, 406, 22, 406, 22, 406, 23, 405, 24, 404, 25, 403, 26, 401, 28, 399, 31, 396, 33, 395, 34, 394, 35, 393, 36, 392, 38, 390, 39, 389, 40, 389, 4, 1, 35, 388, 4, 1, 36, 387, 4, 1, 37, 386, 4, 1, 38, 385, 4, 1, 39, 384, 4, 2, 39, 384, 3, 2, 39, 385, 2, 2, 39, 385, 1, 3, 38, 386, 1, 3, 42, 382, 1, 4, 42, 381, 1, 4, 17, 3, 23, 385, 16, 5, 23, 384, 9, 14, 21, 384, 8, 17, 19, 385, 5, 20, 18, 414, 14, 414, 14, 414, 14, 416, 11, 419, 8, 421, 5, 30320, 1, 426, 2, 425, 5, 423, 6, 422, 8, 420, 9, 419, 11, 417, 12, 416, 14, 414, 15, 413, 17, 411, 18, 410, 20, 409, 20, 409, 21, 407, 22, 407, 23, 406, 23, 406, 24, 405, 25, 404, 25, 404, 25, 404, 24, 404, 24, 405, 23, 29, 2, 375, 10, 33, 10, 377, 5, 30, 17, 411, 17, 411, 18, 33, 1, 376, 19, 31, 3, 375, 21, 28, 5, 376, 20, 27, 5, 377, 20, 25, 6, 378, 20, 23, 7, 381, 18, 22, 7, 384, 18, 18, 8, 387, 41, 390, 38, 392, 36, 393, 35, 394, 34, 395, 33, 397, 31, 400, 27, 403, 24, 406, 20, 409, 19, 411, 16, 414, 13, 418, 6, 13165, 6, 421, 8, 419, 10, 417, 12, 416, 12, 416, 13, 415, 15, 413, 15, 413, 16, 412, 18, 410, 19, 409, 20, 409, 20, 409, 20, 410, 20, 410, 14, 3, 1, 410, 13, 414, 14, 413, 18, 410, 21, 407, 23, 405, 24, 404, 25, 403, 25, 403, 26, 403, 25, 404, 25, 405, 23, 413, 16, 412, 16, 412, 15, 413, 3, 3, 8, 414, 3, 5, 6, 414, 3, 7, 3, 415, 4, 424, 5, 423, 8, 420, 11, 416, 15, 413, 16, 412, 15, 413, 14, 414, 13, 415, 11, 417, 9, 420, 8, 421, 8, 422, 6, 851, 5, 422, 7, 420, 8, 419, 9, 419, 9, 419, 10, 418, 10, 418, 10, 418, 10, 418, 10, 418, 10, 417, 11, 417, 11, 417, 11, 417, 11, 417, 10, 418, 9, 419, 7, 421, 6, 422, 4, 424, 4, 424, 4, 425, 3, 426, 2, 428, 1, 6415, 12, 415, 15, 412, 18, 409, 22, 406, 24, 404, 26, 402, 28, 400, 28, 400, 29, 399, 41, 9, 3, 376, 53, 376, 53, 377, 53, 383, 46, 382, 47, 381, 48, 380, 49, 379, 51, 378, 51, 378, 51, 18, 1, 360, 50, 17, 2, 365, 46, 14, 4, 334, 13, 17, 52, 4, 8, 331, 17, 16, 64, 330, 21, 13, 64, 329, 23, 12, 64, 328, 26, 10, 64, 328, 27, 9, 64, 328, 28, 8, 63, 329, 33, 1, 64, 330, 98, 330, 98, 330, 97, 331, 96, 332, 95, 333, 94, 334, 94, 334, 94, 335, 93, 336, 92, 338, 90, 339, 89, 340, 88, 342, 86, 345, 83, 348, 80, 349, 79, 350, 78, 350, 78, 351, 78, 350, 78, 351, 77, 352, 76, 352, 77, 352, 76, 353, 14, 2, 59, 355, 11, 5, 57, 359, 6, 7, 56, 374, 54, 376, 53, 376, 52, 378, 50, 379, 49, 381, 47, 382, 46, 384, 44, 386, 42, 388, 40, 390, 38, 393, 34, 402, 25, 403, 25, 403, 25, 405, 22, 407, 20, 409, 17, 412, 12, 418, 6, 24507], "size": [428, 640]}, "area": 9083, "iscrowd": 1, "image_id": 19109, "bbox": [216, 234, 366, 116], "category_id": 4, "id": 900400019109}, {"segmentation": {"counts": [428, 33, 395, 33, 395, 33, 395, 34, 394, 34, 394, 35, 393, 35, 393, 36, 392, 36, 392, 36, 392, 36, 392, 36, 392, 36, 392, 36, 392, 36, 392, 36, 392, 36, 392, 36, 392, 37, 391, 37, 391, 38, 390, 38, 390, 38, 390, 38, 390, 39, 389, 39, 389, 39, 389, 40, 388, 40, 388, 40, 388, 40, 388, 40, 388, 40, 388, 40, 388, 40, 388, 40, 388, 40, 388, 40, 388, 40, 388, 40, 388, 40, 388, 40, 388, 40, 388, 40, 388, 40, 388, 40, 388, 40, 388, 40, 388, 40, 388, 40, 388, 40, 388, 40, 388, 40, 388, 40, 388, 40, 388, 40, 388, 40, 388, 40, 388, 40, 388, 40, 388, 40, 388, 40, 388, 40, 388, 40, 388, 40, 388, 40, 388, 40, 388, 40, 388, 40, 388, 40, 388, 40, 388, 40, 388, 40, 388, 40, 388, 40, 388, 40, 388, 40, 388, 40, 388, 40, 388, 40, 388, 40, 388, 40, 388, 40, 388, 40, 388, 40, 388, 40, 388, 40, 388, 40, 388, 40, 388, 40, 388, 40, 207, 21, 160, 39, 203, 31, 155, 38, 201, 36, 153, 38, 199, 42, 149, 37, 190, 54, 148, 35, 189, 58, 153, 27, 189, 60, 154, 23, 190, 62, 154, 21, 190, 64, 155, 18, 191, 65, 157, 13, 192, 67, 160, 6, 195, 68, 360, 68, 360, 69, 359, 69, 359, 69, 359, 69, 359, 69, 359, 69, 359, 69, 359, 69, 359, 69, 359, 69, 359, 69, 359, 69, 359, 69, 359, 68, 360, 21, 2, 45, 146, 8, 207, 19, 10, 37, 144, 14, 204, 18, 16, 31, 144, 16, 204, 16, 21, 25, 145, 18, 204, 14, 28, 17, 146, 22, 203, 10, 192, 29, 199, 6, 194, 32, 396, 34, 394, 35, 393, 36, 392, 37, 391, 38, 390, 39, 389, 39, 389, 40, 388, 40, 388, 40, 388, 40, 388, 40, 388, 40, 388, 40, 388, 41, 387, 41, 387, 42, 386, 42, 386, 42, 386, 42, 386, 43, 385, 43, 385, 44, 384, 44, 384, 45, 383, 45, 383, 45, 383, 45, 383, 46, 382, 46, 382, 46, 382, 46, 382, 46, 382, 46, 382, 46, 382, 46, 382, 46, 382, 46, 382, 46, 382, 46, 382, 46, 382, 46, 382, 46, 382, 46, 382, 46, 382, 46, 382, 46, 382, 46, 382, 45, 383, 45, 76, 9, 298, 45, 46, 8, 16, 18, 295, 45, 43, 46, 294, 45, 42, 48, 293, 45, 41, 50, 292, 44, 41, 52, 291, 44, 33, 61, 290, 43, 32, 63, 290, 43, 31, 65, 289, 43, 30, 66, 289, 43, 29, 67, 289, 43, 29, 67, 289, 44, 27, 68, 289, 44, 27, 68, 289, 44, 27, 68, 289, 44, 27, 68, 289, 45, 26, 68, 289, 45, 26, 68, 289, 46, 25, 68, 289, 46, 25, 67, 290, 46, 25, 66, 291, 46, 26, 65, 291, 46, 26, 64, 292, 47, 26, 62, 293, 47, 27, 60, 294, 47, 28, 57, 296, 47, 28, 54, 299, 47, 29, 46, 306, 48, 29, 16, 3, 13, 1, 6, 312, 48, 31, 12, 337, 48, 33, 8, 339, 48, 380, 48, 380, 48, 380, 48, 380, 48, 380, 48, 380, 48, 380, 48, 380, 48, 380, 48, 380, 48, 380, 48, 380, 48, 381, 47, 381, 47, 381, 47, 381, 47, 381, 47, 381, 47, 381, 47, 381, 47, 381, 47, 381, 47, 380, 48, 380, 48, 380, 48, 380, 48, 380, 48, 380, 47, 381, 47, 381, 47, 381, 47, 381, 47, 381, 47, 381, 47, 381, 47, 381, 47, 381, 47, 381, 47, 381, 47, 381, 47, 381, 47, 381, 48, 380, 48, 380, 48, 380, 48, 380, 48, 380, 48, 380, 48, 380, 49, 379, 49, 379, 49, 380, 48, 382, 47, 383, 45, 384, 45, 385, 43, 387, 41, 384, 44, 381, 47, 378, 50, 377, 51, 377, 51, 377, 51, 377, 51, 377, 51, 377, 51, 377, 51, 377, 51, 377, 51, 377, 51, 377, 51, 377, 51, 377, 51, 377, 51, 377, 51, 377, 51, 377, 51, 377, 51, 377, 51, 377, 51, 377, 51, 377, 51, 377, 51, 377, 51, 377, 51, 377, 51, 377, 51, 377, 51, 377, 51, 377, 51, 377, 52, 376, 52, 376, 52, 376, 52, 376, 53, 375, 53, 375, 53, 375, 53, 375, 54, 374, 54, 374, 54, 374, 54, 374, 54, 374, 54, 374, 54, 374, 54, 374, 54, 374, 54, 374, 54, 374, 54, 374, 54, 374, 54, 374, 54, 374, 54, 374, 54, 374, 54, 375, 53, 375, 53, 375, 53, 375, 53, 375, 53, 375, 53, 375, 53, 375, 53, 375, 53, 375, 53, 375, 53, 375, 53, 376, 52, 375, 53, 375, 53, 375, 53, 375, 53, 375, 53, 375, 53, 375, 53, 375, 53, 375, 53, 375, 53, 375, 53, 375, 53, 376, 52, 375, 52, 376, 52, 376, 52, 376, 52, 376, 52, 376, 52, 376, 52, 376, 52, 376, 52, 376, 52, 376, 52, 377, 51, 376, 51, 377, 51, 377, 51, 377, 51, 377, 51, 377, 50, 378, 50, 378, 50, 378, 50, 378, 50, 378, 50, 379, 49, 379, 49, 378, 50, 378, 49, 379, 49, 379, 49, 379, 49, 379, 49, 379, 49, 379, 49, 379, 49, 379, 49, 379, 49, 379, 48, 379, 48, 380, 48, 380, 48, 380, 48, 380, 48, 380, 48, 380, 48, 380, 47, 381, 46, 382, 17, 6, 23, 382, 14, 10, 21, 383, 12, 13, 19, 44, 13, 329, 7, 17, 17, 43, 17, 353, 13, 44, 19, 355, 7, 46, 21, 405, 24, 399, 29, 396, 32, 395, 33, 394, 34, 393, 35, 392, 36, 391, 37, 391, 37, 390, 37, 391, 37, 391, 36, 392, 36, 392, 36, 392, 36, 347, 11, 34, 36, 343, 18, 31, 36, 340, 22, 30, 36, 339, 24, 29, 35, 335, 30, 28, 36, 331, 34, 28, 35, 330, 36, 28, 34, 329, 37, 28, 34, 328, 39, 28, 33, 327, 40, 29, 32, 326, 41, 30, 31, 324, 43, 31, 30, 324, 43, 31, 29, 325, 43, 31, 29, 325, 43, 32, 27, 326, 43, 32, 26, 327, 43, 33, 23, 329, 43, 34, 22, 329, 43, 35, 20, 330, 43, 35, 19, 331, 43, 36, 17, 332, 43, 37, 14, 334, 43, 40, 10, 335, 43, 43, 4, 338, 43, 385, 43, 385, 43, 385, 43, 385, 43, 385, 43, 385, 43, 385, 43, 385, 43, 385, 43, 98, 12, 275, 43, 95, 17, 273, 43, 94, 19, 272, 43, 81, 9, 3, 21, 271, 43, 78, 37, 270, 43, 77, 39, 269, 43, 76, 41, 268, 43, 75, 42, 268, 43, 74, 44, 267, 44, 72, 45, 267, 44, 72, 45, 267, 44, 54, 8, 9, 46, 267, 44, 54, 11, 6, 46, 267, 44, 54, 16, 1, 46, 267, 44, 54, 63, 267, 44, 55, 62, 267, 44, 55, 62, 267, 44, 55, 62, 267, 44, 41, 2, 10, 64, 267, 44, 40, 4, 4, 68, 268, 44, 39, 76, 269, 44, 39, 76, 269, 44, 39, 75, 270, 44, 39, 74, 271, 44, 39, 73, 272, 45, 38, 71, 274, 45, 38, 35, 1, 35, 274, 45, 38, 34, 3, 33, 275, 45, 38, 33, 6, 30, 276, 45, 38, 32, 9, 27, 277, 45, 38, 31, 12, 23, 279, 45, 38, 30, 16, 17, 282, 45, 38, 30, 19, 10, 286, 45, 39, 28, 316, 46, 39, 26, 317, 46, 39, 25, 318, 47, 39, 20, 322, 47, 40, 18, 323, 48, 40, 16, 324, 48, 42, 13, 325, 48, 45, 7, 328, 48, 380, 48, 380, 48, 380, 48, 380, 48, 380, 48, 380, 47, 381, 47, 381, 47, 381, 48, 380, 49, 379, 49, 379, 50, 378, 50, 378, 50, 378, 50, 378, 50, 378, 50, 378, 50, 378, 50, 378, 50, 378, 50, 378, 50, 378, 50, 378, 50, 378, 49, 379, 48, 380, 48, 380, 47, 381, 46, 382, 46, 382, 46, 382, 46, 382, 46, 382, 46, 382, 46, 382, 46, 382, 46, 382, 46, 382, 46, 382, 46, 382, 46, 382, 46, 382, 45, 383, 44, 384, 43, 385, 43, 385, 43, 385, 43, 385, 43, 385, 43, 385, 43, 385, 43, 385, 43, 385, 43, 385, 43, 385, 43, 385, 43, 385, 43, 385, 43, 385, 43, 385, 43, 385, 43, 385, 43, 385, 43, 385, 43, 385, 43, 385, 43, 385, 43, 385, 43, 385, 43, 385, 43, 385, 43, 385, 44, 90, 6, 288, 44, 88, 10, 286, 44, 86, 14, 284, 44, 85, 16, 283, 44, 84, 18, 282, 44, 82, 21, 281, 44, 81, 23, 280, 43, 81, 25, 279, 43, 80, 26, 279, 43, 79, 28, 278, 43, 78, 29, 278, 43, 78, 29, 278, 43, 77, 30, 278, 43, 77, 30, 278, 43, 76, 31, 278, 43, 75, 32, 278, 43, 77, 30, 278, 43, 77, 30, 278, 43, 77, 31, 277, 43, 75, 34, 276, 43, 73, 36, 276, 43, 71, 39, 275, 43, 71, 39, 275, 43, 71, 39, 275, 43, 71, 39, 275, 43, 71, 39, 275, 43, 71, 39, 275, 43, 71, 39, 275, 43, 71, 39, 275, 43, 71, 39, 275, 43, 71, 39, 275, 43, 71, 39, 275, 43, 71, 38, 276, 43, 71, 37, 277, 43, 71, 37, 277, 44, 70, 36, 278, 44, 71, 35, 278, 44, 71, 35, 278, 44, 72, 34, 278, 44, 73, 33, 278, 44, 76, 29, 279, 44, 79, 27, 278, 44, 80, 26, 278, 44, 80, 27, 277, 44, 80, 28, 276, 44, 78, 30, 276, 44, 71, 38, 275, 44, 66, 43, 275, 44, 64, 46, 274, 44, 63, 47, 274, 44, 62, 49, 273, 44, 61, 51, 272, 44, 60, 52, 272, 44, 60, 53, 271, 44, 59, 54, 271, 44, 59, 54, 271, 44, 59, 54, 271, 44, 59, 54, 271, 44, 59, 54, 271, 43, 60, 54, 271, 45, 58, 54, 271, 46, 57, 54, 271, 46, 58, 53, 271, 47, 57, 53, 271, 47, 58, 51, 272, 47, 58, 50, 273, 47, 58, 50, 273, 47, 59, 48, 274, 47, 60, 46, 275, 47, 60, 45, 276, 46, 62, 7, 1, 34, 278, 45, 64, 5, 2, 32, 280, 44, 66, 3, 3, 32, 280, 42, 75, 31, 280, 39, 78, 30, 282, 38, 81, 27, 290, 29, 87, 21, 282], "size": [428, 640]}, "area": 36639, "iscrowd": 1, "image_id": 12120, "bbox": [1, 0, 638, 289], "category_id": 1, "id": 900100012120}, {"segmentation": {"counts": [488, 34, 4, 1, 4, 4, 2, 3, 4, 3, 1, 7, 4, 10, 1, 5, 3, 6, 2, 10, 3, 7, 2, 2, 2, 4, 4, 12, 8, 2, 28, 22, 222, 163, 3, 4, 3, 35, 218, 212, 215, 214, 213, 216, 211, 218, 208, 221, 207, 222, 205, 224, 203, 224, 203, 224, 203, 225, 203, 224, 203, 225, 203, 224, 203, 225, 202, 226, 202, 225, 203, 225, 202, 226, 202, 225, 203, 225, 202, 226, 202, 225, 203, 225, 203, 225, 203, 225, 203, 225, 203, 226, 202, 226, 202, 226, 202, 226, 202, 226, 202, 226, 202, 227, 201, 227, 201, 227, 201, 227, 202, 226, 202, 226, 202, 226, 202, 227, 202, 226, 202, 226, 203, 225, 203, 226, 203, 226, 202, 227, 202, 227, 200, 228, 198, 231, 197, 232, 195, 234, 192, 236, 191, 238, 190, 239, 188, 239, 189, 239, 188, 239, 188, 240, 188, 239, 189, 236, 191, 234, 194, 231, 197, 227, 200, 225, 203, 223, 205, 221, 207, 219, 209, 218, 210, 216, 212, 214, 214, 213, 215, 212, 216, 210, 218, 209, 219, 208, 220, 207, 221, 206, 222, 205, 223, 205, 224, 203, 225, 203, 225, 202, 226, 197, 232, 195, 233, 193, 236, 192, 236, 192, 237, 191, 237, 191, 238, 190, 239, 189, 239, 189, 240, 188, 241, 187, 242, 186, 243, 185, 244, 184, 245, 183, 246, 182, 247, 181, 249, 178, 252, 176, 253, 136, 14, 4, 18, 2, 255, 133, 296, 130, 300, 127, 301, 126, 302, 125, 303, 125, 303, 124, 304, 123, 305, 122, 306, 122, 306, 121, 307, 120, 308, 119, 309, 119, 309, 118, 310, 117, 311, 116, 312, 104, 324, 102, 326, 100, 328, 98, 330, 96, 332, 94, 334, 73, 12, 8, 335, 72, 356, 71, 357, 71, 357, 70, 358, 69, 359, 68, 360, 67, 361, 67, 361, 66, 362, 65, 361, 66, 360, 68, 359, 68, 359, 68, 359, 68, 358, 70, 358, 71, 356, 72, 355, 73, 354, 75, 352, 76, 352, 77, 350, 78, 350, 78, 349, 80, 347, 81, 347, 82, 346, 83, 344, 85, 343, 87, 341, 88, 339, 90, 338, 91, 337, 92, 336, 93, 335, 95, 333, 96, 332, 97, 331, 97, 331, 99, 329, 101, 327, 103, 325, 105, 2, 5, 316, 113, 315, 114, 1, 4, 309, 121, 307, 122, 307, 121, 307, 122, 306, 123, 305, 123, 306, 123, 305, 123, 306, 123, 305, 125, 304, 125, 303, 127, 302, 127, 302, 127, 300, 137, 289, 144, 51, 17, 215, 150, 45, 21, 212, 155, 39, 23, 210, 162, 31, 27, 207, 170, 21, 31, 206, 178, 11, 33, 205, 186, 1, 37, 204, 225, 203, 225, 203, 225, 203, 225, 203, 226, 202, 226, 202, 226, 202, 226, 202, 226, 202, 226, 202, 226, 203, 225, 203, 225, 204, 224, 205, 223, 205, 223, 205, 223, 205, 223, 205, 223, 205, 222, 206, 222, 206, 221, 207, 221, 207, 220, 208, 213, 215, 211, 217, 208, 221, 205, 223, 202, 227, 199, 229, 196, 233, 193, 235, 124, 2, 36, 5, 24, 238, 122, 8, 30, 12, 15, 241, 120, 15, 24, 20, 6, 244, 118, 20, 19, 272, 116, 26, 13, 274, 114, 31, 8, 276, 111, 37, 3, 278, 109, 321, 106, 322, 105, 323, 104, 324, 104, 324, 104, 324, 104, 324, 105, 323, 105, 323, 105, 323, 105, 323, 105, 323, 105, 323, 106, 323, 105, 323, 105, 324, 104, 291, 12, 22, 103, 288, 18, 20, 102, 286, 22, 19, 102, 283, 26, 18, 101, 281, 30, 17, 100, 280, 32, 18, 98, 279, 34, 20, 95, 278, 36, 23, 92, 276, 38, 24, 90, 275, 40, 26, 88, 273, 42, 29, 85, 272, 42, 45, 69, 271, 44, 47, 67, 269, 46, 48, 65, 269, 46, 51, 63, 267, 48, 52, 61, 267, 49, 54, 59, 266, 51, 69, 43, 265, 52, 69, 44, 262, 54, 69, 45, 260, 55, 70, 46, 257, 56, 70, 47, 255, 57, 71, 47, 253, 58, 72, 47, 251, 55, 95, 29, 249, 53, 106, 22, 247, 51, 117, 15, 245, 50, 126, 9, 243, 48, 132, 7, 241, 46, 137, 6, 239, 45, 142, 4, 237, 44, 385, 43, 385, 43, 385, 43, 386, 42, 386, 42, 387, 41, 387, 41, 388, 40, 389, 39, 389, 39, 390, 38, 391, 37, 392, 36, 393, 35, 395, 33, 396, 32, 396, 32, 396, 32, 396, 32, 395, 33, 395, 33, 395, 38, 390, 43, 385, 48, 380, 52, 376, 53, 375, 54, 374, 56, 372, 57, 371, 58, 370, 59, 369, 60, 368, 61, 98, 25, 244, 61, 96, 29, 242, 62, 84, 42, 240, 62, 81, 46, 239, 63, 78, 49, 237, 65, 75, 52, 234, 67, 73, 55, 232, 69, 71, 57, 230, 71, 69, 59, 228, 72, 68, 55, 2, 3, 227, 74, 66, 56, 230, 77, 64, 56, 231, 77, 63, 45, 242, 79, 61, 43, 244, 80, 26, 2, 31, 43, 245, 82, 24, 4, 28, 42, 247, 84, 23, 5, 26, 42, 248, 84, 22, 7, 24, 42, 248, 86, 20, 9, 22, 43, 248, 87, 18, 11, 20, 43, 249, 88, 16, 12, 19, 43, 250, 92, 12, 13, 17, 44, 250, 96, 7, 15, 16, 43, 251, 100, 2, 16, 15, 44, 251, 119, 13, 44, 252, 119, 13, 44, 252, 119, 12, 45, 252, 120, 11, 44, 253, 120, 11, 44, 253, 120, 10, 45, 253, 120, 10, 45, 253, 120, 10, 44, 254, 120, 10, 44, 254, 120, 10, 8, 2, 33, 255, 120, 8, 6, 7, 32, 255, 120, 8, 3, 10, 32, 255, 120, 7, 3, 11, 32, 255, 120, 6, 2, 13, 32, 255, 120, 5, 2, 14, 32, 255, 93, 4, 23, 4, 1, 15, 33, 255, 90, 8, 22, 17, 36, 255, 88, 12, 20, 16, 37, 255, 85, 18, 16, 16, 38, 255, 82, 23, 14, 14, 41, 254, 79, 29, 10, 14, 42, 254, 75, 56, 43, 254, 75, 55, 45, 253, 75, 54, 46, 253, 74, 54, 48, 252, 74, 53, 49, 252, 74, 53, 50, 251, 74, 53, 50, 251, 74, 53, 52, 249, 74, 53, 53, 249, 73, 53, 54, 249, 72, 53, 54, 249, 72, 53, 55, 249, 71, 53, 56, 249, 70, 53, 57, 249, 69, 53, 58, 249, 68, 53, 59, 249, 67, 53, 60, 249, 67, 52, 62, 248, 66, 52, 63, 249, 65, 52, 64, 249, 64, 52, 65, 248, 64, 53, 65, 249, 68, 49, 63, 251, 81, 37, 25, 5, 37, 246, 80, 42, 6, 17, 38, 245, 82, 63, 41, 242, 84, 61, 43, 240, 86, 59, 46, 237, 89, 56, 50, 233, 91, 55, 50, 232, 94, 52, 52, 230, 97, 14, 5, 30, 53, 229, 128, 18, 53, 229, 135, 11, 54, 228, 143, 3, 55, 228, 200, 228, 199, 229, 199, 229, 199, 230, 197, 231, 197, 232, 196, 232, 195, 234, 194, 234, 193, 236, 192, 236, 192, 237, 192, 237, 192, 236, 192, 237, 192, 237, 192, 237, 192, 237, 191, 238, 190, 239, 189, 238, 189, 238, 190, 238, 185, 2, 2, 239, 184, 4, 1, 238, 185, 243, 152, 3, 29, 244, 150, 7, 26, 244, 149, 10, 24, 245, 147, 14, 21, 246, 145, 18, 19, 246, 143, 22, 16, 247, 141, 26, 13, 248, 139, 30, 10, 249, 138, 33, 7, 250, 137, 36, 5, 250, 137, 38, 2, 251, 136, 292, 136, 292, 135, 293, 135, 293, 134, 294, 134, 294, 133, 295, 133, 295, 132, 296, 132, 296, 131, 297, 131, 297, 132, 296, 132, 296, 132, 296, 132, 296, 133, 295, 133, 295, 133, 295, 134, 294, 134, 294, 134, 294, 135, 293, 135, 293, 135, 293, 136, 292, 136, 292, 136, 292, 136, 292, 137, 70, 2, 219, 137, 67, 9, 215, 137, 63, 16, 212, 138, 59, 22, 209, 138, 56, 29, 31, 1, 173, 138, 53, 35, 29, 1, 172, 139, 48, 42, 27, 2, 171, 141, 41, 50, 25, 2, 169, 146, 31, 58, 23, 2, 168, 151, 22, 66, 19, 4, 166, 157, 11, 75, 16, 5, 165, 161, 2, 84, 11, 6, 164, 251, 8, 7, 163, 254, 4, 8, 162, 268, 161, 269, 159, 270, 58, 4, 30, 13, 54, 271, 56, 7, 12, 3, 9, 17, 54, 272, 53, 10, 8, 6, 4, 22, 53, 273, 52, 13, 4, 34, 53, 273, 50, 53, 53, 273, 49, 55, 52, 273, 47, 56, 53, 272, 47, 57, 53, 272, 1, 5, 39, 59, 53, 279, 37, 60, 52, 281, 34, 62, 50, 284, 32, 63, 48, 288, 28, 64, 48, 290, 26, 65, 47, 293, 17, 72, 45, 297, 4, 83, 43, 386, 41, 387, 40, 389, 39, 390, 37, 391, 36, 392, 36, 392, 35, 393, 34, 394, 33, 395, 33, 395, 32, 396, 32, 118, 3, 275, 31, 118, 6, 273, 30, 119, 8, 271, 30, 118, 11, 269, 30, 117, 14, 267, 29, 118, 15, 266, 29, 117, 18, 264, 29, 116, 21, 262, 28, 96, 44, 260, 28, 95, 47, 258, 28, 94, 50, 256, 28, 92, 54, 254, 28, 91, 64, 245, 27, 91, 80, 230, 27, 89, 83, 229, 27, 88, 85, 228, 26, 88, 88, 226, 26, 86, 90, 226, 26, 85, 89, 228, 26, 84, 88, 230, 26, 82, 88, 232, 26, 81, 88, 233, 26, 80, 87, 235, 26, 80, 86, 236, 26, 80, 84, 238, 26, 79, 84, 239, 26, 79, 82, 241, 26, 79, 81, 242, 25, 80, 79, 244, 24, 81, 78, 245, 23, 82, 76, 247, 23, 82, 75, 22, 1, 225, 22, 83, 73, 22, 3, 225, 21, 84, 71, 22, 5, 225, 21, 83, 72, 20, 6, 226, 20, 84, 72, 18, 7, 227, 20, 84, 73, 15, 8, 228, 20, 84, 74, 12, 9, 229, 19, 85, 74, 10, 10, 230, 19, 85, 75, 7, 11, 231, 19, 85, 76, 4, 13, 231, 18, 86, 77, 1, 14, 232, 18, 86, 91, 233, 18, 86, 91, 233, 18, 87, 90, 233, 18, 89, 88, 233, 18, 91, 86, 233, 18, 92, 85, 233, 18, 94, 84, 232, 18, 95, 83, 232, 18, 97, 81, 232, 18, 98, 80, 232, 18, 100, 78, 232, 18, 102, 76, 232, 18, 103, 75, 232, 18, 105, 73, 232, 18, 106, 72, 232, 19, 107, 25, 2, 43, 232, 19, 136, 41, 232, 20, 136, 41, 231, 20, 138, 39, 231, 21, 139, 37, 231, 21, 140, 36, 231, 22, 141, 34, 49, 4, 178, 22, 143, 32, 48, 9, 174, 23, 143, 31, 48, 14, 169, 24, 163, 10, 47, 16, 168, 24, 220, 17, 167, 24, 219, 19, 166, 24, 218, 21, 165, 25, 217, 22, 164, 25, 216, 25, 162, 25, 215, 27, 161, 25, 214, 30, 159, 25, 213, 34, 156, 24, 214, 38, 152, 24, 239, 19, 146, 23, 405, 22, 406, 21, 407, 21, 407, 20, 408, 20, 408, 19, 409, 18, 410, 18, 410, 18, 410, 17, 411, 17, 411, 17, 411, 16, 412, 16, 412, 16, 412, 16, 412, 16, 412, 16, 412, 16, 333, 1, 78, 16, 334, 1, 77, 16, 335, 1, 76, 16, 335, 2, 75, 16, 335, 3, 74, 16, 336, 3, 73, 16, 336, 4, 72, 16, 336, 6, 70, 16, 337, 7, 68, 16, 337, 8, 67, 16, 337, 10, 64, 17, 337, 12, 61, 18, 337, 14, 58, 19, 337, 15, 56, 20, 337, 18, 51, 22, 337, 21, 47, 23, 337, 27, 6, 6, 27, 25, 337, 46, 17, 28, 337, 50, 9, 32, 337, 91, 337, 91, 337, 91, 337, 92, 336, 92, 336, 92, 335, 93, 335, 94, 334, 94, 334, 95, 332, 96, 332, 97, 330, 98, 330, 99, 328, 101, 327, 101, 326, 103, 324, 81], "size": [428, 640]}, "area": 147779, "iscrowd": 1, "image_id": 12670, "bbox": [1, 0, 638, 427], "category_id": 1, "id": 900100012670}, {"segmentation": {"counts": [14269, 13, 620, 21, 618, 25, 613, 28, 611, 30, 609, 32, 607, 33, 607, 33, 607, 33, 607, 30, 610, 28, 612, 28, 612, 28, 613, 26, 615, 25, 617, 5, 1, 17, 623, 16, 624, 17, 614, 26, 613, 27, 612, 29, 610, 29, 611, 28, 612, 28, 612, 29, 13, 5, 593, 32, 1, 15, 592, 49, 591, 51, 590, 51, 590, 52, 590, 50, 596, 44, 598, 42, 602, 38, 602, 37, 603, 36, 605, 32, 609, 26, 616, 20, 624, 16, 624, 16, 624, 16, 625, 14, 627, 12, 630, 7, 16622, 2, 638, 3, 636, 5, 635, 6, 633, 9, 631, 15, 625, 20, 621, 12, 2, 6, 620, 11, 4, 6, 621, 7, 7, 5, 635, 3, 19190, 1, 634, 7, 633, 7, 633, 7, 633, 7, 633, 8, 633, 8, 632, 11, 629, 11, 629, 11, 630, 11, 629, 11, 629, 11, 629, 12, 628, 18, 622, 19, 622, 19, 621, 19, 621, 19, 621, 19, 621, 19, 622, 18, 622, 17, 623, 17, 623, 14, 626, 14, 614, 11, 1, 14, 613, 26, 613, 25, 615, 23, 616, 15, 625, 14, 626, 14, 626, 13, 627, 13, 627, 12, 628, 11, 630, 11, 629, 5, 638, 2, 65884, 7, 2, 5, 623, 18, 4, 6, 1, 4, 606, 35, 605, 35, 605, 35, 605, 35, 605, 35, 606, 3, 3, 21, 2, 3, 615, 5, 1, 3, 1, 3, 1263, 12, 626, 15, 624, 16, 624, 16, 620, 24, 5, 7, 603, 26, 3, 9, 2, 4, 3, 6, 586, 39, 1, 6, 1, 8, 585, 39, 1, 21, 579, 62, 578, 63, 577, 63, 578, 64, 577, 7, 1, 4, 1, 53, 598, 44, 596, 46, 594, 47, 593, 49, 592, 11, 2, 37, 590, 11, 2, 38, 591, 6, 5, 40, 600, 40, 600, 39, 602, 38, 602, 35, 607, 23, 622, 3, 1, 3, 5, 3, 109233], "size": [640, 480]}, "area": 3957, "iscrowd": 1, "image_id": 12639, "bbox": [22, 144, 287, 83], "category_id": 1, "id": 900100012639}, {"segmentation": {"counts": [94823, 6, 473, 8, 471, 10, 469, 11, 469, 11, 469, 11, 469, 11, 469, 11, 469, 11, 469, 11, 469, 11, 3, 1, 465, 11, 3, 1, 465, 11, 3, 1, 466, 10, 3, 1, 466, 10, 3, 1, 467, 9, 3, 1, 468, 8, 3, 1, 468, 8, 3, 1, 468, 8, 3, 1, 467, 9, 3, 1, 467, 9, 3, 1, 467, 9, 3, 1, 467, 9, 3, 1, 466, 10, 3, 1, 466, 10, 3, 1, 466, 10, 470, 10, 470, 10, 470, 10, 470, 10, 470, 10, 470, 10, 3, 1, 466, 10, 3, 1, 466, 10, 3, 1, 466, 10, 3, 1, 466, 10, 3, 1, 467, 9, 3, 1, 467, 9, 3, 1, 468, 8, 3, 1, 468, 8, 3, 1, 468, 8, 3, 1, 468, 8, 3, 1, 468, 8, 3, 1, 468, 8, 3, 1, 467, 9, 3, 1, 466, 10, 3, 1, 465, 11, 468, 12, 468, 12, 468, 12, 468, 12, 468, 12, 468, 12, 468, 12, 468, 12, 468, 13, 467, 13, 467, 14, 467, 14, 466, 15, 465, 16, 464, 16, 464, 16, 465, 15, 465, 15, 466, 14, 467, 13, 467, 13, 467, 13, 466, 13, 466, 13, 466, 15, 465, 15, 464, 16, 463, 17, 463, 17, 463, 17, 462, 7, 473, 7, 473, 7, 473, 7, 472, 8, 472, 8, 472, 8, 472, 8, 472, 8, 472, 8, 472, 8, 472, 8, 472, 8, 472, 8, 472, 8, 472, 8, 472, 8, 472, 8, 472, 8, 472, 8, 472, 8, 472, 8, 472, 8, 472, 8, 471, 9, 470, 10, 469, 11, 469, 11, 469, 11, 469, 11, 469, 11, 469, 12, 468, 12, 468, 12, 468, 12, 468, 12, 468, 12, 468, 12, 469, 11, 470, 10, 470, 10, 470, 10, 470, 10, 470, 10, 12, 8, 450, 10, 11, 15, 444, 10, 11, 18, 441, 10, 11, 18, 442, 9, 11, 18, 443, 8, 11, 18, 445, 4, 13, 18, 462, 18, 462, 17, 442, 7, 14, 15, 442, 10, 13, 14, 442, 12, 12, 14, 441, 14, 11, 14, 440, 15, 11, 14, 440, 15, 11, 14, 440, 15, 11, 14, 439, 16, 11, 14, 439, 16, 11, 13, 439, 18, 10, 13, 439, 18, 10, 13, 439, 22, 1, 1, 4, 12, 440, 24, 4, 12, 440, 16, 17, 7, 440, 16, 17, 6, 441, 16, 17, 5, 442, 16, 17, 3, 444, 16, 464, 16, 464, 16, 464, 16, 464, 16, 464, 16, 464, 16, 465, 15, 466, 14, 468, 12, 471, 9, 471, 9, 471, 9, 471, 9, 471, 9, 471, 9, 471, 9, 471, 9, 471, 9, 470, 10, 470, 10, 470, 10, 470, 10, 470, 10, 469, 11, 469, 11, 468, 12, 467, 13, 467, 13, 467, 13, 467, 13, 467, 13, 467, 13, 467, 13, 468, 11, 470, 8, 474, 4, 11548, 2, 2, 2, 474, 2, 2, 2, 474, 2, 2, 2, 474, 2, 2, 2, 473, 3, 2, 2, 473, 3, 2, 2, 473, 3, 2, 2, 474, 2, 2, 2, 474, 2, 1, 3, 474, 2, 1, 3, 474, 2, 1, 3, 474, 2, 1, 3, 474, 2, 1, 3, 474, 2, 1, 3, 474, 2, 1, 3, 475, 1, 1, 3, 475, 5, 475, 5, 475, 5, 475, 5, 475, 5, 475, 5, 475, 5, 475, 5, 475, 5, 475, 5, 475, 5, 475, 5, 475, 5, 475, 5, 472, 8, 475, 5, 475, 5, 476, 4, 476, 4, 476, 4, 476, 4, 476, 4, 476, 4, 476, 4, 476, 4, 476, 4, 476, 4, 476, 4, 476, 4, 475, 5, 475, 5, 475, 6, 474, 6, 474, 6, 474, 6, 474, 6, 475, 5, 475, 5, 475, 5, 477, 3, 478, 2, 479, 1, 479, 1, 85626], "size": [480, 640]}, "area": 3773, "iscrowd": 1, "image_id": 15517, "bbox": [197, 248, 264, 45], "category_id": 6, "id": 900600015517}], "categories": [{"supercategory": "person", "id": 1, "name": "person"}, {"supercategory": "vehicle", "id": 2, "name": "bicycle"}, {"supercategory": "vehicle", "id": 3, "name": "car"}, {"supercategory": "vehicle", "id": 4, "name": "motorcycle"}, {"supercategory": "vehicle", "id": 5, "name": "airplane"}, {"supercategory": "vehicle", "id": 6, "name": "bus"}, {"supercategory": "vehicle", "id": 7, "name": "train"}, {"supercategory": "vehicle", "id": 8, "name": "truck"}, {"supercategory": "vehicle", "id": 9, "name": "boat"}, {"supercategory": "outdoor", "id": 10, "name": "traffic light"}, {"supercategory": "outdoor", "id": 11, "name": "fire hydrant"}, {"supercategory": "outdoor", "id": 13, "name": "stop sign"}, {"supercategory": "outdoor", "id": 14, "name": "parking meter"}, {"supercategory": "outdoor", "id": 15, "name": "bench"}, {"supercategory": "animal", "id": 16, "name": "bird"}, {"supercategory": "animal", "id": 17, "name": "cat"}, {"supercategory": "animal", "id": 18, "name": "dog"}, {"supercategory": "animal", "id": 19, "name": "horse"}, {"supercategory": "animal", "id": 20, "name": "sheep"}, {"supercategory": "animal", "id": 21, "name": "cow"}, {"supercategory": "animal", "id": 22, "name": "elephant"}, {"supercategory": "animal", "id": 23, "name": "bear"}, {"supercategory": "animal", "id": 24, "name": "zebra"}, {"supercategory": "animal", "id": 25, "name": "giraffe"}, {"supercategory": "accessory", "id": 27, "name": "backpack"}, {"supercategory": "accessory", "id": 28, "name": "umbrella"}, {"supercategory": "accessory", "id": 31, "name": "handbag"}, {"supercategory": "accessory", "id": 32, "name": "tie"}, {"supercategory": "accessory", "id": 33, "name": "suitcase"}, {"supercategory": "sports", "id": 34, "name": "frisbee"}, {"supercategory": "sports", "id": 35, "name": "skis"}, {"supercategory": "sports", "id": 36, "name": "snowboard"}, {"supercategory": "sports", "id": 37, "name": "sports ball"}, {"supercategory": "sports", "id": 38, "name": "kite"}, {"supercategory": "sports", "id": 39, "name": "baseball bat"}, {"supercategory": "sports", "id": 40, "name": "baseball glove"}, {"supercategory": "sports", "id": 41, "name": "skateboard"}, {"supercategory": "sports", "id": 42, "name": "surfboard"}, {"supercategory": "sports", "id": 43, "name": "tennis racket"}, {"supercategory": "kitchen", "id": 44, "name": "bottle"}, {"supercategory": "kitchen", "id": 46, "name": "wine glass"}, {"supercategory": "kitchen", "id": 47, "name": "cup"}, {"supercategory": "kitchen", "id": 48, "name": "fork"}, {"supercategory": "kitchen", "id": 49, "name": "knife"}, {"supercategory": "kitchen", "id": 50, "name": "spoon"}, {"supercategory": "kitchen", "id": 51, "name": "bowl"}, {"supercategory": "food", "id": 52, "name": "banana"}, {"supercategory": "food", "id": 53, "name": "apple"}, {"supercategory": "food", "id": 54, "name": "sandwich"}, {"supercategory": "food", "id": 55, "name": "orange"}, {"supercategory": "food", "id": 56, "name": "broccoli"}, {"supercategory": "food", "id": 57, "name": "carrot"}, {"supercategory": "food", "id": 58, "name": "hot dog"}, {"supercategory": "food", "id": 59, "name": "pizza"}, {"supercategory": "food", "id": 60, "name": "donut"}, {"supercategory": "food", "id": 61, "name": "cake"}, {"supercategory": "furniture", "id": 62, "name": "chair"}, {"supercategory": "furniture", "id": 63, "name": "couch"}, {"supercategory": "furniture", "id": 64, "name": "potted plant"}, {"supercategory": "furniture", "id": 65, "name": "bed"}, {"supercategory": "furniture", "id": 67, "name": "dining table"}, {"supercategory": "furniture", "id": 70, "name": "toilet"}, {"supercategory": "electronic", "id": 72, "name": "tv"}, {"supercategory": "electronic", "id": 73, "name": "laptop"}, {"supercategory": "electronic", "id": 74, "name": "mouse"}, {"supercategory": "electronic", "id": 75, "name": "remote"}, {"supercategory": "electronic", "id": 76, "name": "keyboard"}, {"supercategory": "electronic", "id": 77, "name": "cell phone"}, {"supercategory": "appliance", "id": 78, "name": "microwave"}, {"supercategory": "appliance", "id": 79, "name": "oven"}, {"supercategory": "appliance", "id": 80, "name": "toaster"}, {"supercategory": "appliance", "id": 81, "name": "sink"}, {"supercategory": "appliance", "id": 82, "name": "refrigerator"}, {"supercategory": "indoor", "id": 84, "name": "book"}, {"supercategory": "indoor", "id": 85, "name": "clock"}, {"supercategory": "indoor", "id": 86, "name": "vase"}, {"supercategory": "indoor", "id": 87, "name": "scissors"}, {"supercategory": "indoor", "id": 88, "name": "teddy bear"}, {"supercategory": "indoor", "id": 89, "name": "hair drier"}, {"supercategory": "indoor", "id": 90, "name": "toothbrush"}]}
\ No newline at end of file
diff --git a/repositories/taming-transformers/data/coco_annotations_100/annotations/stuff_train2017.json b/repositories/taming-transformers/data/coco_annotations_100/annotations/stuff_train2017.json
deleted file mode 100644
index 30f4d50c2eee533ea1abdd48579b5735917a6787..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/data/coco_annotations_100/annotations/stuff_train2017.json
+++ /dev/null
@@ -1 +0,0 @@
-{"info": {"description": "COCO 2017 Stuff Dataset", "url": "http://cocodataset.org", "version": "1.0", "year": 2017, "contributor": "H. Caesar, J. Uijlings, M. Maire, T.-Y. Lin, P. Dollar and V. Ferrari", "date_created": "2017-08-31 00:00:00.0"}, "images": [{"license": 4, "file_name": "000000010442.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010442.jpg", "height": 426, "width": 640, "date_captured": "2013-11-19 21:00:09", "flickr_url": "http://farm4.staticflickr.com/3188/2662753853_11d673a05d_z.jpg", "id": 10442}, {"license": 1, "file_name": "000000010142.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010142.jpg", "height": 480, "width": 640, "date_captured": "2013-11-19 22:51:01", "flickr_url": "http://farm6.staticflickr.com/5171/5408281064_c3f8d3e67b_z.jpg", "id": 10142}, {"license": 1, "file_name": "000000010393.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010393.jpg", "height": 480, "width": 640, "date_captured": "2013-11-15 00:40:34", "flickr_url": "http://farm3.staticflickr.com/2743/4192626071_8f54f4578e_z.jpg", "id": 10393}, {"license": 6, "file_name": "000000010077.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010077.jpg", "height": 259, "width": 640, "date_captured": "2013-11-18 21:04:46", "flickr_url": "http://farm3.staticflickr.com/2112/2273706470_b115ccda6b_z.jpg", "id": 10077}, {"license": 3, "file_name": "000000010123.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010123.jpg", "height": 427, "width": 640, "date_captured": "2013-11-21 05:12:31", "flickr_url": "http://farm5.staticflickr.com/4085/4948947491_1c37f153fc_z.jpg", "id": 10123}, {"license": 4, "file_name": "000000010432.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010432.jpg", "height": 480, "width": 640, "date_captured": "2013-11-19 19:52:48", "flickr_url": "http://farm6.staticflickr.com/5457/9690393462_ba33edf573_z.jpg", "id": 10432}, {"license": 1, "file_name": "000000010192.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010192.jpg", "height": 497, "width": 640, "date_captured": "2013-11-14 20:22:48", "flickr_url": "http://farm9.staticflickr.com/8196/8125509122_e8348e8d89_z.jpg", "id": 10192}, {"license": 3, "file_name": "000000010039.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010039.jpg", "height": 640, "width": 428, "date_captured": "2013-11-15 14:11:21", "flickr_url": "http://farm5.staticflickr.com/4098/4751788411_7b6a63cd92_z.jpg", "id": 10039}, {"license": 2, "file_name": "000000010023.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010023.jpg", "height": 427, "width": 640, "date_captured": "2013-11-20 09:15:13", "flickr_url": "http://farm4.staticflickr.com/3397/3201363151_ea35e217e3_z.jpg", "id": 10023}, {"license": 3, "file_name": "000000010428.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010428.jpg", "height": 640, "width": 427, "date_captured": "2013-11-14 23:33:26", "flickr_url": "http://farm3.staticflickr.com/2685/4078908984_668deecc62_z.jpg", "id": 10428}, {"license": 1, "file_name": "000000010400.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010400.jpg", "height": 640, "width": 427, "date_captured": "2013-11-24 15:06:26", "flickr_url": "http://farm9.staticflickr.com/8323/8134674673_d9489c39b9_z.jpg", "id": 10400}, {"license": 5, "file_name": "000000010249.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010249.jpg", "height": 640, "width": 426, "date_captured": "2013-11-16 21:44:34", "flickr_url": "http://farm9.staticflickr.com/8335/8402579163_8363ba0c7e_z.jpg", "id": 10249}, {"license": 1, "file_name": "000000010290.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010290.jpg", "height": 640, "width": 482, "date_captured": "2013-11-15 04:16:50", "flickr_url": "http://farm6.staticflickr.com/5048/5238196330_aeeaf4d917_z.jpg", "id": 10290}, {"license": 2, "file_name": "000000010205.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010205.jpg", "height": 426, "width": 640, "date_captured": "2013-11-21 22:57:22", "flickr_url": "http://farm2.staticflickr.com/1316/1243676572_54ae68d563_z.jpg", "id": 10205}, {"license": 2, "file_name": "000000010217.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010217.jpg", "height": 480, "width": 640, "date_captured": "2013-11-24 23:26:35", "flickr_url": "http://farm8.staticflickr.com/7284/8742930563_207f40cd15_z.jpg", "id": 10217}, {"license": 5, "file_name": "000000010263.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010263.jpg", "height": 375, "width": 500, "date_captured": "2013-11-22 01:41:21", "flickr_url": "http://farm3.staticflickr.com/2602/3909329409_085a7c9f41_z.jpg", "id": 10263}, {"license": 2, "file_name": "000000010040.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010040.jpg", "height": 480, "width": 640, "date_captured": "2013-11-16 21:57:07", "flickr_url": "http://farm9.staticflickr.com/8213/8264511585_0a5e6212c2_z.jpg", "id": 10040}, {"license": 4, "file_name": "000000010324.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010324.jpg", "height": 640, "width": 425, "date_captured": "2013-11-18 15:34:49", "flickr_url": "http://farm2.staticflickr.com/1059/5150417990_0d7762864e_z.jpg", "id": 10324}, {"license": 3, "file_name": "000000010008.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010008.jpg", "height": 427, "width": 640, "date_captured": "2013-11-20 20:26:10", "flickr_url": "http://farm7.staticflickr.com/6076/6088064448_2690929cbc_z.jpg", "id": 10008}, {"license": 2, "file_name": "000000010149.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010149.jpg", "height": 484, "width": 640, "date_captured": "2013-11-14 21:39:00", "flickr_url": "http://farm8.staticflickr.com/7235/7169336131_521711d854_z.jpg", "id": 10149}, {"license": 1, "file_name": "000000010463.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010463.jpg", "height": 446, "width": 640, "date_captured": "2013-11-15 12:19:52", "flickr_url": "http://farm8.staticflickr.com/7250/6910961220_961bf73cae_z.jpg", "id": 10463}, {"license": 3, "file_name": "000000010138.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010138.jpg", "height": 480, "width": 640, "date_captured": "2013-11-20 21:12:31", "flickr_url": "http://farm9.staticflickr.com/8098/8506152881_ecf43c76c3_z.jpg", "id": 10138}, {"license": 4, "file_name": "000000010014.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010014.jpg", "height": 426, "width": 640, "date_captured": "2013-11-16 15:41:53", "flickr_url": "http://farm9.staticflickr.com/8185/8145478875_28543f87be_z.jpg", "id": 10014}, {"license": 1, "file_name": "000000010219.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010219.jpg", "height": 480, "width": 640, "date_captured": "2013-11-18 12:18:01", "flickr_url": "http://farm1.staticflickr.com/116/293545180_9be1f625c9_z.jpg", "id": 10219}, {"license": 4, "file_name": "000000010114.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010114.jpg", "height": 640, "width": 480, "date_captured": "2013-11-14 18:59:23", "flickr_url": "http://farm9.staticflickr.com/8509/8528792276_824cb10b59_z.jpg", "id": 10114}, {"license": 3, "file_name": "000000010211.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010211.jpg", "height": 424, "width": 640, "date_captured": "2013-11-17 00:23:46", "flickr_url": "http://farm4.staticflickr.com/3032/2751379693_252d3c8e44_z.jpg", "id": 10211}, {"license": 2, "file_name": "000000010216.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010216.jpg", "height": 480, "width": 640, "date_captured": "2013-11-21 03:03:18", "flickr_url": "http://farm9.staticflickr.com/8436/7926050860_db828d8494_z.jpg", "id": 10216}, {"license": 1, "file_name": "000000010115.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010115.jpg", "height": 640, "width": 427, "date_captured": "2013-11-15 03:15:50", "flickr_url": "http://farm8.staticflickr.com/7200/6854804253_f7fa6e98b8_z.jpg", "id": 10115}, {"license": 4, "file_name": "000000010440.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010440.jpg", "height": 427, "width": 640, "date_captured": "2013-11-16 05:51:18", "flickr_url": "http://farm3.staticflickr.com/2865/9443592476_3c3ce98973_z.jpg", "id": 10440}, {"license": 1, "file_name": "000000010256.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010256.jpg", "height": 360, "width": 640, "date_captured": "2013-11-16 22:33:29", "flickr_url": "http://farm9.staticflickr.com/8480/8214519939_ee9d1486b6_z.jpg", "id": 10256}, {"license": 2, "file_name": "000000010056.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010056.jpg", "height": 482, "width": 640, "date_captured": "2013-11-17 00:42:05", "flickr_url": "http://farm9.staticflickr.com/8472/8138318480_cf2cd6fa5a_z.jpg", "id": 10056}, {"license": 2, "file_name": "000000010107.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010107.jpg", "height": 640, "width": 480, "date_captured": "2013-11-18 14:17:26", "flickr_url": "http://farm5.staticflickr.com/4039/4471901818_ac8353d2e5_z.jpg", "id": 10107}, {"license": 3, "file_name": "000000010449.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010449.jpg", "height": 480, "width": 640, "date_captured": "2013-11-18 02:08:04", "flickr_url": "http://farm4.staticflickr.com/3732/9431336544_e6345159b7_z.jpg", "id": 10449}, {"license": 3, "file_name": "000000010012.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010012.jpg", "height": 480, "width": 640, "date_captured": "2013-11-19 21:21:03", "flickr_url": "http://farm2.staticflickr.com/1039/1374745468_a4cb23032f_z.jpg", "id": 10012}, {"license": 1, "file_name": "000000010395.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010395.jpg", "height": 476, "width": 500, "date_captured": "2013-11-18 11:37:47", "flickr_url": "http://farm5.staticflickr.com/4005/4468891744_00bb759e17_z.jpg", "id": 10395}, {"license": 3, "file_name": "000000010104.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010104.jpg", "height": 457, "width": 640, "date_captured": "2013-11-15 21:47:19", "flickr_url": "http://farm7.staticflickr.com/6237/6282305534_1b1fb1f8a1_z.jpg", "id": 10104}, {"license": 1, "file_name": "000000010386.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010386.jpg", "height": 480, "width": 640, "date_captured": "2013-11-18 01:56:20", "flickr_url": "http://farm4.staticflickr.com/3279/2982566887_93e5d316a9_z.jpg", "id": 10386}, {"license": 1, "file_name": "000000010046.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010046.jpg", "height": 480, "width": 640, "date_captured": "2013-11-20 11:50:10", "flickr_url": "http://farm8.staticflickr.com/7363/9675286251_78248282b6_z.jpg", "id": 10046}, {"license": 1, "file_name": "000000010430.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010430.jpg", "height": 480, "width": 640, "date_captured": "2013-11-17 07:44:21", "flickr_url": "http://farm4.staticflickr.com/3678/9477571775_99039714f4_z.jpg", "id": 10430}, {"license": 1, "file_name": "000000010166.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010166.jpg", "height": 351, "width": 470, "date_captured": "2013-11-19 21:19:23", "flickr_url": "http://farm4.staticflickr.com/3248/3015319163_b0a61a7c77_z.jpg", "id": 10166}, {"license": 2, "file_name": "000000010275.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010275.jpg", "height": 427, "width": 640, "date_captured": "2013-11-18 09:32:30", "flickr_url": "http://farm9.staticflickr.com/8177/8029583325_02172d3a6f_z.jpg", "id": 10275}, {"license": 3, "file_name": "000000010321.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010321.jpg", "height": 640, "width": 480, "date_captured": "2013-11-19 03:39:00", "flickr_url": "http://farm9.staticflickr.com/8330/8374378941_80fda27ffa_z.jpg", "id": 10321}, {"license": 2, "file_name": "000000010176.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010176.jpg", "height": 480, "width": 640, "date_captured": "2013-11-22 00:19:33", "flickr_url": "http://farm1.staticflickr.com/164/335653813_61b9a128e2_z.jpg", "id": 10176}, {"license": 1, "file_name": "000000010037.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010037.jpg", "height": 426, "width": 640, "date_captured": "2013-11-19 18:06:00", "flickr_url": "http://farm3.staticflickr.com/2602/3741919994_d9d0343f82_z.jpg", "id": 10037}, {"license": 4, "file_name": "000000010313.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010313.jpg", "height": 640, "width": 427, "date_captured": "2013-11-20 07:30:18", "flickr_url": "http://farm3.staticflickr.com/2595/3691544165_5f8f5d92de_z.jpg", "id": 10313}, {"license": 3, "file_name": "000000010421.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010421.jpg", "height": 480, "width": 640, "date_captured": "2013-11-20 11:47:35", "flickr_url": "http://farm4.staticflickr.com/3286/2704773968_ed600e2d11_z.jpg", "id": 10421}, {"license": 2, "file_name": "000000010161.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010161.jpg", "height": 427, "width": 640, "date_captured": "2013-11-25 13:54:08", "flickr_url": "http://farm9.staticflickr.com/8385/8583133395_109ca97781_z.jpg", "id": 10161}, {"license": 4, "file_name": "000000010405.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010405.jpg", "height": 453, "width": 640, "date_captured": "2013-11-18 01:11:58", "flickr_url": "http://farm6.staticflickr.com/5068/5694693358_890d562b38_z.jpg", "id": 10405}, {"license": 4, "file_name": "000000010444.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010444.jpg", "height": 500, "width": 375, "date_captured": "2013-11-20 16:02:25", "flickr_url": "http://farm3.staticflickr.com/2205/2342560721_8a9545fd1a_z.jpg", "id": 10444}, {"license": 3, "file_name": "000000010084.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010084.jpg", "height": 424, "width": 640, "date_captured": "2013-11-17 07:31:31", "flickr_url": "http://farm8.staticflickr.com/7428/9977327403_ec9211b17d_z.jpg", "id": 10084}, {"license": 3, "file_name": "000000010179.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010179.jpg", "height": 502, "width": 640, "date_captured": "2013-11-23 01:23:34", "flickr_url": "http://farm3.staticflickr.com/2753/5765793056_50a8694618_z.jpg", "id": 10179}, {"license": 4, "file_name": "000000010125.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010125.jpg", "height": 436, "width": 640, "date_captured": "2013-11-24 00:31:28", "flickr_url": "http://farm9.staticflickr.com/8051/8100436361_e70cea2ba0_z.jpg", "id": 10125}, {"license": 2, "file_name": "000000010082.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010082.jpg", "height": 425, "width": 640, "date_captured": "2013-11-24 02:46:01", "flickr_url": "http://farm6.staticflickr.com/5103/5660629949_220d6cb304_z.jpg", "id": 10082}, {"license": 3, "file_name": "000000010318.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010318.jpg", "height": 400, "width": 640, "date_captured": "2013-11-16 21:21:19", "flickr_url": "http://farm4.staticflickr.com/3730/9164843685_66a5f3834b_z.jpg", "id": 10318}, {"license": 3, "file_name": "000000010130.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010130.jpg", "height": 427, "width": 640, "date_captured": "2013-11-24 09:51:10", "flickr_url": "http://farm8.staticflickr.com/7055/6802904874_5313b371f7_z.jpg", "id": 10130}, {"license": 1, "file_name": "000000010403.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010403.jpg", "height": 416, "width": 640, "date_captured": "2013-11-19 18:10:23", "flickr_url": "http://farm5.staticflickr.com/4120/5416067648_a01a1d2872_z.jpg", "id": 10403}, {"license": 6, "file_name": "000000010041.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010041.jpg", "height": 480, "width": 640, "date_captured": "2013-11-19 21:28:20", "flickr_url": "http://farm8.staticflickr.com/7157/6791989693_84a9ec0060_z.jpg", "id": 10041}, {"license": 3, "file_name": "000000010343.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010343.jpg", "height": 427, "width": 640, "date_captured": "2013-11-17 08:20:12", "flickr_url": "http://farm4.staticflickr.com/3768/9317101988_c25e7800c3_z.jpg", "id": 10343}, {"license": 3, "file_name": "000000010388.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010388.jpg", "height": 426, "width": 640, "date_captured": "2013-11-14 21:36:08", "flickr_url": "http://farm8.staticflickr.com/7376/9612632350_fe5f9d2e02_z.jpg", "id": 10388}, {"license": 2, "file_name": "000000010414.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010414.jpg", "height": 420, "width": 500, "date_captured": "2013-11-19 20:53:38", "flickr_url": "http://farm1.staticflickr.com/68/187106012_c60041a5fa_z.jpg", "id": 10414}, {"license": 2, "file_name": "000000010265.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010265.jpg", "height": 427, "width": 640, "date_captured": "2013-11-23 04:52:56", "flickr_url": "http://farm2.staticflickr.com/1153/1340250930_630cae707b_z.jpg", "id": 10265}, {"license": 3, "file_name": "000000010222.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010222.jpg", "height": 333, "width": 500, "date_captured": "2013-11-16 17:51:33", "flickr_url": "http://farm1.staticflickr.com/145/406189235_49af59f7aa_z.jpg", "id": 10222}, {"license": 1, "file_name": "000000010241.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010241.jpg", "height": 480, "width": 640, "date_captured": "2013-11-16 16:15:07", "flickr_url": "http://farm5.staticflickr.com/4034/4618195712_406d349d8c_z.jpg", "id": 10241}, {"license": 4, "file_name": "000000010239.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010239.jpg", "height": 360, "width": 640, "date_captured": "2013-11-16 22:41:38", "flickr_url": "http://farm9.staticflickr.com/8527/8640248164_346015db59_z.jpg", "id": 10239}, {"license": 6, "file_name": "000000010369.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010369.jpg", "height": 424, "width": 640, "date_captured": "2013-11-18 16:30:42", "flickr_url": "http://farm6.staticflickr.com/5210/5279959659_617eb40ec2_z.jpg", "id": 10369}, {"license": 3, "file_name": "000000010136.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010136.jpg", "height": 640, "width": 316, "date_captured": "2013-11-24 12:57:39", "flickr_url": "http://farm1.staticflickr.com/48/143700223_df3abd2533_z.jpg", "id": 10136}, {"license": 3, "file_name": "000000010445.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010445.jpg", "height": 349, "width": 500, "date_captured": "2013-11-19 17:56:31", "flickr_url": "http://farm2.staticflickr.com/1133/973971082_0524b5a07c_z.jpg", "id": 10445}, {"license": 3, "file_name": "000000010015.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010015.jpg", "height": 633, "width": 640, "date_captured": "2013-11-19 18:15:10", "flickr_url": "http://farm4.staticflickr.com/3422/3393927411_26ab4342e9_z.jpg", "id": 10015}, {"license": 1, "file_name": "000000010434.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010434.jpg", "height": 426, "width": 640, "date_captured": "2013-11-15 11:54:59", "flickr_url": "http://farm1.staticflickr.com/8/7845077_9cbe3bd89d_z.jpg", "id": 10434}, {"license": 2, "file_name": "000000010097.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010097.jpg", "height": 360, "width": 640, "date_captured": "2013-11-14 20:26:22", "flickr_url": "http://farm5.staticflickr.com/4018/4255629097_9a832c6dae_z.jpg", "id": 10097}, {"license": 2, "file_name": "000000010058.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010058.jpg", "height": 426, "width": 640, "date_captured": "2013-11-17 19:16:50", "flickr_url": "http://farm9.staticflickr.com/8349/8197861619_93df634c02_z.jpg", "id": 10058}, {"license": 3, "file_name": "000000010108.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010108.jpg", "height": 453, "width": 640, "date_captured": "2013-11-17 02:20:45", "flickr_url": "http://farm4.staticflickr.com/3360/3444049112_fc8b6acf6e_z.jpg", "id": 10108}, {"license": 3, "file_name": "000000010232.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010232.jpg", "height": 427, "width": 640, "date_captured": "2013-11-17 15:38:00", "flickr_url": "http://farm4.staticflickr.com/3675/9453111678_c2cb8874d9_z.jpg", "id": 10232}, {"license": 4, "file_name": "000000010069.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010069.jpg", "height": 640, "width": 427, "date_captured": "2013-11-16 16:09:44", "flickr_url": "http://farm8.staticflickr.com/7229/7167720922_420895b285_z.jpg", "id": 10069}, {"license": 3, "file_name": "000000010024.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010024.jpg", "height": 640, "width": 480, "date_captured": "2013-11-19 20:21:14", "flickr_url": "http://farm9.staticflickr.com/8528/8463731510_53f79a717b_z.jpg", "id": 10024}, {"license": 3, "file_name": "000000010327.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010327.jpg", "height": 480, "width": 640, "date_captured": "2013-11-18 14:53:56", "flickr_url": "http://farm5.staticflickr.com/4025/4461943910_f88106188f_z.jpg", "id": 10327}, {"license": 1, "file_name": "000000010337.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010337.jpg", "height": 640, "width": 480, "date_captured": "2013-11-15 06:08:56", "flickr_url": "http://farm8.staticflickr.com/7064/6801977878_94d6095195_z.jpg", "id": 10337}, {"license": 1, "file_name": "000000010248.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010248.jpg", "height": 427, "width": 640, "date_captured": "2013-11-18 17:00:29", "flickr_url": "http://farm7.staticflickr.com/6013/6015945247_2d03377c4c_z.jpg", "id": 10248}, {"license": 2, "file_name": "000000010276.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010276.jpg", "height": 426, "width": 640, "date_captured": "2013-11-18 19:59:57", "flickr_url": "http://farm4.staticflickr.com/3746/9895771396_45554292ba_z.jpg", "id": 10276}, {"license": 6, "file_name": "000000010196.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010196.jpg", "height": 443, "width": 640, "date_captured": "2013-11-19 19:58:23", "flickr_url": "http://farm9.staticflickr.com/8505/8405340354_90e8d1757d_z.jpg", "id": 10196}, {"license": 2, "file_name": "000000010145.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010145.jpg", "height": 427, "width": 640, "date_captured": "2013-11-22 00:21:49", "flickr_url": "http://farm5.staticflickr.com/4118/4740387609_ce9bb3f14a_z.jpg", "id": 10145}, {"license": 1, "file_name": "000000010407.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010407.jpg", "height": 480, "width": 640, "date_captured": "2013-11-20 07:19:18", "flickr_url": "http://farm3.staticflickr.com/2037/2071235362_a8f850624b_z.jpg", "id": 10407}, {"license": 3, "file_name": "000000010243.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010243.jpg", "height": 500, "width": 375, "date_captured": "2013-11-20 13:39:40", "flickr_url": "http://farm3.staticflickr.com/2212/2416407264_ca4e59c12e_z.jpg", "id": 10243}, {"license": 4, "file_name": "000000010230.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010230.jpg", "height": 480, "width": 640, "date_captured": "2013-11-19 19:40:06", "flickr_url": "http://farm5.staticflickr.com/4094/4788218715_e64dbe9bb7_z.jpg", "id": 10230}, {"license": 6, "file_name": "000000010245.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010245.jpg", "height": 427, "width": 640, "date_captured": "2013-11-21 01:16:08", "flickr_url": "http://farm8.staticflickr.com/7253/7459060240_5e24c744f6_z.jpg", "id": 10245}, {"license": 1, "file_name": "000000010319.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010319.jpg", "height": 427, "width": 640, "date_captured": "2013-11-22 22:31:20", "flickr_url": "http://farm3.staticflickr.com/2521/4128276778_bb74ec1296_z.jpg", "id": 10319}, {"license": 4, "file_name": "000000010229.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010229.jpg", "height": 480, "width": 640, "date_captured": "2013-11-17 20:53:03", "flickr_url": "http://farm6.staticflickr.com/5312/6915913274_7d268c51fd_z.jpg", "id": 10229}, {"license": 4, "file_name": "000000010073.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010073.jpg", "height": 469, "width": 640, "date_captured": "2013-11-21 05:37:17", "flickr_url": "http://farm5.staticflickr.com/4038/4663314869_680cc1b4c9_z.jpg", "id": 10073}, {"license": 1, "file_name": "000000010342.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010342.jpg", "height": 640, "width": 478, "date_captured": "2013-11-16 14:34:19", "flickr_url": "http://farm9.staticflickr.com/8436/7984397533_7a232d7e44_z.jpg", "id": 10342}, {"license": 3, "file_name": "000000010244.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010244.jpg", "height": 640, "width": 475, "date_captured": "2013-11-16 16:56:12", "flickr_url": "http://farm9.staticflickr.com/8453/8055076139_cd811120a4_z.jpg", "id": 10244}, {"license": 5, "file_name": "000000010005.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010005.jpg", "height": 400, "width": 500, "date_captured": "2013-11-18 11:09:03", "flickr_url": "http://farm3.staticflickr.com/2558/4197396435_45d477dc72_z.jpg", "id": 10005}, {"license": 5, "file_name": "000000010083.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010083.jpg", "height": 480, "width": 640, "date_captured": "2013-11-22 00:56:30", "flickr_url": "http://farm3.staticflickr.com/2033/2033435444_1b91e9988c_z.jpg", "id": 10083}, {"license": 5, "file_name": "000000010420.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010420.jpg", "height": 375, "width": 500, "date_captured": "2013-11-17 06:42:55", "flickr_url": "http://farm2.staticflickr.com/1213/1165637087_cf1cc6a168_z.jpg", "id": 10420}, {"license": 2, "file_name": "000000010281.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010281.jpg", "height": 427, "width": 640, "date_captured": "2013-11-22 14:50:06", "flickr_url": "http://farm9.staticflickr.com/8337/8244811136_bd49523ae1_z.jpg", "id": 10281}, {"license": 3, "file_name": "000000010175.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010175.jpg", "height": 480, "width": 640, "date_captured": "2013-11-14 20:17:42", "flickr_url": "http://farm9.staticflickr.com/8103/8549598429_22f161d92e_z.jpg", "id": 10175}, {"license": 5, "file_name": "000000010303.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010303.jpg", "height": 468, "width": 640, "date_captured": "2013-11-19 21:21:59", "flickr_url": "http://farm3.staticflickr.com/2509/4198054407_db61df57cc_z.jpg", "id": 10303}, {"license": 6, "file_name": "000000010346.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010346.jpg", "height": 480, "width": 640, "date_captured": "2013-11-21 20:02:54", "flickr_url": "http://farm4.staticflickr.com/3498/3456906266_7257a4a061_z.jpg", "id": 10346}, {"license": 4, "file_name": "000000010309.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010309.jpg", "height": 500, "width": 333, "date_captured": "2013-11-19 21:48:30", "flickr_url": "http://farm1.staticflickr.com/71/191429048_ad837c4f93_z.jpg", "id": 10309}, {"license": 5, "file_name": "000000010094.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010094.jpg", "height": 500, "width": 500, "date_captured": "2013-11-22 20:54:17", "flickr_url": "http://farm3.staticflickr.com/2289/2461849747_8a952aac05_z.jpg", "id": 10094}, {"license": 3, "file_name": "000000010358.jpg", "coco_url": "http://images.cocodataset.org/train2017/000000010358.jpg", "height": 640, "width": 478, "date_captured": "2013-11-18 15:08:37", "flickr_url": "http://farm7.staticflickr.com/6188/6135058772_4d7a2c78c7_z.jpg", "id": 10358}], "licenses": [{"url": "http://creativecommons.org/licenses/by-nc-sa/2.0/", "id": 1, "name": "Attribution-NonCommercial-ShareAlike License"}, {"url": "http://creativecommons.org/licenses/by-nc/2.0/", "id": 2, "name": "Attribution-NonCommercial License"}, {"url": "http://creativecommons.org/licenses/by-nc-nd/2.0/", "id": 3, "name": "Attribution-NonCommercial-NoDerivs License"}, {"url": "http://creativecommons.org/licenses/by/2.0/", "id": 4, "name": "Attribution License"}, {"url": "http://creativecommons.org/licenses/by-sa/2.0/", "id": 5, "name": "Attribution-ShareAlike License"}, {"url": "http://creativecommons.org/licenses/by-nd/2.0/", "id": 6, "name": "Attribution-NoDerivs License"}, {"url": "http://flickr.com/commons/usage/", "id": 7, "name": "No known copyright restrictions"}, {"url": "http://www.usa.gov/copyright.shtml", "id": 8, "name": "United States Government Work"}], "categories": [{"supercategory": "textile", "id": 92, "name": "banner"}, {"supercategory": "textile", "id": 93, "name": "blanket"}, {"supercategory": "plant", "id": 94, "name": "branch"}, {"supercategory": "building", "id": 95, "name": "bridge"}, {"supercategory": "building", "id": 96, "name": "building-other"}, {"supercategory": "plant", "id": 97, "name": "bush"}, {"supercategory": "furniture-stuff", "id": 98, "name": "cabinet"}, {"supercategory": "structural", "id": 99, "name": "cage"}, {"supercategory": "raw-material", "id": 100, "name": "cardboard"}, {"supercategory": "floor", "id": 101, "name": "carpet"}, {"supercategory": "ceiling", "id": 102, "name": "ceiling-other"}, {"supercategory": "ceiling", "id": 103, "name": "ceiling-tile"}, {"supercategory": "textile", "id": 104, "name": "cloth"}, {"supercategory": "textile", "id": 105, "name": "clothes"}, {"supercategory": "sky", "id": 106, "name": "clouds"}, {"supercategory": "furniture-stuff", "id": 107, "name": "counter"}, {"supercategory": "furniture-stuff", "id": 108, "name": "cupboard"}, {"supercategory": "textile", "id": 109, "name": "curtain"}, {"supercategory": "furniture-stuff", "id": 110, "name": "desk-stuff"}, {"supercategory": "ground", "id": 111, "name": "dirt"}, {"supercategory": "furniture-stuff", "id": 112, "name": "door-stuff"}, {"supercategory": "structural", "id": 113, "name": "fence"}, {"supercategory": "floor", "id": 114, "name": "floor-marble"}, {"supercategory": "floor", "id": 115, "name": "floor-other"}, {"supercategory": "floor", "id": 116, "name": "floor-stone"}, {"supercategory": "floor", "id": 117, "name": "floor-tile"}, {"supercategory": "floor", "id": 118, "name": "floor-wood"}, {"supercategory": "plant", "id": 119, "name": "flower"}, {"supercategory": "water", "id": 120, "name": "fog"}, {"supercategory": "food-stuff", "id": 121, "name": "food-other"}, {"supercategory": "food-stuff", "id": 122, "name": "fruit"}, {"supercategory": "furniture-stuff", "id": 123, "name": "furniture-other"}, {"supercategory": "plant", "id": 124, "name": "grass"}, {"supercategory": "ground", "id": 125, "name": "gravel"}, {"supercategory": "ground", "id": 126, "name": "ground-other"}, {"supercategory": "solid", "id": 127, "name": "hill"}, {"supercategory": "building", "id": 128, "name": "house"}, {"supercategory": "plant", "id": 129, "name": "leaves"}, {"supercategory": "furniture-stuff", "id": 130, "name": "light"}, {"supercategory": "textile", "id": 131, "name": "mat"}, {"supercategory": "raw-material", "id": 132, "name": "metal"}, {"supercategory": "furniture-stuff", "id": 133, "name": "mirror-stuff"}, {"supercategory": "plant", "id": 134, "name": "moss"}, {"supercategory": "solid", "id": 135, "name": "mountain"}, {"supercategory": "ground", "id": 136, "name": "mud"}, {"supercategory": "textile", "id": 137, "name": "napkin"}, {"supercategory": "structural", "id": 138, "name": "net"}, {"supercategory": "raw-material", "id": 139, "name": "paper"}, {"supercategory": "ground", "id": 140, "name": "pavement"}, {"supercategory": "textile", "id": 141, "name": "pillow"}, {"supercategory": "plant", "id": 142, "name": "plant-other"}, {"supercategory": "raw-material", "id": 143, "name": "plastic"}, {"supercategory": "ground", "id": 144, "name": "platform"}, {"supercategory": "ground", "id": 145, "name": "playingfield"}, {"supercategory": "structural", "id": 146, "name": "railing"}, {"supercategory": "ground", "id": 147, "name": "railroad"}, {"supercategory": "water", "id": 148, "name": "river"}, {"supercategory": "ground", "id": 149, "name": "road"}, {"supercategory": "solid", "id": 150, "name": "rock"}, {"supercategory": "building", "id": 151, "name": "roof"}, {"supercategory": "textile", "id": 152, "name": "rug"}, {"supercategory": "food-stuff", "id": 153, "name": "salad"}, {"supercategory": "ground", "id": 154, "name": "sand"}, {"supercategory": "water", "id": 155, "name": "sea"}, {"supercategory": "furniture-stuff", "id": 156, "name": "shelf"}, {"supercategory": "sky", "id": 157, "name": "sky-other"}, {"supercategory": "building", "id": 158, "name": "skyscraper"}, {"supercategory": "ground", "id": 159, "name": "snow"}, {"supercategory": "solid", "id": 160, "name": "solid-other"}, {"supercategory": "furniture-stuff", "id": 161, "name": "stairs"}, {"supercategory": "solid", "id": 162, "name": "stone"}, {"supercategory": "plant", "id": 163, "name": "straw"}, {"supercategory": "structural", "id": 164, "name": "structural-other"}, {"supercategory": "furniture-stuff", "id": 165, "name": "table"}, {"supercategory": "building", "id": 166, "name": "tent"}, {"supercategory": "textile", "id": 167, "name": "textile-other"}, {"supercategory": "textile", "id": 168, "name": "towel"}, {"supercategory": "plant", "id": 169, "name": "tree"}, {"supercategory": "food-stuff", "id": 170, "name": "vegetable"}, {"supercategory": "wall", "id": 171, "name": "wall-brick"}, {"supercategory": "wall", "id": 172, "name": "wall-concrete"}, {"supercategory": "wall", "id": 173, "name": "wall-other"}, {"supercategory": "wall", "id": 174, "name": "wall-panel"}, {"supercategory": "wall", "id": 175, "name": "wall-stone"}, {"supercategory": "wall", "id": 176, "name": "wall-tile"}, {"supercategory": "wall", "id": 177, "name": "wall-wood"}, {"supercategory": "water", "id": 178, "name": "water-other"}, {"supercategory": "water", "id": 179, "name": "waterdrops"}, {"supercategory": "window", "id": 180, "name": "window-blind"}, {"supercategory": "window", "id": 181, "name": "window-other"}, {"supercategory": "solid", "id": 182, "name": "wood"}, {"supercategory": "other", "id": 183, "name": "other"}], "annotations": [{"segmentation": {"counts": "P[Y22^<OaMd0kG\\OU89RF:^1^Ob80ZFg0j0[O\\9h17I6J6Ba`f3", "size": [400, 500]}, "area": 281.0, "iscrowd": 0, "image_id": 10005, "bbox": [187.0, 223.0, 9.0, 83.0], "category_id": 105, "id": 10012149}, {"segmentation": {"counts": "P^R32^<1O4L1O3M00001O00001O2N1O00O100O13M00N22NO12N2N1O1OO12N3M1O1O00001O2N00000000O100001O00O1001O00001O1O1OO1001O00O1O1N2001O00O1000000O1O11]DTO`;n003M001cDnNZ;R1fDnNZ;T10002N2N3MO1O1O1N20000MhDnNX;R1300N21OO100N20000MbDUO];k04001O00N2000000O1M32N00O11O00001OO1N[D[O0Na;g0_D]Oa;c0`D\\O`;e0_D[Oa;e05001O00001O00O1O1001O00001O1O00001O00001ON2H]DBd;>\\DBd;>]D^Of;<[DEi;;YDCg;>500O1000000GoC1S<OmCMW<3jCLV<44M201O_R50TaJ12OM4n;?M1OO1N2O100000000O1M314K[Y4KjfK4L2N001O1O00O1O12N00N2O1O100O1L40PY=", "size": [400, 500]}, "area": 4445.0, "iscrowd": 0, "image_id": 10005, "bbox": [252.0, 0.0, 214.0, 44.0], "category_id": 106, "id": 10012150}, {"segmentation": {"counts": "e2c4P8M00000O10O1001O1N10O1000O102dKnGV4U814L1N10O1001N0100003M0O011O1O0O01000000O010O100000O2O2N1O2M01N21OO11O0O2O00N2jLYLcMg3\\2ZLcMg3\\2ZLeMe3Z2\\LgMb3P2`LmJOS3a3\\1[LbK551HOU3`3\\1fLdK4P3V3\\1gLcK3Q3V3\\1\\McNe2]1[MdNd2\\1\\McNe2]1[McNd2^1\\MbNd2^1\\MbNd2^1\\MdNb2\\1^MeNa2[1_MdNb2\\1^MeNa2[1_MeNa2[1_MfN_2[1aMeN_2\\1`McNa2]1_McNa2]1_M^NPOnLd3d4\\M^Nf2b1ZM_Ne2a1[M`Nc2a1]M_Nc2a1]M`Nb2`1^M`Nb2`1^MaNa2_1_M`Nb2`1^M`Nb2`1^M_Nc2`1^M`Na2a1_M^Nb2b1^M_Na2a1_M_Na2a1_M^Nb2b1^M^Nb2b1^M]Nc2c1XM^KTOR3a3`1UMjNk2W1PMoNo2Q1gL[OJPLY3e4gLAOmKX3b4iLJP3o4J6J7H7J6K5L4L4M4K7I6J4M3M2N3M4L3M4M3L3M4L4L\\Yd04^f[O3M3N2\\OOfD4Y;b0O11O10N2N2O5hDeNR;\\1010O1O00001O1O0100N1100O1000O001N20O010OO2O1N1OKTEiNk:V1VEkNh:T1YEmNh:Q1YEPOf:n0\\ETOb:k0_EWO`:g0`E\\O^:b0dE_O[:`0fEBX:<jEFT:9lEJQ:6PFLo91SF0m9NTF4k9IWF9g9FYF>e9@\\Fb0c9]O]Fe0a9ZO`Fh0^9WOcFB[OX1P:UOdFC_OY1j9SOPGk0o8UOSGj0m8TOVGj0j8UOZGh0e8YO]Ge0c8ZOaG1fN4k9HcGNfN:h9EoG;S:00010O000000001O01O0001O000FCaD=_;F_D9a;J\\D7c;;000010O0000000000N1N5N5K4Ja\\7MhcHMla1OR^N1N3N2N3L1120N2M3K6LojZ2", "size": [400, 500]}, "area": 18332.0, "iscrowd": 0, "image_id": 10005, "bbox": [0.0, 85.0, 308.0, 315.0], "category_id": 124, "id": 10012151}, {"segmentation": {"counts": "doQ21^<100O00100O010O10O1000O100000OO2O1N2O1N101N2002M1O1O001O1OTD6FNP;LVEh0j:WOREn0n:ROnDR1Q;700O10000O10000O100O10000O10000O10000O100O10000O10000O100O10000O10000O10000O100O10000O10000O100O10000O10000O10000O100O10000O10000aLlMaLU2]3mMcLS2Z3PNeLQ2[3oMeLQ2^3mMaLS2Z3jM_I3V3T2`3lM`LT2`3lM`LT2`3lM_LU2a3lM^LT2b3lM^LT2b3lM]LU2`3iM[I0U3W2\\3lM`INR3W2^3iMbI0P3W2]3hMeI1n2W2]3fMgI3k2X2g3iMYLW2e3lMZLT2e3mMZLT2d3nM\\LR2c3oM\\LR2b3QN]Lo1a3SN_Lm1a3SN^Ln1b2kMUK7Y2n1c2jMSK9Z2m1d2iMRK;X2m1f2hMRK;X2m1c2kMUK8W2n1b2lMVK7X2m1b2lMVK8W2l1b2mMWK7V2m1c2lMVK8W2l1c2lMYK5T2o1d2kMcKKh1[2c2mMeKHg1[2e2lMfKGe1]2h1cMgKL6>c3T2`0cMeKM5?e3Q2:_MjK61L1b0j3m1:_MjK8NM0a0n3k1:`MhK\\1m3T1;QOEo0;QOEo0;RObLaNi1JbMc2S4RO`LgN[ODV21kMc2T4RO]LhN^OBV21kMc2U4RO^LeN\\OFT21mMb2U4SO]LdN\\OGT21nMa2U4TO]LbN\\OIR21PN`2U4TO]L`N^OLn11RN_2U4SO]LZNI0c14RN_2U4ROTO`NgL^2V4QOROcNfL]2X4oNPM_Nc06UN\\2X4oNmLcNd03WN[2X4nNmLeNd02WN[2X4mNiL`NmN031f18YNZ2X4nNeL`N[OO^1:YNZ2Y4oN_L\\NH1U1;[NY2Y4PORM]N7<^NW2Y4QORM[N6>_NV2Z4QOPM\\N5>`NV2\\4nNnLaN4U3o2XNiLiN6P3P3WNfLoN7l2S3TNdLSO8j2U3QNbLXO7h2W3oM`L^O6d2[3mM]LB5c2^3jM[LG5`2`3hM[L2G[2n3bM\\L9YO\\2[4ZM\\L;YO[2[4ZM\\L;YO[2[4ZM\\L;YO[2\\4YM[L<YO[2\\4YM[L<XO\\2\\4YM\\L;XO\\2]4XM[L<XO\\2^4RLgKi0c0h0YO]2^4oKnKg0:m0ZO]2P5eLeKo0[O\\2o4fLfKn0[O\\2l4iLiKk0[O\\2f4oLQLc0YO^2f4oLYL;QOf2f4oLaL3hNo2h4mLfLNaNV3k4kLbL0cNU3k4kL_L3eNS3l4jL[L]4\\3iJ`Kj0Q1`4]3lK^LX4a3iK[L[4e3eKVL`4j3aKQLc4o3]KmKg4S4YKiKk4W4VKgKk4Y4UKhKj4X4VKhKj4Y4VKgKi4Y4XKfKh4Z4YKfKf4Z4[KeKe4[4\\KeKc4[4^KdKb4]4^KbKb4^4^KcKa4]4]KeKc4[4[KhKd4Y4YKiKg4W4WKkKi4V4UKhKn4X4SKeKo4[4RKaKQ5_4QK]KQ5c4^100000000001OO11O00O11O0000O12N0000001O0000001O0000001O000000001O00001O000000001OO1O1O100O1O1001O00000000001O0000001O00001O1O001O00000000000000001OO10000000000000000O11OO100000000O100000000M31O2N0000000000O100000000000000000000O1", "size": [400, 500]}, "area": 51864.0, "iscrowd": 0, "image_id": 10005, "bbox": [168.0, 137.0, 332.0, 263.0], "category_id": 154, "id": 10012152}, {"segmentation": {"counts": "ilT23]<6J000000001O01O1O1O5JPgi3", "size": [400, 500]}, "area": 93.0, "iscrowd": 0, "image_id": 10005, "bbox": [176.0, 153.0, 12.0, 10.0], "category_id": 167, "id": 10012153}, {"segmentation": {"counts": "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\\2?O1N012N3L2O1N100O2O2OWFPNP9P2h01O0WFoMS9T2b04QFkM]9X2\\FlMd9_22M2M23N6KN10001O1O1N01dF\\Ml8d2SG`Mk8_2TGbMl8]2TGdMk8]2TGdMk8^2SGcMk8`2SGaMm8_2RGbMo8^2oFcMQ9^2mFcMR9^2mFdMR9m20000O1O11cLnF[3S903M001hLiFU3W9kLiFU3Y90002N2O2NN2N001N3O001LiLmFW3S9300N21OO101M20N2ONeLRGY3o831N3N1OO200000L30N32OO0O114lFcL10e8]3XGjLh8_31OO1NZL\\G0Nb3f8^L^Gb3b8_L]Ga3d8^L\\Gc3c85001O000010O0O1O1010O01O01O1O00001O00001ON3GWLhGi3Y8WLgGj3X8WLdGl3V8ULkGn3V8TLhGm3X8500O012N000GhKXHZ4h7fKTH_4k7bKTH^4l73K^KZHd4f7\\KZHe4e7\\KZHd4f730001O000000001O00001O0000UK]H112OM4Z4S8M10N0O100100O1O10O2L31OBjKbHV4^7>0001O0000001O00001O13L4M1N001O1O00O1O12N01M101O100O1L400M3000O11OO100000000O100000000M31O2N0000000000O100000000000000000000O1", "size": [400, 500]}, "area": 56847.0, "iscrowd": 0, "image_id": 10005, "bbox": [0.0, 0.0, 500.0, 159.0], "category_id": 169, "id": 10012154}, {"segmentation": {"counts": "X7m0c;d1\\N^1bN5J100000000O1000000O100000000O1000000O100000001N1000000O100000000O100000000O1000000O10000000000O1000000000000O10001O000000000O100000000000000O1000000000000O100000000000000O1000000000000SI^Kl5b4nIdKQ6\\4kIiKU6W4aISLR3YO0d4hLYLW3VOOa4jLYLV3YOJb4PMULV3\\OCc4WMRLT3^O_Od4]MnKS3BXOe4eMiKR3ESOf4kMeKQ3HoNf4PNbKQ3JkNf4TN`KP3MhNe4XN^Ko20fNc4[N]Kn24bNa4_N\\Ko28[N^4fNZKn2=VN[4lNXKm2`0TNY4oNWKm2b0QNX4ROVKl2d0PNW4TOUKk2g0nMT4XOUKj2j0jMR4\\OTKi2m0hMP4_OSKi2P1eMm3BSKh2T1bMj3FRKh2V1_Mi3IQKg2Z1\\Mf3MPKg2]1XMd30PKg2a7YM_Hf2b7ZM^Hf2b7ZM^He2c7[M]He2c7[M]Hd2d7\\M\\Hd2k2[Mk01ZLd2k2[Mk00[Le2j2[Mk00[Le2j2[Mk00[Le2j2[Mk00[Le2j2[Mk00[Le2j2[Mj01\\Ld2i2\\Mk0O]Le2h2\\Mk0O]Le2h2\\Mk0O]Ld2i2]Mj0O]Ld2i2]Mj0O]Ld2i2]Mj0O]Ld2i2]Mo1c2QN]Mo1c2QN]Mo1c2QN]Mo1b2RN^Mn1b2RN^Mn1a2RN`Mn1a2QN_Mo1b2PN^M?NUMe2[2]M?0UMd2[2\\M?2^LLOf2T3\\M?1]LO0e2S3[M?2\\L11c2S3ZM?2SLLO7;b2S3YM?3TLKM9=`2S3YM?2ULLL8>b2Q3XM?2WLLM6<e2P3WM?3XLKN4<h2n2VM?3ZLKO1;l2l2UM`03fLLOl2k2UM?3hLLNm2j2TM?4hLK0n2h2SM?4jLJ0P3f2RM`03jLL0P3e2QM`04jLL1o2e2QM?4lLK1R3b2oL`05lLK2R3a2nLa04mLL1S3`2mLa04nLL2T3^2lLb03oLM1U3]2kLb04oLM2U3\\2jLc03oLN3V3=kL>NR23QMN2W39SM<EX22RMO1Y34WM>_OZ22SMO2^3LTMe0]OZ21TM01c3DRMm0ZOY21VM00n4a0QKY20WM1On4a0QKX21XM0Oo4`0PKY20XM2On4`0PKa1NgM2920n4`0oJ_11gMO:40m4`0oJ^14eML<61k4`0oJ]17cMI?71j4`0oJ\\19cMF?:2h4`0oJZO2U19[NEe092i4>SL6YObNCg0:3g4?bKbN1Y1_OmN1JNk0:4e4a0YKjN?e0ZOAN>91g4\\1hL]O^NXOj4_1aL[OeNWOj4a1[L[OjNUOk4c1mKCWOkNm4j4SKgJ1Bm4f5QK]Jk5c5TJYJR6e5nIXJV6h5:N2O001N2O001N101N2O00JaJ\\I]5d6dJ]I[5a6fJ`IZ5_6gJbIW5]6kJeIS5Z6nJhIP5W6QKkIl4T6UKoIi4P6XKRJe4m5]KTJb4k5_KWJ_4h5bKZJ[4e5fK^JX4a5iKaJU4]5mKeJP4[5QLgJm3X5SLkJ>VOP2P6bMmJ7Bl1`5nMPK0XO^2g5cMiI\\OX1=lNR3T6UMgI_OZ1m3n4dLiIBV1j3Q5dLhIEV1e3R5gLhIFT1c3S5hLiIHQ1`3V5ZMhJe2X5iLiIN:^O4k3h5cMnIfN:e3i5QNWJo1h5lLjI=>g2g5SNYJl1h5mLjI>?d2f5VNZJj1e5oLlI>?b2f5bLkI20k0>a2f5ZNZJe1g5QMkI`0?^2e5RMmI`0>^2d5`LmI;0h0?\\2e5SMmIa0>\\2d5_LmI?0g0`0Z2c5^LmIb01g0>X2d5_LmIc01;OYO?Z3d5]LmIf01;3TO;^3e5TMmI:6QO8`3d5VMnI:j0_2W5WMPJ;h0]2W5ZMQJ9h0]2V5ZMRJ:h0\\2T5\\MTJ8i0Z2S5_MSJ8j0Y2Q5aMUJ6j0Y2P5bMVJ6i0W2P5dMWJ6i0V2o4eMXJ5j0T2m4iMXJ3l0T2j4kMZJ0m0U2i4kMZJ3j0Q2m4\\OhJ[MEX3c5QMYJT2:dMJW3d5N\\J1e5O[J1X5QMfJONe24;W5^MeJW259W5`0hJ@W5`MeJY246X5aMdJS25WMNU3Y5:jJcLMR3X5bMeJR24\\MOo2Y5cMdJY10hN2_O1m2X5dMeJX10gN1A3l2W5cMeJZ11nN4T2V5eMeJ11=0G7U2S5fMeJ13;NG9V2S5fMcJ179JI:W2R5eMcJ25=KC=Y2Q5dMbJ30b00]O?X2Q5eM`J30d00[O`0Y2S5`M^J[2`05U5YM_J?K5`0I1Y2X5SMaJe0F;<D5Y2c5POSJgN;X2`5kMnI=9_O:X2]5nMPJ9<A7X2a5lNZJlN5W2f5fMgI>a0D4W2c5gMhI=c0E2W2c5dN]JTO1W2c5bN_J[OJS2g5fMgI:j08]Oh1R6eMgI:m09ZOf1R6gMgIO1Lm0j0WOd1T6fMjIK3Ni0j3Z5\\LoIHO0j0k3X5]LPJF01h0l3W5]LRJFT1m3j4\\LeKb3]4\\LdKd3]4ZLdKe0TNk1M^M[61fKc0XNi1HcM[6OfKc0\\N\\OCW20kM\\6MgKb0\\N_OAV21lM[6LiKg0UN[OGT21nMc8b0eG\\OGS22nMd8b0cG]OHQ22PNc8b0aG^OLo10QNb8a0\\GKNb15RN`8Y3aGgL_8W1[Gd08TN\\8V1`Gd05VN[8S1dGe02WNZ8R1\\GmN8j12WNY8P1`GoNN31e19YNY8k0aG^OL]1;ZNZ8f0\\GI0T1<\\NZ8c0WG43k0=^NZ8[1YG6>_NZ8Z1YG5>aNX8V1`G7T9ERG8o8FUG8k8FXG9h8F[G8e8F`G7a8GbG6_8HeG4]8MQH_OT8b0mGYOV8h0jGXOV8h0jGXOV8h0jGXOV8h0jGXOV8g0kGYOi6NkHi0<YOh60kHg0=YOg62fG3i0b0i0ZOg63dG9g09n0[Oe6f0]HOn0[Od6f0_HOm0ZOd6?gG@n0g0g0ZOd6?hH9b0XOf6?hHa0:POn6?hHi02gNS7f0iHi0N`NY7i0iHc0Y8^OfG>_8YOdFDm0P1b8YOgFCh0P1e8[OhG`0a8TOUF1\\1g0X:K6K4L4N3O01O01O100O100O100O01M2N2N3M01O3L4MST]1", "size": [400, 500]}, "area": 64590.0, "iscrowd": 0, "image_id": 10005, "bbox": [0.0, 83.0, 384.0, 317.0], "category_id": 183, "id": 10012155}, {"segmentation": {"counts": "kd]27W:n2oEmLh9[30000000001O=C>B>B>B6J00000000000000000000000000000000000000000000O100000000000000000000000000PKUH`4\\8O1jL`G_1d8\\NbG`1P9lMWGJHY1Z9aNPG5OQ1S:mNnES1S:kNnEU1U:fNmEZ1R;00000001O0gNYDn0h;QOXDo0h;nNXDL071d0h;XOWDM162c0g;XOXDNO553L5i;DWDOO48ON8i;EQD2:O16e;IPD1l02V;MoC0W=0Sm[20PfcM5[:MZE3`1NdN8a:3PFKEGEe0[:[OmE?<ND;Q:^OhEJOa0c0M_O?W:N[Fd0f9\\OVFg0h9\\OUFf0n9YOPFg0i9@WF`0Z9OfF1k8>TGCh8b0WG^Oh8c0WG^Oh8h0PGbM1f1o8k0PGUOi8ZNWG^4a8:_Oa0000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000RLPHa2f^f1", "size": [427, 640]}, "area": 19514.0, "iscrowd": 0, "image_id": 10008, "bbox": [186.0, 0.0, 324.0, 172.0], "category_id": 109, "id": 10012156}, {"segmentation": {"counts": "ogZ1X4S900000000000000000000000000000000000000000000000000000000000000000000O10000001O000000000000001O000000O1O1O1O1O1N2N2O1N2N2N2N2O1NC]LVGa3k8`LhFO8b3Q9bLlF`3U9`LhFa3Y971O010O2N1O2O0O2O1O001N101O001O0O2O001O001N2O001O001N@QMgFo2Z9`01O1O1O1N2O1O1O1O1N2O1O1O1O1N2O1O1O1O1N2O1O001ROgEiNZ:m0eEZN8h0T:l0QFROQ:n0oEQOR:n0oEQOR:o0oEoNR:0^E^O0n0?CT:0mE0ONV:2kEO0NV:2kE0OMW:3kEONMX:4jEONLY:5iENOKZ:6hEONKZ:6hEONKZ:6iENNKZ:I^GORZW5", "size": [427, 640]}, "area": 15804.0, "iscrowd": 0, "image_id": 10008, "bbox": [102.0, 170.0, 137.0, 188.0], "category_id": 110, "id": 10012157}, {"segmentation": {"counts": "Uhl0b0i<0000000000000000000000000000000^Ob000000000000000000000000000000000nNR100000000000000000000000000000000000000000000000000000000000000000000O10000001O000000000000001O000000O1OiNQE4n:ZOfEe0Y:oNTFQ1S;01O00001O00001O00001O00001O00001O0000001O00001O00000000000000000000001O00000000000000000000001O00000000000000000000001O00000000000000000000001O00000000000000000000001O000000000000000000000000000000000000000000000000000000000000000000000000O10000000000000000000000000000000000000000000000000000O100000000000000000000000000000000000000000000000000O100000000000000000000000000000000000000000000000000000000O1000000000000000000000000000000000000O1L4L4L4L4L4L4L4G9F:F:J6M3h2XM0000000000000000000000000000O1000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000O10000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000O1000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000O1000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000", "size": [427, 640]}, "area": 12266.0, "iscrowd": 0, "image_id": 10008, "bbox": [68.0, 335.0, 572.0, 92.0], "category_id": 115, "id": 10012158}, {"segmentation": {"counts": "Q9Z4Q900000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000^Ob000000000000000000000000000000000^Ob00000000000000000000000000000000mQP7", "size": [427, 640]}, "area": 13158.0, "iscrowd": 0, "image_id": 10008, "bbox": [0.0, 289.0, 102.0, 138.0], "category_id": 156, "id": 10012159}, {"segmentation": {"counts": "R^Y8;P=[1eN[1eN5]H", "size": [427, 640]}, "area": 264.0, "iscrowd": 0, "image_id": 10008, "bbox": [636.0, 238.0, 4.0, 102.0], "category_id": 167, "id": 10012160}, {"segmentation": {"counts": "0X4S9000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000_Oa000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000O10000001O`0@000000001O00_OZG]Lf8c3a000O100O10000O100O10000O100O10000O100O10000O100O10000O1000000000000000000001O0000@YF\\Ml<MdiU4KWUjKf3fM000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000", "size": [427, 640]}, "area": 38981.0, "iscrowd": 0, "image_id": 10008, "bbox": [0.0, 0.0, 640.0, 136.0], "category_id": 172, "id": 10012161}, {"segmentation": {"counts": "X4X4S900000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000_Oa0000000000000000000000000000000000]Nc1000000000000000000000000000000000000000000000000000000000O10000001ZM`dS1BY^lNOSC8k<500O101nN@XD0n0?i:1jDOV;1iD0V;1jDOV;2iDNW;2iDNW;2hDOW;2iDNW;2iDNW;3hDNj:h1G2N1000000000000000000000000_OeMRFZ2l9kMRFY1EWOZ:EmET1KTOZ:JgES11oN[:b1hEZN[:e1gEVN]:i1eERN_:m1`0O1OO12N001N2O1N2O1N3N2N1O100O1O1O100000000O1000O1MdNVD]1i;300O1O010000000000000000000000000000000O11O0000O11O000000000000000000O10000001OO1000000000000000000002N100O0aNVD]1n;N00000000000001O000000002N8H3M3M3M3N1N1N2oDmMh:]2O0VEfMa:d2N1O1O1O2N1O1O1O1O2N1O1O2N3M2N3M2N2N3M2N3M<D7I3M4L4LZNf1]Oc00000O10000O1000000O1000000000000000000000000O100000000000000000000000000000000000000000000000001nMYE^1h:[N^Ee1T;0000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000]Nc10000000000000000000O10000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000O100000000000000000000;E00000YL", "size": [427, 640]}, "area": 50470.0, "iscrowd": 0, "image_id": 10008, "bbox": [0.0, 118.0, 640.0, 188.0], "category_id": 177, "id": 10012162}, {"segmentation": {"counts": "SPR36k<;O1iDA`9c0[FC`9P1lEWOJHX9[1bFoN5OQ9S2mFnMS9S2kFnMU9U2fFmMZ9R300000001O0gFYLn8h3QGXLo8h3nFXLL071d8h3XGWLM162c8g3XGXLNO55d8e3XGWLOO48c8g3YGQL2:e8e3YGQL1=d8b3[GRL0<e8f3[GZLe8V40000000N21OO100000000001O1O000000000000000000000000O1MZG`Kg8_4300O1O100000000000000000000000000000000O11O0000O11O000000000000000000O10000001OO1000000000000000000002N1O1O0WG`Kg8d4N00000000000000000000000000000000001O00O1001O0000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000QN^G[M3`1a8T1hGQNKEGEf8Z2ZGnM?<ND\\8P2]GiMJOa0c0M_O`8V2mG\\Ne8e1[GWNh8g1[GVNg8m1XGQNh8h1_GXNa8Y1nGgNR8j0]HUOd7g0aHXO_7g0bHXO_7g0gHQObM1g9n0jHQOV7h0YFXO_<`0a_[2", "size": [427, 640]}, "area": 30655.0, "iscrowd": 0, "image_id": 10008, "bbox": [235.0, 0.0, 224.0, 150.0], "category_id": 181, "id": 10012163}, {"segmentation": {"counts": "WUl1=n<>B>A?B>A?B4L0O100O1UKZMQOf2<nMCQ21\\NNe10]N0c1N`N0a1NaN2_1MbN3^1KdN4\\1KgN4Y1JiN5W1JkN6U1ImN6R1IPO6Q1HQO8n0GUO7l0HUO8j0GXO9h0E[O9e0F]O:c0E^O;b0CA<>CD=<AF?9AH?8_OK`04@M`03_ONa01_O0a00]O2d0L\\O5d0K[O6e0I[O9d0G[O:e0EZO=f0CYO>g0BXO?h0@XOa0h0_OWOb0i0]OVOe0Q1TOnNm0`1dN`N]1n1UNQNl1]2eMcM\\2k2VMSMm2Z3dLfL]3c61O1O1O1N2O1O1O1O1N2O2N1O1O1N2O1O1O1O1N2O1O1O2N1O1N2O1O1O1O1N2O1O1O1O2M20000000000000000000001O0000000000000O1TOSKWIP5h6RKUIP5j6RKRIQ5l6RKPIQ5o6QKlHS5S7nJiHV5V7`0O1O1O1O001N2O1N2O1N2O1O1O100O1O1O100000000O100000000000000O0100000000000000000000000000000000000000000000000000O100000000000000000000000000000000000000000001O00000O1000000000001O000000006J5K3M3M4L2N1O1]J_HZ5b7cJaH\\5i7iJRHm4W8O2N1O1O1O2N1O1O1O1O2N1N2O3M2L5I6J6J7I6J7H7Hf0VO>B=D=I7I\\N`4dLc00000O10000O1000000O1000000000000000000000000O10000000000000000000000000000000000000O100000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000O1000000000000000000000000000000000000000000000O100000000000000000000000000000000000000000000000000000000000000000000000000000O100000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000O100000O100000000000000000000W1iN\\1dN[1eN[1eNdC", "size": [427, 640]}, "area": 88635.0, "iscrowd": 0, "image_id": 10008, "bbox": [144.0, 108.0, 496.0, 314.0], "category_id": 183, "id": 10012164}, {"segmentation": {"counts": "S^]51o>0M301O000VALh>7YAIc>:00000002N00O1000000L]AKc>4^ALb>4^ALb>4^ALb>3_AMa>3_AMa>3_AMa>3`AL`>4`AKa>58MO11O0O2OoV42mhK2ZAL_>6`AK_>=N00001OO1001O0000O1000000001OO100002NO100O1001O1O00O10000001OO1O11O000000001OO11O00N2001O00O11O00O10000001O1O0000003MO1N20000000000000000001OO1O1000000O1001O000000000000001O000bAA[>a011O000000O1001O00N21ON2000000000000O1001OO1001OO1001O0000O10000001O001O00O12NO1000000000000000000000000001OO1001OO1000000000000001OO10000001OO100001OO1000000001OO11OO11OO1001O00000000000000000000000000000000001OO100000000000000000000000000001O000000O11O00000000000000000000", "size": [480, 640]}, "area": 4226.0, "iscrowd": 0, "image_id": 10012, "bbox": [370.0, 0.0, 270.0, 20.0], "category_id": 99, "id": 10012165}, {"segmentation": {"counts": "n0R>n0000000000000000000000000000000000000000000000001OO100001O00O10000001O00000000000000001O00O1001OO11O00O11O00000000000000000000000000000000000000000000000000000000001O000000000000000000000000000000000000001O0000000000000000000000000000000000000000000000001O0000000000000000000000000000000000000000001O00001O1O00O100O10000000000000000000000001O00O100001O0000000000000000000000000000000000001OO11O00000000N2000UBmNi=U1000O11O00O100N2001OO1000000O11O1OO10000001O00000000001O002NN2O11O000000O13MN20VBlNh=V10002N00L40000O11O1O00N21O1OO1000000000000O11O00000000O1001O00O11O00000000000000001OO11O1OO1001O003MO1N200000000001O00O100O1001O0000O11OO11OO11O00001O00001OO100O1O11O0000000000000000001OO10000001O1O00O12N2N00M30000O1000000MZBlNf=T1ZBmNe=S1[BlNf=T130000O1001O0000O100000000000000N21OO100001O1ON26J2N3M00000000001O000000000000001O0000000000000000000000001OO1001O0000000000000000000000O11O00001O000000000000O11O0000000000O11O00000000OhHkNg0U1jN[OU1f0WLkNeNm0T58UL]OkMg0P6LTLCbMf0Z6GRLJYMf0d6@QLU2o3kMPLV2P4jMnKY2Q4gMnKZ2R4fMlK\\2T4dMjK^2V4bMiK`2V4`MjK`2V4`MiKa2W4_MiKa2W4_MhKc2W4^MgKc2Y4]MgKc2Y4]MdJbNAS4j5\\M[JkNJn3f5WMXJTO0j3d5RM\\JVOMo3a5kLaJXOMS4\\5eLgJZOJX4Y5^LmJ[OI]4T5XLSK\\OGb4Q5RLXK^OEe4n4mK]K_OCi4l4hKaK@Am4j4cKeKA@P5T1PJQ2_1[MB^OT5o0RJT2X1_MC\\OX5j0QJZ2T1`MDZOg6V3eI`MEXOg6X3dI`Mf7`2ZH`Mf7`2ZH`Mf7`2ZH`Mf7`2ZH`Mf7`2ZH`Mf7`2ZH`Mf7`2[H_Me7a2ZHaMf7^2[HaMe7_2ZHbMf7^2[HaMe7_2[HaMe7_2ZHcMn3WOSNV3oMcMR4\\OfMQ3XNeMS4AZMk2cNhMS4BQMf2lNlMR4DiL`2UOPNQ4FaLZ2^OTNQ4HWLT2IWNo3KoKn11\\NP4KeKj1<^Nn3N]Kd1e0bNn30SK^1o0fNm32kJX1X1gNP46_JS1a1gNS4a3mK`LV4\\3jKdLY4Y3gKgL]4U3cKlL_4Q3aKoLc4m2]KSMf4j2ZKWMi4e2WK[Ml4b2SK_MQ5]2oJdMS5Y2mJgMV5V2jJjM[5Q2eJPNa5i1_JWNh5b1XJ^NU2lMN_3nMeNS2RN0R3mMlNR2YNMi2QNnNR2`NFb2XNoNP2fNB[2^NoNP2lN]OT2cNQOn1ROYOm1hNROo1XOROf1POQOn1_OlN`1VOROl1EiNX1[OSOl1KcNR1ASOk12^Nk0FUOk17YNc0LVOk1=SN=3UOi1e0nM69VOh1i0jM1>VOg1n0hMKa0WOg1Q1eMHd0ZOc1S1dMDi0^OZ1V1fM_OP1AQ1W1hM[OW1Dg0X1lMWO]1G>l4BhK@VLQOV8_1YLbNnKKi7c1U5000000001O001O00O11O0000O11OO1000000O100001OO1001O00O100001OO10000001OO11O001OO11OO10000001OO11O0000O1001O00000000000000000000000000000000001O0000O11O00O11O001ON21O0000O12NO1", "size": [480, 640]}, "area": 261093.0, "iscrowd": 0, "image_id": 10012, "bbox": [0.0, 30.0, 640.0, 450.0], "category_id": 145, "id": 10012166}, {"segmentation": {"counts": "0n0R>0000000000000000000000000000000000000000000000001OO100001O00O10000001O00000000000000001O00O1001OO11O00O11O00000000000000000000000000000000000000000000000000000000001O000000000000000000000000000000000000001O0000000000000000000000000000000000000000000000001O0000000000000000000000000000000000000000001O00001O1O00O100O10000000000000000000000001O00O100001O0000000000000000000000000000000000001OO11O00000000N2000UBmNi=U1000O11O00O100N2001OO1000000O11O1OO10000001O00000000001O002NN2O11O000000O13MN20VBlNh=V10002N00L40000O11O1O00N21O1OO1000000000000O11O00000000O1001O00O11O00000000000000001OO11O1OO1001O003MO1N200000000001O00O100O1001O0000O11OO11OO11O00001O00001OO100O1O11O0000000000000000001OO10000001O1O00O12N2N00M30000O1000000MZBlNf=T1ZBmNe=S1[BlNf=T130000O1001O0000O10000oNUBm0k=SOUBm0o=01O000ROPBj0U>WOkAf0W>O1000011OON17J2N3M0LPOXBP1g=QOYBo0g=QOYBo0g=QOYBo0f=ROZBo0e=QO[Bo0e=QO[Bo0e=ROZBn0f=ROYBo0g=5JkNaBU1_=jNbBV1^=kNaBV1^=jNaBW1_=jN`BV1`=500000000000000000dNbBV1^=hNdB4Lo0f=PO[Bn0n=N01O01OO1001O0000O10000000O20N100011NO100O1001O1N11N10000001OO0020O0000000O2OO20O00N2001O00O11O00O10000001O10O000012MO1N20000000000000000001OO1O1000000O1001O01OO11OO11O00O2O000oNTBn0n=11O0001OO02O01O00N21ON2000000000000O0101OO1001OO1010O0000O1000O110O001O0O012ON1000000000000000000000000001OO2O010N10O11O0000O11OO2OO1000O101OO2OO1010N10O1000010N011OO110N0110O01OO11OO10000001OO11O0000O1001O0001OO100000000000000000000000000010O000O11N11N11O001ON21O0000O12NO]O", "size": [480, 640]}, "area": 22444.0, "iscrowd": 0, "image_id": 10012, "bbox": [0.0, 0.0, 640.0, 56.0], "category_id": 172, "id": 10012167}, {"segmentation": {"counts": "gdc6;V>?]MBVFm0g9E]Eg0b:_OTEf0j:AkDe0S;f1N1N2O1N3M2O101N100O1O2O0mNoKVGS4`8WL_Gn3T8[LjGj3R8XLkGo3n7SLQHS4i7oKTHX4f7iKYH]4a7dK]Hb4^7`K`He4[7\\KcHi4Y7XKeHm4W7TKhHP5T1PJ^4Q1\\JT5o0RJa4k0^JX5j0PJh4i0\\Jf6d5[IZJf6f5Z101O000000000001O000000001YL^JnMb5V2cJaMa5^2eJYM_5g2gJoL]5P3iJfL\\5Y3jJ^LZ5b3lJTLX5k3nJlKV5T4oJcKU5\\4RKZKR5f4SKPKQ5P5VKfJj4^5Z23N3L3M4M2M4L3N3L3M4M2M3M5L6I7I\\LcHLW73oHNj61]IJb66dIE\\6:kI_OU6`0RJZOo5e0WJVOh5i0_JQOa5o0fJjN[5U1kJeNU5Z1RKaNm4_1YK[Nh4c1`KVN`4j1fKPNZ4P2lKkMT4S2QLiMo3W2TLfMl3Y2YLcMk3W2[LcMm3S2ZLgMo3o1YLjMP4m1WLlMY4b1Z5POWB_OPoY1", "size": [480, 640]}, "area": 19210.0, "iscrowd": 0, "image_id": 10012, "bbox": [451.0, 77.0, 100.0, 300.0], "category_id": 183, "id": 10012168}, {"segmentation": {"counts": "RXY33W=3M3M4L5K1[C^O`<g0ON28H2N003M4L4L3M3ML42N2NL4ZOUY4ZORhK:K4L3M4K4M4I6L4N01M01200M0ROeDNY;2jDNW;OlDO``P43ekoK4N3WMFfE050T2>l7LTH9g7GXH>e7A[Hd0_7\\OcHg0Y7YOfHh0Z7XOfHh0Z7WOfHc0lMUO]99hH?mMXO[99gH;gM_O7M[98gH=gM^Oj96bH8eMBi98_H7hMAi99_H3jMCe9=cHAeM0520Oc9`0kH@cM0b9a0SI^Om6d0l2001O0O01O12M010O2OOgN", "size": [426, 640]}, "area": 1871.0, "iscrowd": 0, "image_id": 10014, "bbox": [253.0, 0.0, 387.0, 170.0], "category_id": 96, "id": 10012169}, {"segmentation": {"counts": "0a1i;00O1000000M300000000002N00000000002N2N1OO10000000000O10000000000O11O1O001O000000000000O10000000000001O1O001O00000000000000000000000000O1000000000000000000000000O1O1O1O1000000001O000000001O00000000O1O100O1001O0000001O00001O001OO10000000000001O00001O00001O000000O1O100O1O100000000001O00001O001O00O1O1O100O1O10000001O0000000000001O00000000O100O11O00001O0000000000000000000000O1001O000000000000001O0000000000O1O10000001O000000001O000000001OO100O10000000000001O00O11O00000000;E4L2NO100001O0000000000000000000YEmMZ:`23N2N3M4K1nMkDm1Z;ON28H2N0O4M4L4L3hNPDP1Y<K5L3]Ol^i4", "size": [426, 640]}, "area": 14056.0, "iscrowd": 0, "image_id": 10014, "bbox": [0.0, 0.0, 271.0, 87.0], "category_id": 102, "id": 10012170}, {"segmentation": {"counts": "dkP12W=1O1O1O1O1O1O1KHWC9i<4O1O1O1N2M3M3M3M3M3M3M3MJRDVOk;c0_D]O^;=kDCQ;7ZEHe:OeE1j;0000000000000000000000000000000001O0000000000000000000000000000000000000000000000000000000000001O000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001O000000000000000000000000000000000000000000000000000000000000oN9_DGa;Q16J8H8H8H2N0000000000000000000000000000000000000000000000000000000000000000000000CMdC2k<O]kb4", "size": [426, 640]}, "area": 1537.0, "iscrowd": 0, "image_id": 10014, "bbox": [78.0, 372.0, 208.0, 54.0], "category_id": 105, "id": 10012171}, {"segmentation": {"counts": "Rhe382Le<?oCBU;?cDMX;5`D2a;l0O2cDbNj:Q2N001PEgMn:a2G3O\\M\\Ed2d:OO42NK21YM_Eb2g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lD\\N050OU;_1UE_Nm:a1SE^Nn:c1<O1000000O10000O10000M3O100O1000000O10000O100O100", "size": [426, 640]}, "area": 17784.0, "iscrowd": 0, "image_id": 10014, "bbox": [283.0, 0.0, 357.0, 103.0], "category_id": 106, "id": 10012172}, {"segmentation": {"counts": "m:]2m:0O100O1O100O100O100O100O1O100O1O100O100O11O1O1O2N2N2N2N2N1O2N2N2N2N1O2N2[OfDVO\\;g0fDXO\\;e0fDZO\\;c0fD\\O[;b0gD]O[;`0gD_O[;>gDA[;<fDD\\;9dDH];6dDJ^;3bDN`;OaD1a;L`D4b;I^D8d;E]D;d;C\\D>X<0O10000O100O100O10000O100O100002N2N1O001O001O1O001M2O2N2N00001O001OndU7", "size": [426, 640]}, "area": 4012.0, "iscrowd": 0, "image_id": 10014, "bbox": [0.0, 337.0, 87.0, 89.0], "category_id": 115, "id": 10012173}, {"segmentation": {"counts": "]XU33ha42^kK0N>lN^OgDe0S;^OlDf0h:CWEK_O99BZ:>nEEB<3Af:6dEME>LAd:<kEDEb0H_Oh:;kEEEe0V:VOmEa06DGc0V:^OjE9:HE>X:BiE94ML3b8AkH6dN87LI0c8JeH2fN:8MJIa86cHJlN:3>b8_OXGO=S2_8cN\\G^1b8e1\\Od0[Oe02N2N2O1N2O1N2O1N2aKQH^OLn3T8_L[H7Bh0L7X8iN[H0Mg0J9P8oN[HI2o0G3n7VOWIf0nNOm7[OWIKbN8>=j7@iI0aN;h7EgI0eN5f7KfICgM7S17a70UKKm43VKJl46XGDi36P58PKFR58RKDP5`0kJ@V5=m30UCCi<`0O1O00001O00001OO1N2L4K5K[QX4", "size": [426, 640]}, "area": 3830.0, "iscrowd": 0, "image_id": 10014, "bbox": [243.0, 0.0, 70.0, 202.0], "category_id": 132, "id": 10012174}, {"segmentation": {"counts": "^6_4`9[Oh0WOh0YO9F4L4M3L4M3L4M3L4M3L4L4M3L4L5L3L4M3L3Odd\\33_nbLMY<`0\\CAa<h0J3N2N2N2L4G9F:G9G9K5M3N2N2N2L4N2M3000gMjE^1V:bNjEo00eNW:;iEP10eNW:;jEn0i:oNWEP1n:bN^EIE0N022NQ1T;oNSgQ41jZnK4L52MO2J51O3N2MN3O02NN3L301O0VJ", "size": [426, 640]}, "area": 3037.0, "iscrowd": 0, "image_id": 10014, "bbox": [0.0, 184.0, 640.0, 242.0], "category_id": 140, "id": 10012175}, {"segmentation": {"counts": "X_k31V=0O2O1O1N011O0O4M3mCDU;?kDAo00Z8b0fF^OP10X8d0hF]On0>k79TGYOP1?k7=gF@IE0N022NQ1b0\\8?cFc1\\9V1N2O1O1O1O1N2O1O1O1O1N2O1nNR11O1O1O[2eM1O1O001O1O001O1O1O001O1O001O1O001O1O0000000000000000000000000000000000000000O10000000000000000000000000000000000000000000000O10000000000000000000000000000000000000000000000O10000000000000000000000000000000000000000000000O10000000000000000000000000000000000000000000000O10000000000000000000000000000000000000000000000O10000000000000000000000000000000000000000000000O10000000000000000000000000000000000000000000000O10000000000000000000000000000000000000000000000O10000000000000000000000000000000000000000000000O10000000000000000000000VN^MlHb2o6hMlHX2R7nMjHR2U7TNfHl1X7WNfHj1X7ZNbHj1\\7YN^Hl1_7YN[Hk1`7\\NZHh1\\7cN^Hb1X7]2H8OnIQIk5X7O000000000000001O0000000000000000000000000000000000001O000000", "size": [426, 640]}, "area": 33519.0, "iscrowd": 0, "image_id": 10014, "bbox": [296.0, 226.0, 344.0, 200.0], "category_id": 149, "id": 10012176}, {"segmentation": {"counts": "\\hf71Y=4L5K4L5K4L5K4L5L3L5K4M4K4L5L3L1O002M1N2O0O2O2OO10O0mElMR9U2kFmMU9R2gFgM_O7i9R2iFgM^O8h9T2fFeMB7j9Q2eFhMA7k9Q2aFjMC7k9Q2SFeM0520O8k9V2UFcM096GU9e2dFfM2H\\9\\35102M3LO1120MO2M4OO1nM", "size": [426, 640]}, "area": 3511.0, "iscrowd": 0, "image_id": 10014, "bbox": [593.0, 60.0, 47.0, 131.0], "category_id": 169, "id": 10012177}, {"segmentation": {"counts": "a1b32YM:WOQ8_3gGYMf8^2[GlL1d0e8^2^GkLNg0d8]2hGaMY8_2gGaMV8b2jG]MW8c2jG\\MV8d2jGoL]O4j8m2mGjLZO9i8m2QHRMP8n2QHQMQ8m2PHQMQ8o2oGQMQ8n2QHQMo7o2V1O102M3N1N00100O100O10O10000000000O11O1O001O00000001O000O101O1O001O1O01000O010O001O1O001cFoL_8R3^GPMb8P3\\GSMc8n2ZGUMe8k2YGXMf8i2WGYMi8h2TG[Mk8e2SG^Ml8c2PG`Mo8b2nFaMQ9]3O1O001O1O001O1O001O1O1N1O2N2N101O1O1O010OMaG\\K^8`48O1O2MQHdKT7[4n0O1O1O0XOXGoLh8P3]GlLd8S3_GjLa8W3`GeLc8Z3h0N1O10001O00001O001OO1000001O000010O00010O01O0^FfLU9Z3iFiLU9W3kFjLT9W3jFlLT9U3iFmLV9c30010O0001O1O1O1O101N10001N101O0O01O1O10O01O10000001O0000000001O10O01O001O0O2O0O3O1N2N2O1N3M00000000001OO1MoFmKQ9o37N101O2M2O1O1O1N2O1O1N3M2N2N2O1000O2N100000010O000000100O1bFiLg8Y3PGQMm8\\3:O2OO01O010O1I7N2N200O2NlFdL\\8\\3h0020O00001O0;F3LjERMR:l2501O1O2N1O1O1000000O100001OgM_Ej1P;2OL400N1YOlDYOS;e0PEZOP;>YEAg:;]EEc:c0UE\\Ol:=\\ECb:>^EBb:2nDFa06a:2TEE9:c:0UEF94g:6PEG87c:3UED:8b:4TEF:3a:7e1K^Pi4", "size": [426, 640]}, "area": 30075.0, "iscrowd": 0, "image_id": 10014, "bbox": [0.0, 45.0, 272.0, 176.0], "category_id": 173, "id": 10012178}, {"segmentation": {"counts": "S52k0:U;HjDo0U:ROUE0e0g1\\9]NlENg0\\2e8oMZG[2]8fMbG]2[8cMdGa2Y8_MgGd2V8]MhGg2U8]MZGYO:]3Y8^MeGf2X8[MgGh2V8XMiGl2T8UMkGn2Q8TMnGo2o7Y1L4MhKSHV3i7Z1L3L4L4M2M3N1N3N2N2N2N2N1O2N2N2N2N1O2N2mJTId3n6YLTIf3n6WLTIh3n6ULTIk3l6SLVIl3l6QLVIn3l6oKUIQ4m6lKTIU4m6hKSIZ4m6dKTI\\4n6aKSI`4n6]KRIe4o6XKRIh4P7UKPIm4Q7PKPIQ5P7mJQIS5b72O0O2O1N101jHhJX6Y5eIiJZ6X5cIlJ\\6U5aInJ]6S5aIPKm4GoKZ5QOQK`6Q5]IRKb6n4\\IUKc6l4ZIVKe6l4XIWKh6f51O1NXLcIc0^6XOdIk0\\6TObIo0]6PObIR1_6m20O01000O10O01N2O0O2N2N2O0O2NNdIYIZ6d652N2VJYIP5h6WKWIb4k6l02N2N2NZOTJSJj5d0oIU4<SKc5a0[J[45QK^5>fJa4MmJ]5;PKg4i1VKg0LjMm4a1UKb0IVNQ5Y1VKV2j4kMTKV2l4kMRKV2n4kMPKV2P5jMoJW2Q5jMnJV2R5kMlJW2S5P3000000001O00001O00001O001YHiJY7W5fHkJY7V5fHlJX7T5gHoJX7Q5eHRKZ7Q5bHPK^7]51O100O1O1O1O1O1O100O10000O10000O1000000O1000000000000000000001O1O001O001O001O002N2N2N3M2N2O2M3M4L3M2N2N2NZOYIkJe6Q5eIjJZ6T5jIlJ:An4b5lJlJ3CQ5`5oJlJNES5]5SKnJFGW5Z5WKkJ^O1Z5S5[KcJG5n4W5[LeJe3Y5W2O1O1N2N2N2N2O100001O0000000cIPK`4Q5]KmJg4S5WKmJk4S5SKmJo4S5nJnJCO]4T5mKmJHO[4U5QKlJ60`0OY4V5jJWK5Dj0OW4X5bJ\\K1^O3OU10T4d5QK`Jm0LR4c5TK]Jl00P4c5SK\\Jo01n3b5SK]JP12l3`5ZL^JeN2Q5`5[L^JdN1Q5`5Z2I7N2N]HjJV7V5hHkJY7T5fHkJN0U7T5lHlJ31n6S5j0O11O00001]MkJoLU5eMkJT55SMQ5hMjJT59QMm4gMoJY3W1VMROg1g4V1aLlLmNj1a4o3oJmKP5m0oJ[2P5VKoJQ20W1=_Mc4[ORKk1Me1V1ULj3>RKf1NP2R6W3H8H8H8G2O0001O2N1mInJl3T5jKPKZ4R5_KPKd4]5kJdJX5`5cJ^J`5e5TJTJF5Z6U5dIVK8CG0`6U5dIZK1F^6o4cI]KJCf6P5`IfKb6[4^IbKd6^4\\I`Kf6b4YI[Ki6f4XIUKk6m4VInJl6T5TIhJn6h52N2N2N2N2N2N2N2O1N2O1N2O1N2OAiJmHV5R7UKeHi4[7b00001O00001I6I7I8G8I7O2NSOcJQJ[5P6hJPJU5Q6mJnIQ5S6QKlIl4U6S11O0O2O2N110O01H8I7I72M3N2N2N3M2M3N2N2N3L3N2N2ZNf1N3`MlGgMLc1Z8=mGTNI_1\\85kGcNIW1M>1eNT5X1nJeN0o0M_1h46ZK\\NN^1^4?eKRNMa1\\4>fKQNNc1[4<gKPNNe1[4:gKQNOf1Y49hKQNNh1Y47hKQNOj1X44iKRNOk1W44iKQN1l1U43jKQN0n1U41jKQN1o1U4OjKRN1Q2S4NkKQN3R2Q4MlKPN3U2P4JmKRN2U2Q4ImKQN2X2o3\\1RLeNm3FPLPN3[2m3X1SLiNm3W1RLjNm3V1TLjNl3U1TLlNl3S1TLnNl3Q1TLPOl3P1TLoNm3P1SLQOm3n0SLSOm3l0SLUOm3i0ULWOk3h0ULYOk3f0UL[Ok3d0UL]Ok3b0VL^Oj3`0WLAi3VOPLhM7S3i3<WLEi3SOPLhM8U3h3SOPLgM8W3h3ROPLeM9Z3g3POQLdM9]3f3oNQLcM:^3e3oNQLaM;a3d3mNRL`M;d3c3lNRL^M<g3b3jNSL]M<j3a3iNRL]M<l3b3gNQL]M<n3c3dNQL^M;P4d3bNPL_M:P4g3`NoK`M:Q4g3XOXLj0h3VOVLl0j3SOVLn0j3QOULQ1k3oNTLR1l3nNRLT1n3lNQLU1o3kNPLV1P4R40000000000000lIPLdM0`4P4kMRLcMOb4o3kMTL_MOf4m3jMUL_MOg4l3iMQLPM1?3h4k3hMoKUM2:4j4k3nMPLWM5l4k3oMPLSM4n4m3PNPLoL3R5m3PNQLkL2V5m3PNRLgL1Z5m3PNSLdLO]5n3PNVLR2j3g300001O0000000000000000000000000000000O10000000000000000000000000000000000000000000000O10000000000000000000000000000000000000000000000O10000000000000000000000000000000000000001O00000O1000000000000000000000000000000000000000000000000O10000000000000000000000000000000000000000000000O10000000000000000000000000000000000000000000000O100000000000001O00000000000000004L5K5K4L5K4SNmJ[KX5\\4QK_KS5Y4VKaKP5Y4UKcKo4W4VKfKo4T4SKhKT5R4PKhKY5P4lJjK\\5o3hJkKb5g3iJSL_5_3lJ\\L]5U3e2B?KeE`MP:[2hEnM]:l1cEVNa:e1_E[Ne:`1\\E`Ni:Z1WEgNm:U1RElNS;n0mDSOU;j0hDZOX;\\100000000000jNiD9W;CmD>T;\\OPEd0Q;VOUEi0m:QOWEo0c;O00001O001O00001O001O00cI", "size": [426, 640]}, "area": 156542.0, "iscrowd": 0, "image_id": 10014, "bbox": [0.0, 47.0, 640.0, 375.0], "category_id": 183, "id": 10012179}, {"segmentation": {"counts": "bg\\:4cc01b\\OOVc0Ni\\O7Qc0Jm\\O=Rc051N1000O2O000O10O11O0O10000O10001O0O101O1N102V]OSOdb0l0\\]OUOONbb0S1^]OmN20\\b0U1b]OmN^b0X1OO0101OO0101N10000O10000O100O1000000O10000O101O1OO010000O2O000O1000O0KT^O_Nla0]1:L3M4L4L4K4M4L4L4M3J3Ncf>", "size": [633, 640]}, "area": 2767.0, "iscrowd": 0, "image_id": 10015, "bbox": [538.0, 138.0, 78.0, 62.0], "category_id": 92, "id": 10012180}, {"segmentation": {"counts": "c0d0j=0mF0S90mF0S90mF0S90mF0S90mF0S90mF0S90mF0S90mF0S90mF0S90mF1Y50TI2O[OPM3R1c0lNYO00011O0U31Z;d0b4000000000000O2O01N1001OO10000000000000O1000000000000000000000000000000000000000000000000000T@`0lLkN17MI20m=e0VEX1i:hN_EU1\\:kNdEV1\\:iNeEV1Z:kNgET1Y:lNhES1X:nNhEQ1X:oNiEQ1V:nNlEP1U:POlEo0T:QOlEo0T:QOmEn0S:ROnEn0Q:ROPFm0P:SOQFm0n9SOSFk0n9UOSFk0l9WOSFg0n9YOSFf0m9YOTFg0l9XOVFc0n9]OSF?Q:UOZFi0h9WOYFb0m9^OSFb0m9^OTF`0m9@SF`0m9@SF`0m9@TF?l9ATF?l9ATF?l9ATF?l9AUF>k9BUF>Y1ZOi28nK=X1^Oi25PL<U1Bj22QL<T1Dj20SL<Q1El2OSL;Q1Gl2NSL<o0Gn2MTL:n0Jn2LTL;l0HR3MSL:j0HT3ORL9i0HV3OSLG^K4O0Y56Y3OQLG^K4O0X56[3OPLF_K5N0W55^30jKJbK100U55`30iKJcK1N0V53b32c1M^N3[1LlENk86X1MmELl87U1OPFHl89S10QO0m02SONe0:[OFc0<]ODb0<_OD`0=@C`0<AD><CD<=DM22ON030M021OM14O\\O`0e0@SOh0m0XOQOi0P1WOnNj0S1VOkNk0V1UOhNk0Z1UOdNl0]1TOaNl0RI`N[7d0Bk0SIdNZ7a0Bj0UIgNX7?Bj0VIhNX7>Ai0XIjNW7=@i0YIlNV7;@i0YInNW79_Oj0XInNZ78^Oj0VIoN]77\\Oj0WInN`77XOl0VIlNe79TOj0VImNh79ROj0UIlNk7:QOi0RImNo79POi0kHdNA9f8:nNi0gHUO]82lNh0dHYOa8OlNg0[H@k8IkNd1W1\\NjNb1W1^NjN_1X1aNiN\\1Y1dNhNX1[1hNfNU1\\1kNdNU1\\1mNcNQ1^1mNeF\\On7f1]1nNfNP1[1POeNP1[1POfNn0[1ROfNm0Z1SOhNi0Y1XOjNa0Z1_OhN9^1GeN0a10aNId17_NAf1?\\N\\Og1d0g80000000001O00O11O000000O1000000000000000000000000000000000000000000000000000000000000000000000O10000F:00000000000000000000000000000000000000:F00001O0000000000000000000000000000O100F:00000000000000000000000000000000000000:F001O000000000000000000000000000000O100F:00000000000000000000000000000000000_@Gb;9]DIb;a0TD_Ol;a0TD_Ol;b0TD\\Om;d0RD]On;c0nC[OfL2\\?c0nC\\OeL1]?c0mC^OeLO^?c0mCBS<>mCBS<>mCBS<>\\BE\\OKY>`0XBL[OD]>`0UB0^O^O^>b0QB4@ZO_>b0oA7AWO`>b0nA:ASOa>c0mA?^OnNe>c0lAh0SOcNC2^?c0kAP1lN\\NK1^?c0kAR1iN\\NMO_?c0jAS1jN[NMO_?c0jAT1fN^N0K`?c0iAa1X>_NhAa1X>_NiA`1W>`NiA]1Z>cNgA\\1Y>dNgA\\1Y>dNhAZ1Y>fNhAY1X>gNhAY1X>gNiAX1W>hNiAX1W>hNjAW1V>iNjAU1X>kNhAT1aNXNe?d0kAm0dNbN`?a0lAn0cNaNa?a0mAm0bNbNa?a0mAm0aNcNb?`0PB3UO]Ok>`0TBMSO_OUO1d?c0XBGQOESO1d?c0\\BBnNJRO1d?c0cC\\OiL1d?c0fC]OZ<c0fC]OZ<c0^300000000000000000000O100F:00000000000000000000000000000000000000:F1O0000000000000000000000000000000000000000000000000000000Y@\\OY<d0gC]OX<c0hC^OW<b0jC^OU<b0iCAV<?hCDW<<hCFW<:gCIX<7fCLY<4fCNY<2cC3\\<MaC7^<I_C;`<E]C?b<BYCc0f<]OXCf0g<YOXCj0g<VOWCm0h<SOUCQ1j<POSCS1l<lNTCQ1kM]NP?c0RCS1nMYNP?c0RCT1PNXNn>e0oBT1UNVNl>e0nBV1WNUNk>f0lBU1\\=kNbBX1]=hNbBU1XNVNV?e0`BU1]NVNR?d0bBS1_NYNo>e0aBP1cN[Nk>e0bBm0fN^Nh>e0bBk0aNbNKNQ?e0dBg0\\NnNOEQ?f0dBX1a=hN_BX1a=hN_BX1a=hN`BV1a=jNeBP1[=POfBo0Z=QOfBf0c=ZO^B;l=EUB9l=GUB6m=JSB5n=JTB4m=LeB@]=`0n20000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000jA^OU9b0iF_OZKOe=b0QG\\OYK7e==mF]O\\K=f=6nFOR91oFMR93nFMR93nFMR93mFOR91nFOR91nFOR91mF0S90mFOT91lF0S90mF0S90lF1T9OlF1T9OlF1T9OkF2U9NkF2U9NkF3T9MlF3T9MkF4U9LkF5T9@WGa0h8_OWGb0i8^OWGd0VKSO]=9]Gc0VKUOON\\=:^Gi0VKmN20V=:bGk0VKmNX=8bGP1]8POcGo0^8QOaGP1_8POaGQ1^8oNbGP1_8PO`GQ1`8oN`GR1_8nN`GS1`8mN`GS1`8mN`GS1`8mN_GT1a8lN_GT1a8lN_GT1a8lN^GU1b8kN^GU1b8kN]GV1c8jN]GV1c8jN]GV1c8jN]GV1c8jN\\GW1d8iN\\GW1d8iN\\GW1d8iN[GX1e8hN[GY1d8gN\\GZ1c8fN]GY1d8gN[GZ1e8fN[GZ1e8fN[GZ1e8fNZG\\1e8dN[G\\1e8dN[G\\1e8dNZG]1f8cNZG]1f8cNZG\\1g8oNmFl0QL_NR=f0lFg0^9YObFc0b9]O]F`0g9_OZF=j9DUF8o9GRF4S:LlE1X:0gEL]:3dEI`:7`EFc::]E@i:`0TE^OQ;b0S4000000000000000UE[Ob2e0oL[OcH3]:c0oLZO]H013c:d0mLD\\HHh2NS5e0`HZOG1o0OoN002c5b0UMAXMK00`7b3`H^L00017O=0[O6T3Q1lLQO0H211OW1e0iN[ON000O02e0b0h;=RF3_O]Ob:`0oE3_O]Ob:`0oE3_O]Oc:?nE4_O]Oc:?nE4_O]Oc:?nE3@^Ob:?nE3@^Oc:>mE4@^Oc:>mE4@^Oc:>mE4@^OR4", "size": [633, 640]}, "area": 369757.0, "iscrowd": 0, "image_id": 10015, "bbox": [0.0, 0.0, 640.0, 623.0], "category_id": 159, "id": 10012181}, {"segmentation": {"counts": "lnS1V13kN17MI20ia0m17MK10O1O1O1O100N2O001O100O100N200N2O001K5L3N3I601N10001O0000001O000O2O0010OO110N110O002^Ok\\O4O0Wc0Lj\\O4O0Wc0Kk\\O5N0Sc0On\\O100Rc0Oo\\O1N0WZY:", "size": [633, 640]}, "area": 1802.0, "iscrowd": 0, "image_id": 10015, "bbox": [58.0, 113.0, 50.0, 70.0], "category_id": 167, "id": 10012182}, {"segmentation": {"counts": "iVi12ec03M3N2N1O1O1O00O1O10O01O100O2O1NK1e\\OM[c04e\\OJ[c0821N2H7O2O1O01O01O01O010O00A?I7O2N1O1O00000m^ObNn?^1Q@eNm?Z1T@gNk?X1V@jNh?V1X@kNg?U1Y@lNf?U1X@mNg?S1X@nNi?R1U@nNl?R1T@mNm?T1Q@lNQ`0T1m_OlNU`0S1k_OlNX`0S1f_OmN\\`0R1^_OeN@8Ta0S1X_OUOj`0k0R_OWOQa0g0h^O_O[a0^12M3N2L4L4O0O2O1N2O0O2O1N4I6J7G9G8H9JWbl2Nm]SM100001OO1LNa\\O2_c0O`\\O1_c01`\\OO`c050O10_N4V_OKg`0<U_OCi`0a0W_O^Of`0f0Y_OZOe`0i0Z_OWOe`0l0Z_OSOe`0R1W_OnNh`0[1P_OeNo`0c1e^O\\NK1`a0d1c^O\\NM0_a0f1c^O[NMNaa0h1^^O]N1K`a0Q2_^OPNaa0P2_^OoMca0P2]^OoMda0Q23O001O1O001N101O0N2KU^O\\NM1la0a1X^OaNha0_1X^OaNia0^1W^ObNia0j0d^OTOB0M0Qb0d0j^O\\OUO0Vb0>f^OBTO0Zb08d^OHRO0]b04b^OKRO1bc0O^\\O1ec000O`ZV11`eiN1O100M3M3N2M3M3N2K5L4L4L4K5M3M3M3L4MNQ^O_Nma0c1S^O]Nja0e1W^OZNha0f1Z^OYNda0h1^^OWN`a0j1a^OVN^a0k160V^OVNha0h1[^OXNca0h1`^OXN_a0e1d^O[N\\a0c1g^O]NXa0`1k^O`NUa0^1f^OdNINaa0[1a^OnNea0o0W^OXOia0\\1004L3M1L3E<I7N2M2O`0_O9HVeT3", "size": [633, 640]}, "area": 6294.0, "iscrowd": 0, "image_id": 10015, "bbox": [92.0, 53.0, 385.0, 302.0], "category_id": 183, "id": 10012183}, {"segmentation": {"counts": "0R;Y20000000000000000000000000000000000O1001O0000O100000000000000O11O001ON21O00O10000LfMUE\\2k:dMUE\\2k:21O1O1O1O1O1O2hMmDT2U;000000000N2000000O100001O00O100000iMnDU2T;01O0000O10000000000001O00O1NkMnDU2Q;3O100O11ON22N0000O11O0000000000000000000000O11O0000O11O00O11O00000000000000000000000000O11OO11O00O11O00000000000000000000000000000000000000000000000000O11O0000000000O11O0000000000000000000000000000O11O00001O1O1O0000000000O10000oIiMn0W2ROmMj0S2VOnMi0R2WOoMh0Q2XOPNg0P2YORNd0o1[OSNc0n1[OUNd0k1[OXNc0h1]OYNb0g1]O[Nb0e1^O]N`0c1_O`N?`1AaN>_1AdN=\\1CfN;Z1DhN;X1EgN<Y1ChN=X1CUN`KJn4Q2ATNfKGj4U2@SNU1l1jNTNX1k1hNTNTO6iMe1R3VNRO;jM^1T3VNPO?mMZ1R3XNoNa0PNU1Q3ZNnNb0RNS1o2[N[NDdMP1P1R1Q3ZNYNGeMm0o0T1R3YNYNHdMm0P1R1R3ZNZNHbMm0R1Q1R3YNZNKaMl0R1P1R3ZN[NK_Ml0U1n0Q3[N[NL_Mk0T1n0Q3[N\\NN^Mi0U1n0Q3[N\\NO^Mh0T1n0Q3\\N]NO]Mg0U1n0Q3[N]N1^Mf0S1n0Q3\\N^N0^Mf0S1n0P3]N_NO_Mf0Q1n0Q3]N^N0`Me0R1m0o2^N`N0`Me0R1k0n2`N`N0`Me0R1k0m2`NaN2`Mc0Q1k0n2`NaN2^M^OGU1\\1k0m2`NcN2]Mf0R1h0m2aNcN2_Md0Q1i0m2`NdN3_Md0m0iNhNP2W4aNeN2_Md0m0k0n2_NfN3`Mc0k0iNiNP2V4aNfN4`Ma0m0j0l2aNgN5aM`0i0l0n2_NiN5`M`0i0l0n2_NhN6bM?e0jNmNR2S4_NjN6bM>d0o0P3]NiN7cM>b0o0Q3\\NkN8bM<b0P1Q3\\NjN9dM;?Q1S3ZNkN:cM;?Q1R3[NlN9dM;=Q1S3ZNmN:dM:<R1S3ZNmN;cM:;R1T3YNoN;cM9;R1S3ZNoN;dM99R1S3ZNeMg1oNmN9R1S3ZN\\MQ2XOcN9Q1S3ZNZMU2[O_N8R1R3ZNYMX2]O]N7Q1R3[NWMZ2AYN6R1Q3[NWM\\2CWN4R1Q3\\NUM_2ESN5R1P3\\NTMb2HPN3R1P3]NTM`2LQN0mNXOQ2g3aNUM_2NRN1n0k2aNVMo1VOjMj0h0Nn0k2bNVMk1f0dNZOn0i2cNWMi1i0gNVOm0i2dNWMe1o0iNQOn0h2dNXMc1S1kNlNn0h2eNXM_1X1nNhNn0g2eNZM\\1[1QObNo0U2RNaMc0<Y1`1RO_No0P2YNbM>?U1c1VO[Nn0l1_NeM9a0Q1g1XOWNo0j1bNeM7b0n0l1ZORNo0j1eNcM4f0k0n1]OnMP1j1fNbM3h0g0R2@jMo0h1jNcM0h0e0W2AfMP1g1H\\NDZ2EaMP1h1H]N@^2G]MQ1f1J^N\\Oc2IWMR1g1I`N\\Ob2ITMU1i1GaN[Oc2IQMV1b1OiNSOe2GPMW1`11kNPOf2InLV1`12lNoNg2HmLW1^14mNnNi2GkLW1^14oNnNh2GkLW1\\16QOkNj2HhLW1\\17QOkNl2FgLX1Z19SOiNl2GfLW1Z1:TOhNm2FeLX1Y1:UOiNn2EcLX1Y1;VOjNl2CeLX1X1<WOkNk2AeLX1W1>XOlNk2]OfLY1V1?YOmNi2\\OgLX1V1`0YOoNn0_N2i0bNX1V1`0XOTOh0_N7e0bNX1W1b0VOVOc0aN;?eNX1X1b0SOZO`0`N`0<dNX1Z1c0PO]O<^Ng09cNY1Z1d0oN@6]Nn07bNX1\\1e0kND3ZNU14aNY1]1e0iNGNZN\\11_NY1_1f0eNKKVNc1O_NZ1_1g0bNj0a1TM^N[1a1g0^Nj0d1SM^N[1a1i0ZNj0h1QM\\N]1c1`41RJ]N^1c1^42SJ[N_1d1]42SJZN`1e1\\41SJ[Na1f1Z45oIUNg1g1X4`0fIiMR2h1V4k0jKVOT4k0lKVOQ4l0oKUOo3k0RLWOk3k0TLVOb2oM\\Ll2R1VO\\2ZNZLa2Z1VOV2bN[LY2_1WOS2dNWLY2f1UOQ2eNQL[2n1ROo1fNlK\\2U2POl1U2TNmMi1T2WNnMf1S2ZNoMd1Q2\\NQNa1P2_NSN^1n1aNRN_1n1aNRN_1n1aNSN]1n1cNRN]1n1cNRN]1n1cNRN\\1o1dNQN\\1o1dNQN\\1o1dNRN[1n1eNRN[1n1eNRN[1n1eNRN[1n1eNRN[1n1eNSNZ1m1fNSNZ1m1fNTNY1k1hNUNX1j1iNWNV1h1kNXNV1f1kN[NU1c1lN]NU1b1kN_NS1a1nN_NR1b1mN_NQ1b1oN^NP1c1PO^Nn0c1RO]Nj0g1VOZNe0j1[OVNa0n1_OSN<Q2DoM9T2GmM5W2JiM3\\2KeM1_2NaM2_2NbM1^2ObM0_20bMO^21bMTOhL@f5\\1cMTOiL\\Oe5`1cMTOkLVOd5f1cMSOkLROd5k1bMSOmLlNc5Q2bMSOk2n0VMROi2n0XMROg2n0[MQOd2o0]MROa2n0aMQO^2o0cMQO\\2o0eMQO[2n0fMROZ2m0gMTOX2k0iMVOU2j0lMWOT2g0lM\\OQ2d0PN]On1c0SN^Ok1b0VNXNULh0c5P1XNTN[Ll0[5P1[NPN`Lo0S5Q1ROPOk0P1WOPOf0Q1\\OoNb0Q1@oN=R1FmN7T1KlN3T1OlNMV15kNGV1;jNBW1a0hN\\OY1f0iNUOX1m0jNnNW1T1lNlNo0W1ROfNm0\\1TOaNl0b1TO[Nl0h1TOUNl0m1TOQNl0R2TOlMk0W2UOfMk0\\2VOaMj0b2hNkI]Oa3k1f2hNkIXO`3P2h2fNaMWOSLP2_6gN^MXOSLQ2a6^N_IEQ4l1c2\\N^IEo3P2d2ZNaMf1a2XN_Mh1c2VN]Mj1d2SN^Mm1d2PN^Mo1n611O0000000000O1MPNkDP2U;PNkDP2U;PNkDP2U;PNkDP2U;PNkDP2U;300000000000000000000000000000000000000000000000000000000O100001OO1001OO11O00O10000000000000kMmDR2V;0N21OO10000000000000000O1000000000000000000000000000000000000000000000000000000000000002N00N2001O1ON2O1O100000000000000000000000000O1000000000000000000000000000000000000000000000000O1000000001O2N0kMmDR2T;mMmDR2U;1001OO11O0000000000O11OO1001OO11O00000000O11O00N21O000000N2O100O11OO10000000000000000000000", "size": [427, 640]}, "area": 205988.0, "iscrowd": 0, "image_id": 10023, "bbox": [0.0, 0.0, 640.0, 362.0], "category_id": 96, "id": 10012184}, {"segmentation": {"counts": "R;Y2R;000000000000000000000000000000000O1001O0000O100000000000000O11O001ON21O00O10000LfMUE\\2k:dMUE\\2k:21O1O1O1O1O1O2hMmDT2U;000000000N2000000O100001O00O100000iMnDU2T;01O0000O10000000000001O00O1NkMnDU2Q;3O100O11ON22N0000O11O0000000000000000000000O11O0000O11O00O11O00000000000000000000000000O11OO11O00O11O00000000000000000000000000000000000000000000000000O11O0000000000O11O0000000000000000000000000000O11O00001O1O1O0000000000O100000000000000O1O100000000000000000000000000001O1O1O1O1O2N1OO1N20000001O000000000000001O2N0000000000NPNiDP2W;20nMiDP2Y;0N200NnMkDR2U;2O100O1000000O11O00001O0000000000001kMlDQ2W;0001O00000000O11O000000O11O00O100O1M3O100000000000000000000000000001O0000000000000000001OO1000000000000000000O11O00000000000000000000001O000000000000000000000000000000O1O1O1O1O100O11O00000000000000001O2N1O000000000000000000O10000000000001O1O1O002N00000000000000O100O1O100N2O1O1O1O1O15K1O000000001O000000000mMkDP2V;oMjDQ2Y;OO1000000001OO11O0000000000O1MPNkDP2U;PNkDP2U;PNkDP2U;PNkDP2U;PNkDP2U;300000000000000000000000000000000000000000000000000000000O100001OO1001OO11O00O10000000000000kMmDR2V;0N21OO10000000000000000O1000000000000000000000000000000000000000000000000000000000000002N00N2001O1ON2O1O100000000000000000000000000O1000000000000000000000000000000000000000000000000O1000000001O2N0kMmDR2T;mMmDR2U;1001OO11O0000000000O11OO1001OO11O00000000O11O00N21O000000N2O100O11OO10000000000000000000000", "size": [427, 640]}, "area": 45347.0, "iscrowd": 0, "image_id": 10023, "bbox": [0.0, 348.0, 640.0, 79.0], "category_id": 140, "id": 10012185}, {"segmentation": {"counts": "Tgb21Z=4L1O1O1O2M2M3N3N1N2O2M3N1N3N2M10O0AkCJU<2RDJn;6WDFi;9d001OmF7e4JXK<e4DXK?g4AXKb0g4^OXKc0g4^OdJDdMP1g7[OdJHdMm0g7\\OdJIcMl0h7\\OeJIaMm0i7ZOeJKaMk0i7ZOgJL^Ml0j7XOhJM^Mj0i7ZOhJN^Mi0i7XOjJ0]Mg0h7ZOkJO]Mh0g7YOkJ0_Mf0e7ZOmJ0^Mg0c7ZOoJ0^Me0c7[OnJ1_Me0a7ZOQK1_Md0`7[OQK1_Me0^7ZOSK2`Mc0]7[OSK2^M_OFU1g7ZOVK2]Mf0\\7YOVK3^Md0[7XOXK4^Md0X7YOZK3^Md0W7YO[K4_Mc0U7YO\\K4`Mb0S7ZO]K5aMa0P7[O_K5`M`0Q7ZO_K7aM?n6[OaK6bM>m6[OaK8bM>k6[OcK7cM=j6[OcK:cM;i6[OdK:cM;h6[OfK:cM;f6[OgK:dM:e6[OhK;cM;c6[OjK:dM:b6[OkK<cM9`6[ObJh1nNmN`6[OYJQ2XOdN^6ZOXJU2[O`N\\6\\OVJW2^O^NZ6[OVJ[2@YNY6]OUJ\\2CWNV6]OUJ_2ETNU6]OTJb2HQNR6^OUJ`2LQNn5_OVJ_2NSNj5_OWJm1\\OfMf0m0f5@XJj1g0fNo4@ZJh1j0hNk4AZJd1P1kNd4A\\Jb1T1lN_4C\\J^1Y1POY4C^JZ1]1ROa3ROeJc0<W1b1TOX3XOgJ=`0T1d1WOP3^OjJ8b0P1h1ZOi2AkJ6c0m0m1[Od2EiJ4f0i0P2_O_2FiJ3h0e0T2AY2JjJ0h0c0Y2CS2g0eKC[2Fo1h0fK_O_2Ih1j0hK]Ob2He1l0iK[Oc2Jb1l0kKZOd2IX1U1TLSOe2HT1W1WLQOe2HS1X1XLPOf2Ho0Z1ZLnNi2Gl0\\1[LmNi2Hi0]1^LkNj2Gg0^1_LlNk2Fc0`1bLiNl2Ga0a1cLhNm2G>b1dLiNn2D=d1eLjNl2C=d1gLkNk2@<g1hLlNk2]O;h1jLmNi2[O<i1jLoNn0_N2i0V2h1jLTOh0_N7d0W2k1hLVOc0aN;?Z2j1fLZO`0`N`0;[2l1cL]O<^Ng09Y2m1cL@6\\No07Y2n1_LD3YNV15X2n1^LGNYN]11X2P2ZLKKVNc1OY2Q2WLj0a1SM[2S2RLi0e1SMZ2U2nKi0i1QMZ2m5fMSJ[2l5fMRJ\\2m5eMRJ\\2l5fMSJ\\2k5nMjIT2T6c2N2M3N2N3M2dNoH[LS7_3XIZLj6a3]IZLg6d3\\ITLk6j3WIPLo6n3Y1M3M3M3N4L1N10001O0O100000001O0000000O2O001O0010O100O01N101N1O2L3L5K4L5L3M4L3M4N1O2O001N1VOkD^OV;b0lDYOW;g0lDSOX;l0kDnNX;R1jDiNZ;W1=0001O010O00000001O1O1N3N1OmNVDe0i;VO]Dj0a;SOdDm0n;0010O000000000010O00002N2O2M2O000O10O10O10DmC]OS<a0PD\\OQ<d0RDVOR<h0;M2H[CEh<9ZCDi<:5O2M2M\\Sf2", "size": [427, 640]}, "area": 21617.0, "iscrowd": 0, "image_id": 10023, "bbox": [198.0, 45.0, 235.0, 307.0], "category_id": 183, "id": 10012186}, {"segmentation": {"counts": "bij371Mac0;K2E94L5K5K\\]OEia09R1L2MTQ5OYc02kgIMkc0n0XOM2102N1NNm^OVOS?d0P@[O]Ol0Y`0FX@GROE1V1g`0VOQ@EWOi1Pa0XNj_Ok1Va0D<Dj\\h0^OTdWO0O11O4LWa0IP_O:F1OI7001:E`Qg3", "size": [640, 480]}, "area": 878.0, "iscrowd": 0, "image_id": 10024, "bbox": [196.0, 287.0, 93.0, 172.0], "category_id": 105, "id": 10012187}, {"segmentation": {"counts": "d:\\9d:00000000000000000000000000000O1001O1O1O0000000000001O000nF[Ei8e:VG]Ei8c:UG`Ej8`:VG`Ej8`:UGbEj8^:UGcEk8]:UGcEk8i:01O001O001O00001O001O00000000002NO1O10000000000O10000O100000000001O1O00000000O10000000000000000O1001O1O1O1O1O1O0000O11OO1000000O100000000O1I7I71O001O000000000000O100000000001O1O004L5K2NO11O00000000001O1OO10000000000O1O1^ObGaE_8_:a00000000000000000O1000000001ON2000000000000000000000000000000000000001O0000001OO11O0000001O00O1001O00002N1OL4O1O1O100O11O1O000000O11O0000001O00O11O00000000c0]O0gIZEX3i:gLWEW3n:fLQEY3S;eLmDY3S;]L`DXN=6Cc4];RLbD]OY1HlNi4Y;PLkDROE1^10lNm4V;kKaDWOi1OiNo4l:lKUGS4P9hKPGX4Y9_KgFa4\\<01O00001O2N2N1O2N2N2N2N2N2N2N2N2N[OlK`AR4`>QL_Am3a>UL`Ah3a>ZL^Ad3b>^L^A`3c>aL^A\\3c>fL\\AX3e>iLXAX3i>iLUAW3l>jLQAW3o>lLn@T3S?mLk@S3U?oLh@R3V?SMg@m2W?o00000000000DaKWA_4g>>ZM`K`DC=o4l:eKgDHOe4S;iKoDMA\\4Y;oKUEW5d:TKXEl4b:\\2I7M31OI7001OBnFnER9R:>0000000000000000000000000000O100O11O2N00OXFUF]9W:02NN21O001O00000000001O00001O00000gG]EU7d:iH]EW7c:hH^EX7c:dHaE[7^:dHeE[7_;100O100O10000O1O100O11O00001O001hGYDU8g;kGZDT8i;0N2001O001O002N000QH\\Db7R<O1O1O00000000000000000000000RIdCX6\\<gIfCX6Z<dIjC\\6V<eIfC^6Z<cIeC]6\\<bIeC]6P=O2N1O00000000001O002N2N001O001O001OVOcIPD^6n;dIRD\\6m;fISDY6m;iIRDV6m;PJnCP6R<QJiCS6T<oInCP6R<PJnCP6R<PJmCQ6T<nIlCR6V<kIkCU6U<k0002N1OLfHcCY7]<5OaHdC\\7_<1N200N`HdC`7\\<200000^HdC70P7\\<iHeC60P7Z<kHfC31S7Y<jHgC20NNS7\\<lHfC34P7V<mHfC0N26P7\\<oHfCP7Y<PIhCP7Y<PIfCP7d<10000000000O100000000MnHXCR7`<mHaC11U7]<kHbC10T7^<kHaC20T7]<:0000000000001O00O11O001O001O00O1O1000000O100O11O00001O00O100O1", "size": [640, 480]}, "area": 125986.0, "iscrowd": 0, "image_id": 10024, "bbox": [0.0, 323.0, 480.0, 317.0], "category_id": 159, "id": 10012188}, {"segmentation": {"counts": "0d:\\9000000000000000000000000000000O1001O1O1O0000000000001O000nF[Ei8e:VG]Ei8c:UG`Ej8`:VG`Ej8`:UGbEj8^:UGcEk8]:UGcEk8i:01O001O001O00001O001O00000000002NO1O10000000000O10000O100000000001O1O00000000O10000000000000000O1001O1O1O1O1O1O0000O11OO1000000O100000000O1I7I71O001O000000000000O100000000001O1O004L5K2NO11O00000000001O1OO10000000000O1O1^ObGaE_8_:a00000000000000000O1000000001ON2000000000000000000000000000000000000001O0000001OO11O0000001O00FjFiEW9S:lFnET9[9mF_FM652P9W9VGbFN7l8S9hGnFX8e8TH^Gj7b8UH`Gj7a8TH\\GP8e8oGZGR8d8oG\\GR8e8lG[GU8d8kG]GU8b8kG^GV8^8mGdGR8Y8PHiGo7U8SHkGm7S8THnGl7P8UHQHk7m7VHSHk7l7UHVHj7h7YHWHg7g7YH[Hg7c7ZH^Hf7`7[HbHd7]7\\HdHd7Z7]HfHd7Y7^HgHa7X7cHeH]7Z7gHcHY7[7kHcHU7\\7SITHcM7Z9d7i2O1N2O1O1O1N2O1O1O1N2J6K5J6J6K5M31O001O1O001O001O001O1O001O001O001O1O001O001O001O1O001O2N1O2N2N2N1O2N2N2N2N1O2N2N1O1O2N1O1O1O1O2N1O1O1O1O1O2N1O1O1O1O2N1O1O1O1O2N2N2QFYGa8i8\\GZGb8h8\\GXGd8j8YGVGoN6]9f8bG`G^8b8_GaG_8a8_G]Gc8e8[G\\Gd8f8YG[Gg8g8WGZGh8h8UGYGk8i8SGWGm8k8PGVGP9l8nFTGR9n8kFSGU9o8iFRGV9P9gFQGY9Q9eFoF[9l9O00000gG]EU7d:iH]EW7c:hH^EX7c:dHaE[7^:dHeE[7_;100O100O10000O1O100O11O00001O001hGYDU8g;kGZDT8i;0N2001O001O002N000QH\\Db7R<O1O1O00000000000000000000000RIdCX6\\<gIfCX6Z<dIjC\\6V<eIfC^6Z<cIeC]6\\<bIeC]6P=O2N1O00000000001O002N2N001O001O001OVOcIPD^6n;dIRD\\6m;fISDY6m;iIRDV6m;PJnCP6R<QJiCS6T<oInCP6R<PJnCP6R<PJmCQ6T<nIlCR6V<kIkCU6U<k0002N1OLfHcCY7]<5OaHdC\\7_<1N200N`HdC`7\\<200000^HdC70P7\\<iHeC60P7Z<kHfC31S7Y<jHgC20NNS7\\<lHfC34P7V<mHfC0N26P7\\<oHfCP7Y<PIhCP7Y<PIfCP7d<10000000000O100000000MnHXCR7`<mHaC11U7]<kHbC10T7^<kHaC20T7]<:0000000000001O00O11O001O001O00O1O1000000O100O11O00001O00O100O1", "size": [640, 480]}, "area": 164034.0, "iscrowd": 0, "image_id": 10024, "bbox": [0.0, 0.0, 480.0, 431.0], "category_id": 169, "id": 10012189}, {"segmentation": {"counts": "]bi32jc03M3M4L3M3M3M3M3M4M2M3M3M3N2O2N1O1O100O1O2N1O100O103M4L4K4M4L3M4iAQNU:S2kEmMR:W2mEhMQ:\\2nEdMo9_2RF_MSMDX<R3jFTMhLO\\<Q3lFnLdL4_<W3fFRMY9X3\\FjLb9`3jElLP:c3[EjL_:^6J6J6K6L401N2O001N2O1N2O1N2O1O1N\\OSEWHl:h7VEXHh:i7YEWHf:h7]EXH`:i7aEWH^:h7dEXH[:h7gEXHV:i7kEVHU:j7lESHU:n7mEoGT:Q8mEmGS:S8oEjGS:V8nEhGS:U8RFhGn9V8P1O2N1O2N2N2BQDgHQ<U7?M4VMVEXLn:_3eESL_:d3T3G8H9nNa_OXNg`0g1e_OfMc`0Y2d0N1O1O1O1O2N1O1O1O1O1O2N1O1O1O1O2N1O1O1O1O2M3N2M3N2M3N2M3N2M3N2M3N2M3N2M3N2N2M3NXVQ3", "size": [640, 480]}, "area": 16138.0, "iscrowd": 0, "image_id": 10024, "bbox": [194.0, 208.0, 130.0, 313.0], "category_id": 183, "id": 10012190}, {"segmentation": {"counts": "0j4`8K5N2N2N2N2N2N2N2N2N2M3N2N2N2N2N2N2N2N2N2N2N2N2N2N2N2N2N2M3N2N2N2N2N2N2N2N2N2N2N2N2N2N2N2N2M3N2N2N2N2N2N2N2N2N2N2N2N2N2N2N2N2M3N2N2N2N2N2N2N2N2N2Nn^[7", "size": [426, 640]}, "area": 5628.0, "iscrowd": 0, "image_id": 10037, "bbox": [0.0, 0.0, 74.0, 154.0], "category_id": 100, "id": 10012191}, {"segmentation": {"counts": "S<>;0g;0\\DNg<I00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000M_Z`3Oee_L1O2M2O00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001O000000000000000000O10000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000O1", "size": [426, 640]}, "area": 2034.0, "iscrowd": 0, "image_id": 10037, "bbox": [0.0, 386.0, 640.0, 40.0], "category_id": 165, "id": 10012192}, {"segmentation": {"counts": "a<;S5m3`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n<2PCNP=6000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000]1cN_4aK000N20RJ", "size": [426, 640]}, "area": 262818.0, "iscrowd": 0, "image_id": 10037, "bbox": [0.0, 0.0, 640.0, 426.0], "category_id": 183, "id": 10012193}, {"segmentation": {"counts": "oWj01oc00N2O1O1O1O1O1O1O1M3O11OO1O100N200N200O1O1N200N200N200N2O1O1O1O1N200O1O1N200O1O1BiNj]OO4Y1Qb0hNk]O03Y1h`0fN[@0jN14Y1e`0kN__ON:O]OO6Y1a`0mNb_OL:O]O50T1d`0QO[_OE51M1;h1T`0hNd_OF7c1U`0eNd_OI7c1U`0cNd_OJ5f1W`0`Nd_OI5h1X`0_Na_OI5k1Y`0\\Nb_OI5l1Y`0ZNc_OL2j1Y`0[Nc_OM5h1W`0[Nd_OM5i1V`0ZNd_ON6i1U`0jNk_OW1T`0iNl_OX1T`0hNe_OPO3Y2T?gMcAOXOV12T1o>mMdAKYOU12U1n>nMfAR1[OQ1a>cM^A01e08\\ONl3X>SLbAi06a3V>cLiA_3W>`LeAe3[>P1N22NO1O11O1O001OhN`JkB1P1`5Q<_JoB1O2P1^5Q<`JoB5f0KAa5`<^J`C91JKN004b5l;]J[D1B06>L0OA3d5h;\\JTD87f0KAOB2d5f;kJWDa02^OO]5h;fKYDmNO^5g;eKZDmNO_5g;dKYDnNO_5g;X2O1O1FhGeD0001X8Y;nGiDQ8Q;b0O1K5IPGYE10O1o8X:RGhE32T9S:TGkEm8S:a0L4BTFeFNIo9b9SFeFo9Z9<O1O1O1O1O1I^EmFe:o8[EPGj:_8VEgG0Nn:U8REiG5OL2n:S8QEiG124OS;U8TEkGm:R8b0EUDXH24j;W7TDmH0H51j;n6_DTIn;l6c0O100NPCTIP=k63M3M3KiB`IX=[69K5N2HVBUJo=e5QB[JU>`58KdAdJ^>[54K5JXAPKl>m45N2L4Hj@aK[?Y4d@iK2OZ?V4k@jKV?U4;O1O1oNW@jMj?S2Z@SMMa0j?Y2d@TM@?l?Y2m@dMT?Y2QAbMR?^2PA`MP?`2RA]Mo>c2RA\\Mn>d2RAZMP?f2U100000000001O3M3M3M2N6b_OjLf?X3V@kLi?W3m_OeL29o?d3O2N1O001O1O1O2N5K3M2N5K1O5K3M5K5K2N3M4L1eA^JV>g5O3M6J1O2N6J3M6J2N3fBYIU=j6kBVIR=m6RCSIg<S7XCmHN0b<X7^CkH_<a7J000fC^HV<e715K1O3M6RDjGf;[8M3M3aD_GY;e8fD[G10S;j803M3M4SEnFd:Z9\\EfF\\:[9cEdFR60WNb9gK]Fl56YN`9d1fFXN_9oKUFa5g0VNV9i1mFUNV9g1mFWNT9g1oFWNT9f1mFYNV9d1kF[NW9c1kF[NZ9`1gF_N]9]1dFbN`9Z1bFdN`9Z1aFeNc9mLUER4Y1POk9o0WFoNj9P1WFoNm9mLjDi3Z1YOT:g0lEXOU:g0lEXOU:h0kEWOV:h0lEVOW:g0jEXOY:f0gEYO]:YMQDS3c1C\\:UMQD32T3a1Cf:\\MhCg2c1Lf:XMfC12k2b1Kj:YMiC6IX2e18T;_MXC3Ok1e1b0T;aMXC10IOP2e1d0U;aMXC2NI1o1e1d0T;aMYC3NI0o1e1c0X;]MWC8LH0P2f1b0`;bMiBM001o1e1b0b;_MhBO10000Q2e1`0h;`MfBo1c1`0h;`MfBP2b1?R<^OoCa0R<_OmCa0T<_OlC`0V<^OkCa0V<_OjC`0\\<fMnAj0NVO0`0h1i1a<fMgAf00[O3a0e1g1e<SNeAEN0102a0d1g1i<hM`A52KO0O04a0c1f1m<nM]ALOO1030L=h1i1U=kMTAO0O31M=g1h1V=lMSAOO040M=g1g1X=kMRA0O041L=g1f1\\=mMl@050N;d1h1^=lMl@042M;e1f1i=nMc@<c1f1k=nMb@=b1d1l=oMb@=b1d1S>\\NmAc1S>^NlAb1T>_NlA`1T>\\NX@Fd1n1\\>PNQ@101c1m1`>RNl_O3b1k1f>UNZAj1g>SN[Am1]`0O00O10000O1000000O10000O1000oMT^On1Tb0TNk]Oc1aa0[NV_O1YO`1[b0_Nf]O`1^b0bNa]OY1db0fN^]OT1bb0lN^]OT1fb015K2N2QOo\\Ok0Uc0N2N2N006J4L2N6J1OST[1", "size": [640, 428]}, "area": 77791.0, "iscrowd": 0, "image_id": 10039, "bbox": [41.0, 87.0, 317.0, 553.0], "category_id": 96, "id": 10012194}, {"segmentation": {"counts": "0oWj01P\\VO0N2O1O1O1O1O1O1O1M3O11OO1O100N200N200O1O1N200N200N200N2O1O1O1O1N200O1O1N200O1O1BiNj]OO4Y1Qb0hNk]O03Y1h`0fN[@0jN14Y1e`0kN__ON:O]OO6Y1a`0mNb_OL:O]O50T1d`0QO[_OE51M1;h1T`0hNd_OF7c1U`0eNd_OI7c1U`0cNd_OJ5f1W`0`Nd_OI5h1X`0_Na_OI5k1Y`0\\Nb_OI5l1Y`0ZNc_OL2j1Y`0[Nc_OM5h1W`0[Nd_OM5i1V`0ZNd_ON6i1U`0jNk_OW1T`0iNl_OX1T`0hNe_OPO3Y2T?gMcAOXOV12T1o>mMdAKYOU12U1n>nMfAR1[OQ1a>cM^A01e08\\ONl3X>SLbAi06a3V>cLiA_3W>`LeAe3[>P1N22NO1O11O1O001OhN`JkB1P1`5Q<_JoB1O2P1^5Q<`JoB5f0KAa5`<^J`C91JKN004b5l;]J[D1B06>L0OA3d5h;\\JTD87f0KAOB2d5f;kJWDa02^OO]5h;fKYDmNO^5g;eKZDmNO_5g;dKYDnNO_5g;X2O1O1FhGeD0001X8Y;nGiDQ8Q;b0O1K5IPGYE10O1o8X:RGhE32T9S:TGkEm8S:a0L4BTFeFNIo9b9SFeFo9Z9<O1O1O1O1O1I^EmFe:o8[EPGj:_8VEgG0Nn:U8REiG5OL2n:S8QEiG124OS;U8TEkGm:R8b0EUDXH24j;W7TDmH0H51j;n6_DTIn;l6c0O100NPCTIP=k63M3M3KiB`IX=[69K5N2HVBUJo=e5QB[JU>`58KdAdJ^>[54K5JXAPKl>m45N2L4Hj@aK[?Y4d@iK2OZ?V4k@jKV?U4;O1O1oNW@jMj?S2Z@SMMa0j?Y2d@TM@?l?Y2m@dMT?Y2QAbMR?^2PA`MP?`2RA]Mo>c2RA\\Mn>d2RAZMP?f2U100000000001O3M3M3M2N6b_OjLf?X3V@kLi?W3m_OeL29o?d3O2N1O001O1O1O2N5K3M2N5K1O5K3M5K5K2N3M4L1eA^JV>g5O3M6J1O2N6J3M6J2N3fBYIU=j6kBVIR=m6RCSIg<S7XCmHN0b<X7^CkH_<a7J000fC^HV<e715K1O3M6RDjGf;[8M3M3aD_GY;e8fD[G10S;j803M3M4SEnFd:Z9\\EfF\\:[9cEdF^:b9bE]FY:f9O5hEUFQ:m903M1O3M3M2N5K4L4L2N4oFUEe8T;O1O4YGjD\\8^;O1O1O1O3M3M4RHQDc7o;XHQD32c7W<`HhCV7Y<eHfC12Y7\\<fHiC6If6i<YIXC3OX6i<fIXC10IO\\6j<iIXC2NI1[6i<iIYC3NI0Z6m<eIWC8LH0[6V=iIiBM001Y6W=gIhBO10000Z6]=gIfBX6[=gIfBX6d=M1O1O2N1O6QJnAj0NVO0d4Y>[KgAf00[O3d4Z>gKeAEN0102c4]>]K`A52KO0O04b4`>cK]ALOO1030L^4m>cKTAO0O31M]4m>dKSAOO040M\\4o>cKRA0O041L[4S?eKl@050NX4R?gKl@042MW4^?hKc@W4^?iKb@V4^?jKb@V4e?O00000VLX@\\3P`0XLQ@10f3S`0ZLl_Of3X`0O1[Lj_O^3]`0O2N1O2N1O2N1O2N2N1O2N2N1O2N2mMj^OX1Za0bNm^OBHc1aa0dNm^OHB`1ha0_Ni^O0@`1^b0bNa]OY1db0fN^]OT1bb0lN^]OT1fb015K2N2QOo\\Ok0Uc0N2N2N006J4L2N6J1OST[1", "size": [640, 428]}, "area": 192500.0, "iscrowd": 0, "image_id": 10039, "bbox": [0.0, 0.0, 428.0, 640.0], "category_id": 106, "id": 10012195}, {"segmentation": {"counts": "doR52mc05J6K;E3M3M3N1O1O2N1O1O2N1O1O2N1O100O100O1O2O0O100O100O10000O10O010000O100O01000O100000O1000O1000O10000O101N010O100O010O2O0O2O0O2N1O2M2O2N1N3N2N1O2M3N1O2M3N7H7H]je1", "size": [640, 428]}, "area": 3596.0, "iscrowd": 0, "image_id": 10039, "bbox": [260.0, 493.0, 81.0, 84.0], "category_id": 183, "id": 10012196}, {"segmentation": {"counts": "Q5o8Q61O00O1O12N000000000O10O02N010O0O3N2O01N100000000O2O000O1O2O000001O100O1O100OO100O10001O0O10000O100K6GPJ`GP6`88O101N1O1O1O20OO21O06J6I8H=D;Cc0@fGdIQ8W6mGmIT8P6lGQJW8o3jG^MMeN[8c3QHfMChN`8V3WHPNVOnNe8g2`HhNd7l0fHSO\\7b0oH[OU77WIGn6K^I2h6_OcI2TMUO_9:iI@gM5f8LRK5m9O1N2O0100OMWANlb<0okC6oAOk<5lB3P=P1N3L4M2N3N2M2O2M3N2M2O2M3M2L5L4K4L4L4M4K4L4O100000MaDeL_;[3bDdL^;Y3bDgL10\\;W3iDiLX;V382OO12OOO01O3O1M3N2N1O2N2JmC[MU<b2kC^MX<Q2gCnM041M0O^<i1hCWN01\\<e1PD[NQ<d1g0KfBcNY=Z19O1N1N3N2N2N10O1NO2DTBBn=:WBEi=:\\B_Oj===C_A:g>L4L\\ke6", "size": [480, 640]}, "area": 25956.0, "iscrowd": 0, "image_id": 10040, "bbox": [0.0, 154.0, 183.0, 301.0], "category_id": 96, "id": 10012197}, {"segmentation": {"counts": "R_Q23i>4L4L4L4000001OO2O0001OO10000JEGnAN26OOk=K]B7HOk=KUB201O8R>KoA5V>LjANZ>75K5KjP81ToG3J;D:F<C<E;H8M3L4L4M3M2MXORDfN^8C_Ji1XMQNF<d88kI_1PNXNW8e0XIn0kNPN\\8\\1XH?P9J^F2n98`E@Q;j1?G9G:F9G9B8C:G9@`0@mZ40mfS32fngL5N10001N101aBGT<7kCKT<8jCGV<<hCEU<=kCCT<?gC_OTO2S=c0bCYO]O9P=c0QCWOMO0N30M009P=e0QCOn<3QCJQ=T1PCVNf<T2N0O101N200OmMT2N2N3Lmn_2", "size": [480, 640]}, "area": 3294.0, "iscrowd": 0, "image_id": 10040, "bbox": [139.0, 115.0, 330.0, 211.0], "category_id": 105, "id": 10012198}, {"segmentation": {"counts": "aZS21k>4L4L4H8D=M2O100000000O@YB\\ON26Oc=7WB17Hj=0PB0201OY>1jA0[>0fA0Wm00YA4PS>0olA5[AFa><O2OO010Nkn]6", "size": [480, 640]}, "area": 463.0, "iscrowd": 0, "image_id": 10040, "bbox": [143.0, 260.0, 57.0, 48.0], "category_id": 115, "id": 10012199}, {"segmentation": {"counts": "ReS33j>4L5M3TOEmB>m<GTC?a<E_C<]<GdC8Z<JfC6[<IcC9a<C`C<d<@\\C`0h<\\OVCf0P=XOmBg0e=0O23M4L3N3G8GQS60TmI0H3M;^ABV>h0_Ob0@>11M20O10011M1000O11VOlBFT=:nBDQ==SC_Om<a0VC]Oj<b0ZCZOf<f0ZCZOf<f0[CYOe<g0[CXO`<n0`CSO[<HWCU1>TOZ<GXCU1>SOX<MXCP1`0SOY<S1gCnNW<S1jCkNW<U1hClNX<T1hClNX<T1iCkNV<V1jCjNW<U1hClNY<S1gCmNX<T1hClNX<T1hClNX<T1iCkNW<V1hCkNAHc<5\\CP1a0WOW<DXCV1`0ZOS<A]CU1a0\\O_O^Oa<V1PD@o;[O`CQ1a0DT<:lCEV<:jCEW<;iCEV<<jCEV<:jCFV<:jCEW<;iCFV<:jCEY<9gCGX<:hCFX<:hCEY<;gCEY<;gCFX<:hCF^<4bCL_O\\OT=a0]C5e<I\\C7c<I]C7c<I]C7c<I\\C7f<HZC9d<I[C6g<IYC7g<IYC8e<I[C6f<JZC5g<KZC5e<K[C5f<JZC5g<KYC6f<JZC6f<JZC6f<JZC6c<M]C2\\<]OSCf0a0NZ<6fCI\\<6dCJ\\<6dCK[<5eCK[<5eCJ\\<6dCJ\\<6dCK\\<4dCL\\<4eCK[<5eCJ\\<7cCI^<6aCM]<3cCK^<6bCK^<4bCM]<3dCJ^<6bCI`<6`CIa<7_CHb<8^CJ`<6`CHb<8^CHb<8^CJ`<6`CIa<7_CK_<5aCJ`<6`CHb<8^CHb<8^CIb<7]CHd<7]CKa<6^CIc<7]CIc<7]CJb<6^CKb<4^CN`<2aCL`<4`CM_<3aCKa<5_CLa<3_CMa<3_CN`<]OoBO0a0a02f<NZC2f<NZC0h<0XC0h<0XC3e<M[C3e<M[C1_O^OV=a0[C0g<1YCOg<1YCOg<1YC0d<2\\COc<1]COc<2\\CMe<3[CMe<3[CNd<2\\CMe<3[CMe<3[CMe<3[CLf<4[CLe<3[CMe<3[CLf<4ZCMe<3[CMe<3[CLf<4ZCLf<4ZCMe<3[CMe<3[CLf<4ZCLg<3YCLh<4XCMg<3YCMg<4XCMg<3YCK@AW=d0YCKi<5WCLh<4YCKg<5YCKg<5YCLf<4ZCLf<4ZCKg<5YCIj<6VCKi<5WCKi<5WCKi<5WCKi<5WCKi<5WCKi<5WCKi<5WCKi<5WCLh<4XCLh<4XCLi<3XCKi<6VCKi<5WCLh<4XCKi<5WCLh<4WCNh<2XCMi<3WCNh<2XCNh<2YCMg<3YCMh<2WCOi<1WCOi<1WCOi<1XCOh<0XC1g<NZC2e<0ZCOg<1YC1e<O[C1e<O[C2d<N\\C2d<N\\C2e<M\\C2d<O[C2d<K_C0]OBU=<`C2ZOBW=<_C1e<O[CNh<2XCJZOJ_=8cBGe03n<1[BNm00i<1ZBM00QhW20oWhM6f>a0B?@6JN^OhN`CX1^<jNbCW1\\<jNdCU1[<mNeCQ1P<iNhC88R1m;iNfC7=Q1l;XOTDh0l;XOTDh0l;XOTDh0k;YOVDe0k;[OUDe0k;[OUDd0CgNR<f0[De0@fNe;9]D==i0W;iN\\D>=j0V;iN\\D=?i0o:bNbD9N=a0h0P;PO_D8a0h0P;JPE6Q;IoD6m:ORE2n:ORE0o:OQE2m:OSE1m:OSE1m:OUEOk:1VENb3", "size": [480, 640]}, "area": 11579.0, "iscrowd": 0, "image_id": 10040, "bbox": [212.0, 217.0, 428.0, 129.0], "category_id": 124, "id": 10012200}, {"segmentation": {"counts": "Pkd71P?0N2O1O1O1O3M4L4K3N0000000O100001O1OL3012L6OM00O01000000O2OO100001O0O1001O001O0000O1001ON22N000001O00O4M3M3L3M3M1NTA0_]l0", "size": [480, 640]}, "area": 951.0, "iscrowd": 0, "image_id": 10040, "bbox": [522.0, 155.0, 58.0, 25.0], "category_id": 128, "id": 10012201}, {"segmentation": {"counts": "iUf4121h>0WA11Ne>1ZA20Ne>0[A20Ne>5[ALd>4\\ANb>2^AMc>801OO1O1002N000000000000001M202M2O1O0O11O00000000001O000O1O1O1N3N2O00000000000000000000000000O11O0O100N\\AHd>8\\AHd>8\\AHd>8\\AHd>8\\AHd>8\\AHc>9]AGd>8\\AHd>8\\AHd>8\\AHd>8\\AHd>8\\AHd>8\\AHd>8\\AHd>820000000000000000000000000O100000000O1O2N3M2M3O0000000001O0O10000003N2M11O0100N00NjAZOV>f02LhA@X>`0iA^OW>b04O1O1N3E:N2OYfk2", "size": [480, 640]}, "area": 1748.0, "iscrowd": 0, "image_id": 10040, "bbox": [320.0, 177.0, 124.0, 32.0], "category_id": 136, "id": 10012202}, {"segmentation": {"counts": "cRi72n>4L4L3M4L00GcAK0O]>ObA21NO3b>MbA1NOg>1[W^1", "size": [480, 640]}, "area": 93.0, "iscrowd": 0, "image_id": 10040, "bbox": [531.0, 179.0, 10.0, 17.0], "category_id": 142, "id": 10012203}, {"segmentation": {"counts": "RSh4142d>NYA8d>2O2N1MDbA<_>2O1N@dA`0\\>30ON200I\\ORBe0m=\\OQBe0n=\\ORBc0o=\\ORBe0l=]OQBe0m=8L5N100000000000000000000O2N1O1N2O10001O000000001O000bBaNO0Z=d1000000000000O010N\\NhBd1X=\\NhBb1Z=^NfBb1Z=^NfBb1Z=^NfBb1Z=^NfBb1Z=^NfBb1Z=^NfBb1Z=^NfBb1Z=^NfBb1Z=^NfBb1Z=^NfBc1Y=]NgBc1Y=]NgBc1Y=200000000000001O00000000000000WOjBHV=8PCBP=9jB[O3=S=9SCGm<9TCFm<9RCIo<EkBJL0O4>:m<FeC9\\<CfC>`=N2NM33M00O10001O000J64L00O1N21O2N1OO21N1O2NO2K41NN34LN20000N3N11OO10000001O2N4CaAMO0`>1<O`Re2", "size": [480, 640]}, "area": 4449.0, "iscrowd": 0, "image_id": 10040, "bbox": [324.0, 193.0, 134.0, 59.0], "category_id": 148, "id": 10012204}, {"segmentation": {"counts": "\\bT34;Mn==kAFV>`021O001O00N21O00N2^OfA?f>Ff\\`0M^c_O2N4L3M4L001O0000000000001OO1001O00O100000010OO11O01O0000000000000000000000000000000000001O0000O101O0000000001O000000000000000000000000000000001O01O0O10000001O00000000000000000000000001OO11O0000000001O000000000000000000000000000000000001O00000000000001O00000000000000000000000001O00000001O0000000000000000000000000000000010O0000000O10000001O00O100001O0O11O0000000000010O0000O11O0001O1O3MON22ZOlA>\\>N01O100000010;FUALm>06O\\TQ28a\\nMHb>:03M2N21O000000000000000001O000000O1001O00000000O20O0000002N1WG", "size": [480, 640]}, "area": 4373.0, "iscrowd": 0, "image_id": 10040, "bbox": [214.0, 261.0, 426.0, 41.0], "category_id": 149, "id": 10012205}, {"segmentation": {"counts": "P>P1Q>O00O1O12N000000000000O11OO100O11O1O00O100000000O10000O1O10000001O1O1O1O1O1OO100O1000000O10000O100K5HQO_Bo0a=8O100O1O1O1O11OO12N1O1O00001OO12lN^Bf0b=XObBf0^=ZOcBe0^=YOdBf0\\=YOeBg0[=VOiBi0g=01O00000000001O00O1001O1OMXOmAi0P>51O00001O00001O00001O001O00001O000000N2O1N2001O001O001O1O001O000000000000000000000000000000O1000000000000O1]OVOPCj0P=WOoBi0n<WObB1?h0m<]ORCd0n<]OPCd0Q=[OnBf0Q=[OnBf0T=XOjBj0V=VOiBk0V=XOgBi0X=`01OO100000000L_NiBa1V=_NjBb1g<]NXC041M0Of1a<`N_C01c1^<iNbCV1_<e0McMfCZ2Y<71O00O10000001O1ON2D^MXDd2d;aM[D_2d;fMUD]2Y;[MTER3e9mLRG1lNN:U3d9QMTG2TOn2i9oLSG6POl2l9PMSG_3n8aLQG_3o8bLPG^3P9cLoF]3Q9dLiFa3W9`L`Fh3`9YL^Fh3b9YL]Fg3c9ZL\\Ff3c9\\L\\Fd3d9\\L\\Fd3d9]L[Fc3e9^LZFb3[9jLcFW3\\9kLcFU3^9kLaFU3^9mLaFS3l9aLSF_3n9aLQF_3o9bLPF^3P:i0001O00000000001O000000000000@UKkFk4U9VKjFj4V9WKiFi4W9XKhFh4X9XKhFh4X9YKgFg4Y9ZKfFf4Z9[KeFe4Z9]KdFd4\\9^KaFc4_9?0hJcFm4^9nJgFQ5d901O1O00000000000000000000001OROXKdGh4[8]KaGc4]8`KbG`4^8bKaG]4_8eK_G[4b8eK]G[4b8hK\\GX4e8gK[GY4e8gK[GY4d8hK\\GX4d8iK[GW4f8hKZGX4e8iK[GW4e8iK[GW4e8iK[GW4e8iK[GW4e8jKZGV4f8jKZGV4f8kKYGU4h8lKVGT4j8mKUGS4k8oKSGQ4m8Q1O11O1OO1001OO10000000000000000001SJoFi5U9O4L3[JdF`5`9N00O1O1001O00O11OO10000O1001O000`JbF^5`901O0000O11OO1001OO1O11O00O11O000000O11OO1001O00O1001O0000O1002NN21O1ON2O1O1O12NN2002NO12NO1N2001OO12NO1001O1O2NO11ON21O001OO100N2003M00NcJaF]5^9300001O1O00O1001OO10000O11O00O11O00O1001O00O100O11O001ONaJcF_5]921O00001O00O1N21O000000000000001O0000O11O1OO11O1OO11O0000000000001O1O00O12N001O0000001OKhJaFY5_9fJaF[5^94M3L^JiFc5T960000OaKTGl2k8UMWGi2i8WMZGf2e8ZM^Gd2b8\\M_Gc2a8]M`Gb2`8]MbGb2^8^MbGb2^8^MbGb2_8\\MbGd2`8TLlFe0R1W3U8bLRH^3S8VLTHj3a9000O1O1O1O1O\\LTEV3k:iLWEW3h:iLYEW3f:hL]EW3`:jLbEV3Y:nLiEQ3U:oLmEQ3Z:fLhEZ3P;O001O00001O000000001O0000000000O100O1O1WOoLcEQ3Y:m0L4YOg00000000000000000003M4L5K4L4L5K3M00001O00001O00001O001O3M4L5K4L4L001O0000000000001O00000000001O000000000000N2H8H8H8I7001O0000001O000000001O0000001O00001O2N2N2N2N2N2N2N2N4L2N1O1O2N0000000000000000000000000000O1YOfLhEZ3V:oLcEQ3]:SM_Em2b:h0O1N23M1O000000O100OiK^ER4d:kK]EU4g:01O00000000O100001O00000000", "size": [480, 640]}, "area": 78837.0, "iscrowd": 0, "image_id": 10040, "bbox": [0.0, 288.0, 640.0, 192.0], "category_id": 154, "id": 10012206}, {"segmentation": {"counts": "0Z3f;002N1O00001O1OO10000M32NO11OO1N21O0\\DfLb;\\30N2004LO14LO11O1OM31O00M3O11ON2O11\\DfL`;^3ON20_DdL^;_30O13M00O11OO12NO1O100O11O1O00001O00O1001OO1001OO1N2O100O1O1O1O10000O1O1O1O1O1O1O1cN^D\\Ob;c0`D[Oa;e0_DZOb;e0_DZOb;e0_DZOb;e0`DYOa;f0`DYOa;f0`DYOa;f0aDXO`;g0cDVO^;g0gDVOZ;h0kDTOV;i0oDjN\\;m0`10000O1000000000000000000001O001O00001O00001O001O0PDnNR:R1mEVOl9k0SFUOm9k0SFUOm9k0SFVOl9k0RFVOn9j0RFWOm9i0SFWOm9j0QFWOo9i0QFXOn9i0lElNaN;c;i0jE@T:`0jEDT:=jEEU:;_EjNYOo0V;8`ElNUOo0Z;5aE5]:LTEjNK\\1o:KQEoNNW1P;JnDTO1T1o:IkDXO5P1o:>QE_OLaNQ;R2QE]OS;d0lD[OU;f0jDYOW;W2100O1O1O100O1O1O1O100O1O1O100O1O1O1O100G9A?E;0000O100000000000000O100000000000000O10000000000000000>BM31O00001O00001O001O2N4L4RDWM];l2ZDZMf;P30O100O100O1O1]OWDoMi;n1`DkMa;R2iDgMW;V2k000O1O100O10000000000001O0[DlM^:T2bEmM]:S2dEmM[:S2eEnMZ:R2fEoMY:R2fEnMZ:R2eEPNZ:Q2dEQN[:P2cERN\\:n1cETN\\:m1aEVN^:k1`EWN_:j1^EZN`:g1]E]Na:c1REeM2l0j:i1SEZN4VOW:a2bEmN]:`2eEoK]:T46J6O1O1N22N1O000000O11OM34LN2M36JL43M001O00N22NO11OO12NO1O1O11O1OO12NO1O11ON2002NO1O100001O00O1001O000000O11O00O100000000O1O11O0000O11OO11O000000O10000001O0000O100001O00O100O11O00001OO100O10000001O00000000O100001OO100000000001O00000000O1O11OO100001O0000O10000000000001O00001OO10000001O001O0000001OO100001OO10000O1O11O001O001O00000000000000001O00O1001O00O11O0000000000000000O1001O000000000000O1001O002N002N2N1OO1O1O100O1O10000O10000O1BYEcLg:o2hEPMX:P3iEnLX:Q3jEnLV:Q3m00000000`DmLS;S3=00001\\DlLZ;T3eDmL[;S3eDmL[;S3eDmL[;S3eDmL[;S3eDmL[;S3eDmL[;S3eDmL[;S3eDmL[;S3dDmL];T3bDlL^;[30000000001O001O1O1O<D00O11O000000001O1OO100O11O00O100O12NN21O001O000000O1N2O11ON2001OO1O11OO12N3M1O1O00O1N2002NN2000000O11lDULQ;m30O1O13MK500O1N2O1001O002N2N00O1O10000001O2N1ON21OO1001O0000001O0000O10000001O000000001O00O11OO1001O1OO1000000000000001O00N200N21O00000000FmDfLT;Z3nDdLR;\\3mD`LO1U;_3PE`LP;`380000VOcD\\M0?^;U2gDkMY;T2hDlMX;n1nDRNR;o1nDPNR;R2mDnMR;S2oDkMQ;k1dDfM05:;Q;i1jDlM2=T;g1jDhMJ1:?Q;i1[EUNg:k1YEVNf:", "size": [480, 640]}, "area": 70075.0, "iscrowd": 0, "image_id": 10040, "bbox": [0.0, 0.0, 640.0, 144.0], "category_id": 157, "id": 10012207}, {"segmentation": {"counts": "Z3g1Y=02N1O00001O1OO10000M23NO02N01N12M2aNaB\\1b=0N3O04LO14LO1100OM31O01L3O11ON2O20^NdB^1`=ON3O`NcB]1`=0O13M00O20OO12NO1O100O20O1O00010O00O1010gNnBb0Q=^O_C4`<K\\1N00O0101O000000001O1O`g_2?nW`M;E8H7N2bBfNe<3VC]1OfNj<n1G:Kf0ZO1N04N1O0002N0O11NN43LN1N36KK43M001I6LoCYMo;i2QDWMQ<d2TDZMk;h27N012NO1O002000N012NO1O1nCYMl;k2oCUMo;l2RDTMn;o22OPDRMk;T3M2O00O20O0O10011NO1000O11O06I8I2N00000O2N020O00O011OO110OPNjDYN1i1U;MkDYN11Nf1V;0kDYN20Nf1T;2lDXN20Nf1T;2QEXNLn0NUO28V;]1nDXNNl0LTO80I0\\;h1iDXNMm0j;j0^DUOc;k0^DSOc;m0\\DQOFXOm;h1\\DROf;n0ZDQOg;o0[DmND[OR<g1ZDnNh;R1XDoNf;R1ZDlNh;T1XDlNg;U1YDdNDHT<c1XDeNEFT<e1WDdNFGS<e1XDcNDIU<a1YDeNDGT<e1ZD`Nn;_1TD\\No;e1f000O110O00000000O2O0001OO10001O1O0001O00000000O1O11OO100001N11O0O10000000000001N11O01OO10000001O001O0000001OO100001OO10000O001QDZNe:e1[EaN_:_1\\E[NQO3e;a1[EeNe:Z1\\EgNd:W1]EhNd:W1mDcNZOL0O4>b;S1PE_Oo:?PECQ;=nDFP;:PEHn:8REEQ;;oDHn:8REHo:8PEGQ;9nDHQ;9oDGQ;9oDGR;8nDHR;8QE_OT;c0nD^Oo:a0RE^On:d0QEZOP;g0QEUOQ;m0oDROP;n0PETOn:l0PE[NZOj0e;k0QEVOP;j0lD[N@m0b;h0nD\\N^On0b;f0PE@n:`0QE^NBj0_;g0XETOl:l0UETOj:m0^DbN;>Z;6^DGKE`0MA<b;6lDG@Id0JC<_;7QEL9Lf:7SEJ:Nb:7VEH=M_:9VEH>M^:9UEGb0OX:8XEFb02Y:4nFJR95o2M3Nmg4OnS;1ST@2]A2T>>N200O1O001O01000N101N20O01N2O0O21OM300O10O01OO10O1N2O11ON20O2OjClN_:U1`ElN_:U1`ElNa:S1[EQOd:o0SE\\On:b0iDnNVO0O`0U<?_DPO]O1NO3e0T<;]DTO]ONOd0X<:ZDUO_Oe0W<2]D7b;I_D7_;M]D5c;O\\DNf;0ZD0d;2[DOe;0\\D0d;0\\D0d;0\\D0c;1]DOd;mNkCP1c02d;N[D3d;N\\D2c;O]D1f;L[D4_;1aDO_;0bD0];1cDO[;4dDL[;5eDK[;6dDJ];5cDK];5cDK_;2bDN`;0`D0`;1_DO`;0bD0];3aDM_;3aDM_;3aDM_;3bDL_;3aDMa;1^D0c;2ZDNd;7YDJg;8UDIj;:SDGm;;PDFQ<9mC`N0W1S<d100001O0000O10000001O000000010O00O11OO1001O1OO10000000000000010Ok0SO5M3K5N2M1O00000FkKhEV4X:lKfES4[:lKaEO1V4^:oKaEQ4_:71@iEVLX:k3gEUL_9M[F0?o3HSL^93jFd3@YL9N]96jFd3NVLR9<PG^3c9bL\\F_3e9`L[FZ3k9gLSFY3c9\\LnE05:;Z3`9aLUF2=\\3_9bLPFJ1:?Y3a9SM]Fo2c9QM^Fn2W7", "size": [480, 640]}, "area": 30022.0, "iscrowd": 0, "image_id": 10040, "bbox": [0.0, 74.0, 640.0, 184.0], "category_id": 169, "id": 10012208}, {"segmentation": {"counts": "kWl0?`>`0@=C8H8G:G>B=Cc0]O6J4L5TIWLX2m3^M\\L_2i3UMaLg2d3nLgLn2^3fLlLW3X3^LSM^3R3TLZMi3l2iK`MS4g2^KdM2TMU3_5ZLiM@gMh2VOYN`5POROf2_KYN`:f1`EYNb:e1`EYNb:e1_EZNb:e1_EZNb:e1_EZNc:d1_EZNb:e1`EYNb:e1aEXN`:e1eEXN\\:f1iEVNY:f1lEmM_:j1`101O0O101O00001O00001O00O1N3N1N30O00100OmMVNQFOLk1P:\\NjE16c1m9aNdE3?[1j9DVF=g9FXF:P1ZNc6_1\\H7Q1\\N_6_1`H6o0\\N_6a1aH3P1\\N]6d1bH0Q1]N[6d1dH0P1\\NZ6g1eHMP1^NY6g1fHKQ1^NW6j1gHIQ1]NW6k1hHHl0QNbN=g7m1jHFk0SNaN<h7m1kHDj0gNW6h1oHAh0kNS6i1TI]O;PN[Om0Q7k1XIXO<RNWOo0o6k1^IUO;SNTOP1o6k1bIRO0nMI]1_6g1hIoNKRNMZ1Z6j1mIkNGWN0U1X6l1QJhNC\\N3R1S6n1VJ[OFhNn5R2\\JXODdNN`Nl5h3bJUOCcNk5\\2bJROBaNi5a2eJoNA_Ng5f2hJ;U5JiJ7R5NmJ2P52oJNg4=WKDa4e0^K\\Ob4d0]K\\Of1cKi0Q5`M\\Om1iK8o4fMXOX2hKMf9:VFD`1mM`6d2nG\\O\\1XNc6b2nGTOY1bNf6`2oGjNU1oNi6U4QIRLn6n3lHZLQ7g3iHaLT7^3hHjLU7W2]HiL<X1T7U2_H[L;h1T7\\2bHSNT7S5_Oa0C=N2N2M2O2N2N2N0000000O0100gNZJXIf5b6aJ]I_5[6jJdIV5Q6UKoIk4f5aKXJ`4\\5mKcJR4S5ZLlJf3i4fLVKZ3f4kLmJa3o4dLoJ]3n4gLQKY3k4lLTKT3h4QMWKo2f4UMXKV3GcIo0OZ2\\3PMV3i0kIf1S3`Mo2n0[JP1Z1lMVO5V5P1eJ?j0dN\\OLU5U1oJI<_O@BU5[1XKROO9EYOU5_1aK^N\\OW1NjNV5f1dLe0]MdMo5k1[Lg0fM]MP6o1QLi0g4\\OoJi0R5ZOeJk0^5VOTJT1P6cNfIl1^6[LZHS1P1Q3Q7jL_Hk3a7RLmGc4R8V100OoNQHSKo7i4VHVKi7V4WHUK6d0a7S4WIkKV6ZORIg4m0nKn5DnH[4`1iK_56jHl3n1lKU5l4oJSKQ5n4oJQKP5Q5PKnJo4W5mJiJS5[5jJdJU5a5hJ^JX5g5dJXJ\\5U6XJhIj5\\6TJ`In5c6V14_H]I]6f6\\I_Id6d6RIbIn6e0jHe4OmJV7>lH\\5T7dJmH[5S7fJmHY5S7gJnHX5S7gJmHY5S7eJPIZ5Q7dJQI[5P7cJRI\\5n6cJTI\\5m6aJVI^5k6`JXI;Dh3T7iK\\I:0a3e6RL_I73f2UO[MY7YOiH4j0;8h2SOZM\\7@]IMf0U3dN]MY7^O]K5QMV23gM8?k:]1kEaNY:n0VFPOn96jFIW<O0000001O00000000001O001O2N1OaQ`2N\\n_M6M3M2jNHbC;X<OaC4[<3_C0_<2^C1_<1^C2`<O_C4_<M^C7_<K^C8`<I^C9b<E^C<c<P11N10000O[GQN\\4o1eKSNY4:fGk0S4nNS46kGl0R4QOP43nGk0T4SOm31oGl0U4TOk30PHj0V4WOi3OQHj0W4WOh3OPHj0Y4WOg3NPHl0:g0^1`NW6NQHl07T1g0`Nc6TOeH;Jn05\\1;^Nc6\\ORI]1MR11\\NP7UOQI[6n6hIPIX6o6l01O1bHmHn6T7PImHP7T7oHlHQ7<eHZ6:YIQ7<hHZ66ZIR7<iHZ64ZIR7<mHZ6O[IP7=SIX6M[Il6`0XIU6L[Il6>[IV6H]IT74VI_6F]Il7b6TH^Il7b6TH^Il7b6TH^Im7a6SH_Im7a6SH^In7c6QH]IP8i60000000001O00100Nb0ZHcHf6^7TIiHk6g7N2N2UObHRJ_7i5n0L5ZOe001O1N101O1O001O1fMmEES:3SF3j9GXF`0e9]O[Fj0a9SO^FT1_9jN`F\\1]9cN`Fe1\\9YNcFk1\\9m10O1O101N4L4M3L3kKQFT3R:gLoEY3U:bLlE^3X:]LiEg3W:kKSFY4Y:4K4L4L000000001O0000000000001O00000001O01O000000I7H8I7H8M30000001O0000001O0001O01O0000001O001O2N2N2N2N2N2N2N2N2N2N2O1N2N0000000000O10000000000bMlE0T:N]FCc9<nFTOR9j0_GgNZ9>WGSOl8h0ZGTOj8f0l2K5K5K5K5Kjb9", "size": [480, 640]}, "area": 74429.0, "iscrowd": 0, "image_id": 10040, "bbox": [60.0, 30.0, 559.0, 425.0], "category_id": 183, "id": 10012209}, {"segmentation": {"counts": "0_6a81O00000000001O00000000001O000000001O00000000000000001ON2O10000001O001O00002N1OO11O00001ON2001O001OLhG`IX8`6iG_IW8`6jG`IV8`6kG_IU8a6500000000001O1O001O00001O001O00001O00000000000000001O00001ON2001O001O0000000000001O00O100O10000001OM300O10000O1O11O00000000000000O100000000001O00000000000000O11O0000O10000O1NdG`I\\8`6dGbIZ8^641OO100O10000001O00O10000N21O001O2N0000001O00001O00O100O1McGcI]8]6dGbI\\8^6300000000O1001O000000001O00001O00000000001O0000001O00O1000000000000000000000000000000001O00000000000000001OO100002N1O0gG[IW8h6O2NO1001O00N22NN21OO100000000O1O1O100O11O00O11OO1O11OO1001O00001O0000001O1O00000000000000000000O1000000001O1O2N1O00000000000000O100000000001O2N2N2N2N1O1O1OO11O00001O0000O100001O3M1O001O0000O100001OO100001O001O00O1O1O1N2O11O1ON21OO1000000000000000000007I3M0dH_HO0X7b7hHaHW7c71001O1O000000001O00O11OO100N20000000000000000001O0000O100000000001O1O1O0000001O000000000000000000O1005kHRHn6\\8D00000000001O00000000001O000000O1O1O1O100O10000001O00000000001O00H_IPHb6R85M3H8N200O100000000000000O1000000O100000000000000000000000000000000O10000O1O10000000000001O000000001O001O1O001O000000000000O100O100O1O10000001O00001O00001O00001O001O0000000000O1O1O11O0000001O000000000000O10000000000O1O10000000000000000000000O1000000O1O1O1O1N2O1O100O11OO1000000O100O1\\O[HlIf7T6aHcIa7]6a00000000000O10000000000O11TH]IY7b6hH^IX7c6dH]IC4f7_6eH_IF1e7a6cHfI\\7[6_HiIa7X6[HmIc7j6N0000000000N_HeHa7[7200O1O1000000N2O10000000000N200000000", "size": [480, 640]}, "area": 146399.0, "iscrowd": 0, "image_id": 10041, "bbox": [0.0, 0.0, 640.0, 271.0], "category_id": 106, "id": 10012210}, {"segmentation": {"counts": "_6a8`6O00000000001O00000000001O000000001O00000000000000001ON2O10000001O001O00002N1OO11O00001ON2001O001OLhG`IX8`6iG_IW8`6jG`IV8`6kG_IU8a6500000000001O1O001O00001O001O00001O00000000000000001O00001ON2001O001O0000000000001O00O100O10000001OM300O10000O1O11O00000000000000O100000000001O00000000000000O11O0000O10000O1NdG`I\\8`6dGbIZ8^641OO100O10000001O00O10000N21O001O2N0000001O00001O00O100O1McGcI]8]6dGbI\\8^6300000000O1001O000000001O00001O00000000001O0000001O00O1000000000000000000000000000000001O00000000000000001OO100002N1O0gG[IW8h6O2NO1001O00N22NN21OO100000000O1O1OXI[IW5e6iJ\\IV5c6kJ]IU5d6jJ]IU5c6kJ]IU5b6lJ^IT5c6kJ]IU5b6lJ^IT5a6mJ_IS5b6lJ^IT5a6mJ_IS5a6mJ_IS5b6\\J_IZO1X6`6^J`IPOL56\\6^6_J`IoN:c6W6]J^IQO;b6W6ZJaISO9c6V6PJ\\I@8M6d6V6mI\\JT6e5kIRJ^6o5aIPJ`6P6`ISJ]6m5dIUJY6k5hI[JQ6e5QJZJn5f5VJVJj5j5ZJbIWO3_6[6jJbIV5^6mJ_IS5a6PK[IQ5e6e1O1000000001O1O2N1O00000000000000O100000000001O2N2N2N2N1O1O1OO11O00001O0000O100001O3M1O001O0000O100001OO100001O001O00O1O1O1N2O11O1ON21OO1000000000000000000007I3M0dH_HO0X7b7hHaHW7c71001O1O00000^I^Hf5b7XJeHc5\\7[JhHb5X7]JjHb5U7^JmHa5T7]JmHc5R7]JPIb5P7]JQIc5m6^JUIa5k6^JVIb5j6_JUIa5k6_JVI`5j6`JVI`5j6aJUI_5k6aJUI_5k6aJUI_5k6aJUI_5k6aJTI`5m6`JiHFBk5e7`JgHFDj5e7`JfHGEi5d7cJdHFFh5f7hJ\\Hd5d7l00000001O1O1O0000001O000000000000000000O1005kHRHn6\\8D00000000001O00000000001O000000O1O1O1O100O10000001O00000000001O00H_IPHb6R85M3H8N200O100000000000000O1000000O100000000000000000000000000000000O10000O1O10000000000001O000000001O001O1O001O00000000000[JYHQ4f7mK^HR4b7lK`HT4_7kKdHT4\\7kKfHT4Y7kKiHU4V7jKmHU4S7lKlHT4T7mKlHR4T7oKbHcN2^5]7l100001O00001O00001O001O0000000000O1O1O11O0000001O000000000000O10000000000O1O10000000000000000000000O1000000O1O1O1O1N2O1O100O11OO1000000O100O1\\O[HlIf7T6aHcIa7]6a00000000000O10000000000O11TH]IY7b6hH^IX7c6dH]IC4f7_6eH_IF1e7a6cHfI\\7[6_HiIa7X6[HmIc7j6N0000000000N_HeHa7[7200O1O1000000N2O10000000000N200000000", "size": [480, 640]}, "area": 159757.0, "iscrowd": 0, "image_id": 10041, "bbox": [0.0, 206.0, 640.0, 274.0], "category_id": 159, "id": 10012211}, {"segmentation": {"counts": "Wme31o>1O00000000001O0000000@1oA1o=OhAM45T>NgA;Y>EgA:W>HiA9m=CUB8M5m=:WBZOj=e09003N2N6LM5L3XOoA9`>L3L4LTgY16eXfN7J4L2N1O2N1O2N1O20O00001O0000000O2FoABS><nAEQ>:PBFU>4Zjg1JUVXN4M2N3N1N3M210000CeA3Yng1", "size": [480, 640]}, "area": 982.0, "iscrowd": 0, "image_id": 10041, "bbox": [251.0, 232.0, 270.0, 73.0], "category_id": 183, "id": 10012212}, {"segmentation": {"counts": "k6W3i;e0\\O1N1000010O00010O0001O010O000O2L3N3L3N2M4M2M3M4M2M4M2M3N3L3N3L3M3N1N3N2M2O2M2N3N3L3N3L3N3L3N3L3M4M2M4M2M4MQNA`E<a:G_E6a:M_EOb:4]EId:9P23M3M2N1O2O0O2N1O2N1O2N1O2N1O2O0O2TNlNXDNZ1X1]:mNVDL[1Y1]:nNVDJ[1Z1^:RO`EP1]:ROaEP1^:QO`EQ1_:QO_EQ1a:nN]ET1c:lN\\EV1Z:SOdEn0[:UOcEk0]:VOaEj0_:XO`Eh0_:fNhD4f0V1b:hNhD2d0W1d:gNiD2b0V1a3`Nf28XI3>U1W3POP3J\\I0<V1j2@]3YO^I19V1_20j3XN\\I;287U1T2>\\4TNZI94U1n1i0c4iM[I93U1k1o0f4bM^I9OV1j1T1i4]M^I:MU1h1Z1l4VM`I;JU1g1`1n4PMaI;IT1e1g1P5jLbIU2Z1V1S5eLeIR2T1^1bNWLd69fIQ2P1e1fNQLb6:hIn1m0m1iNkKa69jIn1i0S2]5PLkIj1g0Z2]5lKlIh1f0_2]5iKnIg1d0c2\\5gKPJd1c0i2\\5cKQJc1a0n2\\5_KUJ`1?T3[5\\KVJ^1>Z3[5XKUJ^1?^3Z5TKWJ^1>a3Z5QKVJ^1?e3Z5mJUJ^1`0i3Z5hJVJ^1`0m3X5fJVJ^1`0P4Y5bJVJ]1`0U4Y5]JWJ]1`0Y4W5[JWJ\\1a0]4W5WJWJ\\1`0a4X5RJXJ\\1`0e4W5jI]J`1;j4V5eI`J`19o4V5_IbJb17R5U5[IeJb15W5U5VIfJb14\\5`5cJ`J`5^5`J`Jb5_5^J`Jd5`5[J`Jf5`5YJ_Ji5a5WJ]Jk5c5TJ]Jm5c5RJ\\JP6d5PJZJR6f5mIZJT6f5kIZJV6e5jI\\JV6d5jI[JW6e5hI[JY6e5fI\\JZ6d5eI\\J\\6d5cI\\J^6d5bI\\J^6d5aI\\J`6d5_I\\Jb6c5^I^Jb6b5^I]Jc6c5\\I]Je6c5ZI^Jf6b5YI^Jh6b5WI^Jj6b5VI^Jj6b5UI^Jl6b5SI^Jn6a5RI`Jn6`5RI_Jo6a5PI_JQ7a5nH`JR7_5nHaJS7_5lHaJU7_5kHaJU7_5jHaJW7^5iHcJW7]5hHdJX7\\5hHcJY7\\5gHeJY7[5fHfJZ7Z5eHfJ\\7Z5cHgJ]7X5dHhJ\\7X5cHhJ^7X5aHiJ_7W5RHXKn7g4nG^KR8g510000O10000O10000O1000000O10000O2O000O10000O1000OjMdGjM\\8U2eGjM\\8V2dGjM[8V2gGiMY8V2hGiMY8V2hGjMW8W2jGhMV8W2kGiMU8V2lGiMT8X2lGhMT8W2nGhMR8W2oGiMP8W2QHhMP8]1fG\\M;V1o7_1hGWM:Z1m7a1jGQM;^1k7b1lGkL<b1h7d1nGeL<f1g7f1nG`L=j1d7h1iHWNW7j1hHVNX7k1fHUNZ7n1dHRN\\7o1bHRN^7o1aHQN^7P2aHZL]OX1R8^2aHYL3c0]7S3`H[L9<V7Z3aHYL;<T7Z3aH[L;;T7Z3aH[L<9S7\\3aH[L=9R7\\3aH[L>8Q7]3`H[L`08o6]3bH[L`07n6^3cHYL`09k6_3gHWL?:g6a3kHTL?:d6c3_J]L_5e3bJ[L\\5\\3UH]L`27Y5[3ZH^L^26V5[3_H^L\\28R5[3cH]La0Ol08n5^3eHZLc00i08m5_3hHYLb02h06k5a3kHWLb0OSO2c17k5c3mHTLc00SO2b16j5d3oHTLa01UO2_16i5e3QIRLc00WO3[15h5g3TIQLb00XO3Y15g5i3VIoKa00[O4V14e5k3YJmKnN3S15d5l3[JlKPO3Q14b5o3]JjKQO3o05`5o3aJiKRO2m05_5Q4aIhKa00D2j05^5S4bIfKc00D2i05[5T4fIeKa00H1e06Z5V4gIdKb0OI1d05X5X4gIeKd0NJ0c05T5Z4jIeKe0KL0a06R5[4jIeKi0JX15R4^4lIbKl0K30M6U5_4lI_KS1LN2N3R5b4nI\\KV1ML3N2o4d4oIYK\\1NG312j4g4aKTKE205e4d4iKUKl07X3c4oKVKi08T3a4VKWK60b18n2d4XKVK8Nc18j2f4[KTKl26e1h4iJPKe019Oe1:`2g4VKoJ41a0Of1:[2j4TKlJM3>M`01g19V2m4XKnJ;K`00h1;R2n4\\KkJ:L`00i1;m1Q5aKfJ:N>1k1:i1T5dKbJ:O>1l1;c1V5jK^J90=1n1<^1Y5^LZJ61o1<Z1[5QLXJ80=2Q2;U1_5ULTJ70>2R2;Q1a5gLRJ61S2>k0b5lLoI61T2>f0f5oLkI62V2=b0f5_LkIb0072V2>>h5XMhI41X2a04j5mLdI>190Y2i0]Oo5dMWI<1:0Z2P1hNT6XNkH<191[2W1SNY6nN^H;091]2`1\\M]6CRHV11l1Q:oLnET11m1P:PMoES10o1P:oLPFP11R2n9nLQF:110g2n9oLQF84OMj2n9oLQF94MMj2o9QMPF73CN50P3n9RMQF62F0N0T3m9SMQF41I4HMW3n9TMQF21K8o2f9UMPF11M7n2h9TMQFKOJ1;6k2j9VMoEK0G1?5i2j9WMQFI193g2k9XMPFI193e2m9YMPFH0:3e2m9ZMoEH0:3b2P:\\MnEGO<2_2R:`MlE;2S2U:`MkE=OQ2Y:aMiE=NP2\\:bMfE?Mm1`:cMaEB0m0Ol1c:bM`EFMl00j1f:_M`EMJ`3i:^L`E3G]3V;eLjDX3X;hLhDV3Y;lLgDQ3Z;PMfDn2\\;SMdDj2];UNdDj0];ZNbD?2S1^;_N`D=6n0[;fN_D<<f0V;PO^D:a0>R;YO]D9h05m:C[D9l0Lj:m0ZEjNg:7YD<R1UOg:f1W1kNPC`0k<m0N001_EjMV8V2fGTNT8m1hG\\NR8e1]GQO\\8P1bGYOW8h0fG_OU8b0hGFR8:kGOo72nG6k7KSH9k7HQH<n7CQH`0n7_OQHe0m7YORHk0l7TOTHo0k7POTHS1k7kNTHZ1j7eNUH^1j7`NWHb1g7^NYHd1f7_MdGCg0Q3c7VN^Hk1a7SN_HP2`7oM_HT2`7bMjGbNe0Q4_7_MmG\\Nb0Z4_7\\MdHg2Y7XMgHk2V7UMkHm2S7RMmHQ3Q7mLQIU3m6jLSIZ3j6dLXI^3f6aL[Ia3c6^L]Ie3a6YLaIi3]6ULdIn3Z6QLgIP4X6ZKgHOR1h4V6YKhHOS1i4T6WKiHOU1k4Q6VKjHNU1n4P6SKkH0T1n4Q6QKlH1R1P5Q6nJmH2R1R5Q6kJlH4R1R5S6hJlH6P1T5T6dJlH9o0U5U6`JmH;m0V5W6]JlH=n0W5V6[JkH?n0X5W6WJlHa0m0X5X6UJkHd0l0Y5_6dJaI^5`6`J`I`5a6_J^Ic5c6[J]If5c6YJ\\Ii5e6TJ\\In5c6QJ\\IQ6e6mI[IT6e6kIZIW6g6fIYI]6f6bIZI_6e6aIZIa6d6_I]Ib6a6_I^Id6m5ZIiI3:d6l5\\IgIO=g6j5_IdIK`0i6k5_IaIIa0m6k5eISJ^6l5aIRJc6l5^IQJf6n5ZIPJh6o5XIoIl6P6TInIn6Q6SImIo6S6PImIR7R6nHnIR7Q6oHoIR7P6nHPJR7P6mHQJS7o5mHQJT7m5mHSJS7m5mHSJS7m5lHTJU7k5kHUJU7j5lHUJV7j5jHVJV7j5iHWJW7i5iHWJX7g5iHYJW7g5iHYJX7f5gH[JY7e5gH[JY7d5hH\\JY7c5gH\\JZ7d5fH\\JZ7c5fH^J[7a5eH_J[7a5eH_J\\7`5dH`J\\7_5eHaJ[7_5eHaJ\\7^5dHbJ\\7^5cHcJ^7[5cHdJ^7\\5bHdJ^7\\5bHdJ_7[5aHeJ_7Z5bHfJ^7Z5bHfJ_7Y5aHgJ_7Y5`HhJa7V5`HjJ`7V5`HjJ`7V5`HiJb7V5^HjJb7U5_HkJb7T5^HlJb7T5^HlJb7T5^HlJc7R5_HmJa7S5_HmJa7S5_HmJb7R5^HmJb7T5^HlJb7T5^HlJb7T5_HkJa7U5_HkJa7U5_HkJb7T5^HlJb7T5^HlJb7T5_HkJb7T5^HlJb7T5^HkJb7V5_HiJa7W5_HjJ_7W5bHhJ^7X5bHhJ]7Y5cHgJ\\7Z5eHfJZ7Y5gHgJX7Z5hHfJW7[5jHdJV7]5iHdJU7]5kHcJT7^5mHaJS7_5mHaJR7a5mH`JQ7a5PI^JP7b5PI^Jo6c5QI]Jo6d5QI[Jn6f5dH^JBLi7k5YHlJf7W6O1O100O1O101N1O100O1000001O0000001O00001O001O00001O001O0000001O0000001O000000001O0000001O00000010O01O001O1O010O001O1O010O001O1O0010O01O1O0010O01O001O10O01O001O1O010O001O1N101N101O1N101N101O1N3N1N2O2N1N2O2N1N2O2M2O1O2M2O1N3N1O1N2O2N1N2O2M2O1O2M2O1N3N1O1N3L3M3M3N2M3O1N2O1O1N2O1N2ChJ", "size": [480, 640]}, "area": 138160.0, "iscrowd": 0, "image_id": 10046, "bbox": [0.0, 43.0, 640.0, 424.0], "category_id": 183, "id": 10012213}, {"segmentation": {"counts": "0Z3h;00N200001O00000000000000O1N2N20000O100O1O10000O100O1O10000001OO100O1O1O1O13MN20000MjCYMW<g2200N200001OO100001O1OO11O0000000000L4O100000000001O0000001O1O2N1O00O1000000O1001O2N1O00001ONkCXMT<d2PD\\MP<d2PD[MQ<f2500O1001O00O11O0000O100001O0gCZM02Q<e2nCZM10Q<m2000000000LnCXMR<h2nCWMS<h24O1O100O10000NfC\\MZ<d2fC\\MZ<b2hC^MX<b241nC^MJ2];`2jDbMEMa;a2jDcMDO_;^2nDjMR;V2lDkMU;U2kDjMV;V2hDkMY;U2eDmM[;T2cDmM];T2]DSNa;g200000O10000L^DgLc;X3^DhLb;X3^DgLc;Z3\\DfLd;Y33O11OO1N200N21O1OO11O1O0XDiLe;X3ZDhLf;Z3\\DgL_;Z3`DgL_;Z3aDfL^;`3OMbDcL];\\3500O1O1O10000M[DiLe;W331O1O000000N20000O1O1001O00O10VDjLh;X301O1OO1GYDWMg;h2ZDUMOMg;n2ZDTM0Nf;l2YDSM041Mf;l2YDSM04k;i2WDVMj;j27001OO10000002NNoCTMP<l2PDTMP<k2QDTMP<j2RDVMn;f2PDZMV<e24O100000000O100001O001O1hCZMR<f2nC[MOOO1k;j2WDYMi;h2UDWMONk;k2WDVMl;j2UDUMk;Q30001O1O001O1O1O2WDhLd;[3000N200MZDjLf;V3ZDiLg;V33O1O1O1O100O100002N1O1OO11O000`DlLR;T3cDkL83T;U3kDkLU;X3gDjLX;V3gDkLY;V3fDiL[;W3dDjL<Jb:d3ZE`Lf:`3VE]LL5m:^3WEeLi:]3UEbLl:i31001O2N2WElK`:U4_ElK`:U4_EjKb:[40O11O1O1O1dEbKKOT:_4PFfKP:[4QFdKn9]4mEiKQ:d401O002N2N00O11O1O1O1O3WFfJd9\\50001O00001O0aFbJZ9^5dFdJ\\9\\5dFdJ\\9a50O1O100001O1O0000000000000RG_J[8a5ZG^J34a8`5ZG]J28a8\\5[G^J0M09e8Q60O10000001O001OO1002N00O100O1001O00O10000O1N2000000O1000000000000O11OO12N0000002N00001O00O10000001OO1O11O00001OO1001O1O1O0000O1A\\G`Jd8`5\\G`Jd8_5_GUJO6a8c5cGVJK8b8a5jG]JU8`5QHZJR8_5[GbJ^9]54N200O1001O00000000002N000000O1mL]F<2aMa9S2_FfM1[2MgM16b9R2dF0JhMO6d9R2cF0JgM07c9R2cF0JgM07c9R2cF0OlM_9S2cF2LiMLLf9Y2bF2LeM00b9Z2aFfMKW2P:3\\FfMBR2R:8^FI@mMo9Z2bFK]95TFjM7Q2e95SFiM8T2e93SFiM6U2h97YFIHhMm9`2VFNj92VFNj92VFNj92VFNj93UFNj94TFKm95SFLl9^2UFUKm9a4TFbK0Lm9a4SFbKT:]4nEaKS:^4nE`KT:]4:O10000001OO100O1001O1O1O1O1O002NO10000O100O1000000000000001O00001O0000001O1O001O3M00000000001O00000000001O00M30000000000001O1hEXK00Q:o4OO1000000002nEPK0020f9T5ZFmJe9T5ZFlJf9U5ZFjJf9Y5000000000QMYFgM030`2g9D_FkMO\\2b9HlF52dM\\8V2cG6]OdM61OOJ1P9U2dG6\\OeM61KNO103o8R2eG6\\OeM30M=1Di8T2jG6\\OeM3a0M@j8T2iG:^OXNi8^1iGT1X8lNhGT1X8lNhGT1X8mNgGS1Y8mNgGS1Y8mNgGR1Z8nNfGR1Z8nNfGR1Z8oNdGR1\\8nN\\GhMOY3e8oN[GjMOW3f8oN[GjMK[3j8lNYG\\1f8b2N1O001O1O2N1O00O1000000O100UL\\GW1e8iN[GW1e8hN]GV1d8jN^GT1b8lN^GQ1K`Lh8_2^GP1d8oN]GP1d8PO]Gn0d8RO\\Go0c8QO^Gn0b8RO^Gn0b8RO^Gn0b8RO^Gn0b8QO_Gm0c8SO]Go0a8RO^Gf0GnL1Nk8^2]Ga0GQM1001n8]2ZGa0JQMN0o8^2YG9H^M1LN0P9]2YGLGQN1J1KR9^2UGLGmM0J11O00N00T9^2UGGGlM25OK0003U9Z2SGGNnMKN0O01NOV9^2SGG1kMH10O[9^2lFF6lMS9^2gFFj9:VFFj9:VFEk9;UFEk9;UFEk9;UFEk9;UFFj9:VFEk9;VFDj9<VFEi9;WFEi9;WFEi9;WFEi9;WFEi9;WFDj9<VFEi9;WFFh9:XFFh9:YFEg9;YFEg9;YFEg9;YFEg9;YFEg9;YFEg9;YFDh9<YFBh9>XF@DRNT:^2XF]ODVN0OT:^2XF\\OEWNU:]2VF\\On9d0SF\\Ol9d0TF]Ok9c0UF]Ok9c0UF[OCXNW:]2VF[OCXNW:]2WFZOCXNV:^2WF[Ok9f0TF[Ok9e0UF[Ok9e0UFZOl9f0UFYOk9g0UFYOk9g0UFXOl9i0SFWOm9i0TFcNB\\O\\:Q2RFcNBYO_:T2oEbNCnNO:_:W2oE`NEYO\\:W2oE]NBRO53L0^:^2PF\\NASO53LO_:_2oEiMAG0<1C34NM^:`2oEfMAJ004OM78IJ0^:a2oEeM@K0O96h:\\2^EdMAJ>2e:`2]EcM@K>2e:`2]EbM1Oa:_2_EbMNOd:_2^E_M@1>Ne:c2\\E^MA0?Od:c2]E]M@0f;c2jD]MY;", "size": [482, 640]}, "area": 87816.0, "iscrowd": 0, "image_id": 10056, "bbox": [0.0, 0.0, 640.0, 207.0], "category_id": 106, "id": 10012214}, {"segmentation": {"counts": "U;d0`>N0O1O11O10M`AB`>>20^AB_>a01M2001O0100O1N01O11O00O1O1000000O100000001O0000000000000001O0000000000000000O1000000000000O11O000000000000O11O001O0000001O000000000O10000000000O11O0O11O0000000000000000O10000000000000O110O000000000000010O0000000000000O100O11O1CCSB=m=DPB>P>BPB>P>BmANMa0V>AlAb0U>_OiAa0W>6O1O1O1O0000010OL4N2M3M3N2M3M3N3L3M]`P6c0S_oIO10O0101O0O20O0O11O00UE@S7a0mH_OT7`0kHAV7>jHBV7>hHDV7<jHFW78jHHU78lHHR7:nHFQ7;lHHS79mHGS79lHHT78kHIU77jHJW75iHKV76iHKW74jHLU73kHOV7OkH1W7LjH4T7MmH3S7KoH5Q7KnH6S7IlH8T7HlH8U7GiH;W7EiH;X7DhH<X7DhH<X7DhH<X7DhH<X7DhH<X7DgH=X7DhH<X7DiH;X7DhH<V7GhH:X7FeH=[7CdH>\\7BdH>]7AbH`0^7@bH`0c7[O]He0P;00000N2O100O10000O100O10000O11O00N2000000O1N2001O00O10000N2", "size": [482, 640]}, "area": 4787.0, "iscrowd": 0, "image_id": 10056, "bbox": [0.0, 340.0, 640.0, 142.0], "category_id": 124, "id": 10012215}, {"segmentation": {"counts": "SPR61Q?2PA1i>4000000O1001OWAKe><[AB`>`001OAeA7[>HdA8^>HbA6e>11K4000001O2N00o`W10RPhN030a>6^AO\\>>M10000000O1000001O000000001N1H`AO_>0cAO]>1cAKb>3Zc6LQ]I0O2O2N001N101O001O00000O1001O000000000000000000000000000000000001O0000000000000000001O0000000000001O000000001O000000001O01O000001O0001O01O00010O001O000010O01O001O01O01O0\\M", "size": [482, 640]}, "area": 1580.0, "iscrowd": 0, "image_id": 10056, "bbox": [412.0, 71.0, 228.0, 31.0], "category_id": 132, "id": 10012216}, {"segmentation": {"counts": "Qf]51Q?000N0RA0m>3NMUA3k>2N2O1N2N2M3O1001O001OO11O2N001O000000001O00O1O100O100O100O10000O1000000O10000002N00O11O00O100O1000000000000HAiAc0T>7O1001OO1000000000000000000O10000000000000000000000001O000000000000000000000000001O00000000O100000000000000001O00O11O00001O00000000001OO1000000O1O1KSOSB0Om0n=SOSBo0l=ROTBn0k=5O11OO11O0000000000NlNVBT1j=20000O100N2M3L400001O000000001O00O1000000000000MbNbB^1\\=dNdB\\1\\=5O1001OO100O11OO100001OO1001O1O1O1O0000000000001N011N100N2N2000N2ON30N11O002O0O100N20001O000O1O10N020JlN[B00T1c=8HiNeBW1[=iNdBX1[=80O1001O00O2L3OO2L_NgB`1Z=`NfB`1Z=`NdB00`1\\=400000N3N100O1000M2100O10000O3O1N1M3OO1N2O00O2001O00O1000OO`C", "size": [482, 640]}, "area": 9386.0, "iscrowd": 0, "image_id": 10056, "bbox": [368.0, 402.0, 272.0, 80.0], "category_id": 140, "id": 10012217}, {"segmentation": {"counts": "c:651?R3Z:PMdEP3\\:PMcEQ3\\:PMcEQ3]:oLdEP3\\:PMeEo2Z:SMcEEBX3k:SMcEo2]:QMcEEBX3j:SMgEm2Y:TMdEn2[:SMeEm2[:SMfEl2Z:TMfEl2Y:VMgEi2Z:WMeEi2]:VMaEk2`:SMaEm2`:QMaEo2`:QM_Eo2b:PM^EP3c:nL^ER3b:mL_ES3a:mL_ES3a:mL_ES3a:mL_ES3b:kL_EU3a:kL_EU3a:kL_EU3a:kL_EU3a:kL`ET3`:lL`ET3a:kL_EU3a:kL_EU3a:kL_EU3a:kL_EU3`:lL`ET3_:mLaES3^:nLbER3]:PMbEP3]:QMcEo2\\:RMdEn2[:SMeEm2Z:TMfEl2Y:UMgEk2Y:UMgEk2X:VMhEj2W:WMiEi2W:VMjEj2W:UMiEk2W:UMiEk2W:UMiEk2W:UMiEk2W:UMiEk2X:TMhEl2X:SMiEm2W:TMhEl2X:TMhEl2Y:SMgEm2Y:SMgEm2Z:RMfEn2Z:RMfEn2[:QMeEo2[:PMfEP3[:PMdEP3\\:PMdEP3]:PMbEP3^:PMbEP3^:PMbEP3^:PMbEP3^:QMaEo2_:QMaEo2_:QMaEo2_:QMaEo2`:PM`EP3`:PM_EQ3`:PM`EP3`:PM`EP3`:PM`EP3_:QMaEo2_:QMaEo2^:RMaEo2_:QMbEn2^:RMbEn2]:RMdEn2\\:SMcEm2]:SMcEm2\\:TMdEl2\\:TMdEl2\\:TMdEl2[:UMeEk2[:UMeEk2\\:TMdEl2\\:TMdEl2\\:SMeEm2[:SMeEm2[:SMeEm2[:SMeEm2[:SMeEm2[:SMeEm2[:SMeEm2\\:RMcEo2]:QMdEn2\\:SMcEm2]:SMcEm2]:SMcEm2]:SMcEm2]:SMcEm2]:SMcEm2]:SMcEm2^:RMcEm2]:TMbEl2^:TMbEl2^:TMbEl2^:TMbEl2^:TMbEl2^:TMbEl2^:TMbEl2^:TMaEm2`:RM`En2`:RM_Eo2a:QM`En2`:RMaEm2a:QM_Eo2b:nL`ER3`:nL`ER3`:kLPEMa0X3_:jLdEV3^:hLbEX3U;O1O1O1O0cMWDa1i;XN^Dh1b;QNfDn1[;jMlDV2P<0001O0000001O00001O0000001O0000001O00001O0000001O0000001O00001O0000001O0000001O0000001O00001O0000001O000000001O0000001O0000001O000000001O0000001O0000001O000000001O0000001O000000001O0000001O0000001O000000001O0000001O0000001O000000001O0000001O0000001O000000001O0000001O000000001O0000001O0000001O0000001O000000001O0000001O0000001O0000001O000000001O0000000000000000O1000000000000O100000000000000O100000000000000O100000000000000O100000000000000O100000000000000O100000O1000N0UBmNh=U12OVBlNg=W100101N1O2M5M3N2OL3010N11O2N0_OPOnBS1n<QOPCP1P=POQCo0o<QOiBNMN0S1Z=QOeB[1Z=8O0O01O10O010O04lNlB4Q>O10000O1000O10O100000O3N00O11O0O0100O1000O10000000HiAA[><601001OO10O100000000O100000O10O100000000O10000000000O2O00000000000O10000000000O101O000O1000O10O1000000O1000001N10O11O0O101O000O1000001N0100000O0K5E7]BiN0O_=\\1aBeNb=Y1_BgNa=X15O02OO11O00000003M4JSBoNQ>m07KO10O10OO2M3L310001N10000O101O0O01000O10000O10MhA\\OX>b0jA^OU>c05O10O2OO10O011OO0100@a0WOg001O2O1N2O1O0O11O00000001OO11O000N2O1O10YNXCR1j<fN_CX1b<dNcC[1]<cNfC0Jj0b<UOWDi0i;VOYDh0h;WOYDj0o<0000000O1N3O000001O0N200JkA\\O00U>b08HhAFX>:hAEZ>98000000000010O0N2LWAOi>1WAOi>1WAM00i>3401O0001O0000000l[1MWdN001RAOi>51OO100000000000001O1O1O010O1gD", "size": [482, 640]}, "area": 33699.0, "iscrowd": 0, "image_id": 10056, "bbox": [0.0, 339.0, 640.0, 143.0], "category_id": 149, "id": 10012218}, {"segmentation": {"counts": "Z3c2_<0N201O01O000001O0000001N1N2N201O0O100O1O2O000O101N1O10001O01OO100O2N1O1O13NM20000MfL]D[3c;3O0N200010OO100010O1OO11O01O0000001K4O1000001O0001O01O0001O101N1O00O101O000O1010O2N1O00010ON\\LgDc3U;aLkD_3U;aLjDa3V;500O1010O00O11O01O0O100010O0TLmD02d3R;[LmD10d3Z;1O0000000LZLlDg3S;YLlDh3S;4O1O2O0O10000NPLREQ4m:oKSEQ4k:QLUEo3k:41WLUEJ2U3h:RMZEEMY3i:RM[EDOW3f:VMbEj2^:TMcEn2\\:RMcEo2]:oLdER3\\:lLfET3[:jLfEV3[:dLlE[3m:00000O2O000LcLbD^3];cLcD]3];cLbD_3^;`LbD`3];3O110N1N200N30O1OO11O10OYLhDd3Y;[LgDe3[;]LfD^3[;aLfD_33fLj:KSEX3f;OMhL]DX3a;500O1O1O10000M`LdD`3\\;31O1O000000N201O0O1O1001O00O10ZLfDd3\\;01O1OO1G]LSEc3l:^LQEOMc3R;^LPE0Nc3o:\\LPE041Mc3o:\\LPE04h3l:ZLSEg3m:7001OO10000002NNRLQEm3o:SLQEm3n:TLQEm3m:ULSEk3i:SLWET4g:4O100000000O100001O001O1jKXEP4h:PLYEOOO1i3l:YLWEg3j:WLUEONj3l:XLUEk3k:VLTEj3R;0001O1O001O1O1O2XLgDc3\\;000N200M[LiDf3V;ZLiDg3V;3O1O1O1O100O100002N1O1OO110O00_LmDS3S;bLlD83U3T;jLlDV3W;fLkDY3U;fLlDZ3U;eLjD\\3V;cLkD<Jc2c;YMaDg2_;UM^DL5n2];VMfDj2\\;TMcDm2h;1001O2O1UMnCb2S<]MnCb2S<]MlCd2Y<0O11O1O1O1bMdCKOV2]<nMhCR2Y<oMfCP2[<kMkCS2b<01O002O1N00O11O1O1O1O3TNiBg1Y=0001O00001O0^NeB]1[=aNgB`1X=`NhB`1]=0O1O100001O1O00000001O0000mNdB`0\\=UOcB34g0Z=TOcB28g0V=UOdB0M09k0k=1N10000010O001OO2O02N00O2O0O1010O00O101O0O1N3O00001N1000001O00001N11OO21N0000011N00010O00O2O0000010N1O110O00010N1001O100O0001N1AcNYC]1g<cNYC^1e<eNoBO6[1i<iNPCK8\\1g<POWCo0f<WOTCm0d<`N]CY2b<4N3O0O1010O000001O0002OO00001N1E_MUDa2g;]MTD16b2e;^MUDO6e2d;\\MUD07d2d;\\MUD07d2d;aMZDa2f;]MXDLLh2l;\\MTD00d2l;=O01kLTDR3o;O01O0O10mLSDR3n;00001O01OO110HnL\\DT3Z;mLiD0LT3Z;lLiD\\3U;fLiD[3V;fLhD\\3U;;N10000010OO101N1001O100O1O10O02NO2O000O101N10001O00001O0001O01O01O01O000100O001O3NO00001O00010O0000001O01O01L30001O00000010O1mKSE00l3T;0N10001O0002QLmD0020c3W;]LjDc3V;\\LjDd3W;\\LhDd3[;1O00001O00000OnLhD]2W;oLiD61OOJ1Q3V;oLjD61KNO103P3S;PMjD30M=1Dk2T;TMkD3a0M@l2W;UM_Ek2[;2O0000001N10001O00001O01O2N10O01O101N1O01N1000001N100O2O0O1ZEPMZ9Q3mEgMP:VOaE_4_:101O20O01O00O11M22MI^KlE1Nc4P:\\KPF1001g4n9[KQFN0h4g9XK^F1LN0g4[9YKoF1J1Kj4[9TKlF0J11O00N00k4W9UKjF25OK0003j4U9\\KjFKN0O01NOj4Z9`KfFH10On4Z9_KfFf4Z9a000O2OO100O20N3NN3O0001O001N1100O000000000O1001NO11NXJjFh5S9XJnF0Oh5R9YJoFj5P9414L3L0NjIYGU6g8kIYGU6g8lIXGU6h84O1N3L10000O10O^OaIaHa6_7_I^Hd6a7]ISHO:d6c7_I]Hb6_7[IWH53L0c6g7\\IVH53LOd6U7[IjH0<1C34NMc6S7[ImH004OM78IJ0c6T7ZImH0O96m6n6jHlH>2j6R7hHlH>2j6Q7YIPIf6Q7XIoHi6n6iHQI>Nj6Q7hHPI?Oi6Q7hHoHl7P73iL", "size": [482, 640]}, "area": 71752.0, "iscrowd": 0, "image_id": 10056, "bbox": [0.0, 83.0, 640.0, 277.0], "category_id": 169, "id": 10012219}, {"segmentation": {"counts": "m5c0Y4Bh5d1PH^OP8i32O0O10000O2O0O10001N10000101N1O1O1O10O0001O01O00000010O00000001O000O1O2N1O1O1O1O2N1O100O2O00000010O000000010O000010O01O001O010O0000010O000001O0000000O2O0O10001N10000O101O0O10001O0000001O01O000001O0000001O01O0001O000000001O01O0001O000000001O01O0001O0000001O0001O0hFPL^7P4[HWLf7h3SH_Lm7a3lGgLS8Y3gGmLY8S3eGoL\\8P3aGSM_8m2^GWMa8j2\\GXMd8h2YG[Mg8e2VG^Mk8a2SGbMl8^2QGeMo8[2nFhMR9T41oMlFSNS9j1mFYNS9d1mF_NS9Z1RGiNn8n0VGVOj8c0TGFl82TG6l8BSGh0l8^NeGk1\\8kMdG^2\\8]M`Gl2`8oL]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;E<K4N2N3L3N2M3013L4L5L3L4L4M3L4L00O2O000O2O000O2O000O101O0O101O0O101N10000O2O000O2O000O2O000O101O0O1N3VOi0WOi0XOi0VOVS8O\\mG9K5M2dAKj=i0M2N1O1O1N2O1OUO]B6e=J[B6d=K\\B7c=H]B9c=F]B;c=b000000001O00000000001O01O0000000001O0000000001O2dN\\BW1e=gN[BY1h=O0000000001O05K02N2N1O00N2O1O100O2O1O1O001O2N]D", "size": [482, 640]}, "area": 97674.0, "iscrowd": 0, "image_id": 10056, "bbox": [0.0, 189.0, 640.0, 263.0], "category_id": 183, "id": 10012220}, {"segmentation": {"counts": "Q6a5j70O0O1O10_JZH^5j70MO3110N0OOXHbJi7]52cJUH[5m70O3NdJVHY5m703NO000O11O0O20O0000hJPHV5R80002NOmJmGQ5S8oJmGQ5S8nJoGQ5V8N1QKgGm4\\8O00011MMXKgGg4Y851RKeGk4]8001N100000000001UKdGe4a8O00001O0100O2OM`G[Ka8e430OO12JZKcGh4[8YKeGi4X86O2KnJnGT5o741O0010N2N06LO000000010M13N0O011O1N1NiGQKW8o43M21N1O10O3N00O101OO3OO2N2N011N0001O0O012M11L24L3N00N2000001L3O2OO01000O3NN30OO0110O1O01N21K30O100O11OO21M2O1O0O4M002N1O1OO1002NO11N100001OO10000010O000O11N10000N20012L10O20O2M01002NO20OO10O12OO2fKRGU30gMm8TORGV33dMl8UOSGR3NnL50L4O=U9_2kFQM30NO000a0U9]2kFRM303b0o8[2lFSM<b0g8[2]GdMe8DiF`2b0lMe8@jFO1c2a0nMk8S2UGmMk8R2VGoMh8R2YGlMh8@dFa2c0PNh8@fF`2a0PNn8o1TGPNj8EaFV2e0UNh8EfFU2d0TNe8GiFR2h0RNb8IgFR2e:mM\\ER2c:PN[EP2P;01N2N102N0SNeDk1];O104LO12OO01N2N1^N\\D^1g;cNXD[1e;gN[DX1l;fNTDV1P<iNQDS1T<00N1POkC1Nf0\\<XOgCg0^<000OZO`Cd0c<01O1\\O\\C03:l<N01OO1000LIUC7k<35L0O100K50bhR5", "size": [426, 640]}, "area": 33086.0, "iscrowd": 0, "image_id": 10058, "bbox": [0.0, 192.0, 248.0, 188.0], "category_id": 97, "id": 10012221}, {"segmentation": {"counts": "0`5j70000O1001O000000000000000000000000001O00O11O000000001O00000000001O0000000000001OO11O00O10000000iH`Jd6a5WIdJh6\\5TIhJl6X5QIlJn6i5O1O1O000000N2O1O13MO1002N002N001O1O001O2NO1O11ON2N2O1N2O100N21O00001OO11O0000001O00O11O00000ZIiIY6f6N0000001O00001O00O12N00M33M2N00N2000000]OhIiIK5`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^F\\Lb9d32O1000000O10000O11OO1O1000000000000000000O100000000O10000O10000O10000O100O10000O10000O100O1000000O10000O100O100O10000O10000O100000000O10000M3000000001O00O100O1O10000O100O100O100O100O100O100O10000O1000000O1000000000oF^MZ7b2dHaM[7^2fHdMX7\\2gHeM]NJ50^8a2PInMP7R2PInMP7R2oHoMQ7Q2oHoMQ7P2PIQNo6o1PIRNP7n1PISNo6m1PISNQ7m1oHTNP7l1PISNQ7m1PIRNP7n1PIRNP7m1PISNQ7m1oHSNQ7n1nHSNQ7m1oHRNR7n1nHRNR7P2lHPNT7m1oHSNQ7m1oHTNP7l1PITNP7l1PITNP7l1PIUNo6k1QIUNo6k1QIUNo6k1PIVNP7j1PIVNP7j1PIVNP7j1PIVNP7j1PIVNP7j1PIVNP7j1PIVNP7j1PIWNo6i1QIVNP7j1oHWNQ7i1oHWNQ7", "size": [426, 640]}, "area": 96110.0, "iscrowd": 0, "image_id": 10058, "bbox": [0.0, 0.0, 640.0, 222.0], "category_id": 120, "id": 10012222}, {"segmentation": {"counts": "`5a0i<1O0O01O2OXCAe<c0ON2002N00O11O0[C]Oc<e00O10^C\\O^<h003NO00O020O001O0001ObCVO\\<l00002NOgCSOW<m0iCSOX<l0gCVOX<o0O0iCoNU<T1O00002OKoCPOo;Q160iCnNT<T1O10O10000[OnC2S<IRD6n;FVD:j;CZD=C@c<c01N2N100O10N101O13NN1002JVCGk<8VCHl<5501LmBOT=O410OoW13lgN2N2O1N2O2ON04OO0O1100N11O01O00O2N10O3OO0O100MhVj0GdiUO5J3N000O2N1UCEg<;WCE01h<:XCHh<8XCIg<=000O10000O010@XC>j<01MUCEh<1XC:j<FVC80Gj<<20O1M2N0020100000000]C^O]<c0`C^O0O`<h0000001bCWOY<j0fCVO[<l01O5L3M0N100000001O00001OO11OOMZCDe<=[CCf<<ZCCg<=3000000000000000O1001OUCCj<>O101O000000O1O1O1O1DTC9o<0O1O20OO1000000000000O101O000N3O01O0000O1001O0001O01ON200000N22XC_O_<9I3_ji2CaVVM1N100000O010000O11OO1O10001O001O1O001O001N101O001O1N101O0O2O001N2O001N101N101O0O2O1O0O2O0O2O1O1O1N10000O010O100O1000O010O11N011O00O11N01000M3000000001N11N010O1O10000O10O0100O100O100ON34K2O0O1000N1N33MO10O10001O0000000O10O10lE_N_8a1bGaN[8^1gGdNV8\\1jGfNS8[1mGhNP8W1QHlNl7T1SHoNk7P1UHQOk7o0UHQOk7o0THROl7o0RHROn7n0RHROm7n0UHQOk7o0UHQOl7n0THROl7n0UHQOk7o0VHPOl7n0THSOh7P1XHSOe7m0[HWOa7i0^H\\O^7d0bH^O\\7b0cH_O]7a0cH_O]7a0bH@^7?cHA]7?bHB^7>bHB^7>bHB^7>cHA]7?bHB^7>cHA]7?cHA]7?cHA]7>dHB\\7>aHEo4", "size": [426, 640]}, "area": 10027.0, "iscrowd": 0, "image_id": 10058, "bbox": [0.0, 80.0, 640.0, 141.0], "category_id": 127, "id": 10012223}, {"segmentation": {"counts": "Rn[21Y=1O1O2N2mBKm<5SCJn<7QCKm<;M20OO1N3^C^OZ<b0bC_OO1_<f02O1N01^C[O_<d0bC]O^<g0OOgCXOT<f0lC]OT<a0lCAS<a0jC@V<a0iC@V<?lC_OU<k0011ON1020NiCoNX<P12N04DRORDo0o;8N001O1O0UDeNg;^1ZDaNc;]1_DcNa;c1]D]N_;g1`DZN\\;k1O1O0gDTN1No:S2oDPNP;U2000OQEiMm:Z201O1SEeM03c:c2N00000000L`E^Ma:a244L00000000000000O100000000000000000000000000O0101OO1A?\\O4Ma0^Of0SOdC4eU\\4LbWdK2N3M3M2N3M5K7I8H8HP1QO`0BJ7HYFYNW8b1lF]OU9[200fFSLW9P402N002N1OO11O000000001OOXI", "size": [426, 640]}, "area": 7659.0, "iscrowd": 0, "image_id": 10058, "bbox": [182.0, 203.0, 458.0, 150.0], "category_id": 128, "id": 10012224}, {"segmentation": {"counts": "_fa162Kn<>J1O1000002NN21OO1001O1O0O021N00O101N11OO12N1N2O012L102N1O_OdC5[<KeC4\\<LdC4\\<LdC6Z<JfC5[<KeC6Z<=1O001OO11O1O104K2N0O110O0@ZC<f<BZC01=e<C]C=c<C^C<h<3M0O011O2M01002NOO]O]Cd0b<M^CAa<N_Ca01@`<b0aC]O_<c040O1M3O10O11OO1N2^O]C=c<@^C0Oa0g<1O1OO2@YC;h<DXC=j<O10100OO100000002NO1002N3M2N00HaCCa<=51O1000000000000000000000000001O000000O1O1O1O1O101O01O0000000000000000001O00001O00000000000000001N10000000dYi4", "size": [426, 640]}, "area": 2904.0, "iscrowd": 0, "image_id": 10058, "bbox": [119.0, 197.0, 152.0, 41.0], "category_id": 148, "id": 10012225}, {"segmentation": {"counts": "b;h1c;O000000001OM31O2N00N[N_De1a;200002N1O001O00000000O11O000000000000000000O10000001OO1000000001O000000000000O1O1O1O1O100001OMYNbDh1^;21OO12N1O2N1O2N2N0000001O1OO11O000000001OO100O10000O1N`NZD`1f;2N21O0000O10000001OO11O2N2N002N000000O100O11OO1O100000000001O001OO10000O1001O00O11O00001O1OM300000000001OO10000O1000000000000000000O100001OO10000001O000000O10000N2001OO1001O00O10000001O0000O12N00QO_NVF`1j9bNTF^1i9_N\\E50L4O=a1S:_N]E30NO000a0`1R:`N]E303b0Z1P:aN^E<b0R1P:ROoEo0Q:QOoEo0P:ROPFn0P:ROPFn0o9SORFl0n9UOPFl0P:SOQFm0P:ROPFn0o9TOPFl0P:TOPFl0P:VOnEj0P:\\OkEe0R:[100O100O1O1000000O10000001O00O1O1001OO100001OO10000000000000000001OO1000000O100O100K500N20000O100000000000000000000000000O1001ZMaF_1^9n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c0G91O000_MZE^2i:02N002N1OO11O000000001OO1", "size": [426, 640]}, "area": 36705.0, "iscrowd": 0, "image_id": 10058, "bbox": [0.0, 316.0, 640.0, 110.0], "category_id": 149, "id": 10012226}, {"segmentation": {"counts": "hcQ71Y=1O1N2O1O1O2N1O1N2O1N200N200O1N200N2O1O1O1O1O1O1O1N200N2O1O1O1O1O1O1N2O1O00000000ON34L1O000O10N2M33MO10O10001O00000jNoCT1T<2O1MhNPD50m0V<O11O1O01O001N002OO01O10O2O01OO2O1O0001O0O101N100O100O100001OO110OO1000M[K", "size": [426, 640]}, "area": 3016.0, "iscrowd": 0, "image_id": 10058, "bbox": [542.0, 132.0, 98.0, 54.0], "category_id": 172, "id": 10012227}, {"segmentation": {"counts": "SVn12X=00000000OV\\i00kcVO00000000001O000000000000Od`?=\\^@d0\\Od0\\Od0]Oc0\\Od0\\Od0\\Od0]Oc0^Ob0N2N2N2N2N2N2N2N200O1O1O100O1O100O1O1O100O1O100O1O1O1O1O1O1O1O1O1O1O1O1O100000000000000O1000001O00000000008H<D6J4L4L4L1O00001O00001O00000O2O00001O0000001O00001O0000001O00001O0000001O00001O0O10000000000000000000000000O1000000000000000000000O100000000000O10000000000H8D<D<L400000000000000000O1000O1000000000000000000000000000000000000000000000O10O1000000000000000000000000000000000000000000000O10O1000000000000000000000000000000000000000000000O10O100000000000001O001O1O001O001O001O001O1O001O001O0O2O1O001O001O0010O10001N100O10001O000000001O000001O01O0000000000O1O1O1O1O1O1O1O1O1O100O1O1O1O1N2M3M3N2M3M3M3N2M2O1O1000O1000O1000O1000O100000O1000O1000002N3M3M2N3M3M3M2N3M3M2N3M3M3M2N3M7I8H8H<D]1cNL10TOk0UOP]6", "size": [426, 640]}, "area": 82846.0, "iscrowd": 0, "image_id": 10058, "bbox": [149.0, 118.0, 475.0, 287.0], "category_id": 183, "id": 10012228}, {"segmentation": {"counts": "_?S3m`0000O10000000000O11N10N2001N020N001O101OO2O000001O00O1O1O100N3O0O1IhLe_OY3[`0hLc_OW3^`0jL`_OT3d`0310O10O0100O100O10O10O10O1fL__OU3f`001M2O1O1O1000O00100000O0100000O10O0011OO10O1O1O0O2000O10O100000O0CSMh_On2X`0SMf_On2Y`0UMc_Om2]`0TM`_On2_`0:00O1000O0100O10O0100O10eLb_OK1U3^`0lLi_OS3c`0O0O1001Z_OTMV`0Q3;E9G:F:F:F:E:H603M3L4M3M3M3M3M3L3N4L2NdZj00]eUO2N1O2M3N2N1O2N2N2M3L4L3M4K4M4L3M4K5L3M4L4L3L5L4L4L3L5L4N110001O0O10001O0O10001N10001O0O1000000O10000O1000000O11O001OO1010ON3O1O0O2O1O1O1M3M3O0O2O1N2K5M3N2O1O0011ON2N2O10000O010O1O1N2O1N2O1O001N2O1N2O1O1O1N101Mm\\OWOTc0g0400O1O1N20O0fM^OWAa0i>_OXA`0g>BYA<g>EZA:f>GZA8e>I\\A6c>L]AOeMJo`08\\ALfMKn`0:\\AIhMMk`0<]ADj><\\22N101N2N101O010O010O010O10O0101N1O1O2M2O2N1N3N1N3N1O1N3N1N3N1O2M2OO0101N10000j]OZNQb0l1O1N01O12M100Q^OQN31ca0o1Z^OWNea0T2N2M2O1N2O1N3N1O1N100O10O10O010O100O01000O010O1000O0100O10O10O010O101N1O10001O00000010O01NaMT_Ok1m`0UNS_Ol1l`0nM[_OQ2e`0nM]_OEGY2m`0QNa_Ol1_`0mMT_O1a0=_Om0m`0bNo_O?UOE0NO?2DSb06m]OH0M0`01CWb04i]OK5=LBYb01b]OO505>hb0BW]O`0hb0_OZ]O?fb0BY]O8FH0O`c08md1Ib[N1L4M3L5L3L4M3M4K4N2L4L4N3UCfNh6]1UIhNe6[1WIjNg6U1[ImNa6S1`InN^6Q1cIPO\\6P1eInN[6R1eIQOY6o0fIROZ6o0eIQO\\6m0dITO[6m0eIPO^6P1bIPO1", "size": [640, 427]}, "area": 25990.0, "iscrowd": 0, "image_id": 10069, "bbox": [0.0, 205.0, 427.0, 389.0], "category_id": 111, "id": 10012229}, {"segmentation": {"counts": "S8\\7d<02N0OOkHaJM_53aJM_52bJN^52aJO_50bJ0^50aJ0_51aJ0^5OcJ1\\50cJO_50bJ0^50aJ2^5NbJ1^5ObJ3]5McJ2e<10001N010O100O2O0O01O1O010N110O1O2N1NiS80\\XG2hc03O2O0O10000O10O0010O100O01000O100O01O02OO010000O010O10O0100O1000O1O2N3L3Ml_7NZlG1gc07L4O0O100O101N01000O100O01M2100O10000O100O10O02OO001L4M2N3M2N3MXjg41hUXK2N2N2000001O00Ol\\OIab07_]OI`b08Z]OOeb01Z]O1EGY26T>2m_O6l1LT>Mi_OJ1a0=_Om0:l>Lf_Of0?UOE0NO?2D?T`0g0R@jNH0M0`01C`0V`0h0n_OhNK5=LBa0O[OV`0\\1l_OcNO505>g0f?i0l_O_N`0h0c?i0l_OaN?h0d?f0n_OaN8FH0OU1S`0Q1U@UOk?_21000000O00100O1O10000000O100000^Nm_OWOS`0i0R@RO_<hNfFT2PMQOZ<nNcFS2UMkNX<VO_FP2ZMhNV<\\O\\Fm1_MfNU<@YFk1`MfNV<CWFf1fMdNT<JRFa1kMeNT<LkEb1QNaNT<1VEo1gNPNS<4bD]2XObMV<3^D]2\\O`MW4", "size": [640, 427]}, "area": 6549.0, "iscrowd": 0, "image_id": 10069, "bbox": [0.0, 255.0, 427.0, 240.0], "category_id": 123, "id": 10012230}, {"segmentation": {"counts": "bb0^1bb000000000000000000O1000000O11OO1O1O100001O0000000000000000001O000000N2L4O10000O10000000000O10000O10000N2O1O1O10000O1000000O1000000O100O1000000O1O1O1N2000000O1000000O10000000000000000O10M3M3N1N3N2M3N1N3N2H7G:F;\\OfZe03idZOQ1N2N2N2N2M3N2N2N2N2N2M3N2N2N2N2N2N2M3N2N2N2N2N2M3N2N20000O1000000O10000000000O100O100O10000O10000O1000000O10000O1000000O1000000O10000O1000000O1000000O10000O1000000O11O001OO1001ON20000O1000000N2N200O100O1L4N2O10000002NO1O1001O1O001O0000O100O1000000O100O1000000O100NnKV@R4i?31O0000O11O000000O1000000LkK\\@V4c?kK\\@V4b?lK^@T4`?7001O1O1O1O1O001O1O1O1O1O1O1O001O1O1O1O1O1O1O001O1O1O1O1O1O1O001O1O1OhM`LQD_3o;cLPD\\3Q<dLoC[3Q<gLnCX3R<iLmCW3S<eLjA2R2j2nM^M[>HgCh2RN]MX>LeCd2WN^MS>0fC`2ZN^Mo=4gC\\2]N^Mn=6dCZ2`N_Ml=8dCW2aNaMk=NdA311m1\\2dNbMj=NkA4g1Z2eNdMT>2WCW2fNgMU>1UCV2gNiMU>2SCS2iNkMU>2RCQ2jNmMU>2QCo1kNoMU>2PCm1lNQNV>2mBk1nNSNV>2lBi1oNUNV>2kBf1QOXNT>3kBc1ROZNS>5jB_1TO\\NR>6jB\\1UO^NP>8kBX1VO`No=9kBQ1[OfNi=<kBg0BmNc==lB>GUO]=>lB6M\\OW=?lBN3CP=b0lBD:Jj<c0lB\\O`01d<d0XE\\Og:g0WEYOi:h0VEXOj:i0UEWOk:j0TEVOk:m0RETOn:m0QESOo:n0PEROP;o0nDROQ;Q1mDoNS;R1kDoNT;S1kDmNU;T1jDlNV;V1gDkNZ;U1eDkN[;V1cDkN];V1bDjN^;X1`DhNa;X1]DiNc;X1\\DhNd;Y1ZDhNf;Z1XDfNi;Z1VDfNj;[1TDfNm;Z1RDfNm;]1QDcNP<]1nCdNR<]1mCcNY<W1fCjN]<T1bClN^<T1bClN_<T1_CmNa<S1^CnNb<Bi@^1c2QOg<o0XCROi<n0UCSOl<l0SCUOm<l0QCUOo<k0PCVOo<l0oBUOQ=k0nBVOR=k0lBVOT=j0kBWOT=k0jBVOU=k0jBVOV=k0hBVOW=k0hBVOW=l0gBUOY=o0bBRO^=S1\\BnNd=V1WBkNi=[1PBfNP>`1iAaNW>d1cA]N]>i1\\AXNd>m1VATNj>R2o@oMQ?W2h@jMX?_3000000O100O1000000", "size": [640, 427]}, "area": 35123.0, "iscrowd": 0, "image_id": 10069, "bbox": [0.0, 454.0, 427.0, 186.0], "category_id": 140, "id": 10012231}, {"segmentation": {"counts": "0S8m;002N00N20000000000O100O1000000O1001O0000O100O10000O100jL[DPNf;j1fDPNZ;l1lDRNT;l1oDRNR;a1\\E^Nd:b1^E\\Nb:d1_EZNb:f1_EYNa:g1_EXNb:h1^EXNb:h1_EWNa:h1`EWNa:j1^EVNb:l1\\ESNe:n1ZERNf:P2XEPNh:Q2YElMh:[2TEbMl:_2VE]Mk:d2WEYMi:h2YEUMg:l2[EPMf:Q3dD`KH\\1d;[3_D[KKW1g;e3YDUKNU1i;l3UDQKNS1m;n3TDRKKo0R<R4RDQKHm0V<T4SDoJDm0Y<W4RDmJBk0]<Z4QDQLo;R4PDkKQ<W4PDfKP<]4oCaKQ<g4iCVKX<Y60O10000O100O10000O100O10000O100O10000O10000O100O10000O100O10000O100O10000O100O10000O100O10000O10000O1F:^Ob0^Ob0^Ob0M300O100O1O100O100O1O100O1O100O100O1O100O100O1O100O1eN[1WOi0M3N2N2M3N2N2M3N2M3N2N2M3N2N2M3N2N200O1O100O1O100O1O100O1O1O100O1O100O1O100O1O100O1O100O1O100O1O100O1O1O1001O001O001O00001O001O001O001O001O00001O001O001O001O00001O001O001O001O00001O001O001O001O00001O001O001O002N3M3M3M3M3M3M3M3M3M3M3M3M3M3M3M3M3M3M3M2N1O1O1O1O1O1O1O1O2N1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O2N1O1O1O2N1O1O1O1O2N1O1O1O2N1O1O1O2N1O1O1O1O2N1O1cAfKR=e5OM3M3M3N2M3M3M3M3N2M3M3M3M300000000000000000000000000000000O100000000O1000000O100000000O100000000O100000000O100000000O1001O2N4L3M3M3M4L3M3M3M3M4L3M3M3M4L3M3M3M3M2N2NO100O100O10000O11OO1O11O00O11O000000O100001OO100", "size": [640, 427]}, "area": 58654.0, "iscrowd": 0, "image_id": 10069, "bbox": [0.0, 0.0, 427.0, 261.0], "category_id": 169, "id": 10012232}, {"segmentation": {"counts": "Wh2V7j<O0100O0100O010O010O10O10O010O10O010O01000O010jLXISIh6h6dIRI\\6j6jITIU6k6nISIS6`6[J_Id5b6^J\\Ib5d6_JZIb5f6_JYI`5h6`JWIa5i6_JWI`5j6`JVI`5i6aJVI_5l6`JTI`5n6^JQIb5Q7^JnHb5T7\\JlHd5V7oHhFX1o1i5`7YJ]Hg5d7[JXHe5j7]JSHc5n7_JoG`5S8bJiG_5[8hIWFJ[1^6e8cIRFMV1`6o8_ImEMT1d6S9]IjELS1f6W9\\IiEJo0k6Z9[IiEFm0n6^9\\IeECm0Q7`9\\IkFd6Y9[IeFe6]9[I`Ff6c9ZIZFe6i9[IUFe6Z:nHcER7f;0O0100O0100O0100O010O010O10O10O010O10O0100O0100O0100O010O010OjHRI\\1n6aNUI^1l6`NVI`1i6^NZIa1g6]N[Ic1d6[N_Ie1a6YNaIf1_6XNdIh1\\6VNfIi1Z6PNnIP2R6fMXJZ2h5\\MbJc2^5SMmJm2S5iLWKV3i4aLaK_3_4WLkKi3T4nKVLQ4k3eK_L[4`3\\KjLd4V3QKUMn4l2iJ]Mm4l2QKWM]4[3`KhLn3i3oK[L_3W4^LlK_3V4_LmKa3S4\\LPLc3P4[LSLe3m3XLVLg3k3ULYLj3g3TL\\Ll3d3QL_Ln3a3PLbLP4^3mKeLR4[3kKiLT4X3iKkLW4T3gKoLX4R3eKQMZ4P3cKSM]4l2aKWM^4V900O1O100O10L3D<UOl0N1OgNY1YOg0O10O10O10000O100O01000O10000O10O102N1O1O2N1N2O2N1O1O2N1O1N2O2N1O1N3L3L4M4L3M3L5L3M3M4K4M3M4L3L4M4L3M3M30110O001O010O00010O001O010O001O001O00001O001O001O001O00001O0010O010O10O01O100O100O100O010O1O100O100O10O012N2N2N2N2N2N2M3N2N2N2N2N2N2N2M2O2N2N2N2N1O000O1000000000001O0eMSHaHn7^7SHbHm7\\7THeHl7Y7VHgHj7X7VHiHj7U7XHkHh7S7YHnHg7Q7ZHoHf7o6\\HPIe7n6\\HSId7l6]HTIc7j6^HWIb7g6`HYI`7e6aH\\I_7c6bH]I_7`6cH`I]7^6dHbI]7]6dHcI\\7]6cHdI^7Z6cHfI]7Z6bHgI^7X6cHhI]7X6bHiI^7V6cHjI^7U6bHjI_7U6aHlI_7T6aHlI_7S6cHlI^7S6bHmI^7R6dHmI\\7Q6fHoIZ7P6hHoIY7n5iHQJX7n5iHRJW7m5kHRJU7l5mHTJS7k5oHTJR7i5PIWJP7h5RIWJn6XNcHQ6a0gKV8iN^F_4]1gLR8nNaFZ4_1gLl7c4VH]Kf7XOiFm3c1jLa7\\OlFh3e1lL[7k4gHTKU7o4lHQKP7Q5RIoJj6T5XIjJf6W5\\IiJ`6Z5bIeJn0nM\\2`7gLbJk0RNX2^7PM_Je0WNV2]7VM\\Jb0ZNV2Y7[M\\J=^NV2V7^M\\J:_NX2T7`M\\J7aNY2S7bM[J3cN[2R7cM[J0dN]2P7fM\\JJeN`2o6gM\\JGfNb2m6jM\\JBhNd2l6kM\\J_OiNf2k6lM\\J\\OjNh2i6nM]JXOkNj2h6PN\\JTOmNl2f6RN]JPOnNn2e6SN]JlNPOQ3c6TN]JiNQOS3a6WN]JdNSOT3a6YN\\JaNTOV3_6[N]J]NUOX3^6\\N]JVNZO^3X6_N]JlMAe3R6`N^JcMFl3m5bN]J[MLS4f5dN^JRM2Z4k1WKK_3WNiL9a4f1WKHa3YN`L?g4T5jNhLV1X3lNfLT1KXK^Of3f0R1MXK\\Og3f0Q1OXKYOi3g0n02YKVOk3f0l05ZKSOk3f0l09XKoNn3g0j0;XKmNo3f0j0>XKiNQ4g0f0b0YKfNR4g0e0d0YKcNT4g0d0g0XKaNU4g0c0i0YK^NV4f0b0n0WKZNY4g0?Q1XKWNZ4g0>S1YKRN\\4i0<V1XKnM`4j08Y1XKjMc4l05[1YKeMe4n02_1YKaMh4n0Oc1XK^Mj4m00e1VK\\Ml4n01d1TK\\Ml4o03c1QK]Mn4m06c1lJ_Mo4m08b1jJ_Mo4m0;b1fJ`MP5m0=b1bJ`MQ5n0a0_1^JbMR5m0d0_1[JbMQ5n0h0^1WJbMS5n0j0^1SJcMS5n0n0]1PJcMS5n0R1\\1kIeMS5n0V1\\1fIeMU5m0Y1\\1cIeMT5n0]1[1_IfMU5m0a1Z1ZIhMU5m0e1Y1VIhMW5m0g1Y1SInLJ8\\5`1k1X1oHiMW5m0o1X1iHjMX5m0S2W1fHjMX5m0V2W1_HnM[5j0Z2b2gM\\M^2`2bM_Mb2^2`M_Md2]2bM]Mb2`2bM[Mb2b2cMXMb2d2dMUM`2h2eMRM_2k2gMnL]2o2iMjL\\2R3iMhL[2T3lMeLX2X3[7M4L3M3M3M4L3@`0\\Od0[Oe0\\O^G", "size": [640, 427]}, "area": 146164.0, "iscrowd": 0, "image_id": 10069, "bbox": [4.0, 6.0, 423.0, 634.0], "category_id": 183, "id": 10012233}, {"segmentation": {"counts": "ge^17[>4N2M2O1O10000O1O10001OOO3N1O1N2N2XBXO`=S1K4N1O3M3M1O1N101O000O2O001O001N2O1O3L2O0O10000O100O100O1O1N2N2iMfMQH\\2n7fMnG]2Q8eMmG\\2S8dMmG\\2R8gMkGZ2T8gMlGY2S8hMmGX2S8gMmGZ2R8hMlGY2S8hMmGX2R8iMnGW2R8iMmGX2R8iMmGX2R8jMnGU2R8kMnGU2Q8lMPHS2o7nMRHQ2m7oMUHP2k7PNbF@Y1`2T8RNbF_OY1_2T8RNcFAX1]2T8SNdFAW1\\2T8TN_F\\O26Z1Z2R8eNnG[1P8gNPHY1Q8fNnG[1S8dNlG]1U8cNiG^1X8aNjG]1W8bNiG^1Y8`NgG`1Z8]NfGe1[8YNfGg1[8WNcGl1^8RNbGo1_8PNZGYOnNO0j2h9mM[GX2f8gMZGY2f8gMZGY2f8gMZGY2g8fMYGZ2g8fMYGZ2h8eMXG[2h8eMXG[2i8eMVG[2j8eMVG[2k8eMTG[2l8eMTG[2l8fMSGZ2n8eMRG[2n8eMTGY2m8fMSGZ2m8fMSGZ2n8eMTGY2l8gMUGX2l8gMSGZ2m8eMSG\\2m8dMSG\\2n8cMRG]2n8cMRG]2o8bMPG_2P9`MQG`2P9aMoF^2Q9bMoF^2Q9cMnF]2S9cMlF]2T9eMiF\\2X9dMgF\\2Y9fMeFHVOT2V:XN_FC]OT2T:YN_FA@U2R:\\N]FWO\\O36Y2Q:^N_FVOZO06\\2R:^NhEWOm0[2\\9aNdF_1\\9aNdF_1]9aNbF_1_9`NaF`1_9`NaF`1`9_N_Fb1b9]N^Fc1b9]N^Fc1c9\\N\\Fe1e9ZN[Ff1e9ZN[Ff1f9YNZFg1f9YNYFh1h9WNXFi1i9VNjE_OEL1_2`:WNgEX2Z:gMeEZ2\\:fMbE[2^:gM_EZ2b:gM[EZ2e:k01O001O001O001O1O001O001O001O001O001O00000000O10000O10000O10000O10000O10000O10000O100000000O10000000000O10000000000O10000000000O100000000O10000000000O10000000000O10000000000O10000000000O10000000000O100000000O10000000000001O1O1O1O1O1O1O1O1O1O1O1O2N1O1O1O1O1O1O1O1O1O1YNjDMV;3kDLU;4lDKT;6kDJU;6lDIT;7mDHS;8mDHS;8nDGR;9oDFQ;:oDFQ;:PEEP;;QEDo:<QEDo:<RECn:=SEBm:>SEBm:>TEAl:?UE@k:?VEAj:?WE@i:`0XE_Oh:a0XE_Oh:`0ZE_Of:a0[E^Oe:b0[E^Oe:b0\\E]Od:b0^E]Ob:c0^E]Ob:c0_E\\Oa:d0`E[O`:e0`E[O`:d0bE[O^:e0cEZO]:f0cEZO]:e0eEZOZ:f0hEYOX:f0iEZOV:f0lEYOS:g0oEXOQ:h0oEXOP:h0RFWOm9i0TFWOl9h0UFXOk9g0WFXOi9h0WFXOh9h0YFXOg9h0YFXOg9g0[FXOd9i0\\FWOd9i0\\FWOd9h0]FXOb9i0^FWOb9i0_FVOa9j0_FVOa9j0_FVO`9l0_FTOa9l0`FSO`9m0`FSO_9o0`FQO`9o0`FQO`9P1_FPO`9Q1aFnN_9S1`FmN`9S1`FmN`9T1_FlN`9U1`FkN`9V1_FjNa9V1_FjN`9X1_FhNa9X1^FiNb9X1]FhNb9Y1^FgNb9Z1\\FgNc9Z1]FfNb9\\1]FdNb9]1^FcNa9_1]FbNb9_1^FaNa9a1^F_Na9b1_F^N`9d1^F]Na9e1^F[Na9f1_FZN_9i1`FWN_9j1aFVN^9l1cFRN\\9o1iFlMV9V2kFhMT9Z2jFgMT9[2lFeMR9]2nFcMP9`2oF`Mn8c2RG]Ml8e2TG[Mj8g2VGYMh8i2WGXMf8l2YGTMe8n2[GRMb8Q3^GoL`8S3`GmL]8V3cGjL[8X3eGhLX8\\3fGeLW8^3iGbLS8b3mG^Lo7f3QHZLk7j3UHVLg7o3XHQLf7Q4ZHoKd7S4[HnKb7U4^HkK`7W4`HiK^7Y4bHgK[7\\4eHdKY7^4gHbKW7_4jHaKS7a4mH`KQ7b4oH^Ko6c4RI]Kk6f4UIZKi6g4XIYKf6h4[IXKd6i4\\IWKb6j4^IWK`6j4aIVK]6l4dISKZ6P5eIPKY6R5gInJX6S5hImJo1\\OW1j5iLjJe1L[1\\5PMhJc13V1W5WMfJb19o0U5^MbJa1`0h0R5gM^J`1g0`0n4PN[J_1n08k4YNVJ]1_1H_4kNRJ[1l2\\NU39oIZ1X3nMn2g0jIZ1Y:fNgEY1Z:gNfEY1[:fNeEY1\\:gNdEY1]:fNcEZ1]:fNcEY1^:jN_EV1`:oN\\EP1c:VOYEj0d:\\OYEc0g:^OYEb0f:_OZE`0g:@YE`0f:AZE>g:BYE>h:AXE?h:AXE>i:BWE>i:BWE4LbNo:Y1UE4NbNm:Z1UE31`Nl:\\1SE34_Ni:^1SE2Y;NgD0\\;0cDO^;kN`D]2a;_MbDb2^;[MdDe2g;0O00010O0010O000010O0001O000100O0001O00001O0O101O00001O0O2O00001O00000O100000000000000000000000000O1000000000000001O0001O0001O01O1O001O1O1O1O1O1O00100O1O1O1TNaC`1`<^NdC_1\\<aNdC_1]<`NcC`1^<_NbCa1_<^NaCb1`<]N`Cc1i<O1O11O00001O000000", "size": [469, 640]}, "area": 93873.0, "iscrowd": 0, "image_id": 10073, "bbox": [101.0, 46.0, 539.0, 423.0], "category_id": 121, "id": 10012234}, {"segmentation": {"counts": "P6i4l90o0QOQ2nM]PS9", "size": [469, 640]}, "area": 484.0, "iscrowd": 0, "image_id": 10073, "bbox": [0.0, 192.0, 4.0, 153.0], "category_id": 153, "id": 10012235}, {"segmentation": {"counts": "0c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<]1RCcNn<b100000O100000000000000000000000000O1000000000000001O000000001O00000000000000000000001O00000000000000000000O1O11O00001O000000", "size": [469, 640]}, "area": 22352.0, "iscrowd": 0, "image_id": 10073, "bbox": [0.0, 0.0, 640.0, 83.0], "category_id": 165, "id": 10012236}, {"segmentation": {"counts": "ab0Q1d=R2nMQ2oMR2nGkH`4e80O10000O10000O10O10O1000000O1000O010000O10MaGPJ_8o5bGQJ]8o55O1O10O01O1O001O1O001O1O001O1O10O01O1O001O1O001O1O001O100O001O1O001O1O001O1O001O1O1N101O1O001O1O001O10O0100O01O10O01O001O001O001O001O001O1O01O00000O100O100O100O100O10001N100O101N11O1O2O0O1O2N101N1O101N6J2N10O01O1O010O1O010O1O00100O001O100O00100O001O10O01O1O010O1O010O1O1O010O1O00100O1O1O0O2N2O11O1N2O1O1O2N1N2O1O1O001N101O00UN]JeJb5V5aJlJ^5P5fJRKY5i4jJYKV5b4nJ_KR5`4mJcKR5\\4nJeKR5Y4nJjKP5V4PKkKP5T4PKnKo4Q4PKQLP5m2fJcK:a1P5n2nJVK1o1P5l2XKjJC`2T5i2`K^M_4d2YKbMg4_2RKhMm4P601O0O101O001O001N10001O001O0O2O1O001O1O001O1O001O1N101O1O001O001O1O001O001O001O1O001O0O2O001O001O001O001O001O001O00O10000O10000O10000O10O10O10000O10000O10000O10000000000O10000000000O10000000000O1000000000O010000000000O10000000000O10000000000O10000000000O100000000O10000000000O10000000000O100001O1O1O1O1O1O1O1O1O1O1O1O1O2N1O1O1O1O1O1O1O1O10OhM]E>c:B^E=b:C^E=b:D^E;b:E_E:a:F_E:a:F`E9`:GaE8_:HaE8_:HbE7^:IcE6]:JcE6]:JdE5\\:KeE5Z:KfE5Z:KgE4Y:LhE3X:MhE3X:LjE3V:MkE2U:NkE2U:NlE1T:NnE1R:OnE1R:OoE0Q:0PFOP:0QF0o90RF0m90TFOl91TFOl91UFNk91WFNi92WFNi92XFMh92ZFMe93\\FMd92^FMa93aFL^94cFL]94dFK[95gFJY95hFLW93jFMU93lFMT93lFMT92nFMQ94oFLQ93PGMP93PGMo84RGKn84SGLm84SGLl85TGKl85UGJk86UGKj86UGJj87VGIj87VGIj88VGGi8:WGFi8;VGEj8;VGEi8=WGBi8>WGBi8?VGAj8?VGAi8a0UG@k8`0UGAj8`0UG@j8a0VG_Oj8b0TG_Ol8a0TG_Ok8c0TG]Ok8d0UG\\Oj8f0TG[Ok8f0UGZOj8h0UGXOj8i0UGXOj8j0UGVOj8k0VGUOh8n0WGSOg8o0WGROh8o0XGQOg8Q1XGoNg8R1YGnNf8T1\\GiNc8X1bGcN]8_1cG`N[8c1dG]NZ8e1fG[NX8g1hGYNV8j1hGWNV8k1jGUNT8m1lGSNQ8P2oGQNn7Q2RHoMl7T2SHlMj7W2VHiMg7Z2XHgMf7[2ZHeMc7^2]HbMa7`2_H`M^7d2aH\\M[7h2eHXMW7l2iHUMR7o2mHRMo6R3QIoLk6U3TIkLj6W3VIiLh6Y3XIhLd6[3\\IeLb6]3^IcL`6_3_IcL]6`3cI`L[6b3eI^LY6c3hI^LT6d3mI\\LQ6f3oIZLo5g3RJZLj5i3UJXLj5h3WJXLg5i3ZJXLc5j3]JVLa5k3`JUL^5l3cJULZ5m3fJSLY5o3fJRLW5P4iJPLU5R4kJnKh0`N\\2e5kLlKa0kN]2[5SMiK?ROW2W5ZMhK<XOQ2U5bMcK<_Oi1Q5kMaK9Fb1m4UN]K80W1g4`NZK6^1J[30XK4j1\\OR3`0TK3Q9LPG2Q9NoF2Q9OoFOS90mF0S90nFNT91mFNS92mFMT96jFIW9:eFE\\9?aF@_9c0_F[Oc9d0]F\\Oc9d0^F[Ob9e0^FZOd9e0]FZOc9f0^FXOc9h0^FWOc9h0_FUOb9k0_FTOa9l0aFQOa9n0aFPO_9P1bFnN_9R1S2O00001O00001O00001O00001O00001O00001O00001O000O2O00001O00001O00001O001O00001O0000000000000000000000000000000000000000000000000000001O0000001O1O001O1O1O1N2O1O001O1O1O1O1O1O001O1O1O1OVT4", "size": [469, 640]}, "area": 161738.0, "iscrowd": 0, "image_id": 10073, "bbox": [1.0, 17.0, 629.0, 446.0], "category_id": 183, "id": 10012237}, {"segmentation": {"counts": "l4\\1g6001O0O11O0000O2OO10000@\\IAd6>_I@a6=eI@[6`0gI]OZ6c0hIWOC2d6h0c00N21O00001\\OjH9V7HkH5U7JnH1KJX7NWI2ANo70Vi10gVN3THMn61_IOO5a60]IM03c66WIH0MM9j65VIEOM2<i62UIF0M1:k6=TIEj6j0O2ON21O00001O1O001OgNZIU1n6KM51M01K410OO20O01O1012M000000O0100XIgNf6[10L[IiNf6X120000O4L4M0O10J6001O0O2O2NoHPOo6S1N7N1MN2ON12M100O01000001O000OO21MOXIjNh6W14NNkNVIU1j6120N01YOUIA2a0j6GTIL0L0c0n6^ORI0233<j6@QI1401`0[702OO01O0O2M\\HEd7;\\HEf79201OO11000N2KG^H9]7EdHa0[7AeH=\\75N200O100001OO100N2YOYOnIk0R6UOnIj0S6VOmIk0R6UOnIj0S6VOnIi0Q6XOnIi0Q6XOnIi0R6XOmIg0S6ZOmIf0Q6TOcI5<g0o5VOdI4<f0m5UOgI:=>m5FTJ8j5MXJLj55fI[O?=m5:TJEl5;RJGm5;RJEo59QJBEJX6f0QJAW6>iIXOF6a6d0mIYOT6g0lIYOT6g0lIXOU6h0lIVOT6k0lIUOW6h0a02N2O00000O11OO010kHTOR7P10ON1O4L2VInNa6[101O00000O10000O13MN2001ON21OO10000001ON_NbIa1^62001O001OO1001OO1O0100O01O0012N1O0`N_I]1d6000O11O2M01O12NM31N011O00O1N110O1N22M10N21O1O2M4MM311O2J5ON2NN33L2ON1010ON13ONcN^I\\1c62100O3MON131M5K10N3O0O012M10O011O1N101O001ONlNXIP1h6QOUIS1i6nNXIQ1i63013MSIlNj6U1VIkNk6V1M3100N101ON2003MN20NVIkNj6U1VIkNk6S113002HSISOm6n03KoH[OP7e0RIVOS7g0lH[OQ7h0QIWOn6i07OO1N21N101O000001O00O003M2ON21O001OO100O1002OO001O2M1O2N21M010OO23M1O10N1L304mHoNo6S11O01OON11mNRIR1o61M20010N11O2O0O01N2O1O00OO40NOO40TIkNd6\\1N2O1O1O001O1O001N011O1O2N1O2N1O1O1O1O1O00100N01O2N001OGUNXJM1m1f5<000001N10000001O002NWJhMh50YJV2j503M0000001O00O1O100O100O01O01O1O1001O0OO2001OO1O0O3O1N21OO02N00[JfMb5Y2^JgMc5X2\\JiMe5V2[JjMa5Y2_JhMc5[2McM`J]2`53N102O02OaJaMW5f2O12NO10OO021O0IWMTKi2l4WMQK1Nh2Q5XMoJl2P55O11NO1011N01K41O2KSKQMn4m2RKSMR5j2nJUMT5i24OO2O0011M101O00>B6K3K2O2N2N002N3M4L2kN^Ie0f6UO\\Ik0W5oNXL1`NP1Q5SOnJ7]1d0d3GbLZO]N91HO3P5d0YKXO_15W3f0UKYOb11Y3j0QKWO0Ka14]3S1RKjN`14]3X1jKcNL6Y4Y1eK`NB3<MA7l4Z1cKaNA3>5\\4Y1dKgNN3_4T1dKhNM4`4S1aKkNO2_4T1bKjNO2^4U1^K^NI=;0]4V1\\K`NN893[4V1_K^NJ=<NZ4Y1^KmN:GX4X2gKeMCLg4\\2eKiMb4U2^KnM^4S2aKnM]4S2dKnM\\4Q2cKoM]4R2bKnM_4R2aKPN_4n1WKcM113>f4S2WKnMh4f2200FWK[Mh4f2WKZMi4f2WKYMj4e2[KWMe4OUKi26VMe41VKj2Q5UMoJl2Q5TMoJm2T5N1O1OjJWMV5j204L5MM2OMRMQKP3l43012NN2110OM13KO50N01O1O35H1O0aMPK_O0\\2n4hMUKMNO114Z2g4gMhKZ2P5O11O001OO1000O110OO11O1O001O00O10O4M2NO10O2cJ]MT5k21000O02NMVMmJk20TMQ5Q30OoJQMn4Q323MN002N01010OO10001OO20O0O11O01NO22N00000001MSKPMk4P34O101O01O0000O1001O000O01002L04NZL", "size": [259, 640]}, "area": 35220.0, "iscrowd": 0, "image_id": 10077, "bbox": [0.0, 88.0, 640.0, 133.0], "category_id": 97, "id": 10012238}, {"segmentation": {"counts": "X6k1X601O00000000001OO10000000000O100O100O1000000001O000000000000000000001O001O00N2M3M3O1001ON21O00001O1O001O00O11OO100O100N200O1O1003M000000O1000mMRJQ2P60LPNTJQ2m520000O1O100O100000000O1000hMWJV2l5O6J5K001O00O100O10000001O0000N21OMUNmIl1S620000O11OO100000000001O0000O1NQNPJo1P6QNPJo1P620000001O1O1O0000O10000000000000000000000O11OO1000000O10000O1N2O1M3N20000001O5K:FEcNUJX1i5b0O13M3M2WNoI\\1T6[NYJ^1^6H3M00O10000@lNQJT1n5SOkIn0S6ZOgIf0X6c0N2O1N2O1O1000000000000000000000000000000000000000000000000007I8H8H5K1O00O10000O1M3H8H8J61O0000O1000000001O000000O1000000000000O100O1N2001O00UOQNaKP2^4TN_Kl1`4XN\\Ki1d4ZNYKf1f4[NYKf1g4YNZKg1e4ZN\\Ke1d4ZN[KGCl1Q5^NlKW1R4jNoKV1Q4dNRKJn0b1o3eNSKHo0c1n3eNRKHQ1c1n3dNUL\\1k3eNTL[1l3eNTL[1i3gNWLZ1j3eNVL[1i3fNWLZ1h3hNWLX1i3gNXLY1i3fNWLZ1i3dNYL\\1f3dN[L\\1g3[NTK0Y1e1R5000000000001OO1000000000000000_NdI[1[6fNfIY1Z6dNeIO1]1[6cNdI02\\1Z6dNeIO1]1Y6dNgIO0]1U6cNiI0025[1o5dNlIO045Y1P6eNkI25Y1o5kNRJU1n5jNSJV1n5jNnIIN]1S6kNoIHN]1U6iNlIKO\\1U6jNkIJ0\\1U6iNlIKN]1V6iNkIJ0\\1V6iNjIK0\\1U6jNkIJO]1U6nNkIR1U6>O1O100000000000000001O0000O10000001O1O00O11O001OO1O100O1001O00000000001O3M3M1O0000000000000000000000L4L4O1001O0000000000O11O00000000O12N00N21O001O000000001O1O00000000O1O10000BUNdJk1[5XNcJh1\\5ZNbJKIi1d5^NgJ`1X5cNhJ[1W5hNlJS1T5mNlJS1T5nNkJR1U5nNkJR1V5mNjJS1V5lNkJT1U5lNkJT1U5nNiJR1W5nNiJR1W5nN^JA8a1Z5nN^JA8a1Z5_N]Ja00_O4c1a5i0000000000000000000000000O1O10000000000O100000000O11O1O1O1O00000fM[JV2e5jM[JV2e5iM\\JW2d5iM\\JW2c5iM^JW2g500000O11O001iMYJP2m50000000O1N21O000000001O1O00001O001O0000MRNRJm1n5SNRJm1o5SNPJm1P63001O001OO1000000001OO10000000000000000000000001O00001O0000001O000000001O00N2MRNSJl1m553M001OO1O12N00N2N200FQNaJn1`5QN`Jo1`5QN_JP2_5UN]Jl1d5TNZJm1g57001OO1O1OfM[JX2h504L5KO1002NO10000002N1OO1M31OO100002NO1O1002N1OO11O001OO10000001OO11O1O001O00O1003M2NO1001XNhIc1\\601O1O00001_N_I^1d6O0cN]IZ1e614LO1O1O1001O0000001OO11O0000001ON22N00000000NiNZIU1f64O100001O0000O1001O0000O1002NN200", "size": [259, 640]}, "area": 39572.0, "iscrowd": 0, "image_id": 10077, "bbox": [0.0, 156.0, 640.0, 103.0], "category_id": 124, "id": 10012239}, {"segmentation": {"counts": "m\\f01R82N4L2N2NO12N0000O10000000000O100N2O2N[\\m0LhcRO3M2O1O001N2O1O1O2M2O1O1N101O1001O1O1O0O3N1N2M3O2O0O1NN3N1LH]H:c720101NOaHC\\7m0D1JSOUIn0k6ROTIo0l6POTIQ1l631O00O010001OO100N2O1IkN\\IY1c64O10000O10001OO10O11O00O1000_N`I_1`6aN`I_14aNX6^1iIcNV6]1gIbNO1[6]1eIbN02Y6]1gIbNO1Z6\\1gIdNO0Z6X1fIfN0025X6R1gIiNO045V6S1hIhN25V6R1nIoNR6Q1mIPOQ6IhIX17kNINX6OhIW18lNHNW6[1oIfNKOW6Z1oIfNJ0X6Y1mIhNKNT61lIZ16fNJ0S6_1RJaNK0m5OWJ^12cNJOn52QJ^1<`Nb51TJ_1Z6cNeI[1\\65100000O10000000010O0O1O001000001O1O00O11O0010N1O2O2M10100OO100O10001O3N2M1N13UNgIf1\\6O1O2N00O11O1O00001K4L4O2O010O1O1O00O12M11M22kNTIo0Q7O1O1N21N01M30O010N11O1O2N101O0O1O1O1O1N1O2O1O1O2N1NXHIg7;0003K01O0000O1002N000E[H8e7H[H8i7GWH4cfh1", "size": [259, 640]}, "area": 6974.0, "iscrowd": 0, "image_id": 10077, "bbox": [88.0, 141.0, 328.0, 72.0], "category_id": 154, "id": 10012240}, {"segmentation": {"counts": "0l4W3000000O11O0000O10000000fMVK[1j4dNYKZ1g4cN_KZ1a4fNaKX1_4hNcKWOCm1i4mNhKQ1X4oNhKQ1V4QOiKP1X4oNhKQ1X4oNhKQ1X4oNhKQ1Y4[NXK9?\\1Y4\\NYK5`0^1V4\\N]K1KJb0i1W4UNfK2ANf0k1f4UN\\Kk1^50000000000000EUN_Jj1\\5YNZJM:j1^4WNeKOO5Od1\\4\\NcKM031d1\\4bN]KH0MM96b1`4cNZKEOM2<2b1c4`NYKF0M1:1f1e4gNYKh1g4j0O100000000000000000gLZKU3n4LL42NO1K500O100O100001O00000000O1000000000000003M3M0000J6001O001O2NLgL^KY3b4400000000000000000000000000000000001gL]KP3i4lLUKT3k421OO10YMUKA2a2j4gMTKL0L0c2n4^MRK0233\\2j4`MQK1401`2[502N001O001O002N001OO11O1OO1LhM]JX2^5fMcJ`2\\5bMdJ]2\\55N200O100001OO100EZMWKh2[4YMfK4Ig2a4UMcKV3]4jLaKX3_4600O1O100O1N]LeKb3Y4`LfKa3V4^LjKg3U44M3O10000O11OO11OO1000000O13MO1O1000000O11OO100O11ON2N2N2N200000000O10000O13MN2001ON21OO10000001ONeK\\L[4d32001O001OO1001OO1O10000O1O1O12N1O0cK\\L[4f3000O11O2NO1O12NM31OO11O00O1N200O1N22N00N21O1O2N3MM31O1OM31O1OM33M1ON2O100M31ON`KaL`4_321O1O3MO1N22N4L00N200O100O100O11O1O001O001ONeK_LX4`3iK]LZ4b3gK_LX4b34O13MO11OM31O1O001ON2003MN200001ON21O3MO1O1002NM3001OO1N21O001O0000000000O12N1ON21O001OO100O1002N000000O1O1O12NO100N23M1O1OO1L4O11O2N1O1ON20cK\\L[4f30N2001OO11O2N1O00O1000000N22NO12NN2O100000000000000O10000000000000000000000001OO1001OO100FfKgLM1]4V3<000000O10000001O002N1YKfLc4]303M0000001O00O1O100O100O100O1O1O1001O00N2001OO1O1N200O12N002N001O1O1OL42NMZKiLf4W33N2O1002NO1O12NO100N20000ISKXMm4h2SKUM1Nl4m2TKSMP5l24002NO1002N00L41O2N2N4L2N1OO100002N001O00>B6J4L1O2N2N002N3M4L2jL_Kf2e4TM]Kl2V3nLYN1`NQ3P3RMoL7]1e2c1fMcNZO]N91HOT2o2cNZMXO_1U2W1fNUMYOb1Q2Y1jNQMWO0Ka1T2]1SORMjN`1S2^1YOiMcNLU2Z2ZOdM`NB3<MAV2m2[ObMaNA3>T2]2ZOcMgNNQ2a2VObMhNMR2b2UO_MkNOP2a2VO`MjNOP2`2WO\\M^NI=;n1_2XOZM`NN89P2^2YO\\M^NJ=<k1]2\\O[MmN:f1Y29fMG]26bMK`23`MM_24`MM^24cML^23aMN^23aMN_22aMNa20UMcM113\\2h25UMNh2h20000000000OiJXMU5g2kJZMU5f2jJZMW5f2iJZMW5j2O00002N00002N1OMPKSMP5l24O12NN21O2NN22NL41OO1O1O17I00O_KRM_O0^4l2fKWMMNO114Z4g2gKhMY4Q30000000000000O11O0000000000000000O1000000O1L41O1O002NMdK_L\\4b321O001O1OO11OO1001OO10000001O00O11O00O100000000001O0000001O0000000000000000O10000N22N", "size": [259, 640]}, "area": 80740.0, "iscrowd": 0, "image_id": 10077, "bbox": [0.0, 0.0, 640.0, 201.0], "category_id": 169, "id": 10012241}, {"segmentation": {"counts": "elX13P89F;ED=N12N4M3L3jH\\Oh6h0oHEi6S1H2M1O10O010@fIWOZ6h0nIQOR6m0c0O1N2O1N2N3O00000001O0000000000000000000000000000000000000000007I8H8G6K0O100O010000H7I8H8L4N2O001O1N2O1O001N2O1O1O0O2O1O11N2O1O001O2M2O1O12O1N2L5I6Kh^Q3", "size": [259, 640]}, "area": 2631.0, "iscrowd": 0, "image_id": 10077, "bbox": [161.0, 160.0, 93.0, 63.0], "category_id": 183, "id": 10012242}, {"segmentation": {"counts": "]bn31Z=0O1O1N1kBMR=502N00000000000000000000000000000001O00000000000000000000000000000000000000000000000000001O00000000000000000000000000000000000000000000000000001O00000000000000000000000000000000000000000000000000001O00000000000000000000000000000000000000000000000000001OO100O1O100O1J6H82N1O2N3M002N1O00O100O1O100O10000O100N20000:F2JmB2diW2", "size": [425, 640]}, "area": 1121.0, "iscrowd": 0, "image_id": 10082, "bbox": [304.0, 404.0, 163.0, 21.0], "category_id": 104, "id": 10012243}, {"segmentation": {"counts": "l3P1d0i6h5TOT5lJb1^N0000kFJW56jJJU56kJJU56lJJS56mJKR55oJKP55QKJo46RKJm46_LJU26kMKT25mMJS26mMKR25PNJo16b50000000000000000000000000000000000000000000000000000000000000I700000000000000001O0000000O1GTC0R=NVUj01mjUO1000000O1O100000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001O00000000000000000000000000YSe36flZL1AFPD0k;1f0000iC0W;OQ1000Y=0Qd02SlNb0VGDn8P1TFUOP:e0mE_OX:;WETO4d0l:2PE^OMc0Y;IkD`0kbR1POTjmN000000000000000O100000000000000000000000000000000000000QLk3jFULd8a4", "size": [425, 640]}, "area": 2203.0, "iscrowd": 0, "image_id": 10082, "bbox": [0.0, 48.0, 640.0, 377.0], "category_id": 105, "id": 10012244}, {"segmentation": {"counts": "nXd07P=m0TO<C2O1N2N2O1N2VOYNQFh1Q:ZNiEh1Y:YNcEi1]:RNXE65j1i:91OO1]EgMV:Y2aEgM05_:U2_EPNa:\\20000000000000000000000001O00000000000000O1001O00000000O10000001O2N1O2N2N2N1O00002N1O2N1O1O2N1O2N1OX1hNb0^O00000000000000000000000N2O1000000OjXP11nfoN3TCOf<3_CLo<1O1O1N10O100O0010bEOc71\\HOe78SHHn7>kGBU8f0cGZO]8m0\\GSOd8U1TGkNl8\\1mFdNS9c1fF]N[9j1]FVNc9Q2VFoMj9Y2nEgMR:`2gE`MY:h200001O0000000000001ON2N2N2M3N2O1000000000000000000000000001O00001O2N1O1O0000000000000000000000=Ci0WOg0YO1O1O2N1O2N1O1O2N1O1O2N1O2N1O1O2N2N2N2N2N1LlB1V=0_ke12^TZN6K6I6J7J5J7J5J011O001O001O010OO12N000N3O000N\\N_Dd1a;101000O10N06MOM42L2O1OO100N211^NYD^1i;1O0001O2N0O1004K2O001O001\\D^N_;g1O00001]E[N`9e1aF`NR9g1lF^NQ9b1oF_NP9a1PG_NP9a1oF`NQ9a1nF_NR9`1PG_NP9a1PG_NP9a1oF`NQ9a1oF^NQ9b1hFQN_O>i9a1fFTN@;k9_1fFVN^O<P:Z1bFmN`9Q1_FPOe9l0ZFUOh9j0VFWOk9h0TFYOl9g0TFYOl9g0SFZOm9f0SFZOm9f0SFZOm9f0SFZOm9g0SFXOm9h0UFVOk9j0TFWOl9i0VFUOj9k0SFXOm9i0UFTOj9HhEa0>Hg9HoE?;Gf9IRF>d0]O[91UFa0?_O[90\\FJBJO7h05^9NmG2V8KjG5Y8GiG8d:0000000000DWC8m<0M3007FmB0ojW1", "size": [425, 640]}, "area": 16746.0, "iscrowd": 0, "image_id": 10082, "bbox": [48.0, 307.0, 496.0, 118.0], "category_id": 118, "id": 10012245}, {"segmentation": {"counts": "^3>P16W:Hf11O1O1O00010O001aDBQ:>kEFU:;fEJY:7bEN]:3^E1b:1XE3h:NRE7n:ImD=R;DhDa0W;k0M2O1O1O001O100N3N1O1O1N2M4K4L4M3N3J5M3N2005LO0O4M3L4K6K4M3L5K4K5L4Lb0^OT1lNd0\\O3M4L1O2M3O0QJgHm5^7M00N2OO100O10000O100O10000O10000O101N10000006JJ600O01\\JoJP3Q5oLfK\\2Y4dMhK[2X4dMkKZ2U4fMmKX2R4hMPLW2P4iMZLl1h3RNfLa1Z3_NTMS1l2lNoM:Q2FkN_OS1a0ClN;T1:XNEg1?XN_Oh1b0YN\\Of1g0ZNWOf1j0ZNUOe1n0YNROg1X6O100O4M?@2O2M3N2L3N3L3N3L3N2M4M2M]k<5ZTC7QOFgDb0W;C_Dc0];VOZD_1d;5M2O1N101O00001O00001O0JWNmDh1b;H01O0O1010O0000O2O00001O0N210O0000001O00001N2O1O1O1O1N2O1O1N2O1M3O200N4L5K4L5K5K4L5\\FPMe8n3L5K5J6K5K5K5K5K5K5K2NM4M2M3M3N1N4L3M2O1N2N3M2O2MNWKiGg4Z811XKcGf4[834L1O3M3N3L5H7J7IV1iN7JfIgMP2R2SNPNn1j1UNXNl1c1UN`Nl1]1TNeNm1X1SNjNn1S1RNoNo1n0QNTOQ2h0QNXOP2e0SNZOn1c0TN]Om1`0UN@l1<XNCi1:YNFh17ZNJf13]NLe10]N0d1M_N3a1JcMNTL:X6EcM9PL3]6AcMc0lKLR;<iDDT;d0gD\\OV;]1N2M3M3M3N2>Bi0WOi0WOakb1FiT]N3M3M4K4L4K5L4K6K4K5L5J7J5K6I6K6I6K5J5L3E;nNS1L3M3M4M2M3M4L3M3M4L3M3M3M4L3M3M3M4L2N1O1]KgIl12nKX6V2fIm14iKW6Y2fIn1a6QN`Io1`6WLhIo0Hj2`6WLRJ`0DX3Z6XL\\JKEm3o5XL[Kh3e4XL[Kh3e4XL[Kh3e4QL^IM0Mg1W4j4PL_I4c1l3S5]LjJc3P5fLkJQOeNY4Z6oLnJT3k4TMRKm2i4ZMUKg2d4`M[K`2a4eM^K[2^4jM_KX2]4lMgKP2V4TNiKl1T4XNlKg1R4\\NlKd1R4`NmK`1P4dNoK\\1P4fNoKZ1m2eK\\L0d0S33X1m2eKYL3e0b1\\OEj0Q3k2fKXL4e0`1CBg0iNQOW4g3RLmL\\1H^Of0S3e2RLmL]1M[Oa0T3g2SLjL_12UO?Z3U2ULiL2;[1<PO=]3m1^LmLK;Y1b0mN=_3h1aLnLJ9Z1h0hN:c3g1oLVMg0m0bN8i3d1PMSMf0T1^N6l3c1oLSMg0V1\\N4o3oNmKW2m0_Mk0[1XN2S4mNoKFMZ2o0cMk0R3Z2\\MoKDMZ2m2e0X1\\MnKDOe0N\\O1[1m2d1W1\\MSLN7R1a2c1U1]MTLDG4b0X1^2b1U1^MWLG72]O585h2_2U1[M[LH43^OK`0=b2a2P1^MQNIW1h2h0^MSNEiM0^3e0bLZ1Z4]N`16[JZ1T4bNa10aJ[1m3fNb1MfJZ1g3jNd1IjJZ1a3nNf1DoJ[1]3oNd1DSK[1Z3QOg2n0YMPOj2o0VMQOj2o0WMQOi2n0WMQOk2o0TMQOm2R1mLoNT3S1mLiNU3Z1\\41O0O01O1O1O001O1O00FbNjD]1U;gNiDX1W;jNhDU1X;nNeDQ1g<PO7L5LjZ]1", "size": [425, 640]}, "area": 42890.0, "iscrowd": 0, "image_id": 10082, "bbox": [0.0, 26.0, 530.0, 323.0], "category_id": 123, "id": 10012246}, {"segmentation": {"counts": "oSV6160k<0RC3m<NQC4n<4OO2OB3aC2U<6iCKU<6kCJU<d012NO]MoNSIm0n6UOaFMZ2o0U7TO`FMZ2o0W7TO^FOe0N\\O1[1n0W8YOgF7R1?Y8[O[FG4b0X19[8A\\F72]O585b0V9F\\F43^OK`0=:[9Y1]FmNc9U1XFiM0S1i9Z22L3N2M3N2M4M0O3dMmE`1V:]NRFZ1P:eNUFU1n9iNXFQ1l9lNVFP1n9nNVFm0n9QORFl0R:ROPFl0Y;M2N2N3M2N3M2M3N1O2N2N2Nei^1", "size": [425, 640]}, "area": 2248.0, "iscrowd": 0, "image_id": 10082, "bbox": [477.0, 43.0, 50.0, 122.0], "category_id": 129, "id": 10012247}, {"segmentation": {"counts": "Vaf6140l<0RC8k<3O110O2NO12NM42hNB^E>a:F^E9a:J]E7_:M`EOc:7fDBJO77[;\\1O0O1N24M1N3M3M3N1O2L4N1N3N2M3:EM4M3M3N1N3M3M3M2N3M3M3M2N3N\\\\P1", "size": [425, 640]}, "area": 1236.0, "iscrowd": 0, "image_id": 10082, "bbox": [516.0, 338.0, 45.0, 75.0], "category_id": 132, "id": 10012248}, {"segmentation": {"counts": "b1l1];0000000000001O000000000O10000002NO01O10O100000001OO1000000O0O2M4L3N3N1L3O2O11O6J1O0O2O00O01L5N1L4M2011O0000000000000O11O1O1N2N2OO100O10000O100O2O000O10000O10O010000006KI511N10000000000O1001M200000O10001O000000000000000F:O1O010O101N1O100O1O100O1O100O1O10000000000000010_OTDCl;=[D\\Oe;d0\\D[Od;e0\\D[Od;e0\\D[Od;e0]DZOb;g0^DYO^;k0bDUO\\;m0dDSOZ;n0gDSOV;o0jDQOT;Q1?3000IRDSOm;n0XDQOe;n0_DTO[;k0jDWOP;i0h0O1000001O011N2N2N3M3NO2N1O2N6J5J10001TDmN`;S1ZDoNj;Q1VDoNj;Q1VDoNj;Q1VDoNj;Q1VDoNj;R1UDnN8NW;T1aDnN8NV;U1bDmN8NW;T1aDnNk;R1UDmNl;T1SDlNm;T1SDlNm;X10:FlN_Da0b;\\OeD>[;BiD:W;EmD8S;HUE0k:0UE0l:NUE2k:NUE2k:NUE1l:OTE1l:OTE1n:MRE3l:0SE0m:0RE0Q;NoD2o:0QE0n:2QEMP;5nDKR;:iDDY;a0ZDGf;IVDm0h;ROiD>V;CmD:S;EPE9P;GQE8o:HRE7n:HSE8m:HSE8k:JUE6g:BaD8h06g:BaD8h06g:BaD8h05a:6_EJa:6_EJa:6`EI`:7aEH^::bEE]:i0WEVOi:P1mDTOS;l0oDROP;o0REPOm:P1SEPOm:Q1nDSOR;m0oDROR;m0PEPOQ;Q1oDoNQ;T1kDlNV;V1fDjN[;a11OO2NROjDLW;1mDNS;OREJD@[;b0WELm:1WELZOC_;>\\EIj:3\\ELc:3_EJc:5`EI`:6Z1O1O1O1O1O1O1OhTQ20YknM00000000000000001O0000000000000000001O00000000000000001O002N3M3M3M3M3M3M3M3M001O0000000000000000000000001O001O000000000000O1000000000000O1N21O0000001O000000001O00000000O100O1O1O1O1O12N1O00001O0000N2WOdCh0e<G4_OWC020N6k<KYC0NOTkc1", "size": [425, 640]}, "area": 13645.0, "iscrowd": 0, "image_id": 10082, "bbox": [0.0, 0.0, 516.0, 111.0], "category_id": 146, "id": 10012249}, {"segmentation": {"counts": "SgZ2:o<5K0000000000002N000000001O000I73M4NF65M7K0L2O0100000000O010000000000N2LTCKk<6UCJn<3RCMhS_5", "size": [425, 640]}, "area": 613.0, "iscrowd": 0, "image_id": 10082, "bbox": [180.0, 7.0, 39.0, 28.0], "category_id": 164, "id": 10012250}, {"segmentation": {"counts": "0b1g;000000000000000000000000O10000002NO10000O1000000000000000000O1001O001O00O10000000000O10000O1L4O1L4M3O11O00000000000000000000O1O100000000000000001O000000000000O1000000001OO11O0000000000000000N200000000001O00000000000000000000O100001O0000000000000000000000000000000000001O0000000000O1L4N2N2N2N2M30000O11O0000000000001O002N2N2N3M3M0000000000O100000000000002N;E<CS`>MQ`A<D6JO1000000O1O10000O10000001O001O1O0000N2N20000000000000000000000000000000000O1O1O1O100O100O10000O100O100O10000O100O10000O100O10fbd4", "size": [425, 640]}, "area": 7210.0, "iscrowd": 0, "image_id": 10082, "bbox": [0.0, 0.0, 282.0, 51.0], "category_id": 172, "id": 10012251}, {"segmentation": {"counts": "QTd63o;NVE4f:7gD6W;MXDB020N6b0a;NZDD0NO`0g;n00O100O1O1O10000O1000000O10000O10000O10000O1O1O1O1O100O1O1O1O1O100O1O1O1O1O100O10000000000O100000000000000000000000000000000001O0000000000001O00000000001O0000000000001O0000000000001O001O001O001O001O001O0bEQOb8P1ZGSOf8m0XGVOg8k0UGYOj8g0TG[Ol8f0QG]On8c0PG@o8a0nFAR9?kFET9<iFGV99gFJY97cFM\\93`F2_9N^F5b9LZF8e9HXF<g9ETF?l9BPFb0o9\\101O4L4L2N", "size": [425, 640]}, "area": 5063.0, "iscrowd": 0, "image_id": 10082, "bbox": [511.0, 0.0, 129.0, 116.0], "category_id": 177, "id": 10012252}, {"segmentation": {"counts": "RlY22W=;E2N000000000000000000000000000000I73M4LH83M7I2N00O100000000O1000000000001O<CZT_5", "size": [425, 640]}, "area": 589.0, "iscrowd": 0, "image_id": 10082, "bbox": [178.0, 0.0, 40.0, 19.0], "category_id": 181, "id": 10012253}, {"segmentation": {"counts": "Sa0b2h:S5mJ`1`NO001O1O001UHmK^6T4]IPLc6Q4XITLg6l3UIYLj6h3QI\\Lo6e3lH`LS7a3gHeLX7[3cHjL]7b4O1O001O1O001O1O1O2N1O1O1O1O2N1O1O1O2N1O1O1O1O2N2N5K4L4L4L5K4L4XNSIlLQ7n2mIVLX6c3l1KT1kNT1mN5J6K4K6J6K4K6KlP;`0dnDg0ZO1N3N2M2O:E>C>Ak0VOl0SOf0[Oe0ZO4M1N3N1N3N1N3N1N3nMnIiLS6U3]J[Le5c3]J\\Le5b3]J[Le5c3^J[Ld5c3^JYLe5e3]JYLl6^2WI^M\\7Q2eHmM\\7R2gHjM[7U2gHiMZ7W2gHfM[7Y2hHdMY7[2iHbMY7^2i1O1O1000000000000001O@\\E[Nd:e1cETN]:l1dESN\\:m1dESN\\:m1dESN\\:m1dESN\\:m1dESN\\:m1dESN\\:m1eERN[:m1fETNY:l1gETNY:l1gETNY:l1gETNY:l1hESNX:f1\\E[N<OX:f1aE\\N1N^:f1eE^NGLd:e1jEaNV:_1nE]NS:a1_G^NQ7b1cG]N89S8Z1eGbNM;]8S1fGfNC?e8k0hGkNYOa0m8e0iG1V8OjG3T8MlG4S8LmG5R8KnG5R8LmG5R8MlG3T8NkG2U82gGOX89_GHa89^GGb89^GHa88_GHa89YGLg84YGlM7b1`8b0YGlM7c1_8a0ZGlM7c1_8a0ZGlM7c1_8a0ZGlM7d1^8a0ZGkM8d1^8a0ZGkM8d1^8a0ZGkM8d1^8a0ZGkM8e1]8`0[GjM8g1]8`0ZGiM9g1]8`0ZGiM9h1\\8c0dG]O\\8h20QM]GY1b8fNeGT1[8lNiGP1W8oNmGn0S8ROUHg0j7YOVHg0j7XOWHh0i7XOWHi0h7WOWHk0h7UOXHl0g7TOYHm0f7SOZHn0e7SOZHn0e7RO[Ho0d7QO\\HP1c7PO]HQ1b7PO]HQ1b7SOYHo0f7VOTHl0k7[2N\\LjGX2S8gM^Hn1]7RNfHo1V7PNmHS2n6mMSIW2h6iMYIZ2Z3_LPOV1gM`2LXL[270Q1iM_3n1fK7k0kMd3f1ZKgMKf2W1mMi3^1mJ[N4X2V1nMo3P2lJ0U1PNS4l1iJ3T1QNX4h1dJ6T1SN\\4c1_J:U1TN`4^1[J=U1UNe4Y1VJb0Z1RNc4X1RJf0j1bMZ4d1lIi0j1aM\\4g1mId0g1gMW4h1YJ:`1PNS4i1dJOY1VNR4n1lJES1ZNP4U2TKYOl0cNm3V2^KPOe0lNh3W2kKeN>lNPN6c5\\2WLZN:WO]3b2`LPNg5R2aJfM\\5]2kJ\\MR5h2d23RJRM_1P3^NTM^1o2_NVM\\1m2`NYM[1j2bNZM[1h2bN]MY1g2bN_MY1d2fN^MV1e2iN^MR1e2mN^Mo0d2PO^Ml0c2UO`Mc0c2_O_M;d2F_M3d2N^NkNf1V1\\NdNf1^1\\N\\Ng1e1\\NTNg1m1[NnMg1S2[NjMd1X2^NeMb1\\2`NaM`1a2aN\\Ma1c2aNZMa1f2aNVMa1i2aNTMb1k2_NRMc1n2^NnLe1Q3]NlLf1S3[NjLg1U3[NhLg1X3ZNeLi1Y3ZNcLl0GWLf3n2`Lj02TL]3S3^Lh0=PLT3Z3\\Lf0g0kKm2o7[MmGc2Q8fMiGZ2T8_1N1N3M2N3N1a0@i0WOi0XOg0XOi0WOg0ZO0O101N1O2O0N2O2N1O1O2N1N3N1O1O2N1O2N1N1000000000000O101O00000000000000000000000000000000000000000000000000001O00000000000000000000000000000001O00000000000000000001O000000000000000000000000000001O2N2N000000O10000O101O0O10000O100O10000O100000hFMZ53fJ0W50hJ4U5LkJ8Q5ImJ;P5EnJ`0o4@PKd0m4\\OQKi0l4WOSKm0j4SOTKR1i4nNVKW1f4iNYK\\1c4dN[Kc1`4^N^Kg1^4YN`Kn1\\4QNcKT2Y4lMeK[2V4eMiK`2S4`MkKf2Q4ZMnKi2P4WMoKm2n3SMVKk3h4ULkJZ4S5gKkJ]4R5cKmJ`4Q5`KnJc4P5]KoJg4n4YKQKj4m4VKRKm4k4TKUKo4h4QKWKR5f4oJYKT5e1dJJ:_NU5e1gJF4dNY5b1iJB4hNV5d1lJWO8QOo4e1oJmN=ZOg4g1RKcN`0Bb4g1TK`N?E`4i1WKYN?J]4j1ZKUN>M[4k1^KoM<3Z4k1_KkM<6X4l1lMPNW2n1lMnMW2o1n4D:E:]Oc0\\OUW11ohN:E:G9G9G8I7H7K5K5K4M4L4M3L4L4N2MbNnEHQ:9RFEl9;YFAg9>^F_O`9b0dFYO\\9h0hFTOW9l0mFQOS9o0QGlNo8U1UGgNj8Z1YGbNg8_1\\G^Nd8a1_G\\Na8e1bGXN]8i1`14M1N3M2O2N2N1O2gEfM`9c1kETOb0^O]9_1QFUO?B[9Y1WFWO;EX9U1]FYO8GV9P1cFZO4LS9l0hF[O2MR9h0mFa0o8APGa0n8_OSGc0`6jM_Ke1QNc0^6VNQKY1`Nd0]6XNmJT1gNe0[6ZNiJS1kNf0Z6YNhJQ1oNh0W6ZNeJP1SOi0V6YNeJn0UOj0U6[NcJj0XOn0S6ZNcJh0ZOP1R6YNbJf0\\OT1P6YNaJc0_OU1o5ZN`J`0AY1m5ZN_J?Fa02lMg5V1^Je0P1iNQOZO`5X1^Jm0R1VNUOC[5[1\\JS1R1gM^OIR5_1\\JX1Q1ZMGMk4b1[J^1Q1lLN3e4d1ZJd1U2eLa3\\8RO5L5K3L4M3M3L4M4L2M4L4L4L3M4M3L3M4L4L4L3N3L4L4L3M4L4M3L3N3M3M3M2N3L4M3M2N3M3M1O0000000000000000O1000001O0000000000001O00000000001O0000000000001O00000000000O2O001O001O001O001O001dEmN`8S1]GQOb8P1[GROe8n0XGVOg8k0UGYOj8g0TG[Ol8f0QG]On8c0PG@o8a0nFAR9?lFDS9=iFGV99gFJY97cFM\\93`F2_9N^F5b9LZF8e9HXF<g9ETF?l9`1O002M5L5PKS1ZKmN2ZLT1Y8", "size": [425, 640]}, "area": 173393.0, "iscrowd": 0, "image_id": 10082, "bbox": [1.0, 0.0, 639.0, 425.0], "category_id": 183, "id": 10012254}, {"segmentation": {"counts": "nW`52n>0O1O1O1O1O1O1O100O100O1O1O10000O1O100O1O100N200N200O1N2N2O100O1O1O1001O00O1O10000O1O1O100O1O100N20000O1O1O1O1O1O1O100O1O1O100O1O100O1O100O1O100O1O1O1O1O100O1O1O100O1O100O1O100O1O100O1O1O1O100O1O1O1O1O100O1O1O100O1O10000O1O1O1O100O1O100O1O1O100O1O12N1O1O1O1O2N1O1O000000000000000000000000004L7I4L001O000000001O000000001WNWCY1Y=O001O001O0POaBF99W=1oBOR=OPC0P=MXCNi<N_CMa<0cCO`c<O[[C1d>0\\A0O11OY>3hAMN8X>=O1O1O11OM300O100001O[OYOPCh0n<\\OoBe0n<^OgBE9m0n<DoB=n<HPC8n<JnBXO2n0m<Q1N2M3M3M3N2N2M3N2N2N2M3N2M3N2M3M30000O1001O000000001O000000N2N2N2N2N2O1N20000000000O100000000", "size": [480, 640]}, "area": 14408.0, "iscrowd": 0, "image_id": 10083, "bbox": [375.0, 363.0, 265.0, 117.0], "category_id": 101, "id": 10012255}, {"segmentation": {"counts": "V:j4U:1000000000000000000000000001OO1O1N2O1O1001O0000001O00O11O00ATKfFP5X9SK`FL1U5_99O1O11ON20000O1N21O2N1O2N2N2N1O2N2N2N2N2N2N1O00000000O100O100O100O100O100O100O100O1O100O100O100O1YOhJfGX5Y8lJcGU5]8mJ`GT5^8PKXGF2Z5e8TKVGD2Y5h8XKXGh4h8XKVGj4j7fJRIn5n6TJPIl5o6XJVHO>i5[7cJfH\\5Z7dJfH\\5Y7fJgHY5X7hJhHX5X7gJiHZO@m5i7gJgH\\O@m5k7eJgH[5\\7bJdH^5^7`JbH`5`7^J`Hb5`7^JaHa5^7`JbH`5^7_JcHa5\\7`JeH_5[7bJdH^5[7fJbHZ5^7hJbHV5^7mJ^HT5a7oJ\\HR5d7QKXHP5g7SKVHn4i7U1O100O1O1O1O1O1O1N2N2O1O1O1O1N200M31O1O1O1O2N1O1O2N2N1O1O1O1O0]JZHl3f7SL]Hk3b7TLaHk3_7SLdHl3\\7SLgHk3Y7TLiHk3W7ULjHj3W7TLkHk3U7ULmHi3T7ULnHj3S7ULnHj3S7TLoHk3R7TLPIj3P7ULRIj3n6VLSIi3l6WLVIh3j6XLXIf3h6YLZIf3e6[L\\Id3d6[L_Ic3`6^LaIa3_6^LcIa3]6_LdI`3[6`LhI^3X6bLiI]3V6cLlI\\3T6dLmI[3S6dLPJZ3o5gLRJX3n5gLTJX3l5hLUJW3j5iLYJU3g5kLZJT3e5lL]JS3c5mL^JR3b5mLaJQ3^5PMcJo2]5PMeJo2Z5RMhJl2X5SMjJl2V5TMkJk2T5UMnJj2R5VMPKh2o4XMSKg2m4YMTKf2l4YMVKf2i4[MYKc2g4\\M[Kc2e4]M\\Kb2c4^M_Ka2a4_MaK_2_4`McK_2\\4bMeK]2[4bMhK\\2W4eMjKZ2V4eMlKZ2S4gMnKX2R4gMQLW2o3iMRLV2m3jMULU2k3kMVLT2i3lMXLT2h3lMXLT2h3kMYLU2f3lMZLT2f3kM[LU2e3kM[LU2d3kM\\LV2d3jM\\LV2d3iM]LW2f3fMZLZ2h3cMYL]2k3_MULa2l3]MULc2l3\\MTLd2Q4VMPLj2Q4UMnKl2R4SMoKm2Q4SMoKm2P4SMQLm2P4RMPLn2P4QMQLo2o3QMQLo2o3PMQLQ3o3oLQLQ3n3oLSLQ3n3nLQLS3Q4jLPLV3Q4iLoKW3l710000O10000O10000O10000O1000000O1O100O10mLaDe2^;[MdDd2\\;\\MeDc2Z;]MiDa2W;^MkDa2T;`MmD_2S;`MPE^2o:cMRE\\2m:dMUE[2k:eMVEZ2i:fMYEY2g:fM\\EX2c:iM^EV2b:iM`EV2_:kMbET2^:kMdET2[:mMfER2Z:mMiEQ2V:oMlEP2S:QNnEn1R:QNPFn1o9SNRFl1n9SNUFk1j9VNWFi1i9VNYFi1f9WN\\Fh1d9XN]Fg1b9YN`Ff1`9ZNbFd1]9\\NeFc1Z9^NgFa1Y9^NiFa1V9`NkF_1U9`NnF^1Q9bNQG]1o8cNRG\\1m8dNUG[1k8eNVGZ1i8fNYGY1g8gN[GW1d8iN^GV1a8jNaGU1_8kNbGT1]8lNeGS1[8mNfGR1Y8nNjGP1V8POkGo0T8QOnGn0R8QOPHn0o7SOQHm0^3hN\\N:WNn0]3kNXN8ZNn0^3mNTN5^No0]3oNSN2`No0]3POQN1bNP1\\3QOQNNcNR1\\3ROoIBm3;hNQ1\\3TOlMJhNS1[3TOlMJhNS1\\3SOlMIiNT1[3SOkMJiNT1[3ROlMJjNT1Z3SOkMIjNU1Z3TOjMGlNV1Z3SOjMFmNW1Y3SOiMGmNW1Y3ROkMFlNY1Y3QOjMGmNX1X3QOlMFlNZ1X3POkMGlNZ1Y3nNlMGlN[1W3oNlMFmN\\1W3mNlMHmN[1V3nNmMFmN]1V3lNmMHlN]1W3kNmMGmN^1U3lNmMGmN^1V3kNmMFmN`1V3jNlMFoN`1T3kNmMEnNa1U3jNlMEoNb1T3jNmMDoNb1T3jNlMDPOc1T3iNkMEQOb1S3jNlMCQOd1S3iNkMCROe1R3hNmMCQOe1R3hNlMCROf1R3gNlMCQOg1R3gNlMBSOg1Q3gNlMBROh1Q3gNlMASOi1Q3fNkMAUOi1P3fNkMATOj1P3fNkM@VOj1o2fNkM@UOk1P3eNjM@VOl1o2eNkM_OVOl1o2eNjM_OWOm1n2eNkM^OVOn1o2dNjM^OXOn1n2dNjM]OXOP2m2cNkM^OWOP2n2bNjM^OYOP2l2cNkM]OXOQ2m2bNjM]OZOQ2l2bNjM]OYOR2l2bNjM\\OZOS2l2aNjM[O[OT2j2bNjM[O[OT2k2aNjMZO[OV2k2_NjM\\O[OU2j2`NkMZO[OW2j2^NkM\\OZOW2k2]NjM\\O\\OW2i2]NlM[O[OY2i2\\NkM\\O\\OX2h2\\NmM[O[OZ2h2[NlM\\O[OZ2h2ZNnM[O[O[2f2ZNoM\\OZO[2g2YNoM[OZO]2f2XNPN[O[O[2g2ZNmM\\O[O[2g2YNoM[OZO\\2h2YNmM\\O[OZ2i2YNmM\\OZO[2j2YNkM]O[OY2k2YNkM]OZOZ2l2YNiM^OZOY2m2YNjM]OZOY2m2YNiM^OZOZ2m2XNhM_OZOY2o2WNhM_OZOZ2n2WNgM@ZOY2o2YNfM]O[OZ2[2]MiKl0`2^O\\OX2[2hM`Kc0h2\\O]OZ2[2kM\\K`0j2[O@Z2Z2mMZK>l2[O_OZ2\\2PNUK=n2YOAZ2]2PNTK=m2ZOBY2]2SOPNcNCZ2^2TOmMcNDY2_2UOmMaNEZ2_2UOjMaNGY2b2UOgMbNFZ2a2WOgM_NIX2c2YOcM_NIY2d2XObM_NKX2e2YO^M`NLV2h2ZO\\M_NLV2j2\\OXM^NOU2j2^OVM]NOV2j2^OVM\\N0V2j2@UMZN1U2i2CUMXN2U2k2CQMYN3U2l2CPMWN5W2j2BPMWN6X2j2BnLWN8V2l2BkLXN9V2m2CgLYN;U2n2CeLXN>U2m2CdLYN>T2o2DaLXN`0T2P3D_LYNa0R2R3E[LYNc0S2R3EYLXNe0S2S3EXLXNe0R2T3GULWNg0S2U3ETLXNg0Q2W3HPLWNi0R2W3HoKVNi0S2X3GnKVNk0R2Y3aMZJT2a1XNl0S2_3CdK[Nl0R2`3EcKXNn0R2`3G`KXNo0Q2b3G_KWNo0R2c3H\\KWNQ1P2d3I[KVNQ1R2d3IYKUNT1Q2d3KVKUNU1P2g3JTKUNU1Q2i3JPKVNW1P2i3JPKUNW1Q2j3KmJUNX1P2l3LkJSNZ1Q2k3LjJSN[1Q2l3MhJRN[1Q2o3LeJSN]1Q2m3NeJQN]1Q2n30cJoM`1P2m32cJnM_1Q2m33bJlMa1Q2l35cJjMa1P2l38aJhMc1P2l3:_JfMe1P2l3;_JeMe1o1l3>]JcMg1P2h3a0aJ_Mf1P2i3c0_J]Mi1o1`3kMUJj2a0\\Mi1o1`3P1fJQMk1o1_3Q1eJoLl1P2_3R1dJoLl1o1a3S1bJmLn1P2_3U1aJlLo1o1a3U1`JlLn1o1b3W1^JlLo1m1d3V1]JnLm1l1g3W1ZJnLn1k1j39WJQNOMo1i1k37]JoMI2m1h1o3TNQJ]1>cNC6n1e1Q4QNnIF3i1`0_O\\1Q1U4PNUJ]1=@X1S1W4oMWJ[1=BT1T1X4lMZJ^1;BQ1T1\\4hM[Ja1;Bl0U1_4fM]Jb1:Aj0V1X5XOQJAe0W1[5YOoIAd0V1]5ZOPJ@b0V1^5ZOPJA`0U1a5ZOPJ@?U1b5[OoIA=T1e5ZOoIC:T1g5YOPJC8S1g5\\OQJA7S1i5\\OQJA4T1j5\\ORJA3S1j5^OSJ_O1S1m5^ORJ@OR1n5@TJ]ONR1o5BSJ\\OLR1Q6DSJZOKR1Q6FSJYOJQ1R6ITJUOIR1T6ISJVOHQ1U6ITJVOEQ1Y6HRJXOCP1\\6HRJXOAo0_6HPJYO@P1`6HoIYO_Oo0c6HnIZO^Om0e6ImI[O\\Ol0i6HkI\\O\\Ok0j6JjI[OZOk0m6KhI[OYOk0o6KgI[OYOi0R7LdI\\OXOh0V7LaI\\OXOh0X7M_I\\OXOf0[7N\\I]OWOf0]7N[I]OVOe0`70XI[OXOd0a72VI[OWOc0f71RI]OWOb0h71oH_OWO`0k73lHe1U7\\NjHd1W7]NgHb1Z7_NeH`1\\7bNaH^1a7cN]H]1d7fNXHY1j7m21O2N2O0O2N1O101N1O1O101N1O2OYImIa4R6WKXJh4h5WKYJj4e5VK]Jj4a5WK_Ji4`5hJQKW5n4iJSKVOmNF9[5e5iKWKVOZOQ5^5\\KSJ_OW12ZOT5Z5[KiK^OUOR5Q5`KlKZO[OQ5h4fKmKVO_OT5b4fK\\MX4c2iK]MW4c2hK^MY4`2gKaMY4^2hKbMZ4[2fKfMZ4Z2fKfMZ4Y2[KeJBS3S5W2[KUNf4i1ZKXNg4g1YKYNg4f1YK[Ng4d1YK]Nh4b1hJhJ:g3n4g1QKYNP5i1lJXNU5h1iJYNW5i1gJWNZ5j1cJWN^5e5001O00001O001F_JmF12`5l8`JRG0O11a5g8aJZGMNl5d8=O1O1O200L300O2O00010ZOaIhH`6V7dIgH^6U7eI`HE9f6U7kIhHV6U7oIiHR6T7PJhHXO2i6R7Q1N3L3M4L3N3M2M3N3M3M2M3N3L3N3L3L5M2M4M2M3M4M2N3L3N3L3O2OfEaKS:g400OAnEQLQ:l3SFTLm9g3WFZLi9^3^FcLa9Z3bFgL]9V3fFkLZ9R3hFnLX9o2kFQMV9k2mFUMS9i2oFWMQ9f2RGZMo8c2TG[Mm8c2UG]Ml8`2VG`Mj8]2YGcMh8Z2ZGfMV2", "size": [480, 640]}, "area": 105680.0, "iscrowd": 0, "image_id": 10083, "bbox": [0.0, 122.0, 640.0, 358.0], "category_id": 165, "id": 10012256}, {"segmentation": {"counts": "0n62VJj6[5VInI0h0i6Z5[IjILm0h6Y5aIdIHT1f6X5hIhJX6W5jIkJS6V5mIjJR6T5QJlJn5V5QJkJm5W5SJhJl5Z5SJgJk5[5UJfJh5[5YJeJe5]5[JcJc5_5^J`J`5c5_J`J\\5b5dJ]J[5e5eJYJ[5i5eJUJ[5j5iJSJW5l5lJQJU5o5oJlIR5S6QKkIo4W5oIkJS1Mn4X5PJiJU1Ll4Z5QJiJU1Kj4\\5RJgJW1Jh4^5RJhJX1Ie4_5TJgJX1Id4_5VJfJZ1H`4b5WJeJZ1H_4b5YJdJ\\1F\\4f5XJdJ^1C[4h5YJcJ_1DW4i5ZJcJa1DS4h5_JaJb1En3j5dJ]Ja7b5dHYJ]7g5n000O100O100O10000O12N3M5K2N001O1O1O001O1O001O1O1O1O000000001O3MOhGdIn7\\6QHhIl7h6N2N1O5SHjHf7X70001O0000O1K51O0000O1002N1O1O2N2NO`HfH\\7b7M3M2N2N2N3M2N2N00O100O100O10000O100O1O1O100O1O1VJlHg3U7YLQI`3P7`LWIX3j6hL\\IQ3e6nLbIj2`6VMfIb2\\6^MjI[2W6eMoIT2R6kMVJm1k5SN[Jf1f5ZN`J^1b5bNdJX1\\5hNjJo0Y5POoJj0P5VOVKe0i4[O]K`0b4@dK;[4DlK8R4HTL3k3M\\LMc33cLI[36lLFR3:TMAk2?[M\\Od2d0cMVO\\2i0kMROT2n0RNlNn1T1XNfNh1Z1^N_Nc1a1cNYN]1f1eNYN[1g1fNXNZ1h1o500O1000000O1000000O100000000O1000000O1000000O100000000O1000000O1000000O1000000O100000000O1000000O1000000O1000000O100000000O1000000O1000000O100000000O1000000O1000000O1000000O100000000O1000000O1000000O100000000O1000000O1000000O1000000O100000000O1000000O1000000O1000000O100000000O1000000O1000000O100000000O1000000O1000000O1000000O100000000O1000000O1000000O1000000O100000000O1000000O1000000O100000000O1000000O1000000O1000000O100000000O1000000001O001O000hC:V9GTFn0l9RO_Ec1a:]NkDW2U;j001]DmLW;S3hDnLX;S3gDnLX;]300hDZLV;f3jDZLV;h30O1002N001O1O0000O10000O11O000000001O001O00001O0000O1O100O1O11O001O001O00001O001O1O0_EQLC0[:P4mEYLQ:g3kE\\LV:W4000000000001O1O00000000001OO1O1O1O1N2O1O1O1O1L4O1G`ERL`:l3;00O100hNPEiMO6R;o1REhM25l:Q2VEbMH08;k:R2SFbMn9[2]1N2N21]EjMZ8V2bGhMRN3[:U2cGQN[8P2cGSN[8m1bGVN^8j1_GYNa8g1]G\\Nb8d1\\G^Nd8c1[G]Ne8c1[G\\Nf8f1XGZNh8h1oF_NQ9d1eF`MROP1Y:d1^FbNb9a1ZF`Nf9`1ZF^Nh9b1XF^Nh9b1XF]Ni9c1WF\\Nj9d1VF\\Nj9d1VFZNl9f1TFZNl9f1TFYNm9g1SFXNn9h1RFVNP:j1PFVNP:j1PFVNP:j1PFWNo9]301O0000001O0000001O1O001O0000002N1O00001O00001O1O1O001O1O001O00001O0000001O001O00001O1O00001O002N00001O00001O002N0000001O1O00001O001O1O001O1O001O00001O001O00002N0000001O1O00001O00001O001O00001O1O001O001O001O00001O1O00001O001O001O001O00001O001O001O001O001O00001O1O00001O001O00001O001O001O", "size": [480, 640]}, "area": 78263.0, "iscrowd": 0, "image_id": 10083, "bbox": [0.0, 0.0, 640.0, 288.0], "category_id": 172, "id": 10012257}, {"segmentation": {"counts": "P\\g12n>6J7I6I7J6J6J6I8I6J6J6J6I8I6J6J6I7J6J7I6I7J6J6J7H7J6]HjJP5\\5iJgJU5_5cJdJ\\5b5\\JaJb5a5\\JbJb5_5\\JcJc5^5\\JcJc5]5\\JeJb5]5]JeJa5\\5]JfJb5[5]JfJb5[5\\JgJb5[5]JgJa5X5_JjJ`5V5`JkJ_5U5`JmJ^5S5cJoJ[5Q5dJQK[5n4fJSKY5m4fJVKX5j4hJWKV5i4jJYKU5g4kJZKT5f4kJ]KS5b4nJ_KP5b4oJ`KP5_4QKbKn4^4QKeKm4[4SKfKk4Z4UKhKj4X4VKiKi4V4WKmKg4S4YKnKf4R4YKPLe4P4\\KQLc4o3\\KTLb4k3_KVL`4j3_KXL_4i3aKYL]4f3cK\\L\\4d3dK]L[4c3dK_LZ4a3gKaLW4_3hKcLW4\\3jKeLU4[3jKgLT4Z3lKhLR4W3nKkLQ4U3oKlLP4S3PLoLo3Q3QLQMl3P3SLRMl3m2ULTMj3l2ULWMi3h2XLYMf3h2YLZMf3f2ZL[Me3d2[L_Mc3a2]L`Ma3a2^LaMa3^2`LcM_3]2`LcMa3\\2`LdM_3]2`LdM`3\\2`LdM`3[2`LfM`3Z2`LfM`3Y2`LhM_3Y2aLgM_3Y2`LhM`3W2aLiM_3W2`LiM`3W2aLiM_3W2`LjM`3V2`LjM`3U2`LlM_3U2aLkM_3U2`LlM`3S2aLmM_3S2`LnM`3Q2aLoM^3R2aLnM`3R2`LnM`3Q2`LPN`3P2`LoM`3Q2`LPN`3P2`LPN`3P2_LPNb3o1_LQN`3P2_LQNa3n1`LQNa3o1^LRNb3n1^LRNa3n1_LRNb3n1^LRNb3n1]LSNc3l1^LSNc3m1]LSNb3m1^LTNb3l1^LSNc3m1\\LTNd3k1]LUNb3l1]LTNd3k1]LUNc3k1\\LVNd3j1\\LUNd3k1\\LVNd3j1\\LUNe3j1[LVNf3j1ZLVNBULe3e5hLVNCWLc3c5kLVNBXLb3b5kLVNC[L`3_5mLVNB]L`3\\5nLWNC^L]3\\5PMVNBaL\\3X5RMWNCbLZ3W5SMWNBdLZ3U5SMWNDeLW3T5VMVNChLV3R5WMVNBkLU3n4YMWNClLS3m4ZMWNBnLR3l4[MVNDoLP3j4]MWNBQMP3h4]MWNDRMn2f4_MXNBUMm2c4`MXNCWMk2b4bMWNCXMj2`4cMXNCZMi2^4dMWND\\Mg2]4dMXND^Me2Z4hMWND`Mc2Y4hMXNDaMc2V4jMXNCdMb2T4jMYNDdM`2T4lMWNDgM_2Q4mMYNDhM]2o3oMXNDkM\\2l3PNZNDkMZ2l3RNXNDnMY2j3RNXNFoMW2h3TNYNDRNV2e3VNXNDUNU2b3WNZNDUNS2b3YNXNDXNR2`3YNYNEXNQ2^3[NYND[NP2\\3[NZNE\\Nm1[3^NXNE_Nl1X3_NZND`Nl1V3`NYNEbNj1T3aNZNEdNh1S3cNYNEeNg1R3cNYNFhNe1n2fNZNEiNd1m2fNZNFkNc1j2hN[NDmNb1i2iNZNFlNb1j2hNZNEmNc1h2hN[NFlNc1i2gN[NEmNc1h2hN[NFlNc1i2gNZNFmNd1i2eN[NFmNe1g2fN[NFmNd1i2fNZNEmNf1i2dNZNGmNe1h2eN[NEmNg1h2cN[NGlNf1i2dN[NEmNg1h2cN[NGlNg1i2bN[NFmNh1g2bN\\NFmNi1g2aN[NGmNh1h2aN\\NFmNi1g2aN[NGmNi1h2_N\\NGlNk1g2_N\\NGmNi1h2_N\\NGlNk1g2_N\\NFmNl1g2]N]NGlNl1g2]N\\NGmNl1g2]N\\NHmNk1g2]N\\NGmNm1g2\\N[NHmNm1g2\\N\\NFnNm1g2]NZNFoNn1f2]N[NEnNo1g2\\NZNEPOo1f2\\NZNEoNP2f2\\NZNDPOP2g2\\NYNDPOP2f2]NYNCQOQ2f2\\NXNCSOQ2e2[NYNDQOQ2f2\\NXNCROR2f2[NXNCROR2e2\\NXNBSOS2e2[NXNBROS2g2[NVNBTOS2e2\\NWN@TOU2e2[NVNATOU2f2ZNVN@UOV2d2[NVN@UOU2f2[NTN@WOU2d2\\NUN_OVOV2e2[NTN_OWOW2e2ZNTN_OWOV2e2\\NSN^OXOW2e2[NSN]OXOY2d2ZNTN^OXOX2d2ZNTN]OXOY2e2ZNRN^OXOY2e2ZNSN\\OYOZ2d2ZNRN]OYOZ2d2ZNRN\\O[OY2d2[NQN[O[O[2d2ZNPN\\O[O[2d2ZNQNZO\\O\\2c2YNQN\\O[O\\2d2XNQN[O[O]2d2XNQN\\O[O\\2d2XNQN[O[O^2c2WNRN[O[O_2c2VNRN[O[O_2c2UNRN\\O[Oa2W2^N]NRO\\O`2c1QORO^N[Oc2m0CHkMZOc2:6<VM[Od2Dj0Q1bL[Of2oN\\1f1nKZOg2[No1[2ZK[Og2fMc2o2fJZOc8f0]GZOd8e0]G[Ob8f0]GZOd8e0]G[Ob8f0]GZOc8f0]GZOd8f0\\GZOc8f0]GZOd8e0]GZOe8e0ZG\\Of8c0[G\\Og8c0XG]Oj8a0VG@j8`0VG_Ol8?TGBl8>TGAn8=RGCP9;QGEo8;PGER99nFGS99mFGT97lFIV96jFIW96iFKX94hFKY94gFMZ91fFO\\90dFO]90cF1^9NbF1_9NaF2a9M_F3b9K^F5c9J]F6e9H\\F8d9F]F:e9D\\F;f9CZF>f9A[F>g9@YFa0g9^OZFa0h9]OXFc0j9\\OUFe0k9ZOVFe0l9ZOSFf0n9YOSFg0n9XOQFh0Q:VOPFi0Q:WOnEj0S:TOmEl0T:SOmEm0T:ROkEn0W:POjEo0W:QOhEP1Y:nNhEQ1Y:oNfEQ1\\:mNdET1]:kNcET1^:kNbEU1`:jN`EV1`:iN`EW1b:hN^EX1c:fN]EZ1d:eN\\E[1f:dNZE\\1f:cNZE]1h:bNXE]1j:aNVE`1j:`NVE_1l:_NTEa1m:_NSEa1n:]NREc1P;\\NoDe1Q;ZNPEe1R;ZNmDf1T;YNmDg1T;XNkDh1W;VNjDi1W;VNiDk1X;TNgDl1Z;SNgDl1[;SNdDn1];PNdDo1];QNcDo1^;oMdDo1];QNdDm1^;QNcDo1`:QNdEOMn1^:YNaEH3m1\\:_NZF`1e9cNZF[1f9hNXFW1h9lNWFS1i9nNWFP1i9SOVFl0j9VOTFi0l9ZOSFd0o9[ORFd0n9\\OSFb0n9_ORF?o9AQF?o9BQF<o9ERF9o9GRF8n9IQF6P:JQF5o9LQF2Q:MPF1Q:0oEOQ:2nEMT:3lEKV:4kEKV:5jEIX:6hEIY:8gEGY:9hEEY:<gECY:=hEAY:?gE@[:?fE@Z:a0fE]O[:c0fE[O[:e0eE[O\\:d0eEZO]:e0dEYO^:f0cEYO^:g0bEWO`:h0`EXOa:g0`EWOb:h0_EVOc:h0_EWOc:g0]EXOe:g0\\EWOf:h0[EWOf:h0[EVOh:h0i1N3N1O2N4K5Lcag1", "size": [480, 640]}, "area": 91676.0, "iscrowd": 0, "image_id": 10083, "bbox": [118.0, 6.0, 403.0, 433.0], "category_id": 183, "id": 10012258}, {"segmentation": {"counts": "kog56Q=3N0_CIo;8kC1R<c002N0O2LM65J2M2M4L3NMZDgNf;Y1]DeNb;\\141O2N1O1O101N1O1bD\\NU;OhDg11\\NU;g1jD\\N0KP;V2N2OO01O1O100O1O1O1N2O0100N0bMZE]2f:2002OO0O10001O000000010O0O1K54M0O2N01O01O01O000000010O0J62N2O0O002N01O01O01O000010O00000001N1M3005I2101N0010O0001O01O01N11O00M301M26J010O2NN_M_Ea2a:200000010O000001O01O0N23NO01O01O0001O01O0000010O0001O01M23M002O0O1ONaM^E^2a:dM]E\\2c:dM^E\\2a:eM_E[2`:gM_EY2a:600010OK4106J01O01O001O000010O000010O1O00010O002N100OM300010ON`M]E`2b:3100O1O1O100O1O100O1O1O1", "size": [424, 640]}, "area": 14998.0, "iscrowd": 0, "image_id": 10084, "bbox": [444.0, 0.0, 196.0, 195.0], "category_id": 95, "id": 10012259}, {"segmentation": {"counts": "S<U1S<00000001O0000O100O100O100\\NROnFn0R9XOdECQ1U1Z9IYF7e91UFOa9c0SFWN0V1m9Q2M31O1O001O000000O11O000dMSF]1n9\\NYFc1g9YN_Fe1b9XNaFJC_1o9\\NnE3n0:POe0V:lNmE4n01YOn0S:nNVGR1`:0001O0000001O00001O005K9G00000a0mGmN\\L0TO3Z6Q1eIPOh0M[O5MM0N35O1OL0N:O67A3O110NF1:Z6b1fIYNO;2<O\\OO10n1Z6g4M00002N00001O00O10001`ISJd5m5[JVJb5j5^JVJb5j5^JVJb5j5^JVJb5j5^JVJb5k5]JUJd5j5\\JVJd5j5\\JVJd5j5\\JWJd5h5\\J^J^5a5cJ_J]5b5bJ]J_5b5bJ]J`5a5aJ`J^5_5cJaJ]5^5dJaJ]5^5dJaJ]5`5bJ`J_5]5cJcJ]5]5cJcJ]5]5cJcJ]5]5cJdJ]5\\5bJdJ^5[5cJeJ^5Z5bJeJ_5[5aJeJ_5[5aJ`J[OHV6f5`JaJZOHW6g5V1001OO1001N10O1000O20O0000lIYJf4_5`KkJY4l4ZJgJc1>Z49XJY3c1`L]4LRJc3_1cLi4]OjIR4\\1bLW5[3iJeLZ5X3fJgL[5Z3dJfL\\5Z3dJdL_5\\3bJbL^5^3bJcL]5]3dJbL\\5^3eJaL[5_3fJ`L[5g0RI]1d1jMc5:dH[O<;HH5^1^1kNX3E]Ma0SNCAKf0b0E1[1Ig2EmKOH8NH64c1=FOnN2Y1Ld2T1WL_N;>e0OnN3V1Nd2V1ULaN;5l0J\\NKd0>Q11^2AoKg19fN7C]O0n11RN1h0;o03[2CnKe12ZN1?b2_OXN;j04\\2\\2@_MVN4Y2^2C[MVN7V2]2FYMUN;T2_2EUMYN;R2`2FRMXN`0Q2_2h1_MXNb24`MPMNk2a25bMSLBKMO1a0>]3`23dMULG:5]3b2UMRMVO4n1?jMI`0O^3d2RMRMROL2=k1k0aNUO]3h2QMRMROJ1e06ZOW1Y1SOSO[3n2WLhL`0^1^O`N`00@0?Z1@fN90H?j3Z3`LRNJ_N7Q2[3fMYLc6:iK[3[4cLgK]3X4cLiK\\3\\4^LeKc3R7NO02O00001O00N13\\O`F\\M`9c2fFXMY9h2hFWMX9j2c0O11N11O1N11O000001OOYMgEa2`:04YN_E@OB00Nb0i:2aE[OG0;0Kc0a:N`F_OhN<m:4`FEe9<c10O01000000000000004L5LWOdCf0OYOZ<1gCh0\\<17H00000001OO011O003M00N21O0@[C<l<L1O1Ooab0KRQ]OOl<2VC2g<0XCL12g<;O1002NO1O10000O11OO11O00O100]OClC657o;NlCC000?T<c000O11O1OO10000001OO1001O00O1ZOoNTER1k:QOiDY1S;cNgD80H0_1W;`0O1N2O1N2O1N2O1N2O1M300N2N21OO100000000001OO11O00O1O1_OcMUF_2k9?O1K5H800O1000000N2O1000000O1N200LVLkFi3P9:00O1O10000O100001OO10000_OkKoGU4o7nKhGMNW4X8WLfGj3X8d0O1M300O1O1O1O1000000I7M3O1N2O1O1N20000000000000000O1I7M3H8N2FkI`IV6`690000001OO1O1000000O10N3I`IbIW6e67NaI]I]6e63N12O00M33M00O100010O00O20N1^OgISJ[6m5:QJQIQ6P73M32N1O1O00M3M300O1HfIcIY6]6lI^IU6a6M_IlIONd6P6bIoI`60ZIR6i6:N2M3O10000L4O1DZITJNJg6Q6^ITJKLh6m5b0K3dJdHf41PKY74oHi4l70jGQKU8o41J72G56K1L310O0MbG[KZ80hG[4d8hKZGY4e850M\\GbKe8]43N2O1010O00iKUGR4k8lKWGT4h8kK[G0Ko3W9O11M10O12NO11OO1O101MhFTL[9h3fFXL]9d37O1M001N1001O1O1O01N123K5K5J100M33NO0O13MN2kMjEW1]:VNSFg1o9WNRFh1P:VNQFi1Q:TNPFl1g:N100O5M5K1N0000001O0000001000]DaN];a1^D`N3O\\;_1aDiN`;_13M01O1OO3M3M8G2O0O1O2NM32N1O5WOSDOcWX1", "size": [424, 640]}, "area": 64638.0, "iscrowd": 0, "image_id": 10084, "bbox": [0.0, 0.0, 543.0, 424.0], "category_id": 96, "id": 10012260}, {"segmentation": {"counts": "PUl0:n<3M000000000000001O6J00O1O11O00O1O1000000001O0000O100KZCHg<7YCHbi52]cJ:I1O2L201O00O100N4N010O1O1O1N010002M1O011NO3O10N11ON21N20N1000O1O2O_ODPD0H?W<?01O0000O12NO11O00001O00N22N00O1O10000O11O1O000000001OOPDPOj;W104L5K00000000O10000000000000000001ZDaNa;`1_D`N`;d108H0000000000O11O003M00N21O0iDRNT;Q201OL400O100001ONiDSNW;m1200000000O11O1O1O1O2N1O002N2NN2NoDmMQ;S22N2EmDbNR;]1PEaNQ;_1PE`NP;\\1kDbN51P;;kDA11O31?O@6NQ;>mDAL17=KE^;;SEN@D_;?PEC@1h;<iDAA0h;?k000000000000000O11O00N21O001O0000000000O1001OO100001O000000000000O1002N00NZCBf<;ZCFh<9ZCFf<9[CGe<4YCLVbb4", "size": [424, 640]}, "area": 6660.0, "iscrowd": 0, "image_id": 10084, "bbox": [68.0, 0.0, 219.0, 72.0], "category_id": 97, "id": 10012261}, {"segmentation": {"counts": "fZ7130n<3PCNn<9bMF`E0W2c0Q8DiG>V8EfG=Y8HaG9_8I^G9`8I_G7a8J^G6k6VOWIe0M6k6XOTId0JYOkNj0l6YO^IM68e0a0O:`4nNZK8nNH5OJ0b0:e0f0SOPO91_OR1g5nNWJO0010c0m0\\OUO`079L\\Oo04mN7Y1;hNo3=iKc0]OPO59b0F@W1MmN;V15nNP4=mK`0CVOLFa0d1_OZN15:Y15QOY3OcL72W1I?3UO3ROJCP3<TM6F[O7]6JcI3n0:PO]1:TNG:d0M:Nb5KbI4n01YOP1OTO7G@7i0I85h5LjJR1lNoNANg22l5Q1`301O0000001O00001O005K9G00000a0mGmN\\L0TO3Z6Q1eIPOh0M[O5MM0N35O1OL0N:O67A3O110NF1:Z6b1fIYNO;2<O\\OO10n1Z6g4M00002N00001O00O1000100O0000000010O00001O0O11OO2N1O1O1O110M20000010OO2O00002N000001OO1001N10O1000O20O0000OH9G]HgJQ8g0THY3[8\\LgGc3c8PL_GR4n8M3M01O010O00000001WMkF]1]9TN^F[O<;HH5^1Z9QOlFCAKf0b0E1X9NlGOnN2W9MmGOnN3V9JPHJ\\NKd0>R9BYF0n11RN1h0;S;CRE;n;O3M2M2O2MRm_6", "size": [424, 640]}, "area": 22110.0, "iscrowd": 0, "image_id": 10084, "bbox": [17.0, 0.0, 120.0, 408.0], "category_id": 113, "id": 10012262}, {"segmentation": {"counts": "fVU52P=1QC1k<8MFXC:h<3N21O00M33M00O100001O00O11O0000JB`C`0a<3M32N1O1O00M3M300O1HWORDh0n;]OmCc0S<91OKPOPDR1i;:N2M3O10000L4O1DZNTENJh1P;]NUEKLg1n:`0M3JhM]EW2b:80_M\\E^2f:1J63MO100M31O00M[MbEf2S:=1O00MoLoEQ3Q:3N2O1001O001O1OO1002N00O12NO11OO1O100NkLQFU3n9lLRFT3m942N0000O1001O1O1O00O12N0000O100M33M00O13MN2O100000000O100O11O1O000RNQFc0o9[OPFkN2h1n9\\OVFd0j9ZO[FmNFc1o9^O]FPOD7OP1P:H^FROC22T1m9IcFPO]OO2Y1o9EoEROf00\\O2NS1R:POnEa0h0[OYO10>i:@WE3O<n0ARO00a0j:_O^E=j0CQO114Q;3lDHX11lN2Q;3`FLb91`FNb90^F0b9OU^\\1", "size": [424, 640]}, "area": 9215.0, "iscrowd": 0, "image_id": 10084, "bbox": [398.0, 318.0, 134.0, 106.0], "category_id": 124, "id": 10012263}, {"segmentation": {"counts": "TaX24`0OB00NR<:mCG0;0Kn;Q1RDhN<4m:Y1lDmNT;e1N1N10O100O1N2O10004L5L1\\N\\Db1e;O1^O^DVONLe;o0?O1O110N0O2001O1O00O100N1FgC@1On;2XD?R<@mC`0P<DoC=Q<=O2O0001ONPOlCP1T<20O11O000O02N2N2K5LbC[Oa<a05O002M3OM1OCYC=g<3M2E@oC?P<CnC>R<CmC=o;^OoC51=n:^OnD11O31?O@6N>Q;@nDL17=KEk0n:F[E@Dk0S;DoD@1T1P;]OmDA0T1S;j012NO100001OO1O020O0N012AkD^N43R;_1>O0O1O100000O1001N01000O20O1O1O1O00000O0102N00N`N\\D_1b;aN_Db1_;`N`D_1`;bN`D_1Z;_NfDg1V;80A?00O11O0000O1001OO1000000000000O12N0000O1O11OO1O100O11OO100O1O100HVDmN00l;R170000M3002N1O002N00000000O10000^OnC_OO009U<6TDHl;8ZDAg;>b0001OO100000000000000O1002NO12N1O1O00M31O1ON200001OO1O1N200O1O10000000000O1004L1O00O1O100O1O100O1O100O1O100O1O100O1O100O10000000000000000000000000000000000000000O10000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000`V_1", "size": [424, 640]}, "area": 8123.0, "iscrowd": 0, "image_id": 10084, "bbox": [175.0, 0.0, 351.0, 132.0], "category_id": 125, "id": 10012264}, {"segmentation": {"counts": "0S<U1000000001O0000O100O100O100\\NROnFn0R9XOdECQ1U1Z9IYF7i8jNeF0d0W1HOm8mNeFN<h1FWN0V1W9TOQGm2c6iLSI0W2n3f4YLTKh3l4[LPKf3P5_LjJb3V5`LgJa3X5aLgJ_3Y5bLfJ^3c3nK_Le0M^3c3PL\\Ld0JYOkNS4c3PLgLM68e0a0Ob3X1fKbN8nNH5OJ0b0:e0f0SOPO91_OZ4_2fK_MO0010c0m0\\OUO`079L\\Oo04mN7a4Q2mKRNc0]OPO59b0F@W1MmN;]4m1TLUN`0CVOLFa0d1_OZN15:`4X1iKiN72W1I?3[2V1kKkN6F[O7`9?hFYOG:d0M:Nc8LgF87G@7i0I85R9MmENg22XRi7", "size": [424, 640]}, "area": 11182.0, "iscrowd": 0, "image_id": 10084, "bbox": [0.0, 0.0, 39.0, 388.0], "category_id": 140, "id": 10012265}, {"segmentation": {"counts": "`[d12d<0gCOH8NH64h;^1cD_N^:d1_EaNW:J^Eg19fNR:FbEe12ZN1?Y:Z2N10O03N0O1O1mI`M^1_2bNbMSLBKMO1P5n2QOdMULGh4g2TLRMVO4n1?jMIn4c2RLRMROL2=k1k0m2m1RLRMROJ1e06ZOW1Y1]3Q2XKhL`0^1^O`N`00@0?Z1Z4_2aKRNJ_N7R1Z4eNZKc6:iJ[4[5cKgJ]4X5cKiJ\\4\\5^KfJb4P700O10000000]IeLSNQOh4Z4aLhM^3W2QL[No3S1\\KZLd0e2o3P1cKVL=l2o3m0eKVL<n2o3l0cLVO\\3i0eLWO[3j0cLXO\\3h0cLZO\\3f0cL[O]3d0cL^O\\3c0cL]O]3c0bLGU39jL2l2NSM3m2MRM5m2KSM5m2KRM6n2JQM7o2JoL8P3HoL9Q3HnL8R3JkL7U3JiL8V3HiL7Y3HgL9Y3GgL:X3EhL<X3BiL?W3@iLa0W3_OiLb0V3^OiLc0W3]OhLd0X3]OfLe0Y3]OeLc0[3]OdLd0\\3kNWLeLNL?f4[3jNTMV1l2iNTMX1l2gNTMZ1l2gNRM[1m2dNTM\\1l2bNUM_1k2aNTM`1l2aNRM`1n2aNQM`1n2`NQMa1o2^NQMc1o2]NQMc1o2[NRMf1n2oMWLPM1Ol0S5l2mM\\MT2d2kM]MV2b2jM]MW2c2iM\\MX2d2iMZMW2g2iMYMW2g2iMXMW2i2iMVMX2j2hMVMW2k2iMTMY2k2fMUM[2k2fMSM[2m2eMSM[2m2eMRM\\2n2cMRM^2n2aMSM_2m2`MSMa2m2[MVMg2i2VMVLZMQ1a5i2SM[Mm2e2SMZMn2f2RMYMo2g2PMYMT3d2mL[MS3e2lL[MU3e2kLZMV3f2kLYMS3i2mLVMR3l2nLSMR3n2nLQMS3o2mLQMS3o2mLPMT3P3lLoLU3Q3kLnLV3R3iLoLX3P3hLoLY3Q3gLnLZ3R3eLoL[3Q3gLlLZ3T3eLlL\\3T3dLkL]3U3cLkL^3T3cLjL^3V3aLjL`3V3_LjLb3V3_LiLa3W3_LhLb3X3\\LiLe3S3kIZL`2d0d3l2mIbLU2NjM43`0R6i2nIdLL1U2n0Q4^2ZLaMg3^2YLcMf3^2YLbMg3a2WL^Mj3a2WL^Mj3a2WL_Mi3a2WL^Mj3b2VL]Mk3b2UL_Mk3b2TL]Mm3b2TL]Mm3d2QL\\MP4d2QL[Mn3e2TLYMm3g2TLWMl3W2eISM65Z2?l3Y2oISMC000b2d0k3Z2hLeMX3\\2hLcMY3\\2gLeMX3]2gLbMZ3_2eL`M[3`2fL_MZ3b2eL_MV3f2kLXMU3i2jLWMW3j2iLUMV3k2jLUMV3l2jLSMV3o2ZL_Mf3b2ZL^Me3b2[L^Me3m1iIjMd27e3m1jI`Mk2b0^3h1cI`M80H0Q3h0[3g1TMYNl2g1UMXNk2g1XMXNg2h1YMXNg2g1[MXNe2h1\\MWNd2h1^MXNb2g1_MXNa2g1aMXN_2h1bMWNY2@lIW2m3ZNV2l1lMQNS2o1oMPNQ2o1QNQNm1R2QNnMo1R2QNnMn1T2SNjMm1W2RNjMl1X2SNhMm1Y2SNfMl1\\2SNdMg1d2YN[Mf1e2ZN[Mf1g2YNXMg1i2XNXMg1h2YNXMg1h2ZNWMf1Y2aJcMc3KVL00:f5Y2bNnM]1R2dNmM\\1o1iNPNW1i1nNZNQ1g1oNXNQ1h1ROUNn0l1SOSNl0n1TOQNl0P2VOmMj0R2XOnMg0R2ZOmMf0T2ZOkMf0V2ZOjMe0W2ZOiMf0W2[OhMe0W2]OiMb0X2lNbMdKO0098W5T2`JgMY46nKOX5P2TOWN]KI_5Q2SO`Nm0`1TO`Nk0`1WO^Ni0c1VO]Nj0d1VO\\Ni0d1XO[Nh0f1XOYNh0h1XOXNg0j1XOUNh0k1YOTNg0m1YOSNf0n1YORNg0^1fJ]Nc44g0^1iJUNMNg4=b0_1UKRNY4?b0^14_NLa16^NI`1;^NDd1<\\NCd1;^NEb1=\\NCd1?[N@e1?\\NAe1?ZNAg1?YN@h1a0VN_Od1g0\\NYOb1i0_NVOa1i0`NWO_1k0`NUO`1k0aNTO_1l0aNTO^1n0aNRO`1n0`NQOa1o0^NQOc1o0\\NQOe1o0[NPOf1o0ZNQOg1o0YNPOh1T1SNlNn1U610J6N2I7O1GaDjN_;W181O1O1O2N00001O1O1O001O2NF:00O11O001O1O1O1O1O1O1O2N1O1O2N00_OlDnNU;S1?1O1O1O1O1O1O1O1O1O100O1O2NE_DlNONb;R1dDmNb;S191O1O1O1O1O1O1O1O00M30010N10O2O1O00N12O00O1N2N2O1000000000003M2O1N2N1MgCVOY<j03O00100O001O010O001O0O2O01O10N`C\\O_<d03O02OO0O2O00001O00N2O2N1O01K54L1O2N00010O0001O000010O01I62O1N10O02OO0010O01O001O010O0000000O2L3005K0010O0001O01O0001O00O20O00M300N35J001O2NN`C^O`<b020000010O00000010O000N23M0100O1O1O10O01O1O00O2^O_O\\Da0e;_OZDa0g;_OYDa0g;_OXDa0i;_OWD>l;BSDa0k;_OUD`0l;@SDb0l;^OTDb0l;^OSDc0m;]OSD`0FBV<NSD`0I@U<0RD?IBU<OQD?KBT<OlCc02]OR<0lCc0[<]OdCc0]<]OcCb0^<^OaCc0_<]O`C>f<BZC=g<7KO1O100O100O1O100O1O1O10000O1O100O11O0000L4O1O100MnBOS\\5", "size": [424, 640]}, "area": 40533.0, "iscrowd": 0, "image_id": 10084, "bbox": [126.0, 0.0, 502.0, 424.0], "category_id": 147, "id": 10012266}, {"segmentation": {"counts": "Z[W61V=4M3K5L4L1O2O0O1O1O100O2N1O6J6K5J1O1O101N1O100O1VE_Nl9h1aEhN\\:Z1bEgN\\:\\1bEeN\\:^1aEcN^:U2N100O5K7I3N0kHiLX4X3fKgLZM2m6X3hKlLW4V3fKoL]MFg6[3jKQM`MD7OS6]3ULQMbMC22V6[3WLUM`M]OO2[6]3SLaLbMf00\\O2NT6a3WLWMdMYO10>b0_4Q3ZMZMgMRO00a0c0^4S3XM\\MiMQO114?i4W3RMWLPNX11lN2>j4\\3nLgMWNmNj4^3mLgMXNkNj4`3mLeMYNkNj4a3kL[NT3i1hLXNW3l1fLTNY3U2^LlM_3X2`LhM`3X2_LiMb3V2]LkMb3W2\\LjMd3R2_LoM`3S2^LnMa3S2^LnMb3V2eK_KO[2[4m5O100O1O1O1O100O1O100O1O100O1O1O1O100O1O1O1O100O1O100O1O100O1O1O100O1O1O100O1O1O100O1O1O100O1O100O1000000O1O100O1N2O1O100O1O1O1O1O1O100O1O100O1O1O100O1O1O100O1O1O100O1O1O100O1O1O100O1O100O1O1O1", "size": [424, 640]}, "area": 37032.0, "iscrowd": 0, "image_id": 10084, "bbox": [481.0, 76.0, 159.0, 348.0], "category_id": 148, "id": 10012267}, {"segmentation": {"counts": "eiP2Y14QOi:R2]Ob0O2N1O2N2O0O2N1O2N2O0O:F;E1O100O2N1O1O101N1O001O100O2N1O100O2N1O101N1O100O2N1O100O2N1O100O1O101N1O1O100O1O1O010O00100O1O1O100O2N100O1O1O100O1O2N4M0O1O01N1O11O100O1O1O100O1O101N1O1O100O1O2N100O1O010O000010O0001O100O1O100O1O1O100O000010O00010O01O00M4N11O01O00000010O002N2OO00001O00000010O0000K6N100O2O000O101N100O1J7O00001O0000001O00001O00001O00001O00001O00001O001O00001O00001O00001O00001O00001O000O2O001O00001O00001O00001O00001O00001O00001O0O2O00001O00001O00001O00001O00001O00001O00001O0O2O1O1O100O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O001O1O1O1O1O1O1O100O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1N2O1O1O1O1O1O1O1O1O1O1O1002N1O1O0000O10000O100O10000O100O1O100O100O100O1O100O100O100O1N2N2M3N2N200O1O1O1O100O1O100O1O1O100O100O100O100O100O10000O100O1O100O1O1O100O1O1O1O100O1O1O1O100O1O1O1O100O1O1O100O1O1O1O100O1O1O1O100O1O1O100O1O1O1O100O1000000000000O1O1N2O100O100O100O100O100O100O100J600O100OPb`0", "size": [424, 640]}, "area": 56436.0, "iscrowd": 0, "image_id": 10084, "bbox": [156.0, 1.0, 444.0, 303.0], "category_id": 183, "id": 10012268}, {"segmentation": {"counts": "\\S71c?1N2f@MQ?4n@LR?5m@KR?7m@JR?7m@IS?8l@IR?9m@GS?9m@HQ?>O2M2M4M2M3NL4K4M4O10O100O10000O2O0O10000O10001N100O10000O2O0RDGc89[Gg0f7ZOYHn0`7RO^HX1Y7iNeH`1T7`NkHh1n6YNRIm1f6TN[IR2UNfMk68RKX2dMlMU7LXKf3c4[L^Ki3]4WLeKl3U4ULlKn3P4SLQLP4j3PLWLR4e3oK\\LS4a3mKaLT4[3mKfLU4W3kKkLV4R3jKoLX4m2jKTMW4i2iKXMY4e2gK]MY4`2hKaMZ4\\2fKfM[4W2eKjM]4S2cKoM^4n1cKRN^4l1bKUN_4i1aKYN_4e1aK]N_4a1aK`N`4^1`KdN`4Z1`KhN`4V1aKjN`4T1`KkMnNWNb5l3`KjMZOSNV5Q4`KhM@WNP5o3`KgMEYNl4n3_KjMGWNj4n3^KmMHUNj4l3_KoMHSNk4l3]KRNJPNj4l3\\KUNKmMk4c3RJXLX1Z2KjMl4`3TJ[LU1\\2LgMm4^3VJ]LQ1_2NaMo4_3VJ_Ln0b2X6l0XJVOh5h0XJZOh5c0YJ_Og5>ZJDf5:ZJHf57YJKg54YJMf52ZJ0c52\\JOb52^J0_52`J0Z54fJNT58jJJo4<_I^LV1W3X5=bI[L[1U3R5a0bIYLh1l2e4k0cIXLP2g2\\4Q1cIXL^2\\2n3b1dLnM[3Q2hLnMW3R2kLlMV3R2mLmMS3Q2PMnMP3P2SMoMm2o1VMQNi2m1ZMRNf2l1\\MUNb2j1aMUN_2i1dMWN[2g1hMXNX2f1kMYNU2e1nM[NP2d1TKhL`1d1kN]Ne2U3PMlLa1b1hNaNd2o2TMnLb1b1eNdNb2k2VMQMd1`1bNgNb2f2XMTMf1^1^NlNb2`2ZMXMf1\\1]NnNa2]2\\MaM`1T1bNQO`2X2^MkM[1k0eNUOa2S2_MUNU1c0jNWOa2o1aM^No0;nN^O]2g1fMhNi03oNDoNkN[3e2mMQOe0K_N6H`Nm2_2UNUOd0G_N5NcNf2\\2WNXOe0D^N44gN^2Y2YN[Og0A\\N39oN_O2j1P2ZO]Og0@[N1?IS1Z1ZO_Oi0\\OZN0d0YOYOAe1[2YOBj0ZOXNOj0UO]OB\\1_2YODk0XOWNMo0QOCCn0g2]OBl0UOVNLT1oNF93S2OGm0b0_OVMI71X2IKm0=DUMM3N^2B0n08HRM21Jd2]O3o03m2JRL5Q11m2JQL6R10m2KnK7U1Nm2NjK5Y1Ln21eK5]1Jn23bK4`1Io25\\K4e1Go2]3QMbLP3^3PMbLP3_3oLaLQ3_3oLaLQ3_3oL`LR3`3nL`LS3_3mLaLS3_3mLaLS3_3mL`LT3`3lL\\KVObNn3R6lL\\KVObNn3R6mL[KUOcNn3R6mLZKUOdNP4Q6kL[KUOdNP4Q6kL[KUOdNP4Q6kL[KUOdNP4Q6kL[KUOcNQ4R6jL[KUOcNS4P6hL]KUOcNT4o5gL^KUOcNV4m5eL`KUObNY4l5bLaKVOcNY4k5aLbKVOcN[4i5_LdKVObN]4i5]LeKVO`Na4h5ZLhKh4V4XKjKi4U4WKkKk4R4VKnKk4Q4UKoKi4R4YKmKf4T4ZKkKf4U4[KkKd4V4\\KjKb4W4_KiK`4X4aKgK^4Y4cKgK\\4Z4dKfK[4[4fKdKX4]4iKcKV4^4jKbKU4^4lKaKT4`4mK_KQ4b4PL^Km3e4SL[KnMfNh5P6\\L^Ka3e4_L\\K^3f4bL[KZ3g4hLYKU3i4kLXKR3i4oLXKm2k4TMTKj2n4WMRKe2P5]MPK`2R5aMnJ[2T5fMmJW2U5jMkJS2W5nMhJo1Z5SNfJj1\\5WNdJe1^5]NbJa1_5`NaJ^1`5cN`JSN_N[2P7D`JoMcN[2n6G_JlMeN\\2l6I_JiMgN\\2k6L^JeMjN^2h6N^JbMlN^2g62\\J]MPO`2d65ZJZMTO_2c69XJVMXOP1]O1S7l1VJPM]Oo0A0m6S2TJlL@n0EOg6Y2SJgLDm0INa6a2PJbLHk0NNZ6g2nI_LLh03MT6n2nJRMTOLn5U3lJkL]OKh5`3fJaLIKa5l3`JTL6K[5Y4XJhKQOk0Z7d3_I^KVOY1R7b3QJhLh5`3lIiLo5\\3iIlLR6Y3eIoLW6U3bIkMa5[6I6J6J3M4L4L3M4L4L3M4L4L5K5K6I6K0000000000000O1000000O1000000O10000O1000000N2M3aN[DlNh;S1]DgNe;X1`DaNc;^1cDZN`;e1eDUN];k1U101O1AYBPOh=P1XBoNi=Q1XBnNh=R1XBmNi=R1YBlNh=T1XBkNi=U1XBiNi=W1WBiNi=V1YBhNh=X1XBgNi=Y1900O1000000O1000000O1000000O1000000O1000000O1000000O10000000O10000O100N2N2M4M2M3N2M3N2M3N2M3M3M4L3M3M4K6K4L5K4K5L5K4L5K4K6K4L5K4L4jKeKmL`4\\MYK`4:nMa4aMYK]4O`J6[3g4eMYKHKf3NdK9S3i4jMYKALm3IfKd0`2e4ZNoKi2fNnL2@g0T2j4dNgKj2gNmL1Bk0h1m4mNbKi1dN]N3AOEo0[1P5YO\\Kh1eN\\N2BNG4:7d0k5CVKj1fNZN1AOK0<<5o5OPKi1gNXN2@M1N=`0FR6:lJi1gNSN5CJ5J?f0VOV6f0dJi1iNoM9EEX1[7[OoIh1jNiM=IA]1W7XOSJh1jNdMk0]1X6VOSJi16QOg5UOUJi14ROg5UOVJh12TOi5ROWJh11VOh5ROWJh11UOi5ROXJh1OVOj5POYJi1MWOk5mNZJm1JVOm5jN\\JP2FVOo5gN^JS2AWOR6cN_JW2^OVO_8j0`GVOa8k0]GUOc8l0\\GTOe8l0ZGTOg8l0WGUOi8l0VGTOk8l0TGTOm8l0RGTOo8m0oFROR9o0mFQOT9o0kFPOW9P1gFQOY9P1fFoN\\9Q1cFoN^9Q1aFoN_9R1`FmNb9R1^FnNc9R1\\FmNe9T1YFmNh9S1WFlNk9S1UFmNl9S1SFlNn9U1PFlNQ:T1mElNU:S1jEnNV:S1iElNY:T1eEmN\\:S1bEmNa:Q1^EPOc:P1[EPOh:n0XEQOj:n0UEROn:l0QETOR;l0kDTOW;l0gDTO\\;l0`DUOb;k0[DVOh;Y2PM\\LSJb3j5eLnHEAc3_7nLjHCH\\3[7XMfHAOU3X7aMcH^O6n2T7kM`H[O<h2Q7TN]HXOc0a2m6^NZHUOi0[2n6cNSHVOP1T2n6iNlGWOW1m1o62QILP75QIHP79PIEQ7<PIAR7?oH^OR7c0nH[OS7f0nHWOS7j0mHTOU7l0lHQOU7P1kHnNV7S1kHjNV7W1kHfNW7Z1iHdNX7]1iH`NX7a1hH]NY7c1iHZNY7f1gHXNZ7h1hHUNY7k1iHRN[7l1eHRN^7l1dHQN`7l1aHQNc7n1^HoMe7o1\\HoMh7o1YHnMj7P2XHmMl7P2UHnMn7Q2SHlMo7S2RHkMP8T2RHiMo7X2SHdMo7[2SHbMn7^2UH^Mm7b2UHZMm7e2a210O1O010O00100O0L5_Oa0_O`0@a0_O^\\1", "size": [500, 500]}, "area": 117495.0, "iscrowd": 0, "image_id": 10094, "bbox": [14.0, 16.0, 483.0, 477.0], "category_id": 183, "id": 10012269}, {"segmentation": {"counts": "ibl35Q;3O1N3N1N2O1OO101N1010O1O2NO1O1O1O1000N110O1O11O010O00000000001N1000010O2N0O10O2N10000O11010N1O4M01OO001O010O0001O00000000O2OO10O1O1O11O0O1O1O0010O10000O101O2N1O1N10001O0O2O000000000000O101O0O1O2O1B`E2`:MeEO\\:0eEO]:NeE0k:O0O2OU``03VU_OMn95PFMo95nEMP:4oEMP:4oEMQ:3oEMQ:3mEOR:5jEMU:3jENV:3gEOY:`000000000001O000000000000001N1000O100001O000000000000000000000000000000000001O000000000001O00000000001O005K1\\O^E00=h:01OO1O100N2O100000001O0000000001N11O1O001O00O2N10000O3N1O5K4LYk<", "size": [360, 640]}, "area": 3951.0, "iscrowd": 0, "image_id": 10097, "bbox": [354.0, 131.0, 249.0, 35.0], "category_id": 96, "id": 10012270}, {"segmentation": {"counts": "PTf31W;001O0QF0f80YG1g8OXG2h8NWG4h8LWG6h8JjF5_O5f9FdFl0\\9TOeFm0Y9SOiFl0V9TOkFj0V9VOlFc0]O_Og9NmF?@Ad90mF;BF`9OPG6^9JcF2`9Nm01O0000000000000000000000000000000000000000001O0000000000000000000000000000000000000000001O000000000000000000000000000\\ELT:4lEOQ:1nE4n9LRF7k9IUF:h9FXF=e9CZF>f9BZF>f9CYF=g9CYF<h9DWF3GOR:NWF3LJm93WF4JJo92WF2LLm92VF3MJn93UF3MJn93UF2NKm93UF2OJl94UF20Ik95TF2X:NhE2X:NhE1Y:OgE1Y:OfE2Z:NfE0\\:0dE2Z:NfE1[:OdE1]:OcE0^:0bE0^:1aEO_:1`EOa:1_EOa:1_EOa:1_EOa:1^EOc:1]EOc:1]ENd:2\\EMe:3ZEMg:3YELh:4XEKi:5WEJj:6UEJl:6300O1N2O1Obkd1O`T[N0O1O1O0O1N30O10cEKg94XFOg92VF2j9MTF5m9JPF8P:InE8R:IkE9V:GgE<X:BiE?X:@gEa0`:0000000010O20O0O1eK", "size": [360, 640]}, "area": 1554.0, "iscrowd": 0, "image_id": 10097, "bbox": [336.0, 0.0, 304.0, 159.0], "category_id": 105, "id": 10012271}, {"segmentation": {"counts": "he:2P;ORE6k:5LEYE;f:5O0O100O2OO20O02N0000O1O101N1O1O3M3N0O1O010O1O01CgEJY:6jEGW:8kEFW:9jEEX::iEDX:<;O00O101N1O1O1O1O100N2O2O02N3N0O0O2O1O2NO13L2O00O100O1O1O1000O2O1O0O101OO1001O00O2N10000O1N3O0001O1O00001O010O1O001O00O2M2O11O00000000O1O10001OO4N1N2N7FnD0cX53RRKM[E=`:800000001O1`0_O01O000O2O0O2O00001O0O10O101O0O3N5KQj2OmUMNoD4k:MUE7k:4N1O2O00010O01O1O100O1O100O00001O00001OO1O2N100O1N20000010O1O00010N1O101O00O14DWE1mgd00Rc[O2N1O2K4L5M20001O11O0O2O00O00001O001O1O1O00100O00O1000O100O01O1N3N100O3N1O1N11O10N10000O101O00000000001O0001N3M2N2O1N11O1O1O2L3N2N2O1N2O100Na_91_`F1O1O1O2N002M2O1O00000O100O1001O1O0O2O_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]OQFMo92SFMm91UF0j9NXF2h9NXF2i9MWF3j9LWF3i9MYF2f9N[F1e9O[F1e9O[F1f9NZF2f9NYF4f9LZF4f9LYF5g9KYF5g9KZF4f9L[F3e9M[F3e9MYF5g9KXF6h9JXF6h9KWF5i9KWF5i9KWF5i9KWF5i9KWF5i9KWF5h9LXF4h9LXF4h9LXF4h9LXF3i9MWF3i9MWF3i9MWF3i9MWF3i9MWF3i9MWF3i9MWF3i9MWF3i9MWF3i9NVF2j9NVF2j9NVF2j9NVF2j9NXFOi91XFLj93XFLh94YFJh96YFIg97[FFf9:\\FDe9;d0O1001O00001O000000000000000000O100000000002N000000010O00O10000O011N2N1100O0O2N10001N1000000001O4L5K4LkT3NWkL1ZEOS:3kEMU:5>1YEJZ:8dEH\\::aEF`:;_EEa:=]ECc:a01N10000O01000O01001N5L4L4L00`J", "size": [360, 640]}, "area": 9280.0, "iscrowd": 0, "image_id": 10097, "bbox": [30.0, 109.0, 610.0, 94.0], "category_id": 124, "id": 10012272}, {"segmentation": {"counts": "b4_3i701O001O00001O0000001O0000001M2000001O00001O00001O3M00000O1O011OO10000O100O1000000O101N101N100O1O100O10000O10000O0DeLlH\\3S7gLjH[3T7gLjH[3U7fLiH[3W7fLgH[3Y7:0000001O000000000O10010O000010O0O010000000O10000000001O00000O10000O10000000000001O000000001O000000000000001O00000O1O1O2O0001O0000000000000001OO110O00000000000001O0000000000000O2O00O100000000001O1O001O00001O001O00001O0000O10000O10000O001000000O1O1L\\LcHe3\\7_LfH\\3Y7bLeHM4`3X7`LlH`3_7O001N11O01O0000001O00001O00000000000000001O0000000000001O0000001OO11O00000000001O01O000000000000000000000000000000O2O00000001O1O10O000XOSHUNl7j1WHUNi7h1[HVNg7g1\\HXNd7f1^HZNb7d1aH[N_7c1gHXNZ7f1]HoMM:g7d1[HWNL5i7b1[H]NJ1k7:[H40DINl77cH0IMGLm74lHK@8E0i7J[IFWOc0CO1B`75UJY1j5fNWJ[1i5dNWJ]1k5`NTJb1l5]NTJd1l5\\NRJc0]N9a7SORJa0bN;U9@QG?o8]OVGb0j8XO]Gg0c9OUNXOWIg0_6GPIDQOd0e76QI\\OYO?b7=lHZOA8b7c0dH[OI2c7g0\\H\\O0Me7e21O001O001O00001O1O1N101O1O00001O00100O1O001O1O00O01O100000O1O1N3N100O10000O11O1OO10000O10000000000000000001O2N1O1O1O00000000N2O1O100THkLd7V3\\HjLd7V3\\HkLd7T3\\HlLc7U3]HkLd7k0WHj1c7gM^HM1[2b7fM^HN1\\2b7eM]HN2]2b7dM]HN1^2c7`M`H0N`2c7^MaHMG07e2b7\\MmHc2l700000jN\\MhId2Y6\\MfIe2Z6[MeIe2\\6[MbIf2`6ZM^If2c6ZM\\Ie2f6[MYIe2e7000^N[MQI0d004e2W6]M[JNgNf2m6^M[Je2d5]M[Jc2e5_MYJa2g5_MZJ`2e5cMZJ\\2f5dM[J[2e5eM\\JZ2e5eM\\JZ2d5fM]JY2c5gM^JX2a5iM`Ja0dNg0l6hNbJ=dNk0k6fNcJ;fNn0g6gNTJG]O`0KR1d6gNUJG\\O>LT1c6gNUJH\\O;NU1a6hNWJGZO:OW1a6fNWJKXO61Z1a6cNVJKlN0<63[1`6bNZJMTO33^1o7_NnG05a1m7aNaH]1a7dN_H[1^7iNaHV1_7lN`HT1_7nN`HR1`7PO]HQ1b7QO\\HQ1c7PO\\HP1d7QOZHP1g7POWHQ1i7QOUHo0k7ROSHo0m7RORHn0n7SOQHm0o7UOPHj0P8XOnGh0R8YOmGg0S8[OkGf0T8[OlGe0S8]OkGc0U8_OiGa0W8@iG?W8ChG<X8EhG:Y8GeG9[8HeG7[8IfG6Z8KfG3[8MeG3[8NeG2Z8OfG1Y8OhG0X81gGOY82gGMY83hGLX85hGJX86hGJX87hGHX89hGFX8:hGFX8;gGEY8;gGEY8<fGDZ8=eGC\\8d100000O1000000000000000000000000001O1O000000000001O000001O00000jGVMo7m2mGTMR8R3O2N01O000001O00000000001OO1O1010O00000000001O0001O000000001O000MnGoLS8Q32eMoG_1R8_NRH^1n7aNUH]1k7cNUH]1k7cNVH\\1j7cNWH]1i7cNWH^1h7bNXH\\1j7dNVH]1i7cNWH]1i7bNYH\\1h7dNXHZ1j7fNVHZ1j7eNWH[1i7eNUH^1j7cNRH`1n7`NQH`1P8`NoGa1Q8_NnGb1R8^NmGc1R8]NnGd1R8\\NmGe1S8ZNnGf1R8ZNnGh1P8XNPHi1o7VNRHk1m7UNRHk1o7TNRHl1n7TNRHl1n7SNSHn1l7RNTH?I`0S8QOUH;Jd0R8POVH30m0j7PORIP1m6QOSIo0k6ROVIn0j6ROUIo0k6QOUIo0oNcNN0`7=dIP1nNeNLNb7=dIP1nNeNLNc7;cIa1a6[N_Ie1b6ZN^If1e6WNZIj1Q8101N100O0DYNYGg1V8n0N2O1O10O10O100O10000O10O11OO10O100000001ZHlL7OA0M2a6T3gIZMIO_6V40000O10000001O5K1O1O001O1O1O0000001O000000O10000O1O1O1O1O100O1009G6J00O100000000O100O1000000O10000O1000000", "size": [360, 640]}, "area": 66169.0, "iscrowd": 0, "image_id": 10097, "bbox": [0.0, 130.0, 640.0, 230.0], "category_id": 126, "id": 10012273}, {"segmentation": {"counts": "Pnf1>1EW:m0G9N200O1040oN]F6j9\\O^Fa0V:L[ECf::6N5K1O]ab05\\^]O8RFDf8`0kF^OH4[2L[4V1XIQO5Kg1Nl4V1XIVO0Gk1Mk4Y1YIUONGm1Kk4Z2TKfMl4Z2cJfMdN2O21Li6[2iIPOV6R1cISOV6S3O100000000000`McI5]6ClI9CkMa6j1nI0DYN0L^6j1PJMNYNR6j1PJVNC9070J=FW6;\\I61ELJY1:e9J_YP4OdfoK4L3M2GDcE=X:KdE7[:;0010O0O0001000NK7N3M002O2OO0200O00O1001O00VN", "size": [360, 640]}, "area": 3265.0, "iscrowd": 0, "image_id": 10097, "bbox": [156.0, 0.0, 484.0, 150.0], "category_id": 132, "id": 10012274}, {"segmentation": {"counts": "Xei03U;5K4L1O000000000000001O001ZFB`8>^GETOMV9`0bGFVOMV9<eGHSO3R94lGIQO5R92lG9S8FnG:R8HmG7S8ImG7S8ImG8R8HoG7Q8InG9Q8GlG=S8ChGc0W8]OgGf0X8ZOgGg0Y8YOeGj0Z8VOdGk0]8UObGl0^8VOTG_O:[1b8BTG>l8o002N000000O1O1O10000O10000N2N2N2O100O1000000001O00O1K5K5L4N20000O1001OO11O00O1G9M3N2N2FoE\\OR:b0;00O1O11O0000001O00001O2N3M3M1O001O001OO1O100O100O1O1N2K5N2O10000O100O100O100000kEE]9<_FB@4P:;^FHb9:[FHd99ZFHf9=TFEk9l001O001O001O00O1O1O100O1O10000O11O00001O2N1O2N1O0000QO^F9a9DgF8X9HlF4T9KU100N2O1000000Iok36]iKN61ELX9j1M1OO100O1000000001O001O00O1000000O10000O100O100000000001O001O1O0000O1O1000000O1O1HeFaN]9\\19O11O0000000000O1000000001O;E2N001O00001hFWNo8i1PGYNo8Q2O000000O1000000000000001O1O0SGnMd8\\2O1O1O1O0000O100000001^M\\G^2d8bM]G]2h801cNVG8k8FVG:l8CUG=l8AVG>k8@WG?k8QOTGL5S1e902O0OO101N11ON12O0O2N101O1N101O1O001N2O001O1O001O0O2O1O001O1O0M]EG^:8eEE\\:9hEDX:;<N1O2O1N3M3Mmn<1SQC5LNO20000O101O1N2OO010000N22M01O2N4M3M3L4M3M3M9F8I1N3M2N2N3M2N3jFoMP9V2N100MhMVGX2h8jMWGV2i8kMWGU2h86N20O100000000000000000000O1O1N2O1O1O1000000001O0000O10000001O3M1O1O2N2N6J001O1O001O0000001OO1O1O100O100000000O1001OO100001O0000O1O1O1O100O1M3O1N200O100O100O1000000001O00001O001O1O1O0000000000001O00000000000000O100O10000000000001OO12N001O00001O2N1O2N2N1O0000000000000000000000000000O1001O0000000000O1BfGmM[8R2iGjMX8U2kGiMU8V2?HRGUNo8f1YGVNh8j1;001O00O1O10000NYG^Nl7b1RH`Nn7a1oGaNQ8]1oGdNR8\\1mGeNS8\\1lGeNS8Z1mGfNT8[1lGeNS8\\1lGdNT8\\1lGeNS8Z1nGfNR8Z1nGfNR8[1mGeNS8[1mGeNS8", "size": [360, 640]}, "area": 28935.0, "iscrowd": 0, "image_id": 10097, "bbox": [73.0, 0.0, 567.0, 93.0], "category_id": 135, "id": 10012275}, {"segmentation": {"counts": "bg[41V;5L1O001O1O1O0O2O00000000O2O000000000000000001O0001O00000000000000001O000O11O0O10000000000000000001O000000001O0000000001O0000000000O11O01O0000001O000000000001O0000000001O0000000000O01000001O00001O0O2O\\h1NeWN2M2N2000001O2N1O000000O100O1001O1O00N2O101O0000000000000000000000000000000000001O00000002N1N2O1O1O2N2N2M10O2O000O2N100O100O11O00001O0O2O0000O10002M10000O101O00O2O001N100O1O11O1O001O0O1O2O0O100IVE0j:OWE0j:OXE0h:0d_<", "size": [360, 640]}, "area": 1972.0, "iscrowd": 0, "image_id": 10097, "bbox": [397.0, 168.0, 207.0, 28.0], "category_id": 140, "id": 10012276}, {"segmentation": {"counts": "Q8W3Q8000000000000000000000000000N200000000000000000000000000O1O10000000000000000000000001O001O0000000000000000000000O1001O1O0000000000000000000000O1001O0000001O00O10000000000000000001O0000000000000000000000000000000000000000000000000000000000O1O1O100001O000000000000000000001O0000000000000000000000000000O100000000000000000000000000000000000000000000000000000000O1000000000000000000000000O11O000000000000000000000000000000000000000000000000000000000000000000000000001O000000000000000000000000000000O1000000001O1O1O00000000O1000000O100000000006J1oLPHh2X800000000000000000000000001O00000000000000000000000000O1000000000000001O000000000000O1O100000000001O0000000000000000000000000000000000000000000000000000000000000000000\\McG]2]8cMcG]2]8cMdG\\2\\8dMdG\\2\\8dMdG\\2k8I00000000000000000000001O000000O1000000000000000000000000000000000000000000001O000000N200O10000001O000000000000000000001O1O0000000000000000O1001O1O00000000000000000000000000000000O1000000000000000000000000001O1O0000000000000000001O00000oMQGi1S9SNnFl1X9O2N000000001O00000000001OO1O1001O00000000001O000000000000000000MXNeFi1[920000000000001ON21O00O1N200001O00O10000000000002N1O1OO100001O00O100000000000[NeFN0Y1[9iNgFLN[1[9iNgFLN[1i90000000001O0000O10000000000O100000000000000O11OO100000000001mNYF7OA0Ml98dFI_ob0", "size": [360, 640]}, "area": 51123.0, "iscrowd": 0, "image_id": 10097, "bbox": [0.0, 253.0, 586.0, 107.0], "category_id": 144, "id": 10012277}, {"segmentation": {"counts": "amd55R;4L3N001N100000000O2O000O10N3L3O1O1O1O001O010000O10O0100O100nEIS98iFJW98`F0`9l000O10OAiNoFW1Q9iNnFX1Q9iNoFW1Q9jNmFV1S9oNiFQ1W9POhFP1W9TOfFk0[9`010O0010O0DkFgNU9h0m0O3N1O101O0O2O0O10O10O10O10000O01000001O001N10000O1000000M30O1O010O10OJF`E:_:G`E:_:H`E8`:70100002M5L00000O01000O0100000OfEBk9?TFKc95[F3_9M_F5`9O[F2f9h00000O10O10O01000O10O10O10O100000^J", "size": [360, 640]}, "area": 3123.0, "iscrowd": 0, "image_id": 10097, "bbox": [514.0, 178.0, 126.0, 78.0], "category_id": 164, "id": 10012278}, {"segmentation": {"counts": "0b4f6001O001O00001O0000001O0000001O0000001O00001O00001O3M000000[OgI[LY6_3fIPL6>U6a3PJ_Lo5^3fITL;=P6^3UJaLk5_3UJaLk5^3VJaLk5_3UJaLk5^3WJ`Lj5`3UJaLk5`3UJ_Lk5a3UJ_Lk5c3SJ\\Ln5d3RJ\\Ln5d3RJ[Lo5d3RJ\\Ln5c3SJ\\Ln5d3SJ[Lm5d3TJ[Lm5d3TJ[Lm5d3VJZLj5e3m000O1O100O1O100O1001O1O001O00O100O1O1O1O1O4M5I6K2O02N3M1O001O1O2ON13NiMSHR1l7lNWHTOMh1n7POXHVOMg1k7TOYHSO3c1c7[OZHQO5b1b7\\OiHb0W7_OiH`0Z7_OeHa0[7_OeHa0[7_OfHa0Y7@fHa0Y7^OiHa0W7\\OmHc0S7XOSIh0l6VOWIh0j6WOWIi0i6UOZIk0e6SO\\In0d6QO]In0f6dNoG:[1Q1S7eNmH[1`802M1N2000O2N2N10001N101O1M2N3M2O01N1011O000001O0O00L5K5L4N5M2N2M8UG_Ma8e20OO11O01N1G9M3N2N2FoL\\HR3b7;01N1OL6BZHnLP8[2]HdMn7n1m0G:M200O2OiGlN\\6V1cHfN63Z7\\1RHiNa0L`7k1\\HjMC;R8k1XHYNi7h1UHYNl7j1oGVNR8j1mGWNT8^2O1O10O0100O2N1N4I:I5L01000O10O001LcH\\L]7d34O1O1RM^HV2c7fM[H@4h2c7fM`HY2c7dM_H[2b7cM_H]2f7]M\\Hc2T8010O100O100O1N2N1O101N1O101OO002N100O2M4N1O2N10O1O01O1eMaGl1`8QNbGn1_8PNdG5KZ1a8aNiG0G_1`8`NkGNGa1^9N1L\\FdNe9b0o0I\\^2HiaM9\\GCY6?\\IDYN0Y8>ZINf62cGC9070J=R8i0nGKS86gGMZ8`1O101N1000000001O010O00O01001O0O10000O2O0O10000000O1O2O02OO1O000D;M30100000O1O1HeMaG]2\\89O11O01O1O1O4L2M1000002N1019F1N001O00001YMfG^2Z8aMhG^2b8O0001O0O100000O100O1^N\\G`0d8@`G<a8DbG9^8FfG6Z8PO]Gb09=d8B^G>a8A`G?`8AaG7f8H\\G6d8J]G5c8M]G1b82]GMc84^GJb88^GDd8=\\GAe8a0[G[Og8g0XGVOj8k0WGQOl8P1e01O2N2N1O2O1N101N1O2O1N1O1N2O2MM3O200O010O10ZH_Ne5a1jH`Nb00ZO1Y7_1kHbN?O\\O1Y7`1jHbN`0M\\O2Z7_1jHcN?M\\O1[7`1jHbN;1^ON]7_1kHcN72\\OG07b7]1mHbN3`0o6n0mHdN2?Q7m0lHfN4;o6P1lHgN58o6Q1kHiN65o6R1kHjN63o6S1kHkN61o6R1lHoN5MP7T1kHQO4JW7n0fHYO3HW7n0gH[O3EV7Q1fH\\O3BW7R1gH\\O2AX7S1eHeNCO0d0`0DX7T1eHeNEe0=]OANi7[1dHdNGf0;\\O^7Y1gH[OK[O^7Z1iH[OHZO_7[1iH\\Of7e0[HZOd7f0^HYOb7f0_HZO`7f0aH[O]7f0cHZO\\7h0dHWO\\7h0dHYO[7g0gHXOX7g0jHZOT7e0nHZOR7f0nH[OR7d0nHfNoNd0S8e0UIYOi6h0WIWOi6k0UIUOjN@n7[1YIXOg6g0ZIYOe6h0[IWOe6k0YIUOg6IUHb0U1Ai6LTHb0S1SOoN6l73UHb0Z1XOb65WHb0Y1VO`67YHb0\\9]OgFa0]9[ObFg0n9100O1O1O001O1O01O0002N1O1O1O010O001O1O001O100O1O001O1O1O010O1O1O1O1O1O100O2N1O1O2N2iFYNj8h1UG]Ni8b1VG_Nj8b1SG`Nl8o1O001N1000000O10000000000000O11O001N1O2M3N1O2N101O000001O0000O10000001O3M1O1O2N2N6I101O1O001O0000001XOoFVOQ9j0QGTOo8m0SGPOm8P1TGoNm8P1TGoNl8R1TGnNl8R1TGlNn8S1VGiNk8W1UGhNl8X1UGeNl8\\1`001OO1000010O000O1O1O1O100O2L3O1N110O101N100O1000000001O00001O001O1O1O0000000000001O000001O0000000O101N105K1hFRN00Q9T201OO1O2OOO4M101O00001O2N1O2N2N10O0000000000001O0000002NO1N2N1O2N3M2N2O100O10O0000NUFiNk9W1UFhNj9Y13O2N1N2M4O00O1O102N2N00001N1UFkNc9V1\\FjNe9\\1O11O001O01O2N1O0YM", "size": [360, 640]}, "area": 48254.0, "iscrowd": 0, "image_id": 10097, "bbox": [0.0, 0.0, 640.0, 200.0], "category_id": 169, "id": 10012279}, {"segmentation": {"counts": "imj22T;3L4M3M2N3M6JDfEOW:1mEMQ:4QFKg86gGOEJa8?cGGOH\\8j0]G^O:FW8U1WGVO^9Y1010O10001NWG^No7c1mGbNb7m1ZHYNa7k1[HZNc7h1VH_Ni7a2OnM_H3EnNk7f0mH3_OVOc7`0YI1ZO^O]7`0_IH[OFV7a0dIA[OMQ7b0f2O1O2N2M3J^S9JmlF4M3L4M0O2N101SFEi8=UGCk8>TGBn8>PGBR9=mFCT9>jFCW9=eFE]9:aFG`9:\\FHU6EQMb0gLKX6DQMb0cLL\\6CRMS1m2oNSMo0m2ROSMm0m2TOTMj0l2XOTMf0l2[OTMd0l2^OTM?m2BSM<n2ESM8n2JRM4n2MRM2n20UJ]OW2b0c32RJ_O\\2?a33oIB`2;`35kIDf27]36iIFk24[3j0eLVO[3j0eLVOZ3j0gLVOX3k0iLTOV3m0jLSOU3m0lLSOS3n0mLSOQ3m0PMSOo2n0RMQOm2P1SMPOl2P1UMPOj2Q1VMoNi2R1XMmNg2S1ZMmNe2T1[MlNd2U1\\MlNb2T1_MlN`2U1aMjN^2W1bMiN]2W1dMiN[2X1eMhNZ2X1hMgNW2Z1nJWNY2?i2Z1mJZNX2=i2Y1nJ^NW29j2Z1mJ`NX26j2[1lJaN[23h2\\1lJbN]22f2]1lJaN_22d2^1kJaNb21b2`1iJ`Ng2O_2c1gJ`Nj2M^2V2cMkM[2U2fMkMY2U2hMkMW2U2kMjMT2W2lMiMS2W2nMiMQ2W2PNiMkMBV3e2POiMgMDY3d2QOhMaMG^3a2ROhM\\MKa3]2TOhMYMLc3\\2UOhMVMMe3[2WOgMRM0g3Y2WOgMPM1i3X2XOgMmL2k3W2YOgMjL3m3V2ZOgMfL6o3S2]O[Nc0f1]OYNc0g1\\L[Mb2n0Q1g1\\L]Mc2j0R1i1ZL`Mc2f0S1j1YLcMd2`0T1n1VLdMf2<U1P2TLfMg29U1U2PLaMm28T1h2nNUMS1KnK^2Q3fMQ1LQLZ2P3hMP1NTLU2n2mMm0MYLR2m2oMj0O\\Ln1l2RNh00^Lk1m2SNe02^Lk1n2SNc01`Lk1P3RNa02_Lk1S3QN>4^Lk1V3PN<5^Lj1Y3PN86_Li1[3QN55aLi1]3QN16bLi1_3oMO8aLi1b3oML8bLh1d3PNI7dLh1d3RNG6eLg1f3RNE7eLf1g3TNC6fLe1i3UN@5gLg1j3TN^O5hLf1k3UN]O5hLe1m3VNZO5iLd1o3WNWO4kLd1o3YNUO3kLd1R4XNSO4kLc1S4ZNQO3nLa1R4\\NoN2SM^1P4`NlN2WMZ1n3dNlN1YMW1m3gNjN2\\MS1k3jNjN3^Mo0j3mNhN3bMl0`7TOmH>T7BnH<R7DoH:R7EQI8P7HQI6P7JRI3o6MRI1o6NSI0n60U20000O100000oW^13ngaN5K1N101O1N2WNCeH=Y7JbH7[7OaH1]76]HKa7;[HEc7b0XH^Of7h0VHXOi7n0QHSOo7Q1mGoNR8R1nGmNS8R1nGnNQ8S1nGmNR8S1oGlNR8T1nGkNR8V1mGjNS8V1nGiNS8c0^GA?LR8e0_G^O?LR8g0aGZO=OS8g0bGXO;OT8j0bGUO;0T89]GL6I90U8:_GL5H;LT8>_GL3HX99hFN2GW99hF0i9MYF3h9JZF6g9H[F7[:0000O10000O21N4N2M3MOQK", "size": [360, 640]}, "area": 12465.0, "iscrowd": 0, "image_id": 10097, "bbox": [258.0, 2.0, 382.0, 246.0], "category_id": 183, "id": 10012280}, {"segmentation": {"counts": "[5R2W<001O00O10000000000O1000000000000O1O10O100000hNiCYOOS1X<DkC<U<WOiC03i0S<VOkC12i0S<VOlCO2k0R<UOmC02i0R<VOmC11j0Q<UOnC13h0o;VOoC22h0n;WOPD12h0n;WOPD12h0n;WOPD11i0P<UOoC20j0Q<TOoC20j0Q<TOoC20j0Q<TOoC10m0P<ROPD10m0P<ROPD1Ol0KmNV<6PD1Ol0S<SOnC1Ol0S<SOnC1Om0R<ROoC1Nn0R<QOQD1Lo0S<POQD1Nm0Q<ROQD10k0o;TOQD13h0l;WOQD15e0k;ZOoC27d0i;ZOPD2:a0f;]OPD2:a0f;]OPD2:`0g;^OoC2:`0g;^OoC2:?h;_OnC2:?h;_OnC2:?h;_OnC2:?h;_OnC2:?g;@oC1:?g;@oC1:?g;@oC1:?g;@oC19`0h;_OoC19`0h;_OoC19`0h;_OoC0:a0g;_OoC0:a0g;_OoC0:a0g;_OoC09b0h;^OoC09b0h;^OoC09c0g;]OPD09b0h;^OoC09a0i;_OnC09a0i;_OnC09a0i;_OnC09a0i;_OnC06JDg0X<_OnC07c0k;]OnC06d0l;\\OnC06d0l;\\OnC06d0l;\\OmC17b0m;]OlC17b0m;]OlC16c0n;\\OlC17b0m;]OlC08c0l;]OlC08c0l;]OlC08c0l;\\OmC17c0l;\\OmC17b0m;]OlC17b0m;]OlC17b0m;]OlC16c0n;\\OlC16c0n;\\OlC16c0n;\\OlC16c0n;\\OlC16c0n;\\OlC16c0n;\\OlC16c0n;\\OlC16c0n;\\OlC15d0n;\\OmC05d0n;\\OmC05d0n;\\OmC03f0P<ZOmC0J15>U<AlC835Q<ClC684l;FlC5;3i;HlC3?3e;JlCOg03];NlCLn02V;2lCLQ1OS;5lCLR1MS;7jCMS1LS;7jCM`=3`BN_=2aBN^=3bBM^=3bBM^=3bBM^=3bBM]=4cBK^=5bBI`=7`BH`=9`BFa=:7000O01M3N1N3NnVU2OTijM100O100kA:g=5N1000M[BCd==\\BA00d=?^BAa=a021OBaB4^=LiBNW=1kBOS=1oBOo<0SC1k<OWC0i<0WC1h<NZC0g<0YC0g<0ZCOe<1\\COQ<0hC1R=OnB1R=OmB1T=OlB1T=OjB3V=MiB4e=01O0OW>OWZj0OfWUO100O100O10001O015K00N1014YBGT=j0MO2N2N2N1O1OO010O100O10O01000000O10000000000O10000000000O0100001OO10000000000O1K5004ZB[O`=g0000000000000000O100000000000000000000O20O000001O0O101O01O0PO]O[Da0f;@YD`0g;@YD`0g;AYD>g;CXD=g;EXD;g;FYD:g;GYD9f;HYD8g;HYD8g;IYD6g;KXD5h;LWD4i;LXD3h;NWD2i;OVD1j;OVD0k;1UDNk;3TDMl;3TDMk;5UDJl;6SDJm;6SDJn;6RDJm;6SDJm;7RDIo;6QDJo;7QDHo;8QDHo;7RDIn;7SDHm;8SDGn;9RDGn;9RDGn;9SDEn;;RDEn;;RDDo;<QDDn;<SDDm;<TDCl;=TDCl;=TDCl;=TDCl;=TDCl;=UDBk;>UDBk;>UDBk;>UDB\\OMV<a0^DC[OLK0Y<a0aDBZO2U<<`DCYO4V<9`DD[O2U<:RD@O6F3Y<7RD@Nd0o;LQDCNb0Q<KPDDMc0S<IoCEMc0T<HoCFKc0V<GoCFJd0W<FoCe0P<[OPDe0P<[OQDd0o;[ORDe0n;[ORDe0n;[ORDe0n;[ORDe0n;[OSDd0m;\\OSDd0m;\\OSDd0m;\\ORDe0n;[OPDg0P<YOnCh0S<XOkCj0U<VOiCl0W<TOhCm0X<b0000O100000O010000O10000IfC^N[<`18O1001O00O1000000O10000N2O10000WO\\CMd<MeC@C>h<2gCNY<1iCNV<3iCNW<2iCNW<2iCNW<2iCNW<2iCNW<2iCNV<3jCMV<3jCMV<4iCLW<4iCLW<4iCLW<4iCKX<5hCKX<5hCKV<7iCJT<8mCHP<;PDEm;>SDBk;`0UD@k;`0UD_Ol;a0TD@k;`0UD@k;`0UD_Ol;a0TD_Ol;`0UD@k;`0UDAj;?VDAj;?VD_Ol;a0TD_Ok;b0UD^Ol;a0TD_OT<9kCH[<2fCMZ<3fCMZ<3fCMZ<3eCN[<2eCN[<2eCN[<2eCM\\<3dCN[<2eCN[<2eCM\\<3dCMl;DSD?1Mj;HSD;3Mg;MTD65Mg;ORD47Mf;2QD19Me;5oCO<Ld;8nCL>Ld;k0[DVOe;j0[DVOe;j0[DVOe;j0[DVOe;j0[DVOf;i0ZDWOf;h0[DXOe;h0[DWOf;i0ZDWOf;i0ZDWOf;i0ZDWOf7", "size": [457, 640]}, "area": 14541.0, "iscrowd": 0, "image_id": 10104, "bbox": [0.0, 127.0, 640.0, 111.0], "category_id": 113, "id": 10012281}, {"segmentation": {"counts": "d3e0d=0000]O]B00=c=C`B=a=B_B>f=00000O1O1O1N_O\\Ba0c=3001O00O10000000000000000000O100000000N2000O02O0000000O10O100O11O1N1O1000O2O003M001O10OO01N2O2N0L5J6006J6JOYBAf=?20000000001N10000002N1O000O1001OO2]B^O]=f01O100O2N100O11O1O1N200O1N2OJdB@\\=<kBAT=><0000000000000O11O00O101OO1000O010010O1OO20O01O001O0O2O002N4L3M1O00KfBXO]=e05M3O1000001O0000000000000000O11_OYB>1Cb=O^Bc0b=01O1OO_O\\B`0e=11O1O100O2OO010000O100O10O10O100O1000O0100O3N1O0O010O10O1O0101N101O0O1001N20000O001O1N2O1N1000000001O0O10000000001O00000000O100N20000001O0000000001O000001O00001O1O1K4M30cT71\\kH0O2O000O10001N2O2N1N2O1O2OO1M2O2M2O101O001O00O1O100O2N1O3M2O1O1O00000000000001O0000000O2O1KZnd4", "size": [457, 640]}, "area": 4198.0, "iscrowd": 0, "image_id": 10104, "bbox": [0.0, 114.0, 306.0, 31.0], "category_id": 135, "id": 10012282}, {"segmentation": {"counts": "]7l6]701O00O10000000000O1000000000000O1O10000000000001O0000O11O00000000000000001O00NTIgHl6Y72001O0000000000O11O0000O100O10000000000000000000000000000001OO1O10000000000000000O100000000000000O10000000000000000000000000000O100000000000000O100001O001O001O1O001O1O000000O10000O10000O10000O10000O100000000O10000BoHdIQ7\\6VI\\Ik6d6=000000000000001O0000001O0000001O0000001O0eKYIT1h6fNbIU1_6dNlIW1V6cNSJX1n5cN[JX1g5cN`JY1a5bNdJ]1^5]NfJc1[5YNiJf1X5UNmJj1U5UNjJk1W5TNjJk1X5SNhJm1Y5RNhJm1Z5QNgJn1Y5RNgJn1Z5QNgJn1Z5RNfJm1[5RNeJn1\\5QNeJn1[5RNeJn1\\5QNeJn1\\5QNeJn1\\5QNdJo1]5QNcJn1]5TM[He0X2W2^5SMZHf0Y2V2]5SM\\Hf0X2V2`5PMXHj0X2V2\\5PM[HO3j0W2V2e5kMZJU2f5kM[JT2f5kM[JT2f5kMZJU2\\5RM`H8J5[2`2W5TM\\H192L8X2a2Q5UMdH37MN8Y2b2o4^MjHGO2IOb2i2m4\\MSLd2n3YMULf2l3WMVLi2f7O1O001O0000O10000000000O10000000000O100000000O10000000000O1000000N2O1N2O1N2O1O1N2O1N2O1N2O1O1N2O1N2O1N2O1N2O1O1N2O1O11O001O001O001O001O001O001OO1H81O001OcMoKbJQ4^5oKcJP4\\5RLdJm3]5SLbJm3`5QLaJn3^4RLcLn3]3TLbLk3^3VLbLi3[3TL[I4X3h3]3TLYI006Y3e3^3WLYI31KV3k3a3]LYIIV3i3a3^LZIJT3g3b3^L[IMQ3e3d3^L\\IMP3d3e3]L\\I1n2a3g3]L\\I3k1ImNg3m5[L\\I6h1JoNd3m5\\L]I7d1ISOd3m5[L\\I9a1JWOa3k5\\L^I;]1H\\O`3i5]L^I=Y1GA_3h5\\L_I?V1GD]3g5]L_Ia0R1FI\\3R6oLSJFLZ3R6PMPJFOZ3Q6PMnIH1X3Q6QMlIG4X3P6QMjIH7W3o5RMhIG:W3n5RMfIH<W3n5QMbIKa0T3m5RM]IMg0Q3l5RMYI0l0n2k5SMTI0T1m2h5SMPI0[1n2e5SMkHOd1n2b5iMbJW2h8000O10000000000O100000000O10000000000XMZLlJg3S5jL]JV3b5gLbJY3]5dLgJ\\3Y5aLjJ_3V5aLiJ`3W5`LiJ`3W5`LiJ`3W5`LiJ`3W5`LiJ`3X5_LhJa3Y5^LfJc3Z5]LfJc3Z5^LeJb3\\5]LdJc3\\5]LdJc3\\5]LcJd3]5\\LcJd3^5[LbJe3^5[LbJe3^5[LbJe3^5[LaJf3_5ZLaJf3_5ZLaJf3_5ZLaJf3_5ZLaJf3_5ZLaJf3_5YLaJh3_5XLaJh3_5XLaJh3_5XLaJh3_5WLbJi3^5WLaJj3_5VLaJj3_5VLaJj3_5ULbJk3^5ULbJk3^5ULbJk3^5ULbJk3]5ULcJl3]5TLcJl3]5TLcJl3]5TLcJl3]5SLcJn3]5RLcJn3\\5SLdJm3[5TLeJl3Y5ULhJk3W5VLiJj3V5WLiJj3R4TLnIO13P2j3Q4WLeI190Q2h3U4_LjIJQ2g3U4_LiIJS2g3T4^LjIKQ2h3U4\\LkILP2h3U4[LlILP2i3T4ZLmIMo1i3T4ZLmIMo1i3S4ZLoILP2i3Q4[LoILP2i3Q4[LoILP2i3Q4[LoIKR2i3o3\\LPJJQ2j3n3\\LRJJP2j3n3\\LRJIR2j3l3]LRJIR2j3l3]LRJIR2j3l3]LRJHT2j3j3^LRJHT2j3j3^LRJHT2j3j3^LRJGU2k3i3^LSJFU2k3h3_LUJDS2m3h3_LnLa3R3_LoL`3Q3`LoL`3Q3`LoL`3Q3`LnLa3R3^LnLc3R3]LnLc3Q3^LnLc3R3]LnLc3R3]LmLd3T3[LkLf3T3[LlLe3T3[LkLf3U3ZLkLf3U3ZLjLg3V3YLiLh3W3XLiLh3V3YLiLh3R3]LnLc3R3\\LnLe3V3jKdI;V3k3X3ULgLl3Y3TLfLm3Z3SLfLm3Z3SLeLn3[3RLeLn3[3RLdLo3\\3QLcLP4]3PLaLR4^3oK`LS4`3mK^LU4b3kK\\LW4d3iKZLY4f3gKXL[4h3dKWL^4i3bKVL_4j3aKUL`4k3`KSLb4m3^KSKE]On4`5]KSKF[On4a5]KTKFYOn4d5[KTKFVOQ5f5YKTKGUOP5g5YKTKHSOP5i5XKTKIROo4j5XKTKJPOo4k5XKUKJoNn4l5XKVKJlNo4n5WKVKJlNo4o5VKUKLjNo4Q6UKUKMiNn4Q6VKVKMhNm4R6VKVKNfNm4T6VKVKMeNm4U6VKVKNcNm4W6UKVKObNl4X6UKWKN`Nn4Y6TKWKO_Nm4[6SKWK\\5i4eJVK[5j4eJVK[5j4eJWKZ5i4fJWKZ5i4fJXKY5h4gJXKY5h4gJYKX5g4iJXKW5g4jJYKV5g4jJZKU5f4kJZKU5f4kJ[KT5e4lJ[KT5e4mJ[KR5e4nJ[KR5f4mJZKS5f4mJ[KR5e4nJ[KR5e4nJ\\KQ5d4PK\\Ko4d4QK\\Ko4d4QK]Kn4c4SK\\Km4c4UK]Kj4c4WK\\Ki4d4XK\\Kg4c4[K\\Ke4d4\\K\\Kc4c4_K\\Ka4d4`K\\K_4d4aK]K^4c4cK\\K]4d4dK\\K[4d4fK[KZ4e4gK[KX4e4iKZKW4f4jKZKU4f4lKYKT4g4mKYKR4g4RKfId0c1Y4h4RKeIK0h0b1[4h4QKkIe0]1Y4h4PKnIg0Z1X4h4RKmIg0[1V4i4oJoIl0X1T4V5lKjJS4V5nKjJQ4V5oKkJP4U5QLkJn3U5SLkJl3U5ULkJj3V5VLjJi3V5XLiJh3W5YLiJf3W5[LhJe3X5\\LhJc3X5^LgJb3Y5_LaIlNh0d4g5aL\\IPOl0_4h5bLXISOP1Z4h5RMWJn2i5SMWJl2i5UMVJk2i5WMWJh2i5YMVJg2j5ZMVJe2j5\\MUJd2k5]MUJb2k5_MTJa2k5aMUJ^2k5cMTJ]2l5dMTJ[2l5fMSJZ2l5hMTJW2l5jMSJV2m5kMSJT2l5nMTJQ2l5PNUJn1k5SNUJl1d5gKfI_2f0i1b5`N^J_1a5cN_J\\1a5eN_JZ1b5fN^JY1b5hN]JX1b5jN]JV1c5kN\\JU1d5mNZJS1f5nNYJR1f5POYJP1g5ROWJn0i5SOVJm0h5VOWJj0h5XOWJh0i5ZOUJf0k5[OTJe0l5\\OSJd0m5]ORJc0n5_OPJa0P6@oI`0Q6AnI?R6ClI=T6DkI<U6EjI;V6FiI:W6l30000000000O100000000000000O11O00O100001O00N200000000000000000000O11O00O1000000000000000000000000000000O1000000", "size": [457, 640]}, "area": 116902.0, "iscrowd": 0, "image_id": 10104, "bbox": [0.0, 195.0, 640.0, 262.0], "category_id": 140, "id": 10012283}, {"segmentation": {"counts": "0d3e0XM_OVOj8b3gGXM@UOi8c3gGWMAVOh8c3TG]L00=j0GVOh8c3TG`L=g0GVOh8d3SG_L>f0HWOg8i3aGPMHWOg8i3aGoLIXOf8i3aGoLIXOf8h3bGPMHXOf8g3cGPMHYOe8f3dGQMGYOe8d3SG\\La0g0GYOe8c3gGTMDYOe8c3gGTMDYOe8d3fGSMEYOe8d3fGSMEYOe8c3gGTMDYOe8c3gGTMCZOf8b3gGTMCZOf8b3gGTMCYOg8c3fGRME[Oe8c3fGRME[Oe8c3fGRME[O>1YOOR8c3bHQMF\\O=3[7`3bHQMF\\O=4Z7_3cHQMF\\O=4Z7_3bHQMH\\O;5[7^3bHPMI]O:6Z7]3cHPMI]O:6Z7]3cHoLJ^O98X7[3eHnLK_O88X7[3cHoLf0Fg6[3cHoLf0Fg6[3cHnLg0Fg6[3bHoL0_O56Z7]3`HnL1_O56Z7]3`HmL2@46Z7]3`HlL3A26\\7]3_HlL3A26\\7]3^HlL5A15]7^3]HhL9EM5]7]3^HhL9FL5]7]3^HhL9FL5]7\\3_HiL8FL4^7^3]HgLQ1Jc6`3ZHgLS1Ka6^3[HhLT1L_6\\3]HhLT1O\\6Y3`HhLT11Z6V3dHgLS14X6U3eHgLS17U6R3kHcLQ1;T6R3kHbLR1<S6R3lH`LR1>R6R3mH^LR1`0Q6S3dJmL\\5R3eJnL[5R3eJnL[5R3eJnL[5S3dJmL\\5R3eJnL[5R3eJnL[5R3eJmL\\5S3dJmL\\5S3dJmL\\5S3gH^LA4d1<T6R3gHfLS18V6R3gHfLS18V6R3gHgLR16X6S3fHgLR16X6S3fHgLR16X6S3fHgLR16X6T3dHhLS14Y6T3dHhLS14Y6T3dHhLS14Y6T3dHhLS14Y6T3fHfLQ13aNDh7c3gHeLP15[6V3eHdLQ15[6W3dHdLQ15[6V3eHeLP15[6V3eHdLQ16Z6W3dHcLR16Z6V3fHaLR18Y6W3^JjLa5V3^JkLb5U3^JkLb5U3^JkLb5V3]JjLc5V3]JjLc5V3]JjLc5V3]JjLc5V3]JiLd5W3\\JiLd5V3]JjLc5W3\\JiLd5W3\\JiLd5V3]JjLc5V3fH`LP1:Z6W3bHfLP13^6W3aHhLP10`6X3`HiLo0Oa6X3`HiLo0Oa6X3`HhLP1Nb6Z3^HhLP1Ek6c3UHgL\\9Y3dFfL]9Z3cFfL]9Y3dFfL]9[3bFdL_9\\3aFcL`9\\3aFcL`9]3aF`La9`3^F`Lc9`3]F_Ld9a3b0O100001O1OO11O00000000O10000000000000SF^LXO2g9`3nFeLR9[3kFiLT9W3kFjLU9V3kFjLU9V3kFjLU9V3kFjLU9W3jFjLU9V3kFjLU9V3kFjLU9V3kFjLU9V3kFiLV9W3jFhLW9X3iFgLX9Y3hFfLY9Z3gFeLZ9Z3gFeLZ9\\3UF]L>1C1k9a3TF^La:d310000N^E]Lb:c32000000001OO1000000000000O10000000000O100002N1O00O10000O1O1001O001O000000O11O1O000000O100O1000000001O0000000000001O00000000O100N20000001O00000000000000001O00001O1O1aE]LV:c3gE`LY:`3gE`LY:h3000O1O1O100O1000000000000001O00N200O10000O10000O10000O100001lE\\La9e3\\F]Ld9c3[F_Ld9b3YF`Lg9`3XFbLg9^3YFbLg9_3XFbLg9^3YFcLf9^3YFbLg9^3YFcLf9\\3[FeLd9Z3]FfLk0Be7h3`GgLi0Bg7f3bGhLf0Bh7e3cGiLd0Ci7c3fGiL?Ek7a3hGjL;Fm7`3iGjL9Fn7`3jGiL7D[OOd8d3jGjL5B_O0b8d3jGkL9Bm7c3jGlL8An7c3jGmL7@o7c3jGnL6_OP8c3jGmL7@o7c3jGkL8Cn7c3iGjL9Cn7c3iGiL:Dm7c3iGhL;Dm7d3hGhL;Dm7d3gGhL=Dl7e3fGgL=Em7e3aGiLc0Bl7^4UHaKl7_4THaKl7^4UHaKl7_4SHbKm7]4THbKm7]4THcKl7]4THcKl7\\4Q100O100O100O1O100O100O100O100O1H8M3N2O1O1OSF\\LW9b3i0ORF_LV9`3jF`LW9`3iF`LW9`3hF`LY9_3hFaLX9_3gFaLZ9_3eFbL[9^3eFaL\\9_3cFbL]9^3cFaL^9_3aFbL_9^3aFaL`9_3`FaL`9_3e00jEaLb9_3d0000000001O1O2N1O1O2N1O2N2NO10000O1000000O1000000O1000000O1000000O1000000O10000000000000000000000000000000000000000001O001O001O001O001O001O001O001O001O1O001O001O001O001O001O1O001O001O001O1O001O001O1O001O001O001O1O001O1O1O001O1O1O001O1O1O000000000000O1O100000000000000000000000000O11O001O00001O00000SGXKY8h4gGXKY8h4gGYKX8g4hGYKX8g4hGZKW8f4iGZKW8f4iG[KV8e4jG[KV8e4jG\\KU8c4kG^KU8b4kG_KT8a4lG_KT8a4lG`KS8`4mG`KS8`4mGaKR8_4nGaKR8_4nGbKQ8^4oGbKQ8^4oGcKP8]4PHcKP8]4PHcKP8]4PHdKo7\\4QHdKo7\\4QHcKP8]4PHcKP8]4PHaKR8_4nG`KS8`4mG_KT8a4lG_KT8a4lG_KT8a4lG_KT8a4lG_KT8a4lG`KS8`4lGaKT8_4lGaKT8_4lGaKT8_4lGaKT8_4lGbKS8^4mGbKS8^4mGbKS8^4mGaKT8_4lG_KV8a4jG]KX8c4hG[KZ8e4fGYK\\8g4dGXK]8h4b00000000000O1000000000000000000000000000000000000000000000VG]Kn7c4lGfK@C_8g4QHhKo7X4PHjKo7U4RHjKo7V4QHjKo7V4QHjKo7V4QHjKo7V4QHjKo7V4QHjKo7U4RHkKn7U4RHkKn7U4SHjKm7V4SHjKm7V4SHjKm7V4SHjKm7V4SHjKm7V4SHjKm7T4UHkKl7R4VHoKj7n3YHRLg7k3\\HULd7i3^HWLb7i3^HWLb7i3^HWLb7i3^HWLb7i3^HWLb7i3^HWLb7i3]HXLc7h3]HXLc7h3]HXLc7h3]HXLc7h3]HXLc7g3^HYLb7h3]HXLc7P4UHoKl7W4nGjKQ8V4oGjKQ8V4oGjKQ8V4oGiKR8W4nGiKR8W4nGiKR8W4nGiKR8W4nGiKR8W4nGiKR8W4nGiKR8W4nGiKR8g3_GXL?1R8e3cGXL;3R8b3hGYL65R8b3jGWL47R8a3mGVL19R8`3PHTLO<Q8_3SHSLL>Q8_3fH`L[7`3eH`L[7`3eH`L[7`3eH`L[7`3eH`L[7a3dH_L\\7a3cH`L]7`3cH`L]7`3cH`L]7`3cH`L]7`3cH`L]7", "size": [457, 640]}, "area": 86466.0, "iscrowd": 0, "image_id": 10104, "bbox": [0.0, 0.0, 640.0, 209.0], "category_id": 155, "id": 10012284}, {"segmentation": {"counts": "U54U>0O10000O100O10000O100000000000000000000NIRB7n=200O100^BFU=:jBHT=9lBGS=:lBHS=8lBIS=8mBHR=9mBGS=:mBFS=:mBFR=;nBEQ=<oBDQ=<oBDP=<QCDn<=RCCn<=RCCm<>SCBi<b0WC^Oh<c0XC]Oh<c0XC]Oh<c0XC]Og<d0XC]Oh<c0XC]Oh<c0XC]Oh<c0XC]Oh<c0XC]Og<d0YC\\Og<d0YC\\Of<e0ZC[Oe<f0[CZOd<g0\\CYOc<h0]CXOb<i0^CWOa<j0_CVO_<l0aCTO^<m0bCSO]<n0cCROX<S1hCmNR<Y1nCgNT<W1lChN[<R1eCnNa<l0_CTOg<f0YCZOg<f0YCZOg<f0YCZOh<e0XC[Oh<e0XC[Oh<e0XC[Oh<e0XC[Oi<d0WC\\Oi<d0WC\\Oi<d0WC\\Oi<d0WC\\Oi<d0WC\\Oi<d0WC\\Oh<d0YC\\Og<d0YC\\Og<d0YC\\Of<e0ZC[Of<e0ZC[Oe<f0[CZOd<g0\\CYOc<h0]CXOc<h0]CXOb<i0^CWOa<j0^CWOb<i0^CWOa<j0_CVOb<i0^CWOc<h0]CXOd<g0\\CYOe<f0[CYOg<f0YCZOh<e0XC[Oi<d0WC\\Oj<c0VC]Ok<b0UC^Ok<b0UC^Ol<a0TC_Ol<a0TC_Ok<b0UC^Ok<b0UC^O;0f;c0oC]O94e;`0RD\\O79d;;UD\\O6<b;9XD[O4`0a;6[DZO0h0`;O`DYOMo0^;IeDXOMR1Y;HjDVOMR1X;IkDUOMS1V;ImDTOMT1S;IQESOMS1Q;KREROMT1P;JSEROMU1P;GTETOLU1P;GTETOLV1o:FUETOLV1P;ETEUOKX1P;BVEVOH[1R;^OVEWOG\\1S;]OVEWOF^1T;YOWEm0j:SOVEm0k:ROUEn0k:ROTEn0P;nNnDU1V;gNhD[1Z;cNcD_1_;_N`Dc1`;]N^De1b;`0101O0O1000001N100000O00CZDYNe;h1bDQN];P2=1O1O1O1O1O1O2N1O1O1O2O0O100O2O000O101N10001dGYMS5i2gJaMT5`2eJkMU5X2eJQNV5P2eJYNV5i1eJ^NV5d1eJaNZ5a1`JcN_5_1]JeNb5\\1YJiNf5Y1YJfNf5\\1YJeNf5]1XJcNg5_1XJbNg5_1XJbNg5_1XJaNg5a1XJ`Ng5a1YJ^Nf5d1YJ]Nf5c1ZJ^Ne5c1ZJ]Ne55UIQOU1k0e51TIZOV1e0g5MSIBU1b0h5ISIGV1`0g5ESIOU1<i5BRI5T1:i5^OTI9T19i5ZOSI`0T17i5VOSIe0T16i5RORIk0V12i5ROQIk0W14g5POSIl0V14g5POSIl0V15f5oNTI07WOo0k1e5nNUI1c0hNd0Y2d5nNUI5k1P1o4kNVI9f1m0S5kNWI:c1m0U5iNXI<`1m0W5gNYI?]1j0Z5gNXIe0X1f0_5eNYIf0V1f0a5i0]JYOb5g0]JZOc5g0\\JYOd5h0ZJYOf5g0YJZOg5g0XJYOh5g0WJYOj5g0VJYOj5g0UJZOk5g0TJYOl5g0UJXOk5\\NZIP1k0d0k5YN]IS1h0c0l5YN\\IV1;fMJk2_6YN[IW1:hMLg2_6ZNZIX18kMNc2`6YNYI\\16kM0`2b6XNWIc10gM7^2b6_1^I`Nc6XNVIT27Dc6`1]I`Nc6WNVIW26Bd6WNVIW26Bd6WNVIW26Ae6WNUIY26@e6b1[I^Ne6c1ZI]Nf6c1ZI]Nf6c1ZI]Nf6UNUI^25\\Og6VNTI^25[Og6g1XIXNh6YNTI_24VOi6\\NSI^24UOj6]NRI^23UOk6]NSIi07BKV1m6`NPIb0<KES1P7M^IUOBl0R7LZI]OCg0T7HXIFD`0V7HTIZ2m6eMoH^2S7cMiH_2Y7aMfH]2\\7cMeH[2\\7fMdHX2]7iMbHU2a7kM_HS2b7mM_HkNNQ2e7UO\\HkNOn1f7XO[HiN0l1g7\\OYHhNOk1j7\\OXHhNNj1m7^OTHhN0h1o7_OQHiNOg1R8_OPHiNNf1U8AlGjNOe1U8AkGjN0e1Y8`0hG@X8`0gG@Y8`0gGAY8?gG@Y8`0hG@X8?hGAX8`0hG@X8?hGAY8>hGBW8?hGAY8>hGBX8[NfGP11e0Y8ZNgGHO?2X1_8_NaGJN?3U1a8aN^GKN`02T1b8`N_GKNa02T1a8^N`GMMb02S1a8\\NaGOLb03R1a8\\N`GOMc02S1`8ZN^G[OOe01e01P1b8YN\\G_O0c01_O4k0L[1c8WN[GC1`0`0<_O[1e8\\N\\GMb0W1]OCf8WO\\GOc0U1[OFf8UO\\G0e0R1ZOId8VO]GNh0P1XOM`8WO`GLi0m0YO1]8VOaGLh0\\OWO^144[8WObGJi0^OWOY14:X8UOdGJj0^OXOS3Z8eMdGI3C;NDQ3X8fMfGG3K5GLS2JeN[8OgGF3N3GNo1KgNY80hGD312E1k1JmNV8NjGE123E2g1KoNU8NjGE043C5c1JUOR8MkGE133C7`1IYOP8LlGD161B:\\1I]On7LlGD251C;X1HoNJ1R8=nGC26OC>T1IEj7JoGD070B`0Q1GJh7IQHB1e0`04GMf7HRHB1e0a01G1d7HSHA0f0b0OH1d7IRH_O2h0a0LH5b7HTH^O2i0`0KH7b7GTH]O3k0?GI;`7GUH\\O2l0`0CK?]7GVHZO2n0`0^ONd0Z7FVHYO1Q1b0WO0i0W7GVHRONL3_1b0oN3n0T7EWHRO2]1a0iN3V1R7CYHPOO`1c0cN3^1R7_OYHoNOb1d0]N2g1Q7[O\\HnNKe1]15]6XO]Hn3b7XKVH`08Y4Z7YLdHS3JWL_7NaH74f3LUL]7OcH62h3ORL\\71cH50j30QL\\70dH50j31QLZ70fH5Oi30TLY70gH31a4W7\\KiH30a4V7]KiH31_4W7]KiH31`4V7\\KiH50`4V7\\KjH31a4U7[KjH51`4U7[KjH42`4U7[KiH61`4V7YKjH70`4V7YKiH71a4V7XKiH71i17eMIi1V7bNhH81h1b0Ae6^NiH90g1?Ei6[NgH:0f1=Il6WNgH:0f1:Lo6ROgHR1:Lo6SNgH;1f19Lo6QOhHS19KP7ROgHS19KP7ROgHT18JR7QOfHV17IT7POeHX16HW7nNcHZ16HX7mNbH[17FY7nN`H\\17F[7lN^H_16E^7jN\\Ha16E_7iN[Hb16Ea7fNZHe15Da7hNZHd15Da7hNZHe14Cb7hN[Hd13Da7iN\\Hc13Da7iN\\Hc13Cb7jN[Hc13Cb7jN[Hc13Ca7kN\\Hb13Ca7kN\\Hb12Db7kN\\H`12Eb7kN\\H`12Db7nN[H^13Db7nN[H^12Ec7nNZH\\14Fb7nNZH\\14Fa7PO[HY14Fb7QOZHY13Gc7QOYHX14Gc7QOYHX14Gb7SOZHU14Hb7SO[HT12Jc7SO[HR12Jd7TO[HQ11Kc7VO\\Hn01Lc7VO]Hm0ONd7VO]Hj000c7WO^Hf002c7YO`H?O8a7[OcH7LlNJi0g76bH3Na0`7^O`HmNJJ2K5<Hk0g78bHfNOGO32R2^7@iI^NjN=Fk0h7<eI\\NnNQ2]7DdI[NnN=Fl0h7>aIZNQO<Fl0h7`0^IYNTO:Fn0h7a0[IXNVO:Gl0i7c0XIXNXO8Go0h7a0YIXNXO8Go0h7`0YIZNWO7HP1h7?YIZNWO7HQ1g7=[I[NVO6IR1f7=[I[NUO7IR1g7;\\I\\NTO6JT1f79[I^NUO5IU1g77\\I_NSO5KU1g76[I`NSO5JW1g74\\I`NSO4KX1g73[IaNRO5KX1h72[IaNRO4LY1h71ZIbNRO4LZ1g7O\\IdNoN3N[1h7M[IfNnN2O[1h7M[IhNlNO1^1h7J[I^OmNh0h7J[I]OnNi0h7IZI^OmNj0i7G[I^OmNk0i7FZI_OmNj0j7GXI@mNj0l7EWIAmNi0n7EUIClNg0P8FTIe1m6YNTIf1m6ZNSIe1o6ZNQIf1o6ZNQIe1Q7ZNoHf1Q7ZNoHe1S7ZNmHf1S7ZNmHe1U7ZNkHe1W7ZNiHf1W7ZNhHf1Z7YNfHg1Z7YNfHf1\\7YNdHg1\\7YNdHf1^7YNbHf1`7YN`Hg1`7YN`Hf1b7YN^Hf1c7ZN]Hd1f7[NZHc1i7\\NWHb1k7^NUH`1n7_NRH_1P8aNPH]1S8bNlG]1V8cNjG\\1X8cNhG\\1Z8cNfG[1\\8eNdG<mN\\Oa98aG<oNZOb99_G=POWOd9;\\G>POVOe9<[G>QOUOe9=YG?ROROg9>WG`0SOQOg9>VGa0TOPOf9`0UG`0VOnNg9a0SGa0WOmNg9a0RGb0WOlNi9b0oFc0XOjNi9c0oFc0YOhNj9d0mFd0ZOgNj9d0lFe0[OeNj9g0kFc0\\OeNj9g0jFe0\\ObNl9h0hFf0]OaNl9i0fFf0^O`Nm9j0eFg0^O^Nn9j0dFh0l9XOTFh0k9XOUFh0l9WOTFj0l9UOTFk0m9UORFk0n9UORFl0n9SORFm0o9SOQFm0n9SORFm0o9ROQFo0o9QOPFo0P:QOPFo0Q:POoEQ1Q:oNoEP1Q:POoEQ1Q:nNoER1Q:nNoES1Q:mNnES1R:mNnET1R:kNoET1Q:mNnET1R:kNnEU1R:kNoEU1Q:kNoET1Q:lNPFT1P:kNQFU1n9kNSFT1n9kNSFU1l9lNTFS1m9lNTFT1k9lNUFT1l9kNUFU1j9kNWFT1j9lNVFT1i9lNXFS1i9lNXFT1g9lNZFS1g9lNZFT1e9mN[FS1e9lN\\FS1d9mN]FS1b9lN`FT1`9jNbFV1]9iNeFW1[9gNgFY1X9fNjFZ1V9cNmF]1R9bNoF_1P9`NRG`1n8^NTGb1k8]NWGb1i:0000O100O1lNXCf0h<UO]Cj0d<RO`Cn0n<O100O100O100O100O100O100O100O1000000001O0000000000O1O2N1O1O1O2N1O1O1O2N1O1O2N1O1O1O2N1O1OoSi0", "size": [457, 640]}, "area": 68017.0, "iscrowd": 0, "image_id": 10104, "bbox": [0.0, 112.0, 583.0, 256.0], "category_id": 183, "id": 10012285}, {"segmentation": {"counts": "U6e1[b001O0000O10000O20O0O012NO11O000O1N3N2O000O11O000000000000000000O010M3M4J5]OU]OOnb0NY]OEG7Pc0KQ]O1a0N`b0OaVb8", "size": [640, 480]}, "area": 1858.0, "iscrowd": 0, "image_id": 10107, "bbox": [0.0, 196.0, 41.0, 55.0], "category_id": 93, "id": 10012286}, {"segmentation": {"counts": "\\<d7\\<000001O000000000000001O000000O1L4L4L4KRHVDP8j;QHUDo7k;30000001O000000000000000000001O00000000000000000000000000002N2N2N1UHlCh7T<XHmCg7V<000000000001O000000000000001O2N1O00000000000000000000000000001O6J5K2N1O00001O000000000000000000001O00000000001O00000PLSCm0m<QOVCn0k<POWCo0i<oNZCP1f<nN]CQ1c<mN`CR1a<lNaCS1_<mNbCR1_<mNbCR1^<nNcCQ1^<nNbCR1^<nNcCQ1]<POcCo0]<QOcCo0]<QOdCn0\\<ROeCm0[<SOeCm0[<SOfCl0Z<UOfCj0Z<VOfCj0Z<VOgCi0Y<WOgCi0Y<WOhCh0W<ZOhCf0W<[OjCd0U<]OlC4VO_Ml<_2nC1WO`Mj<`2PDNXOaMg<c2PDK[OaMc<f2RDH\\ObMb<f2RDH]OaMb<f2QDH_OaM`<g2QDG@bM_<h2PDECbM]<i2PDDDcM\\<i2PDCEdM\\<h2oCCFeM[<i2nCAHfMZ<i2nC@IgMY<i2nC_OJhMX<i2nC^OKiMX<h2mC_OKiMX<7TCS2g0MNiMW<5VCU2e0MNiM]<NRC[2b0OOhM_<KQC^2a0OOhMg<Y2YC00gMg<Y2YC00gMg<Y2YCO1hMe<[2YCM2hMe<[2YCM2hMd<\\2ZCL2hMd<\\2ZCL2hMc<]2[CK2hMc<]2\\CI2jMb<]2\\CI2jMb<^2[CH3jMb<^2[CH3jMb<^2[CH3jMb<^2[CG4kMb<]2ZCH4kMb<]2[CG3lMb<]2[CG3lMb<^2ZCF4lMb<^2ZCF4lMc<]2YCF5mMb<]2YCF5mMc<\\2XCG5mMc<\\2YCF4nMb<]2ZCE4nMc<]2XCE5nMc<]2XCD6oMb<]2XCD6oMb<Q2fBQNb0o16oMb<P2gBRNb0n15PNc<n1gBTNa0n15PNc<l1iBUN`0o14PNc<k1kBUN>o15QNc<i1kBVN>P24QNc<g1mBXN<P24QNd<e1nBXN<Q22RNd<e1nBXN<Q22RNd<e1nBWN=R21RNd<d1oBXN<Q22SNc<d1PCVN<S21SNc<c1nC:_OSNc<c1oC9^OTNc<b1PD:]OTNd<a1oC;\\OUNe<`1oC:]OVNd<_1PD;\\OVNd<_1QD:[OWNd<^1RD;ZOWNd<^1RD;YOXNf<[1RD=XOXNf<[1RD<YOYNe<[1RD<YOYNe<Z1TD<WOZNe<Z1TD<VO[Nf<W1VD>TO[Nf<V1WD>TO\\Nf<S1XDa0RO\\Nf<R1ZDa0oN^Nf<P1]Da0nN_Nf<n0]Dc0mN_Ng<k0^De0lN`Nf<j0_DFoM3l0]Oe<i0cDDoM4i0@f<f0cDCRN5f0Be<e0dDATN8c0Be<c0fD@VN9?Ec<c0iD]OWN;=Ec<a0lD]OVN;<Gb<=PEARN:<Ib<7UEEnM;;Ic<1YEJkM::Kb<M]EMhM;9K`<JcE0eM:8Ma<CfE6bM98Nj?Ho_O:7Ni?IP@870h?HS@662g?GT@752f?GV@644e?FX@634e?EY@635c?FZ@446b?E[@527b?D^@319`?D`@309`?Ca@3O;_?Bc@2O<]?Be@2N<]?Be@1O=[?Bg@0N?Z?Aj@MNb0X?@k@MNc0V?@WBa0h=^OZBb0f=\\O\\Bd0_`0100O1O100O1O100O1O100O1O100O1O100O1O100O1O100O1O100O1O1O100O1O1O1O1O1O1O100O1O1O1O1O1O100O1O100O1000000000000000000000000O1000000000000000000000000O1000000000000000000000000O10000000000000000000000O1000000000000000000000000O10000000000000000000000O1000000000000000000000000000000000000000000000000O1000000000000000000000000000000000000005K:F:F;E:F8H00001O0000000000001O0000000000001O0000000000VMMgA3X>2dAN[>8`AH_>=]ACb>b0ZA^Oe>h0VAXOi>m0SASOl>S1o@mNf:JlG^1ZMhNj:LhGb1ZMbNn:MfGg1WM\\NS;OcGj1VMWNe9KVE1Z16b2m1TMQNj96XFOg2o1SMlMl99iE>U3b1RMgMm9o1mH`0QMaMR:R2hHc0RM[MV:U2dHc0TMXMX:X2`Hc0VMUM[:Z2[Hd0XMRM\\:^2XHc0ZMoL^:a2THc0\\MlLa:l2eG<hMhLc:Z3VG1UNeLe:g3cF3]9;\\E>d:_400", "size": [640, 480]}, "area": 60927.0, "iscrowd": 0, "image_id": 10107, "bbox": [0.0, 342.0, 480.0, 298.0], "category_id": 118, "id": 10012287}, {"segmentation": {"counts": "0`4Z32Z4MgK3Y4MgK3Y4MgK3Y4MgK4X4LhK4X4MgK3Y4MgK3Y4MhK2X4NhK2X4NgK3Y4MgK3Y4MgK4X4LhK4X4LhK4X4LhK4X4FaHiKNNV3c4\\4F`HkKR3]4b4HeK5_4KaK1c4O]KLeLlKP8X4ZKMgLjKo7Y4[KLh44XKLh44XKLh44XKLh44XKMg43YKMg43YKMg43YKMg43YKMg43YKMg43XKNh42XKNh42UK1k4ORK4n4LmJ9S5GkJ<T5ElJ:T5FkJ;U5EmJ9S5GoJ7Q5IQK5o4KPK6P5JPK6P5JQK5o4KSK3m4MUK1k4OVK0j40SK3m4MRK4n4LTK2l4NSK5k4KUKgKmL\\4l7M\\K4b4M^K3gLbKh7Z4bK4fLcKg7Z4bK6^4JdK4\\4LdK4\\4LdK4\\4LdK4\\4LdK4\\4LeK4Z4LgK3Y4MjK0V40QLIo37TLFl3:ULEk3;WLCi3>WLAi3?XLAg3?ZLBd3>]LBb3>^LBb3?]LAc3?^LcKPLk3b7b0`L^O`3b0_L_Oa3a0`L^O`3b0`L^O`3b0_L_Oa3a0]LAc3?`L^O`3b0cL[O]3f0bLZO^3f0eLWO[3i0fLVOZ3j0gLVOX3j0iL[OQ3e0RM]Oi2c0[M[Oc2e0`MYO_2h0aMWO_2i0dMTO\\2l0iMPOV2P1kMoNU2Q1mMmNS2S1nMlNR2U1nMjNR2V1oMiNQ2W1PNhNP2X1QNgNo1Z1QNeNo1[1RNdNn1\\1RNdNn1\\1SNdNl1\\1UNcNk1]1UN_KlJP3o6a1[N]Ne1d1[N[Ne1e1^NXNb1h1_NXN`1h1aNWN_1i1bNVN^1j1bNVN^1j1cNUN]1l1bNUN]1k1dNTN\\1l1dNTN\\1l1cNUN]1k1bNWN]1i1`NZN`1g1]N\\Nb1d1]N]Nc1c1\\N_Nc1a1[NaNe1`1XNbNh1^1VNdNj1\\1UNeNk1[1SNgNm1X1SNiNm1V1RNlNn1U1oMmNQ2R1mMQOS2Q1jMPOV2n0jMTOV2n0fMTOZ2k0fMUO[2k0dMUO]2k0]M\\KgKj3m6l0QI[KX4i3i2o0oHYK0NS4h3o2X1oHQKn3e3T3]1mLaNU3_1lL`NT3`1lLaNS3^1nLbNR3^1nLbNR3]1PMbNP3^1PMbNP3]1QMdNn2\\1SMcNm2]1SMcNm2\\1TMdNl2\\1UM^KhKf2S7l1UM^KjKe2P7m1VM]KlKe29oLV6o4fM[KnKe24SMX6m4fMZKQLj2JQM^6k4hMXKRLm2EQMa6k4hMVKSLU3U6e1iMTKTLV3S6f1jMRKULW3Q6g1kMPKVLW3P6h1aNWN_1j1aNTN`1k1bNTN^1l1cNRN^1n1cNQN]1o1eNoM[1Q2fNnMZ1R2gNmMY1R2iNmMW1T2iNkMW1U2iNlMV1T2kNkMU1V2kNiMU1W2lNhMT1X2lNhMT1Y2lNgMS1Z2mNeMS1[2nNeMQ1\\2oNcMQ1^2oN`MR1`2oN`MP1`2QO_Mo0a2RO^Mn0b2SO]Mm0c2SO]Mm0c2TO]Mk0c2VO\\Mj0d2WO[Mi0e2WO\\Mh0d2XO\\Mh0d2XOlLYJ^N^6f4YOlLZJ]N]6f4ZOmLYJ]N]6e4[OnLYJ\\N\\6e4\\OoLXJ\\N\\6f4\\OmLXJ]N\\6f4\\OnLWJ\\N]6f4\\OkLUJTN3:\\6g4\\OYLUJTO7MW6f4]ORLVJlN5<20V6e4^ORL]JSOIK8;T6f4]OmKbJVO37n5e4^OhKgJ[OO7l5f4^OeKbKEP5g4]OeKbKEP5f4_OhK]KBT5e4@fK_KEQ5f4_OYKoKNb4h4@WKSLIQN2\\6n4UJPKf45oLLV4R5QJnJj44`3P5bGmJo45]3o4ZGiJ64V51Z3V5\\GkJW>V5gAkJY>_5O0000000000000000000000001O0000001O001O00001O00001O00001O00001O00001O001O00001O001O001O001O00001O001O001O00001O001O0000000000001O00000000001O00000000001O00000000001O00000000001O00000000001O001O001O1O001O1O1O001O1O001O1O1O001O1QC[Ia<S7O1O00N2I7L4M3K5N2O1N200000000000000O100O1O11O000000O100000000000gCcIo:]6QEcIo:]6REcIm:]6TEcIk:\\6VEdIj:^6UEbIj:^6WEbIh:^6YEbIf:^6^10mCbId:^6]EbIb:]6a11O1O0000000000001O00O100O1O1O1N2O1O1N2O10000000000000000001O0000000`CjIhN0\\<V6UDkI31h;j6eCXI9OHM00R<m6lCZI6NKL0OS<W7nCiHO50L0OS<g7lC[HS<e7mC[HS<i70000000000000000UFUHa7k7[GWHMNg8k7PGdH8Bg8j7kFkH<]Oh8h7kFmH@XOk02i8i7kFoH@WO3O;0H4]9S9]FPG;LE;^9T9iFWGS9h8iFbGR9j9L4L2N2N1O2N2N2N1O2N2N2N2N1O2N2N2N2N2N2N1O2N2N2N2N1O0000cLaHYJ_7g5hH`HGaN1Z1a7e7lH[HMHX7m7mHWHN[Oe7^8]HTHh8l7YGhGR9X8nFTG^O9j9c8U1000000000000000000", "size": [640, 480]}, "area": 118022.0, "iscrowd": 0, "image_id": 10107, "bbox": [0.0, 0.0, 480.0, 434.0], "category_id": 133, "id": 10012288}, {"segmentation": {"counts": "gZg03kc07V\\OG[c01h\\Oa0Rc0;K3M3NO10O2O2N2N1OM3O12ON10X]OlNdb0Y100O00020N0001O000100O3N6I3M101N100O100O00001l]OUNRb0l10O110O0O2M14M3NOO4N0O1O1O3N3L3N0M5N4J201M2OWMm^Of2Va0100N2O1O1O0O2N20OQ_OSMh`0V3L3M5M1M3O1N1O2N1O2N10OO10N2O1000O01O002OO01fNP@eNP`0X1V@dNk?Y1[@cNe?[1a@`Na?]1d@aN]?[1i@aN[?Y1k@cNU?[1PAcNQ?X1VAeNj>X1\\AdNg>X1P211O1O001O010O1O1O1O1O1O2N1O0O2O2M1O100O1O100O3N002N10O02O0O2O0O1O101N101N3N2M3N1N100O10O010WZf5", "size": [640, 480]}, "area": 8733.0, "iscrowd": 0, "image_id": 10107, "bbox": [37.0, 175.0, 151.0, 153.0], "category_id": 141, "id": 10012289}, {"segmentation": {"counts": "`4e1Zb0101O0000O2O000O11O00O12NO11O0J]No]ONNc1Sb0_NQ^Oa1oa09O1O00000000000000000000000000O100001O010O1O1O00O10000O10000000000001O00010N11O02N00001O00001O001O000010O01O001O01O0001O00001O001OO1010OO11O0000001O010ON21OO11OO2OW^OmMea0W201OO2O00000O1O11OM4M2N200N200O1O2N1O1O100N2O2N1O1O1N3N1N200N10002N0O4MO220MO200O3oNR]Om0Pc0SOQ]O0Ne0Zc0\\Og\\O?\\c010O10O010O100O10000O2O00O1001OO1O2N1O1O1O020N02O00O11O001N11O00010O01O2O1O1N10O1O000O1000000O1000000O1000O1O1O11O00000000O11OO1001O00O11OO10N5L3L3F[\\O6jc0L3NdZR5", "size": [640, 480]}, "area": 10025.0, "iscrowd": 0, "image_id": 10107, "bbox": [0.0, 138.0, 220.0, 81.0], "category_id": 152, "id": 10012290}, {"segmentation": {"counts": "oXj32nc01O001O0001OMNX\\O3Uc0MY]O7`b0JQ]O5<2bb01X]OIK8ib03[]O3_b03`]OO\\b0l01O4LM3D?J5Q^OQNga0V2O3OOGhMh^OX2Xa0720O10O0O20O01O0N3O001O010O2L5N2J6I7H8H7^OnSm1]OnlRN7L5L3K6M2O2M201O00001O00001N100O2N11O01O000O2O00001O001O00001O1N12OO1O1O1O001N20000O1O1O1O2N1O101N1N2O2i^OTNU`0n1h_OTNW`0n1f_OUNX`0n1d_OTNZ`0o1a_OVN]`0l1^_OYN``0j1Z_OZNd`0c2N101O001O001O001O1O010O001O00hMb_OhN0U2]`0\\Nd_O31a1Qa0lMQ_O9OHM00l1Sa0RNT_O6NKL0Om1]a0TNc^OO50L0On1la0QNV^On1ja0RNV^Oo1ma01O1O1O1O1O1O2N1O00lN\\^OMca0Gj^O8Va0BQ_O<Pa0CR_O@XOk0da0DV_O@WO3O;0HPc0JW]O;LE]c076Lcjk0", "size": [640, 480]}, "area": 7960.0, "iscrowd": 0, "image_id": 10107, "bbox": [195.0, 198.0, 240.0, 238.0], "category_id": 167, "id": 10012291}, {"segmentation": {"counts": "PeQ94lc07I5K2N001Z]OHba08j]O^O9`0ma0U101OO1001O000000000PG", "size": [640, 480]}, "area": 681.0, "iscrowd": 0, "image_id": 10107, "bbox": [463.0, 288.0, 17.0, 55.0], "category_id": 172, "id": 10012292}, {"segmentation": {"counts": "P\\X22lc03N2M3M3M3N2O1O1O1O00100O001O1cH_OYKa0c4E[K<`4J_K5]41aK0Z45eKLV4:hKFT4`0jKAQ4d0oK[Ol3l0RLUOi3P1VLPOVLWOa4n1WOlNVLWOb4o1WOjNVLYOa4o1YOhNTL[Oa4n1[OgNSL\\Oa4n1\\OfNSL]O_4o1]OdNSL_O_4m1^OdNRL@_4m1@bNPLB_4m1AaNoKC_4n1A_NoKD_4n1B^NnKE_4n1D\\NlKG_4o1DZNlKH_4o1EYNkKI_4o1aI_Me5h0[LK]4Q2bI^Md5f0]LK\\4R2bI_Me5b0]LNZ4S2cI`Md5?_LNX4V2cI_Me5=`LNW4X2bI`Me59cLOT4[2cI^Me58dLOR4^2cI]Mf55fL0P4`2bI]Mg53fL1o3b2bI\\Mi50fL2n3Y3[OfLfL1n3[3[OdLgL1m3]3[ObLhL1m3^3ZOaLiL1l3`3ZO_LiL2m3a3XO]LkL2l3c3YOZLkL3l3d3XOZLkL2l3f3XOXLlL2k3g3YOWLkL3l3g3XOVLlL3k3i3XOTLmL3k3j3WOSLnL3j3k3YOQLmL4j3l3XOQLmL3j3n3XOoKmL4k3n3WOnKnL4j3P4WOlKoL4j3Q4VOkKPM4i3S4VOiKQM4i3T4VOgKQM5h3V4VOfKPM5j3U4VOfKPM5i3W4jNQK_Kc0n15i3X4hNRK`Ka0o15h3Z4gNQKbK`0o15h3[4dNRKdK?P24h3[4cNSKeK?n14j3Z4bNUKdK>P23i3[4aNVKfK<P23i3[4`NWKfK<Q22i3\\4_NVKgK<Q22i3\\4_NWKeK<S21i3\\4^NXKfK<Q21k3[4^NXKeK=R20k3[4]NYKeK=S2Ok3[4]NWLhM^Ok3[4\\NXLiM]Ok3[4\\NYLhM\\Ol3[4\\NYLgM]Om3Z4[NZLhM[On3\\4YNYLiM[On3\\4XNZLjMZOn3\\4XNZLjMZOn3\\4WN\\LjMXOo3\\4WN\\LiMXOQ4\\4VN\\LiMXOQ4\\4UN]LjMWOQ4\\4UN]LjMWOQ4\\4SN`LkMSOS4^4PN`LlMSOS4^4oMaLnMQOS4^4nMbLoMPOS4_4kMcLQNoNT4^4jMeLQNlNW4_4eMgLSNkNX4^4dMhLTMoM3k0U5_4aMiLUMPN3i0W5^4`MjLSMRN5f0Y5_4]MkLQMTN8b0Z5_4[MmLPMUN:`0[5_4YMmLPMWN;<]5`4VMoLQMWN:;_5`4QMRMVMSN9<`5_4lLXMYMnM;:a5a4eL\\M^MjM;:b5a4`L^McMhM;9b5c4YL`MjMfM98e5d4PLcMQNbM97g5g4gKdMYN_M96g5j4`KdMaN\\M77h5l4XKfMhNXM76k5n4nJhMPOUM75k5P5\\JUNBgL65m5k9lIRF63n5j9lITF52P6i9lIUF33R6g9jIWF42R6f9jIYF31T6e9iI[F30U6d9gI]F30V6b9gI`F1NY6a9fIbF1MZ6`9eIcF0N[6^9eIeFON\\6]9dIfFNN`6Z9bIiFMNa6X9bIkFLNb6W9`IWHb6g7]I[Hc6d7\\I]Hf6^:0O1O2O0O2N101N1O2O0O1O2O0O2N101N1O101N1O2O0O1O100O1O1O2O0O1O1O1O1O2N100O1O1O1O2N1O1O100O1O2O00000000001O000O1000001O001O001O1O001O1O1N101O1O001O1O1O001PE]Ga:d8UEeGk:i8O1O0O2O001O001O001O001O00001N10001O00001O00001O00001N10001O001O1O001O1O001O1N2O001O1O001O1O1O1O1O2N1O1O2M2O1ZGeEe7]:WHfEh7[:UHgEk7Z:SHhEl7Z:nGjER8W:iGnEV8S:eGRFZ8j:01O001O001O001O001O1O001O001O001O001O001O001O1O1O1N2O1O1O24K9G9G:F9G7IO4L4L5K4L5L1N2N2N1O2N2N2O1N1O2N2N2N2N1O2N2UMlCfMV<W2PDdMR<Y2SDcMo;Z2VDbMl;[2ZD`Mg;^2^D^Md;_2bD\\M`;a2eD[M];b2hDZMZ;b2mDYMU;d2PEXMR;e2TEVMn:g2WEUMj:i2[ESMg:j2_EQMd:j2bERMa:j2eEQM^:k2fERM]:j2fETM]:h2fEVM]:f2fEXM]:d2fEZMa:]2cEaMk:P2XEnMV;c1R4oNf]1", "size": [640, 480]}, "area": 98496.0, "iscrowd": 0, "image_id": 10107, "bbox": [115.0, 182.0, 362.0, 450.0], "category_id": 183, "id": 10012293}, {"segmentation": {"counts": "c`S61S>2O2N7H5K1M32N1O1O1O1O1O1O1O1O1O1O0K]R7Mj_H2j=<G5L1N200O1O1O1O1O1O1O1O1N2N2O1O1O1O1O100O1O1O1O1O1O1O1O1O1O1O1NFeCnNZ<Q1jCmNU<S1mClNR<T1PDkNo;U1RDkNl;T1WDlNh;n0iCQOb01d;n0k01O00001O1O0O2M201O0010OO3O1M3N2N2N2N2N3MO02N1O101N1O\\ORDTOn;l0VDQOi;o0XDoNi;Q1YDmNi;Q1YDjNk;U1d0O0O2O0O2N1O2O0O2O1O0OWOUOZDj0f;XOZDg0e;[O[Dd0c;_O]D`0[;IdD7\\;JcD6];KbD5^;KbD5^;L`D6_;IbD7^;HcD9\\;FeD;Y;FgD:Y;EgD<Y;ChD=X;BiD>X;@iD`0X;_OhD`0Z;_OeDb0\\;]OdDc0];[OdDe0];ZObDf0_;VOeDj0[;SOhDm0W<2O001N101N3N1OCVC\\Om<b0WC[Oj<d0XCZOi<d0=00O100000O2O001O0O1O10O100O0010O01O1000001O0000010O0001O000001O1O00000O1000000000000001O0lD", "size": [453, 640]}, "area": 5915.0, "iscrowd": 0, "image_id": 10108, "bbox": [441.0, 322.0, 199.0, 131.0], "category_id": 94, "id": 10012294}, {"segmentation": {"counts": "0TZS17[flNLk<>gBLS<MmCGe0o0\\;C]D>b;C]D>P1[O[8U2dGnMY8P2iGSNiNAZ3f0Y1f1dL`NU2GV1j1eL`NV2DQ1R2gL\\NW2AR1V2dKhM4c0U3]OT1Y2bKjM2`0Z3[OR1[2aKkM3?[3ZOQ1\\3bKZM]3ZOP1_3aKWM_3ZOP1a3_KUMb3YOo0b3[KQL2T1d3YOo0l4QOSKo0n4ROQKn0n4SORKd0EjKZ5b3QKd0EhK\\5d3oJd0W5_OmJ9U5NhJNX51lJJV56iJJX55hJK[5VL]Jb379g5FYJ:g5FYJ:g5FYJ:h5EXJ;h5FVJ;j5EUJ<k5DSJ>n5AQJ`0Q6^OnIc0U6ZOiIh0Y6VOeIl0\\6_30O1O0001O0001O000001O00O01000000N2O1000001O000001O1O2O0O1O1O1UK^IQ2b6oM^IQ2b6nM`IQ2`6nMcI_M3S4Q1jKm3c2`Ka1b0nK7[1g0T1eN`1=RLFj1V1f0gN^1<SL@P2]1a0eN\\1=TL[OU2b1<fN[1=ULWOW2f19fN[1NQLB61W2h18gN[1LRLB8MV2n16fNY1DoKI8MP3P2@fNZ1DJg1kNeN[1DJg1eLSN\\1b0V2CIi1^LmMH6k1`0V2CJk1[LnMI5k1?W2ALn1XLmMJ6j1>X2@M]2QLfMh1=Z2\\O1a2mKgMg1;\\2\\O1c2kKhMe19a2[OOe2jKgMf18b2\\ONe2jKgMe19c2[ONT3_MaMc2[ONT3^MaMf2ZOLU3XMlLdNf0`8^2gHhM]7X2aHiM`7W2_HkM`7U2`HkM`7U2`HkM`7U2`HkM`7U2`HkM`7U2_HlMZ3oNFU3WLkL;R1g3nNGU3nKVMb0h0h3mNHf3_L]Mi3nNGf3]L^Ml3lN]L0]2g3XM^Mn3kNmK2L8l2]3[M`MP4iNhKk0l2m2ZM`MR4hNeKP1m2i2ZM_MU4iNbKR1m2g2[M]MW48RMbN]Ol3YOYMY4:mLfN[Ol3_ORMZ4>fLkNZOj3FlLZ4a0`LPOXOh3OfLY4d0YLVOVOg37^L[4g0jKW4KoJ]4k0gKW4LkJ_4n0dKY4MhJ`4n0\\Kb43`Ja4_7_KaH`4`7`K`H_4a7aK^H_4c7aK]H^4d7bK[H^4f7bKZH]4f7dKYH]4g7cKYH\\4h7dKWH\\4j7dKUH\\4k7eKRH]4o7bKoGa4Q8_KnGa4S8_KkGb4V8^KgGd4Y8]KfGc4[8\\KdGe4]8[KaGf4`8ZK_Gf4b8ZK^Ge4c8[K\\Ge4e8[KYGf4h8<0O1O0000O1O100N31NN2O1001O001N2O1O1O1O2OO0001O000O0100O2N1O100O2N1011N1O0000001O001O000lJYGj4Q900000000O02O0000000O1000001O001O0ROoJgHR5X7PKgHP5Y7QKQH48l4f7PKoG7:j4h7\\KWHd4i7\\KWHd4i7[KXHe4h7\\KVHe4j7[KVHe4j7\\KUHd4k7\\KUHd4k7\\KUHd4k7\\KUHd4k7]KSHd4m7[KTHe4l7[KTHe4l7[KRHg4n7YKQHh4o7YKnGj4P8WKPHi4P8g0O000O100O10O11OO2O1O01N11N10001O10106J0O00000O0010O00000001PMjG0U80nGMR82QHLo74SHJm76SHJm76SHIm77THIk78UHGl79THEn7;RHEm7<SHCn7=RHBo7>QHBo7=RHCn7=RHCn7=SHBl7?THAl7?THAl7?THBk7>UHBk7>UHCi7=WHDi7bMiGf2>Il77SHJm76RHLm74SHLm74RHMn73RHMn73QHOn71RHOo70QH0R8LnG5R8KnG5S8JlG8S8HmG8S8HlG9T8GnG7R8IPH5P8KQH4P8KRH3o7LRH4m7LUH2l7LVH3i7NWH2h7OXH1h70WH1h70WH0i70WH0h71YHNg71ZHOe72[HNe72[HMf74YHLg74XHLh76XHIh78WHFj7;VHFj79WHFj79VHGj78WHHh79XHG`7eMQHc2`0H_7fMkGg2f0C_7`0aH@_7`0aHA^7?cH@]7`0cH@]7`0cH@]7?dHAYOfMP8h2gHBYOfMP8h2gHCXOeMQ8g2hHGX7OSI0m6OTI1l6NUI2k6NUI2k6NUI2l6MTI3l6MTI3l6MSI5m6aMQHR2S1=[7bN]I^1d91O0L5O1O0001000O1001O001O0O2O001O000fMdDd0c0ZOP;1aD>b0_OQ;1^Da0a<@`C>b<@_C?c<@]C?d<A]C5FJn<2\\C1m<0SCOn<1RCNn<3SCLm<5RCJo<6?10004M1O01H\\BK_Pb2", "size": [453, 640]}, "area": 108332.0, "iscrowd": 0, "image_id": 10108, "bbox": [0.0, 0.0, 455.0, 453.0], "category_id": 132, "id": 10012295}, {"segmentation": {"counts": "[kT39j=2H8N2O1O11O0000001O001O1O001O1O00000000O100O10000O1N2O1N2G\\OoBf0P=800O10000OLTCQOl<m07O2N1N3N3N002M1N2O1001O0000000O2OO01000000000O1N3M2M5KiW2M\\hM1O100O1VON`C3\\<2bCO\\<5`CM_<5]CNQ<KmCa0GF[<JnCo0Q<ROnCo0Q<TOmCl0R<VOlCk0S<d000O11O001O1O1O1AfCQO]<g0jCWO]<a0gC\\O\\<a0fC]Oc<9aCD`<:h0K`h1LfWN3M2N3L201hJI_L8a3K\\L5c3N[L2e30ZLNg32YLNg32YLNf33ZLLg34ZLJg36[LFg3:R500000O10001O00000005K4K5L`Q54ZnJ4L4M5K1N2N2O1O1O100O10000O10000001O00001J6M4Ie]?Hfb@3N3L4M2N5K2WN0[E1`:2`E0`:NaE<T:EkE>R:CmE`0P:AnEb0o9@oEc0k9AUFa0i9@UFP1\\9QOcFR1_OPN^9n0SGb1k8^NUGc1i8_NUGd1i8\\NWGe1g8]NWGe1h8[NWGg1h8YNWGi1h8WNXGj1f8WNYGk1f8UNZGl1d8UN\\Gl1a8UN_Gm1^8UNbGl1]8TNcGl1\\8TNeGm1W8VNiGj1U8XNkGh1T8XNmGh1R8YNnGf1R8[NmGf1S8YNnGg1Q8ZNnGf1R8[NnGe1R8[NnGd1S8[NmGf1R8[NmGe1T8[NkGe1V8[NjGd1W8[NjGd1X8[NhGd1Y8\\NgGc1\\8ZNeGe1`8WNaGf1i8RNXGl1k8QNWGl1P9oMRGo1n8QNWGi1j8WNWGg1j8XNWGg1k8XNVGf1o8UNRGj1o8UNRGj1o8TNSGk1n8SNUGk1k8TNWGj1l8SNVGk1l8QNXGn1n7mMZG6h0l1n7UNRG2n0i1P8WNmF5R1c1R8YNfF9W1^1S8[NdF;V1Z1V8YOhGf0Y8ZOgGe0Z8\\OeGc0\\8^OcGa0^8@aG?_8C`G<a8D_G;b8D_G;b8E^G:c8F]G9d8G\\G8d8I\\G56^NR7]1gH56bNR7X1gH67dNQ7V1gH68fNR7R1eH89gNS7o0aH<>eNR7n0]H?b0bNR7n0ZHb0e0`NR7m0XHc0f0`NS7l0WHd0g0`NR7l0VHd0i0`NQ7k0WHd0i0aNR7i0THf0k0aNQ7j0SHd0m0aNT7h0nGf0P1bNQ7j0nGa0U1cNn6l0mG`0W1cNl6m0mG?X1bNm6o0kG>Z1aNl6Q1jG>X:AhE=Z:CfE=[:BeE<^:BcE=_:BaE=a:B_E=c:A^E>W1mNZ7c0_G?W1ROY7=`G`0V1WOY77cG?V1[OW75cG>W1^OY6^OgHe0I=X1AV6AhH?L=W1DR6CjH<M;X1Go5ElH7M<d0WOTOa0^7HmH3M<d0\\OQO=_7KPILN=d0@mN>`7JUIEJd0d0AkN=`7JXJ6_OEdN?d7GXJ4Ag0X6UOWJ3Bh0X6SOVJ5Bi0X6ROVJ4Cj0W6QOVJ5Ck0X6oNUJ5Cm0S6ROZJ1Cm0Q6SO^JOBn0n5TOaJMAP1m5TOaJLCP1k5UObJJDQ1i5VOcJHDS1i5UOdJFaNG8_1c6TOeJD_NL8\\1d6TOfJB]N18Y1e6TOgJ_O\\N77V1f6TO`KGjMU1e6UO`KGkMT1f6UO]KHmMS1f6VO[KHoMR1e6XOZKFSNQ1b6[OYKEUNP1b6\\OWKEXNn0_6_OXKDZNl0_6POeHMa27[Nl0`6nNeHO_27]Nl0_6mNfH0_26\\Nm0`6kNfH2^26\\Nm0a6jNeH3^26\\Nm0c6gNdH6]26[Nm0f6dNeH8Z26\\Nn0S7oN^J3_N8TOGR8NYJ3aN7ZOBn76TJ0dN6^OBm77hG@V2`0gN6@AP89iIOgN6C_On7>fILkN4X81mHJkNO^88fHImNM_8:cHHoNN^8:bHIQOK^8<aHHTOI^8=\\HLWODa8>lGCbN<d0C^ONf9=eGI`N:e0F^97\\G?UO\\O_93[Gc0TO]Oa9N[Gf0TO\\Oa9M\\Gg0RO]Oc9J[Gj0RO]Od9FYGo0SO[OZ;e0fD[OY;e0iDZOW;f0iDYOX;g0iDXOW;h0jDUOX;j0kDROV;n0Q10O1O1O0kEnNY7R1dHVOV7j0iHZOU7f0jH[OV7e0iH\\OW7d0hH]OX7c0gH^OZ7b0eH^O[7b0dH_O]7`0cH@^7?bHA`7=_HDe78[HHf76ZHKg74XHMj71TH1l7OSH2R8IlG9[8_OeGb0\\8\\OeGd0]8XOdGi0k:100000000O10000001O", "size": [453, 640]}, "area": 21889.0, "iscrowd": 0, "image_id": 10108, "bbox": [227.0, 224.0, 413.0, 229.0], "category_id": 157, "id": 10012296}, {"segmentation": {"counts": "TZS17?Lk<>gBLS<MmCGe0o0\\;C]D>b;C]D>P1[O[8U2dGnMY8P2iGSNiNAZ3f0Y1f1dL`NU2GV1j1eL`NV2DQ1R2gL\\NW2AR1V2dKhM4c0U3]OT1Y2bKjM2`0Z3[OR1[2aKkM3?[3ZOQ1\\3bKZM]3ZOP1_3aKWM_3ZOP1a3_KUMb3YOo0b3[KQL2T1d3YOo0l4QOSKo0n4ROQKn0n4SORKd0EjKZ5b3QKd0EhK\\5d3oJd0W5_OmJ9U5NhJNX51lJJV56iJJX55hJK[5VL]Jb379g5FYJ:g5FYJ:g5FYJ:h5EXJ;h5FVJ;j5EUJ<k5DSJ>n5AQJ`0Q6^OnIc0U6ZOiIh0Y6VOeIl0\\6_30O1O0001O0001O000001O00O01000000N2O1000001O000001O1O2O0O1O1O1UK^IQ2b6oM^IQ2b6nM`IQ2`6nMcI_M3S4Q1jKm3c2`Ka1b0nK7[1g0T1eN`1=RLFj1V1f0gN^1<SL@P2]1a0eN\\1=TL[OU2b1<fN[1=ULWOW2f19fN[1NQLB61W2h18gN[1LRLB8MV2n16fNY1DoKI8MP3P2@fNZ1DJg1kNeN[1DJg1eLSN\\1b0V2CIi1^LmMH6k1`0V2CJk1[LnMI5k1?W2ALn1XLmMJ6j1>X2@M]2QLfMh1=Z2\\O1a2mKgMg1;\\2\\O1c2kKhMe19a2[OOe2jKgMf18b2\\ONe2jKgMe19c2[ONT3_MaMc2[ONT3^MaMf2ZOLU3XMlLdNf0`8^2gHhM]7X2aHiM`7W2_HkM`7U2`HkM`7U2`HkM`7U2`HkM`7U2`HkM`7U2_HlMZ3oNFU3WLkL;R1g3nNGU3nKVMb0h0h3mNHf3_L]Mi3nNGf3]L^Ml3lN]L0]2g3XM^Mn3kNmK2L8l2]3[M`MP4iNhKk0l2m2ZM`MR4hNeKP1m2i2ZM_MU4iNbKR1m2g2[M]MW48RMbN]Ol3YOYMY4:mLfN[Ol3_ORMZ4>fLkNZOj3FlLZ4a0`LPOXOh3OfLY4d0YLVOVOg37^L[4g0jKW4KoJ]4k0gKW4LkJ_4n0dKY4MhJ`4n0\\Kb43`Ja4_7_KaH`4`7`K`H_4a7aK^H_4c7aK]H^4d7bK[H^4f7bKZH]4f7dKYH]4g7cKYH\\4h7dKWH\\4j7dKUH\\4k7eKRH]4o7bKoGa4Q8_KnGa4S8_KkGb4V8^KgGd4Y8]KfGc4[8\\KdGe4]8[KaGf4`8ZK_Gf4b8ZK^Ge4c8[K\\Ge4e8[KYGf4h8<0O1O00mJQKP1o4nNSKQ1n4gNZKX1g4fN[KZ1e4eN\\KY1g4eNZK]1d4dN[KZ1g4fNYKY1h4gNXKY1h4gNXKZ1g4gNXKY1h4gNXKZ1f4hNYKY1f4hNYKY1f4gNZKZ1e4gNZKZ1e4gNZK[1e4dN[K\\1e4dN[K\\1e4dN[K]1d4cN\\K]1d4bN]K^1b4cN^K\\1c4cN^K]1b4cN^K\\1d4cN\\K\\1e4cN\\K\\1e4bN]K^1c4aN^K^1d4_N^K`1c4WNiJSMf0f4b4UNfKm1X4SNhKn1W4QNjKo1V4QNjKo1V4QNjKo1V4PNfKYMQOh4X5oMeKX2[4hMdKZ2[4gMcKZ2]4fMaK\\2[OnJj4g2iKf2W4\\MgKd2Y4\\MgKd2Y4^MdKc2\\4]MbKe2]4\\MbKd2`4[M`Ke2`4[MaKd2_4\\MaKd2_4\\MaKd2_4\\MaKc2`4]M`Kc2`4\\MbKc2^4]MaKd2`4ZMaKf2_4ZMaKf2`4YM`Kg2`4YM`Ki1_OVLR5Q2_Kh1AVLP5R2^Ki1C_K48l4_2[Kl1EZK7:j4a2WKn15`Kd4b2TKQ28]Kd4d2oJR2<[Ke4h7\\KVHe4j7[KVHe4j7\\KUHd4k7\\KUHd4k7\\KUHd4c3kJ;a0RLd4b3lJ<a0PLd4c3lJ=?QLe4b3lJ=?QLe4a3mJ>>oKg4i2iJhM3`3=nKh4f2mJjMOb3=kKj4f2nJiMMe3<lKi4e2PKfMNi3i5nMRJWNa0GFT4g5mMSJXNo0k3n4lMTJXNo0k3n4lMVJWNl0m3n4kMXJVNk0n3n4kMfKU2Y4lMgKT2Z4jMgKV2X4lMgKU2X4kMhKV2W4kMhKU2Y4kMfKT2[4mMdKT2Z4nMWKoMQOS4h5QNoJSNWOl3j5XNgJPN\\Oi3l5ZNeJoM]Oh3n5aN[JkMDf3Q6^NYJ`2a5`MZJe2e5n20000O00SJXJ^3h5`L[J^3e5aL]J^3c5aL`J]3`5aLbJ_3^5aLcJ_3]5_LdJa3\\5_LdJa3\\5_LdJa3\\5`LbJa3^5_LbJa3]5`LcJ_3^5aLaJ_3_5cL_J^3_5fL]J\\3c5b201O10000000O10001O00RMiGOU81PHKP8[MiGf2;Lo74SHJm76RHLm74SHLm74RHMn73RHMn73QHOn71RHOo70QH0R8LnG5R8KnG5S8JlG8S8HmG8g2^MR2Z2VK9f2aMR2V2ZK7b2gMR2R2^K5_2nMo1m1cK4]2PNQ2k1dK3Y2TNT2h1dK4U2VNW2f1fK2R2YNY2c1gK3o1[NY2c1hK2n1\\NY2c1iK1n1\\NY2d1hK1m1\\N[2d1gK0n1\\N[2d1gK0n1\\NZ2e1iKNl1^N[2c1jKOk1^NZ2d1kKNk1^NZ2d1kKMl1_NY2e1jKLn1^NX2f1iKLP2^NV2h1jKIP2_NV2i1iKFT2[NW2P2eKFT2WNZ2R2cKFU2QN^2X2]KGj78WHHh79XHG`7eMQHc2`0H_7fMkGg2f0C_7`0aH@_7`0aHA^7?cH@]7`0cH@]7`0cH@]7?dHAYOfMP8h2gHBYOfMP8h2gHCXOeMQ8g2hHGX7OSI0m6OTI1l6NUI2k6NUI2k6NUI2l6MTI3l6MTI3l6MSI5m6aMQHR2S1=[7bN]I^1d91O0L5O1O0001000O1001O00[E]Mi9b2UFaMj9^2VFeMi9Z2UFjMi9V2VFlMj9S2UFRNg9n1XFTNg9BmEd1c0kN_9@RF_1a0SO]9\\OSFZ1g0Dk8SO_FX1e0Hk8nNaFZ1c0Kk8jNbF[1a0N;cNX74mG5Fh0h0?;bNX73oG1Hk0f0a0:bNV72RHOHm0d0P1NYN]7KYHNHo0c0R1_OPN0;h7G_HLFP1d0b1\\OlMk7G^HJHQ1c0X2W7mLeHm02W2X7mLfHl02W2X7mLeHn01V2Z7PMaHj04W2\\7PM^Hi05X2^7oL\\Hi06X2`7gLRHK7W16X2Y8hMgGX2X8iMhGW2V8jMjGW2T8kMlGU2T8kMlGP2X8PNiGm1W8VNiGj1U8XNkGh1T8XNmGh1R8YNnGf1R8[NmGf1S8YNnGg1Q8ZNnGf1R8[NnGe1R8[NnGd1S8[NmGf1R8[NmGe1T8[NkGe1V8[NjGd1W8[NjGd1X8[NhGd1Y8\\NgGd1BlLc8]1lGT2Q8hMPHZ2U8^MlGa2V8\\MlGc2Y8XMiGf2W8ZMnGa2R8_MoG`2Q8_MPHa2Q8^MPHa2T8ZMmGf2S8YMnGg2R8WMPHi2P8UMSHj2l7UMVHk2k7RMWHn2i7nL[HR3j6iL^H6h0Q3i6PMWH2n0o2j6QMSH5R1j2k6RMmG9W1f2k6SMlG;V1b2n6QNPIo1P7QNPIo1P7RNoHn1Q7SNnHm1R7TNmHl1R7VNmHj1S7VNmHj1S7UNnHk1R7UNnHk1R7UNnHk1R7UNnHk1Q7VNoHj1Q7VNnHk1S7TNlHm1T7SNkHn1W7PNhHQ2Y7mMdHc1WOaMV8k0`H`1YOVM4;KFX8X1^Hc1D_MS8m0XHe1D^MU8l0WHf1E^MT8l0VHf1G^MS8k0WHg1F^MU8i0THj1G]MU8j0SHh1I]MX8h0nGl1J\\MW8j0nGi1M[MV8l0mGi1NZMU8m0mGh1OYMV8o0kGh10WMV8Q1jGg1o8XNQGi1n8WNRGj1n8UNRGl1n8RNSGo1m8PNSGQ2m8nMSGS2m8kMTGV2_OUMR9c0_Gg2`8WM`Gk2_8SMcGm2]8RMcGn2`7\\LgHe0Io2^7`LhH?LQ3Y7cLjH<MQ3W7fLlH7MS3V7iLmH3MT3T7lLPILN=XOo1i7iMUIEJd0XOo1g7iMXJ6UNP2b7kMXJ4XNP2a7lMWJ3ZNP2`7kMVJ5[NP2_7kMVJ4]NP2]7kMVJ5^NP2]7jMUJ5`NP2V7oMZJ1aNo1S7QN^JOaNo1o6SNaJMaNP2m6TNaJLdNo1j6VNbJJfNo1g6XNcJHgNP2f6XNdJH_NV2m6RNeJH[NX2P7PNfJHWNZ2S7nMgJHSN\\2V7lMgJIQN\\2W7lMhJIoM\\2Z7kMfJJnM\\2\\7kMeJInM]2\\7lMeJj2Z5XMeJh2[5YMdJg2Z5[MfJe2[5kLeHMn1Y3^5hLeHOm1Y3^5gLfH0l12SNd2\\7XMfH2k11TNe2\\7WMeH3l1OTNg2]7TMdH6l1NSNg2_7RMeH8j1NRNh2m7]MoIJTNS2NUNR8NkIHVNT23PNn76gIEXNR27QNm77hG@h12aNT24QNP89ZI_OfNU23PNn7>fI`1S7aNmHX1Y7iNfHV1\\7jNcHV1]7jNbHV1_7jNaHU1b7iN\\HW1h7gNlGCbN`1L]Nl9=eGI`N]1P:jN_Ga1c8]N\\Ge1e8YN[Gh1e8WN\\Gi1e8UN[Gm1f8PNYGR2]:0O10O100DSD^No;b1SDZNn;g18O01O000kEWNP8i1mG_Nm7a1RHcNl7\\1THeNl7[1SHfNm7Z1RHgNn7Y1QHhNP8W1PHiNP8W1oGjNR8U1nGkNS8U1lGkNV8=QFDh10[88mEHh10\\86mEKe10_84lELd11b81jENb13d8OjENa14j8IfE2^17S9_OaE9[19U9\\OaE;Z19W9XOaE?W1:e9E[F<e9D[F<e9D[F<e9D[F<e9D[F<e9DZF=f9CZF=f9CZF=f9DYF<g9DZF;", "size": [453, 640]}, "area": 153784.0, "iscrowd": 0, "image_id": 10108, "bbox": [79.0, 0.0, 561.0, 452.0], "category_id": 169, "id": 10012297}, {"segmentation": {"counts": "]_f11_50j60lE0mM30MO30M\\20fM0O1OOW10nN6<Je;6fENTN9a0DF1KO20L31k0O10Ga0KS;CfDKR13dN`01h3b0VLm:IgDGI15OMON11O0013Oa56\\JI0012?_;LaD_62]IM8a;l71G_DPH`;o7fDnG\\;o7:11OO100O1001O00O2MnGVDR8g;nGYDU8\\;QHdDJ0X8[;;0000000000001OO110O000O11O00001O1O000001O0O100000000O11OO1000000001O000O6_G_DZ8i;eGYDj7OYHm;f7TDZHl;f7TDZHl;f7TDZHl;f7TDYHl;g7VDXHj;h7VDXHj;h7VDXHj;h7VDXHj;h7VDWHk;i7511N0100000^HkCY7U<gHkCY7X<aHiCO0_7_<O4LO11O001O0000O1\\CiH`<V7`CjHa<Z7000O20\\FeHa6[7^IfHb6Z7]IgHd6X7\\IiHb6W7^IjHa6W7_IiH`6X7_IiHa6W7^IjH`6W7aIjH`6T7_ImHa6S7_ImHa6S7_ImHa6S7_InH_6S7`InHa6P7^IRIa6o6`IPIa6o6_IRI`6n6_ISIa6m6_ISI`6n6_ISIa6m6_ITIa6k6_IUI`6l6_IUIc6h6^IXIa6i6_IXI`6h6_IYI`6h6_IYIa6g6_IYIa6g6_IZI`6e6`I\\I`6d6`I\\I`6d6_I]Ia6c6_I^I`6b6_IcJ]5^5bJfJeMgNc6XOUIW7d2kJdMeNh0NW4b6]MjJeMfNf0OX4a6]MiJeMhNf0NX4a6\\MWJ^MdNO61I8OHe0m01\\4^6YMZJ]NWOM2\\4^6ZMYJ]NVON3]4\\6XMZJ_NWOK4\\4\\6YMZJcNQOJ:X4\\6[MYJdNeN9>k3d6XMZJ:QO\\2e6YM[J:PO^2@gJS7a2]J;oN_2d6UM^J>lN]2f6UM]J`0`NPM7_5k6PM^Jd0dN^2o6nL\\JcMaNR30\\2T7nL\\JbMbNf8R7hI`JW6a5iI_JW6a5hI`JX6`5hI`JZ6^5fIbJZ6^5fIbJZ6^5XIPIZNa1_8_5VIQIYNb1a8]5QI[IYNW1g8^5oHZI\\NY1e8]5mH^I[NT1i8^5kHWKT7i4mHVKT7k4jHVKV7j4jHVKV7j4jHVKW7i4iH_I]NS1i8_5jHlH\\N?3U1g8`5lHZI^NV1f8`5mHXI^NX1e8`5mHXI^NX1d8a5oHVI]NY1d8a5PIUI]NX1d8c5QISI[N[1b8c5UIPIYN[1J_Nh8V7VIoHXN\\1e8d5TIoHWN^1c8d5XImHTN^1f8d5WImHSN_1e8e5XIkHTNc1a8b5dI]J^6b5bI^J^6b5bI^J^6b5cI\\J^6d5bI\\J^6d5bI\\J^6e5aI[J9_N`3V7WL[J9_N]M0P6V7ZL[JYNbN\\ON2OO62Mn0MoN004o5Q7\\L]JXNRO]O@o0OQO3P6n6[LbJeM]N9o0GoNb00^O;n5i6]LnJfMRO8kNG02d0j5a6`LXLgM`Mj5W6_LYLeMbMl5U6_L[Ma3e2_L[M`3f2`LZM`3f2`LYMa3g2_LYMa3g2_LYMa3g2_LYMa3g2_LYMa3g2`LXM`3h2`LXM`3h2`LXMa3g2_LYM`3h2`LXM`3h2ZL^Mg3b2XL^Mh3b2XL_Mh3`2XL`Mh3`2XL`Mg3a2YL_Mg3a2YL`Mf3`2ZLbMd3^2\\L`Mf3`2XLbMh3^2XLbMh3^2XLbMh3^2XLbMh3^2XLbMh3^2XLcMg3]2ZLbMf3^2ZLbMe3_2[LaMd3_2^L`Mb3`2_L_Ma3a2`L^M`3R2]JZIK3Z2_4`3U2mLkMS3U2mLkMS3S2oLmMQ3S2oLmMR3R2nLnMR3R2nLnMR3P2PMoMQ3Q2oLnMS3Q2mLoMQ3Y1UJXJO`0m2P4m2QOVJ_LOj0j0UOVO13O4b0g2n3o2oNWJaLL011Oc0^1L`1P4n2nNdJSMo0O_1R4SM_Kd5]3lJSMZ1@VNOm2f4g2hNlJRM_4V4e0fNnJTM]4X4b0aN^JgL00c0=]OCR5i4?VN`KXMPOJT5j4mJUKY5n2gKYMoNIU5k4kJVKZ5l2iKUMX4n4OmMiKSMZ4P5NkMZ1T2fNlMZ1T2fNlMZ1T2gNjMZ1V2fNiM[1W2dNkM[1U2eNjM\\1V2eNiM[1W2cNiM_1W2`NkM_1S2bNoMeJTMf6l4fNRN[1n1dNRNeJRMe6k4gNVNX1PM\\I00d4[5\\N`1c1bN^N\\1c1cN\\N^1d1cN[N]1f1bNYN_1f1bNZN_1e1aN[N_1e1aN[N_1d1aN\\N`1d1`N\\N`1d1`N\\N`1d1aN[N`1d1aN[N_1f1SI]K[5l2b1g1SI]K\\5l2`1j1aNTN`1m1`NRN`1n1`NRN`1n1`NRN`1n1`NRNa1m1_NRNb1n1^NRNb1n1^NRNb1Q2[NoMf1n1\\NRNd1n1[NSNe1l1\\NTNd1l1]NSNc1n1[NSNe1m1[NSNe1k1]NUNc1m1[NSNe1l1\\NTNd1l1\\NTNd1k1]NVNb1j1^NVNb1j1^NVNb1j1^NUNd1j1\\NVNd1j1\\NWNc1i1]NWNc1i1^NVNb1j1^NUNc1l1\\NTNd1l1\\NTNd1l1\\NUNc1k1]N\\KbJg2R7l1\\N]KbJg2R7l1^NTNb1l1]NVNb1j1]NVNd1k1[NTNf1l1ZNTNf1l1YNTNi1k1VNWNi1i1WNVNi1k1WNWNh1h1XNWNi1j1VNVNj1j1VNVNj1j1WNUNi1k1WNUNi1k1WNUNi1k1WN\\KeJg2T7o1WNRNh1m1YNSNg1n1XNRNi1m1WNSNi1n1VNRNj1o1TNoMo1Q2PNPNP2Q2mMPNT2P2mMoMS2P2mMQNS2o1lMSNT2m1kMTNT2k1mMUNlJUMU7f4oMUNlJUMU7f4PNVNo1l1PNSNR2j1PNVNP2j1PNUNQ2k1PNTNP2l1PNTNP2l1PNRNR2n1nMRNR2o1mMPNT2Q2jMPNV2P2iMPNX2P2hMPNX2P2fMRN[2m1eMUNY2k1gMUNY2k1hMTNX2l1hMTNX20gHoNQ5R1W2_OQIiMHf1P5S1VMoMk1]1ZLjMEg1P5R1gLUMZNW1o3Q1aLAf1XN=j2XNWMSOa0Oj0R4k0bL@e1YN=k2dMTMH0O0O20Z2S4LaLAd1XN>l2bMO7UMP3W2UMoN\\OYOY2A;n2]MTMOj2<XMR3S2VMjNA^OS2B=Q6gMSJQ3R2WMjNA^OQ2F=n5eMVJT3n1XMiNC_Ok1JjNGg0S6bNlIK<[3m1YMcNGTOF?8FHLW1<ZOOP1j5aNZJ<@Z2Y2kMaNMEG1i0M@0Q1_6bNYIOKf2Y2kM^N2H\\OA0`0o0N_OOR1k6_NjHg2W2lM]N3NYOMQ1M^O0S1P7U1kJmMXN9f03WO_O0S1P7U1kJmMTN=j0OWO_O0S1P7U1lJlMPNa0l0MXO]O1S1P7V1kJlMmMd0n0KYO[O2U1n6V1kJlMSN=j0OZO]OOU1o6V1kJlMkMe0S1DZOBMS1P7V1kJlMgMi0W1@ZOANS1P7W1jJ^OkNfN_O_ONV1n6W1jJ]OlNoMlNa0c0EMX1m6V1fJASOgMjN00g0d0@LZ1n6W1bJD\\OXN\\OCG[1n6V1eJA[OXND_O_Oc1l6U1fJA\\ORNaNFg1c1e5T1hJ@[OPN>]1^5S1iJ[OEoM5b1^5T1iJVOj1G\\3S1jJoNQ2MV3T1hJkNW22Q3R1iJeN\\28l2S1hJ`Na2>f2R1iJ]Nd2a0b2T1hJ[Ng2a0b2S1gJ[Nh2b0a2S1gJ[Nh2b0a2S1iJWNh2f0_2R1iJXNi2e0_2Q1gJ]Nj2", "size": [640, 480]}, "area": 112485.0, "iscrowd": 0, "image_id": 10114, "bbox": [87.0, 17.0, 393.0, 623.0], "category_id": 98, "id": 10012298}, {"segmentation": {"counts": "Yng1>ac0?^O41O000O11OO100000000O14L4LlNW]OT11kNcb00\\]OY1eb003M1O01O0000001O000O12OJ5000M_]OgNab0X1a]OhNoa01n]O25MKT17lNja00[^OS1Pb0mNT^Oo0ma0QOY^O3B9Tb0E\\^OOI4ja0N`11O1a\\OMQc0a02I5L5Mb1LdN1O000ROOn]O0_V11a\\O0[mb6", "size": [640, 480]}, "area": 1407.0, "iscrowd": 0, "image_id": 10114, "bbox": [89.0, 307.0, 53.0, 67.0], "category_id": 100, "id": 10012299}, {"segmentation": {"counts": "d\\f13mc02M2N5ZOEo\\O1030L100`0lb0@S]O<15kb0_OT]OP1lb03ON22NO11OO100O1001O00O1NS]OQOmb0l0S]OTOPc0a0V]O_OJ0Sc0`0;0000000000001OO11O0000O11O00001O1O00000000O100000000O11OO1000000001O00005e\\OYOSc0P1l\\OROjb0R10000000O10000000000O11OO100000[]OnN\\b0R1d]OnN\\b0U1^]OlNO0bb0\\1O4LO11O001O0000O11O1O00001O00001OO1O1O100N22N000000O11OO11O0000O1001OO12NO100O100000000000000000c]O_N[b0c1000002NO12NO1O100000f]O\\NXb0f10001OO1O100O100002N00000000001O00O10000001OO1000000O10000O1001OO11OO1001O000000000000001O1OO1001O0000O100000000000000001OO1001O001O00O10000000000000000000000O1O1000000000000001O0000001ON2001OO1O10000O11O0000001O00001O00O1001ON2O1O1000000O11O001O00000000O100001O1O1O1O1O1O0000000000000000O1001OO10000000000000000000000001O000000000j]OXNTb0h1l]OXNTb0j10O1O10000O1O100000000001O00000i]OYNUb0i1000000000O1O1N21OO1O10000001O0000001O00000000O1O100N200001O00000000000000O11O0000O10000000000O11O0000O1001OO11O1O1O1OO11OO11OO11O0000000000O1", "size": [640, 480]}, "area": 18889.0, "iscrowd": 0, "image_id": 10114, "bbox": [87.0, 0.0, 393.0, 59.0], "category_id": 102, "id": 10012300}, {"segmentation": {"counts": "b[[22mc02K4M4N101O02N3M010O00O101H71O0001M_Oe\\Oa0[c0200A`0M200000001N1001OO20O001O010O000010NPOZ]O1O>hb0@[]OM0c0fb0[Oe]Oe0kb01O00000010O001O01O0MZOm\\Oe0Sc0\\Ol\\Od0Qc0[Oo\\Oj0Pc0WOo\\Oi0Qc030001ON2O1O100O100O1O100O10001O0O1N21O00O10O010000O10000O1O1000O0100O100OO2M201N22M1O10000O10000O100O100O100O01000O1000O13M0N7KN23MoS30PlL2N1O1000001O00000001O00O100O12N01M200L4000000000001O01OO1O100MCc\\O=]c0Gc\\O5]c0Kc\\OM04^c0Kb\\O045[c0Jh\\O7ac0O0000000J^\\R10kolN0jc0300000000O2O00000000001O000000000000010O0000001O000000000001O00000000000001O000001O0000000001O0000000001O01O00O11O00000000000001O0001O0EIm\\O7Sc0Im\\O7Rc0Jn\\O6Sc0Im\\O7Rc0Jn\\O6Sc0Im\\O7Sc0Im\\O8Rc0Ho\\O7Qc0Io\\O7Qc0Io\\O7Qc0Io\\O6Rc0Jn\\O7Qc0Jn\\O6Rc0Jn\\O6Sc0Im\\O7Xc06O10000000000O2O000L4001O1O001O2N00N21O3M001O0000001O0000O20O00O1N2O100O1O1O1O1000000001ON22O2M1OO10ON1kD", "size": [640, 480]}, "area": 4460.0, "iscrowd": 0, "image_id": 10114, "bbox": [120.0, 340.0, 360.0, 48.0], "category_id": 107, "id": 10012301}, {"segmentation": {"counts": "o_l21mc02L400000000000000O1O1O1O1O1N2O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1N2O1O1O1O1O1O1O1O1O1O1O1^NmNV@T1j?a100O100001O1OM32NO1002N002N001O0bLa_O[3_`0eLc_OY3c`0O1O00N22NO10000002NO10000O1MgL__OY3a`03O1M3N2L4000000000000000000O11OO1O1O1O1O1N2N2H82N0000000000O100000000000000000000001O0000001O2NN200000000001O000000000000N2000000000000O1O1002NO1002kKW@o3m?0002N2N2VLl_Od3T`0[Lm_Oe3W`0000000000000000000000N2O11OO100O11O001OO10000O1000000001WLk_Oe3U`0[Lk_Oe3X`001O0000000000003MN200O1001O00N22NO100O100000000000000001O000000000000001O0000000000001O00000000002NO11O001O1O001O00O1001OO100001ON200000000001O1O000000000000000000000kNa_OiN_`0W1a_OiN``0V1`_OjN``0W1`_OhN``0W1a_OiN_`0U1e_OiN\\`0S1h_OjN\\`0]O__OHLW1<ZOe`0F^_Oi0M@h`0Ak^O0`0o0N_Ol`0@W_OQ1M^Oaa0a0_^O_Oaa0a0_^O_O`a0b0`^O]O`a0d0`^O[Oba0c0`^O]O`a0`0a^OB^a0>b^OA_a0>c^O_O_a0Io]Oa0c0E_a0Fn]O00g0d0@da0=`^OC_a0:j^O_OXa0;f]OF`c07;JSn6", "size": [640, 480]}, "area": 31088.0, "iscrowd": 0, "image_id": 10114, "bbox": [147.0, 504.0, 321.0, 136.0], "category_id": 117, "id": 10012302}, {"segmentation": {"counts": "ZRj41mc03T\\OOgc01Y\\OO1Nbc0;O2N101N2I8HZOS]Og0jb09I7N3N10OO3NO12N0OO200L3100000000000001OO1O100M_]OgNbb0X1b]OhNZb00a]OV15jNM0[b0W1d]OiN04\\b0T1c]OoN]b0[1O01O0000102M1OO12N2N1O3M0N3O1O00000001O00001O1OO102M2QOR]O618mb0CR]OO7=ib0A^]O>Rc0O2N2N1O2O002JW\\O1Pf=OPZB014OK`c0b0L000000000O110O0O11O000000f\\O@Uc0?j\\ODTc0h0l\\OSOmb0T1J12OO100N22N0000O1001ON4NN3000NO21ON2O3N2mNX]Oj0Sc0L100O1O1O000002NO20M13OO00N200O2N7KWZb1", "size": [640, 480]}, "area": 4249.0, "iscrowd": 0, "image_id": 10114, "bbox": [246.0, 284.0, 153.0, 74.0], "category_id": 132, "id": 10012303}, {"segmentation": {"counts": "kQP22lc06L3[\\OF\\c0>b\\OB_c0?O4M1N1000000001O001N12NK6O001O1Ca\\O5MK[oh18]QWN>^a0Ba^Oh0Ua0YOk^Of0Ua0ZOl^Of0a`0ROi^OO61I8OHe0m0d`02\\_OMe`03Z_ONe`04[_OKe`08V_OJj`07j^O9Ra0]1MO11O2[_OSMR`0o2a_OPM74W`0o2f_OVMW`0Ie_OR30UM[`0Gg_O`3\\`00c_O`LZ`0`3f_ObLX`0d3O1O00N13NO011OO102M010000O1Mk_O[LU`0e33O1M3N2L311ONdL`_O\\3``021OO1003MO10O11N01O1O1ZO\\_OoMe`0Q2[_O]M0>f`05]_O\\ON2OO62Mn0MoN004:e`09k_O]O@o0OQO38i`0HT_O9o0GoNb00^OPb01T_O8kNG02bc019NZeo3", "size": [640, 480]}, "area": 6278.0, "iscrowd": 0, "image_id": 10114, "bbox": [102.0, 310.0, 173.0, 226.0], "category_id": 143, "id": 10012304}, {"segmentation": {"counts": "ejb82^c09f\\OHXc0;g\\OEYc0a001O000m]O\\Oj`0d0Q_OAo`0?Q_OAo`0?P_OCPa0=k^OGTOFna0c0l^OMSa03j^O2Ta0Nk^O3Ua0Mf^O9Ya0Gc^O=^a0B_^Oa0aa0_O\\^Od0da0]Oa^O=_a0CY^Oe0ga0[OU^Oi0ka0i0MOL4N32M101O0K6K4I6L6I6K5M201O102L10N3RB", "size": [640, 480]}, "area": 1853.0, "iscrowd": 0, "image_id": 10114, "bbox": [439.0, 445.0, 41.0, 79.0], "category_id": 168, "id": 10012305}, {"segmentation": {"counts": "SeQ91nc03S\\OMic09W\\OGcc0>MO1O10010N11ON1200OM32N00NoF", "size": [640, 480]}, "area": 192.0, "iscrowd": 0, "image_id": 10114, "bbox": [463.0, 290.0, 17.0, 15.0], "category_id": 171, "id": 10012306}, {"segmentation": {"counts": "0e8[;000000000000001O00000000000000001O00000000000000001O00000000000000001O00000000000000001O000000000000001O00000000000000001O00000000000000001O00000000000000001O00000000000000fGQER2Ni2R;UKREJLg10mM30MO30M\\20fM0O1OOW10nN6<k:1UEI0a1NTN9a0DF1KO20L31k0O10Ga0Z:0ZEFJ3KR13dN`01h3b0e6]OQE030L100`0OJ7GI15OMON11O0013Oa56\\JI0012o:N\\E1b01m52]IMi:OYmV46fghKK30j0OUO0[b0=o]OCE14<Vb00k]OOJ52_O61aa0>[^Oa0OPOO7ga09Z^Oa0M]Oha0h101M3O001UOgMX_OO`0[2Q>eM_AOj0j0UOVO13O4b0W2_ObMZ>4eAL011Oc0^1LV1f=lM[Bo0OU1e=mM\\BZ1@VNOc2U>mM[BU4c=mK]BS4_=ZKQB00c0=]OCg49YKR=Q1lBPOJj47UKR=R1mBoNIk48TKQ=T1iBo3a=QL]BQ4b=W10000O1O11OO100N30O1`IaB]6a=oM`BdK014OKa1a=_N_BZ4b0SMR=l2nBTMQ=m2oBSMGdNP=W4YCUMGdNo<X4ZCTMGdNo<X4ZCTMGdNn<X4\\CTMFdNn<Y4\\CRMFdNo<Z4[CSMEcNP=Y4\\CTMDcNo<[4\\CRMn<n2RCRMn<n2RCRMn<n2RCfK@V1^=U3QCdKDU1[=W3]CdKSOn0a=]3cC_L\\<`3eCaL[<`3dC`L\\<_3eCaL[<_3eCaL[<]3gCdLW<_3gCaLY<_3gCaLY<_3gCaLY<^3hCbLX<^3hCbLX<_3gCaLY<]3kCaLU<_3iCcLW<^3iCaLW<`3iC_LW<`3hCbLX<^3iCbLV<^3hCdLX<\\3gCeLZ<\\3dCdL\\<^3aBhKj0j0e<i3WCWLh<k3XCTLh<m3WCSLj<m3UCSLk<n3TCRLl<n3TCRLk<o3UCQLk<o3WCPLh<P4WCQLj<o3VCPLj<n3WCSLi<o3VCPLj<P4VCPLk<o3SCSLl<n3TCRLk<o3TCRLl<o3RCRLm<U4nBjKS=a5O21NO10000000000001O00000000M30O02OO110O1O0]IeBa6[=_IeBa6]=0O100O101O1M5M0O100O1000O3N000L40100cKaCV1_<[NfBZNW1Z3S<aMhBSOa0Oj0]3n;mLeBH0O0O20Z2]3Q;kL_E7UMd12]N\\=AdBOj2<XMd11ZNc`02]_O[1J\\N:5a`00__Oa0T1\\O^?0T_OK<h0R1[Oa?0a_O<@GXc0Nm\\OOK3dc0K^\\O4jc0Lmn?", "size": [640, 480]}, "area": 50432.0, "iscrowd": 0, "image_id": 10114, "bbox": [0.0, 0.0, 454.0, 364.0], "category_id": 173, "id": 10012307}, {"segmentation": {"counts": "aUi1c0\\c02O0H^OQ]Oc0lb0CQ]O=nb0;00000000000FS]OAmb0=Y]O_Ogb0a0\\]O\\Oeb0a0>1OO100O1000000001O001O01O01O001OO1O1O100O1O1O2N1002N001OO100O100000000001O4L2M3O1GZOS]Oi02BUb0Fi]O[1Wb0eNi]O\\1Vb0dNj]O[1Wb0eNh]O]1Vb0eNi]O[1Wb0eNi]O\\1Vb0dNj]O\\1Vb0dNj]O]1Ub0cNj]O^1Vb0601N0R^OXN1O\\a0j1b^OYNM0aa0h1]^OcNca0m10000000001O001O0000M_^OhM`a0X2a^OgM_a0Oc^OR2MoMda0O_^OR2NnMha0R230O11O000000O100000^O[^OiNda0X1^^OfNba0Y1_^OgNaa0Y1_^OgNaa0Y1_^OgNaa0X1`^OiN_a0W1a^OiN^a0X1a^OiN_a0W1`^OjN`a0V1`^OjN`a0V1`^OjN`a0V1`^OjN`a0V1`^OiNaa0W1a^OgN_a0Y1a^OgN_a0Y1a^OgN^a0Z1b^OfN^a0V1X^OdN:6^a0l0c^OmNO7^a0k0d^OnNN7^a0j0f^OnNM7]a0k0f^OoNL6]a0k0i^OnNJ6^a0l0h^OnNJ6^a0l0T_OTOl`0l0T_OTOl`0k0U_OUOl`0j0T_OTOl`0n0T_OoNo`0P1R_OoNo`0R1n^OnNTa0R1l01000000000001O0Z]OlNM0O0_b0_10000000001O1O1O1O1O1O1O1O2NO2N1O011N100If]OhNZb0V1i]OeNO0Xb0[1i]OeNO0Xb0Z1:O1O100O1J6JV]OWOib0h08J6O1001OO1000000O101O0O100000000000000000000001O0c\\O_O[c0c000000001O00010O3M1OO12N2N1O3M001O1O00000000000O100000KT]OUOlb0:T]OE14Vc01U]OJnb04a0Oama20]R^M3GJc\\O:_b0T1N002N000KaNh]Oa1Wb04000O11O001O0000O1ZNh]Oc1]b0]Nc]O^1db0bN\\]OV1kb0L01O0101OO11ON21O1OM32N00N2cF", "size": [640, 480]}, "area": 9845.0, "iscrowd": 0, "image_id": 10114, "bbox": [91.0, 283.0, 389.0, 81.0], "category_id": 176, "id": 10012308}, {"segmentation": {"counts": "e8S;m800000000000001O00000000000000001O00000000000000001O00000000000000001O00000000000000001O000000000000001O000000000001O0001O00000000000000001O00000000000000001O00000000000000001Oe2[M0[Nd1T1mNb0^O0O100O100O100001O0000001O0000001O0000001O0000001O0000001O00SNZH`Gf7_8^H^Gb7b8dHUFBg0i7T9gHRFIb0`7\\9aI]F_6c9dHSF78U7S:VImEi6S:XIlEh6o9\\IRFd6j9`IVF`6h9ZITFfN6o7`9aIgF_6Y9aIgF_6X9bIhF^6X9bIiF]6W9eHXF6b0U7V9bIjF^6V9RIYFROj0l7m8RI[Gm6f8RIZGn6k8mHUGS7l8lHTGT7n8jHRGV7P9\\HSFGP1l7o8WH_Gi7\\:0001O0000001O000O1O2N1N2O1O2N1O1O1O2N1O1O1O2N1O1O1O2N1O1O1O2N1O1O1O2N1O1O1O_NbIbE]6_:eI`EY6`:hI`EW6a:iI_EV6b:jI^EU6b:kI`ES6a:mI_ER6b:mI_ER6b:mI_ER6b:nI_EP6b:PJ^En5d:RJ\\Em5e:TJZEk5g:WJXEg5i:YJWEf5j:ZJVEe5k:[JUEd5l:\\JUEb5l:^JTEa5m:_JSE`5n:`JRE_5P;`JQE^5P;bJPE]5Q;cJoD\\5R;dJnD[5S;fJSER5n:nJQGR3m8fL`C7b5S1o6QOo61OM3K_\\O1\\c0;N100000000001O000O1000001O1O1N2O1O1N2N2NcT[1KckdN0O1O1O101N1O1O1O10000000000000001O01O000000000000000001O0001O0000000000000001O000001O00000000000001O0000000001O0000000001O000000000001O00000001O00000000000001O000001O0000000000000001O0001O000000000000000000010O0000000000000000000010O0000iTP1", "size": [640, 480]}, "area": 63322.0, "iscrowd": 0, "image_id": 10114, "bbox": [0.0, 277.0, 428.0, 356.0], "category_id": 183, "id": 10012309}, {"segmentation": {"counts": "ZmY41^34PMM[?2WCNZM60OY?1]CLWM5KIOO1:_?O]CLTMi0_?\\O\\Ck0e<WOYCj0f<WOYCi0g<YOWCh0U<jNbAa0X2f0P<VO_A7_2d0Q<TObA8f1Z1h<`N`A8V1i1Z=nM_A;o0n1a=iM_A;g0S2i=dNoAe1n=^NiAj1V>WNbAS2o=WLZBd5c=^J\\Bd5a=^J]Bd5b=]J]Bd5a=^J\\Bg53WJR=2jBj5NWJX=^6O2OO1000000001O01O00000005K7I2M2O1O00001O0O1nCgH^OOl;[7eDjHX;X7fDmHV;U7iDkHW;U7gDnHY;Q7fDPIV;^7_DcH];R86J00O15K1O000000000000000000000000001O00002N2N3M003M1O00002NM32NO1000F:B>cIUCY5W=eJlBo4^=RKcB`4k=^KWBT4V>kKlAj3_>ULbAa3f>`L[AV3n>YLg@>=R3R?`La@>>Q3S?oLn@W3k>hLVAn2T?RMm@g025X?UOg@6`07Y?AW@Ng06`?IW_OHS26T?NY^o1", "size": [640, 427]}, "area": 20773.0, "iscrowd": 0, "image_id": 10115, "bbox": [220.0, 366.0, 105.0, 274.0], "category_id": 98, "id": 10012310}, {"segmentation": {"counts": "Rlh11oc0001O0NOU\\O1kc0OU\\O2jc0NV\\O2jc0NV\\O2jc03O001O6i\\OD_O<ab04d]O5]b0g00000000N2K5G9E;L4N2N2O100O1000000O100001O002N1O2N1O3M2N3M3M2N3M4L1O5K4L3M1O2N3M3M2N4L4L3M1O7I8Hk0k^OaLS`0g3O000000000000000000001OO11O000000000000001O0000000000000000001O0000000000000000001O0000000000000000001O0000000000000000001O00000000000000000000001O000000000000001O000000008H000000O1000000000000000000O11O@`0O1O10000000000000000000000O11O0000M3J6M3O1O100O11O0000O100000000000000001O1O00O10000O1000000O11OO1O100001OO1I7G92N00O11O000000O1001O000000O100G9I^^OlMfa0T231O00001OO10000000000001OM3O100nMf^O^1la02001O00O1O10000O1001O1O001O000M4Bk0\\Ob\\OG_nW1", "size": [640, 427]}, "area": 24036.0, "iscrowd": 0, "image_id": 10115, "bbox": [91.0, 0.0, 273.0, 136.0], "category_id": 102, "id": 10012311}, {"segmentation": {"counts": "gjb22mc0;F1N2M2d]OMh`07V_OKl`03k^O@[O`0ia00f^O]O@11h0ja0Jd^O=Za0Df^O=Za0B_^Oj0[a0T1J1N2O2M8o^ORM``0Y3L5J4O011SK^LZIb3hK_LX:d0mG@`N`3XO`LKMo9R2nFQN1e3PObLMKo9[2cFgM?g3kNkLR:l1^FfMc0a4P9iMZFiMe0^4Q9iMZFjMg0Z4n8mM[FlMa0Z4T9jM[FmM?Z4V9jMZFmM?Y4W9jMYFnM`0W4X9lMWFnM6`4c9cMUFlML]O1V5n9aMUFi3k9VLnER4R:n20`NPFPIP:o6ZFhHg9W7bF_H_9a7jFVHV9j7RGnGm8S8`101N10O100000O100000O11N2O1O1O00000O10000000O02O0000000O01N2O100000O100000000O01001O000O100O2L3L40001O00000]OkCYIW<b6VDUIk;k6TDWIj;j6XDTIi;l6VDSIk;l6WDSIi;l6f0O10NFRCfIn<Y6TCfIl<Y6VCeIk<[6VCdIj<[6XCbIj<_6VC`Ij<`6;O100000001O00O110O000000000000O10000000000O1O1N2dNfBQL[=[3ZCdLf<i2kBXLa0P1c<ZO^B15a26SMe08PO:f=^O]B1M1169[2G]Md10iN6d=KPB5<k0E_OQ3Kh:FWB2>m0Y3ZOX:G`B=l3DT`n2", "size": [640, 427]}, "area": 30609.0, "iscrowd": 0, "image_id": 10115, "bbox": [132.0, 167.0, 144.0, 319.0], "category_id": 109, "id": 10012312}, {"segmentation": {"counts": "m3c0i3g1`9[N]Fg1b9ZN^Fh1`9YN^Fg1c9YN_EZOhM5Ec2n<QNiCXO002O_O0F60J0l2h0TMW;R1_D]OG0;_4MYKd92YFm2KX3T8bNlGa2_5QObJP2\\4oMhKh2^3YM`Lc3e2^LWMS5[1nJbNi5i0dRY1VOmTgN1O0k\\Oa09Nk?DT_O08i091k0XOfNJO0005061j=o2^BUM^O^2V=a0UCl0i;XOWDU1T;VOjDS1b:ZKTEo34R1Y:a4G`0TJnE<1fKg:FlEY3\\On01PLOHV<Q3kCV10lK]<m2cCT1d<lN[CPM1n3h<TOWCnL2g3l<\\ORCmL2X2OnNOjNV=o2jBQM2_1OWOO[O0S10lNZ=k2kBcNLXOb0L]O^O`=g2eBfNNWOa0LT=V1_B[NI`06h03XO;OW=R1aBnN01D_Oi0a0LC_O0k=`0[BJ412Fc0<L_OQO1R>a0YBI63Z12]=OUAJ6GB0l1?Y=OTAIa27\\<NUAHc2Md<:l@E[b0;f0O0O10000O1O1MYjQ6", "size": [640, 427]}, "area": 54341.0, "iscrowd": 0, "image_id": 10115, "bbox": [0.0, 0.0, 116.0, 640.0], "category_id": 112, "id": 10012313}, {"segmentation": {"counts": "TeY25Xc0MY]O7cb03R]O4gb0h0KO1O100000000O100000000O101OO1O100000O10000O100000000O1000oNoNS_OP1l`0TOR_Ol0m`0WO_^OL4m0[a0[O^^ON1h0`a0^O[^OOOe0da0U11000000O1O1000O1000O1000000O10O100000O9H8H8G9H4L0O101O000O10000O1O100N2O1N2O10000000001O02[]O[Oka0h0R^OYOma0j0P^OVOPb0l0n]OUOQb0n0c]OSO2M]b0W1b]OkN]b0T1b]OnN^b0Z1000001O1O0f]O_NUb0c1h]O^NXb0e1000O20O2N00fNm]O0M:Ub0BQ^O0O>Pb0_OR^OKIOO1:e0ma0[Og^Oe0Yb0201k\\OXOnb0m01O1O1O02OO01O20O002N100N11O20YOQ]O7Rc0CQ]O?Wc0100N2O2N002N100O2N002N2N14K7K0O000O01O10O000O1O1N3O0N2O;EbXP3", "size": [640, 427]}, "area": 5774.0, "iscrowd": 0, "image_id": 10115, "bbox": [117.0, 484.0, 155.0, 148.0], "category_id": 117, "id": 10012314}, {"segmentation": {"counts": "WbT62mc0:_O:XOf0ZOg0YLRNdDd2[;]MPDX3P<iLWCo3i<RLZBk4d=i001N101O0O7JE;D<0O01aNlARMT>[2_BdMa=e1WCUNo<U1gCjNZ<l0QDSOn;o0QDPOP<EaAe0_2Ff<=WC]OP=Fa@8`ae1", "size": [640, 427]}, "area": 3344.0, "iscrowd": 0, "image_id": 10115, "bbox": [314.0, 149.0, 28.0, 201.0], "category_id": 133, "id": 10012315}, {"segmentation": {"counts": "SSo182Ibc0<TONg]O4Xb0Me]O6Yb0Le]O6GB00Ub09R^Od0ma0^OP^Od0Pb0_Ol]Ob0Tb0e000O10000O1O1M3K500O1O100O1O100000000O100000000O1000000000000O10000O100000000O10000O100000000O1000000O1O100000000O1000000O100000000O10000O10000O1000000O10000O1O100N2O1N2O10000000000002eMQ_Oa1Ra0\\No^Oc1Ta0ZNl^Of1Va0XNk^Og1Xa0mMi^O2MS2aa0lMa^OS2^a0lMd^OT2da0000001O1O0PNU^Ok1ma0RNT^On1oa0000O11O2N003M2N1O001O2]Nf]O\\1_b000000002N001O2N1O1O1O2N002N3M002N1O1O1O2N002N1O1O2N002N2N1O002N1O1O001O1O00001IX\\O4kc0O3M00QTR3", "size": [640, 427]}, "area": 10516.0, "iscrowd": 0, "image_id": 10115, "bbox": [100.0, 543.0, 169.0, 97.0], "category_id": 152, "id": 10012316}, {"segmentation": {"counts": "RWb11oc01O0k\\Oa09Nk?DT_O08i091k0XOfNJO0005061j=o2^BUM^O^2V=a0UCl0i;XOWDU1T;VOjDS1b:ZKTEo34R1Y:a4G`0kNoE[Gg:FlEn7\\OQHV<d753J2N2OfMoCeKOjN3j0T<j3jCRLO[O0S10lN3i0Y<g3eCVLb0L]O^O6e0]<f3`CYLa0LGNb<f3\\C]L;OW=^3aBcL9C_O0k=f3^BgLb0_OQO1R>g3\\BhLj>V3VAiLl>V3UAiLl>V3VA]LV?`3f0K6F9E;L4N2N2O100O[OgMe_OY2h?dMX@7KV2m?mMm_O4OP2S`0W1100001O002N1O2N1O3M2N3TJlKfKW4V4TL`Kn3^4SLbKo3Z4SLdKR4V4PLaGMg2U4d5RLcGKh2X4c5kK\\G@[O`0e3Y4`5gKZG]O@11h0e3V4^5dK[G=V3P4]5dK]G=U3Q4]5aKWGj0W3h3^5\\M\\Jg2a5ZM^Jh2_5ZM`Jj2\\5XMbJl2Y5\\MhGRM`2e5c5dM\\J]2`5hM^J_2W5eMhI^L]OU6b6]MPJbL\\Ol6XOaIi5R3aKdLjNL4a8I]Gc4b3hK_L79^ON1Y9_2dF`N`3XO`LKMM<LOOe:U1SE1e3PObLMc<b0ZC?g3kNg9`0fBc0^a0ZOe^Oe0[a0[Of^Og0Wa0YOl^Oa0Wa0_Oj^O?Xa0@i^O?Wa0@j^O`0Wa0_Oj^O6aa0H^^OL]O1Wb03Y1ISdQ5", "size": [640, 427]}, "area": 31745.0, "iscrowd": 0, "image_id": 10115, "bbox": [80.0, 7.0, 88.0, 563.0], "category_id": 168, "id": 10012317}, {"segmentation": {"counts": "gSo2a0`c0k0TO6J0000O11O00O1000001OO11O0000000001O0001N1001O00O10000000001O0O11O1O1O1O00000001O00000O11O00000000O2M2O10000000000000001N1001O000000001O00000010O000000000O11O01O00000N208H000000O1000000000001O00O11N11O@`0O1O10000000000000000000000eBeNP8\\1\\GYOd8f0iFkNXLa0o<d0[C]N15a26SMe08PO]<c1[C`N1M1169[2G]Md10iNZ<`1gCTN5<k0E_OQ3c;POnCoM2>m0Y3o:cNRDUN=l3DgKb;Z8J2O0O11O0000O100000000000000010O1O02M3N3M0O4M1O000O3OL23M010001OO1I7G83OO0OSNbE[J^:c5lESJU:e5UFYJ[O`N^:a6mFoJeN`N^:k5dGfK[8e0gEd0d2gNe7d0hE0X3\\OQ7c0hEXOo34Z6d0hE[Nk4R1[5b0^K^Ob4b0_K^O_4b0bKYOc4g0^KjNg4_1YKQNP5DRE^2\\a031O000O2OO10O10000O100mNT1ZOYf=_OdZB9G9G9G9I7F:F:D<G9C=[Oe0F:E;F:G9F:G9G9G9I7I7E;G9B>F:D<@`0_Oa0_Oa0C=A?D<^Ob0A?C=B>D<A?_Oa0_OaARKn>[4e0_Oa0_Oa0]Oc0fNZ1_Oa0\\OdP:", "size": [640, 427]}, "area": 43895.0, "iscrowd": 0, "image_id": 10115, "bbox": [152.0, 8.0, 275.0, 632.0], "category_id": 172, "id": 10012318}, {"segmentation": {"counts": "0m3c0Lg1`9[N]Fg1b9ZN^Fh1`9YN^Fg1c9YN_EZOhM5Ec2n<QNiCXO002O_O0F60J0l2h0TMW;R1_D]OG0;_4MYKd92YFm2KX3T8bNlGa2_5QObJP2\\4oMhKh2^3YM`Lc3e2^LWMS5[1nJbNi5i0n_`1WObg_N0V\\O1`c0=F6J60O1O2L4IWd[6", "size": [640, 427]}, "area": 4793.0, "iscrowd": 0, "image_id": 10115, "bbox": [0.0, 0.0, 101.0, 640.0], "category_id": 177, "id": 10012319}, {"segmentation": {"counts": "f_P6:Xc0>D<E;C=C=F:G9G9J6007IF:jMVMnBR3c<]MoBQ3g<VMnBX3Q<_KSD`1C^3W<XKSDa6b;jISDa6_;W1@`0H8E;D<00O1aNcF[H]9R7VGmHk8[6mG_IY8k5]HTJd7b5gH]JY7d5fH[J[7Z4VFPL_2F[7j2fGcMl0]Od7[2VH[MgM8`=d1V4O100nMf^O^1la02001O00O1O10000O1001O1O001O000M4Bk0\\Ob\\OGZb03W19G9G9G9I7F:F:D<G9C=[Oe0F:E;F:G9F:G9G9G9I7I7E;G9B>F:D<@`0_Oa0_Oa0C=A?D<^Ob0A?C=B>D<A?_Oa0_OaARKn>[4e0_Oa0_Oa0]Oc0fNZ1_Oa0\\OdP:", "size": [640, 427]}, "area": 37976.0, "iscrowd": 0, "image_id": 10115, "bbox": [307.0, 0.0, 104.0, 640.0], "category_id": 182, "id": 10012320}, {"segmentation": {"counts": "eQZ34kc0:G9G9G8H8H8G9H8H8H4L0O100000000O10000000001N100000000O100000000O10000000001N100000000O100000000O1000000002N101N1O2N1O2C=XOg0G:G8G:G8HSc]1LV]bN3O1N2N2O1O1N2O1O0O2O1O1O0001I6Eegn1", "size": [640, 427]}, "area": 5396.0, "iscrowd": 0, "image_id": 10115, "bbox": [169.0, 405.0, 157.0, 118.0], "category_id": 183, "id": 10012321}, {"segmentation": {"counts": "^YW2;^;GmE>d8R1XGQOd8T1TGSOh8WO_Fe1?\\On8oNlFa14Ad9<ZFEj98SFJR:3lEMZ:O`E5h:DUE>T;XOSEb0U;lNWER1d;M6J>BTml5", "size": [427, 640]}, "area": 920.0, "iscrowd": 0, "image_id": 10123, "bbox": [170.0, 307.0, 16.0, 120.0], "category_id": 123, "id": 10012322}, {"segmentation": {"counts": "0neQ1;QgnN;D<D=D:E3M4M2M4L3N2M4L3ZG_M_6d2]IjMU6Y2hIPNo5S2nIQNa1UOR1m2XMSNd1POR1P3WMSNe1oNQ1R3VMSNg1kNR1T3TMUNh1gNR1X3SMTNh1eNS1Z3SMTNh1cNS1[3SMUNh1`NS1^3SMTNh1_NS1`3TMSNg1^NS1`3UMUNf1[NS1b3VMVNd1YNV1b3TMXNd1VNW1c3TMZNc1RNZ1e3QM[Nc1QN\\1d3QM]N_1PN`1d3PM_N\\1oMd1c3nL`NZ1oMi1a3lLcNV1nMo1`3jLdNS1nMR2_3jLeNP1nMW2]3hLhNl0nM\\2[3fLIJnL`3Z3fLk0Z3UOeLk0\\3VOcLj0]3VObLj0^3XOaLh0_3XOaLg0`3YO`Lg0_3[O_Le0b3[O^Ld0c3]O\\Lc0d3]O\\Lb0d3_O[Lc0d3\\O]Lf0a3ZO_Lh0^3YObLj0[3VOeLl0Y3TOgLn0W3ROjLo0S3ROmLP1Q3POoLR1o2mNRMU1k2lNUMV1i2jNXMX1d2iN\\MY1b2gN_MZ1_2fNaM]1[2dNfM]1X2cNhM_1U2aNmMa1P2_NQNb1m1^NSNe1i1\\NXNe1f1[N[Nf1b1[N^Nh1_1XNbNh1]1XNdNg1[1YNfNg1Z1YNgNg1W1ZNjNe1V1[NjNe1U1\\NlNc1T1[NoNd1Q1YNSOg1k0WNYOh1g0UN\\Ok1c0SNAl1>QNGP26nMNR20lM4T2JjM:W2CgMb0X2\\OfMh0Z2VOdMGfLJe5<ZM2VMC`59\\M0WMI\\55_MJ\\M3T51cMBaM?k4MfMB`Mc0DkN]4n0aNB_Mh0]OmNb4g0dNA_Mn0VOnNg4`0gNB\\Mi1m3aNjN@]MQ2i3\\NlN@]MV2g3VNmNC^MZ2e3oM>S2BjM?Y2_OeMb0]2[ObMg0`2UO`Mm0c2mN_MT1a2iN`MY1a2cN`M_1`2^NaMd1_2XNcMj1]2VNbMk1_2TN_Mn1a2RN]MP2c2PN[MR2e2nMYMT2h2kMVMW2j2U40cIWMX2i2hMYMV2h2iMZMU2f2kM\\MS2d2mM^MQ2b2oM`Mo1a2PNaMn1_2RN_MP2a2]41O0000001O00000000000000O10000000000O1000000000000O1001O001O000N3L3MoLoEo2m9PMWFP3f9PM]FQ3_9nLeFR3X9nLkFS3f9001O001O1O1O1O1O1YOg0J6K5M3N2O1O1O1N2O1O1O1O1NXLbGf2d0XLS7n0]Hk2>ZLS7f0eHQ35\\LT7>lHW3M_LT77SIZ3HaLT70YI_3BdLS7I_Ic3\\OhLS7@fIh3VOjLY8V3eGnLY8R3fGQMX8o2gGSMX8m2fGWMX8P4O1N1O2L4G8H9L42M2O2N2N0O01N1O1O1O1O1iIUJl4l5TKUJj4k5VKWJh4j5XKWJf4i5ZKZJc4g5]KZJa4g5^K[J`4e5aK\\J]4e5bK^J[4b5fK_JX4b5gK`JW4a5iK`JU4a5jKbJS4_5mKbJQ4^5oKdJo3]5QLdJm3]5RLeJl3\\5TLeJj3\\5ULeJj3\\5VLeJh3\\5WLeJh3\\5XLdJg3]5XLeJ[OoNl3]6iLdJYOSOk3Z6kLeJWOTOl3X6mLdJUOXOk3U6oLeJSOYOl3S6QMdJQO\\Ol3Q6RMYKl2h4TMYKj2h4UMZKi2g4WMZKg2g4XM\\Ke2e4[M\\Kc2d4]M^Ka2c4_M^K_2c4`M_K^2a4cM`K[2a4dMaKZ2`4fMaKX2_4hMcKV2^4jMcKT2^4kMdKS2\\4mMfKQ2[4oMfKo1[4PNgKm1Z4SNiKo0[MlNm65iKh1W4XNkKg1U4YNlKe1U4ZNlK@RMj1R7fNnK^OQMk1R7gNmK]OQMl1S7fNmK]OPMm1S7fNoK[OnLo1T7fNnKZOnLo1V7fNmKZOnLS1Q8DUK0X5OhJ0Z5OfJO\\52cJM_52aJN_52aJM`53`JMa53^JLc54]JLc54]JLd53\\JMd54[JKf55YJLh53XJMh54WJLi55VJKk55TJKl56SJJm58QJHo59oIHR68mIHS69lIGT6;jIEV6=hICX6e0`I[Oa6h0[IYOd6g0[IZOe6g0ZIYOf6g0ZIYOf6g0ZIB]65mI5h5F^Jd0W5]OiJl0n4UOQKP1i4QOWKn0i4TOVKk0j4VOWKh0i4ZOWKd0i4]OXKa0h4AXK=h4fN`H`0i2h0g4gNbHb0g2e0g4iNcHb0i2a0d4mNdHc0j2<b4QOeHc0m27^4VOfHc0Q31Y4\\OgHd0X3FQ4FhH9LAc37i3OiH9Q4_OV38jH9d;F^D:a;FaD9_;FbD:];FdD9\\;GeD9Z;GgD9X;GiD9V;GkD9T;GmD8S;HnD6U;HmD6V;GkD8X;EiD9V<O2M2N2NgVj2", "size": [427, 640]}, "area": 199512.0, "iscrowd": 0, "image_id": 10123, "bbox": [0.0, 0.0, 640.0, 427.0], "category_id": 173, "id": 10012323}, {"segmentation": {"counts": "neQ1;o<;D<D=D:E3M4M2M4L3N2M4L3ZG_M_6d2]IjMU6Y2hIPNo5S2nIQNa1UOR1m2XMSNd1POR1P3WMSNe1nNR1S3UMSNg1jNS1U3SMUNh1gNR1X3SMTNh1eNS1Z3SMTNh1bNT1\\3RMUNh1`NS1^3SMTNh1_NS1_3UMTNf1]NT1a3TMUNf1ZNT1c3UMVNd1XNW1b3TMYNc1UNX1d3SMZNc1QN[1e3QM\\Nb1PN]1e3PM]N_1oMa1e3oL_N\\1nMe1c3nLaNY1nMj1b3kLcNV1nMo1_3kLeNR1mMS2`3iLeNP1nMW2]3hLhNl0mM]2[3fLJImLa3[3eLj0[3VOdLj0]3VOcLj0]3WOaLi0_3XOaLh0_3YO`Lf0a3ZO_Lf0`3[O_Le0b3\\O]Ld0c3\\O]Lc0d3]O\\Lc0c3^O\\Ld0c3\\O]Lf0a3ZO_Lh0^3YObLj0[3UOfLm0X3SOhLo0V3QOkLP1R3QOnLQ1P3oNPMS1n2mNRMU1k2lNUMV1i2jNXMX1d2hN]MZ1a2fN`M[1^2eNbM^1Z2cNgM^1W2bNiM`1T2aNmMa1P2_NQNb1m1^NSNe1i1[NYNf1e1ZN\\Ng1a1ZN_Ni1^1WNcNi1\\1WNeNh1Z1YNfNg1Z1YNgNf1X1[NiNd1W1[NjNf1T1[NmNd1S1YNQOf1o0WNUOh1j0VNZOi1f0TN]Om1a0QNCn1<oMIR24lM0T2NjM6V2HhM<Y2AeMd0Z2ZOdMj0\\2TObMQ1]2lN[L`2d3^MiKW3W4gLfK_3Y4_LdKPOdN^4g5`LbKROPO\\45dK`4l0XLTO[O[4HjKc4e0WLVOFP5S4gKTLXO3n4j3fKPL\\O`0k4_3fKoK^On0f4S3hKmKB]1e0lMl2k4iLiKFl11nMa3\\4eL_OZ3?eLHW34iL<l2_OTMP1a2kNaMV1_2fNcM\\1^2`NcMb1]2[NdMg1\\2XNcMj1]2VNbMk1_2TN_Mn1a2QN^MQ2b2oM\\MS2d2mMZMU2g2jMWMX2i2hMVMY2j2gMWMX2i2hMYMV2h2iMZMU2f2kM\\MS2d2mM^MQ2b2nMaMP2`2oMbMo1^2QN`MQ2`2]41O0000001O00000000000000O10000000000O1000000000000O1001O001O0O1M4L3MoLQFm2k9RMYFn2d9RM_Fo2]9PMgFP3V9PMmFQ3o8nLUGR3h8nL[GS3a8lLcGT3Z91O1O1O1O1O1VOj0J6K5O1O1O1N2O1O1O1N2O1O1OWLbGf2c0ZLS7l0^Hk2=\\LR7e0fHQ34]LU7=lHW3M_LU76RI[3HbLT7NYI`3AeLT7H_Ic3\\OhLS7@gIg3TOmLZ8S3eGoLZ8Q3eGRMY8n2eGVMY8j2fGXMY8o3N2N2N1J7G9F92O2N1N3N2N0OO2N1O1O100hIUJn4l5RKUJk4m5TKVJi4j5XKWJf4j5YKXJe4h5\\KYJb4h5]KZJa4g5_K[J^4e5bK]J\\4d5dK]JZ4c5fK_JX4b5hK`JU4a5jKaJT4`5lKaJR4`5mKbJQ4^5PLdJm3]5RLeJl3\\5TLdJk3]5TLeJj3\\5VLdJi3]5VLeJh3\\5XLdJg3]5XLdJ\\OoNl3^6hLdJYOROl3[6kLcJXOTOk3Z6lLdJVOVOk3W6oLcJTOYOl3T6oLeJRO[Ol3Q6RMYKl2h4SMZKk2g4UMZKi2g4VM[Kh2f4XM[Kf2f4YM\\Ke2e4[M\\Kc2d4]M^Ka2c4_M^K_2c4`M_K^2a4cM`K[2a4dMbKY2_4fMcKX2]4iMdKU2]4jMeKT2\\4lMeKR2[4nMgKP2Z4oMhKn1Z4RNgKY1ZM]No6:iKo0[MlNm64jKi1V4XNkKf1V4YNlK@QMk1T7eNlK^ORMk1R7gNmK]ORMl1Q7fNnK]OQMm1R7fNmK\\OQMn1R7fNoKZOoLP2S7fNnKYOoL`1e7VObK2U5NkJ0X50gJN\\51dJN]52cJN^51bJN_53`JM`53`JLb53^JMb54]JKd55\\JKe54[JLe54[JLe55YJLh53XJLi55VJKj56UJJl56SJJm57RJIn59PJGP6:nIHR68mIHS69lIGT6;jIEV6=hICX6e0`I[Oa6h0[IXOe6h0ZIYOf6g0ZIYOf6g0ZIYOf6h0YIB]6NTJ<a5F^Jd0W5]OiJl0n4UOQKP1i4QOWKn0i4TOVKk0j4VOWKh0i4ZOWKd0i4]OXKa0h4AXK=h4eNaHa0h2h0g4gNbHb0h2d0f4iNdHc0i2`0c4mNeHd0j2;a4QOfHd0m26]4VOgHd0T3MU4_OhH:IDa3:n3HiH:M_Od36f31jH:e;F\\D:c;F^D:a;FaD9_;FbD9^;GcD9\\;GeD9Z;GgD9X;GiD9V;GkD9T;FnD8S;HoD6T;GmD7W;FjD9U<O1N3M2N2NgVj2", "size": [427, 640]}, "area": 72382.0, "iscrowd": 0, "image_id": 10123, "bbox": [80.0, 3.0, 343.0, 424.0], "category_id": 183, "id": 10012324}, {"segmentation": {"counts": "WW7=V=1N2FAXC12?f<AWC12>d<AZC0110>^<EaCg0]<9O1L4K5M3O100O1000000O1000000O1000000O10000000000002N2N2N2N000000O1000000O100000000M3L40000O1000000O10000000000O100000000O100000000O10000000000O1000000O1O1N2N21O001O00O1O100O100001O00001O001O0000001O0oM_Do1a;QN`Dn1c;01O001O001O1O001O1O1XNUD30]1U<M00O1000000000000000000000000001O1O001O00000000000000000000001OO11Om0SO000000000000000000000000000000000000000000000000000000000000000000mn2GYQM20O2O00100O01O10O0010O100O100O10O10O100O10O11O0O10001N10000O2O000O101N101N101O0O2OO00101O1NN@TC>7CM1P<0lC971m;7SDIm;6SDKl;6TDJl;5TDKm;5RDLm;7QDIo;9nCHR<:kCFU<m00O0101N10KmChNS<X1PDfNP<\\132M2O000O2OO10O2O000mK^NUH1i3a1S4dNkK^1T3[NPJ0TO:e3\\1Q3ZN]I1h0<i2Z1P3eNQJ3m2Z1P3dNRJ3n2Y1o2fNQJ2o2[1m2eNSJ0P3Z1l2iNQJ0R3Z1j2fNUJOP3]1i2dNXJOo2^1h2cNYJOn2_1i2bNXJ1n2]1k2aNXJ0n2_1i2bNZJNm2`1k2`NXJ1k2`1m2_NYJ0j2a1m2`NYJOh2b1P3\\NYJ4f2`1P3^NXJOk2d1n2[NZJOh2f1n2\\NYJMi2h1n2ZNZJMi2i1n2YNYJNh2j1o2XNYJMi2k1n2XNYJ1d2h1f2nMWJb1P3`0h2PNWJa1P3?i2QNSJc1S3=i2OWM1i2OWM1h21VM0j20VM0i21WMOh22WMOi21YJkMc2T2S3=lLDT3<lLDR3>mLCR3>nLBP3`0PM@o2a0PM@o2a0QM_Om2c0RM^Om2c0SM]Ok2e0UM[Oh2h0WMYOf2j0ZMVOc2m0]MSOc1cMeL\\3h1ROc1oMULR3X2oNc1k1\\NVNd1j1\\NVNc1m1ZNTNf1m1YNSNg1n1WNSNi1o1UNQNk1o1UNQNk1o1UNQNj1P2UNQNk1n1VNRNj1n1VNRNj1n1RN]KnMf2P4m1RNVNn1j1RNVNn1j1RNVNn1j1RNVNn1j1RNVNS2e1mM[N]1ZMQMZ4c1\\NR1eMZMo3@eKP2g2U1gMZMm3AeKP2g2U1hMYMP4b1XNU1iMXMo3b1YNV1iMWMn3c1YNV1jMVMn3b1YNX1iMVMn3b1YNW1iMXMn3a1YNX1hMWMo3`1ZNY1fMXMP4_1ZNY1fMWMQ4]1\\N\\1bMXMQ4]1]N[1bMQM_OOb4e1]N[1bMQM_OOb4d1^N\\1bMPM_OOa4e1^N[1cMQM_ON`4f1^NZ1eMmLB2[4g1^NY1PNoLb3h1^NX1QNPMa3h1^NW1RNPM_3k1_NU1RNPM_3j1`NV1QNPM_3h1bNX1oMPM^3i1cNW1oMPM^3h1dNX1nMPM^3h1dNX1mMQM_3f1eNY1lMPM`3f1eN[1jMoLa3f1eN[1jMoLa3f1eN[1jMoL`3f1gN[1iMoL_3g1hNZ1[2aN^J_NX3P3Z2aN^J_NX3P3Z2aNiMb1S2nLbJ\\1\\3j1n1jLfJ]1[3l1n1eLgJ_1[3n1l1cLiJ^1\\3o1k1cLiJ^1\\3n1l1dLhJ^1\\3n1o1QNQNo1o1QNQNo1o1PNRNQ2m1oMSNQ2m1nMTNR2l1PNRNP2P2PNnMP2U2PNhMo1Y2SNeMm1[2TNdMk1^2TNbMk1`2UN_Mi1c2WN]Mh1d2XN\\Mf1e2ZN\\Mf1d2YN]Mg1d2WN]Mi1b2YN]Mg1b2XN`Mh1`2XN`Mg1a2ZN^Mf1b2YN_Mh1`2WNaMi1_2VNbMj1^2iMYJ\\OZ3k2]2iMZJ[OY3l2]2iM]JWOW3P3UMkLf4l0QKWOT3a3eL]LT5m0cNh2U1ZLVKQ1NoNg3f3U1_MmNa2R1`MnN`2P1bMPO^2P1aMQO_2o0aMQO_2o0aMQO^2P1aMQO_2P1`MPO`2o0aMQO_2o0aMQO_2o0aMQO_2o0`MRO`2n0_MSO`2m0`MTO`2k0aMUO_2k0aMUO_2k0aMUO_2>mMCS2=YMdKEo3R3=WMfKGm3R3=WMgKUO13NOo3b3;WMmKVOK1m3b3:XMnKUOK0n3c3:VMVLVO`3d3:VM:l2DTM<l2DSM=m2CSM=m2CSM=m2CSM=n2EZLdKa0h4U3HiL9X3HeL9[3IcL7]3IcL7^3K_L5a3J`L6`3J_L7a3H[L]KOk4f3H[L^KNj4g3IYL]K0k4g3m41OO10_NbLlH_3i6_McHa2S7jMlHU2l6TNTIR2^6WNaIj1GQL^6Z2`IgM0n3OTLa6k600O1O1O1O1O3M1O1OOgIiHX6X7hIiHV6W7kIiHT6X732N1O2N1O1O1O1O100OM36JL46J1O1O1O1O1O5\\JnG]5U8O1O2N1O3O1N001O001O001O0000O20O00001O000dK`G\\O0T4a8_L_G]O0T4a8_L_G]O384V3[8lLbGDi0_3f7jLcHV3\\7iLeHW3\\7hLdHX3\\7hLeHW3[7iLeHW3\\7hLeHW3[7iLeHW3\\7gLeHY3[7fLfHZ3[7dLfH\\3i816bLeEU3a:L2NM31nLaEm2c:000000001OK\\EWM10c:i25O100O10000N2000000000000O101\\OTEPNM:Q;e1a002N11O0000001O00O1001O0000000M15L2N100000000000XDWNe;l1000G[D]N12d;a1\\D\\N21b;c1\\D\\N20c;`1_D`Nf;^1\\DbNb;_1:00O100O2OO1O1N12O0L312N1O00NOhCiNY<W122NKgCoN[<o0eCQO0M\\<m0dCSO14]<g0bCUO23^<c0gC]O[<a0eC\\OJ0a<c0fC]O`<?aCA_<>bCB_<<bCD^<<<O200000O01000N2fH", "size": [436, 640]}, "area": 72634.0, "iscrowd": 0, "image_id": 10125, "bbox": [16.0, 97.0, 624.0, 339.0], "category_id": 96, "id": 10012325}, {"segmentation": {"counts": "`hX2n0`<6JoNhCR1X<5O10000000000000000O11ON200N2O100000000000000000000001O0000O1O?A1N2000000000SOcDC];:eDG\\;7cDK];5bDL^;3bDN^;2bDN^;2aDO`;OaD1_;NbD2^;NaD3_;LaD5];McD3[;NfD2S;5mDKQ;6PEJk:QOQEV14Id:QO[EY12Fc:QO[EY11Gd:POZEZ12Fd:QOYEX14Gc:QOYEX14Gd:POXEX15Hc:POWEY15Hd:POVEX17Gc:QOUEY18Fc:QOUEX19Gb:QOUEX19GT:SOlENGX1:FV:UOfELJX1;GV:QO_E43V18EV:QOaE5NU1;E]:>eEA]:=cEC_:;aEE_::bEF_:9bEF^:9cEG]:9dEF\\:9eEG\\:8eEG[:9eEG\\:7fEH[:7dEJ\\:5dEL]:3cEM]:4bEL^:5`EL`:1PETO>7CM1Om:j0SESO971Mc:h0dE[OIMc:h0cE[OKMb:g0dE\\OJMb:g0cE\\OKNb:f0cE[OLOW:IZET1f0ROI1T:R1UFjNH4S:R1WFgNF8Q:R1lFnNT9R1kFoNT9Q1mFoNS9Q1nFnNR9Q1oFoNP9R1kFfNhN9]:P1kFjNfN6_:P1mFSOR9n0PGPOP9o0RGPOn8P1RGPOn8P1RGPOm8P1UGoNk8Q1TGPOm8o0SGQOm8n0UGQOk8o0UGQOk8o0UGQOk8n0UGSOk8l0WGSOi8m0XGROh8n0YGQOh8m0ZGROf8n0ZGROf8m0]GQOc8o0\\GROd8n0_GoNa8P1aGoN^8R1R2O01O100O02OLnNfCQ1[<oNcCS1V<mNnCU1R<71N100O100O10000O10000O2O000O100O102M102N1N01MTO^Ck0c<TO^Cl0b<TO^Cl0a<UO^Ck0d<TO\\Cl0d<TO\\Cl0c<2010O2O0O100O2O002N2MO[OUCe0k<2OMVC]Ok<c0301O00000000O10000N110N10100O0101L4OO10OO^ORCb0n<21OO100000O0O11O0100O1O1_OoB?P=BQC=S=O010LlBG]<OXD;[OF]<OdC0?=P<D`CO`0<Q<GoC:P<FaCM:?S<FbCL;=T<GaCK<>Q<LhCFO>Y<LhCFN?X<MjCDKb0[<JjC7W<IiC7V<JfCCOd0\\<JdC:Z<b0N3M2N10O10O000O01O2N02NO2001OO1O1O1CiC\\OX<d0iC[OW<e0lCWOV<g0mCWOQ<k03ROgCQ1NoN[<U111N20O10000O2OO100000O1O00010000C<1\\OoCEQ<9QDGo;9RDUO13NOQ<i0VDVOK1n;j0WDUOK0P<i0^DVOb;j0d000O1000003XOTCa0Q=N3M3N003MO10O0LgBOY=1hBNY=0gB0oiY2", "size": [436, 640]}, "area": 12291.0, "iscrowd": 0, "image_id": 10125, "bbox": [170.0, 257.0, 297.0, 179.0], "category_id": 97, "id": 10012326}, {"segmentation": {"counts": "0PS74XnH6PO<JFS11U64jIK[OEB0035:2f0l0ZOP4S1mJFP1a0VO]Ok0[Oo3T1PKCo0a0XO]O`0lNZO12?Y2AdM733d1X1YNCP1`0WO^O`0mNYO12>V1CkNe0ODO3c1T1\\NCP1`0WO]O>nN[O0110>d07\\O30B14`1S1^NCP1`0WO\\O8SOAg0:6GH0C06^1X2@lNXOYO85LLGH0CO:[1U2ClNXO\\O431KEH1CO9\\1V2BkNXOAM39FDIOE15^1X2eNUMe0f1^OQ17DBWNX1^OoNk2MUMe0f1^OQ185h0WMPOh2MUMf0e1]OR182h0]MPOe2MUMf0e1\\OS1:Ng0bMmNf20RMf0e1\\OS1;Ld0gMlNf23oL21<b1EU1;M>m0TO^L11<d1FS1<NM[MIc3K\\LL8>^1IR1=OI]MHc3NYLM8>a1EP1b0OD]1IkKO9;`1GP1c00_Ob1MmK3JMh11o0d01[Ob10mKNG0R23g0e0T7YOmHi0n900001O0000O100VOXCi0g<WOYCi0g<WOYCi0g<WOZCe0i<\\OVCa0m<_OSC?o<AQC401R=JVC3W=Midh7", "size": [436, 640]}, "area": 10418.0, "iscrowd": 0, "image_id": 10125, "bbox": [0.0, 0.0, 56.0, 423.0], "category_id": 132, "id": 10012327}, {"segmentation": {"counts": "PS74X16PO<JF_:0nEEB0035:2f0X:d0gE]OX:c0iE]OX:b0hE^OX:b0hE]OY:c0gE\\OY:e0gEYO[:g0eE\\OX:c0hEAU:?kEAU:?kE^OY:a0gE^OZ:b0eE_O[:a0eE^O]:`0dE@Z:b0fE^OY:c0gE]OY:f0cEZO^:f0bEZOW:oNhEh11YOU:o0kEQOU:P1iEPOW:W200000O10O10000002N20001O1O0N00000O1000000O100000O11L3L401O0O1000000O10000000000O10000000A_MkE`2T:a000O1O10O10000000000O10N2O11N3M1NEnLXFP3j9oLWFR3h9oLVFR3j9nLVFS3i9mLTF0JS3R:mLQFX3P:hLmEZ3T:30O100001O00001O0010O000001O0nE`LP:`3PFaLo9b301O001O001O1OYLUFe3o9OO1O1SFZL30f9l3K21N101O1O001O000000000000001O01O1O03NO00000001O000000001OO1O20N11OZLVFN143R3g9kLZF01o2\\:J`EWMa:i25O10000000000000000O11ON20@]MoEa2R:cMdEK2a2Z:b0O1O1O1000000O11OO10010O01O1N2N2N1N2000O100000001O000O10001O0001O2L4L6E7N2KmDhMT;Q2lDRNX;n1hDRNX;n1hDQNY;o1hDPNX;P2hDPNY;P2fDPNZ;P2fDoM[;Q2fDnMZ;R2fDmM[;S2eDmMZ;T2fDlMZ;f1hDcNNGX;k1kD[NLJX;m1hDSN43U;k1gDTN5NT;U22kMeDW2Y;iMgDY2U;4O2O0O11O1bMlD[2T;dMlD\\2W;0O10O0gMPEo1Z;0O1000O10000000000F`DXNh;e1500N20F]NbDc1];dN^D\\1b;;N101000O1000001N1001O00000000000<D01O00000O1000O100O1O100O11OLoCeNP<\\1PDbNR<W1nCmNo;LTDW1S<iNmCX1R<50000O100000000000001O0O100000002N002N1OO1MSD_Nm;a1RD`Nn;`1RD`Nn;_1SD`Nn;a1QD_No;a1QD_Nn;a13O10011N1O1O2OO002N2MOoCaNQ<_12O100001O00000O10`NPD[1Q<eNoC[1U<00N201M2O100O1001N2OO100NdClN\\<T121OO10N2000O001O10000000bClN\\<S1eCnNY<W1O02O0XOeC6\\<JdCB0?]<BcC:1DO`0]<BcC:34\\<@aC<3EM:a<E_C:5FL;`<E_C:5FK<a<D_C8:MFOd<3fCNFNe<2gC0DKh<5dC0^<0bC0^<OcCMCOl<4bCLb<2idT3", "size": [436, 640]}, "area": 30894.0, "iscrowd": 0, "image_id": 10125, "bbox": [16.0, 256.0, 387.0, 137.0], "category_id": 151, "id": 10012328}, {"segmentation": {"counts": "gQY82_=ObB5[=4M3O1L203M001N10O1000000O10000NYI", "size": [436, 640]}, "area": 295.0, "iscrowd": 0, "image_id": 10125, "bbox": [622.0, 217.0, 18.0, 21.0], "category_id": 155, "id": 10012329}, {"segmentation": {"counts": "n^7a0m:P1TEFg:=YECS8kNmG733d1X1YNCo6mNTIe0ODO3c1T1\\NC\\6AeI30B14`1S1^NCQ6LPJH0C06^1X2c4VNPJH0CO:[1U2g4VNnIH1CO9\\1V2g4UNmIIOE15^1X2f4@kIWNX1^OoNk2o51QKWMPOh2o5NQK]MPOe2P6KPKbMmNf2T6FmJgMlNf2X6DgJm0Z5TObJn0_5SO]JP1c5QOYJR1g5oNUJU1j5lNRJV1o5kNcJa0_5]OaJc0_5\\OcJc0]5]OcJc0^5]OaJc0_5]ObJb0^5^ObJb0]5_OcJa0]5_OdJ`0b4gLVKg29b0a4gLVKh28b0a4fLWKh29e0nNcL\\51YKi2>i0V4]LYKm2a0g0U4\\LXKo2a0g0W4ZLmJ01l7P5\\HmJf7S5[HjJh7V5;000001O00O1000000000000M3GiJQH[5o7eJRHZ5n7fJQH[5o7eJQH[5o7dJQH]5o7400000000000000O1O100O100O11O0000O1O100O1001O0000000000O10000000000000000O1N2000000XLjJROV5n0kJQOU5o0kJQOU5o0lJ_LYOQ2k5`1QKZLTOV2k5`1UKVLPOZ2k5`1ZKQLkN_2k5`1ZKQLkNU2U6j1RKVNn4j1RKVNn4j1RKVNn4j1RKVNn4T2SJhK:U2b5S2PJlK>Q2b5S2mIoKb0m1a5_2_JaMa5a2]J_Mc5c2[J]Me5d2ZJ\\Mf5d2ZJ\\Mf5d2ZJ\\Mf5d2ZJ\\Mf5c2[J]Me5c2[J]Me5c2[J]Me5b2\\J^Md5b2\\J^Md5b2\\J^Md5b2\\J^Md5b2\\J^Md5d2ZJ\\Mf5g2WJYMi5i2UJWMk5k2SJUMm5l2RJTMn5l2RJYLJ_OT6X4RJXLK@S6X4RJWLLAR6h5nIWJS6h5nIXJR6g5oIYJQ6f5PJZJP6=[Jb3EQLP6=\\J`3GQLm5?\\J_3HRLl5?\\J_3HRLl5?\\J_3HRLl5?\\J_3HRLl5?\\J_3HQLm5`0[J_3MlKh5e0[J_3MmKg5d0\\J^3OlKf5f0[J^30kKe5g0QJh3;`Kd5n5]JQJc5o5]JkIi5U6k0O10000000000UKhHZ3X7eLXIk2i6UMWIl2h6TMYIm2e6SM[Io2c6QM]Io2c6PM^IP3b6PM_Io2a6PM`Io2a6RM^In2b6QM_Io2a6RM^In2b6SMeIe2[6\\MeIc2[6]MeIc2[6^MdIcNWOj3U7dMcIbNXOj3U7dMdIaNWOj3V7fMbI^NXO06f3P7mMkIS2U6nMjIR2V6nMjIa1YOoLo6_1iIc1VOPMP7]1iIa1@lLg6b1jIb1h6^NWH_NU1j2f6kNYIV1f6kNYIV1f6kNbIm0]6TObIn0\\6oNgIQ1Y6iNnIn0Z6QOaIU1_6kN^IX1b6hN^IX1b6jN\\IV1d6jN\\Ia1Y6dN]H[N040L>[3U7jNhHV1X7iNjHc0CiMc7e1iH6DnM14d7e1kH7APN14b7Y1UId0_OjMK9`7Y1SIg0FcMJ:]7\\1QIh03kMl6]1QIg05kMj6^1QIg06jMi6_1PIh09gMg6j1iH<b0iMe6b1PIg0<fMd6c1oHh0?cMb6e1oHh0`0bMa6i4`IVK`6h4W100IVGdKj8Z4XGfKh8Y4YGbKM2k8[4YGcKM1j8[4ZGdKM0i8[4_GeKa8Z4`GfK`8Y4>O1O100O11O3M6JbM`GEc8=[GEc89`GEa8<_GCa8=_GBb8>_G@b8>`GAa8>`GAa8a0^GB^8=dGB\\8;_GVN_O_1R9<_GTN_O`1R9<_GTN^Oa1S9;_GKa85_GKa86]GAm8=UGCk8>VG@j8?VGEg8>VG_Om8a0SG_Om8b0PG]OS9a0nF]OU9h0gFUO[9k0ZFlM4]1b9Y251OO1N2N2O1O1O100O1O100O1O1H800000000000000000000000000001O1O004L000000000000001O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O3M3M3MN2000000O10000000000O100000000000000005KZOgFQMY9e2RGZMn8e2TGZMl8f2UGYMk8g2VGXMj8h2WGWMi8i2XGVMh8j2XGVMh8i2XGXMh8i2WGWMi8i2VGXMj8h2VGWMk8i2TGXMl8h2TGQM_OO]9P3TGQM_OO]9P3UGPM_OO\\9P3VGQM_ON[9P3XGmLB2V9P3cGoL]8P3dGPM\\8o2eGPM\\8P3dGPM\\8P3dGPM\\8P3dGPM\\8P3dGPM\\8P3dGPM\\8P3cGQM]8o2cGPM^8Q3aGoL_8Q3aGoL_8Q3aGoL_8Q3aGoL_8Q3R100003M4L3M2N00O10000001O00000000O100O1O1N2O1N20000000000O1001O000000000SFkL[9d3UF]Lk9e31000000000000O1000000000000O100000000000000000000002N000000001O001O00001O0000000000001OO1001O00O16J1bFQLU9o3hFTLX9T4000000000002N0000NhFhKX9X4hFiKW9V4jFjKV9V441O001O000000000000L45KK55K00000000004hFbKR9`40001O002N2N001O001O001O0000O11O00001O000mGWK\\O0k7j4hHVK]O0k7j4hHVK]O384m6d4UIYKDi0V7o3SIZLl6f3SI[Lm6f3RIZLn6f3RI[Lm6e3SI[Lm6f3RI[Lm6e3SI[Lm6f3QI[Lo6e3PI\\LP7e3nH\\LR7S507mHZIj6l6L2NM31YIVIb6n6000000000000000000000000000000000000000001O00O11O00000000000000000000000000M33M1O00000000000000000000000000N2000000001OO1O1N21O00O12N1O00N200000000M\\IUIe6k62O11ON2000000O10000000000000000", "size": [436, 640]}, "area": 105340.0, "iscrowd": 0, "image_id": 10125, "bbox": [17.0, 0.0, 623.0, 270.0], "category_id": 157, "id": 10012330}, {"segmentation": {"counts": "SV\\7111_=3N100O10000N20001O0000000O100]OI[CM:<Z<b0O1O20O0000001O00O1001O01O00000N2O1O1000001O0000kNdCR1_<000GnNjC12Q1T<oNiC21o0V<oNiC20P1S<ROmCS1Q<oNoCR1o;:00O100O1000000O101K400000000MUN\\Dl1d;22NKTNbDl1^;TNbD0Mk1^;UNbD14j1Z;UNbD23i1X;\\NhDd1X;\\NeDJ0j1Z;]NfDf1Y;[NgDe1X;\\NhDd1W;]NiDc1W;=N3O00000O1000008PH", "size": [436, 640]}, "area": 3718.0, "iscrowd": 0, "image_id": 10125, "bbox": [554.0, 248.0, 86.0, 88.0], "category_id": 159, "id": 10012331}, {"segmentation": {"counts": "USc3:W==]O<I8J6J4M2M3M3N3M2N2O1N2N2O1N2O1N2N100O2O0O1O1000010O00000000001O0001O000000000O100O2O1N2O1N2O1N2O1N2O1N2M3N2M3N2M3N2N3L5I7J5K8HcYi1H^fVNn0RO;F9H8O20OO1O1O2N1O1O2N1O1O1O2N1O1O2N1O1O2N1O1O1O100O1O1O2N1O1O1O1O1O1O1O1O1O1O1O11O000000000001O000001O0000000000000000001O00000001O0000000001O0000000000000000001O00000000000000000010O000000000000000001O0000000000000000001O00000000000000001O0000000000000000001O0000000001O00000001O0000000000000000001O0000000000000000001O00000000000hMd5", "size": [436, 640]}, "area": 21739.0, "iscrowd": 0, "image_id": 10125, "bbox": [270.0, 105.0, 369.0, 326.0], "category_id": 183, "id": 10012332}, {"segmentation": {"counts": "bRZ32X=3N2N0O1O1OHPC8k<IZC5d<K_C4_<KeC3k<O1Njj01Xfi4", "size": [427, 640]}, "area": 76.0, "iscrowd": 0, "image_id": 10130, "bbox": [254.0, 156.0, 15.0, 21.0], "category_id": 110, "id": 10012333}, {"segmentation": {"counts": "RU`11Y=100hB2Q=6N100O100O1O1O1O1O1N2O0O2M3L4M300nC_OZ;a0dDZOc;g0`01O1O1O1O0000000000000000000000000001O0000O100O20O1O00LmNnCU1Q<3E7[DbNe;Z1_DfNb;W1`DiNa;V1_DjN`;W1:0VDiN_;W1bDiN^;W1bDiN];X1cDhN];X1cDhN\\;Y1dDgN\\;Y1dDgN[;Z1eDfN[;Z1eDfNZ;[1fDeNZ;[1fDeNY;\\1gDdNY;\\1gDdNY;\\1gDdNY;2_DU18iN_;V1aDjN_;V1;01O001O0000O10000000000O100000000EiNbDW1^;iNbDW1^;jNaDV1_;jNaDV1_;jNaDV1_;kN_DV1a;jN`DU1`;lN_DT1`;mNaDR1_;oN\\DU1d;90000K_N`Da1_;`NaD`1`;_N_Db1c;\\N]Dd1d;11O1O1O1O001O1O00001O000000000O1A?0000O100O10O010O10MdCWO[<j0fCUOZ<k021O1O1O]OgC6W<KlC3S<NoC0n;3RDLl;6UDJd;<]DDc;;_DDb;7bDI^;5dDL\\;1eD0[;NgD2Z;KgD5Z;HiD8X;EkD:PSh0E^YXO3L3N2L4N3N1N3M3M3M3M3M2N3M3M3^EeNP9\\1mFdNR9_1lFcNT9^1iFfNT9\\1iFgNT9\\1iFdNW9^1gFbNY9a1dF_N\\9c1bF]N^9e1`F[N^9j1_FVNb9k1\\FUNd9m1ZFSNg9o1VFQNj9Q2UFoMj9S2TFlMm9W2PFhMR:Y2lEcMLMX:b2jE_M\\:d242ON100000000000000000000000;E3M000000000000000000000000^OmMlES2T:WN`Ek1_:a000O100000O2XNg1000001N10000TOkCb0V<]OmC`0S<_OPD?P<ARD=o;BRD=`<N1O001O0Od`n2", "size": [427, 640]}, "area": 10664.0, "iscrowd": 0, "image_id": 10130, "bbox": [115.0, 117.0, 298.0, 122.0], "category_id": 123, "id": 10012334}, {"segmentation": {"counts": "iVn2110W=201N0O3N3L4iKFYG3`3a0Q5MjJ3V5MjJ4U5LkJ3V5MjJ3V5MjJ3U5NkJ1V5OjJ1V5OjJ1V5OjJ0W50iJ0W51hJNU4V1fHZOQ1;W6Z4dHZJY7f5gHYJY7h552N5K4NN00GmI[IQ6f6oIZIQ6f6QJTI0No5m6;O0011O0O2O00000O10000O01O1002N4K5oIQI^5m7RKhGo01X1k8hNVGV1Z9UNmFj1c9fM^FY2Y:16I>C<[O^DiNb;MdDn0LRO_<<d0Chdc4", "size": [427, 640]}, "area": 7331.0, "iscrowd": 0, "image_id": 10130, "bbox": [225.0, 207.0, 60.0, 208.0], "category_id": 173, "id": 10012335}, {"segmentation": {"counts": "c2i0b<00O10O1001I9J30000O2OO1ZMHWF4Z14_8KSF7Y1Nd8NnEk0KnNi08_94gET1d0fNe9h207If0ZO:Fc0]O>B`0@4L1O0000001O00aJVI]4j6dKVI[4j6fKTI\\4k6dKVIa4d6_K[Ii4^6WKbIi4^6WKbIj4]6VKcIj4]6VKcIj4]6VKcIj4]6VKcIi4^6VKdIi4\\6YK`Ii4`6VKcIHAd4l6cKgIF^Of4k6dKgIE@f4i6fKgI@[O07i4g6fKTJY4l5gKTJY4Z6XKhIg4_7N2YO\\GeLe8X3]GgLd8W3^GhLc8U3`GkL_8T3cGlL]8R3eGnL[8P3gGPMY8n2iGSMV8k2lGUMT8h2oGXMQ8f2QHZMn7e2THZMm7d2UH\\Mk7b2WH_Mi7]2ZHcMj7W2XHiMn7o1SH_MnN?U9k1SHiMhN4X9o1UHkMeN3V9P2ZHRNg7j1bHPN_7m1Q2L4M3L4L4I7K5K4K6I7N3NjM3PGLQ95nFJS9d0aFQOhN6h:i0cFUO^9l0bFQO`9o0`FPOa9P1]FQOd9o0\\FPOe9P1[11O001iCnNS<V1O5K4RDcNf;b1O1O0000O1O1O1N2O1O1O1O1O1O1N2O1O1O1O1N2O1O1O1001O00000000001O1O2N1O00O1000000O10000000000000000O10000000000001O00001O0nESOf7m0XHYOd7g0ZH_Ob7b0\\HC`7=^HI^78_H1Z7OeH6W7KfH8Y7IeH9Z7HcH<[7EcH<]7DaH>_7C_H>a7C]H>c7C[H>e7CYH>g7>\\GCd8>ZGCf8?WGBi8`20O100O100O1O1N2O11O00001O001O1O000000001O000000000000O1O10000`N[GZNe8c1aGZN_8d1eG[NZ8c1jG\\NU8c1mG]NR8b1PH]NP8b1SH\\Nm7c1VH\\Ni7c1ZHZNg7\\OcGe1g0iNk7B]Ge1j0eNl7FZGd1l0cNl7IXGd1m0`Nm7LVGd1n0^Nm7NUGc1o0^Nm7OTGc1P1\\Nm71TGb1P1[Nm73RGc1Q1YNn74QGc1Q1YNn74PGd1a:\\N_Ed1T;01O00O1001O1O6KO07bNXDl0i;RO\\Di0_;[ObDf0`;UOTDO=l0P<00100O1O2O0O3M2MM4O:FlM_OcG=X8IjG5V8KlG2U8NlG0T81mGMT83mGKT85mGIS88nGFS8:mGFS8:nGDS8<nGBR8?]2000O100000000O1SJJeN6[19gIVOo01X1`0R4m0fJdNV1`0T4[1QJ\\Nj19U4k1aImMY28U4c2lK]MT4i2eKXM[4U3XKkLh4a3cJmKiNb0d6a3YISLn0LROa0g6^4eIjK[6b4XI_Kh6b50000000000HfI^I[6a680000O1000000000000000aJ`IS4`6UK`I@3;OP5^6UKiIBG13P31lN\\6\\NoIEC13m23lMI6`6YOVJF@k24PNFTO0o0`6XO`IKo0h2]ORNG3]6SOeIOl0e2^OTNF5[6QOhI0j0c2_OUNF7Y6QOnJ_2VOWNF8V6ROnJ\\2ZOWND;T6ROnJZ2\\OVND?c7X1lHWNCa0a7V1oHVNBd0_7S1TIUN^Oi0]7Q1XISN]Ol0[7n0WJROi5l0YJTOg5i0[JXOe5f0]JZOb5d0aJ\\O_5b0bJ_O\\5`0fJAZ5=hJCX5:kJFT58oJHQ56QKJo46PKKP54QKLo43RKJQ55PKHS57nJIQ58nJIS55nJK_OQOQ3S1`MM_OPOQ3R1aMN^OQOQ3o0bM0]OQOR3n0`M2^OQOQ3l0bM3]OQOY32WJ;T3b0\\OQOa3:TMe0[OQOa39TMg0[OPOa39TMg0[OPOa38VMg0YOQOa37ZMe0UOTOa37\\Mc0ROWOb35_Mb0oNYOb35aM`0lN\\Oc34bMH]Le0^2_Oc34dM=hN@c3LPJ5f3>gNAa3MTJ3e3>eNCj3MbM?dNDQ2J[M0S2b0_NFQ2JZ1a0cLEo1JiK2d5`0aLFo11`19`LFn14a16`LGo13b15_LHn14d13^LIn14d13_LIl14f12^LJl14f12^LKk13g12^LKk13h11]LMj12i11]LLk13h11]LLk12i12\\LLk12i12\\LLk12i12\\LLk12i12\\LLk1d0W1@nLLk1e0QLQOQ5>SMLk1g0T1]OQMLk1f0U1^OPMLk1e0V1_OoLLk1d0W1@nLLk1c0X1AmLLk1b0Y1BlLMj1`0Z1DlLLj1?[1EkLMi1<]1HjLLi1;^1IhLNi18_1KhLNh16a1LgLNh15a1NgLNg14b1NgLOf14a1NiLOe14`1NkL0c13`1NmL0b12`1OnLOb13^1OPMMc15Z10SMLa15[10TME_1d0Z1IWMC`1d0U1L[M_Oa1e0Q1O^M\\Ob1c0Q11]M\\Ob1c0Q11]M\\Ob1c0Q11]M\\Oc1b0P12]M\\Oc1b0P12]M\\Oc1b0P12]M\\Od1a0ZNRO?Q1C\\Od1`0ZN@3c00]Od1?XNNH3i4O^K:^OEU52\\K;^OCV52[K=^OAV53[K>^O_OW54XK`0@\\OW55XKa0@ZOW57WK8JAo47VK9K@n49UK9L^On4:UK9M]On4;SK9XO^NMn0g5<RK;YO[NNn0h5<nJ=[OZNOm0i5;lJ`0\\OWNOn0k5:gJc0^OVN0m0m58dJf0_OUN0m0o57_Jj0ASN0l0S64ZJg1CUNS66UJh1GSNT69nIg1NPNT6<hIg13nMU6>cIf1FlL?Q1V6oN[Ib13g1<hMV6o3kIRLT6c5O1N20hM`IeM`6Z2eIbM\\6W2lIgMT6V2PJiMP6W2QJhMo5X2RJWL\\OR1b6g2RJoKEY1Z6g2RJoKEY1[6f2PJPLGY1[6f2]JZMc5f2^JYMb5g2^JYMb5g2_JXMa5h2_JXMa5h2_JXMa5h2_JXMb5h2eJPM[5P3hJmLX5S3jJkLV5U3lJiLT5U3oJjLP5V3RKiLn4U3UKjLk4T3XKkLh4T3ZKkLc4e2dIlLl1?`4_2kIPMg1`0_4_2WL`Mj3^2WLbMj3\\2WLdMi3\\2XLcMh3]2XLcMh3]2XLcMh3]2XLeMf3[2ZLhMc3X2]LiMa3X2_LgMa3Y2`LdMa3]2`LaMa3a2^L_Mc3n0^IMQ3UOa3BbIP15FS3HW3A`Ia0MI`07l2NX3^ObIa04C8?i20b3N]IB9?i21`3JUID:09a0g22a3I\\J5S22b3H[J8P21e3JXJ5U2Od3LVJ3[2Nb3OPJ3`2L`3NTJEjN<f3M\\31VJFiN;g3LZ32WJHgN;j3HX35WJGhN<n3CR3=]JLf94^FG_9=j1DXCNk<MRC044Q=LYQl00hnSO5K5O000O2O000O1O100O1O2N1O1000GYCLg<M`C4k<1O001O1O001O1O001bCDk;=RDFm;:RDIl;8RDJm;7PDMo;2PD0o;1oC2P<NmC5S<c0N1O2N1O2N1O2N1O2N1O2N1O2N1O2jDSNj:n1UERNk:P2SERNk:o1UEQNj:Q2TEoMl:R2TEmMk:V2SEjMm:W2REiMn:Z20000000000001O00000PNQEd1o:[NREe1n:[NTEc1Z;000?ZO71O001O000YD", "size": [427, 640]}, "area": 49239.0, "iscrowd": 0, "image_id": 10130, "bbox": [0.0, 0.0, 640.0, 427.0], "category_id": 176, "id": 10012336}, {"segmentation": {"counts": "ce3Z1_9d2eNZ1N2O2M2O1O2M2O0O100O100000O10000000000000000000000000000000000000000000000000000000jN^JcJb5[5`JeJ`5Y5bJgJ^5V5eJjJ[5T5gJlJY5R5iJnJW5P5kJPKU5n4mJRKS5l4oJTKQ5i4QKXKo4f4SKZKm4d4UK\\Kk4b4WK^Ki4`4YK`Kg4]4\\KcKd4[4^KeKb4Y4`KgK`4W4bKiK^4U4dKkK\\4S4fKmKZ4o3jKQLV4l3mKTLS4h3QLXLo3e3SL\\Lm3`3WL`Li3\\3[LdLe3U3bLkL^3P3gLPMY3k2lLUMT3e2QM\\Mo2]2XMcMh2[2ZMeMe2[2\\MeMd2Z2]MfMc2Y2]MhMc2W2^MiMa2W2`MiM`2U2bMkM^2T2cMlM\\2T2eMlM[2S2fMmMZ2m1lMSNT2k1nMUNQ2k1PNUNP2i1RNWNn1h1RNYNn1e1TN[Nk1d1WN\\Ni1c1XN]Nh1a1ZN_Ne1a1\\NaNb1]1`NdN_1[1bNfN]1Y1dNhNoNc3Q1_LZLS6f3i1O100O1001O1N101O1O001N2O001O10O2O000O1000001O1O2N1O00O1iMULnJk3Eb1;^NEb1;^NEb1:_NFa1:_NFb19^NGb19^NGb19^NGb19^NGb19^NGb19^NGb19^NGb18_NHa18_NHa18`NG`19`NG`19`NG`19`NG`19`NG`1:_NFa1:_NFa1:_NFa1;bLPNFe1h3;aLXN@]1o3;_L^N^OX1R4:^LdN\\OR1V4;[LiN[Ol0Z4;YLoNYOf0^4<WLXORO<g4<UL[OTO8g4>SL\\OUO6h4?PL_OXO0h4b0nK@[OLg4e0lK@]OKg4e0jKB_OIg4f0hKB@Ih4f0fKBBHh4g0dKBCHi4W1cJZM5h1?Gi4c1eJgNa0Gj4d1bJfNd0Fj4g3UKZLk4f3UKZLk4e3UK\\Lk4d3UK\\Lk4c3UK^Lk4b3UK^Lk4a3UK`Ll4^3UKbLk4\\3WKdLi4[3WKfLi4[3VKeLj4[3VKeLj4[3UKfLk4[3SKfLm4Z3SKfLm4[3SKdLm4]3RKcLn4]3SKbLm4^3SKbLm4^3TKaLl4_3lJhJIi1[5`3kJhJIh1\\5`3jJiJKf1[5a3jJjJJe1\\5a3jJjJKd1[5b3jJjJKd1[5b3iJlJLa1[5c3iJlJLa1[5c3iJmJL_1[5d3iJmJL_1[5d3iJnJL]1[5e3hJoJM]1Z5d3iJoJN\\1Y5e3iJPKM[1Z5f3gJPKOZ1Z5g3fJPK0X1Z5j3dJnJ2X1Z5l3aJhL_5Y3`JgL`5b50000000000000000O100O100I7RNn1RNUd6lNf^I9F9H9G8N3M2N3N1N2N2N1O2N1ZEUNk9m1QFXNm9h1QF[Nm9g1oE]No9d1nE`NP:b1mEaNR:_1kEeNS:\\1kEgNT:Y2O1O1O1nEdMS9Z3O10000000000O10000000001N10000GoKXGQ4g8:0000000000000hKYGl3T9OBoKcGP4]8YL[Gf3e8[L[Gd3e8]L[Gb3e8^L\\Ga3d8`L\\G_3d8bL\\G]3d8cL]G\\3_4VL]O>UL[3Z4[L_LIS2a0UMZ3V4_LbLGSO0m2?YMZ3S4cLcLFk2f3?gLdLES2<[MW3l3jLdLFS2:`MS3f3PMeLEU28bMQ3a3UMfLDW26dMo2]3YMeLEZ23gMl2W3`MeLB^22hMj2S3eMdLAa20jMh2n2lMcL^Oe2NmMe2i2RNbL\\Oi2MnMc2d2RO^O[NPNa2`2VO@YNRN_2[2[OY1b0eN_O^1?_NDc1:[NHh15UNLo12oM0S2NjM5Y2HdM;^2C`M>c2A\\M?g2^OXMc0k2ZOTMg0n2WOQMi0S3TOlLm0W3POiLP1Z3mNeLT1^3iNaLW1b3gN]LZ1f3cNYL^1j3_NVLa1m3\\NRLe1Q4XNnKh1U4VNjKk1Y4RNfKo1]4nMcKR2`4kM_KU2d4iM[KX2g4fMYKZ2j4cMUK^2m4`MSK_2P5_MPKa2S5\\MmJd2U5ZMcJ@XNV3W7XM`JDXNS3\\7VMZJKXNo2`7TMWJOWNn2d7QMTJ3WNl2h7nLPJ^3S6`LlIa3V6]LiId3n71N101O1O1O001O1N2O001O1O1O001N2O1OJlFVLT9k3PGQLo8P451O1O1N2O001O1O1001O1O1O001O1O001O1O00O10000000O100oMQGFn8:`GYO`8f0nGmNR8R1SHiNn7V1THiNl7V1VHiNj7V1XHiNi7T1ZHkNf7T1\\HjNf7T1SHTOm7k0THUOm7i0UHVOk7i0VHWOk7g0VHXOT4YOnM]1QNYOo3]OPNW1SN\\Ok3@QNR1WN]Of3DSNm0XN_Oc3GTNh0[N@b3ISNe0]N^Ob3NQNa0d4@^K:d4H_K1d40^KJd4:]K@e4b0\\KXOf4k0l33M2M4M2M4M2M4M2M4M3L3N3L3N3L3N3L4M2M4M2M4M1N2O1N2O1N101N101N101N101N10O010O010OQKfLN[31hLMX32kLLT34nLJS35PMIo27TMFm29UMFj2:YMCh2;\\MCc2>^MeKYOY3Z3Q1`McKZOY3U3T1dMaKZOW3S3W1eM`K[OW3o2Z1hM]K\\OU3m2]1jM[K\\OV3i2_1mMYK^OT3f2U1fLPLQ2i2X1c0MYO4d02YONd07YOIf0;XOEe0>ZOBd0a0\\O_Oa0d0^O\\Oc0d0]O\\Ob0e0]O[Od0e0\\O[Oc0f0\\OZOe0f0[OZOd0g0[OYOf0g0ZOYOe0h0ZOXOg0h0YOXOf0i0YOXOg0h0YOWOg0j0XOWOh0i0XOVOh0k0WOVOi0i0XOVOi0j0VOWOj0i0VOVOk0j0TOWOl0i0TOVOm0j0ROWOn0i0ROVOo0j0QOVOo0j0POVOQ1j0oNVOQ1j0nNVOS1j0mNVOR1k0mNVOIfLTOT4S1UOJYMaNb3d1VOKXMaNb3d1UOLYM`Nb3c1VOMXM`Nb3c1UONYM_Nb3b1VOOXM_Nb3b1UO0YM^Nb3a1VO1XM^Nb3a1UO2YM]Nb3`1VO3XM]Nb3`1UO4ZM\\N`3_1WOa1g0`NXOb1g0]NZOn0VMQM_3Q2ZOo0WMPM_3P2[OP1VMQM^3o1\\OP1VMRM]3m1]OR1VMRM\\3l1^OR1VMSM[3j1_OT1VMSMZ3i1@T1VMTMY3g1AV1VMTMX3f1BV1UMVMX3c1CY1TMUMX3b1DX2;gMEg1fLlLd3]1FZ1UMYMS3\\1H\\1VMYMP3[1J\\1UM]Mn2V1M^1UM^Ml2T1O^1UM`Mj2Q11`1UMaMh2o03`1UMcMf2l06a1TMeMd2j07b1VMeMa2i09b1VMgM_2f0;g1RMfM`2d0>c1UMkM[2a0`0d1VMmMX2?b0d1UMPNW2;d0f1UMQNU29f0f1UMSNS26h0h1UMTNQ24j0h1UMUNP22k0j1VMTNn12l0j1VMVNl1On0m1UMUNl1No0W4POhKP1Z4oNfKQ1[4nNdKS1]4lNcKS1_4lN`KU1b4iN_KU1c4jN]KV1d4iN\\KV1f4iN[KV1e4jN[KU1g4jNZKU1g4jNYKU1h4kNYKT1h4kNYKS1i4kNZKS1f4mN[KQ1g4nN[KP1f4oN\\Ko0d4eM^Ij0P2^1d4eM_Il0o1]1c4eMaIl0n1\\1b4gMbIl0n1[1a4fMeIm0l1Z1a4gMeIn0l1Y1_4hMhIm0j1Y1`4gMiIo0i1X1W5fNkJW1W5hNkJV1W5hNkJU1W5jNkJT1W5jNkJS1W5lNkJR1W5lNkJQ1W5nNjJQ1X5mNjJQ1W5nNkJo0X5oNjJo0W5POkJm0X5QOjJm0W5ROkJl0W5QOlJl0V5SOlJk0V5SOkJl0V5TOkJj0W5TOkJi0W5WOjJf0Y5XOiJe0Y5[OhJb0S9M4K6K`\\7", "size": [427, 640]}, "area": 99598.0, "iscrowd": 0, "image_id": 10130, "bbox": [8.0, 32.0, 614.0, 389.0], "category_id": 183, "id": 10012337}, {"segmentation": {"counts": "Sfb0?ac0O11OO100000000O100000000O2O2N2N1N2O1OO;F00001OO11N2ON2N20^\\OD`c0>0N200001OO03NO1001OO1N200O1000000000O1000001O1O0000M300000000O1001N101OO11ON20001N100000002N001N2O1O00O1O1O1N2O10000O100O0100000000001O0^\\OE10O0Xc0b0g\\OBVc0c01N21O1O1ONj\\OZOVc0f02002NO10000M30000O10001O000001OO11O001N3N1O00001OO1001OO20O001OO1O2L3O2O0000000000O10001N1000000O3N00O11O00O2N1000000O11OO3N01O000001O0O110_Ob\\O>`c001O000O10O3M100O1O100N2O1000O1000000O1001OO11O001OO11O00002N0001O7I1N1O2O0N22M011O000O2O1O0O10O108HM23M2N]QS1", "size": [640, 316]}, "area": 3754.0, "iscrowd": 0, "image_id": 10136, "bbox": [29.0, 579.0, 230.0, 28.0], "category_id": 100, "id": 10012338}, {"segmentation": {"counts": "Tc51oc02M1U\\OMic05O2O1Y\\OHcc0;00f\\OEG6ob07Y]OKgb06X]OJgb0:W]OEib0=R]OFmb0g00000O1O10000O1O1H8O11OO100000000O100000000O1000000O10000:F00001OO11O1ON2N20gN[]OW1gb00N200001OO12NO1001OO1N200O100000000000000001O1O0000M300000000O1001O001OO11ON20000O1000000000000O1000000000000000000000000O100000000001O0bNa]O10O0T1fb0kN^]OR1gb01N21O1O1ONnNV]OR1jb02002NO10000M30000O100000000001OO11O001O2N1O00001OO1001OO11O001OO1O1M3O100000000000000001O000000002N00O11O00O1O1000000O11OO1001O00000000O11O00000000O1002N0000000000000000O1000000O1001OO11O001OO11O00002N00000000O1O100N22NO11O00001O1O0000O10000O1O1O100001O000000NnNY]OM0P1ob0O3QOn\\Oj0Vc0NO100N21O001O002NO1N2HVOW]Om0gb07000000000000000000000LV]OQOkb0?hi=", "size": [640, 316]}, "area": 10959.0, "iscrowd": 0, "image_id": 10136, "bbox": [8.0, 590.0, 285.0, 50.0], "category_id": 101, "id": 10012339}, {"segmentation": {"counts": "hjY51o19g?Hl_Oe0o?\\O`^O0T1P1[`0UO[_OU1e`0kNQ_O_1Pa0aNe^Oi1[a0?A>M4O1O2N2N3M1O00000O1O1N2O2O00O1N2kN__OhMda0X22000ON3@`0TOoZ8", "size": [640, 316]}, "area": 2597.0, "iscrowd": 0, "image_id": 10136, "bbox": [271.0, 445.0, 31.0, 114.0], "category_id": 110, "id": 10012340}, {"segmentation": {"counts": "[]51oc00N4N000O2N11NZ@4fLHXO0\\>:RB]4[:mK\\G@lMN1b09T4\\:mKXGd0[Na3]:jK]F3lNc0:a3\\:iKoEP2CY2]:UL]Ei1NTM001o4\\:\\LdEh6k9n1A?C>A?B=DbIlI_KhNR3T7g1dJ`JaNQ3X7h2SKWMg4n2[KQMP4c3QL]Lb3P4^LPLV3\\4jLdKj2h4VMXKb2i0dJ1j2VOU2h0nJZL63:e2]26j1R1mKRLU2=4_2]1Z1`LiKW26Lg2X1P1TMSLh16Kh2Y1e0^M]L^15Kj2Y1>dMcLX14Kl2Y15lMkLP13Ko2X12oMkLn04KQ3V1NSNmLl04KS3T1KVNnLk03LU3R1SOWLiMQ2Lj02MW3P1fNdLTNg1Lh03MX3o0YNQM`N[1Li02LX3P1nM\\MlNo0Li02LX3P1eMeMUOf0Mh01MX3Y1\\M\\M^Of0Mh01LY3[1YM\\M@e0Mh01KZ3]1WM[MAe0Mh02\\O0aM[3[4UM[MAe0Mh03E[3e1SMXMCf0Lh05FV3d1VMXMCf0Lh07DT3f1VMXMDe0Ki0;@P3j1VMXMEd0Jj0;@P3j1VMXMFc0Ik0;@P3j1VMXMFc0Ik0;@P3j1VMRMLi0Ck0;Ao2i1WMlL1P1^Oj0;Bn2h1XMeLImNLZ2Jj0;Cm2g1YM_LHZOMS2Jj0;Dk2g1[MXLGGNl1Ki09Fl2f1[MQLG5Ne1Ki08Gm2e1[MkKFb0O^1Ki07Im2d1[McKGo00X1Ji07Jl2c1hMiL\\OP1Ij06Km2b1aMPMCi0Ij05Ln2a1`MQMDh0Ij04Mo2`1`MRMCh0Ii05Nn2_1aMSMBg0Jj04Nn2^1bMTMAf0Kj04Om2]1dMTM_Of0Lj04Om2]1dMTM@e0Kk040l2\\1dMUMBc0Jl041j2\\1eMUMCb0Jl041j2]1cMUMDc0Jj051j2]1bMVMEc0Jh052j2]1bMVMEd0Ig062j2]1bMVMEe0Hf072j2]1bMVMEe0Hf081i2^1bMVMEe0Hf080j2_1aMVMEe0Hf09Nj2a1`MVMEe0Hf09Mk2b1`MUMEe0Gg0:Im2f1\\MWMFb0Gh0;Gm2h1[MWMFa0Hi0;Fl2i1[MWMG?Hk0;Fj2j1ZMWMG`0Ji0:Hj2h1[MWMFa0Kh0:Hi2i1[MWMFa0Lg0:Hi2i1[MWMEb0Mf0:Hi2i1[MXMDb0Nd0:Ii2j1ZMXMDa0Od0:Ii2j1ZMXMDa0Od0:Ii2j1ZMXMDa0Od0;Hh2k1ZMXMDa0Oe0;Fi2k1YMYMDa0Oe0;Fi2k1YMYME`0Nf08Il2h1XMZMF?Nf07Jm2g1XMZMF?Nf06Kn2f1XMZMF>Og04LQ3c1VM\\MG<Oi03MP3b1WM\\MH;Nj03MQ3a1VM]MJ8Mm02MR3`1TM_MM5Ko02MS3_1RMfMIO0o02MS3`1QMfMIN2n01NR3a1RMfMIL2o02No2b1TMfMHK3o02Nn2c1UMeMHK3o02Nl2e1WMcMIH4R10Nk2g1WMaMKC7W1LNk2g1XM`MHE:V1KNk2g1kLWK<X2KC9Y1JNj2h1lLWK=U2JE9Y1KLj2m1XMXMJG9X1LJi2P2YMUMIJ9W1NEj2U2WMQMKN6W1OEh2V2YMlLL33V11Dg2W2aMeLC;3U13De2W2[MVK_O_1;<2T14Ed2V2[MXK@\\1;>1T14kM@h0T3W3[MnLJ64Q13Fd2U2ZMoLJ65P13Fd2U2ZMoLJ65P12Ge2U2YMoLJ56P11Hf2T2YMPMI47P1NKi2Q2YMQMH38P1MLj2o1ZMSMF1:Q1KMk2l1\\MUMD1:Q1JOk2j1]MUMD1:Q1I0l2h1^MVMC0;R1G1m2g1^MVMC0;R1G1m2g1_MUMB2:Q1H1m2g1`MTMA49P1I1m2g1aMSMA39R1H1m2g1^MUMF_OL`NN8>l2G1m2g1^MUMHQNIV1n0k1VO1n2h1]MUMNWOa0k1WOOn2j1\\MUMNWOa0k1WO0m2i1]MUMNWOa0k1WO0m2i1]MUMNWOa0k1WO0m2i1\\MUM0WO`0k1WO0n2h1\\MTM0YO?k1WO0n2h1[N`KhN09m0?k1WO0n2i1PO]K]Nn0?l1WOOm2j1QO[K]NP1>l1WOOm2j1SOVK^NU1;m1VONn2j1M[LoMm1VONn2j1aMYK_1S1kNl1WOMo2k1_M[K_1T1iNi1ZOMn2l1_M\\K_1P1lNk1XOMn2l1^M^K_1o0mNi1XONn2l1^M^K_1P1lNh1YONn2l1^M^K`1o0kNi1YOMo2m1]M^K_1P1lNh1ZOVOHZNW3Z4\\M^K_1o0mNi1YOQO4nMD3Z3h4YM^K_1o0mNi1YOjNP4Q3ZL]K`1n0nNj1YO`NX4[3RL[K`1Q1lNi1ZO\\N]4^3nKZKa1S1iNi1[O\\N]4^3PLVK`1X1hNh1\\O[N\\4_3[N^LmMh1]OZN[4`3[N^LmMh1^OYNZ4a3[N]LnMi1_OXNX4a3[N]LoMj1^OXNX4a3\\NZLPNm1\\OXNY4_3[N]LQNk1[OYNY4_3[N]LQNk1[OYNY4_3[N\\LRNl1ZOYNY4_3[N]LQNk1[OYNY4`3ZN[LSNl1[OWNY4b3ZN[LQNl1\\OWNX4c3[NYLRNn1ZOVNY4c3ZNZLSNm1ZOVNY4c3ZN[LRNl1\\OUNW4e3[NYLSNm1\\OTNV4g3ZNYLSNl1_ORNT4i3ZNYLSNl1BoMQ4l3[NYLQNl1CoMP4m3[NZLQNj1DoMP4m3[NYLRNk1CoMP4m3[NYLSNj1BPNP4m3[NYLSNj1BPNP4n3ZNXLTNj1BoM<YOX2f4GWLSNk1CmM0Gd2Y4EYLTNj1DoM@1S3m3EYLTNj1FXOa2e2EXLUNk1VOGP3V2EXLUNk1VOGP3V2EXLUNk1VOGP3V2FWLTNl1VOHo2U2FXLUNk1WOGP3T2D[LTNj1XOGP3T2D\\LSNi1YOGP3U2C[LTNi1YOGP3U2C^KSM=R1Y2XOGP3U2D]KRM>S1X2XOGP3U2C^KSM;T1[2VOGP3U2B_KUM:R1\\2VOFQ3U2B_KUM;a0]OFn21FR3XMcLj4n2bKTM=?^OHk22GS3Q2@bKSM>=AJg24FS3Q2@bKRM`0;ALf24FS3Q2@bKQMa0;AMe24ET3R2^OcKRMa08B0c24ET3R2^OcKRMb00H8\\24ES3S2_OZMZMn04ER3T2@YMZMn05EP3T2AYMZMn05Eo2V2BVMZMo04Fo2V2CVMYMn05Fo2V2BWM[Ml04Gm2X2DUM[Ml04Gl2Y2ETM[Ml04Gl2Y2ETM[Ml04Gl2Y2FSMZMm04Gl2W2hLQKn2T2ZMm04Gl2W2hLQKm2U2[Ml04Gl2W2hLQKm2U2[Ml04Gl2W2GVMYMl04Gl2W2GVMYMl04Gl2X2G[KQM\\18Z14Gl2X2G[KRM[17[14Hk2W2G\\KTMY16\\14Hk2W2G\\KSMZ17\\13Gl2W2G\\KSMZ17\\13Gl2W2G\\KRM\\18Y13Hl2W2HUMYMl03Hl2W2HUMYMl03Hl2W2HUMYMl03Hl2U2IXMXMk03Hl2T2JWMYMm01Hl2R2LYMWMm01\\OY3[2B[MTMn01nN7VNn2W4KgMoLn01_N:QOl2j3JhMoLn01QN=Ji2_3IiMPMm01cM?e0g2Q3IjMPMm01TMc0_1c2f2IjMPMm0j0oN]2Z2JiMoLn0?ZOg2P2KhMoLn06CP3g1KgMPMo01GT3d1IgMSMm0NKU3b1mLcK^2S2bMm0NKT3d1mLaK_2S2bMm0NKT3d1lLbKa2P2bMo0NJS3d1mLcK`2P2bMo0NJR3g1fLfKh2n1^Mk02JR3g1dLhKj2l1^Mk02JR3S2]OYM_Mj02KY3j1VObM^Mj02JW3n1YO]M^Mk02JY3l1WO_M^Mk02JZ3k1VO`M^Mk02J_3f1QOeM^Mk02J_3f1POfM_Mj02K_3d1POgM_Mj02K_3d1QOfM_Mj01L_3e1POeM`Mj01L_3g1nNcMbMj01L]3\\1YLQL0Gg2V2bMj01GZ3h1]LjKh2n1_Mi02]O^2hMlMb4B`Kb2Q2`Mh02ROi2TNSMl42UK`2Q2`Mh02hNS3f3oLgJ]2R2_Mi02]NY3T4mLbJY2T2_Mi02SNV1mN>m5dNUJW2U2_Mi02hMa1YOFZ6POfIX2V2_Mi02^Mg1DoM0T1h6U2UKXMQ1JRMP25_Nm6_2XIkNd2f0WMfMW7j2mHkNd2f0XMZMa7U3cHe0j0lNP7`0VHX4\\8dKdGVNJZ4j8`M\\GUNLl3X9iJaFQ3;YNN]3h9ZNZFXN0g2HoK_:R3iEXN0]2f:[OZEXN0j1Y;QOcEVOTOgNJo0KPOh0=^;BjCb15bNT17YO23IP18k=LXBI0:i=KYBKM9l=JXBMM8k=KXBMM7m=KVBNN6m=KUBON5n=LTBON5n=LUBNMOWN0k?3RBML0U>3oAMm?3S@Mm?3S@Mm?3S@Mm?3S@Mm?3S@Mm?4R@Ln?4Q@Mo?4P@Mo?3Q@Mn?MZ^O3h10Qb00O1O10=C0000000O1]G", "size": [640, 316]}, "area": 60429.0, "iscrowd": 0, "image_id": 10136, "bbox": [8.0, 78.0, 308.0, 528.0], "category_id": 156, "id": 10012341}, {"segmentation": {"counts": "\\b0d1]b0O000000O1001OOUOe]O3[b0Mg]O1Zb0Mg]O[OMf0^b0Mg]O3Zb0Kh]O4Xb0Li]O_OHb0bb0Ki]O5Wb0^O_]O007:2EGPc05]]O3fb0L[]O3fb0K^]O3bb0L`]OOfoh5", "size": [640, 316]}, "area": 772.0, "iscrowd": 0, "image_id": 10136, "bbox": [0.0, 588.0, 20.0, 52.0], "category_id": 167, "id": 10012342}, {"segmentation": {"counts": "jf:j6V=O11O0]Mc20H8_Oa0@`0B>A?B>C=G9F:J6[Oe0C<E<D6N8C=E;C=K500000000000000000000009G1O1O001O0000000000000000000000001O000000000000000000001O05K00000000000000000001O000000001O0000000000000O1000000L40001O000000001O000Q_OQMm`0o2S_ORMl`0Q3000001O0000000000001O0000O1N200O1000000000000000000000000001O0000000000000000000000001O0000000000000000O10000001O00000000001O0000000000001O000000000000AX_OfMh`0T2V_OaM5<e`0P2`_OPN``0o1a_OQN_`0n1b_OSN]`0l1d_OTN\\`0l1d_OTN\\`0k1e_OUN[`0k1f_OTNZ`0l1f_OTNZ`0l1f_OTNZ`0l1f_OTNZ`0l1f_OTNZ`0l1f_OTNZ`0l1f_OTNZ`0e1Z_OQNb04T`0e1[@UNlN1j`0i1]@WNc?h1_@WNa?h1`@XN`?g1a@YN_?g1a@YN_?h1\\10000O1Ni]O[NWb0b1i]O^NZb0b1200000000000000001O0000001O0a^O\\NWO0Wa0d1b_OaN]`0`1`_OcN_`0]1^_OhN``0Y1n^OZN<=f`0Y1m^O[N=<f`0Z1l^OZN><f`0Z1l^O[N>:f`0\\1k^OZN?:f`0a1Y_O_Ng`0b1X_O_Ng`0d1l^OoM8>4BY`0T2b_O[N4AZ`0T2b_O[N3B[`0R2d_OnMG9;GZ`0R2d_OmMH<2Kb`0l1f_OZNFLd`0j1f_OfNZ`0[1f_OdNZ`0]1e_OdNZ`0]1f_ObNZ`0]1f_OdNZ`0\\1e_OeN[`0[1d_OfN\\`0Y1c_OiN]`0X1`_OjN``0U1__OnN``0[2NC__OYM0Ga`0o2`_OZMb`0c2c_O[M]`0c2e_O]M[`0a2i_O]MW`0a2m_O]MS`0b2o_O]MQ`0e2n_OZMR`0i2a0000000O14L002P_OQMk`0R30NT_OPMl`0P3T_OPMl`0P3T_OPMl`0n2400000000000000O1000000O1000000001O001O001O001OO1O10000003M", "size": [640, 316]}, "area": 28936.0, "iscrowd": 0, "image_id": 10136, "bbox": [17.0, 0.0, 299.0, 308.0], "category_id": 172, "id": 10012343}, {"segmentation": {"counts": "n>^3b`01O000000O1001QMlM_Bd3b<RK`CU2Mj2Y<cMkC\\2T<dMlC^2R<bMnC_2Q<`MQD_2o;`MRDc2k;^MSDa14eLi;h1YDoMm?g1m_OlMN1b09c?d1RA[No>j0_@lNc0:n>;\\BCS>JTBNTM001i`00Xcg4OokWK0`c0?iNF[^Oh0\\OSOTb06R^OX1la0=L5M130O001O002ON1NlLTNYDS1oLiN071KO1]>?eD6WM[OZ>n0aDEW;]OeA_1V3QOl:^1WE^NW:U2jEjM\\8B[Dh0=[2l2\\Ma7V1cE^1l2\\M_7Y1dE[1m2\\M`7X1bEn0DgM[33_7Y1aEk0LcMU39^7Y1`Ek02]MR3?\\7Z1_Eh0f3mMk6[1^Ei0g3lMk6[1^Ej0e3lMm6Z1WEWN0h2i3bMW7_1PEU1S?k111O00O1O100iNW101O0000000000=C000001O000iF", "size": [640, 316]}, "area": 8502.0, "iscrowd": 0, "image_id": 10136, "bbox": [0.0, 280.0, 316.0, 336.0], "category_id": 177, "id": 10012344}, {"segmentation": {"counts": "X\\g0f0Zc0Y2gM8H0000000000000gLTM^El2V:`MjE`2j9lMVFT2^9XNbFh1U9aNlF^1T9bNlF^1cMlLl:g1aG]1cMhMP:k0]H]1cMcNU90XI]1cM^OZ8UOSJ]1cMAW8ROVJ]1cMAW8ROVJ]1cMAW8ROVJ]1cMAW8ROVJ]1cMAW8oNYJ`1`MAW8hN`Jh1XM@X8bNfJn1RM@X8\\NjGPO\\2T3bM@X8UNjG^O\\2m2bMWOa8XN`GJ_2f2P9ZM`D7`2_2P9SM`De0`2X2P9mL_DR1a2Q2\\9UNeFk1T9\\NlFd1S9]NmFc1S9ZNPGf1P9oM[GQ2e8eMeG[2\\8YMoGg2Q8oLYHQ3[<000001O00000000000001O00000000000001O0000000000000000000000EmLi_OS3o?UMQ@k2g?]MY@c2`?dMa@[2W?mMi@S2a=mLbCY1lNj1]=RM_C\\1TOc1U=XM`CV1Ab1i<^M^CQ10a1[<eM`Cg0=d1l;kMjC8a0m1c1SMk6o0QGJh0T2o0dMm6c0^G]O6ZOIR3Y1UNU7LjG\\ONIJj2m0bN`7YORHYOF;Ka2MB`8RNYHYO]Ok0MX2MC_8jMRId0bNo1M_Oc8gMlHl0lNn1EWNo;KdDn1]OcM^<;_DQ2TOoLZ<3cCe0X1Y2a;\\MWC3`1a2o:gMaC^Oi1k2[:RNlCkNQ2S3i9\\NhFd1Q9aNQG_1o8ZNXGf1h8SN_Gm1a8lMfGT2Z8dMnG\\2R8]M[D7d2\\2R9UMVDl0f2o1T9nLQD_1k2d1P9fNPGZ1P9fNQGY1o8gNQGY1o8gNQGY1o8gNQGY1o8gNQGY1n8dNVG\\1[9mMkFS2a=0000000000000001O000000000000000001O0000000001O00000000000000000000000000001O0000mAXMn:h2fDdMZ;\\2kCUMdNl0`=P2SCWOm<i0cBG]=9cBG]=9cBG]=9cBG]=9dBF\\=:dBF\\=:dBF\\=:dBF\\=:dBG\\=8eBG[=9eBG[=9eBG[=9eBG[=9eBG[=9fBFZ=:fBFZ=:fBFZ=:fBFZ=:fBFZ=:fBFZ=:gBEY=;gBEY=;fBFZ=:fBF[=9eBG[=:dBF\\=:dBF\\=:dBG[=9eBG[=9eBG[=9eBG[=9eBG[=9eBG[=9eBG[=9eBG[=9eBG[=9eBG[=9eBG[=9eBG\\=8dBH\\=8dBH\\=8dBH\\=8dBH\\=8dBI[=7eBD`=<`BVOn=j0RBgN>^Nn<k2dBYN`0XOl<_2dBjMd02h<T2dB\\Mf0l0f<h1iCcNW<^1\\CnNd<R1QCZOj:lMXFj2eNCR;gMVFf2dNGU;gMUFa2dNJV;iMSF]2gNJU;kMSF[2hNJT;lMTFZ2iNIR;nMUFY2iNJQ;mMVFY2iNJQ;mMVFY2iNJQ;mMVFY2iNJQ;mMVFY2iNJQ;mMVFY2iNJQ;mMVFY2iNKP;lMWFY2iNKP;lMWFY2iNKS;hMUF]2hNKV;eMRF`2hNKY;bMPFb2gNL\\;^MnEf2fNL_;[MkEi2fNMa;VMjEm2eNCe==[BYOo=g0QBnNZ>R1fAdNd>]1[AXNP?h1PAnMZ?R2f@cMe?]2[@YMo?g2e00e0[OU2kM_\\g0", "size": [640, 316]}, "area": 35799.0, "iscrowd": 0, "image_id": 10136, "bbox": [37.0, 113.0, 241.0, 367.0], "category_id": 183, "id": 10012345}, {"segmentation": {"counts": "0n0R>00000000000000000000000000000000000000000000000000000000O100000000000000000000000000001O6JM3N2001O1OO1NUGSOg3m0kKTO^K0L3k8h0kKUO[K:j8a0iKEW4<_KN`40VGUOW4n0c4M_K3a4M]K5c4K]K6b4J]K7c4I]K8b4H^K9a4G_K9a4G^Ka0[4_OeKh0T4XOlKj0R4VOnKj0R4WOmKi0S4WOmKj0ZL`NR7f0dLj0ZLhN9Do00m2k0@j0ZLTOFUO094Kf05f3c0A[3bLSLm3c0_O`3oKdK;9X4c0^On3nK`Ke4b0]OZ4d0fK\\OZ4d0gK[OX4f0iKYOV4h0lKVOS4k0lKVOS4k0nKUOP4l0QLSOn3n0RLROn3n0RLROm3o0SLROk3o0VLPOi3Q1VLPOi3Q1WLoNh3R1WLoNi3Q1WLoNh3R1WLnNi3S1WLdNSO\\Lf4Q5VLcNR4^1nKbNQ4_1nKaNR4`1mKaNS4_1nK_NR4b1PL\\No3e1RLYNn3h1QLYNn3h1SLVNm3k1TLTNk3m1TLTNl3l1TLSNl3n1SLSNl3n1ULQNj3P2ULQNj3P2WLoMh3R2WLoMh3R2XLnMh3R2XLnMg3S2ZLlMe3U2[LjMe3W2[LiMd3X2\\LgMd3Z2]LeMb3\\2]LeMc3[2]LeMb3\\2_LbMa3_2_LaM`3`2`L`M_3a2aL^M_3c2aL]M_3c2fLXMY3i2eLXM[3i2eLWMZ3j2gLUMY3k2fLVMY3k2gLUMX3l2iLRMW3o2hLRMX3n2hLQMX3P3hLPMW3Q3iLoLW3Q3iLoLV3R3jLmLV3T3iLmLV3T3lLiLU3W3jLjLU3W3kLkLR3V3nLgLT3Z3lLfLT3Z3lLfLS3[3kLfLU3[3kLeLU3[3kLdLU3]3kLcLT3^3lLbLS3_3mL`LT3`3lL`LS3a3mL_LR3b3nL]LS3c3mL]LR3d3nL[LR3f3nLZLQ3g3oLYLQ3g3oLXLQ3i3oLWLP3j3PMVLP3j3PMULQ3k3mLWLS3i3mLWLS3i3kLWLW3i3hLVLZ3j3eLWL[3i3fLVLZ3j3fLUL[3k3eLUL[3k3eLUL[3k3eLTL\\3l3dLTL\\3l3eLSL[3m3eLRL\\3n3cLSL]3m3dLRL]3m3cLSL]3m3`LVL`3j3^LYLa3g3]L\\Lb3d3[L`Ld3`3\\LaLc3_3\\LcLc3]3^LcLa3]3_LdL`3\\3_LeLa3[3_LfL`3Z3`LfL`3Z3`LfL`3Z3`LfL`3Z3`LgL_3Y3aLYI\\OZ3T4^3`LXI\\OX3P4f3dLRI\\OW3o3i3eLPI\\OV3m3m3iLSLV3n3mLoKS3Q4SMiKl2X4VMfKb2XM`LR7n0fKQ2`MnLL3n6o0eKb1XN^MT6P1dK]1]NcMo5P1dK[1_NeMm5P1dKY1aNfMl5P1dKX1bNhMj5Q1cKU1eNjMh5Q1cKS1gNkMg5R1bKQ1iNmMe5R1bKo0kNoMc5R1bKm0mNQNa5S1aKj0oNSNa5R1aKi0[6WOeIg0]6XOdIf0^6YOdId0^6[OcIc0_6]OaIa0a6^O`I`0b6A]I=e6B\\I<f6EYI9i6GWI7k6IUI6l6JTI6l6JTI5m6KSI5m6JTI5m6LRI4n6LRI3o6MQI3o6JSFTOP>a0QB_OO2V>>lA@X>a0300001O0000001O1O0000O10000000002N3L2N4L2N2O3MfW[19mgdN7M00001O00001O00001O00001O00001O00001O0000001O000010O0010O00001N2O1O001O1O1O1O1O001O1O1O1O10O4M5K4LN2OJfn^3", "size": [480, 640]}, "area": 30131.0, "iscrowd": 0, "image_id": 10138, "bbox": [0.0, 0.0, 403.0, 332.0], "category_id": 98, "id": 10012346}, {"segmentation": {"counts": "ieb52]>0SB0D=d=FfBOC=b=K_BE6e0[=f01N11000001O1O0010O1O1M3N3N0N2O1001O1O1OXO`d]3", "size": [480, 640]}, "area": 907.0, "iscrowd": 0, "image_id": 10138, "bbox": [381.0, 66.0, 25.0, 46.0], "category_id": 100, "id": 10012347}, {"segmentation": {"counts": "o3V2i<10000O100O10XNZCV1e<[N[C<1Y1g<gNYCX1h<hNXCV1j<jNVCT1l<kNUCS1m<mNSCQ1o<oNQCn0R=QOoBl0T=TOlBm0S=SOmBm0S=ROnBn0R=ROnBo0R=POnBo0T=oNmBR1S=mNmBS1U=kNkBU1V=iNkBW1V=hNjBY1T=gNmBY1S=fNnBY1S=gNmBZ1Q=gNoBY1Q=gNoBY1Q=gNoBY1Q=fNPCZ1P=fNPCY1]=N1N2N2O11O1OO11OO11O00001O00O11OO100000000N2G9M300N200O1000000O1000000000O10000O01000@A^B`0b=@^B`0b=@^B`0b=@]Ba0`=[OYB47a0`=\\OXB38b0^=C`B>`=B`B>`=B`B>`=B_B`0`=@`B`0`=@`B`0W=WOdB0590`0W=IhB8U=WOgBa048U=KkB4U=MjBK0BV=d0jBI^=8bBH`=6_BJb=6^BJb=6^BIc=7\\BJd=6\\BIe=7[BIe=7ZBIg=h0000O10001O1mNWBm0P>N1O00000O100O1000O10O10000O01000O10O1O1000O101N01001OO02O0O0102OL21000OO2O01000O02O000O100O10001N0100O010O1OO2N1100O0M41O]O[OnBa0S=_OlBb0T=^OlBb0T=^OlBa0U=_OlB`0T=BjB>V=BjB>U=CkB=U=DjB=U=FhB;W=FgB<W=FbBa0\\=CaB>\\=CcB`0\\=c0MjNkBe0V=ZOnBc0Q=]OSC?l<BYC9m<AWC;j<DYC:f<FZC8k<EUC9j=O1O100000000000J6L4000000000000000000000001O0000000O1001O2N1O00001O0000000IdAJ\\>5<JQkf1<dTYN1O1O1O1O1O1O1O1O10000O1O1002N4L2N00000000000000O101O001O0O13M1N10O1N20000001O1O001O0000N2O2O4J8I6KO2O1J6TO^R^3", "size": [480, 640]}, "area": 8341.0, "iscrowd": 0, "image_id": 10138, "bbox": [0.0, 77.0, 405.0, 120.0], "category_id": 107, "id": 10012348}, {"segmentation": {"counts": "n0e0[>000000000000000000000000000000O100O1000000000000001O000O1000000000001O001O000000000010O6JM4MDiA4V>LjA2Y>MgA2_>HbA6e>O2MgSc8", "size": [480, 640]}, "area": 1062.0, "iscrowd": 0, "image_id": 10138, "bbox": [0.0, 29.0, 53.0, 26.0], "category_id": 130, "id": 10012349}, {"segmentation": {"counts": "U6n5R9^Ob0O1O100O1O100O01N1O1O2N1N2N31N1O101N011N1O100O2N10O02O0O1O100O0001N1O1011N2OO02N011N1O101N1O011N010O1bKnEm3S:eKnE40L3[4Q:fKVFY4Y:O2O0O10001O001O1O007I7I2N000O2kLjD^2V;bMRE9Do0^;gN[EFUO094Kf0S>I6[OdA;k>DURX8", "size": [480, 640]}, "area": 11415.0, "iscrowd": 0, "image_id": 10138, "bbox": [0.0, 178.0, 76.0, 209.0], "category_id": 132, "id": 10012350}, {"segmentation": {"counts": "WiU32m>3QBLS=0lB0m[15jQO8I6J5C=01O000O1N2O1M7L3MO100OH90O10000O100O100O100O100O2O5J5L5J2O0O20O]O^BJa=1nBFP=7n0LkSa5", "size": [480, 640]}, "area": 1692.0, "iscrowd": 0, "image_id": 10138, "bbox": [217.0, 55.0, 45.0, 62.0], "category_id": 139, "id": 10012351}, {"segmentation": {"counts": "^kX11n>2O2M3N2M3M6K9G4K3N000O10000000000000000O2O1YBkNa=\\1N1O00000001O00000O1000001O0O2O1O1O0O2N2OK53M10N0N301M13O0O0O2OOmN`Bi0k=O10O16UOhA`0d>H3JmQV7", "size": [480, 640]}, "area": 2143.0, "iscrowd": 0, "image_id": 10138, "bbox": [87.0, 71.0, 62.0, 47.0], "category_id": 143, "id": 10012352}, {"segmentation": {"counts": "odk01n>100O1O1O10000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001O000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001O00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001O000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001O0000QSP3", "size": [480, 640]}, "area": 1229.0, "iscrowd": 0, "image_id": 10138, "bbox": [58.0, 475.0, 376.0, 5.0], "category_id": 165, "id": 10012353}, {"segmentation": {"counts": "c1\\2d<O10000O100O100O[CiMc<Z2000000000000O100O10001O1O1O2N1O10NeC_MY<a2fC_M[<a2eC_M[<`2fC`MZ<`23000bC`M\\<`2210O0O2O000O1000001N10001OBgMVDX2j;hMTDZ2l;fMSD[2f;gMRDO6\\2g;kMXDj02Od;ZOWDb00RO?j0Z;e0SESOl:m0]EkNc:U1bEfN_:Y1cEeN[:MVD_1^1dN\\:_1cEaN]:c1_E]Na:d1]E]Nc:d1\\E]Nc:d1\\E\\Nd:e1[E[Ne:k1UEUNk:P2oDQNQ;U2iDkMW;Z2dDfM\\;`2^D`Mc;d2XD\\Mh;n2001OO11O00000K6O1O2NONmCYMT<f2mCYMT<e24000001dNfC0Y<0iCOW<1kCDLSO05X<T1oCBZ<>hC]OHZO_<Y1lC[OW<d0oCVOQ<k0XDkNi;U1P10011O2M2OO11O1OO100O11OO02OO100001O0000010OO1001O000001O05LM3O10OO2O10J4D=00001nA]Oe=o00N1E>NO101QB]O_=Q11N21M2^BnNV=R1bB\\O1@T=P2I2UCQNa<W20O11O0000000000001OO100OO20OO101O02O0O1L41O]OYCPOd<P1\\CoNf<P1ZCPOf<P1ZCPOe<Q1[CPOe<o0]CoNc<Q1]CoN`<S1aCmN]<U1dCjNZ<X1iCeNT<^1mC`NT<_1nCZNW<f1mCWNT<f1mCYNU<g1a0O21O00O16JYOPCXO3Cl<OPCT1f0lNZ<0PCT1g0nNV<NSCS1h0ROR<KVCS1h0ROT<o0lCPOW<Q1eCoNa<P1[CoNg<T1TClNl<W1QCiNP=Y1nBfNR=]1kBcNU=Y1;N102[O`BGa=8d000O1010N103L8H6J3NEZB[Of=d0`BWOb=h0:110O2N1O3M3M2M11O000ONkNZBT1]=mNcBO2Y1[=iNcBX1a=300001O0G90001O1O0000O10000000000\\OoNYCR1f<QOVCU1e<lNYCY1c<jNYC\\1b<eN\\C]1c<dN\\C\\1d<gNYCZ1f<`001O000000M3L4M3O10000000000O10000000000O10000000000O10000000000O10000000000O100001O0000001O0000001O0000001O0000001O00001O0000001O001O1O001O1O001O1O001O1O001O1O001O1O1O001O2N3M2N2N3M2N2N8H0000000000000000000WDkLg;W30O1O1002N4L2N00000000000000O100000000\\O_DhMb;g1^DXN0Dn;S2UDWNOCm;R2[DoME6U<k1g000O10000000000001O1O1O1O2N00000000000000B>UOk0UOk0N2001O0000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001O00000000000000000000000000000000000000000000000000000000000000000SR>MmmA3000000000000000001O00000000000^E2R6NeI;[6E]Ic0c6]OTIl0l6TOkHU1U7kNcH]1]7cNZHf1f7ZNRHn1n7RNiGW2W8iMaG_2_8aMXGh2h8XMoFQ3Q9oLgFY3Y9gL^Fb3b9^LVFj3j9VLmES4S:=000000000@jEVLV:f3nEZLR:b3SF]Lm9_3WFaLi9[3[FeLe9Z3]FeLc9Z3^FfLb9Z3_FeLa9Z3`FfL`9Z3`FfL`9Y3bFfL^9Y3cFgL]9Y3dFfL\\9Y3eFgL[9Y3eFgL[9X3gFgLY9Y3gFgLY9X3iFgLW9Y3iFgLW9X3jFhLV9W3lFhLT9X3lFhLT9W3nFhLR9X3nFhLR9W3oFiLQ9W3PGhLP9W3QGiLo8W3]1O1O100O100O100O100O1O100L4L4M3L4M3L4N2001O1O1O001O1O001O1O001O1O001O1O1O1O1O2N1O1O1O2N1O1O2N1O1O", "size": [480, 640]}, "area": 36051.0, "iscrowd": 0, "image_id": 10138, "bbox": [0.0, 0.0, 640.0, 149.0], "category_id": 172, "id": 10012354}, {"segmentation": {"counts": "U=k1a;d1O1O100O1O100oH^Lc2c3]M]Lc2c3]M^La2c3_M]L`2d3_M]La2c3_M]L`2d3`M\\L_2e3`M\\L`2d3`M\\L_2e3aM[L^2f3aM[L_2e3aM[L^2f3bMZL^2f3aM[L^2f3bMZL]2g3cMYL]2g3bMZL]2g3cMYL\\2h3dMXL\\2h3cMYL\\2h3dMXL\\2h3dMXL[2i3dMXL[2i3eMWL[2i3eMWLZ2j3eMWLZ2j3fMVLZ2j3fMVLY2k3fMVLY2k3gMULY2k3gMULX2l3gMULY2k3gMULX2l3hMTLW2m3iMSLW2m3hMTLW2m3iMSLV2n3jMRLV2n3jMRLU2o3kMQLT2P4lMPLT2P4lMPLS2Q4mMoKS2Q4aLkK^N4P5R4[LeKcM?j0JW5S4\\L`LUN]O_5S4[LiLnMUOe5S4]LmLiMPOi5T4^LnLgMnNk5T4_LlLgMPOi5T4eLhLbMTOi5S4gLhL`MUOi5R4iLhL^MVOi5R4jLgL^MVOh5R4lLfL]MXOg5Q4QMcLXM\\Og5Q4VM^LSMAg5P4]MYLlLGg5P4bMTLgLLg5P4hMmKbL3f5P4mMhK]L7g5Q4YNoKf1S4YNmKg1S4YNmKg1S4YNlKh1T4XNlKh1T4XNlKg1U4YNjKg1W4YNiKf1X4ZNhKf1X4ZNgKf1Z4ZNfKe1[4[NeKd1\\4\\NcKd1^4\\NbKc1_4^N`Ka1a4_N^Kb1b4^N^Ka1c4_N]K`1d4`N[K`1f4`NZK_1g4aNYK^1h4bNXK^1h4bNWK^1j4bNVK]1k4cNUK\\1l4dNSK\\1n4dNRK[1o4eNQKZ1P5fNoJ[1Q5eNoJZ1R5fNnJY1S5gNlJY1U5gNkJX1V5hNjJW1W5X4100O1O1O1O1O1O100O1O1O1O1O1O100O1O1O1O1O100O1O1O100O1O1O1QI`J^4`5bK`J]4a5cK]J^4d5_20O1O1TIZJ]4g5cKYJ]4g5cKYJ\\4h5]21jIQJZ3P6`LVJ`3j5aLTJ_3m5aLlIe3U6c20O1O1O100O1O100O1O1O100O1O1000000000000000000000001O00000001O000000000000000000bJbIZ2^6fMfIV2Z6jMjIR2V6nMoIm1Q6SNSJi1m5YM^I_N3Id0]4k5[MSJWN2]4l5\\MRJWN2W4R6aMnIWN0V4T6cMlIWN0S4W6hMgIUN2R4X6nMbIoM6S4X6SN]IjM;R4Y6XNYIeM>k3[OULn6^2VIbMa0Z3BdLM2j6a2TI^Mc0m2:SMP6e2PI[M=Q3h0oLk5`2hI??QMi5^2jI??RMh5^2iI?`0TMg5i1SI`Ng0a2a0VMe5h1^JP1OWMd5i1]Jn01YMb5h1^Jm02[M`5h1^Jk04]M^5i1]Jh07^M]5j1\\Jf09`M[5P2VJ>a0bMY5X2nI4k0dMW5Z2lI0o0fMU5[2kIMR1hMS5P2lHYNn0m1U1jMQ5n1RIYNf0m1X1mMP5m1ZIRN=R2[1nMo4n1WJ2l0PNm4o1VJOo0RNk4Q2kI4\\1kMi4S2dI6d1gMh4U2]I9k1bMh4a2lH1]2^Mg4W5YKiJg4V5ZKjJf4V5ZKjJf4U5[KkJe4U5[KkJe4k3lHXLa3Ob3h3SJVLY12d4f3TJXLY12c4f3SJYLZ11c4e3SJZL[11b4e3RJZL]11a4d3SJ[L\\11a4d3RJ[L^11`4c3SJ\\L]11`4c3RJ\\L_11_4c3RJ\\L_12^4a3TJ\\L`12\\4b3SJ]Lb10[4b3TJ]Lc10Y4c3TJ]Lc10Y4b3TJ^Le10V4b3UJ^Lf10T4a3[40kE_Lk8`3[100O10PIaLmK1S6]3SNoLm1Q3TNnLl1Q3VNnLj1T3VNjLj1b2kI_MZ4Nk1_2ZJZMk35k1^2UOaMk0[2ZOfMd0V2BiM=T2GkM9Q2LnM4o11oMOP22PNNP22PNNP22QNMo13QNMo13QNMo13QNMn14RNLn14RNLn15QNKo15QNLn14RNLn14RNLm15SNKm15TNJl16TNJl16TNJl16TNJl17SNIl18TNHl18TNHl18TNHl18TNHl18TNHl18UNGj1:VNFj1:VNFj1:VNFj1;UNEk1;UNEk1;UNEj1<VNDj1<VNDj1<VNDk1;UNEk1;VNDj1<VNDj1=UNCl1<TNDl1<TNDl1<TNDl1<TNDm1;SNEm1;SNEm1;SNEm1;TNDm1<RNDn1<RNDn1<RNDn1<RNDo1;QNEo1;QNEo1;QNEP2:PNFP2:PNFP2:PNFP2;PNDQ2;oMEQ2;oMER2:nMFS29mMGS29mMGT28lMHU27kMIU27kMIV27iMIX26iMIW27iMIX26hMJY25gMKY25gMKZ24fML[23eMM[23eMM\\23cMM^22bMN_21aMO_21bMN`20`M0b2N^M2d2L\\M4g2IYM7i2GWM9k2EUM;m2YKSMj2Om1Q3fJdMY3[OQ2X3TJiMj3oNR2X3SJjMk3oNQ2W3SJkMl3nNQ2W3RJmMl3lNR2W3QJnMm3kNR2W3PJoMn3jNR2W3oIQNn3hNS2W3nIRNP4fNR2X3mISNQ4eNR2X3lIUNQ4cNS2X3lIUNQ4cNS2g4mMYKS2h4lMYKS2g4mMYKS2g4mMYKS2h4lMXKT2h4mMWKS2i4mMWKS2j4lMVKT2j4lMVKT2j4lMVKT2j4lMVKT2k4kMUKU2k4kMVKT2j4lMVKT2k4kMUKU2k4lMTKT2l4lMTKT2m4kMSKV2l4jMTKV2l4jMTKV2m4iMSKW2n4hMRKX2o4gMRKX2n4hMRKX2o4hMPKX2Q5gMoJY2R5fMnJZ2S5eMmJ[2T5dMlJ\\2T5dMlJ\\2U5cMkJ]2V5bMjJ^2W5aMiJ_2X5aMhJ^2Y5aMgJ_2Y5aMgJ_2Z5`MfJ`2[5_MeJa2\\5^MdJb2]5]McJc2W5cMiJ]2\\4^NdKb1a3YO_Lg0f25ZMJk1P1VNPOo0j1ROVN3d2N\\M0e21[MOe21[M0c21]MOb22^MNa23_MM`24`ML_25aMK:LiK:l3J>MaK7RNIo53`00YK2[NKl53b03PKNeNLi53d05gJKPOMe53f08^JG[OMa54i09jJC]44k0<cJ@b44m09bJCa44o00gJLZ44j2MUM3l2LTM4l2LTM4l2LTM4l2LTM4l2LTM4l2LTM4l80000000QJLRM4m8000000000000000000000000000_ILVN4h1NXN2g1OYN1f10ZN0e11[NOa15_NKg0n0ZOROK_1fIdN_6MPOd1WK_Ni5MUNi1hLXNU5OQNV1_MhNc43mM`0VN[On35lMNhNM\\35[1KeN5[1KeN5[1KeN5[1KeN5[1KeN5[1KeN5[1KeN5[1KeN5[1KeN5[1KeN5[1LdN4]1KcN5]1KcN5]1KcN5]1KcN5^1KaN5`1L^N4d1LZN4g1MWN3j1MVN2l1LTN4\\8000000000000000000000dILlM4T2MQHOY54f2NmG1]51f2OiG3a5Ng2OdG6e5Kg2NhM2c80000000000000000000000000000000000000000000000000000000000000000001O000000000bENn52jI6V6JaI?_6AYIg0g6YOPIP1P7POhHX1X7hN_Ha1a7_NWHi1i7WNnGR2R8nMfGZ2Z8fM]Gc2c8]MUGk2k8UMlFT3T9lLdF\\3\\9dL[Fe3e9[LRFn3n9RLjEV4V::00[J[Ko0e4QO[Ko0e4QO[Ko0e4QO[Ko0U4aJULU:g3oEYLQ:c3SF]Lm9_3XF`Lh9\\3\\FdLd9[3^FdLb9[3_FeLm3Hm0c3VKeLm3Hm0b3XKeLk3Im0b3XKeLk3Im0a3ZKeLi3Jm0`3[KfLh3Jm0`3[KfLh3Jm0_3]KfLf3Km0_3]KfLf3Km0^3_KfLX9Z3hFfLe3Lk0]3aKgLd3Lk0]3bKfLc3Mk0\\3cKgLb3Mk0[3eKgL`3Nk0[3eKgL`3Nk0Z3fKhL_3Nk0Z3gKgL^3Ok0Y3hKhL]3Ol0X3gKiL]3Ol0W3iKiL[31k0V3jKiL[31k0U3c0kL]OT3d0lL\\OT3e0kL[OT3f0lLZOT3f0lLZOS3g0mLYOS3g0mLYOR3h0nLXOR3h0oLWOP3j0PMVOo2k0QMUOo2k0QMUOk2o0UMQOg2S1YMmNd2V1\\MjN`2U1eMkNX2P7LfIfM6X2BRN>n1WO\\NYK\\Oc4Y2HeNfKROb4Z2]OlNSLjN`4[2QOSObLbN]4[2fN\\OoLYN[4\\2ZNC^MQNX4]2oMIlMjMU4]2dMoNYMYOP1[OS4^2XMQOoMQOi0@P4_2WMmNUNPOf0Dn3_2[MfNUNSOe0Ij3_2_M]NXNWOa0Mh3`2fMQN0_OZ2`2oMeMJKW2a2]1_McNb2\\1^MdNc2[1]MeNd2Z1\\MfNe2Y1[MgNg2W1YMiNh2V1XMjNi2S1ZMlNg2R1\\MlNf2Q1^MlNc2Q1bMlN_2?ZMdJ;j4]2FSO7n0mN0P1S7M", "size": [480, 640]}, "area": 211153.0, "iscrowd": 0, "image_id": 10138, "bbox": [0.0, 2.0, 640.0, 478.0], "category_id": 183, "id": 10012355}, {"segmentation": {"counts": "\\7d7\\70000000001O001O1O003M:F6J5K6J3M000000000000000000O12NO1O10000O1O100O11O000000001O001O1O001O001dIeGNJQ6m801O001O1O000000O100O100O100O11O001O001O1O1O1O00001OO100000000O10000000000O100001O00001OO1000000O1000000O1000000O1BPJlGP6S8QJdGO4P6W8RJcGNO61j5\\8SJcGN0X6]88M30000O11O00000000O1O1002N1O2N0000001O1ON2O13M001OM31OLaIgG_6Z831O00O10000O10000O100O10000000000O12NO12NO11O00000000O100O100O1O1O1O1O1J61O001O00001OO1O1O11O0000O11O1OO1O100EoHfHR7U7oHcHO01004Q7S7nHkH8NJ2Q7U7]IiHe6X7a0N2M31O000000002N1eHiHC5Q7V7PIkHO1Q7U7oHSIQ7]711O0000O1000000M3000000O1000000O100000000001OO12N1kHVHR7j7nHWHQ7k710000000000O100O1MmHYHS7f74O1N2JeHdH^7\\7300O100000000000000001OO11O001O:F1O001O00O100CkHlHX7W77O1N2000000K_HlH`7T7aHkH_7W741O1O00N2002N00001gLdHlN]7R1gHkNY7V1iHgNW7Z1jHdNV7]1lH`NT7a1lH^NU7XMbHU4:bNS7ZMcHU4:`NX7a1hH^NY7b1gH]NX7e1hHZNX7g1hHXNZ7g1eHYNX7\\M_H]49VNX7]M_H^49TNW7P2iHoMX7Q2hHnMY7R2gHmMZ7R2gHmMX7U2hHjMX7W2hHhMX7Y2hHfMX7[2hHdMW7^2iHaMX7_2hH`MX7a2hH^MW7d2iH[MW7f2iHYMS7mMcHk4<UMP7P3QIoLn6R3RInLm6S3TIlLm6R3TInLl6R3TInLl6mNhHS3=oMl6hNlHY39nMk6dNRI\\35oMi6`NWI_33PNf6\\N\\Ia32RNb6ZN`Ia31TN_6ZNaI`33UN]6ZN`I_36VN\\6YN_I^38XNX6[N`IZ3<ZNT6\\N`IP3g0cNk5[N^Ii2Q1kNa5\\N_Ic2V1PO[5]N_I^2\\1TOV6g0PJXOP6c0VJ\\Ok5>[JAe57dJH\\5OmJ1S5HjJjNmL]1Y8FnJmNiL\\1Y8DQKQOfLZ1Y8BUKTObLY1Z8@VKXO`LW1Z8^OQKDeLm0Z8\\OkJ1kLb0Z8ZOmJ6iL?Z8XOoJ;gL<Z8WOPK?fL9Z<IfC6Y<LgC3X<OhC0X<1hCNW<4iCK];Q1bDnNU;\\1kDcNS;`1mD_NQ;d1oD[NP;g1oDYNo:j1QEUNm:n1SEQNl:Q2TEnMj:U2VEjMh:Y2WEgMh:[2WEeMg:_2WEaMg:b2WE_Mh:c2WE]Mg:_32N2N2N3nLeEY1]:YNRFd1P:nM`Fn1\\;M3M2N0000000000000ZNnB^1S=`NRC\\1n<dNVCX1j<gN[CU1e<kN_CQ1]=K2N001O00QO_O`C`0`<IWC7e<6SCIg<[1oJQNkLU2W3PNnI82m1o5UN_Ia4a6hKoH_4_6fJfH002:R11]4n6dJSIm6i6PIkH0?P7e6UI[Ik6e6UIlHK;P7j6ZITIf6k6_IQIa6o6^IRIb6o6\\IRId6o6[IQIe6P7YIQIg6P7XIPIh6P7XIoHi6R7TIPIl6S7WIfHj6X7XIgHi6X7`0000aHjHJ0U7V7PIPIP7o6nHUIQ7]701O001O1O001OO1000000O10000OoHVHn6j7RIVHn6P8N003MN21O1O00O1O10000KUISHm6l74O100O100001O1O1O001O1O1O2NO1O11O002NL40000O100000000O10000001O000000O100O1NnHWHQ7i73O12N001ODnH`H0023Q7]7TI`Hn6`79NbHbH^7\\7bHdH`7[73O100O13MO100O100O1003M1OM30\\HfHb7\\7^HdH`7^7000001O9G1O2N000000000000O100002NH800O1H80000O100O11O00007IK53M2\\I`Hd5d7WJ_HZOL[6f7YJeHc5[7\\JjH`5W7^JlH`5Z7QJTHOO1e0n5W7SJUHM10d0P6V7QJ[H0`0m5Y8VJQGc5P9YJSG0Md5X91O\\JhFb5X9]JjFb5Y91O`JgF[5Z9bJgF_5\\91N200N2O1O10000O1O14L7IE;O1O11O00O1O1O1O10000NSJQGm5m842N00O11OO1FPJdGP6Z8RJfGn5Y8RJhGn5X8PJkGo5U8QJ_G18Q2Ii1_8YLcGL0Q2Oj1]8_LdGf12TNL^3]8mNnGT1R8lNPHR1o7oNPHR1o7nNmGW1V8dNlG\\1X8PN]GZN468_3W8oMfGaN5_3T8nMkGbN6[3o7RNnG_N5_3l7RNfHP2[7mMfHT2Z7lMgHS2Y7mMhHR2e5`LbJ_1GQ2e5bLnJV1[OW2f5eLnJU1\\OU2e5gLoJT1\\OU2d5hLQKR1\\OU2c5lLnJm0CU2^5PMoJk0CT2^5QMPKj0BU2]5QMRKi0AW2^5nLRKl0_OV2]5PMTKi0@W2]5oLTKk0]OV2_5oLTKl0]OT2^5QMUKl0\\OS2_5SMRKk0^OS2a5nLRKFVNY1W1S2`5oLSKm0@T2\\5nLUKo0BP2X5SMUKn0Cn1X5VMRKh0LQ2", "size": [480, 640]}, "area": 129209.0, "iscrowd": 0, "image_id": 10142, "bbox": [0.0, 214.0, 640.0, 266.0], "category_id": 159, "id": 10012356}, {"segmentation": {"counts": "0\\7d700000000001O001O1O003M:F6J5K6J3M000000000000000000O12NO1O10000O1O100O11O000000001O001O1O001O001dIeGNJQ6m801O001O1O000000O100O100O100O11O001O001O1O1O1O00001OO100000000O10000000000O100001O00001OO1000000O1000000O1000000O1BPJlGP6S8QJdGO4P6W8RJcGNO61j5\\8SJcGN0X6]88M30000O11O00000000O1O1002N1O2N0000001O1ON2O13M001OM31OLaIgG_6Z831O00O10000O10000O100O10000000000O12NO12NO11O00000000O100O100O1O1O1O1O1J61O001O00001OO1O1O11O0000O11O1OO1O100EoHfHR7U7oHcHO01004Q7S7nHkH8NJ2Q7U7]IiHe6X7a0N2M31O000000002N1eHiHC5Q7V7PIkHO1Q7U7oHSIQ7]711O0000O1000000M3000000O1000000O100000000001OO12N1kHVHR7j7nHWHQ7k710000000000O100O1MmHYHS7f74O1N2JeHdH^7\\7300O100000000000000001OO11OCjHPIV7n6mHRIR7n6oHRIP7m6b000001O2N6J6J3MO1N2000000K_HlH`7T7aHkH_7W741O1O00N2002N00001O1O000000001aHbHZ7]7gHcHY7b701OO1002NMfH_H[7a7eH_H[7`731O1O1OO1000000O11O00O100LeHcH[7\\75O1O11O00001O00000000001O2NO1002N00001OYNeHiK[7U4jHhKV7W4nHfKR7Y4SIcKm6\\4WIaKi6^4[I^Kf6a4^IYKe6e4l1O1O1O1O1O10000O1000000O1000000O10000O1000000O1001]F]Kg8_5O001O1O001O1O1O001O1O001O1O1O001O1O001O1O1O1O1O2N1O7I8H8H8H4L2NO100^OnHfH002:P7o6SISIm6i6PIkH0?P7e6UI[Ik6e6UIlHK;P7j6ZITIf6k6_IQIa6o6^IRIb6o6\\IRId6o6[IQIe6P7YIQIg6P7XIPIh6P7XIoHi6R7TIPIl6S7WIfHj6X7XIgHi6X7`0000aHjHJ0U7V7PIPIP7o6nHUIQ7]701O001O1O001OO1000000O10000OoHVHn6j7RIVHn6P8N003MN21O1O00O1O10000KUISHm6l74O100O100001O1O1O001O1O1O2NO1O11O002NL40000O100000000O10000001O000000O100O1NnHWHQ7i73O12N001ODnH`H0023Q7]7TI`Hn6`79NbHbH^7\\7bHdH`7[73O100O13MO100O100O1003M1OM30\\HfHb7\\7^HdH`7^7000001O9G1O2N000000000000O100002NH800O1H80000O100O11O00007IK53M2\\I`Hd5d7WJ_HZOL[6f7YJeHc5[7\\JjH`5W7^JlH`5Z7QJTHOO1e0n5W7SJUHM10d0P6V7QJ[H0`0m5Y8VJQGc5P9YJSG0Md5X91O\\JhFb5X9]JjFb5Y91O`JgF[5Z9bJgF_5\\91N200N2O1O10000O1O14L7IE;O1O11O00O1O1O1O10000NSJQGm5m842N00O11OO1FPJdGP6Z8RJfGn5Y8RJhGn5X8PJkGo5U8QJ_G18Q2Ii1_8YLcGL0Q2Oj1]8_LdGf12TNL^3]8mNnGT1R8lNPHR1o7oNPHR1o7nNmGW1V8dNlG\\1X8PN]GZN468_3W8oMfGaN5_3T8nMkGbN6[3o7RNnG_N5_3l7RNfHP2[7mMfHT2Z7lMgHS2Y7mMhHR2e5`LbJ_1GQ2e5bLnJV1[OW2f5eLnJU1\\OU2e5gLoJT1\\OU2d5hLQKR1\\OU2c5lLnJm0CU2^5PMoJk0CT2^5QMPKj0BU2]5QMRKi0AW2^5nLRKl0_OV2]5PMTKi0@W2]5oLTKk0]OV2_5oLTKl0]OT2^5QMUKl0\\OS2_5SMRKk0^OS2a5nLRKFVNY1W1S2`5oLSKm0@T2\\5nLUKo0BP2X5SMUKn0Cn1X5VMRKh0LQ2", "size": [480, 640]}, "area": 163206.0, "iscrowd": 0, "image_id": 10142, "bbox": [0.0, 0.0, 640.0, 376.0], "category_id": 169, "id": 10012357}, {"segmentation": {"counts": "Q^l3<b>3O1`AIT>b001O10N4M7H`b8Ao]G4N2N1O2N0O10000000O10000000000000O100000000000000000002M001O0O2O0kKDfI=T6HlI9n5LQJ6i50TJ3f52WJ2e51YJ2e5OYJ4f5MWJ6h5JVJ9i5GTJ=k5DjIg0U6YObIQ1]6oN^IW1a6jNYI\\1f6dNUIb1S5aMWJm0a0h1U5`MWJh0?m1Y5_MUJd0:V2_5[MSJ?5`2f5UMRJ<0\\2jNmLR7?PJ91_2mNiLP7c0lI84_2QOfLl6g2PJf0UOcLi6h2PJf0YObLe6i2oI:KmLT6k2mI92lLo5l2lI:7jLk5m2kI;<hLh5m2jI<`0gLe5l6]JTIa5l6aJTI^5k6dJUI[5k6fJUIY5`6SK`Ik4i5oKVJP4h5SLXJl3g5VLXJj3f5YLZJe3e5^L[Ja3c5bL]J]3b5eL^JY3a5jL_JU3_5nL`JR3_5PM`JP3^5SMaJl2^5XM`Jh2`0WJT4S3[Kb3b4aL]K`3a4`33N1O1N3gFlKa7U4QHZLm7g3eGiLX8R5M3N1OO001O1O1O001OjG^Io7a6PHdIl7\\6THhIi7W6VHnIg7Q6YHSJd7l5[HZJa7e5_H\\Ja7b61N3oN]H`Jj7X5_H_Ji7W5cH`Jd7R5kHeJ]7n4e1SMjE:d:WO\\FJR:HP3_O`0@QP\\3", "size": [480, 640]}, "area": 14568.0, "iscrowd": 0, "image_id": 10142, "bbox": [265.0, 168.0, 144.0, 287.0], "category_id": 183, "id": 10012358}, {"segmentation": {"counts": "fmQ322O10f;1ZDOh0b0k:A\\DNO0e0b0P;G[DHa0a0Q;J^DE02<=i:CQEh03Eb:NYE`04Ac:1VE?OQO6>e:]1VEcNj:n11100O2O1L20001_E\\MY:d2eEfMS:[2kEjMQ:i2O4N10000M4L4L5K3M2N0000000000000O100000000O10000JoKRGQ4n86O1O100O1O100O1O1O1002N2N1O1O001OO10000000000O1000000O1O15K3M0000000000000000000bM^G3b8L`G3_8McG2]8MdG3\\8LfG3Z8LgG4Y8JjGMQOjNU9X1kGLROlNS9W1mGGUOROn8V1nGROW9JiEg0j;UOSDL5o0U<00O7J00000O101O000\\Oc\\l3", "size": [427, 640]}, "area": 11235.0, "iscrowd": 0, "image_id": 10145, "bbox": [234.0, 284.0, 107.0, 143.0], "category_id": 101, "id": 10012359}, {"segmentation": {"counts": "S_V21Z=1O1O001O1O1O1O001O1O1O001O1O001O1O001O1O001O1O1O001O1O1O001O1O1O2N001O1O001O1O1O1O1O00000000O1O10000O100O1000000000000001O000000000000001O0fCUO1LP<Z1N0000000000000000O10000000000NPDiNP<W1PDiNP<W1PDiNP<W1PDiNP<W12O1ImCVOS<i0nCROW<n03000000O10000O1O10000001O00001O000000000000000000000000O100000000001O0000000000000000000000000000AfCG`<:800O10000O100O1001O0000000000000000MUCFk<9VCHi<8500O10000O10000O1000000O100001O001O1O001O1O001O001O00001O0000001O00000]CBY<>aCK\\<b001O00000000000000001cCSOZ<S1L1O002N0000000000001OO100000000000000FmC\\OS<d0mCZOU<f0lCWOV<i0600O1000000000000000000O1000000000000001O00000000000000000000000000000000O1000000000000000000000000000000000000001O000000000000000000000000O10000001O0000000000000000O1000000000000O10000001O00001O0000000000000000OlCVOi;j0WDVOi;j0WDVOi;j0WDVOi;j0WDVOi;j0WDVOi;i0XDWOh;i0XDWOh;i0XDWOh;h0YDXOg;h0YDXOg;h0YDXOg;i0XDWOh;i0XDWOh;j0WDVOi;j0WDVOi;k0VDUOj;k0VDUOj;k0VDUOj;k0PDUO01P<k0mC\\OS<n000000000000000000000000000000000000000000000000FmC\\OS<d0mC\\OS<d0mCXO1LS<k0TDUOl;k0:00000000O10000000000000000000000000000O1O100O100O1O100O1O100O1O10000O1O100O1O100O1O100O1O1O1O100O1O1O100O1O100O1OYP:", "size": [427, 640]}, "area": 11549.0, "iscrowd": 0, "image_id": 10145, "bbox": [169.0, 0.0, 447.0, 43.0], "category_id": 102, "id": 10012360}, {"segmentation": {"counts": "d:g2d:00000000000000000000000000000000gM`E0O2Nc0e:UOcE2N0Oi0a:POhE2MNOP1n;O0000001O00003M00N2006J3M4L1O00O10000000000000000000000N2L4L4L400000000001O0000000000000000001O0000000000000000001O00000000000000001O00000000000000000000001O0000000000001O0000000000000000001O000000000000I71OO11O2NO1O100YNZOhFg0Y9MQF4n9NPF3P:MoE4P:MoE4Q:L`ESO7R1X:L_EWO5n0\\:b10000O10000000000001O1O1cM`Em1Q;N?A000ZNnDS1e;0001OO10000000000000000000000000000000000000000000000000000000000001O000000000000000000000000000000O1oNBUE>k:Q1000000O100001O0000O1000000O11O@PN]EK11NU2d:?0000000000O1000000000mM^E^OO10g1c:bNoE\\1Q:cNPF]1P:cNPF]1P:dNnE]1T:aNlE_1V:`NiE`1W:XNZE6>b1`:YN`Eg1Q;11O1O2N2N00001bNYDV1g;hNcDP1^;nNgDn0l;O4L3M3M3M4L4L5K3MVn6=YQI31O01N100N3O0FAjC>W<BgC?[<@eC`0[<:N2N1O11N010N2O2N000\\OeC8[<GfC9Z<EhC:Z<ChC=c<01O3L4L^UZ4", "size": [427, 640]}, "area": 12634.0, "iscrowd": 0, "image_id": 10145, "bbox": [0.0, 253.0, 308.0, 174.0], "category_id": 117, "id": 10012361}, {"segmentation": {"counts": "_fP421<h<7M000001O0000000000000^OZC?e<B]C;e<D^C9b<GcC4m<L`ie1ObVZN2N001O1PCMf<3XC0f<1XC1h<OVC4i<70001O001O00H\\CI\\<1RD0f<N2O0O0010O010O0jWS2", "size": [427, 640]}, "area": 500.0, "iscrowd": 0, "image_id": 10145, "bbox": [308.0, 274.0, 170.0, 43.0], "category_id": 118, "id": 10012362}, {"segmentation": {"counts": "=W:T30004L7I6J<D8H<D:F<D<D8H=C2N2TIQJX6m6D=CcHdJ0O2NX6P5`IXK2N0Oc6LZIb4LkK2MNO[7c5`0_O9Ga0_O8I8G9G:I8E;C:H9M6G9H5H;D:E;F7I00O100O1O100O100O1O^O[C>j<L3M6JgTW1NljhN3aC5]<M_C7`<I_C98Ei;2nCk0P<g0bMcNPH]1o7@bFiN=g1_OjNn8j3O1O2M110000000000000O10000000000000000000000000000000000000000000001N100000000O1O1N200O11O002N00O11N4lNlGkLU8T3Q101O4L2M101O01O01OO02O1O2N1N3OO@jEcMK11N[:^2?0000000000O100000M300N]OZEUNO<f:_1]ETNNO0:c:e1fEQNH6a:g1jESNE020a:]1_EiN];l0nDROS;n0oDoNR;Q1c01O1010OaNXO[EEi0W1k9Z120001O2N1O2N2001O1ON2N2N2N2N2N2N2N2N2N2N2N2M3N2N1O2N2M3N2N2HjCWOX<8g0J6NRONbD2\\<N\\ONRD2o;Oe07H2XCFc<4_CM`<0cC0]<O`000000\\`=1QmB5]B0000000000000000000000000O100000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000O100000000000000000000000000000000000000000000000000000000000000000000000000000000mKHSK8o4HoJ8R5HmJ8S900000000000000000000000O100000000000000WJG`N9Y1NgN2T12mNNQ14oNLo06QOJn06SOJl07TOIk08UOHj08WOHi08WOHh09XOGh08YOHf09ZOGe09\\OGd09\\OGd09\\OGc09^OGb09^OGb09^OGb08_OHa08_OH`09@G`08AH?8AH?8AH?7BI>7BI>6CJ<3HM9NK25I070G29`600000000000000000000000000000O100000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000O1000000000000000000[JFYN:g1GXN9f0FiI40O`01P56:a0`J_OU50;b0[JCY5K<Y1DgN<Z1CfN=[1BeN>\\1AdN?\\1AdN?]1@cN`0^1_ObN`0`1_O`Na0a1^O_Nb0a1^O_Nb0b1]O^Nc0c1\\O]Nd0d1XO_Nh0a1UObNk0?WJMk4Gn08[J2j1ROi0g0R30aJ6d1SOg0i0T3JeJ;_1SOd0k0X3EgJ=]1UO?l0]3CfJ<^1WO:m0c3^OfJ?]1WO5o0h3[OfJ?]1YO0P1n3VOgJa0Z1[OLQ1S4SOgJa0Z1]OGR1X4POgJa0Z1_OBS1\\4nNhJa0Y1@^OT1a4kNhJa0Y1BYOU1g4hNfJa0Z1DTOV1k4fNgJa0Y1KjNQ1Z5_NdJd0X19ZNg0R6TNgJLUOb0g1m2e5RM[Jn2e5RM[Jn2e5SMZJm2f5SMZJm2f5TMYJl2g5TMYJl2g5TMYJl2g5UMXJk2h5UMXJk2h5UMXJk2h5VMWJj2i5VMWJj2i5WMVJi2j5WMVJi2i5XMWJh2h5ZMXJe2h5[MXJe2h5\\MWJd2i5\\MWJd2i5\\MWJd2h5^MWJb2i5^MWJb2i5^MWJb2i5_MVJa2j5_MVJa2i5aMVJ_2j5aMVJ_2j5aMVJ_2j5bMUJ^2", "size": [427, 640]}, "area": 30325.0, "iscrowd": 0, "image_id": 10145, "bbox": [0.0, 13.0, 640.0, 414.0], "category_id": 123, "id": 10012363}, {"segmentation": {"counts": "a\\W41X=2000000O1RC0a<0[CML5i<;O115I1O1N20001_OWO^Di0b;[OZDe0JGV;DPE[1P;iNlDW1T;`000000001O0O1N2001O01O00001O02O00O010O1N101O001O1O001O1HdM_E\\2a:dM\\E`2c:kM\\El1c:TN^Ek1_:XNbEh1Z:[NfEf1X:ZNiEf1W:ZNiEg1T:[NkEf1T:nMkE21o1U:kMnE6Mn1T:jMSF8Im1R:jMZF8Dn1c:RN]En1c:RN]Em1d:SN\\Em1d:SN\\Em1d:SN\\El1e:TN[El1e:TN[Ek1f:UNZEk1f:UNZEk1f:UNZEj1g:VNYEj1g:VNYEj1g:VNYEi1h:WNXEi1h:=0O10000O10000hMSEQ2n:nMUEP2k:oMXEo1h:PNZEn1e:SN]El1c:SN^Em1P;OBSNaEl1o:O1000@UN`Ej1a:VN_EW1CYOn:@_EV1U;jNkDU1V;kNjDS1X;mNhDR1Y;nNgDP1l;N1N2O1O1N2O1O100000O0L53M3M4L1N4N1N100O1O2O0O01O00001O0O2O000101N010O1M2N2N2NlmY2", "size": [427, 640]}, "area": 6769.0, "iscrowd": 0, "image_id": 10145, "bbox": [324.0, 249.0, 138.0, 104.0], "category_id": 165, "id": 10012364}, {"segmentation": {"counts": "0=n<00004L7I6J<D8H<D:F<D<D8H=C2N2]FhLo8V4D=Ca0_O5\\H]JV7T6Ha0_O9Ga0_O8H9G9G:F;E;E8H9G<D<D9G<D:F:F7I00O100O1O100O100O1OkNnCS1U<0O12N3M000000001O0000000000000000001O0000000000000000001O00000000000000001O00000000000000000000001O0000000000001O0000000000000000001O00000000SIT1[OlNYO_2h0aMPOg2i0`MWO`2j0_MWO_2i0bMWO^2j0aMVO_2l0_MTOa2k0`MUO_2k0bMUO^2k0bMUO^2TOlLWNg0e2\\2VOaM_M4[3[2UOcM^M3]3Z2UOcM]M4_3W2TOfM\\M4`3V2TOfMmLSO7R1h3U2SOgMlLWO5n0l3T2SO^Om0a0TO_Ol0a0TO_Ol0a0SO@m0?TOAl0?TOAl0?TOAm0=TOCl0=TOCl0=SODm0<`NmLeMn2k34`NPMaMo2o31`NPM`MP3P40`NPM_MQ3Q4NaN:_1FbLVMo0T3_2FaLSNAiN=^3a3EaL\\2`3cM_L^2a3bM^L_2b3`M]Lb2c3^M]Lb2c3^M]Lb2c3^M]Lb2c3]M^Lc2b3]M^Lc2b3]M^Lc2b3\\M_Ld2b3[M]Lf2c3ZM]Lf2c3YM^Lg2b3YM^Lh2a3XM_Li2`3WM`Lj2_3UMbLk2^3UMbLl2]3TMcLm2\\3RMeLo2[3PMeLQ3Z3oLfLQ3Z3nLgLS3X3mLhLT3W3lLiLU3V3kLjLU3V3jLkLW3T3iLlLX3S3hLmLX3S3gLnLZ3R3eLnL\\3Q3dLoL\\3Q3cLPM^3o2bLQM_3n2aLQM`3o2`LQMa3n2^LSMc3l2]LTMd3k2\\LUMd3l2fJhN\\5b42M3O0O200O110N0O2002M10O1003M1OO01O00001N002O1O2M2O2M10000000000000UInI]6S6bInI]6R6cIoI\\6Q6dIPJ[6P6eIPJf50\\JS6NnIe5O]JS6NoIQ5KTK6@K6W65nIo4MRK7Jo55mIm41PK7Jn58kI9l;]OQD1L08?b<00FTm7JZSH1hB31Ln<7RCIn<8QCHo<8QCHo<:O10000O1O?BO12NI8L300O2O0O10000O10000001O001O1O1N2O0000000001N11O00O10000O100000002DUC4R=HnB5U=OcUc0M`j\\O;D2O0O1O2N1O1O1O1OO0K:L101O000000000000000N3HnB5X=KP`70Q`H0mB1j<0SC2m<600O2O00000000000000001N3N7I4LO11OK5O110N10000000000000000000000000000000O10O1O1O1O1O1001OO100001O0000O1001O0000100O001O0000000000001O00000O1000O1O2O0O1O100O1O1O1000O2O002N003M7IO02OH8J6O2O00001O0NQCHo<8QCHo<8QCHo<8QCHo<8200000O10000000001N10000O100001O001O001O00000JPC0R=M_P:0hoE000kB1LOo<8QCHo<:01O00002M01O11O8H00O10000O100O10000^OYC`0l<K5K5K`oh0", "size": [427, 640]}, "area": 67241.0, "iscrowd": 0, "image_id": 10145, "bbox": [0.0, 0.0, 580.0, 401.0], "category_id": 172, "id": 10012365}, {"segmentation": {"counts": "b[m32X=1N2ILUC6k<5jMJWG7g8KTFI8?`7CZI7mNG\\O2KNj0?g4BcJO_25YO4VNGa01^O003c0=]O_Oa58^JH]2?UON^NF??A>f5[OeL6UOW2V4[NlKMlNRO1g2h4YMnJR2KSN<l0F\\O7^2o4U5O0O10000O100O1mJkGP5T8QKnGn4R8QKQHl4o7TKVHg4Z8M00000000O10M^G]Kb8b4_G_K_8b46OO001N20O10@^KWHb4i7bKSH]4n7cKRH]4n7gKnGY4R8`00O100001O0O2M3O010O1O001O0gHgJ`6Z5_IQKX6m4gIUKY6j4gIVKY6k4eIVK\\6i4cIYK\\6g4cIZK\\6g4cIZK]6g4aIZK_6f4`I[K`6e4_IaKe1[O_2T5iKSLX1jNo2S5cKYL]1gNn2g6RMXIIGXO1126O9o6HPIM6\\OJP11oN18NH4>18L2N]OQ7b0SIM1]OO=OL8J16NNIM1c0LZOT7d0TICNH6:KKj00]ONIL2f0o6NSIBNK350Mi00ZO231^O7X76VI_O=3j00hNMEOa03]O6[7FeH01>>C<OWO0b12\\N2<L>4VO3^7IiH0J4X3MiL0[11cN4j7OeKKoM1bN5j7JjK0nM1^NOb;0PF1mYT2=[QlMO1N3N2M4M2000O11O00000000O11O\\ONmC4d7<WHJj2NRMNj7>XHGT28eM5o7GVH3m1V1m5DiId0T6AYIQ1`6\\OPIn0o6WOkHj0T7XOkHi0T7XOjHj0U7WOjHj0T7XOiHj0W7XOgHi0W7g2O1ON@UJdIk5[6YJ`Ig4FUK40O`0h6HZIX4f0jKT62QIV4l0cKW6l4S1100O1O100O1O010O1O10000O1O1PIgJn5Y5nIkJQ6V5jIPKT6Q5hISKX6m4fIUKY6l4hISKW6o4gIRKY6n4gIRKX6P5fIRKX6o4hIQKW6P5iIPKV6P5kIPKU6P5kIPKT6R5lImJS6S5nImJQ6X5kIiJT6_5dIlJLUOP6X7N100O1O1O100000000000000000O1O100000000O100000000O1000000", "size": [427, 640]}, "area": 31875.0, "iscrowd": 0, "image_id": 10145, "bbox": [300.0, 0.0, 340.0, 291.0], "category_id": 173, "id": 10012366}, {"segmentation": {"counts": "Rao21Z=1O1O1O0kH0o01POOP12\\NKTK6@Kk65_NMRK7`6L\\N1PK7b6IiI^5U6cJhIa5V6`JjI`5U6aJaIh5_6b00O1O1001OaNfImJEi0h6Z4[1MlGQKT8n4oGPKQ8o4600O1OOSKfGl4[8TKeGl4[8TKeGk4\\8UKdGj4]81010O1VKbGe4`8[K`G[45eK^8O]G\\4k8dKUG\\4m8O10000000>A012NI1330O0011N101O0[HRLl5P4RJWLh5j3VJ^Lc5b3\\J_Ld5b3ZJ_Lg5`3XJ`Li5a3VJ]LPO^Oa6T4_JbLjN\\Oh6R4]JbLhN@k6m3^JbLfNBm6l3\\JbLgNBm6l3]JaLfNCm6m3\\J`LgNCm6m3\\JkLb5V3\\JkLd5V3[JkLc5V3\\JlLd5T3[JnLc5R3\\JoLc5W3WJjLi5Z3RJgLn5Y3oIjLP6W3mIlLS6U3iInLW6m2lIUMT6j2jIULA`0e6Z1]I@<hNBA2KNj0b6`1cIB8gND^O003c0d6c1gI[O3mN0Af6V2YIZOR92]GlNRO1]:AhE<l0F\\O7Yif0MXXYO0d8f0lF[OS9f0lF]OR9c0nF\\OIGXO1126O]6k0dITOM6\\OJP11oN18NH4>18L]6k0dIWOM1]OO=OL8J16NNIM1n6i0cI[OCNH6:KKj00]ONIL2Q7d0cI^OBNK350Mi00ZO231^Ob6m0kIA_O=3j00hNMEOa03]Od6m0XISO01>>C<OWO0b12\\N2<L>4VOd6m0XIZO0J4X3MiL0[11cNf6X1]IW2KoM1bNe6Z1ZIZ20nM1^Na6P5^IbL1^N`6h60^O`IWJb6i5aIQJd6m5_ImId6S6:0000000001OO100000000000000000000000010O0000000000000001O2N7I4LO11OK5O11OO100000000000000000BlHeJT7[5oHbJQ7^5>00000000000O1O1O1O001001OO100001O0000O1001O00001O1O001O000N2N2N2O1O2N1O100O100O000100O00100O1O1O010001O002N003L8JN11OH9I6O1000O1000NiJPHX5o7hJQHY5n7gJRHZ5m7fJSH[5l73O1O1O1O_JXH`5g7`JYHa5f7_JZHb5e7^J[Hc5d7]J\\Hd5e71O1O1O1O00001O0fI[Jn4f5gJdJY5\\5]JnJc5T5QJWKn5S60000000000000O1N2M3N2M3N2M3N2N2M3N2M3_Oa0O1O100001O0000O11O00000000O20O\\O_H\\Kc75mHFJj2NRM_79mHIGT28eMe7?WHF3m1e8^NUHX1T8dNQHi0h:@>J2O1N2O1M4NUjk0", "size": [427, 640]}, "area": 47808.0, "iscrowd": 0, "image_id": 10145, "bbox": [229.0, 37.0, 344.0, 233.0], "category_id": 181, "id": 10012367}, {"segmentation": {"counts": "lVb1m1R:\\1H7101O0O1000000O10000O101O0O1000000O10000O101O0O1000000O10000O10001N10000O1000000O10001N10000O1000000O10001N10000O1000000O10001N10000O1000000O1HZPg0eMXRYO3M3N2M2N3M3N2M3M3M3N2M3L4DQOVDS1h;:N2M3N2N2N2N2M3N2N1I8N2M3M3O1N2O1NiNhE\\OX:b0RFTOP:j0QFVOo9i0SFUOo9i0SFVOm9i0^FlNd9S1Z1010O010O010OO2N2O0O2O1N7J2K3M3M4L4M3M3M3NUZ4c0XeK2N1O2N1O2M2O2N1mDUOe9m0dEI\\:c10000O1000000O1000000O10000000000001O00000000001O00001O001O1O001O1O001O001O1O001O1O001OgMlM\\IR2e6oMZIQ2f6oM[IP2e6PN\\Io1c6RN]In1c6RN^Im1b6SN^Im1U6RNkG1o1n1R6UNoGMn1o1]5oMkH9JIm1P2^5QNdH:1Em1P2^5TN]H;8Am1P2^5iNeHWOl1Q2_5hNeHWOl1Q2_5hNeHWOl1Q2_5hNeHWOk1R2`5gNeHWOk1R2`5gNeHWOj1S2a5fNeHWOj1S2a5fNeHWOj1S2a5fNeHWOi1T2b5eNeHWOi1T2b5fNdHVOj1T2b5fNdHVOi1U2c5eNdHUOj1V2b5eNjJ[1V5eNiJ\\1W5dNiJ\\1W5dNiJ\\1W5dNhJ]1X5cNhJ]1X5cNhJ]1Y5bN_HTOQ2[2`5aN^HWOP2X2b5`N^H[On1U2d5`N]H]Om1T2f5]N^H@l1S2f5]N]HBl1Q2g5]N[HDm1P2h5\\N[HDm1P2h5kNWJV1i5jNWJV1i5jNWJV1i5[N[HDk1R2j5ZN[HEW1CYO^2U7ZN[HEV1e2_6fM[HEU1f2`6eM[HES1h2b6cM[HER1i2c6bM[HEP1k2e6`M[HEo0l2f6_M[HEm0n2h6mMWIT2i6]M[HCk0Q3j6\\M[HCi0S3l6ZM[HCh0T3m6YM[HDf0S3P7iMPIW2P7iMPIW2b6hLkHb0MAg0U3a6kLhH?3]Od0Z3a6gMlHjNg0_3]6SN`Im1`6VN]Ij1c6ZNYIf1g6[NWIf1i6\\NVIc1j6_NTIa1l6`NTI_1l6cNRI]1n6dNQI\\1n6gNQIX1o6iNPIW1P7hNRIW1n6iNRIW1n6iNRIW1n6iNSIV1m6jNSIV1m6jNTIU1l6kNTIU1l6kNTIU1l6kNUIT1k6kNVIU1j6kNWIT1i6lNWIT1i6lNWIT1i6lNXIS1h6mNXIS1h6oLiHc1`0]1g6nLkHe1>]1R7cNnH]1Q7cNQI\\1o6dNQI\\1o6dNRI[1m6mMfH]O=f2m6mMhHROI0:MHT3]7mMeHWO13Oh2Z7oMdH1OP2]7oMaH61j1^7]ObHc0]7_ObHa0^7_OcH`0\\7BcH>]7BdH=\\7DcH<\\7EdH;\\7FdH9\\7GdH9[7HfH7Z7JeH6[7JfH5Y7MfH3Z7MfH2[7OeH0Z71fHOZ71gHNX7_NgH70Z1U7cNkH30Z1Q7gNoH00X1l6mNTIK0X1j6oNVIJ0V1j6POVIJ0V1j6POVIg2j6YMVIg2j6YMVIg2i6ZMWIf2i6ZMWIf2i6ZMWIf2j6YMVIg2k6XMUIh2l6WMTIi2m6VMSIj2n6UMRIk2o6TMQIl2P7SMPIm2Q7RMoHn2R7QMnHo2S7PMmHP3T7oLlHQ3U7nLkHR3V7mLjHS3W7`KiHX10X3X7kLhHU3Z7iLfHW3Z7iLfHW3Z7iLfHd00b0Z7jNfHd00b0[7iNeHd00d0[7_NnHm0Gd0\\7SNYIW1[Og0\\7hMcIa1QOg0h8XOXGi0h8VOYGj0g8VOXGk0h8TOYGl0g8TOXGm0h8ROYGn0g8ROXGo0h8POXGQ1g8oNZGQ1d8QO[GP1b8RO_Gn0_8TO`Gm0]8UOdGk0Z8WOeGj0X8XOiGh0U8YOkGh0S8ZOlGg0Q8[OPHd0o7^OPHc0m7_OSHb0k7_OVHa0h7AWH`0f7BZH?d7C\\H=a7E_H<_7i20lLaH<_7CaH>_7g20jLaH`0_7f2000000000000000000001O1O001O1O1O001O1O1O001O1O1O1O0`LUH`1l7_NTHa1m7^NSHb1n7^NRHa1n7`NRH_1o7aNQH^1P8n11O1O001O1M3L3M3M4UM_Gg0d8YO\\Gd0h8[OYGa0j8_OXG=j8CXG8l8GVG4m8LUGOo80SGKP95RGFQ9:QGAS9>oF]OT9c0nFXOU9h0nFROV9m0VGdNm8\\1b1O01O01O001O00001O00001O001O00001O001O00001O00001O001O01O01]OVH", "size": [427, 640]}, "area": 52754.0, "iscrowd": 0, "image_id": 10145, "bbox": [120.0, 128.0, 520.0, 291.0], "category_id": 183, "id": 10012368}, {"segmentation": {"counts": "k_l15o>000000000O10000000000000000000000000000000000000000`NMoC3P<=VCNi<W11O2N2N2N2N3M4L4L3M1O6J5K2N3M2N2N3M3M1O2N2Neli6", "size": [484, 640]}, "area": 829.0, "iscrowd": 0, "image_id": 10149, "bbox": [127.0, 428.0, 51.0, 56.0], "category_id": 98, "id": 10012369}, {"segmentation": {"counts": "U5g3b3oNf1o0YNQOe1T1WNmNi1S1WNmNP2m0nMTOf28ZMHZ3@jL`0j3aNaL_1S4bMXL^2W80O100002N0ZMgCa2]<`McC[2`<gM`CT2d<mM[Co1h<SNXCh1k<[NTC`1o<bNQCY1R=;3M3M3M3M3M5K4L4L5K4L4L5K4L2N00001O0000001O00001O0000001O00001O00001O0000001O00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000UPb7", "size": [484, 640]}, "area": 3684.0, "iscrowd": 0, "image_id": 10149, "bbox": [0.0, 165.0, 127.0, 319.0], "category_id": 105, "id": 10012370}, {"segmentation": {"counts": "W_R25P?;C=D;E8G01O1O11N1000001O0O1^DfNZ9Z1bFiN_9V1_FlNb9S1[FROd9n0YFWOe9i0XF\\Of9d0XF@f9`0YFC]9nN\\E_1U1H]9iN\\E_1X1LY9eN]E`1Z1OU9bN_E`1]10R92nFOQ91oF5l8JTG;g8FXG<g8CYG`0e8@ZGb0d8^O\\Gd0c8[O]Gh0`8XO`Gk0^8TObGm0]8SOcGo0\\8POdGR1Z8lNhGU1X8jNhGV1Y8iNgGW1Y8iNgGW1Z8hNfGX1Z8gNgGPOeNk1e9TOfGhNoNS2[9UOUHk0l7TOWHi0j7UOWHk0i7TOXHl0i7oN[HQ1e7nN\\HR1e7lN\\HT1d7lN\\HT1e7lNZHT1g7lNXHT1h7mNWHS1j7lNVHT1j7lNWHS1j7mN[Hm0e7TO[Hk0f7TOZHHj88VGFm89SGFo89QGET98lFGX96hFH]95cFJa92`FLe9M_F2e9J\\F5Z<0000O1000000O010000000O100000MKVA6f>50100000000O100000O10O11O00000000000O1000000000O10000O100YMNWF7h9KnECXNf0j;HkE?V:AiE`0V:@jE`0V:AjE>V:BjE>V:BhEa0X:_OgEa0Y:BeE=[:BgE=Y:CgE>X:BdE@aNn0k;J]E9c:H]E8b:I^E6b:K_E]OhN:h;:_EYOmN<d;=]EVOPO=c;=^EQOSOc0[;a0nE^Om9f0TF[Of9k0YFUOd9n0\\FSOd9l0]FSOe9l0ZFUOf9j0[FUOf9j0ZFeN[N>Z;m0_FSO`9m0aFSO]9n0dFSO\\9k0eFUO]9h0dFYO]9d0eF\\O[9b0fF_OZ9?hFBW9<kFDT9;mFGR98oFHQ97PGHP98QGGP98SGFi8=XGBi8>VGBj8?TGCk8<WGDg8;\\GEc89_GGb8^OUE?Z23m8JSG6Q9FQG9P9FPG9R9EPG9R9FPG8P9HQG6Q9IQG5R9GPG7P<N2O0O2O00MOTA1j>1UAOh>9F]1lN000000000006J00001O00001O00000aNaBY1h=M2N1O000000O100000000000000000000000LSBQOm=?TBK1Dl=`0WBIm=7SB@K4T><TB@o=a0:0O2O00000000^OaA?_>@bAa0]>_OcAa0]>_OcA`0^>AaA>`>AaA?_>AaA?_>AaA?b>10N3L5K4M3NceZ20[kdM<`>6O1OO1101O1OO1002M2O0O2ON2O10O01HjABU>5b000000O10001O0001OO100000O11O01N3O1NRANh>OXA=0D]>e0M011O10L6K031O1N10O003M00N2O2N1O1000000003M1O1O00M3O100O\\ADb><^AEa>;_AE>Ob=<PBE;3d=<[BEe=;TBLm=5oAMQ>c0000000000000000000000000O1001O0000001OO10000001O000000", "size": [484, 640]}, "area": 13167.0, "iscrowd": 0, "image_id": 10149, "bbox": [140.0, 300.0, 500.0, 184.0], "category_id": 107, "id": 10012371}, {"segmentation": {"counts": "W]T24o>=C4cA\\OS>k010O1O0010N1002N2M2N101O1O1O001N01O100HSBXOn=h0701O001O00000001O000N200000000O1000000O1O2K4aBjNo<W1QCiNn<X1RChNn<X1RChNo<W1QCiNP=V1PCjNQ=U1oBkNQ=U1oBkNQ=U1oBkNR=T1nBlNS=S1?001O00O1M3L4N11L6M2O0101N1O2N^BTOm<k0SCXOk<f0VCDb<:^CGb<:\\CFf<?SCAn<?QCBP==oBCR=<nBDT=;kBEV=:jBFX=9gBHY=8eBH]=8bBG`=9_BGb=9]BGd=9[BGf=k01N101O1O1O1N200O1O1O1O001O1O1O1O1OeNTO^Dl0a;WO]Dh0c;]OYDc0g;CSD=l;IoC6Q<1iCOW<;_C@f<V1RCXNS=e18O0010O00N3O3NN2O00LcBaNe=i0c0N2O1O1O2N002NO11O01O01O0000000O3M2N4JaPV5", "size": [484, 640]}, "area": 5053.0, "iscrowd": 0, "image_id": 10149, "bbox": [144.0, 341.0, 144.0, 79.0], "category_id": 121, "id": 10012372}, {"segmentation": {"counts": "ZZY42Q?1K6\\AHZ>9eAG[>a00001OM3O02N4M1O00N@^O[B0La0\\9@`F080a31[L4L<`9OYJBZLg0^9GXJb0h5^OXJa0i5_OVJb0j5^OUJb0l5^OTJb0eNnNg4`0cLb0fNPOf4>dLa0gNQOe4>dLd0^LlNO6a1M^5=dLe0\\LkN2;Z1Id5<dLe0\\LkN2;Y1Je5;dLf0ZLkN3;MCV1:l57cLj0cLTOj0MP65cLm0bLeNU1:h4k1TJc2d0TNi2ZObLb2e0TNi2ZOaLc2e0TNj2ZO`Lb2f0TNj2^4VMbKj2^4VMbKk2]4UMcKm2\\4RMjKm2V4YLfH=W3Z3Q810O02N01N100O1[LgD]3d;K40OO000O1O0O2N2O100001O3M3M2N1O0000001O00O01O01I6N300O10000nLQDo2o;PMRDP3n;PMRDP3n;PMRDP3n;QMQDn2P<QMQDn2P<RMPDR3l;nLTDS3d0nLg2j0b3[2bImLk2h1e2_1YJlLQ3c2j1?SKPMR3l2d11XKVMR3j2h1NTKZMS3i2k1IRK`MR3h2n1FnJeMR3e2Q2;oMEQ2;nMFQ2<XM_LUNV3c4;XMcLPNS3h4:XMhLjMo2n4:WMkLfMk2S5;WM7i2IVM8j2IUM7k2IUM7k2JTM6l2JTM5l2LTM4l2KUM5j2LVM4i2MWM3h2MYM3e2O[M0e21[MOd22\\MNd22\\MNd22\\MNd22\\MNd22\\MNd22nM[OT2d0oMYOQ2g0oMYOQ2g0PNXOP2h0QNWOmMVNi1c2:WOlM_Nb1Y2d0WOjMhNZ1Q2l0WOjMPOS1h1T1WOiMYOl0Z1a1\\OcM^Oi0U1d1]OcM^Oi0U1e1\\ObM_Oi0U1e1\\ObM_Oj0T1e1\\OaM@j0T1f1[O`MAi0U1g1[O_M@k0S1[N^L_3i2[MFj0T1[N`L]3g2^MEj0T1[NaL[3i2^MBm0S1ZNcLX3k2`M_On0S1ZNcLW3l2aM^On0[1_1XOcM]On0[1^1YOdM\\Om0\\1\\NVLn2S3iM[Om0`1Z1UOiM[Ol0a1Z1UOjMZOk0b1Z1UOkMYOj0c1Z1UOlMXOi0d1[1TOlMXOf0aMbNT4k2TOmMWOf0g1[1TOoMUOf0g1h0TLkMV3g0oNf0g1h0GbNbNf0g1h0GbNbNf0g1h0GbNbNf0g1h0GbNbNf0g1h0GbNbNf0g1h0GbNbNf0g1h0GbNbNh0e1f0IbNbNi0d1e0JaNcNk0R1_NkLU2`3aNcNl0P1_N`LN;V2b3aNcNl0o0`NaLM214U2g3aNdNm0k0`NdLL222U2i3`NdNm0k0cNeLK3U2i3`NTN]1[1SNdLM3S2j3`NcMn1l14a0oMQM^2^2Bb0UNYLj2V3QOa0U2_OkMa0U2_OkMa0U2_OkMPM[Mo2j41kMnL`Mn2e44lMcLjMX3Y47bNH^18cNG]19dNF\\1<bND^1<cNC]1>cNAeMRM^2^3L_OeMjMi1h2b0]OeM`NU1R2iM_LY3n2hMVOb0]1\\N`LZ3m2hMVOc0a1f1XOgMXOa0a1i1VOeMZOb0`1l1SObM]Ob0`1o1ZL^L\\23cMm0X2d0^1Q2hNaMJ>^1o1jNcMH>^1n1kNdMG>^1m1lNdMG>^1m1lNeMF>^1l1mNfME>fM^Ne3\\3ROgMDb0Z1f1TOgMBd0eMYNa3\\3YOgMAd0eM]N\\3X3_OgM@h0T1_1]OhM@m0o0Z1BiM_On0n0W1EkM]On0o0U1EmM\\Om0P1U1EnM[Om0P1S1GPNYOm0P1o0LSNTOn0P1j01XNoNn0P1f05]NjNm0Q1b09aNfNm0Q1b09aNfNn0P1`0;bNeNP1n0>=bNeNQ1S178hNeNQ1S178hNeNl0eMTN_3X27hNeNj0iMTN[3Z28gNdNk0[1<2jNbNg0_1?OjNbNb0hM\\Nh3X2NkNaN`0h1c0GmNaN`0i1b0FoN`N?j1a0GPO_N?k1`0FRO^N>l1`0FSO]N=m1`0FSOYNa0Q2<GSO\\M\\1m2AGTO_LX2i3eNGm29SMGm29TMFl2:UMEj2<_M[O_2g0cMWO\\2h0SKlKc2Z3Y2j0UKlKe2W3V2m0oMROo1n0SNQOm1o0TNPOl1k0WKRLo2R3j1k0\\NTOd1l0]NSOc1m0^NROb1m0_NSOjMYMY2d3NROhM[NZ1c2o0QOgM]NY1b2Q1QOeM_NY1_2S1QOcMcNG`Nh0k3P2QOaMeN_OgNo0c3R2PO_MiNVOlNY1[3S2oN^MA?_1T2nN_MC=_1U2lN^MF=X1bN_Le3`2^MI;W1Q3[NeL>9W1b4jLVKP27V1X6jNgIW1Y6iNfIW1Z6jNfIV1Z6jNfIV1Z6jNfIV1m3]LSM]2POV1b3mLYMm1UOV1W3^M^M\\1ZOW1S3hM]MQ1@W1n2XNWMa0JX1j2dNTM42Z1S1XLSOa2FM4Z1S1XLoNh2HE6\\1R1ZLiNj2N@6]1kNXLQ2<mMG>l2c0\\O6c1l0dLYN`2e0YO5d1m0eLSNb2k0VO4d1m04POWN3e1l05QOVN3e1l05QOVN2f1m04QOVN2f1m04QOVN2f1m04ROUN1g1m04ROUN1g1m04ROUN2f1l05SOTN0h1m04SOTN0h1m03TOUNOh1m03UOTNNi1m03UOTNNi1n02UOTNMj1n02UOTNNi1m03UOiM;R2`05VOWMm0b2MRLnM50g3]1kLV1YO^Mm3Q2SLVNk3a46dMH[29jMBV2>PN\\OP2d0VNVOk1i0WNUOl1h0VNVOh1l0ZNROh1l0ZNROh1l0ZNQOh1n0ZNPOf1P1\\NnNd1R1_NkNa1U1eNcN]1Z1kN_NW1_1]NoMd2o1ZMeNU2W1kM^Od1`0[NDd1;YNJf17VNMi14SN0l10PN5o1KnM9Q2G^MeLlM`3n4KVM;Q3EoL4X3LhLM_33aLEg3;\\MeMa3[2d4002N1cD_LU;n3K0O000000000000000000O100000O0101N1001OO2OO101O0001O00000TM", "size": [484, 640]}, "area": 83368.0, "iscrowd": 0, "image_id": 10149, "bbox": [290.0, 57.0, 350.0, 411.0], "category_id": 123, "id": 10012373}, {"segmentation": {"counts": "]UU22Q?6K6I7J6I7I6J5K4L4L4J5I6N1000O10QCnMl<U2[OjM\\DV2a;mM_DR2`;PN_DQ2^;SNaDl1];WNcDi1Z;ZNeDf1V;`NiDb1n7]NWJOkMd1l7cNVJHjMj1GfMR8k0ZJElMP2j7_NXJAmMn1m7cNTJ_OoMm1n7gNQJ[OQNl1P8kNmIP2T6RNjIn1V6TNgIm1Y6UNeIc1TNPMY8^1`Id1f6^NWIc1i6^NUIb1l6_NRIb1n6_NQIa1o6`NPI`1P7`NPI_1Q7bNnH^1R7bNnH]1S7dNlH\\1T7eNlHY1U7gNkHY1U7gNlHX1T7iNjHY1U7gNkHY1U7hNjHX1V7iNiHW1W7iNiHV1X7kNgHU1Y7lNfHT1Z7mNbHU1_7nN]HS1c7oN]Hn0d7SO_Hh0b7XO^Hg0c7ZO\\Hf0d7[O\\Hd0d7]O[Hc0e7]O[Hd0d7\\O\\Hd0d7]O[Hc0e7]O[Hc0e7]O[Hc0e7\\O\\Hd0d7\\O\\He0c7[O]Hd0d7\\O[He0e7[O\\Hd0d7ZO^Hg0a7XO`Hg0a7YO_Hd0d7[O^Hd0b7\\O^Hd0b7\\O^Hd0b7\\O^Hc0c7\\O^Hd0b7ZO`HWNnNj1c8MaHXNoNh1a80_HYN1f0`7P1`H[N3?_7V1_HZN5<]7Y1_H\\N76[7^1^H^N:MZ7d1YHVNbN>m1AY7k1XHmNa9R1_F^OR9b0nFMc82]G=U8BlGi0i7WOWHj0h7UOYHl0f7TOZHm0e7RO\\Ho0c7PO^Hn0d7RO\\Hm0e7RO\\Hm0e7TOZHl0f7TOZHl0f7TOZHk0g7nNkFcN^1_2g7kNeHS1[7jNhHU1Y7hNmHS1U7lNlH<l7DUHoNQN5b:k0S30VDmNc9S1]FmNc9S1]FmNc94\\DON?S2^Od91hD=d1CjNZ1P9TOVHT2d7lM]HU2nM\\M]9?eH[2\\7cMeH]2[7cMfH\\2Z7dMfH\\2\\7^M^FIV2i2_3eM;[2aLXM]MO60`2`0[3Y2`LWM`M35M_2a0Z3Y2dL[M9>]M_OV5j2TMZM7=\\MCX5f2]LWMiN3U2=\\MDY5e2ZL[MmNNS2?XMG^5c2XLZMcNH15]2m0f2l1XL\\MdNF11G0e2Q1g2l1XL]MdNE10H1c2Q1h2l1XL^MdNEO0K0a2P1i2l1YL`McNDOOb2R1b2f1^LgM_NBOOa2R1c2d1`LjM]NANOb2S1b2b1bLlM]N_OM0a2S1ZMmNU5f0nJQ1i1kN\\N^Of2W1X2\\OoJS1h1mN[N]OR20TNX1h4[OoJo0l1ROQO^O:1oNV1l4YOoJn0k1VOPO]O92oNU1n4XOoJ6JKR24mN_O83lNT1T5UOPKN2Ml1=gN_O8f1R4cNSKG;Ig1h0_NA8e1T4UNcKM`22fL`0j0H7f1W5oMSM2fLb01[Oa0><f1o8SNQF52Da0?<f1Q9QNPF7e049e1Y9ZNZF3<d1Y9[N[FO<g1Y9YN[F1;i1T9YNaFK>m1o8YNaFM`0i1P9VNaF3`0e1S9UN^F5>h1T9RN_F4<m1T9PN_F3;o1T9PNbF1_OF?Z2_9PNcF<Kd1c9QNaF>Hc1e9ZNZF30c1f9EXF=f9EYF=e9C[F>c9D\\F>b9B]F`0a9@`Fa0^9@bFa0`9VM^FO6a1MZ1`9TM]F2;Z1Ia1^9SM^F2;Y1Jb1^9QM^F3;MCV1:f1FUMf97iFj0Mi1FUMg97ZFU1:[1^:UNZ300O100000001O5VOfA=eUZ3CZYfL003L3WAKK4]>1mALT>3?0O10O100O10000000000000001O0002N1OJTA5k>KUUm0", "size": [484, 640]}, "area": 21744.0, "iscrowd": 0, "image_id": 10149, "bbox": [146.0, 0.0, 432.0, 444.0], "category_id": 132, "id": 10012374}, {"segmentation": {"counts": "hc61S?6XA2NKc=6[Bb0`=A\\Ba04WOS=Y1kBkNS=U1lBoNQ=Q1mBSOQ=b1N000000000000000000000000001O1O1O0000000000000000000000000000000000000000000000000000000000000000N2K5K5L400000000000000000000000000001O9G00O1O1O1O1O1O1O1O1O1N2O1O1O1O1O1O100O1000000O1000000O1000000O1000000O100000000001O00001O00001O000fInNbNS1^1WOWNi0j1]OoMc0Q2^OnMc0R2[OoMe0R2ZOnMf0S2YOmMh0S2VOnMj0R2VOnMj0S2UOmMl0S2SOmMm0T2QOmMP1R2POnMP1S2oNmMR1S2mNmMT1S2kNnMU1Q2jNPNV1Q2iNoMX1Q2gNoMZ1Q2eNoM\\1@ZN_M9Q3Z1aJ]No44[M5a36UJOY5IoL1n93PFLP:IjC;T2KU:7jEGW:<gEBY:b0eE\\O\\:g0n14M2M3O0O2OPOQBi0T>0O100O2O01000HUOUBl0l=60L^BoNX=n0hBTOZ=j0a0000O010O100O101O10O01O1O0O2O1O2M3N2M3M1O10000001O0O10000O1O2M101N2O2OO1M30000:FEQOcBn0k=N10000002NO01O1OO11O1O1OZOSB5m=GYB6h=HZB8e=H]B0BOQ>1^B0CLP>3^B1f=O\\B0b=1_BO`=2g01O101O11O0000000a0_O2M101N1N3M2N2O2O001O00O100O1O1JWBPOk=d0RB_OY>a05O3N0O1O10O0L6M3N2gKCjGHMf0X8GeGE2e0W8KdG@1j0P7XO]H=c0BNl0f6_OjHOe0FSOJKT1LoNBLZ7b0dIMi0IhN2MQ1OQOl67fILj0KaN;Il08mNm64hIM<;gNf16oMn67fIKLj0YOW16mMo67fIL[OQ4NkKR79kHYOc0b5]OmJU7>\\IZ5d6c10000000O1O1000000^OhHQI0LX7e2iHWM080a31[L4LS7i2XIP1BZL_7f2oHP1Z7POfHP1Z7POfHo0[7QOeHn0\\7ROdHn0\\7ROdHm0]7SOcHm0]7SOcHm0]7SOcHm0]7SOcHm0]7SOcHm0]7SOcHl0^7TObHl0^7WNYJ\\NZ8d1gG[NY8e1gGZNZ8f1gGYNY8g1b2001O2N5K001O002N00O100O1N2N22N0000O1O1N2N2O100001OG9O1000000001O09iNaB8Y>M4L1OO2O00O1N2O1O001O1O1O1NiTn3", "size": [484, 640]}, "area": 22296.0, "iscrowd": 0, "image_id": 10149, "bbox": [14.0, 0.0, 359.0, 312.0], "category_id": 172, "id": 10012375}, {"segmentation": {"counts": "XiX21S?000000000aB1]NOf23i79PFEO9S2Nn7a1lGdNT8]1iGeNW8\\1eGgN[8Z1cGgN]8Y1cGgN]8Z1bGeN_8^1_GaNa8b1\\G^Nd8e1YGZNh8i1UGVNl8CRFb14bNK9o9AXFc1FiN2OT:EWFc1ZOoN;HW;R2iDmMY;Q2gDoM\\;o1cDQN_;m1n0N2M3N1M4M2N2N2O1O001O001O10O0010N101O1O001O1O1L6L30O3L01O100O00001O0000O100000O11M2O100O2O00000O1N2N20O0101N100OMaBbN`=^12010O00100O100O1O100O11O0000IbNhB^1U=<K3M6L101NL500000QOoMUDN?S2W;kMXDa0=d1[;^NfDb1Z;^NfDc1Y;]NgDc1Y;^NeDc1[;]NeDd1Z;\\NfDd1Z;^N`D\\OIV2g;S1000O1O100000000000000000000^NdD\\O\\;c0iDZOV;d0oDYOQ;]OaDe0=0Q;QOlDP1O2U;nNlDo0M6W;kNmDn0L7W;kNlDo0M6o;GSDLX<4iC[Oh<KfBc0]>NVQS5", "size": [484, 640]}, "area": 8546.0, "iscrowd": 0, "image_id": 10149, "bbox": [154.0, 0.0, 141.0, 148.0], "category_id": 173, "id": 10012376}, {"segmentation": {"counts": "hVY59k>:FM300O10000O10000O10000000000000000000000000000000000O100O100O100O100O1000000001O00001O00001O1O2N1O2N2N1O2N2N000000000000O10000000000000000000lBZOj;f0WDZOh;f0XD[Og;e0YD^OkNO`<d0dD@hNNc<b0dDI[;7eDKY;5fDLZ;4eDM[;4dDK];5aDM_;5]DMc;5XDNh;7PDLP<<eCG[<a100000N2O1O1O1000000000000000000001O00001ON2N2O1N2N2O1O1000000O100000000001O1O2N1O2NO100000000O1000000O1000000O1L4K5M300000000O11O0000000000001O00O1M3N2N2M3000000001O00O11O00001_B`N\\=`1cBbN\\=^1dBcN[=c1fBYNW=j10O1N21O1O1O0000000000O1000000OXCYNU<g1jC\\NT<d1eCcN[<^1`CgN_<Y1^CWN0`01B[<g1dCPO\\<m0hCROX<Q1fCoNY<R1gCmNY<R1hCnNX<R1jClNV<R1mCmNS<R1PDlNP<T1QDkNo;V1RDhNn;Y1RDfNn;W1UDiNk;T1WDmNi;Q1XDPOh;Q1WDoNi;P1WDQOi;n0TDVOl;o0mCSOS<o1000001O000000000000000000000000M3N2O1L4N2O11O1O000000000000000000002N1`CbMX<k2J1O000000000000000000O1000000O100O1001OO100001O0000000000", "size": [484, 640]}, "area": 16042.0, "iscrowd": 0, "image_id": 10149, "bbox": [358.0, 0.0, 282.0, 94.0], "category_id": 181, "id": 10012377}, {"segmentation": {"counts": "oa16n>d0YEYOh5\\1YI]OhMXO]7o1kIDXNbNh7_2QIHiNmMR8o2VH2e7g3RHnHj7W7VHiHd7]7\\HcH_7b7bH]HY7g7hHYHR7m7nHSHm6Q8TIoGf6U8\\IkG_6Y8bIfGY6^8iIbGR6a8PJ_Gj5e8XJ[Gc5h8:4M3L4L4M3L6J5L4K6K4K5K6K4K2N2O0O1O1O2O0O1O2N1O1O2N100O1O2N1O1O2N1O1O1O2N1O1O2N1O1O1O1O1N2N2N2N2N2N2O1N2N2N2N2N2N2N2O1O1O1O1O1O1O1O1N2O001O1O1O1O1O1O1O1O1O1N2O1O1O1O1O1O100O1000000O1000000O1000000O1000000O100000000001O00001O00001O0kIcNcN]1^1mNWNT1i1WOkMi0U2B`M>a2MSM4m27gLIZ3a0[L_Of3l0nKUOQ4W1cKiNb0_N3k2WOfNf0eNNd2\\OhNd0kNK]2@hNd0QOGX2DhNd0VOCQ2IiNc0]O_Oj1MjNb0B]Ob12mN?E]O]1]NVM`0i1d1H\\O\\1ZNdM6Z1S2J[OX1WNVNZO\\1R3ZO[OT1UNo0Z2TN@l0RNU1X2WNCe0QNX1V2[NG=PNZ1T2aNJ5oM]1S2dNLNoMa1Q2gNMIQNa1n1kN0BSNd1j1oN0\\OWNe1f1UO1UOYNg1c1YO1hNVNcM7_4\\1\\O5aNfNV2P1^O8[NhNZ2g0C?SNjN^2>Gf0jMlNb26Lk0bMoNe2M1S1YMPOi2E4Z1SMQO[8n0dGRO^8m0bGRO_8m0aGSOa8l0^GTOc8k0]GUOe8j0ZGVOg8i0YGWOi8h0VGXOl7GcFQ1`1XOi7NPFG`0Q1g1ZOg77bF?f1ZOh78cF<e1\\Oh79cF;d1\\Oh7:fF8b1^Oh7;fF7b1]Oh7<hF5`1_Oh7<iF5^1_Oi7=jF3\\1@k7<jF3[1Al7;kF3X1Bm7;lF2W1Cn7<kF0V1DP8>hFMX1EP8a0gFIX1FR8?iFJT1GT8>iFJS1HV8<hFLQ1H_Og0V6WOgI9e0H\\Oj0\\6lNiIb0>H\\Ok0R8^OaHG[Om0U8^O]HE]On0V7nNYHa0Q1C^OP1U7QO[H>o0A@Q1T7SO]H=l0_OAS1V7QO^H?h0^OBS1W7QO`H`0d0\\ODT1X7POaHb0a0YOFU1X7PObHb0?YOGU1X7oNdHd0;XOHV1Y7nNeHd09XOIV1Y7mNgHf05WOKV1Z7kNgHi03VOLV1Z7jNiHj00VOLW1\\7gNjHm0LUONW1]7bNnHR1GTONX1n8ERGSO0X1o8EQGRO0Y1P9FoFPO0[1R9EmFPO1[1S9FkFmN3]1W5fN[MP1ZMmN4]1W5hNZMo0ZMkN5^1V5kNZMl0[MjN4`1h2gN[N5^1j0[MiN4a1g2jNYN4`1i0\\MgN4b1g2KXMTOa2h0\\MeN5d1g2NQMTOg2e0\\N9l11mLSOk2b0\\N:m14gLQOQ3a0ZN:R25\\LQOZ3`0WN:W27QLPOb3?UN:d2h0XOnNRN;U38jN]OPN;d3J]NKnM;T4[OoM:lM;X4XOmM=jM;Z4WOmM>hM;\\4UOnM?fM<]4SOoMa0cM<_4ROoMb0aM<^4SOSNa0]M=_4QOVNc0ZM<_4POZNd0VM<`4oN\\Ne0SM<a4mN_Nf0PM=`4mNRL^Ob0W1lN>`4mNQL@e0R1jNa0`4mNRL@h0m0fNf0_4nNTL_Ol0g0aNl0]4QOVL]Oo0a0^NQ1e3IoLeNS1;YNW1Y2dNfLl1g1nM_1LkMf1P2]1gNRMg2a1cN\\1gNSMe2a1dN[1jNSMb2a1dNZ1XNRMdN033\\4a1eN3TMmNU1_O`N051[4`1hNgNgNNBL]N07OZ4`1\\1aNgK:cNEZ4_1^1^NhK?_NDZ4_1\\3mNZHEX4^1^3nN[HCV4_1`3oNYHBW4^1`3QOYHAV4^1b3QOXHBT4]1e3ROVHAU4\\1f3SOUHAT4\\1i3SORHAU4[1k3SOQHBR4[1n3SOPHAR4\\1o3TOnG_OT4\\1o3iNhG15IT4]1P4iNgG15HU4]1P4jNfG06HT4^1Q4jNfGOb4V1i3kNeGOa4V1k3kNdGNb4V1k3lNcGNa4V1m3mNaGMb4U1n3nN`GLb4V1o3nN_GKb4W1P4nN^GJc4W1P4nN^GIc4Y1P4mN^GIc4Y1Q4lNULS1l3mNTLR1m3lNVLR1k3mNWLQ1j3mNXLR1i3mNYLQ1R9K7DVll0HSTSO8I8K2M101O1N2O1O1N101O1O1O1O5K4L1RCaNg2j0U5f0QHbNi2h1W4GmHdNk2c2Z3jNhIeNm2k2R3aNnIfNo2j2S3`NlIiNo2h2U3`NiIjNQ3g2V3`NfIkNS3f2W3TOhLl0X3TOgLm0Y3SOQLhMTNU3k5UOPLjMPNR3P6TOPLoMjMn2U6TOQLRNeMk2Z6TOPL^1o3cNPL^1P4bNPL]1P4eNoK[1Q4eNoK[1P4fNPLZ1P4fNPLZ1P4fNPLZ1P4eNQLZ1P4fNPLZ1Q4eNoK[1Q4eNoK[1Q4eNoK[1R4dNnK\\1R4dNnK[1S4eNmK[1T4dNlK\\1V4bNRLV1o3iN]Lk0e3SO\\Ll0e3SO[Ll0h3ROYLm0h3QOZLn0mMWNa3i0bNP1lM`N\\3>iNQ1kMiNT3[OZLWOh2e2jMQOl2ROmLnN^2o2hMYOe2hNa0n1jL]Ob2dNe0o1iL]Ob2bNh0P2fL^Ob2cNg0o1gL^Oa2gNf0j1iL_Oa2hNf0h1iL@a2hNm0lNhK_2j0Ma2hNl0MTL3?X1a2iNj0m0ZM:l1iNi0f1cLAd2iNg0h1eL_Od2iNf0i1fL^Od2iNe0j1gL]Od2jNc0j1iL\\Od2jNb0k1jL[Od2jNb0k1jL[Od2jNa0l1kLZOd2jN`0m1lLYOe2iN>o1mLYOd2gN`0P2lLYOd2gN?Q2mLXOd2fN>T2nLVOd2fNKUOiLU3h0POe2dNKi2`McNf2`NMm2]McNg2]NNP3[McNj2WNNV3XMcNQ3mMJ`3UMcNX3cMFj3RMcNS7]1mHcNS7]1mHcNS7]1lHdNT7\\1lHdNT7\\1lHdNT7\\1lHdNT7\\1lH_NY7a1gHmMk7S2UH\\M\\8d2gGgLj8Z3`1001O_LlDR3T;mLREn2n:nLWEQ3_;O1O1O1O001O1OUERMa9m2^FjMm8U2SG`NY8_1fGVOf7j0ZHVOg7i0YHWOi7SNUGU11CP1W1l7lMTGU2OoMQ1P2n7hMSG]2U1Lk75UHKi77WHIh79WHGh7:WHGh7:XHFg7;YHEe7=ZHDe7=[HCd7>\\HBc7?]HAb7`0]HAb7`0^H@`7c0_H]O`7d0`H\\O_7e0aH[O]7g0cHYOY7k0gHUOT7P1lHPOP7T1QIkNk6Y1UIgNk6Y1UIgNj6[1UIeNk6[1UIeNk6[1UIeNk6[1UIeNk6[1UIeNk6[1UIeNj6\\1WIcNi6]1WIcNi6]1XIbNh6^1XIbNh6_1XI`Ng6a1YI_Ng6a1ZI^Nf6b1[I]Ne6c1[IjMX7V2iHnLR8R3Q2O001O8I1N3M2N2N2N1O1O1O1O100O00QEYMb9f2]F[Nd8d1\\G]Nd8b1\\G`Nc8_1\\GdNF`NT8k2VHfN^OfN]8c2THjNUOkNh8Z2SHAm7=UHCl7:UHGS80nG0T9lNnFT1`;101O0O10000cMcMSH]2b7SNYHm1\\7dN^H\\1]7oN]HQ1^7_OWHa0d7LTH4g73WHMe7:YHEc7`0\\H@b6hMTIl2:\\Ob6mMlHj2b0YOa6SNdHh2k0UOa6c1`I\\N`6d1`I\\N_6e1aI[N_6d1bI\\N]6e1cI[N]6e1dIZN\\6f1dIZN\\6f1dIZN\\6f1eIYN[6g1eIYN[6g1eIYN[6g1fIXNZ6h1fIYNY6g1hIXNX6h1hIVN\\6h1dIgMn6X2SIVM`7g2iH`Li7_3Q2M6J6J4L2N2N2N2N2N2N2N5I8Hk>UOoAj0UOe0\\O4K5L4L4K5L0B`LZE\\3n:67I7I7Anm?", "size": [484, 640]}, "area": 107185.0, "iscrowd": 0, "image_id": 10149, "bbox": [3.0, 8.0, 603.0, 476.0], "category_id": 183, "id": 10012378}, {"segmentation": {"counts": "i^S53W=100000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001O000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001O00000000001O000000001O000000001O00000000001O000000001O000000001O00000000001O000000001O000000001O00000000001O000000001O000000001O00000000001O000000001O00k2UM9G", "size": [427, 640]}, "area": 1946.0, "iscrowd": 0, "image_id": 10161, "bbox": [392.0, 0.0, 248.0, 120.0], "category_id": 115, "id": 10012379}, {"segmentation": {"counts": "YP:1Z=0O10000000000000000000001O000000000000000000000000000000001O1O1O1O001O1O1O1O001O1O1O1O1O001O1O_OXC`0g<AYC?f<A\\C=d<C]C=b<C`C<_<DbC<]<DeC;Z<EhC9X<GiC9V<GlC8S<HnC8Q<HQD7n;ISD7l;IVD560c:KXE507e:D]E4H>j:^O`E3Ad0n:YObE3ZOh0U;UOcEQ1^:nNdEQ1\\:oNfEo0\\;O2M01O1O1O1O100O010O100O1000O10O1000BlCEU<:lCET<:mCET<;lCET<;lCET<;mCDS<;nCER<8QDFQ<8SDDP<:a0O6J3MYTg6", "size": [427, 640]}, "area": 1545.0, "iscrowd": 0, "image_id": 10161, "bbox": [24.0, 0.0, 100.0, 67.0], "category_id": 132, "id": 10012380}, {"segmentation": {"counts": "0gj0Z8ZZOP5PK000000000000000000000000000001O00000000O10RQP8", "size": [427, 640]}, "area": 1043.0, "iscrowd": 0, "image_id": 10161, "bbox": [0.0, 0.0, 26.0, 268.0], "category_id": 165, "id": 10012381}, {"segmentation": {"counts": "gj0Z8Q5P5PK000000000000000000000000000001O00000000000000000000000000000000001O000000000000000000000000000000001O1O1O1O001O1O1O1O0`IJK7KN93BNa03]OJXLIW2>`1F_LMm1>b1BhLOd1?c81O1O_OXC`0g<AYC?n1Dd8M`E=j1Ma8FfE=g13^8@mE<c1E\\N=o9BSF<_1EfN=c9BZF;[1FmN?X9@bF9W1IUO>T5_OmM1dL9T1I\\O?k4^OPN1gL8P1JC`0b4^ORN0jL8l0JLb0W4\\OVN0mL7h0K4a0n3^OWNOPM7:LYO0]1a0e3]OZNOSM54;o05\\3]O\\NNVM5M?Z11T3]O^NNYM4Ec0g1Ni2^O`NM]M3^Og0Q2L_2^OdNL_M3WOk0\\2FY2@dNLbMQ1f1@T2CcNLdMP1j1\\OP2GbNMfMn0l1ZOm1K`NMhMm0o1WOj1O^NMjMk0S2UOf12]NNiMl0Q7VOUKNiMm0S7TOTKOhMn0U7SORKOhMo0W7ROPKOhMP1Y7POoJ0gMQ1[7oNmJ0gMQ1^7oNjJ0gMR1`7mNiJ1gMR1`7nNhJ0hMR1`2mNg10QN1hMR1_2mNj10nM1iMR1_2mNk10lM1jMQ1_2nNl41eHQ1d:oN\\EQ1d:oN\\EQ1d:oN\\EQ1d:oN\\EQ1d:oN\\EP1e:PO[EP1e:PO[EP1e:PO[EP1e:PO[EP1e:PO[EP1e:PO[Eo0f:QOZEo0f:QOYEP1g:POXEQ1h:oNWER1i:nNUET1k:lNUES1l:mNTES1l:mNTES1l:mNSET1[2kNc51RHT1m:lNSET1\\8kNaI1SNT1Y2kNV30^L1SNS1^8lN^I1SNT1_8kN^I1SNT1Y2kN[30XL1TNT1X2kN]31VL0UNT1`8lN[I0UNT1W2kN`31SL0UNU1W2jNb32QLOVNU1b8lNXIOVNT1W2kNd32nKOXNS1U2lNf32mKOYNR1T2mNg32kKOZNR1S2mNi33iKN\\NQ1Q2nNk33hKN\\NQ1Q2nNl33fKN^NP1o1oNn34dKM_No0o1POP43bKN`Nn0n1QOP44aKMbNm0l1SOQ43aKMbNm0k1SOT43^KMdNl0j1TOT43^KMdNl0i1UOV41]KNeNk0g1VOX42[KMfNj0h1WOW42[KMgNi0g1WOY42YKNgNi0f1XOZ42XKMhNi0f1XOZ42XKMgNj0g1VO\\42VKNfNk0g1VO]42UKMfNl0h1TO_42SKNeNm0i1SO_42SKNdNn0i1SO`41SKNcNo0j1QOb42PKNcNP1k1POb42PKNbNQ1k1POd40oJOaNR1l1nNe42mJNaNS1m1mNe42mJN`NT1m1lNh41kJO_NU1n1kNh42jJN_NV1o1jNi41iJO^NW1o1iNk41hJO]NX1P2hNk41hJO[NZ1R2eNm42eJO[N[1R2eNn41eJOZN\\1S2dNo40dJ0XN^1U2aNP52bJOXN_1U2aNQ51bJOVNa1W2^NS51`J0UNc1W2\\NT52_Je1<ZNU51_Je1<YNW51]Jf1<YNW52\\Jf1;YNZ50[Jh1:WN\\51ZJi19VN]51ZJj17UNa51WJk17TNb51WJk17TNc50VJm15SNf51TJm15RNg51TJn1j7QNVHP22PNl51QJW3kNhLT71QJi3Q6VLoIj3Q6VLoIi3R6XLmIh3T6WLlIi3T6WLlIh3V6XLiIh3W6XLiIh3W6XLiIg3Y6YLfIg3Z6YLfIf3[6ZLeIf3P80\\NZLcIe3^6[LbIe3R8O^N\\L_Id3a6\\L_Id3T8O_N]L\\Ic3V8O10000O10000O100O10000000000O100000000000000O100000000000000O100000000000000O100000000000000O100000000000000O1000000iLZFi2f9VMgF^2Y9bMRGS2n8mM]Gh1c8XNiG[1X8eNSHP1m7PO^He0b7[OjH9V7GW300000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000O100000000000000000000000000000000000iE0S70mH0S70mH0S70mH0S70mH0S70mH0S70mH0S70mH0S72\\H=d7C]H<c7D^H;b7E^H;b7E_H:a7F`H9`7GaH8_7HbH7^7IcH6]7JdH5\\7KeH4[7LfH3Z7MfH3Z7NfH1Z7OgH0Y70hHOX71iHNW72jHMV73Q300000000000000000000000000000000000000000lEMP73PIMP73PIMP73PIMP73PINo62QINo62QINo62QINo62QINo62QIOn61RIOn61RIOn61RIOn61RIOn61RI0m60RI1n6ORI1n6ORI1n6ORI1n6ORI2m6NSI2m6NSI2m6NSI2m6NSI3l6MTI3l6MTI3l6MTI3l6MTI3l6MTI4k6LUI4k6LUI4k6LUI4k6LUI4k6LUI5j6KVI5j6KVI5j6KVI5j6KVI5j6KUI7j6IVI7j6IVI7j6IVI7j6IVI8i6HWI8i6HjHOfM9_7EWJ3c02eM6b9HiH4cM4b7EYJ3a07bM2e9GiH9`M0d7D\\J3?;`MNe7D\\J3>j0Y7VOgHj0U5SO^J3<l0U5QO_J3;m0U5POaJ3:m0U5POaJ39n0U5oNcJ38o0T5nNdJ39n0R5POeJ2:m0Q5POfJ39m0P5QOgJ2:m0n4QOhJ2;l0l4ROjJ2:l0l4ROjJ2;l0i4ROmJ2;k0h4SOmJ2<j0g4TOmJ2<k0e4SOPK2<0]Md0W7ZOPK2=N^Me0T7[ORK2<N_Me0R7[OSK2e0a0W4^OTK1f0`0V4^OUK2g0?R4@WK1h0>Q4AWK1i0>n3AZK1j0<l3CZK1k0;j3D\\K1l0:g3E]K1m09f3F]K1o07c3H_K1o07a3H`K1P16_3JaK0R14]3KbK1R14Z3LdK0T12X3MeK1T11V3OfK0U11T3OgK0W1OQ31iK0V1OQ31iK0V10o20lK0U10o20lK0T11o20mKOT12n2NoK0S12n2NoK0S12m2OPLOR14m2LRL0Q14l2MSLOQ14l2LTL1o04k2LVL0n05l2KVL0n06j2JYL0m06j2JYL0m06i2KZLOl08i2H\\L0k08h2I]LOk08h2H^L0j09g2G_L0i0:g2G`LOi0;f2EbL0g0<f2EcLOg0=e2CeL0e0?d2BgLOe0?d2BgLOd0a0c2@jLOc0b0b2_OkLOTOL^Og0b4_OlLNTOL]Oi0b4\\OnLOSOL]Oj0`4\\OPMNSOL\\Ol0`4YORMOROL\\Ol0_4ZOSMNROL[On0_4WOUMOQOL[Om0`4WOUM0POLZOm0a4WOVM09h0b2WOVM17h0c2WOWM16g0d2WOWM24g0e2WOXM22g0UMUOV51dM310kL`0<\\OR50hM4NOPM>Q9CoINRM=9AV84^JNVM:8DS84^JMZM96FR84^JL]M75Io74QK1RMKl74TKNQMNk74UKLRM0h74QLMn33SLMl33ULLk34ULMj33WLMh33XLMh33YLMf33[LMd33\\LMd33]LMHOXO4Q1MEO[O4Q1MA0_O3Q1L_O1A3Q1L[O2E2X5NiJ2U5OlJ1S5OnJ1P50QK0n40SK0k40WK0h40YK0e41\\KOc41^KO`42aKN^42cKN[43fKNX42iKNU44kKLT46kKJS4:kKFT4<kKD\\1KSMd0`1B]1KPMf0a1_Oa1LiLh0e1\\Oc1MeLi0g1ZOe1O_Lk0j1VOh10[Ll0l1UOj1OULP1P2QOl11mKT1U2kNo12fKX1Z2fNQ24^K[1`2aNT24WK`1c2]NV24QKd1h2XNX26iJg1n2SN\\3S2bLmMT4^1kKbNV4^1iKbNY4^1eKcN[4]1dKcN^4\\1aKdN`4]1^KcNd4\\1[KdNf4\\1YKeNh4[1VKeNk4[1TKeNn4Z1QKfNP5[1nJeNT5Z1kJgNU5Y1jJgNX5Y1fJgN[5Y1dJgN^5X1aJhN`5Y1^JgNd5X1[JiNe5W1ZJiNh5W1VJiNk5W1TJiNn5V1QJjNP6W1nIjNS6U1lIkNU6U1jIkNX6U1fIkN[6U1dIkN^6T1aImN_6T1_IlNc6S1\\ImNe6S1ZImNh6S1VImNk6S1TImNn6R1QIoNP7Q1nHoNS7Q1lHoNV7P1iHPOX7Q1fHoN\\7P1cHQO]7m0dHSO^7j0cHVO^7h0cHXO_7d0cH\\O^7b0cH_O^7=dHC]7;dHE^78cHH^75dHK^72cHN^7OdH2]7KdH5]7IdH7^7FcH:^7DcH<_7AbH`0^7_ObHa0T:V5jJi5WJ", "size": [427, 640]}, "area": 248891.0, "iscrowd": 0, "image_id": 10161, "bbox": [2.0, 1.0, 638.0, 426.0], "category_id": 183, "id": 10012382}, {"segmentation": {"counts": "m>j2T8e3[L2O2M2N2N3N1N2N3N1N2N1O100O1O1O1O100O1O1O100O1O1O1O2O0N2O1N2O1N2N2O1N2O1N2N2O100O1O1O101N1O100O1O1O100O1O100O1O1O100O1O100O1O1O100O1O1O100O00100O100O1O100O1O100O1O100O1O100O100O1O100O1O100O1O100O1O100O100O00100O1O100O1O100O100O1O100O1O100O1O100O1O10000O100O10000O10O010000O1000O0100O1N101O1N2O1O0O200O1O010O1O100O00100O1O00100O1O010O100000O1000000O100000000O1000000O100O100O100O100O100O100O10000O100O100O1000000O10000O1000000O10000O1000000O10000O10000O1000000O10000O1000000O10000O1000000O1O1O100O1O1O1O100O1O1O1O2O0O1O1O100O1O1O1O100O1O1O1O100O1O1O100O1O1O1O100O1O1O1O100O1O1O1O1O1O1O1O1O1O1O1O100O1O1O1O1O1O1O1[OWMSIj2k6cMiH^2U7g0N2O1N2N2N2N2N2O1O1O1O100O1O1O1O100O1O1O1O100O1O1O100O1O1O1O100O1O100O1O1O100O1O100O1O100O10000O10000O10000O10000O10000O10000O10000O100O10000O10000O100O10000O100O10000O10000O100O100O100O1O100O100O1O100O100O1O100O100O1O100O100O1O100O100O1O100O100O1O100O100N3N1N2O1O1N2O1N2O1O1N2O1N2O1N2O1O1N2O1N3N1N3N2N1N3N1N3N2M2O9G<CWO", "size": [351, 470]}, "area": 119224.0, "iscrowd": 0, "image_id": 10166, "bbox": [1.0, 0.0, 469.0, 347.0], "category_id": 183, "id": 10012383}, {"segmentation": {"counts": "0m3S;1O0000000000000000000000000000009G6J1O0000001O1O000000M3O1000000O100001ON2M3K5L400000000O10000000000O1O1000000000000O100000000O10000000000000000O1000000O1000000001O00001O001O001O1O1O1O2N1O1O0000000000000000N2O1O1N2O1O1O1O1001O001O0000001O000000001O000000000000O1001O00O11O00000000000000O1000000000000000000000000000000000000000000O1^LmDW3S;iLPEU3o:kLTER3l:nLWEo2i:QMZEl2f:TM]Ei2c:WM`Ef2`:ZMcEQ1@0n:oNeEm0@2k:QOgEi0D2e:TOkEf0B6b:TOoEc0_O9b:TORFb0[O:b:TOUF?ZO>`:SOYF<XO`0e:nNVFa0TO`0Y<]OgCc0[<[OfCc0^<ZObCf0a<XO_Cg0d<WOaCd0`<ZOcCb0`<[OcCc0^=N11O00MdAA]>>4J`AIc>46NdR_6", "size": [480, 640]}, "area": 22810.0, "iscrowd": 0, "image_id": 10175, "bbox": [0.0, 0.0, 198.0, 144.0], "category_id": 98, "id": 10012384}, {"segmentation": {"counts": "Ui51i>1ZA0f>1YAOf>60OJJcA7X>;L40000001O1O000000M3O0100000O100001ON2M3K5L4000O1000O1000000003M2N001O1O0O101UOnAf0R>ZOnAN2d0R>\\OPBd0V>0O10000000000000O2O1LeAA[>?eAA]>=cAC_>;aAEb>8^AHe>46N1OUYW8", "size": [480, 640]}, "area": 1533.0, "iscrowd": 0, "image_id": 10175, "bbox": [12.0, 125.0, 66.0, 41.0], "category_id": 100, "id": 10012385}, {"segmentation": {"counts": "`Tj85g>003L6K4kN@fCd0Y<@_Ce0a<]OYCg0f<\\OTCh0l<[OlBi0T=b00O001O1N1O2L3J7K4KWBQOm=j06L5L3D=K501OO1_i4", "size": [480, 640]}, "area": 817.0, "iscrowd": 0, "image_id": 10175, "bbox": [601.0, 369.0, 28.0, 68.0], "category_id": 105, "id": 10012386}, {"segmentation": {"counts": "`>`0n>B00000000O10000000000000000000000O10000O10000O1000000O10000O10000O10000O10000O100O100O100O100O100O1_IAaN?\\1EWHMV6?`1L`N4^1NaN3]1NdN2Y11fN0[1OeN1\\1NdN2]1MbN4_1KaN5`1I`N8`1H`N8_1I`N8`1H_N9a1G^N:b1F]N;b1E^N<b1D]N=c1C\\N>c1C]N=c1C\\N>d1A\\N`0d1@[Na0d1@ZJCR2n0d3_OZJDP2n0f3^OZJEn1n0h3UO^HK02k13l1l0j3SOdJ3_1k0m3ROdJ5\\1j0P4POeJ8X1i0R4POfJ7W1j0S4oNeJ8W1j0T4nNeJ8V1k0U4lNfJ9T1l0U4lNgJ8R1n0W4jNfJ8R1P1X4hNfJ8P1R1Y4fNhJ8m0T1[4dNhJ8j0W1^4`NiJ9g0Y1`4^NhJ:f0Z1a4]NiJ9c0]1d4ZNiJ9?a1h4VNiJ9<d1j4SNjJ;8e1n4PNjJ;4i1R5lMjJ;1l1U5hMkJ<IS2[5bMlJ;]O_2g5VMkJ<WOe2n5nLlJ=WOd2m5oLlJ=UOf2n5ZMaJ0@g2o5YM`J1@g2P6XM`J1_Oh2P6XMaJMAl2n5VMbJNAk2m5WMbJMCk2k5XMaJMGi2g5[MbJKJh2d5]MbJLKe2c5_MbJLLd2a5`McJNM`2`5bMcJON]2_5dMcJ0J_2c5aMcJ1J]2b5cMdJ1I\\2c5cMcJ3I[O^Nl2V7eMdJ4KA[N]2U7oMeJ2O_OYN^2S7QNeJ2O]O]N^2o6SNdJ5:f1R5UNdJ5<d1o4WNfJ5=b1m4YNfJ7=^1m4[NfJ7?\\1j4_NeJ6c0Y1h4bNdJ5e0X1g4dNcJ2k0W1V4RNaIf0]1MR1Y1P4XN\\Ic0a1LW1V1k3_NZI`0c1LY1S1k3]OkJ_O]1R1h3@jJ^O_1Q1f3CjJ\\Ob1o0d3EiJ^Od1k0b3IiJ[Oh1j0^3@\\I[O]1;j1i0\\3B^IZO[1;l1h0[3C_IZOX1<n1g0Z3dN_Ij03^OV1=o1f0^3_O]I_OU1;Q2g0[3@aI^OR1:S2h0Y3AcI]OP19U2i0X3gN^Ib05Fn09W2h0Z3cNaIc01Il08Y2i0b3VOYIIl07Z2j0a3VOZIIj07Z2k0b3TO[IKg06]2k0a3TO\\IKe07\\2k0c3SO\\ILd06]2k0c3SO]IKc06^2l0b3SO]ILa06_2l0c3RO]IM`07^2j0e3QO_IN=7_2j0e3QO_IO<5`2l0e3PO`IN;6`2l0e3POaIN88`2k0g3oNaIO71YOAZ3`1e3oNbIO50i2R1`3oNbI04Oi2S1a3nNcIO20k2S1P3kNaI2d00ONm2V1n2]OVJ@NKP3X1l2]OWJ@LIS3Z1i2^OXJ@KEV3^1g2]OYJ_OIGW3]1g2\\OZJAGGW3\\1g2]O\\J@EGY3[1f2^O\\JADF\\3Y1d2@]JAAF^3Z1c2@^J@AIW3E_Lc1[6_O_J@_OALL]3g1i2\\O_JA^O^Ob3f1`2\\OaJ@\\O\\Oe3h1^2[ObJBW3T1W2ZOcJBU3T1X2ZOcJBT3U1Y2YOdJ@T3W1Y2XOdJ]OU3\\1V2WOfJ[OV3^1T2WOgJXOV3b1R2WOhJUOX3d1P2WOiJROY3g1n1WOjJoNY3k1l1WOkJlN[3m1j1WO[Oi0e0VO[Ok0e0UO[Ok0e0UOiKmN_1n1h2UOhKnN`1m1h2UOgKnNb1m1f2VOgKnNb1m1g2UOfKPOb1k1h2TOfKTO`1h1k2SOdKVO`1h1k2SOdKWO`1f1l2SOdKVOa1g1k2ROdKWOc1f1i2SOcKWOe1f1h2SObKWOh1e1f2SObKXOi1e1e2SOaKXOk1e1c2TOaKWOl1f1b2TOaKWOl1f1c2SO`KWOn1f1a2TO`KWOo1e1a2TO_KWOQ2e1`2SOaKWOn1g1a2ROaKVOP2g1_2SObKUOn1i1_2SOcKSOn1k1_2ROdKROl1m1`2QOdKQOl1o1`2oNfKQOg1L`LU2S6lNhKROe1U2b2iNkKQOc1V2b2hNlKQOb1X2b2fNnKQO_1Z2c2cNA]1?bNA_1?`NAa1?^NBb1>]NBd1>ZNDf1<YNDh1<WNEi1;VNFj19UNIk17TNIm17RNJn16QNKo17mMKS27jMJV26iMJX27fMKY25fMLZ23eMN\\23bMN^23`MMa22_MOa2X601OO10000O11OO100O10000N2O12N00O1O1O11O00M3NWMnCh2R<YMmCg2Q<51OO1O1VOSMaEm2_:TM_Em2a:UM]Ek2c:VM[Ek2d:UM]Ek2c:UM]Ek2c:UM\\El2d:TM]Ek2c:UM]Ek2b:VM_Ei2a:WM_Ei2a:WM`Eh2`:WM`Ej2_:WMaEi2_:WMaEi2_:WM`Ej2`:VM_Ek2a:VM]Ek2b:VM^Ej2b:WM\\Ej2d:WM[Ei2e:WMYEk2g:VMXEj2g:XMUEk2k:UMTEl2l:WMoDk2Q;b0O100000000O100000000000000000000000000O100000000000000000000000000000000000000O10000000000000000000000001O0000001O0000001O0000001O0000001O0000001O00001O000fLlDh2T;WMnDh2S;TMPEl2P;QMTEn2l:oLWEQ3i:lL[ES3Z;O00001O00001O001O1O001O001O001O001O1O001O1O00001O1O00001O001O002N0000001O001O001O00O1O11O001O001O001O001O1O001O001O001O001O1O001O001O001O001O1O001O001O001O001O001O1O001O001O001O001O1O001O001O001O001O1O001O001O001O001O001O001O001O001O001O1O001O001O001O001O001O001O001O001O001O001O001O001O1O001O001O001O001O001O001O001O001O001O001O001O001O1O001O001O001O001O001O001O001O001O0000000000000000000000000000001O0000000000000000000000000000L4KMgWc0", "size": [480, 640]}, "area": 45330.0, "iscrowd": 0, "image_id": 10175, "bbox": [0.0, 194.0, 598.0, 286.0], "category_id": 107, "id": 10012387}, {"segmentation": {"counts": "fe_51n>2L3M4L3M4M3O000010O0001O010O1O0010O01O0010O01O1O010O1O00010O1O00010O001O011N0000010O001O010O00ZObBI\\Rb22Wm]M5o=b0I7H8N2L4O11O0000000dN`BX1`=gNbBX1a=gN]BY1b=300002N1O001O00001O0jNYBS1g=mN[BQ1f=oNYBQ1g=oNYBQ1h=nNXBR1l=O002N0ROQBk0S>O2N1O001O1O001O001O", "size": [480, 640]}, "area": 2664.0, "iscrowd": 0, "image_id": 10175, "bbox": [374.0, 359.0, 266.0, 121.0], "category_id": 117, "id": 10012388}, {"segmentation": {"counts": "dXo03k>2VAMf>8KI_A7`>5O100O1N2O100N22O1N2N0000000000O100000001O00000010O0001O001O001O002N2N2K5RBPOh=T1O000002N1O2J6ElAGV>8jAFY>8hAC^>;5N1O00111M10002OO1O1O0\\AK[>8cAIY>;fAE4MP>f0mA[OT>m0J100001OO1000001O1N1O0VBnNg=V100O1O3N0O11N1O02O11]O\\BHb=8^BGb=:_BEa=;_BEb=;^BDb=6_B^O1;a=6dBJ]=3fBK[=MnB1T=HXB0h05R>LjYn6", "size": [480, 640]}, "area": 2370.0, "iscrowd": 0, "image_id": 10175, "bbox": [66.0, 322.0, 99.0, 46.0], "category_id": 121, "id": 10012389}, {"segmentation": {"counts": "n4U3m;N1SMkDm1V;RNkDm1U;RNmDm1S;SNmDl1R;VNoDi1Q;WNPEh1P;XNQEg1o:YNREf1n:ZNSEe1m:[NTEc1m:\\NUEb1m:\\NUEc1l:\\NUEc1U<O1O1N2O1O1O0O2O1O1O1O1O1O1O1O10000O15K2NO001O1N2O1O1O1O1N2O1O1O1O1O1N2O1HiAEW>8mAEV>7<M3M[k9MmeE0h>8M0O1000000O0100000O100O010000000O01000O10000O010U]`0EVc_O1O1N2O0O2O1O001O001N101O001O000O2O001O1O001O001N2O001O001N101O001O001O001N101O001O1N101O001O1O00N1O2N2N2N2N2N2N1O2O1O1O0O2O1O001N2O1O1O1O1N10eBLl;3TDNl;1TD1k;MVD4j;KWD6W=2O2M2N3N1N3M2O1N1O2O0O2N101N2N2O1N2N2O1N2N2O1N2N2N2O1N2N200a0_Ob0]Oa0@001O00000000001O000000001O0000mL0VG0m;0kL0ZG0lW20hnN0WUO00000o>0PA1O1O1O1O1O1N101O1O1O1O1M3M3L310001O1O001O1O1N101O1O001O1O1O0O2O1O001O1O1O00000O10O1aje4", "size": [480, 640]}, "area": 5780.0, "iscrowd": 0, "image_id": 10175, "bbox": [0.0, 158.0, 320.0, 134.0], "category_id": 123, "id": 10012390}, {"segmentation": {"counts": "S8V3j;0000SOXDaNi;Y1^DfNa;U1dDlN\\;n0jDQOW;i0oDWOQ;c0UE]Ok:=[ECe:7`EJ_:1gENY:MmE3S:GSF9o9ATF`0P:\\OQFb0T:ZOmEe0X<_OhA8a>C^A105e>J]A5h>N103MnkP9", "size": [480, 640]}, "area": 1208.0, "iscrowd": 0, "image_id": 10175, "bbox": [0.0, 256.0, 23.0, 105.0], "category_id": 132, "id": 10012391}, {"segmentation": {"counts": "mag03l>3M101O1O00001\\AG_>9aAGN2\\>:dAH\\>>0O1000O10000000001O1TB]OY=c0gB]OY=e0eB[OZ=h0dBXO\\=k0aBUO_=n0]BTOb=n0\\BROc=P1\\BPOd=V100000O2O00001O001O001O1O1O1O2N1N2O00000000000000001O1KmA[OU>`0nA@S>=oABS>;oAER>8PBHQ>5QBKP>2U\\g7", "size": [480, 640]}, "area": 1554.0, "iscrowd": 0, "image_id": 10175, "bbox": [50.0, 119.0, 62.0, 42.0], "category_id": 139, "id": 10012392}, {"segmentation": {"counts": "fW_13l>2ON10O1N2O111N2N3M3M3M2J7DUO^Bn0`=RO^Bm0d=TOWBR1g=4O1[BhNa=\\1eBdNQ=`1lBbNS=]1kBgNR=f1N2N2O1O1O1NPNWCj1i<TNXCo1e<PN[C4Mi1m<TNTCo1R=MN2N1O2N2N2N1O10O10O0OjMZCT2i<000002N0O10O0011O1N3NO10O100O2O000O100O3NO10O3N00O201NO1LkB\\NV=d14N2O1N100O2OO0100O1000O010000O10000000O010000O100001O0O2O0000O001000000O02OO1O001O0N]BhNc=X130O000O20O2N2O0O2O1N2O001O1N2O000000O011N10O10O010O101OO0100O011OO10O010000N2O03N00O1O0011O00M3NnAWOP>j0QBUOo=i051OO003N1N3N1N01000O11O001O001N010000O1O2N101N2O0N3O1L3O4K_lP5", "size": [480, 640]}, "area": 7939.0, "iscrowd": 0, "image_id": 10175, "bbox": [100.0, 340.0, 196.0, 94.0], "category_id": 143, "id": 10012393}, {"segmentation": {"counts": "PWY51o>000000000000000000000000000000000000000000000000000000000000000000000000000000000000001O00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001O001O00001O001O001O00001O001O00001O001O00001O001O001O00001O001O001O1O001O001O1O001O1O001O001O1O001O001O1O001O001O1O001O1O001O001O2N1O2N1O2N1O1O2N1O2N1O2N1O1O2N1O2N1O2N1O2N1O1O2N1O2N1O2N1O1O2N1O3M2N3M3M2N3M2N3M3M2N3M3M2N3M2N3M2N2N3M2N3M2N2N3M2N3M2N2N3M2N3M3M3M3M3M3M2N3M[LfFg1W9[MjFG2Q3Q9mLUHV3h7hLZH[3c7cL_H`3^7_LcHd3Z7XLjHk3S7ULmHm3Q7SLnHo3Q7QLoHP4P7PLPIR4n6SLmHn3R7TLlHm3S7TLhHR4V7VL`Hm3_7XL[Hj3d7_LQHd3n7i1N1O1O1O2N1O1O1O2N1O1O2N1O1O1O2N1O1O5K8H", "size": [480, 640]}, "area": 15619.0, "iscrowd": 0, "image_id": 10175, "bbox": [361.0, 0.0, 279.0, 250.0], "category_id": 156, "id": 10012394}, {"segmentation": {"counts": "\\[71o>1O1XAO_>:\\AE1050o=>kAD\\>a0gA]OS>c0mA]OS>f012O000000O0100000O010000000O01000000O010000000O0100000O1000O1000O1000000O10O100000O1000HnABR><PB@M1R>?;LdAD\\>;5O10O03L3N2OmbW8", "size": [480, 640]}, "area": 1330.0, "iscrowd": 0, "image_id": 10175, "bbox": [15.0, 319.0, 62.0, 38.0], "category_id": 168, "id": 10012395}, {"segmentation": {"counts": "m`X22n>001O010oNNVB1a02^O1h=4iB1W=0fB3KDQ=:lB;1\\OQ=<gBD0h05ZOR=\\1jBhNR=c1N302OO0O11N13MO13NO1O1O0O101OO10O100O1O001O1O1OVNWC_1i<`NZC^1f<aN]C]1b<cNaC[1_<dNeCX1]<gNeCW1]<fNfCX1Z<gNiCW1S=M2N2N2N1N2O2N1O1N2O2N1O1O1N3N1O1N2O0O2N2O1N1O2MiQT6", "size": [480, 640]}, "area": 2630.0, "iscrowd": 0, "image_id": 10175, "bbox": [154.0, 303.0, 67.0, 72.0], "category_id": 170, "id": 10012396}, {"segmentation": {"counts": "d]m81n>2L1WAMi>221O1O3L3M2L5H7K5JSOUBR1e=7\\BgN\\=^1dBdNV=a1jB^Ni<o1WCQNd<U2[CkMb<O_CZ2`<gM_CY2a<fM`C\\2]<4cC`MZ<d2N3B=C<D=C=C<D=B=F;`G", "size": [480, 640]}, "area": 2094.0, "iscrowd": 0, "image_id": 10175, "bbox": [608.0, 271.0, 32.0, 188.0], "category_id": 172, "id": 10012397}, {"segmentation": {"counts": "Pl`13l>2kAMZ=6aBM]=6`BL_=6^BLa=6\\BLc=6ZBLe=6XBMg=f01O010N1000001O00gBQOa<o0^CVO^<j0`C\\O\\<d0cC]O^<b0aC_O_<a0UCSO2>i<?TCUO1=k<h0SCXOn<]10O10O0110N10O11N100O1O1O1O1000O1000001O1O2N00O1K5N21O4L0000001O1O001O00O10O1001O0000000000^NZNiEg1V:\\NhEc1W:cNeE]1Z:eNfEZ1Z:fNfEZ1\\:eNdEZ1[:gNfEX1Z:iNeEW1]:hNbEX1^:hNbEY1]:hNaEY1_:gNaEY1`:gN_EX1c:mNXER1h:QOTEP1k:SOSEm0m:TOQEn0n:ROSEm0n:ROREm0P;ROmDSOAk1c;ROkDP1W;POcDTOIm1e;PO_DT1b;mN\\DS1f;R12@ZDdMg;Z2`001O1O0O2O1O001O1O1O001O1O001O1O001O1O10O<FN101N2N101N1O2O0O3N2M3M3N2M3N2L5CTkk5", "size": [480, 640]}, "area": 8706.0, "iscrowd": 0, "image_id": 10175, "bbox": [104.0, 69.0, 135.0, 121.0], "category_id": 173, "id": 10012398}, {"segmentation": {"counts": "Y;Q3o;c0[IUMOk21VMQOXO`Lb3^4WMlN]OeL]3_4WMeNBlLW3_4XM^NGSMQ3_4YMWNK[Ml2^4ZMPN0aMg2_4ZMiM5hMa2_4[MbM:oM[2_4\\M[M?UNV2`4\\MTMc0]NQ2_4]MmLh0dNk1_4^MhLk0iNg1_4_MgLj0iNg1`4aMfLg0kNh1_4bMeLf0lNh1_4cMdLe0mNh1_4cMdLe0lNh1a4dMaLe0nNg1a4eM`Lc0POh1`4fM_Lb0POh1b4gM]La0QOh1b4hM\\L`0ROh1b4iM[L>TOi1a4jMYL>VOg1b4lMWL=VOh1c4lMVL<WOh1b4nMVL:XOg1c4PNTL8ZOh1b4QNSL7ZOi1c4QNQL7\\Og1d4SNoK6]Og1d4TNnK4_Oh1c4TNnK4_Og1d4VNlK3_Oh1e4VNkK2@h1e4WNiK1Cg1e4YNgK0Dg1e4ZNfKOEf1f4\\NdKNEg1f4]NdKLFf1g4_NaKKIf1f4`N`KJJe1g4bN^KIKe1g4cN]KHKe1i4dN[KFMf1h4eNZKENe1i4gNWKE0d1i4gNWKE0d1i4hNVKD1c1j4jNTKB2e1j4jNSKA3d1k4lNQK@4d1j4nNPK_O6b1k4POnJ]O8c1j4QOmJ\\O9b1k4SOkJ[O:b1k4TOjJZO;a1l4VOhJYO;b1m4UOgJYO=a1m4VOfJYO=a1m4VOfJYO=`1n4VOfJZO<_1o4WOeJYO=_1o4XOdJYO=^1P5YObJZO>\\1P5ZOcJZO<\\1R5ZObJZO<\\1R5ZObJYO=\\1R5ZObJZO<[1S5[O`J[O=Y1T5\\O_J[O=X1U5]O^JZO>X1U5]O^J[O=W1V5^O]J[O=V1W5_O\\J[O=U1X5@ZJ\\O?R1X5AZJ\\O?R1X5BYJ\\O?Q1X5DYJ[O?P1Y5DYJ\\O>o0Z5EXJ[O?o0Z5FVJ\\O`0m0[5GUJ\\O`0k0]5HTJ]O?i0_5JRJ]O?f0b5MoI\\O`0e0c5NnI]O?c0e50kI^Oa0>g54hI^Oa0<i56fI]Ob0:k58dI^Oa06n5=aI]Ob02P6a0]I^Oc0NS6d0ZI^Oc0JW6g0WI^Od0GX6k0TI^Od0[Od6W1hH^Oe0nNo6c1]H_Od0jNS7g1XH_Og0iNQ7h1XH_Oh0gNQ7j1WH_Oj0dNP7l1WH@k0_NQ7Q2TH@m0YNS7W2PH_Od9a0\\F@d9`0\\F@d9`0\\F@d9`0\\F_Oe9`0\\F@d9`0\\F@d9`0[FAi9:XFFl96TFIP:4PFLT:0lE0X:KiE5Z:HeE8`:D`E<c:A]E?g:\\OZEd0j:XOVEg0n:VOQEk0S;POnDP1V;lNjDT1Y;iNhDV1\\;fNeDX1`;cNbD\\1a;aN`D^1d;^N\\Db1b<N2O1O1O001N2O1O1O0O2O1O1O1O1N101O1O1N2O001O1O1N2O001O1N2O1O001O1N2O1O^GCa2=_MF_28bMK\\24dMOZ20fM3X2LhM7U2IkM:S2DTHM^5b0\\2@TH4\\5?^2\\OTH6_5`0\\2HcM<[2CeM`0X2@hMc0T2^OlMd0o1_OQNd0j1]OWNf0d1[O]Nh0^1YOcNi0Y1XOhNk0S1VOnNm0m0UOSOn0g0TOZOn0b0SO_Oo0=ROWJoNP5o1g0SOYJQOn4m1f0SO\\JSOl4k1e0SO_JUOj4h1e0TOaJXOg4e1e0TOeJYOd4d1d0TOhJ[Ob4b1d0SOjJ^O`4_1e0SOkJB]4\\1f0ROmJE[4Z1e0ROPKGY4W1f0SOPKIX4U1f0RORKKW4T1e0QOTKMV4R1d0ROVKOT4P1d0QOXK1S4o0c0PO[K2Q4n0c0PO\\K4P4m0b0oN_K6m3l0a0oNbK7l3k0`0nNeK8j3j0`0nNfK:i3i0?mNiK<f3h0>mNlK=e3f0>mNnK>c3f0=lNPL?c3f0;jNTLa0`3e0:jNWLc0^3d09iNZLd0\\3d08gN]Lf0[3d06eNaLh0X3c05fNcLi0W3b04dNgLk0T3c02aNkLn0R3b01`NnLn0Q3d0N]NRMQ1o2d0K[NXMR1l2d0JZNZMT1k2d0HWN_MU1i2e0FUNcMW1f2f0CTNhMW1d2f0BRNlMY1a2h0_OnMRN[1^2j0\\OkMWN[1]2m0WOhM^NZ1\\2Q1ROdMdN[1Z2T1nNaMiN[1Y2W1jN]MoN[1X2[1oMXLnNQ1m0\\1V2_1_MhLTO>X1Z1V2b3[LSKa1[1T2b3ZLRKd1\\1R2a3ZLSKe1[1R2a3XLTKh1[1P2`3XLWKg1X1R2`3WLjKV1f0c2i5jLXJV3X6ZLgIg3Y6YLgIg3Y6YLfIh3Z6XLfIh3[6WLeIi3[6WLeIi3[6WLeIi3[6WLdIj3n2XLQNNQOj3m2WLTNOoNj3n2ULUN0mNk3]6ULcIk3]6ULbIl3R3WLlMMROl3S3[LfMIWOl3T3[LdMIXOl3U3ZLdMIVOn3W3VLeMLTOn3X3ULdMMTOn3X3ULdMMTOn3Y3TLcMNTOn3Y3SLLn34RLLn34RLeMOPOo3[3RLeMOPOo3[3RLeMOoNP4\\3QLeMOoNP4\\3QLKo35QLKo35QLKo35PLKQ44PLhMOkNQ4\\3QLMo32RLNn31SLOm30TLOm3OUL1k3NVL2j3MVL4j3KWL4j3KWL5i3JXL6h3IYL7g3G[L9e3D^L;b3BbL>^3@cLa0]3_OcLa0]3@bL?_3B`L>`3B`L>`3C_L=a3D^L<b3E]L:d3F[L;e3FZL:f3GYL9g3GYL9g3HXL8h3IWL6i3LVL4j3LVL4j3MTL4l3MSL3l3OSL1m3OSL1m30RL0n31QLOn32RLNn33QLMo32RLNn32QLOn32RLNn32RLNn32RLNn31SLOl32VLLj34_LCa3=_LCa3<`LCa3=`LB`3>`LB`3>`LB`3>`LB`3=aLC_3=aLC_3=aLC_3=aLC_3=aLC_3<bLD^3<bLD^3<bLD^3<bLD^3;cLE]3?_LAa3d0ZL\\Of3i0ULWOk3m0QLSOo3P1nKoNS4o50001O0000001O0000001O00001O0000001O0000001O0000001O\\KULSNk3o1ULoMk3S2ULkMk3W2VLgMi3Z2XLdMh3^2XL`Mh3b2YL[Mg3g2YLXMf3j2ZLTMf3n2[LoLe3T3ZLjLf3X3XLiLg3Z3WLeLi3Y70001O0000001O0000001O00001O0000001O0000010O0000WM\\LVJd3d8O00[M]LmIc3U6]LiIc3Y6]LfIb3[6^LdIb3^6^L`Ib3b6^L\\Ib3e6_LZI`3h6`LVI`3l6`LRI`3o6`LPI`3R7`LmH_3T7bLjH5`N^2h8[MdId2\\901O001O001O1O001O001O001O001O1O001O001O001O001O1O001O001O001O001O001O1O001O001O001O00100O010O0010O010O0100O0010O010O01O010O010O00100O010O010O0010O010O010O10O010O0100O010O10O010O01000O010O0100O010O010O10O0100O010O011N100O2O0O2O0O2O000O2O0O2O0O2O0O101N101N101N100O2O0O2O0O2O0O101N2O1N3M3M2O2M2N3M3M2N3M2N3M3M2N3M2N2N3M2N3M2N2N3M2N3M2N2N3M2O2M3L4E:G:E;E;F:G9I^L^H1[7UOfHlM2R3Q7kNUJX1`4TLWKg2:Y1\\4SLSKf2c0Z1V4SLQKf2j0Z1R4RLmJe2U1\\1k3RLiJf2\\1Y1i3TLeJg2b1V1h3ZOXLh0e3ZOZLg0e3]OWLd0g3_OSLf0l3@mKc0P4CiK`0V4DdK?Z4V5N1N2O1O2M2O1N2O2M2O1N3N1H8C=C>B=C=C>Bd0\\Oc0]O_H", "size": [480, 640]}, "area": 178488.0, "iscrowd": 0, "image_id": 10175, "bbox": [0.0, 1.0, 640.0, 476.0], "category_id": 183, "id": 10012399}, {"segmentation": {"counts": "n][62j>4N20000000000000000000000000000001O000000000000000000000000000000kIJ`M6W2JRN6m1KSN5m1KSN5n1KRNOgI0lg^2", "size": [480, 640]}, "area": 286.0, "iscrowd": 0, "image_id": 10176, "bbox": [433.0, 266.0, 39.0, 214.0], "category_id": 105, "id": 10012400}, {"segmentation": {"counts": "i`k14l>1O1O010O000000000000000000000eNKkC5U<LjC4V<MiC3V<0hC0X<1dC2\\<O\\C8c<JTC>l<ClBd0T=d0O100O11O001O1O1O1O1O001O1O001O001O1O001O001O001O001O001O1O001O001O001O001O001O001O1O001O001O001O000000000000000000000000001O00000000000000000000000000001O00000000000000000000000000001O00000000000000000_OhA:X>ElA8T>HnA6R>IRB4n=LSB3m=LTBNF2W>OWB1i=OXB0h=OYB1\\>O00_X3a0PgL000O1006JN22NJ62N5KJ6M30000O100000000000000000000O10000000000000000000000O10000000000000000000000O100000000000000000000O100000000000000000000O100O1O100O1O100O1O100O1O100O1O100O1O100O1O100O1O10jN]Bo0b=RO^Bn0b=QO_Bo0`=QObBn0^=RObBn0]=ROeBm0[=ROfBn0Z=ROgBm0X=SOiBm0W=ROkBm0T=TOlBl0T=SOnBl0R=SOoBm0P=SOQCm0o<SORCl0m<TOTCl0l<SOVCl0i<UOWCk0i<TOYCk0^=0XOUO\\Cj0]=0001O001O001O001O00001O001O001O001O00001O001O001O001O00001O001O001O001O00001O001O001O001O000NYSP3", "size": [480, 640]}, "area": 7578.0, "iscrowd": 0, "image_id": 10176, "bbox": [126.0, 427.0, 308.0, 53.0], "category_id": 118, "id": 10012401}, {"segmentation": {"counts": "nVd72m>1O1N2O1N2O1N2O1N2O1O1N2O1N2O1N2O1N2O1K5O100O1O100O1O100O1O1O100O1O100O1O100O1O100O1O100O1O1O100O1O100M30000O1000000000000000000000000000000000000000000O1000000000000000000000000000000000000000000000000000000000000000000000000000000000000", "size": [480, 640]}, "area": 5786.0, "iscrowd": 0, "image_id": 10176, "bbox": [520.0, 420.0, 120.0, 60.0], "category_id": 167, "id": 10012402}, {"segmentation": {"counts": "0c>=00O10000000000O1001O00O10000000000O1O1000000O100N2O1N2O1N2N2O1N2O1N2O1N2O1N2O1N2O1N2O1N2N2O1000000O100000000O100000000O100000000O100000000O100000000O10000000000000000000000000000000000000000000000000000000000001O0000000000000000000000000000001O0000000000001O0000001O001O00001O002N1O00\\OcNeC]1Y<fNfCZ1Y<iNdCX1Z<kNeCU1Y<oNeCQ1Y<ROeCo0Y<TOfCl0Y<VOfCj0X<YOfCh0X<[OgCe0W<S1N2N2O1N2N2O1O100O1O1O100O1O1O100O1O1O100O1O1O100O1RHeLV4\\3jKPMi3Q3WLPMh3P3XLTMc3m2]LVM_3k2aLWM]3i2dLXMY3i2gLZMV3f2jL\\MT3d2lL^MR3b2nLaMo2_2QMcMl2^2UMcMi2]2WMfMf2Z2[MgMc2Y2dMcMX2^2nM`Mn1`2TNcMg1]2\\NdM`1\\2bNeM[1[2gNcMX1^2jNaMU1_2mN_MS1a2oN]MQ1c2QO[Mo0e2WOUMh0l2AlL>T3BlL>T3BlL>T3BlL>T3BmL<T3DlL<T3DlL<T3DlL<T3DlL<T3DmL:T3FlL_LAR2c3_1lL_LHk1\\3f1lL^LLi1X3i1lL]LMi1X3j1lL[LMk1W3j1lL[LMk1W3j1lLZLNl1V3j1lLZLNl1V3j1mLYLMl1W3k1lLYLMl1W3k1lLYLMl1W3k1lLYLMl1W3k1lLYLMl1W3k1mLXLLl1X3l1lLYLKk1Y3l1lLYLKk1Y3l1lLYLKk1Y3l1mLXLJl1Y3l1mLXLJk1Z3m1lLXLJk1Z3m1lLXLJk1Z3m1lLYLIj1[3m1mLXLHk1[3m1mLXLHj1\\3n1lLXLHj1\\3n1lLXLIi1[3o1lLXLIi1[3o1mLQLNo1V3P2lLkK4U2P3P2lLdK;\\2i2P2lL^Kk0X2Y2Z2mLWK`1o1c1j2mLQKV2d1n0[3l50000000000O100000000000000000000O100O100O1dNcFUM]9d2mFXMT9g2PGUMQ9k2QGSMo8l2UGPMl8P3WGmLi8S3ZGiLg8V3b100O100O10000O100O10000O100O11O000000000000001O001O1O2N1hEgLg8Z3WGhLh8Z3UGhLj8Y3TGhLl8Y3RGiLm8X3PGkLo8W3nFjLR9W3lFkLS9V3kFlL`2CU3b3YJlL]2GZ3_3UJmL[2I`3[3TJmLW2Le3X3RJmLT20j3T3PJSMk1NU4P3nI:R6HlI8T6IjI8V6IhI8X6X400000O100000000000000000000000000000000000000001O001O001O001O1O001O001O001O1O001O001O001O1O001OO1N2N2N2N2O1O1000000000000O1000000000000000000000000000000000000000000000000O10000001O0000001O0000001O0000001O000000001O0000001O0000001O0000001O000000001OUJYJ]2g5aMkJo1U5oM]Ka1c4]NaKb1^4\\NfKb1Z4]NiKa1W4_NkK_1U4`NnK_1Q4aNQL]1o3cNSL[1m3dNVLWNfMX2T6AWLjMVNT2CgMo5[2XLgM_NQ2ZOmMo5[2XLdMgNo1RORN1Ke5_2cLbMmNk1kNYNMKk5_2TNXOTLaNk5W2WNROnKbNP6[2YNmNgKeNS6^2SOPNjJAT6_2TOmMU1S2nNiMS1V2QOfMP1Z2SOcMm0]2k5000000000000O1000000000000000000O11O1O1O001O1O1O1O001O1O1O1O001O1O1O3M3M2N3M2oDPMn9S3gEUMY:m2]E\\Mb:b3O001O1O001O1O001O001O1O001O1O001O001O1O001O001O1O001O1O0000O1O1O1O1N20000O10000O1mNfE`MZ:`2hESMODY:X3TFhLFMW:Z3dFdL\\9Z3hFdLX9Y3Y1M3M3O1O1O1O1O1O1O100O1O100O100001O0000000000000000001O001oDTMl9l2RF_Me9a2ZFaMe9`2YFaMg9a2UFbMj9`2RFcMm9_2nEeMQ:]2iEgMW:^2`EgM_:^3O001O1XJaKm0_4SOaKm0`4RO`Kn0`4QOaKo0`4PO`KP1a4nN`KR1`4lNcKS1^4kNcKU1^4iNcKW1^4eNeK[1\\4lMeKfL4^5X4`MfL`2[3YMlLf2U3RMRMn2n2lLXMT3j2gLYMY3h2cL[M]3g2bLXM^3i2aLWM_3k2_LVM`3k2`LTM`3n2^LRMb3o2eJmJ^1S2m3Z3TLfLl3T3ZLlLf3j2`JoJS2W2\\3j2TMhLl2X3TMhLl2X3TMhLl2X3UMgLk2Y3UMgLk2Y3UMgLk2Y3UMhLj2X3VMhLj2V3XMjLh2V3XMjLh2V3XMjLh2V3XMjLh2V3XMkLg2U3ZMjLf2", "size": [480, 640]}, "area": 169255.0, "iscrowd": 0, "image_id": 10176, "bbox": [0.0, 0.0, 640.0, 467.0], "category_id": 172, "id": 10012403}, {"segmentation": {"counts": "da61o>0O100O100O100O2N100O1N2O1N2O1N2N2O1N2O1N2O1N2O1N2O1N2O1N2O1N2N2O1000000O101O00000O100000000O100000000O100000000O100000000O100000000000001O0000000000000000000000000000000000000000000001O000000000001O000000000000000001O0000000000001O0000001O001O00001O002N1O1O[OkN`CU1]<nNcCQ1\\<ROdCl0Z<WOfCa0_<C`C5e<N[CIk<:UC^OQ=d0c03M3M3M2N3N2M3M3N2N101N2N1O2O1N1O2N101N1O2N101N1O2N2O0O2QHjMQ3W2oLUNd2m1[MTNd2l1\\MXN_2j1`MYN\\2h1dMZNZ2g1fMZNW2g1iM\\NT2e1kM]NS2d1lM^NR2b1nMaNo1`1PNbNm1_1TNbNj1_1UNdNh1\\1\\NbNb1_1dN]NX1d1lN\\NP1e1QO^Nj0b1XO`Nd0a1]O`N`0`1B^N=c1E[N;e1GYN9g1IXN6h1LVN4j17kMHV2<fMD[2;eME[2;fMDZ2<fMDZ2<fMC[2=eMC[2=fMBZ2>fMBZ2>fMBZ2>fMA[2?eMfKBQ2i2Y2fMeKHk1b2`2fMdKKj1_2b2fMcKLj1_2c2eMbKMk1^2c2eMbKMk1_2b2eMaKMm1^2b2eMaKMm1^2b2eMaKMl1_2c2dMaKMl1_2c2eM`KMl1^2d2eM`KMl1^2d2eM`KMl1^2d2eM`KMk1_2e2dMaKLj1`2e2eM`KKk1`2e2eM`KKk1`2e2eM`KKk1`2e2eM`KKj1a2f2eM_KJk1a2f2eM_KJk1b2e2dMaKIj1c2e2dMaKIj1c2e2dMaKIi1d2f2dM`KHj1d2f2dM`KHj1d2f2dM`KHj1d2f2dMZKNo1_2g2dMSK3V2Y2g2dMlJ:]2R2g2dMfJk0X2a1R3dM`Ja1n1k0b3dMZJW2c16S4l50001O00000O100000000000000000000O2O0O100O1dNYG_Lg8Z3cGbL^8]3fG_L[8a3gG]LY8b3kGZLV8f3mGWLS8i3PHSLQ8l3b100O100O10000O100O10000O010O11O000000000000001N101O1O2_FPLn7Q4PHPLP8Q4nGQLQ8Q4lGQLS8P4kGQLU8P4hGSLW8o3fGSLj4EcMY4aMSLl4DcMZ4_MTLa2ClNOS1\\4^MTL^2EQOKS1^4[MSL\\2IVOFS1_4XMULY2K[OBT1_4WMULU2N@^OT1`4UMVLQ21FYOT1a4SM_Lf1K3UOT1c4QM@l1lKS1e4oL@\\5a0bJ@^5a0`J@`5P5O1000O100000000000000000O10000000000O100O1O100O2O0O2N101N1O2O1N1O2O0O2N101N2N101N1O2O0ORFVKi9h4XFYKh9f4WF[Ki9e4VF]Kj9a4WF_Kh9b4WF_Kg9b4YF`Kd9b4\\F^Kb9d4]F^K`9c4`F^K_9c4aF^K]9b4cF_K]9a4bFaK]9^4cFcK]9]4cFdK\\9\\4cFeK]9Z4cFgK]9Y4cFhK[9X4eFiK[9W4dFkK[9U4eFkK[9T4eFnKZ9l401O00001OQMhF6X9IiF8V9HkF7U9JjF7U9j201O00001O001O001O001O0OfMPGfNP9_1QG\\Nn8k3010O01O0010O0001O010O001O010O00001O010O001OROWGeKj8Y4]GaKc8^4dGZK^8f4`G\\K`8c4]GaKd8]4YGgKg8X4VGlKj8U5010O000000001OUJaJU2_5iMSKg1m4WNeKY1[4eNiKZ1]OlK^3h2YMZ1VO]LU3X2hMY1POXLb3_2`MW1kNVLl3b2\\MW1dNULU4d2YMU1_NVL\\4e2WMS1YNYLc4c2WMoMfMX2=UMj4d2TMbMVNT2BhM6NQ5d2QM_M_NQ2YOnM3MY5d2lL]MgNo1QOSN0M_5c2jL\\MnNj1jNZNLLf5d2fLYMVOh1bNbNe5]2^NkNmKcNj5a2`NfNfKfNm5d2ZOiMiJBn5e2[OfMhJEm5e2^OcMeJGn5e2AaMg0_2\\O^Md0b2_O\\M`00hI^2\\<0000000000O1000000000000000000O11O1O1O001O1O1O1O001O1O1O1O010O1O2N3M2N3M2SEmLc5ObMW3`LSMS:o2cEYM]:f3O1O001O1O0VLeKoL\\4Q3fKlL[4W3eKdL\\4_3dK\\L_4g3`KULa4o3^KlKe4W4[KdKg4^4YK]Ki4g4VKUKl4n4TKlJo4W5QKdJP5`5PK[JR5g5PKRJR5R6nJiIT5\\6kJ]IX5T81N3N1O2M3N1N3JZJoFh5o85O10O00001O0O1100O00100O00nNRGTLm8m3UGfKODl8e4aG[KFMi8h4RHVKm7i4WHUKi7h4X1N2N3OQH]Kb5c4`J]K^5c4`2O0000YKPL`No3f1aKdN`4m500010M2100001O0000000QJ\\Kb1d4aN_KY1a4jNbKP1^4TOdKf0\\4BbK8^40[KNg4U5001iF[KQ8e4nGdKj7\\4THgKk7Z4SHgKm7[4oGhKP8Z4lGiKY4ZOlNo4fLkK[4XOoNQ5^LkKa4TOSOX6j0gIYOZ6d0eI_O[6c41TLeIQO\\6n0eIQO[6n0fIRO[6m0eISO[6m0eISO\\6k0eIUO\\6i0eIWO[6g0hIXOY6f0hIZOY6c0iI]OX61^IcL>\\3U6ARJiLMf3R6YOmJg0T5QOTKn0m4iN[KW1e4fN^KZ1d4aN_K_1b4UNiKk1Y4kMoKU2R4fMRLZ2P4^MWLa2j3PMYI^On2b3k3fLjLZ3W3\\LRMd3U70001O0000000000001O00000000001O00^F", "size": [480, 640]}, "area": 120338.0, "iscrowd": 0, "image_id": 10176, "bbox": [13.0, 77.0, 627.0, 403.0], "category_id": 183, "id": 10012404}, {"segmentation": {"counts": "0k9k51O00000000<DO10000N2000000O1000000O100O100001OO10000001O1O001OO11OO100O11O000`JnE\\5U:1O100O100O11O00000000O100O100O100O13M00O1O11O00O1N21O0000O1000000001O002N0000001O0000000000O100O10000O1N21O00O1001O00000000000000002N1O1O001O1O0000001OO1cNaJcH_5Z7fJdHZ5Z7iJeHW5Y7mJfHR5W7RKhHn4V7UKhHl4V7XKgHi4V7[KiHe4U7_KiHa4U7bKjH^4T7eKkH[4T7hKjHX4U7jKjHV4U7lKjHT4U7oKiHQ4V7QLiHo3U7ULiHk3V7WLiHi3V7YLiHg3V7\\LhHd3W7^LhHb3W7aLgH_3W7dLhH\\3W7fLcHjMMa5_7hLbHgMOa5^7jLbHeMOb5^7oLaHQ3^7d2N2O1O1O1O1O1O1N2O1O1O1O1O1N2O1O1O1O1O1N2O1O1O1O1O1O1N2O1O1O1O100O100O100O100O100O100O100O100O100O100O100O100O100O100O10000O100O100O100gMTGgMm8W2XGfMh8Y2[GdMf8[2^GbMb8\\2cG`M^8_2eG_M[8_2jG]MW8b2lG\\MT8b2QHZMP8e2SHYMm7f2WHVMj7h2ZHVMf7i2^HSMc7k2aHSM_7l2f20000001O000000001O000000001O0000001O000000001O0000001O1O001O1O001O1O001O1O001O1O001O1O001O1O001_EaLi8`3]FZMb9g2dERN\\:e3O001O001O001O001O001O001O001O001O001O001O001O001O001O001O001O001O001O001O001O001O001O001O001O001O1O2N2N2N2N2N2NQMjF_OU9=oFEo88UGEm87WGHj87WGGk89UGFl89UGEm8;SGDn8;SGCo8=QGBP9=QGAQ9?oF@R9?oF@R9`0nF@R9`0nF_OS9`0nF_OS9a0PG\\OP9c0VGWOk8h0[GROf8m0`GnN`8Q1gGhNZ8W1mGbNT8]1RH^Nn7a1WHZNj7e1[HVNf7h1_HUNa7j1dHQN]7n1gHnMZ7P2kHmMU7R2mHlMT7S2mHlMT7R2nHnMR7Q2PImMQ7S2oHlMR7T2nHlMR7S2PIkMQ7U2oHjMR7V2nHjMR7V2nHiMS7W2nHgMS7Y2mHgMS7X2nHgMS7Y2U3000000001O00001O00001O00001O00001O00001O001O1O001O1O001O001O1O001O001O1O001O0]DWMY:k2cE[MY:l2^EZM^:l2ZE[Ma:k2WEXMh:n2PEUMo:k3N1O1O2N1O1O0000001O00001O00001O0000001O00001O00000000001O000000000000001O0000000000001O0000001O0000001O000000001O0000001O001O00001O001O001O001O1O1O1O1O1O2N1O1O1O1O1O1O1O2N1O1O1O1O2N2N1O2N2N1O2N2N1O2N1O2N1O2N1O2N1O1O2N1O1O1O2N1O1O1O1O1O1O1O1O2N1O2N1O1O2N1O1O2N1O2eHiHG@I0P6h7^JVIBQOQ6k7ZJUIEoNR6m7WJUIGnNR6o7TJSIKmNR6Q8QJWIo5k6nIVIR6W80O11O0000002N1O002NO1N23MO1O100O1IoIhFR6X9oIfFR6Z9nIfFR6Y96FgIoF021NY6Q9900001OO10000000000000000000000O1000000000000O1N[ITGd6l8]ITGb6l841O1O0[IRGb6n8^IRGb6n8^IRGb6Q900000002N000000O11O0000O1O10cInFV6R9jInFV6R9jInFV6T9hIlFX6T9hIlFX6T9hIlFX6U9gIlFX6S9iImFW6_9JO100000000", "size": [502, 640]}, "area": 136803.0, "iscrowd": 0, "image_id": 10179, "bbox": [0.0, 0.0, 640.0, 328.0], "category_id": 99, "id": 10012405}, {"segmentation": {"counts": "hZT41f?1O00Oe?0[@0e?0[@0O010O010O10O10O10O0100O01000O010O010O10O01000O0100O010O10O10ZKOfI1]6NbI2c72SGOj7MmE;U2GP8NgEc0S2@V8NbEj0T2XO[8N]EQ1S2QOa8OXEX1R2iNg8ORE`1S2aNk80nDg1Q2YNS9S2iFmMW9X2cFiM^9U2cFjM^9U2bFlM^9S2cFmM^9R2bFmM_9R2aFoM_9Q2aFnM`9S2^FnMc9P2^FPNb9o1^FQNc9o1]FQNd9n1[FSNe9l1\\FSNe9m1[FSNo9c1PF^NU:Z1PDaNl19U:o0oChNm1?U:b0oEAR:3VFNm9MTF2P:IQF7T:DlE;Z:@gE>i<O101O0O110O00001O01OO2N001O1O7IiYY4", "size": [502, 640]}, "area": 2076.0, "iscrowd": 0, "image_id": 10179, "bbox": [269.0, 312.0, 90.0, 166.0], "category_id": 105, "id": 10012406}, {"segmentation": {"counts": "k9k5l9O00000000<DO10000N2000000O1000000O100O100001OO10000001O1O001OO11OO100O11O000`JnE\\5U:1O100O100O11O00000000O100O100O100O13M00O1O11O00O1N21O0000O1000000001O002N0000001O0000000000O100O10000O1N21O00O1001O00000000000000002N1O1O001O1O0000001OO10000001O00O1O100000000000000000000000000000000K`JSFa5m9_JSFa5m9_JRFb5m9`JRF`5o93001O001O001O001O001O001O001O001O001O001O001O001O001O001eMiEUOW:d0SFYOn9`0\\F\\Od9>fF^O[9;nFBR97YGEh84aGI_81kGKV8NTHNl7K^H2c7GgH5Y7HnH4S7IRI4n6IYI3h6J]I3c6JdI2]6KiI1W6LnI2S6KnI6i:0O1O10000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001O0000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001O00000000000000000000000000000000000000000000000000000000YOKmA5S>OiA1W>3eAMZ>9aAG^>>^ABa>j00000O11O000000001OO1O1O1O1O1GQOjAP1V>800O10000O10000O10000O100O100O1M3N2M3N2M3N2M36J8H5K5Km0SO=C0000000000000000000000000000O100000000000000000000000000000000000000000000000000000000000000000000O100000000000000000000000000000000000000000000000000000000000000000000O10000000000000000000000000000000000000000000000000000000000000000O1O1O1O100O1G9XL\\OmGU1R8lN]Ge1d8ZNQGQ2X;2TNnMRFT2n9oMkEU2T:PNcEU2]:b100O100001O1O1O1O1O001O00000000001O000000000000O100O100O100O100iMQLfF7c1i3g7PLeF;a1e3j7PLdF`0]1a3o7oKdFd0Y1]3R8PLdFg0V1Z3Z8iLbGX3^8jL`GV3`8kL^GV3a8mL\\GT3e8lLYGU3g8oLTGR3l8RMoFo2Q9TMkFm2W9SMfFn2[9TMaFm2_9UM^Fl2d9b1O1N23MO1O100O1IoIhFR6X9oIfFR6Z9nIfFR6Y96FgIoF021NY6Q9900001OO10000000000000000000000O1000000000000O1N[ITGd6l8]ITGb6l841O1O0[IRGb6n8^IRGb6n8^IRGb6Q900000002N000000O11O0000O1O10cInFV6R9jInFV6R9jInFV6T9hIlFX6T9hIlFX6T9hIlFX6U9gIlFX6S9iImFW6_9JO100000000", "size": [502, 640]}, "area": 58794.0, "iscrowd": 0, "image_id": 10179, "bbox": [0.0, 286.0, 640.0, 216.0], "category_id": 167, "id": 10012407}, {"segmentation": {"counts": "Wdi11b?5L3M4K4M3M4K4M4L3M3N3M2N2N3M2M4M2N2N3M2N3L3N2N3M2N3M2M2O2N1O2N1O2N1N3N1O2N1O2N1N3N1O2N1O2N1N3N1O2N1O2N1O2M2O2WGWLb5j3WJ`Le5b3TJhLg5Y3SJQMi5P3PJYMl5h2mIcMo5^2jIkMR6V2hITNT6m1eI]NV6d1cIfNZ6[1_IoN\\6R1aIUO[6l0bIYOZ6h0cI_OY6b0dICX6>eIIW68fINU63hI1W60fI1Z6f40O0010O100O100O100O2O0O10000O100O100O100gM^G]Mc8a2bG\\M^8c2eGZM\\8e2hGXMX8f2mGVMT8i2oGUMQ8i2THSMm7l2VHRMj7l2[HPMf7o2]HoLc7P3aHlL`7R3dHlL\\7S3hHiLY7U3kHiLU7V3f20000001O000000001O000000001O0000001O000000001O0000001O1O001O1O001O1O001O1O001O1O001\\OZLVEg3j:ZLTEf3m:[LQEf3o:YLQEh3o:WLQEi3n:WLSEj3j0cL[8CkFk3O]MT9iNmFa5S9^JoFb5o8_JQGa5o8_JQGb5m8^JTGb5l8^JTGc5k8\\JVGd5i8]JWGd5h8\\JXGd5g8\\JZGe5e8[J[Ge5d8\\J\\Ge5c8ZJ^Gf5b8ZJ^Gg5`8ZJ`Gf5`8YJaGh5]8YJcGg5]8YJcGh5[8XJfGh5Z8XJfGi5X8WJiGi5W8WJiGj5V8VJjGj5U8VJlGk5S8UJmGk5R8VJnGk5Q8TJPHl5o7UJQHl5n7TJRHl5n7SJSHn5k7SJUHm5k7SJUHn5i7RJXHn5h7RJXHo5R3oId02[Lo5S3nIUM0V23bMP6U4UJSNLhMo5T3mIlL?]1A\\N38Q6S3lIgLg0U2\\OQNQ6S3mIcLn0\\1QOfN48Q6S3lI_LU1\\1jNjN39R6R3mIZL]1[1aNPO49R6R3lIVLd1[1ZNTO49R6R3mIQLl1Z1QNZO4:S6Q3QLWMhM^O4:S6Q3ULRMeMC3:T6P3YLnL_MH4;T6P3\\LhL]MM3;U6o2`LdLXM14<T6o2dL_LTM73<V6n2gLZLPM;4=W6l2iLWLlLa03=Z6i2kLULhLd04>Z6h2mLjLiL>\\6g2iLlLjL>_6d2gLmLkL?`6b2eLoLkL?b6a2aLPMmL`0c3lNWOb39RMmL`0_3PO\\Og3NhLZM?Y3UO]Ok3JbLdM:Q3]O@m3E]LoM4k2C_OT4^OYL[NLh2G^OZ4fNhLWOSOd2L]O`4ZNjLJeN_21\\Oe4TNiL4]N[27ZOj4PNdL?WNW2;YOP5iMaLV4`NoMU5eM^L[4]NoMa7P2`HoMa7Q2_HoMb7P2^HoMc7P2^HoMc7Q2`HlMa7R2eHhM\\7W2jHcMW7\\2oH_MR7_2UIZMl6e2[ITMf6k2`IPM`6o2eIlL[6T3jIgLW6W3nIfLR6Y3SJbLm5^3WJ^Lj5`3[J]Le5b3\\J]Ld5c3^J[Lc5c3_J]La5b3`J]L`5d3aJZL`5f3`JZL_5f3bJYL_5g3bJWL_5i3aJWL^5j3bJUL_5k3aJTL`5l3aJSL^5m3cJRL\\5P4R33N2M3N2M4M2M38I7H5K5Ll0SO<D01O00001O00001O001O1O001O1O001O001O1O001O001O1O001O0aDUMS:P3fEVMV:P3bEVMZ:Q3]ETM`:R3XEQMf:R4N1O1O1O2N1O1O0000001O00001O00001O0000001O00001O00000000001O0000000O1000001O0000000000001O0000001O0000001O000000001O0000001O001O00001O001N101O001O1O1O1QJYJm1h5QN^Jj1c5TNdJf1^5WNhJd1Y5ZNkJc1V5[NmJc1T5[NPKb1Q5]NQKa1P5]NTK`1m4_NUK_1l4`NWK]1k4`NXK]1j4bNXK[1j4dNZKW1h4hN[KT1g4jN]KS1e4kN_KP1d4nNYKQMXN^3P7@dJXM]Nd2b72nI^M^Nm1Z8c0UIdM`Ng1^8d0nH0V7NgH[NiN`0d8S1_H`NQO5f8Z1VHdNWOJj8b1jGfNg9\\1RFhNP:Y1jEiNX:Z32O001O001O000O1O1O2N100O1O1O1O1O101N1O1N3N1N2O2M2O1N3N1fLaD6OQ1c;fNeEV1]:hNhET1Z:iNkER1W:lNmEQ1U:lNPFo0R:oNPFP1R:mNQFP1\\<N3M3M5K5K3M4L4L3MP^S1", "size": [502, 640]}, "area": 121372.0, "iscrowd": 0, "image_id": 10179, "bbox": [117.0, 74.0, 450.0, 422.0], "category_id": 183, "id": 10012408}, {"segmentation": {"counts": "`ol21`?U1jNe1\\Ne0[O1O00O1000001O000O100000000O10000000000O10000000000O101O000000000O1000000000000O1000000000nFXNjLcN0Nh10i3W3eMWOUM\\NP5\\2lMc0P2UNjJWOV3o2jLmKg4`1nK\\Oa2\\3`1jLfLAj1e3`1aLXMAX1n3`1WLlMBc0W4a1nK_1R4P500000000000000O1000000O10000000000001O0000001O0000001O0000001O00001OO100O1O1O1N2O1O1O1O1O1O1N2O1O1O1O1O1O1O100O100O1O100O100O1O100O1O100O100O1O10000000000O100000000001O00000000001O0000O100O100O10000O100000000000000001O00000000001O0000000000001O001O001O1O1O001O1O1O001O1O1O001O1O2N1O1O00O100O100O1O1O100O11O001O00000000000000000000O1L4L4O100O100000000O100000000O1000000000000O10000000000O1000000000000O1001O00002N2N5K0nHQMb1o2]NUM`1j2aNXM]1h2cNaMT1_2kNiMn0W2SOkM_KfNm4_3DmMWKlNS5W3G[N8e1H[N8e1JYN6g1MVN3j1MVN3i1NWN2i1NWN2i1NWN2h10WN0i10WN0i10WN0i10XNOh11XNOh11XNOg12YNNe14[NLc16]NJb17^NIb17^NIb16_NJb15^NKb15^NKb15^NKb15^NKc14]NLc14]NLc14]NLc14]NLd13\\NMd13\\NMd13\\NMd13\\NMe12[NNe12[NNe11\\NOe10[N0e1O\\N1d1N]N2d1K^N5b1J_N6a1HaN8`1FaN:_1DcNT1\\NUJh1f4NY2^OeMd0\\2[O^M^NdKY2T7SO[Mn0j2TNRNi1P2TNSNj1m7M3M3N1N2N000000000000000001O0[EZN_7g1YH`Ng7`1RHgNn7Z1jGmNV8T1bGSO^8m0[GZOe8g0SGAl8?mFHS99eFN[95[F2e90RF7n9KiE<W:_20001O0O10000000SNiE`NV:_1YFRNf9o1`FgMo8XOdFS3c0ZMT9ATFV3f;iLSDZ3o;32M3N1O1N2O001N2O001O0O101O0O101O0O101O000000000000O100000000O100O1O1001QM`Ck2c<0000001O0001O0000000000001O2N001[MYC^2h<aMYC_2h<_MXCa2l<000`E_MP8a2lGcMT8]2iGfMW8Z2hGgMX8Y2gGhMY8X2gGhMX8Z2gGfMZ8Y2fGgMY8Z2gGgMX8Y2gGhMY8X2gGhMY8X2gGhMY8X2gGhMY8X2gGhMY8X2fGiMZ8W2fGjMY8V2gGjMY8V2gGjMY8V2gGjMY8W2eGjM[8V2eGjM[8V2eGjM[8V2eGjM[8V2dGlM[8T2eGlM[8T2eGlM[8T2eGlM[8U2cGlM]8V2aGjM_8Y2]GhMF", "size": [497, 640]}, "area": 52848.0, "iscrowd": 0, "image_id": 10192, "bbox": [191.0, 0.0, 449.0, 452.0], "category_id": 98, "id": 10012409}, {"segmentation": {"counts": "\\b`71`?000000000000000000000XWP2", "size": [497, 640]}, "area": 12.0, "iscrowd": 0, "image_id": 10192, "bbox": [495.0, 333.0, 12.0, 1.0], "category_id": 123, "id": 10012410}, {"segmentation": {"counts": "RiS6:c>c0G:00O100000000000001O2O5J10O001OO100000O02O1N2O1O002M01M4L3N3N2N1MkAjNU>V1300000O10000O100lBhNo;X1nCkNQ<V1nClNR<R1mCQOR<o0mCROT<l0lCVOS<i0oCXOP<e0RD^Oe;f0]D\\Oa;e0_D^O\\;d0cD@Y;a0fDNk:2UESO]ON_;o0UEQO_ON\\;Q1UEoNBOY;R1UEnNDNX;T1UEkNG0S;U1VEjNI0Q;V1WEhNJ2o:T1XEjNK0P;7fDK`0ML2l:5YGMV<N1O1Oh\\30XcL0000000im[2", "size": [497, 640]}, "area": 2994.0, "iscrowd": 0, "image_id": 10192, "bbox": [403.0, 155.0, 80.0, 122.0], "category_id": 143, "id": 10012411}, {"segmentation": {"counts": "aRc51`?1O0O100O010O010O01000O010O010O0100O0100O010O0100O010O0100O010O0100O010O0100O0100O010O010O01000O010O010O0100O0100O010O010O01000O010O010O010O10O10O010O010O010O10O10O010O010O10O010O10O010O010O10O10O010O010O10O010O10OO20O2O0O_W]2", "size": [497, 640]}, "area": 289.0, "iscrowd": 0, "image_id": 10192, "bbox": [368.0, 433.0, 112.0, 50.0], "category_id": 165, "id": 10012412}, {"segmentation": {"counts": "S;`3Q<POP10O10O100000000O100000000O01O1O1M3000O010000000O10000000O010000000000O101O00O10O2O0000000O10000000000O1000O1O1O0100000000O10O10000000O10000000000O01000000000O10000000O10O100000000O10000000000O010dMmBZ2V=O00000O10000000000O1000O100000O10000000000O1000gNjMWEV2i:kMVEU2j:kMVEU2j:kMUEU2k:mMTES2l:mMTES2l:mMTES2m:lMSET2i:PNWEo1a1]N\\M^O`87cHn1a1bN[M[O1Ng22eM0[4:ZLi1c1DmN^Og2^OeLa1g1`3d0PKbM`1j1`7WN`Hi1i7nMVHS2k7lMUHT2k7kMVHU2k7jMUHV2k7jMUHV2k7jMUHV2k7jMTHW2l7iMTHW2l7iMTHW2l7iMTHW2l7iMTHW2l7iMTHW2k7jMTHW2k7jMUHV2k7jMUHV2k7jMUHV2k7jMUHV2j7kMVHU2k7jMTHW2n7gMRHY2o7fMQHZ2P8cMRH]2o7^MUHb2l7YMXHg2X:100000000O10000000000O10000000000gDaM_9`2_FdM_9\\2_FiM^9W2`FmM^9S2`FQN^9o1`FTN_9l1`FVN^9k1aFXN]9h1bFZN]9f1bF\\N]9d1cF]N\\9c1dF_NZ9a1eF`Nj5bNiLo2]M`Ni5FeKj1bN`Nh5[1RJ56`Nh5P2]I@k0aNg5P5YJPKg5P5YJQKf5o4ZJQKf5o4ZJQKf5o4ZJPKg5P5ZJoJg5P5YJoJh5Q5YJnJg5R5YJmJh5S5YJkJh5U5YJjJg5V5ZJhJg5X5ZJfJg5Z5ZJeJf5[5[JcJf5]5[JaJf5_5[J^Jg5b5[JZJg5f5[JUJh5k5[JoIh5Q6_JcIf5]6g10000000001O000000000000ZMjIgI4j1R6_4^J]Kb5b4bJ[K^5e4eJXK[5h4fJWKZ5i4gJVKY5j4iJTKW5l4jJSKV5l4lJSKT5i4RKUKn4j4TKUK\\6gNTH0Nh10l0c1dNZ7SOmGS2j0iNd8S1]GlNn8kN^Ej1e1ZOR9e0nF[OR9f0nFYOR9g0UGROk8n0dGcN\\8]1l200001OG_BbNa=^1`BaN`=`1aB^N_=b1cB\\N\\=e181O000O101OL4O1O1O1O2N11O1O4M2M4L3fDaN\\8c1ZGVNT9S2PF^NQ:f1bEaN`:W31N3\\LhDl2Y;nLREa2Z;ZMnD`2V;[MPEa2Q;_MSE\\2T<L4L4M3M3N1N2N2O1N2O2M6K4K4M2N3L4M2N3L3N1O2N2M3N2N2N1N3N2N_XU1ObgjN1N3N1N3N1N3M2N2O2M203L5ZCUO]:P1nD_OR;f0hD[OX;f0gDZOY;f0gDZOY;e0hD[OX;e0hD[OX;e0hD[OX;e0hDZOY;f0gDZOY;f0gDZOY;f0gDZOX;g0iDZOiN_OU<[1TEXOg:m0^EmNaNDm;`1cEkN`NFl;`1cEnN\\:S1dEnN[:S1dEmN\\:R1eEoNZ:Q1fEoNZ:Q1fEoNY:R1fEPOY:o0iEPOW:P1jEPOU:o0mEPOS:P1nEPOP:Q1PFoNP:Q1RFmNn9R1RFoNn9Q1oESOP:n0lEUOT:k0jEWOV:j0hEXOV:j0hEWOX:i0eE[NjNl0a;i0eE[OZ:e0fE[OZ:e0fE[OZ:e0fE\\OX:d0iE\\OW:d0iE\\OW:d0iE]OV:b0kE^OU:b0kE^OU:b0kE[OX:d0iE[OW:g0gEXO[:h0eEWO]:i0aEWO`:i0_EYO`:h0]EZOe:>_EDc::^EFf:4\\EKd:5]EJc:6]EJb:7^EJa:6_EJa:6_EJa:6_EK`:5`EK`:5`EL_:5_E\\OoNCb;T1`DZOKL<AY;Y1_D0W<c110100O01000O1001O2M2M2N2O010001O0001O01O0000001O0000001N1OhC`Ma;^2[DPM1f0d;T301O010O0010O01^LkL]HYOh1l3k5PNaIm1b6TN]Ii1f6]NTI`1o6eNSHS2n7oMoGo1T8d23O1DaGcIb8]69000000Oc0lIcFP5P:QKmE^4d:O1O0aNeEYMDKh:j2ZFUMg9DcEg2h0dMS:[2oEdMR:7[Em0e0kNQ:6UHGl79g3N2N^DIl7N\\H4`;Jg@5[?OILm@8R?4O04M00N2N200O1O13MSAK_>4^A1a>`0O0010O01O2M11NO0HUOlAm0S>90O01000O10000O10001N2ON110003L10O12UOeA;]>CdA=^>AbA?_>^OcAb0g>O1O001O00002Bn@:Q?EPA;S?0\\BFaNO]=;QDJo;6QDJo;6PDKP<6oCJQ<6nCKQ<7mCJS<7kCKT<5kCLU<5iCLW<4hCMX<4fCMV<DhB`0P1MV<:hCGW<;gCGY<:dCIW<;gCF[;ZOjDQ1GIAVOf;1_D07R1OJDTOd;2bDO5T1MKFPOd;4cDN5f2X;]MaDN6f2Y;]M_DN8e2W;kMhDU2Y;jMfDW2Y;jMfDW2Z;iMeDX2Y;jMfDV2];iM`DZ2`;eM_D[2b;j01OO1001O0000000000O10000O11O000000001O1OlJ", "size": [497, 640]}, "area": 68658.0, "iscrowd": 0, "image_id": 10192, "bbox": [0.0, 0.0, 640.0, 467.0], "category_id": 172, "id": 10012413}, {"segmentation": {"counts": "0S;^40000O10000000000000000000000O1O1O1M30000O10000000000000000O1000000000000001O00O1001O000000000000000000000000O1O1O1000000000000O100000000000000000000O1000000000000000000O1000000000000000000000000O10TK]Ej4f:O0000000000000000000000O10000000000000000000000gNVKkGj4U8WKjGi4V8WKjGi4V8WKiGj4V8XKiGh4W8XKiGh4W8XKiGh4X8WKhGi4T8[KlGX5mNbJ`87cH\\5lN_J1Ng22eM0[4:ZLY6`0bJg2^OeLn9[2SFbMm=19G1O0O2O0000000000000000O1O1000000O11O2N1O1M3K5Kem]5?]caJDQ>o0PBnN_=c1O1O0`EfNYMDl8g1iI[OV6f0cFdNg2h0e6R1ZIPOe6Q1VG\\Nm0e0l7P1UGV1GbLj8X2_G^1a8cN]G^1c8cN[GW1I`Ll8Y2RG_14WLj8W6PGiIV9Z6OIkFnIY9Q64O13M00@fFgJ\\9Y5dFeJ^9Z5=1O1OM3O1001O000000O1001O1O00002N1O000000000000001O1ON200003M00AdFfJ_9Y5=00J60000000000O11O1O1fEkJ00S:_5L001O1O3MaJVFY5W:I0O10000O101oJeEj4\\:RKiEk4^:10O01OF^EgKa:Y4`EgK`:W4bEiK]:V4eEiK]:V4dEhK]:X4dEfKU:LiE]4c:fK]E[4Z:bKbE0061Y4]:a00N22N1O001OO1001O0000000000O10000O11O000000001O1O", "size": [497, 640]}, "area": 66911.0, "iscrowd": 0, "image_id": 10192, "bbox": [0.0, 0.0, 640.0, 355.0], "category_id": 181, "id": 10012414}, {"segmentation": {"counts": "o>b0c=\\10000O100000000O100000000O10000000000O100000000O100000000O10000000000O100000000O100000000O10000000000O100000000O100000000O10000000000O10000000000O10000000000O10000000000O100000000O10000000000O10000000000O10000000000O10000000000O10000000000O1000O100000O100000000O10000000000O10000000000O10000000000O10000000000O10000000000O100000000O10000000000O10000000000gDbM^9_2`FeM^9[2`FjM]9V2aFnM]9R2aFRN]9n1aFUN^9k1aFWN]9j1bFYN\\9g1cF[N\\9e1cF]N\\9c1dF^N[9b1eF`NY9`1fFaNY9`1gF`NY9`1gFaNX9_1hFaNX9_1hFaNX9_1hFbNW9^1iFbNV9_1jFaNV9_1jFaNV9_1jF`NW9`1jF_NV9a1jF_NV9a1kF]NU9d1kF[NV9e1kFYNV9g1kFXNU9h1lFVNU9j1lFTNT9m1mFRNS9n1nFPNS9P2nFmMT9S2nFiMT9W2nFeMT9[2nF`MT9a2nFZMU9f2k10000000O1000000000000O100QJTMfI4S5h2W1hMeNX2Z1lMcNT2]1oM`NQ2`1PN_NP2a1QN]NP2c1RN[Nn1e1SNZNm1e1UNZNk1b1[N\\Ne1c1]N\\Nc1d1^N[Nb1e1_NYNb1g1_NPM\\NPNU3P5_NcLQOUN`2X5`NTLG[Ni1b5`NiK9\\NW1k5gNXKf0dNb0T6WO`Ji3`5T300001OGjFWJV9i5kFVJU9k5lFSJl5NVMo5PMQJj50VMo5R3QJnLP6P3QJPMo5P3QJPMo5P3QJPMo5P3PJQMQ6m2PJSMl5Q3TJoLk5R3UJnLj5S3VJmLi5T3VJmLi5S3XJmLg5T3YJlLh5S3XJmLj5Q3VJoLn5m2QJTMR6i2nIWMV6e2jI[MY6nN`Jb0WO`0b6^NiIfN0W2Ee0g6fMVJk5n5hIYJZ6^71N2]LUI_Nl6[1_ITNm6g1[ISNh6i1^ISNd6l1`IoMb6P2aIlMa6S2bIiM`6V2cIgM^6X2eIeM\\6Y2hIeMX6Z2jIdMW6[2kIcMV6\\2lIcMT6\\2nIbMS6]2oIbMQ6]2RJ`Mo5^2XJ]Mh5b2]JYMd5f2`JWM`5h2cJVM]5i2gJSMZ5l2jJQMV5n2mJPMS5o2QKmLP5S3RKkLn4T3TKkLl4U3VKiLj4V3YKgLh4X3[KfLe4Z3]KdLc4[3`KcL`4]3aKaL`4^3cK`L]4_3fK_LZ4a3k3O1O100O100O1O1000000000hLcL\\J\\3m800000iLdLYJ\\3g5dLYJ\\3g5dLYJ]3f5cLZJ]3f5cLZJ]3g5cLXJ]3h5cLXJ]3h5cLXJ]3n81jLbLWJ^3i5bLWJ^3n8OmLcLTJ]3l5cLTJ\\3m5dLSJ\\3n5dLQJ[3P6eLPJ[3P6eLPJ[3Q6eLnIZ3S6fLmIZ3S6fLmIZ3T6fLkIZ3U6fLkIZ3V6fLiIZ3W90JfLUDZ3U9gL`IY3U9fLWD[3[:fLQGZ3S9fLaFOQO[3^9eL^G08[3Z8eLaH0RN[3m;0FeL^D\\3\\9dL_H0UN\\3`;dLaD\\3_;dLaD\\3^;dLcD\\3];dLcD\\3Z;fLgDZ3X;gLhDZ3W;fLiDZ3V;fLkD[3T;eLlD[3T;dLmD]3Q;dLoD]3P;bLQE_3m:bLSE^3m:aLTE`3j:aLVE`3i:`LWEa3h:^LYEb3f:_LZEb3e:]L\\Ed3b:]L^Ed3a:[L`Ee3_:\\LaEe3^:ZLcEh3Z:YLfEh3Y:XLgEi3X:VLiEl3T:ULlEl3S:SLnEo3o9RLQFo3n9PLSFR4j9oKVFR4Z8kK]H2ZOU4f9kKZFV4d9kK\\FV4V8gK^H2]OY4S8eK_H3^OZ4Q8cKaH2_O`4^1_KS40nJ2Ad4m0gKd4BnJ2Bj4N[Lb5iNmJ3Cn4YOfLW6ZNlJ1El6^5SImJ1El6^5SIlJ2Fl6]5SIlJ0Hm6\\5SIkJ1Im6[5RIlJ0Jn6Z5SIjJ0Lm6Z5SIjJOMo6Y5QIjJ0Mo6Y5RIhJO0P7W5QIiJO0P7W5QIhJ01o6W5RIgJN3Q7U5QIgJO4P7U5PIhJO4R7S5oHiJO4R7S5oHhJO6S7Q5nHiJO6S7Q5mHiJ16R7Q5mHiJ07T7o4lHiJ18S7o4kHjJ18U7m4jHjJ29T7m4jHjJ1:V7k4iHkJ1:V7k4hHkJ2;V7g5jHYJV7f5kHZJV7e5iH\\JW7c5jH]JV7c5iH^JX7a5hH_JX7`5hHaJX7_5hHaJY7]5gHdJY7\\5gHdJY7[5hHeJY7Z5fHgJZ7Y5fHgJZ7X5fHiJ[7V5eHjJ[7U5eHlJ\\7S5dHmJ\\7R5dHoJ]7P5cHPK^7n4cHRK]7n4bHSK_7l4aHTKb7h4^HYKd7e4\\H[Kg7a4YH`Kk7\\4UHdKo7hNeH^4\\OkLR9S3nFmLb7lN^IU4POoLa7oN`In3oNTM_7QOnI<eNl1NgN^7SOQJ7mNd1DSO\\7VOSJ\\OgN84R2FTO[7XOdJDZNm1GXOZ7YOdJDQNS21POh6aN`Ij0d1Z2TN[Nd6jN^Ic0i1W2UN]Nb64XK_1VN]N`68XKZ1XN_N_69XKX1YN_N_6:WKV1ZNaN^6;WKQOYNm02G^6;WKROYNPO2a103^6:WKSOcN>H5]6;XKSOcN<H6]6;YKROdN:F:^69XKSOeN8F<_67VKUOfN7D>a65UKVOhN5B`0a65UKWOhN3Ab0b64UKWOjN2^Oc0b65VKVOkNo1o5kNVKVOmNm1m5mNVKVOoNk1k5oNVKVOQOi1i5QOVKVOSOg1h5ROUKWOUOe1f5TOUKWOWOc1d5VOUKXOXO`1c5WOVKYOYO^1a5YOVKYO[O\\1_5[OVKYO]OZ1]5]OVKYO_OX1[5_OVKYO]OZ1]5]OVKYOTOc1f5TOWKZOgNl1Q6oLoIU1Y13[NR2\\6dLSJW1V1h3e4oJZJX1Q1i3`5VLaJj3`5TLaJl3_5SLbJm3_5PLcJP4]5oKdJQ4]5lKeJT4[5kKfJU4[5hKgJX4Y5gKhJY4U5hKmJX4P5fKUKZ4h4lKUKT4j4PLSKP4j4ULTKk3i4[LTKe3k4_LRKa3b4SKbIb1j1Z3d4QMZKP3e4QMZKo2f4QMZKo2f4QMZKo2e4RM[Kn2e4RM[Kn2e4RM[Kn2e4RM[Kn2e4RM[Kn2e4RM[Kn2e4RM\\KP1VNgN^69\\Ki0]NnNW69]Ka0cNVOP69]K:jN]Oi59^K2POEb59_KJVOM[59_KC]O4T59`K[OD;l4:`KTOKb0e4:bKkN0k0^4:dKbN5T1W4:fKYN:]1P4:cNF\\1;dNE\\1;dNE\\1;eND[1<eND[1<eND[1<eND[1<eND[1<eND[1\\NlI`1l2aN`Nc1h4YNPJ_1i2TOPNV1i5eNZI2n29eMV1S6[NbI0g2e0YMV1_6SNoH74EW3f3f3UM`Lk2a3VM]Lj2f3UMWLl2h3WMVLi2i3YMVLg2h3\\MVLe2j3[MVLe2j3\\MULd2j3^MTLc2l3^MSLb2m3^MSLb2l3_MTLa2l3`MRLa2n3_MRLa2m3`MSL`2m3aMQL`2o3`MQL`2n3aMRL_2n3bMPL_2o3bMQL^2o3bMQL^2o3bMQL^2n3cMRL]2n3cMRL]2n3cMRL]2m3dMSL\\2m3dMSL\\2m3dMSL\\2m3dMSL\\2m3dMSL\\2m3dMSL\\2m3cMTL]2l3bMUL^2k3bMUL^2k3aMVL_2k3`MUL`2k3_MVLa2j3^MWLb2i3^MWLb2i3]MXLc2h3\\MYLd2h3[MYLd2g3[MZLe2g3YMZLg2g3WMZLi2f3VM[Lj2f3TM[Ll2e3SM\\Lm2e3QM\\Lo2e3oL\\LQ3e3mL\\LS3e3kL\\LU3f3hL[LX3f3eL\\L[3e3bL]L^3e3]L_Lb3g3VL[Lj3f7^N`DSO`;j0dDUO\\;h0gDXOY;e0jD[OV;e0jD[OV;e0jD[OV;e0jD[OW;c0jD]OV;c0jD]OV;c0kD\\OU;d0kD\\OU;d0kD\\OU;c0lD]OT;c0lD]OT;c0lD]OT;c0lD]OT;b0nD]OR;c0nD]OR;c0nD]OR;c0nD]OS;b0mD^OS;a0nD_OR;a0nD_OR;a0nD_OR;a0oD^OQ;a0PE_OP;a0PE_OP;a0PE_OQ;`0oD@T;;nDEU;6mDJ", "size": [497, 640]}, "area": 122983.0, "iscrowd": 0, "image_id": 10192, "bbox": [0.0, 28.0, 640.0, 469.0], "category_id": 183, "id": 10012415}, {"segmentation": {"counts": "bo63:NG4R=MnBR14nNT<h1iCWN01e;S2\\DlM^;V2`DmM\\;U2dDkMY;^2M200N4K4O1N3I6lEkL]9Y3bFhL@O08a9R3mFTMP9BbF\\3<UMn8Q3oFQMo8Q3PGoLn8U3oFkLP9Y3mFhLP9S4O2O0O00100N201N010`GSKQ8m4oGTKP8m4oGTKn7NgGP51QK34k7]5QHdJo7d50001O00000000001O0000000000001O00000000001O00000000001O001O001O1O001O1O001O001O1O001O00001O000000O100O100O11O00000000O11O1O000000002N1O001O0000001O000000000000000000O1000000001O000000001O0000001QIjIW6T7I4L4L3M4L3M3M30000M2O1N1O2O0O1`LdG_1^8^NdGb1\\8]NeGb1]8\\NcGe1]8ZNdGe1^8XNcGh1^8UNeGj1\\8VNdGj1`8QN`Go1a8PN`Go1`8QN`Gn1`8SNaGk1a8TN`Gk1a8UN^Gk1c8TN^Gk1c8SN^Gm1c8SN]Gl1d8RN^Gm1c8SN\\Gm1e8RN\\Gm1d8PN`Go1a8lMmFTOe0n2_8lMfGS2Z8lMiGR2X8lMjGS2W8kMlGS2U8kMnGS2S8lMnGT2Q8kMRHS2o7lMRHS2n7lMUHR2l7mMUHR2k7mMXHP2i7PNYHn1h7QNYHn1h7PNZHo1f7QN\\Hm1e7RN]Hl1c7TN_Hk1a7TNbHj1]7VNeHi1[7WNfHh1Y7XNjHf1U7ZNmHd1T7ZNoH>kM;U9WOSILVNn0h8UOmIi0R6WOmIj0S6VOnIi0R6WOmIj0T6UOkIl0U6UOnIg0R6YOkIj0U6WOkIh0U6XOlIg0T6YOkIh0U6YOkIf0VMROD11Of89iJ:dLBL3f81nJl0\\LkNc8:PKm0Q5SOnJi0^LoNc89oJh0`LmNa8<nJf0W5[OhJe0W5]OhJc0X5^OgJc0X5_OfJb0Y5_OfJb0Y5_OfJb0Y5]OhJd0V5\\OkJe0U5oNVKR1i81O2N1O2N1O2N1O1O0000O100O100OlCeNj;[1SDkNj;f1M3M3M3_OhMYEZ2e:jMWEW2g:`0O1N2O2M2O1M3O1N2N2O10O1O1O1O1O1O1N102NN2O10O2O3L010ON3O20OAjLWFX3i9jLlEN0_3S:70001O001eMZLaJd3^5hLWJY3h5iLUJX3l5gLTJZ3\\N[LR7=aJY3XNdLP73jJj3U5WLjJj3V5ULkJj3T5XLjJi3V5VLlJj3Q5YLnJg3i4bLXK]3h4cLXK]3h4`LgKU3X4iLkKWOcMm3c6hLPLW3Q4cL\\IAc2P4P4^LTLa3k3_LVLa3k3^LVLa3k3_LULa3h3aLVLa3j3`LVL_3T4WLlKi3W4TLjKl3W4QLjKo3Q7010L42MK60OO2L40O01O0O26JH701O03NO1O010O0011ON14MK410N2O1O1O1O1O100L4QGVKl8m4L4O2O0O2M22NM34ML3N3O0O21OL5M103I7J5M4RIjIn5[6jIfI_O1f6e6ZI[I09b6m6_IhH\\6d7JNQJdH^5P8M3N101M2N3N1M3N1N200O100M3O1O10000000O10O1000000OVJTKf2l4WMUKk2k4SMWKbM:\\4^4lM`KaM;`4U4gMhKcM<b4o3[MXMd2g2cMTM[2l2eMUM[2j2cMYM\\2g2bM[M^2e2aM\\M`2c2`M_M^2a2aM`M`2_2`MbM_2^2aMcM_2\\2\\MkMb2U2[MoMd2Q2[MQNe2n1ZMTN]NVMP4f4aMXN[NVMS4a4aMeN\\2[1bMjN[2V1eMmNY2R1eMoN\\2Q1dMoN\\2Q1cMPO]2P1bMPO_2P1`MQO_2P1aMoN`2Q1_MPOa2P1^MPOc2P1\\MQOd2o0\\MPOe2P1ZMQOe2P1ZMPOg2P1YMPOg2P1XMQOh2o0XMPOi2P1VMQOj2o0VMPOj2Q1UMPOk2P1UMPOk2P1UMoNl2Q1SMPOm2P1SMPOm2P1SMoNn2Q1RMoNm2R1SMmNn2S1QMnNo2R1QMnNo2R1QMnNn2S1RMlNo2T1PMmNP3S1PMmNo2T1QMlNo2T1QMkNo2V1QMjNo2V1PMkNo2V1QMjNo2V1QMjNo2V1QMiNo2X1PMiNP3W1PMiNo2X1QMhNo2X1QMhNo2X1QMhNn2Y1RMhNm2X1SMhNl2Y1TMgNl2Y1TMhNk2X1UMhNj2Y1VMhNi2X1WMhNh2Y1XMhNg2X1YMhNf2XLTMQ46Gf2UL]Mn3MNP4MPL3V4GkK9Y4BgK>_4\\OaKe0a4XO_Kh0b4WO_Kh0b4WO^Kj0c4TO]Kl0d4SO\\Kn0d4QO]Kn0d4QO\\Ko0f4oN[KQ1e4nN[KR1f4mNZKT1g4jNZKU1g4jNYKW1g4hNZKW1h4gNYKX1g4hNYKY1i4dNXK[1j4cNWK]1k4`NUKa1o4ZNRKf1T5SNmJk1T5UNlJj1T5WNmJh1T5WNmJa1dNaL`6n1mJ\\1gNfL\\6n1nJX1iNiLZ6o1nJV1jNiLY6Q2nJT1_5lNbJS1_5lNbJR1^5oNcJP1]5POdJn0^5QOcJm0^5SOcJl0]5TOdJj0[5XOfJf0Y5\\OiJ`0X5AiJ>V5ClJ9T5ImJ3V5MlJ0V5OlJMV53kJKW54kJIV57kJGW58jJGV59oJAR5?SK[Ok4h0[KPOg4P1_KhNd4W1_KcNe4\\1]K]Ni4b1XKWNn4i1SKmMW5R2Z3O001O1O0000000000001O0000002NNRNWDn1l;O1O1O1O1O13MO2M2OO0100000O1001OO100003MN22N0000O11OO100N21O0001N10000002N6J3M001O00001O00001O001O001O001O100O1O1O1O2IYCVOi<f0YCZOi<b0YC^Oj<=XCCl<5XCK\\=0000000000000000000000001O000000000000", "size": [443, 640]}, "area": 137173.0, "iscrowd": 0, "image_id": 10196, "bbox": [16.0, 0.0, 624.0, 443.0], "category_id": 122, "id": 10012416}, {"segmentation": {"counts": "0lQ1\\2bYO\\2eM1N1O2N101N1O101N1O2O0O1ULnJ_KNG4U3R5`1kJ[KR14nNZ2V5S2bL`KWN01n1W5^2lLeKlMh1X5b2nLdKmMh1V5a2oLgKkMh1V5^2XMaMh2]2ZMcMg2\\2YMdMg2Z2]MdMd2X2`MgM`2Y2aMfM_2Y2dMeM]2U2iMkLkL<\\5f2mMmLhL@O08`0U5R3mMlLTM2o4o2cJaL\\3<UM2l4o2UNlLQM4k4n2VNmLoL5k4U1_KVOk2]OkL9l4P1aKXOl2\\OhL<k4m0dKXOd3Ki0h0gK\\Ob3Kg0f0jK_O_3Kg0b0nKB[3Mh0=PLEY3Ng0:SLHV3Nh05VLKT30f02YLNR3Of0O[L1o21f0K^L4l2OSKDc58_L4k20TKDc55`L6j20TKEb53bL5jMHP51QK3j5Mo1OiWc00Wn\\O0O001O1N101N101N200O2N2N1O2SFAY6b0cI@^6a0^IAc6?ZIDe6=YIDg6?TICl6>QIDo6>mHDU7<gHFZ7;bHH]79`HIa78[HJe78WHJi7:RHGn7>kGDV8a0`GC`8b0QGIn8=cFL]99TF1l9o1000001O000O1000000010O000000001O01O0001]L^Fk2`:I4L4M2M4L3M3M3NiGhMl4U2TKnMl4P2SKPNo4n1QKSNP5l1oJTNR5l1mJTNU5j1kJWNU5h1kJXNV5g1jJYNX5d1iJ]NW5b1iJ^NY5`1gJaNY5^1gJbN[5[1fJeN[5X1gJhNZ5X1eJiN_5R1aJnN`5Q1`JoN`5Q1`JnN`5S1`JlNb5S1^JmNc5S1\\JmNe5R1[JnNf5P1[JPOf5P1YJPOh5n0YJROh5n0WJROj5m0VJSOj5j0YJVOh5e0mFTO\\37h5c0\\J]Od5b0]J^Od5`0]J@d5>]JBd5<]JDd5;\\JFc59^JGc58]JHc57^JIc56]JJc55^JJc56]JJd55\\JKe53\\JMd53\\JMe52[JNe52[JOe50[J1d5O[J3e5MUJ9j5GQJ?n5AmId0T6ZOkIi0T6WOkIj0V6UOmIi0R6WOmIj0S6VOnIi0R6WOmIj0T6UOkIl0U6UOnIg0R6YOkIj0U6WOkIh0U6XOlIg0T6YOkIh0U6YOkIf0VMROD11Of89iJ:dLBL3f81nJl0\\LkNc8:PKm0Q5SOnJi0^LoNc89oJh0`LmNa8<nJf0W5[OhJe0W5]OhJc0X5^OgJc0X5_OfJb0Y5_OfJb0Y5_OfJb0Y5]OhJd0V5\\OkJe0U5oNVKR1i81O2N1O2N1O2N1O1O0000O100O100O100N2N2O1OChNYDW1h;mNTDQ1m;>1O001O0O2OO11N10001N3N2N2M3N2N2M3N1O3MO1001O2N4L001ON2002MC]OdCd0]<^OXCN0j0h<61O100000fMSOhGk0W8A^G?b8C[G>f8AZG?e8CZG=e8C]G<c8E\\G;e8D\\G;c8G[G:e8E]G:a8I^G7Y82hGMX83hGMX80WHDi7:ZHWOcM<T:9_HFb74kEAc2>b70bHO]71dHO]70dHO]71cHN[74cHN]73cHLg7JYH6j7GWH8k7EVH;e:01OM33MK51OO1M31O0000O17II700003M00001O00002NO14LL41OO100000000001OM3RGB`4=`KD\\4`0cKA\\4?dKC[4<dKF[4:eKHX4:fKH\\45dKLY46fKL^4ObK3Z40eK1Y40fK3Y4MfK4Y4LfK7[4GdK;X4GgK=V4DiK=W4BiKb0P4AoKd0k3_OSLe0j3[OULj0mL_Nn5i0SMm0gL[N_O1f6g0SMZ1WLoM09b6>VMl1YL[M\\6j0ZMW2`2hM^MY2a2hM^MY2b2hM\\MP2n2oMQMn1U3PNjLo1X3RNfLm1\\3TNbLk1a3TN^Lk1c3WN[Lg1h3YNWLf1j3\\NTLc1o3]NnKc1T4]NkKb1X4^NeKb1[4`NdK`1]4aN`K`1a4`N^K`1d4`NYKa1i4`NSK`1P5aNmJ_1U5bNgJ_1[5cNaJ]1`5eN\\J\\1f5fNVJZ1l5gNPJZ1R6hNiIY1X6iNfIV1\\6lN`IT1b6nN\\InNnNS1g72ZIdNXOU1`79VI\\NCW1Z7=mGZNP2V1T6P1gInNY6R1hIlNY6R1jImNV6Q1lInNU6Q1lIoNT6Q1nIlNS6S1nImNR6S1oIkNR6U1oIjNQ6Q1VJlNk5Q1YJnNg5Q1[JmNf5R1\\J]NVM8^8Y1`J[NVM9[8[1kJbNU5\\1PK`NQ5`1e3N200O1O1O100O1O1O100O1O100O100O100O10000O100000000O1000000O100000000O1000000O1000000000000000000000000000000001O0000001O0000001O001O00001O001O1O0[MjNeHW1[7jNcHW1\\7lNaHT1`7PO[HQ1d7VOUHk0k1eNR2?SLl0j1gNR2<ULn0h1gNS2;TLo0a1dNaL4i59ULP1\\1gNfL0j58TLR1X1iNiLOk55TLT1V1jNiLOl53ULU1T1VOg2DULW1S1UOh2ETLW1R1VOj2AULZ1P1VOj2@VL[1n0WOl2^OUL\\1m0XOm2\\OVL]1l0WOo2[OUL_1j0XO\\OQO[16aNb1f0YO[OPO`12aNg1`0ZO\\OoNc1ObNi1>YO[OQOf1KbNm19UOAUOm3g1^LhN2Ba3h1ZLYNa01U3h1VLXNCJSO8T5>gJG]1IGQ1V4^ORK_On02Om0Q4AXK\\Of065j0m3B`KYO=:;f0i3F]MEmNb0f3H]MFRO>a3K\\MHWO9^3NZMJ[O6[3OXMM_O3Y3OTM2EMX30nL8LGV30fLa07]OS31fLb09]OP31hL`0:^On21kL>9@l22lL<:Aj22oL;8Bi23PM99Cg23RM89Ed23TM6;Ea24VM5;F_25WM3<G]25YM2<H[26YM2=GZ26[M2<HX26]M1<HW26_M1:I60PN6`11:I7OoM6b118J61oM4d107K62nM2g106L43nM1h106L53lM0k104M54kMOm1O3N55g1LTNO56g1IVN039e1GXN03:a5F_J9b5G^J9b5G^J9b5G^J9c9N1001OO1000O4MN13N000O011N010OO21O0O10O010000O3N6I4L101N1O101N1O101N101gHkNi1V1WNjNh1X1WNhNi1X1WNiNg1Y1XNgNeNOQO\\1Y2fNeNOQO\\1Y2eNfN0oN]1Z2cNhN0lN_1[2bNhN0lN_1[2aNjN0iNb1UN[NW33K1gNd1SNZN[32J1gNe1PNZN_30J2eNh1mMXNd3OH3eNk1gMWNl3KH3eN_2b1_MH4dN^2d1_MG4cN^2e1_MH4bN]2f1_MG6aN\\2h1_MF6`N\\2i1`ME6aNZ2j1aMD6`NZ2l1bMA6_N[2o1aMA4[N`2T2^M^O6VNa2[2[M^O:kMa2g2VM\\O`0bM_2R3SMZOc0ZM_2[3PMYOh0PM^2g3kLXOo4h0SKUOn4j0TKQOP5o0QKRNm5n1UJ_M\\6", "size": [443, 640]}, "area": 38943.0, "iscrowd": 0, "image_id": 10196, "bbox": [0.0, 0.0, 640.0, 443.0], "category_id": 165, "id": 10012417}, {"segmentation": {"counts": "lQ1\\2^;\\2eM1N1O1O2O0O2N100O2N101N1O2N10001O001O00001O001O00001O001O00001VI_KX4a4eKbK[4_4aKdK_4\\4]KhKc4Y4YKjKg4V4UKnKk4R4RKQLn4P4nJSLR5m3jJWLV5j3fJYLZ5g3cJ\\L]5e3_J^La5b3^J_Lb5a3\\JaLd5`3YJbLg5^3XJcLh5h500001O00000000001O0000000000001O00000000001O00000000001O001O001O1O001O1O001O001O1O001O001O1O001O1O001O001O1O2N1O2N2N2ZLYI4h6IZI7h6EZI;h6A[I>f6_O\\Ia0f6[O\\Ie0f6WO\\Ii0f6SO\\Im0e6QO\\Io0f6mN]IR1e6jN]IV1d6gN^IY1d6cN^I]1h6[NZIe1k6TNWIl1o6gMXIY2m6XM^Ig2h6iLbIW3a800000001O0000000000001O0000000000001O00000000001O000000001O001O001O1O001O001O1O001O001O1O001O001O1O001O001O1O001O2N1O2N1O2N1O2N1O2N2N1O2N1O2N1O2N2N2N2N2N3H7J6I8H7@Tka0K\\U^O6J6K5L4L4L3L3N2N2N2N2M3N3M2M3N2N2M3N1O1N2O001N2O1O1O1O001O001O1O001O1O1N2O1O1O2N10001O001O001O1VEZM]:g2^EcM\\:o2O1O0000001O0000001O0000001O0000001O0000001O1O001O001O1O001O001O1O001O001O1O001O1O001O001O1O001O1O1O1O1O1O1O1O1O10N100O101O0O100O2O0O2O0O2O001N100O2N1O2N1O2N1O2OWIbLU3]3cLoL[3o2]L]M`3b2]LdMa3[2^LjM`3T2_LPN`3n1_LWN^3h1aL\\N^3b1`LcN^3\\1aLgN_3V1aLmN_3Q1`LRO_3l0aLWO_3g0`L\\O_3b0bL@^3>bLD]3:dLH\\36dLL]30cL3_3IaL8a3D`L>b3]O_Le0b3XO^Lj0c3RO^LP1d3lN\\LV1f3eN[L]1f3`NZLb1h3YNYLi1i3QNYLQ2i3eM]L]2e3XMcLh2W7101N101N101N100O2O0O2O0O101N101N101N01000O100O10O0100O100O10O10O100O10000O01000O100O10O1000O0100O10O010000O10O010000O100O2O000O2O0O2O001N10QFTMo8k2oF^Ml8m3K6K4K6K2M1O101N100O1O2O0O101N100O2N010O0100O10O10O100O010O100O01000O100O0]LRHZ1n7cNTH]1m7`NTHa1m7\\NUHe1j7YNXHg1d7[N^Hf1R7[MdH;;[2c6kMXI_O7g2_6oMdJQ2c1oLNT1[Nn1f1ZMCm0cNj1h1_M_Ok0fNf1i1gMZOh0hNc1l1lMVOd0kN`1n1RNQOc0mNX1T2\\NhN`0POU1W2bNbN<UOS1W2eN`N:XOR1V2gN_N9YOQ1W2hN^N9ZOP1V2iN^N:ZOn0V2kN]N9]Ok0V2mN[N:^Oj0U2oNZN9@i0U2oNYN:Ag0U2ROXN8Bg0U2ROWN9Ce0U2TOVN9Eb0U2VOTN;E?V2XOSN;F=W2YOQN<G;W2ZOQN=G9X2[OPN=G8X2]OPN<H6X2_OoM;I6W2AoM:I6W2@PN:I6V2BPN8J6V2CoM7K6U2EoM5M5T2FoM5M5S2HoM4M4T2InM3N4S2KnM1O4S2KmM203R2MmM023P2i3PNWLo1k3PNULP2k3PNULP2k3PNULP2k3QNTLo1l3QNTLo1m3PNSLP2m3PNSLP2m3PNSLP2m3PNSLP2m3QNQLP2o3PNQLP2P4oMPLQ2P4oMoKR2Q4nMoKR2Q4nMnKS2R4nMmKR2S4nMmKR2T4mMkKT2U4lMkKT2U4lMjKU2V4kMjKU2V4kMjKV2V4iMiKX2W4iMhKW2X4iMgKX2Z4gMfKY2Z4gMeKZ2[4fMeKZ2m7100O1O100O1O100O1O100hH`MT3a2lL_MS3b2mL^MS3b2mL_MNNSOd2o0^MNOQOe2P1]MONPOe2Q1]MOOnNe2S1\\MO0lNe2U1\\MOOkNe2V1\\MO0iNe2X1[M00fNf2Z1[MO0fNe2[1[MN2eNd2]1[MM2dNd2_1ZML3eNc2^1[MM3cNc2`1[MK4cNb2b1ZMK5bNa2b1[MK6aN`2d1[MJ6aN_2e1\\MH7aN^2f1^MG5aN^2h1^ME6]Na2m1[ME4YNf2R2XMB9QNd2]2TMA=gMe2g2PM@b0^Md2Q3lL_Oe0UMe2\\3gL]Ok0lLc2f3dL]OT5c0nJZOS5e0oJfNe5Z1\\JgMb6e1TJ[Me6", "size": [443, 640]}, "area": 106770.0, "iscrowd": 0, "image_id": 10196, "bbox": [2.0, 1.0, 638.0, 442.0], "category_id": 183, "id": 10012418}, {"segmentation": {"counts": "0d0f<O10000000000001OO100000000O1001O00O1O1000000001O00000000O100001O3ZC\\O`<j0M001O00000000001O00000000000000001O00000000000000001O000000000000001O00000000000000001O000000000000000000001O000000000000001O000000000000001O00000000000000001O0000000000001O000000000000001O000000000000001O000000000000000000001O000000001O00001QDfNl;^1N0000001OO1000000000000MVDeNk;[1UDeNk;[1200000000001O000000000000001O000000000000001O000000000000001O00000000000000001O0000000000001O000000000000001O000000000000001O000hEmNWOBZ8a1_HPOnN0X8P1jHQOmNOY8P1hHTOmNM[8o0hHROoNOY8o0hHQOPO0X8o0hHPOjN^OOc0_8o0hHPOkN]OL0Nd0c8P1gHPOkN]OKf0c8m0gHPOlN5]8k0gHPOmN4\\8l0gHPOoN2Z8n0gHQOoN0Z8o0hHPOoN0Y8Q1gHPOPONY8R1gHPOQOOV8Q1iHQOQONU8Q1jHQOQOOT8P1kHROPONU8P1kHROoN\\OG`0]8R1mHSOTOKn7R1nHROVOKl7T1nHPOVOLl7T1nHPOWOJl7V1mHQOWOHl7W1mHQOXOGk7X1mHQOXOGk7X1mHQOYOFj7Y1mHROYODj7Z1mHROYODj7[1mHPOYOEj7[1mHQOXODk7[1mHQOXOCl7\\1lHQOXOBm7]1kHQOXOBm7]1kHROWOBm7\\1lHROWOBm7\\1lHROWOBm7]1kHROWO@o7^1jHROWO@o7^1jHROVOAP8]1jHROVOAP8]1jHROVOAP8]1kHPOVOBP8^1jHPOVOBP8^1jHPOVOBP8_1iHoNWOBP8_1iHoNWOBP8_1iHnNXOCo7_1iHnNXODn7^1jHnNXODn7^1kHmNWODo7_1jHmNWODo7`1iHkNYOEn7`1iHkNYODo7a1hHkNYODo7b1gHjNZOEn7a1hHiN[OFm7b1`GTNU1d0^OHk7d1eHdN@Ji7c1gHbN_OKk7c1fHbN_OKk7c1fHaN@Lj7b1gHbN_OLj7b1gHbN_OKk7c1fHbN_OKk7d1eH`NALj7d1eH`NALj7d1eH`NALj7c1gH`N_OLk7e1eH_N_OMl7c1fH_N_ONk7`1aGSNV1?^ONk7`1iHbN\\ONk7`1iHbN\\ONk7`1iHaN\\O2i7]1kHaN\\O3h7\\1lHaN\\O4g7[1mHaN\\O2i7]1kHaN\\O1j7^1jH`N]O1j7`1hH_N]O2k7_1hH_N]O2k7_1iH^N\\O3k7_1iH^N\\O3k7_1iH]N]O4j7_1iH]N]O4j7_1iH]N\\O4l7_1hH]N\\O4l7`1gH[N^O5k7`1gH[N^O5k7`1hHZN]O6k7`1hHZN\\O8k7^1iHZN\\O8k7^1iHYN]O;h7]1jHXN^O:i7^1iHXN^O:i7^1iHXN^O:i7^1iHWN^O<i7]1iHWN^O;j7^1hHWN^O;j7^1iHUN^O=i7_1hHSN@?g7^1iHSN@>h7_1hHRN@`0h7Q1kG]Nm02@?i7a0aJ^O`5b0`J^O`5a0aJ_O_5a0aJ]Oa5c0^J^Ob5b0^J]Oc5c0]J^Ob5a0_J_Oa5a0_J^Ob5b0]J_Oc5a0]J^Od5b0\\J^Od5b0\\J_Oc5`0^J_Oc5a0]J^Od5b0[J^Of5b0ZJ^Of5b0ZJ^Of5b0ZJ_Oe5`0\\J_Oe5a0[J_Oe5a0ZJ_Og5a0YJ^Oh5b0XJ]Oi5c0WJ]Oi5b0XJ_Og5a0XJ@h5`0XJ@h5`0XJ@h5`0XJ_Oi5`0XJ@h5a0WJ_Oi5a0WJ_Oi5b0YJ[Og5e0[JZOd5g0^JVOb5k0`JSO_5m0cJRO\\5o0fJoNY5Q1`31O001O2N2N1O2N1O2N2N1OO1O11O001O00000000O11O0000000000001O0000000000000000000000000000000000002N2N2N2N2N2N3M2N2N0000O1003M2N2N2N1O001O000000001O000000000000001O0000000000001O0000000000000000001O0000001O000000001O00000000000000O10000O10000O100000000O1000000O1O10000O10000000000O1001O000000NVEdMj:[23O100O100O1000000O1000000O1000000O100O100O10000000000O10000O10000O1000000O10000000000N200001ONfDTNZ;k13O10000O1O100O1000000000000O100O10000O100000000O1O100000000O1000000O1O100O1O100000000O10000O10000O100O10000O100O10000O100000000O10000", "size": [426, 640]}, "area": 36996.0, "iscrowd": 0, "image_id": 10205, "bbox": [0.0, 0.0, 640.0, 158.0], "category_id": 102, "id": 10012419}, {"segmentation": {"counts": "nXn41aj01gBOVnX10lQgN1N2N010SCNb<0\\C0h<OUC0L0[kn1", "size": [426, 640]}, "area": 28.0, "iscrowd": 0, "image_id": 10205, "bbox": [380.0, 196.0, 109.0, 195.0], "category_id": 105, "id": 10012420}, {"segmentation": {"counts": "P^c63W=OO2O100]FLP63QJMn55PJMo53PJOo51QJOo51QG=m1CP7c1`H^N`7P2RHQNm7c2^G_Ma8V3jFjLU9Y3iFhLV9g3O001N3K7I6K2]G]K_8c4dG]KY8c4iG\\KV8e4jG[KU8e4lG[KS8f480gGZKm7g4THYKk7h4UHXKj7h4WHXKh7h4YHXKg7X4nGUL;Ce7T4XHVL4Fc7Q4_HVLP8h3THVLl7h3YHULg7k3l0O10O100000O100000000000O1001O01O001N3N1O2ZGoKo7S4oGkKB1]8V4nGiKI0V8Z4oGfKNOQ8^4lGdK^8d40O01000001N100nGXKb7h43>O0O2O0]HjJQ7X5`01O0O2WHdJb7b500O1O100010O1O1O2N1YM[HGf76\\HJd78[HGg74]HKc72`HNa7mNcHkN:V2S7mNoIk0Q6hNTH]OZ2^1a5UOYHTOS4Od3k0\\HUOm;3lCb0]<_OdC`0\\<_OfC>b4_OS33\\H1g<O:O102NTm9", "size": [426, 640]}, "area": 12996.0, "iscrowd": 0, "image_id": 10205, "bbox": [508.0, 65.0, 108.0, 192.0], "category_id": 109, "id": 10012421}, {"segmentation": {"counts": "gjh41X=100M4oBKj<;03L102L1O3dC\\OK0M42Ke;a1L32N3M4M2L5M3M2N3L4M2M4M3L3M4M2M4MggU3", "size": [426, 640]}, "area": 667.0, "iscrowd": 0, "image_id": 10205, "bbox": [367.0, 147.0, 28.0, 54.0], "category_id": 112, "id": 10012422}, {"segmentation": {"counts": "W;S2W;00000001O00O1001ON21O00O10nMkDo1X;01O00O1000000000000KPNQEo1o:SNoDm1o:8O100O10000O1000000O11O0000000000N200001O1O00O100001O2N3M2N2N1nNiDOY;SOfDi041Y;OiDOW;0lDNT;2nDLP;6REHm:9UEEj:<XEBg:?ZE@e:a0^E\\Ob:d0`EZO_:g0cEWO]:i0eEUO[:k0gESOY:l0jEROV:n0S100000001O000O2O1OM300000000O1NQOiCo0W<200000000001N01002N0000000O11OO1000000000O100000O100000eCSOY<m0gCSOY<m0gCSOY<P1O0iCPOT<P1kCQOU<o0kCQOU<o0lCROR<n0mCROT<n0lCROT<n0lCROT<n0lCQOU<o0kCQOU<o0kCROT<n0lCROT<n0lCROS<o0mCQOS<n0nCQOS<o0mCROR<S100000O1000000000000O11O000O10O100000000O100001O00O11OO10O10LlCROT<k0lCTO0O00T<m0lCTO0OV<m0mCSOS<m0500O1000000000000000000O10O11O00dCUO01U<k0jCTO02V<l0jCTOU<m0kCTOT<R1O1O001O00000000O1O100ImCTO0OS<j0mCVO010NT<k0mCUOO2V<i0kCUOO000V<k0mCUOM0V<j0nCVOT<j06000000O01000000000O02MUOeCk0[<1O2O1M212NO1000000000000O11OO11OO101O00000O10O2OO10O1001O00O10000001O00001OO1000000000O0011O00O02O000O11N01O10000000000O1000000O10O10O21N2N1O0POhCm0[<01O0UOdCh0\\<WOeCi0^<0000000O10O101OO10000000000bCWO[<l0000O1000O1000000000000000O10000000O1000000000000000000N3N101O00001N10O100010N11O000000002N0O1O1O1N7J5L00O101O000O1000O1M30O10O1000O011O00O0100000000O1000000O1000O1000O10000O01000O1000000O10000O1000O01ZO[OgDe0Y;]OeDc0`;ZO]Dg0d;ZOZDf0e;]OYDc0i;\\OVDd0l;\\OQDd0Q<]OmCc0R<@lC`0T<;2N2N1O001O00001O00DYOVDf0j;@PD`0Q<<3M2N2M4M2N3L3N3L5K6KkRk1MR`TNNT=5O00001O00000NJPC7o<3OO11O000O1NEUC;k<20000000001O0000000O101O01OK5K6M2N20000N21OkCQOo;o0QDQOL1P<o0QDPOO2P<o0QDoNO3Q<l0PDQOO3Q<l0PDQO02P<m0PDQO02P<n0oCQO02P<m0QDPOO3P<m0PDQO02P<W1N0O2OO2O1O1O1ORE", "size": [426, 640]}, "area": 17250.0, "iscrowd": 0, "image_id": 10205, "bbox": [0.0, 317.0, 640.0, 109.0], "category_id": 115, "id": 10012423}, {"segmentation": {"counts": "jlR3o0c<I0O000000F910000O1001O00O101OO1014K00100NM4I70000002M6K4L1O0O2O000OC?OO1O10000000000O10O1000O10O10O5NO1N1O0gCQOW<Q101O0kCnNR<R1mCoNS<T10000000O100001OO3N1nNjCn0Y<01O000000000000O0L5N21O1OO15KO1N2O1O1O10UOlC`0U<[OPDc0P<ZOSDg0V<2L4L40POmCi0R<UORDj0T<3L4O1OO3N10001O<D1N10O1N2ZOD[D`0];DcD>^;_ObDe0`;UO`Dn0_;POaDS1h;32N6L4J2N000M012ML]OdCd0c<L4M]X1DlgN600O00N3MR[^3", "size": [426, 640]}, "area": 3965.0, "iscrowd": 0, "image_id": 10205, "bbox": [237.0, 291.0, 137.0, 56.0], "category_id": 118, "id": 10012424}, {"segmentation": {"counts": "Y^R3c0\\=Ond1BlZN3M00000000000000001OO10jko4", "size": [426, 640]}, "area": 126.0, "iscrowd": 0, "image_id": 10205, "bbox": [236.0, 273.0, 19.0, 39.0], "category_id": 123, "id": 10012425}, {"segmentation": {"counts": "_`P64kj0O\\B3M3N1N3M2O1N2N1O2OO0O100O2O0O110O0000007I10OO20O00001O001O1XOF\\D<]ODg<?YCAg<a01O000000000O100O100O20O000000O1O1000001O000000O100000O11N13M9Gbj]1", "size": [426, 640]}, "area": 868.0, "iscrowd": 0, "image_id": 10205, "bbox": [462.0, 323.0, 67.0, 38.0], "category_id": 141, "id": 10012426}, {"segmentation": {"counts": "fZ_32X=0O1O3N2VC6o;o0D0000000000000001O0O2O1O1nNoCi0R<VOnCi0U<TOlCl0V<ROkC9P=K3M^m`4", "size": [426, 640]}, "area": 738.0, "iscrowd": 0, "image_id": 10205, "bbox": [267.0, 261.0, 24.0, 45.0], "category_id": 143, "id": 10012427}, {"segmentation": {"counts": "]`h02X=3M2M3N2N2N2N2N1O3M2N2N2N2M3N2N00000000000O1000000000000000000000000000000O1000000000000O11OO10000000000000000O100000jNnCT1R<lNnCT1R<lNnCT1U<O0nNkCo0U<POlCP1T<POlCP1T<QOmCm0S<ROmCo0S<QOmCo0S<QOmCo0S<QOlCP1T<POlCP1T<POmCo0S<QOmCo0S<QOmCo0S<QOmCo0R<ROmCo0S<QOnCn0W<00000O1000000000000O11O000000O100000000O100001O00O11OO10000LnNPDR1m;nNRD0O00R1o;nNRD0OT1o;oNQDQ1o;500O1000000000000000000O10000000fNSD01S1m;lNRD02T1n;lNRDT1n;lNSDS1S<O1O001O00000000O1O100InNSD0OR1k;nNUD010NS1l;nNTDO2U1j;lNTDO000U1l;nNTDM0U1k;oNUDS1k;6000000O10000000000O11O00O10000O12NO1000000000000O11OO11OO100000000O1001OO1000000000000000000000000000000000000O1O11O00O11O000000O1000000000000000000000000O1001O000000O1000000000000000000O1000000000000000\\N]Da1f;000O100000000000000000000O100000000000000000000000000001O001O00001O00O100001O00000000000000O1O1O1N2O10000001O00000000O1M30000O10000O11O00O10000000000000000000000O10000000000O10000000000000000000000O100000000000000O10000000000000000000000000000000000O10000O100OJkDYNT;e1oD[NQ;b1RE^Nm:`1VE`Nn:X1VEhNo:P1TEPOQ;h0QEYOS;`0PE@U;8nDHW;0lD0W<0000000O100000000000000O100000000000000O1N2N2O1ZNC]F?b9DZF>f9DWF=h9FTF<l9FPF<o9GmE;T:FiE;W:FfE<Z:EcE=]:DaE=_:C_E?a:A]Ea0d:_O\\E`0c:CYE?h:BSEZOJU1S;BoDc0Q;^OmDc0T;i0001O1O0nMjDP2V;PNjDP2X;00000000000O100O100O11O000000O1O100000000000000000000O11O00CnMbER2^:WNYEi1g:=00O100000000O100000000O100001O000000001OO1001O000000001O000000001O00O100NaM[E]2e:aM]E_2h:O000000001O001O1O00000000000000000TNUEANf1m:iNXEV1i:iNWEW1i:iNWEW1i:iNXEV1h:jNXEV1h:jNXEV1h:jNXEV1i:gNQEE7c1h:hN[EW1f:`NPE49]1h:^NoD5:\\1j:dNVE\\1j:dNVE\\1n:_NSEa1n:^NREb1[;0000000001O00000000001O0000001O00000000000eNWDU1i;kNWDL1V1i;kNVDO2W1h;jNVDO3V1g;kNVDO3V1g;kNVD02U1h;kNVD02U1i;jNVD02U1h;lNUDO3U1h;kNVD02U1R<N001OO100000000", "size": [426, 640]}, "area": 29274.0, "iscrowd": 0, "image_id": 10205, "bbox": [58.0, 341.0, 582.0, 85.0], "category_id": 152, "id": 10012428}, {"segmentation": {"counts": "lmQ31V=i0kB4a;K`D4_;M`D4`;l0O0iDWNm:i1kDXN0O21S;o1kDQNS;T2O1O0103L11N1N2O11OO1GkMXEN10OW2h:mMYELOX2g:lMYEM0W2f:PNZEP2f::0001bEkMe9U2ZFlMf9l201O000O1O0101N1O10O010000000O100000001O010O1N2\\FaL[9`3dF`L\\9b3aF_L^9d3`F]L9e0T8U3gGnLY8R3dGPM\\8o32N2NO1000000O100000000O10002N1dGTKX8a0hG_O0X4X8YLiG^O2W4V8XLoGg3T8jKkG13U4h800000001N1K5N21O1OO15KRNnFCS98UGCm8<WG@j8?YG^Oh8b0[GZOf8f0]GWOc8i0`GUO_8k0cGRO^8n0eGmN]8S1fGgN]8X1fGbN^8^1hGXNlNOS9i1QHSNlN7T9h1THYNn7g1oGZNU8G^Fl1[1\\NlNIY9g1lG]NkNN[9d1dFYNn01TO6S9IeFk18UNR1:Q8o0TGiNS1;h7l0WGhNo0?i7i0jHZOT7e0h20100O`EjNo8T1XGlNjN0V9R1UHROe8>W2L3N1N2N2N102M2N2O0Odkc3", "size": [426, 640]}, "area": 11891.0, "iscrowd": 0, "image_id": 10205, "bbox": [235.0, 152.0, 126.0, 164.0], "category_id": 156, "id": 10012429}, {"segmentation": {"counts": "n^f49Q=7PCCe<b0c0]OTW10e[N089U<McCa0V<:1N1O2O1O0M4L4L3M4L4L3M4L^c11c\\N3M3M4L3M4L000000000O10000O10000`D\\OY:e0V1000O100jDZOh9f0]1KaC@`<b0320N6@i[m2", "size": [426, 640]}, "area": 873.0, "iscrowd": 0, "image_id": 10205, "bbox": [361.0, 243.0, 54.0, 82.0], "category_id": 165, "id": 10012430}, {"segmentation": {"counts": "UhS53W=4L3M4L2N0000O100000000000000000JD]C=e<41EhYQ13[fnN2M4M3O1O0010DcCKP=N2Mkfh1", "size": [426, 640]}, "area": 434.0, "iscrowd": 0, "image_id": 10205, "bbox": [393.0, 261.0, 110.0, 31.0], "category_id": 167, "id": 10012431}, {"segmentation": {"counts": "d0c:f2100000001O00O2OO2ON21O00O01\\M]Ea2f:10O0O001000000001O0K^McEa2]:aMaE_2]:8O010O101O3dERMU:U3M001O0O11O000001O000N200001O1O00O2O0001O2N3M2N2N1O2SMdEi2^:00000N2O1O1O1O2O0O100000000000001O01O00001O1OM300000001N1NkLoEU3Q:200000000010OO1002N00000001O0000000000000O101O0000000000001O000000000000001O000000000O10001O000000000000000000001O00000O101O00001XF_Le9e3N0000001OO10000000O1000M^F]Lc9c3]F]Lc9c3200000000001O000000000000001N11O000000O10001O000000000000001O00000000000000001O00000O1000001O000O1NTLfFl3Z92N2O1M4O000000000000001O000[HZLWOBg5T4RK]LnN0<]O`4V4VL^LmNO:6\\O4:D51S1d3IbLlNL>4\\O49b6Q1[IkNK>5\\O4:a6Q1[IkNK>U1Ee5R1]IiNI`0U1Df5S1\\IgNUONd0d0U1\\OXN0O2a7X1[IfNKd0Y1\\Oa5Z1TJXOP8h0PHWOQ8i0oGVOa0@Y5Z1VJVOa0@Y5[1UJXO>^O^5Y1TJYO?]O]5Z1VJVO>@\\5Z1WJSO?CZ5Z1XJQO?EY5Z1XJRO>DZ5Z1ZJoN=GY5Z1^JfNRN10OW20Y5Z1`JgNPNOj9Z1UHfNRN0i9Z1XHfNh7Z1T200000000010O00000000000001O0000000001O0001O0000000001O01O00001O001hNoCU1U<N10O000O100001O0000O20OO1MkNPDV1P<2UFhNlM0e9X1_HiNkM2c9U1aHPO^7P1SHiN[N613a9n0TH^Ol7b0RH@n7`0QHAo7?PHBQ8=oGDP8<PHDP8=oGCQ8=oGCQ8=oGCQ8=oGCQ8=nGDR8<nGDS8;mGES8;mGES8<lGDT8<mGCS8=mGCS8=oGAR8>mGCS8=mGCS8=mGCS8=mGCS8=lGDT8<lGES8<bFhNS1l0\\8;`FjNU1i0\\8=_FiNU1l0gNTOb9l0_F\\O2IV1P1V8JiFWOKOU1R1V8GQGXOh0l0iNROc9h0XH6k7HVH7l7GSH;n7DRH<o7BQH>Q8@QH?P8_OQHa0P8]OPHe0P8YOQHi0n7UORH[1`7cNaH`1]7^NeHc1Z7[NfHg1Z7XNeHj1\\92O002O0N2O0O1O1O01O2O0O1aNgDl0Z;SOgDl0Y;UOgDk0Y;ROkDFKQ1[;XOPEg0Q;YOREd0n:[OSEd0o:[OSEb0n:\\OSEe0n:XOUE3\\OK0M42^;2b[5MQfJ11O001O00000000O11O000000000000\\e;1cZD2M3N210O00000O1N2O2M3M3M3M3N2M3M3M3M3M2N3M3N2M3M3M3M3M3M3N3L5K4L5QG\\Mn6h2kHZMV7k2bHWM_7m2ZHVMf7n2SHUMm7P3jGSMW8Q3bGRM_8R3YGQMg8i300001O000000001O00000000000000O1000O010000O01N200000O0100000O]LTGj2k8WMTGj2l8TMVGl2j8SMWGm2h8TMXGl2h8TMWGm2i8RMYGl2h8TMXGk2i8UMWGk2i8UMWGK=m1[8XNkHX1V7hNXIj0i6UOkI6V6J_JAa5>bJ@^5`0d3^O\\Cb0c<3O01M2N3N11000000O1000O10O10001OO0100O100O1000O100000O10000O10000O01001O0O1000O100000N20O101ON[O_Ce0`<2010000O00100OD=00O100O1000O010O10O1O010001O1O1N3M2TF@l6a0QIAo6?SI@l6b0oHBP7>mHER7=iGGkN:Z9OiGS1o7mNdGXO]OZ2a8^NRH\\OTOS4Q7bLiI^OUOQ4P7bLSIVOb0c4Z6VLUIXO`0b4Z6WLUIYO>b4\\6VLVIYO1n4h6iKWIa4i6_KVIb4j6^KVIa4k6aKSI`4k6P10000O2O;E?@;RJ^H`5b7`JhHU5i7N1O1N2O2M10000O10O11O00O001000jN", "size": [426, 640]}, "area": 100478.0, "iscrowd": 0, "image_id": 10205, "bbox": [0.0, 17.0, 640.0, 349.0], "category_id": 172, "id": 10012432}, {"segmentation": {"counts": "W]R3b0Q9_OeJf1T4]NkK]2Z3dMfL\\2Y3eMgL[2Y3eMgL[2Z3eMeLj1hLUNc62dLi1m3XNRLh1n3ZNPLf1Q4ZNnKf1R4[NmKe1T4[NkKe1U4\\NjKd1W4[NiKe1W4[NiKe1X4ZNhKf1X4ZN`HGS3o1^4_NaKa1^4aNaKl1R4TNnKl1R4UNmKl1S4TNlKl1T4TNlKGQM^1]7lNbKE]MR1R7ZOXLf0i3YOWLh0h3YOXLf0h3ZOXLh0g3VOZLo0a3QO_LS1]3mNdLS1[3mNeLS1\\3lNdLT1\\3lNfLR1Z3nNlMLU23a50000000O100000000O100000O10O1000000O100000000O1000000O10O100000O10O1000O100000O10O10000000O01000000nF_OZ5a0bJG[59dJKY55fJNX52hJ1U5OkJ4SNXO]5e0`L6lMYOd5`0aL9eMZOk5=`L<^MWOV6=\\L?XMSO_6?XLa0SMPOi6?TLg0iLjN4Me6b0_Lg0cLjN:Oe6?aLi0kLhNh6?\\Lg0lLjNj6`0ZLf0iLjN_6LgId0P3g0gLiN[55]KN[O=V3@cLn01SOP5f0iKCWO6\\3CbLP1U5FcK:S2WOWMMNg0V5M`K9T2XOWMMOa0X52\\K9V2JVMHd0]Of3i0YL8V2MWMb0Z4ZOWL8W2OXM>Y4\\OVL7Z21XMMOoNZ4n0SL6[24[M[O3]OW4o0nK6\\2:\\MbN<OQ4P1iK6^2>_MUN67W4Q1dK6_2h0bMQN^4R1_K6`2h0`MPNd4S1ZK5c2i0[MoMj4U1VK4d2R1X2kNSK3d2V1V2:iMMQ23oM3k1MTN6j1IVN8i1IWN3_MmLd01m2o2oN4VNTMf2h2SO_OSNaM1>f2b2VO_OhNQNR2_2VOAeNSNU2\\2UO6PN`Mj2[2VO[1k0eNTO\\1l0dNSOZOeMiMNm00EZ3[2RO]OhMbMM9h0;a2\\2SO@mNRNP2_2ROAnNoMQ2_2POEmNlMS2`2oNEkNmMW2`2jNFkNkM\\2a2fNGjNjMa2a2bNFjNkMe2a2]NGjNjMi2a2[NGhNjMn2a2VNGiNiMR3c2QNGiNhMW3b2nMHgNhM[3c2jMGhNhM`3a2fMIfNgMf3b2`Mn0b2SOZMn0g2SOWMm0k2TOQMl0R3UOkLiMYO[1m3m0gLiM@V1k3R1bLhMFS1i3W1]LgMNn0f3\\1YLgM4i0d3c1ULdM;e0`3i1RLcMa0a0]3o1oK`Mh0=Y3o3jLmKW3S4lLjKT3V4nLhKR3X4nLgKS3X4nLhKR3X4mLiKS3W4mL]JmNj0W4h4mL_JnNf0U4k4mLaJoNa0T4n4mLbJPO>S4o4nLeJoN9T4R5mLfJoN7T4S5mLhJoN3T4T5nLjJoNOT4W5mLkJPOLS4Y5nLlJoNIS4Z5oLnJoNES4]5eLYKWOYOT4a5YL`KDmNS4i5iKfK4^NT4P8lKnGT4S8mKkGS4W8mKgGS4b7fKnH8]OS4f7eKnH7[OT4g7fKnHd4S7]KmHa4S7`KnH^4S7cKlH\\4U7dKjH\\4X7cKgH]4\\7bKcH_4]7bK`H`4a7aK[Hb4d7`KYHa4g7bKTH`4m7?001O00010O001O001UMlG<V8]OlGg0V8QOmGQ1V8gNmG[1U8]NoGf1S8RNPHP2S8hMPH[2Q8^MRHe2P8SMSHP3o4bLWM6lM\\3i4aL[Nb3a1aL]Nb3_1`L`Nc3]1WLjJ26MV3m3\\1RL^K1M43MS3n3[1QLbK0O320o2o3W1PLjKM1312j2Q4T1nK_LND4f2T4P1lKhLLD0C0n2[4m0iKRMKAOD4i2\\4i0hK\\MH]O0H5c2^4f0fKeMGXO0L7^2_4c0cK[NGfN3_2f49bKZOF5j4<`KBIOk48]KLJHo4W2kJ]L9Y1P5V3RKhLS5IbJ[1<jNV5D`Jc1<gNY5^O]Jl1<bN]5[OYJT2<]Na5XOVJ\\2:YNf5ROTJe28UNj5oNPJm28PNo5kNlIV36lMS6gNjIb2[OUNj0>l6V1_H^Nf09m6Z1ZH_Nk03m6_1XH^Nl00m6d1WHZNn0Nm6i1UHXNP1Km6o1SHTNR1Jl6U2PHQNU1Fm6S3TIjLn6]3kHbLV7_3jH^LX7b3hH\\LZ7d3gHZLZ7f3fHXL\\7i3R100O1kMnFbNIR1Y9nNaFS1?VO08o8_ObFP1c0K_O^O_9E_FR1e0=m8`N^FS1f0=l8GTG:k8FVG:h8HlFiNJ_1Z9<dFD\\9>aFD^9T2O1000000000hMcFc0]9^OfF>Z9ChF:Y9GfF9Y9IgF5Z9JhF4Y9KhF4X9MhF2Y9MgF3Y9MgF3Z9LfF4[9KeF5[9KdF6]9I`F:a9E\\F=e9d11O3M4L4KOkLSFQ3m9RMQFm2o9UMPFj2Q:VMoEi2Q:XMoEf2S:80TMmEa2S:`MmE_2T:aMlE^2T:cMlE[2U:fMkEY2f9\\MgF;CX2a9eMiF4FV2^9lMiFOJS2[9RNiF^2V9gMgFY2Y9f0510eM`Fk0a9VO_Fh0b9XO`Fe0a9\\O^Fc0b9_O^F?c9B]F<d9E[F:f9F[F8f9IYF6h9KXF4h9MXF2h9i1O00UNYF3h9LXF4h9KYF5h9JWF7k9FVF:l9gNTF90P1m9eNVF8MS1P:cNQFC0e0NT1U:bNlEI0=0Y1W:]NjE0N71]1a:lN_EU1a:iN`EX1_:j00000001O000hMdEb15PNj9n0PFc0OfNR:T1oE5Q:8QFYOP:S1RF^Nn9KPFn1Q:XNhEh1Y:WNgEi1Y:WNgEj1Y:eMjE:MY1W;gNiDY1W;gNiDW1Y;iNgDV1Z;=010O01O4L4L5K4L4L5K3M000010O00001VOcEeN]:[1hE`NY:_1kE\\NV:d1oEVNR:j1RFRNn9o1e01lD_N`:U20O10005K>B?A?Anh6", "size": [426, 640]}, "area": 52539.0, "iscrowd": 0, "image_id": 10205, "bbox": [236.0, 30.0, 387.0, 390.0], "category_id": 183, "id": 10012433}, {"segmentation": {"counts": "kWe74Q=4H700O102ON15KO100O1O100O10000O101N1O2O0O100O101N100O1O100MoBNR=1_`6", "size": [424, 640]}, "area": 413.0, "iscrowd": 0, "image_id": 10211, "bbox": [592.0, 112.0, 32.0, 22.0], "category_id": 96, "id": 10012434}, {"segmentation": {"counts": "S<U1b<A001O00001O8H00000000000000000000000000K500000000000000001OnNFVE:e:4RELn:<jDDV;e0bDZO];Y11O00O100001O00001O00O1000000000000000000000000000000000000001OO100000000001O00000000O1000000O11O0000000000000000001O0000O11O0000O11O1O0000000000O10000002N00O11O00000000000000O1001O000000001O001O00O100000000001O00000000O1001O0000O1001O00000000000000O1O12NO11O00O1001O000000001O1OO10000001O0000000POYD31Gf;OdD4HMb;G[D0g07]O2i<000000000000000000000^OOmC1j;9UDGa;d0^D\\Oc;d0\\D\\Od;e0[D[Og;d0XD\\Oh;e0WD[Oi;f0VDZOk;f0TDZOk;R11O1OO1O11OO1001O000000O1001OHhN\\D\\1V<B;E1O1O1CaCOl<00000000000O1bMJ^E1Y25Q85lF8XOCg9;mE_Oo0d0\\OBi9JkE>OCR1d0YOAn9<hF4YO@o9=fF6YO]OQ:=fF7XO\\OS::hF:TO\\OT:<hF8RO\\OV:=dF;UOXOV:>eF<SOVOX:=fF>QOUOY:=gFe0Y9ZOhFf0W9[OjFd0V9]OiFc0X9\\OhFd0X9[OhFf0X9WOgEAS1W1T9ZOnFf0Q9ZOQGe0o8WOTGj0l8WOTGh0k8YOWGe0i8[OWGe0i8[OUGAfNT1U:[OUGg0k8YOUGg0k8YOTGh0l8XOTGh0l8XOTGh0l8YOSGg0m8XOTGh0l8WOUGi0l8WOSGi0m8WOSGi0n8VORGj0n8VORGj0n8UOTGj0m8SOUGm0l8POVGP1j8oNWGQ1j8jNfE1`1V1_:01OO100001OO1\\NkNeEN^1W1k8POUGo0j8UORGl0n8UOQGDgNU1X:XORGh0n8ZOoFg0Q9\\OlFd0T9^OjFb0V9@hF`0X9@hF`0X9@iF?W9AhF`0X9_OjF`0V9_OjFb0V9\\OlFd0T9[OnFd0R9[OoFASO9H0V:6_GD^N6]:KaG5j:O000000001O0000001O00`MKmG5P80nG0o76nGJo7:QHEm7a0cG\\O`N3m9c0aG_O]NN^91jFf0g0WOPOh0^9f0aGA^8a0aG_Oi87WGIV9IkF7b9\\O^Fd0S;06J5K4G_CCc<9_CGc<5_CKc<2^CNd<N_C1l<0000000\\:O_E`0F0iDI]98cFJZ95gFMW93hF0V90jF0V90jF0V90jF0W9NjF2V9OiF1Y9MXEC]1`0]9MVEC[1`0_9OaF1`91]F0b91VE^OV1a0c94\\FLe94ZFMe95YFKg94ZFLf93[FMe93[FMe93[FLf94[FKe96YFKg95YFKg94ZFLf94ZFLf94ZFKg95[FHf98YFIg99WFGi99WFGi99WFFj9<TFDl9=SFCm9;SEXOQ1<m9;UFEk9<TFDl9<TFDl99WFFj9:VFFj98XFHg9:XFFh98ZFHf98ZFHf98ZFIf96ZFGi9:XFCi9:oD@Z13h9=nD@]10e9a0b1O11O00002NO20ON22N1O01O01O002N0000O1001O00001O01O01N01N21O00O12N00O1001OO11O00O101N11O00O11O1O0000O11O00O1O1O11OO1N2000000O100001O00O11O001OO1O10000O10O100000000000000000000000001O1O01O000000000000001O0O11O0000001O00O1000010N100O100O100O100001O0VCDh<`0M1000000000O1O12NO2N12NO20OA`C6Pi51acJ7XCBg<?01N110OO2O0O11OO1O12NO1N200100O1O1O0000001O00O2O001N10000000000O1O0N21O1LGXC;i<14L4L5Kll20nRM6N2M2O3O00O2OO2O0000010OO01102M41M2K2N11OO1001N01001O00O020OO2LVCGk<57KRd4", "size": [424, 640]}, "area": 19581.0, "iscrowd": 0, "image_id": 10211, "bbox": [0.0, 303.0, 628.0, 121.0], "category_id": 111, "id": 10012435}, {"segmentation": {"counts": "dQl22S=7K4L1ON43MN002N10N1100001O000010N3N2JWo4MjcJ0S=1mB2m<424L0O1O10K;LI8M001OM7OI4O11N110O03N01NN2YCAe<a01OO1O02O0N2N13M0O1010OO1100O100O002O0O1O100O1O100O3M2N2NcYi02[fVO00101oBKm<9N101O1N10O1N110O10O0100O010000O2OM33L100O10000O2O1N2OO10O01O010000O100OM45KO02OO03N000O010O1000N1O23L01001N01O11N2O0O100O101N2O0O002OK46K0O1OO2N15K10O02N010O5K10O0100O1O010O1O01M24NO3L4N2L301M3L4M3M3L3N0000000O10O1M3M1^Od0NeN[DX1j;010O1M4NNYDcNg;]14MO5NN1O1O02eDcNi:_1UEcNi:[1XEeNFNm:\\1\\EgNEMN1Q;Z1\\EmNCIQ;[1[EoNc:EQEY1=TOa:n0`EROa:n0]ESOc:n0\\EROc:n0^EdND<n:o0aEQO_:Q1^EPOb:o0_EQOa:m0aESO^:Q1aEmN_:S1aEmN_:S1bElN`:2SE?=@P;`0PE@];4cDK_;1cDO];3aDM\\<100000O20N100000000000000001O008Hbh0N`WO0nB0l<1SCON0O0k<1XCON0O0k<1UCO1000O0k<3VCM02j<1UCN00i<?L101O01L\\CBe<=[CCe<=lDDbN1h:<aFHdNNj:;bFM`92cFL[96bFL_93_FO`9OVEC\\1;`91bF0^90aF1]91aF1^91`FOa93[FMe95[FJf96ZFIh96XFJh96XFJg97WFKi96WFIj96WFIi96YFIg97XFJh95ZFJe97[FHg96[FIe97ZFJf95ZFLi91h1N7I5LgQ<2WnC9F12NL50ON20O102ON13MM32N02NN4M2M4L3Nnh4", "size": [424, 640]}, "area": 7204.0, "iscrowd": 0, "image_id": 10211, "bbox": [222.0, 123.0, 406.0, 85.0], "category_id": 113, "id": 10012436}, {"segmentation": {"counts": "`c=1Q=6J6O011O00O100001O01O01O00O101O000000000000000000001O00000000000001OO100000000001O00000000O1000000O11N1000000O100001O00010O00O020N1000O20O10O000000O11N10000002N0O0110O000000O11O001N1001O000O100N3O0O2N10O2O00O1001OO2O0001O002M2O01O01O0O1001O0001O00O11OdJDnM:m1HgHO[59m1FhH120Y57l1MkHLY59j1MkHKZ57j14VNMi13WNMe17\\NGf19YNGi1HmH=Z5Lh1GoH<Y5Mj15TNLl14TNLm14RNLn14QNNn12SNNl13SNLo11RN0l1DmH<W50j14UNMk13oH@R5`0n12TNLl13UNMk14TNLl12VNNk11VNNj13VNJiJFP7a0XNDo1;_5O7L0M100100RCHh<=N23N0N10010O\\HMS23gM3Y2MgM5W2JjM6V2JkM5U2LjM5U2KkM4U2NjM3l1@UIb0o4Oj1_OYIa0m4Oj1_O[Ia0l4Oh1A]I>m42d1_ObI=k43o1LRN4o1ISN8l1GUN9k1FVN:k1DXN:h1DZN;h1CZN<f1C[N>Z7EWCMk[52XQK;D001O00O1001O2NM4O0001O001O0000O11O0010N11O2N00NZCBf<>201N100000001O000000O110O000000O1001OO100010OO1001OO01UCEi<=0O1000000001OO110N10O2O0RNFnF2ol43V^K2N3M>B5K1nMAZG>m8ZOTG5eNB\\<>70O100003MXc4DT]K0^N2jENV:?^E@c:`0\\E@c:a0]E_Od:?lDAO0U;?lDCMNW;?lDFJKZ;?lDHHI\\;?lDK[;5eDMY;3gD0V;0jD2T;NlD2T;NmD1S;0mDOS;1mDOS;1nDNR;2nDNR;2nDNR;2nDNR;2nDNS;1lD0S;2lDNT;1mDOS;1mDOT;0lD0T;0lD0g5AhI1X5>XK1h5GlN:YK0k5EmN:YK1j5FmN8YK2j5DhIKU5?YK2j5DoN:VK3k5CnN;WK2k5CoN:VK3k5BPO<TK2l5BPO<TK2l5ARO<RK3l5AQO=SK2l5ARO<QK4m5@RO<QK4m5@RO<QK4m5@RO<QK4m5@RO<QK4m5@RO<QK4m5BPO:TK3l5CPO:QK6m5[OjI7V5:PK5l5\\OnIN08U5;nJ4o5[OnI9T5;lJ1R6HROP1n0POQOQ1o0oNQOQ1n0POSOo0l0ROTOn0k0SOTOn0l0ROTOn0l0SOSOm0m0SOSOm0Q1oNoNQ1R1`NlI;R5V1V1gNkNY1X1cNhN^1a600000N200001O00001O001O00001O001O00000000000000000000000000000000000000001O000000000000000000001O00O100001O0000002N003MN21O1OO11OO11OO100000000001O00N20000O100O11O00000000O11O00O100O11O002N001O1O001O0000000000N2O100O1O10000O10000O1O100O1O1M_N^Db1b;2O10`NeDS1Y;kNlDT1d;0000000@oNhDR1V;a02_NhDn0X;lNnDT1c;000O100O1N21O2N00001O0000000000O100O100002N00O1O1000000000000000000000000O100000000001O0000000lNSDm0T<000O1000000O100O100O1O1O10000O10000000000000000000000000000000000000000001O00001O000000001O000000000000000000O1", "size": [424, 640]}, "area": 17185.0, "iscrowd": 0, "image_id": 10211, "bbox": [32.0, 173.0, 608.0, 251.0], "category_id": 124, "id": 10012437}, {"segmentation": {"counts": "fZm31WQ7;_[I5M33M[oR4", "size": [424, 640]}, "area": 67.0, "iscrowd": 0, "image_id": 10211, "bbox": [302.0, 284.0, 21.0, 22.0], "category_id": 125, "id": 10012438}, {"segmentation": {"counts": "kim134Ol<ORC40Mi<;O11OL6N10O0001O0100N0O31NL5N12OMeg3HfkK2KLf<`0000001N01O1@ZC?j<ML41@YC<k<0O0L4eCAn;>kCBNON2n;0YDm0Z;RO_DO31O03P1[;UOaDKO22Q1];SOaDO0P1_;b0O1E82001OO01O1O2O0000O1O1@mDiNQ;Z1RE[NM1V;d15ZNgDg1Y;41OO1M32MO20O3N1O1EcDiN^;S1iDiNY;T1?N^DlNR;S1kDSOFI\\;0cDQ1>UOBJ01\\;o0PE_Ol:a0TE_Ol:c0RE_Om:a0SE^On:`0SEAn:>RECm:=SEDl:<UEBl:>TEAj4Bi0m0]JB`4KTKEj5o0cJ^OZ48S1;aJ]O[4:U1:^JZO\\4?V19XJZOa4a0U1g0kN[OU1d0iN_OX1a0dNA]1?aNC`1<]NGc18\\NJd17YNJh16WNKi15UNNj13TNNl11SN1m1ORN0P2BRJjN0=0D1O20\\3d1a2@QJlN2>MB30Z3f1b2\\OXJ@Z3U1^2[O\\J^OE@[3h1_2XNZJR17@o2W1Z2XN_J00P19@m2Z1\\2VN\\JV1<ZOk2[1]2_OcJZOP3X1^2C^JSOR3\\1Y2CmJnNi2`1Z2BmJmNh2c1[2@nJlNf2e1]2^OjJoNh2e1b2\\OeJlNh2i1d2VOhJPOd2l1c2mM\\Jo0>WOb2o1e2gM\\JO0T1;XOd2o1e2fM_JR19WOc2R2f2eM^JR18WOd2R2g2dM^JR1:TOb2W2l2fNbJQOc2Z2k2fN`JPOf2Y2i2fNcJmNA1T3]2lLbMj5R1gJnNd2a2c2cNhJlNf2`2a2dNRN\\1n1cNSN\\1n1eNQN[1P2dNeJmNj2`2b2bNbJoNl2`2b2`NmM`1T2_NlM`1V2^NlMa1S2`NnM`1U2]NjMd1V2ZNlMf1S2[NmMe1T2ZNkMg1U2YNkMg1T2ZNlMf1T2YNlMh1T2YNkMg1U2YNkMg1X2VNgMk1W2WNiMi1W2WNiMi1X2VNhMj1Y2TNhMY1k2bN\\I@j3^1Z3aNmHO010OO0U4l0e3TOgL5]j>Oj]A:oNQ1L4M3M3M3M3L5L3K7I7J4K6M2N2N3M2N2N2N2N2N2K5O10000O100O11ONWLeFi3[920000O101N1000000000O10002N011N1O1O0000000000000010O0[O\\LfGd3Z8]LWGK;i3^8`L_Ga3a8bL[G`3d8b0000000000000000000000001O00O10001OO1001O001O1O001O0gKTGV4l8iKXGT4n8001O000000000000000000000000000N22N0000000000000000K51O0000000000O1O1N20O011O001O00000000001O00000000nMhKTKm0TNX2d6oLXKf0WN\\2_6PMYKc0YN]2\\6RM[K`0[N]2Y6RM]Kb0ZN\\2X6SM^Ka0[N[2U6VM`K?ZN\\2U6XM_K;\\N^2T6VMbK;[N_2Q6YMbK8^N`2n5YMdK7_N_2j5]MgK3`N`2g5^MkK0_Nb2e5_MnKL^Ne2d5_MmK]OQN6b0n2U5hLdJ0Fb0m1@RNN17d0Q3R5hLjJ?a1AoM7d0Q3R5hLjJ?d1I_NP3T5hLgJ>ABU29^No2U5hLfJ>j1LZNn2V5hLgJ>h1K]Nn2T5iLfJ?i1J`Nl2c5VMnKMbNk2`5XMnKLfNi2[5\\MoKJiNh2Y5]MnKLkNd2W5`MnKLmMIi0i2]5`MnKNjMLm0c2[5cMnKNiMLQ1a2W5fMoKMhMLU1_2Z5^MmK<lNT2f7lM\\HR2V7bM_G0:<S1P2T7fMiG:U1m1m6oMnG4MGn0T2P7WNVHNKIP1P2o6YNVHNILR1l1P7YNUH1FMU1g1P7[NTHa0l0S1o6^NUH`0k0Q1P7^NVHb0j0o0P7_NVHd0i0l0S7^NTHg0i0k0Q7_NVHh0h0h0S7_NUHj0i0UOSOl0o7UOUHl0h0POZOl0h7XOVHm0h0lN^Om0d6oNlH4OM04962o0`1D\\5QOjH9OK9:2n0`1^O`5OeHWO0?:n0c1iNo5V1^H3e9J^F7`9IaFSOCf0k95TGKk84UGNk80UG1l8kNTFQ1h0UO\\Ol0g9nNUFQ1U11e8nNVFQ1U11g8OYG2d80\\G1f8LYG5R1UOS5f0iI8i0]O^5:iI9d0Bc56hI9c0Ac5WOiHP1P19c0Ac5VOjHR1f0PO]OW1_1Bd5<]I<n0YOe5`0WI7S1ZOf5?VI9S1XOg5b0TI6S1YOj5>UI9R1XOi5=WI<o0WOg5SOlH\\1;<R1TOg5b0XI:Q1TOg5a0YI<P1ROg5`0[I?m0QOi5>ZIc0l0POk59YIj0k0mNl59XIl0k0kNm59WIm0l0jNm59WIn0k0iNn59WIn0k0iNn57YIQ1h0hNP67WIQ1j0gNo59VIQ1i0gNQ69UIP1j0hNP67WIR1g0hNR67VIQ1j0gNn59WIQ1m0dNl5TOlHV1:S1o0aNm5;UIT1P1_Nl5;UIV1Q1]Nj5T3XJjLh5W3YJgLi5W3YJgLg5Y3ZJeLg5[3\\JaLe5`3]J\\Ld5d3^JVLWN0\\7j3P2OO21OO1O0O2N2MQLkFP4T93oGTLd6m3TI]Li6d3RIbLl6^3PIhLm6Z3mHmLP7T3mHPMR7P3mHVMn6k2QIUMP7j2oHWMQ7i2oHXMo6j2PIUMQ7l2oHSMS7l2lHTMS7n2mHQMU7n2lHQMS7dNhHZ46PMT7Q3PIiLQ7NgHU2d0`Md6;QIm1<iM`69UIQ2:hM]69WId2J]L0;m6`0^Ih22cLa6f0\\Ih23`Lb6f0]Ik21_La6f0]Im21\\Lc6g0\\In2N]Lf6g0[Il2GTLN<o6c0]Io2F`Ln6d0YIa3h6[L[If3c6]L[Ic3d6\\LgIZ3Z6]LoId3j5bLVJ^3i5cLXJ]3g5cLYJ]3g5cLYJ^3f5bL[J]3e5cL[J]3e5cL[J]3e5cL\\J\\3d5dL]JZ3d5eL]J\\3c5dL\\J[3e5eL[J\\3d5dL\\J[3e5cL]J]3c5dL\\J\\3d5dL\\J\\3P1", "size": [424, 640]}, "area": 49663.0, "iscrowd": 0, "image_id": 10211, "bbox": [149.0, 78.0, 491.0, 276.0], "category_id": 142, "id": 10012439}, {"segmentation": {"counts": "Ve]1110T=20NPC0L2k<<VCB02O1Z<j0O1O1O1OO11O1OO1000O1000O10000O2QOhCl0_<J7ZO[C=j<GUCLZia6", "size": [424, 640]}, "area": 630.0, "iscrowd": 0, "image_id": 10211, "bbox": [110.0, 115.0, 24.0, 35.0], "category_id": 150, "id": 10012440}, {"segmentation": {"counts": "ae_14T=1N3N1N3OO01LEZC:f<F[C:d<F[C;d<G[C9e<51CYCO04g<M]C3d<0XCM0Nh<4YCNk<OYCOgj<0VUC0n<1QCOj<OWC4NOi<NWC8k<3N2O1L3L^ObCb0]<@bC>`<6O0I_OgCa0Y<700oCSOg;n0XDROh;o0SDSONN40e;P1YDTO1Me;P1XDTO0K01i;Y1WDgNi;\\101O00O11O1O2ON10MaN\\Da1c;20N2O1001O00M30001O0O1O1O10000000000L40OLQDPOQ<o041OO1M32NN3O02N1O1O1O2N1O002oCiNm;[1NO1O100001O00000000O2O01O00O1001O001O000000000O1000001OO100JUDoNl;j0UDUO00k;l0UDROR<n050OI700001O4L1O0mCROl;0oCm01TOO0P<OPDn01VOn;LRDn00VOm;LSDo0OVOm;KTDV1l;jNTDW1n;O110N2NOmNPDR1P<oNoCQ1R<3O1LjNTDV1l;jNTDV1l;jNTDV1l;jNSDU1o;kNQDX1l;hNTDX1l;hNTDW1m;iNSDX1l;hNTDX1l;11000O103MN2001O1O00L40NN4N03NO2N02011OO00LQDPOn;P1UDmNk;T13200O11NO3PDjNj;Z102OO0NWDeNj;Z111010O00010O0gNUDT1P<jNPDU1o;kNQDU1o;kNQDT1P<lNPDT1S<2M2NM300001O1O000NiNSDX1l;hNTD\\1g;eNYD\\1e;eN[D\\1a;;J2N1O2002N0N200J530N1O000M_D`Nb;V1_DiNN120f;OYDQ11POn;P15M31O000100ON30M21O1OO1O11OM3O100GeCA_<?51O01N0O\\C@d<`021O10OO20O1L[CBe<?21O1O001O00100NM^CCa<=`C@b<a021O001000O10OL\\CDe<;14OL[CCg<=21O001O1O001OO1NZCBg<=12O1OLZCFf<:[CDf<3YC000i<0XCO0Nk<3UCOn<NQC0cV61bVJ5kBKm<802NO1002O0O1O1OO100O1001OO02O001OO11OO100O11O00O100003M3M1O2^C[O\\<i00O1N2O1O1O1O2N1000000>BEnC@R<`0RD\\On;a0iCAa<?40000O1O1001O00O1O10010N1CWC;i<EWC8n0HSO0h;0WD50Ln0ORO1i;OWD12On01PO1h;N_E6h;10O10O1MJTC6k<4N23L2OO10ON31O000000O100O100O1O2N1LA^C`0]<_OcC00e0\\<\\OdCf0[<4O1@TO[D00n0e;ROWD0023l0f;ROWD10T1i;lNVDX1j;3000001O0000O1000O12N00000000000000000001YEdN_9]1aFcN_9\\1aFeN`9Z1`FeNa9[1^FfNmNOc:[1`FhN`9X1_FjN`9W1_FiNa9V1_FkNb9U1\\FnNb9R1[FfNTO8a:R1\\FPOd9P1ZFROf9n0\\FPOd9P1\\FPOd9P1\\FQOc9o0\\FQOe9o0[FSOc9m0\\FTOd9l0\\FTOd9m0[FROf9n0YFROh9n0WFQOXOC`:]1WFQOg9BdE\\1f0ROf9R1ZFnNg9R1WFoNi9Q1WFnNj9Q1VFoNj9R1QFSOo9n0oESOQ:m0TFoNj9R1UFoNk9Q1UFnNk9S1TFnNl9R1SFQOj9P1VFoNk9Q1TFROj9n0VFRO@ZOV:f1ZFROe9o0ZFROf9n0ZFROe9P1YFQOe9VOnEi1<ROf9Q1YFoNg9Q1XFPOg9Q1YFoNf9R1YFoNg9Q1YFoNh9Q1VFPOh9R1XFnNi9Q1VFPOi9Q1VFQOj9n0VFQOi9Q1TFoMNP1n9R1SFQOk9Q27N0100O10000O100O110O0O100001OcN", "size": [424, 640]}, "area": 17899.0, "iscrowd": 0, "image_id": 10211, "bbox": [115.0, 44.0, 525.0, 112.0], "category_id": 151, "id": 10012441}, {"segmentation": {"counts": "PYl62V=O14L000000001OO13M00O11O1O001ON200001O1O00O100N2LPC1o<O5O12N000000O100Oll22RSM1N0200001nBKo<8O1OO1001O1ON21ON21O0000001O00N200O1O1N2M31OO1001g_>", "size": [424, 640]}, "area": 424.0, "iscrowd": 0, "image_id": 10211, "bbox": [532.0, 0.0, 73.0, 11.0], "category_id": 157, "id": 10012442}, {"segmentation": {"counts": "T4b1[2m0`4ROSL6S4IQLNU41PLEW49^4M1O4L1O2N2O0O2N3M3M2N2O4K2N2N2O1O1O1O1N2O1O001O001O00U^10jaN2M3ZO0RD2k;5oCMm;;mCGo;k0L4M4K4L4L4SMWNXJl1f5bNjIb1T6iN`IZ1^6lN[IW1d6mNTIV1l6POlHT1R7QOhHR1V7TOcHn0\\7XO\\Hl0b7YOXHj0g7[ORHg0m7[2N3M2WHcJe7_5[HbJb7c5N2O0O3M2N2N2N2O1N2N3M2N1O2N3N1M3M31O00O20O0O010000O11N1000000000O2O1N1NWJiIf4U6YKmIg4o5[KSJe4m5YKUJg4k5YKUJROFb5W6ZKSJSOHb5W6XKRJVOHa5U6ZKSJUOI`5T6YKUJWOG_5T6\\K[JXO^ONOT5Y6dK\\JZO^OR5V6dKgJY4Z5nJfIg0o0[4]5eKcJ[4[5RKfIL2>o0c4f5PKYI02OQ1R5Q7N101000000001N01000O011OO101O002N1O`NTKcJm4Y5YK_IIL0S1m4c5bK[J_4d5bK]J]4b5bK`J^4a5eK[J[4g5cKYJ]4h5^K\\Jb4d5_K[Ja4e5`KXJb4h5^KWJc4j5\\KWJc4i5^KVJb4j5^KTJe4k5\\KTJd4l5]KTJb4l5`KRJa4m5_KRJa4n5aKQJ_4n5bKRJ^4n5bKRJ^4n5bKRJ^4P6`KoIa4R6^KnIb4R6^KnIb4R6_KnI`4S6^KkI@\\OR5i6_KjI_O]O>OS4l6oKhIA]O71HOa4j6RLWI]O:<DD120]4i6\\L`I_OKHLZ4n6^LZI@NFK\\4h6eLVIPO0:=Q4\\6VMdIj2^6PMfIn2_OfKi6_1hIj2\\6VMSIaN=Y4_6WMTIaN<Y4a6VMaIh2_6YMaIg2_6ZMaIe2`6ZM`If2^6\\MbIe2[6]MfIc2Y6^MdId2\\6fKZI52KLS10mN<[4l6fLXI[3@dKU7Q1[IoN@]4T7dL^I\\3c6cL\\Id2]7]MdH`2]7aMaH^2c7bM\\H[2g7eMZHW2i7hMXHU2j7mMUHQ2n7nMQHP2R8kMUHP2n7^M]G3e0]2Q8YMZGNON2<e0^2b8_M`G^2c8`M_GIVOa2^9dM`GZ2b8fM^GX2e8fM]GX2e8gMZGX2f8jMZGT2h8lMYGQ2j8nMVGQ2j8PNVGn1l8RNTGl1n8UNPGj1S9UNmFa1c1^NS52ZIT1h1POn4MYIh0n1Ah4IZI;W2M^40TIKe26g3_OYIa0<Bj2a0_3_OPIM1e0]4Oc29]MIc26]MKc24\\MOc20^M0b2OcMM]23gMIX28mMCS2=QN]OP2d0TNZOj1f0\\500O10O10UOfCh0_<YO`Cb0c<]O]C01>j<MO11OO6KO2N2M10011NO2O1N1O2N1011O0O10O11N100O2OO10O4CUC1O2Q=MoB3S=02MojY14jTfN7H8L4La0@O10O10hNVDi0LWO13S<g0801VOdCf0`<O001O1O00O1N200O100O100O10000001MbCZO[<e0gC[ON0^<b0dC^O^<b040000O1O2N2O1OO10O01O10000000000L45KO11OO12N0000O1000000N2N32L01010OO2N20O10O0]C^O`<b0_CB^<d0001O1O00O11OK55K0000N2N24L00O11M13N04L00O1000000O10K51N1M33M00O11N003L20O011O0O2O0000000O101O000N2001O001O1O2MOBZC>f<2O13MO001O11O2NN10O102OM1111N2OO1VCDg<?2M01N23ON001N0101MXCDf<>4H`hb1", "size": [424, 640]}, "area": 32586.0, "iscrowd": 0, "image_id": 10211, "bbox": [0.0, 115.0, 517.0, 227.0], "category_id": 162, "id": 10012443}, {"segmentation": {"counts": "0T4T9O1O1O1001OO12NO10000O1001O1O00003M000000000000000000O100O100O12NO1O1O1O1O1O100O1O1O1O1O1O1N2O1O1O1O1O1O1N2O1O1N2O1N2O1N2N2O1O1N2O1O1N2O1O1N2O1O1N2O1N2O1O1N2BXE[Ni:]1aE_NR1Ok7b1UG\\Nn05m7_1aHdN`7\\1]HhNb7Y1ZHjNf7V1WHlNj7T1SHoNm7Q1RHPOn7P1QHPOP8P1oGROP8n0oGSOQ8n0nGROR8n0mGSOS8m0lGTOT8k0lGVOT8j0kGVOV8i0jGXOV8h0iGXOX8g0iGWOY8i0gGVOZ8j0gGQO]8n0dGRO\\8n0dGRO\\8m0fGTOX8l0hGVOV8k0iGTOX8m0hGROX8n0hGQOY8P1fGQOY8P1gGoNY8Q1gGoNY8R1fGnNZ8S1fGlNZ8U1eGjN\\8W1dGgN]8W1eGiN[8W1eGiN[8V1gGiNY8W1gGiNY8V1iGiNW8X1gFYNe0?d8X1hFYNd0>d8V1]F^N:0c0<f8V1]F_N:Oc0;f8W1]F^N;2`08i8W1^F^N94`06i8X1lFdN94k8Y1_F\\NO049b01l8Z1_F`N37`0On8[1bF[NM0N<f0Lm8]1aF\\NN`0c0GZOHa9f1aF_NO>e0Jf8Z2[GiM_8Y2aGbMb8_2`G_M_8b2`G]Ma8d2]G\\Md8e2ZG]Me8e2XG^Mf8c2XG_Mg8b2VG`Mj8d2QG]Mo8h2kFYMU9^3O0000000000AmFaLO2T9^3kF^L40MOU9b3>O11OL4001O0000001O1OeN\\FlM0P1e9T1\\FkMOR1d9n0[FQN4NOQ1b9P1\\FoM8S1[9o0hFPOX9o0jFQOU9o0lFiN[9T1hFkNY9Q1UFXNb0h0X9o0XFXN>j0[9n0kFROT9n0lFQOU9h0ZF^Na0k0T9g0SGYOm8g0SGfNSON71^9[1YGeNROOU:\\1jF`NSONN4W:^1iF`NTO1S:_1jF^NTO0K0X:b1RG^Nn8b1\\10000000000001O00M]D`Nd;`1200000000000000001O00000000000000000000O11O00000000001O000000000000000000000000000000000000001O000000000000000000000000O10000000000001O00001O000000000000000^D\\N`;d1`D\\N`;d1`D]N_;c1aD]N_;c1aD]N_;c1aD]N_;f10001O1ONcDZN\\;f1eDYN[;h1200MbD\\N^;d1bD\\N^;d1bD\\N^;d1bD[N_;e1aD[N_;e1aD[N_;e1aD[N_;e1aD[N_;e1aD[N_;e120000O1000000000cF\\NV7d1eH\\NRN1Y9c1nG_NhNNO010OO0<Y9X1PHcNgN?X9l0oGBkNlNQ9b1RHGo77THGm78RHIo77oGJR84PHKQ85PHJP88oGFR8:nGES8;iGaNPOR1X9=hGdNmNn0\\9>hGEY8:iGEW8<iGBX8>iG@X8b0fG]O[8b0hG`NjNh0^9h0lGXOT8h0nGUOS8l0lGTOT8l0jG_NeNe0a9m0iGUOW8j0jGUOW8k0jGTOV8m0iGSOW8m0iGSOW8m0jGROV8o0iGQOW8o0`F]NT1f0[8Q1_FYNU1e0\\8R1_FYNU1e0\\8R1_FYNT1f0]8Q1_FYNT1g0\\8S1fGjNZ8V1hGhNX8X1eGkN[8U1eGkN[8U1eGlNZ8T1gGkNY8U1hGjNX8V1hGjNX8V1hGkNW8S1bFXNX1c0W8U1aFXN\\1?S8X1bFYN\\1>R8X1cFZN\\1=Q8V1ZHfNf7X1\\HhNd7V1]HkNc7S1_HmNa7S1_HlNb7V1\\HjNd7V1ZHlNf7T1ZHlNf7T1THROl7m0WHQOi7Q1UHoNk7R1THnNl7R1THnNl7R1QH`N`N<`9V1fG_NiNN120;`9[1_FYNQ11PO`0`9Y1`GlN`8T1]GoNc8Q1^GnNb8R1^GnNb8R1^GnNb8S1^GkNc8V1\\GjNd8T1_GkNa8V1\\GlNd8T1]GkNc8U1^GjNb8V1]GkNc8U1\\GlNd8T1]GjNd8V1YGmNg8S1XGnNh8R1XGnNh8R1oFTNAn0`9n0oFWOQ9i0PGVOP9j0PGVOP9k0nFUOS9j0lFTN@R1d9j0lFXOT9h0mFWOS9i0nFVOR9k0mFUOS9j0oFUOQ9l0nFTOR9m0jFRNBQ1e9l0jFVOV9j0kFUOU9k0lFTOT9l0lFTOT9l0mFSOS9m0mFSOS9m0nFROR9o0nFPOR9o0kFSNCl0c9Q1jFTN@m0f9o0kFSOU9m0lFROT9n0lFROT9n0mFQOS9P1mFoNS9R1lFnNT9S1lFlNT9T1hFPNDk0e9V1fFnNZ9o0jFPOV9P1fFRNCP1g9n0fFTOZ9l0gFSOY9m0gFSOY9m0hFROX9n0iFQOW9o0iFPOX9P1iFoNW9Q1hFPOX9P1fFRNBn0h9Q1eFQO[9n0gFPOZ9P1gFoNY9Q1cFRNFl0h9R1bFSNDl0j9Q1YFRN000n0h9P1XFSNO0Nn0l9P1WFRNOP1j9o0TFPN0U1m9T20O1O100O100O1O100O100O1O100nNlEoNU:Q1PFQNK7d:h1dEYN[:g1gEXNX:h1gEZNX:f1hE[NW:e1kEZNT:g1lEYNS:g1nEZNP:f1QFZNn9f1QF[No9e1QF[No9e1PF\\NP:d1PF\\NP:d1QF[No9e1PF\\NP:c1RF\\Nn9d1RF[No9e1RFYNo9g1PFXNR:h1oEVNR:j1mEUNU:k1kETNV:l1iETNX:l1iEQNY:o1`000000^ETNh9l1SFYNm9g1VFVNj9j1WFUNi9k1YFQNi9n1i00000000000001O000000000]EVNh9j1XFZNd9f1YFQNA97IGOg9n1jF^NGL\\9f1mF^NFM]9e1mF_NDL`9e1kF`NEJa9f1iFaNEKa9d1jFaNDKc9d1jFbN@Nc9`1mFSOS9m0lFSOU9m0jFUOU9k0kFUOU9k0lFUOS9l0kFUOU9k0^FPN;T1X9l0]FPN8n0HSO090K<MZ8n1ZGPN50Ln0ORO1Q1e8o0ZGPN12On01PO1]1W8c0gGWO67Q8j1PHVNP8j1oGeNc7[1]HeNc7Z1^HgNa7V1YHlL6n1a7U1cHjN^7T1dHlN\\7W1`HjN`7W1_HkN_7T1bHkN_7U1`HmN_7P1dHPO\\7Q1cHoN]7Q1cHoMGPO4OM049a7i2cHPNFoN9OK9e7i2aHPN?nNWO0k7o2`HSN\\8n1dGRN`8i1aGVN`8j1`GVN`8i1aGoMC]Ol8c2cGoMA\\O0Mk8g2eGPNA[OONl8b2UG]M`0h0@[OONk8^2UGaM00e0h0\\OXOO100n8Z2SGeMf0h0\\OYOM0n8Y2mG^NXOXON0m8Z2nGZN_8U1eF\\N00n0>_8U1cFYN0023l0?_8U1cFYN10T1b0Y8T1cFYNX1d0R8U1QHlNn7T1RHlNn7T1RHlNo7S1RHkNP8T1PHmNo7S1QHlNo7U1PHmNo7S1QHlNn7V1RHkNi7[OnFh1Z1mNh7Z1VHdNk7]1UHbNn7\\1RHdNn7\\1RHbNP8^1PHbNP8^1PHcNn7^1RHbNn7^1RHaNn7`1RH`Nl7b1TH_Nk7a1VH^Nj7a1WH_Ni7a1VH`Nj7a1UH^Nl7b1TH_Nk7a1UH_Nk7AmFi1X1fNk7@nFj1X1hNl7Z1SHhNl7Y1THlNf7T1ZHnNe7TOPGk1[1ROg7m0YHQOj7n0VHTOg7m0YHSOg7m0YHSOh7l0XHSOj7l0VHTOh7n0XHQOj7n0VHROh7P1YHoNh7P1XHoNi7Q1WHnNj7Q1WHnNj7n0iF]N\\18gNOU9[1XHcNgN1Q9]1gHbNX7`1hH`NX7`1hH_NX7a1W21N2M22OO0101NO2000N2O100O1NaD[N_;e1200O1O1001ON21OO11ON2O1N21OO100O10000O100O11O00O100001O", "size": [424, 640]}, "area": 53268.0, "iscrowd": 0, "image_id": 10211, "bbox": [0.0, 0.0, 640.0, 176.0], "category_id": 169, "id": 10012444}, {"segmentation": {"counts": "RoX2884T<`0O11OO1000O2O00dCVOY<P1NK50dCUO[<l0N10L50OFeCHX<OgCJO31O03Y<2lCJKO22Y<5kCIO0Y<6a00]mf5", "size": [424, 640]}, "area": 530.0, "iscrowd": 0, "image_id": 10211, "bbox": [176.0, 97.0, 22.0, 33.0], "category_id": 177, "id": 10012445}, {"segmentation": {"counts": "Yj>b0f<000000000000010O000000001O000N20000O10000O10010OZO`Cd0`<\\OaCc0b<000O11O0000000000001O00O1001O00000000001O00O01000000O010001O000010O1N110N100001O001O000O10O11O000000000N10101O000001N02O001N11OO1000O100N200O1O1001O00O1001OO100001O011N1O01OO2O00000000001O00O11N3NO02L31O0000NSOiCn0V<2001OO010000O200O0O2O1UOdCg0`<M22c0[O1O_^72VaH9I7N3O00000001OO100001O00000002L22O5JP\\5_O\\WJg0^<10ZOaCa0`<]OaCc0c<O1O00N22N1O01N100O1001O00O1M]O`Cd0`<2O1L50O000000000001O0001OO1O20O00000O02M2N2O1O1O10000O10001O02iNoCS1T<O3M1N2O2N3M2N2N000000O1O2M2O00100OM8Big36jWLa02N3M1O4L2O2M1O1O0O0100Ri5?_VJ010N011O00000O11O0QOiCm0Y<SOgCk0[<03M1XOaCe0b<O1O2NO1O10001OO2O00O2O00000002OO0002M20M@\\C`0d<210OON4O0N21ON20000001OMZOdCf0\\<ZOdCf0]<2O02O01O02M011ON22N1N100O2O002N0N20O1010O00010O01O01OO2M210O0O12N00O1001OO11O00O100O11O00O11O1O0000O20O00O1O1O11OO1N2000001N10O101O01N11O0011M1O4MN21N2OO11OO11OO100000000001O00N201O1N100O11O00000000O11O0O02O0O11O011N0O2O1O010N100O100O10N10100O2N1TOfCj0^<N0O10000O1O01O03M0N^C_Ob<b04M011M2I[X1KjgN5O55Ihd10V[Na0TC[OX<k0OUOeCk0[<01O010OO100O11OO0012M01N202OO1N2N2O0000001O00O10000O100000O1000O1O1M301O01O0000000EWm2OUSM4L6I02O00O0101O0000001OO1001O005K4L1O00000000O100000001N11OO2L^O_Cc0]<6L4O101O000O11OO11OO100000OPE", "size": [424, 640]}, "area": 12646.0, "iscrowd": 0, "image_id": 10211, "bbox": [35.0, 331.0, 605.0, 55.0], "category_id": 178, "id": 10012446}, {"segmentation": {"counts": "YgX15Q=2N2N200UCFh<:WCHg<9YCHf<8ZCIc<9]CGd<?_C^ONOX<e0jC^OV<m0NO00O20N2O1N2O1OO11O1OO010000O10O10001N102M2OOM50N11M24L00L41O1M2O110O0N3O101N0O2O0000000O100010NNbC\\O_<c0aC]O_<c0bC]O_<1`C:l<N1O3GQC0hmT6", "size": [424, 640]}, "area": 1703.0, "iscrowd": 0, "image_id": 10211, "bbox": [98.0, 141.0, 67.0, 37.0], "category_id": 182, "id": 10012447}, {"segmentation": {"counts": "f5^1\\3jNh4f2cI[Oa4i0VK^Oh4g0nJ@P5^4M2N2N2TJlHb5^7N2O1N2N2N2N2N2O1N2N2N2OL41O1O1N2O1O1O2N1O1O1OlNgK[IY4`6ULXIi3c6eLUIZ3T6X2O1O1\\OUIkJl6R5\\IhJf6T5bIfJ_6W5j0M3N2M3M3M2O2TMPH>S8BYHOj70^HFe79aH_Ob7`0cHZO_7e0gHTOZ7l0kHnNW7R1nHfNU7Y1PIaNQ7_1UIZNm6e1Z201O00010O1O001O1O010O1O001O1O0010O01O1O0OEcNPE[1k:oNQEP1R;ROkDl0X;?3L5L3M3N2O1O1O101N1O1O001O001O02O000O2O0O1010O1O010O1O010O1O100O2O0O2O0O2O0O2O0O1O2O0O2O1N2O10O01001O0O2NN2N2N3L3N2N7IY[`0Z1\\c_O3L4\\E[ND0X9i1lFbNFG[9k1fFjNJ\\O]9n1aFYO]9Z2M3XLcLbM`3]2cL^M`3a2aL^M`3a2aL]Ma3b2`L\\Mb3c2\\L^Mf3b2ZL\\Mi3b2[LZMX3YM_L]5:WMP3fMeLS5;UMh2SNlLh4<SMa2_NRM^4=RMX2kNZMS4>PMQ2WO`Mi3?nLk1BeMa3?kLh1KgM[3a0`Ll1<bMU3a0TLR2c6mMRI[2V7`M`Hi2f7mLWHY3j7hLnG_3Q8dLiG_3X8bLfG_3Y8dLeG[3[8gLdGY3\\8hLbG[3\\8gLbG^3Z8bLeGc3W8]LhGi3S8XLjGn3R8RLmGS4o7nKoG_4e7W1ZOg0ZO;E1O1O1ZJiHY5c700006JO1O1OgMbJjL\\5P3ULgKj3Q4lLbKT3V4VMhKi2Q4`MnK`2m3fMQL[2m3iMQLV2m3nMRLR2l3RNRLm1l3WNSLi1j3[NULe1i3_NUL`1i3Q4M3N101O0O2O1N101N2O001N2O0O2O0O2O1N101O0O2O0O1010O010O2N100O2N100O2N10O00001O01O10O01O010O10O01O10O01O100O1O0fJnLkKGX4[3cMRM[O9dN^O\\4X3RMnMZO1c3U2]LgN\\O^OV4o1gKW1Y4m3N1O1O1O1O2O0OcNRLbIm3a5TM\\Jk2`5\\M_Jb2_5dMfI]N>n3j5WNQJi1m5^NnIa1P6gNlIW1U5NfJ1U5T4J6J5Y1hN=C=CS1lNe0\\O5K4RLTGX3n8dLTG\\3n8`LTG`3n8]LSGd3n8XLTGh3V9000010O00nLUMgKk2n3lNfJT1V42hKOS48jKHS4=kKCR4c0lK\\OQ4i0mKXOn3o0oKPOo3U1oKkNn3[1oKeNm3a1QL_Nj3b5L4K5L4N1O2O1N2fNSFWOo9e0XFVOj9e0^FVOd9f0cFUO_9k0dFPO^9P1\\1010000O10000O10O10O10000O10000O010O10000O10000O01000O10000O100O01000O100O10000O10O010000O100O10000O010O10000O100O1000O0100O1000O010O010O1O01O010O01O01O010O00010O0010O00010O0B?1N2N2O1N2N2O2M2N100O12N3M3M3M2M4L4K5L4K5L3M1N2O1O10nMVEe1k:SN]Ek1Q;M3[Oe0N2ORGPOf5o0XJTOg5k0ZJVOd5k0\\JVOc5j0^JWO`5i0aJWO^5h0dJWO\\5i0dJXO[5h0eJYO[5e0fJ\\OZ5b0hJ^OX5`0iJBd5N]J3c5M\\J4d5KYJ9h5EVJ>j5AWJ?i5@YJ?h5@YJ?g5@[J?e5@[Jb0d5]O]Jf0P93M4L3O2MPOPDj0n;UOSDn0j;QOVDR1n;3M3N2N2NkNWDm0g;QO]Do0a;oNbDR1\\;mNfDS1i;O010O001O1O0010VDTOS2O\\6m0^GZOn1Nd6h0[G^On0FZO?_8U1SHCn7=mGGU8:WG7j8]1hNjLkIn2[6VM^HCf0W3BoLZ6h0UJ_2TOnLg6c0WJS3h5dLbJ\\3^5fLYI\\Ol0m3k5SMVJk2k5UMUJm1\\OZMa6i0TJh1HTMV6T1SJX1Z7gNgHk0g7SO\\Hf0j7ZOVHd0m7[OTHd0m7[OTHc0o7ROZHn0l7`NdF0e1^1m901N2O00100O010O100O1O1O001O001O001N101O02N2O1N2N2N2O1N2N1O3N2M2N3M4SFoM\\8T2[GQNf8S2jFXNW9l2001N2O1O1fM^Fk0d9PObFn0_9mNhFMMH\\96mFMOFX98oFNV:0mELV:3a1O0O1010000O1001O103L=C16[ObC2l<0oB0e<=M0000N2M4HUCLP=4n[8", "size": [424, 640]}, "area": 55673.0, "iscrowd": 0, "image_id": 10211, "bbox": [0.0, 51.0, 619.0, 372.0], "category_id": 183, "id": 10012448}, {"segmentation": {"counts": "WSf41o>3I5OO2O000001O0001O4Lf\\1:PcN001OO001O[OEgB:P>0YOFhB:V>JSm0;aROb0^O00001O0000001O0000O101N1001O002N0000001O1O0000001O3M2N11N:F3M011NN21OO10000000001O000000000001O0001O0001O0000000000001ONSOQBm0o=30OLROVBn0j=400000000000O10000001O0001O000000000001O00000001O000001O000000000001O00O1O11O0001O00001O01O0O11N101O01O000000000001O00000000000000000102M2N00000000000001O00000000000000001O0000000000000000001O00000000000000001O0O11O000000O2O000O10000O2O0O10000O10001O01O7I1mNVBn0k=QOXBl0n=0O2O0001O00000000000001O0000000001O0001O000000000000000O110O0001O1O001O00O10001O01O00O101OO1000O1N3N1O1001O0000000001O000000000001O00000001O0000000000O10001O000000001N1I7O1DnACY>5=Oc[2L^dM4G9^L", "size": [480, 640]}, "area": 10201.0, "iscrowd": 0, "image_id": 10216, "bbox": [320.0, 71.0, 320.0, 74.0], "category_id": 92, "id": 10012449}, {"segmentation": {"counts": "W1m0U>GnA]OV>b05O001O01O000O01O001O000100_OB_B?Q>0NKiAEW>;7O010O01@E[B;e=E[B;T>010O10000O011N2O1O10KcAF\\>8iAGU>9oAEo=:<00O2O004L1O2N1OQ]10obN001O0Tnf43iQYK0O4K2I7O01O01000O10000000001O000001O000O11OO20O01O0001O01OO1000000000000000000NcAA^>=3O1ObAEY>;60001O1L3Lf]1NgSN0b>2\\ANd>704L1OO]OEcB;]=EcB;]=EcB;]=EcB;T=EaB0;;T=EaB09=V=CaB07?X=AaB15?Z=@aB15?Z=@aB14`0[=@aB0Kh0d=XOaBm0`=RO`Bn0c<ROQD0\\Oo0b<QORD0\\OP1a<PO\\DQ1c;oN]DQ1c;oN]DQ1c;oN]DQ1c;PO\\DP1d;QO[Do0e;QO[Do0e;QO[D7^OMW<LQDN@5=0R<NPDM@3b02m;OcCL20J3f01l;1`CM3N90M4W<3\\CN6K;4F1]<8fCC79@Nb<5hCD?<j;OgD1Y;ImD5U;JmDLZN6j<MoDMWN3k<0QENP;2o1000000001O2N2N0000001O2NMWCJn:3SE0l:MUE6j:IUE8l:GoD>R;BkD`0V;@gDc0Y;]OdDe0];[OUDR1l;oNRDQ1o;oNQDP1P<POPDo0Q<QOoCo0Q<QOoCP1P<POPDP1P<POPDQ1o;oNQDR1n;nNXDl0h;SOZD3PO1N6g<F\\D1YOOC3k<MYD0`<0X10PZ32ndLOQ1Oil10`PO0ldN0k=100O1N2M301N1TOGjB0@;f=FiB?W=AiB?W=BhB=Y=EdB:^=GaB9_=I^B8b=MWB5i=a000000I7O1O1N2J6@hA7Y>HiA6b>OlAK]=5cBL\\=3eBNZ=2fBNZ=2fB0X=0hBM[=3eBMQ=O`B5?LQ=1\\B5c0JQ=>nBBR=`0lB@T=b0kB]OU=d0c02N000000001O2N1O000VBWO]=i0cBVO^=h0dBXO\\=g0?O100FmAGS>8;O1000000000000001O2N2N2N4L000PC[Oe;f0XD]Og;d0VD]OQ<=_C@I3k<;\\CCH1n<;ZCEF0R=:lBD220OT=;iBD32OOV=;TCFFNW=<RCGFMY=<oBIHK7Ob<=_CJFK9Nb<=_CKE:l<J_Ce0a<[O_Ce0`<\\OXCCNR1j<[OXCDLP1MoNo<=XCj0j<WOTCj0m<VOQCk0o<c00O1O1N2M3O1000001HhBfNX=Y1jBfNV=Y1lBfNT=T1hBmN4OT=f0jB@0J21>On;d0gC^O523LQ<c0iC^O360IT<?nCAN70IS<3eCOm06YOIU<0hCOk09UOJX<MPE9hNJc=MZB93Jc=MZB:2Ic=OYB:2Hd=0XB94Gb=d0^B\\Ob=d0]B]OQ<", "size": [480, 640]}, "area": 5850.0, "iscrowd": 0, "image_id": 10216, "bbox": [0.0, 0.0, 640.0, 88.0], "category_id": 123, "id": 10012450}, {"segmentation": {"counts": "Xf[81o>1N2O0O2O1]ANK3Q>0oA:R>91O10QBWOf=i0[BWOe=e0^B\\Ob=<fBDZ=:hBHW=7hBIY=NTB020a02Z>NO1O0010OWANe>2\\ANc>2^ANb>1VQe0", "size": [480, 640]}, "area": 277.0, "iscrowd": 0, "image_id": 10216, "bbox": [571.0, 37.0, 24.0, 37.0], "category_id": 132, "id": 10012451}, {"segmentation": {"counts": "e6[8d61000000O1000000O10000O10000O10000N2O100O1O100000000O100O10000000000001O000000001OO11OO1001O00O11O00000000O11O00000000O100N2N2000000000000000000000000000000000000001O0000000000000000O1O1N2N20000O10000000000O1N2O100001O000000000000O10000O100O1O100000000CjFPKV9o4kFQKU9n4lFRKT9m4nFQKS9o4nFoJS9Q5oFkJS9T5=0000O1O100000000O11O0000O100O1000000O1004L3M3M2N3M3M2N4L2N3M2N3MlNWGSLg8m3\\GTL`8m3cGPL\\8P4eGSLW8m3mGQLQ8o3PHTLl7l3XHQLg7o3[HSLa7m3cHPL\\7P4eHSLW7m3mHQLQ7o3PITLl6j3ZISLe6l3^IVL^6i3fIULY6i3kIYLQ6f3TJYLi5g3YJZLd5f3_JZL^5f3dJZLZ5g3hJYLU5g3mJYLQ5g3SKYLi4h3XKXLf4h3]KXL`4h3cKWL[4j3fKWLW4i3mKULQ4k3QLWLk3i3YLTLf3l3[LWLa3i3cLUL[3i3hLZLT3f3PMWLo2g3UM[Lg2d3^MYLa2e3bM^LZ2`3lM^LR2a3PNbLl1]3YN`Lf1_3]N`Lb1`3aN^L^1b3dN\\L\\1d3hNXLX1i3iNULW1k3lNRLT1n3nNPLR1P4QOmKo0R4UOkKk0T4VOlKj0T4VOlKj0S4WOmKi0R4XOmKi0R4XOnKh0R4WOoKi0P4XOPLh0o3YOQLg0o3ZOPLf0o3ZOQLg0o3YOQLg0P4XOQLg0o3YOQLg0o3YOPLh0P4XOPLh0P4WOQLi0n3XORLh0n3XORLh0l3ZOTLf0k3[OULe0l3ZOSLg0k3ZOVLf0j3ZOVLf0j3ZOVLf0i3\\OVLd0i3\\OXLd0g3]OXLd0g3]OZLb0f3^OZLb0e3_O[La0d3@[La0d3@\\L`0c3@^L`0b3@^L`0a3A_L?`3B`L>_3CaL=X3aJ^LS59=W3KiL5Y3HhL8X3IgL7Z3HfL8Z3GgL9Z3FfL:[3EeL;\\3DdL<]3CcL=^3BaL?n2]JWMl2FSO5d3m2`JWMc2MWO0f3l2`JXMa2NXONg3l2`JZM^2NZOLh3m2`JXM]21ZOJi3d3lLbL[OIj3f3iLcL\\OGk3h3gLaL^OHj3g3hL`L_OIi3h3gL_L@Hj3k3cL^LCGj3l3bL\\LDIj3k3aL\\LFIi3l3`LZLHJh3n3]LWLMKf3o3[LVL0Ke3o3ZLVL2Je3^3[JhLm1O3Le3]3]JhLh1N8Mc3]3]JiLf1L<Na3\\3_JjLb1K`0O_3Z3cJjL]1Lc0>o2j2TKmLX1Ig0e0h2e2ZKmLU1HiNHd1Q1R3b2\\KnLS1FmNM\\1Q1V3^2`KmLo0GPONS1X1]3V2bKmLl0FTONc0h1k3g1cKmLf0AjN3a00?j1m3e1dKmLc0H^OL<S1XOYOg4S3eKmL?IAL;S1@RO_4Y3gKmL<IDL8U1FlN[4]3hKmL;GEN5W1m4W2oJmL9GFM5k0WO\\Of5X3PKmL8IBL7j0[O[Oe5Y3PKmL6KAJ7l0]OYO<Ie4`3eKmL5K_OJ9n0[OXO:Ng4[3gKlL5M\\OH:^1BnNi4S3lKkL30YOF:a1@ROl4l2nKlL40TOF<_2\\4o0QLlL40QOE=c2\\4l0SLlL46TO[2e4=[LQMM]3h3^O_LVMI[3h3^OaLWMGi2VOnLb4P3dLYMEa2^45PLYMB\\2c4j0\\KkL2V2e4P1YKjL2U2e4m0^KnLOR2c4c0mK[M@P2c4e0mK\\M^Ob1mNWNi5l2lKZM\\Oc1QOWNg5m2lKYMXO_1\\O\\N_5j2cJmLn0>@Z1G`NY5k2aJoLn0=]OV11cNS5k2aJPMn0;]OS14hNP5j2aJPMo0;ZOP1:kNl4k2aJnLQ1<VOl0`0oNh4n2^JkLT1=TOh0f0ROd4o2_JgLU1?ROf0i0UOa4n2SLVMaNg0l0TOa4o2SLWM_Nd0m0WOa4n2SLXM_Nb0m0XOa4n2TLWM`NKD7V1Ib4o2ULVM^NGI=o0Ge4o2VLVM]NDLb0i0Eh4o2VLWM]N@0f0b0Cl4P3VLVMnN9MBn4o2YLVMkN:LAP5[3nKjLVO;J@R5]3lKiL[O8EAU5^3mKgLA4[OGW5P3UJQMi15B1VOJZ5o2]LVMUOOSOL[5P3]LUMVOLSOOZ5o2_LUMWOJPO2Z5n2aLWMUOGPO4Z5m2cLXMUOCoN7Z5m2dLYMTOAmN:[5l2eLXMHLc3l2fLYMEKe3m2fLXMEJe3Q3dLUMEKg3W3^LnLJJi3X3^LnLHKi3n2UJmLZ2:GKj3n2jLWM[OKk3n2kLWMYOJm3P3jLVMXOJn3P3kLVMUOKP4o2lLVMSOKQ4o2mLVMQOKR4P3mLVMoNJT4P3nLUMnNKT4P3oLUMlNJV4P3PMVMiNJW4P3QMWMeNJZ4n2SMXMcNIZ4o2TMWMbNJZ4P3UMUM_NL\\4o2VMVM\\NK^4Q3TMUM]NJ_4Q3UMTM\\NK_4Q3WMTMXNKa4Q3XMTMVNJc4S3VMTMUNJe4S3VMRMUNKe4S3XMRMQNKg4S3YMRMoMKh4S3YMSMoMIh4U3YMQMnMJj4X3WMnLmMJl4Z3VMlLmMKl4Z3VMlLmMJm4Z3XMjLkMKn4[3XMkLhMJP5[3YMkLeMKR5[3YMjLdMKS5[3ZMiLcMLS5[3[MjL`MKU5[3\\MjL^MKV5[3]MjL\\MJX5\\3]MjLYMKZ5[3^MjLWMK[5[3_MjLUMK\\5[3`MjLSMK]5\\3`MiLRMK^5\\3aMiLPMJ`5]3aMiLnLK`5\\3cMiLlLKa5\\3dMhLkLLa5\\3eMgLjLLb5]3eMfLiLMb5]3fMfLfLNd5\\3hMdLdL0d5]3gMcLeL0d5]3gMcLeL0d5]3gMcLeL0d5]3hMbLdL0e5^3gMcLbL0g5]3gMcLbL0g5]3gMcLbL0g5]3gMcLcLOf5^3gMcLbL0g5]3hMcL`L0h5]3hMcL`L0h5]3hMcL`L0h5^3gMbLaL0h5^3gMbLaLOi5_3fMbL`L0j5^3gMbL^L0k5^3gMbL^L0k5^3gMbL^L0k5^3gMbL^L0k5^3gMbL^LOl5_3gMaL]L0l5^3hMcL[L0l5]3iMcL[L0l5]3iMcL[LOm5^3hMcL[LOm5_3gMbL[L0n5^3hMbLYL0o5^3hMbLYL0o5^3hMbLYL0o5^3hMbLYL0o5^3hMbLYLOP6_3hMaLWL1Q6^3hMbLVL0R6^3hMbLVL0R6_3gMaLWL0R6_3gMaLWL0R6_3gMaLWLOS6`3gMaLUL0S6_3hMaLUL0S6_3hMaLUL0S6_3hMaLULOT6`3gMaLULOT6a3gM_LTL1U6`3gM`LSL0V6`3gM`LSL0V6`3gM`LSL0V6`3gM`LSL0V6`3gM`LSLOW6a3gM_LQL1X6`3gMmLY2S3gMmLY2S3gM`LQLOX6a3gMmLY2S3gMmLY2S3hMlLX2U3gMkLY2U3gMkLY2U3gMjLZ2W3eMiL[2W3fMhLZ2X3fMhLZ2W3gMiLY2X3fMhLZ2X3fMhLZ2Y3eMfL\\2Z3eMeL[2[3eMeL[2[3eMeL[2[3eMeL[2[3eMeL[2[3fMdLZ2\\3fMdLZ2]3eMcL[2]3eMcL[2]3eMbL\\2^3dMbL\\2^3eMaL[2_3eMaL[2_3eMaL[2_3eMaL[2_3eM`L\\2`3eM_L[2b3dM_L[2a3eM_L[2a3eM^L\\2b3dM^L\\2b3dM^L\\2b3eM]L[2c3eM]L[2c3eM]L[2c3fM\\LZ2e3eMZL\\2f3eMYL[2g3eMYL[2g3eMYL[2g3eMYL[2g3fMWL[2i3fMUL[2k3gMQL[2o3[41O0000000000000000001O00000000000000001O0000000000001O00000000001O0000000000001O0000000000001O0000000000000000001O000000000000001O0000000000000000001O0000000000000000001O000000001OO10000001O00000000000000001O000000000000000000001O0000000000O1000000000kEeKe9[4ZFgKe9]4UFfKj9[4TFfKl9g400000O1O1ERFaKQ:^4QF`KP:]4;O1I7O1H800", "size": [480, 640]}, "area": 201399.0, "iscrowd": 0, "image_id": 10216, "bbox": [0.0, 103.0, 640.0, 377.0], "category_id": 145, "id": 10012452}, {"segmentation": {"counts": "hUi51o>000O1ZAO]>1dAO[>1:0[AO[>1;0O0O10000000000000000JN^A2b>9MEaA2i>N100010O000000000000000WlR3", "size": [480, 640]}, "area": 79.0, "iscrowd": 0, "image_id": 10216, "bbox": [395.0, 17.0, 34.0, 18.0], "category_id": 146, "id": 10012453}, {"segmentation": {"counts": "g214h3T;N2O00000O1000000O10O1O02O000O1000OO1010O01O0100O1O10O10O0010000F]LWEc3h:^LXEb3g:`LWEa3i:`LUEb3i:;O1O11O010N11OO1010O00O20O01OO100O011N1O1002N1N101M2N2000000XOPEnL1;o:`2_E_Ma:V2kEiMU:U2oEiMP:R2WFmMi9R2YFmMg9l1aFSN^9i1hFVNW9i1lFVNT9i1nFVNQ9j1PGVNP9g1SGYNn8]1TEcNP2Ol8]1]GcNd8Z1^GfNb8X1d2O2N002N100000^DnNV9R1hFPOW9Q1_FYO`9h0^F[Oa9d0ZFCe9=[FE\\9XOQET1o1[Om8ASET1V2VOg8FQEO0U1Z2TOe8Q1\\GmNe8S1\\GlNd8T1]GkNc8P1cGoN]8P1dGPO\\8o0eGQO[8n0fGQO[8n0gGoN[8P1j2100O100`CoNgNOU<R1RE[Oh:f0TE^Ol:b0RE@n:`0lDFS;<kDFS;<jDGQ;?lDDGoNl:^1[EFAlNL2Y;\\1YE3g:NVE4j:LTE6l:KoD9Q;HmD9S;IiD9V;i1000O1OC]L_Eb3a:`L^E_3d:`L]E_3d:`L\\E`3R;OCaL[E_3e:aL[E_3e:bL[E\\3f:dLZE[3g:eLYE[3f:fLZEZ3f:fLZEZ3g:fLXEZ3i:eLXEY3X;1O0000QO`DdM2b0`;Y1]DUNe0a0n:j0[DgN[1:[:c0WF[Oi95\\DA[26Z9L\\DNf25T<KTi2:`VM5M5J4L5M2011M3_OoAKc>1O01OQMMQG3m8OjDLe15a93bD2e06AEW;P1gDLLTO];a2cD_M];b2bD^M^;c2aD]M_;d2`DZMZ;n2fDQM[;P3dDoL];Q3cDmL_;T3_DlLb;T3^DlLb;U3]DkLb;;bD\\2^;cMdD]2\\;aMfD^2Y;bMiD]2W;cMiD^2V;aMlD^2T;bMmD]2S;cMnD]2Q;dMnD[1HUOZ;@oDW1MVOT;DoDR12XOn:HPEm06YOk:JnDl09YOi:KoDj09YOj:NlDh0;ZOh:0nDc0;[Oi:3kDb0<ZOj:4kDa0;YOl:7hD`0<WOn:9gD?:XOP;:fD=9YOR;;eD<7YOT;=dD:7ZOV;;dD=1ZO[;:dD2AB=3^;:dD0M3^;NdDN:IQ;:fDL;Ho:=fDJ;Io:>fDG;KP;?dDF<JQ;`0dDE:LX:DgEl0FD;KS;b0cD@;MS;d0aD_O;Mf:FnDn02^O:Ne:HnDm02\\O<Ne:ImDm03[O:Og:IkDo04XO:0g:IkDP14UO;1f:KkDP13TO;2g:IlDQ13SO:4f:HmDR13QO96g:GmDS13nN:8e:HnDS12mN;8e:HnDS13lN9:e:HnDT14hN:;e:HnDV1m0ROT:IoDV1k0POY:IlDW1k0oNZ:JkDX1j0oNZ:IlDY1h0mN^:IjD\\1g0kN_:IjD\\1f0lN_:IkD\\12bN28R;KiD[14hNJ1Z;KiD^12mNBJd;KgD_14TOU;]OhD`12SOV;]OhD`13QOV;_OgDa13nNW;AfDb13kNW;DfDb12iNY;EdDc14gNX;EeDe13dNY;GdDf13aNY;KcDe13_NZ;LdDe13WNC1g;3bDg1U<YNkCh1_;SNiD4Ij1];SNiD3Jj1];UNZDN643j1\\;YNbDM2j1\\;TNfD3Nk1Z;QNiD4Ll1[;oMjD5Km1Z;mMlD6Jm1Z;mMlD6Jm1Z;mMlD6Jl1[;nMlD5Im1[;nMmD5Fn1R<SNlCo1S<SNiCT2R<?N001O1N3M1N3L4O2N0BfLVEh20XME2T;OUEQ2j0RNQ:LUES2j0QN[:P2fEPNZ:o1gEPN[:o1eEoM]:P2Z100O100O10O0100000001O0000000000O1000000iCYNU;g1^DeNb;\\1]DcNe;]1cDZN^;f1VDPN`<P2^CQNc<T1_C^OL@e<Q1aC^OIAc0N];R1YDD7]O`;b0XD[O3e05_O_;;`D@Le06A];8QE7B_O_;9PE7C_O];9QE8B_O];8RE9A_O];8RE1IGU;8SENJJS;8TEMo;3QDOm;1SDOm;1SD0l;0UD0j;0VD1i;OWD1i;0VD1i;0VD0j;2TDNl;5QDKo;6PDJP<8nCHR<;kCEU<<jCDV<?gCAY<a0fC^OZ<d0dC\\O\\<d0dC[O]<e0cCZO^<f0bCZO^<f0bCYO_<g0aCXO`<h0_CYOa<g0^C[Oa<e0^C^O`<a0`C@`<:dCH\\<7dCJ\\<2gCOY<0gC1Y<NgC3Y<JkC5U<JmC5S<FRD:n;BVD>j;AZD<f;@^D`0Q=1O01O00000000000000001O0000GAQB?n=BRB>l=DTB<X>2N:F3ITOTBl0Q>1NN21OO10001O000000000000000010O0000000O2I60POWBi0i=VOXBj0h=TOZBl0m=00001ONPBTOP>l030OLoAYOQ>g040000000000000000000001OO2N1N2O10XOoAa0S>]OmAc0X>04L1OO10001O0000000000000000001O01N2N20O0000000000001O01OO0100010N100N2G]OTBd0k=]OoAO3e0o=^OoAc0R>50XOnAb0X>000000000000DlALT>4=N1O1O3N1N`m0OaU53llI2WAMf>Nmn00[QO001O1O1N3M3N2_OKTB7i=HXB:f=E[B=c=B^Ba0_=^ObBc0]=]OcBd0\\=[OeBe0[=[OfBd0[=[OeBe0k=O10000O101O0O100O10000O101O00CBXB?j=BRBe0j=\\ORBe0OYOj=o0UBTOh=R10O1000@lNTCU1n<kNoBU1T=kNiBU1a=0LkN]BU1V=kNUC0CU1h=1CjNQCU1Q=kNmBU1_=0000000010O001O00001O001O0000O011N11O@POPCQ1P=mNQCT1]=010N10O1000002ON1O0101O00001N1N2O7I10N3N1N2O1O10000001O00000001O0NPOTBP1l=20QOWBg0i=VO[Bi0n=000000000000010O000004L100O00000000000O1O1O1O1O1000XM", "size": [480, 640]}, "area": 33283.0, "iscrowd": 0, "image_id": 10216, "bbox": [0.0, 0.0, 640.0, 213.0], "category_id": 174, "id": 10012454}, {"segmentation": {"counts": "j0=c>2N1O1O0O2OO100000O02N101N4M2ML4N112kAFb=9_BI^=8aBL\\=4dBL\\=3dBN\\=2dBO[=0fB1Y=OgB3W=LiB6W=IiB7W=IiB7W=HjB8V=HjB7X=GhB9Y=GhB7Y=IhB6Y=HiB7Y=GhB8Y=FdBOC:Z>KeA3[>1cAL]>8cAE]><dAB[>a011O000000O10000O101O0O2O1O2M3N1O2N1N2OfeZ12lldN<Y;8nC=b;KSD<i;LnC9o;]1OBTDjMk;o1^DQNT;McDm1?VNk:1dDi1b0VNi:4cDd1i0UNd:Y2_EeM`:Z2cEeM^:[2dEbM\\:`2Q14M3QD\\M[;U30O1RM[D_2S<00fMPDf00BX<[OSDg09@m=O001O1O1O001O001O0OXV45aiK6J5L4M1O2N1O1000O100O1O2N1N5LHATB;m=1:<E1O0O1_M[OgFd0V9_OkFa0T9@lF?S9ClF>S9BnF>P9DPG;P9FoF;P9FPG:o8FRG9i8YOmD`0Y28g8OYG0h80XG0g80ZG0f80YG0g81YGOg80ZG0f80YG0h8OYG1h8NXG2h8NWG2j8OUG1S9ZOdD8Z2=X9BhF>X9BkF;U9ERG3o8LZGLg83S30000O1000000O10002KZ[56^dJ6N2N1O2O1N100000O010O001N1000lLL]G4m7c0SH[Oo7e0PHZOQ8g0nGXOS8i0lGVOV8j0iGUO17Q5e0nJROLe0Q59RKROjMIc1Q1^55TKPOmMN[1Q1b51UKoNPNOR1X1h5KUKmNSN0a0h1V6\\OQKiNgM2b01>l0QOJV7?QKnN[NM;l0C\\Of6m0oJoN]NL;k0IYO`6Q1nJoN_NL8k0_7:kImN_NN5j0d7;hImN_NN4i0g7<gIPOYNK8i0j7<eIROWNI8h0<WO[6V1kJROTNJ9f0;^O[6P1nJTOPNG;g09F[6h0RKVOlME;g0:M[6a0VKWOfME=f094\\6:ZKWObME=g01>f6O\\K\\OeM\\2n6lMkKFRMc2S7ZMYL[3g3^LaLm2S4RMPLf2V4WMSH4_3_2c4\\MoGo0f2^1`5aMkGR1g2V1c5fMhGi0R3\\1_5ZNoJb1R5^NlJc1U5^NhJ_1\\5bNaJZ1f5fN^GAU2c1d6mNVGBR2`1l6nNRGBR2\\1P7ROnFBQ2Y1m6]OSGYOP2W1n6CQGVOP2T1S7FbI8b6F]I:d6F]I9d6F]IClL4i98]I@mL9e97^I]OPM>_95bIZORMc0Z92eIWOVMh0R91ZJ1c54YJMe5b0mI_OQ6c0TJXOj5g0^JTO`52]KMa42bKL^44eKI[47gKFZ4:iKCV4>mK_OS4`0PL^Oo3c0W51M9I8O0=\\Ocd4AbZK0bC2]<OcC1]<OcC1\\<0dC0\\<0dC0[<1eCO[<1eCOZ<3eCNZ<4cCM\\<6bCJ^<7gBDd05e<8fBEc03f<:fBBe04e<>]CA`<a0aC_O_<`0bCA]<?cCA]<?cCA]<?dC@\\<`0dC@[<a0R10iBYOU<h0gC[OY<e0fC\\OZ<d0eC^OZ<b0fC^OZ<c0eC]O]<>jB_Of04b<;hBFa0Og<;hBFa0Oh<:gBGa0Oh<:gBGa0Oi<9fBHa00h<2hBIO4b01h<7fBHb01g<6iBI`01f<8jBFa01d<:kBEa01b<<mBD`00c<<mBD`00c<=lBCa00d<<kBEa0Oc<<lBEb0Hi<b0eBGo=9RBFe0H`<b0kBFd0J`<`0lBFb0Lc<=kBGa0Me<;jBH`0Nl<4dBN`0Nm<3cB0?Mo<1cB2>MP=0bB3>MQ=OaB4>MQ=OaB4>M[=2fBN]=0bB0S=N^B5?MQ=1^B3?FA6`=3`B1>GB6]=4cBO=1P=0dBO:JE2\\=6eBN:JE2\\=6eBN:2P=1fBM:2h<:mBE:1h<;nBE6NLGP=f0nBE26P=6nBC08T=5lBC07U=6kB6V=JjB5Y=IgB7Z=g03ZOcBCa=9_BGa=9_BGb=8^BHb=8^BHb=8^BHc=7]BIc=M^BIO:g=G\\BOM7l=HWB1M6l=KVBOO4k=NVBNO4j=OWBMO1a=NbB4OLi=a0WB_Oi=a0WB_Oh=b0XB^Oh=b0XB^Oh=b0XB^Oh=c0WB^Oh=b0XB^Oh=b0XB^Oi=a0WB_Ok=?VB@o=;QBEU>5kAK6:T=KfBJ3`0W=EfBI2f0W=AgBGMO3j0[=@dBFN?2N^=C`B27>0Ge=;`B@_=j061O2M201O1O001XBmNa=Y100000000N2M3jBfNf<Z1ZCmN^<T1aCPO\\<P1cCSO[<m0dCTO]<i0eCWO[<g0fCZOZ<d0gC]OZ<`0gCAZ<7lCJV<2kCOW<LlC4U<^O^CJ>i0]=1O1O1O1001O001O1O001O1O1O1O2N1O001O1OI_OPB`0n=BTB<k=EXB8f=J\\B4c=M_B1`=0cBM\\=4fBIZ=8hBFW=<f00O10000O100HEjA=T>FjA:U>900O1O1O101O000FXO\\Bh0d=ZO[Be0e=\\OZBd0f=OZB^Of=e0[BXOd=k0]BPOe=o06O1000000ARBLo=6QBGP>;PBBQ>OoANO70IS>NQB2L90EZ>3fA9\\>GdA9[>IfA4Z>NeA1[>;O0J^OWB9i=IUB7k=KRB6n=KPB6o=MmA6R><01O0O101O0O11O1N2O1O001^OUBNk=M\\B0Y>1O1O010O0M4H7O20OH^OnA20a0P>9oATOP>m0SBSOi=l0WBUOh=k0YBUOg=k0XBVOh=i0XBXOh=g0XBZOi=d0WBDc=8_BGc=8]BHe=HXB82Oo=0QB0Q>OnA1S>NmA3S>MlA4T>KlA6T>JkA7U>HlA8U>FRm?", "size": [480, 640]}, "area": 17604.0, "iscrowd": 0, "image_id": 10216, "bbox": [0.0, 0.0, 606.0, 326.0], "category_id": 183, "id": 10012455}, {"segmentation": {"counts": "d`72Z>3TB11KX=<]BN7G\\=l0`BVOm3M^5P1`FUOi3KWL9]9o0YJXOf5i0XJYOg5g0XJ^Od5b0[J@d5a0UJEk5;hI1Y60ZI:h6GlHc0U7^ObHi0_7WOmGiNgNK5X2X9UO_GWOXOd1Z9VO]GWOWOe1[9TO^GWOVOf1\\9RO`GXOTOZOO10V2\\9WO`GTOTO^O:V2R9WOaGTO@f1n8UOdGSO@d1o8XOcGRO@c1o8ZObGTO^O`1R9[OaGXOmN_O010o1h9YOYG_1i8aNWG^1j8bNVGR1V9oNhFQ1Y9oNhFo0Y9UOaF`0WO[NY:V1_F=\\O[NU:Z1]F:CVNR:b1jE`N4g1`:IZEbN5d1c:LTEeN6^1g:;WEDj:X21N2O1O1N2O1@jDSMY;m2gDkLa;T36I700M3N2N2O1N2N2O1O1K5O1M3O1O100000000O100O1000000O100O100O100000000O1000000O100000000O1001O0000000000000000000000002N000000O10000000000001O000000000000001O1O00O11O000000000000000000001O00000000000000\\NlC_OCd0`<LXD4h;KYD5g;JZD6f;I[D7e;I[D7e;I[D7e;IZD8f;G[D9e;G[D9e;G[DEnNa0f<I]DEoNa0d<JaD8^;GcD8^;HcD7];HdD8\\;HdD8\\;FeD;[;DQ21ONYAKg>430000NVANj>2Rlc6", "size": [480, 640]}, "area": 12525.0, "iscrowd": 0, "image_id": 10217, "bbox": [16.0, 0.0, 172.0, 184.0], "category_id": 110, "id": 10012456}, {"segmentation": {"counts": "iYQ2410e>601O2N1N2N2O2N0eA]O10U>h000jAYOR>l0O001O1O2N1N2O3N1N4L3M3L1jB]No<f1lB`NP=j1N2N2N1O1O001O3L4M2N1O1O2N003M2N1O2N2N1O2N2N1O1O2N101N1O2jDeLc:\\3[EfLd:\\3XEgLg:[3WEfLh:\\3UEgLi:i3O100O1N2O1N1O1001N101O0100OMZEmKe:P47O2N1O3M3M3M3M003N0O1O3N0O1O1O1O2OO02N2N1O0O2O1O2M3N2N1O2M4M1_McC^2a<N3L3hMYCT2k<N1O2N1O2M3N2NNYNlBg1V=01N3N2N1O4K7J7I6J6Ieb_5", "size": [480, 640]}, "area": 10335.0, "iscrowd": 0, "image_id": 10217, "bbox": [139.0, 146.0, 126.0, 141.0], "category_id": 121, "id": 10012457}, {"segmentation": {"counts": "[1k0m1k1e7UNZHj1g7WNYHh1h7XNWHh1j7XNVHg1k7YNRHj1n7VNnGm1S8SNfGT2Y8nM^GY2c8gMTGb2l8_MmFf2T9[MeFj2\\9WM\\Fo2e9T12O1O1O1N200UOPFeLQ:R3oEWL9>HAS:g3\\FdLk9Z3WFcLk9\\3ZF_Lg9`3[F]Lh9\\3Q1C>C=D<G8\\OUCgNK5U=g0_CXOc<g0^CWOc<i0]CVOc<l0]CTOZOO10R=l0_CTO^O:Q=c0`C@_<b0_C@_<b0_C@_<a0bC^O]<c0fCmN_O010R=Q1j00001N113J3O2N2_OjA4V>JlA5W>GnA6`>NlRc8", "size": [480, 640]}, "area": 4582.0, "iscrowd": 0, "image_id": 10217, "bbox": [0.0, 41.0, 53.0, 176.0], "category_id": 132, "id": 10012458}, {"segmentation": {"counts": "i6m1Q=4M002N2O1N3N0O2O0O2N1O2M2O1O2N1N201N1JXMQDl2k;6N2O1N3N1O1N3N1O2N1O110O01O0010L3N2N3I6N2O2C<O1O1C_KZFc4c9aK[F_4d9fKVF\\4i9>N3N1O1O1N3N1O1N2O2_O]J`Gf5`8ZJXGn5g87H701L3N2N3N1N2N3N1O1K6N1M3O2N1000N2NZHjHf7V71100O2O002N0O100O100O100000000O10O1000O100000000O0101O000000000O100000000000002N000O11N10O1O100001O100O1O001O1O1O0001TKdH\\M0b4\\7PNjHS2S7mMnHS2P7nMRIQ2n6nMSIR2l6mMVIS2i6lMYIT2f6lM\\IS2c6mM]I`M]O10^4U7QNcIP2\\6PNdI`MYO^4R7QNjIn1V6RNjIo1U6QNlIn1T6RNmIn1S6QNoIn1P6RNQJm1o5RNSJm1m5SNVJk1i5VNXJh1h5XN\\Je1c5[N`Jb1`5^NcJ`1]5^NdJiM]Nc3n6dNhJdM`Nd3h6hNRKW1m4iNUKU1k4kNWKT1h4lNYKT1f4lN[KS1e4mN[KT1d4lN]KS1c4mN`KQ1`4mNdKP1\\4POfKo0Z4POgKP1W4QOjKo0U4QOmKm0S4SOmKn0R4ROQLl0n3TOTLk0k3UOVLk0i3UOYLj0f3VO\\Lh0d3XO]Lh0b3XO`Lg0_3YOcLf0[3[OfLd0Z3\\OgLd0X3\\OjLc0U3]OlLc0S3^OnL`0R3@oL`0P3@RMRNeLl1W63UMoMfLn1T63XMkMgLQ2Q64ZMiMfLS2o54]MfMgLU2k55nMJR26oMJP27PNIo17RNIm16UNIk17VNIh17YNIg16ZNKe15[NLd15ZNLf14ZNMe14ZNMe13\\NLd15\\NKc15ZNaMmKZ2h55[N0d10^NNb13_NL_15cNJfKeMd5c2fN1Q11oNMQ15ZNYMnL`2h49WNWMUM]2c4<XNWMXM\\2_4?UO^Ol0b0VO\\Oj0d0XOZOh0h0XOVOh0j0ZOTOf0l0\\OROd0n0^OPOb0P1@nN`0S1AkN?T1DkN;V1FhN:Y1HcN9]1IaN7^1L`N4`1N_N1a11]NMf14XNLi15VNJk17TNHl1:RNFo1;PNDR2<lMDT2lJjL`5Q1CW2?gMAY2nJhLa5l0A\\2oJhLN0a5k0B\\2oJlL`5g0@]2QKkLa5g0]O^2SKkLa5f0\\O^2SKlLc5e0YO_2UKlLc5d0WO`2WKlLc5b0WOa2VKnLd5`0UOb2YKkLe5b0QOc2[1\\MeNc2]1[McNe2`1YM_Ng2c1WM]Ni2e1UM\\Nj2f1TMZNl2h1SMWNm2k1QMUNo2m1oLTNP3n1oLQNQ3P2nLPNR3o60RKnLoMQ3Q2oLoMQ3Q2oLoMQ3Q2oLoMQ3Q7O0000001O01O01O0000001O0000001O0000IVMXDj2k;UMTDj2l;6O02N3M1O00001O00001O00O100O101N1O1O101O01O1OO20O000001O00000010O0000O101O0000001O0000001N1000001jJjL_I0m4V3]1VMbNj2\\1WMeNi2V1jLlI?n4g2U1]MkNd2S1]MmNc2R1^MlNd2S1]MoNa2P1`MoNb2o0_MoNc2P1^MPOb2P1^MQOa2n0`MROa2l0`MTO`2k0aMTO`2k0aMUO_2j0bMWO^2g0cMYO]2f0dM[O[2d0eM]O[2b0fM^OZ2a0gM_OZ2?gMBX2=iMBX2=iMCW2<jMEV29kMGU28lMGU28lMIS26nMJS24nMLR23oMMQ23oMMQ22PNNQ20PN0P2OQN1o1NRN2n1MSN2o1LRN4n1KSN5m1JTN7k1HVN8k1FVN9k1FVN;i1DXN;i1DXN=h1AYN>h1AXN`0h1_OXNb0h1]OYNc0h1[OYNe0g1ZOZNg0e1XO[Ni0e1WO[Ni0f1UO[Nl0d1SO\\Nn0d1QO_Nn0`1QO`NP1a1nNaNR1^1mNbNT1^1kNcNV1\\1iNhNU1X1iNiNW1W1hNjNY1U1fNlNZ1T1eNmN[1T1cNmN]1S1bNnN_1Q1`NPO`1P1_NQOa1P1]NPOd1P1[NQOf1n0YNSOg1m0WNUOi1l0UNSOm1m0RNTOn1l0QNUOo1k0PNVOQ2j0lMXOT2h0kMZOT2f0kM[OU2e0jM\\OV2e0hM]OW2c0hM]OZ2b0dMA[2?dMC[2>cMC]2<cME]2;bMF^2:`MH`28_MIa27^MJc25\\MLd24[MMf22XM0h2OXM2i2MVM4k2KTM6l2JSM7n2HPM:P3FoL;R3DmL=R3DmL=S3XMcJW1Z2b1S3dLWKi1d1e1Z4ZNeKg1\\4XNcKi1^4VNaKk1`4TN_Km1c4QN[KQ2f4nMYKS2h4kMXKV2i4iMVKX2k4gMTKZ2m4eMRK\\2P5bMnJ`2R5`MmJa2T5^MkJc2V5[MjJf2W5YMhJh2Y5WMeJk2\\5TMcJm2_5QM`JP3a5oL^JQ3d5nL[JS3f5lLXJV3i5hLWJY3j5fLUJ[3l5dLSJ]3n5bLQJ_3P6`LnIb3S6]LlId3U6[LjIf3W6YLhIh3Z6ULfIl3[6SLcIo3^6PLaIQ4`6nK_IS4b6lK]IU4d6jK[IW4f6hKXIY4j6fKUI[4l6cKTI^4m6aKRI`4n6`KQI`4P7`KoHa4Q7_KmHc4T7\\KkHe4U7\\KiHe4W7[KhHf4X7[KfHf4[7YKcHi4]7XKaHi4_7WK`Hj4`7VK_Hl4a7TK]Hl4d7TKZHo4e7RKYHn4h7RKWHo4j7QKTHP5l7PKSHP5n7QKoGQ5Q8oJnGR5R8nJmGS5T8l00O10001O0O10001N10000O2O0O100O2O0O100O2O0O100O2O0O10001M2N200010O000001O000O10001\\LYGm0g8RO_Gh0b8WOcGe0]8[OfGa0\\8]OhG`0X8_OkG?U8@nG=S8CPH:Q8DSH9m7FVH7k7HYHhNcN`0T9g0^HbN^N]O3W1R9i0_HaNdNc0n8k0bH^NbNf0l8k0fH[NaNg0j8m0iHXN`Ni0h8m0lHVN^Nk0g8n0dIoN]6P1cIPO^6o0dIPO]6n0dIQO]6n0dIRO\\6m0eISO[6l0fISO\\6k0eITO\\6k0fIoN_6P1eIaNg6^1_3N1O1N201N1O1O1O2N1O1O1O101M200O1O2N1O1N2N3N3M2N101M2N3O0N3N1Ofe`0", "size": [480, 640]}, "area": 96568.0, "iscrowd": 0, "image_id": 10217, "bbox": [0.0, 64.0, 604.0, 335.0], "category_id": 143, "id": 10012459}, {"segmentation": {"counts": "f8Z6h8NO11O1O1O2N001O001O001O00001O00001O001O0000001O00001O001O00001O0000001O00001O00001O0000001O0000001O0000001O0000001O00001O001O00001O00001O00001O00001O0000N2NbJbF^5^9200001O002N0000000000000000000000O100000000000000O1000000000000O1000000000000000000O11O00O1O100001O1O1O1O001O1O1O00001O3M1O1O1O1O1O1O1O1O1O001O001O1O00001O001O001O[FmK_OCe8_4kGYLU8g3jGZLW8e3hG\\LX8d3gG]LZ8b3fG^L[8a3eG_L[8a3eG^L]8a3bG`L^8`3bG`L_8_3aGaLEnNf8a4dGbLEoNg8^4dGgL^8X3aGiL`8V3`GkL_8U3`GlLa8S3_GmLb8R3\\GoLf8P3YGdMU8[2lGdMU8[2iGfKKo1\\8[2hGhMY8W2gGiMZ8V2fGjM[8U2cGiKNQ2`8V2bGlM_8Z41O1O001O1N2O001O1O001O1O1O001N101O1O001O1O001O1O1O001N2YJhFd5c9G00000O2OO100000000000000001O00O100001O0N201O1O001O001O0000100O1O100O1O010O1O10O01O0001O01O00010O000010O0000001O0000001O01O01O0000010O0000001O01O0ITFZKl9i4SFVKl9j45011N3M1O000010O0001O00O2O0O100O1O1O2O0001O1OO20O00000O1O1O1O1O2N1O1O001O1O100O1O1O1O1O1O001O1O1OlJbFG0m4]9VKoFi4P9VKQGk4P9RKRGn4`90000N22NO1N2001O0000O1001O001N1000001OO1001O00O11O0000000000000000O100001O00O11OO11OO01O1000001N10001N12ON110N1012NO001O0000001O000O101O000N2000001O01O00001OO20O1O000000000001O001O001O1O001O001OO10000K5L4N2N2N2N2N2N2M3N2N2N2N2N2N2N2N2N2N1O2N2N2M3N2N2N2N2N2N2N2N2N2O100000O10O1000O100000O10000000000O2OO11OO2O000O100000000O2O00000O10000O2O000O100O100O2O0O100O100O101N100O10000N2N201O01O0000000001N10000]LZLlKg3R4_LhKb3W4cLeK]3[4fLaK[3^4iL_KX3_4kL_KU3`4nL]KS3c4PMZKP3e4TMXKm2f4VMWKk2h4YMhIcN`0T4g5^MbI^N]O3W1R4i5_MaIdNc0n3k5bM^IbNf0l3k5fM[IaNg0j3m5iMXI`Ni0h3m5lMVI^Nk0g3n5dNoI]1P6cNPJ^1o5dNPJ]1n5dNQJ]1n5dNRJ\\1m5eNSJ[1l5fNSJ\\1k5eNTJ\\1k5fNoI_1P6eNaIg1^6_3N1O1N201N1O1O2N1O1O1O2N100N201N2N2N2N2M3M4M2\\O^FgKd9X4]FfKe9Y4[FgKf9Y4ZFdKi9[4WFcKl9\\4UFcKl9\\4TFbKo9^4QF`KQ:_4oE`KS:_48O100O1O1O100N2O1N2O1O2M2O1N2O1N2O1N2O1O1N2O1N2O1N2O1N2O1O1N2O1N7J7H7JgL", "size": [480, 640]}, "area": 116375.0, "iscrowd": 0, "image_id": 10217, "bbox": [0.0, 3.0, 640.0, 477.0], "category_id": 165, "id": 10012460}, {"segmentation": {"counts": "hdS33l>3M3M3M3M3N2M4L3M3M3M3N2M3M3M3M3N2M3M3M4L3L4M3M3L4M3M3M3L4M3M3_MoLnHT3P7mLmHX3P7hLoH[3o6fLoH]3o6dLoH_3o6aLPIb3n6_LPId3n6]LoHg3o6YLPIj3n6WLPIl3n6ULPIn3n6RLQIQ4m6PLPIT4n6mKPIW4m6iKRIZ4l6gKRI\\4l6gKoH]4o6hKkH[4S7kKfHX4X7mKbHV4\\7oK]HU4a7f1O0000001O000000001O0001O000001O000000001O000000001O01O00000001O000000001O00000000010O000001O01O0001O0001OO2N1O1O1O2N1O1O1O1O2N1O1O1O2O0O1O1O2N1O1O1O2N1O1O1O1O2N1O1O1O2N1O1O1O2O0O1O1O2N1O1O1O1O2N1O1O1O2N1O1O1O2N1O1O101N1O1O1O2N1O1O1O2N1O1O1O2N1O1O1O2N1O1O1O2O0O1O1O2N1O1O1O2N1O1O1O2N1O1O1O2N1O1N2O2N1O1O1N3N1O1O1O2N1N2O1O2N001N2O1O1O1O1N2O001O1O1N2O1O1O1OfMgMnGX2a7\\N^Hc1P7QOoHn0n6WOQIh0n6[OQId0n6_OQI?o6EoH:P7IoH6P7MnH3Q70nHOP75oHJP79oHEQ7>nHAQ7b0nH]OQ7f0nHYOQ7j0mHVOR7m0mHQOS7R1lHmNS7V1lHiNS7[1kHdNT7_1kH`NT7c1kH[NU7h1jHWNU7l1iHTNU7P2jHoMU7T2jHkMU7X2jHfMV7]2iHbMV7a2iH^MV7e2iHZMV7i2iHVMV7n2gHQMY7R3fHmLY7V3fHiLZ7Y3eHfL\\7[3cHdL^7]3aHaLa7`3^H_Lc7b3\\H]Ld7e3ZH[Lg7e3YHZLh7g3WHXLj7h3VHVLl7j3THULm7k3SHTLm7n3SHPLn7P4RHoKo7Q4RHlKP8T4PHkKQ8U4PHiKQ8X4nGgKR8Z4nGeKS8[4nGbKT8^4lGaKU8`4kG^KW8a4iG^KX8b4iG\\KX8d4hGZKZ8g4fGWK[8i4eGVK\\8j4dGUK]8k4l010O000001O0000010O0000001O0001O01O0000001O01O0001O0000000010O000001O0000010O0000010O0000010O0000010O0001O01O0001O01O0001O01O0001O0001O01O0001O01O00010O0000010O0000010O1O1O100O1O2N100O1O1O100O1O1O100O1O1O100O1O1O1O1N2O1N2O2M2O1N2O1O1N2O1N2O1N2O1N2O1O1N2O1N2O1N2O4K7J6I8lNaM", "size": [480, 640]}, "area": 65157.0, "iscrowd": 0, "image_id": 10217, "bbox": [212.0, 17.0, 428.0, 385.0], "category_id": 183, "id": 10012461}, {"segmentation": {"counts": "0ekY11YcfN3M3M4L3M3N2M4L3M3M4L3M3M3N3L3M3M4L3hCdNg:_1jDQO\\2ZOa5h1dG_Ob2SOd5S3TJVMg5n2RJ[Mi5h2oIaMl5c2lIgMn5]2jIlMQ6X2gIRNS6S2eIUNV6o1bI[NX6i1`I`N[6d1]IaNc6n4O1O1O1O1O1O1O1O100O1O1O1O1O1O1O100O1O1O1OM3G9F:G:G8M4M20100O1O100O1O010O1O100O1O10O01O100O100000O100000O100000O100000000000O010000000000O1000O10000000O10000000O2O001O001O00001O001N101O001O001O001O0O110O00ZJbIk2]6UMdIj2\\6VMeIj2Z6VMhIh2X6XMiIh2W6WMkIg2U6YMlIg2S6YMmIg2S6YMnIf2R6ZMoIf2P6ZMPJf2Q6YMPJg2o5YMQJg2o5YMRJf2n5[MQJe2o5[MQJf2n5ZMSJe2m5[MSJe2n5ZMSJe2m5[MSJe2m5\\MRJe2m5[MTJd2l5\\MTJd2l5\\MUJc2l5]MSJd2l5\\MTJd2l5\\MUJc2k5]MVJb2j5^MWJa2i5`MVJ`2j5`MWJ_2j5`MXJ^2h5bMZJ[2g5fMYJY2g5gMZJX2f5hMZJX2g5gMYJX2i5hMWJW2j5hMVJX2k5gMUJY2l5gMTJW2n5hMRJX2n5hMSJW2n5hMRJX2o5hMPJW2R6hMoIW2R6hMnIX2S6gMmIY2T6gMlIW2V6hMjIX2V6hMjIX2W6hMiIV2Y6iMgIW2Z6hMfIX2[6hMeIW2\\6hMdIW2^6hMbIX2_6hMaIW2`6hM`IX2`6hM`IW2b6hM_IW2b6iM]IW2d6hM\\IX2e6gM\\IW2f6iMYIW2h6hMXIX2i6gMXIX2h6iMWIW2X4nKUMj1cNX2W4SLSMd1gNX2U4XLRM`1hNX2V4[LPM\\1kNX2T4`LoLW1mNY2S4eLmLR1oNY2S4iLlLm0ROY2Q4nLjLi0UOY2P4SMhLd0WOY2Q4UMgLa0XOZ2P4XMgL=ZOZ2n3[MhL;YOZ2n3^MhL7ZO[2m3`MiL4ZO\\2l3cMiL1[O[2l3eMiLO[O\\2k3gMjLL[O]2j3jMkLHZO^2j3lMmLDYO`2i3oMnL_OZOa2h3QNnL^OYOa2h3TNoLYOYOc2g3VNQMUOXOe2f3XNSMROWOe2e3\\NSMnNXOf2d3^NUMjNWOh2d3`NUMgNVOi2d3bNWMcNUOk2c3eNXM^NVOl2a3hNYM[NVOm2`3jN[MXNTOn2`3mN\\MSNTOP3`3nN]MPNTOQ3^3RO]MlMUOR3]3TO_MiMSOS3]3WO`MdM[NYO`0l3d3YObM`MZN[O`0l3d3ZOcM]MYN]Oa0k3b3^OcMZMYN]Ob0k3a3@dMWMYN^Ob0k3`3CdMSMZN@a0j3`3EeMPMYNBb0i3_3HeMlLZNCc0h3^3JeMkLYNCd0h3]3LfMhLYNDd0h3\\3OfMdLYNGd0f3\\31gMiLnNU3Z35gMeLoNV3Y37hMbLoNW3Y39hM^LoNY3X3;iM\\LnNY3X3>iMXLPOZ3U3`0kMULPO[3T3b0lMRLPO\\3T3d0kMPLPO\\3T3f0lMmKPO]3S3i0lMiKRO]3Q3l0kMiKSO[3Q3o0jMfKUO[3P3Q1iMeKWOZ3P3R1gMfKXOX3P3U1eMdK\\OV3n2X1dMcK^Of2BmL[3m4cM`K@d2]3n1QM`KA_2`3T2lL^KD[2c3X2gL^KGV2d3^2cL]KIR2f3d2_L[KJn1i3i2[LZKLj1k3o2VLXKOf1n3S3QLXK1b1Q4X3kKWK5]1S4]3gKWK5Y1X4`3aKXK7U1[4e3[KWK:R1]4h3WKWK<n0a4l3PKWK`0k0a4o3nJWK`0i0c4R4jJVKc0g0d4T4gJVKe0e0f4Z5ZKeJg4\\5XKcJi4]5WKbJk4]5UKbJl4^5UKaJk4_5UK`Jm4`5RK_Jo4a5QK^JP5b5PK]JQ5c5oJ\\JS5c5mJ\\JT2dN4Q7gMZJU2hN2n6iMZJT2kN2j6jMZJT2nN1h6lMXJR2TO0e6mMVJS2XONb6oMUJS2\\OM^6oMVJT2^OL\\6PNUJS2CKX6QNVJR2FLT6QNUJS2JJQ6SNTJR2NJn5TNSJQ23Ij5VNRJQ27Hf5WNRJP2<Gc5XNQJo1?H`5YNQJn1a0I]5YNRJl1c0K[5YNRJi1g0MW5ZNQJf1m00Q5ZNRJb1R13m4ZNQJ_1V17i4[NPJZ1\\1:d4\\NPJU1b1?]4\\NQJS1e1`0Z4]NQJR1f1a0Y4]NPJQ1j1b0U4]NQJP1l1b0S4^NQJo0n1b0Q4_NQJm0P2d0o3_NQJl0R2e0l3_NRJk0T2e0j3`NRJi0W2f0g3aNQJi0Y2g0e3`NRJh0[2g0c3aNRJf0^2h0`3bNRJe0`2i0]3aNTJe0`2j0\\3aNTJc0c2k0Y3aNUJc0d2k0W3bNTJc0g2k0T3`NWJd0f2l0S3]NZJg0d2k0R3[N]Ji0b2m0Q3UNaJn0_2l0^4SOcKm0]4SOdKl0[4TOfKm0Y4SOgKm0Y4ROiKm0W4SOiKn0V4QOlKn0T4ROlKn0T4ROlKm0T4SOnKk0T4TOlKk0U4TOmKj0U4UOkKj0V4UOkKj0W4UOjKi0W4VOjKj0W4UOjKi0W4VOjKi0X4VOiKh0X4WOiKh0Y4WOgKh0Z4WOhKg0Z4XOfKg0[4XOgKg0Z4XOfKg0[4XOeKh0]4WOaKj0a4UO^Kk0d4SO[Kn0g4QOWKP1k4nNUKR1m4mNRKT1o4jNRKU1P5jNPKU1^9O1O1O001O1O100O1O001O1O1O100O10O10O100O100O10O010000O100O010O100O10000O010O100O10000O010O100O100O1O100O2O0O101N100O2O0O100O2N100O2O0O10fkc1", "size": [480, 640]}, "area": 232570.0, "iscrowd": 0, "image_id": 10219, "bbox": [0.0, 0.0, 640.0, 480.0], "category_id": 124, "id": 10012462}, {"segmentation": {"counts": "ekY11n>3M3M4L3M3N2M4L3M3M4L3M3M3N3L3M3M4L3hCdNg:_1jDQO\\2ZOa5h1dG_Ob2SOd5S3TJVMg5n2RJ[Mi5h2oIaMl5c2lIgMn5]2jIlMQ6X2gIRNS6S2eIUNV6o1bI[NX6i1`I`N[6d1]IaNc6n4O1O1O1O1O1O1O1O100O1O1O1O1O1O1O100O1O1O1OG9G9F:G:F910O00100O1O100O1O010O1O100O100O00100O1000000O01000000000000O10O1000000000O100000O1000O1000000000000O0100000001O001O001N10001O001O001O001N101O001O00001O00ZJcIj2]6UMdIj2\\6VMeIj2Z6VMhIh2X6XMiIh2V6XMlIf2T6ZMmIf2S6YMmIg2S6YMnIf2R6ZMnIg2Q6YMPJf2P6ZMPJg2o5YMRJf2n5ZMRJf2o5ZMQJe2o5[MQJf2n5ZMRJf2n5ZMSJe2m5[MSJe2m5[MTJd2m5\\MRJe2m5[MSJe2m5[MTJd2l5\\MTJd2l5]MTJc2k5]MUJc2k5]MVJb2k5]MVJb2j5^MVJb2j5_MVJ`2j5`MWJ_2i5aMYJ]2g5cM[JZ2g5fMYJY2g5gMYJY2g5gMZJX2g5gMYJX2i5hMVJX2k5gMVJX2k5gMUJY2l5gMSJX2n5hMSJW2n5hMRJX2o5gMRJX2o5hMPJW2R6hMnIX2S6gMnIX2S6gMmIY2T6gMkIX2V6hMkIW2V6hMjIX2W6hMhIW2Z6hMgIW2Z6hMfIX2[6hMdIX2]6gMdIW2^6hMbIX2_6hM`IX2`6hMaIW2`6hM`IW2b6iM]IW2d6hM]IW2d6hM\\IX2e6gM[IX2g6hMYIW2h6hMXIX2h6hMXIX2X4kKVMm1bNW2W4PLUMh1dNX2V4ULSMb1gNY2U4YLRM^1iNX2U4]LPMZ1kNY2T4bLnLT1nNZ2S4fLmLP1POY2R4kLkLl0SOY2Q4PMiLf0VOZ2P4TMhLb0XOY2P4WMgL?YOZ2o3YMhL<YO[2n3[MiL:XO[2n3^MiL6ZO[2l3aMjL3ZO\\2l3cMiL1ZO\\2l3eMiLO[O\\2k3hMjLJ\\O]2i3kMlLGZO^2i3mMnLCYO`2h3PNoL^OYOb2h3QNoL]OXOb2h3TNPMXOYOc2f3WNRMTOXOe2e3ZNSMPOWOf2e3\\NTMmNWOg2d3^NVMiNWOh2c3aNVMfNVOi2c3cNXMbNUOk2b3fNYM]NUOm2a3hNZMZNUOn2`3kN[MVNUOn2`3mN\\MSNTOP3_3PO]MnMTOR3^3RO^MkMTOS3]3TO`MhMSOS3\\3XOaMcM[NYO`0l3c3ZOcM_MZN[O`0l3c3\\OcM[MZN]O`0l3b3^OdMYMXN^Ob0k3a3AdMUMYN_Oc0j3_3DeMRMYNAb0i3_3FfMoLYNBb0i3^3IfMkLYNDc0h3^3JfMjLXNDd0h3]3MfMfLYNEe0g3[30gMcLWNIf0d3[33gMgLnNV3Z35hMdLnNW3Z36hMbLoNW3X3:hM^LPOX3W3<iM\\LoNX3W3?jMWLoN[3U3`0lMTLoN\\3T3c0lMPLQO\\3S3e0lMoKPO\\3S3g0mMlKPO]3R3j0lMiKRO]3Q3l0kMiKSO[3Q3o0jMfKVOZ3o2R1iMeKXOY3o2T1fMeKZOW3o2V1eMdK\\OV3n2X1dMcK^Of2ClLZ3n4cM`K@d2]3n1QM`KB^2_3U2lL^KEZ2b3Y2gL^KGV2d3_2bL\\KJR2f3d2_L[KJn1i3i2[LZKMi1j3P3VLXK0e1m3T3QLXK2a1Q4X3jKXK5]1T4\\3fKXK5Y1X4a3`KWK9T1[4e3ZKXK;Q1]4i3UKWK>m0`4m3PKWK`0k0a4o3nJWK`0i0d4Q4iJWKd0f0d4Z5\\KeJe4[5[KdJg4\\5XKcJi4]5WKbJj4^5VKaJl4^5TKbJl4^5TKaJm4`5RK_Jo4a5QK^JQ5a5oJ^JR5b5oJ\\JS2bN5R7hM[JS2fN4o6hMZJT2iN3m6iMZJS2lN2j6kMYJR2QO1f6mMXJR2TO1d6lMWJS2XOOa6nMVJR2]ON]6PNUJR2AMY6PNVJR2DMV6QNVJQ2GLT6RNTJQ2LLo5SNTJQ20Jl5UNSJP24Ji5VNRJo19Ie5XNQJo1=Hb5XNQJn1`0I_5YNQJl1c0J\\5ZNQJk1d0LZ5YNRJg1i0OU5ZNQJd1o01P5[NQJ`1T14l4[NPJ\\1Y1:f4ZNQJX1^1=a4[NQJT1c1`0\\4\\NQJS1e1a0Y4\\NRJR1f1b0X4]NPJP1k1b0U4^NPJo0m1c0R4^NQJn0o1c0P4_NQJl0Q2e0n3_NQJk0S2e0l3`NQJi0V2g0h3`NRJh0X2g0f3aNQJh0Z2g0e3aNQJf0]2i0a3`NSJf0^2i0_3aNSJe0`2i0]3aNTJd0a2k0[3aNTJc0c2l0X3`NVJc0d2l0V3aNUJc0g2k0T3_NXJe0e2m0R3[N\\Jh0c2l0Q3YN_Jj0a2m0_4SObKm0]4ROdKn0\\4ROeKm0Z4TOfKl0Z4SOgKn0X4ROiKm0W4ROjKn0V4ROkKm0U4ROlKn0T4ROlKm0T4SOnKk0T4TOlKk0U4TOmKj0U4UOkKj0V4UOkKj0W4UOjKi0W4VOjKj0W4UOjKi0W4VOjKi0X4VOiKh0X4WOiKh0Y4WOgKh0Z4WOhKg0Z4XOfKg0[4YOfKe0\\4YOeKg0[4YOdKg0^4WOaKj0a4UO^Kk0d4SO[Kn0g4QOWKP1k4nNUKR1m4mNRKS1P5kNQKU1]9O1O1O1O001O1O100O1O001O1O1O10000O010O100O100O10O10O100O100O01000O100O100O010O10000O100O010O100O100O100O101N1O101N100O2O0O100O2O0O101N100Ogkc1", "size": [480, 640]}, "area": 74104.0, "iscrowd": 0, "image_id": 10219, "bbox": [89.0, 15.0, 440.0, 451.0], "category_id": 183, "id": 10012463}, {"segmentation": {"counts": "0cQa06_n^OLk9>J6J6K5M3O1N2O1N2O100L4M3N2N2M3ZNXNbJm1T5ZN^II32l0R2P5oMQJa00G;Z2a5iN[JX1e5kNXJV1f5oNWJP1`5\\McJ^4]590001O00000000001ON22N1O0000001O0000O1O11O00001O1bJSKZ5Q5O1mJVKa4j4_KWK`4j4\\KYKd4h4\\KWKd4k4ZKVKe4Y5000O1001OO11OM31O2N1O000000000000004Le0[O1O00b0^O001O00000000000000001O001TMkH^2U7bMkHg1;XNX74jH0BCf\\<1cmC1K52NO1O100N2N2O1FDcF<V9EdF21MO<[9b0M300001O001O002N2XOf0O\\V43biK1O1O002Ng0YOg0YO00000000000000000000000000000000000O1000000000000000000000000000000000000000000000000000000000000000000000000000000L4WOi0WOi0B>0000O100O100000000001OO1O1O1M3O1O100O1O1O1O1000000000000000000O1O11O1OJ6M3O12NO1O1O1000000O11OO1L4K5O1O100000000001O000000001O1O0000000000000000000000001O0000O1M3O1O1O1FmJYKT5f4nJUKM0V5j4:O100O11O00001O1O2`JUKZ5Q5N1O1O2N1O1O1O2N1O001O001O001O1O001O001O1O1O001O1O001O001O0000000000000000000000000000M^K[Jb4e533M0000000000000000000000000000O1O100000000000000000000002N3M4L1OO100000000000000000000O100O100O100O100O10000000000000000000000000000000000000000", "size": [333, 500]}, "area": 70259.0, "iscrowd": 0, "image_id": 10222, "bbox": [0.0, 0.0, 500.0, 333.0], "category_id": 96, "id": 10012464}, {"segmentation": {"counts": "cQa062Lk9>J6J6K5M3O1N2O1N2O100L4M3N2N2M3ZNXNbJm1T5ZN^II32l0R2P5oMQJa00G;Z2a5iN[JX1e5kNXJV1f5oNWJP1`5\\McJ^4]590001O00000000001ON22N1O0000001O0000O1O11O00001O1bJSKZ5Q5O1mJVKa4j4_KWK`4j4\\KYKd4h4\\KWKd4k4ZKVKe4Y5000O1001OO11OM31O2N1O000000000000004Le0[O1O00b0^O001O00000000000000001O001TMkH^2U7bMkHg1;XNX74jH0BCQg54UcJ2O0O2O0MHPF:f0F_8>aGDi3N2`0dKC56?J^1NjNO`3a0aKF55OKMIN3X22`NKWO2l3NTL16W1NVO16b0H]O6NM82`3h1[L^N5YOFl2i3f3O1L400MYI`La00@:d5U3mJdL^O9\\1DE:f1U3kMTMh0@\\Oc2ZOYMb0`31SMe0C\\Ok2H?8SMA^Oi07Ei2I?9TM\\OAj05AGDR3=8=bMAVON81S3MKe0fMZOYO043S3N2<`M_O[O0EN<5W30M=fN@^14M:^2GcM8]2HgM4W2NjM0U22mLHoM6S53nL1fMKR5HfJ<b21fMJl4JfJ21MO<h21gMHk4i0^M@hMDg4P1aM\\OP3i0PMWOP3i0PMWOo2j0QMMnNPOY1U1Io1OTNRNiNl0W4OoLSOSOg0R7XOmHj0S7UOdI5\\6LR20000001O0000001O00001O00000000000000000000000000O10000O1O1O1O1O100O100O1000000000000001O1O00000000001O00001O1O1O001O1\\GXOa7h0ZH^Oe7c0THDk7=nGJQ87hG0W8T1O1O0iHmMT6T2fIRNY6o1`IXN_6i1ZI^Ne6c1UIdNi6]1PIjNo6[2O1O1O1O1O1O1O1O1O1dKeLd1\\3bM]M^2e2gLTNY3_4100O100000000001OO1O1O1M3O1O100O1O1O1O1000000000000000000O1O11O1OJ6M3O12NO1O1O1000000O11OO1L4K5O1O100000000001O000000001O1O0000000000000000000000001O0000O1M3O1O1O1FmJYKT5f4nJUKM0V5j4:O100O11O00001O1O2`JUKZ5Q5N1O1O2N1O1O1O2N1O001O001O001O1O001O001O1O1O001O1O001O001O0000000000000000000000000000M^K[Jb4e533M0000000000000000000000000000O1O100000000000000000000002N3M4L1OO100000000000000000000O100O100O100O100O10000000000000000000000000000000000000000", "size": [333, 500]}, "area": 72704.0, "iscrowd": 0, "image_id": 10222, "bbox": [52.0, 0.0, 448.0, 332.0], "category_id": 106, "id": 10012465}, {"segmentation": {"counts": "djd15W:2O0O2O0MHPF:m94O2O1ZO_OkF16k0l8=O001UGfN_8g1OONZN_Ga00@:9`88YG^O99^89iGEX8<gGDY8=gGAZ8`0gG\\OV8i0e028G1O2N2O4L1N101O0O2N3MdgR3", "size": [333, 500]}, "area": 724.0, "iscrowd": 0, "image_id": 10222, "bbox": [162.0, 106.0, 34.0, 67.0], "category_id": 130, "id": 10012466}, {"segmentation": {"counts": "d_f11\\:2TJNi03RK6?J^1NjNOU47QK5OKMIN3X22`NKWO2T59PK316b0H]O6NM82j4>QKH5YOFl2S5\\20nJmJj4\\5OO1F:0hKPKO:f1e4[NTKDc2ZOYMb0P5>VKDk2HP2b0WKEi2IR2?WKAGDR3;S20mJN81S3Jk12oJ043c8I]G0EN<5j9MWf2NhYM500O2oIe0dMYOY14R1h2fN\\MROI3P4HVL33Nm5ORJ3P6\\NWJb0]O`1[6mMYJd0[Ok1l6UNTI]3Z5cLfJn4i3RKWL`6W2aIhMn6i1RIXNm6h1SIXNm6h1SIXNm6h1TIWNl6i1TIWNl6i1TIWNl6i1l00008H1O1O002Ng0YOUo2ZO`QM1O00100O010O10O10000O1000001OmXa2", "size": [333, 500]}, "area": 4124.0, "iscrowd": 0, "image_id": 10222, "bbox": [167.0, 20.0, 83.0, 313.0], "category_id": 132, "id": 10012467}, {"segmentation": {"counts": "^Vn14Y::F00001O000000J6O1O100001ojo2", "size": [333, 500]}, "area": 152.0, "iscrowd": 0, "image_id": 10222, "bbox": [191.0, 91.0, 14.0, 15.0], "category_id": 157, "id": 10012468}, {"segmentation": {"counts": "g[R2S1Z9a1_Nb1^Na1_Nb1^N?BO0000000O100000000000000000000000000000O10000O10000O10000O10000O10000O1000000000000000000001O00001O1O1O001O1TI`Mi5`2QJgMn5Z2kImMT6T2eISNY6R3O1O1O0bJTL[4m3^KZLa4g3YK_Lf4b3SKeLl4\\3mJlLQ5Y4O1O1O1O1O1O1O1]Oc0VOj0VOj0VOj0VOk0TOg`T2", "size": [333, 500]}, "area": 18367.0, "iscrowd": 0, "image_id": 10222, "bbox": [204.0, 22.0, 85.0, 253.0], "category_id": 183, "id": 10012469}, {"segmentation": {"counts": "j6U1k=O1000O1001O000000O1001N01O2O01]OVBMl=2WBLh=3]BFHKk=<hBDX=<f0ONDbA;_>F`A;`>22NMEaA;`>2100OO02N11N2O1N2O002N1N11N1N110O100000000O1000O1O1N2L5L4M3O1<^OXUm6A[kRI3N001N2N1M4M3L3N2N21N100O11O00O11OO10001O000000001004K6I2O01O0000O100001O00000000000000N20OO200000000000000000000000000001O1O001O0000000O10000000O1O100O11O000000000000K5002N3M000DRBFn=9UBEk=;VBDj=NVB1Y>MR_6", "size": [480, 640]}, "area": 3797.0, "iscrowd": 0, "image_id": 10229, "bbox": [0.0, 103.0, 626.0, 152.0], "category_id": 96, "id": 10012470}, {"segmentation": {"counts": "\\g71o>3L5\\AHU>c002M1O11O000000001O00O0020N2O1O1O002N1O00O1N200O100000000O100000000001O1O1BRRa8", "size": [480, 640]}, "area": 784.0, "iscrowd": 0, "image_id": 10229, "bbox": [16.0, 230.0, 41.0, 24.0], "category_id": 119, "id": 10012471}, {"segmentation": {"counts": "0i3W;0000O11O0000000000N2O1O1O1O100O10000O100000000001O001O00000000001O002N2N1O2N001O001O1O1OO1O10000000000O1L4O1O1O1N2001O1O00JhDcLY;]3gDb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fMmBi1d>WN_A0N2c_6c0enIV1QBYNY<W201O0000001O00001O00001O0000001O00001O00001O00001O0000001O00001O00001O00001O1O00N2O1O10000001OO11O2N000000001O000000000000000000O100O1000000001O001O000000000000000000O1N2O100O100O100O1001O00000000000hC^MR<b2mC_MS<g200000000000O100O1O1O11O2N000000O100000000M3N2O1O1O10000000000002N000000002N1O1O1O00000000001O000000O100O10000001O1O1O00O1001O00O100N21OO11OO1001O00O1O1O1N2O1O100O100001O001O1O1O002N001OO10000001O0000000000O1O100", "size": [480, 640]}, "area": 55361.0, "iscrowd": 0, "image_id": 10229, "bbox": [0.0, 0.0, 640.0, 127.0], "category_id": 120, "id": 10012472}, {"segmentation": {"counts": "i3f0Z>1O0O11O01ON2002N0N3N001O1O101oAROm=R1OO11N10O100O1O1N30O010O00O11ON2100O02K5O0M4N010O010O1O1OO1O2OO11O00000O0M5N2N1O2M20N31OO00JUOVBk0k=UOTBl0m=210O2O0O10O12N0000000001N2O0001O000O100O10000ONXBnNi=Q1VBPOj=P1300000OO22OO0001OO10OM44L1O01O0004L6I01O1000O1000O100000000O100000000O101O00000O010000000O011O001O001N1000O01N1O2N2N2N2N2N2N3M2M3FaA1a>N9O100O1O00e]g3=_SXLB^>o1bATN0Ng<Y2M00000000000000000000001e0ZOV1eNYoP3", "size": [480, 640]}, "area": 6190.0, "iscrowd": 0, "image_id": 10229, "bbox": [0.0, 0.0, 433.0, 155.0], "category_id": 132, "id": 10012473}, {"segmentation": {"counts": "b:^4b:0000000O1001O000000O10000O100LbKfE^4W:aKjEb4V:_KiEa4X:_KhE`4Y:3N21O1O000000O10000N2O100O1O11OLXKoEi4R:2O1001O0000001O00O13MO11O1O0000O1002N00N2O11VKkEg4X:O1O001O0PLWFi2c9nLPGn2d:M4L3M4L3M4L4L3M4L3M1O00000000000000000000000000000000000000000000000000000000000000000000000000000000O10000000000000000O1O1O1O10000O1O1N2O1N2N2K5K5K5K5K5J6K5N20000000000000000000000000000000000000000000000000000001O0000001O1O1O00001O00001O001O00001O00001O001O00001OIcLkD]3V;dLhD\\3X;eLhDZ3X;hLf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b0M3N21O00001O0000001O000000001O0000000000001O0000000000001O0000000000001O00000000001O0000000000000000O1000000O100000000O1000000O1000000O100000000O1", "size": [480, 640]}, "area": 72626.0, "iscrowd": 0, "image_id": 10229, "bbox": [0.0, 305.0, 640.0, 175.0], "category_id": 149, "id": 10012474}, {"segmentation": {"counts": "_4[2f<O00000O2ON3O02N001N0101OO1O2jMWCS2l<0N20O00O100ONXCnMg<S2ZClMd<W231NM[CkMf<U221N20NO30O1N10M22ON2001N110O0O1000001OO11O00000O002M3K4M4M20NM7N0O10O010O0101N100O012M100O10000O010O101N100O100O100O10MmNYBR1h=mNYBQ1i=12N2N2N004L2N2N3M1O2NN44K2O0O1O104KUQ[5KondJ2O2N101O1O100N2001OO1O1O1O100O1O001O1O1O<D4L3M3L4M3L4M3M3M3M3N1N3N1N2N2N2N2N2NO1O1O1O00O1001O010O001K[CnMe<R2\\CmMe<R2\\CmMf<Q25O1O1O1N2ZOnB\\OT=BlBk03@R=DlBl02@X=`0iB_OW=`0kB^OV=a0kB_OU=>oBAQ==RCBn<;UCEj<:XCFh<8ZCHe<:YCGf<:ZCFe<;ZCFg<9ZCFh<8XCHh<8WCIi<7XCHh<8WCIh<8XCHh<8XCHh<9WCGi<9WCGi<9WCGf<<ZCDd<>\\CBc<`0]C_Ob<c0bCXO]<i0iCQOW<n0n0001O0000O3N0001O0002N1O1O1O000N200N201O000000O100O10000001O1O1O01N2O010O0O100N21OO11OO10O2N10O002N1N2O1O100O10K5011N3NO1O]BYOn<f0QC]Om<c0SC^On<`0dB@17V=9gB5Z=h0O1000O101O0000000000O2N2O2[M", "size": [480, 640]}, "area": 12027.0, "iscrowd": 0, "image_id": 10229, "bbox": [0.0, 78.0, 640.0, 140.0], "category_id": 169, "id": 10012475}, {"segmentation": {"counts": "o7c2\\<10000000O1001O000000O01001N10OMeCcM\\<Y2cChM`<X2aCgM_<Z2aCfM^<[23N21O1O000000O1000OO3N010O1O11OL[ClMf<U22O1001O0000001O00O13MO11O1O0000O100oMmCW1Q=L3K6K5MnASOR>m03M3L3N6I6DSh\\8", "size": [480, 640]}, "area": 4527.0, "iscrowd": 0, "image_id": 10229, "bbox": [0.0, 253.0, 66.0, 85.0], "category_id": 172, "id": 10012476}, {"segmentation": {"counts": "UQj0d0g=i0H7fNnN[D[1a;V1K4M4K5L3L8gEgL]8_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jH_HX7_7iH`HX7_7iH`HY7^75O2N1O1N1O1010000O1000000000000000M3M3M3M300L4J6K5I7bJhGl3`8SL`Gg3g8YLYG_3o8`LQG[3U9X1000001O00000000001O\\Od0N2O1001O0000001O000O101O00000000001O0000000000001O0000000000001O0O10000000001O00000000001O0000O100000000O1000000O1000000O10O1000O10000000001O2M2WL[O", "size": [480, 640]}, "area": 151339.0, "iscrowd": 0, "image_id": 10229, "bbox": [55.0, 73.0, 585.0, 341.0], "category_id": 183, "id": 10012477}, {"segmentation": {"counts": "d4S1\\8[OgLe0Y3ZOhLf0Y3YOgLg0Z3XOfLh0Z3XOfLh0Z3WOgLi0X3YOgLg0Y3YOgLg0Z3XOfLh0Z3YOeLg0Z3ZOfLf0Z3XOdGFR5R1Z3XOdGFR5S1Z3VOhLj0X3VOiLi0Y3WOeLi0[3UOfLl0Z3mNbG6Q5n0b3oN_LQ1a3oNaLo0`3PO_LQ1b3nN_LQ1i8M22NG9O1O12N7I4K11O01OO10001O0O0HWB[Oi=e0WB[Oj=a0<001O2M1mAWOo=k0oAZOl=m00000NnNVBR1i=3N200O102NN2000000001OO1000O1O11O1O00000001O01O00001O00000000000000000iNYBU1g=kNYBU1j=O000lNVBR1l=00000KnN\\BR1d=POZBP1g=oNYBQ1f=PO[Bo0f=oNZBR1e=POZBP1f=oN[BQ1e=PO[Bo0e=QOZBP1f=POZBP1g=oNYBQ1f=QOYBP1g=oNYBQ1f=500O20K3L500003M1VBnNc=X11N11OO100000000000001OO11O001OO10000001O00O1001O0000O10000O101N11O0F`BTO`=j0cBVO\\=j0gBROZ=n0<1]BQOS=P1bBYO`=S10O1M3N2O2M2N2O1M3O11O2N1OO1000001O01N9HL41O1O2M2O0RKgNYG103c4U1S4SOlKl0T4UOjKl0W4SOhKn0X4ROhKn0X4ROfKP1Z4POeKQ1[4oNgKo0Y4QOhKn0X4ROhKn0W4SOjKl0V4TOiKm0Y4QOgKo0Y4QOgKo0X4ROhKn0Y4QOgKo0\\4nNeKQ1^4iNRGNb4X1T901M3M1011OO100O4M1O1O1O1O1O1O001OhVc0]Oii\\O4M3N2N00000GCiAc0T>600O101O01ON2O100O1KSOVBn0Q>N2M4JkXj0BagUO8H5K4L5K4L5J5E<O001O001O0O20O01O001O001O010O0O2O1O2N1O2N1OYO]CSOa<k0gCPOX<o0m0O100O10000000000gN^BT1b=lN^BU1a=kN_BV1e=100O3M2NO1N2O1O100O1000000000K501O1O0000000000000000000000000001O00nBgN]<X1cCkN[<U1eCkN[<U1eCkN[<U1eClNZ<T1gCkNX<V1hCjNY<U1fClNZ<T1fClNZ<U1eCkN[<U1eCkN[<U1eCkN[<U1eCkN[<T1gCkNY<U1gCkNY<V1fCjNZ<V1fCjNZ<V1fCjNZ<V1eCkN[<U1eClNZ<T1gCkNY<U1gCkNY<U1gCkNY<U1gClNX<T1hClNX<T1hClNX<T1hClNX<T1hCkNZ<T1fCmNX<U1gCkNY<T1hClNY<S1hCmNW<S1iCmNW<S1iClNX<T1gCmNY<S1gCmNY<S1gCnNX<S1gCmNY<S1gCnNX<Q1jCnNV<R1kCmNU<S1kCmNU<S1kCmNU<S1lClNT<T1lClNT<U1kCkNU<U1kCkNU<T1lCkNU<U1jCmNU<S1kCmNU<S1lCkNU<U1kClNT<T1lClNT<T1mClNR<T1nClNR<T1nCmNQ<S1nCnNP<G]C[1c0nNP<T1PDlNP<T1oCmNQ<S1oCnNP<S1PDlNR<Q1oCnNR<R1nCnNR<R1oCnNP<R1PDnNP<R1oCoNR<P1nCPOR<P1nCPOR<Q1mCoNS<Q1mCoNS<R1lCnNT<R1mCmNS<S1mCmNS<R1n000000000000000000000000001O01OOPCgN_OO02b<X1kCWOS<i0nCVOR<j0nCWOQ<i0oCWOQ<i0nCXOR<h0nCXOR<i0mCWOS<h0nCXOQ<i0oCVOR<i0PDVOP<j0PDWOn;j0TDUOk;k0UDUOk;k0UDUOk;k0UDUOk;k0UDUOj;m0UDTOj;m0UDSOk;n0TDROl;DdCK1V17jNMa0W<CjCS1LoNO<\\<AiCS2Y<jMhCV2a<lMXCP2i<nMYCP1b0[OU<EZCm0d0]OW<b0kC]OU<b0mC]OT<b0mC]OS<b0nC^OZ<IjB>l0Ia<3`CLb<2^CNb<2^CNd<0[C1e<O[C1e<0ZC1e<0YC1f<1XC0h<1VC0j<3eBEMM0>_=o00001O0000iNfBh0Z=WOkBe0U=ZOnBd0S=[OmBe0S=XOPCh0P=XOPCh0P=XOPCh0P=XOPCh0P=YO_B39d0X=e0O00000000000000000O1010O0jNbBj0^=WOcBg0]=YOjB`0V=ZOdBL7j0U=ZOPCf0P=ZOPCg0P=YObBL:k0W=VO`BO9j0W=VObB0O4Nf0h=<6I1O0000L4001OUOSOcCm0Y<ZOSCE>Q1[<_OVC@=S1Y<AZC\\Oa0o0U<QOZCb00^O;U1[<kNZCb00^O:V1^<\\OXC^O21023l0c<EZC@8k0]<KfC4Z<KgC5Y<KgC5X<KhC6X<HjC8V<IiC7W<IiC7W<KgC5Y<NdC2\\<Z1O01UN^CV1c<cNdC\\1[<bNhC^1Z<]NXC0?c1[<]NeCc1m<O01N11O1O0000N2O11O01O0N200000001O000000000000001N100nJ", "size": [480, 640]}, "area": 23891.0, "iscrowd": 0, "image_id": 10230, "bbox": [0.0, 123.0, 640.0, 357.0], "category_id": 96, "id": 10012478}, {"segmentation": {"counts": "0m1S=2N00O100O1O11OMoB[Nm<f1UCVNl<k14LmB_No<`1UC\\Nl<e17O13M1O00I73M1O1O3M00O1M3M3001O00000000NgB]NY=c12002N00O1O12N1O00MiB]NW=d12000000000000001OO100O1O11dB]NY=c1gB^NX=e10002N1O003M000000002NK5ElBhNV=X1kBfNV=V1hBhN22V=U1jBhN02W=U1kBiNN1W=V1kBhN00V=U1fBmNe=R15O11OMVBoNk=P130000005K00N200N2000000000000001OO11O0000000000001O1O2ZBnNZ=^1NO1O1005K001O3M0kBXNR=k102N1OO1M31O00N2J62N1O002N00002N1OO1M3N2O100O11O00N2N200000dB_NY=e1O1OKiBaNW=^16NbBcN]=]1cBcN]=\\1400KbBhN^=X151ON[BiNe=W12O1000000O1O1002N1O000000O100O1O1O100O10000001O00001O0000001fBPOb<P1]CmNg<S1XCTOb<m0[CSOg<U1PClNP=_13M31O2N6J2NO13M1ON2O1001O2NJ600L4MmBYNS=f14M3L4O100O1O12N1O1O1bB_N[=a1eB_N10W=c1iB]NW=d1hB\\NX=f10000000O1000000O1L4J6L40000O10000N200O1O11O00002N1O4L2N5K2N001O3iBZNn<f1PCZNN0R=l102N5K1UClMh<V2YCkMc<V2\\CjMd<Y20O11OO11OO10000E[C[Ne<d1\\CZNf<d1[C^Nd<b1=O1O1O1O11O2N00001O00O1O1O100L400O13M0hBZN:0`<f1VCZN1061b<j1XCVN20f<k1XCTN12g<j1WC[Ni<n101O1O0000001O3M0000001O00M^ChMb<T271O00003MO1O12N1OO100O1O1M3001O00N20000O13M1O1O1O0000O1O1N2N2000000O11O3M2N1OO11OO1O100M3L4004L1O1O1O002N3MO11ON2M300002N2NO1N200O100NXClMh<T221OM300O11O1O1O1O00001O1O1O002NO100O11O2NJ^CkM00b<U2^CkM00b<R2^CnM30_<R29N22NO1002XCnM^<U2_CkMa<T2_CmMa<R2_CnMb<Q2]CQNc<U2^CgM_<X2`CjM`<Z2aCdM\\<[2dCgM[<Y2cCiM]<\\2100O1O10000M3M3IZCTNf<k1[CTNf<m1YCQNi<i1WCYN2Ne<i1ZCXNO2f<d1^CYNM1f<d1_C[NMOd<_1YCeN6Mg<\\1XCdN1Oh<]1b00000N2O100N22N2NO`BeN]=[1bBeN_=^100cBaNY=]1hBdNOOU=c1kB]NU=b1kB`NT=f10001O000000O1O1O1O1O1O100O100000000001O00000000001O00000PC^Nb<d1WCbNh<m1NO1N23TCkMg<X2ON200O11O002[ChM^<Z2`CfM`<[2_CgM_<^2O1OIdCfM`<Z23I^CnMd<R2]CmMc<S25M33M001O3M003MN2N2O1N2O1O100O100O1O1O100K500O1000000O100O100O10000O1001O001O0000N21ON2001O00O100O1O12N1O2N1OO13M1O1OO11ON200O10000002N2N1O1O1PCSNk<S2N00O1000000M3000SCPNj<T2OO10\\CmM[<W2`CjM0O\\<W2cCkM1N\\<`2fC`MT<a2hCeMU<e2SDUMa;j2`DVM`;k2[DYMe;Q306JN2O100001O1O00N\\DhLd;X320000001O001O00000000000000N2002N00N21O000000O1O1O100O1000000O100", "size": [480, 640]}, "area": 39340.0, "iscrowd": 0, "image_id": 10230, "bbox": [0.0, 0.0, 640.0, 108.0], "category_id": 106, "id": 10012479}, {"segmentation": {"counts": "g5?h13Y4MF0QL3X4NGOQL3X4NGOQL3X4NGOQL3X4NGOQL2X40GNQL3X4NG0PL3X4MH0PL2Y4NG0PL1[4NE1PL1[4NE1PL2X4_ORJ>f52oK0Z4@QJ>g52mK1Z4OI0mK0[40H0mK1\\4MG2mK1Z4OI1lKN^40E4lKL^41F4kKK_41F4kKK_41F4kKK_4APJ>f56kKL`4NCF_J8_14_4NEB`J<[14`4NMMdK5^4ONLcK5^41LMfK2^41OJcK6]4_OSJ>m5McK6`4MG3iK0_4NH2iK0a4LF4jKO_4NH2iK0_4NG2kK0^4NG2kK0^4NG2lKO]4OG2mKN\\40F3nKM\\40F3nKM]4OE4nKM]4OJOgKD]N>R6OKNfKE]N>Q60LMeKF^N=S6NJOeK5a4MH0hK2`4ND4lK_O]N=S60D4nKL_4OC5nKL^40D4nKM^4ND5nKM^4ND5nKN]4ME5nKN]4ME5nKN\\4NF4mKN_4MF3kKO`4NC5mKM`4NC5nKL_4OC5nKM]4OE4mKN^4NE4mKN`4LD5lKN`4NC5mKMa4MB6lKNb4LB6mKN`4LC6nKM^4ND5nKM^4ND5nKM^4ND5mKN_4MD5mKM`4NC5mKN_4MD5mKO\\4NH1nKO[40G1nKO[40G1nKOZ41H0nKOZ41H0nKOZ41H0nKOZ41H0nKOZ41H0nKOZ41H1mKN\\40G2mKN\\40G2mKN\\40G2mKN]4OF3mKN]4OF3mKN]4OF3mKN^4NE4mKN^4NE4mKN\\40G2mKN\\40G0oK0Z40GKTL5U40GFYL:P40n10RN0n10RN0n10SNOm11SNOm11SNOm11SNOm11SNOm11SNOm11SNOm11SNOm11SNOm11TNNl12TNNl12SNOm11SNOm11RN0n10QN1o1OQN1P2NPN2P2NQN1n10RN0o1OQN1o1ORN0n10QN1o1ORN0n11QNOn11SNOm11RN0o1OQN1n10SNOm11SNOm11TNNm11RN0n10QN1o1OQN1o1OQN1o1OQN1o1OQN1o1OQN1o1NRN2n1NRN2n1OQN1o1OQN1o1OoM3Q2MQN1o1OQN1o1OQN1o1OQN1o1OQN1o1OQN1k1BUH>P60k1BUH>P60k13UNMk14TNLl14UNKk15UNKl14TNLl14UNKk15UNKk15TNLl14SNMn12RNNn12SNMm13TNLJDSLa0S4KGHVL;T4MDKVL:U4KDMVL7W4LAOYL4V4M_O3_JAh1>X40C5SLJZ41IOmK0Y42NKhK3Y431HfK5Y432GeK6Y432GeK6Z42@DlJ4Z16[41_OGjJ2\\16Z42@LdJLb16Z42@9VLEZ42@9VLEZ42A8ULFZ42I0mKNZ42E5oKK[4AQJ=f56nKK\\4BPJ=g55mKL[4CQJ<i53kKN[4BRJ=i52jKO^4OH2jKO^4BnI01032f5=kKNj4C[O?jKOg:1YEOg:1YEOg:1YEOg:1YEOg:1YEOg:1YE0f:0[EOe:1[EOe:1ZE0f:0ZE0f:0ZEOg:1YEOg:1ZENf:2ZENf:2ZENf:2ZENf:2ZENd:4\\ELb:6^EJa:7_EIb:6^EJa:7_EIa:7_EI`:8`EH`:8_EIa:7`EH`:9_EGa:9^EHb:7`EH`:8`EH`:9`EF`::`EF`:OkE1b<01O01O00000000000000000000O100000004L5LnVo1MTiPN2N101N1O2M2O1OO01O001jBEf;;YD2];LdD7Z;HfD8[;GeD9\\;FdD:];EcD;];EcD;];DcD<_;@dD`0n<010N101O1N10001O000000000O0100001O00000000N2O1000O10000000000000O0101O00O100006I3N1O1O001O4L00N11UBmNi=U10000000001OO10000O00100001O00001O00001N10O1000000O10O10001O1O001O000000000O1YOfNhCZ1W<hNhCX1V<eNWC3c0W1U<mNkCS1T<oNkCQ1U<oNkCQ1U<POjCP1U<QOjCP1V<QOiCo0X<POhCP1X<POhCQ1W<oNiCP1X<POhCP1X<POhCQ1W<oNiCP1Y<POfCP1Z<POfCP1Z<POfCQ1Y<oNfCR1Z<oNeCP1]<oNcCS1[<mNeCS1[<mNeCS1[<mNeCT1Z<mNeCR1]<mNbCS1_<nN`CS1_<mNaCS1_<nN`CR1`<nN`CR1a<mN_CT1`<lN`CT1`<mN^CT1b<mN]CT1c<lN\\CT1d<mNZCT1f<d0OO1O1O1N3MSCQNm<o1201O1O01O0000O2N10001O000001O0VCUN_<l1^C]N[<T20002N01O0O10010O00000O10001O000N]ChMb<X2^ChMc<W2300O101O00000000001O01O00000010O0001O001N12OO1O00001O00O101O00000O1O1000001O00000001O00001O0001O00100O0O2O0O100000001O0O1001OO100000000O1O10000010N020OO1001O00001]H", "size": [480, 640]}, "area": 22429.0, "iscrowd": 0, "image_id": 10230, "bbox": [0.0, 177.0, 640.0, 303.0], "category_id": 111, "id": 10012480}, {"segmentation": {"counts": "V6h1b0k3a6UL_Ik3a6UL_Il3`6TL`Im3_6SLaIm3_6SL`In3`6RLaIl3a6SL`Il3`6TL_In3`6RL_Io3a6QL_In3b6RL_Im3b6RL]Io3d6PL]IP4b6PL]IQ4c6oK^IR4`6nK`IR4a6mK]IU4AXJQ7b1^IZ4a6eK_I[4a6eK_I\\4`6dK`I]4CRJh6a1fI`4Y6aKgI_4Z6`KfI`4Y6aKgI^4Z6aKfI^4\\6bKdI^4\\6bKeI]4_OSJi6`1hI`4X6`KhI_4Y6aKgIa4W6`KhI_4Y6aKgI_4X6bKhI^4X6bKhI^4X6cKgI]4Y6dKfI\\4Z6dKfI\\4Z6dKfI]4Y6cKgI]4Y6aKYI]N>R6Y6aKYI]N>Q6Z6aKYI^N=S6X6_KjIa4W6`KhI`4X6`KYI]N=S6Z6bKfI_4Y6aKgI^4Z6bKgI^4X6bKhI^4X6bKiI]4W6cKiI]4W6cKiI\\4X6cKhI_4W6aKhI`4X6`KhI`4X6aKgI_4Y6aKhI]4Y6bKhI^4X6bKhI`4V6`KiI`4X6`KhIa4W6^KjIb4V6_KjI`4V6aKiI^4X6bKhI^4X6bKhI^4X6aKiI_4W6aKhI`4X6`KiI_4W6aKjI\\4W6fKgI[4Y6eKgI[4Y6eKgIZ4Z6fKfIZ4Z6fKfIZ4Z6fKfIZ4Z6fKfIZ4Z6fKfIZ4[6eKeI\\4Z6dKfI\\4Z6dKfI\\4Z6dKfI]4Y6cKgI]4Y6cKgI]4Y6cKgI^4X6bKhI^4X6bKhI\\4Z6dKfI\\4X6fKhIZ4S6kKmIU4n5PLRJP4i5ULWJk3i5ULWJk3i5ULWJl3h5TLXJl3h5TLXJl3h5TLXJl3h5TLXJl3h5TLXJl3h5TLXJl3h5TLXJl3h5TLXJl3h5TLXJm3h5RLXJn3h5RLXJm3i5SLWJm3i5SLWJl3k5SLUJl3P6PLPJP4P6QLoIo3n5TLRJm3Q6nKPJR4o5PLPJP4l5TLTJm3k5SLUJl3k5ULUJl3k5SLVJl3i5TLWJm3i5SLWJl3j5ULUJk3k5TLVJm3i5SLWJm3i5SLWJn3h5SLWJl3j5TLVJk3k5ULUJk3k5ULUJk3k5ULUJk3k5ULUJk3k5ULUJk3k5ULTJl3l5TLTJl3l5TLUJk3k5ULUJk3k5ULUJi3m5WLSJk3k5ULUJk3k5ULUJk3k5ULUJk3k5ULUJk3k5ULUJk3k5QLhIoM>P6j5QLhIoM>P6j5QLYJo3g5QLZJn3g5QLYJP4f5PLZJP4f5QLYJo3g5PJkIk1>V4l5jKTJV4m5iKSJV4n5jKRJU4l5oKSJQ4l5PLTJQ4k5oKUJR4j5mKWJS4j5mKTJT4m5jKUJU4l5jKSJW4m5jKRJV4P6nIaIh1>X4Y6fKfIZ4Z6fKfIY4\\6fKdIY4]6gKcIY4]6gKcIY4]6gKcIZ4\\6fKdI[4[6eKeIZ4\\6fKdIZ4\\6fKdIZ4\\6fKdIZ4\\6fKdIZ4\\6fKdIZ4]6dKeI[4AQJh6d1fI\\4BPJh6d1fI[4CQJg6d1fI[4BRJh6c1fI^4Z6bKfI^4Z6cKeI]4[6bKfI^4Z6bKfI_4Y6aKgI_4Y6aKgI_4Y6aKgI_4Y6aKgI_4Y6aKhI^4X6cKgI]4Y6cKgI\\4Z6cKgI]4Y6cKgI_4W6aKhI`4X6`KhI_4Y6bKfI_4Y6aKgIb4V6^KjIe4EfIN0Z6e1SJk4m5UKSJk4m5UKSJk4m5UKSJk4m5UKSJk4m5UKSJk4m5UKSJk4m5UKSJk4m5UKSJk4m5TKTJl4l5UKSJk4n5TKRJl4n5SKSJm4l5UKSJk4m5UKSJk4n5UKQJk4o5UKQJk4X6lJhIT5Y6kJgIU5[6jJdIV5^6hJcIW5_6gJaIY5a6eJ_I[5c6cJ]I]5e6aJ[I_5g6_JYIa5h6^JXIb5j6\\JVId5l6ZJTIf5n6XJRIh5P7VJPIj5R7TJmHm5W7oIiHQ6\\7jIdHV6a7eI_H[6f7`IZH`6S8oMgG[MZ8T2]HeMc7k1SIoMR7\\1dI^Na6m0VJlNo5?gJ[O^50XKJm4AiK9f9I6JP?0[mh1OgcVN010Q?000o@0O001M2O2M2O2M2ZBFc<=WCGH2X<8jCKN8m;OoCM4<e;HRD09`0];BTD2?c0V;4jD4m:LSE<c:G]Ea0Z:@fEg0S:ZOmEj0m9VOTFQ1e9oN\\FT1`9lNaFU1\\9kNeFX1W9iNiFY1T9hNmFY1g8SMiFg1`0X1a8SO_Go0`8PO`GS1]8mNcGT1\\8lNdGU1Z8kNfGX1X8hNhGY1W8gNiG[1V8dNkG]1S8cNmG_1Q8aNoGa1n7`NRHa1m7^L`Gl1c0h1R8VNlGm1S8SNlGo1S8QNmGQ2R8nMnGS2m1TLb3h1bJU2j1TLd3g1bJW2g1SLg3f1bJ[2b1PLl3e1bJ_2\\1nKR4c1bJ`2Z1nKT4b1bJb2V1nKX4`1bJd2R1nK\\4^1aJe2R1nK]4\\1bJg2m0PLa4Y1cJh2g0RLf4V1cJk2=VLP5o0bJP3K`Lc5`0bJh4^5XKbJh4_5WKgJc4Y5\\KjJb4V5^KkJa4U5_KlJ`4U5_KkJa4U5_KlJ`4T5`KPK\\4P5dKPK\\4o4eKoJ]4R5aKoJ]NmNP6T6cKQK]4o4cKQK]4P5bKPK^4P5bKPK^4o4cKQK]4o4cKRK\\4n4dKQK]4P5bKPK^4P5bKPK^4Q5aKnJ`4R5_KnJb4R5^KnJb4R5^KnJb4R5^KoJa4Q5_KoJa4Q5_KoJa4Q5_KPK`4P5`KPK`4o4aKQK_4P5`KQK_4o4`KRK`4m4aKRK`4o4_KQKa4o4_KQKa4P5^KPKb4P5^KoJc4R5\\KnJd4R5[KoJe4Q5[KoJe4Q5[KPKd4P5\\KQKc4P5\\KPKd4P5\\KQKc4n4^KRKb4n4^KRKb4o4]KQKc4o4]KQKc4o4]KQKc4o4\\KRKd4n4eJhI1Z1Z5n4dJjI1X1[5n4bJgI@3c0W1\\5o4_JnI5S1\\5o4^JPJ5Q1]5o4^JPJ5Q1]5P5]JPJ5P1^5Q5[JPJ6P1_5Q5ZJPJ6o0`5R5ZJnI6P1`5S5YJmI7Q1_5R5ZJmI7P1`5S5YJmI7P1`5T5XJlI8Q1_5S5YJlI8P1`5T5YJlI6P1a5S5ZJmI5P1a5S5ZJmI5Q1`5R5[JmI4R1a5P5\\JoI2P1c5P5]JoI0S1a5m4`JPJOS1a5m4`JPJOS1a5o4^JnI1T1`5n4_JoI0R1b5P5^JmIOS1d5P5]JnINS1d5o4^JnINS1d5o4^JoIMR1e5o4^JoIMR1e5o4_JnILT1d5m4aJoIKT1d5m4aJPJIT1f5m4`JPJIT1f5m4aJoIHT1g5m4aJPJFT1i5l4aJhK^5X4bJgK_5Y4aJfK`5[4_JdKb5\\4^JbKd5^4]J_KDQNo5`6]J_Ke5a4[J_Ke5a4\\J^Kd5b4]J]Kc5c4]J^Kb5b4^J^Kb5b4^J^Kb5b4^J]Kc5c4^J[Kc5e4]J[Kc5e4]J[Kc5e4^JZKb5f4^JZKb5f4^JZKb5f4^J[Ka5e4_J[KIUN]5`6jJ\\KF]NY5X6PKlKP5T4PKlKP5T4PKnKn4R4RKnKn4R4SKmKm4S4SKlKn4T4RKlKn4U4QKlKn4T4SKkKm4U4SKkKm4U4SKkKm4U4SKjKn4V4RKjKn4V4RKjKn4V4SKiKm4W4SKiKm4W4SKgK_OhM]5a6TKgK_OhM]5a6UKfKn4[4QKeKo4[4QKdKP5\\4PKdKP5\\4QKcKo4]4QKcKo4]4QKcKo4]4QKcKo4]4QKcKo4]4QKcKo4]4RKbKn4^4RKcKm4^4RKbKn4^4RKbKn4^4RKbKn4^4RKcKm4]4TKbKl4^4TKbKl4^4TKcKk4^4TKbKl4^4TKcKk4]4TKdKl4\\4VKcKi4^4VKcKi4]4WKcKi4]4WKcKi4]4WKdKh4\\4XKdKh4\\4XKcKi4]4WKcKi4]4XKbKh4^4XKbKh4^4XKbKh4^4XKaKi4_4WK`Kj4`4VK`Kj4`4VK`Kj4`4VK`Kj4a4VK^Kj4b4VK^Kj4b4VK^Kj4b4VK^Kj4b4VK_Ki4a4WK_Ki4a4WK_Ki4a4WK`Kh4`4XK`Kh4`4XK`Kh4a4XK^Kh4b4XK^Kh4b4YK^Kf4b4[K]Ke4c4[K\\Kf4d4[K[Ke4e4[KZKf4f4ZKZKf4g4YKYKg4g4YKYKg4g4YKYKg4g4ZKXKf4h4ZKWKg4i4YKWKg4i4YKXKf4h4ZKWKg4i4YKWKg4j4XKVKh4j4XKVKh4j4XKVKh4j4XKUKi4k4WKTKj4l4VKTKj4m4UKSKk4m4UKSKk4m4VKSKi4m4VKSKk4m4VKSKi4m4WKRKj4n4VKRKj4n4VKSKi4n4VKRKj4n4VKRKj4n4VKSKi4", "size": [480, 640]}, "area": 111701.0, "iscrowd": 0, "image_id": 10230, "bbox": [0.0, 185.0, 640.0, 295.0], "category_id": 124, "id": 10012481}, {"segmentation": {"counts": "oXm71o>2M011N2O1N100O1O100O1O101N1O1O1O1O100O11O1O1O1O1O1O001O1N10100O001O00001O01O3M01M200002N1O1aAGU>:hAHX>9gAHX>`03\\OdA>]>AcA=_>CaAa0[>_OeA;a>E_A:b>F^A210f>0PPd0", "size": [480, 640]}, "area": 656.0, "iscrowd": 0, "image_id": 10230, "bbox": [540.0, 146.0, 57.0, 37.0], "category_id": 166, "id": 10012482}, {"segmentation": {"counts": "m1g2[<N00O100O1O11OM[MoCa2R<aMjC`2W<4LYMSDc2l;aMPD`2Q<7O13N0O0OJ55M11ON4NO1N1M3M3001O00000O11MRMRDn2n;2002N01N1O03N10O0MSMSDm2n;20O100000000NQDSMo;l24M110O01O11UMlCh2T<XMmCg2V<0002N1O003M0O1O11O11NK5E]MWDe2i;\\MUDe2g;YMWD22e2f;[MWD02g2e;[MYDN1g2f;[MXD00f2e;VM]DV3a;5O11OMeL`D\\3_;30000005K\\DfLb;Z3^DfL`;_3O0N2cD_L[;c300000KeDeL[;[3gDcLY;^3fDbLZ;]3gDdLY;\\3eDdL[;\\3gDcLZ;\\3eDeL[;[3fDeLY;[3gDdLZ;\\3fDdLZ;]3eDcL[;\\3gDcLY;^3fDbL[;\\36O2iL_Dj2n;ON001005K001N4NO[MhCb2[<02N1OO1M31O00N2J71M20O02OOO102N1OO2L3N10101N11O0OO2N20O100TMoCh2V<00OKYMQDg2n;6NRMSDm2m;SMSDm2l;401JQMYDo2g;51ONjLZDV3f;3N1000000O1O1002N1O000001N10O01O1O100O01000010N1O101M2O12N10VM`DR2`;mM]DX2b;gMeDR2];kMcDW2e;`M\\D`2o;3M3101O5J2ON22L2ON2O_OgC]NY<_1TDXNm;g0dCOW=JUCKi<;WCBl<>TCCg<a0ZCWO@3R=KnBj0a0WOGOg<n0bCQOK0_<R1m04O011N1O21O0O100hNYBV1f=jNZB10R1h=nNXBR1i=mNWBR1l=000000O01000000O1L5I7K401O1N4MHoBbNP=^1RC`Nl<a1VC^Nj<b1VC^Ni<b1XC_Nf<b1[C]Nf<a1=1O02OoB`Na<_1`CcNa<Y1_ClN^<j15NO3K6Ke0]OVMmC`2T<^MlCN0d2Z<02N5K1cM^CQ20PNj<0WCi1P=VNPCj1S=O011OO20M10100OFYN]Cb1g<_NWCa1i<`NYCU1Q=Dh01O001O02O2M100O2O0O01O0010OM40O004M0YOiA:0NX>HhA10610\\>JdA203^>KaA124]>JhA5b>01N2O0O11O01OQj2OlUM7NO00003NN1O12O0OO100O2N1M3010O00N201O0O402L4L4L4K5K5J9G1N2N200000O020O3M2N1OO11OO2N10ON3L4004L1O1O1O002N3MO11ON2M300002NdBaNY=]1gBcNX=^1hBbNV=e101N100NVNnBj1R=21OM300O11O1O1O1O000K6O1O1O002NO100O11O2NJaNhB00_1X=aNhB00_1U=aNkB30\\1U=9N22NO2O02ZNlB\\1W=aNiB_1V=aNkB_1T=aNlB`1S=_NoBa1W=`NeB\\1[=cNgB^1\\=cNbBZ1]=fNeBY1[=eNgB[1^=100O1O10000M3M3I\\NRCd1m<]NRCd1o<[NoBg1k<YNWC2Nc1k<\\NVCO2d1f<`NWCM1d1f<aNYCMOb1a<[NcC6Me1^<ZNbC1Of1_<c0OO10N3N100N22N2NOaMdC\\2\\<cMdC^2_<00dM`CX2^<iMcCOOT2d<lM\\CT2c<lM_CS2g<0001O000000O1O1O1O1O1O100OO]CgMc<Y220000002OO00000001O01O00000PN^Cb1d<WNbCh1m<NO1N23TNkBg1X=ON200O11O002[NhB^1Z=`NfB`1[=_NgB_1^=O10HcNgBa1Y=3I]NoBe1Q=\\NnBd1R=5M33M001O3M0O4MN2N101N2O1O10O0100O1TCSNK1f<m1^CXN_<j1`CVN`<l1]CUN^<Y2dC`MX<a2fCaMX<`2hCaMW<d20001N108kCPMi;X3L2M102N0OLaDiL_;W3cDgL^;W3bDiL_;W3bDhL_;V3600OO21NO1101N1O010O00003M2N3N1N022N0000N200M201N2O001O11N2O0O1O1SMPDi2U<N00O101O000M3000TMoCi2U<OO10]MlCU2]<fMdC0OR2a<mMaC1Nn1n<TNRCb1S=ZNSCc1J]N[=8eBo00iNZ=9fBP1[=kNiBU1a=06JN2O010001O1O00NmNWBS1i=201eBnNf<S1SCTOl<k0RCXOo<i0lBiN0?T=j0lBVOU=[1O00O11O1O0000N01111N00N03O000000O1O1O100O1000000O010kL", "size": [480, 640]}, "area": 44478.0, "iscrowd": 0, "image_id": 10230, "bbox": [0.0, 33.0, 640.0, 134.0], "category_id": 169, "id": 10012483}, {"segmentation": {"counts": "jn:4j>3E90O11215GjV4L[iK0M=F10O2N0DdPn05^oQO32N24OET]`03]T_OOW>3hANX>6cAK]>94HbAJbV42TXL9I5M10N2N2O1NZ[`0Kgd_O=gNA\\Ch0Y<D\\Ca0c<_OZCe0e<[O\\Ce0c<[OVCA2T1f<]OVCGOj0k<^OUCLOf0k<]OUCT1j<kNVCW1S1nNm9\\301N2O1O1N2O1O1N1O2M3N2NhLbEP2\\:PNeEQ2X:PNhET2T:lMlEY2o9gMQF^2j9bMUFd2f9\\MZFi2g4SLL]5_OZK;i4QOmKj0W4aN_LY1f3RNQMh1S3cMcMY2`2RMVNj2m1aLiNQ3SKmLb;l2`DRMf;g2\\DXMf;f2ZDZMd;g2]DZMb;f2_DYMa;f2=1O0VDYM[;h2eDZMX;f2hD]MU;c2lDbMn:V325K6J6TE`Ll9\\4N2N2N000000000000001O000000000000nNUFoLk9m2\\FQMc9e2jFXMV9o1gGkM[8U2X2O100O100O100O100O100O100O101N1O100O100O100O100O100O11O00001O0000001O00001O0000001O00001O0000001O00001O0000001VF]MU7c2gHhMR7\\2eHjMZ7\\2\\HiMc7\\2THhMl7]2kGhMT8]2cGgM]8^2ZGgMe8Z401O001O001O001O001O001O001O001O001O1O2O0O2N2N1O2N1O2N1O2N1O2N1O2N1O2N1O2N1OYOkGeJS8n4ZHSKe7j4]HYKa0\\Og5[5fI\\K=Ib5k4TJ[K4:\\5_4QJ[J12Ok0>g0W5_5cJlJV5U5dJXKT5a7I8H7I8H7I8H4EjDdLX;^35N2N2O2M2N2N2N1O2N101N2N1O2N2N2O0O1O1O2M2N2N4L6I4M3L3N3L2N3L6I8_Od0[OncW1O^\\hN2N3O1O100003M2N2N`f;5ZYD5K2O0M4O0000AdA:h>IlR6LYmI1N7CJaA7_>5004LMG`A8_>JaANc>637I02N1M3I7N2OTj25cUM5M2O100O1N21O0O4M2Leh?", "size": [480, 640]}, "area": 60272.0, "iscrowd": 0, "image_id": 10230, "bbox": [23.0, 74.0, 583.0, 406.0], "category_id": 183, "id": 10012484}, {"segmentation": {"counts": "0f3e9WMVH1j7TOQIl0o6TORIk0n6UORIk0n6UORIk0n6UOSIj0m6VOSIj0m6VOTIi0l6WOTIi0l6WOTIi0l6WOUIh0k6XOUIi0j6WOVIi0j6WOWIh0i6XOWIh0i6XOXIg0h6YOXIf0i6ZOWIf0i6ZOXIe0h6[OXIe0h6[OXIe0h6[OXIe0h6[OXIe0h6[OWIf0i6ZOWIf0i6ZOWIf0i6ZOVIg0j6YOVId0m6\\OSI`0Q7@nH=V7CjH1XM4Q:KmH0W70R3000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001O2N0000000000000000000000000000000000000000000000000000000000000000000000000000000GL[C4e<2UCNl<800000000001O000000000000000000000000000000000000000000001O000O10000000000000000000000000000000000000001O000000000000000000000000000000GMZC3n<16J000000]K0kK0U48cKH]4?\\KAc4g0VKYOj4h0UKXOk4h0UKXOj4i0VKWOj4i0VKWOj4k0TKUOk4l0UKTOg4o0ZKQOh4>gKB^40kK0h8000lN0mD0V;4cDLa;7XDIk;<mCDW<?bCAa<c04L3M4L0000000000000000000000000000000000000000000000000000000000000000000000O1000000000O100000000000000000000000000000000000O100000000000000000000000000000000000000000000O10000000000000000000000000000000000000000000000O10000000000000000000000000000000000000000000000O1000000000000000000000000000000000O1000000000O10000000001[C5h;h0O00001O00000000001O0000000jNfN[F[1d9[OfEe0^:R19H9F9G:F9H0O0000000[OkC5T<KlC5T<JmC6S<JmC6S<InC7R<InC7R<HPDGO5MHU<<oCEb<;70000MSCGn<5RCKR=1PbX2", "size": [427, 640]}, "area": 6471.0, "iscrowd": 0, "image_id": 10232, "bbox": [0.0, 0.0, 466.0, 427.0], "category_id": 105, "id": 10012485}, {"segmentation": {"counts": "RXa5>n<2M3M3N2M2N3M3M3M3N0O2N1O2N2O0O2N10O0O1002O1N2N2N1O2O1N1O2N1O2N010O0000O100000000O100000000O10O1N3N2N2O1O000000O10N21OO1000000000O1000011N2N0000000000O100000000O11OO1kDmMS;Y2lDhMl:c2I01O1O001O0000000O20OO10010O0lMVEf1k:UNUEL1P2i:QN_En1c:oM^EQ2o:O000000001O0000000002O1M4N1NNnDnMP;m19O2N1000000000000000000001O0101N1O000000O100000O2O000001O002N100O100001N02OO01M2O101O001O2N4LN2N20001O000000010O0001O01O01O2N4L3M3M002NK5O1O2N1001O01O0000010O01O1O001O1O1O100O1O001O00100O002eM", "size": [427, 640]}, "area": 14108.0, "iscrowd": 0, "image_id": 10232, "bbox": [425.0, 31.0, 215.0, 129.0], "category_id": 132, "id": 10012486}, {"segmentation": {"counts": "WjT61Z=000M30000000000000000000000O10000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000O100000000000000000000000000000000000000000000CKdC5]<LaC4_<O^C1b<2[CNd<5ZCKe<>001O1O00O11O0000000000000000005@TC7U=K]ch0", "size": [427, 640]}, "area": 795.0, "iscrowd": 0, "image_id": 10232, "bbox": [471.0, 407.0, 109.0, 20.0], "category_id": 167, "id": 10012487}, {"segmentation": {"counts": "X>k1e:k00001O001O00001O00001O00001O001O00001O00O100O1000^FWMb8h2[G^Mc8b2YGcMf8]2VGiMh8V2UGoMj8Q2SGTNk8l1QGYNn8f1QG^Nm8a1SGaNl8^1UGcNj8]1UGfNi8Y1WGiNh8V1XGmNf8R1[GoNd8Q1[GROc8m0]GUOb8j0^GYO`8f0aG[O^8e0aG]O^8b0bGA\\8>dGD[8;fGGX88hGJW86hGMV82jG0U8OlG3R8LnG6Q8JnG8Q8GoG<o7CRH>m7ASHb0k7^OTHd0k7[OUHh0i7WOXHj0g7VOXHl0g7SOYHP1e7PO[HQ1d7nN\\HU1b7kN^HV1a7iN_HZ1_7fNaH[1^7eNaH^1]7aNcHa1\\7_NdHb1a3eLKh1dLf1\\3jLK`1iLg1X3mLN[1jLk1R3PM2U1lLl1n2SM5P1mLP2j2SM8m0mLR2g2VM:g0PMU2b2WM=d0PMW2`2XM?`0RMY2\\2[M`0<SM[2T2bMh02UM]2a1SNY1@UM^2P1eNj1lNWM_2k0kNl1fNXMa2g0nNP2`NYMc2c0ROS2[NZMc2?WOV2UN[Mf2:ZOZ2PN\\Mf26^O^2lM[Mg26^O_2jM\\Mi23_O`2hM\\Mj23_Oa2fM\\Ml22@a2dM]Mm20@c2bM]Mo2NAe2`M]Mo2MCe2]M^MY7b2gH^MY7b2fH^M\\7a2dH^M^7a2aH`M`7_2`H`MQ34G[2WMbMd7]2\\HbMe7^2ZHcMg7\\2YHcMi7\\2VHdMl7[2THeMm7Z2SHeMo7Z2PHgMV8S2jGlM^8m1aGSNg8f1YGYNn8a1QG_NW9Z1iFeN_9T1`FlNg9n0b1IfDYOW:`0YKA@5`0O\\O1e03VOMm05mNL]1O^N1l1JoM6c7002N1O0000000O100000000000000000000O100000000mNDWE<k:DSE<o:EnD;T;EjD;X;EfD;\\;F`D;b;E\\D;f;EXD;j;FSD:o;FoC:T<FiC:Y<FeC:]<FaC:a<G\\C9f<52N001O001O001O001O0000000000000000000000000000001O000000000000000000000000000OTJGhN9X1JeN6[1FiN:`1GVN9k1JQN6Q2JmM6T2LiM4X2NfM1[20cM0_21^MOc24YMLh28SMHn2<mLDW3=dLCa3<[LDi39VLGn36QLJT42kKNY4OfK1_4K`K5k800000000000[MJUH6S8NaG2f82oFNY96[FJm99iEF_::YEFn:8mDH[;6_DJi;3RDMV<1dCOc<OXC1P=16J000000]K3hKMX4:aKF^4c0ZK]Of4j0SKVOl4R1mJnNS5Z1eJfN[5a1^J_Na5j1WJVNi5Q2PJoMW5Q3bJoL^58^H_2l1YMf52iH<i1_OA3i60kK0h80hN0UE0o:3jDMP6M[N:^KId;;TDEb6M\\Nb0kJAj6M^Ni0c1XO^Ne0d1[O^Na0c1_O_N>b1C`N9a1G^N9d1GZN9h1FWN:j1GTN9n1FQN:Q2EnM;a70000000000000000000000000000000O100000000000000000000000000000000000O10000000O10000000000000000000000000000000000000000000000O100000000000000000000000000000000000000000000O10000000000000000000000000000000000000000000000O100000000000000000000_IB5>JE4;LG29MJ16OLO40APJ5o5:1A<??AfN?Z1EbN;^1FaN:_1FaN:_1FaN:_1F`N;a1D_N<a1E^N;b1E^N;b1E^N;b1E^N;b1E^N;e0@fJ5e4;e0@fJ5e4:f0AeJ5e4:f0AeJ6d49g0AeJ6d49g0AeJ6d49g0AeJ6d49g0AgJ4b4;g0AjJ4\\4;j0AmJ4V4;m0APK3Q4<o0ASK3k3<eKOT4B_L3e3g1JVNdL2`3k1ISNjL2Z3n1IPNPM2T3Q2d1nMYNU2HjMbM0`2Z2KfMnM0n1]21cMZ2`2cMaM\\2b2aM^M_2d2_M]M`2d2_M\\Ma2f2]MZMc2h2[MYM=3gMe2k1XMh0?gL[2_2WMR1k0iKP2S3UM]1W4bNiKh1o3VNRLR2f3mMZL]2^3aMbLh2W3VMjLS3m2lLSM]3f2aL[M_3e2`L[M`3g2^LYMb3i2\\LXMc3j2[LVMe3l2YLTMg3n2WLSMh3o2VLQMj3R3SLoLk3T3SLlLm3V3QLaLX4a3h22N3M2N2N2N3M2N2N3M4L5K5K4L5J6H7XHfJW7_5bHeJ\\7f5O2N2N2N2N1O2N2N1O2N1O2N1O2N1O2N1O2N1O2N1O2N1O1O1O1O1O1O2N2N2N2N2VJdHR1OW3_7dKeHQ1OZ3e7bL^H]3d7_L_H_3d7]L`Ha3b7[LaHd3a7XLbHg3`7oKiHP4Z8O1O1O1O1O3M3M3M4L3M3M1O2N1O2N2N1O2N2N1O2N1O2N2N1O2N1O2N2N1O2N1fNUE2m:LVE1l:LWE2j:LZE1h:L[E2f:M]E0Q<N3M3M2N3L3N3M2NTaP1", "size": [427, 640]}, "area": 177389.0, "iscrowd": 0, "image_id": 10232, "bbox": [1.0, 2.0, 560.0, 425.0], "category_id": 183, "id": 10012488}, {"segmentation": {"counts": "^W\\4121Q;300SELe:5ZELe:5ZEMd:5YELS8M^J=c5FYJ;f5KTJ6k5OoI4o5NoI4P6MoI2Q60nI0Q63iI1V63aI3_6OXI7h6LUI5e6mN[HQ1m01h68bHPOMe0JoNg7_1_HoNOb0d7X1YHiNf7Z1XHgNf7Z1ZHeNf7[2ZHdL00e7]3[HcL00d7^36M1RMZHZ2f7aM_H_2a7`M_Ha2a7YMeHh2Z7XMfHh2Z7XMfHh2Z7XMeHi2[7VMfHj2Z7VMfHj2l70O10000000O02O00000O100000000O3N4L1O0O101OO100O100000000O100000000nLQHk2X8N2O0O2N2N2N1O2N4L4L4L4L4L4L4L2N2N1O2N2N1O001O1O001N105K5K6C`EC0Nf::<I2O001O00ie0<nYO60G:Ff\\]1", "size": [360, 640]}, "area": 7551.0, "iscrowd": 0, "image_id": 10239, "bbox": [398.0, 209.0, 112.0, 122.0], "category_id": 100, "id": 10012489}, {"segmentation": {"counts": "hZW63=0X:9]EIc:?O1O1O1O001O1O1O001O1O001O1O001O1O1O00O11O0jEPOT:S1O1O001O1O1O1O1O001O1O1O001O1O001O1O001O1O1O001O1O1O1O001O1O001O1O001O1O1O001O1O1O1O001O1O1O001O00", "size": [360, 640]}, "area": 3276.0, "iscrowd": 0, "image_id": 10239, "bbox": [567.0, 0.0, 73.0, 71.0], "category_id": 102, "id": 10012490}, {"segmentation": {"counts": "0`5h50000000000000000001O000000O100000000000000000000000000000000001O000000000000000000O10000000000000000001O000000000000000000000000000000000000001O00000000000000001O00001O00001O001O00001O00001O000000001O000000000000001O001O001O0000000000O1N2O1O1N200iNnJYLQ5g3X1N2N2N2N2N2N2N2N2M3N2N2N2N2N2N2N2N2N2M3N2N2N2N2N2N2N2N2N2M3N2N2N2N2N2N2N2N2N2M3N2O10000000000000000000000000000000000000000000000KbF`N^9`1cF_N]9a1cF_N]9a1eF]N[9c15000000O1000000000000001O000000000000000000001O1O0000001O003M00000000000000000000000000000000000000000000000000001O000000000000000000000000000000000000000000000000000000000000000000lNdF<\\9_OkF?U9@mFCHf0k:XOXU81ojG2XELZ:8aEOX:4eE1V:e0L3L6H7VNMTH7Q6]30001O00001O00K50ZJVLn3j3RLVLn3j3RLTLP4l3PLQLS4o3mKoKU4Q4kKmKW4T4hKlKX4W4eKiK[4Z4bKfK^4Z4dKeK[4[4eKeK[4[4eKeK[4Z4fKfKZ4Y4gKgKY4X4hKhKX4W4iKjKV4U4kKoKQ4Q4oKSLm3m3SLTLl3l3TLULk3i3WLXLh3j3VLVLj3n3RLSLm3Q4oKPLP4d501O1O002NN2O1N2M300000000000000000000001O000000N2L4M3000000O10000O100M3M3N2J6000000000000000000000000O11OO1\\Od0F:0000000000000000000000000000000000000000000000000000O100000000000000000000000000000000O100000000000000000000O100000000O1O100N2O1O1O1O1O1001O001O000000000000001O1O1O1O1O00001O2N1O00001O0000001O0000001O0000001O0000001O00001O0000001O0000001O000000kMTJnNl5R1TJnNl5Q1VJnNj5R1VJoNi5`0UJkM4d1g54UJRN515f1a5nNXJ7OQO530i1e5jNgJI0^OK5Ej0N]Oo53QMa0QMZOi`6i0UiI;YFWO\\8S2iGXM]7k2UHmM=l0k4Z4O001N01000O10003MO11NO2O1O1O010O1O001O10O105K>B1N0O110O\\I^Kb6e4O1N101O1N2O1N2J50100O10OO2O10O01O1O001O14fJoIT5X6J10O1O1O1O001O1O001O1O001O1JjJVJY5b5:K6O1O1O1O001O1N2O001O00iM", "size": [360, 640]}, "area": 87762.0, "iscrowd": 0, "image_id": 10239, "bbox": [0.0, 0.0, 640.0, 254.0], "category_id": 106, "id": 10012491}, {"segmentation": {"counts": "l9;m:10OO1001OO100000001O2N2N3M2N1_EZO10[:j00000000000000000000000000000OKeE[O^:f02I8M200O1000O11O0O101LSEKn:3hec2M]o[MO_8Y1WH6MaNo4i4lJ^KW4]5hKeJCHb3o7\\LXHo1n8UOl0SOo^7JRbH2XELZ:8aEOl1IX6;iG1c1Mb6h0UI^Oj6e0SI\\O7FnNf02NQ3`0cMWO[O6GMJe1^3XObMPO^OW1A?i1WOZN7m0lN^Od8b0\\G]Oe8c0ZG^Og8a0YG^Oh8b0XG]Oi8c0WG\\OX1Ei5n0oH\\OS1Ln5h0oHZOR11Y6;eHB[1MX7NPlj1OojTN0N0P;6O100000000O10000O10009HO00000000O101O001ON2000000O1J[EE01f::\\EEe:;40O2N101[IBoMO[2`0EAPNO[2`0DCPNM\\2`0DC_2;RMCkL4d1K[2>iNCRM515f1E]2?aMEWOOQO530i1I[2>_M5iN0^OK5Ej0N]O0n3=dLCAP3a0QMZO0o3<eLm4]3iJeK0n0W5[3kJgKKONP1]5Y3iJiKLP1[5U3iJiKJ20R1Q7j0jI\\OQ90O10eNUFY1m900000O1000000000000aFJl76SH5NWOb6e0aI^12XMU32dNX1VNa1CmM=T2h1^NXNk5g1UJZNk5d1VJ]Nm5_1SJaNm5_1SJbNk5_1XJ\\Ni5d1aJQN`5o1l11SOoMVH5Gl1T8PNPHa2Q8^MkG_2LaM00X82gG`2`84000O2O000000001O1O6J?A2N00001O01O1OVNfFf1Z9XNhFh1\\91O0014NM3M2N0000000O0100IhF[NO0Y9c19O100000000O1000000000000O1000000003M6J1O00005K2NO103N0I6O100000nG", "size": [360, 640]}, "area": 24616.0, "iscrowd": 0, "image_id": 10239, "bbox": [0.0, 0.0, 640.0, 360.0], "category_id": 132, "id": 10012492}, {"segmentation": {"counts": "`5b1f900000000002NN20001O000000O10001O00O1000000000000000000M3M3O110O000000000000000000O10000000007I2N000001O00000O1001O2N0000O1O1O100N2K5N200O11O101N1O002N002N001O2O0O1O01O00001O001O00001O00001O000000001O0000000K50000010O01J5N3M2M3O1N2O11NNcE[O[:f031O1N20O2Og0YOcba23Y]^M1N2M300000000000000000000001O00001Oac[2GdQdM0P;0nD2R;2O100O6F5001O001OO10O2O00000000005CRE6V;JkW9", "size": [360, 640]}, "area": 6157.0, "iscrowd": 0, "image_id": 10239, "bbox": [0.0, 176.0, 613.0, 79.0], "category_id": 135, "id": 10012493}, {"segmentation": {"counts": "]jf32V;1O000M3000000O10000O1NISE7h:JXE105h:NXE2h:7000000001O2N0EUE9m:00000000000O100000000O100000000O10000000000O10000000000O100000000O1ICbE>]:7N2N2O1O100001OO1O100001O1OO1000000O100O1MQOlEP1S:3001OO1LmNQF00S1o9mNQF00S1o94O10000001O00000000000000000000O11O00000000000000002N4L1O00001OO100OEmECS:<oECQ:=oECQ:<PFDP:<PFDo9<RFDn9<RFDn9;SFEm9;SFEm9:TFFl99UFGk98VFHk96VFJj96VFJj9L`F4Z:00000001O1O1O1O00000000001O000000000000000000O10000000000O1O100O10000O1A?M30000000000001O001ON2000000O1JVOjE01k0U:WOjEj0V:4001O001O1O0000N20000000000HROVFn0j9ROSFONP1n9ROTFP1j9nNTF20R1l9600O10eNUFY1m900000O100000000000000O1000000O1000000O1O100O11OJcN^F00]1a970000O1000000000000000000000000001O1O00001O001O3M3M2N00000000O100IfN]FO0[1a99O100000000O1000000000000O1000000003M6J1O00005K2NO1001OJ6O1000000", "size": [360, 640]}, "area": 9155.0, "iscrowd": 0, "image_id": 10239, "bbox": [337.0, 304.0, 303.0, 56.0], "category_id": 140, "id": 10012494}, {"segmentation": {"counts": "U^n33R;NQE3l:6L5N2N000ZE@d:`0\\EAc:?]EAc:b000000000000000E]OQFc0LAf9l0VFXOf9W1M5N2N3M2M3N002M2O3J5K8I7E8N3M`0@3M1O1O2N000000O1O1O0\\MdH]1^7bNeHZ1e7\\NaH^1`7aNeHY1b7aN`H]1`7dNaH[1_7dNcH[1\\7eNgHU1\\7kNhHl0]7UOeHb0b7]OaH`0`7ZObGFQ1m0]7\\OlHNPOMT84oHLoNOR84hIIY66jIHV67kIIU62_G0T;OiR\\2", "size": [360, 640]}, "area": 3805.0, "iscrowd": 0, "image_id": 10239, "bbox": [359.0, 193.0, 64.0, 125.0], "category_id": 143, "id": 10012495}, {"segmentation": {"counts": "`7\\2;eNU7[1lHdNT7]1kHcNU7\\1lHdNT7\\1lHdNT7]1kHcNU7\\1lHdNT7\\1lHdNT7\\1lHdNT7\\1lHdNS7]1nHbNR7^1PI`NP7`1RI^No6a1TI\\Nm6c1UI[Nk6e1VIfMZO10>`7l1ZITNf6l1ZITNf6l1ZITNf6l1ZITNf6l1ZITNf6l1ZITNi6i1WIWNi6i1WIWNg6k1YIUNf6l1ZITNd6n1\\IRNd6n1\\IRNg6k1YIUNg6k1YIUNf6J`Hg1j0_NQ7`1jHQN[Oc0k7[1kHgNU7Y1dHnN\\7S1aHoN_7Q1aHoN_7Q1`HPO`7P1`HPOa7o0_HQO_7P1bHPO^7Q1aHoN_7Q1`HPO`7P1`HPO^7POXHQ26oMKP1b7QO_HS22PO_7mN_H]3a7cL_H]3`7fL^HZ3a77000000O100O11O1ON21OO10\\LcH]3f7NO10000000000001O0000001O00O11O00O11O000000000000002N00002N6J5KO100000000000000000000O10000M30000M3O10000a0_O000000000000A[MWH0Ie2P8ZMXH2Gd2Q8ZMXHj2h7VMXHj2T8O6J0000000000000000O100O1O1O1O1O10000001O0000001O1O1jMbG`1_8PNPHP2e8O00000000000000003M001O001O1O001O001O001O1O001O001O001OO^N]Fa1c9200O100000000000000000000O100000000000000000000O100000000000000000000O1000000000000000000O100000000000000000000O100000000000000000000O100000000000000000000O100000000000000000000O1000000000000000000O1000jNhF\\OOk0m8WOXG8Xb;?T^DN\\8_12N00001O1OM3L400001bNVG<m8_OVG`0Q9XOPGh0i9O001O001O001LPFnNP:R14O00ON2100O1O02O00WO[OPGc0m8l0fFVNW9h1jFXN10Q9l1RGTNj8P2164L6RNjF^1d9L10O2OOYFaNe9a1^N`N[Ia1c6gNSHNf0\\1V7TOiHm0W7UOfHl0Z7[NPHh0e0m0[7[NQHi0a0l0_7[NPHl0=j0f7YOVHh0j7ZOSHg0m7\\OPHd0P8_OlGa0U8Y10000000TMoG`2R8\\MRHd2Y802N2N3M2M3N002N1O3M2M6K5K2N3M`0_O4FiE\\OY:b08M4L3DWE6n:O1O1O1O2Mndb2", "size": [360, 640]}, "area": 27754.0, "iscrowd": 0, "image_id": 10239, "bbox": [0.0, 239.0, 404.0, 121.0], "category_id": 149, "id": 10012496}, {"segmentation": {"counts": "YZl6210k::K5000000000003L2O0000000dH", "size": [360, 640]}, "area": 248.0, "iscrowd": 0, "image_id": 10239, "bbox": [626.0, 236.0, 14.0, 22.0], "category_id": 157, "id": 10012497}, {"segmentation": {"counts": "_lV1:l:<D;D7Ka0^O3N11O001O1]OQGkNP9P1UGoNP9g0VGXOi86`F1U89eIEg6i1kGVNT8S4[N2N2N2N2N2N1O2M3N2N1O1O1O1O1O1O3M2M3N2N3M2N2N3M3M3RKZJV4i5ZKfJd4P6M3N2N2N2N2N2N3M2N5K2M4M2O2O001O001O1O001O001O001O1O00OdLWH[3i7200000000000000O100000000000000000000O10000000001O000000000O100000000000000000000O100000000000000000000O100000000000000000000O1000000000001O0000000O1000000000000000000O100000000000000000000O1000000000000]NbL\\K^3i3^2UOk0TOl0UOk0UOk0UOof9^O^YFa0WFMZ9S1M2N2O1N1OL4K5L32N9`GVNR7S2eHoMY7Z2_HhM^7R3M4M3M201O0010O01OO1O1O102M5L4MOYOUHQNl7j1]HRNc7i1P1L5K5J5L5JZFnNa9b1cFYNm8U21K7J5J6J6kNWF>[:N1O3M3N2M4L3M4L4MiV<7\\hCd0F>LO100O1O1O100000000000000003M3M1O0fFfNf8Z1VGjNj8V1UGkNk8U1UGjNl8V1TGjNl8V1SGkNm8U1SGkNm8U1SGkNm8U1SGjNn8V1QGkNn8V1RGjNn8V1RGjNn8W1PGiNQ9W1oFiNQ9X1nFhNR9X1nFhNR9X1mFhNT9Y1kFgNU9Y1kFgNU9Z1jFfNV9Z1iFgNW9d11000000O1EgFgNX9e1000000000OdJUN]1k1bNVN^1j1bNVN^1j1aNWN_1i1aNWN_1i1`NXN_1i1aNWN_1i1`NYN_1g1aNYN_1g1`NZN`1f1lJUN[36h1P2UNQNj1R2SNoMm1R2RNnMl1V2hMTNW2o1cMUN\\2n1bMRN]2P2bMPN]2S2aMmM^2X2^MiMa2[2[MfMe2]2WMdMh2`2TMaMl2b2PM^MP3f2lLZMT3j2hLVMX3l2fLTMZ3n2dLRM\\3o2cLQM]3Q3aLoL_3S3_LmLb3S3]LmLd3R3\\LnLe3R3ZLnLg3Q3YLoLh3Q3WLoLi3Q3VLPMj3Q3ULoLl3U3oKkLT4W3gKiLY4]3aKcL_4b3\\K^Ld4h3VKWLl4i3SKWLm4i3SKVLn4k3QKTLP5l3PKTLQ5k3oJULQ5Q3hJkL63S5U3eKkL[4X3bK^Li4j3nJkK]5_4a01O01O0000001O00001O0000001O000O101O0O10001N1000001O00000O20OO101O8]OP3eLbX5IYgJT10O1O03LN3O1N2N6J2@`0L4KoZg0", "size": [360, 640]}, "area": 55914.0, "iscrowd": 0, "image_id": 10239, "bbox": [110.0, 57.0, 463.0, 299.0], "category_id": 183, "id": 10012498}, {"segmentation": {"counts": "^8b6^800000001O1O0000O1000000O10000000000O10000O11O0000000000000000001OO11O00O1O10000001O00O11O00M3N]IjG`6[8000001O00N2O1O1O100002N00000000000000000000001O1O1O000000000000000000000000000000b0^O00000UOUGmJL10<o8e4^GZKb8d4_G]Ka8d4]G]Kb8d4^G\\Kb8b4bG\\K]8e4bG\\K^8d4bG\\K_8d4aG[K`8c4aG]K^8c4dG\\K\\8d4dG\\K\\8d4dG\\K\\8n3SGYLa0I\\8l3RGhK4;LE00d00\\O9o8g3RGPL00=0C020e05Y8R1oFiN60LY2j1gMV7U1^GR1`:mNbEQ1_:oNcEQO_OLMe1Q;]OdEmN[O33NMd1Q;_OfElNZO110Oc1P;@RFb0l9^OTFb0l9^OUFa0k9_OUFa0k9_OTFb0l9^OUF`0l9@TF`0l9@UFa0i9^OWFc0i9]OXFa0i9_OXFnNiNO001\\1m:VOVE@k1S1g9kN\\FT1h;O3L6QOPBf0Y>LO1001O1OO2O01N11O010O0O11N2HdAJ]>OiA0f>NTWV7", "size": [480, 640]}, "area": 24449.0, "iscrowd": 0, "image_id": 10241, "bbox": [0.0, 264.0, 148.0, 216.0], "category_id": 94, "id": 10012499}, {"segmentation": {"counts": "UWV8514_>700O2O000010O01O0001O01O00010O00010O00010O0000010O000010O10O000010O0001O010O0001O01O01O01O010O01O00010O00010O0010O00010O01O01O01O01O0000001O01O01O01O01O01O01O0VI", "size": [480, 640]}, "area": 1544.0, "iscrowd": 0, "image_id": 10241, "bbox": [559.0, 195.0, 81.0, 43.0], "category_id": 132, "id": 10012500}, {"segmentation": {"counts": "0^8b6000000001O1O0000O1000000O10000000000O10000O11O0000000000000000001OO11O00O1O10000001O00O11O00M3N]IjG`6[8000001O00N2O1O1O100002N00000000000000000000001O1O1O000000000000000000000000000000b0^O00000UOUGmJL10<o8e4^GZKb8d4_G]Ka8d4]G]Kb8d4^G\\Kb8b4bG\\K]8e4bG\\K^8d4bG\\K_8d4aG[K`8c4aG]K^8c4dG\\K\\8d4dG\\K\\8d4dG\\K\\8n3SGYLa0I\\8l3RGhK4;LE00d00\\O9o8g3RGPL00=0C020e05Y8R1oFiN60LY2j1gMV7U1^GR1`:mNbEQ1_:oNcEQO_OLMe1Q;]OdEmN[O33NMd1Q;_OfElNZO110Oc1P;@RFb0l9^OTFb0l9^OUFa0k9_OUFa0k9_OTFb0l9^OUF`0l9@TF`0l9@UFa0i9^OWFc0i9]OXFa0i9_OXFnNiNO001\\1m:VOVE@k1S1g9kN\\FT1h;O3L6QOPBf0Y>LO1001O1OO2O01N11O010O0O11N2HdAJ]>OiA0f>NWSS39`llL:F;D;F;E:E<E:F:E<E:F;D;F;E:E;F;E:E<E7I1O0O10O10O10000O10000O10000O10000O10000O10000O10000O10O10O10000O1000000O10000O10000O10000O10000O1000O010000O10000O10000O1N2M3M3M3M3L4M3M3M3M3M3M3M2M4M3M3M3M3M3M3L4000000001O000000001O000000001O0O100000001O000000001O000000001O000000001O00000O101O000000001O000000001O000000001O000000001O0O1000001O000000001O000000001O000000001O000000001N1000001OS1mNd1\\Nd1\\Nc1]N][1dMUXN4_>700O2O000010O01O0001O01O00010O00010O00010O0000010O000010O10O000010O0001O010O0001O01O01O01O010O01O00010O00010O0010O00010O01O01O01O01O0000001O01O01O01O01O01O01O0VI", "size": [480, 640]}, "area": 233972.0, "iscrowd": 0, "image_id": 10241, "bbox": [0.0, 0.0, 640.0, 480.0], "category_id": 157, "id": 10012501}, {"segmentation": {"counts": "SlX5:f>:F;D;F;E:E<E:F:E<E:F;D;F;E:E;F;E:E<E7I000O10000O10000O10000O10000O10O10O10000O10000O10000O10000O10000O1000000O10000O01000O10000O10000O10000O10000O10000O10000O1N2M2N3M3M3L4M3M3M3M3M3M3M3L4M3M3M3M3M3M3L40000000O2O000000001O000000001O00000000001O000000001O0O1000001O000000001O000000001O000000001O000000001N100000001O000000001O000000001O000000001O00000O101O000000001O000000001O0000005Kd1\\Nd1\\Nd1\\Nd1\\NbSW1", "size": [480, 640]}, "area": 47062.0, "iscrowd": 0, "image_id": 10241, "bbox": [360.0, 40.0, 196.0, 296.0], "category_id": 183, "id": 10012502}, {"segmentation": {"counts": "gad01b?10001O001N10001O001O0O101O001cNG]C9c<KYC5g<0SC2l<1QCOo<2PCOo<1QCOo<1QCOo<1QC0n<OSC1m<ORC3m<EYBOl0;k<BWB0X1=n=0O001O00001O001N10002N2M5L4L4L4MN2GbAXO`>h07N10O010000O10O001O001OJZA\\Oh>e07L100N200N20O01O1N2O1O1O1N3O2Lomm3", "size": [500, 375]}, "area": 1295.0, "iscrowd": 0, "image_id": 10243, "bbox": [42.0, 0.0, 75.0, 82.0], "category_id": 96, "id": 10012503}, {"segmentation": {"counts": "i9k5h9100000000O10000O1000000O1000000O10000O1000000O10O100O010O100O10O0100RJ\\Fk5g900O10002N002N5J6K5K5K5K5K5K5J4M3M3M3M3M3M3L4M3M3M4L3M3L4M3M3M3M3M3L4L4L4M3L4M;E4L4L4K4M4J6E;I7K4M]ji0NbUVO2VN9gCHV<j0YCYOd<X1lBiNQ=n1N11M5K6YOf0F;M2N3M3N3OO10000000000O101N5L4L5K4L4L4K6K4L4L5K4L4K6K4L4K6J5J6JhLhCU3W<kLjCT3V<lLlC6Mb2W<WMoC10f2f<N2N1O001O00001O1O1O2N2N2N2N1O2N2N1O1O3M1O2N2N1O3M2N2N1O3M2N1O2N1O2N1O3M1O1O3M2N3M1O2N2N1O1O2N2N2N1O3MO10Le@OU\\S2", "size": [500, 375]}, "area": 16971.0, "iscrowd": 0, "image_id": 10243, "bbox": [0.0, 304.0, 237.0, 196.0], "category_id": 98, "id": 10012504}, {"segmentation": {"counts": "]6R1`0^OR=a0oB_OQ=`0PC_OQ=`0PC@P=?QCAP==QCC`=L`B4k>EZo0ObPO6MO2O1O110Ke@O`ci00PlVO1O1O100O10001O000O1g@NP?3m@OQ?;I7M3O1001O4L4L3M4L2N00000000O100000000000000I7G92N3M2N2N2N1O1O1O1O1O1O1O1O1O1O00001O00001O001O00001O001O1OQVe3", "size": [500, 375]}, "area": 1135.0, "iscrowd": 0, "image_id": 10243, "bbox": [0.0, 205.0, 134.0, 295.0], "category_id": 105, "id": 10012505}, {"segmentation": {"counts": "_8Z1Z>O010000000O108HK4O21OM3N1O200HeNRB1O\\1n=601N2O001O11O2M4M2N2N2M3N3M2N2N2M3N3MWAWOi>i02N2N3M=E3Kfk[1O[TdN0000009G5J100ZACU>=kAGQ>8PB3e=M[Ba0W=_OkBj0j<UOWCV1^<iNcCb1MSNc;;`Dm1_OlMP<8`DV2^;hMdDY2[;hMdDZ2Z;fMfD[2Y;eMgD]2V;dMjD^2T;dMiD`2T;`MlDa2S;_MmDc2P;^MPEd2n:\\MQEg2m:XMTEj2j:VMVEl2h:TMXEY1BIU;nNYEY1CJR;lN\\EZ1DIo:mN\\EZ1GJk:lN^Eh0BYO6T1g:lNaEg0FTO4Z1c:jNdEg0MkNOf1^:hNgEf0<b0k9hNhEg0>a0i9hNiEg0`0a0e9hNiEh0c0b0b9fNjEi0e0b0_9eNlEh0g0d0[9eNmEg0h0f0X9dNoEg0i0f0W9cNPFg0i0h0U9bNPFg0l0h0R9`NSFg0o0g0m8bNTFg0o0h0l8bNRFf0V1f0h8fNoEe0[1c0cNUNo9Q2^GJcNVNn9o1aGJaNYN6M\\9o1oGJ^N]N10`9h1SH?k7@WHa0g7_OXHc0g7]OYHc0g7XOgFWNc1a2e7WObHh0^7XOaHi0_7VOaHl0^7SObHo0]7QOcHP1\\7oNdHT1Z7lNfHV1X7jNhHX1V7hNjHZ1T7eNnH[1Q7eNPI\\1n6dNRI^1l6bNTI_1j6aNXI_1g6aNYIb1d6^N\\Ic1c6]N]Ie1a6[N_Ig1_6XNbIi1]6WNcIl1Z6TNgIm1W6SNiIo1U6QNjIQ2U6oMkIT2R6lMnIV2P6jMoIX2P6hMPJZ2n5fMRJ[2m5fMRJ\\2l5dMSJ^2l5cMSJ`2j5`MVJa2i5_MWJb2h5_MVJe2g5[MYJ_NVNo3_7bM[J[N]NS4U7bMlJ]2S5dMlJ_NiMe3Y7lMoJZNPNh3o6oMPKYNRNh3m6oM`KQ2_4PNaKQ2\\4SNaKo1]4SNaKo1b4nM\\KS2g4iMYKZ2h4bMYK\\2j4bMVK^2_MRLV7`1[Kd2c4]M]KcNbMd3Q7iM]KbNdMd3o6jM]K`NgMe3e6XLWIc1\\2`NjMd3b6TNdKWNlMd3`6UNdKVNnMd3^6QNUI\\N^2NQNd3\\6PNXI]No2b3h3PN\\I^Nl2a3h3QN\\I`Nk2^3i3QN^IcNf2\\3l3eMWIQO8Nd2\\3n3mLTIO:G3Ob2^3n3iLVI2:DB0>2c2_3o3`L[I;f0DQ2a3\\4jLhLV3X3iLiLW3W3hLjLX3V3gLlLX3T3hLlLX3T3hLlLX3T3gLmLY3S3gLmLY3S3gLmLY3S3fLmLKULm2n6XMmLJWLm2l6YMmLJWLm2m6WMlLMWLl2m6WMlLLXLm2n6UMiLOYLl2o6TMhLOYLn2[;RMfDn2P<0XOSMnDh2R;WMPEh2o:YMREf2n:ZMSEe2l:\\MUEc2j:^MWEa2o5^MfM3[L^2o5bMaM2bLZ2m5gM[M2iLV2k5lMVM0QMS2h5PNTMOTMP2f5SNVMERLNQ1Z2f5TNVMBWL0l0Z2h5SNdMAdL\\2j5QNcMDaL[2m5PNcME_L[2m5QNeMD]L[2o5PNeMFZLZ2Q6oMhMEWL\\2P6QNhMBYL]2o5QNiNo1W1QNiNo1h0eMjI>^5m1d0jMjI;c5k1`0nMiI:g5h1?gNBX1?fNBZ1=gNCY1>eNC[1=eNC[1<fNDZ1<fNC[1>dNA]1?dN^O^1b0bN]O_1h0\\NVOf1m0VNSOk1o0SNoNo1Q1QNnNP2S1oMkNS2U1mMkNS2U1lMnIZO\\4j2f1lMlNT2R1oMmNQ2R1PNnNP2T1nMlNR2U1mMkNS2U1nMjNR2Y1kMgNU2X1ZM]I<Z5Z2W1YMbI=V5[2b1eM[N]2e1dMYN]2g1cMZI@g4l2o1eMXI@i4k2o1mMQNS2n1nMRNR2n1nMRNR2m1oMSNQ2o1mMQNS2n1nMRNR2n1nMRNR2m1oMSNQ2n1nMRNR2o1mMQNS2o1mMfMWOfKl2d6mMfM^2Z2bMeM_2[2aMeM_2[2aMdM`2\\2aMcM_2]2aMcM_2]2aMbM`2]2bMbM^2^2bMaM_2`2aM_M_2a2bM]M_2c2bM[M_2g2_MYMa2h2`MVM`2k2aMSM_2n2`MRM`2n2`MRM`2n2`MSJSOh1]3U4`MSJSOh1]3U4`MSJTOg1\\3V4`MSJUOf1[3W4`MRJVOg1Z3W4`MRJVOg1Z3W4`MRJVOg1Z3Y4^MQJXOe1Z3\\4\\MnI\\Oc1Z3b4WMkI@_1\\3g4SMjIB^1[3i4]LfHc0S1F[1\\3n4nLhIEY1^3o4mLhIGW1\\3Q5mLgILS1X3V5lLgIMmN@S2g3Y5lLgIOP1U3Y5lLgIOP1U3Y5mLfIMR1V3X5mLfIMR1V3X5mLgIKQ1Y3", "size": [500, 375]}, "area": 32839.0, "iscrowd": 0, "image_id": 10243, "bbox": [0.0, 199.0, 375.0, 301.0], "category_id": 107, "id": 10012506}, {"segmentation": {"counts": "eba31b?0d?0\\@0010Ojo10WPN00000O1gMOnD2R;NmD4P;NPE2l:LoB002T23m:1lD7S;IkDJWN<n<IPE7P;HRE7m:IVE:d:F`E6a:IaE5_:LaE3_:M^CLj15i:O\\CLl15h:O[CMm14h:0YCMn14i:OWCOP22i:0UCOR21h:8XEHjNJP;=WFIiNKo:<XFIhNLP;;XFIgNNP;9XFKfNMQ;9YFKeNMFJl:>iFKbNG\\O8g;6[FL`NF_O9d;6]FL^NFA9c;5^FM\\NED<_;2aFM[NEF;i:DhE?^1NUNHN6f:FhE?`1MnMK63d:FhE>a16VNEb:GfE?b12XNI]:IhE=c10ZNHZ:GXE4`0>e1NZNHY:HXE5=?i1J[NHX:1cE>j1H\\NIW:1bE?l1E^NIT:NgEd0g1BbNKP:NiEd0e1BdNKn9NiEg0c1@c9JbDOJi0Q2]Oe9J`Do0j1WOS:i0mEVOT:j0kEWOU:i0kEVOV:k0hEUOY:l0fESOZ:n0fEROZ:o0dEQO]:P1bEoN_:V1[EjNf:W1YEiNg:l0dCYOe1Kg:k0iCVO_1Oi:j0lCTO[12j:g0dEYO]:f0bE[O_:d0_E_Oa:OcC5\\1EkN;W<HdC7\\1FfN<[<FeC6[1IcN<]<DgC5Z1M^N<a<BiC3X1b0`;\\O`Dd0W=1O11O1OXNZOdDd0Z;^OgDa0Y;^OiDa0W;_OjD`0W;_OiDa0U=10O00001O000001O00001O010O00001O000O2N100O2N100O2O0O2O0O101O1O2N1O1N2O7gAgNi=a11N2N2J61N010N2N101N2J^NVB00d1l=5M0O10O01ZF", "size": [500, 375]}, "area": 4035.0, "iscrowd": 0, "image_id": 10243, "bbox": [232.0, 237.0, 143.0, 168.0], "category_id": 121, "id": 10012507}, {"segmentation": {"counts": "UUQ24`?5K45K10K4L1O0O10002M1O1001ON21O0000002M1000000O01000000O10O1000O101OO100N2O101O0O101N01001M4Mj_n2", "size": [500, 375]}, "area": 659.0, "iscrowd": 0, "image_id": 10243, "bbox": [133.0, 225.0, 48.0, 29.0], "category_id": 132, "id": 10012508}, {"segmentation": {"counts": "aim01d?3L5K4L5J<EOIVOeAl0V>ROgAX1W>4O00000000000000O1000000O10000001O1O001O00O100000000O1O1O1O1O1G9001ON200O103L6K6J5K5JQmo3", "size": [500, 375]}, "area": 1832.0, "iscrowd": 0, "image_id": 10243, "bbox": [61.0, 0.0, 52.0, 46.0], "category_id": 139, "id": 10012509}, {"segmentation": {"counts": "m3`2T=01OO11O;F3VOi0L4L4L1O2N1O2N1O2N1O2N1O2N1O2N1O2N1O2N2N1O2N1ObXk04SgTOMm@8P?:K4M4K4M3M4K5L4K5L4K7J:_OhMnB`2P<eMeDc2W;`MeDe2X;ZMhDi2S;YMkDj2T;VMlDk2R;VMlDl2T;TMlDm2V:jLUF9En2V:nLPF4Io2W:SMjENMR3X:UMfEI1S3Y:YM`EE6S3Z:hMeEZ2Z:fMdE\\2\\:eMcE\\2\\:fM`E\\2`:\\101O00001O001O00001O001O00001O00001O001O00001O001O000000000000001O0000000000000000000000001O0000000000000000000000000000001O00000000000000000000000000001O000000000000000000001O00001O00001O001O00001O001O0000VOTFmKk9S4UFmKk9S4UFmKk9S4UFnKj9R4VFlKl9S4UFYK>N^9d3SF\\Mh:b2]E`L3=`:S3oEiLQ:V3VFdLj9\\3W11O001O3M5K1O4L5REjKT:Z4`EoK_:U4[EmKe:a40O11O00N22NN2000000O1000000000000001O000000001O0000O1O11OO100001O000000O11O1O002N0000000000000000000gERKl9o4RFVKj9k4TFYKi9f4WF^Kf9c4XF_Kg9b4XF^Kh9a4WFaKi9b4TF]Km9V5O0000O11O00001O000000001O000000001O00000000001O000000001O000000001O0000001O0000001O0000001O0000001O1O1O00001OO11OO10000001O00O100001O0000000000001O0000", "size": [500, 375]}, "area": 48266.0, "iscrowd": 0, "image_id": 10243, "bbox": [0.0, 0.0, 375.0, 206.0], "category_id": 172, "id": 10012510}, {"segmentation": {"counts": "gX^43`?7I2N2N2O001N020O00O0110O00014K3M2N0010O00000N2O21N1O003MO]AWO]>g0bA\\O_>m0O00000001N100O12NO2O0O11O10O0000001O0000001N1001O01O11O0O1O10OO11OO10000001O02M3N3M100cAjNZ>X100000000001O0000kI", "size": [500, 375]}, "area": 2387.0, "iscrowd": 0, "image_id": 10243, "bbox": [291.0, 182.0, 84.0, 55.0], "category_id": 173, "id": 10012511}, {"segmentation": {"counts": "d0Q1o=d00000kC_NW:`1QEDd:<YE2\\:NaE9?ZOZ7=TH?>`0S6QO]Id0<e0n5gNcIi0<<V6jN\\In0<4]6nNTIQ1>Nb6QOmHU1?Je6QOjHW1`0Hg6QOfH[1a0Dj6QObH^1c0Al6PO_Hc1c0\\OP7QO\\Hd1c0[OR7Y2lHgMU7Z2iHfMX7Z2gHfMZ7[2dHeM]7[2bHeM_7\\2_HgM_7Y2`HjM^7W2`HmM\\7T2cHoM[7R2cHQNZ7P2eHSNY7n1eHUNX7W5M2M3N2M3N3L3N2M3N3M_HcIX6P6hIPJ]6k5cIVJa6e5_I[Jf6_5[IaJj6Z5UIhJo6S5QImJT7n4lHSKX7h4hHXK]7c4cH]Kb7^4^HcKf7W4mFdKT16R8S4jFgKT17T8o3hFjKT17W8l3eFmKT17Z8i3bFPLT18\\8e3`FSLT18_8b3]FVLS1:b8\\3\\F\\LP18g8Y3YFdLk03o8V3VFkLg00U9R3TFRMc0L]9n2PF[M>Hd9j2nEbM9El9e2lEkM3@T:b2iERNO]OZ:^2gEmN\\:P1dEQO^:l0bETOa:j0^EVOe:g0[EZOg:c0YE]Oj:`0VEAl:<TEDo:9QEGR;6nDK\\;KcD5a;G^D;e;AXDb0l;ZORDh0Q<^12O100000O1000O10O010M2M3M4L4L4L4L4L:F7I4L4K4K5C:M41N3N1N2N2O0O1O100O100O1O100O0010O0010O00010O010O0001]GdJm6[5SIiJj6V5VInJg6R5XISKd6l4\\IXK`6i4_I[K]6e4cI`Kb3VO`MZ5nNdK[3TOhMY5lNgK]1oNhN2P1X5kNmKS1[OaNBb1W5iN[Lb0]OcNSOR2U5hNgL4CaNcNd2S5gNSMEHe1U3fN^MVO_OV2S3dNiMgNTOi2S3`N6d1J\\NAY2?gMXOb2i0]MVOd2j0\\MUOe2k0ZMUOg2k0YMUOg2k0YMWOe2i0[MYOc2g0]M[Oa2e0_M]O_2c0aM_O]2a0cMA[2?eMCY2=fMFX2:hMKS26lM0n10RN7g1IYN=a1C_Nc0[1]OeNj0T1VOlNP1n0POQOW1i0iNWO^1b0bN^Od1<\\NDj16VNJQ2OoM1W2IiM7]2CcM=d2\\O\\Md0j2VOVMj0P3POQMo0U3kNkLU1j8000000000000O1000`CkNa:U1^EoN_:Q1aEQO]:o0bEUO[:k0dEWO\\:h0dEXO\\:h0cEZO\\:f0dEZO\\:f0dE[O[:e0eE[O[:e0dE]O[:c0eE]O[:c0eE^OZ:b0fE^OZ:b0eE@Z:`0fE@[:?eEBZ:>fEA`NZOd;U1lE@^N@e;o0lEA^NEc;j0oE@^NHb;h0PF_O^NJc;f0oE_O^NMb;d0PF_O]NNd;b0oE_O]N0d;a0oE^O^N1c;a0oE^O]N2e;?nE^O]N5d;<PF6Q:IPF^O[N8e;:PF]O[N:e;9QF\\OZN;f;8PF]OZN;f;8PF]OZN;f;8QF5P:JPF6P:JPF6ROTOU97XF>a1]OYN>g0Cj7EgHg0hN6h16nNZOAEl73QH3a0a0[O4h16mN\\O]OKm7=aH3A2h16kN^O^OKk7c0ZH0INi16kN_O\\OLl7h1UHfNj15hNB]OLl7g1THfNm13eN5Z7R1THfNQ2NaN;[7Q1SHjNl1IfNL^OKR8a1nGSOh1EjN=c7h0kGZOd1@oN>d7g0iG[Oc1_OQO?h7BbHb0e0\\ORO`0k7TOeHS1>YORON[OM7DP8OnHZ1?VOSO0YOM6FY9k0eFVO`1;TO1XOM4IY9i0dH_OhN2WOM3KW9i0gH\\OiN3VOM2MV9h0iHZOjN4UOM1OU9g0fHlNVN;k05SON01T9e0gHoNVN6n07QOM04R9d0gHQOXN3m06SOd0o83iHPO[N51C>?_Of0m84jHPO^N2KGa0;@h0l82_HlNjN7M8`0H@j0i81\\HVOmNOM9d0D]Om0i8ATGDZ13kNOK9T1a0f7_OXGCZ16hNOJ:V1>e7_O\\G@Y1;fNNJ:g0\\OEQ1_8^O^G@X1?dNd0`0QOGm0_8]OSI2WNc0>UONb0Z8BTI6UNa0=ZOM=]8@VI8SNb0:_OL7a8^OXI:QNb09BK4d8XO[Ia0nMDKb0<OK2Q;JlDBMc0:0K3Q;HmDAMe0:OK2Q;JmD_ONf080L1Q;IoD^OMh061MOR;JnD\\OOj031OOP;KWE5BUO5m02MQ;LVE7G02MQ;LUE9GN3LR;LUE:FN3LR;LUE:EO4KR;LUE:EO5HS;OSE:EO6F?_Og9a0PF;C07D=Eg9<RF;C08B:Kh98SF;C09@71i93UF;C19^O74g92VF;C19[O99d90WF;C1:UO=`0^9NYF;C2Y1Gi8LZF<D1X1Ii8I\\F=C1X1Jh8G^F=C3U1Jj8D`F?A4b0ROFg0h9B`Fa0@4?VOGd0j9@aFb0_O58\\OM=k9@aF?B93^OM:h9EfF:B81CN7g9EhF9B80EN5g9DlF8@:NGN3h9^OUFFi0h0XO\\OMm07KL0j9^ORG>SO]ONl06LMOi9_OSG<ROAOg060MLi9@SG<QOB0f060MLi9@SG;ROc05GMKh9@VG9QOe03IMIi9@WG8POGNb057MHi9@WG7POg03KLGi9@ZG4POi01MLFi9@[G0RO1I>4=MCh9BPHl0ZN0NBh9CoGj0\\N1MBh9CVGDGV1^O2LAi9CUGIDP1B4M^Oi9ESGMBl0E5MZOl9GnF2Bg0G7NWOl9IlF4Bb0J>JQOo9LiF4D<Oc0EoNP:OVFF5?0MEI=X1ClNQ:1SFH6>1LFG<[1DhNR:4PFI6?2IGF<\\1L`Nj9=nEL4=5IH@>a1P:mNaEN2;8G;R1j9oN`E019:E;R1k9PO^Em0=oN;T1k9oN]En0?lN:V1k9C\\EH>B9S1m9A]EK<C9Q1n9oN\\E22;:F8o0P:]O_EN8H8m0Q:]O^EO8I8k0S:]O\\EO8J9j0S:]O\\EO7L9h0U::aEoN;f0U:iNYEX17YO;g0T:fN\\EZ14[O;e0^:0VE\\O<e0]:OWE\\O<e0]:OVE^O<c0^:OVE^O<c0^:OUE_O>a0]:OVE@=a0]:OVE@=a0]:OUEA?>]:1TEA?>]:0TEC`0;]:XOPE538`0:^:YOoD538`0:^:YOoD538a09]:YOPE539`08^:WORE809`08^:UOPE>24b08]:TOnDa043a07^:4QEDb08]:3REDc07]:4PEEc07]:3QEEc08]:2PEEe07]:2oDGd06_:1nDHe05^:2nDId04b:RO`Dk0;Oc03d:NjDOc01d:MlD2a0Ne:0jD1d0Lc:3iD1e0Jc:5hD1f0Hc:7gD1h0Dc:;eD1Q=OoB1Q=OoB0R=0nB0R=0oBOQ=1PCNP=2QCMo<3RCJP=6QCHP=8PCFR=:QCAQ=>TC]Om<c0R10001O0000nG", "size": [500, 375]}, "area": 68270.0, "iscrowd": 0, "image_id": 10243, "bbox": [0.0, 0.0, 375.0, 500.0], "category_id": 183, "id": 10012512}, {"segmentation": {"counts": "Pld31oc01R\\O0jc00V\\O3gc0302N1O1O2N00002N3M0i\\O^OPc0b0P]O_OOO21LON0_b0b0b]O_O12O`0Zb0Og]OEO=Yb08g]OHZb0o003M0000000005K11N00O1O1001O2k]OaNfa0a1V^ObNMIha0g1W^OfNha0j1O2N1O000000O11O1OO110N002O02N00YN_^Oo0aa0nNb^OS1ca0eN_^OY1ga0aN[^O^1Tb000L51N1O1OO11OZOa]OYO0?^b01n]OORb00P^OMQb02R^OLna03S^OMma03T^OMka02W^OMob0N1M4L21hmV4", "size": [640, 475]}, "area": 3271.0, "iscrowd": 0, "image_id": 10244, "bbox": [187.0, 0.0, 72.0, 84.0], "category_id": 96, "id": 10012513}, {"segmentation": {"counts": "\\_Z21nc001000O010O10O10O100O0101O0010O10O01O10O01O002N3M3L3N2M3M10O010O10O02O0Mc\\OC]c097O2G]\\O2\\PV6", "size": [640, 475]}, "area": 286.0, "iscrowd": 0, "image_id": 10244, "bbox": [119.0, 102.0, 40.0, 22.0], "category_id": 119, "id": 10012514}, {"segmentation": {"counts": "PlS45kc01O0000000000001O0000000000000000000000001OO11O00000l\\OL\\b04a]OO_b07Y]OKgb09S]OImb0e00001O000000000k]OPOYO0ma0P1c^O\\O\\a0e0c^O]O[a0c0d^O@Za0`0e^OA[a0?e^OBZa0>e^ODZa0b1O001OM30000N2O1O1O1000000O1000000O11O1O00002NKT^OVNla0j1T^OVNla0h172N00001O001O001OBR^OjNna0>X^OZO4LF=ma04c^OBL:aa0Kl^OKB=`a0@V_O3YO9ib0Gkl;NoSD0O1O10OO2O1N1_O5o\\OKib0a0R]O@ib0n00O100000000O1000000O100000000O1000000O100000000O1000000O1000000O100000000O1000000O100000000O1000000O100000000O1000000O100000000O1000000O1000000O100000000O1000000O100000000O1000000O100000000O1000000O100000000O1000000O100000000O1000000O100M3M3LTTb0", "size": [640, 475]}, "area": 6262.0, "iscrowd": 0, "image_id": 10244, "bbox": [211.0, 0.0, 235.0, 73.0], "category_id": 120, "id": 10012515}, {"segmentation": {"counts": "SX]52nc02N001O0O1BMo\\O3Pc0Oo\\O1Vb0K[^O6\\OOWb0Kc]O051=6C1Tb0Lg]O2<6E0Xb00P^Ob0n`0ROh_O>XOa0X`0WOe_O4LFn0g1X?iNm_OLh0^1R?oNV@B>h1V?nN\\@YO9o1Z?iN]@o1c?W100000M3K5K5K5K5L4K5K5K5K5K5K5K5K5K5K5ZOn]OYOWb0g0i]OTO\\b0k0e]OQO_b0n0b]OmNcb0S15N2O1N2^O[]OIeb0Og]OLZnn2", "size": [640, 475]}, "area": 3039.0, "iscrowd": 0, "image_id": 10244, "bbox": [277.0, 0.0, 47.0, 139.0], "category_id": 132, "id": 10012516}, {"segmentation": {"counts": "g3Y`0g30001OO11O000000000000000000000000000000000000000000000000001O00000000000000000000000000001O00000000O1000000O11OO10000001O000000002NO1O10000000k_OYLQ`0m3N1O2N1O1O1O001O1O1O00O1000000O1000000O1000000O10000O1OZ@SL[?Z4O2N1O1O2VAcK5Km<]5oBhJP=Y5lBlJR=T5jBQKU=V6G2N2N1O0000K5FkBdIZ=\\651O00H8N2N2M3L48He0VB`Ij<l6O1O1O1O1O1OC[C_Ie<_6`C^I`<b6aC\\I`<d6aC[I_<e6cCYI]<g6eCWI[<b5e1M3L42N001O0000\\NaAcL2b0]>k2jAWLNk0Y>m2VBRMj=n2WBQMi=n2YBQMg=o2YB\\LWO=a>V3`BjL`=V3SBYLFa0W>V3RB[L_OO0a0_>U3RB[L^O1O`0a>U3QB[L^Ob0e>S3mA[L^Ob0e>S3mA[L^Ob0e>T3lAZL_Oc0d>T3lARMT>n2lARMT>n2lARMT>o2jARMV>n2jARMV>o2iAQMW>o2iAQMW>o2jAPMV>o2kARMT>n2lARMT>m2mA^Mh=b2XB_Mg=`2ZB`Mf=`2ZBaMe=_2[BaMe=^2\\BcMc=\\2^BdMb=\\2^BeMa=[2_BeMa=[2_BfM`=Z2`BfM`=Z2`BfM`=Z2`BfM`=Z2`BeMa=[2_BdMb=^2\\BbMd=]2]BbMd=^2]B`Md=_2]BaMc=_2]BaMc=_2]BaMc=_2]BaMc=^2^BbMb=^2^BcMa=]2_BcMa=X2kA`Ld0X1a=W2mA`Lb0Y1a=U2jAaL31b0X1b=S2iAdL70=[1b=l1nBPNV=^1\\CcNc<]1^CbNb<\\1_CdNb<[1]CfNd<Y1]CdNf<]1WCaNm<_1SC^NP=a1QC`Nn<d1nB\\NR=e1nBZNR=f1nBZNR=f1nBZNR=f1nBZNR=f1nBZNR=g1mBYNS=g1oBWNQ=h1PCXNP=h1PCXNP=l1lBTNT=l1lBTNT=m1jBUNU=k1kBZNP=j1kBXNT=m1eBWNY=c1mB^NR=c1lB^NT=b1lB^NT=b1lB^NT=b1mB]NS=c1nB\\NR=d1mB^NR=h1hBWNY=e1kB\\NT=b1nB^NR=b1nB^NR=f1jBZNV=o1bBoM_=X2ZBhMf=X2gA]L`0\\1h=X2gA]L11O24Y1U>d2lA]MLbNT>o3nAdMM]NU>n3lAjMT>Q401O0000001O00000000001O0000001O00001O00000000001O0000001O0000N2O100N2O11O1O00000000O10000000000001PBZJd=R6O2N00003M1ON21O1OO11O1OO10000001O2NO11O001O0000001O001O2N2N1O00000000000000001O00001O00001RETIf8m6YGSIg8n6VGUIi8k6XGUIg8l6XGUIg8l6XGUIg8k6XGVIh8k6XGUISNJ`:R7\\GUISNIa:S7ZGXITNF_:S7]GZIb8f6\\G[Ie8f6[GYIe8h6YGYIg8h6XGWIi8j6VGWIi8j6UGXIj8j6SGWIm8k6PGVIP9k6iFbHkNf0[:l6cFbHQOc0[:T7WFWIi9g6XFYIi9o6dETH3m0Y:Z7cEhH\\:]811O1OO11O00O11O3eEZFV:i9O4L001O1O001O001O1OO12NO1000TFoEi9V:N003M00001O001O001O001O1O00001O1OO11O1O0000000000O11O0000O1O12N00O11O002kF\\Ej8e:oF\\E05n8k:O1O001O001OO11OO11O000000001YGkDa8U;\\GlD00c8[;aGdDN1U8\\;iGdD0000X8\\;hGiDY8_;00000O11O1O1O1OO11O001O00002N001O001O001O2N0000001O1O1OO100", "size": [640, 475]}, "area": 203961.0, "iscrowd": 0, "image_id": 10244, "bbox": [0.0, 82.0, 475.0, 558.0], "category_id": 140, "id": 10012517}, {"segmentation": {"counts": "^\\P32;OVc03b\\OO01\\c00c\\O1O8Vc0Hj\\Ob0Rc0^On\\Ob0Rc0^On\\Ob0Rc0^On\\Oc0Qc060000O10000001O01O00;E1O001O001O001O001O000000O1O100O2N100000000001O000000O02OL410OO0O11M1N4M31O01O00000000002N00000000O2O5J2M4N1N100001O1OO2OO11O000hNfNj_OZ1V`0oNb_Oo0]`0ZO\\_Of0d`0ZOi^O[O`0\\1e`0[Oj^OZO11O24Y1Va0Ck^O>LbNUa0n0m^Oe0M]NUa0n0l^Oj0Ta0Q110O0MXMn^Oh2Ra0XMn^Oh2Ra04O01O00000TMn^Oj2Ra0VMn^Ok2Qa0UMo^Ok2Ta0O0001O00010O00000000001O00000MUMQ_Ol2o`0SMQ_Om2n`0TMR_Ol2n`0TMR_Oj2Qa0UMo^Oj2Qa0XNP_Of0Qa0YOQ_Oc0Qa0]Oo^Ob0Ra0^Oo^Ob0Pa0^OP_Oc0o`0\\OR_Od0n`0nNo^OH3Z1n`0mNQ_OH1[1n`0nNS_ODO^1n`0nNT_OC1[1m`0lNW_OE0_1i`0iNi_OW1X`0gNi_OY1_a0000N201g]OcNma0i1O2NUNT^Ob1la0YNY^Og1Qb02M1ON30O1OO20O10N10001O01O2ON11O010O0001O01O03N3N2N0OO01O1O1O1O001O1O100O00100O1O10__OgNY>Z1fAfN[>Z1bAiN^>V1cAjN]>V1bAkN^>U1aAlN^>T1aAmN_>T1aAlNSNJW`0[1eAlNSNIW`0]1dAnNTNFU`0]1gAPOW>Q1gAPOZ>Q1fAnNZ>S1dAnN[>T1dAkN]>V1bAkN\\>W1bAkN]>W1`AjN`>X1]AiNb>Y1WATNkNf0m?Z1QATNQOc0l?c1f@hNZ?V1g@jNY?_1T@dM3m0j?i1R@YNm?l211O10N11O01N11O3R@mKj?U4O4L010O1O010O00100OO12ON1000\\@gKa?^4OO03M01O01O010O0010O01O10O000100OO1100O0001O00000O20O0001N1O12OO0O110O02g@`Kn>a4k@`K05S?f4O1O010O0010N11OO20O000001O01PATKj>l4SAUK00m>Q5WAnJN1_>R5_AnJ0000b>R5^ASKd>T500001N11O100O1OO20O0010O0002N010O0010O01O2OO000010O1O10N10\\I", "size": [640, 475]}, "area": 29198.0, "iscrowd": 0, "image_id": 10244, "bbox": [154.0, 93.0, 321.0, 295.0], "category_id": 149, "id": 10012518}, {"segmentation": {"counts": "Xld83jc03L3010000000O1000000O100000000O1000000O100000000O1000000`0@0000", "size": [640, 475]}, "area": 275.0, "iscrowd": 0, "image_id": 10244, "bbox": [443.0, 0.0, 32.0, 21.0], "category_id": 157, "id": 10012519}, {"segmentation": {"counts": "a0S1mb00O1010N11N011O001ON21OO01N02010O1OO21M1O011N0101O2N001O001O0UOU]O`0nb0^OT]O`0kb0@W]O?lb0\\OW]Oc0Uc0L3N5G[\\OOfc0M^\\O2[Rb0O^a^O2Y\\O2Xc0;01O00000000000000000000001O0000O1O11O1O0000M31O1ON21ON200O12N00O1O1001OO11O00M3001O000e\\OEQc0<n\\ODRc0>k\\OBVc0>j\\OCUc0=l\\OBTc0>j\\OF1Ojb0;T]O1kb0b000000O1001O000000000000O1O10S]OPOjb0T1O002N2N001O1OO10000N21O1OO11\\]OhN^b0]10O12N1O4LO11O2N000000000Q^O[Nca0d1^^O\\Nba0e1\\^O\\Nda0a1U^O]N72da0b1]^O`Nba0a1\\^OaNca0b1Y^O_Nga0a1V^OcNia0j10001O00O1001ON21O00000000O11OO10010O1lMV^OP2ja0PNY^Om1ma002M200O2M10002M4NOdNc]OU1]b0kNd]OOO21LON0f0[b0[Of]O12O`0a0ha0@l]OO=c0na0]OR^Ob0gb012Ni\\O^Onb0k0O00O14L1O02N00O1O2O01N3ZOR]O6Qc0FR]OMI<Sc0CZ]O<Vc001N13L5KJ61N11O1OO11OO7IM4N4N00001M02O014H42M2O10N11Lb\\OD_c0;a\\OE`c0:2101O01O00]\\OE`c0<`\\OD]c0?c\\OAZc0e0I\\OP]Od0Pc0600N2O2N1O1000000O1000000ONV]OPOkb0P1T]OPOmb0n0T]OROlb0n0T]OROlb0o0S]OQOob0l0NTOV]Om0ib0SOW]Om0gb072N00001O0GS]O_Onb0>R]O[O05Pc0?T]O\\OQc0c06O2N1M5CWfa3", "size": [640, 475]}, "area": 8575.0, "iscrowd": 0, "image_id": 10244, "bbox": [0.0, 0.0, 293.0, 102.0], "category_id": 169, "id": 10012520}, {"segmentation": {"counts": "0a0S1b1V?^Nj@b1V?]Nk@c1U?]Nl@c1S?^Nk@b1V?^Nk@b1T?]Nl@d1T?\\Nm@c1S?]Nm@c1S?]Nn@b1R?^Nl@d1T?\\Nm@c1S?]Nl@d1T?[Nm@e1S?YNm@i1S?WNm@i1S?XNm@g1S?YNn@f1R?ZNm@g1S?ZNn@d1R?[No@e1Q?ZNo@g1Q?YNPAf1P?YNPAh1P?XNPAh1P?YNo@g1Q?[Nm@e1S?[Nm@e1S?\\Nl@d1T?\\Nl@d1T?]Nk@c1U?]NP@A`0T2_?]No_OA`0R2a?\\No_OD?P2b?_Nj_ODc0m1c?kNY@W1g?jNW@W1i?mNn_OkNOX2S`0nNj_OnN2T2T`0`100000000000001O00000000O1000000O11OO10000001O0000000020M3_Ll_OQ3b`001O00000K5I7I7K5O1O1O1O1O2N1N2O00011N2N1O10M22N2ON12EQ^ObNna0`1X^OZNha0g1712OO1N2N1001N011O00M30O2O000\\Nn]OZ1Sb0eNm]OZ1Vb0cNj]O^1Vb0bNk]O]1Ub0dNj]O\\1Vb0bNn]O1OR1Sb0lNY^OR1[b01O1O0O2O010O1O001O001O1N1O2OcN`]OZ1db00O11N2OO10O10N2O2N1M4O000N3O]Ng]O^1]b02M210d]O\\NYb0e14K020OQ^O_Naa0_1[^OgNca0Y1\\^OiNca0X1T^OcN26ja0i101OVNV^O_1ha0bNX^O^1ia0`NX^O`1ea0YNY^O72a1ea0`N]^O_1da0_N^^Oa1da0[N]^Oe1ca0XNa^Og1la0O101N10O0101ON12N10000000O11OO10011NO100O10O11O0O01000Ke]OeNZb0[1h]OdNYb0Y1d]OfN31Yb0V1d]OiN70Wb0P1`0^Oe0MN03N2MhQX5", "size": [640, 475]}, "area": 14585.0, "iscrowd": 0, "image_id": 10244, "bbox": [0.0, 0.0, 206.0, 126.0], "category_id": 172, "id": 10012521}, {"segmentation": {"counts": "kS]12gc09G8H9I6N1O2N2N2N2N1N2O1O010O1O1O010O1O10O01F^NU^Oc1ka0cNo]O]1Rb07101N2MQ^O_Nda0o1OSOU^OGia09W^OHga08Y^OJfa06Z^OLb0UO5KW?S1R@i0c0YNZ?n0S@j0?]N\\?h0U@l0;aN^?d0W@R2`?nM`@T2^?kMb@X2\\?hMd@X2[?hMe@Y2[?gMe@W2]?iMc@R2b?mM^@m1i?SNW@m1h?SNY@m1h?RNX@l1k?SNT@g1S`0XNn_Of1U`0YNk_Oe1W`0ZNi_Oa1^`0^Nb_OY1h`0fNX_Ol1V`0SNj_Oc20jLW?b0i@P3V?oLk@R3U?mLk@T3U?jLk@X3T?iLk@X3U?gLk@Y3U?hLj@j23hLT?>i@i25gLS??i@i25gLR?`0i@h27gLQ?a0h@g29eLQ?c0g@h1f`0XNZ_O_1Pa0`NQ_O\\1Ta0cNk^OZ1Ya0eNj^OY1Va0fNl^Om01eNSa0;P_OZ1Qa0dNR_OZ1Pa0dNR_O8@>_a0XOY_O1[Of0]a0WOi_Og0Y`0WOh_Og0Z`0WOh_Oh0Z`0VOg_Oi0ha0WOP]Ob0Wc001N6K7IXkf2MfTYM5K5K6J5K5K6K4K5K6J5K6J5K5K6J5K5K6J5K5L5J5N201O0O4M4L4L1N2O1O1O1O0O2O1O1O1N101O1O1O1N101O1O1N2O001OO10O10O100O010O1000O0100O1000O0100000O2O00001O0O101O000O2O00001O0O101O000O2O0000001N10001O0O101O000O2O00001O0O101O000O2O00001O0O10001O0O101O00001N10001O0O101O00001N10001O0O10001O000O2O00001N10001O0O101O00001N10001O0O101O00000O2O00001N10001O000O2O00001\\1cNQ2oMP2QN\\J", "size": [640, 475]}, "area": 34017.0, "iscrowd": 0, "image_id": 10244, "bbox": [72.0, 5.0, 403.0, 228.0], "category_id": 183, "id": 10012522}, {"segmentation": {"counts": "_eW51V=4K5I7WO@aDg0^;c00000000001O0000000000001O00000000000000000000000000000000000000000000000000000000000000000000000000001O0000001O00001OO10000000000000000000000000000003M4L4L4L3M2N0000000000O10000000000O1000000_NlDk0T;UOaE6_:JlEJU:6kEJU:6lEIT:9jEGV:f0]EZOc::iEEX:<]E_O]O6V;;]E_O^O5U;<]E_O_O4T;>]E]O@3T;`0\\E]OA2S;b0[E]OA1T;b0[E]OBOT;d0ZE]OBOT;e0ZE[OCOS;f0ZE[OR;e0nD[OR;f0mDZOS;f0nDZOQ;g0nDYOR;g0nDYOR;h0mDXOS;n0gDROY;S1cDmN\\;Y1^DgNb;b1O1O1O2NO10000O100O1O1O1O1O100O1O1O1O100O1O1O1O1O100O1O1O1O100O1O100000000000000O10000000[Sk0", "size": [427, 640]}, "area": 7566.0, "iscrowd": 0, "image_id": 10245, "bbox": [402.0, 0.0, 173.0, 67.0], "category_id": 156, "id": 10012523}, {"segmentation": {"counts": "W=4W=000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000O1000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000O10000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000O1000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000O100000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000", "size": [427, 640]}, "area": 3844.0, "iscrowd": 0, "image_id": 10245, "bbox": [0.0, 419.0, 640.0, 8.0], "category_id": 167, "id": 10012524}, {"segmentation": {"counts": "fdh4<<EZ;c1100O10000O100O100O10000O100O100O10000O100O100O10000O100001O000000001ONQDhNo;T1UDlNk;o0ZDQOf;h0aDXO`;a0R2_OWO000000010O000000001O01O000001O000000001O000000000000000000000000000000000000000000000000000000000001O0000001O0000[j]1P1eTbN00000000O10000000000000000O10000000000000000O100000000000000001O0000000000001O00000000001O2N1O1O1O1O1O2N1O1O1O1O2N1O1O1O1O1O2N00", "size": [427, 640]}, "area": 4199.0, "iscrowd": 0, "image_id": 10245, "bbox": [366.0, 0.0, 274.0, 55.0], "category_id": 172, "id": 10012525}, {"segmentation": {"counts": "02Y=0000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000O10000000000000000000000000000000000000000000000000000001O0000001O000000001O000000001O000000001O000000001O000000001O000000001O0000001O000000001O000000001O000000001O000000001O000000001O000000001O000000001O0000001O000000001O000000001O000000001O000000001O000000001O000000001O0000001O000000001O000000001O000000001O000000001O000000001O000000001O0000001O000000001O000000001O000000001O000000001O000000001O000000001O000000001O0000001O000000001O000000001O000000001O000000001O000000001O000000001O0000001O000000001O000000001O000000001O000000001O000000001O000000001O000000001O0000000000O1WOfD@VVb3", "size": [427, 640]}, "area": 7583.0, "iscrowd": 0, "image_id": 10245, "bbox": [0.0, 0.0, 367.0, 54.0], "category_id": 181, "id": 10012526}, {"segmentation": {"counts": "2U=60000000000000000000000000000000000000000000000000000000000000000000000000000000000O1000000000000000000000000000000000000000000000000000000000000000000000O10000000000000000000000000000000000000000000000000000001O0000001O000000001O000000001O000000001O000000001O000000001O000000001O0000001O000000001O000000001O000000001O000000001O000000001O000000001O000000001O000O101O000000001O000000001O000000001O000000001O000000001O000000001O0000001O000000001O000000001O000000001O000000001O000000001O000000001O0000001O000000001O000000001O000000001O000000001O000000001O000000001O000000001O0000001O000000001O000000001O000000001O000000001O000000001O000000001N1000001O000000001O000000001O000000001O000000001O000000001O000000001O000000001O0000000000O100O100O10000O100O100O10000O100O100O10000O100O100O10000O100001O000000001O00000000001O000000001O000000001O000000001O000000001O000000000000000000000000000000000000000000000000000000000000000O100000000000O100000000000000000000000000001O3M4L4L4L3M1O0000000000O10000000000O1000000_NTEc0l:]OiENW:2TFBm9>SFBm9>SFBm9f0kEZOU:R1`EmN`:b0fEYO^O4m:d0eEWO]O6n:c0eEWO^O5m:d0eEWO_O4l:f0dEVOA2l:h0cEVOB1k:j0cEUOA1l:j0cEUOBOl:l0bEUOBOl:m0aETODNk:n0aETOk:l0VESOj:n0UEROk:n0UESOj:n0UEROk:n0VEQOj:R1SEnNm:X1mDhNS;^1gDcNX;c1bD]N^;i1O1O3M00O10000O100O1O1O1O1O100O1O1O1O100O1O1O1O1O100O1O1O1O100O1O100000000000000O1000000000000000000O10000000000000000O10000000000000000O010000000000000001O0000000000001O00000000001O2N1O1O1O1O2N1O1O1O1O1O2N1O1O1O1O2N1O1OXN", "size": [427, 640]}, "area": 249726.0, "iscrowd": 0, "image_id": 10245, "bbox": [0.0, 1.0, 640.0, 422.0], "category_id": 183, "id": 10012527}, {"segmentation": {"counts": "0md:5WhE:E;E7J6I8H5L3L3M3N2O100O1O100O100O1O100O1L4L4L4L3M3SO\\MQGh2o8\\MhFh2X9]M`Fg2`9a001O01O0O2O0O101O0O01M2O2N2M2O20O100O20O2N2YOYLgGi32^LV73`H`33oLP7i3nHeLd6^3ZIgLa6[3^IiL^6X3`ImL[6T3dIRMW6U5L4K5L5K4K5L4K6K4L4K5L4L5\\LWG[2n8\\MYGb2k8VM[Gh2i8oL]Go2b9N2M2O2N2M2O000O100O100N2N2N2N2N2O1N2N2N2N2O1N2FhD_NZ;`19O1O1O1O1O1O100O1O1O1O1O1O1O1O1O1O1O10eNQOZFn0a9XO_Fg0b9ZO]Fe0e9ZO[Fe0f9[OZFd0g9]OXFb0i9^OWFa0k9_OTF`0m9@SF?n9BQF=P:CPF=P:DoE;S:DmE<S:ElE:U:GiE:W:FiE:X:FfE;Z:FeE9\\:HaE:_:F^E=c:CXEa0h:P11000001O0000001O000000001O000O1N3L3M3M3L4O2O0000001O000000001O0000001O00000HPDUOQ<g0RDYOn;e0UDZOo;`0SD@[<0gC0k<O00000000000000000000000000000000000000000000000000000000000001O0XC0U<0fC5Z<KbC:]<F_C>a<7O1O001O00000000000000000000000WOXOoDh0P;[OnDe0Q;^OmDb0R;AlD?S;DkD<T;GjD9T;KjD5U;NiD1W;2gDNX;5fDKY;8eDHY;V1O010O010O10O010O0101N2O0O2O1N2O1N01O00010O001O001O001O001O001O001O00001O001O0010O100O100O100O100N101N2N2O1N2O1N2O1N2O1N2N2O1N2H8B>000000000000O1000000000000O1000000000000O1000000000000O100PI]LX4c3gK_LW4b3hKaLU4`3iKcLU4^3jKeLS4\\3lKgLR4Y3lKjLR4W3mKlLP4U3nKoLo3R3oKQMo3P3nKUMo3l2XKRLZOV1[5i2[KSLUOX1^5f2]KTLPO[1`5b2`KVLkN\\1c5^2cKWLeN_1f5[2eKRNX4o1hKSNU4n1QKmK5V2h4d2WK_Me4b2[KbM`4`2`KcM[4_2dKeMW4]2hKgMS4[2lK7b1I^N9^1JbN7[1KdN7Y1KfN6X1LgN6V1KjN7S1KmN6P1LoN6n0LQO6l0LSO6i0LWO6f0LZO5c0M\\O5a0M^O5?M@4>MB59OG224MNM72a601O1O1O1N101NVnl2", "size": [427, 640]}, "area": 157853.0, "iscrowd": 0, "image_id": 10248, "bbox": [0.0, 0.0, 640.0, 427.0], "category_id": 124, "id": 10012528}, {"segmentation": {"counts": "md:5T=:E;E7J6I8H5L2M3M3N2O100O1O2O0O100O1O100O1L4L3M4L3M4RO\\MQGg2P9]MgFh2X9]M`Ff2a9b0O0001O0O2O0O101N10O1M2O2N1N3N110000O21N2N2YOYLgGh33_LU73`H_34PMo6i3nHeLd6^3ZIgLa6[3^IiL^6W3aInLZ6T3dIRMW6U5L4K5L5K4K5L4K6K4L4K5L4L5\\LWG[2n8\\MYGb2k8VM[Gh2i8oL]Go2b9N2M2O2N2M2O000O100O100N2N2N2N2N2O1N2N2N2N2O1N2FhD_NZ;`19O1O1O1O1O1O100O1O1O1O1O1O1O1O1O1O1OhNnNYFR1b9TO]Fk0a9XO_Fg0b9ZO]Fe0d9[O\\Fd0f9[OZFd0g9]OXFb0i9^OWFa0j9@UF?l9ATF>n9BQF=P:CPF=P:DoE;R:EmE<T:DkE;V:FhE;X:EhE;X:FgE:Y:GeE9]:G_E<a:D]E>c:CPEi0P;h02O0000001O000000001O0000001O0O1N2M3M4L3L40001O000000001O0000001O0000001O000GPDWOP<f0TDYOl;e0VD[Oo;>TDAZ<OhC1j<O00000000000000000000000000000000000000000000000000000000000000YCNU<2gC3X<MdC7\\<I`C<_<801O1O001O000000000000000000000WOWOPEi0o:ZOoDf0P;]OnDc0Q;@mD`0R;BmD>R;ElD;R;IlD7S;LkD4T;OjD0V;3hDMW;6gDJW;:gDFX;W10O010O0100O010O010O2N2O0O2O1N2O1N01O0010O01O001O00001O001O001O001O001O001O001O0100O1O100O100O1O1O0O2O1N2O1N2O1N2N2O1N2O1N2O1N2^Ob0M30000O1000000000000O1000000000000O100000000000000O1000000PI[LZ4e3eK^LX4c3gK`LV4a3hKbLV4_3iKdLT4]3kKeLT4[3kKhLR4Y3lKkLQ4V3nKlLP4U3nKoLo3R3nKSMo3n2VKQL]OU1[5k2XKRLYOW1\\5h2[KTLSOY1_5d2_KTLnN[1a5b2aKULiN^1d5]2cKQN[4P2eKRNX4o1hKSNU4n1fJXLa0j1g4e2XK_Mc4b2]KbM^4`2aKdMZ4^2eKgMU4[2kK6c1K\\N7a1J_N8]1KbN7[1KdN7Y1KgN6V1LiN6T1KlN7Q1KnN7o0KPO7m0KSO5j0NUO4h0MXO5e0MZO5c0M\\O5a0M_O4>NA4;NE451J1O50MJ86^601O1N2O1N10a[m2", "size": [427, 640]}, "area": 114974.0, "iscrowd": 0, "image_id": 10248, "bbox": [25.0, 1.0, 391.0, 422.0], "category_id": 183, "id": 10012529}, {"segmentation": {"counts": "0`1`b000000000000000O100000000OQ^OfNZa0Z1b^OdNC2la0[1`^OhN`a0X1W^ObN46ea0X1V^OcN55ea0m1O001O3M0000003M00O1001O0000001O0000001O0000001O00O1001O00N2001O0000O1N22N000000L4O11O1O1OK51OL4N2O10000O1N2001O000000Ko]O[NQb0e1500O100K56J0000O1N2O100000000O100O1O1001O1O00Lf]ObNZb0_1300O1J6001O0000O1O100O1N2N20U]OoNgb0V1O1O001O002N001O1O0000O12N1O00000000N200O1L400MZ]OlNfb0S1\\]OmNcb0S15001O1OLY]OoNgb0R1Y]OmNgb0S13SOU]OM1NOO2303jb0Oc]OMC1mb0MQ]O2g00Xb0NodZ5", "size": [640, 426]}, "area": 8489.0, "iscrowd": 0, "image_id": 10249, "bbox": [0.0, 0.0, 153.0, 78.0], "category_id": 94, "id": 10012530}, {"segmentation": {"counts": "X2R5m>10dM\\2jNW10N2N2O1N2N3M2O1O100N2O1O1O2O0O1002NO1O1O12ON1N6K6K2M001O100O1O00100O1O00100O1N2O010O1M3O100O2N1N2M31OO1O1O1K5N2000000J7O1J5M4MNZ@nKd?m3:O01N2001O000O10KTLV@l3j?500O100K56J01O0O2M102OO100000O100O1O010100O00LjK^@V4c?6M1N4G6010O0000O1O100O2M2N20PKTAl4Q?O1O001O002OO01O1O0001N12N1O0001O000N02N1ON22NO\\Kj@b4W?`Ki@]4Y?32N3N2NMeKc@Z4_?fK`@Y4a?20O2O1O2N001O2M3N00001O2N0O101O2M2O00N2OSNR@Jk?9\\@Ad?>c@\\O^?b0m@gN`?Y1d1O0O11O100001000O2M2O1O1O1O010O000N20000O20O1ON2O10010O0002N10O1O0011OO1N2M31N01O1O1b_OdMV?]2f@fMY?\\2c@gM_?W2]@mMc?T2Y@oMf?Z2n_OkMQ`0R32N2OO0O2O1O1O000100O1OO10000O12N0001O02N10OO4M0001O01OO11O001O0000O2O000O100000000N21O00O11O0000000000O13L011O1O10O1O0N21O1O4M1M3N2N00001O00001O0N12L20110O000O1N3O00001N0O22N__OcL``0^32N10O2OO010O5K00MkL^_OS3a`0kLa_OT3f`0001N101M3M4N1O1b_O`LZ`0b33M11O1O1N11M11O3N12M001N1O10O10Ld_OdL\\`0V391002N0lLY_On2h`0QMZ_On2l`00001N102N1O0O3M2O1N10001N100O1O11O01O0O2O01O01O00O010O1O1001O1O1O002N01O000O10O100OKhMb^ON0Y2_a06O1001N2O1O03M0L5N1O2O10O001O2OOON4O00N30000O0O2N2N2O0O1000001N10iM", "size": [640, 426]}, "area": 44975.0, "iscrowd": 0, "image_id": 10249, "bbox": [0.0, 17.0, 426.0, 217.0], "category_id": 97, "id": 10012531}, {"segmentation": {"counts": "n_c42jc04O1O1O100O1O1001OO11O000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000008Go[>0mcA8M00000000000000000000000000000000000000000001O000000000000000000000000000000000000000000000000001O00000000000000000000000000000000000000000000000000001OVX;", "size": [640, 426]}, "area": 1296.0, "iscrowd": 0, "image_id": 10249, "bbox": [235.0, 629.0, 172.0, 11.0], "category_id": 105, "id": 10012532}, {"segmentation": {"counts": "Vb0h0Yc0OO01001N10O11N101OO010O1O001O1000O10N2O010002M10N20O10O10O01000O2OO1O02O000O2O00N1101O1ON110001N2ON20OO21O1N10KQ]OYOob0h035L1OO02O00N1012N0`DROm3n0oKZOn3f0QL]Om3b0TL@i3b0VL@h3a0WLCe3;]LJ^36bLOhHYOT:i0SM;m2ESM;m2DTM<k2EUM:l2BlETOW7Z1m2BlESOX7[1k2CmEQOY7\\1j2DXM<h2DXM<g2D[M;e2FZM:f2FnEQOZ71hHm0o95\\MlNgHn0m96dMI]27dMG\\2;bMF^2:bME_2;aMC`2>`MB`2>`MB`2<bMC^2>bMB^2>bMA_2?aMA^2a0aM_O_2`0lEhNc7g0b2e0]M[Oc2f0\\MZOc2i0XM`NdHg0T:h0PGaNR51iIf0U:h0jFdNRONU6`0mIAS:]1iFkNT58QJ^OS:_1gFlNU57Z4m0bFkNT58Y4n0bFkNU56Z4o0aFkNU56Z4P1_FkNW54Z4R1_FjNW54Z4R1_FjNX53Y4R1`FkNW54W4R1bFjNW54W4R1bFjNW55V4R1cFhNW56U4S1cFhNX55U4S1dFgNW56U4S1cFhNX56S4S1eFgNX56S4S1fFfNX57Q4S1gFfNX57P4T1gFfNY56P4T1gFfNY57o3R1iFgNX55Q4U1eFgN[51Q4Z1cFeN\\5OS4[1bFfN[5MU4]1_FgN\\5JV4_1_FgN[5HX4a1]FgN\\5EY4e1QFZN_OO2=U6CZ4h1PFYN@OO>k`0[1c_OoN]`0Q1a_OQO^`0R1__OoNa`0Q1^_OPOb`0P1^_OPOa`0S1\\_OnNd`0U1Y_OkNg`0W2O10000O10000O10000O1000000O10000O10000O10000O\\M__Ok1a`0UN`_Oj1``0QNa_O^OOa2_`0RNf_On1Z`0PNh_OP2X`0QNj_Ol1U`0TNl_Ol1U`0QNn_On1Sa0O002N001O0000O11OO1O10000N20000O11O2NO10000O1N20000000000M3M3K5001O001O00001O00O1O1O1001O1O000000N2O11O2N1O2N2_Oi^OUNYa0e1k^O[NVa0`1n^O`NTa0Y1Q_OgNca0a0a^O_Ofa06^^OJia0J\\^O6gb000N2L4O1O1O100OQX10RXT16PliNJfc0800000000hBIg67YI3W63iI3h56XJJ_5?aJ@V5j0kJUOl4T1SKmNd4\\1\\KdN[4e1eK[NU4k1kKUNU4k1lKTNT4l1lKTNS4m1mKSNY4g1hKYN`4^1`KbNj4T1VKlNS5k0mJUO]5a0cJ^O[JE\\:d0YKGZJGe:5aDH`6<YJJi:HXK<nI0ca01]^O1aa0O_^O2`a0Ma^O5]a0Kd^O2^a0Mc^O4\\a0Ld^O3]a0Mc^O3\\a0Nd^ODUO<Wb0Oe^OETO<Wb00i^OJZa07h^OFXa0:i^ODWa0>U100000O1000000W^OA[NOda0`0Q@Dn?<R@Em?;Q@Go?9Q@Gk3Ka6?aEIn3J_6>VE_OUO:V5K]6?TE^OVO;X5I]6b0QE[OVO=\\5HZ6f0nDJg4CY6d0oDIh4EW6b0QEJh4DV6b0REJh4FT6`0SEKj4FQ6`0TEJk4GP6?UEIl4Jm5=WEIl4Lk5@hDa0a05k4Li5_OlD?b05h4Ni5^OnD>b04h42f5\\OQE=a0J\\OJ\\5e0d5YOVE;?BZO46NW5j0b5WOYENI4f0KZO36MX5m0^5VO\\EMI3f0MZO1j5n0o4TO^EMI2g0MZO2i5P1m4ROaELH2i0MYO0i5W1i4nNdEMHOQ1Oj4Y1g4lNmEKc0Oj4\\1d4jNPFJb00j4^1b4hNZMX1d2hN\\M[1b2bN`M`1`:1N2M301K4N2N2N2M4M2O1N2N2N3M2N2O1N2N5K5K6I7J5K6UAVLT=P4fBVLU=n3eBSLX=V4aBkK_=[4ZBfKg=^4SBcKn=b4kA_KU>g4eAYKZ>W500O11OO1002NO1O100O100", "size": [640, 426]}, "area": 24633.0, "iscrowd": 0, "image_id": 10249, "bbox": [0.0, 187.0, 426.0, 453.0], "category_id": 111, "id": 10012533}, {"segmentation": {"counts": "`1h0Xc0O100001OO1O10O01O1O1000ON3Ld\\OC^c0<3G^\\O4bc0K_\\O5Y^b2Iia]M1NOO230Vc0=i\\OC10jb0NU]Og00[Okb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o]O\\Nfa0o101O2N00M300001O1O1O001O1O1O000000001O00", "size": [640, 426]}, "area": 9848.0, "iscrowd": 0, "image_id": 10249, "bbox": [0.0, 0.0, 426.0, 72.0], "category_id": 120, "id": 10012534}, {"segmentation": {"counts": "nb0R1nb00O1001O00O11O001OO100O1O1O1O1000000N2O100002N00N20000O100O100001OO1O11O00001O00N2001O1ON200001O1ON200N21O1O00KfNd]OZ1]b044L1OO11O00N2O12N00O100O11O1ON2000000O100LcNf]O\\1Zb0dNe]O]1[b0cNd]O^1]b0200O11O0`Ne]O[1U7dNP4^1PLcNo3]1PLfNn3Z1QLhNo3W1QLhNP4X1oKiNQ4W1mKkNS4U1mKjNT4V1lKjNR4X1mKiNS4W1mKhNT4X1kKiNU4W1kKjNU4U1kKkNQIHo:0PE[1o6nNQIGU;Z1jKROW4m0iKSOY4k0gKUOX4h0iDmNo6:Y4c0kDRON0e62\\I;U;a0jKUOoH:X;`0jKFV4;iKEW4:iKEY4;gKD[4:eKG[49eKG[49eKGZ4:gKEY4;gKFX4:iKEX4;gKDZ4;gKC[4>dK@^4?dK^O^4b0bK]O_4d0aKYOa4g0_KWOc4h0^KVOd4j0X711N2OO10O01001Fn\\O_OO2Sc0?n\\O@OOUc0>:N4M1O102M4M]Z>NeeA1J1Y\\OOgc05N212L1\\OEa]O>^b0c0N102N0O2O000O011OO1O01000N3O1O1N1101ON2O001N2M3O0000001O0M3M3K50cDFT3;gLH[37bLL_34\\L0e3OXL3i3MSL7n3InK9T4FhK>Y4@eKb0\\4]OeKc0\\4[OeKd0]4[OcKe0^4[OaKd0a4\\O^Kd0b4\\O_Kb0c4]O]Kc0d4\\O\\Kc0f4ZO]Ke0o;01N2O1N100O2O000O1000000000001O00001OO100001O00000000000000001O000000000000000000000000000000000000O101O001000O01O001O10OO2O001O1O001O1O1O1M301M2O101N1O1O101N1O100N3N2NgN[]OZ1db03M3N3NOO01O0000010N0]Oc]OE]b0;d]OUOO;^b0?c]OTO1<]b0`0g]O^O[b0a0f]OZO_b0e0b]OVOdb0h0;N_B]Oh7McBb0f5Fc7;]HJ]77cHOX70hH6S7ImH<o6CQIc0i6]OWIh0b6ZO^Il0\\6TOdIQ1Y6nN\\FAi0e1h8iN`FBg0f1h8iN_FDg0e1h8gNaFFe0e1g8hNbFDf0e1f8jNbFCf0e1f8hNaFGh0c1e8fNcFHg0d1`8mMfDg0R2Kf0b1[8mNPGAd0d1Z8jNSGCb0e1Y8gNVGDb0f1V8fNXGDb0g1T8fNZGBc0j1Q8PNWE4U22c0l1o7nMYE5T22c0m1n7lM[E4U2NkMAk2`2h7bM\\E816V2Oh0S2c7`M_E717T2Oj0T2`7^McE7O7U2Nl0V2\\7^MgE3M;8A_1=Z1T2[7aMgE2M<6B`18^1W2X7_MjE3J=2Fe12_1X2b7bMXE>0Ig1La1[2a7aMWE?OJi1Gc1_2_7`MWE?LM_5P2d5bN\\J^1d5bN\\J_1c5aN]J_1l4cM[Fm0i4a1j4dMlEN9m0P5b1l4bMiE2:j0Q5b1l4bMhE3;i0R5a1k4cMiE38j0KSOP5]2S5dMjE50JOl05UOR1O`2]2`6QNhEC1k05VOQ1O`2\\2b6QNfEC2k05WOo0Oa2\\2c6TNhE:4ZOm0MPOL^3`2i6SNiE93\\ONIg03XOM`3]2f6UNjEGO44KMIg02YON`3[2e6WNkEC271KMIg02XOOb3Y2d6XNkEA3:0KLHh03WOOc3V2e6ZNkE_O2=0JMHf0:k2n1e6ZNVFLG1c0Kk2n1e6ZNUFMH1b0Jm2E[LU2Y:^NUFMH1b0JP3l1_6\\NWFMH1a0KQ3k1_6\\NWF]OC<37c0IR3j1^6]N[FIB7c0HS3l1\\6\\N[FJD5b0IT3k1\\6]NYFJE5b0IU3i1\\6_NXFJE6a0GW3l1Y6^NYFHH6?HX3i1Z6aNWFHH6?HY3h1X6cNWFHI5?H]3d1T6hNWFFJ4?J^3b1Q6kNYFDI5?Ia3`1n5oNXFDK3=Kc3^1m5POXFDK3=Kc3_1l5kNlE\\O<<M3<Jc3a1l5jNmE[O;=O1:Lc3a1k5kNnEZO;=O1:Mc3_1k5lNnEZO:=118Nd3^1k5kN]FHL27Me3^1n5eN\\FOI28Le3_1m5dN^FNH38Lf3^1m5dN]FNI47Mf3\\1m5eN^FMH65Mh3[1n5eN[FOI4G_O9`0m3Y1P6dNZF0J2GB7?m3Y1R6dNPFCM<74EA8?m3Y1R6dNoEDO;54EC8=o3X1Q6fNnEC0<44DC9=Q4V1Q6fNmEE0956CD7=S4V1o5fNlEH435d0CEZ4R1R6jNgEI604\\1n3OQ6ZOmEZO5\\1n30m5\\ORFVO3^1n31m5ZOSFVO6\\1i34o5ZORFTO8]1g34P6[OiFb0W32o5]OjFd0S3OS6^OjFd0Q30S6]OlFc0Q3OT6^OkFc0Q3OS6_OlFb0Q31Q6^OmFa0R30R6_OkFb0S3NS6_OkFc0S3MQ6BkFa0T3LR6CjFa0T3L^1", "size": [640, 426]}, "area": 21789.0, "iscrowd": 0, "image_id": 10249, "bbox": [0.0, 141.0, 426.0, 499.0], "category_id": 124, "id": 10012535}, {"segmentation": {"counts": "[8k3U`0jLW@U2j`0]Ob0WOh02O1N1O2O1G\\NT^Of1ka080O0100O1O]OS^OUOla0g0[^OVOea0f0b^OXO]a0e0i^OYOWa0d0o^OYOPa0e0U_OXOk`0f0X1N3M2N2O3L3M3N3L5Kbgg0<lWXOl0m]OjNR`0d2M3K4N300000O010000000O100000eNRM_@2n0k2b>UM^@2P1g2d>^M^A`2a>bM_A]2a>bM_A_2a>aM_A_2a>aM_A^2b>bM]A_2c>aM]A_2c>aM]A_2b>bM]A^2d>bM\\A^2e>aM\\A^2d>bM\\AFoNd2e?fM\\AEoNf2e?eM_AZ2e>cM[A]2f>_MU@MU1d2i>\\MS@OS1f2j>[MYAe2P`001000N2O10O1aMl^OR2Ta0lMR_OP2n`0nMY_Om1h`0PN^_Ok1c`0SNc_Oi1^a0L5K4L4K5L5K4L4L3L4M3M2N3M2M4MoSd3ORl[L1T\\OOgc01W\\O1<Nob06o\\OLPc05k\\OOUc02f\\O2Zc0;N1000000000001OO10001O001O002M3N0000000O01O1000O]OBf]O>Zb0Bg]O=Yb0Cf]O>c4BT8ORC16?d4AT80QC17=d4CR81RCO7>U2@F7k:OoBM;8AHd2OA9o:KPCM<6BHc22[O<P;APC93J=2FL_23VO`0T;HSCG>0IM\\25ROb0W;GSCE?OJN[26mNe0\\;DRCE?Ld27ZNk0^;BjFDcMn0b;_OkFE^Mo0g;\\OjFGZMQ1l;YOiFGQMY1W<_NUC9c30fLa1c<TNWC:`3[2Y9ZMXC;_3\\2X9ZMYC8`3^2W9ZM\\C0JOc3g2d9XMjB1a3h2f9VMiB2a3g2k9YMVFe2k9\\MTFg2i9YMnBOk2i2V:XMlB2n2g2U:WMkB3Q3g2S:WMjB2T3g2R:bMnE^2R:aMmEb2kLPMT=>RFd2n9\\MRFd2n9\\MlBC<OW2S3`:_MYCBS2o2d:^MZCDQ2o2e:\\MZCEQ2P3d:[M[CEQ2P3e:ZMYCHQ2OcMg2R=bMZCHQ2MhMg2l<cM\\CIP2LjMk2g<`M^CJ^2g2R:`M_CIa2g2n9_MbCK_2g2n9^McCK_2g2j9QM[C<<M`2d2j9TMYC;=O^2b2l9TMYC;=O_2b2j9TMZC:=1^2b2j9bMhCL^2b2g9dMlCI^2c2f9eMkCH_2d2e9dMkCI`2d2d9dMkCHa2d2e9bMlCI_2e2e9bMlCJ^2c2g9ZM^CM<7_2b2g9YM_CO;5_2d2g9WM_C0<4_2e2e9WMaC095_2f2g9RMeC435_2g2i9oLfC604`2j2V:RMYC5_2k2W:RMXC3`2l2X:RMWC6[2j2_:PMTC8Y2k2c:eM^E[2a:eM^E[2c:eM\\E[2e:eMZE\\2f:dMXE^2h:bMXE^2i:aMVE`2j:_MWEa2h:`MXEa2h:^MXEb2h:^MXEb2P5", "size": [640, 426]}, "area": 15179.0, "iscrowd": 0, "image_id": 10249, "bbox": [0.0, 151.0, 426.0, 259.0], "category_id": 150, "id": 10012536}, {"segmentation": {"counts": "f\\Q41nc01M21N22M1O1N22N1N12N00O0102N20N0O10O100004MN2O1OO2O2N\\hl0MgWSO1O4M1M3N2M1O1O2O01O01O01N1M3O20N11O0O1N3O1OO11N1N22N1O2O0N1O2O0O2O5J1ONg\\OBVc0>h\\ODWc0c0O1O0100N2N111O1O11OOO1N20O111M2N01O4M0010O1O0O10O1O01002OOi\\O\\OSc0c0l\\O_OTc0f0O101O011M2N111O0O1O00001O01O001O00010O01O0O1102M1N2O1N6J2O10O100N111N0O1000O1000000Jj\\O@N0Wc0a06O1001N2O1O0001K7L2N1000[^<", "size": [640, 426]}, "area": 2648.0, "iscrowd": 0, "image_id": 10249, "bbox": [207.0, 14.0, 199.0, 60.0], "category_id": 169, "id": 10012537}, {"segmentation": {"counts": "QkS66jc01N3N2N1O2N2N1O2N2N101N2N1O2MO2O1O0O2000O101N1000000m\\OUOPc0l0P]OTOQc0P1N2M102M000k]OkN_a0U1a^OlN^a0T1a^OnN^a0R1b^OPO\\a0P1e^OPOZa0P1e^OROZa0n0g^OROXa0n0i^OROVa0n0j^OROVa0n0j^OROVa0n0j^OROWa0l0j^OTOYa0h0i^OWO[a0e0e^O[O`a0?a^OA`a0>`^OB`a0>a^OA_a0?a^O@`a0?a^OA`a0>`^OB`a0>_^OCaa0=_^OC`a0=b^OB^a0>c^OA]a0?b^OA_a0>W^O@F2Rb0>W^OEDNSb0>Y^OEBNTb0>Y^OOfa02Z^ONea03Y^OOfa02Z^ONea03Z^ONfa02Z^OLga05X^OMga02Z^ONfa02Z^ONfa02Y^OOga01Y^OOga01Y^OOha00W^O1ia0OW^O1KZOea0e0`^O1I_Oda0`0c^O1HAca0?e^O>Za0Be^Oa0Ya0^Oh^Ob0Xa0^Oh^Ob0Xa0^Og^Od0Xa0\\Oh^Od0Xa0\\Oi^Od0Va0[Ok^Od0Wa0[Oi^Oe0Wa0[Oi^Oe0Wa0[Oi^Oe0Wa0[Oj^Od0Wa0ZOj^Of0Va0kN\\^O9`0k0Sa0mN^^O7?l0Ta0lN]^O8?l0Ta0kN_^O8=m0Ua0iN_^O9=o0Sa0hNa^O7=o0aa0kN_^OT1Xb0N1O1O2M2M402L2N1O1N200O10000000_G", "size": [640, 426]}, "area": 4091.0, "iscrowd": 0, "image_id": 10249, "bbox": [313.0, 220.0, 113.0, 87.0], "category_id": 178, "id": 10012538}, {"segmentation": {"counts": "l`0Z1j:ROQJc3m1>`KV2Z3nNnKk1T4SNmKm1T4PNnKP2S4nMmKR2U4kMmKU2T4hMnKX2T4^MYJXKe1Y7T4]MSLc2n3\\MQLe2P4YMQLf2Q4YMoKg2R4WMoKi2R4UM[KbKTOX7c5TMUKkKUOR7h5PMoJULWOk6k5nLkJ]LXOd6o5nLfJdLXO^6S6lLcJkLWOZ6W6iL`J_4b5`K[Jc4f5[KXJh4i5VKUJm4m5QKQJP5T6lJiIW5\\6dJaI_5d6]JYId5i6\\JSIg5m6[JnHh5S7a32O1O1N101O1N2O001N2O1O1N101O1N2O1O1N3N1O1O1N2O1O1N2O1O1N2O1O1N3N2N2M3N1O2M3WFQH7UO`5l8TJeHDdNX6j8mIiIR6\\6gIgIY6^6YIjIh6g9O1000O10O10000O01000O1000O010000O10O10O1000O010000O10O10O100000O010000O10O1aMRIlGm6S8YIiGg6U8`IfG`6X8fIcGZ6\\8lI`GT6^8RJ^Gn5`8YJ[Gf5d8`JXG`5f8fJUG[5j8jJRGU5m8RKnFn4P9XKlFh4R9^KjFa4U9dKgF]4W9hKfFX4X9mKeFR4Z9RLdFn3Z9WLcFi3[9[LbFf3\\9_LaF`3^9b3N2N1O2N2N1O2N2N1O2N2N101O1O001O1N101O1O001O1N101O1O1O001N2O001N2O001O1QN^D^Kc;a4dDYK];e4jDUKW;i4TE`JY;`5d1O1N110000O101O1N3N1O1N200O0100O1O001O1O010O1O1O001O01O0000010O1dDjIl8V6PGnIP9S6kFPJW9P6dFTJ]9l5_FWJa9j5ZFYJh9g5TF\\Jm9c5oE`JS:`5iEcJX:]5dEeJ]:\\5^EhJc:X5YEjJi:V5REnJo:Q5nDQKS;S5eDQK\\;W5XDkJj;e61N2O001N3N1N3N2\\JlCW3W<cLmC]3T<^LPDb3R<WLSDh3b<`KbC`4e<UK_Ck4h<iJ]CW5f=00000000000000000000000000000000000000000000000000000000000000000000000000000010O01O1O001O001O1O001O001O1O001O1O1O1O2N1O1O1O2N1O1O1O2N1O1O2N1O3M3M3M3M4L4K?YO?A;E:F;D<E:F;M3M2O2N24K8I]I]Fo2[9oLkFT3m8kLXGZ3`8dLfG_3S8_LRHe3g7YL_Hf3]7YLiHa3Y7]LlH^3V7`LPIZ3Q7eLTIV3n6iLSIU3o6iLSIU3n6jLSIU3o6iLSIV3n6hLTIV3m6iLTIV3m6iLUIU3l6jLVIT3l6jLVIT3k6kLVIT3k6kLWIS3j6lLXIR3i6mLYIQ3h6nLYIQ3i6mLYIQ3h6nLZIP3g6oL[Io2f6PM[Io2f6oL]Io2d6lLbIR3`6hLeIW3\\6eLiIY3X6bLnI\\3S6_LSJ_3n5\\LWJc3j5YL[Je3f5VL`Jh3b5RLdJl3]5lKkJS4V5cKUK[4l4[K_Kd4o9O2N2N2N1O2N2N2N3M2N2N2N2N3M2N2N2N2N3M2N2N2N2N3M2N2O1N2N3M2N2N2N2N3M2N6J6J5K6J6I6J7I7J6I6J7I7Ic`7", "size": [640, 426]}, "area": 138877.0, "iscrowd": 0, "image_id": 10249, "bbox": [0.0, 81.0, 413.0, 552.0], "category_id": 183, "id": 10012539}, {"segmentation": {"counts": "0l1S1X811O100N01001O0NjEROV:n0021O1N9TO_EO0`01Aa:`0^E@b:c001O1_E[O]:h0OXOdEf0_:1M3N21OOO1UOeEk0[:0O2O3LROoENMf00ZO]:h01\\OaE<b:@dE=[:DdE=LB]:0fEc0_:02N1O001L3O10O2N3O0N2J6OO3LM^OeEd0X:_OeEa0[:7O0001OfEUOV:P1iEQOS:U1N0TFjNd9X1YFiNh9X1VFhNi9\\1100OO2M3O01O01M4O0O1NSOfFXOLc0_90nFOQ91PGNQ91PG0n8NTG2l8HbFEg0b0j8EYG;WO_O\\94_Gb0b8ZO_Gi0TOVOX90fGi0QOYOY:l0OO0100FXOVFf0k9<N2L30001M2aF`NV9`1fF`NO0]9g101OXNbF02b1`92O110O]N`F00[1k9O1M`NoNcGLm0Z1^7SO`Hk0c7WObG]O40=[1l7[OdG\\Oa0X1k7@UH`0k7BTH>FgNK0l7k0dH?^OeN32M0o7j0aHc0AhNn7e0`Hm0a7TO_Hk0a7UO_Hi0c7WO\\Hk0d7TO\\Hl0d7VOXHn0g7ROWHR1e7ROVHnNNo1l7Z13O4KO1O2O2NOO2O1O1O001O013OOVHbLg7_3I8N2N01100O0001O010N03N104K000O1lHPLQ7S41N4M0O100O12N00O2O001O0N20O010AYLVIM0l3i6ZLUIJO10k3l6>01O1O0000O101OO010O100ON21O00O12ON6L000kKQIP4o6QLQIo3R72O12N00L41O0000O10O2O0O1O20O2ON2N012ULlH^3a7N1_LZH]3i700O2M12O00000000O100O100M2N23MN32N0O02N05L00O1O1O1001M2J4N401OO1O010001O0^H]L_7c3`H^L`7b3`H^L_7f30MaH\\L`7d3`H\\L`7c330N3O1O00N200000O2O00O101OO100KMgHaLU7c3mHZLT7e34ZLfHg3X7[LfHf3[7301N2NO2O110O0OIULTIj3l6WLnHn3o6QLPI11o3o64202M12OO0O0100000O11O02M20K40001O1O00O10O100O02O1N3N000M3000O1O1O1O01010O2N012L11O1N2N100001ONNRLnHi32VLo6m3710OMTLjHl3V7TLjHm3V7O30000101N01N1N1100N2001N11M4M011OO1O2N1001N101N10MlHQLT7P4211N0O2O01OO001O12ON11ONgHULY7k32O1N2O002N1000000OfIbLl4_3nJhLQ5Y3hJnLW5R3cJUM\\5k2`J[M^5d2bJ^M]5a2bJbM\\5]2dJfMZ5Z2fJhMX5Y2fJjMY5V2fJlMW5V2gJlMV5V2iJkMV5V2jJkMU5U2jJmMT5T2kJmMS5V2lJkMS5U2lJlMT5T2kJnMT5R2kJPNT5Q2jJPNW5o1hJSNW5o3010O01O10=Ba0_O5K5J5I8J6J2N2N1O2N2N2N2O001O1O1O1O1O001OO100000001O000O1000000000000O10000000000O1000001O00000O100001O00000000M3M3M3M3M3M3M3M4K4I7H8I7H8I7I7ROn0002N001O1OO1001oIjJn5Y53L8G:H8G8eKmHR4V7N01O1O00000000O3N030nKiHn3X70005K01O001O00N21N10100O0N4N1N110O0O20lKoHo3T71OOOQLmHo3S7QLlHo3T7311ON5L0LhHWLZ7e35102OOO11N02OO2O02MkLcHa21nL2NQ7=mHEOX31hLU77TIGGS3OPM2MQ70mH003?o2AQM2MU72YIU3AjLV7O]IU3\\OnLi7NWHT30oLh7Z3000000XHbLf7`30000010ONgL]HJOT3e7QM[HL0T3R8OO01OO101O2N00000010OO100N2KgLZHZ3b780O11O02N00001ON3O2O0O0O00cLbHR3e7fL\\HY3l7L3OoGkLP8V3O101O0O2O001ON21OO01THfLj7Z3VHgLk7OWHW3n70PHiLl72THQ31mLm70RHT30lLS8U3N0100N02N1O2O0100O0O100O11O00001O001SHhLh7\\30O20OOkJ", "size": [360, 640]}, "area": 150874.0, "iscrowd": 0, "image_id": 10256, "bbox": [0.0, 0.0, 640.0, 360.0], "category_id": 119, "id": 10012540}, {"segmentation": {"counts": "l1S1T:11O100N01001O0NjEROV:n0021O1N9TO_EO0`01Aa:`0^E@b:c001O1_E[O]:h0OXOdEf0_:1M3N21OOO1UOeEk0[:0O2O3LROoENMf00ZO]:h01\\OaE<b:@dE=[:DdE=LB]:0fEc0_:02N1O001L3O10O2N3O0N2J6OO3LM^OeEd0X:_OeEa0[:7O0001OfEUOV:P1iEQOS:U1N0TFjNd9X1YFiNh9X1VFhNi9\\1100OO2M3O01O01M4O0O1NSOfFXOLc0_90nFOQ91PGNQ91PG0n8NTG2l8HbFEg0b0j8EYG;WO_O\\94_Gb0b8ZO_Gi0TOVOX90fGi0QOYOY:l0OO0100FXOVFf0k9<N2L30001M2aF`NV9`1fF`NO0]9g101OXNbF02b1`92O110O]N`F00[1k9O1M`NoNcGLm0Z1^7SO`Hk0c7WObG]O40=[1l7[OdG\\Oa0X1k7@UH`0k7BTH>FgNK0l7k0dH?^OeN32M0o7j0aHc0AhNn7e0`Hm0a7TO_Hk0a7UO_Hi0c7WO\\Hk0d7TO\\Hl0d7VOXHn0g7ROWHR1e7ROVHnNNo1l7Z13O4KO1O2O2NOO2O1O1O001O013OOVHbLg7_3I8N2N01100O0001O010N03N104K000O1lHPLQ7S41N4M0O100O12N00O2O001O0N20O010AYLVIM0l3i6ZLUIJO10k3l6>01O1O0000O101OO010O100ON21O00O12ON6L000kKQIP4o6QLQIo3R72O12N00L41O0000O10O2O0O1O20O2ON2N012ULlH^3a7N1_LZH]3i700O2M12O00000000O100O100M2N23MN32N0O02N05L00O1O1O1001M2J4N401OO1O010001O0^H]L_7c3`H^L`7b3`H^L_7f30MaH\\L`7d3`H\\L`7c330N3O1O00N200000O2O00O101OO100KMgHaLU7c3mHZLT7e34ZLfHg3X7[LfHf3[7301N2NO2O110O0OIULTIj3l6WLnHn3o6QLPI11o3o64202M12OO0O0100000O11O02M20K40001O1O00O10O100O02O1N3N000M3000O1O1O1O01010O2N012L11O1N2N100001ONNRLnHi32VLo6m3710OMTLjHl3V7TLjHm3V7O30000101N01N1N1100N2001N11M4M011OO1O2N1001N101N10MlHQLT7P4211N0O2O01OO001O12ON11ONgHULY7k32O1N2dNdHkN\\7o0lHoNV7m0nHQOS7n0oHQOQ7n0QIQOo6n0SIQOm6o0TIoNm6P1UIPOk6n0WISOh6l0YITOg6j0[IVOe6h0\\IXOe6g0\\IXOd6g0^IWOc6h0^IXOb6h0_IXOa6f0aIZO]6f0dI[OY6f0iIYOV6h0kIWOU6h0lIXOS6h0oIWOo5k0QJVOn5i0TJVOl5j0UJUOk5j0VJVOj5i0XJWOh5a0`J^O`58jJHV53PKKR51T3L4M4L3L4M3LTQh0h0RnWO6J8dNPOVHV1f7]OeGj0V8W1K3M4L5PHiLi7^3K4L3M5K4M41N01O1O00000000O3N030nKiHn3X70005K01O001O00N21N10100O0N4N1N110O0O20lKoHo3T71OOOQLmHo3S7QLlHo3T7311ON5L0LhHWLZ7e35102OOO11N02OO2O02MkLcHa21nL2NQ7=mHEOX31hLU77TIGGS3OPM2MQ70mH003?o2AQM2MU72YIU3AjLV7O]IU3\\OnLi7NWHT30oLh7Z3000000XHbLf7`30000010ONgL]HJOT3e7QM[HL0T3R8OO01OO101O2N00000010OO100N2KgLZHZ3b780O11O02N00001ON3O2O0O0O00cLbHR3e7fL\\HY3l7L3OoGkLP8V3O101O0O2O001ON21OO01THfLj7Z3VHgLk7OWHW3n70PHiLl72THQ31mLm70RHT30lLS8U3N0100N02N1O2O0100O0O100O11O00001O001SHhLh7\\30O20OOkJ", "size": [360, 640]}, "area": 57292.0, "iscrowd": 0, "image_id": 10256, "bbox": [0.0, 55.0, 640.0, 219.0], "category_id": 175, "id": 10012541}, {"segmentation": {"counts": "_RV43n:8L4N2N2N2O1dGAl5a0nIFo5<kIKS66gI1V60gI6U6LjI6T6KjI8S6IlI:Q6HnI:P6GnI<o5EQJ=l5ERJ=l5ERJ<n5DRJ=l5ERJ<m5FQJ<n5DRJ=l5ERJ<n5EPJ=n5DQJ=n5EPJ=h5JWJ8^53`JN[58cJJY5T3L5L3Mb0]Oe0\\O8G9H7I8J6J1O2N2N2N2N2N101O1O1O1O1O001O00000000O101O000000000O10000000000O1000000000000O2O000000000000000001ON2M3M3M3M3M3M3M3M4I6H8I7H8I7I7WNi1J6J6K5gNRIRNT7j1W1L4L4L4L4L<D<D<D=C`Ta1", "size": [360, 640]}, "area": 22040.0, "iscrowd": 0, "image_id": 10256, "bbox": [381.0, 48.0, 119.0, 271.0], "category_id": 183, "id": 10012542}, {"segmentation": {"counts": "0o1h91O1O001O1O00001O1O00001O1O0000001O001O1O0000001O1O1O1O001O1O001O001O001O001O001O00001O0000O1O13M1O1O1O1O001O1O001O001O001O1O1O0000001O001O001O00001O000000000000000000O1O10000O1000000O1001O1O0000000^GmL`8V3O00000000000000O1001O001O00O100000000000000MaGmL`8S3200O1000000000000000000000000O10^GoL^8Q3`GRM_8n2aGRM_8S301O0000O1001O000000000000000000000000000000000000O1001O00000000000000000000000000001O000000000000000000000000001O00000000000000O11O00YNhGfNLo0\\87XHGh78YHHg76[HJe75\\HKd70dGkNk0U1a70dGkNm0R1`72eHMZ73gHKZ75gHJY76gHJY76hHJW76iHJW76lHGT71dGZOX1e0T70gGYOS1i0V7MiGYOR1i0U7NVI2i6NYI3d6NZI3f6MZI2g6NYIOj61XINg61ZINg62ZINe61\\IOd60^INd6DdG1g1;e6[OcG07:a1;S7ElHFZNa0l8EPI;P7ATGNl1a0Q7^ORG0Q2b0R9000001OI@WE`0h:DTE=m:61O100OmMZOUIf0\\6ZOhG011J0m1f0_6[OfG3NK07i1b0b6ZOeG`0g16b61^I0a60`IO`60aI1^60`I1`6O_I2a6N_I2a6N^I2c6N_I0a60^I2a6OXIVOeNg0MXOV8k0UIUOeNo0Y8LRHUO`0S1_7JnGUO?T1c7HlGXO=Q1g7HkG^O6k0o7JgGA3g0V8Q2O00000000O11OO1000000001O00O11OO100000000001O1O00O11O001ON21O00000000O10000000000001OO100000000000000000000000000001O0000000000O100001O000000O11O00000000000000O10000001O0000000000000000002N00N20000001O000000O1000000O11O0000N2O10000O1O1001ON2O100O100O100O100O10000O1000000O1O1O100O1FWG_MP9T2`000O1O100O1O100O1001O00O11OO1N[FRNe9n12001OM3O100O1N2O100O100O1O100N2O1O1O1O11OMjEdNW:\\1200O100O100000000000000O10000O100001OO1M3N2N2M300001OM31O00000000O1O10000O1O100O1000000O100O100", "size": [375, 500]}, "area": 42392.0, "iscrowd": 0, "image_id": 10263, "bbox": [0.0, 0.0, 500.0, 111.0], "category_id": 102, "id": 10012543}, {"segmentation": {"counts": "iWc01f;000`E0j61QJ0YNOM0K67KHO162Jg75VHMW1OiNc00n0h7kNXHE0N0141ONN020Na2d7_11N11O10O000010ON2L5000O1O1O01VLUHe3T8K1O00O11O01N0110O00000000000O10O02O02M100000O2N100O0020O0010O01O001O0GaLZH_3f7aLZH_3f7cLUH`3k73UHZLk7f3UHZLj7f34O2OPH\\Lm7g301O1O1O00O02OO1001NO201OO02O001O0000RHYLk7j300O1O2O00000O0022M0O1ULVHj3i7VLWHj3k7000001N01000OO2O12N01ON2O3NO1010O01ODTHlLn7Y1RHaNN02Ob13\\6NRH0Y30f4O`K0T80Z`60e_I1VOOVE11O10<1\\:3VEL110O:1_:9VEH1N61b:>XEA0031d:`0XE_O050L0Oe:P1N3]GmN`6V1aGlNR1OPOON006ON14Q8Y1nG`0R8c1O000000102M00YLRHe3Q8XOoGfMT8X2QH`MS8Y2i00000000O11O00002N1OL41ON2N23M10M2O01010O00001OO101O07RG^MZ8U3J0O2OO0101N2N01O100000000WOoGZM:KG2P8X1oGhN1N510OY11c6<oGF0Mj20W54oGO4Mk20Z50anh2", "size": [375, 500]}, "area": 19073.0, "iscrowd": 0, "image_id": 10263, "bbox": [52.0, 125.0, 205.0, 131.0], "category_id": 112, "id": 10012544}, {"segmentation": {"counts": "_h\\21e;5gDL`:i001O1O00O100001O001O0000000000O2O00O10NWEVOj:i023MO2OO2OO1N9XOl[o2", "size": [375, 500]}, "area": 781.0, "iscrowd": 0, "image_id": 10263, "bbox": [209.0, 224.0, 30.0, 30.0], "category_id": 115, "id": 10012545}, {"segmentation": {"counts": "i_T42e;1O2K4O1N4MO010002M100O21OO2O0O100N2O2N0011OO00001O000000005K3L3`ElNY:W1dEiN\\:Z1O1O1N10101L308HO001M202N007H101O10N1010ON3LbNmE_1Q:5N3HZNZFh1c9702N000023MM2L30\\NcFn0]9QOeFn0\\9POgFn0Z9POgFP1[9mNgFR1P:O3M0000010O1O000O2O1ON3N001O1O1O1002N00000000000O1001O0<D1O0O110O001O000000001O1VOTEf0m:ZOTEe0R;M2N00001O00000000000L4L9Ine3", "size": [375, 500]}, "area": 4635.0, "iscrowd": 0, "image_id": 10263, "bbox": [361.0, 291.0, 128.0, 75.0], "category_id": 117, "id": 10012546}, {"segmentation": {"counts": "Y9^2Y90O1O11O0000O10000O11OO1O100002NO10000O100O11O1OO1M_MPGa2o84N2O1N200O1O1O100O100001O00O1M300JPMcGR3]84001O0000M3001O00O1_OhLcHX3]7iLUH0:V3c7RMSHF1Y3l7?O11O1O0000001ON2L41O1O1O1O00O100000000001OO11O000000000000O100O1002N000000O1O100O1O11O001O001O001O00000000MYLVHg3j7YLVHg3i74O10TLXHi3k701O1O1O00O11OO1001ON20000O1000000000VLUHh3m7000001O000000O11O00O11O00000000O10000O10000001ON2O100001O001OO12N00O12N00O11OO11OO11O00O1O100O100O11O00000000001O000000O100O1N21O1O0000001O3M0000N2N2001O1O00O100001O001O0000000000O1000000NWLVHi3j723MO1001OO1N2K56J001OO1001O1OO1O100000000O1000ULVHi3n7XLRHc3n7]LUH`3R8O1O00O100O1001O00C`L_Hd3a7\\L^He3b780000000000000000000000001O0000000000000000000000000000000000O11O0000000000000000000000000000000000000000000000000000000000000XLXHa3h7^LYHb3g7^LYHb3g7^LZHa3f7_L\\H_3d7`L]H`3m701O1O00000000O11O00O11O00N20000001OO10000001O1O0cMlGW1T8iNmGV1T8iNnGU1R8hNRHW1n7iNSHV1n7hNVHU1j7kNUHV1l7hNUHX1l7gNTHY1l7gNVHW1j7hNWHX1i7hNWHX1j7fNXHY1f7kNYHT1h7kNZHS1\\7UNTHl0`0n0V7TNYHW1b0d0U7VNXHW1c0b0U7AmH>T7AnH=R7CnH=R7CoH<Q7FnH9R7GnH9S7FmH:S7FnH9R7GnH9R7GnH9S7FmH:T7`NWHk0e0e0V7]NZHj0e0d0R7aNXHl0i0`0o6dNYHj0k0YOlNR1[8ElHVOiNU1[8EoH:R7EoH:Q7FPI9Q7[OcGK]1j0P7ZOdGEd1P1h6[O^Ic0a6^O`Ia0`6^OjI9U6GlI9U6[OlG0P2e0f82000000O100001O001O001O1O1O2N1O002N00002N2N1O000000000000000000001O000000O100001OO10000000000000000000000O1001O000000O11O0000000000000000O1000000000000000000000000001O0000000000O11O00000000", "size": [375, 500]}, "area": 47014.0, "iscrowd": 0, "image_id": 10263, "bbox": [0.0, 246.0, 500.0, 129.0], "category_id": 118, "id": 10012547}, {"segmentation": {"counts": "`e91f;0[D020];8aDJY;;O000O10001O0L41]OmD`0U;000M@nDa0g:@ZEO0f0g:44K5L3N20N22N00000O100000O1O12@kD0N014oag10X^XNLk:d0M1N2mFYOVN1k8g0mH_Om6IZGk0h1_Ol6F\\Gm0e1Ak6d0RI\\Oo6e0PI\\Oo6e0oH\\OQ7d0oH\\OQ7e0oH\\Oo6d0QI]On6f0oHVOm6BmGX1V1WOk6DmGS1[1VOi6IkGR1[1SOl6X1TIjNi6X1TIjNl6T1UIjNm6V1TIhNm6X1SIhNm6Z1QIgNm6Z1RIgNn6Z1QIfNn6[1QIfNn6\\1QIdNV6CmH11g1m0cNV6EcH007:a1m0cNV63mHY1FZNT18Z67hH]1n0\\NZ67dHANl1U1YN[6;_H_O0Q2o8e0000001OISGdMm8[2WGaMj8`261O1O1OmMnFa1R9PNnF4011J0m1Q9TNPG13NK07i1l8XNPGO`0g1[9001N11N1O100O12NO2IQFeNn9X1QFeNV:;jE`0]:]OeE?_:_OeE=]:BjE6Z:FlE3ohc2", "size": [375, 500]}, "area": 4274.0, "iscrowd": 0, "image_id": 10263, "bbox": [26.0, 0.0, 246.0, 169.0], "category_id": 130, "id": 10012548}, {"segmentation": {"counts": "hai4131015OK7c0IS99YFf01PO64]9`1[FkN`9f101O0000000O10001O0O10O1OJlMiFV2V96O100000000000000O11O01O01N2O001OO100O10000O1O1L_MQGb2o8301O00000001N11O0O1000O11O00011OO01N1O2O01O03M\\MWGZ2j8^MVG4160k1l8kMWG3NMN1100m1\\9RNcF1100k1h9lN[F6d9J^FDJ2j9BWF90K:LE0031N10O20Mn98SFHO0a01H0FOR:3nEMY11gNOT:1WG0Z:OUe1", "size": [375, 500]}, "area": 5669.0, "iscrowd": 0, "image_id": 10263, "bbox": [419.0, 94.0, 76.0, 94.0], "category_id": 133, "id": 10012549}, {"segmentation": {"counts": "Y_`41a;0bD1^;0`D1`;40O00000001O4L0O1G95J4M1N1O10O10O10000O20O1O1cDG[;;N20O01O1O1O0O1000001OO20O0001O000000000O02O010O0O1000000001ON@mDa0R;3OO011N3OO0O1O2O1O01OO100000001O01OO02NN201N200O2N20N1010O00KUE[OQ;b02^OPE=P;CPE=U;01O0O1001fDEV;>0O11OO1001O00N21mI", "size": [375, 500]}, "area": 1711.0, "iscrowd": 0, "image_id": 10263, "bbox": [394.0, 181.0, 106.0, 32.0], "category_id": 156, "id": 10012550}, {"segmentation": {"counts": "nPb41e;1]D5Z;5iJEPO9Q1ImN9Q1IcJLY1a0GL[4LQJH;OJ1^1c0jN\\O88YO6a5BkJ?2AC7OI10O022NNP1l0ZOYO8g0m4VOkJ>3<D[Oc0f0EXOHLOY1_5oNcJ8=h0OGFWOMY1[5kNdJ7:^OG^1:DFi0Z5S1nJ:j4GUK:k4FUK;i4EXK<]4eL[KV375R4;nKER4;nKFP4;oKDR4>nK^OJiLX4i3oK\\OT4ZNdKcN1015OK7c0IK8@GW20jM5?W4l0nKeNf01PO6477Q1c0jNTOG[4Z1WMeNkN9NV1`0jNf3S1bMnNWNU1a0jNf3S1cMmNWNU1a0jNf3S1bMnNXNT1a0jNe3T1bMnNYNS1`0kNd3U1cMmNYNS1=eNTO;_4o0gMmNZNT1;SOd3l0hMmNYNU1?iNb3J_K[1W2mNYNT1a0iN`3X1gMjNYNS1a0kN_3X1gMjNXNT1c0iN_3W1gMmNWNQ1d0kN[3Z1iMkNXNo0e0lNY3[1iMkNYNn0e0kNZ3U1dKhNV2;VNn0e0jNZ3U1QNSOPNn0e0jNX3V1TNROoMn0h0gNT3Z1UNQOoMo0T50mLQOoMo0S51nLPOnMP1T50nLoNPNP1Q52oLnNPNP1P53PMnNPNn0P54PMnNPNn0f0gNP3^1[NlNPNP1b0hNR3^1[NjNQNP1<QOT3V1`NhNPNQ1;SOYODOO]3b1POgNPNT1NcNB02i0NYOO1Z3a1UOfNRNS1L1CeNU3a1YOgNRNT1J4i2]NmLe1^2dNbMGa0\\1K7g25ZOaNWNR1H:e22]ObNUNS1I9d23^O`NVNT1H9d22_ObNSNT1J8c23@cNRNQ1K9c23@cNRNR1J8d22AcNRNS1I8d21BcNSNS1G9d2MQM_Nb28SNR1F:d2MFgNPNQ1G;c2MGgNnMo0Ij0V2@3fNoMR1Fi0V2@5eNoMb3l1iM5eNoMb3l1iM5eNoMc3j1jM5dNRNa3i1kM6cNQNc3h1jM7dNPNb3j1hM7eNoMd3i1hM8eNmMd3h1jM:`NPNg3d1kM=\\NPNj3`1lM`0XNRNm3XOPLP2n1f0UNRNm3XOQLo1n1h0QNSNU4R1kMg1V2XNiMg1X2VNjMk1V2VNhMl1X2SNhMm1X2SNiMg1PNaLZ4h1fMg1\\2YNdM`NPN[23jMY4Z1fMYNnM4160l12WNY4Y1jMZNnM3NMN1100m16_NU4NgKk0`2jN]M1100k19`NR4KkKo0f2BWM66]OX4EhKT1c2DYMDJ2;Mc4h0QNmNRM90K:LE0031N10O20M<<CEj4R1XNUOPMHO0a01H0FO`0?Z4c0YNQOmLMY11gNO`0>[4d0ZNoNVN0WO>X4c0JnNnK>X4e0>[OCc0>^OAb0?^OAa0`0]OBc0>^OAK", "size": [375, 500]}, "area": 17180.0, "iscrowd": 0, "image_id": 10263, "bbox": [398.0, 23.0, 102.0, 342.0], "category_id": 171, "id": 10012551}, {"segmentation": {"counts": "o1i6o4O1O0O2O1N100O2O1O00001MiJdIX5[62O100O2N101N2O0O100O2O1O1\\LZJT1h5kNXJeM020X3h5QO`JeMJT3g5VOdJh0^5WObJh0_5XOaJh0`5VOaJj0_5VOaJj0`5UOaJi0`5WO`Jh0b5SObJm0^5TOoInM`0n2b5UO^Jk0b5UO^Jj0d5ROlIQNa0l2d5hNlI]NO0f0l2_5gNeJ[1]5eN^J^1c5cNYJ`1g5`NWJb1h5_NXJa1g5^N[Ja1i5^NUJa1m5^NSJb1n5]NRJc1o5\\NPJd1R6[NnI1B0a6OmI1B0b6NlI2BD0TOd6g0]IZO>k0BTOOM0K67KHO162JNg6o0^IPOM1>c0A\\Oc00g7o0cHTORON020NX9m0`10O01O100O0001O01O0100O01O0001XESOe:T1K1O00O11O00O1001O0O1O10000O1000O11N10O2O1O0001O00iNbEU1a:N11O00001O00000O10GbEYO_:f0aEZO_:f0cEUOa:j060O01001O000000000MTOZEm0f:110O1000O1001OO11O001O0000O10O01SOZEh0g:VO\\Ei0d:WO\\Ei0i:01N1O13L100UEVOj:i0VEWOj:k0000001OO10000N2O12N0000002NO1001O0O1cHUOn3l0WJTOaNN02Ob13]Nm6l0QIUO0Y30gLo6k0PId20aLQ7^4oHdKo6b40O11OO11OO11O00O1O100O2O0O11O000000YMmHj0S7UOnHk0R7[NmHPO11O10<1X2Q7ZNRIPOL110O:1[2Q7ZNXIPOH1N61_2Q7YN]IROA0031b2Q7XN_IRO_O050L0Oc2S7ZNmIf1T6WNoIV1mN_NU7;QJWOlNR1OPOON006ON1\\7i0QJVOg7k0b10000000O20O00WETOg:o0O1O00E[E\\O14d:>bEA^:>eE@Z:`0`0N010O1010O0000001O0102L10O2L31OO02O0O0101VE_O\\:a0dE@[:a0cE@]:`0cEA\\:`0aEB_:j0100O100O1O1000001O0000QHUOZM:KGZ7h0PJWOhN1N510OY1Z7ZOSIXOF0Mi2Z7oMjHXOO4Mj2[7QNeHX2[7R11O000000O1001OMhKgHX4Y73O1000000001O00000000O11OO10001O0001O00O11OO100000000001O1O00O11O0O20M21O00000000O10000000000001OO100000000000000000000000000001O0000000000O1000010OO1000O11O01O00000000000O10000001O000O100001O00000002N00N2000bNiHQMOY1X7\\1hH[M<Y1l6a0iHVN0044KL03a0U1g6b0TIXNGNb0W1c6c0RJ@k5DmHI[1\\OcNN1n0S7KmHVO5e0`1:^5KVIJ^1_OXN`00_OU7h0WIH]1_OXNb00^OT7h0XIHa12Z56VIHf1MT5:VIIg1LS5;WIHg1LR5<XIGf1MP5>ZIDg1Nn4?[ICg1Om4=^IVO^O30N000OW2=m4=aIXOk1:c4?cIUOj1=b4?WLAh3?XLBkLFc6f0dLEfLJ`6c0kLCdLIa6e0lLAcLK`6c0nLMQ34PMKP34QMMm24SMLm24SMMk24UMLk24VMKi2IfIIb3<i2JgIGa3`0h2HiIF`3a0g2InIBZ3O]Le0Z6ZOfI<W46X2HiMGiKOS72TMOQ3O][Q1", "size": [375, 500]}, "area": 38352.0, "iscrowd": 0, "image_id": 10263, "bbox": [0.0, 63.0, 409.0, 217.0], "category_id": 172, "id": 10012552}, {"segmentation": {"counts": "h8a0V;00O0011N100O010000O11M_OnD`0S;10100O3M010O10O010O02O1OO0NSE\\Om:c04N100O200O1O00010O1000O2N10O0N210JlDGV;940O2N1000M21O2O0O0011BjD:_;F_D1nSX2OSlgM0a;0^D31M\\;5dDNY;9O1O00O100O1001O01K4O_Rg0KfmXO10001O2M1M30100N10000000O20O00O11O00N20000001OO1000O101O10O0001O00001O001O1O01O00012K21OFgD3jT]1", "size": [375, 500]}, "area": 1626.0, "iscrowd": 0, "image_id": 10263, "bbox": [0.0, 249.0, 377.0, 48.0], "category_id": 177, "id": 10012553}, {"segmentation": {"counts": "[fW421OX;0hD<]:EcE0044KL03a0Y:KfEGNb0[:e0TOhNXG[1\\OcNN1n80QG5e0`1Z8dNeG^1_OXNe8<jG]1_OXNf8<jGa1U8`NkGf1o7ZNQHg1n7ZNQHg1n7ZNQHf1o7ZNPHg1P8YNPHg1o7[NcG^O30N000OW2]8^NeGk1Z8VNdGj1]8i0O0O2O1O1NbNiG^OW8=iGXN1\\1T8=lGUN1^1S8=QHBo7?PHAP8?PHAP8?PHBo7>QHCl7?UH_Ok7ROoGo0:gN7<b7LmGJ1m09QO:8_71mGC7OI10O022NNd0X1Fl63hHDn08Z66eJE^56iGGm20h[Q1", "size": [375, 500]}, "area": 3085.0, "iscrowd": 0, "image_id": 10263, "bbox": [370.0, 127.0, 39.0, 118.0], "category_id": 181, "id": 10012554}, {"segmentation": {"counts": "V4U9V400000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001O00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000O100000000000000000VGnK[8R4aGTL]8l3_GZL_8[4N1O0000O100000000O100000000O1O1M3M3M3M3M3N2O11O00000000000000000000000000000000000000000000000000000000000000002N6J3M4L1O1O1O001O1O1O0000O100O1]OaGZL_8f3fGULZ8k3iGQLX8o3jGoKV8Q4mGkKT8U4a0000000000000000000001O0000000000000000000000000000000000000000000000000QGmKf8S4YGoKf8Q4YGPLg8P4XGRLg8Y40DYGSLh8W41O1O1O1N2LRGiKP9W4200000000000000000kKlKSOT4l0nKSOR4l0oKTOQ4l0oKTOQ4k0PLUOP4k0PLUOP4j0PLWOP4i0PLWOP4h0QLXOo3h0PLYOP4f0QLZOo3f0QLZOo3f0PL[OP4d0QL\\Oo3d0QL\\Oo3d0PL]OP4b0QL^Oo3b0QL^Oo3b0PL_OP4`0QL@o3`0PLAP4?PLAP4>PLCP4=oKDQ4<oKDQ4;oKFQ4:oKFQ49oKHQ48oKHQ47PLIP47oKJQ46oKJQ45oKLQ44nKMR43nKMR42nKOR41nKOR41mK0S4OnK1R4kMkKG2_2S4hMPLENc2R4fMSLFJe2S4dMULFHf2S4bMZLDCk2S4_M^LD_Om2S4_M_LC^On2S4_M`LA]O]OLd2W4^NaL@\\O]O0b2S4aNbLZO@BLc2R4aNbLXOBDJc2R4aNbLVODEId2Q4aNbLSOEIIc2P4aNbLQOGKGc2P4aNbLoNILFd2o3bNbLkNJ0Ec2o3bNbLiNL1Dd2n3cNdLaNK:Db2m3dNiLWNGe0C`2m3eNmLnMDn0C_2l3fNfMmN^N]2l3fNdMmNbN]2j3gNaMeNnNd2a3hN^MiMN_3d2hNSOX1m0gNTOY1l0gNTOY1l0gNTOY1l0gNUOX1k0hNUOX1k0hNUOX1k0hNUOX1k0hNUOX1k0hN\\NTNjMT3j3hNeMlMcN?Jn2n3gNdMRN[N?0i2Q4fNdMSOXNX2T4eNdMUOUNW2W4dNcMj2^2UMbMk2^2VMaMj2^2WMaMj2_2VMaMj2_2VMaMj2_2WM_Mj2b2UM^Mk2b2UM_Mj2a2WM^Mi2a2XM_Mh2a2XM_Mh2b2WM_Mh2a2YM^Mg2b2YM^Mg2b2YM^Mg2b2YM]Mh2c2YM\\Mg2d2YM[Mh2e2XMZMi2f2XMYMh2g2YMWMh2h2ZMVMg2j2ZMUMf2k2[MSMf2m2[MQMf2o2[MPMe2P3\\MnLe2R3]MmLb2S3_MlLa2T3aMkL^2U3bMkL^2U3cMkL\\2U3eMjL[2V3fMjLY2V3hMiLX2W3iMiLV2W3kMiLT2W3mMhLS2X3nMhLQ2X3PNgLP2Y3PNhLo1X3XNbLg1^3YNbLg1^3ZNbLe1^3\\NaLd1_3]NaLb1_3^NbLa1^3`NaL`1_3aNaL^1_3bNaL^1_3cNaL\\1_3eNaLZ1_3fNaLZ1`3fN`LY1`3nNYLR1g3POWLP1i3QOVLo0j3ROULn0k3SOSLn0m3ROSLn0m3QOTLo0k3ROULn0k3ROULn0l3QOSLP1m3oNTLQ1l3oNTLQ1l3oNTLQ1k3oNULR1k3nNULR1l3mNTLS1l3lNULT1k3mNTLS1l3mNSLT1m3lNSLT1m3lNSLT1m3lNSLT1m3mNQLT1o3lNQLT1o3lNQLT1o3lNQLT1o3lNQLT1o3mNoKT1Q4lNoKT1Q4lNPLS1P4mNPLS1P4nNoKR1Q4mNPLS1P4mNQLR1o3nNQLR1o3mNRLS1n3mNRLS1n3mNSLR1m3mNTLS1l3mNTLS1l3mNTLS1l3lNVLS1j3mNVLS1j3mNVLS1j3mNVLS1j3mNWLR1i3nNWLR1i3mNXLS1h3mNYLR1g3nNZLQ1f3POYLP1g3SOWLl0i3WOTLi0l3XOTLg0l3ZOTLe0l3[OTLe0l3[OULd0k3]OTLc0l3_OSL`0m3HkK8U4_40000000000000000000000000000000000000000000000O10000000000001OO100000000000000000000000000000000000000000000000000O11O00000000001O00O1000000000000001O00O100000000000000O1000000001O00002N1O00001O2NO10000NSGfKm8Z4200000000000000000000000000O100O100000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000", "size": [427, 640]}, "area": 179685.0, "iscrowd": 0, "image_id": 10265, "bbox": [0.0, 134.0, 640.0, 293.0], "category_id": 140, "id": 10012555}, {"segmentation": {"counts": "0V4U9000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001O00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000O10000000000000000000000O1L4K5J6J6J6L4L4L4L4L4L4C=K5N2O1N2O1N2O1O1N2OjF_Nd6`1ZIdNe6[1YIhNg6V1XImNh6R1XIoNh6P1ZIoNf6P1\\IoNd6Q1]InNc6R1]InNc6R1^ImNb6S1_IlNa6T1_IlNa6U1^IkNb6U1^IkNb6U1^IkNb6U1^IkNb6V1]IjNc6V1]IjNc6V1]IjNc6X1[IhNe6Z1XIgNh6\\1SIfNm6]1oHdNQ7^1jHeNV7]1`HkN`7V1aHhN_7Y1bHeN^7\\1cHbN]7_1dH_N\\7b1eH\\N[7j1aHTN_7U2ZHiMf7Z2YHdMg7_2YH^Mg7e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", "size": [427, 640]}, "area": 83087.0, "iscrowd": 0, "image_id": 10265, "bbox": [0.0, 0.0, 640.0, 142.0], "category_id": 173, "id": 10012556}, {"segmentation": {"counts": "Ukc14S=6J6J6J4K5J6J6J6L3M4L4L4L4L3D=K5N10O0O2N1O2N2O0O2N2N2N1O22N2N2O001O1O00010O000001O00002M4K5L4L2H9K41O1O1O1O19G3M1O10O4M3M001ON2N2M3N2N1O1O1M4M2M5L6J6I4M3L3N_]?1_b@2N1O1O10O00001O2M3M^Z42aeK1O2O0O010O10O0100O01000O01000O0100O010O10O0100O010O010O010O10O0100O010O10O0mMNoF2o83mFMR96mFJR98mFGR9>jFCT9a0jF^OW9c0hF]OX9d0fF\\O^OMl8h0eG[O^O1j8d0cGAALl8c0aGCCJl8d0^GCFJl8c0[GDJJk8b0YGFLHk8b0WGHMGm8`0TGKOEm8`0RGL1En8b0iFI<Fl8f0_FEg0Ek8j0VFBP1Ek8`1WG`Ni8^1WGdNj8Y1oFPOR9m0SF0m9NRF5m9JTF7l9FVF;j9CWF>i9@WFb0i9ROYFTOOk1h9QOaFP1_9PO`FQ1`9PO^FQ1b9oN]FBF3=G`9d0\\FCKJ?L[9g0ZFD1@?3W9j0WFCS1@g8m0VFCX;=gDDY;=eDC];<bDE^;;bDE^;<`DDa;<^DEc;Q1000010O0000010O00O1O2O0O1O2O1N2O1N2N2O1N3N100O2OO0100O100O100O100O10OUOfCf0`<O010O1000O01000O0100O013M3L2M2O1O1O00O0100000O01000O1000O011O000O10001O000O10001O0000010OO10001N10000O2O000O101O0000001N101O02O2N2M010O01O10O8H4Mbf]2", "size": [427, 640]}, "area": 10107.0, "iscrowd": 0, "image_id": 10265, "bbox": [124.0, 44.0, 329.0, 271.0], "category_id": 183, "id": 10012557}, {"segmentation": {"counts": "U5n78]5IO001O000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000IOTC1l<OTC1l<0SC0m<0SC0m<1RCOn<3PCMP=4oBLQ=7000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001O0000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000003M2N0000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001O0000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000VH`4iM", "size": [427, 640]}, "area": 269429.0, "iscrowd": 0, "image_id": 10275, "bbox": [0.0, 0.0, 640.0, 427.0], "category_id": 183, "id": 10012558}, {"segmentation": {"counts": "meV83S=3L5M3UCCe<c0N2N2O101mJ", "size": [426, 640]}, "area": 152.0, "iscrowd": 0, "image_id": 10276, "bbox": [631.0, 162.0, 9.0, 26.0], "category_id": 111, "id": 10012559}, {"segmentation": {"counts": "Ukf52V=OlB0S=6N1M200HG]C;c<7N1O10O010OO2000O2O1O00001O0000O10000000000002NN2001NN4O0D\\OTDb0e;AoCM01<`0e;IUDGL?OAl;>TD60\\O02f;=ZD40Ee;6[Da0c;d00N20O2O0\\D]Na;0_D`10_Ne;c1N2000O200I[DdNh;[17J3K5L5J5L4K5N110000L4O11O1OVD@SO1c;`0WEEi:;UEHk:7nD0R;0mD0T;0iD7T;IgD;Y;DbD>c;c011OO2ON3L3O1000O1N2N21YD^Nd;e1O1O1O1OO100O2O000O101O0000O1000N2O2O1O00000001M1110N11OO110N2OM3O10N2010O01O0O1bNVD\\1m;O1OO1O11O00M12MTDiNm;T1TDjNn;U1RDmNm;S1SDlNn;S15M5N01O1O00O00011O0O1O101ON1000FhCCY<:iCE[<9gCDZ<<91O1001L4O00M3N3K4ON_C]O`<d0aC[O`<4aCN0OO01OR=0f^;", "size": [426, 640]}, "area": 6060.0, "iscrowd": 0, "image_id": 10276, "bbox": [439.0, 178.0, 173.0, 66.0], "category_id": 116, "id": 10012560}, {"segmentation": {"counts": "X2k0c0FKT8j1WH[NDKU8j1WH[NDLT8i1XH[NDMS8h1YH[NDLT8j1XHYNDLU8k1WHYNDLU8k1WHYNDMT8i1YH[NCKT8j1YH[NBLU8i1ZHZNAMU8j1YHYNBMU8i1ZHZNAMU8j1YHYNBLV8k1XHYNBKW8l1[GhMk0a0BIZ8n1YGhMk0a0DGX8P2YGhMk0b0FBW8W2RHWNGBW8W2PHYNJ_OV8Y2oGYNJ^OW8Y2PHXNJ]OW8[2oGXNK\\OV8]2mGXNh8h1YGWNg8h1YGYNg8h1XGXNh8h1XGXNh8h1XGXNh8h1WGYNi8g1WGYNi8g1TG\\Nl8d1QG_No8]1bFhM>m0P9g25N200O100000PG_LW8a3iG_LW8b3gG_LY8a3XG]L74`8_3YG^L63a8_3XG_L4NH508]8^400000001O000fHTKV6l4iIUKW6k4]IUKB2Q7i4]ITKA5R7h4\\ITK^O7V7e4UIhKj6\\50O10000O1000000O1FkH`JT7m4gHPKg00c6l4Q1O100001O0000001[HRKl6l4VITKj6m4TIVK\\OMZ7m4ZIZK^OIR7m4_I\\K\\OIU7l4\\IcKc6]4YIRKCc01]On6n4]ISKDg0n6V4^IoKCSOl6m4cIQL]6Q4_IQLa6T4ZImKe6X4UIiKk6[50001O1OO1001OhMXIoMg6S2ZInK6[1`6j2[IgKb0R1S6Z3UJcLk5_3SJbLl5a3RJ^Ln5e3oIYLS6j3kI[K]OKOe0i6W4dI_KE8i6\\4UIfK3Mh6U5XIjJh6W5WIiJi6W5WIiJi6X5WIgJi6Y5VIhJj6Y5UIgJk6f4VIZK0Nj6j4aIkJE:k6b4WIPKe0=T6a4TIRK30e0=T6_4UJaKk5_4VJ^Kl5b4TJ]Km5c4UJYKm5g4TJTKP6l4W12N00N21O00O11O1O0000O11O1O004L00001O00O11O1O00LnGoJS8Q53001O1O0000M3O1O1M300M300000000000WHYKo6g4oH`Kl6a4RIfKh6Y4WIlKf6R4ZIPLf6o3ZIQLg6o3WISLi6n3TISLm6m3RIhKDK[7_4mHgK_7Z4_HeKc7]4YHdKh7n41O1NhHhJb6W5[IoJc6P5[IUKc6i4\\IZKd6e4ZI]Kg6c50O1O1O1O100O1O1O100O1O1O1O1^Ob0000000001O1O0000000000O11OO11OO11O00O1jMRHQOm7o0THQOk7o0VHQOi7n0YHQOg7P1ZHnNf7R1[HhNIWNm7Q3[HgNi7Y1XHfNh7Z1YHeNg7\\1YHcNg7]1ZHbNf7^1ZHbNf7^1[HbNd7^1]HaNc7_1^H`Nb7a1^H^Nb7c1]HQNGoNl7P3^HkMOQOc7U3^HeM4UO^7V3_H^MC\\Ob00[7V3aHTMKF90[7U3fHjLM1T8T3]10000000000000000000000001O0000001O00000000O11O00M3001O4L000000000000000000000000O1000000ORJQMc1n2\\NXM`1h2_N]M]1c2cN_M[1_2gNcMW1[2jNhMT1W2mNkMQ1R2QOQNm0P2RORNl0o1SORNl0n1SOTNl0n1ROSNm0n1QOTNn0m1QOTNn0m1QOTNn0m1POTNP1l1POUNo0k1POVNP1k1oNSO3m0M^OH`09BF>:CE=;@H?8YO1d0ULPMj3X25h0RLoLh3n1b0S1AcNi0^1VO`Nl0`1SObNl0^1TOeNi0\\1UOQO?o0ASO=n0BUO;l0CWO;h0FYO9h0EZO:f0F[O9e0F]O9c0G^O8b0G@8`0HA7?HWO_LmMi3l2HVOaLnMf3l2HWOcLmMd3l2InNnLUNX3m2ZLlL]3o1UMXNS3m2[LlL]3l1XM\\No2o2IbN\\M_Nj2o2JbN\\M`Ni2n2KbN]M_Nh2n2nMjLTO5;d1K^Ni2P3gLhLH1l00]OOE>n0]1L[Nj2R3cLoLg0i0Ok0MYNl2U3ZLTMn0c00j0MWNm2h3TMYM1i0h3o1nKaM9`0i3o1kKdM<b0d3m1cKlMh0c0Y3_3fLPMl2o2TMRLVOYO_3d4[MSL^OUOT3i4\\MQLHPOl2o4[MPL1jNe2V5XMQL:bN_2\\5WMQLY4o3eKRL\\4n3bKUL]4k3cKVL\\4e3SK^Ja0P2Z4e2XKYKN`0OA3=0i2d3^1cL[KF63JN12`0Mj2c3n0aLlK=KKQ4V36dLmKj0o3`2GbL[LNM8Oj0R4]2EgL[LJO]1S4P2BlLYLI1[1T4P2AaN>`1@bN`0^1@bN`0^1@bN_OiNRMf2_3aNYOe2g0\\MSOi2m0XMmNm2S1SM^LnMY2Q5Y1RM\\LnMY2R5[1SMbNP3^1QM_NQ3a1oL]NS3b1nL\\NT3d1lLWNY3i1gLRN^3P2`LkMe3U2ZLjMh3W2WLhMj3X2VLgMk3Y2ULfMl3Z2SLeMo3Z2RLeMo3[2PLeMQ4\\2nKbMT4^2kKbMV4]2jKcMW4^2hK`MZ4`2eK_M]4a2cK]M_4c2`K]Ma4c2_KmLQ5S3nJjLV5V3jJgLY5X3hJeL[5Z3fJcL]5]3cJ`L`5`3`J\\Ld5d3Q2O1O1001OO11OO1002N1O1O1O1OO11O1O000000000000000000O1O100O10000000000001O00O11O00001dIYLS3h3jLWLoL0R62nIj3m2ZLR3NPJi31SLi27S3S4iLoKV3R4jLnKV3S4aLdKgM;g5R4bLULZ3n3eLSL[3n3cLSL\\3n3dLQL]3o3bLRL^3n3bLQL_3o3_LSL`3n3`LRLa3m3_LRLb3n3_LQL`3P4_K]KoN1LOa0b0V5P4\\KaKPONK2b0?W5o3ZKRL_OOW5P4YKRL@MX5P4WKTLALY5o3VKULALZ5m3UKXL@L\\5k3UKVLAOZ5l3SKVLCN[5k3lJcKYOe0`0M]5BQJQ4e0hLMEd5c3\\JkL0Bd5c3[JlL1Ad5c3XJoL6\\Ob5e3WJoL6^Oc5c3VJPM7]Oc5c3WJoL7\\Oc5e3TJQM6^Oe5b3QJSM:[Oe5c3iIXM7aN5b0k5[5iISJ01<`0k5c5oImIL?OAR6X6nI\\J0\\O02l5W6TJZJ0Ej5Q6VJfJj5]60N2001O0TJeHi5[7WJdHj5^700000O11O3M00O1001O00O100000000O100000000001O00O14L00L4O11OO1001O000000O1N2N21RJeHk5^7bKeHk2\\7SMhHj2Y7TMjHj2W7TMlHj2S7WMoHg2Q7XMRInNEZ3X7hMVImNFV3T7lMYImNCV3T7mM[IlN@W3T7mMlIR2T6mMnIR2R6nMnIR2R6nMoIQ2Q6oMPJP2P6PNPJP2P6PNQJo1m5SNTJl1k5UNUJk1k5UNVJj1j5VNWJi1i5WNWJi1i5WNWJi1i5WNWJi1j5VNVJj1i5WNWJi1j5VNWJi1h5YNWJTOdNn1V7mNVJSOiNm1P7QOWJPOmNm1m6ROVJPOPOl1i6UOWJmNROn1g6UOTKj0l4VOTKj0l4VOUKi0i4ZOVKf0j4ZOWKe0j4ZO[Ka0e4_O[Ka0e4_O[Ka0d4@\\K`0d4A[K?e4A[K?e4@\\K`0d4@\\K`0d4_O]Ka0c4_O]Ka0c4^O^Kb0`4@`K`0]4TNnHZ1f2b0[4UNmHZ1i2a0Y4UNPIY1g2b0Y4UNoHY1Y2iNlMn1k6^OXKn0e4TO\\Kl0d4TO\\Kl0e4SOZKn0g4POZKP1f4POYKQ1f4POYKQ1f4oNZKR1f4nNYKS1g4lNYKU1f4lNYKU1f4lNYKU1g4jNYKW1g4iNWKY1h4hNWKY1h4gNXKZ1]4WMhI`1j1Z1[4XMjI`1j1Y1\\4YMgI_1l1Y1]4ROaKo0_4RO_Ko0a4ROZKR1f4oNWKS1f4QOZKn0f4SO[Kk0e4VOaKc0_4^OcK?^4AcK=]4DcK;Z4[MjI\\2k18[4]MiI\\2m15j3`M[J0OO01O]2l12l3kMXJ[2j1Hn3h0QLWOo3l0oKSOR4n0YKgL\\O\\2[5Q1TKjLZOV2c5R1nJPMXOo1j5U1hJ@V5[4001O00N21O1O001O1OO100M32NO1000`HXJ^7j500000001O", "size": [426, 640]}, "area": 155253.0, "iscrowd": 0, "image_id": 10276, "bbox": [0.0, 72.0, 640.0, 354.0], "category_id": 124, "id": 10012561}, {"segmentation": {"counts": "R[^511020P=601TCJb<7[CKf<4ZCLf<6WCLh<;001O0O1000O1001O2N0OO21O000O11O1O0000002NO1002N1N4MM300100N0N3PGZOdL1LO^8f0QK]OdLNK2]8e0QKKn46RKKm46QKKo45QKKo46oJKQ56oJGS5:kJGT59gJSOPMe0Y87eJ1[5ObJ4_5L_J5a5K\\J8d5IZJ7g5JWJ7i5JWJ5i5KUJ7k5JQJ9o5HRIPOTO^1k7DoHROnN_1R8_OmHVOnN]1U8\\OkH\\OkN[1Z8YOkH[1T7gNiH\\1V7cNkH[1W7eNiH[1W7eNiH[1W7eNhH[1Y7eNgH[1Y7eNfH\\1lNaM[8R1gHc1X7^NfHb1\\7^N^Hh1d7WNWHm1i7XNoGl1P8\\NfGg1Y8e1O4M00001O000000O101OOM3WKdGc4_8]KaG^4e8`K\\G[4i8eKXGU4m8kKSGP4W9KhFULY9f38H^FfLe9Y36L5K4M3L4L4K5K3M3M4M2M4M2N3L5K5L4K5K5L4I8K4L4M3BZC5i<IZC3P[3MndL2V<NgC884P<0oC0P<NPD3R<KiC<W<;00L4O1000000000000O1N2N2000000O100N2000000O1O1002N1NO200O1O1N21N11O0O1001N101eNVDT1j;jNXDV1o;O2N4L0O1000001O000N21O00001O0O3M1O100000O1O2O01O0ON3N2KeCZO\\<b0dC^O`<b0aC\\O`<c0aC]O_<b0bC^O^<a0600000000000000000000000000000oN", "size": [426, 640]}, "area": 11415.0, "iscrowd": 0, "image_id": 10276, "bbox": [419.0, 20.0, 221.0, 171.0], "category_id": 132, "id": 10012562}, {"segmentation": {"counts": "hVP11Y=0O3kBMo<7SCIf<9WCIg<?K_O^Cc01]O[<1dCg0[<5dCSOZ<n0N201N14L5K01O10N1001O1O000IWOmCi0S<80O0N3TOiCLNe0c<N3O1O00001OO1002N0O2O00001M2O3L3Kb`T42__kK2M3N100O1M300O1000000O11O00O11O00001O1O1O1O001O2L[oW2", "size": [426, 640]}, "area": 1241.0, "iscrowd": 0, "image_id": 10276, "bbox": [77.0, 171.0, 389.0, 255.0], "category_id": 150, "id": 10012563}, {"segmentation": {"counts": "jh`02X=0O1ANeC4Z<MeC3Z<NcCNH508S<h000000001O000000O2O0000000N3N2O1N1O1O10O2O1O2M3M2N1C=9H1N3M2N2O2K4O1N3M2O3L5K5K5J6MSg32lXL5LLcT40]kK<D=C;E2NGTDPOP<n0mCRO30Q<k0:O0000000011N00N21O00O11O1O0000O11O1N1N6L00001O00O11O1O00LnCoNS<Q13001O1O0000M3O1O1M300M30002N2N2N2N2N101M3L4M3N2OZ`9;Y_Fb0_Oa1_N000000001O1O0O11O00001N3O1M3O0N200O1N3O00000O2N1JhDWNZ;h15O1O1O1O00100O1O1O0D_DoNb;k0gDQOZ;j0kDUOV;d0ZD\\Ob00T;:cDF90X;OfD1^RW5", "size": [426, 640]}, "area": 5839.0, "iscrowd": 0, "image_id": 10276, "bbox": [40.0, 88.0, 199.0, 85.0], "category_id": 166, "id": 10012564}, {"segmentation": {"counts": "\\^h64V=2M0\\D0R:OhE9U:HiE9V:IhE8X:IfE8Y:IZE0\\O8Y;JVEf0j:ZOQEk0R;ROiDS1V;a0N2N201N1000001BPN`EP2`:UNYEm1e:<0100O101N20O001ON21M3O100O00N2O200NN3OO20010O0010O00\\E\\Mc:f2O10N1O20O01L3dMXEM3S2Q;2OO00001O02KjMREV2n:kMQEU2o:kMRET2n:mMQES2P;21O11O0000001ON2O1O1002N00O1O1001N2N11L3N2L4O1ONTNgDn1W;9FiMTEX2i:601O001N11M1101O00N21O1O010000N100M4N1L4K4N3L3O1O2O10fL", "size": [426, 640]}, "area": 8519.0, "iscrowd": 0, "image_id": 10276, "bbox": [520.0, 100.0, 120.0, 91.0], "category_id": 167, "id": 10012565}, {"segmentation": {"counts": "0a0e0a0]:_OcEa0]:_OaEc0_:]ObEb0^:^ObEb0^:_OaEb0^:^ObEb0^:]OcEc0]:]ObEc0_:^O`Eb0`:^O`Eb0`:^O`Ec0_:]OaEb0`:^O`Ec0_:]OaEc0_:]O`Ed0@kNn:a0^Eh0DgNn:a0XEn0JaNn:b0TES1k:nNSES1m:f0O00001O00O11O0000kMVEj1j:VNVEj1j:VNVEj1j:UNWEk1i:UNWEa14YNg:4VEb1T;^NlD`1V;_NkD_1X;`NhDT1d;lNbDMFP1g;ROiDl0W;RO`D6Ik0f;oNeDW1l;KL4N1O1O1N2EeNbD1M[1a;dNaD2M[1b;cNbD`1^;`N_Dc1`;31O10000M3002L4OO1O001N1O2G8\\Od0@a0K4O10cS2<_kMb0oCSOe;m0ZDTOa;R1ZDSOe;n0XDUOi;U1IaNcD_1S;bNmD2O\\1T;bNlD5NZ1U;aNlDj1T;WNkDi1U;WNjDj1V;VNjDj1V;VNiDk1W;5O000000001O00000000O1O1O1O100O1O1O1O1O11O001O1O001O001O1O001O001O001O1OO1O1O1O1O100O1O1O1O100001O00001O00001O00001O00001O00001O001O001O001O1O001O001O1O001O001O001O1O00001O001O00001O00001O001O00001O001O00001O2N3MO10000000000000000O11O00001O2N2N2N1O1O1O1O000000O11O000000001O00000000001O1O001O0000O1O10000000000000000000000001O0000001O00000000O11O00M3001O4L000000000000000000000000O1000000O1O10000N2N2O1M31O1O002N1O1O1O1O00001O00N20000O1MlEPMT:P3mEoLS:Q331O00001O001O1OO11O0000000000000000000mElLP:T3PFlLP:W300000O11O001ObMTF^1l9aNVF\\1l9bNWFQ1HaNQ:9\\FV1F_Nm9;]FV1F_Nm9;]FU1G^Nn9>ZFS1J^Nl9?[FQ1K\\Nm9c0XFo0L^Nm9b0XFQ1S:oNmEP1T:POlEk0Y:UOgEd0`:\\O`E?e:A[E9k:BgDE>a0S;J`DD=<Y;0[DC<7_;7UDA=2c;=g0001O001dEKa75\\HNd72eG@^N31N0e0l9JeGe0[8\\OaGe0a8[O\\Gh0d8YOWGl0h8TOaF]OC06i1e9jNbFd1^9\\NbFe1]9[NcFe1]9ZNcFg1]9VNTFA`0W2@iM50`9?mFh1EiM^9`0kFS2S9mMmFS2S9mMnFS2Q9mMoFS2Q9mMPGR2P9nMPGR2P9nMPGQ2Q9nMPGR2P9nMPGS2o8nMPGR2P9nMPGQ2Q9nMPGS2o8mMQGS2o8mMQGS2o8mMQGS2o8mMQGS2o8lMRGT2n8lMQGU2o8kMQGT2P9mMoFR2R9nMnFR2R9nMnFR2R9nMnFR2R9oMmFP2T9oMmFP2T9oMmFQ2S9oMmFR2R9nMnFQ2S9oMmFR2R9nMmFR2T9nMmFQ2S9oMmFS2Q9nMnFS2Q9mMoFT2P9lMPGU2o8lMPGU2o8kMQGT2P9mMoFT2P9lMPGU2o8jMRGV2n8jMRGV2n8jMRGV2n8jMRGV2n8kMQGU2o8kMQG_O@0020\\2_9SNYGm1g8TNYGDYOn1^9^NZGAZOQ2\\9^NZGAZOP2]9_N[G^OZOQ2\\9`NbG`1^8`NbG_1_8`NbG`1^8aN`G`1`8[N^FHQ1m1a8[N^FHQ1m1a8ZN_FKn0k1c8[N_FIn0l1c8[N_FF0Nn0R2b8[NeGe1[8ZNeGf1\\8ZNdGg1[8XNfGh1Z8XNfGh1Z8XNeGj1Z8VNgGj1X8VNiGl1T8UNkGl1jNhMW9<oGQ2o7oMQHQ2o7PNRHo1m7SNPHn1P8VNlGj1T8VNnGi1Q8WNPHh1P8XNSHe1m7\\NoGg1Q8ZNnGf1R8ZNoGe1Q8\\NoGc1Q8]NnGd1R8\\NnGd1R8\\NmGe1S8[NmGd1T8\\NlGe1S8[NnGd1R8\\NnGd1R8\\NoGb1R8^NoGa1Q8_NPHa1o7_NPHb1P8^NoGd1P8\\NPHd1P8\\NQHc1o7]NQHc1o7\\NSHc1m7]NTHb1l7^NUHa1k7_NUHa1k7_NVHa1i7_NXHk0gNSOP91\\Hj0hNnNo88YHg0lNnNm8;VHe0ROkNk8a0RHd0b8[O`Gb0b8^O]Gc0c8]O]Gc0c8\\O^Gd0b8\\O^Gc0c8]O]Gc0c8]O]Gb0d8^O[G`0h8@XG>j8BVG8P9HQG3S9MRGKQ95WGAk8?P20000000000000000000000O100000000O1O1O1O100O1O10000001O1O1O001O1O2N00001O0000O1O11OO1O1O10000O10_EDT8<hGHnMIV:?hGMQNEV:>eG1VNBS:=fG3VNAS:<_G<]NXOT:<`G;]NYOR:<bG;[NYOS:;bGc0]8^OcG?_8AaG`0^8@bGa0]8_OcGb0\\8^OdGc0[8]OeGc0[8]OeGa0]8_OcG`0^8@bGa0]8_OcG`0^8[OmEIe1l0^8[OgGc0[8]OeGc0[8^OdGc0[8]OeGc0[8]OeGc0[8^OdGc0[8]OeGc0[8^OdGc0[8]OeGc0[8]OeGd0Z8]OeGc0[8]OeGd0Z8\\OfGd0Z8\\OfGe0Y8[OgGe0^NmNM3b9:THR1l7nNSHT1l7mNSHS1m7mNSHS1m7mNSHT1l7oNQHQ1o7POPHm0_NbNa9a0oGn0aN`N`9c0oGl0aNbN_9b0PHl0bNaN^9c0PHm0S8SOmGm0S8TOkGm0U8SOkGm0U8TO`F\\OT1`1\\8TO^F^OV1^1\\8[OcGe0]8]OaGc0_8A]G?c8A\\G`0d8@\\G`0d8@\\G`0d8@\\G`0d8A[G?e8A[G?e8AYGa0g8_OZG`0f8@ZG?g8AYG>h8BYG>f8BZG>f8A_G;a8D`G<`8CaG=_8CaGTOkNP10lNe9P1gG2\\8NjGH^NYOi9o0iGE]8<cGC]8=cGC]8=dGoN]Na0o9?gG@Z8`0fGAY8?gG@Z8`0fG@Z8`0fG@Z8`0fG@Z8`0fG@Z8`0fG@Z8`0fG@Z8`0fGAY8?gGBX8>hGCW8=hGDX8<hGDX8<hGEW8;iGEW8;iGEW8;hGEY8;gGEY8;eGF\\8:eGD\\8<cGE]8;bGF^8", "size": [426, 640]}, "area": 45740.0, "iscrowd": 0, "image_id": 10276, "bbox": [0.0, 0.0, 640.0, 136.0], "category_id": 169, "id": 10012566}, {"segmentation": {"counts": "a0e0m1NdNKe87hHNbNKf85jH0`NLe85jHOaNMd84kHOaNLf84jHO`NLg85iHO`NLf86jHN`NMe84lH0_NKf84kH1^NLg83lH0]NMg83lH0]NMg83lH0]NMg83lH0]NLh84kH0]NKi84lH1ZNIl82nH5XNGj8NTI<TNBj8NUI`0QNBk8LSId0SN_O[;b0dD^O];a0dD]O];c0dD\\O[;e0f0OO1000000TM\\OQIe0o6[OTIb0l6^OSIc0l6^OTIb0l6^OTIb0d6\\OgF3e2<i6AbF3e2:k6C_F4e2:l6C^F3f29m6D]F9MFa2=U7D\\F?\\2MX7EZF56IZ2=V7EZF9c23S77gHJX73kHMU72lHNT71lH0T7OmH3Q7KPI6P7_O]F21Mb2c0P7^O]F12Mb2d0o6^O]F2f2a0m6]O]FOi2d0j6\\OSHO^Of0U1ZOo41]J0^Of0U1ZOP50]J0^Of02YO@1B2Q7NbKh0ZNWOA0A5R7L_Kk0]NUO@0^O7V7I`Kj0\\NVOYOd0j6\\OaKj0\\N:l7kNQHj01<V6jN_J2XOi01<X6jN^J2YOh00<Y6jN^J2YOi0N;\\6kN[J2lNW1<M]6jNZJ3^Ne1j0_O^6jNQJW2AoN^6jN]Ij27ZN]6lNlHZ3h0_MWOXOT70hH]3o0fLPOJg00c63fH]3S1`LX73eG\\3b951C=kNU1L400VOSEWOm:7eEFSOGX;c0eEETOHW;>kEESOMQ;>mEB^:`0U1IXCLj<0YCOg<0\\CNd<1<O0O100OodU31^niL5b:JlD1l00]OOE>R;IlEi0m9]OSFc0^7SOmI<cNc0^7SOlI=]Ni0d7mNbIHaNb06l0b7oNgIF_Na0KU1n7dNhIF_N_2i7kMgIG^N_2m7iMdIJ^N\\2o7kMaIK\\N^2R8gM`IL^N^2Q8gMbId2^6\\MaId2`6\\M[Ii2e6WMTIP3l6PMoHU3Q7kLiHV3TO^Lo7NTH>a0a2_OQMN`0OA3=_7C`H=<e2LmLF63JN12251W7NbH<7_2LXM=KK3O0U7O`H=2a24TMj0MR7Y3PHkLNM8On8W3PGkLJOb9T3gFjLI1`9T3b0WOXFlMj9Q2YFoMg9Z1kE^N31N0e07_9Z1PGfNQ9V1PGlNP9Q1SGoNn8l0WGSOi86hEC06i10_87oGIQ87PHHP88PHHo78RHGl7JkE`0W2@iM5V:=QHEP89a2001O001O000O1001O00O100000000O10O101O0000001OO1O10000000O11O01O00001O001O00O10000001O00010O0001O0O100O20OK]OdCb0\\<^OdCb0[<_OgC>[<BdC>\\<BaC0N?a<9M10N12O00000001O00001O2N4L00000O2O1O0010OO1M3O1N2O10000000000O100001O0O10000000000O100001OO01000O1000000000O1002N000[IFhI5Z65MO30K26NE7;IA<>D]Oa0c0_OYOd0h0\\OSOi0m0WOQOk0o0UOQOk0o0UOQOk0o0VOPOi0Q1VOPOj0o0VOfIGc41mKR1j5VOfIG^47QLl0k5WOdIG[4<ULa0@SO^69bIHU4`0\\L9R6_O]IHP4e0aL2S6GWMAULh0`0IZ6OlL`0jL@Z61dL^OfLS1<[O\\66aLf0SMoN`6;YLj0VMfNf6b0oKm0d4TOYKo0g4QOUKS1k4mNQKX1n4hNmJ[1V5dNeJ@dMc1k7jN^JHiM[1k7jNYJOlMo00dNk7=VJ6nMc0[8TOdI>RN<\\8TO_Ic0WN7]8ROZIk0ZN1]8SOUIP1_N@0\\O]84oHT1fNWOl8DZHY1jNnNQ9IPHa1lNaNY9NfGe1j8\\NRGf1o8[NjFj1Y9VNbFn1]9SN_FP2a9QN\\FR2c9oMoEI5[2l9lMmEL3[2P:8PF`Mo9a2VFXMl9W2oERN^:j1fEnMI3b:k1jEQNE3a:h1nEVNBOa:j1oEVNAO`:[1]EhNn0Mj9c0XE]O88o0I`9b0eEDk0I_9d0dECh;=VD^OP<c0;00L4O1000000000K@`C`0`<41N3M20001O0O2O0N201O000O1O10O3N10M200O4L2M210O00X^f0", "size": [426, 640]}, "area": 13369.0, "iscrowd": 0, "image_id": 10276, "bbox": [0.0, 0.0, 586.0, 242.0], "category_id": 182, "id": 10012567}, {"segmentation": {"counts": "`b<2U=3M3VONZD3f;i0N2N2D<1N2O1O2N1O1N100O100O1O100O100O1O10N1N1301N3M2OO00I7B_h3T1gVL6N3M2N2N3N1N2N3M2N4M4K5K5K5K6J5L4K5K000001O000dNcFkN]9W1SGXNl8k1XGnMg8V2[GeMd8^2R14L4M3XFVMi8m2kF]MT9g2_FdM_9U3N1O20UNoF\\OP9d0\\GPOe8o0hGeNX8Z1TH[Nk7e1n1O001O1O001O001O001O1OO1O1O1O1O100O1O1O1O100001O00001O00001O00001O000010O0001O001O001O001O1O02O1N2O1mERNg8o1UGXNf8i1XG^Nc8c1YGbNd8`1XGaNj8`1SG`Nn8b1nF`NS9a1iF`NY9a1dFTNHHe9V2_FSNm9n1QFQNR:P2jEQNW:`22M2OXFXMP9g2mF_MR9^2mFgMQ9X2mFjMT9U2jFmMX9R30O2N1O1O2O0O2N1O101N:Fb0^OQ2PNRXT1MogkN6K5L2N2M3N2M3N2N1N2O1N2O1O1N100O2On0RO;D2OO1K4J7J5B?L4N13N3L=D2N3L3N1N2O1N2O1N101NF_EnM`:R2aEnM]:l1lESNS:i1RFWNm9f1WFZNh9c1[F^Nd9a1^F_Na9a1_F`N`9`1aF_N_9a1aF]Na9c1`FZNb9g1]FWNf9h1[FVNf9j1m01O5K<C?APOhEWOP:h0XFUOd9k0cFnN]9Q1kFhNT9Y1SG`Nm8_1^11O4N1N3MU1]NQMcFS3W9PMgFR3V9d0M2O1O0000O1ROWG[Mi8_2T1K6J6^M[EY2i:fMXEX2n:N2N3M2N2M3J6J6M201O1O1N101O0O2O000O2N1O2O0A`0M2N3M3M3L4MYcf12g\\YN4L3M3N2M3]CER<>mCFm;=RDBn;`0QD@m;P1N101O1O1O001O1O001O00001O00000001OgDeNc:[1[EjNb:V1\\EnNb:S1\\EoNc:Q1[EROd:j101O10O01O5K000001O00O100O100O100O1O10O0A`00O0100O010O1O001O001N2O001O1O001O1O1O2N2M3K5O1001O6J2N1O01O012M2O0O1N3ZOaDZOd;`0cDZOa;a0h0Ddk8", "size": [426, 640]}, "area": 23621.0, "iscrowd": 0, "image_id": 10276, "bbox": [30.0, 51.0, 588.0, 314.0], "category_id": 183, "id": 10012568}, {"segmentation": {"counts": "[XQ633Ok<8SCIj<>O0002N002M0O23M00O2O0OO3N1O1O1002N101NO11O1ON2O1O1002OO0000000001O00O10000O100O100N21O001O1O01OO10001O000000O2O0M31OO1001O0O11OO02O000001O1O00ON3N3M3O2L3O1O01O0O2mBJn<7RCLj<930N3N00O21N0O100001O1O00O1O10O3N6I2MlBMgb>0Z]A1Q=2nB0o<8L4M3L311NM31O1OOO\\O]Cd0c<21O0O01O10000000003M01OO12N10O1M4N2M12N2NO12NO11O00O1SJ", "size": [427, 640]}, "area": 2449.0, "iscrowd": 0, "image_id": 10281, "bbox": [463.0, 185.0, 177.0, 32.0], "category_id": 96, "id": 10012569}, {"segmentation": {"counts": "e;f1e;00000_N\\N[Gd1e8^NZGa1f8`NYG`1g8aNXG_1h8bNWG^1i8dNUG\\1k8dNVG[1j8eNVG[1^:O00001O001O00001O00001N1O]NdNZGZ1e8iNZGW1_8PObGm0`8SO`Gl0a8TO`Gj0a8WO^Gh0c8XO^Ge0e8ZO[Gd0h:M6J3M3M3M2N2N2N2N2N2OY:6aXOIl<:SCFl<=K500O1VD]Ol:c0SE^On:a0QE@o:`0jD@^O2h;>jDGV;9kDEV;;jDEV;;hDGX;9iDFW;:eDI\\;7cDJ];6`DM`;4_DL^;9_DGa;=\\DCd;>[DBe;`0XDAg;Q1L50O10000O100O2N010O1O1000000O01000O10;E5K0001@UO^Dl0a;a0O0O2O4K1O001O001O0O36MM00O_OYNZEj1e:`0aEcMm9]2PFeMQ:[2mEfMT:Z2kEfMU:^2fEcMZ:g200000010O01O000000000001O001O000O1O101M201O1N2O1O0001lLoEl2R:RMQFl2X:O1O00O100001O00000000O2N10000O2O001O00N10_FRMg8m2ZGTMe8l2[GVMc8j2]GWMb8i2^GYM`8h2_GYM`8g2`GZM_8a1hFZOi0WO]8^1mFZOe0YO]8]1PGYOa0\\O^8Z1UGXO<@]8X1YGWO8C^8U1[GXO6E]8S1]GZO4E\\8S1`GYO2F]8P1aG]OMGa8l0bG_OGJf8f0dGg0Z8YOeGk0Y8SOhGm0X8SOgGn0Y8QOhGo0X8POiGP1W8POhGQ1X8nNiGR1V8nNjGS1V8lNkGT1T8lNmGT1S8kNnGU1Q8kNPHU1P8jNPHW1o7jNQHV1o7iNRHW1n7dMcGe0`0g1m7`MhGg0<i1l7_MjGf0;k1k7_MjGe0<l1h7`MnGb0;n1f7aMoG`0;P2f7`MoG`0;P2e7aMPH><Q2c7aMRH=<R2b7aMRH<=S2`7cMRH9?T2_7dMQH7a0U2]7eMRH5b0V2\\7eMRH5b0V2[7fMRH4c0W2\\7dMPH6d0V2\\7dMPH5e0W2[7dMPH4f0X2Z7dMQH3e0Y2[7YMeG7;7f0Y2Z7YMeG7;7f0Y2\\7`MeGI5=k0Z2[7`MdGJ6<j0[2\\7bMjG3k0[2[7bMiG3m0[2Z7dMfG2P1Z2Z7dMcG4T1X2Y7VNgHj1Y7UNhHk1X7TNiHl1X7SNhHm1X7RNhHo1X7QNhHo1X7dMhGGQ1e2W7cMkGEo0h2V7aMnGFl0i2V7aMXI_2h6`MYI`2g6`MYI`2g6_MYIb2h6]MXIc2h6]MXIc2h6]MQHIe0j2Z7]MQHJd0i2Z7]MSHKb0h2Y7_MVHI`0h2Z7^MWHK>g2R7VMSH9=K=f2R7ZMPH6a0K<e2R7iMbHC;d2S7iMbHD:c2S7iMdHE8b2T7iMeHE6b2T7iMgHF4a2U7iMgHG3`2V7iMgHH2_2V7jMhHH1^2W7jMhHI0]2Z7gMgHK0^2Z7fMgHKO_2[7dMgHMN_2]7bMeHNO`2]7aMdHOO`2^7`McHO0a2]7`McHO0a2^7_MbHO1b2]7^McH00b2]7]MeH0Nc2]7]MeHOOd2\\7]MeHOOd2\\7\\MgHNNf2[7\\MgHNNf2Z7]MgHNOe2Y7^MhHL0f2X7^MiHKOg2W7_MjHI0h2V7_MkHHOi2U7_MmHGOj2T7_MmHGOj2T7_MmHGOj2U7^MmIb2S6^MmIb2S6^M[HHQ1j2d6^M[HHQ1j2c6^M]HHP1j2d6]M[HJQ1i2d6]M\\HIQ1i2c6^M\\HIQ1i2c6^M\\HIQ1i2c6^M[HKQ1g2d6^M[HKQ1g2d6]M\\HLP1g2c6^M\\HLQ1f2b6^M^HKQ1g2a6^MSJb2l5_MTJa2[800000SOXMWGh2h8YMZGe2f8ZM[Gf2e8ZM]Gd2b8]M_Gb2a8^M`Ga2`8_M`Ga2_8`MaG`2_8`MbG_2^8aMbG_2]8bMcG^2]8bMdG]2\\8cMeG\\2S7`MnH41[2P7bMoH31[2X8eMiGZ2V8lMfGS2Z8QNbGo1^8UN^Gk1a8XN]Gh1c8XN]Gh1c8XN\\Gi1c8XN]Gh1c8XN]Gh1c8XN\\Gi1k6cMSJd0ROi1f5aM_Lf0jMj1f5aM`Le0jMj1f5aMjJ2RONo0d0_Ok1l6PNfI5_Oj1i6SNhI3_Oj1i6RNjI2_Ok1j6PNgI5Ai1k6nMeI9Bg1Q7gM_Ib0Bd1X5jM^KOHb0Dd1W5mMiL?QNc1V5RNTKJ3`0Cd1V5SNkJ0=9Bd1W5RNeJ6b04Ae1X5QNeJ5c05_Of1Y5PNfJ3c07_Oe1X5QNfJ3c07_Oe1X5QNgJ1c09]Of1Y5PNhJ0b09^Og1X5QNgJNd0:]Og1X5PNiJNb0;]Og1X5PNiJMc0<jNBOU2k5PNjJKc0>hNDOS2l5PNkJJb0?hNEIW2R6kMkJIc0`0gNEC]2W6gMjJHe0?gNZ2j5_MhJIh0>fNZ2k5^MfJKi0=fNZ2k5^MdJLl0;gNZ2i5_MbJMo0:fNZ2i5_MaJNP19fNZ2i5_MaJMR19dN[2i5_MaJMR19dN[2j5^M_JNT19cN[2j5_M^JK[OMg1>fN[2j5_M^JK^OMa1>iN[2j5^M_JKANZ1>lN[2j5^M^JJGOR1>oN[2j5^M^JJJOl0>RO[2j5_M]JHO0e0>UO[2j5_M]JH20?>XO[2j5_M\\JH708?[OY2j5`M\\JH904?]OY2i5aM]JG^1?]NA_Oe2X6eMbKFVO?DU2d5fMbKH_O2[O`2e5eMaKK7`2X4eMaKM5^2Z4eMaKO4[2[4gM`KN5[2\\4fM^KO8Z2Z4gM^KN9[2Y4fM_KN9\\2X4gM^KL;]2W4gM^KK=]2U4hM^KJ>^2T4hM^KJ>^2T4iM\\KIa0^2S4hM\\KJb0^2R4hM]KHb0`2Q4hM]KHa0a2R4gM]KGa0c2Q4gMhMY2X2hM\\KEc0d2Q4gMeMZ2\\2eMcM\\2]2dMcM\\2]2dMbM]2^2cMaM^2`2aM_M`2a2`M_M`2a2`M^Ma2b2`M]M`2c2`M]M`2c2_M]JGW2j2\\3_M\\JIW2h2]3_M]JJ\\OMb2j2e3YMeI1h05n1a2k3ZMWJ4n1b2l3YMVJ5n1b2l3YMWJ3o1c2j3YMXJ4n1c2k3WMYJ4m1e2g700000000000jKWMjMi2W2WMhMi2]60000000000000000000000000000000dMeMhI[2]4WM_K`04X2]4\\M[K<8X2]4`MWK7=Y2\\4cMTKEiN6h1b2[4fM[KE;e2Y4gM]KC:f2Y4gM^KA:h2W4hMaMX2_2hMaMX2_2iM`MW2`2iM`MW2a2iM^MW2b2iM^MW2b2iM^MW2b2iM^MW2c2hM]MX2a2jM_MV2c2hM^MW2b2iM^MW2c2hM]MX2d2gM\\MY2e2fM[MZ2d2gM\\MY2d2gM]MX2c2hM]MX2c2hM]MX2e2fM[MZ2e2gMZMY2e2hM[MX2d2iM]MV2c2jM]MV2c2jM]MV2c2jM]MV2c2jM]MV2d2iM]MV2a2lM_MT2a2\\MgI=h3W2a2\\MhI<g3X2f2gMZMY2f2gMZMY2f2gM[MX2e2hM[MX2e2iMZMW2e2jM[MV2c2lM]MT2f2iM[MV2e2jM[MV2d2kM\\MU2d2kM\\MU2c2mM\\MS2e2lM[MT2e2lM\\MS2d2mM\\MS2d2mM[MT2e2lM[MT2e2lM[MT2f2kMZMU2f2kMYMV2g2bMfILc3b2g2aMhIM`3b2h2`MjIM^3c2h2_MdMa2\\2_McMb2]2^McMb2]2^McMb2^2]MkIOZ3d2k2]MkIOY3e2l2\\MkIOY3e2l2]MiIO[3d2l2^MgIO]3c2l2`McI0`3`2m2jMTMU2l2jMUMV2k2jMVMU2j2kMVMU2j2kMWMT2i2lMWMT2i2lMXMS2h2mMXMS2h2mMYMR2g2oMXMQ2h2oMYMP2g2PNgLAYM_2P6PNgLAZM^2o5RNYMn1h2QNYMn1f2SNgL@ZM]2o5SNgL@[M\\2n5SN\\Mm1d2TN\\Mk1d2YNaI^Ob3Y2m2_NSM`1m2aNRM_1o2aNQM^1o2cNPM]1P3dNoL\\1Q3cNoL^1Q3bNoL^1Q3aNoL`1Q3_NeLWORMZ2X6_NgLZOkLZ2_6ZNgLn1X3PNkLP2V3nMkLR2U3mMkLT2U3kMlLU2T3jMkLX2U3eMiL`2W3_MgLd2Y3[MgLf2V3]MiLd2U3[MmLf2Q3[MPMb2S3\\MSM^2m2dM]JAj1i2k3eM[JBk1d2[M\\Mb6>oLU2U3iMlLW2T3iMlLV2V3_MXI7b3X2X3aMYI4`3Y2\\3iMdLV2^3jMaLU2b3iM^LW2b3jM]LV2b3lM]LT2e3kMZLR2i3PNULo1l3WNnKf1U4\\NiK`1[4cNbK\\1_4cNaK_1^4aNbK_1^4_NdKb1[4]NeKd1[4\\NeKc1[4]NfKb1[4^NdKa1]4`NcK`1]4`NcK_1]4cNkH]OX2Q2m4bNjH^OY2o1m4eNhH\\O\\2m1n4XNbH32I]2l1o4XNdH4LIb2j1o4XNeH<\\2\\1o4WNgH<Y2^1o4WNhH;Y2^1n4XNiH9Z2_1n4WNhH9Z2b1n4TNhH:Z2b1o4SNgH9\\2d1n4RNfH9\\2g1l4RNfH6a2g1h4UNeH5c2f1h4WNaH4h2e1f4iN[KW1d4hN]KX1b4iN^KW1b4iN]Ka0TM[O^74^Ka0UMZO]75^K?XMXO^78ZK>[MTO_7?VK<]MnNc7f0QK:_5G`J8a5H_J7b5I^J6d5I\\J6e5[OcGNh2f0f5YOhGNc2f0g5\\OfGNc2e0f5BeGGf2f0i5@^GMi2c0i5MXJ1j5OVJ0l5OTJ0n5OSJN^O]OR4e0_LM@^OQ4d0`LM_O@S4a0^LN_OBP4b0bLK^ODQ4`0cK]OiN>b1GS4=bK_OhN<c1IR4=cK@gN8d1LR4:eKCdN6e1NR49fKDaN4f11S46hKE^N3h12Q47iKF\\N0l12o37kKHXNNo13n3LVL4mMLo14n3KWL7iMKR23n3JXL:fMIT23n3IYL<cMIV22n3IYL>`MF[23h69kF_OJOf29f69hF_Ob2O[M9]99cF^Og2<[4DWKd0>H[4FVK`0`0JY4GYK<?MW4H[K8`00n3DYI6Z25?1l32dKLa02k32eKJa04j33hIAZ18T14j34gI@[15WNKm2<j35dIA^13WNKm2<j37`IAb10X18f3f0QKQOZ19e3f0QKPO[1:d3g0kInN>2S29d3h0jISO^OJg02^29", "size": [427, 640]}, "area": 55829.0, "iscrowd": 0, "image_id": 10281, "bbox": [0.0, 200.0, 640.0, 227.0], "category_id": 154, "id": 10012570}, {"segmentation": {"counts": "R6R2Y;0000000000000000O1000000000010O0000001O01O1O0O2O1O1O1O001O1O100O001O1O100O1O1O100O1O1O1O1O1O2N0000O2O10O01O00001O2N1O1O2N1O2K4N2O2N1N2J7N1O1O1O1O1O100N3N1O1O1O1O1O1N2O1kNmCP1f<A3DkV6KeiIO10O130oQ21kmM3M3M2N4L3MO1O1N6K2M5L`_5Oa`J3jB0l<2RC1l<8M3N11N1000010O001O001N1O2OO10OoMHQG8k8LUG4k8LTG5l8KSG6m8JQG7P9IoF8Q9HoF8Q9IoF6Q9JPG5P9LPG3P9NPG1P90PGOP92`EDU19[95\\EDZ16[9=gFBY9=iFBX9=iFBW9:nFDT92VGKQ\\a01ma^OOS=7N2M6K1N10O010O1O1ObCBQ<=oCED1W<:SDCF8V<4TD1j;OUD2l;LUD5j;KUD6k;IVD6k;JUD6k;IUD;i;DVD=j;BWD=i;DVD5HIR<1WD5IHQ<3UD5S<LmC4T<KkC3X<MhCO\\<1cCO^<1bCO^<1bCO^<1aC0_<ObC1^<OcC0^<NdC1\\<NeC2[<MgC2Y<MhC3h<101N1O1N100O2O0N1O3M2O001O1O1dC]On;f0PD[Ol;i0RDYOn;g0PD[OP<e0PD\\Oo;e0PD\\Oo;d0QD\\OP<a0RD^On;c0RD[OQ<o00O100O10000000000O01001O00000YOTDGV<6>06Ko_23o_M001O0000000^CM_ONZ<5UD3j;MVD3k;LUD5k;JTD2BNY<1UDLG3T<0^D1`;O`D2_;McD4\\;KdD6\\;IS10fk01P_21mdL;01N101O10O01O001O1O00O1N2O2M2N2O10O10O2O00000O1000000O101O01O001O0O200O0000000000K5O1N3O_U40`jK2O2O2M1O00O11O001O0010O010N2Ihb>0^]A1O1O2N001O1O1O2N1O001O1O2N10O0K5O1N200O100O1000000001O00100O1O2N6J001O10O0000000IWCG02k<NgS20glM001ag3O_XL001O001O1O1O2O0OO10001O0001OLlB1QZd1", "size": [427, 640]}, "area": 9616.0, "iscrowd": 0, "image_id": 10281, "bbox": [0.0, 194.0, 515.0, 91.0], "category_id": 155, "id": 10012571}, {"segmentation": {"counts": "0R6Y70000000000000000000000000000001O0000001O000000O1000000000000001O000000001O0000001OYNRJQLn5k3WJTLi5h3\\JWLd5f3aJXL_5d3fJ[LZ5a3lJ]LT5a3oJ^LQ5`3RK_Ln4_3VK_Lj4`3XK`Lg4_3[KaLd4^3_K`La4`3`K`L_4_3dK_L\\4`3h200O1O10XGcLb7\\3[HiLd7V3YHoLf7Q3UHVMi7i2TH\\Mk7c2SHbM:UOa6Y3SIeM6WOg6S3SIhM0ZOm6m2RImMKZOS7j2oHjNQ7V1nHkNR7U1mHlNS7U1kHmNT7T1jHmNV7S1iHnNW7S1gHnNY7S1eHnN[7S1cHnN]7`30000000001O001O001O001O001O1O1O001O1O2N1OHUIRJi6m5YISJf6l5\\IUJb6j5`IVJ_6i5dIUJ\\6k5b0001O001O001O3M3M0000000000000000000000M3M3O100001O00001O00001O001iHnIS7U6000cJTI[4l6dK\\IU4d6jK]IV4c6jK]IV4c6jK^IU4b6lK]IT4c6lK]IT4c6lK]IT4c6lK]IT4c6lK]IT4c6lK\\IU4d6lKZIU4f6kKYIV4g6W1000001O00000000000000000000000000001O001O001O000000000000O10000O1O100J600O100O11O000000001O000000001O1O0000O100O11O2N4L1O1O1O001OO100O100O100O1000000000000001O000000001O00O10000O11O0000UOoH\\KQ7d4PI[KP7e4PI[KP7e4PIdJ1b0o6k4TIUKl6k4QIYKn6b50000000O1N2N2O1N2O1O1O1O1O1C=N2O1O1N20lHhJU6X5bIQK^6o4\\IWKd6i4XI[Kh6g4SI\\Km6b5OO11O0000000000QOSI`Km6_4VI_Kj6a4QIdKo6\\4QIdKo6\\4QIcKP7^4oHbKQ7]5O0000002NN2N200O1001O001O1O00000000000000001O1O001O00001O00FVIRJk6n5900001O00001O2N1O00001O0000000000000000000000O1001O00000000001O000000000000000000O1O1001O0000001O0000000000000000000000O100001O0000001O000000000000000000N2O1000000000000002N001OJ6J6O1O100O10000O11O000000002N4L4L3M00000000O1YNQIRMo6n2PIRMQ7m2PISMP7m2oHTMQ7k2PIUMP7k2oHWMP7i2PIWMP7i2PIWMP7i2PIWMP7i2PIXMo6h2QIlKOl0S7Y3nHiK0m0S7Z3nHgK0m0T7]3mHaK0R1U7_3oHbLQ7`3kHbLU7g4300002NO1O10000001O0000001ON2000000002N002NO10000001O00N2O1O1O1002N1O2NO11O1ON2O1O1002N00000000001O00O10000O100O100N21O001O1O000000001O000000O100M31OO1001O000000O10000001O1O00O1001O1O2N1O1O0000O1O1O11OO100001O00O100001O1O00O1O100MiHRJW7o52N2O1dNeHhL[7W3gHgLZ7X3fHiLZ7W3fHiLZ7W3eHkLZ7U3fHlLY7T3gHkLZ7U3gHhL[7X3fHgLZ7Z3nH_LP7a3QI_Ln6b3QI\\LQ7e3lH_LR7d3jH\\LW7l401O0000O1O1000000cMbHlN_7S1bHmN^7R1cHnN]7Q1dHmN^7S1bHlN_7S1bHnN]7Q1dHoN\\7Q1dHnN]7R1cHPO[7P1eHPO[7P1eHPO[7P1eHROY7n0hHnN[7R1eH_Nj7b1UH]Nl7c1TH]Nl7c1UH\\Nk7e1TH[Nl7e1TH[Nl7e1UHZNk7f1UHZNk7g1THlMNPOn7T3UHjMMSOn7S3UHiMMUOn7R3VHgMLXOn7R3UHeMNYOm7S3THcMO[Om7S3TH`MO^Om7S3SH^M0@m7S3THZMODm7S3UHVMNIl7R3WHRMMMl7R3XHnLM0k7S3oHnLQ7T3lHmLT7T3jHmLV7T3hHmLX7U3eHlL[7U3cHlL]7U3]HPMc7\\400000000000000000", "size": [427, 640]}, "area": 121829.0, "iscrowd": 0, "image_id": 10281, "bbox": [0.0, 0.0, 640.0, 207.0], "category_id": 157, "id": 10012572}, {"segmentation": {"counts": "`mh51Y=3L2N3O1O1L210002NO3M10O1O101O0000010M0MTCKl<0[YY10gYfN0V=8H3UCDd<b000O0100O2O1OO0O1111O00M31M00101N100O01O10001OO010000N2O20O00O12L23MJZCD1Og<8[CHf<9H<2O2M31O00000000NYC@g<`021O00O1O1000001O000001OO12N1O00N200O12N2NO12NO11O00OgJ", "size": [427, 640]}, "area": 1527.0, "iscrowd": 0, "image_id": 10281, "bbox": [443.0, 169.0, 197.0, 39.0], "category_id": 169, "id": 10012573}, {"segmentation": {"counts": "T8a3j9lM\\Fa0d9_OaF<_9DfF7Z9LhFOX93hFLW96hFIX99gFFY9:hFEW9<kFBU9?jFBU9>kFBU9[2O00010O001O000010O1O01hG[Li6d3VI]Lk6R1iGG0i0Z1_Nn6o0lGDOm0W1`No6m0PHZO2Z1l0aNR7k0fHd07bNT7i0eHf05bNW7h0dHe04dNY7f0cHg01eN]7e0`Hf01gN_7e0^He00hNc7d0\\Hg0KgNj7e0WHe0LhNn7f0RHc0MiNR8h0iGc02gNV8j0_Gc09eNY8l2eGVM\\8i2bGYM_8f2_G\\MZ49bNZ2RM_MX4<fN9QM40XOU4`0jN1SMZ1P4jNlNeNZNa2f2oNPO]N\\Nd2`2UOTOTN\\Nh2^2WOf0i0XOZOg0f0WO^Og0b0XO@g0`0XOBg0>XOEf0;ZOFe0:ZOIc08\\OJc06]OKb05]ONa02^O0`01@UNcLe0l3V1@RNiLi0e3U1AoMoLl0_3U1BjMVMQ1U3V1DfM\\MT1o2V1DdMbM:UOKd3W2EbMeM6WO3]3U2FbMhM0ZO:V3U2G`MmMKZOa0Q3T2I]MjNa0Z1S2L[MkNc0X1R2MZMlNe0V1Q2OXMmNg0R1R22UMmNj0P1Q23TMnNl0m0Q26QMnNo0j0Q29nLnNR1h0P2;lLnNV1d0o1l2RNRMo1n2RNQMn1o2SNPMm1P3TNoLl1Q3UNmLl1T3TNkLl1U3UNjLk1W3UNgLl1Y3UNfLk1[3UNRKoN3l2k4VNQJ3o0g1Q5HoJ8Q5HPK7Q5SOZJnMf0n2Q5kNnKS1S4hNRLW1n3gNVLW1k3gN[LT1f3kN[LT1g3jNYLV1h3iNYLV1_3\\MRJ]1^2W1_3^MSJ[1^2V1^3aMUJY1[2V1_3cMVJ[1X2P1a3hMWJk1h18Q4QNTJh1k15Q4UNTJO1P1i1k0S4VNTJk0;iNa1U2P4\\NTJHO3h2JXM\\1n5CSJSOk2KVM_1l5BSJUOl2HVMa1l5@RJWOn2GTMb1o5]OnI[OS3AQMg1Q6XOmI_O\\3Y1j2TOjID\\3X1m2POgIH]3X1o2lNeIK]3Y1P3iNdIM^3Y1R3eNaI1^3Z1T3aN`I3]3\\1U3_N^I5]3\\1h3bNYL^1g3bNYL^1g3bNYL^1g3cNXL]1i3cNWL\\1g3gNXLY1g3hNZLW1f3jNYLV1g3kNXLU1f3nNYLR1b3XNmHi0a3o0b3TO^Lk0a3WO^Li0b3WO_Lh0b3WO_Lh0a3^NoHc0a3n0i3POXLo0i3POWLP1j3oNVLQ1j3POULP1UMeNd5;WMP1TMmN^53^MP1SMnN_53]Mo0TMnN^54^Mn0TMoN]53_Mn0UMnN\\54_Mn0UMnN\\55^Mm0VMnN\\55^Mm0VMnN\\55^Mm0VMnN\\55^Mm0VMmN]56]Mm0WMkN]59[Ml0XMjN_59ZMl0m3TOSLl0m3TOSLl0n3SORLm0n3SOSLl0n3SORLm0n3SOSLl0m3UORLk0o3TOoKn0Q4ROnK5aLOa7LmK6cLMa7MkK6fLK_70jK5hLJ`70gK6kLH`71eK6lLHa71bK7nLGc70YJ@oNi0WOEb72VJCPOe0YOEa74UJDPOa0\\OF`75RJHPO<@E^78QJIPO8CF]79nIJRO6EF[7;lIIVO4EGZ7<jIIXO2FHX7>hIH]OMGLT7?hIH_OGJ1o6`0gIIg0Eb5b0gIHk89SGHm88SGGn89QGHo88PGIk7EcGb0b0Hh7KeG=b0Ig7LgG:b0Ja73mG3a0KU7H]G:o02?MU77aHL9NU73fHN50U7NjH200W7NiH111V71]GAQ1=;2X78\\HE<4Y7<UH@a05c71PG[Oj0>c07d72iGGb08e73gGDd0:e75dGAf0:i75^GAj0:f78_G^Oj0;b7=cGWOk0=`7?dGTOk0>`7`0dGQOl0`0_7`0eGPOk0a0`79XGmN=8k0c0_78YGSO82P1c0_78YGUO6OR1d0_7i0]GcNT1d0`7j0ZGaNV1f0b75UG^O1GW1g0l7KlFF2GW1h0k7KlFE3HU1i0l7<oFkNU1i0m7:PGkNS1l0o7_OkF24CR1k0Q8^OiF45BQ1m0S8ZOgF76Bo0n0V8VOeF;7@n0o0[8PO`Fa08_Om0Q1`8AdF^Ol0Q1]8_OlF_Og0S1[8_OPG]Od0U1\\8]OQG]Od0V1Z8]OSG]Ob0V1[8TO_FLf0J`0V1Z8TObFIm1T1`7SOeFFl1X1_7ROgHn0X7ROhHo0X7POiHP1V7QOiHP1W7oNkHo0U7ROlHm0T7SOmHl0R7TOPIk0P7TORIk0m6VOTIi0l6VOVIi0i6XOXIg0h6XOZIg0f6YO[If0e6YO]If0c6ZO^Ie0b6ZO`Ie0^5TOhH7k1d0]5VOgH6m1c0\\5WOgH5o1c0Z5XOiG1h05V2a0Z5]O`H3V2?Z5[ObH8T2<X6EgI<X6FgI:X6GhI9W6HhI9W6IhI7V6KiI6U6LkI4T6LlI5R6MnI3Q6MPJ3o5NPJ3o5LSJ4l5KUJ6j5IXJ7[55fJKX56hJKW55jJKU55kJLS54oJLQ5NTK3l4D]K<c4^OcKb0]4ZOfKg0\\4TOgKl0[8100O100M4M2O1N2VLhN`KY1_4jN]KX1c4cNaK^1_4bN_K`1a4`N]Kb1d4]N[Kd1U81O100jMcN]Ha0WN0[9B[H>]NNV9HZH;aNLl8^OPG>`19fNJi8d0QHBXOJe8c0UHCXOIb8a0XHGWOHa8:UGUOP1i0]OG]8:[GSOi0m0AD\\89^H4WOC`82YH;YOC^8O[H>WOD]8L]Ha0VOD]8F`Hf0TOE^8D[Hh0WODa8CUHj0YOEe8@oGj0]OGS::mEFR:;nEER::oEFm8YO]GR1FEi8^O`Gm0GDg8C`Gi0JDc8FcGf0JD`8JdGc0LB]8OgG?KC[8c0WGK>An7\\OdGV13N;@n7B]Gn0=17^OP8i0kGJ5]OP8e0oGN1]OQ8f0kGO3ZOS8j0gGL6ZOS8m0cGJ:XOU8U1XGEb0VOZ8R1SGHb0VO_8^2aGbMc8i0lF3`0SOi8>nF`09ROm82RGl01QOR9DWG\\1GPOR:Q1nEnNS:R1mEnNP9A^Gb1BmNP9B]Ga1BmNQ9C\\Ga1ClNQ9C\\Ga1CkNR9DZGb1DjNR9B[Ge1ChNS:X1mEhNR:Y1nEgNR:Y1nEgNQ:Z1oEfNQ:Z1oEfNP:[1oEgNo9Z1QFfNo9Z1QFfNn9\\1QFdNo9\\1QFdNn9]1RFcNQ:Z1oEfNT:W1lEiNW:T1iElNZ:Q1fEoN]:n0cERO`:k0`EUOc:h0]EXOf:e0[E[Of:c0ZE]Og:b0YE_Oh:?XEAj:=VEDk::UEFm:8SEIn:5SEKl:5TEKk:6UEKi:5XEKg:6YEKe:6[EJd:7\\EIc:8^EGb:9^EH`:9`EG_::aEF^::cEF]::cEE]:<cEC]:>cEB]:>dE@\\:a0dE^O\\:c0dE\\O]:c0dE]O[:d0eE[O[:f0eEYO\\:g0dEXO\\:i0eEVOZ:j0gEUOZ:k0fEUOY:l0gETOR9IYGS1DUOR9IZGQ1CXOQ9J[Gn0CYOf8BQG6f0o0A[Od8ETG2f0m0B]Ob8GUGOh0k0@Ab8FPGG18m0h0@Ea8DPG6d0l0I\\Oc8CnF7?o00XOc87UGf07UOd86nFj0>QOd8P2\\GQNd8o1ZGSNf8h1nF\\M<m0h8f1YG\\Nh8c1WG^Nj8`1UGbNd8e1[G\\N_8j1_GXN`8i1_GXN`8i1^GYNb8f1^G[Na8f1^G[Nb8e1\\G]Nd8c1[G^Nd8U31O000000002N4L4^M`G1d8H`G8c8]OeGb0_8UOgGj0\\8POiGn0[8kNiGT1[8dNjG[1j5\\NlLc1T3\\NoLb1P3_NQM`1o2_NSM`1l2aN`LOjL_1f6aN`L0lL^1d6bN^L2PMZ1b6dN\\L3TMX1`6eNZL5WMU1_6fNXL6[MS1]6gNSIOg28jMQ1]6hNPI0g28oMo0Y6jNoH0g29RNl0Y6lNiH0l29TNj0Y6QOcK6UNh0Z6nNcK9VNg0[9ZOdFg0[9YOfFh0Y9YOeFh0]9XOaFh0`9YO^Fg0c9[OYFf0i9\\OSFd0n9_OmEc0S:@iE`0Y:AcE`0^:C^E=d:EWE<j:CVE>j:AUE`0m:^OSEb0o:[OQEf0Q;XOoDh0S;UOnDk0T;SOkDo0V;oNjDQ1X;lNhDU1Z;iNfDW1\\;fNdD[1[;fNeD[1Z;eNeD\\1Z;;00O10\\NgDV1X;lNhDT1W;lNjDS1U;oNkDo0V;QOkDn0T;SOmDl0S;SOoDl0Q;SOQEk0P;TOQEl0o:ROTEm0l:ROVEm0k:QOWEn0i:QOYEm0h:ROZEm0f:QO]En0c:QO_En0a:RO`El0a:UO_Ej0a:XO^Eg0b:ZO^Ee0b:\\O^Ec0b:^O^Eb0a:_O_E`0a:A_E?`:C_E<a:F_E9`:J_E4b:M]E3b:0]ENc:5\\EJc:8\\EGd:9^EFa::aED_:<aEE^:;]ETOBa0Q;;]ETOBb0P;;]ESODa0]9_ObF0X1m0UOSODb0Y9FQHb0ROVODb0Z9ETH?mN[OEb0]9^OWHa0gN_OEb0`;GkDGFb0g;^OcD0Fb0Y<^OgCc0W<^OiCb0W<]OjCc0V<]OlC`0U<@mC>S<BPD:P<GSD6m;JVD0m;0g0000O100000000O100000005J100000000O101O00000O1000000O100O1O00100O001O10O01O1O0oJ_OZMa0e2AYM`0f2@ZM`0g2@XMa0h2^OWMd0i2\\OVMd0k2\\OTMe0l2\\OQMf0o2[OoLe0S3CaL`0_3A\\Lc0e3\\OWLg0k3VOSLn0P4nNoKT1S81O110O0001O00001OfJfNcN[1[1fNeNZ1Z1gNgNX1X1iNhNW1X1iNhNW1W1jNhL0dNU1c4lNfL5dNP1e4kNgL5eNo0d4lNjL3aNQ1e4lNjL0eNS1a4mNoNR1Q1nNQOP1o0QOQOn0o0ROSOm0m0ROTOm0l0SOUOl0k0UOVOi0k0VOVOi0j0WOXOg0h0ZOXOe0h0[OZOd0f0[OiJNU3f0R2]OgJMZ3d0o1_OfJM]3c0m1AdJLb3a0k1BbJNe3>j1C`JOh3>h1C^JOm3;g1E[J0Q49e1HWJOV47e1KRJN\\44d1OmIMb42b12iIMf4Oc1k0`NROc1k0`5N2N3M2N2JZd3", "size": [427, 640]}, "area": 46829.0, "iscrowd": 0, "image_id": 10281, "bbox": [0.0, 23.0, 631.0, 404.0], "category_id": 183, "id": 10012574}, {"segmentation": {"counts": "YfU83ic08L2M1O1O2O1O0O2O1O1N2O4L4L101N100OO11N1001ON3O0O1001O0003W]OiNab0b1K1N2N3M00O100010M2000000O10On]OVNO0oa0j1R^O\\N0K1Oca0Y21O2M100001O001N10000O2O0o^OjMX`0V2Z_OXNf`0a2000000PB", "size": [640, 482]}, "area": 3211.0, "iscrowd": 0, "image_id": 10290, "bbox": [418.0, 445.0, 64.0, 103.0], "category_id": 107, "id": 10012575}, {"segmentation": {"counts": "RV:5hc04e]ONb`07R_O4j`00U_O2k?EV_OI9l0c0Gh?2]_O?OTO7a0[`0a1`_OdNV>ZObBL1W2WOcNP>IUBQ3LTMb1XOb:a0RDT4]1YKa:e0PDV4[1WKd:f6[D_Ie;g7F`0Ac0]Ok0bNQFfFP;g8h0_O`0^Of0YO8IOYJSC]4m<dK[CS4b<lJoB[15h3k<]LTCd3k<]LUCc3k<]LUCc3k<^LSCc3m<]LTCb3m<]LSCd3l<\\LTCg3i<YLWCe3l<QKPCo03Q4m<mJPCK3U10T4m<kJVCP1MW4k<iJZCo0KX4k<hJ]Cm0G[4n<gJ[Co0FV4T=jJWCR1DR4U=lJXCY6W=01oJ[Be3R?NH7I9N10O11O00000000000000000000002NN22N0000O12N00O100O11OO12N1O0000M300000000000000O1001OO1O1O10000003M1O2NN2I71OO11O00O2O01O0000O1002\\ElJmKK30M2V7OaKc5Q1cJXL1m6ITI000f2c5P1oJV3^OSI6e2^5P1oJcNb0T1SOnNg5\\1fJ\\LZO:^4EdK_10aN9l1Q6i1>lLWI^O;c1Y6P2]1eMmNS2d9L3O]_OjL_`0\\30N6H50OON^O]_OmMa`0o1e_OQN[`0g1P@nMXOOn`0j1V1J6Gf]OeNab0W16K7H6J6K\\V^6", "size": [640, 482]}, "area": 48768.0, "iscrowd": 0, "image_id": 10290, "bbox": [16.0, 46.0, 136.0, 488.0], "category_id": 109, "id": 10012576}, {"segmentation": {"counts": "0RV:5f]F4e]ONm;n0VFYOaM4S;FiDR1[2XOhM2S;DjDR1\\1oNhMI9l0c0GU;DjDQ1X1]OnM?OTO7a0h;^OkDR1Y1Q1oMdNk;D`Do0=cN1W2WOcNk;KVDT14k1LTMb1XO\\8e0QFT17l2]1YK\\8j2UFQ2[1WK_8k8`FZG`9l9G?Ac0]Ok0UOb0^Oc0]Ob0^OQ1oN=aL\\Ah0b>UNRA7n0U1[?^NPAT1Xa0Fb0^O<D^S6IilI010h\\Z8", "size": [640, 482]}, "area": 18395.0, "iscrowd": 0, "image_id": 10290, "bbox": [0.0, 0.0, 55.0, 640.0], "category_id": 112, "id": 10012577}, {"segmentation": {"counts": "bUV5>ac03M2O00O10O1O10000O100O0BBU]OK4c0gb0?0000O10000h^OoNa?R1U@mNlN5l`0m0W@Ba?b0P@LS`03b_O6^`0MZ_OfNK`1k`0LW_O7i`0`1000000001OO100000000000000000\\MU_OS2k`0lMZ_OP2f`0oM__Om1a`0SNb_Oj1^`0UNg_Og1Z`0WNk_Oe1V`0ZNl_Od1U`0ZNn_Od1S`0ZNP@d1Q`0[NR@b1o?\\NT@b1m?\\NV@b1Va0N2N3M2N2N1O00000000000000000000000000000000000000000000000000001O0000000000000000000000000000YNlNb@T1^?]OQ@c0o?O__O1``0h10000O10000O1000000O1O100000000001OKRMW_Oo2i`040000O1O1O100O1O100O10000O11O000000001O1ON24L4L6J6J4L1OO^Oe^OaNWa0[1k^OXNN212Va0d1m^OVN74l`0f1Z_O\\Nj`0`1R_OdNo`0[1n^OhNPa0Z1m^OiNQa0Y1m^OiNUa0U1n^OhNPa0Z1k00O1O13M4WNS^OW1\\b0O1OO13M001OOWO^]O4dTl0", "size": [640, 482]}, "area": 11381.0, "iscrowd": 0, "image_id": 10290, "bbox": [265.0, 531.0, 172.0, 109.0], "category_id": 117, "id": 10012578}, {"segmentation": {"counts": "]Qg06jc0?A>B:H`0A9F6G1O1003MO1O]Of^ObNYa0V1Q_OiNo`0o0Y_OQOg`0f0b_OZO]`0?k_OAV`05S@Kl?H`^O3l15ia000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001O000000000000000000RL4hCLl;`0TD@b;j0^DVOX;T1hDlNa:k1_EUNb:j1^EVNb:j1^EVNa:k1_EUNa:k1_EUNa:k1_EUNa:k1_EUNa:k1_EUNa:k1_EUNa:k1_EUNa:k1_EUNa:k1_EUN`:l1`ETN`:l1`ETNa:k1_EUNb:j1^EVNb:j1^EVNb:j1^EVNb:j1^EVNb:j1^EVNb:j1^EVNa:k1_EUNa:k1_EUNa:k1_EUNa:k1_EUNa:k1_EUNa:k1_EUNa:k1_EUN`:l1`ETNa:k1_EUNb:j1^EVNb:j1^EVNc:i1]EWNc:i1]EWNb:j1^EVNb:j1^EVNb:j1^EVNb:j1^EVNb:j1^EVNb:j1^EVNb:j1]EWNc:i1cDQO\\;P1kCIV<6oBe0P=]OUB]1k=\\21O00000000001OO10000000000O11O000000O100000000000000000000000_KSBo2m=hLeBo2[=lLPBROi0P4W=bLoA_OU1P4k<ULeC4cNS3h?jLW@W3l?dLW@[3X`0cL^_OZ3d`00000000001O1O1O00001O001O001O2NMnLX_OR3g`0400000000L4YOgLX@02^3e?c0M3K500jNVLTBl3f=ZLYBg3g=XLZBh3f=WL[Bi3e=ZLjAZO11M_4X>WLhA[O0d4X>o0N23M1O1O003M2N2N0nJ`Ad4a>TKhAj4Y>TKiAk4h>N2NOaKm@e1:2i>[No@^1;5h>TNXAb129^?Bb@>a?_O^@b0g?YOY@g0l?SOU@m0n?TN`_Oh0c0T1T`0SNY_Oc0c0Z1X`0dNh_O\\1X`0cNi_O]1Z`0`Nf_O`1[`0^Ne_Oc1_`0RNn^O4c0j1\\a00000000000DS^OeNna0Q1Q^OlN50ia0S1h0A`0E<H\\\\OKic02VbQ22U]nMN]]O6_b0L^]O6Sb0K`]OK2972Wb0O_]O171[b0Q1M2O13M00O2N2ON15K5L6]Oc0I5K2N0l@WMS=e2iB]MXNN21c>e2SC^MVN7d>\\2VCjMl<Z2PCbMV=]2iB`MZ=^2hB_M[=_2gB_M\\=b2bBaM\\=\\2fBVNh<j1XCVNi<h1XCXNi<f1XCZNi<h1TCXNm<k1ZAfMW1?c=V2\\BjMh=S2WBmMj=Q2WBoMk=R2RBnMo=Q2QBoMP>Q2oAoMP>Q2WA]M4d0f>n30000001O00000000003WKi@c4`?J2N2N3M00O100001O0000000000OkKY@O0R4g?oK_@0K1Od3X`0000O100001YOP@\\MP`0[2X@fMi?n1b@RN^?\\1g_OYNY1;P?S1]AmNc>k0eAUO\\>a0mA_OS>6XBJh=HfB8a`00000000", "size": [640, 482]}, "area": 29354.0, "iscrowd": 0, "image_id": 10290, "bbox": [36.0, 449.0, 446.0, 191.0], "category_id": 123, "id": 10012579}, {"segmentation": {"counts": "PTl51oc01O1O1O1O3M2N5K3M00000000000000000000000000I7N2M3O1O100OQPo2", "size": [640, 482]}, "area": 342.0, "iscrowd": 0, "image_id": 10290, "bbox": [301.0, 0.0, 29.0, 18.0], "category_id": 130, "id": 10012580}, {"segmentation": {"counts": "nPg0?^c03@E^]O8cb0b0O3N3M2M02N1003MO001O101OO0111jN^]Oi0JVOPc0i03I7H;FmPd11^[[N2dc00\\\\O0dc01[\\OOec01[\\OOdc02\\\\ONdc02\\\\OOcc01]\\OOcc0700000000000000001O01O000000000000000010O00000000O101OO10N2O101O000000000001O0000000000000010N1000000000001O0000000000000O12N2N2N2N^]OF^a0:`^O6Sa0In^O6Ra0Fm]OMP1?Sa0Cn]OMo0a0Ra0AS_Oa0l`0^OT_O?o`0AQ_O?o`0AQ_O?Pa0@n]O0l0`0Ua0Bm]OOn0?Ua0Bm]OOn0?Ua0Bm]OOm0`0Va0Gi^O4TOCVb08f^O6]a0IR^OK1>la0FQ^O109oa0Go]O5N4Tb0Ij]O7ONYb0Nd]Of0_b0<11OO2O0O11O010O00L4N2O1O1N10001O1O0O2O0OO24L1O4M1N101M2N3M2O1O3L3OSRU4", "size": [640, 482]}, "area": 3402.0, "iscrowd": 0, "image_id": 10290, "bbox": [36.0, 38.0, 233.0, 527.0], "category_id": 132, "id": 10012581}, {"segmentation": {"counts": "`af83kc04L:E3M5M10000001O1XOj\\Od0Vc0\\Ok\\Od0Xc0N3O001O0O2O0O101L3L5L4O_f7", "size": [640, 482]}, "area": 395.0, "iscrowd": 0, "image_id": 10290, "bbox": [445.0, 421.0, 24.0, 27.0], "category_id": 139, "id": 10012582}, {"segmentation": {"counts": "VY;U1jb05L000O1NN1M52M12O1O2M10002[OS]OD\\c0;52M11N5LYZ[3JU_dLL[If0\\6ZO_I1hIi0g<VOUI0UJU1g<kNgHe1kJcNW7HeL0SL0^40YK0011O010P26VNU72jHGe31]LOT40eK0O10e22dMY7a0lHFM@L94HMNh11_NOQ13iNV6NgIV7j2WJVMdN0114OJ00k7V7oJRIWMJ02W9Q7a3D]CZId<c6>01O1O1O00001JeBcI]=^631N01O2LOfI`BX6a=311OO100OM5XObI]C02c6`<d0L3K323gNoH[ES7^O]H`:U1QFb6Z9YHUFf1b0P6Z8QKgGQ5\\7mKSH\\L11Mk7l6kLTI[K0P8c1`H]N^O0:b07b0[>ERCWOQN22Oi0OUO30g0m>_OQEGWL`0l>@`E0_K7bW^4", "size": [640, 482]}, "area": 15164.0, "iscrowd": 0, "image_id": 10290, "bbox": [18.0, 28.0, 237.0, 513.0], "category_id": 143, "id": 10012583}, {"segmentation": {"counts": "Pl;8hc0001O00;E7I001O1O1O2N00002N0000O11O4L;E000000000000001O3MN2001O2N1OO1L4001O000000O11O00O11O00000000000000000000002NN22N0000O12N00O100O11OO12N1O0000M300000000000000O1001OO1O1O10000003M1O2NN2I71OO11O00O1001O0000O1002bAfNmKK30M2j=h1nE]NXL1i=b1nEiNQ:X1mEiNS:U1nEmN\\LZO:d1U;5UHS1k7lNRHX1n7hNjG`1V8`NfGd1Z8[NeG\\NjLV3a;]NkGc1U8]NiGe1W8_NaGe1_8\\NaGc1_8]N`Gd1`8YNnF_NmMX3U;WNjFgNQNR3U;kM`DgN22P2e0nMXOOe3a;cMbDiN02g1h4g9]LcDhNO3a1n4m9WLcDhNO3X1MeNX5a;oKdDiNN3T1[5Z:iKdDiNO2n0a5_:dKdDiNO4f0e5g:^KjDmN9k5m:XKjDmN4P6R;SKjDb5V;^JjDb5V;^JjDb5V;^JjDb5V;^JjDb5V;^JjDb5V;^JjDb5V;_JiDa5W;_JjD_5W;aJiD_5W;aJiD_5W;aJiD_5W;aJiD_5W;aJiD_5W;aJiD_5W;aJiD_5W;aJiD_5W;aJjD^5V;cJiD]5W;cJiD]5W;cJiD]5W;cJiD]5W;cJiD]5W;bJjD^5V;bJkD]5U;cJjD^5V;bJjD^5V;`JlD`5T;_JmDa5S;_JnD`5R;`JnD`5R;`JnD`5R;`JnD`5R;`JnD`5R;`JnD`5R;`JnD`5R;aJmD_5S;aJmD_5S;aJmD_5S;aJmD_5S;aJmD_5S;aJmD_5S;aJmD_5S;aJmD_5S;bJmD]5S;bJnD^5R;bJnD^5R;bJnD^5R;bJnD^5R;bJnD^5R;bJnD^5R;bJoD]5Q;cJoD]5Q;cJoD]5Q;cJoD]5Q;cJoD]5Q;cJoD]5Q;cJoD]5Q;cJoD]5Q;bJPE^5P;dJnD[5S;gJkDZ5T;hJjDo0Dl1c;WMgDEFN9VOGe3c;bMgDC6B;EFM@L94HMNh1c;EfDC6_O[2m0VMdN01c;\\1bDbNMP1?_OY2IWMJ02b<[1aCbNMo0a0]Ob?R1o_OFh0nNZ?]1m_OESa0;m^OESa0;m^OESa0<l^ObN0l0Ta0a0n^OaNOn0Sa0a0n^OaNOn0Sa0a0n^OaNOm0Ta0b0S_O]Om`0d0R_O\\On`0e0Q_OjNK1Ta0V1P_OgN10o`0Y1Q_OeN5Nj`0^1S_O`N7Of`0a1V_O\\N`a0e1=00001O0000001O000R_OZNb?f1\\@_Na?a1^@`NjNIf`0g1_@dN`?\\1^@eNc?Z1]@iNa?V1_@lN`?T1_@mNa01]N^O0:b07S<S1\\DoNH[1WOQN22Oi0OUO30X<d1eDROYOX3GWLR<_1mDVOQOg30_Kj;c1TEU4e:hK]EY4c:jKYEW4g:iKXEX4h:kKUEU4k:lKSEU4m:kKSEU4m:lKPEV4P;jKnDX4R;iKkDY4U;gKjDZ4V;fKiD[4W;gKeD[4[;fKdDZ4\\;U2000O11O1O001OO1F^DnGj;f6ZD[IL11Oh<T6XCkI1001O1W=b5iB]J1O0001OO60V=S5jBmJK0000001O000005OW=d4cB]K2O0090G000000001O01O41V=Z4fBeK0000000090G0<0D090R=[4eBeK000000Q?[4o@eK]?U4a@kK111O]?R4b@nKO0g?Q4[@oKg?P4500N2O1O1O1N21O003M1O2OO1O1O100O3M2N5K3M00000000000000000000000000I7N2M3O1O100O1M30000000000000000000001O0000000000000000000000000000000000O1K5Mn_OWLS`0i3200000000000000000000000fBWL]:i3ZEXL`MOO1[20]8g3fGYLbM00000Y1OoN0<1T:h3dGeLmNBoN0<1T:h3bGWLeM>V1BlN070:0]O11Oe:i3dGiLgM^O100040<0@070:0]O11O>0f9i3eFWLd0e0dN[O0041;OA00030=1^O01O<2h9i3kEVLe0Oi0e0dN[O0000000000000000O11O311OK055JK:5k9k3ZFPLC0000000i00ZOe0L\\O1O00000000000000O11OO1000O11OO100:2NQ:j3nEnK10N00000000000000040L0101f0L@1K10OO10000001N011O0070h0U:P3oEoK00N000000000000000000000101e3Q:=nEnK01N01OO11O000001O0000000000g3T::mEoKNO1]5T:\\4000000000000000000000O1000000000000000000000000000000000000M3[OoEVGV:X8R1M3O1O100O1000000001O00001O00001O00001O00000000001O0000001O00001O000000001O000000001O00001O000000001O00001O000000001O00O1O100", "size": [640, 482]}, "area": 76821.0, "iscrowd": 0, "image_id": 10290, "bbox": [19.0, 0.0, 463.0, 314.0], "category_id": 172, "id": 10012584}, {"segmentation": {"counts": "ZgU99^c08F;^OUOa]OY1Vb0=H8G9J60000000000l^O", "size": [640, 482]}, "area": 755.0, "iscrowd": 0, "image_id": 10290, "bbox": [469.0, 548.0, 13.0, 79.0], "category_id": 173, "id": 10012585}, {"segmentation": {"counts": "aZf0;lc06BO2K3F_\\O8cc01Wh3HQXL01OO0Z\\O0`c008000cn\\11TQcN0\\\\O000ec00Z\\O6o>o0]DSOc;`2RAYM_10aN9m>b3dATL^O;n>e4Ob0^O1O00O10000000000000000O1001O1O0000000000O1000000000O10O11O1O001O00O100000000000000O11OO1001O00000000001OO10000000000O11O000000O1000000000000000000000000eJfAP5Z>PKfAP5Y>QKgAo4Z>PKfAo4[>QKeA^40YK0011O010]>7bAT40eK0O10o>8PAQ13iNQb04l]OJ00PYb0>lY^OBT]OP1la0R1UOk0nNR1VNj10N14L2N2O003M2N2N0bBlIP=U6hBTJV=m5hBUJW=\\6N2NOUCYI_<g6cC[IY<g6^CdI^<T7K3M5J6L2TDlGg;Z8XDfGb;8^Dl7a;SH_D10H206\\7\\;dH\\D0n0k2SOb1h<VN`Ce1a<YNfC]1]<cNiCW1W<iNoCQ1Q<oNUDPOnN8m<h0\\DfNkN`0j<j0aD\\NkNh0d<l0gDSNiNP1`<m0mDiMiNX1Z<o0SE_MiN`1T<Q1PFkNS:U1oElNn9T1SFkNm9U1UFiNk9W1VFhNj9X1XFfNh9Z1YFeNh9Z1YFeNj9X1WFgNj9X1YFfNe9[1_F`Nb9`1bF\\N^9d1fFXN[9g1iFUNW9k1mFQNS9o1QGmMo8S2TGjMm8U2TGjMk8W2VGhMl8V2UGiMk8W2VGhMi8Y2XGfMi8Y2XGfMg8[2_401O00O02O0000000000O1O1000000000000O100001O000000000000000000000000000000O100001OO100000000000000LfMb^OZ2]a0gMc^OY2]a0500WA`MR<`2mCaMS<_2lCcMS<\\2mCeMS<[2lCgMS<Y2lChMT<X2kCjMT<V2kClMT<T2kCmMU<S2jCoMU<Q2jCPNV<P2iCRNV<n1iCSNW<m1hCUNW<k1hCVNX<j1gCXNX<h1eC]NY<c1dCcNZOoNa;^2QEjNYOjNf;^2lDnNXOgNl;]2fDROYOdNQ<]2`DUOYOaNW<\\2ZDZOYO^N\\<[2UD]OYOVNJAi<n2nCBYORNm<^2bC<^<GWCa0i<Z31O1O100O1O1GPCbIP=h5nBfJ[=Z5gBcJ[=n4fBeJK297W=R5jBdJ17W=U5P1N2O13M00O1O100O1O100C=O1O1O10M2\\O^KgA_4L\\Kl=b583O0001O01000O10001N10001O01O010O0001N1001ON200O2O0AdA[K[>c4iA[KW>c4SBUKn=g4UBWKm=f4h0O2O000001O1g@[KV?d4j@]KU?g401O0010O01O0ZAUKX>j4hAXKV>i4fA[KY>e4cAaK[>`4aAcK_>^4`AbKa>n400001N10000O101O00O1O100\\G", "size": [640, 482]}, "area": 49288.0, "iscrowd": 0, "image_id": 10290, "bbox": [35.0, 265.0, 447.0, 279.0], "category_id": 176, "id": 10012586}, {"segmentation": {"counts": "f`[51oc00Z\\O0H206mb0KS]OM0n0_b0ROa]Oc1m`0aNT_OL11Oc2Y`0]Mf_O1001O1R3g?nLX@1O0001OO60Q3X?oLh@K0000001O000005OR3i>hLXA2O0090G000000001O01O41Q3_>kL`A0000000090G0<0D090m2`>jL`A000000l4`>TK`AX5Z>fJfA111OX5W>gJiAO0b5V>`JjAb5U>500N2O1O1O2M5N0OO4M1O2OO0000010OO12N00O11OO1001O00O11O0000000000O1O1000000000000O100001O0000000000000001O0000000000000O100001OO100000000000O1K1UBSJh=0VBm53SJg=o550000000000000000000000lLQB7o=@RB`MOO1[20WNm=l1SBbM00000Y1OoN0<1Nn=j1_BmNBoN0<1Nn=h1QBeM>V1BlN070:0]O11O?o=j1cBgM^O100040<0@070:0]O11O>P>;PBd0e0dN[O0041;OA00030=1^O01O<T>CmAe0Oi0e0dN[O0000000000000000O11O311OK055JK:\\>5dAC0000000i00ZOe0L\\O1O00000000000000O11OO1000O11OO100:2W>O_A10N00000000000000040L0101f0L@1K10OO10000001N011O0070P?4g@00N000000000000000000000101na0OR^O01N01OO11O000001O0000000000Pb01P^ONO1ckk1", "size": [640, 482]}, "area": 19606.0, "iscrowd": 0, "image_id": 10290, "bbox": [274.0, 121.0, 113.0, 201.0], "category_id": 180, "id": 10012587}, {"segmentation": {"counts": "RWh0h0jb0YOb]OR1Sb0POk]OR1Sb0>0000001O0gNc]Oo0]b0POe]OP1[b0nNf]OR1R3QOi;LXAQ1f2\\OS<AZAQ1[2F[<ZO^Al0m13f<PO]Am0e1;o<gN\\An0\\1d0Y=]N\\AR1DUOP1d1P>UN\\A\\1<h0h>VORAR1e`09H8G9H8O1000000000000000000000000000001O000000000000000000000000000000000000000000000000000000000000000000000000000000000001O000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001O0000000000000000C=VOj0WOi0VOSim1[OZXRN8J7I6J6J6_^OnNh?Y1n_OkNQ`0[1e_OkNY`0[1^_OiNa`0]1U_OiNi`0]1m^OiNQa0T2O2N1O2N1O2N1O1O1O3M4L4L4L4L4L4L3M1O1O1n@fK\\>[4bAjKZ>W4dAnKX>S4gAPLV>Q4hATLT>m3jAXLR>i3mAYLQ>h3mA[LP>g3nA\\LP>e3oA^Ln=c3PB`Ln=a3PBbLn=j4N2N3M2N2N000000001O00000000000000000000000000000000000000000000000000000000000000000000XL^BR1b=mN`BR1`=mNaBS1_=lNcB]O3^OY=U1eB\\Oe0mNf<f1gB\\OU1\\NT<W2hB]Oo>b0SA\\On>c0TA\\Ol>c0UA]Ok>b0WA\\Oj>c0WA]Oi>b0YA]Og>b0ZA]Og>b0[A]Oe>b0\\A^Od>a0^A]Oc>?dA\\O^>a0hAYO[>c0lAXOY>b0lAYOY>a0nAYOX>`0oAZOV>a0PBYOU>a0RBYOT>a0QBZOT>;o2FZSi1", "size": [640, 482]}, "area": 31277.0, "iscrowd": 0, "image_id": 10290, "bbox": [38.0, 395.0, 352.0, 236.0], "category_id": 183, "id": 10012588}, {"segmentation": {"counts": "2P6d8\\8dG00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000O10000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000O100000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000]LVb0", "size": [468, 640]}, "area": 293345.0, "iscrowd": 0, "image_id": 10303, "bbox": [0.0, 1.0, 639.0, 461.0], "category_id": 183, "id": 10012589}, {"segmentation": {"counts": "0j1j=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", "size": [500, 333]}, "area": 18387.0, "iscrowd": 0, "image_id": 10309, "bbox": [0.0, 0.0, 333.0, 62.0], "category_id": 97, "id": 10012590}, {"segmentation": {"counts": "b5T2d3]1g2cNYM]1g2dNXM[1i2eNWM[1i2eNWMZ1j2fNVMZ1j2fNVMZ1j2fNVMZ1i2gNWMX1j2hNVMX1j2iNUMW1k2iNUMX1j2hNVMX1j2hNVMX1j2hNVMX1j2hNVMW1k2iNUMW1k2iNUMW1k2iNUMX1j2hNVMX1j2hNUMZ1j2fNVMZ1j2fNVM[1i2eNVM]1i2cNWM]1i2dNVM]1i2]N]Mc1c2\\N^Md1b2[N_Me1a2YNbMf1^2YNcMh1\\2XNeMh1Z2YNeMe1]2[NbMe1_2[NaMe1_2[NaMe1_2[NaMd1`2\\N_Mb1YLaLX6n1^Ma1e2_N[Ma1e2_N[Mb1d2^N\\Mb1d2^N\\Mb1d2^N\\Mb1d2_N[Ma1e2_N\\M`1d2`N\\M`1d2`N\\M`1d2`N[Ma1e2_N[Mb1d2_N\\M`1d2`N\\M`1d2`N\\M_1e2aN\\M]1e2dNZM\\1f2dNZM\\1f2dNZM\\1f2dNZMZ1ZLeL\\6R2YMY1i2gNWMY1i2gNWMZ1h2gNWMY1i2gNWMZ1h2fNXMY1i2gNWMZ1h2gNWMZ1h2fNXM[1g2eNYM[1g2fNXMZ1h2fNXM[1g2eNYMZ1h2fNXMX1j2iNUMW1k2iNVMV1j2jNVMT1l2jNbIlMb3Z3l2jNbImMa3Z3l2iNcImMa3[3k2hNXMX1h2hNeIlM`3]3i2hNZMW1g2iNYMW1g2iNZMT1h2kNYMU1g2kNYMU1g2kNYMW1e2iNZMX1f2gN\\MV1f2jNZMW1e2iN[MY1d2fN\\MY1e2gN[MY1e2gN[MY1e2gN[MX1f2hNZMW1g2kNXMT1h2lNWMV1h2jNXMV1h2jNXMT1j2lNWMS1i2mNWMT1h2lNXMT1h2lNXMU1g2kNYMU1g2kNYMV1f2jNZMU1g2kNYMU1g2kNYMU1g2kNYMT1h2lNYMT1f2lNZMS1g2mNYMS1g2mNYMS1g2lN[MS1e2mN[MR1f2nNZMS1e2mN\\MR1d2nN]MS1a2mN_MU1_2kNaMX1\\2hNeMW1[2iNeMZ1X2fNhMZ1X2fNhMX1Z2hNgMW1Y2iNfMX1Z2hNhMV1X2jNgMV1Z2jNfMU1[2gNiMY1W2cNmM]1S2_NQNb1n1[NUNc1m1YNWNg1i1UN[Nk1e1QN_No1a1nMcNQ2]1kMgNU2Y1gMkNY2U1cMoN]2Q1bMPO^2o0cMQO]2o0bMRO^2n0aMTO^2l0aMUO^2k0bMVO^2j0bMVO]2k0bMVO^2j0aMWO`2h0_MZO`2f0`MYOb2f0]M[Ob2e0^M]Oa2c0^M^Ob2a0^M@b2>`MB`2;bMF_29`MH`28_MIa27^MJb26]MLb24]MMASJo2P6_MNBRJn2Q6_MOBPJP3P6]M3c2N[M3d2M\\M3e2MZM4f2MXM4h2MVM4j2NSM2[1kI1T6bN3Y1RJOW<OoCNP<0SDOm;NVD2j;LXD3i;KYD5c=0000000001N100000000000000001OO100001O001O0cN\\OjCd0S<@lC>R<FnC;m;JRD6k;MUD4f;1YDOg;1YDNh;3WDLo;0PD0Q<OoCOS<2lCNT<2lCNU<2jCNV<2jCNV<3iCMX<2hCMZ<3eCM[<4dCL[<5eCK[<6dCJ\\<7cCI]<7bCK]<6bCL\\<5bCM^<3aCL_<6`CJ`<9\\CGe<=WCCi<d10001cKWMnKh2R4YMmKf2S4\\MlKd2S4^MlKb2T4^MkKc2T4_MlK`2T4`MmK_2R4cMmK\\2S4eMmK[2R4fMoKZ2o3gMRLV2n3lMQLU2n3lMRLV2l3jMTLU2l3lMTLT2l3lMULS2k3mMULT2i3lMXLT2h3lMYLT2f3lMXLU2i3kMWLV2h3jMXLW2g3iMYLU2i3kMWLU2i3kMWLV2h3jMXLV2h3jMYLT2h3lMYLS2f3nMZLR2f3nMXLT2h3lMXLT2h3lMYLT2e3mMZLT2f3lMZLT2f3kM[LU2e3kM[LU2e3kM[LU2Y3WNfLj1T3\\NlLe1m2aNSM_1m1lMnIk0U4X1n1BSN=m1CSN<n1DSN:n1FRN:n1FRN:n1FRN;n1DRN<n1DRN<n1DRN;o1ERN:n1FRN:o1EQN:P2FQN:n1FQN<n1DRN<m1ESN;m1ESN=k1CUN=k1CUN=k1CUN>i1DVN<j1DVN=i1BXN>h1CWN=j1BUN>l1AWN<j1DUN<l1DTN;n1DRN:P2FPN:P2FPN9R2FnM:R2FnM;Q2EoM;P2FQN:n1FRN:n1FQN;o1EQN;o1EQN;o1EQN;n1FRN;m1ESN;m1ESN<l1DUN;k1EUN;k1ETN<k1EUN;k1EVN:j1FVN:i1GVN:j1FVN:j1FWN9i1GWN9i1GWN9i1GWN8j1HVN8j1HVN8j1IUN7k1HVN8j1HVN8j1HVN8j1HWN7]L", "size": [500, 333]}, "area": 45695.0, "iscrowd": 0, "image_id": 10309, "bbox": [0.0, 168.0, 333.0, 312.0], "category_id": 111, "id": 10012591}, {"segmentation": {"counts": "j1h3T2L]1UMYNo2:L]1UMYNo2;K[1WMZNn2;K[1WMYNo2<JZ1XMZNn2<JZ1XMZNn2<JZ1XMZNn2<JZ1XMYNn2>JX1YMYNn2?IX1YMYNn2`0HW1ZMXNo2a0GX1YMWNP3a0GX1YMVNQ3b0FX1YMUNR3c0EX1YMUNR3c0EW1ZMVNQ3c0EW1ZMVNQ3c0EW1ZMVNQ3c0EX1YMUNR3c0EX1YMUNR3c0DZ1YMSNS3c0DZ1YMRNT3d0C[1XMQNU3d0B]1XMoMV3d0mLZNY1S3dNoMV3P1^LRNg1P3dNoMV3S1WLRNo1l2dNnMW3R2TN0eNoMV3R2TNOfNoMV3S2TNMfNPNV3T2SNMfNoMW3T2TNMdNoMX3U2SNJgNQNV3V2QNIjNQNU3V2QNIjNQNU3U2RNJiNPNV3T2SNKhNQNU3S2SNJQNaLg0c1T3P2VNLiNVNo2l1ZNNgNXNm2i1]N0eNWNn2g1_N2cNWNn2e1aN4aNXNm2b1dN6_NXNm2a1eN7^NXNm2`1gN7\\NZNl2]1jN9ZNZNl2\\1kN:YNYNm2\\1jN<YNYNl2Z1lN>WNXNm2Y1nN>UNYNm2W1PO`0SNYNm2V1QO`0SNZNl2U1SO?RN\\Nk2S1UOa0PN\\Nk2R1VOb0oM\\Nk2n0ZOf0kM\\Nk2m0[Oe0oLeLk0i1k2m0[Oe0lM]Nj2n0ZOe0lM\\Nk2o0YOf0kMZNm2P1XOf0kMXNo2R1VOg0jMWNP3S1UOe0lMWNP3T1TOf0kMVNQ3T1TOg0jMTNS3U1SOh0iMRNU3V1ROh0iMRNU3W1QOg0jMRNU3W1QOh0iMRNU3V1ROg0jMSNT3V1ROe0lMTNS3W1QOe0lMUNR3V1SOd0kMVNR3W1ROa0nMWNQ3c0RL_No2g2nMWNQ3c0RL_No2h2mMXNP3a0TL_No2i2lMXNP3`0WL]Nm2k2lMYNo2?WL^Nn2k2kMZNm2>[L]Nl2j2mM\\Nk2=\\L]Nl2j2mM\\Nk2=\\L]Nm2g2nM_Ni2<]L^Nl2g2nM`Nh2;]L_Nm2f2nM`Nh2;]L`Nl2g2mM]Nk2<\\L`Nk2h2nM\\Nk2;]LaNk2e2oM^Nj2<\\LaNk2f2nM]Nk2<[LbNl2g2lM[Nn2;ZLcNl2f2mM\\Nm2;ZLdNk2e2nM\\Nm2;ZLdNk2e2nM\\Nm2;ZLdNk2d2oM]Nl2;ZLdNk2c2PN^Nk2U1VO<oM_Nk2V1TO=PN]Nl2V1TO=PN]Nl2V1TO;RN_Nj2V1UO:QNaNi2U1VO;PN`Nj2U1VO;PN`Nj2V1UO;PN_Nk2V1UO;PN_Nk2V1UO<oM^Nl2V1UO;PN_Nk2V1UO;PN_Nk2]1nN4WN_Nk2i1bNGdNaNi2k1aNDeNaNj2R2ZN\\OmNaNj2T2XN[OnNbNi2U2WNYOPObNi2W2VNVOQOcNi2Z2SNROUOeNg2[2RNQOVOdNh2\\2RNoNVOeNh2^2QNnNUOeNi2^2QNoNTOcNk2_2PNQORO`Nn2a2oMnNSObNm2a2oMPOQO_NP3b2nMoNRO`No2a2oMmNTObNm2`2QNmNRObNn2a2oMnNSOaNn2a2QNlNQOcNn2a2PNlNSObNn2b2oMkNTOcNm2b2oMkNTOcNm2a2PNlNSObNn2b2oMmNROaNo2a2PNlNSObNn2b2oMlNSObNn2b2oMlNSOaNo2b2oMmNROaNo2b2PNlNQOcNn2a2QNlNQOcNn2a2QNlNQOcNn2`2RNmNPOcNn2`2RNmNPOdNl2`2TNlNPOdNl2`2TNlNPOeNk2^2WNlNnNfNk2^2WNkNoNgNi2\\2[NmNlNhNh2W2`NPOiNiNg2T2cNSOfNiNg2S2dNUOdNhNh2R2fNUObNiNh2Q2fNXOaNfNj2P2gNYO`NgNh2i1QO_OWNgNi2j1PO_OWNgNh2k1QO^OWNgNf2m1SO\\OWNfNd2R2TOYOWNeNe2R2TOYOWNeNe2R2TOYOWNdNf2S2SOYOWNcNg2T2SOXOVNdNg2U2ROUL_Mo2h0gNg2U2ROUL_Mn2i0hNf2X2POQLaMP3h0gNg2Z2POoNYNgNh2\\2lNlN]NhNf2^2kNkN_NgNf2_2jNjN`NgNg2_2hNjNaNfNi2`2eNjNbNfNk2_2aNlNdNeNl2_2`NkNdNfNm2_2^NlNdNdNn2b2]NjNeNdNm2d2]NhNfNdNl2f2]NeNhNeNj2h2]NcNiNdNj2k2nMoNXOVNi2m2bMYOEiMi2P3UMC2]Mh2R3iLM?RMf2P8ZMRHc2n7^MRHa2o7_MRH`2n7`MRH_2o7aMQH_2o7aMQH^2P8bMPH^2P8bMPH]2Q8cMoG]2Q8cMoG\\2S8cMmG]2R8dMnG[2S8eMmG[2S8eMmGZ2U8eMkG[2U8eMkG[2V8dMiG]2W8cMiG\\2X8dMgG]2e1lLU3i0VK[2d1oLU3e0WK\\2d1RMR3b0ZK\\2c1UMR3>[K\\2c1YMP3;]K\\2c1[MFD4f0eNY2`1aMAC9d0fNX2_1dM\\OI:;lNW2^1gMVOL=8oNU2]1jMQOMc04POS2\\1PNjNMj00POS2\\1RNcN1P1JROR2Z1VN^N2V1FROR2Y1YNYN3\\1BSOQ2X1]NQN6b1]OTOQ2X1_NlM8g1XOUOQ2W1bNfM:n1SOUOQ2V1U1FjLSOR2W1T1FjLSOR2V1U1GiLSOR2U1V1IgLROb1f1g1ZOeLoNc1h1i1ZOcLnNb1i1m1WObLPO`1j1m1WOcLnN_1l1o1UOcLoN\\1m1R2TObLoNZ1o1T2RObLoNZ1n1U2SOaLoNY1o1V2SO`LnNZ1o1W2RO_LoNY1Q2W2oNaLPOX1R2V2nNbLPOW1T2V2lNcLPOV1U2V2lNdLoNV1V2V2jNdLPOU1W2X2hNcLQOU1X2W2fNeLROS1Y2X2eNeLROS1Y2Y2eNcLROT1Y2Z2bNdLTOS1Z2X2cNeLROT1[2W2eNcLPOV1[2W2dNdLQOU1[2W2dNdLQOT1\\2Y2bNcLQOU1]2X2cNbLPOV1\\2Y2dNaLoNW1]2Y2dN_LPOW1\\2X2eNbLnNW1]2W2fNaLmNX1]2W2gN`LlNY1]2W2eNbLnNW1]2W2eNbLoNV1\\2X2fNaLnNX1[2W2gNaLnNX1[2X2eNaLPOX1Z2X2eN`LQOY1Y2W2fN`LQOY1Y2U2hNbLPOY1W2U2iNbLPOY1W2V2iN`LQOZ1U2U2kNaLPOZ1U2U2kNaLPO[1S2U2mN`LPO[1S2U2mN`LPO\\1R2T2nN`LPO\\1R2S2oNaLoN]1Q2R2QO`LnN^1Q2R2QO`LnN_1WOVN\\1k3>aLnN_1PO]Nd1d3=`LoN`1kN`Ni1`3<aLPO_1kN`Ni1a3:aLRO_1jN_Nj1a3:aLQO`1jN_Nk1`3:aLQOa1iN^Nl1`3;`LPOc1hN^Nl1_3<`LPOh1bNZNR2^3<`LPOn1\\NTNX2^3;aLQOl2d0d0:`LSOk2c0e0:`LROm2c0c0:aLROm2d0c0:_LROn2d0b0<_LoNP3e0a0<_LoNo2f0b0;_LnNP3g0a0=]LlNR3g0a0=]LlNR3g0a0=]LlNR3g0a0>\\LkNR3i0a0<]LkNR3i0a0=\\LjNS3h0b0>[LjNS3i0a0=\\LjNT3h0?>^LjNS3g0b0<\\LmNR3g0a0<^LnNP3f0b0;_LPOo2d0b0:aLSOl2c0c0:aLTOk2b0d09bLUOk2a0c0:bLUOk2a0c0;aLTOl2a0c0;aLTOk2b0e0:_LUOk2a0f0:_LUOk2a0e0;`LSOl2b0d0;`LSOl2b0d0;`LSOl2b0d0;`LSOk2c0e0;_LROl2c0e0;_LROl2c0e0<^LQOm2c0f0;]LQOn2d0e0;]LQOn2d0d0<^LPOm2e0e0;^LPOm2e0f0:]LQOm2e0f0:]LQOl2f0f0:^LPOl2f0f0:^LPOl2f0g09]LQOl2f0g09]LQOl2f0g09]LQOl2f0g08^LROk2f0g08^LROk2f0g08^LROk2g0f07_LROk2f0g08^LROk2f0g08^LROk2f0g08^LROk2f0h07]L", "size": [500, 333]}, "area": 77284.0, "iscrowd": 0, "image_id": 10309, "bbox": [0.0, 50.0, 333.0, 450.0], "category_id": 124, "id": 10012592}, {"segmentation": {"counts": "co;a0Y>JSB:f=0RB4m=k0N2M3N10100O1OO1N2O1N3N1N2N2N2O1N3M2O1O1O1O1N3N1O1O1O2M2N2L401O01O01O00000010O0001O0000010O00000010O00000000001O0O1000001O0O100000001O0O100000001O000000001O00007I<D3M7I1O2M3N3M2N1O2N1O1O2N1O00000000O1000L4L4L3N2M4L4L4M2M4L4L4O010O1O1OO1M4L3000001O0K5M31O2O0O1O1O2N1O1O1O2N4L3M4L2N2N2N2N2RHQLT4Q4hKXLQ4j3lK\\Lo3f3nK\\LQ4f3kK^LS4d3jK^LU4d3hK^LW4d3ZJjMe5X2nITNQ6n1bI^N]6d1VIhNi6R5O1O100O100O101N100O100O100O100000eNdI^I[6_6iIaIW6^2YI`0e0QMR6_2\\I:e0WMo5^2_I5g0\\Mj5^2bI0h0bMe5^2fIKi0fMa5_2iIDj0mM]5^2lI_Ol0RNX5^2oI[Om0VNT5_2RJTOn0]NP5^2UJoNP1bNj4_2YJhNR1iNe4_2[JbNU1nN`4_2_J[NV1VO[4^2bJVNX1[OV4_2dJoM[1BQ4^2hJiM\\1Hl3^2aNaM_1_2aNaM_1^2cNaM]1^2eN`M\\1_2eNaMj0P3WOnLh0S3ZOlLd0U3^OiLb0X3_OgL?Z3FbL8`3l511O010O010O01O01O010O001O1O1N2O1000O010000O1000000000001O001N2O001O001O0O2O001O0A`0I6cNdC\\Oc<;_1H9N101O001N4M6J\\Wj0", "size": [500, 333]}, "area": 24819.0, "iscrowd": 0, "image_id": 10309, "bbox": [24.0, 119.0, 255.0, 319.0], "category_id": 183, "id": 10012593}, {"segmentation": {"counts": "lhY55fc06J5N3O00001N1WOAP^O`0ma0En]O>oa0Ij]O:Rb0l0N1100O100O1N2N2N3M2N4M1N3L5I7J8I9DiPn07RoQO9H6K5K7I5K4L4M4L2N2N1O2O001O1N101O0010O0000O1000O100000000O100O100O1O2O0O1N3N1O1O2M3M4K5N1L6J6I<BTP:O]\\EJ]c0d0I6J5K4M3L4M2N1O2N2O1N1VD", "size": [640, 427]}, "area": 4152.0, "iscrowd": 0, "image_id": 10313, "bbox": [271.0, 362.0, 156.0, 80.0], "category_id": 96, "id": 10012594}, {"segmentation": {"counts": "Sdl122O04_c0;O1O1O1O1O1O00000000O11O1O1O1O1O1O001O1O1O1O1O1O00001O001O0000O100O1O1O1O1O11O1O000000000000O100000000001O1O001O1O0000001O000000O100O^]OUOQb0k0n]OWOQb0j0m]OXORb0h0n]OXORb0h0m]OYOSb0h0l]OXOTb0i0j]OXOVb0Y1000O1N2O1O1N2001O1`]OcN[b0c1N000000000000000000O1I700O100O100O100O1O1O100O100M3O100001O001O00000000001O000000000000O100O1000000001O0000000000N2N2G9O100O10000O100O100N2O10000O10000002N001O000000000000000000O1O1He\\OK[c049000000001O1O001O0000O100O100O10000O1000000000000001O00001O001O001O1O1O1O00001O00O1O11O2N2N2N001O000000000000O100000000000000O1001O3M1O2N2N3M2N00000000O100N2O1O1M3N2O1O1O100O1O100O100000000O1000000001O2N1O001O0000O100O1O1002N000000001O000000000000O1O1O10000O100O10000O1001O001O0000O100001O00001O000000000000", "size": [640, 427]}, "area": 8139.0, "iscrowd": 0, "image_id": 10313, "bbox": [97.0, 0.0, 330.0, 53.0], "category_id": 106, "id": 10012595}, {"segmentation": {"counts": "]?c4]?000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000O100000000000000000000000000000000000000000000000000O1000000000000000000000000000000000000000000000000000000O10000000000O100000[Kj@`4\\?O1O001O001O001O1O001O001O001O001O1O001O001O001O000000000000000RL[@a3d?_L_@_3a?aL`@^3`?aLb@^3^?bLc@]3]?cLc@]3]?bLe@]3[?cLe@]3[?cLe@]3[?cLd@^3[?cLe@]3[?cLe@]3[?dLc@]3]?cLc@]3]?dLa@]3_?cL^@`3b?_L_@a3a?^L`@b3`?]La@c3_?\\Lb@d3^?\\La@e3_?ZLb@f3l?0O10000O100000000]OWLo@i3Q?WLo@i3P?XLo@i3Q?VLPAj3P?VLPAj3c?0000000001O1O1O0000O10000O100O1O1M300O10000ZOQL[Ao3e>QL\\An3d>RL\\An3d>SL[Am3e>SL[Am3e>SL\\Al3d>TL\\Al3c>UL]Ak3c>UL^Aj3b>WL]Ai3c>WL]Ai3c>WL]Ai3c>WL^Ah3b>XL^Ah3b>YL]Ag3c>YL]Ag3c>YL^Af3b>ZL^Af3b>ZL^Af3b>ZL_Ae3a>\\L^Ad3b>\\L^Ad3b>\\L^Ad3b>\\L_A6ZOo2W?kL_A6@i2Q?QM_A5Bi2o>SM^A4Dh2n>TM_A2Dj2m>TM_A2Dj2m>TM_A2Ck2n>SM_A1Dl2m>SMaALEQ3j>TMRBl2n=TMRBl2n=TMRBl2n=TMRBl2n=TMQBm2o=SMlAR3T>oLkAQ3U>oLkAQ3U>oLkAQ3T>PMlAP3T>PMlAP3T>PMlAP3T>PMlAP3T>QMkAo2U>QMjAP3V>PMjAP3V>PMjAP3V>PMjAP3V>PMjAP3V>QMiAo2W>QMiAo2W>QMiAo2W>QMiAo2W>QM[AGHX3m>QMZAHHX3n>PMYAJHV3o>PMXAKIU3o>PMXAKIU3o>PMWAMIS3P?QMVALKR3o>RMVALLQ3n>SMVAKNQ3l>TMUAK1P3j>UMUAJ3P3h>VMUAJ3P3h>VMUAJ3P3h>VMUAJ3P3h>VMTAK4o2h>WMSAI5Q3g>WMTAH5Q3g>WMTAH5Q3g>XMSAH5P3i>WMQAK4o2k>VMQAMOP3P?TMPA`3P?`LPA`3P?aLn@`3R?`Ln@`3R?`Ln@`3R?`Ln@`3R?`Ln@`3R?`Lm@a3S?_Lm@a3S?_Lm@a3S?_Lm@a3T?^Lk@c3W?[Li@e3X?ZLh@f3Y?YLg@g3Z?XLf@h3Z?XLe@i3[?>0000001O0000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000O10000", "size": [640, 427]}, "area": 59013.0, "iscrowd": 0, "image_id": 10313, "bbox": [0.0, 487.0, 427.0, 153.0], "category_id": 140, "id": 10012596}, {"segmentation": {"counts": "]<P3Pa000000O100O1000000O10000000000000000O100000000000000000000000000000000000000000000001O00001O1O00000000O1000000O100000000000000O1000000000O100000000000000000000000000000000000C=O1O1000000005J101O001O001O1O001O001O1O001O001O1O001O001O001O000000000O10000000000O1000000M3J6N2O1O1O1O1O1O1O1O1O1C=O1N2N2O1N2N2O1N2N2O1N2N2O1N2N2O1N2N2O1N2N2N2O1N2N2O1N2O1N2N2O1N2N2O1Nf[i4", "size": [640, 427]}, "area": 15327.0, "iscrowd": 0, "image_id": 10313, "bbox": [0.0, 378.0, 181.0, 115.0], "category_id": 143, "id": 10012597}, {"segmentation": {"counts": "0n33^4`7bKfHX4Z7iKkHQ4U7PLQIi3o6WLWIb3j6_L\\IZ3d6gL_IT3b6mL]IS3c6mL]IS3c6nL\\IR3d6oL[IP3f6PMZIP3f6QMYIo2g6RMXIn2h6RMXIn2h6SMWIm2i6SMWIm2i6TMVIl2j6TMVIl2j6TMVIk2k6VMTIj2l6VMTIj2l6WMSIi2m6WMSIi2m6XMRIh2n6XMRIh2n6YMQIg2o6YMQIg2o6YMRIf2n6[MQIe2o6[MQIe2o6[MQIe2o6ZMRIf2n6ZMRIf2n6ZMRIf2n6ZMRIf2n6ZMRIf2n6ZMRIf2n6YMSIg2m6YMSIg2m6YMSIg2m6YMSIg2m6YMSIg2m6YMSIh2l6WMUIi2k6WMUIi2k6WMUIj2j6VMVIk2i6UMWIk2i6TMXIl2h6TMXIl2h6TMXIl2h6TMXIk2i6UMWIk2i6UMWIk2i6TMXIl2h6TMXIk2i6UMWIk2i6UMXIj2h6VMXIj2h6VMXIj2h6UMYIk2g6UMYIk2g6UMYIk2g6UMYIj2h6VMXIj2h6VMXIj2h6UMYIk2g6UMYIk2g6UMYIk2g6UMYIk2g6UMYIk2g6TMZIl2f6TMZIl2f6TMZIl2f6TMZIl2f6TMZIl2f6TMZIl2f6SM[Im2e6SM[Im2e6SM[Im2e6SM[Im2e6SM[Im2e6SM[Im2e6RM\\In2d6RM\\In2d6RM\\In2d6RM]Im2c6SM]I`2P7_MQI`2P7`MPI_2Q7aMoH_2Q7aMoH_2Q7aMoH_2Q7aMoH_2Q7`MPIQ1_8oNaGm0c8TKYFOO04a3Q1X1W9gNiFU1\\9jNdFR1a9mN_Fo0f9POZFl0k9SOUFj0o9UOQFi0Q:WOoEf0T:ZOlEc0W:]OiEa0Y:@fE=\\:DdE9`:F`E8c:G]E6g:IYE4k:KUE3n:LRE1R;OmDNV;2jDLY;3gDJ];5dDG`;8`DFc;9]DDg;;YDBk;=UD@n;a0QD]OQ<c0oCZOU<e0kCWOY<i0gCRO_<m0aCoNc<Q1]CjNh<V1YCeNj<]1WC\\Nn<d1TCUNP=l1RCnMQ=S2QCfMS=[2nB`MU=a2lB[MV=f2kBWMX=h2hBVM[=j2eBRM^=n2bBPM`=P3aBmLa=S3_BkLc=U3d1000O10000000O101O1O001O1O0000001N100000O100OYMZ_OV2f`0iM\\_OV2e`0hM]_OW2c`0iM]_OW2c`0hM^_OW2d`0hM\\_OX2e`0fM\\_OZ2Ua0000O1N2O1O1N1101O1]Mf^O^2`a0N000001O0000001O000O1I8O0O100O101N100O1O2N100O101L3O100010OaCjLT8V3jGlLW8S3gGoLY8Q3eGRMZ8n2dGTM\\8l2aGWM_8i2_GZM`8f2^G\\Mc8b2\\G`Md8`2ZGbMf8^2XGeMg8[2VGhMj8X2TGjMl8V2RGmMm8S2QGoMn8R2PGPNP9P2nFSNP9n1mFUNS9k1kFWNU9i1iFZNV9Q1]GQOc8m0]GUOd8i0[GXOf8g0XGYOk8e0UGXOP9g0oFWOU9h0jFVOZ9h0fFWO[9j0dFlNf9U1XFjNc9]1]FbNe9^1ZFcNg9\\1XFeNQNnNZ:]2dGfNj9Z1UFfNl9Z1TFfNk9Z1UFgNk9Y1TFhNk9Y1UFfNl9Z1SFgNk9[1TFfNk9Z1VFfNj9Z1TFhNl9X1SFiNl9X1SFiNm9W1RFjNn9U1QFmNo9S1PFnNR:P1mEQOS:o0lEROU:l0jEUOW:k0hEVOX:j0gEWOY:i0fEXOZ:h0fEXOZ:g0gEYOY:g0fEZOZ:f0fEYO[:g0eEYO[:g0eEYOZ:g0fEZOY:g0gEYOQ:_NXCX2g2YOP:P1oEQOQ:o0oEQOQ:n0PFROP:n0oESOQ:o0mEQOT:o0kEQOV:o0iEQOW:P1gEQOZ:o0eEQO[:P1dEPO\\:Q1bEPO]:S1aEmN_:T1`EkN`:W1^EjNb:W1]EiNb:Y1]EgNc:Z1\\EfNd:[1ZEfNe:]1YEbNh:_1VEbNj:_1TEbNl:_1REbNn:_1QEaNo:b1mD_NS;e1hD\\NX;h1dDXN];l1]DUNc;n1YDRNh;o1WDQNj;o1TDRNl;o1RDRNo;n1PDRNP<o1nCRNS<l1mCUNT<j1kCWNV<g1kCYNV<e1jC\\NV<d1iC]NW<b1jC^NW<`1iCaNW<_1hCbNW<^1jCaNV<`1iCaNX<]1hCdNZ<Y1gCgN[<W1dCjN^<S1bCnN^<R1bCnN_<P1aCQO_<n0aCRO`<n0_CSOa<l0`CTO`<k0`CVO`<j0_CWOa<h0`CXO_<i0`CXO`<g0`CYOa<f0`CZO`<f0_C[Oa<d0_C]Oa<b0`C^O`<b0_C_Oa<`0_CA`<`0_CAa<>`CBa<<_CEd<8[CIf<5[CKg<3XCN\\O\\MS=c2aC=b<_O_Ca0c<]O\\Cf0b<YO^Ci0a<VO`Ck0_<UO`Cl0`<SO`Cm0`<TO`Ck0a<TO`Ck0_<VObCj0]<WOcCi0\\<WOeC>c<E]C9c<H^C5d<K]C5b<L^C4a<L`C4`<L`C4_<LbC4]<McC3]<McC2]<OcC1]<OcC1]<OcC1]<OcC1]<NdC2[<OeC1[<OeC1[<OeC1[<OeC1[<OeC1\\<NdC2^<LbC3`<KaC5_<KaC5`<J`C6`<J`C6`<J`C6_<KaC5_<L_C5`<M_C3`<N_C3a<N]C3e<LZC4f<LYC5g<LWC5i<LVC4j<MTC4m<KRC6n<KQC4P=MnB4R=MlB4T=LlB4T=MjB4V=MhB4W=MiB3V=OhB2W=0gB1Y=0fB0Z=0eB1Z=1dB0\\=1cBN]=4aBM_=Z300O1001O001O0000O100001O00001O00000O100000_O", "size": [640, 427]}, "area": 85831.0, "iscrowd": 0, "image_id": 10313, "bbox": [0.0, 0.0, 427.0, 397.0], "category_id": 169, "id": 10012598}, {"segmentation": {"counts": "jXa41lc04J5L4L5L3D<L5L3O1O1O2N10001O0O101O00001O00001O00001O0O10001O00001O0O1RB\\N`9e1ZFeNa9[1ZFnNb9R1ZFSOe9n0ZFSOe9m0\\FROd9n0\\FSOc9m0iEWN]Mk0j<o0cE[NcMg0i<n0_E`NhMb0i<n0ZEeNnM=g<o0UEhNUN9f<o0WEeNTN=d<n0ZE`NUNb0a<o0]EXNUNj0]<n0`FRO`9n0eDSNKQ1_;m0bDUNOn0^;n0`DWN1m0^;l0^DZN4j0^;m0ZD\\N7i0^;l0WD^N9h0`;j0TDaN:h0a;h0QDcN<g0b;h0nCeN=f0d;e0lChN>e0f;d0gCmN`0a0h;c0dCoNb0`0j;]2RDgMHWNY;T4hDjMNTNY;T4cDmM2PN[;W4\\DlM8nM\\;\\4PDmMb0hM];n6aDSI_;n6_DSIa;o6\\DRId;o6ZDRIf;P7WDQIi;P7VDPIj;R7SDoHm;R7QDoHo;a701O2N1O1O1N10000000001O000O1000000000000O100000000000000000000000nG]Dg7n;0002N1O1O00OQMTDlMk;Q2^DjMa;T2eDiM[;U2jDhMV;V2QEeMd:d2aEYM]:g2gEWMV:j2nETMR:k2RFRMn9m2TFRMl9m2VFRMj9m2WFSMi9l2YFSMg9m2YFSMf9n2[FQMe9o2\\FPMd9o2]FQMc9o2^FPMb9P3^FPMb9P3_FoLa9R3^FnLb9R3^FnLb9R3^FnLb9Q3_FoLa9Q3_FoLa9Q3^FPMa9Q3_FoLa9Q3_FoLa9Q3_FoLa9Q3_FoLa9Q3^FPMb9P3^FPMb9P3]FQMc9o2]FQMc9o2\\FRMd9n2[FSMe9n2ZFRMf9n2YFSMg9m2WFUMi9l2UFUMk9k2TFVMl9j2SFWMm9j2PFXMo9j2nEXMR:j2iEYMW:i2gEWMY:j2cEYM]:j2]EYMc:j2VEZMj:n2hDXMX;h500000000O1000000000000000000000nLhDdJJd2^;c2VEXMj:f2\\EVMd:h2aEUM_:i2eEUM[:j2hETMX:j2lETMT:k2nETMR:k2oEUMP:k2RFTMn9k2TFSMm9m2TFRMl9m2UFSMm2", "size": [640, 427]}, "area": 36024.0, "iscrowd": 0, "image_id": 10313, "bbox": [232.0, 222.0, 195.0, 273.0], "category_id": 172, "id": 10012599}, {"segmentation": {"counts": "n33mc06K5K6I6K6J2NO001O1O001O1O0010O01O00001O001O001O001O00001O0000O10000000000O10000000000O2O0000000O10000000000O10000000000O10000000000O100000000O101O0000000O10000000000O100000000O10000000000O1YB>P7BlHc0S7]OiHg0W7YOeHl0Z7TObHQ1]7oN_HU1a7kN[HZ1d7gNXH\\1h7dNVH_1i7aNTHc1k7]NRHf1n7ZNPHi1P8VNmGm1S8SNjGQ2U8oMiGS2W8mMfGW2Y8iMdG[2[8eMcG]2]8cM`Ga2`8^M]Ge2c8[M[Gh2d8XMYGl2f8TMWGo2i8QMUGR3j8nLSGU3m8kLPGX3P9hLmF[3T9dLjF^3V9bLgFa3Y9_LcFe3]9[L^Fj3b9VLZFn3f9RLUFS4k9oKoEU4R:lKgEY4Y:iK`E\\4`:fKZE^4f:dKSEa4R2VJi5Z1PHd4R2XJm5U1mGf4P2[JS6o0kGh4P2\\JT6m0iGi4R2[JV6k0eGm4T2ZJV6j0cGn4V2ZJV6h0bGP5W2YJW6h0_GQ5Y2YJW6f0]GT5[2WJX6Z7gIhHX6X7gIiHY6W7fIkHY6U7ZIYIe6g6ZIZIf6f6XI]If6d6XI^Ih6b6WI`Ih6`6VIbIj6^6TIeIk6[6TIfIl6Z6RIiIm6W6QIlIn6T6QImIo6S6oHPJP7P6nHRJQ7o5nHRJR7n5lHTJT7l5jHVJV7j5iHVJX7j5fHXJZ7h5dHZJ\\7f5cH[JZOeMe7P8oH]J[OfMd7m7oH_J]OeMc7l7nHaJ^OeMb7k7oHaJ_OdMb7k7mHcJAcMa7j7lHdJCcMa7i7kHeJDcM`7h7jHgJF`Ma7i7hHhJG_Ma7i7fHjJI\\Mb7j7cHlJKZMb7j7bHmJMXM`7l7aHnJOUMa7m7^HoJ3RM`7o7\\HPK4nLc7R8WHRK5mLd7^;[HcDe7];ZHdDf7\\;YHeDh7Z;XHfDh7[;VHeDk7h;001N1000001N10000\\ORHVEn7h:THXEm7d:VH\\Ej7b:WH_Ei7a:WH_Ei7a:VH`Ek7_:THaEm7_:RHbEn7^:QHcEo7]:nGgER8X:mGjER8S;O1OO2QOnGnER8[3PH\\2OXJQ8Z3RH^2MWJR8Y3SH`2KWJR8W3VHa2HXJR8U3XHc2FWJS8T3YHe2DVJT8R3\\Hg2@VJU8Q3]Hi2^OSJW8S3]Hj2\\OSJW8Q3_Hl2ZOSJW8o2bHm2WOTJW8m2dHo2UOTJW8j2gHR3SOSJV8i2jHS3POTJV8g2lHU3nNUJU8d2oHW3lNUJU8b2RIX3iNVJU8`2TIZ3gNVJU8^2VI\\3eNWJT8Z2[I^3aNXJ_7m2RJk2_NXJ]7m2VJk2^NWJ[7m2YJl2\\NXJY7k2^Jl2YNYJW7k2bJl2WNYJV7i2fJn2TNZJT7g2kJn2RNZJQ7h2oJn2PNZJP7g2RKo2nMZJo6f2VKo2kM\\Jl6e2[Ko2iM\\Jk6d2^KP3gM\\Jj6b2cKQ3dM\\Jh6c2eKQ3cM]Jg6a2gKR3bM]Jg6a2gKR3bM]J7[OP6V3YLP3aM`J5@k5o2`LQ3`M`J4Bk5m2aLQ3aM_J3Ck5l2cLQ3_M`J2Dl5j2dLR3^MaJ1Cl5j2fLR3]MaJ1Cl5i2hLR3[MbJ0Dm5g2iLS3ZMdJKER6c2jLT3ZMUKl5e1mLU3WMVKk5e1oLU3VMUKl5e1oLV3UMUKl5d1QMV3SMRKP6f1oLX3QMQKP6g1PMX3QMPKo5g1QMY3PMPKo5f1SMY3nLQKo5f1SMY3nLPKP6f1SMZ3mLPKo5g1UMX3lLQKo5g1UM`1cL]M9\\Oo5f1VM]1kL\\M1@n5g1WMW1SM^MHDn5g1WMR1\\M_M_OHm5h1XMm0`McM[OHm5g1ZMh0dMhMUOIm5g1ZMc0iMmMPOHn5h1YM>oMQNkNHm5h1ZM;RNUNgNHl5i1\\M4WNZNaNIl5i1\\MO\\N_N\\N\\OGGW6\\2[MMaNcNVN\\OHHW6[2[MIdNhNRN[OIIW6Z2ZMFiNkNmM[OKIV6Z2YMDlNnNjMZOLJV6X2ZMBnNQOfM[OLIX6X2XM_OROTOcM\\OLHX6X2WM]OVOVO`M[OLLW6S2YM\\OWOZO]M[OLMW6Q2YMZO[O\\OYM\\OKOX6n1YMXO^O_OVM\\OJ1Y6k1ZMUOABRM]OI3Z6g1\\MSOBFoL\\OJ4Z6f1[MQOFHkL]OJ3\\6e1[MoNGLhL]OJ3^6c1ZMmNJOdL^OJ3_6a1ZMlNL2bL]OH5a6^1[MiNO5^L^OH5b6^1R2oNdG^OH5c6\\1S2oNdG_OG5f6X1P2SOdG@G2k6W1k1UOdGBHNP7U1e1ZOeGA^7P1o0]OdGC_7m0o0^OdGD^7m0o0]OdGF^7k0Q1\\ObGH_7j0Q1\\OaGJ_7i0R1ZO`GM_7g0S1ZO_GO]7g0W1WO]G2\\7g0X1VO\\G2\\7h0Z2XOeMh0\\2XOdMh0\\2XOcMh0^2WObMi0_2WOaMh0`2XO_Mi0a2WO_Mh0l:0010O0010O00010O00010O01O00010O00010O0010O00010O01O00010O00010O0100O1O100O100O1O100O100O1000000000000O100000000000000O1000000000O2O1N1O2O1N1O2O1N2N101N2N2O0O2N2O0O2N2O1N1O2O1NZX>", "size": [640, 427]}, "area": 64197.0, "iscrowd": 0, "image_id": 10313, "bbox": [0.0, 92.0, 404.0, 448.0], "category_id": 183, "id": 10012600}, {"segmentation": {"counts": "Pgn12]<5dCJT<:02M2O0O11O0001O01O00001ORDDh;<XDCm;9QDGR<29N_kc5", "size": [400, 640]}, "area": 233.0, "iscrowd": 0, "image_id": 10318, "bbox": [160.0, 219.0, 19.0, 23.0], "category_id": 94, "id": 10012601}, {"segmentation": {"counts": "l1h1k:M0000O1O11OO100O1O13MNB[EUOg:i0YEVOj:h0VEVOk:k0UEQONKR;P1QETOR;l0:OO11N10O11OO1000000000000eDQOT;P1lDQOLOT;R1lDoN02R;R1PEoNP;n0REROn:l0TETOl:j0VEVOj:j0VEXOh:h0XEXOh:h0XEXOh:i0WEWOi:i0WEWOh:j0XEVOh:i0YEWOg:i0YETOj:l0WESOi:m0WESOk:k0UEUOl:j0UETOn:j0REVOk:m0=O100M11N22NO11O00O1000002N4L1O1O00O10000O1_OfDMY;2iDKY;4iDKV;6jDJW;5jDIW;7iDEIN];>iDEJM];>iDEKM[;?hDENKZ;a0fDE1JX;l060000001O]O`D6`;IaD7_;HbD8k;00000CFdD:\\;J`D6`;J`D6\\;@eD;N5T;j0mDhNj:a13OM1O11M20M41OO2N0000000O0100N00202N00N30N1O1O1CXEQOi:n0YEPOh:o0ZEPOf:o0\\EPOc:Q1]EoNd:P1\\EPOd:P1\\EPOh:k0YEUOg:k0YEUOc;O]D1P<1hNf0ZEZOg:h0VEXOj:h0VEXOi:j0UEWOj:k0VETOi:l0YESOi:k0WEUOg:m0YESOi:k0WEUOi:k0WEUOg:m0YESOh:l0XETOh:m0VETOi:n0UESOj:P1REROP;n0mDSOR;n0nDROR;V1O1001O0000000010L25MQEeNg:[1YEfNc:]1]EcNe:[1[EfNd:Z1\\EfNc:[1]EfNc:Y1]EgNc:Y1]EgNb:Z1^EfNc:Y1\\EhNc:Y1]EgNc:Y1\\EgNe:Y1\\EfNd:Z1ZEcNN2i:[1XEdNl:T1TEPO0KV;j0nDYOKM^;0bD=;EEN`;0`D7W<Lne4NoYK0mC66II6a;h0O1O0M33M0OO21O0O0001O2OL5OoDUO_O0l:j0cE\\O\\:d0dE^OY:g0_EQOI9h:f0[EROM;g:d0YEoN0b0g:a0YE_Og:V1O00O020OO002OO10O12NO10000000A^EPOb:o0`EPOa:n0aEQO^:P1bEoN_:Q1aEPO_:o0aEQO`:n0`ERO^:Q1`EPOa:P1]EQOb:_10WN[Eg1g:0O1001OO1O11O00000^O_EUOa:j0aEUO_:j0cEUO]:j0eEUO[:k0eEUO\\:j0dEVO\\:j0dEVO\\:j0dEVO\\:j0dEVOZ:l0fETOY:n0eESO[:n0cESO]:o0aEQO_:n0cEQO]:n0eEQO[:n0gEQOY:o0gEQOY:o0gEQOY:o0gEQOZ:n0fEROZ:n0fEROZ:n0fEROZ:n0eERO\\:n0cESO]:HZES17WOc:h0[EYOf:g0XEZOi:e0WE[Oi:d0YE[Og:d0[E[Oe:e0[E[Oe:e0[E[Od:f0\\EZOd:f0\\EZOe:f0YE[Of:g0XEZOh:W1003M1`NQE\\1X;G4N1O011ON21O000O100O1000O1EPOnD0O20o0T;<L101O101N1O0O1000000002N00000oNUEHO:l:ATEMO0;6HKM=c;DcDNL=`;Gj0Lg[k3", "size": [400, 640]}, "area": 12799.0, "iscrowd": 0, "image_id": 10318, "bbox": [0.0, 45.0, 324.0, 71.0], "category_id": 95, "id": 10012602}, {"segmentation": {"counts": "0_1Q;O100001O1ON21O000000000000O11O2N1O3MM3O14L1OL4O11O00005KM33ML4\\OUEROO`0S;6VE@U;9PEAQ;??111NN201N11O00O2O00O114K3MO11N0102MM2400N001OO100000KTDFl;9VDEk;;41000000O11OO1000000000000000003MM300002NN2000J91OOH510001O1O00N201N2O00O4L21N02NM120000010O0OK43O4L01O00001O0000001OO13M0000O1O1002N0000N13NO10010N100O02OO100N21ON20O5LM3003M0O02OO012OOO01100NN300O0014LL52N0NN40ON2O11\\D@V;j0`DXOa;j0^DUO];Q11N1O2N1OO1L44KM45KO1L44I^DWOd;c0245J3NN1TD@j;b020O13M0000000000001O03TD@b;c0[D@b;g0^DTO_;P1M2O02OO02OO11OO10O20OOO14MO11NO4NO2OO1001OO101N01001O1ON21OO2OaDQO\\;R10O2OO2OO00200O00O11O00O10IeDYO[;g0eDZO[;LcDf04]O[;`0hD@Z;`0dD@];;gDC[;=<0N0300OO021M3M011OO100003XD]O];c0bD^OLN`;g0`D@];k0O10O0000O10O10O20N1000InNPEU1l:lNPEY1OfNf:n1J1O00O11ON21O000000000000O1E_E_N0O20a:b19M3O1M3N2N200N200O1O100NeDPOZ;P130000OYEQOZOO02_:m0TFZOn9e0RF\\On9d0RF\\On9d0RF\\On9e0QF[O^OF_:o0QF_Oo9a0RF]Oo9c0PF]OQ:c0nE^OR:b0nE_OQ:b0mE_OS:a0mE_OS:a0lE@T:`0fEPO0c0U:=iEPOLl0Z:5hEPONZ1k9KUF5k9KUF5k9Z1`F\\MP9d2PG\\MP9e2nF\\MR9e2nFZMR9e2PGZMP9f2PGZMP9f2PGZMP9f2PGZMP9f2PGZMP9f2PGZMP9f2PGZMP9g2nFZMR9g2lFYMU9]2dFbM011320X9W2QGeMD7\\9X2c000000000000000000000000RFaMk9_2UFaMk9_2UFaMk9_2TFcMk9a2000N22N001ON2001ON21O001OO12N001O1O1O00000004MO1O2ZFYM_9l21TM]F_20iMc9H]F_20dM00e9Z2]FdM10c9Z2=MmEiMS:W2mEiMS:V2nEjMR:V2nEjMR:V2nEjMZ:k164M000bERNY:o1gEQNY:o1gEQNX:P2hEPNX:T200002N00002N1O00000O2O2NMSNbEl1`:4N00000O1000O10O1N22N00O10001O01ON2O100NkMiEU2V:3MhMmEY21hMn9^2JaM[F_2e960001OOaF[MU9d2jF^MV9a2kF_MU9`2mF_MS9a2mF_MS9a2mF_MS9a2mF^MT9b2lF^MT9b2lF^MT9b2lF^MT9^2PGbMP9`2?2NN200O1000000000000O11O001O0\\FdMT9\\2jFfMV9[2gFgMY9Z2eF_M02[9c2hF\\MX9c2jF\\MV9c2kF]MU9b2mF]MS9c2mF]MS9c2mF]MS9c2mF]MS9c2mF]MS9c2mF]MS9c2mF]MS9d2lF\\MT9c2=000000000000L4M36JM3O1O1O10000000000001O1O2NO1O1O10000000000001O1O2N1OO1O1O1O1000000000000O1O1O1O100000000N2O1O10000HhESN2052e9i1TFTN5122e9h1\\FWNM2g9f1^FXNI3i9d1`FbN`9^1aFaN_9_1aFaN_9^1j0O1N2O100001O3MO11O002N001OO14L1O1OO1O1N`ETN`:l12O100O1O1002N00O1001O1O00001O000000000000O10000N2O1J6KUEbNn:^1200000000000000000000001O`0XEQNo9Z2nEfMj9`2000000000000000000000001O1O", "size": [400, 640]}, "area": 32257.0, "iscrowd": 0, "image_id": 10318, "bbox": [0.0, 0.0, 640.0, 100.0], "category_id": 96, "id": 10012603}, {"segmentation": {"counts": "hha71_<000000001O001dCOP<OPD;i;9K\\O[Dg0a;6HSOgDS1o:<J6O1O11O2N9GN12O]E", "size": [400, 640]}, "area": 594.0, "iscrowd": 0, "image_id": 10318, "bbox": [618.0, 328.0, 22.0, 56.0], "category_id": 97, "id": 10012604}, {"segmentation": {"counts": "Shj084In;=O2O001O0N21O001O00O2O2NO3O3BVDLo;1chk19UWTN74LL50OO1001O000000O11ON2O100O13M1OO14LL47II76JJ65KK45MN0101OO12NN2:EH99GJ6V[2JeXMNX<2hCNW<61OK55LE<8GB=3M004LK52N003M1ON012N11O000000N31M01N200H\\OfDb0Z;^O^D06b0\\;AcD?];B_Da0a;700O100000O10000000O100000000000000O100O1O1O1ON30001O00000000001O000O10O2O0`DRO];Q1000001OO100001OO1000000McDSO];OcD60>0]O_;5aD>b;]O`DO7<i;0O12NM35KJ65KK45LL45KM300M32NoCDm;`01O00O03NN25KJ66JJ2SDCT<4\\^>2aaA9NN22N000O10000000000O10000001O1O002N001000M20100N1[D_O[;b09O2L2O20^DDR;;mDET;7^DIX<3iUa0MY^^O0G0k;1SDO78b;JWD00O0;g;>M2L21O01O0O01010O000000O010BdDF_;9lDAP;<c00ZDEW;;?100N;RDXO`;a0fD@Y;`0iD_OV;b0jD^OU;b0lD^OT;4bD6k;JWD4i;LWD4i;LYD1i;NXDNJ1o;O]DOf`T1", "size": [400, 640]}, "area": 4785.0, "iscrowd": 0, "image_id": 10318, "bbox": [68.0, 172.0, 479.0, 41.0], "category_id": 113, "id": 10012605}, {"segmentation": {"counts": "hoh01\\<4N2M3N1O2M5L100001O0000002NO12N1O00O100O1O1N2O100001OO1000000000LVDBj;=6LPDFQ<8PDHR<4US:MilE4oCOP<91O00O1O1O100O1O11O1O0O10000000000O100001OO1O1O1O1N20000O10021ONOO300\\di5", "size": [400, 640]}, "area": 1062.0, "iscrowd": 0, "image_id": 10318, "bbox": [63.0, 303.0, 101.0, 97.0], "category_id": 124, "id": 10012606}, {"segmentation": {"counts": "\\Xi11X<0nC0K0T<1QDOL0o;5SDK10k;5ZDKe;97001O2L5O1NOO1O11EUDNP<JoC2\\<02NSV90_]F0W<8N1N4M1NBRD?m;200000M33M00N21O00O1O4EoC0U<MnC0Y<0afX5", "size": [400, 640]}, "area": 449.0, "iscrowd": 0, "image_id": 10318, "bbox": [146.0, 34.0, 62.0, 211.0], "category_id": 130, "id": 10012607}, {"segmentation": {"counts": "oob1184i;:1O0001O000NBRD>n;3O2N1O2N2O1N2_EWOX9i0[FUO_O0J5[:7eE4d0A^O;W:IgE02:a02h9AfE32<=0Q:BeE=91S:BdE=80a:0^E0d:O[E0h:0VE0k:0TE0n:1oDOS;3iDMo:n00000O1N25LOlN[E=e:B`E9`:G`E:_:G`E:`:E`E<_:DaE=^:DaE<`:C`E>_:BaEO@8`6BmM6TLM_OM2=^6CbI0V46ZLL_ON0>_6FaNOUK9n5ETJ4X2K`M3Z2NoMOI7l41lKM[O2W1IhN0b0>Y1BUN1:3CLm4>RKB=;EEN7?J8;M2Q2F\\2<oJ@7l07WO79O3X73WH>:TO7901Y7Y1_H_N6731X7Y1^HaNOIF`0e0MX7Y1^HbNFGO0002d0b0LW7`1WH\\O?RO\\7`1VH\\O`0UOZ7R3bHnL^7R3bHmL_7S3aHnL^7R3]H_LH`0l7Q3VHXM\\1ZOjN7Y5V4mJbK;l0f4_3oJlK5f0k4[OaJf3>_L0l0AeNa11o1OaL166II6^31_M2c0Gg08iMU3i4cLWK0h40XKh2Q1YMf7b2[H^Md7b2XHdMf7\\2ZHjM`7U2`HiMc7W2\\HjMd7V2[HmMc7R2ZHUNc7j1]HUNe7k1[HVN8En5V2gIXN:Bo5R2kI^N2DR1B_N1n3[2VLdM_O03m0NRO20>?m0ClN8K5=D_O2n0K_O2o0[2ROjMCg0NSO3O>`0l0GdNJO948;DBOg06XO2[1P2TOlMAe00SO3ON1O19?KCn03oN8OU12\\O1\\OV1U2nN`MI]10bN0?0A0P1l0XOUOf3<SLC0229^2HaMM_10OQ1hNPOR40fK0V3OjL0\\3m0lLSOV40kN0OP1\\8JJ64KM43MM22O00O00K3XO^Db0e;^O[D0Oc0d;_O\\Dc0d;^O\\Db0d;^O\\Db0d;53M0BbDJ];2[D41Jc;3jDLV;3c0003M4MGoCM08k;KZDMm;2SD3j;KWD1V<1Zbi02]]VO?F1010OSOL_E3`:2]EMb:5]EMQ:\\OgE0:i0FWO7b0m8^OoGZ1UOWOl8_OjG0SOd0O\\O1d07Km8AhG1UOb0214Jm8BYGa0D0441Hd80`G4F07O4L^82bG2F07M6M\\8R1VGUO8L7L_8c1\\G_N6MY8i1bGYN7LX8i1cGZNX9f1iFYNW9T1ZFUO`0FW9T1YFVOa0DW9k0XFYO0G0?a:<dE]OKH0?b:O^EL6LN4MFd::_EN4LM5h:GTE2446LJ4o:KRE4j;LVD5V<K1O`iS10\\VlN0jC0Q<0oC0N6n;:N1000O2O10N100000O11O10O0GRDI050Kl;2UDNO0482Jh;6RDI12m;>0O20OO10N20M2001N0101O010AQD70IT<0lC0O0Q=0ZO1eC1Z<3M4O1L4NDQD9m;EVD<h;EXD:g;EUD059g;G\\D9c;G_D6b;J_D6n;02L10jg0NjkN1X<6XDIU;7kDIU;7b0O0011N1O2N002O0O1OMPDF00Q12]9NeE118NGn07\\90fEO4Je00UO017\\:8fFBmNO17\\:8dFFPO2[:8hFElN3\\:8hFNX92fF0Z90hFNX92fF0Z90hFOW91gF1Y9OiFNX92fF0Z90hFOV9KgEHOOR1a0Y9DSG9m8DYG7i8HZG5d:L3NQk_1", "size": [400, 640]}, "area": 11617.0, "iscrowd": 0, "image_id": 10318, "bbox": [130.0, 48.0, 387.0, 352.0], "category_id": 132, "id": 10012608}, {"segmentation": {"counts": "`l<8X<7I00O11O00001O0000001O00O11O000000O1002NL42N2N001OO10000000000O1000000O11OO100000000000000000000000000000000J64L2NO10000000000001O1O00O1O12N0000N20000001O00J61O4L00000000000000000000000000000000000000O10000001O0000O1000000000000O100000000O11OO1001OO1001OO10000O100001O1OO11O000000003]\\i0MccVO1OO100O11O1O00O11O00O100001nCCo;?02N1O0000N21O1OO11OO1O100000000000000O1001O0000O10000O11O0000IQDMo;2RDJRPY12X\\gN0000001eCLX<4hCLX<600000000000008HO10000O100O10000002N00002N1O0000001O2NMUD@l;`02000000O1000000O1N22N00O10000001ON2O100NmCGS<83MjCKW<53Oo]Y2", "size": [400, 640]}, "area": 3617.0, "iscrowd": 0, "image_id": 10318, "bbox": [33.0, 0.0, 419.0, 20.0], "category_id": 135, "id": 10012609}, {"segmentation": {"counts": "V;Z1V;02NNiNkDW1R;51O1OO11ON21O002NNNmDkNP;X1PEhNR;V1nDjNR;V1nDjNR;V1nDjNS;U1mDkNW;Q1iDoNY;o0gDQO^;j0bDVOc;N^Dd0O^On;4YDKek31]`L3O1O1L4LERD;o;4N11N2OO3J3100O2000kDCW:<cE_O^O8n:<\\E^OFN0:Q;8QE^ON02a0P;k0O11O002N00001O002N2N2FeD[O\\;a0hD^OZ;?hD@X;>kDAX;;iDEX;9jDFV;8e0L2O2N2NRc20m\\M1oN4eDK1O>1B1Z;>PE@G2Y;i0eDUO22Z;P11OO1O100002NN23MM24M^IRO_1Q1`NRO`1l0]NQOWK3Z6n0^NPOXK2Z6n0\\NXOe1g0YN[Oh1d0WN]Oj1b0UN_Oj1b0UN_Ol1`0SNAl1`0UN_Ok1a0TN@m1?TN@l1`0UN_Ok1a0UNYO^KL]6k0VNWO^KN^6i0VNWO\\K0_6h0UNXO\\K0`6g0VNWOZK2`6g0XNUOXK4`6g0[NYOe1f0^NXOb1h0_NWOb1h0j4XOiDi0V;ZOhDf0Y;[OeDe0Z;9[GmNn5R1PJPOQ6o0oIQOS6m0mISOT6l0kIWOT6h0lI\\Oo5e0QJ\\Om57`GFh26TNCc6`0cKLkMH^6=fKLfME[O1Y7>fKLaMC^O014OO[7>fKLaME_O7Z78fKM_M1k62fKL`M2j60eHAQ3>_M1k60hK0[M1m6OhK0[M1m6OgK1^MNk61gK2]MMl61gK1\\M05@]O0d6`0]L1[M04E@O\\6;eL2PM9>Cm53lK_O^N`1HoNn52kKAkM1;^11nNn52iKJXNV1G_NN0N01`0[63gKHZN\\1C]N0=\\62gKH[NT2n5UNeKH\\NT2o5TNeKH[NU2P6SNdKI[NU2R6QNcKJZNV2R6QNdKIXNX2U6lMeKLVNX2T6oMdKIXNX2U6oMbKIXNY2U6nMdKIWNY2V6lMdKKXNW2S6nMfKNVNR2U6nMfK0TNS2U6lMjKNTNU2S6lMiK0TNS2R6mMjKn2U4SMkKm2V4QMkKo2T4SMkKm2V4RMjKn2V4RMiKo2V4RMjKn2V4RMjKn2V4RMjKn2V4RMjKn2V4fLeI7U2]1kMM\\6^NdI50JU2e1mMMo8MPGcN15O1Od0Q9BoF=0UO104OK425NG2i0V9_OiFIO00525NF11=6Ga0U:WOoFi0\\:000000000000000O11OO1000000d0VOhC4Pa4M]_K1o:0nD8K5=D_O2n0K_O2P85cGJO948;DBOg06XO2\\8JeG8OU12\\O1\\OV8LhGf3<SLC022Y75fHR40fK0d0n6DQIV40WLn6k40000O10000O1000iJTI51R4j6jKUI22U4j6hKTIH054IL^4m6kKSII0O?[4_6lKRII2N>]4_6kKQIJc0\\4]6iKPIJc0^4^6gKoHKc0^4_6fKeIY4\\6eKgIY4Z6fKeI[4]6cKbI^4X7000O12NNcKQH]4o720000`NdKaIN08n0U4a5SLaJk3_5ULaJk3_5UL`Jl3`5TLaJk3_5UL`Jl3`5TL`Jl3`5TL`Jl3`5TL`Jl3`5TL`Jl3`5TL`Jl3`5TLaJk3_5ULaJk3_5UL`Jl3`5TL`Jl3`5WL]Ji3c5ZLaIYOf0^4i5ZLUJi3k5VLVJj3j5ULSJo3m5QLSJo3m5QLSJo3m5QLSJo3m5QLSJo3l5RLTJn3m5_KaI<a0V4m5_KbI<?V4o5^KbI=<W4R6\\KbI?8W4V6WKbIO0a10c2^6mKbIO0a10oN1P3]6aLbIO0a10iN7EK]3\\6eLbIO0a10hN:U3T6cLbIO0a10gN;V3S6bLcIO0b1OeN0I=]3Q6eLbIN1b1OeN0I=]3b6jMRIPOOI<^3e8OO1001O00kMdLTI:e1R3o4nM\\JR2d5SN_IbNMN00`0]3T6TN^IhN;T3W6TN^IhN;T3W6VN\\IfN:GG]3c6VN\\IfN:GF^3d6UN\\IgN1JOL0`3d6Z20000000000000jJ^I^4b6]KdIb4Z6_KUJS4l5kKUJU4k5lKTJT4l5oKmI_OAb4b6PLhICE]4c6QLaIEL]4c6hKnIC@e4c6gKRJX4m5jKRJ\\3_OiL_6MPJT30fLP68_I`3b0XLo5_4RJ`Kn5a4QJ_Ko5d3mI^L3NP6c3RJ\\LM1Q6b3TJ\\LM0o5T3]IdL0:i0FWO7b02Q6m1]ImNZ1ROZO4o5m1]IhN0SO]11XO8n51]IK7T1IC1UOX24Z44nIo0_OUOa0Da88aGF_Oj0;A4Fb89eG>FD2Fb89eG=e0YOg7<`Ga0k9@lEFJc0_:J_ELN;b:EfEDJo0`:\\OmEc0S:^OnEa0Q:@mEa0S:BeEd0Z:]O`Ei0_:d001ON2O1O10M211O000000000000O1000OIaE]Na:c1`EZNc:e14ZNXEc1k:O1eNTEQ1m:oNUEn0m:POVENi;3:0010OO1000000MImC7S<ImC7S<3VLGfG005\\34m43SKMm43SKMk45UKKm43SKMm43SKMn42RKNl4JmGOW37l4IoGOT39n4FPH0R3:l4GTHNP3;m4FSHOP3;m4FSHOP3;n4ERH0P3;m4FSHOP3;h8EnC8U<00000O1000O1000001O1O2N1O000002O3L00001O0O101O0000001O0O101O0000001O00001O0000001O000O2O0000010O00001O0000001O00001O0O10001N10001O0O10001O0O1000T2lM000O13M00001O01O000cFQMY9o2fFUMW9l2lFQMS9o2iFUMW9R31OLkLmFU3R95TGeLd8Z3\\GfLc8[3]GeLd8[3[GeLM5_8W3bGdLM9Y8HhG]30cLN;Y8DiG_30SMT8Q3jGRMV8f3M2N000O105K2N1@ZKTIh4j6XKVIh4j6YKmHo4S770000jHiJS7>nH[4R7aKSI`4k6ZKnHL017i4=_K\\OHR60oIOO17j4<aKe5EoIi4=bK[2D90oLm43fKe2]Ob0l5nNXJjMMX3k5kN\\JkMJZ3j5QOXJaM0^3h5TO^Jm0a5TO^Jl0b5UO]Jj0d5WO[Ji0e5WO[Jj0d5VO\\Jj0d5VO\\Jj0d5VO\\Jh0f5XOZJg0g5YOYJf0h5YOXJh0h5WOZJg0g5YOYJg0g5ZOXJg0g5ZOXJf0h5[OWJd0j5\\OVJd0j5\\OVJe0i5[OWJe0i5_OSJa0m5@RJ`0n5P3100N25K00000000HXJhIh5Y6UJfIn5^6000000000000000`LQJ`0n5@RJ`0n5@RJ`0n5XOQJ_M2Y3m5XORJ]M2\\3k5WO[Ji0e5WO[Ji0e5VO\\Jj0d5UO]Jk0c5SO_Jm0a5SO_Jl0b5TO^Jl0b5TO^Jk0c5UO]Jk0c5UO]Jk0c5UO]Jj0d5Q30O1000000000000000000001O000000000000000000O10000O10000O10000O1ZLlIo0U6QOkIn0V6ROjIj0OdLX6b2jIi0Y6VOiIeMLk2[6AjI`MMQ3W6_OSJa0m5]OUJc0k5]OUJ=BYMV6Z2XJg0g5ZOXJg0g5XOZJh0f5XOZJf0MmLZ5]2iJf0LoLZ5[2jJf0LoLZ5[2jJf0LoLZ5h2\\J9:QMY5o5gJUJU5k5kJXJR5h5nJYJQ5g5nJYJS5h5lJVJV5k5hJWJW5j5hJVJX5j5gJWJY5i5gJWJa4_OQLZ6^OWJa4_OQLZ6^OWJa4_OQLZ6^OWJa4_OQLY6_OXJ`4_OQLX6@YJ_4@PLV6BZJ^4@PL_2Jf0H[M^4A\\KO?_2Of0H[MW4_OcK;>U20f0H[MQ43WLl10<B[M6:l3_OmKg0?i10<DYM3>h3:aLc1O=2fMV3]OZLS1?]1O=2fMl2l0SMQ1O=EUM6a0m2R1YMk0Oi0GYMP3T1ZMj0Oi0GYMo2U1[Mi0Oi0GYMP3T1ZMj0Oh0HZMQ3R1XMl0Oh0H[MY3h0PMV1Of0I[MV3j0SMV1Me0J\\MV3h0SM", "size": [400, 640]}, "area": 62866.0, "iscrowd": 0, "image_id": 10318, "bbox": [0.0, 77.0, 640.0, 323.0], "category_id": 140, "id": 10012610}, {"segmentation": {"counts": "QVl33_;NbD1g04f:OWE4JKZ:3mE1H3S:NPF4LNT:OmE6MLV:NmE7LKW:0kE6MJX:1jE7LHY:3jEh0V:YOiEg0d9jNeF`0Ff0e9jNeFa0Df0\\9iNhF0G0?o1n8VNkFKH0?o1b8QNYG6LN4MFo1b8QNhG3N4LM5n1Z8RN]GL2446LJ4Q2[8UN`GK4l2[8ZMaGJ5k2Y8iMbGX2^8hMaGY2^8m01O000000000000O10000HPLnGR4R8oKkGS4U84O1O1O1OkKYHc3Z801O001O001O1O001O001O1O3M1O1O1O0000O100O100001O001O001O001O001O001O00001O001O00^IjLk3U3ULkL]M010R6U3^LmL]M10MO0Z6Q3XL\\M^MC]6n2RLRMbM440X6j2RLRMbM361U6j2RLSMcM181S6k2RLSMcM0:1Q6l2RLSMcM0:1Q6l2oKZMPNJQ6l2RLWMmMMQ6l2RLWMmMLR6m2QLRMdM190S6l2PLSMdM190S6l2PLSMdM190S6l2PLSMdM190T6k2SLUMiM0T6k2SLUMiM0U6j2RLVMiM0U6j2RLVMiM0U6j2RLVMiM0V6i2QLXMgM0X6h2QLXMgM0X6h2QLWMiM0V6i2QLWMiM0V6i2QLWMiM0V6i2PLXMjMOV6i2PLXM^4h2bKXM^4h2`KZM`4f2bKYMhMMZ6j2nKYMhMMZ6j2nKXM^4h2bKXM^4h2bKWM_4i2aKVM`4j2_KVMb4j2b2000[IVMd3j2[LWMe3i2YLYMg3g2YLYMg3g2YLYMg3g2XLZMh3f2nKVMfM5\\6e2nKXMbM5`6c2nK\\MdMK^6i2nK[MfMK\\6j2nKZM\\4f2dKYM]4g2cKYM]4g2cKWM_4i2d20000000000000M300000000000000mHWMkMLZ6l2mKWMgMO[6j2PLWMaM1_6N^If2`2fMR4CbId2\\2_McM1`6O^I`2^2`MfM1]6KcId2Z2_MhM1b6`2fK^MjM1`6a2fK]MlM1^6b2fK]MlM1^65WIT2_2fMlM1^65WIT2_2fMlM1^65WIT2^2gMmM0_64VIM5n1Z2oMmM2`62TIM9n1V2QNmM2a60TIN;m1`2VNR4OdIg1^9WNeFg1U:M2N0000005K2N1O2N000000000iNjDT1Z;O3M0ClD\\OW;c0:13FiPm0KcoROO10000000O1O200N0oCJi;6YDIi;49010O1O1RX67f[ICo;?001DVD0i;0YDOg;0[DOe;0]DOc;1]DOc;1]DOc;1]DOc;1]DOc;1]DOc;1]DOc;2[DOe;2YDOg;3VDMjT6", "size": [400, 640]}, "area": 17233.0, "iscrowd": 0, "image_id": 10318, "bbox": [317.0, 76.0, 308.0, 324.0], "category_id": 149, "id": 10012611}, {"segmentation": {"counts": "d3Q3_90000001OO100O10aFQM]9Q3000001NO0N0QMeFo2\\95I234LM3O12NO1O100002N001OO200hLiFT3Z9O2O0001O02OO1OO11O003MON5JfLRGX3P9hLPGX3P9hLPGV3NjLR91nFU3U9kLkFT3U941N012N1O02N00002M01001O00000O0O20O0CUGUMIMR9m2?O011ON20N21N101O01N101O02O3L3L3N1O1OO001000LhLPGZ3n8fLQGY3Q96MM33MbLSGV3U920L2001M3O10N201M1001O000O102L3M3O000001O10OGQGPMP9o2SGoLm8R3TGlLl8S391O1O2N1O1OO1O100000000000000000000000000000000000001O000001OO11OO11OOJiLUGX3R9VMjFI3Q2U9UNlFCK14X2T9SNXGk1j8TNWGl1h8SNWGn1k8QNUGn1l8SNSGn1l8RNTGm1m8SNRGo1m8RNSGm1m8RNSGn1m8SNSGm1m8TNRGl1n8TNRGe0C4[9XOPGd0GRO5e0T9FoF?FTO?4AK3;Z94kF4HA0Nh:=`E1m:NTE1m:NTE1m:NTE1m:NSELFI[;;PEED002g;0c01Xm3NlRL0K6N6IN21O3J7LO21M3O2NM31O0PF[OT8d0iGFR8M^FF0OO0a1:W80[FFd1?m7K_FFb1=T8MPH:j7DXH6m7JTH:i7EWH7m7IRH;j7FUH8o7GQH:m7GWH5j7JXH3S:O2LjCLV<1jCO0O[<03000aj32]UL2M2iCKQ<:000O100000000001OO1000000000\\DHP;9kDEF6\\;7kDNT;2eDD0>Z;2fDNZ;e0O0000001OJ63M00000O2O00O1O100L4O1O10000000000001O1O1N0N4NN210000000O10O^Oc01N0100000eE@e8a0ZGAh8?SGFcNJ1OP:Q1kF]OV9c0nEoN070O7:k9`0nEPO0?62n9?lE_O53P:=kEB13T:m0lETOT:l0kEUOU:b111O1OO1O1000jEhMS92UGO:[2Z8lMZGKKM=^2Z8mMZGKNL=\\2[8lM\\Gm2c8UMYGm2g8>0YLYGe3i8O01O1001O0TM\\Gl1d8SN^Gl1b8SN`Gl1e8nM]GP2e8oM[GQ2T9`MlF_2U9aMkF^2V9bMjF]2g9M100001O00000000001O000O1\\ObMdF011<[2m8PNfFD7]2W9b0100YMfFX2Z9gMhFX2X9gMjFX2V9gMlFX2T9hMlFX2T9hMlFX2T9hMlFX2T9hMlFX2T9hMlFX2T9hMlFX2T9dMaF29[2V9cMaF37[2IaM60o81kF53]23]Mo80mF0O0005_2KcMO7_9f20O12NM35KJ65KK54LL45KM300M32ON20O00O12NN25KJ57IKPGUMC2o8o2UGXMj8m2\\GjLb8U3`GkL_80UGR3=nL^80UGQ3[9oLeFQ3]9O2O000O100000O01000000O100001O000000001O00000N3O1O1O1O0QGkLc8T3=O1eNeF@\\9;dF`N0T1?iN]8^1TGeN0N6h00XOX9\\1RGTO^9k0cFUOL@Q9[1SGUO]9k0bFVO^9k0aFUO_9l0aFRO`9m0aFSO_9m0aFSO_9m0aFSO_9m0`FTO`9l0aFSO_9m0bFQO_9P1`FPOa9o0VFcNI050K<Q:Q1QFdNNO0482l9f1TFTNI1S:l1=1O2N1N2OO11O0000O10O2O001fElM70e9X2TFhM0O03k9b2N1O5K2N10M2000000O10000001O00000000ZO]FkM1a0b9a1dF^N\\9b1dF^N\\9b1dF_N[9a1dF`N\\9_1dFbN\\9^1eFbNZ9]1gFcNY9\\1iFcNW9\\1iFeNW9Z1kFeNU9[1kFeNU9Z1lFfNT9Y1iFUNF00b0a9l0_FeN118NGc0`9i0gFdNO4J`0_9i0PGBn8>RGBn8=RGFl8:TGFl8:TGDn8<RGFl8:TGDn8<RGFl8:TGDn8<RGFl8:TGDn8<RGFl89nFdNHOOR1^9<gF^OK_O0G\\9\\1fFAOQOO7a9W1`FA1QO00O0b9`2:11M21O0000O1001O00000O11N1001AbMjF_2c9eMRFT2n9lMSFS2m9lMUFS2l9jMQFO3X2S:O100OImMSFR2n9nMRFQ2o9oMQFQ21mMf94VFP24nMd9Y2\\FgMd9Y2\\FiM1K]9Z2aFlMNJ10`9[2_FQNc9_2O00000000000000000000001O0B?4L0O1000ON8H3O1O1N100000iFXMY80cGh2j8\\MVGd2j8\\MWGb2\\90000000000O01O1N201O1O10O01O1O001O3M0cFPMY9Q33002N0000J6N2M300000000O100000000000000O1000000001O001N110O001O1O0O10000000N2N200O2O0OPM", "size": [400, 640]}, "area": 49633.0, "iscrowd": 0, "image_id": 10318, "bbox": [0.0, 62.0, 640.0, 153.0], "category_id": 155, "id": 10012612}, {"segmentation": {"counts": "PhT2>R<2N1O2N1OO1L44LL45KO1L44L1OK54L1OM3N200O13M0000000000001O000000O1N2O11OO11OO11OO1001O00N23MO11ON2001OO1001OO100O10000000000001OOakk4", "size": [400, 640]}, "area": 1022.0, "iscrowd": 0, "image_id": 10318, "bbox": [176.0, 0.0, 65.0, 20.0], "category_id": 158, "id": 10012613}, {"segmentation": {"counts": "e6a4o7002NNTH`Km7\\442O1N01aKQH^4P8N21O011L0YIbK\\5_4gJaKW5c4gJ]K[5a4eJ_KX5d4hJ\\K\\5a4cJ_KZ5h4bJXK]5k4aJUKa5n4ZJRKe5T5_ImJd0Ol5`5fIgJY6j5N02N001OO1WKTJZNMc4n5oLYJ^NJa4n5RMWJ]NL_4n5PM]JQ3`4bKRL\\1@Q3[4fKULX1AS3X4fKWLY1_OQ3Y4gKXLV1AT3R4jK]LS1@T3n3^KdK;o0S1_OS3o3nKaLl0CW3j3nKcLl0BV3l3lKcLn0POdN>f4m3gKeLo0POdN>e4m3kKcLo0_OX3h3lKjLk0_OW3i3nKhLh0BZ3f3mKiLi0CV3]O_KX4b0gLc0KZ3b3SL\\Lh05R3b3ULSL_O^O^1i0Q3e3ULlK^OFN0_1k0n2g3UL`K^ON02f1j0f2g3TN[Lk1d3TN]Lm1d3TNXLP2f3PN\\Ln1f3PNYLQ2g3oMZLR2d3nM\\LQ2f3nMYLS2g3mMYLT2h3eM]KlMk0_4j3bM]KmMj0a4k3`MZL`2g3_MYL`2j3^MVLb2j3^MVLa2n3\\MRLd2o3[MQLd2P4\\MPLe2R4XMnKh2S4WMmKj2T4TMlKl2V4aKhJ^1R1Q3Y4oLfKR3Z4nLgKQ3K`K_3_1fLQ3JlKW3Q1nLT3KbL_2;fMT3JbLa2;bMU3K`Ld27dMX3GcLe25dMY3EcLf25eMX3PO\\K>1BV1[37cM]3\\OUKGW1Y36fMi3oNjJ2W1X37gMU5W2mJiMS5V2QKgMo4Y2TKbMn4^2lJhMT5Y2kJgMU5X2lJhMT5X2mJiMQ5W2PKhMP5X2PKhMP5X2nJfM_NUOc6U3nJ]M_5c2`JZMd5g2[J\\Mb5e2]JZMd5h2]JTMd5o2ZJQMe5n2\\JRMd5m2_JnLd5Q3ZJTMd5k2]JUMc5l2\\JTMd5k2]JUMc5l2]JSMc5n2\\JRMd5n2UIdLl0>P6o2RIdLl0?R6o2PIbLn0?R6o2PIbLn0=T6S3lH`LQ1<S6V3jH^LS1>Q6W3oIiLQ6Y3nIdLT6]3kIdLT6^3jIbLV6^3jIbLV6^3kIaLU6_3jIbLV6R1kHk0R1PNS6S1mHm0P1PNS6S1mHm0P1oMT6T1lHm0o0QNT6o0RIn0i0TNU6c0PIRO9S2b0\\NQ6>gIU1:[No5a0iIMVO6R1YOR6d0eIN[O1n0Bm5>lIIWO[O1l0m0CP6<lIETO^O014OOn0o0Bm5<nIFUO_O7m0h0Cn5;oIDA>b0Bo5;nIFA=c0@n5=PJE_O>b0@P6>oIB@`0a0Bn5=PJAA`0`0AQ6>nIC_O>c0Bn5=QJB^O?b0Co5<PJAA5@]O07P18Q6>oI_OA4E@n0b0l5IRI3o0CJ>c02c5EoHJ54K3o17^O4f5@YIOF1Q2<ZO3c8CPFN0N01b1>b8APF0\\1a0]:03L10O2O0O12N00O20L43nEBVN3T9>fHCoMK14Z9;hH1V72hHOV72iHM]7NdH2X73hHL[71eHO\\7OeH0Y74gHLY72gHNX74hHLf0BlN0U3c0ZMJe0CiN0_N2i49WKJS28nNCd00UO0J1\\O0T50nJ2R28mNGRO5i0I^O19421_1Jm00PM0T2:hNFTO?f0B[OO50J1a0513]OIn10^36aK;A;NEKKMC]10bN15O1O_46UL>A:De02_N;N=0UO104OK425NG2e43WL?C:AV18mMIO00525NF11=6G]40WL?Bc3BRMf4kNULa0Cm4Y4`JULc0Bm4Z4_JTLc0Dm4Z5PKdJR5Y5PKhJP5\\5lJdJT5\\5lJcJU5]5kJdJT5\\5gJUJH`0`5]5bJlJ\\1ZOjN7n2a6XMWI;l0X2m5]M^I5f0]2l5]MdI0l0AeNa11@j6VORJ2c0Gg08Q6HhG0cj3;`aL0N4nNFREK20>?_:EPEM3O>`0^:DQEM3ON1O19?c:ITEF0?0A0a0_:AbE0OO0:0j0_:oNaEd1`:\\N`Eb1c:4O2O1000O1O1004TGQNiN1Z7O]HQ2^1oMiN2]7X2WIlMYOJH054ILf7[2TIRN_OCI0O?c7l1VISN^OBI2N>e7l1TITN_O@Jc0d7h1TIVN]O_OJc0f7h1SIWN\\O^OKc0f7h1SIXN[O4a7g1QIVN\\O6a7d1eHkMJ<O4b7e1aHcM41Je0M1f7d1`HSNLR1d7k0_HIa76_HJb75aHK]74gHGDPNe7Y2^HhMM0c0_1T7`N[HT21RN?Z1U7`N]HR2NTNa0T1]OfN10n72RHk1OZNh0IYO08`0W8f1fH[NjN_O]8X2gH[NX7f1hHZNX7g1gHVN\\7j1dHVN\\7j1dHVN\\7j1dHVN\\7i1eHVN]7j1bHVN]7i1eHXNZ7g1gHXNo4CRLU2oNYNn4BSLT2POYNm4ERLU2nNVNm4HUL9lMV1T1cNP5LQL6iMF6a1T1^Nn49QLORN]1U7eNhJGiM0>_1U7jNhJGSNf1l6eNdKS1d4lN\\KZ1^4fNbKT1c4mN]KX1`4fNaKT1f4jNTK`1iMXNY69QL[1gM\\NY69oK[1hM\\NX6:PL[1gM[NY6:PLZ1eM\\N@0a10f3:eM[1bM[NB0a10oN1V44XN^1bM]N@0a10iN7EKc42\\Nj1TMRNO0O0a10hN:^4FWNf1]MZNF0O0a10gN;`7d1ZHQNN1O0b1OeN0I=c7`1fHSNI0N1b1OeN0I=b7]2eIWMPOOI<b7a4OO1001O00XMbHkMN;0E:e1V7`0bHkMN3Z1m0W6S1eHPNY10bNMN00`0c7c1cHPN[1OhN;[7`1gHVNV1OhN;Z7f1dHoM\\1NfN:GGd7g1oHXNP10fN:GFb7U2\\JkM\\N1JOL0d7h1R33M004KL52J421N0ON102QOaEJ`:5bEK[:5jEPOJ1Oa0m7_OcJk0nMF^7@eJN_M070O7::Y7DhJK_M0?629X7IiJHmM53<S7ImJFoM13?R7J^KGaM=Q7M_KEaM>P7MVLLTL1c74ZLJSLJ06b77\\LHUL0_78[LIWLK_7O^H>i3IP4:PLFP4:PL_OVL4k7=SLAl3`0VL^Oi3c0WL]Oh3c0S40001O0N21OO1O101N100O1JnC0P<NPDLY<2hCN]<000h\\d20kV[M2V<NiC5V<3O2N2O1O0O;E0O10010001O3MO11O011OO2OOOK\\D@h;;ZDDg;:9OO1O1NEoC;Q<3N2O5J000120M10OF\\DIe;6]DHe;6]DN_;0cDO];1cDO];1cDO^;0bDO_;1aD0^;0bD0^;0bDN`;3^DNb;3\\DNd;4XDOf;=1000N2O1J5LTOcDo0];201OO1000000000000000001O`0WORD0Y<Mf\\5", "size": [400, 640]}, "area": 36923.0, "iscrowd": 0, "image_id": 10318, "bbox": [0.0, 29.0, 626.0, 371.0], "category_id": 169, "id": 10012614}, {"segmentation": {"counts": "jom21110OR<=0CnC<R<00ON4OO1000O1001OO20OO0105K1N2L4OVVd4", "size": [400, 640]}, "area": 259.0, "iscrowd": 0, "image_id": 10318, "bbox": [240.0, 242.0, 20.0, 17.0], "category_id": 172, "id": 10012615}, {"segmentation": {"counts": "Ti42^<00000O2O00000O1000000000000000000000N6NL2N2N2N20O10001O0000OJiC5W<MiC1W<1iCMW<600000000001ON4N000000000000000005K1Nn]4Mc]K0eL<P3EoLR1Z2oNeMQ1[2POdMP1\\2QOcMo0]2RObMn0^2RObMn0^2RObMn0^2RObMn0_2PObMP1k1nNYJ2l3P1j1oNZJ1l3P1^1nN^J280l3P1^1[OfJDm3Q1]1[OfJDm3Q1_1YOeJEl3R1h1POlNP1V1nNjNR1V1nNiNS1Z600000000000000000000000000000000000000000O100eKkN[MU1d2lN\\MT1c2mN]MS1b2nN^MR1a2oN_MQ1W2kNhI4Q4Q1W2lNgI3R4Q1V2mNhI2R4Q1V2mNhI2R4Q1U2lNkI3P4Q1U2kNlI4o3Q1U2oNhI0S4Q1R1IgJWO40R4P1c1B[J^OR4P1V2oNhI1R4P1V2oNhI1S4o0U2oNiI2R4o0U2oNiI2R4o0U2oNiI2R4o0U2oNiI2S4n0T2POiI2U4l0R2oNlI5T4j0P2QOlI5V4h0o1SOjI5Y4f0m1WOhI3]4d0l1XOgI4_4b0j1ZOgI4a4`0i1[OfI5c4=h1^OeI5Q4KZL`0Y6E]MLZL>Y6F\\MOZL:Z6G\\M0ZL8Z6H\\M2YL5[6I\\M4XL2\\6J]M5WLO\\6L`M3ULO[6NcM2TLLY62eMOS31g4O0000lGMk33UL2f3NZL7a3I_L6SL3g6GVM6RL7e6CYM5RL8f6CXM4SL8f6DWM4RL8h6DVM3RL9i6CWM3PL9j6DVM2PL:k6CVM2oK<k6BVM2nK<m6AVM2nK=l6@WM3lK=n6@VM1SL:RP`1Gg[`N6N?A11O3GYDBi;7`0ImC0`;a0XD@h;d0101DZDIQl3NPTL7h;:N2TD_Og;e01N3GVDHl;2\\DLd;0;6iCJo;=002L2E_o80kPG2nD9V9JaE]1_:;O010O001O001O001O002N3M1O1O001OO100O10000001O001O00001O001O001N101O001O001^OiMiFW2V9mMgFT2W9mMiFS2V9nMjFV2R9kMQGT2k8mMWGQ2i8oMWGQ2i8oMWGQ2h8PNYGo1g8QNYGn1h8RNXGn1h8RNYGn1e8RN\\Gn1]5bMWJOR3a0ZMn1\\5eMZM=ZMo1\\5cMZM>[Mn1a8SN_Gm1a8SN_Gn1`8RNaGm1`8QNaGP2^8PNbGP2^8PNbGP2^8PNcGo1]8QNcGo1^8PNbGP2^8PNcGo1]8QNcGo1]8QNcGo1^8PNbGP2_8oMbGP2_8oMaGQ2`8mMaGS2_8mMbGR2]8oMcGQ2\\8QNcGo1\\8RNdGn1[8SNfGl1Y8UNgGk1Y8UNgGk1Y8UNhGj1X8VNhGj1X8VNhGj1X8WNgGi1Y8WNhGh1X8XNhGh1X8WNiGi1W8WNjGh1V8XNjGh1V8XNjGh1V8WNkGi1U8WNlGh1T8XNlGh1T8WNmGi1S8WNnGh1R8XNnGh1R8XNnGh1R8WNoGi1Q8WNPHh1Q8WNoGi1R8VNnGj1R8VNnGj1S8TNV3O001M15I3N3O02N2N2N2N101NDK_D3_;0bDN_;2bDL\\;0WD5W<3O2N1O1O1O1O1O1O01O000000001O1N1Ob^>IfaA;C2XM_OaI;e6FZIa0^6AcI5^MIg83VGLO3j20X6:P3OgFL^6LaI5`6J`I7_6I`I8`6H_I9[91O2N100O101O0000001O00O100O10000O2O0O2O1N1OmCFS<;1ON2N20000O10000mLDRJ<P6BPJ>Q90O10000000000O1000O101O1N2O001N2O0O3M4LmZ26kdM4L4ON2O[DHT;8kDIU;7kDJS;7mDIS;7lDJT;6lDOo:1QE2l:NTE2l:NSE1o:0PE0P;1nD0Q;1oDOQ;2mDOS;1mDOS;1mDOS;0nD0S;OmD2R;MoD3R;KoD5Q;KoD5R;JnD6R;JnD6S;ImD7V;FjD:X;@kDa0b;000C@jD`0R;IjD6T;LlD5Q;LPE4P;KQE5n:LRE4n:LSE4k:MUE4R5", "size": [400, 640]}, "area": 15198.0, "iscrowd": 0, "image_id": 10318, "bbox": [12.0, 47.0, 628.0, 261.0], "category_id": 183, "id": 10012616}, {"segmentation": {"counts": "P7f0e<O1O10000O100001OO1O11OO10002M5L2N4LUYb0Mhd]OOjE271\\80dFOS1O]O0B2l06QOGe03b73jGOm0O]O1IMJ0:N7>3O\\73SH@N6b00^ON0d00YO03>;31?@j5o0fIf0F\\NLa0>O>B]57dIh0>k0CkN?I0FEMS6^1VJV1B[M9a05GCKW6\\1lI`NOl3:gLi5[5UJjJg5W5WJlJg5`6LO11O00001O00N2[ITJk5l5UJVJ\\ONW6m5XJ`J\\5k5bJXJ[5k5bJWJ\\5m5`JXJ\\5l5^J`JV5e5gJ_Jn4T71M01O1ZOgGTL_8l3bGmKd8R4;00O1O0hMbGD`88kGiNYOJl86]G1f0^O[O:b86^G1\\1@QNN14T9<`GA[O;c;3WEKj:5WEHk:8UEAVO3f;;Y1CQC5Pk3MoTLNk<>XJB`N`0bK@b40YJ2e3MSNc0]OAnNM[56TNI^L2\\1l0G_OY4LTNLaLNR1n0UOUO:9h4KgJMn2:TNQ1]OnNj44eMMSNS1\\OmNl42fMNPNU1^OlNk41gMNcMB_O0Ne19nNP5MgMNbMe1^OhNY5FeMNdMd2g4]MfMObMe2h4\\MfM0aMd2i4ZMhM1`Me2h4YMiM3]Me2j4XMiM2^Mf2i4WMXMAnMd00c2k4XMVMCjMe06`2j4XMUMFfMe0:\\2m4YMQMIeMc0>9Ci0Y5cN_LC`NS17MGc0U5jNZLH^NP1<KL>X7jN_Hm0c0DHd0g6`NiH55:Ml0P1TOAn0`6PORI`1R1\\N]OS1_6V1VJeM[Oo0e6]1PJ]MgN4c0n0k6c1cJ_N\\5b1cJ]N]5c1dJ]N\\5a1fJ`NY5`1gJ_NZ5b1eJ^N[5a1fJ`NV5c1jJ]NT5d1kJ^NT5c1lJ]NU5_1mJ`NW5[1jJdNX5_1cJbNY5]1hHaMo1Q1X5a1nJ]NT5c1mJ]NR5c1oJ\\NQ5d1nJ\\NS5d1kJ]NV5c1jJ]NU5e1jJ[NV5c1lJ]NT5d1kJ\\NU5d1lJ[NS5f1lJ[NT5f1kJZNj4oNZIh2j1YNl4SOWIc2d0XM>R1h5R2aI[Ma0a0o5T2aI\\M>a0P6T2bI[M=d0n5R2fIWM?e0j5U2iIVM=d0j5V2fIZM?`0j5W2hIXM<c0l5U2hIXM;d0m5S2hIYM<d0l5T2fIYM>c0l5T2fIXM?d0k5U2eIWM`0c0m5V2aIXMc0a0l5X3TJeLo5\\3RJ`Lo5]3QIhKP1j0P6]3TJdLk5\\3TJeLm5`3lIaLU6]3lIcLT6]3kIdLV6[3jIeLQ6]3RJcL_5o3]JRLc5o3\\JQLd5n3]JRLc5o3[JQLf5m3\\JSLd5j3RJWKHP1V6j3nIaKDe0^6k3mI[LCjN31^6i4mIWL`6i3aIVL_6j3`IVLa6j3_IULb6j3aISL`6m3aIRL_6R4PISK8n0g6T4TIoKl6X50O1001O1O0000001O2N1O2N1OO1O11O2N00O100O11ON29G3M5K1O00O10000O1M3M3004LO1O10000O11O001O00SOcImJ\\6R5eInJ[6Q5gIlJ[6R5gIlJ[6S5fImJZ6R5hInJW6P5kIQKT6n4mIQKT6n4nIQKR6m4PJSKP6l4QJTKo5k4SJTKm5j4UJSKn5X3hI^M;]Oj5T3mI\\M<_Og5R3RJjLCLe07g5o2VJjLCMa0;e5m2YJfLGN>>b5l2^JbLG1:a0a5h2[LWMf3h2\\LWMd3e2`LZMa3f2_LZMa3f2`LXMa3h2_LXMa3i2^LVMc3j2^LVMa3j2_LUMb3k2^LTMc3l2^LTMa3l2_LRMc3n2]LQMd3P3\\LQMb3o2^LQMb3o2^LRMa3n2`LRM_3n2aLTM]3i2fLWMZ3g2hLZMW3_2PMaMP3a2oL^MQ3b2oL^MQ3_2RMaMn2]2TMcMl2\\2UMdMk2]2TMcMl2^2RMcMn2[2TMeMl2X2WMhMi2X2WMhMi2X2WMhMi2X2XMgMeLFc5b2jMiMXL0l5W2mMYNR2g1oMXNQ2h1PNXNo1i1QNWNn1j1RNVNm1j1TNTNm1l1TNSNl1m1TNSNl1m1UNSNj1m1WNQNj1P2VNoMj1P2XNPNg1P2ZNmMh1S2YNiMj1V2WNgMl1W2WNeMl1Z2VNcMl1^2SN_MP2a2]400O100001O00000000000000000aEdMo9m2O1WFkLZ9V3bFnL]9S3aFVMW9l2dFgL0=\\9_300000001O00001O1O001O1O001ONiFQLV9j38J61OO100O11O3MO1M30^G_LZ7a3eHlLo6T3QIlLo6T3QIkLP7U3QIhLQ7X3oHeLT7T3iGcLY16P7P3ZIkLj6T3g10000000000O1000000000000O11[FmLP9T3iFSMV9n2dFXM[9]3O00000000O100000000O10000O10000N2002N1ON200O10000O10000000000nN^F_MN40;e9Q2kFnMU9R2kFmMV9R2nFjMS9U2RGfMo8Z2QGdMQ9[2P1K5O1J6O1000000O100000000N21OO1L4H80000000000001O00O100002NOlF\\Nc6h1mF]N7Ma11Z7m1TGTNc03IO_8l1QGVNd07BLg8S2fGTN@Ki8Q2cGcM_OW1m8W1]GdMFZ1l8T1WGVOi8f201O002N00001O1fH^Ki5b4UJ`Kk5`4UJaKUONQ6a4gJeKUONR6]4fJiKUONS6Z4eJkKUO<g5j3QJ]Ko0>WO<i5l3nJkKVO;l5i3nJnKTO:n5g3oJPMQ5P3nJRMQ5n2PKQMP5o2cJ]KJb1c5P3dJ_KHa1d5Q3dJ]KGc1e5P3cJ]KIc1d5P3_J`KNY1j5W3WJbKDJ7_1l5U3ZJdKDG2g1j5o2^JcMb5^2WJhMi5[2TJgMj5n4OO1000000000000O10000O1001O000000O1000000MSJiHl5W7UJiHj5W7WJiHh5W760000001O00DUJXIk5g6=O1O100O1O1O1003MN2001O1O001O001O4L1OM3N21O00M3N200I7L40000O10000O1[OSIlJm6d4XIRKL:k6d4eI\\K[6`4iI`KW6W4SJhKm5Y4UJdKk5\\4WJaKj5_4XJVKkN6m6c4]JZKe5f4`1000000000000O1000000O11O1O000000O1N2O11ON2000000O11O1O000000O100O1O1O10000O1O11OO100O1N2M3N200000000000000001O0000002N1ON2O100", "size": [427, 640]}, "area": 85116.0, "iscrowd": 0, "image_id": 10319, "bbox": [0.0, 0.0, 640.0, 271.0], "category_id": 113, "id": 10012617}, {"segmentation": {"counts": "V9U4V901O0000O10000000000O11O00001OO1O1N20000N2O100O11O1O0000O1O1O1O10000001O00O11O000000000000O11O00001O2N2N2N001O1OO1O1O100O1001O0000000000O1O100000000001OO1O100001O000000O1N21O00001O0000001O0000001O0000001OO100O1L4H8O100O10000N2O100O10000IZKhGg4Q8=O1O100001O0000000000000000O100N200N2O1O1O1O1J6K51OO1001OO1001O000000N2M3N2N2N21O00O1O100000000000000O1O1N2002NO100000000000000O100N2O1O11O000000000000000000O1000000G`I`I31^6Z6=00O1O1O1001O0000O1001O1O0000001O2N1O2N1OO1O11O2N00O100O11ON29G3M5K1O00O10000O1M3M3004LO1O10000O11O001O001O00N2N2001O1OO100000000M33M00O11O0000O100O100O100O11OO1O11ON2O12N001O1O2L2M4M2O1N2O100O1O1O1O10000O100]JhHR5[7cJPI[5a7N101N2N2N001O1O2M101O2N2N5K3M9G3M3M2N2N3L5L3M2N2O002N2N3N1M3M3L4M3N2M3M4XO[ERNY;l15N2N3M3M3M2N3M3M5K7I6J7I7I7Ifo<2XPC2M1O1O1O1O1O1O1O1O100O1O10000N2O1O1O100O1O1N200N2O1O1N2O1O1O1O1O100M3O1O1O1O1N2N2O1O1N200O1O1O1N200N2N2O1N2O1N2O10000O11O000000002N00O11O1O1O3M3M2N2N1O1O1O1O001ORLaNPL_1P4dNmK\\1R4hNkKX1U4kNgKV1Y4nNcKR1\\4_OUK`0f4bN]HS1j29f4gN_HR1k26`47bKG]4:dKE[4<gKBY4>hKAX4?jK_OV4a0kK^OU4b0mK\\OT4c0nK[OR4e0PLYOP4g0SLVOm3j0VLcN_L7[7V1YL^N_L;X7W1`LiNY3^1jL_NX3^1RMYNT3`1\\4O1O3M2N001O1OTDhNg;W1YDiNh;V1XDjNi;U1WDkNj;U1501O1O001O2N1O1O1O010OZESOP9l0PGYOl8f0PGBj8CgEi0^1Le8\\OmEf0^17]8TOUFc0^1b0U8DfFVOf0^1]8]OmFTOe0d1Z8XOQGUOc0f1Z8UOTGTOa0j1Z8ROTGVO?k1[8oNWGVO=l1o7VN^Gh07VO;m1n7[OfGjN:m1m7ZOjGmN4j1P8[OkGjN3o1Q8WOmGkNNR2R8TOQHhNL[2m7oNVHdNNa2h7lN[H`NNg2e7iNkHY1W7eNgH]1V7eNhH^1W7cNaHf1^7YN[HP2d7oMVHZ2NYLc7d5O1N2jH\\Jb6e5\\I[Jh6d5TI]Jm6d5QI\\Jl6i5RIWJl6U61O00M3N200I7L40000O10000O1001O00000000O1001O2N2N4L5K2N3M001OO1O11O0000O100O1O100O100001O001O1O00O1O1O1N200000000O100001O1O001O00001O000dIaIl5o60001O00000000001O000000000000001OO1", "size": [427, 640]}, "area": 88490.0, "iscrowd": 0, "image_id": 10319, "bbox": [0.0, 201.0, 640.0, 226.0], "category_id": 123, "id": 10012618}, {"segmentation": {"counts": "kUb15o<7K6O1O001O00O100O2OO11N011OO10O01[DCZ:?eEGU:9kELP::iEHY:b0ZEAd:d0VE]Oj:e0TE[Om:e0lDgN4c0S;^12OO1N2001OO100OO20M2N14KLQNQEo1P;601O1OO1N201ON21O01OO2O1N0gNjD>W;YOXEa0g:@ZE>g:BYE=h:DUE?i:CWE=h:@\\E?d:B[E<g:DYE;g:EYE<h:BYE>g:BXE?i:@WE`0j:^OWEc0i;011JPOkCP1T<40O7I010O10M33L2OO11NO2MFfCH\\<4oCDR<;>101OO10O3N13_OUC8R=KiTh06SkWO2L5J5N2M5I30M013M30001O0O1000000O101O000O1000M3N1J72N00M3N2O02O1ON2M3000O10O100000O2O1O0000000O101OO10000O1N2O11O0000O100001O000000000001O0000POUD`0\\<O1F\\CKe<1_COa<O`0KkB0fX6O^TJ8G610N2O1N1102NN1M6M<DO1000XDnNW;S1fDPOY;P1dDSOU;JbDY16oNQ;[1jDjNT;g10001OO100O010O100O100O10O02`NhDk0Y;nNnDP1T;jNREU1c;N100001O0O101O00001N10001N10000N3O02N10M200O2O000O2O00001O000nN\\NaEN40;h1n9hNQFX1o9hNPFY1o9kNmEV1R:oNiES1V:mNhEV1V:P1K6N2I6O2O1O001N2O001O1O0N300N1L5G9O1O1O1O2N1O2O2M4K5L3M4N2K]MkG5V8[MoG7Ma11P1W8^MjGc03IOX2S8XMoGd07BLc2W8jMPH@Kh2R8dMbG_OW1P3T7ZMgGFZ1R3n6QM\\IR3]83N2M3ON0O101O1UNoE9R:EPF6O`NQ:Z1QFUON93WOo9X1TFUON4[:d0kEUON4Y:d0lEUOQ;HbDo0>WOT;g0oDVOR;j0PETOP;m0g0O11OCgCJV<7kCHV<8iCGX<8hCIX<3kCNU<1mCDJ7Y<6oCDG2\\<8c0I9Mc\\i1", "size": [427, 640]}, "area": 16475.0, "iscrowd": 0, "image_id": 10319, "bbox": [120.0, 52.0, 382.0, 201.0], "category_id": 142, "id": 10012619}, {"segmentation": {"counts": "0P7f0`J_3`5`LaJ`3_5_LcJ`3]5`LcJ`3]5`LcJ`3]5_LcJb3]5^LcJb3]5^LcJb3]5_LbJa3^5^LcJb3]5]LdJc3\\5^LbJc3^5\\LdJc3\\5]LdJc3\\5]LdJc3^5ZLdJe3`5WL_Jj3c5TL\\Jm3h5oKVJS4a7000N2O100O11O1O0000O1O1O1O10000001O00O11O000000000000O11O00001O2N2N2N001O1OO1O1O100O100ZLjKXNU4BjKfM271`2S4QNmK_NOS1O]O0B2l06QOGe03g1S4UOPL`NOm0O]O1IMJ0:N7>3Ob1i3\\OZLaN@N6b00^ON0d00YO03>;31?@1V4?iLPOf0F\\NLa0>O>B1S44TLlNh0>k0CkN?I0FEMj0S41[M^OV1B[M9a05GCKl0R44ZMSO`NOl3:gL`0c32h1\\OjJ>^36i1ZOlJ>[38U3DkL<T3ElL;U3EjL;V3DkL<U3ClL=U3BkL>U3BkL>S3ElL;_LROk5c0fM;_LTO\\ONW6c0nM<ZL^O\\5a0ZN1XLA[5?^N1TLB\\5=bN3nKE\\5:eN4iKNV51QO3fK0n45\\OV1e0iNZOV1f0kNZOU1e0lN[OT1O`MTLb1m3n0OaMmKh1S4g0OD1<OD1<NE2;MG28fMTNDe1f28bM^NiNYOJQ2k38[LPN1f0^O[O:g1l38ZLQN1\\1@QNN14Z2R47ZLSNA[O;h4k3jMXLXNKo3m3iMXLYNHo3Q4hMWLYNAVO3f4Y4kMRLQ3V4TM]KZM5`5_4S30O10000N2O100O1bMYKoINV2i4c3eK\\L[4mMWKa5`0bK@@3[Oi40VKe3MoL4:?7AnNM2_5]3`J^L2;99c0?_OoNV5^3bJaL`0`0<GUO:o5_ORJn2m0aM>0n4_2dJ`M`0Ol4b2eJ\\Ma02j4b2iJRMjNNe19l4g2gLPMb3n2]3100N2O100O_OjEnMW:Q2kEjMY:U2jEfMY:X2jEeMY:i1bE`Nk:]1ZE^NX:P2kEoMV:a1_EZN55:MS:_1kEdNg:[1e0000000O100000000M3N2O11O2NO1J6M30000000000O1000000O100EhCCX<<lCAT<?<O1O1O1N200O100000000001O001O0000001O1O001OK^lj4BeSUK5`CL_<`0L4G:02M2N2kCkNo;Z1TDhNc;b1N2N4L5K2N3M0O2OO1O11N11O1N100O1O01N1011ON3O001O1N11N2N1O1N20O100O1O0010001N2N110N100O2M2M3YElMU:f20001O000000000010O0000002N1ON2O2OOgK", "size": [427, 640]}, "area": 36932.0, "iscrowd": 0, "image_id": 10319, "bbox": [0.0, 0.0, 640.0, 295.0], "category_id": 169, "id": 10012620}, {"segmentation": {"counts": "[[[31Y=1O2M2O1O2M2O1O2M2O1O2M2O1N3\\O\\O^De0_;@^Da0^;E^D>^;G_D:^;k0L5L3M301O000O2O00001N10001O0O101O000O10001O00000O100jKaM`M^2_2dMaM\\2^2eMbM[2]2fMcMZ2[2hMdMY2[2hMeMY2Y2hMgMY2X2gMhMZ2V2gMjMZ2T2gMkM[2S2fMmM[2Q2fMoM[2o1fMQN[2m1fMSN[2k1fMUNZ2j1gMUN[2h1gMXNZ2f1gMZNZ2d1gM\\NZ2b1gM^NZ2`1gM`NZ2gLPMm3g0\\OY2dLUMm3c0_OY2`LYMn3>CZ2ZL^MP49FY2WLcMP45IY2SLgMQ41LX2oKmMR4LO]3LeL4]3IdL7^3FcL:_3CbL=`3A`L?b3_O^La0d3]O\\Lc0f3[OZLe0i3YOVLh0k3VOULj0m3TOSLl0o3ROQLn0Q4POoKP1S4nNmKR1U4mNjKT1W4jNiKV1Z4gNfKY1\\4eNdK[1^4cNbK]1`4aN`K_1c4_N\\Ka1g4\\NYKe1i4XNWKh1k4VNUKj1n4SNRKn1P5oMPKR2T5jMkJV2\\5cMdJ^2a5\\M_Je2g5TMXJm2o5lLQJU3U6eLjI\\3[6mKVJT4`7001O1O1O001O1O003M`0@<D00001O00001O00001OO100O100O100O104K01O001O001N101O1O2N1O1O2N1O1O2N1O1O2]OQJgIP6i61O1O2N1O1O2M2O1O2N1O1O2N1O1O2N1O1O2N1O1O1O2N2N1O2N2M2O2N2N1O2N2N1O2N2N1O2N2N1O2N2N2N2QOmGcLU8X43QOhGfLZ8S44SObGhLb8T3^GlL7[OT8^4iGfK[8\\3aGTM2Ba8o3_GQLf8R3XG[M1Ek8k2TG`M0FP9g2PGPNU9k1kFVNX9g1hF[NY9a1gFaN[9h22_NcFmN_9o0aFTO`9i0`FXOb9d0^F_Oc9=]FEf96ZFgN0b0g9c0YF3i9IWFPO0e0j97VFTO1f0l91SFYO1i0Z:]OeEJ2j0]:VOaE03l0[:RObE23n0Z:oNcE43o0X:lNeE54P1V:jNfE64S1T:fNhE75T1R:dNiE85W1P:aNkE76Y1n9_NlE87[1k9\\NnE:6]1j9XNPF;7_1g9UNRF<8b1c9QNUF=8f1`9lMXF>9i1\\9hM[F?9m1Y9cM^Fa09S2Q9\\MfFa0:V2l8YMjFa0:Y2i8VMmFa0;Y2g8VMnFa0;Z2f8UMoFa0;[2e8TMPGa0;\\2d8SMQGb0:\\2d8RMRGb0:]2c8QMSGb0:^2b8PMUGa09`2a8oLVGa09a2`8nLWGa09b2`8mLVGa0:c2_8lLWGb09c2_8cMaG^2^8bMbG_2]8aMbGa2]8_McGb2\\8^McGd2\\8\\MdGe2[8\\McGf2\\8ZMdGg2\\8XMdGALh2_8gMeGA0d2[8kMfG@4a2U8oMgG@8^2P8RNhG@?X2h7gMcG56Kh0Q2^7PNfG14NQ1i1U7WNiGO11Y1b1n6^NhGM13^1^1i6bNjGJ05`1]1f6eNjGG07d1Z1c6gNkGFM9h1X1`6jNSHM`1W1]6mNQHLe1U1[6nNoGMi1S1X6QOmGLo1P1U6SOlGLR2o0R6VOiGLX2l0o5YOfGL^2i0m5ZOcGNc2f0j5]O`GNi2c0g5M]J0d5N_J0a5NcJ0^5HkJ6U5CTK;l4_O]K>j8N2N2N2N3L3N2NlkS1", "size": [427, 640]}, "area": 43357.0, "iscrowd": 0, "image_id": 10319, "bbox": [257.0, 123.0, 296.0, 299.0], "category_id": 183, "id": 10012621}, {"segmentation": {"counts": "0h1S1\\On>d0RA\\On>e0QA[Oo>d0RA\\On>d0RA\\On>d0SA[Om>e0RA\\On>d0QA]Oo>c0SA[Om>e0SA[Om>e0SA[Om>e0SA\\Ol>d0TAZOn>f0RA[Om>e0SAZOn>f0RA[Om>1Q@^OR1a0m>^OQ@@0947P1AmNO9<h?3S@AK1O06>d1AYN5P`0:e@AZO0N5Zb0:U1O100000000O11O00002N00000eABT9>kFDT9<XB@B1Y28ALYNKO1j=`0aBDi1b0nNNlN\\Ol=b0^BEi1a0]NWO1i0Z=MZBFc1f0cNPOFO>i0\\=MXBH^1h0`NlN030KOO117i0d=c0cCJaNA2YONk0l=c0bCd1^<]NaCc1_<]NaCd1^<]NaCe1]<ZNdCf1\\<[NcCd1^<\\NbCe1]<[NbCg1]<YNcCf1^<ZNbCf1^<ZN_Ch1b<XNXCk1nNdLk=a1SCo1o<QNSB_Na01Cb3j=XN[BWNHa3o=\\NSBUNN^3P>^NoAj1R>TNPBk1Q>P22O1001O0000J6M3O10000000QBbJ\\=^5dBdJZ=`5_B]JH3OMh=b5bB_JF6f=c5\\BWJN7e=c5\\BVJO9c=b5]BaJc=m511OO11OO1O11O0000O1O1O1001OO1N2001OO100N2001OO1O1N21O0000@[BaJHNo=a5>0000M32N00N21OI71O6JO10000O12N1O2N1OO11O0eA`JV>f5O00O1001OO10000002N0000GmAbJV>^560eA_JW>e50N2001OO11O00N2O1O13M1ON22NO1O12iA\\Jn=d5nA]J0OR>o5L0000O12N1O0000O12N1O00O11O00O13M00N2O10000001O1O00001O0000O1002NO1O100N2O11O0000001O00001O00001O00000000O1O1CUBZJV>a5700000000000000000000YOcAkK]>a1cAjM2=O_13fNZ>d1iATNJ`15gNY>d1SBkM@e18iNX>f1XB^OAjNX>i1^BUO[OPOX>k1bBoN[OoNV>o1bBnMgNS1b0mNV>R2aBnMhNQ1\\?Q1kAoMiNP1\\?Q1oAoNQ>Q1oAoNQ>P1PBPOP>P1oAQOQ>o0oAQOQ>o0PBoNQ>P1PBPOP>>b@bN1<[1g0R>;e@bN03MM]1T1Q>:e@bN03a1N\\N;01_?P1dBcNnM:0I0MO4`?Y1fBkNmMIMO34]?V1kBkNSNMR?V1nBkNe=T1d2O1001O0000000000000g_OjNh=W1VBjNj=V1nAiNmM7S`0P1PBVOn=j0RBXOl=n0P@bN06d1:cNAIO01a?h1_@WNa1?fNIY?c1PBVOP>j0mAZOR>f0kA^OT>b0PBVOT>h0UBUOg=l0YBROh=m0YBTOf=m0YBSOg=l0ZBUOe=k0[BSOg=m0YBUOe=k0[BPOj=Q1UBTOf=l0WBVN]Nl0g2D]9Y1`EWN[No0>ROf1a0P:W1UEIPOaNj0OG?\\;[1nDOnNXNn08D8`;Y1RE6TOTN3655d;^1]DYNAS2MnM85HF;<k;]1PDUNLNN=1o1OmM8?I[O0>T<l1kCPN0]26oMKOT<V2jCL0YM2h0NMV<X5jCfKV<R6000000000O110NjCZHU<h71YHiCc7`<\\H`C]7c<N1O000N1010001OO101O01E]CVId<c6YC^I21d<a6]CZI14a<c6eC\\I^<a6cC_I\\<b6eC^IZ<b6eC^I\\<]6ZCaI:2\\<Z6lCdIV<\\6jCbIX<^6hCbIW<_6e0004aB_IW=f6O00001O0nBXI1Oe<l6WCVI3Nf<U704L0lCgHa;Y7\\DkHd;T7WDnHl;R7SDoHm;R7PDRIP<^710O00O0200MVHnCj7R<2002NNVHnCj7R<20N3OO2O00LnCYHV<d710211N1OM3001O0001N11O00O1O11O03L111N2N0000000O010000000001O1OOOnCVHQ<j7PDVHR<h74M111M4N1OO1111MMbHdC^7[<50O1OO1O12N00O1N21O00J\\HlCh7T<200001O00000000000000001O00000002N1O00001O00O1001OO1O10O1000N2MkCZHV<f7020000000000jCXHT<j700001O000000000002N1O00000000000001O000000000000^O", "size": [640, 480]}, "area": 89493.0, "iscrowd": 0, "image_id": 10321, "bbox": [0.0, 0.0, 480.0, 273.0], "category_id": 96, "id": 10012622}, {"segmentation": {"counts": "o3^5b>B>0000O1O100O10000O2ON21OO11O0000000VKm@g4T?XKl@h4S?YKm@g4S?YKn@f4S?YKm@g4U?1000001N12N1O1O000iLj@d1V?[Nl@d1T?[Nm@fNKO1f2W?\\N]AlN\\Oh2X?hMg@1i0V2`>dMi@FO>i0Y2^>YMk@030KOO117i0a2^>WM`A2YONk0j2X`00010O2OO0O20000N2O1N1MQMT_Oo2m`02O0O1000001N2e_OlLi?U3a001O0001I7L5M3NOQ@\\Ld?c3^@^L`?b3?mLf_O`2Z`0`Mh_O^2\\`0[Ma_OH3OMl2^`0^Mc_OF6j2_`0XM[_ON7i2_`0XMZ_OO9h2]`0XMf_Oi2g`011N011N00011N1000O001O1001OO1N2001OO100N2001OO1O2M210O001_OWMe_OHNS3]`0>0000M32N00N210H80O6JO10000O12N1O2N1OO11O0_Lf_O\\3``0O01N1001N010000002N0000GgLh_O\\3X`060_Le_O]3_`00N2001OO11O01M2O1O13M1_O>13M0O103WMn^O_2Sa0]Mn^O0Oa2`a0K1O1O003N1N100O012M2O0O011N10O04M0OO2O0100O101O1O00001O0O10O1002^Ok]OUOTb0j0X^OlNga0R1e0N001O3M2N2M3N3M2N201O0O101O00000O10O1O1C^OQ]Ol0kb07000001O001O00001O001h^OjNm>OX@W1g0oNn>NX@T1f0ROP?LY@T1b0UOP?K[@S1a0WOS?GZ@T1=[OX?BY@T1;_OZ?BT@S1=@_?^OQ@S1<Da?Q1[@TOd?m0W@XOh?`201O1O1O1N3N4L4L4K5K3NO1OO11O100O1O101N10000O100000O100O12N1O001O1OL44LO11O010ON31NK64KNRNZAgNg>oNj@f1a0YOk>lNf@j0OG?e0S?cN_@n08D8h0[?nNX@3655l0d?cNT@85HF;<S1e?bNT@8?I[O0>\\1Q`0iNW@KO\\1j?bNa_O2h0NM]1Q`0cNm@]1k`0000000001N11OOcNa]O[1bb00010O0001O0N3N2O001O1O01N10FaNR^O_1ha0^NY^O21^1ga0cNT^O14\\1ha0jNW^OV1ia0kNW^OT1ja0mNV^OQ1ka0nNU^OR1ga0dNW^O:2R1da0VOZ^Ol0fa0TOX^On0ha0ROX^On0ha0e0004jMV^Oo1na00O0001O0UNQ^O1O^1Sb0^No]O3N_1\\b004MOROa]O:`b0Cd]O<\\b0_Of]Od0Zb0[Og]Oe0Zb0XOj]Of0hb001O00O11O1O0000000001O0N2001O00LVOQ]Ok0ob03N12O0000000000001O0000000000003NO02N2M10000O1O0010000000000001MZOj\\Og0Tc0ZOl\\Of0Tc03N3O0N3O00O102M10001N100000000000000000000000000001O001O0000O10000O10000O102N1O00001O00O1001OO1O101O000N2MVOo\\Ok0Qc0110000O10000UOm\\Oi0Uc0000000000000000002N1O00000000000000000001O001O001aG", "size": [640, 480]}, "area": 36615.0, "iscrowd": 0, "image_id": 10321, "bbox": [0.0, 126.0, 480.0, 181.0], "category_id": 99, "id": 10012623}, {"segmentation": {"counts": "c:]9R;A003M1O1O1O1O001O1O001O001O00001O0000000000000000000000000000O1001O1O000000000000O1O1O1000000000000O1O1N2006J00000000O100J6C=O1O17I8H5K1OO1O1_O\\GfEd8i9VHmEk7T:THlEl7U:SHjEn7Y:oGfER8\\:eGiE]8\\:]GfEb8[:\\GgEc8Z:[GhEd8Y:[GgEe8j:000000000O100O1OZLkDcNT;nMoD]1<a0e:mM[Ee0j0Z1j9mMhE5n0k1Z9kMUFOe0U2W9fM`FJ=`2S9cMjFC7i2o8aMTG[O1T3k8_MdIb2\\6\\MeIe2[6YMgIg2[6UMfIl2[6PMgIQ3Z6lLhIT3Y6jLgIW3Y6hLhIX3X6gLiIY3W6gLPJR3P6mLWJj0cJQ1V;TN^Ja0cJX1o:WNfJ4cJb1g:YNdK9bI`0j:VOhK5bIc0f:XOcLg0\\3YOeLg0[;O001O001O00001O00000000000000000000M3M3N2N21O001O001O\\NXOP@h0i?DR@<d?WOQ_Oi0V10^?c0]@]O^?n0[@SOa?T1\\@lN`?[1]@eN`?`1^@`N^?g1^@ZN_?l1^@TNa?o1]@QN`?U2\\@lM`?Z2^@fM`?^3N2M3O1O1O1O11O1O1O1O1O1O1O1O00O100000000000000001O0000000000000000001O000000000000001O001O001O001O0\\O_@iLb?T3f@fL[?V3j@hLW?U3m@iLS?U3QAiLP?T3RAlLn>R3TAnLm>o2T1kNZ_OlNh`0l0d_OiNe`0P1a1B?Al^47j`K8H8J6N2N2N2O1N2SO\\N\\_Of1_`0bNY_Oc1c`0cNW_Oa1e`0fNS_O_1h`0o0L4M3L4L4L4M3O10000O10oNR@PNm?o1X@mMi?P2]@kMe?S2a@eMb?X2U1B=C>L4C<F;L3Lkf51VYJ8H7I7_]OfNQb0f1M3M3I7I7\\Od0N2M3N2M3M3N1N3NIc_OnL\\`0S3g_OgLN1X`0[3j_OdLN0W`0^361O2M3N2N2N2N2M3M3N2N3M2M3N2N2N2M3M3N2N2N2N2N2N3L4L2K:K1O2O1N1O2O1N2N2N100O1O100O100O100O100O100O10000O100O10000O10000O10000O10000O1001O001O1O001O001O2N3M4L3M1O1O1O1O1O1O1O001O1O1O001O00001O001O001O1O1O001O1O2N3M3M3M3M2N1O1O2N1O2N2N2N1O3M2N2N00000000cKgKSIY4m6hKSIW4m6iKSIW4[;0000000000000kKhKbHX4^7iKbHV4^7lKhDO[3U4m7mKeD3[3P4o7oKcD5\\3l3S8_LkGa3V8_LiGa3X8_LgGa3Z8_LmD\\OQ2V4S9_LhD@R2Q4W9`LcDDR2m3\\9`L^DHR2i3`9aLZDLQ2d3f9]MWFc2j9_MRFb2n9`MoEa2R:aMjE`2V:cMgE]2Y:hM`EZ2a:kMVEX2j:mMoDU2Q;nMjDT2W;nMdDT2\\;oM]DU2c;oMTDV2l;o20000001O00001O000000000000000000000000000000000000O100O1", "size": [640, 480]}, "area": 78280.0, "iscrowd": 0, "image_id": 10321, "bbox": [0.0, 329.0, 480.0, 311.0], "category_id": 116, "id": 10012624}, {"segmentation": {"counts": "h1S1mb001OO10001OO1O12N0000000000000\\OnNZ^OR1Sa0nN\\^O0947P1AmNO9<GT`04`_OK1O06>d1AYN5O[`0f0`_OZO0N5Y2LgM\\`0_3c_OaL\\`0_3e_OaL[`0_3e_ObLZ`0_3e_OaL[`0a310O11O0O120OO1O000000kLe_OB1Y28SNS`01i_Oi1Qa0TNR_Oi1Ra0RNR_Oc1Xa0[Nk^O^1Pb0N2O0O001OO11O000O10000M3J6L4ROP]Oa01C]c05d\\OHbc02`\\ONac0O72X`[3NlkcL2=Omb06P]OJob0a0g\\O@Wc0h0N7H5H3[]OgNcb0Y1]]OhNbb0W1_]OiNab0[100O10O1001N1^ObNR^O1<[1ea0gNo]O03MM]1Rb0fNn]O03a1N\\N;0o`0S2e^OnM:0I0MOSa0V2U_OmMIMO3Pa0X2U_OSNg`0P2W_ORNg`0d2O1001O0001O00000000YOX_OVNi`0h1X_OXNh`0`1W_OmM7`0c`0c1c_O[N]`0e1e_OXNb`0Dn^O06d1:cNAIO0]a00c^Oa1?fNVa0Y1i0M3M34J9J0N11OO10000001O0Mi]O]NVb0d1j]O[NWb0Y1a0K221]O]]OAcb03W]OLNN=1ob0OQ]O0ac0OQid3", "size": [640, 480]}, "area": 7279.0, "iscrowd": 0, "image_id": 10321, "bbox": [0.0, 14.0, 293.0, 128.0], "category_id": 130, "id": 10012625}, {"segmentation": {"counts": "PTl51oc01O1T\\ONhc0;W\\OEac0?N1O0000O100001OO100001O0000O12NO10000000000O10000000000000000000000001O00002N1O0000O11ONe\\O_O[c0a02002NNe\\O_O[c0a02001OO100003MN2002N1OM3001O0000O11O00O1O11O00O11O0000000000O100000000001O1OO1O12N1OO11O2N1OO11O3MMm\\OYOSc0f041O1OO1O12N00O1N21O00Jh\\O@\\c0`0200001O00000000000000001O000000000000000000000000000000O100000000N200000000000000001O00000000000000000000000000001O000000000000", "size": [640, 480]}, "area": 3256.0, "iscrowd": 0, "image_id": 10321, "bbox": [301.0, 0.0, 179.0, 27.0], "category_id": 157, "id": 10012626}, {"segmentation": {"counts": "Q^Q11ec0;M3d\\OJ93ha0]1O010O1I7OO2D<J501ROc]O9_b0Dc]Ob0Wb0\\Oj]Ol0Sb0nNo]OV1ma0hNU^OW1la0hNT^OW1\\b0O5QOb]O1[c0K100O1NWY8O\\cG3bB<U=0nAj0i=BcAn0U>_O`Ae0Z>G[A=b>MTA6i>4l@0R?_2O2N1O1N2O2O1O002O7H7YBcJ^<d5XCcJe<e5nBcJn<[6VCbIk;b6PDbIn;X7O2N101N2N101N1O100O100O100O1000001N10N2M3N1N3001O000O2O00YN^DQKa;e4nDVKR;b4[EXKe:`4iE[KW:a4RFZKn9b4YFZKh9b4^F\\Kb9a4dF\\K[9a4lF\\KT9c4oFZKR9c4TGZKl8b4ZG\\Kf8a4^3NlM[LQDc3P<bLlC\\3U<hLhCW3W<oLeCP3[<UMaCi2`<\\M\\Cd2b<bM[C^2c<fMZC\\2c<eM]CS1GnMj<P1`Co0IQNe<Q1cCj0MTN_<Q1eCh00WNY<R1hCd03YNT<R1kCa05\\No;T1oC;6_Nk;V1mC:<_Nf;W1lC;b0\\Nb;Y1iC;i0[N];Z1hC<o0XNY;\\1eC<V1VNU;_1bC<]1SNQ;`1`C>b1RNl:a1_C>i1oMh:X4dD_KUO8V<Y4bDiKTOLZ<Y4`DULQOA^<Y4`D[LROZO^<Y4`DaLSOTO[<Z4bDfLPOQO^<X4`DXM`;f2`D\\M`;b2YDgMg;W2XDlMh;R2XDPNh;n1XDTNg;l1XDVNh;h1WD[Ni;c1WD_Ni;_1SDgN];UMaDR42kN[;_MYDc3=QOV;a1kDaNS;]1oDfNn:Y1TEhNj:W1WElNf:R1]EoNa:P1aERO[:n0gESOW:k0lEWOQ:h0QFYO`2fLb3P4QJ[OZ2mL_3h3VJ]OW2PMa3c3XJ]OU2TM`3`3[J]OR2WMa3\\3]J^Oo1XMd3Z3]J^Om1ZMf3W3]JAj1ZMi3U3]JBe0bLlNi0Q6T3^JA=nLiNd0\\6m2^JC6hN\\5e1^JLHdNj5`1]J7ZO^NX6\\1^Jb3b5]L_Jc3a5]L^Jd3b5\\L[Jg3f5XLXJj3h5VLVJl3k5SLSJo3m5QLQJW3i6hLVIR3S7mLkHm2]7SM[HR3i7XJmGR15i4P8QJPHW1Hm4Y8hISHi8P8RGUHk8m7QGXHl8k7oFZHR8XOlGa8N\\HQ8ZOiG`80\\HQ8[OiG]82]HP8]OgGZ85^Ho7b8lGcGo7Y:L5K5K5L4M6L4K4M4K5LbE`I`7\\6^HiImMFn8\\6SITJjMBW9V6mH^JdM@`9P6iHUKV7l4\\HbK^7gN^F[5f1\\Lk7V4RHnKm7S4fGZLZ8e3\\GfLc8[3YGiLg8bMaFj4c0iMm8TM`FG071d8`9TGbFL99Gg8k9PG]F6Jj8i9PG^F4Jl8h9QG^F0Lo8f9QG_FMMR9d9QGTF6:i8b9QGRF9;f8b9aG]FY71mH`9l7nFnGo8R8XGiGh8U8_GgG`8j0YFW6^1jHZ8i0^FZ6Z;YIaDI5k6^;YIbDMLf6f;XIgDl6c;PInCKb05^Oo5i<SJlCJ[OQ6i<QJXC1f0k5T<]JkC`5X<aJgC]5[<fJbCW5a<kJ]CR5f<PKXCn4k<SKSCj4P=XKmBg4^=QKaBm4c=hJfBV5S>M3NoN[KkBc4W=aKeB]4]=hK^BU4e=PLVBn3l=ULQBi3Q>Q12M1O011O2N2N2N1ZKkA`3Z>kK_A6a0h3Q>[LUBb3l=aLSB]3n=dLRBZ3o=gLQBW3Q>iLoAU3S>]LWA6f0[3U>]LYA6b0[3c>cL]AZ3g>eLYAX3l>fLTAW3P?hLPAS3V?lLj@S3Y?kLg@T3Y?lLh@T3S?YMe@f2Z?^Md@a2[?_Mh@`2W?\\Mn@c2R?ZMRAe2o>WMUAh2k>VMXAj2d>YM]Af2c>ZM^Ae2c>ZM^Af2b>YM_Af2c>WM_Ai2i?2H9K5O010O0010O01O010O1N1L5K5N110000O10000O2bLj_OP3V`0mLn_OR3T`0jLo_OU3_`000O10N11mL\\_Oi2c`0WM[_O03a2``0jM^_OV2a`0kM__OV2X`0[Mh_Oa00V2f?XMd@i0FR2c?WMe@h0GU2`?RMi@k0FV2^?oLm@k0EV2m?jMS@V2l?P11iLS@X2Q`0j01hLn_O^2S`0bMm_O^2U`0XMR@i2Q`0RMQ@n2X?lLdAU3W>RMgAm2X>VMfAk2X>XMgAg2U>^MQAM1f2l>_Mo@N5c2l>VNTAj1l>VNTAk1m>iLn@V15Q2Q?gMWAZ2i>YMcAh2]>QMiAo2d?1O1O1O1O2N3M3M3L4K4L3M3M4L3M4M3M3N1O3N11N1O8G2N2N1O1O001OYOjKdAV4Y>mKhAR4X>nKiAR4V>nKkAQ4U>oKlAP4T>PLnAn3S>QLnAn3R>RLoA6VOZ3k>`LQB2YO[3f>cLRBO]O[3a>fLSBL@\\3]>hLeBW3S?00WM^_OQ2e`0kM__OR2d`0jMa_OS2a`0iMd_OS2_`0iMg_OR2Wa0M3L3M4L4L5K7H7I6I6K5K5HP\\2LjcMb0M4Dl0[O0O21O1N2ZOf02Nb0@N3L1nNc]O`0^b0^OX^OLla0OV^OO__6", "size": [640, 480]}, "area": 68911.0, "iscrowd": 0, "image_id": 10321, "bbox": [53.0, 214.0, 417.0, 426.0], "category_id": 183, "id": 10012627}, {"segmentation": {"counts": "Qb0o1Qb000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000@`0000000000000000000000000000000O10000000000000000000000000000N2D<N200000000001OO10000000000001O00000000000000O1L4L400000000M3O11O0000000000001O1O1O000000000000001O2N1O2N1O000000001O00000000000000000000001O000000000000000000001O000000000000000000001O000000000000000000001O00000000000000000000001O000000000000000000001O000000000000000000001O00000000000000000000001O000000000000000000001O000000000000000000001O00000000000000000000001O000000000000000000001O000000000000000000001O0000000000000000000000001O000000000000000000000000000000000000000000000000000000000000000000000000000000001O000000000000000000000000000000001OO10000000000000000000000001O001O001O00001O00000eM^^OX2ca0gM^^OX2fa0O1kMX^OO0n1Rb0MO11OO1001OO11O000000004L6J3MO1O10000O1000000O1002N1O1O1O2N00000000O100001O00O1", "size": [640, 425]}, "area": 34790.0, "iscrowd": 0, "image_id": 10324, "bbox": [0.0, 532.0, 425.0, 108.0], "category_id": 118, "id": 10012628}, {"segmentation": {"counts": "Pla27ic00000000000000000000000O100000000000000000000000000000000000000000000000000O100000000000000000000000000000000000000000000000000O100000000000000000000000000000000000000000000000000O100000000000000000000000000000000000000000000000000O100000000000000000000000000000000000000000000000000O10000000000000000000000000000000000000P`b2", "size": [640, 425]}, "area": 624.0, "iscrowd": 0, "image_id": 10324, "bbox": [131.0, 0.0, 162.0, 7.0], "category_id": 143, "id": 10012629}, {"segmentation": {"counts": "a4n2Ra00000001O000000000000001O000000000000000000000000000001O0000000000000000000000000001O000000000000000000000000000001O0000000000000000000000000000000001O00000000000000000000000001O000D<C=C=B>C=C=CmQ9b1\\gFc5]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`0@P1POP1POP1POP1POP1QOo0POP1POP1POP1POP1POP1POP1POP1POo0QOP1POP1POW[6", "size": [640, 425]}, "area": 168490.0, "iscrowd": 0, "image_id": 10324, "bbox": [0.0, 0.0, 414.0, 560.0], "category_id": 183, "id": 10012630}, {"segmentation": {"counts": "0PZ]18fTcN3M3M2O0N3M2O2M2N2O1N2N101N2N1O2N2N1O2M3N1O2N2N2M3N2N1O2M3M2M2O1N4N2O001O2N6J=C6J4L4L3lFaLd6b3WIeLc6_3WIhLd6\\3WIgLh6^3QIfLm6b3hHbLW7o3cGgL\\8o401N100O100O100OM3iNX1K5N2O1O12N3WGPK\\7S5_HRK^7Q5]HTK`7W6O000O2O00000000000000fN^HVKb7b4kHYKU7d4RIXKn6f4XISKk6j4g1N2M3M4M2M3M4M2O1N2O2N1O101N1O1OmGWLR5i3jJZLV5g3gJ[LZ5e3YJhLf5\\3QJiLP6^3cIgL^6g3mH_LS7f3^HdLc7[5101O00001O0K5lNT1RNeFSN^9m1cFRN\\9o1fFPNY9P2iFnMW9S2iFlMW9T2kFkMT9U2\\G[Md8e2`GWMa8i2aGRMb8n2`GmLc8S3e10RNZLbHf3_7[L_He3b7\\L\\Hd3d7^LZHb3g7`LVH`3k7aL_G1\\O_3]2]Lm34VJ4[O^3]2`LQ40TJS4d1ULX4JoIS4o0nLR5POlIT4=aM\\OQNo5;TJ`7l5bHQJa7n5aHnI_7T6bHiI_7X6c0101N2N2O001eNQJcIP6Z6UJcIl5Y6YJeIg5Y6^JdIa5W6hJfIV5V6SKgIk4V6o1H9J61O0O2O02O2M4M3M3L4M3L4M3K5C<L5N2M2N0N2L4K5D=POnDjMY;W2jD^M];b2`01O010O00O1O1UNQDe0P<VOYDd0j;XO]D:m;FWDOP<0VDGn;8[D]Oh;c0[1N100O101N7JnSQ5", "size": [480, 640]}, "area": 279057.0, "iscrowd": 0, "image_id": 10327, "bbox": [0.0, 0.0, 640.0, 480.0], "category_id": 124, "id": 10012631}, {"segmentation": {"counts": "PZ]18f>3M2N2O1M2N2O2M2N2O1N1O2O1N2N1O2N2N1O2M3N1O2N2N2M3N1O2N2M2N2M2O2M4N101O1O3M6J=C6J4L4L3lFaLd6b3WIeLc6_3WIhLd6\\3WIgLh6^3QIeLn6c3gHbLW7o3cGgL\\8o400O100O100O100OK5iNX1L4O1O1002N3WGPK\\7S5_HRK^7Q5]HTK`7V601O0O10000000000000mNSHSKm7`4fHZKZ7c4nHXKR7e4TIXKl6f4h1M3N2M3M4M2M3N3M2N2N201N1O101N1O10lGWLR5i3jJZLV5g3gJ[LZ5e3YJgLg5]3PJhLQ6_3bIfL_6h3lH^LT7g3]HcLd7\\5001O00001N1kNU1oMgFTN`9k1bFSN^9m1cFRN\\9o1fFPNY9P2iFnMW9R2jFnMU9S2lFkMT9U2]GZMc8g2_GUMc8k2`GoLc8Q3f1000RNZLbHf3_7[L_He3b7\\L\\Hd3d7^LZHb3g7`LVH`3k7aL_G1\\O_3]2]Lm34VJ4[O^3]2`LQ40TJS4d1ULX4JoIS4o0nLR5POlIS4>aM\\OQNo5<SJ_7m5cHPJ`7o5bHmI^7U6cHhI^7Y6c0101N2O1O0fNPJdIQ6Y6TJcIn5Z6WJcIj5Z6[JcIe5X6bJfI]5U6lJhIR5U6VKdIl4Y6m1D=001O0O2O02O3L4M3L3N3L4M3L4G9F:M3N2M1O0L4L4J6hNUEnMZ;R2iDdM];\\2fD[M`;f2<0010O000O1WNlCe0V<VOSDe0n;UO\\Dd0h;[O\\D4Q<KTDMP<3XDAl;>[101N1O100O2O:EkSQ5", "size": [480, 640]}, "area": 27740.0, "iscrowd": 0, "image_id": 10327, "bbox": [96.0, 123.0, 200.0, 292.0], "category_id": 183, "id": 10012632}, {"segmentation": {"counts": "jbS26jc04L1O01O[\\OHac0<VMFoA9Q>:\\AGc>:\\AFd>:[AGe>9ZAHf>9YAHVNAX`0g0bA5Y>KiA4V>MhA5W>KiA5W>KiA6V>KiA6V>JjA7U>JkA5U>KVBHl=9TBEm=;SBDn=<TBCk==UBBl=>TBBl=>UB_Om=b0RB^On=c0QB]Oo=Ki_OJNb0Z2H^>6cAI^>7aAHb>6^AKb>4^AJd>7[AJd>6]AKa>5^AN`>2`AO_>2_A0`>0_A2`>O^A3a>M`A2`>N`A2_>O`A2`>O`A0`>0`AOa>1_AOa>2^ANb>2]AOb>2^A0`>1_A1`>N`A1b>O]A0e>O[AMj>2WAKl>5SAFRNIk`0a0UA@TNNh`0b0^21N11O00O2O01O00O101O0101N3M2N2O1OO00001O1O1O2NN11d\\O_OZc0a0e\\OBXc0b011OLh\\O_OYc0a0g\\O_OYc0a04O1O1O10O00002M10O11O0000001O001OO2L3K6M3KnfS5", "size": [640, 480]}, "area": 3052.0, "iscrowd": 0, "image_id": 10337, "bbox": [108.0, 0.0, 110.0, 127.0], "category_id": 102, "id": 10012633}, {"segmentation": {"counts": "S^b61lc0301N2O1O1N2N6K1O000L_Oi\\Oa0Wc0@h\\O?Yc041O1O0O2O1N1000L31O2G:K5NRZY2", "size": [640, 480]}, "area": 341.0, "iscrowd": 0, "image_id": 10337, "bbox": [336.0, 441.0, 26.0, 22.0], "category_id": 104, "id": 10012634}, {"segmentation": {"counts": "Rfe57gc0202M102N001O0O101OO04M2NM21M5D;O1O10OO10001N02OO10000O1OUOZ]O>gb0@[]O`0db0@\\]O?eb0@]]O?db0@]]O?cb0A]]O>eb0BZ]O>fb0ZOc]Of0lb0O001O0000001O0001O]Of\\O`0]c0010000002N2f\\O\\OWc0c02O000000O011O0001O001O00001O1O0100O1OO110O0000001O01O0001O00000010O00N201N1O1O20O000001O0000001O01O00100O1O1O01O01O0001O01O0001O01O01O00mNEa^O;_a0Ea^O:aa0F^^O:ba0F^^O9ca0G]^O:ba0G\\^O9fa0FZ^O;ea0E[^O;ea0FZ^O:ga0EY^O;ga0EX^O;ia0FV^O<ha0DT^O`0ma0_On]Oe0Sb0\\Oj]Of0Vb0ZOj]Of0CZO_b0Om]Oi0CXO`b00l]Oh0DYO_b0Ol]Oi0EXO1N[b00o]OP1EPO30Sb01S^OP1GoN30Sb01S^OQ1FoN30Tb00Q^OU1KkNTb00Q^OW1IiNWb0Oo]O]1Qb0dNn]O[1Tb0dNk]O]1Ub0cNk]O_1Tb0aNk]O_1Ub0aNj]O^1]b0OMcNd]O]1`b0eNa]OU1fb0OPOjNV_OV1l`0mNP_Oo0Ua0QOi^Oo0Wa0QOi^Oo0Wa0QOi^OP1Ua0ROj^On0Va0ROj^On0Ua0SOl^Ol0Ta0UOk^Ol0Ta0TOl^Ol0Ta0UOk^Ok0Ua0UOk^Ok0Ta0VOm^Oi0Sa0oNV^O6l0f0UOSOea01Z^O6l0g0TOROfa00[^O7j0j0UOoNga08R_Oi0XOoNO15OXa08[_Oj0XOSO2NZa06Z_OQ1ZOkN]a03Y_OR1ZOkN]a03X_OZ1g`0hNW_OZ1i`0fNU_O[1k`0fNS_O[1m`0eNR_O\\1m`0fNQ_O[1m`0hNQ_OZ1l`0iNR_OX1Qa0eNn^O\\1Ra0eNl^O]1Ua0bNi^O_1Xa0aNg^O_1Ya0bNe^O_1[a0d0WB", "size": [640, 480]}, "area": 5342.0, "iscrowd": 0, "image_id": 10337, "bbox": [290.0, 428.0, 190.0, 97.0], "category_id": 107, "id": 10012635}, {"segmentation": {"counts": "[Sf0o0Qc03YOoNg]O3<o0ma0c0O100_OZNg^Oh1Wa0YN\\^OO:h1Za0]Nc^Oe1]a0]N]^Oh1ba0;0000000O11O00000bCfMV7Z2bFmMZNIXO6k;f2]DUMd0>kNAJ20`01A<4l;f2UD\\MAGY1m1QO[No;f2RDeMH]Ol0T3g:KcDYN:i1S;i1bDVKIP3f;W50O2O0O010O2O000VJXD\\3h;gL_Do2a;PM`Do2a;fJTD01U2;bN4HHi2i;mL`DX1LYOm0GgNZ2P<^McEK\\N7>>_1[OSNc2f>QNdCd1`<XNoCX1m?[N[]O00f0dc0^OWjj7", "size": [640, 480]}, "area": 5888.0, "iscrowd": 0, "image_id": 10337, "bbox": [35.0, 196.0, 43.0, 266.0], "category_id": 109, "id": 10012636}, {"segmentation": {"counts": "n>R5n>01O1O1O1O1O001O1O001O1O001O001O1O1O001O2N1O1O1O1RLn@l2S?RMTAh2m>WM[Aa2f>]MdAZ2\\>fMnAP2T>mMnAMfNd1b?WNjA2lNb1Ra0\\NS_Oa1fa002N2B>L3M3O3K3O2N2L3N2N3L3NnP^8", "size": [640, 480]}, "area": 4871.0, "iscrowd": 0, "image_id": 10337, "bbox": [0.0, 478.0, 47.0, 162.0], "category_id": 110, "id": 10012637}, {"segmentation": {"counts": "a6U2h3Ae8?[GAe8`0ZG@f8`0ZG@f8b0XG^Og8c0YG]Og8c0YG]Og8c0jCiMW3d1o8l0^DoMW1T1[:m0^DWNP1l0b:m0\\D^Nm0e0g:n0ZD_Nn0c0h:n0YDaNn0a0h:R1UD`NR1>i:R1SDcNS1;j:R1RDeN:hNN`1f;T1QDfNKeNJN67:_1k;o0nClNKgNM2>]1m;k0mCQOIUO4ZOLg1Y<j0kCSOKTO3XOMh1Z<l0gCOO\\O[<oMfC]7`<fH^CT7d<jH\\CV7g<O1MXCmHi<S7WCmHi<R731OO010O100001O]ISDS5l;nJWDR5j;iJWD[5U=3M1O2OO01O100O0000000O11O01O00WOQBbKn=V2YBZNIXO67Z>1aAd0>kNAJ20`01A<46\\>KfAAGY1m1QO[N9\\>HoAH]Ol0ka0ZO]_O:[b0E]\\OIchV8", "size": [640, 480]}, "area": 10808.0, "iscrowd": 0, "image_id": 10337, "bbox": [0.0, 205.0, 60.0, 259.0], "category_id": 112, "id": 10012638}, {"segmentation": {"counts": "T>j0Vc0001O1O1O1O1O0O2O1O001O1O001N1010000O001N3N1O1N2LbNc]O`1`0oNk`0Ae^Oa1>UOf`0o0Y_OYO``0g0^_OCX`0>h_OLn?6o_OLMfNa?e1Z@G2lN_?U3_@PM^?i311N21O2M2N02OOOXL`@R3_?iLg@W3X?dLm@\\3S?bLo@^3P?`LTA]3m>bLVA[3k>dLXAZ3h>eLZAZ3f>dL]A[3c>eL^AZ3c>dL_AZ3b>fL_AP3UOQM``0m2;M3N2M3N2N20OO2O^_ObMc?^2\\@cMc?^2[@fMc?X2]@iMc?W2]@iMd?V2Z@mMFDa?_2i@nMECc?]2i@PNCDe?[2g@SNBDg?=]@m0:ROBDi?Mj@\\1JUO@Dj`0V1f_OVOACj`0V1d_OYO^ODo`0R1c_O\\O\\OBQa0S1b_OD^`0<a_OE_`0;a_OfN^Om0Qa0=c_OC]`0>b_OB^`0>b_OB^`0>a_OC_`0=a_OC_`0=b_OB^`0=c_OC]`0>b_OB^`0>b_OB^`0>c_OA]`0b0Z_OdNFk0o`0e0R_O^NOU1o`0i10000000000O10000O100O100O10000O100O10000O100O100O1EkLk_OU3Q`0RMl_On2o?ZMm_Og2n?h01O1O1O1O1O000000O1000000O1O1O1O1MPLU@Q4g?6O10000001OO11O000000O1000000000000002N0SLX@`00W2W`0QMi_O3143HMl2V`0UMj_O0O0167IIh2V`0ZMi_OO1O06<JD135L00J050O0Z1X`0cNi_OO42LNP13@3Fh0R`0ROh_O150J0V2l0S>TOh_OO51KOV2l0S>TOP@1F0V2k0T>TOP@1GOW20kMe0d`0[OZ_O1`2?V>AZ_OO`2`0W>AX_OOb2?V>BmA=T>BlA>T>CjA>W>AjA>V>CjA<W>CiA=W>DhA<Y>ChA<n`000E^\\O00O13ac0Me\\O0J0n_k5", "size": [640, 480]}, "area": 16120.0, "iscrowd": 0, "image_id": 10337, "bbox": [0.0, 445.0, 180.0, 195.0], "category_id": 115, "id": 10012639}, {"segmentation": {"counts": "ggV51jc05J7iNDd^Oa0Ya0AR^ON7c0fa0^OT^O01g0ja0YOT^O2JMKP1Tb0ROl]ON75JK0P1Tb0VOn]OX1Rb0:010O1O0100O020N4N2L6K1V^OkMga0W201O00iM`^On1ia011N0?C\\1bN:GO01O10OO13N1MN4O20N1O1N2O0M32NO2OO0100O1O100O1O2OO01O10O01O2N1O100O1O100O1O101N1O101N2N2O3L4PMR_Oh2Ra010O1O1O1O011O0O1O1O1O1O1O100Mc^ObM^a0^23O1O1J]^OlMca0U2^^OiMda0W220000O100O2N100O100O1O101N1OOOQ^OTNPb0l112N0002O0O100O1O100O100O2N101N2N2O1N1O101N1O100O2O0O1O101N1O1O100O0100O11OO11O000000O12N00O2O00nNW]Om0jb0ROV]On0jb0ROW]Om0ib0SOW]O1Nh0Qc0WOo\\O30`0Qc0]Oo\\O30`0Rc0\\Oo\\O30`0Tc0@l\\Oa0Uc0]Ok\\Oc0Yc00O1002N01M2O1O10001O0M3000101NO11O01N1001O0001TOn\\Oh0Sc0WOm\\Oj0Tc0VOl\\Oi0Rc0XOn\\OO15O:Rc0AS]O2N<Vc0Bl\\O>Tc0Bl\\O>[c0O2O0001O0001O000010O0000010b_O", "size": [640, 480]}, "area": 13531.0, "iscrowd": 0, "image_id": 10337, "bbox": [266.0, 448.0, 214.0, 150.0], "category_id": 123, "id": 10012640}, {"segmentation": {"counts": "m_a11mc03O2M2O1N100M4J6L3O10000010O00000100O0000XOl\\O>^c004L3J4MWXZ7", "size": [640, 480]}, "area": 423.0, "iscrowd": 0, "image_id": 10337, "bbox": [79.0, 109.0, 26.0, 27.0], "category_id": 130, "id": 10012641}, {"segmentation": {"counts": "anU51oc0000O1U\\O3\\c0<L3010O10001O00000O10000000001O0000001O9AX\\O3XfT3MemkL1000000001O0001O01O0000PW:", "size": [640, 480]}, "area": 402.0, "iscrowd": 0, "image_id": 10337, "bbox": [265.0, 324.0, 198.0, 93.0], "category_id": 132, "id": 10012642}, {"segmentation": {"counts": "iiP71nc03U\\OM\\c00h\\O6JJ_c00c\\O:^c041O1O2E\\\\O5hc001O001N4MTRS2", "size": [640, 480]}, "area": 102.0, "iscrowd": 0, "image_id": 10337, "bbox": [359.0, 431.0, 13.0, 17.0], "category_id": 143, "id": 10012643}, {"segmentation": {"counts": "]_m41oc04K3a\\OImb0=P]ODmb0b0o\\O_Omb0f0Q]O[OPc0j0O1002N00000000001OO11O0000O11OO11OEUOX]O0003m0_b0]O_]Oe0ab0=00001OO1001OO100O1O1O1O100N200O1N27I001O1O00001OO11O2N0000O100M32NO100O100O1O100O1O100O1O100O1O1O1O100O1O100O1O100O1O100O1O100O10000O1O1O1O10000O1O1O1O1O1O100MfM_^O[2aa020000KcMf^O^2Za0cMd^O^2]a020000O100O1O100O100O1O100O1O1MWMn^Oj2Ra021OO1O100O100O1O100O10N10001N100O2O00001N100101N100O2NWNU_O?k`0@V_Oa0h`0_OX_Ob0g`0_OY_Oa0f`0_O[_OLKBi`0a0]_OBK@1<f`0c0^_O@O\\O0c0``0`0b_O@91_O[Ob`0T1f_O^O;9k?8k_O]O=:h?9k_OZOa0<d?9l_OXOd0>`?:l_OVOf0`0^?:m_OSOh0b0[?:n_OPOk0f0W?:n_OoNl0g0V?:XAFh>:XAFh>:XAFi>9UAIk>7TAJl>6g@POTOk0U`05f@QOkNO6m0Y`03e@UOPOi0[`02e@WOnNg0]`02c@;]?Kd_OjNN4=Y1Q`05o_OKR`04o_OKQ`05P@JP`06R@Hn?8S@Gm?9U@Ek?;T@Fm?9R@Hn?9P@HP`08Q@Go?9o_OIQ`07P@HQ`07n_OJR`07j_OLV`07g_OIY`09e_OG\\`09a_OIa`0j1O11O00O1001O0000001O1O00000000O100001O00000000001O0000001O", "size": [640, 480]}, "area": 16104.0, "iscrowd": 0, "image_id": 10337, "bbox": [251.0, 527.0, 229.0, 113.0], "category_id": 165, "id": 10012644}, {"segmentation": {"counts": "d_f0<bc02N200N20000N2O1O1E]OW]Oe0hb0]OV]Od0jb09000000O1GSO_]Om0`b0:N2O1N2O1O11OO10XOh]OHYb07h]OHYb06k]OGTb09m]OGSb09m]OGSb07P^OFDD\\b0f0Q^OECE[b0g0R^OCDFZb0f0T^OBPb0>P^OBPb0=R^O@Pb0`0P^OAoa0>S^O^OPb0b0R^O\\Ooa0c0g0O10d\\O^OZc0d01O000O1001N11O00012Ge\\OF_c01_\\OOUic7", "size": [640, 480]}, "area": 1612.0, "iscrowd": 0, "image_id": 10337, "bbox": [35.0, 588.0, 55.0, 52.0], "category_id": 167, "id": 10012645}, {"segmentation": {"counts": "f0d1S1`1R=`NmBa1S=`NkBa1U=_NkBa1U=_NjBb1W=]NhBc1Y=\\NhBd1X=]NfBd1Z=[NfBf1[=YNeBf1\\=[NbBf1^=YNbBh1^=XNbBh1_=XN_Bh1b=WN^Bj1b=VN]Bk1c=XNYBh1i=XNVBi1i=ZNQBZND_3Y>[NoAg1R>[NkAe1U>\\NjAe1L`LU>m1lAi12XLm=n1PBm11ULP>m5000000001O0O2O001O00O11N1NjAZJV>f52O2O0_OfA[K[>d4fAoJO:[>g4jAVKY>i4iAQK[>o4;01O00mNPKVCP5j<QKTCP5l<QKSCo4m<VKUB0a0i4Z=aKeB_4[=aKeB_4[=bKdB^4\\=bKeB^4Z=aKgB_4Y=bKfB^4Z=bKgB]4Y=cKgB]4Y=dKfB\\4Z=dKfB]4Y=cKhB\\4X=dKhB[4Y=fKfBZ4Z=fKgBZ4X=fKhBZ4X=gKgBY4i1UKR9`0XEZ4i1]Ke89cEY4g1_Kf89bEX4]OTK01U2;bN4HH`8<dG]4B`KX1LYOm0GgNQ8f0SHY4DdLK\\N7>>_1[OSNZ8g0XHm62fJ[7^NbHP7OQKP7oMQIk:B^D00f01n4f0kJf;EfC`5d0kJS<U5mCkJh1^Oh7h5`FjJf1Ai7e5aFjJf1Cg7c5dFiJd1Fg7a5eFjJc1Fg7`5cFmJe1Dg7d5_FiJj1Cg7n5SFaJS2Ei7j5RFcJP2Im7d5RFdJn1KP8b5QFcJn1LP8a6oG_IQ8a6oG_IQ8a6oG`IP8a6PH^IP8b6PH^Io7c6QH]Io7d6PH]Io7c6RH]Im7c6SH]Im7c6SH]Il7e6SHcHUN>n9o6QHQIo7o6UHmHk7T7WHfHl7Z7UHcHm7]7\\20O001eDfHo9Y7QFkHk9U7UFlHj9T7VFlHk9T7UFkHkNHm:]7WFoHi9Q7XFnHh9R7XFoHh9Q7WFoHi9Q7VFPIj9P7UFQIl9n6TFSISOAa:\\7[F`I_9a6cF^I\\9b6cF`I[9b6dF^Id7WOlH[7@_Ib7ZOWHIE_7b0_Ia7@^G@0010944_7b0^Ia7LaGYO<]7c0]I`78kG]6o0nHX7e0iG]6P1lHX7g0gG^6Q1jHY7i0dG^6U1hHV7j0eG^6U1gHW7k0cG`6U1eHX7l0aG`6X1aHY7o0^Gb6X1_H[7n0]Gd6W1^HZ7EZGZ13n4OhIN`1\\1jN[7X7fGlIP1kNY7Z7hGjIo0kNZ7[7iGhIm0nNY7Z7kGhIk0lN\\7\\7iGhIk0mNZ7\\7kGgIl0nN]7U7gGmIk0QO[7S7jGmIj0QO[7R7kGmIi0SO[7P7lGmIh0UO\\7m6lGnIg0WO[7l6nGmIh0VOZ7m6nGnIg0UO[7m6mGoIg0UO\\7l6mGPJg0SO\\7m6mGPJg0ROa7j6hGUJf0QO_7m6kGRJf0QO_7m6kGRJe0ROc7i6gGVJf0SO^7j6lGSJf0UO^7f6mGUJd0TO\\7k6QHPJc0TO^7k6PHQJa0PO`7R7PHnI`0mNe7R7lGQJ>hNPOI`8d7RHlI?aNSON^8d7PHmIb9P6^FPJ]9U6bFmI\\9T6eFkI_9Q6aFoI]9S6bFnId9l5\\FTJ_9Q6bFmI^9T6bFlIe9m5ZFTJ`9R6`FoI^9R6bFnIc9m5^FRJ_9Q6cFmI\\9T6gFiIZ9V6hFhIW9Y6kFeIU9[6mFdIS9[6mFeIS9[6mFeIS9[6mFfIR9Z6nFfIR9Z6nFfIS9Y6mFgIS9Y6mFhIR9X6mFiIT9V6mFjIR9V6nFjIR9V6nFjIR9V6nFkIQ9U6oFkIQ9U6oFkIP9V6QGiIn8X6SGgIn8X6SGhIk8Y6VGfIj8Z6VGfIk8Y6UGgIj8Z6VGeIl8Z6SGgIn8X6RGiIk8fNSFa7R1jIj8X6VGiIi8W6WGiIk8U6UGkIk8U6UGlIj8T6VGlIh8X6VGiIh8[6UGeIk8\\6UGdIh8_6WGdIe8^6[GcIb8^6hFQH>b1h8]6gFTHa0_1h8f6dFgGb0c1k8e6cFhGb0a1m8g6aFiGa0_1P9g6_FjGa0_1o8h6`FhGb0`1n8g6aFjG`0_1P9f6`FkG?_1R9f6^FlG`0_1Q9e6_FlG`0_1Q9j6nFVIS9l6_FeG6_1[9l8O0O1O2N1N2O100K^ElFc:R9_EmFa:S9_EmFb:P98O10010O00001O04L4M2M4L4M2M004WN\\FnHi9k6^FPIf9k6bFnH^9P7RGcHn8[7SGeHn8Z7SGeHn8Y7SGgHm8^OoEK2g7Q1PIn8YOPF04f7o0QIW9o6iFQI[6YO_Kc7WNSIY6ZOaKb7VNTIY6ZO`K8RML0o6U1cIX6[OeH3T3P7nMbIR6JPLb6PNeIk5LWL]6oMgIi5MYLY6QNiIf5M[LY6oMkIe5L\\LX6PNlId5L[LY6QNkIe5KZL[6PNkIe5JZL\\6RNiId5KZL\\6SNiIb5J\\L]6RNiIb5J[L^6SNhIb5J[L_6RNhIb5I[L`6TNfIa5J\\L`6RNfIb5J[La6SNfIb5H\\Lb6RNeIb5I[Lc6SNdIb5I[Ld6SNbIb5J[Ld6SNbIc5IZLf6RNaIm5ZOiH3Y3R7RN`I\\9a6cF`I\\9`6eF_I[9b6dF^I[9c6fF\\IZ9d6fF]IY9d6fF\\IZ9d6gF[IY9f6gFZIW9g6iFYIW9h6hFXIX9h6hFYIV9h6fEcHm0d0]9j6eEbHn0d0]9k6dEaHo0e0[9k6dEcHQ1a0X9V7eEWHP1c0Q9b7PG^HP9b7QG]Ho8c7QG]HP9b7QG^Hm8c7SG]Hm8c7SG^Hl8b7UG]Hj8d7VG\\Hk8d7TG\\Hk8e7VG[Hi8e7WG[Hi8e7XGZHg8g7YGZHe8h7ZGYHf8f7ZGZHf8g7ZGYHe8g7[GZHe8Q6YFZI0]1R1cNlNc0h9n5dFkJk0VOb8n5dFlJn0QO^8S6dFlJFdNV1`0X8R6mFfK3[NDBK=252[OL4P7_6_IbK2Z2i4W2VK^K1c3_O\\J3Lc3f6jL_K2n5T1dNkN]K1W66eGKg6N^K0X62hGNb60^K1^7_O^FOf6b0]K0^>0bA0^>1aA0^>OcAO_>2aAL`>4`AL`>5]_O\\On1>f>;YADh>=WACi>>VABj>?VA@j>`0VA^Ol>b0TA^Ol>b0TA^OnNFQ?m0o@SO0?JJS?d0i@1AFb0IQ?`0k@j03gNR?`0j@h05iNP?`0i@i08fNn>a0j@o01bNZ8BnMm0mIm0@oN`0VO[30WL1W3NeL024a6k0lIU2DnLM4e3>UO]OWM1O?c6=iIk2EZLT3OjLN1g0OYO]20cMV1b6HgIQ3GVLY13gNI0OY11gNO20Oi0X2d0X4AdI^3S1PL0g0SOPO6>3o0l5]OdIb3S1PLMo0lNhN12O>0_O2K6^1HbN`6b5gJXKiNVO44OL3h1K@OiN6_1\\6S4nI_JG4IV10eN3X3OYNa6R4^I:0eKb6R:]IoEc6f;100002N00N2M3M3N2000000001O00000000005K9G0hI_BQ6`=PJ`BP6`=PJ`BP6`=PJ`BP6`=PJ`BP6`=PJ`BP6`=800001O000000000iI_Bo5a=QJ_Bo5a=QJ_Bo5a=RJ_Bm5a=SJ_Bm5a=SJ_Bm5b=RJ^Bn5b=RJ_Bm5a=TJ^Bl5b=TJ^Bl5b=TJ_Bk5a=UJ_Bk5b2UJS81ZEj5_2PKbMPO4N^98]Fj5_2aNo3eKcIi5^2?aMaI0O\\49dKh5^2l0g1\\IkKh5^2l0h1^OWNc0i1[IjKg5]2n0i1[IjKg5]2n0i1]OVNd0j1[IjKe5\\2P1S1UIRM6_Oe5\\2P1R1XIQMT6m1d0R1ZIoLR6n1e0S1YIoL2Cc5[2R1T1WIoLT6m1e0T1UIQMV6j1f0U1RITM8]Ob5Z2T1U1RITMW6g1g0U1RITMW6g1g0U1SISM8^O`5Y2V1V1RISMV6g1h0V1RISMV6g1h0W1RIQMV6h1h0W1RIQMV6g1i0X1QIQMV6g1i0X1RIPMU6h1i0X1RIQMT6g1j0X1RIfLK3Y6n1k0Y1RIeLK4W6n1l0iMlH^36fLM4U6o1n0X1PIdLM5U6n1m0[1QIaLO6R6n1n0[1QIaL05Q6o1n0[1\\IeLf5o1o0\\1]IdLc5P2P1\\1bI_L^5U2P1\\1JdN6\\1JdN6\\1JcN7\\1JdN6\\1JdN6\\1JcN7]1IcN7]1IcN7^1HbN9]1GbN9_1GaN9_1\\I_Ld5Q2Q1a1ZI^Le5Q2Q1a1YI_Lf5P2Q1a1XI`Lg5n1R1c1UI`Li5m1R1c1UI`Li5m1R1c1TIbLi5k1T1c1QIcLk5i1U1d1mHeLo5g1T1d1gHlLT6_1V1e1fHlLT6_1V1e1fHlLT6_1V1e1fHkLU6`1U1e1fHiLW6a1T1f1eHgLY6c1R1g1dHiLW6`1U1g1dHiLW6_1V1h1cHkLU6]1X1i1bHkLU6[1Z1k1`HjLV6[1Y1m1`HhLW6", "size": [640, 480]}, "area": 142220.0, "iscrowd": 0, "image_id": 10337, "bbox": [0.0, 0.0, 480.0, 457.0], "category_id": 172, "id": 10012646}, {"segmentation": {"counts": "0f0Zc0000000001O0000001O0000001O0000001O00001O00001O1O000000001O001O001O00O11O0000001O0000001O00001O01O1N2O5ROR]Oa0Wc0O001O01N11O01O01O0001O01O01O01ON3O10O003NO10O0001O00000001O01L3YO^Oo]Oc0Qb0Gc]O;]b0Ea]O=_b0C`]O>`b0C_]O=ab0b0O00001O00001O0000001O00001O00001O001O0000001O04Lc0]O10O0001O01O01O01O01O010O010O00000001O01000O0001O00010O0000010O001O0001O010O0010O00001O010O01O100O1O01O01O0001O000O100010O01O0000001O10O000100O1O2NN3OXOk\\Oe0Vc0ZOm\\Od0Vc011OL\\Ok\\Of0Tc0ZOl\\Of0Tc030000010O00002M10O200O1O00010O0YNXOW@h0h?[OT@f0l?[OP@i0o?XOk_OP1R`0QOk_OS1S`0mNo^OJa0_1^`0gNn^OMa0_1a`0mNk^O@b0c1c`0mNk^O@b0a1e`0oNi^OAa0_1g`0POh^OAa0_1g`0POh^O@b0`1f`0oNi^OB`0_1g`0oNi^OB?_1i`0oNg^OC`0_1h`0nNh^OC`0_1h`0SOW_Om0i`0VOi^O[O6_1Qa0V1O00001O000000001O001O0000001O00001O00001O00001O0000[NR_O8n`0FY_O5g`0Ja_OO_`0OQ@Dn?;S@Em?;T@Dl?;U@Ek?=T@Ck?<V@Dj?<V@Dj?=W@Bh?>X@Bh?>X@Bh??W@Ai?=Y@Df?;[@Ee?9^@Fb?:^@Ga?8`@H`?8`@H`?9_@H`?8a@G_?9b@G]?9c@G]?9c@G]?:b@G]?9d@F\\?;c@E]?;c@F\\?;d@D\\?<d@D\\?=d@B\\?>d@B\\??c@A]??d@@\\?a0c@@\\?`0e@_O[?b0d@^O\\?b0e@]O[?c0e@^OZ?c0e@]O[?c0f@\\OZ?e0f@[OY?e0g@[OY?f0f@ZOZ?f0f@[OY?e0c_OfNm0d0`?g0b_OeNn0d0`?h0a_OdNo0e0_?g0`_OgNQ1a0_?o0^_O^NP1c0b?Q1_@oNa?Q1`@nN`?R1`@nN`?R1a@nN^?R1b@nN^?R1b@oN]?Q1d@nN\\?R1d@nN\\?S1c@mN]?S1d@mN[?S1e@mN[?S1f@lNZ?T1f@mNY?T1f@mNY?S1g@mNY?T1g@lNX?T1h@mNW?_Og_OL0]1R1cNlNc0g`0i0`@VOk8B^LX1kJQO_80eLo0dIdNV1`0X81nLg1RJ[NDBK=252[OL4P7>@c1QJZ2i4WLV1^1QJc3_O\\J3Lc3f0j2_1RJn5T1dHk4]1QJW66eGKg0n5^1PJX62hGNc0o5]1RJ^7_O^FOg0a6\\1QJ_8o5aGQJ_8P6`GQJ^8o5cGoI_8R6aGlI_8U6aGkI_8V6^E[In1>e8\\6ZGcIf8_6YGaIf8a6YG_Ig8b6YG]Ig8c6YG[Ii8e6WG[Ie8F`Eo6k1[InNFj81\\Fo6j0PI0?JJl8k6PGjIAFb0Ik8f6QGdJ3gNm8e6oFcJ5iNk8e6nFdJ8fNj8e6nFkJ1bNZ8BkGP70jJ@oN`0VO[30WL1W3NeL024>n6ORLDnLM4e3>UO]OWM1O?`0`6LhLEZLT3OjLN1g0OYO]20cMV1;o5NjLGVLY13gNI0OY11gNO20Oi0X2d0QNh5KWMS1PL0g0SOPO6>3o0Ed5K[MS1PLMo0lNhN12O>0_O2K6`10Z:o0PEiNVO44OL3h1K@OiN6b10Y::VDG4IV10eN3X3O^N0Y:OnI0o=O[`W2", "size": [640, 480]}, "area": 25773.0, "iscrowd": 0, "image_id": 10337, "bbox": [0.0, 0.0, 365.0, 449.0], "category_id": 177, "id": 10012647}, {"segmentation": {"counts": "dbQ88dc0k0^\\OPO4N[b0c4kLm1e@aI0OX=m6I0O10000O1000000O10000O1000000O1000000O1000000O100VClHh<V7OO2000O100000000O10000O1000001N010O10000O1000001N100O1000000O10000O100O10OUN", "size": [640, 480]}, "area": 15214.0, "iscrowd": 0, "image_id": 10337, "bbox": [412.0, 60.0, 68.0, 243.0], "category_id": 181, "id": 10012648}, {"segmentation": {"counts": "RSj01kc06J5N2M4M3M3M2M3O1N2O1N2O1N2O1N2O0O2O1N2O0O2O0O2O001O1O1N2W^OfNj`0[1g^OTOXa0k1O1O0O2O00000O100000000O10O100000O100000O10O100000O10O010000O100O2O0O101O0O101N100\\Nd^Od0\\a0ZOf^Oe0\\a0YOe^Og0[a0XOe^O<2POZa0c0e^O89ROSa0d0d^O6Sb0Go]O7Sb0Hm]O:Sb0Dn]O<Sb0Bn]O?Qb0@Q^O`0na0^OT^Oc0ka0\\OW^Od0ia0[OW^Od0ja0\\OV^Od0ja0[OY^Oc0ga0]O[^O`0fa0@\\^O>da0B^^O<ga0^O\\^O?eb0M5K6JPZb3MSf]L101N2O001el`0L^S_O0O101O0O1O3F8[OAg]Oa0Yb0@f]O`0Yb0Be]O`0Zb0@f]O`0Zb0Ae]O?Zb0Ce]O=[b0EZ]OH5e0ab0E_]O;cb0D\\]O<cb0E]]O;ab0H]]O:_b0L^]O4_b0Oa]O1]b0k0O00UOd]O2\\b0Nd]O2\\b0j00O000SOd]O6\\b0h0O0009G9HKROY]Ok0fb0VOZ]Oj0fb0VOZ]Oj0fb0VOZ]Ok0eb0UO[]Ok0eb0UO[]Ok0eb0UOZ]Ol0fb0TOZ]Om0eb071O01OlN[]Om0eb0SO[]Om0eb0SO[]Om0eb0TO[]Ol0db0TO\\]Ol0db0TO\\]Ol0db0TO]]Ok0cb0UO]]Ol0bb0UO]]Ok0db0TO]]Ok0cb0UO]]Ok0cb0UO]]Ol0bb0TO_]Ok0ab0VO^]Oj0bb0VO^]Oj0bb0VO_]Oj0`b0WO_]Oi0ab0WO`]Og0ab0YO_]Og0ab0ZO^]Of0cb0YO^]Of0bb0ZO^]Of0bb0[O]]Od0db0\\O]]Oc0cb0]O]]Oc0la0XOc^O6@b0la0[Ob^O3C>na0A]^O1E:Rb0DZ^O3C5Xb0EW^O6B3Xb0EX^O9_O2Yb0DZ^O9]O2Zb0FX^O8_O1Yb0GX^O9^OO[b0HW^O9^OO[b0IV^O9_OL\\b0KU^O9_OL]b0JT^O;_OJ]b0LS^O:@I^b0MR^O:@I^b0MR^O;@F_b00Q^O9@G_b00f]OK3?GEab01e]OL3=HE`b03c]OL5=GCcb03a]ON4<HCcb04_]OO6:HAdb06^]O059IA32Zb0?i]O8Vb0Ij]O6Vb0Lh]O4Xb0Ng]O1Yb0Og]O1Zb0Mg]O4Xb0Kj]O4Vb0Lj]O4Vb0Lj]O4Vb0Lk]O3Ub0Ll]O5Sb0Kn]O4Rb0Ln]O4Sb0Km]O5Sb0Kn]O;ka0DV^O;ka0EU^O:la0FU^O8la0HT^O7ma0IT^O5ma0IU^O7la0FV^O9ka0B[^O<fa0B]^O<da0D]^O:da0F\\^O9ea0G\\^O7ea0I\\^O5ea0K\\^O3fa0LZ^O3ga0MZ^O1ga0OZ^OOha00Y^ONia01T1OjB", "size": [640, 480]}, "area": 10240.0, "iscrowd": 0, "image_id": 10337, "bbox": [41.0, 412.0, 439.0, 134.0], "category_id": 183, "id": 10012649}, {"segmentation": {"counts": "V3d1<GY`09g_OGY`09h_OFX`0:g_OGY`09h_OGV`0:j_OFV`0:i_OGX`09f_OIX`08i_OGW`09i_OGW`09h_OHX`08h_OIW`07j_OHV`08j_OHU`08l_OIS`08l_OHV`06j_OMT`02n_O0o?OP@4o?KR@4o?[Nj_OY17<V`0Dj_O;W`0Ei_O;X`0Dh_O<X`0Dh_O;Y`0Eh_O:X`0Fh_O:Z`0De_O=e`0YO[_Og0i`0UOW_Oj0k`0UOV_Oj0j`0VOV_Ok0i`0UOU_On0j`0ROV_Om0k`0SOU_Om0l`0ROT_Oo0k`0QOT_OQ1k`0lNX_OU1g`0kNZ_OT1f`0lNZ_OU1e`0kN]_OT1b`0mN[_OS1g`0jNl^O@>c1h`0mNj^O@=c1j`0mNi^O@0O;c1m`0nNh^O@0O;c1m`0nNk^O_O8c1m`0mNi^OAOO;d1l`0lNj^OAOO;d1l`0mNi^O@0O;e1k`0QOU_On0l`0ROT_On0[`0Dd_O;``0B`_O=d`0@\\_Oa0c`0_O]_Ob0F]Ni`0Q1a_Oe0a`0YO__Og0``0ZO`_Oh0\\`0ZOd_Oc0^`0^Ob_Od0]`0[Oc_Od0``0ZO`_Oe0d`0XO\\_Og0k`0SOU_Ol0m`0SOS_O>FXOWa0:S_O>GWOVa0;S_O=HXOUa0;T_O;IZORa0;U_O;I[OQa0:V_O:KZOPa0<U_O:KZOPa0<U_O9MYOo`0>T_O87ROd`0f0U_O8`a0H`^O79ROb`0g0U_O79ROc`0f0T_O7:SOc`0e0S_O9ea0E[^Oe0\\a0ZOd^Oh0FmN[a0:o^Oi0FmN[a0:o^Oi0GlNYa0;V_Oc0BSOWa0;R_Of0HnNVa0<Q_Og0ImNVa0<R_O^OFT13ROTa0;X_Oc0EQOSa0<T_Og0JkNSa0>S_Og0KjNRa0?S_Og0KlNPa0>T_Of0LmNo`0=U_Oe0MmNPa0=S_Of0MnNo`0<T_Of0MnNo`0;U_Og0LnNo`0;U_Og0FmNM0Ya0=S_Of0GSOWa06R_Og0FUOWa04S_Og0FUOWa04T_Of0EUOXa04T_Of0EUOWa07S_O?KZORa09Q_O:0_Om`07R_O;1\\Oo`08Q_O<0^Ol`07T_O;0^Om`06T_O;O_Om`06T_O;O_Om`06T_O;N@n`05W_O8KCo`04T_O;MAn`0Dh^OO767e0LBo`0Cg^O0767e0LB[a0Hi^Of0LB[a0Hj^Oe0KB\\a0Ii^Od0LB^a0EX^O0>i0N@Vb0G]]Oa0=HZb06g]OAD3fb0<j]OCVb0>j]O@Yb0?e0O10N3N01O00001O0ATke02iaYONnb07Q]OIob07Q]OIob07Q]OInb08R]OHmb09S]OGmb09S]OGob07Q]OInb08R]OHnb09Q]OGPc08P]OHob09Q]OGPc08n\\OHH0Zc08n\\OHH0Zc08n\\OHH0Zc08n\\OHH0Zc08n\\OHH0Zc08n\\OHH0[c07m\\OIUc07k\\OIH0Zc08n\\OHH0Zc08P]OHob09Q]OGob09Q]OGPc08P]OHob09Q]OGPc08P]OHPc08P]OHPc09o\\OGPc0:P]OFPc0:P]OFPc09Q]OHPc06P]OJPc06P]OJPc06<11O1OO10ON3O100004L001OO0001O2N001O1Oc\\>0ScA1e\\OOVc06j\\OJH0[c07m\\OKSc05m\\OKTc04l\\OLUc03k\\OMVc02j\\ONVc02j\\ONVc02j\\ONVc02j\\O0Tc00l\\O0Tc00l\\ONVc02j\\OLH0^c04j\\OLH0^c04<00O10001O000001O2O0N2N2O101M2O2OO0Id\\OJ\\c02h\\OMXc04g\\OMZc01g\\OOXc02h\\ONXc02h\\ONYc01g\\OOYc01g\\ONZc02f\\ONfc00E0f\\O0Yc01f\\O0Yc00e\\90gcF00O10\\\\O0Wc01i\\O0Vc00j\\O0Vc00j\\O0Uc00l\\O0Tc00l\\O0Tc00l\\O0Tc00l\\O0Sc01m\\ONTc02l\\ONTc01m\\OOSc01m\\OORc02n\\ONRc02edn0Of[QO4L4L44MN1000010O000O1010O00000O1O1O1O1O002O00O1]\\OMYc0=001OO10000001OO11OO11O00O11OO1001O000000001O0000O1000000O100001O000000001O10N1O11O0000000O1Aa\\O=_c0Ca\\O;cc0M]\\OHdc01\\\\OO10Q_d0", "size": [640, 478]}, "area": 11220.0, "iscrowd": 0, "image_id": 10342, "bbox": [0.0, 100.0, 446.0, 125.0], "category_id": 95, "id": 10012650}, {"segmentation": {"counts": "0V3j`000000000O1001OO10000000000O1002N1O1O1O1Z_OlL^`0[30001O0000002N:F4L1O000000001O00000000000000000000000000000000_Oa03M3M00002NO1N2O11O2N3M6J1O00000000000000000000001O1O2N1O1O00O10000000000000000001O000000001O000000O1000000O11O0000001OO11O0a0@h0WOl0SOi0XOo0fN_\\O6Peb0HQ[]O0_b02c]O2NLO003008h0K@MF031MQa01P_O6N30E4T2LlMe`0o0\\_OR2j?X1J001O000000O11O0000001O00O11OO10000O1O1002NO1001OO11O00000000001O00O100O1001OO11O0000O100002N0000O11O1OO100M3O100004L001OO1O1001OO10000O1O1O1O1O1N2O1O1O1O1N2O1O1O1N2O1O1O1O1N2O1O1O10000000Y@cLk>]3PAhLP?X3k@nLT?R3h@SMW?m2d@YM[?f3000000000000000000000001O000000001OO1O1001OO1001O0000O11OO1001O00002NO1O1Ob@bK70k>`4VA`Kj>`4VA`Kj>`4VA`Kj>`4VA`Kj>_4WAaKi>_4<00Ok@bKj>]4WAcKi>\\4XAdKh>[4YAeKg>Z4YAfKh>Y4YAgKg>X4ZAhKf>W4[AiKe>V4c0O1O1O1O1O1O1O1O1O1Of@TLb>k3j0N2Oe@XL`>h3_AYLa>g3[A\\Lf>d3VA`Lj>`3SAcLm>]3o@gLQ?Y3l@jLT?V3h@mLY?S3d@PM\\?j301O00O1O1O1O100O1N2O1O1O1N2O1O1N2O1O1O1N2O1O1O1N2O1O1N2O1O1O1N2O1O1O1N2O1O1O1N2O1O1N2O1O1O1N2O1O\\@\\Mn=c2oA`MR>^2lAfMT>Y2iAkMW>U2eAPNZ>P2bATN^>l1^AXNb>h1ZA[Ng>e1UA_Nk>a1QAdNn>\\1n@hNR?X1j@lNV?T1f@POZ?k20000000O11O00O100001OO10000001OO11OO11O00O11OO1001O000000001O0000O1000000O100001O000000001O1OO1O11O0000000000000000L4@`0C=G9D<G9D<G9C=F:F:F:G9FZX;", "size": [640, 478]}, "area": 55830.0, "iscrowd": 0, "image_id": 10342, "bbox": [0.0, 0.0, 460.0, 156.0], "category_id": 106, "id": 10012651}, {"segmentation": {"counts": "cb0]1cc0PO1O00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001O000000000000000000000000000000000000000000000000000000000000000000000000SHFTL:m3JnK6S4MiK3V43eKM[4FXD`0X7Jb4;YKEg4?UKAk4d0PK\\Oo4j0lJVOT5o0gJQOX5U1cJkN]5Y1_JgNa5^1ZJbNe5^1\\JbNe5\\1\\JdNd5Z1^JfNb5Y1_JgNb5X1^JhNc5V1^JjNb5V1^JjNb5U1`JjNa5<WD0X6D`5;bDHo5M`58cDKm5M`55fDMk5N`52hDG_O1Y66a5OiDH@2V67d5IREOZ58Y=000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001O0000000000000000000000000000gIIiH7V7JjH6T7LlH4S7NlH2d6HdC7h51c6KbC5k50b6`0^I@b6`0^I@b6a0]I_Ob6b0^I^Ob6c0]I]Od6c0[I]Oe6d0ZI\\Oe6f0ZIZOf6g0YIYOh6h0VIXOj6i0UIWOk6j0TIVOm6j0RIVOn6k0QIUOQ7k0mHUOT7k0kHUOT7n0jHROW7o0gHQOZ7P1dHPO\\7Q1cHoN]7S1aHmN_7U1_HkNa7W1]HiNc7Y1[HgNe7Z1ZHfNf7\\1XHdNg7_1XCYNj48n7a1UCZNk45P8g1oGYNQ8i1mGWNT8i1kGWNT8l1jGTNV8m1iGSNW8o1gGQNY8P2fGPNZ8R2dGnM^8Q2bGnM_8R2`GnM`8R2`GnM_8S2aGmM`8R2`GnM`8R2`GnM`8R2`GnM`8R2`GnM`8R2`GnM_8S2aGmM_8S2aGmM`8R2`GnM`8R2`GnM^8T2bGlM^8T2bGlM^8T2bGlM^8T2bGlM_8S2aGmM`8IRCW2^4PNb8IQCT2]4SNf8l1_CmMV37\\9k1^CnMV37]9j1]CoMV37]9j1\\CPNW36]9j1\\CPNW36^9i1[CQNW36^9i1[CRNV35_9i1[CRNV35`9h1ZCSNV35_9i1[CRNV35_9IiBn1b0RNX37_9g1UGYNk8g1UGYNl8f1TGZNm8e1SG[Nn8d1RG\\Nn8d1RG\\No8c1QG]No8c1QG]Nn8d1RG\\Nn8d1RG\\Nn8d1RG\\Nn8d1RG\\Nn8d1RG\\Nn8d1RG\\Nm8e1SG[Nm8e1SG[Nn8e1WDYNP12h:f1XDkN=_O[;f1WD_OKkNn;f1VD2ZOXN`<g1TDb0aNaM86U=e1QDY1o;gNPDZ1S<cNmC]1V<`NiCa1X<_NfCb1Z<iL\\Bb1Y1f1\\<gL[B10_1X1j1c<VN\\Cj1b<XN]Ci1c<WN\\Cj1e<UNZCl1d<WNZCj1e<WNZCj1h<TNSC]NWO_3f=TNSC^NUO_3f=UNUCo1k<RNTCn1l<RNUCm1l<RNSCo1l<RNTCn1l<SNRCn1o<QNPCP2P=PNoBQ2P=QNnBP2R=PNmBQ2T=nMkBS2U=nMiBS2W=mM\\BYN5k3_=lM[B]N2h3d=kMXBaN0e3i=iMVBd2l=g1001O00000000O100001O00O100000000002N1O1OO1001OO1001OO1O10000O100000000001oIRBn5Q>O2N003M000000000000O11O001O1O1O2N1O1O0000001O0000O1000000000000001O1O000000O11O001OO1O11O0000001O001O2N3eJZAV5j>N0000001O00O11OO11O1O00O1O100000000O11O00O11OO11OO1000\\OXAPLi>m3ZARLg>l3ZATLg>i3\\AVLe>n0SAZ1:hMj>c0YAZ10mMEHY?e0_AY1HQNJ0e`0f1[1I7K5K5L1OO001O1O1O001O00K5K6J6J6Jl^c0", "size": [640, 478]}, "area": 37283.0, "iscrowd": 0, "image_id": 10342, "bbox": [0.0, 376.0, 446.0, 264.0], "category_id": 116, "id": 10012652}, {"segmentation": {"counts": "Y=X4XP`2kKfc`M4K5L4L3L5L4K4MN2N1O2N1O2O1N1O2N2N1O1O1O1O1O1O1O1O100O1O1O1O1O1O1Ojmf11URYN100O1000001O000000000O100000001O00000001O1O001O^OMX]O3Zc0100\\OL[]O4Yc00O10000O1000O1O1O2N10eod3", "size": [640, 478]}, "area": 843.0, "iscrowd": 0, "image_id": 10342, "bbox": [0.0, 425.0, 290.0, 180.0], "category_id": 123, "id": 10012653}, {"segmentation": {"counts": "jQ91Zc00g\\O010=0]b04]]OLH001>OC0eb07Z]OI379ID10Oo`00d@g0]NYO87g`0La@g0dNAj`0L]@f0eNSO0;m`00\\@P1d?SOX@o0g?UOU@k0j?XOT@h0l?YOT@g0k?YOT@h0l?XOU@g0k?YOT@i0j?XOV@h0j?XOV@h0k?WOU@i0l?WOS@j0k?WOU@i0k?WOT@j0m?UOn_O]OYO\\1l`0VOk_OP1U`0oNk_Ob0[OjNN8m`0=i_O?h`0Km^O4Ua06`^OJ^a0\\1ON31O100001N101OO100O1O11N2cM_^OY2da00002NM32NO1O1O1O10001O01O1OO1g^OUN]`0h2N200N110O1O0O4N4K4L5KGP@gLl?X3[@aLe?_3\\@_Le?a3>0O0O1002O0OO110O000O1010O00O2N1002OM22N01OO100O1O10000001N1O10000OLk_O]LV`0c32ZOj_ORMNb0Y`0=h_Ol05eNS`0>k_Ok01gNV`0`0g_Oh04gNV`03n_O^OJh12fNT`07\\@\\O[O\\15QOV`05TA1`N]O6<W`05^AHUNG7;V`06XBEi=:o21>^Okc:4okDM\\<5fB7gMDg?0aBo0]=TObBl0R=CkB=X=;lAFT>T1RAmNm>d1X@[MOX1i?l201O0HkKe@V4Z?lKc@V4\\?kKb@V4^?7O1O1N101O1O1O001N1015K6KN2NMg@aKW?_45001N1000001O0000001O0O101O0000001O00001N1000001O00001O000O2O0000001O00001O0O101O00MRATKn>k4SATKm>\\4c0Cb@QLN5`?k3<1O2N1OJ7N2[Ln_O\\3Q`0cLS@X3P`0fLT@X3l?gLW@W3i?hLX@Y3g?fLZ@Y3W`0N2MZ_OlLf`0T3Z_OlLf`0T3DkLS@T3m?nLR@Q3n?PMQ@Q3o?PMP@o2P`0RMo_On2R`0SMl_On2T`0SMk_Om2U`0>11ON20000O1N2000O101OO1001O00000000000O10O1001OO10000001O002N1O2M100000000000cLj_Oo2W`0PMj_OP3Y`0lLi_OS3S`0hLV@W3k?iLV@V3j?jLV@V3i?jL[@S3e?mL\\@Q3e?oL[@Q3e?oL[@Q3d?PM\\@P3d?oL]@P3d?PM[@Q3d?PM\\@P3d?PM\\@o2e?QMZ@P3f?PMX@R3g?oLZ@g2_O]MX`0LY@e2A_MV`0LY@e2B]MU`0OZ@c2o?]MQ@b2P`0^MP@b2o?`MR@^2n?cMS@Z2n?fMT@Y2k?hMT@X2k?fMX@Z2h?eMY@[2g?dMY@]2f?dMT@c2k?\\MU@d2l?[MU@e2k?[MU@e2j?[MW@8Cj1U`0nMX@2JQ2l?mMZ@0NQ2HmMb?3h@ONR2GoM`?0k@OMT2GoM_?Nm@ONS2^OZNe?Dn@0Ng2Q?ZMQAONg2Q?ZMQAOMh2R?YMQAOMg2R?\\MPAMNg2R?\\MPAMNg2R?\\MPAMNf2S?^Mn@LNg2T?]Mn@LNf2U?`Mk@JOg2V?`Mj@I0g2V?bMe@J4d2W?eM_@K:`2W?]N_@d1b?]10O10000O1M30000001O0000O10O10O100O10000O10000O10000002N000O1V@nKf?V400000WMb@P1]?POd@P1\\?POe@o0Z?ROf@k0]?VOc@h0]?WOe@i0[?gNVAX1i>hNXAX1g>iNYAW1e>kN\\AT1c>mN]AS1b>nN^AR1a>oN_AQ1b>nN^AS1`>nNaAQ1_>oNaAP1`>QO_Ao0a>QO_An0b>RO^An0b>RO^An0b>RO^Am0c>SO]Am0c>SO]Am0c>SO]An0b>QO^AP1b>oN_AQ1a>oN_AQ1a>oN_AQ1a>oN^AR1b>jN^@QO2Ok0W2d>hNe@TOd0U2f>gNeAY1[>gNeAX1\\>iNo@RO0U2Q?kNj@SO5R2Q?lNh@SO8P2P?oNa@VO?k1P?^OPAc0o>]OQAc0o>]OQAc0o>]OQAc0o>]OPAd0P?\\On@g0Q?ZOm@g0S?YOf@n0Z?l1000001O0000PNb@^O^?b0b@]O_?c0a@QN2g0^?W1`@bM0=2i0_?X1_@bM2:1b0h?b1U@bM4744P`0S2i_OaM383LZ`0[2`_OaM47i`0X2S_OaM40Pa0^2;I7C=H`D_NQ5V1YKRO`4f0eKAW42WLOg3GcL9_;3H8I8J53NL30gN`]Oo0MPOhb0R12100O1VMXO]Bg0^=^O`Bc0\\=BeB>[=AcB=fMoNg?e0dBLeMAR`0e0ZBHb<KQB;^1E[<8PB<h1RO[<j0^A?Xa0S10ON36JM3000dM^^OZ2da00O1000000000O10dG", "size": [640, 478]}, "area": 47426.0, "iscrowd": 0, "image_id": 10342, "bbox": [14.0, 180.0, 464.0, 282.0], "category_id": 126, "id": 10012654}, {"segmentation": {"counts": "i_Z41nc01S\\OOZc0d0B_OT]ON5e0fb0=0010O0I7N201L300010OO1N3L\\Nj]Od1Vb0\\Nj]Od1Vb04N1O101N1O10001N11ON3O000O1N3O000010OO10010O00001O0000001N10010N10001O01O002O0O2N01O0000001O0OO[^OiMea0V230O10001O0O101O000O2O000O101O0GbMo^O_2LaMo`02U_Ob2j`0_MT_Ob2l`09O2O000O110O00000010OO101O01O2OOZMS_OY2n`0fMU_OW2l`0hMV_OV2j`0bM`_O]2Sa0N0001N10001N100O101O0O4M6I4MO010000O1M30000001O0000O10O10O100OO2O10O10001N10001O02N0001OlLX_OP3l`0001O00001O0M3O101O00001O00000010O00O2O0OK__OkLc`0V3\\_OjLd`0V32100001N10000000001O1O00O101O1O00100O0000N201ROX_OhM0g0h`0_1\\_OaNe`0^1[_OcNe`0\\1\\_OcNe`0]1Z_O[NKDo`0o1X_OnM]a0P2:gNS^O6Vb0Il]OHbb08c0N2DWZn0", "size": [640, 478]}, "area": 17145.0, "iscrowd": 0, "image_id": 10342, "bbox": [221.0, 308.0, 208.0, 122.0], "category_id": 132, "id": 10012655}, {"segmentation": {"counts": "j4<S4d3^7\\LbHk0W:VOhEk0W:TOjEl0V:UOiEk0W:UOiEk0W:TOjEl0W:ROkEm0U:TOjEm0U:SOlEl0T:SOkEGmKV1X>SOjEP1V:QOjEn0V:ROnEj0Q:WOiE7PL4X>DiEGoK=09W>CjEFPL>OC0c0W>HkE0nKD10Od0W>GlEEmK87<P>HnEHVL?l=IPFDiK0;c0l=I_F8`9HaF7_9IaF7_9IaF7_9JaF5_9KaF5_9JbF7]9IdF6\\9JdF6\\9KcF5]9KcF5]9IfF6Z9JfF6Z9JfF7Y9IeF9[9FgF9V9J[FEdKN8d0i=JXFf0h9ZOWFg0i9[OUFd0m9YObF9\\9ZOPB>d46]9\\OoA=g44[9_OnA0O;i44[9AmA0O;k41Z9GkA8l4OY9GlAOO;m4MY9IkAOO;o4JY9KiA0O;P5H^98cFF^9:U5O00000000000000000000000000000001O000000000000000000000000000gAEl8a0oFFPKM`=8dGLjJ2]=1jG`0R8AdBFV5m0P8BPHb0l7ZOXHg0g7YOYHh0f7XO[Hf0g7YOYHg0eJmNP=<[Hi0e7WO\\Hh0d7XO\\Hh0c7YO]Hf0d7ZO\\Hf0e7YO\\He0e7[O[He0e7[O[He0e7\\O[Hb0e7_O[Hb0e7]O[Hc0g7[OZHd0f7[O[Hf0c7[O]Hf0b7ZO_He0a7\\O]He0c7[O]Hf0b7ZO]Hf0d7]OXHe0g7YO[Hg0T7lNaC767T5g0T7kNbC767T5h0R7UOiC3V5j0c2XOlKJ[L5U5i0d21SKQOTM5U5c0j2S1PJXNlMD0>U1Bm2k0X4Q2RIZNdNiNm01U3f0]4U3cGTL6\\1c0fNUO0h3NYLh0_8n4ZHaJm2d0j4j4ZHbJT2KQMNb0j0P8l4XHcJ5Hl05eNd0R8m4WHbJ5Kk01gNe0S8n4UHbJ8Hh04gNc0U8n4mGlJ1G^OJh12fNb0W8o4lGjJ45\\O[O\\15QOa0X8Q5jGkJ3m01`N]O6<b0Y8P5jGkJ2X1HUNG7;`0[8Q5iGkJ2R2ERNa8R5gGjJ2R3X8T2WGYKb0a2X8i3VGZLj8]71000000O1000000O1000000O1000000ZFRGgN0`9Q9bFSGNLO003008h0K@MF031MV43^On8]LVGN30E4T2LlMi36nN7gMb9CRIR3PMCW>dMjAS228T>eMlAe1?c0f=gMkAi1W1Do<cNkA7R1Hn0d0U<]OlA6R1H^1J[MO`>8fA8Q1FT`03l^O6P1GU`02k^O7o0HV`01k^O7Q1FT`03k^O7S1CS`05k^O8S1AT`06k^O7cb0I]]O7bb0J^]O6cb0ET]ON:<Vc0000000000000000000000`BGY79gHJfJ2^;4lIM]J6f;MnIl0R6TOnIl0R6UOmIj0T6VOlIi0T6XOmIg0T6XOlIh0T6XOlIg0U6YOkIg0U6YOlIg0S6YOmIg0S6YOmIf0T6ZOmIe0S6[OmIf0R6ZOnIf0R6ZOnIf0R6ZOoIf0P6ZOPJf0Q6YOoIi0o5WORJi0m5WOSJh0l5ZOTJg0k5YOUJh0j5XOWJg0i5YOWJg0i5YOWJg0i5YOXJf0h5ZOXJf0h5[OWJe0i5[OXJd0h5\\OXJc0i5]OWJc0i5]OWJc0i5]OXJb0h5^OXJc0g5]OYJb0h5^OYJa0g5_OYJa0g5_OYJa0h5^OYJa0g5_OYJc0e5]O[Jd0d5\\O\\Jd0d5\\O\\Jc0e5]O\\Jc0c5]O]Jc0c5]O]Jc0c5]O^Jb0b5^O^Jb0b5^O^Ja0c5_O]Ja0c5_O^Ja0a5_O_Ja0a5_O_J?c5A^J>b5B^J>b5B^J>b5B_J>`5B`J?_ISOn;>cJa0_IROk;=gJd0W5]OiJd0U5]OkJd0S5]OnJb0S5]OmJc0S5^OlJc0S5lN[D?b6e0U5[OlJd0T5\\OlJe0S5[OmJd0T5\\OmJc0\\InNe;?oJe0Q5[OoJe0Q5nN[D9d6j0R5kNZD;e6j0R5VOnJk0m4kN_D:d6k0k4jN`D11:e6k0i4jNaD11:e6k0h4nNcD7e6j0i4oNbD7e6j0i4nNcD8e6i0h4oNcD8e6i0f4QOeD6e6i0e4ROfD5f6h0b4UOhD3f6g0a4WOjD2e6g0`4XOkD1e6h0`4VOkD2f6f0a4WOiD3f6f0b4VOhD4f6f0c4UOgD5g6e0c4TOgD7f6g0c4POgD9f6g0d4oNfD:g6i0g4WOYKl0d4TO\\Km0c4SO]Km0VIhNZ;;aKm0TIhN10W;;dKS1[4mNeKQ1]4oNdKP1\\4POdKQ1[4oNeKo0]4QOcKn0^4ROcKo0[4QOeKo0[4QOcKETIX1[;SO`KP1`4PO`KP1`4PO`KQ1_4oNaKP1`4POaKo0_4QOaKP1^4QOaKo0_4QObKm0_4SOaKm0_4SOaKn0^4ROcKm0]4SOcKm0]4SOcKm0]4SOcKn0\\4ROeKn0Z4ROfKP1X4POhKFRIV1V;TOmKl0R4TOnKl0R4TOnKl0S4SOnKl0S4SOmKm0S4SOmKl0T4TOlKl0T4TOmKk0S4UOmKl0R4TOnKl0R4TOnKk0R4VOoKi0Q4WOoKi0Q4WOoKi0Q4WOPLh0P4XOPLh0o3YOQLi0]3mNhE:l6i0[3mNiE:l6j0Z3kNkE;k6i0Z3mNkE:k6i0Z3mNkE:l6i0X3mNlE:l6h0Z3mNjE;l6h0Z3mNjE;m6h0W3mNmE;l6g0W3nNnE;k6f0W3nNPF<j6e0U3oNRF<i6e0i3[OWLd0j3\\OVLd0j3\\OWLc0i3]OWLc0i3]OZL`0f3@`L:`3FcL8XH\\OR;<eL<Z3DfL>X3CgL=Y3CgL`0V3@jL`0V3@jL`0V3@jL`0V3@jL`0V3@jL`0V3@jL`0V3@jL?W3AiL`0V3@jL`0V3@iLb0V3^OjLc0U3]OkLd0T3\\OlLf0R3ZOlLi0S3WOlLk0T3TOlLl0T3TOlLl0T3TOlLl0T3TOmLl0R3TOnLl0R3TOnLl0R3TOoLj0R3VOnLj0R3VOnLj0R3VOnLj0R3VOoLi0Q3WOoLi0Q3WOoLi0Q3WOPMh0P3XOPMi0o2WOQMj0n2VOSMi0m2WOSMi0m2WOSMi0m2WOSMh0n2XOSMh0l2XOTMh0l2XOTMi0k2WOVMg0k2YOUMf0l2ZOTMg0k2YO]EDe7S1n2[OTMd0l2]OSMc0m2\\OTMe0k2[OVMd0j2\\OVMe0i2[OQMC^HV1_:WOTMB]HZ1OfN[:>YMS1d2nN\\MR1d2nN\\MR1d2nN\\MR1d2nN[MT1d2lN\\MT1d2lN\\MS1e2mN[MT1d2lN\\MS1e2mN[MT1d2lN\\MU1c2kN]MU1c2kN]MT1d2lN\\MS1e2mN\\MR1d2nN]MQ1c2oN]MQ1c2oN^MP1b2PO_Mn0b2RO^Mo0a2QO_Mo0a2QO]MP1d2PO\\MQ1c2oN`LBXI0g0^1b9PO]LFQJ\\1a9nN]LFSJ[1a9oN[LGSJ[1b9nN[LFkIKDe1V:jNYLH^Il1_:\\NRLj1R4VNeJBeK_2h9oMbJDWKk2W:aMkGA5a3P8nLkGA]O0gNc3Q:lLkGAQN2g0NPOn3^:bLiG@bM0=2i0W4_9WLiG@bM2:1b0`4i9mKiG@bM4744j4X:^KkGAaM383LU5_:TKlGAaM47d5]:fJkGBaM40j5e:`JiGk5W8UJbGR6^8nIUG^6l8bIlFU6_NjIe:1aF_6ROYI]:8YFd6AoHV:>lEQ7X:_HcE`0K[7b:UHaEd8a:]G\\EOH]8T;dGmDOO10Y8W;8Ec0@:J9kE\\Fb9U:I8E<G8DcMPHeIe7`6`H^IX7g6mHcG\\On0\\7j7WIXG]Ol0U7T8_IPG[On0k6Z8lIhFXOP1i6P7bIZH;@OC[O08e0`6d7kI]H<GKAFb0V6e7UJRH?623Z5h7gKVHnN2[5h7fKWHoN1[5e7iKZHlN1[5k7cKTHRO1[5h7fKXHnN0\\5h7fKXHnNO]5i7YIfGZ2b0POO]5k7cKWHoNN^5j7dKYHnNM]5j7eKZHmNL^5j7eKZHlNM_5i7eKYHmNM_5j7dKYHmNM_5j7cKZHnNL_5j7cKZHnNL_5", "size": [640, 478]}, "area": 43943.0, "iscrowd": 0, "image_id": 10342, "bbox": [0.0, 0.0, 478.0, 477.0], "category_id": 181, "id": 10012656}, {"segmentation": {"counts": "a6n1Rb00o_OU7m4lHSKS7m4mHSKS7n4lHRKT7n4mHQKS7o4oHoJQ7R5PIlJP7T5PImJo6S5QImJo6S5PInJP7S5nHnJR7R5nHnJR7R5mHoJS7R5kHoJU7R5iHPKV7T5fHlJZ7W5bHjJ^7Y5^HhJc7Z5YHgJg7]5THdJl7_5PHcJo7`5mGaJS8c5iG]JW8d5iG[JW8f5hGZJX8f5iGYJW8g5iGZJV8f5kGYJU8g5jGZJV8f5jGZJV8f5jGZJV8g5iGYJW8g5iGYJW8g5iGZJV8f5jGZJV8f5jGZJV8f5jGZJV8l5dGTJ\\8X6WGiIi8f81jN_GlFb8P9cGnF^8R9dGkF]8U9dGiF]8W9eGfF\\8Z9eGdF\\8[9fGcF[8]9gG`FZ8`9gG^FZ8b9gG\\FZ8d9Q10000O100000000000000000000TMfERLZ:n3fESLY:l3iESLW:l3jETLV:l3kESL`0]Mi8_6hFPMX9P3iFPMV9o2kFRMT9m2WGjLh8V3XGjLi8T3XGmLg8S3ZGmLe8R3\\GnLd8X3VGmLe8Z3lFSMo8m2RGVMj8j2VG[Me8e2\\G_M_8`2bGeMY8[2gGiMU8W2lGmMo7S2RHQNi7o1XHUNc7k1]HZN^7f1cH]NY7c1hHaNS7_1mHfNn6Y1SIlNh6T1XIPOd6P1\\IUO_6k0ZIVI2T6_6f0_IF\\6:dIJX66hIOS61mI2P6NPJ1Q6NPJ1Q6JTJ4n5ITJ7m5ISJ7m5ISJ6n5JRJ6n5JRJ5o5KQJ\\OGhJX6l5QJZO2`JZ1Fj2`6jKXO4bJY1Jd2\\6oKUO7dJX1N^2Y6SLSO9^J@0g1:X2U6XLQO;^JA1d1`0S2P6\\LoNi0`Ji0?S2R6[LlNk2lJo0W6WLgNQ3mJl0\\6SLaNW3oJj0`6oK\\Nj7d1VH[Nk7e1UHZNl7f1THYNm7g1SHYNm7g1SHXNn7l61O1O10SN^D\\Ka;f4bDVK];k4hDPKW;R5mDiJR;Y5REcJm:]5XE^Jg:d5\\EXJd:h5aESJ_:n5bEPJ^:Q6`EPJ_:Q6`EPJ`:Q6^EPJb:Q6\\EPJd:P6[EQJd:Q6[EoIe:Q6ZEPJf:Q6XEPJh:Q6VEPJi:Q6VEPJj:Q6TEPJl:P6SEQJm:o5RERJn:n5QESJP;k5PEVJP;j5oDWJQ;i5nDXJe4jN[1n6oIYJf4kNY1l6QJYJf4kNY1l6PJZJg4kNbKR1n3h5hK\\Jh4kNaKR1n3g5hK]Ji4jNbKQ1m3h5gK^Ji4jNcKP1n3g5eK`Jj4iNcKP1n3g5dKaJk4hNcKP1o3f5bKcJl4gNcKP1P4e5`KeJl4gNdKn0Q4e5_KgJl4fNTKI=U1U4b5_KkJk4hNaKk0V4`5^KnJk4gNaKk0W4_5]KoJk4gNaKk0W4^5]KQKk4fNaKk0X4\\5\\KTKk4eNaKj0Z4[5ZKWKk4dNaKj0[4Y5YKZKk4cNaKj0[4X5YK\\Kk4bNaKj0\\4V5XK_Kk4^NTKI=T1\\4V5WK`Kl4]NTKI=T1]4T5VKcKl4\\NTKI=Z1Y4k4ZKgKm4ZNSKJ=`1U4b4^KkKm4YNSKJ=`1V4:]KY20kMm4XNSKJ=`1Y4\\O\\KH0^3OlMl4XNTKI<a1[4ROdKNHa3MnMl4WNTKI<a1]5nN[Jc3KoMm4VNTKI<a1]5mN]Jb3IRNm4UNTKI<a1^5kN]Jc3HTNm4TNTKI=`1`5iNZJe3HVNm4SNTKI=`1S6]2_IXNm4RNTKI=_1T6]2^IZNm4QNTKI=_1U6[2]I]Nm4PNTKI=_1U6e4YNSJTKJ>^1V6d4XNTJTKJ>^1W6c4WNUJTKJ>^1X6b4VNYJbKU1Y6S2YIjNl4nMbKU1[6P2WInNl4mMbKU1\\6^4RN]JbKT1^6^4QN]JaKU1_6]4PN^JaKU1`6\\4oM_JaKU1b6Z4lMbJbKT1c6Y4kMcJbKT1e6W4iMeJbKT1g6U4gMgJbKT1h6T4fMhJbKT1j6R4dMjJbKT1l6P4bMlJbKS1o6o3_MnJbKS1P7n3]MPKcKR1R7l3[MRKcKR1T7j3YMTKdKQ1U7i3WMlJUK7?T1W7g3UMXKdKQ1X7fNZHQ10d2j4YLUK9?S1_7EUHd2h4fLdKQ1`7ETHc2i4fLcKQ1c7DSHc2e4jLeKo0d7CTHb2b4mLfKn0f7BRHb2a4oLgKm0g7ASH\\OGk2g4\\MhKl0i7_ORH\\OFn2f4\\MiKk0j7^OSHZODR3e4\\MjKj0l7\\OSHYOBV3d4\\MkKi0m7ZOTHb2R4]MmKg0n7YORHd2R4]MnKe0o7ZOPHe2R4]MoKc0P8[OnGf2R4]MPLa0Q8\\OlGg2R4]MQL?R8]OjGh2Q4^MSL<S8^OhGi2Q4^MTL:T8_OfGj2Q4^MUL8U8@eGj2P4_MVL6V8@dGl2o3_MWL5V8@cGm2n3`MYL3ImNS8c0lGn2n3`MZL2GQOS8?mGo2n3`M[L1EUOS8;nGP3n3`M\\LOEXOR89nGQ3m3aM^LMDYOS88mGR3m3aM_LLCZOT87lGS3m3aM`L;g7QOkGT3m3aMaL:g7QOjGU3m3aMbL9g7POkGV3j3cMdLIAXOV86kGV3i3cMfLHAYOU86kGV3h3cMhLGBYOS87kGV3g3dMiLECZOR87kGV3g3[MXLTOa0j0DXOR89jGW3f3UM]LZO>h0CXOS8:iGW3f3PMbL_O9h0DWOR8;jGV3e3mLfLC5h0EUOR8=iGV3e3hLkLG1h0U8B[GW3d3dLoLKMh0U8B[GW3d3_LTM0Hh0U8B[GW3d3[LXM4Dh0U8B[GW3h4_LhKh0U8B[GW3h4_LhKh0U8B[GW3h4_LhKh0U8B[GW3h4_LZKI7o0\\8B[GW3h4_LZKI7o0\\8B\\GV3g4`LZKI7o0\\8A]GW3f4`LZKI7o0\\8A]GW3f4`LZKI7o0\\8A]GW3f4`LZKI7o0\\8A]GW3e4aLZKI8m0]8B\\GW3e4aLZKI8m0]8B\\GW3e4_L\\KK6m0]8B\\GW3e4]L^KM4m0]8B\\GW3e4\\L_KN3m0]8B\\GW3e4ZL`K12l0]8A^GW3P5\\LUKl0^8@]G>5`1k4fMUKl0^8@]G=i0n0W4YNUKl0^8A\\G;^1<c3lNUKl0^8A\\G:Q2KP3^OUKl0_8AZG9f2YO[22VKk0_8BYG7Q8\\OWHk0_8BYG7Q8\\OWHj0`8DWG5S8]OVHj0a8DVG3T8_OUHj0a8DWG1m7AQH0:j0a8EWGNj7GSHM;i0b8EVGLk7IRHM;i0b8EWGKi7KSHL;i0b8FWGHj7MRHL;i0b8GWGEk7OQHL;i0b8GXGCk70QHM:i0c8GWGAl72oGN;h0c8HWG^Om74nGN;h0c8IWG\\Ol76oGM;g0d8KVGYOm78nGM;g0OUOW8g0H@gGM;g0NVOY8g0E_OhGN<f0DVO51^8g0C_OjGM<f0D@c8?B^OkGM<e0E@OEb8j0D^OkGN;e0ECa8:E@jGN;e0DG_85HBjGM<c0DI^84IChGN=b0DI_82IEgGN=b0DJ^80JGgGM=b0DI_80IIgGL=a0DK`8@_G8Y80hGL<a0DK`8@bG4V85hGK<a0DK`8_OfG1R8:gGK=?EKa8^OgG0o7>gGJ=?EHd8AN>]GK;>FIXa0Og^OJ;>FIWa00h^OI;=GKTa0Ok^OI:=GKSa00k^OI;<GKRa01l^OH;;HMo`01n^OG;;HMn`01P_OG:;INj`01S_OF:;JOf`01U_OF;9K2b`0OY_OF:9L0a`02Y_OE:9I3c`00Z_OD:8I5b`0O\\_OD98H0L@f`0f0]_OB98HOg`08W_OB:7G0h`03[_OF66G2h`0O^_OI36G2h`0Kb_OMO6F2j`0Hd_O0L5G3i`0Dh_O4H5G3i`0Aj_O8F4G3i`0]On_O<B4G3fa0Ic^O3H4ea0Ic^O3H4ea0Ic^O3H4ea0Ic^O2I5ca0Jd^O1I5ba0Ke^O0J4_a0Ng^ONJ3_a00g^OLK4]a01h^OKL3[a03i^OJM2Xa06k^OHN1Va08l^OGO0Ta0:m^OF1IVa0b0i^OE4CTa0j0h^OCja0>V^OBia0?W^OAga0a0Z^O^Oea0c0[^O]Oda0d0\\^O\\Oca0e0]^O[Oaa0g0_^OXOaa0i0_^OWO`a0j0`^OVO_a0k0a^OUO]a0m0c^OSO\\a0n0d^ORO[a0o0e^OQOYa0Q1g^OoNXa0R1h^OnNWa0S1i^OmNVa0T1j^OlNTa0V1l^OjNSa0W1m^OiNRa0X1n^OhNQa0Y1o^OgNo`0[1Q_OeNn`0\\1R_OcNn`0^1R_ObNm`0_1S_OaNk`0a1U_O_Nj`0b1V_O^Ni`0c1W_O]Ng`0e1Y_O[Nf`0f1Z_OZNe`0\\1Q_O_N:5d`0Y1V_ObN65b`0W1\\_OdN25a`0T1a_OgNN4a`0R1f_OiNI5R1Ne=Q1dAlNE5Q10c=m0kAnNA4R11a=j0PBQO]O4R12`=e0VBTOXO4T12^=b0ZBXOTO4R15_=;_B\\OPO3c0f0n=GcB@lN2c0i0m=AiBCgN1e0k0k=]OmBGcN0f0l0j=YOQCK_NNh0n0h=UOUCO[NMi0P1f=POYC4@l0]?UOc@k0\\?VOd@j0[?WOe@i0Z?XOg@h0W?YOi@g0W?YOi@g0W?XOj@h0V?PORAo0Pa000000O1O10O1000LPOW]O2Oj0jb0ZOZ]Ob0fb0<001ZOY]O1ib0KY]O6gb0G[]O:Uc000000000O1N2O2K4IYP10loN3N1N2UMIkA:c<OhA4Z1OnMF]>c0PB4X1IQNIf>Q2PChNo<]1jBfNT=`1gBbNW=c1dB^N[=g1aBYN_=g1aBYN^=g1bBZN^=e1cB[N]=d1dB\\N[=d1fB\\NZ=c1fB^NZ=a1gB_NX=a1hB`NX=_1cBgN]=X1^BnNb=R1XBTOh=l0RBZOo=f0jA@V>a0cAE^>n21O001O001O1O001O1O001O001O1O001O001O10O01O1O001O001O1O001_O_5", "size": [640, 478]}, "area": 87913.0, "iscrowd": 0, "image_id": 10342, "bbox": [0.0, 88.0, 477.0, 552.0], "category_id": 183, "id": 10012657}, {"segmentation": {"counts": "S5k1`;10O0O1WN`Dh1a;O01O23L0ON4N1000001O001OO11O02NO002O000000TNcDk1^;O1VNaDi1`;00001`DVN];i1dDWN];k101O00001O0eDSNX;P201O00000000000iDoMT;R2kDnMU;R2kDnMS;V2N3O00000O20N100001O000002N1O00M300000NQEhMn:m0UE:m:^O\\E`0d:XOeEi0[:ROiEn0V:SOjEm0V:SOjEm0W:ROiEn0W:ROiEo0V:QOjEn0W:ROiEn0W:SOhEn0W:ROiEn0W:ROiEn0W:ROiEn0W:ROiEl0XOkNo:9iEl0Z:SOfEm0Z:SOgEl0Z:SOfEm0Z:SOfEm0_:nNaER1_:nNaER1[:lNmD3h0Q1[:ROeEn0[:ROeEn0[:ROeEn0[:ROeEn0[:SOdEn0\\:QOdEo0[:ROeEn0[:ROeEn0\\:QOdEo0\\:QOdEo0\\:QOdEo0[:ROeEn0[:ROeEn0[:ROeEo0Z:QOfEn0[:ROeEn0[:ROeEo0Z:QOfEn0[:ROeEo0Z:QOgEn0X:SOhEm0Y:SOfEm0Z:SOfEm0Z:SOfEm0Z:SOfEm0Z:SOfEm0Z:SOfEm0Z:SOfEm0Z:SOfEm0Z:SOfEn0Y:ROgEm0[:ROeEo0Y:ROhEm0X:SOhEm0X:SOhEm0X:SOhEm0X:SOhEm0X:SOhEm0W:UOhEk0X:mNPE4h0o0\\:QOdEo0\\:mNlDN00i0T1[:lNkEU1T:kNlEU1U;O11O0000000001O0000000001O000001O0000000LmCPOS<Q13000001O01OO11O00O1000001O02N00000hCoNV<Q1jCoNV<Q1jCoNV<Q1jCoNV<Q1jCoNW<P1iCPOW<P1iCPOW<P1iCPOW<P1iCPOW<S1O0oCnNi;R1WDnNi;R1RDmN22l;Q1RDmN22m;R1RDoNn;X1O00O11O0000O110O00000000000O11O01O0001OO10000000001O00000000000000010O0O10000000000001O01N1001O00O1O1002NO2O00000000000KmCROS<n0mCROS<n0mCROT<m0mCROS<n0mCQOT<o0lCQOT<o040000000001O00000000000O10001O00000001O2N000001OeCSOX<Q1O0000O1O11O0001O0000000001O000001O0000000001O000001MgCROY<n0gCROY<n0gCROY<n0gCROY<n020eCROY<P101O01O00000001O000000N201O00000001O00000001O0001O000000001OO20O0000000000000gCQOV<P1iCQOW<n0iCROW<Q101O0000001O000lClNR<T1mClN00Q<T1oCoNP<Q1PDmNR<X1NO12N00O110O01O1O001O000000010O0000010OO11OO2O000LQDkNP<U1PDkNQ<T13O11O01O01O000001O01O00010O000001O00000010O000010O0001O01O01O00LlCQOU<n0kCROU<n0lCQOT<o0lCQOT<o0lCQOU<o0kCQOT<o0lCQOT<o0mCPOS<P1mCPOT<o041O0001O0001O01N100O101N1N21OO20O00O1O2O02NO110OhCYOl;o0lCROS<n0mCROS<o0lCQOU<n0kCSOT<n0kCROU<R1010O00000010O000010O0KkCTOU<l0kCTOU<l0mCROT<m0mCROS<o0400010O000001O0001O01O01O0001O01O0001O00000010O0000N2N201N1000000O1001O010OO100001OO11O00010O0000`0@1O000010O00000001O00001O01O01O00000000001O01O00000001O001O01O0000000LSDiNn;V1SDkNl;U1UDiNl;W1TDiNl;W1TDiNm;V141O00ON4L4VMcN`I`1^6cN_I`1_6dNRGH[2d1e6fN]IV1gMbNLN51W8;PJW1dM]N0<\\84kId1T6]NlIc1U6\\NlIc1R6_NmIc1R6]NmId1S6\\NnIc1S6\\NnIb1Q6`NoIa1P6_NnIb1P6`NSJ^1m5bNSJ^1m5`NSJb1l5_NTJa1o5]NPJd1n5XNXJf1i5lMoG7W2l1S6SNZJa1f5ZN`Jc1e801N1001O000001O01O000_O]NZEc1f:^NYEc1d:^NPEO9a1i:`NlD27a1l:a0M32NO1O2M23dMQEV2R;1NSNoDb1Q;\\NQEd1P;[NSEb1l:_NUE`1j:`NXE_1k:]NUEd1l:[NTEe1l:ZNUEc1k:_NXEMD_1i;02N00O0101O1O1ON3N10010N1\\G", "size": [427, 640]}, "area": 27686.0, "iscrowd": 0, "image_id": 10343, "bbox": [0.0, 161.0, 640.0, 166.0], "category_id": 124, "id": 10012658}, {"segmentation": {"counts": "W39R=01O01O001O1O000000O1001OO2O000002NO100N3N1N2M300000[e11dZN001O1O1O000kBLS=4mBL10o<90N2003M1OM31O00O1O1N3N1O1O1O1000eVU26nhjM7O0100000000001O000O2O001O10M30UCAj<?1O1N3N000001O00000000000000N2000O10L22000001O1N101O00O11O0O1002N1O0FQC8Q=000000000000O100000000000000000`P:0`oE001O000O10O1000O11O01O00001O000RCFk<:TCGl<<01O002NO100000000000000O11O00O1001O1O000000O10000000000001OO13M00002N000[C]Ob<f000000\\O^C`0b<]OcC?f<04J5HmB0W=0lbc00S]\\O1jBOQ=6M4O1N01O11O00003MO1001O1O0O20O0N200O11OO11OO100O1001OO11OO11O001ON200O100001O1O00O1000O20OO2O01N1000010N1O10000000100ON310M2N3M03N100O0@WC`0i<00O1001O2N00N21O1O1O1NN4M0AXC>i<111O000O1O1000O1O11N101OM30O2N101O00O1O0MZCAh<?XCBg<=4O10000O12M02OO1001N10O100000001O3MO1M31TCBi<`00O11WC@e<`0ZCAf<b001ZC\\Oc<d0]C^Oa<e01001O001O001OO11O0000O12N0000O11O0O2O1OO1001O1O00001OO1O1O10MSOgCl0[<30N1N22OO00000O1O100012MO1O1001O1ON2O0O21O0000O10O010_CXO_<j002M4M001N1000N200O0101OO1O12NO1O11O00O11nL", "size": [427, 640]}, "area": 6566.0, "iscrowd": 0, "image_id": 10343, "bbox": [0.0, 84.0, 640.0, 51.0], "category_id": 126, "id": 10012659}, {"segmentation": {"counts": "n6]6o6O00O11OO1001O00O11O0000001O001OO11O0000O11O0000001OO11O00001hInHU6Q7lIoHT6T701O00001O0nIjHo5Y701O00000000000RJfHk5[7TJeHl5[7TJeHl5]701O0000001OO100001O000000000000000000NVJcHj5]72O11O000000001OO1001O000000NUJdHk5\\7200000000000000000000001O00000000000000001OO1001OO11O000000000000000000001OO11O000000000000000000001O00O11O000000000000000000001O00000000000000LZJcHf5^73000000001OO11O00O1000000002N00000WJ`Hg5`7YJ`Hg5`7YJ`Hg5`7YJ`Hg5`7YJ`Hg5`7YJ`Hg5`7YJ`Hg5`7YJ`Hg5`7YJ`Hg5c7O0_J^HY5b7gJ^HY5b7bJ]H22\\5a7bJ]H22\\5c7cJ^H]5i7O00O11O0000O11O000000000000O11O0000001OO1000000000000000000000000001O00O10000000000001O00O1001O00O1O1002NO1000000000000KdJ[H\\5e7dJ[H\\5e7dJ[H\\5e7eJZH[5f7eJYH\\5g7dJYH\\5g740000000000000000000000O10000000000001O2N0000000`JXH]5l7O0000O1O11O000000000000001O00000000000000001O000000NeJTH[5l7eJTH[5l7eJTH[5l7eJTH[5l720cJTH[5n701O00000000001O000000N200000000001O0000000000001O000000001OO11O0000000000000iJoGT5R8kJoGT5Q8lJoGT5T801O0000001O000oJiGn4X8QKhG00m4X8SKkGl4U8TKiGn4\\8NO12N00O11O001O1O001O000000001O0000001OO11OO10000LYKcGh4]8XKcGh4]83O11O00001O000000001O00001O000000000000001O00001O00001O00001O00L\\KaGd4_8\\KaGd4_8]K`Gc4`8]K`Gc4`8]K`Gc4a8]K_Gb4a8^K_Gb4a8_K^Ga4b8_K^Ga4b841O000000001O00O100O100O1N21OO11O00O1O1002NO11O0aK`GS4h8eKYGZ4g8fKYGZ4h8eKXG[4h8eKYGZ4h8eKXG[4l801O000000001O00001O00KhKWGX4i8hKWGX4i8jKUGV4k8kKTGU4m8400001O00000000001O00001O0000001O0000000000001O0000N2N200O1000000O1001O001OO100001OO11O00001O0000`0@1O00001O000000001O00001O00001O00000000001O00000000001O001O0000000000L^L^Fc3a9^L`Fa3`9`L^Fa3b9_L^Fa3b9_L^Fa3b941O00O11O1OO100000^L_FLN51o2d9lL[F0<T3i9000001O0000O11OO1000000001OO1O100N200O1001O000000001O0000001OO11O000000001O00001O00000000000000M300000000O1000000001O00001O00", "size": [427, 640]}, "area": 102577.0, "iscrowd": 0, "image_id": 10343, "bbox": [0.0, 222.0, 640.0, 205.0], "category_id": 154, "id": 10012660}, {"segmentation": {"counts": "0a2j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`EWM^:k200000000000000000000000000000008H00000000000000000000000000O100001O0000000000000000000000O1000000O11O000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000O11O00000000O11O0000O1NhEQMX: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size": [427, 640]}, "area": 59278.0, "iscrowd": 0, "image_id": 10343, "bbox": [0.0, 0.0, 640.0, 103.0], "category_id": 157, "id": 10012661}, {"segmentation": {"counts": "a2f09m0`:SO`Em0a:SO^En0a:RO`Em0_:TOaEl0H^Nf:e0cEn0\\:SOeEk0\\:UOdEj0^:UObEo0Z:QOfEo0[:POdEn0_:ROaEm0_:TObEk0]:VObEk0_:UO`Ek0`:UO`Ek0b:SO^Em0b:SO`Ek0`:UO_El0a:TO_El0b:SO\\EQ1b:PO\\EP1e:POYEQ1h:oNUET1k:lNUET1l:kNTEU1l:kNTEU10XNj:_200VEaMg:b21O1PNXE[1h:eNXE[1g:fNZEZ1f:eN[EZ1d:gN]EX1d:gN\\EY1d:gN\\E_OLh1h:iN\\E_OL10d1i:kN`EU1`:kN^EW1b:iN^EW1a:jNbES1_:lNbES1^:mN_EV1a:jN`EU1`:kN`EU1a:jN^EW1c:hN\\EY1d:gNZEY1i:gNUEX1l:iNSEX1m:hNREY1n:gNQEZ1P;eNPEZ1o:hNQEY1o:a01O1O001O00O3O00O0M4O0000O02O0O20OO1001N11O001OO1001OO11O10O0010O05K01KmDoMT;Q21200001O01OO1010N1001N1000001OO1001O001N20N1O1001O000000O1O1010N1000O1O11OO010kDnMR;U200nDkMN0n:U2REPNn:o1REQNn:V200O11OO10000000000O101O0001O001O00O1000000O1000000000001O00000O100001OO11O00001OO1001O000001O000000000000000001OO11OO1000000O2O01O00O100O11O0000000000010OO100000000000010O000O100000000010O00001O001O1ON21^MXE`2i:000O10000001O0001O000000000N2000001K40000001O1O001O01N11O00000000000001O000000000O1000001O000000000000001O00000000000000001O00000O100001O0001O00000008H001O0001O0O1000000001O00010O000]M[E`2e:_M\\Ea2g:01O002NO2O0000000000000O110O0O1001O1O000000O2O000000000001OO22M00002N000bMVE[2m:00001[OdMZF[2b9eMkE5?W2f9lMZFS2f9RNTFo1k9VNmE_O0[2T:YNlEg1T:h00001O0O11O0000O1NjLoEV3P:3O2N100O1000000000001O01O00O100001OO101O00000000000000001O0001O00O1000001NSMZFW2g9gMZF_OOf2g9jM_FV2a9gMcFX2]9hMdFW2\\9hMdFY2\\9gMcFZ2]9fMdFY2\\9gMdFZ2[9fMeFZ2[9fMhFW2X9iMgFX2X9iMhFW2Y9hMhFW2W9jMjFU2V9kMjFU2W9iMkFV2U9kMjFV2T9kMjFW2V9iMjFW2V9iMiFX2W9hMjFW2V9iMiFX2W9hMjFW2V9iMiFX2W9hMiFY2V9gMiFZ2X9eMhF[2W9fMjFY2V9gMiFZ2W9fMjFY2V9gMiFZ2W9fMjFY2V9gMjFY2V9gMkFY2U9fMiF\\2W9dMiF\\2W9dMhF]2X9cMhF]2X9cMhF]2W9dMjF[2U9fMlFY2T9gMlFZ2T9eMkF\\2U9dMkF\\2U9dMkF\\2U9cMmF\\2S9eMlF[2T9dMnF[2R9eMnF[2R9fMlF[2T9eMlF\\2T9cMlF]2S9dMnF[2S9eMkF\\2T9eMkF\\2T9eMlF[2U9dMkF\\2U9dMkF\\2U9dMkF\\2T9fMlFZ2T9fMkFZ2U9dMnF[2R9gMnFW2R9hMPGW2o8iMTGU2m8iMSGX2m8jMQGV2o8kMQGU2m8mMRGS2o8lMaF^O`0d2o8PNQGP2n8QNQGP2P9oMPGQ2o8PNRGo1n8QNTGm1l8SNTGm1k8TNSGo1l8QNUGn1l8QNUGn1k8RNVGm1i8TNXGk1h8TNVGo1i8SNTGo1l8oMeF[O>g2l8oMUGS2k8lMVGS2j8mMVGS2i8nMWGR2i8mMXGS2i8kMXGU2g8lMYGT2g8lMXGU2i8jMVGW2j8iMWGW2h8hMYGX2g8hMZGW2f8iMWGZ2h8gMXGY2h8fMZGY2g8eMZG[2f8eM[GZ2e8fM[G[2d8eM[G\\2e8dMZG]2g8aMVGBAo2Y9_MVGBBn2X9`MUGd2k8\\MTGf2j8[MVGe2j8[MVGe2j8[MUGf2k8ZMWGd2i8[MWGg2h8ZMWGf2i8YMXGg2h8YMXGg2h8ZMVGg2j8YMVGh2h8XMYGh2h8WMXGi2h8WMXGi2h8WMXGi2h8WMYGi2e8XM^Ge2b8[M]Gf2c8ZMZGi2g8VMZGBBU3T9YM\\Gg2d8YM[Gi2d8WM]GC@R3S9[M]GBAS3R9[M`Ge2`8[MaGB\\OP3S9^MaGB^On2Q9`MdGa2\\8_MdGa2[8`MfG_2Z8aMfG_2Z8aMgG_2X8aMhGa2U8`MlGa2Q8`MnGc2Q8\\MPHf2m7ZMSHh2j7YMVHk2f7UMYHo2d7QM^Hh0VOm0W8\\NcHd0YOT1Q8WNfHc0]OlNH[2P8VNjHf0@V1b7TNoHc0AX1`7VNoHb0ES1]7ZNPIa0DU1\\7ZNQIa0BV1\\7YNQI`0EV1\\7YNoHa0EU1]7ZNoH`0DW1\\7YNQI`0BX1[7YNSI=DZ1Y7YNSI=DX1[7[NRI9E_1W7XNSI:F^1W7XNRI;E]1[7XNoH;G]1Y7YNPI=EY1\\7VNRHBl0P1B^1_7PNRIa0QOWO7W2f7RNPIi0YOb1[7eMZIk0VOf1_7_M]Il3c6UL\\Il3c6TL]Il3c6TL]Il3c6TL\\Im3d6SL[In3e6RL[In3e6RL[Io3d6RL^Ik3a6VL^Ik3c6TL\\Im3d6SL\\I\\3VObL^72]I[3VObL]73^IX3VOYLO9\\76]I[3XOTL27[7:ZI\\3W7cLhH\\3[7dLfH]3X7cLhH\\3Y7dLgH[3Z7eLeHZ3]7fLcH]3XOTLo7>iHb3W7^LiHAXOn3:RLZ7?VI_3^OTL\\7=XI^3[OXLZ79_I[3XO]LX78`IZ3XO`LW76bI\\3SO^L\\75bI^3QO]L]75bI^3PO^L^74`I]3TObLMDZ7=eIR4[6nKdIU4Z6jKgIV4X6kKiIS4X6mKgIT4Y6lKfIV4Y6jKiIU4V6kKiIW4V6iKiIV4Z6iKgIU4Y6lKgIT4Y6lKfIV4Y6jKhIT4f3", "size": [427, 640]}, "area": 60830.0, "iscrowd": 0, "image_id": 10343, "bbox": [0.0, 81.0, 640.0, 198.0], "category_id": 169, "id": 10012662}, {"segmentation": {"counts": "inl04o<8G9L5O000000000001O000000000000000000000000000000001O0001O0000000000000000000000000001O00000000000001O000000000000000010O0000000000000001O00000000000000010O00000000000000001O00000000000001O01O00000000000000001O00000000000001O0001O00000000000000001O0000000001O000001O000000000000000010O00000000000000010O0000000000000010O00000000000000010O00000000000000010O00000000000000010O00000000000000010O00000000000000010O0000000000000001O01O000000000001O0001O000000000001O0001O000000000001O0001O000000000001O0001O000000000001O0001O000000000001O0001O000000000001O0001O000000000001O0001O0000000001O000001O0000000001O00000001O00000001O01O0000001O0000010O0000001O00000010O000001O0000001O01O0001O0000001O0001O01O0000001O0001O01O0000001O0000010O0000001O00000010O000001O00000010O000001O0000001O01O0001O0000000010O000001O0000001O01O0001O0000001O0001O01O0000001O0001O01O0000001O0000010O00000000000010O00000000000000010O000000000000010O00000000001O0001O0001O000000001O01O000001O00000000010O00000000000001O0001O2N3M2N2N3L5H8I6J7InXk0", "size": [427, 640]}, "area": 16099.0, "iscrowd": 0, "image_id": 10343, "bbox": [69.0, 173.0, 505.0, 94.0], "category_id": 183, "id": 10012663}, {"segmentation": {"counts": "ojn31=0U<0dD1mNO61L17OU<2cD2oNK2101N0O00Oa<OjC0N6F73=U<l11O1OO20OO1001O001N020O01O0O2O1N20ON30N10001O01O01ON3O000001O0001O010O01O0O01000O10000O10000O01000O2OO010000ON3O02OM300O06K1N0kMgCe1[<YNeCg1^<UNcCj1`<TNbCj1_<oM^C34n1`<oM\\C06P2h<O3QNTCf1T=J5A?oNTB>`>G7Mlnc0NRQ\\O1ZA002d>;I212N2N2M2O001ML6M20N_OhA?T>@lAf0P>[OoA00g0i==00001OO1000O2O000N1110O0N3O10OO1001O2NNmNWBS1i=2mDlNbM;R:i0WGkN`N0g0?YONY:h0iFkNSO1K92M2k0S:4hFoNPO?1D3j04nNf9W1oF2VOI0oNMO0000l9W1RGmNROn022NSO0Im9U1XGNkN=l9GXGLlN=l9GXG^1g8cNYG]1g8cNYG]1g8cNYG]1g8bNZG^1f8cNYG]1g8aNYGa1g8`NZG^1f8cNZG\\1h8bNXGkNkNS20WNn9k0WGjNkNT20WNP:h0XGP1iNYNo90nGg1WNUNl9IWHR2mMTNn9IUHR2nMoM^<P2oMoMeGQ2[8oMeGP2\\8PNdGP2\\8PNcGQ2\\8PNPF;d0c1]9RN`Ej0S1T1Z9WOeFj0Y9WOgFi0Y9WOgFi0Y9WOgFi0X9XOhFi0W9WOiFh0X9XOhFh0W9YOiFh0U9YOjFh0W9nMdEX1T1P1mNhM00R:0mEU1R1X1oNfMS:Q1lElNm0_2SOfMS:KmER10oNj0h2[9XNkEROg0g2_9\\N_Fe1a9[N^Ff1c9YN]Fg1c9ZN\\Ff1d9YNjEkN`0l2f9]NXFd1h9\\NYFc1g9^NYFa1g9_NZFFAf0U:EZFA_OTO3g0M5Y:;[FEAG1]OL0N10`0Y:e0\\F^O_O]O3a08Nl9a0`FAC05Jh9a0eESOm0b0BN6Kf9FeE70M12o03POIc01]O30Ie0<k9EaEO000114Q110NF6R:GXE0MO_21^N0hSV2", "size": [480, 640]}, "area": 13954.0, "iscrowd": 0, "image_id": 10346, "bbox": [270.0, 14.0, 221.0, 300.0], "category_id": 100, "id": 10012664}, {"segmentation": {"counts": "oo>1n>100O100O100O1N21O0000O100O10000O10000O100O10000O10000O100O1000000O10000O1O1000000O10000O10000O10000O100O100O10000O1O100O100O10000O100O10000O100O10000O1000000O100O100O100O100O100O100O10000O1O100O100O100O10000O100O1O10000O1000000O1O10000O100O10000O100O1O100O100O100O1O1O100O10000O1O100O10000O10000O100O100O1O10000O100O1O1O100O100O100O1O100O100O100O100O1001O1O2N2N2EZCTN1Ng<g1[CXNP=_1mBfNY=P1gBkN70I2]=P1=11000O11O0O10000O100000O0010JTBTOQ>e0PB[OU>e0kB]OoNN32h<b0TD\\OQO80Jk<b0mC1T<MeC;[<EeC;[<EeC;[<EeC;\\<CeC=[<CeC=[<BeC?\\<@dC`0\\<^OfCb0Z<\\OhCd0X<YOjCh0W<SOQCNj0o0U<QOoCo0Q=000001O002N001O0000O11OO11O001ON21OO1lKTOeF1i2k0b6TO[F0O1R3m0b6VOZFMKO33T3l0Z6RObF708R3?Z6TO`FN1m0U31Z69eIG[6:dIF\\68gIGZ67gIIY67hIHX66kIIU67kIIU66lIJT65mIKR67nIHS66oIIQ68nIHS67mIIS68nIFS6:lIFU69kIGV68kIGU69kIGV68jIHU6:iIGW69hIPOcLe0e9<hInNcLf0e9=iICX6;gIoNcLf0f98iIKW61PJLQ61QJOo51PJ0P6OQJ1o5NSJ1m5OSJ[OWLc0f91WJOj50WJOi50YJOh50XJ0h5OZJ0f50ZJ0e51[JOe50\\J0d50\\J0c50^J0b5O`J0`50aJO^52dJL\\54cJJ`55iJBW5>f400O010O010O10O100O2OO1O1N1010O1000O0100XFDXK1_98YK0f4L\\K6c4E_K?Y9_OnAd0P>^OoAc0Q>6O100001O00O1000000O10000O100O1002NO100000000O1001O000000001ON2O11O@QOaB0<o0S=UOcBMNS1]=ROcBS1^=7O100N2O10000O100O1K\\NmBe1R=5O100N200O1O1000000NoMUCQ2k<21O0000001O00002oMRC1O10c1]=L1O1O0000O1001O00000000001OO11O0000O100000000000000000000O10000O1VNaNSF_1l9dNRF\\1l9hNTFV1j9mNmDDi0a1Y:nNfDIh0IVOc1\\;ZOkDZOIf1\\;c11O000000001O0000001O0000001O0000001O0000001O0000001O0000001O0000001O0000001O000N201N1O1O2N1DWDaMi;]2>O2N1N2O1O2M2O1O1N3N1O1N2O2N1N2O1O2M2O1O1N3N1O1N2O2N1N2O1O2M2O1O1N3N1O1N2O2N1N2O1O2M2O1O1N3N1O1N2O2N1NkkX1", "size": [480, 640]}, "area": 24534.0, "iscrowd": 0, "image_id": 10346, "bbox": [31.0, 302.0, 521.0, 178.0], "category_id": 101, "id": 10012665}, {"segmentation": {"counts": "ecP71n>2DNgA4X>LdA9l0I[<NfB00:k0FTO3g0Mj;a0[DDG1]OL0N10U<a0^DC]O3a08e;3[DL05a;O_DMN6b;MXD^OIc01]O30Ie0R<NTD`0NFm;U1PDkNS<o1200001O0001O0000O1O1001O00000O3N01M22N00O1O01O0O200L5KeCdMZ<[2gCdM[<[2501O1O1O1O2HWCUNo<LQC0102l0MUO0111ONZ=OnB0N4K0OM90I0c[d1", "size": [480, 640]}, "area": 3605.0, "iscrowd": 0, "image_id": 10346, "bbox": [479.0, 72.0, 50.0, 95.0], "category_id": 105, "id": 10012666}, {"segmentation": {"counts": "Zh]421<S=CmBi1a;e1fNY1bN^1ZOf001O001O00O10000000000000000O100001OO1000000M3O11OM300O15K1OO11ON2O1jJkGkNOD059h3o7bLmHBkNO00h0j3Z9VLmFh3W:Bl0_NU1ZOVXY4", "size": [480, 640]}, "area": 8652.0, "iscrowd": 0, "image_id": 10346, "bbox": [302.0, 0.0, 45.0, 222.0], "category_id": 112, "id": 10012667}, {"segmentation": {"counts": "U2k<U20M]IoN]OU1?kNAX1\\JhMm5Q1GY19gNGY18hNHX18hNHX17jNHV18jNHV17kNIU17gNVJQOc5X26hNNX12gNOY10hN0g0a0YO_Ol0;UOER14nNLo0XJnMl5T1Lk0YJQNm5T1Jf0ZJVNo5T1Hg0;YOEe0<\\ODd0;]OEd09]OGc07_OI>9CG=8DH>5CK=3EM?NB292HM83WO`JVO\\5d13SOfJXOW5e14QOgJYOT5c1[J[Nk5g0hJZOR5a1=UO:l0GTO8k0JTO5l0LTO2n0OQO2n0ORO0n00RO0n01QONP13oNMQ13oNLQ16mNKT15jNLV1bJZNf4>h0Y1bJYNh4<f0[1bJYNj49e0_1]J[No44d0c1ZJZNU50b0h1VJYNZ5L`0m1lITNI8Y<d1nC`NR<a1jCbNU<U20001N010O1000000O10000O1O10O1O11fD`MQ:a2nESN_9k1bFjNj8V1UG_OV8b0iG3c7M\\Hg0Q7YOnHk2n4UMSKk2m4UMSKk2m4UMSKk2l4VMTKj2l4UMUKk2j4VMVKj2j4VMVKj2i4WMWKi2i4WMWKi2i4WMWKi2h4XMXKh2g4XMZKh2f4XMZKh2e4YM[Kh2d4ZMZKf2e4[M[Kf2d4YM]Kh2b4XM^Ki2`4XM`Kh2`4XM`Ki2^4XMbKh2^4XMbKh2^4XMbKg2^4ZMbKf2^4SMiJYMi0d5]4PMjJWM0005i0d5]4PMnJ\\Me0d5]4PMnJWMM1i0h5[4nLVKYM?h5\\4oLQLQ3o3nLRLR3n3nLRLR3m3oLSLQ3m3nLTLR3k3oLULQ3k3oLULP3k3PMVLP3j3PMVLP3i3QMWLo2i3QMWLo2h3QMYLo2g3QMYLn2g3RMZLn2f3RMZLn2e3TMaKYM2c5]4TMRKVMN230L0e0d5\\4VMPKTM00O80Je0c5\\4\\MTKYM?[5\\4`MUKRM?^5\\4_MUKSM`0^5Z4`MWKQM?^5[4oMeKQ2Z4QNeKo1[4QNeKo1Z4QNgKo1Y4RNfKm1[4RNfKn1Y4SNgKm1Y4SNgKm1X4TNhKk1X4WNgKi1Y4VNhKj1X4VNhKj1W4XNhKg1Y4]NcKc1]4]NcKc1]4\\NdKd1[4^NdKb1[4^NfKa1[4_NeKa1[4^NfKb1Y4]NiKc1W4]NiKb1W4^NjKb1V4^NjKb1V4^NjKb1U4_NkKa1U4_NkK`1U4`NlK`1S4bNlK^1T4bNlK^1S4bNnK]1S4cNmK]1R4dNnK\\1R4eNmK[1R4fNnKY1R4hNnKX1Q4iNoKW1Q4iNoKW1P4jNPLV1P4jNPLU1Q4kNoKU1P4lNPLT1o3mNQLS1o3mNQLR1o3oNQLQ1o3oNQLQ1o3oNQLQ1n3PORLo0o3^NaKeL`0m4o3^NgK_L:S5n3_NeLa1[3_NeLa1Z3`NfL_1[3aNeL_1Z3bNfL]1Z3dNfL\\1Z3bNhL^1X3bNhL]1X3cNiL]1W3cNiL]1V3eNiLZ1W3gNiLY1V3hNjLW1W3hNjLX1U3hNlLX1T3jNjLU1V3kNkLU1U3kNkLT1U3mNkLS1T3[OaKcK0012d0P5j3DVL;j3FVL:j3FVL:i3HVL7k3IUL7j3JVL5k3KUL4k3MUL3k3MUL2k3OUL0k31ULNl32TLKn36RLIn37SLIm36TLIl37ULHl37ULIj39ULFk3=SLCm3=SLBm3?TLVOV4i0mKhN_4Z1RLhMZ4Y2kLXL]3i3hMkJ_2U5e3mJXFP5o9M1NFTK\\FQ5f9nJXFU5f9301N2N011KdJdF]5Z9eJeFZ5\\9gJcFY5]9500O10O0010O4L6KO02O0O1O100N200O1O1O1O100O1O1O100O100O1O101gNeIXI1L[6l5dIQJ040I0O16\\6n3cIiL1mNO61L17O33HMKO338[6o3eIhL2oNK2101N0O00O201708O@1`6Y4`IoK0N6F73a0MSO1m02i5h1YIO0VN010002o3Ni2T1XI^O1^O004OK23NY<1eC60I_>1>OO1[eX5", "size": [480, 640]}, "area": 72027.0, "iscrowd": 0, "image_id": 10346, "bbox": [0.0, 46.0, 280.0, 434.0], "category_id": 123, "id": 10012668}, {"segmentation": {"counts": "Wlh5621k=JZBk0b;VOcEOmN_1\\:aN]F1oN0o0Q2b9SN_EK04g0MYOO>1B`20aMS:0oE`0N^Ob00_O2O20h20VMR:i0WFVOHa3Q:Z11G2N5K7H8I8N3O000000000000O10000N2001O001O1O00000001O0000kNnDbM;4g:U1mD`N0g0?YON;f:g0mDSO1K92M2k06Q:e0REPO?1D3j04nNIT;l0UFVOI0oNMO000U;n0SFTOLNSO0m:c0aF]OP;=UECk:=l100O10O11ON3PBD[=<e0N10XBC[10]9<VEDYO0T20]9<UEG]1N_9;SEG^12[97WEG^12TNKn:<\\EKa1OoM0T;6oDD;4b1;T9N_EG]1;T9M`EH[1<V9K_EI[1<V9K_EH\\1=T9LVE2d13V9f0jFZOW9e0jFZOV9f0jFZOV9f0jFZOV9f0jFZOV9f0kFYOU9g0jFZOV9f0jFZOV9f0kFYOU9f0lFZOU9e0QGmNhM002W;o0VGoNfMOU;0hDm0_2SOo80cDj0P>VORBg0U>N1O101OO^OfA`0^>M2O2D^A7c>I]A3h>LXA100``Y2", "size": [480, 640]}, "area": 6035.0, "iscrowd": 0, "image_id": 10346, "bbox": [394.0, 51.0, 90.0, 169.0], "category_id": 156, "id": 10012669}, {"segmentation": {"counts": "^[R32m>2O2M3M3ZCETN1Y<<\\EFXN5Y<8VE@fN=T<4lD_OkN70I2>V<7jD8U;GlD:T;FlD:S;GmD9S;FnD:Q;HnD8R;HmD9R;HnD8R;HnD8Q;SOmCb0Q1<Y;AgD?Z;@fD`0o:QORDe0o0:P;POoCg0Q1:o:HRE8n:HlDWOTOU1o;FfDVO[OY1n;BgD5]OoNN3Q<i0cD3\\OQO80e;0UDl0b0Lk;ZObCk0a0DU<BYCj0b0DU<a0kC_OU<a0kC_OT<c0jC^OV<b0jC^OU<c0jC]OW<d0hC\\OW<e0gC]OX<d0fC^OZ<b0cC@]<b0^CfNNj0d<`0\\CDc<]1O100N3O0O3M1O2N100O002N011N101NO12OO01OO10JUMXDl2h;6O1O1N3N101M3O0O1O110N20O0011OO0001O0001N1OaDcL^;]3aDcL`;^3OOaDcL\\;_3O7K20O01O2O0lDWLP;m301N2O0O2O000O100O1O2O1O2NOfEiKl9`4kEaKT:f400O100O100O10000001OO1O1N2O1000000O1000GUFXK10g9h482L21KQF\\KY:[4iEdKW:]46O0100O2O0O010O1O10O0100O010O01002NO100000000O1001O000000001ON3N11O@dEnK0<\\:f3hEPLMNa:o3dEQLa:P47O101M2O101O0O100O2JkD^LV;a35O101M200O1O2O00000N\\DhLe;W321O0001O01O00011YDhL1O10Y;g3L1O10O000O2O01O0001O0000010N11O0001N1000001O0000001O00000O2O000O1VNcD1^;F`DiN5_1[;FbDiN7a1U;EeDhN9:Di0`;4UEkNCh0IVOc;U1aEROSOIf;T1k1N`BmNX=Q1<O1O1N3N1O1O1N3N1O1O1O2M2O1O1O2M2O1O1O2M2O1O1OkT[2", "size": [480, 640]}, "area": 27569.0, "iscrowd": 0, "image_id": 10346, "bbox": [209.0, 300.0, 270.0, 180.0], "category_id": 165, "id": 10012670}, {"segmentation": {"counts": "naW72n>0O1O1O1O10000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000_KLVJ4k5KUJ5k5KUJ5k5KUJ5[:000000000000000_KKWJ5f4o0YKQOc3g3iJYLP5^4`JbK", "size": [480, 640]}, "area": 1184.0, "iscrowd": 0, "image_id": 10346, "bbox": [493.0, 251.0, 147.0, 229.0], "category_id": 167, "id": 10012671}, {"segmentation": {"counts": "0U2k<00M34L3WChMd<Z20000000000000000000_Oa05K7IMWCnMj<o1WCQNk<j1VCVNn<k13N2001O00M3002N004LK5O10000VOVCTOC31MV=l0ZCSOm<l0SCUOm<k0SCTOn<l0c01O000000001O00001ZCQOY;n0hDROX;P1fDPOZ;m0mCSO73l;k0lCSO53P<^1mCcNS<^1cC_NI8d<Y1cCkN]<V1_CmNa<i10001OO100000000000000000000O1O11O00N200000000O100iNXC6h<H_C3a<LdCO]<1cCO]<1cC\\OJ3c<`0dC_OKMa<b0fC@G1c<?fC@FJLOOOi<j0dC^OGK1Md<i0fC]OGIK0P=k0]C[OLJg<h0PCYO<5MJg<i0_C^Ol<d0RC\\On<e0QCTOC4]=g0SCZOl<g0TCWOm<g0UCYOk<f0VCYOk<h0UCWOk<j0TCUOm<l0c01O4L00O100JUBVOl=h0700O100O100O10000O11O001gA[OU>m0KLVBXOd=i0XB\\Of=e0ZBZOf=g0YBXOh=i0WBVOj=P10001O0000N22NM30000002hBlN`<Y1lBgN00;5d<P2J5K4L00O11OO100O1N200O100000000O11O00O100001O0000000000000000000000000000]O`CfN`<Z1fC`NZ<`1c00000000000N200O1001O0000O1O12NO1AfBnNOO13Z=P1gBlN52T=R1?NVBnNj=R1200O1000000O11O000000N2002NO1002SBnNh=U103M0000O10000O1O11O000WCjNh;U1RDTOl;l0mC[OS<e0mC\\OR<c0oC^OP<d0nC[OS<b0oC@P<`0PDAo;?QD@P<?QDAo;b0mC_OS<g0gCYOY<m0aCSO_<f11O1003M5KO11O000000000000000000000000000000000000001O0000001O1`E\\MO0VN01000X8d2lH\\M^O1^O004OK23N_1T8R1mG]M60If3S8mNnG_1S8bNlG_1S8`NnG_1S8g201O001OO11O0000O100O11ON21O00001O00001ON200_OmGgIa8n4^GRKn9\\3d1gNY1bNRoj0ETRUOc0ZOUOeB]1e<aN[CW2Q<e0G:J6O1TNdL[FO61X1\\3\\7aM]GROS1a3h6VNnGZN30N14NK093GM0l3Q8Q300000000000O10000000000000000000000fJnGe3S8SLnGfN002a42`K14m7T1[H`2B]Lk0NX7R1]Ha0ADOmN_1Kd6S1_H\\O^O?1oN0o0Q2TOQ6S1aHWOACK04g0MYOO>1B`20e5V1`HkN\\O1`0N^Ob00_O2O20h20Z5U1^ITO]NHe;T1S200N200O1MUBROj=i0UBWOQ>e0PBZO00Q>?=00001OO100001O0000O11O00N2001OO1001O2NNcAA]>?20000O100001OO100O1000000000000002N1O2N1O1O1O1O00000000O100M3O10000O10000O1O11`AA]>b0cA^OZ>f0N0hAZOV>i0O1O001O0000000000000cFUOe4l0lGVOS2NQ6l0kGWOS2NR6l0fG[OW2IS6l0bGSOSO:X3GS6l0fFSO70M12<R3CR6S1fFnNO000114=S3BS6\\1fFfN0MO`0j2CWM6Z9o1]IYNc6g1]IYNc6g1]IYNc6g1^IYNa6g1_IYNa6g1_IYNa6h1^IXNb6h1^IXNb6h1^IXNb6g1_IYNa6f1`IZN`6f1`IZN`6g1_IYNa6g1_IYNa6g1_IYNa6g1^IZNb6h1\\IXNd6h1]IWNc6g1_IYNa6i1]IWNc6i1]IWNc6h1^IXNb6g1^IZNb6f1]I[Nc6d1\\I^Nd6b1\\I_Nc6a1YIcNg6^1TIjMdMg0X9_1SIkMdMg0Y9_1RIfNn6Z1RIfNn6[1QIeNo6\\1PIdNP7]1oHcNQ7^1nHbNR7`1fHaMUNo0U9f1gF[M0102l0MUO0111ONX1X9_1gFaM0N4K0OM90I0Y1X9Z4000000000000000001O0000000000000000000000001O000000000000001O00000000000000001O000000000000001O00000000000000001O000000000000001O000000000000001O00000000000000001O000000000000001O00000000000000001O000000000000001O000000c0]O", "size": [480, 640]}, "area": 54528.0, "iscrowd": 0, "image_id": 10346, "bbox": [0.0, 0.0, 640.0, 223.0], "category_id": 172, "id": 10012672}, {"segmentation": {"counts": "ZVn41`>0nA0P>5lALS>7kAKm8OXGOD059h31SLE34o7R1oGkNO00h0j3FVLA06a7a1_HYOh3Ji3P2iK_NfLlNX7T3aJZO^5i0lIJT6a4iHZHT7j7OO1O1L4M3N2N2N2N2N2N2N2N2N2N200E;]Oc0ZOTGfJ^9d4`F\\KX:P4f0F:J6O1TNbD]NO61X1^;ZO_E_OROS1c;fNTF0ZN30N14NK093GM0nbl3", "size": [480, 640]}, "area": 6255.0, "iscrowd": 0, "image_id": 10346, "bbox": [337.0, 0.0, 38.0, 252.0], "category_id": 173, "id": 10012673}, {"segmentation": {"counts": "ZV1Q1o=0O100O100O100OMVBROj=n0XBPOg=Q140O100O10O0100O100O1O1O1N2O1O1O1N2O1OFeBQOZ=e0RCZOm<f0UCYOl<e0VCZOk<e0h001O1O010O1O010O1O0O2N2N3M2O2M3M3M2NPh8:fWGd0\\Oe0[Oe0[Oe0[Od0\\OT2mM00000000000000000000000000001O1O1O001O0000000000O1000000000000O10000000000O10000000000O1000000O100000000O1000000O100000000O1000000O1000000O100000000O1000000O100000000O1000000O100000000O1000000O100000000O10000O100O10000O100O10000O10000O100O10000O100O100O100O1O100L4O1O100O100O100O1K6Ca0^O`1bN`1dNR\\h1EQdWN?@?I7M4L3M4M2O2N1O2N1O1O2N1O2N1O20000000000O100000000000001O0000001O0O10001O0000001O0000001O0O10001O0000001O000H8H9G8G9H9M2000001O0000001O0000001O0000001O00000010O000001O0000001XMhKfJX4i4YLWKg3d4^L\\Kc3c4]L]Kc3c4]L]Kc3b4^L^Kb3b4^L^Kc3a4]L_Kc3a4\\L`Kd3a4oKkKQ4Y4XLZKi3a4[L_Ke3a4[L_Ke3a4[L_Ke3hNeKl30TKe0Y2f3dNgKY3d0jKOY2f3cNhKP3m0TLEY2f3bNiKQ3m0SLYOe2Q4VNkKQ3k0ZL^OT2m3_NkKT3i0TLDY2h3]NmKV3g0SLEZ2g3\\NnKW3f0SLD[2h3ZNPLW3?YLHW2j3WNPLY3NiLFY2\\4cMRLV8n3iGSLW8m3hGULW8l3gGULY8k3eGWL[8i3dGXL\\8h3cGZL\\8g3bGZL^8f3aG[L_8e3_G]La8c3^G_La8b3]G_Lc8a3\\G`Ld8`3ZGbLg8^3WGdLh8\\3WGeLi8[3VGfLj8Z3TGhLl8Y3RGiLm8W3RGjLn8V3QGkLo8c401O0000001O0000001O0000001O01O0001O00000iISJ[3n5bLYJY3g5fLZJZ3f5eL[J[3e5cL]J]3d5aL]J_3c5`L^J`3b5^L`Jb3`5]LaJc3`5[LaJe3_5YLcJg3]5XLdJh3\\5WLeJi3\\5TLfJl3Z5SLgJm3Y5RLhJn3X5PLjJQ4V5mKkJS4U5lKlJT4T5jKnJV4R5iKoJW4R5gKoJY4Q5eKQK[4o4dKRK\\4n4cKSK]4n4`KTK`4l4_KUKa4k4^KVKb4j4\\KXKd4i4ZKXKf4h4YKYKg4g4WK[Ki4e4VK\\Kj4e4TK\\Kl4d4RK^Kn4b4QK_Ko4a4PK`KP5a4mJaKS5_4lJbKU5]4jJdKV5\\4hJfKX5[4fJfKZ5Z4eJgK[5Y4cJiK]5W4bJjK^5W4`JjK`5V4^JlKb5T4]JmKc5S4\\JnKd5S4YJoKg5Q4XJPLh5P4WJQLj5n3TJTLl5`60000000000001O00000000000000001O000000000000001O00000000000000001O000000000000001O000000000aKnI0R6NPJ2P6MPJ4Q6IQJ7o5HRJ8n5FTJ:l5EUJ;k5DVJ<j5EUJ;k5EUJ;k5EUJ;k5FTJ:m5ESJ;m5FRJ:n5FRJ:n5FRJ:n5GQJ9o5GQJ9o5HPJ8P6HPJ8Q6GoI9Q6HnI8R6HnI8R6ImI7S6ImI7S6ImI7S6JlI6T6JlI6T6KkI5V6JjI6V6JjI6V6Z400000000001O0lJ\\Jl0d5TNXLP1h3^MAk0Q9YN\\A", "size": [480, 640]}, "area": 84564.0, "iscrowd": 0, "image_id": 10346, "bbox": [2.0, 23.0, 638.0, 451.0], "category_id": 183, "id": 10012674}, {"segmentation": {"counts": "0h3a0WNa0dNQ;U3^DWNb0bNQ;W3]DWNa0eNP;T3_DWNc0bNo:X3]DVNe0aNn:X3^DWNe0_Nn:[3\\DVNg0_Nl:[3\\DXNg0]Nm:[3\\DXNg0^Nl:X3_DZNe0]Nm:Y3_DYNc0`Nm:[3[DVN1PN8?\\;d3RDmM:PN8?\\;i5\\DhI8`0[;i5\\DhI9>[;j5[DkI7=];h5ZDnI2`0d;c5TDSJ5<g;T6WDmIi;V7OO10000O11O00000000O11OO100000000O1O11OO100000000001O000000002N2NO13MO1000000OUDoGi;T80N2O1O10000O11OPHTD1Oh7\\<H5E^CnHf<c6d0N3K3O0OO011O1OcIbBV6_=jIdBT6]=kIcBm2O5NlL^=2gBf0MA0[14^1Y=PMeBf00XOO13>L>9Q2T=TMeBf0?FEGHK0027>f2P=SMeBg0?EHFDN55=g2U=kMoB\\OZO1h0OL>Ca1`=eNVCZOL2L;Ec1]=eNTD\\OjN:Hd1[=eNSD[OmN:Gg1W=dNoDDlMh1U=cNPECmMi1S=dNlE]1S:bNoE]1Q:XN]BOc3i1P:VN]B2d3g1o9XN]B0d3h1P:TNZB004g3g1o9SNbFk1k=1OO1O1O11NO2O1O1O1O100O001O100O010Of]O]NXb0e111OO10O010OO2O001O1O1O00100O100N1O2M3N2M3N10100O01000O01000O0100000O01000O10O101ON20O01O1O010O10O0100N101O1O1N101O1O1N14MN2O010000O01J6O1N110000O10O1N101O00100O012M3Mf_]5", "size": [640, 478]}, "area": 24873.0, "iscrowd": 0, "image_id": 10358, "bbox": [0.0, 0.0, 200.0, 274.0], "category_id": 96, "id": 10012675}, {"segmentation": {"counts": "kmc21oc00O1O1O2FLf\\O3[c0Nd\\O2\\c0O_\\ON04`c0:OO11N1001OO100O2O01OO12M;E4MTW60lhI1S\\O0;Olb01h\\O43L30ob02k\\O222NNQc06P]O7mb0IR]O9lb0>1O1OO100O1100N2O01M11O3ZOP]OI1MO008Pc01f0O1I]\\4OhcK0T\\O0kc0301N3II`\\O8\\c0Hd\\O?Yc0Ae\\Ob0Zc021N10001O0O2O00001N8IVPS1OmolN8I7F8G2O0000000N3O001O001O0N03M2L4N3N101N200O100N1O100O2OO111O0OO2M2Ca\\O8ac0H^\\OO06fc0JZ\\O4jc0M_i3OdVL2M2\\ONX]O5fb0MZ]O2fb00W]O1ib01R]O3kb03R]OKnb06Q]OLPc02P]ONnb0c001O00001O2N0000000000002OO1O02OO000010O0001O0O11O00XOX]O7ib0IY]O6fb0I\\]OJH6kb02c]ON\\b02c]OOZb04e]ONYb02n]OIPb08P^OJla08S^OJla07Q^OKAC]b0a0S^OJAGZb0?U^ONla02S^OOma01S^O0ma0OR^O2ma0OS^O1ma0OS^O4ja0LV^O3la0LQ^O:ma0ER^O<na0VOi]O:8a0Pb0TOi]O:1GOl0\\b0^Oc]Oh0\\b0?O000O100002OM2O1000O001O2M2O00101M31N00O10001O01O1O001OO0110N1jNaNk_O_1U`0cNi_O^1V`0bNk_O\\1V`0eNj_OZ1V`0gNi_OX1Y`0gNg_OMoNX1[a0kNi_OY1S`0gNm_OW1U`0iNk_OW1U`0iNk_OV1V`0jNi_OW1W`0iNi_OW1W`0iNi_OW1W`0iNi_OV1W`0kNj_OT1V`0lNj_Oh0nNZOVa02k_Od0QOZOn`0JP_O7Q1d0SOVOo`00j^O9U1=kNYOo00\\?b0k@3K\\OQO2R`0`0QA2KES?:RAOKHPOES`0d0QANLLR?7QAMMLS?6QAMLMS?6QAMKMU?6PANKLS?7SAKJOS?6SALJMT?6RAMKMR?6SANIMT?4TAOIKU?5RA0ILT?0n_OBV1c0GLR?0[A5_O\\OVO`0o?0]A3YO]O\\Od0m?M^A5UO3\\?H_A5TO5Z?UOV@a0]13SO8Z?GcA1RO;Y?DfA0QO<Z?CeA0PO=]?CcA0PO`0Z?@fA0oNa0[?_OfANbN@<T1\\?^OfANoNe0\\?\\OeA0nNa0`?_OcAOlNe0_?\\OeAOkNe0b?[OcA0iNh0c?XOdA0iNh0d?WOcA1iNi0c?VOdA1jNh0a?XOeA0iNk0JfNa??lA0iNj0LgN]?`0oANhNk0JmNY?<UBLgNn0JiNZ?=UBMfNW1S?mNVBMfNV1T?nNXBJdNW1T?POWBKcNW1U?nNXBMaNV1U?nN[Bh1e=WN[Bj1c=WN\\Bi1e=WN\\Bh1e=WN\\Bi1d=VN\\Bj1d=VN\\Bi1e=WN[Bj1d=VN\\Bk1m8", "size": [640, 478]}, "area": 10132.0, "iscrowd": 0, "image_id": 10358, "bbox": [134.0, 148.0, 344.0, 156.0], "category_id": 97, "id": 10012676}, {"segmentation": {"counts": "lcV4:]c0;NJf\\OB^c0<50a\\OFbcQ5", "size": [640, 478]}, "area": 76.0, "iscrowd": 0, "image_id": 10358, "bbox": [215.0, 243.0, 5.0, 22.0], "category_id": 105, "id": 10012677}, {"segmentation": {"counts": "aim21kc05K5K5b\\OKnb0c010001O11N0O2N2O000000001a]OnNH0oa0Q1X^OROGNPb0R1m]OmN66IJ0111la0S1S^OA0\\O\\a05k^Oo0I@0\\O\\a08g^Oj1h`0lMV_O000O159Lk1i`0YNW_ONOi1j`0P1O00000000000000000f_OlLh?Y3i_OgL08V`0Q3j_OgL08V`0T3j_OlLV`0T3j_OlLV`0_30001O00O11OO11OO1000000001O00000000001OO11O0000000000000000O100000000001OO1001O001OO1002N001O00000000000000001ON2N2N2N2N2N2N2O1O1O1O1O1O1O1O1N2000000000000001O001O001O001O001O001O001O0W@cLo>^3o@cLQ?]3m@cLU?^3f04L<D0000JQ@[Lo?d3R@\\Ln?d3T@ZLl?f3T@XLn?h3Q@YLo?e37O10g_O\\LV`0g30O11O001O5K2N2R@nKj?X4LN2001OO1O1000000O10000000000O100N2N2O100O12N1O00JR@XLP`0h3P@XLP`0h34000000_Ol_OVMT`0i2P@SMQ`0l2S@nLHLU`0T3W@mLEOT`0S3]@lLd?S3e0O1000000000000000000001O00^MU_Oo1=bMm?>k_O=L?:iNn?;P@7Kf0OmNW`06S@2KX1Q`0fNT@11T1k?kNT@14Q1h?mNU@23Q1h?mNU@24Q1f?mNW@12T1f?kNX@03U1e?kNX@03T1f?lNW@NACb0e1d?jNY@M6U1\\O_NV`0?X@M6R1h?QOS@N3T1g?nNV@N3T1g?nNV@N2T1i?nNU@N2T1i?kNe_OE`077Y1d?jNZAj0R?UOQAh0Ua000N11O000O100000000O2OO11OO01001N2N2N111N1OO10000O101O0O100000N101O1O002O1El\\OHTc08m\\OHUc0Jk\\O520Sc0Kl\\O430Qc0JQ]O1M7`c0200012M00O2N110OF_\\O5gc0M5M00]\\>0go@0hc05O0O101KI]\\O9`c0Ga\\O;^c0Ga\\O8ac0F`\\O:`c04OON3O10Mc\\OB^c0>2C`\\O9bc0F^\\O:fc0M10Y\\OIec09O013K1002OO1kL", "size": [640, 478]}, "area": 25615.0, "iscrowd": 0, "image_id": 10358, "bbox": [150.0, 0.0, 328.0, 137.0], "category_id": 106, "id": 10012678}, {"segmentation": {"counts": "X6R1c0Q;e5PEYJQ;g5nD\\JP;d5REYJo:g5REXJn:h5SEVJn:j5SEVJl:k5SEUJm:k5SEVJl:j5TEUJm:k5REWJm:i5\\DhI8?\\;i5\\DhI8?\\;i5\\DhI8`0[;i5\\DhI9>[;j5[DkI7=];h5ZDnI2`0d;c5TDSJ5<g;T6WDmIi;V7OO10000O11O00000000O11OO100000000O1O11OO100000000001O000000002N2NO13MO1000000OUDoGi;T80N2O1O10000O11O1O00JSDYHm;\\7a01OO11O00O11O1ON2000bC`H\\<b70001O00eMlCjLT<T3oCkLQ<S3RDlLn;S3TDnLj;Q3XDoLg;P3[DoLe;d2hD]MW;c2jD\\MV;c2lD]MS;c2nD\\MR;b2jC_KT1o1R;a2kC`KT1n1Q;b2SE\\Mn:d2SE]Mk:[2^EdMb:Z2aEeM_:[2bEdM^:f2YE[Me:f2ZEYMg:g2ZEXMf:h2[EWMe:i2\\EVMd:j2]EUMc:j2_EVM`:m2^ERMb:W3UEiLk:W3VEhLj:Y3VEgLi:X3YEgLg:W3[EjLd:U3^EjLbN]Nm;h4cEmL]:R3TDZK\\1e1^:[3^EdLb:]3^EcLa:_3bDmJ;d1S;a3UDPK0Ni0a1Q;P4nDoKS;Q4nDnKR;S4UD[Je0b1V;W4jDhKV;X4kDgKU;Y4lDfKT;[4kDeKU;[4lDeKS;Z4oDeKQ;\\4oDdKP;d3XDbKi0h0P;f3YD_Kh0j0P;f3ZD`Kf0g0R;h3[D_Kd0g0R;j3\\D]Kc0f0S;h3`DaK>e0S;j3`DaK<e0T;j3`D`K>f0Q;j3aD`K?e0P;m3_D^Ka0f0o:j3aDbK`0d0n:j3bDaKb0d0l:m3`D`Kc0d0l:l3`DaKe0c0j:l3aDaKf0b0i:k3dDbKc0d0h:i3eDdKd0c0f:i3fDdKe0c0d:i3fDfKf0`0d:j3fDfKf0a0c:j3gDdKg0b0a:j3gDeKi0`0`:g3mDfKe0c0]:g3XE[K<o0[:f3\\EXK:S1X:d3mFZLT9f3mFZLR9f3nFZLR9f3oFYLQ9g3PGXLP9h3PGYL`0VOT5a4]JXL9CT5U4dJXL6HR5P4hJXL5KQ5m3kJXL2NQ5j3]HYKY2m093o4g3_HZKK0;O`1m0k05o4f3\\HYKN43L30f1k0k07n4j3[HXK22NNj1f0l0;m4n3^H^Kg17k0XO]MY1`7j3`H`Kg15h0\\OaMX1]7]4[JnJf0R1j4P4aJmJd0T1k4o3bJkJ`0\\OfMk1X7n3bJoJ=V1P5k3dJmJ=X1o4k3eJlJ<Y1o4l3dJlJ;Y1Q5j3fJmJ6\\1S5g3hJmJ4[1U5i3fJlJ4\\1V5g3hJgJ7b1Q5f3jJgJ4d1R5Q3iHnJ1MO008R264f1Q5P3`K[K]Oj1o4k2eK[K[Oo1k4f2kK[KYOQ2j4b2oK^KVOQ2j4c2oK[KWOT2h4a2RLYKVO\\2c4[2WLYKVO_2`4X2[LXKUOa2RNYMR6n4gLXKUODoMm20ZMU6m4hLXKSODPNV3U6n1iLWKUOj2R4o1iLXKTOk2Q4m1nLUKQOo2P4m1QMQKPOQ3o3P2[OnMf0R2QJiJO2M2j4R3X1T2nIgJ19i4k2Y1e2fN[M[1e2fNZMZ1g2bN]M]1c2^NWJ_KW3S6b2[NdMf1\\2WNeMk1[2TNgMk1Y2TNiMk1W2SNlMl1T2SNmMm1S2RNmMo1S2PNnMP2R2kK^JJ`3\\4Y2kIoI\\1c0:V3_4`2RKTJiNBi1j3]4a2PKQJf0^3[4l2eJfIXOFS1R4iNlKW6e=20000N2O1MdIaB]6_=cIaB]6_=2N2N2N200VLlIgIU6V6oIhIR6V6oIkIZOlLd6X9TJnIl5Q6VJmIk5R6VJmIk5S6VJmIi5R6XJjIl5V6TJjIl5U6VJkIi5U6TJnIl5S6lISJU6k91001VIkBg6W=0O10000000YIkBa6W=^IjB2NZ6^=eIcB0OU6h=N2nIVBm5l=RJTB00O0h5o=XJWBf5S7ZJCa5b0gJWOT5l0UKkN_OdIX5d7aKaNWOkIU5e7mKYNQ4i1XLPNf3Q2cLXIXOa2Q4Z4oLcH[OS3c3\\4iNfKS1\\4nNdKo0^4RObKj0a4WOWK[LiL\\4R8ZOUK^LeLU4X8^OSKe0o4^OoJ`0S5AlJ=X5BfJ<^5E_J;c5E]J9e5H[J5g5K[J1h5NXJ0j51VJMl53SJKo56PJHS68mIEU6<kIAU6d0hIZOX6j0eIVOX6o0gIoNY6V1eIhN[6[1`IfNb6`1WI_Nj6P2iFgJ0Of1[3c7V2fFVK1ZOf0Z3e8Y3RGhLQ9R70O101N101O1N4L3N0O4IQFWFR:g96M4MO10O2O1N110O00OXGbEP8_:nGcER8\\:lGfES8^:hGeEW8\\:iGcET8b:iG_EV8d:eG`EZ8d:cGZEZ8GdGP;1[EW8HgGn:2ZEV8[;01N1000101M1O1001OO1@UDjHl;U7ZDeHi;W7a0M311M100N2N30OO100O0OUCoHl<P720O11OO0\\NQIZFn6e9VIXFi6i9YIVF_6aN[IZ;;TFW6Q:hIRFY6]9]I\\E<W1W6\\9^ITEN30L>`1B`Nb6m:aISEN1b0]1^OdN`6j:dIUE?a1o5Y9YJgFg5Y9YJhFe5X9]JgFc5Y9\\JhFc5Y9]JjFa5U9_JjFb5U9`JmF\\5T9eJkF\\5T9dJWEQOa1[6W9fJWEPOa1Z6X9kJhFT5Y9kJhFT5Y9kJgFU5Y9kJgFU5Y9jJhFV5W9kJiFU5W9kJiFW5U9kJjFT5V9jJlFU5V9iJkFW5T9jJlFV5T9jJkFW5U9hJkFX5W9gJhFZ5X9gJeF[5Z9fJeF[5[9dJeF\\5\\9dJdF]5[9dJbF^5^9cJcF[5]9eJcF[5]9eJbF\\5^9dJbF\\5^9dJbF\\5^9eJaF[5_9eJbFZ5]9gJdFX5]9gJcFY5]9gJdFX5[9iJdFW5^9gJcFY5]9hJcFW5]9hJdFX5]9gJcFT5dNhIk:R1bFU5bNjIl:R1`FU5f9kJYFU5h9jJWFW5i9iJkDoNX1Y6m9kJWFQ5i9oJUFS5k9mJUFS5k9mJTFU5k9jJVFV5j9jJVFV5j9jJVFV5j9jJUFW5k9jJTFV5l9jJSFXOkN]5T;ZKSFoNhNNOO5j5P;ZKdFf4\\9[KeFc4XNXJo:V1hFlNZN_5N`JQ;T1oF[4PN`JS;U1lFkNPN^50eJT;Q1nFa4P9^KPGb4P9_KoF`4R9`KmF`4QN\\JP;T1PGb4P9_KoFa4Q9_KoFb4P9^KQG`4P9`KPG`4o8aKPG`4P9`KQG_4k8\\JTEV1R2]4i8gKWGY4i8hKVGX4j8hKWGW4i8iKXGV4h8kKWGT4j8lKXGR4h8oKWGm3nMfJk:]1UGo3o8QLTGm3k8SLUGo3i8QLWGo3i8QLWGo3i8QLTGQ4m8PLTGm3m8SLRGm3o8SLRGl3n8TLRGl3n8TLSGk3m8ULSGk3m8ULUGcNlMQ5o:\\LTGeNmMn4o:^LSGbNTNl4i:bLUGaNQNl4k:cL^G\\3b8cL^G]3c8eLZG\\3f8cL\\G\\3d8dL]GZ3d8gL\\GX3d8hL]GV3d8iL\\GW3e8jLZGW3e8jL[GT3f8lLZGT3f8lLYGU3g8kLZGT3f8lL[GS3Y3", "size": [640, 478]}, "area": 149846.0, "iscrowd": 0, "image_id": 10358, "bbox": [0.0, 135.0, 478.0, 505.0], "category_id": 124, "id": 10012679}, {"segmentation": {"counts": "bho22nc01O4MO0O1000000000000000O1000NR`c3MR`\\L5P]OLla08n]OKQb09j]OKSb05l]O1oa0OQ^O4ka0MU^O3ka0MU^O4ja0MU^O2la0NS^O3ma0MS^O3ma0MQ^OACb0\\b0MP^O6Pb0JP^O6Pb0KQ^O3oa0MQ^O3oa0MQ^O2Pb0NP^O2Pb0Nk]O7Tb0k0110OO1N1O11O0O100000000O1@d]O\\O\\b0d0d]O\\O\\b0b0f]O^OZb0Og]ON22[b0Ke]O23HI4_b0J`]O5643IG3db0K^]O0682HH0N1hb00`]O6_c0L5OO1lbb1", "size": [640, 478]}, "area": 1614.0, "iscrowd": 0, "image_id": 10358, "bbox": [153.0, 15.0, 244.0, 138.0], "category_id": 132, "id": 10012680}, {"segmentation": {"counts": "glc21kc06L2M3O4M01O02OM2011N0001M2O10001O011N1KUT8NmkG5O1OO010O02O1OO100O11N2O1O00N2002N0EER]O;hb0JX]O7GIeb03e]O9[b0Ie]O9Yb0Gg]O9Xb0Hi]O7Xb0Hh]O8Wb0Ij]O6Wb0Ii]O7Vb0Jk]O4Vb0Lj]O2BCdb0;j]O1Yb0Oh]O0Xb00h]OOZb00g]OJ@Njb08f]OF@2nb08d]OF^b0:d01l\\OEab0;c0OO110O00001O00001O000O100000000001OO1001O001OO1002N001O0001O00O1N2N2N2Nn`<0R_C3N2O1N2O0ON]\\OHcc083M2O20O1W\\OOac08O2O1N4L2O101M1M5M3O1O0W]OoNcb0X1IhNd]OX1[b0iNe]OW1[b0kNc]OV1\\b0jNb]OX1^b0gNc]OZ1Zb08N2OZNi]Od1Yb01N11O00104J3O1bN^]O[1gb0LN3O0]]OgN`b0Y1`]OfNO0^b0^1b]ObN]b0a101O000O100000O11O1N10Gh]OhNXb0W18G_]OQOfb0n0\\]OoNN0gb0R1Z]OnNgb0R16N1N11IQOY]OQ1gb0oNY]OQ1gb0310000]OV]O0jb0M[]O1eb0N_]OOab01_]OOcb0O_]OOdb0N_]OMBKQc04b]ONoRb0NT`^O802M2M3O001d\\O@M0Wc0a0j\\O@OO41L0kb0k0U]OYOlb0m0O0LoNY]OS1gb030001OO2O1O3kNT]OQ1mb001VOR]OI2b0lb0@R]ON9a0fb0@]]O?Sc0O00000000000000O011O00O11O00Hb\\ON^c01d\\OIN3_c03f\\OLZc04h\\OJYc01k\\OOcc00jRY1", "size": [640, 478]}, "area": 4930.0, "iscrowd": 0, "image_id": 10358, "bbox": [134.0, 118.0, 278.0, 68.0], "category_id": 142, "id": 10012681}, {"segmentation": {"counts": "cU[22ac0O`\\O60N`c0;O001a\\OA[c0c0O00001N10O20O02M2O2N2M2N2N2O1O1KW\\O0iY=5iYC<I5K3M3M3M2O1N3JkN\\]OY1]b0kNc]OX1Yb0=J1LVNQ^Ok1Pb03O000O1N3OO0100O100O5L5K2N1O2M6K3M1Q_OYMc`0JZ_Om20ZMf`0IZ_OV3i`0O02N10O1NhJkLcD2M2j4R3d6kLcD9i4k2e6YMZIg2g6YMZIf2e6\\MWIh2h6XMWIi2i6WMVIi2k6WMUIg2l6ZMTIg2k6YMUIh2j6XMVIi2i6WMWIi2h6XMXIg2i6YMWIg2i6YMWIf2V7UMVDDZ4W3k7RMPHm2b<O1NX_OlLh`0T320000N2O1MS_ORMn`0n2R_ORMn`0n22N2N2N20AdMY_O\\2i`0<01W_OlLg`0V31N1O2\\MW_OP2h`0iMa_OQ2c`0kMa_OU2``0fMd_O[2Pa000O1O10O2\\^OeMaa0]2O010O100O1b^ObMa60S4_2[EbM2NZ63Y4a2\\E]M0OU66^4f2`K\\M_4e2_ESMm59c4f2_EQM00O0h5;i4f2_EUMf56j4m2QKYMm4i2nJ\\MQ5c2cEfLX5k0T5`2dEeLU5n0V5^2hJfMW5[2gJgMU5]2gJgMY5Y2dJjM[5X2aJkM_5U2^JnMb5R2ZJRNf5f1lEiL\\4d1h5c1PFeLU4k1k5`1XJdNi5Z1UJiNk5V1SJmNm5Q1QJSOP6j0PJXOP6h0nIZOS6f0jI\\OV6f0fI\\OZ6d0dI^O]6b0aI_O`6`0^IBc6=[IEf6;WIGj69SIIP74nHNT7OlH2U7MiH5Z7IdH8^7CcH?c7ZO\\Hh0R8kLeD0Of1[3`1Y8iLSE1ZOf0Z3b2\\9UMeFm2V=1N2O1N2O1O1N2N2O1N2lEYLj3MWFl3n5YLi3k3VLXLg3i3WL\\Lf3d3WL^Lj3b3UL_Lj3b3VL_Li3a3WL`Lg3a3YL_Lh3`3XLaLg3_3YLaLg3_3XLaLi3_3XLaLh3^3YLaLf3`3ZL`Lf3`3QLoKdJb0Y9b3PLhLo3Y3kKmLU4S3jKlLT4nNgFW4U5jLR4ROhFU4V5kLo3[3RLbLP4^3QLaLn3`3TL^Ll3b3TL^Ll3b3SL_Ln3`3RL`Lm3a3SL_Ll3b3TL^Ll3b3VL\\Lk3c3TL^Lk3c3UL]L[3TOgG`4P5ZLX3\\ObG[4W5XLU3Y4oLoJ_J?`8e4RMjJ`J`0_8e4VMUKZJH_8T5XMPKXJJf8V5TMlJn2V5l521OO100O1NeJ_A[5a>2000000O100O1IaJiAc5U>53M1O001O1O00O100O11O00O11O00000000000000002N00O1000000O1000000O11O0000000000000000000000O1000000K_JkAa5U>^JlAb5T>50000000000001O0000000000O11O000ZJjAb5V>]JkAc5U>]JjAd5V>[JmAc5[>M00O1O`JdA^5_>0001OO10000000000000000O100LaJgA_5Y>41O2N0000O1O1O100000000000000O100O10000O100O1O11OO100000000", "size": [640, 478]}, "area": 46583.0, "iscrowd": 0, "image_id": 10358, "bbox": [120.0, 165.0, 358.0, 475.0], "category_id": 150, "id": 10012682}, {"segmentation": {"counts": "UYa24kc011NO3N2OOZ\\OLOO_c06b\\OKO2[c0=2O01O101N1O11O00001O1O000O2K6J5K[Va3Oii^L4L4K4M3M1O011O0000001O00O10000010OO1M3N4HgTb0Mbk]O1O1D4`\\OM]c0<01O5K110OL41O3MN21O010OJg\\ODZc0;g\\OEYc0:72L01002N1O1000N30O01N11OLc\\OD^c0;5N11OO2O001MY\\OKgc02Y\\ONkc013OhTo0", "size": [640, 478]}, "area": 1296.0, "iscrowd": 0, "image_id": 10358, "bbox": [130.0, 28.0, 298.0, 97.0], "category_id": 157, "id": 10012683}, {"segmentation": {"counts": "nXX5111jc05K7J4N3M\\Oj\\O>[c0010002N000N3OO11O1O1O0MTle0LQTZO2T\\ONkc01U\\O0ic0401N201N00O01000002NO1002OO1L\\\\OIfc062K[\\OOfc00je54WZJ2M2\\\\OH^c0>OO1O07J00O2M21O00O100O1000000M3O1O1J\\\\OOgc0Oj\\m02RPROO_b03W]OLGOO5Qc02P]O<mb0]OR]Oo0lb021O001000000O20N020O10N11O01O1Lo\\OVORc0e0n\\O\\OYc0`04O001O10OO200OOCa\\O<`c012O0O1O1N200001OO10000O2O0O101N3N_\\5", "size": [640, 478]}, "area": 1600.0, "iscrowd": 0, "image_id": 10358, "bbox": [269.0, 152.0, 200.0, 117.0], "category_id": 162, "id": 10012684}, {"segmentation": {"counts": "h3a0_c0000001OO11O0O10N2013K:GmcQ1OYhmNOfc0>H5K4L3M4L2N1OO100000V]OoNeb0P1[]OSOcb0n0\\]OROm2Od<o0_@TOf0MA0[14k=l0c@ROf00XOO13>L>9^>g0g@ROf0?FEGHK0027>R?d0g@QOg0?EHFDN55=S?i0_A[O\\OZO1h0OLU?g0_ABZOL2LT?f0_A`0\\OjNU?g0^A?[OmNV?d0_AY1a>hN^AY1a>gN_AT1f>lNYAU1g>kNn@eNO_2T?mNk@dN2_2S?mNl@dN0]2IaM[?_1h@aN004^2JbMZ?_1f@_1]?b10H8N200:F1O00000000O13M9G001OO1N2O1O1Ol@ZKP?P5K1O2N1O00000000000000O1PLYAb11YNf>4ZAa14ZNa>6YA`18WNa>7ZA`17XN_>7\\A`16kMCNl>g0ZAPOLOOY2>nMG0g>i0ZAQOKO2V2;oMI2e>h0dAY1OiMK1g>m0^AY1e?fN\\@Z1d?eN]@[1c?eN^@Z1b?eN_@[1a?dN`@\\1`?eN_@[1a?eN_@[1a?eN_@[1a?fN^@Z1b?gN]@Y1c?gN]@Y1c?gN]@Y1c?hN]@W1c?kN\\@T1d?mN\\@POId1k?]Oh@d0X?\\Oh@d0X?\\Oh@d0X?]Og@d0X?]Oi@a0W?_Oi@a0W?^Ok@a0U?^Om@a0S?_Om@a0S?_Om@a0S?_Om@a0S?_Om@b0R?^Oo@[OnNH0f0S`08n@YOROGNh0Q`08QAmNmN66IJ0111f0P`0;SAnNA0\\Og0P`0JU@Fo0I@0\\Oh0o?IY@Bj1d0m=ZOo_O0000O159Lk1h0Z>XOm_OOi1i0Qa00O1O001O1O2N1OO100AQ]OMTc0Dl\\O08;lb0El\\O08;ob0EQ]O;ob0EQ]O:[c0K5MgPj2N[oUM5^OKT]O9fb0Gn\\O0<9eb0Ho\\O1:9OE10ka02j]O488MBK17OI52K?1i`0;j^O7NA44JJ`00h`0\\1i^OeNO::DH1Wa0\\1g^OUO6]OJ9Xa0l1j^OXNVa0Y2010O00O2O000001OO1001\\Oj^OZNWa0f1m^OWNTa0h1n^OSNUa0j1P_OSNQa0m1n^OSNSa0_1c^OhN<FTa0a1a^O`NN30LQb0`1R^O`NN1Tb0_1P^OaNPb0`1:NLbNe]O^1Yb063M0YOd]OJ[b0Om]O0Tb00n]ONk>^OPDc0WMNh>AdCk0gMBe>EbCj0iMAZ>2lC<kMVO@M08h>9lC=mMSO@OO41Lh>`0mC;VNUOQ>?jC=S=DlB;U=EkB6[MZOl?`0iB6Z=JfB6Z=JfB6Z=JfB6Z=JgB6X=JSA@`0>^NJn?8TA@a0=^NMOFi?`0WABb0<`NMg?5d@CN2W1NdNNM<4CI2[?a0m@B23HIi1<WNCN9Z?:f@]O5643IG32^1<^NNX?9g@]O0682HH0N14e1f0b=Ni@@6a0^11c=9k@Jc1Lb=;j@K^1Nh=6k@M]1Lh=[1\\BbNd=^1ZBdNf=\\1[BcNe=\\1\\BcNe=]1\\BbNd=^1VBjMdNi0V?\\1VBlMdNh0V?\\1UBkNk=U1WBiNi=W1UBkNjNPOg>T2_BmNiNQOINl>T2cBmNgNTOe>o1dBmNgNVOc>l1gBoNaNYOh>i1fB\\OZ=e0gAkM:d1m=a0iAlM:b1m=e0[AiM52b0`1n=e0[AjM43`0_1Q>e0YAnM1Mh0_1n=T1PBZMYOc1g>S1oAQOQ>o0nAROR>Q1lAnNT>R1kAnNV>Q1kAoNU>P1lAmNW>T1fAWM_Oa10bNj>f2WAYM8NGc11]Nl>o2VASMN^1T?b1k@QM0]1U?b1k@cNU?V3N001O0000O1001O00001O00N2O1O100Nk@YKU?g42001O000000gNj@cL0Q1V?\\2o@dMP?\\2PAdMP?[2QAeMo>[2RAeMm>W2l@fL9S1k>T2l@jL;S1h>R2o@jL8U1i>R2m@jL:S1j>S2ZAlMf>T2YAlMh>Q2[AmMg>R2ZAnMf>R2WAmLBQ1X?R2VAoMk>Q2h@mL9R1P?R2f@lL:R1P?V2m@jMT?V2l@hLIQ1Z?W2o@hMR?W2n@jMR?V2QAhMn>V2SAlMl>S2TAnMl>S2SAmMm>R2TAnMl>", "size": [640, 478]}, "area": 27133.0, "iscrowd": 0, "image_id": 10358, "bbox": [0.0, 0.0, 478.0, 206.0], "category_id": 169, "id": 10012685}, {"segmentation": {"counts": "P\\a25ic04M2N2O1N101O1N101O001O001O001O00001O0000001O00000001O00000001O00O101O001O000O2O001O0O2N2O001O2N2N00O100000000000001O001O00001O001O001O00001O001O0010O00XBBn7?nGDR8<kGGV89hGHX88gGIY88dGJ]85bGL^85`GL`8;XGFi8`0oFAQ9k0bFVO^9U1VFlNj9`1iEaNW:j1REbNn:X5000000000O10000000000WOkBgJU=o4S1N2O1O2N1O01]BUKo;l4kC]KQ<e4kC_KS<b4jCbKT<`4hCdKV<`4aCgK]<n5O2N1O2N1O2N101O001O00000000O0^L^CIc<6eCB\\<<mC\\OT<fNXC6m0l0l;lNYC7S1d0f;SOZC6Z1>];\\OZC4a1SO[<h0VB3g1cN_<X1lA3[`0Me_O2]`0Le_O2\\`0Nk_OJW`04T@@n??]@VOe?i0e@lN\\?R1k1N1N3M2N3N1N3M2O2M<DX`h3", "size": [640, 478]}, "area": 12009.0, "iscrowd": 0, "image_id": 10358, "bbox": [130.0, 103.0, 155.0, 244.0], "category_id": 183, "id": 10012686}, {"segmentation": {"counts": "R8V5R800000N2000000O11O00001O00O11OO1001O000000001O000000000000O1O1N2O100001O001O001O001O001O001O1O9Gc0]O00000000000000000000000000_KjK1U4NlK2T4JQL4P4KQL4P4LoK4R4GgKoK8Z4Q4FiKoK6[4Q4GSL:l3GRL:n3FSLmKHW4T4MdLH\\39cLG]39dLF\\3;dLE[3;eLE[3<eLCBVLY3X4UMA^O]L[3R4XMAXOcL^3m3YM@UOfLc3k3WM1i2OXMOi22VMNj23TMNl22TMNYOeK`3^4UM1k27mLIS3b0bL^O^3U5100006aMVFT1j9lNVFT1j9jNXFV1h9jMWFh05^1d9iMXFg07_1d9lMTF0O40O<P2a9mMUFON11O01?Q2^9mMTF1O11Nd0R2`9oMfFP2Z:O0000000000O1OaNlDl0W;POkDo0b4jNP1K`K[1`3nN^KHV53oKQ1]3TO]KLS53_LH]Nb0d411]OeK<\\14n222F[K5f12n220:RMDn22O<RMBo220;QMCo211=oLBP32Oe0hLZOY300f0\\KnNR1<b300d0^KROn0:e3ONf0\\KjN071Hm0b0h3O_K_OV4j1WLSOT4C^KBU4j1mKYNNi0b4B[KEU4j1lKYN2g0b4A[KFe3L\\LO3\\2NbNc4KjNZ3V1fLjNZ3W1eLhN\\3X1dLYKXO[3U4\\1eLeN[3[1eLeN[3[1dLfN\\3Z1dLfN\\3Z1cLgN]3Z1bLfN^3Z1aLfN`3Z1`LfN`3Z1_LgNa3Y1_LgNa3Y1^LjN`3V1`LiNa3X1]LiNc3W1]LcNi3]1WLbNj3^1ULbNl3^1TLaNm3_1RL`NP4`1PL`NP4a1nK`NR4`1nK_NS4a1lK`NT4`1lK`NT4`1kK`NV4`1jK`NV4`1iKaNW4`1hK`NX4`1gKaNY4_1gK`NZ4`1hK\\NZ4d1hKYNY4g1iKlMB_Mf4e4iKkMB^Mf4h4hKhMC^Mg4j4gKgMB]Mi4l4eKfMb4Z2_KeMa4[2`KcMa4]2_KcMa4]2_KbMb4_2\\KTK_OZ2U5b2aKVK[OP2T5j2bKUK[OP2S5k2cKTK[Oo1S5m2jKRMV4n2kKhLnN]N01W5j4kKeLa4\\3^KSKoN^1c5_3_KRKnN_1c5_3aK`L`4`3[KRKTO^1a5`3[KSKRO^1c5_3\\KSKPO^1d5_3\\KeLc4\\3]KcLc4]3^KbLb4^3^KbLb4^3^KaLc4_3]KaLc4_3^K`Lb4`3^K`Lb4a3]K^Ld4b3ZK_Lg4b3WK]Lk4c3UKSKUOR1f5l3TKQKXOR1d5n3]KoJmNm0e5T4eKlKZ4U4fKmKW4S4kKjKV4W4jKhKV4Y4iKfKX4Z4gKgKY4Z4fKeK[4[4dKfK\\4[4cKeK]4\\4bKdK^4\\4aKeK_4\\4`KdK`4b4YK_Kg4V5cJkJ]5`6000d0\\Oe0[Oe0[O3M00001O00001O0000001O00001O0000001O00jNV1BZKZHf4n8VON2J6J6K5J6J6K5J6K5J6N2O1O100O1O100O1O1O100O1O100iNVJoJk5n4YJPKh5l4\\JTKd5i4_JVKb5c4eJ\\K\\5Y4oJgKQ5o3YKPLh4n3[KPLf4o3[KQLe4o3[KQLe4o3[KQLe4n3\\KRLd4n3\\KRLd4n3]KQLc4n3^KRLb4n3^KRLb4n3^KRLb4m3_KSLa4m3_KSLa4m3`KRL`4m3aKSL_4m3aKSL_4l3bKTL^4l3bKTL^4l3bKTL^4k3dKTL\\4l3dKTL\\4l3dKTL\\4l3dKTL\\4l3dKTL\\4l3eKSL[4m3eKSL[4l3fKTLZ4l3fKTLZ4k3gKULY4k3gKULY4k3hKTLX4l3hKTLX4l3hKTLX4k3iKULW4k3iKULW4l3hKTLX4k3jKTLV4l3jKTLV4m3iKSLW4l3jKTLV4l3jKTLV4l3jKTLV4k3lKTLT4k3mKULS4k3mKULS4k3mKULS4j3nKVLR4j3nKVLR4j3oKULQ4k3oKULS3@eK[4X1ULS3CmJJb0^4^1ULS3F_KU4^1ULR3I^KR4`1ULQ3L]Kn3d1ULo2O[Kl3f1ULo21YKh3j1WLm24UKe3o1WLk28RKb3S2VLk2h4VMXKi2i4XMVKg2k4YMUKf2k4[MUKe2k4[MUKe2k4[MUKe2j4\\MVKd2j4]MUKb2k4_MUKa2k4_MUKa2j4`MVK_2k4aMUK_2k4aMUK^2k4dMTK[2m4eMSKZ2m4gMSKX2n4hMRKW2o4iMQKV2o4kMQKT2P5mMoJR2R5nMnJQ2R5PNnJo1S5QNmJn1T5RNlJk0BfL0M2d0b5m1jJj0GdLL02a0N@h5]2dJl01XMb5i1]Jo01XMe5f1ZJS10WMf5f1ZJT1NWMh5e1ZJU1NUMh5g1YJT1NVMj5e1XJV1MTMl5f1WJV1LXMj5b1ZJV1LXMj5b1ZJW1LVMj5c1ZJW10RMf5h1YJV1OUMg5e1ZJV1MUMk5e1XJV1MTMl5KmIZ1:i1KlLKEU6OjI0;f10k1JiLMFg6f1bIl1JgLMHf6f1bIl1JeLOHf6g1aIl1KdLOHe6h1aIm1J_LMLo6h1ZIm1L\\Ln6g1VIn1R7RNnHn1R7SNmHn1R7RNoHm1Q7SNoHm1Q7TNnHl1R7ZNhHg1W7WMbIR3[8O2M22N01O0O100O000001O0O100O10O001O0O20O0cNULSH7S1d3m6VMWHk2i7Y1020N3N10O0N10100\\HnJ^6NUI0>T5MQK_6NcIR5KSKb6KVIM300W52TKd63WIl40UKf6P6O110N11O1O01M1100H_IhIb6Y6420000DdIkI_6l5`IQJk6i5UISJ42h6k5[ITJg6k5YITJh6j5;1000O11OO11O0DUJZIk5f6VJZIk5e6[JVIe5h61WITJN4i6f5\\IUJNOi6l5ZIUJj6l5VITJj6l5VITJj6l5QISJ20n6P6QIQJP7m5QIRJP7n551O00QJoHh5X710^JdHV5]7iJeHT5\\7kJjHn4Y7PKjHMDm4X8SKgGj4]8N2M2O11OO11N10O02O0O3N0O10O1O01O001O001O2N100O2N10000O1O1fJRKZ2n4aM[KZ2f4aMcK[2]4`MlK[2k3]KUK01T2X1[2b3aKTK10o1d1Z2X3gKTK00j1m1Z2o2nMTMo1l2QNUMo1j2QNXMm1j2QNXMn1h2QNZMm1h2QNYMo1g2QNZMm1\\2TLdKn1R2l1Z2XLcKk1T2k1Y2^LaKf1X2j1X2aL^Ke1\\2h1V2fL[Kb1a2f1U2iLYK`1c2g1S2fNoMW1P2kNRNT1n1kNSNT1o1kNRNT1n1kNTNS1m1lNUNR1m1lNTNS1m1lNUNR1l1mNVNR1k1lNVNS1l1kNVNT1j1lNYNQ1g1nN]Nm0f1RO]Nj0d1VO`Ne0b1ZOaNb0`1^OcN>^1BeN:\\1EiN6X1JkN3W1KkN2V1NkN1V1NjN1W1NkN0W1OjN0V10jNOX10iNNX12hNNX12iNLX13iNMX12iNLX14hNKY15hNIY17gNHZ18gNF[18fNG[19fNE\\1:dNF\\1:eND\\1<dND\\1<eNB]1=cNC^1=bNA_1?aN@a1`0_N_Oa1b0^N]Oc1c0^N[Od1e0[NZOg1e0ZNYOg1h0XNWOj1i0VNVOk1i0UNVOl1k0SNTOn1l0SNSOn1m0QNROP2n0QNPOQ2P1nMPOR2Q1PMaNWM<j5S1nLcNWM:k5T1lLeNWM5XOEe6a1kLgNXM2P6W1gLiNWMOT6X1cLkNYMKU6Z1bLmNWMHX6\\1`LmNXMFY6\\1^LQOXMA[6_1]LPOXM@[6a1]LPOVM]O_6c1\\LPOW4P1iKoNW4Q1iKPOV4Q1jKnNW4R1iKnNV4R1kKmNU4T1jKlNT4V1lKkNo3Z1oKgNn3]1PLeNm3^1QLcNo3^1PLbNP4^1oKdNP4]1nKdNR4]1lKeNR4]1mKcNS4^1kKcNU4^1jKcNU4^1jKbNU4_1kKbNT4_1kKbNR4a1mK`NT4_1kKbNU4^1jKcNW4oNVIOM0OR2f2POd4Q1[KPOd4P1\\KkN[MRO]7S2VKhNP5Y1oJdNT5]1kJbNW5^1hJaNY5`1VJjM_Ne0\\7b1TJjM_Nc0^7c1SJkM^Na0a7d1ZIjM\\O2Dd0f7a1WIlM^OO@h0l7^1TImM_Ok0_7X1PIoM@i0`7POZHl1d0]N@f0R8^O`Gn0m0PO@c0U8XOeGT1e0SO@`0X8ROjGZ1=WO^O>l8;dGYO_O;o8;`G^O_OC5Lm8S1]GA_OB9Fl8W1ZGE_O^Og9o0hFF_O[Oi9o0fF6Z9IeF9[9FfF8\\9HdF8\\9GeF9[9EhF:X9GgF9Y9HfF8Z9GgF9Y9GgF9e4", "size": [424, 640]}, "area": 108444.0, "iscrowd": 0, "image_id": 10369, "bbox": [0.0, 81.0, 640.0, 343.0], "category_id": 101, "id": 10012687}, {"segmentation": {"counts": "0[7m51O001O00001O001O001O000000000000IZJiHi5U7VJjHn5V720000[KTJm1m5SNXJh1h5WN_Jc1a5]NdJ]1]5cNdJ\\1\\5dNeJ[1[5dNfJ[1[5eNfJX1\\5hNeJV1\\5jN\\JaMDc3R6kNXJeMG^3R6mNSJjMJY3S6mNPJmMNU3R6nNlIRN2Q3P6lNnIUN1P3P6kNnIVN3m2P6mNmIWN3k2P6nNlIXN4k2o5lNnIZN3j2n5lNnI[N5i2l5lNoI\\N4h2m5kNoI^N5g2k5kNoI`N5e2l5kNoIaN5d2k5kNoIbN7b2j5kNPJdN5b2j5jNPJeN7`2i5jNQJgN6`2g5iNRJhN7`2f5gNSJkN7]2f5hNSJkN8\\2e5iNRJmN8[2e5gNTJoN7Z2d50\\J1c5eNUJSO8W2c51^JNb5fNUJVO8U2b52_JN`53`JL`54`JLGbL5OZ5d3jJNJ`LMN\\5e3mJiLHk2[5<mJhLIo2W5:PKbLNS3R5;PKaLOT3Q5<PK_LNU3S5<PKYLF28X3IfLW5h3TKYLL\\3P5;_KDb4=\\KDd4=\\KWL^OW3T5b0oKSOWOUMY4i3_LQOVOYM[4f3`LPOSO]M\\4d3aLnNQOaM]4a3bLPOkNcMc4^3bLA]3`0cL_O]3a0dL_O[3b0dL^O\\3b0dL_O[3b0eL[O]3e0cLYO_3h0_LXOb3h0^LWOc3i0\\LWOe3h0\\LWOe3i0[LVOf3j0aLnN`3Q1cLmN]3S1dLjN^3U1cLkN]3U1dLjN\\3V1eLjNZ3U1gLlNX3T1iLkNW3U1iLlNjNXMk3k3]MlNhNYMk3k3]MmNdN\\Mn3g3_MmNaN^Mo3d3aMoN]N_MR4b3bMnNZNcMS4^3dM@\\2`0eM@Z2`0fM@Z2?hMAW2?jMAU2?kMAU2>mMAS2?mM@T2`0SMZLlN84m2m3`0TMZLoNe3n31]MNd21\\M0d20\\MOe21[MOe20\\MOe21ZM0f20[M0d2O\\M2d2N\\M3c2M]M3c2L^MbL\\N[3V43]MaL_N]3S41_M_LaN`3P41_M_LbN_3o32iMMW22jMOU21kMNV22jM_LRN[3U45lMLT24lMLT24kMMU22lMNT22kMOU20lM0T20kM0V20jM0V2OjM2V2NjM2V2NiM5U2KkM4V22jKbLb1]3d2h0lLROZ3n0fLQO[3o0dLQO]3o0cLPO^3P1aLoNa3Q1_LoNa3Q1^LPOb3P1^LoNc3Q1\\LPOd3P1\\LPOd3P1[LPOf3P1ZLPOf3P1YLQOg3o0YLQOg3o0XLROh3n0XLQOi3o0YLmNi3S1YLjNh3U1[L^NPO_Mf4S4[L]NPO^Mf4U4[L[NPO^Mg4W4ZLZNoN]Mi4Y4XLYNo3g1RLXNn3h1SLVNn3j1RLVNn3j1RLUNo3k1PLhKkNZ2U5n1ULjKgNP2T5V2VLiKgNP2S5W2WLhKgNo1S5Y2^LfMb3Z2_L\\MZN]N01W5V4_LYMm3g2SLhKZN^1c5j2TLgKYN_1c5j2VLUMk3k2PLgK_N^1a5k2PLhK]N^1c5j2QLhK[N^1d5j2QLZMn3f2SLYMm3g2TLXMl3g2ULYMk3g2ULXMl3h2TLXMl3h2ULWMk3i2ULWMk3i2ULVMl3j2RLWMo3i2PLVMR4j2nKlK\\NR1f5R3nKkK^NR1d5S3XLjKRNm0e5Y3`LgL_3Y3bLiL[3W3gLfLZ3Z3gLeLY3[3gLdLZ3\\3eLeL[3[3eLdL\\3\\3cLeL]3[3cLeL]3[3cLeL]3[3bLfL^3Z3bLfL^3Z3aLgL_3Y3`LhL`3X3kL]LU3b3aMiK_2W4Y3000000000000000000000000000000000000YOTGRMl8n2]GiLc8X3eG_L[8a3f0001O0000001O00001O00001O0000001O00001O0000001O00001O001O1O2N1O2N1O2N1O2N1O2N1O2N1O1O000000O100O10000O10000O10\\GfKZ8Y4gGgKY8Y4gGgKY8Y4fGhKZ8W4gGhKZ8X4fGhKZ8X4eGhK\\8W4eGiK[8W4dGjK\\8V4dGjK\\8U4eGkK[8U4dGkK]8T4dGlK\\8T4dGmK[8S4dGmK]8R4dGnK\\8R4dGoK[8Q4dGoK]8P4dGPL\\8P4dGPL\\8o3dGQL]8o3cGPL^8P4aGQL_8n3bGRL^8n3bGQL_8o3`GRL`8m3aGSL_8m3aGSLa70oHl3@ULa72WHJb0o3FULa75iHf3HSL^7:hHb3LSL[7>gH_3NRL\\7a0dH]3k7eLSHZ3n7iLnGX3Q8lLkGU3U8n001OgNiGbMW8_2jG`MV8`2kG_MU8a2lG]MU8c2lG\\MT8d2mG[MR8f2nGYMS8g2nGXMR8h2oGVM3TOa7g3]HTM1VOb7f3^HSM0WOa7g3`HPMOYOa7g3aHPMNYO`7h3cHnLMYO`7j3dHkLL\\O_7j3fHiLK]O^7k3mHaLFC]7m3SIZL_OJ]7m3ZISLYO0\\7n3hIQLX6P4gIQLX6P4hIoKV5ObI0M2d0Q4g0nKT54`IL02a0N@R4l7[LeHi3[7WLeHi3\\7VLdHj3]7TLdHl3]7TLbHl3^7SLcHm3^7RLaHo3_7PLeHm3[7SLeHm3\\7SLcHm3]7WL_Hi3a7ULbHj3^7TLbHn3^7RLaHn3b7PLXHKEV4T8nKUHMFW4V8lKSHMHV4V8lKQHOHV4W8lKPHOHU4Y8kKkGML_4Y8fKhG^4Y88001O0000001O0000O13M1O1O001O0000001N10000N2N100O1OYHhK`6Y4_I]Lm5a3SJaLl5^3TJeLk5Z3UJgLk5W3UJjLk5U3UJmLl5m2WJUMh5d2^J^Mb5Z2dJhM]5n1jJTNV5d1PK^No4e1nJ\\NR5h1hJZN`N_N42V6;gI]2X1mNeNYN15OKZ6;lIc2n0WOV5SNoIk2d0SO_5oMPJQ3;ROg5hMRJZ30QO_6T1ZInNf6V1UIjNm6g3100`KTIe2k6[MVIe2j6ZMVIe2j6\\MVIfNJ`3Q7jMUIeNLa3f6YLYIb15dNMa3e6QN_I[NNd3c6QN_IZN0d3a6RN`IXN1f3^6RNbIVN1g3`6QN_IWN3`3CeLk6d1oI`1VOhL42h6f1nI`1]OiLg6f1mI`1\\OiLh6g1mI]1^6dNbI\\1^6dNbI\\1^6dNcIZ1^6fNbI[1]6eNdIY1]6gNcIZ1\\6fNeIY1o5oLZIi1h0V1n5RMZIh1h0W1m5WMVIa1n0X1j5VOWJ<PO_MN4i6R2ZJ7RObMNOi6Y2XJ4ROdMj6Y2UJ2QOeMj6Y2VJ1POfMj6Y2VJ1kNeM20n6Z2VJ3kNdMP7X2VJ2kNeMP7Y2UJhMhNW2X71SJ0l50TJ0l5gMoHQ2U19S6GlI;T6kMdH^1X1g0U6jMeH]1U1i0W6iMjHY1o0l0Z6iMjHMDY1X1P1P7gMgGW1X1R1U7mNjHS1X7lNgHS1[7mNdHS1]7mNbHU1]7kNcHT1^7lNaHV1]7kNbHV1^7jNbHU1^7lNbHU1]7kNdHT1[7mNeHU1Y7kNhHT1W7mNjHR1V7eMfGV1T1T1\\7jNeHT1]7kNcHU1]7kNdHS1]7mNdHR1\\7nNdHQ1^7nNcHQ1]7oNdHQ1[7oNeHQ1[7oNfHP1Z7POgHP1X7POhHP1X7POiHo0X7POhHP1X7POiHo0W7QOiHo0W7QOiHo0X7POiHo0[7mNfHR1^7jNbHl0aN]N01o8e0aHl0`N]N10P9g0_Hk0aN^N00Q9f0^Hk0k7UOVHi0l7VOTHi0m7WOTHi0k7WOUHi0k7WOUHj0k7UOUHk0k7UOVH?\\NVO^9;VH>^NVO\\9<WH;aNWOX9>]H5\\N[OY9?ZH6`NXOW9b0YH6aNWOW9b0XHCVNM0;P:5jGBWNM09T:6eGB]N8n96eGA\\NOJ;T:5oGMR82nGNQ83oGNP82PHNP82PHNP82PHOo71QH0n70RH0n70RHOo71RHMo73RHJo77RHGn7:SHDm7=TH@n7`0SH]Oo7c0[2000O11OO100000000O11O001E]CG00i<0YC1[SV1", "size": [424, 640]}, "area": 89131.0, "iscrowd": 0, "image_id": 10369, "bbox": [0.0, 0.0, 548.0, 424.0], "category_id": 105, "id": 10012688}, {"segmentation": {"counts": "[j`52U=2O1M2O2N2N1O2N2O00100O000010O01N101CFjC;U<<01O1O1O1O1O1O001O1O0SDhNj;[100000O100O10000O1O1O1O1O100O1O100001O001O1fNlN^FT1a9oN]FR1a9PO^FP1a9QO_FP1_9QOaFP1]9QOcFo0\\9SOcFn0[9SOeFn0Y9SOgFm0Y9SOgFn0W9SOiFn0U9SOkFm0U9SOkFn0T9SOkFm0T9TOlFm0R9TOnFl0R9TOnFl0Q9VOnFk0Q9YOkFh0S9]OiFc0W9^OhFc0V9^OjFb0U9@jF`0U9AkF`0T9AkF?T9BlF?S9AmF?R9BnF>R9CmF=R9DnF=P9DPG<n8FRG;l8FTG`0e8BZG=f8DZG<f8EYG;CVNM0j8a1fG9BWNM0k8b1dG7B]Ni8]1eG6A\\NOJk8d1eG?\\8CcG=\\8CeG=Z8DfG<Y8EgG;Y8EgG;X8FhG:X8FhG:X8FhG:OmMf7k1[H9MnMf7j1]H9JoMh7h1_H:GPNh7f1bH;DPNi7e1dH<@QNk7d1eH<]ORNm7c1fHh0Z7XOfHj0W7WOiHi0W7VOjHk0T7WOkHi0T7WOmHj0R7VOnHj0Q7WOoHj0o6WOQIi0o6WOQIi0n6WOSIi0m6XObI9\\6HdI8[6JjHjMG005`0V2n6LbHkM1;?n1m6a0SI_Ol6b0TI_Oj6b0VI^Oi6c0XI]Og6c0XI^Og6c0[I[Oe6e0ZI\\Oe6e0\\I[Oc6e0[I^Oc6c0YIAf6`0WIDh6<UIGj6:TIHk69RIKl6Z3N1O2O1N1O101N1O2O0O100N[IaIe6_63N2N1O101ISIPJn6P6SIoIm6R6RIlIP7T630O00100O1]OjIRJW6l5mIoIV6l5oIoIW6n5lImIX6S6?N2N2O2M101O1O1O1O00000C=M3O1O1WO_HfKl6MeH3c0R4`7UL_Hk3^7YLaHg3^7[LaHe3^7]LaHc3^7^LbHb3]7`LbH`3]7aLdH^3\\7cLcH]3\\7dLdH\\3[7eLeH[3[7fLdHZ2EhMg7NdHZ2IdMb73eHY2LaM_77dHX23[MY7`0aHU27[MX7g0YHn1>\\MZ7l3fHTLZ7l3fHTL\\7j3dHVL]7i3cHWL`7f3`HZL^4ImMj3eM]L]4OnMBXMh3=gL]4OTNY3_MhL\\40VNW3^MiLZ43WNT3_MiL[42VNU3_MiL\\41VNU3^MjL[42YNR3\\MlL[42\\No2", "size": [424, 640]}, "area": 25690.0, "iscrowd": 0, "image_id": 10369, "bbox": [427.0, 0.0, 213.0, 424.0], "category_id": 115, "id": 10012689}, {"segmentation": {"counts": "[7g0b<O001O0N201O001N110O0001O00O11OOJBaCa0]<^ObCg0]<20000O101O0O10000O1N2O0O10O2O102N10N1010O100O010O010O0100O9Ia0^OO00001O1O1O00001O1O000_N\\D5OR1i;lNYDMNT1R<12M0O1O000QOkCm0X<0O2O02OO^OkC1T<MoC3Q<KRD4n;JUD6l;DXD<[<OcCEm;;oCKP<5kC1S<OiC4Y<=N1O2O1O1mCnNo;V1O1O1O010N206WE`N^9`1bF`N_9_1_FcNb9\\1^EcNh05j9X1]EdNg07l95^Ea01RO0O40O<]:2_Ea01SOON11O01?_:JdEe0KSO1O11Nd0a:BfEn0JF`:YOhEQ2W:mMlER2e:01O001O1N1O]EPNP:S2lEoMR:\\1bEF^:9fEJS:6QF6H]Nc9o0]F[O<\\1U9YOgFRO5f1R9XO\\Gh0d8WO^Gh0a8ZO^Gf0b8ZO_Ge0b8YOgG>Z8BfGSOnNR1]9JcGVOROn0[9KdGTOjN071Hm0c9LWHHU85mG_OYNNk9`0oG^OYN2i91QFMO3\\2Nd7Mk\\c6", "size": [424, 640]}, "area": 5327.0, "iscrowd": 0, "image_id": 10369, "bbox": [0.0, 222.0, 129.0, 184.0], "category_id": 165, "id": 10012690}, {"segmentation": {"counts": "RV95S=5J7J5K1O0O2O1O00GeCFY<7kCIU<4oCLP<0TD1k;MXD2g;N[D2d;N\\D3c;L_D3a;L`D5^;LcD4\\;KeD5[;KfD5X;KiD6V;JkD5U;JmD6R;IoD7P;JQE6n:ISE8k:IVE6j:IWE8g:IZE7e:H\\E8k;0gNHaE8g;0fNHeE8e;1cNGjE8c;10O01000O0100O010O10O010O10O10O0100O01000O010O10O010O01O1N9H;E;D3N1N101O0O2O001N101O0O2O1N101O0O2O001N10lNZEGe:9T1100O10000O2O000O10000O101N10000O10000O2O000O100O10001N100004La0_O8H01O0000000000001O0000000000001O000O100000001O0000000000001O0000000000001O00000000000O2O00000000001O1O001O001O1O001O001O1O001O001O1O6Je0[Oe0ZOf0[Oe0[Oe0[Oe0[O2N1O00001O0000001O00001O00001O0YOfJ`IZ5`6oJWIQ5j6WKmHj4R7fMP3\\1eNW1hNK5J6J7J5J6J7J5J6K6I600O1O2O0O1O2O0O1O101N1O101iN[FhNf9U1]FjNe9Q1`FnNa9n0bFQOa9i0cFVO`9>jFBW93SGLP9JXG5j8JWG4l8JTG6m8HTG8n8FRG:o8EQG;Q9BPG>Q9APG>R9@nF`0S9^OnFb0Q9_OoFa0Q9_OoFa0Q9^OPGb0o8_ORG`0n8_OSGa0l8@TG`0l8@TG`0l8_OUGa0j8@VG`0j8@WG?i8@XG`0g8AYG?g8AYG?f8A[G?e8A\\G>d8B\\G>c8B^G>b8B^G>b8A_G?`8B`G>`8BaG=_8BbG>]8CcG=]8CcG=\\8CeG=[8CeG=[8CfG<Y8DhG<X8DhG<X8DhG<W8DjG<V8DjG<U8DmG;S8EmG;S8EmG;R8EoG;Q8EoG;Q8EoG;P8ERH:n7FRH:m7GSH9m7FTH:l7FTH:k7GUH9k7FWH9i7GWH9h7GYH9g7GYH9g7GYH9f7G[H9e7G\\H8Z6@aH9U17Z6A`H7W18Y6B_H6X18Y6C]H6Z17Y6C]H5[18X6D\\H4]17W6FZH4_16W6GYH2a17V6HWH2c16V6IVH0e17U6IVH0e17U6JTH0h15S6MTHMj16R6NSHLk16R6OQHLm15R60PHJo16Q66iGEV25Q6<cG_O]25o5b0^GXOd26n5n0RJROn5n0RJROn5m0SJSOm5m0SJSOm5l0UJSOk5m0UJSOk5m0UJSOk5l0VJTOj5l0VJTOj5l0WJSOi5l0XJTOh5l0XJTOh5l0XJTOh5k0YJUOg5k0YJUOg5k0ZJTOf5k0[JUOe5k0[JUOd5k0]JUOc5k0]JUOc5k0]JUOc5j0_JUOa5k0_JUOa5k0_JUOa5j0`JVO`5j0`JVO`5j0`JVO`5i0bJVO^5j0bJVO^5j0bJVO^5i0fJTOZ5l0kJoNU5P1iIK_65XHU1g7W2O2N1O1O2N1O1O0000O010O10N1O2O0O2NVLWHX2i7gMSI_1k6aNRJc0m5]OoJIP56P4000M3J7I6I7J65K7I7I7I6K6I7I7I6Jc[n06TdQO9G9G9G9F:G9K3M1O2N2N1O2O1N2N1O2N2N1O2N2N1O2O1N2N1O2N2N1O2N2O3L4M3M4L3M3M3L5L3M2N1O0O2O001O001O0O2O001O001O0O2O001O001O0010O010O10O010O0100O010O0010O0100QO]GWMc8h2_GXMa8f2bGXM^8h2dGVM]8h2eGXM[8g2gGWMY8h2iGXMW8g2kGWMU8i2lGWMT8i2mGUMT8k2lGTMT8l2mGTMS8l2lGTMT8m2lGSMT8m2kGTMU8l2kGTMT8m2kGTMU8j2lGWMS8i2nGWMR8g2oGZMQ8d2QH]Mn7a2SH`Mm7_2SHbMl7]2VHcMj7[2WHfMi7X2YHhMg7W2YHjMg7U2YHlMg7S2ZHmMf7Q2[HPNd7P2]HoMd7P2]HoMd7P2]HoMd7P2]HoMd7P2]HoMd7P2\\HPNe7o1\\HPNd7P2]HoMd7P2]HoMd7P2]HoMd7P2]HoMd7_1TG`NY10d7_1TG`NX11e7^1TG`NX11d7j0RG[O3GY14c7g0TG]O1B^19^7f0UG^OO_Ob1=[7d0VG_Oe1MV7b0WG@d1MV7a0XGAb1NV7`0ZG@a10U7?\\G@`10T7?_G_Oc1Mn6b0aG_Om:?WE_Oi:?[E_Oe:?^E_Od:>U1MYa3", "size": [424, 640]}, "area": 35421.0, "iscrowd": 0, "image_id": 10369, "bbox": [22.0, 23.0, 609.0, 300.0], "category_id": 183, "id": 10012691}, {"segmentation": {"counts": "m^h33g>N]A3c>500000000000000000000000000000000000000000000000000000000000000001O00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001O0000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001O0000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001O0000000000000000000000000000000000000000000000000000000000002NSXW2", "size": [480, 640]}, "area": 1610.0, "iscrowd": 0, "image_id": 10386, "bbox": [256.0, 471.0, 231.0, 9.0], "category_id": 105, "id": 10012692}, {"segmentation": {"counts": "0h5X90000O1001O000000N2O10000O1000000000000000000O100000000O11cF^JZ9d5000N23MO1O1O10000O100O1O100O1000000O10000000_FcJ_9`5O1OO11O00001O0000001ON21OO1^NcF_M_9_2cFSLOm00]O_9h2`FgM62N\\O]9j2cFcM47MUOM2_9R3eF[M2<2UOX9S3bFPML;8<f9X2YFoLI<;;c9Z2ZFnLH?:8d9Z2^F\\M08c9[2^F]MCC<c0c9]2]F`MDA9b0f9]2`FbMG1j9\\2_FaMAA6b0j9\\2`F_MJ5f9]2_F`MJ2g9^2`FaMIOg9`2`FaMH0h9_2_FbMH_OK;o9_1oEXO?_OKKh9[1nEYN050K0g0`0[O@;8Nk9Y1UF_NILOg0a0YO@00?5BJ6U:^1SF]NKNNg0b0YO_O>:Cl9d1RF]NJe0c0FQ:X1aE^NKd0b0GR:X1`E\\NOc0?IR:X1kE\\N_O;f01P:X1kE\\NA=?K^OHg:e1kEZNA<c0HY:a1lEhNIG[:_1nEmNBG_:_1mEiNFF]:a1QEYNi0`0JD^:f1iEdNKD\\:R1oDlN2?h00P;e0ZD]Oe0MQ;g0[DZOe0Oo:f0[DZOi01k:d0_DYOIDk0?m:e0^D\\Od0Mo:h0]DYOe0No:i0\\DXOg0Mn:k0ZDnNL4m01o:`0SDA9IJ1m02POKm;h0TDA8GL1l02oNMo;h0RDB>Fc02P;c0SDGa=:`BCa=>aB]OHLg=h0aB[Oa=e0^B[Oc=c0VBZOV>e0kA[OU>5kA9W>HhAJ29W>IgA028X>HfA053W>MnA0T>0lANV>2<N20ee<1fkBOW10`N2k<OVCO43X12_;OXC0X12oNKR<4gC0U16oNGIOV<5mC0n0NnN85On;LQDOk0`0TOF_;i0]EASOH^;i0[EAWOG];j0YE_O[OG\\;j0bDmN>c0BH];S1oDUODKZ;m0eDbN<d0EM[;k0dDaN?0A^1\\;1TE1m:0RE1A_N10V;_1YE4@^NW;_1VE8j:HWE6j:KSE7m:HkD`N1k1T;ChDcN025g1R;CmDdN1h0M6U;a0jDeN1b028LVOo:Z1RE\\O0UO8U1e:U1aEkN_:T1aEmN_:S1aEmN_:S1`EnN`:R1`ElNb:T1^EmNa:R1`EoN_:P1bEnN`:S1_EmNa:R1`EmNa:R1`EPO^:n0dEPO^:R1_EQO_:P1`EPO`:P1`EQO_:o0`EPOb:P1WEXOh:i0TE[Ok:c0XE[Oi:f0UE[Ok:g0REYOo:g0PEZOP;Z21000000000000O10nDULo:P4QEoKk:T4O1O2N2N2N1O1O0000O1O10000000000O11bEeKU:[4kEeKU:Z4kEgKU:\\4fEfKZ:Z4eEgK[:_400000O1NdEaK[:_4eEaK[:_43O1O1O1O100O1000000001O3M0000O1O1O10000O1000000O11O00000000001O00001O00001O00000000O10000000oEdK^9]4]FgKc9k40000000000000O11OO1000000O10000002NO11O00001O00001O1O1O001O00001OO100MXFnJh9S52001O00001O00001O0000O10WFkJg9X5O001O001O00001O002N00O11O1ON`FdJ`9\\52O100O10000O100O10000O100000000O10000001OO1000000001O00O1O1O100001OO100O1O100O11O00O1002SFnJh9Q5YFoJg9T5YFkJe9U5[FlJd9X500000000000001OO1001O00O1N2M3N2O1N2000000000000001O000000O1001O@RFmKo9R4RFmKo9R4SFlKn9T4SFjKn9W4RFgKo9Z4mE`K11O2S:`48N2O12N00O1O1MbEdK^:Y4eEgK[:Y4600dN\\ERM0b002d:Z2_EZMMO1;d:m1[EeM024JL42NNO4`0a:n1aEgM1LMNM13a0`:f1kEhMB170O0N;KHc:S2lEjMM0LOO0K0O0O014b:S2[FjM11d9U2[FjM11d9U2[FiMi:W2P1000000001fDiMM0_8W2UGiM^O1DOo0OQO1020Mg9X2oFiM[O493c0ITO4OMe9V2mFlM@03OK6n02h8m1kFlMA414k00h8l1jFoM@1068B<<S9l1hFZNCK5CI0505?[9i1^FkMMg00D2J1L3<_9\\3]F^LNL1?c9m3]FTL1[OO3V9_4iFUL0YO22U9Z5iFeJ02W9c500000O11O00001O001OO11ONkFYJU9g5200000000001O00O100001OO100001O00000000001O00001O00001O1O001O1O001O001O001O00001O001O000000NUGoIk8Q621O1O000000001OMVGPJj8P6WGoIi8R6VGoIi8P641O0000000000000000O10000N200002NO1O11OO11O1RGoIk8S6000O11O00000000O1001O00002NO100001O00000000O1O1001O00O10000O1001O0000001O001O0000001O000000001OO11O00001OO100001O00001OO1M3N23MO1NWGmIi8R63M300O11O2N", "size": [480, 640]}, "area": 94799.0, "iscrowd": 0, "image_id": 10386, "bbox": [0.0, 0.0, 640.0, 202.0], "category_id": 106, "id": 10012693}, {"segmentation": {"counts": "h5m0S>000O1001O000001M2O01000O101O0000001O2N1O1OO0O2N24L3M1N20dN]BV1h=N20N50NNO1O110O1]ObBD]=<jB_OU=`0e000100O1000000O10O11O1O[OgAc0\\>000N20O01O10O0000001NO30N01N2BZOaBf0_=]O^Be0a=\\OXBM2g0f=AXB`0h=<0N2N11000O01000O10000O0^OTB2l=GYBHKa0m=E\\B9h=A\\B:CHb>5_AIO0b>2^A1h>NYA0_ko01\\cPO1O3L1N6L4K01O3M101ON2010L12001M02O11OcAEV>9gAFN31MV><jAOQ>EoA910NHn=1RB:1No=7PBJ5Fa=OZBP13POc=T1[BmNf=Q1[BoNd=Q1[BQOe=U10O10O1N21OM3O11OL6N1M3O1O1lB]Nj<n1SCmMh<W2O0000001O00001O000N3N1N203N3M1N000O1O10000000000O11]MjCY2W<gMiCY2V<gMkCY2X<bMjC^2V<aMkC^2\\<00O10`CdM[<^2dCbMZ<OgC]20fMW<MiCc2W<3O6J1O2N100ON3O1002N1002M0O1N1O11O3N1OKRDWMn;j24001O0OO30O1OO11O0001FmCaMO0T<X2oCgMOOX<\\29N10001O00000O11N10000000VN]CW1d<dN`C[1S=001OO1N200000O11OO0100000O10O10002ON200O00100O0010O1M3L3M4M2M3N3L2N3NOUOQBh0S>20O2O0O101O0O101N100O010\\OfAb0]>N101N101O0O101N102N0O011N2ONKYA5g>1010O010O10O10O010O10O10O100000O101O0001OO10001O0001O00O2N1O100001OO2O0O1O100O20O00O1002_ObA=\\>DdA<_>D`A:`>FaA9c>00001O000000010N1001O00O1N3L3N2O1N3O2N3M3M3M3M1O01O000000O10\\O\\BKf=3`BGb=8g0H^AIi>1Vaf0Nl^YO1UAOj>6]OLTB6j=HkA566n=OnA2R>;33M5K2N3NO0M2N3O101L2105K01L3O1MmAYOS>d0lA\\OX>d02M43L2N0hA[OV>i0NM30100O0O2O1O0O2O0O20O010O00010O010O001O0N_OeAb0Z>21O1N100O1O101OMBdA?[>BdA>]>AdA>[>410O000000O1HcAO[>0gAKNO[>6gAI00Z>6hAJN0X>1fAO005ON0W>0RB1GOf>0YA2j>00Nao>3_PA2UAKe><K2O00O0103N1N000001N101O00000L5L3N2Ml^<", "size": [480, 640]}, "area": 16488.0, "iscrowd": 0, "image_id": 10386, "bbox": [0.0, 139.0, 613.0, 99.0], "category_id": 135, "id": 10012694}, {"segmentation": {"counts": "P9f2T1@l7`0TH@l7`0WH]Oi7c0VH^Ok7a0UH_Ok7a0UH_Ok7a0UH_Ok7b0TH^Ol7b0RH@n7?TH@l7a0TH^Ol7a0VH^Oj7b0VH^Oj7c0WH[Oi7e0WH[Oi7`0PGbMX1Z1jNdNN>P9R2QG`MY1X1fNhN5>j8i1QGjM10^1V1kNVOe8j1QGiMT2d0ZNHb8h1XI_NXNH`8h1YI`NYNE_8k1XI`NZN@b8R2RI^N_N_O]8S2TI^Nf7a1\\H^Ne7b1ZH^Nf7a1[HZNgMFm9Q2\\HWNiMHk9R2[HWNiMGl9R2[HWNiMFm9R2[HWNiMHk9P2]HoMfM52Kl9S2[HlMgM63Jk9U2ZHkMiM51Ll9V2XHiMkM52Jm9W2[HiMfM2T:W2`200M34L0\\CfMb<\\20001ON12O001ON2O100M3N2O1O1000001N11N10hNYC7g<IYC7g<IZC5g<CVCA4l0e<]OVCC1O00O26n0e<^OVCB70NP1e<]OWCD`0m0X<@WCBc01\\Oi0j<DmCB[Oj0g<AVD1ROHn<7RDKY<KfB7Q1HPO2`=NgC1foV1Om]iN0UA100000O20[>0cA3030M\\>MdA`0[>AeA?[>3001OM32NO11O1OO12N2NO10000O1K@iAa0m;@XENI1RO`0m;AVDNN1g01WOOj02VO?o;_OUD1LOe093JXO>h;_OZDON2241KO0NO4`0b0DZO>_9AeHNnM>0A131?`06l8^O`FJn11TN?OA033?a03j8GYHFXN`00^O143=`05k8FZHEWN`01_OO52>d01i8HYHg0ZN_Ob04k8GXHe0ZNAc04k8FWH]1nNmNj8GXH\\1nNmNl8ETHa1oNjNk8HVH^1mNkNn8FVH^1nNjNj8YOcF?e1^1lNlNl8HXH\\1mNkNj8KYH\\1jNiNl8LZHZ1kNjNk8LRG^Oh0k1ZOlNl8KoF@m0h1XOmNj8MmFCR1a1UOQOl8LhFEX1^1TOQOm8;fFjNX1g1QO[NIh0R9VOnFZ1U1>QO[NJg0R9e0RH9TOYNFk0U9a0RH;SOXNGl0T9a0RH<ROVOk8?SH<QOUOk8`0TH;ROTOh8b0WH;POSOg8ROQGa1W18ROVOf8d0XH7SOSOe8f0XH6SOUOe8e0XH6TOTOd8f0YH6ROTOe8g0XH6SOROd8i0VG[NM2O10Nb0k1_OTN6l0h8k0RGYN0:OF31NO5P20mNi8h1QG^O6jNi8m3WGSLi8l3RGWK10Ol0n8P4RGTLn8j3SGWLm8j3SGULm8j3SGWLm8i3QGYLo8l4O100001O0000O11O00O11OO1001O0000001O00O10UOTGbKl8\\4YGaKg8]4^G`Kb8^4cG_K]8^4iG_KW8_4mG_KS8^4PHbKP8Z4THfKl7U4YHkKg7Q4]HoKc7l3bHTL^7h3fHUL]7f3iHXLX7d3T2D<A?G9L4L6J3L6K4L3M4L4L4N200O100O10000O100O10000O100O101OO0101N010O2O000O010O2OO10O100O01O10O2QOPBl0R>O10000O100O100O10000O100O1000M2100O10N2O010O010O10O3N1N1N4L1N5L2N3M2M2OXTl02ckSO6K401O010O010O1O001O001O010O0010O01O10O0N3O10N04M003H[AOf>O61][i1NadVN5VAMb>9N2O100L4F^OPB1Nd0R>700000000O1000000000000000000@`000000000O1000000O100000000001O001O1O1OO11OO10000O1000000O1N2I7J600N200001O001O000000O11OO1O1O100MPNUCQ2k<20000001ON22N0000000000", "size": [480, 640]}, "area": 26500.0, "iscrowd": 0, "image_id": 10386, "bbox": [0.0, 282.0, 640.0, 198.0], "category_id": 148, "id": 10012695}, {"segmentation": {"counts": "e6[2f2iNU6W1kIiNU6W1kIlNR6T1nIkNS6V1lIjNT6V1lIjNT6V1lIjNT6V1mIiNT6V1lIhNV6X1iIjNU6W1lIiNS6W1lIkNS6U1mIkNS6U1nIlNQ6S1oImNQ6S1jIfMbMX1Z1jNdNNT7Z2\\KgM`MY1X1fNhN5T7T2SKgMjM10^1V1kNm5n1SKhMiMT2d0ZNa6i1oJ1_NXNb6f1nJ3`NYNE^Ob6U2YK4`NZN@Af6R2\\K1^N_N_O\\Oe6V2^KO^NeNR6^1_KO^NcNW6[1[K1^NdNZ6X1WK5ZNgMFl0j6V1WK7WNiMHj0k6U1WK7WNiMGk0h6X1ZK4WNiMFl0g6Y1[K3WNiMHj0f6Z1ZK4oMfM52Kk0j6V1YK6lMgM63Jj0l6U1YK6kMiM51Lk0i6W1]K2iMkM52Jk0i6Y1^K4iMfM2R1g6T1`K\\1_4eNaK[1l3hLQJm1P2^1n3lLmIe1Y2[1i3[OWLSMfM`3R6^OZLb0f3^OZLb0f3^O[La0e3_OYLc0g3\\O[Lc0e3]O[Lc0e3]O\\Lb0d3^OZLd0e3]OZLd0g3[OYLe0h3ZOULi0k3WOSLk0n3TOQLm0P4ROoKo0R4POnKP1R4POnKP1S4oNmKQ1T4oNjKR1V4nNkKQ1U4nNlKR1T4nNdJ\\M7f3U5nNdJ\\M7f3T5oNeJ\\M5f3W5mN\\JZMA4l0d3V5oNWJXMC1O00O26n0d3W5nNWJYMB70NP1d3o2VLWNh2WNZMD`0m0W3o2YLSLOR2h2\\NXMBc01\\Oi0i3X2WLgM6Y1b2\\NoMB[Oj0f3W2\\LcM4^1`2YNWN1ROHm3P3XL[M2c1`2YNYNKX3m2kKPML;8c1`2PNXM7Q1HPO2Y4P3mKoLI<;`1e2SNXN1T3Q3lKnLH?:[1a8W1aG\\M0\\1_8W1bG]MCC<g1_8Y1aG`MDA9e1c8Z1cGbMGU1f8X1cGaMAA6f1f8X1dG_MJX1c8Z1bG`MJU1d8[1cGaMIR1d8]1cGaMHR1f8]1aGbMH_OKk0NEn8Z1RGXO?_OKKFK5HKa0n8W1RGYN050K0g0`0[O@;8NJJ79f8V1YG_NILOg0a0YO@00?5BJ67I6:CHo8c1YG]NKNNg0b0YO_O>:C?6VOIO0o8e1[G]NJe0c0Fd0EeN1T9b1UG^NKd0b0Gf0CeN0T9e1SG\\NOc0?IR:X1kE\\N_O;f01P:X1kE\\NA=?K^OHg:e1kEZNA<c0HY:a1lEhNIG[:_1nEmNBG_:_1mEiNFF]:a1QEYNi0`0JD^:f1iEdNKD\\:R1oDlN2?h00P;e0ZD]Oe0MQ;g0[DZOe0Oo:f0[DZOi01k:d0_DYOIDk0?m:e0^D\\Od0Mo:h0]DYOe0No:i0\\DXOg0Mn:k0ZDnNL4m01o:`0SDA9IJ1m02POKm;h0TDA8GL1l02oNMo;h0RDB>Fc02P;c0SDGa=:`BCa=>aB]OHLg=h0aB[Oa=e0^B[Oc=c0VBZOV>e0kA[OU>5kA9W>HhAJ29W>IgA028X>HfA053W>MnA0T>0lANV>2<N20ee<1fkBOW10`N2k<OVCO43X12]:KVE4TO0X12oNKn:OgD100000O20a05A0U16oNGIOo11n6NRG3030Ma03C0n0NnN85Oh1HP7MSG`0`0GDOk0`0TOFj1Jl6KWGe1o0POSOHh1Io6d1RHmNWOGe1Nn6`1SHkN[OGd12k6[1_GZN>c0BHd10k6i1mG_NDKa1LQ7e1]GnM<d0EMd1In6g1]GlM?0A^1e1oNo6f1lG]OW1lNn6g1jG^OA_N10^1>U7e1lG@@^N_1>V7c1hGFS1cNZ7e1dGFQ1gNZ7e1bGFT1eNZ7d1ZGoM1k1\\1_N[7e1UGRN025g1X1bN]7c1ZGRN1h0M6Z1^OY7D\\GR20RN1b028LVOU17POEn51SKNI1ROP20jN0UO8U1l0UOmNFN31Mo52TJNN1g01WOOj02VOj2?XNe0WOmNOm5FVJ1LOe093JXOh2?ZNc0jNoN910NHg50[JON2241KO0NO4`0b0DZOh2?ZN?lNRO:1N`3IcNNnM>0A131?`0`2JUN?ZOSOJ5FU35fLJn11TN?OA033?a0]2IUN?kNSOP13POW3=^NFXN`00^O143=`0_2JUN=NVOmNY3<_NEWN`01_OO52>d0Z2IYN<KWOoNX3<^Ng0ZN_Ob0\\2LVN<MUOQOY3;]Ne0ZNAc0]2KTN=3^2VO\\N]1nNU1LTN=3]2XO\\N\\1nNT1MWN:1a2VOXNa1oNo00WN94^2XOZN^1mNR1OWN83c2UOZN^1nNR1LXN:2`2gNgL?e1^1lNT1NWN64d2UO\\N\\1mNS1LWN86b2VO]N\\1jNQ1H_N?Ma2XO^NZ1kNS1CbN>Od2ROXM^Oh0k1ZOS1GaN<2b2QOVM@m0h1XOU1EaN<4a2POUMCR1a1UO[1D]N`05_2POQMEX1^1TO[1C^Na05B]Nc2Q1ZMjNX1g1QO[NIf4a0[M]OmM\\20fMZ1U1>QO[NJe4`0_Mi1XOkN9TOYNFi4b0]Mj1VOkN;SOXNGj4a0]Mj1VOkN<ROT39ZMi1WOlN<QOS3:[Mg1WOnN;ROR39\\Me1XOQO;POP3;]Mb1hMkMa1W18ROS3GUK`0X2d1ZOSO7SOU3FQK<Y2h1ZOSO6SOZ31VMi1ZOSO6TOZ3MYMk1WOUO6RO\\3KXMn1WOTO6SO\\3GYMQ2VORN[NM2O10Nb0k1_OTN6k7i1lIQNYN0:OF31NO5P20P6f1eJTN^O6o5d1hL\\NX3d1gLWNWK10OQ8i1kLWNY3i1eLXN\\3h1eLXNZ3h1eLXN\\3h1dLVN^3j1l4O1000O2O0000O02O0O01UNoBh1Q=WNoBh1Q=YNQCe1U=1O05K0010O00ON3O1002N1O1O00O1N2N2002N1OK_NjBb1V=4001O00N211ON020O0O10GYNUCO0h1d<[N[COOl1h<8O10000000000O11O0000000000O1010N01N3OO1001O000O01001OO10O1000O2N2O10YChMd<Z21O1O001jM]Cm1m<N3M3M3M3M3M2N3M2N2N2K6M2N2N2L5L3N2M3N3L2O11O00O11ON12OO100O1N1OnQm01QnRO1O2N101N1O1O100O1O1O2N100^NB`Da0];D[Da0Q;ZOXD<?>Y;YOSD>=>_;WOoC`0;=0nNS;V1eD17iNT;Y1^D3>dNS;h2mDYMS;j2jDWMa:ObEk2MVM`:5]Ei2KoLO4f9KeF13e3AeLf9FeF5LJIg31dLc9HeF:Hl3c9kKcFh4]9XKaFi4`9SKZF11O2n4e98N3N12OO0O2N1MdJbF]5Z9fJfFZ5Z97O0dN\\JRH0b002e5Y7^J[HMO1;e5l6ZJfH024JL42NNO4`0c5l6_JiH1LMNM13a0b5d6iJjHB170O0N;KHf5P7iJmHM0LOO0K0O0O014e5P7XKmH11h4Q7WKnH11h4Q7WKmHn5R7P101O001O01_IPIM0g3o6mKQI^O1DOo0OQO1020Mo4P7gKQI[O493c0ITO4OMn4m6dKUI@03OK6n02Q4d6bKUIA414k00R4b6`KYI@1068B<<]4b6^KdICK5CI0505?f4^6SKVIMg00D2J1L3<j4Q8RKiGNL1?o4a8QK`G1[OO3b4S9]KaG0YO22b4m9\\KRF02d4V:<Di0WOb0]O5M4K4L5L3LeIRHi5j7SJWH_OOb6e7oIaHS6_7gIcH^6[7]IhHf6o0UIk41WJo6i0SIZ5T7aJmHb5T7WJoHn5P7kISIW6o6bITIa6l6ZIVIf6b7M3M3O100M3005K00M3O1MfG`IZ8]6eGcI_8]62M34L2N0bGaI[8d6NM3001O00O10000O100O11O001O00001O001O001O001O0000O100O1O100001O0000001O000000O1HfGlIX8S6jGhINOX8Y6jGfI00W8Y6kGgIN0V8S6hGmI005ON0m70VHR66oIGOj75THo5=lID2i7Y6iHgIU7[6kHcIV7^6f02J3G]IQH1Nd6R88OO11O1oGRIn7P7O10O11O00000000O1001_O`00002NO10O101O000O1000O1O14L3jHVHQ7P8L10O1N2OO4N1M10000O0100000O1NNM4M42M1O30010N110O0001OO011OO2N1O101LfG_IZ8b6O3N23MO2MZIjGd6W85K300O11O2NnI", "size": [480, 640]}, "area": 120895.0, "iscrowd": 0, "image_id": 10386, "bbox": [0.0, 0.0, 640.0, 480.0], "category_id": 169, "id": 10012696}, {"segmentation": {"counts": "UYc34j>5K5K5J6K4K2M4K5L4K5L4K5L4D<A?G9L4L4L4K5L4L4L5K4L4N200O100O10000O100O10000O100O10000O100O100O10000O100O10000O100O10000O100O10000O1PM[KeKe4X4`KeKa4Y4dKdK\\4Y4jKcKW4Z4oKcKQ4[4TLbKl3[4ZLaKg3]4]LaKc3\\4cL`K^3]4gLaKY3]4kLaKV3[4oLbKR3^4QM_Ko2`4TM]Km2c4UM[Kk2d4XMZKh2f4[MVKf2j4\\MTKd2k4_MRKb2n4`MPK`2o4dMnJ\\2R5eMlJ\\2S5fMlJZ2T5gMjJZ2V5gMgJ[2X5gMeJ[2[5eMbJ^2]5dM`J^2`5cM]J_2c5bMZJ`2e5bMWJa2i5_MUJc2j5_MSJc2m5X300O100O10000O100O100O10000O100O10000O1000000001O00000000001O01O000001O00000000001O000000001O000000001O00000000001O0_JTJn1l5mMYJS2g5iM^JV2b5eMcJ[2^5cMdJ\\2\\5cMeJ]2[5bMgJ]2Y5cMgJ]2Y5bMiJ]2W5bMkJ]2V5aMkJ_2U5`MmJ_2S5aMmJ_2S5`MoJ_2R5_MoJa2T5[MnJd2U5YMkJg2X5UMjJj2Y5`KiIc0R1m3X5[KlI`0Q1T4V5WKnI=R1[4S5SKPJ;Q1b4R5mJTJ9o0i4n5PKVJP5n7O001O010O001O001O001O001O001O001O001O001O001O001O001O001O001O001O001O001O001O001O001O001O001O00h0XOj0VO7I4L5K4L5K4L4M4K4L5K4L4L5H7I6J7J6Ii^T2", "size": [480, 640]}, "area": 46646.0, "iscrowd": 0, "image_id": 10386, "bbox": [245.0, 175.0, 248.0, 300.0], "category_id": 183, "id": 10012697}, {"segmentation": {"counts": "kQU21X=10000@`0O1O1001O000000001O0000O100O1O10000O1A[OYDe0c;AZD`0c;e0M300O11OO1O1000000000000kN`NTF0C`1X:eNdEKN75Z1X:nN`ENOY1a:Q11O0000000000000000001OO100N2O1N2O1GVMmEo2S:41OO1001O00O1O1O11O001O00KkLTFV3k9kLSFW3l94L4N2M300M3O1001O0000O1N2N2N2O1N2O1L400O1O1O1N2O1O1O100001OO100L4M3000000000000000000001O1O1O2N0000001O000000000000O11OO12N8H6J2N1O0000000000O100YOVLnGj3Q8aLdG`3\\8g00000000000003M4L2N2N1O2N2N2N2N3M1O00^OQLiGo3W8TLfGl3Z8WLcGi3]8YLaGg3_8ZL`Gf3`8[L_Ge3a8\\L^Gd3b8]L]Gc3d8]L[Gc3e8^LZGb3f8_LYGa3g8a00000000000000000O1000000000000000000000000000000000000001O0000001O2N1O0000001O0iKSGQ4P9lKRGR4n8oKRGP4n8QLSG10c3n8\\LSGM1g3m8YL[Gg3U9O0000001O1O001]L[F_3e9`L^F^3b9bL`F\\3k9L3M1O1O001O2N1O1ON2LQMnEn2R:5O12N2N2N1O2N1O2N1O2N2N1O2N1O2N1O2N2N1O2N1O2N1O2N1O2N2N1nNcD0OZO`;1aDN355JG72HO002`;1^EMRO0;0D0c;1XWa2", "size": [426, 640]}, "area": 30208.0, "iscrowd": 0, "image_id": 10388, "bbox": [165.0, 274.0, 279.0, 152.0], "category_id": 95, "id": 10012698}, {"segmentation": {"counts": "TX7a0j<O000000O1O1001O00O11OO11O0000N20000O1000000000000001O000000O11O00`^Z1^OnaeN5L4JGVC>h<20000000000000000O1O1N2O1N20L4O01001O0O101OO0110NO120OO1010M201O000000100N10000001O00O10000100N101OO100001O00000000000000000000nbP5_O`]oJ0kB122j<8O20000O11OO11O0000O1000O1000008C\\d5", "size": [426, 640]}, "area": 2085.0, "iscrowd": 0, "image_id": 10388, "bbox": [17.0, 183.0, 609.0, 26.0], "category_id": 96, "id": 10012699}, {"segmentation": {"counts": "fbl31Y=0O0J0TC0g<84I50100O200000000000100010N3M7I01Kn`T4", "size": [426, 640]}, "area": 62.0, "iscrowd": 0, "image_id": 10388, "bbox": [299.0, 183.0, 22.0, 18.0], "category_id": 105, "id": 10012700}, {"segmentation": {"counts": "W5T1U<10000O1000000O10000O100000000001^Ob00N10000O00010O2O00O11OOOYObCg0]<3001VOaCe0c<0002M1000001O00O1N201O01O000O110O1b0\\O1001O00001OO11OO1001OO11O00O100001O00O1001O00O100O11O000O1000O12NO2O1O0000000000000O11OO1000000000O0011N11O0000000000O100O10O11OO11O1O1N100000000O10000O1001OO1O11O0000000O10O100001OO1O1000000001O1OO100O10L5L1MQDlNQ<U11100000000000000O02N1N2O0O20001N0101O000O2OO1001ON21O01OO2ON2000000000O20N10O10001N10O2O0010O1O001OO10N201O01O1O0000O1001O00O1Oc0_O0O01O0000001O1OO1000O1001M1101O1O0O01003M000000O11O00O10000O1001O0100M21O1N011O00O1O0010000001O001000O001O0000O10O0100O2O0O100O100001O001O1N10010O1NN3O1O1N20O01IVDnNj;m0<00N2O2N2O1OO1O11O00O200O2OO1OO101lCSOi;W101O1O002N3M3MO002O000O011N1000O0101O01O1N1010N2O0000O1O02O00000000O0100100N1001OO10000001O1N011O00000000O1000000001O1O00000000001O00O10010O000001O0001O0O100001OO11O0010O01OO10000000000000000000000O11O00010O00O2O01O000000O100000000O10000000O1O1O100000N201N100001O00001N11N0001O10O11O0000000000000001O003MM3001OO11O001OO0100001O001OO1001O0000O110O000001O000O10O1O1001OO100000010N01O200N20O0O0010O1001O01O2N1N101O01O2OO01N10000001O001OO11O0O100O001O1N200000002N001OLmNQDS1o;mNoCU1Q<20000001O10O1O10O000000001O0000000000O1000000000000O11O0000O11OO100001O00000001O0O100001O0O1000N200O11O1GPDVOQ<f0oCTO12U<g08O11001N11N011N1N2O0010000000008eCPOk;Z1N1O00000001O1O1O001O0000O1oJ", "size": [426, 640]}, "area": 27351.0, "iscrowd": 0, "image_id": 10388, "bbox": [0.0, 149.0, 640.0, 60.0], "category_id": 135, "id": 10012701}, {"segmentation": {"counts": "0W5S8O10000O1000000O10000O100000000001O1OO10000O1O100O100000000NhGSKW8m43001fGQKW8Q50002N0000001O00O1N200001O0000001O1OO1001O00001OO11OO1001OO11O00O100001O00O1001O00O100O11O00000000O12NO10000000000000000O11OO10000000000O100O11O0000000000O100O1000000000000O100000000O10000O1001OO1O11O0000000000O100001OO1O1000000001O1OO100O1001ON2O1O100000000000000O11O0000O100001OO1000000O10000000000001OO1000000000000O11O00O10000O1001O001O0000000000N200001O1O0000O1001O00O1O11O001O0000001O1OO100000000N2001O1O00O1003M000000O11O00O10000O1001O001ON21O1OO11O00O1O1O10000001O001O1O000000000000O100001O000000000000000000O1001O00O100000000O100000000001O1O1OO1O11O00O11O001O1OO1000000000000000000O11O0000O11O0000O1001O0000O1001O1O0000O1O11O00000000O100001OO1001OO10000001O1OO11O00000000O1000000001O1O00000000001O00O1001O0000001O000000O100001OO11O001O001OO10000000000000000000000O11O00001O00O1001O000000O100000000O1000000000000000000N200O100001O00001O00O1O10000O11O000000000000000000000000000000000000O100001O001OO1001O0000O11O0000001O000000O1O1001OO10000001OO1001O1O1O00O1O1000000001O2N1O001O00001O00O10000001O001OO11O000000O10000000000002N001OLmGQKS8o4mGoJU8Q520000001O1O00001O000000001O0000000000O1000000000000O11O0000O11OO100001O0000000000O100001O000000N200O11O1O1O1OO1O1001O00O100O1N2O1O10000000000O100000000001O1O1O001O0000O1", "size": [426, 640]}, "area": 101248.0, "iscrowd": 0, "image_id": 10388, "bbox": [0.0, 0.0, 640.0, 167.0], "category_id": 157, "id": 10012702}, {"segmentation": {"counts": "[6o6[60000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000O1000000001O1O0000000000000000000000000000O1O11O00000000000000000000O11OO11O1O1O0000000000000000000000000000000000O10000000000000000000000000000001O1O1O2N00000000000000000O1O10000@`0OM30101O000O10001N11OO0011N00101O0O2@PIdJP7X5VIeJk6X5e0M4O1N11OO1O1000000000001jNRHbL0Cm7k3XHQLKN75g7k3aHmKNOf7T4Q11O0000000000000000001OO100N2O2M2O1GhF[L]9e341OO1001O00O001O20N101O00K^FaLb9_3_F_Lc9`34L4N2M300M3O1001O0000O1N2N2N3N2M2O1L310O1O1O1N101O1O100001OO2O1K4M4O0000O100O100O100O101N2O1O2OO001O10O0001mM_E[1b:_NhE\\1U:bNUFY1R;LgNkNaFP1_9POmFf0X;BXo4<]PKO3N2N2N2N3M4L4LhQ23mlM3SE3f:5SE1g:8PELl:<lDGP;^1L3L4M000O100001O1O010OO100000000O1000000000O01001O1O0000000000000000O100000000000001O0000001O2N1O0000001O0]E_M]:d2`E^M^:b2cE^M\\:b2eE_M10o9b2PF_MM1T:`2lEhMT:h2O000000100O00]OoEnM0_Om9a2WFnMKDl9]2\\FmMHHj9Z2aFWN[9h1hFYNU9g1mFXNR9h1PGWNP9g1SGWNm8i1UGVNj8j1XGVNf8i1\\GWNc8i1_GVN`8j1aGSNa8l1bGnMPOJ]9X2dGmMa8S2eGfM\\8Y2[10000O10000000001O00010O0000000000000000000001O00000^HiM[MOj5Y2XLhMWM355JG72HO00a6Y2ZLeNUMRO0;0D^6[2]LXOb3h0]LYOc3g0_LWOa3i0aLUO_3k0bLTO_3k0cLSO]3n0cLQO]3o0eLoN[3Q1fLnNZ3R1hLlNX3T1jLjNV3V1kLiNT3X1nLfNS3Y1mLgNR3Z1nLfNQ3[1oLeNP3\\1PMdNP3\\1PMdNP3]1nLdNR3\\1nLdNR3\\1nLdNR3\\1nLdNR3\\1nLdNR3\\1nLdNR3]1mLcNS3]1mLcNS3]1mLcNT3]1jLdNV3\\1jLdNY3Y1gLgNV3\\1jLdNV3]1iLcNX3\\1hLdNW3]1iLcNX3]1gLcNY3]1gLcNZ3\\1eLeNZ3\\1fLdNZ3]1eLcN[3]1eLcN[3]1eLcN[3^1dLbN\\3^1dLbN\\3^1dLbN\\3_1cLaN]3_1bLbN^3^1bLbN^3^1bLbN^3_1aLaN_3_1aLaN`3^1`LbN`3_1_LaNa3_1_LaNa3_1_LaNa3`1]LaNc3_1]LaNb3`1^L`Nb3`1^L`Nb3a1]L_Nc3a1]L_Nc3b1\\L^Nd3b1\\L^Nd3b1\\L^Nd3c1ZL^Nf3b1ZL^Nf3c1YL]Nh3b1XL^Ng3c1YL]Nf3e1YL[Nh3d1XL\\Ng3f1XLZNi3e1WL[Ni3e1VL\\Ni3f1VLZNj3f1VLZNi3h1VLXNj3h1VLXNk3g1ULYNk3h1TLXNl3h1TLXNl3i1SLWNl3j1SLWNm3i1SLWNm3j1RLVNo3i1QLWNQ4h1nKXNR4h1nKXNS4g1mKYNS4h1lKXNT4h1lKXNT4i1jKXNV4h1jKXNV4h1jKXNV4i1iKWNW4i1iKWNW4j1hKVNX4j1hKVNW4k1iKUNW4l1hKTNW4m1iKSNV4o1hKRNW4o1iKQNU4R2jKnMV4R2jKnMV4S2iKmMW4S2iKmMW4S2iKmMW4T2hKlMX4T2hKlMX4U2fKlMZ4T2fKlMZ4U2eKkM[4U2eKkM[4V2dKjM\\4V2dKjM\\4W2cKiM]4W2cKiM^4V2bKjM_4V2_KkMb4T2^KlMb4U2]KkMc4U2]KkMc4V2\\KjMd4V2\\KjMd4W2[KiMe4W2[KiMe4X2ZKhMf4X2YKiMg4X2XKhMh4X2XKhMh4X2XKhMh4Y2WKgMi4Y2VKhMj4Y2TKhMl4X2TKhMl4Y2RKhMn4X2RKhMn4Y2PKhMP5X2PKhMP5Y2nJhMR5X2mJiMS5W2mJiMS5X2kJiMU5W2iJkMW5V2eJmM[5S2aJQN_5P2]JSNc5m1YJWNg5j1UJYNk5g1TJZNl5g1SJYNn5f1RJZNn5g1QJYNo5g1oI[NQ6e1mI]NS6d1jI^NV6b1hI`NW6b1fI`NZ6`1dIbN\\6V4000000000000O10000000000000000000000O10000001O2N1O000000000000000000000000", "size": [426, 640]}, "area": 94421.0, "iscrowd": 0, "image_id": 10388, "bbox": [0.0, 201.0, 640.0, 225.0], "category_id": 178, "id": 10012703}, {"segmentation": {"counts": "c`j3>g<:F:I6L4K<D>B<E8D:C9L2O1N3O00O10000lNeFXN\\9]1YGYNh8i1_GlMd8T2Z10005KN3M4L4M3M3L21O1N3dNYEOl:JZE2l:FYE7n:@VE>P;ZOSEf0k;L3N3L3M3M1N2O1O1O1O1O1Og^f00WaYO4M3M3M3N2N2M3N2N2M2O2N1N3N1O6I4M2N2M2O2N1O2N1O2N2N101N1O2N1O2N2N1O2N1O2N1O2O0O2N2N1O2N1O2N1O2N2N1O2O0O2N1O2N2N1O2NO100000001O0000000O101O00001O0000001N10001O0000001O00001N10001O0000001O000O2O00001O0000001O0O2O00001O001O00001N101O00001O001O000O2O001O00001O001O0O101O001O00001O001N10001O001O001O001N10001O001O001O001O0O2O00001O001O001O0O2O001O001O00001N1O2N101N101N101N100O2N1M4K4M4K4M4L301O001N1N2N3M2N3M20Z_?", "size": [426, 640]}, "area": 17089.0, "iscrowd": 0, "image_id": 10388, "bbox": [294.0, 184.0, 308.0, 207.0], "category_id": 183, "id": 10012704}, {"segmentation": {"counts": "P3S2k<3O000001OO10O1O02K32O0N4NGoM`CU2\\<nMdCR2[<oMcCJ0V2^<;N01001O1OM32N00N2]Oc000000000000000000O11OO1000000000000000001O0000000O11OO10000000000000000000000000000000000O10000000000000000000000000000000000O2O00000000000000000000000000000000000000000O10000000000000001O2N2N2N2N2N2N2N1O1O1O1O001LXMnCi2Q<XMnCi2Q<9JM3002NL4N1N3O100O10000000000000O100K500O100O01000O10000O10000O1000000O1000001O2N1N101O000O2O1gDdLl:^3oDeLQ;e300O100000000O1000lLlD\\2T;dMmD[2S;eMnDZ2R;eMoD[2S;cMmD]2Z;ZMjDd2_;RMiDg2i;O00001N101O0fNeCiN1O1m0Y<9RDFP<8PDHS<YOaCb0=5W<SO]Ch0<5[<KeC5\\<IeC7c:TOcFe0iN8d:VO^EL@4j0b0E7b:[O[E2>8E<b:^OUE4b0IOc0[:CPE3f0A4i0V:9gFA_9?aF@`9a0_F]Ob9d0_FZOb9e0_F[Oa9f0]FZOd9f0^FXOb9h0`FSOc9n0[FTOd9l0\\FnNj9R1WFiNUN3b;8^D>c0WO]O2?M61m:;^DF0d0d0ZO\\O5;4EGP<=PDE1d0<[OC:92f;ORDD3e01[O1?2Ng;OYD;J]OO=0@O?o;I]D=F^OOS1a0lNf:2YEh0@;`0lN_ONS;OcES2NPN\\O0k;o1hDRN]OO0Of;P2mD_NQ;a1oD^NR;b1nD^NR;b1nD^NR;b1nD^NR;c1mD]NS;e1jD\\NV;d1jD[NV;f1jDZNV;e1kD[NV;d1jD]NU;`1_DYNM7e;d1ZDjNe;W1[DiNe;X22O3L7J4L1O1O1O1O2N1O1N2O1O2N100001OcNfCUOJMON0b0c<:WDGk;4WDLk;0VD1j;OUD1l;NTD2n;KSD6m;HTD7n;HSD7n;GSD9n;FSD:n;CSD=n;CQD<Q<CoC=R<BnC=S<CmC<V<BjC<`=L2N3K4M\\c;1f\\D3M4L4L3M2N1O002N2N3N2M2NM4O000000001O000_ORB0_>0000001O1O1O1O1O1O1O1O1O1OO10000O100O10000O2OO10O10000O100O10000O100O100O100O100O100OSQ7LRoH7I001O001O001O1M210O001O001O001O001ACYB=b=d0O001O001O0O200N101O001O1O0100O01O10O3NN101O1O00000NbNbB_1[=cNeB]1[=cNeB]1[=cNeB^1Z=40001N10001O00001O00ZNmB_1S=_NnBc1X=0N1O2NdNoBl0o<TOQCm0o<iNZCY1W=01OO01000O1N110N2O1O0O2N2O1O010O1A`NVCM1d1g<cNUCc1j<;O1N101N2FfMeC02^2S<iMmCW2R<=O1O1O001O1O1O001O1O1O0O200O1O001OiL^DQ3i;O`DPMP;Q3lDRMT;o2hDTMX;m2dDVM\\;W30000000000000000O1000000000O1000000000000O11O0000N2000O0bDbL\\;^3dDbL\\;^3dDbL\\;]3eDdLP;0oD20Z31dLQ;d3PE\\Lo:e3QE[Lo:e3QE\\Ln:b3TE^Ll:a3UE`Lj:_3<O2M102N10O02O0O1O10000000000O1000000000O1001N100000O1000O1N3oN_DVNGNk;k1P1O10000O100O11O00001O0VDnMf:S2XEnMh:T2VElMj:U2TElMl:T2SEmMl:U2RElMn:V2YDgM:1GN15a;]2_DSNe;b201O`NZDFf;8\\DIc;7]DIc;7\\DKc;5]DCOmNd;[1\\DbN1S1OjN130Mc;a1cDbNIi00UO;OX;a1QEQO^O_Oh<3VC=d0H\\OMa>JdA0K2Ta<", "size": [480, 640]}, "area": 39322.0, "iscrowd": 0, "image_id": 10393, "bbox": [0.0, 32.0, 614.0, 153.0], "category_id": 96, "id": 10012705}, {"segmentation": {"counts": "\\P`81m>2PO7^BGN3c=P1O10000O10OhBmNd<T1e0O00001O0O2N3N1N1O2N3WOjA:1GN1\\>0mfa0", "size": [480, 640]}, "area": 630.0, "iscrowd": 0, "image_id": 10393, "bbox": [580.0, 102.0, 22.0, 58.0], "category_id": 97, "id": 10012706}, {"segmentation": {"counts": "kW[21n>1O1O1GMbA6^>6N201O1N3K33NOO1001N0100O10010N200O001O0N1120N0O1O100O10000O100O100O1O1O1N3N0O2O1000000000000O1O100O1O1000O01O00100MnNWBR1j=200O10O010OaBlNT=T1lBlNT=T1lBmNS=S1jBlNK0[=T1jBnNX=R1gBnNZ=R1fBmN\\=R1cBnN`=0]B0Oh01YOn=k01N4L3M4L2N2N5K3M]OOZB1g=4TBJl==mACT>c01O2N1O2N2M3J6M4Mfa58coIJMON0T>j0N2N11O00O1O101N101N11O00000000O3OO3NVBoNe=o0XBSOj=o03M2N3M3M2N3M3M2O2M3M5IXe6OokH1h>7M2N30O10GDhA?V>BjA`0S>AmA?R>8O1O1O1O1O1O1O001O1O1O1O1O001OO1001O100N2O1O1O1O1O1O0020O1N2O100O20OWO_B1b=KbBGL3c=2cBHO5^=0XCJh<3[CMkcP4", "size": [480, 640]}, "area": 4717.0, "iscrowd": 0, "image_id": 10393, "bbox": [160.0, 129.0, 206.0, 135.0], "category_id": 105, "id": 10012707}, {"segmentation": {"counts": "\\Pk21o>1O1N1YOO_B1a=6VBNi=:mANm=c0O0L4100OO20OaCjNiN1O1Q<U1UEVOi:m0UESOk:P1VDdNb0=X;T1PD`Nh0<X;X1hDhNX;Y1fDhN\\;V1dDiN`;2jC@4j0b0Eb;MRD>8Ek;GSDb0IOc0YOe:2QEf0A4i0TO[;i1_D]Na;c1^D^N\\ONo;c1cDiN];X1aDiN^;X1bDjN];T1cDQOY;Q1eDSOW;o0dDVO[;h0gDXOX;h0bD_O];b0^DUN3X1:hNd:c0oD]O2?M61d0<gNe:d0nD\\O5;4EGh1=XNf:=nDC:92]1O\\Ng:3mD1?2N`1G`NR;JnDO=0@O?i1@bNV;GnDOS1a0lNm0[;bNjE`0lN_ON[1c<YO_C\\O0[1MgN0OY<b0kC]OO0O`1V<SO[Di0e;WOaDe0];[OiD`0V;@mD?Q;AQE=P;BoD?S;^OoDd0n:\\OREe0m:[OTEOlN1P<1TENQOLk;6UEDhN1?Nc;1YDMU>O>0`Xe5", "size": [480, 640]}, "area": 3263.0, "iscrowd": 0, "image_id": 10393, "bbox": [194.0, 50.0, 59.0, 186.0], "category_id": 132, "id": 10012708}, {"segmentation": {"counts": "P8h3X;0O11O10N10O100O10O2O010O00000001O00O01000HYLUEi3j:71N2O3M2O1N0O10000001O1O1O1O1O00000O2O000O1000O1000000O1O1O1O1O0O3OO1001OOOiD[LW;a3iD`LZ;`3fD_L\\;`32O10O10000001N1000O100000000000000O1001OO11O000O1000000000O1000000000000000000000000000000000000000O1000000000O11O0000O11O01O00000O1000000000O10000000000\\ObDfM^;X2dDhM];V2eDiM[;U2gDkMY;T2iDkMU;U2nDiMT;U2lDlMT;R2mDoMR;P2oDQNQ;k1SEUNm:g1VEZNk:a1XE`Ng:9cD5i0Cd:8kDIe0O`:8SE]O`0<]:7^FJb95_FKa95]FMc93[FMVNYO_;j0YFOXNWO_;0aDh0f13ZNUOa;g0oE;S:EiE?W:AeEc0[:]O`Ej0^:VOaEk0_:UObEj0_:UObEj0_:UObEj0`:TOaEk0_:UOaEj0a:UO`Ej0_:XOaEf0`:\\OiE8Y:IRFIn9:UFAk9`0UF@i9d0UF[Ok9h0TFVOm9m0PFROo9R1oEmNR:U1lEjNT:V1nEhNR:Y1nEgNQ:Y1PFfNn9]1j102OOUO`B6`=IcB5]=KeB4e0E^;6oC4b0H^;4RD3>J`;2UD2:N`;0XD161a;M\\DO36`;K^DL3:_;IaDI1`0];GdDE0f0[;DaE>^:AbE`0^:@`Ec0`:]O^Ee0`:]O^Ee0a:\\O^Ed0b:]O\\Ee0c:[O]Ef0b:[O\\Eg0c:ZO\\Ef0d:[O[Ef0WOgNn:d0iEg0XOfNn:d0iEf0YOgNm:d0iEf0YOgNm:f0hEb0[OiNl:f0jE?ZOkNl:e0lE`0WOkNm:d0nE`0UOlNm:c0QF`0ROlNl:d0TF?POnNk:b0WF?nNoNj:b0YF`0mNlNk:c0ZF`0kNnNj:a0]F`0iNoNi:a0`F?hNoNh:a0bF?fNPOh:`0cFa0eNnNh:`0eFa0cNoNg:a0oE^OEQ1EPOg:`0PF_OEP1BSOh:7oDKl1k0ZNVOk:4oDIo1l0TNZOn:1PEGP2n0oM\\OQ;LoDKU2X1m8mNPEIS2Y1n8mNXGP1j8POWGn0j8QOXGl0k8QOXGm0^;O2M3N2I6K6IVlZ3KRTeL3M300O010O1O100O100O10O0100O1O100OO20O100O1O010O100O100O1O101N2O1N2O1N3M100O100O100O100O1O100O10OO001N2O1O2N2N2M3N2WN_NQFc1o9^NnEd1R:^NjEd1V:hN\\EZ1e:jNSEY1j:POnDR1T;QOaDU1_;V111M2O2N0001O00O11O0000000000000001O1O00000O110M2001N2O001M200O1O100O001O2OO10000000000000010ON22N00O11OM30000O1O11O0001O0O12N000000O11O00O10O011LkL[DV3d;kL[DU3e;21001OO101O00OO30N12L21OO1002NO1010O020L12O11NN0201O0fH", "size": [480, 640]}, "area": 35086.0, "iscrowd": 0, "image_id": 10393, "bbox": [0.0, 230.0, 640.0, 151.0], "category_id": 140, "id": 10012709}, {"segmentation": {"counts": "h;X3h;000001O00O100O1001O001O000000000000O100000000O100001O00O10000001O1O1O1O1O0000001O00000000O1000000O1O1O1O1O1N200000000NfL^DZ3^;fLcD]3];cLbD^3^;2O10000000000O100000000000000000000000000000000O10HeDkL[;o2QEkLo:o2WEQMi:i2j0J6N2N2N2O1N2N2N2N2O1N2N2N2N2N2O1N2N200O1O001O1O1O1O1O1O100O1O1O1O2N1O1O1O1O100O1O1O001O1O1O1O10000O100O10O10O100O10000O10000O100O10N[OiAe0W>[OiAd0X>\\OhAd0X>110002M100000000O0100O1O1O1001N1000000000001N10N22N1O001O0O101O00001O00001O001O00001O00001O00001IYA3g>MZA2[>MnA0H2f>N[A1Z>OlA0K0Y>1QBOP>0PB0P>0PB0`>0Pn00]dk02`]SO3M2O2M3M2O2N2N1N3N2N1O1O1O1O1O1O1O1O1O1O0O2O1O1O001O1O1O001O1O1O001O1N2O1O001O1O1O001O1O1O1O1O2M2O1O1O1O1O1O1O1O1O1O1O1001O0001O00000O1000000000000O1000000000001N1000000000000O100000001O1O0O2O13L4M4L4KZCnMb<n1\\CRNj<j1TCVNQ=f1nBZNX=b1fB^N`=\\1`BdNj=T1:TO\\BH^>0000000O1000000000000000000000000000000000000O10000000000000000\\\\17PcN9F;F:2N4L4K5L4L4L4CaA3d>L_ANe>2500O10000O1000m00nN4L5K1014O0LjZ8J^eG2O2N2N1N3N2N1O2N1O2N2N1N3N1O2N2N1O2L3O2N2N1O2M2O2N2N1O2N00O1O2N2O1N2N2O2M1O100O1O10000O100000000O01N2O1O1O01000000O100000000O100000002M2O2N00000O1000000O100000000O101O1O000O10001O0000O10000N201N1O100O1O1O100000001O0000000001ON22N01N11OM30000O1O11O0001O0O12N000000O11O00O2O0O11O10O0O1010OO2O00001M210O0N30OO1011NO2O01O011NO2O02OM20010O1^E", "size": [480, 640]}, "area": 26007.0, "iscrowd": 0, "image_id": 10393, "bbox": [0.0, 333.0, 640.0, 147.0], "category_id": 149, "id": 10012710}, {"segmentation": {"counts": "0P3P<N20000001OO10000O1L41O002NGoC`MT<]2oCcMQ<\\2PDbMJ0V<^2:O10000000000000000000000000000000000O11OO100000000000000000000000000O11OO10000000000000000000000000000000000O10000000000000000000000000000000000O1000000000000000000000000000000000000000000O1000000000000000000000000000000000000000000MZClMf<T2[CkMe<U230000000000O100000000000000000000O100K500O100O10000O10000O10000O1000000O10000000000O1000000O100000000O100000000O10000000000O1001O1O00L41O1OO11OO1O1000000000000O10000000000O1000000000000000mBaNe<_1[CaNF0k<^1`CbNE1j<]1`CcND3k<Z1aCjN^<V1_CnN`<R1\\CSOc<m0YCXOf<b1O0000000000000000O1E\\CZNd<`1dC_N[<_1iC_NW<_1mC_NS<_1h0N200O1O1O1O1O1O1O1O100O1O1O1O100O100O10000O10000O1000000000000000000001O000000000000001O00001O00001O001O00001O1O1O1O1O1O1O1O2N2N1O000000000000000000001O00001O000000001O000lBeNc<l100000000000000000000000000000000000000000001OO1000000000000000000000000000000000000005KO100O100001O1O001O001O001O1OI_CoMa<Q2700000000000000N21O0000000000000000B[CaNe<Z1c00000000000O11OO100000000001O1O00001O3MO10000000000NhB\\NX=b1jB^NV=b1jB^NV=b1jB^NV=b140000O10000000000000eBbNT=^1jBcNW=d1OO1O1ORCaN]<^1cCbN^<^1XCkNi<g1O2N001O1O00O11OO10000O1O100001O00B]CYNM1f<e1aCWNe<h1:00O100O1GhBcN02[=V1lBjNT=U1<0000000000000000000000O11O0000000YBnN`=Z1O000000000000000000000000O1000000000000000000000000000000000000O1000000F`BnN20^=S19O100000000001OO11O00O11O00000000000000000000000000O1001O000000O10000001O00000000000000000000000000000000O100L44L000000000000000000c0]ON20000LgBaNY=_1gBaNY=_1gB`NZ=`131OO10000TOfBDL4^=NfBGL0>=Q=G\\COVO3`>KaAOO2ZQ6", "size": [480, 640]}, "area": 36137.0, "iscrowd": 0, "image_id": 10393, "bbox": [0.0, 0.0, 628.0, 96.0], "category_id": 157, "id": 10012711}, {"segmentation": {"counts": "mVk81m>2000O10\\BOT<0fB1S1OjN130T=6lBIi00UO;U=9\\C^O_O:U<JnCJ=d0H\\OMc0m;V1mC\\N0K2e0Q<W2O0LUMSDl2l;TMTDm2k;SMTDo26RM[;R3:OO10000TOPMZEL4U3W:oL^EL0>=e2j9oLmEi0OVO3Q3Q:SNjEmNOO2Q3U:d101O1O1O1O00000000000000", "size": [480, 640]}, "area": 3554.0, "iscrowd": 0, "image_id": 10393, "bbox": [604.0, 0.0, 36.0, 159.0], "category_id": 169, "id": 10012712}, {"segmentation": {"counts": "h5X2h<00O11O1OO010000000O1001O000O10010N1N110O10H^CPNd<o171O14NM2N2N00000000000O100000000000O10000O100000000000000000000O11OO1001OO100001O0000O100000O2O0000O100000000000000O1001OO11O00000000000000O1000000000000000000000O1000000000000000000000000000O11O0000O11O00000000O100000000000E;M3002N9G0001O0000N21O00O100001OO1000000000000000000O1O1GWCXNl<h16N2000O01M33M000000O0100AVNhCj1g<001O1O1O001O00O12N00O1O100O10000O100O100O1O1O1N2O0O2O1000000000000O1O100O1O10000O1dNdBS1e=O102KSBROR>j06L2M2O1N2N2O00OO4H7L^k11cTN7J6J3M2NO11N101O010jA[On=Q1KO10O010O1000001OO1O1N2N2N101N2O1O2N2O2M2N3M2N2O0O1O2N00100O2N1O010Occc10_\\\\N2O0O02NO0WY30lWL1k>0\\R[10cmdN3O2H7L4L4M3[Oe0O2N1O1O1O1O1O2N1O1O1O1O1O2N1O1O1O1N2O2N1O1O00000O110OM32N001OO10000010O00O110O001OL400000000000000000O11ON2001O1N101O0000000000O1001OO1000000000000001O000002NN200O100000000000001O00000000000000000000O100M^ChMb<X2_CgMa<Y230O100003NL30O11OO12N0000000000001O002MO21O1O00N2000UK", "size": [480, 640]}, "area": 25764.0, "iscrowd": 0, "image_id": 10393, "bbox": [0.0, 155.0, 640.0, 102.0], "category_id": 176, "id": 10012713}, {"segmentation": {"counts": "S5d0]>O0000000000000000001N8I2M4JnS5Z1kjJ0000000000000000000000000000000000000001O00000O1000000000000000000000000000000000000000000000000000000000000000000O10000000[KiNaKW1Y4UObKj0X4\\OhKd0R4BnK>l3HTL8j3JVL6h3LXL4f3NZL2e3O[L1c31]LOa33_LM_35aLK]37cLI\\38dLHZ3:fLFX3<hLDV3>jLBT3`0lL@R3b0nL^OQ3c0oL]Oo2e0QM[Om2g0SMYOm2g0SMYOl2h0TMXOk2i0UMWOj2j0VMVOi2k0WMUOh2l0XMTOg2m0YMSOf2n0ZMQOf2P1ZMPOe2Q1\\MnNd2R1^MlNa2U1aMiN^2X1dMfN[2[1gMcNX2^1jM`NU2a1mM]NR2d1PNZNo1g1RNXNm1i1TNVNk1k1VNTNj1l1WNSNh1n1XNRNg1o1ZNPNe1Q2\\NnMc1S2d51O1O1O100nHdM0EYLg2e3fM0FYLe2d3hM2EYLc2d3iM1FZLb2e3hM0HZL`2e3iMOI\\L^2e3iMNI]L_2e3hMLJ`L^2d3hMJKbL^2d3gMFOfLZ2d3gMB2kLW2c3gM^O5oLU2c3fMVN@lMh0<R2b3fMVNH`Md0h0n1b3gMUNOUM?T1l1b3fMUNY1ZNQ1a3fMTNY1[NR1a3eMTNW1]NT1_3eMTNU1_NV1\\3fMUNR1`NY1[3eMUNm0eN^1V3eMTNj0iNb1S3dMTNe0nNg1n2dMTNa0ROk1j2dMTN=UOP2g2cMTN>TOo1h2cMTN?SOn1i2bMUNa0QOm1j2bMUNb0POl1k2bMUNb0oNm1l2aMUNc0nNl1m2aMUNd0mNk1n2`MXNd0iNl1o2`MYNn0]Nb1Z3`M[NY1nMW1h3_M\\N]1gMT1o3]M\\N^1cMV1R4[M^N]1_MX1T4ZM`N]1ZMY1V4YMeN[1TM\\1W4YMhNY1PM^1X4YMjNX1mL_1Z4XMiN[1kL]1]4UMkN^1fL^1a4PMkNc1cL]1j8cNUG]1k8eNSG[1n8eNQG[1T8lLUHi1G[1S8oLTHh0CG6b2S8QMSHd0FI2c2T8RMSHa0II0d2T8TMRH=KKOd2S8WMQH9OLLd2T8YMPH52MJe2S8\\MnG27LHf2S8]MkG2;LFe2S8`MhG0a0JDf2S8bMdGOg0HBg2R8]N^HlN_Og2R8\\NaHmN]Og2R8[NcHnNZOg2T8YNdHoNXOh2U8WNeHPOVOi2V8VNdHROUOh2X8TNeHSOSOi2X8TNfHROROj2Y8SNfHSOPOj2[8QNfHUOoNj2\\8PNfHUOnNk2]8oMfHVOlNk2_8mMgHWOjNl2a8lMdHXOkNl2d8jMaHZOjNl2e8lM`HWOkNm2d8nM`HVOkNl2d8PN`HTOlNl2c8SN_HQOnNl2b8_MYG0V1FnNk2b8`M[GNU1GnNk2a8aM\\GNU1EnNl2`8bM^GMS1FnNk2`8cM`GLQ1FoNk2_8eM`GKQ1EPOk2^8gMaGHQ1GoNj2^8hMbGHQ1EoNk2]8jMcGFP1FoNj2^8jMcGGo0DQOk2\\8lMcGEP1ATOn2X8gNcHXNXOQ3S8jNdHRN\\OT3o7lNRIT1l6oNRIR1m6oNRIR1l6QOQIQ1n6QOPIP1o6ROnHP1Q7QOnHP1Q7ROmHo0Q7TOlHn0R7UOlHl0R7WOgHo0W7SOdHR1Z7QO_HU1_7W3N2N2O1]JeH_3]7[LkHa3W7]LmH_3U7_LoH]3S7aLPI\\3R7bLPI\\3R7dLoHY3S7fLQIT3Z1^K_3]1^Ki2X1jK[3\\1bKc2U1RLY3Z1dKa2T1ULY3Y1eK_2S1YLX3W1gK^2Q1[LY3V1hK\\2P1_LX3T1kKY2n0cLX3T1kKV2n0fLX3S1lKU2l0iLX3Q1nKS2k0mLW3o0PLR2i0PMW3m0RLQ2g0TMV3k0ULn1e0YMU3h0XLn1c0[MT3g0ZLl1b0^MT3e0\\Lk1`0bMS3c0`Lg1=hMR3a0eLb19oMQ3>jL`15RN]N^Oa3P1nM^14VNXN@f3l0oM\\13ZNSNCj3f0SNZ11^NmMGn3a0UNX10aNiMKQ4<WNW1OcNdMNV48XNU1OfN^M1[44YNS1OInMVNR3n0SOP1MMmMWNR3l0UOo0LNlMZNQ3i0XOm0L0kM\\No2g0ZOk0M3iM]No2e0\\Oi0M5gM`Nn2b0@e0M9dMcNm2`0C`0N=bMdNl2?E=Na0aMeNj2=J8Mf0_MgNh2;N5Li0^MhNg2:02Lm0]MiNe283OMP1[MjNd274MOR1YMlNb257JOV1XMlNa258F0Z1WMlN`249D1]1UMmN`22;A1a1TMmN_22<]O2e1SMlN_22<\\ONk1WMhN^21>YONo1VMhN]21?UOOS2UMhN\\20c0nNO[2RMgN\\21Q4Y1cIgN[20Q4Z1dIgNZ2OQ4[1eIgNY2OP4[1gIfNY20n3[1iIfNX20m3[1kIfNW20l3[1lIgNW2Ok3[1nIfNW20i3[1PJfNV20h3[1RJfNU20g3[1TJfNT22d3Y1XJeNT24a3X1[JeNS25_3W1^JeNR28[3T1cJeNQ2;W3Q1hJeNP2>S3n0mJdNP2a0o2l0QKdNo1a0n2l0SKdNn1a0m2l0TKeNn1?m2m0UKdNn1a0j2m0WKcNn1c0g2k0[KcNm1e0c2j0`KbNl1f0a2i0cKbNk1g0_2h0fKbNj1c0b2l0dKcNh1a0c2m0eKcNg1`0c2n0fKcNf1?c2o0gKcNe1>c2P1hKcNd1=c2Q1iKcNc1<c2R1iKdNc1:c2S1jKdNb19c2T1kKdNa18c2U1lKdN`17c2V1mKdN_16c2W1nKdN^16b2W1QLcN\\17a2W1TLbNZ18`2W1XL`NW1:_2V1\\L`NT1;^2T1aLbNo0;^2R1fLcNk0<]2P1kLdNg0=\\2o0nLeNe0=[2m0SMfNa0>Z2k0XMgN==[2l0ZMgN:=[2k0^MhN6=]2h0`MkN2<`2g0`MmNO<b2e0`MPOM:e2c0aMSOI:g2a0bMUOF:i2?cMWOC9l2>cMYO@9n2<dM[O]O9P3:eM]OZO8S39dMAWO6V37eMCTO6W36gMDQO5Y36hMEnN5[34iMGkN4]34jMHhN4_32kMJeN4`31lMLcN2c30lMN`N2d3OnMO]N1g3NnM1ZN1h3NoM1XN0j3NPN3TNOl3MRN5PNMo3MSN6mMMQ4KSN:jMJT4JUN=eMIV4I[N;]MKY4J_N7WMOZ4IfN4nL2_4GjN3eL6b4EPO0]L:e4DUONTL>h4BZOMlKa0j4AAIdKg0k4^OHE]Kn0j4\\O4h1MVN>dNVKT2\\4VOX1j0hNVOX1k0hNSOY1m0hNQOY1P1gNoNY1R1gNlNZ1T1gNkNY1V1hNgNY1Y1jNdNV1]1iNbNX1^1iNaNW1`1iN^NX1b1hN^NW1d1iNZNX1g1hNXNX1h1hNWNY1j1gNUNY1k1iNSNW1n1jNoMW1Q2kNmMU1T2jNkMW1U2iNkMW1V2gNjMZ1V2gNhMZ1Y2jN`MX1`2d51O001O001O0012NO0@YMgDh2j:cMYE]2Y:mMkET2f9WN]Fj1[9ZNhFf1Y9VNjFk1V9QNmFo1T9lMPGU2P9gMSGZ2m8bMVG^2k8^MXGc2h8PMoE3]1m2e8oLQFN]1T3b8nLbGS3^8mLaGS3`8lL`GT3a8lL\\GV3e8jLVG[3j8dLRG`3o8`LlFd3U9\\LfFi3T:4M3L5K4L4[MXLnIm3P6PLSJQ4m5kKXJT4i5gK\\JY4e5bK^J^4S8O10000OjL`KmK`4Z700O1000000O10000VL`ES3a:jL`EV3n6[L_L<eLZ3h6_LcL4fL^3b6WLYI:`3MgLc3^6eLjLEjLf3\\6fLkLAjLj3Y6hLXMY3g2iLWMX3g2jLXMV3h2lLVMU3h2mLWMT3g2oLWMR3h2PMVMP3i2RMVMo2i2SMUMn2i2TMVMl2j2VMTMk2j2XMTMi2k2XMTMi2j2ZMTMf2k2]MSMd2l2]MSMd2k2_MSMb2l2_MRMb2m2aMQM`2n2bMPM_2n2cMQM^2m2eMQM[2o2fMPM[2n2hMPMY2o2gMQMZ2SOaKP2U2nNZ2POdKQ2Q2PO\\2mNfKP2n1TO]2hNiKR2k1VO]2cNmKV2e1XO^2_NQLW2a1[O_2ZNTLZ2]1\\O`2eMcKJc0S3Z1_O`2aMmLn2b0Bb2\\MPMP3?Db2XMSMR3;Gc2SMVMU36Id45]KKd44\\KLf41ZK0g4OYK1lMQMj6l2[K5f4I[K7f4HZK8g4FYK;h4CYK=i4_OYKa0h4\\OZKd0g4YO[K9cMhMS7l1[K;bMkMT7f1]K=`MnMT7b1^K?]MQNW7\\1^KV1c4gN^KZ1c4cN_K]1b4`N`K`1a4\\NbKd1h4PNZKP2l4hMUKY2P5`MRK`2T5XMnJh2e80VIkLi1U3WNkLh1V3XNjLh1V3WNkLi1V3VNjLj1V3VNjLj1V3UNkLj1V3VNjLj1V3UNkLk1U3UNkLk1U3UNkLj1V3TNlLl1T3SNmLm1S3RNnLn1R3QNoLo1Q3oMPMQ2Q3nMPMR2P3mMQMS2o2mMQMS2o2lMRMT2n2lMRMS2o2lMRMT2n2kMSMU2m2kMSMU2m2kMSMU2m2kMSMU2m2kMSMU2m2kMSMU2m2jMTMV2l2jMTMV2l2jMTMW2k2iMUMW2l2hMTMX2l2hMTMX2l2hMTMX2l2hMTMX2l2gMUMY2k2gMUMY2k2gMUMY2k2gMTMZ2l2fMTM[2j2fMVMZ2j2fMVMZ2j2fMVMZ2j2fMVMZ2j2fMVMZ2j2fMVMZ2j2fMVMZ2j2fMVMZ2j2fMVM[2i2dMXM\\2h2dMXM\\2h2dMXM\\2h2dMXM\\2h2dMXM\\2h2dMXM\\2h2dMXM\\2h2dMXM\\2h2dMXM\\2h2dMXM]2g2cMYM]2g2cMYM]2g2cMYM]2g2cMXM^2h2bMXM^2h2cMWM]2i2cMWM]2i2dMVM\\2j2dMVM\\2j2eMUM\\2j2dMVM\\2j2dMVM\\2j2eMUM[2k2eMUM\\2j2eMUM[2k2eMUM[2k2fMTM[2k2eMUM[2k2fMTM[2k2fMTMZ2l2gMSMY2m2hMRMY2m2gMSMY2m2gMSMZ2l2fMTMZ2l2fMTM[2k2fMTMZ2l2fMSM[2m2eMSM\\2l2dMTM\\2l2dMTM]2k2dMTM\\2l2eMSM[2m2fMRM[2m2l401O001O00001O?A]C", "size": [480, 640]}, "area": 121073.0, "iscrowd": 0, "image_id": 10393, "bbox": [0.0, 31.0, 640.0, 449.0], "category_id": 183, "id": 10012714}, {"segmentation": {"counts": "S\\W7l2P<O1OjD", "size": [476, 500]}, "area": 273.0, "iscrowd": 0, "image_id": 10395, "bbox": [497.0, 359.0, 3.0, 92.0], "category_id": 93, "id": 10012715}, {"segmentation": {"counts": "W6e8Q9VM_5aJ00001O0000000000000000000000000000000000000000000000001O000000000000000000000000000000000000000000000000001O0000000000000000000000000000000000000000000000001O000000000000000000000000000000000000000000000000001O0000000000000000000000000000000000000000000000000000000000000000000000000000O10000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000O10000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000@`0O1O1", "size": [476, 500]}, "area": 4798.0, "iscrowd": 0, "image_id": 10395, "bbox": [0.0, 199.0, 500.0, 277.0], "category_id": 141, "id": 10012716}, {"segmentation": {"counts": "Pd0m3n:`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`0@000000O100000000O10000000000O100000000O10000000000O100000000O100000000O10000000000O100000000O10000000O10O100000000O100000000O10000000000O100000000O100000000O10000000000O100000000O10000000000O100000000O100000000O10000000000O100000000O10000000000O100000000O100000000O1000000000jNQ[1", "size": [476, 500]}, "area": 176138.0, "iscrowd": 0, "image_id": 10395, "bbox": [1.0, 66.0, 496.0, 402.0], "category_id": 183, "id": 10012717}, {"segmentation": {"counts": "k=T1lb0O1N23M001O1O1OO11O00O1O1001O1NN210O101M2002N00000001O1OO2O002OO11N00O01O2O2N01N1000000O11O1O1O0000O12M2O010O00O10011NO10O1O100000O110LfN`]OY1`b04O2N10001N11O0_Nc]O_1]b0aNc]O_1_b00O2O`Nc]O]1]b0bNd]O^1_b01O000O1001O002N1O010M^]OfNab0[1210OPNeN[A[1d>gN[AY1`>eNf_O2NNh1\\1a>dNg_O:h1R1`>eNg_O9j1R1`>QO^AP1c>POe_ODd1\\1f>SOYAo0h>POXAP1T>bNg@`0U1@]N]1`?bNc@2;0C:f1DYN[1^?iNc@Ob08V1Q1c=iNd@O>MB=i1m0c=nNRAJD8h1o0`=QOh@E85H6g1o0a=QOh@E94F8h1o0a=UOk@6f1e0\\=4dBK\\=6dBK[=5eBK[=5eBK[=6bBL]=1k@cNi1\\1\\=LjB5W=HgBjNTN`1U?DiB?V=BjBa0S=_OmBa0T=^OkBc0U=\\OmBc0Q=_OoB`0S=_OmBb0n<BPC`0P=@PChNRNe1o>BRC`0l<@TC`0l<_OUCa0k<_OUCa0k<^OVCb0k<\\OXCb0k<[OUCf0d=`N]Ba1`=^NbBc1_=[NaBd1mMZN[?O_@0Y2f1lM\\Ni01a=MlCh1fM\\Nm0Oa=MlCi1eM]Nj00c=KnCP2_NXN_=GSDR2eMnMe0;b=ETDQ2fMoMa00^O;V>FiAOZ2U2YNkM]O<V>FjANZ2W2SNjMD>2Am=4UCLOW2YO[NFAh=3UBJn00TO1Q1V2[O[NG_Of=8QCITO0S1V2YO]NE^Oi=6cBHC03006P1o1\\OeN0UOM0]<;cCIELO010O8S1l1]OgNLYO0MD1d<;cCOINT1j1\\OiNCTOMO3475V<6`CA1k0S1W1ZOZOCgN96MGo;b1_D^N[O28k16LEWNM;1FO2U<_1aD^N[O25Y2J@0UN000e00[OR<c1bDiN\\Ol30jK0f0P<n0RDZNN21NOj5o;U2O2N1N2O2M3N1N2O1N2O1O1N2N2O1N2O1N2O2N0O1O3N003M1N3N2M4L101N3N3L2OO03NO11N3N1N101O1M3O1N2OO0101O000O2N2O002N1M3O1O0O2N3N1M3N2O1NbNSGmGl8T8VG_F2T1g8]8`GbG_8_8cG_G\\8b8dG^GZ8e8eG[GY8f8iGYGW8e8jG\\GS8g8nGXGQ8j8nG\\FD>\\8l8bGhFa0KB`0Z8k7cG_H2ZO=I@44>X8Y7mG[HGJ6125HJ1O63:0BM0d0W8Z7kJ_HkLCX8P8RKXHo4i7TKTHk4m7UKSHi4o7ZKnGd4T8\\KlGc4U8]KiGc4Y8a31N200N2N2dNaGUGa8k8`GSGa8l8bGoFa8k8ZGYF6a0N]Od8h9ZGVFN17?IB4Nf8i9YGWFN08<l8]9n012N1O1O1O1O1O1O^G_Fh6_9VIeFi6Z9TIkFk6V9RImFm6P9UIRGj6m8VIUGi6j8VIYGi6f8WI\\Gh6c8YI^G`6g8`I[G^6e8bI]G[6d8fI]GX6c8hI_GV6a8kI`GR6a8nIaGP6_8QJbGm5^8TJcGj5]8VJeGh5[8YJeGg5[8YJdGg5\\8ZJdGe5\\8\\JdGc5]8]JcGb5]8_JcGa5X8eHeFk1R1`5[8cJeG\\5_8aJaG^5`8bJ`G]5a8dJ]G]5d8bJ[G^5g8bJWG_5i8bJUG^5m8aJSG_5m8bJQG^5Q9bJmF_5T9aJjF_5Y9`JeFa5\\9`JaF`5a9`JZFd5g9_JoEf5T:R21O1N2O1O2`FcEX9f:M00N21O1N2O2N2N1N3N1kIoDXM000k5T;eLYE_MCh5V;fLZEZ3g:eLYE[3j:`LXE`3i:^LXEb3j:[LWEg3i:_JlDS1<_4h:]JmDk0FSOe0e5j:ZJmDk0JRO?h5m:YJjDm0KPO`0g5o:ZJgDm0d0i4U;VKlDj4U;UKkDk4U;TKlDl4U;SKkDm4V;QKkDo4U;aJgC00KV1NkNO0_5Z<dJjC2LN`11cNm4Y<PKkC0K0`10eNn4g0SKf:N_D1m1k4jNZKP;JZFi4fN^Kb2IX7g4UF`Kc2IX7g4UF`Kc2IdL0a:h4WF_Kd2MT7e4RFcKj2IS7f4PFbKl2IT7f4nEbKk2KW7h4`EoJ7b0L_O61Y28m7e6nE[IEFU1:X9f6aE\\J2dN==o9c6bE\\J1fN8NI>\\:W8cE\\G02O<_:U8bEcGO9_::aEm61RI_:OaEk7\\:XHcEZO0X8k:gGWEM0O5]8Z;N3O0O100000O11OO10002N00000000000O001OO020OO30O1N2O1O1hK^D0c;G[DVL41MP4g;WNXDWO6aNM0Mn3j;VNXDUNOH00Oi0=VOCn3j;RN\\DXN0HLh0>\\O@c3e;_NaDQN5KE315a0I_O90EO00T3a;ROkDjM41E6=K^O10M11:P3V;SOiEmMhN0`0OA0MO1011:Q3T;SOPFlMZOOVO050:Q3R;QOUGoMjMP3o:ROjGm0W8SOjGk0V8VOjGj0W8UOiGk0W8UOiGl0V8TOjGk0W8TOkGk0U8UOmGi0T8UOlGk0U8UOlGh0V8VO[DSN^3e2Y8ROYDXNf3>[Lg1g;^OSH]NUL95S1KSO0Ei;h0ZDFj3_OSL47S1MUOMEj;<UDM5GL:m3DRL00491G2000460MEj;3TD41JN55LK:S4DnK0:1E0053MOMO30N:4i;LnC2OOe0NZO9j4GWK3b0MK1m;OeD8P4Ic9O]A5lc0KT\\O0j[8", "size": [640, 427]}, "area": 79352.0, "iscrowd": 0, "image_id": 10400, "bbox": [0.0, 132.0, 414.0, 487.0], "category_id": 96, "id": 10012718}, {"segmentation": {"counts": "W[f541Mjc04O1O3JI^\\O7bc042O0ON21O1O0000O1000000000O5M2M2M2OcTV2", "size": [640, 427]}, "area": 217.0, "iscrowd": 0, "image_id": 10400, "bbox": [291.0, 481.0, 23.0, 14.0], "category_id": 103, "id": 10012719}, {"segmentation": {"counts": "cm[41oc02S\\O2cc07O2N01OO2M1110Oh\\OD`b0Nb]Oa0KB_a0M[_O2ZO=I@44h`05Z_OGJ6125HJ1O63:0BM0n`0R3K5K3M003M000b_OaL[`0b30000000O10000O100000O0O210O000000000010O0001OO11O001OO4M0O100O1O1O1O1000000000000001O1O1O1O0N20000002NO1001O0000000O11O00001O1O000O11O00O100O1O100L46J00000O1O1O1N2O10000001O0O100000000M4OO3OO01Lb_OdL^`0\\3b_OdL^`0\\3b_OdL^`0\\3300O1M7jLX_O61Y2ha0kM`^OEFU1ka0iNa_O2dN=ma0A__O1fN8NIcc0O^\\O02Oac01c\\OO]c01QR]1", "size": [640, 427]}, "area": 13034.0, "iscrowd": 0, "image_id": 10400, "bbox": [223.0, 488.0, 131.0, 122.0], "category_id": 112, "id": 10012720}, {"segmentation": {"counts": "lSo14mc0OO1L40000M31O001O00NH]\\O7bc041ONF^\\O:bc021O1OKHa\\O9_c04O1O10000001O00O1N2003MN@d\\O`0\\c021O1O002N1O0C_\\O;cc0E]\\O9ec00O1O100001O000000O11O0000O1EEn\\O0K;Rc0KR]O8nb0In\\OJO=Qc0Bo\\O809Pc0=001O00001O1O0000000000000000000000001OO1O11OO12N001O1O2NO1001O2N00N21ON2001O00O10000000000N2O100O1O10000O11O000000O1001O0000000000000000000000000000000000NTOP]Ol0Pc02000000000000000000O10000O1000000O1001O00000000001O00001OO11O001OO100O100O1O1O1O1000000000000001O1O1O1O000000000000000000000000O11O00001O1O0000000000000000000000000000O1O1O1N2O10000001O0000000000001OO11O0000001O1O1O00001O001O00001O00000000N200O1000000000000002N000000000000O100N200O11O00O1000000N20000O13M001O1O1OO1O100001OO10000O1N2N21O2NO13M002N0000O10000N2O100N20000000000O11O", "size": [640, 427]}, "area": 9079.0, "iscrowd": 0, "image_id": 10400, "bbox": [100.0, 603.0, 327.0, 37.0], "category_id": 140, "id": 10012721}, {"segmentation": {"counts": "X:[1eb0O13M1O0O2OO01ObNe]OX1\\b0iNd]OV1\\b0hNf]OY1`b010O0NhEjNj10jER1Z8VOg1GoER1Z8XOj1h0VNYOj1IlEf02VO50d7>V2GPFNO?1_O03NNj7<X2C\\FQ1[7[OY2FYFP1_7ZOW2F\\FP1\\7ZOj2e0WM\\Oh2e0WM\\Og2f0XM\\Oh2a0YM@f2?[MAd2a0[M^Of2a0[M_Oe2a0[M^Ok2>TMCi2>XMDf2<ZMFd2[ObEm0j7Fi2]O_Ef0j7MZH]OY:f0]MMYH^O[:d0\\M;`2D`M<e2_O[Ma0d2A[M>g2AYM?g2AYM?f2BZM=f2DZM=d2E[M;f2DZM<f2C[M`0a2C]M<c2E]M9_2SOjEd0g78_2TOkEc0g78\\2SOkE21c0i78[2SOkE3O:T8`0lGSOT:0lE233X8j0i1SOPF4W8j0g1TOPF2Z8k0e1SOQF4X8j0g1ROoE4[8i0f1TOoE3[8h0f1@ZN>h1AYN?f1BZN`0f1_OYN?g1BZN?f1BXN>i1AWNa0g1_OYN`0a1QOYF>W8`0hGQOM0:0n83iF=8BI0g7T1WHkN61R9d0iFZO00O00124e7T1YHnNY9?hF[OO10004g7_1^1ZOkFXO0Ih7e1]1CeN<_1B`N?_1AaN>a1B^N`0`1@`N`0b1]O_Nc0`1^O`Nd0a1YO_Ne0\\H[No8P1eNf0\\HYNP9o0eNh0[H[NKOQ9l0jNj0VHZNO5Q9h0iNm0VHZNR9i0hNT1W1kNkNU1U1kNkNV1S1lNlNU1R1kNoNU1WHUNNG[8o0@f1`0ZN@e1a0[N_Of1?[NAe1`0ZN@f1b0YN]Oh1`0XNBh1>UNeGVOm7d2>VNgGVOj7e2=QNgG\\O2Nj7e2>mMfG^OV8e27iMdGAV8f26jMLW23jMLV24iMMW22kMMU24gMOZ20fMgGCT8e28mMGU27fMeGBV8h23fMiGDR8f24ZMgG85Ho7f25ZMgG94FQ8h29\\M2h2a81O6S_OlLb`0Z3O0]_OfL``0]3Q@cL[?^3d@cL@0g?^3Z@aLN180J0N0e?_3\\@cL4ML2O3e?\\3[@cL2NN7f?^3^@eLLEb?h3^@WL0?b?n3O00N200Ob@fKX?[4d@eK02Z?Y4e@jK\\?^4f@^KT?d4j@]KU?g401O0aFWKg3i4XLRLn2n3QMRLP3m3nLXK`Jl0`8l3oLZLP3e3QM[Lo2b3UM]Lk2c3oL\\KcJQ1_8b3kL\\KfJ1NR1b8b3fL[KhJ10O0V1b8`3dLgL]3Y3_LjLb3V3^LjLb3V3[LkLg3U3[LiLe3Z3VLgLk3Y3ULfLl3\\3nKhLR4Y3mKfLT4[3iKfLX4]3eKbL\\4_3UGPK[4OfK_1j8e3cKZL^4f3TGlJ]4[1a4k3]KVLd4j3\\KkJgKU1n8P4aKmKa4T4ZKoKg4R4XKlKj4T4PGhJn3T1S5V4jFfJ11R4P1U5[4hFfJl1O8o0U7`4oGdJUOOg1j0W7b4oGcJmNOL2d1MfNk0o8c4lGiJmNIN3c10\\N00j0[9c4bGaJQO=2BN013e12[NMOk0_9c4iFaJ65C80FOO1Mb15]Nk0a9_4dFfJ82Di0^17S8X4bFeJ94Eh0NQO00\\1Q1X8\\4^FbJO2?6Bm0Y1MZ8]4[FgJ?\\1l0^O\\8e4iFPLg0\\Oa8d4hFoKf0^Oc8c4WF\\J=e1DZNc00GP1`9j4aFkKD]N9ONO6l0`9m4aFoKJZNM16i0c9o4\\FoKMXNM35h0f9k4^FTLHXNN0ML40Nk0n9m4\\FTLIYNK43I0e0o9P5RFYJ4o1IhNO2S:o4PFXJ10Mo11AS:W4lEYJO12e3U:W2iEjMX:m52O1N2O1N2O1O1N2O1N2O1O1M3O1N2O1N2O1O1O1N2N2O1O1O1M3O1O1N2N2O1M3N2O1N2O1O1O1O1N2N200M3O1N2O1N2N2N2O1O1N2N2O1N2O1N200N2N2dNnBhKT=X4mBfKT=Y4oBbKT=X4gBlJ6a0N]OW=U5gBiJN17?IB4NY=V5fBjJN08<`=i4P1O1O1O1O1O1O1O1O1O1O11OM3O1O1O1OWKcKQJ\\4o5eKQJZ4n5hKRJW4n5jKRJU4n5lKRJS4m5oKSJP4m5QLSJn3m5SLSJl3m5ULSJj3m5WLSJh3n5XLRJh3m5YLSJf3m5[LSJd3m5]LSJc3l5^LTJa3m5_LSJ\\3aKnK\\:g0SJ\\3o5eLQJ^3k5cLUJ]3j5bLXJ^3h5\\LiE]O`4X4e5]L_Jd3`5[LaJe3^5[LcJf3\\5VLlE@i4Z4Z5VLjJk3U5WLiJj3U5XLjJj3T5TLnJm3P5ULoJl3P5ULoJl3o4ULQKm3m4SLSKn3l4QLUKP4i4QLWKP4h4PLXKQ4g4oKYKS4WKWKX9g0aKZ4\\4fKdKZ4\\4jK`KT4b4oK[KR4d4PLZKQ4d4QL[KP4d4PL\\KR4b4mK_KU4_4kKaKV4]4gKgK[4W4kKcKV4\\4jKdKY4Y4gKgK[4W4eKiK\\4V4dKjK_4S4aKmK`4R4`KnKb4R4\\KnKf4hKaJe7h0cLh4gKaJk0Fe5Q1iMj4dKaJk0Jc5l0nMk4bK_Jm0K`5m0QNk4`K^Jm0M`5j0SN[5\\LmI`5g0UN]5ZLlIUOEX6R1YN]5YLRJ]5a0ZN^5XLRJ]5`0[N_5VLTJ]5=]N_5ULVJDYOF0O0_5o0VOd5PLUJ\\OYO4:OHm4l0Df5mKTJ@WO1>NIn4g0Gi5jKVJC0i4MNn5dK^IB?2<g4I1n5gKYIAc0Ob0f4C3Q6VKUId0n1S4lN3S6jKQKT4kN2T6lKoJS4gN6[6fKnJV4dN5^6fKmJV4cN5a6cKmJ]4TNoJ7Q5h6bK\\I@R1Z:d5SFZICS1Z:b5oEaIGAMO1T1\\:l5nEZIEE02<R1Q:m5PFWIFH<T1m9Y5dERJb0F4o0e9Y5fEdIO:c0G]ONg0T1c9X5oEkI\\1e0h8_5nEkI^1KWN01Om:Z6`EgIe<Z6`0O10000O11O0000O1O12NO100A[BXJ21ONd=h5eFXJ^5h5cJXJ\\5h5RJgJWLBf9g5RJnJl5R5TJoJk5Q5UJPKj5P5VJQKi5o4WJRKh5n4XJSKg5T4cFeKf38f5T4cFdKg39e5S4dFdKg3:d5n3dFSK011c0g3<c5l3mFgK`3NZLKX9`4oFfK_3O[LKV9_4RGfK]31[LKT9W4iF\\Ka0`0W33[LIT9X4]GkKV31ZLFY9]4XGlKV3Oo5m3VFeK04i0LYO100o36k5S4UFfKO1[54a4U4WFfK\\50_4X4h5002NO11OO11O00N20000O1VNY@SO0aN04e?i1[@_N2FNS10kN3OM3e?i1]@aNOGOf00[O1MQ`0j1Q@`NND000a0OF1M1MNOP`0k1U@^N39HE1L3Ol?i1U@`N138EHMj?k1W@^NOOKI?2h?h1Q@VNION0<191d?e1g_OZN:0P10P?f1f_OYNUb0f13001O0000J60000001O00", "size": [640, 427]}, "area": 71309.0, "iscrowd": 0, "image_id": 10400, "bbox": [0.0, 0.0, 427.0, 495.0], "category_id": 157, "id": 10012722}, {"segmentation": {"counts": "0X:[1PHIQ=6PCIQ=:lBDV==iBFT=:lBFS=<lBES=:nBGP=:PCGn<kNQAX1R2Ln<mNPAV1R2Nm<jNSAY1o1MU=4jBKX=4hBKY=3iBMm2mNU50jER1T22j2TOS5GoER1U24g2TOX5h0PH2k2WOU5IlEf02VO50]1U1[3YOR5GPFNO?1_O03NNc1R1^3ZOQ5C\\FQ1T12_3YOQ5FYFP1Y1N_3\\On4F\\FP1V1N`3\\O`5e0QG1]3[Oa5e0QG0^3\\O_5f0RGN_3^O_5a0SG1^3_O^5?UG2]3_O]5a0VGO]3_O^5a0WGO[3@^5a0VG1[3]Oe5>oF5\\3^Ob5>SG6Y3^Ob5<VG5X3A`5[ObEm0g18U3^Og5]O_Ef0g1?S3^OZK]OY:f0ZG>T3_OXK^O[:d0XG`0U3K_5D[Gc0U3Ie5_OVGj0S3Gf5AVGh0T3Fh5AUGg0T3Hg5AUGg0T3Hf5BVGf0T3Gf5DVGe0T3Hd5EWGb0V3Id5DVGd0U3He5CWGf0S3Jb5CYGd0T3Hc5EYGc0T3F_5SOjEd0c1c0T3E_5TOkEc0c1c0T3E\\5SOkE21c0e1e0Q3C^5SOkE3O:o1o0h2AYKSOT:0lE233S2W1a2C]5SOPF4S2V1_2D\\5TOPF2V2V1^2E[5SOQF4T2S1a2GZ5ROoE4W2S1`2FZ5TOoE3W2T1_2D[5@XHj0]2D]5AVHl0]2C\\5BWHj0^2F[5_OVHj0`2EZ5BWHi0_2FZ5BUHh0a2F[5ATHi0a2HY5_OUHj0b2FS5QOYF>T2k0_2E\\KQOM0:0n83iF=8BI0a1N^NY1g4MXKkN61R9d0iFZO00O00124^1X1X3LXKnNY9?hF[OO10004_1Y1X36^4ZOkFXO0I_1`1X35^4C\\H8V34b4BWH;V34b4AWH<W32d4BUH;W35b4@WHjNaN_1f47d4]OVHmN`N_1f47c4^OYH;T39d4YOWH`0T35`K[No8P1]HPO_N]1e49`KYNP9o0]HQO_N^1d49`K[NKOQ9l0eHb0n28]KZNO5Q9h0dHa0o2<\\KZNR9i0cHa0o2c0]4kNeHc0n2b0]4kNeHc0n2c0[4lNgH`0n2e0Y4kNjH`0m2e0_KUNNG[8o0]I>k2X1h3ZN^I=j2X1i3[N]I>i2X1h3[N`I:UNfNc4e2i3ZN^I<k2Z1i3YN[I>k2Z1g3XNaI=h2[1g3XNaI=h2Z1g3[N`I;i2[1e3\\N`I:k2Z1f3[N_I;k2Z1i3WN]I?f2^1m3TN[I?i2]1k3UNdGROd1\\1l2^1l3VNWI>i2`1o3SNXI=i2`1n3SNYIoN]N]1[4b1h3PMPHS1d1mNYN[1X4f1k3SMnGo0h19_2f1h3SMPHP1i16_2g1h3YNjIO`2g1c3^NlIKa2g1c3^NlIL^2eNjKQ3l7\\NnINZ2n1b3VNTJK\\2o1^3WNVJKY2eNlK_33aLW7`1aJKY2\\2U3iMbJKZ2[2lKZLU7a1bJL^2[2\\LXLo5^1_IcNi1\\1X2eNoKh3a0WL@0[6X1cK5S2a2PLQLN180J0N0Y6U1dKjNTN`1P4a2PLRL4ML2O3Y6o0fK?Q2`2PLQL2NN7Y6l0fKa0n1c2WLSLLEU6T1jKa0n1e2SLeK0?V6e0hKc0o1X3X2TLkKc0n1X3U2WLmK`0o1W3W2XLjKb0m1W3U2[LlK`0m1V3ZLRKg5X1RLhNRNe1l3Z3ULQK02i5U1WL`0l1U3SLYKk5R1VL`0j1YNULT52mJc5U1]La0i1a3ULjJd5T1^La0j1d3h1jK_Lb0j1d3g1hKbLb0g1f3h1gKaLd0h1c3f1jKcLd0f1a3f1jKfLf0c1XNTLU5a5lJ`JP1X2f0mMZN`3OZLS5`5QL`JlNY2f1lM\\Ni01h1o4k2RLjLd0fM\\Nm0Og1m4P3SLfLf0eM]Nj00i1k4l2PKcJS1Y2m0_NXNf1g4l2PKfJ1NS1\\2n0eMnMe0;i1f4i2oJhJ10O0W1\\2j0fMoMa00^O;_2f4f2^LjJaNZ2U2YNkM]O<`2e4b2bLnJ]NZ2W2SNjMD>2AW2Q5h2dLSL[NOW2YO[NFAT2n4h2gLQKXNn00TO1Q1V2[O[NG_OQ2Q5k2gLkKYNTO0S1V2YO]NE^OV2Q5e2fLaKWNC03006P1o1\\OeN0UOM0k0T5e3gL`KYNELO010O8S1l1]OgNLYO0MD1R1T5d3kLaK[NINT1j1\\OiNCTOMO3475d0o4g3jL[KnM1k0S1W1ZOZOCgN96MG30L0KY5a4mMSLjL[O28k16LEWNM;1FO2530Y5Z4fMWLkL[O25Y2J@0UN000e00[O27OJO_50aJ[4OfKS3c0YM\\Ol30jK0g00_O0^5^4_MdKkLN21NOn;0RD]4a?N20^@gK]?_40K5O0jK]@n3f?mKZ@11R4f?mK[@l1O8j?TOY@UOOg1f?VOY@mNOL2d1MfNg?k0`@mNIN3c10\\N00i?m0U@QO=2BN013e12[NMOl?8S@65C80FOO1Mb15]Nm?5S@82Di0^1Z?eNj_O94Eh0NQO00\\1U`0fNj_OO2?6Bm0Y1S?eNn_O?\\1l0l>UOWAg0k>YOTAf0n>jN__O=e1DZNc00Gg`01n@D]N9ONO6d`01TAJZNM16d`0OSAMXNM35a`04VAHXNN0ML40Ne`0:WAIYNK43I0``01^_O4o1IhNOo?3Z_O10Mo11_?Ob^OO12kc0NYZa20lQ^M000bc0=]\\OC^c0`000N2O1O1O10000O20O1O0\\Oh\\O`0]c0O1O2NiNCn]O0d0;\\a0Jm]OLh00UO4Vb03i]OKk0OUO1Yb05f]OLP1O\\a0:l]OC>2ga0?e]OAc0Ola0O_]Od0Zc0002OO010N1OQ]O@bb0=^]OCbb09d]OGAMO1db0:f]OEE02<bb01d]OFH<oa0A`^Ob0F4j1ZOn=1T@O:c0G]ONg0m1ZOo=8[@\\1a1[N]N0`?;a@^1KWN01Om04TOM0O5d?7]@[3d?a0N10000O11N11OO01O12NO100AeKn@21ONZ4R?On@TLR?m3n@RLR?\\3]AWLB;R?]3cA`L^>`3cA^L^>b3cA[L_>e3bAZL^>f3n00WOZLXAf3i>YLWAg3i>YLWAg3e>XLf@011c0g3`4ZLm46bF`3NZLf3NiK41MP:8aF_3O[LT2MjN6aNM0Mm9;dF]31[LP2OgMOH00Oi0=VOCf90ZFa0`0W33[Lj13kM0HLh0>\\O@\\9d0oFV31ZLm12jM5KE315a0I_O90EO00i8e0eGV3O`Li11jM41E6=K^O10M11:]8EaG04i0LYO100o36\\Ln1m0mMhN0`0OA0MO1011:\\8EcGO1[54RKP2S1lMZOOVO050:[8DeG\\50RKR2U2oMjMW8h5kIQMU6o2kIRMV6k2kIUMT6m2kISMV6l2jITMU6m2kISMV6l2iIVMV6j2jIXMR6k2lIUMU6k2kIVMT6j2jIgISN^3R8l2eIfIXNf3>[LY5OiK0aN04Z6^1aM]NUL95S1KSO0E2l2N]M2FNS10kN3OM3Z6i0iIFj3_OSL47S1MUOME2m2O^MOGOf00[O1Mf6<YIM5GL:m3DRL00491G2000460ME4l2L`MND000a0OF1M1MNOf63[I41JN55LK:S4DnK0:1E0053MOMO30N:43f2G^M39HE1L3Ob6O\\I2OOe0NZO9j4GWK3b0MK14h2CbM138EHMe60UJ8P4IhLh2_N`MOOKI?2e7MkG5l6d2]IXMION0<191f7NeG0k6f2UI[M:0P10Q>e2e@ZMVa0e212O2O0N20J60000001N11\\N", "size": [640, 427]}, "area": 99212.0, "iscrowd": 0, "image_id": 10400, "bbox": [0.0, 0.0, 427.0, 640.0], "category_id": 169, "id": 10012723}, {"segmentation": {"counts": "hbT52kc04K5M3N2N2M3N2O1N2N2O1N2O1N2O1O1N1000000000O1000000O101N1O2N2O0O2N2N2N3N1L6Hdm]2", "size": [640, 427]}, "area": 1053.0, "iscrowd": 0, "image_id": 10400, "bbox": [263.0, 197.0, 39.0, 37.0], "category_id": 183, "id": 10012724}, {"segmentation": {"counts": "V3a3^9QN\\GDd8BVH>j7_OYHb0e7\\O^Hd0b7YOaHh0]7WOeHj0Z7SOiHn0V7oNmHQ1R7mNQIS1o6jNTIW1h6jNZIV1e6jN\\IW1c6iN]IW1c6iN]IW1c6jN\\IW1b6jN^IW1V6TOkIl0U6SOkIn0T6ROlIo0S6QOmIP1R6QOmIP1R6POnIn0T6mNbGEZ2]1T6lNeGGW2\\1U6kNfGIU2[1W6jNeGLS2Z1Y6gNgGNP2Z1Z6fNhG0n1Y1Z6fNkG0k1Z1l6fNTIY1n6gNQIX1\\900O100O2N100O100O1O1M3N2M3N2M3M3N2M3O1O1N2O[EJb85_GJb84_GNa80`G1_8NaG2g:0O2O0O100O102M4M0O100O100O2O0O100O102N3L3N1N10000O101N10O0101O0O1`DoNQ;Q1oDPOl3Ni2T1ZInNl3Oj2R1YISOk3Kl2S1WISOl3Kl2S1UIVOm3Hn2d1QM]Nn2f1oL[NQ3e1oL]No2c1PM_NbMHj3j1bN`NbMHl3i1aN_NaMJm3i1aN]N_MMP4f1aN]N_MMP4g1`N\\N_MNP4f1bN\\N^MNP4f1bN\\N^MNo3g1cN[N^MOn3e1eN\\N_LE114:f4c1hN]N\\LF0268f4b1iN^NYLH1178e4`1kN_NWLI1098Q1DP2k1`0`NTLJ10:7c4_1nN`NRLL3N0N28i4a1PO`NQLK4NN055h4b1PO`NPLK40NN84f4c1POjNRLBNN<3\\1H[1l1m0iNQLCOL=4\\1IX1k1o0\\O]LPO]1KS1j1S1[O]LQO]1KQ1j1T1YO`LQO\\1Mn0i1V1YO`LQO^1NZNJT2o1d1XO`LQO_1OVNLU2l1f1XOTLkN47g10RNMV2j1i1VOSLmN57h1:4]1k1UOSLnN66i1:1_1l1SOSLnN87h163b1j1ZO[LnNi125g1f1YOULeN19d4k1UO\\OWLkNc4j1UOMj05UOKk05UOLj04VOLi06VOKi05WOKh07WOJh06XOJh07WOJg06ZOJf05[OL_OgMjM]2h2L_OfMiM\\2j2O\\OgMgMZ2n2O\\OgMeMX2Q32YOfMfMW2R33YOfMcMV2V34WOgMbMT2X36VOfM`MS2\\37UOfM^MR2^39SOfM^Mo1a3;ROeM\\MR2a3:ULaM`24iMP2c3;TLaMa24fMn1h3>PLaMb23eMj1m3b0lKaMc23cMa1W4l0bK`Me22aM`1[4n0`K_Me23_M_1]4P1^K^Mg22^M`1]4P1^K^Mh21\\Ma1_4Q1\\K]Mj20[Ma1`4R1[K]Mk20YM`1b4S1mNmNS1T1ZK\\MW3`1`1T1YK\\MW3_1a1V1WK[MX3_1a1V1WK\\MV3^1d1W1VKZMV3^1e1X1UKZMV3^1e1Y1UKYMT3^1h1Y1aNfN`1[1_NdNb1\\1]NeNc1\\1\\NcNe1]1[NbNf1^1ZNbNf1_1XNaNi1_1WN`Nj1a1UN^Nl1b1SN_Nm1b1RN]No1c1QN\\NP2e1nM\\NR2d1mM\\NT2e1lMYNU2g1kMXNV2i1iMWNW2i1iMVNX2k1gMTNZ2l1fMTNZ2m1eMRN\\2o1cMPN^2P2bMPN^2Q2aMnM`2R2`MmMa2T2^MkMc2U2]MkMc2V2]MhMd2Y2[MfMf2Z2ZMfMf2[2YMdMh2]2WMbMj2^2VMaMk2`2TM`Ml2`2TM_Mm2b2RM]Mo2d2PM\\MP3d2PM[MQ3f2oLXMR3i2mLWMS3i2mLVMT3k2kLTMV3l2jLSMW3n2hLRMX3n2hLQMY3P3gLnLZ3R3fLnLZ3S3eLlL\\3T3eLjL\\3g0jI\\1i2lM^3d0iITO2Z2h2lM^3e0iIUO6V2b2oM`3e0TJ]1]2jMa39gI0<l1l8SNkFO:n1j8RNnFO9n1f4iMkN2oK2:46P2d4jMPO1WL49o1^4nMQONZL0;T2Z4oMQOK[L1;V2W4PNBJXLV2T4PNc0P2[OPNg0Q2VOoMm0Q2POoMR1R2kNmMX1T2eNlM]1U2`NkMb1V2[NiMg1Y2VNgMl1[2PNdMR2_2jMaMW2c2dM]M]2g2^MXMd2k2YMTMh2P3TMnLn2U3^32M2ObNSGjNk8Q1]GlNd8o0bGoN^8l0jGPOX8k0nGROS8i0UHTOj7i0ZHVOg7f0^HYOb7d0bH[O^7c0eH\\O]7?fHA_78dHG]75fHKZ73hHNW7OkH2e2MnN0_N3^2:h1GTN?j1AQNc0P2]OnMe0Q2\\OmMe0T2[OjMg0U2ZOiMh0W2XOgMi0Y2XOeMj0[2VOcMl0\\2UObMl0_2UO^Mm0a2TO^Mm0b2SO\\Mn0e2ROZMo0e2ROYMP1g2POXMP1h2QOVMQ1j2oNUMR1j2oNTMS1j2oNTMR1j2QOUMo0i2TOVMm0f2WOXMj0f2ZOXMg0i18VNHj19TNIj19UNHi1:UNGk1;eMmMVMk1S5:eMnMTMj1V5;cM1]20`M3^20_M2_20VKbMi1_2Q31\\M1b21RK^Mo1e2m2OPK^MR2h2i2MQK\\MV2h2h22WM0h20VM4g2MXM5f2KZM6f2JYM8e2IZM9e2GYM;f2EZM=e2CZM=g2CXM=h2DVM<l2CTM>k2CTM=dNeLh3n2cM>dNfLg3m2cM?fNdLg3l2cMa0eNdLg3l2cMc0cNbLj3k2bMe0cN`Lj3l2aMf0^NXL1:P4h2`MV1_2kN`MV1`2jN_MW1`2jN^MX1b2hN]MY1c2gN\\MZ1c2gN\\MZ1d2fN[M[1d2fNZM\\1f2eNXM\\1g2eNXM\\1h2dNWM]1i2cNUM_1k2aNTM`1l2`NSMa1m2_NRMb1n2^NQMc1o2]NoLe1Q3[NnLf1R3ZNmLg1S3YNlLh1T3XNkLi1U3WNjLj1V3VNiLk1V3VNhLl1X3TNgLm1Y3SNfLn1Z3RNeLo1[3QNdLP2\\3PNcLQ2]3PNaLQ2_3oM`LR2`3nM^LT2b3lM]LU2c3kM\\LV2d3jM[LW2e3iMZLX2f3hMYLY2g3gMXLZ2h3fMVL\\2j3dMUL]2k3\\30000000000001O001O001O00001O001O001O001O000000[OVGTMj8a30001O1O00001O1O001O001O2N1O001O2N001O1O1O1O1O1O1O1O2N1O1O2N1GSK]Hn4`7UK_Hk4^7XKbHg4]7[KcHd4[7_KeHa4X7bKhH_4U7cKkH]4S7eKmH[4R7fKnHZ4Q7gKoHY4P7hKPIX4o6iKQIV4o6kKQIU4n6lKRIS4n6nKRIP4o6QLQIn3o6SLQIl3o6ULQIj3o6WLQIh3o6YLQIg3n6ZLRIe3n6\\LRIc3n6^LRIa3n6`LRI`3n6`LRI_3n6bLRI^3n6bLRI^3m6cLSI\\3n6dLRI]3l6dLTI\\3l6dLTI\\3k6eLUI[3k6eLUI[3k6eLUIZ3k6gLUIY3k6gLUIY3j6hLVIX3j6hLVIX3i6iLWIW3i6iLWIV3i6kLWIU3i6kLWIU3i6kLWIU3i6kLWIU3i6kLWI_2KkLn6f0WI`2LhLm6h0WI`20WLV7Y1jH`2^7`MbHa2]7_McHa2]7_McHa2]7_McHb2\\7^MdHb2\\7^MdHb2\\7^MdHc2[7]MeHc2[7]MeHc2Z7^MfHc2Y7]MgHc2Y7]MgHc2Y7]MgHd2X7\\MhHd2X7\\MhHd2X7\\MhHd2X7\\MhHc2Y7]MgHc2Y7]MgHc2Y7]MgHb2Z7^MfHb2Z7_MeHa2[7_MeH`2\\7`MdH`2\\7`MdH`2\\7`MdH_2]7aMcH_2]7aMcH_2^7`MbH_2_7aMaH_2`7`M`H`2`7`M`H_2b7`M^H`2b7`M^H_2d7`M\\H_2e7aM[H_2f7`MZH_2g7aMYH^2i7aMWH^2j7bMVH^2k7aMUH]2m7cMSH[2P8dMPHY2S8gMmGW2V8hMjGV2X8jMhGT2Z8lMfGR2]8mMcGP2`8PN`Go1b8PN^GP2d8nM\\GR2e8mM[GR2h8lMXGT2i8kMWGU2k8iMUGW2l8hMTGX2n8fMRGZ2o8eMQGZ2R9dMnF\\2S9cMmF]2U9aMkF_2LQMY9>kFc2V9\\MjFm2o8QMQGU3j8jLVGV3l8hLTGX3n8fLRG[3n8dLRG[3Q9cLoF^3Q9aLoF]3LaLW90mF_3[91O1OL42N0000ZNaFK_96hFBX9?PGkN]9V1eFiNY9X1gFfNZ9[1fF`N^9a1bF^N^9b1lFSNU9n1n01O2N8H9Gi0WO4L3M4L3M1O000000001O0000000000000RJSLi2m3WMSLi2m3WMTLh2j3ZMWLe2h3\\MYLc2f3^M[La2d3`M]L_2a3cM_L]2`3dM`LQ2LlJc3T3bLh12TK[3V3bL_19\\KT3V3dLo0f0kKd2X3fLi0j0oK_2Y3hLe0k0RL\\2Z3jL`0m0VLX2\\3kLhLHC0^3X1kLS2^3nLeLJn2X1oLo1_3oLZLI44o2W1TMl1a3UM]LHj2Z1XMh1b3WMYLAL5o2]1[Md1c3[MSLIl2Z1^M_1f3kMjNg0`M\\1i3mMdNi0cMW1l3RN]Ni0gMR1o3WNVNi0kMm0S4ZNoMk0nMh0V4`NgMj0SNc0Z4`M]Kn0S2Q1VN>c4aNUMR1XN:f4dNPMT1ZN6h4gNkLU1]N1k4jNfLW1_NLn4PO^LV1dNGQ5TOYLV1fNFQ5SOZLX1dNEQ5UOYLW1fNEP5UOXLW1hNCR5UOULY1iNC;PN^O0Y4X1QMV1mNB8ZNi3o0QMU1nNB7[Nj3n0PMV1oNA6\\Nj3o0oLU1QO@5]Nk3n0nLV1RO@4]Nk3o0lLU1UO_O3^Nc3@XL]1m0V1UO_O3^Nb3DWLY1m0W1WO^O3^Na3GTLX1P1V1XO^O1^Nb3[1SMj0ZO@N\\NX1^Oi0m1fNj0[O_OGkM3a0[1@ENNo1Ji0^OBIYN\\1ACLOm1Ml0\\OCGVN6Eh0NOJSO5`0O@L6Q2c0j0@7IZMk0LJ4DOFj1i0f0_O7IZMi0NL3]ODL>0g1h0g0@e0b0iLM4YOD0e2f07Ac0b0kLMQ3OBBa0c0mLKQ3NCC?d0mLKQ3MEC=e0oLIP3NDD<e0RMGo20DC;f0RMGP3NED9g0RMHo2MFD9g0RMHo2LHD7i0QMGP3LIC6j0QMGP3LIC6i0RMHo2KKC4j0RMHo2LK", "size": [416, 640]}, "area": 111033.0, "iscrowd": 0, "image_id": 10403, "bbox": [0.0, 0.0, 640.0, 416.0], "category_id": 122, "id": 10012725}, {"segmentation": {"counts": "Qie61n<109F7J00001OO11NOeC_O[<a02100OL42N00000001BL2PD0o;7iCHY<;5O:EURT1", "size": [416, 640]}, "area": 360.0, "iscrowd": 0, "image_id": 10403, "bbox": [526.0, 79.0, 25.0, 35.0], "category_id": 123, "id": 10012726}, {"segmentation": {"counts": "0V3a3X610001N101N2O1O0O101L3O2O0eGULa7k3]H[L_7f3]H_L`7c3]HaLV7k3gHWLZ7i3cHZL\\7g3bHZL^7g3_H\\L`7b401O0O1001O1O00O1O1N2N2N2O1N2N2N200O100O100O100O010O0100O010O10O2OO11O1OO1001O000^MTJPNl5i1^JTNb5f1gJWN[5b1mJ[NV5_1PK^NP5`1TK]Nm4`1VK`Nj4^1XKbNh4[1[KeNe4Y1]KgNd4W1]KiNc4U1_KkNa4T1`KlN`4S1aKmN_4R1bKnN`4n0bKROa4j0`KVOb4g0_KYOb4e0_K[Oa4d0`K\\O`4c0aK]O_4b0bK^O^4`0dK@]4?cKA]4>dKB[4?eKA[4>fKB[4=eKC[4<fKDZ4`N`I]1V23Z4`NaI[1V2`NhM\\1b6hN`I\\1V2_NiM]1`6gNeI[1R2aNiM]1a6eNeI\\1R2aNiM^1`6bNiI^1n1aNjM_1Q70UK`NkM`1R7MTKbNkMa1Q7MTKbNmM_1o6OTKaNoM^1n60`KNc41]KMf42ZKKi44XKLi43WKLi45WKKi44XKLh44XKLg45YKmNWM114^7P1ZKjNXM026[7Q1[KgNZM117Y7R1\\KeN[M109W7S1^KbN\\M10:V7S1^K`N^M3N0N2V7[1aK_N]M4NN05S7Z1bK^N]M40NN8R7X1mK`NTMNN<P7V1nK_NUMOL=Q7U1aLkN_3U1aLkN`3S1`LoN_3Q1aLoN_3Q1aLoN_3Q1aLcN\\L4S7Y1`LbN^L5T7X1^LbN_L6T7W1]LbN_L8T7V1dLjN]3U1cLdNVL1X7Z1gLfN[3Y1W4O00001O001O001OO1O1O1N2O1N3N2M3N2M3N1N300N2L4K5001N2O2N2N1O1O1O0000O100O100O3fFlMc7U2ZHlMf7V2XHjMh7Y2RHjMn7W2nGlMR8X2hGjMX8X2cGkM]8\\300000000000000000000000000000000000000000000000000000000O10000O1000000O10000O100O10dLUGc2l0lLj6>aHc2a0oLTO2g7:iHc2;RMUO6b74PIc28_Mg6NSIb2FUM0<U7LXIb2BYMO:U7K\\Ia2_O\\MO9V7H^Ib2UOVM2:46X7F_Ig2TO^M49W7@cIh2QOaM0;]7ZOcIk2nNaM1;e8c2[G^Md8`3O101N1O1O100O0010O000000000001O00TLiGP3V8hLSHX3l7dLXH^3f7`L]H`3b7^L`Hd3^7ZLeHf3Z7YLgHg3Y7XLiHh3X80O102M6K1N100EmKhGR4c8O2N1O2M2O2M3N1O2N2O0O2O1O0O2O1O0O2O1N2O000O2O0O2O001N2O0O2O000O2O1O001N3N4L0O2O2N1N101O1O0O2OO1ON\\EiMc:S28K4_NSEl0R;SOPEg0U;YOkDb0Z;]OgD>`;_OaD<e;C[D8j;APD1:9Z<K`\\d0Kjc[O1000OWd11j[N1N2O2M2O9kC_OV;S1WDmNk;S1101O0O0010O1O010O00101N1O010O2N010O1O100O1O100O1O2O0O1O2O0O1O01N1O1010O0001O00001N100O2M2O1O2N1O100O2N1O101N10001N11O01O00001N10001O00001N10001O001O2N4_O^o[1OPQdN001O1O1O1O1O000010O01O001O100fCHC0U<9WDJh;6nCI44l;8UDHj;9SDAL5R<<nCIQ<d000O2N2O0O3NmNWDn0n;0O001O003M1`JmNQOR1m0QOROQ1j0SOUOn0g0TO[Ok0c0XOfJD^O0Y4X1]1WOiJKi3o0Z1YOlJIj3n0W1\\OnJGj3o0T1]OQKEk3n0Q1ASKBk3o0m0BWK@c3@XL]1k4FZK]Ob3DWLY1i4I^KZOa3GTLX1i4L`KVOb3[1j0EaKQOX1^Oi0m1[2G]K]N3a0[1@ENNo1\\3MeKgN\\1ACLOm1]34cKaN6Eh0NOJSO5`0O@L6Q2Q4i0kKbMk0LJ4DOFj1T4h0mK_Mi0NL3]ODL>0g1P4[1jLjLM4YOD0e2n3j0kLkLMQ3X33lLmLKQ3X32mLmLKQ3X32mLoLIP3Y30nLRMGo2[3OnLRMGP3Z3NoLRMHo2Y3OoLRMHo2Y3OPMQMGP3Y3OPMQMGP3Y3OoLRMHo2Y3OoLRMHo2Y3", "size": [416, 640]}, "area": 51105.0, "iscrowd": 0, "image_id": 10403, "bbox": [0.0, 0.0, 640.0, 416.0], "category_id": 165, "id": 10012727}, {"segmentation": {"counts": "ga0i0];j0M3M3M3N2M4L3M3M3N2O100`GfMZ6Z2dIlMY6S2dIRNZ6n1dIVNZ6j1dIXN\\6h1bI[N]6e1bI\\N^6d1`I_N`6`1_IaNa6_1]IdN]6ROUHZ2]1eN[6TOXHW2\\1fNZ6VOYHT2\\1gNZ6VO[HS2Z1gNY6XO]HQ2Y1hNX6ZO^Hn1Y1iNW6\\O_Hk1Z1iNU6^OaHi1Y1jNT6@cHe1X1lNS6AeHc1X1lNQ6o1nIRNQ6o1oIQNo5Q2PJPNn5GlHZ1U1POm5HnHX1U1POm5HnHX1T1QOm5HoHW1T1QOm5HoHW1S1ROm5HPIV1R1SOn5GPIV1o0hMQO[1o6HRIT1l0XOS6DQIU1h0lMWOW1o6IRIT1f0[OY6AQIT1c0^O[6_ORIS1`0@_6^OPIR1?Ba6\\OPIR1<Dd6[OQIP1:Fe6ZOQIP19Fg6ZOPIP17Hh6YOQIo06Hj6YOPIo05Ik6YOoHn04Jn6XOnHn03Ko6WOnHn02KQ7WOnHm0ONS7UOnHS3S7nLlHR3T7nLlHQ3c4iKhMV1eMQ3\\4SLlMl0hMP3W4]LnMc0kMP3R4eLPNg4n1_KoM6QNm2n3QMnM3TNk2l3UMPN0TNk2j3WMRNNTNj2h3[MTNT4j1nKVNQ4j1PLVNP4h1RLXNGTNg2c3cMYNFTNf2c3eMYNETNf2b3fMZNi3e1YL[Ng3d1ZL\\Nf3c1[L]Nd3c1]L]Nc3b1^L^N_OTNa2^3QN^N`3a1aL_N^3`1dL`N\\3`1dL`N\\3_1eLaNZ3`1fL`NZ3_1gLaNX3`1hL`NX3_1iLaNW3_1iLaNV3_1kLaNU3_1kLaNT3`1lL`NT3_1mLaNS3_1mLaNR3`1nL`NR3_1oLaNP3`1PM`NP3`1PMaNn2_1SMaNm2_1SMaNm2_1SMaNl2_1UMaNk2_1UMaNj2`1VM`Nj2_1WMaNi2_1WMbNg2_1YMaNg2_1YMaNf2`1ZMaNe2_1[MaNSOQNP2^3mNaNlNRNmNOW3_3POaNhNRNb2]3fNaNdNUNf2[3fN`NcNTNBLc2`3XOaNbNRNEN^2`3[O`NaNRNH0Y2^3^O`NaNPNK3T2]3@aN_NoMO5n1[3EaN^NnM17j1Z3GaN]NnM48WOJT2`34`N]NnM5:ROLU2\\37aN[NnM9c0S1n29aN[NmM;e0n0n2<`N[NmM<e0l0n2=aNYNlM`0b0k0R3<`NYNlMa0>m0V39`NYNlMV3d3aNaNV2`1iMaNW2_1iMaNXNjMW3f3aNaNWNiMX3f3aNaNWNiMk11jNf3T1aNVNhMm11hNf3U1aNUNiMo10fNf3V1bNTNhMQ20cNg3X1aNTNhMQ21bNf3Y1bNSNgMS20`Nh3Z1aNSNgMT20^Nh3[1bNRNfMV20\\Nh3\\1bNRNfMV21ZNh3^1bNQNeMX21XNh3_1bNQNeMX22VNi3`1aNQNdMZ21UNk3_1`NRNdM[21SNl3_1_NSNdM[22QNm3`1^NSNcM]22oMn3`1]NTNcM]23mMn3b1]NSNbM_23kMo3b1\\NTNcM_21jMR4b1[NTNcM_21jMR4b1ZNUNcM`21hMS4b1ZNVNbM`21fMU4c1XNWNbMa21dMb1Jc0i19WNbMb2OdM`1Od0d1;WNbMc2OaM`14c0`1=WNbMd2MaMa17a0\\1?YNaMd2M`Ma1;`0W1b0YNaMe2K`Ma1>`0S1c0ZNaMf2K]Ma1c0?o0d0[NbMe2J^Ma1e0>l0f0[NaMS3Z1hM?i0f0]NaMP3Z1kM`0g0f0]N`MQ3Z1lM`0e0f0^N`MQ3Y1mMb0c0f0_N_Mo2Y1QNb0`0f0`N_Mo2Y1QNd0>e0bN]Mo2Y1SNf09e0eN\\Mn2Y1UNh05e0hNZMm2X1WNl00c0mNXMl2Y1WNn0Ke0QOTMm2X1XNR1De0XOQMj2X1[NT1_Oe0\\OoLj2X1[NW1nNQ1M`Li2W1]NY5kN_Ig2X1_NZ5iN^Ih2W1`N[5iN^If2W1aN\\5hN]If2W1cN\\5hN\\Ie2W1dN^5fN\\Ie2V1eN^5fN\\Id2V1gN_5eNZId2V1hN`5dN[Ic2T1jNb5bNZId2T1jNb5cNYIc2T1kNd5aNXIe2R1kNf5`NYId2Q1lNg5_NXIe2P1mNi5^NVIe2P1nNj5]NVIe2P1nNk5\\NUIf2o0oNl5\\NTIe2o0POn5ZNSIf2n0QOo5ZNRIe2o0QOP6YNQIf2n0ROR6T1mImNS6S1mImNT6R1kIoNV6P1iIQOW6o0hIROY6n0fIROZ6n0eISO\\6l0cIUO^6j0bIUO_6k0`IVOa6i0^IXOc6g0]IYOc6h0[IXOg6g0XIZOh6f0WI[Oj6d0VI[Ok6e0TI\\Om6c0RI^On6b0RI]OP7c0nH^OS7a0lH_O6oMU6c2cI^O5VNU6\\2eI^O4[NV6W2dI_O5\\NW6V2`I_O9]NV6a4iIbKW6\\4iIfKV6Z4iIhKW6V4iIlKW6R4iIPLV6P4iIQLX6m3gIULZ6i3fIYLZ6e3fI\\L\\6a3dI`L^6]3bIdL`6Y3_IjLb6S3^InLe6n2[ISMg6j2XIYMi6d2WI]Ml6_2SIcMQ7X2oHiMT7S2lHoMW7m1gHUN\\7h1cHYNa7c1]H_Nf7\\32N3MiHTKl4b0VKW4F_KP5b0VKo3FdKP5c0XKh3FgKn4i0XK`3HjKl4l0YKZ3ImKi4Q1[KQ3KoKg4T1]Km2KQLd4W1`Kh3^4\\LaKd3]4_LbK`3\\4cLdK]3Z4fLeKZ3X4iLhKV3W4lLjKS3S4oLnKb25hJl3h2oK\\2`4fMaKU2b4mM^KP2c4QN^Km1a4VN_Kh1a4YN`Ke1_4^NaK`1_4bNaK\\1_4eNbKY1^4iNbKU1_4kNbKS1^4oNcK5HRMf4i2cK1IWMd4i2cKKM[M`4k2dKE0`M\\4l2dK_O3eMZ4l2dKlNd0XNg3n2eK[O2fMZ4o2eK\\ONfM\\4P3fKZOMeM^4o2hK]OHdM`4l2lKAAcMc4k2oKB]OcMd4i2RLEXObMe4h2WLFROaMh4k1UMY1k2gNVMX1i2hNYMV1h2iNZMT1g2mN[Md0nMUMh4V2\\Mc0RNPMd4]2\\M?P3ARM=o2BTMSNcMf1Z57UMoMbMj1Z56VMmM`Mm1[56XMiM^MQ2[56XMfM_MS2Z56_MJa26`MHb28^MGc28^MGb29^MGc29]MEe2:\\MEe2;[MDe2=ZMCg2<ZMCg2=YMAf2VNZJY2Q3@`2]N_JS2P3@]2bNcJm1Q3@X2R1hMlNU2X1lMgNP2^1oMbNm1b1TN]Nh1h1XNVNe1o1[NPNa1T2`NkM\\1Z2dNeMW1`2jN^MX1b2hN]MY1b2hN]MY1c2gN\\MZ1c2gN[M[1e2eNZM\\1e2eNZM\\1f2eNXM\\1g2eNXM\\1h2dNVM^1j2bNUM_1k2aNTM`1l2`NSMa1m2_NRMb1n2^NPMd1P3\\NoLe1Q3[NnLf1R3ZNmLg1S3YNlLh1T3XNkLi1U3WNjLj1V3VNhLl1X3TNgLm1Y3SNfLn1Z3RNeLo1[3QNdLP2\\3PNcLQ2]3oMbLR2^3oM`LR2`3nM^LT2b3lM]LU2e1aJOZ3[NV2g1`JMZ3[NW2h1`JLY3[NX2i1_JLY3ZNY2j1_JKX3ZNZ2l1^JIX3ZN[2m1]JIX3XN]2o1\\JHP98QGGo8:PGFP9:QGDP9<QGCo8>PGBP9>QGAo8?RGAm8`0RG@n8a0RG_Om8c0RG\\On8e0QG\\On8f0QGYOo8h0QGXOn8j0QGUOo8l0PGUOo8^201O0000cNTGgNk8[1VGbNj8_1XG_Ng8c1[GXNf8i1`GPN`8R2oG\\MR8e2T12N1O2N1O00001O00001O00001O00001O0000001O00001O00001O00001O0HfLjFZ3T9hLlFY3P9jLPGV3n8lLRGT3k8oLUGR3g8QMYGo2e8SM[Gm2c8UM]Gl2a8UM_Gk2`8VM`Gj2_8WMaGj2]8WMcGi2\\8YMcGg2\\8ZMdGg2Z8ZMfGf2Y8[MgGe2X8\\MhGe2V8\\MjGd2U8]MkGc2T8^MlGc2R8^MnGb2Q8_MoGa2P8`MPHa2n7`MRH`2n7`MRH`2m7aMSH`2l7`MTH`2k7aMUH_2k7aMUH`2i7aMWH_2i7aMWH_2h7bMXH_2g7aMYH_2g7aMYH_2f7bMZH_2e7aM[H_2d7bM\\H^2d7bM\\H_2b7bM^H^2b7bM^H^2a7cM_H^2`7bM`H^2`7bM`H^2`7bM`H_2_7aMaH_2_7aMaH_2_7aMaH`2^7`MbH`2^7`MbH`2^7`MbHa2]7_McHa2]7_McHa2]7_McHb2\\7^MdHb2\\7^MdHb2\\7^MdHc2[7]MeHc2Z7^MfHb2Z7^MfHc2Y7]MgHc2Y7]MgHc2Y7]MgHc2Y7]MgHc2Y7]MgHc2Y7]MgHb2Z7^MfHb2Z7^MfHb2Z7^MfHb2Z7^MfHa2[7_MeHa2[7_MeHa2[7_MeH`2\\7`MdH`2\\7`MdH`2\\7`MdH_2]7aMcH_2^7`MbH`2^7`MbH_2`7`M`H`2`7`M`H_2b7`M^H`2b7`M^H_2d7`M\\H_2e7aM[H_2f7`MZH_2g7aMYH^2i7aMWH_2i7aMWH]2l7bMTH\\2n7dMRHZ2Q8eMoGY2S8gMmGW2V8hMjGU2Y8kMgGS2[8mMeGQ2^8nMbGP2`8PN`Go1b8PN^GP2d8nM\\GR2e8mM[GS2g8kMYGU2h8jMXGV2j8hMVGW2l8hMTGX2n8fMRGZ2P9dMPG\\2Q9cMoF]2S9aMmF_2T9`MlF_2W9_MiFa2X9^MhFb2Z9\\MfFd2\\9ZMdFf2]9YMcFg2_9WMaFh2a9WM_Fi2c9UM]Fk2e9SM[Fm2l900000000001O00kNTFVOl9k0[FmNe9T1bFdN^9]1iF[NW9f1Q11O1O001O1O2N8H9Gi0WO4L3M4L3M1O00000000000000000000001O00N2O1O1O1N2O1OF]LYGb3a8dL`G[3Z8lLfGR3P8ZMPHe2k7aMUH^2j7cMWH\\2g7gMYHW2f7lMZHS2d7PN\\Ho1c7SN]Hl1a7WN_Hh1_7[NaHb1`7`N`H^1a7cN_HZ1b7hN^HU1c7mN]HP1d7RO\\Hk0e7WO[Hf0f7\\OZHa0g7AYH<i7EWH8j7JVH4j7NVHOk73UHJl78THEn7<RHAQ8?oG^OS8c0mGZOU8g0kGVOW8k0iGROY8o0gGoN[8Q1eGlN]8U1cGhN_8Y1aGdNa8]1_G`Nc8a1]G\\Nf8d1ZGXNi8j1VGSNl8n1TGoMn8R2RGkMP9V2PGfMS9[2mFbMU9_2kF^MW9c2iFYM[9g2eFVM]9l2:1O2N2N101O1N2O0O2O1O0O2X1hN", "size": [416, 640]}, "area": 102291.0, "iscrowd": 0, "image_id": 10403, "bbox": [1.0, 0.0, 639.0, 416.0], "category_id": 183, "id": 10012728}, {"segmentation": {"counts": "]YP11T>4L8H2N3M1O1O00000000001O00000000O1O1O100O1O10\\BAb=a002N2QD^O_NOd;d0kD_O86l:;hDD=0j:a0eD_O>2m:R1TEnNk:T1oDoNR;Q1nDPOQ;P1aDaN9>W;]1cDcN\\;U2N1O001O00001OO1O1O1L4G]DiMj;U24000000O1O1O1N2O1001OO1O1O11O00O1001O1O1O001O001O2N3M1O2N2NM3N27VDQNOAn:U3SElLg:V3WEjLi:\\3WEcLd:`3\\EcL^:b3^F]LBO_8e3nGnLQ8S3VGgMh8l3O00001O00O1O15KN2K500O1AXGjK_8\\3^HXLk7Q3lHnLU7n2S26JN2K56JL4M36JCSEZMU;f2kDYMV;f2kDYMV;g2jDYMV;e260000000000O10000O1000000O10000O100O10_D_M^;a2bD_M^;a2bD_M^;`2bDaM^;_2bD`M_;`2aD`M_;_2aDbM_;^2aDbM_;^2`DbMa;^2_DcM`;\\2aDcM`;c2N00O100O100001O00001O001O001O00001O001O1O0fDWMX;Q3JO12N1OO12NN21O2NM3004LL41YFTMS8o2TGgMl8_2gFhMY9b301O0000000000000000001OM3L4K5L4O1YOUFdLOL1010N0Q:V3RFPMMM]:S3nEjLU:S3`EmLn:R330000001O0000O11ON2O100002N000000001OO1O1002NN2O1003MM3O1004L6J00O1L41O2NO1O1001O00O1O11OO1O11O0000AQEgMP;Y2PE]M00V;c2jD\\M[;c2fD\\M[;d2300001O1O001O1O2N001O001O1O001O1O001O001O1O00O11O001O1O0TEUM3GL0010O01O010O10O01O010O010O010O010O5050F08e6k2[IR1e6W30000000000000K54LNZIeHf6Z5ZIgJ90G^1i6k3WIgJ9OH_1h6b2ZIRMNnN8OH]1OcNi6P4[IcLLK2B70Hn0O_O0Ci6o3^IbLI@0:`0?@XO070Ik6l3^IaLHBO80G`0i0BWO:0a6m3_I]LR9c3PG^Lm8a3UG^Lk8a3WG]Lj8`3o01OO1O12N00N21O2NO12N1O001O00001O001O001O000000000000O100O10000001O1O1O1O001O1O2N1O1O2YFULLB080I3f0MA0IV7k601O1O000000N2O1N21OO1001O2NO10000000000O1^Ob0001ON2nIWHJNl5X801Nl0WJoF024KT4Z:kKgE\\3l:gLRE27GNl0L8o:lN_G<[;Enh`2", "size": [453, 640]}, "area": 42462.0, "iscrowd": 0, "image_id": 10405, "bbox": [73.0, 0.0, 384.0, 239.0], "category_id": 157, "id": 10012729}, {"segmentation": {"counts": "0l<Y100O1001OO11O0000000000000000O1O1O1000000000000000000000000000000004L00000000000000000000000000O1000000O100000000O1000000O100000000O10000000dNYCZ1i<001O4L8G3N3M1O1N100000000O2O00000000O1O0010O01O10lCQNR<Q2O3O1aEnM_NOS:U2\\FnM86[9l1YFSN=0Y9R2VFnM>2[9d2fF\\MZ9e2`F^MP9B`FO;R35^Mo8DkFn2HoL9>U9FaFK0000O^35QMZ9FbFK00OY4]9>N12ON10001N01O1O1L4G`FfKg9X44hKVF1OO3m3h9SLVF12n3k9RLUFn3R:N1O1O1N2O2O01OO1O1O20O01N01O2M3O1N101O001O2M4M1O2N2MN3N27SFTLOAQ9R5PGoJl8Q5RGoJm81SGS50kJP9T5PGoJk8U5QHjJBOl6X5aI[K^6f4iHTLU7_5O000O2N10O1O06KN2K410O1AoHSJg6T5VJ`JS6i4dJVK]5f4R27JN2K47JL4M36IDnF_KZ9a4fF^KZ9b4gF]KZ9c4fF]KY9b460O100O1000O01000O1000O10O10O10O10O010bF\\KZ9e4fF[KZ9e4fF[KY9e4gF\\KY9d4gF[KZ9e4fF[KY9e4gF\\KY9d4gF\\KW9f4hFZKZ9e4fF\\Kc1>a5U4mH\\Ka1a0a5Z4\\JfKe5Z4[JfKe5Y4]JgKa5Z4\\JiKd5V4`JfKa5Z4_JfK`5[4^JgKb5Z4^JeKa5\\4`JcK`5]4_JdKa5]4\\JeKc5\\4^JcKY5]O`IQ5X1aKU5@cIo4V1aKX5@bIQ5V1_KW5@cIQ5V1_KV5AdIP5V1_KU5BdIQ5[8RK`GQ5`8;N2M[JdGe50[JZ8o5I0120OOO12MO30O2NM3004KM32XIUJT5n5SJhJl5_5gIhJX6c601O01O1YHdIc7\\6]HdIc7\\6]HcId7c6LN`H_I`7a621O01UO_H[JOa0_7V5eHWJMa0[7Y5nHgJm6Z5jHUJ;>i6_5kHTJ?:g6c5[ISJ_OO11OOb61bIOL1010N0S6T7PJRIMM_6Q7lIlHV6R7_InHo6Q74O0000010oHmHn6U700O11NO10100002OO00001O01OO1O1002NN2O1003MM3OO204K7I1O01L4101NO1O1001O00O1O02N01O11O0000ARIfIo6Z6QI\\I00U7d6kH[IZ7d6gH[IZ7e6301O01N2O001MXIgHi6X74OOO2O001O1O001O1O001O001O1O00O11O001O1N1XIQI3GL0010O01O010O10O01O010O010O010O010O5050F08`2P7`MmLa2[70000000000000K54LN^MaDb2^9^McF90G^1e2o7[McF9OH_1d2f6^MnHNnN8OH]1OcNe2T8_M_HLK2B70Hn0O_O0Cf2R8aM_HI@0:`0?@XO070Ii2n7`M_HHBO80G`0i0BWO:0_2o7aM[HP5e7RK\\Hk4c7WK\\Hi4c7YK[Hh4b7o01OO1O12OO0N12O2NO12N1O001O00001O001O001O0000000O100O001O001O12N10K5N2O1O05L00O2N1OgGXJ38d7d5WHaJj7GeHLB080I3f0MA0Ie3\\:01O1O01O1O0N101N21OO10O2O2NO10N2006J1O0O2]Ob00010M2nIaK`LNl5a4]MkKc2T4]MnKa2R4_MjLhLjN024KT4\\4POVNkL_M\\3[4IiNnLjL27GNl0L8\\4U2kNgLWO<n1HcKU3l5hLiN^3o5aL^E[3j:gLTEQ3m:nLSER3n:mLSER3Q;O1O1O0000001O000000001OO11O000000001O00O11O00001O0000O11O1O00001O00N2001O0000001O0000000000MWMjDi2V;WMjDi2V;3O10000001OO11O00O11O001OO100O1O1O1O1N20000001OO10000002N0000001O1O1O1VMiD00M0`2X;`MQE0G^2e;00000001O0000001O0000000000O11O0000O11O001O000000001O001OO11O000000O100000000001O0000001O00002NO1000000001O0000001O0000000kMRDS2P<0O11O001O1O00000000001O000QNmCl1S<TNnCk1S<TNmCl1S<TNmCl1S<TNnCk1V<00VNhCg1X<YNhCg1[<000N20000001O00001O1O", "size": [453, 640]}, "area": 192613.0, "iscrowd": 0, "image_id": 10405, "bbox": [0.0, 0.0, 640.0, 413.0], "category_id": 158, "id": 10012730}, {"segmentation": {"counts": "l<Y1l<0O1001OO11O0000000000000000O1O1O1000000000000000000000000000000004L00000000000000000000000000O1000000O100000000O1000000O100000000O10000000dNYCZ1i<0000000O1000000O100000000O1000000000000O100O1000000O11OO10000O11O_OaNnCO;a1g;cNXD]1h;dNoCK0000Oa1R<dNoCK00Od1R<=O12NO10000O100000000000nMPD1OO3g1n;YNPD12h1Q<XNoCh1X<O000000001O000000001O001OO1O1N200O100000000O1000000O10000002NOXNhCe1`<L1O0000000000O1O10000O10000O10000O10000O10000O1000000O100O100O100O100O10000O100000000O10000O10000O100O100O10000O100N21OZHZNm2f1QM\\Nn2e1QM[NP3e1PM[NP3e1QM[Nm2f1PM]NP3c1SMYNn2g1RMYNm2h1QMZNo2f1RMYNm2h1TMWNl2i1SMXNm2h1QMZNn2g1SMXNd2]O`I[2n3WN_2@cIY2l3WNb2@bIZ2m3VN`2@cIZ2m3VN_2AdIY2m3VN^2BdIY2S;jMhDY2X;:O1NUMjDk2V;2O1002N0000O11O00000000O1O100O1O100001O1QMlDk2T;UMlDk2T;UMkDl2[;LNXMgDh2Y;21O00VOXMbEOa0i2l9[MaEMa0i2k9`MUF`2g9WMhE;>`2h9TMkE?:_2k9cMkE_OO11OOQ3V:iNlE5T:JRF1m90XFKh95]FFd98_FFa9:_FFa99`FG`99aFF`9cNiEd1g0Ib96_FJa96`FI`97`FI_97bFI]98dFG\\99dFG\\98eFH[98fFG[98eFH[97fFIZ97gFHZ97fFIZ96gFJY96hFIX97hFIX96iFJW96jFIV97jFIV96kFJU96lFIT97lFIT96mFJS96nFIP99PGGP98PGIo88QGHn89RGGm89TGGl89SGHm88SGHn86SGJm86SGJm86RGKn85RGKn85RGKn84RGMn83RGMn83RGMm84SGLl85SGLm84SGLm84SGLm84SGLm84RGMn83RGMn83RGMn83RGMn83QGNo82QGNo82QGNP91oF0Q90oF0P91PGOP91PGOP91oF0o8bNoE^1R10o81RGOo80QG0n81QG0o80QG0o80QG0o80QG0o80PG1P9OPG1P9OPG1P9OoF2Q9NoF2Q9NoF2Q9NoF2Q9NnF3R9MnF3R9MnF3R9MnF3R9MmF4S9LmF4S9LmF4R90kF0T94hFMY96cFJ]99`FG`9=[FDe9?XFAh9b0UF^Ok9f0QFZOo9^2000000000000001O1O0000000000000000001O00O10000000000000000000000000000000000O100O1O1O1O1O12N1OL4O100005K1O00000oKPF38[3m9`LXF_3V:10000001O1O00O100000000O1000000N2006J1O001O00001O00000000000000JkL\\EU3d:60aL^E[3j:gLTEQ3m:nLSER3n:mLSER3Q;O1O1O0000001O000000001OO11O000000001O00O11O00001O0000O11O1O00001O00N2001O0000001O0000000000MWMjDi2V;WMjDi2V;3O10000001OO11O00O11O001OO100O1O1O1O1N20000001OO10000002N0000001O1O1O1VMiD00M0`2X;`MQE0G^2e;00000001O0000001O0000000000O11O0000O11O001O000000001O001OO11O000000O100000000001O0000001O00002NO1000000001O0000001O0000000kMRDS2P<0O11O001O1O00000000001O000QNmCl1S<TNnCk1S<TNmCl1S<TNmCl1S<TNnCk1V<00VNhCg1X<YNhCg1[<000N20000001O00001O1O", "size": [453, 640]}, "area": 51880.0, "iscrowd": 0, "image_id": 10405, "bbox": [0.0, 131.0, 640.0, 322.0], "category_id": 178, "id": 10012731}, {"segmentation": {"counts": "bfh33Q>6K5K5J2O1N10001N10001N10001N10001N10001N10001N10001N10001N10000O1000O01000O0100000O10O1000O1000000O10000O1000000O1000000O10000O0100000O1000000O1000000O10000O1000000O2O3M3M4K4M4L3L5Lifi3", "size": [453, 640]}, "area": 2905.0, "iscrowd": 0, "image_id": 10405, "bbox": [272.0, 370.0, 92.0, 44.0], "category_id": 183, "id": 10012732}, {"segmentation": {"counts": "0V3j;2N001O1O0000002N004LN200N2O1N20000000000000000O1O1O1O1N2O1O100O100YOg0_Oa0O1O1O1O100O1O11O4L4L4L1O1O1O1O001O1O1O1O1O1O001O1O00O100O1O1O100O1O100^NYCMM<k<E^CHL`0g<GaCDMa0c<KPD4P<JTD3m;LUD1m;NUD1k;OUD1k;OVDOk;1VDNj;2WDLj;4VDLj;4WDKi;5XDIi;7WDIi;7WDIk;5UDJR<0nC0`=101N2O2O0O100O2N100O1O1O1O1OO2N1O2N2O1O1O1OO2O0O101N10001N2N2N101M2QB[Ob=f0]B\\O`=g0^B[OS=OnBg0N\\OR=`1O2N01N2O0O2O1N101N2O100O01O1_ObN_C_1`<dN]C^1^<^NXC81a1f<?N2M2O2L4O001O001O1O001O1O1O1O1000O001O2N1cMjCn1W<lMlCV2_<0O02OO10000O10O100000O1000O0100001N20OhM\\Cl2l;TMTDn2n;01O00001O00001O000000O1O100O1O100O100O1O1O101N2O0O2O0NhL\\DY3c;2O2O001O10O01O1O1O01000N101O1O2O1N2N2N10000O010O1O100O001O1\\EVLQ:j3eE^L[:S42N1O2O0O1O1O001O100O1N2N2N2N2N2N3M2N1O11M3O1O1N2M300O100N2M4N2O1O1O1O2N6J?A001O1O1O1O001O1O1O1O1O001O1O1O1O1O1O00c1]N=C3kH[Hi6f7YI[Hc6e7_IZH`6f7bIYH]6g7>1O000000001O00001O00001O00001O00001O001O00001O00001O00001O001O00001O00001O001O00001O001O001O00001O001O00001O00001O00001O00001O00001O00001O2N3M3M2N2N2N2N2N2N2N3M2N2N2N2N2N2N2N3M2N2N2N3M000000O10000001OWFUK]9k4bFWK]9i4bFXK^9m4ZFPK04f9T51O2M5L6J1N10001O1N2O1O1O1O001O2`KbEZ4\\:6O10M2N3M2010O100O1O100O100001O00001O00001O0000001O00001O00001O00001O000ULZE\\3f:^L[Eg3i:6K5K5K5K5K5K5K5K5N2000000O1000000000000O10000000000O1000000000000O10000000000O1000000000000O10000000000O11O001O1O001O001O1O001O001O1O001O001O1O001O001O1O001O1O001O001O1O001O001O1O001O001O1O001O001O1O001O001O1O001O001O1O001O001O1O001O1O001O001O1O001O001O1O001O001O1O001O001O1O001O001O1O001O001O00N200001O001O001O0000O11OO11OO11OO1000000000000", "size": [480, 640]}, "area": 130731.0, "iscrowd": 0, "image_id": 10407, "bbox": [0.0, 0.0, 640.0, 360.0], "category_id": 121, "id": 10012733}, {"segmentation": {"counts": "oXi41n>1O1O1O1O1O100O1^OJUB;k=FnA`0R>700000000LYOoAg0P>5K5M3N200001O00001O0000001O0000001O00001O0000001O0000001O0000001O0000001O0000001O0000001O0000001O0000001O0000001O0000oK^OdIb0\\65nHJR79kHGU7=gHCX7b0dH^O\\7e0bHZO^7j0^HVOb7m0[HSOf7n0XHROh7P1WHoNi7R1VHnNj7S1UHmNk7U1SHkNn7U1RHjNP8V1nGjNV8S1iGmN]8o0aGQO`8o0`GPO`8R1^GnNb8S1]GmNd8S1[GmNf8T1YGkNh8U1WGkNj8V1TGjNm8V1RGjNo8W1oFiNQ9f1]F]Nd9\\2_EgMc:VOXEP4V;F6J3M3M3M4L3M3M3M3M3M2N2N1O2N2N2N2N2N1O2N2N2N2N2N1O2IjB`NX=\\1jBdNX=W1kBiNV=S1mBmNU=o0mBQOU=j0nBVOS=g0oBYOS=b0PC^OQ=>RCBP=:RCFo<6TCJn<2TCNo=000000000000000000000000000000000O100oMNTE2n:OoD1S;OkD1V;1gDO[;1cDO_;1_DOc;2ZDNg;3WDMk;4RDLP<4nCLT<4jCLW<6fCJ\\<6bCJ`<7]CIe<7YCIi<8TCHm<:PCFR=;kBEW=<fBD\\=>`BBa=b0ZB^Oh=d0TB\\On=j02N1O2N2N2N1O2N2N2N1O2N2N2N0000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000\\LJ^H6`7L`H4`7M_H3a7M_H3b7M]H3c7N\\H2e7M[H3e7NZH2g7NXH2h7NXH2h7OWH1h70XH0i70VH0i72VHNk71UHOj73UHMl72THNk74THLl75SHKm75SHKm79oGGQ8R1VGnNj8k1]FUNc9T2TFlM", "size": [480, 640]}, "area": 8866.0, "iscrowd": 0, "image_id": 10407, "bbox": [326.0, 323.0, 314.0, 157.0], "category_id": 122, "id": 10012734}, {"segmentation": {"counts": "V3j;X3N001O1O0]NXNnEh1Z80fG0b6h1^IZNi4]3WKcLQ3U5oLoJV1h6jNVI0R80_30000000a`]40__bK1O2N1O101N1O1O2O0O1O2O0O1O2N100O000001O00001O0LcAE]>:5K6L\\\\Y4", "size": [480, 640]}, "area": 3308.0, "iscrowd": 0, "image_id": 10407, "bbox": [0.0, 102.0, 346.0, 378.0], "category_id": 170, "id": 10012735}, {"segmentation": {"counts": "]h2b1f;h1XNh1YNg1XNh1YNg1hNX1O1N2O1N20000000000000000O1O1O1O1N2O1O100O100YOg0_Oa0O1O1O1O100O1O11O4L4L4L1O1O1O1O001O1O1O1O1O1O001O1O00O100O1O1O100O1O100O1O1O100O1N20000O100O10000O10000O100001O2N1O1O1O2N1O1O1O1O1O1O1O1O1O1O1O2N1O1O1O1O2N1O1O1O1O1O2N1O1O1O1O1ON2O1N2O1O1N2O1N2O1N2O1N2O100O10000001O001O1O001O1O001O001O1O001O1O1O1O1O1O00000000000000O10000000000O10000000000O1000000O1d0\\O7I000000000001O0000000000000000000000000000000001O1O001O001O001O001O1O001O1O1O001O1O001O1O2N3M2N1O1O1O1O001O1O1O1O001QFcKY9^4]FjKd9h41O1O1O100O1O1O1O1O100O1N2N2N2N2N2N2N2N2N2000000000000000000001O1O1O1O1O2Nd0\\O1O001O1O1TMUKaKl4W4aKcK_4b4`KZKa4j4[KUKf4o4VKPKk4T5QKkJP5Y5lJfJU5[5jJdJV5_5hJ`JY5a5fJ^J[5c5dJ\\J]5f5aJYJ`5c71O1O00i1WN:F0nH^Hf6c7\\I]Ha6c7aI]H]6c7eI^HX6b7jI_HS6b7nIZHT6f7c00000001O0_NWHkKi7U4XHjKi7U4XHjKh7W4YHgKg7Y4ZHfKg7Y4ZHfKf7Z4\\HdKd7]4\\HbKe7]4\\HaKe7_4]H^Kd7b4]H\\Ke7d4[HZKf7f4\\HWKf7h4[HVKf7j4[HTKf7m4[HQKf7n4[HPKf7P5[HmJg7T5ZHdJm7[5TH^JR8b5f0010O01O00010O000010O01O01O01O0010O01O00010O001O01O01O01O01O00010O000010O0001O01O01O3M3N1N2N3M2O1N2N2N2O1N2N2N2O1N3M2N2O1N2N3M2Og0XO;E4M2M4L3M4M2M1O2N101N1O1O2O0O2N1O2O0O2N1O101N1O4Jm0RO[k0KlTO7J3L4M3L5L3L4M3L4L4M2M3N1O2O1N2N2O1N1O2O1N2N2N2O0O2SE`MV9b2fF`M[9a2`FbM`9_2\\FdMd9^2XFcMj9^2QFeMo9\\2nEfMR:\\2iEgMX:2XE]1<dN\\:IXEHLk1<fN`:@ZE6Jc18jNd:WO[Ec0I\\14lNl:0PET2k:RNUEm1f:ZNZEd1a:cN_E[1\\:lNdER1W:TOjEj0Q:]OoEa0l9FSF9h9NXF0f93[FKe97[FGe9;ZFDf9k2O10000000lM[FmNe9T1\\FjNd9X1]FeNb9^1^F`Nb9b1_F[Na9f1`FXN`9j1aFSN_9o1aFoM_9S2aFkM^9W2dFfM\\9\\2dFbM\\9`2eF]M[9e2eFYM[9h2gFUMY9m2gFQMY9Q3hFlLW9W3iFgLW9[3jFbLV9_3lF^LT9d3mFXLT9j3nFRLR9P4PGlKo8V4TGfKl8\\4VG`Kj8b4i02N1O2N2M3N1O2N2N2N1N3O1N10O1O001O001O1O001O001O1O001O001O1O001O001O1O001O1O001O001O1O001O001O1O001O001O1O001O001O1O001O001O1O001O001O1O001O001O1O001O1O001O001O1O001O001O1O001O001O1O001O001O1O001O001O1O001O001O1O001O001O1O001O1O001O001O1O001O001O1O00a0_Oi0WOh0XOi0WOdA", "size": [480, 640]}, "area": 157842.0, "iscrowd": 0, "image_id": 10407, "bbox": [5.0, 50.0, 635.0, 430.0], "category_id": 183, "id": 10012736}, {"segmentation": {"counts": "j0Z<j000000000000000000000000000PF[Oa7f0PHLl74mG7o7IoG=m7CRHe0g7[OWHn0b7RO\\HU1_7kN_HZ1^7fNaH^1\\7bNbHc1[7]NcHf1\\7ZNcHi1[7WNcHl1\\7TNbHP2\\7PNbHS2]7mMbHU2]7kMaHY2]7gMaH\\2^7dMaH_2]7aMaHb2^7^M`Hf2^7ZM`Hi2_7WM`Hk2_7UM_Ho2R4]LdNd0YMQ3m3dLfN;\\MS3g3kLjN2^MU3a3SMmNHaMW3\\3ZMoN_OdMX3W3cMQOUOgMZ3Q3jMUOmNhM[3m2QNWOdNkM\\3h2ZNYOZNnM^3b2bN\\OPNQN`3]2iN^OgMTNa3X2QOA^MVNc3U2VOAXMXNc3U2ZO@e1>\\NBd1=^NBc1;`ND`1;bND_1:bNF^19dNF\\18gNGZ17gNIY16iNIX14kNKU14lNLU12mNMS12oNMR10QOOo00RO0o0NSO1m0MVO2k0LVO4j0KXO4h0KZO4g0I\\O6d0I]O7c0H_O7b0FA9?GA9?FC9=GD8=FD:<FE9;FG97POYJg0a586ROYJd0b5:5IL64IN62JN62I060J15OJ26NJ35MJ55KK64KJ66JJ75IJ95GK95GJ;5EK<4DK=5CJ?5AJa05_OJc05]OKc05]OKd04\\OLe03[OLf04ZOLg03YOLi03WOMi03WOLk03UOMl02TOMn02RONn02TOKn04SOGVJ1h67Q601O1O1O001O000000000000000000000000O10000000000000000000000000000000000000000000000fHNb12^NNb12^NNb12^NNb12]NOc11]NOc11]NOc11\\N0d10\\N0d10\\N0d10[N1e1O[N0f10YN1f10ZN0f10YN1g1OYN1g1OXN2h1NXN2h1NWN3i1MWN3i1MVN4j1LVN4j1LUN5k1KUN4l1LSN5m1KSN5m1KRN6n1JRN6n1JQN8n1HQN:n1FQN;o1EPN=o1CPN>o1CPN?o1APN`0o1APNa0n1@QNa0o1_OPNc0n1^ObILFJm3m0k2]OaI3c3b0j2\\ObI4b3a0k2\\OcI3a3c0k2ZOdI3`3e0j2YOfI3^3e0l2XOfI3\\3h0l2VOhI3Z3h0m2VOiI2X3k0n2SOjI3V3k0o2SOkI2T3n0P3POlI3R3n0Q3POmI2Q3P1P3oNoI2n2Q1S3mNoI2m2S1R3lNQJ2j2U1T3iN_JE\\2c1T3iN`JDZ2f1T3gNbJDW2g1W3eNbJDV2i1V3dNdJDS2j1Y3bNdJDR2l1X3aNfJDo1m1Z3`NgJC]2`1k2mNhJDh2S1_2ZOiJCm2o0Y2^OjJDQ3i0T2DkJ0i28Z2ImJ8c2M_2KnJ7g2KZ2OoJ4l2KT21PK3P3Io15QK1T3Hi18SKNW3If19SKMZ3Ia1;UKK\\3I_1<UKI`3JY1>WKGb3KU1?YKEe3JR1a0YKCh3Lm0b0[KAk3Li0c0\\K_On3Me0e0]K]OQ4M`0g0_K[OS4N=g0`KYOW4N8j0bKVOX404k0dKTOZ410l0fKSO[40Mo0iKoN\\42HQ1lKlN^44CQ1oKjN`46]OR1SLhN`48YOR1WLeNb4:TOR1ZLcNd4<nNS1^LaNe4=iNT1aL_Ng4?eNS1dL]Ni4a0_NT1hL[Nj4b0[NT1kLYNl4d0UNU1oLVNm4g0PNU1SMSNo4i0kMU1R3lNjLV1W3kNeLW1\\3jNaLW1_3kN]LW1d3jNXLX1i3jNSLW1n3mNkKU1U4oNeKS1\\4QO^KP1c4TOQKS1P5QOcJW1]5mNWJ[1j5iNiI_1W6fN\\Ib1e6^2001O0fLWI9j6YOiIa0W6SO[Jg0e5ROhJh0Y5UOoJe0Q5YOTKd0m4YOWKe0i4YO[Ke0f4WO_Kg0a4WOcKg0]4WOgKg0Z4VOjKh0V4VOnKh0S4UOPLj0P4TOTLj0m3SOWLk0i3SO[Lk0f3RO]Lm0c3RO`Ll0`3SObLl0^3SOeLk0\\3SOfLl0Z3SOiLk0W3TOlLj0U3TOmLk0S3TOPMj0P3UORMj0o2TOTMj0l2UOVMj0j2UOYMi0h2UOZMj0f2UO]Mi0a7N1O2N1O1O1O001O001O001O001O001O1O001O001O001O001O001O001O001O001O001O001O0000001O000000000000000000`V?0`i@010O10OeH0b11[N1e1OZN2e10YN2f1NYN3h1MUN5k1LSN5m1LQN5o1LoM6P2KmM7S2IlM8T2IjM8W2HfM;Y2FcM=]2D`M?_2B^M`0c1UOfJ;e3c0d1ROgJ<a3e0g1POhJ<^3g0i1mNiJ=[3h0l1kNiJ>X3j0m1iNkJ>U3k0P2gNkJ>R3o0R2cNlJ?o2P1T2bNmJ?l2R1V2_NnJa0h2R1Y2^NoJa0e2T1[2[NPKc0a2T1^2ZNQKc0]2W1a2VNRKd0[2W1b2VNSKe0W2X1d2TNUKe0U2X1f2SNUKg0R2X1g2RNWKg0o1Y1j2PNWKi0l1Y1k2oMYKi0j1Y1m2nMYKk0f1Z1o2lM[Kk0d1[1o2kM]Kk0a1\\1R3iM]Km0]1]1T3gM_Km0Z1_1V3dM`Ko0V1_1Y3cMaKQ1Q1_1]3`MbKS1l0a1`3]MdKT1h0a1c3\\MeKV1a0c1i3WMfKX1:g1n3RMhKY13j1U4mLhK_1Hj1^4hLjKf1ZOi1k4aLkKo5T4RJlKo5R4RJnKo5Q4QJoKo5P4RJPLo5m3SJSLn5k3SJULn5i3SJWLm5h3TJXLm5f3TJ[Ll5c3UJ]Lk5b3VJ^Lk5`3VJ_Lm5^3TJbLn5[3SJeLo5X3RJhLP6U3QJkLQ6R3PJnLR6o2oIQMR6m2oISMS6j2nIVMT6g2mIYMU6d2lI\\MV6a2kI_MW6\\2lIdMV6W2mIiMT6S2oImMS6n1PJRNR6i1QJWNQ6d1RJ\\NP6_1SJaNo5[1SJeNo5V1TJjNm5R1VJnNl5m0WJSOk5h0XJXOj5c0YJ]Oi5>ZJBh59[JGa90000000000000000000000000000", "size": [420, 500]}, "area": 51197.0, "iscrowd": 0, "image_id": 10414, "bbox": [0.0, 13.0, 500.0, 407.0], "category_id": 143, "id": 10012737}, {"segmentation": {"counts": "0j0Z<00000000000000000000000000001O000000000000000000000000000000000000000000000000000000001O00000000001O00O100O100O1O100O100O1O100O100O100O1O100O100O1O10000O10000O100O10000O10000O10000O1000000000000O1000000000000O100000000000000000000000000000000001O00000000000000000000001O00000000000000000000001O00000000000000000000001O001O001O001O001O001O00001O001O001O001O001O001O001O00001O001O001O001O00Dn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size": [420, 500]}, "area": 9994.0, "iscrowd": 0, "image_id": 10414, "bbox": [0.0, 0.0, 500.0, 31.0], "category_id": 172, "id": 10012738}, {"segmentation": {"counts": "lj55a<b0E;J6K8G9G7I5L4K5K3N3L3M4L3N2M4L3N3L3M4L3N2M4VK]LNe3LdLO^3JkL2W3GSM4n2G[M4g2EcM6_2CjM9W2BRN9P2@ZN;h1^ObN=_1^OjN=X1\\OQO`0P1]OWO>k0_OYO>h0AZO=h0AZO>f0@]O>e0@\\O`0d0_O^O?d0_O^O?c0_O_Oa0b0]O@a0a0^OAa0`0\\OCb0>]OCb0?\\OCc0=\\OEb0<\\OFd0;ZOGd0:[OHd09YOIf08YOJe08YOJf06YOLe06XOLh04WONg03XOOf02XO0h01WO0h00WO2g0OYO2f0NYO3g0NXO3f0NYO4f0JcNfJg0`5e0K\\O7c0I]O8b0H]O9b0H^O9a0G^O;a0E_O<?E@<`0D@=?C@?>BB>>BA`0>@Ba0=_OBb0>^OBc0=]OBe0=[OCf0;[ODf0<ZODg0;YODi0;WOEi0;WODk0;UOEl0:TOEn09SOFn0:SOEn0:ROEP1:POFP1:oNFS19mNGT18lNGU19kNGV18jNGX18hNHY17gNHZ18fNH[17eNH]17cNI]17bNI`16`NJ`16`NIa17_NIa18^NGc19]NGc19]NFd1:\\NFd1:\\NFd1:\\NFd1:\\NFd1:\\NEe1;[NEe1;[NEe1;[NEe1;[NEe1<ZNDf1<ZNDf1<ZNDf1<ZNDf1<ZNDf1<ZNDf1<ZNDf1<ZNDf1<ZNCg1=YNCg1=YNCg1=YNCg1>XNBh1>WNCi1=WNCi1=WNCh1>WNCi1=WNCi1=WNCi1=WNCi1=VNCk1=UNCk1=UNCk1=TNDl1=SNCm1=SNCm1>QNCo1=QNCo1>PNBP2>PNBP2?nMBR2>nMBR2?mMAS2?lMAU2`0jM@V2`0iMAW2?iMAW2`0gMAY2?gMAY2`0eMA[2?eMA[2`0cMA]2?cMA]2`0aMA_2?aMA_2`0_M@b2`0^M@b2a0\\M@d2`0\\M@d2a0ZM@e2a0ZMAe2?ZMCe2>YMCg2=XMEg2<WMEh2<WMFh2;VMFi2;VMGi2:UMGj2:UMHi2:UMGk29TMIj29TMHl28SMJk27TMKj27SMKm25RMMl25QMMo23PMOn23oLOP32oL0P31mL1R30mL2R3OkL3T3NkL4S3NkL4T3LjL6U3KjL7U3JhL8W3IhL9V3IgL9Y3GfL;X3GeL;[3ETMMj25aM_O^2b0gMZOX2h0kMSOT2b1]MZNb2g1`MWN^2i1fMTNY2k1lMSNS2l1QNQNn1o1VNoMi1o1ZNPNe1P2^NoM`1Q2bNnM^1P2fNoMX1Q2jNoMU1P2nNnMQ1Q2ROoMm0P2VOoMh0P2[OoMd0Q2_OnM`0Q2BoM<P2HnM8Q2JoM3R2OnMNT23kMLU26kMGV2;kMAV2`0lM]OU2d0lMXOU2j0lMROU2P1lMmNU2T1lMhNU2Y1mMcNT2_1mM^NT2c1mMYNT2i1mMTNS2m1oMoMR2S2oMiMS2X2nMeMR2]2oM_MR2b2PNZMR2g2oMVMQ2l2QNoLQ2R3RNhLo1Z3UN`Ll1`3XNZLj1g3ZNPLi1R4[NaKm1`4VNTKQ2m4TNfJT2[5oMYJY2g5lMlI[2U6Z21N101QLkIl0V6gN]JS1d5`NnJZ1R5_N[KZ1g4cNaKW1_4gNfKU1[4iNiKU1X4hNlKV1T4gNQLV1Q4gNSLW1m3gNWLW1j3fNZLW1g3gN]LW1d3fN_LY1a3eNcLX1^3fNfLX1[3eNiLY1W3eNlLY1V3eNmLY1S3fNoLY1Q3fNRMW1o2hNSMW1n2gNUMW1k2hNXMU1i2jNYMU1h2iN[MU1e2jN]MT1d2kN_MS1b2kN`MT1`2kNcMS1]2lNeMS1\\2kNgMS1W7M2O2N1O1O1O001O001O001O0O2O001O001O00WJ]OROb0h60UJ^OUOa0g601O001O001OnIB^O>e60kIBC=b60jICF<a6010fIDJ<`6O000001O0000000010O00000001O0000001O00010O00001O00001O00001O00001O00001O00001O00001O1O001O001O1O0aIWOc0j0k5001O001O1aISOe0m0ZOTOf0m0WOUOi0k0VOVOj0k0TOWOk0i0SOYOm0h0QOYOo0h0oNYOQ1h0mNYOS1h0jN[OU1f0iN[OW1e0hN\\OX1e0eN]O[1d0bN_O]1b0`N@_1b0^NAa1`0[NCd1>ZNEe1<XNFh1;TNIj19SNIm18PNKo16nMLQ25mMNR23kMOT23hM1W21eM1[20bM3\\20`M2_20^M3a2O[M3d2OZM3e2NYM3f20VM3i2NUM3j20SM2k20RM2n20oL2o20nL3Q3OkL3T3OiL4U3OgL4X3NdL4[3OaL4^3O]L4a3O[L3e3OSL8k3KnK;P4IhK<X4LZK;d4OnJ7Q5n3O2N2N1O2N2N2N1O2N2N1O3M3M3M3M2N3M3M3M3M3M3M2M4K5K5K5K5K5K4L5L4K5K5K5K5K4LmX6", "size": [420, 500]}, "area": 148085.0, "iscrowd": 0, "image_id": 10414, "bbox": [14.0, 7.0, 470.0, 413.0], "category_id": 183, "id": 10012739}, {"segmentation": {"counts": "c7T4c70001OO1O1O1O1000000000000005K4L1O1O1OO1002NN2001O1O1O8H000000001O001O001O000000O1O100O1H8O100N2O1O100O1001O003M2N6YLmG^3Y8M1O00O10000N2001O002N001OLgLkGX3m7hLSH`3i79001OO11OO1000000001O3M005ZLoG\\3W8OO11O1OO11O00O1N200N2DjLVHW3j7;O1002N2N1O001O00KZLYHf3g75001O00000000O1000000001O2N003MO12N1O1ON2O100N2N22NNYLTHg3l722NO1N21O1OO100O11OO1000000000000O1000000000000MULZHk3f7300O11O3M0000O1O100O100O100001O00000TLYHh3g7XLYHh3g7XLYHh3k700000000000000000000000O100O10000O10000000000001O000TLWHj3l7O00000000000000000000000000000000000000000000000000000000M300000000000000001O001O1O2N0YL[H\\3e7dL\\H[3P80001O2NO1001O00O100DgLXH[3d7eLTHg3k730000O10000O100000000002N2N0000000000000000000000O1N20000O11O00000000001OO11O1O0000002N0000000000000000N200NWLVHi3j720000000000000000000000001OO10000O11O00O10000O100002N001XLUHb3l7]LVHa3l7^L[HZ3f7fLXH[3R80001O1O00O11O000000000000O11O00O1001OJ6I`LUHb3j7_LUHb3k7^LTHc3l7]LTHc3k7^LTHc3l7]LTHc3l7]LTHc3l740000001OO1001O00000000O1O11O001O00000000001O001[LQH`3T8O3M1O1O000000N2000000002N00FfLRHN2\\3h7dLVH3O_3k780000O100O11ON21O001O1O1O00000000000000O10000001OO1001O001OO11OO1001O001O0000002N2\\LRH[3T812NO100O10000002NNeLhG[3X82000000001O00O12N3MLiLgGX3S8gLQH\\3n7eLRH[3n7eLPH]3P8cLPH]3o7dLQH\\3o7dLoG^3o7`LQH20^3n7aLRH11]3m7bLRH10^3m7bLSH00^3n7aLRHc3m74001OO1", "size": [375, 500]}, "area": 59902.0, "iscrowd": 0, "image_id": 10420, "bbox": [0.0, 240.0, 500.0, 135.0], "category_id": 113, "id": 10012740}, {"segmentation": {"counts": "S4`3V810001N0JaLUH_3l752N3N000000001O00O6BYLaHl3W7VLcHO5l3W7YLgHi3Y7WLfHj3Y7WLeHi3\\7902NN3O01JhKjHZ4S77N9H00000O1O2N101O0IPKgIQ5X6PKgIP5X68O11N2N100O1H9N101M4M1O01O010010O21M2O5SISKd6S5M1O00O2O000N3O010O11OO10OL[IWKc6b4]I^Kk6^490O2OO11OO1_KlH_4V70001O0120L105kH^Kl6g40N20O1OO200OO06I3aKkHZ4[7L8^OeH[L]7d3<N2O200kLjGg2X8UMiGk2Y8SMiGj2[8RMgGm2b8O1JXG[Mi8d25001O0gLYMcMf2]2ZMnL<aM]2_5[M_JKO0j1m3h3[L^JI\\1X4l2gKQM<LS4R3bKRM;WONFX4Q4_KQM=PO4LQ4S4_KPMY1lNX3T4_KoL[1kNX3T4_KoLZ1lNDb0P2a3TMcLd1ZOlNo0\\OSOi2Q4\\MRLS2MfMi1l2Q2JUL[MP2j2e1JcL[Mi1m2b1_NeLROd2`2f0WNnLPOe2i2<gM_M[OoN3W3h2=iM^M\\OoN0Y3i2;jMm0V2]O`Md0]2_OaMc0\\2_OdMa0^2]ObMb0]2\\K\\MS45b0_2A^Ma0d2W400OO41OOO101N11OO101O00N2O1YOYMYH1Ag2R8]M[Hg2a7^M_Hb2_7aM`H_2`7aM_H`2a7_MaH`2^7bMaH^2_7bMaH^2_7aMbH\\2BXMl7<aH]2b7cM^H]2b7dM\\H\\2e7dMZH^2d7bM\\Hb2c7`MYH`2j7]MTHe2m7YMSHg2n7oLoG62k2V8TMhGm2Y8701O0OeLhG[3X8201O0O10gLjGQ32iLR82RHQ3LmLR81THQ3JnLR80VH1FQ3d8O00L4N21OO11O4L=C001O3]MlF\\2]9K2MoNlMaHT2\\7kMfG2m0S2\\7lMfG4l0o1_7VN^Hk1b7UN^Hk1^7lMiG;h0i1_7ZNbHe1[7^NeHb1[7^NoG_O`0S2`7eN`H\\1^7hNoGSO144S2f7iMXHW12P1KiMi7`1WHk0i6eMSJn3k5TLUJl3k5TLVJk3j5ULUJl3k5TLUJl3k5TLUJl3k5TLUJl3k5TLVJk3j5ULVJk3j5ULVJk3j5ULSJn3m5RLYIN?0GQ4a6QLYIN>3DP4e6oKYIO<3DQ4g6PLeIV4[6kKYIK4ON[4b6fK\\I90G000[4d6eK\\I<0R4b6dK^I^21Ia6=^IC[6fMiI`2g0WO`5ZNfIOO\\2o0[Oh4YNeJ0H1MY2R1]Oc4ZNZJO0250L2OU2T1^Ob4bN]JIM^2T1WOa4dN\\JHO]2T1WO_4gN]JW2T1RO_4^1aKbN_4]1bK`Na4a1^K_Nc4_1^KaNb4`1]K`Nc4a1\\K_Nd4`1]K`Nc4`1]K`Nc4`1]K`NY4\\NdJ02V3R1]NT4]NiJ0O10V3T1]NR4]NkJO00OW3T1]Ni3]NXK1KNO20V3T1^NU3[NWL0C4NZ3S1XNT3YNSL0H20e3Q1RNQ3XNVLOE0031f3S1PNlN`Ng3GYL21O50J41d3S1PNlNaNf3FZL21O50JP4T1TNa2oM]LLO03T4P1QNa2oM\\L00S4S1oM`2_2`McM^2[2^L]KV1U2]2^2]L]KU1V2^2]2dMdM[2]2dMcM]2]2bMbM]2@_L]N0d2T1_O^2@^L\\N0g00R1T1OkN^Na3H`L]1`0mNCL2KJV10UOU1Z1kNZNi30WL00_1f0gN2<[OKNDOO05V1`1d2_NVLn04nNM4h0M50RO9<HHO1OS1e1b2B`LmNc1M`0d1]1_OV1`0kN]OX1c0hNZO[1f0eN\\OY1c0hNZO[1f0eNiNTLL0I02V5`1fNiNXLFNN20R5c1fNhNYLE]5c1ZNkNj1G[JV1k3VOg1C`JONS1i3VOW2GTJQ1c3ZOX2DUJR1c3SO]LJj5OXJT1b3QOh2o0P4000O11lN_EP1b:oN_EP1d:00PO[En0h:O0O2M2^OVE7i:JYE4V;N20O00000000001N011O1O0N200O3N01OlM_ORIa0m6@^G0T1a0^7_O]G1U1`0]7BXGMK1`1`0\\7CYGLK10N:1G0i0a0R8CYGLK76IK2h0?S8CXGMK770b07V8EUG9MDO9e09Z8AUGk0>E^O[Oo88QGk00mN105j0k8^OoFR2m8>00O1001O001O00O1O1N2N3L2O2000O11O00O2OO10O101OO3N001`GmLZ8T3eGnLX8U3gGRMQ8o2oGoLR8[30010O10O0O11N10000O1O100O0110O0O1001OJ5JkGjLW8U3jGjLV8W3jGhLW8X3iGhLW8W3jGhLW8X3iGhLU8Z3kGfLS8\\3401ON2010NO20bLjG10W3\\801O001O1N1O020O01O02N2NOTM]Gf2e8XM\\Gg2e8XM\\Gh2d8WM\\Gi2d8WM\\Gj20UM_81aGo2_8PMbGe0Ne1]8gMfG>MB0Z2]8eMeG`0NA10OV2b8WN`GB00NW2b8WNhGi1X8WNhGi1X8WNiGf1Y8YNhGg1f7lM^H<Lh1e7mM_H;MHEf1l7ZNbH7NGGg1V7XNPI1D2^1MiNi1R7^NgHH39MHg1g1X5\\NUIh0b1b0\\NcNN2o6\\1^JKcNgN3OQ7b1WJHQ68oIHP69SJDm5<SJCn5=QJDo5<PJDQ6=nIDP6=TI_N7R1a6jNmHe1:`N9Q1Z6kNQI02c16lN6f0b6j0PIhN>>`6l0PIhN>>_6h1`IZN]6j1WInL0NO]1j6^3O1000000O10O10000001OO2UKVIg4n60O010OO20OO10O2O06KN1O1O1021OQI]Kg6h430NO100O10000002NNWIVKi6j43O00000001O00O12N3MTKYId41ZKg60]I]4KgKU7U4lHkKV7S4jHkKX7U4hHkKX7T4iHlKW7T4iHjKY7T4eHlK20Y7S4fHmK11X7R4gHmK10Y7R4gHnK00Y7S4fHmK^7R440L5M1\\L", "size": [375, 500]}, "area": 59215.0, "iscrowd": 0, "image_id": 10420, "bbox": [0.0, 0.0, 500.0, 270.0], "category_id": 169, "id": 10012741}, {"segmentation": {"counts": "aQ71e;101O1O2N1N1ROI\\F8c9KXF:e9FPFf0k9f0eNbNfGO^OS2k8nMYGc2f861O1O0100OJUGbMk8^2VGaMj8^270F_MXG00g2g87O0000001O001O2N00O1O100001O2N001O000000001O00001O001O1O1O1O00O10000O10000000dGgLZ8[30001O003MO1001O1O1O00001O^OnG`MQ8InGH0O0g1?[NB01=b8P1WHcNWO=c8:[GXO9OJ1i01VONY:0hE1i01VON];2cDNc;0`mP21^RoM2`DNT;7lDMP;4oDLQ;4mDNS;;00000OO3oD\\Om:e0RE\\Oh:OXEk0T:f0lE_NQ:e1fK]NYJNT4d1b1aN]Na1b1`NaMKlL0a0f1D[NS53mM4ZM\\1G_NP52ULMcN4X16XOg1g4SNZKL_ONa0a0I^O37D9;g1j3kMeL6D4Kb0FXO7f0JTOO_2V4iM_L0J5BC3]1MT2U4iLPLF00L9<_5h3bJjKF11OO6>7Z5i3nJmKH8[5k3nJlKG2B1i5Q4e11000000N200O1001OlMgKjLY4U3hKVK0T1Y4f3gKUK1U1X4e3jKPKMK1`1X4d3kKQKLK10N:1G0i0Y4Z4kKQKLK76IK2h0W4[4kKPKMK770b0Q4\\4kKoJ9MDO9e0S4`4gKoJk0>a3c4gKkJk00mN105f4o4bKkJn5Q5>00O1001O001O00O1O1N2N2M3N200000000001OO100001OO1000000O10000001O001O0000O100[N[InLI;O3OJn6LfJL\\N:221J?0C5LKn6LQINf1:ZN35DK1Q15WOIh62PINf17^N45Eh06]6JcH0e10ZN4734If00c60]H0g13eNNOO[80ZG1g1OgN0N0]80oH0l900000cR^1", "size": [375, 500]}, "area": 19770.0, "iscrowd": 0, "image_id": 10420, "bbox": [19.0, 0.0, 355.0, 240.0], "category_id": 175, "id": 10012742}, {"segmentation": {"counts": "0S4d7O10000O1I]HYLd7g361O2N000000001O00O1GWHcLj7Y3XHaLO5i7Z3\\HdLe7h2UHhM6_Oe7h2VHiM6]Oe7j2VHhMS8X2nGgMR8Y2PHeMP8[2QHeMn7Z2SHfMm7\\1lGQO8]OIHT8j1nGlN:]On7g1hGaNf0Fc7e1SIZNm6<eG5O^OS20Y6=eGGP;970000O100O10000O3FdD0l_c01Pi\\OOTG0O0g1?[NB01a8>lH;cNWO=2_7=kH_OXO9OJ1i01VONb01]Oh7g1WH[N1i01VONh0g7R2[HVMNn0a7R2_HoM`7U3O1O2N2PIeK`6_4\\IbKc6a4[IaKb6c4ZI_Kd6k4O1O1O000000gL^I^1a6bN_Ii0<aMX6d1`IZNKO0j1h7M`HYNI\\1T8POkGm0<Lo7VOfGn0;WONFT85cGm0=PO4Lm77cGl0Y1lNT78cGk0[1kNS79dGj0Z1lNDb0i5H[I<d1ZOlNo0\\OSOb68cIKS2MfMi1b6[NTLK[MP2W9XNhFi1o7WN_Hd2Z7dM]He2R7kMiHoN3W3R7kMkHoN0Y3S7X1\\OgKjIZ4U6gKiI[4V6eKjI[4V6eKjIZ4W6gKgIZ4Y6gKdI[4\\6?1O1M32N00001O0000001O00N2O1YO^ITL1Ab6W4bIVLc6e3bI[L^6c3eI\\L[6d3eI[L\\6e3cI]L\\6b3fI]LZ6c3fI]LZ6c3eI^L[6b3eI]L\\6c3dI]L\\6c3eI[L\\6e3dIZL]6e3cI[L^6g3dIUL\\6n3aIPLa6Q4]IoKd6Q4RIlK62i6X4VIfKk6[48O1O1NeHhK[7X420000O10hHiKP7Y4lHmKP7S4oHoKP7Q4oHQL1Fl6i4O00L4N21OO11O4L=C001O3bIgJX6a5K2NnNoI^LQ6_3nIcK2m0P6_3oIcK4l0m5a3XJ\\Li5d3WJ\\Li5`3nIgK;h0g5a3\\J`Lc5]3`JcL`5]3`JmK_O`0Q6b3gJ^LY5a3kJlKSO144P6i3lIULW12m4i3\\KRLe4o2kImMh7Q2ZHoMf7Q2ZHPNe7P2[HoMf7Q2ZHoMf7Q2ZHoMf7Q2ZHoMf7Q2ZHPNe7P2[HPNe7P2[HPNe7P2[HmMh7S2XHSMN?0Gk7g2WHSMN>3Dj7k2UHSMO<3Dk7m2VH_MP8a2QHSMK4ONU8h2lGVM90G000U8j2kGVM<0l7h2jGXM^21c3g2cLXM]3a2lIcM`2g0Q3f1`J`MOO\\2o0U3n0_J_N0H1MY2R1W3i0`JTNO0250L2OU2T1X3h0hJWNIM^2T1Q3g0jJVNHO]2T1Q3e0mJWNW2T1l2e0dM[O\\2e0cM\\O]2d0dM[O\\2f0bM[O^2e0cMZO]2f0dMYO\\2g0cMZO]2f0cMZO]2f0cMZO]2<_JaN02V3R1Z27`JfN0O10V3T1Y26aJgNO00OW3T1Y2MaJTO1KNO20V3T1Y2ZO`J20C4NZ3S1R2ZO_JM0H20e3Q1j1YO`JNOE0031f3S1h1VO_JF21O50J41d3S1h1VO_JF21O50JP4T1`1UOcJILO03T4P1]1UOcJH00S4S1Z1UOTOk0l0UOROG]KV1_5SOTOG]KU1`5TOSOn0m0ROTOm0l0TOTOk0l0SOVLI]N0d2T1i2TOVLH\\N0g00R1T1e3PO`JL]1`0mNCL2KJV10UOU1T5lN`JO00_1f0gN2<[OKNDOO05V1`5jN]K4nNM4h0M50RO9<HHO1OS1h6RO\\Ic1M`0d6c2M3M32NM3_OiJ[J0I02V5j5nJUJNN20R5l5oJTJ]5o543MK52NI_JYJ`5_5[JeJn5[5700000N200K^JRJe5Z5f0O1gKfIPONT4[6kLjIT3W6lLjIW2KlL0a0\\6<hIX24ZMS6>jI>McN4X16XOo5?kIBL_ONa0a0I^O37D9;Q6?gID4Kb0FXO7f0JTOOo6e0YIJ5BC3]1Mf65mG00L9<Q:BfE11OO6>7X:FhE8Z:GfE2B1`Pf04`oYOI;O3On6d1dH\\N:221J?0C5Ln6OgHf1:ZN35DK1Q15WOm6HlHf17^N45Eh06[Oj6DPI10e10ZN4734If00@Q7_OoH10g13eNNOOX1Q7\\NoH11g1OgN0N0X1P7^NPIf10l0P7YOPIg0P7YOPIg0P7YOPIg0P7o1100O1000000N2N2001ON2001ON20hHdK10P7c401O001O1O00O11O00002N2NO[IVK_6l4_IUK`6l4_IUK`6l4_ITKa6l4_ITKa6l4_ITKa6m4^ITKe0NT5n4XJUK>MB0h5o4WJSK`0NA10Oc5U5jJmJB00Nd5U5jJUKV5k4jJUKV5k4jJVKU5j4jJWKV5W4]JmK<LW5V4^JnK;MHEU5]4kJQL7NGGV5g3iJ_L1D2^1MiNV5e3QKTLH39MHg1T5k1oJbLh0b1Y4l1ZLkMf3T2]LjMc3V2]LjMc3U2^LnM_3R2aLnM_3R2aLmM`3S2`LlMa3T2`LkM`3T2aLPM_N7P5c2lJkLe1:`N9n4]2nJnL02c16lN6c4e2mLmLhN>Z4d2PMlLhN>Y4d2mM[MT2c2PNQMnL0NOW5P3^3O1000000O10000000000001ZGQMb8S3O001O001O0000O1006JO1O1O1003M1O2N0000000000000000001O00000000000000000kGbLQ8^3mGgLP8c3M2N00000000000000000000L4N2", "size": [375, 500]}, "area": 48613.0, "iscrowd": 0, "image_id": 10420, "bbox": [0.0, 0.0, 500.0, 212.0], "category_id": 181, "id": 10012743}, {"segmentation": {"counts": "0;Z1h8n2XGmMl7T2THZO^6f0aIi0S5UOmJZ2c3gM]Lg3T2ZLkMQ5k0oJUOQ5k0oJUOR5k0mJTOT5k0mJUOS5l0eFmNU47W5m0iJROX5n0hJROY5n0fJROZ5m0gJROZ5l0hJTOY5l0fJTOZ5j0hJUOY5k0gJUOZ5k0eJUO[5m0cJRO^5P1`JPOa5n0`JQOa5o0_JQOb5o0]JQOc5o0]JPOe5o0[JQOe5o0[JQOf5n0ZJQOh5n0XJROh5n0XJROi5m0WJSOi5l0XJTOi5k0WJUOi5k0WJUOj5j0VJVOj5j0VJVOk5i0UJWOk5i0UJWOl5g0UJYOk5g0UJYOl5f0TJZOl5f0TJZOm5e0SJ[Om5d0Y4O001O1N101O001O001O001N0100O100O10O01O100O10O0100O10O0100O010O10O1O11O0O10O2O2M101I^ALd>N]A1`X33WVMLXA4g>4M200]AG^>;aAE_>;aAE^>`0N100O100O100O100O010O10O0010O2OO0001O1O0O100000000000002N1O1O001O1O1O2N0010O11WBmNd=R1[BoNe=Q1ZBPOf=U100^BjNZ=V1bBiN02_=T1aBjNO3`=S1`BPO`=X1001O00000000XGcNn3\\1RLdNn3\\1RLdNn3\\1RLdNn3\\1RLdNm3]1SLcNl3^1TLbNl3]1ULcNj3]1WLcNi3V1^GlNj4Ng3W1aGiNh40g3X1^LhNa3V1bLkN\\3V1dLjN\\3U1eLkNZ3W1eLiN[3W1eLiNZ3W1gLiNX3X1hLhNX3X1hLhNW3Y1iLgNW3X1jLhNU3Y1kLgNT3Z1lLfNT3[1kLfNS3[1mLeNS3Z1nLfNQ3[1oLeNP3W1kGiNU50P3X1TMhNk2Z1TMfNl2Z1TMfNk2[1UMeNk2]1SMcNl2^1TMbNk2^1VMcNi2]1WMcNh2_1WMaNi2`1VM`Ni2`1XM`Ng2b1XM^Nh2b1XM^Ng2b1ZM_Ne2a1[M_Nd2a1]M_Nb2b1^M_Na2a1_M_N`2b1`M_N_2`1bM`N]2`1dM`N[2a1eM`NZ2a1eM_NZ2`1hMaNW2`1hM`NW2`1jM`NU2b1jM_NU2a1kM_NT2b1lM_NS2a1dHUNV5:U2f1jMZNV2g1iMZNU2g1kMYNT2j1jMWNU2i1hHkMQ5<V2m1iMSNW2m1iMTNU2m1kMSNT2n1lMRNT2m1mMTNQ2l1PNTNP2l1PNUNn1l1RNTNm1l1TNTNl1k1UNVNi1k1WNUNi1k1WNVNg1i1[NWNd1j1\\NVNd1i1]NXNa1i1_NWNl0_O^J[2f4WNl0AXJX2n4WNk0DPJV2LdMX5b0m0V2kIXMX5c0m0T2VOlMk0S2UOnMk0R2TOnMm0o1UOQNm0m1SOTNm0k1SOUNn0j1ROWNn0h1ROXNo0f1ROZNo0e1QO[NP1d1POZNS1e1mNXNW1f1jNYNX1f1hNXN\\1f1dNXN_1h1`NWNb1g1_NWNd1g1]NXNe1h1ZNVNi1h1XNWNj1h1VNWNl1h1TNXNm1g1SNZNm1c1UN^Nk1a1UNaNk1_1SNbNm1]1SNcNn1]1QNcNP2]1oMbNS2^1lMbNU2\\1lMcNV2[1kMeNV2Y1kMfNW2W1kMiNV2V1jMjNW2U1iMjNZ2T1fMlN[2S1eMlN]2S1cMmN^2R1bMmN`2R1`MnNa2P1`MoNa2Q1_MoNb2P1^MoNc2S1[MmNf2R1ZMmNg2R1ZMnNf2R1ZMmNh2T1VMlNj2U1a5O05LO01O01O001O001O0O101O001O001O00001O001O0010OO101O010O001O0NoAUOQ>k0oAVOg6Oa0i0CWO=i0n6O0010lAYOMOV60jI14OY7h0fH\\Om5LRJ6NL]7g0eH]Oj5LUJ8^7`0aH^O_5IhJ?X7e0mMPOhJ;]7f0hMQOjJ9^7h0fMC\\2<cMD^2=`MD`2<\\MjNPKi0f7<ZMHf28WMiNSKo0h76UMMk22UMNm2OSM2n2NQM3o2MPM3R3KoL5Q3JPM5R3JnL5S3KmL5T3JlL5U3KkL5U3KkL4W3KiL5W3KiL4Y3KgL4Z3MeL3\\3LdL3]3McL3]3LdL3^3MaL3_3MfGWOh4k0d3McGYO34OLN0W4i0W43cGAU4<Y42aGCV4:Z4e0aGbN0003h3f0g4l0`G^Ni3e0i4P1XKPOh4P1XKoNj4Q1UKnNl4R1TKnNm4Q1SKnNn4R1RKnNn4Q1SKnNo4Q1QKnNP5R1PKnNQ5Q1oJoNQ5R1nJnNS5P1nJPOR5P1nJPOR5P1nJPOS5o0mJQOS5o0mJQOT5n0lJROT5n0lJROU5m0kJSOU5m0kJSOU5m0kJSOV5l0jJTOV5l0jJTOV5m0iJSOX5l0hJTOX5k0iJUOW5k0iJUOW5k0iJUOX5k0gJUOY5j0hJVOX5j0hJVOX5j0hJVOX5k0gJUOZ5j0fJVOZ5j0fJVOZ5j0fJVOZ5j0fJVO[5i0eJWO[5i0eJWO[5i0eJWO[5i0eJWO\\5h0dJXO\\5i0cJWO]5i0cJWO]5h0dJXO]5h0bJXO^5h0bJXO^5h0bJXO^5h0bJXO^5h0bJXO_5g0aJYO_5g0aJYO_5h0`JXO`5h0`JXOa5f0`JZO`5f0`JZO`5f0`JZO`5e0aJ[O`5e0_J[Oa5d0`J\\O`5d0`J\\O`5d0`J\\Oa5c0_J]Oa5c0_J]Oa5c0_J]Oa5c0_J]Oa5c0_J]Ob5b0^J^Ob5b0^J^Ob5c0]J]Oc5c0]J\\Oe5c0[J]Oe5c0[J]Oe5d0ZJ\\Of5c0[J]Of5b0ZJ^Of5c0YJ]Og5c0YJ\\Oh5c0YJ]Oh5c0WJ]Oi5a0YJ_Og5c0WJ]Oi5b0XJ^Oh5b0XJ^Oi5a0WJ^Oj5b0VJ^Oj5c0UJ]Ok5d0TJ\\Om5c0SJ]Om5c0SJ]Om5d0RJ\\On5d0RJ\\Oo5c0QJ\\OP6d0PJ\\OP6d0PJ\\OP6d0PJ\\OQ6c0oI]OQ6d0nI\\OR6c0oI]OQ6c0ZFnNc3>T6f0lIZOU6g0iIYOW6g0iIYOW6i0gIWOY6j0fIVO[6i0eIWO[6i0eIVO\\6j0WFiNZ3=_6m0aISO`6m0_ISOa6Q1[IoNe6Q1[IoNe6Q1[IoNf6P1ZIoNg6Q1YIoNg6Q1YIoNg6R1XInNh6R1XInNi6R1VInNj6R1VInNj6S1UIlNl6T1TIlNm6T1RIlNn6T1RIlNn6U1QIkNo6U1QIkNP7U1oHkNQ7U1oHkNQ7U1oHjNR7V1nHjNS7T1nHlNR7U1mHkNS7U1mHkNS7U1mHkNS7U1mHkNS7T1nHkNS7U1mHkNT7T1lHlNT7V1jHjNV7W1iHiNW7W1iHiNW7X1hHhNX7W1iHhNX7[1eHeN[7X1hHhNY7W1gHiNY7W1gHiNY7V1hHjNX7V1hHjNX7W1gHhNZ7W1gHiNY7W1gHiNY7V1hHjNY7V1fHjNZ7V1fHjNZ7U1gHkNY7T1hHlNX7T1hHkNY7T1hHlNX7U1gHkNY7R1fEiNQ35Z7Q1fEiNP36Z7Q1iHoNW7P1jHPOV7P1jHoNW7R1hHnNX7Q1iHoNW7Q1iHoNX7P1hHPOX7Q1gHoNY7Q1gHoNY7Q1gHnNZ7Q1gHoNY7P1hHPOX7Q1gHoNY7R1bEjNR34]7S1cHmN]7S1cHmN]7T1bHkN_7U1aHkN_7W1_HiNa7X1`EfNk22e7X1`EgNj21f7\\1`EbNg22j7]1`E`Nd23l7`1TH_Nm7`1TH`Nl7`1TH`Nl7`1TH`Nl7`1TH`Nl7`1TH`Nl7`1TH`Nm7^1THbNl7]1UHbNl7^1THbNl7]1UHcNk7Z1\\EeNj21j7W1\\EhN10i21j7W1\\EhNm21g7W1[HiNf7U1[HkNe7U1[HkNe7U1[HkNe7U1[HkNe7U1[HkNe7U1[HkNe7U1[HkNf7T1ZHlNf7U1YHkNg7U1YHkNg7U1YHkNg7U1YHlNf7U1YHkNg7V1XHjNh7W1WHiNj7Y1SHgNm7Y1SHgNm7[1QHeNo7Z1RHfNn7Y1SHgNm7Y1SHgNm7Y1SHgNm7X1THhNm7W1SHiNm7U1YEiNj22m7T1VHlNj7S1WHmNi7S1WHmNi7Q1YHoNg7P1ZHPOf7m0]HTOc7k0]HUOc7k0]HUOc7k0]HUOc7k0]HUOc7l0\\HTOd7l0\\HTOd7l0\\HTOd7l0\\HTOe7l0ZHTOf7l0ZHTOf7l0ZHTOf7l0ZHTOf7k0[HUOe7k0[HUOe7k0[HUOf7j0ZHVOf7j0ZHVOf7j0ZHWOe7i0[HWOe7h0\\HXOd7h0\\HXOd7h0\\HXOd7h0\\HXOe7g0[HYOe7f0\\HZOd7f0\\HZOd7f0\\HZOb7g0_HYO`7h0`HXO^7i0cHWO\\7i0eHWOY7k0gHUOW7l0jHTOU7l0lHTOR7m0oHSOP7n0PIROn6o0SIROk6n0VIROh6o0YIQOf6P1ZIPOd6Q1]IoNa6R1`InN_6S1aImN]6T1dIlN[6T1fIlNaLN_9X1PJ", "size": [480, 640]}, "area": 32199.0, "iscrowd": 0, "image_id": 10421, "bbox": [0.0, 0.0, 640.0, 394.0], "category_id": 139, "id": 10012744}, {"segmentation": {"counts": "h:X4g:1002NO1O100001OO11hKYEU4i:0001OO1N21ON2001O2N2NO1001O00000000000000O1000000000000O100000000O1000000O100000000000000O10000000000O100000000O1000000O10000O10000O1]OcKbF^4^9bKcF]4\\9dKdF\\4\\9cKeF]4Z9dKeF]4Z9eKeF[4[9gKbFZ4]9gKcFY4]9hK\\FILa4g9gKWFI1f4h9>O100O100O10jJ^Fn4a9QK[FO4P5a9PKcFo4g900RKRFj4o9UKQFk4o9UKQFk4S:O0000000000000000O10000O1O1001OO1O10000O1O1O1O1O1O1O1O11O0000O10000001OO100O11lJXFn4h9QKYFo4g9PKZFP5k900TKTFd4l9XKSF02h4k9XKSFO3i4j9WKYFi4o90000000000000000000000000O1O1IWKVFj4j9XKTFh4m95M300O11O00O1000000O100001O00O100KkJ_FU5b941O00002N00O1001O1OO11O00O100O1000000O1O1001ON21OO11O00000gJ\\FV5h9O1O002N0mJUFQ5n9O000000O1O10000O1O10000N200O10000N2NgJ^FX5b9hJ^FX5b93001ON200000000O100VGcJQ8]5mGeJS8[5jGiJT8X5kGiJU8W5iGkJW8U5gGnJY8Q5fGPKY8Q5eGQKZ8P5eGRK[8m4cGUK\\8l4cGVK\\8j4cGWK]8i4cGWK]8i4dGWKY8k4hGTKX8l4jGSKW8k4jGTKV8l4jGTKW8k4iGVKW8i4hGXKY8g4gGZKW8g4hGZKW8g4iGYKV8h4iGZKT8h4lGXKT8h4lGYKS8g4lGZKT8f4lGZKT8f4kG\\KT8d4lG\\KT8d4kG^KT8b4lG^KS8c4lG^KT8b4lG_KS8a4lG`KV8^4jGcKU8]4jGdKU8]4kGcKU8]4jGeKj0jNa6_5eHgKj0jNb6g6\\IYIe6g6[IYIj6b6UI^Il6c6SI]In6c6PI]IQ7d6nH\\IR7f6kHZIV7h6hHXIX7i6fHWI[7k6cHUI]7l6aHTI_7o6_HQIa7Q7\\HoHe7T7XHlHh7U7100O100O1O100O100O2OO01O101N100O1OOVInGj6R8VIoGg6R8400000O2YIlGMOV60jI14V8OmGm5LRJ6NT82lGj5LUJc8OcG_5IhJU9U5oFhJT9U5nFjJT9T5<O2N1LXFPKh9P54MRFSKn9m420O02O1O01O100000000000000001O0000O11O0UKnEh4R:WKoE34OLN0W4V:jKWFU4i9jKXFV4Z:kKWE0003h3m:WLSEi3Q;1001O000000O10000001OO100000000000000000000001O00O100001OO100001O0000000000000000001O00O11O0000000000001O00O10000O11OO10000000000000000001O0000001OO1001O00O11ON22NO10000001O1O00001O0000000000001OO10[LgDc3[;02N002N1O00000cL`DZ3c;01O4L00000000001O001O001O001O001O001O000000O11O000000O100002N1O001OO13MM30000O1001OO100O11O00O1O100O11OMoLWDQ3i;PMVDP3j;3O1001OO100001O0000O1O11O1kLVDR3l;0001O002N1QMQDk2o;UMRDj2R<VMmCg2U<ZMjCd2Y<0O10000000000O1O100O1MWMnCj2o;VMQD10i2o;VMQDm2o;2O1000000000000001O0000001O1O1O3M002NO1O10000O100NVMnCj2Q<3O100N2O1M3000000001O0000001O000000O1000000000000O100000000O10000O100O1O100O1O1O100O1O1O100O1O100O1O100", "size": [480, 640]}, "area": 90341.0, "iscrowd": 0, "image_id": 10421, "bbox": [0.0, 0.0, 640.0, 480.0], "category_id": 167, "id": 10012745}, {"segmentation": {"counts": ";g0Y>^1bN^1aN_1bN_1aN^1aN[1fN001N10001N10001N10001N10001N101N101O0O2O001N101O001O001O001O001O1O001O001O001O001O001O001O001O001O00O100O100O100O100O100O100O100O1O100O10O0100O100O100O100O100O100O100O100O1O100O100O100O100O100O100O100O100O100O100O1O100O100O1O1O1O1O1N2O1O1O1O1O1O1O1O1O1O1O1O1O1O100000000001O000000001O0000001O0001O0000000O100O1O100O100O2N100O100O1O100O100O100O2N100O100O1O100O100O1O2O0O100O1O100O2O0O101N1O2O0O101N1O2O0O101N1O2O0O101N1O2O0O101YLcHH^74WHmL=e2]7:XHZM>R2[7>ZHjM=]1Z7e0[HWN:n0\\7f0[HfN7>_7h0\\HSO21b7i0]H@NAg7n0YHJMSOk7R1VH5LeNo7U1SH`0KUNS8Z1QHi1o7WNoGj1S8TNkGn1V8j1kG[JX8e5kGUJW8l5mGmIU8T6<0O100O1O2O0O100O1O100O100O1O101N1O100O100O1O100O10hJgGh3X8XLhGj3V8ULjGm3V8RLjGo3U8PLkGR4U8mKkGT4T8kKlGW4S8iKmGX4S8fKmG\\4R8dKnG]4R8aKnGa4Q8_KoGb4P8]KPHf4o7YKQHh4n7WKRHk4n7TKRHm4m7RKSHo4m7QKSHP5m7nJSHS5m7mJSHT5m7jJSHW5m7iJSHX5l7gJUHY5l7fJSH[5m7dJTH]5l7bJSH_5m7`JTHa5k7_JTHb5m7\\JTHe5l7ZJSHg5o7VJRHj5o7TJQHn5P8PJPHP6Q8nIoGT6R8jInGV6S8hImGZ6T8dIlG\\6W8`IiGa6[81N101N101N1O101N101N101N1O101N101N1O2O0O101N101N1O2O0O101N101N1O2O0O101N101N1O2O0O101N1O2O0O2O0O101N1O2O0O2O0O101N1O2O0O2O0O101N1O2O0O2O0O1O2O001O001O00001O001O001O00001O0000001O0000001O0000001O000000001O0000001O0000001O000000001O0000001O0000001O0000001O000000001O0O10001O0000001N1000001O00000O101O0000001O000O101O0000001O0O1000001O00000O2O0000001O000O101O000000001N1000001O0000001N100000001O000O10000000001O0O1000000000001N100000000000001N1000000000000O2O00000000000O2O00000000000O101O000000000O10001O000000000O10001O0000000000001O0000000000000010O00000000000001O000000000000001O000001O00000001O0000000000001O000000000001O01O00000000000000N2N2O1N2O1N2O1N2N2O2M2O1N2O1N2O1N2WFTK\\9n4aFoNd5l0", "size": [480, 640]}, "area": 183847.0, "iscrowd": 0, "image_id": 10421, "bbox": [0.0, 0.0, 640.0, 350.0], "category_id": 183, "id": 10012746}, {"segmentation": {"counts": "PlZ32nc0001V\\O0M2_c0<O0000000000000000000000000000000000002N5K7IO11OL42N5KO100O11b]OROha0P1U^OWOea0c1O3M1O1O001O1O1O2N3M2N5K1O00O10000Mc^OcM]a0]231O1O1O00O100M3O1001O1O0_^OeM]a0`2O1O0000O100N21O00O11OO100O1B>N20000O11OO10000000000000000000000000000?A009G1OO1000000000000000000O1000000000000001OJ65K002N001OO1001OO1O1O1O100O11O1O00O1O100O1O1O1N2O1M_^OgMaa0X240000O100001O00001O00001O1O002N1O2N1O3M1O2N00O13M00N2N2O1O1O12NL4O10000@b^O^N^a0b1d^OUNN0^a0k1e^OQNM012^a0m1h^OPNK2]a0n1?0000N21O003MM31O2NN2O11O001O1O1O1O2N2N1O1O1ON200O100O1O1O1O11O5KN2O1O1N200001O000000000000O100L40000O100000000000000000000000000", "size": [640, 427]}, "area": 17439.0, "iscrowd": 0, "image_id": 10428, "bbox": [171.0, 0.0, 256.0, 91.0], "category_id": 102, "id": 10012747}, {"segmentation": {"counts": "kXZ256LYc0?N1N31O1O002Lb\\OC_c0=21O000O2e\\OFnb0h0N1JROX]OP1eb0QOY]OR1eb06N3L3L300O1O0100O2O000O]NbNd@`1Z?jN^@V1c?lNZ@S1h?mNi_OIAZ1f`0oNd_O_ODo1h`0cNX_O_ONY2k`0XNW_Ok1i`0UNV_Ol1j`0d0100O001N2O1O1N2O2N2iMj^O`1Xa0_Nk^OM1CK13Q1Xa0XOo^OO;c0g`0^OP_OL?`0b`0DQ_OI?`0b`0FQ_OJ=>c`0HR_OH==b`0JU@0P`00Q@HV`07j10000005K001O01O1O1O010O1O00100O001O1O010O1O001LYQ^4", "size": [640, 427]}, "area": 3149.0, "iscrowd": 0, "image_id": 10428, "bbox": [118.0, 458.0, 81.0, 107.0], "category_id": 132, "id": 10012748}, {"segmentation": {"counts": "n11Y1_2d3QOeK5l6_NiI[2e3ROfK3k6\\NdH5U1Y2h3ROeK2g6jNjIS2j3QOeK2d6oNjIo1l3ROdK0g6POhIn1m3ROeKOf6QOiIm1l3SOeKOg6POhIn1k3TOeK0j6iNhIS2i3TOeKOP7aNfI\\2e3TOeKOo6bNhIZ2d3UOfKNS5iNgK0IU2g3TOeKNU5QOlJ=?a1k3SOeKNU5ROiJa0`0Z1m3VOfKNR5QOlJ?`0\\1l3VOfKNR5POlJ`0a0\\1k3VOeKOS5QOiJ?d0[1k3VOeK0R5TOeJ=g0Y1e3cMbKd1:1R5VOaJf3c4gM]KOn4[3DRO<o0BRO>S1SKeJ=G@K30N0f2g4Z2m4\\KVKd4S:00000001O1O0000O10000006J000WKl@f4T?YKm@00d4R?]KPAd4V?000O11O0000\\O\\KkAe4S>]KnAb4Q>`KnA`4Q>bKnA^4R>cK_AH9h4NXKI031l=>UB_4L\\Kn=6UBj4k=VKUBl4i=UKVBk4k=f0O0O100O100O10000O12N1O2N1012M4M1NO0000O2O001N100O10O11_BdIf<1kCd6P<]InCf6o;]ImCe6R<^IlCe6Q<]IkCh6Q<ZImCh6R<ZIkCf6U<]IiC_6[<d0N102M2N2OO0M41N3N1N3M1O1O2ONUDoGj;R81100O1O102MN31N010O1O1O2O0O10000O01000oDgGZ:Y8eEmGV:T8kEoGP:S8lERHR:n7mETHQ:m7SFoGl9Q8UFoGj9R8TFPHl9R8nERHQ:o8O1O1O101N012M4L100O10000hFQF^8o9bGSF]8m9bGUF\\8l9cGVF[OLl8o9iGZFU8g9jG[FT8f9kG\\F`7LSHi9<]Fa7JRHk9;\\F]7OVHk97]F]7JYHo94XFb7_;O1N200O11NnHSDo5P<PJPDQ2CbNOmN1e2]<iMRDP2D`N0ROMe2^<gMTDk1]OiM7d0J]O<o1DPNO`0`<MRDe1LaN3]1JVNW<7QDc1M`N3X1@YN=2Q<9SDd1M`N3d0]OlNO1022Oe00i;>SD`0_OJb0D1e0BPOLMZ=`0VC?_OQOO02a0b0M0e0Y=^OcB=N]O91Nb0^=B_B2]OFa0193Kd0Z=PORBa0a0KEK;M<8Gd0Z=QOQB`0a01B@NNb01?;Be0[=BbBMd1IaNf0Y=EcBMd1EaNg0\\=E_BNd1GaNf0]=G[BJb2f0R;@\\BK_2h0V;ZO]BNc2a0R;DaB]Oa2P1n:AcB^OZ2U1S;]OkE7VLWOS>>jE6c:BZAMX4OhKO05f>LYA21MX41iKN41Zc0O[_d4", "size": [640, 427]}, "area": 64087.0, "iscrowd": 0, "image_id": 10428, "bbox": [0.0, 0.0, 189.0, 491.0], "category_id": 133, "id": 10012749}, {"segmentation": {"counts": "VoW41oc04S\\O2_c09N100O1B?0O1OO2M2_^OnNV`0Q1i_OPOV`0P3KH[O=N8OMO110N1001O1OO2O000O11O1O0nL^_Of2a`0RMi_Om2e`0O1000000O101O]OUMQ@T2A^N``0]OP@l14VNh`0<U2G^\\O0hb0KR^O1ZO2hc0O1O1OZTV3", "size": [640, 427]}, "area": 3172.0, "iscrowd": 0, "image_id": 10428, "bbox": [217.0, 336.0, 46.0, 114.0], "category_id": 139, "id": 10012750}, {"segmentation": {"counts": "`?=[1Je`07Z_OJf`07Y_OIf`09X_OHg`09X_OHi`07W_OIi`07W_OJg`07Y_OJg`09S_OIm`0b0f^O@Za0a0e^O@Za0a0e^O_OPa0Ea^Oo0>^OO2B\\OX`0^1V@TOO>GPOd?b1c@PO1m0S?9g@jN5o0R?T1k@nNT?]3B00000O100000000001O1O0000O10000006J000WKl@f4T?YKm@00d4R?]KPAd4V?000O11O0000\\O\\KkAe4S>]KnAb4Q>`KnA`4Q>bKnA^4R>cK_AH9h4W>gKdAZ4[>gKdAZ4[>fKeA[4Z>fKeA[4[>e000O100O100O10000O12N1O2N1O2N1O2N1O0000O10000O100O1000_N^AdMl=bNgBi3_OdMj=dNeBh3BdMh=gNbBf3GcMf=jNaBa3KeMd=lN]B`30dMb=nN\\B^34cM`=QOYB\\38cM^=TOXBX3;dM]=n2dBRM[=n2fBRMZ=l2hBTMW=l2kBSMT=m2mBSMS=l2nBTMQ=l2PCTMP=k2QCUMn<k2SCUMm<j2UCUMj<k2WCUMh<j2ZCVMe<j2\\CVMc<j2^CVMb<i2_CWM`<i2aCWM^<i2dCVM\\<i2eCWMZ<i2gCWMX<i2iCWMW<g2kCYMT<g2mCYMS<f2oCYMP<g2QDYMo;f2RDZMm;f2TDZMk;f2VDZMi;Z2nBbL[1T1g;U2jDjMU;T2nDlMR;P2REPNn:P2REPNm:Q2SEoMm:P2TEPNl:Q2TEnMk:S2UEmMj:T2VElMi:U2WEkMi:U2WEkMh:U2YEkMf:V2ZEjMe:W2\\EhMd:S2SCiLY2T1c:S2UCgLZ2V1`:S2eEmMZ:S2gEmMX:R2jEnMV:o1nEPNQ:Q2oEoMQ:P2PFPNo9P2RFPNm9Q2SFoMm9Q2SFoMl9S2TFlMl9T2TFlMl9T2TFlMk9a0^CHi2Gi9?hCB_2Oh9a0kC]O]22g9c0lClNIAd2P1f9d0oCfN_ODZ3Q1W9f0QDYN_ONe3S1W8XOYD_1<XNW3Q1U8XOWD_1=WNX3R1o7^OZDZ1\\4XOZ7@WDY1_4WOY7_1hH`NX7_1iHaNV7`1jH`NW7_1iHaNW7_1iHaNY7]1gHcNW7_1jH`NW7`1hH`NX7a1gH_NY7a1ZEcMmNKT3Q1c8d1XEhMR2d0f8d1WEiMS2c0g8c1UEkMU2a0f8d1VEjMT2b0f8d1VEjMT2b0f8d1VEjMT2b0e8e1XEhMS2c0e8V1gEWNd1c0e8T1jEXNa1d0c8U1mEWNa1c0b8T1oEYN_1c0b8S1PFZN^1c0`8U1SFXN\\1c0c8R1RF[N[1c0c8R1RF[N[1c0c8Q1SF\\N[1b0Q8YOREa1m1cNP1c0R8W1oFVNo0c0o7o0]G^Nd0c0n7P1^G]Nd0c0o7o0]G^Nd0c0n7P1^G]Ne0b0l7S1^G[Nf0b0l7S1^G[Nf0b0l7S1^G\\Ne0a0m7S1^G\\Ne0a0j7SOSER2]2ZNf0a0j7W1`GXNg0`0k7V1UEUNl15T1`0g7[1XEVNg1OZ1`0`7b1_ERNd1L]1`0e7]1YEYNd1J^1`0e7T2lF\\M_1`0d7V2lFZM`1`0c7X2lFXMb1?b7Z2kFWMc1?`7]2kFVMd1=_7`2kFTMf1<`7`2bEoLd06Z2;`7R3UFdL[2:_7T3UFbL]29]7V3UFbL^28\\7X3TFaL`27\\7Y3RFaLb26_7V3nEeLc25]7Y3nEcLe24^7X3lEeLg22^7Y3cElLo2K^7a4aH_K_7a4aH_K^7H]EW4U3QL^7HaE@2Z4o2^L_7FkEk3f2_L`7DkEm3f2^L_7EkEm3f2^L`7CkEo3e2^L_8a3aG_L_8a3aG_L_8a3aG_L`8`3aG_L_8d0ZDa1V3kMa8c0ZDa1U3lMa:a1_E_Ng:[1YEeNe:]1[EcNi:Y1XEfNh:Z1XEfNh:Z1XEfNi:Y1WEgNi:Y1WEgNi:Y1WEgNi:Y1XEfNh:[1WEeNj:Z1VEfNj:Z1VEfNj:Z1VEfNj:Z1VEfNj:Z1VEfNj:Z1WEeNi:[1WEeNi:[1WEeNh:\\1XEdNh:\\1XEdNh:\\1XEdNh:\\1YEcNf:_1YEaNg:_1YEaNg:_1YEaNf:`1ZE`Na:e1_E[N^81YER2Z2lM]82ZEQ2Y2mM]82ZEQ2Y2mM]82[EP2X2nM]82[EP2X2nM]82[EP2X2nM]8D[D5P1Y2Y2mM]8B]D7m0[2X2lM_8@]D7n0]2V2lMh8ESE_2U2lMT:T2lElMT:T2lElMT:T2lElMS:U2nEjMR:V2nEjMR:V2nEjMR:V2nEjMR:V2nEjMR:V2nEjMR:W2nEhMR:X2nEhMR:X2nEhMR:X2nEhMS:W2mEiMS:W2mEiMS:W2nEhMR:X2nEhMR:X2nEhMR:X2nEhMR:X2nEhMR:X2nEhMS:X2lEhMT:X2mEgMS:Y2mEgMS:Y2mEgMR:Z2nEfMR:Z2nEfMQ:[2oEeMQ:[2PFdMo9]2QFcMo9]2QFcMo9]2QFcMo9]2QFcMV9LmCb2l2bMh8IoC<:Y2P3aMf8?[DP2o2aMe8`0\\Do1o2aMd8a0]Dn1o2aMc8>QDYO=h2o2aMc8>QDXO>i2n2aMU7FiFh0XOQ2k2`MT7HhFg0YOQ2k2`MS7IhFg0ZOP2k2`MS7KfFi0XOl1o2`MP7OhFf0XOk1P3`Mo62fFd0WOQOIi2U3eMX7LaFe0YOo1d2jM`7F`Fb0\\On1Y2UNh7_O]F]O\\OR15m1P2_NW8QOVFi0Cg1f1iNa8hNSFj0Fe1[1TOl8^NPFl0Hc1Q1]OQ9]NQFe0HaN0R3k0E\\9UNmEe0L`NOR3a0Ni9dMjE[OK21NOc11b147Y:UMhEBN`1MU5`:eIfEEN\\8]:nGfEEM]8^:iGiEJI]8\\:gGoEKF^8[;O00000000000000000000000000000000000SHcDW7T<O00001O1O00001ON22NO11\\HjC\\7^<O001O1O000000000000000000M300002N6J0000O13M1O00000000001O0000000[JVCT4j<lKXCR4i<YKQCK;_OEW5o<_KWCE7l4b<iJRCa0<D4o4^<lJQCb0U1b4j;lJRCf0o0^4o;lJSCd0o0`4R<_KoCa4T<^KjCb4V<_KiCa4W<_KiCa4X<_KgCa4Z<^KfCb4[<_KdC`4[<_KiC_4X<WKPDj4S<TKkCm4W<RKgCR1kNlN0c3i>bM\\AjNL_3g>hM]AgN1^3a>oMcAQ2`>lM`AT2`>hMeAW2[>gMgAY2Y>gMgAY2Y>gMgAY2Y>gMgAY2Y>gMgAY2Y>gMgAY2Z>fMfAZ2Y>gMgAY2W>iMiAW2V>jMjAV2X>hMhAX2X>hMhAX2W>iMiAW2", "size": [640, 427]}, "area": 67583.0, "iscrowd": 0, "image_id": 10428, "bbox": [0.0, 330.0, 427.0, 310.0], "category_id": 165, "id": 10012751}, {"segmentation": {"counts": "Yio42fc0d0oN:;1O001O000009G;E4L2N1O000O10000000000000000001O000000000000001O000000O10O0100O01O10000KK^\\O6ac050000LCe\\O=[c0Cd\\O>[c0400004L1O0L`\\OI_c07501O00001O0KY\\O0hc00Y\\OOlc0000kbP2", "size": [640, 427]}, "area": 796.0, "iscrowd": 0, "image_id": 10428, "bbox": [255.0, 396.0, 68.0, 43.0], "category_id": 167, "id": 10012752}, {"segmentation": {"counts": "_jc11nc01O2N1N2O1O1O1O2N1O1N2O1O1O1O2N1O1O1O1N2O2N1O1O1O1O1O2N1N2O1O1O1O2N1O1O1O1N2O1O2N1O1MZNk]Og1Ub021O01O0>B1O1O2N101N1O1O000001N100O10000O101O0O10000O101O0O100O1O1N3N1O1N2O1O1jMZNPBh1o=[Ng@O;h1h>_Nm@M5e1n>`Nk@01a1T?aNi@O0b1V?aNi@NOb1X?bNg@NNa1Z?dNf@LJf1_?_Nf@LCl1o>PNXA96[2`>^MZA76[2_>_M[A66[2]>aMbA00_2]>bMcAO0`2\\>aMdAO1_2Z>cMfANO_2[>cMfANO_2Z>eMfAL0_2P>PNoAA1_2o=QNPBA0_2d=\\N\\BTO0`2d=]N[BSO1`2d=]N[BSO1`2d=^NZBRO2`2e=^NYBQO2a2e=^NYBQO2a2e=_NXBPO4a2c=`NXBoN5a2d=`NVBoN6a2d=RMPBW17UO5b2d=QMYBQ1M\\O6b2e=PM^Bm0FA7b2e=oL`Bm0DB7c2d=lLdBo0]OE;`2[>aNeA_1\\>`NdA`1]>_NcAa1^>]NcAc1^>[NdAe1\\>ZMWA=>Y2n>eMSA[2m>dMTA\\2m>cMSA]2m>bMTA^2m>`MTAa2l>]MUAc2k>\\MVAd2k>ZMVAf2j>XMXAh2i>mLaAS3g?O01O00000000001O00000001O01O0000000000001O000001O0001O0000000nJiL[IW3\\6RMdIo2Y6SMgIm2X6TMhIl2X6TMhIl2W6UMiIk2W6VMiIi2W6WMiIj2V6WMiIi2V6XMjIh2V6YMiIg2W6]MeIc2Z6`MQEXO\\4X3c6bMmDYO_4U3c6jM\\IW2b6kM]IU2c6mM[IS2d6oMVEiNl3X3n6PNSEkN74T3P3a7SNREjN7`0k2c2l7\\NWEVOi2_2n7@PH`0P8AoG?P8BPH>P8BPH>o7CQH=o7CQH>m7CSH=m7CSH=m7CSH=m7CTH<l7DTH<l7DTH=k7CUH=l7BTH>m7ASH?n7@RH`0n7@RH`0o7_OQHa0P8^OPHc0P8\\OPHd0P8\\OPHd0P8\\OQHc0o7]OQHc0P8\\OPHd0o7]OQHd0n7[OSHe0m7[OSHe0m7[OSHe0m7ZOTHf0l7ZOTHf0m7XOTHi0k7WOUHi0l7hNTEVNQ3R3k7bN`EVNf2W3k7UNUIk1k6SNWIm1i6SNWIm1i6RNXIo1h6oMYIQ2g6oMYIQ2h6mMYIS2h6lMXIT2h6lMXIT2j6iMWIX2j6fMVIZ2k6eMVIP2V7mMkHi1a7UN_Ha1l7XNlCZOY4S2W8`NPHV1[8hNfGm0g8oN\\Gf0P9UOTG`0X9[OlF9e>DmSQ2", "size": [640, 427]}, "area": 24870.0, "iscrowd": 0, "image_id": 10428, "bbox": [82.0, 329.0, 240.0, 300.0], "category_id": 183, "id": 10012753}, {"segmentation": {"counts": "^]h55i>3O1O1N;E3M2O3M2VBXOX=h0gB\\OV=h0dB\\OZ=U1eB^NX=b1hB^NY=`1hBaNW=d11O001O00001O0O101O00N2L4KYNnBe1S=122101O0000001O00O1O10O1O2L3010O1oB]Nc<d1ZC\\NH0k<Q2UCoMk<T2O0000002N00000000000O10001O00M3M4L6KL240;D3O0N100O0101O00001O1O2N5K1OYNiBe10\\N\\=b11O0_NdBO2X1Z=hNjBX1_=O10001O0000000000000^Ob0O2O1N3O0O2NM31O0000O10AbA=]>CcA=]>CcA=]>CcA=]>CbA>`>1O1N10101M12N000N2001O1OO10O1001O001O003C_A4g>10M2KWA2l>Ne\\b1", "size": [480, 640]}, "area": 5635.0, "iscrowd": 0, "image_id": 10430, "bbox": [393.0, 184.0, 139.0, 75.0], "category_id": 96, "id": 10012754}, {"segmentation": {"counts": "0Z3f;1O000000000000000000001O1ON200000000001O000000001O00O100001O00001O001O1O001O001O00001O00001O1O1O0000001O0000001O00001O000000000000O10000O100O100000000001O0000000000O1001O00000iDZLT;f3lDZLT;g3kDYLU;i30O1001O001O1O2NO11O00001ONPETLP;l3200001O0000000000001O0000000000O100O100O10000O10000O100000000O100000000000000001O00O11O001O00jLlD`2T;`MlD`2T;`MmD`2R;aMmD_2S;aMmDK0R2S;SNnDJOS2S;PNQEMMR2R;QNQENLR2k;aMbDOC80g1R`5oMlnJ?ZABl=?PBT1LkNa0F_OKY;`0jDg2o:YMPEh2P;XMoDi2Q;VMoDk2Q;TMPEl2P;TMoDn2P;QMPEP3P;PMoDQ3Q;?000001O000000001O0000001O00000000001O0000001O00001O0000001O000000001O00001O0000001O0000000000001O001O00001O00001O1O001O00001O0000001O00001O1O001O00_NdE^N\\:b1dE_N[:a1eE_N[:[1eEYM0]1Z:Z1fEYM0]1Z:Z1gEVM2^1X:]1eEVM2d0O0Z:V2gETMOc00_O0c0Z:W2jETML51K123c0U:X2iESMM1e0b0f9Z2eFeM[9\\2hF_MY9`2iF`MV9_2gFeMY9\\2gFTMmN<\\:V2o1I71O001O00:iCZMG8e0Mj9c2gEeMM`0V:k1mEeMM`0V:k1mEeMLa0W:k1lEcMNc0U:k1lEbM0b0T:l1lEaMNDNo0X:m1kEaMNe0W:k1iEhNV:X1jEhNV:X1jEhNV:X1jEiNU:W1kEiNU:W1jEjNV:Q3O00001O001O00001O00001O0000001O00001O00001O000000001O00000000001O0000001O00001O001O001O000000001O000000O100001O1O1O00001ON21O1ON200O1O12NN22N1O1O001O1O00002N00001O00000000001O000000001O1O00001O0000001O00001O0000001O0000001O0000001O00001O000000001O000000001O0000001O0000001O00O1O100001O001O0000M30000000000000000000000O100000000001O3MJ600;E2N1O0000O1000000000000000000001O0000O100O11O0000000VGlIh8V60N2N23M1O1O001O00O1001OK50UGoIg8V6OO11O1O0000000000000000001OO10000000000O100000000K55K1O00001O00000000000000O11O00000000O1000000O10000O100O10000O10000O10000O10000O1000000001O1O001OMWGnIj8R620000001O00L4000000000000001ON2O100000000003M2N00O11O00O100000000O1000000O1000000HQG_Jo8R5WGnJKJO10OP9W5YGmJJLP9W5XGjJn8V5>0000N200001O00001O0000000000000000", "size": [480, 640]}, "area": 98825.0, "iscrowd": 0, "image_id": 10430, "bbox": [0.0, 0.0, 640.0, 203.0], "category_id": 106, "id": 10012755}, {"segmentation": {"counts": "e8[6e8fI]GW6c8iI]GW6e811O00000000000000O10000O11O000000000000000000000000000000O1000000000000O10000N2O100001O2N00000000000000O100N22N0000000000000000000000001O00O100O100N2O100001O00000000000000O1001OO11O0000O10000O1O1O1002N1O1O0000002NO12N00O1000000000000O11O00O11O0000O100McIbG^6]83000000O10000001OO100N20000O1M300000000000000000000000000000000O100000000O1000000O100O100O11O001O001O0000O10000O1000000O1000000O100002N001O00O100O10000000000O1000000O11O001O1O1O0000O10000000000O11OO10000O11O1BnGaI2000O0R8]6`0M100O1O1O1O1O10O1002N00O1O1002N00N2OO2L4LSGTJk8o5UGPJi8R6202O0O1O1O10000O1O10000O1O100O3M2N2N2N2O2L2000O0100O1O01O02O0O00002O0O00104KN33MO0M40lJWFP5m9ON3003MN0114K00N21RKPFk4T:O0ON24K20L3100O1O5LM]KhEd4W:33MN\\KhEd4W:33LO]KgEd4W:LmEaKV:\\4kEdKQ:^4PFbKm9`4;N2N10000O1O100O1O100O1O002O000O2OO02N011N0010O01O1O200N01O11M3O0O1O1O1O1O1O10000N200O2OO0101N00100ONbLdD^3Z;dLeD\\3[;50O1O100O11OO11O000N2K6O010ON1HjDaL13[;V3hDhL];T3eDkL];R39nLUDn2l;RMTDn2k;SMVDk2P<0000O1O10000O0101O00M3N2O1N2O10O02N010O100`MbC]2_<cMaC\\2`<dMaC[2_<eMaCZ2`<fM`CZ2`<fM`CZ2c<O010fM\\CX2f<0M12O100O1000YCiMg<U2YClMf<W20N200O1001O0010O01LO\\CmM`<n1`CPN1Oc<Q271M310O11N0100O1001N10001O1O2BUC^NNNO0S=^1RCcNT=Z1nBfNR=OhBW18iNT=V1<O0000OO200000000O10000DVBYOO0R>f0Da00010O02M21O001O00000001O01O000001O1O0O2O2M100001M20O200OO101O00001O00000O20NO100O10100000000000000N22N00O101N100001O1O002N1O00000001O00000O1NjN[BU1e=4O01O001\\OYBLi=2\\B_OK6l=9]BFd=MWB35OP>LTBOR>0cdo0", "size": [480, 640]}, "area": 84422.0, "iscrowd": 0, "image_id": 10430, "bbox": [0.0, 235.0, 572.0, 245.0], "category_id": 113, "id": 10012756}, {"segmentation": {"counts": "Z3T3m;N1000000000000O1000001O1ON20O1000000010O2N6J4L01O00O1000YMdD]O4Q2W;`NUE_1k:_NWEb1h:[N_DCj0R2h:YN_DEk0P2f:ZN`DFj0P2g:XN^Eh1c:WN]Eh1d:WN^Ef1e:VN_Ef1jNXNg;0aEe1gN[N20P=f1RCZNm<[1oBnN5Gk<W1RCiN07V=P1nBoNS=P1QCjNQ=W1<O010O0001O1M2I8J5N3O1N1O2O101O0O10O1000O10O10O010O2OO100N2002OO0000000000O1001N11O1OPNSCm1m<SNSCm1n<RNRCn1P=1N10O2O001O1O2NO11N11O01ONWNmBi1S=2000010O1O1oBSNm<P201O1SCoMi<T210O000000000O2O0O2O0O101O0O2O001N1000001O0O1000001O0000O1O1O2O0O002N1O2N1G[CVNf<j1\\CQNg<n17J51O01O1O1JiBaNZ=:eBd01RO[=2dBM6m0KSO]=2kBa0GC2Il=`0WBEMKm==]BCk=0iA200ce48PiK2N2N2^OB_B`04^Oj<:jB8:@k<R1nBUO?1`;k0jCkN0=d0Ob;Z1\\DZN^O<V<Z1\\DhNd;Y1kCYN=>g;h1nCYNQ<[200000010O000001O000000000LlC[MV<b1mCoNMAOO]<`1kC_NH3041KON^<a1SDaN_OOe<`1nC_N^O1d<_1QDaNo;^1j001O00000010O00000010O0N22O5aB`NNM10n<o101O01O0000000O10001O0O2O00001O01O01O1O0010O00010O001O01O00001M3K401O00001O0O2O00O1000000O100O11O000LaBeN00U=Z1QCiNT=W1kBiNT=W1<M2M4M7I8I10001O0000001O0O101O00001O01O2O0O2O1N2N100O00010O002N3N2M2N2O2M1OO2L300001O01O00000001O01O000O1N3L3O1O2O001O001O000000010O0O11O0O100001O1]OgAON;V\\h0E\\RXO0^A010O21OO1]>?1O0000010O01O010O00001O01O01O00N2L5J]AJf>2QkP1NZfnN0h>6L5HEdA?Y>7LO310OK50VOnAe0X>OfA]OV>d0jA\\OW>c0iA]OX>b0hA^OX>b0hA]OY>e0000O1001OO12N01OO1000000000O0101O00O1OL46J2N1O101N1O100001O001O0O020O1O00000O101OO10O0100O0010O01000O101O0O2O000O101O0O100000N2N3M3M2O2NNF_A;a>02000010O01K401O1O0000000001NO2O01O10000O112N1OO1N200O0O3N1O1O1O0O100000O0100000OIYOWBf0i=[OQBO10Og0o=\\OQBa0T>60ZOmA=\\>O1O1JaAH1O]>4bANe>16NWo4", "size": [480, 640]}, "area": 22291.0, "iscrowd": 0, "image_id": 10430, "bbox": [0.0, 106.0, 629.0, 119.0], "category_id": 135, "id": 10012757}, {"segmentation": {"counts": "cT^3112000Ob><N100O1O1O1O1O10000000000000000000000N2L4LWOPBj0P>VOoAk0P>3O100O1O1O10000O1O10000O1O100O1O1O1O1O100N20000O100O1O100O100O1O1O100O1O100O10000O100O1O1000000N200O1O1O1O100O1O1N200O100O1O1000000000000O11OImMaCS2_<nMaCQ2^<PNbCP2]<:O1O10000O1O100O1O100O1O1O10000O100O1O100O1O100O1O1O11OO10000N200O1O1O1O1O1O10000N200O100O100O1O100O100O100O1O100O11OO11O0000001O001ON2G]LnD13c3n:`LPEb3o:`LPE`3o:9O100O10000O1O10000O1001O00M3N2O1N2O100O1O100O100O1O100O10000O1000000N2O100O1000YKiEe4W:[KjEd4Y:0N200O1001O001O001OL\\KmEc4k9]KSF1Oi4k99O1O10000O100000000O100000000DlJgFNNO0W5Z9nJgFW5W9kJiFU5V9mJiFS5W9;000000N200000000O10000DWJXGO0Q6g86O1O100O1N21O001O00000000001O0000000000O100O100001O00O11O00001O00001O0000001OO1O1O1O10000000000000000000000001O00000000000000000000001O00000000001O000000O1N2O100000000001O000000000000001OO100O10000001O1O000000001O00000kJ\\Gn3d8QL\\GPO02Oa4f8YLZGQO323NJ1000_4f8^LPHROZO`4e8_LSHa3l7`LTH`3l7`LTH`3k7cLSHmNZO^4c8fLTHZ3m7cL\\GoNg0^4m7cLRHPOZO]4d8bLSHa3n7_LQHa3P8`LmGa3S8^LoGa3R8^LnGb3R8^LmGc3S8\\LnGd3S8\\LkGe3U8[LkGSO]O_4h8`KYGh0f0XOYO`4l8`KTG028OOg0Y4[8hKoFLg0\\4Z8iKPGHg0_4[8iKmFHg0_4\\8iKiGW4X8gKiGY4X8iKeGW4\\8eKgG[4Z8aKiG_4\\9000000000000000N`KdE`4[:aKeE_4\\:`KdE`4[:3", "size": [480, 640]}, "area": 54183.0, "iscrowd": 0, "image_id": 10430, "bbox": [234.0, 275.0, 406.0, 205.0], "category_id": 136, "id": 10012758}, {"segmentation": {"counts": "kch82m>0WA02OX>0cA323NJ1000U>f0jAZOV>h000002NhAZOW>g0NYOkAg0U>OlAZOS>g04O2M02O00O011N10fA]OT>g0kAYOl=1[Bg0f=ZOWBg0j=ZOSBg0o=XOQBg0o=5O13MM3M3J62N2N2NN200N2O11MXBlNi=R1XBnNg=T1XBlNg=T1lF", "size": [480, 640]}, "area": 1167.0, "iscrowd": 0, "image_id": 10430, "bbox": [598.0, 294.0, 42.0, 43.0], "category_id": 142, "id": 10012759}, {"segmentation": {"counts": "^6W2h<1YCjMd<V2\\CjMd<X211O00000O10000000O1000O01VO\\CcN1`0b<e0]CnN7<\\<0cDO];LiD3X;LP2N6I^g24iiL4W><M2MWOnAj0Q>WOoAk0n=VORBj0m=500O2L4M20O1O1O2O000O2O1N2O0O1O1O2L3H8J6M4N1N2N3N1000RiY11gQfNO]K3lJOa9NbK9]4H]K=d4B]K>b4BPG0l3>T5CoFOS20VO>e7FRGMR20VO>FA`64cHLR21oN0aN>;C>OYOOP7h0oHEOC80g14[NLd01RO`05_OJ9OG10O1002O1?l6c3UInKM6m0KSOZ2m6h1UIVLa0GC2I_2i6Q2gIcKEMK_2i6R2dIiKCY2i6R2WIYK200T3g6Q50O1000000O2O?VHfHo6\\7mHWJLkNa0F_OK\\4]7gKjKd0SMU1I`N8DE2a70gKd0TMT1HcN8DV70gKd0UMS1GeNk7DZKc0VMS1G^NJLP81YK1hL9`04^Oc09mNP81SK1nL28:@d0b8oNiJ0gM6UO?f8ZO_J1gMOkN0=d0T<]OUEa0ZN^Oa<1UEa0m:_OTE0YN=k>ElfP1MWYoN000J0\\A0d>0\\A0d>1YA2g>MZA2d0OY=1QBOc00_O0l=4RBL51K123m=NQBM1e0m=;03K2NM50UBmNh10WN0a;d1iEbNiNLW;j1PFYNiNMX;h1oEcNQ:^1nEbNS:]1mEcNS:^1mEaN]:M\\DG8e0R1G\\:JgDMc15f9NgDMc15f9NgDLd16f9MeDNe15g9LdD0e13g9McDNDNS25h9NbDNi14f9LmF3S9MnF2R9NnF2R9NnFOVM0l;1P3ORQS5", "size": [480, 640]}, "area": 7494.0, "iscrowd": 0, "image_id": 10430, "bbox": [0.0, 0.0, 292.0, 280.0], "category_id": 164, "id": 10012760}, {"segmentation": {"counts": "\\U`0110f>9CIiA=U>8MZOlAf0Q>YOoA20d0R>^OnA`0h=\\OaB5G?c=^O]B07j0\\=ZOcBi0Z=[O`Bh0`=<O100O1O10000O100O2O001N100001O0000O1000000O100O1001M101OO2002N0000000000000000O11O1O00000011N1N3N0000O10000O011O00000000000O2O1O1^NdBk00@2E]=k0d0O1UOmAg0V>01O0O1000000O2O001O000IdAFa>84M4Ki[29UdMOON11O0O1I8A@\\B`0c=DYB=f=GWB8i=ITBFOc0l=GTB=j=?M2N2N2N2N1001O001O00001O0QO`B>b=\\O\\BJ9OG10O1002O1\\P70^^I1O102\\AJY>8bAN[><000000000010O00000010O000000000O100000L_OhAb0V=A[CMAOOi0T=_OkBH3041KONj0U=GmB_OOQ1T=BkB^O1o0T=FlB;R=j000000000001O0000001]Ob0OO22M7mNTBNM10a0\\>01O00000001O0O10001O0O012SB^OX=b0]BZON119c==]BJb=k000O00000001O000O2N110N10000\\OZBNf=_OYB`03NMBn>00000Zfb41eY]K1M2N3M2N2O2N2O0N200010O001O001O10ON2M`AFP`70jnH0UA100h>8K5K3N2N100nA\\Oh=g0TBZO00e=f0ZBZOOON6d=b0_BEa=<`B@b=`0\\BAe=`0ZBBc=o0N2RCdNh:5jEW1]OfN]OM02T;7jEZ1EgN`:1iEZ1CgNd:1QED=j1_OVN8=i:6_E_1C\\Nn:5^EU2_:mM[EZOH1Oi2m:kMWE[ONS3j:aMWEf2i:YMUEe2o:h0N2O1NN200N2O11O1OO1OiJ", "size": [480, 640]}, "area": 10650.0, "iscrowd": 0, "image_id": 10430, "bbox": [34.0, 166.0, 606.0, 134.0], "category_id": 169, "id": 10012761}, {"segmentation": {"counts": "XZf3X1d=40000000O10O10000000000000000000O1000O1000000000000000O100000O10000000000O010O100O1000000000O1000000000f0ZO^X33_gL?AO2O00000000001O00000O1F;AVX4OdgKe00000000000<DiW;j0]gD000000000O1000000O100000O1000000000001O1O001O8H;E;CfTb3", "size": [480, 640]}, "area": 4304.0, "iscrowd": 0, "image_id": 10430, "bbox": [252.0, 190.0, 144.0, 61.0], "category_id": 183, "id": 10012762}, {"segmentation": {"counts": "Ugm71o>1O0O101N101N101N101N1O2N2NOaAC^>:WOFVC;k<FSC;l<HPC:P=IjB:T=LdB7]=e030L0112NO01O100O10000000OO3CVBAj=<WB\\O1OO5h=`0`00N10030MMjA^OV>b05N011N1NbAB^>=32jAAi=a0RB@n=b0lA]O120Oo=h0QBYO4Oc=h0YBYO22c=l0[BUOf=R1N2000001O00N2O2E\\BWOe=g0\\BYOe=;VBG9Mc=9fBG\\=5eBL\\=4dBK^=NPB0d01W>0]P=", "size": [480, 640]}, "area": 1732.0, "iscrowd": 0, "image_id": 10432, "bbox": [541.0, 86.0, 71.0, 55.0], "category_id": 100, "id": 10012763}, {"segmentation": {"counts": "o5Z1[3R3d2iKYJ3T4h3c1TLZJ4S4h3c1SL[J5R4h3c1RL\\J6Q4h3Q2WLoMi3R2VLnMj3R2VLnMm2ZLTMh5OnMi2aLUMa53mMo1^MkMe46mMh1T4XNkKe1Y4\\NfKa1]4_NbKa1^4aNaK]1`4dN_KZ1d4gN[KV1g4kNXKU1h4mNWKQ1i4ROUKn0h4WOVKj0i4XOUKh0l4YORKg0o4[OnJf0R5[OlJe0U5]OhJc0X5AdJ>^5B`J<c5I\\GiN_2\\1X6LTGlNb2Y1Z6:nH:T7HfH;[7FbH<]7F`H:b7IUH<k7GQH9P8ImG8S8V34N3O1N3N100O1O100O10000000000000000000O2O001O1O1O1N2O2oF`J]8d5]G`Jb8P6N3iGhIb7[6THPJg7f6O1N2N2N3fHgHh6]7RIiHj6m7H2O2N2O1O1O1O2N3M4L000000000VM^GVOb8e0dGZO\\8e0gGXOZ8c0lG\\OU8`0nG@R8>nGDS8;lGEV8:iGGX89fGG\\89bGH_88_GHc89[GGf89XGGj89TGHm88RGGQ97nFIV94jFKc9I]F6d9J\\F5e9K[F3g9MXF3i9MWF1k9OUFNn91SFKQ:4PFKQ:3RFNl9OWF3g9LZF5e9K[F7^2lMD1d1k1ZL:W2SNENg1e1]L;Q2ZNGHj1c1^L=j1`NIDm1_1_L?e1fNK^OP2]1`L`0^1nNOUOS2^1^La0Z1TO1oNV2\\1_Lb0T1\\O2hNZ2[1^Lb0P1D5_N]2[1^Lc0j0J8ZN_2Y1^Le0e0M>UN_2Y1]Lf0e0M?TN_2Y1]Lf0d0N`0SN_2Y1\\Lh0c0Mb0RN_2Y1[Li0c0Mc0QN_2Y1[Li0b0Nd0PN_2Y1[Li0`00f0nM_2Z1ZLh0`01g0mM_2Z1ZLh0`01g0mM_2[1YLg0a01g0mM_2\\1XLf0b01g0mM_2]1WLd0d02f0mM_2]1VLe0e01f0mM_2Z4mLiMd0mM_2Y4nLkMb0lM`2Y4nLkMb0lM`2[4lLjMc0kMa2\\4iLkMf0iMa2]4fLmMh0fMb2^4dLnMi0dMc2_4aLoMl0bMc2_4_LRNm0_Md2`4]LRNn0_Me2_4[LUNo0\\Mf2_4YLWNP1[Mg2^4XLYNP1YMh2^4VL\\NQ1VMi2W8WMiGi2X8VMhGj2X8VMhGj2W8VMiGk2W8UMiGk2W8UMhGl2W8UMiGk2W8UMiGk2W8UMhGl2W8TMjGl2V8TMjGl2U8UMmGi2S8WMnGQOXO[3j8cM^HZ2a7fMaHY2_7gMcHW2]7hMeHW2Z7iMgHW2Y7iMiHU2V7kMlHT2T7lMnHR2Q7nMQIQ2\\3oLVNQ1^NP2\\3QMTNo0cNm1Y3VMQNl0iNm1V3XMPNj0mNl1S3\\MmMg0SOl1o2`MlMc0WOl1l2dMjM`0\\Ok1i2gMjM<@l1e2kMhM9Ek1Y2\\MYKb0g27Ij1X2WNoMNKj1W2XNlMOOg1V2YNkMO1g1T2[NiMN6e1R2kNWKfNi1j0o0d1R2jNXKhNf1i0S1c1P2jNYMCk0_1l1lN[MEl0\\1j1mN[MFm0\\1i1lN[MHn0Z1h1lN[MJn0Y1h1jN\\MMl0Y1i1hN\\MOl0X1i1gN\\M1l0W1i1fN\\M3k0W1i1dN_M4i0W1i1cN_M6h0W1j1aN_M8g0W1j1`N`M9f0W1k1]NaM<d0W1k1\\NaM>d0V1k1[NbM?c0V1l1YNaMb0c0U1l1WNcMd0a0U1m1UNbMg0a0T1m1TNcMh0`0T1Q5lNoJU1Q5kNoJU1P5kNPKV1P5jNoJW1P5iNPKX1P5hNoJY1P5gNRKX1n4hNRKX1eNnMQ4i0\\MX1cNRNn3f0aMV1aNXNj3a0hMU1^N^Nf3=oMR1[NdNc3:TNP1YNgNb39XNm0UNkNc37[Nl0RNlNd38[Nk0RNkNd3:\\Ni0QNkNd3<\\Nh0QNkNc3=^Nf0oMlNd3>^Ne0nMlNe3?_Nc0lMnNe3?`Nb0kMnNf3`0aN`0iMPOf3`0cN>gMQOg3a0cN=fMQOh3b0dN;dMSOh3b0dN;dMROi3c0cN;dMROi3c0dN:bMSOl3b0bN;aMTOm3a0bN;_MUOP4`0aN;ZMYOV4<`N;[MXOU4=aN:\\MVOT4`0`N:^MSOS4c0_N:_MQOS4d0_N;_MPOR4c0aN=h3AXL`0h3^OZLb0f3]O\\Lb0d3]O_La0a3^OcL?]3@eL?[3@hL>X3AjL>W3@kL?U3@lL`0T3_OnL`0R3@oL?Q3_OQMa0o2^OSMa0m2^OTMb0l2^OTMb0l2]OUMc0d80000000000000O1nM\\OhEd0X:F]E;c:E\\E<d:D[E=e:CZE>f:BYE?f:BXE`0h:@hDJWOi0P<_OhDn0W;SOiDm0V;UOiDk0V;WOiDi0V;YOhDh0V;[OiDe0V;]OhDd0Z;[OdDf0^;YO`Dh0b;WO\\Dj0g;TOWDm0k;SOQDo0Q<ROiCQ1Y<j01O0000001O0000001O000000;El0TO3M1O2N1O001O1O001O00000000000000000000O100O1O1O1O1O1mMnN`D1L0V1S1`:IRE:o:k12N1O2N1O2N1O001O001O001O1O001O001O001O1O0000O100O1O100O1O1M3001O00001O0000O1O1M3I7O1000000000000O1FkLhDV3X;nLcDS3\\;fM`Ef0`:XOkE?T:@ZF4f9JgFKY94jFJU95nFJR94RGJk87XGHe89^GF_8;eGCY8=jGBW8;mGCT8;nGDR8:RHDo79THFm77VHHj77YHGh77b3O1N1O2O1N10oZ20PeM2O0O2OO01O2O0O2O1N2O01000O100;DYoe07_PZO2O1^AF[>=23J41N2O1O1O1O1N101O1N2O001O001O001O0O2N100O100O0100O0N300001N2NR_g1", "size": [480, 640]}, "area": 51708.0, "iscrowd": 0, "image_id": 10432, "bbox": [0.0, 64.0, 521.0, 416.0], "category_id": 104, "id": 10012764}, {"segmentation": {"counts": "\\hc82n>0O1O3DMeA5\\>JbA9^>F`A>^>20O1000001O00N101FDoA=o=DQB=j=GUB;i=FWB;i=CXB=j=BUB?k=@TBb0l=]OTBd0l=7100000001@QBMo=0UBOk=LZB3j=GYB9W>01N10000M[AKe>45O000O101O1O0Oke2", "size": [480, 640]}, "area": 775.0, "iscrowd": 0, "image_id": 10432, "bbox": [588.0, 109.0, 46.0, 38.0], "category_id": 114, "id": 10012765}, {"segmentation": {"counts": "a\\W81o>2N3M3M3ZBF`<;^CFb<<ZCFf<=TCFk<?mBES=T1O10000O10000O1O1O100O1N2O1N2M3O100O1O1O1O11O0000000000000000000000O10000O1O1O100O1N2O1N2M3N2N2M3L4H8000000000000000000000000000000O100O100", "size": [480, 640]}, "area": 5309.0, "iscrowd": 0, "image_id": 10432, "bbox": [561.0, 380.0, 79.0, 100.0], "category_id": 115, "id": 10012766}, {"segmentation": {"counts": "^em75k>0O101O010O001O001O00100N2O100N2O01NLEdA<]>120L6L01003M0100000N2O100O10O11OO2OO10N3N120M0110OZOjAa0\\>0O0000002OO00000O20ON201B`A80Gd>8\\AHd>8\\AHd>8\\AHf>66J_Z`0", "size": [480, 640]}, "area": 1079.0, "iscrowd": 0, "image_id": 10432, "bbox": [541.0, 62.0, 64.0, 31.0], "category_id": 121, "id": 10012767}, {"segmentation": {"counts": "[YT81k>6M10001O0O2N12NN3O001N1O2O0O100000000N2D]OZBd0b=]OVB1OO5a0g=`000O1O12MMPOXBP1h=4O10O01NjNZBR11oNd=T1\\BlNf=:YB:2\\Oi=5VB>R>\\OmA120O51NY>MgA41MZ>OeA23M`>1^`d0", "size": [480, 640]}, "area": 868.0, "iscrowd": 0, "image_id": 10432, "bbox": [555.0, 102.0, 41.0, 45.0], "category_id": 142, "id": 10012768}, {"segmentation": {"counts": "bc^82n>001O001N101O001N2O1O1O1O1O2N10101M4K8I000000000O10000000000O10001O0O2O001O001O001N2O1O1N2O2N1XCcNh;O`C`1d0dNj;c1PDaNo;c1iCcNU<U2N3N1O001N101O001O1N2O0jK", "size": [480, 640]}, "area": 2471.0, "iscrowd": 0, "image_id": 10432, "bbox": [577.0, 134.0, 63.0, 95.0], "category_id": 143, "id": 10012769}, {"segmentation": {"counts": "PUm31o>0PU50PkJ1O1O1O2N4L1O00001O1O2NN2O1O1O1O1O1O100O1O100O10000O100000000000000000000000000O1O10Pc[1:RncNFb>`0L1O00001O000000001O0oA]Oe=Q1N2N1O1gBPO`<P1ZCXOd<f1L00O100O1O100O10000O1000003L4M0O2O0[NhBa1Y=_NhB`1[=10O101O00000000000000000000000000001O5K000000000000000001O01O010O001OO1000000N20001O0O2O0N30O01O02O0000O1O1O1O0N23MO020N1O11O00O1O1O2O00015K1O00O10O0010ON2N23MO100001O1O0000000K50N1O2O12ON1001OO1O1O110M101O2OO104M1InB[NS=f112OM5[NhB^1]=01O2N3N00000N2O01O010000001OO2O100O02NO11O0ZBjNb=[1O0O1O100O2O0O1N2N3O00001ON2O1M212J400K_BjNb=R18O100O;POjAN1`0]>NOO3M4LVW;", "size": [480, 640]}, "area": 9935.0, "iscrowd": 0, "image_id": 10432, "bbox": [267.0, 0.0, 349.0, 83.0], "category_id": 156, "id": 10012770}, {"segmentation": {"counts": "Paa63m>3M001O0XAKe>5[ALd>7100001O00000000000000000000000000001O5K000000000000000000001O001O001OO1000000N2000000O100N21O00002N1O1O00000000N23MO11OO1O11O00O1O1O100001O1O1O1O1O00001ON2N23MO100001O1O0000000000N2N2O12NO1001OO1O1O11ON2O1O100004L2N00N2O11bAAY>b01001O2N00000000O1O100000000001O1O1O000000000000O1O100O100O1N2N200000[CA_N0o;?^EFbNKP<?]EGcNJP<?ZEJfNGP<>[EMcNFQ<?VE3cNAV<<WE9i:GRE@jNj0<VOX;`0^Eb0WOUOW;9aEd0TOVO[;6aEd0TOWOZ;5aEf0nNkN0=a;<aDVON1`0i1Q;WOmDk0S;ROlDR1T;mNiDW1W;iNeD[1[;U101O0000M3M3M3K5M3O100005KO1N22NN20000O1000000O1", "size": [480, 640]}, "area": 6030.0, "iscrowd": 0, "image_id": 10432, "bbox": [447.0, 0.0, 193.0, 111.0], "category_id": 172, "id": 10012771}, {"segmentation": {"counts": "XYZ14h>;F6K5K5K4M3M2N3M2N2O2M2N2O0O2O1O1O1O1N2O1O004L3N1N2N1O2VFlMc6V2VISNg6n1SIZNj6h1oH`Nm6b1mHfNP7[1iHnNS7S1hHUOU7l0eH]OV7e0cHDZ7=`HJ]78]HMb74]HMc73\\HNc73\\HNc73\\HNd72[HOe71YH1g7OXH2h7NXH2h7NXH2h7NXH2h7NXH2g7M[H3e7K]H5c7K]H5c7L\\H5d7K[H6d7LXH6h7KUH8j7ITH8m7GQH<n7EoG=R8ClG?S8AkGb0U8]OiGe0W8[OhGg0W8YOgGi0Y8P31N`0@4L3L3N2N2hKiFfN0c3Y9gMoFY2S9fMlFY2W9gMgFY2[9fMdFZ2^9eMaFZ2b9eM]F[2e9dMZF\\2h9cMWF\\2l9cMSF]2P:W13N3L3N2ZLeEb2_:\\MaEc2a:[MaEb2b:\\M`Eb2b:\\M`Ea2c:\\M_Eb2d:[M_Ec2c:ZM`Ec2c:ZM`Ed2c:XM_Ef2_;O0O2O0O2N`0`MZCh1e<ZN[Ce1d<<N2O1O2N1NVNdCT1[<nNeCQ1Z<QOfCn0Y<m0N2O1O1O1N2TFgMP7f0jF8U2XOl6T2RIRNi6n1VIWNe6k1YI[Nb6e1]I`N_6a1_IdN]6\\1bIiNY6Y1eIkNX6U1hIoNT6R1kIQOR6o0oITOm5m0SJUOk5k0TJYOi5g0WJ\\Oe5e0[J]Oc5c0\\JAS3cMXMl2ECo2PNPLYO4U3l0Fk2[NbKQOf0n2m0Hh2iNZL_1n0Ke2hNZL^1P1Mc2gN\\L\\1Q1Na2hN]LZ1R10^2hN^LY1R12_2gN]LW1R16^2eN_LU1P1:_2cN`LS1o0>_2`N`LS1Q1>]2`NbLb0DeM_10POZ2Y3@cLQ1T1SM^OR2h2KeLe0a1\\M_Oj1Y26gLP1U1nL0k1R2dNkL>NU2U1mL4i1m1dNSM?HU2T1mL9h1h1dNXM`0DV2S1lL?f1a1fN^M`0AV2Q1lLd0e1\\1fNdM`0]OW2o0mLh0d1V1gNjM?ZOX2Z3_NAiNPN>VOX2\\3^N^OkNRN>VOX2\\3\\N[OnNUN=UOX2^3ZNXOPOWN=UOW2_3ZNSORO[N=TOU2a3YNoNTO_N=ROU2c3VNoNUO^N?QOU2e3SNl0H_KU2h3PNj0J_KU2l3kMg0O]KV2]4ZM7?]KW2n9gMSFY2n9fMSFY2n9fMRFZ2o9dMSF[2n9dMRF\\2o9^MVFb2U;O0010O01OO0O2oD\\Mc9g2[FaM\\9a2dFfMU9[2iFmMo8U2oFSNj8m1VGXNe8i1YG[Nc8f1]G^N_8c1_GaN]8a1bGbNZ8_1eGeNW8]1hGgNS8[1kGiNP8Y1QHiNl7X1THjNi7V1XHmNd7T1]HmN`7T1`HnN]7R1dHPOY7Q1hHQOT7P1lHROQ7n0PITOn6l0QIXOk6h0UI[Oh6f0WI]Og6c0WIAf6?ZIXMBW1Q7a1\\ImL2a1_6b1_IlL6b1Y6a1`InL:a1S6a1cInL>^1o5c1eIlL`0_1j5e1hIjLa0`1f5e1jIiLd0`1a5g1mIgLf0_1]5j1mIeLj0_1X5m1mKRNR4n1QLoMo3Q2SLmMm3S2ULlMj3T2XLjMh3V2[LgMe3Y2]LeMc3[2_LdM`3\\2cLaM]3^2PKiL@g0`5_2\\JbM3MY6h1cI_N4H]6c1_IiN4_Oe6b1UISO6WOh6d1QIZO6nNm6e1lHA8dNP7h1hHI7[NU7i1dHOl8OUG4j8IVG:i8EWG>h8@WGc0h8]OWGf0h8WOXGl0h8ROXGP1g8oNXGS1i8jNXGX1g8gNYGZ1g8eNXG^1h8_NXGc1h8\\NWGg1i8VNWGl1i8QNYGQ2f8mMZGU2g8gMZG\\2e8aM\\Gb2d8YM^Gi2b8TM_Go2`8nLbGS3_8hLcG[3\\8bLeG`3m92N0O2O001O1O0O2O001O0012NGZLZEb3f:eLWEX3h:oLYEi2f:_M\\EY2c:kM\\EQ2d:SN\\Ei1e:YN\\Ed1d:_N\\E]1e:fN[EV1e:mN\\EP1d:SO[Ej0f:XO[Ec0g:@XE<i:HVE7i:KXE2h:1WENh:4XEJg::YEDf:?YE^Oh:d0XEZOh:i0XESOh:P1XEmNi:V1VEbNP;a1oDXNV;j1jDTNU;P2jDnMV;T2jDjMV;Y2iDeMV;T3O]OkD[MU;h2kDTMV;n2kDnLU;U3mDfLT;Z3oDbLR;^3eE`Mf8_2YGlM]8U2aGYNS8f1lGgNi7Y1VHjNh7V1VHmNh7R1XHROf7k0[HXOd7e0]H^Ob7?_HE^7<`HG_7:^HJ`76_HL`75]HO`73]H0b70\\H3c7N[H5b7M\\H5c7L\\H4d7L[H5d7MZH4f7MYH3f7NYH3f7OYH1f7e3O1O1O1N\\L_HM`73aHM^74cHK\\75eHKZ75hHJX76gHKX75iHKV76kHIU76lHJT75nHJQ77PIHP77RIHm6:RIFn6;RIDm6=TIBl6?TI@l6a0TI^On6`0RI@n6a0RI^Om6c0TI\\Oj6f0WIYOh6h0XIXOg6i0\\ITOb6n0`IPO_6P1dInN\\6R1fIlNY6U1jIhN[6S1gIkN\\6Q1gImN\\6n0hIPOY6n0kIoNS6R1PJlNn5T1VJjNi5V1ZJhNe5W1`JfN_5Z1dJdN[5R1QIgLh1U2V5V1TKhNj4Z1YKcNf4_1[K_Nd4b1^K\\Na4e1aKYN^4h1eKUNZ4l1hKRNW4P2jKnMV4R2lKlMT4T2nKjMQ4W2RLfMn3Z2TLdMl3[2WLcMi3]2YLaMh3\\2\\LbMd3]2`L`M`3_2cL_M]3`2fL^MZ3b2hL\\MW3e2kLYMU3f2nLWMR3j2QMSMo2m2SMQMl2P3VMnLj2R3XMlLh2T3ZMjLf2U3]MiLc2g2YIZMW4L`2i2^IVMU4O]2k2_ITMW4OZ2l2bIRMW40W2>XIl1<eMV40W2;\\Im1m;nMWDn1`<01O2N3M2N2M3N1O1OnN^NgD_1X;dNhD[1U;jNkDS1T;QOkDn0S;VOlDh0S;\\OkDd0S;@lD>R;GmD8Q;LmD3R;1mDMR;5nDJQ;8PEFn:=SEAm:`0SE_Ol:c0UE[Ok:f0UEVOm:l0SEQOo:o0SEoNm:R1SEnNk:T1UEkNk:V1VEiNh:Y1YEeNg:\\1YEcNf:_1[E^Nf:c1[E[Nd:g1Z11N2O1N2O0O10000O1000000000000000TN\\C\\1c<bNaC]1_<`NeC_1[<]NkCa1U<\\NoCc1h<O2N100O2N1O101N1O1O2O0O1O2O0O1O1O100O1O1O100O1O1N2N2N2O1N2N2N2O1N2N2N2O1N2N2N2Oabh0", "size": [480, 640]}, "area": 66460.0, "iscrowd": 0, "image_id": 10432, "bbox": [90.0, 72.0, 497.0, 408.0], "category_id": 183, "id": 10012772}, {"segmentation": {"counts": "h[]1;j<7N0O2O1fD_Oj9`0UF_Om9b0QF_Oo9j0iDSOl05Z:j0fEVOZ:i0fEXOZ:j0cEXO]:g0bEZO^:f0`E\\O`:d0_E^O`:b0`E^O`:b0_E_Ob:a0]E@b:`0kDWO0K2ON00a0V;b1N1O1AlM]E0O]2`::0001O001O1O1O2N1O1O1O0000001O00000000001O000000001O000000L40kEPMP:P35O10000007I000000J600000000007I1O0000001O001O001O001O001O001O1O001O001O001O001O00mL[Fb2i9WMZFi2f9oLdFP3Z9PMfFQ3k9\\NiEdNOX1Z:2WFCj9:^F_Oc9@nEMI6h03j9B\\E34620d02m9EYE3g0MYO4k06R:FoE23NRO1TP21j\\NOPCO08l04X:FUE:c02S:HZE7c00ZOC\\:<dE4d0N[OC[:>eE1e0N[OC3OK0n9a0oE_O1d0e0J]OBM22N20Q:\\1_FAe9`0YFAg9?XFBHQOe9]1_FcNDP1X:=kEOU:2gE1Y:e1K2N00O100O100O100O1N2K5O100O10000O1O1O100O1O12N1O1O00000kEXMh9h2mEXM37o9a2nEdMR:\\2nEeMQ:j201O001O1O00O10000O100001O000000001O000000N2jN_FoMU:a0mE^Oj02Z9N]F0:1k;O10lW11ShN2OO1O00000000N7\\O?SO^OfDn0`:1VEmN0U1c:`1^EZM^:j2M202L3jFRMm7Q3kGUMU8l2^GlLD`0n8f2XGbMh8`2RGdMn8\\300000000000000000O11O0000YL`GT1BGn8TOaGU1L\\Oi8WO_G[1H^Oi9XOkEl0>Kh9WOkEn0?Ig9XOkEm0?Kg9UOmEn0?Jl;UObCb0d<O004L00003H]S2KjlM3`K3]GKON0e0T3D`5n0\\GlN3k00YO41:J_ON31M9c8U1]Gb06gN`8j301O1O1O001O001O0000O1O11OO100O11O00UOjG\\LELa8f3VHZLj7c3YH]Lg7`3]H_Lc7^3`HbL`7\\3bHeL]7X3fHhLZ7W3hHhLX7X3hHiLW7W3hHiLY7W3gHiLY7W3]10000O1000000000QGiLk7W3THjLl7V3THjLl7V3THjLl7V3SHkLm7U3SHjLn7V3RHjLn7V3QHkLo7U3QHkLo7T3RHlLn7T3QHmLo7S3QHmLo7S3RHlLn7T3RHlLn7T3SHkLm7U3THjLl7V3UHlLh7T3YHkLg7U3ZHjLf7W3ZHhLf7Z3XHfLh7\\3VHdLj7^3P12N2N2N2N2N9G;EO100O1O1M3O1K53M1OO12N00O11O000000000000O1001OO1O100002N0000000mFkKQ9X4OO11OO1000000000000O11O001O1O001O1OO10000000000O100000000N20000000oFjKn8Y40000000O1000000O1O1O10000O1J6N2N2O10000000000000000000000oMoF_NCP1^9:ZG_N[OW1[98bGG_89aGG_8iNaF60K1n0S16j8GYG9i8ROPF7[1e0g8SOnEJ63JM\\1R1h8ROiG>TNJX<2oCHY<JYU41QiK:UEGc19EEW8`0UGD\\O12f0S1YOX8<VGm0CmN9Ln89VGo1S9QNgFV2X9kM[Fa2e9b000000O1000hF^Lh8b3XG^Lh8b3WG_Li8a3WG^Lj8a3WG`Lh8`3WGaLi8_3WGcLg8]3YGhLb8X3^GmL]8S3cGRMX8m2iGSMW8m2iGTMV8l2jGSMW8m2iGSMW8m2iGSMW8m2iGSMW8m2iGSMW8m2iGSMW8m2iGSMW8m2jGRMV8n2kGQMU8o2lGoLU8Q3eGaLH>c8Q3]G]Mc8b33O1N2001O000000000000000000OgGfKd7Z4YHiKg7Z4UHhKj7X4UHjKj7Y4lG_K2:R8Z4lGiKS8h4O00O11O00001O0000000000000000SKPH`4a8VKPHR4h8F9XL^F_3j90O10N4fLTFn2T:01O1M3N2OdNUMZHj2`7dMYH[2f7jMXHU2f7QNVHn1Z7dMjGa0m0h1Y7mNdHlNTOh1U8]OgH^NTO:1j1T8@gHfNVOGOo1S8DkHbNYOh1m7JeHYNXO26i1n7NcHVNFl1g7NTI2l6NTI2l6NTI2l6NUI1k6NVI1k6OUI2j6NWI1i6OXI0h60YINh6OiG[Nb1d1d6LfI2Z6", "size": [426, 640]}, "area": 54366.0, "iscrowd": 0, "image_id": 10434, "bbox": [109.0, 0.0, 531.0, 168.0], "category_id": 96, "id": 10012773}, {"segmentation": {"counts": "fhT24V=>C2MN2O2N10010O0000000000001O0000000001O001O1O01O0000000O100000000000001O2N1O2N1O2M200O2N1OO1O1O001O100O1O0102N1O1Nhi?;lU@20000O12N00N3N10000O10000O10000010O0O2LXCDi<;PX6DUhId0GO00000000000000000000000000]C]O_<d0_C_O_<f0000001O001O0O11O000000000000002N1WOaCe0d<OON201O00002N0000mW6AbhI0L<G1O20OO100O11O0000000O10001O000010N2O1L3O2OO10O10001N0M400000000O2O1O1O2N00000000000003M0000000000O202M00O1N2O10N1001O01O0N201K44L2N012M1O011N1O100O100O10O1001OO00100002N0O10000ZO^Cd0e<OO11OO1000O10000000O11O001O1O001O1OO1000O100000O100000000N20000000^O[C?h<0000O10O1000000OAXC>k<O1O000000001O01O0000000000000001O000000010O1O002N2N1O2N00000Od\\5N^cJ8H7I1O101N2N1`C[O[<j0000000O11O002N5L4K5K001OO101O1O1O1O1O1O1O1N2O1O1L4N2M3O100O1O1O1O1O1O1O1O1O1NYOTD3k;JXD7j;EXD;g;D[D<g;ZOQD2:f0f;XO]Dh0S<0O2M200O2N100O00O10000O100O1001O0J7L41O1OO001O000N2O10000001O00003M010OO1oCiNn;Z10000000000O110O000O11O00mJ", "size": [426, 640]}, "area": 8628.0, "iscrowd": 0, "image_id": 10434, "bbox": [165.0, 129.0, 475.0, 78.0], "category_id": 126, "id": 10012774}, {"segmentation": {"counts": "X6[3o9O1O1011NO2O0O100002N01N1001O010O002N0010O0O2O00010O001N10001O0O10OO2N110O103L2O1O1O1O1O1O2M20N100O12N2UOZEoNe:n0^EROb:l0aESOIZOd:`1fEXOY:g0jEXOV:e0QFVOP:h0ZFQOe9l0cFoN]9o0eFQOZ9d0`E[OY11W9c0bEYOY14U9a0UG_Ok8`0VG@j8>XG@j8?WGAh8?YGAg8=[GAg8>ZGCe8;]GEd89]GHb83\\EJT23a82bGNm:00001M2N2N3M2BFkC<S<>@VO_Dm0];WO`Dj0^;[O^De0a;c0N1O1OZNcDb1\\;^NfDa1b;00000010OO1001O00000010N2O1O1N1001O0000001O001O000001N3M3mDVNa:m1\\EVNa:m1\\EVNa:m1[EWNb:k1\\EXN`:\\2N010O01hMfE^1Z:aNnEX1R:gNVFR1k9lN\\Fo0c9PO_Fo0a9POaFo0`9oNcFo0]9QOdFn0\\9ROfFl0g0kNd68fHo0`0lNj66fHo04iNfN7<HC1l79]In0OVOlN0f7M`Im0gNgNT1>kNB2?h7MbIk0eNjNOO0;6HKNOO1d0OLk7KbIX3^6iLbIV3_6iLcIU3]6kLdIT3\\6lLfIR3[6mLfIR3Z6oLfIP3Z6QMgIm2Z6jLTH2b1S3Z6iLWH4_1R3b6PM^In2c6RM]Im2g6QMXIn2i6RMXIl2i6UMVIj2k6XMSIg2m6\\MRIb2o6`MoH_2R7aMnH^2S7cMmH[2S7fMmHY2U7eMmHY2T7ZM\\G4g1a2m8OaN\\MZHb2g7^MYHa2g7`MYH_2g7aM[H]2e7dM[H[2e7fM[HY2e7hM\\HV2d7kM\\HT2e7kM\\HT2d7mM]HQ2c7PN]Ho1c7RN]Hm1c7TN^Hj1b7WN^Hh1b7YN^Hf1c7ZN^Hd1b7]N^Hb1b7_N^H`1b7aN_H]1a7dN_H[1a7fN_HY1a7hN`HV1`7kN`HT1a7lN^HT1b7mN]HS1c7nN]HQ1k6]NTHc0P1P1l6_NRHb0Q1o0m6`NQHc0P1m0o6bNoGb0Q1l0P7dNnGa0Q1j0Q7gNlG`0R1i0Q7jNkG?R1g0S7lNiG>S1f0T7nNgG=T1e0U7oNfG>S1c0W7QOdG=T1b0X7SOcG<S1a0\\7SO_G=S1a0d74YHMl7ORH2P8MmG5S8MjG4V8MhG4X8NeG3[8ObG2^82]GOc85WGMh88SGIn8Y2O100000000O1M3L4L4N21O00000000000000000000001OO13M3M3_KVG\\4l80001O000000001O0XMRGT1n8iNVGV1j8fN[GY1f8iMSG9:n1c8fMWG6<S2]8cM[G6<W2Y8_M`G4<]2g8aM\\G^2i9O0000O100001OO1001O0000O1001OO12N0000O11OO10_MYE_2g:aMYE_2g:aMYE_2i:0000000O1001O00O1O1001O00O100000_MYE_2i:0O1O1001O00O10000000000000000000000O10000O1O1K]MaEe2^:400001O000000O100000000000000O10UMcEi2_:02N3M2N000000001O001OO1000000004L1O1O1O4L1O00000000O1O1001O00000000O1O1E;O1O1O1O1O11OO11O0000000000O11O003M5K5K2N00000000000000000000O10000001WNQEU1P;gNUEW1m:dNVE\\1k:`NXE`1Y;0001O00001OO10000001O000000O10000O11OO11O000000000000000000001aNYDY1g;dNZD00[1l;eNSDY1Q<gNoCU1Q<kNoCU1S<01OO11O000000O1001O00O1000kNmCS1V<O1O1O000000O10000001O000000O11OO1O100O1000000000000001O0000000000009G4WO^Cb0e<01O00O1000000001O001O00000O1N7DSna1", "size": [426, 640]}, "area": 39637.0, "iscrowd": 0, "image_id": 10434, "bbox": [0.0, 200.0, 519.0, 226.0], "category_id": 140, "id": 10012775}, {"segmentation": {"counts": "U3S3Y:dNXFROh9I`G4`8LcG1\\80dG1[8OeG1\\8NdG3[8LfG4Z8LeG5\\8JcG7]8IbG8^8HbG8^8HaG:^8FaG;_8E_G=b8B]G?b8B]G`0a8A_G?a8A_G?a8A_G?b8@^G`0b8@^Ga0b8^O^Gb0b8^O^Gc0a8]O^Gd0b8\\O^Gd0b8\\O^Ge0b8ZO^Gf0b8ZO^Gg0a8YOeGa0[8_OfG`0[8_OeGb0e8SO\\Gl0b:O0O2O0O1GeCC[<9=M3M3M3Mg[31[dL2M10000O010O1O1K5L3L5K5L4K5L4K4M4001O100OZDeN^;[1aDfN_;`13N1N3N1N3N1N3N1N2N2N2M3N1N2N2O0O01oD]O]9b0dF^O]9a0V1_OWD`0W1@P91fE>Z1Ai92Sk3NgVL1O1O1O10N13M101N2N5L4K2N2O0O1O2O4aCSOX<S1L101N1O2O0O1O2O000010O0010O01N1O2O01O010O01O010O0010O0001M2MZDQOX;l0kDTOT;j0oDVOQ;g0REYOm:e0UE[Ol:b0WE^Oh:?\\EAd:<_EDc:7`EHc:3_EOn;0jBMT=2c98UF4M3M4M2O1N3M2N2N2N2N2N2N2N1O2N2N2O1oEcNQ8_1jGfNU8Z1fGkNZ8V1aGoN]8S1]GSOa8o0ZGVOd8l0VGZOh8h0SG[Om8f0oF]OP9f0jF^OV9d0eF_O[9X2000O100000000002N6J5J3OO0000000000O011OO1000001O0O2L3L4L4N210O00O100001O000000O100011M04M3N2dFQLW9Q400O2O0001OO101O0O10001O0000001OO10O22M0001O00001O010O00000000000000N2O20OM4^Na1fNVo5^OQfI]OT;0lDR2o9W1UOk0O1000001O000000008nFcKg8_411O0000aKYG\\4g8bK\\G]4h82N3M5L1N4M2M3M5K1O1O00001O000000O101ON2000000000O10PHhJn7Z502N3M2N000000001O001OO1000000004L1O1O103L1O00000000O1O10O2O00000000O2N2D<N1[NTHkMn7b0XHWOK4m7=gH[O\\O5n78TI^OnN8o73`IA`N<o7M^JQOcMP1Q8HmJ8Q5GRK8n4GTK8l4HUK7k4HWK7i4HXK8h4HYK6i4HXK9h4FYK9h4EYK>f4@ZKe0a4ZO`Kk0[4UOeKm0Y4ROhKn0X4ROhKn0X4QOiKo0W4QOiKo0W4POjKP1V4nNjKT1V4kNjKV1V4hNkKY1U4fNjK\\1V4bNkK_1U4`N]K0RM`1c7]N]KQ2f4kM\\KT2m7001]EkMo9V2mEoMQ:S2jEPNV:Q2fERN[:^20001O000010N10000001O01ZMcE]2]:aMhE\\2X:cMkEZ2V:eMmEY2d:N3L3O1M3O2M2NO1O100O1O1O1N2M3N3QEiMg:Y2VEhM00f:a2ZE^Mb:h2^EXM[:k2eEUM[:m2010N110O001O0O1010O01N101O_EYM_:j2O100O01O000O10000001O000000O02OO1O100O100000000000O101O0000000000009G4kEjLn9Y301O00O1000000001O001O00000cFdLl8\\3RGfLn8_3fFhLZ9d300000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000002N2N2NfLZLRMd3m2^LTM`3l2bLSM]3m2cLUM[3j2fLWMY3i2gLWMY3h2hLYMW3g2hL[MW3f2hLZMX3f2hL[MW3f2hL[MW3f2gL[MY3f2fLWJOb2[3X3eLUJ2b2Y3Y3eLTJ4a2X3\\3dLRJ5a2W3]3eLPJ6a2V3_3eLoI6a2U3`3fLmI7a2T3b3fLiI:d2P3c3ZM\\Lf2d3[MZLf2f3[MYLe2g3\\MWLe2i3\\MVLd2j3^MSLc2m3^MRLb2o3^MoKc2Q4_MkKc2U4\\LeIP1m1l2`4VMWKQ3i4PMPKV3P5TM`JR3`5_MiIg2W6V200001O0000000000", "size": [426, 640]}, "area": 59480.0, "iscrowd": 0, "image_id": 10434, "bbox": [0.0, 101.0, 640.0, 325.0], "category_id": 149, "id": 10012776}, {"segmentation": {"counts": "]fk72m<?TC2T<f0L3PDfNj;_1N1O2N4_D[NU;m101O1O1O1OdNkDd0T;VOZEc0e:\\O`Eb0^:]OhE?c:TO`Em0c;MnCTOa;l0RDTO:1e;k0[DVOGOi;n0\\DYOg;b0TDXO26k;a0RDFn;k0000001N10001O1O1LaN[Db1];:\\N", "size": [426, 640]}, "area": 1657.0, "iscrowd": 0, "image_id": 10434, "bbox": [605.0, 0.0, 35.0, 111.0], "category_id": 169, "id": 10012777}, {"segmentation": {"counts": "Xb0a0d;X1M3M00000O10O1O1O100O1O1N2O1O100000000000000O1000000006J1O001O1O001O000TEXNV:h1fE`NV:`1gEfNV:Z1gEjNX:V1eEnNZ:R1cERO\\:o1O1O1O2N1OO100O1000dGQM]6n2aIUM_6k2_IXM`6h2]I[Mb6e2]I^Mb6b2[IeMa6Z2^IPNZ6o1dIYNW6b1lI`NT6\\1dI^MlNY1`7T1hIeMeNY1c7m0kI]OU6?nIBR69QJIo53TJNl5MXJ4h5HZJ:f5FYJ;U4WNWK_1a0<l3aN_K@jNd1i1<n3`N^KAkNd1f1=R4]N[KDmNc1d1=T4\\NZKEnNc1a1>W4ZNXKGPOb1_1>Y4YNXKGPOb1]1`0[4WNXKGPOc1[1>_4XNVKd194c4XNTKe172g4ZNRKc153\\5iM_JU23EQO\\Na7o1\\IFR7:lHXOUOkNP8m1iHXO[OhNm7P2eHYOCcNm7P2^H]OT8c0jG^OX8a0fG@[8`0dG@]8?bGB_8FoF\\Oc0n0`8FmFZOb0Q1b8GlFVOa0S1d8IkFQO`0W1g8IjFmN>Z1i8a0jFAV9Y21O001O01O01O0000O001O1O1O1O2N1O1O20O2N100OoN_L\\H_3e7aL[H_3e7aL[H^3f7bL[H\\3f7dLZH\\3f7dLZH[3h7dLYHZ3h7ULiHk3\\8N1O\\H]Lc5c3\\JeL^5Y3bJoLW5P3iJWMQ5i2nJYMR5f2mJ\\MR5d2mJ_MR5a2mJ`MS5_2mJcMR5]2mJdMT5Z2mJfMS5Z2lJhMS5W2mJjMS5V2lJlMR5T2nJmMQ5S2PKmMo4T2PKnMo4Q2QKPNn4Q2QKQNm4o1SKRNl4o1TKQNk4o1VKRNh4o1oJ_LjNb1W6n1mJcLlN_1V6n1lJeLPO]1R6o1kJgLSOZ1Q6o1iJkLWOU1o5Q2gJmLZOS1n5o1fJQM]Oo0l5Q2dJSMBj0i5S2cJUMGg0d5U2bJWMLb0]5[2dJWMO>\\5eNTJb3>^M3;Y5_2_JYM88W5c2\\JWM=8U5P4dJWL[5\\O^JV3OfMb5TOmJ\\3_OPMi5^OUKU3RO]Mh5^OXKS3QO_Mf5^O[KQ3oNcMd5]O_Km2nNfM\\5ChKe2mNhMZ5CkKc2lNkMW5CoK_2kNnMU5CRL^2hNPNU5BUL\\2gNSNR5AYLZ2fNUNW5ZOUL_2eNWNV5YOWL^2dNZNS5XO[L\\2cN\\NQ5YO]LY2cN^No4YO_LY2bN_Nn4WObLX2aNaNl4WOeLV2`NcNk4VOgLU2_NfNh4UOkLl0TNYO:f0g4TOmLh0WN\\O6h0e4TOoLc0[N@2j0c4RORM?^NCNl0a4ROUM9bNHHl0b4SOUM4fNKDm0a4TOWMNjNO_Oo0`4SOYMJmN2[OP1`4SOYMGQO4WOQ1_4TO[MATO9TOP1^4UO\\M]OWO<POQ1]4VO\\OHXLQ1\\4VO^OGWLR1[4WO@EWLR1Z4XOADVLS1Y4YOCBULT1Y4YOCBVLS1W4\\OD_OVLT1W4^OC]OWLS1W4@C[OWLS1Y4Bf0;]ODc0:_OG`06DI=4FK:3IM70LO5NL19JH5=EF9?BB=g6N100O2N1O1O2NjNLWDOl04n:NSD2n0NP;7oDHR;8nDGS;9o0001O05K`Pi18cbVNEY<g0M3M4L3N2M3O2O00O1000\\HgNW3X1XKnNaNKU6W1QK]OfN\\OV6X1lJJiNnNZ6W1gJ8kN`N]6X1bJU1[NcMR7X1]Jf1b5YN]Jj1b5PNcJR2\\5mMeJT2Z5lMeJV2Z5jMeJW2[5hMfJY2Y5gMfJZ2Z5fMfJ[2Y5dMgJ]2Y5cMgJ]2X5dMgJ]2Y5bMhJ^2X5bMgJ_2Y5bMfJ^2Z5bMeJ_2[5bMdJ^2\\5cMbJ^2^5cM_J]2c5dM[J\\2f5eMWJ\\2k5dMSJ[2Q6cMmI^2V6`MiIR2QORMY7j0dIV2l6iMRIY2o6eMoH]2S7aMlH`2V7^MhHd2Z7ZMeHg2d7PM\\HP3n82N1O100O1O101N1O1O100O1O2eFXLR9h3lF]LP9d3oF_Ln8b3QGaLm8P4M4M2M3N2N3M2NO00100O1O1O0O2M3N2N2M3N2M3000O2O001O001O00001O001O001O00001N101O00000000000000000000O100000O10000000000@^KXHb4f7aKYH^4g7dKXH\\4h7dKXH\\4h7dKXH\\4g7eKYH[4a7kK_HU4_7nK`HR4^7PLcHo3\\7RLdHn3\\7RLdHn3\\7RLdHn3\\7RLdHn3\\7RLdHn3\\7RLdHn3\\7RLdHn3\\7RLdHn3\\7SLcHm3]7SLcHm3]7SLcHm3]7SLcHm3]7SLdHl3\\7ULcHk3]7VLbHj3]7WLcHi3\\7ZLbHf3^7\\L`Hd3_7^L`Hb3_7aL_H_3b7`L^H`3b7`L^H`3b7`L^H`3e7]L[Hc3S8oKmGQ4g8O000000000000000000ZOdK^H\\4W7nKjHR4n6ULTIj3l6ULUIk3k6TLVIl3j6SLWIm3i6SLWIm3i6RLXIn3g6RLZIn3f6RLZIn3f6QL[Io3e6QL[Io3e6PL\\IP4d6PL\\IP4c6QL]Io3c6PL^IP4b6PL^IP4b6PL_Io3a6QL_Io3a6QL_Io3`6RL`In3`6RL`In3`6RL`In3`6RLaIm3_6SLbIl3^6TLcIk3]6ULdIj3\\6VLeIi3[6WLfIh3Z6YLfIf3Z6[LfId3Z6]LfIb3Z6XLcHHT1o3Y6QLVJn3d7O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O2N1O1O2N1O1O20O1O100O10OO2N1O001N2O001O1N10mLZFb2f9]M\\Fb2d9]M^Fa2d9^M]Fa2d9]M^Fa2d9^M]Fa2d9]M^Fa2d9[M`Fd2T:O1N2O1N2O2M2O1N3L3G:G8I`0@g0YOc_3", "size": [426, 640]}, "area": 60453.0, "iscrowd": 0, "image_id": 10434, "bbox": [1.0, 50.0, 630.0, 324.0], "category_id": 183, "id": 10012778}, {"segmentation": {"counts": "0oT^2:RkaMGn<=RCHe<e0K4K4L1O00001O01O01O00O1MjCPOW<;kC6W<EnC:S<CVD7`<N01000O001001O1LFTC<1Dg<?XCAi<`001O2M101N2B=0001O00M3O10010O0\\OhC5X<KiC4W<LjC40@P<=PD2OBQ<<PD3NAR<<PD3NBQ<;RD2NDn;:TD3MEn;5XD?f;YOUD16f0f;XOUD06i0d;WOVDN8k0b;WOVDN8k0c;VOaDk0^;TOcDl0];TOdDk0n;1O0O10M31O001O11NbCVO\\<i0dCWO]<k00O01O10N1110O0001O10M3N2M4jCmNn;[1M2M4L2O1N21O000000000O100[N\\Dd1e;fN]Dl0d;kNZD37o0_;mN\\D17S1InNm;S1RDoNj;S1VDoNh;]1NO3MG801000001O000MWNcDj1];VNcDj1];21O00TEVNW:k1dEZN[:g1`E^N_:c1^E`N`:b1\\EbNc:^1\\EdNc:\\O]Ef2d:O01OO0101ON1101N10N110C^EmM1J`:X2bEgMh:d0VEB2Q1m:]ORE@3R1m:[O\\Ee0d:lNPE8`0h0b:oNoD8?j0a:oNPE5`0l0`:oNTEO>R1_:nNTEO=S1^:oNlEQ1T:mNnES1U;001O000000OPOmNkES1R:POPFn0P:SOoEl0Q:UOPFd0nNZOS;1oEe0V:\\OiEd0W:\\OiEd0W:]OhEc0X:]OiEb0X:^OfEb0[:_OeE`0[:@eE`0[:@hE=Y:BfE?Z:BfE=Z:CgE<Y:DiE=U:BlEa0P:_OSF`0k9ATFa0j9_OVF?m9@SF`0m9^OUFd0X;00000O10000010O0000O1O1M3001M3N2M210O10N03NkN\\OdEc0X:]OdD3S1?Z:CfE=Z:CfE>Y:ChE;X:EiE:W:FjE9V:HiE6Y:JiE0[:0fEI`:8Z1O01O000010O000O2O1O1O001O00eDO]90cF1]9MdF5X;001O001O1O1O0010O00000000101N2M2NVTU11ikjN3N00001N101O1O0O3N00O1O10O0100O10O010O0105FmB1fiY1", "size": [427, 640]}, "area": 264767.0, "iscrowd": 0, "image_id": 10440, "bbox": [0.0, 0.0, 640.0, 427.0], "category_id": 157, "id": 10012779}, {"segmentation": {"counts": "0c1g;000000000000G9N2N2N2POQDe0o;YOTD6L1Q<ITD3N3n;IVD3M2n;KUD210X<0jCNV<1b0000[Z=OgeB1N3L4M3M2M3M8F6M1N2O2OO00000001O000000001O000000000000O1O>B=@kg30QXL8UCEf<=ZCDj<6UCLP=46K3Mlh00SWO0000000VD4V:LeE;Y:E_Ec0a:]OWEk0i:UOPER1P;nNdD1NV1^;iNaD6MS1b;=000O1001O00001O000000000000000003M1O00002OO000000000001O000000000kNZDf0i;POVDN6R1o;1O1N10N1010O3N1]n2jNcRM6J6Rd1C[\\N000i^e20RaZM3mBMT=5nB3e<901O0M3O@nCKo;4e0N]eX3", "size": [426, 640]}, "area": 4051.0, "iscrowd": 0, "image_id": 10442, "bbox": [0.0, 0.0, 388.0, 199.0], "category_id": 92, "id": 10012780}, {"segmentation": {"counts": "RoW21Y=000`D4b9LYF8h9HSF>l9BoEc0Q:]OiEi0W:WOdEn0\\:ROaEP1`:PO^ER1b:nN]ET1b:lN\\EV1d:jNZE8]Oe0Y;SOXE8Bd0V;TOWE7Fd0S;UOUE8Jb0Q;VOSE:M?P;WOQE<0<o:YOiDb094n:3RELn:4SEIo:7RECS;>mD\\OX;d0jDUO[;k0b0001O00001O0000001OO1K5L41O00001O001O00001O4L4L1O1O1O1O1O1OHZDROD2h;j0d000O1N200O100N2N200O100O100001O001O1ON2N2O100O1000000001O1O1O1_C]OO1OO92V;U1hDmNW;U1fDnN0Io:[1mDXOR;`1000000000000000000000000000001O00000000000000000000001O00000000001O00001O0000000000O100O1O1O11O1O00N21O1O0000O11O1O00000000O1N2O1O100000kDoMQ;Q2nDQNQ;o1oDQNQ;o1nDRNR;n1nDRNR;V1SEXOKBR;T1VEYOHCR;T1]EROAJR;S1aEUO_:k0gEoNY:P1Q100001O001O1OCUD]Oi;a0[D^Od;b0]D]Oc;b0_D]Oa;c0`D\\O`;c0bD[O_;e0c00000000000001O001O001O001O1O1O2N1O2N3M1O1O2N001O1O001hDdN`:]1^EdNb:^1TEbNJ2R;P20000000000000000000000000002N1O1O001O2N00000000001O00000000000000N2001O000000001O0000001O0000000000001O000000001O00000000001O1O001O001O1OM3O1A\\EWNe:i1[EWNe:i1[ETNh:l1XESNi:l1YESNg:k1[ESNg:l1<N2N2N2N2G^DhNd;U1:N2N2O1O10000000UEoN]9R1_FRO`9n0`FSO_9m0aFUO]9n0`FRO`9P1^FPOb9Y1UFgNk9]1QFbNP:P2]EQNc:Z2100O10000000000[O^E]Ng:a1`EXNb:h1`0O100O1O1N200O100O[E^Nd9b1\\F]Ne9b1\\F^Nd9b1\\F^Nd9U1bEPO`;n0?00O100O100001O001O1O1O000000001O001OO100001O00M3N200O100O1000000O10000001O001O1O2mCQOQ1K`9\\1aFeN_9Y1aFiN_9W1`FjN`9U1aFlN^9T1aFmN_9R1aFoN_9Q1`FPO`9P1_FQOa9P1RFgNC;[:n0QFjN_O;`:l0oE]OQ:d0iEAW:a0`EcN0n0`:f110000O1O1000ZE^Md:d201O0000001O00000000000000000000000000M3O1004L1O0000000000000000O100001O0000O1000000001O000000000000000000000000N2002N0000001O001O0000000000", "size": [426, 640]}, "area": 26581.0, "iscrowd": 0, "image_id": 10442, "bbox": [173.0, 0.0, 467.0, 90.0], "category_id": 96, "id": 10012781}, {"segmentation": {"counts": "d^n12U=3YCLV<f0N11J5KV>EYB1ONkB1P=4N3L6L5O1O21KYnd1_OZR[N001nBOj<1UC1h<1WCOj<0WC1g<0XC3d<O\\C2b<N_CL0O00Y<5gCKh<5XCKi<380QCMg<2YCNg<1ZCOf<1ZCOe<2[CNe<1:1000O01000O0100O0000O1OSb42j]K100100O100O100O10J0QC1Q=2NMoB3Q=12ON101N1101O10O10000001OXEK[85bGM_83^GOd80XG4i8KTG`0e8_OXGb0f:1N2O1Xa`3", "size": [426, 640]}, "area": 608.0, "iscrowd": 0, "image_id": 10442, "bbox": [150.0, 50.0, 219.0, 191.0], "category_id": 105, "id": 10012782}, {"segmentation": {"counts": "gPd04W=6JO000000000000O1O1N2N21GUCMP=1QCMUZ88jRH1O00001O000000000O1E;0[O_Ca0b<_O_C`0`<_ObC3M7g<HZC8g<GYC9l<0000O101O0000O2J50000000001O000O1001O0O101O0000000000000001O0000000001O000000000000000000000001O000001O0O[m7FnRH3M2M4O0O202M2N000O1001O000O1N200000000O101O00000000000001O0000O11O00000000000000000000000002N1O1O1O000001O0000000000000O1O100O100O100O1000000005K1O1O1O00000000000000O1O2O000O1O1O11O000000000001O000000000000000001O00000001OO1000000000010O00000000001O00001O0000000000O101N1O1O11O1N11M21O1O0000O11O1O00000000O1N2O1O2OO11O@ZC<f<C\\C<d<D\\C<d<C]C=c<C]C=c<C]C=c<C]C=c<C]C=h<00C`C0oS>9cXB200N2N2O1O10001O00000000000000000000001O000002N1O1O001O2N00000000001O00000000000001M2001O000000010O0000001O0O1001O000001O000000001O01OO1000001O1O001O001O1OM3O1O101L3O100N32M1OO100000000000000000000000CXC8n<O1O2N000000O100O100O2O0000000001O1O1O1O01O000000000O11OO2O000O1000001O00O11O00000O10000000000000000O1O1O1N2M3N2002N1O1]O`C:i<O1O1O000000000001O000O1O1O10000001O00000000000001O000O10000O1O1000BVC<l<01O0000001O01O00000000000000000000000M3O1004L1O01O0000000000000O100001O0000O1000001O01O000000000000000000000001M2002N0000001O001O0001O0000OWM", "size": [426, 640]}, "area": 6858.0, "iscrowd": 0, "image_id": 10442, "bbox": [48.0, 47.0, 592.0, 56.0], "category_id": 111, "id": 10012783}, {"segmentation": {"counts": "c1g;c100000000000002N2N1O1O3M1O2N004L1O3M1O1O1O1O1O000000000000O1O1N21O1O001O1O00000000000000O1O1N2N2L40000000000000000000000HiDYN[;f1fDXN\\;f15000000001ZHZNf3f1WL^Nh3b1ULaNk3_1RLdNn3\\1oKgNQ4Z1`KUO_4k0oJGQ59kJKU55iJMW53fJ1Y50dJ2\\5OaJ3kNTNf5i1]K5hNeN]5V1jK7cNTOW5n0mKNgNZO[5i0lKg1S4YNmKh1R4XNmKj1R4WNmKi1S4WNlKk1S4UNmKl1R4TNmKn1R4RNnKn1R4RNlKo1U4QNhKP2Z4PNbK]1mNPMb5c1^K^1Z5cNaJ_1a5aN\\J`1f5`NYJ_1i5aNVJ^1l5bNTJ\\1n5dNQJ[1Q6dNPJZ1R6fNmIY1U6gNjIX1X6hNhIU1g0hLS4S2VKS1KhMo4V1UKf2j4ZMWKe2i4[MWKe2i4[MWKe2i4[MXKd2h4\\MXKd2h4]MWKc2i4]MXK\\N[O_3]5UNXKYN_Oa3Y5VNXKVNCd3T5VNZKSNFe3P5XNZKPNJg3l4XN[KnMM[3\\OkL\\5l1\\KkMOT3DSMQ5n1YLf0QOYMf4Q2ZL<[OaM[4S2[L3EgMP4V2\\LI]57fJB^5>fJ_OY5a0kJ[OU5e0oJXOP5i0SKSOm4m0WKPOh4P1\\KmNc4S1aKjN^4V1fKfNZ4Z1jKcNU4]1QL\\NP4d1PL[NQ4e1_LjMb3V2i300000000000000001O00000000001O4LO1O1O11O00MSEiMm:W23N2001O1^G\\N\\5f1[JcNc5[1]JiNa5Z1ZJjNd5V1YJoNe5o0[JUOc5i0\\J\\Ob5f0ZJ^Od5c0ZJF^5:aJK[56cJOY51fJg1c3ZN[Lk1a3XN[Li1e3YNXLi1g3WNfK]2Y4cMfK^2Z4bMeK`2Z4`MeKb2Z4^MeKc2[4]MeKd2Z4[MgKe2Y4YMiKg2W4YMiKg2W4YMiKY1;dLl3S2iKP1m0bL\\3^2gKk0S6TOnIi0U6WOkIg0W6YOiIf0X6ZOiIf07VMe4T2TKi0OdMa4c1`Km0EhMi4[1cKo0[OmMQ5T1XLl1f3TN\\Ll1b3UN^Lk1a3UN`Lj1`3VNaLj1^3UNcLl1\\3UNdLk1[3UNeLWNnN\\2^4]OdLYNnNT2b4CaLZNkNECe1Z5<[LYNfNGMU1X5k0XLXNaNH7f0W5Z1TLXN[NH>6_5j1kKWNUNJd0Lb5S2hKWNoMJk0Cc5\\2fKfNZ5Z1iJcNW5]1lJnMfM3^7o1nJkMeM5^7P2PKfMdM:\\7P2ZKoMg4Q2UKRNl4n1QKkM]MKa2Lb2^2]MPNG6f2j1aMTNYOd0R3X1bMZNlNl0a3k0_M^NaNT1P4>\\McNZNW1Z46YMhNSN[1c4MWMc1i2]NTMf1l2ZNTMf1l2ZNTMg1k2YNTMh1l2XNTMh1l2XNSMi1m2WNSMi1m2WNSMe1Q3[NnL_1Y3aNgLY1_3gN`LT1f3lNZLn0l3ROTLh0R4XOmKc0Y4]OVKhMSOh2j5@QKPNoN]2S6CmJn0V5ROjJk0Y5UOhJk0W5UOiJU1m4kNSK_1c4aN^Kh1X4XNhKR2n3nMSL[2c3eM]L_2_3aMbL^2^3bMbL_2]3aMcL_2]3bMcL]2]3cMcL^2\\3bMeL]2[3cMeL^2Z3bMfLQNkNh3_4WNgLmMnNk3[4XNgLjMROn3V4XNiLfMUOQ4R4YNiLcMYOP1Ic1U4JaMdNfNj0[OYN]4Y2cMeNmNZOP5Q2TLfNaNXOe5S2jKfN^NZOf5P2mKgNTN@o5i1nKhNnMDR6d1QLHP48SLDn3<WL^Oj3b0[LXOf3h0_LROb3n0bLmN_3S1fLgN[3Y1jLaNW3_1nL[NS3e1RMUNo2k1VMoMk2R2XMhMj2W2XMaMWLNe6a2Q40lFfMX7Z2gHjMV7V2iHQNQ7o1oHQNQ7P2nHQNQ7o1nHSN`1^OoMO\\3`2UMSN_1@oMN\\3_2UMUNPO@g10fNN]3]2VMUNPO@f12eNN^3[2VMWNoN_Og13dNM`3Z2VMXNnN^Og15RN=S4h1UMYNoN^Oe1V1V2S1VMZNoN^Oa1W1Z2Q1UM\\NnN]Ob1W1[2P1UMTOChN\\OT1m3P1SMXOVOm0g3KSMZOQOn0l3HSM\\OkNo0S4EQMhNgN[2Z4mNoL\\2T3dMkL[2W3fMhLX2Z3hMfLV2\\3jMcLS2a3mM_Ll1h3TNWLg1o3XNQLi1o3XNPLg1Q4YNoKg1Q4YNoKf1R4ZNnKe1S4[NmKd1T4\\NlKc1U4]NkKb1V4^NjKa1mN_L1EW4[2lLc2S3]MmLd2R3\\MoLc2oNYK\\3T2eMY3[2gLfMX3Z2hLgMX3X2hLmMkMdNg4_3^MQNdMcNn4\\3_M]Na2c1^M_Nb2`1_M_Na2a1_M`N`2`1`M`Na2_1_MbN`2^1`MbN`2^1`MbN`2^1`McN\\2`1dM`NY2c1gM^NU2e1kM[NR2h1aMaLiMh1a4k1nM[Nn1h1SNYNh1j1YNXNa1k1`NVNZ1n1fNSNV1P2jNRNQ1Q2oNlM^MdM`3Z1aLk1a2WOm0kNjLm1Y2XOj0hNSMP2S2UOj0gNZMT2k1TOj0dNcMW2b1SOk2m0SMTOo2k0kL[OU3e0QMUOo2k0RMSOP3l0QMSOo2m0RMROn2n0SMQOn2n0RMSOm2m0SMSOm2m0SMSOn2l0RMUOoMlMZ4o2gMVOiMSNT2l0dNj1_1XOdMVNY2a1kMQ1W2ONZ21gMOY21hMOW20kMOU21kM0T20mMNT21mMOS20oMNR2:fMFZ2:gMDZ2<gMA[2?eM@\\2`0dM]O_2c0\\M^MUMk1c5g0YMlM_NDW4`2ZMlMcNAR4d2ZMlMX4T2hKlMX4T2eKoM[4Q2dKQN[4o1cKUN[4k1eKYNW4g1iK\\NT4d1iK_NW4a1gK^N\\4b1eK[N]4e1dKWN_4i1bKTN`4m1_KPNd4P2]KmMe4S2\\KjMf4V2[KfMh4Z2T300000000000000000000000000000000001O0000000000001O0000000000O1001O0000000000000000001OO100000000000000000000000PJ[M[1e2bNdMX1]2eNkMU1U2hNUNQ1k1lNZNR1f1lN]NS1d1iNaNU1_1jNdNT1\\1lNeNS1[1lNhNR1X1mNlNP1T1oN_O?a0AA=?BDWMeMP2g2h0GZLgM?:Q2X2U16hLeMP2U2U1b0bL]MU2Q2W1f0bL[MU2o1V1k0cLWMV2n1S1o1mNQNo0S2QOmMk0X2TOhMh0\\2XOdMe0`2ZO`Md0b2\\O^Mc0c2]O]Mc0d1XM^LU2n1c0c1YM_LT2n1c0d2\\O\\Md0d2\\O\\Md0U1XM\\L1b0S2m1d0U1ZMQLOV1S2e1c0T1YMUL1P1S2g1c0]1YMkLT2h1c0c2]O]Mc0`1YMdLe0ROg0j2\\1`1YMbLc0VOg0h2]1a2kMjJf0e2_1a2jMlJf0c2`1a2iMnJf0a2a1a2hMQKe0^2h1X2fM\\Ka0]2n1m1dMhK=[2X2i1UMoKa0X2\\2o2cMQM^2P3`MPMa2T2gLcKg0Y2c2X2nLSK>e2e2\\2nMdMS2_2iMbMW2b2dM^M]2f2^MZMc2j2XMVMi2n2RMRMo2R3lLnLU3T3hLmLW3U3gLkLY3W3eLhL\\3Z3bLgL]3[3aLeL_3]3_LcLa3`3\\L`Ld3b3ZL^Lf3c60kL^LbLb3X3dLhL\\3R3jLnLV3l2PMTMP3g2UMYMl2_2[MaMg2R2dMnM]2VM_LT4[1fNi2o0_MQOb2g0eMYO[2?mMAT28RNHo1=kMCU2c0eM]O\\2h0\\L\\L0l2d3m0RL]L9f2e3S1hK[Lc0b2f3j1ZLVNf3j1ZLVNg3i1YLWNh3h1XLXNj3f1VLZNl3e1SL[No3c1QL]NR4`1nK`Nb4hNdJ>j0k0h4^NbJe0f0m0Q5TNfJc09Y1Z5iMaJm04Z1d5_M[JU11\\1m5TMVJ^1M^1R7`NnH`1T7]NmHc1U7UNQIk1R7lMTIT2Q90000000000001O0000000000000000000000000000001O00000000000000000000000000000000001O000000000000000000000000000000001O000000000000000000000000001O000000000000000000001O0000O1", "size": [426, 640]}, "area": 174508.0, "iscrowd": 0, "image_id": 10442, "bbox": [0.0, 51.0, 640.0, 375.0], "category_id": 145, "id": 10012784}, {"segmentation": {"counts": "m^l02U=3M3M4L3B>^Ob0L5M2mKiNPLZ1P4gNhIAk0j1]5fNgI@j0m1jNTNf5`0dJ@j0n1gNeN]5OoJ_Ol0n1cNUOV5KmJSOZ1m1^N[OZ51^JkNi1d3h3nMXLS2g3mMXLT2h3lMXLU2g3kMXLW2g3iMYLX2f3hMYLZ2f3fMZLY2g3gMWLZ2k3aMVL`2l3oLaL]2YNfLW5d0fLg2S4^MdKd2^4bMQJkNd0d3]5gMhIfNj0b3`5nM`I`No0a3c5SNYI^NS1]3f5ZNYJe1i5[NWJc1k5]NTJb1n5^NQJa1Q6_NoI_1>iLS4h1_K]1BiMo4;nK`3R4[LTLd3l3RL^Ln3b3kKeLU4[3cKnL]4Q3ZKcJN^2h4o2WK[Mc0VM_3^8^LeGa3[8\\LiGc3W8ZLnGd3S8YLPHf3P8WLTHh3l7ULXHX3_OoLV9g2VGVMk8a2_G^Ma8X2kGeMV8Q2UHmMl7k1Q2L4M3L4M3L4M3M3L4M7H1N8HSP4GRPL4L4L3M41N2O1NREGo88mFLT92iF2X9o11O1O1N2J6J6J6K5JeG\\O\\4>cKK[4i0oJ[Oo4Z1ZJjNd5T1[JQOc5m0]JWOb5g0\\J^Ob5`0^JD`5:aJOW5OjJ6R5ERK`0j4[OZK]2o2^MUMf2h2UM\\Mm2c2nLaMT3^2fLUMm3k2oKXMS4g2kKZMW4e2gK\\MZ4d2dK]M^4b2`K`Ma4_2^K[K^Ok1T5j2\\K[KCj1Q5k2ZK[KHi1d3]NnL_4D\\KLg1Y3YOdLe36\\KNe1P3i4oLeI2a1l2f5UMYJh2k5XMTJg2m5ZMiHDc0P3f6]MaHHj0m2HVMa55gIMP1k2@dM]5EmI1V1j2VOhMe50`KP2bNWNm5IlKV3S4iLnKX3P4hLQLW3o3iLRLW3n3hLSLX3l3hLTLY3k3hLULW3k3iLULDaNX2]5SNRLG_NP2d5ZNoKF[NFF_1S6ZOoK@VNG1P1l5MPL[OQNH:`0o5;jK]OjMH`01X6j0aK\\OdMJg0HX6R1_K]O_MIV8i0^JLb54aJYOXMNX8h0cJUOWM3V8h0fJPOVM7U8i0hJkNUM<U8f0PKYOU5b0hJA\\5<aJ^OBG^3i0nLETO6i33RMKeNd0V4@RM2XNl0f4POPM9kMT1V5aNmL`0cMW1d5UNfLi0\\MZ1[6_MWLR4]630000O101N10N2J5K6J5J7J6J5MCWMaFf2`9bMZF[2h9a03M42M:F:G9F:G9F4MO00010O010O0dKfGh3Z8VLiGj3W8RLmGm3S8PLQHo3P8mKTHn0El1V8SMYHR1M]1b8dNkG1LlNY8T1lGnNQ:S1dEXO[:i0bEZO]:g0ZE@g:a0TECl:\\16J6J6J6J6J5K6J6J6J6J3Ich01`WO4L6K1O1N1\\F_OnMO_7c0aJ@oMN^7c0TH@g11eNN_7b0TH_Og13dNM`7c0TH^Of14dNMa7a0UH^Oe16RN=S81VH[Od1X1V6^OVHYOb1[1W6]OXHWO`1]1X6GmGlNP1:WOT1k7GoGkNc0c2^7bNPHkN>f2b7_NPHlNL06f2o7_NPHkNJQ3Y8VNlGiNKm2[8]NhGh1Z8YNfGgNMe2^8fNdGeNNa2`8lNaGdNOY2f8TO[GcNOQ2l8]OVGbNNm1n8CSGg0m8YOTGf0l8[OTGc0m8]OSGb0n8_ORG?o8BQG<P9EoF:R9FoF8R9InFaNOS11RO0EQ8d1oG`N0Z2P8VOPH_N1[2P8UOnG_N3]22gMW7\\1dHaN3S3Y7[NcHcN3[12J[7c0cHcN5g1]7lM^Hg0M\\O293n1b7]MbHo0G]O3T3P8d11O010O0010OcKlGd3T8WLPHh3S8VLmGh3V8XLiGf3[8YLeGd3^8\\LaGa3d8]L]G`3g8_LXG_3n8^LRG_3S9_LlF^3Z9`LfF]3`9^LbF_3e9N3LcGhLn6W3nHPMQ7n2lHYMQ7f2kHaMT7\\2iHkMU7W4N1O101O001O00TMPH>P8_ORHc0m7ZOUHg0k7VOWHk0i7[MVHV1?lN7R1S7gN^HV19TOm07n6b0VHYOT:c0mE@R:<PFFQ:6PFMP:f1001O0O2O0O2N2M2N3L3mN_E[O]<M3MlU1?aiN6K6I6J1003L4M3L3N3L4Mihg0E[RXO1TM8f2HXM`0b2@[Mj0^2VO_Mo0^2RO`MQ1]2RO_MS1^2RO]MQ1_2QOaMP1\\2ROcMQ1Y2QOfMS1U2nNkMe1Z1hM_Je0W4e1U1eNjN^1fLeMW3o02_1iKgM?:V3b0a0l1WLeMT3`0b0W2QL]MX3=e0Z2QL[MX3<c0^2SLVMZ3<?b3@^L<[1ZLFZ3oN8[1^LKZ3iN4P4LPL1S40kKOW43gKL]3gLbK0f0^3ZOeLh0V3d3nLYL`31Bf3nLYLm3DUOS4oLXLW4ZOjN^4nLYLc7Y3VH\\L6<d7X3\\HVLNd0f7V3VHUL1O2g0g7^3VHkK1h0i7_3VHgK0k0j7_3VHdLe0RO]6Z4oHbLc0VO]6Y5bIjJ\\6V5cIlJ\\6o3UIgL?\\O\\6i3[IgL9C`6c3VIcL>Lc6h3kIZLY6T4SIoKm6W50QOgHmKX7W4PI]KP7g4XImJh6W5d03MTK]HT4_7nKZH[4b7k0L4L4L2M2N2N2N2N2N3M2NN2J6J6K5J6J7F;El0VO8G9H7H9M35J6K5VG\\Lb7i3TH\\Ll7j3jGZLW8g1`G`0`8AaG>_8V21O2N3M2N8fKPGb10>\\9^OfFa0b9TOlF`0]9UOgFi0b9mNaFQ1h9dN]FY1l:N2O1I7H8HWkS1", "size": [426, 640]}, "area": 48503.0, "iscrowd": 0, "image_id": 10442, "bbox": [68.0, 0.0, 485.0, 425.0], "category_id": 183, "id": 10012785}, {"segmentation": {"counts": "X4V;^401O00000000000000000000000O10000000000000000000001O000000000000000000000000000000000000000000001O000000000000000000000000000000000000000000001O00000000000000000000000000000000000000000000001O000000000000000000000000000000000000000000001O000000000000000000000000000000000000000000001O00000000_NUEYNk:d1XE\\Nh:a1\\E^Nd:a1^E^Nb:a1_E_Na:_1bE`N^:_1dE`N\\:_1eEaN[:]1hEbNX:]1jEbNV:]1kEcNU:[1nEdNQ:\\1PFdNP:[1RFdNn9Z1UFeNk9Z1VFfNj9Y1XFfNh9X1[FgNe9Y1[FgNe9Y1\\FgNc9Y1^FfNb9Z1^FfNb9Z1_FeNa9[1`FdN`9\\1cFaN]9_1eF_N[9a1gF]NY9c1jFZNV9f1lFXNT9h1nFVNR9j1QGSNo8m1SGQNm8o1UGoMk8Q2XGlMh8T2ZGjMf8V2\\200000000000000000000000000000000000000000000000000000000000000000000O10000O100O10000O10000O100O10000O100O10000O10000O100O10000O100O100O100O100O1O100O100[OZB[Og=e0]BWOc=h0aBTO`=k0dBQO]=o0fBnNZ=Q1e000O1O100O100O100O100000000000000000000000000000000000000000000001O0000000O1000000000000000000SBTOR=l0mBUOS=l0kBVOT=l0iBVOV=m0eBVOZ=l0cBUO]=m0`BUO_=m0]BVOb=_1O001O1O1O001O001O001O001O00001O001O001O001O001O001O00001O001O001O001O001O0000001O0WEbM\\8^2]GiMc8W2VGPNj8P2PGVNP9k1hF\\NX9d1cFaN]9_1aFcN_9]1^FfNb9Z1[FiNe9W1YFkNg9V1UFmNk9S1RFPOn9P1PFROP:n0iEYOk6S4mHULX2Z6", "size": [500, 375]}, "area": 149232.0, "iscrowd": 0, "image_id": 10444, "bbox": [0.0, 21.0, 375.0, 473.0], "category_id": 183, "id": 10012786}, {"segmentation": {"counts": "_[S15h:2N3N115J10O100M3N2M3N1N3OeQd02Tn[O3J6M3O10010O011ON3N10001O01O01O0N2O2M2O1N3N1OZPV3", "size": [349, 500]}, "area": 446.0, "iscrowd": 0, "image_id": 10445, "bbox": [103.0, 220.0, 97.0, 72.0], "category_id": 105, "id": 10012787}, {"segmentation": {"counts": "kl:2k:0N114K1O1O100O1001O000000001O00000000O1O1O1000000001O000000001OO100001OO11OO10000O11O00O100000000O1O1O1001O00O1001O1O0000000000O100O10000O100NBhE;V:DmE<P:ESF:l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mMXGQ2h8oMXGQ2k8ON2000000000000000000000000000000O100000000000000000000000000000000000000000000000000O11O0000O1000000000000000000000000000000000000001O000000000000O1000000000000000000001O0000001O00", "size": [349, 500]}, "area": 19435.0, "iscrowd": 0, "image_id": 10445, "bbox": [31.0, 280.0, 469.0, 69.0], "category_id": 154, "id": 10012788}, {"segmentation": {"counts": "0Pl0T2h\\O1O1N101N2O1O1N2O001000000O10O10O10000O01000O10000O01000O1XJ`MS3`2mL`MS3`2kLaMU3_2lLeMP3[2oLfMQ3Z2nLgMQ3Z2nLgMR3Y2nLgMR3Y2mLhMR3X2oLhMQ3X2PMgMP3Y2PMgMo2Z2QMfMo2Z2QMfMo2Y2RMgMm2Z2TMeMl2[2TMeMl2[2TMeMk2\\2UMdMk2\\2UMdMk2[2UMfMk2Z2TMgMk2Z2TMgMl2Y2TMgMl2Y2TMgMk2Z2UMfMk2Y2VMgMj2Y2WMfMh2[2XMeMh2[2XMeMh2[2XMeMg2[2ZMeMf2[2[MdMe2\\2ZMeMe2\\2[MdMe2\\2[MdMe2\\2\\McMc2]2]MdMc2\\2^McMb2]2]MdMb2]2^McMb2]2^McMb2]2]MdMb2\\2`McM`2]2`McM`2]2_MdM`2]2`McM`2]2`McM`2\\2aMdM^2]2bMcM^2]2aMdM_2\\2`MeM_2\\2`MeM`2[2`MeM`2Z2bMeM]2\\2cMdM]2\\2bMeM^2[2bMeM^2[2cMdM\\2]2eMbM[2]2fMcMZ2]2fMcMY2^2gMbMY2^2gMbMh1JlKd2\\2bMc13lK\\2`2bM`19mKT2d2cM[1Z3dNgLY1\\3gNdLU1`3kN`LR1c3mN^LS1a3nN_LQ1b3fLSLf1=d1^3hLWL`1=h1_3eLWL]1=n1S4oMnKP2[4fMgKZ2\\4aMfK_2\\4\\MgKd2\\4VMgKi2Z4TMiKl2X4PMkKP3U4lLoKT3R4hLQLW3P4eLTL[3n3`LUL`3d5O1UOlHjMW7T2XI]Mj6a2j0N2N2N2N200O1O100O100O100O100O11O0000001O000XOjMiHV2V7lMiHU2U7mMjHS2V7nMiHR2V7oMjHR2U7mMlHY2?hMS51]Jc3a5^L_Jd3_5[LbJf3\\5[LdJf3Z5[LfJg3X5YLhJh3V5YLjJh3T5YLlJi3R5WLnJj3n1QL87iMi3o1SL35nMj3m1SL33PNl3k1SL12UNn3g1RL02YNj3i1VLJ2]Ng3k1XLD3aNd3k1\\L@2eNa3k1`L\\O1iN^3i1fLZONmN\\3f1kLXOLROY3e1nLSOMXOU3f1oLkN1_OP3g1PMcN5Fk2h1QM\\N7Mh2h1RMTN;4c2h1TMnM<;`2h1UMfM?c0]2g1XLfMW2b0a1i1TLhM[2?a1i1nKnMa29a1j1jKPNe26a1k1hKPNg25a1^2_NbMa1_2^NaMb1_2^NaMb1a2[N`Me1_2\\NaMd1_2[NbMe1_2YNbMg1_2XNaMh1`2VNaMj1a2TN_Ml1b2TN]Ml1d2SN\\Mn1b2TN]Ml1c2TN]Ml1b2TN_Ml1a2RNaMn1^2SNbMm1^2SNbMm1^2SNbMm1]2SNdMm1Q1ULPOl1Oo1U1RLlNn1OP2V1RLkNn1NP2X1RLkNl1MR2Y1RLjNi1OU2X1RLiNh1OV2X1SLhNg10V2Y1SLfNf12W2Y1SLdNe14Y2W1SLdNd15Y2Y1RLaNe16Y2[1QL^Ne18Z2[1QLYNh1<W2[1RLVNi1?U2[1SLSNj1b0S2Z1ULQNi1f0R2Z1ULmMk1i0P2Z1VLiMl1n0n1Y1WLfMm1Q1l1Y1dNkN\\1U1cNlN]1T1cNlN]1T1cNlN]1T1cNlN]1T1cNlN]1T1cNlN^1S1aNnN_1S1`NmN`1S1`NmN`1S1`NmN`1S1`NmN`1S1`NmN`1S1`NmN`1S1aNlN_1T1aNlN_1T1aNlN_1T1aNlN_1U1aNjN_1V1`NkN`1U1_NlNa1T1`NkN`1U1`NkN`1U1`NkNa1T1`NkN`1U1aNjN_1V1bNiN^1X1aNhN_1X1bNgN]1Z1eNdN[1\\1dNeN[1\\1eNdN[1\\1dNeN\\1[1dNeN[1\\1dNeN\\1[1cNfN\\1[1dNeN\\1\\1dNcN[1^1dNcNZ1_1fNaNY1`1fNaNY1a1fN_NY1b1gN^NW1e1hN[NW1e1jN[NU1f1jN[NU1f1kNZNT1g1kNZNT1g1lNYNR1i1nNWNP1k1POUNn0m1QOTNm0P2POQNm0Q2ROQNl0Q2TOoMj0R2WOnMf0U2ZOkMd0W2[OjMc0X2]OhM`0[2_OfM?\\2AdM=^2BcM<_2CbM;`2DaM:a2F_M9b2G^M7d2I\\M5e2I^M6D^Me1[2hN5BcMf1V2jN5@gMf1S2kN5^OiMh1P2lN5\\OmMh1m1mN5ZOPNi1i1oN6WORNk1g1oN6UOUNl1c1`MPNP1X2GRNi1e1RO8XOPNg1f1SO9ZOnMc1h1TO:\\OjMa1j1UO;]2EdM;\\2lM]KY12mNW2m1[2mM^KS14ROT2m1[2nM^Kn07UOR2o1Y2mMaKi08ZOo1o1Y2nMaKe0:\\On1P2X2oMbK?<Ak1Q2W2oMcK:Q3f1]1PNdK5R3j1[1QNXOo1h0QNXOn1l4O1000000BSGlNm8Q1XGmNh8Q1\\GlNe8R1_GlNa8Q1dGmN\\8R1gGlNY8S1i0O1N2O1O1O1O1O1O1O100O1O100OTN]O`Ic0X6EhI;o5NQJ2n5NSJ2l5NUJ2j5OVJ1i50WJ0i5OXJ1o4\\OjId0W10j4EjI;]10c4NlI3a1O]48mIJf1NX4a0mIAl1OT4]1lKcNP4a1PL_No3b1QL^No3b1QL^No3b1QL^No3b1QL_Nn3`1SLbNYOYOV3U2aMcNWOZOW3S2bMdNUOZOY3R2bMfNSOYOZ3Q2cMgNQOZOZ3P2eMhNnNZO\\3n1fMiNmNZO\\3m1gMkNjN[O]3j1iMlNhN@ECP3Q2cNPOcN8d2h0iNUO]N0n2k0eNYOXNLU3j0cN^OTNHY3j0cN_ORNH[3i0cNP1]1POcNP1]1POcNo0^1QObNo0^1POcNP1]1POcNP1]1oNdNQ1\\1oNdNP1]1QObNo0^1QObNo0^1ROaNn0_1ROaNn0_1ROaNm0`1ROaNn0^1TOaNl0^1UObNk0]1VOcNj0[1XOeNg0[1ZOeNf0Z1ZOgNf0l0oL[MMJ^2o1f0n0oLYM[2i1\\NQN^1n2jMXM\\2i1[NSN]1W38fN[NSN]1W38fNZNUN]1U38gN[NTN]1T39hNZNUN\\1S3;gNYNWNZ1S3<gNZNWNY1R3=gNZNXNX1Q3>gNYNYNY1P3>gNXN[NY1m2`0hNUN^NY1k2a0hNUN^NZ1j2`0iNTN`N[1g2a0iNSNaN\\1f2`0jNQNcN_1c2`0jNoMdNa1b2a0gLcMQ2:gNb1a2a0gLcMQ29gNd1a2c0gNhMjNd1_2b0iNjMjNb1]2d0iNiM`NLEe1S3f0hNhMaNNDc1R3h0iNfMaN1Bb1T3g0iNeMaN4A`1U3g0iNdMbN5@`1U3g0iNdM`N8A\\1W3h0hNcM\\NZ2k24iNaM[N]2l22iN`M[N_2l21VO1i00VO0k00iN_MSNf2T3KiN<W1DiN<V1EjN;V1DkN<U1DkN<T1ElN;T1ElN;T1ElN;T1ElN;S1FmN:S1FmN9T1GlN9S1HmN8S1HmN8S1HmN8S1HmN8R1InN7R1InN7R1InN7Q1JoN6Q1JoN6Q1JoN6P1KPO4Q1LoN4Q1LoN4Q1LoN4P1MPO3P1MPO3P1MPO3o0MRO3n0NQO2o0NQO2o0NQO2n0NSO2m0NSO2m0NSO2l0OTO1l0OTO1l0OTO1l0OTO1k00UO0k00UO0k00UO0j01VOOj01VOOj01VOOi02WONi02WONi02WONi02WONh03XOMh03XOMh03XOMg05XOKh05XOKh05XOKh05XOKg06YOJg06YOJg06YOJf06[OJe06[OJe06[OJe06[OJd07\\OId07\\OId07\\OIc08]OHc08]OHc08]OHb09^OGa0;^OEb0;^OEa0<_OD`0=@C??@A`0?@", "size": [349, 500]}, "area": 90466.0, "iscrowd": 0, "image_id": 10445, "bbox": [0.0, 0.0, 500.0, 347.0], "category_id": 155, "id": 10012789}, {"segmentation": {"counts": "Pl0S2i81O1N2O1N2O001N2O1O10O1000O10000O01000O10000O01000O1000O010000O1000O010000O1000O010000O10O10O1000000O01000O10000O01000O10000O01000O1000O010000O1000O010000O1000O010000O10O1000O10000O010_O]MhHc2S7fMhH[2T7lMiHT2S7m0M2M4M300O1jKQL^1d2XLmMc2BU1^2YL_NS2UOd1Z2YLoNe1jNP2V2ZLVO`1gNV2P2[L]O[1eNZ2o1XLAX1dN_2o1RLCY1aNd2X4VMkKj2X4PMkKP3X4iLmKV3V4dLmK[3V4_LnKa3U4YLoKf3d5O1UOUIaMm6^2bISM`6k2i0O1O2N1O110O0010O0010O01O010O002O0O1O101N100O1O2O0O1O3cImLl4[3eJmLY5]4N3N1N2O2VLQKa1Q5_NUKY1m4gNYKR1i4mN]Kk0e4UO`Kd0b4[O`Kc0b4\\O_K`0f4^O[K>i4AXK;h4GZK5g4MZKOg43[KHf4:[KBf4`0[K\\Og4d0[KVOi4k0XKoNl4R1UKgNP5Z1QK_NT5b1mJXNW5h1kJQNZ5P2gJiM^5X2cJbMb5^2^1001O1O1O1O001O1O1O2N1O1O2N1O1O2O0O1O2N1O1O2N1O1OeNkHPOS7T1jHlNU7U1kHkNT7V1lHkNR7V1nHjNQ7V1PIiNP7X1PIgNP7Z1PIeNP7[1QIdNP7\\1PIcNP7_1oH`NQ7b1nH\\NS7e1mHXNU7h1lHUNV7j1lHRNW7n1jHoMX7Q2iHlMY7T2hHiMZ7W2gHfM[7Z2d000001O000001O0000000001O0000000000000000001O01O00000000O100O101O0O100O100O1N_MhGa2W8`MiGa2U84O\\MlGa2R86O1O1O1O1O2M2N2N2N2M3N2N2M3N2N2M3N2N1O2N2N2O1N2NSM\\Ie1[2UMi1U1mKg1Y2UMh1T1QLg1U2WMh1R1ULg1R2XMh1P1WLi1o1YMh1n0[Li1l1ZMh1l0^Li1i1]Mh1h0aLl1f1]Mh1h0bLk1d1aMh1g0aLh1f1dMf1g0aLf1g1dMh1j0^Lb1i1eMi1l0ZL`1k1fMj1m3UNSLl1m3[L]KY11nNg0]3l3\\L^KS13SOc0^3m3\\L^Kn06VOa0`3k3[LaKi07[O>`3k3\\LaKe09]O<b3j3]LcK><A8d3i3]LdK9]1Z3P3]LeK4^1^3n2^LeMb3[2^LdMb3]2]LdMb3]2^LbMc3^2]LbMc3^2]LbMc3^2]LaMV3fMUMi4E`MT3lMUMn6k2VIRM`4J^MS3VNQM\\4L]MQ3\\NQMW4NbMk2ZNUMT40bMj2W3VMiLi2X3WMhLg2g501O1O1O1O001O100O1O10O0I8H8K5O1O1O1O100UOmLkIT3P6UMkIl2o5^MlIc2]2gLg0o0hL[2`2mL<b4G^K2e43YKJg4;WKBi4c0UKZOk4k0TKQOl4S1SKjNn4W1SKeNP5QOQKo0NNR5QORKR1MJR5RORKW1LDT5SOQKZ1KAU5SORK]1J]OV5SORKb1HXOW5UORKd1HTOX5UOUKf1BRO\\5UOWK^OKc1CG`5ROoKS1bNHc5POfKa1fN]Oh5nNaKh1gNYOj5kN`Ko1eNTO]7n0bHQO^7Q1bHmN^7V1aHVNO2_7k1bHnMO:_7?`H90B`77aH6OA`7;bHm0^7VOcHf0\\7]OeH`0[7CeH:[7HfH5Z7NgHNY79fHAY7f0fHUOZ7R1dHhN]7[21N2\\O^HlMb7W2`HdMa7]2`H_Mb7c2^HZMNKb7m2`HYMV5JoLn2jMXMV5LnLV3S3jLmLV3R3kLnLU3R3lLmLT3S3mLlLS3T3nLjLS3V3nLiLQ3X3oLhLQ3W3QMhLo2W3SMhLm2V3VMhLk2W3VMiLj2U3XMkLg2U3ZMkLf2R3]MnLc2P3^MPMc2o2^MQMb2n2^MSMb2l2aMRM^2o2dMoL\\2P3YMZKFd1R3Q3YM\\KDc1S3P3YM_KCa1T3o2YMbKB_1U3n2ZMcKA_1T3o2UMiKFW1V3o2TMgMl2X2SMjMl2V2TMkMl2T2SMnMm2R2PMPNQ3Q2iLTNV3j40000O1000000O10000O0100000O10000O10000O1000O10O10000O1000000O10000O1000000O10000O10000O1000000O10000O1000000O10000O1000000O10000O10000O100O1O100O1O100OUM", "size": [349, 500]}, "area": 63195.0, "iscrowd": 0, "image_id": 10445, "bbox": [2.0, 0.0, 498.0, 340.0], "category_id": 183, "id": 10012790}, {"segmentation": {"counts": "]lS21142LN2`>5aAO]>2bAN^>;0N2ON111OO11OO100001O00000O1O1000000000N3OaAA]>?cAA]>a00O11OO1O100O1001M2O0101O00000000001O001O000N03O0000000O00101M110O1O10010OO10000000O11O1O001O00000000000000O11O0000O10000001O00O10000O1000000000010OO0102NN2001O01OK^OkAc0T>^OlAb0n=]OTBi0k=WOUBi0l=5O10000001O00MQOUBo0k=3O100O12N003M00001O002ON1N1N3000001OO2O1O3M000FPBDQ>:QBDQ>;<M11OO2O0000N04OM21O0N^AFd>8\\AHe>73O00fCKi92iCN^20j90iC4Y2Ln91hC4Y2Ko9OjC61Nl1O[:6fCNl1M^:>bEB]:?bEB^:?aEA_:?aEB_:=`EE^:IiC`0i1Ga:9_EGa:9_EG]:IkC`0h1G]:JjC?i1G]:=bED^:<bED^:<bED^:<bED^:<cEC^:<bED^:<bED^:<bED^:<bED^:<bED]:=bED^:=aEC`:=_EC`:=aEC_:>`EB`:>_EC`:?_EAa:JkC?d1Gb:IjC`0c1Hd:GiCa0c1Hc:GkC=0Ba1;i:LlCI\\1;j:IaE7_:IaE7_:GcE9_:EcE9a<100N20000O10000000000O10000000000000000O10000000000000000O1000000O1000000O100000000000000O11OO1M\\OjAd0V>]OiAc0W>]OiAc0X>2O1O100000000O11OO100O100000WOlAf0W>[OiAa0X>^OhAb0\\>O1O00001O00001O00001O00XOCmB=n<IQC7P=HPC8P=JnB6m<BbB<98e<]O^C?I7i<[OSCN01OO0`01:m<YOQC40I1^1m<b000000000000000000000000000O10000001O000000000000000000O11O00O100O1001O00O11O00O11O00O10000002NO10000001O1O000000O12N00000000001O00O100O1NQNSCo1m<2001OO100001O000000001O000TNQCg1P=WNQCi1P=VNQCi1S=01O000XNjBf1Z=N00001O00000000000000000000000000O10000001O000000O11O00000000000000001O0000000000O11O000000000000000000000000000000O11O0000000000000000000000000000O11O0000000000001OO11O1O0000O1O1001O1O0000O1000000O1O12NO1001O00000000M300001\\NeBa1^=O00N2", "size": [480, 640]}, "area": 17992.0, "iscrowd": 0, "image_id": 10449, "bbox": [144.0, 374.0, 496.0, 106.0], "category_id": 111, "id": 10012791}, {"segmentation": {"counts": "gaT41m>3O1DLjA7S>JdA020N6]>:01O101M10010N1O11M22NN21O002M2O10O00nAWOn=i0PBXOP>h0QBWOo=i0oAYOR>i0010O010001N0QBSOl=Q100OPOTBj01VOn=j0SBUOl=l05O00001OO20O0000N20O10O2N0011OO001UBQOe=P1ZBPOe=R1ZBnNf=Q1ZBPO=0g<U1RCQOIIS=X1QCTOn<l0RCSOo<m0oBVOP=l0nBTOR=^1N11N0101O0000001M10100I70O3L111OOO1010O20OO03N0O2O1O1ON10100MRNSCP2k<4N2N200O0001NLjM`CW2_<lM^CT2b<60O004L1O0010XCiMf<X2N3O000O2000OO2N100O4L1O111M10N21N1O10O10O10O100O1JjCaMV<`2501N1O1O0100O1O1O1O10001O01N010000O1O10\\MfCb2]<OO1O100O1001O1N2O00N201O0O1O1O100O12N0000O011M20ON33L10N30ON1110O1N20O00O10O10011NOM41O0010001N00O10O12N02N000000N3O00O010O3LjCZMV<f22001N0100001O000O100010O00nCWMn;i2PDXMP<i2oCXMR<j22O0O00lCVMT<l2N0N201N1001ON2001OO1001O1OO100N2O0O2N2002O0OO1N20O11N11OO11O000kLYDQ3n;M1O010O1O2N001O0O11OUMmCi2U<0O1000000O1O1O1O1N200MQMUDo2k;3O10O11O00000000000001O2N0QMSDk2Q<01O2N000O021N2N1O001ON201OO201O0O0[MgCc2\\<NN_MfCa2[<2O200N1000O1000000O0003M02O01N10000001L4O1OM^MgCd20\\MU<j2O01M]E", "size": [480, 640]}, "area": 27105.0, "iscrowd": 0, "image_id": 10449, "bbox": [282.0, 322.0, 358.0, 109.0], "category_id": 124, "id": 10012792}, {"segmentation": {"counts": "\\i^4230:0FOl==ZBHHNl=;UBB1=j=e00010O0O1110OL40KZBnNh=P1211O1N3NO2N100O2O10OVBoNh=P1WBQOh=o0XBROh=m0XBSOi=m0WBTOg=n0XBQOj=P1VBnNj=R12O2O1O1OMSOTBk0m=43HPBYOQ>d0nA\\OU>`0mA_OO0V>?9O0O011O0O11O000000000M30000O100O2D^ORB10a0n=DQB=m=;[BROX=Q1fBSOV=n0cBnN1:W=]1O0N5L0oBVNl<P2K5YChMe<Z2M2O1O10001HbMlC_2o;fMmCJ1`2Q<<N3L3N2010JVDTMh;n2[DoLe;Q351KKXDSM14h;c2_DaM^;^2aD]MLOa;d2dD\\MMNa;e2bD]Mf;b2>N1N2O0O3OO01OO1O2O0O1cC`M\\<^2dCbM\\<^2dCbMZ<c21ROdC\\N0g0[<n0eC[N0f0\\<n0eC\\N3a0X<5dC40VO6a0W<1iCO;Nm;1mCN6BB7]<:jCM?BYO1^<<lC2b0Bd;:kC3a0De;5kC8`0@SO0b<3`CM;`0X=CmBM^O=f=CnB0[O?]>AcA?]>AcA>`>O01^BBb<?]CBa<?bB@8263n<>gBC;Om<a0eBA=2j<g0TC[Ok<f0SC]On<d0kB@U=U1KZNPCg1o<ZNPCe1P=]NoBc1R=50O1O2OkBWNR=m10N1WOnBQOO=T=a0oBPONOO>T=b0PCRO09R=e0VCZOk<e0VCROC6W=JiB20d0c0ZO]O0W=OjB20OOc0X>\\OiA00`0\\>2N011O1O110NO11O2O3MO12N100OO1L42M00WOkAe0Y>11L1101O]OfAa0]>00O001O2M100I_A0`>0bAN^>19O010O1LVA1k>O30ZPV2", "size": [480, 640]}, "area": 7871.0, "iscrowd": 0, "image_id": 10449, "bbox": [304.0, 236.0, 186.0, 108.0], "category_id": 134, "id": 10012793}, {"segmentation": {"counts": "m2f1Z=0O11O1N200O01N1002N1O000000N2O0O3N3iBWN01P=j1PCWNP=h1QCXNm<i1RCYNm<o1OM12O001O01OO010N2010N4M1QNSCj1R=01UNmBf1R=ZNnBg1;[NZ<e1YCYN1:g<l110O2M1ON40N101O0O101N1001N2O\\CgMa<X2`CiM\\<[2cCgM[<`202M010M[MiCe2W<4M200O000O1O2ARM`D23m2];=01O1N1000000O10010N11LbDeL];[3dDdL];\\3cDdL\\;\\34000001O1N201N0001N2N0N41N0001M21O1OO2O0000000O1O02O0N2O2O00O110O00O10O1McLcD]3];30O3NO02O2O0O002NO10000NgL^DX3b;31ON20000O1O11O0100O6I3N5LO010O01ON200001N1005KLZLnD\\1Ok0T;VOnDi0T;QOSEm0l:SOVEl0k:ROWEl0j:POZEo0f:QO[En0f:ROZEm0g:SOYEm0g:TOXEm0g:SO\\Ej0d:VOZEn0d:RO]Em0c:POaEP1^:oNcEQ1]:ZNmD8e0]1_:ZNmD8e0^1]:ZNPEIK9i0e1[:YNUE1a0f1[:VNkDJ9:b0c1\\:XNVEKC7h0i1`:PN[E65n1R;QNoDP2o:PNPES2o:oMmDT2P;nMPER2P;mMoDU2Q;kMoDU2P;kM^DI>^2R;nMnDR2R;oMmDQ2Q;PNPEP2P;oMoDT2l:_MgD?=Q2l:SNgD^O6_2U;SNeD]O5a2V;VNfDXO0b2[;P100ON3O1O0O1O2N0100O1O11OO100000O021N2OO0003M0000O1O1001O01OOO2101NO010NgL]DY3c;kN^DoNb;S1]DlM0o0b;T1cDlN];S1gDQNHa0a;]1hDmMF13d0`;[1REdNn:]1]DoMb0d0R;_1mDbNS;[1oDdNQ;[1QEeNm:\\O^Dm1g0fNk:^O_Dk1f0gNj:_1UEbNk:]1UEcNj:_1UE`Nl:a1iDmMNd0V;^1mDnMIOMf0\\;_1lDmMKNLg0];^1lDPNHa0^;a1hDPNJ=^;e1fDfNY;\\1gDcNY;^1gDaNY;`1dDcMNl0^;d21O001OO1010N02N10O100O2N011O10O0O2OO1O21N0O10O011O0NOeLaD[30eL[;b3O010J_LlD`3O^LR;j3nDVLP;l3201OO1002N01OO11N2M22NPESLn:h0REY2OPMm:i0UEX2l:YMRE22JNb2o:aMREH0NO20N259\\2d:dMQEK42713MB00[2o:jMREK40825Y2]:hM^FY2U;00O1O101O[CiMa<[20000O10O002M^CgMe<T2\\ClMd<T2\\CkMd<U25N2O2CRCdNn<\\1RCcNS=X1PC`NN1S=`19001O10M2O11NO201OMbNcB_1]=CcBPO06]=k0jBROW=m0kBPOW=o0<1OO2CUB_OQ>b06M3O002O00O10000001O000010N20O04L1jAZOP>Q1JO11N010000001ON12O2N0010OYBlNO0_=_1M3M30N010O00ZC^Nn;b1QD_No;c1nC_NR<`1mCaNT<^1kCcNU<\\1jCfNV<[1hCfNW<\\1gCfNU<\\1lCdNT<]1jCdNW<\\1fCgNR<AhCd2R<`MnCb2P<9M201O1O000O100000000000O011O00O1N2O1O101O01O1O10O1N1N3O01O1I7K5J6L401N001O3M00MaMdCa2X<51OO1O10000OTNjCUO1<On0V<@kCVO13N00M2[1Q<[OSDZO=_1Z;UOoDQ1k:nNWEQ1g:nN]EQ1d:mN]ES1c:lN^ET1c:jN^EV1c:fN`EZ1`:`NXDOZ1a1_:_NeEa1]:^NbEb1n;00001OO100000000001N11O0000001N1001N10O1001O0ON3J7M2N2N4K3O1O001O1O2N1VOlAh0V>000N20000O11O0000O1O100000010O1O10OO20O00O101aEPOk6Q1UIoNm6n0mHSOiL1[:j0kHWOgL1`:LbE4NL000;Z30c7OeGF_M0`00_O=l:KgGNjMKC=k:JdG5jM5b:EdG9aM@5d0c:DeG:cM^O021b0g:DfG8cM9i:_OeGS1[8mNeGR1\\8nNeGQ1[8nNfGS1X8nNgGQ1[8oNeGQ1\\8nNcGT1\\8lNeGR1U5gN^K9]OP1T5iN^K7^OQ1S5iNbHMe29FR1Q5jNbHNe26IP1P5nN_H2d2OOP1m4\\OTK@\\MNc2S1n4@UK\\O]MOa2S1n4AQN=o1InM4S24gMKY29cMI[2QO_HV1U5I[2QO`HU1U2kNi0o0e48_JkNk0k0i49\\JmNk0i0i4:ZJjNPN5l2f0k4:YJSOj0c0n49UJZOj0?P57UJ\\Ol0:o4;UJ[Ol0;o49UJ]Ok0:Y5YOQH>m1Oj09ZMVOk72UH?m10j0?X5_OoI2i0`0W5VOPH0o1:k0AVMj0R8YOoGKT2a0i0<S5SOoG0[2`0f0<^7DbH>\\7BdH>\\7BfH=Z7BbHBRMl0\\:BaHc0_7]ObHb0QMoN[:>dHf0]7ZObHe0_7\\O`Hd0a7[ObHb0^7XOfEHl2P1^7XOhHg0Y7TOaEMY3n0W7ROPIk0d:N1NlN[ORDd0l;]OVD?lN^Om<3XD?j;CVD;j;FVD:i;GWD9g;IXD8f;I\\D6b;M]D3c;M]D3_;1aDO_;1bDN];3QD_OH`0]:AkFa0nNBI<[:GjF;POEJ;Z:HjFEbN?`0JBH0c0O\\O_:<RGE_NON013`07BGMi0a:CRGDeN09:Ac0^:APHMcMb0W9SOgG>`1o0c6bNnF5m0<`1m0c6lNnG9^1i0d6oNnG8^1k0c6lNoG9^1m0`6mNPH6a1m0^6nNPH5b1m0_6mNoG7`1n0c5cNeH:75b1k0d5fNcH:75b1k0X5fN^H1`0:84b1k0X5ROlH1:3a1k0X5ROlH0;2b1n0T5ROlHO?Ob1R1Q5QOmHO`0Ie1Y1^MZN^7f0mHOb0Cj1a1g4lNlH1e3o0QL[N_7c0mH3c3o0b3nNiH3f3o0d3jNiH2g3U1^3jNkH0h3U1^3kNhH2j3S1^3kNgH4j3Q1^3lNhH3k3P1]3mNhH3k3P1]3mNhH3l3o0\\3oNhH0m3Q1Y3QOjHNf3^OULb1Q7_NPIf03LY3]OhL4G^1W7_NmHe06KW3@fL5I[1V7bNmHb0:JT3CfL4H]1S7eNnH=>KS3BeLi1l6ROmHSO`0`0Q3D_LM2j1R7RO^IEl2GcLb1S7RO^IDm2HbLb1Q7TOaIAm2HaLc1Q7TOcI@j2h1b3hNdI@j2i1a3gNeI_Ok2k1^3hNfI\\Ol2j1`LiMP7P1eI\\Ol2i1b3jNeI[Oi2k1b3jNdI[Ol2k1g0", "size": [480, 640]}, "area": 50948.0, "iscrowd": 0, "image_id": 10449, "bbox": [0.0, 85.0, 640.0, 263.0], "category_id": 142, "id": 10012794}, {"segmentation": {"counts": "e9b1e1^OT9b0lF^OT9b0lF^OS9c0nF]OP9d0PG[OQ9e0oF]Oo8c0QG]Oo8c0QG\\OQ9c0oF]OQ9b0PG]OZOTOd8^1RH_OYOSOd8_1SH^OXOTOe8^1SH_OWOSOf8^1SH_OVOTOg8\\1THAnNoN_O4_9\\1THAnNQO]O2a9\\1TH@kNPO^O00213b9[1SHAjN_OT9o0SHBgN@U9n0UHCdN@W9m0THDdN@X9l0UHCcNAX9l0UHDbN@Y9l0THEeN]OV9n0VHEdN]OV9n0VHDfN]OT9o0VHDfN]OT9o0VHCfN_OT9m0WHDeN_OT9m0WHDfN^OS9m0WHGfN[OR9n0YHGeN[OQ9P1YHFfNYOS9o0WHGhNmN_O9b9R1XHIfNlN@9b9Q1YHJiNTOn8o0\\HMfNTOm8o0_HMcNTOl8P1cHO]NQOP9o0eHN\\NSOo8o0dHO]NROP9n0cHO^NSOn8n0eHO]NSOn8o0dHN]NTOo8o0bHO^NROo8o0dHO]NROP9n0cH0]NROP9n0dH0[NROR9m0bH2\\NQOS9l0`H3]NROT9j0`H3\\NSOT9k0_H2]NSOW9H\\Fi0Q2<\\NSO^9`0UH`0PNfN2O0;i9?VHa0oMhN0M2;m99PF\\OS2a1mM_N3;m98UHQ1mMhNn96WHQ1jMjNo95XHP1hMlNP:4XH`1f7nMRFb0W2a1g7aNYH_1h7`NWHa1i7_NWHa1i7_NVHb1j7^NVHb1j7]NWHc1i7`NTH`1l7dNPH\\1o7gNoGY1Q8hNnGX1S8lNiGS1W8nNhGR1X8POfGP1Z8POfGP1[8oNeGQ1[8oNeGQ1[8oNdGR1\\8mNeGS1[8mNeGS1[8mNeGS1\\8mNcGS1\\8nNdGR1]8mNcGS1\\8oNcGQ1]8nNdGR1]8mNcGS1\\8nNeGQ1^8mNaGS1`8kNaGU1h8aNYG_1g8bNXG^1h8aNYG_1g8aNYG_1g8aNYG_1e8cN[G]1b8lMiEf0f1^1]8jNbGV1^8jNbGV1W8POjGP1V8POjGP1V8PNSFj0g1V1]8iNcGW1c8cN]G]1c8cN]G]1b8cN_G]1[8lMRFf0c1^1[8jNdGV1\\8jNdGV1\\8jNdGV1\\8iNeGW1o7UNZFe0g1U1P8VOoGk0T8ROlGn0Y8mNgGS1[8kNeGU1[8kNeGU1[8lNdGT1\\8lNdGT1\\8kNeGU1Z8lNfGT1V8nMSFn0h1S1T8TOiGm0V8TOUFhNc1T2Q8]OoGc0m7ASH?j7DWH;c7QNjFc1c1<b7mMkF07b1\\1a0o7_OPHb0n7@RH`0n7@SH?n7@RH`0o7^ORHb0n7^ORHb0n7^ORHb0n7]OSHc0m7]OSHc0n7\\ORHd0n7\\ORHd0n7[OTHd0l7YOWHg0j7nN\\FQO01k1P2i7kN\\FSO50g1R2f7nM]Fm0Y2T1Z7oNeHQ1[7oNeHQ1[7oNfHP1Y7POgHQ1Y7oNgHQ1Y7oNfHR1Z7nNgHQ1Y7oNfHR1Z7mNhHR1X7oNfHR1Z7oNeHQ1[7nNfHR1Z7`N^FIZ2f1X7`N_FKX2e1Z7_N^F@08Y2i1X7`NVI`1j6`NVI`1j6_NWIa1i6_NWIa1i6_NWIa1j6^NVIb1j6^NVIb1j6^NTId1l6\\NTIAYMQ2c9^NTIAYMQ2c9^NVIb1j6]NeFCH3X2n1[7\\NdFHT2k1X7^NcFHKJT2Q2^7\\N`FBM:0IR2P2a7QO_Ho0a7QO^HP1b7oN_HQ1a7PO_Ho0`7SO_Hm0a7SO^Hn0b7oNaHQ1`7nN`HR1`7nN`HR1d7jN\\HV1l7aNUH_1k7aNUH_1k7aNUH_1k7aNUH_1k7aNUH_1l7`NTH`1l7\\NQFAS2S2l7ZNPFBV2V2j7WNZHh1f7XNZHh1f7XNZHh1f7QNPFK01Z2S2f7QNSFLW2S2g7mMUF0S2T2R8lMnGT2R8lMoGS2P8nMoGS2Q8mMoGS2R8kMoGU2Q8kMoGU2R8jMnGV2Q8kMPHT2P8kMPHV2P8kMoGU2Q8jMPHV2P8jMPHV2]:000RMoMmHQ2n6VN\\FDa2V2o6^NPIb1P7^NPIb1P7^NPIb1P7]NQIc1o6]NQIc1o6]NQIc1o6]NQIc1o6]NQIc1o6\\NRId1n6[NSIe1m6[NSIe1m6[NSIe1m6[NSIe1m6ZNTIf1Q7UNoHk1X7nMhHR2Y7lMhHT2X7lMhHT2Y7jMhHV2Q:000000000000000001OO1002NN2001O000000001O00000000000000000000000000000000000000001O00O10000001OO100000000FmMgCS2X<oMfCR2Z<:O11OO1000000N21O0000N_MeCa2[<_MeCa2[<2000KfCdMZ<\\2fCdM^<X2bChM_<W2aCiM_<1dCl1ORN_<OeCl1MUNg<k1YCUNg<j1ZCVNg<i1YCWNg<i1ZCVNf<i190000000000O10000001O0000000000O2O1OO11O0O2O000O100O101OO11O02ON10OO201O0O10001OO1000O10O1N20000N2O1001N10O100O1000000O10O101O1O1N2O1O000O011O001O0000000O110eN\\BW1g=MYBmNh=R1YBmNg=S1YBmNg=T13N2N101O00003L11OO3N0OO21O00lAWOQ>l0PBTOk=n0TBROl=R1O1N100O2IlN_BU1a=501N1O1O2O00XOdBL\\=OjB0V=1iBOW=1kBMU=NcBA<9W=F^B=?IZ=LVB01OO0`01^=NYB0I1^cP3", "size": [480, 640]}, "area": 33414.0, "iscrowd": 0, "image_id": 10449, "bbox": [0.0, 295.0, 434.0, 185.0], "category_id": 148, "id": 10012795}, {"segmentation": {"counts": "c4R5b1cL^O_Oc3n3oLcL^O_Oc3n3oLcL^O_Oc3m3PMeL]O]Ob3n3RMeL[O^Od3l3QMfL]O\\Ob3n3QMfL]O\\Oc3m3PMgL\\O]Od3m3oLfL]O]Od3m3nLgL]OZOh3P4jLfL_OYOg3P4kLgL^OXOh3Q4jLgL_OWOg3R4jLgL_OVOh3S4hLhLAnNoN_Oh4k4hLhLAnNQO]Oe4n4iLgL@kNPO^O0021g4n4gLgLAjNU4@jJ01i4^1nLBgNX4BiJg4]1QMCdNX4EhJe4]1QMDdNX4EiJe4[1SMCcNa4X4lKUMDbN^4[4nKRMEeN\\4W4oKUMEdN\\4W4oKUMDfN]4T4oKVMDfN]4T4oKVMCfN_4T4mKWMDeN^4U4nKVMDfN]4T4nKVMGfNZ4S4oKXMGeN[4Q4PLYMFfNX4T4PLVMGhNmN_OX5c4SLWMIfNlN@Y5b4QLYMJiNU4m3nK]MMfNV4k3mKaMMcNW4JfJ^3W1WNO]NU4AcJ1:k3n0XNN\\Nf4\\3\\KWNO]Nf4\\3ZKWNO^Ni4X3XK[NO]Ni4X3YKZNN]Ng4\\3\\KUNO^Nf4[3[KXNO]Nf4\\3ZKWN0]Ng4[3YKYN0[Ng4]3XKWN2\\Nf4^3WKUN3]Nh4^3TKVN3\\Ni4^3UKUN2]Ni4a3RJRLi0Q2<\\Nh4i3kJjM`0PNfN2O0P6NPJS4j0nMa0oMhN0M2o5ORJU4c0iK\\OS2a1mM_N3P6NSJT4`0PNQ1mMd4R4ZJSNQ1jMi4P4VJWNP1hMk4Q4UJWN`7f1nGRLb0W2a7g1aHYNa7f1^HYNd7f1\\HZNe7e1[HZNf7f1ZHZNf7f1YH[Ng7e1\\HXNe7g1_HUNa7j1bHTN^7l1cHSN^7m1fHoMZ7P2gHoMY7Q2iHmMW7S2iHmMW7T2hHlMX7T2hHlMX7T2hHkMY7U2fHlM[7S2eHmM[7S2eHmMX7[NhGh3P1lMX7]NgGW2?eNc0g0X7]NfGS2c0iN>g0Y7a0]HfN<h0W7?aHhN7j0X7=bHjN5i0Y7<cHjN5k0W7:eJEZ5:gJGZ58eJI[56gJIY57fJJ[54fJL[53eJMZ53gJMZ52eJO[51fJNZ52fJN[51nH_N3a1o60oH^N2b1o60PI]N1c1o60dJ0]5OcJ1]5OcJ1]5ObJ2^5NbJ2^5NcJ1]50bJ0]51cJO]51bJ0\\52eJNY53gJLZ54fJLZ55eJK[55eJK\\54dJL\\54eJKZ56fJJZ57dJJ\\56dJJ\\56dJK[55gJHY5:fJQNQMc1Y8<hJDW5?gJAY5a0kHlMe0d1`6a0iHlMf0c1a6c0[HnM]OL07b1\\1f6g0PH^NZ1j0g6i1[IWNe6i1[IXNd6i1[IWNe6j1YIWNg6i1YIWNg6i1YIWNg6i1XIXNh6h1XIXNh6i1WIWNi6i1WIWNi6i1VIYNi6g1TI\\Nl6e1iHaLQO01k1V8c1eHbLSO50g1Y8_1gGdLm0h1SOXN42LN21\\8U3bHaNWOZN`8S3YHcNXOYNd8o2THhNV8W1jGkNV8T1jGmNU8S1kGkNX8T1hGlNV8V1jGjNV8V1iGkNW8U1jGjNW8U1jGjNV8V1iGkNW8U1[GSMIi1l8T1ZGTMKg1a6XN[Kn2XNSM@08h1c6YN\\Kl2ZNYOU6RN_Ke2\\NXOU6UN^Kc2\\NXOV6WN\\Kb2^NWOR6[N_K_2_NVOQ6\\N_K`2_NTOR6\\N^Ka2`NSOR6\\N^Ka2`NSOS6[N^Ka2_NUOR6]N\\K^2bNUOR6[N`K^2^NWOR6\\N_K]2^NXMCH3g1Z6PObKX2^NXMHb1W6oNcKW2_NWMHKJc1]6SOaKY2_NSMBM:0Ib1Z6VObKg0WNJn09X5WOdK?`NMd0=W5XOeK8jNO9b0X5XObK4UOO2d0W5VOeK6WOKOf0V5YOhK3WOJKi0V5[OiK1ZOGDP1X5WOmK0\\OF^OT1Z5ROoK3]OCZOX1Z5ROoK2l0l0T3QORL3i0m0U3PORL2j0n0S3^NgI>^25h0o0W3lNQL4i0Q1V3jNQL5i0Q1U3kNRL4i0R1S3iNWL3c0@lLd1\\6iNTL2b0@mLi1^6XNdI6_29b0Z1^3WN`I5a2:a0Z1b3XN[I0b2=b0[1\\6dN]IBTM01j1_9cN\\IFUMg1]9eN[IGXMc1^9eNXIKZM_1_9fNWIKZM_1^9gNXII[M^1^9jNWIH[M^1]9kNXIHYM]1`9kNWIHYM\\1a9lNVIIXM[1b9lNWIGXM^1`9lNWIFXM_1a9kNWIFYM^1`9mNVIEYM_1a9lNVIEYM_1a9mNUI]1j6dNVI[1l6cNUI^1j6bNWI]OXMo1a9bNeH_O^Nn1m8bNbHDaNk1l8`N_HJeNf1l8`N[HNhNc1m8_NYH0jNa1m8`NXHOkNa1m8`NXHOkNa1m8`NXHOkNa1m8`NXHOjNb1n8`NWHNjNb1Q9_NRH2mN`1o8_NQH4PO]1o8_NPH5QO\\1P9_NQH2nN_1R9_NhH_OQNR2V9aNUI_1k6bNoH\\ObMS2_9bNRI_1f5mMjHf0^1^1i5kMhG0o0f0^1_1l5PNfHa0^1^1l5UNjGHa0e0h1^1m5VNeGF13d0d0g1]1n5aNZH2h1]1o5lMeGb0d06g1\\1S6]NWH6e1^1R6^NXH5f1]1P6`NZH4d1]1P6aN[H3e1]1o5`N\\H4R1]OcNo1P8_N\\H5k0EeNg1U8_N[Hc09_OSO_1Z8^NYHo0Kb1m7TMfGNd0_1@b1U8RMgGIOMf0h1]O`1X8PMgGKNLg0j1\\O^1X8QMjGHa0m1\\O[1Y8nLlGJ=o1^OS1OnL\\84bHl1SOP10PM\\84`Hn1ROh01XMc82YHo1SOf01YMd8O[GNl0R5P9O100000010OONTFRKm9m43hNPF\\L0?0MP:U3YFkLLBL8o9X3[FlL1Jd9Y3`FaLA1O2<3c9Y3bFbL@O2185d9X3dFbL10]O2o9\\3cFcL0O^O2P:[3cFdL11\\9Z3]GdLe8[3_11O12N01O1NN30]DeLa;]3N20aLbD\\3a;O0101McLaD]3_;cLaD\\3`;111O00001O10OO20N20O00bLaDZ3`;eLaDZ3b;10WO^D`M0:0Fb;Y2jDmMHH_;[2jDfMB1n;Y2d0101N1O011OaMcC]2_<M31^DgMnNN[;]2eEiMY:U2hEmMV:T2iEnMU:S2iEQNS:BPE]2k0SNS:AREZ2m0UNQ:AQEY2o0WNo9l1QFUNn9j1TFWNk9h1VFXN_9]OhE[2h0gMoN>a:@gE]2h0dMQO?_:C`EN1]2o0cMRO?^:W2_FZMSOa0]:U2`FZMTO`0]:T2aF[MQOb0_:S2aF[MnNb0_:U2cFmM\\9T2cFmMoN[O]:h2cFQNY9R2fFoMY9R2fFoMROUO20o9i2PFXMk0n0UOROo9h2PG\\N_8kNcF06m2`0TMYOU1^9QOfFf2`0SM\\OZ1^9oNcFd2?UM_OO0Z1_9P2PGXNP9i1oFWNo8k1PGVNc8^NSGa3;QNc8V2\\GiMb8Z2]GhMa8Y2`GeM`8\\2`GdM`8\\2`GdM`8\\2`GdM`8\\2`GdM`8\\2`GdM`8Y2cGeM_8[2aGdM`8\\2`GdM`8[2aG^Mf8b2ZG^Mg8`2[GaMd8S2jFeL10a0W1e8T2oFdL=Y1g8m1dGRMROe0\\9UOcFl2R1XMSOc0d9n1ZGXMnN1:f0]9P2PHmMR8S2nGmMP8S2UHhMl7X2THYMVN<d9[2\\HdMd7X2aH]MhM9g9Z2cHfMZ7Z2hHgMX7W2iHgM[7V2fHiM[7W2eHiM\\7V2eHfMaMMk93UFO0k11WN_2Mi7f1mEYNJ1`2Ol7d1P3L2M4M200[O]BNb=1_BMe=0]BOc=0^B0b=O_BMg2DSM1h:=`EO_2KR84`E2]2I]ML`:7hE2[2L\\MMNK_:;lE2Z2K]M0]:1mE5X27k7APF9S24f8MXG4h8MWG3j8LUG4l8NSG2m8MSG4m8LRG3o8LRG3o8LSG3m8MSG2n8MSGC`M<^;3PGAbM;_;4oFAbM<^;1UGNl83VFA\\N0g0<f:3XFA[N0f0=g:2WFB\\N3a09m:1XEB40VO6a07n:1SEHO;NMP;0QEMN6BB7=X;NRELM?BYO1<_;1mDM2b0B@_;3kDL3a0D_O_;5fDL8`0@SO0<b;5aDAM;`0R1R;ROaD1M^O=^1V;TO]D20[O?^1U;K\\DgN?_1U;I\\DhN>`1V;JiD7X;GiDFBBf;h0iDEBBe;h0jDiN@8263M\\;S1REeNC;OKY;Y1XE^NA=2GQ;c1fEdN[OGm:h1iE_N]OGk:l1iEVNj:k1U1KPCZNP=f1QCYNo<f1SCYNm<h16O0001OO11O0lCYNQOOi;h1UE[NPONOOj;h1VE\\NRO0h;c1VEeNP;U1oDlNROCQ<\\1oCdN20d0c0ZO]Oj;]1UDdN20OOc0h0T;n0XD[N00`0g0X;S1jDjNX;U1hDlNX;T1iDkNW;U1jDiNW;W1jDgNW;[1iDbNX;^1gDcNY;]1hDaNY;_1iDaNV;_1mD]NR;e1mD[NS;f1oDVNR;k1oDSNQ;o1o01O100O000VClMh<V202NO0100[ChMc<[2O1N101011M1OcCeMU<[2kCgMT<X2kCjMU<U2jCmMU<R2lCnMU<Q2jCQNV<n1fCjM1:X<l1gCYNX<f1hCZNY<U22O001O11OO001O1M3O1O1N2O7K5I9G00F^NTCc1l<_NRCb1l<:0000O10O10000000^MQNSHh1bMZN[:1QHd1fMXN[:6bEL4NL000m1h1e8nNZG:TNQO0`00_Oc:f0XG:[N]OKCa:g0ZG4aN^OY:>WG2cNWO@5e:b0YG0dNYO^O021c:f0ZG0aNZO\\:f0TG0^90cFO]91dFO\\90eFO[90gFNZ92gFMZ93eFL]9F_D3U27\\9BbDNL4Y2;Y9AgD4o1<g9CYF=h9_OPD1X2a0T:_O`Ec0\\:\\OjC0j1e0Z:^OjCMl1f0W:BhE>X:BhE?W:BhE?W:AiE?V:BjE?U:BkE<W:CiE=X:AiE`0X:^OhEc0X:\\OhEc0[:[OeEd0]:ZOdEh0Z:XOfEh0[:WOeEi0[:WOeEj0Y<01N10kAXOP>l002N00002NLSBUOm=j0510N2O0003cMXOXDLR2l0f9ZOYFg0b9SO]D:P2c0`9C_F?^9B`Fa0_9@_Fa0a9@_F`0`9@_Fb0`9_O^Fb0b9_O^Fa0a9@^F`0b9A]F?c9B\\F=e9DYF>f9CZF<f9EiEVO[OU1l:FeETOCV1h:HbESOFV1g:HaEROId0_OHY;DhDj0d0WOMa0BI];=QEZO0>EJ];<lD^O_OL?`0JBH0f;j0aD_OIFON013`07BGMi;c1WDeN09:Ad;^2]DcMa;^2b0N2OO00001O01N11O0001OO1N1L3M5G:O00O1000000001O001O00ITDZMl;Y2TDlM4Gi;\\2TDkM5Ig;[2UDlM4Hh;]2TDjMT<U2nCdMM2V<Y2RDfMn;Z2RDfMn;[2QDeMo;[2;0000O11O001nI", "size": [480, 640]}, "area": 70755.0, "iscrowd": 0, "image_id": 10449, "bbox": [0.0, 117.0, 640.0, 363.0], "category_id": 150, "id": 10012796}, {"segmentation": {"counts": "PPW41130Mg>8O1O3M0_ADO0]><cAG]>9cAH\\>?O001O000000001O00O10000O1O1000000000000O10000001O001O000SB^O\\=c0bB^O^=d0`B]O_=d0^BYOI3i=d0^B_Oa=b0^B^Ob=d0XBWO13i=Q1O002NNYBkNg=U12O1O11O00001OO1001OO1001O0000001ON200O100KWBSOi=m05O100N2O100O100O1O10000000000NiA[OW>e02O1N2O1000000000000002N0000001O0000001hA[OS>k0N002N1oAROn=n0RBSOm=m0SBUOk=P10O10000001O0000O11OO1O1O1O1C=O1000000000000000000000000000000001O1O1O1O1O001OO1O1L41OO1001ON2O10000M32N00001O00O100O1N2002NO12NM`AEa>;21O0000O1004LO100001O1OO11OO100000000001O00O1000000001OO11O00001OO100N2001O00001O1O00O1001O1O2N000000000000O11O001O001iAZOR>l0mAUOm=o00O11O0SBQOi=S100000O11O0VBmNg=V1000O11O0YBiNe=Y1000000000O100O100O11O00001O00001ON2O1FZBXOh=g0\\BVOd=k0[BUOe=f0WBZO6Oc=e0ZB[Oo=e060000O100O1001OO1O1001O00001OO100N2000000O1001OO100000000O11OO100001O0000O11O00O11O1O00001O1O001O1O3M1O1O1O00O12N1O002N002N1O2N2NN2002NO100O1O10000O100O1", "size": [480, 640]}, "area": 8549.0, "iscrowd": 0, "image_id": 10449, "bbox": [288.0, 0.0, 352.0, 42.0], "category_id": 157, "id": 10012797}, {"segmentation": {"counts": "`al3130?0b=9[BLBLj=:bB1[=4YBA1O2<3EW=`0dB@O2185FV=b0dB10]O24W=>hB0O^O24W=?hB11@V=Y1hBiNW=`102NO1N3L4OM30\\NfBb1\\=O1N20OFbBTO^=l0cBkN020N^=U1:0O1O20N10010N2N110OO11O00O1O010100O01O00010OO102M2O1O1N3M2O1NjAZOV>g01N11O00010OO11O0001M20000O1O1_OcA>b>O0N2E\\A201h>NYA1hV]4", "size": [480, 640]}, "area": 2504.0, "iscrowd": 0, "image_id": 10449, "bbox": [265.0, 323.0, 74.0, 59.0], "category_id": 162, "id": 10012798}, {"segmentation": {"counts": "jg`6111<OP>1dA13N00M2V>4jA=R>9O2L4N1O1O1M3JhNbBZ1^=4McNdB\\1\\=4O1O1O100J]NlBO0e1S=600O1N21NO30OO1L401N11O0O2O001O4L0O1001O0M302IQNYCo1g<50O100001O0YCiMe<Y2000N20000O11O01O1N2N10001ON30OO1O20N20O00O01L3M4N1O2M2O3N0100O101OO`MeC]2_<0O2O01O0100hCaMm;`2oCcMNLQ<`2oCiMR<LmC`2S<^MPDa2M\\MQ<0XDd2R<0O2M14L7I4L2M003M100NTCPNl<P21M4O1000N2O1O1HPNaCo1_<RNZCT2a<kM_C[2`<3000001O00000NaMcC`2[<301N100O1O10000000000000000002N001O000000001O000000000lNfCBZ<8eCiN5m0V<8oCIQ<6PDJP<7oCIP<7SDGm;9TDFl;;SDEm;^OkC::7l;^OjC;:7Q<XOfC`0:8\\<FfC:[<EeC;\\<BeC?[<@fC`0[<^OfCb0Z<]OgCc0W<_OiCa0W<]OiCe0U<^OfCf0Z<ZOeCg0[<WOgCi0Y<VOiCi0W<WOiCi0W<WOiCi0W<WOiCi0X<WOfCj0Z<VOfCj0[<UOfCj0Y<XOeCi0Y<ZOeCg0Z<ZOgCe0V<lNZC`0`0d0V<^OlC`0T<@kCa0U<@jC`0V<BiC=W<CiC=W<ChC>Y<AfC`0Z<AeC?Z<DdC<\\<CdC>d8", "size": [480, 640]}, "area": 13133.0, "iscrowd": 0, "image_id": 10449, "bbox": [445.0, 92.0, 195.0, 108.0], "category_id": 163, "id": 10012799}, {"segmentation": {"counts": "0m2S<00O11O1O1O1O00O1002N1O000000N2O1N2O1001OO1001OO10000001OO100N2001O3M1PDTMj;R3O2VDlLd;T3\\DlLd;Y3O001O00O1O11OO10000O100O1001O1O00M3001O001ON[DiLe;W32O11O0000O1O1BWD[M23h;b2=0000O1000000O1001OO11O001O000000001O1O1O2N0000O1O1M32N0000N21O1OO100000000O1O11O00001O00O11O000000O1MiC]MW<c23002NO11O2N1O002NO10000NlCYMS<g231ON20000O1O11O001O00O1001O001O0000000000O1005KLoCYMQ<h231OO11O00O100000000000000000UDYM];g2bDZM^;e2bD]MIK`;h2gDaMY;`2dDWMJ9b;`2cDdMKC_;j2aDiM_;l20O100N20000O1O100N200L[DmLe;T331O001O1O001O1O00001OO100O1O11OO1000000O12N2N00003M0000O1O1001O0000N21O2NO100N_DeLa;[32O11O1O1O1O001O1OO1NaDcL_;\\3bDeL];Z351OO10000O100O1O1O10000O11O001OO1001OO1O10000001OO11O1O00O100001O00O100O11O00MZDlLf;S34O101nLVD0Md22]MT<_29M5M0gM\\CO0V2c<jM`CU2a<kM`CT2g<1N01O000O1N22O0lCnMX;P2jDQNT;P2]DnM22JN^;S2eDnMH0NO20N259X;h1hDmMK42713MB00W;S2nDnMK40825T;b1mDYOU;Y200000001O0O1000O101N1103L00eLlDi2V;UMkDl2V;RMkDn2U;oLhDI3Z3S;mLPER3Q;mLoDW3o:cLlD13]3];O002NNeL_D\\3`;2O1O02O01OMaDdLa;[3101100N01O2O0000001NO3O0O010KgLcDY3];5O2O0N2O2OO02O0O1O100000O1000NXLlDh3T;2O1N101000001O00O1O120N01O0001O01O1O01TLoDg3W;M1M5N1_LbD_3];aLdDW3OiLb;0aDo21QMe;m27O2000001O0O10O11OO1O1O1O0D>N10O1N2O1O100001O1O1O00O1N2001O1O100000001OO0102L1ONaDdL\\;]360N01O2ON2O0100ON41N0N210O01N2O0O2M2002M003MNcLbD^3^;LcDfLO0`;X3600O0O213J12OO1L5O1N020OO2O001O4L0001O00ON300KUDUMk;k2501OO11O00001OO100N2001O00010000O0O1010M301M1O11O1O0000O0M5K4N3M2M4jMYCo1o<PNRCi1S=0O11O0WNmBf1V=OjB[NS=i10O0110OVNmBg1V=1OO002O0ZNhBc1[=O1O1O100N1010O1aBaN]=_1cBaN\\=b110O0010O000O20M;iBUNf<m1XCTN^<0dCU2Z<oMcCQ2^<nMbCT2W<hMiC6OR2V<kMjC^2V<60000O100O10NlCXMU<f221O0101N1O101OO100N2000000O102OOO2O0000000O20OO100001O0000O11O0O011O1O01OO2O1O0O2N2O3N0O1O11NO012N1N102N002L3DXC\\Nl<c1TC\\Nn<`1TC_Nk<`1ZC]Ng<c1=NO10N11O1000O02O0O1nN", "size": [480, 640]}, "area": 61295.0, "iscrowd": 0, "image_id": 10449, "bbox": [0.0, 0.0, 640.0, 149.0], "category_id": 169, "id": 10012800}, {"segmentation": {"counts": "RbY12o>0O9G0001O000N2MYAMW\\18^RO000_AGk>0O6J00O20j`i7", "size": [480, 640]}, "area": 130.0, "iscrowd": 0, "image_id": 10449, "bbox": [88.0, 308.0, 19.0, 26.0], "category_id": 178, "id": 10012801}, {"segmentation": {"counts": "bfV1a0\\>4L4L4N1O1Om0RO201N100O101N100000B>F:2N1Oe0[O0000000001O00000C^C\\Nb<f1;3N4K2N0000O2O000K5O2N1G;J6J6I8Emae0H]^ZO:F:G8L5L4L4K5L4K`0A7H2O0O10000O100O101N1000000O1000001O00000001O001O010O0IjCdMU<Y2RDdMn;W2?L4M3L5L401O0000001N1M3M3102Mh0YO7IO10000O1O1O1N101O0O101O0@]CbNe<X1dCdN_<U1iCgNi<b0T1D9K20000O101N1000XnX32bQgL3J7M3M2N3L4N1O2N2N2N1O2O1O1O001O1O1O1O1O1O1O7I7I9GFUC]Nm<b1UC\\Nm<a1WC\\Nk<b1;O001O0O10001O00O100O100O2O1N101O1O1O1O1N2O1O001O1O0000O101O000O2O0010O0001O00O2O00000O1000000O2O2N000O101O000000000O20O1O001O001O1M2O2M3N1N3N2N2N2N2M3M3M3K5N3M2M3N3M4K<E`kd0", "size": [480, 640]}, "area": 13284.0, "iscrowd": 0, "image_id": 10449, "bbox": [82.0, 206.0, 513.0, 118.0], "category_id": 183, "id": 10012802}, {"segmentation": {"counts": "VVZ51m=00001O00001O01O0001O00001O0000001O000001O0001O0000000000001O00000001O000001O000000000000000001O0000000000000000000000001O00000O100000000000N2N3M0aZ\\2", "size": [446, 640]}, "area": 331.0, "iscrowd": 0, "image_id": 10463, "bbox": [390.0, 337.0, 74.0, 10.0], "category_id": 105, "id": 10012803}, {"segmentation": {"counts": "0^4`9000000000000O100O10000000000BbF\\L^9c3hFWLY9g3`000O11OO11OO13MN2001O00O11OO11OO1N200O11O00O1003M00N21O1OO1000000000000NmEYLS:a3nE`LV:]3iEbL11W:OhE2260HOe2a:[MhE3N\\2HaM[;0fD[2];eMcD01U2a;lM]DX2c;N5N2N3I6NO1OO101O11OjNoC`0NlNo;b0ZDb0b;^O`D`0EnN10f;=hDj0Y;UOhDi0X;WOhDk0W;nNREP1o:oNSEo0m:QOVEl0j:SO\\Eh0e:WOaEc0P<M1O1O1O0HeBJOOaUa7", "size": [446, 640]}, "area": 8762.0, "iscrowd": 0, "image_id": 10463, "bbox": [0.0, 0.0, 87.0, 142.0], "category_id": 113, "id": 10012804}, {"segmentation": {"counts": "^4`9^400000000000O100O10000000000BbF\\L^9c3hFWLY9g3`000O11OO11OO13MN2001O00O11OO11OO1N200O11O00O1003M00N21O1OO1000000000000NmEYLS:a3nE`LV:]3iEbL11W:[38N`EfL`:Y33N23MN21O1OM31O1OO100002N3^EcL]:c3L0gE]L10S:h30O11O00000000000000000000000000000000002N00O1001O0000O10000001O0000000000001O00000000O100O1@oESMQ:k2WFmLk9T3?1OM32N00N21ON2001OM32N2NM31O4L00L43M2NK52N2NK54L3MH84L1OJ64LN22NL4O12NN2001O00001O00001O001O0_EPMNLZ1OU7V3dGTMW1GT7U3^G[M]1BT7S3aG]MW1AW7i3hHYLW7e3kH\\LT7c3mHbLn6^3QIiLi6W3WIoLc6Q3]IUM]6k2bI]MW6c2iIcMQ6\\2PJhLfN?T7i2UJjLkN;m6k2XJkLPO3i6R3WJlLTOLf6W3VJoLYOCb6]3VJPM]O^O]6a3WJRMf6m2ZIUMe6i2]IXMb6g2^I[Ma6e2_I\\M`6f2^I[Ma6h2[IZMd6g2[IZMd6e2]I[Mc6_2bIcM]6a2_I`M`6a2_I`M`6a2^IaMa6_2_IbM`6^2`IcM_6_2^IcMa6]2_IdM`6\\2_IeMa6[2_IfM`6[2_IfM`6Z2_IhM`6X2`IiM_6W2aIjM^6V2aIlM^6T2bImM]6S2dIlM\\6T2eIlMZ6T2gIlMX6T2hImMW6S2jImMU6S2lImMS6T2lImMS6S2nImMQ6S2PJmMo5S2QJmMo5S2RJmMm5S2TJmMk5S2UJnMj5R2WJnMh5R2YJnMf5R2ZJoMe5Q2\\JoMc5Q2^JoMa5Q2_JoMa5Q2`JoM_5Q2bJoM]5R2bJoM]5Q2dJoM[5Q2fJoMY5Q2gJPNX5P2iJPNV5P2kJPNT5P2mJoMS5Q2mJPNR5o1PKQNo4o1RKQNm4n1TKSNk4m1VKSNi4l1YKTNf4k1[KVNd4j1]KVNb4h1aKWN_4h1bKYN]4h1cKXN\\4j1cKVN\\4k1cK\\NV4d1kKRO^3o0bLAm2b0PMAm2?TMDh2<YMFd2;[MHb28_MJ^26hKTLf1e3b2>\\MBd2>]MAc2b0[MfMbNAS4i2\\MfMiNWOl3S3[MgMQOmNd3[3]MhMWOdN\\3e3]MgM_O[NT3m3^MiMFPNm2W4^MiMQ4V2QLiMo3X2PLiMo3W2RLiMm3W2TLiMk3W2ULjMj3V2WLjMh3V2YLjMf3V2ZLkMe3U2\\LkMc3U2^LkMa3U2_LlM`3U2`LkM_3T2cLlM\\3T2dLmM[3T2eLlMZ3T2gLkMY3U2hLkMW3U2iLlMV3T2kLlMT3T2mLlMR3T2nLmMQ3S2PMmMo2S2RMmMm2R2TMoMk2R2UMnMj2Q2XMoMg2j1oJULY2R2g2i1QKTLY2S2e2i1RKPLO0\\2W2b2j1dMWN[2i1fMVNZ2i1hMWNW2i1iMXNV2h1kMXNT2b1SN^Nl1b1TN_Nk1a1VN_Ni1a1XN_Ng1`1ZNaNe1_1\\NaNc1a1\\N_Nc1b1]N^Nb1`1`NaN_1_1bNaN]1_1dNaN[1d1`N\\N`1e1`N[N_1e1bN[N]1f1bN[N]1e1dN[N[1e1fN[NY1e1gN\\NX1d1iN\\NV1e1jN[NU1e1kN\\NT1e1lN[NS1e1nN[NQ1f1nN[NQ1f1oNZNP1f1QOZNn0f1RO[Nm0c1VO\\Nj0d1WO\\Nh0d1XO]Ng0c1ZO]Ne0c1\\O]Nc0c1^O]Na0e1]O\\Nb0d1_O\\N`0d1A\\N>e1A\\N>d1C\\N<d1E\\N:d1F]N9c1H]N7c1J]N5b1L^N4c1L]N3b1O^N0b10_NOa12_NMa14_NK`1OhN0X1HQO7o0@[O?e0YODf0<QONn02jN7U1IbNa0]1@YNj0f1VORNS1m1mNPNV1P2jNoMX1P2hNoMZ1P2fNPN[1o1eNPN]1o1cNPN_1o1aNPNa1o1_NPNc1o1]NQNd1n1\\NQNf1n1[NPNg1o1YNPNi1o1WNQNj1n1VNQNl1n1TNQNn1n1RNQNo1o1QNQNP2n1QNPNQ2o1oMPNS2o1mMPNU2o1lMPNU2o1kMoMX2P2mMhMW2W2gM`MeK7g6X2fMeM^2Z2dMbM_2]2b4O1O1O1O1O001O1O1O1O1O1O1O1O1O1O1O1O1O1O001O1O1O1O1O1O1O1O1O1O1O1O1O1O001O1O1O1O1O1O1O1O1O1O1O1O1O1O001O1O1O1O1O1O1O1O9G9G000000000000000000O1000000000000000000N2M3N2N2M3O100O1O100O1O100O1O100O1O100O1O100O1O100hGVO[3k0eLUO[LLl5P1iMUO[LNg5n0mMVO[L0d5j0RNUO[L4^5h0WNUOZL8Y5d0]NTO[L;T5a0aNUOZL>P5>gNSOZLc0i4;lNTO[Ld0`0UO`2S1e0TO[Lh09VOd2o0h0TO[Ln0NTOl2k0k0SO[LU1DROT3f0m0TO[L[1YOPO\\3b0Q1ROZLc1oNnNc3>T1QO[La2_2^NW1QOZLa2]2_NY1POZLc2Z2^N\\1POZLc2X2]N_1oNYLT1PO0n1cNTO[1e3oNYLi0ZO;a1bNWO\\1e3nNYL`0Ce0U1bN[O[1d3oNYL5MP1f0dNBV1b3QOZLK5\\18dNl00]26kMW1hNdNR1N[27kMY1cNfNW1I[29kMY1^NhN\\1F[29jM[1\\NiN^1C]29gM^1YNjNa1@_28gM`1TNkNf1]O_28fMf1nMhNl1ZO`29dMS3KdLa29dMS3JeLb29bMT3KcLc29^LX4i0eKi24]LX4a0lKR3L^LX46UL\\3D^LV4N^Ld3\\O^LW4DfLn3TO^LV4_OjLS4PO^LV4_OjLS4QO^LU4]OkLU4PO_LT4[OmLV4oN_LU4YOmLX4oN_LT4WOnLZ4nN_LT4WOnLZ4oN_LS4UOoL\\4nN_LS4TOPM^4mN^LS4ROQM`4lN_LS4nNSMc4kN^LR4nNTMd4jN_LR4jNVMg4iN^LR4iNVMi4hN_LQ4fNYMl4fN^LT6b3lI^LT6b3mI^LR6b3nI^LR6b3nI_LQ6a3PJ_Lo5a3QJ_Lo5a3RJ_Lm5a3SJ_Lm5a3TJ_Lk5b3TJ^Ll5b3UJ^Lj5b3VJ_Li5a3XJ^Lh5b3XJ_Lg5`3[J_Le5a3[J`Ld5^3^JcLa5]3`JbL`5^3`JcL_5[3dJdL\\5\\3dJeL[5Z3gJfLX5[3gJeLY5[3hJeLW5Z3jJfLV5[3jJeLU5[3kJeLU5[3dIYKT1\\1W5\\3dIXKU1]1V5[3fIWKU1]1U5`3jJaLU5_3kJaLU5_3lJaLS5_3mJbLR5^3oJaLQ5`3nJaLQ5_3PK`LP5`3PKaLo4_3RKaLm4_3SKaLm4_3TKaLk4_3UKaLk4`3UK`Lj4`3VK`Lj4`3VKaLi4_3XKaLg4_3YKaLg4^3[KbLd4]3]KcLc4]3^KcLa4[3aKfL^4Y3dKfL\\4Z3dKgL[4Y3fKfLZ4Z3fKgLY4Y3WJVK\\1`1]4Z3VJWK]1`1\\4Z3VJWK]1`1\\4Z3VJWK^1^1\\4[3VJXK]1^1\\4_3fK`LZ4d3bK]L]4c3dK]L[4c3eK]L[4c3fK]LY4c3gK]LY4d3gK\\LX4d3hK]LW4c3jK\\LV4e3iK\\LV4e3jKZLV4c3]JlJ]1b1U4b3nK^LR4a3PL_Lo3`3RLaLm3]3VLbLj3X3\\LiLc3V3_LiLa3W3_LjL`3U3bLkL]3V3bLjL^3V3cLjL\\3V3dLjL\\3V3eLjLZ3V3fLkLY3U3gLkLY3V3gLjLX3U3iLkLW3V3iLjLV3V3jLjLV3V3kLjLT3V3PMgLo2Y3ZM^Lf2", "size": [446, 640]}, "area": 140959.0, "iscrowd": 0, "image_id": 10463, "bbox": [0.0, 98.0, 640.0, 348.0], "category_id": 117, "id": 10012805}, {"segmentation": {"counts": "a\\h013260HOV=0jB93No<b0nBWOo<i0QCWO01k<k0VCSOk<P103M3M4L3M0000001O1O1WDbNP;_1nDhNl:X1TEjNj:W1QEmNo:T1PEmNo:R1QEnNP;R1iDWOU;j0jDXOT;h0lD[OQ;e0nDAm:`0REFh:k1M1O1O1O0cEjLJOT:g3O0000000000002N00O1001O0000O10000001O0000000000001O0000kNTFXNl9j1WFQNi9l1bFkM_9U2]FoMc9T2]FhMd9S2eFiM[9Y2iF_MY9b2Q1L42NO1002N2NL42N3MM3002NO100O1O1O11O00001OO12NO1001OO11O001OO11OO100O1M32NN2O11O2NM31O1ON21O`MfDZ2k0iMYOLT:[2dFnMa9n1WFYNQ:]1SFeNj9[1QFiNd9YOiE18h1?kN`9e1]F\\Nf9c1YF]Ng9f1TF\\Nn9e1oE[NS:g23M5J5L8H6J6I9G8H8H8G9H6K7K5L2L3KmXQ6", "size": [446, 640]}, "area": 12003.0, "iscrowd": 0, "image_id": 10463, "bbox": [56.0, 0.0, 140.0, 126.0], "category_id": 156, "id": 10012806}, {"segmentation": {"counts": "ZRl6?[=5A>G:_O?F;E;C=I8O01O00@mLTFT3l9RMjER3V:;0000XObLaFJ063^3\\9oLcFQ3]9PMbFP3^9QMaFP3^9PMaFQ3_9oLaFV3Z9o0H00000000000000000000001O00000000001O0000000000000000001O00000000O100N20000N200O11O00O11O0000N2M3JgFeK[9Z45000000O1N2001O0000O1O1_OhFYL[9e3gF[LY9d3a0bNkE`NMZO0>Y:d1UFWNBK6MJ20NZ:Q2[FPN\\:R2bElM`:S2f0O1000000000000L4N2L400O10000000000O11O0000001O002N2VDUN];m1_DaNU;T20001O0000O100001O00000000O100", "size": [446, 640]}, "area": 15322.0, "iscrowd": 0, "image_id": 10463, "bbox": [505.0, 0.0, 135.0, 158.0], "category_id": 161, "id": 10012807}, {"segmentation": {"counts": "acY21j=4O5K8H1OEfBI18W=M?2N002N2N3M5K4L8H6J6J8H7I7I7I7I5K7I7I5K4L6J4L1O1O00002N0000001O00000000000000000000001O00000000000000000000000000001O00000000000000O100O100O1O100N2O11O2N1O001O3M00001O000SFTLh9S4N003M0000O11OO100O11O0000000000000000001OO1001O0000000000000000O11OO1I\\FULc9k3^FTLb9l3^FPLO0d9Q4600O10000J6000000O1002N1ON200005K1O001O000000001O001O001O1O0000N200000000002N00001O0000000000O11OO1000000O10000000000001O00000000000000000000001O0000000000001O0000BgFXLX9g3`0O^F[Lm8e3UGYLk8f3g0O1O100O1O1M3L4L4K5N200O1O1O1O1O1O1O1O1O1O1O1O1O100O1O10000O100O100O100O100O100O100O10000000000000000001O001O1O1O001O1O1O001O1O1O1O2N2N2N2N2N2N2N2N3M4L4L3M4L4L3M4L=C1O1O001O1O00001O001WG[KW8e4iG\\KV8d4jG\\KV8e4iG\\KV8d4jG\\KV8e4iG\\KV8d4lGZKT8g4lGYKS8g4b01O001O001O00KSGZKl8f4TGZKl8f46O11O0000001OO1000000UOXG`Lh8\\3]GdLb8n2lGRMT8e2VH[Mi7U2fHlMZ7i1QIWNo6^1\\IcNc6P1jIPOV6i0RJVOn5j0RJVOn5k0QJUOo5k0QJUOo5;nFET30n5;TG[OR3:j5;aJE_5;aJF^5:bJFe5DgF063^39R6FnI:S6EmI;T6DmI;S6DnI<R6DSJ7V[d0Jfn[O1YBOd=4]BK]=0dB;Z=5000000O2M2001O0000O1O2^OF[C=c<E]C;b<a0bNiNbEMZO0>[1b:SOYEBK6MJ20N[1P;ZOQE\\1R;bNlD`1S;d01010000000nDYMm:g2REZMn:g2QEZMj:k2UEVMh:l2XEUMc:T314K100000001O0O110O1OMdEdL\\:\\34N1hM_EmN5n1_:lNkE1D0e:]OYE4?9JOf:@RF:_O4i;D_D;a;@dD>_<L5HWe4", "size": [446, 640]}, "area": 43527.0, "iscrowd": 0, "image_id": 10463, "bbox": [169.0, 0.0, 460.0, 171.0], "category_id": 172, "id": 10012808}, {"segmentation": {"counts": "ghY21m=1N2O1N2O1O5J7J6J6I8I6JTOcC=V<EoC7o;JUD0l;1YDHg;9^DAc;`0bDZO^;g0k02O1N2O1O1N2O001N2O1O1N2O1O1N2O0O2O1O1N2O1O1N2O0100000O10000O1000O010000O10000O1000O010000O10000O10O1000O10000O10000O01000O10000O7Jf0ZO`0_O3N3M2M3N2NN101XNjEFW::QF]OP:c0XFTOh9m0`FiNb9W1fF`N[9_1nFXNR9i1b1000O10000O10000O10000O1000O01000000O10000O10000O10000O1000O010000O10000O10000O1000000O10O10O10000O10000O10000O10000O10O10O10000O1000000O10000O10000O01000O10000O1000\\NgDb0Q;CoD`0h:EZE=\\:JdE9S:MnE5h91YFhNMb0a9l0cFdNN>U9k0hFWN6c0O9i8W1SGiM6i004_8c1[G]M6o010Z8h1fG\\N0JX8l1eG_N2DW8o1dGcN4\\OW8R2cGfN5WOV8k3jGSLT8R4jGmKT8U4nGiKP8Y4oGhKP8Y4PHgKn7[4RHeKl7]4SHdKk7^4UHbKj7_4UHbKi7`4WH_Kh7c4WH^Kg7d4YH\\Kf7e4ZH[Kd7g4[HZKc7h4]HXKa7i4_HXK]6CXJQ6f5RJVJQ6h5RJSJR6l5oIQJT6m5oInIW5LoJT6MlIV50nJR6n5nISJQ6m5oITJo5m5RJRJm5o5SJRJi5R6VJoIe5U6[JlI`5X6aJhIY5]6gJdIV5^6jJcIU5]6lJcIR5^6nJcIP5^6PKcIn4^6RKcIl4^6UKbIi4_6WKbIg4_6YKbIe4_6\\KaIb4a6]K`Ia4a6_K_I`4b6`K_I^4b6cK^I[4c6eK^IY4c6gK^IX4b6hK_IV4b6jK_IT4b6lK_IS4a6nK_IQ4a6oK`Io3a6QL`In3`6RLaIl3a6SL`Il3`6TLaIj3`6VLaIi3_6XL`Ig3a6YL`If3`6ZLaId3`6\\LaIc3_6]LbIa3_6_LbI`3^6`LcI^3^6bLcI]3]6dLcI[3]6eLdIZ3]6eLdIZ3\\6fLeIY3[6gLfIX3Z6hLgIW3Y6iLhIV3X6jLhIV3X6jLiIV3V6jLkIU3U6kLlIU3S6kLnIU3Q6kLPJU3o5kLRJT3n5lLSJT3m5kLTJU3k5kLVJU3i5kLXJT3h5lLaJl2^5TMjJe2U5[MkJf2T5ZMlJg2S5YMmJi2Q5WMoJk2o4UMQKm2m4SMSKo2k4PMVKR3h4nLXKT3g4kLYKW3e4iL[KZ3b4fL^K^3^4bLbKa3Z4`LfKd3S4_LmKe3P3XLkJ7S2d3n2^LeJNCIf2o3S3]LlJEl1S4_4mK]KV4hMjK_61dKb4`4\\K\\Kg4f4YKUKj4n4UKlJn4W5QKeJR5[5nJbJU5^5jJaJW5_5iJaJW5_5jJ_JX5`5hJ_JY5QOfJn51QKZ5POeJo52oJZ5ROeJn51oJ[5SOdJn51oJ\\5ROdJo50mJ]5TOcJo50lJ_5TObJo5OmJ_5VO`Jm51lJa5WO^Jl50mJc5e5]J[Jd5YO[Jh50oJf5[OZJd50PKh5]OYJi6g5f02N101N1QITJk5m5UJWJg5j5YJYJb5h5^J]J\\5e5dJ^JX5b5iJaJR5a5mJdJm4]5TKfJd0UO]2V6nLiJ>VOa2R6RMmJ4VOg2o5TMRKKTOo2j5WMYK_OROW3g5ZM]KUOPO_3d5\\M^La2c3`M^L^2c3aM_L\\2b3eM_LX2c3hM]LQ1RMQOa6NmJPO0m1dNTOa6OaJZO;_1dNXOa6OWJCf0R1cN]Oa6NlIMQ1c0eNE[6KfI6\\15eNo0V57VLgNeNT1T56XLaNhNX1Q56YL]NiN]1n45\\LZNiN`1l45]LVNkNd1h45`LRNkNh1f45jLgMcNS2d44QNKo15QNKP2oNVOn0l0SOSOe0V1ZOkN<^1EbN2g1MYNJP27PN@Y2`0gM^OZ2b0fM]O\\2c0dM\\O\\2d0dM[O]2f0cMXO_2g0bMWO_2j0`MVOa2j0_MTOb2l0^MSOd2m0\\MPOf2P1[MnNg2R1XMlNj2U1VMiNl2V1UMgNm2Z1l4O0100O0010O010O0100O010O010O10O01O010O010O10O010O0100O010O010O1O010O010O010O10O010O0100O0010O0100O010O010O010O10O01O010O10O010O0100O010O010O010O1O010O010O10O010O0100O010O0010O0100O010b0]Ol0UO`G", "size": [446, 640]}, "area": 61598.0, "iscrowd": 0, "image_id": 10463, "bbox": [169.0, 75.0, 471.0, 365.0], "category_id": 183, "id": 10012809}]}
\ No newline at end of file
diff --git a/repositories/taming-transformers/data/coco_annotations_100/annotations/stuff_val2017.json b/repositories/taming-transformers/data/coco_annotations_100/annotations/stuff_val2017.json
deleted file mode 100644
index 02769c6c0b86a75fb2079eab476fbee18d5be549..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/data/coco_annotations_100/annotations/stuff_val2017.json
+++ /dev/null
@@ -1 +0,0 @@
-{"info": {"description": "COCO 2017 Stuff Dataset", "url": "http://cocodataset.org", "version": "1.0", "year": 2017, "contributor": "H. Caesar, J. Uijlings, M. Maire, T.-Y. Lin, P. Dollar and V. Ferrari", "date_created": "2017-08-31 00:00:00.0"}, "images": [{"license": 1, "file_name": "000000017627.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000017627.jpg", "height": 480, "width": 640, "date_captured": "2013-11-16 15:13:53", "flickr_url": "http://farm5.staticflickr.com/4145/4977243989_c2efb1b911_z.jpg", "id": 17627}, {"license": 3, "file_name": "000000016228.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000016228.jpg", "height": 440, "width": 640, "date_captured": "2013-11-19 00:09:53", "flickr_url": "http://farm4.staticflickr.com/3737/10031812195_372ae7538f_z.jpg", "id": 16228}, {"license": 2, "file_name": "000000011813.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000011813.jpg", "height": 500, "width": 333, "date_captured": "2013-11-24 04:49:37", "flickr_url": "http://farm4.staticflickr.com/3545/3411897599_c3ae826bbb_z.jpg", "id": 11813}, {"license": 3, "file_name": "000000015079.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000015079.jpg", "height": 424, "width": 640, "date_captured": "2013-11-20 14:57:40", "flickr_url": "http://farm8.staticflickr.com/7064/6970424809_69d949181f_z.jpg", "id": 15079}, {"license": 3, "file_name": "000000011197.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000011197.jpg", "height": 427, "width": 640, "date_captured": "2013-11-17 03:59:36", "flickr_url": "http://farm8.staticflickr.com/7002/6732385791_cd9fcb9572_z.jpg", "id": 11197}, {"license": 3, "file_name": "000000016010.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000016010.jpg", "height": 471, "width": 640, "date_captured": "2013-11-18 21:20:54", "flickr_url": "http://farm2.staticflickr.com/1134/950664990_8070cd6e3a_z.jpg", "id": 16010}, {"license": 3, "file_name": "000000018150.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000018150.jpg", "height": 480, "width": 640, "date_captured": "2013-11-21 03:12:35", "flickr_url": "http://farm8.staticflickr.com/7155/6476497205_c725257677_z.jpg", "id": 18150}, {"license": 3, "file_name": "000000011760.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000011760.jpg", "height": 425, "width": 640, "date_captured": "2013-11-18 18:15:02", "flickr_url": "http://farm3.staticflickr.com/2477/5856512283_9abe8e859f_z.jpg", "id": 11760}, {"license": 3, "file_name": "000000015278.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000015278.jpg", "height": 480, "width": 640, "date_captured": "2013-11-19 23:14:33", "flickr_url": "http://farm4.staticflickr.com/3633/3291104715_bdec9818d3_z.jpg", "id": 15278}, {"license": 6, "file_name": "000000011051.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000011051.jpg", "height": 536, "width": 640, "date_captured": "2013-11-18 05:42:28", "flickr_url": "http://farm4.staticflickr.com/3155/3281278459_15063a4662_z.jpg", "id": 11051}, {"license": 3, "file_name": "000000016958.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000016958.jpg", "height": 428, "width": 640, "date_captured": "2013-11-21 20:49:33", "flickr_url": "http://farm8.staticflickr.com/7108/7435713066_78b44aae33_z.jpg", "id": 16958}, {"license": 2, "file_name": "000000010977.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000010977.jpg", "height": 375, "width": 500, "date_captured": "2013-11-15 02:16:38", "flickr_url": "http://farm9.staticflickr.com/8060/8241415983_02d80c2fca_z.jpg", "id": 10977}, {"license": 1, "file_name": "000000017436.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000017436.jpg", "height": 640, "width": 481, "date_captured": "2013-11-15 14:11:51", "flickr_url": "http://farm4.staticflickr.com/3265/2922590857_cc7c32cfaa_z.jpg", "id": 17436}, {"license": 4, "file_name": "000000013659.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000013659.jpg", "height": 480, "width": 640, "date_captured": "2013-11-17 06:35:46", "flickr_url": "http://farm7.staticflickr.com/6144/5987177856_785814a990_z.jpg", "id": 13659}, {"license": 1, "file_name": "000000020059.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000020059.jpg", "height": 427, "width": 640, "date_captured": "2013-11-18 21:44:47", "flickr_url": "http://farm1.staticflickr.com/177/393601030_4451879068_z.jpg", "id": 20059}, {"license": 3, "file_name": "000000012576.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000012576.jpg", "height": 640, "width": 480, "date_captured": "2013-11-21 01:24:50", "flickr_url": "http://farm3.staticflickr.com/2813/9012011605_cbb7999e2b_z.jpg", "id": 12576}, {"license": 4, "file_name": "000000014439.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000014439.jpg", "height": 404, "width": 640, "date_captured": "2013-11-22 17:25:30", "flickr_url": "http://farm4.staticflickr.com/3544/3506151605_2a3530b2f9_z.jpg", "id": 14439}, {"license": 3, "file_name": "000000018575.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000018575.jpg", "height": 480, "width": 640, "date_captured": "2013-11-20 01:42:34", "flickr_url": "http://farm4.staticflickr.com/3252/2464830020_ecf3bcf1c3_z.jpg", "id": 18575}, {"license": 2, "file_name": "000000013546.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000013546.jpg", "height": 425, "width": 640, "date_captured": "2013-11-20 06:39:19", "flickr_url": "http://farm3.staticflickr.com/2689/4091707765_2f3bf944a7_z.jpg", "id": 13546}, {"license": 3, "file_name": "000000013923.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000013923.jpg", "height": 427, "width": 640, "date_captured": "2013-11-21 21:17:47", "flickr_url": "http://farm9.staticflickr.com/8550/8973469217_ddac383dc5_z.jpg", "id": 13923}, {"license": 1, "file_name": "000000017379.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000017379.jpg", "height": 640, "width": 478, "date_captured": "2013-11-15 01:16:56", "flickr_url": "http://farm6.staticflickr.com/5123/5316824279_9f0a0584c7_z.jpg", "id": 17379}, {"license": 1, "file_name": "000000010363.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000010363.jpg", "height": 361, "width": 640, "date_captured": "2013-11-15 07:08:52", "flickr_url": "http://farm4.staticflickr.com/3027/2477308902_443e5baf08_z.jpg", "id": 10363}, {"license": 2, "file_name": "000000017178.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000017178.jpg", "height": 427, "width": 640, "date_captured": "2013-11-15 08:30:55", "flickr_url": "http://farm4.staticflickr.com/3354/3626704406_be9d80e909_z.jpg", "id": 17178}, {"license": 1, "file_name": "000000015338.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000015338.jpg", "height": 424, "width": 640, "date_captured": "2013-11-16 17:43:06", "flickr_url": "http://farm9.staticflickr.com/8526/8519097250_f635a04651_z.jpg", "id": 15338}, {"license": 3, "file_name": "000000011122.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000011122.jpg", "height": 480, "width": 640, "date_captured": "2013-11-17 09:24:02", "flickr_url": "http://farm4.staticflickr.com/3278/3105013044_2f108fed95_z.jpg", "id": 11122}, {"license": 5, "file_name": "000000010707.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000010707.jpg", "height": 480, "width": 640, "date_captured": "2013-11-21 23:35:53", "flickr_url": "http://farm1.staticflickr.com/147/434840964_a9c5bc3b58_z.jpg", "id": 10707}, {"license": 4, "file_name": "000000012120.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000012120.jpg", "height": 428, "width": 640, "date_captured": "2013-11-23 03:42:48", "flickr_url": "http://farm4.staticflickr.com/3492/3179857331_fdd8bbd30f_z.jpg", "id": 12120}, {"license": 1, "file_name": "000000018193.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000018193.jpg", "height": 480, "width": 640, "date_captured": "2013-11-20 13:24:37", "flickr_url": "http://farm1.staticflickr.com/242/516677812_f03351fffd_z.jpg", "id": 18193}, {"license": 3, "file_name": "000000019109.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000019109.jpg", "height": 428, "width": 640, "date_captured": "2013-11-17 02:08:36", "flickr_url": "http://farm8.staticflickr.com/7223/7017616831_24cd0b6131_z.jpg", "id": 19109}, {"license": 4, "file_name": "000000013291.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000013291.jpg", "height": 335, "width": 500, "date_captured": "2013-11-18 16:17:44", "flickr_url": "http://farm4.staticflickr.com/3394/3491087208_8efdfeb6d4_z.jpg", "id": 13291}, {"license": 1, "file_name": "000000017714.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000017714.jpg", "height": 480, "width": 640, "date_captured": "2013-11-24 14:14:00", "flickr_url": "http://farm7.staticflickr.com/6133/5943387737_ffd52a7f27_z.jpg", "id": 17714}, {"license": 4, "file_name": "000000012062.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000012062.jpg", "height": 427, "width": 640, "date_captured": "2013-11-16 20:16:30", "flickr_url": "http://farm4.staticflickr.com/3775/9086068242_2064357878_z.jpg", "id": 12062}, {"license": 4, "file_name": "000000015517.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000015517.jpg", "height": 480, "width": 640, "date_captured": "2013-11-16 23:41:40", "flickr_url": "http://farm9.staticflickr.com/8184/8407007011_9456edeb6d_z.jpg", "id": 15517}, {"license": 1, "file_name": "000000010764.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000010764.jpg", "height": 424, "width": 640, "date_captured": "2013-11-19 19:23:45", "flickr_url": "http://farm3.staticflickr.com/2714/5810596234_b5090d75f6_z.jpg", "id": 10764}, {"license": 4, "file_name": "000000015272.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000015272.jpg", "height": 640, "width": 424, "date_captured": "2013-11-16 12:18:43", "flickr_url": "http://farm9.staticflickr.com/8386/8584364941_15ab4eb0e4_z.jpg", "id": 15272}, {"license": 3, "file_name": "000000010583.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000010583.jpg", "height": 612, "width": 612, "date_captured": "2013-11-20 14:18:50", "flickr_url": "http://farm9.staticflickr.com/8497/8288720209_c13ea818f8_z.jpg", "id": 10583}, {"license": 4, "file_name": "000000017029.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000017029.jpg", "height": 640, "width": 640, "date_captured": "2013-11-18 14:33:10", "flickr_url": "http://farm8.staticflickr.com/7304/8746020648_f1e2075b86_z.jpg", "id": 17029}, {"license": 2, "file_name": "000000012748.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000012748.jpg", "height": 640, "width": 480, "date_captured": "2013-11-19 01:51:12", "flickr_url": "http://farm8.staticflickr.com/7295/9090127695_6dc690f776_z.jpg", "id": 12748}, {"license": 1, "file_name": "000000015597.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000015597.jpg", "height": 640, "width": 433, "date_captured": "2013-11-20 01:36:59", "flickr_url": "http://farm7.staticflickr.com/6128/5960989557_660cd5f173_z.jpg", "id": 15597}, {"license": 4, "file_name": "000000013348.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000013348.jpg", "height": 427, "width": 640, "date_captured": "2013-11-16 14:52:33", "flickr_url": "http://farm9.staticflickr.com/8286/7733450942_0da3e941b4_z.jpg", "id": 13348}, {"license": 3, "file_name": "000000011699.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000011699.jpg", "height": 640, "width": 480, "date_captured": "2013-11-18 10:35:30", "flickr_url": "http://farm7.staticflickr.com/6010/5946492599_6248b4620e_z.jpg", "id": 11699}, {"license": 1, "file_name": "000000010995.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000010995.jpg", "height": 480, "width": 640, "date_captured": "2013-11-20 21:22:47", "flickr_url": "http://farm4.staticflickr.com/3438/3815505319_1c077a82b6_z.jpg", "id": 10995}, {"license": 1, "file_name": "000000015497.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000015497.jpg", "height": 480, "width": 640, "date_captured": "2013-11-18 00:19:02", "flickr_url": "http://farm3.staticflickr.com/2197/2269556612_b210991e62_z.jpg", "id": 15497}, {"license": 2, "file_name": "000000017115.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000017115.jpg", "height": 640, "width": 443, "date_captured": "2013-11-18 18:09:41", "flickr_url": "http://farm6.staticflickr.com/5189/5672755536_fd276c33d6_z.jpg", "id": 17115}, {"license": 6, "file_name": "000000019742.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000019742.jpg", "height": 374, "width": 500, "date_captured": "2013-11-24 03:31:54", "flickr_url": "http://farm1.staticflickr.com/70/194348221_493851f79b_z.jpg", "id": 19742}, {"license": 4, "file_name": "000000011149.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000011149.jpg", "height": 375, "width": 500, "date_captured": "2013-11-15 04:15:40", "flickr_url": "http://farm4.staticflickr.com/3625/3323418866_ea5017538d_z.jpg", "id": 11149}, {"license": 2, "file_name": "000000017207.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000017207.jpg", "height": 480, "width": 640, "date_captured": "2013-11-15 10:53:00", "flickr_url": "http://farm6.staticflickr.com/5131/5537426848_a4508fe44f_z.jpg", "id": 17207}, {"license": 6, "file_name": "000000018737.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000018737.jpg", "height": 427, "width": 640, "date_captured": "2013-11-15 18:31:53", "flickr_url": "http://farm5.staticflickr.com/4026/4511471485_c1791c0767_z.jpg", "id": 18737}, {"license": 1, "file_name": "000000015751.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000015751.jpg", "height": 427, "width": 640, "date_captured": "2013-11-17 05:52:45", "flickr_url": "http://farm8.staticflickr.com/7351/9419515091_a211b75ce5_z.jpg", "id": 15751}, {"license": 1, "file_name": "000000014888.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000014888.jpg", "height": 480, "width": 640, "date_captured": "2013-11-17 18:50:25", "flickr_url": "http://farm9.staticflickr.com/8380/8634330041_a47c2cdaeb_z.jpg", "id": 14888}, {"license": 3, "file_name": "000000020333.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000020333.jpg", "height": 640, "width": 427, "date_captured": "2013-11-18 04:03:40", "flickr_url": "http://farm5.staticflickr.com/4027/4697218785_7f0414cdf8_z.jpg", "id": 20333}, {"license": 4, "file_name": "000000012280.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000012280.jpg", "height": 640, "width": 480, "date_captured": "2013-11-18 07:29:51", "flickr_url": "http://farm9.staticflickr.com/8516/8544258219_95e49e415e_z.jpg", "id": 12280}, {"license": 5, "file_name": "000000010092.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000010092.jpg", "height": 426, "width": 640, "date_captured": "2013-11-21 00:20:22", "flickr_url": "http://farm9.staticflickr.com/8276/8710590452_08a7a8f59c_z.jpg", "id": 10092}, {"license": 1, "file_name": "000000019221.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000019221.jpg", "height": 478, "width": 640, "date_captured": "2013-11-19 22:51:01", "flickr_url": "http://farm7.staticflickr.com/6156/6237638989_9d2c007615_z.jpg", "id": 19221}, {"license": 1, "file_name": "000000018770.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000018770.jpg", "height": 428, "width": 640, "date_captured": "2013-11-18 02:44:01", "flickr_url": "http://farm9.staticflickr.com/8020/7499130232_34bd20c007_z.jpg", "id": 18770}, {"license": 3, "file_name": "000000012639.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000012639.jpg", "height": 640, "width": 480, "date_captured": "2013-11-22 21:09:08", "flickr_url": "http://farm5.staticflickr.com/4102/4795012771_81c0b6b502_z.jpg", "id": 12639}, {"license": 3, "file_name": "000000012670.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000012670.jpg", "height": 428, "width": 640, "date_captured": "2013-11-24 01:39:03", "flickr_url": "http://farm5.staticflickr.com/4144/5072551441_fd302a2c44_z.jpg", "id": 12670}, {"license": 3, "file_name": "000000014038.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000014038.jpg", "height": 427, "width": 640, "date_captured": "2013-11-24 11:18:00", "flickr_url": "http://farm9.staticflickr.com/8507/8561635383_4bc0c679dd_z.jpg", "id": 14038}, {"license": 4, "file_name": "000000020247.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000020247.jpg", "height": 457, "width": 640, "date_captured": "2013-11-18 14:33:42", "flickr_url": "http://farm8.staticflickr.com/7027/6473127891_50c1ca2c03_z.jpg", "id": 20247}, {"license": 3, "file_name": "000000015956.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000015956.jpg", "height": 480, "width": 640, "date_captured": "2013-11-18 23:12:04", "flickr_url": "http://farm5.staticflickr.com/4057/5078510649_c131c12862_z.jpg", "id": 15956}, {"license": 3, "file_name": "000000011615.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000011615.jpg", "height": 640, "width": 480, "date_captured": "2013-11-17 07:15:52", "flickr_url": "http://farm1.staticflickr.com/47/151953288_f6b6a50f78_z.jpg", "id": 11615}, {"license": 3, "file_name": "000000014380.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000014380.jpg", "height": 427, "width": 640, "date_captured": "2013-11-17 09:54:28", "flickr_url": "http://farm6.staticflickr.com/5463/8750190611_325720f243_z.jpg", "id": 14380}, {"license": 1, "file_name": "000000018519.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000018519.jpg", "height": 640, "width": 515, "date_captured": "2013-11-20 01:22:59", "flickr_url": "http://farm8.staticflickr.com/7199/6892325386_145a03efa0_z.jpg", "id": 18519}, {"license": 4, "file_name": "000000019924.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000019924.jpg", "height": 500, "width": 458, "date_captured": "2013-11-18 01:01:49", "flickr_url": "http://farm4.staticflickr.com/3407/3642510664_1c89fbc90a_z.jpg", "id": 19924}, {"license": 3, "file_name": "000000016598.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000016598.jpg", "height": 640, "width": 478, "date_captured": "2013-11-18 03:39:49", "flickr_url": "http://farm6.staticflickr.com/5091/5391571396_41823477da_z.jpg", "id": 16598}, {"license": 5, "file_name": "000000013774.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000013774.jpg", "height": 480, "width": 640, "date_captured": "2013-11-18 18:22:53", "flickr_url": "http://farm8.staticflickr.com/7167/6837122127_de8b1f8d77_z.jpg", "id": 13774}, {"license": 5, "file_name": "000000018491.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000018491.jpg", "height": 427, "width": 640, "date_captured": "2013-11-19 17:54:54", "flickr_url": "http://farm3.staticflickr.com/2134/5814907667_496ae24446_z.jpg", "id": 18491}, {"license": 4, "file_name": "000000012667.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000012667.jpg", "height": 480, "width": 640, "date_captured": "2013-11-15 15:06:56", "flickr_url": "http://farm1.staticflickr.com/197/499050773_995cd1e9bb_z.jpg", "id": 12667}, {"license": 3, "file_name": "000000013201.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000013201.jpg", "height": 640, "width": 427, "date_captured": "2013-11-19 21:57:52", "flickr_url": "http://farm9.staticflickr.com/8391/8577059857_69c575151f_z.jpg", "id": 13201}, {"license": 1, "file_name": "000000017959.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000017959.jpg", "height": 427, "width": 640, "date_captured": "2013-11-22 18:46:36", "flickr_url": "http://farm4.staticflickr.com/3031/2389030002_e3a04076c3_z.jpg", "id": 17959}, {"license": 1, "file_name": "000000017899.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000017899.jpg", "height": 640, "width": 480, "date_captured": "2013-11-14 15:45:58", "flickr_url": "http://farm8.staticflickr.com/7031/6746562893_cbf91d331c_z.jpg", "id": 17899}, {"license": 4, "file_name": "000000015746.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000015746.jpg", "height": 640, "width": 427, "date_captured": "2013-11-16 21:36:18", "flickr_url": "http://farm4.staticflickr.com/3643/3517907686_509f46055f_z.jpg", "id": 15746}, {"license": 4, "file_name": "000000014473.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000014473.jpg", "height": 427, "width": 640, "date_captured": "2013-11-17 11:06:09", "flickr_url": "http://farm9.staticflickr.com/8401/8607706408_e00725a54d_z.jpg", "id": 14473}, {"license": 1, "file_name": "000000014831.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000014831.jpg", "height": 640, "width": 480, "date_captured": "2013-11-17 19:58:56", "flickr_url": "http://farm9.staticflickr.com/8113/8656986875_366dec25c9_z.jpg", "id": 14831}, {"license": 1, "file_name": "000000016502.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000016502.jpg", "height": 500, "width": 375, "date_captured": "2013-11-16 18:53:28", "flickr_url": "http://farm1.staticflickr.com/153/388880244_55dd5edd8e_z.jpg", "id": 16502}, {"license": 3, "file_name": "000000015254.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000015254.jpg", "height": 427, "width": 640, "date_captured": "2013-11-20 01:29:32", "flickr_url": "http://farm1.staticflickr.com/140/385285808_120242fcc0_z.jpg", "id": 15254}, {"license": 1, "file_name": "000000015660.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000015660.jpg", "height": 348, "width": 640, "date_captured": "2013-11-22 08:41:16", "flickr_url": "http://farm4.staticflickr.com/3356/3414127711_6772851b0d_z.jpg", "id": 15660}, {"license": 5, "file_name": "000000011511.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000011511.jpg", "height": 464, "width": 640, "date_captured": "2013-11-16 13:42:03", "flickr_url": "http://farm8.staticflickr.com/7124/7556029168_519acfdf09_z.jpg", "id": 11511}, {"license": 3, "file_name": "000000017905.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000017905.jpg", "height": 640, "width": 480, "date_captured": "2013-11-16 18:01:33", "flickr_url": "http://farm1.staticflickr.com/44/173771776_53b9c22bb6_z.jpg", "id": 17905}, {"license": 1, "file_name": "000000013597.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000013597.jpg", "height": 425, "width": 640, "date_captured": "2013-11-19 20:18:39", "flickr_url": "http://farm5.staticflickr.com/4040/4474734743_bc5e8973c7_z.jpg", "id": 13597}, {"license": 3, "file_name": "000000020107.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000020107.jpg", "height": 500, "width": 333, "date_captured": "2013-11-16 23:28:30", "flickr_url": "http://farm2.staticflickr.com/1311/968078473_4a7d696ec6_z.jpg", "id": 20107}, {"license": 1, "file_name": "000000018833.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000018833.jpg", "height": 426, "width": 640, "date_captured": "2013-11-17 16:35:54", "flickr_url": "http://farm1.staticflickr.com/92/217798870_1d648607b4_z.jpg", "id": 18833}, {"license": 1, "file_name": "000000016439.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000016439.jpg", "height": 480, "width": 640, "date_captured": "2013-11-22 19:14:13", "flickr_url": "http://farm1.staticflickr.com/52/171945415_f37fb5d1b2_z.jpg", "id": 16439}, {"license": 3, "file_name": "000000016451.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000016451.jpg", "height": 612, "width": 612, "date_captured": "2013-11-20 20:12:37", "flickr_url": "http://farm8.staticflickr.com/7460/9050893513_1fb9508000_z.jpg", "id": 16451}, {"license": 5, "file_name": "000000019432.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000019432.jpg", "height": 480, "width": 640, "date_captured": "2013-11-21 04:39:45", "flickr_url": "http://farm6.staticflickr.com/5131/5533564723_6f1633b85e_z.jpg", "id": 19432}, {"license": 3, "file_name": "000000019786.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000019786.jpg", "height": 375, "width": 500, "date_captured": "2013-11-22 00:20:10", "flickr_url": "http://farm1.staticflickr.com/115/317277248_05f5ac031e_z.jpg", "id": 19786}, {"license": 1, "file_name": "000000017031.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000017031.jpg", "height": 334, "width": 500, "date_captured": "2013-11-16 22:09:32", "flickr_url": "http://farm4.staticflickr.com/3431/3811840964_b913f6f140_z.jpg", "id": 17031}, {"license": 5, "file_name": "000000018837.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000018837.jpg", "height": 480, "width": 640, "date_captured": "2013-11-17 18:58:56", "flickr_url": "http://farm9.staticflickr.com/8219/8372455764_124e052754_z.jpg", "id": 18837}, {"license": 3, "file_name": "000000014007.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000014007.jpg", "height": 426, "width": 640, "date_captured": "2013-11-24 11:03:51", "flickr_url": "http://farm8.staticflickr.com/7161/6701691801_dc2cee1aa9_z.jpg", "id": 14007}, {"license": 1, "file_name": "000000018380.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000018380.jpg", "height": 426, "width": 640, "date_captured": "2013-11-25 14:13:45", "flickr_url": "http://farm6.staticflickr.com/5474/9701242334_0797348359_z.jpg", "id": 18380}, {"license": 3, "file_name": "000000016249.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000016249.jpg", "height": 365, "width": 500, "date_captured": "2013-11-14 12:26:50", "flickr_url": "http://farm4.staticflickr.com/3168/2931904036_1c5828e037_z.jpg", "id": 16249}, {"license": 4, "file_name": "000000013004.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000013004.jpg", "height": 500, "width": 375, "date_captured": "2013-11-19 21:30:33", "flickr_url": "http://farm5.staticflickr.com/4049/4208826429_3432119060_z.jpg", "id": 13004}, {"license": 4, "file_name": "000000019402.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000019402.jpg", "height": 480, "width": 640, "date_captured": "2013-11-21 04:31:37", "flickr_url": "http://farm5.staticflickr.com/4135/5435897535_a9b3e0a053_z.jpg", "id": 19402}, {"license": 3, "file_name": "000000013177.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000013177.jpg", "height": 427, "width": 640, "date_captured": "2013-11-15 20:03:10", "flickr_url": "http://farm8.staticflickr.com/7127/7607618094_78c23b097e_z.jpg", "id": 13177}, {"license": 3, "file_name": "000000014226.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000014226.jpg", "height": 480, "width": 640, "date_captured": "2013-11-17 04:22:03", "flickr_url": "http://farm1.staticflickr.com/6/6710769_d74169f5b7_z.jpg", "id": 14226}, {"license": 2, "file_name": "000000015440.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000015440.jpg", "height": 640, "width": 404, "date_captured": "2013-11-17 09:50:00", "flickr_url": "http://farm4.staticflickr.com/3261/2870016347_39ffc0ca45_z.jpg", "id": 15440}, {"license": 4, "file_name": "000000017182.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000017182.jpg", "height": 428, "width": 640, "date_captured": "2013-11-19 22:30:23", "flickr_url": "http://farm4.staticflickr.com/3020/2906486794_80400b009e_z.jpg", "id": 17182}, {"license": 6, "file_name": "000000019042.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000019042.jpg", "height": 371, "width": 640, "date_captured": "2013-11-17 01:03:41", "flickr_url": "http://farm8.staticflickr.com/7289/9652593344_4469e9a6f1_z.jpg", "id": 19042}, {"license": 2, "file_name": "000000013729.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000013729.jpg", "height": 480, "width": 640, "date_captured": "2013-11-21 20:10:40", "flickr_url": "http://farm2.staticflickr.com/1009/559099100_98414a0e66_z.jpg", "id": 13729}, {"license": 2, "file_name": "000000015335.jpg", "coco_url": "http://images.cocodataset.org/val2017/000000015335.jpg", "height": 480, "width": 640, "date_captured": "2013-11-25 14:00:10", "flickr_url": "http://farm6.staticflickr.com/5533/10257288534_c916fafd78_z.jpg", "id": 15335}], "licenses": [{"url": "http://creativecommons.org/licenses/by-nc-sa/2.0/", "id": 1, "name": "Attribution-NonCommercial-ShareAlike License"}, {"url": "http://creativecommons.org/licenses/by-nc/2.0/", "id": 2, "name": "Attribution-NonCommercial License"}, {"url": "http://creativecommons.org/licenses/by-nc-nd/2.0/", "id": 3, "name": "Attribution-NonCommercial-NoDerivs License"}, {"url": "http://creativecommons.org/licenses/by/2.0/", "id": 4, "name": "Attribution License"}, {"url": "http://creativecommons.org/licenses/by-sa/2.0/", "id": 5, "name": "Attribution-ShareAlike License"}, {"url": "http://creativecommons.org/licenses/by-nd/2.0/", "id": 6, "name": "Attribution-NoDerivs License"}, {"url": "http://flickr.com/commons/usage/", "id": 7, "name": "No known copyright restrictions"}, {"url": "http://www.usa.gov/copyright.shtml", "id": 8, "name": "United States Government Work"}], "categories": [{"supercategory": "textile", "id": 92, "name": "banner"}, {"supercategory": "textile", "id": 93, "name": "blanket"}, {"supercategory": "plant", "id": 94, "name": "branch"}, {"supercategory": "building", "id": 95, "name": "bridge"}, {"supercategory": "building", "id": 96, "name": "building-other"}, {"supercategory": "plant", "id": 97, "name": "bush"}, {"supercategory": "furniture-stuff", "id": 98, "name": "cabinet"}, {"supercategory": "structural", "id": 99, "name": "cage"}, {"supercategory": "raw-material", "id": 100, "name": "cardboard"}, {"supercategory": "floor", "id": 101, "name": "carpet"}, {"supercategory": "ceiling", "id": 102, "name": "ceiling-other"}, {"supercategory": "ceiling", "id": 103, "name": "ceiling-tile"}, {"supercategory": "textile", "id": 104, "name": "cloth"}, {"supercategory": "textile", "id": 105, "name": "clothes"}, {"supercategory": "sky", "id": 106, "name": "clouds"}, {"supercategory": "furniture-stuff", "id": 107, "name": "counter"}, {"supercategory": "furniture-stuff", "id": 108, "name": "cupboard"}, {"supercategory": "textile", "id": 109, "name": "curtain"}, {"supercategory": "furniture-stuff", "id": 110, "name": "desk-stuff"}, {"supercategory": "ground", "id": 111, "name": "dirt"}, {"supercategory": "furniture-stuff", "id": 112, "name": "door-stuff"}, {"supercategory": "structural", "id": 113, "name": "fence"}, {"supercategory": "floor", "id": 114, "name": "floor-marble"}, {"supercategory": "floor", "id": 115, "name": "floor-other"}, {"supercategory": "floor", "id": 116, "name": "floor-stone"}, {"supercategory": "floor", "id": 117, "name": "floor-tile"}, {"supercategory": "floor", "id": 118, "name": "floor-wood"}, {"supercategory": "plant", "id": 119, "name": "flower"}, {"supercategory": "water", "id": 120, "name": "fog"}, {"supercategory": "food-stuff", "id": 121, "name": "food-other"}, {"supercategory": "food-stuff", "id": 122, "name": "fruit"}, {"supercategory": "furniture-stuff", "id": 123, "name": "furniture-other"}, {"supercategory": "plant", "id": 124, "name": "grass"}, {"supercategory": "ground", "id": 125, "name": "gravel"}, {"supercategory": "ground", "id": 126, "name": "ground-other"}, {"supercategory": "solid", "id": 127, "name": "hill"}, {"supercategory": "building", "id": 128, "name": "house"}, {"supercategory": "plant", "id": 129, "name": "leaves"}, {"supercategory": "furniture-stuff", "id": 130, "name": "light"}, {"supercategory": "textile", "id": 131, "name": "mat"}, {"supercategory": "raw-material", "id": 132, "name": "metal"}, {"supercategory": "furniture-stuff", "id": 133, "name": "mirror-stuff"}, {"supercategory": "plant", "id": 134, "name": "moss"}, {"supercategory": "solid", "id": 135, "name": "mountain"}, {"supercategory": "ground", "id": 136, "name": "mud"}, {"supercategory": "textile", "id": 137, "name": "napkin"}, {"supercategory": "structural", "id": 138, "name": "net"}, {"supercategory": "raw-material", "id": 139, "name": "paper"}, {"supercategory": "ground", "id": 140, "name": "pavement"}, {"supercategory": "textile", "id": 141, "name": "pillow"}, {"supercategory": "plant", "id": 142, "name": "plant-other"}, {"supercategory": "raw-material", "id": 143, "name": "plastic"}, {"supercategory": "ground", "id": 144, "name": "platform"}, {"supercategory": "ground", "id": 145, "name": "playingfield"}, {"supercategory": "structural", "id": 146, "name": "railing"}, {"supercategory": "ground", "id": 147, "name": "railroad"}, {"supercategory": "water", "id": 148, "name": "river"}, {"supercategory": "ground", "id": 149, "name": "road"}, {"supercategory": "solid", "id": 150, "name": "rock"}, {"supercategory": "building", "id": 151, "name": "roof"}, {"supercategory": "textile", "id": 152, "name": "rug"}, {"supercategory": "food-stuff", "id": 153, "name": "salad"}, {"supercategory": "ground", "id": 154, "name": "sand"}, {"supercategory": "water", "id": 155, "name": "sea"}, {"supercategory": "furniture-stuff", "id": 156, "name": "shelf"}, {"supercategory": "sky", "id": 157, "name": "sky-other"}, {"supercategory": "building", "id": 158, "name": "skyscraper"}, {"supercategory": "ground", "id": 159, "name": "snow"}, {"supercategory": "solid", "id": 160, "name": "solid-other"}, {"supercategory": "furniture-stuff", "id": 161, "name": "stairs"}, {"supercategory": "solid", "id": 162, "name": "stone"}, {"supercategory": "plant", "id": 163, "name": "straw"}, {"supercategory": "structural", "id": 164, "name": "structural-other"}, {"supercategory": "furniture-stuff", "id": 165, "name": "table"}, {"supercategory": "building", "id": 166, "name": "tent"}, {"supercategory": "textile", "id": 167, "name": "textile-other"}, {"supercategory": "textile", "id": 168, "name": "towel"}, {"supercategory": "plant", "id": 169, "name": "tree"}, {"supercategory": "food-stuff", "id": 170, "name": "vegetable"}, {"supercategory": "wall", "id": 171, "name": "wall-brick"}, {"supercategory": "wall", "id": 172, "name": "wall-concrete"}, {"supercategory": "wall", "id": 173, "name": "wall-other"}, {"supercategory": "wall", "id": 174, "name": "wall-panel"}, {"supercategory": "wall", "id": 175, "name": "wall-stone"}, {"supercategory": "wall", "id": 176, "name": "wall-tile"}, {"supercategory": "wall", "id": 177, "name": "wall-wood"}, {"supercategory": "water", "id": 178, "name": "water-other"}, {"supercategory": "water", "id": 179, "name": "waterdrops"}, {"supercategory": "window", "id": 180, "name": "window-blind"}, {"supercategory": "window", "id": 181, "name": "window-other"}, {"supercategory": "solid", "id": 182, "name": "wood"}, {"supercategory": "other", "id": 183, "name": "other"}], "annotations": [{"segmentation": {"counts": "m:Z1R<N0O010O0100O0100O10O1O010O10O01N2O11O00001O1OO1010O0004eFdN`6\\1^IgNa6Y1_IhNQ2LSL0m2]1lNlNk1M[LL]OM\\3^1oNTOcMDm31gLL^OM[3_1oN]OFVOC5POL\\ON[3_1PO@POL1[OXOL[OO[3_1PO_OTOLL[OZOL[OOZ3a1POTOgNCUOh0T1UOZOM\\ONY3b1ROYOeMYO8h1P1^NXOMi2\\1QOk1jNSMUOH3F]2f1_OX2]NbLB2LDe2b1^OZ2ZN_LG>`2j0^OZ2kMlK;e0Jb04kNk1i10]2TNaLHP2Q1TOR1[2SN@h0WNS1Z2SNFd0QNX1Y2TNIa0PNY1W2VNGc0[NP1o1YNO?RNY1n1XN07hKNZ2f1V6[NaGNY2j1V6UNbG1X2k1U6SNeG1V2k1W6RNdG2W20cMh1j8WNcI0dMj1[;0013M4M11M4L2N2N1O4L3nDnMf:R2ZERNb:o1]ETN`:l1`ETNR1DoN0V9X2iFTNm07P8f1RGSNo07n7T2PHoMo7Q2WHlMf7U2\\HgMe7X2\\HlMVO@i7d2QITNnNCf7X2]ISOY6m0fIYOlNSNS7h2nIBj5P1dIVOA_M`6l6I1O4L6J5PJfHf5h7`JVHn4Z8I<D7I3M1O1O001\\LhGT2X8kMhHV1Y7hNlHIPNQ1T9TOnHKoMo0T9UOnHKPNm0T9WOmHKTNh0GSOY97nHMTNg0Q9[OlHKWNh0m8]O`Ib0`6]ObIa0_6]OcIb0^6]OcIb0]6^OeIa0Z6_OfIb0Y6]OiIOkLb0\\9^OlIb0S6^OoIa0]90O11O00aL_OVJ`0Z901O01eFARMh0]7HnJl1b4TNnJAPNl2h1XM_2;hMT3@jLVNA12S4`0TNd3SOcLe2JXNg3mNbLi2IXNd3mNjLg2EYNa3QOgLh2HWNb3oNeLl2IUNb3nNiLj2EXNb3nNiLj2EXNc3mNgLl2FWNc3oNeLj2HWNc3ZNeK5P1[3HVNc3WNSLMa0g3IUNc3VN[Mf3ROTNd3PN_Mk3nNUNb5l1^JTNa5m1_JSN`5n1`JQNa5o1_JRN_5n1bJoM`5R2`JnM_5S2aJmM^5T2bJmM]5S2cJmM\\5T2dJmMZ5U2eJkMZ5V2fJjMZ5V2fJoM[3SNYMo3\\OmM\\3hNcL\\31nMYOkM41Z3]1dLj2e0dNd2cNeLi2g0iNZ1PMkN`1ROh2i0\\Oe0jLROa59UN5o2KUM^OjK3R7?X5O000000000000001O00000000001O00000000001O000000000000001O000000001O00000000000000001O000000000000000000000000000000000000000000000000000000002N00O10000O1000000001O00O11O000000000000000000000000O100O100O^O]C?c<A]C?c<A]C?c<@^C`0b<@^C`0b<@^C`0f<0001O000000000000000000000000000000000000000000000000000000IAcC?U<AhC0N43;W<BgC707Y<CfCh0Z<400O10000000000O100O100000000O100O100VEoN[9Q1eFPOWOM^9T1[GROTOK`91QFn0[1WOiNM1Mi90SFo0Z1XOgNI61f9OSFo0Y1ZOgN4l9BUFP1X1[OfN3m9BVFo0W1Ki85WGJj86VGKi85WG2b8N^G3a8M_G3a8M_G3a8M^G5a8K_G5\\8lN^Fo0V13\\8nN[F01Q1X14X82gG0U83lGIV88iGOl76THNnNjNY8Y1iH>V7BiH=Y7CgH=Y7ChHl0h6TOXIm0g6SOYIm0g6SOYIo0e6QOZIR1d6nN\\IR1d6nN\\IQ1e6oN[IP1f6POZIP1f6POZIP1f6POZIP1f6POZIP1XObMj6^1nIP1YO_Mk6a1lIS1kN\\M5:l6W1TJl1j5TNVJP2f5PNZJQ2f5nMZJT2d5lM\\J\\2\\5dMcJ`2Z5`MfJ`2Z5`MgJ`2X5`MgJb2X5^MiJb2V5^MiJd2V5\\MjJd2V5\\MjJf2T5ZMlJf2T5ZMlJJkN^1Z6hNkJGPOc1Q6fNoJAUOk1k5dNPK@ZOg1g5iNPKiNkN0OIR1>mNU1X6KPKbNTO1HMo0>N8X5j0nLmNkM;T5h0dNUO]1k0dNPO`1P1_NPOb1P1^NQOa1o0bNmN_1S1dNkN[1U1eNkN[1U1eNkN[1U1eNjN\\1V1dNaNQK5[6Z1eN`NQK1^6_1aN`Nd10[I^1n4eNg1M[I^1h;bNXD^1h;bNXD^1k;O0LUDiNk;V1VDjNj;U1WDkNi;U1WDkNj;S1XDlNh;T1XDlNh;S1ZDlNl;l0\\a2TOi_M1000000O100O1gN7`D:T;T1N2M3N2N2N2M3N2N2M3N2N2M3N2N2N2I7]Oc0]Oc0^Ob0001O0dKlGb3Y8XLiGg3Y8SLkGm3i80O1000000N2O1001OO11O1ON2O1O1O1001O1O1RLiFg3`9M2N1O1O00H800000000000000000000FTLVGl3j8TLUGm3k8TLTGl3k8XLPGj3n8:O100L4O100001O000000000000002N1O0000O1N2000000001O1O3M3M00000000000000000000000000N2O1N2N2N200000000000000001O000VLVGT3j8lLYG_11YOf8XO[G]10ZOe8YO^GY10\\Ob8[OaGg0=MR8\\OcGJX1h0T7_OkI?U6AnI<R6DmI=S6CmI=S6CmI=V6@jI`0Y6]OUJ5n5HTJ6o5GQJ9R6DmI:lL@c<<7KSf1IZZN00000000000nG2h2NlL>T3BjK<kL3[2Bc1;VO2jL4C^O`02Q18Z2d0GAkL:0_Oo03P2R16ROjL;NEHMa00h2R17QOjLe0@9?QOQ3o06SOiLU2MjMT3n05TOjLU2KjMV3m0iN\\OXNHMX2KfMe2JYMT1m1B[NDNY2HdMf2L\\MQ1j1D`NAL\\2DcMj2L\\MP1[14mNSOMn2_2kL\\MP1Y17hNV3b2dK]MLNb0Z1m5[1eI]MJ1c0W1n5[1eI]MK2`0V1P6[1eI^MJ3?T1R6[1eI^MJ5=R1T6[1eI^MJ7;Q1U6Z1fI^MJ8:o0V6\\1fI]MJ:8m0i1gNn1f2UL]ML;6k0f1nNQ2_2WL]MLZ2l0iM@Oa3a2WL]MMY2j0kMBN_3a2YL9f0ZM@La3a2lKf0S1lL@OZ2FTNa6MlICOZ2GTNZ63QJ^OOKC[1NhN9?W63SJ^ONCJd1KdN3d0Y61SKTOgNa1KeN3d0Y61`Kh0UNbN4d0V64bK[OnMh0;QO1g0T66bKVOVNh0g0K`58kLXOgM>]5=nLnNnM?T5b0PMmNRN<n4g0PMmNTN9o4h0d5O00000POhCn0b9ROTIn0f6XOZIN[MMOc0]9\\OcF2R3=k9DXh:", "size": [426, 640]}, "area": 60894.0, "iscrowd": 0, "image_id": 10092, "bbox": [0.0, 39.0, 614.0, 387.0], "category_id": 109, "id": 20000603}, {"segmentation": {"counts": "VcP75U=1N32M01N2O2NN2OL400OLUeT1", "size": [426, 640]}, "area": 102.0, "iscrowd": 0, "image_id": 10092, "bbox": [539.0, 375.0, 12.0, 17.0], "category_id": 117, "id": 20000604}, {"segmentation": {"counts": "W<S1W<0O100O10000O100O10000O10000O10000000000000000001O00004L00001O000000001O001O2N2N1O1O2N2N1O2N9G0000O100001GRC4n<KSC5P=1O13M4Lmi?ORV@2NNlB2S=3O100O1O1O10000000000000000000000001O2N00001O1O2NRh;?_WD2NN2O11O000O100O2O3_O[OPDNM30]1Y;h0iDnMc:i2C10001O000000000O100001O0O100000000fMXFU1e9iN_FW1_9iNcFW1[9hNhFV1X9kNhFS1Y9nNfFR1Z9nNgFQ1Y9SOdFm0[9WOdFg0[9[OfFa0[9@eF`0Z9@fF`0Z9AeFN]O^On9e0`F2AYOn9g0_F1CXOn9h0]F0FXOm9i0\\FOGXOl9j0]FNGXOl9k0]FKH[Oj9k0^FHJ\\Og9n0^FCFRO6:i9R1[FANZOi9X1XF]OOXOl9]1TFYO1VOn9e1oETO\\:n0cEQO[:S1dEkN[:Y1cEgN]:[1aEZN2L]:W2gEcM\\:[2:N4O0L3N3_NkDm0S;QOfE6\\:JVF@n9?`1O1NmeS2D`ZlM00000O2O0SDNb:2]EOc:1\\E0d:0\\E0d:1ZE1e:O[E1e:OZE3e:3SE0l:W100000000000O1O1000000O01N1NjNdD3o\\l0FmoSO0O1M5L3I8J6K4WOm0J8I00001O4Ll0TO4K2O1O1O001O00000000000000O10001O0000000O10QFhLl9X3TFiLj9X3VFiLi9\\301O00001O000O2O0000001O001O00000000O1000O2O0000000WLeLbM\\3]2eLcM[3^2cLcM^3S6011O00O101O0O101O0O100000000O1E]FoLc9e2iF[MW9c1gFiN3DV9b1iFhN4CT9d1iFiNo9W1QFhNP:X1PFhNP:X1QFgNP:W1QFiNS:GUF;HMV;1lDNS;4lDLU;2mDMT;L`_h0", "size": [426, 640]}, "area": 17223.0, "iscrowd": 0, "image_id": 10092, "bbox": [0.0, 60.0, 581.0, 366.0], "category_id": 123, "id": 20000605}, {"segmentation": {"counts": "0c0g<0000001O0000001O0000001O00001O00001O00001O0000001O0000001O00001O0000001O00001O0000001O0000001O0000001O0000001O0000001O00001O000000001O00001O0000000000001O001O00001O00001O00001O00001O000000001O00001O0000001O00001O0000001O0000001O0000001O00000000001O001O0000001O00001O00001O00001O00001O00001O000000001O0000000000O100000000000000000000O100000000O100000000O10000000000A_DUOa;j0aDUO_;k0bDTO^;k0dDSO];j0fDVOZ;i0gDWOY;g0jDXOV;g0lDXOT;i0kDWOU;i0jDQOF1a;n0oDQOQ;o0PEPOP;o0QEQOo:P1e0N200001O1O1O2N1O1O1OO1001OO1000000000000O1O1N2N200O100001O000000001O0000O10000O1001OO100000000000000000000000000O1001OO100000000O1000000000000001O1O1O00000000000000O1000000O1000000O1N22NO1O10000000000000000O100000000000000000bCXOZ<m0O1O00O1000000O10000O10000O1002N2N1OO10gCROV<n0jCROV<R1O1O000000002N1O0000001O000000O10000000000000000000000000000O1001OO11OO1000000000000^OYDAg;?^D\\Ob;d0b00000O11OO11O0000O10000O11O0000O100000000001O00000000001O00O11O1O00N2O100001OO11O0000O100000000O10YCI[<g0L1O0000000000000000000000O1000000000000000000000000000000EkC@V<:jC@N124V<;QDEo;:VDAk;?`0O100001O00O1001OO100001OO1001[CKS<f00000000000000001O000000O10000O1000000000000000000O1000000000000000000^OfC4Z<Lb0001OO1N22N1OO1N22N00001OO11O1O1O1OO1O11O00O1000000O11O00O100001O1O001O00O10000O100O100O1O1000000002N001O1O00M300O1000000O1O11O1O000000O12N7I6J0000000000O100O1O1O1O1O1O100O1O1O1O1O100O1O1O1O1O1O1O1O1O1O1O100O1Oao9", "size": [426, 640]}, "area": 19264.0, "iscrowd": 0, "image_id": 10092, "bbox": [0.0, 0.0, 616.0, 60.0], "category_id": 151, "id": 20000606}, {"segmentation": {"counts": "cQo1b0h<2N1O00O100O1O1O1O100O1CWC8j<HVC7k<HVC6k<KUC4l<LTC3m<MSC2o<MQC2T=OW\\50bcJ:M3N0O2N1100N3M2N2M2O000O10000O102Nc^X30]agL5J5K5M001O3fD[Oi9f0kEHR:i0VE@h:c0VE]Oi:b0WE@h:<`E@`:>fEVOc:i0]EWOc:i0]EWOc:i0]EWOc:h0^EXOb:h0_EWOa:i0^EXOb:h0_EWOa:i0_EWOa:i0_EWOa:i0_EWOb:h0^EXOZ:IUEo0a0WOT:LWE01o0e0TOR:3XEk0i0mNQ:_1m000010O0O10000000dN[DKOU1g;lNdDP1l;N100000O1000000O100001OO101O0VDnN\\;R1dDoNZ;R1eDROY;b1M02cNaDk0`;oNcDS1^;iNeDW1g;0003SOUD5e<K1O000000000N2O1N3Mbg^1", "size": [426, 640]}, "area": 3078.0, "iscrowd": 0, "image_id": 10092, "bbox": [151.0, 202.0, 376.0, 107.0], "category_id": 165, "id": 20000607}, {"segmentation": {"counts": "oYX12X=1N2N5K2L4M3O1O00001OO10[C]Oc<e001O00O11O001O001O001O000000000000000000000000000000O100000000O100000000001O000000000000000000001O000000000hKXOnKh0R4XOnKh0Q4YOPHOf3i0Y4XOPHOh3i0X4XOoGOj3i0W4XOnGOl3i0V4XOmG0m3h0V4XOlG0o3h0U4\\OlKe0S4ZOnKf0R4ZOnKf0R4YOoKg0P4YOQLg0n3XOTLh0k3XOVLi0h3WOYLi0f3VO\\Lj0e3TO\\Ll0Q8000000001O000000001O01O00000000dIUO9k0HTO8l0HTO8m0FTO:l0FTO:l0FTOgJ3g4i0a0UOcJ8k4c0a0WOaJ7o4b0=ROWJ6:8S5`0<ZO^J7X5?:ZO[J9\\5=8[OXJ;a5:70J06OK140L04OM1HTOkJk0]51HXOeJh0d50F::FD<<DE:<FA=?C_O>b0B@<`0D^O=c0CTOUOTK\\1h5_OUOd0l0\\OSOe0m0[OQOg0o0YOQOf0P1ZOoNg0Q1YOnNh0R1ZOmNe0S1[OkNg0U1XOlNh0T1XOlNh0T1XOkNi0U1VO[NZOQLa1d5UOYN_OmK_1j5ROVN`1j1`NSNc1m1]N^M\\OnLZ2d5[NXM\\2h2dMXM\\2h2cMYM]2g2dMXM\\2h2dMYM[2g2eMZMZ2f2fMZMZ2f2fMYM[2g2eMYM[2g2eMYMZ2h2fMXMZ2h2fMXMZ2h2fMWM[2i2eMWM[2i2eMVM\\2j2dMVM\\2j2cMWM]2i2cMVM^2j2aMWM_2i2aMVM`2j2_MVJUOm2\\3m2_MVJUOm2\\3m2_MVJUOl2]3n2^MUJVOl2]3o2]MUJVOl2]3o2]MUJVOk2^3P3`MPM`2P3`MoLa2Q3`MnL`2R3`MmLa2S3_MlLb2T3^MlLb2T3^MkLc2U3]MjLd2V3\\MjLd2V3\\MiLe2W3[MhLf2X3ZMhLf2X3ZMgLg2Y3YMfLh2Z3XMfLg2[3YMdLh2\\3XMcLi2]3WMbLj2^3VMbLj2^3VMcLi2]3WM_Lm2a3SM_Lm2a3SM^Ln2b3RM]Lo2c3QM\\LP3d3PM]Lo2c3QM^Ln2b3RM^Ln2b3RM^Ln2b3RM_Lm2a3lLTJ[O\\2h3`3eLTJ@0N43Z2h3^3gLUJ_O70W2i3]3hLVJ^Oi2i3Q3iLTMV3l2jLTMV3l2iLUMW3k2iLVMV3j2jLWMU3i2kLWMU3i2kLXMT3h2lLXMT3h2kLZMT3f2lLZMT3f2kL[MU3e2kL\\MT3d2lL\\MT3d2lL\\MT3d2lL]MS3c2lL\\MV3d2jLZMX3f2gL[MY3e2gLZMZ3f2fL[MY3e2fL]MY3c2gLVJDT3d3f2hLUJEU3c3f2hLUJEV3b3e2iLTJFW3a3e2iLTJGU3a3g2mLXMT3h2lLYMS3g2mL`Ml2`2TMaMk2_2UMaMk2_2UMaMk2_2UMbMj2^2VMbMj2^2QMTJG\\3Z3`2mLTJF01a3Z3Z2nLmMQ3S2lLmMW3S2hLTNR3l1iL]N6PL]2^:ON200`0@1O002N2N00O1O10000000^NlDn0T;SOiDo0Z;cNfD5:P1i;N4L1O2N8H3M001O1O1O1O002N00O14L4L1O005Ked]3", "size": [426, 640]}, "area": 21217.0, "iscrowd": 0, "image_id": 10092, "bbox": [96.0, 57.0, 280.0, 353.0], "category_id": 167, "id": 20000608}, {"segmentation": {"counts": "c0T1i5Y2WMgMi2[2UMeMk2[2UMeMj2[2XMdMh2\\2XMdMh2[2YMeMg2[2YMeMg2Z2[MeMd2\\2\\MdMd2\\2\\MdMd2\\2\\MdMd2[2^MdMb2[2_MeMa2[2_MeM`2[2bMcM_2]2aMbM`2]2aMaMa2]2bMaM\\2WOYJX3\\3_M\\2ZOXJX3[3]M]2g2dMXM[2i2eMWM[2j2dMVM[2l2dMTMY2n2iMRMV2n2jMRMU2P3jMQMT2P3lMQMS2o2nMoLR2R3nMlLS2U3mMkLX2P3iMmLZ2BYJ\\3]3PM\\2DWJ[3^3PM\\2DWJ[3^3QM[2CXJ[3_3PMZ2EWJZ3`3QMY2EWJY3a3SM[2m2fMRM[21RJ]2c3bM_2HTJe2]3cMl2\\2TMdMR3N\\Ik1c3VNa4g0_KZOc4c0]K]Oi4=WKCl4:UKEi4=WKEo43QKNn4J]GNe3;k4FaGNf3=i4BbG1e3>h4@eG1c3>j4_OdG2c3`0Q5_OPKa0l8O100010O3M4M1N2N1O4LO20O0001O0000000000010O001O00010OhCSOS<n0lCSOa40l2m0cHSO_48i2e0fHTO`4=`KDP7P1_MJgKTOX6R1QN:i1DXNh0\\1YObNo0W1QOiNR1U1nNjNS1U1mNkNT1T1lNkNU1U1kNkNV1T1jNlNV1U1jNjNW1U1iNkNW1U1iNjNX1W1gNiNY1W1gNiNZ1V1gNiNY1W1gNiNX1Y1gNgNY1Y1gNfNZ1Z1fNfNZ1[1fNdNY1]1gNcNX1^1hNbNW1_1iNaNW1`1hN`NW1a1iN^NW1c1jN\\NU1e1kN[NT1g1kNYNU1g1kNXNV1h1jNXNV1h1jNXNU1j1kNUNU1k1jNVNU1k1mNSNR1n1^NRJJQ4c0SLNk5DcN;kKUOA12=o51eN<SL^OZ56WNf0bLQOX59UNd0jLnNS5=SNf0gLnNW5<SNd0eLROX5:SNc0iLoNU5>RNc0T3\\OmLc0S3]OmLe0Q3[OoL0eK5^7JmLNSLMP75mLMk33VLGP48a40001N11OM4O00010O01O00005SGNY40hKd0d3^OYHG41Z3k0X44eKK[4:[JlNkNk0j6;XJXO`N>X7:kHSO;g1i6WOjHXOFC3o1]7VOiHY2W7gMjHX2V7hMjHX2V7hMjHX2V7gMkHY2U7gMkHY2U7fMmHY2S7gMPIV2P7\\M[HXONM30]1f2W6TNaJXOnMP2a7hNXKl0h4SOYKm0g4SOYKn0f4RO[Kn0d4RO\\Kn0c4SO]Km0c4SO]Km0c4SO]Km0c4SO]Kl0d4SO]Kl0c4UO]Kj0d4VO\\Ki0e4XOZKg0g4YOXKi0g4WOYKi0g4WOYKg0Z4^NSIk0c2f0Z4aNRIi0d24QM_OZ7EPIh0h2IXMFQ7KmHg0l2EYMFl61oHd0n2CWMGl63oHc0n2BYMEj69nH`0n2B[MDi6<mH>o2C[MAk6=kH?o2Dh4LSHF1k0U3De41UH;W3Dc42UH:W3Fb41WH9X3Eb4P1^KoNa4S1_KlNb4T1^KkNc4T1^KkNd4T1\\KkNf4T1ZKkNh4S1YKhNn4V1SKdNS5[1_3N2OO0101N010N2N200001O0O3M2M2N3hDbNb:^1\\E[Ol9f0TFH^98aFJ^97aFHa98]FGe9o10O1O110M2000O01_OSFdMn9Z2WF`Mk9^2b0M4ZO\\EaNf:[1g00000001O1O00O100000000O10O0101OO0100O1000O01O100O100O100O2N2O1N1O100O1O100O1O010O1O1O0101OL40O0O13M02N2O00001O1O2N1O1O1N10001O1O001O001O00fNTDV1Q<00O00O2ON2N20OYOoC6R<JoC5Q<KRD2m;OTDFM1n;9VDDI6R<5WDDS<=nCBR<>?N2N1OO10FSC7m<ISC7Q=N2O00000O111M2O00Z^=2g\\B9VLIi3<hG@920OU34j4>jGI1HY37g4V1WKhNh4X1WKiNh4X1TKlNm4S1RKnNm4S1TKlNm4S1RKnNn4R1SKmNm4S1SKmNl4T1TKlNl4T1SKmNm4S1jJ_N[M>j7T1kJ_NVMa0P8n0kJWOU5i0kJWOU5i0^JDa5=_JCa5=]JEc5;_JCa5=kJWOU5i0lJVOT5j0lJVOU5i0kJWOU5i0kJWOU5i0kJWOU5i0kJWOU5i0kJWOU5i0kJWOV5h0jJXOV5h0jJXOU5i0kJWOV5g0kJYOV5f0jJZOV5f0jJZOT5h0lJXOS5i0mJWOS5i0mJ`NTMc0o7m0mJ_NUMd0o7l0PKTOo4m0QKSOP5l0PKTOP5l0PKTOP5l0PKTOo4m0QKSOo4m0QKSOo4m0QKSOo4m0QKSOo4m0QKSOn4n0RKROn4\\O[HV1g2^O^5>bJB_5=aJC_5=aJC_5=aJC_5=aJC_5>`JB`5>aJA_5?`JB`5>`JB`5>`JB`5>`JBB\\OZ3R1TMBoNOm3?TMB\\Nc0^4LVMAjMU1BcNd4g0PNBVMl10mMi4d0RN^2oLRMo4`0RNR3n1mLSNR3n1nLRNR3n1nLRNR3n1nLSNQ3m1oLSNo2o1QMPNo2Q2QMoMo2Q2QMoMP3P2PMPNo2Q2QMoMP3P2PMPNQ3o1oLQNo2f1RL^KP1l2U1PM]Oa4^N]KN124o0o2T1SMZO]4jNaKh0n2S1YMWOX4SO\\Kd0S3P1^MVOS4:_N`0^MWOQ4:aN?_MWOo3:bN?bMUOk3<cN?cMnNQ4b0\\N`0TNVNg3Z1UN`0WNTNb3]1WN?VNTNd3]1VN?RN\\Ne3T1YN`0PNbNb3o0^N`0oMaNc3o0^N`0oMaNc3o0^N`0oMaNd3n0]Na0nMbNd3n0^N`0nMcNc3m0_N`0nMbNe3@dKk0j2S1mMcNU4:nMS1mMcNU4:nMS1mMcNU4=^KQOb1P2kNbNU4]1ZKcN7`19_NW4a1UKeN5U1@UNo0`0W4c1QKgN1P1JRN01o0c0U4c1QKiNKQ16oMk0c0T4f1mJiNLP1_1_Nk3g1iJQ1\\1WMl3i1fJQ1^1VMl3i1fJR1^1PMP4n1bJR1^1oLP4o1cJQ1^1PMo3l1hJR1Y1RMo3j1kJS1V1TMm3d1UKV1n0VMm3`1[KX1\\OnLKOU1;Y4]1_K[1TOXMP10]4e0\\L^2UOnL_4c0dLX2mNUM_4c0dLX2mNUM_4c0dLX2mNUM_4c0eLV2mNYM\\4a0kLR2iN^M[4`0hMV1mMZNZ4a0mMQ1iM^NZ4`0oMP1hM_NZ4a0oMo0gM^N\\4c0nMn0fM_N\\4c0nMo0eM^N]4c0oMm0eM`N\\4c0oMm0fM_N[4d0oMm0fM_N[4d0oMm0fM_N[4d0oMl0gM_Ni3EdKP1l2m0o2EQM;o2EQM=nLUN`5]1aM`0iLVNg5[1`M`0eLXNi5Z1bMj0`2UO_Mk0b2TO^Mo0PMhMi4Z1WNh1b1ZN^Ng1c1WN\\Nj1d1VN\\N]NhLZ3l4YN\\N]NiLY3l4XN\\N_NiLX3j4ZNbNf1_1YNbNf1_1YNaNg1`1XN`Ni1`1VNaNi1^1XNbNh1]1YNcNg1^1WNdNh1\\1XNdNh1[1YNeNg1[1YNeNg1[1YNfNf1Z1ZNfNf1Y1[NhNe1X1ZNhNf1X1ZNhNf1W1[NiNe1W1[NiNe1W1[NhNf1Y1YNgNg1Z1XNfNh1Z1WNhNg1Z1XNfNh1Z1XNfNb1ZLVMo4Y1gN`1^LTMk4\\1gN`1`1_NaN`1_1aNaN_1_1aNbN]1^1cNbN^1^1bNcN\\1]1eNcN[1\\1fNcNZ1^1fNbNZ1^1gNaNX1`1hN_NY1a1gN_NX1d1gN[NY1e1gNZNY1h1fNXNZ1i1eNWN[1i1eNWN[1f1hNZNX1f1hNYNY1f1hNZNX1f1hNZNX1f1hNZNU1i1kNWNSO_LQ1b5MPNoNdLo0\\53PNkNkLHMa0Z5k0XN^N_M?\\4R1FkN;V1EhN<\\1BcN=_1D]N<a1I[N;T1\\6G8I1000000001N010O1O1O1O1O1UIXOS1m0hNVOV1f0kN^OS1c0mN]OR1f0lN[OR1g0lN[OR1e0nN]OP1d0oN^OP1a0QOAl0?UOGd0:\\OK>6AMoJoNR5W1M52MN21OO10000O1iJdNW5Z1O51KWMWOkMLS1S1l3IVM1bMfN20R1a1T4HUM8gNOV4HSM9fNOX4iNeJd0]2d0eN0Z4bNjJi0W2e0dN0h4ZOdLf0dNOj4WOfLi0`NMn4oNlLT1VNLX84hGLX84hGKY85gGJZ86fGI[87eGH\\89cGF^8:bGE_8;aGD`8<`GCa8=_GAc8?]G@d8`0\\G_Oe8a0[G]Og8c0YG_Oe8a0[G@d8`0\\G@d8`0\\G_Oe8a0[G_Oe8a0[G_Oe8a0[G^Of8c0YG", "size": [426, 640]}, "area": 51377.0, "iscrowd": 0, "image_id": 10092, "bbox": [0.0, 0.0, 640.0, 394.0], "category_id": 173, "id": 20000609}, {"segmentation": {"counts": "g1i5a70000O100000000O10000000000O01O1N2ONbHYJ]7h5dHXJ[7h54O100O1M4O0O2N2O1N2N2YORHSLn7d3XH[Lo7[OPHm31gLX8^1cGC5PO\\8g0YH1[OXO[8j0ZHL[OZOR8<QHUOh0T1UOZOZ8\\OeG8h1P1^NXOi:d0^EUOR;<lDB\\;9eDG\\;IRD;e0J^;3fDH];6m0N10003L10\\a63^QI2k<MUC7g<JXC9e<G[C9R1DoN0[;3dD9m07U:AmD8o07S:OkE4T:LRF1k90WFLj93WF1VO@o9>kF:nNCl92WGY1_8gN`G_1lNSNY9b0hGh1P8jN^G\\1A_Mg8e4I]K^G_4b8`K_Ga4_8_KaG_4b8_K_G`4g8O00O11N101O000O10001O000O10001O00000O101O000000000O2O00000O10001OO_LmFNh0d1k:@`0@7J?ASWV3BWiiL5H505Hc0lCmNR;S1lDNZ:OeE2Z:a1N1OO1003M3M000000001N10M3G900N22N<D1O0000000000000000000O10000000000fETMV:l2iEUMW:o2000000000000000000000000000000001O01OO10000000000000O100O1000001O00O10000000000000O2OO10000000000000000000000000000000O100003`MmEb1T;ZN^E7f:E_E5e:G_E1g:O[EKk:2WELj:3X1NnX[14jfdN7nNOiDOV;2kDNT;4jDLV;3jDOT;3jDOU;2jDOKC[1Nd7c0TGNCJd1K`7_1mFgNa1Ka7l1aHUN`7l1UGnMh0;Q8g1SGVNh05R8m2YGSMi8o2PGTMo8n2oFSMQ9m2oFRMR9c301O00001O0VOiKkGOc0Y4\\7QL_HS4`7RLTHW4j7e0\\HeJY7\\5aHjJ]7c5N2L4N3NE:K5O1O1O100O1O1O001O10O22N1O00O10000O1PO", "size": [426, 640]}, "area": 30018.0, "iscrowd": 0, "image_id": 10092, "bbox": [0.0, 31.0, 640.0, 209.0], "category_id": 181, "id": 20000610}, {"segmentation": {"counts": "S]W1m0\\<P1PO4K2O1O1N2O1O1O1N2O1O1O1N2O1O1N2O2N12NO1O1O100O1O1O100O1O1O1O100O1O1O100O1O1O100O1O1O100O1O1O1O100O1O1O100O1O1O100O1O1O100O1O1O1O100O1O1O100O1O1O100O1O1O100O1O1N2O1O1N2O1O1N2O1O1ULPLZNR4d1RLYNo3g1SLVNn3i1ULSNm3n1RLQNo3P2QLnMP4T2oKiMS4[2jKcMW4`2iK\\MX4e2jKVMX4k2gKTMZ4l2fKSM[4n2cKQM_4P3]KRMd4o2ZKQMg4P3XKnLj4S3UKlLl4T3TKkLm4V3SKgLo4Z3PKeLQ5\\3oJbLR5`3mJ]LU5e3jJYLW5i3hJULY5m3fJQL[5Q4dJmK]5U4aJkK_5W4_JhKb5P60O10000O100O100O100O10000O1000000000000000000000000000000000000000000O100000000000000000000000000000000000000000001O000000000000000O10000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001O0000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000N2N2N2M3N2N2M3N2N2N2M3N2N2M3N2N2N2M3N2B>]Oc0]Oc0]Oc0]Oc0]Oc0]OUZY1@nffN3M2N3M3[D7c9K`EQ1_:m001SOdMjF]2T9gMiFQ2_9QN_Fe1j9_NSFc1k9`NRFa13dNc8OWGd2h8_MUGa2j8cMSG\\2;mLo7R4mGSLR8m3iGYLW8]4O100O100O10000000000000N1_Ob0_Oa0^Ob0^Ob0_O`FSNe8R2cFkM23[9R2_FoM60Z9Q2`FoM60Z9Q2`FoM60Y9R2bFmM51Y9R2bFmM51Y9R2bFmM51LNl8T2SGmM51J1m8Q2TGmM51K2j8Q2UGlM61K4h8o1WGlM61K6f8m1ZGkM52K8d8k1\\GkM52K9b8k1^GjM52K;`8i1`GjM43L<^8g1bGjM44Kj0P8X1QHjM44Li0o7Y1QHjM5`1j7f0QHjMHm1W8:hG6X8^2O1000000000000000000000mNiGSMW8m2iGSMW8m2iGSMW8c2SH]Mm7\\1ZIdNf6\\1ZIdNf6\\1ZIdNf6\\1ZIcNg6]1YIcNg6]1YIcNg6]1YIcNd6`1\\I`NZ6j1fIVNo5U2QJkMd5`2\\J`M^5f2bJZM\\5:SIR1a1dNZ5<UIP1a1dNX5>WIn0b1cNV5n2jJRMT5P3lJPMR5R3nJnLP5T3PKlLn4V3RKjLl4X3TKhLk4Y3UKgLk4Z3TKfLl4Z3TKfLl4Z3TKfLl4Z3TKfLl4Z3TKfLl4Z3TKfLl4Z3TKfLl4S2bLeMRM", "size": [426, 640]}, "area": 68723.0, "iscrowd": 0, "image_id": 10092, "bbox": [94.0, 104.0, 546.0, 322.0], "category_id": 183, "id": 20000611}, {"segmentation": {"counts": "h3V5R610000O100001O0N3O3K003N2M2N3N1J403K5ON22NM2O20N4M2O4M0lKPIh1N:Y7UNgHj0<n0o6SNdHN22Mj0g0i0Q7^NYHj0h0e0m6_NZHO4k0f0f0V7_NQH00?n0Q1i8O3M;E4L3M2K2O3G\\EFR;1nDNS;1nDO[b=3d]BMR;:JM40N3N20OPFE60d7`0SHF@5G4c0E]OL0000d7h0hHDGb01SOL21<`7?gH`1Y7bNdH_1_7bN\\H_1^7Y15K1OM24M00N21O1O00O1N22NO1O11O0000000000O11OO100000000O10000O11OO10000O100002N00hHZLm6f3jHZL43R7c3jHZL08Q7m31O5K7J0O1O1O100O1O1O011N1O100O1O1O10O01O1O10M21O1O3lLSHh2U8PMmGm2X8O1O100O1O100O1O1O100O1O10K\\MdGd2U8\\MjG003Ob2W8:1OO2K43N1N00100O2N:G1N100O1O100O1O100O100O1O100O100O1000LgMVGZ2j83000000O01000000O0100000O10001O1N2O002N0O102NJ6L33NN2002nNSGSO2Ok8h0PHSOT86cHF_76mFGd13a73bHL`73`HMc7O^H1]9001N5k_?HW`@3MOoDOT;1N2O11O001OO10OO2H8TO<RFEn9k0000000O1000O10O1DfNiFZ1V9mNdFS1\\9=00O1001OO010001O2N00000000001O0O10000000O1000000001N01O1N2N2000O100000000O100000000O2O00O100O02ON22NN20O1000001O0O2O1O1O1O1N2O002NO1K50O20O0001BVFRO1On9k0?A`0L2GPE2W;NaYf2", "size": [361, 640]}, "area": 24545.0, "iscrowd": 0, "image_id": 10363, "bbox": [0.0, 57.0, 395.0, 229.0], "category_id": 98, "id": 20000612}, {"segmentation": {"counts": "P^n1111n:9K4000N2011M20OO0011O02N1YE@6:j96PF@6;i99QFLo9g01O00000000000000000000000000000LPFmNR:R13000000O1000001O0O100000O10001O1O1N102N01OO3NJ6L33NN2011ON14K3O1N1N4RFeNi9]1000010N1001NJdN^F`1[9aNeFd1[9]NdFc1\\9^NcFb1]941000J66J00000000O100000000O11O1O0000000000000000000000000000000000000000N21OO11O001O1O00000000L43OM1O100O10O1000000O;ZO[FTOj9d0VFRO74d9e0YFWOT:c0lE]OZ:a0hE_OY:=;0000005MI5ITEL21j:OTE1jY^3", "size": [361, 640]}, "area": 6462.0, "iscrowd": 0, "image_id": 10363, "bbox": [177.0, 12.0, 151.0, 66.0], "category_id": 100, "id": 20000613}, {"segmentation": {"counts": "QUd01W;5L6J0O3N1N0O12BXE9f:G]E8k:O001O0NQEJo:6QEJo:63O0KnD3R;MnD3T;0001ObfU6", "size": [361, 640]}, "area": 203.0, "iscrowd": 0, "image_id": 10363, "bbox": [57.0, 58.0, 22.0, 20.0], "category_id": 107, "id": 20000614}, {"segmentation": {"counts": "feh01X;?A2N001O1O00M3M3N2M30000O1000000O1JoD4Zd5LmfJ0iD0T;1lDOS;401O1O1O1O1O1O1O1O1O2N1O1O1O1O1O2N1O1O1O1O1O1O1O2N1O1O2N1O1mElN0Om9Z10001OO11OO1002N1OO1G9M3O100M30000003M1O1O3M4Ld0QFTNY9n100000000000NgFTNY9l1gFTNY9l120000O1DjFaNJ0^9]1<O1ROUFC2d0k9ESFH0O1a0O^Oo9:VFFM2N5W:3nEFN1M2[:7b00000001O000000O11O001O00O1M300O1000014Hef00YYO1PEOP;3nDMR;6O2N001O1O0SEFk::UEFk::UEFk::UEGj:=O000000MWEEj::WEFi::30000O11O0000001O1O0000000XEFa::_EG`:9`EI^:6bEM\\:3dE1X:OhE2W:NhE3X:MfE4[:>O00O11O00001OO1001OIgE[O\\:?eEA`:?aE@_:`0bE_O^:a050000J66J00000000O100000000O11O1O0000000000000000000000000000000000000000N21OO11O001O1O00000000L43MO1O100O10000000000:F5_FSO\\8n0_GVOa8j0YGVO]O1Y9i0XGXO_ONZ9k0SG@m8`0SGAl8?TG@m8`0SG@m8`0XG[Oh8g0QG_On8a0kFoNL21`0V9?iFoN1j0V9V100000001O0000000000O1000000001OO1O1N2N20000000000000000000000001O00O100O11ON22NN2000000001O001O1O1O1O1O1O002NO1K5001O00001O001ON20000O1N2IcF`N_9Z1bFfNOM01_9\\1cFeN0N^9]1bFdNc9[1_FdNa9\\16000000000000000000000000000YFdNL120NO[9]1hFmNX9a11O1O1O100000000001O0000002NM3001O001O2N001O0000001OO10000MfFWNZ9h141O0000O100000000000000000000FbFhN_9S1bFgNh9Y140000O10000001OO10000O[FmNV9d1O1O3M001O00O100O100O100O100O100fNjF`0W9\\OTG=l8@[G;f8A_G>a8_OcG?f9N1N3N1N3N1N3N1NUV_1", "size": [361, 640]}, "area": 15248.0, "iscrowd": 0, "image_id": 10363, "bbox": [70.0, 0.0, 436.0, 67.0], "category_id": 113, "id": 20000615}, {"segmentation": {"counts": "n8[2n80000000000000N200N2N2O1N2N2O1J6N2L4000000O100N2O1001O1OO1N21O00O10000000000M3N2HdL^Ha3b7_L]Hb3b7_L^H?0`2b7cM^H^O2j2`7gMdHY2\\7fMfHY2[7fMfHY2Y7hMgHX2Y7eMjH[2V7cMkH^2V7_MmH`2T7\\MoHd2Q7YMQIh2h7000000000001O001O00000000001O0000000000O1O100O10000O100000000O100O10000ZM]MfLc2Z3]MeLP1SNLX5UOeLj0gNAd4DfLg0YOVOQ44dLd0[5[OgJ`0]5@cJ=_5E_J8e5H\\J3h5NWJOl51TJKP65PJGT69lIDW6=Z26WE\\Oa:g0O001O0000000000O100O10000O10000E;00O11O00000000000000O10000GRE4Q;LoD0P`a5", "size": [361, 640]}, "area": 9618.0, "iscrowd": 0, "image_id": 10363, "bbox": [0.0, 180.0, 137.0, 181.0], "category_id": 115, "id": 20000616}, {"segmentation": {"counts": "i2o0Z:O10000O011OO20O01LSOjEP1R:230O1O0101N1M21O1ON22NM3N2002N1O4L1lEPOm9X1ZFfNZ9Z1aFeNN22M[98cFS13fNY90dF34S1MiNO4Z9LgF0004`1c9O00001N10001O7dNRFm0W:N6YO_E;e:A\\E?n:H6IkD0XUY11PkfNOd:9TEIj:;00NCXE=h:CXE=h:20000O1DFeEJ0b0Y:<O1ROQOgF2d0o0a8oNlF0O1e0P1`8TOjFM2N`0k0^OWOW93iFN1Ma0c0^O^O003OU9a0VG2U9NiF4W9LiF2Y9OhFJ^94dFMZ94eFMZ92fFN[91fFOY93fFM[92gFMX93iF@@6k99eFA@6k99iFA[9`0d04O0O100001OG@fEc0Z:6000WOfEc0\\:[OdEe0^:YObEg0a:O2N001O1O0^O[E`0e:@[E`0e:@[E`0e:@\\E?h:O000001LA[E`0d:A\\E?d:3000O011O00000O2O1O01OO100DZE5f:K[E3f:M\\E1c:O`EO`:1<00O1NdY\\4", "size": [361, 640]}, "area": 3308.0, "iscrowd": 0, "image_id": 10363, "bbox": [0.0, 0.0, 242.0, 153.0], "category_id": 123, "id": 20000617}, {"segmentation": {"counts": "0[1n900O1000000000000000cFgNA0U9Y1ZGgNA0U9Y1ZGgNA0O0S9Y1]GkN_OM00014Nc8Y1jGUO_OG1La8X1oGAP8?PHCn7=RHDm7<SHDm7<SHDm7;THEl7<SHDm7<THCl7<THEl7;UHDk7=SHDm7;THEl7;THEl7=RHCn7=RHCn7?lGeNGm0\\8?lGEOnNk7a1PHCNlN34l7f2PHWM0004W2L_3W3TLUMl3k2TLUMl3k2TLVMk3k2TLUMl3k2TLUMl3k2TLVMk3k2TLUMl3R3\\JcL[1;Y4Y3gKgL7IU2e3]LSLl0b0QO_OX19^2f3YLQLP1P1M\\Oh2m3TMPMJTOR3Q4[LaKb0b1HmN[3U4jLUMDgNa3Y4fLdM[3d2\\L]Md3T501O11N1N2M3N3]JRLg3o3WLVLX3KSK010N20n3e1[LR3U4lLkKP3F]Ka4a1lKP3X4oLiKo2Y4oLgKR3Z4kLgKV3@_Kf4]1\\KcN9c4\\4gL[KiN8`4\\4PMcKQ3\\4oLdKQ3\\4PMcKP3\\4QMbK[NJd4g4nL_K^NJe4h4kL^KV3c4kL\\KU3e4jLVK`N3g4g4hLVKaN3g4g4jLYKV3d4mL\\Ki2MkKd4`1^Kd26eKZ4X693000^KXKVNMZ4k4aM_KZ2a4eM\\K_2d4aM\\K^2e4`M^K_2a4aMaK]2Z4kK^Ki17SOE60R1d4HlKmNF@5G4c0E]OL0000m0e4S1SL\\NDGb01SOL21g0a0k3[1YL[Nk19m1\\1ZLXNk1:l1a1ZLPNj1`0l1Z1^OeNb0`1XOaNh0`1WO`Ni0]1[OaNf0b1WO]Nj0c1WO[Nj0c1XO[Nj0f1UOZNk0g1TOXNm0h1TOWNl0JSKk1X4TNf01RKO0h1Z9XNgF0Oj1]9O2N200O1O1O1O1O1N201M2O1O1O1O1N3N1O1N200N2O1O1N3N1O110N1O1YO`E0Oa0f:L4013K7KOO1002N1OO1G_OQF:l9BPn30klT5", "size": [361, 640]}, "area": 15684.0, "iscrowd": 0, "image_id": 10363, "bbox": [0.0, 0.0, 161.0, 245.0], "category_id": 130, "id": 20000618}, {"segmentation": {"counts": "iRi41W;1I72NO0100O100O1O100OM4_OJcEa0]:61N100O00100O100O100O100O1O100O100O100O1O100O010O100O2OO02O1N4M0Ojog1", "size": [361, 640]}, "area": 568.0, "iscrowd": 0, "image_id": 10363, "bbox": [434.0, 57.0, 47.0, 31.0], "category_id": 143, "id": 20000619}, {"segmentation": {"counts": "Z]?1X;0jD2P;4O2O1O0M3N1O3L3M2M4N2O100CTOSF010N20j0m9>LgNWF[1h93O2O00_NZF_1h90001O01O0001OO1001N1O10FdNgF[1Y9mN`FS1_9<0O10O10000O010O02OO100O002O00O11O0O002N1N200O10N2L5L3L5L300O100O1O0N3N1O2M3N1O2N2MH92N1O3L3N2N2M3N2N2N2M2O2N3L3N3MaTa5", "size": [361, 640]}, "area": 3682.0, "iscrowd": 0, "image_id": 10363, "bbox": [43.0, 190.0, 94.0, 83.0], "category_id": 152, "id": 20000620}, {"segmentation": {"counts": "[1P1Y:0O0100000000001OO1BhEA06X:9hEA07W:8iEA0O05W:;mE_OM000Y:a0<000000000O11O01N0110N01002N002JXEGi:88JTP7NPPI1O1O1O2N1O1O2N1O1O1O1OO11OO10001NXoj1EZfTN2R:k0XOg0J\\N_Fd1`93000000000O2L3F:F920O0001O0O101O001O2N2N2N2N1ON200O100001O00000000001O00000000O1O1N2O1O11O1O001O1O1OO1N2NRFlNk9X11N2O11O001OO100N2HT1SOTXm00h\\RO1O3f:<TOAbFl0[9ZOaFf0_9\\OXFG2P1f9]OXFc0g9`0N2I^NeFe1T9\\NlFOM01d1W9^NjF0Nb1Y9^NhFf1X9\\NgFd1Y95100O100O100O1O100O100O100O1^N_FL120NOU1c9nNgFR1g9001O0010O100O1O101N100O102MN30O2LhETOY:k0hEUOY:c0<0NO2O0000001OO1000L4DUY5OX\\J0b:1XE9h:40000O100000O2OO10001NJi]i1", "size": [361, 640]}, "area": 6225.0, "iscrowd": 0, "image_id": 10363, "bbox": [0.0, 0.0, 477.0, 132.0], "category_id": 172, "id": 20000621}, {"segmentation": {"counts": "X\\P1=h:d0\\Oc0^O;D4M3L4L4M3L4L4M3L4L4M3L4M300O100O1O1M3N2N2M3N2N2N2M3N2N2N2M3N2N2M3N2N2N2M3N2N2M3N2N2N2O1O100O1O1O100O1O1aKjJj2W5oL^Kc2b4[M`Kd2a4\\M_Kc2b4ZMaKf2_4ZMaKe2`4[M`Kd2a4\\M_Kc2b4`M[K`2e4aMZK^2g4cMXK\\2i4iMRKV2o4nMmJR2S5Q21O1O100O1O1O100O1O100O1O1O100O1O100O100O1O100O100O1O100O1O100O100O1O100O1O100O100O1O100O100O10000O100000000O1000000O1000000O1000000O1000000O1000000O1000000O1000000O1000000b0ZI[Kd5X4jJgKV5m3WKSLh4c3bK]L^4Y3mKfLS4[3mKdLS4\\3mKdLS4\\3nKdLQ4]3nKcLR4]3m1001O002N1O2N2N2N2N1O2N2N00000000000UJRL[4n3eKRL[4o3dKQL\\4n3eKRL[4l3gKTLY4k3hKVLW4h3kKXLU4g3lKYLT4e3oKZLQ4e3PLmL^3Q3eLnL[3Q3gLnLY3Q3iLnLW3S3iLmLV3T3jLkLV3U3kLjLU3W3kLhLU3Y3lLeLT3\\3lLcLT3]3mLbLS3_3mL`LS3a3mL_LR3a3oL^LQ3b3PM]LoNKl2h3WN[LlNMn2h3YNXLk2h3dNXLMh33XLMh33XLMg34ZLKf36YLJg36YLJg3ZOaLdMHR3g3]O]LbMMP3f3AYL`M1o2f37ZLIf37ZLIf37ZLIf38YLHf3n300000YL[LJe33^LMb30`L1`3LcL4]3IfL7Z3FiL:W3ClL=S3APM?P3^OSMb0m2ZOmLWM@_3c3WOeLeMHT3c3TO^LRNOj2c34]LLc34]LLc34]LLc34]LLc34]LLc34]LLb35]LLc34]LLc34]LLc34]LLc34]LLc34]LLc34]LLc34]LLc30aL0_3KbL9]3CcLb0]3YOcLl0]3oNdLU1\\3fNdL_1\\3\\NdLi1\\3QNfLS2Y3iMgL\\2Y3_MgLf2Y3ZMbLk2^3UM^Lo2b3QM^Lo2a3RM_Ln2a3RM_Ln2a3]2000dJ_LP3`3QM`Lo2`3QM`Lo2`3QM`Lo2`3QM`Lo2`3QM`Lo2_3RMaLn2_3^20WKaLh1_3RNgLn1Y3Q3O10000000YKbLc1]3^NcLb1]3^NkLZ1U3fNiLTMJo3]3mNfLWMNk3\\3mNeLZMOi3[3^OfLa0Z3_OfLa0Z3\\NdLaN2S3Z3[NeLbN0T3[3YNeLdN0S3[3YNeLdN0S3Z3ZNfLcN0S3[3YNdLeN0S3\\3XNdLeN0S3\\3]OdLc0\\3e3001fKcLf0]3YOdLg0\\3WOeLj0[3UOfLk0Z3SOhLm0X3ROhLo0Y3oNfLS1Z3kNfLW1X3jNhLW1W3hNiLZ1W3eNhL]1W3aKfLl23e1W3_NiLb1V3]NjLe1V3ZNiLh1V3WNkLj1U3UNjLm1U3RNkLP2T3PNlLQ2T3mMlLU2S3kMlLW2T3gMmLZ2R3fMmL\\2S3bMmL`2R3`MmLb2R3]MoLd2Q3[MnLg2Q3XMoLj2Q3UMoLl2P3SMPMo2P3e2O[JRMQ3n2oLRMQ3m2PMSMP3l2RMTMm2l2YKTMl00k3k2[KUMj00j3k2^KTMg01k3j2aKUMc01k3j2YMVMg2i2ZMXMe2h2\\MWMd2h2]MYMb2f2`MYM`2g2`MYM`2f2aM[M^2e2cMZM]2e2dM[M\\2e2eMZM[2e2fM[MZ2e2U3OcIcMP4\\2QLeMn3[2RLfMm3Y2TLhMk3X2ULhMk3W2VLjMi3V2WLjMi3U2XLlMg3T2YLmMf3R2[LnMe3Q2\\LPNc3P2]LPNc3o1_LQN`3o1aLQN^3n1cLRN]3n1dLPN]3o1eLoM\\3Q2eLlM]3S2eLkM\\3T2eLkM\\3U2eLiM\\3V2l200O100O1O100O100O100O100O100O100O1O10O0100O100O100O100O100fNkF?V9]OUG<k8A\\G:e8B`G=`8@dG>]8@gG>Y8BhG<Y8DiG:W8FjG8W8HkG6U8JlG4U8LmG2S8NnG0S80c1000000000000000000000000000000000000000000000000001O00000000000000000000000000000000000000000000000000001O000000000000000000000fEN]92aF0_90UF1I0R:OTF6ELW:NSF>m9BRF?n9AQF`0o9@PFa0P:_OnEc0R:]OmEd0S:\\OlEd0U:\\OjEe0V:[OiEc0Z:]OeEb0]:^ObEa0`:22M3N2AYE:>Ek91gE7a0Hh91gE5c0Jf91gE3e0Ld91gE0h0Oa91bFO^91bFO^91bFO^91bFO^91bFO^91m000000000000000000000000000000000000000000000000000000O1000000000000000000aE0e9j00000000UO", "size": [361, 640]}, "area": 142545.0, "iscrowd": 0, "image_id": 10363, "bbox": [91.0, 0.0, 549.0, 361.0], "category_id": 183, "id": 20000622}, {"segmentation": {"counts": "j7Z;i71O100O1O1O1O100O1]JdD]3];cLiDV3X;jLoDn2R;RMTEh2l:XMZEa2g:_M_EZ2b:fMdES2]:mMiEl1X:TNnEe1S:[NSF_1m9aNYFX1h9hN^FQ1c9oNcFj0^9VOiFb0X9^OnF<R9DTG5m8KYGNh82]GHd88]GFd8:]GDd8<\\GDd8<]GBd8>]G@d8`0\\G_Oe8a0\\G]Oe8c0\\G\\Od8d0\\G[Oe8e0\\GYOe8g0\\GWOe8i0[GVOf8j0[GUOe8k0\\GSOe8l0\\GSOe8m0\\GQOe8o0\\GoNe8Q1[GnNf8R1[GmNe8S1[GlNf8T1[GjNf8V1[GhNf8X1ZGgNg8Y1ZGfNf8Z1[GdNf8\\1ZGcNg8]1ZGaNg8_1YG`Nh8`1YG^Nh8b1YG]Ng8c1YG\\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``e00`_ZO00001O00001O00001O001O00001O00001O00001O00001O00001O00001O00001O001O00001O00001O00001O00001O001O001O001O001O001O001O001O001O001O001O001O001O001O001O001O001O001O001O001O001O001O1O001O001O001O001O001O001O001O001O001O001O001O001O001O001O001O001O001O001O001O001O001O1O001O001O001O001O001O001O001O001O001O001O001O001O001O001O001O001O001O001O001O001O1O001O001O001O001O001O001O001O001O001O001O001O001O001O001O001O001O001O001O001O001O1O001O001O001O0HnL", "size": [612, 612]}, "area": 346583.0, "iscrowd": 0, "image_id": 10583, "bbox": [0.0, 1.0, 612.0, 611.0], "category_id": 183, "id": 20000623}, {"segmentation": {"counts": "lbP1Y1e=3M4L3M4L3L5L3M4L3O2N101N00000001O000000010O1O1O1O0000000001O00100O1O1O001O1O0000000000010O00000001O000000010O0PN`C`1`<`N`C`1a<_N`C`1a<_N_Ca1b<^N_Ca1b<]N_Cc1b<\\N_Cc1b<\\N^Ce1b<ZN_Ce1b<ZN_Ce1c<YN]Cg1d<WN^Ch1m<01O001O10O01O001O001O001O1O010O0011N2N2M4M2N2N2PCPNj00WO0l;T2jDmMZOOk;V2hDmM]OMk;W2fDjM_OOO1l;[2_DeMH1j;Y2eDgM[;Y2fDfMY;[2f0001O0YOcMkD]2T;dMkD]2Q;gMoDZ2l:kMSEW2h:lMXE]2[:gMeE]2U:fMiE\\2T:fMlEZ2S:gMmEY2S:hMlEX2S:iMmEV2S:kMlEV2T:kMkEU2T:lMlES2U:mMkES2T:oMkEQ2T:PNkEP2V:PNjEP2U:RNjEn1U:SNkEl1V:TNjEl1U:VNiEk1V:VNjEj1V:VNjEi1V:YNiEg1V:ZNjE?MYOY:9hE>8POP:c0fE>d0fNf9l0cE`0P1\\N\\9V1cE=R1\\NP9LVFW3j0mLP9LVFV3j0oLP9LTFU3m0oLo8MRFT3P1nLo81nEP3S1PMn8Z3RGfLn8Z3RGeLo8[3QGeLo8[3PGfLP9Z3PGeLQ9[3oFeLP9\\3oFdLR9\\3mFcLT9^3fFeL\\9]3_F\\Ll9d3g01O1O100O100O101OO01001@lDRMT;n2REkLn:V3WEeLi:[3=1O1N2O1O0010000001O00001N2O2jN]DkNe;Q1^DnNc;n0bDPO`;l0cDSO^;i0fDVO\\;e0iDXOZ;e0hDZOZ;c0hD\\OY;b0jD[OY;a0jD]OY;`0jD]OW;b0kD]OV;a0mD\\OT;c0oDWOV;f0d1N1O2N2N2M3N2N2N2M2O1O2N4LS[[5", "size": [480, 640]}, "area": 17042.0, "iscrowd": 0, "image_id": 10707, "bbox": [69.0, 186.0, 205.0, 186.0], "category_id": 93, "id": 20000624}, {"segmentation": {"counts": "dbl3120h>0WA100h>60O10000O1000000O10O10O10000O1000000O1000O01000000O10000O_i20bVM1O00Ojbj14aPUNLU:8UEK]NO62OLO;a;NmE0bNLLK1c0c;J\\DIX1U1[:^O`Ed0^:@_Ea0`:LUE3j:OTE2k:OUE1i:2VENi:3WEMh:4XELg:6XEJh:6XEJh:7WEIi:7WEIi:7WEIi:8WEGi:9WEGh::TEZNE\\1W;;mDOS;o100000000000000000000000000cNZL`Gf3W8mL[GW3[8]MWGg2_8mMTGV2c8o1]GUJY8m5oGSJg7o5aHQJY7n5nHRJQ7g5WIYJk7S4gHmKo7j2dHVMc<000000000000000000000000000000000000000000000000000O100000000000000000000000000000000000000000000000000jNM_C3^<4^CL_<<\\CDa<d0ZC\\Oc<KVCb02C]=<^BDh=;SBEi<JZDh0c;YOeDa0Y;@mD;P;FXE4e:MbEN[:3hELU:5mEKP:6RFJk97WFIf98\\FHb98`FH^97eFIY97iFIU96nFJP95SGKk85WGKg84\\GLb84`GL^83eGMY83iGMU82nGNP82RHNl71WHOg71[HOc70`H0_7OcH1V;0000000001O0000000000000000Qam0", "size": [480, 640]}, "area": 6241.0, "iscrowd": 0, "image_id": 10707, "bbox": [265.0, 251.0, 311.0, 229.0], "category_id": 105, "id": 20000625}, {"segmentation": {"counts": "Tn67f>9J8H1O1O10000000O1000000000O1000O11OO100000000000O10O100000000000000O10002N2N2N2N00bA@Z>c0O7J5QBYO[=\\1J0O1000000O10000O100000@nBROQ=l0TCPOn<>hBL;BQ=b0fBIh=7ZBGg=9[BEe=:^BCc=<]BEb=<^BCc=<]BEW=g0iBYOR=k0oBTOQ=m0nBTOP=m0QCSOn<n0RCROl<o0TCQOl<P1TCPOk<P1UCQOn<l0RCSOR=i0oBWOT=e0lB[OW=c0iB]OZ=?fBB]=;cBE`=7aBHb=6]BKf=1[BOh=NXB2k=JUB7[>N4M000000O1L47J[R62dmI5K4K5L4KHkAFT>8<N1NPBH\\=6cBK@0h=3gBN_O1k=1dB6^=HbB8_=G_B;c=D[B=g=CTB0H>U>ElA`0V>41O?A1O000000000000003Mn^?1Pa@2L3M3O1O1O11OO10000O1001ODAWB?h=DVB<j=EQB?o=AoAa0P>8000000O10000O1000000O10000O10000O1000000O10000O10000O1000000O10000O1000000O10000O10000O1000000O10000O1003MO1001O1OO[OdBH\\=E[B:a00V=0iB1V=0jB0X=NhB2[=LdB4^=JbB6_=J_B7c=G]B9d=G[B9e=GZB:f=FZB:e=HZB8f=HZB8f=JXB6h=1QBOn=b0000000O1QMQOmGo0T8QOkGo0V8QOhGP1X8ROfGn0[8ROdGn0]8ROdGl0\\8UOjGd0V8XOmD4R3=S8DSH5m7LWHOh73XHLg77WHIi78VHHj79UHGk7;SHEl7=SHCm7>RHBn7?QHAo7a0oG_OQ8b0nG^OR8c0mG]OS8c0mG]OS8d0lG\\OU8d0jG\\OV8e0mDWOj24Y8i0eGWO\\8i0cGWO^8i0aGWO_8j0hFNX94gFKY96gFIY98gFGY9:^EmNd0h0l9?^EiNg0g0j9b0^EgNi0f0l9a0QEPOV1>Z:>gEC^:6cEKb:N_E3f:F[E;k:\\OWEe0a<1O1O1O1O1O1O1O1O1O1O1O1O1N200O1O1O100O1O1O1O1L4XNTOVER1f:f13M2N2N2N3M2N1O1fNUD]Ol;a0[DYOf;d0_DYOa;f0`DZOa;d0`D\\Oa;c0_D]Ob;b0^D^Ob;b0^D^Oc;a0]D_Od;`0\\D@d;?^D@c;?]DAc;>^DBc;=]DCc;<^DDb;;_DEa;;^DFa;:`DF`;9aDG`;7aDIa;4`DLb;0`D0b;M_D3c;J^D6d;G]D9e;C]D=d;A\\D`0f;]O[Dc0g;ZOZDf0h;WOYDi0o<000000000O11O00000000000000000000000000000000001O00M3]OTOPCn0m<VOPCl0l<ZOoBi0m<\\OQCe0k<jNRCd01c0l<ATC>l<k01O1O0000001O1O001O001O000WOmBET=GoBBO62OLO]=<lBDLLK1d=a0f0001020N101O00001O001O000010O00LcAE^>4Xgg2", "size": [480, 640]}, "area": 13769.0, "iscrowd": 0, "image_id": 10707, "bbox": [14.0, 352.0, 438.0, 128.0], "category_id": 118, "id": 20000626}, {"segmentation": {"counts": "hZY38g>2O00O01000O02O1O1O0O2O1F910000O101O000O2O000O2O001M2O1O2N1O2O1N2N1O2N2NY?0e@3M2nMMWE0RNO_<>ZEAWN010]<d0j17J5J5M2M4M2M4M2M4M2N3L3N2M2O1N2O001N2O000[CgMc<[2001N1O10001O0O10001O4L3M1O00100O1O01000O001O1O1O0N3N2hNSC>j<@]1L4Lbfe4", "size": [480, 640]}, "area": 3628.0, "iscrowd": 0, "image_id": 10707, "bbox": [224.0, 282.0, 96.0, 93.0], "category_id": 121, "id": 20000627}, {"segmentation": {"counts": "kTP69^>9WN_OkDf0W;HSDNYO1e<?\\CF3Hb<V1dCeN_<W1fCgNZ<V1UCgNd01W<X1mChNR<X1k01O10O001O01O10O01O001O00dNbBV1o7lNWMNnJT1W=lNjBQ1W8oN_L0^K0Ie0[=[OXC`0g=LUJDY600000O100O100O100O010O10O010O02N101N2O1N2N2N2N2N_OgA>]>1O1O101N2N3N1N2N3M;F5JfN`BS1c=kN^Bb0Y>H9J[k_1JjT`N3L6N300O20OO11O01O02N1O100O00000001O0N2O1O2O0000001O00000O1O2O000O101N10000010O000001O000001O01O0000001O0000001O01O00eH", "size": [480, 640]}, "area": 2771.0, "iscrowd": 0, "image_id": 10707, "bbox": [409.0, 197.0, 231.0, 255.0], "category_id": 123, "id": 20000628}, {"segmentation": {"counts": "dVV24P>NjB2W=NgB2S>1000O01000O01001MUAOk>1K1]AOR>OPB;OEW>;ZZn6", "size": [480, 640]}, "area": 90.0, "iscrowd": 0, "image_id": 10707, "bbox": [149.0, 344.0, 16.0, 32.0], "category_id": 132, "id": 20000629}, {"segmentation": {"counts": "`VR16g>5_AGV>:fAJY>?O100O100O10000O1000000O10000O100O1000000O100000000O10000000000001O4L8H7I3M00ZR6NhmI9G6I2OO1O2N100N3N3K]AHh>1k\\Z7", "size": [480, 640]}, "area": 1194.0, "iscrowd": 0, "image_id": 10707, "bbox": [72.0, 450.0, 67.0, 30.0], "category_id": 143, "id": 20000630}, {"segmentation": {"counts": "[8e6m8^Ob0^O5K6J5K5K6J5K6J5K5K6J5K6nLgDQ2^;gMkDS2Z;hMnDP2W;kMjDT2W;kMjDT2R<O1O1O1O001O1O1O1O1O1O1O002N001O1O1O1O1O1O1O001O1O1O1O1O1O1O001O1O1O1O1O1O1nNTBn0o=06J5\\OnA0gk_8", "size": [480, 640]}, "area": 4627.0, "iscrowd": 0, "image_id": 10707, "bbox": [0.0, 267.0, 60.0, 213.0], "category_id": 156, "id": 20000631}, {"segmentation": {"counts": "ile17i>00000001O0000000000001OBNnA2V>0dA0b>42N0N2NRo70PQH4L0001O00000000000000000000000000001O000000@MSB3m=4lALS>a0000000O10000O1000JmA_OS>`06N3M3O001O11N010000O01010O2N1K4Njk\\6", "size": [480, 640]}, "area": 682.0, "iscrowd": 0, "image_id": 10707, "bbox": [114.0, 452.0, 88.0, 28.0], "category_id": 165, "id": 20000632}, {"segmentation": {"counts": "QVV34k>5WAHb><O003M;Eo0QO>B:[CPNf;W3^Oj0VOe0eEoJ]9e5E00O100O100O11O0000001O00001O1O2N2N1O2N1O2N2N2N1O2N2N001O001O1O001O1O1O1O1O1O1O00001O3M00O1O1O1N2O1O1N2N2M3N2N20000O10000O10000O10000O10000O10000O100000000000000000000O1O1ZOWGUKi8_4R1O1O1N2O1O1O1O1O100O100O1O100O1O10000000000000000000000000000000000001O000000001O001O001O1O001O1O1O2N2lFRKR8P5iGTKV8n4eGUK[8n4\\GYKc8i4UG]Kk8]5O00001O001O1O001O1O001O001O1O001O001O1O001O001O00O1N2N2N2N2O1O1O1O100O1O1O1O100O10000O100O100000000eMmGiMS8^1lH\\NT7\\1ZI^Nf6]1eI]N[6a1lIZNT6d1TJVNl5h1\\JRNd5l1dJnM\\5Q2jJQNo4m1YKRNb4l1Z4N2N2O1JlB^NV=`16NjBcNm<]1TCbNO1[15\\9X1dEiNg06a9Q1hEPO=6g9k0lEUO46l9e0oE]OK5R:>nEIF0X:7mE`1n9aNlEh1P:h1L4L4L4L3MM3M3N2M3N2M3N2M3M3NYG]KU7`4jHdKV7Z4kHgKU7U4nHlKR7P4QIRLn6k1_GDf1cNk6e1fG@c1lNg6`1nG\\O^1VOc6Z1WHWOY1A`6T1^HTOU1I]6o0fHoNQ13Y6j0mHkNn0=T6c0WIhNg0g0dMYOX8S1XLB^K\\OY8o0aLE_39hLCW3;PMBn2<YMZOl2e0h5N2N2N2N2N2O1N2N2N2O10000O1000000O10000O10000O1000000O10000O10000001O00000000000000001O00000000000000001O000000000ZCO]:1`E2`:N]E6b:KZE9e:IVE;i:GQE=o:FkD>T;EfD?Y;DaD`0^;B]Da0c;BWDb0h;ARDc0m;@lCd0T<U1O1O1O001O1O1O001O1O1O001O001O001O001O001O001O00001O001O001O001O001WHiLg3W3RLPMn3Q3kKTMV4l2dK[M[4f2_K^Mb4b2ZKcMe4^2VKfMj4Z2QKkMo4V2lJPNR5P2iJVNV5k1eJZNZ5f1aJ`N^5d1ZJ`Nf5f1PJ^NP6g1fI^NZ6h1\\I\\Nd6i1RI\\Nn6j1hHXNZ7f41O100000000000000O1O10000O100O10000001O0000000000001O000000000000000000001O00", "size": [480, 640]}, "area": 59311.0, "iscrowd": 0, "image_id": 10707, "bbox": [218.0, 0.0, 422.0, 240.0], "category_id": 172, "id": 20000633}, {"segmentation": {"counts": "0`7`7N200000000O100000000O100000000O100000000O100000000O100000000O100000000O100000000O100000000O100000000O100000000O100000000O100000000O100000000O100000000O100000000O100000000O100000000O1\\MmGkK000X2?fMS7W4^HkK0O13OP2?mMR7V4_HkK0O040o1?nMQ71`Hk30TL000O040o1?nM]7e3THZL1000O40n1a0nM]7c3SH]L0000O40n1a0nM^7b3RH^L0000O31o1`0nM_7a3QH_L000O01O01R2`0nM`7a0]Hm1CaM11OO01O01R2a0mM`7;dHQ2CeMH0O01Q2b0nMc8[2WGeMD10Q2b0oMS9g1lFXN@1Nn1c0RNT9a0eF5;YO[Om1a0TNb92gF5Oe1ISNh;k1YDUNf;l0fCUO0h0d0WOf;k0lCUOIf0e0ZOi;h0nCK9]Oh;h0SDHe<6jC[OZ<`0iC_OX<`0hC@c<3_CMj=0YI3iGOg>a0YA^On5OZ2k0iGUOe50b2S1hGnN2OX50n2^1hGdN1O0Oj40]3j1hGXN0O2Og<Y2WCiM1O0On30Z4Y2gGQN0G10f3Ob4P3gGRM4OMO_;c3`D^L10h2O`5c3gGmL0B1NU20T6g4m1>kEkJc9`5\\FaJ0OV9R6E1UGoIe8Q6[GQJc8o5\\GSJc8m5]GSJc8V6O001O0000001O000000EhGnIX8j5cGVJj8k551O003MO10000000000O100000000000000000000000000O11OO1NoFUJQ9k52O100000000000000O1000000O10000000000000000001O00N200NiF[JW9e5iF[JW9e5iF[JW9e5iF[JW9e52001OhJXGX4h8fKnGEjNn3X9^LhFCm10SNV3X9[MTIT2dM[MX9c0jIZ1X6cNbJ9TLXO0004Z9:UKDeKNL004Z9:gKCY4<V5BcA9f>HWA1m>Oc\\T6", "size": [480, 640]}, "area": 41000.0, "iscrowd": 0, "image_id": 10707, "bbox": [0.0, 0.0, 221.0, 240.0], "category_id": 180, "id": 20000634}, {"segmentation": {"counts": "XX[113000d>0\\A0O13O\\>0bA0O040W:0bI0TL000O040S:;_IEZL1000O40S:=\\ID]L0000O40S:>[IC^L0000O31T:>ZIB_L000O01O01W:?ZFNm1CaM11OO01O01W:`0SF5Q2CeMH0O01W:d1SHhNeMD10W:U2`G\\NXN@1NT:W2]FUN5;YO[OS:c2PFWN5Ok9Q4OOjFVKUO0o8j4kG\\KUOIm8n4kG^KR8a4nGcKo7^4oGQLc7R4XHQLg7P4XHPLh7[4kGgKU8b54ZIfG^6j8bIUGn5T9SJkFe5]9ZJdF2OX5h9fJZF1O0Oj4T:UKnE0O2O]4c:aK_E1O0On3d:QLfE0G10f3Z;YLgD4OMOU3m;jLTD10h2m;WMbD0B1NU2S=F=VN`BX1k=gNVB0Ok0]>E1@dA9]>GeA7[>HgA7Y>IgA7b>O0O2O0000001N100000E6`AJX>1hA;Z>51O003MO0100000000O010000000O100000000O10000000O02OO1NCaA=_>2O0100000000O1000O1000000O10001O0000001O000001O01M20N0^OfAb0Z>^OfAb0Z>^OfAb0Z>^OfAb0Z>1101OhJNbK2\\4d0oFjNn3b0T5^OmFm10SNV3b0Q6j1^IdM[Ma0Z9a2cHaMZ7Y3bGTLXO0004b0R9m3lFeKNL004a0S9`4jF`KU9U51O1O10N14M;DP1QO>B:YLREg2V<^Oj0WOd0cNQB?c>D10O01O0O2O00Xgl5", "size": [480, 640]}, "area": 8050.0, "iscrowd": 0, "image_id": 10707, "bbox": [92.0, 0.0, 145.0, 215.0], "category_id": 181, "id": 20000635}, {"segmentation": {"counts": "`7`0^>o0SOb0^O5K6J5J6K6J5K6J5J6K6J5K6J5J6K5K1O1O1N2O1O1O1O1N2O1O001O1N2O1O1O1O1N2O1O1O001N2O1O1O1O1N2O1O1O1O0O3N3M3M3M3L100000O10O1000O1000O10O100bJbHZ3^7dLeHZ3[7eLiHY3W7eLlHZ3T7dLPIZ3P7dLSIZ3iNWL_7=lIZ3eN[L\\7:RJZ3aN^L\\76WJY3^NcLY72\\JZ3ZNfLY71^JW3XNiLZ70_JV3WNiLZ72aJn2YNQMV71aJb2eN\\Mk63_J`2fN^Mk62_J^2hN`Mi62_J]2hNaMi63_J[2iNbMh63_JY2jNdMh64]JV2mNfMf64]Jo1eMjMV14h63^Jf1dM^NS1Hk65^J^1dMoNo0_Oo64^JQ1fMJi0ROS74]J_2^N\\MV75\\Jb2ZNZMZ74\\Je2WNVM]76\\Jg2SNTMa75\\Ji2QNRMc76[Jk2nMoLh76[Jm2jMmLk76[JP3fMjLo77\\J^3b5cL^J_3_5aLbJ^3^5bLcJa1eMBi7mNbJ`1eMDh7mNdJ^1dMZOOkNi7>dJ\\1dMBl7RO`J[1dMKe7jNgJ[1dM2^7cNnJ[1dM:U7\\NWKY1dMc0n6UN]KX1eMe0l6SN_KX1eMe0l6SN`KV1eMh0j6RNaKV1dMi0j6RNbKU1dMi0j6SNaKS1fMj0i6SNaKS1eMk0j6RNaKS1eMk0j6RNaKb0dMQO2[2h6TNaK?iMoNMQ1O0j6AaK>dMkN220U14Jd6FcKh0eM^N1\\17B`6LcK;bMZO5o0:ZO\\62cIUO`1U1RN@0l0d0ROV6:cITO`1m0YN]1b0RNR6a0bISOa1R1TNY1l0kMm5h0aISOa1b3UO]Li5o0`ISOa1d0UNV2l7TN\\HSOc1b0UNY2j7TN]HROc1a0VN[2h7SN^HQOe1`0UN^2f7RN_HQOe1e3k5[M_HPOf1>VNc2c7PN`HPOf1=WNd2a7QN`HoNh1<WNf2_7[NYJnNYNi2\\7ZNZJmNYNk2\\7XN[JmNYNm2Z7jMfHQOf1k3b5bM_Jl2R5UMmJk2S5UMmJk2S5VMlJj2T5VMlJj2S5gLXITOd14\\N]3g6mMlJh2T5XMlJE^N^1f6nNkJfN^N63V2d6nNkJfN_NO8]2]6[NZIXOb10`N32[2b6oNlJ]ObNd1b6POkJ[OdNe1a6POmJYOaNh1b6oNPKVO^Nk1a6POSKRO]Nn1`6YN[IZOj1=ZNQ2a6oNWKnNXNS2a6nNZKPNQNi05Y2`6nN^KhNRNZ2_6oNaKeNoM^2_6VN]IZOW2FWNj2U6mNeKnMZN0BU3_6lNXLQNXMT3`6kNiKaNfMd2`6lNjKaNeMc2a6lNjKaNdMd2b6kNjKj1V4UNlKj1T4VNlKj1S4aM_IXO^2W3S4]McI\\OZ2W3S4ZMfI_OX2f2fMoL\\6h0kIAZ2b2fMnLU6k0oIE]2[2S4mMdIH[2X2R4oMeIHZ2X2Q4oMfIIY2X2Q4oMfIHZ2Y2P4nMhIHX2Y2P4oMiIHV2Z2Q4nMiIHV2Z2Q4mMkIHT2Z2R4mMkIHS2\\2R4lMkIHS2\\2R4kMmIHQ2]2R4jMnIIo1]2T4jMmIIo1]2T4iMoIHn1_2T4gMoIJl1_2V4gMnIJl1_2V4fMPJJj1`2V4fMPJJj1_2X4eMoIKi1a2X4cMQJKg1b2X4cMQJK`0NXOd2W6bMSJJ>9oN[2a6aMSJH?f0dNQ2k6`MRJG`0Q1[Nh1S7_MoKi0nLh1T7^MoKj0mLh1T7^MnKk0nLg1U7\\MmKm0oLf1W7ZMjKQ1nLf1Z7WMgKT1oLe1c:ZN^Ef1b:ZN]Ef1d:ZN\\Ef1d:ZN\\Ef1d:YN\\Eg1e:YN[Eg1e:YNZEh1f:TN\\En1d:lM_EV2c:dMWEg2i:h00O10000O10000`LVEEMm2n:hMXEQ2h:PNYEo1g:PNZEP2f:PNYEP2Y:XMjEh0MP2Y:^McEb05P2X:cM^E=9P2Z:^NgEa1Y:^NiEa1W:_NjE`1V:`NjE_1W:`NkE_1T:YNVFf1j9[NUFd1l9]NTFb1l9_NTF`1l9aNSF^1n9cNRF\\1n9fNQFY1o9hNQFW1n9kNQFT1P:nNoEQ1m8fMaGZ1Bo0k8jMbGX1Bm0j8PNbGU1Cj0i8TNcGS1Dh0f8YNeGQ1Bg0f8]NeGn0Dd0g8`NdGm0Dc0g8bNdGm0Ca0h8eNbGl0E>h8iNaGl0F:h8mN_Gl0G7i8oN_Gl0F4k8SO\\Gk0H2k8VOWGn0LLk8i1SGWNl8n30UNUGcMj8[2ZGdMe8[2]GdMb89`GKNKa87iGKFM`87QHH_OO`89WHDXO3_8:]HATO4^8:cH@oN5]8<fH^OlN5^8>hH\\OjN5]8?mHZOfN6[8b0QIWOdN5[8d0UITO`N8Z8e0XIRO^N8Y8f0]IPOZN9W8i0aImNWN9X8j0dIlNTN9W8l0hIiNQN:U8o0lIfNnM:U8Q1oIeNlM9R8U1SJbNkM8P8W1XJ`NhM`0f7R1cJ]NgMh0_7j0lJ^NeMn0X7d0TK^NdMT1P7?^K\\NaM\\1j69eK[NaMb1c63mK[N`Mf1]60ULYN^Mf1]61VLYN\\Mf1^62VLXN\\Me1]64XLWN[Md1]65ZLVNXMe1^65[Lc0d3_O\\L`0d3@]L?c3B^L<a3I\\L6d3MZL2f3O[LOd32]LMc34]LKc36]LIc37_LGa3:_LEa3<_LfMiMH`6c2gKdMfMMb6_2jKbMaM3d6\\2kK`M_M7e6Z2lK^M\\M<g6V2oK\\MXMa0g6U2QLYMUMf0h6S2SLUMTMk0R6iN^JX3JTMU2m0W3Q3bJ`Mi0@e4R3^JfMf0YOl4S3ZJlMc0ROR5i5gJXJY5o5^JSJb5e70000001O001O00001MVJoFj5S9001N2O100O1O1O100O1O1O1O1YNnIZJX6]5SKZIT5]6bIdIP8S6QHXJl7e5TH`Jj7_5UHeJi7Z5WHiJh7T5YHPKd7o4[HUKc7j4[HZKd7R6QJjHR4W7lKPIo3P7nKTIP4l6oKVIQ4j6mKWIS4j6lKVIU4i6kKWIV4i6iKWIW4j6hKVIY4i6gKWIZ4i6dKXI]4h6bKXI`4=XJW5W1]Jc48XJ[5U1]Jf42YJ`5P1_Ji4K[Jf5k0`Jl4B`Jm5d0aJg5_5XJbJi5]5VJdJj5]5TJdJl5^5QJcJP6^5lIcJU6m1PIi0h0[MY6i1oHP1e0XM]6e1nHW1b0UM`6`5\\IbJe6\\1mHe1<PMh6^5UIcJk6_5RIbJo6o0kH]23eLR7i0mHl3U7nKmHU4T7eKnH^4R7`KmHc4T7ZKjHj4V7TKjHn4W7oJiHS5X7jJhHX5X7fJiH[5X7cJhH^5X7`JjH`5i7kIXHV6V7iI\\IX6c6RJTIn5j6WJRIj5l6[JPIf5n6^JnHe5o6W1O1O1O1O100OL4I7H8\\NgHZKb7e4kHgJ\\7Y5V1O10O01O0101O00001dMXK^Jh4i4WLQKj3f4dLTK\\3g4oLSKQ3k4VMPKk2m4]MmJc2Q5eMiJ[2U5mMeJS2Z5SNnJ_1Q5iNQKn0n4ZOoJb0P5FjJ9U5JjJ5V5LjJ3Q5UKPKj4OOQ5[KPKd4O0P5_KQKg11e0OT2P5aKoJO3W16Q1HW2Q5lKXKb06W1AZ2R5mK^K86\\1[O^2S5mKcK06_1UOc2S5kKlKI3e1nNg2S5fKdM^1ZMl2R5aKmM_1QMP3`8kL`GV3d8fL\\GZ3i8`LXG`3l8\\LSGe3Q9VLPGj3Q9ULnFl3o8VLSGi3j8ZLVGf3h8[LZGd3c8_L^G`3`8aLbG^3[8dLgG[3W8fLkGX3S8jLoGU3n7mLTHR3j7nLYHQ3d7QM^Hn2`7SMbHl2Z7WMhHh2T7ZMPIS1YO]Nf5oNjKe1[Ob0I]NQ5CcKb1F2:[N]49[K]12Cd6XOQIX1?TOg4lNeKo0nNT1k0cNd4B_Ko0iNP1o7YNTIn0eNl0R8]NTIP1bNf0S8bNVIc36oLi20nLY3LeMm1DVNn2Ci2;^M[Og2c0`MSOe2l0h5N2N2N2N2N2O1N2N2N2O10000O1000000O10000O10000O1000000O10000O10000001O00000000000000001O00000000000hNLdC4Y<4aCM\\<;_CF]<b0^C^O_<G]Cb0OGa<DlCd0^OHd<DVDc0QOIT2EZ80hFl0k0XOY8MTGe0`0AY8L]G>7JX8IiG8J4Y8ETH4^O;Z8BZH6VO;]8@_H8nN<_8]OeH:fN=a8ZOkH;_N?c8VOPI>WN?g8SOTIa0oM`0j8nNZIe0fMa0m8jN_Ig0^Mc0Q9eNdIa2Y6^MjIc2S6]MPJc2l5]MWJc2g5]M[Jd2b5[MaJf2\\5ZMfJg2W5XMlJh2R5XMPKi2m4VMVKk2g4UM[Kl2b4SMaKm2]4SMeKn2X4QMkKo2S4QMoKP3n3oLULQ3i3nLZLS3c3mL_LS3g71O001YJkMaNU2\\1jMhNW2U1gMoNY2n0fMVOZ2g0bM@_2:QM_J2`5m2KSMe0n2SOSMS1m2gNRM`1o2XNQMo1o2kMoL]2R3[MQMi2o2QMRMT3o2dLXM\\3h2^L^Mb3c2VLeMi3[2RLjMn3W2mKmMT4R2gKSNY4n1bKWN]4i1_K[Na4f1YK_Ng4a1UKcNk4^1oJhNP5Y1kJkNU5Z1aJkN_5[1WJiNi5\\1mIiNS6]1cIhN\\6]1ZIhNf6^1PIfNP7e401O0000001O000000001O0000001O0000001O0000010O000000001O0000001O0000001O000000", "size": [480, 640]}, "area": 146305.0, "iscrowd": 0, "image_id": 10707, "bbox": [0.0, 2.0, 640.0, 478.0], "category_id": 183, "id": 20000636}, {"segmentation": {"counts": "0_8i400001OO1000000001O00O11O000000000000001O00O1000000O100O10000000000O10VKdGh4_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_GiLa8o2hGPMX8l2mGTMR8j2QHUMo7i2THVMl7h2WHUMk7i2XHTMj7j2YHRMj7l2Z1N2N2N2N2N200O100000000O100000000O100000000O1000000000000000000001O00000000000000000\\H`M`4a2_KcMcMOQ6^2]LgMUM4]6U2_L[Na3e1_L\\N`3d1aL\\N^3e1bLZN^3f1bL[N]3f1cLZN\\3g1dLXN\\3h1dLYN[3h1eLWN[3j1dLWN[3j1eLVNZ3j1cLYN]3h1^L]Na3d1[L_Ne3b1WLbNh3^1TLgNk3Z1RLhNn3Y1oKjNP4V1mKnNR4S1kKoNU4R1lJUMBm1a5o0hJYMDj1d5m0gJZMCl1e5k0gJZMAn1g5i0gJYM_OQ2j5g0fJYM]OS2l5e0fJYMZOU2P6e0cJWMZOW2R6e0aJL^58^JHb5;[JFd5=YJDf5d0RJ\\On5P4O001O00001O001O001O00001O001O00001O001O00001O001O0000O100000000000000000000O1000000O1O1O1N2N2M3M3K5N2001O00000000001O00O100O100O100O100O1O100O1O100O100O1O100O10]J^I`4b6_K^Ib4b6\\K`Id4`6[KaIe4_6YKcIg4^6WKcIi4]6UKeIk4[6TKfIl4Z6RKhIn4X6PKjIP5W6nJjIR5Z6hJhIX5Z6_JVI3a0^5T7O0000O11OO10000000000000000O10000O100O11OO1001O0000001O000000O100O10000001OO1001O00001O00O100O100O11OO10000O1001O00000000001OO10000O11O00001O0000O100000000O100001O00000000000ZJ`Hd5b701OO10000000000O100000000O100O1001O000000000000001O000000O100000000O1001O1O0000000000000000000000O100O100001O000000O10000001O00001O00N2", "size": [424, 640]}, "area": 142873.0, "iscrowd": 0, "image_id": 10764, "bbox": [0.0, 0.0, 640.0, 273.0], "category_id": 145, "id": 20000637}, {"segmentation": {"counts": "_8i4_80001OO1000000001O00O11O000000000000001O00O1000000O100O10000000000O10VKdGh4_8O00O1MXKfGh4Z83001O1O0000M3000000001O00001O000000000000O1001OO100O10000LTKlGl4T8TKkGm4U830000001ON200000000000000O100000000O1001O000000001O000000000000001O0000001O00000000O100M3O1000000000000N2001jJoGS5S80002NO1O10000O10000NlJPHT5P8lJPHT5P8200001O0000000000000000000000001O0000000000O10000000000000000O1O10000001O00O11O000000O1001O1O001O00O10000001O0000O10000O100O1O10000O1O100000000000000O100001O00000mKYHe2g7YM_Hc2a7[MfH`2Z7hLcIS3]6kLjIP3W6nLnIn2Q6QMSJm2n5PMVJn2j5PMZJn2f5QM]Jm2Q8N2N2N2N2N2N2N2N1O00001O001O00001O001O00001O00001O001O00001cNPE`0P;[OVEd0k:UO\\Ej0d:QOcEm0]:nNiEQ1X;O1O1O1O1O1O1O1O0000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000VOUOWEk0h:VOXEj0h:VOXEj0g:WOYEi0g:WOYEi0f:XOZEh0e:YO[Eg0e:YO[Eg0d:ZO[Eg0e:YO[Eg0d:ZO\\Ef0c:[O\\Ef0d:ZO\\Ef0c:\\O\\Ed0d:\\O[Ee0d:\\O\\Ed0c:]O\\Ed0d:^OXEd0g:^OWEc0i:_OTEb0k:@REb0m:APE`0P;BmD?R;l000O1O100O100O1O100O100O1O100O1O100O100O1O100O100O1O100O1O1O1O1N2O1O1O1O1N2O1O1O1O1N2O1O1O1O1N2O1L4L4K5K5K5L4M3N2N2O1N2N2O1N2N2O1N200001O00001O0000001O1O1O1O1O1O1O1O1O0000000000000000000000000000000000001OO1M3N2M3N2M3M3L4M3L4M3M300O100000000000000000000001O0000O11OO10000000000000000O10000O100O11OO1001O0000001O000000O100O10000001OO1001O00001O00O100O100O11OO10000O1001O00000000001OO10000O11O00001O0000O100000000O100001O00000000000ZJ`Hd5b701OO10000000000O100000000O100O1001O000000000000001O000000O100000000O1001O1O0000000000000000000000O100O100001O000000O10000001O00001O00N2", "size": [424, 640]}, "area": 86705.0, "iscrowd": 0, "image_id": 10764, "bbox": [0.0, 240.0, 640.0, 184.0], "category_id": 154, "id": 20000638}, {"segmentation": {"counts": "mnj26P=6J7SOm0I7J5J4L4L4M3L4L4M3L4L4L4M3J6L2N3M2M4M2N2N3M2N2M4M2N3M2N2N3L3N3M2N2N3L3N2eHPK\\6S5^ISK_6o4[IXKb6k4XI[Ke6g4VI_Kh6b5M4L3M3N2M3M3M2O1N2N2O1O1O1O1O1N2O1O1O1O1N2O1O1O1O1N2O1O1O1O1N2001O00001O00001O001OXO\\HlKd7l3eHSL[7i3jHWLU7g3nHXLR7f3QIYLo6e3TIXLn6f3UIWLm6g3VIULm6i3Z1N2N2N2N2N200O101O00000O100000000O10VOcLiG]3W8cLiG]3V8dLjG\\3V8dLjG[3V8fLjGZ3U8gLkGY3U8gLkGY3T8hLkGY3U8gLkGY3T8hLlGX3S8iLmGW3S8iLlGX3S8iLmGW3S8iLmGW3R8jLmGX3Q8jLnGV3R8jLnGV3Q8kLnGV3R8jLlGX3S8kLjGV3U8mLhGT3X8mLeGU3Z8nLcGS3\\8oLbGR3n2ZLhMMg3f4`NaK]M1R4_4bNcKoL8^4U4dNVL[1k3eNVLY1k3hNULW1l3hNTLW1m3jNSLU1n3kNRLS1P4lNPLS1Q4nNoKQ1R4oNmKP1U4oNlKP1U4nNmKP1T4lNPLS1R4iNRLV1o3fNTLY1n3bNWL]1i3aNZL]1h3_N[L`1g3]N\\Lb1d3[N`Lc1b3`MbJAn1o2a3ZMgJDk1P3`3XMiJDj1S3^3YMhJBm1T3\\3YMhJ@o1U3[3[MeJ]OS2X3Y3ZMeJ[OU2Y3Y3ZMcJYOW2\\3Z3XMWMf2m2WMTMf2R3VMnLi2V3TMkLj2Z3RMgLl2k6O2N1N3N1O1O2N1N3N1O2N1O1N3N1O2J5K5K6J5K6K4N2N3N1N3M2O1N101N2N2000000001O00001N10001O1N2N2N2M3M3L4L3K5N2001O00000000001O00O100O100O101N100OO1N3N1N2O2M2M4M2M3N3L301O1N2O1N2O2M2O1N2O1N5K4M3GTC0SiP2", "size": [424, 640]}, "area": 41455.0, "iscrowd": 0, "image_id": 10764, "bbox": [219.0, 81.0, 265.0, 315.0], "category_id": 183, "id": 20000639}, {"segmentation": {"counts": "Rd[3:];0`MGbI9^6GbI9^6M[I4o5LXHi0P1[Oh63QHU1d0iNZ7`2SH`Mm7U300001O1O00001O001O1O2N1O001O00001O000000O10000O100001O001O1O001O001O2N001O1O00001O001O0O101O001O00010N2O01O0000O2O001O2N001O1O001O001O00001O00001O001O001O0010O0001O1O1O00001O001O1O1O001O00000000001O00O1L400L4O1O10000O11O0000001O4dNmFTOJLO=]9`0ZG_Oh87aGHb83aGLg8K\\fl0", "size": [375, 500]}, "area": 10676.0, "iscrowd": 0, "image_id": 10977, "bbox": [293.0, 233.0, 128.0, 113.0], "category_id": 98, "id": 20000640}, {"segmentation": {"counts": "bc\\38`;6I0001O001O0001MDiD=h:H\\ENM9c:O\\EK06c:g0O0O200N3N1O0O2O00001N11O1O1N3N000O2O0001O00100O001O010O2N00100O00010O001OUOgE8Y:GhE9Y:FhE:W:FiE:W:FiE;V:EkE:V:EjE;V:EkE;T:ElE<S:CnE=S:BnE=R:CnE=R:CoE<Q:DoE=P:CPF=P:CQF=o9BQF`0m9_OUF`0k9@UFa0j9_OVFb0i9^OXFa0h9_OXFb0e9@[F`0g9^OZFb0e9^O[Fb0e9^O\\Fa0d9^O]Fc0b9]O^Fc0b9]O_Fb0a9^O_Fc0`9]OaFb0_9^OaFc0^9]ObFc0^9]OcFc0]9[OdFe0_9XObFh0]9UOQFOb0l0^9TOeFl0T:0O100O0003NO010O100O010O0001O000010O00O2K401K4O2N10001N11O000002O3GfTn0", "size": [375, 500]}, "area": 3987.0, "iscrowd": 0, "image_id": 10977, "bbox": [296.0, 197.0, 121.0, 81.0], "category_id": 107, "id": 20000641}, {"segmentation": {"counts": "laP33e;0kN5kELM4j9`0VF]ON5_9l0^FlNO701X9X1hFkN0Mo8\\2D<E;F:H8C=E;fMZKWMR5i2nJjL_5V3aJ_Lk5`3UJRLY6n3gIiKb6V4_I`Kk6`460000000000000000000000000O1kNcKPK]4Q5iKhJI@Q4h5]LaJP4_5WLZJi3g5S1010O0O1OO[JRJe5`5`JdJNM`5\\5hJcJK0\\5]5f000O20OO10001N100iJ[J\\4e5dK]LNXMa0O900\\6XOaNOSK2_6NV5O0000b]P2", "size": [375, 500]}, "area": 9505.0, "iscrowd": 0, "image_id": 10977, "bbox": [263.0, 26.0, 61.0, 242.0], "category_id": 109, "id": 20000642}, {"segmentation": {"counts": "m0l9k100000000000000000000000000?A01O000000000000000000000000000000000000000000000000000A?00000000000000000000000000?A00000000000000000000000000A?00000000000000000000000001>AdFmNT8S1kGoNT8X1dGnNW8]2L3M4LO1000000N21O0ZHTMXO0d0Hk00_N0^4T3[LcNB\\N=d0R3\\2cLmLLh2JVNf3T2eLU1\\3jNdLV1]3kNbLU1^3kNaLW1^3jNaLW1^3jNaLV1_3kN[LlLMY4h3lNYLZ1g3hNWLW1m0cKk1h7RN[Hn1e7QNmH^O\\OO6a0HFi79UIZO\\OO0f0GHi7CUH523i1KVNN06a8JcG6j1IRN01002i84ZIIR76oHJP76Y2O3MRgZ3NQYeL00000O1WK1iM0X23VI3o01TOJLOX20U4P1`JGZ1YOi3_1cJAc1PO3N9=kM\\11\\Og1mNO9\\NS2FEO_Od1`N;<TN[6a1YILo73RH\\O_8d0aGiNR9W1nFXNc9h160000000000000000001O00000000000000000000000O10000000000000000000000000000000000000000000000000000000000000000000000001O000000000000000000000000000RO", "size": [375, 500]}, "area": 58276.0, "iscrowd": 0, "image_id": 10977, "bbox": [0.0, 29.0, 500.0, 331.0], "category_id": 112, "id": 20000643}, {"segmentation": {"counts": "eZY1230];0bD100Z;OeD9X;GhD8Y;30O1NfDGZ;9110000O1O002N100O100001O010N1000O1O1000O100000001N1000000O100000O01000O100O100O11O000O0100O110O01O000O11O000000N1NgDGZ;93O000O01000O1000000O2OO0100000000O01000000O101O1O001O1N101O2N000mDCl:=SEHh:9XEIf:6\\ELa:4^EMb:3^EMb:3^EMb:2_ENa:2_ENa:2_EN`:3`EM`:2`EO`:1`EO`:1`EO`:1`EO`:1\\E3c:N\\E3d:`000O1000000000000O1000000000000O10000000000O1000000LlNeET1\\:31O0WOjE1V:FTF9f:O1O2N1O1O1O001O00001O00001O000000O100000000O100000000O1000000O100000000Y_[2", "size": [375, 500]}, "area": 2967.0, "iscrowd": 0, "image_id": 10977, "bbox": [112.0, 303.0, 181.0, 42.0], "category_id": 117, "id": 20000644}, {"segmentation": {"counts": "oiW13a;3Ob0iD\\OO6e9f0UF\\OO0k9`1N4L4L1O1O1JkMiFV2V96O001O1O000O11O010O100001O001O0000O1O100000000000O2O0000000001OO11N10000O100O100O02O0000O010O11O001O000O100000000O01001O0000O100000000000O2OO10000000000O1000000O100000O1000O10000000000O10O100000000000O10000000O10O10000O1000000000O10O1000000000OoNnF_OR9=VG^Ok8>\\G_Od8?aG^O_8a0eG\\O[8c0hG[OX8e0iGZOW8e0kGYOU8h0lGWOT8h0oGVOQ8j0PHUOj7LRGn0X1SOf7ORGn0]:RObEn0_:ROaEn0_:ROaEn0^:SObEm0^:SOaEn0_:ROaEm0`:SO_En0a:4000I`EWO`:j0`ETOc:9^EM4J`:6]EN5L_:1YEO7010`:0cE0M0[;O1000000000000000fXX11YggN010O0010O010O00010O0010O00010O010O0010O0010O0001O00Vln0", "size": [375, 500]}, "area": 11437.0, "iscrowd": 0, "image_id": 10977, "bbox": [108.0, 222.0, 307.0, 104.0], "category_id": 123, "id": 20000645}, {"segmentation": {"counts": "icX12d;1O100O1O100O1NJcD6];200000000O11O0000000000001OO10000000000O100000000O1000000O1000000O1000000000000000000O10000001O000000O11O000000N2MBlD?T;20000O10000O1000000O100O100000000O100000000001O1O001O1O001O2N000EkD4U;KPE1P;OREOm:3;O1000O1000000O01000000L4OiPT3", "size": [375, 500]}, "area": 1458.0, "iscrowd": 0, "image_id": 10977, "bbox": [110.0, 322.0, 116.0, 23.0], "category_id": 131, "id": 20000646}, {"segmentation": {"counts": "[gS1120a;6G823JK5K31O2N2N2O000001O000N3NmT]4", "size": [375, 500]}, "area": 206.0, "iscrowd": 0, "image_id": 10977, "bbox": [97.0, 206.0, 17.0, 22.0], "category_id": 132, "id": 20000647}, {"segmentation": {"counts": "mUZ4_1X:O100000000N2K5O11O2B=N2M2J7O1O4L2N5KO1000000000000000\\GhMi7X2VHkMh7T2XHmMh7S2RHTNm7l1QHZNC\\OV8Z2UH^Nj7c1QHbNo7b210000000O01O11oGeLd70VH_2=kMn73SJFR62Xal0", "size": [375, 500]}, "area": 3283.0, "iscrowd": 0, "image_id": 10977, "bbox": [377.0, 103.0, 45.0, 124.0], "category_id": 133, "id": 20000648}, {"segmentation": {"counts": "kbV4292m:?H^OTEe0k:4M3O1001O00M3J6O101O3M0fEgNV:Z1iEgNT:_1K5O11O2M2O1O001O1O4L2N5J010000000000000O1CaEB_:=dEA[:?fEAZ:9mEFS:8SFCn9;h0K`]P1", "size": [375, 500]}, "area": 1584.0, "iscrowd": 0, "image_id": 10977, "bbox": [367.0, 164.0, 44.0, 56.0], "category_id": 143, "id": 20000649}, {"segmentation": {"counts": "]Zm01f;00000O2O0YE8_9H`F:_9MYF9b9m01M3L4001O1O1O1O1O101O0O2N1O1O2N1O1O1O1O2N002N0000000000000000001O0000000000000000000000000000001O00000000000000001O000000000001OOE^FcNb9\\1fF]NZ9a0_1L400000000000000000000001O0000000000000000000000000000001O00000000000001N10000000000001O00000000000000001O000000000000000000001O000000000000000000000000001O0000l0TOe0[O000O2O0000000000000000000000000000000000000000000000000000000000000000000001H7C=E;B>G9F_j<d0WUCR2REXMa0O90`6m4gHSK20]6V5O1O1O2N00001O0O11O0001O00000O2O0000001O00001O000000001O0000001O0000001O00000000001O0000000000_OPJUKV6i4=J\\IVKg6i44M3O1001O00M3[Ne10000000000000000000OE<O2M2J6000000000000000000000000000000000000O1hMdFU2\\9kMdFU2\\9kMdFU2\\9kMdFU2\\9jMeFV2^9O10000000000A?_Oa0]Oc0_OfRk0", "size": [375, 500]}, "area": 21980.0, "iscrowd": 0, "image_id": 10977, "bbox": [80.0, 28.0, 346.0, 183.0], "category_id": 172, "id": 20000650}, {"segmentation": {"counts": "dYP15_;6I8LO2O1O1O1M300O`ENXO0d0H[88hG_1B\\Nd0=h5FfIJLh29gMV6DfIZ32jLZ6JdI^3MkLa6GaI_3InLV7R3kHoLT7P3nHPMQ7o2QIPMj6ClH\\3<PMg6V3YIjLg6V3YIiLf6Y3ZIgLh6X3WIhLi6X3WIhLi6X3UIkLFA52b6c3aIoLIAf6h4000O10000O2N1O1O1O1O1N200O10000000000000001O000000000000000O2O0000000001OO11O00000003M>B4L1N2O1_LTHU3l7jLUHV3W8N5L3M003M00000O100000000O10000000000000000000000O10000000000000000000000000000O1000000000001O00000O1000O1000000000000000O2O00000000O1000000N1L5mNeMbHc2^7k00000000O100000000000000O10000J\\HYLd7g3\\HYLd7g3\\HXLe7h3[HXLe7h3[HXLe7g3\\HYLd7g3\\HXLe7h3[HXLe7h3[HWLf7i34000000J6B>C=E;G9D<E;F:H8C=EXna2", "size": [375, 500]}, "area": 20554.0, "iscrowd": 0, "image_id": 10977, "bbox": [88.0, 69.0, 188.0, 181.0], "category_id": 176, "id": 20000651}, {"segmentation": {"counts": "bSh14b;8gNS1K403M>B4L1O1O1kFhMi8X2VGiMj8c2O4L3M012M000000000000000000000000000001O00000000000000O10000000000001O00000000000000001O0001O0000000000O10001O000000000001O00000000O10001O0N2KQ1QNhF_OU:Lk0000jSo2", "size": [375, 500]}, "area": 7445.0, "iscrowd": 0, "image_id": 10977, "bbox": [153.0, 44.0, 87.0, 98.0], "category_id": 181, "id": 20000652}, {"segmentation": {"counts": "j`h26];8H7K5L4L3N1N2O1N3N1N2O1N100VFlNQ9T1fFVOY9e1O1N3N1O1O1O0O101O00001N10001OO1000000O100000000O1000000O100000000O1000000O103Mc0]Oc0]Oc0\\Oa\\`0@oc_O10001O00001O001N10001O001O00001N101O00001O001O000O2O001O00001O001N10001O00001O001N10001O001O00001N101O00001O001O0O101O001O00001O0O2O00001O0OL4_Ojjm0", "size": [375, 500]}, "area": 5281.0, "iscrowd": 0, "image_id": 10977, "bbox": [241.0, 214.0, 177.0, 126.0], "category_id": 183, "id": 20000653}, {"segmentation": {"counts": "dcb1<f>N000000000000001O0000000000000000000000001O000000000000000000000000000000000000000000000000000000000000000000001O00000000000000000000000000000000000000000000000000000000000000000000000000000000000000001O0000000000000000000000000000000000000000000000001O0000000000000000000000000000000000000000000000000000000000000000000000O10000000000000000000000000000000000000000000000O100000000000000000000000000000000000000000000O1000000000000000000000000000000000000000000000000000000O1000000000000000000000000000000000000000000000000000000000000O100000000000000000000000000000000000000000000000000000000000000O100000000000000000000000000000000000000000000000000000000000000001O000000000000000000000000001O00000000000000000000000000001O00000000000000000000000000001O00000000000000000000000000001O00000000000000000000000000001O0000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000O1000000000000000000000000000000000000000000000000000000000000000000RN?]DAQ;`2^Ob0@`0", "size": [480, 640]}, "area": 4191.0, "iscrowd": 0, "image_id": 10995, "bbox": [107.0, 360.0, 533.0, 120.0], "category_id": 93, "id": 20000654}, {"segmentation": {"counts": "0PhQ13mfnN8H8H:F7I002N7I9G<D;E>B9G:F9G:F:F9G`1`N3MOaGRJn7`60>Bc0]O9G7I6J4L9G5K8H9G7I5K6J5_Oa0I7I2TLoDf3^;F:G7H:F:G8G8H<E9F:F8I2M3M3N2Md_^1?l_aNR1SO8HN1?E2Fh0oNn0WEhLh8Y5_O`0@9G;E;E7I9G8H5K7I5K6J6J2N9G5K<D8H6J0000O10000O100O100O100O100O10000O100O100O100O100O100O100O100O100O100O100O100O100O100O100O100O1O100O100O100O100O100O100O100O100O100O100^OfJ]G[5a8iJ]GW5Q8gJoG5MU5b7hJ_H4K40^>N2Mblb4", "size": [480, 640]}, "area": 75666.0, "iscrowd": 0, "image_id": 10995, "bbox": [0.0, 0.0, 326.0, 480.0], "category_id": 109, "id": 20000655}, {"segmentation": {"counts": "RYf42l>4\\ONUB5h=1QB4k=c0N2N1OO100O100O100O100O100O100O10N1C>0O100O1O100O100O100O100O100O100O01mNR1100O100O100O100O1O100O100O100O100O1O100O1O100O1O100O100O1O100O1O100O100O1O100O1O100O100O1O100O1O100O100O1O100O1O100O100O1O100O1O100O100O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1OM4[OJ[B:W<V1dCmNh:_300001O1O1O1O1O1O1O1O100000000001O0000000000001O0001O00000001O000000000O110O0000000000001O0000000001O01N1O1O1000000010O0000000010O000000000O101N100O100001O010O000000001OO20O0000000000010O0000000000010OO1001O000001O0001O00000001O000001OO100010O00000000O110O000001O1ON2010O1O1O00002OO01O00010O001O01O0000001O0000001O0000001O00000010O000001O0000001O0000001O01O0001O0000001O0000001gL", "size": [480, 640]}, "area": 30251.0, "iscrowd": 0, "image_id": 10995, "bbox": [320.0, 87.0, 320.0, 217.0], "category_id": 123, "id": 20000656}, {"segmentation": {"counts": "nkX5b0m=a000000000000000O100000000O1000003M8^B7Z;i1M30000000000000000O100000000000TNiDOW;IT2L40003M1O1O1O001O1O1O003M3M8H2N1O3_OaA004biY3", "size": [480, 640]}, "area": 3331.0, "iscrowd": 0, "image_id": 10995, "bbox": [360.0, 100.0, 55.0, 116.0], "category_id": 130, "id": 20000657}, {"segmentation": {"counts": "dlc12m>8I7H6K7H8I6I6K9G7H8I5K0O1O1O104KO2M2N3N1N3005J10O001O10O01O0010O01O10O01O00100O1OO1100O1O01O01O0010O01O3M10F91O100O1O00101N1O00100O1O100O2N1O2O1N1O1O100O0010O02N010O100O10000O100O010O010O010O01001N100O100O101NO2K5N1M4O001N110O0100O10O1O0100O010O01O010002M100O01O0014K7J6I3N9G5J=D8G7JXgP15gPoNV8F00000000000000000000000000000000N2B>00000000000000000000000000000000O1SL]IZNf7U1kHkNU7U1kHkNU7U1kHkNU7U1kHkNU7U1kHkNU7U1kHkNU7U1kHkNU7U1kHkNU7T1lHlNT7T1lHlNT7T1lHlNT7T1lHlNT7T1lHlNT7S1mHmNS7S1mHmNS7S1mHmNS7S1PIjNP7V1XIkL_8R3i1M30000000000000000O100000000000lEQMW8o2aGPOh6P1TITOl6l0TITOl6l0TITOl6o0QIQOo6P1PIPOP7Q1oHoNQ7R1nHnNR7R1nHnNR7S1mHmNS7T1lHlNT7U1kHkNU7U1kHkNU7X1hHhNX7[1eHeN[7c1]H]Nc7e1[H[Ne7f1ZHZNf7i1iGQL004Z2S8\\400000000000000000000000000000000000000000000000000000000000000000L4[OlGYJY8X4WIcKm6h2`3O1O100000000000000000000000000000000000000000000001O000000000000000000O11O0000000000000000000000001O00O1O1O1000000001O000000001O0000000000O100O100O100001O001O000000001OO11O0000000000001O0000000000001OO1001O00000000001O000000000000001OO100001O00000000O11O0000001O1ON2001O1O1O00002N001O00001O001O00000000000000000000000000000000001O0000000000000000000000001O00000000000000000000", "size": [480, 640]}, "area": 52412.0, "iscrowd": 0, "image_id": 10995, "bbox": [110.0, 0.0, 530.0, 418.0], "category_id": 172, "id": 20000658}, {"segmentation": {"counts": "PhQ13m>8H8H:F7I002N7I9G<D;E>B9G:F9G:F:F9G`1`N3MOaGRJn7`60>Bc0]O9G7I6J4L9G5K8H9G7I5K6J5K5K5K000000O100O100O10000O10000O1O1O100O100O100O10000O100O10000O100O100O10000O100O1000000N2000000O1O100O100O1002N00F:00000000O1001O00O100000000001O001O1O00000000O100O11OO10000000000000000O100O100O100O1001O00000000001ON2K5N2L4O1O1N200O100_GfJ\\7Z5`HhKb6X4_IPLW6Q4jImKV6T4jI[Lg5e3]J]L]5c3_JUMl4Q7ZKeFe2\\;^Oa0_O:G;E;G5I9G8G6J8HX[i5", "size": [480, 640]}, "area": 45406.0, "iscrowd": 0, "image_id": 10995, "bbox": [72.0, 0.0, 172.0, 339.0], "category_id": 181, "id": 20000659}, {"segmentation": {"counts": "bRc13k>3L3M3N2M3M3N2M3M3N2M3M3N3L3M3N2M3M3N2M3M3N2M36JO101N1O1O100O1O1O100O1O100O1O1O100O1O1O100O1O100O1O1O100O1O1O100O1O100O1O1O100O1O1O100O1O100O1O1O100O1O1O100O1O100O1O2O0O100O10000O100O100O100O100O10000O100O100O100O10000O100O2O0O100O100O10000O100O100O100O10000O100O100O100O101N10000O100O100O100O10000O10O0100O100O100O10000O100O100O100O100O100O100O100O010O100O100O100O100O100O100O100O1O100O100O100O10O0100O100O100O100O100O100O100O1O100O100O100O100O010O100O100O100O100O100O100O1O100O100O100O100O100O100O100O10O0100O100O100O1O100O100O100O100O1O100O1O100O1O100O100O1O100O00100O100O1O100O1O100O100O1O100O1O100O100O1O100O1O100O100O1O010O1O100O100O1O1O1O1O1O1O2N1O1O1O1O1O1O1O1O1O1O1O1O100O2N1O1O1O1O1O1O1O1O1O1O1O10001O000001O0000000000001O000001O000001O0000000000001O00000001O0001O0000000000001O00000001O0001O00000000001O0000000000010O0000000000001O0000000000001O0000000000001O0000000000001O00000O1000001O0000000000001O0000000000001O0000000000001O0000000000001O00000000001O0000001O0000001O0000001O0000001O0000001O0000001O0000001O0000001O0000001O0000001O0000001O0jMkIaKU6l3i2^Oc0]OnH", "size": [480, 640]}, "area": 95635.0, "iscrowd": 0, "image_id": 10995, "bbox": [108.0, 213.0, 532.0, 262.0], "category_id": 183, "id": 20000660}, {"segmentation": {"counts": "c<R1d?2M3K6J5L4J6K5O12K3hNaf;DS[D00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001O0000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000ANX@2h?3S@MS`095K4Lhen3", "size": [536, 640]}, "area": 3652.0, "iscrowd": 0, "image_id": 11051, "bbox": [0.0, 372.0, 397.0, 164.0], "category_id": 105, "id": 20000661}, {"segmentation": {"counts": "0c<R1Q32M3K6J5L4J6K5eC[Nb9f1WEBj:U300^Ob0hNX1iNW1lNT1O100O100O100O100O100O100O100O100O100O:F100O100O100O100O100O100O100O100O100O100O100O100O100O1O100O100O100O100O100O100O100O100O100O100O100O100O1O100O100O100O100O100O100O100O100O100O1\\Od0A?N2N2N2M3N2N2N2N2N2M3N2N2N2N2N2M3N2N2N2N2N2N2M3J6D<D<E;D<I7N2N2N2M3N2N2N2M3N2N2N2M3N2N2N2O1000000O1000000O100000000O1000000O1000000O1000000O1000000O1000000O100000000O1000000O1000000O1000000O1000000O11O001O1O001O1O1O001O1O001O1O001O1O001O1O001O1O1O001O1O001O1O001O1O001O1O0UEWOg5j0UJYOk5h0RJ[Om5f0PJ\\OP6d0nI^OR6c0kI@T6a0iIAW6?gICY6>dIE[6<bIF^6:`II_68^IJb67ZILf64XIOg62VI0j61SI1m60PI3o6MoH5Q7LlH7S7JjH8V7HhH:X7GeH<Z7EcH=]7C`H`0`7A]Hb0b7_O[Hc0e7]OVHh0j7YOoGn0P8SOhGT1X8lNaG\\1^8eNZGb1f8_NRGh1n8YNkFn1T9RNeFU2[9lM]F[2c9fMUFb2j9^MoEi2Q:XMgEP3X:h10001O00001O00001O001O00001O00001O001O00001O00001O00001O001O00001O00001O001O00M3L4O1O1O1O1O1O1O1O1O1O1O1001O0000001O0000001O0000001O0000001O001O1O000000000000000000jNoEdKR:\\4PFaKQ:^4VF\\Kj9a4X1M3N2000hKTKdLl4^3WK]Li4d3[KWLe4j3_KQLa4P4cKkK]4U4hKfKX4[4lK`KT4`4QL[Ko3f4TLVKl3k4XLPKg3Q5^LjJb3W5bLdJ^3]5fL^JZ3b5kLYJT3i5PMRJP3o5TMlIl2T6YMgIf2[6^M`Ib2a6bMZI^2f6gMUIY2l6kMoHU2Q7PNjHP2W7TNdHl1]7RNdHo1[7QNeHo1\\7VN^Hk1b7YNYHg1g7]NUHc1l7d310O01O1O010O1O001O001O1O001O1O001O1N101O1O0O2O0O2O1N101O1N110O001eJ\\G`2e8YMfGb2Z8XMlGh2U8QMQHP3n7lLVHT3k7fLZHZ3f7bL^H^3c7\\LbHd3^7XLfHi3Z7QLkHo3V7lKnHT4R7hKRIX4o6bKVI_4_N^K^OCN0a0O^6=ZKS8V3gGoL`8j2YG]Mj8W53M3M7IN2N2N2N2N2N2N2N2N2M3N2N2N2N2N2N2N2N200O10000TL\\GDd8:bGB^8<gG@Z8>lG^OT8`0QH]Oo7a0WH[Oi7b0]HZOd7d0bHXO^7f0gHWOY7g0mHUOS7i0SIROn6m0[IkNe6T1gIaNY6^1jI`NV6`1kI^NV6a1mI]NS6b1PJ\\NP6d1RJYNo5f1SJYNm5f1VJXNj5h1XJUNi5j1ZJTNf5l1`JnM`5Q2jJfMV5Y2UK\\Ml4d2]KSMc4l2gKkLY4U3e4O1O1K5M3M3M3O10000O10000O100O10000O100001O1O1O1O1O1O1O1OO1N2N2N2O1O100O10000O1000000000000O100000000001O001O1O001O1O001O1O001O1O001O0000000000000000001O1O1O1O1UFaM[5`2`JjMZ5W2bJnM\\5S2_JRN`5o1\\JVNb5k1YJZNf5g1VJ]Ni5d1RJbNl5b1mIbNR6d1dIbNZ6_1bIeN\\6^1^IhN`6Y1]IjNb6W1ZInNc6U1YInNf6S1WIQOg6P1VISOi6n0SIWOk6k0QIXOn6i0UH[L2P3g7f0RHfLMg2P8d0mG?Q8U4O1O2N1O2N1O2N1O2N1nLUGUNm8d1ZGZNg8_1aG_Na8Z1eGeN\\8T1kGkNW8n0nGROS8h0SHWOn7b0YH]Oi7<]HCh72_HMe7JaH5d7@dH>a7XOeHg0_7nNiHQ1\\7aNoHf0kLQOZ:LUIQ1jLiNX:HZI\\1fLcNU>X1i0L4L5K4M3L8H9GXT2", "size": [536, 640]}, "area": 110016.0, "iscrowd": 0, "image_id": 11051, "bbox": [0.0, 0.0, 640.0, 536.0], "category_id": 172, "id": 20000662}, {"segmentation": {"counts": "`^h51g`02N2N1O2N2N2N1bAD^<>SC0l<2eB=S<YOkC=Jb0Y<SOjC=Kb0Y<XOfC8Md0X<f0eC\\OW<i0fCYOW<j0fCZOX<h0eCZOZ<h0bC\\O\\<a2O1O2N1O2N1O1O2N1N3N1O1O2M2O2N1N2O2N1O2N1O1O2O0O2O0O1O2N101N1O1O2O0O2N1O1O2N1O2N1O1O2N1O2N1N2OO1N2N1O2N1O2N1O2N2N1N4M2N2N2N2N2N3M2N2N2N2M4M2N2N2N3nK]D^OCN0a0OS2c?]N9G5K5K5JZfS3", "size": [536, 640]}, "area": 13914.0, "iscrowd": 0, "image_id": 11051, "bbox": [352.0, 157.0, 97.0, 207.0], "category_id": 181, "id": 20000663}, {"segmentation": {"counts": "ZU4<U?W1hNX1hNX1iNW1hNX1iNW1lNT1O100O100O100O100O100O100O100O100O100O1O100O100O100O2O0O100O100O100O100O100O100O100O100O100O1O100O100O100O100O100O100O100O100O100O100O100O100O1O100O100O100O100O100O100O100O100O100O1\\Od0A?N2N2N2M3N2N2N2N2N2M3N2N2N2N2N2M3N2N2N2N2N2N2M3J6D<D<E;D<I7N2N2N2M3N2N2N2M3N2N2N2M3N2N2N2O1000000O1000000O100000000O1000000O1000000O1000000O1000000O1000000O100000000O1000000O1000000O1000000O1000000O11O001O1O001O1O1O001O1O001O1O001O1O001O1O001O1O1O001O1O001O1O001O1O001O1]EnN^5R1`JPO`5Q1]JQOc5P1ZJSOe5n0XJTOh5l0VJVOj5k0SJXOl5i0QJYOo5g0nI\\OR6e0kI^OT6c0iI_OW6a0gIBX6?eIC[NROc5\\1oKD]NQOd5[1mKG\\NPOg5Z1jKH^NnNi5[1fKI_NnNk5Z1cKKcNiNj5\\1`KNhNdNh5_1]K0l61QI1o6OoH3Q7NlH5S7LjH6V7JhH8IUNe4d1_K:KSNe4d1\\K<0PNc4d1VKc09iM_4e1QKi0`0bM^4f1jJo0j0ZM[4g1dJW1R1RMX4h1^J]1[1kLV4i1WJc1i8^NoFj1m1\\LQ4j1lIP2V9QNbFV2^9kMZF]2e9cMTFd2l9U2O000VI[Ia3f6_LZI`3f6aL[I]3e6cL\\I\\3e6dL[I[3e6eL]IY3c6hL\\IX3e6gL\\IX3d6iL\\IV3e6iL[IW3e6jL[IU3e6kL\\IT3e6lL[IS3e6nLZIR3f6nL[IQ3f6oLZIP3f6PMZIP3g6PMYIo2g6QMZIn2f6SMZIl2g6SMYIm2g6TMYIk2g6UMZIj2g6WMWIi2i6XMWIg2i6ZMWIe2j6ZKQIb04T4k6[KQIa04S4l6[KQIc02Q4m6]KQIb02P4m6_KQIa01P4o6_KQI`00P4o6`KRIa0Nn3P7bKRI`0Mn3R7bKQI`0Mm3R7dKRI>Lm3S7dKRI?Kl3S7h3MYJPIl1l6YNTIf1k6\\NUIc1j6_NVI`1i6X4O1O1O_JZIR1e6a4O1O1O1O1001O0000001O0000001O0000001O0000001O001O2N2N2N1O2N2N2N1THRIl5P7eI^IZ6d6YIiIe6Y6WIkIh6U6VImIi6U6SIoIk6S6RIoIm6S6oHPJP7Q6nHRJP7P6lHSJR3[Nh0d7SLTJT3\\Nd0]9]OfF>[9BiF9Y9FjF6W9InF2T9MPGNQ92SGIn86WGEk8:YGAh8?\\G\\Of8b0_GYOb8g0bGTO_8l0eGoN]8o0hGlNY8T1kGgNW8W1nGdNS8\\1QH_NP8a1THZNn7d1WHWNj7i1ZHRNh7m1\\HnMe7Q2`HjMa7V2cHeM_7Z2eHaM\\7^2iH]MY7b2_3O101O0O2O000O2O001N10001N101N10001N101O0O10N2M2N3M3M2N3M2N3M3M2M4M3M2N3SEnLP8T3jGWMo7l2jGZMT8h2fG^MX8e2cG_M[8c2`GbM^8a2]GcMa8`2YGeMd8^2XGfMf8]2TGhMj8Z2RGjMl8Y2oFkMo8W2lFnMR9U2iFoMU9T2eFQNY9Q2`FVN^9c4N2M3N2N2N2N2N2N2N2N2N2N2M3N2N2N2N2N2N2N2N200O1000OUL]GCc8;cGA]8=hG_OY8?mG]OS8a0RH\\On7b0XHZOh7c0^HYOc7e0cHWO]7g0hHVOX7h0nHTOR7j0TIQOm6n0\\IjNd6U1hI`NX6_1kI_NU6a1lI]NU6b1nI\\NR6c1QJ[No5e1SJXNn5g1TJXNl5g1WJWNi5i1YJTNh5k1[JSNe5m1aJmM_5R2kJeMU5Z2VK[Mk4e2^KRMb4m2hKjLX4V3e4O1O1K5M3M3M3O10000O10000O100O10000O100001O1O1O1O1O1O1O1OO1N2N2N2O1O100O10000O1000000000000O100000000001O0O2O1O001O1O001O1O001O1O001O0000000000000000001O1O1O1XFXM^5i2]JgMW5Z2eJjMZ5W2aJoM]5R2_JRN`5o1[JWNc5j1YJZNf5g1UJ^Nj5c1RJbNl5e1iI`NV6c1dIbNZ6_1aIfN^6\\1]IiNa6X1\\IkNc6V1ZInNd6T1WIPOh6Q1UISOi6n0TIUOk6l0RIXOl6j0VHWL4S3e7g0RHbLOk2m7d0oGmLJb2V8b0jGb0T8R4O1O2N1O2N1O2N1O2oLSGSNn8g1YGWNi8a1_G]Nb8]1dGbN^8V1iGiNX8Q1nGnNT8k0QHUOP8d0WH[Ok7>[HAh76_HIe7NaH1d7EbH:b7\\OfHb0_7TOgHk0]7iNlHV1Y7\\NRIj0kLnNY:JWIV1hLfNW>V1j0K4L4L5L3L5K9G9GUT2", "size": [536, 640]}, "area": 214508.0, "iscrowd": 0, "image_id": 11051, "bbox": [7.0, 2.0, 628.0, 534.0], "category_id": 183, "id": 20000664}, {"segmentation": {"counts": "[no43k>2HLaA6`>J]A;a>21M03N1L44LODaA9_>GaA9c>1L1SMDVG`0j8DRG<n8BTG?k8D_DMU2MQNb0\\;HbFIRN>\\;GdFLjMN1?a;FdFd0^9@\\Fa0e9_O\\F?e9@[F`0f9^O\\DIk1f0k9@ZDKk1f0j9EXF;g9EYF9i9IUF9i9GXF7i9HXF7i9GYF9g9GYF:f9FXF;i9DVFEQNg0i;BWDGl1m0l9\\OUFf0j9XOWFj0h9SOYFP1k;O12O2M10M2000jNkN]C2P1S1c;oN]DQ1a;kN_C5m0:UO5_<F`CFO0f0>^O4\\<NVDN^O5[<LWDMC4X<MVDMYOF:?U<AaC`0c0KL2n;DcCa0b0JOOj;;VDG0Lk;<WDGOG^OM_<e0UDB2Jm;d0SD^Oh<a0XC@h<a0WC^Ok<a0VC^Oj<b0VC]On<a0QC_On<b0RC]On<d0h0N100O100OO@eA`0U>@lA2N?U>_OmAc0U>^OiAe0U>30105lAQOl=U1K4M10O11M200O2GcNgBO3_1U=dNiBT1a=510N3M20H72N4J4N1OO1OO1M]NlB`1V=aNjB]1X=cNfBZ1^=31M`BfNa=X14O00102BXBAh=>YB@h=a0XB[OOKj=g0<100O2O00N200O1N211ON1B[OVBO9f0_=ZOXB3;b0[=\\OZB1>d0U=_OiBc0W=^O]BJ107f0[=B]BJ5e0_=@]BK4e0_=@\\BL6c0^=A\\BL7b0]=G\\BE2f0b=E\\BF1d0d=K\\B4d=M[B3e=>\\BoNe=Q1\\BmNe=P172N1O4L000000O10000000000000000N22XBjNb=Z10O100\\O[BLf=4ZBKg=4[BKe=5^BHb=7aBG_=9aBG_=8bBDIJe=b0]BJf=6^BEc=:`BE_=;bB@D3h==eB_OC2j=>d00000001O2N003M1O1O003M002N1O1O1OO1N21OGPBDn=<SBBn=?SBAk=`0WB^Oh=d0YBXOh=c0UB]OS>c052N00HhAGY>971O1OM31O000000001O0000M3L4N2O11ON21OO1N2O100O11O1O1O2N0000002bDKm74UHKk77RHKm77kGFnL71IS;:mG0oLGT;9mG3S8LoG3Q8LRH2n7OlG6T8JnG4R8LQH1o7OQH1o7OQH1o7NQH5m7LQH3Q8MlG4V8JjDIS39T8LoG5jLGT;0TH?U;0dA@Y>i0hAXOn=i0PBWOo=ORBi00ZOk=n0100O20LQOUBo0l=1101OM31O11UDnNc9T1]FkNfN0R:W1bEkNU1N_N4`0OY:U1_EnNP19WOD[:W1XEoNW16VOC\\:e1]FiNmNBLN13i:d1]FiNlNEMNm:c1YFVOiNWOQ;`1ZE`N<i0YOWOP;a1[E`NEOa0n0_:d0ZEdN8g0^:j0UE_N;j0_:h0UE`N:h0a:i0QEbN>e0a:Y1`EgN_:Z1_EgN`:Z1`EfN`:Y1`EhNFUO[:Y2iEQOW:S1cEYMOe1Y:[NiEa4W:_KiEa4W:_KiEb4V:^KkEa4U:_KkEa4R:_KkEg4k9_KWFc4d9?0001N1O100^M", "size": [480, 640]}, "area": 11680.0, "iscrowd": 0, "image_id": 11122, "bbox": [341.0, 0.0, 299.0, 254.0], "category_id": 96, "id": 20000665}, {"segmentation": {"counts": "hRd721Ok><G2YCC[N6l;9aDDN4_OO06a;C_D0<a05X1P;dN_D6?W1R;d1N100O2O0M4OM4OZMaL[G5c1IkNb3T8nLjHAcN1?a3P7]LhH0I0c0`0b0CmNNN1108a3c6dLWIIM:OG1OO03121M`0S1HkN26W3m5]LRJl0KVO0<1D2100M`04]Oj0e4b4[K`J320Nn12TN0i00RO57JMOi30lL[5k1eJcM1i0OQO011\\40ZLX5n7gJ\\G2e0W5i7gJaG1OO06e0Q5i7[KWHe4h7\\KWHe4h7\\KXHd4i7]KUHb4n7\\KQHd4Q8ZKQHf4o7XKRHi4l7YKSHg4h7hJfGc0c0c4c7jJlGe0c0`4a7eK_H[4^7iKaHW4^7jKbHX4\\7hKdHU4`7jKlGRO81H0OT5T8kJlGn00SO9Z5j7gKmGRO5W5n7PLmGS4T8lKlGT4S8mKmGS4T8\\10O3NM31001ON01N21O3M2O1N1NO13NN2001OL4O11O00N4KVIPHh6S8WInGf6n7[ITH0Nc6i7bI[Ha6KZIg76YHI3j6d7aIYH^61WIb7`7_HaHX7_7fHaHW7c7jH_Ho6MQIj7P7WHoHi7R7111N1M2WNRHcLm7]3SHcLm7]3SHVL2kNm7o4RHUL4gNl7i3nG[M0mN2g0OE5Bo7R2lGAORO0G9=JA\\8h1aGfN001]O172L5?O]O[88_GU1N_N4`0O7O[O48NM7ME51K8O[85bGP19WOD9NXO88MN;J_O20O427M]82aGW16VOC30@NO8;IDO5a0L@0OOm9X1cFmNBLN13?0@NO71H11O36>Ib9X1\\FlNEMNa01^OM071IO94FO?Mf9W1gFiNWOb04^O20H0a03R:8_E<i0YOWOb0W1_O[9:_EEOa0n01R;HVD8g00X;CQD;j01V;CRD:h02X;@SD>e02h;OXD0i;NXD2h;NXD2g;NZDFUOMg<7cDIa;1kBOi[6", "size": [480, 640]}, "area": 21241.0, "iscrowd": 0, "image_id": 11122, "bbox": [520.0, 120.0, 107.0, 321.0], "category_id": 97, "id": 20000666}, {"segmentation": {"counts": "Y8f4`0kLi7U3XHjLi7T3XHlLi7R3XHnLh7S3XHlLi7S3VHnLk7Q3UHoLk7R3THnLj7T3VHlLk7S3THnLo7iNbGo00c1>eNP8iNcG<OJ1Z2OWM>`1X8`N]G0M00415032]2>fNY2gNXM7f5D]J9OJM^2`0`N_OQO03h2f0XMYOg5K\\J0M065K]2?`NAoN05g2n0^3QOkI?OW1?TOAQO67a2m0j3QO`I50`1>UOi25e3SOTI10V1>Aj24aNSO_3MiJOO20n0>Lk24aNXOWNHa55^J2O2`0c0j25`N]OXNMQ5EmJO13O015a04^O0[3g0^NOXNROW5LhJ016a0N_O105Z3j0^NOWNQO060Ed00c30kK0N0f04]O006X3g0bNl0SNeNU5_OQK007W3i0_Nd1^3[MnJ5V3m0^Nc1h3\\M\\J0_3R1\\Nc1\\NaMb8l0QId1[NcM9FB6o7l0lIe1YNgM62M@J0[7Q1`Jf1UNZM0=73NJJFR7P1jJe1UN]M0<1A0b02Ol6a0kJk1UNbM1D0>O@0b05C]6n0XKX2WNiL0S12UOOD4OJ1n5o0RJQOj1[3VNkL0Q10iN1;1F22i5l0QLS4RN_K0^O15214JR5P1gJUO0L30[1i5bNhJ5H6FX4V1]KjN33Y1\\6dNbI2<1DN3_4U1bKjNY1R7fNmHO:X4m0iKgNO2\\1f7dN\\H3Lj3Y1SLlN\\1S8\\2YIbMQ7fN_G0OZ3_1SNX7l1iHSNX7m1gHQN[7o1fHQNZ7n1eHRN]7m1cHSN^7QOSG^2`1_N^7ROSG_2_1_N_7POSGa2^1WM`NW1n8oNVGc2^1_Ng7a1XH^Nj7b1VH_Ni7a1VHWN`NVO[9c2XHTNaNUOW9g2XHRNdNUOU9j2UHRNT8m1nGmM]N[Og9h2nGkMU8U2kGlMT8T2lGmMS8T2lGmMS8S2nGkMS8U2lGkMU8U2lGjMT8V2mGiMS8W2nGhMR8X2nGgMS8Y2mGgMS8Y2oGgMo7Y2THdMl7\\2UH]MSNJk9i2RHXMSN10Ml9j2VHXMl7h2V200001O000000000000000000000PEVMSO1]:j2`FUMQO4]:f2dFVMnN3_:h2_FYMQONa:i2^F_Mc9`2^FaMa9`2^F`Mb9`2^F^Md9b2\\F`Mb9`2\\FcMc9]2]FaMe9_2[FaMe9_2[F`Mf9`2ZF`Mf9`2ZF^Mh9b2lETMG;\\:a2mETMB0K029e:c2eETME42LM315g:d2cEVME23<e:\\2cEPNMUOV:k2lEQNMTOX:k2mEoMIWO[:j2kEoMIXO]:j2hEPNHXO`:h2hEWNY:k1eEVNZ:m1cEUN[:n1aEWN[:R3500001O1O001OeNfEnMMnN[:S3jEUMM;0[OY:T3nEnLHN2f0\\:\\2mEnLF010Mf0]:[2]FdMb9Z2`FQMUO`0O@\\:n2dFbM^9^2bFcM]9]2cFbM^9^2bFdM\\9\\2dFeM[9[2fFaM]9^2dFaM]9`2bF_M_9a2aF_M_9a2aF`M^9`2cF_M]9a2cF`M\\9`2eF_M[9a2gFSMlN5^:h2nFTMR9l2a1001O00000000000000000000001O00O1000000000000001O0\\EPMX9P3hFQMfN0^:o2kFUMU9k2jFUMW9k2hFUMjNK\\:P3jFXMV9h2iFZMV9f2iFYMY9g2QFQMO1C8\\:g2PFQMMNH108\\:g2PFSMKNH009]:f2oETMKOHO06`:h2lEWMDI34MM19]:f2mERNT:n1lEQNU:o1lEoMU:Q2jEmM0SOl9P3UFlMMXOl9l2WFkMJ[OQ:j2WFiMH\\OR:l2UFhMGZOX:n2QFjMCXO^:n2oEoMQ:Q2QFlMP:T2QFiMQ:W2PFgMQ:Y2RFdMn9\\2RFcMo9^2PFcMo9]2RFaMo9^2RFbMn9_2QFcMm9]2TFcMk9]2UFfMBXOm9R3_FjM_OZOo9l2bFkM]OZOQ:k2cFnL\\OT1P:n1fFRNZ9n1eFSN[9m1fFRNZ9n1bFoL^OT1P:o1QFlL=W1b9k1SFnL<V1a9m1PFmL00`0U1`9R2`FmMa9S2cFeMa9[2`FbMb9]2`F_Mc9a2^F]Mc9d2]FXMf9i2SFiL@O0`0[:i2TFhLAb0[:f2TF^Ml9b2TF]Mm9c2RF_Mm9b2RF^Mn9a2eEgL9i0S:`2cEhL:g0T:a2bEhL=d0C[O\\:\\3PF\\MP:c2mE`MT:`2gEfLMj0]:`2gEbMZ:_2eEbMZ:^2bEdL1n0^:`2bE`M^:a30000000O11ON20_EcK_:_400000O1O12N]OmETLX:j3_GVLl6i3TIbLb6`3ZIlL\\6T3cIVMT6i2lIYMS6g2lI[MS6e2kI^MT6d2iI^MV6b2iI`MV6`2hIcMW6\\2iIfMV6[2hIgMW6X2iIjMV6W2gIlMX6S2hIoMW6Q2hIQNW6n1hIUNW6l1gIVNX6l1eIVNZ6m1aIVN^6j1aIXN^6i1`IYN_6g1`I[N_6d1bI]N]6b1dI_N[6a1eI`NZ6_1gIbNX6[1kIfNT6\\1jIdNV6\\1jIdNV6^1hIbNX6_1gIaNY6_1gIaNY6a1eI_N[6a1eI_N[6`1fI_N[6b1dI^N\\6a1eI_N[6_1gIaNY6_1gIaNY6_1gIaNY6]1jIbNV6^1jIbNV6]1kIcNU6_1iIaNW6^1jIbNV6^1jIbNV6^1jIbNV6_1iIaNW6`1hI`NX6a1gI_NY6a1gI_NY6`1hI_NY6b1fI^NZ6c1eI]N[6c1eI]N[6b1fI^NZ6a1gI_NY6X1dH]L1MS1]2Y6Y1PJeNQ6\\1oIbNR6X1UJfNl5X1WJfNj5X1YJfNh5\\1WJbNj5`1UJ^Nl5b1UJ\\Nl5d1UJZNl5e1VJYNk5d1YJZNh5e1ZJYNg5e1\\JYNe5f1]JXNd5h1]JVNd5j1]JTNd5l1]JRNd5m1^JQNc5P2]JnMd5R2]JlMd5T2]JjMd5W2b3O100000000001O003MO11O00O12N00N21O00000000001ON200001OO1001O0_DcL_;`3O3cD]LW;c3iD_LU;a3kD_LU;a3kD_LU;f30000000001O1O00M3001OK5O100N2O1O1N20000000000001OO1001O2NO1O1O1000000000000O1001O000000O11O001O1O1O00001O0N21OOO21O000O0N3O0O\\LhDe3V;4N5L0O110O1O3M01O000O10O01O11O1N3M2N21NO3N2N1ON20O021L10N`EeKb:X432N20O00000O10JhKbEZ4^:5O2L21ON20O2O00O20NN3O11O0000O2jEWKS:l4O000nEUKo9m411N01O1O11O1NO2000000000nLUKoKl4o3WKoKh4j3SK^I8g2e4k3bKTL^4]3UKSJ>`2\\4W1SKTN5SN?o0]O8l4`2UKWLNT14gNO2MN<2;m0ZOTO2MM202010K14O=m4\\3UKYL2n0:lNi0`0iNFo0K]O?`4[3dKYMZ1oN9:i2]3eK]M^3VOl0`3cKZMb3VOk0_3dKXMe3WOh0_3eK]M`3TOm0^3cK\\Ma3WOj0^3fKZMa3UOmKHj4j3hKWMc3WOkKGk4j3iKWM^3[Ok0]3gKYM]3[Oj0^3iKiL\\OHP42j0\\3lKgL^OHP43f0c0XKY1OjMP1<f3L4;YK^OO_1OdNd65XN7oKKYOIQ70lM:nKEoNMS==[1BbA11OO1l>Nhjd1", "size": [480, 640]}, "area": 171607.0, "iscrowd": 0, "image_id": 11122, "bbox": [0.0, 88.0, 527.0, 392.0], "category_id": 113, "id": 20000667}, {"segmentation": {"counts": "0d1\\=O1MdBaN]=_12001O1O0000LcBcN_=]121O002NNbBcN]=]13001`BbN\\=c1N001O001O001O1O00000000O10000001OO11O1OO11OO1N22N1O001O0000O1000WC]NU<c1hC`NX<a1`CVN1=^<^1`CfN`<Z1aCeN_<n10000000O11O0000001O0000O11O0000000000000000001O00O1001O0000000000O11O00000000001OO10000001O0000O11O00001O001OO1002N1ON20000O11O0000O1000000001O00000000001O00000000000000000000O1001O0000000000001O00000000000000000000000000001OO11O00000000000000000000000000001O00000000000000000000000000001OO1001O000000O1O1O1000000O1jNgCAOSOZ<[1hCiNNK2a0N_O[<Y1eCgN150K0b0O^O[<W1eChN54MMO>OC1NZ<X1mClNJLO?0B0OZ<V1eCjN<0F0O>0C^<U1WDWO[OC_<W1TDkN\\O54I\\<W1UDjN[O6f<o0PDjN\\O5e<Q1SDoNm;R1UDiNTO0g<X1o0O1001O0000VO[B7O\\Oe=>\\BN0Gg=8]B0MIX>7hAHY>LeA67KW>8fAG\\>6<M01Q_k5", "size": [480, 640]}, "area": 17456.0, "iscrowd": 0, "image_id": 11122, "bbox": [0.0, 0.0, 240.0, 89.0], "category_id": 120, "id": 20000668}, {"segmentation": {"counts": "mQm71o>1KOYA2`>N_A1OO06\\><O1O1110N1N2N02J_OiAc0R>]OPBe0Q>5M4N1001ONRORBn0l=3002OO02N102NM31N2O1OO1N21O3M2N2N1ON2iAYOW>h0M1101NM4O02O00N1NVOPBj0Q>UOPBj0j=WOXB0Nh0d=]O`Be0a=\\OZBI3j0d=AYB?e=?1OL6JbNeB_1[=bNdB]1^=3O0O0N3VN]NXFb1h9^NXFb1h9^NkE2kNc1Y;\\NkE4gNa1T:YNPG0mN2g0OE5Bb1U:XN`F0G9=JAn1j9PNQF172L5?O]Oj1R:PNlE48NM7ME51K8Of1W:RNfE88MN;J_O20O427Mc1V:TNlENO8;IDO5a0L@0OOa2Z:bMgENO71H11O36>IT2[:^MeEM071IO94FO?MW2[:aMeE20H0a03c2X:UNfEi1]:\\11000O2O00000000O100O10010O0000000O10001O000000RH", "size": [480, 640]}, "area": 6655.0, "iscrowd": 0, "image_id": 11122, "bbox": [539.0, 252.0, 101.0, 168.0], "category_id": 126, "id": 20000669}, {"segmentation": {"counts": "U2T6f4\\JoNe5Q1\\JmNf5Q1[JPOe5n0\\JSOc5n0]JROc5m0\\JTOe5k0[JTOf5m0YJTOd5n0\\JPOg5o0XJROk5eLfIo00c1>h0m5fLfI<OJ1Z2OWM>c3U6]L`I0M00415032]2>j05cL\\O7f5D]J9OJM^2`0`N_OQO0Y2b0`N^OYOg5K\\J0M065K]2?`NAoN0\\2`0gNBlNM31Ng54YJ?OW1?TOAQO6Z2JfMb0T1@TONLo51RJ50`1>UO1Q16TOZO\\O:@;1[OOU57lJ10V1>A1U13BEiNHM0JY5MiJOO20n0>L2V12C@FOeNa55^J2O2`0c01X1_OjM?_3CnLQ5EmJO13O015a04^O0b0n1Jg1ESLW5LhJ016a0N_O105b0P2Hi1EQL060Ed00c30kK0N0f04]O006`0R2Ga2FeKU5_OQK007>T2GX3o4\\JnJ5>W2DY3Z5\\J\\J0g0[2CZ3MaJi5U2[JZ3JdJ9FB6V5V2UKZ3IhJ62M@J0b4\\2hKZ3F[J0=73NJJFY4[2RLY3F^J0<1A0b02OS4k1TL`3EcJ1D0>O@0b05Cd3V2bLP4FiI0S12UOOD4OJ1T3\\2WMn3ElI0Q10iN1;1F22P3X2WMg5E_H0^O15214JY2\\2kM^6EhG5H6F^1]2bNP7EdF2<1DN3e1Y2eNe7FPFO:_1P2lNZ8E^E3LQ1]2VOi8e0WG[OR9FbD0Oa0]2IX96hFJY95gFLX94gFMZ92gFMZ92fFN[90fF0Z9OgF1Z9MhF2X9LjF4Q<00000000001OO10O11O01O0001O0000O11O0000000000000000010O00O1001O0000000000O110OO11O00O110OO10000001O0000O11O00001O001OO1011N11L5M3M1N110O000O2O00N2O1O2M2O1N2O10001O00000O11O00000000000O1001O01O0000000001O000000000001O00O1000000000001OO20O000000000000000001OO100000001O0000000001O000000000000001O01N02O01O000000O1O1OF^A8b>G_A9a>G_A;0E`>8bAH^>9_AG00a>=00000O1001N2O1O01OO101OOdA@[>?dAB[>?eAA\\>a00O100N21NO22OOO003N000001O1N1@bA=_>CaA<b>003MO100M310OO2N2NObAB^>>N44ML4M11\\OfAb0^>M012OOO2N1100O1O1O2M3M]OhA>OA]>?O202L311OO1O0KgAA[>?32MN320N10N2J^OjAe0U>26KO03M1OO12O10N011O03K02JFfA:[>5002N4L2N]AG_>9`AH`>;100N22\\AE_>=12O1M1000ON0027H3N1000000O11O00010N001MF_A;_>4OBdA:]>EeA9a>1N1O4NN1N4L3L3M^Q70cnH2TANi>6N4ZAF_>;aAG[>;eAE\\>:dAF]>>N22NO1O10100N10M4O100J6N012L5Lnb=0l\\B7J7M3N20O1OO2O000O11M12O01O2M100AbA;`>DaA<d>N12NN0001O1O3M0K5005J100O2N11O1O2N1O1N1N42NN1ON2]OeAa0^>N12M02LbAC_>;41O1O0O1O10O100001O2N0O1M33L2O0001OO1000001ON2N3eA\\O\\>c0O000eA^OX>d020N02N1O02K52K4N00000000bHA;`0CC<;]O@^I8S79_ON<6UO]OSJ>l60kL_OTN5SN?o0]Oe4?SNBWLNT14gNO2MN<2;m0ZOTO2MM202010K14Oj4`0oNBYL2n0:lNi0`0iNFo0K]Ol43nN1YMZ1oN9f4]NQO1]M^3b3`LTOOZMb3b3_LSO0XMe3d3[LRO2]M`3`3aLROO\\Ma3a3`LTO0ZMa3_3`LXOOWMc3e3ZLSO5WM^3j3\\LnN6YM]3i3\\LPO7iL\\OHP4`4\\LnN:gL^OHP4a4XLULFY1OjMP1<h0CWNOi2i4cMlKH^OO_1OdNh36dN^4QNhK>KYOIU48`MC[NR58]Kc0EoNMj4>iKDN4_OO0l4e0\\KP5A[J0<a05l55dIZO11OO1h5;XJ6?a5l0lI_4g7\\110O0100M3001OZMXHdK5c1IkNj7l3fHRMAcN1?g7j2WHnL0I0c0`0b0CmNNN1108g7]2^H]MIM:OG1OO03121M`0S1HkN26_7<WHc0NPOl0KVO0<1D2100M`04]Oj0l8DVG1N]O320Nn12TN0i00RO57JMOk8`0WG@J400d2LcM1i0OQO011_9O_F;`0E@0m=0ZB1>OXi]1", "size": [480, 640]}, "area": 20345.0, "iscrowd": 0, "image_id": 11122, "bbox": [0.0, 65.0, 543.0, 415.0], "category_id": 132, "id": 20000670}, {"segmentation": {"counts": "nZU62o>OO1000000O1M3O1NJZA6e>3O100O11O0000001O000000O1000000O1O1O12NO100000000O11ON2O11OO1O11O00000000JCgA?Y>400N21ON2001O00O11OM3O11O0000O10000000000O100000000O10000000000000000000000001O000000O1001ONTOQBk0o=UOPBl0P>200000000O1K5I700001O00001O001OM3001O1O00001O1O00000000O10000O11O00001O1O00000000\\OjNUC1H0OW1S=iNVC^1h<aNoB45\\1k<iNPCZ1P=fNPCZ1o<gNQCY1n<=000000O10000000000000000000oMSCo1P=OO100O100O10000O1000000O1000000002N00O11O00O10000001OO10000N2M3N2L4O10000M3O100001O00000000O100O1001O00000000O1000000000000", "size": [480, 640]}, "area": 9426.0, "iscrowd": 0, "image_id": 11122, "bbox": [420.0, 396.0, 220.0, 84.0], "category_id": 140, "id": 20000671}, {"segmentation": {"counts": "_Y\\32o>0L4B>O1O001O00001O00LiA_OW>a0iA^OX>c02002NO1L45OH8Mgke5", "size": [480, 640]}, "area": 372.0, "iscrowd": 0, "image_id": 11122, "bbox": [231.0, 0.0, 21.0, 23.0], "category_id": 157, "id": 20000672}, {"segmentation": {"counts": "d1a0^>0N@eA`0\\>3O1000O0O11K]OiAc0Y>21N102OM^OgAc0W>4bCYOVN31NQ<b0nC]Oj18VNLP<LQD=k13^N@;1[OOo:?hF3aNMHMm:3iF5]N>j:^OSE@c1j1Z9kNdFT1]9kNcFU1]9lN^FX1c9gN^FX1c9gN]FY1c9gN^FX1b9hN_FW1a9iN^FX1b9hN]FY1b9hN^FX1b9hN_FW1a9iNaFV1^9kN^F[OeNj1n:iN]F[1b9fN^F\\OiN0L30[1n:VOcF\\O^N33Y1n:WOdF_O^NY1n:XOdF^O[NO2\\1P;WObF]OaN\\1l:XOkFf0T9\\OjFg0W9WOjFh0W9WOiFg0Y9XOiFh0W9XOgFh0Z9XOfFh0Z9XOgFf0Y9[OgFe0Y9\\OfF\\OUNW1T;UOREDg1X1V9QOVEGc1W1Z9jNjD1Z2V1l8hNWGP1cMVOW;KXGl0eMUOe=i0^BUOb=l0:JmA[OW>c031O1O10N002O1O1O0O10201M1I_AJd>1\\A10Mj>2Ta6OXPI1^>O`A4_>L`A3^>1aAN`>811O00N22L3ON200O100NFFRB;m=EnA0K029o=GPB42LM315P>JoA23<n=<WBUO^=l0aBTOb=j0\\BWOd=i0[BXOe=j0XBXOh=o011O2M5LK501O01O10O011LXBnNg=8[B;0[Oh=2WBN2f0l=ZOSB010Mf0Z>OO[OhA`0O@\\>`021OO12N10L3O1O10010O010O2D^A5k>Lof;NSYD1RA0i>4O000YAKe>8O2NO\\OH_B1C8l=H]BNH108l=J[BNH009l=J[BOHO06n=ORBI34MM19j=h01O2N0N]BSOZ=l0dBXOZ=g0cB[Oa=c0_B\\Ob=d0\\BZOh=h0TBXOn=m02M2M3N4M0O110N1002O0O3nAXOd=l0WBZOf=g0XBZOi=IWBT1j=0O1oNkNXDT1g;mNUD0TOT1g<lNVDV1i;lNWDS1h;nNYDQ1a:lN`EOSO4l0P1a:nNgDNK2a0N_O5Q1g0^:TOaD150K0b0O^O5R1d0]:WO`D54MMO>OC1N4T1`0_:CaDJLO?0B0O4U1>]:]O^D<0F0O>0C8U16_:7hD[OC8Q1HPOO0`0c;4^D\\O54I5Q1HPOb0c;2]D[O6?j08b:O]D\\O5?k05d:4aDFk08d:4\\DTO0`0P18e:g0YEZOg:h0XEWOi:h0[EUOoN[Oe;`1ZEXOf:h0VE[Ok:e0PEaN[Oj0d;UO\\D_1>bNFl0Z;TOeDa1<_NDm0Z;TOgD]1:dNAM1n0_;TOdD]1=dN@k0T;ROQE7KZ1?Cd:mNUE3G]1`0De:lNQEg1=[Ob:ROnDb1`0\\Ob:SOoD_1?]Od:n0\\ESOc:n0\\EPOb:POmDQ2?PO@nNR;1nDR20mM=U1f:P1mDkM<V1g:Q1YEoNi:o0ZEmNf:T1YElNd:X1\\EjNg:Q1VE_N1\\OX:3iEV2OXNh:FVER21WNj:[2XEfMf:Y2\\EfMd:Y2[EWMB?T;Z2ZEhMf:T2^ElMb:X2[EiMc:T2aEkM_:S2bEnM^:R2nD\\Mb0a0a:W2\\EkMc:T2^EkMc:W2\\EiMc:W2\\EiMe:X2YEiMg:W2ZEgMg:Z2YEfMf:Z2[EgMc:Y2^EiM_:W2cEgM]:X2fEgMY:X2TEXM>OAa0m:\\2bEeM_:X2cEhM^:W2dEhM\\:U2gEjMZ:W2gEdM\\:]2cEeM[:\\2dEdM\\:\\2_EWMAa0n:X2aElM^:T2dEjM\\:U2bEPN\\:P2fEnMZ:T2dEkM]:V2cEjM\\:U2fEiM[:R2SE\\Me0?Z:U2hElMX:Y2cEgM]:X2cEgM_:[2^EfMb:Z2]EfMd:X2]EkMa:V2^EiMc:X2]EgMc:X2]EiMc:W2^EiMa:W2_EjM`:Y2[EjMd:T2^ElMb:n1TE]M:d0c:P2bEQN]:o1cERN\\:P2bEPN^:T2^EkMc:W2[ETMG`0o:\\2ZESMH8OJ1Mo:n2\\E\\Ml:d2TE]Mk:c2SEYMS;g2oDRME0^;n2a0001OO10000M3M36J1O000000000000001OO1O1MPDUMQ<i24OlCZMP<g2oC[Mo;k20O1O11O002N0000001ON2O1L44LORDSMk;m2UDSMk;Q30M5J8LN23ZMhCU2MQN`<n1cCRNZ<P2gCjMN1Z<U2`0J21N0ORNVCk1h<UNYCk1m<2O02N01N1N2000N1OSCQNk<0UCl1P=1O1N1M50001O001O0000jNXCPO2P1f<0\\C0d<NXCRO5m0:UOh;l0SDSOFO0f0>^Og;i0[DIN^Og;i0ZDIMCg;e0ZDIMYOF:R<b0nCTO`0c0KLd;<RDUOa0b0JO_;:kDFG0];:kDHGOG^OM1a;j0TEIB2J^Oa;g0SEK^O9_;LREK@9^;LSEJ^O;_;LREJ^O:`;LREJ]O;a;NPEF_O<a;MQEG]O<c;LQEk0o:VOoDl0P;TOPEl0P;TOoDm0Q;SOoDm0Q;SOnDm0S;QO]DUO`0h1T;nN\\D[O2N?a1Z;VOUD[Oc0[1^;ZOPDYOe0Y1];^OfDa0[;_OfDa0Y;@lDYOQOo0R<ISE2n:MVE0j:0WEOi:2VEMk:4VEKi:4YEJh:7XEHh:8ZEGe:2nC]OO3_1>b:3RD]OT1i0j:K[E5eNVOm;f0^E4dNWOm;d0bE9[:FgE;W:FaEc0]:]OeEa0[:_OiE=W:AmE=S:CnE<R:DmE=S:ClE9Y:EhE<X:@UDF`1Q1V:YO[DF]1R1X:YO[DCZ1X1[:UOhEm0W:SOfEBfN[1d;TOdEP1\\:POcEQ1]:nNcES1]:mNcES1]:oNUE@A_1[;ROSE@@]1_;SORE@[OOKc1d;nNSE_1m:`NTE`1l:`NTE`1XOPNb;?XEc1g:]NYEc1g:]NWEd1j:\\NVEe1i:[NVEf1j:ZNTEh1l:XNUEg1k:[NTEd1l:[NUEd1l:nM_DNO9f0j1m:mM]D03;b0g1P;nM]D>d0a1S;]NoDc1R;QNVD107f0g1V;QNVD5e0k1T;QNWD4e0k1T;PNXD6c0i1V;QNWD7b0e1_;TNmC2f0m1Z;QNQD1d0P2_;PN`DP2a;oM_DQ2R<PN[CQ2e<PNYCQ2d<72N103J2N24L0O10001OO11O00O1L43K40jMXCP2l<0O100\\OmMZDT2f;lMYDV2e;lMZDS2g;PNVDl1m;WNQDm1k;SNUDn1i;SNSDIJT2S<nMYDV2f;nMUDR2k;QNTDn1l;SNoCD3W2n;VNnCC2Y2o;c01O1N24M01M103M100N112G6N4D;O1O1N01N21OGSNaCk1_<VN_Ck1b<VN^Ch1c<ZN[Cd1h<]NTCd1g<YNYCQ2a0oM^;V2bDjM`;U2^DoM@J40c;X2YDhM;`0E@];0hDY2KnM1>\\;k22O1OM31N1O11O1O01N1000M3L6LO202NO12OO1N2O10O011O10010M1000002FgDiLX;Y3gDhLZ;U3hDjLZ;n2cDnL71IO^;Q3lDoLW;Q3;2N3OJ52N3M000O02N1M1PMTDS3g;3M2011O100NZDkLe;U3300001OMZDlLf;U32001OM31O1OO1O1001O1O1O2N3MO1000000000000O1000000L[DmLe;S3[DmLe;S3[DmLe;S3[DnLd;R3\\DnLd;o2\\DnLj;h2\\DZMf;a2?0000O1O100", "size": [480, 640]}, "area": 43201.0, "iscrowd": 0, "image_id": 11122, "bbox": [0.0, 0.0, 640.0, 185.0], "category_id": 169, "id": 20000673}, {"segmentation": {"counts": "o[e31n>;D<E:F2N2M3N2N2M3N2N2N2M3N2N2M3N2N2M3N2N2N2O1O1O1O1O0000000000000000000000000001O0O10000000000000000000000000000O1O2N2N2N2N2N2N2N2N2N2N2N2N2N2N2N2N2N2N2N2N2NXTa4", "size": [480, 640]}, "area": 5176.0, "iscrowd": 0, "image_id": 11122, "bbox": [250.0, 163.0, 80.0, 81.0], "category_id": 183, "id": 20000674}, {"segmentation": {"counts": "aU82c;2O1N2N2O11N2N2N1O2NSmZ5", "size": [375, 500]}, "area": 59.0, "iscrowd": 0, "image_id": 11149, "bbox": [22.0, 111.0, 11.0, 10.0], "category_id": 104, "id": 20000675}, {"segmentation": {"counts": "0V1a:E;D<E;00001O000000001O00000mEKd86ZGKf85XGNg82XGOh83UGOj84QGOn84nFMR96jFLU97gFKX9Z101O11N01dGVNk6R1eGC]1^On6l0hGEY1AP7f0jGIS1DS7`0kGMP1EZ77jG3i0I^71jG7e0Ja7J_GH=c0a0Le7HoG<:Km7FhG`0;CU8KaGa0:Cn8>RG@P9?QG@n8b0RG\\Oo8e0PGZOm8j0SGUOn8l0QGTOo8m0d0UOaE10j0\\:=O1N101O1N1O2O1N2G\\NXF1OO0e1d9=nFnM_8Q2`GPN\\8T2cGmM]8S2bGnM[8U2eGkM\\8T2cGnMY8U2fGkMZ8V2fGjMX8X2gGhMZ8X2fGhMZ8MZGW2;mM_8S2aGlM^87ZGD1d16`N`8:]GW12_Na84hGY1FbNf81hGLZO10Y17hNf9NTF00X15iNn9X1QFhNP:Y1nEfNT:\\1iEdNW:^11OO001`FfNb8[1[GhNf8V1YGlNe8V1ZGkNf8U1YGlNg8R1[GnNb8FkFb00GP12T85\\HMd73ZHOd73ZHOf71oG:R8ElG<U8DhG?Z8_OdGb0Y8BeG`0_8\\O`GBZOn0V9@fG>Z8CfG=]8@bGa0^8_ObGa0^8_OcG`0]8@XGBGn0Q9@WGCIl0P9AVGDKj02TO`8<bGGMi0OTOc8<`GHNj0LROg8<^GI0i0k8^OTGJ2i0g8^OVGJ4i0BPOP9<YGL5<AGT9NWG055HHn80WG53L0Kh81WG;0Bc92[F`0V:^OkEc0e:2M?B5J9G9H2NL4M3M3M3L4M3M4L4L3L5Lhk5BfTJ<D:F3M2N2N3N100O101O01OO101N1000001N12N002N10O000O1100O0001N3N100O2M2N2N3M2N201N1O2N1N2O2N1O7H;F:F:FeN_HgNZ7V1mHgNR7V1VIaNkNDP8g1\\IbNg6[1aIaN^6\\1iI`NW6^1S2O0O100O1O2O0O2O0O3NO00BkE\\OU:d0lEZOU:f0kEYOW:e0jE[OV:e0jEZOW:f0<11N01000000N20000O100001OO1O20O1O2N002ON1M31O3NO0O1002O2M013M1N2O1O1NTHD^4<VKDVM2d7:TKGWM0d7:TKFSM5j75QK2o4NPK3Q5KoJ7Q5HnJ:R5DoJ<R5AoJ`0T5[OmJe0R811O1MoD^OP80PKa0PM_Oo72QK>PM@o74PK;QMAQ83oJ9QMEn75PK5RMFn76PK3T5NlJ1T5OmJ3P5MoJ4Q5LoJ4R5LmJ4d4DkH8a24d4DiH:b22f4DeH=e20e4EbH>h2Lg4F_H`0j2Jg4<XKEg4=XKCg4>YKBg4>YK@i4`0VKAj4`0UK_Ol4a0TK@j4N_H6g2MZMC42n61TI<`2;^4XORI>[2>c4SOSI?V2c0f4nNTI?Q2g0m4iNRIa0l1k0Q5eNRI`0k1l0S5eNRI?j1m0T5dNRI?i1n0U5cNRI`0g1o0V5bNRI?h1o0V5bNRI?g1o0X5bNQI?f1o0Z5bNPI`0d1o0\\5aNPI`0d1R1Z5^NQI`0d1V1X5ZNTIa0b1[1XNRNj62\\Ia0a1_1S5PN\\Ia0a1_1S5QN[I`0a1`1T5PN[Ia0_1a1U5nM\\Ia0_1a1V5mM[Ib0^1b1V5nM[I`0^1c1W5mM[Ia0\\1c1Y5lM[Ia0\\1c1Y5lM[Ia0[1c1\\5kMYIc0Y1c1^5jMYIc0X1d1_5iMYIc0X1c1a5iMWId0W1d1b5hMWIe0U1h0gNZOl6ZOYIc0T1e0kN_Og6YOZIc0S1a0ROBa6ZOZIc0R1>WOFZ6[O]Ib0P19^OIY6YOZId0o06CLS6[O[Ic0n03HOn5\\O\\Ic0l01L1k5[O]Ic0k0O14f5YO_Ic0j0N45c5ZO_Ic0i0L:5fNXOg63`Ie0g0J`04bN[O03`6OhId0e0Je0=m4UOiId0e0Gj0?g4VOkIc0c0Go0?b4WO\\J414V1?]4YO_K67`0Z4ZO^K6;>W4\\O]K5`0=R4_O]K4d0;o3AlILW16R1;j3DmIKV16V1:e3GmIJV4>m1GmIKX4=k1GmILY4=i1GmIM\\4:h1GdN7\\1GiIOk4;]6O22MO21N00O2N1O20OO21N100O1O01O001O0100OO[JKZJN\\5692GO63JN44KMKGlJ?Y5KHHmJ?[5JFGoJ`0[5IE?:CD=<CD>;DD>:@He02\\OIh07XOHj0VKiNm4>Ml03UOMj03VOMi04ZOJe06[OJf0eLgNP2e0Y1c0gLjNo1d0V1e0jLiNo1b0W1e0iLkNo1`0X1e0iLlNn1>[1e0eLPOo1;[1f0`LVOT24\\1f0ZL]OY2M]1f0TLD^2F^1f0mKLd2^O_1f0gK3i2WO`1e0bK;m2PO`1d2_N\\M`1f2^N[Mb1_O_KL0o2l2hMi1WO^K0NS3h2gMU2[2fMhMZ2Y2cMhM^2Y2^MiMb2Z2ZMgMf2[2VMgMj2[2SMeMo2]2lLgMS3Z2iLgMZ3Y2cLhM^3Z2]LiMc3Y2YLkMe3[2RLZMnNTOQ5g3jKTMXOTOT5h3\\KUMBROR5o3UKoLKROo4U4nJjL6oNl4d5VK[Jg4g5[K_J]4a5dK`J\\4^5eKcJ[4[5eKfJ^4V5cKiJ_4U5bKkJb4P5_KPKa4o4`KQKa4m4`KTKb4h4_KYK`4f4`K[Ka4b0VK_2:oL`4?nKl1CeM`4;dLl0\\OiNR46YMMUOKe34GK:3HN7OK25KN52H18OE4;JD9=`5O11N10O100O1000L3L6J7I6J6J6J4M00M3M3M3L4M3M3M6I8I:FUV5;XiJ?H8I7O1O1N2O1O1N3N1O1N4M1YH", "size": [375, 500]}, "area": 24970.0, "iscrowd": 0, "image_id": 11149, "bbox": [0.0, 0.0, 500.0, 314.0], "category_id": 129, "id": 20000676}, {"segmentation": {"counts": "iXP4163T;OkD2R;2mDNS;2lDOS;2mDNS;2mDNS;3kDNU;2kDOS;;O12N001O1O01O01O00O1000001N01000001O0000001O001N4M6J001Ldg00_XO0iiW1", "size": [375, 500]}, "area": 552.0, "iscrowd": 0, "image_id": 11149, "bbox": [350.0, 95.0, 41.0, 22.0], "category_id": 139, "id": 20000677}, {"segmentation": {"counts": "Z8]3[8O1O1O1O1O1O2N1O1O1O1O1O1O1O00000000O1000000M3N2N2N2N2O1M3N2O100001O000000001O00000000000000000000001O3M2N6J8H5K4L4L4L4L3M3M2N2N2N2N2N2N3M2N2N3M1O1O1O2N1O1O1O1O1O2N1O1O00001O001O00`JXO1h00[OMd03@I`08CE<;I@7a0K\\O5d0MZO3g0NWO2i00UO0k02SONn03POMP14oNLR14mNLV13hNM\\1OdN1`1L_N4d1J[N6i1GVN9n1DQN<R2DjM=Y2BeM>\\2DaM<`2E^M;c2F[M:e2HYM8h2IVM7k2JSM6n2JQM6P3KnL5S3LkL4V3MhL3Y3NdL3]3MaL4`3M]L4d3MYL4h3MUL4l3MQL4P4MmK4T4MiK4X4MeK4\\4MaK4`4M[K6f4MRK7o4NgJ6Y50^J3b54oI4Q6i21O0000000000000000000000000000001O0000000000000000O100O100O100O10000O100O100O100O10000O1O100O10000O100O100O100O100O100O1@RKWJn4j5[KkIf4Q6PKoI^5R631O002N3M2N1O2N2N1O=C00000000N2N2N2N2N20000001O001O00001O001O00001O00001O001O00001O001O000000O100O1O1O1O100O1O\\KZIY4e6]K[I72\\4f6cK[I\\4e6_K`Ia4k60001O1O0000O10000N_KnHa4R7_KnHa4R7_KnHa4R7_KnHa4Q7`KPI_4P7aKPI_4P74003M00000000O11OO100000000N200O11O1^KnH42o3]71001OO11OO100001O00O1O1003M3M5oKXHi3k7000001O0000000000O10000O100001O001OO100001O2N000ZLQHd3o7\\LRH03]3U8O1O1O0000000000001O1O1O1O00003MO13MO12N00O1O1O11OO12N1O1O1O000000001O1OO1O11O1O1O1O1O002N002NO11O001O1O003M002N1O0000O100000000000000000000001O00000000O12NO1001O3M0000001O00007I1O1O00O10000001O1O00000000N2O11O000000001OO11O00O100O10000O11O2N1O1O1OO1LSN_Fl1f901O00001O0000000000000000O10000O100O10000O100O1M300000000000000001O00002N00", "size": [375, 500]}, "area": 51784.0, "iscrowd": 0, "image_id": 11149, "bbox": [0.0, 173.0, 500.0, 202.0], "category_id": 140, "id": 20000678}, {"segmentation": {"counts": "eiR18^;1EJUE6k:HWE9GK4MLOb:0^E1003<3HZ:P1nEkNb9W1WFnNi9Q1]FjNc9g1N000000O11O1O000VFYNd9g1[FYNf9h1XFYNh9j1100O10000000000O10000001O1O001O1O001O1O1O001O1O1O001O1O001O1O1O1O1O1O1O1O1O1O1O2N00O100001OO100O1000000O12N002N1O0000O11O1O0000O1TOPGQOP9<WGSO6GD<n8i0[GQO94\\8k0\\GPO93\\8o0ZGmN:3]8Q1YGkN;M\\O2Q9T1]GjN:0Z8T1^100003M001OO10000O1O1O1N2F]E@c:`0]E@c:`0_E\\Oc:c0_EZOc:i0[EVOe:i052NO11O0ZEUO`:k0\\EVO00d:m0]ESOb:Q100^EoN^:U10001O1O1O00O100OhFiNlN0S9X1jGiNUO2Q9U1iGjNSON15Q9S1mGSOR8l0]GnNDK3=l8j0^GEb8<\\GEd8=SGfNOm0P9`0nFcN2m0P9e10O100001OO1O11O1O2N002NO1M31O3M00O1002NcNSGTOO?k8:[GSOJF0m0k8DbH=Z7AjH=V7BlH<U7DkH;V7EiH;X7EhH;X7FgH9Z7FgH:Y7GgH9X7FjH9V7GjH9V7GgH<Y7DRHBTO93f0g8_OoGU1P8lN`GANi1b8fN[GCLR2h8ZNYGC0W2g8ZNXGh1g8XNYGX10eNf8n1\\GQNd8P1dGROLM`8o0hGQOKN]8Q1fGQOON[8P1aHPO_7R1_HmNb7Q1aHeNhN30Nh8Y1c100O1001OO1006JO1O1O1O1N2N2O100MaEmN`:n07O1N2EXECl:=TEAn:?500N2O1O100002N00002N0000O100MjDEV;8600O100JcSg00\\lXO8ROF]F9d9HZF:e9HcENb0:k90QF1n90oE21EENd9<fF1OKGI^9<kF1M3W9LlF2J2\\9LjF3F4`9IjFk0V9UOjF5F1_9InF0J3Y9LnFOM3U9NoFMM5T9MTGJI8S9NiG2X8MiG2X8MhG2Z8EoG9h90jDGL0o:<oDG0NQ;d0N2L4M2M3M3K6M3K6M2L4L3NO01M21N3M2O2NL[NYF0Oa1h982O2M3N2M3O1M3K5M3L4L4O1O10000O1O1^OSEL07P;IXE5W;Kj\\c0", "size": [375, 500]}, "area": 14678.0, "iscrowd": 0, "image_id": 11149, "bbox": [95.0, 0.0, 352.0, 96.0], "category_id": 169, "id": 20000679}, {"segmentation": {"counts": "f`92e;002^EM\\94aFO^93^F0a91\\F0e95TFOj93SFNm92nECH=Y:1nE4Q:0lEOT:3jENU:j001OO100L40000GfEPO10Y:m0<00O10000O1O100O1HTE@1OO0l:=<2NM31ON22NN200O11O1hDDS;a0OOXECD1^:<PFGP:9jE2Q:1kE3LZO10P:a1oE`N00n9f101O1O001OO1O1O11ON20000MRF]Nn9b1400N2001O002NL44LhNUFd0k9[OVFd0n9YORFj0i9XOXFGK4MLOa0Q:_OPF003<3H9Q;O3I46Rkk0CcTTO7WE6GDb:9dE9Z:HeE9\\:FcE:^:FaE;M\\O\\:>dE:Y:c001O03M001N010000O001O1N2FcNZF]1f9cNZF]1f9eNVF^1h9dNUF^1n9`NQF`1n952ON11O0^NQF\\1o9`NRF00`1Q:aNoE_1T:00aNlE\\1W:002ON2N2O00O2O0OIhElN02Y:k0hEUO2OW:k0hESON150T:n0<_O`EDK3g::`0N3GfDO_;NaD2he7NfZHOc:6YEJF0[:]1K4M2O00O101OO110N2O00M3[OlETO93U1M[7i0YIWOg69WGNX2L^61\\GLZ;1eD0c;OTG0S60oH0RO1i91iENM0\\9:iFLMKMO[9>hFKNHO1Y9:jFONFO0Y9V1hFjNO0[9T1eFmN0OY9W1]FhN30N39NX9e1gF\\NY9e1fF[NY9f1gF[NX9c1`FZN83Y9b1_F[N81Z9e1^FZNm9f18JO1O00001N2N2O10ONZNTFh1f98N1N2EoMlFV2S9jMkFX2U9410N2O1O100002N01O02N0000O100M`MoFa2m8510O100J7L30001O00O10000N23MO1O1001O001O00000000000000O100O1O11O1O001O00O100O1000000O1O12N001O1O00001O00O1000000O1H_GXMc8f28N2O1GPGhMS9V2oFhMS9W2mFiMT9V2700001OkNeFOZ9JnF1W9NhF2Z9LhF4Y9TO]Fb0:;`9AaF>`9_ObF1EEN5k95bFOKGIOk9:bFM3H[9;cFJS:6nEFU::d00YEFP:=jEJR:7lEMR:4lEMS:8iEIV:l0DfN[F[1d9fN[FZ1e9gNRFb1m9401O00O1M3N2M3M3K5N2L4O1M3MTGUOR7i0mH[OR7b0oHFk65WINi60UI4k6KRI9n6EQI;R7DlH>U7[OVGN0Oa1m0X7ROPIR1h85J4M6I3O2L6H5M4K4L5N3M2O000O1O1^O[MdG07h2Q8`MmGj1MnML>Y8NiGi17kM\\8c2=O1O1TOoFQOR9h0XGVOg8c0cGZO]8`0lG]OT8a0PH^Oo7`0VH]Oj7b0ZH[Of7c0^HYOd7e0h1N2N2N2N2N2O1N2N2N2N2N2N200000000000000000000000000000000000000000000000000000000<D", "size": [375, 500]}, "area": 18690.0, "iscrowd": 0, "image_id": 11149, "bbox": [26.0, 0.0, 474.0, 112.0], "category_id": 174, "id": 20000680}, {"segmentation": {"counts": "X1i0b1n3f2i2C=D<O1O100O1O1O2N100O1O1OkHmLg5S3VJoLj5Q3UJQMi5P3VJQMj5R3RJPMm5S3nIPMQ6S3kInLT6V3hIlLX1Bo2e3dKlLZ1AQ3h4mLZKQ3i4lLYKR3j4kLXKR3k4mLVKQ3m4lLUKR3n4kLSKT3o4jLRKU3Q5hLPKW3R5gLoJ[3Q5bLPK_3Q5^LPKc3P5[LQKf3P5ZLmJh3T5WLfJP4Y5QLeJP4\\5PLbJQ4_5oK`JQ4`5oK_JR4b5mK]JT4c5lK\\JU4e5jK[JV4f5iKYJX4g5l01O1O001O1O02O2N1O5K^J^JU5[5jJfJZ5V5eJkJ^5R5aJoJb5n4^JRKe5k4ZJVKi5g4WJZKj5d4UJ\\Kn5b4RJ^Ko5a4PJ_KR6`4mIaKT6^4lIbKU6]4jIcKX6\\4gIcK\\6\\4cIdK`6Z4_IeKd6Z4[IeKh6Z4WIeKm6Y4RIgKo6[4nHdKT7_41O2N1N_IgKg5X4VJlKj5S4SJPLn5n3RJSLo5l3PJULQ6j3oIVLS6f3nI[LR6k2\\ISMo03f5c2jJ_MV5a2hJaMX5_2eJdM\\5[2YJPNg5P2WJQNETO^5j2kJTN=POT4l2\\KVNa0ROo3h2_KWNc0TOk3d2aKVM[Om0Y1^Of3_2lKRN`0Ba3[2oKSN`0D_3Y2QLSNa0E\\3X2SLSNCQO=g0j3U2lKTMFo06RO?g0g3T2mKUMHm04TO`0g0f3S2mKVMJk03UOa0h0c3R2nKWMLi01WOb0h0b3Q2nKXMMi0OWOe0h0^3Q2PLXMOi0LUOk0g0Y3S2PLYM1h0IVOQ1b0U3W2oKZM3h0ESO[1a0m2Z2oK[M4i0BPOc1`0g2\\2oK\\M6;AGi13`2]2QL_M64HHg16X2\\2ULdM4K0Kd19Q2\\2TLkM5_Oc2:Q1[2XLmMX3I>Z2[LmMV3L<V2_L[Ni2@f0U2bLaNb2ZOk0T2dLjNZ2SOP1S2gLROQ2lNU1S2jLZOi1cN\\1S2lLXOj1fNX1R2oLTOm1kNR1Q2RMQOo1oNl0P2WMnNP2SOg0o1ZMkNR2WOa0o1^MfNU2\\O:o1bMbNW2@4P2eM]NZ2DNQ2iMWN\\2HIR2mMRN]2MCS2PNlMa22\\OT2TNfMc27VOT2^2mM^MV2b2kMXMZ2h2kMoLY2Q3lMeLX2[3oMYLW2f3UNeKU2Z4e2O001O1O001O1O1O1kKUI]3k6`LbIU3_6hLnIm2S6PMSJl2n5QMTJo2m5nLUJR3l5kLVJU3k5hLXJW3T7000001O0000001O000O101N10000O101N100O101N100O2O2M2O1N3N1O1N3N1N3N1N2O2M2O1N3N2M;F:E;F:FaN5]G_Oa8h0R18H7I8H7J7K3L3N1N3M3N1N>C0O100OOCZMmGd2U8]MiGc2X8]MgGb2Z8_MfG_2\\8aMcG^2_8bMaG^2_8>N10001O001O00001O001O000010O0001O0010O0001O010O00O1O2O0O2N2N^OkLdHT3\\7oLcHP3\\7SMcHk2]7XMaHg2`7YMaHe2_7]M`Hb2a7^M`Ha2a7_M_H_2b7aM^H^2c7cM[H]2f7dMYH[2h7eMWH\\2j7dMSH]2n7cMPH^2R8c0010mLjGd2V8ZMmGe2S8ZMnGf2R8YMPHf2o7ZMSHe2l7[MUHf2i7ZMXHf2g7ZMZHf2e7ZM[Hf2e7ZM[Hf2f7YMZHg2f7YM[He2f7[MZHe2f7[MZHe2f7YM\\Hg2e7VM^Hh2c7VM_Hj2W801O0O10001O0O^OZMUHf2e7`M[H_2f7aM[HY2i7hMWHT2m7lMSHo1R8QNoGj1V8UNjGk1V8UNjGj1W8VNiGi1X8WNiGg1Y8XNgGh1Y8XNgGg1Z8YNfGf1[8ZNfGd1[8\\NeGd1\\8[NdGd1]8\\NdGb1]8^NcGb1]8^NcGa1_8^NaGa1`8_NaG_1`8aN`G_1`8aN`G^1b8aN^G^1c8bN^G\\1c8dN]G\\1c8dN]G[1d8eN]GY1d8gN\\GX1e8iNZGW1f8iNZGV1g8jNZGT1i0hN[64lHT1e0lN_60lHS1a0ROc6LkHQ1?XOe6GmHo0:_Oi6BmHo06Cm6^OmHn03Io6ZOnHk02MP7XOnHj001R7UOnHj0N5R7QOPIi0L;R7mNRIf0Ka0Q7iNTIf0Ie0Q7fNUId0Hk0Q7aNWIc0GP1P7nNiH05W1P7KnH8Q7GoH<o6CPIa0n6nMiHV19o0l6jMlHW16S1l6fMnHV16W1k6bMoHV4o6iKRIY4l6gKTIZ4k6eKVI]4h6cKYI^4f6aKZI`4e6_K\\Ib4c6^K]Ib4c6]K^Id4a6\\K_Id4j610O0001O001O0010O01O00001O0010OO2O00000000000000000O1000000000000000aLWIP2i6oMYIP2f6PN\\Io1d6PN^Io1b6QN_In1a6QNaIn1_6nMfIQ2Z6iMmIV2S6cMUJ\\2k5^M\\Ja2d5YMcJf2]5TMjJk2U5oLSKP3k6O1N2M5L3L4L5L3L4M4K4M3L5L3L8I7HdFQOZ8g0fG\\O_8=aGEd83]GOh8JXG9Q:2N1O1O1O1O1O1O001O1O1O1nKGeL:X3>RLCk3T1QK[Om4\\1oITOn5\\3N2M3M3M3M2N3A?O1N2O1iNkIjLW6o2SJmLm5m2]JoLd5k2eJPM]5n2gJoLU5T3PKgLm4]3WK_Le4d3_KXL^4k3c13M3M3N`MiHf0U7VOSIh0k6UO\\Ij0a6SOgIj0W6ROQJl0m5QOZJm0d5POdJm0Z5POmJn0Q5nNWKP1m4gNYKZ1m4YNZKg1[70000000O100O10000O100O100L4TOl0H8H8L4O1N2O1O1N2O1O1N2O^4bKbK", "size": [375, 500]}, "area": 75732.0, "iscrowd": 0, "image_id": 11149, "bbox": [0.0, 2.0, 500.0, 349.0], "category_id": 183, "id": 20000681}, {"segmentation": {"counts": "0[2P;000000001OO11O00O1000000O100MPEjMQ;V220000000000NmDmMR;S232N1OO1LmDPNS;P2nDnMS;S2lDmMT;S22O1001O0jDmMT;S2lDnMS;U20M300001O00001O1O0000001O1OO1O12N00N2O1O1001OO1O11O00O11O000000000kDmMR;U21O100N21O001O00000000000000003M:F2NO11OO10000L[EaMf:_23001ON21O1O000000O12NOYEdMa:a2200O11O1O00O1000000O11O00M31O5K00O1000_EYMO0]:l2hEVMNNl9n2SFVMOM\\10R7m2cGVMOMV17W7f2bG\\MW1OV7Q3cGfLR1<[7P3dGeLa0OE?V8m2cGeL1O:7J8X8P3cGbL1O86L:W8o2dGfL81M:W8U3eGlL10Y8h3gGVL[8k3cGUL^8\\40O12NN21OO10000O1O1001O001O001O000000O100O1000000O10000001O001O1O1OIcG`K]8_4dG^K_8b4bG\\K_8d4400O1O1N2dMiGDW8<iGCX8=hGCX8=hGCX8>gGBY8>gGAZ8TOVG>`0>Z8TOVG>`0>Z8SOXGh2g8TM_Gj2a8VM`Gi2`8UMbGm1D\\Nj8GbGm1E[Ni8HcGl10PN]84cGl10PN]84dGm1MoM_84eGg1K[N`8MfGh1LYN^8MnG@I2i:NVX26biM2nBHo97ZF533KU1\\9YOgFCF]1]9VOiF_OJ[1]9VOiF_1V9aNjF`1V9_NjFa1U9`NkFa1T9_NlFa1U9^NkFc1S9^NmFb1S9^NmFc1R9]NnFd1Q9\\NoFd1JYMU9T1PG]OKf1T9mNQG]O;V1AYNP9T1TG]O;U1BZNo8T1UG\\O:Z1a8ZOUG[O;Z1b8ZOSG\\O;[1a8YOTG\\O;\\1`8XOUG]O:[1a8XOUGB4Z1d8TOXGHNQ1m8WOUGj1m8TNSGl1m8TNSGl1]8XMgGk0LP2Z8UMTHO4j2XORM`85THO6h2h7YMkG=;R2YO\\Ma85gGl0Mj1JVMb84gGl0Mj1KUMa85eGn0Oh1_8ZMbGn0Og1`8[MaGn0Oh1_8[MaGm00h1_8[MaGm00h1_8[MaGm00h1_8\\M`Gl01g1`8gNdGW1Z8iNfGW1Z8iNfGW1Z8TN[Hm1d7RN]Hn1c7QN^HS2^7lMcHS2^7lMdHHlNi1`8_NdHHlN>74Y8FeHGkN32^O=R1Q8FfHFjNC1OO9f0i0j7FgHDPOFg0n0c7HfHA5g0U7HgHZO:n0o6H^J6c5J]J6c5J]J2g5K\\JLm53UJJm55QJMP63nIOR61nIKQMCY9OlFa0[2MZ:0000000001O000O1000000000000000001OO1NDUC<j<3NEXC7d<M\\C3k<FUC:k<FUC:n<00000000000RCMd<3\\CJg<6YCJg<<2_O_C4n<N11O100O1OL^j0OgUO2hB00Nn<3QC2o<501O2N4L3M5KN3L3N2O1N]NBTFe0m9\\O_EW1a:jNWE\\1k:a02OO11N010O100O010O1N1J8O0O100O100O3M101OjDeN\\:\\1`EjN^:U1bEnN\\:R1cEQO[:n0eEUOY:j0gE_O\\9WOcF\\10BW9TOiFY1NK`8_ObGg0KMn71UH3JHInNm7b1_HJGIOhNl7e1]HKGIR8<VHMFHT8;VHMEIU8:VH[1j7eNVH[1j7eNWHZ1i7[NbHe1^7[NcHd1]7`N_H`1a7gNXHY1h7gNYHT1k7lNTHR1o7nNQHR1o7nNQHQ1P8oNPHn0B]M_8e1PHj0U8VOjGj0W8VOiGj0W8VOiGj0W8WOhGi0X8PN^Gc0:]1X8mMaGf06^1Y8lMaGe07_1X8lMaGe07a1V8jMcGe07`1W8kMcGd06a1W8kMcGd06b1V8jMdGd06b1V8jMdGd06>DCb8[OdG`0ITO?]1DD_8[OfG?9a0EC\\8]OgG0KM<8AA02O88:b8DcGNk1KRN9i8MmII\\M:f8MnIG_M;c8N_J2a5M`J3`5M`J4_5M`J3`5M`J3`5M`J3`5McI2lMc0o7JcIH[N?R8LaGUOi0060UO>7c0S8JbGUOe02[OO12d0M\\O10`08`0V8NSH_OMCBh077X81jGROG;0B:o0K1[81_GoN33N0O>;?I3[80dGQOMa03]O0Q1O0^82aGG2UO0Y2]8LaG:_8F`G;`8X20O10000O1000000bK_GR4a8jKdGNKR4`8oKlGR4S8jKRHU4ClKg8[41O001O3N0O1O2NO1101O00001O00N2O1]OjK`G2<U4T8SLaGF1X4^8b0O0000000000000000001O0000iMcG]MOP2^8d0jGWOV8i0jGWOV8i0jGWOV8h0mGVOS8h0QHVOo7j0QHVOo7j0QHVOo7k0PHUOP8i0RHVOo7h0aGaMa0f1n7i0THWOl7i0THWOl7i0THWOl7i0SHZOk7e0VHkM]OU1]8P1WHjM\\OK0X1[8R1ZHdM\\O22MO]1V8m0iGfMd00Ia1f7b0\\I]Od6c0\\I^Oc65mGeN`1V1c62VH]NG2a1`1a60PJOP61PJOP61VJIj57WJ_NhMO1R1P8?XJ_NlMQ1m7`0WJ_NmMP1l7a0`J^Oa5a0`J_O`5a0`J_O`5:lGlNd2i0a52mGTOO0c2k0a51UHSOZ2l0a50nJ2Q5LQK5n4JSK6m4LQK5n4JRK7n4JQK7n4IRK8m4HSK8m4HSK8m4HTK7l4IUK5l4KTK4m4LSK4m4LTK2m4MZHYOT2k0b5MVH[OY2H^M10g0S85mGRO3>e2;[5:mG\\Oi24WM^OT8a1lG_Na2g0h5j0WJYOj5i0TJUOn5m0PJSOP6m0PJSOP6m0PJPOS6o0nIQOR6m0PJSOP6m0RJQOn5o0RJQOn5o0QJTOm5l0RJSOP6l0QJTOo5l0QJTOo5l0QJTOo5n0oISOP6m0QJQOP6P1oIPOQ6P1oIoNR6Q1`G[N\\2c0U6T1lIkNT6U1lIjNU6V1lIhNU6Y1kIgNT6Y1mIeNT6Z1oIdNQ6\\1hIoNT6n0mIUOR6j0oIUOR6k0nIUOR6l0oIROQ6o0nIQOR6o0mIROS6l0oI`NcM=_8U1lI[NdM11>`8V1kI[NeM00=b8X1iI[NhM=_8X1oIgNR6X1nIhNS6Y1nIfNQ6Z1nIgNR6X1QJ^N[M4d8\\1WJ_NUM1e8`1R3MZDaNf;_1300O1001O1O001O1OO1O100000000O1000000O100NkDdN_:`1ZEcNf:^1UEcNn:]1QEeNJJo:c1REfNNHP;b1QEkNP;R1QEoNP;Q1mDROS;c1000000lDlMQ;T2PEkMP;X201O00O11OO1MoDkMR;U2200MlDnMU;R2200000000000000N21O1O00O13M2N00N200001ON21O00O1001OO1N2001OMiDRNW;n13O11O0000000000MfDTN[;k1300", "size": [427, 640]}, "area": 55456.0, "iscrowd": 0, "image_id": 11197, "bbox": [0.0, 0.0, 640.0, 171.0], "category_id": 96, "id": 20000682}, {"segmentation": {"counts": "jbi01Y=102L20000O1O100N2N101N104OM2O002N00M4N12MO2O20O2N0001N1O2O00O2@WC<l<0O10M4NO21N10001O1OO11OO2VCH^<9bCG^<9]CHOOd<9\\CI0Ne<>[CBf<=ZCCf<=ZCCf<>YCBg<a000001O1OO12N00000000003M1O1O1O0000001O001L6M[ig42bVXK100jBMS=6000000LJRC8k<5O1N20000M3000000002NN20000001OO100O1O100O1O11OO1004L1OO011O001O0O1O1N200O1aCYO1NT<S11MiCPOW<P1iCPOX<n0iCROW<n0iCROW<n0310K5N100N4M100O010M200101NI7110OKUCJl<5UCKj<47O2O0Ng[=1YdB1O2N1O100nBLn<=K1N011M2000000001N1N2M3JTCJP=5PCJR=3nBM00U=0_]4", "size": [427, 640]}, "area": 2582.0, "iscrowd": 0, "image_id": 11197, "bbox": [61.0, 142.0, 568.0, 47.0], "category_id": 97, "id": 20000683}, {"segmentation": {"counts": "abU4:o<3LL5IcdS4", "size": [427, 640]}, "area": 34.0, "iscrowd": 0, "image_id": 11197, "bbox": [320.0, 138.0, 4.0, 18.0], "category_id": 105, "id": 20000684}, {"segmentation": {"counts": "fT_52U=5oBKi<<L5N1000O1002N00001ON2001O00001O00N2O2^OZC<Q=EmB1`ab2", "size": [427, 640]}, "area": 412.0, "iscrowd": 0, "image_id": 11197, "bbox": [420.0, 0.0, 23.0, 22.0], "category_id": 130, "id": 20000685}, {"segmentation": {"counts": "ib]22Y=0O2QGM`49^KGb4:]KG`4;kIDSO1R7;kIEPO2`NMc8=lI0eNH_78lI0eNH_79mIMdNJ_7:gIKPOKX7;hILnNIX7c0`GI2j2m7o1O101O000000SNYHWNf7h1\\HbLHc1l7kN[HK533KU16`N4i7OPIJCF]1;_NNa73VIJ_OJ[1<aNJ_76VIJ_18gMJd74VIJ`19bMKh73UIIa1;^MKl70VIJa1h0Z5^OUIJa1f0\\5ARIIc1e0[5ASIJb16[MH0O0OP8:SIJc12hMJb7:SIJd1Om57_HJd1JYM2e8:_HIf1Lk5;_HIf1Lk5;_HIe1Ml5:_HJ]:6cEJ]:7bEI^:7bEI^:7bEI^:7bEI^:7bEI^:7bEI^:9`EG`:9`EGf:4YELg:>]D4c;L]D6a;CmD;n:AaEMf:3ZEMf:1\\EOd:1\\EOd:1\\EOe:0[E0e:0[E0e:0[E0f:OZE1P<4L03M2N1OTFLb63\\IMiL0l92PIO\\M2c90eFMi16iMNi9OeFM>747i8FdFL32^O=R1Oi8GcFKC1OO9f0i0Hi8HaF1Fg0n0Al8F]FW1g0SOl8GVF\\1n0mNl8^1RGcNn8]1RGcNn8]1nFlNm8T1jFWOT9j0iFYOV9d0lF^Oj2mNg2c1aJYOAnN^1<UOD05[5d1]JXNCT15kNo0b0@_O14`32jKOa08HZ15CGeNm0e0C]OK0A4T4[1SL76EDdNk0i0FVO^O393G0T4\\1RL67J9_OKoN_O465IMU4\\1QL78I5COSOC2KMT4[1RL78I2F1UO@6S4o0RL78I0INLh3;RL87KLHHcN7Y1k39SL87KLHHdN1]1Q44SL86LJZNKS3R4_NSL86MG[NMR3S4^NSL86MFb1Q4YNSL86Z3g3^LSL86Z3g3^LSL86Z3g3^LSL86Z3g3^LSL86Y3h3_LRL86Y3h3aNXL_1h3bNWL^1i3aNXL_1h3_NlK^L<S5h3^N[Lb1e3\\NPLaL7S5i3XNTLeL3S5i3_NmK^L:S5i3_NmK^L:R5j3cNVL]1j3cNVL]1j3cNVL]1j3cNVL]1j3cNVL]1j3cNVL[LMh4m3mNVL[LJk4P4jNVL[LJ]15e1k3c0\\LhMOYO@_1U4a1YLiM6lNId1h3g1XLiM<bN10WOc1T4R2YLhMc1cNoMY1U4\\2ZLfMh1Ln1^2[LaMn1GbMWOU4a3\\LdLj26fLJOKU4a33^LiK412NKU4a33^LhK182INT4a33ZLhK202:2GOT4a33ZLjK0N4?LC4T4b38]LVL1b3b39ZLWL0[O1U4e3]500000RI[LV4e3h200001O000000[N^FXOk9g0VFeN^:Z1cE^Ne:b1b01OO11OO1000000O10000O11O00000000002N001O001O1O1O1O\\OmDoNT;P1nDPOQ;=bEC^:IWF5`;O1O1O10000001D;013L7I01OO10000001OO10001OiNF]E:`:I`E6a:J^E7b:I^E7b:I^E7b:J]E6c:J]E6W2CS57fH6W2CS57fH6>D00gN0T86cHITO?]1DK3mNKKOW88dH9a0EKOnNNR86UHKM<8AA02O88:3ZO7U8KSHk1KRN9n0h7h1nG\\M:k0h7i1lG_M;h0i7Z2WHfMh7[2XHeMh7[2YHdMh7[2XHfMg7Z2YHeMh7[2XHbMk7a1THlMc0ARO<g8e1iG[Nb00h7CVGi0060UO>7e01h7DUGe02[OO12d0M\\O10`08f0Oi7:^GMCBh07a0Kj77oFG;0B:o0Ke9J\\E33N0O>;?Ig9O]EMa03]O0Q1Og9ORF2UO0Q=N6OVPo2", "size": [427, 640]}, "area": 21474.0, "iscrowd": 0, "image_id": 11197, "bbox": [186.0, 0.0, 226.0, 295.0], "category_id": 132, "id": 20000686}, {"segmentation": {"counts": "e4e0i4c2L]M5b2K^M5b2K]M6c2J]M5d2KTM_KHf4T3KRMbKIc4U3LnLdKM`4U3LlLfKO_4T3KkLhK0^4U3X50O11O001O00000000000000000000O10000000000N2A?00YM[LUI5e09>W3h5hLfI1b0W3h5hLfI1b0W3h5hLfI1b0W3h5hLfI1b0W3h5iLeI0c0W3h5iLeI0c0W3h5iLeI0c0W3i3`LjL9jN0c0W3e3fLlL3lN1b0V3f3fLkL4mN0b0V3f3fLkL4mN0b0V3j3bLgL8mN0b0V3i3bLiL8lN0b0V3j3aLhL9lN0b0V3i3bLiL8lN0:^3Q4[LgL5nNP5[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[3hInJ1h1V6]3jIbLW6^3iIbLW6^3iIbLW6^3iIbLW6^3jIaLV6_3jI`LW6`3iI`LW6`3iI^LY6b3gI^LY6b3gI^LY6b3gI^LY6b3gI]LZ6c3fI]LZ6c3fI]LZ6c3fI]LZ6c3gI\\LY6d3gI\\LY6d3gI\\LY6d3gI\\LY6d3gI\\LY6d3gI\\LY6a3jI`LU6Z3hITK4a1T6Z3SJfLm5Z3SJfLm5Y3TJgLl5[3RJeLn5Y3TJgLl5Z3SJfLm5Y3UJfLk5Z3UJfLk5Z3UJfLk5Y3VJgLj5X3WJhLi5X3WJhLi5Y3VJgLj5Y3VJgLj5Z3UJfLk5Z3UJfLk5[3TJeLl5[3TJeLl5[3TJeLl5[3UJdLk5[3VJeLj5[3VJeLj5Z3WJfLi5Z3WJfLi5Z3WJfLi5Z3WJfLi5Y3XJgLh5Y3XJgLh5Y3XJgLh5Y3YJfLg5[3XJeLh5[3XJeLh5\\3WJdLi5]3VJcLj5^3UJbLk5^3UJbLk5h3kIXLU6h3lIWLT6i3lIWLT6i3lIWLT6j3kIVLU6j3kIVLU6j3kIVLU6j3kIVLU6j3kIVLU6i3lIWLT6g3oIXLQ6h3oIXLQ6h3oIXLQ6h3QJVLo5j3TJTLk5l3fIlJ>V1l5n3fIlJ`0T1j5P4fImJa0R1h5Q4gImJa0R1h5U4fIhJ`0S1j5W4VJiKj5W4VJiKj5W4VJiKj5W4VJiKj5W4VJiKj5X4UJhKk5X4UJhKk5X4UJiKFlNm5\\5\\JiKBkN20P6\\5\\JPLc5P4]JSL`5m3`JVL]5k3bJUL^5k3bJTL_5k3bJUL^5k3bJUL^5k3bJUL^5k3cJTL]5l3cJSL^5m3bJSL^5l3cJTL]5j3eJVL[5j3eJVL[5[3SJZK0O0Ob0\\1\\5Y3`J[K4\\1\\5W3XKiLh4V3YKkLf4U3ZKlLe4U3ZKlLe4T3ZKnLe4R3[KnLe4Q3\\KoLd4R3[KnLe4S3ZKlLg4T3YKlLg4W3VKiLj4T3YKlLg4T3YKlLg4T3XKlLi4T3WKlLi4W3TKiLl4U3TJYKP1c1k4T3WKlLi4Q3VJ\\KR1c1g4Q3VJ]KT1c1d4P3YJ\\KT1c1c4Q3`KPM_4o2bKPM_4Q3_KPMa4P3_KoLb4Q3^KoLb4Q3^KnLc4Q3^KoLb4S3\\KlLe4T3[KlLe4T3[KlLe4U3ZKkLf4U3dJZK5_1Y5[3\\JVKG0d0_1W5^3]JVK;_1U5`3ZJRK?L_Ob1g5i3hJeJAd1f5h3iJZLU5h3kJXLT5i3lJWLT5i3lJVLS5l3mJTLR5m3mJTLQ5n3oJQLQ5U4jJkKV5W4hJiKU5[4jJeKV5\\4iJbK[1cNU2e5^LUJ4f1?ZN05n2j5bLQJKf1d0[N14n2k5aLPJLf1d0\\NK0A4a3m5`LnIOe1b0ZN^O393G0`3S6`L^Ka0YN_O465IM_3U6bL\\Ka0aNC2KM^3T6cL\\K`0fN@6[3j5eL[K;_Oo2R7fLgG7Y1Q3Q7hLgG1]1V3c800N2N200O100O001O1O1000000N2M300O0O2O1O1N200O1O1O1O1O10YNaMbH]2`7jMmFHY1]2l7oMRHP2P8RNfGBWO[2Y9oMRGGJOKZ2[9oMdFH412NKZ2Z9PNeFF182INW2]9QN^FG202:2GOU2a9QNZFK0N4?LC4T2e9UNZF91`1g9XNUF:0[O1S2i9Z1100O1N2O010N2O1O2N1N201N1O1O1O2N1O1O2O0N201O0NG\\MoEc2]:]MZE_2h:aMXE_2j:N201MlMjMVIU2g6PNYIP2h6oMYIn1j6QNUIP2m6nMSIR2n6mMRIQ2Q7nMoHR2U9O002N100N^MSNjGM_10RNm1_8UNdG76Ge10RNm1_8WNaGb0m1ZOSNk1a8TO[Ik0f6XOXIf0i6]OTIb0m6_ORI?P7AoH?R7AoH=R7CnH<S7DnH;R7EnH:S7FnH8T7GlH9T7GmH7U7HlH5V7LjH3V7NjHOX71iHMX73iHKX75hHJY76hHHY78hHFY7:hHDY7<gHCZ7=iH_OX7a0hH^OY7b0fH^O[7b0eH]O\\7BcFj0Q2D\\7BcFj0Q2B^7DbFi0P2B_7EaFKOi0S2G^7EbFa0S2Je73^HKd74]HKd74^HJc76]HId77\\HAdM1S:>e201OO10GXCOR=01O000000000000O1O11O0000000000001O00000000000O1N2O11O1O0001O1O2N0000hR20XmM000000000O1000000XR<OimC001N3N1N4SCJJMc<d020O100010O1O0010O01O12M000002M11O]OZCa0h<00000001O00000001O00000000000000O1000000001O00001O000000000000000O11O001O0001O00002N]O]C1N;l<2LAXC`0g<@YC?g<BYC?f<AZC?f<31NM_O^Ca0a<_OcC?f<N1O1OM3L5K5GZOhCi0W<6001O5KO1O11O1O001O2N2N1A\\C5e<J\\C5e<I\\C7e<G\\C8l<O10000000J71N1O1O_OERD;m;EUD:k;FUD:k;FVD9k;FUD:^<000000O02N1BDlC=S<EkC<V<EeC>\\<800O11O0000001O00000XObCc0h<K1OO11O00000001O0O1N2M2K]OaCh0]<XObCi0\\<WOdC00h0Y<ZOgCj0Y<5OO101O00000000010O0OdJ", "size": [427, 640]}, "area": 67660.0, "iscrowd": 0, "image_id": 11197, "bbox": [0.0, 148.0, 640.0, 279.0], "category_id": 140, "id": 20000687}, {"segmentation": {"counts": "V^j45S=301N1MGSC:j<5O10000000000002NO1001O000N3J513IWh30jWL21OO4L4PCGg<>3NO10O100000O10000O10O1O1O1O2O1N1N`mi2", "size": [427, 640]}, "area": 533.0, "iscrowd": 0, "image_id": 11197, "bbox": [370.0, 149.0, 54.0, 20.0], "category_id": 143, "id": 20000688}, {"segmentation": {"counts": "Z5R1Y<000O10001OO1000O11O2N2N1O001O0O1_OiCMW<1bCIm<4XCKh<1mX6OYZI004h<:10O0000001[OBPD7K7U<EkCh0U<8O0O101O01O1=B1O01O00O10000O10001O0O1O100000001O1O1O2N00000001O00000000000000000O2O0O1O10000000000000001O01O0000001O0000000001O0000000001OO1000001O0O100N2000000O1000001O0000000000010O0000000000001O0000000000000000000000001O0000000000000000001O000000000000001O00000000000000001O00002N3N1N2N2OO0000000000000000001oClNi;U1RDkN20l;\\1O3M3M00O1000001O0O100000000O100001O1O1N2O000000O10000000O0100001O01001OO01OO010O100O100O1000000N20QJbN1a1L_N2OVJb1g5`N1NXJd1f5_N2c1L_N4a1K`N5`1K`N5_1JcN6]1IdN6]1HeN8Z1HgN8Y1HgN8Y1EjN;V1EjN;T1FmN:i0OXO1b05^OK?7BI9;HE2?0ALc04]OId09\\OEe0<\\OAe0`0[O@d0a0\\O\\OXJK\\6i0]OYOYJMY6l0^OVOe0l0m50O100O1O1O1O1000000N2M300O1N2O1O1N200O1O1O1O1O100N2O1O1O1O100N2N2O1N200O100O1N2O100N2O1O1O1N200O1O1O1O1O1OO1M4M1N4G9M10nLVNfJh1T5cNWHBV2k1]5lN\\HYOW2h1X5XO_HQOW2e1U5DdHfNW2b1P52gH^NV2`1Q56iHYNU2b1S56gHXNV2a1T58dHYNU2`1X58bHWNW2`1X5=hJAZ5?dJB]5>cJAa5<^JDc5;]JEc5=[JCe5>[JCd5=ZJEf5;ZHRNm1d1i5:ZHRNm1d1j59YHSNk1f1o56PJKS63kIOW6NhI3Y6LeI6[6IeI8[6IcI8]6HbI9^6HaI9^6GaI:_6G_I:a6F_I:a6G]I:c6GZICiNXOm7U1YIBlNWOm7W1TICQOTOl7Z1QIBUOmNP8b1iHAYOfNT8i1bHAn7`0PH@Q8a0mG@S8a0kG_OV8a0iG_OX8d0dG]O\\8c0cG]O^8b0bG_O^8a0aG_O`8a0`G^Oa8b0]G_Od8a0[G_Oc8c0]G]Oa8f0_GYO_8j0_GWOa8j0^GVOb8l0\\GTOe8l0ZGTOg8l0QGdM1c1n8d210000O10000O100O10000O10000O1O100O1O100O100000000O1000O1N2O11O1O000O10000O100O100O100O1O2O0O10000O1000L[LbFe3^9[LbFe3^9[LbFe3^940000000O1000000O100O10001N10000O10000O102N2N0000000000000000O1O1O1L^L]Fd3c911000001O0000001O0000]FiLU9T3jFmLV9S3iFnLW9R3iFnLW9R3iFnLV9S3iFoLW9P3iFPMW9P3iFPMW9b300000001O00O101N100000000001O0O1000000001O000]OlFoLU9P3c000000000000000000000000001O00002N2N2N1OO11O0000O1001O000000010OQFVM^9[300000000000000000000000000000000O2N1000O10001O0^O^F[Mb9e2^F[Mb9e2^F[Mb9e2^F[Mb9e2_FYMb9g2^FYMb9g2_FXMa9h2kFlLU9T3a0000O11O01O0000000000001O00000XFkLZ9V3^FSM`9]3O1O00000000O1000001O000000O4O0N2M3N2O0N10000000000001O00O1dI", "size": [427, 640]}, "area": 51902.0, "iscrowd": 0, "image_id": 11197, "bbox": [0.0, 165.0, 640.0, 262.0], "category_id": 149, "id": 20000689}, {"segmentation": {"counts": "[2Z2Q;00000001OO110O0O1000M3M2O2ONnMlDS2T;3O1OK5M3M3M1WNcDd1a;05K4N00McNZD[1h;fNVDZ1l;eNTDZ1m;2O1001O0dNSD[1l;eNUDZ1n;0_DfNn:^1PEeNn:[1REgNl:[1REgNm:Z1oDjNo:l1MaNnDQ1g:nN\\EZOO2NY1h:ZO[EZO001[1d:[OgEd0Y:\\OgEh0U:XOkEg0W:XOiEi0W:WOhEh0X:YOfEi0Y:U12K30N3N1O10010N1O3OM2O011M20000O1O1`MXE]2j:O1O2OO03N12O0O002N00M3O12NN2O4N9H0NO11]NlDl0T;UOlDj0U;VOkDj0U;WOjDi0R;mNeD;9i0GlNW;>REi0n:^OkDa0W;i0N30M30O00001N21M0QNlDj1X;11O011O10O1N1000000O110O1L2213K00O1000SNeDO0i1`;\\NbDNN[1_;dNeDOM\\1_;eNdDOMV1e;iNdDW1h;iNnCR1W<oNjCa0OEZ<IgC1O:7JV<KdC1O86LW<KhC81M]<LnC1f<01NeaT41TQkKOU=8K000O3L4N0001ON2N@YCa0f<200000O01cC]OR<d0mC\\OK0R<e0lC\\O22MOQ<OoCd00Ij;R1O0C`NoD`1n:iNgDG2a1V;a0O006J1PEhMO1g:X2YElMg:T2YEmMf:\\2O100I\\MgEd2P:^MoEO0c2R:fMmEZ2R:?O2O12NO02O00000001O1O00001NWMTFT2n9hMUFY2H^M10j90TF3>e2c9XM^Fi2o9WMcEa2d:O6N2N0000O1N202N00O1O0100002N010O000bMUE\\2m:1O00100O1N3NI41110011OO0OO2XEcMd:[2YEdM11f:[2YEeM00g:[2YEhMg:^2O021NO03\\E[M40P:k2kEUM11T:R3MnLmER3S:4O1N10011O_NYF\\Og9d0YF\\Oh9c0XF\\Oj9c0VF]Oi9d0WF[Oi9f0WFYOj9g0VFYOj9g0VFXOk9h0UFWOl9i0TFXOj9i0VFWOj9i0VFXOi9h0WFXOi9U2OMRFlLm9S34cMlEg1X:RNkEn1V:mMkEW2T:hMnEJJY2Y:hMPFNH[2W:fMVF\\2f9eM[F]2d9`M_Fa2U:00001O]M[E`2e:aMZE_2i:010dEaMj9_2VFaMi9a2SF^MG1W:`2RFdMl9^2SFcMi9JnEe27bMk9c2oEaMP:m2MhLRFY3n93O00O11O1O00O11M21L4N1O004L3N01M20O101M021O00O1010OO1N20100LiLRFW3n93O11O01O0000000MeLUF\\3j94O0TN", "size": [427, 640]}, "area": 24243.0, "iscrowd": 0, "image_id": 11197, "bbox": [0.0, 29.0, 640.0, 148.0], "category_id": 169, "id": 20000690}, {"segmentation": {"counts": "hW24U=3L3N2N2N5J5K5L3N1001O1OK4SDnN`;T1]DQO_;R1]DRO`;o0^DUO^;^1M3M3O1O1N2O1O101O000001O000VNoDZ1R;eNPEY1P;fNSEX1o:fNSEX1P;_NYE^1^;ROSD6\\:ZOiG8kMN\\:LdE01Nj13m80VE001k<:01OO101O]X`0Mfg_O00O2O0000UUP1HRkoN1jB;f<EYC<g<DXC=h<3O1O1N2dMLaG5^8KaG5`8K`G3b8M^G3c8L]G4c8L\\G5d8K\\G5h8GXG9n:O000000000000000000Yh3@WXL8JO1L4LXX3MmgL?A000O100002N6J6JQ_70Z`Hg0M2N2N1O101O000000000000000M3O1O100k0UO2N2N000000000000000000000000000000di64WVI6hC@]98aG<gNIa9LgG`0^N1d9@mG]1R8cNmG_1R8aNjGd1U8]NnFe2l8[MeFT3[9=1200OSLiFe3^9F]FlLc9j2_F^Mb9W2^FSNe9`1[FiNj9i0[F\\OR:h1?AG9H8G9M3N3M2O1N2O2O000O2O0O101N100OGeFdLY9U3PGlLh8S3aGlL_8l2jGSMU8e2UHZMk7]2_HbM`7V2jHiMV7m1TIRNl6P2TIoMk6S2UIlMk6U2U2^NREi0m:VOVEh0i:XOZEf0f:YO]Ej0]:VOkEb0U:^OPF<R:BUF8k9E]F5e9HbF3^9JnFLT91T2O2N1O__?2aS@Nd<=00O1O1O1O101OO001001O000000000O1O1O1O100O100O2N1O1O100O1O1O2N2O2N000000000000000001O0000000O1N200001O01O000000000000000000001O00001O1O1O1O1O12N10N001O001O001O0O2N3L4I8Ic[l0OadSO4L3L3NOnMNoFOT91kFLY94gFI\\97l10000000]D2i9NWF1j9OWF0i90RFIiN7T;0SFJhN6U;0RF8_;2F@gCb0V<@gC5O3X<4hCN7Ag;a0RDO6@i;?QD34@k;Q1SDPOn;V11O1[OnCOS<d00K5M4JdC_OX<k0OAjCIV<4lCNT<OmC3R<MnC3S<JiCF4`0S<IPD7Q<HoC8S<DoC<a<O000O2N2N2M5K_e?", "size": [427, 640]}, "area": 12286.0, "iscrowd": 0, "image_id": 11197, "bbox": [5.0, 20.0, 597.0, 233.0], "category_id": 183, "id": 20000691}, {"segmentation": {"counts": "0k5e8SNbGhM^8c1YH[Ng7P1PInNP7<hIBX64TJJl57UJGk5;UJCk5>VJ@j5a0WJ]Oi5d0XJZOh5g0YJWOg5k0YJSOg5n0ZJPOf5Q1[JmNe5T1\\JjNd5X1\\JfNd5[1]JcNc5^1^J`Nb5a1_J]Na5d1`JZN`5h1_JWNa5j1`JTN`5m1aJQN_5P2bJnM^5T2cJiM]5X2]31O1O1O2N1O1O1O3M3MiNaDVOOZO^;_1gDSOc;l0aDoNa;P1cDkNX2F[6]1bGhNR21Y6V1iGeNl1;X6o0dJWOY5h0eJ]OY5c0dJCY5=cJG]5:`JH`58^JJb56]JKc55\\JLd55ZJLf54YJMg53ZJKg56ZJGg59[JEe5;]JCc5>_J_Oa5a0aJ]O_5c0cJ[O]5f0dJWO]5i0eJUO[5n0eJoN[5T1dJjN\\5X1dJfN\\5]1j33M1O1O2N1O1O2N2N4L3M3M3]D^MR;e2gDbMV;P3L5gEnLj8Y3gFUMS9Z4J6J6J5K4L1O2N1O00[LSHg0m7XOWHe0i7cNdGQOf0Z2f7^NmGUO`0Z2d7_NPHTO?[2a7`NSHQO?^2^7`NUHPO>V2nNkM_8o0WHmNf0n1gNUN\\8P1XHkNk0j1cNYN[8R1XHjNo0f1_N[N\\8U1UJ:V6FoIOSMlNQ9U1SJGQ69VJ@j5`0_JUOc5k0Q40000001O001O001O1O3M2N1O1O1O1O1O1YEnNn7_1^GjN`8S2`FSN]9P2^FTN`9S2WFQNg9a3K9G:F1O0000O100Ob1_N1O`0_O100KmDmLS;R3PElLP;S38O0O100O2O=PNTDa0n;^ORDb0o;TORD\\O03O[1;cNX;b0lDABOOf0?XOU;`0UE_OZO10`0<@V;?ZE@TO2M:`0EU;>aEBjN:a<E_C4\\14n<JQ]5O^_Jo0e21O001O001O2N1O1O1O1O1O1VEfN\\8[1^GmNlMGa:]1]GQORNF0L[:_1]GSOXNA10OMV:`1^GUO\\N^O0101Q:]1`GYO_NHi9P1cG\\OdNDi9R1SGHTOWOh9R40000000O1000000000000001O002N1O1O00O100000000O100000000001ON2N2O1N2O10000O100000000O100YOQFmLo9S3QF[L0008o9]3QF[L00050Io9f3RF\\LON0O08R:_3oEZL94h9b3]F[Le9d3d00000000000000000N2N2O1N2O1001OO100L41OO1001OMmDhLT;U35001OkNhDWN0>[;V1eDWN050=\\;W1dDWN0000000=\\;m0dDkN0K0N22N0000;^;n0hDgNJ06OJ100041IO1_;W1mDiNDO;0E30O1N22b;Q1lDmNO2ANO061`;R1^EnNP;R1W10000000000000000000000O100O100O100O1O100000000O10000O1O10PPm0XOhPSO`0@0000000000000000001O00001O00001O0]BZOf==YBDm=6SBIm=9QBHn=9QBGQ12g;;oCB]O7c<:kCA_O8f<8jC_O_O<f<4lC0T<1kCOU<1lCOS<0mC1S<OmC1S<OmC2R<NnC2R<NnC2R<NnC2R<NnC2R<MoC4P<LoC5Q<OjC3U<2dC0\\<5^CMa<R1O001O1O2N1O1O3M1O2N4L3M3M4L2`1^N1N2N01000N21OO100O100O10000002N1O1OO1001OO11O00O10000O100001O00000000O100000000000000O1000000O1O1L5B^BFi=OkiY2", "size": [464, 640]}, "area": 32239.0, "iscrowd": 0, "image_id": 11511, "bbox": [0.0, 0.0, 477.0, 192.0], "category_id": 99, "id": 20000692}, {"segmentation": {"counts": "Pdm1a1o<1O`0@000000001O0000001O=dD`Mn9a2QF_Mo9b2gE_M\\O03Oj:b2fEmMABOOf02i1NY4c2fIVN_OZO10`0Ol13^4_2eI[N@TO2M:7NN^10S5_2dIbNBjN:>GO`1GZ5h2TITM4\\14FW1SO]5_4VIeLY1kNa5T1TIf3S1WKi5Q1XIn3g0PKR6Q1[IT4;kJZ6P1_IX41hJ`6o0cI]4A`J23k6n0gI_OHY4a6XLjIUONa4X6YLkJe3U5[LQK^3P5bLTKZ3l4fLYKU3g4jL_KQ3a4oLfKi2T8M7I6I7_MfD`1`;RNkDHOd1a<L:E:G:F6J6I4MO1dLM[H2f7NTE0T>0kA10OP>0PB010T>0[B2T=NlB2T=NmB1i=O100000O10O100000f\\;4jbDMSBb0a=;J5M30001N11O00^MiNoE1=5ZOT1X:fN_E00082`0<YOBM0O\\1c:dN`E00050I4c0M]O?0T1`:]NbEON0O08:6U2R:bMaE9412EI20_2^:nMdEONC1i2[:Y1O1O2N1O1O1O1O1O0N2N3N1N2O2O01OO2O0L41OO2O01OVHXKi5N]Hl4h1WKh5n4XJQKi5o4VJRKk5n4TJRKg4MkI0>T5o0PKc4LkI050=U5P1PKb4KlI0000000=V5T1oJT4K`J0K0N22N0000;X5U1nJcNJY56dJJ06OJ100041IO1Y5b1oJ]NH11Z5;fJDO;0E30O1N22\\5`1PKd32jJO2ANO061[5`1oJ`N@Q5T1oJk4_1fJa3a6_L_Ia3b6]L_Ic3a6]L_Ic3a6\\LaIc3`6\\L`Id3`6\\L`Id3`6[LbId3_6[LaIe3_6ZLcIe3]6[LcIe3^6YLcIg3]6YLbIg3`6WL`Ij3`6VL_Ij3c6TL]Im3c6SL]Il3e6SLZIn3f6QLZIo3h6PLYIn3h6QLYIo3h6PLYIo3g6PLZIP4g6nKZIR4f6mKYIVOSOk4f7mKXIYOQOj4g7lKZIYOoNk4h7jKZIZ4d6eK^I[4b6cK_I]4a6bKkIS4V6kKlIV4R6iKoIU4T6iKmIW4S6hKnIW4T6gKmIX4a0cKU34[LZ4=gKV3M^L\\4;lKS3GcL]49nKT3CdL_47oKU3AeL`45oKW5P4iJQLW5m3jJTLV5l3iJULW5k3hJYLU5g3kJYLU5h3iJXLX5h3gJYLY5g3gJXLZ5i3dJXL\\5h3cJXL^5i3aJULa5k3_JSLc5n3[JoKi5R4TJlKP6T4oIkKS6V4jIlKV6T4fIPLZ6Q4cIQL]6P4`IQLa6Q4PIYKZO00i0f7P4\\H_K1m0c7`5000HZHaIO000h7\\6XHeIo7[6QHfIn7Y6SHeIO3k7W6YHjIf7U6_HgIa7X6<1OO10000N2nNiGlKW8R4lGlKT8_3hGVL69R8a3jGSL6:Q8a3lGTL59o7b3oGQL7:j7d3QHPL;6e7d3XHSL3:e7c3YHRL65c7i3YHmK39XOI]8P4VIQLQ7o3nHQLS7o3PInKP7R4g100O100O12N3M0000000000000000001O00001O00001O0oEhKj9Y4UFhKj9Y4VFfKj9\\4TFeKk9\\4TFdKQ12d7^4RH_K]O7`8]4nG^K_O8d8Z4lG]K_O<R9h3`G\\Lh8_OeFl3b0eLl8Z3UGfLj8Y3VGhLj8X3VGhLj8X3VGiLi8W3WGiLh8X3XGhLh8X3XGhLh8X3XGhLc2]Om2j3aJjLa2]Ol2j3bJjLb2]Ok2m3^JhLe2]Ol2P4XJeLh2WOSM5l5T4SJbLg2UO[M1O5j5S5kLoJYMOl5Q5kLZKT3g4jLZKW3f4hLZKY3g4eLYK[3h4dLXK]3h4bLXK^3k4^LWKa3j4^LUKc3m4[LSKe3Q5VLPKi3T5SLmJn3U5nKlJQ4Y5iKiJV4Z5iKeJW4^5^JnIe0i0j4Z5WJjI6;a0h0l4Z5^JSJ`0k0k4R6eJTJ[5`701O0000oFfJl8[5RGeJn8_501M13L3O1dKPG72`1R9SN\\GMFn1Q9QNbGDDZ2l8lMjG_OlN1`0b2o8hM`HU2c7`M]F1U2`2h9O20O01N1hMSDl1o;oMXDn1FQN_<o13M2O2VO]CAf<<]C@f<>ZCCh<9YCFj<8WCHk<5UCKn<1SCOo<OQC2Q=JQC5Q=IoB7T=DnB>a=01O00000O20O000001NZGFX39_L2`3N]L5d3H^L8g3@\\L`0j80O10000000MTBBl=>30OO2NhBl0Q:>kEBY:>cEC]:?_EBb:?QEMo:j1001O0O110JoDiLS;k0RF\\On:c0SE\\On:c0SE]Om:b0TE]On:b0RE^On:a0SE^On:a0SE_Om:a0SE_O];0dDOo<000004LW^i1", "size": [464, 640]}, "area": 61552.0, "iscrowd": 0, "image_id": 11511, "bbox": [136.0, 0.0, 377.0, 379.0], "category_id": 132, "id": 20000693}, {"segmentation": {"counts": "k5e8k5000000000000000000000000000000000000000000SHWJS7i5lHXJT7h5kHYJU7g5iH[JW7h5eHYJ[7P6[HQJe7]6000001O0000000000000aJ]IQ3c6nL`IP3`6oLdIn2\\6QMhIl2X6SMlIj2U6TMoIi2Q6VMRJh2Q5UKRKR21f2m4ZKPKo17d2i4_KnJk1>c2d4dKkJi1e0`2`4hKjJg1i0_2]4lKhJd1o0]2Y4QLfJa1U1[2T4WLeJ]1[1Y2o3\\LdJ[1`1W2l3`LbJX1f1U2h3dLaJV1k1S2d3hL`JT1P2Q2`3lL_JS1R2P2_3nL]JR1U2P2]3PM]JP1W2o1\\3RM\\Jo0Y2n1[3TM[Jn0Z2n1[3UMZJl0]2n1Y3oNgLQ1[3mNfLR1[3mNeLS1\\3kNfLT1Z3lNfLT1[3kNfLT1[3jNfLV1[3iNfLV1[3iNeLW1[3iNfLV1Z3iNgLW1Y3iNhLV1X3jNhLV1X3jNiLU1W3jNjLV1V3jNkLU1U3kNkLU1U3jNmLU1S3kNmLU1S3kNnLT1Q3mNoLS1Q3lNQMS1o2mNQMS1n2nNSMQ1o2mNQMS1P3kNRMT1n2lNRMT1n2lNSMS1m2lNTMT1l2lNTMT1k2mNUMS1k2mNUMS1k2lNVMT1j2lNVMT1j2lNVMT1i2mNXMR1f2oN[MQ1c2QO]Mo0T2jLgJ18V2m2o0S2nLQKR2m2P1R2mLRKS2l2P1b2PO^MP1b2PO^MP1b2oN`MP1`2PO`MP1b2nN^MR1b2nN^MR1c2lN^MT1b2lN^MT1c2kN]MU1c2kN]MU1c2jN_MU1a2kN_MU1a2kN_MU1a2jN`MV1`2jN`MV1_2kNaMU1_2kNaMU1_2jNbMV1^2jNcMU1]2kNcMU1\\2lNdMT1\\2kNeMU1Z2lNfMT1Z2lNfMT1Y2lNhMT1W2mNiMS1n1VOSNi0m1WOSNi0m1VOTNj0k1WOUNi0k1WOUNi0j1XOVNh0j1WOWNi0i1WOWNi0h1XOYNg0g1XOZNh0f1XOZNh0f1XOZNh0f1XOZNh0f1WO[Ni0e1WO[Ni0e1WO[Ni0e1WO\\Nh0d1WO]Ni0c1WO]Ni0c1WO_Ng0c1VO_Ni0a1VOaNi0_1ROgNm0_1mNbNR1d1hN]NW1e1fN^NX1g1dNYN[1n1]NTNb1T2VNmMi1[2oMfMP2`2jMaMU2f2_MkH2a4]2^7O1O1O1O1O2N1O1O1O1O1O2NnH[NZ1d1gN_NV1`1jNjNm0U1SOTOe0k0[O_O<`0DF88HN31L32LN34LL45KK84FK;6DH>9AXOeN\\Kj1^5@VOn0k0QOUOo0l0POSOQ1n0nNROQ1o0oNQOQ1o0oNQOQ1o0oNQOP1P1POoNQ1P1POPOP1P1POPOP1P1POPOo0Q1QOnNP1R1POnNP1R1POnNP1R1POoNn0R1ROnNn0R1ROPOl0o0UOROj0n0VOSOh0n0XOROh0n0XOQOi0o0WOQOh0P1XOPOh0P1XOPOh0P1XOPOh0o0YOPOg0Q1YOoNg0Q1YOoNg0Q1YOoNh0P1XOPOh0P1XOPOi0o0WOROh0n0XOPOk0o0UOoNm0P1TOoNn0P1ROnNP1R1POmNf0cKmM_5]1nN:`KTNV6b1ZN5o1KPN2S2OmMOT22lMOR22nMOP22PNNo13PN0m11SN0k11WNXOeKZOT6_1XNPOlKYOBM0OZ6l1ZNhN]K]O?0P6l1WNaNb2`1_MYNXKI20V7o1aMQNXK1^7o1n41O1O1O1O1O1N2O1O1N2O1O1N2O1O1N2O1L4F:G9F:C=I700O10000001O000lKlEl3V:nKjE11Q4[:01oKcEm3a:O0000001O00001O001O0000O1OkNaE[N^:LhE<0HW:EXF7F4R:]OfF3^O`0k9ZORGJXOl0e9[OSHe0n7ZORHf0n7ZORHf0o7YOQHg0o7YOQHg0o7YOQHg0m7oNcE9a2g0l7QObE8b2g0m7POaE9b2g0m7[OSHe0n7ZORHf0n7ZORHf0l:000000000000000000000000000000000000000001O00000000000000000000000000000000000000000000000000001O000000000000000000000000000000000000000000000000001O00000000000000000000000000000000SNSO\\D5d1h0S:G]E9c:7mDIS;g0]DYOc;j100O10000kIXMb0h2^OYM`0h2@YM?g2@[M?e2]OWMfJ5l5d2XOWMmJ1O5j5d2ZO^MjJOl5b2ZOiMe0W2ZOjMg0U2YOkMh0T2XOlMh0T2XOlMi0S2WOmMi0S2VOoMi0Q2WOnMj0R2VOnMj0R2UOoMj0R2UOoMl0P2SOQNl0P2RORNm0o1TOPNl0P2^MZMPM2e0i0j4l1`MZMgLN6;a0h0l4e1eMbMiL6`0k0k4^1lM`NiL8[5W1lM6T2JjM9U2GlM8T2HkM9U2PMfJb2U3>V2nLeJe2T3=[2CeM=\\2BcM=^2DbM>Z2DfM=Y2CgM>YOoL;d2><mMXMELn1a2a09mMaMWOLZ2Z2c06oMiMiNYO1`0b2Y2d05PNcNY1Y1g03QNRMhN_1a2\\1g01SNcNU1]1g00UNdNT1[1h00TNeNT1[1h0NUNiNQ1Z1i0NVNhNP1[1j0MVNiNo0]NdMk2W30TNgNU1Y1f02UN`NY1^1b03TN[N_1a1=3VNXN`1e1;3SNWNc1g1:2SNUNf1h173SNTNf1j172SNTNg1i163SNSNh1j145RNRNj1i145RNRNk1h136PNSNn1g126QNRNm1h125QNTNo1e107QNSNP2f1O8oMSNR2e1O8oMSNR2e1N9PNQNS2f1M9oMQNT2g1M8oMPNV2g1K9oMPNV2g1J;jMnLROR1[3e1H<\\M_Nm2U1C`0^M\\No2U1]Og0^McLI`1\\3W1ZOi0`MUNV3R1ZOk0\\MUNn2WNXMk2^2j0[MSNQ3\\NnLj2f2g0ZMTNR3[1D\\2<dMD[2=eMC[2>dMB\\2>dMB\\2>dMB[2?eMA[2`0dMA[2>fMCX2=iMD[M^NS3n1BDZM^NU3m1BDYM`NU3l1BCXMbNW3j1BEjLmNd3]1C]1>bNB^1>aNC_1=aNC^1?`NBa1=_NC\\1gLgKi3k2Aa0_1^ObNa0_1_OaNa0`1]OaNb0`1^O`Nb0`1]OaNb0a1\\O`Nd0a1[O_Ne0c1XO^Ng0d1XO\\Nh0e1\\MPKo0\\3e1e1YMXKi0U3n1d1VMaKb0m2X2c1SMeK?k2^2a1PMiK<i2c2n2WMUMi2n2PMVMP3m2jLVMV3R71O001O0000O100O1O100O1JlDPMT;e2a000000000000000000000000000J6H8M3000000000000000000000000004L7I6J00000000O100000000000000O100001O00000000000000000000000000000000?A00001O000000000000000000001PEnL3Ol9S3hEXM3MU:k301O00000000000000000000001O00000000000000000000000000001O", "size": [464, 640]}, "area": 119634.0, "iscrowd": 0, "image_id": 11511, "bbox": [0.0, 69.0, 640.0, 395.0], "category_id": 140, "id": 20000694}, {"segmentation": {"counts": "mTW63\\=O`C4_<O]C3a<1YC4e<1UC0k<3jB@1c0O]OS=[1O100N110001N200OO20N100000001N100N21N0101N10000O10001JeBRO^=?a0N4JoAIQ>67L3O0OdYX2", "size": [464, 640]}, "area": 1326.0, "iscrowd": 0, "image_id": 11511, "bbox": [439.0, 195.0, 41.0, 47.0], "category_id": 150, "id": 20000695}, {"segmentation": {"counts": "mkX43\\>2O0TOl002NN200O1OiDVOj8k0SGZOj8f0UG_Og8a0XGAg8?XGBh8>WGBj8=VGDj8:WGGi89VGHj88UGLh84XGLh85VGKk85TGLl84TGKm86QGKo85PGKQ96nFHT98lFFV9;hFB\\9?aF_Oc9a0\\F^Of9c0WF_Oi9a0SFCm9>PFDP:=mEDT:>]ElNZO00i0Y;=iDRO1m0V;m1000HgDTMO000[;i2eDXMb;h2^DYMa;f2`DXMO3^;d2fD]MY;b2lDZMT;e2<1OO10000N2nNVD_Oi;`0ZD^Of;MVDH69d;OXDE6:c;OZDF59a;0]DC7:\\;2_DB;6W;2fDE3:W;1gDD65U;7gD_O39XOIo;>dECc:=\\ECe:=^E@b:`0g100O100O5NjZj3", "size": [464, 640]}, "area": 3674.0, "iscrowd": 0, "image_id": 11511, "bbox": [302.0, 0.0, 68.0, 119.0], "category_id": 175, "id": 20000696}, {"segmentation": {"counts": "^b07d=g0YOg0XOh0D<00O1O1O1O100O1O1O1O100O1O1O100O001hElLQ9U3lFmLT9T3iFmLY9T3bFnL_9Z3WFgLj9a3lE`LU:m32N1O1O1O2N3M3MhN]L[GO[Ob3Y9cLXG1]OZ3Y9iLUG4@P3Y2bLc4>_I6DX15ZOT2mN`4`0^IP24`Nm1TO]4`0^Ij1>_Nf1[OZ4R3UJaM`1AW4l2_J`MY1HU4d2hJbMR1MT4^2oJcMl03S4W2TKeMh08Q4R2ZKdMc0?P4l1`KcM?e0n3g1fKbM;j0n3b1iKbM9P1k3]1oKaM5V1i3[1RL]M4\\1h3X1TLZM3a1g3W1ULWM3f1e3U1WLSM4l1c3S1WLPM5Q2a3Q1YLmL6S2`3R1YLjL6U2b3R1WLhL7W2b3R1VLeL9Y2c3S1RLcL;[2e3Q1hMPO[2o0cMRO_2]1oLcNS3\\1lLeNT3Z1kLgNV3Y1iLhNX3W1gLiNZ3W1dLkN\\3U1cLkN_3S1aLnNa3o0_LQOd3l0[LVOh3f0XLZOk3eMlIa2Y2Km3^MQJc2R2O_4M`K5b4ZN[I4S2c1g4jMeI=d1i1n4[MlIf0U1Q2]6iMcIW2c6cM]I^2h6\\MXId2l6XMSIj2P7RMPIn2Y91O0O2O0VJaMC_2<eM@\\2HQMhKf0^4Y2CZMlK`0_4W2B]MlK?`4T2C`MiK?b4R2DaMhK=W2oNNS3S2cMeKf0n1gN9P3T2dMcKk0j1cN>n2U2eMbKo0f1^Nb0o2V2CRM_Mh0n2V2HlL[Mn0n2U2NeLUMU1n2V24]LoL]1m2V2=nKlLl1g2V2Z1jMfNV2Z1iMgNW2Z1hMfNX2\\1fMdN[2[1eMfNZ2[1dMfN]2Y1cMgN]2Z1bMfN_2Y1aMgNa2W1^MjNe2S1[MmNf2Q1[MoNf2P1YMROg2m0YMSOh2l0XMTOi2k0WMUOj2i0VMXOl2`NmK^OW1R2d3cM\\K9P1T2V4oLnJk0l0V2Y4jLoJm0h0[2k5bMVJ^2S6YMmIg2]6nLcIS3f6cL[I]3l8000O101O000O10000O1aM\\LRJd3m5_LQJb3m5_LSJa3m5_LRJb3m5_LSJa3m5_LSJa3l5`LTJ`3l5`LSJa3l5`LTJa3k5_LUJc3g5_LXJc3f5^LZJc3f5\\LZJe3j5VLVJk3n5PLQJR4V82N1eIkKY2V4dMnKY2T4eMPLX2Q4fMSLW2n3gMVLU2m3gMXLnLHY4Q4hNZLeLNa4i3hNZMV1g2iN_MP1c2oNaMm0`2ROeMi0]2TOiMg0X2XOmMb0U2^OoM=R2CTN6m1IYN0i10\\N_MkL`1k4o0`NSMPMk1a4R1XOlNi0U1TOkNn0U1QOkNP1U1oNkNR1U1mNkNU1T1jNkNX1U1gNkNZ1U1nLQMXOj1k3U1gL[MYO_1R4V1`LeMWOV1[4U1WLoMWOn0c4S1TL[NmNc0P5S1PLo0R4QOdKX1]4iNPKh1Q5k30O10000O1000000O10O1000O1000000O1000O01000000O100001O0O2O001O001O001O0O3N1N2N2N3M2N2N2N3M2N2N2N3M2N2N2N3M2N2N2N2M3N1O1N3N1O1N3N1O1N3N1G9G9G:C<C=L5N100O2O000O2O0O100O2O0O101O0O101N100O2O0O101N101OZFnNn5Q1YHUO_O0Y8j0PHE[OFe8d0iG4VO^OR9=dG`0nNXO^97eGa1\\8^NdGb1\\8]NeGc1\\8\\NdGd1\\8[NeGe1\\8YNeGg1[8XNfGh1[8VNfGj1Z8UNgGk1Z8SNgGm1Y8RNhGn1Y8PNhGP2X8oMiGQ2X8mMjGR2V8mMkGS2V8kMkGU2U8jMlGV2U8hMlGX2T8gMmGY2T8dMnG]2Q8bMPH^2Q8`MPH`2P8_MQHa2o9000000000000000000000000000000000000000000001O00000000000000000000000001O00000000000000000000000001O00000000000000000000000000000000000000000000000000001O05K`0@`0@`0@`0@hof0E_PYO7I6J7I6K6cCPOR;U1kDmNQ;W1mDjNQ;Z1kDgNU;[1iDeNV;_1gDaNY;b1cD`N\\;b1bD^N];f1_D[Na;g1]DYNc;j1XDYNf;Y20000O2N1O100O2N1O1O2O000O101O0O10001O0]JmLHT38mLGS39nLFQ39RMEo2:SMEl2?PMBo2a0nL@R3o5O101N1O101N101NhIZNHf17[NId17]NJa16`NJ`16`NJ_16bNK]14dNMZ14fNNW12jNOU10lN1WOoJn0Q5K1TOXKf0i470PO_Ka0b4>2nNaK>_4c03kNdK<[4h0c1RO`Nm0d1lN`NT1^7001N1000001O1OkNoBl0P=POVCo0i<POZCn0f<RO[Cl0f<SO]Cj0d<VOhja1", "size": [464, 640]}, "area": 60044.0, "iscrowd": 0, "image_id": 11511, "bbox": [1.0, 32.0, 529.0, 402.0], "category_id": 183, "id": 20000697}, {"segmentation": {"counts": "0`?`4KbKf@`4X?_Kh@10`4X?5000000000ZKj@b4X?2O1O100M31OO12N1O000000002N0000mH`KeNa4o0lKjLFVM`4e5XL`IWOj1g0kNj3j5XLXIEc1g0YO\\3m5fMiIO5^2P6gMYI<f0m1n5kMWI=i0i1Z4fKaJD<e2M;k0f1R1ZKg0j0\\Mm1N;l0d1>RLlNYOd1h0TNR2O:R1]1QOaM0QNl1f0oMT208T1\\1lNgM2mMo1f0lMS229S1[1lNiM2kMj1l0RNn127S1\\1kNkM2jM_1W1]Nb138U1X1iNoM1nM2Ke0U1VOd128W1W1hNPN1eNh08UOe138X1T1fNTN1cNj0c3UOcNZ1S1fNTN0cNm0b3SOdNZ1S1fNUNO`41XLZ1S1eNVN0_41XLY1T1fNa50\\IY1T1gNU9W1k42N1O01N11O1OO1001O1O0_]OeN]b0[1b]OgN]b0\\121O3M2NO1010ON22NO1002d]O]NWb0f101O00O1O100Nh]O\\NXb0c1i]O]NWb0d13N12N00O100002N2NO11ON22N1O2NO2O0001O00O12NO1O1O100OoAaNQLIY=e1gFlNX9S1iFmNW9S1iFmNW9T1hFlNX9U1gFkNY9T1hFlNX9S1iFmNW9S1iFmNW9R1jFoNU9P1lFPOT9P1lFPOT9o0mFQOS9o0mFQOS9n0nFROS9m0mFSOS9l0nFUOQ9j0PGVOP9j0PGVOP9i0QGWOo8i0QGWOo8h0RGXOn8d0VG\\Oj8d0VG]Oi8b0XG^Oh8b0XG^Oh8b0XG^Oh8a0YG_Og8a0YG_Og8`0ZG@f8`0ZG@f8`0ZGAe8?[GAf8=[GCe8=[GCe8=[GCe8=[GCe8<\\GDd8<\\GEc8;]GEc8;]GEc8;]GEc8;]GFb89_GGa89_GGa89_GGa89_GGa89_GGa89_GGb88^GHb88^GHb88^GIa87_GIa87_GIa87_GIa87_GIa87_GCVKB[=k0_GCVKB[=k0_GCYK^OY=o0^GCg8=YGCg8=YGCg8=YGCg8=YGCg8=YGCh8=WGCi8=WGCi8=WGBj8>VGBj8>VGBj8>VGBj8?UGAk8?UGAk8?UGAk8?UGAk8`0TG@l8`0TGAk8?UGAk8`0TG@l8`0TG@l8`0TG@l8`0TG@l8a0SG_On8?SGBl8>TGBl8>TGBl8=UGCk8:XGFh8:XGFh89YGGg89YGGg8;WGFh8<VGDj8;WGEi8`0RG@n8<VGDj8:XGFi89WGGi87YGJf83]GMc83]GMc82^GNb82^GNb81_GOa81_G0`81_GOa8OaG1_8NbG2^8McG3]8LdG4\\8JfG6Z8JfG6Z8JfG7Y8JfG6Z8LdG4]8IeG7[8FhG:X8FhG:X8EiG<V8DjG<V8EiG;W8HfG8Z8GgG9Y8GgG9Y8HfG7[8IeG6\\8IeG7[8IeG5^8JbG5_8L`G4V>00000000000000000000000000000m\\O]OH0ob0c0Y]OEab0m00001O00000000000000001O0000000000JY]OoNkb0n05J6O10000O10000000000000000002N3M3M3M00001O00001O000000000000000000001O0000000000001O000000000000001O00000000001O0000000000001O0000000000001O00000000001O000000000000001O0000O1N200O1N20cJlNnGT1R8nNlGR1U8nNjGR1S8lNgB3T5Q1V8UOgGk0Y8WOeGh0\\8ZObGf0^8\\O`Gd0`8_OeD_Oc0R1h:@_DBj0n0g:^O\\D1e0a0o:@XD1i0?o:AVD3i0<Q;GPDLP1=P;MfCLX18Q;5VCJj12o:k1QEUNo:j1REWNm:f1hBSM[2W1m:e1WE[Ni:c1YE]Ng:c1YE]Nh:`1fBYMb2W1h:`1ZE`Nf:^1\\EbNd:\\1hB]MN11O]2W1l:^1fBaM^2Q1l:f1TEZNl:_1[EaNe:_1[EaNe:_1[EaNe:_1[EaNe:_1[EaNf:`1XEaNg:_1YEaNg:]1[EcNe:\\1\\EdNd:[1]EeNc:Z1^EfNb:T1gBeMi2W1`:R1iBgMg2W1`:S1hBeMi2X1_:S1eEmN[:R1fEnNZ:R1fEnNZ:R1fEoNY:Q1gEoNY:e0gBXN1Ln2W1Z:c0kBYNMKo2\\1V:>RCYNk2\\1P:NmBnN8Gl2`1l9KQCdNOO`01d2c1j9DSChNn3d1o8CUG>j8BVG>k8AUG?k8@VG`0j8@VG`0j8@VG`0j8@VG`0j8@VG`0j8@VGa0i8_OWGa0i8_OWGa0i8_OWGa0i8_OWGb0h8nNUCHS4Z1h8kNZCIn3[1i8kN`CDg3`1j8kNbCBe3d1h8iNmGW1S8hNnGX1S8hNlGX1T8hNlGX1T8iNkGW1U8iNkGW1U8iNkGW1lMZNb7?bJW1kM[Nc7?aJV1lK`NS1Mo1Ng2?[NV1kKjNj0DY2Mg2?[NV1jKROb0_Oa2Jh2?[NV1jKZO9XOk2Hg2?\\NW1hKD0POT3Fh2`0[NV1hKMGjN]3Dh2?\\NZ1cKa0X3fMi2<eIDf4b4kN^Kj2=eIBf4e4iN\\Km2;^N[4bN[KP35cNd4YNWKT3NjNT5iMoJe8Y5RGhJn8_5jFbJV9b5fF^JZ9b5eFaJY9_5eDcJ]1No9g5\\DcJ\\1HW:a6`E`I`:i6UEZIj:n6mDTIR;n7O002N1QEfGV:\\8hEkGQ:Y8jEhGV:Q90003M1O00001O000000001O", "size": [640, 480]}, "area": 47869.0, "iscrowd": 0, "image_id": 11615, "bbox": [0.0, 0.0, 480.0, 565.0], "category_id": 120, "id": 20000698}, {"segmentation": {"counts": "Xl?5_c0=WAF_9h0lBWOj1g0S;2jBEc1g0b;R1]CO_<5oB<m<HnB=[;_MYDD<e2M;T8PM`Jj0\\Mm1N;`7fMfHYOd1h0TNR2O:X6nNkIQNl1f0oMT208U6SOmImMo1f0lMS229S6TOnIkMj1l0RNn127R6WOnIjM_1W1]Nb138Q6WOnInM2Ke0U1VOd128R6WOnIeNh08UOe138P6XOoIcNj0c3UOcNR6WOnIcNm0b3SOdNR6XOmI`41XLQ6YOnI_41XLQ6e6nI\\IR6Y:O1000001O00000000000000000000000000001O0000000000000000000fNQBcLo=X3VBhLj=[2bBfLHl0f=X2nBeL_OQ1c=V2dChM]<R2jClMV<Q2oCmMQ<P2TDnMl;P2WDoMi;o1[DoMe;n1_DQNa;m1cDQN];R1RBQOc2L[;R1SB_NNM07g2:X;T1[BYNF103k2=T;T1dB]NZ2?R;V1cEkN]:V1bEjN^:X1`EhN`:W1bEhN_:W1bEhN^:X1cEgN]:Z1cEeN]:[1dEdN\\:[1eEeN[:]1dEbN\\:]1fEbNZ:]1hEbNX:]1jEbNV:^1jEbNV:]1lEjNZL@Y=e1_FTO`9k0bFTO^9l0cFSO]9n0bFRO^9o0bFPO^9o0dFPO\\9o0fF]NkK`0_=S1gF\\NjKa0_=R1jFoNU9P1lFPOT9P1lFPOT9o0mFQOS9o0mFQOS9n0nFROS9m0mFSOS9l0nFUOQ9j0PGVOP9j0PGVOP9i0QGWOo8i0QGWOo8h0RGXOn8d0VG\\Oj8d0VG]Oi8b0XG^Oh8b0XG^Oh8b0XG^Oh8a0YG_Og8a0YG_Og8`0ZG@f8`0ZG@f8`0ZGAe8?[GAf8=[GCe8=[GCe8=[GCe8=[GCe8<\\GDd8<\\GEc8;]GEc8;]GEc8;]GEc8;]GFb89_GGa89_GGa89_GGa89_GGa89_GGa89_GGb88^GHb88^GHb88^GIa87_GIa87_GIa87_GIa87_GIa87_GCVKB[=k0_GCVKB[=k0_GCYK^OY=o0^GCg8=YGCg8=YGCg8=YGCg8=YGCg8=YGCh8=WGCi8=WGCi8=WGBj8>VGBj8>UGCk8=UGCk8>SGCm8=RGDn8<RGDn8<QGEo8<PGDP9<oFFP9:PGFP9;nFFR9:mFGS99mFGS99lFHT99jFHW96jFKU95jFLV94jFLV93jFNV9OlF2T9NlF2T9MlF4T9LkF5U9MhF5W9MgF3Y9LgF5Y94]FMc94^FJb97_BgNk3R1g9<VFDj9=TFEk9;TFFl9;RFFn9;PFFP:;nEFR:;lEFT::lEGS::jEHV:8iEIW::eEG[:;bEF^:;_EGa:9^EHb:9[EIe:7ZEKe:3[EOe:3oDUNgMj1[=1kDYNfMh1_=1gD9Y;HfD8Z;JcD8\\;JbD6^;L`D4`;K_D7a;H`D8`;GaD9_;FbD9_;GbD7_;HcD7];IcD5`;IbD5_;kN\\BUO0M3`0S2c1h;`N`BLi1c1U<QNUB<f1NjMS1S?lNUCDUN`1oa001O001ZO`]OJ`b05a]OK^b06b]OJ_b05a]OK_b04b]OL^b04b]OL^b03c]OM]b03c]OM`b0Oa]O14]OH0\\b0=h]O64ERb01j]Ol0Zb0oNg]OQ1Yb0oNg]OR1Xb0nNh]OR1Wb0nNj]OR1Vb0mNk]OS1Ub0mNk]OS1Tb0nNl]OR1Tb0mNm]OS1Rb0nNn]OR1Rb0mNo]OS1Qb0mNo]OT1Rb0iNo]OW1]b000000000JY]OoNkb0n05J6O10000O10000000000000000002N3M3M3[OPOT^OP1ia0UOU^Ok0f3FV9AUCj0d3FU9DTCf0g3FU9ESCe0h3GS9FTCd0h3FU9ESCe0h3GR9GUCb0i3GS9GSCb0k3FR9JQC`0m3GQ9JQC?n3GQ9LoB=P4HP9KPC=P4Io8Q1QGPOn8P1RGQOm8o0SGQOl8Q1TGoNl8P1TGPOl8P1TGPOl8P1TGQOj8P1VGoNk8Q1UGnNf8SOYCR2Q4jNc8^1\\GaNd8`1\\G`Nd8`1]G_Nb8b1^G^Nb8b1^G^Nc8a1]G_Ne8_1[GaNi8[1WGeNi8\\1VGdNi8]1WGcNi8]1XGbNh8^1XGcNf8^1ZGbNe8_1[GaNd8a1[G_Nd8b1\\G^Nd8b1\\G^Na8VO^C]2Q4]N`8f1aGYN_8g1aGYNc8c1]G^Nb8c1aGYN_8g1cGWN]8i1cGWN]8i1cGWN^8h1bGXN^8h1bGXN^8h1bGXN^8i1bGWN]8i1cGXN\\8h1dGYNZ8h1fGYNX8h1hGYNT8j1lGWNS8j1lGVNR8l1nGTNQ8m1oGSNP8n1PHQNP8P2QHoMn7R2SHlMm7U2VHgMj7Z2XHcMh7_2ZH^Md7d2^HZM`7h2j41O002M2O011eLjLkEW3U:kLgEW3Z:iLbEZ3[:dLgB3i2\\3a:jLZEX3f:jLVEW3k:kLPEX3P;jLlDX3T;kLeDY3[;hL_D]3a;aL\\Dd3d;^LXDd3h;]LVDd3j;bLPD^3P<hLfC]3Y<lLVC]3i<m101O00000001O0000000000000000000010O00000000000000000000001O00003M3M3M2N001O01O000000000001O0000001O00O1O11O0001O00000000gEPIb7P7]HQIc7o6]FVIS1Mo1Ng2o6VJ`Ij0DY2Mg2o6UJhIb0_Oa2Jh2o6UJPJ9XOk2Hg2P7TJZJ0POT3Fh2P7SJcJGjN]3Dh2S7oIWKX3fMi2l9VMTFj2n9TMRFm2o9PMRFP3R:lLnET3[:cLfE\\3b:[L_Ee3h:SLYEm3k:oKUEQ4k:nKXEQ4g:mI[E]1Nf4P;eIZE\\1Ho4i;hJXDX5Q<]JRD`5X<WJjCh5X=FXBZJh=f5ZBVJj=h5VBWJl==YBc4KoJo=<]B^4l=]KUBc4d>0003M1O00001O0O100O1O2OYF", "size": [640, 480]}, "area": 224221.0, "iscrowd": 0, "image_id": 11615, "bbox": [25.0, 0.0, 455.0, 640.0], "category_id": 132, "id": 20000699}, {"segmentation": {"counts": "`?`4[?bKf@`4X?_Kh@10`4X?5000000000ZKj@b4X?2O1O100M31OO12N1O000000002N0000O1N20000M300O100004L00O1O12N002N000000O1O1N22N1O00O11O1OO1001O1O0^Kf@^4Z?aKh@^4[?21O3M2NO1001ON22NO1002dK]@W4f?01O00O1OgNZ@ZNf?a1_@_N_?hN\\@l1a0]NHl0Z?lN]@e1m0\\N_OQ1X?a0oA]OP>?WB_Ok=<ZBBf=;_BC`=<cBC]=;gBCY=:kBEW=7mBGX=3jBLV=AV@M07g2:\\=]Om_O103k2=_=AcB?^`0100001O1O1O1O1O001O1O1O1O001O1O1O1O001O1O1N2OQ`U10o_jN0O1O100O100O100O1O100O1O100O100O1O100O1O1O100O1lKDcD=Y;HhD6V;Mk@Fk3=Z;3cDM[;6cDKZ;8eDI[;8cDI\\;9bDH^;9`DH_;:_DGa;9_DGb;9[DIc;9\\DHc;<YDEf;>WDCh;?UDCi;?VDBj;?SDCm;=RDDo;9QDIQ<7cCiNgMR1d>8bCjNfMP1e>;aCO_<2`CN_<5^CLb<6\\CJe<7YCIj<3UCOj<1WCOi<0XC0i<NXC2h<NYC1f<O\\C0d<0\\C0d<O^C0c<oNXAE0M3`0S2o0\\<TOlALi1o0i<eNaA<f1NjM?g?@aBDUNl0cb001O001N1O011O0O100O103L6K4L4K1000O01O10O10O010O102Mko>1RPA5K204M1N20ON300O2N1O2N003M000O11O0000O100JVOS]Om0ib07O100O1001O2N4L00O10000O1O1O1O100MlNY]OU1fb03004L000000001O0000000000O1O1M300N2O1O1O1O1O1O1O1N2N2N2N2N2M3N2O1O1N2M3M3N2N2M3N2O1O18H002NO12NO100O1MeMa^O[2^a04N200N^Mf^Ob2Za02N2N\\Mj^ON11O]2Xa0bMn^O^2Za00I7000000002N00N2O1O1O1JYMQ_Oi2m`0YMS_Og2n`0XMQ_Oi2o`04O1000000DPMd_O1Ln2^`0UMe_OMKo2^`0XMe_Ok2n?mLZ@8Gl2o?mLP@OO`01d2k?mLT@n3k?40000O1000000000000000000@mKSAS4j>RLTAn3k>YLo@g3P?\\Lm@e3R?e0O11O001O0000001O000000O11O00MYKn@f4S?ZKl@f4S?4K5I7O10000001OO11O00N200GgJkAY5U>iJgAY5Y>gJfAZ5Z>fJeA[5[>50000000000000000O100O1O100", "size": [640, 480]}, "area": 33426.0, "iscrowd": 0, "image_id": 11615, "bbox": [0.0, 458.0, 480.0, 182.0], "category_id": 169, "id": 20000700}, {"segmentation": {"counts": "V]e5h0Xc01O00001O0010O0001O1O1O001O100O000000O1O1O1O101O000001O00000001O0000000001O0001O00006J00000001O00010O1O1O1O1O0000000001O2L4L5M2OSn]2", "size": [640, 480]}, "area": 1639.0, "iscrowd": 0, "image_id": 11615, "bbox": [290.0, 166.0, 65.0, 37.0], "category_id": 183, "id": 20000701}, {"segmentation": {"counts": "0X8h;001O001O0000001OXOh00000001O000000000000001O00000000000000O1O100O100O1000000001O0000001O0000001O1O1O1O001O00001O0000N2M3N2M3N2M3N2M3N2N2O10000000000O1M3L4M3L4M3L4M3L4M3M3L4M3L4O10000000000000000000000000000000000000000000000O1M3001O1O0000O100O100O100iN^AoLc>P3lAbLT>\\3Y1O1N2O1N2O1O1N2O1N2O1N2N2N2M3N2N2M3N2M3O100O100O10000O100O100O10000O100O10000O100O100O10000O1000000001O000000001O00000000001O000000001O0000001O2N1O2N1O1O2N1O2N1O1O2N1O1O2N1O2N1O1O8HN2M300O1O1O1O1O1O1O1O1O100O1O1O100O1000000O1000000O1000000O100000000O100000000000000000000000000000000000000000000001O1O1O1O1O1O1O1O1O1O1O1O001O1O1O1O1O1O1O2N1O1O1O1O1O1O1O2N2N2N2N2N2N3M2N3M5K4L4L4L1O1O1O1O1O1O1O001O1O1O1O1O1O001O1O0000000000001O00001O0000001O00001O00001O1O1O001O1O1O1O001O1O1O001O1O1O1O1O1O2N1O1O2N1O1O2N1O1O2N1O1O2N1O1O2N1O1O2N1O1O2N1O1O00N2O1O1O1O1N200000000004L001O0000001O001O2N0000O1O10000N2O1O1O1O1O1O1O1O1O1O1O1O1O1O1O100O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O100O1O1O1O1O1O1O1O1KnA[JS>c5600001O00O1O100O1O1O1O1O1", "size": [640, 480]}, "area": 80310.0, "iscrowd": 0, "image_id": 11699, "bbox": [0.0, 0.0, 480.0, 267.0], "category_id": 102, "id": 20000702}, {"segmentation": {"counts": "X8n6R=O2N1100OO1lIoBS5W=lJlBl4[=QKjBf4_a0fL6K5J7I6J6K6I6J6L5O001O00001O00001O0O100000O100O100O100O1O100O100O100O100O00100O100O100O100N2K5K5K5K5K5KJ6G9J7H7_Oof:LjYE10O10O10000O1000000O10000O1000O010O1O1O00100O010000O1000000O10000O100000O1O1O1O10oi\\13mUcN2N3L3N3M2M4L3M3M4L4L4K6K4L4O1Y1hN1N2N2O1N2M2MUXR5", "size": [640, 480]}, "area": 3893.0, "iscrowd": 0, "image_id": 11699, "bbox": [0.0, 184.0, 220.0, 302.0], "category_id": 105, "id": 20000703}, {"segmentation": {"counts": "Q_g64lc02U\\OKgc0;L6J3M4K8I4L3M5K4L2b]O^NZb0b1f]OcNUb0]1k]OgNPb0Z1P^OkNja0V1V^OnNea0g1[^OkM^a0V2b^OoMWa0S2i^ORNV?I\\Bj2`=\\MYBi2c=]MUBf2nNhLh>g0TBc2SOjLe>m4[AYK_>g4aA\\K\\>d4dA_KY>a4gA`KX>U5O000VBYJ[=g5dBaJW=]5iBhJ\\=o4_BeJK?d=j4aBgJJd0c=m4]BXK_=g4aB[K^=e4aB[K`=d4aB[K`=d4`B\\Kb=b4]B_Ke=]52N1O2N1O2N1O2N3M1O1O3M1O1O001O1O1O002N2NL47I2N1O1O1O1OO11O001O000000003M=C1O0000000000000000001O00000`Kc@]4]?bKd@^4^?12N000000001O00001OO11O00002N001O1O000000003MO11O1O2N3M1O2N2N001O001O1O1O1O002NO10000", "size": [640, 480]}, "area": 19204.0, "iscrowd": 0, "image_id": 11699, "bbox": [344.0, 414.0, 136.0, 226.0], "category_id": 117, "id": 20000704}, {"segmentation": {"counts": "Ymm52ic07I8H8H7G:G8H9G8G:F9D=C<J7O001O001N1h@^LP>d3lA\\LV>e3gA[LZ>h3bAXL^>l3^ATLa>f400000000000000O100000000000000O1004L2VKj@f4\\?L6J3M4L7I4L3M5K4L2bL^_OZ3b`0fLc_OU3]`0kLg_OQ3Y`0oLl_Ol2T`0TMP@h2d`0XMn^Ob2Ra0^MS_O]2m`0cMX_OX2\\a0L4L1QNR^Oj1oa0UNU^Og1ka0YN[^Oa1ea0_N^^O^1ba0bNa^O[1_a0eNb^OZ1Sb0O000TO[]O=eb0Bc]O7]b0Ih]O2Yb0I[]OK?8Vb0M[]OJd05[b0Kj]O0Vb00n0O01O00Oo\\k1", "size": [640, 480]}, "area": 7641.0, "iscrowd": 0, "image_id": 11699, "bbox": [303.0, 470.0, 81.0, 170.0], "category_id": 132, "id": 20000705}, {"segmentation": {"counts": "gTl31kc04K5L5K4L4K5L5K4L4K5L5L3O10001O01O00010O00000001IKP^OfNPb0j0e^OQO_a0`0T1I6K4I`al4", "size": [640, 480]}, "area": 1168.0, "iscrowd": 0, "image_id": 11699, "bbox": [198.0, 347.0, 31.0, 67.0], "category_id": 144, "id": 20000706}, {"segmentation": {"counts": "^a0b2ic0eM000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000O10000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000O1000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000cILnH4S76bHJ^7a0WH_Oi7l0lGTOT8W1aGiN`8a1UG_Nk8l1jFTNV9W2_FiMa9c2SF]MQ:j2dEVMa:P3TEPMQ;V3dDjLa;\\3SDeLT<_3aCaLf<c3oB]LY=f3\\BZLl=i3iAWLa>j3TAVLX?S4=C=C=C`0@`0@a0_O`0@a0_O;E1O000000000000000000000000000000001O0000000000000000000000000000WhR3", "size": [640, 480]}, "area": 4837.0, "iscrowd": 0, "image_id": 11699, "bbox": [0.0, 426.0, 321.0, 214.0], "category_id": 167, "id": 20000707}, {"segmentation": {"counts": "QWT43ic04M3L4M4K4L[Ok\\Og0Rc06K4M37I3M1I7O22MM4K4Kaal4", "size": [640, 480]}, "area": 356.0, "iscrowd": 0, "image_id": 11699, "bbox": [211.0, 321.0, 18.0, 37.0], "category_id": 172, "id": 20000708}, {"segmentation": {"counts": "f_`64jc04L4MO010O2N2N101N1O2O000101N2N1O2N2N1O2O1N2N3M3M3M3M3M4M2Mk0UO1O1O1O1O1O100O1O1O1O1O3M;E3N2M2O1N2O1N3N2M3N1N3N1N3N1N3N3M1N2O3mLPLoER4n9QLQFP4m9RLRFn3m9ULPFm3n9ULQFl3m9WLPFk3m9XLRFh3n9YLQFi3m9YLPFj3n9WLPFf3T:\\LiEl3P:ULoEm3S:PLkER4T:PLiER4W:nKgET4X:mKfEU4Y:mKeER4\\:oKbES4^:nK_ES4a:nK]ET4c:lK[EU4g:kKVEV4j:kKUEU4k:mKRET4m:nKQEV4k:lKREc4a:_K\\Ec4c:^K[Ec4c:aKYEa4f:aKXE`4g:bKWE_4h:dKTE^4k:dKSE]4l:hKoDY4P;kKkDW4T;nKgDS4X;RLcDP4[;[2O1O1gD\\GU;e8jD\\GU;g803M100O1O1O2N1O1O2N002N1O1O3M1O2N2N1O100O1O4L002N2N3M4L2JlE]FW:_98N0010O01N2N2O1N1O3M001O1`J", "size": [640, 480]}, "area": 25339.0, "iscrowd": 0, "image_id": 11699, "bbox": [333.0, 176.0, 147.0, 349.0], "category_id": 181, "id": 20000709}, {"segmentation": {"counts": "gS1R4l?2K5J6K5lI`KbLf4]3]K[Li4c3\\KTLj4a2DVJb0j5_OPJf0P6ZOjIl0V6TOdIR1]6nN\\IX1d6hNWI]1i6cNQIc1o6^NjHh1V7XNfHl1Z7TNfHl1Z7UNeHk1[7UNeHk1\\7UNcHk1]7UNcHk1]7UNcHk1]7VNbHj1^7VNbHj1^7VNbHj1^7WN`Hj1`7VN`Hj1_7WNaHi1^7WNcHi1]7WNcHh1]7XNdHh1\\7XNdHh1[7XNfHh1Z7WNgHi1Y7WNgHi1Y7VNhHj1X7VNhHj1Y7TNhHl1X7TNhHl1X7SNiHm1W7RNjHn1W7QNiHo1W7PNjHP2V7PNjHP2V7oMjHR2W7mMiHS2X7kMiHU2X7jMhHV2Y7hMhHX2X7gMiHY2X7fMhHZ2X7eMiH[2W7eMiH[2X7_MmHa2S7ZMRIf2n6VMVIj2h6SM]Im2`6QMeIo2Y6nLlIR3Q6lLiI_3U6^LbIP4[6nK_I]4_6`KZIl4c6SKkHc5S7f3N2O10000000000O1M3L4M3LYIXI>e6E[I;a6I_I7^6KcI5Y6OgI1V62jINS65mIKo58QJIl5:TJFh5>XJBg5>ZJBf5>ZJBf5>ZJBf5=[JCe5=[JCe5=[JCe5=[JCe5<\\JDd5<\\JDd5<\\JDd5;]JEc5;\\JFd5:\\JFd59\\JHd58[JIe57ZJJf56YJKg54ZJLf54YJMg53YJMg52ZJNf52ZJNf52ZJNf51[JOd52\\JNa55_JKa54`JLa53_JMb52^JNb52^JNb51_JO`53_JMa53^JNa53^JNb52]JOb52]JOc51]JO\\4mHgL[>X3TBZLl=d3Y1O1N2O0O2O1OmHnKZNP4h1PLXNo3i1RLVNl3l1ULSNj3n1WLQNg3Q2XLPNf3R2ZLnMd3T2[LmMb3W2]LiMa3Y2`LfM^3\\2cLcMZ3`2gL_MW3c2jL\\MS3g2nLXMQ3i2PMVMP3j2QMUMn2l2SMSMm2n2SMQMl2P3UMoLk2R3VMlLj2U3VMjLi2X3WMgLi2Z3WMeLh2\\3YMcLg2^3YMaLf2a3YM_Lg2b3WM_Li2a3UMaLj2a3TM`Ll2a3QMaLn2Y:0000O100O100O1000ZIUMWLj2j3VMVLj2j3WMULi2m3VMRLj2o3UMQLk2b:1O000000001O00000000001O000000001O000jBRMZ9n2eFUMY9m2dFUMX2MQ1o2dLXMU2MW1l2bLYMQ20]1i2_LZMn12b1e2]L\\Ml13g1c2ZL]Mi14m1`2VL`Mh14R2]2SLbMe17W2Y2PLdMc17]2V2mKgM`17c2S2jKkM\\16j2Q2fKmMZ17o2m1cKQNX16U3k1_KTNV14[3i1aKRNn09a3f1kLhL^Og1f3c1nLdLWOl1k3a1QMbLmNQ2R4i1iLTLPOV2W4c1PMULcN]2\\4\\1WMVLVNb2c4U1]MXLkMf2h4R1aMVLaMl2n4m0KiN5V1OhN0W12]NWIRNg6`34mMjI^NR6d37aMo0^2SOXMV1g2mNoL[1P3gNdLd1[3`710O1O1O100O1000000O1000000O1000000O100000000O100000000000000000dIlLlKT3T4WMaKi2`4aMUK_2k4lMjJT2V5XN^Jh1b5cNSJ]1n5mNgIS1Y6XO\\Ih0d6CQI=S7JbH6c70RH0S86bGJc8=QGCV9a0_F_Oh9e0mE\\OZ:g0[EZOl:i0iDXO`;i0UDXOV<T4<D<D<D?A?A`0@?A`0_O;FO1O100O1O1O1O2N1O1O1O1O1O1O1]MnKVET4c:TLZEn3^:ZL`Eh3Y:`LdEb3U:fLhE]3n9lLPFV3h9SMUFo2c9YM[Fj2\\9_MaFe2U9dMhF`2k8kMSGZ2]8TN`GP2R8\\NlGf1h7nJlF]3Y1f1`7fJjGd3d0g1W7gJ_Hc38f1o6lJoH^30g1h6oJ^I[3Gg1a6SKmIW3@g1Y6WK]JT3VOf1^6PKbJ\\3lNd1P9_NmFb1R9bNjF_1U9g4O1O1N2O001cLbEVM_:g2eEWM[:h2iEUMW:i2lEVMT:i2kEYMU:e2lE\\MT:d2jE]MW:b2hE`MY:_2eEcM[:\\2eEeM[:[2cEgM^:X2`EjM`:U2`ElM`:S2`EnM`:Q2aEnMa:Q2`EnM`:Q2bEnM^:Q2cEoM^:o1dEPN\\:o1fEPNZ:o1gEQNZ:m1hERNY:m1hEQNZ:m1hERNX:m1kEQNV:m1mEQNT:m1PFoMR:o1QFnMQ:P2RFmMo9S2SFiMP:U2RGiLP9U3WGdLk8Z3[G_Lg8`3`GYLb8e3dGUL^8i3n3N2O1N2N3M2N2N3M2N2N3N1N2O2M2O1N3N1N2O2M2O1N3N1O1N3N1N2O2N1N3N1N2O2N1N3N1N2N3M2O1N3M2N3M2O1N3M2N3M2N2O2M2M3N3M2M4M2N2M4M3M5J5L5KPgm0", "size": [640, 480]}, "area": 163320.0, "iscrowd": 0, "image_id": 11699, "bbox": [1.0, 83.0, 431.0, 550.0], "category_id": 183, "id": 20000710}, {"segmentation": {"counts": "c:f2c:000000[M^Ec2e:ON200000000O10000002N00001O00000000001OO10000O100003M2`MWE\\2m:N2N00000000000000O100001OO1001O00000000000000O11O00000000LhMWEX2i:hMWEX2a:hMbE1MW2^:?00000000001O5K5K5K6QNREW1`;000001O00000000001O00000000001O00M3L4K5L4K5L4K5CmM`EW2`:900O11OO11O0000O100001O1O0000O1000000000000000`NhEOX:DVF;j9XOcFh0]9lNoFT1c:000000000000000001O000000000000000000L4]Oc0]Oc0H80000000000001OM300000000000000000000001O000000O1001O00001O0000O11OO1001O1O00O11O0000M30XMaEf2a:000O11OO1001OO11O0000000000]O_M^Fa2b9_M^Fa2a9_M`Fa2Y9eMgF\\2Y9dMhF[2X9fMgFZ2T9kMlFU2T9iMnFW2S9jMkFV2U9jMkFV2V9hMkFX2T9iMlFW2T9jMkFV2W9gMjFY2W9gMiFX2Y9eMhF[2Y9dMgF\\2\\9]MVFN?e2Q:100O1001O2NO11O00000000000000M]M`Ec2a:2000000N20000002NN2001O00N22N1OO11O1ON20000000000002N00N21ON20000000000O1001O00001O00O1O100O100O1O12N001OO11O0000001O1O1O0000NZMaEf2_:200l0TOb0^O=C1O00O100000000O10000000000000000001O0000000000000000000000000000000000001O000000lNXOgEh0Q:JeE6S:=cEBV:o0`EQOX:T2H8I7N2N2001O0000001O003M2N1O2N001OO1000000000000001OO11O001O0000001OO100001OO1001O1O00M3000000001O000000000000000000000000000000O11OO10000000000001O00O1001OO100000000000000000000002N002N0000000000O11OO100O11O00N21OO1O1O100001OO11O001O00O1001O000000O1000000000^OPFkMQ:P2TFoMk9n1ZFQNf9k1_FTNa9CTFP2?\\Nf9]1_FbNc9T1fFkN]9i0mFVOV9?SG@P94ZGKh8JbG5j:OO1O100O1O100O1O100O1O100O1O100O1O100O1O100O100O1O100O1M3M3L4M3001O001O1O1O1O001O1O1O002N2N2N2N2N2N2N00O1O1O1O1O1O1O100O1_N^OZFc0[91[F0Z9c0]F^OY9U1]FlNc9^OPFI0O2e12VOm9UOQF08f2W:00000000000000000001O00O1O12NO11ON21O001OO1O12N000000N2O1000000000000O10000000000O11OO1001O0WMbEg2`:0O10000O100O1O13M000000JXMhEk2T:UMkE00n2R:SMnEQ3P:nLPF10Q3P:QMPFo2P:50000000000000000001OO1", "size": [425, 640]}, "area": 49155.0, "iscrowd": 0, "image_id": 11760, "bbox": [0.0, 306.0, 640.0, 119.0], "category_id": 111, "id": 20000711}, {"segmentation": {"counts": "`9S1V<0000000kCnNS<U1ON200000000O10000002N02N3NO00000O10010OO01000O10O112M2lCkNP<Y1M3N000O100O100O11N0100O2OO01102M1O2N2N2N1OO11N02N11O00N2OMRDmNn;S1RDmNj;o0VDTO1Mk;j0d0K5K5K5K5KhU;7RjD=A7J0000O20N011O0O10O10000100O0O11N01001OO1004L6J5K6J5K[a95T^F<K5K5O1O100101K301O1O2N0000N20000000010O000000O10N3O000010ON20O11OO1001O1O01N20O01TOfC30=e<M4OWCBf<a01O0O20OO10O2OO20O0O10000004L00O1O0110O00N22N00O1001OO110ACoC>o;EPD;n;JkCHN?V<`02OO00110N3NO20O00000001O1O003JgCVOY<k0O33MO11M20O100O3OM3OMXOeCi0[<20N22OTOdCj0a<K7KO1ON201O1O001OO102OO0N12MBYC<h<300O1N2O10O1001O00010O0O00001O0010O0003N0O2OO11O0O1O110N200N100OOhCSO^<g0;Gegd0E`X[O4L6J7N10000102M12O00O10OO01OO10O11O1OO11O1O010N111N010OO10001OO0100010N0101O10OON3000000010O000O1001OO1001O00000000001O00NBYC=h<30N10000000000001N1O02OO2OO10000000O1001O00O1000002N0H]OdC30c0V<[OjC50`0V<CjC=U<<00000O11OO100O11O00N21MPOkCo0U<2O1010001OO11O0O21N0O102OO02N1O0O3N1OO1O100011M01O12H7G;FjWl06`[SOHI0O2V<?jCA08W<f000000000O1001O000010O00O2N03ON11ON21O001OO2N12N01OO12L01011ON20000000O10000000001N11OO01O2O0fCSOV<Q11N10000O100O1O22M000O11IgCYO\\<c0dC\\O00_<a0bC_Oa<`0^C@10_<b0cC^O\\<c041001O00000000O11O01OOdF", "size": [425, 640]}, "area": 12464.0, "iscrowd": 0, "image_id": 11760, "bbox": [0.0, 283.0, 640.0, 69.0], "category_id": 124, "id": 20000712}, {"segmentation": {"counts": "0`9i3000000000000000000000000000000002N3M000000O1001O00O1000000O11O0000O1UL\\LQNd3l1bLQN^3m1gLoMZ3n1lLoMT3o1QMmMP3P2WMlMi2Q2]MkMd2S2aMkM^2R2hMjMY2T2lMiMT2T2RNhMo1U2XNgMh1W2^NdMc1Y2eNcMZ1[2lNdMQ1Y2WOcMh0\\2]ObMa0]2E`M9^2M`M1_25]MJb2;YMFf2`0VM_Oi2f0RM[Om2k0mLVOR3P1jLoNT3W1hLiNW3]1bLeN]3`1^LaNa3e1ZL[Ne3k1RLYNm3b5O1N2O1O1O100O10000O10000O10000O10000O10000O10000O10000O10000O11iKjLYNV3f1nLWNS3f1PMYNP3f1PM[NQ3c1PM]NP3a1RM_No2_1RMaNn2^1TMaNm2\\1TMeNl2Z1UMfNl2X1UMhNk2V1VMkNk2S1VMmNj2R1WMnNi2P1YMPOh2n0YMROg2m0[MROf2o0XMQOh2R1TMoNm2S1QMlNo2X1lLiNU3[1fLeNZ3a1aL^N`3f1ZL[Nf3k1TLUNm3c500000000K5G9G9001O001O1O1O1O1O1O001O1O1O1O1O1O001O1O1ON2N2N2N2NULgLjMW3S2mLnMQ3o1TMQNj2l1\\MSNb2j1eMTNY2j1kMVNV2d1PN[NP2c1SN\\Nn1b1UN\\Nk1c1VN]Nk1a1VN_Nj1_1VNcNk1[1VNeNj1Z1WNfNj1X1WNhNi1W1XNiNi1T1ZNkNf1T1[NlNf1R1[NnNe1Q1\\NoNe1o0\\NQOd1m0^NSOc1h0_NZOh19^NGk1KZN5o1\\OWNd0i1VO^Ni0a1ROcNP1\\1dNQO\\1n0XN_Oh1`0kMNU21_M<a2CUMh0k2WOUMj0k2UOVMk0j2TOVMn0i2QOXMP1g2oNYMR1g2mNZMT1TOTK30S3g3fMa1Z2^NgMc1X2\\NhMd1Y2[NhMf1W2YNjMg1V2XNjMj1U2UNlMk1T2TNlMm1T2RNmMm1T2RNlMo1T2QNlMP2S2PNlMQ2T2PNkMo1V2QNiMP2W2QNhMo1X2QNgMP2Y2QNfMm1\\2TNaMn1_2RN\\MR2e2oMVMn1Q3RNjLS2V3nMeLV2[3kM_LU2f3g301O001OO1002N1O00N2N2O11OO1O11OO1001O000000001O1O003M00M33MO11O00O100O11O1OM_L^Fa3c9200001\\L\\Fa3j9L6J0000001O1O001OO100TJkLTL4Q5P3kNlLnK:W5j2jNmLlK=X5g2lNlLkK?V5@jJU3T4mLlK>U5h2oNjLlK?T5g2oNkLmK>S5h2PO^Mm0d2SO\\Ml0e2SOgLnKa0n4h2TOgLnKa0n4h2UO]MUL4^3_2=^MRL;[3W2d0]MoKe0V3n1k0^MlKl0T3e1Q1ZOkNf0U1^OfNc0Z1AaN`0`1C[N>e1EWN<j1GQN:o1InM7S2KiM6V2NfM3Y21dMO[25aML^28_MH`2<]MDb2`0[M@d2d0XM]Og2g0UMZOj2j0TMUOj2P1RMQOm2T1PMkNo2Y1mLhNQ3e1dL[N[3P2ZLQNd3k5O1N2O1N2O1N2O1O100O100O100O100O1O100O100O1000000001O00001O0000001O00001O00001O7Ib0^O5KO10000O10000O10000O10000O10cKRL_Om3;ZLEf37^LIb33cLL^3OiLNX3MmL2T3cLlK_2V1l0o2bLoK\\2X1P1j2aLQLZ2[1S1d2`LULZ2Z1U1b2^LWL[2X1W1b2[LZLZ2V1[1a2XL\\L[2T1]1a2UL^L\\2Q1`1\\3^NfLa1\\3[NgLd1Z3ZNfLg1[3WNgLh1Z3VNhLi1Z3SNhLm1Y3QNiLn1X3PNiLP2X3nMkLP2`3cMbL]2R4nLPLQ3c4[L]Kf3S700000O100001OO100001OO1000000001O0000O1001OO1001O00000000001O00N[L`Fe3`921O00000000000000O1O11OO10000000000O1001O00O100000000HWLjF30g3R9WLnF50d3R9_LnFa3Q9<00iLYLlLg3S3hL_LX3_3YMRLg2l3^MQLb2V2kKhMf11_2g1\\LVNW12]2g1^LTNX14Z2g1`LRNZ15V2i1aLoM\\17S2i1bLnM^18P2i1dLmM^17hMdNV4W3fLkM_18n1m1dLhMa18m1o1dLfMb19k1P2eLeMc19h1R2eLcMf1:e1R2gLaMg1<b1R2iL_Mh1>_1S2jL\\Mj1`0\\1S2lL[Mj1`0[1T2mLYMk1d0V1R2PMXMn1d0R1U2PMVMo1d0Q1W2PMSMQ2g0m0W2RMQMR2g0l0Y2RMnLT2j0h0Y2TMlLU2k0f0[2SMiLZ2j0c0^2h0cMVO^2j0bMUO_2n0\\MSOe2o0WMROj2P1SMPOn2R1oLnNR3T1mLjNT3X1hLiNY3Z1bLgN_3[1`LcN`3c51O001O001O00O1N2N2N2O1N2N2N2N2M3N2M3N2000000O1000000O10000O10000001O2N1O2N1O2N1O1O2N1O2N1O2N00O1000000O1000000O10000O1000000O1000000O10000O1000nKVMeMj2R2_MnMa2j1gMVN\\2c1hM]N^2\\1cMdNb2U1_MlNg2m0ZMSOk2g0WMXOk2e0VM[Ol2b0UM^Ol2?WM@k2=VMCl2:UMFm27UMHl26TMKn21UMNm2OTM1m2MTM3n2JSM6o2GRM9P3CRM=P3@QM`0R3\\OPMc0R3ZOoLf0S3WOnLi0U3ROnLm0T3POlLQ1V3lNmLR1V3jNiLX1Y3dNhL]1[3_NgL`1[3]NdLe1^3XNcLh1`3TNaLl1a3PNaLP2b3lM_LT2c3iM^LW2d3fM]LZ2f3bM[L^2g3^M[Lb2h3TM_Ll2c3bLnL]3_6000O1O100N21O0000000000001O0000O11O0000O1N2O1O1001O00000000O11O0000", "size": [425, 640]}, "area": 131385.0, "iscrowd": 0, "image_id": 11760, "bbox": [0.0, 0.0, 640.0, 324.0], "category_id": 169, "id": 20000713}, {"segmentation": {"counts": "cf>2T=6K5J6K5J7I6K5J6K5J6J7J6I8I6I8J5K6I6K6J5K6J5K6J6I6K6J5K6J6J6J6I7J6J6J6K5J6K5K5J7YJjHo4h700O10000O101O0O10000O101O0O10000OL5L4K4gMhJ_L]5_3RKnKS5P4]K^Kh4`4gKmJ^4R5n1M2O2N1N3N1O2N1N3N1O1N3N1O22M3N2M4M4K6K4K6K4K6J5]JWJ]3n5PL_JP4f5^KgJ\\4e5RKgJe4S7G9010O001O1O1O1O1O1O001O1O1O100O1O001B>\\Od0\\Oa0_Oa0L4M3M3M3M3M3M3M3M6L3O2N1N3N1O2N1O2N1N3N1O2N1O2N1N5G?A?A<D5K5G9D<D<D<C=M31O001O001O001O001O001O001O001O0100O2O0O2O0O2O1K4K6K4L5JVkf0MUhXO4]<L]C:b<G[C=d<C[C>d<B]C?b<A^C?a<B_C?`<A_C`0a<@_Ca0_<@aCa0^<_ObCa0_<5iC4o:LoD<l:DQEe0h:bNUEg01o0e:o0L4L4M3L3N3L3N2L4L4L4L4L4L4L4L4L4K5L5K4K=D;D;F:E]1dNc0\\O?B1N2O1O10O0100O100O100O1O100O100O1000000001O00001O0000001O01O01O00001O?A?A00O2O00hNYKnIf4k5mKjIS4n5`LgI`3Q6o1J7H8I6K6J6J5K6J5K6L4L5K5K5fLgF^2^9_MfF[2_9bMdFZ2_9dMdFZ2^9bMfF\\2\\9aMhF[2[9bMhF\\2T:N2N3L3N2N2N3L3N2N2Me0\\Of0ZOf[k05UdTO?@a0_O5TN]NjGf1e7kNYHW1e7kNXHX1f7jNWHZ1g7fNWH]1g7eNVH^1h7dNVH^1h7dNUH_1j7bNSHa1k7aNRHb1l7_NSHd1k7]NRHf1l7\\NQHg1m7[NPHh1o7YNnGj1P8XNnGj1P8WNnGl1P8VNmGn1R8RNmGo1S8QNkGQ2U8oMjGR2V8nMhGT2X8kMhGV2Y8iMeGZ2Z8[10003M2N2nKkJKW5NnJ2T5GQK9Q5@TK`0o4SMfJo0?n1m4iLnJR1:T2k4_LUKS19^2d4UL]KQ19i2c5lLfJT3\\5`LnJ_3T5VLVKj3T70O00O2N1O101N1O2N1O1N3N1N2O20O1O10O01O100O00100O00100O1L6K4K5M4O001N101O000O2O001OO0O2O2N2N2M3N2N2N1N10O1O001O1O1O00100O1ZN`LbI`3T6TMbIm2S6fMdI[2S6WNcIi1U6hNbIY1V6Q3G9H8H=J7I6I7J6J2N3M3L3N3M3M2M4M3M3M2N3L4M4L3M3M4K4M4L3M3M4K4M3M4L3L5L3M3M4L3L4M4_O`0_Okm=", "size": [425, 640]}, "area": 78351.0, "iscrowd": 0, "image_id": 11760, "bbox": [35.0, 88.0, 571.0, 330.0], "category_id": 183, "id": 20000714}, {"segmentation": {"counts": "c<h1k=11OO010000O100000000000000O10O100O100O100O00100O1O1000000O0ZCVNd;j1YD[Ne;e1WDaNf;`1XDdNf;\\1ZDfNd;Z1\\DgNb;Z1^DeNd;Z1\\DeNe;Z1\\DeNd;\\1[DdNf;[1\\DdNc;\\1^DcNc;[1_DdNb;[1_DdNb;Z1`DeNa;Y1aDfNXOFl;d1mDeNWOHf;OUDb1P1fNUOJf;e1VE`NTOLe;c1ZE_NQOOe;`1`E\\NkN5e;^1kEcNT:\\1mEeNR:[1nEfNR:X1oEiNP:V1QFkNo9T1QFmNo9Q1SFoNm9o0TFROl9m0UFSOk9k0VFVOj9i0VFXOi9g0YFYOg9e0ZF\\Of9c0[F]Oe9a0\\F@d9>]FCc9<^FDb9:_FG`99aFG_97cFI]95dFL\\93eFM[91gFOY90gF1Y9MiF3V9LlF4U9JlF6T9HnF8R9GoF9S9EmF;U9CkF=X9@hF`0[9]OeFc0V<0O10000jL\\O\\Gd0c8_O[Ga0d8A[G?d8DZG<f8F_DEc2f0m8G_DCd2f0m8I]DAf2f0l8LTG4l8LTG4l8LTG4l8LSG5m8KSG5m8KSG5m8JTG6l8JTG6l8JTG6l8JTG6l8JTG6l8JSG7m8ISG7m8ISG7m8ISG7m8ISG7m8ISG7m8ISG7m8ISG7m8IVG4i8MZG0f80\\GNd82dGF[8;kG_OU8a0_3000000000O10O100000lC_OT9b0hFCeMKR;b0VGGdMJU;`0VGEaM1W;<YG3f8NZG2d80\\G0d80\\G0c81\\G0c81]GOb82^GN_85aGK]87cGI\\88dGH\\88VGQOlML3l0j:8TGSOnMJ3l0k:7cFnNjN:Ha0k:7aFQOhN:K?l:6_FTOfN9O>l:5\\FWOgN[1m:^OZFR1g9mNWFU1i9jNUFY1k9gNSF[1m9eNQF]1o9cNoE_1Q:aNmEa1S:_NjEd1V:\\NhEf1X:ZNfEh1[:WNcEk1\\:VNbEl1Y:YNeEi1a:QN]EQ2c:oM[ES2e:mMYEU2g:kMWEW2i:iMUEY2k:gMSE[2m:eMQE]2o:T1O100000000000000O10000N2M3N2N2O1001O00000VK\\Ed4d:YK_Eg4i:N001O000000000000000000O10000O1000000000000O100O1003MO1O1O12N]Oc0M31O000^O5ZKgEMMl4\\:9N20O17J`0@0O100NoLaF1a9LcFXMLb2d93`FYMI03d2f92]FZMOd2e90]F\\MOc2d91]F\\MOc2c9cMZFU2;8[9dM\\FP2:<[9bM`Fn16?Z9^MhFP2Mc0R:ZOnEf0V:WOhEj0\\:ROcEo0`:nN`ER1c:lN[EU1c:mN]ES1d:kN]EU1d:kN\\ET1e:kN[Ee0EYNP;R1[Ea0EPN1=o:Q1]Ea0H]Nk:R1^E`0H]Nl:Q1\\E`0L]Nk:P1YE`0O`Nk:m0mDiN1W1j0TO[:i0jDlN1R1P1XOU:j0jDlN0Q1T1XOS:i0gDnNN04P1U1YOQ:j0hDnNMO4o0X1ZOP:k0eDoN2l0Y1[OP:P1gDDY1]OQ:P2QERN3Ml:P2mD^N2AR;P2iDeN0]OX;l1gDhN2\\OX;j1fDlN0[O[;g1eDoNMnNM=a;C_DP26SOL]Oa;`1cDSOJ_Ob;T1bD^N2d1\\;OdDZN0g1\\;0kD1U;1iDOV;1mDMS;4nDJQ;9oDEP;>oDAQ;?lDDS;<nDDR;<jDHU;6nDJR;4PELP;3QEMn:1UEOk:OWE1i:ZOlEf0\\:^NaDN[1d1Y<00000000000001O000000000000O10000000000000M3N2oNTB8n=GTB5o=IUBObk3JbTL4a;6aD34GK1\\;7eD13HLO^;8bD05IJ0_;7aD07II0_;7aD07II02", "size": [500, 333]}, "area": 29532.0, "iscrowd": 0, "image_id": 11813, "bbox": [0.0, 307.0, 333.0, 193.0], "category_id": 115, "id": 20000715}, {"segmentation": {"counts": "e1n:P2SFEn8eNeFh1<Cg8SOhFX1b0E]8\\OPGP1c0DT8EYGg0c0Dk7NbG=d0Ei7NdG=c0Dj7OcG=c0Dj7OcG=c0Ei7NdG=c0Dj7OcG=c0Dj7OcG=c0Dj7OcG=c0Dj7NdG>b0Dj7NdG>b0Dj7NdG=c0Ei7NcG>d0Dh7OdG=d0Dh7OcG>e0Ch7OcG>e0Ch7NcG`0e0Bh7NcG`0e0Ai7OaGa0f0@i7O`Gb0g0_Oi7O`Ga0h0_Oi70^Gb0i0^Oi70]Gc0j0]Oi7O^Gd0i0]Oi7O^Gd0i0]Oi7O^Gd0i0]Oi7O]Ge0=lMD`1a80^Gd0:QNE[1c80^Gd06WNGU1e80]Gd06[NFQ1g8O^Ge05]NDP1h8N_Ge05^NCo0i8N^Gg05[NEP1h8N_Gf04[NFQ1g8N_Gf04ZNGR1f8N^Gg04YNIR1e8N^Gg05XNHR1f8N]Gi05VNIT1d8M^Gi05UNJU1c8M^Gi05TNKV1a8N_Gh05SNLW1`8N_Gh05RNLY1`8M^Gi06PNL[1`8LXG`N3Y29oML]1`8JYGP1;hML_1`8IXGQ1=eMKb1`8HXGQ1a0`MGh1`8GXGQ1k0YOm7FWGR1k0YOn7X2QHiMo7W2PHjMP8U2PHlMP8T2oGmMQ8S2oGmMQ8S2nGnMR8R2nGnMS8Q2lGQNT8n1kGSNV8l1jGTNV8l1iGUNX8j1hGVNY8i1fGXN[8g1dGZN]8e1cG[N^8d1aG]N`8b1`G^Na8a1_G_Nb8_1^GbNc8]1]GcNe8[1[GeNf8Z1YGgNh8X1XGhNi8W1WGiNj8aNfFj1`0Ek8`NeFk1`0Fk8^NeFl1`0Fm8^NcFj1`0Hn8P1RGPOo8o0QGQOT9j0lFVOX9f0hFZO]9a0bF@b9;_FEb9:^FGa99_FG`9:`FF_9;aFE^9<bFD^9<aFE^9<bFD^9<bFD]9=cFC]9=cFC]9=cFC]9=bFD^9<bFD^9<bFD^9<bFD^9;cFE]9;cFE]9;cFF\\9:dFF\\9:cFG]99cFG]99cFG]99cFG]99cFG]99cFG]99cFG]99cFG]99fFDY9=jF@V9`0lF^OT9a0UGWOj8j0\\GPOd8P1_GnN`8R1`GnN`8R1`GnN`8R1`GnN`8R1`GnN`8R1`GnN_8S1aGmN_8R1bGnN^8R1bGnN^8R1bGnNc7^NhGd21YMKe1[8cNdG`22]MKc1_8aNcG_20aMJc1a8_NcG]22`MFj1e8YNaG^26IW8kMaG\\29IV8kM_G]2<HU8kM_G\\2=JS8jM_G\\2>KS8iM^G[2a0LQ8iM]GZ2d0Mb4[MQNZ3_M\\O^4\\MRNV3bM^OZ4`M`K1b1P3fN_OT4ROPKhNS1e2]OnL@L3i2k4NlK?CQMBJ3i2i44jK:ETMH`2f4=aK2MTMKb2OoL01\\4Q7fK_IOl2V4f3iKUMo3]7I6K3M6H;gMbCc0S=mNgCJT=0^1ImZe2", "size": [500, 333]}, "area": 48387.0, "iscrowd": 0, "image_id": 11813, "bbox": [0.0, 50.0, 158.0, 450.0], "category_id": 123, "id": 20000716}, {"segmentation": {"counts": "eSd34_?4d@LR?4l@I03U?3k@OT?2l@OS?1m@OT?8O101OO101N1O101N10O110O0nBEk:;QEEPN1n<<QEHn:9QEHn:8PELn:4oDOQ;HUC1g1:U;ETC1l16o:IUC0o16n7EmH3VMN04P25l7GnH2WMMO4P27l7FPI0WM2m18m7GTI1kN;R8DRK?l4CYK8e4J]K1\\4IaF:Q5N]4L^F8U5J^4<bKAkJM[90lFb0n4@Y4NhFc0o4]O[40cFe0e>[OZAf0f>ZOXA00f0h>221XAXOb>h0]A[Oa>f0^A[Oa>n0N2NkNeAR1a>N0UO]Ad0c>XO`Aj0^>UOcAl0b>001OO1O11O003M0O2HQOfAT1R>jNoA^1P>bNPB^1P>3000O10000O100001O001O001O1O0000000000]NVB\\1T>L1001O00O1O11O1O00N20gF", "size": [500, 333]}, "area": 2986.0, "iscrowd": 0, "image_id": 11813, "bbox": [237.0, 286.0, 96.0, 204.0], "category_id": 132, "id": 20000717}, {"segmentation": {"counts": "YZo16]?5L1N2O0O2O000000000cLBdG?Z8CeG;[8IQE1b16Z9[1`DhNS1M\\:Y2[EfMc:a2XE`Mg:k2mDWMR;f34L3M100O000000O1O100O101OO1K56J00000000000000O2O0000001O001O0O101O0N2M4M2N2O10O20O00O1TD^Lf;b3WDaLi;g3N001M2O1O1000000000000O010000O101O1O0000001bMZDaN0X2g;WO^Df0b;[O_Db0b;^OaD?_;AcD`0Z;^N\\D`0>_OHi0`;UO]Da0;@Kh0];WO_D`09AKg0\\;ZObD5>IEj0Y;XOfD3<GL8f;LSD5:GN5h;OPD4;HK8i;KSD3:JJ8i;KSD39KKH_O=[<KTD38M1KXOM^=4XC6f=I]B>Z=BgB<Z>N3M2Ih@N[?0eR_1", "size": [500, 333]}, "area": 10454.0, "iscrowd": 0, "image_id": 11813, "bbox": [129.0, 216.0, 107.0, 139.0], "category_id": 143, "id": 20000718}, {"segmentation": {"counts": "_ab32a?3K5N1N01NMHm@:R?4O110_ADi=;XBGe=9ZBLb=4YB4d=LVB=g=l0L4L3M3MN12O1N2O000000ZNlBn0S=oNoBT1n<lNRCW1a2mNm6AVF005=`1X2TOP7WO^F3:c1V2WOg7j0WHXOh7g0WHZOj7g0THYOn7f0QHWOS8j0kGgNf8X1YGdNm8\\1RGaNR9_1lF`NW9`1gF`NZ9a1dF_N]9a1`DYNP26a9c1_F]N`9c1VF[N^N2];c1VF]NZN0`;c1WFdNh9^1ZF`Ng9G`Db1i1jNe9CeDa1g1mNc9AgD`1i1oNg9n0\\FQOe9m0[FPOi9n0WFSOj9l0VFPOo9n0oEUOS:i0kEYOV:f0iE[OW:d0gE_OZ:`0dEB[:?PE6o:3UDjNN[1n;o1003M001O2N1O01O000O10000O10O101O001O001O1OZElLl8T3RGnLn8R3QFlL84g9P3PFnL55k9m2nERMO7S:a400O1001O00O1O\\NiEoL43T:<RFC34GK10T:;SFF13HLO1U:;SFD05IJ02R:=UFA07II02R:=UFA07II02", "size": [500, 333]}, "area": 7877.0, "iscrowd": 0, "image_id": 11813, "bbox": [234.0, 284.0, 99.0, 216.0], "category_id": 165, "id": 20000719}, {"segmentation": {"counts": "0e1o=000000000000O1001OO10000000000000000000000O10000O10000000000000000001O0000000000O11O0000000000000000000000000000000000001O00000000000000000000000000000000001O00000000000000001O000000000000000000000000000000001O00000000000000000000000000001O00000000000000000000001O001O00001O0000001O00001O002N4]BnM01X=\\2oFgM_4]2YKjMd4Y2UKlMj4V2QKnMn4R2oJQNQ5R2gJSNY5S2^HRMc0X1o6n1nGdMJT1Y8\\1gGLZ89_GIa8d310000000000000000000000001O0000001O001O00001O00001O000000PMfGnKJ13^2^8c1mG^NR8b1nG^NR8b1nG]NS8c1lG^NT8b1lG^NT8b1lG^NT8b1lG^NT8b1lG\\NV8d1jG[NW8e1iGZNX8e1hG\\NX8c1iG]NW8c1iG]NW8d1hG\\NX8c1hG^NX8b1hG^NX8c1gG\\NZ8d1fG\\NZ8d1eG]N[8c1eG]N[8c1dG^N\\8b1_GdN`8]1[GhNd8T4000001O00000000001OO1TNaGYKi9a4^F^Kb9e4[F[Ke9c4]F]Kc9c4]F]Kc9b4_F]Ka9b4`F^K`9b4_F`Kg02V7^4UHRMj7\\50000000O100O1O1O1O1O10000000000O1O100O100000000000000001O000000000000000000000000001O001O001O001O00O11O1O1O2N1O1O1O0000O1000000000000O1O10000000000000000000000B>0000000000000000000eGUIm7k6oGZIP8g6mG\\IR8d6lG_IS8R7O1O000000001O00", "size": [500, 333]}, "area": 48456.0, "iscrowd": 0, "image_id": 11813, "bbox": [0.0, 0.0, 333.0, 241.0], "category_id": 173, "id": 20000720}, {"segmentation": {"counts": "[>8e<2hENP:`0jE_On9i0RFXOe9Q1[FoN\\9Z1dFfN[9[1dFfN\\9Z1dFfN\\9Z1dFfN\\9Z1dFfN\\9Y1eFfN\\9Z1dFfN\\9Z1dFfN\\9Z1dFfN\\9Z1cFgN]9Y1cFgN]9X1dFhN[9Y1eFgN[9Y1eFgN[9Y1eFfN\\9Z1dFfN\\9Z1dFfN\\9Z1cFgN]9X1dFhN\\9X1dFhN\\9X1dFhN\\9X1dFhN\\9X1dFhN\\9X1dFgN]9Y1cFgN]9X1cFiN\\9X1dFhN\\9X1dFhN\\9X1dFhN\\9X1dFhN\\9X1dFhN\\9X1dFhN\\9W1eFiN[9W1dFkN[9U1eFkN[9U1dFlN\\9T1cFmN]9S1aFoN_9Q1`FPO_9P1`FRO`9n0^FTOb9l0]FUOc9k0[FWOe9i0ZFWOg9i0WFYOi9g0UF[Ok9d0UF]Ok9c0SF_Om9a0RF@n9`0PFBP:>nEDR:<mEES:;kEGU:8jEJV:6iEKX:4fEN[:1dE0]:OaE3_:M_E5b:J]E6e:HZE:g:EWE=j:BUE?l:@REb0o:]OPEd0Q;[OmDg0T;XOjDj0W;TOiDm0Y;QOeDQ1\\;nNcDS1^;lN`DV1a;iN]DY1d;fN[D[1f;dNXD^1i;`NWD`1l;aNQD_1P<cNmC]1V<cNgC]1]<bN`C^1e<f05K2N2N1O2fMnBn1T=PNlBP2U=oMkBQ2W=mMiBS2X=lMhBT2\\=00O1000000000O100000000000000000O1000000000000000000000000O1000O100000O10000O1O1O1O1O1O1N2N2N2N2M3N2N2N2N2N2N2N2N1O2N2N2N2N2N2N2N2N2N2N2N2N2M3N2N2NSV`2", "size": [500, 333]}, "area": 10710.0, "iscrowd": 0, "image_id": 11813, "bbox": [0.0, 333.0, 168.0, 134.0], "category_id": 183, "id": 20000721}, {"segmentation": {"counts": "0P8[50000O1O100O100O1O100O100O1O100O100O1O100O100O1O100O100O1O100O1O100O100O1O100O100O1O100O1O100O1O1O100O1O1O1O100O1O1O100N2O1O1N2O1N2O1O1N2O1O1N2O1O1N200O1O1O100O1O1O100O1O1O100O1O1O100O1O10000O10000O10000O10000O10000O10000O10000O10000O10000O1000000001O001O001O001O1O001O001SI[JR6e5j01O001O8H00O1O100O100O1O100O10oHWJ`6h5aIXJ_6g5bIYJ^6g5aIYJ`6f5aIZJ_6f5aI[J^6d5bI]J^6b5cI^J]6b5cI^J]6a5eI^J[6b5eI^J[6a5gI_JX6`5jI_JV6`5lI`JS6_5m000O1O1O1O100O1O1O1O100O100N2M3N2M3M3N2M3N2M3M3N2M3N2N2O1O1O1O1O1O1O100O1O1O1O1O1O1O1O100O1O1O1O1O1O1O1O100O1O1O1O1O1O1O1O100O1O1O1O1O1O1O1O100O1O1O1O1O100O100O10000O100O10000O100O10000O100O10000O100O10000O10000O100O10000O1000000000000000000O1000000000000000000000000000000000000O1000000000000000000000000000000000000O1000000000000000000000000000000000000O10000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000WJTN3l1M[NLe14[NLe13^NKb15^NKb15_NJa16bNG^19iN@W1`0jN_OV1a0hNAX1?gNBY1>gNBY1>gNBY1=hNCX1=hNCY1<eNF[1:eNF[1:dNG\\19dNG\\19bNI^17bNI^17]NNc12ZN1f1NZN3f1MZN3f1MXN5h1KXN5h1KYN4g1LYN4h1KYN4g1LYN4g1LWNlJBV5W2MYNlJ@W5W2M\\N3d1MYN6g1JZN5f1K[N4e1L[N4e1L[N4e1L[N4e1L\\N3d1M\\N3e1K[N6e1JZN7f1IXN9h1GWN:i1FWN:i1FVN;j1EUN<k1DVN;j1EUN<k1CVN=j1CWN<i1DWN<i1DWN<i1DWN<j1CVN=j1CWN<i1DXN;h1EWN<i1DXN;h1EXN;h1FWN:i1FVN;j1EVN;j1FTN;l1ETN;l1ERN=n1DQN<o1DQN<P2CoM>Q2CnM=R2CnM=R2CmM>S2BmM>S2ClM=T2ClM=T2CkM>U2CjM=V2CjM=W2BiM>W2CfM?Z2AfM?Z2AfM?Z2BeM>[2BfM=[2BeM>[2Y5000000001O00000000001O00000000001O00000000001O1O001O1O1O001O1O001O1O1O001O1O1O001O1O001O1O1O001O1O1O001O1O001O1O1O001O1O1O001O1O2N1QJbLo1_3mMfLQ2\\3kMhLS2Y3iMmLT2T3fMSMX2o2aMXM]2i2]M^Ma2c2YMdMe2^2TMgMl2Z2nLlMQ3U2iLPNW3R2bLTN]3m1]LWNd3j1VL[Nj3c5000000000000000001OO10000000000000000001O2NO1O100000000N2O11O00001OO100hJoJW2P5eMUKZ2k4cMYK\\2g4aM]KdM\\Oe4W5fM_KdM\\Od4U5fM`KgM]Ob4R5fMaKiMZOe4T5`MbKVNYO\\4k4_M[K`N1Q4GWKi4T2XLlM^Oh4Z4\\MXLlM@f4X4]MYLmM@e4W4^MYLmMAe4U4^MZLmMBd4T4^M[LnMAd4T4^MZLoMBc4T4]M[LPNBc4R4]M\\LPNCa4R4_M[LQN_O\\4_OcKO0h4P2[LS3nNmJ0Og4Q2lKb3]O^Jj4Q2XKgN0ZOM0c0U4\\4ZNcK`M7U4V4nMPLnMIP4[4nMPLRNEV4U4dMSKUNS11EX4S4bMUKUNS11EW4T4bMUKVNR11EW4T4bMUKVNR11EW4T4bMUKVNR11EW4T4aMVKWNR10EW4S4bMVKWNR10EW4S4bMVKWNo0X5j3`LXKXNa0e5W4SLXKXN4i0J_O?Q4Z4_MWKRO<UO<Z4Q4RNoKdM0[4P4lMWKmMg0b0\\Oe3f4", "size": [427, 640]}, "area": 89208.0, "iscrowd": 0, "image_id": 12062, "bbox": [0.0, 0.0, 640.0, 275.0], "category_id": 99, "id": 20000722}, {"segmentation": {"counts": "m<>W=F00000000000000000000000000000000000000000000000000000000000000000000O10000000000000000000000000000000000000000000000000000000000000000H8H8J6N2M3M3N2M3M3N2M3M3N2M3M3O1O1O1O100O1O1O1O1XN`NeG`1Z8iN]GX1b8QOUGP1k8h1O1O1O1O1O1O1O1O1O100O100O1O100O100O100O100O1O100O100O100CaKmG`4S8<000000000000001O3M3M2N2N000000000000O1000000000000O100000000000000O1000000000000000000001O000000000000000000001O00000000000000001O0000000000000000G`KhG_4Y8cKdG]4]8cKcG\\4_8dK`G[4b8dK]G\\4d8cK]G\\4d8cK]G\\4c8dK]G\\4j8O1O1O001O1O0000O1000000000000DhKeGX4[8hKdGY4[8gKfGY4f80000000000000O100000000K5L4N2O1O1O1O1O1N2O1O1O1O1O10000O1ROnJcIR5]6nJcIR5^6nJaIR5_6nJaIR5\\70O1000000XOnJWIR5j6nJTIS5m6mJRIS5n6oJPIQ5Q7oJnHQ5S7PKkHP5V7PKiHP5X7a02N1O2N1O2N1O2N1O2N0000O1O100O1O100O1O100O1O100O1O100O100000000000000O1000000000000O100000000000000O100000000000000O100000000N2N2N2N2M3O100001O00000EjHaJW7]5jHcJV7[5lHeJT7Y5nHgJR7W5PIiJQ7U5PIkJP7S5RImJn6Q5TIoJl6o4VIQKj6m4XISKi6k4XIUKh6i4ZIWKg6f4[IZKe6e4\\I[Kd6d4]I\\Kc6c4^I]Kb6b4_I^Ka6`4aI`K_6_4bIaK_6]4bIcK^6\\4cIdK]6[4dIeK\\6Z4eIfK\\6X4eIhK[6W4fIiKZ6V4gIjKY6U4hIkKX6T4iIlKX6R4iInKW6Q4jIoKV6P4kIPLU6n3mIRLT6l3mITLS6k3nIULR6j3oIVLQ6i3PJWLQ6g3PJYLP6f3QJZLo5e3RJ[Ln5d3SJ\\Lm5c3TJ]Lm5a3TJ_Ll5`3UJ`Lk5_3VJaLj5^3WJbLi5\\3YJdLg5[3ZJeLf5Z3[JfLd5Z3]JfLc5Y3^JgLb5X3_JhLa5W3`JiL`5V3aJjL_5U3bJkLh5j2YJVMn5b2SJ^Mm5b2SJ^Mo5_2RJaMn5_2RJaMo5]2RJcMQ6Y2PJgMW6R2iInMX6P2iIPNU6R2kInMT6R2mInMS6Q2nIoMR6Q2nIoMR6P2oIPNQ6o1PJQNn5Q2RJoMn5P2SJPNl5Q2TJoMl5P2UJPNi5Q2XJoMh5Q2XJoMc5U2^JkM_5X2aJhM^5X2cJhM]5W2dJiMZ5Y2fJgMZ5X2gJhMZ5W2fJiMZ5V2gJjMZ5T2gJlMY5T2gJlMW5KSHV2g2oMW5JRHW2g2oMZ5P2gJPNV5R2kJnMV5Q2jJoMW5o1jJQNV5o1jJQNV5n1kJRNU5m1lJSNU5l1kJTNU5k1lJUNS5l1mJTNR5l1oJTNo4m1RKSNm4n1SKRNm4m1TKSNk4n1UKRNj4n1WKRNj4l1WKTNh4m1XKSNh4l1YKTNh4k1XKUNh4j1YKVNg4i1ZKWNf4i1YKXNg4g1ZKYNg4f1YKZNh4e1XK[Ng4e1ZK[Ng4d1YK\\Ng4c1ZK]Nf4c1ZK]Ne4d1[K\\Ne4c1\\K]Nc4d1]K\\Nc4d1]K\\Na4e1`K[N`4e1`K[N`4e1`K[N_4e1bK[N^4e1bK[N^4d1cK\\N\\4e1dK[N\\4e1dK[N\\4d1eK\\N[4d1eK\\NZ4e1fK[NZ4d1gK\\NY4d1gK\\NY4c1hK]NV4e1jK[NV4e1jK[NV4d1kK\\NU4d1kK\\NV4c1jK]NV4b1kK^NT4c1lK]NT4c1lK]NT4b1mK^NS4b1mK^NT4`1mK`NS4`1mK`NS4`1mK`NS4_1nKaNS4^1mKbNS4^1mKbNS4]1nKcNR4]1nKcNS4[1nKeNR4[1nKeNR4[1nKeNR4Z1oKfNQ4Z1oKfNR4Y1nKgNR4X1oKhNQ4X1oKhNQ4X1oKhNR4V1oKjNQ4V1oKjNQ4U1PLkNP4U1PLkNQ4T1oKlNQ4S1PLmNP4S1PLmNP4S1PLmNQ4Q1PLoNP4Q1PLoNP4P1QLPOo3P1QLPOP4o0PLQOP4o0PLQOP4o0PLQOP4o0PLQOQ4n0oKROQ4n0oKROQ4o0nKQOR4o0nKQOS4n0mKROS4n0mKROS4n0mKROS4n0mKROS4o0lKQOU4n0kKROU4n0kKROU4n0kKROU4n0kKROV4n0iKROW4n0iKROW4n0iKROW4n0iKROX4m0hKSOX4m0hKSOX4n0gKROY4n0gKROY4n0gKROY4n0gKROY4n0gKROY4n0gKROY4o0fKQOZ4o0fKQOZ4o0fKQOZ4o0fKQOZ4o0fKQOZ4P1eKPO[4P1eKPO[4P1eKPO[4P1eKPO[4P1eKPO[4P1eKPOY4HQHY1e3oNZ4S1fKmN[4R1eKnN[4R1eKnN\\4Q1dKoN[4R1eKnN[4S1dKmN\\4S1dKmN\\4S1dKmN\\4S1dKmN\\4S1dKmN\\4T1cKlN]4T1cKlN]4T1cKlN]4T1cKlN]4T1cKlN^4S1bKmN]4U1bKkN^4U1bKkN^4U1bKkN^4U1bKkN^4U1bKkN^4U1bKkN^4U1bKkN^4U1bKkN^4U1bKkN^4U1bKkN_4T1aKlNa4R1_KnN`4S1`KmN_4T1aKlN_4T1aKlN_4T1aKlN_4T1aKlN_4T1aKlN]4V1cKjN\\4W1dKiN]4V1cKjN]4V1cKjN]4V1cKjN^4U1bKkN]4V1cKjN]4V1cKjN^4U1bKkN^4U1bKkN^4U1bKkN^4U1bKkN^4U1bKkN_4T1aKlN`4S1`KmNj4i0VKWOl4HbGm0b3[On4e0RK[On4e0RK[On4e0RK[Oo4d0QK\\Oo4d0QK\\Oo4c0RK]On4c0RK]Oo4b0QK^On4c0RK]Og4IgGO0k0b3]Og4IgG0Oj0c3]Og4IgGm0b3ZOg4j0YKVOg4j0YKVOc4n0]KROi4h0WKXOk4f0UKZOj4g0VKYOj4g0VKYOj4g0VKYOj4g0VKYOj4g0VKYOj4i0TKWOm4i0RKWOn4k0gJ^OZ5c0YJHg59lI3U6N]I>c6DoHh0", "size": [427, 640]}, "area": 63697.0, "iscrowd": 0, "image_id": 12062, "bbox": [0.0, 232.0, 640.0, 195.0], "category_id": 167, "id": 20000723}, {"segmentation": {"counts": "Q8c2g:c2^M0O1O100O100O1O100O100O1O100O100O1O100O100O1O100O100O1O100O1O100O100O1O100O100O1O100O1O100O1O1O100O1O1O1O100O1O1O100N2O1O1N2O1N2O1O1NN3I7J5M4O1N1O3N1N2N2O2M2N2O1N3N1000001O01O0001O01OXNlHZMU7e2TIRMm6m2\\IjLd6V3i100O001O1O001O1O00100O010O10O01O10O0100O010O10O01O100O101BWFWMj9i2=O010O0SNeEj0\\:TOeEl0\\:SOcEn0]:QOdEo0]:PObER1]:nNcEU1[:jNeEY1X:fNhE]1W:bNiE_1V:aNiE`1X:^NiEb1_:VN`Ek1`:UN`Ek1_:UNaEl1^:UNbEk1^:UNbEk1]:UNcEl1]:TNcEl1\\:UNcEk1]:UNdEk1\\:UNcEl1\\:UNdEk1\\:UNdEk1[:UNeEl1Z:UNfEk1Z:UNeEl1Z:TNgEk1Z:UNhEi1W:XNjEg1U:YNmEf1S:ZNnEe1Q:]NPFa1P:`NPF_1o9cNQF\\1n9fNQFY1o9iNPFW1o9kNoEW1P:jNoEV1P:lNoET1P:lNPFU1o9lNQFT1n9lNSFT1m9lNTFS1k9mNXFQ1g9PO[Fn0d9RO^Fm0a9@UF?j9a1O100N2M3N2M3M3N2M4M2M3M3N2M3N2NQOZG]Me8e3O1O2N1O1OBcGPL\\8S4hGgKX8[4hGdKV8`4iG_KU8d4jG[KU8i4iGWKU8k4jGVKT8k4lGUKS8l4mGUKQ8l4nGVKP8k4PHVKo7j4QHVKn7k4RHVKl7j4UHWKi7j4WHVKh7k4XHUKg7l4YHTKf7m4ZHSKe7n4ZHSKe7n4[HRKe7n4[HQKe7P5[HPKd7P5]HPKb7Q5^HoJa7R5_HnJ`7S5`HmJ_7T5`HmJ_7`5O010O1O1O1O1O1O_NjHoLU7R3iHPMV7P3iHQMX7n2hHQMY7o2gHmL]7T3cHiL_7W3aHiL_7W3bHhL^7Y3aHgL`7X3aHgL_7Y3bHfL_7Z3`HeLa7[3`HdLa7[3`HdLa7\\3_HcLa7^3_HaLb7`3]H_Lc7c3]H[Ld7e3\\H[Ld7f3[HZLd7h3\\HWLd7i3\\HWLc7k3\\HTLe7l3\\HSLd7n3[HRLd7k40UO\\HoKc7m40O1XO\\HiKc7Y4^HeKb7\\4^HcKa7^4aH`K_7a4aH]K`7d4`H[K_7f4cHXK]7j4bHUK^7l4?3N1N3N1O2M2O2N1O2NO100O1O100O00100O1O100O1O100O1O100000000O10000000O100000O100000000000000O1000000000000O010000000000000O100M3N2N2N2N2000000000O2O0_MoJhLQ5W3PKiLP5U3RKkLo4R3SKnLm4P3UKPMk4o2VKQMj4m2XKSMi4j2YKVMg4h2[KXMe4f2]KZMc4e2^K[Mb4c2`K]Ma4`2aK`M_4_2bKaM^4^2cKbM]4]2dKcM]4[2dKeM\\4Y2fKgMZ4X2gKhMY4W2hKiMX4V2iKjMX4T2iKlMW4S2jKmMV4R2kKnMU4Q2lKoMU4o1lKQNT4n1mKRNS4m1nKSNR4l1oKTNQ4k1PLUNQ4i1PLWNP4g1RLYNn3f1SLZNm3e1TL[Nm3c1TL]Nl3b1UL^Nk3a1VL_Nj3`1WL`Ni3_1XLaNi3]1XLcNh3\\1YLdNg3[1ZLeNf3Z1[LfNe3Y1\\LgNd3X1]LhNc3V1_LjNa3U1`LkN`3T1aLlN_3S1bLmN^3R1cLnN]3Q1dLoN[3Q1fLoNZ3P1gLPOY3o0hLQOX3n0iLROW3m0jLSOV3m0jLSOV3l0kLTOU3l0kLTOU3k0lLUOT3j0mLVOR3k0nLUOR3j0oLVOQ3j0oLVOQ3i0PMWOP3h0QMXOo2h0QMXOo2g0RMYOn2f0SM[Ol2e0TM[Ol2d0UM\\Oj2e0VM[Oj2d0WM\\Oi2c0XM]Oh2c0XM]Oh2b0YM^Og2b0YM^Og2a0ZM_Of2`0[M@e2`0[M@e2?\\MAc2`0]M@c2?^MBa2=`MC`2=`MC`2<aMD_2<aMD_2;bME^2:cMF]2:cMF\\2:eMF[2:eMF[29fMGZ28gMHY28gMHY27hMJW26iMJW25jMKV24kMLU24kMLT24mMLS24mMLS23nMMR22oMNQ22oMNQ21PNOP21PNOP20QN0o1ORN1n1ORN2l1NUN2k1NUN2k1NUN2k1MVN3k1LUN4k1KVN5j1KVN5k1JUN6k1IVN7j1IVN7k1HUN8k1GVN9j1GVN9j1GVN:j1DWN<i1DWN<i1CXN=i1BWN>i1BWN>i1AXN?i1@WN`0i1@WN`0i1_OXNa0i1^OWNc0h1\\OYNd0g1\\OYNd0h1[OXNe0h1ZOYNf0g1ZOYNf0h1YOXNh0g1WOZNi0f1WOZNi0g1VOYNj0g1UOZNk0f1UOZNk0f1TO[Nm0e1RO[Nn0e1RO[Nn0e1QO\\No0d1QO\\No0d1QO\\No0e1oN\\NR1c1nN]NR1c1mN^NS1b1mN^NS1c1lN]NT1c1kN^NU1b1kN^NV1a1jN_NV1b1hN_NX1a1hN_NX1a1hN_NX1a1gN`NY1a1fN_N[1`1dNaN]1^1cNbN]1^1cNbN^1^1`NcNa1\\1_NdNb1[1^NeNb1[1]NfNd1Y1\\NgNe1Y1YNhNg1X1YNhNh1W1XNiNi1V1WNjNj1V1UNjNk1V1UNjNl1U1TNkNm1T1SNlNn1T1RNkNn1U1RNkNo1T1QNlNP2S1PNmNP2T1oMlNR2S1nMmNS2R1nMmNS2R1mMnNS2S1lMmNU2R1kMnNV2Q1jMoNW2P1jMoNV2R1iMnNX2Q1hMoNY2P1gMPOY2P1gMPOZ2o0fMQO[2n0fMQO[2n0eMRO[2n0eMRO\\2m0dMSO]2l0cMTO^2k0bMUO^2k0cMTO^2k0bMUO_2j0aMVOa25WLeLX1V3b21\\LfLR1Y3c2N_LgLo0Z3d2JbLjLj0\\3e2EgLmLd0^3f2_OmLQM=`3h2XORMVM6b3i2ROYMYMNe3j2lNjNS1X1fNmNZ1T1`NQO`1P1ZNUOf1m0SNXOm1i0mM\\OS2e0gMAX2i500000001O00000000001O00000000000000000000000000000000000000000000000000YIgL\\3Y3`LlL_3T3^LPMa3P3\\LSM\\M]OR6_3bLVM[M]OQ6]3bLWM^M^On5[3cLWM`MXOS6a3[LQMSN@W5_3hL_L\\N9j4X3eL`MbM^Of5R3hL_McMAc5P3iL`MdMAb5o2jL`MdMBa5n2kL`MdMC`5m2kLaMeMB_5n2lL`MeMB_5n2kLaMfMA_5n2kL`MgMD\\5l2mL`MhMWOg5Y3aLXMU4h2kKiLd4W3[LaLiLM0c0X5o2aMoLWM7X5j2TM\\MeMIW5k2PMaMhMDX5k2lL^LkMS11DX5k2lL^LkMS11EW5j2lL_LlMR11EW5j2lL_LlMR11EW5j2lL^LmMS10EW5j2kL_LnMR10EW5j2kL_LnMR10EY5h2iLaLnMi0a6a2cKgLlM;o6a2aKTM`MNo01XO?V5Q2]NkMUL=`5Z1[NeNULXO5`0\\5V1WOaNh1R1dK", "size": [427, 640]}, "area": 119289.0, "iscrowd": 0, "image_id": 12062, "bbox": [0.0, 59.0, 640.0, 363.0], "category_id": 183, "id": 20000724}, {"segmentation": {"counts": "f_6114Q=MoBO0:k<50001O00O4L002O00O02OO10000000000000000`CGm;:PDHo;;nCGP<<mCFQ<=lCDS<?iCDV<j0O1O0O2O1O001O1O1O0012NO1O0000000000000000000O01002N00000TDcNi;]1WDcNi;`10MXDbNg;^14000000001OO1O10000001OO10000002NZOiDXOU;d0l0M3N2F:N101O3M^a71a^H8H6XOBWDc0c;@[Dg0_;[O]Dm0^;TO^Dn0a;`00O0001O0O110N02N100O1O1O1_NWD_1i;aNWD_1i;aNWD`1j;0000OO31N00O2N10000001O1N01OO21O_NWD^1l;1M13NN3N04NL211O000011N0O3M10O20O0O1O0200OO101O0010N02N11L400L4O000O12NN22NN1101OO12NN31MO31NO2M31NN20001O0000O100001OO100000000YN]Dd1d;\\N\\Dd1d;\\N\\Dc1g;1O00000000O11M22M01O2N10O1O12NO1ZN\\Dc1e;]N[Dd1d;\\N\\Dd1d;\\N\\Dd1d;\\N\\Dd1f;0000000000O11M\\N\\Dc1e;]N[Dd1d;111ON21O00000O11OO100001N0101O00ZN\\D]1k;cNUDT1V<00000000O1O11MmNkCS1U<mNkCS1U<2000O3NN300N12OM210MiNoCV1R<jNnCU1S<3OO10000O1001OO11O1OO11OhNnCX1P<hNPDX1T<1M0002N1OO1O1O10O2OO10000001O_OoN^DZ1b;8O100000O11O00O1000000000000O1YN]Dd1g;M3O0000000000000001O00000O1000000000O10000001O00O100000O11O0000002N0O100O200N20OO11OO1000000000000000O10000000000000000000N31N00O11OO1O11nCgNm;0TD[1k;4OO02OO11OOOaNWD`1h;1110O0O100000000O100000000000O2ON21O000000O1001O0O1WD_Ng;c10001O00O11O00O2ON^NZDc1e;]N[Dc1e;111O00O3NN1100O10O100000000000O110OO11ON2002N000N2I7N2O2ON22MO20O100@nCFR<9PDEQ<:QDBS<=nC^OU<c0;N102NNAWC>:FP<<oCFQ<9oCGP<;nCGR<8nCGQ<<mCEU<:hCGW<e05F6020O02OK5KXOcCk0Z<5000000000M3J5N30O02O000O11O1O1O2N1O1O2N1O2TDROU;P1hDROX;o0dDTO\\;m0aDUO_;l0^DVOb;l0ZDVOf;X10000001O1O0O100000000000O1000000000000O100000O100000O1000000000000O10000000000001O3NOO011OO1O11O00000000000000000O10000000000010O000N`NXD`1h;201ONYD_Ng;a11010000000000000000000000000O010000O10000000O2O0N2000000001OYJ", "size": [428, 640]}, "area": 27143.0, "iscrowd": 0, "image_id": 12120, "bbox": [15.0, 182.0, 625.0, 93.0], "category_id": 138, "id": 20000725}, {"segmentation": {"counts": "Y4e6h0QKa3o4_LQKa3o4]LSKc3m4]L\\I\\Od1W4m2lK\\Ma0SN\\Od1W4h2RL`M;TN\\Od1W4b2YLeM7I`3k1ULcL;h1LWN]Oc1W4k1RMZNZOXN]Oc1W4k1SMYNXOZN]Oc1X4k1TMWNWOZN]Od1X4k1VMUNUO[N@c1U4m1WMTNaN\\Na0h1g3h1YMTNQO3f3i1\\MPNeNbNJNNg1W4Q1hKXM4`0h1i0WNgNM321Ob1W4o0iK[MO0::b1n0WNo0U4>WLcM_1\\3Z2QOWLbM_1^3Z2POXL_M`1a3X2POXL^M`1c3X2oNWL]Mc1d3U2SOTLYMh1d3T2ROTLZMh1e3T2ROSLXMc11VNe3T4ROSLWMc1o3Z2iNSLXMc1P4Z2iNPNX1P2gNQNY1o1gNPNZ1P2fNoM[1Q2eNnM\\1R2dNmM]1S2cNlM^1T2bNlM^1T2bNkMdMPN[3U4QOkMbMRN]3S4QOkM`MUN^3o3ROlM_MWN_3m3QOmM^MYN`3j3QOnM^MYNa3i3POnM^M[Nb3g3POnM\\M^Nc3d3QOmM[MaNd3b3QOcNo0]1POdNP1\\1POdNP1\\1POdNP1\\1POdNP1\\1POdNP1\\1POeNo0[1QOeNo0[1QOmMaM^N^3e3TOeMdMeNX3f3TO`MjMiNR3g3TO_MlMiNP3h3TOVMYMjNe06o2j3SOVMZMhNf07m2k3SOVMZMgNh06l2m3ROVM[MdNj07j2o3QOVMZNhNg2S4nNUM\\NeNh2V4lNUMY2j2hMVMX2j2hMVMX2k2fMUM[2k2eMUM[2k2eMWMY2h2hMXMX2h2RKjKd2^1Z2j2fMVM\\McNk4W4iMVM\\McNk4V4jMZMV2f2jMWM\\MbNj4X4hMVM\\2i2eMWM[2g2gMYMY2g2gMYMY2g2gMYMY2g2gMZMX2f2hMYMY2g2QKkKd2]1\\2h2PKkKd2]1\\2h2PKkKd2]1\\2j2dMVM\\2j2dMWM[2j2dMUM]2k2cMUM]2n2`MRM`2o2_MQMa2P3mKfK?\\1b3f2RKPLf07e0T1b3e2SKQLc08g0V1`3b2VKmK1M>b0j0T1_3`2ZKTL52LGV1g1[3]2]KTL234CQ1k1Z3Z3RMhLl2V3UMlLj2S3WMnLh2P3\\MoLc2g2hMYMW2d2kM^MT2b2lM_MS2a2mM_MS2a2mM`MR2a2mM_MS2b2lM^MT2b2lM^MT2c2kM]MU2Q3]MoLc2R2hKlKOl0`1V1i2R2hKlK3j0Z1X1k2W2mK`LW1Y1l2W2QL]LR1\\1m2P2iKoK`0=j0d1m2Q2gKPLm0O?P2m2X2eLhK=P2n2X2fLgK<P2o2Y2fLfK;Q2o2Y2VNeMk1[2UNdMl1\\2SNcMo1]2oMcMS2]2mMXM^2h2cMWM]2i2bMYM]2f2eMYM[2f2fMZMZ2c2iM^MV2b2jM^MV2b2iM_MW2a2jM_MU2a2jM_MW2`2kM^MV2b2jM^MV2^2nMaMS2]2oMbMR2]2oMbMR2\\2PNSLSM_1m4[2SKVLn20RM_1m4[2SKVLn20RM`1l4Z2VNfMj1Z2VNfMj1Z2VNeMk1\\2RNgMm1X2VNfMj1Z2VNfMj1]2RNeMm1\\2QNeMo1W2UNiMk1W2UNiMk1X2TNhMl1X2UNgMk1Y2VNeMk1[2TNfMl1Z2SNeMo1[2TKULi2a1R2Z2UKULi20UM^1n4]2TKULk2_1P2`2nMaMS2_2oM_MQ2a2mMbMR2_2lMaMU2_2nM_MQ2b2kM`MV2`2jMaMU2`2jM`MV2a2iM_MW2a2iM`MV2a2kM]MU2d2jM[MW2f2jMWMW2k2gMUMY2m2dMUM[2m2dMRM\\2n2dMQM]2o2cMPM^2P3aMRM^2m2dMSM[2m2eMRM\\2n2dMRM\\2m2fMRMZ2m2gMSMY2n2gMRMX2n2gMRMZ2n2gMPMZ2P3fMQMY2o2dMUM[2k2fMUMY2k2dMYM[2g2eMYM[2g2eMYM[2g2dMZM\\2f2fMXMZ2g2eM[M[2d2hMZMX2f2fM[M[2d2fM\\MZ2c2hM\\MX2c2hM^MX2`2lM^MT2_2mMdMR2W2UNfMl1\\2PNgMo1X2TNfMl1Y2TNiMk1X2RNkMm1U2UNiMk1W2SNkMm1U2SNkMm1U2SNlMl1T2TNlMl1T2TNlMl1T2TNkMm1U2SNkMm1U2SNkMm1U2SNlMl1T2TNkMm1U2SNkMm1U2SNkMm1U2SNkMm1U2SNkMm1U2SNTLTMd1j4R2XNZLnLd1j4X2RNTLTMc1k4Y2SNhMl1X2TNhMl1X2TNhMl1X2TNhMl1T2XNlMh1V2VNiMk1X2TNiMk1X2RNjMn1V2TNgMm1Y2RNhMn1T2WKXLj2f1n1T2SNmMm1U2QNjMP2V2PNiMQ2W2oMkMo1V2PNiMQ2W2oMSLVMc1l4Z2nMSLVMd1k4Y2oMSLVMd1k4Y2oMSLVMd1k4Y2oMSLVMd1k4Y2QNgMo1Y2QNgMo1Y2QNgMo1Y2QNgMo1Y2QNgMo1Z2PNeMQ2\\2nMeMQ2a2gMkK^Mc1l4b2fMkK^Md1k4a2gM`MZ2a2eM`MZ2b2dM\\M^2f2`M[M_2e2aM[M_2e2aM[M_2e2aM[M_2e2`M\\M`2d2aM[M_2e2`M\\M`2d2`M\\M`2d2`M\\M`2d2aMZM`2f2_M[Ma2e2_M\\M`2d2`M\\M`2d2`MfKdM]1S5m2YMfKdMT1\\5V3RMjLn2V3RMjLn2V3RMjLn2V3RMjLn2V3RMiLo2W3QMhLP3X3PMiLo2X3nLeKeMS1]5W3oLfKdMS1]5X3nLjLR3U3oLkLQ3U3oLjLR3V3PMhLP3X3nLkLQ3T3QMlLn2T3QMmLo2S3SMlLl2T3RMnLn2R3SMnLl2R3RMgKdMV1[5S3QMgKdMU1\\5T3PMoLo2Q3QMnLP3R3PMnLP3R3PMnLP3R3PMmLQ3S3oLmLQ3S3oLnLP3R3PMmLQ3S3oLnLP3R3PMoLo2R3PMmLQ3S3oLnLP3R3PMfKeMX1Y5R3RMfKeMX1Y5R3VMoLg2Q3YMoLg2Q3YMoLg2Q3[MmLe2S3\\MlLd2T3[MmLe2S3ZMnLf2R3YMoLg2Q3YMnLh2R3YMmLg2S3XMnLh2R3XMnLh2R3XMnLh2R3XMnLh2R3YMmLg2S3hLlKQNZ1W5j2hL^MX3b2gL_MY3a2gL_MY3a2gL_MY3a2gL^MZ3b2fL_MY3a2gL_MY3a2gL^MZ3b2fL^MZ3b2fL^MZ3b2fL^MZ3c2eL]M[3c2eL]M[3b2fL^MZ3b2fL]M[3c2eLfKRNd1Z5f2gLWM[3i2fLVMZ3j2fLVMZ3j2fLVMZ3j2fLVMZ3j2fLVMZ3j2fLVMZ3j2fLVMZ3j2fLVMZ3j2fLWMY3i2gLWMY3i2gLWMY3i2gLWMY3i2fLXMZ3h2fLXMZ3h2fLXMZ3h2fLXMZ3h2fLXMZ3h2fLWM[3i2eLWM[3i2eLWM[3i2eLWM[3j2dLWM[3i2eLWM[3i2eLVM\\3j2dLVM\\3j2dLVM\\3j2dLVM\\3j2cLWM]3i2dLVM\\3j2dLVM\\3j2dLVM\\3k2cLUM]3k2eLSM[3m2eLRM\\3n2dLRM\\3n2dLQM]3o2dLQM[3o2fLoL[3Q3fLoLY3Q3gLnLZ3R3fLoLY3Q3gLnLZ3R3fLnLZ3R3fLnLZ3R3fLnLZ3R3fLnLZ3R3fLnLZ3S3eLmL[3S3]LdKZNY1Y5S3\\LfKZNW1Z5S3\\LfKYNX1[5R3\\LfKYNX1[5R3\\LfKYNX1[5R3\\LeKZNY1Z5S3cLmL]3S3cLmL]3S3cLmL]3S3cLmL]3S3cLmL]3S3cLmL]3S3cLkL_3U3bLlL\\3T3dLlL\\3T3dLkL]3V3bLkL]3U3cLjL^3V3bLiL_3W3aLjLoMgN]5_4bLaKRN[1[5T3cLPM\\3P3dLoL]3R3aLPM^3P3bLoL_3Q3aLPM^3P3bLoL_3Q3_L`KUN`1[5P3`LQMa3o2_LRM`3o2`LPM`3P3`LoLa3Q3_LoLa3R3dJ^Kh1`1d3T3\\LlLd3T3\\LlLd3T3\\LkLe3U3[LkLe3U3[LkLe3U3[LkLe3U3[LkLe3V3ZLjLf3V3ZLjLf3V3YLlLf3T3ZLjLh3U3YLlLf3T3ZLlLf3U3YLkLg3T3ZLlLf3T3ZLkLg3U3YLkLg3V3XLkLg3U3YLkLg3U3XLlL[N_N[5e4ZLnLf3R3ZLnLf3R3ZLoLe3Q3[LoLe3Q3[LnLf3R3ZLoLe3T3XLlLh3V3VLiLk3X3ULgLk3Z3RLTKcNc1Z5Z3RLSKdNc1Z5[3QLfLP4[3oKfLP4Z3PLfLP4[3oKdLR4\\3PLbLP4^3nKcLS4]3mKcLS4]3mKbLT4^3lKbLT4^3kKcLU4]3kKcLU4]3kKcLU4]3kKcLU4]3kKcLU4\\3lKdLT4[3mKdLT4\\3lKeLS4Z3oKeLQ4Z3oKgLQ4X3QLgLo3X3PLjLP4V3PLjLP4V3RLhLn3X3RLhLn3X3RLfLP4[3aJWKZ1W1\\4f3dKXL^4h3bKWL_4j3aKULiNkNa5P5dKWLiNlNn1Nb1o4iMTLfNQOl10d1k4hMVLhNoNj12f1i4hMULiNPOf16h1e4iMULhNROd17j1b4jMULhNROa1;l1_4jM]L8VOn1]4jM]L5YOQ2Z4kM\\L0\\OW2X4hM^LL[O_2W4fM]LG^Oe2U4dM]LUOdNNk0[3T4dM[LROhNJk0b3k3gMVKaN\\1;iNGk0i3k3cMcLlNJc3c3bMULaN\\O8k0g3d3_MdLfNIn3d3\\MbLcNKS4c3XMmMk2T2VMhMl2W2SMhMP3X2RMdMP3]2mLbMV3^2jLaMW3`2gL`MZ3_2hL^MZ3b2aLbM`3b2WLoJSOa2g4c2PLbMP4b2lK^MT4c2kK^MT4b2lK_MS4a2mK_MS4a2mK`MR4`2nK`MR4a2lKbMR4_2mKcMQ4]2nKfMP4Z2QLgMm3Y2SLiMk3W2ULkMi3U2WLmMg3R2ZLoMe3P2\\LnLEeNm3]4^LfL2jN^3_4aLbL;lNR3b4cL`Lb0kNi2e4eL^Lg0lNb2f4gL\\Lk0nN\\2g4hLZLo0oNW2g4iL[LR1nNS2g4kL[LT1nNo1g4mL[LV1nNk1h4nLZLY1oNf1f4RM[LZ1QO`1e4UMZL\\1QO^1e4VMZL^1PO[1f4WMZL`1nNY1h4WMZLb1mNV1i4YMYLc1lNT1l4YMWLe1jNS1n4YMWLg1hNQ1Q5XMWLi1eNP1T5WMWLl1aNn0X5VMWL[5j3dJVL\\5j3dJVL\\5j3dJVL\\5j3cJWL]5i3cJWL]5j3bJVL^5j3bJVL^5j3bJVL^5j3bJVL^5i3cJWL]5h3cJYL]5f3dJZL\\5e3eJ[L[5d3fJ[L[5f3dJZL\\5g3cJYL]5h3bJXL^5i3`JXL`5h3`JXL`5g3aJYL_5f3bJZL^5e3cJ[L]5e3cJ[L]5d3dJ\\L\\5c3dJ^L\\5c3cJ]L]5d3bJ\\L^5e3aJ[L_5f3`JZL`5g3_JYLa5i3XJ\\Lh5e3SJ_Lm5a5OO100000000000000000000000000O10000000000001O000000001ONTJdHl5\\72O100000000000000000000000000O10000O1000000001O00000000000000", "size": [428, 640]}, "area": 144022.0, "iscrowd": 0, "image_id": 12120, "bbox": [0.0, 119.0, 640.0, 309.0], "category_id": 145, "id": 20000726}, {"segmentation": {"counts": "PTX11[=7I2N1O2N3M1dCAi;?RD@F002X<>lC@02L0X<`0jC^OO5OMX<`0jCFNJX<a0iCEOJX<a0hCJX<6hCJX<6hCJX<6hCKW<e00000000000000SOPD>P<_OVD>j;AXD>h;AZD>f;A^D<_<J3M2N1Obb_35V]`L4M3CEdC?[<;N3gCSOm;V1000000000001O0000000000000000^OXD@h;<_DAa;<cDC];<eDC[;8kDGU;6R1O1O1O1O1OmPi2", "size": [428, 640]}, "area": 1566.0, "iscrowd": 0, "image_id": 12120, "bbox": [96.0, 0.0, 331.0, 41.0], "category_id": 161, "id": 20000727}, {"segmentation": {"counts": "c0f3T:B00001O001O001O000000000000000000001O001O0O10001O00001O0000000000000000000O1000000000000000000000000000000000000000000001O00O1001O0000O10dERMZ:P3000O1001OO1N2000000000cESM[:m2eESM[:m2eESM[:o20001O003M1O1OEnE[MR:e2oE\\Mo9d2SFYMo9e2UFXMk9h2<O11N101O1N1EVMSF4L_2R:eMmEF002a2U:cMiE02L0^2Z:dMdEO5OM]2[:eMkENJ]2\\:dMjEOJ]2\\:cMoE]2Q:cMoE^2P:bMPF`2n9`MSF`2\\:2N5K9G1O00O100O100O1000000002N2N10O1O1O001N100L4N2O1N2M[E^Mg:a2YE_Mg:a23O0001OO2O03N0OL5O01O0001O0000UEcMg:]2YEcMg:]2YEcMg:a2000001O001O001O1O001O1O1O2N2M3N\\OmEoMS:S1lERO:Hj9S1RGlNn8S1TGkNm8T1UGkNj8U1XGjNh8n0aGPO_8o0P2O1O1O101N1000001O001O000001O010O1O1O001nDUOb9k0`EVOMNI9h:e0^E3c:OYE2h:Z1O00000000000000000000000000000000J66J00000000L42N1O1O0O10LUEgMk:[222N00001O00000000000001O000001O1O6J01O01O2N2OO001O00000000000000000000000000000000000001OO11OO1000000O1000000000000000001O0000001O0000010O000000000000000000000000000000000000000000000000000000000001O00O1000000000000000000000O10000000000000000000000O101O00000O1000000000001O000O1000000000000000000O1O10001O0000000O1O101N1OZNTFNk91YFMe94]FKb95`FJ`94cFK]90hF0X9MkF3U9LlF4T9KmF6R9IoF7R9GoF9Q9FPG:P9FPG:P9EPG<P9DPG<P9DoF>P9BPG>P9BPG>P9BPG>P9BPG>P9BPG>P9BPG>P9BPG?o8AQG?o8BPG>Q9BnF?oNfNP:k0QGa0P9_OoFa0Q9@nF`0R9AmF?S9BlF>T9BkF?U9AkF?U9BiF`0V9@iFa0W9@fFb0Z9_OeFa0[9@cF`0^9@aFa0_9@_Fb0`9_O]Fb0d9AXF`0h9CRF`0n9`1O00000000000000\\NVFFj99XF]OQ:c0oEZOT:e0mEZOT:?fEdN7`0NAV:X1RFWO]:i0`EYOa:f0`EYOa:b0QEoN=?c:a0cE^O_:a0aE^O`:a0`E@`:`0hEWOY:h0kEUOU:l0oEoNQ:Q1U16J1O1OO1O1O100000001O00mDkNo9U1PFmNo9S1PFPOn9P1QFSOm9m0RFUO1Hi8S1UGVOG2T9h0UG]O\\OO_9e0SG:m8FQGlN]OU1b9OPGlN@T1a90ZG0f81YGOh81WGOi83UGMk86\\FdN?V1T9<nFDR99QGGo86TGJl86UGIk87UGIk87UGIj89UG@R99VGGi88XGHi86XGJi8N^G3a8L`G4a8KhFVONo0Y9JiFYOMl0[9KgF]O^OL0P1k9GgFP1X9QOgFo0Y9ROSFXO<g1`9XO`Fg0a9ZO]Fg0c9^ORFl0j9^1MUOXFVNd9<cFGOM]99gFJLM\\98jFJKNZ98lFJJNZ98lFJJNY98nFDDYO5l0W92WGSOD57f0m81YGSOD66f0m81YGROF64g0m81YGQOG82f0n80iGZOYOf0n80iGZOYOf0n80hGBTO<U93fGBTO:W96cG@VO:W9<]G[O[O8Y9>[G[O[O7Z9?ZG\\OZO5\\9?kFgN2g0F2]9a0gFjN5b1R9FjFfN4d1Q9HZG8e8JYG7g8JPG?o8BWG6j8KVG5i8KXG4h8KZG4f8G`G8`8GbG8^8FcG<\\8DdG;]8EcG;]8EcG;]8EbG<^8DbG=]8DbG<^8EaG;_8GhFdN`0f1h8MjF_N3d1S9MjF_N3e1R9MkF^N2e1S9NjF^NNh1X9JkFZN1k1T9LRG3o8NPG1Q9OPGOQ9MSG2n8JVG5k8JVG7j8GXGUO_O:X9a0YGSOC;S9b0ZGQOG<n8c0\\GoNH=l8e0[GmNK<k8h0YGjNO<i8k0XGgN1<g8n0XGeN3<e8n0iF_N86;<e8h0nFgNI=f02d8i0lFjNK9f05b8h0nFRODNl09a8g0PGkNJ3f0<_8f0QGhNM6c0=^8f0_GkN4`0\\8e0eH]OVN]Oh8W1TGgNh1f0WNAl8W1mHFP7:QIEo6<QIBP7>PICo6?PI_OQ7d0]GaNo0j0e7h0WG`NT1i0d7T1\\HlNd7T1\\HlNd7T1\\HlNd7T1\\HlNd7T1\\HlNd7T1\\HlNd7T1\\HlNd7T1\\HlNd7T1[HmNe7S1ZHnNf7R1ZHnNg7Q1XHPOh7Q1WHoNi7Q1WHoNi7R1VHnNj7S1UHnNj7U1RHlNn7W1PHhNP8Y1oGeNWOlNj8_2PHdNR8\\1oGcNQ8[1QHeNo7T1YHkNg7P1^HoNc7o0`HPO`7o0aHQO_7n0cHQO]7n0eHQO[7n0fHROZ7n0gHQOY7n0hHROX7n0hHROX7n0hHROX7n0hHROX7n0hHROW7o0iHQOY7m0gHSOZ7l0fHSO[7n0dHRO]7m0cHSO]7n0aHSO_7m0`HTO`7l0`HTO`7m0^HTOb7m0\\HTOd7l0[HTOe7n0XHTOg7n0VHROk7k0YHUOe7l0\\HTOa7o0^HROb7n0^HROa7P1]HRO\\6", "size": [428, 640]}, "area": 54333.0, "iscrowd": 0, "image_id": 12120, "bbox": [0.0, 17.0, 640.0, 172.0], "category_id": 173, "id": 20000728}, {"segmentation": {"counts": "[n15R=6I7XOLPD;o;b0O101N2N1O2N3M8HOO2O0O2O1N101OJiDXNW;h1mDUNR;k170001O100O1O1O1O100iN]De0<lNh:=oDe08QOi:8PEg05TOl:2QEi01WOP;MQEj0N\\OR;FREn0K]Ob;b0\\D@e;>ZDDg;;WDGi;n0100O001O1O1O0[OUDIl;1ZDNg;K`D5`;HcD6^;IdD6];HeD7\\;GfD7[;GhD8W<M3Lmo;1lnCNPE;n:GoD;P;F\\DL8b0Y;E[D242MHc;6ZD221m;m01N1O2G9L3001L5L3O2O1O0010^OkC9i;DYDA0j0c;C]DC2j0`;HcD6h;WOSD2<`0f;@cD8[;IlD0S;2mDMR;4oDKQ;5PEIP;8R10O1000O10002M3LVb18d]N00O100000000000000000000000004LYjh15bUWN00001O00000O10001O100:D^j05^UOO11O00003M1O000000100Vl?GRT@001O001O2O2N01O0N4MRC1l<1G9O100000000001O1O4IRX17`gN:0000O1000B]C3c<M]C4b<L^C4b<;0I`CBa<7c0KUc0Lm\\O1000000Q_f04j`YO100O1IKVC6g<45O01KC[C=d<510O10O01104K1O100O1J6JZZ10ndN1YD3g;NWD4h;MUD5l;JPD:P<GiC?W<;0N3XOka0MkZO6dJMUNNl4OZI7c3NTNOn4;nLFRN2n4:oLDPN6o47PMDoM8o45aL@WM3V1;Q53aLAUM4V1;Q53bL@SM5W19S55cL^OnL8X17V56dLMRNO\\54aLNoMOc53^LO\\MVONi0Z63[L[OSM59EKj0a60WL\\OVM46HGj0a65\\LTOXM43k0Z6N[LSOYM31j0]6XOZI;o2^OZM5Lk0]68]LhNYM7Ij0b68]LeNYM`1]6SO[INP3[OZMc1\\6TO\\ILY3P1^3TO^IYOZ3i1Z3nNnLR1U3lNlLR1^3fNaLZ1`3fN`LY1d3`N`L_1g7O10\\M\\N`Ie1Q5bN]INb1a1m4dNaIKb1a1k4fNbIJc1a1i4fNdIJ_1c1m4cNdIJY1j1Q5]N`IDYO5S2o1l4^NSIE46m1i1i4]NUIE55l1l1h4ZNVIF64l1n1f4WNXIH73j1P2d4fNbIZOi1S2c4PN[IO9Nj1T2`4bNmISOd1\\1lMYOc6C]I2a0Ed1T1XN_OV68nIUOf1j0cNFm55kI[Oe1g0iNGf5FZI3d0Ge1e0nNH^5H[I4e0Fe1c0QOKX5EaI6d0Dc1c0TONS5DcI6S34[N2n4FaI4W30_N6h43lLD`N9c4MSMF_N=]4JWMG_N`0Z4M[O5`0MA2>OCO<3EL5:LE2<0AOa03\\ONc05ZOLf06WOKi08SOIm0[601N2YOaC?a<]ObCb0e<B[C3e<M[C2g<NYC1h<N;1jB1n<61EPC8Q=1K5L5N1O101O000100_O[C9f<BZCc0e<B[C9d<G]C1d<4^CKb<6^CHd<9dCDV<<dCJ\\<7`CLa<a0N60KO00N3M2O4EXCJm<1;KfWc00[h\\O2G8O101O7JOgL", "size": [428, 640]}, "area": 10413.0, "iscrowd": 0, "image_id": 12120, "bbox": [4.0, 7.0, 636.0, 317.0], "category_id": 183, "id": 20000729}, {"segmentation": {"counts": "c5[3n`0G0000000O100001O01O0000000001R_OoLk`0Q3U_OoLk`0S301O00O100002OO000N200000000O10000000001MR_ORMn`0n2R_ORMn`0n2R_ORMn`0n22O11O00O1001OO11O00000J4L0Z_OQMa`0S3a_OlL_`0R3`_OlL02c`0P3800O2OO1J66J1O000000000000002N0000000000000N21O00001O01OO10nMS_OQ1n`0jNo^OYO5o1j`0gNR_OZO4o1l`0eNY_OY1g`0gNY_OY1i`0dNY_OZ1j`0eNU_OZ1l`0fNV_OX1j`0hNV_OW1k`0iNU_OW1k`0hNV_OX1j`0hNV_OX1j`0hNV_OX1j`0hNV_OY1i`0gNW_OY1i`0gNW_OY1i`0fNX_OY1i`0gNR_OBIh1Ua0eNR_OCIg1Wa0eNP_OEGg1Ya0dNP_OEFh1Za0cNP_O`1Pa0bNn^O^1Ra0dNl^O\\1Ta0eNk^O[1Ua0gNh^OZ1Xa0hNf^OX1Za0g000000000000Lh^O`MXa0a2g^O_MYa0a2g^O_MYa0b2f^O^MYa0c221O010O0000000004F7N3N1O2O0MFoMk^OP2Ta0PNn^Oo1Pa0RNQ_OS2i`0mMY_OQ2e`0QN\\_On1c`0SN__Ok1``0VNa_Oi1_`0WNb_Oh1^`0XNb_Oh1^`0XNb_Oh1^`0XNb_Oh1^`0XNb_Oi1]`0WNc_Oj1\\`0VNd_Ok1o?aNo_O_O\\OP2i`0]Nk_OC\\O^16jNm`0Ma_OS1\\OmN72m`0N^_OT1@iN82l`04Y_OP13jNe`0n1]_OPNd`0o1g0N2O11O000000001O00000c^ORNh`0n1R_OTND0Ya0l1S_OZNl`0g1R_O[Nm`0j1n^OWNQa0k1m^OVNRa0j1n^OVNn`0DP_OW21UNo`0DP_OX2OVNPa0BQ_Oh2Pa04000000000000000000000O10000002OM47H3L01O0000000MU_OQMk`0n25N1[OV_ORNl`0n1Z_OjMh`0V2a000ZN^^On0ba0QOb^Ok0_a0UOc^Oi0^a0TOX^OB9[1`a0gN\\^O26V1_a0hN[^O26V1_a0gN]^OO7Y1^a0gNk^OY1Ua0gNj^OZ1Va0fNk^OY1na01O0O2_]OdN\\b0]1c]OcN]b0`10000015J1O12NO_]OfN\\b0`1O4n]O[N`a0i1]^OWN``00W@g1]OYN\\`00W@g1]OZN[`0O`_O0=i1HXNZ`00a_O08l1KYNJJY`0a0k_Oo1KaMZ`0\\3e_OcL[`0a300O101O000O10000000000@aL_@_3a?`0O100O010000000O100000000002N0SLTAa1SOYNO70=i?CVA_1SOYNN0g03YOMj?8YA[1M]NPO0k?6[AZ1K`NQ?1WA[1\\a0M2N2N2M3N3L3N2M4M2N3L3N3M3L3NhX67WgI2JNO1H8L52M9_Oc\\OMRc0a0k\\O_OQc0g03N2O0O2]Of\\O>\\c0Bd\\O>\\c0Bd\\O>]c0Af\\O<ac0NbcU3", "size": [640, 480]}, "area": 24439.0, "iscrowd": 0, "image_id": 12280, "bbox": [0.0, 160.0, 317.0, 136.0], "category_id": 96, "id": 20000730}, {"segmentation": {"counts": "0c5]>@`0lNTAZLf?f3:001O0000001O000000001O0000001O0000001O0000001O0000001O0000001O000000001O0000001O0000001O0000001O0e@gKo>Y4l@mKS?a4O1VA[KW>f4bAbK\\>Q50001OO1J66J1O000000000000002N0000000000000000000000001OO1001O002N002N2N00000000000000000000001O1O0000000000000000000000000000000000O100001O0000000000000000000000O100O100N2O1O100000000000000D<4L8H000000000000000000000000000000000000000000LeAcJ[>]5eAcJ[>]5eAcJ[>]540000000000000000000000000000001O2N1O001O00000000001O000000000000001O1O000000O100N200O1O1000000000000001O1O1O00000000O100000000O1000000O10000000000@iAWKW>i4`0O100O100000000O100000000002N00CSAfKn>Y4SAgKm>Y4SA]KO7o>\\4ZA^Kf>b4<00O1O1001O1OL4N20000000000O11O002NO11O000000O1000000O1H_@PLb?g3^@YL0K33^?i3n@ULQ?j3c000N2O100001O0000002NN2M32N00O15K00N21O2N4L001O00O1L400000000000000000000@Q@oLo?P3R@PMn?n2U@QMk?o2U@QMk?n2W@oLk?P3]@hLd?X3b0O100O1O100O100O1002N00001W_OlLd`0X30000\\_OfLb`0\\30000000000000M3O100O1O1O100000000LX_OoLi`0o25N2N2N2N2O100O100O100O100O1O100O1000000000000Hb^OnM^a0R2b^OnM^a0R2c^OjM`a0V2`^OhMba0X23000000NZ^OjMfa0R26JU^OTNna0j150000O1000000000000001O0000001OO1001O000000Ln]OYNSb0f14O100L41ON2O11O002N1O1O1O1O001O1O00000000000000001O1O1O002NO1001O00000000000000", "size": [640, 480]}, "area": 66282.0, "iscrowd": 0, "image_id": 12280, "bbox": [0.0, 0.0, 480.0, 186.0], "category_id": 102, "id": 20000731}, {"segmentation": {"counts": "fdQ67ic04MO00O1O1O20OdG6ZI`1aL^NY9V1hEZNg0`0o1c1RNcN_9V1_FdNa1Z2YNmMe9X1`F_Nd1Y2]NlM[9i1XFWNj0_OkN4g0f2MgM^9o1PFYNm0F^Om2HXMm9m2hFRM[Ol3l9POmFSMYOk3l9[OPFWMd0R3[9R1PFkJNa4R:[5nLUFoKk9n3XFSLg9k3^FRLb9j3nFaK\\9^4dFbKQ9i4oFWKR9h4nFYKQ9g4PGXKQ9g4oFYKQ9g4oFYKQ9g4oFYKR9f4hF^L@jL5Km7m6YHTICZ4l7a2aHWIBW4m7R2THoI<JCT4`0kKe6W6mHPJ<`4B_KU7o5PIQJ:m4g6R1oHQJ;l4e6S1RInI=l4b6U1YIgI7R5a6W1RJhNm5X1TJZNjNcKS7S6VJQNnNkKl6S6YJnMnNmKi6U6[JmMmNmKh6V6]JkMlNmKh6W6^JlMS6U2oIiMR6U2QJhMP6X2QJhMn5Z2RJdMo5[2RJ\\JSNO0n1l7f3SJYJUN2N54h0f7i4SHkIl1=ZN1M67d0d7P5oIUJZN0L69b0d7R5oIUJXNJJL2`0:a0e7S5nGhIX25oM0ML1a08d0f7S5VJlIoMN5>0d0h7S5VJkI_N>Ae0j7S5WJiI]N`0Cb0j7U5XJhI\\N?Cc0i7V5QKZJXM>CoNQ8Y6WKVJWMa0@QOR8X6WKVJYM>_OSOR8V6ZKWJVM`0]OTOS8T6[KXJUM`0\\OTOU8T6XK[JVM`0b7T5ZK[JUM?a7V5[KZJUMFA?n7`5^KZJSMDEb0i7`5TM\\Jl2d5TM[JjJBP8S6WMZJiJCP8S6YMXJfJGo7n5RHPJ[5:dJHo7l5cM_J]2_5dMbJ\\2\\5fMdJZ2Z5fMgJ[2W5fMjJZ2U5dMnJ\\2R5dMnJ[2R5gMfJZJJo7`5gMfJZJH0Mo7d5iMfJYJG0On7d5jMeJYJGO0m7d5Z62O0O110O01000000000000HiAgJW>Y5iAgJV>Z5kAbJX>^5hA`JZ>`53000000NbAbJ^>Z56J]AlJg>Q541000O10000000O1000010N10000O20N102OO01O00NNVAQKl>m44O10OM32M01002N1O3M2JnJ[AT5b>7N2O010O10O0cJaAY5`>fJ`AZ5`>fJ`AZ5`>fJ`AZ5`>fJ`AZ5c>00001O1O1O002M01001O00000001OLcJeA]5\\>30RN", "size": [640, 480]}, "area": 66579.0, "iscrowd": 0, "image_id": 12280, "bbox": [309.0, 48.0, 171.0, 503.0], "category_id": 112, "id": 20000732}, {"segmentation": {"counts": "n8R;n80000000O100001O0000000000001oDRGn:n8RERGn:P901O00O100002N0000N200000000O10000000000NQESGo:m8QESGo:m8QESGo:m82O11O00O1001OO11O000000N2JmD^GT;`8nD_GS;^8lD`G02U;^8800O100000000000000000000000000000000000000N21O00001O000000002N000000O1O100O1000000001O0000O11OO10000000000000000000000000000LbDfG^;[8aDeG_;[8aDeG_;\\8`DdG`;\\821O001O0000000000J6O100001ON21O1O6J00000hFdGl6\\8lHmGS7S8eHVHZ7j7^H_HoNTOP8]8iHgHWOlNP8]8aHoH_OeNo7]8YHVIH]No7^8PH]I0VNP8^8gGdI9nMP8j:PHWEo7j:PHVEP8j:PHVEP8j:PHVEP8j:oGWEQ8i:nGXER8i:lGXET8h:lGXET8l:gGUEY8Y;000000000O100^OdG`E\\8`:kGYEU8h:QHQEo7o:a000001O\\MRGVJn8j5SGUJm8l5RGnH0hN0l0n8]7TGmH5Eg8^7UGlH3Hg8\\7\\GeHM0g8[7cG]HG>`8U7ZHjGWOY1X8m6PIVIn6j6QIXIn6h6QIXIP7h6oHXIR7h6nHXIR7h6mHXIT7h6lHWIU7h6mHWIS7i6mHVIT7j6mHTIT7l6lHVIR7l6mHUIQ7i6QIXIn6Q7jHPIT7T7hHmHW7S7jHlHV7T7jHlHV7T7kHlHT7T7mHkHS7U7nHjHR7S7fG\\GZ1_1P7T7VIjHj6U7YIiHg6U7]IhHd6V7`IhH`6X7aIfH`6[7aIcH_6]7cI`H^6a7cI]H]6c7eIZH\\6g7eIWH[6i7gG^GK0l1f0c6m7eGaGg1a0d6Z8\\IcGe6_8[I_Ge6b8\\I[Ge6e8]IYGc6e8aIXG`6h8aIWG_6i8cITG^6l8dIRG\\6o8Z200001O000000000000O10000O10VEPGd:P9[EQGe:Q9ZEnFf:V9000001O000000000000000000000000O1000000000000000000000000MXEmFi:P9XEPGO0i:P9XEPGj:P9300001O00001ON2000VEPGL0f:P9^ERGb:n8^ESGa:n8]ETGb:U901O00001O0000001O00001O1O0000000000O100000000KkLmFgKR9Z4RGbKn8_4QGaKP9^4PGbKP9]4QGcKP9[4QGeKn8[4TGdKk8^4TGbKl8V1jHnL^Nk1h8mMXGo1c1RNcNR2c8lMWGa1Z2YNmMY2d8lMSGd1Y2]NlMS2Q9`MoFj0_OkN4g0f2MgMW2V9WMRGm0F^Om2HXMe2U:PNjE[Ol3e2W6TNlEYOk3c2d6YMnEd0R3R2[8YMbCNa4i2d<00000001OE;1O001O00001O001O00000000O10000O10000O10000O11O00001O1O001O1O1O1O1OO10000000000000000O10000000000O100000000000000O1000000O1N2O1N200O10000000000000000O100000000000000001OO1000000000000O100001OO10000O11O002N001O00N21O0000O1O1N2O1O1O1O1O1KbLg_O_3W`06O1001O001O0\\Lh_O`3Y`0_Lg_Oa3Y`0_Lg_Oa3Y`0_Lg_Oa3Y`0_Lg_Oa3\\`00000000000000O10000000000001OLaLg_O_3Z`0300", "size": [640, 480]}, "area": 126347.0, "iscrowd": 0, "image_id": 12280, "bbox": [0.0, 263.0, 480.0, 377.0], "category_id": 117, "id": 20000733}, {"segmentation": {"counts": "ooP48hc06J3M1O0O2O001N1l\\O[OK0Pc0f0T]O^Oib0o0N2N1O01M201oNT]Ol0mb0SOS]Om0Pc01O00010O0003M0VOo\\Oe0Wc0O4Ed\\OJ_c03a\\OL\\c08d\\OH\\c08e\\OG[c09e\\OF\\c08jgb0Gjd\\OO70Xc02`\\ON0g0Qc07N1O1000001N100O2ON1100Y]OmNL0cb0S1a]OoN^b0R1b]OoN]b0R1a]OPO^b0Y1O2O0OUOV^OBla0<U^ODla09W^OEla00]]O1d02Rb0JZ]O3e03Ub0Mj]O5Wb0Ii]O7Qc0OE0f\\O1Yc0<0O8H0000001N10HJf\\O6]c0Gc\\O9ec0M2ImnY3", "size": [640, 480]}, "area": 1910.0, "iscrowd": 0, "image_id": 12280, "bbox": [206.0, 250.0, 104.0, 54.0], "category_id": 146, "id": 20000734}, {"segmentation": {"counts": "ZYX5o0\\b0ROX^Oo0fa0ROZ^OR1ba0nNk]OO71OU1ma0SOn]OM0S1oa0h0M2M3M3N2O2N1J6L5M2N3M2N3M3L3O2o_OXMd>j2ZAWMg>i2VAZMg>j2n@_L1d0U?o2g@]L3e0V?R3j@mLW?U3g@kLY?o3NFo@gKQ?Y4;F:6I3FhKg@X4Q?hKo@0K33l3[?`Lc@[3`?f0M9EG:M40001N10^@eK_?[4a@eK_?[4a@eKa?Z4^@iK]?`4KN7SOk0O006G4OO30N302L001mLX_Ol2h`0QM[_OP3m`0M<[O9000010O000000JT1d^OjL5K\\?1]@S3OoM[?POe@Q3<bMn>_Of@n2m0RM^>W3V1O101N12N3L5N1N2M3Q@VLb?m3Y@SLh?S41N2M201N1gLn_OO0n1T`0oMP@2N54h0o?QOP@1M67d0m?UOP@0L69b0n?WOm_OJJL2`0:a0Y`0POa_O0ML1a08d0Z`0oNa_ON5>0d0]`0nNP@>Ae0a`0lNm_O`0Cb0c`0mNk_O?Cc0[a0@g^O>CoNfa0?i^Oa0@QOga0>k^O>_OSOha0=j^O`0]OTOia0<j^O`0\\OTOia0?j^O`0Wa0_Oj^O?Wa0@j^OFA?ea0Jj^ODEb0Uc0L0Ob\\OB]c0=c\\OC_c0;`\\OG`c08`\\OHac0;O100N101M3001Hd\\OJ\\c06d\\OH0M]c0:d\\OG0O]c09d\\OGO0nRf1", "size": [640, 480]}, "area": 11409.0, "iscrowd": 0, "image_id": 12280, "bbox": [269.0, 119.0, 125.0, 176.0], "category_id": 161, "id": 20000735}, {"segmentation": {"counts": "Y^S45jc04M2LF^\\O9Xc0Ll\\O6Tc0Jl\\O6Sc0Li\\O7Uc0Bk\\Og0Tc0ZOl\\Oe0Uc0[Ok\\Of0Rc0500O0010000000006J1O1000O1O0O2M200XOQ]O=Qc0CP]O8dc0Ijie4", "size": [640, 480]}, "area": 791.0, "iscrowd": 0, "image_id": 12280, "bbox": [210.0, 179.0, 30.0, 39.0], "category_id": 173, "id": 20000736}, {"segmentation": {"counts": "Si0h0Tb0YOU^Ob1ja0:001O0000001O000000001O0001O01O0000001O0000001O00000010O000001O000000001O0000001O01O0001O0000001K4gNe]Og0ab0TOe]Oe0ob0I7I7ISne11jQZN9G9G8^]OUOga0S1Y^OmN_a0\\1`^OdNXa0d1h^O]No`0k1P_OVNh`0R2X_OnMb`0X2^_OhMP`02k_OX25fMh?Mo_O72W27fMf?Nn_O82V2:dMe?>o_OP2:cMf?>o_OP2:cMf?T3Y@mLe?V3Z@jLe?[3V@fLk?f3100000001O000fLo_OS25eMl?1P@G0c2;^Me?8R@D0e2``0gMf_OX2]`0eMd_O[2_`0aMb_O^2_`0aMb_O^2^`0bMc_O]2^`0aMd_O_2\\`0_Me_OZ1FEXa09j^OFXa08g^OI`a0O`^O3fa0E[^Oa0^b08H3M1O0010O001O2O0O2O02O1N101N10O001O1O2N2O1N1N3N2M3N2M3N1N3N2M3N2M3N1N3N2M3N2M3N1Nh]d14Sb[N4M2M2N3O0000O1O8Ijf=J]YB<Ca0W]OVO[a0Z1n]OlNRb0`12O1N5L4Ab]OSOcb0b0e]O]O_b0;f]OC`b07b]OGcb04_]OKeb01j_^2", "size": [640, 480]}, "area": 8522.0, "iscrowd": 0, "image_id": 12280, "bbox": [1.0, 127.0, 353.0, 246.0], "category_id": 183, "id": 20000737}, {"segmentation": {"counts": "f9R3m`0100OPNRle8XOcVZG400000000000000000000000000000000000000000000000000000000SMm2E;", "size": [640, 480]}, "area": 1004.0, "iscrowd": 0, "image_id": 12576, "bbox": [0.0, 308.0, 480.0, 332.0], "category_id": 100, "id": 20000738}, {"segmentation": {"counts": "Zc[34kc02O101OO02cLInB7Q=KnB5Q=NlB3S=Nh_OJR37V=9l_O_Oa29b=b0i_OVO^2:g=h0k_OmNY29l=Z1fAgNY>k1m_OkMU1:n>U2m_OaMl0:V?`2b@aM]?f2n_OPM2O5<j?a301O1O00GQ@bLn?^3S@aLm?^3U@`Ll?_3V@^Ll?a3V@]Lk?c3V@]Li?b3;O100O1O1O100O1OS@dLX?\\3i@dLV?[3k@eLU?Z3m@fLR?Y3k000O1O1O100N2]NX_OMi`02]_OGe`07c_OA_`0=i_O\\OX`0c0o_OUOS`0i0U@oNm?o0[@jNf?U1c1N2N2O1N2O1O1O100O1O100O1O100O1O100O1O100O1O100O1O100O1O11O001O1O1O1O001O1O1O1O001O1O1O1O001O1O1O1W_OWOY>i0SAOi>2m@:P?Gf@e0W?\\O_@m0a?TOU@T1l?mNk_O[1U`0X10O1O100O1O1O1001O001O001O001O0000000000000000001O001O001O1k_OWLQ`0l301O001O1O1O1O1O001O1O1O1O001O1O001O1O00]Oc0N2O1O1O1N2O1YOb_O[M0:_`0X2S@^Mn?g1`_OVN0O20R13]?Me@0kN0O0k12Rb0Nfld2", "size": [640, 480]}, "area": 13576.0, "iscrowd": 0, "image_id": 12576, "bbox": [172.0, 0.0, 172.0, 140.0], "category_id": 112, "id": 20000739}, {"segmentation": {"counts": "fjo68gc07Z\\OB_c0?M4N1N2L4N201N1N200O10001O00000NnNV]OR1ib03O2N100O10001O0O10000O1010O3M2N0001NJ[]OnNjb0R111000000010O002M100O3N1SOo\\O11b0Tc0]Om\\Oe0Yc0OOO0O2O011O0N2N>DiPV1", "size": [640, 480]}, "area": 1937.0, "iscrowd": 0, "image_id": 12576, "bbox": [358.0, 62.0, 61.0, 47.0], "category_id": 119, "id": 20000740}, {"segmentation": {"counts": "coZ85jc03M3O1M3N3N1M3N2N2N2N5K5K2N3N1OkN\\]Ok00QOib0o051O0O1OO1O100N21O1N0100000000000000O0100000001O00001OO12M10O100002N00OWL", "size": [640, 480]}, "area": 1691.0, "iscrowd": 0, "image_id": 12576, "bbox": [427.0, 113.0, 53.0, 48.0], "category_id": 165, "id": 20000741}, {"segmentation": {"counts": "0k4U?003MM32N1OO11O00O1002N1O1O1[APKX>T5`ARK^>X5aAaJ]>a50N2001O1O2N2N1O2N001O1O2N1O1O2N1O1O1O2N00O1O100O1O100O1O100O1O100O1O100O1O100O1O100O1O100O1O100O1O100O1O100O1O100O1O100O1O100O1O100O1O100O1O1O100O1O1000000000000000000000000000000000000O1000000000000000000000000000000000000000000000000O1000000000000000000000000000000000000000000000000O10000000000000000000000\\NRAVNn>i1TAVNl>j1UAUNk>l1VARNj>o1UAQNk>o1WAoMk>P2UAoMm>P2SAoMP?n1QAQNP?jNh@R37TN[?nN]@a29`Nd?kNT@^2:eNj?mNk_OY29jN\\`0h0e_OWOm`0oNi^OU1:KXa0PO^^Ol0:3ca0E^^O:ia0QOm]O2O5<g0db001O1O001O00O1N2O11O001O001O001O001O1O1O001N10001O00001O001O001O00001O001O001O00001O001O00001N101O010O00001O0010O01O00001O010O001O000010O00000000001O0RV^1I[VaN0ac0b0lN^O`]O0O20R1f`02Z_OkN0O0k1e`0_1O00O1O1O1O1O1O1000000001O0000@^_ObMb`0]2e_O\\M\\`0e2?M3O1N2L4N200O1N200O10000000000NW^OmMia0R23O1O100O1000000O10000O1001O3M2N0000O100O1000000001O002N00002N1X^OjM11^a0X2a^OiM_a0_2M1O001O002N1O1OO1UOk0]Oc00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000", "size": [640, 480]}, "area": 41284.0, "iscrowd": 0, "image_id": 12576, "bbox": [0.0, 0.0, 480.0, 198.0], "category_id": 172, "id": 20000742}, {"segmentation": {"counts": "h<a4_?g2YM0oMQ2ULk3O100O1O100O1O100O1O100O1O100O1O100O1O100O1O100O1O100O1O100O1O100O1O1O100O1O100O1O100O1O100O1O100O1O100O1O100O1O100O1O100O1O100O1O100O1O100O1O100O1O100O1O100O1O100O1O1O100O1O1000000000000000000000000000000000000O1000000000000000000000000000000000000000000000000O1000000000000000000000000000000000000000000000000O100000000000000000000000000000000000000000000000000O100000000000000jNn@^MR?b2o@]MQ?b2QA]Mo>b2SA]Mm>b2UA]Mk>c2VA\\Mj>c2XA\\Mh>c2YA]Mg>c2ZA\\Mf>c2[A]Me>b2]A]Mc>b2^A^Mb>b2_A]Ma>b2`A^M`>a2bA^M^>b2cA]M]>b2dA^M\\>a2fA]M[>b2fA^MZ>b2gA]MY>b2hA^MX>a2jA^MV>a2kA_MU>a2lA^MT>`2nA`MR>m0o@MP1VOQ>l0UAGm0\\On=k0]AAg0Dl=i0eA\\Oa0Jj=i0lAUO<2h=g0TBoN79e=f0\\BjN0`0d=e0oC[OQ<c0RD\\On;b0TD^Ol;a0VD^Oj;`0XD@h;?ZD@f;?[DAe;>\\DBd;>]DAc;>^DBb;=`DAa;?_DAa;>`DB`;=bDC];=cDC];<eDC[;<fDDZ;<gDCY;<hDDX;;iDFV;:kDEU;:lDFT;9nDFR;:nDFR;9PEFP;9QEHn:8REHn:7TEHl:7UEIk:8UEGk:9UEGk::UEFj:;UEEk:<SEEm:<REDn:<REDn:=QECo:>PECo:>PEBP;?X4001O1O1O1O001O1O1O1m^OBb>>n@6n>Ki@`0T?Ab@j0\\?WO[@Q1e?POQ@X1P`0\\1000O1O100O1O1O1001O001O001O001O0000000000000000001O001O001O4L001O001O1O1O1O1O001O1O1O1O001O1O001O1O00]Oc0N2O1O1O1N2O1O1O1O1O1O1O1O1O1O1O1O1O1000000001O00000000001O000000001O00000000001O00000000001O000000001O00000000001O000000001O00000000001O00000000001O000000001O00000000001OWOo_OcMQ`0b1k@]NU?P1^APOb>P1_AoNa>Q1_AoNa>Q1`AoN_>Q1aAoN_>Q1cAoN[>Q1kAlNR>T1nAnNP>R1PBPOn=P1RBROl=n0TBTOj=l0VBVOh=j0XBXOf=h0ZBZOd=f0\\B\\Ob=d0^B^O`=b0`B@^=`0aBE[=;eBKU=5kBNR=2nB0P=0PC3m<MSC5k<KUCPOkMl0n>4XClNmMS1h>1\\ChNPNX1b>0hC3U<MlC4m;1RD3`;8`DJ^;6bDM[;3eDOY;1gDOY;1gDOY;1gDOY;1gDOY;1gDOY;1gDNZ;2eDO[;1eDO[;1eDO[;1eDO[;1eDO[;1eDO[;1eDN\\;2dDN\\;2dDN\\;2cDO];1cDO];1cDO];1cDN^;2bDN^;2bDN^;2bDN^;2bDN^;2bDN^;2bDN^;2aDNo:c0QE]Oh5POeHV5c1jK7[OgMc2MSMc0j3", "size": [640, 480]}, "area": 244514.0, "iscrowd": 0, "image_id": 12576, "bbox": [0.0, 15.0, 480.0, 625.0], "category_id": 183, "id": 20000743}, {"segmentation": {"counts": "i^Y11nc02U\\ONec0:L20NMGa\\O;\\c0602ON1O6J7I1O1O001N10nW12ogN1O2OO002N1O00N2O2N200O102_OGW]O;fb0Do\\O091^c0301O11O2O0NQTg02fXXOLgb04X]OLnb01P]OOPc02n\\O3nb0MP]O4Pc0Mo\\O4Pc0Mo\\O2Rc0Om\\O0Tc01j\\O0Vc01i\\OOVc0?0OL\\Ol\\Od0Tc0\\Om\\Od0cb0\\Ol]Oe0hb0Df\\O0Zc0Nj\\O0cc01O001O01O00O1FHl\\O9Sc0Ij\\O8ac0NO01000000000003HY\\O1]`l5", "size": [640, 480]}, "area": 713.0, "iscrowd": 0, "image_id": 12639, "bbox": [66.0, 197.0, 113.0, 45.0], "category_id": 105, "id": 20000744}, {"segmentation": {"counts": "fjb61mc02O2U\\OLhc0600_[4JgdK5L1N101N110N20OgQ\\2", "size": [640, 480]}, "area": 98.0, "iscrowd": 0, "image_id": 12639, "bbox": [337.0, 194.0, 21.0, 24.0], "category_id": 123, "id": 20000745}, {"segmentation": {"counts": "Q8g0Xc02O03N03K9H5U]OjNO>n1AT>X2R@jMS1:a>X2SATNk>o1RAQNm>n1[AiMf>X2VAnMh>S2]@ZMe0b0o>Z2SAcMm>]2QAdMU6Ak1k2PHdMU6Bj1k2QHaMV6Hd1h2UH_MU6AVI7c8h2PHdMV6Gj1d2TH`MR6Ni1c2SH`MT6Og1`2WHRMYO303k69b1`2]HSMXONN000k6c0^1\\2HgM5Y2KaM<b2@^M?b2B_M=c2A]MYM@kJ0P3S3l4^MWM2]Ma2\\5]MWM3\\M`2^5ZMUMBnJd0_2`2^5XMSHCR51nJd0`2`2\\5XMXMDlJd0`2a2[5WMYM;[M^2]5VMPMFYKg0n1YOSNU3l7iLoG1m43UKf0P2\\OnMQ3Q=RNRCgNi15SNQ3Q=[NmDl1o:cLSCb1P2h1m:RN\\En1e:PN]Em1d:SN]En1b:QN_Eo1b:PN_En1b:RN^En1a:SN_Eg0_NWNR<R1_Ef0bNUNQ<T1^Ee0dNUNm;W1_Eb0hNUNi;Z1^E`0l;ASD>n;DPD<Q<IiC6X<LfC4Z<0bC0]<1cCO[<3eCM[<4dCL\\<5TCSNoNh1m=7YBSN2NHh1l=<VBnM6NHh1l=<VBnM6NHh1l=<VBnM6NHh1l==TBnM8MHh1l=>RBnMi0d1U=`0mAoMn0a1U=?hAUNd0EHg1l=<mAUN?IHe1n=9PBVN=JEg1o=5SBZNh0`1U=6TBUNA0V1e1V=4bCL_<3aCM`<ChAlNi1a1^<CkAkNg1b1^<BmAkNe1c1^<BoAhNd1f1]<BRD>m;BTD>l;BTD>k;CUD=k;CUD=j;DWD;i;FVD:j;HPBcNg1e1Y<IkAgNk1`1Y<LfAeN1JP2e1Z<4hCLY<3gCMY<3gCM[<0eA[Nk1f1_<0eC1[<OeC1Z<0eC1[<OdC3[<LfC4Z<LeC5[<JeC7Z<JeC7\\<EgC;X<EoA^NM0Y1n1k<ChC>W<BjC?U<@kCa0U<_OkCb0U<]OkCd0U<[OkCe0U<[OkCf0U<YOkCh0T<YOVBdNT1T2e<XOVBdNV1T2c<ZOSBdN\\1R2`<ZOSBeN]1R2_<GaC:^<FbC;]<FbC>Y<CgC?W<AjC?U<AkC>V<CiC=W<EgC<X<GeC9\\<\\OnCe0R<YOhBWNc0`2e<YOoCg0P<ZOPDf0P<ZOPDf0P<ZOPDf0P<ZOPDf0o;[OQDe0o;\\OPDd0Q<]OmCd0Q<^OnC`0T<@lC`0S<AmC`0S<_OmC`0S<AlC?U<BjC=W<CiC<Y<CfC?Y<AfC`0Y<BfC>Z<SOWB_N14]1Y2\\<bMUB`1;_NU1`2^<POoCP1R<oMQB;1XOj1a2S<_MRB90<5ZOc1e2Z<eMnA20C12R2d2]<[MgA1T2b2n>2N100001N1O101Kf^OaM\\a0^23L`^OgMaa0W26M23NN1N2O11O01O0000O101O00O11O0_MmMUCS2k<nMjBOoMO1S2W?oMhB\\2X=dMhB^2T=dMlB[2T=fMlBZ2U=eMkB[2V=eMiBZ2W=gMiBY2X=fMhBY2Y=hMeBY2[=lM`BT2`=QN[Bn1e=WNWBj1Q=cMRBe0k0h1o<hMUBc0i0f1P=iMlAKLi0V1c1P=mMlAk0Q1X1S=oMkAk0P1V1U=PNjAl0n0U1W=QNiAm0n0R1Z=EbB=]=H^BHAPNJMY>c2RB[O1UNQ>h2eARO;VNo=i4SBVKo=h4RBXKn=f4TBYKm=f4TBZKl=d4WB[Kj=c4WB]Ki=a4YB_Kg=`4ZB`Kg=]4[BcKe=[4]BeKc=Z4^BgKa=W4aBiK^=W4cBiK]=U4eBkKZ=U4X1O101N2N2O1N2N2O1N2N2O1N2N2N102N2O0O1UOh_OnMY`0i1S1K4O3N002O00Z_ORNZ?n1b@XN\\?h1`@_N]?a1`@lNV?U1f@nNZ?R1c@QO]?o0_@\\N@8Q`0^1V@^NO1k?b1o_O`N=Jd?\\2a@`M`?]2f@`MZ?^2l@^MS?b2U110O010O011N010O101N100O100O100O01O1000\\Nh^OUONT1[a0CR_O[ODMNe0]a01b_OFf`0:Z_OEUOEYa0f0b_OCWOGVa0g0c_OBWOGVa0g0c_OBWOGVa0h0c^OZOb0O^O:7ETa0Q1g^OoNN00g07YOTa0V2l^OjMSa0n0o^OOMSOTa0l0R_O1ISOVa0j0R_O4HROVa0i0R_O6GQOWa0e0Z_O_O_O?L^OZa0c0]_O^O_OOK123KM\\a0`0j_O^OoN025IL]a0`0i_O_O^O3i`0=`@C_?=b@B^?>b@C^?=c@B[?`0l^O^OM0a10e?Z1[1000l_OhN_=Y1_BoN[=R1a@eNe1;i=P1b@eNd1<j=Q1]@fNg1;l=Z1SBhNl=X1_AWN[Oc0N_Oe>:l@F0V1U1iNZOc0KCi>6o@CNX1R1lN\\Ob0FFo>2n@BO62j0m0UO]OU1g>SOQAG5l0IPOc06FU1h>QORAG5R19SOEV1k>mNRAF7o09SO[O37Y1^?bNo@6h0NjN[1fa0cNT^O24]1ha0hNW^OX1ka0hNS^OY1ka0iNT^OW1ma0>1N2O\\ORNS_OR2i`0RNT_OP2i`0RNY_Om1e`0UN[_Om1d`0QN]_Oo1d`0PN^_Ol1e`0RN[_Oo1V>nMgC2TNn1U>`NVC@iNP2P>gNhA0l0V1^=POaAK3XO<n1o=7`AkMb0n1o=:YBBh=a0TB_Om=`0SBAP>3TAWNl0g1P>1YASNg0n1o=M\\B2c=N^B1b=O`BUNeNi1k>1cBOa=N^B3b=M]B3c=YNQA_1]17g=JYB5h=KWB5j=KUB5k=KUB5l=OoA1Q>0oAOR>2kANY>0fA1Y>1dAO]>2aAO`>1_AOa>3h@TN=j1i>5h@RN?i1i>=TADk>i0h@XOX?j0f@VOZ?h20O1O1010N1O11N20O0001O002NO1O1O2OO01N2O100O1O10O101O001N110O3M1O1O2N000O2O0O1001N10O10010N10001N4jMW@Ni?2X@Of?QNZ@c079JS1k?bNo@EYOi1h?aNcA[1^>dNiAV1V>iNlAV1T>iNmAW1S>iNnAV1S>hNoAg0`>YOaA4R?Lb2N00100O100OR\\1GSdN5L4L3M5L2bP:]OPPF2N2N2N1nNMW^O5ia05k]OMUb0=_]OEab0m001OOZOfNh^OZ1l`0gNb^O1Ob0Of0_a0hNc^O0Oo1]a0UNb^OLOo1_a0WNb^Ol1^a0<O0000O100000000000000000UJ", "size": [640, 480]}, "area": 47295.0, "iscrowd": 0, "image_id": 12639, "bbox": [0.0, 110.0, 480.0, 524.0], "category_id": 124, "id": 20000746}, {"segmentation": {"counts": "]P6<bc040ON0ROB\\^O?aa0E\\^O<_12^O^OZN000`=2\\C>Z17XN]OB5a0<L[OU<3aCMa0c0c15QNL5=EPO]<0`C60Ka0e0b11mMYO6Y2JhM4NV<0gC:b0n0P1\\OjNh1CdM02W<b0WDe0T1_O_NXO6b2K`MP<`0]Df0T1_O_NYO2k2o;gMmC^O303Y1^1BaNo1k;mMRD[ONN000Z1c1^OeNR2h;AeE\\NcNT2h;^OjEYNdNU2c;_OmEYNbNW2`;^OTFWN_NY2_;[OXFWN\\N]20]Md:m1UGTN[N`2H`M_O000T;i1[Hf0RMaM_OO11S;f1_Hi0kLkMf:Y1dHj0dLbMC;V;U1gHm0`LPNj:o0kHo0[LRNi:m0PIP1VLTNj:i0TIX1fKbM1?U;e0XIn1g6RN[Im1f6PN^In1a6RNaIm1_6RNbIn1\\6RNgIm1Z6QNhIn1X6QNiIo1V6PNmIo1S6PNnIP2R6nMQJQ2n5oMSJQ2m5kMXJT2h5hM\\JX2e5cM_J]2c5]MaJc2a5XMaJi2[;0O100O10000O10000000000001O1O001O1O001O1O001O1O001O1O001O1OO1000000O1000000O1000000O1VO\\MX@d2Z;[MTFO15^Na2];]MPFO46]N^2_;_MlEO86ZN]2b;aMgEN<7YNZ2c;dMcENa07VNW2f;fM_ENd08UNT2h;iMZENh07SNS2k;jMVENk09QNo1n;lMRENo0R3n9SMnDNS1P3o9UMiDMX1n2o9WMdDN\\1l2P:XM`DN`1j2o9[M]DMc1i2P:]MWDMi1f2P:_MSDMm1d2P:dNoE]1Q:cNoE]1P:dNoE]1Y8eLTGn1c0]1[8\\MYFX1Z1]1^8ZMXFY1Z1]1]8[MXFY1[1\\1]8[MXFY1Z1]1_8YMWFZ1Z1]1_8XMXF[1X1^1a8VMWF\\1X1^1a8VMWF\\1X1^1a8VMWF\\1W1_1b8UMWF\\1W1_1b8TMXF]1U1`1c8SMXF]1U1`1c8SMXF]1T1a1d8RMXF]1T1a1d8QMYF_1R1`1f8PMYF_1P1b1e8QM[F]1P1b1e8QM[F]1o0c1g8nL[F_1n0c1f8nL]F_1m0c1e8mL`F`1j0d1e8lLcF_1h0e1d8kLgF_1d0g1f8hLhF`1b0h1f8hLiF_1`0j1g8gLiF_1`0j1e8hLmF]1>k1e8hLmF^1<k1f8hLoF\\1;l1g8gLoF\\19n1h8fLQGZ17P2h8fLRGY16Q2g8fLUGX13S2i8dLUGX12T2i8dLVGW10V2j8cLWGV1OW2j8cLWGV1NX2j8cLXGU1NX2i8dLZGS1MY2i8dLZGS1LZ2f8gKSFm0\\1Q1K[2f8fL`GP1H[2d8eKXFQ1]1n0G\\2b8kLgGi0G\\2`8mLjGf0E^2d8hLhGj0D^2c8iLiGi0C_2b8jLlGf0B`2a8jLnGf0@a2c8hLnGf0_Ob2c8gLoGg0^Ob2c8gLPHf0\\Od2d8fLPHf0\\Od2d8fLUGlNNj18e2e8fLSGSOJb1=e2g8fLoFZOH[1b0e2g8eLoF]OHZ1?e2i8eLoF]ODc1?[2o8dLlFAEe1:W2U9cLkFCEh16R2Z9dLiFDGj10o1_9dLhFEDR20e1d9dLhF\\7Z9cHdF^7[9cHdF^7\\9cHcF]7]9cHbF^7]9dHaF]7_9b1O10000O11O001O00O10000001OLhE_FY:a9jKbFPN^9l1gFSNX9j1mFUNS9g1RGXNm8d1YG[Ng8`1_G_Na8S1nGlNR8i0[HUOe7i0^HVOb7g0bHXO^7f0eHYO[7e0hHZOY7b0jH^OV7`0lH@T7>nHBR7<PIDn6;UIEl68UIIk65WIKi62ZINe61]IOb60`I0_6NdI2[6McHZLTOi3X8LdHaLoNd3\\8IgHiLhN^3`8HhHPMdNX3d8FhHYM_NQ3h8cNlIbNWMk2l8_NPJlNPMe2o8[NTJWOhL^2S9WNXJZ3g5bL]J_3b5]LaJe3b5SLaJo3i5bKZJ`4P6QKSJQ5W6`JlIb5W:2N2N2N2M3M3M3N2M3M3M3N2M3N2N2N2O1N2N2N2O1N2N2O1N2N2O1N2N2O1N2N2N2O[KnC^1Q<`NRD`1P<\\NRDd1Q<VNSDi1o;SNSDm1o;nMTDR2o;hMUDW2m;eMUD[2m;`MVD`2m;ZMWDe2k;VMXDj2j;QMZDn2h;lL\\DT3g;fL\\DZ3f;aL^D^3d;]L_Dc3d;WL_Di3c;QLbDn3`;mKcDS4`;gKdDX4b=0001O0000kIeKfLZ4Y3RL\\Ln3c3_LQLa3m3lLhKT3W4YM]Kg2b4eMWJRNZMY4\\8oMPJlMcMU4X8VNPJjMfMP4U8]NPJhMiMk3T8cNmIfMnMg3U8eNhIiMQNb3W8iN`IkMWN\\3Y8mNYIkM]NX3Z8QORIlMbNS3]8TOjHnMgNn2_8XOcHnMmNj2`8]O[HnMSOe2b8ATHoMXO`2c8FnGnM_O[2c8KgGoMDV2d80aGoMIQ2f84ZGoMOm1f8o6001O000]LaGeKa8Y4eGaK[8_4eGaKY8`4hG`KX8_4iGaKV8`4jG`KV8a4iG_KV8b4jG^KV8a4kG_KU8b4jG^KU8b4lG^KS8c4mG]KS8a4oG_KQ8b4nG^KS8b4lG^KT8b4lG^KS8c4mG]KS8c4mG]KS8c4mG]KT8b4lG^KT8b4lG^KT8b4lG^KT8b4lG^KT8a4mG_KS8a4lG`KT8`4lG`KT8`4mG_KS8a4mG_KR8b4nG^KS8a4mG_KR8b4nG^KR8b4nG^KR8b4nG^KR8b4nG^KR8b4nG^KR8b4mG_KS8a4nG^KR8c4hGZHHS3`8X8O1O1001O0000O11O000000000000001OO1000000000000O1000000O11O000000000000O100O1001O00O1001OO10000O1O11O0000001O00000dDQHg:d80000000000000000000^O\\EhGd:U8bEhG^:X8eEeG[:[8hEbGX:]8f01Oe0[O0000000000000000000000@YEgGg:X8\\EfGd:X8aEeG_:Y8eEeG[:Y8iEeGW:Z8lEdGT:\\8h00000O1000000001OO1000000O1001O0000O1000000000000000000", "size": [640, 480]}, "area": 120268.0, "iscrowd": 0, "image_id": 12639, "bbox": [9.0, 267.0, 471.0, 373.0], "category_id": 125, "id": 20000747}, {"segmentation": {"counts": "h8X;i8O001O4L8H5XEgFO>n1AQ6[:UHgES1:_6Z:UIRFh6R:UIZF^6e9jIVFT6j9hI[FW6f9nHXEe0Q1^6m9WIPE0Q1h6o9VITENo0R2_O^O^OZN000]13VOm:DRE2Q1m1DXN]OB5a0<L[O243m:DREMM0T1T2DQNL5=EPO=7Oi:EPEL60K1U1R2AmMYO6Y2JhM4N20J7b0i:CPELW2b1]NjNh1CdM0268;f:A]G`1[N_NXO6b2KmM>b:BZGa1\\N_NYO2Z3:f7FZG`1aNaN_27f7F]G0PO0_O2b24b7J[GOl34i4MYGNk3;_=01O0J\\\\O0gc0MZ\\O0hc00W\\O1QXi8", "size": [640, 480]}, "area": 5407.0, "iscrowd": 0, "image_id": 12639, "bbox": [0.0, 280.0, 30.0, 360.0], "category_id": 132, "id": 20000748}, {"segmentation": {"counts": "0Q8o;O1003M1OO10000O11O00O10000000000O1001O0000kM_DjKb;o3fDPLZ;o3jDnJEOa;Q5mDmJD3^;o4PElJC4^;o4QEkJC5\\;Z4SDVK0?o0LB5\\;Y4fDWK<0N>Q;[4hFdKX9Y4RDZKf2=X9Y4RDZKg2=V9Y4SDZKh2<U9Z4oFdKR9S4SDhKn21P9W4TDfKQ3Nk8X4cGgK]8W4g3MY@lKh?S431OO1001O00O1001OO10000001O0000O1N2000000O1000000000000001O1O001O1O1OO1000000O100O100O1000000O11O1O002NO100O100000000O11OO100O100001O1O001O0000O100000000O100000000001O1O00O1000000O1001OO100O11OO100001O00O10eAQL_N1P>m3oAQL`1;_NFk=Q4eC]L[<c3dBnK;1XO>i=c3WBnK90<5ZO:j=g3`BjK20C12j0h=i3WBcK1k0h=R5000001O00000000D<O1O100O10000000000001O003M2NN2N2N2M3\\OgA[K_>e4>0000N2O11O1OO11O000000O1YOQA]Lo>_3VA`Lj>_3XAVLKLn>l3[AVLn>i3TAVLl>j3UAULk>j3WATLj>m3d0001O1OO11O0000001O00001O000000O100O1O1O1000000000000000000000000O11O1O00001O00O11OO11O000000000000000000000000O1O100O100O11OO100001O000000000000O1O100001O1O0000O10000N200O1001O1O00001O1O1O00001OO1000000O100000000000[@SLF0]?l3n@VLCN_?l3o@ULBO62a16];d3YCXLG5S1KkN6S=b3WCYLG5R19Q<Z3UCXLF5T18nN^OQ=_4mBfKd18^;U4kDbKUNLP=^4PEeKU;X4oDhKP;V4SEgKo:W4^2O12N3RBTLb;o3\\DnKf;R4YDPLf;P4ZDPLf;P4YDQLg;o3YDSLe;m3ZDSLg;n2fB^Mc1Ef;l2WCPMT13e;l2^CQMk01e;o2fCkLg01d;S3gEkLY:V3gElLV:T3jEkLW:U3jEiLW:Z3fEeL[:[3fEcL[:]3eEcL[:\\3gEaL[:^3fEaL[:_3S34L1O0i_O\\LR`0i301O1O001O001O3M00001O00N200O100000000001OO1O11O1O00001O002NO1O1O100O1N2O100O1O100001O001O001O3M1O1O2N00001O000000O10000001O00001O3MO1O[@kKa?[4O001O0000001OO1001OO10000000000E;O1O1O1O1O1O1001O001O001O001O001O00c0]O1O1O1O1O1O1O1O1O1O1O1O1O1O1O3M3gAWKU=l4hBhJD01Oc=\\5eBdJH10Oc=_5bB`JO0LOd=d5^B\\J41a=f5XBYJS>j500000000000000000000000", "size": [640, 480]}, "area": 69895.0, "iscrowd": 0, "image_id": 12639, "bbox": [0.0, 0.0, 480.0, 260.0], "category_id": 169, "id": 20000749}, {"segmentation": {"counts": "eU>^1l0fNc`0`1Y_OeNR:4WK]1bJeNQ:1YK_1cJbNR:0XKd1bJ_NS:NXKi1`J\\NW:LVKm1^J[No94_Kb2]4aM`Kc2\\4aM`Kd2^4]M^Kg2`4[M\\Kj2b4WMZKm2e4SMXKQ3f4QMWKS3g4oLVKS3i4nLTKV3j4lLSKV3m4iLRKX3n4hLPK[3o4fLoJ[3R5dLmJ^3R5bLlJ`3T5UKgF11l0S4P4U5PKkF02n0j1NROW4V8lJWGn0`12POU4l8gKSH7kNW4R9aKQH<gNW4Y9\\KoG`0bNW4b9WKkGe0]NX4m9nJfGl0XNX4T:kJcGi6^8VIbGj6_8UIaGj6a8VI^Gj6c8UI]Gj6e8UI[Gk6f8TIZGl6f8TIZGl6f8TIZGl6f8TIZGl6f8TIZGl6f8TIZGl6f8TIZGl6f8TIZGm6^7gHnG<d0m6[7lHoG7e0o6X7oHQH2g0P7T7RITHNh0P7T7SISHMi0Q7R7TITHKj0R7Q7TITHJk0R7b6gHmG`0d0Gm0S7`6iHmG>e0Fn0T7^6jHnGb0`0AS1S7_6lHkGh0<YOZ1T7^6SJWHiN[1U7\\6SJYHhN[1U7\\6TJXHgN\\1V7[6TJWHgN^1V7Z6SJWHhN_1U7Z6TJUHhNa1T7[6oIWHnN^1S7]6dHoGl05^O_1Q7`6_HQHP10A_1P7i6]HaGR15Ca1n6j6]H`GP15Fa1m6j6]H`GP14Ha1j6l6aIbGEc1j6j6`H`Gn01Hf1j6i6_HbGl00Le1i6i6_HcGh010d1h6i6_HdGe023b1i6h6_HeGd013d1o5UNQJ`8YOkG?M8c1o5XNnI]8]OQH7G>d1b1UNI124^NZ8ASH3H>b1e1WND074\\NX8CTH2I<a1g1YNAO:6ZNQ8JnGJK2Lj0l1[1[N]OO>6XNn7NlGL1i0f1Z1]NXONb07VNk7a0lG5h1Z1^NTONf08SNg7f0kG4k1W1`NQOOi07SNc7i0kG3m1W1aNmNOm07QNa7k0kG4m1U1dNiNNQ1c8nNkF5Q2R1eNfNNU1a84lH0hNbNNY1_85lHOiN_NN]1]8`NhFU1U2>kNZNNb1\\8_NiFU1S2?lNWNNf1Z8]NjFX1R2>nNSNNi1Z8[NiF\\1R2;5NP7XNlF_1P2:4N_7H^H93O_7IgHNJ9`7HiIkNhN\\1`7IhIkNiN[1_7JhIkNiNZ1`7KgIkNiNZ1a7JfIlNiNZ1e7FbIoNjNZ1f7F_IQOkNY1\\7iNVFd0c3ZOkNX1\\7lNUFc0c3YOlNX1\\7lNUFc0c3YOlNW1]7mNUFb0b3[OkNV1^7lNVFc0`3\\OlNU1^7mNVFa0`3]OlNT1_7nNXF>]3@lNT1_7POXF:]3BlNS1a7QOVF9\\3DnNQ1c7^NRF=0?]3EnNQ1j7kNkE>]3FnNP1k7lNkE=\\3GnNP1k7nNjE9]3JnNn0l7QOhEEM9`34oNm0m7QOgEB0;[38POi0R8PObEA4=X3;oNg0S8QOiEMS3>POd0U8QOfEMU3?POb0W8ROcELW3a0nNa0Z8QO`EMX3b0mN?]8RO]ELY3d0mN>_8TOWEI^3f0lN<a8SOUEI`3i0iN:e8ROSEC21^3Q1gN9h8POjHi0\\N6l8POhHk0[N5n8oNfHn0[N2Q9nNeHQ1YN1S9mNdHS1XN0U9lNcHT1XNOY9hNaHZ1UNNX=2hBMo8iNXI[1hMLo8kNXIY1iMKP9lNWIY1iMKn8oNWIX1kMHl8RO^EI_3]1WNGk8VO\\EIa3[1WNFc8PO_Ed0g3f0WNEc8NTI?XNCc8OUI>XNCc8OUI?WNAd81TI?XN@d81TI?XN_Od8I_E]Oe3]1WN]Oe8HaE]Oc3^1WN\\Of8IbE[Oa3a1VN[Oa8:XI_OaLLf1;b89XI^OhLH^1?c8;XI[OkLIZ1a0a8=YIYOnLC_OKl1g0]8d0ZIVOoLDe1=P8j0]ISOQMFf18i7R1`IoNSMFi13_7^1fIfNUMFm11n6m1PJ\\NP:d1PF[NQ:e1PFYNQ:g1oEXNR:h1oEWNQ:h1PFWNR:h1oEVNR:j1R402N6J5K3L3N2N2N2M4M2M3N3L9Hg^OEV?1e@6Y4Fk6;QIJl68PIMn64nH1o61lH5R7LiH:T7H^Hg0`7ZOUHS1g7oNWHT1g7mNVHW1g7kNWHX1f7kNWHX1g7iNVHZ1h7hNVHY1j7hNTHZ1j7hNTHZ1k7gNRH\\1l7fNRH\\1m7eNQH]1m7eNPH^1n7cNQH^1o7bNPH`1n7aNPHb1n7_NQH?[LSOc;>QH?cLnNZ;d0QH?kLiNR;i0RH?RMcNk:n0RH?YM^Nd:T1PG`1cNWM[:[1mFa1oNoLR:a1kFd1c:\\NYEg1h:YNTEj1n:UNnDo1R;VNeDm1b:eL\\Dh1c0f1k:GfD<W;3ZD0f;T41O1O2M3ZIRDW5Q<eJVDU5m;gJ\\DR5f;kJeDj4_;RKcDl4`;RKbDk4a;oJlCC5[5T<iJkC2NS5Z<dJkC`0Gi4W=\\KeBc4Z=cKcB\\4\\=jK`BT4a=T12N101N1O2O0O2N101N1O2O0O2N101N1O2OnD[Jk7e5RH_Jm7c5oG_JQ8d5iG_JW8c5eG`JR8JYEh5`2aJV8HZEj5Z2aJZ8G\\Ej5V2bJ]8C^Em5P2cJb8A]Eo5k1cJh8^O]EQ6f1eJl8ZO^ES6a1fJQ9WO^EU6[1iJW9QO^EY6U1iJ^9mN]E\\6P1jJd9iN]E^6j0mJi9eN\\Ea6e0mJT:]NXEg6>PK[:XNXEi68SKc:P5WESKj:R70O00100O1iIUEW3l:gLaEm2a:oLmEe2U:hKSE7U1e3i9QLUE8_1[3^9ZLUE8j1V2lMaMV;NVE6o1V2iMfMS;KVE4U2V2gMiMn:LXEOZ2W2eMlMi:LZEL_2W2cMoMe:LZEIf2U2`MUN`:K]EEl2T2\\MZN[:LZIb1`L`NV:M_I\\1_LfNS:KdIX1^LkNi7GiG1N4W4R1^LPO_76UG`0<WOg4l0]LVOV7g1bGUNQ5g0\\L[Om6n1eGoMW5a0\\L@e6T2hGiM\\5<[LF_6X2jMjMlKLX6]2n821O3VOh0K3L41O1O1O1OOa]ObN^b0]1c]OgNXc0:g\\OKYc00h\\O4Wc0Hi\\O;Yc0Af\\Oa0[c01O2F\\OU]Oe0jb0@Q]Ob0lb0:O2N1O20O16I10O1O10OeN_]OW1fb0O1TOZ]O<hb0CZ]O9hb0FZ]O8hb0GY]O6ib0IX]O6jb0IW]O4kb0KV]O3^c0N1N3N]^P17[aoNd0\\Od0]O7H1O2O1e_OeNn=]1]AWOb>j0k@IU?a2O1O100O100O1FJj@VLk>P4]AmKb>P4eAmKZ>o3nAnKQ>o3VBnKi=o3U1N2Mf@kLQ>R3ZAfMg>i3001O001O001O001Ob0oNc@[L^?b3f@\\L[?a3i@]LX?`3l@^LU?_3o@_LR?^3RA`Lo>]3UAaLl>\\3n0M3N2M3mN]_OeNf`0Z1d_OZN_`0e1o0O2N3fNP^O>Sb0_O`^ONca0OZ1M3M3M3MWZ7", "size": [640, 480]}, "area": 58167.0, "iscrowd": 0, "image_id": 12639, "bbox": [22.0, 125.0, 446.0, 431.0], "category_id": 183, "id": 20000750}, {"segmentation": {"counts": "o0V9W1^JH`59aJG^5;aJE^5<bJD]5>bJA]5b0bJ^O\\5e0cJ[O[5g0eJYOZ5h0fJXOY5h0hJXOW5j0hJUOW5m0iJSOV5Q1gJoNW5V1fJiN[5X1dJhN[5Z1dJfN[5\\1dJdNZ5^1fJaNZ5`0_GFU3J[5=bGiNO60b0R32\\58eGnN52Hf0R31]53dGUO;OBi0R30\\54ZHLZ20[54\\HUOVOc0S33[55\\HUOXOb0Q34Z56cHGR23Z57dHFR23Y57fHFQ23Y56fHHQ22X57gHGQ22W57hHIP20W57jHIo1OW58lHHm10W58lHHm10V51UI0d1OW50WI1a1NX50YI2_1NW50[I2^1NV50]I3\\1MW5O_I3Z1MW5OaI4X1MW5NbI5W1LW51aI[OZNf0n2OV50eI0U10U51fIOU1OU53fINU1OT54gINT1NT55gINU1MU54eI0V1LV53dI1V1KT57fINV1KS57gIOV1JR58hINV1JQO]OQ4j0iKNV1KoN_OP4e0RLNo0NmNCo3;\\L4h0MhNIU43^L7e0MfNLV40_L7e0MgNKT42^L9f0JhNLR42`L8f0JgNMS41aL7e0JhNOP41aL8g0HhNOP41bL7f0IhNOQ40aL8f0IgN0R4NbL9e0IgN1Q4MdL8d0IhN2P4MdL8d0IhN3o3LeL8d0IhN3P4KeL7d0KfN4Q4JaLCkMd0m2JgN6l3LdL8j0FfN6m3KbL:k0EgN5k3McL_OlMg0P3HfN6j3MfL4j0IgN5e3^OdJa0U22l0IhN5b3@eJ`0U22l0IiN4`37kLKm0JhN5a34jLMm0JhN5a33kLNl0JgN6c31fL[OQNc0o2JiN6`33fL]OPN?R3KhN7_32fL4R1CiN7[3]OkJd0P25Q1CiN7Z3^OlJc0o16R1AjN9X3^OlJa0Q27Q1AjN9W34oL2P1AjN9W34oL1Q1BiN:aN[OU4f0bM2P1BhN=\\NXOJ2_4d0cM2R1CRNGVOi0G]O[4=eMNlM^OW3:oMS1kNoN[43mM5Q1FlMe1U3_NoM4R1HiMg1T3^NPN2U1IeMj1U3[NQN1V1J_MP2Y3UNRN1V1J^MQ2Z3TNQN2W1I\\MS2[3RNSN2V1I]MS2Y3SNRN2X1G]MU2X3SNRN3X1E]MV2X3SNSN1Y1E]MW2W3RNSN3Y1D\\MY2W3PNTN2Z1EZMZ2X3oMSN4Z1C[MZ2X3oMSN3[1C[M[2V3PNTN2[1CZM]2V3nMTN3\\1BZM^2T3nMUN4\\1_O[M`2T3mMUN6Z1]O]Ma2S3XMRLc0R2:Z1ZO_Ma2S3XMRLc0Q2=Y1VOaMc2U3kMPN=Y1POfMh2R3kMoM>Y2i1DUMTLd0o1?W2i1EVMTLb0o1a0W2g1GkMQN?W2g1HjMPN`0W2g1IiMoM`0X2h1IhMoM`0W2h1KgMnMa0W2i1HPMVLf0k1a0W2i1HQMVLd0j1b0X2j1IgMnM?`1iN]MR3U3fMmM?b1iN[MS3V3eMmM>c1kNYMQ3X3fMkM>e1lNVMQ3Z3eMkM=f1mNTMQ3\\3eMiM=h1nNPMR3\\3eMlM;i1nNnLS3\\3dMmM;j1oNkLR3a3aMjM>k1POhLR3c3_MkM=l1ROfLQ3d3`MiM>m1ROdLQ3f3^MjM>m1TObLo2h3_MhM?n1SOaLP3i3^MgM?P2VO\\Lm2n3]MfMa0P2VO[Ll2l3`MiM=Q2a26QMiM?Q2_27RMgM?S2^27RMgM`0R2^27QMgMa0S2]24^LTLc0e1b0T2\\23aLSL`0f1c0U2\\22TMiM`0V2[21TMkM`0U2\\20SMkMa0V2[20QMmMc0T2\\2ORMkMa0X2\\2MTMjM`0Z2\\2KUMjM?\\2[2KVMhM`0]2Y2LWMgM`0]2Y2KXMhM>^2Y2KYMeM?a2X2JYMeM?a2X2IZMeM>c2W2I[McM>e2W2H[McM>e2V2H]McM<f2W2G]MaM>h2T2H]M`M?i2S2H^M^M>l2T2E`M^M:o2U2DbM[M6U3W2BbMYM1[3]2\\OcMWM0^3\\2\\OdMUMOa3\\2[OeMSM0b3[2[OeMRM2b3X2]OfMQM1c3Y2ZOnLlKh0W11c3Y2ZOgMTM0b3X2[OhMSM0b3X2[OhMPM3e3T2\\OiMnL4f3S2\\OiMnL3g3T2[OhMnL5g3S2YOPMlKh0T15g3R2ZOkMnL4h3Q2ZOkMmL4j3P2ZOkMlL7i3m1[OmMkL8i3k1\\OlMlL9h3j1]OlMkL;h3i1[OTMlKg0P1=i3g1\\OnMlL;h3g1\\OnMkL<i3f1\\OkMmL?h3e1\\OkMmL`0g3e1\\OjMmLb0g3d1\\OiMnLb0g3d1ZOkMoLa0h3d1YOkMnLb0i3c1YOjMnLc0j3c1XOiMPMb0i3e1VOiMQMb0j3f1ROVMnKa0W1a0k3h1oNWMoK>W1d0l3g1nNgMVMb0m3g1lNfMXMb0n3i1iNfMWMa0Q4i1hNfMWM`0R4j1gNfMVM?U4k1dNgMVM?V4j1cNiMUM=Y4i1cNjMTM=Y4i1cNjMSM=[4h1cNkMRM=[4f1eNmMoL=]4d1eNPNmL=^4b1eNRNlL<`4a1eNSNkL<`4_1gNUNiL;a4c0aJUOV4NhL9b4c0aJUOV4NeL<f4`0_JUOW4OcL;j4?]JWOU4OeL:j4?]JWOU4OfL7l4a0]JUOR43eL5n4b0\\JVOP44cL5T5>[JYOn3^OhKd0j07V5<[J\\Om3]OhKd0i07Y58\\J_Om32^L6[54]JEg3]OiKd0h05^50]JJd3^OhKc0h06e5FZJ2c33]L7k5\\OWJ;a32\\L8o5VOVJ`0_32]L6n6IeL1]L6n6IbL\\OjKe0f06n6IaL]OkKd0b0[O_Ok0b7JbL4\\LXO@i0c7KaL5ZLXOBh0c7L_L4]LXO@i0d7K_LDmK2a0F_Oi0c7L_LDnLGPOh0c7N_LClLc0h6J[LDlL`0m6KWLEkL>Q7MTLDlL;V7OmKGmL5]72fKIlL6_70dKKkL7b7NbKKkL8c7MaKLmL7c7K`KMnL8h5\\O]IOe1>W20oL7h5_OSK9V21nL8h5@SK7W21nL7i5BRK6U22PM6i5BRK6U22lLA[Oe0b6CRKHZN;j33nL;l5_ORKH[N;h33PM:k5ATK2P25PM8l5BSK2o16QM6m5BRK3P26PM5n5CRK2o17PMM[ODd66QK3P25QMNZODd66QK4P23QMN\\ODb68PK4m1@`Lb0e0M^OD`6:oJ3n1@`Lb0e0LX6OeJ3m15WM@[ONn6:dJ2m15VM@a6:[J1n15VM@a6;ZJ1l1@bLc0h0B_6:[J1l1@bLc0h0B_6:[J1k1AcLb0h0B_6:\\J0k15[MA^6;[J0k14\\MB]6;[JOl14\\MB]6<[JMi1AgLd0h0B]6=ZJLi1BhLc0i0B[6<\\JNg1AhLe0i0@\\6<]JMe1;bM\\O\\6<]JMd1=bM[O\\6<]JMd1<dMZO[6=^JLb1?dMXO\\6=^JLa1a0dMVO]6=^JM`1`0eMWO\\6<_JM_1`0gMWO[6=_JK^1b0iMUOZ6>_JK]1c0jMTOZ6>_JL[1c0lMTOY6;nH^Ob1`0Z1e0lMROZ6;cJMV1g0YMoN92e6:dJNU1V1^MbNi69eJOR1]1[M[Nn69eJOQ1b1ZMTNP7;eJOP1g1[MkMP7?fJNj0D^MY2S7UNeJOg0V2`4kMiJNf0]2]4fMmJLf0b2Y4bMQKKf0h2U4]MUKJg0m2P4YMZKIc0U3o3RM^KJ>Z3R4lL`KJ;^3T4hLbKI6d3W4dLbKH2j3[4^LcKINm3^4ZLdKILo3`4XL]K01k3a4UL]K10m3b4RL^K2Mn3e4QL]K1LQ4f4oK\\K1MR4f4PLZKNNT4h4nKYKNNW4h4jK[KOKY4j4gK[K1I[4k4cK]K2F]4m4aKaKJaNBP1U5n4_KfKD^Oo4l4]KQMf4n2ZKQMg4o2YKoLi4Q3VKnLl4R3UKkLm4U3PKlLS5S3jJnLX5R3hJlLZ5T3fJjL\\5V3dJiL]5W3cJgL]5[3cJcL]5_3cJ_L]5c3cJ[L]5g3cJWL]5k3cJSL]5o3cJoK]5S4cJlK\\5V4dJhK\\5Z4dJeK\\5\\4eJbKY5a4gJ^KX5d4hJZKX5h4hJWKW5k4iJTKW5m4jJQKU5Q5kJnJV5R5kJlJU5U5lJiJV5V5kJaI^Og0h5h5kJVI^O0NO3Q1h5i5YKVJh4j5XKUJi4k5WKTJj4l5WKQJk4o5UKPJl4P6TKoIm4Q6SKnIn4R6RKmIo4S6QKlIQ5S6PKkIQ5U6oJkIQ5U6oJjIY5o5gJQJY5o5fJQJ[5o5eJQJ\\5n5cJSJ]5m5cJRJ^5n5aJSJ_5m5aJRJa5m5^JTJb5l5^JSJc5m5]JSJc5m5]JSJc5m5^JQJc5o5]JQJd5n5\\JQJf5n5ZJRJf5n5[JQJe5o5[JPJe5Q6[JoIe5Q6ZJoIg5Q6YJoIg5Q6XJoIh5R6WJoIi5Q6VJPJj5P6VJoIk5Q6UJoIk5Q6UJnIl5R6TJnIl5R6TJnIl5R6TJoIl5P6TJQJk5o5UJRJj5n5VJRJj5n5VJSJj5l5VJUJi5k5WJVJh5j5XJVJg5k5YJVJf5j5[JVJd5j5\\JWJb5j5_JUJb5j5^JWJa5i5_JXJ`5h5`JYJ_5g5aJYJ`5f5`J[J_5e5aJ\\J^5d5bJ]J^5b5bJ^J^5b5cJ^J\\5b5dJ_JZ5b5fJ_JZ5`5fJ`JZ5`5fJaJY5_5gJbJY5]5gJdJX5\\5hJfJX5X5hJkJW5S5jJoJV5n4kJRKV5l4jJTKW5k4jJTKX5j4iJVKW5i4iJWKW5i4jJWKV5h4kJWKU5i4kJWKV5h4kJWKU5i4lJVKT5j4lJVKT5j4mJUKT5j4mJTKT5l4mJSKS5m4PKPK`NSO]6m5UKnJ]NVO0O\\6m5XKRKh4n4ZKQKd4P5\\KPKd4P5\\KPKd4P5\\KQKb4P5^KPKb4P5^KQK`4P5aKoJ_4Q5aKoJ_4Q5aKPK_4o4aKQK_4o4bKPK_4o4aKQK_4o4bKPK^4P5bKPK^4P5cKPK\\4P5fKnJ[4Q5fKnJZ4R5jKkJU4U5TLbJn3\\5RLfJk3[5ULbJn3^5RLbJn3^5RLbJP4\\5PLeJn3\\5QLeJn3\\5RLdJo3[5QLeJo3[5QLeJP4Z5PLfJP4Z5PLfJP4Z5QLfJm3[5SLeJm3[5SLeJm3[5TLdJm3[5SLeJm3[5oKWJjM>X6Z5lK[JkM<X6Y5mK[JkM<X6Y5lKnJU4Q5iKQKW4o4eKVKZ4h4fKZKZ4e4fK\\KZ4b4hK^KX4`4jK`KV4_4kKaKU4^4kKdKT4Z4mKgKS4X4mKiKS4V4nKjKR4T4PLlKP4R4PLQLo3n3RLRLn3m3SLSLn3j3ULULl3h3VLYLj3e3^LSKZMo0Y6k3cLSL^3j3dLWL\\3g3eLYLQMROX6c4jLZLnLWOT6^4oLbLP3]3QMcLo2[3SMeLm2Y3UMgLl2W3VMgLj2Y3WMhLi2U3YMkLg2S3[MmLe2R3\\MnLd2P3^MPMb2o2_MnL]LQOS6P4bMnLd2n2^MRMb2m2`MRM`2l2bMTM^2k2cMUM]2i2eMXMZ2g2gMYMY2e2iM[MW2c2kM]MU2b2lM^MT2`2mMbMQ2^2PNbMP2]2QNcMo1[2SNeMl1Z2VNgMh1Y2YNgMf1Y2[NgMd1X2^NhMa1V2aNkM\\1W2eNjMT1mN[JZ3U4eLULT1\\5W2`NPMiKl0f5R2cN[N\\1f1cN[N]1f1aN[N_1d1bN\\N^1b1dN_N[1d1bN\\N]1d1dN\\N\\1c1eN]NZ1d1fN]NY1X1`JVMOKW4g1Y1X1hJPMR4h1V1V1VOjNg0V1\\OkN?Y1AgN=Y1FfN6\\1McN3U1nJYMR5a1NW1QKSMO0U5e1ZO[NaK0;m2ESM10W5d1WO`NcKo2^5_NoNi1R1WNmNi1T1VNkNk1U1UNkNj1V1WNiNi1V1XNiNh1X1XNhNh1X1XNgNj1X1WNgNh1Z1XNcJ\\Nj3]3c1XN^J\\N0Oo3]3c1XN^J[N11k3_3e1UN_J[N02b3O]LW30jL1N50\\5f1^J_Nc3U3`2[NnI`N`3W3a2aN^M^1d2bN[M_1e2bNZM_1e2aNZM`1f2`NkLUNVM0LZ3U6aNiLm1W3SNiLm1W3TNgLn1W3SNhLm1Y3SNfLm1[3RNfLn1Z3RNcLRNXMl3U6SN`LRN\\Mk3T6SN]LS2d3nMZLR2h3mMWLT2j3iMWLY2i3fMVL[2k3eMSL_2k3bMRL_2o3aMoKa2P4`MnKb2R4^MlKd2T4\\MjKf2V4[MgKg2Y4YMeKi2Z4XMdKj2\\4VMbKl2^4TM`Kn2`4RM^KQ3a4nLgJQNHR5b5mLbJUNJQ5c5kLbJXNEo4i5iLaJYNDP5k5hL^JYN^OF4\\5o5fL\\JYN@G3\\5R6cL\\J^N@R5S6`L]JR4f5lKZJU4e5kKZJV4f5jKZJV4f5jK[JU4e5kK\\JU4c5kK\\JV4a5kInIo1`0W4a5kInIo1`0W4a5mK]JU4b5lK\\JU4e5kKYJW4f5jKZJV4f5jKZJV4f5jKUJjMM\\6o5jKTJkML[6P6kKSJkMLZ6P6lKTJX4l5hKSJZ4l5fKTJY4m5gKSJZ4l5fKTJZ4l5fKTJZ4l5fKTJ\\4j5eKSJgM0c6m5iKSJW4j5]IVJR9i53L3N3N110000O11O1OO02O00O20O0O1kN", "size": [480, 640]}, "area": 186809.0, "iscrowd": 0, "image_id": 12667, "bbox": [0.0, 0.0, 640.0, 480.0], "category_id": 110, "id": 20000751}, {"segmentation": {"counts": "cmh01n>2M4H5O30O01N101O000O101O001O0O101O01O010O1O1O01O0O11O01O00001O00001TB[OZ=g0aBXOJ2P=0\\Ci0G]Ol<V1QCoNm<c1N2M3J6N1N210N1O100O2N10000O2O1N101O0O1O002N100O1O1O1O00100O1VOoLdER3\\:nLcES3]:nLbEQ3_:PM_EQ3a:oL^EQ3c:PMZER3f:nLYES3g:nLWER3j:oLTER3l:nLTEQ3m:PMQEQ3o:PMPEo2Q;QMnDP3R;SMjDm2W;TMhDl2X;>0O1O100O1O100O100O100O100O1O100O10000O100O100O1O100O1O100O1O100O10000O100O1000000O100O1O100O100O10000O10000O1000000001O0000001O00000000O100O1N2N2O1O1N2SOcCUO_<j0bCUO`<j0`CUOb<i0_CWOa<h0`CWOb<g0bCUO_<j0bCVO_<g0cCYO]<e0dC\\O]<`0fC_O\\<<gCE[<6hCJ^<MeC2a<FaC;b<@`C`0_=10000O10001O0O1O1001O002N2N1O10O02N2N1O0010O01C^A;a>E`A;b>001N110N101O1O0001O0001O0001O01O01O00010O00010O0001O01O0001O01O000000001O01OO2O0O1O2O01O01O0001O0I7O101O0O100O010O1004HeABa>8igh4", "size": [480, 640]}, "area": 14917.0, "iscrowd": 0, "image_id": 12667, "bbox": [53.0, 0.0, 261.0, 125.0], "category_id": 132, "id": 20000752}, {"segmentation": {"counts": "nfm62m>1N2N2O1O1N2O1O1N2N2O1O1N2N2O1N2N2O1N2O1O1N2N2O1O1N2N2O1N2O1N2N2O1N2O1N2O1N2N2O1N2O1O1N2N2O1O1N2M3O1O1M3N2001OO1N23MO1O100E\\MXDOKd2l;dMSD_2k;<M300N2N2HfLiD[3W;fLdDO0]3Z;cLfD10^3Y;6000000O100O1001OO100001OAZLYE1N50`3h:`01ON2001O00N200001OO1O1000000N2N2O100O12NO10000000000000000001OO11O00001O001OO11O0000001O00000000O1000000O10000001OO11O00002NO1000000O100O10000O11O1OO11O00O11O0000", "size": [480, 640]}, "area": 17166.0, "iscrowd": 0, "image_id": 12667, "bbox": [472.0, 336.0, 168.0, 144.0], "category_id": 139, "id": 20000753}, {"segmentation": {"counts": "U:W1g=102N1O2M3M3M2O1O002M2O4K5M1N2N2M2OQO\\CFa<7aCiNO60b0]<:fCnN52Hf0]<4fCUO;OBi0\\<4\\DLc;4^DUOVOc0[<8_DUOXOb0X<:fDGX;:hDFW;:jDFV;9jDHU;9kDGT;9lDIR;7oDIP;7REHn:8REHm:1[E0d:O^E1`:NbE2]:NdE2[:NfE3Y:LiE3V:LlE4T:KmE5R:MmE[OZNf0i;OPF0o91QFOn92RFNm93SFNk93TFNm91RF0o9OQF1l92TFNk92UFOj92VFNj91WFNi9O^FNa9LfF4Z9IiF7W9IiF7V9JhF9V9HjF8V9HkF7T9JjF8V9HkF7V9HjF8V9GkF9U9GlF8T9HlF8T9HlF8U9GlF7U9IgFCkMd0[;LiF8Y9GfF:Y9GgF_OlMg0^;KhF4T9\\OfDa0U22U9^OfD`0U22T94lFKW93iFMW92jFNW91eF[OQNc0Y;3eF]OPN?\\;4cF4X9WOiDd0P25V9XOjDc0o16W9XOiDa0Q27U9NlF2T9NlF1U9MmF2T9MlF2U9MlFNlM^OY;?PG5S9JnF4S9MlF2V9NjF1W9OiF1W9OhF2W9NjF2V9OhF2W9OhF3V9NjF1W9NiF3W9MiF2X9NgF4X9LhF3X9NhF2X9NgF3X9NgF4X9LhF6V9VOfDc0R2:V9SOhDc0Q2=W9DhF=W9CiF>S9nNnDd0o1?Q9oNoDb0o1a0R9BmF?S9AlF`0T9@kF`0V9@jF`0V9_OjFa0T9iNQEf0k1a0T9jNQEd0j1b0W9AhF?Y9AfF?[9AeF>\\9BcF>^9BbF=_9C`F=^9EbF;^9EbF;a9B_F>b9A_F=c9C\\F>d9A]F>d9B[F?e9AZF?g9@YFa0d9B\\F=e9B[F?e9AZF?g9@ZF`0f9_OZFa0d9kNgDc0e1b0d9mNfD`0f1c0e9@[F`0e9_O]F`0d9_O\\Fa0e9\\O^Fc0c9^O[Fa0f9@YF`0g9AXF?i9AVF`0j9@VF`0i9AWF>j9BTF?m9ASF?l9BSF>n9BQF>P:BPF>o9CQF<P:DnE>R:AnE?S:AlE>U:DjE:X:GfE6_:IaE1e:0YE0h:0WEOk:1TE0l:0SE2l:NTE1k:WOnCh0W11k:0VE0j:0VE0j:0SE3m:MRE4n:LRE3o:LQE5m:SOoCh0T15m:MRE4n:LQE4P;KPE7n:JQE8n:GSE9m:FSE;k:mNTDg0P1=l:EUE;k:ETE<l:AVE?k:@VE`0j:_OVEb0j:]OWEb0h:_OXEa0i:_OVEb0j:]OVEc0k:\\OWEb0i:^OWEb0h:lNRDa0W1a0h:oNQD>W1d0i:^OWEb0i:]OXEb0j:_OTEa0m:_OSE`0n:@QE?P;BoD?P;CnD=S;CmD=S;ClD=U;CkD=U;CjD=V;DiD=V;DiD<X;DhD<X;DhD;X;FhD9Y;FeD<\\;DcD;^;EcD:^;EdD7_;IaD5`;L]D5e;UO`Cd0j07g;UO_Cd0i07k;IUD6i;VO_Cd0h05j;XO]Cc0h06l;KSD7m;IRD8n;HSD6n;JRD6k;UO_Ce0f06j;VO`Cd0b0[O_Ok0_<NQDXO@i0`<0nCXOBh0_<0PDXO@i0`<_O`C2a0F_Oi0_<@aDGPOh0`<A^Dc0d;^O[D`0h;@WD>l;AUD;n;FRD5S<KlC6S<KkC7U<IjC8U<IlC7S<HnC8Q<IoC7Q<InC8R<HnC7R<JnC6R<JjCA[Oe0k<IkC;U<ElC:S<HlC8S<JlC6T<KkC5U<LjCM[ODk<>kCNZODl<=jCN\\ODg<LXCb0e0M^ODe<MXCb0e0L\\<7eC@[ONS=a0bC@f<`0ZC@d<LeBc0h0Bb<KfBc0h0Ba<LgBb0h0Bb<?_CAa<?_CB`<>`CB]<JkBd0h0B\\<KlBc0i0BZ<KlBe0i0@Z<d0fC\\OY<f0fC[OY<e0hCZOW<h0hCXOW<j0hCVOX<j0hCWOV<i0kCWOT<j0mCUOR<l0nCTOQ<m0oCTOo;n0PDROo;o0QDQOo;o0QDQOm;l0XDTOg;h0`DVO_;e0kDWOP;1bCd0R>13M5K4L5K2N1O2MYic02fV\\O4L6J5K6J5K5K5K5K5K5K5K5K6J5K5K<D5N2N3M2N2N2N3M2N2N2O1N2N3M3M2N2N1O2N2N2N1O2N2N2N2N2O1N2N3M2N2N2N3M2N2O001O1O002N1N21O01O01O01O01O0O110O00010O02N2N3M2N1O2N1O001O001ZOVJTHj5l7VJTHj5l7WJSHi5m7WJSHj5k7WJUHi5k7XJTHh5l7VJcGOa0k5l7UJdG0?l5b800O10000O10O0100001O0EUJaGl5^8TJaGm5_8SJ_Go5a8RJ]Go5d8PJZGR6f8oIXGS6g831O020MN30002ON1O11O001O0001N100001O0010O0001O01O00000000001O000000001O0001O100O1O100OfI\\GX6d8hI`GT6g8O000001N020O0000000MlIZGS6f86N00000001O000000001N10000O2N1O1O1O1M4IoF[JS9f54O0O1000001O0O100O2O0O100M4K4N2L401N1_ORFaK0;P:T4TFcKS:]49O0O101O0O100O2O0mK[Ej3g:QLZE0Oo3g:QLYE11k3h:TLWE02b3O]LP;0TEc3o:^LQE`3X;O1O2O0O1AbDVM0L_;n2>01N1O1O010MnCXMS<e2nC\\MR<a28N1OO11O2N3M2N2N2N2N3M2N2N2N2N2N1XOhBHZ=4jBJY=5kBEW=:jBDY=9hB^OF4e=;fB@G3e=>hB@Z=`0c000O1001O1OO1O1O1N2N2N20000K\\AMe>3\\ALe>3\\ALd>44O10000000001MTA0n>0Pk7", "size": [480, 640]}, "area": 53973.0, "iscrowd": 0, "image_id": 12667, "bbox": [0.0, 80.0, 623.0, 306.0], "category_id": 143, "id": 20000754}, {"segmentation": {"counts": "0o0Q>00000000O10000000000O10000O1000000O10000O10000O100000000000000O1000000O1000000O100O11O00O100000000O10000000000O100000000O100000000O1000000O100000000O100000000O1000000O1000000O1000000000000000000O100O1000000O10000O10000O1KU`g7", "size": [480, 640]}, "area": 2173.0, "iscrowd": 0, "image_id": 12667, "bbox": [0.0, 0.0, 112.0, 31.0], "category_id": 172, "id": 20000755}, {"segmentation": {"counts": "Q[U46e>9G:F9F:G9G9G9G9G9G9G9G7H7J6J6L3N3M3M2N3M3N1N3M3M2N2N2N3M2N2O1N3M2N2N2N3M2N2OO000000000001O01O001O1O1O001O1O1O1M3L4L4L4L4L4L4L3M4L4L4L4L4L4L5K4L5L3O2N2O0O2N1O2N1O2N2N1O2N1O2N2N1O2N1O2N101N2N1O2N1O2N2N3M3M3M2N3M3M3N1O2N2N2N1O2M3O1O010O1O10O01O100O00100O1O3M3M2N1O00001O001O00001O001O00001O001O001O0O101O001O00001N101O0N2O2M2OV\\j2", "size": [480, 640]}, "area": 14715.0, "iscrowd": 0, "image_id": 12667, "bbox": [284.0, 49.0, 163.0, 241.0], "category_id": 183, "id": 20000756}, {"segmentation": {"counts": "0n0^<00000000001O002NIkCXOT<g09O10000O1000000000000O1000000O1K50000000000O10000000000000000000000000000000000O1O11O000000000000000000000000000000O1000000000000000000000000000000O1000000000000000000001O000000000000000000000000000000000000000000000000O106FjRh6", "size": [428, 640]}, "area": 1978.0, "iscrowd": 0, "image_id": 12670, "bbox": [0.0, 0.0, 123.0, 34.0], "category_id": 96, "id": 20000757}, {"segmentation": {"counts": "ZXi12T=6000000QOIiD7T;IoD7S<00000\\N0lE0k97WFI`9>bFB]9>dFBh7LUHb05Bd7LlFMX1e09Bk7JiGc0>Ci7LeGa0d0Ch7MgF868\\1Ch7`1ZH`Nf7_1[HaNe7]1]HcNc7[1_HeN`7[1aHeN_7X1dHhN\\7V1fHjNY7T1jHlNS7U1oHkNn6U1UIkNQ7m0QISOV7d0mH[O`75cHK_:0000000000000000000000000000000000000003M2NWh30iWL2N2N1O00000000000000000000000001O0000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001O000000000000000000000L]=0eB101N1ecl1O]\\SN2N0000000aNLfE4Y:4`EL`:;YEEg:b0RE^On:8^DO=IU;5cD71D[;4hD=F_Ob9OVH2hNf0S9HdG_O]Oh0o8;RGkN1j0V9d0SG[OP9d0nF\\OV9=nFBU99nFFV93mFMV9MnF2U9ImF7W9BmF=V9]OmFc0o:O001O001O001O1O001O001O001O001O000000000000O100000000M31O1O1O1O2N001O1O1O1O1O001O1O1O1O001OjXf14WZYNNZ<i0aC^Oc;Z1N00000000000000000jD", "size": [428, 640]}, "area": 3599.0, "iscrowd": 0, "image_id": 12670, "bbox": [136.0, 295.0, 504.0, 133.0], "category_id": 105, "id": 20000758}, {"segmentation": {"counts": "Vi36U=3M10001N1000000000000O10O10000000001O0O2N3M2L22[oe3<UPZLc0^O0OH9O01000001N2O1O1O1O1O12M3M3N2N2N2N1O2N2O0O2N100O2O000O100000000N200O1O1O1O1O1N10Zmd3", "size": [428, 640]}, "area": 1052.0, "iscrowd": 0, "image_id": 12670, "bbox": [9.0, 22.0, 351.0, 151.0], "category_id": 132, "id": 20000759}, {"segmentation": {"counts": "mSk31T=53011N2N1M2O1N2N2O1XC_Ob<c0]C]Ob<f010O0010ON3K5J5K6K2M3[DbN[;a1iD_NY;\\1?]OQD@U<=mCBU<<lCC>IT;=dDH:LR;;dDG<NQ;6hDJS<0d?OUA0O0Z_20Wj\\30`f`L000O1O100O1O2O1N1O2N2N2N1O3M3M6_C[Oo;X1I4L001O1O5K4L7HYNnDONk0Q;TOhEL^O<g:CcF<\\9DdF<[9EeF;[9EeF;Z9FfF:Z9FfF:Y9GgF9Y9GgF9X9HhF8W9IiF7", "size": [428, 640]}, "area": 1686.0, "iscrowd": 0, "image_id": 12670, "bbox": [294.0, 198.0, 346.0, 230.0], "category_id": 140, "id": 20000760}, {"segmentation": {"counts": "n0U1W<I7N2N2O1O2N1N4N1N1O1O1O2O0O100O1O10000O10O10O10CIeC7V<NjC2V<NjC2V<OiC1W<OhC2X<OfC2Z<0cC1\\<1`C2`<MbC2^<>0000000001O0000001O001O001O001N00O3O0O1N2O100O100O1O10000O10000O10O10000000000000000000000000001O0O10001O001O001O001O1O010O1O1O1O1O1O1O1O2N2N1O1O1O2N0000F:0001O0O6POaC40?j<M1O001O0OY_h6", "size": [428, 640]}, "area": 2987.0, "iscrowd": 0, "image_id": 12670, "bbox": [0.0, 12.0, 122.0, 55.0], "category_id": 151, "id": 20000761}, {"segmentation": {"counts": "T^^1:R=000mNGSE9m:GRE:n:KRDK40?:[;6bDJ^;7aDI_;7aDI^;9aDG_;9`DHT;J`D\\1`;8000000000000000000000000000000000N2N2O1O1O1N200O1O1O1O100O100O1O10000O10000O10000000000000000000000000000001O0000001O001O001O001O1OO1N2O100O1O100O100000000000000000000001O001O1O0000000000000000000000001O001O001O001O001O1O1O1O1O2N0000000000000000000000001O001OoNgD0X;MnD1Q;MSE2l:LXE3g:K]E4b:K`E6^:IdE9Y:FiE=S:BoE?o9@SFb0j9]OXFf0d9ZO\\Fm0]9ROeFm0[9ROgFj0\\9VOdFg0_9XOcFd0`9\\OaFc0_9]OcF`0^9@cF?]9@eF>\\9BeF=[9CfF<Z9DgF:Z9FgF9Y9GhF8X9HjF5W9KkF3U9MmF1S9OoFNR92T200001O00001O001O001O1O001O1O1O1O2N1O000000O10000000000000000000000000000000000O1N2O1O1O1O1N200O1O1O1O10`Q?0`n@1O001O1O1O1O1O1O1O2N2N1O3M3M3M000000000000000000001O0000001O001O001O001O001O1O001O1O1O1O1O1OO1O10000O10000O100000000000000000000000000000000000000000000000000000000000000000000000000000000000000001O0000001O001O001O001O1O001O1O1O1O1O1O00O1O10000O1O1O1O100O1O100O1O1O100O100O1O10000O10000O100000000O10000O10000000000000000000000O1O1O100O1O100O10000O10000O10000000000000000000000000000001O001O001O001O001O1O0000001O00000000O100O1O1O100O100O1O10000O10000O1000000000000000000000000000000000000000000000000000000001O0000001O001O001O001O1O001O", "size": [428, 640]}, "area": 14368.0, "iscrowd": 0, "image_id": 12670, "bbox": [111.0, 0.0, 529.0, 85.0], "category_id": 169, "id": 20000762}, {"segmentation": {"counts": "Q71_12o1R1g3nNPJ82R3m5iLlI7SOU4P7fKiI7WOT4P7gKdI7]OR4P7hK_I8@Q4R7gKZI;Dn3R7iKUI<Hl3T7hKQI>Jk3V7hKkH`0Oh3W7^11O001O1O1O1O2N2N2N2N1OO100O100O100N2O1O1N2O1O1N2O1O1O1O1OWJQIW5n6fJYIW5e6hJ`IS3@eNo6UNhIS3ZOmL2_1k6^NoIT3SORM3Z1j6]NUJU3oNUM2X1P8a1nGYM2T1Q8b1lG]M3o0R8b1lG`M2m0T8a1jGdM2i0U8b1iGgM2e0W8b1gGjM2c0Y8`1fGoM1?e8V1ZG]N1;e8X1ZG^N19f8X1YGaN1\\O1;f8f1YGeN0TON536g8k1XG\\O2cNg8P2WGC2WNh8U2VGIm87PGOi8`2I3M3M3M3L4N2N2N2N2N2N2O1O2M2O1O1O1O1O100O100O100O100O100O100O100O10000O10000O10000000000eNQKYJo4e5UKYJk4f5WKYJi4f5YKYJg4f5ZKZJf4e5[K[Je4e5[K[Je4d5\\K\\Jd4c5]K]Jc4b5^K^Jb4b5^K^Jb4a5_K_Ja4`5`K`J`4_5aKaJ_4_5aKaJ_4^5bKcJ]4\\5dKdJ\\4S5nKlJR4o4X2N2N2N2NWJYKQ3W4YJTLi2El2Q4kJoK\\21f2o3QNQLm1n3VNSLg1m3\\NSLa1l3cNSL[1l3iNTLT1k3oNULo0j3UOVL?S4DmKO0kKR4Y4nKA0oK3:n3Z4oKRO4^LM8o3\\4oKiN5eLK:P4[4PLaN3`Ml3R4RLXN1jMl3R4RLPN1QNm3R4RLPOn3f3WL_Ii3P4RLmL<TOc3n5dLTJ\\3g5gL[JZ3Q4PLPLf00Z3n3TLnKb06Z3j3TLfJLW1f0:[3P4QLbKd0`0[3o3RL^Kb0e0\\3n3iMSLX2m3eMUL\\2j3cMWL^2g3aM[L`2P2kKeMc1=d2j1iKkMb1<f2g1hKmM`1?i2a1hKQN]1`0l2^1fKQN]1d0n2m2SMUMP3c2bKUK\\1[2]3o1WKRLT1Q2f3c2QL_MP4m2`KVMa4j5001O1nGUKa7l4[HXKd7i4XH[Kg7X5M1O00001O000bJ[JU3f5jLZJV3f5jLZJV3e5cL^JSNNZ5e5cLaJ]3`5bLaJ]3_5cLaJ]3`5aLaJ`3`5^L`Je3b5UL_Jm3d5nKaJo3b5lKcJo3b5iKgJQ4`5fKbJZ4e5\\K]Je4j5RKXJn4S70000O2IiGWKW8i4lGTKT8l47O0000001O00001O002N1O2N2N1O2N2N1O2NVOfKcHY4Z7UL]Hi3a7dLVHZ3g7TMUHe2i7eMVHT2g7RN^Hf1`7^NaH_1\\7gNdHV1\\6iLVJU2_Oo0U5mLcK5AT2Gg0S5XM^K4_OS2O?S5`MYK6\\Oo198Q5hMUKl2KYOo4PNQKj2OUOn4XNmJf24QOn4Z2RKdMm4_2RK`Mm4c2SK[Mk4j2SKUMn4l2RKRMn4P3QKoLo4S3oJnLP5g500001O00000000001O0000000WMlJZMT5_51UMkJ]MU5c2kJ]MU5]51TMjJ`MV5[51SMiJcMW5Y5gMRHUOP5mMeK0U1k2[NVOh5oMlKg2bNYOa4PNnK0n0d2iNYOX4SNUL0i0a2oN[On3UN[N`2jMYOf3YNgLM>`2YOXO^3YNQMO7`2]OXOT3XN^M1O^2CXOk2YNgM2I]2HWOb2\\NQNOD^2LVOZ2^NYNO_O\\21WOQ2`NbNNZOl1bN]NZ2b0d3UO^KNfNf12cN^2>[3\\O[KMlNe13aNe2:P3DXKMQOb16aNj25g2KUKMUO`19aNk24a2ORKNYO]1<_Nm25[22oJN^OZ1S4dN`1NbK]1R3dNj2[1[McNc2]1HcNmM]1R2cNoM]1P2cNQN]1o600000000000TJhNDX1:hNHX19iNEW1<kNAU1a0lN\\OT1d0mN[OS1d0oN[OQ1e0POZOP1e0ROTJAo4]1l0TOSJ_OR5]1k0UOQJ_OT5_1g0TOTJ]OU5d1a0RO@S1;mNEX15hNLZ11gNOZ10eN1\\1HjN8U1DPO<i0E\\O<?DF<6aM`NSO]1_3O`MQ2XMaLU5\\1_KgNV1[3ZNbLQ5[1XKQOZ1Q3`NcLn4Y1SKZO\\1\\2YNZM?Gi4X1nJB^1U2^NYM>Hh4W1lJG\\1S2bNWM?Hg4W1lJHY1n1kNYM;Kd4W1lJGX1P2mNUM=Mb4W1kJIV1l1VOTM71b4]2nK;7XMSONV4_2^L5mN_M_1Nh3_2iK6e0[MoNOl3`2^LOQ1bMW3`2eKKY1eMR3a2bKJW1jMPNJW5b2aKHY1PNV3Y2_KF[1SNV3X2^KDY1jMFOZ2c2fLEX1hMM3P2a2iLDY1iM22i1a2kLDi0hMiN130m11`1f2iLAk0hMQONK2U21X1j2gL]OP1gMXO0m12Q1m2gLZOS1fMk138\\5mMaJ[5_5dJaJ]5^5dJbJV50gI^5R1cJW5OgI^5R1bJX50fI^5R1bJX50fI]5R1dJX5OfI]5R1cJY50eI]5R1cJm20WN0jM\\5S1dJk21XNOjMV40jLW5ROnLNkMR45mL_7QO\\HQ46nL^7QO\\Ho37QM]7PO\\Hn38RM\\7PO\\Hm2LPN>3i0PO^5j2`IPN88j0nNi20cNg2fMQN3<k0mNf21fNc2jMoM0a0k0kNc24bNLgMb2?nMIg0m0iN_25fNKfM_2e0lMDm0d4mN_MKdM\\2j0lM_OQ1b4lNiMT2oNROR3mNQNQ2kNTOU3hNQNU2jNTOV3cNPN[2iNTOR6n0mISOR6n0nISOQ6m0oISOQ6m0oIUOo5k0QJVOG[Nk4_2^KVOG[Nk4_2^KWOm5i0SJXOl5h0TJYOk5g0UJZOj5f0VJ[Oi5e0WJ\\Oh5d0XJ]Og5d0YJ]O?YNPOJh2`2iM^O8bNQOAo2`2gM_O4nNiNTO]3`2gM_ON\\O_NfNm3c2eMYOKEa2d1VMdN;D`2j1XMTMSOg0b1ET2b2oMkM>oNd1Y3kMjMa0jNe1^3iMiM`0iNg1`3gMkM?cNk1e3cMkM?`Nn1g3aMjMa0^Nn1k3^MiMb0\\NP2g4bLPM\\1YNQ2o4\\LgLc1ZNQ2W5TL_Lk1ZNQ2T7QNlHn1T7RNlHn1T7U2O100O10000O10000O11O001O1O1O1O1O001O1O1O1OERJXIP6e6RJ[IP6b5lIUK6XOn5`5PJUK4[Oj5_5VJSK3]Of5^5[JRK1_Od5]5_JRKNA]5MWJ^5b0QKMCY5_5nJkJKFV5^5QKjJLGR5]5VKiJJIP5]5YKhJIJm4]5]KfJIMh4]5aKcJP44SLX5V5oJcJQ5]5VK\\Ji4c1eJa1j0eLa4l1eJ]1=WL19l4U2fJZ1:]L8Nh4]2fJV18cL?Cb4f2gJSO:KKoNj5U2QJoNl0[OVOEk5l3`KlKdN:l5S2SJROc1oNY4R4aKQL_4o1TJSOV1ROe4R4VKPLk4S4nJQLQ5R4iJQLW5R4dJQL[5S4^JPLc5c1UJWO2ZOh5`1VJQ1i5POXJn0i5ROVJo0i5ROWJm0i5\\30O01O0010O01O`J^JV3c5fLdJW3[5fLkJX3T5eLRKX3o4dLXKXOQOR3f5cM_KYOlNQ3g5`MfK\\OcNS3l5XMhKD\\NR3Q6PMlKKTNS3U6iLoK0mMV3Y6`LSL7eMW3T9dLmF[3W9bLjF\\3Z9`LhF^3d9O2\\NSFVO^:h0eEeNLNc:[1dEcNMLe:_1aEaNLLh:`1`E_Nm:_1a0N3M3N2M3N2M3N3L4L4Doj4GbUK2N3N2M3M3N2M3ZOf0L4M3M2M4Mg0XO4M2G`McEb2V:dMhE`2U:`MkE`2V:_MjE`2W:91O2O0O100ZMeE[2j9`MdF4CZ2k9jMYFMLX2l9ZNTFd1n9\\NRFc1S:YNmEe1[:UNdEk1c:oM]Eo1e0hM[96PFQ2d0kM^92nEQ2d0oMa9MkET2b0PNh9Q2UFPNl9Q2QFPNP:Q2mEPNS:Q2kEPNV:Q2gEPNZ:Q2cEPN^:_20OkNWMjGi2U8WMmGg2S8ZMmGe2S8[MoGc2Q8^MoGa2R8^MPH`2P8aMQH]2a4`MaM2oM]2k3gMSKMS30PNZ2d3RNRKH[3KPNZ2_3bNcNTOoMY2Y3gNiNPObM4QOT2`4lNnNnNRM>Bf1]4nNPOQOfLc0O\\1Z4\\NGFgKi09]O2l0V4WOLh0iLO[3WOOi0gLOY3XO2i0fLMX3XO5i0fLMT3[O5i0hLKS3[O7i0hLKo2]O:f0jLKl2^O=e0SMA_2K`0a0S2_OQN<o1DVN8i1I[N2g1L^NMe13_NEe1;f50hKJ[K5a41]KO`47]KIa4;]KEa4?^K@_4e0_K[O_4i0_KWO_4m0`KRO_4o0aKQOb4l0^KTOd4g0_KYOb4b0cK^O\\4=iKDV48nKIR41ULNj3N\\L1c3KcL4]3JfL5Y3JiL6V3HnL7U8O2N1O1O01O2\\D0g91YEm0h:TOTEn0m:TOnDn0S5iN`0;XJn0W5iNa0;SJn0[5iNb0f1]O[Nf0b1YO_Ni0_1VOaNm0=jI;X5XOR19hI>U5YOV16gI`0R5ZO[1OgIg0m4ZOm1e0RN[OP2d0oM\\OS2c0lM]OV2b0hM_OY2a0eM@]2?aMBc2;ZMFl25SMKU3OiL1]3KaL5d3HZL8\\80O100N2O03M3L5Kgf0", "size": [428, 640]}, "area": 120237.0, "iscrowd": 0, "image_id": 12670, "bbox": [0.0, 40.0, 638.0, 388.0], "category_id": 183, "id": 20000763}, {"segmentation": {"counts": "PTh22nc02N2N2N3M2N2N2N2N1O2N1O2N2N2N3M2N2N2N2N2N2N2N2N1O1O000000O1000[^O`Nj`0`1U_ObNBNl`0a1a_OaN@2n`0\\1b_OcNZON24Qa0\\1a_OcN[ON43o`0]1__OdN^OO11Qa0^1[_O]NCe0Ra0P1U_OXOj`0i0S_OXOn`0g0R_OZOn`0e0R_O[Oo`0k100000000000000000001O7I4U_OiLe`0]3L1O5K2N4L1O2N1O00001O001O00001O2N2^@nKR?U4i@oKU?S4h@PLV?_4N2N2N1O2N1O00000000001O0000000000000[AoJ[>Q5`AoJM2d>o4_ARKb>n4]ARKd>o4ZAQKg>S51O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O100O100O100O100O1O100O100O100O1O100O100O100O1000000000000001O000000000000001O0000000000001O1O2N1O1O2N1O1O2N1O1O2N1O1O2N1O1O2N1O1O2N1OL]AlJb>S5`AnJ^>R581OO10000000000O10000[OVASLk>k3XATLh>j3[AULe>h3^AULFFk>S4bAULc>h3aARLd>l3_AQLc>m3i0O1N2N2N2N2M3N2M3O1N2N2N2N2N2M3N2M3O1Lk^O[MWa0e22O11O1O1O001O001O001O1O000000N2M3O1N2N2O1N2N2N2dN]^O9ea0E]^O:da0C_^O;ca0Ba^O;aa0Ca^O;aa0Ca^O;aa0Cb^O;_a0Cc^O;_a0Be^O;]a0Ce^O<\\a0Ah^O<Za0Bh^O<Za0Aj^O>\\b0O1O2I_\\OJec036Mi^_1", "size": [640, 480]}, "area": 29638.0, "iscrowd": 0, "image_id": 12748, "bbox": [141.0, 0.0, 263.0, 173.0], "category_id": 102, "id": 20000764}, {"segmentation": {"counts": "]`Z45gc01Z\\ONbc0Nd\\O2ec00O100O1000O0100O10000O100O010O01000O010O10O10O010O10O010O1O00bC4j4KZK3d4M]K3c4M]K3c4L^K4a4L`K4`4KaK5^4KcK5]4JdK6\\4IeK7Z4IgK7Y4HhK8W4IiK7W4HjK8U4HlK8T4HlK8n;00000000000000000001O000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000SNIQ@7n?JR@6n?JR@6m?JT@6l?JT@6l?JT@6k?KU@3TNKga01V@4k?MU@2l?NT@2k?OU@1k?NV@2j?NV@2i?OW@1i?NX@2g?OY@0h?0X@0h?0X@0g?0Z@0f?0Z@0e?1[@Oe?1[@Od?1]@Oc?1]@Oc?0^@0a?1_@Oa?0c@M\\?4X2O010O10O010O0100O010O0100O0100O010O010O010O10O010O0mno0", "size": [640, 480]}, "area": 1616.0, "iscrowd": 0, "image_id": 12748, "bbox": [221.0, 381.0, 207.0, 259.0], "category_id": 105, "id": 20000765}, {"segmentation": {"counts": "ffg31nc03K5N2N2N20ON3M3N2N2O1N2N2O1?TO`\\OO05YlZ5", "size": [640, 480]}, "area": 270.0, "iscrowd": 0, "image_id": 12748, "bbox": [191.0, 338.0, 16.0, 34.0], "category_id": 112, "id": 20000766}, {"segmentation": {"counts": "QbQ25kc0b0^O6h\\OUO040K30M1^b0h1b]OWNSb0l1O2N01O000000O100000000000000000000O100001O00O1O11O1O1OFVN^^Oj1ea073M1OO1000000000000000000O100001OO1N2O1N2N21O00O13M00001OO1O1O11O0000000000001OO1001O0000001O001O0000O1000000000000000000O10000O11O00O1000000000000NiM[^OW2ea0200O100O1O1O100N2O1M300L4O1HZMT_Oh2l`0XMT_Oh2l`0XMT_Oh2l`06000000001OO1000000000000O100O100O100O1M3O100N21XOb_OlM``0l1T@fMm?T2S1I7J7H6K5J6J7J5J6K5J6JXcT4", "size": [640, 480]}, "area": 11569.0, "iscrowd": 0, "image_id": 12748, "bbox": [104.0, 533.0, 163.0, 107.0], "category_id": 116, "id": 20000767}, {"segmentation": {"counts": "\\UU371Kec0;M0O101OO1O1O02O000001O001O01M1O2O2N10000O2O0O2O000O01000O10000O100O1000O01000O02O00O0100O100O1N2OOQ]OSOmb0o002N1O2N1O1O101N0010N12NN30OJ]]OQOcb0Q1]]OoNbb0R1^]OnN`b0T151N2N2O1=D0N2O2N1O1O1O2N1N2O1N2O2M2O1N2L5M2O\\o]4", "size": [640, 480]}, "area": 2270.0, "iscrowd": 0, "image_id": 12748, "bbox": [161.0, 498.0, 91.0, 73.0], "category_id": 131, "id": 20000768}, {"segmentation": {"counts": "V[W73lc01N3M3M2O2M3M2M4N2M`0@2O2M2N2N2N2O2M2N2N2N2O2M4L:G3L5K200O1O1JWMR_Ol2l`05N2N2N2N2N2o_OeL[?]3a@hL\\?Z3a@jL[?Z3b@iL[?Z3_@kL_?k3O2N2N2N2N3N1N2N2N2O1O12Ne0UOU2lKZ^O099DG31MO0413]cQ1", "size": [640, 480]}, "area": 4889.0, "iscrowd": 0, "image_id": 12748, "bbox": [370.0, 4.0, 57.0, 162.0], "category_id": 157, "id": 20000769}, {"segmentation": {"counts": "V]Y63lc02O4M0N3N1O1O2N1O3e]O^OQa0f0i^O\\OXa0h0b^OYO_a0j0[^OXOea0l0V^OVOja0m0P^OUOQb0n0j]OSOTb0Em]Od1Sb04L4O2N100O100O2O0OnMV^OQ2ia0300O1O2N1O10000O2O0N2O1O1O1O2N1O1O1O101N22N0O2O1O001O1O001O1O>A101O000000001O0O1000001O2N8H2N3M0O10000000000000000O1UO_]O7ab0Ed]O;[b0Bi]O=Xb0@k]O?Vb0[Oo]Oe0eb0101O000001O01O0000002N1O24SOj\\Oe0_c0K001L_\\ODac0;6EY\\O3iQo0", "size": [640, 480]}, "area": 5648.0, "iscrowd": 0, "image_id": 12748, "bbox": [322.0, 103.0, 109.0, 120.0], "category_id": 169, "id": 20000770}, {"segmentation": {"counts": "0c4W7V80TDSIe:U8O0F:O100O10000O100O100O101O0O100O10000O100O101O0O100O100O10000O100O1O1N2N2N2N3M3M3M3M3L4M3M3M3M3M3M3M3M3M3M3M3M3N1N3M3N2M3N2M3N2M3N2M3M3N2M3N2M3N2MPDmHX;o7O001N2O1O001O1O1O0dEdGS9]8gFgGZ9Z8_FkGb9V8XFoGh9Q8SFSHn9n7kEWHV:i81O100O1O1O100O001O1VGlE^OBU8c:YH]EAT1d7`9jHhFP7Y9oHgF:UO\\5V:YJeF;Gj4e9iJeF>KXOiN040K30M1f4i:aKeF`0g0YOjM[4k:jKeFa0j0c3c8kKcFa0m0b3a8kKcFb0m0b3b8jKbFd0l0b3W:\\LjEc3W:\\LjEc3W:\\LjEd3X:YLiEf3Y:YLgEg3[:nKSC5c2l3`:PLbEP4^:oKcEP4W=00O10SARLh=m3WBXLf=h3YB\\Ld=c3\\B`Lb=^3^BgL_=W3bBmL[=Q3fBSMW=k2jBYMS=d2nBaMo<]2RCgMk<W2VCmMg<Q2ZCSNc<k1]CYNa<e1`C_N]<_1dCdNZ<Z1bCmN]<Q1]CYOa<e0ZCCg<9SC1m<KmB>T=^OfBl0Z=QO_BX1c=c20O100O10O010O0100O101N100O100O100O100O01O0O?A1O1N1000RJiBS5W=lJkBBNV5W=WKkB@2X5R=XKmBZON24\\5Q=VKnB[ON43X269l<mMoB^OO11[267m<jMhBCe0o13e0a<cMdCh1Jf0b<_MeCm1Hc0c<`MfCn1Ha0`<aMhCP2G>h=CYB:h=GYB6h=LgBBZ=?UC[NYNOc>g1lCUNU<l1o21O1O1O0115I4hMV^OV2la0M004K2O3M001O0L31O01OeBkNSKBO0`<c1^H\\O[7c0dH@Z7a0fH@X7`0gHCX7:fH^NXKZ1R<3gH5Y7hNnCc0a4UO^Ke1T<^NQDf0U4[1j7lMTDh0P4_1Y8_NeGe1[8YNcGk1]8SNbGP2]8PNaGS2_8lM`GV2_8jM_GT2d8lM]Go1g8RNWGm1k8SNUGi1o8VNQGe1U9ZNjFb1]9\\NcF_1c9`N^FZ1h9eNXFV1n9iNlEC]L]1n=oN`EJbLQ1T>TOZELdLh0X>[OPE3hL;^>@lDP1Z;kNUAMe3R1[;QOcC9RNf0\\>POiC4iMn0_>lNbC<oMh0Wa0[Og^Ob0_a0ROg^Oo0o`0@m^O>Va0Fb^O<_a0A`^O]OHP1ha0D`^O^OFn0ha0G_^O>ca0EX^O<ja0k03M3N2M3N1N3N2M3N2M3M2O2M3N2M2O1N101N2N2O1N2O1N2O0O2O1N2N2O1N2O1N101N2N2OkVj3", "size": [640, 480]}, "area": 52566.0, "iscrowd": 0, "image_id": 12748, "bbox": [0.0, 0.0, 284.0, 639.0], "category_id": 171, "id": 20000771}, {"segmentation": {"counts": "bco1?2BYc00f\\OT1hb08J0U^OPOf`0P1Z_OPOe`0Q1\\_OnNb`0T1]_OmNb`0T1]_OmNc`0S1\\_OnNc`0S1\\_OnNc`0S1]_OnNb`0S1\\_OnNe`0Q1Z_OPOg`0o0X_OROi`0o0T_OQOn`0o0Q_OQOPa0P1e^O]N5d0Wa0S1e^OoN[a0Q1d^OPO]a0i11O1O1O1O1O1N2O1O100O1N2N2000001DU^O_Nna0a1:00N2L3N4L4L2N2N1O1O001O0O2O0010OO2M2O2M3M210O0Ej\\OIZc06801N101O001O1N10gbP6", "size": [640, 480]}, "area": 3283.0, "iscrowd": 0, "image_id": 12748, "bbox": [101.0, 489.0, 70.0, 89.0], "category_id": 172, "id": 20000772}, {"segmentation": {"counts": "\\Xa41dc0k0AOO25I1N105NG66L5H0O_\\OH`c08b\\OF_c07:Kokf1OQTYN3M3L3N3L4M3M2O0O001O001N1m\\OWOQc0i0120O00000001O1N101N02ONSOS]Om0ob001O3M3M3M2N3N1N3N1O2M3M2N2OTV60kiI2M3M4M2N2N3M2M4M2M4M2M4M20001O1O1OO1N2N2N2N2N2M3N2N2N2N2N2N2N2O1NoDJ\\24dMN\\20eMN^21bMO_20aMOa23\\MMe2h0oHAlKFV31P8g2jDZM099DG31MO0413S1g2mNZ8f5cGZJ^8i5SEVJm11Q9j5QEXJj1MW9o5dDQJ36P2JZ9^6cFbI^9_6`FaIa9a6[FaIe9`6YFaIg9g6oE[IQ:j6hEXIX:l6aEVI`:o6XETIh:Q7QEQIo:S7jDPIV;U7cDmH];k70000fNdGPG\\8S9dGjF\\8Y9cGeF^8^:2N2N2N2N2N1O2N2N2N2N2N1O2^OiFWFY9h9gFWF[9h9eFWF]9P7PGZKR9b4]GQKd8m4jGfJX8V5VH^Jl7^5cHUJ_7g5PIlIR7P6VIjIl6R6UIoIl6CVFT6h2jIYMa0k9]OWFV6f2cJY7[5iHeJX7X5jHhJW7U5kHkJV7R5kHoJU7o4mHQKT7m4mHSKT7j4nHVK", "size": [640, 480]}, "area": 18210.0, "iscrowd": 0, "image_id": 12748, "bbox": [232.0, 0.0, 248.0, 627.0], "category_id": 177, "id": 20000773}, {"segmentation": {"counts": "WdX31Pd0a0_Oa0^O2N1O2O1N2O1lLPOTCR1k<oNSCS1^<ZOcCg0\\<XOeCi0Z<VOeCm0Z<TOeCm0Z<ROeCQ1Z<POcCS1\\<nNaCU1_<ZOoBi0P=FjAYNOT2V>;fAHY>Q3O1O1O0OO3K304L1O11NO2N2O1O10L31O01O0000O20OOONPAXKm>i46Ho@^KT?Z4>N1N2N2O2N2O1aLU@f2l?RM]@n2c?jLf@S3U`0O2M3L3N3N1N3Gg^OgMZa0R2l^OnMVa0Y1d^OVO7CTa0k0n_OUOS`0j0c1M3G94KH9O13MD<00O1M2M^Pb4", "size": [640, 480]}, "area": 7871.0, "iscrowd": 0, "image_id": 12748, "bbox": [167.0, 157.0, 79.0, 185.0], "category_id": 181, "id": 20000774}, {"segmentation": {"counts": "gP1:b`0T3lLT3jMV2O100O101O0O100O100O10000O100O10000O2O0O10000O100O100O10000O100O2N1N2N2N2N3M3M3M3M3L4M3M3M3M3M3M3M3M3M3M3M3M2O2M3M3N2M3N2M3N2M3N2M3M3N2M3N2M3N2MhCUI`;g7O0O2O1O1O001O1O1fEbGo8^8lFfGU9[8dFjG]9W8]FmGd9T8UFRHk9n7PFVHQ:k7hEZHY:f81O100O1O1O100O001O1O1O1O1N2O1N2O1N2O1N2O1N2N2O1N2O1N2O0O2O1NcEaG\\9^8bFgG\\9W8dFmGZ9R8eFQHZ9m7eFXHY9e7hF_HV9^7kFfHS9W7nFmHP9P7PGUIn8g6TG]Ij8`6WGdIg8Y6ZGjIe8S6[GRJc8k5^GYJ`8d5aG_J_8]5aGgJ`8T5ZGUKh8f4RGdKo8W4kFRLW9i3cFaL]9[3^FmLd9n2VF\\Mk9\\62O1N01N101N101N2O0O2O001N101N101N101N101O0O7Ja0^Ob0_O9F2O1N2O1N2O1O1L4L4L3O2M3N2N2M3N2N2M3N2N2M3N2M3N2N2M3N2N2M3N2N2M3N2M3N1O2M3N2N2M3M3L4L4L4L4L4M3L4L4L4L4LjI_LSL^3f3kLVLDRJ]3c9XMSL@[JW3`9]MnKBbJo2a9bMeKDkJh2a9gM\\KGSKa2a9jMUKKZKY2b9oMlJMcKR2b9SNdJ1iKl1c9VN[J4SLd1c9ZNSJ8ZL]1c9^NkI;bLU1d9bNcI>jLn0d9gNZIa0RMW1T9[NcIb0ZMT1Q9]NbI`0]MT1o8_NbI=^MV1n8_NbI;aMX1j8`NbI9cMY1i8aNbI5fM[OJ8l8;aI3iM[OH9l8<aIOkM_1a8eNaIMnM_1_8gNaIJPN\\OH<e8`0aIHRN\\OI=b8b0aIETN[OJ`0^8c0aIBWN[OKa0[8e0`I@ZNZOJc0Z8f0`I]O\\NYOKe0W8h0_IZO_NYOLg0S8i0`IWOaNXOMi0P8j0`IUOcNXONj0m7l0`IROdNYOOj0\\2iMd2U3ZLoNgNYO0k0T2QNg2o2\\LlNhNYO2m0l1WNk2i2\\LkNjN[O0l0h1[NQ3f2ZLhNnNV2a1aLW3b2YLgNnNX2[1eL]3_2WLdNQObNKh2Z1hMc3\\2ULbNROcNLg2T1nMh3Y2SL`NTObNNg2m0TNn3T2RL^NUOdNMg2i0XNR4Q2QL]NVOcNOg2b0^NW4l1RL[NVOeNNg2>bN\\4h1PL[NWOdN0h26gNb4d1oKZNYOcN0h20mNh4_1nKXNZOdN0b5g4dMmKWN[OdN1a5g4eMlKUN]OdN0c5f4fMkKTN^OdN1b5e4gMlKRN^OeN1c5e4gMjKRN@dN1c5d4iMjKSOCT3c4jMhKSODT3c4kMgKQOGT3b4lMfKPOGU3b4mMeKnNJU3a4nMdKmNKU3a4nMcKmNLV3a4nMbKlNMV3a4oM`KlNNV3b4oM^KkN1V3a4PN]KjN1W3b4c1_K]Nb4b1^K^Nb4b1]K_Nc4`1^K`Nb4`1]KaNc4^1^KbNb4^1]KcNc4]1]KcNc4\\1^KdNc4[1\\KfNd4Y1]KgNc4Y1\\KhNd4X1\\KhNd4W1]KiNc4W1\\KjNd4U1]KkNd4T1[KmNf4R1ZKnNh4o0XKROi4m0WKSOj4k0WKUOk4i0TKXOm4f0TKZOm4e0RK\\OP5b0PK^OQ5`0PK@Q5?nJBT5;mJET5:kJGV58jJHX55hJLY53gJMZ51gJO[5hNcJVK1S6]5eNbJYK1R6^5cNbJZK0T6`5_N`J^K0S6a5PK^JR12jM0T6a5mJcJP1MoMNU6d5gJiJo0EVNNT6e5dJnJm0_O[NNU6f5_JmJS1_OZNNT6h5[JaJnNHk11iNNT6i5VJWJA2W10oNNS6j5]KZJ\\ONUONR6l5^KYJVOL[OOR6n5iIRJ<78JBOQ6Q6cIQJe070HGOR6T6\\IPJl2LhM0P6X6VImIR3KiM0o5[6PIlIX3IiM0P6^6jHkIY2G^NOa01n5a6dHjIa2GXNMe01o5Q7VKRIGMS5P7XKTImMKo00m5P7YKVI^OKY5n6[KXIZOJ[5n6\\KYIVOI_5m6]K[IROHa5l6_K]InNFd5l6`K_IiNFg5j6bK^IhNHf5j6bK\\IiNJ90S3i6mM[IhNLe5h6eKYIgNOe5g6iMYIW2f6jMYIX2e6hM\\IaJ5_7_6PNbIR2]6nMbIT2]6mMaIU2^6lM`IV2_6jMaIW2_6iM_IY2`6d5OYKaIoK^6S4bIlK]6V4cIiK\\6Y4dIfK\\6Z4eIeKZ6]4fIbKY6`4fI`KY6b4gI]KX6d4iI[KW6f4iIYKW6h4iIWKW6i4jIVKU6l4kISKU6n4kIQKU6P5kIQINAW6^7lIPINAV6`7lInHNBV6a7kIlH0CU6a7lIkHNEV6a7kIiH0FU6b7iIiH2ET6k9mIUFS6k9lIVFT6j9lIVFT6i9mIWFS6i9mIWFS6h9mIYFS6g9mIYFS6f9nIZFQ6\\7kIoH3FR6f9nIZFR6e9oI[FQ6e9oI[FQ6X7kISI4FQ6V7kIUI4EQ6U7kIWI4DQ6T7lIWI4EP6S7lIYI3EP6S7lIXI5Eo5a9PJ`FP6_9QJaFo5_9PJbFP6^9PJbFP6Q7mIUI3KP6P7RJPIN0P6P7RJoHN2P6Z9PJfFo5Z9QJgFo5Y9QJgFP6X9oIiFQ6V9PJjFP6^7lIQH3b0Q6]7mIoG3d0P6]7nInG1f0R6]7lIkG3h0Q6_7kIhG4i0Q6Q9nIPGR6f0kIW54SJQ6d0mIY51TJS6a0mIZ51UJR6`0oIZ5NWJS6>PJ[5MWJS6=nI^5OVJU69lIb5OUJV67jIe50UJX63hIi50TJY61gIh4NiI2_1[6MdIl41gI0`1]6JbIP52eINb1_6GaIR52ZL_6aN^IW52YLa6^N]IY52ZLc6ZN[I\\52\\Lc6VN[I_51\\Lf6QN[Ib5OZJO<m6AVIi5N]J14P7_OSIo5LbJ3KR7]ORIU6IgJ5BU7ZOoH^6FjJ7ZOX7XOmHc6DoJ6TO^7ROkHk6@SK7mNc7nNhHQ7^OXK6fNi7jNfHW7[O]K5`Na8l8^GRGb8Q9^GlFb8W9]GgFc8\\9\\GbFd8a9\\G\\Ff8e9YGYFi8^:2N1O2N2N2N2SGkFk6V9UIlFh6V9WIjF[6NSHZ9`1hF^6NQH\\9`1fF_6NPH^9`1dF`6NoG`9_1bFc6OlG`9a1aFc6OkGb9a1_Fn35iLJgMd9a1\\Fn3c0_L]OQNf9`1[Fm3Q1VLnN\\Nh9`1YFk3`1mK`NfNi9a1VFj3P2cKPNROk9`1VFS2NPN_2POa7l0RFR2NQNn2eNS7W1PFQ20SNZ=cNeBP12X2OTN^=`NdBT1OU20VNf=EYBT21VNh=FXBQ21XNj=GUBo11YNm=HQBm13[Nn=GPBl12\\NP>InAh13^NR>JkA2`0IC:U>JhAYO[1`0iN<m3", "size": [640, 480]}, "area": 166269.0, "iscrowd": 0, "image_id": 12748, "bbox": [1.0, 51.0, 479.0, 582.0], "category_id": 183, "id": 20000775}, {"segmentation": {"counts": "0S`0P2`Mf6[IP6PJ1O00000O1000000000000O0100000000000O10000000000O10000000000000000000000000000000000000000000000000000000000000000000O1000000000000000000000000000000000000000000000000000000000000000000000000000O1000000000000000O100000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000O10000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000O010000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000O100000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000O1000000000O1000000000000000000000000000000000000000000000000000000000000000000000000000000000O1000000000000000000000000000000000000000000000000000000000I", "size": [500, 375]}, "area": 11564.0, "iscrowd": 0, "image_id": 13004, "bbox": [0.0, 0.0, 375.0, 500.0], "category_id": 165, "id": 20000776}, {"segmentation": {"counts": "S`0P2c=f6[IP6PJ0000000O1000000000000O1000000000000O10000000000O100000000000000000O100000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000O10000000000000000000000000000000O10000000000000000000000000000000000000000000000000000000000000O10000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000O10000000000000000000000000000000000000000000O100000000000000000000000000000000000000000000000O10000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000O10000000000000000000000000000000000000000000000000000000O100000000000000000000000000000000000O10000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000O10000000I", "size": [500, 375]}, "area": 175753.0, "iscrowd": 0, "image_id": 13004, "bbox": [1.0, 7.0, 374.0, 477.0], "category_id": 183, "id": 20000777}, {"segmentation": {"counts": "WQ]51X=2O3N001O000O2O1O0O1000000O100000O1000O10O1000O10O0100O10O0100O001N2N20O10001O0O2O0O2O00001N1O2N2N101N2O00`ZW2", "size": [427, 640]}, "area": 571.0, "iscrowd": 0, "image_id": 13177, "bbox": [414.0, 396.0, 54.0, 23.0], "category_id": 100, "id": 20000778}, {"segmentation": {"counts": "Pon1<l<30000000001O00000000001O00000000001O00000000001O000000001O1O1O1O1O1O001O1O1O1O1O1O1O001O1O1O1O1O1O1O1O001O1O:F0000000000001OO1000000000000000000O100000000001O00000000O1001O00000000000000O100000000000000000000000000O100000000001O000000O[D_Nb;a1^D`Na;`1^DaNb;`1]D`Nc;d100000001O000000O10000001O0^DYN`;i101OO1N2N2N2O1O1O1O1O100O1O1O1O1O1O1O1O1O1O100O1O1O1O1O1O1O1O1O100O1O1O1M3N2N2M3N2N2M3N2000000O1000000000000000000000000O1000000000000000000000SCMb<3]COb<0^C1b<O\\C3d<M[C3g<L\\C1d<OV^:3naEMQC:f<7000000000000000000000000000001O1O1O8H3M000000001O00001O001OO11OO11O3jCjN03l;]1M00001O000000001O001O0000002N6J6J00O1000000O1000000O10000O1000000O10000O10000O1000000O1000000^OdDTO];g0hDYOX;`0QE^Oo:=WEAj::[EFe:9]EFc::]EEd:;]EDc:<]EDc:<]ECd:=\\ECd:=\\EBe:>[EBe:>T10000000000001OK6I7N2N]ij1", "size": [427, 640]}, "area": 11505.0, "iscrowd": 0, "image_id": 13177, "bbox": [151.0, 0.0, 348.0, 66.0], "category_id": 113, "id": 20000779}, {"segmentation": {"counts": "Pj[2111T=5M2N2M4M2N3M2N2N2O1N2O1N102M2lJnNnMR1S2POiMR1W2oNgMR1Z2nNcMT1_2lN^MV1c2iNZMY1h2gNUMZ1k2gNRM[1eKbNW73RMa1l2aNPMb1o2_NoLb1Q3`NkLa1W3_NgLb1Y3_NdLc1\\3_NaLb1_3_N^Lc1b3_N[Lb1d3`NYLb1g3_NWLb1h3aNUL`1k3aNRLa1n3aNoK`1Q4aNlK[1PMnMV7h0hKZ1]4hN_KV1d4lNZKP1k4ROQKP1o4QOoJR1o4oNnJT1Q5nNlJT1T5lNiJU1X5mNeJT1[5mNbJT1_5mN_JT1a5nN[JT1e5mNYJT1g5mNVJX1XN]M]7\\1XJ[1g5gNVJZ1j5iNSJY1l5hNQJZ1o5gNnI[1Q6hN`Ie1`6\\NQIR2o6V2O1O0O1000O01G90000000000000O11O0QIfI01j6Y6VIfI11h6[6WIfIi6^60000000KXIgIh6Y6XIfIi6Z6WIfIi6X6YIgIh6Y6XIgIh6Y6XIgIl69VI[5m6]J`Ia5V7O2N1O2M2N3M3N1O2M2O2N101O001N20OO10_GWK^8k4001O000O101O0O101O000O0jNaGRMLMb8m2jGQMG1_8l2oGnLF2IGa8Z3aHlLb7U3`HhLa7W3aHhL^7Y3Y110O01O010O10O01O10O01O001N2O001O001O1O001O1O001N101O1O001O1O001N101O1N101O0O2O1O0O2001N2O1N2O1O1N2O0O10000O100O1O1N2O0O100O010ON31N2O2M3N2N2M2O2M3H8ZOf0[Oe0E;N2O1O0O2O1O1N2O1O1O1N2O1O0O2O1O1N2O1O1N2O1O1O0O2O1O1N2O1O1N2O1Oa\\l2", "size": [427, 640]}, "area": 25959.0, "iscrowd": 0, "image_id": 13177, "bbox": [181.0, 147.0, 237.0, 211.0], "category_id": 115, "id": 20000780}, {"segmentation": {"counts": "XnQ3334j<8TCBX<4SD<k;HRD9g;_OTD;29i;\\OSD>36i;OUD2k;f02O000O10O10000000N2001O1O0RDhNh;_10OXD`Ne;0ZD01_1OaNc;0]D11`1O_Nb;0`Dc1d;00O1000K_N`Da1`;_N_Dc1`;]N`Dd1];^NbDc1^;]NbDd1];\\NcDd1\\;600000M30O001O10000O101OO200O1O002N001WN_Df1c;00001O0O10000O10001O0O1@UN`Ek1^:XNaEh1]:\\N_EIGk1e:bNaEb1a:^N\\Ec1g:[NXEe1k:=0O1O001O001O001O001O10O01O001O001O001O001O00100O001O001O001O001O001O001O001O1O0000001O0000001O0000010O0O11O00O2O01OO101O0000001O0000001O000000001O0000001O0000001O000000001O0000001O0000TDcNj;_11N100000001O0000010O01O00NTDfNl;W1VDiNj;V16000001O0O10001N10001O0O2O001O001O0O2O001O0O2O0O2O0O2O1N101N1O2M2N3O001O010O010O010O0010O010O10O10O10O010O0O2O010NXS7JnlH2jBNP=71O05L2QCDi<?1O010O001O001O0000O2O001N1aCAR<`0kCGP<j0O01001N10O10O10000O1aGkNh4U1]JPOhMNj7R1]JTOeMKm7R1^HkNXO160O18L2>KJo7o0]HTOZOE05532K6=JKQ8m0\\H^O@EOK7;KMQ8l0[H@_OD1O260MR8k0YHZ1ClMT8k0VHP2g7TNVHn1g7UNVHm1h7m1M2N3M3N1N3M3M2N3M3M3M3M4LnJ^I`3`6^LcIc3Z6XL_IlN8m4V6YLPJh3m5ZLSJh3j5YLVJh3i5WLXJj3g5ULZJl3e5RL]JP4a5oK`JR4_5nKaJS4]5oKbJR4]5mKdJT4[5iKoInNf0[5Y5fKkJ[4T5_KRKb4m4^KSKc4l4]KTKe4i4\\KWKe4h4ZKYKg4f4YKZKh4e4YKZKh4e4XK[Kj4c4UK^Kl4a4TK_Km4_4UK`Kl4_4SKbKo4\\4QKdKP5[4PKeKQ5Z4kJeJXOR1n5X4jJkKW5T4kJjKW5R4lJmKU5P4nJoKS5o3nJQLS5l3oJTLS5i3nJWLS5f3oJZLR5d3PK[LQ5b3QK^LP5`3PKaLR5[3QKdLP5Z3PKgLQ5V3QKjLP5T3RKkLP5Q3RKoLa1\\OKc3eNQMU1H6T3hNSMc08f0c2hNUM`0:l0[2fN[M=:R1V2bN`M<9W1Q2^NgM;7\\1m1XNnM;5b1g1UNTN94g1c1PNZN93k1]1oM_N63P2Y1kMdN56Q2P1lMjN28U2i0iMPO28X2b0hMVO17[2>eM[O08^27dMANCiMJl4m0eKeN02<P1Z1AlMGo4k0`KUO@0c0E_OR1R2BnMAX5l0XKOJPOFS1R2[OUNCY5i0SK6[OPO20L01O0U1T2ZOS47`Ja0XOiN140T1T2UOY43]JZ2[1aM]40YJ_2Z1WMkM5g6OWJd2W1WMmM4j6LSJi2\\1YMe5f2`201O001O00001O00001O0000001O00001O00001O001O", "size": [427, 640]}, "area": 40440.0, "iscrowd": 0, "image_id": 13177, "bbox": [234.0, 0.0, 406.0, 427.0], "category_id": 132, "id": 20000781}, {"segmentation": {"counts": "Wcm02Y=0hBO00T=50000000000O10000000000O10000O10000O10000O100O10000O10000O10000O10000O10000O10000O10000O10000O100O10000O10000O10000O1O1O100O1O1O1O1O1O100O1O1O1O1O1O100O1O1O1O1O1O100O1O1O1O1O1O100O1O1O1O1O1O100O1O1M3N2N2N2N2N2N2M3N2N2OaMXE]2l:O00001O001O000000000000O11O00O10000001O00000gMQEV2T;N1O00O100000000000000000000JlMSEV2l:5L4K5002N1O1OO100O10000003^MWE\\2l:_NVEVO4Q1e:IcETOBP1l:ZOXE1<e0\\:XO\\E09h0[:QOTE2;29j0X:ROTE0>36k0X:QOgE22m0d;10000O10000000000N2001O1O0POjCk0\\<O0VObCg0b<XO^Ce0e<[O[Cb0g<000O10000001O1O001O00000000M300O100000000001OO11O00000000000000001O0000000000001O00000000000000001O000000000000000000001O00000000000000000000001O00000000000000000000000000000000000000000000000000001O00O11O00O1001OO10000000000000000000000000000000000000000000000000000000000000000000BUC<m<0O1000000000000001O001O001O0000001O00001O1O0000000000000000O1000000O1000000O1000000O10000O1000000O1000000O100O1000000O1O1O1O100O10000O100O1001OO1I71O001O001O001O001O001O1O001O001O0^O[C>f<@\\C2N9n<0005K3JkB0X=000nbc07h\\\\O3IIYC8g<60000O1O1N2O1001OO1MZOaCf0^<4J6000000O1001O00O1001OO10000LoNmCR1S<32N1O1O000000001O00O11OO1001O00001O000000O1N200O11O0000O1000000WNoNTF2<P1_9XOfE0c0E_OR1Y:NQFPOFS1Z:MdEPO20L01O0U1]:2bEiN140T1^:b1001O001O001O001O00001O00001O0000001O00001O00001O001O", "size": [427, 640]}, "area": 16552.0, "iscrowd": 0, "image_id": 13177, "bbox": [70.0, 333.0, 570.0, 94.0], "category_id": 149, "id": 20000782}, {"segmentation": {"counts": "VeX21Y=3M3M3M3L4M3M3M3M3M3M3M3M3M3M3M3M3M3M3M4L2N3M1O1O1O2N3M3M3M1O2WOh0000001O000001OO0100O10000000000O10O1000001O0001O00000001N01O2O000O100000000O01000O1G90000000000000000000O100000000001O000000OSMgEj2Y:VMhEm2T:RMmEk2W:TMiEc2d:K5^NVE?n:_OUE=n:ATE<P;BQE:S;DPE8S;FoD7T;GnD5U;KmD1V;MlD0W;NkDNY;0jDLZ;2gDK\\;MlC4U=Nb[j0NadUO2M3N2N2M3N2N2H7001O1O1O1O1O1O1O1N2N1O2M3N2KQi^3", "size": [427, 640]}, "area": 9336.0, "iscrowd": 0, "image_id": 13177, "bbox": [174.0, 50.0, 200.0, 112.0], "category_id": 173, "id": 20000783}, {"segmentation": {"counts": "f9n2d1SMY1j8eNP3M3N3M2N1O00000000001N100000001O000000001O0O1000001O000000001O0O1000000000000000001O0O1000000000000000000O1000000000000000000O0100000000000000O1000O100000000000O10000O01000O10000O10000O1000O0100O10000O10000O10O10O10000O10000O1000O0100O10000O10000O1000O0100O1O1O100O1O1O1O001O100O1O1O1O1O1O10O01O2N1O1O1O1O100O2N1O1O1O1O101N1O1O1O1O1O2O0N2N2N2M3RH^MQ5e2mJ^Mo4d2PK_Ml4c2SK`Mi4b2VKaMf4a2YKbMb4b2\\KaM`4b2^KaM^4b2`KaM\\4b2bKaMZ4b2dKbMW4a2gKbMU4`2jKcMQ4a2mKbMo3a2oKbMm3a2QLbMk3a2SLbMj3`2TLcMh3`2VLcMg3_2WLdMe3^2ZLeMc3]2[LeMb3^2\\LeMa3]2]LeM`3^2^LeM_3]2_LeM^3^2`LdM^3^2`LeM\\3^2bLdM\\3]2cLfMY3]2eLeMY3X6N3L3N3L3N2M4M2M4M2M3N3L3N3L3N2M4M2M4M2M3N3L3N3L3N2M4M2M4M2M3N3L3N3F9A?AhQ<1ZnC=C<J7\\NlNiFY1T9iNiF\\1S9gNiF]1U9dNgFb1U9`NhFe1T9^NhFf1U9\\NgFj1V9WNgFm1V9VNfFo1V9SNfFR2W9PNfFU2W9mMeFW2X9Q1L4L3N1N3M2N2O2N1O1O1O2O0O1O1O1O0QK[KY1e4cNcKY1^4eNgKW1Y4jNjKR1V4oNlKn0U4QOoKk0Q4VORLg0m3ZOTLd0m3[OTLd0l3]OULa0l3^OULa0k3@VL>j3CWL;j3DWL;i3FXL8h3IXL6i3IYL6f3KZL4e3M]L1c30]LOc32^LJd36]LHd39]LDd3<]LAe3`0\\L]Oe3d0[LZOf3f0\\LVOf3k0[LROf3o0ZLQOd3P1^LoNa3R1_LnN`3R1bLlN^3U1bLkN]3V1dLiN[3W1fLiNY3X1hLgNW3Z1iLfNV3Z1lLeNR3]1nLcNQ3]1QMbNn2_1SM`Nk2b1UM^Nj2b1XM]Ng2d1YM\\Ne2e1]MYNc2h1]MXNb2i1_MVN_2k1bMUN]2l1cMTN\\2m1dMSNZ2n1gMRNX2o1iMPNV2P2kMPNS2R2mMnMR2S2nMmMQ2S2QNlMPNgM^3c4bNfMoMiM\\3b4fNdMnMkM]3`4eNeMlMmM`2MVN`4_1eMjMoM]2N]N\\4]1gMhMQN[2KdN\\4Y1hMfMSN\\2HhN[4W1jMdMTN[2FnNZ4T1ZNL[MTOX4S1]NGZMYOW4Q1_NX1_1jNaNW1]1jNcNW1Z1lNeNU1Y1lNhNT1U1nNkNS1S1nNmNS1P1POoN]MdMn2[3FROXMmMl2n2NUOSMRNn2g20WOoLWNo2a24=G@;c0C[O>e0CXO?h0BPOe0P1\\OZNZ1e1gNfMm1Z2TN[MV2e2kMYMV2g2jMYMW2f2jMYMV2g2kMWMV2i2kMVMU2j2lMUMU2j2lMTMU2l2lMSMT2m2mMRMS2n2nMQMR2o2oMoLS2P3mMPMS2P3nMoLR2Q3oMmLR2T3nMkLS2T3nMkLR2U3oMiLR2W3oMhLQ2X3PNgLQ2X3PNfLQ2Z3PNeLP2[3PNeLP2[3QNdLP2[3QNcLP2]3QNbLo1^3RNaLn1_3SN_Ln1a3SN^Ln1a3SN^Lm1b3TN\\Lm1d3TN[Ll1e3TN[Lm1d3TNZLm1f3l30001O000000001O001O001O001O010001N100O101N100O101N100O101N100O2O0O100O2O0O10001N100O101N100O100O10O010O0100O010O10O010O10O10O0100O010O010O10O0100O010O010O10O01000O010O0100N101O1N101O0O2O1O0O2O1O0O2O001N2O001N2O001N101O1N1O2M3N1N3M2N3M3M2N3M3M2N3M3M3N2M4L3M3M3M4M2O1N2O1O2N1O1O1O2M2O1O1O1O2N1O1N2O2N1O1O1O1O2M2O1O1O2M2N2M3N2M4M2M3N2M3N3L3N2M3N3L3M3N3L6K5J6K5J6K5J6K5J6K5J7J5J6K5J6K5J6K5J6K5J6Kmf9", "size": [427, 640]}, "area": 153862.0, "iscrowd": 0, "image_id": 13177, "bbox": [0.0, 3.0, 616.0, 419.0], "category_id": 183, "id": 20000784}, {"segmentation": {"counts": "m4h5W>1O2O0NVJnAi5S>2O1O1O100O1N21O001OO11O1O1N1000001O00O100001O0000MUJQBk5b=`0GeIjB^6U=dIiB]6T=9O1O1O11O0000O1O1O1O101O00000000000000aIQCm5P=QJQCo5P=oIQCQ6`=N2O002N2N3M00O100O10000O1O1O00101O0000000000ODhIoB5Jm5U=SJmB01l5Q=TJlB15i5P=`JQC`5o<_JSC_5m<aJXCZ5h<gJXCX5h<hJXCX5i<hJVCX5j<hJVCX5j<hJVCX5k<fJVCZ5j<fJUC[5k<eJUC[5l<cJUC]5k<cJUC]5l<aJUC_5l<`JUC_5m<^JTCb5o<YJTCS5AYKQ>b4ZBXKf=c4eBWK[=d4S1K5K4O011O1O1O1O2N1O00BVLg@j3Y?>RDbK[8^4eGcK[8[4gGeKX8[4iGfKU8Z4lGfKT8Y4mGhKR8W4oGiKR8U4oGkKR8S4oGmKQ8R4PHnKR8o3oGQLT8k3mGULV8g3kGYLX8c3iG]LY8`3hGaLZ8\\3fGjLW8R3jGUMQ8i2oGWMR8h2mGXMU8f2kG[MV8d2jG[MY8b2gG^M[8a2cGaM^8^2aGbMa8\\2_GdMc8[2\\GeMf8Y2[GfMg8Y2ZGeMh8Y2YGgMi8W2WGhMk8W2VGgMl8W2UGhMn8V2RGiMP9U2RGiMQ9U2oFjMS9T2nFkMP8FSE\\2o2lMm7KTEW2P3kMm70UES2o2iMo75REP2U?RNl@k1U?UNl@i1V?WNk@g1T?[Nl@c1U?]Nm@`1U?`Nm@]1S?dNRAU1o>kNVAn0m>ROXAe0k>\\OYA=j>B\\A6f>K^ANd>3aADb><a21N2O1O2N3K5L4M3L4N1N2N3M2M3K5M101O1O0101N10O10O0100N101JVNR^O0Oj1ma08O1O0O2O010O1DkMc^O06T2Ua0?O1N101O001O01OkBYMQ9g2nF[MR9d2mF^MR9b2mF`MS9_2mFbMR9^2mFdMR9\\2nFeMf8GdCd2e3fMe8IdCa2g3hMc8IdC`2i3fMa8MfC]2h3fMc8NbC^2k3cMeME[9;TE^2k3cMfMEZ9U3PIUMgMGY9S3oHVMiMHW9R3PIVMiMIV9Q3PIVMkMJT9P3PIVMmMJS9o2QIVMmMNo8k2UIWMlM3i8e2]IWMkM0j8j2[IWMkMOf8HcER3m3VMjM1e8k2cIUMfM1g8i2dIVMdM2g8g2fIXMdM0f8g2gIYMdMNg8g2fI[MbMOi8d2eI^McMMh8e2eI^McMMi8e2cI^MeMLi8e2bI_MeMLj8d2`IaMfMKk8d2^IaMgMKm8b2\\IcMgMKl8c2\\IbMiMKi8f2]I_MkMJi8f2\\I_MlMKh8f2\\I_MlMJi8h2YI^MoMJg8i2XI^MRNJf8h2UI_MVNIe8h2SIaMXNGd8i2RIaM[NGb8i2PIcM]NDb8j2oHdM_NBa8k2nHeMaN@`8m2mHdMcN_O`8m2kHfMeN]O_8n2jHgMgN[OW8W3oHaMiNXOX8X3lHeMiNSOZ8Z3jHdMmNROX8\\3hHdMPOPOW8_3eHdMSOlNX8b3aHdMXOjNW87PFk2g1lLgNZ1\\1hNU8MSFN4V3`1SM_NV1f1fN^8InEY3]1lN8QN]8KfE03[3`1hN;QN^8NgE_3Y1bNc0RN[8NiEc3T1\\Ni0RN\\8MiEf3o0\\Nl0QN\\8LjEh3m0ZNn0RNe8d3\\FYNQ1QNd8g3[FVNS1RNb8i3\\FRNS1SNc8k3\\FPNQ1UNc8l3]FmMk;T2UDjMl;W2UDgMl;Y2UDeMk;\\2nCSLSO^1o<`2lCULUOZ1_<bNhCR4b0ULWOT1_<iN]CM1U4j0TLYOo0^<[3VDjK\\O<M]Ob<_4SDlK]O6k<o3fCnK^O2l<P4eCRL^OKn<S4bCVL@Do<W4_CYLA]OQ=Z4]CjLd<V3\\CjLd<V3\\CiLe<X3ZChLf<X3ZChLf<X3[CgL^2_NY7j4YFgL\\2bNZ7h4YFfL\\2cN[7g4YFgL[2cN[7f4[FhLX2bN]7g4ZFkLT2`Na7e4[FmLR2_Nb7d4]FPMm1\\Nf7d4]FRMk1ZNh7e4]FQMj1[Nh7d4^FRMi1ZNi7d4^FRMi1ZNi7e4^FQMh1ZNj7f4]FPMi1ZNj7g4]FmLj1\\Ni7g4]FlLk1]Nh7h4\\FkLl1]Nh7i4\\FhLm1_Ng7j4[FfLo1`Nf7k4ZF[LZ2jN\\7S5SFRLa2kN]7f6bHZI^7f6aH[I_7d6aH]I^7d6aH]I_7c6aH]I_7c6`H^I`7a6aH_I_7a6aH_I_7a6aH_I_7a6bH^I^7b6dH\\I\\7e6dHZI\\7g6cHYI]7h6bHXI^7h6bHXI^7h6bHXI^7h6bHXI^7g6cHYI]7f6dHYI]7Q7oElHS23o7Y7oGgHR8X7nGhHR8Y7mGgHS8X7nGgHS8Y7mGgHS8X7oGgHQ8Y7oGgHQ8Y7oGgHQ8Z7nGfHR8Z7mGgHS8Y7mGhHR8X7nGhHR8X7nGhHR8X7nGhHR8X7nGhHQ8X7PHhHP8h6nE^IR2Jo7f6XFYIj11n7d6]FUIh18j7d6fH\\IZ7d6fH\\IZ7e6eH[I[7g6cHXI^7i6aHWI_7k6_HUIa7m6]HSIc7n6\\HRId7n6\\HRId7n6\\HRId7n6\\HRId7n6\\HRIc7n6^HRIZ7T7]FgHQ26_7Q7bFnHl11]7j7`HVH_7l7`HTH`7l7`HTH`7l7`HTHa7l7^HTHb7l7^HTHb7l7^HTHb7l7^HTHb7m7]HSHd7k7]HUHc7k7]HUHc7k7]HUHc7k7]HUHc7j7^HVHb79dFg6k1PIe73eFi6g1TIg7OeFk6d1VIDJ^6N\\HQ7c1WIBOm6g6dIZI_O2k6`6iI^I\\O4i6Y6PJcIWO5h6W6RJdIUO;d6Q6WJdIUO;d6Q6WJdIUO;d6Q6WJdIVO:c6R6WJdIUO;d6R6VJcIVO;d6R6VJcIVO;d6R6VJcIWO:b6V6UJ`IZO9a6b6jIUIE9a6a6kIVIC:b6_6lIWIB:b6]6nIYI@:b6\\6oIZIA8o5AgHn6i1YIB7n5D\\HV7S2oHC8m5P7`JhHD7l5n6cJkHA6n5o6`JkHC6l5o6aJkHC5m5P7`JkHC6m5o6_JkHD6n5n6^JlHD6o5n6\\JlHD7P6m6\\JlHC7Q6n6\\JkHC7Q6n6\\JkHB8R6m6\\JkHC7Q6o6[JjHD7Q6o6[JjHD8Q6n6ZJjHE8Q6n6ZJjHE8Z3", "size": [640, 427]}, "area": 74554.0, "iscrowd": 0, "image_id": 13201, "bbox": [0.0, 68.0, 427.0, 410.0], "category_id": 113, "id": 20000785}, {"segmentation": {"counts": "e:X3h`0010O0O2O00001O1O001O001O001O001O1N101O001O00001O001O001O001O001O001O001O001O001O001O0010O01O001O001O1O001O001N101O001O1O00001O001O001O001N1010O01O001O00O01`@bK^?a40OO20O01O001O001O001O00001O001O000010O01O0XAYK\\>c4[A\\KN5l>Y4VAlKo>P4l@TLW?Z40O2N1O1YOf@fL\\?W3f@hL[?V3g@lLW?R3l@lLV?Q3k@mLX?Q3i@oLX?5a@P28kMX?3a@Q29kMX?2_@S29kMY?1_@R29lMZ?1]@R2:lM[?1\\@o1<oMY?2[@m1?oMW?4Z@k1a0QNV?3Y@j1d0SNR?3Z@T1LfNT12f>4Z@P1`1jNX>5X@P1a1kNW>5X@n0d1lNT>6X@3L6k1AQ>5Y@3M3l1Dn=6_@LJ7k1Fm=6o@2W1Fj=8PA0X1Hg=7TAOV1Jf=5VA0U1Je=6WANV1Lb=5ZANUa0On^O0Ra0OQ_OOo`00gQ7OkoH0T\\O0ic00V\\O0hc07L3M1N4M2N2N2O12O0O1O100O1N01O10O01O0mN4T^OKma06Q^OKoa06P^OKoa06o]OKQb05n]OMQb04n]OLRb05l]OLTb05j]OMUb03k]OMTb0Q1L3M3N3K4O2N1O1O100O1O0001O01O003M2O0O2N1O1O2O0O2N1O101N2N1O1O1O1\\NoLXBR3g=PMVBR3h=QMVBP3h=SMVBm2i=UMVBl2h=WMVBj2h=YMVBg2P=hLdBc0:f2Q=iLbBd0;d2R=jL`Bd0=c2S=jL]Be0?a2T=lLZBe0a0`2T=lLYBf0b0_2U=lLVBg0d0^2U=mLUBf0e0^2U=nLSBf0f0]2W=UNhBl1X=TNgBm1X=TN\\BbNG[3m=SN\\BX2c=hM^BX2c=fM^BZ2b=eM_B\\2`=eM_B[2a=fM]B[2c=dM^B]2a=dM^B\\2b=dM^B\\2b=eM]B[2c=eM]B\\2b=dM^B\\2b=dM^B\\2b=dM^B\\2a=eM_B[2a=fM^BZ2a=gM_BY2a=gM^BY2b=hM^BX2a=iM`BV2_=kMaBT2`=lM`BT2_=mMbBR2_=mMaBS2_=mMaBS2_=mMaBR2`=nM`BR2`=nM`BR2`=nM`BR2a=mM_BS2d=jM\\BU2d=lM\\BP2h=PNXBP2i=oMVBR2i=oMWBR2g=oMYBQ2f=PNZBQ2e=PNZBo1f=RNYBP2e=QN\\Bm1d=TN[Bm1e=SN[Bm1d=TN\\Bl1c=VN[Bk1d=WN[Bi1d=XNZBj1f=VNZBj1f=X2O10001N101N101BcIUC_6k<=N0O1O2N1O100O100000iKUCY1k<eNXCZ1h<eNYC[1h<dNYC[1i<cNWC\\1n<_NTC`1n<^NSC`1R=\\NnBc1U=[NkBe1V=ZNkBd1W=[NiBe1W=[NiBe1X=ZNhBf1X=ZNhBe1X=\\NhBd1W=]NiBc1W=]NiBb1W=_NiBa1V=`NjB`1l<jNTCV1l<jNTCV1m<iNSCW1m<hNTCW1l<iNUCX1j<gNWCY1i<gNWCY1i<fNXCZ1g<gNYCY1g<gNYCY1g<gNYCX1h<iNWCX1h<jNVCV1k<jNTCV1m<iNSCW1n<hNRCY1m<gNSCY1m<gNSCX1n<hNRCX1m<iNSCW1l<jNSCW1W=ULfBS23i1_=UNaBk1_=UNaBk1`=TN`Bl1_=UN`Bl1`=TN`Bl1_=VN`Bj1`=VN`Bj1`=VN`Bj1a=UN_Bk1a=TN`Bl1`=TNaBk1_=UNaBk1_=UNaBk1_=UNaBk1_=UNaBk1^=VNbBk1m<SLYCR2Jk1j<\\LUCj11j1h<aLQCh18f1h<jNXCV1h<jNXCV1i<iNWCW1k<gNTCY1n<fNRCZ1P=dNPC\\1R=bNnB^1S=aNmB_1S=aNmB_1S=aNmB^1T=bNlB_1S=aNmB_1R=bNnB]1Q=ZLjBQ26e1k<\\LTCl11h1_=UNaBk1`=TN`Bm1_=SNaBl1`=TN`Bm1`=RN`Bo1_=QNaBP2^=PNbBQ2]=oMcBQ2^=nMbBS2\\=nMdBR2\\=nMdBS2[=mMeBS2[=mMeBR2[=oMeBR2Y=oMgBQ2X=PNhBP2X=PNhBDJb0]=KiBBO?U=2lB_O2=n<7PC\\O4;g<>UCWO5:e<`0VCUO;6_<e0VCUO;6_<e0VCUO;6_<e0VCVO:6_<d0WCUO;6`<d0UCVO;7_<c0VCVO;8^<b0WCWO:a0W<6_CZO9c0`<HWCE9c0_<IXCC:d0]<JYCB:d0[<L[C@:d0Z<M\\CA8c0[<L]CB7b0]<K\\CC8a0\\<L\\CD7`0Z<O_CA6a0\\<M^CC6>]<O]CC5?^<N]CC6?]<M]CD6>^<N\\CD6>_<M[CD7?^<M[CC7a0^<L[CC7a0^<L[CB8b0]<L[CC7a0_<KZCD7a0_<KZCD8`0_<KYCE8`0_<KYCE8`0X2", "size": [640, 427]}, "area": 61167.0, "iscrowd": 0, "image_id": 13201, "bbox": [0.0, 305.0, 427.0, 246.0], "category_id": 124, "id": 20000786}, {"segmentation": {"counts": "m=S6m=01O001O00001O1O001O001O001O001O1O001O001O00001O001O001O001O001O001O001O001O001O001O001O001O001O001O1O001O001O001O001O1O00001O001O001O001O001O001O001O00O10SKo@k4T?O001O001O001O001O001O00001O001O00001O001O0mKd@b3]?ULb@N5l3Y?VLl@j3U?QLo@o3b?O00000000003M00N200000000O1O1O1O1001O00O100001O0000001O1O001O1O2N3M2N00001O0000N2O1N200001O1O0[Li_Oa3X`0^Lh_Ob3]`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size": [640, 427]}, "area": 53319.0, "iscrowd": 0, "image_id": 13201, "bbox": [0.0, 445.0, 427.0, 195.0], "category_id": 140, "id": 20000787}, {"segmentation": {"counts": "ScW52ib03e]OLH3M4cb00c]O7HJdb02_]O7N_O03ab0Q1a]OoN]b0R1d]OnN]a0OY_OU1XOnN\\a0O[_OT1XOnN\\a0:P_OS1n`0oNP_OR1Pa0oNo^OQ1Pa0POP_OP1o`0ROP_On0Pa0ROP_On0Pa0SOo^Om0Pa0TOo^On0Pa0SOn^On0Ra0ROm^On0Sa0SOk^O?OROVa0b0i^O<5gNM1Ta0Z2;1O2O1O1N2O1O100000000000000000000000000000000000000000000000001O0000O100000000000000000000000002\\Me^O_2ca0K2N2OO11N002N2N1O00000000O100O1000O100O1000000000000000002N2N2N1O0O1H8N2K7M10O1010O1O1O1O01O01O2N1O00f]OSOba0P1X^OUOda0o0X^OTOea0R1T^OPOma0a1010O1O0bNR^Oj0Pb0ROU^Ok0ma0oNY^Oo0la0jNX^OT1\\b0N2N2N1N3N1O001O1O4ZOh\\O;ac0Eam9", "size": [640, 427]}, "area": 9264.0, "iscrowd": 0, "image_id": 13201, "bbox": [268.0, 0.0, 143.0, 105.0], "category_id": 157, "id": 20000788}, {"segmentation": {"counts": "0m4S?O1O100Nl@XKT?h42O1O1O100O1N21O001OO11O1O1O0000001O00O100001O0000Mi@]KW?V4`0GY@VLj?i3X@ULi?h39O1O1O11O0000O1O1O1O100000000000000000V@\\LX?e3f@\\LZ?e3d@\\L\\?U4O1O002N2N3M00O100O10000O1O1O1O100000000000000C[@\\L5JZ?h3f@ZL01X?e3h@XL15V?c3SA^Ll>c3SA_Lk>a3UAdLf>\\3[AdLd>\\3\\AdLd>]3\\AbLd>^3\\AbLd>^3\\AbLd>_3ZAbLf>^3ZAaLg>_3YAaLg>e2l@fM;Fi>c2UA_M2Ni>b2\\A[MI4k>`2^A\\MF5k>^2aA^MA5n>\\2cAaMZO6R?Y2eAWN[>h1gAWNY>h1iAWNW>h1kAWNU>h1mAWNS>h1oAVNR>i1PBTNR>k1PBTNP>l1PBTNP>k1RBTNn=n1QBQNo=P2QBPNn=R2PBnMP>S2PBkMQ>V2oAZMUOLm>l2lAWMc>j2]ATMd>n2[AQMe>P3ZAoLg>S3XAkLi>V3P12N1O2N1O2N1O2N1O1O2N1O2N1O2N1O2N1O2N1O1O2N1O1O2N1O1O2N1O2N0000N2N200O1001OO1001O00001O001O00000000O1000000N2O100O11O000000O1M3O10000001O2N1O001O001OO100K\\@lK0Od?S470000O1001O00EV@XL06i?`3>00O1000000001O001O001O0000Ek_OkLU`0S3n_OkLS`0T3o_OjLR`0S3Q@mLo?S3R@jLP`0U3Q@jLP`0V3=1O0000000000O1N2LW_OPMj`0o2400O1001O1O001O1O1O1O2NO1N21O0000O11O00O100O1O1O100O1H800O1O1O1O100ZOi^O`NLC\\a0l1k^O\\NZa0c1i^OXNZa0e1b0N2QOj]O3Wb0Kk]O4Vb0KV^OIka06W^OHja08W^OGia08X^OHha07Z^OHfa08Z^OHfa08[^OGea08\\^OGea09\\^OEea0;[^ODfa0;[^OCga0;\\^OCea09S1N2N2O1O1N2OQXj01RTUO0jc08K2N2N002N002N2N1O00000000O100O1000000000000000000000000002N2N2N1O000000002N00O1001O1O1O1O00001O2N1O001OO1O11O001O1O0g]OmNea0U1W^OPOfa0R1T^OTOja0Q1o]OSOoa0a1N2N2N1O2N1O001O1O4^^OaM[a0b2000001O00001O1O1O00O1000000001O00", "size": [640, 427]}, "area": 38645.0, "iscrowd": 0, "image_id": 13201, "bbox": [0.0, 0.0, 427.0, 157.0], "category_id": 169, "id": 20000789}, {"segmentation": {"counts": "TWj11nc09G7I2N2N2QC@\\6a0^IJ[68_I3Z6O`I<Y6FaIe0X6]ObIm0X6UOeIP1W6ROgIP1W6ROgIQ1W6oNhIS1V6oNhIS1kNgM^3U1eMW1jNfMa3S1cMY1jNfMd3o0aM]1iNgMe3l0`M`1hNeMj3j0\\Mc1hNdMm3h0ZMf1gNcMQ4f0VMi1gNbMT4e0RMm1hN^MX4c0oLQ2gN]M[4b0lLS2gN\\M^4a0iLT2fN_Mb4;gLX2dN_Mf49dLY2dNaMi45aL[2dNcMk41`L^2nMiLQNT1b7E\\L_2mM[Nh5XOWL^2PN\\Nj5WORL_2QN]Nm5VOnK^2ZMdLBk1W7SOjK_2]MeL^Ol1[7ROgK^2^MgL]Ok1_7QObK^2aMhL\\Ok1a7PO`K^2bMhL[Om1b7mN`K^2bMkLZOk1e7kN]K`2dMkLYOk1f7jN\\Ka2cMlLYOk1i7hNXKb2eMmLYOl1h7eNYKb2fMnLXOm1i7bNXKd2eMPMXOm1i7_NXKe2fMGR7dMWKe2fMIT7aMTKh2fMHW7`MRKh2fMJX7^MPKj2gMH[7^MlJj2hMH^7^MiJj2hMGc7^McJm2hMDh7`M^Jl2iMDk7`MZJm2jMDn7_MVJn2jMEo7^MUJn2kMEQ8^MQJn2mMFR8\\MoIP3kMGV8[MmIn2jMJZ8XMjIm6Z6TIcIk6_6VI_Ii6d6XIZIf6i6ZIUId6n6^IoHa6T7aIiH]6Z7hI_HZ6`7kIYHY6f7lIQHX6o7lIjGW6U8oIcGT6]8PJ\\GS6d8RJSGQ6m8`22O1N2N4jNiF]GZ9^8]GlFg8Q9ZGmFj8P9WGoFl8n8UGPGo8m8QGRGR9l8oFSGS9k8nFSGU9k8lFTGW9i8iFVGZ9f8R1L3M4L4M2O2N1O2N2O0O2N2N101N1O2N2N101N1O2N2N101N2N1O2O0O2N1mLWCnNj<Q1WCnNj<Q1XC[MM4m<`2XCYMN5k<b2XCVM06i<c2YCTM17g<d2ZCRM28e<f2ZCPM29e<f2ZCoL49b<i2[CkL6:`<j2\\CiL7;^<l2\\CfL9<\\<m2]CdL:=Z<n2^CcL:=Y<P3^C`L<>W<Q3^C_L>=V<T3]C\\L`0>T<]OeBf3i0\\La0?R<W3kDhLU;Y3PEaLP;_3WEZLh:h3g21O0O2O1N2O001O10O2O00001N10001O0O101M2N3M2N2O2M2N2N3M2N2N3M3M3M3M4L3UOf^OkN^a0T1h^OcN\\a0[1k^O\\NYa0c1g0J6K4K2O1N200001N2O1O001OKY]OQOfb0l0]]OUOab0j0b]OUO^b0i0d]OXO[b0e0j]OZOUb0d0n]O\\OQb0b0S^O]Ola0a0W^O^Oha0`0\\^O@ba0?b^O@]a0>f^OBXa0=V100010O0000010O00010O000010O01O01O100O010O1O100O;FjcR2", "size": [640, 427]}, "area": 36044.0, "iscrowd": 0, "image_id": 13201, "bbox": [93.0, 87.0, 227.0, 436.0], "category_id": 183, "id": 20000790}, {"segmentation": {"counts": "V4;T:1OO10000002N001000N1O1010O1ON21O1O2OO01O0000000100O00000000000O110O1N20O10O1OO1O1000001O001N1N12O1O1O1O1O0OS^a4", "size": [335, 500]}, "area": 665.0, "iscrowd": 0, "image_id": 13291, "bbox": [0.0, 134.0, 55.0, 22.0], "category_id": 106, "id": 20000791}, {"segmentation": {"counts": "f5i4e51000000001O000000O100O1O100O11OO100000000O1002N003M00O1001ON2000000001O001O000000O11OO100000000001O0000000000000eL_Jh1a5QNmJh1S5QN\\Kg1d4RNnKc1R4VNgLX1[3_NaMl0R6O00000000000000000000000000000000O100O1O100O1O1O1O1bNjNaIX1]6nN]IT1b6QOXIQ1f6SOWIn0h6YOaHWO<b1Q7FcH<\\7\\1O1O1O1O1M3N2M3M3N2M3M3N2O1O1O1O1O1O100O1O100001O00001O00001O001O001O001O0_M`IS1a6kNaIT1_6kNcIT1^6iNfIU1[6iNgIV1Y6hNjIW1W6eNmIZ1S6bNSJ\\1n5`NVJ_1j5^NZJa1g5[N]Jd1c5RNiJl1n6O2N1OUMTNTMk1j2XNVMg1j2ZNUMf1l2ZNTMe1j2^NUMb1k2_NTMa1k2aNTM_1l2bNTM]1k2eNTM[1l2fNSLDXOf1e4gNmK1UOX1o4gNeK9[Oo0o4jN`K?^Og0R5kNgJH6g0Nf0U5\\OQKOHe0X5[OQK2Dc0Z5[OTK3Aa0[5\\OUK4^O`0]5[OWK6ZO?`5ZOWKP2g4QN[Kn1c4RMcJn0k0o1j4lMYKR2k4iMWKV2l4gMUKX2o4cMRK]2R5_MoJ`2T5\\MmJ;mNe1Z6kMkJ<QOf1]7VNfHk1[7SNfHm1[7RNdHo1]7oMdHQ2]7nMbHS2^7lMcHT2^7kMaHV2l70TOjM^IW2b6jM]IV2b6lM]IT2c6nMZIS2f6nMYIR2h6nMVIS2l6mMRIS2o6mMoHT2S7lMkHT2V7mMgHT2Y7mMfHS2Z7oMcHR2]7PNaHP2_7QN_HP2a7?00000000O100000000O1000000O1O1O1O1O1O1O^MhHo1W7QNkHn1T7RNmHn1S7QNoHn1P7PNSIP2m6nMVIQ2i6nMZIQ2\\6cM^I:8S2Y6dM_I7;T2a6iMbIW2^6gMeIX2Q6eM^I1d0Y2n5fM]JZ2m6OdNiM`JW2a5hM_JX2b5hMjI2JV2]6gMgI9HP2a6gMeI;Jn1Q7UNnHk1c6gM_I`0Ni1b6hM^Ic0Ne1d6hM\\Ig0Na1e6jMZIh00^1e6nNYIR1g6POWIP1h6\\1O1O1001O@\\LUJd3[60O10000OfL[Il2e6SM`Ii2`6UMeIh2Z6XMgIh2X6XMjIg2V6XMkIh2T6WMnIi2n5ZMSJf2l5ZMVJe2j5ZMWJf2j5UMZJk2f5QM^Jo2b6O0000001O0^McHR2]7nMdHQ2]7mMeHR2[7nMeHR2\\7mMeHR2\\7lMfH5L\\1^7_NfH4O[1\\7`NeH50Z1\\7`NdH52Z1[7_NdH62[1Z7_NeH43\\1Y7_NdH44]1Y7^NcH44_1f7`N[H`1e7^N\\Hc1c7\\N^He1V80O1O1O100O1O1O1O1N2CQNYHP2f7SNVHo1i7<00O1aNaMWJ9EW2T6`MRJ`0GQ2W6_MQJd0Dn1[6^MPJ]3P6cLPJ]3P6cLoI^3Q6bLmI`3`61N2O1G\\LfIe3Z6[LeIf3`62O1O1O1O1O1000\\L^J[2b5cMbJ>QOS1]6^NeJ<XOm0S6eNhJ;ZOm0n5gNkJ9XOP1l5fNPK6VOT1j5eNSK3TOY1`7dNaH\\1Y80O10000O10000O10000000000000000000000000000QOdNkH\\1R7iNjHY1T7lNeHX1[7jN_HZ1a7iNXH[1h7f01O0000001O0000001O0000001O001O001O00cMcMiL\\2W3fMgLZ2X3hMhLW2W3lMhLS2X3nMgLR2X3QNgLn1X3UNfLk1Z3VNfLi1Z3YNdLg1\\3[NcLd1]3]NbLc1_3^N`La1_3aNaL^1_3dN_L\\1b3eN]LZ1e3eNZL[1f3gNYLX1h3jNVLU1j3mNULR1k3POTLo0l3SORLm0n3UOQLj0o3XOPLg0P4ZOPLe0P4\\OoKd0R4\\OnKc0R4^OnKa0R4@nK?Q4CoK<Q4EnK;Q4GoK8Q4IoK6Q4KoK4Q4LPL3P4NoK2Q4OoK0Q41oKNn36RLIn39PLGP4<nKCR4`0lK_OT4c0kK\\OT4h0jKWOV4l0gKTOZ4n0dKQO[4S1cKlN^4T1bKkN]4X1bKgNb4W1\\KiNc4Z1\\KeNb4_1]K`Nc4b1\\K]Nd4d1[K\\Ne4f1ZKYNf4i1YKVNi4h1XKWNh4i1YKVNh4i1XKWNh4i1XKWNh4i1XKWNh4i1XKWNh4i1WKXNi4h1WKXNi4h1WKXNi4h1WKXNi4h1WKXNi4h1WKXNi4h1WKXNi4h1WKXNi4h1WKXNi4h1VKYNk4f1UKZNk4f1UKZNj4g1VKYNk4e1VK[Nj4e1VK[Nk4d1UK\\Nl4b1UK^Ni4d1WK\\Ni4d1WK\\Nj4b1VK_Ni4b1WK^Ni4b1WK^Nh4b1YK^Ng4b1YK^Ng4b1YK^Ng4a1ZK_Nf4a1YK`Ng4`1YK`Ng4`1YK`Ng4_1YKbNg4^1YKbNg4^1YKbNg4]1ZKcNf4]1YKdNg4\\1YKdNg4[1ZKeNf4[1YKfNg4Z1YKfNg4Y1ZKgNf4Y1ZKgNf4Y1YKhNg4W1ZKiNf4W1ZKiNf4W1YKjNg4U1ZKkNf4U1ZKkNf4U1ZKkNg4T1XKmNh4R1YKnNg4R1YKnNg4R1XKoNh4Q1ZKmNe4S1^KkNb4U1aKhN`4W1bKgN^4Y1dKeN[4\\1gKbNZ4\\1hKcNX4]1gKdNX4^1fKcN[4]1dKcN\\4]1cKdN]4f1YKZNg4i1kJZMDn0", "size": [335, 500]}, "area": 51289.0, "iscrowd": 0, "image_id": 13291, "bbox": [0.0, 174.0, 500.0, 161.0], "category_id": 124, "id": 20000792}, {"segmentation": {"counts": "RY_13Y:4M3M2N0B`001ON201N101N100001OO2O0001OO2O0010ON3M3O0O2N100O1O011N2N010O2N100O1O100OUbV3", "size": [335, 500]}, "area": 646.0, "iscrowd": 0, "image_id": 13291, "bbox": [144.0, 153.0, 42.0, 30.0], "category_id": 128, "id": 20000793}, {"segmentation": {"counts": "0V4Y600000000000000001O1O00001O00000000001O00000000001O1O000000000000001O1O1O001O1OO1O1@WJZLi5b3\\J]Ld5_3aJ`L_5^3dJaL\\5^3fJaLZ5]3iJaLX5^3m0O1N2O1N2O1N2O1N2O1N2O1N2O100O1O1O1O1O100O1O1O1O10000001O001O002NN2M3N2M3N2M3N2N2M300000000O10000000000001O000000000000001O1O1O1O1O2fHjMW6W2dImM\\6T2`IPN_6P301O00001O001O001O001O1O001O1O1O001O1O001O1O001O1O1O3M3M4L3M4L4L8H0000001O001O000000001O0000001O001O001O1O001O000000O11O1OO1001O00000000001O00O1O100O100O1O100O10000000000000000000000000000O1O1N2N2N2N2N2O1N2O1O1O1O1O10000000000001O0000001O1O2N2SJgK`5V4eJjKY5U4a000O100O100O100O1000000001O0000001O1O001O1O2N2N1O2N6J000000O1O11O00001O00001O00001O001O001O1O0000N200O1001O1O000000000`JSK^5o400dJRKW5P5gJPKY5S50001O1O2N000000M3000hJlJU5X5OO1O1000000001ON2O1O1000000EiJ^KW5`4=O1O1N2O1N2O1N200O100O100O100000000000000001O000000000000001O00003M5`J^Ki4g4lJ_KT5n40O1001O00O100N2O10000O11O001O1OO1O100O1001O00O1O10000O1O10000001ON2001OO1001O1O000000001OO1N2001O001O00000000000000001OO1001O001O000000O100001O00001O1O001O0000000000001O0000O10000N2O100003M00O1O1001O1O00O1001O00O100O1002N1O00001O1OO10000000000001OO1001OO10000000000001O", "size": [335, 500]}, "area": 70893.0, "iscrowd": 0, "image_id": 13291, "bbox": [0.0, 0.0, 500.0, 170.0], "category_id": 157, "id": 20000794}, {"segmentation": {"counts": "a4U1[9N010000002OO01O1OO0010O2N2ON1200N3N001O000O1002OO03M00O1001ON111O00O1100O01O000000O110N101O00O10001O00000O1H81O1O1O1O1O1O11000G8H9GiYX2L`fgM3^FMf84WGNh83WGNi83VGLl84SGL]ON_96RGNo85nFJS99jFFW9h0100VO]Fh0e900ZO\\Fa0f9]OZFc0i90001O1O2N000000M3000^OVF?n9OO1O01000000O2ON100010O100O1O4M^i:IiVE1O1N2O100O1N2O1M3N101OO011N1O2O1N000100O10010OO01O2O2N0O2N10000001ON20010N1001N2O0O1000001OO1N20M4O001O000O1001OO11OO14MN0O201O001O02N001N100001O00001O1O001O00000001O00O20O001N2ON20N3N0100O4M00O1O1001O1O00O1001O00O100O1002N1O00001O1OO10001O00000001N01010OO011O00O11O0001OhJ", "size": [335, 500]}, "area": 4562.0, "iscrowd": 0, "image_id": 13291, "bbox": [0.0, 145.0, 500.0, 44.0], "category_id": 169, "id": 20000795}, {"segmentation": {"counts": "ek=7T:5K5M3N2M3N2N2M3N2M3N1N3N2M3N2`IeNh3^1PLPOh3R1PL]Of3f0RLLb36VLc0X3^O`L^1j2eNmLb1R3aNeLf1Z3\\N^Lk1a3XNVLo1i3\\200O1O2N1O10000001O001O0O4LN3M2O1N3N1N3N00dNbLiK]3Y4hLaKZ3]4lL^KU3`4QMZKQ3e4TMUKn2i4\\MmJe2S5\\1O1O1O1O1N2M4M2M3N2M3M3N2N2O2N2N2N2N2iLiIS2Z6gMkIX2V6dMmI[2U6`MPJ^2Q710O00010O0010O010O0100O010O100OjIUMh4l2VKVMj4i2UKYMk4g2RK]Ml4c2SK_Mm4a2PKbMP5]2mJgMR5Z2iJlMV5S2gJQNY5o1cJUN]5j1`JZNc5=oIB3=o5LQJ]1R6]NQJd1P6WNSJi1P6QNSJo1W700O1O1O100O1O1O1mNaHUO_7d0jHYOW7`0RI]Oo6a0UI\\O`71aHM[77fHGZ7:gHDX7>iH@W7a0kH]OS7e0nHYOR7h0Y12mNVO_Hk0^7[O]Hh0`7]O[He0c7_OZHa0f7CUH?i7FSH:l7JPH8;mNP7o0`H6<POQ7T2kHoMU7S2iHnMW7S2hHmMW7U2gHkMY7V2gHjMY7W2eHiM[7Y2dHgM[7f21TOeHdNZ7]1gHaNY7`1hH_NX7`1kH]NU7e1kHZNT7h1lHVNU7l1kHRNT7P2mHnMS7T2lHjMT7W2nHgMQ7Z2PIdMQ7\\2QIbMn6_2TI^Mm6b2TI]Ml6c2`00000000000O10000000000O01O0O1O1O1O1O100nHYM\\6h2dIYM[6c1RI^Oc0QOY6f2fI\\MX6^1VIA`0TOY6[1WIB>VOY6W1YIZO0\\O=5Z6T1YI\\O1ZO:9[6P2[IgM8<\\6m1\\IgM5?_6i1]IhM2b0`6f1^IiMOc0c6d1^IhNb67\\I@29b67\\I@2:a66]I@2GOKb6n0]IA2C5J]6P1^ICOA:JZ6Q1]IEN^O=LZ6o0[I5>kNY6JWJOROn0OYOa00T6OYJISOo0OWOe0OP6<RJ^OYOo01VOh0Ol5>SJ^OWOn04SOk01f5b0UJ9>TO]5d0UJ7U7KjH4W7MhH2X70hHNY72mHIQ78bHZOBM1=j7=^HIFFm7a0\\H9d7HYH:g7GWH9k7GRH;n7FoG<=eNU7[2kHjMQ7V2mHoMQ7o1oHRNR7l1nHUNR7k1mHVNT7h1kH[NU7_1oHbNS7\\1lHeNV7Z1hHgNZ7W1bHnN^7R1]HROi7g10001O0O1O2eH[Mk6d2UI]Mj6d2TI]Ml6c2TI^Ml6b2SI_Ml6b2RI`M5LV6d2eI`M4OV6a2eI`M50U6a2eI_M51V6`2dI`M52W6^2cI`M53X6^2bI_M54Z6\\2aI`M44\\6i2cIXM^6g2`IZMb6d2]I]Md6c2ZI_Mg6S32RMYIQ2i6mMXIR2i6mMXIS2i6lMVI]OMe2o6mMVIR2l6mMSIS2R7iMmH2Mc1[7WNhH8M_1i77XHQNj7n1YHmMj7R2:`NhGi0X8WOhG72AW88hGM=Gl7<gGKa0Fi7`0eGIg0Cd7d0eGIV97jFHW98iFGX99hFEZ9;fFD[9<eFB]9>cFA^9?cF_O^9a0bF]O`9h01O0M^FZOc9f0]FYOd9g020O100eF6P8KnG9=QOh6g0jH;;XOb6=QI>:ZOa69TI`08WOe69QId06UOh68QIf03TOS6\\O]Jb2]ORNU6ISJn2l5k0O0O2O1N101N200O010O100O1000000000000000ROVJWMk5f2YJXMg5e2_JUMd5j2`JoLd5R3]JhLg5X3h00000010O0005WMmHl1X7iMnHX2e7O1O1O100O1O100O10O10O101N101N2O0O2O1N101O0O2O1N101N2O1O1O1O1N2O2N1OO0100000000O10000000O01000000O3N1O2N2N2M3N2N1O1O1N101O1O1N2O001OO1O000000000000000000O1000000000000000O01000O10000O10000O10O10O1000O10O1000O01000O01000O010000O01000O0100000O0100002M3N3M2N2N2M10O2O1N1Of0[Of1", "size": [335, 500]}, "area": 38520.0, "iscrowd": 0, "image_id": 13291, "bbox": [42.0, 66.0, 457.0, 234.0], "category_id": 183, "id": 20000796}, {"segmentation": {"counts": "Phk01Z=0O1L1lBOT=7M3NO000L40001O000000001O000MkB0U=OlB1`cf50hiYJ0N1O100jBNS=5N210L33NO0M301N11OO1010O00O101O01O00013L01O1O1O1O1O1M3O[O\\Cd0d0]O[;c0dD^O[;c0cD@[;a0cDA\\;a0aDB[;a0cDA^;>`DE];=aDEo:l0nDVOo:m0PEROP;P1nDPOS;f1N00O10000000000000^EiMP:\\2kEdMU:b2aE^M10^:l2MO100000000000000TOdEiN\\:W1eEhN[:i0dEkN2;Z:g0hEhN4`0T:0dEM6A5a0Q:1dEMi0Mg96aEAN0OJl09TOGe:e0^E_O8La04P:a0YE[OX<e0hC[OX<e070000000000000000000002N1O000000000O10O2O000000O1000000O1O10000O1000000O13M00O1000TJ", "size": [427, 640]}, "area": 4128.0, "iscrowd": 0, "image_id": 13348, "bbox": [66.0, 186.0, 574.0, 93.0], "category_id": 96, "id": 20000797}, {"segmentation": {"counts": "0c61PJS6P6oInIQ6R6PJmIP6S6QJlIo5T6RJkIn5U6RJjIo5V6QJjIo5V6QJjIo5V6QJjIo5V6QJjIo5V6QJjIo5V6RJiIn5W6RJiIn5W6RJhIo5X6QJhIo5Y6PJgIP6Y6PJgIP6Y6PJgIP6Y6QJfIo5Z6QJfIo5Z6QJfIo5Z6QJfIo5Z6QJfIo5Z6QJfIo5Z6QJfIo5Z6RJeIn5[6RJeIn5[6RJeIn5[6RJeIn5[6RJeIn5[6RJeIn5[6RJfIm5Z6SJfIm5Z6TJeIl5[6TJeIl5[6TJeIl5[6TJeIl5[6TJeIl5[6TJfIk5Z6UJfIk5Z6VJeIj5[6VJeIj5[6VJeIj5[6VJeIj5[6VJeIj5[6VJeIj5[6VJeIj5[6WJdIi5\\6WJdIi5\\6WJdIi5\\6WJdIi5\\6WJdIi5\\6WJdIi5\\6WJdIi5\\6XJeIf5[6ZJnI]5R6cJnI]5R6cJlI_5T6aJlI_5T6aJlI_5T6aJmI^5S6cJlI]5T6cJlI]5T6cJkI^5U6bJkI^5V6bJiI^5W6cJbICOi5_6eJaIB0i5_6fJ_IB2h5_6iJ\\I_O5h5_6oJ^IQ5b6l0000000iM]IfMc6Z2bIaM^6_2gI\\MY6d2lIWMT6i2QJRMo5n2VJmLj5S3[JhLe5X3`JcL`5]3eJ^L[5b3jJYLV5g3oJTLQ5l3TKoKl4R4XKiKh4Y4ZKcKf4^4S21O1O2N1O1O2N1O1O2N1O1O1O2N1O1O2N1O1O1O2N1O1O2N1O1O2N1O1O1O2N1O1O2N1O1O1O2N1O1O2N1O1O2N1O1O1O00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000O10000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000O10000FRIWJn6i5UITJk6l5WIRJi6n5:00001O001O1O1O1O1O001O1O1O0000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001O0000001O0000001O0000001O0000001O0000001O0000001O000000001O00WOYIYJOb0h6U5]IjJc6V5^I]JJL2OO3f6e5`I[JJN10O1f6c5aI^JKM00O2e6c5aI^JKM42`6b5cI]J3OD0e6c5eI^J5OV6b5YJ^Jg5b5Q11XI]Jf5c5Q1O11O003M1O2N1OMaHZJ_7f5aHZJ_7f53O1002N3MO1O11O001O001OO1O1O1MaHZJ_7d5cH\\J]7a5`H`Je7`53001O002ZH\\Ja7d5_H]J`7h5O000000000000000000000000000000000000000000000000000000000000000000000000000000000000002N1O000000000000O1000000000000000000000000000000000000000000", "size": [427, 640]}, "area": 127758.0, "iscrowd": 0, "image_id": 13348, "bbox": [0.0, 0.0, 640.0, 242.0], "category_id": 106, "id": 20000798}, {"segmentation": {"counts": "_7l5g7H00O10000O10000001O001O000000OQL_HV2a7^M]HnN5c3^7]MbHjN4h3Y7^MTI_2l6_MVIa2j6]MXIc2g6_MXIa2h6`MWI`2i6cMTI]2k6eMTI[2l6fMSIZ2m6hMQIX2o6iMPIW2P7iMPIW2P7iMPIW2P7jMoHV2Q7jMoHV2Q7jMoHV2Q7jMoHV2Q7jMnHW2R7iMnHW2R7iMmHX2S7hMlHY2T7hMkHX2U7nMeHR2[7o100000000000000000000000XK^HRO2`4^7_LhH_3Y7`LgH`3Y7`LhH_3V7bLkH^3U7bLlH]3T7bLmH^3T7aLlH_3T7aLlH_3T7aLjHa3U7`LkH`3U7`LkH`3U7`LkH`3V7]LlHc3T7^LkHb3U7`LiH`3W7`LiH`3W7`LiH`3W7`LiH`3W7`LiH`3W7_LjHa3R7bLoH^3Q7aLQI^3o6bLQI^3o6dLmH^3S7cLlH]3T7cLmH\\3S7dLoHZ3Q7fLoHZ3Q7`LhHSO7]4Q7^LjHUO5]4n6cKhHj06VO4]4m6dKiHh0d0d3b6`L_I`3b6_LjH[O7X4o6WLoHA3X4n6XLnH@4X4n6XLnH@3Y4o6WLnH@5W4n6XLmHA6V4m6YLmHA6V4m6YLmHA6V4m6YLmHB6T4m6ZLmHC5S4o6XLmHH0Q4S7WLmHINQ4U7VLmHJMP4W7ULlHKMP4W7ULlHKMP4X7TLkHLMP4X7TLkHMLo3Y7TLkH`4V7_KjHa4V7_KjHa4V7_KjHa4W7^KiHb4W7^KiHb4X7]KhHc4X7]KhHc4X7]KhHc4Y7[KhHe4X7\\KgHd4Y7cK`H]4a7f00ZK_Hb3b7\\L`Hc3`7]L`Hc3`7]L`Hc3a7[L_Hf3a7YL`Hg3`7YL`HTOM`4d7ZLeHd3[7gK\\H;b0g3S7mK\\H;a0h3S7lK_H:>j3S7lK_H;1KKn3f7cLZHk3f7hKZHe4f7SK[HM2o4d7nJYH051OQ5S80000000O100O100O1O100O1000000O1000000001OO11OO100000000000000001O1O2N1O1WKiG\\4Y8[KgG22c4X8ZKgG21d4`81001OO1O11O2N1O0000001O0000001O[O`K`HOAOMY4P8mKaHJB04R4i7ULnHk3R7WLlHi3T7WLlHi3T7WLQHGg0S4X7ZLgHf3Y7]LdHc3\\7]LdHc3\\7]LdHc3\\7]LdHc3\\7]LdHc3\\7]LdHc3\\7]LdHc3\\7]LdHc3]7\\LcHd3]7\\LcHd3]7\\LcHd3]7\\LbHe3^7[LbHe3^7[LbHe3^7[LbHe3^7\\LaHd3_7\\LaHd3_7`LZHc3f7]LTH\\ONX4n7\\LTH\\ONX4n7\\LTH\\ONX4n7\\LTH]OMW4o7\\LSHj3m7VLSHj3m7VLSHj3n7VLQHj3o7VLQHj3o7VLPHk3P8ULPHk3P8ULPHk3P8WLnGi3R8h00000000000000000000lJnGo4Y8N1O2N1O1O00O100000000002N00001O1OO10000O11O0000000000001O1O0000K[KdGe4\\851O0000001O0000001O002N1O2N1O2N1O2N1O1O000fKUGT4Q900000000000000000000000000000000000000000000000J60000000000000000000000000000O1O1N2N2N^K\\Ga4j8_KUG\\4k8dKUG\\4k8dKVG[4j8eKVG01W4P90000000O100000000001O0000000000L4^OeKfG0051V4Y8eKeG1140W4Z8eKdG1130X4[8dKdG1130X4[8dKdG114OW4\\8hKdGa4\\870000000000000000000000000000N2N2M31O00000000000000000000001O0000000000000000000000000000000000000000O100000000000000000000O11O000000001O00000000001O000000001O00000RKgGj4Y8VKhGi4]8000M31O1SKcGj4`8O0000000000O10000GWKPHi4P8YKnGg4R890000000000000000000000000000000000000000000000000000O100000000000000000000000000000000000000000oJkGl4U8SKmGl4S8TKlGm4T8SKmGl4S8TKnGk4R8TKoGl4Q8TKPHk4P8UKPHk4P8UKQHj4X80000000003MO1O1O100N200001O00O11O00O1001OO1O100001O1O1O000000O1000000001O001O0YKaGb4a8\\K`Gc4`8]K`Gc4`8]K`Gc4`8\\KaGd4c80000000000000000000001OO100001OO1000000000000000000000000000000001OO1001O00000000000000000000002N1O3M1O00N2003MO11O2N2N1O001OKmKQGT4i8:002NL4O10000001O3ML400", "size": [427, 640]}, "area": 101610.0, "iscrowd": 0, "image_id": 13348, "bbox": [0.0, 232.0, 640.0, 195.0], "category_id": 140, "id": 20000799}, {"segmentation": {"counts": "ln[42V=6kBIn<9RCGl<;TCFi<<WCD01c<d0N2O11O0O2O00001O001^Oa0M31O1O1O001jB7i<GWC051d<NXC140f<NWC130g<OVC130g<OVC14Oj<0mWc3", "size": [427, 640]}, "area": 359.0, "iscrowd": 0, "image_id": 13348, "bbox": [335.0, 265.0, 28.0, 26.0], "category_id": 149, "id": 20000800}, {"segmentation": {"counts": "kUk61Z=000I0SC0m<4oBLo<7PCIP=900O2O0O011O0O01001N01O010001N2O1O0O100O001O1O1O1100O2O0O]C]O`<c0^C^Ob<a0^C_Od<?\\CAe<>ZCCg<`0O1O12N00O1000000000001OL[CAk<:UCFQ=0oB10Ocbi0", "size": [427, 640]}, "area": 855.0, "iscrowd": 0, "image_id": 13348, "bbox": [525.0, 247.0, 54.0, 32.0], "category_id": 161, "id": 20000801}, {"segmentation": {"counts": "l]i622OT=5O1QCJL2f<5\\CJN1c<6_CKM0d<5_CKM0c<7^COa<2_CN`<=210L33L11L303L201M2OMEVC<i<DWC<i<201002O2MO1O5MO001O001OO1OO11VCAe<1[Ca0b<A^C?_<^ObCg0^<30N31NO3YO]C3R=MoB0jd91\\hF1@OdC2X<2bC4e;HhD6A5f;EiDi0MXOe:0REN0OJl09TOG6T;MPE8La04AP;HlDi1T;WNlDj1S;61001O00000000000000000000002N1O3M1N10N3O03MO11N3N2N1O0O2NL`E^Ma:\\2:0O3NL4O10O13M01N4ML40cI", "size": [427, 640]}, "area": 4280.0, "iscrowd": 0, "image_id": 13348, "bbox": [521.0, 178.0, 119.0, 115.0], "category_id": 169, "id": 20000802}, {"segmentation": {"counts": "c61c07l;KRD=f;DYD;h;FWD:i;FWD9j;GUD:k;GTD8m;HSD8m;HSD8m;HSD9l;GTD9l;GTD:k;FUD:k;FTD<k;ETD:n;ERD;n;ERD;Z1Dj81lE:o0CmN5W:NmE:m0HiN4\\:JnE:l08T9^OPF9k0;U9]OoE8j0=W9[OoE8k0<V9\\OoE7P18R9AnE7P17S9BmE7Q1EfN=\\:GmE7U12n8GmE6V13m8HlE5W13m8HlE5W13m8HlE5W13m8HmE4W13l8ImE4W13l8ImE4W13l8ImE4W13l8JlE3X13l8JlE3X12m8KkE3X12m8KlE2W13m8KlE2Y11k8MlE2S<NmC2S<OlC1T<OlC1T<OlC1T<OlC1T<OlC1T<OlC1T<OlC1T<0kC0U<0kC0U<0kC0U<0kC0U<0kC0i0FQO2T;8TEJj03Q:3^FMb94]FNa92^FOb91^FOb91]F0c90]F0c90]F0c90]FNe92[FNe93ZFMg93XFMh94UFNk93UFLk94WFJi97VFIj97VFIj97VFIj97VFIj97UFJk97SFKe7GVI>TOMe7JRI9YOKg71kH4AGe7:eHOFGe7?`HKJHd7b0]HFOIc7f0YHA4Jb7j0UH\\O3BnN8j8o0PHWO6EoN5k8T1kGSO9EQO4k8Y1fGnN=3m7T1aGiNa03P8X1ZGeNf0_O[O8h8f1SGcNd0DA3j8]2eG_MA4k8\\2dG`MA4l8[2cGaMA6l8W2cGcMA7l8U2cGdMA7m8U2aGdMB7o8S2_GfMC6o8S2^GgME4n8T2\\GiMH0o8V2XGjMJOo8V2WGkMKNo8V2VGlMKNP9V2TGlMLNR9T2RGnMLMT9U2oFnMf9Q2ZFoMh9o1XFQNi9o1VFQNk9n1UFRNl9m1TFSNn9l1QFTNP:k1PFUNQ:k1nEUNT:i1lEWNU:h1jEYNW:g1kEVNW:h1g0O10cEUNY9j1fFXN[9g1dFYN]9f1cFYN_9f1`F[Nb9d1\\F\\Nf9c1ZF]NUON^:d1\\FeNc9[1gE\\N;b0i9P1lE_N:a0k9P1iEbN:>n9o0hEbN=O[:]1o00cDaNm:^1QEdNQ;Z1nDgNS;Y1jDjNV;U1iDlNX;T1eDnN[;R1dDnN];R1cDmN^;T1aDlN_;T1`DlNa;T1_DlNa;U1^DjNc;V1]DjNc;^11O100000000000000000000000000O100000000001O1O1O2N1O1O2N1O1O0000O1O100O100O100O1O1O1O1O1O1O1O1000000001O000O1000000000000000000000000000001O000000000000000000000000000000001O0000000000000`DZN[;f1bD_N\\;i1O2N1O0000O100O10000O100O100O100O10000O100O1001O1O1O1O1N2O1O0FPNYEQ2f:RNWEn1i:TNUEl1k:;O0001O010O1O1O1001N101O001N1O2N1O1O0000000000000000000000000000000000000000O10000000000000000000000000000000000000000O1N2N2N2N2N2O1N2N2N2N2001O00001O00001O0lD`N]:a1_E`Nc:`1^EaN`:_1aE`N_:`1bE_N^:a1bEUN4I[:R2e00000000000000000000000000000000000000000O1M3N2O100000000001O0000000000000000000000001O00000000000000O10000000000000000000000O100000000000000001O000000001O00000000001O00000000001O00O100000000O1000000O1000000000000000000000000000000000000O10000000000000000000000000001O0000001O0000001O0000001O0000001O000O101O0000001O000000001O0000001O000O101O0O10001N100O101O0O10001N100O101O0O10001N101N2O1N2N2N2N2N2N2O1N2N3M2N2N2N3M2ORdn0", "size": [427, 640]}, "area": 32373.0, "iscrowd": 0, "image_id": 13348, "bbox": [0.0, 142.0, 566.0, 166.0], "category_id": 183, "id": 20000803}, {"segmentation": {"counts": "ce415000^<7^C8Y<a0WD\\Oh:e0WE\\Oi:h0QEZOn:P1iDPOV;b100F:00O10000O101MbMYE^2g:200O10007ZE]MX:l2N>C0O0O12NN21O1O01O00000N20000000000000000000000O1O10O11O00O1000M24A<000O02OO20N1000001OO1001O000002N5nEgL0Oh9Z3XFkLf9]3O000000O10000000001N3iLWFKOj2Z:KfEZM[:e2fE[MZ:d2iEZMW:h2gEXMY:j1jE^No:?WEYOF1k;>jDAW;b0iD^OX;a0h0000000000000C^CMOOmb_20V]`M3e<OZC1e<2YCOe<3YCNh<9iEBc7>YHFf7;ZHEg7:YHFbML50f9`0bHDcML41g9?bHDcML41g9`0`HM`73^HOb72WG^O35lN3j99VGB11RO3h99UGC@:@F54f9:PGNUO60\\O77b9;`F_O1>G5O]O66b9?\\FDNo0MiN84`9b0[FEMm00hN83`9e0XFE1o07fN`9k2aFTM_9l2aFSM`9n2_FRM`9o2aFPM`9o2<100000001O0ZFmLU9T3hFlLX9W3hFiLo83fFX3;eLo8`3PG`LQ9a3nF_LR9a3nF_LR9a3nF_LR98eFo29iLR95eFL0V39iLR95eFL0V39iLR95eFL0V39iLR95iFQ35jLR95iFQ34lLR9NfF34S34kL^9T3cFmLc9l2]FTMc9l2]FTMc9l2]FUMa9l2_FUM`9k2`FUMGLg9o2bFWM]9j2bFVM_9j2aFVM_9j2aFVM^9k2bFUM^9k2bFTM_9l2aFUM^9k2bFUM]9l2cFSM^9m2bFSM^9m2bFSM^9m2aFTM`9k2`FUMa9j2_FUMc9j2]FVMd9i2\\FWMd9i2\\FWMd9i2\\FXMc9g2^FYMb9g2^FYMb9g2]F[Mb9e2^FZMc9f2]FZMb9g2^FSML0f9m2`000000001O000000000000000000000O10gETMT:P30010N2O101OO1003M001OM32N01XMoET2R:iMRFKKO43LH0011O]1Q:nNTFDK41O4O?N\\OV1S:ROmEI63KL0042b0NYO07f0Q:AQFMHL0042b0NYO07f0S:_OoEOKL12m0d0Z9]OnE0JM12[16n8ImE11OU15^9J]E1U1ObN1Z;0YF0g90i101OmD0Qli2", "size": [425, 640]}, "area": 19250.0, "iscrowd": 0, "image_id": 13546, "bbox": [11.0, 0.0, 413.0, 193.0], "category_id": 123, "id": 20000804}, {"segmentation": {"counts": "S4`5i700O1O101O00O1001NO3L22O2QKbHo3^7PLcHP4a7jKaHU4i7bKWH^4Z800O100O11O00O101O000000000O8fKQGl3X9N>DOO000001OO10001O01OO12NN2N200N2N200O10000000000O10O11O00O10O11N1D<0O10O0101OO100000O1000000000002N5bLSF0OT3n9lLWFQ3R:O000O10O1000O1001OO0011JPMPFQ3o9PMQFP3n9SMPFn2Q:PMoEo2T9TMTG_3o7gLiGF1^4W8kKhGY4X8gKhGY4X8<0O11OO10000O10000001O00001ON2001OO1001O0000001O1O0000000000000000000000000000K5L4N2M31OO12NgK`Gh3c8mKgGS4g80000000000000000OC>J6000O1O11OO10O100000000000O1O1100O0000O10000O100O11O0000O11O0001N10000000001O00000O1000001O2N1N100001OO10016H5M0O0O1001O1O1O0SOPL^HS4b7mKWHZ4i7fKoGc4P8]KoGc4R8]KnGc4R8]KnGc4R8]KnGc4R8]KnGc4R8]KnGc4R8]KnGc4\\80F]KnGc4R8]KnGc4R8]KnGc4\\800O100001OO100000O100000001OO1N2K5N2O1hKRGT4R9ONlKPGT4Q9lKoFU4P920NbMSLdKo3Y4RLgKn3Y4RLgKn3Y4RLgKm3Z4SLfKn3Y4RLgKn3Y4RLgKn3Y4RLgKm3Z4SLfKl3[4TLeKl3[4ULdKl3[4SLfKl3[4ULdKk3]4TLcKl3]4TLjKe3V4[LQL^3o3bL^II`2d3R4gLnKY3S4fLmKZ3S4gL]IC]2f3V4iLjKW3V4iL`Ka3`4_L\\Ke3d4[L]Kd3c4\\L\\Ke3d4[L\\Kf3c4ZL]Kf3c4ZL^Ke3b4[L^Ke3b4[L_Kd3a4]L^Kc3b4]L^Kc3b4]L^Kc3b4]L^Kc3b4]LeI@a1R4j4^LeIBKO_1R4P5]LfIHZ1j3P5^LfIH[1i3P5`LcIG]1i3P5oLoJR3S5kLmJW3R5jLmJV3S5jLlJW3T5iLkJX3U5jLiJV3W5iLiJX3W5iLhJPOeNU4e6iLhJW3W5iLjJW3U5kLjJU3W5iLjJW3V5aLSK^3l4bLUK^3k4bLUK^3k4^LZKX3TN\\Lg65WK\\3XNYLI0P73PKc3^5ZLbJg3^5ZL`Jh3`70O0100WLjF_3`9O11OO10000OK6M4OO10O1000O11OO100000001N0110OO101O1O0^L[F`3g9N11000001N11O00O100000N21N10001O01O00001OO10O1M301O1O1N01OO2OM5I3\\FaLc9`33O11OO1000O2M200^F\\L^9e3bF[L]9g3bFZL]9f3cFYL^9j3OO2OO1012M001OM32N00N2dMWLSI43LH0011O]1h3d5YLQI4O>W1U3i5YLnH0042>X1S3i5[LmH0042>Z1Q3g5`LmH12>[1n2h5cLkH12>[1m2i5lLkH7]1j2j5oLiH7]1h2l5QMgH7\\1i2m5PMgH7\\1j2l5oLhH7\\1i2m5QMfH6_1]OaNY3Y7hMaJo1`5QNaJn1_5RNbJm1^5RNdJm1\\5SNdJm1\\5SNdJn1[5RNdJn1\\5SNdJm1[5TNZJWO^Nd2Y7SNXJ[2h5eMWJ]2h5cMXJ^2g5_MiHTO`1\\3h5`MhHTO`1\\3i5_MgHUO`1\\3i5_MhHTO_1^3h5]M\\Jb2e5^M[Ja2f5_MZJa2g5]MZJc2f5]MZJc2g5^MWJb2i5]MXJc2n5YMQJf2R6YMmIe2Y6WMfIi2Z6WMfIi2X6YMhIh2Y6WMeIj2Z6WMeIk2Y6VMgIj2Y6VMgIj2Z6kLZHJN0^1[3[6gL\\HNK0^1[3\\6eL\\HOK1]1Y3]6gL\\HO\\1Z3X6gLRJY3m5gLTJZ3h5hLYJW3h5hLYJX3f5hL[JW3l71O:HO9F1O0000PNhLoIX3c5gLkH2b1W3_5PMhHL`1\\3h5SMRJS3n5mLRJS3i5YLiHf0^1R3f5YLlHg0^1P3d5XLnHk0^1m2d5XLnHj0_1n2o4XLRJ0@k0`1m2m4ZLPJm0S1^2W5fLeIm0T1\\2X5hLcIl0U1\\2X5hLdIk0T1]2X5iLdIg0V1`2V5iLeIf0U1a2U5jLgId0S1b2X5jLeIb0T1c2X5lLdI?T1e2Y5nLbI`0P1d2\\5nLdI=m0h2_5lLdI:n0?oNk0^6]NeI9m0>VOf0X6fNdI5m0`0XOe0V6gNeI2m0b0[Oc0T6iNdI2k0b0_Ob0S6jNcIOl0JhN?h0n0R6iNbIOn0IgN?j0b0UOQOQ71ZI3P1IfN`0k0<YOWOR7LTI7Q1HgN`0l07[O^On6LRI4V1JcNa0m05[O@m6MRIM\\10]N`0n04[OAm60lJJoM?o05YOBm61lJHoM`0Q12XOEl62mJEoMa0Q11XOGk63mJDoM`0S1OWOJj65kJBQN`0T1NVOJk66RK1nNNUOKl64SK1oNNTOKj66SK1ROKd64[J0TOIa67[J0UOH_69]JNUOH]6;_JLTOI]6<^JJVOI]6=\\JK^75aHLHE_5?iJLGEa5?gJMGEc5>dJNYOEhNO[7>cJN\\OGaNO`7<bJO]OI\\NMf7;aJO]O1R60`J0^O0R61^J0ANQ62]J1CJR65[J0LBj58YHFP2a0l7DPFNZ2:j7HlEK01[24R8KdE010UY41mSL5K6J5K5M3L5K4L4L4L5L1NO11O0010M21ON23MN22M10002N001O1N2O1O1O1O1DWNWEj1d:^NWEd1h:>1O10O1O011N0100001O0O10O10O100O2N1O1O11O01N1001O00O1000O011O0O1000O1O1000000O100O10O10O02O00N3N10000001O000000O100O011OO02OO_L", "size": [425, 640]}, "area": 75417.0, "iscrowd": 0, "image_id": 13546, "bbox": [0.0, 15.0, 640.0, 292.0], "category_id": 124, "id": 20000805}, {"segmentation": {"counts": "UcV11X=001O=C1O00000000000000001OO1000000K5Ioc`0NZ\\_OO11O0K5E<OO1000000L41O1O2N0000OWRS6", "size": [425, 640]}, "area": 480.0, "iscrowd": 0, "image_id": 13546, "bbox": [93.0, 0.0, 77.0, 19.0], "category_id": 132, "id": 20000806}, {"segmentation": {"counts": "c9f3c90O1O10000000000O11OO1000000O10000O100O11O00O1000000000000O100002N0000001OO10000001OO12NN2N200N2N200O10000000000O10000000000O11O0000O100O10000000000O10000000000000000O10000O1000000O1001OO1O1kIhKZMOk5X4bLWL^3i3bLWL^3i3cLVL^3i3aLXL^3i3bLWL^3i3bL]LX3c3iLYLZ3g3fLVL]3j3aLYL^3g3cLXL\\3i3dLWL]3h3cLXL\\3i3dLWL\\3i3dLWL\\3i3dLWL[3j3eLVLdMDMOOMY5Z4XMgLh2Y3YMfLg2Z3ZMeLg2Z3XMgLh2Y3XMgLh2Y3XMgLi2X3WMhLg2Z3YMfLg2Z3YMfLh2Y3XMgLg2Z3YMfLg2Z3YMfLh2Y3XMgLh2Y3XMgLh2Y3XMgLh2Y3XMgLi2X3WMhLj2W3VMiLj2W3VMiLj2W3VMiLj2W3UMjLk2V3UMjLk2V3UMjLk2V3UMjLk2V3UMjLk2V3UMjLk2V3TMkLl2U3TMkLl2U3SMlLm2T3UMjLk2V3WMhLi2X3oLkKiMm0S5]3TMkLh2Y3XMgLf2[3ZMeLc2^3]MbLd2]3\\McLc2^3^MaLd2]3\\McLc2^3]MbLoMkNW4b4jMcLd2]3\\McLd2]3\\McLd2]3\\McLd2]3\\McLd2]3\\MbLe2^3^M_Lb2a3`M]L`2c3`M]L_2d3aM\\L_2d3bM[L^2e3bM[L^2e3bM[L]2f3bM[L]2f3dMYL]2f3bM[L]2f3cMZL]2f3cMZL]2f3cMZL]2f3cMZL]2f3cMZL]2f3cMZL]2f3cMZL]2f3cM[L[2f3eMbLR2_3nMaLS2^3mMcLS2\\3mMdLS2\\3mMdLS2\\3mMdLR2]3nMcLR2]3nMcLR2]3nMcLQ2^3oMbLQ2^3oMbLP2_3PNaLQ2^3oMbLQ2^3oMbLQ2^3oMbLP2_3PNaLQ2^3oMbLQ2^3oMbLQ2^3oMcLo1^3QNbLo1^3QNbLo1^3QNbLo1^3QNbLo1^3QNbLo1^3QNcLn1]3RNcLn1]3RNcLn1]3RNcLm1^3SN]L_MlN^4g4SN[LbMmN[4h4SNYLdMoNY4h4SNWLfMROV4g4TNVLhMTOR4f4WNULgMVOP4f4ZNSLfMWOP4f4YNTLgMVOP4f4YNTLgMVOQ4e4XNULgMVOP4f4YNTLgMVOP4f4XNULhMUOQ4e4WNVLhMROS4i4VNTLX2k3gMVLY2j3gMVLX2k3hMULX2k3hMULY2j3hMULY2j3hMULY2j3fMWLZ2i3fMVL[2j3bMYL^2g3`M[La2d3^M]La2d3_M\\La2d3_M\\La2d3_M\\La2d3_M\\La2d3_M\\La2d3_M\\La2d3^M^La2b3_M]Lb2c3]M^Lc2b3^M]Lb2c3_M]L`2c3^MXKfMU1k4d3_MWKfMU1k4d3_MWKfMU1k4d3_M^Lb2a3^M_La2b3_M^La2b3_M^La2b3`M]L`2c3aM\\L_2d3cMZL]2f3dMYL\\2g3dMYL\\2g3dMYL]2f3cMZL\\2g3dMYLZ2i3hMULS2P4VMQKjNo0n3R4YMnJjNo0l3T4hLlKPOO0ROl3T5hLSLSOjN;l0LUOm3S5iLPLSOkN00<m0LSOl3V5hLPLTOjN>m0JSOm3U5hLPLSOkN>m0Q4X4\\LQLEHm3Y4_LVK\\O18`0o3W4]LWK]O27`0o3W4]LWK]O27`0o3W4]LWK]O27`0n3X4^LVK]O36?P4W4]LWK]O36?P4W4]LWK]O45>Q4W4]LWK]O71<T4W4^LVK]O71<S4X4_LUK]O9H[O1R1[4U4_LUK^O;G<]4S4^LVK]O<H;\\4T4_LUK^O<F;]4T4_LVK]O;G;]4T4_LVK]O`1T4Z3_LVK]O`1T4Z3_LVK]OCIi1[4^3_LVKA\\1P4^3_LWK@[1Q4^3_LWKABCf1]4a3`LVKBY1n3a3aLUKAZ1I]Nh3T5nLUKAZ1HZNm3W5cLiJYO03<<Z1H[Nl3V5bLmJ\\OLO<?Z1HZNm3W5aL^KJQ1HZNn3V5]LbKMn0HZNn3V5]LbKMn0H[Nm3U5_LaKLo0H[Nm3U5_LaKLo0H\\Nl3T5`LaKMn0G]Nl3T5]LdK0k0G]Nl3T5\\LeK1k0F\\Nm3T5^LjJ_Og0`0Q1DZNo3T5^LjJ_Og0`0@@Z10dNR4Q5_LjJ_Og0`0@BKO_1R4d3`LbKM@HZ1j3d3aLbKM@H[1i3c3bLcKN]OG]1i3c3cLQK_O6m0U1R3d3cLPK^O9j0S1W3c3cLoJ]O:j0S1V3d3dLmJ]O<i0R1W3e3kLYKNh0ROROV4m4jLYK0b0TOXOR4m4jLYKOc0UOVOS4n4iLYK0b0TOQOL4W4P5jLZKMa0VOQOL4W4P5jLYKMc0VOPOL4W4P5kLWKNc0d3V4^LXKLa0h3W4]LWKDj0n3o3^LVKGh0l3R4]LVKG7C@Z4S5]LUKNHC0i30hKS5o0VKJHFOf36eKI0U5X1PKb0Ka2a5nLbJb0M`2a5oL_JQ5`5\\10O10000O11OO10000O1001OO1000000O11OO100000000O1001OO100000000N20000001O000000000000N21O00001O000000000000O1M3000000O100N2O11OMSJjHm5V73O11OO100001O000QJiHl5W7TJiHl5X7SJiHl5W7TJhHm5[7OO100001O000000000000aIUJ`5k5\\JYJd5g5\\JYJXOKW10iN0o4l5gKTJ[O6V1MiNM07b4f5TL\\Ja0JiNM07b4f5TL\\Ja05Y3a5VL[J`0b0k2T5eL[J?b0i2U5hL[J=`0mNVOg3Q6oLZJ<d0e2R5mL\\J>d0d2o4oL\\J=e0c2P5nL]J?c0d2o4mL^J>d0d2o4oL\\J=e0d2o4PM[J<f0d2o4QMZJ:h0e2n4QMZJ:h0e2n4QMZJ:h0f2m4nL]J<f0e2n4PMZJ<h0d2n4QMVJZOFR1V1b2o4VMkIVO100R1W1b2m4`NSKa1l4_NTKb1k4[NmIPNX1d3l4\\NlIPNX1d3l4\\NlIPNX1d3l4\\NmIoMW1f3k4ZNYKe1h4[NXKd1i4\\NWKd1i4[NWKf1i4ZNWKf1i4\\NUKd1k4[NVKe1j4]NTKVNgNS3U6iNRKTNjNQ3U6lNPKSNkNQ3U6lNPKSNkNQ3U6lNPKSNkNR3T6lNPKRNkNS3U6kNPK`1o4`NQK`1o4`NQK`1o4\\NlIPNU1d3o4\\NlIPNl0N[Of3m5[NmIQNj00[Ob3P6]NnITN=1E^3P6kNTJkMLZ3P6nNmIlM3W3o5@QJ?P6APJ?P6APJ>Q6BoI?P6ZOmITM3l3f5nN[JTMOo3e5jNgJV1Y5hNiJX1W5dNmJ\\1S5UN[Kl1e4TN[Kl1e4TN[Kl1e4UNZKk1f4UNZKk1f4UNZKk1f4TN[Kl1e4TN[Kl1e4TN[Kl1e4SN\\Km1d4RN]Kn1c4RN]Kc1n4]NRKb1o4^NQKb1o4^NQKb1o4\\NSKd1m4\\NSKd1m4\\NSKc1n4\\NSKc1n4\\NSKc1W4_LRKR2d0_1Y4_LSKR2e0^1X4`LSKQ2f0`1S4_LcJ0d0R2e0^1d3bL_K18o1d0`1d3aLYKMK6d0k1d0b1c3cLVKJN007e0k1b0b1e3QMiK[1c0f1a2ZLUMh0FX1f0d1_2\\LSMj0HV1g0c1Y2_L]LOi0j0JU1h0b1V2bL_LM0Od0m0OX1d0\\1^1_LTN4VO1a0l03U1d0Y1Z1lLRN1Cn0=n0c0V1[1lLoMi136c0T1[1CRN[Oc0P1HfL5OlN01W3c0UOc0n0HgLSO7c0W3OoNa0k0JkLQO4d0X30oNa0i0JVMDT30oN?h0MUMDT30QO<e02XM_OS33QO:i0GfL_O?1_O3c3<RO8l0DZMBVO3NLe3c0SO5o0BVMFT3b0hN4T1_OS1=kM1V1_Oo0`0QNK0CQ3b0VMCn5=TJ@m5`0TJ^Om50hF`0g<000000000jI@QK0m2a0U2_OkJ0P3a0X2_ObJ3V3>Z2@]J2Y3>]2@WJ2\\3>]2CTJO_3>^2EPJMb3>^2BSJ0_3>c3B[LMcKa0R8B[LMcKa0R8CZL?f3AYL`0g3@XLa0h3_ORLLlKi0R8[ORLg0m3ZOULd0k3\\OULd0k3[OVLe0j3ZOXLe0h3XO[Lh0S8000001O[KYOhLg0X3ZOhLe0X3[OhLe0Y2[OPJ0d3h0Z2]OoILe3h0\\2_OlIIh3h0[2DhIEn3f0Z2HeICP4e0X2NdI^OV4b0V24^I\\O\\4`0S2d0lM]OV2b0jM]OS2f0mMZOT2e0lM[OS2b0RN]Om1?aIkNb4g0n18^ITOh4d0i19_ISOh4d0i19_ISOh4d0i19`IQOh4f0h19\\NGd19\\NGd19\\NGd13_IZOl4d0e12_IZOl4d0e12_IWO00g40[Kg0_62_IWO00a40`K30d0_63aIUOa4U1m1GVN9j1GVN9j1FWN:h1GXN9i1FVN;i1GUN:k1FWN8i1HVN9k1FUN:k1FVN9j1GUN:j1GUN:k1FUN:k1FTN;l1ETN;m1DRN=n1CoM`0Q2@kMd0U2\\OiMf0W2ZOiMf0X2YOgMh0Y2XOgMh0Y2XOgMh0Y2XOgMh0Y2XOfMi0Z2WOfMi0Z2WOeMj0Z2WOfMi0Z2WOcMl0]2TOdMk0[2VOcMl0]2UOaMl0_2TO_Mn0a2SO[MP1e2POXMS1h2lNXMU1h2kNXMU1h2kNWMV1i2kNVMU1j2jNWMV1i2jNVMW1i2jNWMV1i2jNWMV1h2kNWMV1i2gNdISOd3V2h2gNdISOc3W2j2dNWM^1i2cNTM_1l2aNSM`1m2`NPMc1P3]NPMc1P3]NoLd1Q3\\NoLd1Q3\\NnLe1R3[NlLg1T3ZNjLg1V3YNjLg1U3ZNiLh1X3VNiLj1V3XNhLi1Y3VNfLk1Y3VNfLk1", "size": [425, 640]}, "area": 103887.0, "iscrowd": 0, "image_id": 13546, "bbox": [0.0, 16.0, 640.0, 409.0], "category_id": 140, "id": 20000807}, {"segmentation": {"counts": "n2U1T<01O000001O01N10010M2^OiCF000M[<3lCLl`T8", "size": [425, 640]}, "area": 428.0, "iscrowd": 0, "image_id": 13546, "bbox": [0.0, 94.0, 13.0, 38.0], "category_id": 149, "id": 20000808}, {"segmentation": {"counts": "0i0f1Fe8<YGDg8;[GEd8<ZGEf8c0RG]On8f0UFmN9<b9]1XFeNh9d1QF[Nn9`20001O001OO1O100O1O100F:00O100^MQFj1j:L6K2bN[DR1MkNT<Q15^OkCA03R<a0RD^OR<=PDAF0U=0`Vi1a0QiVNO11O00000000000000001OO100000000000000000000000000001OO11OO1001OO1001OO11O0000O1O1001OO100000000O1O1M3ETC007P=JYW6Oh[I0O091]<b0N000000O1001OO1001O00001_C\\O[<e0cC\\O]<e0bC\\O]<h000cCVO[<l001O000000O11ON21O000000000000000000000000000000000000001O1O00IgCYO03Y<b0kC\\OLOY<e09M3001O0000M3O12XC_Od<a0\\C_Od<a0\\C_Od<c00000000O1O1O11O00O11OO1000000001O1O00O1O1O1O1O10000O11OO10000000000O1M7H]e10dZN0mB0S=0mB0S=0mB0S=4O0KLUC4j<607I0000O10000O10000O1000000O10000001O1O1O1O00000000000000O100000000001O000000000000000000000000O1O100N2O10000000000000000001OO1O12N1O2N2N2N2N00N21ON2001O1O1O0003K9I6JfP<L]oC9lBGh<>O001O1O1OE`CM`<1dCL]<4<001O1O001O004L4L2N1OO1O100000000O11O0000O10000O1001O1O2N7I6J1O000000000000001O000000O1O100000000001O000000000000000SDTO];l0^DPON5c;k0_DPOK016d;l0[DoN0106d;]1N2N1O2N3M5K1O2N2N2N2N2N1O2N2N2N2N2N1O2N2N2N2N1O2N2N2N2N2N1O2N2N2N2N2N1O2N2N3M3M3M3M1OK5O10000O1O100O100000000O10000O100O1O1O1O11O00O1001O00O10000O11O000000O1O1000000O100O10000O11O00N2O10000001O000000O100O10000O100", "size": [425, 640]}, "area": 20207.0, "iscrowd": 0, "image_id": 13546, "bbox": [0.0, 0.0, 640.0, 138.0], "category_id": 169, "id": 20000809}, {"segmentation": {"counts": "agk22l<OUC072LNa<3]C61G10V10gN002n:?REB0M020432OI2=i:8SE51UO3>i:8SE82Bk:V1TEkNl:U1SEmNl:i1O000000000000O100O2gMREO091`1_;OONaDZN_;f1aDZN_;f1aDZN^;g1201OO10100O2N10XNcDb1^;\\NcDd1^;[NcDd1a;00\\N]Dd1c;YO^DGc;9\\DHc;F\\ENk;GWC;f<EYC00<g<DYC>g<BYC>f<2100000001O001O3L102GQC1U=O101O00She4", "size": [425, 640]}, "area": 2517.0, "iscrowd": 0, "image_id": 13546, "bbox": [221.0, 0.0, 58.0, 85.0], "category_id": 171, "id": 20000810}, {"segmentation": {"counts": "\\h[61X=000M0mB0b<2jC1V<0cCMK6a<0`CJN007b<>nN[O^Eh0a:XO]Ej0^:XOgDOi0j0^:YOiDM0Od0m0g9SO^F4VO1a0l0f9ZO]F1Cn0P:POZFi1f9m0]NQM`HOlN01W3c8jL^G7c0W3n7eL]G4d0X3o7nLPHT3o7lLQHT3o7nLnGS3Q8oLcG_O3c3\\8nL^G@3NLe3f8jLZGb3e8a000O1000000\\G]Kb8e41O0000000^K]G]4d8cK\\G]4h8000000000000NXGcKh8]4XGcKi8\\42O1O1JSGlKQ9T422N0O1O2L3N20000N3N101O00M4N10010O02N00O20O000L5J^KbGb4Z8\\KiGh4W8XKiGh4W8XKiGh4W8YKgGh4Y840000JSKPHl4Q8TKoGl4Q8TKlG00g40[KT8NlG00a40`K30Q80kGa4b8300O1000O2N12NO1001OO1O100O100O1M3L4N200O1000000O100O100M31ON3N1N3L3M20100O2OO10O10000ON]L_Fd3a9\\L_Fc3a913M2O1M300O100O1N3N100N111N1O1OeH", "size": [425, 640]}, "area": 18783.0, "iscrowd": 0, "image_id": 13546, "bbox": [490.0, 234.0, 150.0, 174.0], "category_id": 172, "id": 20000811}, {"segmentation": {"counts": "i0f1e;NO20N9H3SOSD9b<J;IQU6<`jI4M5K2kCROk;Z1L3eDdNA0l:Y1hEkNX:X1cEjNAFj:[2VEfMi:]20001O00O12NN21O1O00000000N20000000000000000000000O1O1000000000000M33M0000O11OO11O0000001OO1001O00000000000000000000000001O0010<EOO0001OO100001O00N30N1000000K5I7L5O1O1OO100000000000000000000000000000000O10000000000O100O12N2NH^EiMb:W28000000001O0000SNVE[1j:eNUE\\1k:dNVE[1j:eNVE[1j:`N[E`1X;OO4M2N000L50O1O2M11OO011OO11O00000000000000001OO100000000000000000000000000001OO11OO1001OO1001OO11O0000O2N2OO2OO1000O1001N001O11PNkDj1V;UNjD10V10gN0028U;HlD0M020432OI2=4m:H`E1UO3>3n:IbE2B5l;JUD3n;LTD1hgc21jd\\M7UCGX<?bCB6Ok;j0kCVO1001S<R100000001O000000001O001O006J3M5K00N22NO1O1001O1O1OO100O1]N\\D^1Q<dNoCP1V<N101O1N2N1O011O3N1N1001OO1J^C@f<<[CD[<1nC8FGa<1hC7GHc<0fC5GKQ=25NlfP2", "size": [425, 640]}, "area": 16634.0, "iscrowd": 0, "image_id": 13546, "bbox": [0.0, 0.0, 484.0, 84.0], "category_id": 177, "id": 20000812}, {"segmentation": {"counts": "ckZ1<m<00000000000000O10000000000000000000000000000000000000000000000000000000000000000000000000000000_CIP<7gC2Y<?000001O0O100000M3_Oa00000O100000000000000O10004JQd;OP\\D2N3M2O101O00O1000000000TC1^<a0L00000000000000K5H8K5000000O10000000000000000000000000000000000000000000000000000000000000000000000O1000000000000000000008H7I001O00O100B>00000000000000O10002N2MgQ70XnH2N2N30O0001O000010O00UCO^<?O00000001N1F:00000001O0000000O1000000000O100000O10000000000000000O1000000000000000000O10O10000000000QCLi<<N1O1O00O100O1G9O1000000O10000N101NkQ74SnH2N1O0O10000000002N9G1O1O1O00000000B>O100000000000000000O1000000000000000000000000`NJmE6P:MgEKTO8T;NgEKUO7S;OlDN;0F3S;?PEBMOR;c0jDB4KQ;V1oDiNQ;X1oDhNP;Y1PEgNl:X1mDeN63m:U1REfN15l:T1_EoN]:o0fE[Oi8BaGR1H\\Oe8FaGa07IX8H_G9>0S8J]G4a02S8KZGC_O4V1>R8KgGA87^89hHFZ7:eHF[7;dHEb76]HJi71VHOj72UHNl72SHNm73RHMn74QHLP84oGKR86mGJT86kGJU87jGIV88iGHX88gGG[89dGF_89Y2000]FMo53oI3n5KRJ6n5JPJ9o5GoI;Q6ERIEdNh0[8BPIFdNk0a0UO_6:[JGdNk0<YOf65XJIdNk08\\Om60`HMS1LXOm05[OQ7OYH3Y1FWOn04[OU7a0^IVOXOP14YOW7a0\\IWOXOQ12XO[7a0YIWOYOQ11XO^7`0XIVOYOT1NWOc7?TIWOZOT1NVOd7`0SIWOZOT1NUOd7c0QIUO\\OV1MTOd7c0PIUO^OW1KVOb7?SIUO@Y1HWO?YOY6T1nITOAY1H[O;ZOX6P1QJTOCW1IhNL9:G[6n0QJTOEW1HC2TOa6o0mITOGW1HfNNb02^Od6U1gImNNh1EXOf6:_IfN7g1EYOe6<\\IeN:j0LXOIn0e6<ZIdN=i0OVOEQ1f6k0gIPOOPOEU1e6j0gISO1iNEZ1c6i0hIVO82P6g0iIYO50R6g0iI[O3NU6e0iI^O4JS6h0iIC2CT6i0jIMMWOY6k0kI0MROX6o0fI62hNY6R1]I?;\\NZ6P4fImK[6T4gIhK[6Y4bIgK`6Y4^IgKc6]4VIeKo6X53L3N1O2N2cMdHiN^7S1gHiN\\7S1iHjNY7R1mHjNT7T1PIiNR7U1QIhNQ7U1TIbNPOgNo7d2UIbNlNhNS8c2UIbNo6[1UIbNl6]1XI^Nk6_1YI^Ni6_1[I^Ng6_1^I]Nd6`1`I\\Nb6b1bI[N`6c1cIYN`6d1eITNa6i1b2M3M2M4M3M3M3M3M2M5L4L4L4L4K6K4LWPj0", "size": [425, 640]}, "area": 13794.0, "iscrowd": 0, "image_id": 13546, "bbox": [103.0, 5.0, 474.0, 254.0], "category_id": 183, "id": 20000813}, {"segmentation": {"counts": "o\\g03U=4L2M3N1O2N2N2N2O02O0O1O000000004L8H_ZZ7", "size": [425, 640]}, "area": 286.0, "iscrowd": 0, "image_id": 13597, "bbox": [56.0, 158.0, 19.0, 22.0], "category_id": 121, "id": 20000814}, {"segmentation": {"counts": "0n;8XEP1^9APFP3`7YMVH_3T7_LlH`3V7_LjH_3Y7`LgH^3]7`LcH_3_7`LaH^3b7aL^H]3f7bLYH\\3j7cLVH\\3l7cLTH[3P8cLPH[3S8dLmG[3V8cLjG[3Y8dLgGZ3\\8eLdGZ3^8eLbGY3b8eL^GY3e8gLZGX3h8gLXGW3k8hLUGV3o8hLQGW3Q9hLoFV3T9iLlFU3W9jLiFT3[9jLeFU3]9jLcFT3`9kL`FS3c9mL\\FR3f9mLZFQ3P:N2O1O1O1O100O1O100O1O100O1O100O1O100O1O100O1O100OlGdM]5[2cJiMZ5W2eJkMZ5T2eJoMZ5P2fJQNZ5o1eJSNZ5l1fJVNY5i1gJYNX5g1gJ[NX5d1iJ\\NW5c1lJ\\NS5d1nJ[NR5d1PK[NP5d1QK\\No4d1XJPNcN<U7c1WJ[N\\N2]7b1WJeNTNIe7b1VJ[Oj5d0VJ]Oj5b0UJBi5>VJJc55]JMb52^JOb51]J1b5N^J4a5K_J6a5J_J7`5H`J9`5F`J<_5D`J=`5BaJ?^5@bJa0^5_OaJc0^5\\OcJe0\\5ZOdJg0\\5YOcJi0\\5VOdJk0\\5TOeJm0Z5SOeJn0[5QOeJQ1Z5nNfJT1Y5lNgJT1Y5kNgJW1X5hNhJY1X5gNhJZ1W5eNiJ\\1W5cNiJ_1V5aNiJ`1W5_NjJb1U5]NkJe1T5[NlJe1T5ZNlJh1S5WNmJj1S5VNmJk1R5TNnJm1R5RNoJo1P5QNoJP2Q5oMoJS2P5lMQKU2n4kMQKV2o4iMRKX2m4hMRKY2n4gMQK[2n4dMSK\\2m4dMRK^2m4aMTK_2l4aMSKa2l4^MTKd2k4\\MUKd2k4[MUKg2j4YMVKg2j4XMVKj2i4VMVKk2j4UMVKl2i4SMWKn2i4RMWKo2h4PMXKR3g4nLXKS3h4lLYKU3f4kLYKV3g4iLZKX3e4hLZKY3f4gLZKZ3e4eL[K\\3e4dLZK^3e4aL\\K`3c4`L\\Ka3d4^L]Kc3b4]L]Kd3c4[L]Kg3b4YL^Kg3b4YL]Ki3b4VL_Kj3a4VL^Kl3a4SL_Ko3`4QL`Ko3`4PL`KR4_4nKaKR4_4mKaKU4^4kKaKV4_4iKbKX4]4hKbKY4^4gKbKZ4]4eKcK]4\\4cKcK^4]4aKdK`4[4`KdKa4\\4^KeKc4Z4]KeKd4[4[KeKg4Z4YKfKg4Z4YKeKi4Z4VKgKk4X4UKgKl4Y4SKhKn4W4RKhKo4X4QKhKP5W4oJjKQ5V4oJiKS5V4mJjKS5V4lJjKV5U4jJkKW5T4iJlKW5T4iJkKY5T4fJmKZ5S4fJmK[5R4eJmK\\5S4cJnK^5Q4bJoK^5Q4bJnK`5Q4_JPLb5o3^JQLb5o3^JPLd5o3[JRLe5n3[JRLf5m3ZJRLg5n3XJSLi5l3WJTLi5l3WJSLk5l3UJTLl5k3SJULn5k3RJULo5j3QJVLo5j3PJVLR6i3nIWLR6i3nIWLS6h3lIXLU6h3kIXLV6g3jIYLW6f3hIZLY6f3gIZLZ6e3fI[LZ6e3eI[L]6d3cI\\L]6d3cI\\L^6c3aI]L`6c3`I]La6b3_I^Lb6a3^I^Lc6b3\\I_Le6`3[I_Lf6a3ZI_Lg6Q50O2O001N2O001N101O0O2O001N2O001N101N101O1N101O0O2O001O0O2O1O001O0O2O001O001N2O001O0O2O001O001N2O001O001N101O001O1N101O001O0O2O001O1O0O2O001O0O2O001O1O0O2O001O001N101O1O001N101O001O010001N101N2O0O2O0O2O001N101N101N101N101O0O2O0O2O0O2O0O2O1O0O2O0O2O0O2O0O2O001N101N101N101N101O0O2O0O2O1N101N101O0O2O0O2O0O2O0O2O001N101N101N101O0O2O1N101N101N101O0O2O0O2O0O2O0O2O001N101N101N101N2O001N101N101N101NSJWJ[4i5dKWJ^4h5aKXJ`4g5`KZJa4e5^K[Jc4d5]K\\Je4c5ZK^Jf4a5ZK_Jh4`5WKaJi4^5WKbJk4]5TKcJm4]5RKdJo4Z5QKfJQ5Y5nJgJS5X5mJiJT5V5kJjJV5U5jJlJW5S5hJmJY5R5gJnJ[5Q5dJPK\\5P5cJPK_5n4aJRK`5n4_JSKb5k4^JUKc5k4\\JVKe5h4[JXKf5h4YJXKi5f4WJ[Ki5e4VJ[Kl5d4SJ\\Kn5c4RJ^Ko5a4PJ_KQ6`4oI`KS6_4lIbKT6]4lIcKV6\\4iIeKX6Y4hIgKY6Y4fIgK\\6X4cIiK]6V4cIjK_6U4`IkKa6T4_ImKb6R4]InKd6Q4\\IPLe6o3ZIQLg6n3YIRLi6m3VITLj6l3UITLm6j3SIVLn6j3QIWLP7g3PIYLQ7g3nHZLS7d3mH\\LT7d3kH\\LW7b3iH_LW7a3hH_LZ7`3dHaL]7^3cHcL^7\\3aHdLa7Z3_HfLb7Z3]HgLd7W3\\HiLe7W3ZHjLg7T3YHlLh7T3WHlLk7S3THnLl7Q3THoLn7P3QHPMP8o2PHRMQ8m2nGSMS8l2mGUMT8h2mGXMT8g2lGYMV8e2jG\\MV8b2kG^MW8`2iG`MX8_2hGbMY8\\2gGdMZ8Z2gGgMZ8W2fGiM[8V2eGjM]8S2dGnM]8P2cGPN^8o1bGQN`8l1aGUN_8j1aGVNa8i1^GXNb8g1^GYNd8f1[GZNf8e1ZG\\Ng8c1XG]Ni8b1WG^Nk8`1UGaNk8_1TGaNn8]1RGdNn8\\1QGeNP9Y1PGhNP9X1oFiNR9U1nFkNS9T1mFmNT9R1kFoNU9P1kFQOV9n0iFROX9m0hFTOY9k0fFVO[9MhFXOMl0[9_OUGD@m0]9]OTGE_Oo0]9\\OTGABT1[9YOSGADW1Y9XOSG^OF[1Y9TORG^OG^1\\9POmF_OIb1]9kN\\GV1g8gNWG[1l8aNTG_1P9^NoFc1[:1O1O001O1O1O1O001O1O1O001O1O1O001O1O1O1O001O1O1O001O1O1O1O001O1O1O001O1O1O001O1O1O1O001O1O1O001O1O1O001O1O1O1O001O1O1O1O2N2EgLbFZ3\\9hLcFZ3Y9hLgFZ3V9hLiFY3U9iLjFY3S9hLmFY3Q9hLoFZ3n8gLRG[3k8fLUG[3i8fLWG\\3e8fL[G\\3b8eL^G\\3`8eL`G]3]8dLcG^3Z8cLfG^3X8cLhG_3U8bLkG`3R8aLnG`3o7bLQH`3l7aLTHa3i7`LWHa3g7`LYHb3d7_L\\Hc3a7^L_Hd3^7]LbHd3\\7]LdHe3X7]LhHe3U7\\LkHf3R7[LnHf3o6\\LQIf3k6\\LUIf3g6\\LYIf3b6]L^Id3_6^LaId3[6^LeId3V6_LjIb3S6`LmIb3o5`LQJb3j5aLVJa3g5`LYJa3d5aL\\Ja3`5aL`Ja3]5`LcJb3Y5`LgJa3V5aLjJa3S5`LmJb3o4`LQK", "size": [425, 640]}, "area": 161062.0, "iscrowd": 0, "image_id": 13597, "bbox": [0.0, 0.0, 640.0, 425.0], "category_id": 123, "id": 20000815}, {"segmentation": {"counts": "n;8V1P1^9APFP3`7XMWH_3T7`LkH^3X7aLhH]3[7bLeH]3^7aLbH]3a7bL_H\\3d7cL\\H\\3g7bLYH\\3j7cLVH[3n7cLRH\\3P8cLPH[3S8eLlGY3X8eLhGY3[8fLeGY3]8fLcGX3`8gL`GW3d8gL\\GX3f8gLZGW3i8hLWGV3m8hLSGW3o8hLQGV3R9iLnFU3U9kLjFT3X9kLhFS3\\9kLdFS3_9lLaFS3a9lL_FR3d9mL\\FQ3o9O1N2N200O1O100O1O100O1O100O1O100O1O100O1O100O1O100O1O100O1O100O1O100O1O100O1O10mFQNl6n1SI\\Ne6c1[IfN]6Z1bInNW6Q1iIXOo5g0PJCh5=WJKb54^JNa51_J0a50^J2a5M_J4a5K_J7`5I`J8_5GaJ:_5EaJ=^5CaJ>_5AbJ`0]5_OcJb0]5^ObJd0]5[OdJe0\\5ZOdJh0[5XOdJj0[5UOeJl0[5SOfJn0Y5ROfJo0Z5POfJR1Y5mNgJT1Y5lNgJU1X5jNhJW1X5hNhJZ1W5fNiJ[1V5dNjJ]1V5bNjJ`1U5`NjJa1V5^NkJc1T5\\NlJe1T5[NlJf1S5YNmJh1S5WNmJk1R5UNnJl1Q5SNoJn1Q5QNPKP2o4PNPKQ2P5nMPKT2o4lMQKT2o4kMQKW2n4iMRKW2n4hMRKZ2m4fMRK\\2m4cMTK]2l4cMSK_2l4`MUK`2k4`MTKb2k4^MTKc2l4\\MUKe2j4[MUKf2k4YMVKh2i4XMVKj2i4UMWKl2i4TMWKm2h4RMXKo2h4QMXKP3g4oLYKR3g4nLXKT3g4lLYKT3g4kLYKW3f4iLZKX3e4gL[KZ3e4fL[K[3d4dL\\K]3d4cL[K_3d4`L]K`3c4`L\\Kb3c4^L]Kb3c4]L]Ke3b4[L]Kg3b4XL_Kh3a4XL^Kj3a4UL`Kk3`4UL_Km3`4RL`Ko3`4QL`KP4_4PL`KQ4`4nKaKS4^4mKaKU4^4jKbKW4^4iKbKX4]4gKcKZ4]4fKcK[4\\4dKdK]4\\4cKcK_4\\4aKdK_4\\4`KdKb4[4^KeKc4Z4\\KfKe4Z4[KeKg4Z4XKgKh4Y4XKfKj4Y4UKhKk4X4UKgKm4X4SKhKm4X4RKhKP5W4PKiKQ5V4oJjKQ5V4nJjKT5U4lJkKT5U4lJjKV5U4iJlKW5T4iJlKX5S4hJlKY5T4gJlKZ5S4eJnK\\5Q4dJnK]5R4cJnK^5Q4aJPL_5P4aJoKa5P4_JPLa5P4^JQLc5n3]JQLd5o3\\JQLe5n3ZJSLg5l3YJSLh5m3XJSLi5l3VJULj5k3VJTLl5k3TJULl5k3SJULo5j3QJVLo5j3QJVLP6i3PJVLR6i3mIXLS6h3mIXLT6g3lIXLU6h3jIYLW6f3iIZLW6f3iIYLY6f3fI[LZ6e3fI[L[6d3eI[L]6d3bI]L^6c3bI]L_6b3aI]L`6c3_I^Lb6a3^I_Lb6a3^I^Ld6S50O2O1O0O2O001N101O0O2O1O0O2O001N101O0O2O1N101O0O2O001N2O001O0O2O001O001N2O001O001N101O001N2O001O001N101O001O1N101O001O0O2O001O1O0O2O001O001N101O1O0O2O001O001N101O1O001N101O001O0O2O1O001O0101N101N101O0O2O0O2O0O2O0O2O001N2O0O2O0O2O0O2O001N101N101N101N101O0O2O0O2O0O2O1N101O0O2O0O2O0O2O0O2O001N101N101N101N101O0O2O0O2O1N101N101O0O2O0O2O0O2O0O2O001N101N101N101N101O1N101N101N101N101O0O2O0O2O0O2O0O2O001N10QJWJ^4g5bKYJ_4g5`KYJb4e5^K\\Jc4c5\\K]Je4b5[K_Jf4`5YK`Jh4`5WK`Jk4^5UKcJk4]5TKcJn4[5RKeJo4[5PKfJQ5X5oJhJR5X5mJiJT5U5lJkJU5U5jJkJX5T5gJmJY5R5gJnJ[5Q5dJoJ]5P5cJQK^5n4aJRK`5m4`JSKb5l4]JUKc5j4]JVKe5i4ZJXKf5h4YJXKi5f4WJZKk5e4TJ\\Kl5c4TJ]Kn5b4QJ^KP6a4PJ`KQ6_4nIaKS6^4mIcKT6\\4kIdKV6\\4iIdKY6Z4gIgKY6Y4fIgK\\6W4dIiK]6W4bIjK_6T4aIlK`6T4_IlKc6R4]IoKc6Q4\\IoKf6P4YIQLg6n3YIRLi6m3VISLk6l3UIULl6j3SIVLn6i3RIWLP7h3oHYLQ7g3nHYLT7e3lH\\LU7c3jH]LW7b3iH^LY7a3fH`LZ7_3eHbL]7]3bHcL_7\\3aHeL`7Z3_HfLb7Z3]HfLe7X3[HiLe7W3ZHiLh7U3XHlLh7T3WHlLk7R3UHnLl7R3SHoLn7o2RHQMo7o2PHQMR8m2nGTMR8k2nGUMT8i2lGXMT8f2mGZMU8d2kG\\MV8c2jG^MW8_2jGaMX8]2hGcMY8\\2gGeMZ8X2gGhMZ8W2fGiM\\8U2dGlM\\8S2dGmM^8P2cGQN]8n1cGRN_8l1aGTN`8k1`GVNa8h1_GXNb8h1]GXNe8f1[G[Ne8e1ZG[Nh8c1XG^Nh8b1WG^Nk8`1UG`Nl8_1TGbNm8]1RGdNn8[1RGfNo8Y1PGgNQ9X1oFiNR9V1mFkNT9S1lFnNT9Q1lFPOU9o0jFQOW9n0iFSOX9l0gFUOY9j0gFWOZ9BSGCBk0\\9@TGD@m0]9]OTGCAQ1[9\\OTG@CU1Z9YOSG@EW1Z9XOQG]OH\\1Z9SOoF^OI`1[9oN^GR1e8jN[GW1h8fNWGZ1m8bNSG_1Q9]NnFd1[:1O001O1O1O001O1O1O1O001O1O1O001O1O1O1O001O1O1O001O1O1O001O1O1O1O001O1O1O001O1O1O001O1O1O1O001O1O1O001N2O1O001O1O1O1O1O1O2N2EgLbFZ3\\9gLdF[3X9hLgFZ3V9hLiFY3U9hLkFZ3R9gLnFZ3P9gLPG[3m8fLSG\\3j8eLVG\\3h8eLXG]3d8eL\\G]3a8dL_G]3_8dLaG^3\\8cLdG_3Y8bLgG_3W8bLiG`3T8aLlGa3Q8`LoGa3n7aLRHa3k7`LUHb3h7_LXHb3f7_LZHc3c7^L]Hd3`7]L`He3]7\\LcHe3[7\\LeHf3W7\\LiHf3T7[LlHg3Q7ZLoHg3n6[LRIg3j6[LVIg3f6[LZIg3a6\\L_Ie3^6]LbIe3Z6]LfIe3U6^LkIc3R6_LnIc3n5_LRJc3i5`LWJb3f5_LZJb3c5`L]Jb3_5`LaJb3\\5_LdJc3X5_LhJb3U5`LkJb3R5_LnJc3n4_LRK", "size": [425, 640]}, "area": 110089.0, "iscrowd": 0, "image_id": 13597, "bbox": [0.0, 0.0, 640.0, 420.0], "category_id": 183, "id": 20000816}, {"segmentation": {"counts": "a=_1a=000O100001O000000001O001O001O0gN_Bf0NYO07i=?XBZOO7j=>WBZO42g=d0=N2O1O2NbA@\\>`0dA@\\>?eAA[>?dAB\\>a000001O001N9HcJLeHNY1OT67aHKTNNa<:WEMe:9WEH]NJV<a0iDCWO4IIW<>XDAM2N=j;2\\DK_OK2GOa0O_OV<b0ZD:@TO0b0X<1WDP1h;oNYDQ1g;oNXDR1h;mNUD_OEe1V<lNUD_OEd1X<mNQD@Gc1X<mNPDBGa1Y<nNoCW1Q<iNnCX1S<hNlCW1U<iNjCX1[<dNdC\\1a<_N^CX1MfNj<0VCZ12eNj<`1YC_Ng<a1YC[Nk<d19O1O100001O001O000000O100000O10O0100]JcNdM\\1]2lNYMU1i2hNWMX1l2dNUM]1k2aNWM^1k2cNSM]1l2cNVM[1j2hNSMX1n2jNUH0NC10`02m2a1^4M[HgNV3X1_4g0\\KZOd4j0XKfMlLS1U8]1TIcMPOMn0P1hNQOe73eHb2FZM`0016EKT1l0^6XOaHd27WMF206NJV1j0_6YO`Hg2ITMN153MN1b0X17^6\\O]HV4V1eKiN6KJm79UHT4[1bKlNOl7n4dIRK\\6n4j11O10000000O11O1O00O1O1N2O100M3O10000001N10001O00lLQFYNL16b2m9TO^Fj0b9VO_Fi0c9UO_Fi0b9UO_Fk0c9RO^Fn0g9lNZFT1f9oNWFQ1j9nNVFS1\\ObMa:T1SF`1m9]NSFe1k9[NUFf1j9TN\\Fm1a9VN^Fj1d9XMcEj0f0UOZOi2b:WM`Eh0a0S2U:kMmES2S:mMmES2[:dMfE\\2`:\\McEc2]:XMkD0j0i2T;5N300000000O2O00001O000000001O0000001O001O1O2K51NOZO`DnM^;Q2jDiMU;V2SEdMl:[2\\E^Md:b2m0O1O100O1O1O100O1O1O100O1O1O100O1O1O100O1_O[CfNf<X1]CgNc<V1aChN`<V1dCgN]<X1eCfN\\<Z1dCfN\\<Z1dCdN^<\\1c0O2O01O0000000000kN_Bk0a=POiBk0l=J5K6F8L1OO100O2O1DMhA4X>:0000000000000000000000011N3M2N2M2O000O020NK6O2O000000B`A<`>D`A<g>K2N0KZA0W`?2dn@0O0SAOj>3YN7XDHb;e0SCG?G^<i13N00N1101N100O2O1O1N2O0O3N2M1oNeB>]=CWCCm<>RCAn<b0QC\\OP=f0e00O01O3M7_OeAO\\ch4", "size": [480, 640]}, "area": 19150.0, "iscrowd": 0, "image_id": 13659, "bbox": [0.0, 89.0, 315.0, 391.0], "category_id": 100, "id": 20000817}, {"segmentation": {"counts": "jd_82^>1?4L4L4L3M10O01O000000000fA^OV>o0GRORBk0Q>00001O0000001O00001O0000<E0O1O1O1O00M3G9H8H8G9HXk24XTMNZBOmX6", "size": [480, 640]}, "area": 1061.0, "iscrowd": 0, "image_id": 13659, "bbox": [579.0, 234.0, 48.0, 53.0], "category_id": 105, "id": 20000818}, {"segmentation": {"counts": "o9[1e=0O1OJbBmN\\=S1eBkN]=T1dBlN[=U1eBkNZ=V1fBjNY=X17O0100O1O1\\BgNo<1_C[1_<iN^CX1a<kN]CU1b<oNUCC3_1g<ROXCm0g<UOWCi01eNh<OWCn12RNj<n141O1OO1000O10200N0eDoMf9R2YFQNe9n1mERNVO4j:j1oESNVO7j:c1QFVNTO;i:]1SFZNnNJ1d0l:T1fEXNJV1^:>dEbNHJKW1k:IYEWO:3CGNW1j:WOWEM2N=2@P1j:TOcE_OK2GOa0O_O>0n0l:TOPF@TO0b0n1[:aNfF^1Y9cNgF\\1Z9cNgF]1X9`NTEEe1k1W9`NTEEd1k1Z9^NSEGc1k1Z9]NUEGa1l1[9\\NjFd1V9[NkFe1V9ZNiFf1X9YNiFg1YOTNj84mGh1VOTNQO0l93dGMfNg0I=m0SOQ9IcG2eNf0J;h0nNXO8_;LfDc0M9e0[Oi:IaDf035?@l:c0hDK;Cl:b0kDJ8El:`0PEI4Gl:?REI2Hl:>UEINIn:<WEIJKP;;XEIHZOA`0`;:[EKEKP;9]EKBMQ;7`EK^ONR;6bELVO5U;OhEk0Y:SOjEl0V:SOmEm0Q:ROQF\\1`9cNcF`1W9_NmF_1S9`NoF_1Q9`NRG^1n8aNTG^1l8aNWG]1h8bN[G]1e8bN^G\\1a8dNaG[1_8dNdGZ1[8fNgGY1X8fNlGX1T8gNoGW1P8jNQHU1o7lNRHR1d7YO\\Hf0l7SO\\Hd0n7SORHl0R8ROlGn0V8QOjGn0W8ROhGn0Y8ROgGm0[8SOcGm0^8SObGl0_8TOaGk0a8TO^Gl0c8TO]Gk0d8VOZGj0g8VOYGi0h8WOWGi0j8WOVGh0l8WOSGi0n8XOQGg0P9YOoFg0R9YOnFf0S9ZOlFf0U9[OjFd0U9^OkFa0T9AkF?U9BkF=T9EkF;T9HkF7T9KkF5T9MkF3b8a0]G_O\\8j0bGVOP8Y1oGgNR8Y1mGgN\\2@`KL16U5h1mLSNmM`0`40d0^1nLQNoM4O7`47c0_1VMZNW29`0_1XMXNW2:?a1YMUNW2;a0`1WMUNW2<b0a1UMSN\\29?e1UMQN\\2:?f1TMPN]2:`0f1RMPN^2:`0g1QMoM_2:`0g1QMoMY2`0f0a1QMoMZ2?e0b1QMoM[2<dKZOP5[2QMoM^29d0g1nLPN^2;b0e1PMPN^2;b0e1PMPN]2<c0e1oLoMl1OYL`0k4b1PMoMl1OTLH0j0P5`1PMoMj1k0]1U1iLPNj1h0`1X1fLPNj1h0`1X1fLPNi1i0a1W1fLPNi1i0a1X1eLoMj1i0a1X1eLoMj1h0c1X1cLPNj1h0c1X1cLPNj1h0c1X1cLPNj1h0c1X1cLPNj1h0c1X1cLPNi1i0e1W1aLPNk1h0d1X1aLPNk1h0d1X1aLPNj1i0e1V1bLQNi1i0e1TOcJh1P2[Nh1i0e1TOcJg1Q2\\Nh1h0e1nNjJl1i1^Nd1l0i1kNhJ0Jg14XNi1:k1i0T2nNTJ:0n01gNl1:k1i0T2mNUJ93Q1i1POk1i0X2TOWJQ1f1ROk1i0Y2POoIM:V1c1TOk1f0]2ROWJT1a1TOk1i0k22ZKUOk1i0k23YKTOl1i0k23YKTOl1i0k23YKTOl1i0k23YKTOl1i0k23^IjNh1:o1i0k23^IjNh1:o1i0k23YKTOl1i0k23YKTOl1i0k24XKSOm1i0k24XKSOm1i0k24XKSOn1h0j25YKROm1i0j25[KPOh1n0m22]KnNf1P1m22^KmN_1W1S3M]KlN^1Y1S3cN_IX1Q2kNa1V1l24cKfNZ1]1Q3OfKcNW1`1S3MfKcNX1_1S3MfKcNW1`1S3MfKcNW1e0gL2\\6f0fKcNW1e0iL0Z6g0hKcNT1g0oLJU6k0iKdNU1d0SMHo5P1VOXOoJDl5R1TO\\OUK]Oi5N`IS1e5@UK\\Oi5T1ZOWOc1h0^NVORJG`7S1^NUOSJH`7Q1^NWORJHg62mIn0[5XOi0KXIMO0a0P1_5XOh02hIg0_5ROQJ0f6;hIe0_5POTJOc6=kIf0\\5mNl0`0hIe0Z5kNn0`0hIg0X5hNR1a0eIi0W5eNU1a0eIk0Q<VOnCj0R<VOnCk0Q<UOoCk0Q<UOoCb0CRO^<=mC`0IQOZ<?lC?MROV<`0kC<4ROP<c0jC>5oNR<c0fC?;lNo;e0dC`0>kNn;n1fIZN>e1C[N>c1C]N=^1HbN8Z1LgN4U1OkN1Q13POMk07UOIh0:YOFe0;[ODd0>\\OBc0?^O_Oa0c0_O\\Oa0e0_OZO`0h0AVO?a0L^O2>FXI@17X6c0f0@QIF27U6b0k0@TIMQ6b0l0ARIOQ6>o0BQI1o5<R1]OUI:e57X1^OTI>a53\\1_OSIb0]5Mb1@RIe0[5IU27kMGW29iMFY29hMDZ2<fMC[2=fM@\\2`0dM_O^2a0aM]Oc2`0P6M2O2N100000000000O1O2O000000001O1N2N2O1I7M300O1O4L7I1001N0000000O1000003L3N1O17F3N1dBeNT=Z1kBfNV=Z1hBhNW=Y1hBgNZ=^11000001O0O11N2OOB^NSC60l00Bh<j0^CROb<m0bCoN^<Q1fClNY<T1kCjNU<S1QDiNo;V1UDhNj;W1ZDiNd;T1cDnNW;P1]1O20O00000002nNRB41c0R>ZOnAOOe0Y>0000]OhA?W>@jA`0Z>103M6I7J6J3LN3M2O2N2N3L3N2N2N3M2M3N2N2N2N2MTjW4", "size": [480, 640]}, "area": 20896.0, "iscrowd": 0, "image_id": 13659, "bbox": [0.0, 100.0, 350.0, 380.0], "category_id": 110, "id": 20000819}, {"segmentation": {"counts": "gYS32l>4L3L5OOO3M101N1O1001O0O2O001O001N101N1N3MUIKoN3S10kNMW15i62N1O0000O11OJ6OGOeA1Z>1eAO\\>0dA0[>2dAN\\>2dAN\\>2cAO]>1cAOf>100O100`^g0MeaXOd0[OO2O1N1O000O]D@h85^D4f2G\\M0V;8hDL15n1b0Z9AiDLh1j0d9SOeD3b1o0S:QOiET1V:kNkEU1U:kNaELmNY1b;kN]E0QOV1`;kN\\E2TOS1`;kNYE5WOP1`;kNVE8ZOn0_;jNTE;]Ok0_;jNRE=_Oj0^;iNPE`0Bg0^;iNmDc0Ee0^;hNiDf0Ic0];gNgDi0L`0];hNcDk00>\\;hNaDl03=[;1eD0Z;0fD0Z;0fD1Y;OgD2X;NhD2X;NhD3W;MiD4V;LjD5V;JjD6V;JjD7U;IkD8T;HlD9S;GmD;Q;EoD=o:CQE?m:ASE`0l:@TEb0j:^OVEd0h:\\OXEf0f:ZOZEh0d:XO\\Ej0c:UO]El0b:TO^En0`:RO`EP1^:PObER1\\:oNcES1[:mNeEU1T:ZMiEa13V1X:jNhEX1V:hNjE[1T:dNlEa1P:^NPFg1l9XNTFl1h9TNXFQ2c9oM]FR2b9nM^FR2b9nM^FQ2c9oM]FQ2c9oM]FQ2c9oM]FQ2c9oM]FQ2b9PN^FP2b9PN^FP2c9nM^FR2c9lM^FT2R9`LUG[1KT2a9jM_FV2\\9`LaFX13Y2\\2cLV4M[IU12]2\\2aL_M1_6NVJ?@R3[2_LbM2]6NoJc3Q1]LdM2\\6OmJc3R1[LgM3Z60kJc3S1YLjM4W6n0gIm2W2QLlM4V6j0lISOGf3Z2XLoM5T6e0[Jk2P2`Ld3b0bJk2k1cLc3:jJS3c1cL]5]3cJcL\\5^3dJbL\\5^3dJbL\\5^3dJbL\\5^3dJbL\\5^3dJbL[5_3eJaL[5_3eJaL[5_3eJaL[5`3dJ`L\\5`3dJ`L\\5`3dJ`L\\5`3dJ`L[5b3cJ_L]5a3`JbL`5^3^JdLb5\\3[JgLe5Z3WJiLi5W3UJkLk5U3RJnLn5R3oIYLkMe0U8S3nIXLoMe0S8T3jIWLVNe0P8T3gIXL[Nd0n7T3eIXL_Nd0l7T3cIXLbNe0k7m1mG_NS2Dm5k1WH^Nm1Gm5f1]H_Ng1Kl5a1eH`N`1Ok5\\1mH`NZ14i5W1UIaNR19i5R1[IaNn0=g5P1`I_Nj0a0f5o0dI\\Ng0e0e5m0YLSOg3k0ZLVOf3i0[LWOe3g0]LYOc3f0^LZOc3c0_L]Oa3b0_L_Oa3?aLA_3=cLC]3<dLD\\3:fLFZ39gLGY37hLJX35iLKW33kLMU31mLOT30lL0T30kL1U3OkL1V3NjL2X3MgL3[3AfGKo4d0]3VOnG5d4f0l3ZOTLf0l3ZOSLg0m3ZOQLg0o3YOPLh0P4YOnKh0R4XOnKh0R4YOlKh0T4YOjKh0V4XOiKi0W4XOgKi0Y4WOfKj0Z4WOdKj0[4XOcKi0]4WObKj0^4WO`Kj0`4VO_Kk0`4XO]Ki0c4YOZKh0f4ZOWKg0i4[OTKf0l4[OhJP1X5ROPJd1P6^NkIe1U6]NfIf1Z6\\NaIg1_6[N\\Ih1d6ZNWIi1j6XNQIk1o6WNlHl1T7VNgHm1Y7TNbHP2^7RN\\HR2d7oMXHT2h7mMSHW2m7iMPHZ2Q8fMjG^2U8PM\\G^O;f3`8QLiG9Di3a9XLZFl3f9TLVFP4j9QLRFR4o9?0000000000000000001O000000000000000000001O000000000000000000000000000000000000000000000000000000000000001O000000000000000000000000000000000000mNbKdG^4P8ULiGk3k7gLoGY3f7V2O11O001O1O001O000000O1O1O100O12N4L5K5K6J[OeIjHV6V7mIkHo5V7TJgHk5Y7ZJbHf5^7aJXHb5h7k0000QISHk6Z8G1O00001O0000001O00001O0oKbG`1^8XNjGh1b8cMgG]2Z8ZMnGf2S8QMTHP3m7fL\\HZ3e7]LcHc3]7TLkHm3T9000O100O1N2F:G9F:F:G9F:F:M3N2000000000000000000000000", "size": [480, 640]}, "area": 47163.0, "iscrowd": 0, "image_id": 13659, "bbox": [211.0, 171.0, 429.0, 309.0], "category_id": 115, "id": 20000820}, {"segmentation": {"counts": "WYW41n>1N2^ONmA2M1V>9hAGY>a002M1O1O2O1N1O10O0HRB_Om=2dBLY>01NQl11mSN10000\\`h4", "size": [480, 640]}, "area": 334.0, "iscrowd": 0, "image_id": 13659, "bbox": [288.0, 274.0, 26.0, 37.0], "category_id": 123, "id": 20000821}, {"segmentation": {"counts": "0Y6g81OO11OO11OO100O100001O00O100001O0000000000001O0000000000000000001OL\\GkIe8m5[GSJ020Mf8l5aGTJb8l59O1L4000000N2000000O1O10;XOZF_KT:l3lElK1N0:c:[3hEaLe:Q3^EjLBOb;f2W1]O<nMjBd1c=Fa0^O8I0B>N2N110O10PT51S]J3`>:M4000N2OKBfA=Z>CfA=Y>6N1O1010O<D1O102M3Lj[o7", "size": [480, 640]}, "area": 10957.0, "iscrowd": 0, "image_id": 13659, "bbox": [0.0, 0.0, 95.0, 203.0], "category_id": 132, "id": 20000822}, {"segmentation": {"counts": "`Sk11g>b0F>B2N0O10O10N200000O01000O100000000O8I8D4AcA0\\P7`0l]I40O20OO10O20OO1O10000000[OhAb0X>_OgA?[>@fAb0X>^OhAc0W>]OjAb0V>^OkAa0Z>01O000000N@dA`0\\>@dA`0\\>@dA`0\\>2O11OO10000O1000_OWef0M][YO0O6K2RNKmD5YNKd<2RE9m:GSE9m:HRE8m:IQE9o:GQE9o:GRE5Q;KoD5g:5XELg:5YEKg:4ZELf:3[EMe:3[EMe:3[EMe:3[EMe:3YEA]N;[<3XE0h:0XE0i:NXE2i:LXE4i:KVE6k:C[E=d<2N000001O0000004L00O10010O1O1OF:JfQ\\10_ncN2M3N1N3N2M3N1M4L4L3N3M3MKVBXOh=g0]BXO_=i0=N2O2N1O2O0O2N1O2O000O2O_OCaB;S>N1000000O101O0O2O001O0O2O001O0010]X]29YgbM0^AFh<7cD4cNFj<7bD6];MaD3^;McD4[;MeD3\\;LcD5\\;LdD4^;JbD6^;KaD5b;J\\D6`;KbD6^;a0kC_OW<a0iC]OX<c0XCLi<V1N1O1001O3M1ON22N5K0000000001O00HoB[NW=T1iBlNh=f0`0ZOkV4", "size": [480, 640]}, "area": 4584.0, "iscrowd": 0, "image_id": 13659, "bbox": [126.0, 14.0, 505.0, 188.0], "category_id": 139, "id": 20000823}, {"segmentation": {"counts": "W<Z1f=0000O1000O20O000000010O01O001O0_BgN[=_1^OeNUCN0M1a1i<b001M3O1O20NO100O12N1OOfN\\C;b<EaC9^<YObCH4m0Y<ZOdCH7S1m;UOlCG;S1@jNR<;UDBJ1d0X1^;TOTEh0l:ROnCKW1R1o:oNkCNW1S1P;mNTEU1l:jNSE<nN3]=NcB1]=OeB0Y=2fBOY=1hBOW=1iBOV=2jBNV=2jBNV=2kBMU=3kBMU=3kBLU=5lBJT=7kB@C2g>MYAOVmP24baoM3L4M4M2O000cA_O[>b000101N10000O10000000000OM401O1O1O10O1O00000000000000000001O000001O000000O10000O11OO1010O00000000000000000000000001L301O2N1O1O1O2N1O1O1O2N1O7I6^C\\N\\;j1]DXNb;l1YDVNh;i1UDZNj;g1TD[Nk;g1QD\\Nn;e1oC^NP<c1kCbNT<V2O1O1O1OmNTDSOk;l0[DPOd;a0TDoN:`0b;b0TDkN;b0c;c0RDdNO2>f0b;d0SDdN=h0b;c0dD[Od;ZOTD?=5^=N5DdAMi>NQ\\R5", "size": [480, 640]}, "area": 5976.0, "iscrowd": 0, "image_id": 13659, "bbox": [0.0, 197.0, 293.0, 270.0], "category_id": 143, "id": 20000824}, {"segmentation": {"counts": "Y6Z1g=OO0_BiN\\=]1O3OOO3N8G;F0001OWClM02a<U2_CmM^<T2bClM^<T2bCmM]<]2ON1M32N3M0eC]MY<e2001O0000000O1002N00O101OOO2M3O1L4O100M310OO100000001O0000001O00O1O1[OYC^N0`0h<R1ZCaNO6h<Y1YCaN04h<Z1aCdN`<Z1cCeN]<]1dC_N\\<b1a00000L40000O10000001O1O2N000000000O1000000000000K^NlBb1T=`NjB`1V=50AlBROW2Fd80RE`0016ET2Kf8?PEF206NR2Lk82kDN153MN1`a0M`^O3a>8000000000000KGcA9_>32N1N104LM3O10000000001O000O1011N22M01N2N1O2M3L30Qgb00mX]O3M1N3FIhA8X>HgA:X>GfA:\\>DbA?`>O2O2M2O2M2O2M2OXSU4OhljK00000O100000001cM7`EJ\\:`0]EA_:i0YEYOb:R1WEoNh:\\1lDfNT;R2SDoMl;KoCa2Q<aMTDY2k;kMYDl1h;PNdDo1T;nMQ3LYRW2", "size": [480, 640]}, "area": 7126.0, "iscrowd": 0, "image_id": 13659, "bbox": [0.0, 187.0, 488.0, 141.0], "category_id": 165, "id": 20000825}, {"segmentation": {"counts": "WT`01g>0[A020M0b>7^ALb>9O1L4000001M1100000O1O100O100O11OPD_Oa9a0^FE^9:mE[O^N0`0`0Q;5QF]OaNO6b0V;2SF]OaN04b0X;1RFFdN:Y;1QFHeN7Z;1SFI_N7^;OTFb0m9]OSFc0m9]OSFc0m9]OoEg0Q:YOoEg0P:ZOPFf0o9[OPFf0Q:YOoEg0Q:YOoEg0R:XOnEh0R:XOoEg0R:XOoEg0Q:YOQFd0Q:[OoEe0R:ZOnEe0S:[OmEe0T:ZOlEg0T:XOlEg0V:WOkEh0W:WOiEh0Z:VOfEj0[:UOeEk0\\:TOdEm0]:QOcEn0_:QOaEo0a:oN_ET1`:jN`EU1V<0XBkNf=W102N001N1`BiNW=X1hBlNT=`101OlNmB;S=DoB;Q=DRC:n<EUC9k<FWC4G[OQ=b0ZC3j<KYC5f<I\\C7c<H`C6_<JdC8W<HkC4V<MmC0S<0PDNP<1RDNk;4XDJg;6\\DHd;7^DHb;7aDG_;8dDF];8fDE[;:f101N1001O1O1O1O1O1O1O10@bA>b>N1O0000O1O1O1O1O1O1O100N2O1O1O1O1O2O0O1O100O1O1O1O100O001O010O0100O2O1N101KnAXOS>f05N1N6CaA0c>M]A2j>N1NVfi6", "size": [480, 640]}, "area": 4591.0, "iscrowd": 0, "image_id": 13659, "bbox": [34.0, 178.0, 141.0, 136.0], "category_id": 167, "id": 20000826}, {"segmentation": {"counts": "P^g0;l4Fl4g0_JYOlK1N0g9V1nIUOo5W1cIlNjLBO0\\9S2ZIbNf6P2gHRNX7Y2fFoLd1h0f7f2PHZMP8W3^GjLb8]3WGbLj8^3hFPMX9P3fFRMZ9n2dFUM[9j2fFVMZ9j2eFWM[9i2eFWM[9V41O11O001O001O001O1O00_MiFlL<n1j8Q1hGgNW8W1jGkNU8W1gGlNX8V1dGnNZ8T1fGkNY8Q1PGQMf0P2Y8n0QGRMi0P2T8l0THVM[ONC10`02Ok8Z2YI]MQN6e8[2QJaMo5^2VJ^Mj5b2[JYMe5m2T3O10000N20000O1O1000000000000000000000000000000000000O10000000000002N1OdNmCLR<L`DIa;7mD[OS;e0oDVOT;j0nDjNmN5V<P1a10000N2000000O1000000000000001OO1O100001O000000O1000000000000004LL4001O0000O11OO1O10000C=00O1O100O1@gAOM0cTg6", "size": [480, 640]}, "area": 11117.0, "iscrowd": 0, "image_id": 13659, "bbox": [50.0, 0.0, 132.0, 195.0], "category_id": 172, "id": 20000827}, {"segmentation": {"counts": "eQb2=c>0^AE^><cAC\\>>cAC]>=cAC\\>>dACk=2UBM0e00]Oh=S101O000000NYBkNg=U1YBkNg=U1YBkNg=U12LWBPOj=P1WBPOh=P1XBoNi=Q1WBoNi=Q1WBoNi=Q130000000000000000000000000000O1000000000000000000000000000RBQOk=o0UBPOl=P1TBZOb=R10001O2N1OO1GaBkNe=P184LM300000000000cEnNl6R1TIFT6:lIEU6;kIFT6:lIEU6;kIEU6;kIoNhL<]9e0X4000000000bFROj4n0VKROj4n0VKSOi4m0[KoNe4Q1e400000000000`FQOo4o0QKROn4k0fFQO\\45n4j0fFQO\\45n4j0fFQO[46o4i0fFQO[46o4i0RKWOo4i0PKXOP5h0PKXOP5h0oJYOQ5g0oJYOQ5g0nJZOR5f0nJ[OQ5e0jJ@V5`0kJ_OU5a0kJ_OU5a0kJCQ5=oJCQ5=oJBR5>mJCS5=mJDR5<nJDR5<nJDR5<mJES5;mJES5;mJES5;UK^Oj4b0WK\\Oj4d0VKVOP5j0PKUOQ5k0oJSOS5m0mJROT5n0_400000000O100000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000O10000000000000000000000000000000000000000000000000000000000000000000oASOo=m0QB]Oe=i06M3000000000000000000000000000000MRBSOo=m02O10000000000000000000000000000000000000000000000O1O1N200O1O1O1O1O1O100H800O1000000002N3M3M3M2N2N2N2N3M2NN2O1N2O10000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001O000000O100000000000000000000000000000000000000000000000000000000000000000000O100000000000000000000H8L41O00001O000000O12N00O100000000O1O1O11OO12N003ML4002N1O1OO1O1001O3M1ON22N5K0000000000000aCVO[N5V<e0nDVOlNf0V<4`D<`;DjCS1U<m0O1O001O1O1O001O2N", "size": [480, 640]}, "area": 14776.0, "iscrowd": 0, "image_id": 13659, "bbox": [175.0, 0.0, 465.0, 186.0], "category_id": 173, "id": 20000828}, {"segmentation": {"counts": "mTk11n>6K0M4AGkA05a0i=`0L1000N22TBmNg=X1XBjNa=]1N008GM400N2000O0oNcNaD]1^;lNVDX1i;iNhCC0o1X<e0001O000O11N10O100001OL43M0e0[O00O11OO1001O00000000000000N22O0O0M300J6N24LI7N21O00000YO_NoC`1R<aNmCa1Q<_NoCa1Q<_NoC_1S<aNmCe0JFW<EoCe0m<[OSCe0m<[ObBV1^=600000000000000000O1000O1000000000000000000000000000cN`BZ1`=fN_B[1a=eNiBQ1c=0001O2N1OO1GROZBT1g0mNo;P2iCPN@1Z<Q2PDnMF2V<T2SDPNl;P2TDoMm;Q2RDPNn;Q2lCUNS<k1lCVNT<j1lCVNT<j1kCWNU<Z20001O0000001ON2O10000O1000000F:O100000000000MdMaC^2^<bMbC^2^<bMbC_2]<aMcC`2\\<3O1O2N1O000000001O2N000000000000003M2N1O1ON2O11O00O11O0000001N1000O100ARD_M60m;]2>00O1O11O00001O1O001O000000002aC_M41P<e2mC[MOOR<l20000PDUMk;k2TDVMl;n210000O10000O100O10000LQDWMo;a2<I7N21O001O001O1O1O7I0000O100O11OO10000O001000000O100000000001O000000IgCgMY<W2jCiMU<U2nCjMR<V2<O1O100O100O10000O1000000000000O1PNRCn1n<RN\\Cd1j<5N3000000000001O001O@YCgNg<W1b0O1O1N2OYCiNg;V1ZDlNa;N_CU1R1nN^;R1dDmN\\;R1fDmN[;R1Z1N2N2N2O1N2N2O1N2N2N2O1M3N2N2O1O100O1O1O100N1OVe<3hZC5J5L4M4L2N2N1O1O2N1O2N1O2N1O2N1O2N1O2N1O2N3M1ON2M3O11O0000001O2N00001O0000001O001O0nD[Ni8f1VGZNj8f1VGZNj8g1W22N2N2^CYN>2P:g1[E`N:0[:c1TEFl:Q2SEYLm:d3900000000000000000000O2O000000O1000O1000000000000ORE^L`:b3`E^L`:h1VEL;\\N_:g1[EI6aN^:e1`EF3eN]:d1eEBNkN]:b1iE@JnN]:a1mE]OFSO]:_1PF[ODWO[:\\1TF\\O@YO\\:Z1VF[O_O[O[:Y1WF\\O]O\\O\\:X1XFZO\\O_O\\:W1XFYO]OAZ:V1ZFXO[O\\OFOd:]1[FWOGZOn9_1\\FUOa:k0_EUOa:k0`ESOa:m0_EROb:n0^EROb:n0_EPOb:P1^EoNc:Q1^EnNb:R1^EmNc:S1]ElNc:V1_EdNYOKg:a1iF_NW9b1gF_NY9a1gF_NY9b1eF_N[9a1eF^N\\9c1P2001O001G8L50O000010O00000O21N00O10B_C]Na<c1_C]Ni<[1XCcNj<\\1?NO1000O10001O2NM2h0YO20O@ja=", "size": [480, 640]}, "area": 30394.0, "iscrowd": 0, "image_id": 13659, "bbox": [126.0, 15.0, 485.0, 148.0], "category_id": 174, "id": 20000829}, {"segmentation": {"counts": "oei01P?0O2M2N3M2M4M2N3M3M2M4M2N3M2N3L3N3M2N3L4M2N3M2N3L3N3M2N3M2M4M3N101O0YOcMjDe2k:gMmDW2Q;oMlDQ2R;RNmDn1P;UNPEj1P;WNPEi1o:YNQEe1o:\\NQEd1^9ZMmGS1eNb1]9]MmGR1fNa1[9`MmGQ1gN^1[9dMlGo0jN\\1X9gMmGn0kNZ1X9jMkGm0mNY1V9mMkGk0oNW1U9PNjGl0QOS1S9TNjGj0SOR1Q9WNjGh0UOP1P9ZNjGg0VOo0n8]NjGe0XOm0m8aNiGb0\\Ol0i8dNjG?_Ol0f8hNiG;Cm0b8kNiG8Fl0`8nNjG4Im0[8ROjG0Mm0X8VOiGL1m0U8ZOhGG6n0Q8]OiGoNl0d1Z7_OhGgNV1h1Q7DiI;W6FiI9X6HgI7Z6JeI5\\6KeI3\\6NcI1^60aIO`61`INa63_IKb64_IKc63^ILc63^ILc63^ILc63]IMd63[IMf62ZINg61YIOh60XI0i6OWI1j6OUI1l6NUI1l6NTI2m6MSI3n6LRI4o6KQI5P7KoH6Q7IoH7R7HoH7S7GmH9T7FlH:U7EkH;V7EiH;X7DhH<Y7CgH=Z7BgH=Z7BfH>[7AeH?\\7AcH?]7AcH?^7@bH`0^7@bH`0_7_ObH`0^7AaH?`7@`H`0`7@`H`0a7_O_Ha0a7_O_Ha0b7^O^Hb0n5]NYIR1i0`0m5fNTIi0o0a0l5nNnHa0V1a0k5VOiH8\\1POfM`1T8_OcH1c1oNgMa1R8CbHLe1QOfM`1R8G`HIg1POgMa1Q8H`HFh1g0e5G`HBl1h0a5IbH^Om1j0_5LaHZOP2l0]5LbHWOQ2n0[5OaHTOS2o0Y50cHPOT2R1V52cHlNW2S1U53cHiNY2S1S5U1mJkNR5V1nJkNP5W1oJiNQ5W1oJjNo4W1QKiNn4X1RKiNj1nMlNZ3ZOhNj1PNjNW3\\OkNi1SNeNP3DnNf1XN`Ni2LoNd1\\N\\Nc21ROc1`NVN\\29UO0VNaN<R1X2?VONkNkMHg1Y2>UO0jNkMIf1X2?UO0jNkMIe1Z2>TO2iNkMId1\\2=SO4hNjMJd1\\2>RO4hNjMJd1]2>PO4iNjMJc1^2a0mN2kNjMJc1_2b0jN1mNjMJc1_2d0hNOoNiMKc1`2f0dNNQOiMKc1a2g0aNMSOiMLb1`2i0`NKUOjMKMSO[1^3R1_NLUOiMLI\\O[1T3X1^NKUOjMb0m0l1]1]NLUOjMg0g0h1c1\\NLUOjMm0a0c1h1[NMUOiMR1?]1l1[NLVOiMR1`0\\1k1\\NLVOjMP1a0^1h1\\NMVOjMP1b0]1g1]NMVOjMo0c0_1e1\\NNVOjMo0c0_1e1\\NNVOjMe0NXNf0b3c1[NOVOjMe0m0j1Z1[NOVOjMe0m0k1`0aLmNi1l1VOjMe0m0k1<eLQOe1l1VOkMd0l0m18hLSOb1n1UOkMd0l0m14lLWO^1n1UOlMc0k0n10QMZOY1o1UOlMc0k0o1LSM^OV1o1UOmMb0j0P2KUM^OT1P2UOmMb0k0n1IYM^OS1Q2TOmMb0k0n1H[M^OQ1R2TOnMa0j0n1G_M]Oo0T2SOnMa0j0m1GaM[OP1V2QOoM`0i0m1FeMYOo0Y2oNoM?j0m1EgMWOP1[2VNgNU12n1CjMVOo0R4YOUMm1C7Y3kMTMm1B:Z3iMTMm1A;\\3gMSMn1_O=a3bMPMQ2^O>e3^MmLT2\\O`0k3XMiLX2[Oa0o3TMgL[2WOc0V4[LdKLo0V3VOd0o4ULkKX3SOe0S5RLjKY3ROf0T5QLjKY3POh0W5nKiK[3nNh0Y5mKiK[3lNj0`4nKmKMg0\\3jNj0a4\\L`K]OV1]3gNl0b4dLgL`2fNm0a4dLjLn6U3SIkLm6T3SImLm6S3SImLm6S3RIoLm6Q3TInLl6R3XIkLg6U3YImLe6S3[InLd6R3\\IoLc6R3]InLb6R3^IPM`6P3YIaKUO`1a7o2YIdKTO^1b7n2ZI[Me6f2YI]Me6d2ZI`Mb6a2\\IfM8gMS5d4dJkM2cMY5c4cJQNM^M_5b4cJRNJ_Mb5`4bJSNJ_Mc5_4bJSNI`Md5^4aJUNG`Mg5a4[JQNIcMk5d4TJaNk5[5O1O1O1OmN\\JkHc5T7cJhH\\5W7eJjHZ5U7hJiHY5V7iJiHW5U7kJkHU5T7mJkHS5T7nJlHR5R7QKmHo4R7RKnHn4Q7TKmHm4Q7UKQIi4n6XKUIe4i6^KWIa4h6`KYI_4e6cK\\I\\4c6eK_IY4`6iK`IV4^6lKcIS4\\6nKdIR4Z6QL]IgN_OW5S7SLoHWOLf4S7RMmHm2R7VMlHj2n4gJUMk7i2[HQMg7n2]HnLd7Q3`HjLX2\\Nb2i4ZKgLT2bNa2e4aKeLm1gNb2c4eKbLj1lN`2a4jKULPO_Oa20e2Z4QLQLiNFj1f0\\3b3YLjKbNNe1k0_3\\3aLdK\\N5_1P1d3X3iLgKCS1c3V3jLgK[OHlN[1o4V3jLgKWOLPOW1o4V3jLgKSO0TOT1n4U3kLgKPO3WOQ1n4U3kLgKmN6ZOn0n4U3kLfKkN:\\Ol0m4T3lLfKhN=_Oi0m4T3lLfKfN?Ah0l4T3lLeKdNb0De0l4T3lLeKaNe0Gc0k4S3mLfK]Ng0Ka0j4X3hL`K`Nj0N>j4^3bL[KbNl02<i4c3]LVKeNm05;h4g3YL[LoNOg4g3ZLYLoN0g4d3mKhJEd1G1f4`3aL_LiN2e4]3dLaLgN2e4[3fLcLeN3d4X3iLeLcN4c4T3mLhL`N5b4Q3PMjL^N5b4o2RMlL\\N6a4l2UMnLZN7`4i2XMPMXN9^4d2]MSMUN:]4a2`MUMSN<[4]2eMVMPN?Y4Y2iMXMnMa0W4U2mMZMlMc0U4P2RN]MiMe0S4e1]NfM`Mf0R4a1aNiM]Mh0P4\\1fNlMZMj0n3X1jNnMXMl0l3U1mNoMWMn0j3R1POPNVMP1h3P1ROPNVMQ1g3n0TORNTMR1f3l0TO^LRMd14T1d3j0RObLWM_13W1b3g0\\ORNRMY1`3e0^ORNRM]1\\3a0CQNQMc1W3;JQNoLh1S37NQNoLm1n223QNoLQ2j2N7QNoLQ2j2N7QNoLQ2j2N7QNoLP2k2O6QNoLP2k2O6QNoLP2k2O6QNoLP2k2O6QNoLP2k2O5RNPMo1k2O6QNoLP2k2HUMhLg2O^M_1LR2[1_LMW3jNjL_21eM\\1KS2^1fL^On2XOkL[22hMZ1JU2a1lLPOh2Y2kL_MZ1GW2g1RM_Na2OlLc21VMW11[2k1UMoMYO2R3\\3oLgLT11^2n1oLnMe2\\3kLYM?_OS3R2hLlMj2Z3mLQNc3j0dLlMl2Q2iLPO57c3m0`LmMn2o1kLPO?JZ3Z1\\LoMP3k1nLQOo0ROm2S2ULPNQ3i1PMPOj0YOROGf3X2ZLQNS3f1SMQOc0Hj2`1[LSNU3c1UMQO=Nl2l1BSOXMQO38S3d1BROZMk0T33BPO\\Mm0R33BmN`Mo0n24BjNdMP1k26@iNgMQ1i26@fNkMS1e27@dNnMT1b28_OcNjMcNVOb2a38_OaNiMjNVO[2c3:^O_NiMd1j2L]O`NgMf1l2J]OaNeMf1n2I]OaNdMg1o2H]ObNbMg1R3C@eN^Mh1R3ABhNZMh1T3]OFjNUMj1U3YOYOjMVMS1:j1Y3VOWOC`MW1Y3SOYOH]MU1Z3PO[OLZMU1[3mNYLlMn2m0PME2N;g2\\3jNZLQNl2l0SM_O427h2]3fNYLXNk2k0VMXO483h2_3cNYL[Nj2l0ZMPO5=Oi2_3aNYL_Nh2l0mM[OBh2a3iMULE3Ef2l0PNXOAg2]2mL\\Nm0nNC2Je2k0QNXO_Og2Z2SM^Ng0QO@2Nc2k0TNUO^Oh2V2ZM^Nc0TOZO32b2m0TNRO_Oh2Q2bM_N=YOTO19a2l0WNPO\\Oi2n1iM_N9^OmN0>a2k0XNoN\\Oi2i1QN`N4BfN0d0_2k0UNSO@c2f1WN`N1HYO[2k0nMkNDO5k2c1[N_Ni0W2YOeMA0S2d1^N\\Nf0Y26dMjM3l2b1R1e0jNhM4a1T1e0[NhMeM2l2`1U1e0kNjM0_1V1e0mNmML]1X1d0oNnMI\\1Z1d0oNPNG[1[1d0POPNEZ1]1e0oNQNDX1_1g0W2XOiMh0oNQNBT1a1j0X2UOiMj0X2TOjMl0V2SOkMk0W2SOjMl0X2ROjMk0Y2UOgMi0[2WOeMi0\\2VOdMi0^2VOaMj0b2UO]Mj0f2jN\\HKn4Z1i2`NbH6e4Y1[O_NA8S1Y1Y3gNfLZ1Z3gNdLR1d3nN[LS1e3nNYLR1h3nNWLS1i3nNVLR1j3oNTLR1l3nNSLS1HaNI==V1H]NJ==Z1FYNM>;]1EWNN=;_1EUNO<;b1DSNO=;d1CnM3>9g1BkM5`06h1DfM6e03h1EbM9h0Oj1E^M<j0Ll1f3VN[Kk2`4XMXKQ3e4PMXKV3d4lLWKZ3e4iLVK[3i4gLRK^3l4dLoJ`3i0YKSO2;W1Dc38gKFI7P1Fd39fKF03j0Ie39eKF61b0Kh3:eKD;O<Mj3;dKGh0^OL<l3>cKEU3H^1b0_KCjNQOR4f0j1h0WK@ROUOg3?T2o0oJ]OXO[Oj2LVM9o5T1fJ\\OWOGR24RNAT6Y1_J[OUOP39XLX6]1WJ[OUOT3<PL]6b1PJ_3d6_L[Ia3h6]LWIc3i6^LVIb3h6aLWI_3f6eLYI[3f6gLYIZ3g6eLYI[3g6fLXIZ3h6hLVIX3j6jLTIV3m6oLmHQ3U7TMdHl2\\7TMdHl2\\7TMdHl2]7TMbHl2^7TMbHl2^7TMbHm2]7SMcHm2^7RMbHn2^7RMbHn2_7QMaHo2_7RM`Hn2a7QM_Ho2a7QM_Ho2c7oL]HQ3d7nL\\HR3f7lLZHT3h7YKcH;1[4^7TKkH20j4Y7lJlIT5j7nJ^FR5_9:O00000000jJ`Fl4`9TK`Fl4`9TK`Fl4`9TK`Fl4`9TK`Fl4`9TKaFk4^9VKbFj4^9VKbFj4^9VKbFj4^9VKbFj4^9VKbFj4^9VKbFj4^9VKbFj4]9WKcFi4]9WKcFi4]9WKcFi4]9WKdFh4\\9XKdFh4\\9XKdFh4\\9XKdFh4[9YKeFg4[9YKeFg4a7PKRJ:]Nf4`7UKoI5bNe4^7ZKlI2fNd4]7_KhINkNc4\\7cKfIIoNd4Z7gKcIETOd4X7jKaICXOb4U7nKbI_OZOc4S7PLaI^O\\Ob4R7QLbI\\O]Oc4Q7RL`I\\O_Ob4Q7RL_I\\OBa4o6TL^IZOEa4m6UL]I[OGb3HfLT7>[IZOJV39lLb6d0[IZOKj2j0QMP6k0ZIZON]2[1WM]5S1XIZO1o1S9gNlFYO2e1]9RO`FZO4W1h9@RFYO6l0KfNg9U1XFXO42@Nc0J`9n0XFYO23F_Oj09U9m0WFXO34i0Hn8n0PFYO73j0Hn8d1WFfNF_Ok08Y9b1TFhNGEf03^9_1TFkNHDe02`9]1RFnNIEc02a9[1QFPOKCc06^9U1QFK=UO^9Q3[FSMb9g3WFiKe9W4_FiK^9V4fFjKV9V4nFkKn8T4VGmKe8S4_GoK[8Q4hGQLS8o3QHQLl7n3XHoKg7Q4_HgKc7Y4dH]K_7c4`1N1L4M3MkEjKf9T4UFSLl9i3QF]Lo9a3lEfLT:S4001O0000000000XIYKW3g4aLaK_3_4XLiKi3W4oKQLQ4o3gKYLY4g3^KaLd4^3TKjLl4V3kJSMU5m2cJZM^5f2YJcMg5]2QJjMP6V2hIRNX6n1_I[Na6e1WI\\NP7o0oH]O[7b0hHSOa7m0\\HlNn7S1RHdNX8\\1hG[Na8d1`GRNk8m1UG\\Mb9c2_FeLY:Z3gE`L`:^3bEcL]:\\3dEeL\\:X3fEiLZ:T3hElLY:R3hEoLW:o2kERMU:l2lETMU:i2mEXMS:e2oE\\MP:c2QF]Mc<M_C4n=InH", "size": [480, 640]}, "area": 119114.0, "iscrowd": 0, "image_id": 13659, "bbox": [54.0, 10.0, 586.0, 469.0], "category_id": 183, "id": 20000830}, {"segmentation": {"counts": "[6S4m:00O10000O10000N2000000O100000O10O10O01OLTLUEm3h:60100000O100000001O0O100000000000G8011O1O1O4L1N3N2N5J10001O000000O1000O100000000000O1O1N2O1O11N1000001O000O101N100O1O1_MUEl0m:SN[F8WO1`:GZF6YO0_:IXF9YOJ\\<0kCM_R[51T[eJ2O001O1O1O100O1kMJ_E6P9IYF5a08R9GXF5a0:T9DWF5j04j8JYF5l04g8JXF68D\\O`0P:ITF;=_O\\Oa0m9JmEb0d0ZOA<h9Z1`FbNG6j8ROcGY2GMh8nMYGm4i8XKoFh4T9b000O110N1O10001N1VOjJjGV5j7V1D=eNkHnIL8a7`5^ITJlN3g7e5_1XOPG]KT9Z4UGeKk8[4UG`KP9`4i0N1O2O0000000O102NO11OL`KhE`4X:`KhE`4X:`KiE_4W:aKiE_4V:cKiE]4k7aKSH100T22mM\\4l7nKPJGTNZ4k7QLQJDTN\\4j7PLbIBgNOL:1U4i7SL_IBhNl4i7bKSIEVOP5g7[KiHO_Of4i7\\KbH^5]7cJ[He5e7k0000000D<N2O1O1N2E;O10000001XHVIX7R7`HQI]7Z7bHdHV7c70000000O1001O001ON2K5D<G9F:F:H8G9H9H7HZFQKk9n4jHRKc4a4T3^OgERLM0d:d3_E[Lo:Y3c0B=D<G:D;E<F9F<E9E^TS1", "size": [480, 640]}, "area": 33220.0, "iscrowd": 0, "image_id": 13729, "bbox": [0.0, 21.0, 565.0, 337.0], "category_id": 98, "id": 20000831}, {"segmentation": {"counts": "^:e2[<O010O10O0100OO2000O10O01000000N110O000100O10001O0OO2O1O1O1O10O1000000000001N10O12N00001N4M1O1N2O1O1O2N2N2N2N3M3L3N2N3M3M2N3M3M1O1O1O2N0O2O2N002N1O1N101N10M2001O03N2MiEhKi9W4XFjKg9T4[FlKe9S4\\FlKd9T4d0OjEnK`9R4aFRLZ9m3hFRLX9n3hFRLX9m3iFSLW9m3iFSLW9m3iFSLV9l3lFTLT9i3oFWLQ9e3SG[Lm8[3]GeLc8]2[HcMe7f0^F\\OP3No65iIKf:000000000001O000000M3L4M3L4M3M3M3N2M3M3N2M3M3N2M3M3L4aN[N^Ej1]:`NYEe1c:dNSEa1h:iNnD\\1n:]1M3O1O1O1O1N200000000O100000000O100000000O1000000000000002N2N3M2N3M2N4L3M3M3M4L3M3M3M3mMjCV1X<gNjCX1Y<dNjCZ1X<cNjC\\1Y<aNhC^1Z<aNfC^1]<_NdC`1^<^NbCb1a<[N`Cd1b<ZN^Cf1o<O1OYMbNlG^1S8hNiGW1U8mNjGR1T8ROjGn0j7@UH?S6hNcH[1Y1LV6UOdG_1U2\\OY6b0bGeNT2h0\\6?dGiNn1h0`6:gGnNh1g0c67gGUOd1c0g63hGBY1;g7YORGe0n02_8gNfF_1c0J_9a0WF_Oi9j0mEWOS:R1dEnN[:V1bEjN^:Y1^EhNb:k200O100O100O1VOhKlFX4k8XLmFi3k8eLnF\\3Q9Z1001O0000001O00001O0000001O00001O00001O001O0000001O0000001O00001O00001O000000001O00001O00001O00000gJbF^1Nm1a9dL_F_12m1_9dL^F]16o1\\9cL_Fo0M[O;S3`9`MUF]O;S3`9`MVF\\O:T3_9`MXF\\O:S3^9aMYFZO:U3]9aMYFZO:U3\\9bM[FXO9V3\\9bM[FXO9V3\\9aM]FXO8V3[9bM]FWO9W3Y9cM_FUO8X3Y9cM_FUO8X3Y9bMaFUO6Y3Y9bMaFUO7X3X9cMbFSO7Z3W9cMbFSO7Z3W9cMbFSO7Z3V9cMWG]2i8cMXG\\2h8dMXG\\2h8dMWG]2i8cMWG]2h8cMYG]2g8cMXG^2h8bMXG^2k8_MUGa2W9RMiFo2X9PMhFP3]9kLcFU3c9eL\\F\\3d:000O10000O100O10000POdL[F]3`9kL]FU3]9UM_Fk2b9WMZFj2f9ZMVFf2j9]MSFc2m9`MoEa2Q:W11O0000000000001O00001O00001O0000001O0000001O00001O0000001O0000001O00001O0000001O00000nLdE`1]:_NiE[1W:dNkE[1U:eNkE[1V:dNjE\\1V:cNkE]1U:cNlE\\1T:dNlE\\1U:bNlE^1T:bNlE^1T:bNlE^1T:aNmE_1T:`NmE_1S:aNmE_1S:lMaEN=V2h:jMXEV2i:iMWEW2i:hMXEX2i:gMXEX2h:hMXEX2h:gMYEY2g:gMYEY2h:fMXEZ2h:eMYE[2g:eMYE[2g:eMYE[2f:eM[E[2a:iM_EW2\\:mMeES2Z:mMgES2Y:kMiEU2W:jMjEV2U:jMlEV2S:jMnEV2T:gMmEY2o9jMRFV2R:eMoE[2[;000000000000000000000000000000000I7E;J6J6J6J6J6K51O1O1O001O1O1O1O1O1O1O1O1O1O001O1O1O1O1O1O1O1O1O1O001O1O1O1O1O1[N^DJc;4^DLc;1`DNa;OaD1_;McD3^;KcD5^;HdD8^;DeD;^;AcD?`;\\ObDd0a;WOaDi0b;mNeDS1^;aNkD_1\\<O00O1aMdNZG\\1d8gNZGZ1c8kNZGV1f8lNXGT1h8nNUGS1k8oNRGR1o8oNoFQ1Q9QOlFP1T9ROhFP1X9QOeFQ1\\9oN`Fg0nNbNb:h0]Fg0ROaNa:i0ZFh0UO_Na:j0VFi0ZO]Na:j0RFj0^O\\N`:k0oEj0B[N_:l0kEk0GYN^:m0hEl0JWN_:m0cEn0OUN^:n0`En03TN]:o0]En07SN]:o0XEP1<QN\\:o0VER1>oM]:o0QET1c0mM\\:U3eEkL[:T3fElLZ:T3fElLZ:S3gEmLY:R3hEnLZ:o2gEQMX:o2iEQMX:n2m0O1O2N1O100bMlCF0U2V<RNRDk1o;TNSDj1n;SNVDl1_<O2N1O1O100O1O00GXNVC00i1e<^NZCf1h<WNYCh1f<900100O1O1O1O10O01O1O010O1O2N1O101N2N2N2N5]NbB\\1_=cNbB[1c=N2N1O2N1O1O2N100O2N1O1O1O2N2N1O1MgA^OZ>b03O0O100O1O1O1O1O1O1O1O1O1L4MUA1nan0", "size": [480, 640]}, "area": 60941.0, "iscrowd": 0, "image_id": 13729, "bbox": [0.0, 267.0, 575.0, 213.0], "category_id": 101, "id": 20000832}, {"segmentation": {"counts": "j`g42m>6K5M3M3MN2N3M7IeW`4", "size": [480, 640]}, "area": 107.0, "iscrowd": 0, "image_id": 13729, "bbox": [323.0, 121.0, 9.0, 23.0], "category_id": 105, "id": 20000833}, {"segmentation": {"counts": "Q]\\81n>3N1O0KN\\A2c>0\\A1c>601O1O001O01O1OHDlA<T>EkA;S>HlA8T>HlA8T>IkA6V>JjA2MKY>3jA1NLX>3hAKO72IW>6gAJ063JW>5fA3_>7000000001O0001M1N3M32M4MO12N003M00M4OEbA4f>O1O010OO1001O0000M3O11O0O101O000O1O1M4N1OVT1", "size": [480, 640]}, "area": 809.0, "iscrowd": 0, "image_id": 13729, "bbox": [572.0, 268.0, 65.0, 29.0], "category_id": 121, "id": 20000834}, {"segmentation": {"counts": "0W2n2oNg5Q1YJoNg5Q1YJoNg5Q1YJoNg5Q1YJoNg5Q1XJPOh5P1XJPOh5P1XJPOh5P1XJPOh5P1XJPOh5P1XJbNWMAa8n1WJ`NZMA_8o1WJ_N[MB^8o1WJ^N\\MC]8o1WJ\\N^ME[8o1VJ\\N`MEZ8o1VJ[NaMEZ8P2UJZNbMFb8g1lIbNcMGi8JjFb1l2jNbMIi8KiFb1R3bN^M1j8]1^J_NXL0\\9`1P4001O00000001O000000000003J[NkBj1o<XNPCm1k<6N000eEZN\\7f1dH[N[7e1eH\\NZ7d1fH]NY7c1gHaNU7_1kHbNT7^1lHdNR7]1mHeNQ7Z1PIkNk6U1UIkNk6U1UIkNk6U1UIlNj6AoFh0W2Gj6@SGb0W2Ni6oNfF:;8FGd2h0o6oN[F0`0<FIJKh2o0c7YOoHg0R7UOQIk0P7RORIn0`:00000000ETBBe>O1O2M2OVQ8;XnG:L2M2N33M0O10O1001O0O103K7KT]f1CPbYN0YC8h<InB`0S=AbBi0^=WOYBQ1h=32N10nNmNVDR1j;POUDo0l;QOUDn0j;TOVDj0c;^O]Da0b;B^D<b;F^D9a;H_D7a;KcDO];3aDN_;2`DN`;4_DKb;6\\DKc;6ZDLf;6YDJe;8ZDHf;:YDEg;=XDBh;?WDBh;`0WD_Oi;b0WD]Og;f0XDZOe;j0[DVOb;l0^DTO_;n0oC^Na0c0];R1fDoNV;T1WDXN`0c0W;W1ZDVN>c0^7^OeKl1_LTN=c0]7\\OTH0`3o1cLRN:d0_7[OUH1[3P2gLPN9e0e7[OYKQ2iLPN8d0`7ZOYH1T3R2lLoM5e0j7YOUKS2lLPN4e0U;[1gDPN3f0V;Z1fDQN2g0X;X1fDQN4f0[7ZOjHOe2R2oLPN7e0S7AZLk1[LoM8e0S7AZLR2cL^NQ7@^LQ2bL^NP7A^LQ2bL^NP7@aLQ2^L_NJ[On65kLU3VLdLQ77jLT3ULdLR77lLS3QLgLS76oLW3o2hLUMU3k2kLYMP3g2QM]Mk2c2TMaMi2_2WMeMd2\\2[MhMb2X2^M19OG28NG38NH37MH57KI57KI66JI86HJ86HJ94IL73IM73IN71I07OI17NJ36LJ47KI6`71O001O001O1O0WbQ1HQ]nNP1I8K4F;H7K500O2O0O100O[N^Cm0c<nNcCm0`<POfCi0_<SOgCj0Y<ROnCl0R<POTDn0R=L4L4O:@Ycd0Of\\[O7I7I7I7K5N2N2N2M3N2NO1D<F9GVWf1DY_YNM_KO53X9:eJGQL61IR9a0QKKmKEo8b0PKOoK@i8h0SKMTL\\Oo75oGm0h3HZLUOl7e1dK]O^LoNn7d1aK@aLkNo7e1^KJb46^KJb47]KIc47_KGa49^KHb48^KHb48^KIa47_KJ`46`KBcLhNm7f1`KBdLgNl7g1`K@gLhNi7h1`K@gLhNi7h1aK_OgLhNh7i1aK_OgLhNh7i1aK_OgLhNh7i1aK_OgLfNKOm7m1`K^OiLeNJ0m7m1`K_OhLdNj7m1^K_Oi4a0XK^Oh4b0XK^Oh4b0XK^Oh4b0XK^Oh4b0XK_Og4a0YK_Og4a0YK_Og4a0YK]Oi4c0XKYOk4g0UKVOn4j0RKXOl4h0TKUOo4l0PKoNU5Q1kJkNY5V1fJbNb5_1^J[Ng5e1YJVNl5j1TJPNR6P2_3000000000000000000O1000000000000000000000000000VN", "size": [480, 640]}, "area": 17691.0, "iscrowd": 0, "image_id": 13729, "bbox": [0.0, 0.0, 640.0, 365.0], "category_id": 123, "id": 20000835}, {"segmentation": {"counts": "iVQ22n>1O2N1O1O1O1O001O1O1]AE^>a0G_OkAd0V>]OiAc0W>]OiAc0W>]OiAc0W>]OiAc0W>7MK4O1[OgAc0\\>N100_OcA?`>N200O00000001^AC_>=aAD]>=cAC]>=cAC]>`00000000000000KcAG]>NcA80H0O^>0bA100020N0O^>0bA1000O10O100]>OcA100000O10O1cee6", "size": [480, 640]}, "area": 699.0, "iscrowd": 0, "image_id": 13729, "bbox": [139.0, 49.0, 46.0, 28.0], "category_id": 130, "id": 20000836}, {"segmentation": {"counts": "_\\m7140dT5;_YK=mAYOl=h0TBXOl=i0SBXOk=j0TBVOk=Q1O0^OPOSCR1k<ROQCP1m<TOPCl0o<c001O001O0000000000SOPCQOO536n<5SCC61I7n<:^C_OE2KLS=>cCB@2MMn<;jCF\\O1P=3jCLUO2[>LiA1W>OjA0dWm0", "size": [480, 640]}, "area": 1083.0, "iscrowd": 0, "image_id": 13729, "bbox": [540.0, 232.0, 38.0, 62.0], "category_id": 139, "id": 20000837}, {"segmentation": {"counts": "PgZ71o>00000O2cMN`C100T22YNND0j;<RFG]N5^;6UFD\\N8^;4fEBgNOL:65_;3dEBhNQ1d;]OXEEVOV1a;UOoDO_Om0b;UOiDe1V;\\NbDl1^;k0000O1F]DQM00P<^2WDaMm;a2PD`MQ<]2:O100000000000N1I8K50O1XOQD_No;^1XD^Nh;^1]DbNa;]1cDaN];\\1gDcNZ;Y1jDgNU;W1kDlNU;U1gDnNV;BQD1022k0h0Bi;=aCWO5c0V<7bC<^<P14N1N\\CjMc<T2`CkM_<T261N2MlN^C0^<4cCL\\<oNcCo021[<POcCo04ROKg0_<WOcCn05POLk0]<VObCn0:ZOE7e<=fC[OK4_<a0fCZOO2\\<c0eCZO11\\<e0aCZO8MW<i0aCXO=LR<l0aCYOX=f0hBWOGNa=k0jBUOE1a=j0iBUOG0`=k0a000001O00000010ORCTOQO38Oc;k0SEWOoN16KI1P<l0[DSO8=^OC25m;h0YDVONNO<KFO2R<h0VDWOHL00O35;MMQ<b0VD8GVOU<a0UD7GXOU<`0UD7EYOW<`0UDb0m;]OSDc0m;]OTDa0m;@QDa0o;_OPDa0Q<_OoCa0Q<@nC?S<AnC>R<BnC=R<DoC;m;hNjCm0;8k;lNjCm0:7m;mNgCl0;7n;nNgCk0;7n;NRD1o;0oC1Q<OoC0R<0mC1S<]1100O101N010O10O0100O100000001O0000001O000000000000000000O1000000O1O1M3O1O1O2O01OTG", "size": [480, 640]}, "area": 9469.0, "iscrowd": 0, "image_id": 13729, "bbox": [500.0, 268.0, 140.0, 120.0], "category_id": 165, "id": 20000838}, {"segmentation": {"counts": "S=m1R=100O100O100O10000O100O1000000N200O1O1000000001O00N2O1O1O1O1000000000000001O00O12N00001O3M1O1O1O1O1O2N2N2N2N3M3M2N2N3M3M2N3M3M1O1O1O2N001O2N002N1O1O001O003M1O1O2NiIb0PHUO]48b3f0nGUO`43d3h0hGXOe4Ni3e0_G@h4Jk3T1TLlNl3T1TLlNk3U1WLiNi3W1P50O10000000O0100N20eNgBo0Q>Dh[l7", "size": [480, 640]}, "area": 5472.0, "iscrowd": 0, "image_id": 13729, "bbox": [0.0, 267.0, 101.0, 213.0], "category_id": 167, "id": 20000839}, {"segmentation": {"counts": "W2n2R<00000000O1000000000fDWM^:h2aEZM^:f2aE[M_:e2`E\\M`:d2^E^Mb:a2^E`Mb:`2]EaMc:_2\\EbM\\8LQIb2bNcM]83TG@b1l2jNbM`82TG@b1R3bN^Mh83fHU3[7lLdHT3\\7lLdHS3]7mLdHR3\\7nLdHR3\\7nLdHR3\\7nLdHR3\\7nLdHR3\\7oLcHQ3]7oLcHQ3]7oLcHR3\\7nLdHR3\\7nLdHR3\\7nLdHR3\\7nLgHo2Y7nLRG_Oj1^3T7RMTG_Om1Z3o6WMWIg2i6YMWI^2R7bMnHm1c7SN]Hm1c7SN]Hm1c7SN]Hm1c7SN]Hm1c7SN]Hm1c7SN]Hm1c7SN^Hl1b7TN]Hm1c7SN]Hm1c7SN]Hm1c7SN]Hm1c7SNjFFh0W2^8SNiFJb0W2e8RNXF]O:;8FGd2_9XNXFRO0`0<FIJKh2g9kNaFg1_9ZN]Fi1c9XNZFj1f9h100000000mFlJZ8m50001O00001O0000001O0000001O0000000YH[JS6f5lHbKl6^4TIcKk6^4TIbKl6Y60M3N2M3M33M0000O1001O]M`IfL`6W3aJkK_5S4_KTK^4k4l2TN]FZNd9f1RGbMP9]2RGaMo8^2TG`Ml8`2UG^Ml8a2VG]Mk8b2XG\\Mh8d2YGZMh8e2[GXMf8h2\\GVMd8i2^GVMb8i2`GVM`8j2aGUM_8j2cGUM]8j2eGUM[8k2m1O100000000O100000000000000000000001O0000001O00mNnCYOQ<g0PDXOP<h0RDWOm;0oCH57l;1QDF49j;1[D]OLb0h;1ZDYOGK9h0h;4^DSOA19g0i;5aDSOHb0E\\OU<o0nD@mNAY<o0kDROlN5`<i0_1O11O0000O1000000000000000UBROd=o0[BQOe=P1ZBPOf=S1WBnNh=R1XBoNg=Q1YBPOf=P1ZBPOf=V1O001O0000000000001O000000L]BkNc=T1_BkNa=U1_BkNa=U1`BjN`=W1_BiNa=W1400000000001O000gDjNh8V1XGkNg8U1ZGkNe8V1ZGlNd8T1]GlNb8T1_GmN_8S1aGnN^8R1XGYOg8g0mEg0Q:U2O2N1O1O3M4L3M3M3M4L3M3M4L3M3M4L3M4L4L3M4L3M4L3M=C000000001O001O0000O10000000000N2M3N2M3M3M3N2VL\\GU1g8jN[GS1g8mN[Go0g8QOZGm0g8RO[Gl0f8TOZGl0f8TOZGl0f8SO\\Gl0d8TO]Gk0c8oNhGl0X8lNnGV1R8jNUHo0k7PO\\Hk0c7UOcHe0]7[OdHf0Z7ZOfHg0Y7YOhHg0W7YOjHLWOWNm7m1lHROj8n0WGgNS9Y1mFeNU9Z1mFcNU9]1kFaNW9_1jF]NY9c1gF[N[9e1fFXN\\9h1dFVN^9j1l1000000000000001O0000000000000000000000000000>B1O001Oc0]O1O2N1O2N1O2N1O2N1O2N1O2N1O2N1O2N1OO1002N2N2N2N3M2NK5F:]Oc0_Oa0]Oc0@`0K50000000000O100000000000000O10000000000O1O1O1O10000000000001O00O11O00000000001O1XBiNe=Y10000000000000000000000000O100O1000000000000000000000000000000000000O1O100000000O1000000000000000000O10000001O1O00001O000000O100O10000000000MWBoNi=i0;JSKH^K8W43iKMl3>TLBY3Q1gLoNX3R1hLnNU3U1kLkNR3Y1mLgNP3\\1PMdNP3]1oLcNR3W1SMhNn2S1WMmNi2X1RMiNm2V1TMjNl2=cH\\Oc47k2:eH\\Ob49j25kHA[4>f2JWIGS4b0b2BhNb0i73M001O0000000000000000N21O1O00N200H80000O1O100000000O100000000000000000000001O000000O1000000000000000000000000000000000000000`KA_J?^5H^J8^5MaJ3]51aJO\\55cJKZ59eJGY59iJGY55iJKU56lJJV5@kF5R4;m2EgJHo2c0Q2L04F1?OWO7m0IkN<X1D_Nd0b1\\OVNk0k1VOmMQ1S2oNeM\\OQKj1\\2VN_2d03`1XMmMd2d0GP2`M]Mh2c0VO^ORLM0k2Q2YMRNK]4g0TO]O[LV3Q2mLoMLa4d0hNT3hNaLXNEm3g0eN_3VOULWNKj3a0]Nk3BiKVNOh3=WNb3mMRLo10TN1h3<lMk3]NdKm12RN8d37eMU4lNWKi15PN=c33^M\\4b1TK^M=a33UMf4l1jJ[MGNf0f35jLm4W2cJYME1e0e36_LX5c2WJXMG0c0g3b6QOoIo0Q6QOoIo0Q6QOoIo0Q6ROnIn0R6ROnIn0Z2^L2d2dMn0Q2gL;[2dMn0j1nLb0U2cMm0i1PMe0R2bMn0b1WMk0l1bMn0j0dLSNm0a3a1kLVM8i3U1RNi1n0hLZMNNOi3b1QNf1R1jLWMHL00O35h3d1PNd1T1lLb1`1ZMd1T1mLa1_1\\Mc1S1oL`1^1]Me1Q1nLa1]1^Md1R1oL`1]1^Md1R1PM_1\\1_Md1S1nL_1^1^Md1S1mL`1_1]Md1S1mL`1_1]Md1T1lL_1`1]Md1T1mL^1_1^Md1T1mL^1_1^Me1S1mL^1^1_Me1S1oL\\1\\1aMe1T1nL[1]1aMe1T1mL\\1^1aMd1S1nL\\1^1aMd1S1nL\\1^1aMd1T1lL\\1`1`Me1S1kL]1`1`Me1S1jL^1a1_Me1k5[NUJe1k5[NUJe1k5[NUJe1k5[NUJe1k5[NUJe1k5[NUJf1j5ZNVJf1j5ZNVJf1j5ZNVJf1UOfL\\5d1`Ke1oNkLb5`1_Ke1kNQMd5Z1bKd1bNZMl5R1cKd1[NbMo5j0fKd1VNgMT6e0fKd1PNjM]6b0cKm2]4SMcKj2`4VM`Kj2`4VM`Kj2`4VM`Kj2`4VM`Kj2`4VM`Kj2`4VM`Kj2`4VM`Kj2`4VM`Kk2_4UM`Kl2`4TM`KT3X4lLhKU3W4kLiKU3W4kLiKU3W4kLiKV3V4jLjKV3V4jLjKV3V4jLjKV3V4jLjKV3V4jLjKW3U4iLkKW3U4iLkKW3U4iLkKW3U4iLkKX3T4", "size": [480, 640]}, "area": 66294.0, "iscrowd": 0, "image_id": 13729, "bbox": [0.0, 0.0, 640.0, 361.0], "category_id": 172, "id": 20000840}, {"segmentation": {"counts": "PcR21o>2N9GNeAGK0Q11k;>SCA10P13k;`0TC^Ol0ORO7m<a0mCXOVO>j<:kCOS<0iC7T<C[C@d0Q1o;^O^C@c0T1m;\\O`C@c0`0A0Y<0cC@c0>OHh;;fC_Oc0<3N^;7fDFN:T;1iDG69o:0jDH95n:3TD^Oc09=ODIg:d0VEEa0JR;a0]DEb0FT;e0iC[O?:m<KdB<=IQ=JbB>=GR=JaB`0<FV=G_Bb0;GV=G`Ba0:GW=H`B`09HW=H`B`0:@C5i=:dBAD4h=:eBBC5h=8eBCC6g=7iBIW=7iBJV=6jBKU=5kBKU=5kBLT=4lBMT=2lBNT=2gBDG;a=1]BD80H0O<c=1]BC100020N0O<_=DcB=NC1000O10O100<\\=FdB;OC100000O10O1<\\=FdB[1[=fNdBZ1]=eNcB\\1\\=4TC^NZ<c1eC_NY<b1fC_NY<a1fCbNX<_1gCcNW<]1iCdNW<\\1gCgNW<Y1iChNV<Y1iCfNX<\\1eCeN[<\\1dCeN\\<\\1bCdN^<^1_CdN`<o1O00F:dNbii1[O_XVN6J5AEoAd0c=h0]Od0SOl0VOk0SOd0_O?L50001O00000O101O00000000001N10000000001N1O1O1O10001O00000001O01N11O0mMaFeN_9Y1eFeN[9Y1hFfNX9Y1jFgNU9Y1mFeNT9[1lFdNU9VN_FS3=fNV9[1jFdNV9\\1lFbNT9_1lF`NT9`1mF`NR9`1PG^NP9c1PG\\NP9d1QG[No8f1RGXNn8h1RGXNn8i1QGXNn8EfFe0<Fn8]OQGk00Ho8TO]GQ1DKn8SOcGo0_ONn8ROgGn0ZO0n8SOkGj0WO4m8ROoGh0SO6n8QORHg0PO8n8QORHg0VO2h8WOSHd0DHY8CTHc02]Oj70UH`0a0SOY7=VH?U:AlE<V:CkE;W:EiE9Y:GhE6Z:IgE6Z:JgE3[:MfE0\\:OiEKY:5kEEW:;W2O01O100000001N100000000000001O000O10000001001N2N2iB[OP<e0mC^OR<d0iCAU<a0fCCY<d0]C@a<a1O2M2O1O1O2N1O1LcMcC^2T<=H7O1O1O2N1O1O1O2N1O2M6K4M4K4L5J501NN3L3M001O0O11OO1O10000O1VOfEnLX:h2W1C=eNhCQOL8d<=[DWOlN3h<d0f1F4O0KmgV2", "size": [480, 640]}, "area": 18869.0, "iscrowd": 0, "image_id": 13729, "bbox": [142.0, 19.0, 347.0, 199.0], "category_id": 180, "id": 20000841}, {"segmentation": {"counts": "YPU16i>2O1N3N1N2O1N2O001N2O1N100O10000N2WGLc28YMi0e1[OYNc1a0gN^Oe1\\KWMS3R2ERNUNl33ULeNP3S3eM]M\\4CgNo2oL\\Mj8c2YG[Mg8e2ZGYMg8f2[GXMf8g2]GWMc8i2^GUMc8j2`GSMa8m2aGQM_8n2cGQM]8n2eGQM[8o2fGPMV8S3lGlLP8W3RHhLj7\\3j12N3M3N2M3M2O2M3M3M3N2M3M3K5aNcKVHb4f7gKPH^4k7lKkGY4P8QLfGU4U8]1O1O1O2N1N3O001N101O1O0O2O2N2N2N3L3N3M[OiHcIU7[6YI]Ic6a6aIcIX6]6jInIj5P6YJPJZOZOS6f6eJhI_OBk5e6hJbIDIc5e6kJ^IEN^5d6nJZIH2Z5c6lK]IU4`6mK_IU4_6lKaIV4\\6kKcIW4[6jKeIX4W6[2N4L3M3N2N3L3N2N2M3YK^Fk3c9RL_Fn3c9nK_FQ4c9lK_FT4^3^K_2;UJV4Z3aKb28TJV4\\3aKTMOd47lJZ4\\3_KWM1_44oJ[4]3`KVM3\\4ORK^4\\4cKa0MSK`4`4bKg0^4\\OaKkMOL`4kMaKP4OUN;GU4SNcK`1Mb00O?KQ4SNdKa6>[Kn3TNfKZ6c0aKg3SNiKY4JhLX1k0T3TNkKZ46hK]1h1c2UNlK[4`2_Mc1UNoK]4d1ZKfNS2g3UNQL^4^1^KiNn1j3TNPLa4X1bKnNh1k3RNQLe4R1cKYO_1f3WNQLg4k0eKGV1]3]NRL_50PKj0k0n3b4fJfJe1>g3c5bLTJ_3j5lLlIV3R6oLhIS3W6PMfIR3X6PMfIQ3X6n2N3M3mLTGKQ92oFLU92kFMX92hFK]92cFoN=gNS9X2`FgNl0jNh8]2]F^NZ1oN]8`2YFWNb;g1_DnMm;P2SDfMZ<W2;M2N3M2mNSCOo<MSC2P=JQC7P=GQC9Q=RObCm0Y=1000O05LN1O2O1M2N3O1N101O1N100O100O01N^OfAa0Y>30]OgA`0X>AhA=Y>ChA<c8BnL3_J9d8ElL2`J9c8GlL0bJ7c8JjLOdJ5b8NiLMfJ4a80hLLgJ2b84fLIiJ2b86dLHjJ5^85iLCjJ8\\88PMHP3?iLAV3f0dLZO\\3l0^LTOa3n0]OlNjMT1T2nNlMS1P2QOnMP1ULBh2VOhK;[5n0ZK?_3XNlK;Z5o0QKk0f3lMnK:[5P1nJP1d3gMSL9[5P1lJV1b3aMVL:[5Q1jJY1b3]MXL:[5P1hJ`1a3VM\\L:Z5R1fJc1a3RM^L9[5S1cJm3Z1hJhN8[5S1aJo3\\1gJfN8\\5g6mKQIgN9[5g6mKQIgN8[5h6mKQIhN7[5i6lKQIhN6\\5j6kKPIhN7\\5j6lKPIgN7\\5j6lKoHhN7\\5k6jKbIW4]6iKcIW4^6hKbIY4^6fKbI[4]6eKbI\\4_6bKbI_4^6`KbI`4_6_K`Ic4e6WK[Ij4j6oJWIQ5o6iJPIY5U7aJkH_5[7[JeHf5Y82O001N2O00b0]O3N1N3QO[I_If6[6bIbI`6V6jIeIX6U6PJhIR6P6XJlIi5n5TIoIo0OP6k5QI\\JQ1Fo5g5QIhJT1]Om5d5oHTK[8e4fGaK[8Y4dGlK]8o3bGVL`8U50[N_GiLc8R3]GQMg8g2ZG]Mk8Z2UGiMP9n1PGVNU9i34K6K4K7I9G:G8G9G:FM4N100010O000001O01O0001O000010O000001O0001O01O0[FWLS8i3mG]Lm7d3QH^Ln7b3RH^Lo7a3QH_Lo7a3PH`LP8a3oG_LQ8a3oG_LQ8a3nG`LR8`3nGaLR8_3mGaLS8_3lGbLT8^3lGbLQ6BiJm3UOaLP6FiJ`3_OkLf5HjJT3IUM[5IlJj20]MT5KjJa28eMo4JhJZ2`0lMh4KgJR2h0SNb4KeJP2j0VNa4LcJm1m0WNa4LaJm1n0WNa4MaJj1n0ZNa4N_Jg1Q1[Na4N]Jg1R1[Na4O\\Je1UOiMU1d0[5NZJe1QOoMY1>\\5NZJd1nNVNY19`5LZJd1iN]NZ14`3@fL;Gc1eNeNZ1O\\3IlLOId1aNkN[1JV34RMCLa1`NUOX1DX38QM^OO]1_NAU1]O[3;PMYO1Z1_NLQ1WO_3=mLVO5]3ROQMl3?kLRO7i3fNhLV4`0kLoN9S5l2nKkLVO2l4S3oKhLDH@SOU4\\4hLgL2]OoNAX4[4gLeLa0SO^3X4RLcLY6]3gIaL[6^3gI_L[6a3eI]L]6c3dIZL^6e3cIZL^6f3cIWL_6i3bITL`6k3eIoK]6Q4gIiK[6W4W2O10@`0J6J6J6J7I6J6O11O1O001O100O1O1O1O1O1O11O0O2mGXJk6i5PI[JP7f5kH_JT7a5iHbJn5F_Ji5^OeJl5HgJh6R5_InJb6k4dIUK\\6d4kI\\KU6]4RJcKn5Z4UJfKl5W4VJhKk5V4WJjKi5S4ZJmKf5Q4\\JoKe5n3]JRLc5l3_JTLa5j3aJVL_5g3cJZLOlNd3h4_L\\LKmNh3d4_L_LFoNl3_4aLbL@QOP4Y4dLeLZOUOR4R4hLiLTOVOV4k3kLoLlNXOZ4W2_JhN_2JdNYOb4o1^JjN`20\\NXOj4g1^JlNa27RNYOS5]1]JoNc2<hMZO\\5U1[JQOg2a0XM@k5f0ZJUOh2f0gLH[66ZJPOQ3V2j2aN]N]1h1[N]Nc1g1UN_Nh1g1oM_Nn1f1jM`NS2Y7N2M3M2O2M3K4M3LPDPOW:m0jETOW:i0kEWOV:e0lE\\OU:a0lE@U:=lEDU:8mEIT:4nELS:0oE1R:LoE5R:HoE9R:CPF>P:@RF`0o9\\OSFe0S<1O100O1O1O1O100O1O1O1O100O1O1O100O1O1O1O100O1O1O1O1`J\\OdLd0Z3^OeLc0Z3^OeLc0Z3^OeLc0Y3_OgLa0n2JQM7n2JQM7o2IPM8P3HoL9Q3jN^H`0a4f0R3iNbH6a4R1_3dNiL]1\\8100O1O1O1O100O1O1O1hKWNXKj1T4lNjKT1T4oNjKR1T4POkKQ1P4TOoKm0m3XOQLi0l3ZOSLg0l3[ORLf0m3XOULi0k3UOVLl0j3jN`LV1l3nMcLS2T81O1O1O1O1O1O1O1O1O1O100O1O1O1O1O1O1O1O1O1O1O100O100O100O1O100O100O100O100O100O100O100O100O100O010O100O100O100O100O10000000000WMdLnI\\3i5nLVJR3j5oLUJQ3k5oLUJQ3k5oLUJQ3k5oLUJQ3k5oLUJQ3k5oLUJQ3k5oLUJQ3l5nLTJR3l5eL]J[3d80000000000000000000001O0000^D", "size": [480, 640]}, "area": 90814.0, "iscrowd": 0, "image_id": 13729, "bbox": [78.0, 54.0, 562.0, 425.0], "category_id": 183, "id": 20000842}, {"segmentation": {"counts": "g:Y4g:0000000000000000000000000O100MgK_EY4a:3O1N20000001OO1000000001O00002N1O2N1O001O0000000000000000000000000000000000O1O10000O100001O00O100001OO100000000000000000000002NO10000001O0hKZEV4f:jKZEV4i:OO1002N1O00O1000000000000O1001O00O100O100O1000000NiK[EW4e:200O11O0000O100000000000000000000O100000000000000000000000L]EiKf:i3a0OO00000000O10O010O10000ODkDnLV;R3jDeL12OMW;b2iDTNd;j1eDnM\\;o1\\DdM71IO0O00f;1YDT20mM>NE010c;1YD00Q2l<1OL310O1000O01NQCSNP=l10BPCeN05P=T1SCgNM4Q=S1oBfN81H1S=W1b0O1O2N2OO10O2O0O1O10000O10O10O10O010001N100O1SOoAl0P>TOPBj0T>02N0O2O000N2N20000O10O1O001O010NN0_OgAc0Y>O300O1000000O10000O1000O010OONBdA`00@Z>d0NOhA^OX>b04N010O01001OO0100000001O2@`A<c>N2H[A3g>KZA3j>0o^c2Oo`\\M3WAKc>;N2N3O000O11O0O11O001O00O1001O00O120ObABX>e0N3M00100N10001O01OO3N0RBPOm=o0SBQOm=Q10O100000N3BnNbB15R1S=YOhBj0X=`0N2N101O001O0O1000O12N1N2O10OUCPNe<U2101O0N22N1O10MXClMh<T222NO1010O2N0001N1N12O0000001O000001MWCmMi<S211NlMXCT2X<mMhC090GT28kMM0_;7`DJLX23gM00N5OK`;9`DG1P3_;\\M`Dd2`;>O2O0000O1001O01N02OO100O11O001N011O0O010000000000O20N100O1000000dD^L:0d:0REb31_L50j:d3SE[LN1P;c3RE]LM0Q;e3oD_L0KP;n31N1001O00O100000000002N00N20PETLm:l3RETLO030f:U4YEkKd:X410O0O11O00O020O0O10O02OO102NN201iEfKf9[4nEeK00N5S:X4mEdK08U:`4N2001OO100000000001O00O12N0000O1003M00M30VKmEg4S:YKmEg4V:000N20000003MNZKjEf4V:21O000000O100000000001O", "size": [480, 640]}, "area": 44745.0, "iscrowd": 0, "image_id": 13774, "bbox": [0.0, 317.0, 640.0, 163.0], "category_id": 154, "id": 20000843}, {"segmentation": {"counts": "Z9]1c=00000000000000000000000000O100M]BiNc=W13O1N20000001OO1000000001O00002N1O2N1O001O01O0000000000000000000000000000000O1O10001N100001O00O100001OO01001OO100000000001O00O3ON10000001O0\\BfNb=Z1^BfNb=]1OO1002N1O00O1000000000000O1001O00O10O0100O101O000N]BgNc=Y1200O11O0O11N10001O000000000000000O100000000000000000000000_FjNiK1\\9V1]JYOc5i0\\JVOd5j0\\JVOd5j0\\JVOd5j0\\JVOd5j0\\JVOd5j0[JWOe5i0[JWOd5i0\\JXOe5g0[JYOe5f0\\JZOd5f0\\JZOd5f0\\JZOd5e0PJjNnLb0R9d0PJjNeL12OMc0\\9c0VIjNTNP1f86TISOnMh0n85QIkNdM71IO0O00R1\\93eFjNT20mM>NE010o0Z95gFhN00Q2R2Y7[OhHd0X7\\OdHh0\\7WOeHi0\\7VOcHk0\\7VOdHj0\\7VOdHj0\\7UOdHl0\\7TObHeNSNW2[9UOaHm0_7QOSHfNeN05Z2S9oNQHjNgNM4[2T9nNoGgNfN81H1]2Y9lNnGf1R8ZNmGg1T8XNkGi1V8WNhGj1X8WNgGi1Y8VNhGj1X8VNgGk1Y8VNfGj1Z8VNeGk1[8UNdGl1\\8TNdGl1\\8TNdGl1\\8TNcGm1]8SNcGm1]8RNdGn1\\8RNcGo1]8QNcGo1]8PNcGQ2]8oMcGQ2]8oMcGQ2]8PNaGQ2^8PNbGP2^8PNaGQ2_8oMdFgNl0Y3_8QNeFfNj0[3a8QN_Go1a8SN]Gm1d8RN[Go1d8SN[Gm1e8SN[Gm1f8RNXGP2h8PNVGR2j8nMVGR2k8mMUGS2k8mMTGT2l8lMTGT2l8kMUGU2j8kMVGV2i8jMXGV2h8iMXGX2h8hMXGX2g8gMWG]2h8`MgFiNc0g3f8`MYGc2g8]MYGc2h8\\MWGe2j8ZMVGf2j8ZMVGf2j8ZMVGf2j8ZMUGg2k8YMUGg2l8XMTGh2k8YMTGh2l8XMTGh2m8WMSGi2m8VMSGk2m8UMSGk2m8TMRGn2o8QLcFi00VO`00@n3^9RLcFi0c0U3j8RLdFf0a0ZO^On3b9gLPG\\3Q9dLmF]3S9bLnF^3S9aLlF`3T9_LmFa3S9_LmFa3S9`LlF`3T9_LlFb3T9^LlFb3T9^LlFb3T9^LlFb3T9^LlFb3T9_LkFa3U9aL[F[O<T4Y9dLeF]3[9dL]FWO3U4`9fL[FVO3U4b9hL^FX3`9U10000001O000000001O00000000000VK^FV4V:01OO1L^LkEa2Q:aMRF^2Y:VMhEj2W;01O001O00J6E;H8H8006J9PLZET3Y;01O00001O]NQMnFV3P9UM[FW3j9SLSFa4W:OTO^K[Ga4j8]KSGc4m8`KoFa4Q9fKhFZ4X9g00000000000000000O11O000000000000000000001O00O10000000000000000000000000000000000LeJbF\\5^9dJbF\\5^9300000000000000000000000000001O0000001O1O000000000000000000000000000000000000000000000000000000000000NdJ`F\\5`9dJ`F\\5`9dJ`F\\5`9dJ`F\\5`9dJ`F\\5`9dJ`F\\5`9dJ`F\\5`9dJ`F\\5`9dJ`F\\5`9dJ`F\\5`9dJ`F\\5`9dJ`F\\5`9dJ`F\\5`9dJ`F\\5`9dJ`F\\5`920000000000000000000000000000000000000000000dLbFV1^9gNeFQNKV3`9eNkF[1U9cNmF]1S9aNPG^1P9bNPG^1P9bNPG^1P9aNQG_1o8bNPG^1Q9aNnF`1R9`NoF_1Q9aNoF_1Q9aNPG^1P9bNPG^1o8cNPG^1Q9aNoF_1P9bNQG]1P9bNPG^1P9bNoF_1Q9aNQG]1o8eNPGXNBm2^9kNWGS1i8mNZGP1f8POZGP1e8QO\\Gn0d8SO\\Gl0d8SO]Gm0c8SO]Gm0c8SO^Gl0b8TO^Gl0b8UO]Gk0c8TO`Gj0`8VO`GWNPOa2`9YO_GVNQOa2`9YOaGg0_8YO`Gh0`8XO`Gh0`8XO`Gh0`8XO`Gh0`8VOcGi0]8jNaFeN15R1\\2\\8dNmFkNj0a2Y8dNWH[1i7dNYH[1g7dNZH\\1f7dN[H[1e7eN[H[1e7eN\\HZ1d7fN\\HZ1d7eN]H[1c7eN]H[1c7eN\\H\\1d7dN^HZ1b7fN_HY1a7fNaHY1_7gNbHX1^7iNaHPOPNR2_9mNfHT1Z7lNfHT1Z7mNeHS1[7mNcHU1]7kNeHS1[7mNfHR1Z7nNgHQ1Y7POdHmNlMS2`9POdHR1\\7nNfHP1Z7POeHQ1[7oNeHQ1[7POeHo0[7QOgHm0Y7SOgHm0Y7SOgHm0Y7TOeHm0[7SOcHo0]7POeHo0[7QOeHo0[7QOeHo0[7QOeHo0[7QOfHn0Z7ROfHn0Z7ROfHn0Z7ROfHn0Z7SOcHkNmMR2`9SOcHo0]7POdHP1\\7nN`FnNT2T2]7]N`F_O090GT28kMM0]1`9nNgFDJLX23gM00N5OK_1`9POiFAG1P3^1`6POlFAd2_1`6POnIP1R6oNPJP1P6POPJP1P6POPJP1P6POoIQ1Q6oNoIQ1Q6oNPJP1P6POPJP1Q6POmIQ1R6oNPJP1P6POoIQ1Q6oNoIQ1Q6oNnIR1R6nNoIQ1Q6oNoIQ1Q6oNPJP1P6oNPJR1P6nNQJQ1o5oNQJQ1o5nNQJS1o5mNQJS1o5mNQJS1o5mNQJS1o5mNQJS1o5mNQJS1o5mNPJT1P6mNPJR1P6mNQJS1o5mNQJS1o5lNRJT1n5lNRJT1n5lNRJT1n5lNRJE^L:0g0`9ZO`FEb31_L50j0_9^OUJE[LN1o0`9^OSJE]LM0P1_9_OVJA_L0Kn0`9B^J>a5D]J=c5C]J=a5E`J:`5F`J:`5F_J;a5E_J;a5E_J;b5D^J<b5D^J<a5E_J;a5EaJ9`5SObFa0n3<b5D\\J>a5E_J^OTLi0]9UOdFb0k3_OTLO030a0]9\\OdFc0T4_OkK>]9@dFd0W4LU5@dFe0W4JY55gJKY54hJLX55gJKY55gJKY54gJMY54gJKY55gJKY54hJLX54gJMY52iJMW53hJNX52hJNX54fJLZ52hJNX52iJLX54iJ@eK00Ng9a0fJ_OdK0j9c0`4N2001OO100000000001O00O12N0000O1003M00M30dA_OY>a0gA_OY>d0000N20000003MNhA\\OY>c021N11OO11N1000O1001O01\\F", "size": [480, 640]}, "area": 66820.0, "iscrowd": 0, "image_id": 13774, "bbox": [0.0, 298.0, 640.0, 182.0], "category_id": 155, "id": 20000844}, {"segmentation": {"counts": "0Z9f500000000000000000000000000000000000000000000000000000000000000000000000000001O000000000000000000000000000000000000001O000000000000000000O1001OO100000000001O00O11O00000000000000000000000000000000000000000000000000O10000001O00000000000000O11O00001O00000000000000000000000000000000000000000000000000000000O11O000000000000000000N21O00001OO10000000000000000001O1O0000000000000000000000000000000000O10000O100001OO1001O00001O000000O1O10000O1O100001O1O000000001OO1001O0000001_JcF]5^9bJcF]5i6gJdKLdM\\5c6oJlKP5S4QKnKn4Q4SKoKm4Q4TKoKk4P4VKPLj4o3XKPLh4P4XKPLh4P4XKPLh4P4XKPLh4P4WKQLi4o3WKQLi4o3WKQLi4o3VKRLj4P4SKQLm4P4QKQLo4P4oJQLQ5P4mJQLS5n60N20000001O000000001O000000000000L4K5L4O100O1000000O1HWJYGi5d8;00O10000001O1TJUGa5l8YJZGf5P9000001O003M3M1O5K0000000000000000000000O11O000000000000000000001O00O10000000000000000000000000000000000LeJbF\\5^9dJbF\\5^9300000000000000000000000000001O0000001O1O000000000000000000000000000000000000000000000000000000000000NdJ`F\\5`9dJ`F\\5`9dJ`F\\5`9dJ`F\\5`9dJ`F\\5`9dJ`F\\5`9dJ`F\\5`9dJ`F\\5`9dJ`F\\5`9dJ`F\\5`9dJ`F\\5`9dJ`F\\5`9dJ`F\\5`9dJ`F\\5`9dJ`F\\5`920000000000000000000000000000000000000000000000000000000000001O00000000O11OO11O000000000000O100000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001OO1000000000000000000001OO1000000000000000000000000000000000000000000000000001OO100O100N2000000001O00O1001_JbF^5`90M30_JdF^5\\9bJdF^5\\9bJdF^5\\9bJdF^5`9O00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001O00O11OO11O0000O1001O00", "size": [480, 640]}, "area": 193810.0, "iscrowd": 0, "image_id": 13774, "bbox": [0.0, 0.0, 640.0, 310.0], "category_id": 157, "id": 20000845}, {"segmentation": {"counts": "kj^35f>6N2N100O2N1000000000O100O3M2N2NaR8LPmGd0K5L12MVCVOf;i0YDYOf;S1nCoNQ<P20001N1HjMfCU2X<1:F9I8I74L6K8UCUNP<0gCS2d<O01O00cN_C9d<\\OoC3Z<O]12eA_OP>h03L3M3L=DkSn4", "size": [480, 640]}, "area": 1722.0, "iscrowd": 0, "image_id": 13774, "bbox": [236.0, 210.0, 66.0, 158.0], "category_id": 183, "id": 20000846}, {"segmentation": {"counts": "idX13W=001N20O0100N110000O10001N10001N2OUgj6", "size": [427, 640]}, "area": 87.0, "iscrowd": 0, "image_id": 13923, "bbox": [97.0, 199.0, 18.0, 10.0], "category_id": 93, "id": 20000847}, {"segmentation": {"counts": "ecW1130S=2kBOT=5O001O001O001O1O00001O001O001OO11O00001O00001O001O001O00001O1OO11O0000001OO1001O00N2O1N200000000001OO1001OO100001O1O1O00001O00001O00O11O000000O1000000O11O1O0000000000000000000000O1000000000000000000000000001OO1O100000000001OO1001O00O1001O0000O10000000000O11O000000000000000000000000000000000000000000000000000000000000O100001O000000000000000000000000O1000000000000000000000000000YC_Od<a0\\C_Od<b0[C^Oe<b0[C^Oe<d000000000000000000000000O1O100O1O1000000000000001O00000000001O0000NYC@g<`0YC@g<`0YC@g<?3001OO100001O00000000000000O1O1O1001O00000000000000O100001O1O00001O00O100001O00O10000000000001O2WC^Oe<b0[C^Oe<d000000N200000000000000000000N2O1O11O0000000000O11OO10000000000001OO11O00O1001OO10000001O00O1K5N2N2N2N20l\\h2", "size": [427, 640]}, "area": 5807.0, "iscrowd": 0, "image_id": 13923, "bbox": [95.0, 0.0, 333.0, 21.0], "category_id": 102, "id": 20000848}, {"segmentation": {"counts": "n_T21Z=0O1000001ZO3lCMf;o0lCQO]<j00000I75K1OO1000000000001O4L00N200O11O1O00O100000000000000O1002N00000000O1000000000000O10000000lE@nMN1CO44Ml7n0oH^O@i0^OSO9Hj7o0mH_OAT1BdNMHP8R1oH^O]OZ1F^NNHP8S1]HQOM=1i1d7jN[H0KlNL]2m7hNZHh2e7YMZHg2f7]MSHf2m7Z13N100O011DnG\\Kb8U4_GjKb8Q1dG\\1J\\M0017b8P1dGj0b0oMVO4k8g0dGW1;RNY8>^GDJ;I6f0]OZ8=THKQO6b0@[8?VHFnN;k9O\\G@kN`0j9OjG1V8OjG1V8OkG0T81lGNV82jGMV83jGMQ89nG^OPNL03W:<kGEoMKO4X:;jGAnM242T:;lGCPN2U::kGDPN1]:3dGLoM1`:OcGOnM0l<O80V`o4", "size": [427, 640]}, "area": 4903.0, "iscrowd": 0, "image_id": 13923, "bbox": [164.0, 78.0, 93.0, 180.0], "category_id": 112, "id": 20000849}, {"segmentation": {"counts": "e:b0h<100000O1000000000000000000000101OO1O000O0101O000O1O0100000O4L4N4J[R5NhmJ2K4N1O1O1O1N200O1O1O1O10000N200O1N200N2O1N20000N2O100O1O1O1N200O100N2O1O1O1O1O1O1O1N200O1O1O1O1O100O100O1O1O1CYNUEh1i:ZNVEg1j:ZNSEh1m:YNQEh1o:XNoDj1Q;80000000000000000001O0000000000001O000000000000000000001OO10000000000000000000000001O00000000000000001O0000000000000000000000001O0000000000000000000000000000001O0000O1001OO10000001O000000000000001O00000000000000000000000000001OO10000M300O1O100000000O1001O001O1O1O1O1UNPEY1P;hNREU1o:kNSER1n:nNTEo0m:QOVEk0k:UOWEh0i:YOXEe0i:[OXEc0i:]OXEa0i:_OXE?i:AXE=h:CZE;e:G\\E7R:@aE:>4c:L_E2`:0aENR:FXE<g0M^:4bEK]:6cEJ]:7cEH]:9cEF]::dEE^::aEFa:6bEIa:1cEN_:NdE1k;01O1O00m\\:0_PF000dB010O10O01O010O010O1O01O01O0O2O00001O0O101O000O2O0BGjC:V<FiC:X<FgC:d<2EDeC<\\<DcC<^<DaC=_<C`C=`<D^C=c<B]C>d<B[C>f<BYC>g<22O0105J01O001O1O001O001O001O001O00001O001OSMKdH5X7OiH0S73nHMU70lHOT70mH0S70nHOR70oH0Q70oH0Q70PIOP71PIOP71PIOP71PIOo62QINo62QINo62QINU7LlH3V7KjH5V:000000000001O000000000000001O0000000000001O00000000000000000000000000001O00_jm2", "size": [427, 640]}, "area": 13562.0, "iscrowd": 0, "image_id": 13923, "bbox": [0.0, 313.0, 414.0, 114.0], "category_id": 118, "id": 20000850}, {"segmentation": {"counts": "S9b1h;0100000000000M3L]N_Dd1`;4O1O10000O1O2O2N0ObNhD_O0O1W1W;XOQEh0o:YOQEe0P;[OPEe0P;[OQEe0m:XOiDF:Q1n:XOiDF9`0FNX;MiDC<>F1V;LhDG;;H1V;MgDH:8MEH:[;0fDFO0>9L1Y;FRE7G3n;MTD0m;MVD2m;KVD0`U17liNHn<>M6J`0A3jCiNP<Z1O6QD`Ng;b100011M2O1O1O1N2M4N1N:G8G8GTn]2HURbM0jBOS=3lBMS=4mBLR=8N1O10000QCFm<:SCFm<<00001O0O2Oi[P2J]doM000000000SMMbH3^7MbH3^7MbH3]7NcH2]7NcH2]7NcH2\\7OdH1\\7OdH1\\7OdH1[70eH0[70eH0[70eH0Z71fHOZ71fHOZ71fHOY72gHNY73fHMZ73fHMZ73fHM[72eHN[73dHM\\73dHM\\73dHM]73bHM^73bHM^73bHM^73bHM_73`HM`73`HM`73`HM`73`HMa72_HNa72_HNa73^HMb73^HMc72]HNc72]HNc72]HNc72]HNd71\\HOd71\\HOd71\\HOd72[HNf71ZHOf71ZHOf72YHNh71XHOh71WH0i70WH0i71VHOk70TH1l7OTH1l7OTH1l70SH0n7ORH1n7ORH1n70QH0P8OPH1P8OPH1Q8OnG1R8MPH3Q8KPH5V5KdM5\\2LcM4^2LiJO64R5N_M2a2O^M1c2O\\M1e20YM0g21XMOi21VMOk22SMNn28kLHU38kLHU37lLIZ3NiL2X800000000000000000001O0000000000000000000000O100000000000000O100000000000000HLXC5h9JQI3VM3i9KPI3VM2k9JnH6VM0h<2WCNh<;WM_OhH6^MNk9LeH4eMN\\<2gCLY<4kMLPH3c:1giV1", "size": [427, 640]}, "area": 3683.0, "iscrowd": 0, "image_id": 13923, "bbox": [0.0, 248.0, 546.0, 179.0], "category_id": 123, "id": 20000851}, {"segmentation": {"counts": "im`11Q=:M3aCE0OZ;>cDFN2];9`DLL1`;8`DHO1c;W11OO20O0000O100mDZNa:g1^EYNc:f1]E[Nb:f1]EYNd:g1[EZNe:m1TESNj:2PE10j1Q;SNVEj0I6[;IiDVOL10h0`;LfD\\OJh0b;H^D_O61Jg0];NTEMl:6REIm:7nDOR;1nDOU;NlD1\\;EfD;S;GVE5j:1RENQ;FRE?l:EVE5o;13Mbce3Co\\ZLNROe0800000000002O0O1O1O000006JM]O]Ce06[OQ<3iCb06[OQ<2jCc04]OQ<h0oCVOS<j0mCVOS<2iCc0^<]ObCb0a<\\O`Cc0d<OO23M0000O001005H7ML3102LekU2", "size": [427, 640]}, "area": 2357.0, "iscrowd": 0, "image_id": 13923, "bbox": [117.0, 132.0, 355.0, 85.0], "category_id": 130, "id": 20000852}, {"segmentation": {"counts": "i;b1i;00000000000001O1OO1O10000000000L4O1E;00O1000000O1O1000000O1O11OO1001O000000001O00XOlMXFU2c9SNYFn1f9SNZFm1g9RNYFn1f9TNYFh1@PNW:9UFFDQ22nMU:<jED6S2W:\\NhEd1X:]NgEc1X:`NdEb1]:e00O20O0O1G91O10000N200O1N200N2O1N20000N2O100O1O1O1N200O100N2O1O1O1O1O1O1O3L:H6I1O1O1OBbNQE]1o:`00O01O01O000DfDhN[;V1gDiNY;X1hDeNY;\\1hDbNY;^1gD`NZ;a17100O1O100O1O100O1O2O0O1O100O1O101N1O100O1O100O1O100O1O2OO01O100O1O100O1O100O1O100O2N100O1O100O1O100O2N11O00000jM`Ea1`:VNcEF1T2\\:UNcEH1S2\\:UNcEHM01S2_:UNcEKMQ2_:TNeEKMP2^:UNeEILN1T2_:nM`E24NLN4Q2n:oMRER2S;01OO100000000O100000000O1000001O04L5J3N100N101O0001O000001O00000001O00000O1000000O1m0SO5K4L1O1O1O010N2O1O1L301N2N2O1O001O1NRoR10nPmN5L3L4M3L4M3L4M3L4M3M2M3AgNgD\\1T;gNjD\\1S;fNkD^1Q;dNnD]1o:fNoD\\1o:fNoD[1h:i0000O01O10O2O000O2O00000O10000O4M1iMnDS2R;mMnDR2Y;M0O2O1lNeD5\\;JgDLa;2cDHb;6`D^Ok;a0a0N100O2N1O4M3L5K`e6O`ZI2M3N2N3O00001O010O001O01O02N2O0O2N2N2;]OSCOekP3", "size": [427, 640]}, "area": 18089.0, "iscrowd": 0, "image_id": 13923, "bbox": [0.0, 254.0, 408.0, 173.0], "category_id": 152, "id": 20000853}, {"segmentation": {"counts": "nQ[41Z=000N0iB0=Q1X;UOgDj0Y;VOfDk0Z;XO\\Dn0d;<10O2O00001O0001O00000003M1`DWN];h1cDXN\\;o1M00100O000010O01O000010O03M3M4M2MO1O101O00000O2O0000000O20O000gNiM^GV2a8RNZGk1h8ZNSGc1P9^NoF`1S9`NmF^1U9bNlF]1T9cNlF]1T9cNlF]1T9cNlF]1T9cNmF]1l8UNZF>j0]1l8iNTGW1l8iNSGX1m8hNRGZ1m8fNSGZ1g8mNWGT1i8lNVGV1i8jNWGV1j8iNUGX1l8kNoF7H^O]9^OZFe0><0A^92aF>1@^93bF;0Ac90_F>MAd92aF95\\O[9;cFNg:2\\EFk:9R11O12M10002N3L6K4Kali2", "size": [427, 640]}, "area": 5419.0, "iscrowd": 0, "image_id": 13923, "bbox": [333.0, 154.0, 91.0, 130.0], "category_id": 156, "id": 20000854}, {"segmentation": {"counts": "n7U1V<O10000000O1000M3LiCSOX<l0I]OoCa0Q<<0100MiN^Dj0R<O10O10OSDWO_O0O1f;h0iD@W;`0jD@U;`0kD@T;a0lD@T;>iDXOF:`;?iDXOF9`0F^:h0]EYOC<>Fa:f0\\EXOG;;Hc:d0[EYOH:8MEHm:h0]EYOFO0>9Ld:f0SEE7Gg:Z1YEhN0EY:c1dEkN2FX:`1cEmN0KX:IoEY2`:1O`MaENHT2h:lMfEQ2Z:oMlEk1T:UN\\F[1d9fN_FOiNV1g:kNcFT1]9lNiFO`N50`0h:\\OjFOfNa0b:_ORGa0l:01O1LWCDj<1UC6R=O1O2Lkn\\1OXQcN1O100\\CNl;5kCNF1^<MbC290H1]<2kCMHM01\\<6lCLKMY<7lCMKMW<7nCLILN1Z<9hCF24NLN4V<i0jCWOU<P1O2OO01N2N2M3FdN^D02]1a;83M0000N12O1O00O12210J3H73MN1J70O001O001OO21N1VNhD_1\\;]NdDc1b;0001O000O1000000O1A`0L3O100O100O2O0O2O1N2O0O10N10100O100O10000O1001O00O1N101003FbCA]<?cC^OO1]<a0fC]OM2\\<a0hC]OL2T;NmD0o03RO;6Cl:0lDOP11SO=6Cj:3mELSO>7Ci:3QF;VOBi:3PF<WO@j:4nE=XO_Oj:5lE=`:C`E=`:C_E>^:EaEJVO;V;MjE5U:LkE5T:Kf1000O11O00000000000002N1O1O2N000000000001O000000000000000O1O10000000000O100000000000010BTC<m<001O00000000000000O100O11OO2L310O1O1O00000000000000000000000006J1OO4FTPl0<goSO2MO1OAJlC5\\<73N0200000000000O11O001O001O0010ON3N101O1M3000N4M4L4L3L2O1O00OO2O1N2O1N2OO0EM\\C8e<63NO02N1O2N2N00100O1O2NhaY2", "size": [427, 640]}, "area": 7657.0, "iscrowd": 0, "image_id": 13923, "bbox": [0.0, 201.0, 463.0, 157.0], "category_id": 165, "id": 20000855}, {"segmentation": {"counts": "0X7S60000000000001O1O2N1O1O000000O1O1N2M3N2O1000000O100000000O1000000O100000000O1000000O1000000O100000000O1000000O100000000O1000000O100000000O1000000O1000000O10000O10000O10000O10000O10000O10001aIWIY6l6eIUIZ6o6L3O2N1N3O0O2O1M20001O001O010OO110O1O10N1XNaHdM_7S2kHkMW7R2lHhLE0O2`7U3nHfLFN26Z7U3oHbLLL1;T7U3TI`LHO1=S7U3TJkLl5V3SJkLl5T3VJkLk5U3TJlLl5S3TJmLk5T3UJmLk5Q3VJoLj5Q3VJoLj5Q3WJnLi5S3VJmLk5R3VJmLi5T3WJkLj5U3]JdLd5Y3X2]Oa02O1N2000O1000001OO1001OO100001O1000eERMW:P3110N11O01N10O11LfEVMY:j2gEVMY:i2hEVMY:d1oEVOHVOX:k0hEUOQ1g0oNYOX:j0iEVOS1d0mN[OW:k0iEWOo01TO;X:<cGVO\\8k0S2O1O0000000000000000000000O1000002N001O0000000000000001OO1O100000000001OO1001O00O1001O0O10O100000O1000O11O0O11O000000004L00O1000lHCZ1=hN@Y1?hNAX1`0fNAZ1`0eN@[1`0eN@[1a0dN_O\\1?fNAZ1?fNAZ1?eNB[1>eNB[1>eNB[1>eNB[1>eNB[1>eNB[1=fNCZ1=eND[1<eND[1<dNE\\1;bNG]1:cNF]1:cNF]1:cNF^19bNG^19bNG^19bNE`1;`NE`1;`NG^19bNF_1:aNE`1;`NG^19bNG^19bNG^19bNG^19bNG^19dNFZ1;gNDY1;iNDW1<kNBU1>kNBU1>kNBiJ@W6n0POBU1>kNCfJ_O\\6n0nNCfJ@[6m0oNFQ1:oNFQ1:oNFQ1:oNFQ1:oNFQ1:oNFQ1]OeIj0Z5IQ1]OeIj0Z5IR1\\OdIk0Z5IR1\\OdIk0Y5JU16jNJW16iNJW16iNJW16iNJW16iNJW16iNIX17gNJY16gNJY16gNJY16gNIZ17fNIZ17fNIY18gNHX19iNFW1:jNDV1=jNCU1>lNAT1?lNAT1?lNAT1?lN@U1`0kN@U1`0kN@U1`0kN@U1`0kN@V1?iNAX1?hNAX1?gNBY1>hNAX1?gNBY1>dNE]1:dND]1<cND]1<cND[1_O_Im0V5D[1_O_Im0V5D[1_O_Im0V5C[1`0eN@[1`0eN@\\1?dNA[1`0eN@[1`0eN@[1`0eN@\\1?dNB[1>eNCZ1>eNCZ1=fNDY1<k500001N1O1O1001O04LO1N2000000O0100001O1N10001O0O0100001O00O1000LhCUOX<k04O10001O2VO_Cf0a<ZO_Cf0c<N]C\\Oc<c03OO110000000000001[C]O`<g00000PEYOA0XO1n8h0YHWO[O8WONT9i0oG[O]O?c87mG]O@<d86lG^O@<d86lG_O_O;e86lG_O_O;e86lG_O^O<f86kG^O^O=g85jGCZO9k85iGC^O7j85gGDA7f87jG_OA?`85mG[OCa0_83XGkN>a0Ka0_8a0aGoNO`0`8b0`GnN0a0_8b0`GmNE^OM22V1d2SOT3[1_Ji0GcMe0><0k4T4hJkK>1i4U4jJkK;0l4X4eJjK>Mm4X4fJmK9MP5W4gJoKN8U5e1[J^Ob0hNF:^5]1bJ]O97V5;fJZO5;U59hJ[O4<T57jJ_OO;W5JYJUNf0f1J;X5IPK1H6X5HTK1B7Y5IUK3^O5X54TKK_O1[5L_K8QOM^5M`Ka2^4aMaK`2]4cMbK]2^4oMVKQ2h4TNTKn1k4TNRKh0G\\MY5n1mJg0KYMY5R2iJf00VMW5P6jJoIV5Q6kJlIW5S6lJlIS5a5kJRJ3=Q5a5mJPJ4<H[OT5c5fKeJ@<o4o4bKfJ^O9Q5Q5cKeJ\\O9Q5R5eKdJ[OL\\5`5]KaJT5_5PK^Jo4b5]11O00000000006\\JXJY3e5fJYJc11bN8U5[5iJ\\JQ1b0lNLX5V5jJ]JS1o0T4d4hK^JTOo0S5c4iK^JTOo0S5c4jJ]Ji03YOn0S5b4kJ]Jb0;_Og0S5c4jJ\\Jc0a1d4W4[KjKe4T4]KlKc4W4ZKiKf4W4ZKjKe4V4[KjKe4U4[KkKf4T4[KjKg4V4YKiKh4\\4PKeKR5_4kJ_KV5^4mJ`KU5a4kJ\\KW5g4eJYK\\5Y62O1O11O0000001O0YM]HFd78]HHd77]HHc75`HKa72bHM_7MgH2Y7BSI>n6UO_Ij0b6QOcIn0]6nNgIR1Z6iNkIV1V6eNoIZ1R6PNTH1n1o1n5PNdJo1\\5PNfJo1`5hMdJW2Y8O1O1O1O00N2O1O1O1N2O1O1O1O1N2O1O1N2N2N2N2N2N2N2O1N2N2N2N2N2N2N21O001OSKkLDU3KcMO\\2MkM1T2KQN4o1IVN5j1G\\N7d1EbN:]1EdN=Z1BgN>Y1AhN?m0PLUMa3n1`0i0TLWM[3Q2b0f0VLWMW3T2d0d0L[O4c0O[O3c0N\\O4<4BM<6BL;6DL97EJ99EI99DJ99EH9:FH7;FG8<EH7;FH7<EH8;DI8:EI8o0oNVOm0f6M4L4L3NeG^OQ4>\\K=`4_O`Kj0Z4SOgKU1T4gNlK`1Q4\\NoKg1h74L4M3L3M4M3N2M2O1N101O0O2OO001O1O0010O03M3M2N3N1N3M2N3M2N3N1N3M2N2N3J5L4L5K4LaFVOd6f0VIJe60VI`0d6\\OVIT1d6hNXId1e6WN]Ik1c6QN^IS2j83M4L3M4M2M4L3N3N1N2O0O10000O100O10000001O0000oK", "size": [427, 640]}, "area": 110700.0, "iscrowd": 0, "image_id": 13923, "bbox": [0.0, 0.0, 640.0, 427.0], "category_id": 172, "id": 20000856}, {"segmentation": {"counts": "Qkh1150X<c0eCDj0Id:m0[EWOVOL10V;R1^ETO\\OJV;T1ZElN_O61JV;n0_EC[:>hE@W:?iE\\O]:d0cE\\O]:g0`EZO_:n0WETOi:e0YEDc:<cE@[:d0YE_Ol:?XEC5ZOk9V2VFlMi9V2TFkMl9k20O1RMeEk2\\:110N110O0O101O010O0O2O001O0O101O3M3M4L3M3M4L3MNcFWL\\9i333M0001M6MO1M20O03O4LN1J55LO1J61O001O001N0120OfFXLo8l3mFTLS9R40001N1000000000000O2O000000nM^GnMN1CO44MJP9Q1\\G@i0^OSO9HHQ9o0]GAT1BdNMl8o0\\G]OZ1F^NNm8=oFM=1n:OeEKlNLb;7`1O4Ige[5", "size": [427, 640]}, "area": 8857.0, "iscrowd": 0, "image_id": 13923, "bbox": [136.0, 113.0, 92.0, 141.0], "category_id": 174, "id": 20000857}, {"segmentation": {"counts": "f\\Q31Z=1H0oB001R=5nBIo<:00UCFe<:YCHg<=1N2O1003MO1O1O1MVCIf<7XCJi<6VCJk<:1O1O1O100O1ORCLg<:200I520O3NO0012N000000001O000000000O101MiB0W=0\\le4", "size": [427, 640]}, "area": 441.0, "iscrowd": 0, "image_id": 13923, "bbox": [233.0, 234.0, 47.0, 19.0], "category_id": 177, "id": 20000858}, {"segmentation": {"counts": "hfU2:W<HkCQ1Q<POoCS1n;mNSDo0o;M:9G0000I75K1OO100000000000000000002N001O000000000000000000000000000000000000000000000000O100000000O100000000O11O0001O0004K11N`0A1`EeNn8\\1QGeNn8a1fFfNX9c1_F_NDJ;IX8n1hGVOKQOZ8h1kG[OFnN_8g1kG@@kNe8e1jGOT83lGMT83lGNS81nGOR82nGNQ82oGNQ8MUH2k73nGOR82mGNAnM_8U2PHOP82oGNQ89gGHY8;cGG\\8e2O00N2N2O100UO_GaL2I_8]3iGgLLJ[8Z3oGkLGI[8]3RHeLDNY8^3SHbLE0X8^3SHbLFOW8^3THcLEOW8\\3fHcLZ7[3hHeLX7Y3jHgLV7W3lHiLT7V3mHjLR7X3aGQM8N4IS8f3cGhL8BU8[4jGeKV8[4jGeKV8\\4gGfKY8[4cGhK]8c40001O0000000000000000QOaGXL3b0\\8V3hGkLX8T3hG`LI1_8n2bGoL>0B2^8e2bGTM61;3_O3^8e2hHRMoN0M2\\8l2XISMh6l2YITMg6m2XISMh6m2XISMh6k2YIVMg6i2ZIWMf6h2PHXMg00Y7i2mGXMj00Y7i2bGVM22c0OE1DL]8S3eGRMd00D2S8U3XHkLD0T8V3VHmL]O4]8o2mGeLIj0Z8a2lGgMT8Y2lGgMT8Y2lGgMT8Z2fGbL0T1Z8`2eG`M[8`2eG`M[8h300000001O001O1O1O000000000000000000MgGTKZ8k4fGUKZ8k4fGUKZ8i4fGVK01Z8g4701AfGkKZ8Z3aHeL`7h2TIWMl6i2TIVMm6k2RIUMn6k2RIUMR7g2oHXMP7i2PIWMn6k2RIUMn6k2RIVMm6j2SIVMm6j2TIUMk6l2UITMk6l2UITMk6l2UITMk6l2VITMi6l2j110001O0O100000000000000LRMkEn2U:4O10001O00000NkLnEU3Q:3O1O10000000000001gLQFT3S:1_FjLk8V3e0[NnEA0XOU:W1kE[O8WOW:S1kE]Oh:`0[E@e:`0[E@e:`0\\E_Od:a0\\E_Od:a0\\E^Of:a0ZE^Og:a0^EZOc:d0^E^Oa:a0_EAa:`0\\EAg:=XECg:GhD>a0KU;0lDOU;0kD0V;OjDE^OM22k;7efP3", "size": [427, 640]}, "area": 22608.0, "iscrowd": 0, "image_id": 13923, "bbox": [167.0, 39.0, 241.0, 171.0], "category_id": 181, "id": 20000859}, {"segmentation": {"counts": "e_6;l<4N2M3N2O0100000O2O001O001N101O001N101lCZOa;X10000O1000001N10001O1N3N1O1O1O1N2O1O2N9F9H7I00000O1000000O100000000O1000000O1000000O10000O10000O10000O10000O3N8H6I1000O0BeFnL[9Q3`00O01O10O01O10O01O10O01O10O01O10O01O10O01O010O2N101N1O2O0O2N101N1O2O0O2OO01O100O1O100O00100O1O100O1O101N1O101N1O2O0O2N101N1O2O0O1O2M\\D]Nd;b1\\D^Nf;`13O2O0O2N1O1O3M4L5L3L4L5J5J6Kdd>V1UZA8J5L1N2O0O2O1N2O1N101N2O1N2O001O1O1O1O001O1O1O1O1O0^EiMo9Y2QFiMl9=QFm03hNi9;[Fg0LPOf9=aF=IYOb93YFM4`02B^91\\FX16hN\\90]FZ17gNZ9N`F\\16gNX9MbF]16gNV9LdFc00_O73S9KeFf02YO68Q9IgFg03VO6;n8GjFi03RO7?j8FlFl01nN9b0h8DmFo02iN:e0e8CoFR10dN=h0c8AQG_1=oNa8CRG^1=PO`8BRG_1?oN\\7d0UH<a0POT5oNhJ2S1b1aN=`0POk4YOnJLS1_1cN`0?lNg4_OSKGS1^1dNb0;mNk4@nJDV1^1fNd05nNQ5[OmJFW1\\1gNg00nNU5YOmJGV1[1gNW2T5iLnJEW1[1gNX2Q5YMXL?gNY2n4ZM[L<hNZ2k4]M\\L9hN\\2i4]M_L7hN[2i4VL\\Kk0T1d0gNX10TNj4oNaK616m0X1hN\\2k5[L]KY1gN[10QNm5ZO\\KZ1gN]2m5YL\\KZ1gN\\10oMn5XO\\K^1fN\\2o5QL_Kb1bN^2a7bM_H^2a7bM_H^2a7bM_H^2a7aM_H`2a7`M_H`2a7`M_H`2a7`M_H`2a7_M_Hb2a7^M_Hb2a7^M_Hb2a7^M_Hb2\\5mKfJ2h1_1VNc2\\5RL_L[1UNc2[5SLnJIV1a1aNc2j4mK]K>0BW1`1bNc2`4mKbK61;3_OX1_1bNe2`4RM`KoN0MV1^1jNn10TM`4S1fLKjNn10TM_4U1fL\\3kN^K`4T1fLJjNQ20QM`4T1eLKkNQ20oL^4W1gLIkNQ20oL]4W1iL^3kNZK[4MhKh0R1;jNT21kL\\4KhK;KIV1R1jNT21kLc4CaKc0OER1W1jNT21iLd4BaKd00DQ1Q5jNUKm45ZKAR1U5hNTKm4KSKLh1\\1VNR10`N1[On4KTKHj1`1SNk09eNI\\Om4LSM]3PNcL6g0J\\Om4MSM]3oMgL6a0K^Om4MSMX1nMa00dN260@n4FQK1Q2^1nM`00hN211AS5GlL_1PNZ11mM0CS5FlLe1oMT10nM3BW7[4eHRL4DV7a1gHk0OnM5FU7a1jH_12oLT7a1nH]1NRMT7a1QIZ1KUMS7a1VId0AQN8IQ7b1YIS1F[MQ7b1\\IP1C^MQ7a1`In0@`MP7b1cI<TOWN9Ko6b1hI:POWN;Lm6c1kI8mNXN;Mm6b1nI8jNXN;Nm6b1PJ6hNZN<Ml6b1RJ7fNYN<Nl6T1jHnN\\1f1bNXN=0k6Q1mHPO[1g1`NWN>0k6o0nHQO]1g1]NXN=1l6l0oHRO]1j1[NVN=2m6i0PITO\\1l1YNTN?3m6e0RIVOZ1P2XNQN?4Q:k1aEPN>5Q:l1PFUNo9l1QFTN\\52]Nk1_1POaNP1l0CSO?l0ATO`0l0@SO`0m0@ROb0m0^OSOb0m0^OSOc0l0^OSOb0m0^OROd0m0\\OSOd0m0\\OSOe0l0[OSOf0m0[OROf0m0ZOSOf0m0ZOROh0m0XOSOh0m0YOQOi0n0WOROi0o0VOnMYNSNc2n3TOnM\\NRN`2P4UOkM_NSN]2Q4TOkMaN\\ML;`2^4SOjMoNfMo1_4ROjMPOfMo1`4ROiMoNgMP2_4QOiMPOgMP2`4POhMQOhMo1`4POgMROiMn1`4POgMROiMn1`4QOeMROkMn1_4POeMSOlMm1_4POeMSOlMm1`4oNcMUOmMl1`4POaMUOoMk1`4POaM[OiMe1f4POaM]OgMc1h4POaM\\2_2eM`M\\2_2dM`M]2`2cM`M]2`2cM`M]2`2dM_M\\2a2dM_M\\2a2dM^M]2b2cM_M\\2a2dM_M]2`2dM_M\\2b2XL[Kc0T2T3a2YLaK=n1Z3a2YLdK:k1]3a2YLeK:i1]3b2YLeK:j1\\3a2ZLeK:j1]3`2XLgK;i1]3`2XLgK<i1[3`2YLgK<i1[3`2YLgK<j19hMh1g4^MlKa0e17jMj1e4^MlKa0f16iMk1e4^MlK`0g18gMj1g4]MkKa0g19fMj1P5SMcKi0j18cMl1i5jMgL8aMm1h5jMjL7^Mo1h5jMlL5\\MQ2h5iMnL5ZMR2X6XMaLd0WMT2U9UMkFh3S99N2N2N2M3\\JcKX2_4fMiKT2W4jMnKS2R4kMPLU2P4iMRLW2n3]MaKaMf0R5i3]MPL\\MH\\5X4WMnK^MOW5X1hJV1c2^M`M7V5S1hJX1i2kM_2m0hJX1_2TNk2c0fJY1_2SNl2c0fJZ1^2SNm2b0eJ[1d2mMg2h0eJ[1l2dMa2o0dJ]1m2aM`2R1cJ]1o2^Md0HTN\\1YN^1R3ZMb0MRNZ1[N`1R3VMb02PNW1\\Na1m6YMgJU1\\Nb1m6ZMgJR1]Nd1k6]MgJn0^Ne1X6XMQJ6W1n0`Nd1X6[MmI5;H5S1]Od1V6]MkI6<K1o0Ac1W6^MlI491Oj0Ec1W6`MkI294Lh0Ib1W6bMjI1L`09;Ka1V6gMgIn08KJ`1W6kMdIi0;LJ`1Y6XOlIZOL]1Z6WOiI^OL[1[6WOgI@NY1\\6VOeIBOX1\\6WObIC3U1[6UOdIH0S1\\6UObIJ2Q1\\6_M^I?0C9I3f1JP1]6^M^I53JNOh0e1\\Oo0]6^M^I1^1c1fNn0^6^M_I0\\1e1hNl0]6_M`IOZ1g1iNk0]6_M`I0X1g1kNj0]6]McI1T1j1kNh0j6[M\\Jn1jNg0W8ZOiGf0W8ZOiGf0V8\\OiGd0V8]OjGc0U8^OlGa0R8AnG?Q8CnG=P8EQH:n7GRH8m7JSH6l7LSH3l7OUH0i72WHMi74WHLg77XHIg78ZHHd79\\HGe78[HHe79[HFe7:[HFe7;ZHEg7;YHDg7<ZHCg7=XHCj7;WHDj77ZHIg72^HMd7KcH4^7^OoHb0R7UOWIj0k6POZIo0g6lN]IT1d6gNaIX1a6bNdI]1U901O1O100O1O1O1O0aNWE<g:LSE4l:3mDNR;V1O1N2O1O1O1O1N2O1O1N2N2N2N2N2N2N2O1N2N1O2N2N2N2N21O0O2OSKRM]On22jMHn1eKUM_4T1Jf1gKVM[4Y1M`1jKVMV4_1OY1mKWMP4f12R1oKVMl3m14l0RLVMi3o16i03XOMg03ZONe02[OOc01^OOa01@0?1@0=3AN=4BN;5CL;7CK:7EK97EJ99DJ99EI8:FG8;FH7;GF8<EH7;FI6;EI8;DI8>AE<h6L4M2M4L4LeGAo3<^K?^4]ObKl0Y4QOgKW1S4fNmKa1P4[NPLh1g74L4M3L3M4M3N2M2O0O2O1O0O10O001O1O0010O03M3M2N3N1N3M2N3M3M2O2M2N2N3K4L4L5K4L4LaFXOb6c0YIMb6NXIb0b6ZOXIV1b6fNZIf1c6UN_Im1m84L3M4L3M4M2M4L3N3N1N2O0O10000O100O10000001O0000oK", "size": [427, 640]}, "area": 67512.0, "iscrowd": 0, "image_id": 13923, "bbox": [15.0, 34.0, 625.0, 393.0], "category_id": 183, "id": 20000860}, {"segmentation": {"counts": "n0\\<n00001O00001O00001O00001O00001O00001O001O00001O001O00001O001O001O00001O001O001O001O00001O00001O001O001O001O00001O00001O001O00001O001O001O001O00001O001O1O00001O1O0000001O001O00001O001O001O001O00001O001O001O00001O001O001O001O001O001O001O001O001O00001O001O001O001O00001O001O001O001O00001O001O001O001O001O1O00001O1O001O001O00001O001O001O001O00001O001O001O001O001ON20QFhLl9[301O001O001O001O1O001O001O001O00001O001O001O00001O001O00001O001O1O001O001O001O1O001O001O1O001O001O001O001O00001O0000001O001O001O001O001O001O1O001O1O1O001O1O00001O001O001O001O0000001O00001O001O1O001O001O001O001O1O002N001OO11O001O001O003M000ZH`La0oN`0ET1l4kMVON^Lh1]4QN_O2gL]1e8cNjGn0W8POYHa0f:B1O000000000000000000000000000000k^S4", "size": [426, 640]}, "area": 99863.0, "iscrowd": 0, "image_id": 14007, "bbox": [0.0, 30.0, 323.0, 396.0], "category_id": 98, "id": 20000861}, {"segmentation": {"counts": "0n0\\<00001O00001O00001O00001O00001O00001O001O00001O001O00001O001O001O00001O001O001O001O00001O00001O001O001O001O00001O00001O001O00001O001O001O001O00001O001O1O00001O1O0000001O001O00001O001O001O001O00001O001O001O00001O001O001O001O001O001O001O001O001O00001O001O001O001O00001O001O001O001O00001O001O001O001O001O1O00001O1O001O001O00001O001O001O001O00001O001O001O001O001ON20QFhLl9[301O001O001O001O1O001O001O001O00001O001O001O00001O001O00001O001O1O001O001O001O1O001O001O1O001O001O001O001O00001O0000001O001O001O001O001O001O1O001O1O1O001O1O00001O001O001O001O0000001O00001O001O1O001O001O001O001O1O002N001OO11O001O001O003M000ZH`La0_Nm3Q5bKdM^4]2YKlMf4\\50001O00001O0000001O00001O001O00001O00001O00001O00001O0000001O001O00001OiNkKYIU4e6ZLnHf3Q7\\LnHd3Q7^LoHa3P7aLoH_3o6dLPI\\3o6fLPIZ3o6iLoHW3P7kLoHU3P7mLPIR3n6QMQIo2n6SMPIn2o6TMPIl2o6VMPIj2m6ZMRIf2k6^MUIa2Z6kKiIi1G`2^6nKbIT6\\6>N2N2N2A?M3M3O1O1O1O1N2N2N2M3N2N2N2M3N2002N1O2N1O1O2N1O2N2N4L5K2N1O0000001O00001O00000001O1N2O1NhIaJ^O4V4Z5YLgJ@0V4W5YLmJ_OLY4V5WLQK_OH[4U5XKhJ>>MD_4U5TKmJ==O_Ob4X5nJPK><0\\Oe4Y5iJSK?;_5V5]JmJb5T5WJSK4QOS5Y7gJjH[5a72O1N2O0O2N2O1N2N3N1N2jHoIQ7R6nHoIQ7V6N10O0011N2O1N2O1O1O1O1O1O1O100O1O1O100O1O1O100O1O1O1SJTI[5l6`JWIa5l6VJ[Ii5S7O1M201O1O2N001OO21N011N2N2N2O1N3M2N3M1O2N3N1N2N2O1N2N2N2OYMaKdL]4]3cKPJ0^2\\4c3kK\\LS4e3oKYLP4h3QLWLn3j3SLVLk3k3WLSLh3n3YLQLg3o3ZLPLe3Q4\\LnKc3S4^LlKa3U4`LjK`3V4aLhK_3Y4aLcI]OR2Q4\\4eLcKZ3^4hL`KX3`4jL]KV3d4kL[KU3e4mLYKT3f4mLXKP3YNSL`6o0UKm2o4UMlJm2U5TMkJj2hNSLJ0V6U1VKg2kNbLe5h0_Ke2mNcLd5j0\\Kc2QOcLc5k0ZKb2SOdLc5l0WKa2VOcLc5m0UK`2YOcLb5n0SK_2\\OdL`5n0RK^2@bL_5Q1oJ^2BcL]5Q1nJ\\2FcL\\5S1kJZ2IeL[5R1jJZ2LgLV5Q1kJY2OhLT5P1kJY21hLS5R1gJX26hLQ5R1dJU2JPLb0k0n4X5VKkJg4S5nJoIH1On0[5l4dJTJO09a1R5\\4RKeKm4[4SKgKk4Y4UKhKj4X4VKjKh4U4YKmKBWNQ5l5^KPL`4o3`KSL_4m3aKUL]4j3dKVL\\4i3eKXLZ4h3gKYLW4g3iKZLV4f3jK[LU4e3kK[LU4e3jK\\LV4^3QLaLo3Y3YKeJ=0ENN03T2d4Y3fKXMZ4i2eKVM\\4i2eKWM[4h2fKXMZ4h2fKXMZ4g2gKYMY4g2gKYMY4f2hKYMY4f2hKZMX4f2hKZMX4f2hKZMX4d2jK\\MV4d2jK[MW4e2jK[MU4a2oK`MP4_2PLbMP4Y2XKTKj0c2n3Y2eKWN[4i1eKWN[4i1eKWN[4h1fKWN[4i1eKWN[4h1fKXNZ4g1gKYNY4f1hKZNX4f1hKYNY4g1gKYNY4g1gKYNY4g1fKZNZ4e1gKQNc4P2]KiMi4X2UKhMl4Y2TKgMk4Z2TKfMl4\\5000000000000000000000001OO1_Oa0000000000000000000000000000000_Oa000000000000000000000000000000000000000000000000000000000", "size": [426, 640]}, "area": 102772.0, "iscrowd": 0, "image_id": 14007, "bbox": [0.0, 0.0, 640.0, 289.0], "category_id": 102, "id": 20000862}, {"segmentation": {"counts": "XkU43W=0O1PFOf61[IOd61]IOc61]IOb61^I0a61_IOa60`I0_61aIO^61cIO]61cIO\\61eIO[60fI0Y61gIOX61iIOW61iIOV61kIOU61kIOT61mIOS61mIOT6OmI1T6MmI3e90000000000000000000jKNTK2m40PK0[5N\\J2b5MgF0h33`5OaJ1_5OaJ1^5OcJ1]5OcJ1\\5OeJ1Z5OgJ1Y5OgJ1X5OiJ1V5OkJ1U5OkJ1T5OmJ1S5NnJ2Q5OoJ1P5OQK1o4NRK2m4OSK1m4NTK2k4OUK1j4OVK2j4MWK3h4NXK2g4NZK2f4M[K3d4N\\K2d4M]K3b4M_K3`4N`K2`4MaK3^4McK3]4McK3\\4MeK3Z4NfK2Z4MgK3X4MiK3V4NjK2V4MkK3T4MmK3R4NnK2P4OQL1m30TL0k31ULOi32XLNf33[LMc35]LKb35_LK_37aLI_36bLJ_34bLLc3O]L1e3L\\L4]8000000000000000000000000000000000O100000000kJLTM4l2LTM4k2MUM3k2NTM2k2OUM1k20TM0k21UMOk21UMOk22TMNl22TMNl22TMNl23SMMm23SMMm24RMLo23QMMo23QMMo24PMLP34PMLQ33oLMQ34nLLR34nLLR34nLLS34lLLT34lLLT35kLKU35kLKV34jLLV35iLKW35iLKX34hLLX35gLKY35gLKZ35eLK[35eLK\\34dLL\\35cLK]35cLK^34bLL^35aLK_35aLK`35_LKa35_LKa35_LKb35\\LLd34\\LLe33[LMe34ZLLf34ZLLg33YLMg34XLLi33WLMi34VLLk33ULMk33ULMk34TLLm33SLMm33SLMn33QLMo33QLMo34PLLQ43oKMQ43oKMR43mKMS43mKMS43mKMT43kKMU43kKMV43iKMW43iKMX42hKNX43gKMY43gKMZ42fKNZ43eKM\\42dKN\\43cKM^42bKN^42bKN_42`KN`42`KNa41_KOb41]KOc41]KOd40\\K0d41[KOf40ZK0g40XK0h40XK0i4OWK1i40VK0k4OUK1l4NTK2l4ORK2o4MQK3o4NPK2Q5MoJ3R5LnJ4S5LlJ4U5KkJ5V5JjJ6V5KiJ5X5JhJ6Z5IeJ7W9000ULIcJ7]5IcJ7]5IcJ7X900000000000000000000000000000000000000000000000000000000000000000O1000000000000000000000000HH^C8c<I[C7d<MYC3e<:002N1O004L2N6JlYb0", "size": [426, 640]}, "area": 2925.0, "iscrowd": 0, "image_id": 14007, "bbox": [321.0, 252.0, 274.0, 174.0], "category_id": 123, "id": 20000863}, {"segmentation": {"counts": "ndW51Y=1O1O1O1O1O1O1O1O1O1O1O1O001O1O1O1O001O1O1O1O2N1O1`CYO[<h0dCYO[<l0O00O1O11O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1hD_Ne:b1VEaNk:b1lDeNS;i1000O100001OO100N21OO11O1O1O1O1O2N1O2N001O2N1O1O1O1O1O1O100O]M^E^2i:O1N1O101N1O1O1O1O1O1N1lMlDR2W;O2N2M2O2N1N3N2K401N1O2M2N3M2N2N2N2N3M3M2N3M2N4K4K`n^1", "size": [426, 640]}, "area": 5774.0, "iscrowd": 0, "image_id": 14007, "bbox": [403.0, 0.0, 124.0, 90.0], "category_id": 130, "id": 20000864}, {"segmentation": {"counts": "d_m3`0j<R1oN1ZD`N^;`1bD_N_;b1_D_Nb;c11O101N100O101N100O2N101N100O2KRDlNn;T14O2N1O101N1O101N1O100O2O0O2N100O2O0O100100O1OO100O101N1O010O102M:F0O3OVoY11fPfN3O1O2N1O2N1O101N100O2N1O101N1O2N1O1O2N101N1O2O0O1O2N101N1O1O2N1O2N1O101N1O2N101N1O1O2O0O2O010KWNfDj1Y;5N1OXNiDJ0b1U;eNYER1e:oN[EQ1d:PO\\EQ1b:oN_EQ1a:oN_ER1_:oNaER1]:oNdEP1[:ROcEP1\\:POfEn0Y:SOgEn0W:ROkEn0T:SOoEi0Q:WOQFh0n9XOSFg0m9YOUFf0f9iNaEb0k0d0b9@aF>\\9WOfEH1On0S1T9lNlEO09a1k0c8YO^Gf0b8ZO`Ge0_8[ObGf0\\8ZOfGe0X8\\OjGBWNn0o9AmG=R8CPH<P8DRH:m7GSH;j7FWH9i7HXH6h7JYH6f7J[H6d7J\\H9a7F`H<X7KgH8P7POnF=0ENN03T2o0n6[OcIh0[6WOdIm0X6TOhIn0U6SOkIP1R6POnIS1n5nNRJT1l5lNTJW1h5jNWJZ1e5gN[J[1c5eN]J^1`5bN`J`1\\5bNdJa1Y5_NfJe1W5\\NiJe1Q5_NPKc1l4]NUKf1c4bMjHj0c2f1a4mMoKV2n3jMRLY2k3gMUL[2h3fMWL^2f3bMZL`2c3aM]Lb2_3_MaLd2[3]MeLe2Y3[MfLi2W3WMiLl2T3TMlLn2R3QMoLR3m2oLiL^3U3bLdLf3[3XLeLl3Y3TLgLn3W3QLiLQ4W63M2N3M2N1OO1N2002N1O004M1M7YOe0L00000000000000000000000000000_Oa000000000000000000000000000000000000000000000000000000000", "size": [426, 640]}, "area": 20109.0, "iscrowd": 0, "image_id": 14007, "bbox": [301.0, 153.0, 339.0, 273.0], "category_id": 172, "id": 20000865}, {"segmentation": {"counts": "_am3>k<`0A`0_O`0A?@?A100O100O100O100O1O100O100O100O100O1O100O100O100O100O1O100O100O100PNhLmIY3Q6VMbIj2]6XMaIi2^6YM`Ih2_6ZM`If2^6]M`Id2_6^M`Ib2_6`M_Ia2`6bM^I^2a6dM]I]2a6fM]IZ2c6hM\\IX2c6jM[IW2d6kM[IU2b6oM\\IR2a6RNTI]NIb3b6]LdIi1G`N3Z3`6dL]IS23Z1^6TOaIm0]6TOdIl0Z6UOfIl0X6VOhIj0i5DWJ=f5FYJ;d5G]J9b5G^J:a5G^J:a5F`J:_5FaJ;]5GbJ:\\5GeJ9Y5IfJ8W5JiJ7U5JlJ6R5LmJ5Q5LPK4m4ORK2l4OTK2m4LTK4m4KRK6o4HQK9Q5DPK<Q5CnJ>T5_OlJb0U5]OkJc0V5[OjJf0X5WOhJj0[5SOeJm0`5mN`JT1d5hN\\JX1e5fN[J[1f5cNZJ^1f5bNZJ^1f5aNZJ`1f5_NZJb1g5[N[Je1e5YN\\Jh1d5WN\\Jj1e5SN]Jl1d5RN]Jo1c5PN]JQ2c5mM_JS2a5kM`JV2`5iMaJW2^5kM`JV2`5jM_JW2`5iLYJ@6g3a5fL^JA0j3a5eLbJ@Kl3c5cLeJ_OHn3b5eK[J>>NCP4d5bK`J<<0_OS4g5]KbJ=;2[OT4j5YKdJ>:3WOW4`6aK_J3RO]4a6XKeJ4oNd4j7VKYHk4R8100O100O1O100O1O100O100001O00001O001O00001O00001O001O00001O0O101O00001O001O00001O00001O001O00001O00001O001O00001O00001O00001O001O00001O00001O001O00001O00001N101O00001O00001O001O00001O00001O00001O001O00001O00001O001O00001O00001O001O00001O00001O00001O2N2M4M3M2N3M3M2N3M2N3M3M2N3M3M2N3M3M2N3M2N3M3M2N3M3M2N3M3M2N3M2N3M3M2N3M3M2N3M3M2N3M2N3M3Mbde0", "size": [426, 640]}, "area": 40916.0, "iscrowd": 0, "image_id": 14007, "bbox": [301.0, 154.0, 286.0, 268.0], "category_id": 183, "id": 20000866}, {"segmentation": {"counts": "_Q53m<N[C9l0GSO17OX:6aE5h06VOFc9M[FLN0047504c0a0\\OTOK00N00Q9a0SGKO7`0=A_Ok8g0RGA>:D^Ol8d1^GoNE]Om8e1]GBc8?\\GAd8b0XG_Oh8f0RG[On8a2000000000000000O11O0000000000000000000000O100EoFVL01Q9c3QGYL03N1Q9]1oF2g0`NYO1Q9i0SG]O21L:S1[OnN3n8=oFG811OM;V1@f74YGM2NDO<2M>T1@e74[GLe00YO>X:DcGNiM6khW10dWhN2c2KR62XG?d0]Oe03`70VGd0b0YOf06b7MlFj0e0E2AD5h8m0dGJ@XON3o8j0cGR1\\8mNcGV1V8POjGn0m7hMiGW18R1l7kMjGT11Z1R8cMnGR4R8oKdGJO21W4[8nKcGK11OU4^8ZMbGf1^8YNfHc0Z7]OfHb0[7^OeHb0\\7mM`G^1T1k0Y7TOgHl0[7ROeHo0\\7oNdHP1^7oNbHP1b7mN^HS1b7mN^HR1d7mN\\HR1e7nN[HR1d7oN\\HP1e7PO[HQ1d7oN\\HP1e7PO[HP1e7PO[Ho0f7QOZHP1mNlM[8]1hHj0X7WOhHi0S7\\OnHc0Q7^OoHb0P7_OPIa0P7_OPIa0k6iMRHg1S1a0j6hMRHh1T1`0j6hMQHi1U1?j6hMQHi1U1?j6CVI=j6CVI=j6CVI=j6CVI>kNhMk7j1ZIa0f6_OZI`0g6@YIa0g6]OZIc0i6ZOWIe0l6YOTIh0n6UORIl0o6ROQIn0R7oNnHQ1T7mNlHS1T7mNlHS1T7mNlHS1T7mNlHQ1V7oNjHQ1W7nNiHR1W7nNiHQ1X7oNhHR1W7nNiHR1W7nNiHQ1X7oNhHR1W7nNiHR1W7nNiHR1W7nNiHR1W7nNiHR1W7nNiHT1U7lNkHT1U7lNkHT1U7lNkHU1T7kNlHU1d6bMdHY1h0V1c6dMbHV1k0X1a6cMcHU1l0X1a6dMbHT1m0X1`6gMaHQ1o0U1c6kM]HP1P1U1c6lM\\Ho0Q1U1c6nM[Hl0R1X1a6mM\\Hk0S1Y1`6mM\\Hj0T1X1a6PNYHh0g0f1Q7mNYHh1g7n10O1eLXHT1g7kN\\HnMLP3i7QOaHn0_7ROcHm0]7QOeHm0\\7ROgHl0Y7SOiHm0V7UOiHi0Z7VOeHk0`7PO_HP1a7QO^Hn0c7RO]Hn0c7SO\\Hn0c7oMSH49l1e7kMZH70n1f7kM[H7Nn1g7fMPHA;m0Ll1i7gMoG_O>n0Il1l7dM_Ha0Dk1n7fM[H`0Ek1Q8dMZHc0Ci1U8bMXHf0Bh1V8aMYHf3i7XLWHh3i7YLVHh3i7YLVHf3k7ZLUHf3k7ZLRHj3m7VLgGD9V4HjKU8`0QHg3IjK32l7=WHQ4k7nKTHU4j7lKUHQ4n7PLQHn3n7ULQHj3o7XLQHh3m7\\LQHd3o7l0OMRHiJP8n4RHTKR8l4oGSKR8m4oGRKP8o4PHQKo7P551N1H85J8kKgGc21bLV8o0gG=1>3WNT8j0oG55f0KZNQ8k0THWOJ;7X1KZNS8i0QHYOK=6^O3U1[8EaGWOOa0f0GXO4GCl8i0nGI9^OmN4<7d8<jGL<^OXO8d8=iGL<^OXO7d8?hGLo0BZ7b0gGLY:4gELY:2iENV:0mE0S:0nEOR:2mENS:2mEOR:0oE0Q:2nELU:EXF:];O2N101M3N2N1O3M2OgdW4", "size": [427, 640]}, "area": 24460.0, "iscrowd": 0, "image_id": 14038, "bbox": [12.0, 0.0, 302.0, 416.0], "category_id": 98, "id": 20000867}, {"segmentation": {"counts": "bQg13Z=1N1EL\\C5HLg<3^C6_<M\\C5d<90009G5KO12N1O001OO100O100O100O1O100O1001O00001jCnNQ<W1O1O001O1O001O1O001O001OO1000000000000000aDhNk:X1REmNl:U1oDoNP;R1mDnNU;e1O00000000000000000000000000O1O1O1O1O1O1N22N2N1O1O0000O1O1N21O1O1O1O1O1O1O00000000O11O00O1000000000000000nDTNe:Y2000000000000000000000000000000000O1O1O1O100000000000000O11OO1000000001OO10000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000fMUET2R;O00O1N2O00oMQEh1n:SNXEm1R;1001O1O003M1O00N2O10000O10SNdDk1_;O1O2NO11O001O002N1O3MO1M35K1ON21O00003M0000N20000M201004L1O0000H8N28H3M1OO1I71O4L2N2NO11O1O00O10000000000O1001OO1O11O1O2NO100O11O0000O1M31ON21O1ON200N22N1OLdNZD[1f;5001O00001O00M300M31N4L000O4MO0SDfNk;\\1O41M10O00012KbNYD^1a;:0O012M010OG]NjDb1Y;^NeDa1^;EbDRO`;n0aDoNa;P1cDkN_;R1<3N0JiN[DU1g;mNUDT1l;41OO12N2OO0N21O1O1LPDlNQ<S14N2O11O1O00O1O1O100O1O1O1O1O1O1O1O1O1O1O1O1O100O1O1O1O1O1O1O1O1O1O1O2Nend1", "size": [427, 640]}, "area": 20558.0, "iscrowd": 0, "image_id": 14038, "bbox": [132.0, 0.0, 381.0, 81.0], "category_id": 102, "id": 20000868}, {"segmentation": {"counts": "Xk_3=l<?B103L401J2O1001N1001OO22MM301O01OO4K010000001N2O100O10000010O01O1W1jN0OOG:JfMYEZ2g:fMVE]2`0cMh9`2WF`Mi9`2WF`Mi9n2iERMW:Q30000O11O01O001O001O0O2O001OLoEnLQ:R34000000000000000000O100NiEPMW:P3200O100000000001O00O10000001O0O2O00100O0000000000000000000001O000nLiEP3W:PMiEP3W:PMiEP3i:\\MaQ^3", "size": [427, 640]}, "area": 8144.0, "iscrowd": 0, "image_id": 14038, "bbox": [268.0, 87.0, 108.0, 105.0], "category_id": 109, "id": 20000869}, {"segmentation": {"counts": "e]W5211U=3M2M4M3L4J6N10O2OO03NOO2100OO13N1N0OJQD3m:OQE3m:NQE4n:OoD2g:VOVEP1JLn:TOQE23o1l:92N00O1L4O100001O2N10O01N2]OTEdNW;m0PEoNR;KbDP1a0QOP;N_DQ1X<oNhCo0Z<001O00O11O00000O1O100000O112EhCYON1_<9b0Ml]Z2", "size": [427, 640]}, "area": 2416.0, "iscrowd": 0, "image_id": 14038, "bbox": [401.0, 186.0, 60.0, 80.0], "category_id": 110, "id": 20000870}, {"segmentation": {"counts": "eR^33T=4L4O1O12NM3N2N2000000L_O\\Cc0[<_OgCf0X<[OgCf0Y<[OfCe0Z<[OfCe0Z<600O1O1N2QLoN[K1bLP1U8mNXK4dLn0P8UOWK0hLk0Q8ZORKLlLj0R8ZOQKT1o4lNWJMQNGC`1U8lNVJ0PNV1j7jNUJ2oMU1l7iNUJ3mMU1n7hNUJ5jMT1Q8gNUJP2k5QNTJo1j5SNVJm1g5VNYJj1g5VNYJj1h5TNYJl1g5TNYJl1f5UNZJk1h5SNXJm1[5`NeJ`1\\5_NdJa1]5^NcJb1]5^NcJb1]5^NcJb1^5]NaJd1_5\\NaJd1_5\\NaJd1_5\\NaJd1_5\\NaJd1_5\\NaJd1_5\\NaJd1`5[N`Je1`5[N`Je1`5[N`Je1`5[N`Je1`5[N`Je1`5[N`Je1a5ZN_Jf1a5ZN_Jf1a5ZN_Jf1a5ZN_Jf1a5ZN_Jf1a5ZN_Jf1a5ZN_Jf1a5ZN_Jf1a5ZN`Je1`5[N`Je1d5WN\\Ji1e5VN[Jj1f5UNZJk1g5TNYJl1h5SNXJm1i5RNVJo1j5QNVJo1j5QNVJo1j5QNTJBiM]2S8QNPJBmMa2S8mMnIBoMc2S8kMmIX2S6hMmIX2f5UNZJk1f5UN[Jj1e5VN[Jj1e5VNZJk1f5UNYJl1g5TNZJk1e5VN[Jj1e5VN[Jj1e5VN[Jj1e5VN\\Ji1]5]MeHj0n1i1]5_McHh0S2f1Z5aNgJ^1Y5bNgJ^1Y5bNhJ]1X5cNiJmNiM]2]7gNjJlNiM]2]7gNlJY1T5gNlJY1T5gNlJY1T5gNlJY1T5gNlJY1T5gNlJY1T5gNlJY1T5gNlJY1T5gNlJY1T5gNlJY1T5gNlJY1T5gNlJY1T5gNlJY1T5gNlJY1T5gNlJY1T5gNlJY1S5hNmJX1S5hNmJX1S5hNnJW1R5hNnJY1R5gNnJY1S5fNmJZ1S5fNmJZ1T5eNlJ[1U5eNjJ[1V5eNjJ[1X5eNfJ[1Z5iNbJW1_5kN`HhNh10VN]2a7POYJR1g5oNSJcN\\N`2a7oNQJT1P6mNnIS1Q6QOlIo0U6SOhIm0X6VOeIj0Z6YO_HSNS1d2_6\\O_Id0`6_O^Ia0b6A\\I?d6DXI=h6EWI:i6HUI8k6LQI4o6N_H^M>d2S71lHOT73_HYM8e2Z76cHJ]77_HTM0e2`7<_HDb7R30YJ^He5d70N200M3O11Oh0XO1O00000000000000000000000000001O001O1O1cKdGi3o804L0000001O00001O001O001O1O1O3M1O2N3M001O002N6JO10000N2O1O1O1O1O1N2O10000000000001O000000000000001O1O1O000000001O00O10SN]F2d9J`F5_9HeF8\\9fN[FD2c0:R1X9fN_FB2e07S1Y9dNgF81i0EiNb96kF6Nd0MoNY97nF5M=3WOR97PG2L:8]Ok87SG1J4?Dc87VG0IMe0L]8NaFOg08FHl03V8N`G6n0Lb7N`G6n0Lb7N`G6n0Lc7M_G6o0Mc7L^G7P1Lb7M^G7P1Lc7L]G8P1Ld7L[G8Q1Ld7L[G8Q1Lg84YGLg83ZGMf83ZGMf83ZGMf83ZGMf83QGK[N2d:3RGMXNOf:4SG4m8KUG4k8LUG4k8IYG6o:O1O001O1JlB3X=N1O]fY1", "size": [427, 640]}, "area": 28078.0, "iscrowd": 0, "image_id": 14038, "bbox": [263.0, 237.0, 276.0, 190.0], "category_id": 118, "id": 20000871}, {"segmentation": {"counts": "Zh?:8Ga<a000O26J6J4M2MVPh0OioWO010O1O10O01O1O001N2O0O2N1O21N2O0O2O1O1N4MY]c21fb\\M2N1O001O1O1O1O1O1O1N101MCVC>i<3O1N4M2N2N3M2N2O0O0O10000001O001O0]OhC3Y<LgC4Z<KfC5Z<GjC9V<GjC9V<GjC9V<GjC9V<GjC9V<GjC9V<GjC9V<GjC9V<GjC9V<GjC9V<GjC9V<GjC9V<GjC9V<AeC2S=NmB0U=0kBOT=2mBOT=Oga20]^M001ZOOaC0O0N010N3T;2fEo0W:V1N001OO10O101OO11N12M020N1XORE^N1a0m:R1VEmNj:Q1XEoNf:P1^EoNb:o0aEPO`:l0dESO]:g0iEXOW:g0jEYOV:g0iE[OV:f0iEZOW:e0iE[OX:g0fEZOY:e0fE]O\\O_Ol:T1iE^OW:c0iEZOY:f0fE[OZ:e0iEUOZ:l0gEQOZ:o0fEROY:m0aE\\O]:c0eE[O\\:d0eEZO^:d0eEYO]:<kDAP1JW:c0iD\\O23Z<a0:1N00O1L6M4M1O0101N1O0O1^EDT8<[GHaNNV<9fCJ;JX;a0YD:g;i0L0001O01N11O01O001O001O1O1N4N0GUNlDN1P2h:c0L1O20O02O5LM01000N3N`0@1O2N2N1N3N101O1O001O00100O00L4N2O100000100O10O0RNlDe1^;O1O0\\O`D^Od;<^DCd;;\\DDf;:[DFf;6TD^O5=g;4UD]O6=g;5]DKn;GgCL70L;o:K^FKbN:o:KeF3^9KcF3_9KcF4]9LdF2\\9Mied0<m[[OO11O1O001OO100002N2NN2002N1OO100O1005KO100O100O100O100O100O100O1000000000000001OO11O00000000Ilm<", "size": [427, 640]}, "area": 8771.0, "iscrowd": 0, "image_id": 14038, "bbox": [37.0, 0.0, 572.0, 364.0], "category_id": 123, "id": 20000872}, {"segmentation": {"counts": "TnW44U=3O00O1N2O1OoB1h<NSC8m<21001O1O003M1O00N2O10000O10SCDk<?O1O2NO11O001O002N1O3MO1M35K1ON21O00003M0000N20000M3O1004L1O0000H8N28H3M1OO1I71O4L2N2NO11O1O00O10000000000O1001OO1O11O1O2NO100O11O0000O1M31ON21O1ON200N22N1OLcC[O\\<e05001O00001O00M300M31O3MO1O12NN2N2M32N00O1O100MYCBg<891O003M001OGWC0i<2WCLi<651O1O1ON2O1IPC4o<MSCOn<2500N21O1OOVTb1Olk]N1jDOg84YEMP20X8f0hG\\OV8e0iG]OV8d0gG_OX8a0gGAX8`0gGUOTN:T:a0kG^OU8c0gGUOUN9S:b0hGBW8?iG@W8`0iGAV8?iGBW8>hGDW8<hGEX8;cGSO_Nd0m9:bGTO_Nc0o99cFTO9g0U94_F[O9b0W93_F^O6a0\\90\\FB8=\\91[FE7;]90[FG79]91[FG79_9OYFL55a90ZFK64_92ZFL71^94[FL7O\\97\\FL8LZ99^FL9JY9:^FM9HY9;^FM;EY28W35UJ0:Cg1BPNj0j50VJ18EQ17jN0e54XJ17D9o0C[Oc50[J25FG_16iNd5OZJ34T2LZMX6?hIg3R7YLmHe3V7Y1O00O10O100O1RN", "size": [427, 640]}, "area": 7599.0, "iscrowd": 0, "image_id": 14038, "bbox": [326.0, 0.0, 314.0, 300.0], "category_id": 130, "id": 20000873}, {"segmentation": {"counts": "\\Yh11U=5O10000O100O100O100O100O1JIXC7h<600N_OZCa0e<3M3N2L4K51OO11OO11O00O1006J001OO1O100O1O100O11OO100O11POhCk0[<100000000001O0000000000001TOcCh0`<0O11O00O11O1O0000000000N20000O11O00O11O000000002N00001O001O2N0000M300002N1OO1O10000O11OO1001OO1001OO11O00O1001OO1000000000000000000001OO10M4K3CVC7n<HSC3eak4", "size": [427, 640]}, "area": 3147.0, "iscrowd": 0, "image_id": 14038, "bbox": [134.0, 392.0, 133.0, 35.0], "category_id": 131, "id": 20000874}, {"segmentation": {"counts": "U`X35Q=8M1JEZC;g<DXC>f<43M0000O1001O1O000M3DSC9Q=N2O0O2O1O0O102Na[h4", "size": [427, 640]}, "area": 284.0, "iscrowd": 0, "image_id": 14038, "bbox": [250.0, 253.0, 24.0, 20.0], "category_id": 139, "id": 20000875}, {"segmentation": {"counts": "Vbg32P=;UOJ]D7a;J`D6^O3[;KWEf0e:\\O[Ef0^OmNS;=`Ei0^:XOaEi0^:WObEj0]:VOcEj0^:UObEk0^:UObEk0^:UObEk0^:VOaEj0^:WObEi0^:WOcEh0]:XOcEh0]:YOaEh0_:XO`Ei0`:WO_Ej0a:WO]Ej0c:VO]Ej0c:WOZEk0f:VOXEj0i:WOUEj0k:VOTEk0l:WOQEj0o:WOPEi0P;i000000000000000000000000000000001O000000000000O10000NPEiMP;S2PEmMT;Q2lDoMV;P2300001O00O1O11O0000001O003M1O001O1mDiMP;W2PEiMP;Y2000000000000000000000000000000000001N0100000001O002N4L3oMgDh10VN];j13JaD\\Nc;b15N3M3M3M2iNlCS1X<N2N2N3L3O1N4L2^OWC>l<O2CRC8T=M1LkB0Y=Ohgn2", "size": [427, 640]}, "area": 7662.0, "iscrowd": 0, "image_id": 14038, "bbox": [286.0, 274.0, 126.0, 76.0], "category_id": 152, "id": 20000876}, {"segmentation": {"counts": "^mo61Y=3N1O1O0000000000000000000000000000000000000000000000000000000000000000000000000000000000Kl]:1VbE2N2000000000000000000000000000000000000000000000000000000000000", "size": [427, 640]}, "area": 455.0, "iscrowd": 0, "image_id": 14038, "bbox": [536.0, 421.0, 104.0, 6.0], "category_id": 165, "id": 20000877}, {"segmentation": {"counts": "Uf=6U=2lB4e<L\\C<:A`;c1H=B000001O1O1O2N1O3M4L4L4L4L4L4L4L4L4L4L5K4L1O1O00001O00001O00001O005KYk\\7", "size": [427, 640]}, "area": 1280.0, "iscrowd": 0, "image_id": 14038, "bbox": [32.0, 357.0, 39.0, 70.0], "category_id": 167, "id": 20000878}, {"segmentation": {"counts": "0_Q53^[KN[C9l0GSO17OX:6aE5h06VOFc9M[FLN0047504c0a0\\OTOK00N00Q9a0SGKO7`0=A_Ok8g0RGA>:D^Ol8d1^GoNE]O;Fm7o1RHB1QO[7^1cHA2Ci6o0QI_O64W6b0]I[O<e0e5l1[JfNS5Z1mJXOa4h0_KJo36QL3f3MZL3f3MZL3f3MZL2g3NYL2g3MZL2g3OXL1b1kKOT4_N1b1mKPN4`1n3`NRL0U4k1gKVN<:A;[9n0XHiNo7Q1\\2VOWDCQ<7h0InQ3IcbL8^NM_:OjE21L:=CDl9MSF811OM;;e9JlE2NDO<2M>8e9LkEe00YO>7e9d1mEiM6l0c9k2I1O001O0000000cF\\LS9d3mF\\LS9d3mF]LR9c3nF^LQ9b3oF^LQ9c3nF]LR9c3nF]LR9c3mF^LS9m300000N200O100GlF[LT9c3QGYLP9f3QGZLo8e3RG\\Lm8Z3]GhLa8S3cGoL\\8Q3dGRMY8o2fGRMY8n2gGTMW8l2iGUMV8k2jGUMV8k2jGUMV8T3`GmL`8Z3YGfLg8\\3WGdLi8]3VGcLj8o3000000000003M5K2N3M00000000O100000000000000000000O12bG[KR8P5O001O1O1O3M1O001Oe0[Oe0[Ob0^O:FcH[J^7[5dHfJc7P5^HQKj7f4VH[KHLW8c4nGfKZ8R4aGPLi8T4:F:O1K5E;H8G9F:G9E;FWKZNkI2c2KdMc1P6_NfI?d0]Oe03QOb1Q6^NdId0b0YOf06RO^1T6_NXIj0e0E2AD56G^Oe1i6AoIJ@XON3;\\1Z6^OmIR1DBa6[OjIV1AA`6_OoIn0^OFZ6RNnIW18V3h5gKnIT11^3n5_KRJW6m5jIiIJO21[6W6jIgIK11O[62bIR6V700O001N2M2N300_ObHkJ1O]7V5cHhJ8NT7Y5QIgJP7X5PIiJQ7V5nHiJS7X5kHkJT7U5lHkJT7U5lHlJS7T5mHlJS7T5lHnJS7R5mHmJT7S5lHmJFJ\\7Y5nHoJR75iHX45aKT74lHY40bKW70mH\\4McKX7NkHj1;WODQOCN=2i7g1fHUO9ROR7i1eHUO9QOS7j1dHUO9POT7j1dHVO8POT7j1dHVOc8j0]GUOd8k0\\GUOd8k0\\GUOd8k0\\GUOd8k0\\GUOd8k0\\GUOd8k0\\GUOd8k0\\GUOd8k0\\GUOe8j0[GVOk8]OXFE2o0m0Ml8ZO^Fg0V1_O^8G^Fi0c;SO_Dl0P<10O00001O5KO4M7GH82O1O1000000O1O100O1O2O0O010O1O1O1O1O1O1O2O0O1MeNUD\\1j;30000000000000O10000000000000000O10O1O1O1O100000000000000O11TOdDF\\;9l0O100O13NM101001O0000001O00001O00003MN2O1O1O1000000000000001O001O1O2N1OG9KXCGh<9XCDk<<20000000000O11O000000000000O1000000000000000O1000000000000000000000000000000TGDi4<XKFe4:[KHc48]KK`45`KM^43bK0[40eK2Y4NgK5V4KjK7T4HmK8S4HZKFPMb0f7HZKFPMb0f7IYKEQMb0f7IUKIUM>f7IUKIUM>f7IUKIUM=g7JTKIUM=g7JTKIUM=g7JTKIUM=g7JTKIUM=g7JTKIUM=g7JTKIUM=g7JTKIUM=g7JTKIUM=g7KSKHVM=g7KSKHVM=g7KiGCR30QMZ1S8TOjGBR30oL\\1U8ROjGBR30nL]1V8CVHQ1FPNO[1U8U2jGoMV8c30O100000000000000[OiGgK0O0N010NX8o2mGkMT9R2T100000000O1000000O12NO11O000000N2001O1O0000000000000000000000000000001O1O00001O0000002N3M1O001O0O1O00O0100OiLPFX3o921OO100O1O12OO01LQFjLo9V341N010O0_FjLENP9Z3YGgLH2l8Z3XGlLFO]8JnG^3JZMU8YOnGa4P8cKkGe4l7?0OO2N3NO01O1O2NlNU10O1O1O1O1L_K]Gb4b83O2O20O1O00O2N1O100O2N1O1O1O2N1O1O1O1O2NcMSH^Ol7`0WH@h7?ZHAf7=\\HDb7;`HE_7:cHG[78gHIW76kHKS73PI4h6KcInNSO[OZ7f1eInNRO\\OMWOY7^2kInNoN^OV7e1lIkNPO]OU7h1lIfMPOf01LR7h1oIdMoN4K868i6YOTI_2U1aMPO2GN3>49i6o1QKhMUNE44f6^2RKiMTNB:3a6a2SKiMgNCW6c2WKfMiMGf00X6b2[I`M[1;Z6U2[H`MZ1<[6S2\\HaMY1<[6S2\\HaMZ1;Z6T2\\HaMZ1:[6U2ZHbMZ19]6P2^HgMU18^6k1cHmMP17]6h1gHQNl06^6i1fHnMP17[6k1bJUN^5k1bJRNXMLW8R2dJnM_5R2m2M3N20000O1N2O1N2N2O^E]N^9a1bFfNY9Y1gFlNU9R1mFoNR9P1nFQOR9m0PGSOP9l0QGTOo8j0RGWOn8h0SGXOm8f0UGZOk8f0UGZOR5BXMc1WMlN_5BZMf1SMhNb5B\\Mj1oLcNc5E^Ml1kL_Nf5E`MZ2\\LRNQ6FcMX2\\LRNR5BbK6Q3m2_McMi1@_M4Y3j2SMnMR23l0P2oLPNQ23P1n1lLSNn13d1_1YLaNf1;Q2[1PL_Ne1>\\2Q3^MbLbNdNo3j4XMhLQOVNg3T5UMgLTOUNh3V5oLgLYOSNg3[5iLeL_OQNh3b5PL`J0Q28mMh3]8XLbGk3\\8ULdGm3Z8SLfGk3\\8ULdGk3\\8ULdGm3[8RLdGP4[8PLeGo3`8mK`GS4l8O10O6KO101N10NaKXG^4i8cKWG\\4i8dKWG[4j8eKWGZ4i8gKVGX4k8hKUGX4k8iKUGU4l8kKTGU4l8kKUGS4[2kKR43bIR4\\2lKFOV23hKR4S2oKoMMP20U23iKP4e1cL=\\OT21jKP4d1dL=]OT2OkKP4d1eL:_OV2MlKn3d1fL9AU2LnKm3d1gL8UOTN:Q4NoKl3c1gL<^OR2T4cM_L7UOUN9P4T4cM^L8BT2P4dM_L8@T2Q4dM_L8AS2P4eM_L7BT2o3eM_L6DT2f3mMfLNEU2[3WNPM_OSO_Nd0j3Y3XNQM]OTO_Nc0l3X3XNQM^NTO9g0Q3T3YNPMZN[O9b0S3S3ZNPMYN^O6a0W3Q3[NoLVNB8=W3R3[NoLUNE7;X3Q3\\NoLTNG79Y3Q3[NPMTNG79Z3P3\\NoLRNL55]34ZLe1Q2WNSNK64\\32`Le1j1[NSNL71\\31eLd1d1_NTNL7O\\3^OYL4?T2_1aNUNL8L\\3_O\\LLa0\\2X1bNWNL9J[3_OZMY2k0bNWNM9H[3@ZMY2k0bNWNM;EY28YM[OQ1Y2k0aNWN0:Cg1BPNj0KWOS1Y2j0`NXN18EQ17jN0GZOT1Y2i0aNYN17D9o0CCk0X1i0`NZN25FG_16QOk0X1j0_NYN34T2LZM9Hc0X2k0_NYNg3S1dKGV2m0_NXNe3W1hKAU2P1\\3^O_JBU2P1\\3^O_JBU2P1[3_O`J@V2Q1Z3_O`J@V2P1[3@`J_OU2Q1[3@`J^OV2Q1[3A_J_OU2", "size": [427, 640]}, "area": 81815.0, "iscrowd": 0, "image_id": 14038, "bbox": [0.0, 0.0, 640.0, 427.0], "category_id": 172, "id": 20000879}, {"segmentation": {"counts": "cX]2j0T1DQOCN1a:k0]FLc94]FLc94]FLb95^FKb95^FKb95]FLc94]FMb93^FMb93^FMb93^FNa92_FNa92_FO`91`FO`91`FO`91`FO`93^FMb9c0nE]OR:n1010O00000O10003L8IF9100002N2N2N2N2N1O2NOkEmLU:R320O10O1000000000001O00000000000001O00000O3N6J0000000000000O102N1O1O2N002N0000000000O11N2O0003J6K6J200O10O0101N004LN12G94M5IO02O1O4L3M2M4M2O0O100000O2O0000X1hN2[NZEa0f:_OXFEg9:e100O2O00>B[PY4", "size": [427, 640]}, "area": 11429.0, "iscrowd": 0, "image_id": 14038, "bbox": [185.0, 125.0, 126.0, 120.0], "category_id": 174, "id": 20000880}, {"segmentation": {"counts": "od76U=b0^Ob0^Ob0^Ob0^Ob0^Ob0^Ob0^O9GO100O10000O1006J7I7I7I7I7I7I7I7I5K4L5K4kNYI^Kk6V4TIRK1h0o6l3WIZKKj0R7h3\\IWLh6e3XI[Ll6a3TI_LP7e3`HaK3j0a7g3^HYLf7d3YH\\Lk7a3TH_LQ8[44L1O12M2N2O2M2N3N1N3M010O1O100O1O1O00WNaHfM_7X2eHgMZ7X2gHhMY7W2hHiMX7m1RISNn6h1WIXNi6h1WIXNh6j1WIVNi6j1WIVNi6j1VIWNj6i1VIWNj6i1VIWNj6W2hHiMX7Y2fHgMZ7[2dHeM\\7\\2aHfM_7S400000000000000000000000O1000000O10000O10000O010O100O100O10000O1000000:Fe0[Of0ZO=C:F:F:F:F:F:F:E;F9F;F:E;F:E;F:E;F:EhY5J_fJT1lN00000000000jMlNWF1e1S1T8mNTF8b1k0Z8YOhGg0W8YOjGg0V8WOlGi0T8WOlGi0T8VOmGj0S8VOmGj0S8VOmGj0S8VOmGj0S8VOmGj0S8VOmGj0S8UOnGk0R8UOnGk0R8UOnGk0Q8VOoGj0Q8VOoGj0Q8VOoGj0Q8VOoGj0Q8VOoGj0Q8VOoGj0Q8VOoGj0Q8VOoGj0R8UOnGk0R8UOnGk0R8UOnGk0S8TOmGl0S8TOmGl0T8UOiGl0W8WOfGi0Z8YOdGg0\\8\\OaGd0_8^O_Gb0a8A\\G?d8CZG=g8BYG>g8BYG>g8BYG>g8BYG>f8CZG=f8CZG=f8CZG=e8D[G=d8C\\G=c8D]G<c8D]G<c8D]G<e8B[G>g8@YG`0j8]OVGc0l8\\OSGd0n8[ORGe0o8ZOQGf0Q9XOoFh0R9WOnFi0T9UOlFk0U9TOkFl0V9SOjFm0X9QOhFo0Y9POgFP1Z9oNfFQ1\\9mNdFS1]9lNcFT1^9kNbFn0Q;[OQY1GlgN3kBLn<:O2M2N3M21N2N001O001O1O1O1O2N1O1O1O2M3NYWb0>Yh]On0RO?A1O00000000001O000001O001O010O001O001O00001O001O1O1003M00000O1000O1O2N2N2N3L3N3@`0M2N3M3M2N3M2N3M2N3M___1a1P_`N001O001O1O0O2O010O001O10O0O1K501O0O11O01O01O001\\DaNZ;i10ObMUN\\Il1`6WN`Ii1^6YNbIh1[6[NeId1S1TNe1:XMb1aNSNa15c28\\M`1_NTNUO11Oo18n26_M]1^NZNUOMm0MVO2c0:U45RNDSOKPN70Lj00VO2e07S48TNClN:YNAg0:L0j0Ff1m0ZNSOC6f0:ZN@e0=LNd0Ll1h0[NROE4e0;[N@b0?ML>3Q2d0\\NoN^OOZO6^1?`N[Oa0a0KL:7X2?ZNoN^O0]O3]1a0`NZO?c0LJ4?]28ZNPO@N^O3[1b0aNYO=e0MIMe0d23YNkNjNOi02]O2[1d0aNXO41Lg08FHl0j2MZNjNjN0h01^O3Z1d0bNWO3o05o0b2ZOZNhNjN1h01_O2Y1e0cNWO1o06o0a2\\OZNfNkN1i00_O2X1g0cNVO0o07o0`2]O[NjNDM^O1Y1g0cNUO1P15P1`2ZN`M>k0ZOlNOi02_O0X16`N63A1P15P1_2[NeM9g0^OlNOj01^O1Y1g0bNVO1P15P1_2ZNjM6b0BlN0i01^O0[1f0aNWO1P15P1^2[NoM1>FlN0i01^O0:NANh0i0XOYO3n04Q1^2[NVNJ8LkN1j00]O1;N_O0h0f0YOZO3n04R1]2ZNDFgM0i01]O1<N]O1i0d0YO[O3n04R1]2ZNDFgM0i01]O1=26`0XO\\O3n03S1Y2^NIAgM0i01]O1=18`0VO]O3n03S1S2dNO[OgM0i01]O1=18`0VO]O3n03S1o1hN3WOgM0i02\\O0>18`0VO]O3n03j0H]NW2c04XOfM0i02\\O0>18OTO;2C3n03j0KZNT2f0^NVOo02]N0i02\\O0>18;TOC2O3n03k0MVNR2i0XN\\OU1L]N0i02\\O0>18;TOC2O3n03l0V2nNjMD[1D^N0i02\\O0>18;TOC2O3n02n0V2lNeMK`1]O_N0i02\\O0>18;TOC2O4m01o0S2HXO\\N^N1j01\\O0>18;TOC2O4m0NS1S2G[OXN^N2j01\\O0>18;TOC2O5U3o1cN[OWN_N2j01\\O0>18;TOC2N7V3l1`N_OZN\\N2j01]OO=28;TOC2N8U3j1[NG`NUN2j01]OO=28;TOC2N9U3f1TN3gNlM2j01]OO=28;TOC2N:T3d1gMc0UO]M2j01]OO=28;TOC2N;S3a1iMf0TO\\M2j01]OO=28;TOC2N:T3`1jMh0RO\\M2j01]OO=280ZONJ02N:T3_1jMj0RO[M2j02\\ON>O;3WONJ02N9U3^1kMl0PO[M2j02\\ON>O;3WONJ02N9U3]1lMm0oN[M2j02\\ON>O;3XOMI12N8V3\\1lMP1nNZM2j02\\ONa11eNOFN8V3[1mMQ1mNZM2j02\\ONZ2NiMW3Z1mMT1lNYM2j02\\ONf2U3fNnMU1kNYM2j02\\ONe2V3eNnMX1jNXM2j02\\ONe2V3dNoMY1iNXM2j02\\OMe2X3bNPN[1gNXM2j02\\OMe2X3kNgMU1RO\\NOc1Y3SO`Mm0XO[1X3lM\\Mi0\\OZ1Y3QNWMe0^O\\1[3bNdLZ4]3dKfLZ4[3k20O100O1000000\\OhLUGX3k8oLmFR3S9nLmFR3S9nLmFR3S9nLmFR3S9nLmFR3S9nLlFS3T9mLlFS3T9mLlFS3T9mLlFS3T9mLlFS3U9lLjFU3V9jLkFV3a9^L_Fb3e9000O10000\\F_L_9f300NWLbFj3]9VLdFi3\\9XLcFh3]9XLdFg3\\9YLdFg3\\9ZLcFf3]9ZLdFe3\\9\\LcFd3]9\\LdFc3\\9^LcFb3]9^LcFb3]9^LdFa3\\9`LcF`3]9`LcF`3]9aLcF^3\\9cLdF]3\\9dLdF[3\\9:0000000_LdFS3\\9mLeFP3]9PMdFm2^9SMcFk2^9VMbFg2`9YMaFf2_9ZMbFe2^9[McFd2]9\\MeFb2[9^MgF`2Y9`MkF\\2U9dMoFX2Q9hMSGT2m8lMVGQ2j8oMYGn1g8mM]FKn0V2e8nMaGR2_8nMaGR2_8nMaGR2_8mMbGS2^8mMbGS2]8nMcGR2]8mMdGS2\\8mMdGS2\\8nMcGR2]8oMbGQ2^8QN`Go1`8SN]Gn1c8RN]Gn1c8RN]Gn1c8RN\\Go1d8QN\\Go1d8QN\\Go1d8QN[GP2e8PNbHi0RN", "size": [427, 640]}, "area": 52693.0, "iscrowd": 0, "image_id": 14038, "bbox": [18.0, 39.0, 622.0, 388.0], "category_id": 183, "id": 20000881}, {"segmentation": {"counts": "a^W4=c>2N00000000000000000000000000000_N4^C^O1>a<<UCCM2n<`1O11O001O00001O001O00001O001O00001O001O00001O001O00001O001O00001O001O00001O001O00001O001O00001O001O00001O001O00001O001O001O00001O001O00001O001O00001O001O00001O001O00001O001O0\\OSB5]>O^ma3", "size": [480, 640]}, "area": 4536.0, "iscrowd": 0, "image_id": 14226, "bbox": [288.0, 415.0, 108.0, 65.0], "category_id": 105, "id": 20000882}, {"segmentation": {"counts": "glj69g>000000000000000000000000000000000000000000000000000000000000000000IZA4i>0001O001O001O001O00001OQRk1", "size": [480, 640]}, "area": 357.0, "iscrowd": 0, "image_id": 14226, "bbox": [466.0, 471.0, 47.0, 9.0], "category_id": 110, "id": 20000883}, {"segmentation": {"counts": "\\Ti55a>;0=B01O00001O001O00001O001O00001O001O00001O001O001O00001O001O00001O001O00001O001O00001O001O00001O001O0000000000000000000000000000000000000000S\\b1O^U]NIV>8jAIT>8kAJT>7fAF0102Y>b0O12M101N1O2O000000001O0002NO10000000O100N22N8HO100O100O100OO2F\\k=", "size": [480, 640]}, "area": 1701.0, "iscrowd": 0, "image_id": 14226, "bbox": [394.0, 214.0, 216.0, 266.0], "category_id": 115, "id": 20000884}, {"segmentation": {"counts": "g[W14k>103L3OO00001O00000000000000000000000000000000000000000000000000002N001O2N1O1O1OSXS6:eglIOO05JRn00kQO4K5K5N200001OZOAlB>o<GQC9k<KVC4e<1[COd<3\\CKe<5[C^OS=a0f00001O01O0001O00010O0O1O9H000O2JWA010eYm0", "size": [480, 640]}, "area": 923.0, "iscrowd": 0, "image_id": 14226, "bbox": [83.0, 214.0, 495.0, 266.0], "category_id": 123, "id": 20000885}, {"segmentation": {"counts": "0T:l400000000O100O10SKoEk4T:OO100O100O100O1O1O100O100O100O100O100O10000O1O1LiJ_FW5`9jJ_FW5`9iJ`FX5^9jJaFW5^960000O100000000O1O10]JeFa5^9ON21OL_JhFb5W9_JiFa5W9^JiFc5V9^JjFb5U9^JkFc5T9^JkFc5T95O1O10000O10000O10000O1O1O1O1O10000O1O10000O100O10000O100O1O1O100O1O1O1O1O1000000O1000000O1O1O100O1O100O10000O100O100O100000000N2O1O100O1N200O1O1000000O10000O100O100O1O1O100O1OhH]HU7c7kH]HU7c7jH^HV7a7kH^HV7b7iH_HW7`7jH_HW7a7hH`HX7_7iH`HX7a7fH`HZ7b71O10000O1LcHdH^7[7cHeH]7[7bHeH_7Z7bHfH^7Y7cHfH^7Y75O10000O10000O10000O100O1O100O1O1O1O1N2O1M3O1O100O100O1001O00000000O1000000O10000O100O100O10000O1O1O100O1N2O1L4O100O100O100O11O1O00001OO100O100O10000O100O100O100O100O1O100O1O1O1O1O1N21OO1000000O10000O1KeFeJ[9Z5fFeJ[9[55O100O100O10\\FgJa9X5`FhJ`9\\5000O10000O100O100O1K[FnJf9Q5[FnJf9Q55N2000000O100O100O100O11O1O1O00002N001O001O0000O100O100O1N2G9H8UNaEmNg:P1`EeNg:Y1`E^Nd:`1bEYNa:d1fEUN]:i1jEoMY:o1]1M3N2N2M3N2N2M3O1O1O1O1O1O1O1O1O1O100O1O1O1O10000O10000O1000000O10000O1000000O10000O1000000O1TO_BNE0Qcm3", "size": [480, 640]}, "area": 78429.0, "iscrowd": 0, "image_id": 14226, "bbox": [0.0, 0.0, 373.0, 325.0], "category_id": 147, "id": 20000886}, {"segmentation": {"counts": "ijZ71m>20001O001O001O001O00001O1O00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000", "size": [480, 640]}, "area": 1209.0, "iscrowd": 0, "image_id": 14226, "bbox": [500.0, 471.0, 140.0, 9.0], "category_id": 165, "id": 20000887}, {"segmentation": {"counts": "k>5k>000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000mNMYC3g<0VC0k<1SCOn<3oBMR=5kBKW=6fBJ[=8bBH_=:^BFd=:ZBFg=<VBDk=>RBBP>?mAAT>?kAAV>d02N1O1O2N1O1O2EcAO^>OcA1^>NeAO_o>0W_A001O2N1O1O1O1O00000000000000000000000000000000000000000001O00000000000000000000000000000000000000000000000000000000000000001O1O0KXA2l>ORmS5OoRlJ1N1[M6SFKn9>hEB\\:d0YE^Ok:g0kDYOZ;3eC5f0Ij;O_C`1P=0O1100OO10001O0ZOZNRDf1i;_NWDb1c;cN]D]1_;hN`DX1_;jN`DW1_;iNaDW1_;jN`DV1`;jN`DV1a;jN]DX1b;hNQDe1o;f001O0001O01O000010O00O2N11O1O10O00001N100O2N1000001O00001O02NO2O0000001O00001O0000001O00001O0N2F;M200O101N100010O0001O01O0010O01O10O01O010O1O0010O01O10Of0[OT1kN3fH", "size": [480, 640]}, "area": 11476.0, "iscrowd": 0, "image_id": 14226, "bbox": [0.0, 219.0, 640.0, 261.0], "category_id": 167, "id": 20000888}, {"segmentation": {"counts": "T:Q1o=1O001O0O2O1N10jNXBU1j=OO2O0O2O1N101N1O2N101N2O0O2O0O2O0O2O1N101O0O2N1LRNVCo1h<RNWCP2g<PNYCQ2e<QNZCQ2d<601O0O2O1O001O0O2N10^MdCa2^<ON300K]MjCd2U<]MkCd2T<[MlCf2S<[MmCf2Q<ZMoCg2P<ZMoCh2o;6N1O2O001N101O1N101O0O2N1O2N2N101O1N2N2O1O1N2O1N2O1O1N3N1N2N1O01O01O001O0010O100O01000O10O001O010O0010O01000O010O0101N101O1O001M3N1O2O1N2M201N2N101O1O0O2O1O1N2O1N3N1N3M2N2O2M2N]JhFb5V9^JjFc5U9\\JlFf5Q9[JnFg5Q9XJPGj5m8WJRGk5m8TJTGn5i8SJVGo5j8oIWGS6i8XMVG]Oh8d0YG]Oe8b0\\G_Oc8`0^GB_8>cGBX8TMdG[34CU8SMgGY36DR8RMhGZ37Fn7QMkGX38Hk7QMlGW3;If7<ZHEd7;^HF`79aHH^77cHJ[76gHIY77gHIY76iHHW78jHHV77kHIU76mHHS79mHGS78oHGP79QIGn69SIGm69TIFk6:VIFi6:YIE]ORMk6Y3iIE\\OSMi6X3lIE[OSMD0P7Z3RJBZOXMb6V3UJAZO[M\\6V3\\J^OXO\\M[6V3^J^OWO\\MZ6V3`J^OVO\\MZ6V3aJ]OUO]MY6V3cJ]OTO]MY6U3eJ^OQO]MY6U3gJ^OPO]MY6T3hJ_OoN]MZ6S3hJ@mN]M[6R3iJAlN]M[6Q3kJAjN^M[6P3lJBiN^M[6P3lJChN]M[6P3oJBfN^M[6o2PKCeN^M[6n2RKDbN^M\\6m2SKEaN^M[6n2TKDaN^M[6m2VKD_N_M[6l2WKF^N]MZ6m2ZKE\\N^MZ6l2[KF[N^MY6m2]KEYN^MZ6l2^KFXN^MY6e0mIl0c1Q1WN^MY6e0RJf0`1V1UN_MY6e0WJ`0\\1]1SN^MY6e0^J;W1a1RN_MX6f0eJ3R1h1QN_MW6g0iJNP1m1oM^MX6f0mJKn0P2mM_MW6g0PKHl0R2mM_MU6i0TKCl0T2kM`MT6j0WK_Ok0X2iM_MQ6m0_KXOh0\\2hM_MP6n0bKTOh0^2fM`MP6n0cKSOg0`2eM_MP6o0dKQOi0`2cM`MP6n0fKPOh0c2aM_MP6o0gKnNi0e2_M^MQ6o0hKmNi0f2]M^MQ6o0jKmNh0g2\\M]MS6n0jKlNi0P3^3SNiKmNi0P3^3SNaNm1_1RNcNm1^1RNbNn1]1SNdNl1\\1TNdNl1[1TNfNl1Z1TNgNk1X1VNhNj1X1UNjNj1V1VNUJnN\\4l2^1WNkNi1U1WNoKnNj0j2V3XNnNh1R1XNoNg1P1ZNPOf1P1ZNPOf1o0[NROd1n0\\NROd1m0]NTOb1k0`NTO`1l0`NTO`1k0aNVO^1i0cNWO]1h0dNYO[1f0gNYOY1f0hNZOX1d0jN]OU1d0jN\\OV1c0lN]OS1c0mN]OS1c0mN^OR1b0nN^OR1a0oN_OQ1a0PO_Oo0a0QO_Oo0`0ROAm0:_MeJc1R5n08`MeJc1S5m08XOIg06ZOJf06YOLf03[OMe03[OMe02[O0d00bMgJg1Y5g0OcMhJf1Z5f02YOOg01WO1i0NWO4h0LWO5i0KUO8j0GVO:j0FUO;k0DUO>j0BTO`0l0_OTOc0k0XOgMnJ^1k5k0VOhMnJ\\1n5l0SOXOo0g0oNYOS18YJXNd4^1V19YOEi0;WOCk0=TOCn0<ROBP1>oNCR1<nNCS1=lNDT1<lNCV1<iNEW1;jNCX1<iNCW1=jNAW1?iN@Y1?gNAY1?iN^OY1a0gN_OY1POiI<^O1a5a0Y1ROPJ3CMU5n0Y1QO8m0n600O0010O010OZOiSj09V^UOE00P>j00000000001O0000000000001O0000000000001O0000M3O10000O10000O100O10000O10000O10000O100O10000O10000O10000O10000O100O10000O1000000000000000000000000001O0000000000000000000000000000000000000000000000001O0000000000000000000000000000000000000000000000001O0000000000000000000000000000000000000000000000001O0000000000000000000000000000000000000000000000001O0000000000000000000000000bG^O^2b0^MBb2>YMHf28VMLj24QM1o2OlL7S3IiL;W3EdL`0\\3@_Le0a3[O[Lj0d3WOVLn0j3TOoKQ1Q4POjKU1U4lNeKY1[4hN_K]1a4dNZK`1f4aNTKc1m4^NmJf1T5[NgJi1Y5YN`Jk1a5VNYJn1h5SNSJS2k5nMoIW2Q6jMiI\\2V6eMeI_2[6bM_Id2`6^MYIg2g6ZMTIj2l6WMnHn2R7SMhHR3X7oLcHV3\\7kL^HZ3b7gLXH^3h7cLSHa3m7aLlGd3T8d1000bKiHS1W7gNRIU1o6eN[IW1e6fNaIU1a6iNdIV1Z6iNkIS1U6lNoIj0X6TOnI`0Z6_OkI5]6JgIL`62fIBb6=cIXOd6g0aImNg6Q1aIaNg6^1^3O1N2O1O1N2O1O1O1O100O100O10000O100O10000O100O10000O100O100", "size": [480, 640]}, "area": 52611.0, "iscrowd": 0, "image_id": 14226, "bbox": [0.0, 0.0, 640.0, 480.0], "category_id": 173, "id": 20000889}, {"segmentation": {"counts": "U;f3[;O001O001O1O001O001O001O1O001O001O001O1O001O001O001O1O001O001O001O1O001O001O001O1O001O001O001O001O1O001O001O001O1O001O001O001kNeCC\\<=fC@Z<a0hC\\OY<d0iCYOW<h0kCUOV<l0kCQOU<P1mCmNT<S1mCkNT<V1mCgNS<Z1oCcNR<]1PD`NQ<a1PD\\NQ<d1nC\\NS<d1e01O00001O000010O0010M2N1O100O100O100O100O10000O100O100O100O100O10000O100O10010O01O1O001O1O001O1O1O001O1O001O1O001O1O1O1O1O2N1O2N1O2N1O2N1O2N1O1O2N1O2N1O2NWMSO`Gl0`8TObGj0]8XOcGg0\\8ZOfGd0Y8]OhGb0X8_OiG?V8BkG=T8EmG9R8HoG7P8KQH3o7MRH2m7OUHOj73VHLi75XHJg78XHHh78XHHg79XHHg7:XHGf7:ZHFe7<YHEg7;YHEf7<ZHDe7>ZHBe7?[HAe7`0ZH@e7a0[H_Od7b0\\H^Oc7d0\\H\\Oc7e0]H[Oc7f0\\HZOc7g0\\HZOc7g0]HYOb7i0]HWOb7j0^HVOb7k0]HUOb7l0^HTOa7m0`HRO_7P1`HPO_7Q1aHoN_7R1aHmN^7T1bHlN]7U1cHkN\\7W1cHiN]7W1dHhN[7Z1dHfN[7[1eHeNZ7\\1gHcNX7_1gHaNY7_1gHaNX7a1gH_NX7b1iH]NV7d1jH\\NU7f1jHZNV7f1kHYNT7i1kHWNm47RJc1Q1VNm4<lI_1W1UNm4a0fI\\1]1RNl4h0bIV1b1RNl4n0[IR1h1PNm4Q1WIP1m1nMk4V1TIm0Q2mMk4X1RIl0R2lMl4Z1oHk0U2kMl4\\1lHk0X2hMk4a1iHh0\\2gMk4c1fHg0_2fMk4d1eHg0`2dMk4e1dHh0a2cMj4g1cHh0c2`Mj4h1bHi0e2^Mi4j1aHi0f2\\Mh4l1bHh0X:XOgEi0Y:XOfEi0Y:WOgEi0X:Q2O00WMiEi0W:WOiEj0U:WOkEi0U:]MkEc10Q1T:VOlEj0S:WOmEi0S:^MmE`10S1R:]MnE`10S1R:VOPFi0n9YOSFe0m9]OSFa0m9ASF>l9DTF:l9ISF6l9oNRF[O2d1l9POXFYOLe1m9POUF@N_1l9PORFI1U1m9oNSF00P1l9kNWF9Mj0l9jNXF`0Ld0m9f0RFYOm9i0SFUOm9m0SFROl9Q31O01O001O01O01O0O2O00001N101O0O101O0O2M2O1O2M2O2N1O1N3N1O2M2O2M2N2N3M2OoJ]Gh3`8]100O2O0O2OlJdGd3[8]LeGc3[8]LeGd3Y8]LgGc3X8^LhGc3W8c1O10kJjGa3T8`LlG`3T8`LlGa3R8`LnG`3R8`LnG`3Q8aLoG`3o7aLQH_3o7aLRH_3l7bLTH^3l7cLSH]3V7ZMjHg2l6bMTI^2c6kM]IV2Z6RNfIn1_4jJPM`3bNe1[4RKhL`3mN_1X4XKbL]3VO[1V4^K]LZ3]OY1R4dKZLV3DV1P4kKTLS3KR1o3;RLEj3>VLBh3`0XLAe3a0[L_Ob3d0^L\\O`3f0`L[O]3g0cLYOZ3j0gLVOW3k0iLUOV3l0jLTOU3n0jLSOT3n0lLROS3o0mLROQ3o0oLQOP3P1QMoNn2R1RMoNl2R1TMnNk2S1UMnNj2R1VMnNi2S1WMmNh2U1WMlNg2U1ZMjNe2W1[MjNd2V1\\MjNd2V1\\MjNc2W1]MjNb2V1^MjNb2V1_MjN_2W1aMiN_2W1aMjN^2V1bMjN^2V1bMjN]2W1cMjN\\2V1dMjN\\2V1eMjNY2W1gMiNY2W1gMiNY2W1gMjNX2V1hMjNW2W1iMjNV2V1jMjNV2V1kMiNT2X1lMiNS2V1nMjNR2V1nMkNQ2U1oMkNP2V1PNjNP2V1PNkNo1U1RNjNn1V1RNkNm1U1SNkNm1U1SNkNm1U1SN[JOS3n1a2SN\\JJjN3X4P2b2TNZJI\\3R2Z2UNZJH]3S2Y2UNlJMT2n1o2VNlJKX2n1l2WNkJJ[2o1j2WNkJG_2R2e2WNQOi1o0XNQJGS4Q2l1XNQJGT4P2k1YNPJHU4o1k1YNPJHV4n1j1ZNPJGW4o1i1ZNoIHX4o1h1YNPJHY4n1g1[NoIGZ4n1g1[NoIG[4j1i1_NmIFZ4j1j1`NlIFZ4j1i1aNVIGc0M50`3j1b2gNiI_OO0B0T4i1c2aNnIGIOB0U4h1b2[NUJNBOB0U4h1g3YNbHOB0U4g1g3[NbHNB1U4e1g3\\NaHOC0U4d1h3\\NaH0B0V4c1f3^NbHOB0V4c1f3^NbHOBOW4c1f3`N_HODNX4b1d3bN`HNR4`1^3bN`HNCN[4`1c3dN_HNS4^1_3cN]H0T4]1`3bN]H1S4[1a3dNQHN44Y4Z1c3eNnGM73Y4Y1d3iNVHMV4Z1d3iNVHLX4Z1W4eNiKZ1o801OoKiNYJV1h5kNXJT1h5mNWJS1j5mNUJR1n5mNRJR1P6mNoIS1R6lNoIR1T6mNkIS1Q:OoLoNVHP1i7POXHP1g7QOZHm0f7SO[Hm0e7SO[Hm0m:O001O00001OXDTOlM3U;i0QG^Oo8a0SG]Om8c0SG]Om8c0TG\\Om8c0SG^Ol8c0SG]Om8c0TG\\Ol8d0TG\\Ol8d0UG\\Oj8d0VG\\Oj8d0WG[Oi8e0WG[Oi8e0XG[Og8e0YG[Og8e0YG[Og8e0ZGZOf8f0ZG[Oe8e0\\GZOd8f0\\GZOd8f0]GYOc8g0]GZOb8f0_GYOa8g0_GYOa8g0_GYOa8g0`GYO_8g0aGYO_8g0bGXO^8h0bGXO^8h0cGXO\\8h0dGXO\\8i0cGWO]8i0dGVO\\8j0dGWO[8i0fGVOZ8j0fGVOZ8j0gGUOY8k0gGVOX8j0iGUOW8k0iGUOW8k0iGUOW8k0jGUOU8k0kGUOU8k0lGTOT8l0lGTOT8l0mGTOR8l0nGTOR8l0Q3000nDTOP8l0R300000000001O000000000000000000000000000[ESOW7m0jHSOU7m0lHSOS7m0nHSOQ7m0oHTOP7l0QITOn6l0SITOl6l0TIUOk6k0VIUOi6k0XIUOg6k0YIVOf6k0ZIUOe6k0\\IUOc6k0^IUOa6k0_IVO`6j0aIVO^6j0cIVO\\6j0dIWO[6i0fIWOY6i0hIWOW6i0jIWOU6i0kIVOV6j0kIROX6n0WOROZKn0]1SOTM0_1m0a1YOfLKh1l0g1_OULFT2k0l1DfKA^2k0Q2IVK]Oi2j0V2NfJZOS3h0\\23WJUO]3h0a28gIQOh3g0f2W1YMiNm2Q1SMoNR3l0nLTOW3h0hLYOU3i0kLWOP3n0PMROl2R1TMnNg2X1XMhNc2]1]McN_2a1aM_NZ2g1eMYNV2l1jMTNR2P2nMPNm1V2RNjMi1[2WNeMe1_2[NaM`1d2`N\\M[1j2dNVMX1n2hNRMS1S3mNmLn0Y3QOgLk0]3UOcLf0b3ZO^La0g3_OYL=l3BTL9Q4GoK4V4LlKNY41hKI]47dKCa4=`K^Oe4a0\\KYOi4g0XKSOm4m0TKnNP5R1QKhNU5W1mJcNW5]1jJ]N[5c1fJWN`5h1aJSNc5m1^JmMg5S2ZJgMl5X2UJcMo5]2RJ]MS6c2oIVMV6j2kIQMZ6n2gIlL^6T3cIfLb6Z3_IaLf6^3[I\\Lj6d3WIVLn6j3TIPLQ7o3P200000jGkLb4T3XKUMe4k2UK_Mg4a2VKeMh4Z2VKkMg4U2XKPNi0VNf0j3`NTN`0XNQ1d3]NZN6ZN^1[3[N`NK\\Nk1T3YNdNB_NU2m2WNjNXO`Nc2e2TNPOnNbNn2^2SNUOcNdN[3W2PN[OYNfNh3n1nM_1S2aNlM`1T2`NjMa1W2_NhMb1Y2]NfMd1Z2\\NdMf1\\2ZNcMg1^2XNaMi1_2WN`Mk1_2UN`Ml1a2SN_Mm1a2SN^Mn1b2RN^Mo1a2QN^MQ2b2nM^MR2b2nM^MS2a2mM^MT2c2kM]MV2b2jM]MX2b2hM^MX2c2gM]MZ2b2fM]M[2c2eM]M\\2b2dM]M^2c2aM]M_2c2aM]M`2b2`M]Ma2g2[MYMf2k3ULTLl3Q5oJoJR5U6iIoH", "size": [480, 640]}, "area": 154124.0, "iscrowd": 0, "image_id": 14226, "bbox": [0.0, 17.0, 640.0, 458.0], "category_id": 183, "id": 20000890}, {"segmentation": {"counts": "]ci0131Q=5MJPC110OOo<551SDKa:6]EIkN11Oc;9\\DGR16nNJa;?WE0h:2WENj:1cDC:<S;OaDCN66KJ>a;?aDTON=a;X1K62MhN]NhFc1W9eNaF\\1a9]NbFe1[9RNmE5m0h1U9^NnF]1S9cNlF]1Q9fNoFZ1Q9hNnFW1P9nNnFQ1S9POlFn0T9VOiFj0X9nNoFR1Q9lNRGS1o8nNoFR1R9oNmFP1T9kNmEEm0a1V9hNQGX1P9hNoFX1T9gNlFW1lN_NW:5jEMR1c1R9aNmEKR1b1R9aNSG[1S9dNkF_1U9_NjFc1W9\\NiFe1e9\\NjEd1X:]NdEc1`:]N\\Ee1e:]NZEa1g:^NYEc1c:aN]E^1]:fNeEZ1[:fNeEZ1Y:hNgEY1R:[NaE>;X1V:ZN_E>:Y1V:ZN_E>:Y1W:hNjEX1X:gNiEX1W:ZN^E9=[1V:\\N_E6;`1U:YNbE59d1T:WNdE1:f1U:XNbE0;h1P;2M00O3N1OM[NbDb1_;5001OO100001O00O1O10N110000002N00004LI7002N2NL]NbDa1_;42O1O1NM400N1101O001O1OO11O001O001O0bN\\DU1e;gN\\D[1h;2O1VDaNo;Z1J7H_NcDb1[;`NgD^1Y;cNbDO1\\1];eNbD_1];eNbD\\1];dNcD\\10]N\\;6dD`1Z;92N000O0O11N2O12N001O1O0000009G3M00000O2O0000001O0001OO10001O000000000000000000000gNRDU1n;kNTDS1m;lNSDU1l;kNTDU1l;kNTDU1l;jNUDV1k;jNUDU1l;kNTDU1Q<0000OLlNRDU1n;kNRDU1n;lNQDT1o;lNQDT1o;lNQDU1n;40O10000000000000000000000O1000000000000000000000000000000000000000000NoCjNQ<U1PDkNP<U1PDkNP<U1PDkNP<U1oClNQ<T1oClNQ<T1oClNQ<T1oCkNR<U1nClNQ<T133MO11OO100001O0000002N00M32NO100001OO1O1001nChNo;Z1OO2OL6O00O13M00M32OO01O2NM300O1O200OON30OhNQDX1Q<O0000001O00O1O12N1O000NmClNP<W1PDiNm;Z1SDfNm;Z1SDfNm;Z13N3N000001O000000000001O2N00kClNS<V100000000001M24KO20mCjNQ<V1oCkNP<U1PDkNP<U1PDlNP<S1PDlNQ<X1OO1N2lNmCN1n0S<TOlCN3j0MVOT<1mCO0l0U<UOmCl0S<TOkCn0V<QOiCP1W<POjCo0V<QOQDj0m;VOQDj0Q<VOoCj0Z<OM3O10000KSOkCn0NROU<0lCo0OROT<0lCQ1R<QOnCP1Q<42O1O0000000000000000000000000000001O000000000001OO11O1ON20000M30000000000001O00000O1O100O11O001O2OiCnNS<U10O11O0000100O1N02M2O10000000000MkCPOU<P1kCPOU<Q120000O11O0N2O1O11O2N0nNiCP1Y<001O001O0O1O1000O1O13MNO4N1001O1O5K01O00N3O01OO10000001O00000000000000O10O2M20O1000O101O0000OO2O101NO3O10012L1O2N1O00000000000001OO1001O00OO2N2O001O11O02N0O10000000000002N1O1O1O2N000001O000000000000001N1O1O1O1O1N2O10001O01O0000000000002M;G0O02N0000O100001O0001O0000000000dCSOZ<o0gCROU<n0jCSOV<m0jCSOW<l0iCTOW<P1000001O00000000O101O00000000000000001O00000000000001eI", "size": [427, 640]}, "area": 24481.0, "iscrowd": 0, "image_id": 14380, "bbox": [61.0, 117.0, 579.0, 119.0], "category_id": 95, "id": 20000891}, {"segmentation": {"counts": "Z5m3_9OO1100OO020N10O13NN0100001O1O10OO1O0O2O1001N11O0O10O2O01L_F[LS10PO4P8b3QI[LoN4MKl71UHf3a0ZLK1F5h7e3fH]LDJN4h7R1WHV1>lMCI06e7n0^HT19SNA62[O`7X1dHT1KeM9]1U7HhH\\11mNW7HgH]1OlNZ7GfHa1LiN^7EjHkNIS4\\7RMlHjNKS4X7SMWIj2i6XMVIg2j6YMUIi2j6UMWIkN]On3\\7WM]IoNXO_3[7cMhIZ2W6fMhIjNnN]3[7hMkIjNjN\\3[7iMQJU2n5kMVJQ2j5kLeHQ1d1Q2g5nLeHQ1h1m1d5QMdHR1h1n1c5PMgHP1f1o1h5RNWJn1h5SNUKoNaMi1Z7WO_Kh0b4WO]Kj0c4VO^Ki0b4WO\\Kk0d4UN`H21h0j2Q1l4nNXKo0b4YN`HOOi0S39Q5LPKOU50jJL[55fJH\\5ZO`Gk0U3ZOoL6`8=QKXO\\L0g8g0j3]OaC:]<DaCN66KJP=2QCNb^i01anVO2jBMT=5mBNk<=MO1O10000001O00O100002NM30O2O2NN12]C_OZ<a0`C_O31]<c0`C^O`<c0`C]O`<h0O1O00O100000000001O2UDSOT;o0dDQOI0c;Q1bDROHNe;R1_DSOKKf;OZDb1g;01O0001O0N210O\\N[Db1h;O0O1000O1N20B>O10000O10000O100001O00000O1000000[ETOk8l0TGVOk8k0SGWOl8i0SGZOl8e0SG\\Ol8e0SG]Ol8c0UG\\Ok8d0UG\\Ok8e0TG[Ol8e0UGZOk8f0UG[Oj8e0VGZOk8f0VGYOj8g0VGYOj8g0WGXOi8i0VGWOj8i0VGWOj8i0UG]Of8c0XGDc8<]GEb8;]GGc88\\GJc87[GLc84]GLc84^GL`85`GL_84aGK`85`GK`85`GK_87aGH_88aGH_88aGI^87cGH]88cGH^87bGJnNPOU9V1nGInNQOR9X1oGMiNROQ9Q1VH9j7GUH:k7FUH:k7FUH:k7FUH:k7FUH;j7FVH9j7GUH:k7FVH9j7GVH:i7FWH;g7FXH<g7DZH<e7EYH<g7D]H9oNZNY8]1hH`0iNSN^8]1jHcNgNn1^8_OPI`0o6@SI:bNXN[8^1SI<`NWN\\8^1SI:Q7FoH:Q7FoH:Q7FPI:o6FSI8m6HSI7n6ISI6m6KQI7n6ISI6m6JTI6k6JUI7j6IVI8i6HXI8e6XNgG`1d19c6XNiG`1c19c6WNjG`1c19c6WNjG`1c1;a6TNmGa1b1<`6SNnGa1b1<`6SNnGa1b1=_6RNnGc1c1<]6QNQHb1b1<^6GbI;]NPNP8b1cI>]NPNo7c1eI>\\6AdI>\\6ChI_NXNg1o7KTJNk52VJMk52VJKl55TJJm56VJHi58WJ^NmMR1n7>`J@_5a0`J@\\5c0dJ^O]5`0cJA[5`0eJ@[5`0eJAZ5?fJBZ5=fJCY5?fJAY5`0gJ_OZ5a0fJ_O[5kNTHc1a2B]5MTH[O_2j0Y5MXHlNN116`2=_MKk7f0ZHkNN0N1a2c0_MJk7f0^HlNZ2g0]M[O0N20M1l7S1VKPOPMLf8T1\\JoNk5n0VJROi5n0WJQOg5R1ZJoNf5o0[JoNf5Q1[3O2NM300O0011O01L3002N0O1000000001O000000000NlNmCT1S<lNmCP10QOT<OlCP11POS<0lCQ10POS<R1nCmNQ<U12000001O000001O001O0000N2lNkCR1W<O1N20000000N24LN20QOfCm0Z<SOgCl0Y<TOgCl0Y<TOhCl0W<TOhCm0\\<0N1N2O3LmNlCR1U<3O2NN22N11NN201O002N00O1N2hNoCR10oNR<OoCQ1OPOU<P14OO10O1O100001O00000000000000001O1O000000001O1OO1010N02O1ON20000M30O1O1O1001O01O000O10iCnNU<T10O100003M0oCkNl;Z1SDhNh;NWD]1k;0O11O01O01O1O1OO1N2O101O0000000M`N[D`1e;`N[D`1f;20000O11O000000000000000100O01O0NZD_Nf;a1ZD_Nf;a1ZD_Nf;a1ZD_Ne;b1[D^Nd;c15MN21O0001O000ZD^Nc;b1]D^Nd;d101M20010N10000001O000000000001OO0O7ZNXD01[1S<J1O000NkCnNU<R1200000N20000001O0O01J7I7N2O2N0000O10000O10001OO3NO2O0O06K7_DQNX;R2N10000002N000001O000000000000O100O11O0001O0001OO100001O01L31O1ON2FeD]Nb;^18001O01N1000000000000000O11O002N0000O100001O000000000000000eNRDY1P<hNQDT1=kNR;0bDU1<kNR;0bDU19nNU;MbDV13SO^;]100001O000001O0O10000000O1000000001OO11O000000O100O2MfH", "size": [427, 640]}, "area": 35506.0, "iscrowd": 0, "image_id": 14380, "bbox": [0.0, 166.0, 640.0, 231.0], "category_id": 96, "id": 20000892}, {"segmentation": {"counts": "0Z5Q81OO11O1OO11OO100001OO100001O1O1O0000O1000000O11O0000O1001OO1LSHjJm7U5500000000000000O1000000000000001O1O000oGiJn7W5RHiJn7X5QHhJo7X5QHjJm7Z50O1001O00000000MSHgJn7X5300O1O1O1O11O000000K53MhNRHSMn7l2ZHlLg7V3RHmLP8P3gGXL5m0S8j2SHYMh7h2XHWMh7f2[HZMe7f2]HYMb7e2cHYM\\7h2eHWMZ7h2jHUMV7l2bH[M^7e2`H^M_7c2bH[M^7f2cHYM\\7h2_HYLEm0m7j2\\H]Md7d2\\H[Md7h2[HXMc7i2XHTLMR1k7j2YHULKR1j7j2YH[Mh7f2WHXMk7i2SHVMo7k2PHUMP8Z3QHULP8l3QHPLQ8R4oGjKS8W4oGhKo7Y4PHgKP8V4THjKk7P4YHRLg7n3YHRLg7l3[HTLe7f3oGmK>;e7i3mGlK>:e7j3nGkK>:d7l3]HULd7l3[HULd7k3nGjK9=g7j3PHkK6;j7k3oGlK59l7l3oGlK1:P8k3nGlK0;Q8g4O00002N1OMTHiJj7W55001OO100001O00O1O100O10000002N00004LI7002N2NLSHlJk7U551O1O1OL400N2001O001O1OO11O001O001O0UHiJb7X5ZHiJh7[52O17IJ6IUHmJl7Q5VHQKh7o4YHlJO1f7S5[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^G^Ka8b44001O000000O10[G\\Kc8f401O00O100N200O1N21O000000000000000000O10000O1N200001O1OM3N2N21O000000001O00N2001O0000O1001O0000O1O100O1O12N001O00000000000000O1O100000000O11O1O00N2MmFmKT9S421O1O002NO1001O00000000000000O1N200001OO1001O", "size": [427, 640]}, "area": 100491.0, "iscrowd": 0, "image_id": 14380, "bbox": [0.0, 0.0, 640.0, 181.0], "category_id": 106, "id": 20000893}, {"segmentation": {"counts": "og[21Z=0hB0U=0jB200o<9M35K0O01O1OO20O001OO101N11OO11ON2N3OiR2<jlM20O0001O01O01O0010O000O2M200O1O2N1001O01O000010O000010O0001O01O0O101O0000010N11O01O0O11ON3N10fkd05ST[O2001O00010O00000010O000001O01O01M2N2O1Nen21\\QM0O2O0O2LKoB6j<KXC:d<8O2O0010O01O01O00010O1O010O0]O[C`0e<@[Ca0e<^O[Cb0R<B]D?b;B]D>d;B[D?d;BZD?f;@\\D`0d;@[D`0e;_O\\Db0c;_O]D`0c;@]Da0DZOl;4aDa0B]Ol;2bDa0B^Ok;2bDe0^;[OcDd0^;\\OaDf0];ZOcDf0];[OcDd0^;\\OaDd0_;\\OaDe0^;[OcDd0];\\OcDd0^;[ObDLCh0i;\\OeDKBi0i;\\OgDd0Z;[OdDg0\\;YOdDi0Z;WOfDh0[;XOfDg0[;XOeDh0[;XOeDj0Y;VOiDh0W;XOiDh0X;WOhDk0V;UOjDk0k;0000SOdCm0Z<SOfCm0\\<01O02NO1010O0010O0002OO0000O2O10IgCZO[<c0fC\\O[<d0fC[O[<c0gC[OZ<f06O00000O1010O0YO]OfDb0[;_OeD`0[;BdD>DA[;3oD=EA[;2PE>DB[;ORE`0^O\\O14_;1QEa0@_O_;0QEj0o:VOQEl0m:TOTEk0m:TOSEl0m:TOTEk0l:VOSEj0^OPO];6VEk0j:TOWEl0j:TOUEk0l:TOUEl0k:UOTEl0k:UOUEi0l:XOSEg0n:YOREf0P;ZOPEe0P;[OPEe0P;[OPEf0o:ZOREe0n:[OREe0o:ZOREf0m:ZOSEf0m:ZOSEf0m:VOWEk0h:XOWEg0i:XOVEi0j:WOVEi0j:WOVEj0i:WOXEf0i:ZOWEf0i:\\OmDMEe0a;]OjDNEd0b;_OhDMFe0a;_OhDMEe0c;^OhDMEe0c;]OjDi0W;UOjDl0U;UOlDh0U;XOkDi0T;WOlDi0T;VOmDi0U;VOlDj0GROR;3XEk0FUOo:1ZEj0GUOo:0[Ek0FWOm:N^Ej0]OSO2006R;O^Ei0_OSOOO15T;2ZEU1AdNS;7mDI=^1f:kN[EGB[1S;POYEED[1S;QOZEo0g:PO[En0e:RO[En0e:TOYEl0g:TOYEl0g:UOXEk0h:TOYEl0h:SOXEm0h:SOXEm0h:TOWEl0i:TOWEHFk0S;_O^Ea0c:_O\\Ea0d:@[E`0e:AZE?f:A[E>e:B[E>f:@[E`0e:@[E`0e:@\\E?d:A]E>c:B^E=b:C]E>d:A\\E?d:A]E>c:B^E=b:C^E=b:C^E=b:C^E=b:CZEB^O00k0X;CZEB^Om0X;@WEf0j:XOWEh0i:XOYEf0g:YOZEg0f:YOZEg0f:YOZEg0f:YOZEg0f:YO[Ef0e:ZO[Ef0e:ZO\\Ee0d:[O\\Ee0d:[O\\Ee0d:[O\\EK[Oc0Y;B]EJ]Oa0V;E]EJ]Oa0V;E]EK\\O`0W;E]EK^O>U;BeEKRO04c0c<O000000002N0000001OO11O00001O1OO1001O0000002NN22N00001O1O001O00001Ibfo0", "size": [427, 640]}, "area": 8250.0, "iscrowd": 0, "image_id": 14380, "bbox": [181.0, 276.0, 382.0, 151.0], "category_id": 125, "id": 20000894}, {"segmentation": {"counts": "bla27T=3M0O1O101M2O12N01M21O1O1O0001O000O1001O00OEXCN0OShl02ReSO1iBNS=8M0N2N21N20N02O01O10N100N21N1O200O002N2N0000000001OO20O0000001O00O2O10N010OYC@f<c0O0O100O2O00O2NO4O0O0000M2N301O01N3N00000O11O0000000001O2N000000000001O01O00O1000000001O0000000aCXON1Z<g0hCXON3X<e0iCYOO0[<f0fC\\O[<d0eCZO]<g0bCXO_<h0aCYO^<g0bC@W<`0iC^OY<b0gC^OY<k00L2010000KdCZO]<f0cCYO_<f0bCYO^<e0dC[O\\<e051O10O00O1000000O11O00000000O100O101O00000001OO10000O11ON2001O00O1O1000000000000O1O01OO20O202MO2O2N000000000O110O00O02O00000O1N2O11O1O2N000000001O0N3N00O3M4N0jCmNS<V1O12NO12N1N1O101OO1O11OON4N2O01N2O5K0O11O10NUOTD9R<@oC`0R<_OoC`0^<00000001O00000000O1O1O1YDAQOL01[;b0fEKS:4oEMP:3oENQ:2oENQ:2oENQ:1PFOP:1QFNP:1PFOP:1PFMR:3mENS:0oE0Q:OPF1P:OQF1n9NQF4P:LoE3P:0PFIX:6iEB^:a0U1001N10OO100000000000000002N2N1M4L5L4LWcV1", "size": [427, 640]}, "area": 6792.0, "iscrowd": 0, "image_id": 14380, "bbox": [196.0, 147.0, 351.0, 91.0], "category_id": 127, "id": 20000895}, {"segmentation": {"counts": "\\^T76U=1O1O1O0000001O00O1000000M35KGhD4c9L[F6e9LUFOaN5Z;KXF7h9JQF<f8e0RFoNa0S1Q9oN[Fl10YOd9o0\\FQOd9o0\\FQOf9Y2O1M\\L`Fc3]97001O2XL`FON10O020U3b9kL_FNO10Ol1_1g7aN_F0N1n1;RNe0c9oN_F0O0n16fN;R9_OZF0n15lNKUO35Mb90ZF0n13PONQOOQ:0RI1mLOQ:0RI0nL0X=0_Sf0", "size": [427, 640]}, "area": 1621.0, "iscrowd": 0, "image_id": 14380, "bbox": [547.0, 304.0, 39.0, 123.0], "category_id": 132, "id": 20000896}, {"segmentation": {"counts": "W9T4W90001O00O11O00O13MO100000000001OO1O1N2O1001O0000O1001O00MlKnFS10PO4d2n8]NoFoN4MKd2S9nMnFK1F5a2l8mMVGDJN4a2l8lMXGCI06^2i8nM\\GA62[OY2S9fMnF9]1n1j7PNWHP2k7mMVHS2m7iMTHW2`901O00001O00001O001O1O0000001OO100000000000000005K5K4L1O1O2N4L6J4L2N3M3M7I002N2N2N3M3IoBNU=NkB2Y=Nj]W7", "size": [427, 640]}, "area": 7158.0, "iscrowd": 0, "image_id": 14380, "bbox": [0.0, 291.0, 84.0, 136.0], "category_id": 140, "id": 20000897}, {"segmentation": {"counts": "\\U^48i<:O100O1O20O001OO11O00O20N101O001_OZC9i<F\\C4e<L]C1dhd3", "size": [427, 640]}, "area": 351.0, "iscrowd": 0, "image_id": 14380, "bbox": [340.0, 387.0, 20.0, 23.0], "category_id": 143, "id": 20000898}, {"segmentation": {"counts": "TYk62Y=2N1J5J7O001\\C_O\\<d0_C_O`<g0O000000001O00000001O0000006J1N2N20O000001O01N10O1001L35KG:OO3ON020OMc0\\O4000Q[30bdL0ZC7d<I[C8j<2L401O01O01O00010O0000010O0000010O001O0000001O00001O0000001O001O0000001O00001O0000001O00001O00001O0000001O00001O000PD", "size": [427, 640]}, "area": 2091.0, "iscrowd": 0, "image_id": 14380, "bbox": [525.0, 362.0, 115.0, 50.0], "category_id": 144, "id": 20000899}, {"segmentation": {"counts": "S[_11Z=1O1O2LLmB5Q=3O200O1N1000M26K001O0000000000AXC;l<DSC9P=HSC2V=5gBIm<=M10000001O00O100011NM3001OZC@c<`0[CAb<a0^C_Oc<5\\C8c<B]C31<d<A]Ca0b<_O^Ca0g<00O00O100000000001O2N2M3N2LfWR1K`hmN5L010lBKn<<N2N0001OO12OO2M101OO5ML4O00010O000001O00000010O0001O01O004YC\\O`<o0H1O10O03M0kCROm;Y1NO0000001O00001O000000YNgN]GX1c8hNiEN11]10iNo0P:UOiEN0N1O_1P1g8ZOiEK_1i0j8CVG?eNROL5e9;jG?dN[Oa96lG?cNZOa97lG?bN\\Oa95mG`0cNYO`97mGa0aNZOa96mGP1S8POnGo0R8QOnGo0R8QOnGo0R8QOnGo0Q8ROoGo0P8QOQHn0o7SOPHm0Q8ROmGS1bNYN\\9d0PHU1dNXN[9c0PHY1P8gNnG]1o7dNQH^1m7bNSH^1m7cNRH]1n7cNRH^1m7bNTH]1m7bNSH^1m7bNSH_1l7aNUH=gNLT9GUH=gNLT9HTH;jNLR9ITH;jNLR9IUH9kNMl8TOPGg0Y15fNTO6m0j8TOPGf0Z1NgN]O:j0f8TOPGf0Z1KXO:_8VOnFe0[1JZO9_80VHG[O9_80VHG\\O7_82VHGZO6a83UHG[O6_83VHG[O6_83VHHZO6_83WHFZO6a83UHG[O6_83VHHZO5a82VHIXO4c83UHIYO4a83VHJXO3`85XHHeN^O`0f0b85YHFeN_Oa0e0`87ZHFdN^Ob0f0_O\\Oi8l0gHARO7]OAe8g0mH@QO8]OCc8e0PI_OQOe0m7LSI]OQOg0l7LRI_OROd0l7NRI]OROf0k7MRI^OTOd0j7NSI^OROd0k7NSI^OSODZOl0`82SI_OROB]Ol0^83SI_OROB^Ok0^83SI_OVO>g74RI_OVO>h72SI_OWO=f74SI_OWO=f74TI_OUO>h71TI@TO`0f71VI_OUO?f71UIATO?h7OSIBUO?f71UI@\\NCh0k0h70TIC[NBi0k0i7OSIFTO<h7NTIDVO>f7NTIDXO=d7NTIEWO>e7NSIEWO>g7KRIGWO>g7KRIGYO=c7MTIHWO;c7OVIFWO:`7UOfGl0c1EYO:\\7UOhGl0c1EYO;[74XID]O9Z73ZIC\\O<X71\\IC_N]Om0n0V73^IB_N]Om0o0T73`IC\\O;SOROl7P1fICZO?P7NfIC\\O=o60dIC\\O>P7OdIC\\O>P7OeIC[O=P70eIC[O=P70eIC\\O<o61fICZO<P71fICZO<P72eIB]O:n64eIC\\O9P73eIC[O:Q72dID[O:Q72dIC]O:o63dIC^O9n65dIBWO\\OZOm0k75dIDTO[O\\Om0m73cIETO\\O[Ol0o72cIFQO^O[Ok0P82dIERO>Z7MdIFQO=\\7LcIGQO=[7NdIDQO>[7NdIDPO?\\7MdIDPO?\\7MdIEPO=\\7NeIDoN>\\7NgIBlNa0]7MhIBjNa0]7OjI_OiNDAa0k7=mI\\OhNEAa0i7?nI[OiNDB`0g7a0nI\\OjN^O\\O14d0i7`0nI\\OkN@_Od0h7`0nI\\OTO4n6`0nI\\OVO2l6c0mI\\OVO1l6d0nI[OVO1l6]OSHU1k1^OUO0o6b0mI]OTO^OPOa0n7e0nI]OUONm6e0mI^OVOMm6e0nI]OTOOQ7b0iI@VONQ7@PHm0j1DVONU7@mGh0i1JSOO^76`IJQO1_75`IJPO2`74aIJnN2a74aIJnN2b74_IJPO1a75_IKoN0b75_IKoN0b75_ILoNNb76_ILoNNb76_ILoNNb76[I0TOIa78]INQOIc78\\INROJb78\\INROJb78\\INSOIa79]IOoNId78]IOoNId78_IEVNEe0`0f77^IDWNEd0a0g76_ICVNFe0`0f77`IBVNEe0b0e77`IBVNEe0b0e77_IDRO5_77^IETO3^78_IFPO3a78^IERO2`79^IERO2`79]IFRO2a78]IGROGROO_8c0\\IHSOFUOL\\8f0]IGROGUOL]8e0[IISOFWOJ\\8g0YIJSO]OSO2006O[8h0[IIRO_OSOOO150\\8h0^IF]OAT70`Gi0o1WOQN=n9CRFi0Q2EoMBk90UFi0S2CmMDk90UFj0S2Ch7CUFj0S2Ef7=ZHCf7=\\HAd7?\\HAd7?]H@c7`0\\HAe7?ZHAf7?ZHAf7DVFh0U2Ce7EVFh0U2CPNF_9g0cHJ^75cHJ]76dHI\\78dHG]78cHI\\77dHI\\77cHJ]76cHJ]76cHK\\75dHL[75dHL[74eHK\\75dHK\\75dHL[74eHM[72eHNZ73fHMX76gHJX77hHEjM^O00^9m0hHEjM^O`9m0eHBa7>^HC`7@\\Fm0T2E^7=aHD_7=`HC`7=`HC`7=`HC`7=`HD_7<aHD_7<aHE^7;bHE^7<aHD^7=bHCTN[OS9R1iHDSN]OR9n0mHCQN_OS9m0nHBPN@S9m0nHBoMCS9i0nHGjM\\O04X9j0nHNQ72oHNP73QIVOiM?V9;TIQOiMc0S9<ZICe6>[IDc6<]IDc6=\\ICd6=\\ICf6;[IEc6<]ICd6=\\IDb6=^ICg68YIHn6\\ObFd0_21Q7NoH3P7MPI2Q7NoH2Q7NoH3P7MoH4Q7MmH4S7LnH3R7MnH5Q7JoH4S7LmH6Q7JoH6Q7JPI5P7LoH5P7KoH7P7IPI7o6JRI6m6JSI6l6KTI5m6JTI6k6KSI6m6JTI1eMYOW9e0TI5n6LRIMU7;h2EkC]OZ<b08011NON0\\C@d<<8ON3EAaCh01YOON10O020f;Q1ZDPONO10O3i;o0WDPO0NR<Q1nCQO0O_2<d6h0mFlN_2a0aM^O35^8Q1_GkN_2c0bM\\OO6`8S1oI[ObM\\OO6`8S1`GgN^2c0bM^O05`8U1nI[ObM@`8U1nICR6=oIBQ6>oIBQ6>oIBQ6>PJAP6`0oI@Q6`0oI@Q6`0oI@Q6`0PJ_OP6a0PJ_OP6a0PJ_OP6a0PJ_OP6b0PJ]OP6c0PJ]OP6c0QJ\\Oo5d0QJ\\Oo5d0QJ\\Oo5d0QJ\\Oo5d0RJ[On5f0QJZOo5f0QJZOo5f0RJYOn5g0RJYOn5G`G`0b2In5G`G`0b2In5DcGc0`2Hn5_OhGh0Z2I^68bIG^69bIG^69bIG^69bIG^69cIF]6:cIF^69bIG^6:bIE^6;bIE^6;bIE^6;bIE^6;cID\\6=dIC\\6=dIC\\6>dIA\\6?dIA\\6?dIA]6>dIA[6`0eI@\\6?dIA\\6`0cI@]6b0bI]O^6e0`I[O_6i0^IWOb6k0]ITOb6o0\\IQOb6T1[IlN", "size": [427, 640]}, "area": 23505.0, "iscrowd": 0, "image_id": 14380, "bbox": [113.0, 227.0, 527.0, 200.0], "category_id": 147, "id": 20000900}, {"segmentation": {"counts": "Wbb01X=5nBLf<a0LOZC@b<e0]C[O`<k0M4L3L5L000000m0PDPNk:Y20N110M2003M4L1OO12M2`E_MT:Q3gElL00n9Y314L4LN24MN1iLYFm2d9nLbFo2`9oLbFT3[9kLfFW3X9iLhFY3LkLl8LXG]3IeLP9NWGm3b8TL]Gj3e8VL[Gn3a8RL_Gi3e8XL[Gj3c8VL]Go3^8SL`Go3^8SL_GQ4^8a0M3M2N3MN2oJhGP5[8N1O1N2VKbGf4^8ZKcGd4d8N101ON3N1O1eGZKo7g4lGaKR8m4N3K4O1O1N2O2N2N00]J[H^5f7aJ[H^5f7bJZHO0[5g7eJ]HZ5j701OO11O00O1O11O1O001O1O1O001O2mJkGN0k4V8UKoGj4Z8O001O001O1OO11ONZK_Gf4`83001O1O1O1O1O1_KZG[4g8dKZG[4g8cK]GZ4j800000O100O11O001O1O002N1mKmFl3T9SLmFl3T9TLlFk3V9SLkFl3\\9N1O2N1OO1001O1O1O1O1O1O1O1O1O1O003M1O1O00001O1O1O1O001O1O1O1O1PMhEk2]:O2N1O00NXMaEh2_:21O1O1O1O001ON[M_Ed2a:]M_Eb2^:bMbE]2_:cM`E]2a:6001O1O001OO1O11OO1000000001O00001_M_EV2a:kM_ET2g:fM`ES2l:0000000001O0000001O1O1O1O001YNeD\\1b;^N^Da1g;O1OLbN[D^1g;`NZD_1e;400001O00O1001O001O1O1O1O1O1O002N1O001O2NO12iNlCS1T<mNlCS1V<0O11O1O1O1O1O001O1O1SOeCh0`<O001O001O001ON21O3MO11O1O001O001O1O001O1O001O1O1O001O1O1KmBNV=1300O\\SR4", "size": [427, 640]}, "area": 26764.0, "iscrowd": 0, "image_id": 14380, "bbox": [44.0, 222.0, 283.0, 205.0], "category_id": 149, "id": 20000901}, {"segmentation": {"counts": "ln=3W=4M0N12O0O04nBIi<8VCKh<?OOOOO1101N100O1000000O10001O00O101O0010OO100000HYCLh<1WCJ11O1i<4]CJmR<0QZD600mBKn<6RCKm<;M2M4NO101O0002N01M12000OOGYC1f<O[C1d<O]C1d<M^C2NHa<2]CN58LI_<2hC6IHO0]<0mC8FLj<:10OO11L35J1O20O1O001O1O1O0O2O2ZC^ON0f<O[C9OMj<:O001O001O1OO11ONZC_Og<?3001O1OBXC:i<DXC<g<DYC=k<0WCBd<>[CCe<=YCGd<`01O000O100O11O001O1O00_O\\C=b<C^C>4^OZ<1cCa02_O[<OcCc02^O^<c0`C^O_<i0N1O2O0OO1010O1O1NTOgCi0X<WOhCN0i0W<YOiCM200f0T<[OUDe0Y<000O10O12L200O01OO2O1N200N110M3M3O1K@YDEIFi;d0`DJ_;5cDL[;3hDLX;2iDNW;A[E<nNDk<<21O1O100N101ONXCBg<>ZCBe<;_CE`<<`CCe<9;K10000O2OOOf_i0OZ`VO3N20O010000kBMQ=70O010O10O001NO21O3OM^OGRD<m;ERD<n;DfC0N20M1=V<f00O0100O010O1O001O1O10O01O12hNlCT12lNN1l;OUDT10oNn;R1RDmNP<R1oCnNS<T100001O00001O00000000001O0UOQD:`<00000001O0000000000001OO1000000N2O1O10000001O0000000000001O00001O1O001O001O001O001O001O001O00O12N1O1O2N1O1O1LhZU3", "size": [427, 640]}, "area": 5509.0, "iscrowd": 0, "image_id": 14380, "bbox": [33.0, 205.0, 363.0, 222.0], "category_id": 164, "id": 20000902}, {"segmentation": {"counts": "URk2320O0i<>001O1OWCBd<?\\CC`<`0_C@b<?^CAc<>]CBd<=[CDe<<[CDe<<[CDe<a02N00MN\\CBd<?\\CAe<>\\CAd<?\\CAd<?\\CAc<`0402NO11O1O00O1O10000O11O001O00001O00O11OO1O100O10000O100000000O1000000O1001OO11OO10000O11O0000003M1_OXC=n<M000000O11OO1001O1OO100N21O001O1O0O3Nnn]22gPbMLZC5e<MZC4e<9001O0001O0N2N2001N1O3M1O3M0100O1001O01O00000001ON201ON22N1O1N2O0011MN]O_Cb0a<4001O000000O10ZO]Cf0c<2O0O2M4M3K201N1N21N10000000000002N1O1N2O2N0O1N200001O1OM3N2N21O0O1O1O1O2N1N01101O0001N1001O0000O1O3N:E2N12N001O000000000001O0O1O100000000O1100O00N2MPNjDQ2V;21O1O002NO1010O00000000000000O2M200001OO1001OjK", "size": [427, 640]}, "area": 7648.0, "iscrowd": 0, "image_id": 14380, "bbox": [218.0, 132.0, 422.0, 70.0], "category_id": 169, "id": 20000903}, {"segmentation": {"counts": "Zcj0g0`<9F9K200N3M502N0J42OL404G23O000UNlD]1V;`NhDd1e;LZD^N[;h1aDXNc;h1I61N5K2N3M3M3M2N3MN21O0000O11O00001O1O2N1O1UOjC?W<\\OQD`0_<N3M2N2N2N2N3M3MU`j6", "size": [427, 640]}, "area": 2178.0, "iscrowd": 0, "image_id": 14380, "bbox": [63.0, 330.0, 52.0, 97.0], "category_id": 172, "id": 20000904}, {"segmentation": {"counts": "U`o01U=6M2O10000001O0000001O1OelX1=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]O`b4h0b\\K7O00000010O00000001O0001O01O0000001O01O0001O00000001O000000000000001O0000000000001O0000000000001O00000000001O2N3M2N2N3M2XOTF", "size": [427, 640]}, "area": 20698.0, "iscrowd": 0, "image_id": 14380, "bbox": [75.0, 228.0, 565.0, 156.0], "category_id": 183, "id": 20000905}, {"segmentation": {"counts": "WZZ2<\\<L4L4N_;5YD7J5K4L2O0OO0O2N3L3N2M2O1O1N3N2NfVm0`0PiRO2NK5L4L4L3NSaY4", "size": [404, 640]}, "area": 379.0, "iscrowd": 0, "image_id": 14439, "bbox": [188.0, 125.0, 103.0, 40.0], "category_id": 105, "id": 20000906}, {"segmentation": {"counts": "]2>V<1O0000O10O10010O0000000000O02N100O3N1O01O00O11O000O1HPDLP<4PDLP<4QDKP<4:GeC001T^;0^nDO^C0011OX<1gCO011OX<1gCO0103S<0mC3U<MkC3U<7O1O00O10000000000O10020OO0000000000O2O001OO0101OO10O1002N1UDC\\;?bDC];>\\D@N013e;>ZDC0Of;i00000000000001WOYDc0f;^O[Da0e;_O\\D`0d;_O^D`0b;_O_Da0a;]ObD6J2n;LUD3g;O[D1c;1]DNb;4_DFK0c;=bDCa;?;O3GjCNX<OiC1]<0AM]D3d;2UD0j;KPD72Nn;KPD`0P<@QD?R<0000O2N0020O0001ON21O1O001O01N100O11O000000O10O100O1010M11DjC:V<FjC;Z<JfCLTf60mWMOVkL1ZC4L2N;E100O1O2N2N1O002N2N00000N2O010001O1O10O0001O00010000ON1N3N2N1N2O2M2O2N1N3N201O00000001O0001O0001O00002N2N3oCL\\;d021O2NO2O0010OL^DVOb;i05N30OO0100O100O2M200O100000000000M3O1O1GYDDi;9[DCg;<811N1000000H9O1N1Oc00lN0a01O20OO110O2N3M2N2N3ZDCo:`0a03L8I000001O001O1[O]D8b;G`D8`;GbD8^;GdD8\\;HdD8[;IeD7[;IfD6[;IdD9Z;HfD8Z;HfD9Y;HeD9[;HcD8_;I]D9c;<0O4NO01N100O1001O001OO10O100001ON21OO002O0MWO[Dh0e;400001O0010O0O1001O0VOfD;DCc;0lD>S;BoD>d;O00001OO01001O01O01OO11OO3N1N1HgC3[<MeC1`<OTh20dWM;H60011K4UDZOf;f0ZDZON0g;e0[D]Oe;e0YD[Oh;d0XD\\Oj;f001O00O01000010NO11O102M1000001O0O012OPO]Dm0e;O0010000000O1N2000000O11O1O\\DROb;d0<00O1O100O1O2N010O00010O101N011O0000010O010O10O0O2O001N11O01N3N0001OO101O000002M2N2N2O00000001O00O1O1000000000NEkC;U<200O01000100OO0101O0000O11O01O000000001O0000000000O21M010O011O01O00000000N201O01N011OO20O00000O11O0mCAQ<a001N1000O10O01100O0O11O000000O1001O0O1000000O10000000001OO100000001O00010O0000000001N11O0000O100OO200O110O001O0000O11O000000O11O001O00O2O3MO1N2000001O^OQD?o;@RD00=T<01O000DjC:X<00M5N001OO1O10000000001N1O100O2O0O10000001N10O1000000001MZM", "size": [404, 640]}, "area": 10407.0, "iscrowd": 0, "image_id": 14439, "bbox": [0.0, 74.0, 640.0, 40.0], "category_id": 111, "id": 20000907}, {"segmentation": {"counts": "Q1\\1>>;\\58VJ^O=:]58VJ^O<<]56WJ^O<<]56WJ]O=>[55XJ]O==\\56VJ^O><\\56VJ^O><\\56WJ^O<<]56WJ^O;=^55WJ^O<<]56WJ^O;=^55WJ^O;=^55WJ^O;=^5BPI0W10<>]5BPI0V12<<^5CPIFM2W1:=;`5CPIEM4T19==b5AWIKg0:=NhNCl6:XIMDEo0d0`0LT6MjH1a06a0LT6LkH2a05`0LU6MjH2a05`0LU6MjH3?5c0JT6NiH4a03b0KT6MjH5`035_OVO<[7OhH3b020JWO2_72dH1f0IROLl0:XOM`76`HOj0HROLj0=YOHc7o0YIPOROKh0`0ZOFb7o0[IoN0>RODc7m0_IhNlN001R1h0nNBe7l0hJDcM^Of7l0ZHkN]2k0cM^Of7j0kJI_MYOi7o0gJIcMTOf7R1gJK_55aJL^54bJM]53cJM]53cJM]53cJM]53cJL^54bJJ`55bJJ^55eJI[5JTHBe2a0W5MTHAh2c0Q5JYHBh2d0n4I\\H_OH0P3g0l4I]H@j2g0h4HfK7Y4HjK6V4InK5Q4KQL3o3MSL2l3MdHET3<X4ObHHV38X4ObHNS32hLKV74PI0R30fL0V70RI2U1E89[N2T7OSI1W1D_N0011O`18bN6S7NSI1V1DaNO011O`17cN7R7ORI0W1DaNO0103_12dN9S7OQIOU1GdN3_1KaN14<R7OPI0V1FdN3^11fN7R7OQINV12OMfN4T71oHJZ15KMgN4U71oHHZ16JNhN3U72mHH\\14J0hN2U74kHF^14H2jN1T7`0ZJ]OF4lNOT7`0ZJ]OE4mN0T7>[J^OD4mN0T7?ZJ]OD5nNOT7>[J]OD6mNOT7b0WJYO]Oa0XODT7b0WJ[O[O?[OCS7`0\\J]OUO`0\\OCS7`0\\J]OUO?]ODR7a0[J\\OWO>]ODQ7c0ZJ[OXO>[OFS7a0ZJ[OXO>ZOGT7?[J\\OWO1[OJN;U7>[J[OXO2^O;P78[JZOWO3B7l6<[JZOWO3C6k6=\\JYOVO4C6k6=[JYOXO5@6m6<[JYOXO5@KGNV7h0]JYOVO6@LGLV7i0\\JZOWO5@MGJV7j0\\JYOXO6_O6m6:]JZOWO6AOP7?ZJ^OSOT1_6\\O`JAdNCOe0OKn67dJH^NC1NL<43m68dJIWN@N013g02E8j6:dJJUNC0Oh01_O0K8U7;dJ5fNYO_O<3JT7<dJ5fNYOD501R7<dJ5fNXOE225P7<cJ5fNXODNK615W7:cJ5fNXOCOL416X7:RJYOSOl03XO_O30O17Y7:PJ[OSOj04A_OI18X79PJ]OTOg04B_OI18X79nI_OUOf05I@IX79mI_OWOf04I@IX79lI_OYOf03IAHW7:kI_O[Of02IAHW7:_I[O\\O7<7BH?=AHW7:jHYO=6A6=3GH<>AGX7;hH\\O;e0KIOF:6_OL02Z7<hH^O9^1LcN<6]OI55U7f0RIb0L^NK0a0;HOn6i0oH?O^Nd0a0XOHV7j0PI>U1hNdN0W7j0UI9W1fN^N5W7l0TI9a1hN\\5m0VI:P9FPG:P9FQG^OD3[9>UG[OB7X9?WGYO@FH`0a9a0ZGXO[OGJ`0a9a0]GVOWOIL?`9b0`GSOWO;Y9NcF1P1BUO?X9NjFEE1_1;S8ObHO_70aH0`70aH0^70bH0^7OcH1]7OdH0\\7OcH3]7MdH2\\7NeH1[70dH0\\71dHN\\74bHM]72cHO]71cHO]71bH0^70cHO]71cHO]7L_FIU2;\\7MgH3Y7LgH5Y7KgH4Z7LfH4Z7LfH3[7MeH3[7NeH2Z7NdH3@Io54aJGiM:f1Mo53aJFjM;<0?Ni61gJKeML:o0FBc7HWK_1i4bNTK`1l4aNQKb1n4`NoJ`1R5cNkJ]1NRNi2a0VMc1LoMm2>UMj1GkMS3;VMl1BlMX38TMS2]OhM^35SMW2[OgMa32TMW2XOkMc3NRLFFd2b0kMe3KQLIEc2c0lMf3InKKI`2`0oMi3FmKLI`2?QNj3CoKKHa2<UNl3_OPLKFc2<VNm3[OSLARO6a0h2<YNm3WOZLG[Ok2;ZNP4SO]LEYOl2:_No3bNYK7l1i2jNaN\\4^NkLQ3fNeN_6[1_IhN`6X1^IkNa6U1\\InNd6R1ZIQOe6n0YIVOg6i0WIZOh6d0XI_OU5dMYK8J\\2NKo4aMWK:LZ2LNo4`MXK8MZ2J1n4_MXK:OX2F3R5[MiJL<`04W2A5U5[MUK95W5b4YJoJ7;84o0@i2i4\\LcKL4n0Ak2Z4hLVLZOOR1Bm2X4gLWLZOOo0EQ3T4fLYLWO0R1DQ3R4fLZLTO3S1CS3P4fL[LQO4l0J_3f3dLVMMTO`3e3cLWMMUO`3c3cLVMOWO_3b3bLVMOYO`3`3`LXM0WOa3a3_LXM0VOc3a3]LZMOTOf3a3[L\\MNSOh3`3ZL^MNQOi3`3XL`MOQOi3^3YL`MNPOk3`3WLaMMmNo3a3TLbMMjNS4c3QLbMMhNU4e3nKdMMcNY4h3jKfMV5Z2jJfMW5Y2iJfMY5Y2fJhM[5W2hIfL<S1m5l2nISMT6n2hISMY6m2bIcK1b1^6m2aIRMa6`41N2O00100O1O1O1O0bMXIPOi6o0YIoNh6P1XI[M0m0h6g1cIhLF02P1KPO0;k6l2PJgMZOROK8k6o2RJdM]O\\Ob6o2QJeM]O\\Oc6n2QJdM]O^Oc6m2RJcM[O@d6l2`JPMQO4`6k2TKVMkNIn4Q3VLWMmNHm4P3VLXMoNGk4P3TL[MPOGl4m2TL]MoNGm4k2TL^MoNHl4j2TL`MPOGk4h2ULaMlNAVO7i5f2UKSMROd03Lf0MP5_2UKTMPOg03Jf0OR5[2UKoMROHg0NR5Z2UKQNROFf01R5X2WKPNoNHi01Q5V2WKeNHWOP5S2WKfNJYOn4P2XKgNJ[Om4m1YKgNL]Oi4l1[KeNNBe4h1]KfNNDd4e1^KfNOGb4b1_KgNOIa4_1`KhNOL_4[1SKeMFT1h0N]4Y1UKfMES1i00\\4V1WKfMDT1i02[4S1WKhMES1f0hNBn0j4^1YKiMES1Y1Gh3\\1YKkM@H4Z1]1Gd3\\1[KmMDP1_1Ga3[1ZKPNFo0_1fN^Nn0Q5\\1\\KTNBm0j1AX3]1[KVNCk0Q2[OS3c1XKFR2YNeMHm4X2[KH_2QNV2W2ZKI`2QNV2U2[KI_2RNW2T2[KI^2TNW2R2ZKL^2RNY2Q2\\KKZ2UNY2P2aKGV2YNAN<Q2RNnM_O]1a2gN_O4;i1_N[Oh1iN\\O9UOL^Of19[Oc1PO@8kN6BV1`0[Od1RO]O=aN>Hg0f0]Oc1QO]Ob0WNg0L9m0\\Od1SOYOf4SOXLd1QOXOh4TOWLe1QOUOi4VOVLg1oNROl4WOULh1oNoNm4YOTLk0lNiN1R1Q5ZORLj0POgNMT1R5\\OPL1hNA>IHX1S5]OoKLPOWOf04WO\\1U5]OmKL:YOeN\\1V5_OkKLk0d0[3@jKLk0c0\\3AiKLj0d0]3_OkKLh0e0]3_OkKLh0e0]3_OlKKf0g0^3^OmKKd0g0_3^OmKKd0f0`3_OlKKd0f0`3_OlKKc0g0a3^OlKKc0g0a3^OkKLd0f0a3^OkKLc0g0b3]OlKLc0d0b3@jKMe0b0a3AjKNe0`0a3BjKNf0?`3CjKMh0?^3DkKLh0?]3EkKLh0>^3FjKKi0?]3FmKIe0a0^3FmKIe0a0^3FnKGe0c0]3FnKGe0c0]3FnKFf0c0]3GmKFf0c0]3GmKGe0b0^3GmKGf0a0]3HkJPO`0h0W1`0^3HiJTO`0c0Z1a0]3HgJXO`0?\\1`0^3IeJ[O>=_1?^3IeJ]O<;a1?^3IcJH50j1?^3I`JL8Kj1`0^3J^J6NBV2>^3J^J7N@V2>_3K]J7M@X2>^3K^J6KCX2<_3K_J5JDX2<_3KcJMGSOOh0Y2=^3KcJEN7R28]3LdJ\\O5`0j17^3McJYO8c0g17^3MdJVO:e0d18^3MeJTO:g0c18^3MfJQO<j0_18_3MdKKm08_3McKLn06`3NUKTOXOh0S26`3NTKVOYOe0S26a3ORKYOXOc0LSOV2Q1e3O^JWO17Ga0U22e3O\\JV1n1jNg30[JV1n1iNh30ZJX1n1hNh30ZJX1o1fNh32YJX1o1eNi33XJW1P2fNh34WJV1Q2eNi35VJW1P2cNk36VJV1o1dNk37UJU1P2cNl38VJT1m1cNn39YJo0j1hNm39[Jn0g1hNo3:ZJm0h1hNo3;ZJl0g1iNo3;[Jj0g1jNo3<ZJb0A`NW2b0o3<ZJa0@_NX2e0n3:\\Ja0^O_NX2f0o39^J`0e1WOn3:`J;`1]OP48hJ3W1ER48gJ5T1CV48gJ=i0\\O`47gJ=h0\\Ob47^JRO[OZ1e1\\Oc48ZJVO]Oe0NBh1;c48WJZOC?LAg1=d4:RJZOC1M57Ec1`0e4:PJa0N_N]1f0e4:oId0i1QOY4<lICN2HZOP2d0_4=fIQOIc0:1HZON0o1e0b4;eIK5JG]Om1b0c4<dIK5LE[Oo1a0d4=cIL5JE\\Oo1a0d4=cIS1i1_Ne4>cIS1g1^Ng4<\\ISO<m1a1dNg4<mIYO1i0\\1Ag4=mI]O1?Z1Gi4=mI^ON>\\1Gi4=SJYOGc0]1Fj4>XJSOBi0[1El4?jJK<Fj4?iJL=Dk4b0eJK`0Ck4d0cJK`0Am4d0bJLa0@m4d0bJL`0Bm4b0cJL`0Bm4b0cJM?An4b0cJM?An4b0bJMa0Am4b0bJO?@n4a0dJoNdNm0j1Cn4a0fJK=Dm4a0eJK?Dl4a0eJK>Em4`0eJK>Fl4?fJK6XO_N>e6?fJKKDiN2f6?eJLANTOGf6?cJNXO8^O\\Oh2_OR1o0`MNTO<BXOe2BU1l0`MNTO<BXOb2FW1h0aMNTOQ1Q2\\NZ1e0`MOTOQ1o1^N]1b0aMNSOS1EnMR2c0d1=dMNdNRO;R2GoMn1e0h1:dM_2fNbLk1i0j16eM_2fNcLg1k0n13eM`2eNcLc1n0S2OeM`2eNdL_1Q1V2KfM`2eNeL[1S1Z2HfM`2eNfLW1U1^2FeM_2fNgLS1X1a2BgM_2dNhLP1Z1e2_OfM`2eNhLl0\\1i2\\OfM`2eNlLe0\\1o2XOfMa2fNVM7T1]3UOeMb2gNZOd3TNeMb2gN[Oc3SNfMb2gN[Oc3SNgMa2fN\\Oc3SNfMb2gN\\Ob3RNhMb5X2^JhMQ5iNZJ_3e0hMl4PO]JX3g0hMh4WO]JR3k0hMb4]OaJk2m0hM]4DdJd2o0iMX4^3hKcLU4_3kKaLT4`3lK_LS4c3mK]L:dNZ2Q5\\M[L9fN[2n4\\M[L:gN_2i4WMaL9gNg2`4PMiL9hNn2W4iLPMBeN26j3m3bLXMBCS4m2[L`MBCV4j2XLdMACV4i2YLcMBET4h2ZLcMCEQ4i2\\LbMCEW1POm0i3]N^M_OJV1oNn0i3_N\\M]OMU1QOl0f3bN^MZOMe0mNYO6R2b3gN^MWONc0PO]O6n1^3kN_MUOOa0RO_O7k1Z3PO_MTOO=UOD7g1V3TO_MTO08XOI7c1R3XO_MTO05\\OK7`1n2\\O_MTO12]ON9\\1j2_O`MVOO0_OO<X1f2C`MVON1AMn0f0S24bMWOM2@MP1c0R25cMXOK4@LR1a0P24fM[OG4BLS1?n15gM\\OE5DJT1>l15iM^OB5FJU1<j17iM^OA6GIW1:h1c0_MTOJ6IIX18f1g0[MgNjN;S17JHZ15e1j0ZMTOL5LH[13c1m0YMSOL6MG]11a1o0XMSOM6NG^1O_1Q1XMSOL7OF`1M]1S1XMSOK71Fa1K[1U1YMSOH82Fd1HY1W1ZMROF94Ef1FW1Y1YMSOG84Fd5o0aJSOF95Ed5m0dJTOC95Gd5f0jJZO\\O:6Fd5e0kJ[O[O97Gc5e0jJ\\O[O99Dc5g0jJ[OZO9:Eb5h0iJ[OYO9<Db5g0jJ\\OXO8<Fb5f0jJ\\OXO6>H`5f0jJ\\OXO4`0J^5g0iJ[OYO3`0L^5f0iJ\\OXO0d0M[5g0iJ\\OWOOg0NY5f0jJ]OVONh0OX5g0iJ\\OWOLi02W5f0iJ\\OWOJk04U5f0iJ\\OWOHm06S5f0iJ[OXOHm07R5f0jJ\\OUOEN1;8h5f0iJ\\OWOCN774k5f0iJ\\OMM\\O1n5f0hJ\\ON1ZOJR6i0fJ]ON6TOCX6j0fJ]ON?jN[Ob6i0gJ\\OMa0iNZOc6i0gJ\\OMb0hNYOd6i0gJ\\OMc0gNXOV2HT2Q1RM\\ONc0eNXOS20T2i0VM\\ONd0dNWOP25W2d0UM^O0c0cNVOR14ZO6l3?UM_OOk0DWOUO4S4;UM_OOk0A7[3_OVM]O0l0\\O;^3\\OUM^O2j0cNU1U4cNWM]O1k0cNU1U4cNVM_O2h0cNV1U4cNWM^OYOHIP1BW1U4cNWM^OUO1Hg0GX1T4bNXM^OTO4Fe0JW1T4bNXM]OUO6Bg0LT1U4bNXM^OSO7Bf0OR1T4cNXM^ObNA>g0Df00Q1T4bNYMAoNh1C6T4bNYMAoNg1D6T4bNXMBPOf1D6T4aNYMCoNg1C^O`0]Ne3k0YMBPOh1BTOm0dNX3n0YMBPOh1BnN\\5HQLBROP3o4_MnKBROm2R5aMmKBPOk2U5cMkKBPOi2W5eMhKCROd2Y5iMfKBROb2Z5kMeKCQO_2]5nMbKCRO]2]5PNaKCSO\\2\\5RN_KCVOZ2[5SN_KCWON]Oi1k5fNbKBYOG_OT2c5cNeKALm1]4bNgKALP2Z4_NjKAMQ2W4^NlKAMT2T4[NoK@9k1g3eNPL@a0d1^3lNQL@e0b1X3nNSL@j0^1R3ROTL@o0@dMg1X5YOUL@o0@dMi1V5WOXL_Oo0@cMk1U5VOXL@R1[1e2bNmJ2\\11R1\\1d2`NoJ3[11R1MeMm0m4RORK2Z12R1MlMg0e4WOTK3Y13Q1LRNc0_4[OUK3Y13Q1LVNa0Y4^OVK3Y12R1LYN?U4BUKDO8]17P1L^N<P4CXKC0;Z17P1L`N<l3AaK0S17P1LaN<j3AbK0S17P1KcN=g3AcK0S17P1KdN=e3AdK0S17P1KeN>b3@fK0T15P1MeN>`3@gK0T16o0LgN>^3@hK0T16o0LhN?[3_OjK0T16o0LiN?Y3@jKOU15P1MiN?W3ChKLX15P1MjN`0T3BjKLX14Q1NjN?S3DiKKW16S12eN8X3FhKKW15T12fN6X3DkKNS17T11gN5W3EkKNT17R11iN4V3FkKNT17?B^NNGa0X1=k2\\OVL0R17<E]NNL=Y1<j2]OVL0R1794\\NMY1:j2^OVL0R17Jc0kN^OZ19i2_OVL0R16J[16nNi2@WL0P18G\\19lNi2@WL0P18E^1<iNh2AWL0P18C_1`0gNf2BWL0P18XOE]Oe1d1lN`2BWL0P17WOg1R1`N`2BWL0P18TOk1R1ZNd2CVL0P18SOm1R1XNe2CkKE9;R19QOn1S1RNj2GgKE9;R19POo1T1lMo2NkKNR18QOP2S1fMU34eKNS17POQ2R6jMkINV14mNT2R6jMlILU17nNR2Q6kMlILS19POP2Q6oMjIDW1=oNo1P6PN`K1aNm1P6nMcK6`N^OCP2\\6\\NaKDnM?j0`1h5^N_KBPN00=j0_1j5bNbKOdNj0YOXOa6NdKOcNe0]O]O]6OcKOcN`0BBX6OcKCgM:P17DNR6NeKMhNOF6m5NeKJoNN_O:m5NgKHPOM\\O=m5NgKHM:\\4NhKGL:]4OfKHL:^4NeKIM9^4NeKIM9^4NeKIM9^4NeKIM9^4NeKIM8_4OdKJT76kHKU75jHLV74jHLV74iHNV72jHNV72iHOW72hHNX72hHNX71iHOW72iHLX74hHLX73hHNX73gHMY73gHMY72hHNX73gHMY76eHG]7", "size": [404, 640]}, "area": 178383.0, "iscrowd": 0, "image_id": 14439, "bbox": [0.0, 1.0, 640.0, 403.0], "category_id": 124, "id": 20000908}, {"segmentation": {"counts": "YTm23X<1jC2P1KPO0o:<oEIROKl:>oELP:4PFLP:5nELR:6lEJT:e0XE@h:U12N100N20000O100iNnDK1Nc03_:MUEOKOd03i:M^E2b:N_E2a;NaQg4", "size": [404, 640]}, "area": 644.0, "iscrowd": 0, "image_id": 14439, "bbox": [236.0, 15.0, 21.0, 55.0], "category_id": 132, "id": 20000909}, {"segmentation": {"counts": "nZ6120^<0aC3[<OeC3Y<6gCEU<>O00001N10000O1O11N2OOO2O1NkCER<<32J42OO11O00O11O000O1O1CQD4Q<KoC4Y<O2LaC0`<0[[X7", "size": [404, 640]}, "area": 487.0, "iscrowd": 0, "image_id": 14439, "bbox": [16.0, 17.0, 35.0, 27.0], "category_id": 138, "id": 20000910}, {"segmentation": {"counts": "j]g64`<0000000000000N21OO10000001O001O000000000Ma\\l0", "size": [404, 640]}, "area": 98.0, "iscrowd": 0, "image_id": 14439, "bbox": [546.0, 16.0, 22.0, 6.0], "category_id": 143, "id": 20000911}, {"segmentation": {"counts": "^To51X<0RD0n;0;000O100001O0000O10000O11O0O10fC0o;1QDOo;1QDOo;1QDOo;1QDOo;1QDOo;1QDOo;1QDOP<0PTc1", "size": [404, 640]}, "area": 51.0, "iscrowd": 0, "image_id": 14439, "bbox": [484.0, 177.0, 26.0, 14.0], "category_id": 167, "id": 20000912}, {"segmentation": {"counts": "0Q1c;00000000000000000000000000]ObD0^;0bD0^;1bDFMNOOb;=cDEMNNOc;>lDBT;>a0O1O1000000O12N3M3M6J1OO10000M3O1K51OO11O00O11O000000001O00O1O1O1O1O10000OWDE[;;cDH\\;7dDNX;1iD0V;0jD2T;OkD1U;0jD1U;0jD0V;1iDOW;1hD0X;0iDNX;4fDJ\\;7dDH\\;9bDH^;:`DF`;g0000O11OO14L00M3001O1O00O1000000000000O10000O1N2N2K50000000000000000000000000000000XDYOc;g0\\D\\Ob;d0^D^O`;k003M000000N20000O11O000000\\OcD1];OjDEE1a;:d0O10000O100O100001O1O2NO100000000KlCIU<83O1000000001O00001O00001O1O2N3M00000000000000001O000000O1O1O1BoC7_<Hmh20TWM0O2M200bCL\\<7LIiC7n;<C=00000000000000000O100000000O11O00001O00000000O1SOjD<l;K2L2KcC1b<Oi`30W_L2N00;E1O0000000000001O0000000000000VDAK0^;?`DIOJa;i0^DXOb;h0^DXOb;n0O00O100000000000000AdDF\\;:eDE[;<dDD\\;;eDE[;;eDE[;:fD@H4b;<hDDX;:jDFV;:mDBT;=mDCS;<c0O1O11O1OO13M4L5QD_Oa;j00000000000000000000001O0000000000O100000000000000000000000000000000000000000000000000000000000000001O00000000000000000000000000000000O10000O10000001O00001O0000000000000000O1O11O000000000000001OO1001O00O100000000M_DSOa;m030000000000002N2N000000000000000000000000000000000000000000O10000000000001O00000000000000000000000000000000000000000000000000000000000000[Oe0N200O1N200O10000000000001O002N1O2N4LO100000000001O002N7I8HO100001OO100001O0000O11O000000000000000000000000000000000000000000000000000000O11O00O10000001O00000000GdDYO];e0:00O1L400000000000000N21OO1000000O1O100O1001O2NN2N2000000000000001O3M001O1OL40000O1000000O10000000000000iCEU<;kCEU<=0000000004L00L4001O00O100000000000000000000000000000000000000001O00O11O00O11O00O11O3M", "size": [404, 640]}, "area": 15481.0, "iscrowd": 0, "image_id": 14439, "bbox": [0.0, 0.0, 640.0, 37.0], "category_id": 169, "id": 20000913}, {"segmentation": {"counts": "go93[<:J4L3N2N2O1N2O1O1O1N101O1O0000O1N201O3M2N21NO10002M2O1O2M1O2N1O10mCKf;4XD0f;O[D3d;K\\D6d;J\\D7d;G\\D:b;CZD05=`;I_D7`;L^D4a;M^D4a;L`D4_;MaD3];OcD1[;1eDOZ;2eDO[;1eDOZ;2fDNZ;1gD0m:;SEEm:;TECm:=SECm:=SECn:<SECm:=SECm:OoDM54l:OSEI27k:1VEDO;k:1bEN^:2bEN^:2aEA_O;P;4`EC@8P;5`ECA6P;7_EE@3Q;8_EG_O0R;9_EOa:1^ELf:b0mD@P;7YEIU;IlD6U;HlD8T;GfD1J8X;HgDOO84OW;JkDN78n:JkDLJ486T;IjDOJ614[;GiD0K326\\9D_H4SN30O17\\9E_H;TNI17[9GUGLg0=hNI17Y9HXGLe0<iNI17X9HZGLd0<iNI18V9G\\GNc0:jNI26U9I\\G0b08kNI26i8FYG6>Oc06kNI26f8JYG6=Lh05jNI26e8<aGAQ14gNK13f8n0eHSOPON\\8k0iHTOlN0\\8i0RIZOYNLh8e0XIYOoM2l8e0^IZOb6i0VGPOQ20n6Q1nFPOZ;b0gDK[;MoDOS;1QEIR;5PEGT;8f0N2LUo=NlPB3PL2VK0e48UKLh48TKKi4:RKIl49PKKb3D]Jc0o1La3C_Jc0m1La3DbJS1W1]OU4AcJ`1i0ROd4]OZJH[OP2^1oNl4YOYJU2f0eNQ5kNeI6c0\\2d0eNY5nNSJ]2a0iNW6W1gIlNY6R1fIQOY65XI]N=b1Z6N]I^N8g1Z6J`I]N5k1[6J[J:f5EWJ>j5BQJb0R6]OiIf0Y6[OaIg0U5mMkJ_1Ih0Y5kMoJ`1Bh0^5hMQKc1[Oh0`5hMVKc1SOi0d5fMZK9PO<Lh1i5cM]K5WOX3]5bL]K4]OU3V5fLaK0_OV3S5fLdKL]O]3n4fLnLY3R3dLRMZ3n2dLRM_3n2^LRMe3l53L4M3L3N3M3L4M3M2M4M3M3L4M3L3N3M3L4M3M2M4M3L4M3M3L3N3O1O1O1O00100O1O1O1O010O1O11O2mIoI]5e60000000000O101O00000O1000001O1O2M2O1O1O2N1N2O1O2N1OaNUKUKj4f4[K[Kc4d4`K]K^4Y4mJjJh0m0Z4V4RKiJg00UOn0Q5W4ZLgKf3X4`LaK`NZOm4T5RMTKQNIm4R5^MnJc2Q5\\MQKd2n4\\MRKe2e4UKXKV24e2c4cM]K]2b4WKZKS25f2_4TM^KXN3k10;^4mM]Kf0e0QOAn15\\OHYO_4R1ULUN@R2:RO0_OP4\\1\\L2IhN9Cc3f1XLM2^Na0IT3P2ULG=TNh0Oe2Z2SLAT7a0iH^O[7c0bH[Oa7e0]HZOf7KQHSO9o0k7jNmG97KOR1Q8jNiG6;JKU1U8gNiGKi02ZO[1X8`NnGMc07VO[1\\:^NdEa1h:O10O10O10O1000000O01000O12N1O1N2O1O1O00000O10000000000O2OmKiNiLW1U3mNiLS1U3QOiLo0V3TOhLl0X3VOfLi0Y3B^L>_3F`L:_31WLOi31WLOi32VLNk31ULNm30TL0P4DXL<h3\\OaLc0`3VOfLj0Z3UOgLk0Z3ROhLm0Z3POhLP1Y3nNhLR1]700O100O1O100O1O101N1O100O1O100O1OXLYOTH7R3`0h4JXK5h4MVHDb2>X5e0aJ[O_5e0aJZO`5f0_JZOb5g0\\JZOe5e0ZJ[Og5e0XJ[Oi5f0TJ\\OT6<kIDV6>gICZ6e0\\I[Oe6e0ZI[O_6ZOlGZ1e1\\O\\6^OnG?5Ga1;Z6BWHK;4U1>W6CSH3g0Go0c0U6AQH>j20U5NVHCd2>V50XK0c4^OUHb0X30b46^KIc48\\KHd49[KFf4h0lJWOV5h0jJXO]5GUH6^22_5F\\K9f4KTK6P5HnJ7Y5CgJ<i800O1O11O000O10001O0000001O000O100HCYD>[;NdD2Q;9oDGf:d0ZE\\Oh2@m4X1[HXOe2CP5V1ZHXOb2FS5g1jJ\\NU5e1hJ^NX5b1_HoMQ2c0_5_1_HnMo1f0j4T2WIWMk1h0n4Q2WIXMg1k0P5n1YIXMb1n0T5k1ZIXM^1P1X5i1YIXM[1S1Z5f1[IXMW1U1]5d1\\IXMS1W1`5b1]IXMo0Z1c5^1^IYMk0\\1f5]1^I[Me0[1l5[1_IeM7T1Y6W1`IJ_67aII_67aIJ]66dIJ\\66dIJ\\66dIK[65eIWO6oMV6j2dIRO=RNP6k2cIoNc0TNj5m2cIjNk0UNd5P3aIfNR1XN]5R3aIdNg7[1YHcNi7]1WHaNl7YOSHX11^Om7ZOSHW10]OP8[OQHW1O]OQ8\\OPHW1OEi7SOZHW1MNb7jNbHW1L7i7SN\\He1K`0S8_OmGi0k7WOUHk0j7TOVHl0k7SOUHm0l7ROTHn0l7ROTHn0m7QOTHn0m7QOWHj0k7bNRHZO7P2g7bNVH^O7l1d7aNZHB6i1a7aN\\HF7e1^7`N_HK7a1Z7`NdHN6]1X7dNbHO:Y1T7hNbHOn0e0_6]OdHMo0d0]6^OeHNP1b0Z6AfHMR1`0W6DgHLT1>T6FjHKT1<R6JjHJV1:o5LlHJW18l5OmHHZ17h51oHH[15e53QIH\\13b56RIG^11_58TIG_1N]5<TIFa1LZ5>VIFb1JW5a0WIEd1HT5c0YIE_9;aFD_9<bFD^9<bFD]9<dFD\\9<dFD[9<fFDZ9;gFEX9<hFDX9;iFDX9:jFFV98lFHT96nFJR95oFKP94QGMo81SGOm80TG0l8NVG2j8LXG3i8KYG5g8JUF2<4U:0hE0X:3eEMZ:7cEI^:=[ECe:f0QE[Oo:f0PEZOP;g0oDXOR;i0mDWOW2Ia6P1WGWOT21a6i0ZGVOQ25e6f0YGUOS15YO5[8a0YGUOP1;UO4a8<ZGUOm0[1j7_OYGVOj0^1n7[OXGVO3W2e8cNXGVO3W2f8jNZGW1m7iM`Ho0CX1i7RN_Hg0GW1i7UN]He0JV1i7VNZHf0MU1g7WNZHf0OR1f7YNZHf00Q1f7JZH6f7KYH5g7KYHD8ZN^7R2ZHZOe0aNQ7V2YHoNS1hNd6Y2YHmNo8S1QGkNQ9U1oFiNS9Z22M4POgF`N]9Y22M4O1O100aM^Fl1e9jM`FY2m91N3N16K4J4L3N4K2TNXEg1g:XNZEj1g:1O1OaN[EP1d:POcEk0[:UOlEe0S:[OPFc0o9]OUFa0i9_O[F>d9B_F<`9DaF=]9CdF=[9CfF=Y9ChF=W9CjF>T9BmF>R9BoF>P9BQG?m8ATG?k8AVG?i8AXG`0f8@[G?e8FWG8j8IVG6j8JWG4j8LWG3V8^OlFMHb0W1=g7WOWGMK?X1<c7GUGMY1:T78cG^OZ19R7P1oHoNo6S1QImNm6V1SIiNk6T1[IjN[6@SHd1e1lNV6`1jI`NT6f1hIZNW6h1hIWNX6k1gIoM^6R2bIhMd6X2^I`Mh6`2f101O001N2jMmEg1a:O1L5^OZETOf:f0`EZOa:a0cE_O^::hEF\\:NkE3X:HjE7[:EeE;^:BbE>Z;000O10O1000000FVn6", "size": [404, 640]}, "area": 46783.0, "iscrowd": 0, "image_id": 14439, "bbox": [25.0, 39.0, 597.0, 301.0], "category_id": 183, "id": 20000914}, {"segmentation": {"counts": "m0n3]9000000000010O000000000001O000001O0cLdFk2]9nLcFJ1X3\\9lLlFT3e9000001O000001N10000O100O101O00000001O0010O01O1O0000O101N1O1O11OO100O2O0O10000000iN[FhNe9V1`FhN_9X1bFgN^9?\\FWO7XOIo0e9?]FZO86\\9XOZFJ0S12E86f95RFE95d96TFE85d96TFD97a96VFC:6`97VFC:ZODh0l9;VFC>B@4m9e0VFCL[Ob08_O5n9d0UFCa0D\\O5n9c0VFBi0_OTO;o9FoEf09Gl0LW93XFGf04W9LhG3Z8IhG7f:0L400fMDWF8N0YO5`:JiEKI<2MOMM6R:;PFOMG4IN5h9T2YFgMO5g9W2XFeM04h9W2XFeM04h9X2WFdM14h9c2XF]Mh9c2XF^Mg9S3000000000001O00000000000O2O00000O1000001N1000000000001O000O1000000N3O00000000000O2O0001OO1000001N1000000O1O101N1O100O1O1O1O2OMRGiKm8W44001O0O1001O001O001O0VOmF\\MS9d2nF[MS9d2mFnLO2T9Q3mFmLO2T9Q3mFmLO2T9Q3oFkLN4R9Q3PGkLN4R9g2mFUMn9l0VFCL80RO17l9l0ZF^OND0O`:m0iEoNZ;P1a0O1O10000000002OhFTOkL1P9k0YITOXN=_8?RHTOQO5MO1>8]OG01?P9>^Gn0IYNH;R9?[Gn0JXNI<R9a0WGk0MYNJ;S9b0UGj0ITN15N<S9b0PGmNNl10TN0i0R9:oFnNNl11SN0i0S9=mFh0ORN0j0U9Z310OQLkFh3V9ULlFONi3W9XLnFg3\\9O1O001O3M101N1O0000001O1O01O000000000O1000001N100O10000O1O10001N1WOeL`G]3^8fLaGZ3^8hL_GZ3a8gL\\G[3d8f00001O00000O11O0000O1010O00O11OO1001OO110N11O00000000000001O000000000000001O000001O1OO1001O01O01O001O00001O0010O0001O00001O00010O1O0000001O000001O0000000001O000001N11O000000O100010O00O10000000000O20O0000000000001O01O01OO10000O10001O0O100O10000000001O01O00000000001OO2O00000001O000010O00000001OO10000O2O0000000001O1OO101O0001O1O000000010O00O100000000O101O01O1O00O11O000RNaGWM0d1_8m0oGSOo7l0THQO^O]N^8b2UHaN_OVO`8Z2QHgM]OH0`0o8Q2QH]MQOb0n8b0RG8S1UOk7a0]G2g0]Ol7`0^G3f0\\On7?PGRO:R1h0]Om7?QGRO;R1g0]Om7c0]GOg0]Ok7d0_GOf0\\Ol7e0_GAAGT13l7=QGZO>?AGT10lNEQ9j0PG[O??@GT10lNEQ9h0QG^O?=_OHU1IS8d0jF^O`06@D29_:?_EYOY<`0>1M10O10XF^Og6a0cG_O25fN00LOO]9a0VGDE7n01jNH\\9<QGLG2Q15W80nFIH4S13U82oFIG4T12U81PG>k0AW8OnFa0j0@X80mF`0k0AY8OiFb0NiNM120c0e0[OZO[9e0eF\\O1W1OROe0<[O[O:Oe8h0QG`00SOb0E^Ok01XO1N<2m7\\2UHlNLjNo7[2kGTO3cNS8Y2hGVO3bNU8X2eG[O2_NY8U4dGmK\\8a41O000010O000000000O1000001O01OO1001O000001O0000000000000O110O0000000000000002OO000001O1O1O0010O0001O1O0000000001O0001O001O1O4YKZGb4g80001OO100O2N1O10000000001O000O1000001O00000000001O0001O000010O00100O1N2O1O1O2M200N2O101O000O1O1O1O2O0O1O1N2O2M2O1O1O1O1O2N100O100O1O2O0fHUJOLQ7Z6N1O1O2OO00100001NO1000000001O1O1N1XI_If6d6O1N101ON21O3M00O10N103M5K103L4L3I5O1O10000O10]H]J^7j5N1O1O00iM", "size": [427, 640]}, "area": 88911.0, "iscrowd": 0, "image_id": 14473, "bbox": [0.0, 29.0, 640.0, 278.0], "category_id": 124, "id": 20000915}, {"segmentation": {"counts": "Rlg11:1d<0XC4g<801O1O1O0000O100O10000O100000000O100000000002N00001O1O001O00000000O1O1000000001O@WC00036V=JcUh03VjWO5O2N3M3M3M00O1O10000001O000000000000000000O10000O11O0000000000000000001O001O001OO1001O1O00000000000000N200O100O1000000O100001O0000001O1O0000000000000000001O0000000000000000000001O100O00001O01O0001O000O2O001N101N10001N1O100001O1O001O001O1N2O00000O100001O000000000O101O01O1O1O00000000000O2O01O00000000000010O00001O01O001O0O10001O0010O001O1O1O0010O0100O01O01OO2M2O2O00001O001O01O001O1O00O1000O1001O001O100O1O010N2M3Mbfa2", "size": [427, 640]}, "area": 4149.0, "iscrowd": 0, "image_id": 14473, "bbox": [134.0, 0.0, 310.0, 36.0], "category_id": 127, "id": 20000916}, {"segmentation": {"counts": "kai01X=5M1UOJ_D7XOIT<2dD8k9GcEOJ0S12E8k91`FGE9j9OaFIE8j9OaFID9j9NcFIC:i9MdFIC:ZODZ::iFHC>B@e9<TGGCL[Ob08_Of9=SGFCa0D\\Od9?TGEBi0_OTOi9a0XF]Of09Gl0Y9JfFEGf0_9LaFS1]9PO_FR1a9\\10L6N0fMXFC8N0YOb9S1]FVOKI<2MOMMb9f0oF\\OOMG4INb91YF2^2NgMOl90`HOeM0m9O^H1eM0d<O\\C1o<000\\Yj00cfUO2O01O000001O000000000VOM\\D3d;N[D3d;MnCO25P<LnCO25P<LnCO25P<NlCN43P<OlCN43f;LVDo0n0SOd72mGL80RO17o08FZ8AhGND0Oj2c8_MYGc3g8a0O1O01000000M3M4hNTGkL1j0NYOn8V2SGXN=4j8=iFQO5MO1>8]OG01?8V96VHIYNH;1`9<mGJXNI<0c99iGMYNJ;0e98gGITN15Nd:3WENl10TN0Q;1PENl11SN0U;0iFORN0gdn21[[QM0i<>M3kD^O_9c0QF_OVO1i:`0XE]OH0`0`0`:`0nDQOb0?Q9AiGS1UO\\OP9LcGg0]O]Oo8MdGf0\\O^Oo8@bF:R1h0]O_Om8_OdF;R1g0]O_OQ9KaGg0]O^OQ9LbGf0\\O_OR9LTGAGT13_Oj8^OmF>?AGT10lNEd0W9]OnF??@GT10lNEd0U9^OQG?=_OHU1IGP9VORG`06@D29T2j8jMnFP4i8=2N00O10aNUG^Nj8LVG25fN00LOOT1j8_O[GE7n01jNHS1e8ZOcGG2Q15NY8WO`GH4S13OX8UOcGG4T12OW8VOXHk0AOW8VOYHj0@0X8UOXHk0AOY8SOXHNiNM120c0e0[OZOP1P9POQG1W1OROe0<[O[O:O9T9]OTH0SOb0E^Ok01XO1N<n:2\\FLg9JbF3a9KaF3`9JeF2\\;Moc_12o[`N4N001O1O3L011O00O1N1010000000K5NBWC?l<M2M3O2N000O2Kfo?", "size": [427, 640]}, "area": 6536.0, "iscrowd": 0, "image_id": 14473, "bbox": [61.0, 37.0, 540.0, 258.0], "category_id": 132, "id": 20000917}, {"segmentation": {"counts": "^6V3h:]O001O0010O0001O001N10001O001O01OO2O010OO101N100O1OeM^Eo1`:QNfEk1X:VNiEj1V:TNnEk1V;B1000mN`NSFi1o:HnNcNWF[1j9eNVF[1k9eNeENN]1]:gNaE8HQ1h:hN\\E;Kl0j:Q11N3N1O0O10000002N1O100SEkMb:a2N1O1O001O0000010O00O1000000001O010O001O000000000010fMfECNe1\\:fNiEAOh1X:eNTFZ1m9dNVF[1j9cNXF\\1i9cNUF_1l9_NTFb1m9^NQFc1P:]NoEc1S:\\NnEc1R:\\NVF]1j9cNWF\\1i9dNWF\\1i9dNWF\\1i9dNUF^1k9dNgEh1Y:\\NaEf1_:c010O000000000000O1I8N1N2L4001O00000001O00000002N7UEaM\\:;aEn1]:lMlEQ2T:nMoEP2R:oMPFn1Q:RNQFl1o9TNWFe1j9[N]F^1c9bN]F^1c9bN]F]1d9dN[F\\1e9eNZF[1f9fNPFc1Q:]NRF_1n9cNUFX1k9jNTFU1l9QOoEn0Q:XOiEh0W:C^E=b:V101O01N100O1O100J600O101I6000000000000oNkDLU;1PEMQ;LWE2i:LWE5j:IUE:k:CTEa0l:^OSEd0m:ZOVEe0j:ZObE:_:EbE;^:CbE>`:_OaEb0_:]OaEc0`:]OVEn0i:ROSER1m:nNoDV1h:l0]Ob0O2O1N2O1O3L7J1O1OdMSFZ1j9]NcFc1Y9TNSGm1f8eMdF2S1Y2h901O01O0REeM73Y:h2N0010O01OL4N20001O000`MaET2_:jMlEn1S:RNnE1Bc1`:]NYF_1i9`NYF^1g9bN[F[1f9fNZFZ1e9fN[FZ1e9gNZFY1f9iNYFV1h9kNVFV1i9lNUFT1k9nNSFS1l9oNRFQ1n9QOQFo0n9SOPFo0_O_N\\:c0QFT1o9POlER1S:SOfEo0Z:P11O1O2K4O1O100O101O0002N00N201O00000000001O000000001O000001O0001O01O001O00010O0000000001O01O00000001O00000000001VNVEQ1j:mNXES1h:kN[ET1e:jN^EV1a:hNaEW1`:gN`E[1a:^NfEb1Y:\\NlEa1T:_NVFW1j9hNWFX1i9hNWFX1i9hNXFW1n:N20kNkNUFS1l9mNTFS1l9ROkER1U:PObEV1`:jN_EV1a:kN[EX1e:lNSEX1m:c000001O00000001O1N2O4L2N1O1O0YE^Me:d2000000O100000aN]E7b:GaE8_:]OcEXO0[1]:fN_E9e0Q1m9dNcF\\1]9dNcF]1e:0I6K6UOWN[Fb1f9^N[F`1e9cN]FX1d9lNXFT1g9POUFP1k9@eE`0\\:C^E?b:U10010O1O000001N1O1O1O1O1O2O000001OO110O0001O1TEcMh:e2LO01O1O000001N1K5O10000O2N100O10001O00000001O01O01O001O1O10O01O5K002O0O;E00000001O000O1000001O00000O101N100O1O1O1O2N1O1001O00100O00000010O0O1O100O1O2O00000000001O01O1O1OO1010O002N0001O000O1O1O1O2O00000000001O000001O01O000O\\EjMU:V2jEjMW:V2gElMY:X2aEjM`:`20001O001O100O1O1O0001O01O00001O010O0000000001O000O1O1O2N1O1IaE`Ma:^27K5O2N10000000001O0001O0001O000001O0001O0O1O1000O10000O101O0O100O10000O10O0101QEiMh:W2WElMf:^2O1O1O001O002N1O1O000000000000000000O0100O2N100O1O101N1I6O12N3N010O1O3MO11O00O1N101000000000100VFkL[9W3`FmL^9`3100000O2K4011NO2O1O001O1000GiF\\LX9a3:^LYF^3l9M3N1O2ON30N4NO0O2O2M21N4MO12M3N1N0000000O10000001O00000]F", "size": [427, 640]}, "area": 51714.0, "iscrowd": 0, "image_id": 14473, "bbox": [0.0, 205.0, 640.0, 208.0], "category_id": 147, "id": 20000918}, {"segmentation": {"counts": "Rn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size": [427, 640]}, "area": 5651.0, "iscrowd": 0, "image_id": 14473, "bbox": [326.0, 0.0, 314.0, 35.0], "category_id": 157, "id": 20000919}, {"segmentation": {"counts": "n]R32Y=0oB0f<3UC0k<8O1M3O1O1O011O0001N1001O00O11OO1001OO02OO11N11O0000000000000000001O001O001OO1001000OO1001O00001O0N3O0O101N101O000O2O00010O00001000O000001O000000000001O0001O000000O11O000000O101O01O00O101O0000000O02O000O100O10001N1O101OO2O1O0O2O001O1N2O0O10000000000001O00000O100010N2O1O000001O000010O00000001OO10000O2O00001O0001O1OO01000001O10O001O1O10O01N101O1O000O2OO1N3N2O00O11O000001N101O1O00O10001OO100O10001O000O2N00O3N010000000001O1M5MO3EH^C9a<;N1O1O0O2O1O0O1N2M4M21O10O01O1O1O001N10001O00100N3N3N0O0000001O00001O001O0O20O01O00000000000002N01OO110O100O001O00001N2O0O13M4L1O1O1O100L4N1O2M3MYR_1", "size": [427, 640]}, "area": 6558.0, "iscrowd": 0, "image_id": 14473, "bbox": [236.0, 17.0, 291.0, 48.0], "category_id": 164, "id": 20000920}, {"segmentation": {"counts": "0m0^<0000000000001O0000000000000000001O0UDROY;o0`DQOJ1f;n0^DZOa;X1000001O000000O10000O100O100000000001O001O001O1O0000O100O1O1O11OO100O100O1000000000000001OO1000000O100O1O100N2O1O1O1O100002N001O001QDkNf;_1N2N1O000000000000000000000000000000O1000000O1000000O10000000000000000O1000000N2000000000000O2kNUDh0l;TOXDk0T<10N2O1O000O0100O10O1O010O1O100O001O1O100O102N000O2O10O010O001O000O2N0100000001O@[OlC0036b0n;IlC7T<`00000000000000002N001O1O1O1O1O1O001O1O1O0\\D`N];a1`DaNON^;b1cDcN\\;g1O1O001O3M1O2N1O0000001O1O000000000000O1000000O100O10000O1O10000O1WOTEQOn:m0UEROk:KiD>NH`0LT;;\\DGc0KU;;[DEc<8;L4Jjl`20YS_M1O1O1O001O1O1nBJm<:0000000001N2O3MO1GYCMg<3:O00000O1000O01Ndj00]UO2N1O2N2O1N1O2O000O2O001O1O201N00O1O1O100O0010O0100O10O010O0001O001O00001O01OO11O010O0001OO11O0O100O22M4L100O100O1eC_Ok;a0SDAl;?SDCm;=PDIJDP<d0SDLn;h0010N100O1O1O101OO100O1J600O010O1O1O1O1O1O1O100000001O00010O00010O01N1000001N11OO0101N2O1O0O1O1O1O2N1O1L3100O2O001OO1000001O001O01O10O01O1O1O10O00100O10O2N1O1O001O00001N3N2N1N1000000000001O1M2O2N1O2O0O2N1K5O1O01000O01dCVOW<Q1N100O0nN", "size": [427, 640]}, "area": 17537.0, "iscrowd": 0, "image_id": 14473, "bbox": [0.0, 0.0, 640.0, 79.0], "category_id": 169, "id": 20000921}, {"segmentation": {"counts": "k4S1X<S1mN001O00010O00001O001O010O00001O001O01O0O100O100O1ORERNb:k1^E[N^:c1cE^N]:b1`EbN_:^1^EQOV:o0jEQOV:R20nNjEfN`:Y1aEPOU:Q1nElNQ:T1oElNQ:T1PF[N3Jm9l1QFVN:He9S2Q1100O1O1O00001O01O00000000000010O000000000001O0001O00000001O00000001O000001O00aEmMBNk9U2aFPN@On9Q2`F[N`9e1^F]Nb9d1[F]Nf9c1YF[Nk9d1SF]Nn9c1RF[NP:e1PF[NP:e1PF\\No9d1PFdNi9\\1WFeNh9[1XFdNj9\\1UFcNl9]1TFWNMHQ:Q2UFRNW:n1c0000001O0001O00000001O000001O000001O0000000001O01O00VEoMZ:Q2`EXN]:h1bE[N\\:e1dE]NZ:c1fE_NX:a1hEeNS:[1lElNm9T1SFlNm9T1SFlNm9T1TFkNl9U1UFhNm9X1TF_NT:a1mEcNo9\\1SFgNh9Y1^FbNa9_1bF]N^9c1nFRNQ9n1V10001O00000001O0001O0000000000010O000000lEnMo8R2nFSNP9m1jFZNU9f1iFYN[9f1cFYN`9h1\\FWNh9i1WFXNi9h1UF[Nj9e1UFgN`9Y1_FgNb9Y1\\FgNg9X1WFiNj9W1UFbNS:^1mE^NW:c1hEYN\\:c1hEYN\\:Y1R1ZOg0ZOXP3KVPM=C<C=D<D<H801O000000000010O0000000001SEmM`:S2^EXNY:h1gEYN1Bn9U2RFdNl9[1TFgNj9Y1VFiNh9X1XFgNh9Y1XFhNg9X1[FfNe9Z1]FdNc9\\1^FcNb9]1`FbN`9^1aF`N_9`1cF^N]9b1eF\\N[9d1gFYNZ9g1iFUNX9k1mFnMU9S2Q10001O000001O00000000010O00000000001O01O00000001O00000001O01O00000000001O01O00000001O00000001O01O000000000010O0000gEgM`9Y2^FiMb9W2\\FlMc9U2ZFmMf9S2XFnMi9R2UFnMm9R2mEWNQ:h1mE]NP:c1PFfNg9[1WFgNh9Y1XFgNh9Y1XFgNh9Y1XFhNg9X1YFhNg9X1ZFgNf9Z1YFfNh9Y1^FYNj9g1WFVNk9j1VFTNk9l1XFmMl9S2XFdMm9]2`00001O00000000000000001O0O100000000000001O00VFhMa8W2^GkMb8V2RGmMXOOf9T2[FiM9e0[9b1[FnNe9S1ZFmNf9X25EYFlLk9S3QFmLT:Z2lEfNm9Z1TFgNj9Z1[FeN`9[1dFaN\\9_1SGRNm8o1VGnMi8R2\\GdMj8\\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[c0", "size": [427, 640]}, "area": 45696.0, "iscrowd": 0, "image_id": 14473, "bbox": [0.0, 155.0, 593.0, 159.0], "category_id": 183, "id": 20000922}, {"segmentation": {"counts": "X5l2l;W5WDYH[;g7eDWL]7i3cH4`3L_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_C0R50nJ0R50nJ0R50_7000`C0P50PK0P50PK0P50PK0P50PK0P50PK1o4OQK1o4OQK1o4OQK1o4OQK1o4OQK2n4NRK2n4NSK1m4OSK1m4OSK1m4OSK2l4NTK2l4NUK1k4OUK1k4OUK1k4OUK2j4NWK1i4OWK1i4OWK1i4OWK1i4OWK2h4NYK1g4OYK1g4OYK1g4OYK1g4OYK2f4N[K1e4O[K1e4O[K1e4O[K1e4O\\K1c4O]K1c4O]K1c4O]K1c4O]K1c4O^K1a4O_K1a4O_K1a4O_K1a4O_K2`4NaK1_4OaK1_4OaK1_4OaK1_4ObK1]4OcK1]4OcK1]4OcK1]4OcK1]4OdK1[4OeK1[4OeK1[4OeK1[4OeK1[4OfK1Y4OgK1Y4OgK1Y4OgK1Y4OgK1Y4OhK1W4OiK1W4OiK1W4OiK1W4OjK0V40jK1U4OkK1U4OkK1U4OkK1U4OlK0T40lK1S4OmK1S4OmK1S4OmK1S4OnK0R40nK1Q4OoK1Q4OoK1Q4OPL0P40PL0P40PL1o3OQL1o3OQL1o3ORL0n30RL0n30RL1m3OSL1m3OSL1m3OTL0l30TL0l30TL1k3OUL1k3OVL0j30VL0j30VL0j30VL1i3OWL1i3OXL0h30XL0h30XL1g3OYL1g3OYL1g3OZL0f30ZL0f30ZL1e3O[L1e3O\\L0d30\\L0d30\\L0d30\\L1c3O]L1c3O^L0b30^L0b30^L0b30^L1a3O_L1a3O`L0`30`L0`30`L0`30`L1_3ObL0^30bL0^30bL0^30bL0^30bL1]3OdL0\\30dL0\\30dL0\\30dL0\\30dL1[3OfL0Z30fL0Z30fL0Z30fL0Z30fL1Y3OhL0X30hL0X30hL0X30hL0X30iL0V30jL0V30jL0V30jL0V30jL0V30kL0T30lL0T30lL0T30lL0T30", "size": [640, 480]}, "area": 305085.0, "iscrowd": 0, "image_id": 14831, "bbox": [0.0, 0.0, 480.0, 639.0], "category_id": 183, "id": 20000923}, {"segmentation": {"counts": "l7T7k71001O0000000000002N1OO1M31O0mHVHP7l71N21OO1O1001O00001O00001O0000001O000000O1O1O1O1O1O1O1000000000000001O1O2N1O1O1O1O0000001O0000O1ARIkHo6R7UIoHi6o6\\ImHe6R7^IkHc6U7`IhH`6X7_IiHa6V7aIiH_6W7e0O1O1N24LO1001O00O10000O1001ON200O10000O1M3N21OO12N1O1O0000N21OO100N20000O1N2001OO100000000000000001O00000000000000000000001O0000000000000000000WHSI_7P7\\HRId7U7000001O1O0000O1O1001O1OO100M3N2G9O12NO1K51O00N21O2NO1001O1O00001O001lGVIP8m602N00O10000N2M3N2O11O1O001O001O1O6J5K001O00001O00001O0000000000001O1O000fIeHiN0R6Z7QKgHmN4Q6T7QKTIn4`6RJUIT1;j4`6SJTIS1<j4_6`KaI_4_6aKbI^4^6bKbI^4]6cKdI\\4\\6dKcI]4\\6eKdIZ4[6gKeIY4[6gKeI[NAl5j6jKfIV4Y6kKfIV4[6iKdIX4]6fKcI[4^6QKUI_O>`5^6oJWI^O`0_5Y6QKZI^O=b5Z6oJ[I]O:e5[6nJ\\I[O:g5[6mJ\\IZO9j5^6iJZI[O8m5d6bJVI_O6o5d6bJkI_5U6aJjI`5V6_JkIa5U6_JjIb5V6]J]IAFS6m6\\JZIEGP6o6[JYIHEn5R7TJiHHOO`09El5S8VJkGk5T8WJjGj5U8a0O1N2O1N2O1O10000000eI^HX5c7]JiHa5\\8O2N1O2N3M2N1O8H>B1O00000000O1000000O100000000O11UKmE150NV4S:iKPF0O`0J@31M[3W:dLXF2BNO90G;W3d:iLTE1M00O=U3b:kLTE0?V3]:jLhEV3S;N0000000000000000000000lNmLjEOOT3V:SMdE`3[:cL\\EF5g3_:eL`E\\3`:eL^E\\3b:dL]E]3c:`00000000000000000001ON2L4M\\LTFZ2e9lLkEn0`0V2e9mM[FS2e9mM[FS2e9mM[FS2e9mM[FS2e9mM\\FR2d9nM\\FR2d9nM\\FR2c9oM\\FR2d9nM\\FR2d9nM]FQ2c9oM]FQ2h9kMWFU2o9eMPF\\2Q:cMmE_2T:`MlE`2T:bMhE`2X:cMdE^2\\:eMaE[2_:S1001OO1000000O11O00000000001O000000O1000000000000000000O1000000O100O15`K]EY4f:O000000000000O1000000000000FgKmEY4R:jKlEV4Q:hKiE53S4T:QLkEo3T:?00LXKQFg4o9YKRFf4n961UKmEg4Y:M00N2O11O001O00001O4nLlEP1W:gNoEY1T:cNnE\\1U:YNZEXOd0_2S:WNSFi1o9TNRFl1T:kMPFT2V:dMlE\\2\\;00000001O00001O00O100O1WLfMbH8a0T2k6hMVHa0m0h1l6hMQHg0S1`1i6lMTHe0ElNI6S1]2[7lMTHf0DSOm0[2X7oMWHg0AnNQ1[2W7PNWHS1c0l0U7SNWHQ1d0l0T7TNXHP1c0m0S7UNZHn0c0m0o5_MYIOD0?i02l0c0m0n5cMSI28c04k0d0l0m5fMnH49b08h0e0k0k5VNUI9:g0f0j0j5XNVI79g0h0i0i5ZNTI7;e0j0i0g5]NPI7?c0i0j0i5_O^IGh0k0i5_O_IF2a1[6jLVIP23SNO[5h6cJTIn7l6300000000000000000000000000000000O1O1O1O1O1N2O1O1O1O1O1O1O1O1O1O1O1O1O1O1N2O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O100O1O1O1O1O1O1OhGeJm6Z5jHeKa6[4TIfLV6Y3`IWM[6j5M3M4L3M3M3M3M3M3M4L3M3M3M3M3M4L3M3M4L3]MSFMQ:KTF4o9JPF6T:EnE:U:ClE<X:@hE`0[:]OeEc0_:YOaEg0b:VO]Ek0g:ROXEn0k:oNUEQ1o:kNPEV1S;gNmDY1V;eNhD\\1\\;`NdD`1_;]NaDc1j<D2N2N3M2N2N3M1O1O0000O1000000000000O1000000000000O1000000000000O100000000K5^OcNbC\\1\\<gNdCX1Y<kNgCU1V<nNjCR1S<QOmCo0P<TOPDl0m;WOSDi0j;YOWDg0f;\\OZDd0c;_O]Da0a;A_D?^;DbD<[;GeD9X;JhD6U;OiD1T;5iDKT;:iDGT;?hDBU;d0gD]OW;W2M3[I]Lj1f3PNdLi1_3PNlLi1W3QNSMh1P3RNZMg1i2SNaMf1b2SNiMf1Z2TNPNl0l2nN^MbNn4W1]KWNQ5c1YKkMU5P2P4N2N2N2N2O1N2N2N2N2N2E;C=C=G9", "size": [480, 640]}, "area": 125543.0, "iscrowd": 0, "image_id": 14888, "bbox": [0.0, 12.0, 640.0, 468.0], "category_id": 132, "id": 20000924}, {"segmentation": {"counts": "km\\3a0_>1N10O0100O100000001O1O00010O3M2jAVOl01UOOo<l0aCE]<;cCE]<<bCE]<;cCF]<9cCH\\<8cCI]<8bCI]<7cCJ\\<6eCI[<8cCI]<8aCI_<\\11OjCYNS;h1bDdN\\;a2101N10O2M2N107H>B1O00000000O2O00000O100000000O20O1O00O11O000001O01O0000gNTE^Nl:W1]EkNc:U1_EjN`:U1`ElN`:T1aEkN_:U1aEkN_:U1bEjN^:W1bEVN@No:l1aEVNA_OO:P;Q2aEUNGEi:U2bEUNIAf:Y2bEUNIBe:U2oDlMc0<n:g1XEXNh:h1XEWNi:i1WEVNj:k1VETNj:l1WERNi:n1ZEPNf:P2_EkMa:U2U102N3M2N000000001O0000001O000001O?B>_CVM0h0R;X3J1O10O0000000010OO1000001N11O0001O0000O2O00000O01000O1000000O100000O0100000O010O15SEjKe:Z4O00000O100000O2O3M2N2N2N2N3CmDgLU;V3nDhLT;S3jDgL53R;U3REjLn:U3?H\\DTMd;k2^DUMa;i2aDXM^;g2>00M300O1L4N2001O0OSOjC[NOb0V<o0TDROl;j0[DSOe;m0ZDVOd;f0aDYO_;g0aDYO_;g0bDWO_;h0aDYO_;d0dD[O];a0hD^OY;1eCKU13W;1fCFX19T;OUE0n:LTE4l<O1O2O0O3NjeQ3", "size": [480, 640]}, "area": 18719.0, "iscrowd": 0, "image_id": 14888, "bbox": [232.0, 184.0, 199.0, 159.0], "category_id": 143, "id": 20000925}, {"segmentation": {"counts": "3i3W;h3XL000000O10000O1000000O10000O1000000O1000000O10000O1000000O10000O1000000O10000O1000000O10000O1000000O1000000O10000O1000000O10000O1000000O10000O10000O1000000O10000O10000O1000000O1000000000000000000000000001O00000000000000000000001O00000000000000000000001O00000000000000000000001O000000O10000O100O10000O10000O100O10000O100O10000O100O10000O10000O100O10000O100O10000O100O10000O10000O101N10000O100O10000O100O10000O100O10000O10000O100O10000O100O100O100O100O100O100O100O100O10000O100O100001O0000000000001O000000000000001O00000000001O2N3M2N1O00000000001O00000000000000001O00000000000000001O000000000000001O00000000O10000000000O10000000000O1000000003M3M1O001O000000001O0000001O000000001O0000001O000000001O0000001O000000000000O1000000O1000000O1000000O10000O1000000O1000000O10000O100002N3M2N2N2N2N3M2N2N1O00000000O1000000000000000000000000000000000000O100000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000O1O1O1O1N2O1O1O1O1O1O1O1O1O1O1O1O1O1N2O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O100O1O1O1O1O1O1OdGjJP7U5gHjKd6V4QIkLY6T3]I\\M^6c2XIkMe6[5M4L3M3M3M3M3M3M4L3M3M3M3M3M4L3M3M4L3M4L3M4L3M4L3M4L3M4L3M4L3M3M4L3M[1eN2N2N3M2N2N3M1O000000000000O1000000000000O1000000000000O100000000000000M3]Oc0M3M3M3M3N2M3M3M3M3M3M3M3N2M3M3M3M3M3M3M3]IULo1m3kM]Ln1f3kMeLn1^3lMlLm1W3mMSMl1P3nMZMk1i2oMaMj1b2PNhMi1[2PNPN2j3H`L`NT5Z1VKUNW5e1SKjMZ5S2k3N2N2N2N2N2N2N2N2N2K5D<C=CY?", "size": [480, 640]}, "area": 146843.0, "iscrowd": 0, "image_id": 14888, "bbox": [0.0, 3.0, 639.0, 438.0], "category_id": 183, "id": 20000926}, {"segmentation": {"counts": "g4a8g40000000O100000000O100O1000000O100001O0000001OO11O000000000000001O00000000O100000000O10`I^KTN0O0d5b4WLaKQN42Hf5c4YK]KCm0S5e3XKcKCk0R5a3[KgK^Oo0S5Z3^KjK]Oo0R5W3_KnKUOW1X5k2aKPLWOX1U5h2cKPLXOZ1T5e2dKoKYO`1Q5a2fKoKWOd1OoMm4^4mKPLUOm1j4S2nKeNo3[1PLjNl3V1SLkNm3W1QLiNo3X1RLfNn3h1fKWNY4k1aKZN^4[5M1O1O2N2N3M001O1O001O1O2N2N2N001O1O002N001O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O00001O001O1O001O1O1O1O1O1O1O001O1O0000001O1O001O00001O00001O001O001O00001O1O1O001O001O1O000000001O001O001O001O00001O000000001O0000001O00001O0000001O0000001O000000000000001O0000000000000000000000000000001O00000000O100000VNSEY1n:cNWE[1i:cNYE]1h:aNYE_1g:aNYE_1X;0001O000000001O0000001O1O001O001O001O1O001O1O00000000000000KmNUDS1i;7N2O1O1000000000000O10000O11O000_N]D]1k;L1O1O1O001O0000001OO10000O1001OO1000000O10000O10000O1N2001O1O00001O001O001O1O1O2N1OO1000000001O00001O1O0000001O1O2N3M2N00O11O00000000000000000000O1O1O1N2O1O1O1O100O100O11O001O1O00001O0000001O00000000001OO100O1O100O100O100O1O11OO1000000O100000000O100001O0000001O000000000000000000O100000000O100O100EmN`DT1`;:O1O1O100O1O1O100001O00001O0000004L6J3M0000000000000000O100O1O1O1O100O1O1O1O1O10000000000O10000[OcN^E^1b:cN]E]1b:eN]E[1d:hNYEW1g:mNSEU1m:c000O1O1001O1O001O00N2001O1O001O0000000000001O1O00000000O10000004L0000000000O100O100O10000O1@PNhEP2X:RNeEo1[:TNaEm1^:?1OO1O1O1001O00000000O1001O00003M1O1O00000000O100O1VJ^Mn0b2QOaMl0`2SOdMj0\\2UOfMi0[2VOfM:F]Ke2X4fM9KZK`2\\4fM43\\KX2_4fM7m2HTM8l2GUM:j2EWM<h2DXM<h2EWM<h2DXM<h2EWM;i2FVM;i2EWM;i2DXM<h2\\OkLjK>n4c2UOSMkK;S5_2ROnMm0o1ROUNn0h1QO`Nl0\\1TOcNP1Z1POfNS1W1lNjNX1R1hNnN[1o0eNTO[1i0eNVO_1g0aNWOe1e0[N[Oh1b0XN^Ok1?UNAn1<RNDPLeN72Z5a1_NHoKfN72^5]1\\NKnKhN8Oa5[1XNNQLhNNM0022j5Y1UN0PLTOk5k0UNc0k1]OVNc0i1]OWNc0i1]OWNa0k1_OUNb0j1^OVNb0j1^OUNc0k1]OUNc0k1]OUNc0l1\\OTNe0k1[OUNe0ZLRNI0h10_1Y1f0e0oKRN74INl10]1W1g0X1mMdMY1T1j0X1mMfMW1R1l0W1mMkMT1n0o0W1mMmMR1l0Q1Y1XL^MI3Z1<b1j0S1Y1XLcMS1>_1f0V1Y1QL^MO:j01ZO:e2c0W1j1eL[NQ2KZ1i1_LoLAd1d2D[1j1`LgNR2_O^1j1_LjNQ2\\O`1k1^LlNo1YOc1k1^LnNm1WOf1i1^LSOi1TOj1h1]LVOg1ROm1g1\\LYOf1oNo1g1\\L\\Ob1mNT2e1ZL@`1kNX2c1VLG_1fN]2a1SLL^1cNc2]1PL2Z1aNh2[1oK5W1`Nk2Z1mK:U1\\NP3X1lK=R1[NS3f4jLZKW3g4hLXKY3i4eLWK]3j4`LVKb3j4\\LVKf3k4WLUKk3k4SLUKP4k4mKUKV4j4hKVKY4l4dKTK^4UOPKT5>iKd4POQKX55kKk4kNRK[5NmKQ5fNSK^5HnKW5bNRKV7`501O000000O12N1O000000", "size": [424, 640]}, "area": 61323.0, "iscrowd": 0, "image_id": 15079, "bbox": [0.0, 128.0, 640.0, 296.0], "category_id": 143, "id": 20000927}, {"segmentation": {"counts": "ZP82V=0O1O1O2OO02N100O1O100O100O2N100O1O10O01O100000O1000O10O001000O100O01000O100O102M2OD;001O010O001O010O01O10O0100O010O100O1O100O100O10O0100O001000O100O010O1O10000O01000O10000000O0100O10000O010O100O100O10O0100O10000O010O100000000O01000000000O0100000O10O010000000O10O10NZCBf<>2000O01O1O1O0010O1O0010O1O100000O10O10000OWCFf<:YCGf<9ZCIOMc<;]CKa<?O010O100000000O101O001O01O100O1O010O10O01O0OO101OEbCM^<HQD0T<0a00QlR40oSmK4N1N3M2O1N3N1N3N1O2M2O2M2O2N1N2OO0O2N2N1O2N2N1O1O1O2N10010O01O1O01N1H]CJd<3`CKb<49O2N101OX_h06a`WO3M2N2O1L3010O00001N21O0O01O00^J", "size": [424, 640]}, "area": 3599.0, "iscrowd": 0, "image_id": 15079, "bbox": [19.0, 80.0, 621.0, 114.0], "category_id": 165, "id": 20000928}, {"segmentation": {"counts": "0g4a800000000O100000000O100O1000000O10000O100O1O1O100O1O100O1O100O100O1O100O1O100O1O10000000000O100O10000O100O10000O100O100O100O1O1O100O1O1O100O10000O10000O1000000000000000000O10000O10000O100O100O1O10000O10000O100000000O100O10000O100O100O100O100O100O10000O100O100000000O10000000000O1000000O100O10000000000O10TFTM\\9l2dFUM[9k2eFTM\\9l2dFTM\\9k2dFVM\\9j2cFWM]9i2bFXM^9h2aFXM`9g2`FZM`9f2`FZM`9e2aF[M_9d2aF]M_9b2bF]M_9c2`F^M`9b2_F_Ma9a2_F_Ma9a2_F_Ma9a2_F^Mb9a2_F_Ma9a2^F`Mb9`2^F`Mb9`2^F`Mb9`2]F\\MF0n9d2\\F[MG1m9c2\\F\\MIOl9d2\\F\\Mj9b2?O100O100000000O1000000001O1O1O1O001O1O001O00N2O1O1O100O100O1O1O1O1O1O1O1O1O1O1O100O1O1O1O1O1O1O1O1O1O1O1O1N2O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1N2O1O1O1O1O1O1O1O100O1O100O100O100O100O100O100O10000000000000000000000O100000000000000000000000000000000000000000000O10000000000000000001O00000000000000001O00000000000000001O00000000000000001O00000000000000001O000000000000001O00001O001O00001O001O00001O00001O001O00001O00001O001O00001O001O00001O001O001O001O001O00001O001O001O001O00001O001O001O1O001O1O001O1O1O001O1O001O1O1O2N1O2N1O2N1O`NeDT1Z;mNiDQ1U;oNnDo0Q;ROREl0l:UOVEi0i:XOZEg0c:YOaEg0[:ZOgEh0T:YOoEg0m9YOVFg0g9ZO]Fe0_9\\OdFc0Y9^OjFa0S9_OQG`0l8AXG>d8C_G<^8DfG;W8FmG8P8ISH7i7JZH5c7KaH4\\7MhH1U70nHOo61UIJl67WIE_MBW9j0^ID`6=cI@\\6`0hIAS6`0PJ_Om5b0WJ^Od5b0`JZO`5g0cJUO]5l0_31O001O1O001O1O3M2N2N3M2N3M2N3M2N3M2N3M2N2N3M2N3M2N3M2N3M2N3M2N2N3M2N3M2N3M1OO11O00001O001O001O1O001O001O1O001O001O1O001O1O0000001O00001O0000001O00001O001O001O0000000000000000000000O100001O00000000O10000000000O10000000000000000O10000001O1O1O1O2N2N2N4L2N1O2N1O1O1O2N2N2N2N3M3M1O2N2N1O1O2N00001O00001O1O1O001O00", "size": [424, 640]}, "area": 49874.0, "iscrowd": 0, "image_id": 15079, "bbox": [0.0, 0.0, 640.0, 178.0], "category_id": 173, "id": 20000929}, {"segmentation": {"counts": "dof06k<;E<E;J5K6mMWO\\Go0^8TO`GP1\\8SO`GQ1\\8SO`GP1^8RO_GP1_8TO]Go0a8TO[Gn0c8UOZGn0d8UOXGn0e8UOXGm0f8m1N1N3N1N3N2M2O2M2N3N2M2O2M2O2M3N1N2N101O1O1O1N2O001O1O1O1N2O001O1O1O1O1N2O001O1O1O1N2O001O1O1O1O1N101O1O1O1O1N101O1O0000000O10000000000O100000000O100000000O100000000O2O001O1O1N101O1O1O0O2O1O1O001N2O1O001O1N2O001O1O1N1000000000000001O001O00001O00001N101O00001O0000AaHVK]7_4RI^Kn6b4VI[Ki6d4\\IXKd6g4]IYKc6f4_IZK`6e4aI[K^6e4cI[K]6d4eI\\KZ6c4gI]KY6b4hI^KX6a4jI^KV6a4kI`KT6_4mIaKR6_4PJ`KP6_4QJaKo5_4QJbKn5]4TJbKl5]4UJcKk5\\4VJdKi5\\4YJcKg5\\4ZJeKe5Z4\\JfKd5Y4^JfKb5Y4_JgKa5X4`JiK_5V4cJiK\\5W4eJiK[5V4fJjKZ5T4iJlKV5S4kJmKU5R4lJnKS5R4oJmKQ5R4PKoKn4Q4SKoKl4Q4VKnKj4Q4WKoKh4Q4YKoKg4P4[KPLc4P4^KPLb4o3_KQL`4o3\\20WIQL[4n3fKRLY4n3a2O00ZISLS4l3d20001O001O001O00001O0O2O001O00001O001O010OnI]L`2c3b31PJ\\L\\2c3eM]LZ2d3gM\\LX2c3iM]LV2d3jM]LU2b3mM]LR2d3nM]LP2c3QN^Ln1b3SN]Ll1c3UN^Lj1b3VN^Li1b3YN^Lf1b3ZN^Le1c3[N^Ld1b3]N]Lb1d3^N]L`1d3aN[L_1e3aN\\L]1e3cN\\L\\1d3eN[LZ1f3fN[LY1e3gN[LX1f3iNZLU1f3lNZLT1f3lN[LR1f3oNYLQ1g3oNZLo0g3QOYLo0g3ROYLl0h3TOXLk0i3UOXLj0h3WOWLh0j3XOWLg0i3YOXLe0i3\\OVLd0j3\\OWLb0j3_OUL`0l3@UL?k3BTL=m3DSL;m3ESL:n3GRL8n3IQL6P4JQL4P4MoK3Q4NoK0Q41oKOQ42oKLR45mKKS45nKIS48lKGU4:kKEU4;kKDV4=jKBV4>jKAW4`0iK_OX4a0gK^OZ4b0gK\\OZ4e0eK[O[4f0eKXO\\4h0dKXO\\4i0dKUO]4l0bKTO^4l0cKRO^4o0bKoN`4Q1_KoNa4R1_KlNb4U1^KjNb4W1^KgNc4Z1\\KfNd4[1\\KcNe4^1[K`Nf4a1ZK^Nf4c1ZK[Nh4e1WK[Ni4f1WKXNj4i1VKVNj4k1VKSNk4P5500O1QMoJgMQ5Z2oJeMR5W510000O10000O10001N10000O10000mNhJUJY5j5hJVJY5h5iJWJW5h5jJWJW5h5kJWJV5f5mJYJS5f5nJYJT5n4dJjJ:7R5P5oJ\\J1c0P5S5cKlJ_4T5aKkJ_4V5aKiJ`4X5dKaJ]4`5e1101N1O100O2O0O1O2OO00001O010O00001O001O00001O0O2O001O001N101O000O2O001O001N101O00001N101O001O1N101O1O001N2O1M2O2M3N1N3M3N2M4M2M4L3N3L3NaNkKnIR4U6QLiIk3Y6XLeIf3]6]LaI_3b6cL]IZ3e6iL[IQ3g6SMYIg2j6[MWI`2k6cMUIW2m6lMSIo1P7UNnHf1U7]NkH]1X7fNgHU1[7oNdHm0^7WO`Hd0c7_O]H;e7IZH2i72UHIn7:QH^OU8e0kGROZ8R1eGfNa8^1]G[Nh8h1WGPNn8T2RGcMT9`2i04L3L4M3M4K4M3M4L3L4M3M4K10O2O1N3N1O2M2O1N3N1N3N1O2M2O2M2O2N1N2O2M2O2M2010O2O0O100O1O101N100O2O0O100O2O0O1O101N100O101N100O2O0O1O101N100O101N100O2O0O1O101N100O101N100O101N1O101N100O101N100O101N1O10O0N3M2N3M2O2M2N3M2N2N3M2N3M2O2M2N3M2N3M2N2N3M2N3N1N3M2N3M2N3M2N2N3N1N3M2N3M2N3M2N3M2O1N3M2N3M2M4J5K6J5L`c4", "size": [424, 640]}, "area": 110265.0, "iscrowd": 0, "image_id": 15079, "bbox": [55.0, 8.0, 573.0, 377.0], "category_id": 183, "id": 20000930}, {"segmentation": {"counts": "0d:d1S10O12NN2M3K5L4K6K4L5J5L5J6K5J6K6J4K7J4K6K4K7J4L5J5L4K5L4K5L4L4K6K4K5L4L4K5L4K5[OWIdJm6X5UIiJl6S5XIlJi6S5[IiJf6T5_IhJc6T5aIkJ`6Q5eImJ\\6o4R1fLhG^1[8bNfGZ1]8eNdGX1_8hNbGR1c8mN_Gl0h8SOXGk0j8UOWGg0l8YOUGc0n8\\OSG;V9EkF9V9FkF9V9GjF7X9IhFLEiNf9[1eFKd95\\FJe96[FIf97ZFGh9:WFSOGHU:U1UFQOHHT:W1VFnNJET:]1RFlN]:V1cEgN^:Z1cEcN^:]1cEaN^:_1dE]Nd02_8a1RH`NQ8`1kG`NZ8_1bGaNb8_1[G`Ni8`1SG`NQ9`1kF`NY9`1cFaNa9^1[FbNh9^1UFbNP:[1nEeNV:X1kEfNX:X1o0M2O2N200000001N100O10000O1000000O1A?0001O00000000000000000000000000000000001O000000000000000000000000000000001O0000000000000000000000000000000000000000001O0000000000001O001O1O2N1O1O1O2N1O1Oajk3b0mTTL000000000000000000000000000000000O100000000001O00000000000000000000O10000000000000000000000000000001O]C^O]<b0bCEX<;hCFW<:hCGX<9hCGX<g0O000000001O00000000001O00000O10000001O1O001O01O0000001O1O001O001O2N003M2N2N2N1O2N2N4L3L2O;E4L2N2N1O002M101O1O00002N5K4L3M8H5K5K3M5K4L3M2N3M2N4L3M4L5K5K3M5J7kH_JT6f5ZIiJd6\\5VIeJi6n5100O12N2N3M1O7I3M6J7I3M7I6J8G9H4L5K2N0O10001O00000OmG", "size": [427, 640]}, "area": 31858.0, "iscrowd": 0, "image_id": 15254, "bbox": [0.0, 0.0, 640.0, 427.0], "category_id": 115, "id": 20000931}, {"segmentation": {"counts": "bQd03X=1N101N2O001O1N101N100000000000O2O0O1O2N3Mmd]7", "size": [427, 640]}, "area": 207.0, "iscrowd": 0, "image_id": 15254, "bbox": [48.0, 30.0, 22.0, 13.0], "category_id": 130, "id": 20000932}, {"segmentation": {"counts": "d:d1g;0OfNlD?V;ZOPEe0n:YOWEf0i;N1O1O100O10O001O1O1O1O2N002N001O002N001O000000000000001O0000000000000\\IVOg0j0UOXOm0h0oN\\OQ1d0nN@P1?mNFhJ]OT6m0POJkJYOV6m0kNOX11dN3\\1MaN5`1K]N7d1IYN9h1GSN>m1BmMc0T2^OiMd0W2\\OfMg0Z2YOcMi0^2WOYMR1g2nNXMS1h2mNWMT1i2lNUMU1l2kNiLSOkLU2\\6hNhLa1X3_NgLa1Z3_NeLb0iL^Ob60ZKRO[O^1oN^O\\62ZKROhN2H[11]OT64ZKTOgN2HV19^On56YKWOeN3ER1e0[Oh5:WKXOeN^1c0mNa5=VK[OcNX1l0nN[5?SK@cNP1V1nNT5b0SKAaNl0^1oNn4d0SKC]Nd0k1TOe4e0SKh0=bN`4f0SKd0b0eN[4h0RK?h0hNV4i0RK<l0jNR4j0RK8R1lNl3l0RK4W1oNg3m0RK0\\1ROb3o0QKKb1UO]3o0QKIg1WOX3Q1nJFn1YOT3Q1kJGS2XOR3P1iJJV2VOR3o0eJK\\2VOo2n0dJJa2XOk2n0bJHg2ZOg2n0bJDk2^Oc2n0bJ@o2B_2n0bJ]OR3E\\2n0bJ]OR3E\\2o0`J\\OU3E[2o0`J[OV3FZ2o0_J[OX3FY2o0_JZOY3GX2o0_JZOY3GX2o0^JZO[3GW2o0^JYO\\3HV2o0^JXO]3IU2o0^JXO]3IU2o0]JXO_3IT2o0nIFo3[OS2o0nIEP4\\OR2o0nIEd:<[ECf:=ZEBg:>YEAh:?XEAh:?XE@i:`0WE_Oj:a0VE^Ok:b0UE^Ok:b0UE^Ok:b0UE^Ok:b0UE^Ok:b0UE^Ok:b0UE^Ok:b0UE^Ok:b0UE^Ok:b0UE]Ol:c0TE]Ol:c0TE]Ol:d0SE\\Om:d0SE\\On:c0RE]On:c0RE]On:c0RE]On:c0RE]On:c0RE\\Oo:d0QE\\Oo:d0QE\\Oo:d0QE\\Oo:d0QE\\Oo:d0QE\\Oo:d0QE\\Oo:d0QE\\Oo:d0QE\\Oo:d0QE[OP;e0PE[OP;f0oDZOQ;f0oDZOQ;f0oDZOQ;f0oDZOQ;f0oDZOQ;f0oDZOQ;f0oDZOQ;f0oDYOR;g0nDYOR;g0nDYOR;g0nDYOR;g0nDYOR;g0nDYOR;g0nDYOR;g0nDYOR;g0nDYOR;g0nDYOR;g0nDYOR;g0nDXOS;h0mDXOS;h0mDXOS;h0mDXOS;i0lDWOT;i0lDWOT;i0lDWOT;i0lDWOU;h0kDXOU;h0kDXOU;h0kDXOU;i0jDWOV;i0jDWOV;j0iDVOW;k0hDUOX;m0fDSOZ;n0eDRO[;o0dDQO\\;P1cDPO];R1aDnN_;S1`DmN`;T1_DlNa;_100000000000000000O100000000000000000000000000000001O00000000000000000000000000000000ZIZNg1f1YNZNg1f1YNZNg1f1XN[Nh1e1XN[Nh1e1XN[Nh1e1XN[Nh1e1XN[Nh1e1XN[Nh1e1XN[Nh1e1XN[Nh1e1XN[Nh1e1XN[Nh1e1XN[Nh1e1XN[Nh1e1XN[Nh1e1XN[Nh1e1XN[Nh1e1XN[Nh1e1XN[Nh1e1XN[Nh1e1XN[Nh1e1XN[Nh1e1XN[Nh1e1XN[Ni1d1WN\\Ni1d1WN\\Ni1d1WN\\Ni1d1WN\\Ni1d1WN\\Ni1d1WN\\Ni1d1WN\\Ni1d1WN\\Ni1d1WN\\Ni1d1WN\\Ni1d1WN\\Ni1d1WN\\Ni1d1WN\\Ni1d1WN\\Ni1d1WN\\Ni1d1WN\\Ni1d1WN\\Ni1d1WN\\Ni1d1WN\\Ni1d1WN\\Ni1d1WN\\Ni1d1WN\\Ni1d1WN\\Ni1e1VN[Nj1e1VN[Nj1e1VN[Nj1e1VN[Nj1e1VN[Nj1e1VN[Nj1e1VN[Nj1e1VN[Nj1e1VN[Nj1e1VN[Nj1e1VN[Nj1e1VN[Nj1e1VN[Nj1e1VN[Nk1d1UN\\Nk1d1UN\\Nk1d1UN\\Nk1d1UN\\Nk1d1UN\\Nk1d1UN\\Nk1d1UN\\Nk1d1UN\\Nk1d1UN\\Nk1d1UN\\Nk1d1UN\\Nk1d1UN\\Nk1d1UN\\Nk1d1UN\\Nk1d1UN\\Nk1d1UN\\Nk1d1UN\\Nk1d1UN\\Nk1d1UN\\Nk1d1UN\\Nk1d1UN\\Nk1d1UN\\Nk1d1UN\\Nk1d1UN\\Nk1d1UN\\Nk1d1UN\\Nk1d1UN]Nj1c1VN]Nj1c1VN]Nj1c1VN]Nj1c1VN]Nj1c1VN]Nj1c1VN]Nj1c1VN]Nj1c1VN]Nj1c1VN]Nj1c1VN]Nj1c1VN]Nk1b1UN^Nk1b1VN\\Nk1d1UN\\Nk1d1UN\\Nk1d1UN\\Nk1d1UN\\Nk1d1n4000000000000001O001O00001O0XIYNl1h1SNXNm1h1SNXNm1i1RNWNn1i1RNWNn1j1QNVNo1j1QNVNo1k1PNUNP2k1PNUNP2k1PNUNP2l1oMTNQ2l1oMTNQ2m1nMSNR2m1mMTNR2n1lMSNT2m1lMSNT2n1jMSNV2m1iMUNU2m1jMTNU2l1jMVNU2j1jMXNU2i1jMXNT2S2aMPN]2k2hLVMW3_3SLcLl3g3iK[LU4Q4ZKVLe4T4fJYLX5R4RJZLm5c5N2Ob0^Oh0XOi0TOQIUM\\4P2\\K`N[5`0iJEo5[OSJn2Q72M2XHYMj4i2lJIj29SMd0Q2]OmMl0j1WOSNQ1f1POWNY1b1hN\\N]1`1dN^N^1a1bN]Na1a1aN]Na1b1_N]Nc1b1^N[Ne1d1[N\\Ng1b1ZN\\Nh1c1YN\\Nh1b1YN^Nh1a1YN^Nh1a1XN_Ni1`1XN_Ni1`1WN`Nk1]1\\N]Ne1b1[N]Ng1c1XN]Ni1d1UN\\Nl1d1SN\\Nn1h1lMYNT2h1kMXNV2g1jMYNV2f1kMZNU2f1kMYNV2f1kMZNU2f1jM[NV2e1jM[NV2d1kM\\NU2d1kM\\NU2d1kM\\NU2c1kM^NU2b1kM]NW2c1hM]NX2e1fM[NZ2g1dMYN\\2h1cMXN]2j1`MWN`2k1^MUNb2m1\\MSNd2n1[MQNf2P2YMPNg2P2YMPNg2P2XMQNi2n1WMRNi2o1VMQNj2o1VMQNj2o1VMQNj2P2TMPNm2P2SMPNm2P2SMPNm2P2SMPNm2Q2RMoMn2Q2RMoMn2Q2RMoMo2Q2QMnMo2Q2RMnMo2R2QMnMo2Q2RMoMn2P2SMPNm2P2SMPNm2o1TMQNl2n1UMRNk2n1UMRNk2n1UMQNm2n1SMRNm2n1SMRNm2n1SMRNm2n1SMRNm2o1RMQNn2o1RMQNn2o1RMQNn2o1RMPNo2P2QMPNo2P2QMPNo2P2QMPNP3P2oLPNQ3P2oLQNP3o1PMQNP3o1PMQNP3o1PMQNP3o1PMQNP3o1PMQNP3o1PMQNP3o1PMRNo2n1QMRNo2m1RMSNn2l1SMTNm2l1SMTNm2l1SMTNm2l1SMTNl2>bI:b3XOl2>bI:b3YOk2=cI;a3XOl2=cI<`3WOm2=cI=_3VOn2=cI=_3VOn2=cI>^3UOo2=cI?]3TOP3=cI`0\\3TOP3<dIa0[3SOQ3<dIb0Z3ROR3<dIc0Y3QOS3<dId0X3POT3<dId0X3POS3=eId0W3oNT3=eIe0V3nNU3=eIf0U3nNU3<fIg0T3mNV3;gIi0R3lNW3;gIj0Q3kNX3;gIj0Q3kNX3;gIk0P3jNY3;gIl0o2iNZ3;gIm0n2iNZ3;gIm0n2hN[3;gIn0m2gN\\3;gIS1h2bNa3;gIY1b2\\Nf3<hI^1\\2VNl3<hId1V2PNR4<hIi1Q2kMW4<hIo1k1fM\\4;iIU2e1`Mb4;iI[2_1ZMh4;iIa2Y1TMn4:jIh2R1nLT5:jIl2n0jLX5:jIQ3i0eL]5:jIU3e0aLa5:jIZ3`0]Le59kI^3<YLi59kIc37TLm5:lIf33PLQ6:lIk3NkKV6:lIo3JgKZ6:lIT4EbK_6:lIX4A_Kb69mIh4S6XKmIc0Ee2^6hLmIb0Ka2X6mLmIa01]2R6SMlI@^Oj0i0^2m5XMlI_OEd0h0`2g5]MlI_OFc0l0\\2b5bMlI^OGc0Q1Y2[5fMmI^OGc0V1T2U5lM\\JOd0P2P5QN\\JNj0l1j4VN\\JNo0g1e4[N\\JMU1c1_4`N\\JMZ1^1Z4eN]JK_1\\1S4iN^JKd1W1n3nN^JJi1T1i3RO^JJn1P1c3VO_JIT2l0]3[O_JIY2h0W3_OaJH]2e0Q3CbJGc2b0i2HdJFg2?d2KeJEm2;^2OfJFP38Y22gJFU34S26hJHW3OP29iJJY3In1>hJK]3Cj1c0hJL`3_Of1e0jJNh1@kNM^4f0nJOf1BoNHZ4g0RK0a1GTOBT4g0WK2^1JWO^Oo3f0\\K4Z1N[OXOk3f0`K6W10@SOd3h0fK6R15DnN_3g0lK7n09HhNZ3h0PL9j0=LcNU3g0VL:e0a01^NP3g0ZL<b0d05XNk2h0_L==i09SNf2g0fL=1U1T5^NkJ?_Of1c5kMQK>kNZ2P6XMWKX4e4hK]KY4`4gKbK[4`0mJg1h0jM^4?jJc1h0PN`4n3aKTL`4i3`K[L`4`3aKcL`4W3aKkLb46cJc0l0BY4LkJ<n0LW4HkJ7o03X4FiJ1Q1;X4h1iKYNY4b1iK^N[4]1fKeN\\4U1eKlN_4o0bKRO`4i0bKXOa4c0`K]Od4>]KBg4DnIVN]1X2f4DmInM^1c2d4_OnIhM`1m2b4[OnIcMa1U3b4YO\\Ko0_4RO]KS1b4nNYKW1f4iNVKZ1j4gNRK^1m4cNnJa1R5_NjJd1W5]NdJe1]5ZNaJi1`5TN_Jn1c5nM]JV2c5gM\\J\\2d5aM\\Jc2d5ZM[Jk2d5QM\\JT3c5iL\\JZ3d5cL\\Jb3c5[L[Jl3`0_JX4a1XKU4NiJl4n0VK^4HeJU5k0QKS5T5iJlJW5X5fJgJY5^5cJbJ_5_5]JbJe5`5VJaJm5`5nIaJS6a5iI`J^6]5]IdJf6]5UIdJQ7Z5kHfJ\\7a53M7I6J8G9H4L5K2N0O10001O00000OmG", "size": [427, 640]}, "area": 54209.0, "iscrowd": 0, "image_id": 15254, "bbox": [0.0, 0.0, 640.0, 426.0], "category_id": 143, "id": 20000933}, {"segmentation": {"counts": "cU1b0d<6K5K5J7J5J6K5K4K5L4K5L4K5L4L4K5L4K5L4K6K4L4K5L4K5L4K5L4L4K5L4K5L4L4K5L4K5L4O1O1O1O1O1N2O1O1O1O100O10000O1000000O10000O2oN^IWKb6e4dIYK\\6c4kI[KT6a4RJ]Kn5`4XJ]Kh5_4_J^Ka5^4eJ`K[5\\4lJaKT5[4RKcKn4Y4WKfKi4W4[KhKe4T4`KkK`4Q4eKnK[4n3jKQLV4k3oKTLQ4h3ULVLk3g3YLXLg3d3^L[Lb3a3cL^L]3^3gLbLY3Z3kLfLU3V3oLjLQ3S3RMmLn2o2VMQMj2k2ZMUMf2g2^MYMb2c2bM]M^2`2eM`M[2`2eM`M[2_2fMaMZ2^2gMbMY2]2hMcMX2\\2iMdMW2\\2iMdMW2[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[Nh1e1XN[Nh1e1XN[Nh1e1XN[Nh1e1XN[Nh1e1XN[Nh1e1XN[Nh1e1XN[Nh1e1XN[Nh1e1XN[Nh1e1XN[Nh1e1XN[Ni1d1WN\\Ni1d1WN\\Ni1d1WN\\Ni1d1WN\\Ni1d1WN\\Ni1d1WN\\Ni1d1WN\\Ni1d1WN\\Ni1d1WN\\Ni1d1WN\\Ni1d1WN\\Ni1d1WN\\Ni1d1WN\\Ni1d1WN\\Ni1d1WN\\Ni1d1WN\\Ni1d1WN\\Ni1d1WN\\Ni1d1WN\\Ni1d1WN\\Ni1d1WN\\Ni1d1WN\\Ni1d1WN\\Ni1d1WN\\Ni1d1WN\\Ni1d1WN\\Ni1d1WN\\Ni1d1WN\\Ni1d1WN\\Ni1d1WN\\Ni1d1WN\\Ni1d1WN\\Ni1d1WN\\Ni1d1WN\\Ni1d1WN\\Ni1d1WN\\Ni1d1WN\\Ni1d1WN\\Ni1d1WN\\Nj1c1VN]Nj1c1VN]Nj1c1VN]Nj1c1VN]Nj1c1VN]Nj1c1VN]Nj1c1VN]Nj1d1UN\\Nk1d1UN\\Nk1d1UN\\Nk1d1UN\\Nk1d1UN\\Nk1d1UN\\Nk1d1UN\\Nk1d1UN\\Nk1d1UN\\Nk1d1UN\\Nk1d1UN\\Nk1d1UN\\Nk1d1UN\\Nk1d1UN\\Nk1d1UN\\Nk1d1UN\\Nk1d1UN\\Nk1d1UN\\Nk1d1UN\\Nk1d1UN\\Nk1d1UN\\Nk1d1UN\\Nk1d1UN\\Nk1d1UN\\Nk1d1UN\\Nk1d1UN\\Nk1d1UN\\Nk1d1UN\\Nk1d1UN\\Nk1d1UN\\Nk1d1UN\\Nk1d1UN\\Nl1c1TN]Nl1c1TN]Nl1c1TN]Nl1c1TN]Nl1c1TN]Nl1c1TN]Nl1c1TN]Nl1c1TN]Nl1c1TN^Nk1b1UN^Nk1b1UN^Nk1b1UN^Nk1b1UN^Nk1b1UN^Nk1b1UN^Nk1b1UN^Nk1b1UN^Nk1b1UN^Nk1b1UN^Nk1b1UN^Nk1b1UN^Nk1b1UN^Nk1b1UN^Nk1b1UN^Nk1b1UN^Nk1b1UN^Nk1b1UN^Nk1b1UN[NVK1e6d1UN[NVK1e6d1UN[NVK1e6d1WN\\Ni1d1WN\\Ni1d1WN\\Ni1d1WN\\Ni1e1VN[Nj1e1VN[Nj1f1UNZNk1f1UNZNk1f1UNZNl1f1SNZNm1f1SNZNm1g1RNYNn1g1SNXNm1i1RNWNn1i1RNWNn1j1QNVNo1j1QNVNo1k1PNUNP2k1PNUNP2k1PNUNP2l1oMTNP2m1PNSNP2n1nMSNR2m1mMTNS2m1lMSNS2n1lMSNT2n1jMSNV2m1jMTNU2m1iMUNU2l1jMVNU2j1kMWNT2j1jMXNU2`2RMcMk2X3[LiLd3c3oK_LP4l3dKVL[4T4QKWLm4U4]JXLa5j5O1O4Ki0XOi0VOj0TOgHaMf4a1SKoNe51^J4[6jNhI_3n62M2XHYMj4i2lJIj29SMd0Q2]OmMl0j1WOSNQ1f1POWNY1b1hN\\N]1`1cN_N_1`1bN]Na1a1aN]Na1b1_N]Nc1b1^N[Ne1d1[N\\Nf1c1[N[Nh1c1XN]Ni1a1YN^Nh1a1XN_Ni1`1XN_Ni1`1WN`Nj1_1VNaNl1\\1\\N]Ne1b1ZN^Nh1b1WN^Nj1c1TN]Nm1f1oMZNR2h1kMXNU2h1kMXNU2h1jMYNV2f1kMZNU2f1kMYNV2f1kMZNU2f1kMZNU2f1kMZNV2d1jM]NV2c1jM]NV2c1jM]NV2b1kM^NU2b1kM]NV2d1iM\\NW2f1fM[NZ2g1dMYN\\2i1bMWN^2k1`MUNa2k1^MUNb2m1[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[2hLeMX3[2hLeMW3]2hLcMX3^2gLbMY3_2fLbMY3_2fLaMZ3a2dL_M\\3g2^LYMb3m2XLSMh3S3RLmLn3Y3lKgLT4_3fKaLZ4e3`K\\L_4i3\\KWLd4o3VKQLj4U4PKkKo4[4lJeKT5`4gJ`KY5d4cJ\\K]5i4^JWKb5m4ZJTKe5Q5VJoJj5U5RJkJn5Z5mIfJS6^5iIbJW6c5dI]J\\6g5`IYJ`6l5[IUJd6Y60kKbI]1]6cNiIX1W6gNoIT1Q6lNTJo0l5POZJk0f5UO_Jf0a5YOeJc0Z5]OkJ>U5BPK9P5FVK5j4K[K0e4OaKL_44fKGZ48lKDS4<RL_Om3a0XL[Oh3e0]LWOb3h0dLSO\\3m0iLoNV3Q1oLkNP3T1VMhNi2X1[MeNd2Z1bMaN^2_1fM^NY2b1lMZNS2h1oMUNP2m1RNoMn1S2UNiMj1Y2cLdMfN2c4]2cLgMkNL]4_2dLkMoNFY4a2eLmMTOBR4c2fLQNXO\\On3e2gLTN[OWOj3g2gLXN@QOd3i2iLZNDmN_3k2iL^NIgNY3m2kLaNLbNU3o2kLeN0\\NQ3Q3kLhN6WNj2S3lLlN:QNf2U3_L]Ol0^M`2W3SLN]1kL[2S7gMlHU2X7kMiHP2[7PNeHl1_7UNaHf1b7[N^Ha1f7`NYH\\1k7dNVHV1n7kNRHo0T8ROlGh0Y8XOgGb0_8_O`G<d8E]G4i8LWGOn82RGGT99lFBX9`0gF[O^9e0cFTOc9m0\\FnNi9R1XFgNm9Z1SFaNR:`1mEZNY:f1hETN]:m1bEmMc:T2^EfMg:Z271O0O2O00001N101ON1N3L4M3L3M4M3L4M2M4M3L4M2M4M3K5K4L5K5K5K4Mff6", "size": [427, 640]}, "area": 185855.0, "iscrowd": 0, "image_id": 15254, "bbox": [2.0, 9.0, 621.0, 390.0], "category_id": 183, "id": 20000934}, {"segmentation": {"counts": "0dYj0T2XXVO1O000000000K8J4N100000000000000000001O0O100000000000000000000O100000000O2O0000000O1000000001O000000001O0000010O0000001O0000001O000000001O00000001O0001O000000000000001O00000000000001O00000000000000000000000001O000001O00000O1000000O10000O1000000O1000001O0001O0000001O00001O00001O00001O0001O01O000000000000000000001O0000000001O0000000000000001O000000000000000001O00000001O00O1000000O1000000O1000001N1000000O10000001O00001O001O001O0010O01O00001O0000000000001O1O3M2L7I;I7eN]jk2", "size": [640, 424]}, "area": 251847.0, "iscrowd": 0, "image_id": 15272, "bbox": [0.0, 0.0, 424.0, 640.0], "category_id": 169, "id": 20000935}, {"segmentation": {"counts": "dYj0T2la00000001O000K8J3O2O000000000000000000000O1000000000001O0000000O100000000O100000000O1000001O01O000000001O0000001O0000001O0000010O000000001O0000000000001O0000000001O0001O000000000000000000000000000001O0000000001O000000000000O1000000O101O0O1000000O100000000001O0000001O00010O00001O00001O0000001O00000000000001O000001O0000000000000000000000000010O00000000000000000000000001O00O101O000O1000000O1000000O1000000O10000010O00001O001O001O001O001O00001O000001O000001O2N3K4L:H9TOS^l2", "size": [640, 424]}, "area": 19357.0, "iscrowd": 0, "image_id": 15272, "bbox": [42.0, 39.0, 234.0, 98.0], "category_id": 183, "id": 20000936}, {"segmentation": {"counts": "iki02n>8TAG`>>O3M00000O2O00000O01OG^OVBa0k=@TB?l=DRB;n=KmA4R>>H8O100OO100O12N01O01O001cBjNE1V=X1TClNi<V1UCmNh<U1UClNk<V1RCaNN9o<Y1QCiNn<f1N1O1O1O1O1O100O1O1O100O100O1O1O100O1O1O100O100O1O100O100N200O100O002N2N2O1M3O1N2O1N2O1N2O1N2N2O1O002M2B]L[Ef3d:<O1O1O1O1O1O1O1O1O1OO1000000O1000000O100000000O10000O10000000000O10000O100O1O1O1N2O1N200O100O100O10000O1O100O100O10000O1000000O10000O10000O10000O1iNeDiN[;V1WEYNi:f1jEgMW:X2Y1O1O1O1O1O1O1O1N2O1O1O1O1O1O1O1O1O1O1O1O1O1O100O1000000O10000O1000000O10000O10000O1000000O10000O1000000O10000O11O001O1O1O001O1O1O1O001O1O1O1O001O00001O00001O0000001O00001O00001O0000001O00001O0000001O00001O00001O0000001O00001O0000001O00001O00001O00001O00001O0000001O00001O00001O00001O00001O00001O00001O0000001O00001O00001O00001O00001O00001O00001O000100O1O00100O1O01O01O001N1O101N1O100O2O0O1O2O0O1O2O0O1O101N100O2N100O2N100O2N100O100O1O2O0O1O100O1O100O100O1O10F9J6100O1001O001O001O1O001O001O1O001O001O0`0AO12M01O001O1O001O001O1O0010O01O1O001O010O001000O100O11O1O2N110O001N1O2O1N1ON2O1O1N2O3M2N2M2O1O2M3N2N4K3N1O001N10000N2L4G900O010O10000O2O1M3M4K4Mh\\]1", "size": [480, 640]}, "area": 34278.0, "iscrowd": 0, "image_id": 15278, "bbox": [55.0, 0.0, 488.0, 142.0], "category_id": 121, "id": 20000937}, {"segmentation": {"counts": "RY`02k>5J5eAEm=>RBCh=c0UB@g=e0VB]Oe=h0YBZOc=U1K6I6K4nBSNk<S2M3L4M6I4fC_MR<i2nCTMl;R3M5LaN^DCf;8dD_O`;=hD[O\\;`0nDWOW;d0QESOT;h0f1L4K6K4K5K6KmWl01RhSO0100O1O1001O001O001O1O0O101O000000001O2N2N00000O100N2N2N2N2N2N2M3N2N201N2O1O0O100O10000O100O10000O100O1000CaBYO^=g0fBUO[=j0=01O10OO20O101O0O100O2O001O01O00001N1O1O2C<M4O00001O001O01O01N101OO101O0O10001N10000O010000O01000O010O1O010000O01N2O001O1O1O02OO10O01000O2O01O01O1O002N10O01O1O001O010O00001O1bBNl;2RD0n;0gC@\\O15`0i<OeC;[<EdC<]<CcC=^<BaC?`<@QCC:n0e<@oBC<m0f<CXC>i<BVC>k<ATC`0m<@RC`0o<_OPCb0P=^OPCb0Q=[OQCe0P=[OoBe0R=YOPCf0Q=XOPCh0P=WOQCi0P=VOPC8HOY=GQC6I3W=ERC4J7U=CSC2L:U>01O00001O001O00001O00001O001O00O1N2N2N2N2N2N2N2N2N2N2N2N2N2M3N200000000001O000000001O000000001JXBROh=m0600O1MnAWOS>i03O0001O0001O0O1O10010N1001O1O101N1O1O3M1O01O000000001O0N2N2N2O2N1N200O100010O00001O01O00000002OO01O1O002NO2O0NfA^OZ>a03M300O001O1O1O001O11O0O10O11N10O11O0O2O001O1N012N0O2O001O1N1000000O10O10ON300O010O02OO0100O103L5L4K5L0O100O100O100O100O100O1O100O100O100O100O100O100O100O100OM4HgBfN[=Z16O001O001O01M4L4L4M3L4L4L4M2M4L3M3N2M3M3N1nC^Mf;b2\\D^Ma;d2_D]M_;d2aD]M];d2cD]M];Y2cD]M59X;Z2nDfMS;Y2lDhMT;W2mDiMT;V2lDjMT;V2kDkMU;U2kDkMV;T2jDlMV;S2kDmMU;S2lDlMU;S2kDmMU;S2kDmMV;R2jDnMV;c1ZD_Na0NV;b1^D]N<OV;b1fD\\N03Z;_1hDaNLO\\;`1hDdNJK_;`1gDWOY;h0hDXOY;h0fDXOZ;h0fDXOZ;i0eDWO\\;h0dDXO\\;h0dDXO];g0cDYO];f0dDZO\\;e0eD[O\\;e0cD[O];e0cD[O];f0bDZO_;f0`DZO`;g0_DYOa;i0\\DXOe;i0WDYOi;i0TDXOl;n11O0MRDTMn;k25L3G9O2O00001O0O1VO]CWOd<i0]CUOc<l0^CROb<k0dCnNa<P1aCkNc<V1^CdNf<[1[CeNf<Z1[CeNe<[1`001O0O101N1O3N1O5LM2O100TBnNj=T1O1N12O001N100NUBoNl=P1112N0O0KQB[Oo=0QB50G80H1Q>OoA01000WQg0", "size": [480, 640]}, "area": 17533.0, "iscrowd": 0, "image_id": 15278, "bbox": [34.0, 290.0, 557.0, 190.0], "category_id": 170, "id": 20000938}, {"segmentation": {"counts": "n5U2l<P3PMO001N2O1O00QOo000O1000000O1000000O100000000001O00WIPJb4P6\\KTJc4k5\\KWJc4i5\\KZJb4f5]K\\Jc4c5\\K`Jb4_5^KcJa4]5^KeJb4Z5]KiJa4U5`KmJ`4n4cKUK[4h4gKZKX4c4jK`KU4[4nKgKQ4V4QLmKm3o3VLSLj3i3XLZLf3b3\\LaLc3\\3_LfL`3V3cLmL[3P3gLRMW3k2lLXMR3e2PM]Mn2`2UMaMk2\\2WMeMh2Y2ZMhMf2T2]MmMb2Q2`MPN`2P2oKVLCj1^4T2jK\\L_Oa1g4V2fKaL[OZ1n4Y2bKgLWOQ1W5\\2]KkLSOk0_5^2YKUOg4n0UKSOj4Q1QKQOo4S1lJmNT5W1gJkNY5X1bJjN]5[1^JfNk4TLfKX9Z4g0O1000O10O10000O1O1N101N2O1N2O1N2N2O0O2O1O100001O000O100000000000000O100000000000000O100000000000000000000O100000O11O1O1O1O1O1O1O1N2O1O1O1O1O1O1O1O1O1O1O1N2O1O1O1O1O1O1O1N2M1N3N2N2N1O2N2N2O00100O1000O0100O10O10O100O10O10O10O010O1O0010O01O00100O001O00100O001O2O0O2O0O101O0O2O00001N10001N101O0O101O0O1iN_FoL`9Q3RG^Ln8a3eGlK\\8S4X101O1O001O1O001N2O001O1O1O001O1O001O1O001O1O010O1000O10O10O10O1000000O101O0O10000O101O000O10000O2O00000O101O1N20O01000000O010000000O10O1000jMZDQ1f;kN^DT1c;hN`DY1`;cNdD\\1\\;aNgD_1Z;]NiDd1W;XNmDg1T;UNoDk1R;QNQEP2P<O001O01O01O010O00010ON2N2N3M2N2N3L3N2N2N3M2N2N3M2N2N3O01O0001O00010O0000001O01O01O000010O0001O00010O0000001O01O01O000010O0001O00010O00001O00010O00000010O0001O00010O00001O00010O00001O01O01O000010O000001O00010O0O1000O1O010O1O010O100O00100O1O010O1O010O100O00100O1O010O1O010O100O00100O0010O01O010O010O0010O01O010O0101N101N101N2O0O2O0O2O1N101N101N101N2O0O2O0O2O1N101N1O2O1N101N101N2O0N3L3M4L3N3L4L3M4M2M5K6J5K6K4K6K5K4L5J5M3ON10001N101O000O2O001N10001N10001O0O2O000O2O000O2O001N10001O0O2O000O101O0O101O000O2O000O2O00001O000O2O00001O00001N1000001O00001N10001O00001N1000001O0000001O0000001O00001O0000001O00gFfJl8Z5RGjJl8V5RGmJn8R5oFRKP9n4nFVKP9^5O101N1O1O1O2N1O101N1O1O2N1O1O2N100O00O1O100O1O1O2N100O1O1O100O2N1O1O100O1OROZM", "size": [480, 640]}, "area": 205928.0, "iscrowd": 0, "image_id": 15278, "bbox": [0.0, 40.0, 640.0, 433.0], "category_id": 183, "id": 20000939}, {"segmentation": {"counts": "k^i526OX>1dAO0;[>701O0000000001O2N1O1O2M2O1N2O1O05K7^AAU>e010O1O000L3L5L2M2N3N2M2N3M2O2M2N3M3N1N3OO3M3M3M3M3M3M4L3M3M3O1O1O1O100O1O2N1O1O1O100O1O1O1O2N1O100O1O1O1O1O1O101O01O001O01O010O010OdEGR6:lIIS66fIFVL7T1Id7;QKHTL6U1Jd77TK3iLDDOE6g85TK5kLH^O1c81TK9hL9o7_OXK?bL4DRO_8:[Kc0VLSO8R1T8YO^Ki0YLOX8WO_Km0VLMZ8VO`KT2`4lM_KU2`4kM`KU2a4kM^KU2b4kM^KV2a4kM_KS2c4lM_KS2`4nM`KT2^4kMcKV2[4kMfKT2Z4kMgKV2W4kMjKU2U4jMlKW2R4jMnKV2R4iMPLV2o3jMRLV2n3jMRLU2n3kMTLT2l3lMULS2j3mM[Lo1e3QN^Ll1a3TNcLi1]3WNfLP1\\KYOn7GkLa0dKFa7InL4mK1T7KSMGUL<h6M]N1b1ObNN]13eNI]16fNHY18jNFV1:g6010O010O010O010O010O010O01OCGTB9m=HjAO48S>IgAO0147V>NjA1V>0iA0X>0hA0X>0gA0Z>0eA0\\>0dAO]>1bAO_>81O1O1O100O1O1O1O1N2O2M2OmZU1", "size": [480, 640]}, "area": 5558.0, "iscrowd": 0, "image_id": 15335, "bbox": [395.0, 163.0, 165.0, 253.0], "category_id": 105, "id": 20000940}, {"segmentation": {"counts": "Rc[23V?NG7L3N1100O01M3L3M4L3M2N2N1O2N1O2N1OUOdB4Z=LhB3W=MlB2R=NQC0n<1SCOk<1XCMg<3\\CLb<4`CK_<5dCH\\<8V1001O000O10000000001O000000000000001O000000000000001O0000000000001O000000000000001O000000000001O01O000000000000010O0000000000001O0000000000001O3M3M3N2M3MO1L4L4M3M3M3M3M3M2N2Nff;O^YD000000001O0000001O0000010O00000010O0000010O001O0010O010O1^OOTB0k=3SBNm=3RBLm=9PBGm=OjA?4CQ>d0kA[OT>j0O1N101O0O2ERObBi0`=[O^Bd0d=\\O\\Bb0f=_OXB`0i=AVB>>ZOm<o0UCSOh<l0ZCVOd<h0^CYO`<g0aC[O]<d0dC^O]<=fCCY<<hCFV<9kCJS<5mCNP<2PDNP<1QDNP<2PDNP<2PDNQ<1oCOQ<KUD5m;EWD;i;CYD<i;AYD?T=0000000000O10001O00000O1000000000000O10000000000O10000000001O0O10000000000O1000000000000O10000000000O2O00000000000O10000000000O1000000000000O101O00000001O000000000000000000000001OO101O0001O00000000000000000000000001O00000000000001O00000000000001O000000000000000001O0000000001YON^B2b=O]B1c=2ZBNg=5UBKT>0hA0\\>0`A0c>1YAOi>32N000000000001O00000000000O10000000000000001O0001O1MVANj>0VA0fU_1", "size": [480, 640]}, "area": 4010.0, "iscrowd": 0, "image_id": 15335, "bbox": [161.0, 106.0, 379.0, 94.0], "category_id": 123, "id": 20000941}, {"segmentation": {"counts": "^o71m>3M5dAJg=:UBJg=:VBLd=6WB6OZO]=j1D7oBmMi<_2F7I<D4UDkLd;Z3O7I6K1M_LREQ3P;iLTEU3`;lLVDi2R<TMXD^2_<@`0B>C9F9G4M3L4M3N2O2M[k11fTN001O1N3K4N2N2N0]OBUB1;?_=G^B9c=H\\B8d=I[B6e=KZB6f=JZB5_=B\\B=42_=3aBL^=6bBJ]=8bBG]=;bBF]=<bBC^=>bBB^=?aB@b=n04K3N1N7J00000000000000O1O1O1O100O1O10nNTBP1l=POUBo0n=0O11O001OO100O11OO1O10001N100NVOWBG1g0i=^O]Bb0b=]ObB`0^=@dBLE>f=GiB9W=FiB<U=ElB:T=FkB;T=FlB:T=EmB<R=DnB<R=CoB=Q=CoB=Q=CoB>P=BPC>P=B_B36;[=B]B669]=0_B2`=g0000mNeBa0[=_OfBa0Y=_OgBb0X=^OhBc0X=\\OiBd0V=[OkBf0T=XOoBh0P=XOQCh0n<XOSCh0m<WOTCi0k<WOVCi0i<WOWCj0i<UOXCk0g<UOZCk0e<SOhBOd0m0e<SO]C7^O:l=EWB8i=HZB5f=K^B2b=0g01N1O0_OF\\B:d=HZB8e=JZB5g=KYB5h=KWB0ELW>5SBNQ>1oAOR>5hAKZ>6bAL_>:0O2N2N2N2O2N100001N014[AFZ>;HEUB>h=DUB=g=KUB4l=MPB6o=>0O101O0O101O0O100O2O2N000000O1000000001O2N3MO1001O2N00001O00O10000O100O100O1O2O1N1O2N2O1O1N2O0O2OLTBUOk=k0WBTOh=l0YBTOe=m0]DTO`9l0^FWOa9i0[EVOJ6j:d0\\EXOSOi0a;N]EZOoNk0c;K^E?a:A`E9e:E]E3k:LVEMQ;MjC^OW1b0R;MjC@U1a0S;KdCDa1`0m:HXE6j:IVE7l:HTE6n:JSE4o:LQE2Q;NnD2R;NoD0S;0lDOV;1iDOX;1gDJ\\N2o<4gDKZ;3gDL\\;1_Qg0O\\PYO3^O`00000001OZBCh<=SCGo<:hBKY=n0HeNlBZ1S=iNkBV1U=jNmBU1R=kNQCT1l<mNXCQ1f<oN[CR1c<nN^CS1`<nN`CS1^<nNaCMCQ1i<^OYC@0R1f<^OZC@1R1c<_O]C_O0R1b<^O`C_OOT1`<[OlCf0R<ZOoCg0o;YORDh0l;XOVDf0k;XOVDg0k;XOVDi0h;WOYDh0h;YOWDe0YOlN_<b0VDb0l;_OSD`0m;CPD>P<CmC>S<DkC=U<DhC>X<DeC<\\<E`C40UO`<i1dCUN]<k1cCUN^<j1cCTNa<n0XCH8YOb<1WCf03NQ=[OoBb041U=MnB2R=LPC3Q=LPC4P=JSC8k<EWC;h<FYC9g<FZC;e<C]C=b<C`C<`<]OgB4k0>a=001O002N0010N1NgN^OYD`0g;BYD;i;GVD7j;JWD5i;KXD3h;MZD1g;OZDOf;1\\DMe;3XDOh;1UD4j;LZD0e;1^DLb;3bDK];5g10O010O10O01001OO1O5K10O100001O4NM100000001O000000000010O01O001O10O01O00010O1O0010O01O1O2O0O00100O1O2O0O010O01O00001N1fBN`;9]DJb;6\\DMb;5[DMe;5VDOi;2TD1k;0RD2n;0nC3Q<NlC4T<\\1O1N3N1L401O0M301OZNcCi0_<UOaCk0`<SObCl0^<ROdCn0_<nNbCR1`<lNaCS1b<jN^C`0JHj<F\\Ca0MGg<G^Ca0MFf<H]Ca0NHd<F_Cb0MHe<D`Cb0LJV=5kBKY=OjB0V=NlB2W=JjB6\\=BfB>Y=ChB<W=DjB<V=BlB>k=01O01O000O2O00001O001O1O001O1O0O2N2H8A?K4B?M4I6^Oc0VOWMoDI3Q3m:WMnDJOMOV3S;e0N201OJ5F;D<nNR1L4L4iNkB_O3e0[=Mn0N1M301L31\\Yg1", "size": [480, 640]}, "area": 14370.0, "iscrowd": 0, "image_id": 15335, "bbox": [17.0, 7.0, 505.0, 142.0], "category_id": 132, "id": 20000942}, {"segmentation": {"counts": "cV;3l>01000O0100O01000O010000O001L4L4K5L3K6J6BTObBR1]=90O0100O10000O010O100O100O10000O100O100O10000O10000O100000000O1000000O100000001N101O1O001O1O001O002N1N3N1O2N5K0000000000001`CaM]<_2cCdMZ<d2MO1O11OO11O1OO1002N00000001O0O2N101O0M42M012M100O2O0hCYMU<j200O10001O1O4LO1O1O1O1O1O1O1O1O001O00001O001O001O001O001O00001O001O001O001O001O00001O001O001O001O0000001O000000000000000000000000@hMXDX2i;hMVDX2k;iMSDW2n;jMPDV2Q<jMnCV2S<kMkCU2V<kMiCU2X<lMfCT2[<mMcCS2_<lM`CT2a<51O1O1O1O1O1O2N1O1O1O1O1O1O2N1O1O1O1O1O1O1O2N1O1O1O1`NcB[1^=dNcB[1h=I1O1O1O2N2N2N1O2N2N2N2\\OdA`0f>FZAJg>4[]e5", "size": [480, 640]}, "area": 14525.0, "iscrowd": 0, "image_id": 15335, "bbox": [23.0, 370.0, 229.0, 110.0], "category_id": 137, "id": 20000943}, {"segmentation": {"counts": "djR71m>3O1O002N1O001O001N2O00011M110N101O001O1O3M1O001OO04NO00O3N1O1O1O3M003M1O4L2O2M1O000O10000000000000000000000000000000000001O00O11O002N1O1O002N1O1O1O2N2N1O1O1O1O1O1O1O1O2N002N2N1O2N001O1O1O00002N001O2N001O001O2N1O002N1OQbe0", "size": [480, 640]}, "area": 3040.0, "iscrowd": 0, "image_id": 15335, "bbox": [483.0, 432.0, 110.0, 48.0], "category_id": 139, "id": 20000944}, {"segmentation": {"counts": "aQ_32n>1O7KO000001O1O1O00100O1O1^AB_>g0gAYOj=g0TB[Ol=l01O2N1O1ZOZB0[>00000000000000000000000000000000000000000000000000000000000O10000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000O100000000000000000000000000000000000000000OQ`f0LWQYOOk>4UALh>600000000000000O100001O2N00O100N2N2O1O100O100O10000O100O1N2N2M3N2N2M3N2N2M3L4H8H8I7L4M3M3L4M3N2O1M3N2N2N2NWMPDf2o;YMTDf2T<O1O1O1O1O002N1O001O0000000000O1001O2bN_C9c<C`C<`<DaC;c<A]C?d<hNXCf06`0f<^O[Ca0f<^OZCb0f<^O[Ca0e<_O[Ca0e<_O\\C`0c<@_C?`<B`C>`<B`C>^<EcC9]<FdC:^<EbC:_<DbC<^<DcC;^<DbC<_<CbC<_<CbC<^<DeC9[<GfC8Z<HfC8Y<IhC6X<JgC7Y<HkC5U<LjC4V<LjC4V<KmC3R<NoC1Q<OPD0P<0QDOo;1TDLl;4TDLl;4WDIg;9ZDFf;:^DBb;>`D@a;`0aD]Oa;a0a1N001N10001O00001O00001O00001O001O00001O00010O0O110O02N100O002O0O1O101N2O0O1O100O1O100O1O2OO02N2N1N3N001N2O1O0O102N0O2O2N0O2O001N3N1O0O3N1O1N10000O10000OVOk0_Oa0N1O2N2O1O1O1O1O10000O1000000000000O100000000000jT6", "size": [480, 640]}, "area": 8162.0, "iscrowd": 0, "image_id": 15335, "bbox": [236.0, 384.0, 390.0, 96.0], "category_id": 165, "id": 20000945}, {"segmentation": {"counts": "0e4[:1OM3O1N2O100001O0000000000000000nKaEc3_:[LdEd3\\:ZLiE4Jn2]:mLmE1Jo2Y:oLQFOLl2S:UMSFJ6OZOe2]:bM]GS2c8lMdGJmMR2_:TNPHb1P8^NWH[1i7eNcHo0]7QOgHKkLl0^:ZOlHd0T7]OSI;m6FYI3g6OZIOe61jELQ32U75dEMU3OW7g0eEPOi2<`7k0dESO^24l7`1oG]No7m1mGPNP8Z2kGcMS8c2mGYMQ8l2o100000000001O00N2O1N2^OmCUNU<k1kCTNV<l1kCQNW<o1jCoMW<P2lCmMU<P2oCnMR<Q2PDnMP<Q2`0O1\\OSCdN1;n<P1XCmNi<S1XClNh<T1YCkNg<T1ZCkNg<U1YCkNg<m0PCnN=4d<m0aCSO_<k0cCUO]<j0eCUO[<i0gCVOZ<i0hCVOX<i0iCWOW<i0jCVOV<l0n04L2N1O6J00000000000000O1O1O1O100O1O10VBlNh=T1XBmNg=V10O11O001OO100O11OO1O10000O100N_BnNG1^=R1gBTOX=l0gBXOV=h0jBZOLET=P1QC_Oo<a0PC_OQ=`0PCAo<?QC@P=?QCAo<?PCBP=>PCBP=>oBCQ==oBCQ==oBCQ==oBCQ==oBRO36n<h0oBPO66l<j0]CROd<n0g0000[BWOS=i0mBXOR=h0nBXOR=h0nBXOR=i0mBXOR=h0mBYOS=g0kB\\OT=d0lB]OS=c0mB^OR=c0mB^OR=b0nB_OQ=a0oB_OQ=b0nB_OQ=a0oB@P=`0nBmNOd0m0\\OV;T1mCB7^O:La;l1TD[N8Kc;i1UD_N5Je;f1VDdN2Gg;e1YDTOd;m0]DUO_;k0bDWO[;i0eD[OW;4_DUO:j0T;1dDSO8P1P;LjDSO5U1n:HmDSO5Y1j:EQEQO0ELk1P;_OUEPONh1i:YOXEoNOb008f:8_EhNKh0U1XO]9Y1dEcNLj0W1YOdNMb:^1mE[Og;e0XD[Oi;f0UDYOm;h0QDXOP<i0nCWOS<j0lCTOV<n0hCQOY<P1gCnNZ<S1fCjN\\<W1eCfN\\<Z1dCeN]<\\1gC\\N\\<d1eC[N[<X1ZCPOV=P1lBmNU=o0SCmNm<S1TCiNo<V1>00001O00001O0000001O2N000000O1000000001O2N3MO1001O2N00001O00O10000O100O100O1O100O1O1O10000O100O10WCiNi;W1WDkNg;U1YDlNf;S1[DoNc;Q1]DoNDOV;R1VEPOB0W;P1WESO^ON[;o0VEWO[OK_;n0VEZOVOJd;l0VEKi:5UEMk:3TENl:2nDiN^OW1d;0kDlN@U1e;OgDhNDa1e;HcD=];CbD=_;D`D<`;D`D=_;DaD<^;EbD:^;FbD;];FcD9];HcD7];JcD5J\\Na;`1eD6Z;KdD6\\;LaD5_;f10O1AcD[M];]2oD^MR;`2h0M3L4K5K5L4O1O1O1O1O1O1O1O1O1N2N2N2N2O1001O00000000000000000000000000001O1O1O1O1O1O1O1O1O1O1O001O1O1O2N1O2N00HTC]Nk<b1XC\\Nh<c1XC_Ng<`1YCcNe<[1\\CiNa<V1_CkNa<T1_CmNa<R1`CnN`<Q1aCnNMC\\<^1SDdN@0\\<\\1TDdN@1[<Z1VDfN_O0Z<Z1VDhN_OOZ<Z1UDROj;n0VDSOi;m0WDTOh;l0XDVOf;k0XDVOh;j0WDWOi;h0WDYOi;g0XDXOh;g0[DWOe;i0\\DVOd;i0_DTOb;l0_DQOc;n0_DPOb;P1_DmNc;S1_DjNb;V1_DfNd;Z1Q1001O3_C`N\\;a1fC`Nf03b;]1gCcNb04f;`1XDcNg;]1WDeNi;[1VDfNj;Z1TDiNk;X1RDjNn;U1SDlNl;T1SDmNm;S1QDoNo;P1QDROn;n0kCYN4k0P<o1000000000001O00eNmCJR<5PDKo;5SDJl;5UDLj;4VDMi;2WD0h;0XD1g;NYD4f;LZDGMSOl;U1WDFV<:jCBZ<=gC_O]<a0bC]Oa<c0_CZOd<f0\\CYOe<f0[CZOf<f0ZCYOg<f0ZCYOg<g0XCYOi<f0XCYOi<g0WCXOj<i0UCVOl<i0UCWOk<h0ZCSOg<l0ZCSOg<m0XCSOi<m0WCROj<n0VCQOk<o0VCoNk<Q1b02N000000001O00000000001O001O001O1O001O00001O1O001O001O1O2N1O001O1O1O2N1O001O00000000O1J600O1000000000000O1N2O1L40000M3000VCgNm;[1QDeNo;\\1oCfNP<Z1nChNR<[1jCfNV<\\1hCeNW<^1fCbN`0Jl:f1bD`Na0Mk:c1cDbNa0Mj:b1dDaNa0Nk:a1cDbNb0Mk:b1aDcNb0Lm:S2REnMn:V2lDmMS;S2kDoMU;T2gDmMY;Y2_DiMa;V2`DkM_;T2aDmM_;S2_DoMa;h201O000000O100000000000000000000O1O1I7B>L4B>N2K5_Oa0XOeBAI3b=<fB_OJOMOf=c0e0N20000O1000000000VBDP=<j01RB_O[=a0bBB^=>bBB^=?^BEa=;`BD`=n0000001O0000001O00001O00001OO1O1N2O1N2N2M3M3O100O10000O100O100001OAkAIM:f>IelV1", "size": [480, 640]}, "area": 34477.0, "iscrowd": 0, "image_id": 15335, "bbox": [0.0, 0.0, 557.0, 158.0], "category_id": 172, "id": 20000946}, {"segmentation": {"counts": "e>;k:j2XEhM`4_8aKaG^4`8aKaG]4`8cKaG\\4_8eKaG[4_8dKbG\\4]8dKdG]4Z8dKfG\\4Z8cKgG]4X8cKiG]4W8cKiG]4V8cKkG]4T8cKmG]4S8cKmG]4R8cKoG]4P8cKQH]4o7cKQH]4n7cKSH]4m7bKTH^4k7cKUH]4j7cKWH]4i7cKWH]4h7dKXH\\4h7cKYH]4f7dKZH\\4f7dKZH\\4f7cK[H]4d7dK\\H\\4d7cK]H]4c7cK]H]4b7dK^H]4a7bK`H^4`7bK`H_4H_K`62hIa4D^Kd62hIb4A\\Kh61hIl3lN_La0Eh63kIe3VO_L3Jh67oI]3^O_LHMh6;RJU3H`LZO0h6`0VJl21aLmN5g6b0\\Je2`0iLP5g0`J\\2c0mL\\4LkIQ1V1S2[7YNeHe1\\7\\NdHc1]7\\NdHb1]7_NcH`1^7`NbH^1`7bN`H]1`7dN`HZ1b7fN^HX1c7hN^HW1c7iN]HU1d7lN\\HS1e7mN[HR1f7nNZHQ1f7POZHP1f7POZHo0f7ROZHm0g7SOYHm0f7TOZHk0g7UOYHk0g7UOYHj0g7WOYHi0g7WOYHh0g7YOYHg0g7YOYHf0h7ZOXHf0g7[OYHd0h7\\OXHd0h7\\OXHc0i7]OWHc0h7^OXHa0i7_OWHa0i7_OWH`0j7@VH`0i7AWH>j7BVH>j7BVH>j7BVH>j7BVH>i7CWH=i7CWH=i7DVH<j7DVH<i7FUH;k7FRH<n7DQH=o7DoG=Q8DmG=S8ClG>T8CiG?W8AhG`0X8BeG?[8BcG?]8C`G>`8C]G?c8CZG>f8n20000000000000001O000O101O0000001O0000001O01O0010O01O0010O01O0010O100O100O1000000000000000000000001O2M4M2M4M2M4M2M4M2M4LiNnGcKP8[4RIhJj6V5]IiJ`6U5cIlJZ6S5hIPKU6m4oIUKm5j4XJUKf5i4_JWK]5g4hJZKU5d4lJ_KQ5`4PKdKl4Z4VKjKg4T4ZKnKd4Q4]KPLc4m3_KULj2SO]Lg4j0WL^4g3cK[L[4e3eK\\L[4b3fK`LX4`3hKaLX4^3hKgKPN3V6U4kKhKQN2S6V4lKhKTN1o5V4mKiKWNOk5W4oKkKWNNi5V4PLlKZNMd5W4RLlK]NK`5X4TLmK^NJ]5Y4ULmKaNGZ5[4VLnKP5R4PKnKP5R4PKoKo4Q4QKoKo4Q4QKoKo4Q4QKoKo4Q4QKoKo4Q4QKoKo4Q4QKoKP5P4PKPLP5P4PKQLo4o3QKQL_4D\\I[4U2QL`4DZI[4V2QLa4EWIZ4X2QLb4FTIY4Z2QLc4FRIY4[2QLd4GoHX4]2QLe4HlHW4_2RLe4GkHW4`2RLf4HhHV4b2RLY1CA4cLW4c2RLU1FG]4TOmKR1HLZ4ROnKc06=k3POoK>:d0f3nNPLa06c0i3lNRLb02d0k3jNSLe0Md0o3gNTLh0Id0Q4dNVLj0Fd0T4aNVLn0Bc0X4^NVLU5i3kJWLV5i3hJYLX5g3gJYLZ5g3eJYL]5f3bJZL_5f3`JZLa5e3_J[Lb5e3]J[Ld5e3[J[Lf5e3YJ\\Lg5d3QJkKUNa0k7d3aHmKg01lN>n7b3^HWL`0KSO<P8c3ZHYLINN2N4R8e3UHZLI`00AS8g3oG_MMjNU8i3jG_M0WN0?W8`5PHaJP8V5XHjJj7m4]HSKd7i4_HWKb7f4`HZKa7d4`H\\Ka7a4aH_K`7_4aHaK`7\\4bHeK^7Y4cHgK_7V4bHjK_7U4aHkK`7S4aHmK`7Q4aHoK`7P4`HPLa7n3`HSLd1^OP3^4]KSLb1@R3[4]KUL`1@V3X4[KXL^1AX32`J_3j0nL\\1Co3^3fJoL3_OH5_5n2`IVMV181CG1c5f2kIYMk0>1BC2f5c2^KlMXO^OB4g5`2bKQNTO[O@5j5[2fKYNnNVO@6m5V2jKaNhNSO]O8Q6o1oKiNbNPO[O9T6j1SLkNcNPOTO;W6i1SLhNhNTOkN<Z6g1TLeNXODe4f1TLcNYOGd4e1TLaNZOKb4c1UL_NYO0c4`1VL\\N\\O1_4b1VLZN@2Z4c1VLYNF0U4f1ULXNKOQ4g1ULXNOOl3g1VLXNP7f1V3N2O1001O0000000000000000000dE\\N\\7d1P30cE\\N^7d1o20bE\\N`7e1_H[Na7f1]HZNd7g1[HYNe7h1i21O1`EVNh7k1WHUNi7l1VHTNj7m1RFSNb10\\8n1QFTNa1N^8o1QFTN_1N_8b2`G^M`8c2_G]Ma8d2^G\\Mb8e2_F\\MO0`9f2ZF`M6J`9h2TFbM<F`9l2fEgMk0]O_9c3aF^L^9b3bF^L^9a3dF^L\\9b3dF^L\\9c3cF^L\\9c3cF]L]9d3bF\\L^9e3bFZL^9g3aFZL^9h3`FXL`9i3`FVL`9k3`FTL`9m3_FSLa9o3^FQLa9Q4]FoKc9S4\\FmKc9U4[FlKd9V4[FiKe9Y4YFhKf9[4XFdKh9a4SF`Kl9e4PF[Ko9k401O1O001O001OK5N2N2NWIZK\\3d4eI]K\\24n3]4iI`KT28Q4V4mIdKl1<U4n3QJhKe1:]4m3oIjK_19f4l3lImKX12cNFm5Y4ZJQLR2Ib3U4]JTLo1Hc3S4_JXLk1Fe3R4`JZLi1Dg3R4`JZLi1Ef3P4bJZLi1Fe3P4bJZLi1Gd3o3cJZLh1Hd3o3dJYLh1Ic3h3kJ_Lb1Ic3d3oJcL^1Jb3a3SKcL\\1M`3^3VKeLZ1M\\3HoIb3]1iLX1NW3IVJ^3]1kLU1OQ3N^JW3]1lLT10g24jJn2]1nLS1O]2=SKf2^1nLT1NS2c0]Ka2\\1mLV1Nj1j0eKZ2\\1nLV1Nb1o0mKT2\\1oLW1LZ1V1TLn1\\1PMW1LR1Z1]Li1[1QMW1Lk0\\1fLW1C^Mf13X12<Y1UMQ1G_M_14[1:MT1eMk0L_MW18\\1a0PM7V1h0]Oe01`MP1;^1V2bM]O1?5aMi0=`1b2oLSOf0:m1QN`0g4eMWMl1RN`0d4fMYMk1SN`0`4hM\\Mi1SNb0]4gM_Mh1TNb0Z4hMaMh1TNa0W4jMdMf1UNb0S4jMgMe1VNb0o3lMjMc1WNb0l3mMmMa1VNe0g3nMRN^1WNe0a3RNWNZ1XNe0\\3UN[NW1YNf0U3XNaNS1ZNf0P3[NeNP1[Nf0j2_NjNl0\\Ng0d2aNoNi0\\Nh0`2cNSOf0]Nh0]2dNVOd0]Nj0Y2dNZOb0]Nk0V2eN]O`0]Nl0S2fN@>]Nn0o1fND<\\NP1m1fNG:\\NR1i1fNK8\\NS1f1gNN6\\NT1c1hN15[NU1`1hN53[NV1^1hN81ZNX1[1iN;OYNZ1Y1iN>MYNZ1W1jNa0LXNZ1U1lNc0JXN[1R1mNg0GWN\\1P1oNi0EWN\\1n0QOk0CVN^1l0QOn0AVN^1j0SOQ1^OUN]1j0VOR1]OTNY1l0\\OP1[OTNT1n0Co0YOSNo0Q1Jm0VORNk0S12k0SOQNh0V16j0ROPNc0Y1=g0POPN>[1d0g0mNnM:^1k0d0kNnM6_1R1c0iNmM0c1X1b0gNjMMf1_1`0dNjMHi1e1>cNiMDk1k1=`NhM@m1S2;]NhM[OP2Y2:[NfM[Om1^2=WNfMZOj1b2b0SNcM[Oh1f2e0oMcMZOe1j2i0lMbMYOb1l2P1jM^MYO_1o2U1WL]MMOl0\\1R3[1QL^M0Kl0Y1T3a1nK^M1Il0T1W3h1iK]M4Gk0Q1Z3o1cK\\M7Dk0P1Z3T2`K\\M:@k0o0[3X2]K[M=^Oj0n0\\3]2YKZM`0[Oj0m0X3f2QK^Mm0oNi0m0R3^4VLdJg0m0m2g4\\L\\Jf0l0k2n4^LVJg0j0i2T5`LRJf0i0i2X5aLoIe0h0i2]5aLkIe0g0h2b5cLgId0g0g2e5eLdIc0f0g2j5eLaIb0d0i2m5eL_Ia0c0i2Q6fL\\I`0b0j2T6fLZI?a0k2W6fLXI??k2[6fLVI>?k2]6gLTI=>l2`6gLRI<=m2c6gLPI;<n2f6gLnH:;o2j6fLkH::P3m6fLiH99P3Q7gLfH89P3S7iLcH79o2V7jLaH6:n2W7lL_H5:n2Y7mL^H3:n2Z7oL\\H39l2^7QMYH29l2`7RMWH29j2b7TMUH19j2d7UMSH19h2f7WMQH09g2j7XMmG19f2k7YMlG09g2l7YMkGO:g2m7YMiG09g2o7YMhGO:g2o7ZMgGO9g2R8YMeGO:g2R8ZMdGO9g2U8YMcGN9h2V8YMaGO8h2X8YM`GN8j2Y8XM^GN9i2[8XM\\GN9j2\\8XM[GN9i2^8XMYGN9k2_8VMXGO9k2`8UMWGO9m2a8SMVG09n2`8RMWGO9P3a8PMVG09P3c8nLTG19S3c8kLTG29S3e8iLRG39V3e8fLSG38W3g8dLQG48Y3h8bLPG57[3l8\\LmF88\\3o8XLiF;8_3_8ZLYGH0?8`3S8hLeGUO0b08f3c7RMYHXO4i3^6SN\\ISN6n3W6TNbInM7Q4o0\\Ke3i2UKiM7W4`0dKk2DUMR3XO_M8Z43mKW3W3^LaL8_4FULb3j2aLbL7i5V3eMcLaL7n5S3aMgL`L6Q6R3^MhL`L6U6P3[MjL`L5X6o2XMlL_L6[6l2VMnL_L5^6k2SMPM^L6a6i2PMQM_L5b6j2oLQM^L6c6i2oLQM^L5e6i2mLRM]L6f6i2lLQM^L5g6j2kLQM]L6i6h2jLRM]L5j6j2hLQM]L6k6i2hLQM\\L6n6h2fLSM[L5o6i2eLRM[L5Q7h2eLSMZL4S7g2dLUMXL4U7e2eLWMVL3W7d2dLYMTL3Y7b2eL[MRL2Z7a2[L^MXL120]7]2PLbMmL1F0]7Z2gKdMcM2XO0_7Y2iLfMhK0a7Y2gLgMgK0c7X2gLhMfKOd7Y2fLoM\\3P2dLoM]3P2dLoM]3Q2cLnM_3P2bLPN^3P2bLoM_3Q2aLnMa3S2]LlMd3U2[LjMf3W2YLgMj3Y2iIdM=2k5[2gIgM:LQ6^2bIeM`0LP6a2SHbMg01c9m2ZFRMj9o2SFPMQ:o2lEQMe1NR6P3WHSMe10V6k2THTMf12X6h2RHVMd14\\6e2oGVMe13b6f2gGWMe13h6f2aGWMe12o6g2ZGVMg12T7g2SGWMg12Y7c2SG[Mb11`7_2QGaM]1Of7_2lFcMY10n7\\2jFdMT10W8[2eFfMP1O_8Z2aFgMm0Nf8[2^FfMi0Om8Z2ZFgMg0NS9Z2WFgMc0NZ9[2SFfMa0O`9Z2PFfMX;Z2gDfMY;[2f0000lCeMb;]2ZDcMi;]2<2cCaMW<_2fCaM]<]2310O10O10O10O10O10OaNZDFf;IkD7U;GmD9S;EoD<P;BRE>n:ASE?m:@TE`0l:_OUEa0k:^OVEb0j:]OWEc0i:]OWEc0i:]OWEc0i:\\OXEd0h:\\OXEe0g:[OYEe0g:[OYEe0g:[OYEd0h:\\OXEc0i:]OWEc0i:\\OXEc0i:]OWEd0h:\\OXEf0f:ZOZEf0f:ZOZEe0g:[OYEe0g:[OYEd0h:\\OXEd0h:\\OXEc0i:]OWEc0h:_OWE`0j:@VE`0j:@VE?k:AUE?k:AUE>l:BTE>k:CUE<l:ESE;m:ESE:n:FREm4[6SKeI", "size": [480, 640]}, "area": 212218.0, "iscrowd": 0, "image_id": 15335, "bbox": [0.0, 15.0, 640.0, 465.0], "category_id": 183, "id": 20000947}, {"segmentation": {"counts": "ob5110T=200000002\\K5gKKX46hKKW46hKKW45iKKV47iKIW47iKIW47iKHW4:gIFGOc6;aH1EEW10a6<bHOFEW10a6>`HNGDX10`6?hGC>:1FX1Mb6b0eGC0G0919:^OH:c1Gh6^1bGWO6[OL:d1F_6`2kG`M21N:a1F`NNT8a2lGaMO>g1@^6f2kGkMf1_O_6X3bIhL]6Y3i100O10O100000000O2O0WGRMX7o2RHfMn7_2PH^MQ8l35K5G9OL02MO110O2O000001O0O11NPLVGE0n37SLh8a1QG^N01:i1MWNR83XH\\1Nh0b7QN`HW1N?DUNk78RHD`0_1O=IVNf7<bHP1O=JXNd7;dHP1O:K[Nb7:eHQ1M9N]N^7;fHo0N70_N\\7<gHl0NM@mNb0MZ7=fHl0NM@kNd0OX7=fHE_Oo0?5@iNf03T7;iH@]O0003Q1=7ChNd04T7;SI=G:^OkNd04T79SI7N>4QOl6;SIL7=jNXOW12h6=PICRONa135NY6a0aL_O_3g0[LXOe3l0XLTOh3l0Y41O2O000O2O0O1O1HmNZDT1i2mNl5Y1mHgN[N1l02SOM0O_8[1WIhNYNO0Nk05UOOa8U1VIgNZN6OJ24NM0i0b8?PIjN^NM0`1a8JQI=o6BSI=m6CSI=m6CSI=l6CUI=k6DTI<l6DTI<l6ERI<m6FRI:n6FSI9m6GRI:n6GQI9n6GTI8l6HdGbN30K0c05]ON4e1Z8FcGdNO4b00^Ok1^8]ObGPO?HAk1^8OnG:R8GlG:S8HiG;W8EhG<X8EfG<Y8EfG<Z8Y20001N10000eHTKY6m4gIVKV6j4jIVKV6j4jIVKU6k4jIWKU6i4kIWKV6h4jIWKW6i4iIWKW6i4iIWKX6h4hIYKW6g4iIYKX6f4hIYKY6g4gIYKY6g4gIZKV6h4kIVKV6m4iIQKV6V5dIjJ\\6\\5^IeJa6`5ZI`Jf6d5VI\\Ji6e5WI\\Jh6c5YI\\Jh6d5XI\\Jg6d5ZI\\Jf6d5ZI]Je6c5[I]Je6c5[I]Jd6d5\\I\\Jd6d5\\I\\Jd6d5\\I[Je6e5[I\\Jc6e5]IZJd6f5\\I[Jc6e5]I[Jb6f5_IYJa6g5_IYJb6f5^IZJa6g5_IYJ`6h5`IXJ`6h5`IXJ_6i5aIXJ^6h5bIXJ^6h5bIYJ\\6h5dIWJ]6i5cIYJ[6g5eIZJZ6g5eIWJELe6n5fIUJ]6k5cIUJ]6k5ZISJ41b6m5ZITJ3Md6o5YIUJ2Le6o5YIUJ0Mh6n5WIWJOLi6n5aISJ]6m5eIQJ[6P6dIoI]6R6bInI]6T6bIoI[6R6dImI]6T6bIjI`6W6_IiI`6Y6_IgIa6Z6^IfIb6[6[IiIc6_3]I^LO30L15OT20gMb6c3cI]LN1N3O10o1b6]1jI`LF22NLV20kM2ON0R6b3YJ^LG07OGR11oN8b0HHP6S3oIcL=0E09P121d5[2eJeLEo030a5`2RKaM^ON`5]2WKfMWOMU5UOdJ[3m0bM[OMU5n2_KVM]OLS5n2_KXM]OJT5o2_KUM^OMR5n2`KVM]OLS5n2`KTM_ON@PO1040P5o3kKRMBN^OQO14ONT5l3nKSM_OSO_O710OF07T5h3lKPLPO?5H<O@35<m4[3TLYLSOKe0MZOc0j4\\3ZLQLa0b0U3]3[NcLe1Z3^NeLc1\\3]NcLc1V3dNjL\\1U3eNkL[1U3eNkL[1T3YMQMYNJ^4S3YMUMYNH^4S3ZMTMVNK`4o2^MTMTNK^4R3]MSMUNK^4Q3^MTMTN@ZO4T5[3\\MPMVN^O[ONO9T5Z3]MPMl3Q3ULmLmMDX5`3kLlLaMOd5R3eMmL[2S3fMlLZ2T3fMlLZ2T3fMmLY2T3fMlLZ2T3eMmL[2S3fMlLZ2T3eMmL[2S3eMmL[2S3eMmL[2S3\\J]LU3`0_2S3[J_LV3=_2T3ZJ_LX3>]2W3bMjL^2U3dMiL]2W3j3O100000000O100002NM300000dG^LR7a3kH^LnN1a18h4Y3hJ_LmN5^1:h4U3iJdL`07g4[3`JbL@CM1l0a0V5^3[J_LDH;1F<o5b3nIkKOO0c0OE0H12O1075:P6g3lIeKOQ10VO1n0S6W3lIPM0JT6m5OGmI[IU6e6kIZIV6f66G9K500O1O1000000000000000000000000000000000000000000000000N20000000000000000000000000000000000000000000000000000001O00001O0000001O00001O1O001O1O001O1O001O2N1eI^In5c6QJ\\IP6f6nIZIR6g6mIYIS6n600000001O3M5K2N1O000000001O00O11O00O1MVJhHj5X7VJgHk5Y72O100001O001OO11O001OO1M31O000RJjHj5V7VJkHi5]7M001O000000000000N[JaHe5_721O0000O1001OO100000000000000O1000000001O00000000000000000000000000O1001O0000000000O100000000000000O1C=C=C=C=C=O10000000000000000bKgHm2j800000000000000000001O00000YMdFb1\\9kMgFM0V2Y9lMmFGM\\2_9cMSGR2YOQNd:m1dElM\\:T2=O1O0O11ON31N0001O00001_OjDbN26T;W1jDdN07V;l0iDRO1K25U;l0k0O3NO10O20OO101O0O100O2N10dCVO[<0eC001090F15^<KcC56KH2_<NcC0N000R=0VRd1", "size": [424, 640]}, "area": 80858.0, "iscrowd": 0, "image_id": 15338, "bbox": [13.0, 0.0, 501.0, 260.0], "category_id": 96, "id": 20000948}, {"segmentation": {"counts": "V6l1l;@1O000000000000000000001fNSDW1o;0000006LN0J7O1O000O10O1O11O0000O1M300LjNUDW1i;kNWDU1i;kNWDU1i;kNWDU1i;5O10001O0000O1000N11O11O1O000L44L00O1O1001O00000000O11O1fN[Do0e;QO[Do0R<M000000001N10O2OO3NO1RDoNf;Y1OM2M4100O100O1000006D60cN`DR1k;0000O1K500000O1000000000000000000O10000000O100000000000O10000000000001O3M00001O01N106JN3M21N4M01OO1000N3N1N2O2OO101O10N100O1001N11OO20O01O00N2O11O00011N10N003OgCROW<o0NQOkCo0T<300O1O01N1N302M12M2OM6NN2M2000O111OOROhCm0Y<12ROgC2ON0?Y<BQD=]<01O0000O11O0O20O00DVC104l<KTC14Oi<0TC0dV60^VJ1mBOo<a0E011O00O101O00000000000000001O00O0011O00000000000000000000000001M10OO3N11O10001O000_OgC2h<N2000000000cC1a;O_D4^;LYD3AM04V<LSDOH602U<ISD8GOW<IRD8GOW<ISD7F0W<ISD7F0W<ISD7F0W<ISD7F0W<ISDa0m;_OmCg0S<700OTOnCd0R<ZOPDf0P<ZOoCg0Q<YOoCg0Q<YOnCh0R<XOnCh0R<XOnCh0R<XOnCg0S<YOmCg0S<YOmCg0S<YOmCg0S<YOmCg0S<YOmCg0S<YOlCh0T<XOlCh0T<XOkCi0U<WOkCi0U<WOkCh0V<XOjCh0V<XOjCh0V<WOkCi0U<WOkCi0U<WOkCi0U<WOkCi0U<WOkCh0V<XOjCh0V<XOjCh0V<XOjCh0V<XOjCh0V<XOjCh0V<XOjCh0V<XOjCg0W<YOiCg0W<YOiCg0W<YOiCg0W<YOiCg0W<YOiCg0W<YOiCg0W<YOiCg0W<ZOfCg0[<3000VOhCd0X<\\OhCd0X<\\OhCd0X<\\OhCd0X<\\OhCd0X<\\OhCc0Y<]OgCc0Y<]OgCc0Y<]OgCc0Y<]OgCc0Y<\\OhCd0X<\\OhCd0X<\\OhCd0X<\\OhCd0X<\\OhCd0X<\\OhCd0X<\\OhCd0X<\\OhCc0d;^OjDOBc0c;GcDFJc0c;HbDGIa0e;l000000O101O3M5K2N1O00000O101O00O20O0O01MoNoCP1R<POmCQ1S<2O100001N101OO20O001OOOO31IlCXOT<h0lCWOU<MmCh0N[OU<MnCg0M\\O]<`0dC^O^<b0bC]O`<b0300O1000000N210O1O2M3M4IoB1ffR3", "size": [424, 640]}, "area": 11867.0, "iscrowd": 0, "image_id": 15338, "bbox": [0.0, 198.0, 402.0, 83.0], "category_id": 97, "id": 20000949}, {"segmentation": {"counts": "fmi1`0h<1O00001O00000000000000000000O100O1L7J7K10cnU6", "size": [424, 640]}, "area": 335.0, "iscrowd": 0, "image_id": 15338, "bbox": [139.0, 382.0, 22.0, 18.0], "category_id": 132, "id": 20000950}, {"segmentation": {"counts": "R8=a0k3a7UL_Hk3a7UL^Hl3b7TL^Hl3b7TL^Hl3b7TL^Hl3b7TL^Hl3b7TL^Hl3b7TL^Hl3b7TL^Hl3b7TL^Hl3b7TL^Hl3b7TL^Hl3b7TL^Hl3b7SL_Hm3a7SL_Hm3a7SL_Hm3g7kKiGM`0X4g7kKiGM`0X4a7QL`HP4`7PL`HP4`7PLoGG?Y4b7PLoGG?Y4a7QLPHF?Y4a7QLPHF?Y4a7QLPHF?Y4a7QLPHF?Y4a7QLPHF?Y4a7QLPHF?Y4a7QLPHF?Y4a7SL_Hm3a7SL_Hm3a7SL_Hm3a7SL_Hm3a7SL_Hm3a7SL_Hm3a7SL_Hm3a7TL]Hm3c7SL]Hm3c7SL]Hm3d7RL\\Hn3d7SL[Hm3e7SL]Hk3b7WL]Hi3c7XL\\Hh3d7XLZHj3e7WL[Hi3d7XL\\Hh3e7XLZHh3g7VL[Hi3e7VL]Hi3c7VL_Hi3a7WL`Hh3`7WLaHi3_7VLbHj3^7VLbHj3^7VLbHj3^7ULcHk3]7ULcHk3]7ULcHk3]7ULcHk3]7ULcHk3]7ULcHk3]7ULcHk3]7ULcHk3]7ULcHk3]7RLPHHd0V4\\7RLeHo3[7QLeHo3[7QLeHo3[7QLeHo3[7QLeHo3Z7RLfHn3Z7RLfHn3[7QLdHP4[7QLdHP4\\7PLdHP4\\7nKQHIc0Y4e7gKXH0@Z4T8jK\\HX4_7mKaHS4_7mK`HT4`7lK_HU4a7hKQHJ;0E`4o7eKRHK:b4d7cKRHK:b4d7bKSHL9b4d7bK`H^4U8000000000C^KTHb4d7aKTHM8b4d7dKQHJ:c4e7eKQHH8c4g7eKQHH8c4g7eKQHH8c4g7eKQHH104c4j7eKQHH102e4l7fKQHE2f4m7eKQH_4o7=0000000000000000O1000000000000000SKRH`4n7`KRH`4n7`KRHG0e4n7dKRHE3f4k7eKXHZ4h7cKRHI7c4g7dKRHH8d4f7dKRHH80Hb4n7dKRHJ00;a4c7eKRHJ=a4a7dKSHK<a4b7aKcH_4]7`KdH`4Q8100000000000fNkGaMT8`2mG_MS8a2mG_MS8a2mG_MQ8XO_GY3a0^MP8d2PH\\MP8d2PH\\Mo7e2QH[MP8d2PH\\MQ8c2oG]MP8d2PH\\Mo7e2QH[Mo7e2QH[Mo7e2QH[Mo7e2QH[Mo7e2PH\\MP8d2PH\\MP8d2oG]MQ8c2kGaMU8b2eGaM[8PO`Ga30`Mb8`2^G`Mb8c3000000000000000000O1000000000000000000000000000000000000000[K_Gc4c8000000000O1000000000000000000000000000000O100000000000000000000002NO1O100O10000000000000000000000000000000000O1000000000000000000O100000000000000000000001O000[KcG_4]8aKfG\\4\\8bKdG^4c80O100000000000000000000000000000000O1000000000000000000O1000000000000O1000000000000O100000000000000O100000000000000O10000000000000000000000O1000000000000O10000000000000000O10000001O00O100O10000000000O100001O000000N200JUKoGk4Q8UKnGl4R8TKnGl4R8TKnGl4Q8UKmGm4S8SKlGn4T8300O1000000N2^OkJRIT5n6nJPIR5Q7nJnHR5R7PKkHQ5U7QKhHP5W7TKbHF1V5]7a01OO100000000000000O1000000001O00000000000000000000000000O1001O0000000000O100000000000QKhHn3X7eKUI[4j6YKcIg4_7000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000O1000_KbGM0U4]8hKcG310OU4]8gKdG4O10T4]8gKdG4O12R4^8mK`GL025NKS4a8oK`GNO2=k3T8ULbG0>N_Og3a8YL_G0k0f3h7ZLXHf3e80N200O10000000000000000000YLcFc3b9O1O00000000000000O11OO10000O1O1O1O100000000001O00000000000000O11OEYLSGi3j8ZLQGONh3P9<000000LkKUGU4j8mKUGS4k85LfKZGZ4f8gKYGY4c8fK^G1OY4d870000001OO1O100O10000_OiKoGY4Q8gKoGY4Q8gKoGY4Q8gKoGY4Q8?0000005KK500000000001O000000000000000000O11O1O000000O100001OO100", "size": [424, 640]}, "area": 96795.0, "iscrowd": 0, "image_id": 15338, "bbox": [0.0, 238.0, 640.0, 186.0], "category_id": 140, "id": 20000951}, {"segmentation": {"counts": "_8a0g<00O1000000000000000000000O10000N@\\C`0d<@\\C`0d<1100_O[C?e<A[C?e<A[C?e<A[C?e<A[C?e<A[C?e<A[C?e<A[C?e<A[C?g<00000000000001N1000001O02OO1O0N20001OO2N2N2O1N1O10000O10000000000000000M]O`Cd0`<11000000000000O1O10000M]C@d<`0200O1ONB[C;0Ef<1ZC:h<FXC:g<GYC9g<41O00000000ODYC9f<HZC8i<EWC:l<FTC8l<HTC8l<HTC8l<HTC104l<KTC102Q=MoB2R=N[Q70inH000lB0P=0nB3Q=3LJTC7k<ITC8l<HTC80Hh<0XC00;g<EYC=f<DZC<d<5O14KO2N21O3M00000O10N2OOZCBd<`0110O11000N0010O1001O00001O001OWCCg<?N2O11O00002N1OO1O11@YC=i<O10000000O1N2M300O12N1OM31O1O0000O11O1O1OYCAf<`0000000000O100000O11O00O11O001O000000O1000O10000000000000000000O111NO00100O10000000000000000000O10000000000000O100000000000001OO1N110O1O100000000O18H000001O000000JYCKh<1ZC0o<O1000000000Z\\d4", "size": [424, 640]}, "area": 4036.0, "iscrowd": 0, "image_id": 15338, "bbox": [0.0, 252.0, 281.0, 37.0], "category_id": 149, "id": 20000952}, {"segmentation": {"counts": "fQ<:d;Y1M400000O10000O1000000O100000000O10000001O5K5K5K002NN200O10000001O0000O100JoDUNQ;f1;M300O100O100000000001O001O0000O100O1O10000O100O1O1HWDPOj;P17O10000O1000000O1000000O1000000O10000000000O10000O1000000O10000O10000O1001O00001O001O0000N200O1000000O10000O1000000O1000000O10000O1001OO1O100O10000O1000000O10000O1000000O1000000O1000000O10000000000O10000O106JX`\\3<^^cLn000000000000000000001O00000]E`N^9`1oEcNM0R:]1PFiNGMX:c1gEoNY:Q2O001O1O0000001O00001O00001O00001O002NO1001O00001O0000001O001O00LoEQMQ:o24VMlE`2T:[MSFc2Y:00N11O1_E[M_:h2O0bEYM[:i2100O1LdE\\M]:j0bE[O1T1OgN30Z:b0fE_O8;E60@3Ma:a0nEA^O095GJ0020N2h:0YE0\\1OdNN;3[:OoFNf92h1N20^V63_iI00001O1O1O0000001O1OO100002NN21O0000O12N00001O00001O1O1O001O1O1O1O1O2N2N3M5K1O001O1eDQO[:o0^EoN]O1O2V;n0]EPO]O6V;j0WEoND=U;c0XEPOB=W;c0WE_Oi:a0XE^Oh:a0YE_Og:a0YE@f:`0ZE@f:?[EAe:?[EAe:?[EAe:>\\EBd:?[EAe:`0ZE@f:`0ZE@f:a0YE_Og:o1BO100000000O11O2NO1O100000XEcMe:`200eEaMm9f2iE`MT:a2lE_MS:m2N001O00O10000000000000000", "size": [424, 640]}, "area": 15919.0, "iscrowd": 0, "image_id": 15338, "bbox": [29.0, 0.0, 611.0, 102.0], "category_id": 157, "id": 20000953}, {"segmentation": {"counts": "foj0110h<?jDA^N01:c:6lFOT92lFNT9A\\E`0_1OW91hFOX92hFOV92jFMX92hFNY93eFNM@R8b0QHNM@R8b0jF_Oo0?5@R8c0eF]O0003Q1=7CQ8k0cGG:^OT8k0[GN>[OW8h0QG7=jNXO>[9a0gFRONa13\\O[9j26I4M000017H9G3NO0H8C=O1DUFZMk9_1UF[N1l02SOM0l9f1VFYNO0Nk05UOk9g1PFZN6OJ24NM0f:b1\\E^NM0];b162O000O11O001N2O01OO2OO2O`N\\D30K0c05]ON4c;M\\DO4b00^Oi;0_D?HA[<<:N2L3O2N1O_Pm0O_bROO30L15O]<5^CN1N3O10T<<hCF22NLY<;gCG07OGR11oN8a:G_E=0E09P12a99`EEo03b9c0_F^O\\9g0eFWO^9f0aF[O_9d0bF]O]9b0eF]O\\9c0bF^O^9b0cF]O]9c0aF_O`9`0_FB_9`0aF_OSO_O71c9o0RFPO?5H<O@35a9Q1_FSOKe0MZOh9T1XFa0g9`1M310H7O100OdNZGYNc8g1_GYNa8h1^GVNb8n1\\GTNc8k1]GUNb8l1^GTNe8j1ZGVNe8k1ZGUNh8l1VGmMR9S2nFaMZ9Y3O1O0001O0O11OO10000gLUFU3k9jLWFV3h9iLXFX3l9O02O?@2O0000000O100002NM300000[MgF[1X9bNgFnN1a18QOP9?hFmN5^1:oNn8b0kF`07mNU9:hF@CM1l0a0YO[98bFDH;1F<0a9MlEOO0c0OE0H12O1075:1f9KfEOQ10VO1n04V9KQG0J5l;0FK]C7c<I\\C8d<Kagg2OjXXM8I1O000O11ON22N0000000000@FfC268S<FhC07:h;EVD1K258i;k0O10000O11OO10000O100O1O10eNUDY1R;gNnD01090F15Y1k:iNPEO56KH2Z1j:fNVE3NO0N000i1l:bNTEb1R8TNeJY2V5nMhJR2U5QNkJo1U5QNiJQ2W5nMjJS2U5lMlJG[M\\2h7mMPKS2o4mMQKGYMZ2e7oMTKT2j4lMVKU2i4kMVKW28`MSM0U49\\NH\\Ma2g2^M\\M10OU4:bLF[O1T1OgN30_2e2_M^MO10T4:ZLI_O8;E60@3Mf2c2WM]M14O12R49ZL7A^O095GJ0020N2o2_2TMbMML025NKS4:PLG0\\1OdNN;3e2\\2TMcMLNO2=k3LUL]1NQ2^2^LdMN0>N_Og39[LW5U2kJiME0k0f3^OZLZ5i1WKWN1f3^OZL\\5Z1_L\\2m3VM^Ll2c3fLjLZ3a610000000000000000000YLcFc3b9O13L00001O1O1O000O110N2OO10O1O3MO12O0000O12N01O01O00001O1O1O0O200DiKcGZ4Y8iKbGONZ4^8=O2N2N3IQKoGT5k7nJTHS5k75LfJZH[5e7fJZH[5k0eJg5O_I1O[5d0cJ]O1O2c6a5PJ]J]O6c6]5jI\\JD=b6V5kI]JB=d6V5jIlJW6S5jIlJU6T5lIlJS6U5mIlJR6T5nIlJQ6T5PJlJP6T5PJlJP6T5PJlJ_5NPJW5b0kJ^5NPJX5a0jJ_5NPJY5`0iJ`5NPJY5`0iJ`5g5_JYJa5U7BO105KK5000O11O2NO2N100000]J^H`5e700jJ\\Hh4k7nJ[Ho4f7QKZHn4R8N0O20O1N100000O100001OO10kL", "size": [424, 640]}, "area": 39402.0, "iscrowd": 0, "image_id": 15338, "bbox": [65.0, 0.0, 575.0, 308.0], "category_id": 169, "id": 20000954}, {"segmentation": {"counts": "0k5]700001O00000000000000001O000000000000XLoHY1Q7fNPIZ1P7fNQIY1o6gNQIZ1n6eNTIZ1l6fNTIZ1l6fNTIZ1l6eNVI2FnNT7P1VImN1EE>T7o0XImNOFE?S7;QIkN:c0NGDa0R7PO[J5ROJC>:1Fc0e8TOjGHC0G0919:^OH:Q1_8TOfHHWO6[OL:V1l:hNjD21N:a1a:`NUEO>c1a:]N`Eb1R;1000O010000000000001M2B?ZOj0NXTd7", "size": [424, 640]}, "area": 6428.0, "iscrowd": 0, "image_id": 15338, "bbox": [0.0, 0.0, 50.0, 211.0], "category_id": 172, "id": 20000955}, {"segmentation": {"counts": "k5;m<`0@01O00000000000000001O0000000000000000001O000001O0001O2N2N3M4FX`>Mn_A4M2N2N2N2M3N2N2O100O1000000O100004K9H9G]=1TB=D=O1O100O1O1000000000O1000000000000000000O100000000000000000001N100000000000000000000O1000000000024K5J5K6J1O00O100O2O00000000000000000000000000000000000000001O01O000FUC7k<IXC4h<LYC3g<MZC2f<NZC0Pj09gB10O1O1O1O1O1O1O1OZRo08QmPO=cC@e;m0[DSO`;Q1aDPO^;P1bDPO];Q1cDoN\\;R1dDnN\\;R1dDnN\\;R1dDnN\\;R1dDnN\\;R1dDnN\\;R1dDnN\\;R1dDnN\\;R1dDnN\\;R1dDnN\\;R1dDnN\\;R1dDnN\\;R1=00000XDhN`;X1^DjNb;V1^DiNc;W1]DiNc;W1]DiNc;W1]DhNd;X1\\DhNd;X1\\DhNd;X1\\DhNd;X1\\DiNc;W1]DiNa;Y1_DgNa;Y1_DfNb;Z1^DeNc;[1]DeNc;[1]DeNc;[1]DeNc;[1]DeNc;[1]DeNc;[1]DeNc;[1\\DfNd;Z1\\DfNd;Z1\\DfNd;Z1\\DfNd;Z1\\DfNd;Z1\\DfNd;Z1\\DfNd;Z1\\DfNd;Z1\\DfNd;Z1\\DfNd;Z1\\DfNd;Z1\\DfNd;Z1\\DfNd;Z1\\DfNd;Z1\\DfNd;Z1\\DfNd;Z1\\DfNd;Z1\\DfNd;Z1\\DeNf;Z140001OVDgNe;Y1[DgNe;Y1[DgNe;Y1[DgNf;X1ZDhNf;X1ZDhNf;X1ZDhNg;W1YDiNh;V1XDjNh;V1XDjNi;U1VDlNk;S1UDmNk;S1UDmNl;R1TDnNm;Q1SDoNm;Q1SDoNo;o0QDQOP<n0PDROQ<3]D4BIS<1[D6BIT<0bDO]ONR<1_jW14PVhN=C=B>C=C=C=C=O10000000000000000000001O000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001O000000000000001O1O1O1N3N1N2O2M2O1O1N3N1N2C=B?A>B>A[lZ1", "size": [424, 640]}, "area": 15438.0, "iscrowd": 0, "image_id": 15338, "bbox": [0.0, 172.0, 536.0, 100.0], "category_id": 183, "id": 20000956}, {"segmentation": {"counts": "0m6S=00000000000000000000000000O1L400N200O100O10000000000O10000000000CmBlIT=S6=LVBQJk=n54001O00O100000000O100001OLRBVJn=g57GkAfJV>X5mAeJU>[5kAcJW>]5hAdJX>\\5iAcJW>]56O100000000001OO11O3M1O1O0000001O000000O10000O1N2001O001O1O001O001O001O000000O100O1O100O1003M2NO1000gA[JW>h5O00001O4mAXJ1N]=[6000A?00cNUBeLk=Z3fBVLZ=i3VChKj<W4XCiKg<V4[CjKd<U4_CjK`<U4bCkK]<T4eCkK[<U4fCkKY<T4iClKV<S4mClKR<S4PDmKo;R4SDnKl;Q4R2O1O1O1O100O1O2N3M3cLo_Of2S`0WMQ@f2S`0VMP@g2S`0VMP@g2S`0VMm_Oj2d`002O0O1O1O1O00011O1mMg^O]1[a0`Nk^O[1Va0cNn^OZ1Ta0dNQ_OW1Pa0gNQ_OY1o`0gNQ_OY1Sa0bNo^O]1Ra0bNo^O]1ja001OO1O100N200001ON20000O1M3N2N2O1N2N2O1N2N2N2N2N2O1N2O1N2N200001O001000O10000N3M2N2O1N2N2O2I6J6N2M3N2N2M4L3L4L4N2M3M4N1O1O1O1O1gA\\KT=f4eB]K]=d4]BaKc=`4WBeKi=\\4PBiKQ>X4iAmKW>n4000000000000000002N0000001O0000001O0000001O000000001O0000001O00U1kNa0_OO1N2O1O1N200O1O1O1O1001O0000000000000000N2N2J6NQCSIo<l63N200000000E;M33MN2N2000000TOcBcJJ>c=o4dBaJK?a=n4kBdJB:c=R5TCfJn<Z5SCcJo<\\5k0O11O00001OL4001O00000000I_AnJb>R56O1N200O10000O11O1OO10000O11SARKf>n4YATKf>n4XARKh>V5M1O3M0000001O0000O1000000O10nAiJBKe=\\5fBlJDMb=X5fBoJGJb=Y5_B[Ka=b50000000000000000000000000001O?AO100001O2N000000001O0000000000O1O1000000000000001O1O000000000000", "size": [640, 404]}, "area": 68724.0, "iscrowd": 0, "image_id": 15440, "bbox": [0.0, 0.0, 404.0, 244.0], "category_id": 96, "id": 20000957}, {"segmentation": {"counts": "VdY52mc03GL^\\O8bc0310N2N1O101O0N4N0f\\O]OWc0h0i\\OAhb0l00000LlN\\]OT1`b09N10O10OLb]OgN`b0X1TOQOn^OP1o`0TOP_Om0Ra0POm^OP1Ra0ROn^Oo0o`0TOP_Om0o`0SOQ_On0n`0SOQ_Om0o`0SOo^OP1T`0dNb_OJ>b0JT1T`0aNb_OK?`0OQ1m?kNf_OB:a06P1j?VOh_OL>m0k?XOd_OMa0k0j?0W@0g?1Z@Ng?1Y@Og?2X@Mi?3W@Mj?2T@0h?4V@Oi?2R@2o?MQ@2P`0NP@2P`0Nn_O4R`0n1X@[LS?e3c@eLV?Hd@X4\\?6O2M200O10000O11O1OO101ON1N4`Ke@W4]?hKe@T4`?jK`@T4j?M1O3M00000O20O000O1000000O2OaLV@BKS3n?XMZ@DMP3j?XM]@GJP3k?QMi@o2T`000000000000000000000O1001O011=AO100001O2M100000001O0000000000O3M1000000000000001O1O000000000000PI", "size": [640, 404]}, "area": 11975.0, "iscrowd": 0, "image_id": 15440, "bbox": [271.0, 159.0, 133.0, 153.0], "category_id": 97, "id": 20000958}, {"segmentation": {"counts": "VXa11kc00V\\O2010M01nb00R]O42LN60N0L40M0OO00^b0g0b]OE0Noa0f1O0000001OO1O020OO10000O11O00011N00O02OO11ON21O0000O01000O11O001O00O11O000O10O2O001O0000O100O02O0O13MO11ON120O10O000O1O201M4M2N2N1O001O00N22N1O0000000000000_Nc]O_1_b0000001O000000000000O11O0000000001O001N100000001O00O101OOaNc]O]1`b00O1O2N1O03M01O11O00O12ON010O1003MO12M11O1OO10000O012ON3NM31OO12N0000O10000000O1M32NO13M000000000O11O01N010001OO10000O11O1OO100N21O1O000000O1O12M02O01OO11OO1001N1O1O1O11OO010N3O02N1OO1O20M21O02NO11O3M0O11NO^]OfNcb0Y121OO1000O2N2OO12NO01L400O0024\\Nb]O\\1ab00000O11ON21O001OO12N00O1N^]OfNbb0Z12000000001O1O00O1O100001OO100000O1001O00O1K52NN24L2NO1N2001O2N000000000000000000000001O00O100002N1O1NOa]OcN_b0]13O0O11O000N1O22O0N00101O1O0000]]OfN`b0Z1`]OfN`b0]10002N1O00002NO1O10000O11\\@", "size": [640, 404]}, "area": 16199.0, "iscrowd": 0, "image_id": 15440, "bbox": [78.0, 478.0, 326.0, 72.0], "category_id": 124, "id": 20000959}, {"segmentation": {"counts": "fSX22oc010OT\\O1fc051N100O00000O10000O1000jBN^62aHN^J0g00WO0P=2bH0`JOf0OWO10Oo<2cH7WKOS<JfH8UKNV<JdH:RKE]O8l<IdH;SKM[<HbH>oJL^<FcH=nJO_<DcH\\1\\7dNUHNoJk1l<WNSGNQL101=NE1OP2b;oMdE2d10ZMNb0`0ZOn1b;cMcE1g07aN0BG081KOM020T3b;lLYE:P1j4f9lJjDe0M]OHMc0S6l:_KbD^N?W6n:^KmDi4S;UKfDS5Y;mJcDW5];g10O0N22N1O00000001O0000cD_G[;c80000010O00000000000O110O0000000000001N10000000000nNbDbH0?7B\\;_6^EoIVOB\\;j1]Dk1b0_La01ROLi;^1^DZ1GYM=M44`00RONh;]1`D=6XNBOP11L1TOMh;Y1fDIg0POlNNP12J0W;k0iCXOf0Kf10cMN73P10KNW;:]EEi00aM00082o0OiNOR10[=1QDOdM061Q1O[>0jB0dM070Vc00c\\O070Vc00c\\O0hc00cho3", "size": [640, 404]}, "area": 13504.0, "iscrowd": 0, "image_id": 15440, "bbox": [115.0, 213.0, 84.0, 284.0], "category_id": 132, "id": 20000960}, {"segmentation": {"counts": "[i^11kc05K5N2O1N2O1O100O1O1O01OO1O11O00000O100O1O1O2N11O1N101O00000000001N1000000000O10000O1000O10O2O0010OO10O100O11O0000000000O11O1O0000O2N11OO1000O10000001OO11O1O000000O10000000000000000000000O10000000000000001O001O000000001N10O100000000010O00O2N0100000000O20N1000O1O1000O20N20N01000O1001O2NM31OO03N0000O102N0000O1000000O100001O000O11O02M100001OO10000O11O1OO100N2100O0O1O10O0003NO1001OO02OO1001O0000000000O10O20O00O10001ON210O00000000O011OND`\\O<`c021OO1000O2O10N12M02OO1000O11M22N000O11NO30O001N012N01N1NE_\\O:bc03O00O11O10M30O0O1O100001OO1000000000O10000000000O1001OO11O0000O100000000O12NO10000O101O00O0100020OO1ONI[\\O7ec020O020O000O01010OO1O0100000000G\\\\O4fc0LZ\\O5hc0O102O0N1NT\\O0hR5", "size": [640, 404]}, "area": 4824.0, "iscrowd": 0, "image_id": 15440, "bbox": [74.0, 542.0, 322.0, 24.0], "category_id": 140, "id": 20000961}, {"segmentation": {"counts": "m6S=m60000000000000000000000000O1L400N200O100O10000000000O10000000000CmBlIT=S6=LVBQJk=n54001O00O100000000O100001OLRBVJn=g57GkAfJV>X5mAeJU>[5kAcJW>]5hAdJX>\\5iAcJW>]56O100000000001OO11O3M1O1O0000001O000[M]JmFc5o8bJPG]5m8hJRGX5m8jJRGV5R8^JXF=e1T5P8_JaE2010M01?>o1P5a7bJQF42LN60N0L40M0OO000`0]2o4a7XKRFE0`0\\2d4S7RMlHn2T7SMkHn2T7SMkHn2T7RMlHn2T7RMlHo2S7QMmHo2S7QMmHP3Q7QMoHo2R7PMnHQ3P7PMPIP3P7oLQIQ3o6oLQIQ3n6oLSIP3o6nLRIR3n6mLSIR3n6mLSIR3P7mLoHS3Q7nLnHQ3R7PMnHP3S7PMlHS3P7nLPIT3o6kLQIT3n6nLRIR3o6mLQIS3eM[JX9b2SIV3l6kLSIU3l6lLTIT3l6lLTIU3k6kLUIY3hMXJ1N\\8b2kIi3V6VLjIj3V6ULkIP5Q5oJoJU5m4kJSKW5j4iJWKX5fMXHm6_2]K`5b4`J^K`5b4`J^Ka5a4^J`Kb5_4_JaKa5_4_JaKa5_4`J`K_5a4`J`K`5`4`J`K`5`4`J`K_5a4aJ_K_5a4aJ_K_5Y4PH_Ib2X2^5X3PH^J0g00WO01b2[2^5V3RH`JOf0OWO10c2]2\\5U3YHWK`2d1W5U3ZHUK`2f1V5T3\\HRKE]Oi2]2V5S3]HSK`2j1S5S3`HoJ^2n1R5S3_HnJa2o1P5S3oKmLQ4d2PHoJ\\4]2d3b1PHQL101=NE1Ob4[2]3?RHZMNb0`0ZO`4[2P3AYHaN0BG081KOM020e5\\2Y2_OEa07lMXHHMc0_8i1JeMgG?`8m1MnM8R2HgM?Y2AcMc0]2a8OO11O1O000000O10000000000000000000000O1000000000000000000000000000000O100mLbMcA0?7BMo1Z2_;cNPCVOBLo1[2k6bMkJb0_La01ROL:m1o1a6nMZJGYM=M44`00RON8OBl1^2b6PN]I6XNBOP11L1TOM<k1l1]6VNjHg0POlNNP12J0Kk1a1X6ZNkGf10cMN73P10KNKo1`1P6aNPHa10aM00082o0OiNOR10KQ2`1P6`NPHa1OdM061Q1Oi0S2:n5bNoG`10dM070V3P2nNR6P1nGeL070U3Q2oNR6o0mGfL0f3R2eNQ6o0mGa0Q2`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^5XJbJg5_5YJaJe5a5ZJ`Je5a5[J_Jd5b5\\J^Jb5d5^J\\Jb5d5^J\\Ja5d5`J\\J_5YNgH^6k1YK]5YNkH[6i1\\K[5SNhH]O8S7f1]KZ5SNSIa6b1\\K\\5SNRI_6c1^K[5RNTI_6`1`K\\5PNUI^6b1aKY5PNVI_6a1aKY5PNWI`6]1aK\\5oMWIa6\\1`K]5mM[I`6Y1cK\\5mM[IA]Ok6m1gK\\5lM_IBA^6c1TL^5jMkIR6h0SL^5jMjIS6h0SL_5iMiIV6f0QLc5cMcHLT1_6g0RLb5_MPJ_6=RLT7Q4hHPLW7Q4iHoKW7Q4iHoKR7^MXGc6g1oKQ7V4nHjKR7V4nHjKS7U4mHkKR7V4nHjKS7U4lHlKU7R4lHnKU7P4mHoKS7jMXGd5e1bLT7gMZGf5a1dLW7dMYGg5a1dLW7dMXGg5a1fLZ7`MUGf5e1jLW7_MUGf5d1kLX7^MTGf5e1lLX7]MSGf5d1oLY7[MSGk5FVJh1b2k7[3UHeLl7Z3THfLl7Z3THfLl7Z3SHgLm7Y3THfLl7Z3THfLl7Z3THfLm7Y3RHhLn7X3RHhLn7X3RHhLn7X3SHgLm7Y3SHgLm7Y3RHhLo7W3RHhLn7X3RHhLn7X3QHiLo7W3RHhLn7X3SHgLm7Y3QHiLo7W3RHhLn7X3RHhLn7X3RHhLn7X3RHhLo7W3QHiLo7W3QHiLo7W3QHiLo7W3QHiLo7W3QHiLo7W3QHiLP8V3PHjLP8U3QHkLo7U3PHlLP8U3oGkLR8T3nGlLR8S3oGmLQ8n2THRMl7P3RHPMo7m2RHTMm7Q3oGoLQ8S3nGlLS8R3mGoLS8o2PHPMP8P3PHPMP8Q3oGoLQ8S3lGnLT8R3lGnLT8R3lGnLT8R3lGnLU8Q3kGoLT8R3kGoLV8P3lGnLT8R3kGoLT8R3lGnLU8Q3kGoLU8Q3jGPMV8P3jGPMV8P3jGPMV8P3jGPMV8P3iGQMW8o2iGQMW8o2iGQMW8o2kGoLU8Q3kGoLU8Q3kGoLU8[MUFc5g1RMV8n2jGRMV8o2hGRMX8n2iGQMW8o2iGQMW8o2iGQMX8n2gGSMY8k2iGUMW8i2lGVMT8l2jGTMV8m2iGSMX8k2hGVMX8j2hGVMX8k2gGUMY8l2fGTMZ8l2fGTMY8m2eGUM[8k2fGTM\\8j2cGWM]8i2cGWM\\8j2eGUM\\8j2cGWM]8i2aG_J_Nh2P:i2`GZM`8f2bGXM^8h2aGYM_8g2`GZMa8e2_G[Ma8e2_G[M`8f2_G[Ma8e2`GZM", "size": [640, 404]}, "area": 123191.0, "iscrowd": 0, "image_id": 15440, "bbox": [0.0, 173.0, 404.0, 467.0], "category_id": 149, "id": 20000962}, {"segmentation": {"counts": "ZSa51mc03M3I7MCa\\O?\\c03N200000O110N2O0O101O001O\\^OGa?7_@L`?2b@O]?5_@K`?8W@AbN7Wa08V@CbN6Wa07W@DaN5Xa08W@C`N5Ya09V@BaN5Ya09V@Nk?1T@1k?OU@1k?MW@3i?KZ@4g?G]@9c?G\\@:d?F\\@;c?C_@=ca0M2FnT8J_kG3N2M3N201O0000001N11O0001OO1001O01O1O0000000001OO11O00O11O00000O1001O002N000000]\\OEac0=00O1000001O000000001O0002N000O1002N00O11O00000000001O00O10_F", "size": [640, 404]}, "area": 1862.0, "iscrowd": 0, "image_id": 15440, "bbox": [283.0, 219.0, 121.0, 103.0], "category_id": 172, "id": 20000963}, {"segmentation": {"counts": "P^W2d1\\b02aN_N[@b1c?oNm_OS1P`0]Oa_Od0]`0^Oc_Oc0Z`0_Of_Oa0X`0Bg_O>W`0Di_O;V`0Gi_O9W`0Hi_O7V`0Kj_O4U`0Oj_OOV`03j_OLU`06k_OIT`09l_OFS`0<m_OCR`0`0m_O_OR`0c0n_O\\OQ`0f0o_OYOQ`0h0o_OWOP`0k0P@TOo?n0Q@QOn?R1Q@mNn?U1R@jNm?X1S@gNl?[1T@dNk?^1U@aNj?b1U@\\Nk?f1U@jMY`0X2g_OXMh`0i262N3M1O1O1000001O000000000000001O0000000000001O000000000000001O0000000000001O00000000000000O1O[@iLc>V3i@`MV?f30O100O1O1O2N1^NX@jNi?f21O1O1O2N1O1VNP@CP`0;P@FQ`08o_OIR`05n_OLS`02m_OOU`0Nk_O3V`0k11O1lMh_O>Y`0@g_Oa0Z`0]Of_Od0\\`0YOd_Oh0]`0VOc_Ok0^`0SOb_On0_`0POa_OQ1``0lNa_OU1``0fNc_O[1_`0^Nd_Od1]`0VNh_Oj1Y`0PNl_OP2U`0jMo_OW2m`000000000000000000002N0000001O0001O01O0000001O0000000O2J5L40001O00Rbh2", "size": [640, 404]}, "area": 15074.0, "iscrowd": 0, "image_id": 15440, "bbox": [114.0, 116.0, 148.0, 145.0], "category_id": 183, "id": 20000964}, {"segmentation": {"counts": "01o>000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001O00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001O000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001O000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001O00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001O0000000000000000000000000`BY2g9V5ZL7I", "size": [480, 640]}, "area": 2689.0, "iscrowd": 0, "image_id": 15497, "bbox": [0.0, 0.0, 640.0, 252.0], "category_id": 123, "id": 20000965}, {"segmentation": {"counts": "l\\X421Ok>30000000000000000O100000000O100O1O100O100000000O100O100O100O1O100O100M300O100O100O100O10000O100O100O100CYO`Bh0a=XO^Bh0b=XO^Bh0c=XO[Bi0e=XOZBh0e=ZOZBf0f=[OXBf0g=\\OXBd0h=:O10000O100O100O10000O100O100O10000O100O10000O1001O1O1O001O1O001O1O001O1O001O001O001O001O001O001OoKUOnIj0P6]OkIc0T6DgI;X6LbI4]62_IM`6:ZIFd6b0WI]Oh6i0SIWOl6P1oHoNP7W1kHiNT7^1gHaNW7g1cHYN\\7m1`HRN_7U2\\HjMc7]2UHeMj7a2oGaMo7g2iG[MV8i2eGYMZ8k2aGWM^8l2^GVMa8m2[GUMc8o2YGSMf8Q3UGQMj8R3RGPMm8b4O1N2M3M3M3M3L4M3M3O13M3M2N3M3M3M3M3M3M3M2N3M3M3M3M3M3M3M3M2N3M3M3M3M3M3M3M2N3M3M3M3M3M3M3M2N3M3M3M3M3M3M3M2N3M3M3M3M3M3M3M2N3M2N3M2N3M2N3M3M2N3M2N3M2N3M2N3M3M2N3M2N3M2N3M3M2N3M0000000000000000000000Qme1", "size": [480, 640]}, "area": 16078.0, "iscrowd": 0, "image_id": 15497, "bbox": [290.0, 263.0, 234.0, 217.0], "category_id": 167, "id": 20000966}, {"segmentation": {"counts": "1a2^:^OdEo<0000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001O00000000000000000000000000000000000000000000000000000000000000000000000001O00000000000000oBOS;1lDOU;1jDOW;1l10000000000000000000000000000000000000000000000000000000000000000000000000dL3eGMg;000RL1kHOo65SIKm63UIMj63WIMh63YIMf62\\INd61]IOb61_IO`62`IN_62bIN^61cIO\\62dIN[62fINY62hINX61iIOV62kGNB0b82oGN]O0c83RHMYO0e82THNVO0e83WHMRO0h81XHOoN0i81ZHNlN1j81[HNjN1k81oIOQ60PJ0P60PJ0P60PJ0P60PJ0h4OQH1W30h4OaL1^30bL0^30bL0]31cLO]31cLO\\31eLO[31eLOZ31gLOX31iLOW31iLOV31kLOU31kLOT31mLOS30nL0Q31oLOQ30PM0o20dHOl21`40dHOl21`4OeH0k21i90`B0V8O]L1]K0`=0`B0`=0`B0`=0`B1_=OaB1_=OaB1_=OaB1_=OaB1^=0bB0^=0bB0^=0bB0^=0bB0^=0bB0^=0bB0]=1cBO]=1bB0^=0bB0^=0bB0]=0dB0\\=0dB0\\=0dB0[=1eBO[=1eBOo7MfL4[KOo7MjI0;6kMMo7NlIO9:hMIR8OnIO6<gMFT80QJN1`0gMBV81hLa0nJ^OY83gLc0mJZO\\83gLk0V2oNdI7U4k0X2PO_I6X4k0Y2RO[I4\\4j0Z2TOVI3_4j0\\2UOQI3b4IoJg0^7_OoH1c4IUKc0Z7EjH0g4GYKa0W7JeHOU3BXN4kN>S70aHM[3BSN1SO=o6c0PLoNjM1[O:l6c0SLSObM0C7i6c0WLWOYMNK5e6d0[L[OPMK53a6d0_L_OfLJ<2`6c0aLCgMJh5a0fLIlKHQ1N^6>jL6iL\\O^6;oL4dLA]69TM2_LF^65YMO[LL]62]MNWL0]62nNNT10lN0U10jN0X1NhN2S80jAM_=3aBM_=3aBM_=3aBM_=3aBM^=4bBL^=4aBM_=3aBM_=3aBN^=2bBN^=2bBN^=2cBM]=3cBM]=3cBM]=3cBM]=3cBM\\=4dBM[=3eBM[=3fBLZ=4fBLZ=4fBLZ=4fBLZOOn=5hBLZOOn=5hBOW=1iBOW=1jBNV=2jBOT=2lBNT=2lBOS=1mBOS=1mBOS=1mB0R=0oBOQ=1oB0P=0PC0P=0PC1o<OQC1o<OQC1n<0RC1m<OTC0l<0TC1k<OUC1k<OUC2j<NVC2j<NVC2j<NVC3i<MXC2h<NXC3g<MYC3f<NZC3d<N\\C2d<N\\C2c<O]C2a<O`C0`<0`C1^<0bC0^<0bC0]<1cC0[<1eCO[<1fCOP:CZG?fNNR:CVG?hNOS:CQG?lNNV:BkFa0POLW:DfF`0TOLW:DbFa0XOJX:F]F`0[OKZ:EWFa0@I\\:ERFb0CI]:ElEc0GHj;8WDGh;:YDFf;:ZDFe;;\\DEc;;^DDa;=_DCa;=`DCh:GfDf0c0Cg:FeDh0d0Cf:FeDg0f0Bf:FcDi0h0Ad:GcDh0i0Bd:FaDi0l0Ab:FbDi0m0@b:G_Dj0P1_O`:G`Dj0P1@`:F_Dj0R1@^:F_Dl0S1^O^:F^Dl0T1^O^:G\\Dl0W1]O]:F\\Dm0X1]O[:G\\Dl0Y1^OZ:G[Dl0]1[OW:K[Dj0_1[OU:L[Di0b1ZOR:NZDi0e1YOo9Q1SFnNl9R1UFmNj9T1XFkNg9U1ZFkNd9V1^FiNa9W1`FhN`9X1bFgN\\9Z1fFeNY9[1hFeNV9\\1lFcNS9]1nFbNQ9_1X2000O100O100O10000O10000001O1OXE_NQ8a1PH_NP8`1PHaNP8^1QHaNo7_1QHbNo7]1RHbNo7]1QHcNP8\\1j201O001O001O001O1O001O001O00PLlNUJS1j5SOQJm0m5[OnId0Q6CiI=V6IfI6Y61aIO]69^IFa6`0[I_Od6h0VIXOi6o0RIPOm6V1nHjNP7^1kHaNT7e1gH[NX7l1cHSN\\7T2^HlMa7Z2XHhMf7`2RHbMm7d2lG^MS8f2hG\\MW8g2eG[MZ8h2bGZM\\8j2`GXM^2eNR3W4[JVMc8m2YGUMf2aNR3]4TJTMj2_NQ3S6nLmIQ3U6oLkIo2W6PMjIm2Y6SMfIk2]6TMdIh2`6XM_If2d6YM]Id2f6\\MZIa2i6_MVI^2n6aMSIa2k6_MTIe2i6ZMXIi2e6WM[Ik2c6TM]IP3`6PM`IS3]6lLdIm1NgL[6\\1fIj12nLU6W1jIh13UMP6S1lIe16]Mk5n0oId16bMh5i0SJd14gMg5e0TJd15kMd5`0XJd14PNa5<ZJd14UN_56^Jd13ZN\\52aJb14`NX5MdJc13eNV5HgJb13jNS5CkJb12oNP5_OmJb12SOo4[OoJa12XOl4VORKb12\\Oi4ROUKa11Bg4lNYK`11Hc4hN[K`12L`4cN_K`102^4^NbK_107[4YNeK`1O;Z4UNgK_1O`0W4PNjK`1Od0T4lMmK@10Mi2R4gMPL[O81GQ3n3bMSLROOnN9[1GW3k3^MaMUNmNS1F_3i3YMaMYNPOl0Gf3e3VMaM\\NSOe0Gl3c3TM_M_NWO>GT4`3oL_MdNZO6G[4]3lL_MfN]O0Hb4Y3iL^MjNAGHk4V3dL^MnNDAHQ5S3aL^MPOHZOGZ5P3]L]MTOKSOH`5m2YL]MXOOkNHg5j2WL[M\\O2cNIo5g2SL[M_O5\\NIV6d2oK[ME6SNL]6`2WNdMYKKe6_2TNdMTKNl6[2RNmMR2P2PNlMU2Q2lMmMW2Q2kMlMY2Q2hMlM\\2R2\\5M2N3M2N3M3M2N3M2N3M2N3M2N3M3M2N3M2N3M2N3M3M2N3M000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001O0000000000000000000000000]AS5S9T4YLg3YLlB", "size": [480, 640]}, "area": 284100.0, "iscrowd": 0, "image_id": 15497, "bbox": [0.0, 1.0, 640.0, 479.0], "category_id": 183, "id": 20000967}, {"segmentation": {"counts": "S5_1a=00000000O100001O0000O11OYOdBJ\\=1bB_O2?]=0iB1W=NkB1Z=JfB6[=@^B18?Z=@nB>k=CaA9c>0000L]AJe>35000O10001N1O0L5J6M3OO20O010001OO1O12M101O00010L3O10008HZQ7NfnH2N200N2M4M2N1100002NN20000O11O0000O101OiACm=<nAEK0Z>8lAKS>5lALT>3mAKU>5jALV>5iAKW>6iAIW>8hAIW>`01O0001O0001N100000\\C^Ok:b0kC^Oc06b;<jC_O9OZO?d<3^C@40??P<2[C@23b0;Q<9[C_Oa0:R<b0nC@P<`0PD@Q<?PD@P<`0PD_OQ<a0PDWOW<m0eCSO[<m0eCSO[<o0cCQO]<n0dCRO\\<o0cCQO[<Q1eCnN]<Q1aCeNE:k<P1_CgNF9j<Q1`CfNF9h<S1bCdNF9h<T1`CPO`<P1_CPOb<P1^CPOb<P1^CPOb<P1\\CbNK>j<o0VCXOj<h0VCXOi<a10O1N2O0000001O00000O2OO100K5@UNkCk1S<ZNiCh1U<b000000000000O6K2N0`MdC1OW2f<ON16KO1000001O001O1O2[CgM_<]20O10000000002N0fM`CT2f<0000O100M33MO1O10000000000O10000L4ONTNQCl1n<35L00N22N0000001O0001O001N2O0000OO2O1N20O010000N2N200O10000001O0001O0101N1O3M004LO02N00O1101OO1000000O1N200]CSNR<n1PDRNn;o1QDQNo;o1RDPNn;P2a0000010O01N20O000O1O1O100O2N2O000O1000O1N12OO10000HWNYCi1f<XNZCh1f<9000000O1000000M300000000O01O1O1000O11OO1001OO11O000001O1O0000000000000015K10N4LfM_CU2j<DYCXNe<g1^CYNa<f1aCYN_<f1?N011O0O10L]NkBb1V=4O100O100O11O0000001O000000101ON3N3M00000000000O1001N10O100000O100O1O10O1O100O1I7O2OO`CmMU<S2jCnMU<R2mCmMS<S2mCmMS<S2mClMT<T2lClMT<T2<01O0001OO1001OVNVC^1P=[NQCf1o<YNQCg1T=O2O0O1O1001O02N00O03M1N2cNdBX1Z=hNeBY1[=gNgBW1Y=iNdBZ1]=eNcB]1_=0MbNdB_1[=aNdBa1Z=4O10O1N10N1M\\NkBf1T=ZNlBf1S=40O2O001OOEmBkNQ=P1lBmN70k<T1[CmNc<V1a0211L13N01O001OO11OO100001mBdN`<\\1[ChNf<U1[ClNg<T1XClNh<T1XCkNi<U1WCoNe<S1XCnNh<S1UCaNN<n<d10QNSCk1m<VNRCi1o<WNQCi1S=1NO3N11M05MN22N2NM300N2N200O100O1000OM400O1001O00SOoMYD07Q2`;oMYD000JS2m;k0AYMdDh2[;\\MaDe2^;]MaDc2_;?O100O100O1O100O100O011N100O101N1O101N1O2O0O100O10O11N100O100O0KnK^ER4\\:nKdE0OR4^:nKcE0NS4_:mKbE1NR4`:nKbE0NR4`:nK`EOOX4`:6000000O100000000O2O001O1OO6J1O11O1O0000001ON2O020O00000000000002NM4M2O1O11OL4000000001O1N10001XEiKe:[4N0000O1001OO1J6M30NKYEULg:j380000000O100N200VERL]:0^EP44QL^:S4_ElKN1a:O`EZ4`:301N023N0N101M11O1]KdE_4]:aKdE^4_:01O1O2N0fK\\EW4g:001O0001OO100000O1000001O000jKXE00R4Y;DO00001N2O002M01001O0O100QL", "size": [480, 640]}, "area": 46570.0, "iscrowd": 0, "image_id": 15517, "bbox": [0.0, 98.0, 640.0, 179.0], "category_id": 96, "id": 20000968}, {"segmentation": {"counts": "0Q1U1g0m9ZOSFf0l9ZOUFf0j9ZOWFe0i9\\OVFe0i9^OTFb0l9_OSFa0m9_OTF`0l9@TF`0l9@XF<h9DYF;g9EYF;g9CRElNW1a1g9B]F=c9B^F=c9C\\F>d9A]F?c9A]F?c9A]F?c9A\\F`0d9_O^FPOcN^1o:B`F>`9AbETO5_1Y:]O_EYO5\\1[:\\O^E^O1V1b:\\O]EAMT1f:ZO]EEIS1j:XO]EGGQ1l:XO]Ec1c:]N\\Ee1c:ZN^Eg1a:YN_Eh1`:XN_Ei1a:VN`Ek1_:UN_Em1a:RN`En1`:RN_EP2`:QN^EP2b:QN]EP2b:QNZER2f:nMZES2e:nMYET2f:lMZET2f:mMYES2g:mMYES2g:S101O00O11O1O1O000000cL^E\\2b:dM_EZ2b:fM^EZ2b:bMcE]2]:_MgE`2Z:_MgEb2X:^MhEa2Y:\\MiEe2W:ZMjEf2V:ZMkEd2V:ZMmEe2S:XMQFg2o9XMRFh2n9XMQFi2o9VMRFj2n9VMRFi2o9XMnEh2T:XMlEg2U:YMkEg2U:YMjEh2V:XMiEi2W:WMeEm2[:SMeEk2]:UMdEj2\\:VMeEh2\\:YMcEg2]:ZMcEe2]:\\McEc2]:]McEc2]:^MbEa2_:_MbE`2^:aMdE\\2\\:eMdEZ2\\:gMdEY2[:hMdEX2\\:iMeEU2[:kMfET2Z:lMgES2Y:mMgES2Y:mMhEQ2Y:oMgEQ2Y:oMiEo1W:QNlEk1U:TNmEk1S:TNnEl1R:UNmEk1S:PNPEFn0Y2R:oMSEEo0Z2n9mM[FS2e9lM\\FU2c9lM]FR2d9mM^FR2b9mM`FQ2a9mMaFS2`9lMaFS2V;1O0O2O01O10002NO000022MO010L30001O00010O1O2N1O000001O001ON200O100O100000100O000O2M21O]O^MlD_2R;bMoD]2^:bM]E16\\2]:eMlDO3O32N1:Z2c:kMQE3NF13=X2b:mMQE==f1T:gM^EM7T17V1S:lMfEn0;R1m9RNgEm0<Q1l9SNfEn0>o0l9UNdEl0?P1m9_OSFa0m9_OSFb0l9]OUFc0k9\\OVFd0j9_OQFd0n9[OSFe0m9VOeE`N?Z2l9VOXFj0h9WOWFi0i9VOXFj0h9TOZFl0f9TOZFl0f9TOZFk0g9UOXFl0h9TOXFl0h9TOXFl0h9VOUFk0k9WOSFi0m9XORFh0n9YOdE[N91HV2\\:CPF;Q:FnE:R:GnE7U:HkE6W:JhE5Z:JgE5Y:KhE4Y:KgE5Z:JeE7]:GfE7Y:JfE7Y:IgE8Y:HgE7Z:HfE8[:GeE9\\:GcE9]:HbE8`:F`E;_:F_E<a:D]E>b:P201O00010O01O1O001O2O02M3TEkKh:X4N1O1M2O1M3N21ON2J510O1O10000001O3M1N_FaKd8^4\\GdKb8\\4\\GgKb8Z4]GjKa8U4_GkKa8U4`GnK\\8S4bGmK_8T4`GmK_8T4`GkKa8U4^GjKd8U4\\GlKc8U4\\GmKd8R4\\GmKd8T4\\GlKd8T4\\GlKd8U4[GkKe8U4[GjKg8V4XGhKk8W4UGiKk8X4TGgKm8Y4TGfKm8Z4RGfKo8Y4QGgKo8Y4QGgKo8Y4QGgKo8Y4QGgKm8[4SGfKk8[4UGeKk8[4UGWKC;X9_4WGbKg8_4YGaKf8`4ZG`Ke8a4[G_Ke8b4YG_Kg8a4XG`Kh8`4WGaKi8`4UGbKf8b4YG`Ke8a4YGaKg8c4TG^Kk8\\5000O1N03OO10000HWGYJi8f5XGZJh8f59000000O1000000M300000000O100000000O11OO1001OO11O0000000000000000000000006J2N000mFXJn8Q6LO100001OO11O0000LVGRJj8n54O100O100O11O0000001O0000001O2NO100000000000000O1001O00O100000000000000O1O100O1I7O10000O100000000001O00000000000QG[Jc8k5VGVJj8k5UGUJk8P601O00000000002N00O12N000YGnI`8R6`GmIa8S6_GoI_8Q6aGlIb8U6]GkIc8Y60M\\GjId8V6\\GiIe8V63001O1O00N2LXGoIi8Q6WGoIi8P64001O001OO1N2N2N2N21ON22N00000000000000000000001O000000001O0PGTJl8l5UGSJk8m5UGSJk8Q60O11O00N23MN22N2NM300N2N200O100O10000L400O1001O00SOiF_K07W9Z4iF_K000JY9g4k0ASFjKn9U4VFgKk9X4WFgKi9Y4?O100O100O1O100O100O100O100O100O1O100O1O100O100O10000O100O100O1JjDbLV;X3jDhL0OW;Y3iDhL0NX;Z3hDgL1NX;Z3hDhL0NX;Z3hDfLOO];[36000000O100000000O1000000005K000000000000000000O11O00000000000000001O00000000000000000000O1000000000000000000000000N20000000000O100N200K[DmLg;S33NVDoLi;Q3300O11O2N001OO11O1WDjLf;V3ZDkLe;X301O1O2N0_DcL_;_300000001OO100000000_OaDaM_;W2aD]M1MO?_;P2bDeMON4>Z;P2bDcM2N2?Z;o1cDdM1M3`0K@00Z;_2gDdM0N3Q1i:\\1TEeM1M3R1h:\\1TEeM9n0c:\\1TEgM8n0d:[1TEgM7n0f:[1SEfM8o0e:Z1UEfM6P1e:Z1REdM>Q1a:]1^EdNb:[1`EdN`:Z1bEfN^:Z1bEeN_:[1aEeN_:", "size": [480, 640]}, "area": 89857.0, "iscrowd": 0, "image_id": 15517, "bbox": [0.0, 0.0, 640.0, 206.0], "category_id": 106, "id": 20000969}, {"segmentation": {"counts": "odS91g>0]A1MO_>1eAON4^>LcA2N2^>MdA1M3_>LdA0N3^>MdA1M3_>LdA9Z>GgA8Z>HfA7[>IdA8[>JdA6\\>GbA>`>O02M20000dM", "size": [480, 640]}, "area": 215.0, "iscrowd": 0, "image_id": 15517, "bbox": [622.0, 76.0, 18.0, 20.0], "category_id": 132, "id": 20000970}, {"segmentation": {"counts": "i^7114l0Ml<8iBHA5_=9mBOR=3lBNU=2iBOW=2hBNX=4`B\\O1b0_=k01O001O0000001O0000001O000000001O00001O000000001O00001O00001O1O001O4L0000O10000000000000000001O0000001O001O0000001O001O00001O0000001O001O0000001O00001O00001O001O001O00001O001O000K5K^AJe>2ZANZbf7", "size": [480, 640]}, "area": 3064.0, "iscrowd": 0, "image_id": 15517, "bbox": [15.0, 433.0, 99.0, 47.0], "category_id": 140, "id": 20000971}, {"segmentation": {"counts": "`8V11lN5\\2`1_Nk68iGX1[1aNl67jGV1Z1dNk66lGW1X1cNl66mGU1W1fNl65mGT1X1gNk65nGU1U1fNm65nGT1V1gNm63oGU1T1hNl64QHS1S1iNm63QHR1S1kNl63RHQ1R1lNl63RHQ1R1lNl63SHP1P1nNm61UHQ1JPNAOl0o0g7PO^Gk0e0U1FPND;i0a0XOTOg82XGc0f0\\1\\OoM50I?g02ZOAS9b0bG^1ZOoM7h08Em82dF@00f0U2@dN;Cn82cFBd0V2ZOmM1f0`0AQ9I^FM0003k0S3GSMY9HUFOl00WOP3a0QM\\O1P:NRF0l0o2Q:QMPFo2o9QMRFn2n9RMSFn2l9RMTFn2Q;N6J10O0000010O000dNZM^Ff2[9[M_EOV1f2S9\\MdE32KX1f2m8mMSGS2m8mMSGS2m8mMSGT2l8lMUGS2j8nMVGR2j8nMVGR2k8mMUGS2j8nMVGS2h8nMYGQ2g8oMYGQ2g8oMYGR2f8oMYGQ2g8oMZGP2g8oMYGR2h8lMXGU2h8jMXGV2i8iMWGX2i8gMXG\\2d8dM\\G\\2f8bMZG^2e8cM[G\\2e8eM\\GZ2d8fM\\GZ2d8fM\\GZ2d8gM[GY2e8gM[GY2e8gM\\GX2e8gM[GY2e8gM[GY2f8fM[GY2d8hM\\GY2c8gM^GX2a8iM_GW2a8iM`GV2`8jM`GW2_8iMbGV2^8jMcGV2\\8jMdGV2\\8kMcGU2]8kMcGU2]8kMbGW2]8iMcGW2]8iMcGX2\\8hMcGY2]8gMcGY2]8gMcGZ2[8gMeGY2[8fMfGZ2Z8fMfGZ2Z8fMfG[2Y8eMgG[2Y8eMgG\\2W8eMiG[2R8RM^Fd0`1Z2R8RM]Fe0a1Y2R8RM]Fe0a1Z2Q8QM]Ff0b1Y2Q8iMnGX2Q8iMoGX2P8hMPHX2P8hMPHX2P8hMPHY2o7hMPHX2P8hMoGZ2o7gMQHY2o7hMPHY2n7iMQHW2k7mLhFm0\\1V2e7UN[Hl1d7TN\\Hl1d7TN\\Hm1c7SN\\Hn1f7PNZHP2POlLW8S1hHQ2lNiLJ5b8Q1hHR2hNgLN:b8m0hHb2W7_MiHa2]7YMcHg2^7XMaHi2_7XM`Hh2`7XM_Hi2a7WM_Hi2b7VM]Hk2c7UM]Hk2c7UM]Hk2c7UM\\Hl2d7UM[Hk2f7YMTHh2l7hMdGX2\\8V20000000000000000000000000000O100000lI[Gm5l801O0000001O0000001O0000001O000cMmFmNS91mFdN8S1k87oFeN:Q1f89SGeN9P1c8EkG[OFl0^8DSH_O@l0]8_OYHEZOl0n7XNjGW1o0D[Ol0l7YNiGX1Q1B[Ol0h7]NkGV1R1A^Oi0e7`NjGV1U1@\\Oj0d7aNjGV1V1^O_Oi0a7cNiGW1W1]OAg0_7eNjGV1W1]OEc0Z7jNjGU1X1^OFZOjNT1^8oNjGU1X1_OI<U7POjGU1X1DD7Z7POjGT1Z1E`N[Of0k0f7QOjGT1Z17PODl7QOjGT1Z1=jN^OR8QOkGS1Y1f0^NSN3R1Z8oNcG19T1[1o1i6kLdG28T1[1o1f6nLgGO8T1[1o1i6kLdG28S1\\1P2g6lLeG18S1]1o1e6nLfG09R1\\1P2e6nLfG09R1\\1P2e6nLfG09NIn0e1U2c6oLfG0?k0X1V2c6oLfG0?k0X1V2c6oLfG0?j0Y1W2a6PMgGO?j0Y1W2a6PMgGO?k0Y1U2a6QMgGO`0j0X1V2c6oLeG1`0j0X1V2d6nLdG2`0j0X1V2g6PMQHj0Y1U2f6QMQHj0Y1U2f6QMQHj0Z1T2e6QMRHk0\\1Q2c6SMQHm0\\1o1c6TMRHl0[1P2c6TMRHl0\\1o1b6UMRHl0\\1o1W7QNjHn1V7RNjHn1V7RNjHn1V7RNkHm1U7TNjHl1W7SNjH<oM6W9^OjHIoMK2m0U9_OiHJQN[OO60J5[1R9@hHKRNYO00O0118[1n8@gHLgNd0b8@fHCB=h70fHEC8g73fHBF;e73dHAG=e72dH@I=c73cHBI;d73cHAG?f70dH^OGc0e7OdH@Ea0g7OdH_OGa0e70dH^OM=`75bH^ON=`75bH\\O1>]76cH[O0?]76cH[O1>[78gHWONa0[77jHUOLd0[76jJJV56jJJV56jJJV56jJJV56jJJV56jJJW55gJMY53fJNZ52eJO[51jJJV57iJIW58gJIY57fJJZ57aJM_53_JOa51_JOa51_JCYLHX9e0^JC[LHW9e0^JC[LHW9e0_J_OWLE28X9d0^J\\OZLGO028W9e0^J]OaLNQ9f0]J[OcLOQ9e0[J]OfLLo8g0\\J\\OdLNP9f0[J]OdLNQ9e0[J]OeLMP9f0ZJ]OfLLR9g0ZJZOfLMQ9i0YJWOjLNn8l0WJTO`6l0j30000000000001O00000001O00000001O000000O100O1O1O1000001O01O0000000000001O000000000001O01O00000000001O1N2O1GQO_BP1`=800[EgNc71`ER1m2nNb7NdET1i2oNi7Q1VHPOk7o0UHSOi7m0VHUOPNITOO01a9R1XImNWM96IK366Y9g0YIYO^MN04W9f0]IVO\\M1M4Z9e0]IWO\\M1J4^9c0\\IYOUME5l0Z96\\I\\OWMBOk0^97\\I]OVMBOj0`97XI@YM;_95WIa0i6_OWIa0i6_OWIa0i6_OVIb0j6_OUIa0k6_ObHPO\\Na1R9_O_HTOYNIJe1^9^O]HVO[N`1X9ZOZHmNWN;5^1[9ZOTHA^NV1^9YO`FXOU1Y2[8UO\\Gl0d8[OTGf0l8FfF<Z9e200000000000000000000000000000000001O00]OdJbG\\5^8dJbG\\5Q900000B>0aJoFo4Q9lJTGT5m8gJWGY5^7gJmIY5S6iJYHM]1Z5Y6PKfIP5Z6PKfIP5Z6PKWHF]1Z5\\6PKWHF]1Z5\\6RKdIn4\\6RKdIn4\\6RKdIn4\\6RKdIn4]6QKcIo4\\6RKdIn4\\6RKdIn4]6QKcIo4]6QKcIo4^6PKbIP5^6PKbIP5]6QKdIn4\\6RKdIn4[6SKeIm4Z6TKfIl4Z6TKfIl4[6SKeIm4[6SKeIm4[6SKeIm4[6_KYIa4f6bKXI^4g6cKYI]4e6eK[I[4g6cKYI]4g6cKYI]4g6cKYI]4g6cKYI]4i6aKWI_4i6aKXI^4i6aKWI_4j6`KVI`4k6^KVIb4k6]KUIc4k6]KUIc4j6^KVIb4j6^KVIb4i6_KWIa4j6^KVIb4j6^KVIb4j6^KVIb4i6_KWIa4i6_KWIa4i6_KWIa4i6_KWIa4]6kJhHd0k0a4V6hJlH70`0n0a4U6UKnH;l0`4W6SLiIm3W6SLjIl3X6RLhIn3W6TLhIl3X6TLhIl3Y6SLgIm3Y6SLgIm3Z6RLfIn3Y6SLgIm3Z6RLfIn3Y6SLgIm3Y6SLgIm3Y6SLgIm3Z6VKhH:n0`4Z6QKlH`0j0_4W6RKPI`0i0^4X6QKoHb0h0]4Y6QKnHh0d0W4^6QKRId0`0[4`6nJQIg0?[4a6kJdHN<l0`0Z4R7fKnHZ4R7fKnHZ4Q7hKnHX4R7hKnHX4S7gKmHY4Q7iKoHW4P7jKPIV4Q7iKoHW4o6kKQIU4o6kKQIU4o6kKQIU4o6kKQIU4o6kKQIU4o6kKQIU4o6kKQIU4o6kKQIU4o6kKQIU4o6kKQIU4o6kKRIT4n6lKRIT4n6lKRIT4n6lKRIT4m6mKSIS4m6mKSIS4m6mKSIS4m6mKSIS4m6mKSIS4m6nKRIR4n6nKRIR4n6nKRIR4n6kKUIU4k6kKUIU4k6kKUIU4k6kKUIU4k6kKUIU4k6lKTIT4l6lKnHPO\\OT5f7lKoHROYOQ5h7iKYHSOg0Y5P7bKVI^4j6^K[Ia4e6^K\\Ib4d6^K\\Ib4d6^K\\Ib4d6^K]Ia4b6`K^I`4c6_K^I`4b6`K^I`4b6aK]I_4c6aK]I_4d6`K\\I`4d6`K\\I`4d6`K[Ia4f6^KZIb4f6^KZIb4g6]KXId4h6\\KXId4h6\\KXId4h6\\KXId4h6\\KXId4g6]KYIc4h6\\K_H\\O<X5U7\\K_H\\O<X5U7]K^H\\O<W5V7]K_H[5a7eJ_H[5`7fJbHX5^7hJSIg4l6ZKTIf4l6ZKSIg4l6ZKTIf4l6ZKTIf4m6YKSIg4m6YKSIg4m6YKSIg4m6YKSIg4m6ZKQIg4o6ZKPIf4P7ZKPIf4P7[KoHe4Q7\\KnHd4R7]KmHc4T7]KkHc4U7^KiHc4X7^KfHb4[7_K_He4`7_KVHh4i7X1O11O1O0000001ON2O11O00000000000nMUHoLk7R2UHhL0WO0o1m7P2XHcL1\\1d7S2\\HQLJ:7b1a7P2YHQL72I:7c1_7P2kHZLHf1\\7P2mHYLJe1Z7P2lHTL_OON2<l1W7T2fIlMZ6T2fIlMZ6T2fIlMZ6T2gIkMY6U2gIkMZ6S2gImMZ6S2fIlMZ6U2fIjMZ6V2hIhMY6WNZHn3]1kM]6T2cIkM]6U2dIjM\\6X2bIhM]6X2dIhM\\6Z2cIeM^6\\2aIcM^6_2bI`M^6`2cI_M]6a2cI_M^6`2bI`M_6^2bIbM^6\\2cIeM^6Y2dIfM\\6Y2eIgM[6X2dIjM]6U2cIkM^6U2bIjM_6aNQHL7U3^1[Nk6d1WIQM\\No0^8k1\\IQMYNT1[8i1iIUNW6Z1_GdM030Mb2o0o5]1`GcMj2P1g5]1\\JcMRMm0b8T1cGRNKNo2l0c5T1eGoMl2m0_5T1jJlNV5T1jJlNV5T1jJlNV5T1jJlNV5S1kJmNU5S1kJmNU5S1kJmNU5S1kJmNR5V1nJjNR5V1nJjNl4TOmGo1W3mNk4]1UKcNk4]1UKcNk4]1UKcNk4]1VKbNj4^1WKaNi4_1VKbNk4]1UKcNk4`1gGeMX3k0P5a1hGdMW3l0Q5d1nJ\\NR5]1VKbNj4^1VKbNj4^1VKbNj4]1YKaNg4_1YKaNV4\\O^HLKW2a3aNV4\\OYHX20dMa3h0W4b1XHhMh3f0P4_1XHhMn3i0j3_1XHhMn3i0k3^1WHjMl3i0n3\\1WHiMl3k0m3\\1VLdNl3[1TLdNk3]1VLbNj3^1VLbNk3]1VLbNj3^1PLhNP4X1nKjNR4V1oKiN", "size": [480, 640]}, "area": 88471.0, "iscrowd": 0, "image_id": 15517, "bbox": [0.0, 232.0, 640.0, 248.0], "category_id": 149, "id": 20000972}, {"segmentation": {"counts": "Q1U1h10W90iFOW92iFMW94hFLY93hFL[91eFO\\90dF0\\91cFO]91cFO]95_FKa96^FJb96^FJ`9oNoDW1a1J_9:`FFWOSO[9X1]GFSOPO_O2Q:W1^GGPOXOb9R1^GFoNZOb9P1_GKjNUOg9P1_GL`NmN18P:n0`GM_N]OP:h0PFcN^1o1cNRNk9^1bGd0]8^NXF5_1]1Y8[N]F5\\1_1X8ZNbF1V1g1W8XNfFMT1k1U8XNjFIS1o1S8XNlFGQ1P2T8YNgHg1Y7XNiHg1V7ZNkHd1V7\\NkHb1V7]NkH^1Y7cNhHV1^7hNdHU1^7lNbHS1_7lNcHS1^7kNcHT1_7kNbHT1_7hNdHX1\\7hNeHX1[7fNgHX1Z7hNfHW1\\7hNdHZ1Z7fNfHZ1`:10O00O20O1O1O08H6JOdL\\N^Id1b6]N\\Id1d6\\N\\Id1`6aN_I_1]6eNbI\\1]6eNdIY1]6gNbIZ1[6hNfIX1Y6iNgIW1lLYNY9a0iIW1nLYNV9c0kIT1nL[NS9e0nIP1PMZNQ9g0oIo0nLXN04Q9d0RJV1o5kNQJS1Q6mNnIR1U6kNkIW1U6iNjIV1X6jNhIS1[6lNfIR1\\6mNeIQ1]6kNgIU1Y6kNeIW1[6jNdIV1\\6kNbIV1_6iNaIW1`6iN_IW1b6iN]IW1c6iN]IW1d6hN[IY1e6hNZIX1g6jNVIV1k6jNTIV1m6jNSIV1m6iNSIY1l6hNRI[1k6fNTIZ1l6gNSIY1m6gNSIY1m6hNQIY1o6gNQIY1o6iNoHW1Q7lNkHU1T7mNkHT1S7mNmHS1T7lNlHT1o6oMgFn0Y2S1n6RNfFo0Z2o0l6ZOTIg0j6ZOWIe0j6[OTIf0k6\\OTId0k6^OSId0j6^OVIb0f6lMVGc1S2b0f6B[I>d6B\\I>d6A^I>b6B^I>b6C]I>b6C^I<b6E^I:b6I\\I6d6J\\I6d6J\\I6d6J^I4b60[IOe61[IMg64YILf61]I0b60^IOc61^ILd64\\ILd64\\ILd65\\IJd66]IIc67_IGa69`IG_69aIG_69aIF`6:`IG_6:`IG_69aIH^69aIG_67cII]67cII]66dIK[65eIK[64fILZ64fILZ64fIMY63hIKY66gIIY67gID^6<bITOkMROc8i1dIROnMQO_8k1eISOm6n0RIROn6;`F\\O_28Q7;aF^O]27R7H`FL16\\26S7HbF[OO3O32N1:Z2?S7EhF@3NF13=X2?T7DiF@==f1d0o6QOhFMM7T17V1f0o6nNmF5n0;R1b0kMmN1Oo80WG6m0<Q1k0T7gNmG>o0i0X7gNiG?P1o0]8QOcGn0^8ROcGm0\\8TOdGl0[8UOeGk0^8POeGo0Z8ROfGn0U8dNaE?Z2m0U8WOkGi0V8VOjGj0U8WOkGi0S8YOmGg0S8YOmGg0S8YOlGh0T8WOmGi0S8WOmGi0S8WOmGk0S8ROnGn0eMcNY:=RHV1o7iNQHW1P8[NdE91HV2e1Z8gNdGZ1]8eNcG[1^8eN`G\\1c6XNUI<6]1e6XNUI:5_1f6XNTI:5^1g6XNTI;4]1h6YNSI:5]1h6ZNRI87^1g6\\NPI97Z1i6]NQI87Z1h6^NQI88Y1g6`NQI77Y1h6aNPI68Y1h6bNoH59Y1h6cNoH39W1RN[Ng8;oH28V1m6jNiH0;Y1h6gNnHN<Z1f6iNnHK>Y1f6lNZKV1d4jN]KU1c4kN]KU1c4kN]KU1c4lN]KT1b4lN^KT1b4mN]KS1c4nN\\KR1d4nN\\KR1d4oN[KP1f4ROYKm0g4TO[Ki0e4WO^K[OkKY1g8]OaK?a4A`K=a4C`K:b4D`K<`4CaK<`4AcK?]4_OeKa0[4@dK>^4@dK>^4\\OgKe0Y4[OgKd0Z4[OgKe0Y4ZOhKf0X4ZOhKf0X4ZOhKg0W4YOiKg0W4ZOhKf0X4]OeKc0[4^OcK>b4C]K=c4C]K;e4D\\K;e4FZK:f4FZK;e4E\\K9e4G\\K8d4H]K7c4I]K6d4J[K6f4I[K6f4KYK5g4JZK6f4JZK6f4J[K5e4K[K5e4L[K3e4NZK2f4N[K1e4O[K2d4O\\K0d41[KOe41[KOe41[KOe41[KOe4O]K1c4N^K2b4N^K2WKCY9:aK5_4JbK6^4IcK7]4HdK8\\4HeK6\\4JdK5]4KcK4^4LcK2^4JfK5[4JfK4\\4LhKOY40\\500000PWY5", "size": [480, 640]}, "area": 20233.0, "iscrowd": 0, "image_id": 15517, "bbox": [0.0, 0.0, 279.0, 201.0], "category_id": 157, "id": 20000973}, {"segmentation": {"counts": "]<f1Z=O002OO0012NO10000O100000O2lNfBAOl0Z=@eBD;b0E^O`=2`B50I?=U=C]B7f>O2JZA1gPS20S^mM8G4M2N4L1O0O2O1O3M0O3N2N5K2WBjNe=Y14K1nN]Bf0T>J6J8DVA3RXf6", "size": [480, 640]}, "area": 1348.0, "iscrowd": 0, "image_id": 15517, "bbox": [0.0, 395.0, 183.0, 56.0], "category_id": 167, "id": 20000974}, {"segmentation": {"counts": "b6n1R=1O00O10O1000010OO11N11O00O103QCoMi<V2YCiMa<\\2O0nCdM@0l;`2bDbM^;`2WD^MM00032i;i2UDUMO2l;l2TDTMh;OXDR3f;5000001O01O00O1000000N200OJ_DoLa;i2`DTM32];f2c000000O01001OO2NN3O100000101M2O1O1O002NO0010000PMTDl2l;TMUDk2k;TMWDk2i;VMVDk2i;SMUD04k2P<0OO100O101O00O100002NN20000O11O000O01000aMkCS2V<hMkCK0]2U<iMnCV2R<iMoCV2Q<kMmCR2MjMW<3mCS2LiMY<3kCT2LiMZ<3iCT2MhM\\<3hCT2c<O10001O0000O0100O1DVC^N2Ji<>UCc0]=\\OdB9OZOg=2[B40?i=[OXB23b0l=\\OWBa0T>00001O0013L002NO11O000N_AEa>:`AF`>:`AF`>:`AFa>84O10000NYAKg>070ngT10_WkN0UB2j=OgAO60J5X>LgA00O0118X><Fj0B1J3O0020OON113NO2N6K0N3O001GUN[Ck1e<WNXCj1h<7001O0000000N3N1O15K00O1O2K4N2000]CWNo;h1QDYNP<f1PDZNP<g1lCVNE2`<g1hCYNGO02`<f1jC`NV<`1iCaNX<]1iCeNU<\\1jCcNW<\\1jCcNW<]1iCdNV<[1jCeNLFS<f1nCfNMEU<e1kCjNNBW<d1iCSOW<k1001O000000001O00000000001O000000001O000000001O000000001O000000001O000000001O001O1O1O1O1O00000000O1000000O100000aN]Ca0c<_O^C`0b<@_C?a<@`C`0`<@bC>]<BeC2ITOO013b<d0]CYO96IK366JX<f0hC@N0h<c0XC]O1Mh<f0XC]O1Jh<i0XCVOE5_=f0oBWOBO^=j0QCVOBO\\=j0UCWOm<h0e01O10O01O^OQB5o=HUB2IJR>3WB3l=KkAN;5_>Mn]R3MWamL0kB0WO0n=5dB1Z=0VBJ:7[=JXB72I:7[=:bBH]=9bBJ[=6^B_OON2<c=m0000001O0O11000O2N010O11NO12O1O00dKjNlJW1P5lNPKT1P5mNoJS1j4hNTG6P4S1k4UOSKl0n4UOPKm0o4SOPKn0P5ROoJm0S5TOlJl0T5UOlJk0S5VOXGZOL7U3^1d5B[J`0QM\\NX83bGj0P3m0QMYN]8OcGk0m2Z1_5hMcGN1P1\\2POdM030Mb2`8^NlIQOcMj2a8VNlIm1cMRM_8P1bIUORNKNo2]8RNcIVOoMl2b8jM_I_2c6_M]Ia2d6^M\\Ib2f6\\MZId2f6\\MYIe2g6[MYIe2f6\\MZId2f6\\MZId2f6YM]Ig2c6YM]Ig2c6SM[GFo1W3f6RMdIn2[6SMeIm2[6SMeIm2\\6RMdIo2[6QMeIP3Z6PMfIo2[6RMdIn2\\6RMgI@eMX3d8WMhIAdMW3e8XMkIg2T6ZMeIn2Z6RMfIn2Z6RMfIn2Z6RMeIQ3Y6oLgIQ3`6dLYGKW2a3`6_LeI0dMa3n:_LTEh3i:XLTEn3l:RLTEn3l:RLUEl3l:ULREl3n:310O1O001OJ6N21kE", "size": [480, 640]}, "area": 26070.0, "iscrowd": 0, "image_id": 15517, "bbox": [0.0, 193.0, 640.0, 287.0], "category_id": 169, "id": 20000975}, {"segmentation": {"counts": "f9125e>4O0O2O001O001N2O1O1O001O1N2O0000001O0O2O1jAZOQ>k0O1O1O1O002N6J001O0000000010O000001O000000001O0000001O01O000001O0000001O0000000010O000001O00001O001O1O001O1O01N10000O10000O1000O100000000000000000000000O10001O00001O1N2O00000000001O000O10`NSOmDm0S;SOmDl0T;TOmDk0S;VOlDi0U;WOkDi0U;WOkDi0T;XOlDg0U;ZOjDf0V;ZOjDe0W;[OjDd0V;\\OVE8j:HhEEX:<V20000000001O00O1000001O000000O01000NfAES>d000010O000001O00000O2O0000001N100ZDTOX9k0gFWOY9h0fFZOZ9e0PF3P:]O]D2^1g0U:WO]D2X1n0[:oN]D2X1P1\\:gNbD8S1Q1]:eN`D9T1S1c:lN]EU1c:jN^EW1a:iN_EW1a:iN_EW1a:iN^EX1b:hN^EY1a:hN^EX1b:hN]EY1c:gN]EY1c:gN]EZ1b:fN^EZ1^:^N\\D8U1[1^:^N]D7U1[1^:^N]D8T1[1^:]N^D8S1\\1_:\\N^D8S1\\1_:\\N^D8S1]1^:[N_D8S1]1^:[N_D8OJm0d1e:ZN_D?k0W1f:ZN_D?k0X1e:YN`D?j0Y1f:XN`D?j0Y1f:XN`D?k0X1e:YN`D?k0X1e:YN`D?k0Y1d:XNaD?k0Y1d:XNaD`0j0X1e:XNaD`0j0X1e:XNaD`0j0Z1b:WNcD`0k0\\1_:TNfD`0l0\\1]:TNgD`0l0\\1]:TNgD`0l0\\1]:TNgD`0l0]1\\:SNhDa0k0\\1]:SNhDa0k0]1\\:RNiDa0k0]1\\:RNiDa0k0]1\\:RNiDa0l0]1Z:RNjDa0l0]1Z:RNjDa0l0]1Z:RNjDa0l0\\1[:SNiDb0k0Z1]:TNhDb0k0Y1^:UNgDb0k0Y1^:UNgDb0k0X1_:VNfDb0k0X1_:VNfDb0l0W1^:WNfDb0l0W1^:WNfDb0l0W1^:WNfDc0k0V1_:WNfDc0k0V1_:WNfDc0k0V1_:WNfDc0k0V1_:WNfDc0k0W1^:VNgDc0l0V1]:WNgDc0l0V1]:WNgD219k0^1]:WNgDd0k0U1^:WNgDc0l0W1\\:VNhDc0k0X1]:UNhDc0k0X1]:UNhD0O?l0\\1]:UNhDc0k0X1]:UNkDa0g0Z1^:UNkDa0g0[1]:TNlDa0g0[1]:TNlDa0g0[1]:TNlDa0g0[1]:TNlDa0g0[1]:mNcET1\\:mNeEQ1[:oNeEQ1[:oNeEQ1[:oNfEP1Z:POfEP1Z:POfEQ1Y:oNgEQ1Y:oNgEQ1\\:lNdET1^:jNbEV1`:hN`EY1_:gNaEY1_:gNbEX1]:iNcEW1]:iNcEW1]:iNcEX1\\:hNdEX1\\:hNdEX1\\:hNdEX1\\:hNdEX1\\:hNeEX1Z:iNeEW1[:iNeEW1[:iNeEW1[:iNeEW1[:iNeEX1Z:hNfEX1Z:hNfEX1Z:hNgEW1Y:iNgEW1l9VOTFj0l9VOTFk0k9UOUFk0k9UOUFk0k9UOUFk0k9UOUFk0k9UOUFl0j9TOWFk0i9UOWFk0i9UOWFk0i9UOWFk0i9VOUFl0j9TOVFl0j9TOUFm0k9SOTFn0m9QORFP1n9PORFQ1m9oNSFQ1m9oNSFQ1m9oNSFQ1m9oNTFP1l9POTFQ1k9oNUFQ1k9oNUFQ1k9oNUFQ1k9POTFP1l9POTFP1l9POUFP1j9POVFP1j9POVFP1j9POVFP1j9POVFQ1i9oNWFR1h9nNXFS1g9mNYFT1f9lN[FT1d9lN\\FU1c9kN]FU1c9kN]FT1d9mN[FS1e9mN[FS1e9lN]FR1d9TNbE9k0b1c9TNjE3d0h1b9UNPFL`0n1`9VN[Gi1e8WN[Gi1f8VNZGi1g8WNQFI?P2`9WNoELa0l1`9XNmENc0j1`9XNkE0e0h1`9XNiE3f0e1a9XNiE3f0e1a9XNiE3f0e1a9XNiE4f0b1b9ZNhE4f0b1b9ZNhE4f0b1b9ZNhE5e0a1c9ZNhE5e0`1d9[NgE5f0^1d9]NfE6e0]1i9VNdE>c0[1j9UNeE`0a0Z1^:fNbEZ1e9fN`E0l0X1e9hN_E0l0W1f9iN]E1m0V1f9iN\\E2n0T1g9jN[E2n0T1g9jNZE3P1Q1g9lNYE3a1?W9^OXE3i16P9GWE3P2Mk80UE3a=M_B3a=M_B3a=M_B3a=N^B2b=N^B2b=O]B2b=O]B1c=O]B1c=O^B0b=O_B1a=O_B1a=N`B2`=L_B7a=d0000^O\\BHd=8\\BHd=8\\BHd=8\\BHd=8\\BHd=8\\BHd=8\\BHd=8\\BHd=7]BIc=7]B\\OP>d0PB[OQ>d0PB\\O:0V=d0_B]O64[=>_B_O28^=Q10O100000000001O000000_O_BCa=6_B@0:a=6`B_OO;a=7`B]OO<a=M_BM0J0<a=M_BM0J0<a=M_BM0J0<a=M_BM0J0<a=M_BM0I1=`=M_BM0I1=`=M_BM0I1=`=M_BM0I1=`=M_BM0I1=`=M_BM0I1=n=JQBI1=n=JQBI1>m=HSBJ0>m=HSBJ0>m=HSBI1?l=HTBH0`0l=HTBH0`0l=HTBH0`0l=HTBH0`0l=ISBG1`0l=ISBG1`0l=ISBG1`0k=JXB6h=JXB6h=JXB6h=JXB6h=JXB6h=JXB6h=JXB6h=JXB6h=JXB7g=IYB7g=JXB6h=JXB6h=JXB6h=JXB6h=JXB6i=IWB7i=IWB7i=IWB7i=HXB8i=HVB8j=HVB8j=HVB8j=HVB8j=HVB8j=HVB8j=HVB8j=IUB7k=IUB8j=HVB8j=GWB9i=GWB9i=GWB9j=ARB04?o=AQB?o=AQB?o=AQB?o=AQB?o=AQB?o=AQB?P>@PB`0P>@PB`0P>AoA?Q>BnA>R>CmA=S>DlA<T>801O0000000000000000000000000000000001O01O0M3000\\OQB7o=JTB2l=NWBOj=4VBHj=?SB]Om=f01ZOnAg0o=[OQBe0k=:O2O00VOWB=i=CWB=i=CWB>h=>O01N100000000O100000000000000WOVB<j=DVB<k=CVB<j=Di01O2N`0@00000000O1000001O1O001N2O1O1O1O2M4FXa_1", "size": [480, 640]}, "area": 28078.0, "iscrowd": 0, "image_id": 15517, "bbox": [0.0, 263.0, 538.0, 144.0], "category_id": 183, "id": 20000976}, {"segmentation": {"counts": "]8c;]8000000000O1N200000000O100N200N2O1O1M3001OO1O1000000O1002N0000000QDQHm;Q8000O1000000O100O100O1O100O100N200O1N2O1O1O100O100O1O100O100O100O1O100O100O1000000O10000O1O\\DoH[:Q7]E_I[:a6^EmI[:S6]E\\J\\:d5aEbJ\\:]5cEiJY:W5fEnJV:S5gERKV:o4gEWKU:^7L3M3M3M2N3M2N3M3M2N3`GXEb7l:YHYEc7k:XHVEh7n:SHSEm7R;lGPET8^;0000jG[DQ8e;nG\\DR8h;10000000000000000O10000001OZORH[Em7g:THVEl7l:QHVEn7i:SHXEl7g:THZEl7f:TH[Ek7f:SH[Em7d:SH]Em7d:RH\\En7d:QH\\EP8Q6PHlM0SLP8Q6QHjMOVLP8P6RHhMNYLP8o5SHgMMZLP8n5UHfMK]LP8m5VH^Nj7b1WHcMI`LQ8m5WHRNI`KP8^6XHQNHaKP8^6ZHnMHcKn7^6]HmMFcKn7`6^HkMDdKo7^6mGaIa0[4EdKn7`6bHjMf7V2[HhMf7X2\\HfMd7Z2]HeMc7[2_HbMb7^2_H`Mb7`2`H^M`7b2bH[M_7e2cHXM^7h2cHVM^7i2eHUM[7k2gHRMZ7m2iHPMX7P3jHmLW7S3kHkLU7U3mHWJ_Ni1e8n3PIXJaNb1`8V4PIXJeN[1\\8_4oHUJfNZ1[8a4PIUJfNW1[8c4QIVJfNS1Z8g4QIVJfNP1Z8j4QIVJfNn0Y8l4RIUJgNl0X8o4RIUJgN9[OCm8o5RIUJgN7ZODn8P6RIUJfN5ZOGn8o5SIUJfNc0W8W5UIVJdN1YOK3De8Z6\\IVJdN?YOSOg8X6]IVJdNd0o7U5_IXJaNb0P8V5`IXJaN`0o7X5aIYJ^N?Q8X5bIYJ\\N?R8X5cIZJXN`0T8V5eIZJVN`0U8V5gIZJRN?X8W5gIZJPN?Y8W5iIZJkM?]8V5jI[JhMOFIh8m5lIZJeM0GHi8n5mIYJbM0IIh8m5PJYJ^M1JHi8n5QJXJ[M2KHi8n5SJWJXM2NGh8Q6SJUJWM2NHh8Q6UJTJTM20Hh8R6UJTJRM21Gi8S6UJTJQMO4Gh8V6TJTJnLN8Fh8Y6SJRJkLN<Eh8[6RJYJa6g5`IRJf6n5[IQJe6o5\\IQJc6o5^IQJa6o5`IQJ_6o5bIlIb6T6`IgIc6Z6a3001O00000000001O00001O0000O11O00001O0000001O0000001O00000000000000001O001eB]IW=g6iBZIR=i611O0000002N00O100000000O10000O1O1O1O11O001O00001O00001O001O001O002N00O1001O1O00O11O001O001O001O001O002N001O00001O001O1O001O001O001O1O001TJaHUO_7j0fHRO[7m0gHQOZ7m0gHSOY7l0hHTOY7j0iHUOW7j0jHVOW7h0jHXOW7f0jHZOV7e0kH[OV7d0jH\\OW7c0iH]OX7b0hH^O[7?eHA\\7>dHB^7<bHD]7=dHB^7<bHD_7:bHF_79aHG`78`HHa77_HIc75]HKe73[HMf72ZHNg71YHOi7OWH1j7NVH2k7LVH4k7KUH5n7`J_GV5b0;R8DnG<S8CmG=T8BlG>U8AjG`0W8_OiGa0X8^OhGb0Z8\\OfGd0[8[OeGe0]8YObGh0`8VO`Gj0a8UO_Gk0a8UO_Gk0`8VO`Gj0_8WO`Gj0`8VO`Gj0_8_JRGc4?n0_8^JSGc4?o0`8\\JPGf4?o0c8RO\\Gn0`8WO_Gi0_8ZO`Gf0^8]OaGc0\\8@cGa0[8@fG`0X8BhG>W8BjG>T8DkG=U8BlG>S8ClG>S8BmG?R8BnG>Q8CnG>R8AoG?P8]OfGkJ9i5R8ZOgGkJ7l5R8XOTHh0k7XOUHi0j7WOWHi0i7VOWHk0h7UOYHk0f7UOZHl0f7TOYHm0f7SO[Hm0c7TO]Hm0c7SO]Hm0b7TO]Hm0a7UO_Hk0a7TO_Hm0`7TO`Hl0_7UOaHk0^7UOcHk0\\7VOdHj0V7\\OiHe0V7\\OjHd0V7[OkHe0S7]OmHc0P7@PI`0o6@RI`0m6ASI?l6BTI>l6BTI>k6CUI=k6CUI=j6CWI=h6DXI<g6EXI<h6DXI<g6EYI;g6EYI;g6DZI<d6F\\I:d6F\\I:c6G]I9c6G]I9c6G]I9c6G]I9b6H^I8b6H]I9c6G]I9c6G]I9c6G]I9d6FTIVJHU6S7FUIVJGT6U7ESIc0n6W6000000000000", "size": [640, 433]}, "area": 160791.0, "iscrowd": 0, "image_id": 15597, "bbox": [0.0, 202.0, 433.0, 438.0], "category_id": 96, "id": 20000977}, {"segmentation": {"counts": "0]8c;0000000000O1N200QNbD\\K^;d4dDZK\\;e4gDYKY;g4iDVKX;j4jDTKV;k4lDRKV;n4lDPKT;P5nDlJT;S5nDkJS;U5dDjIGP1f;U5dDoIAj0n;W5bDPK^;P5[DjILV1h;o4]DnIHR1l;P5[DQJHm0n;R5ZDSJFk0P<Q5[DYKe;g4[DYKe;g4[DXKf;g4[DYKe;g4ZD\\Kd;d4\\D\\Kd;c4]D]Kc;c4]D]Kc;c4]D]KDaNm;Q6_DaKa;_4_DaKa;_4^DaKc;^4^DbKb;^4^DbKb;^4]DcKc;\\4^DcKc;]4\\DdKd;\\4\\DcKe;]4ZDdKf;\\4ZDcKg;]4YDbKh;^4XDbKh;^4XDaKi;_4WDaKi;_4WD_Kk;a4UD_Kk;a4VD]Kk;c4UD[Km;e4SDZKn;f4RDYKo;g4RDWKo;j4PDVKP<j4QDTKP<l4PDTKP<l4PDSKQ<n4oCPKR<P5nCPKR<P5nCoJS<Q5nCnJR<S5mClJT<T5mCkJS<U5mCjJT<V5lCiJU<X5kCgJU<Y5kCfJV<Z5jCfJV<Z5jCeJW<\\5iCcJW<]5iCcJW<]5iCcJW<]5jCaJW<_5iCaJW<`5hC`JX<`5hC_JY<a5hC]JY<c5gC]JY<c5hC\\JX<d5iC[JW<f5iCYJW<g5kCVJV<j5kCUJU<k5lCUJS<k5nCUJ[OEd<W6S1000000J[BQJe=l5^BTJb=k5_BUJa=j5`BVJ`=j5`BVJ`=i5aBXJ^=g5cBYJ]=g5cBYJ]=f5eBYJ[=g5eBYJ[=g5eBYJ[=g5?O10000N2O1O1N200O100000000000000000000O1001O00001O001O006J3M001O001O002N2N2N5K7I00000000000000000000O100O1000000N2002N00O100000000000000O10000O10000000000000000O10000O100001O000000001O00000000000000001O001O00000000001O00001O0000O11O00001O0000001O0000001O00000000000000001O001eB]IW=g6iBZIR=i611O0000002N00O100000000O10000O1O1O1O11O001O00001O00001O001O001O002N00O1001O1O00O11O001O001O001O001O002N001O00001O001O1O001O001O001O1O001O001O1O001O001O1O001O1O1O3M1O2NO12N1O1O1O1O2N2N1O1O2N1O1O1O3`D_G[;d8O1O1O1O1O1O2N1O2N2N1O00O1O100OTERGh:n8WESGi:o8UEPGl:R90L4N2N2M3N2N2O1N200O1O1O1O100O11O00O1O100O1O100O1N200O1N200O1O1O1O1J6O100N2M3O1O1O100O100O1O1O100O10000N200O1000000O1000000001OO11O1O000000000000", "size": [640, 433]}, "area": 98546.0, "iscrowd": 0, "image_id": 15597, "bbox": [0.0, 0.0, 433.0, 292.0], "category_id": 157, "id": 20000978}, {"segmentation": {"counts": "[j58hc02M3N2N2M2O2N2M10Hk\\OETc0<P]O@Pc09j\\OM]c03e\\OKZc06h\\OIWc07k\\OGUc09810O10O10000O10O10O10O10O0100O010O100O1000000001O000000001O00010O001O01O01O00010O001O01O01O0001O01O000010O0000U^OIm?8j_O8n?Ij_Oe0o?\\Oh_OS1R`0mNj_OY1S`0iNi_O]1S`0dNk_Oa1Q`0`Nl_Oe1Q`0\\Nm_Oi1P`0W1L3M3M3GTLX@n3c?UL]@n3_?SLa@o3\\?RLd@Q4Y?oKg@T4U?mKl@T4Q?mKo@V4f0aK]=9mAZ4a0bK]=5RB]4<_Kb=4RBa47\\Kg=3RBf40YKn=d5O10YMhBfNYOTNk=m2ZCjNjNZNj=j2dClN[<S1lChNS<X1RDdNl;]1]D[Nc;d1_DZNa;g1`DXN`;i1`DVN`;j1aDUN_;k1aDUN_;k1aDUN_;l1`DTN`;l1`DTN`;l1aDhMiMBg=f2iD^M`MOe=c2XE^Mh:c2WE^Mg:c2XE_Mg:a2YE`Mg:_2XEbMh:_2WEbMh:^2WEcMj:\\2UEdMl:\\2TEdMm:=\\Bl0f2gNR;:YBo0d2gNX;5UBS1b2iNZ;3UBS1`2kN[;2VBS1^2kN];1VBS1\\2mN^;0WBR1Z2fN`MIP>=XBT1W2fNaMIP>=YBS1U2gNdMHP><YBS1S2iNdMHS>9XBU1o1kNgMEV>8UBW1m1lNjMC\\>3oAb1e1hNPNBU?k1dBcNg=`1TB`Nm=a1PB`NR>`1kA`NW>a1gA_N[>a1bA_N`>a1_A^Nd>b1T@SNj0;U?m1h@TNZ?k1e@TN^?l1_@TNd?k1[@TNh?l1U@UNl?k1R@UNQ`0k1m_OTNV`0k1h_OVNZ`06]_Oh07QO_`06`_Oc0OWOd`05__Oc0JXOi`05^_Ob0HYOk`05^_Ob0DYOPa05]_Oa0BYOSa06]_O>_O\\OVa05]_O<]O@Wa04]_O:\\OAYa05[_O8\\OC[a05Z_O6[OD]a06X_O4[OG^a05X_O2YOJ`a04X_O0XOL3CXa0a0]_ONXOO1EZa0>^_Od0c`0]O\\_OIXO;]a0L\\_O`0e`0AY_OEYO?_a0LW_O?j`0BS_O?n`0AQ_O?Qa0Am^O?Ta0Ak^ONAKga07e^OODJia06b^OOGIja07^^O0HIla06[^O1IHoa06W^O1KHPb07T^OCI83NRb06Q^OCJ95LSb07n]OBJ<5KUb06k]OBK>5IWb07i]O01HXb08f]ON5GYb0:`]OM:GYb0<[]OLWc078L4000000M3L_dS2Lg[lM5L2M1O1O1O1O1O1O101O0000000000000O100000001O000000O10O100000O100000000O100000000O1000000O10O1001O1N2O1O00O10O010O010O010O1O01KFd\\O9\\c0Ja\\O7^c050001O001O010O0010O1O10O01000O1000000O10000O10O10000000O10000000000O10O10000000000000000O1Ie\\OI[c07f\\OHZc07h\\OGYc07To3", "size": [640, 433]}, "area": 14027.0, "iscrowd": 0, "image_id": 15597, "bbox": [9.0, 177.0, 417.0, 286.0], "category_id": 183, "id": 20000979}, {"segmentation": {"counts": "djP21e:0]E3a:6O0_EG`:?L1dEAQ:0SFd0i9@TF`0j9DUF:n9:1FQFBR:<nEDR:<oEDP:=PFBOLP:b0SFAm9?TFAj9a0VF^Oj9b0WF]Oi92SFO065Ii90[F0FO00e:1[EOj:1kUa4", "size": [348, 640]}, "area": 395.0, "iscrowd": 0, "image_id": 15660, "bbox": [190.0, 253.0, 22.0, 35.0], "category_id": 132, "id": 20000980}, {"segmentation": {"counts": "e4W6e40000000000000000000000000000000000000000000000000000000000000000000000000000000000QN\\KfMd4Z2\\KfMd4_1fKQMH`1b4]1YLcNg3\\1[LcNe3]1\\LbNd3^1\\LbNd3^1[LcNe3]1[LcNe3]1[LcNe3^1ZLbNf3^1[LaNe3_1[LaNe3_1[LaNe3_1\\L`Nd3`1\\LRMQOo0c4o1\\LjLYOW1[4o1_LfLWO[1Z4o1_LfLWO9Fg0d4Z2_LfLWO1H2OMOn0d4Z2\\KhLe10^N0Mn0d4Y2[MgMe2Y2[MgMe2Y2[MgMe2Y2[MgMe2Y2[MgMe2Y2[MgMe2Y2[MgMe2Z2ZMfMf2Y2PMiLeNn0[4Z2nLjLeNm0]4Y2nLkLeNk0]4Y2nLnLdNi0^4Y2mLQMbNg0a4o1\\KRM00f1\\1n2^1ZMbNf2^1ZMbNf2^1YMcNg2]1YMcNg2]1YMcNg2]1XMdNh2\\1XMdNh2]1VMdNj2\\1VMYMVNU1d4b1VMUMVN20W1d4a1YMWMTNX1c4b1XMVMUNX1c4c1\\KTMl1Z1h2d1YM[Ng2f1XMZNh2f1WM[Ni2e1VM\\Nj2d1UM]Nk2c1UM]Nk2c1UM]Nk2c1VM\\Nj2d1TM^Nl2b1TM^Nl2b1TM^Nl2b1TM^Nl2b1VM\\Nj2d1iKQMR1W1U3Q2`KhL]1U1S3Y2QMcMo2]2TM`Ml2_2UMaMk2_2UMaMk2`2TM`Ml2_2UMaMk2`2TM`Ml2`2TM`Ml2`2SMaMm2]2UMcMk2^2TMbMl2^2TMbMl2^2TMbMl2^2PMfMP3Z2jLlMV3S2fLRNZ3m1gLSNY3m1gLSNY3m1gLSNY3m1hLRNX3n1hLRNX3n1hLRNX3n1fLTNZ3l1fLTNZ3k1gLUNY3k1gLUNY3k1gLUNY3k1hLTNX3l1hLTNX3l1hLTNX3Z2ZLfMf3Z2ZLfMf3Z2ZLfMf3Z2ZLfMf3Z2ZLfMf3Z2ZLfMf3[2XLfMh3Z2XLfMh3Z2XLfMh3Z2XLfMh3Z2XLfMh3Z2XLfMh3Z2XLfMh3Z2XLfMh3Z2WLgMi3X2WLiMi3U2YLkMg3T2ZLlMf3T2ZLlMf3[1aKfMi0a0SOXOc4Y2mKjM=e0TOWOb4X2PLjM;f0TOXOa4V2SLlM7f0VOWO`4V2TLmM6e0XOWO^4U2SLRN7b0YOVO]4U2QLUN:?ZOVO[4U2PLXN<<ZOVOZ4U2oK[N<:\\OVOY4T2nK^N;YOSOo0<UOX4T2nK`N7YOXOo0<TOW4T2lKcN8WOXOn0=TOW4S2kKeN5=IkNW4R2jKhN6:JlNV4Q2iKlN76JmNV4P2hKoN74LmNU4o1gKRO82LmNU4o1gKRO90LoNT4n1gKSO:kN[OS1`0QOT4m1gKVO;KKROS4l1hKWO:KKROS4k1iLVN[Ol0JSOR4j1kLVNYOm0JSOR4j1kLVNYOMXOl0b0XOR4h1lLWN^Of0D[OR4g1nLWN_Od0A^OR4f1oLWN@c0@@Q4e1PMXN@b0_OAQ4e1PMXN_Ob0AAP4c1QM[N]Ob0B@P4b1RM]N[O`0DAo3a1SM^NZO`0DAo3`1TM_NXOa0E@o3T1^KmNh10TO?G@o3R1bKlNe13SO?G@o3R1bMPOfN>J@n3S1aMoNgN>J@n3S1aMiNkM0o0d0G@n3S1`MhNPOe0B@n3S1fN]O\\M@n3S1VMkNC`0YOBn3T1TMmNDJdN`0f0En3T1RMPOG7YOEn3T1PMQOJ6XOEn3T1nLQON6VOEn3U1kLTOO2XOEn3U1iLXOVO^O5>OGm3U1gL\\OVOZO7>OGm3U1eL^OXOYO6=0Gm3U1cL@[OWO4=0Ho3T1^LD_OUO0<4Gn3U1]LEATOO;5Gn3U1SLNMkN_OL8?;Gn3V1oK01iN]OL=;8Jn3Z1iKNFcNO065IK<<8Jo3W3]KlL0FO00:?16Lo3d3]KULO;`005Lo3g3]K\\L?15Lo3T4mKnK5Nn3U4kKnK6No3U4jKmK7No3U4jKmK7No3T4kKnK6No3T4kKnK5OP4m3^KkK>94OP4n3PLSL0OP4P4oKPL10P4Q4nKoK20P4R4mKnK21Q4R4kKmK51P4R4eKdKK9`01P4R4cKhKI50Gd0:P4T4`KQLMAc0:P4T4^KSLO_Ob0;Q4T4\\KSL1^Ob0;Q4g4^KnJa0;Q4g4^KnJa0;Q4g4^KnJ`0<R4e4_KoJ?<R4e4_KoJ?<R4e4SL[Km3d4TL\\Kl3d4SL]Km3b4TL^Kl3b4TL^Kl3b4TL^Kl3a4TL`Kl3`4TL`Kl3`4TL`Kl3_4ULaKk3_4ULaKk3_4TLbKl3]4ULcKk3]4ULcKk3]4ULcKk3]4ULcKk3]4TLdKl3\\4TLdKl3[4ULeKk3[4ULeKk3[4TLfKl3Z4TLfKl3Z4TLfKl3Y4ULgKk3Y4ULgKk3Y4TLhKl3X4TLhKl3X4TLhKl3X4SLiKm3V4TLjKl3V4TLjKl3V4TLjKl3V4SLkKm3U4SLkKm3T4TLlKl3T4SLmKm3S4SLmKm3S4SLmKm3S4SLmKm3S4RLnKn3Q4SLoKm3Q4SLoKm3Q4RLPLn3P4RLPLn3o3SLQLm3o3SLQLm3o3RLRLn3n3RLRLn3n3RLRLn3m3SLSLm3m3RLTLn3l3RLTLn3l3RLTLn3l3QLULo3j3RLVLn3j3RLVLn3j3RLVLn3j3QLWLo3i3QLWLo3h3RLXLn3h3QLYLo3g3QLYLo3g3QLYLo3f3RLZLn3f3QL[Lo3e3QL[Lo3e3QL[Lo3e3PL\\LP4c3QL]Lo3c3QL]Lo3c3QL]Lo3c3PL^LP4b3PL^LP4a3QL_Lo3a3oKaLQ4_3nKbLR4^3lKdLT4[3kKgLU4Y3iKiLW4W3hKjLX4V3fKlLZ4T3dKnL\\4Q3cKQM]4o2bKRM^4n2`KTM`4l2^KVMb4j2\\KXMd4`400000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000O1000000000000001OO1001OO1001O00000000000000000000000000000000O1000000000000000000000000000000001O00000000O11O00O1000000001OO11O00000000000000000000O10000000000000000001OO11O0000O10000000000000000000000001O000000000000O100001O0000000000O10000000000000000000000000000000000000000001O000000000000O100000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000", "size": [348, 640]}, "area": 114556.0, "iscrowd": 0, "image_id": 15660, "bbox": [0.0, 148.0, 640.0, 200.0], "category_id": 155, "id": 20000981}, {"segmentation": {"counts": "0e4W6000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000jLoIa1Q6]NUJ_1k5`NYJ]1g5bN[J]1e5bN]J]1c5bN_J]1a5bNRJmN1`2m5bNQJUONX2Q6bNoIZO0S2Q6bNoI^O0n1Q6dNnI@1k1Q6dNoIB0i1Q6eNnIEOf1S6dNoIGMe1T6dNoIY2Q6fMPJZ2P6gMoIY2Q6gMoIY2Q6gMoIY2Q6gMoIY2Q6gMoIY2Q6hMnIX2R6hMnIX2R6hMoIW2Q6jMnIV2R6kMmIU2S6lMmIS2S6nMlIR2T6nMmIQ2S6PNlIP2T6QNkIo1U6\\100000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001OO100000000000000000000000000000000000000000000oLoIW1Q6hNUJS1k5mNXJP1h5PO[Jm0e5RO^Jl0b5TO_Jk0a5UO`Jj0`5UOaJk0_5VO`Jj0`5WO^Jj0b5XO\\Jh0d5ZOYJg0g5]OTJd0l5V20000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000O1000000000000001OO1001OO1001O000000nNlI\\MT6b2oI]MQ6b2QJ]Mo5b2SJ]Mm5d2RJ\\Mn5d2mIaMS6_2nI`MR6a2lI`MT6d300000`LmIW2S6fMRJX2n5fMmISO2T3R6hMlIZONm2V6hMlI]OMk2W6gMlIf2T6YMlIh2T6XMlIh2T6WMlIj2T6VMlIj2T6VMkIk2U6UMjIl2V6TMjIl2V6TMjIl2V6TMkIk2U6UMkIk2U6UMkIk2U6VMkIi2U6XMkIg2U6ZMjIg2U6ZMkIe2U6\\MkIc2U6_MjI`2V6S10O11O00O1000000001OO11O00000000000000000000O10000000000000000001OO11O0000O10000000000000000000000001O000000000000O100001O0000000000O10000000000000000000000000000000000000000001O000000000000O10000000000000000000000000000000000000000lNkIaMU6_2kIaMU6^2mIaMS6_2UJYMk5g2UJYMk5h2TJXMl5h2TJXMl5j2RJVMn5l300000000000000000000000000000000000000000000000000000000000", "size": [348, 640]}, "area": 94506.0, "iscrowd": 0, "image_id": 15660, "bbox": [0.0, 0.0, 640.0, 150.0], "category_id": 157, "id": 20000982}, {"segmentation": {"counts": "_nm06d:6K4L2N2NEjE3T:LSF0k9N[F0c9NaF2]9OeF0Z90gF1V9OlF0T90nFNQ92m00O11O0001O0001O0000100O1O10O010h]<3TZCLP5`0lJAQ5b0mJ_OQ5c0oJ^Oo4b0QK@l4?WKBg4=ZKEd4:^KG`48aKI^46dKKZ44gKMY41iKNW41kKOT41lK0S4OoK1P4NQL3n3MSL3m3LSL5l3KUL5j3KVL6i3LVL4i3m0VKTOi4m0WKSOi4n0UKSOj4n0VKROi4o0WKQOh4P1WKQOh4P1XKPOh4P1WKQOg4Q1YKoNf4R1YKoNf4R1ZKnNe4R1\\KnNX4]ORJg1f1lNV4AQJd1i1kNV4`1iKaNX4^1hKbNX4^1hKaNY4_1gKaNY4V1lI]Nj1=Z4T1QJ]Nd1?\\4Q1TJ_N_1`0]4o0YJ^NZ1c0]4m0]J_NU1d0^4k0YLUOh3h0ZLXOf3f0[L[Oe3c0]L]Oc3a0_L_Oa3?aLA`3;cLE]39eLG[3OoL0T3KoL5X3_OmLa0g600O10O100000O1000000O100000O10OO]OkEc0T:^OlEb0T:^OlEb0o1Bo5LRHa0o1Ii5GXH`0o1Lf5D[H`0o1Oc5A^H`0n12a5_OaH?n13`5^ObH>o15^50aJ0_51bJN^52cJM\\53gJJZ56jJEW5;mJ@S5a0d200O010000O10O1000000O010000000O0100000O1000O01000000O01000O100000O0100000O010000O10O1000O1000O01000000O01000O10O1000O1000O10O10000O010N2N2O0O2N2N2N2O0O2N2N2NQf\\12mYcN3N2N20N1L401MZn03bQO5LJfE0W:0lEOR:1<00010O0100O1000000001O010000O101OjjW11TUhN1N2O8H01O00d]:", "size": [348, 640]}, "area": 6017.0, "iscrowd": 0, "image_id": 15660, "bbox": [88.0, 27.0, 521.0, 316.0], "category_id": 183, "id": 20000983}, {"segmentation": {"counts": "TQW12nc0017G4L6J1IAl\\O`0Uc050O1O2O00N2012MO2N00N22OOO3O1M110N101O3N0O011N3M2OO1O1NHoNe]OR1[b0mNe]OT1Yb0nNe]OT1Zb0nNb]OT1^b08O03M1N0N23N0O1M5L2O2O00003M00N120N10O1O02O018G1O1O1N2N201N2L4O100L4M3O000K500N2M3L4N2O1O100O1Jn^]5", "size": [640, 427]}, "area": 3005.0, "iscrowd": 0, "image_id": 15746, "bbox": [62.0, 254.0, 87.0, 61.0], "category_id": 97, "id": 20000984}, {"segmentation": {"counts": "T4\\2fa0N0000000O1O100000000O10011nNW^OIja05Z^OHga06h^O]OVa0c0T1M3001OO100001\\OSOS^On0ja0UOU^Ok0BQOVb07U^Og0FVOQb03Z^Of0GVOna03\\^Oh0EYOka00W^OH1^1ea0kNZ^OIN^1fa0jN\\^OHMa1ea0f010OO0hNZ^OPO6P1]a01h^OH[a09j^OAWa0?i^O@Xa0`0h^O_OZa0>h^OBYa0=g^OBYa0`0f^OQO_O<la0c0i^OZOXa0e0i^OYOWa0h0k^OWOo`0KZ^OP1g0TOm`0U1R_OhNPa0X1Q_OhNi`0Ma^O\\1Zb0fNf]O]1Wb0601O1O0O10001O0000XOl]ODTb0;n]OATb08j]O\\O29Vb0;T^ODla09V^OH6Lc`0:W_OK6Lc`0Gh^ON2:?53Le`0Em^O9=42Nm`0IU_O9MOn`0GW_O:I3o`0AZ_O9F:ka0HT^O<ha0CY^O<ha0DW^O>ha0BX^O>ha0BX^O>ga0EW^O;ha0DZ^O;ha0CY^O<ea0HZ^O8ea0H\\^O7da0I_^O4]a0_Oo]O<g03Za01f^OO[a00f^O0Za0Mi^O2Xa0Nf^O5Za0K]^OBCg0oa0F^^O`0ca0^O[^Oe0ea0ZO\\^Of0da0ZO\\^Of0da0ZOZ^Oh0fa0XOZ^Oh0ea0ZOZ^Of0fa0ZO[^Of0ca0]OZ^Od0fa0]OZ^Oa0ha0_OW^Od0fa0]OX^Od0ia0kNQ^O?5e0JnNQb0=T^Oh06iNYa0`0`^OIMb06UOK0aa0b0b^OCNLMh02_Oaa0;o^O4@BNHaa0a0P_Of0Pa0ZOP_Og0o`0ZOP_Om0i`0ROY_On0h`0oNZ_OP1e`0QO[_Oo0f`0POY_OQ1g`0nNZ_OR1g`0mNY_OS1h`0lNX_OT1h`0lNY_OS1h`0mNW_OS1i`0mNX_OR1i`0nNS_OU1m`0lNl_ODgNc0[a0Jj^OCU17W`07^^OH\\1J[`0f0`100O1O14g\\OYOQc0n0MN22N1O2N00003M00O1002N00O1LX]OPOhb0P1Y]OnNhb0R1X]OnNQ>0nA2a4o0aKoNl=8cFi0`9YO_Fg0_9[O`Ff0_9[OaFf0jKmNX=`0lFc0kKROT=b0kF;lKnN16MJV=h0PGo0o8QOQGP1m8QOZGh0e8YO[Ge0g8[OYGf0f8ZOZGf0e8[O[Gd0f8\\OZGa0i8_OWG`0i8AXG?g8AXGWOfKR1P=GeGTOWKn0W=NbGTOWKm0X=OcG2\\8NeG1[8OfGO[81fGOY81hG0V80jG0V80jG0V80mGOR80jG3V8NjG2V8NjG2W8MjG2V8NjG2X8LgG5Y8KeG7[8IeG7\\8HdG8]8GcG8_8GbG<\\8BfGa0W8]OiGb0Y8]OjGc0oJlNQ==oBDU5R1o7mNlB2_5o0h7ROYHm0g7SOYHl0]=1O001000LS]OTOnb0j0R]OUO01nb0h0V]OXOjb0c0R]O\\O023Olb0b0U]O]OO1mb0`0>NM4L3N3L221O00000P]OTOlb0T1M0OO110O0O1000000O2POR]O;0E0007fc0O2O1JW\\O0mco00W\\PO1cc0O\\\\O2dc051OO20O01N1O11NLHb\\O9]c0Hc\\O7]c061O010H_\\OO_c0Na\\ON0O0012_c0Ob\\OO42fc00i]O0_`0Na_O4Tb03K3O2KDd\\O<\\c0Ec\\O;^c0Ea\\O;_c031O00cNBW_O?i`0AS^O0l0?Qa0BQ^O0m0?Ra0AQ^O0m0?d`0@d^O5ILOOo0?a`0G_^OK10O41NON0Ol0`0f`0?Y_OAh`0>X_OBg`0?X_OBh`0=Z_OBg`0=Y_OCg`0=Z_OBf`0>Z_OBf`0>Y_OCg`0<Z_ODf`0;\\_ODd`0<\\_ODc`0=^_OBb`0D^^O=0C0O0OP1>d`0B\\^O<0D20T1>``0HZ^OK1O[17a`0KS^ON00c16Yb0L0OX\\m0OhcRO3E0`\\O5d9K[F3`9631AEnFO39^93_ONnFE1:b93_O=`0D@<`0D^O>b0B^O>b0A_O?a0B]O?c0A]O?c0B\\O>c0D\\O<d0FZO:f0G_F]OY89hGd0`9J`FXOS8\\1W1fNeFk0T8?R1LkN5U1LjN4X1LfNE[HROH0Y9X1dNYOT2_OnEY1n7WOU2j0jMXOW2g0kMXOS2k0lMRORHBP:^1nMPOR2R1nMnNR2S1mMmNS2T1mMlNQ2T1PNlNP2T1cF^Nj6;f2W1UF_NKOO030Y7:e2X1RF_NM0031M[78e2Y1RFiNNF30R75j2JPFT20mM51KOYa0U2g^OjM1022MOYa0W2g^OiM08Xa0R2h^OnMYa0Q2g^OmM[a0S2e^OoMYa0]2O000O100O1O1002N1ON21NSG", "size": [640, 427]}, "area": 15343.0, "iscrowd": 0, "image_id": 15746, "bbox": [0.0, 97.0, 427.0, 543.0], "category_id": 119, "id": 20000985}, {"segmentation": {"counts": "e9h3=^2X9aMkF]2T9dMnFZ2R9dM]F[L?S6R9dMQG[2Q9cM^F\\L>P6U9gMiF[2W9bMlF^2S9cMmF]2R9cMmF_2S9aMmF_2T9cMiF]2W9bMjF^2V9bMlF\\2T9cMmF]2S9fMiF[2U9dM_FXL=T6T9lIaFd3N\\L<U6Y9_M[F\\L<U6[9\\MhFd2Y9ZMhFf2Z9XMfFcL@S6k9YMgFg2Z9XMeFi2]9TMeFk2[9WMbFj2\\9XMdFh2[9YMfFf2Z9\\MdFd2\\9\\MdFd2]9ZMdFf2Z9[MgFe2Y9[MVFbL`0R6[9]MaFg2[9jIZF`3<e2Z9`McFa2\\9`MdF`2[9`MfF\\LFo5d9dMhF[LDR6d9bMkF_2V9aMgFa2W9cMfF^2Y9aMhF`2W9`MjF`2W9^MjFb2V9^MkFa2U9^MmFa2S9\\MnFf2S9YM^FbL8V6Z9]MgFc2Z9[MhFd2X9_MeFa2\\9^MeFa2\\9^MeFa2[9_MeFa2Y9_MjF`2X9`MeFa2[9_MeFa2[9]MfFd2X9jI[Fb3=d2X9_MjF^2V9bMhF`2Y9_MhF`2W9aMiF_2W9`MnF\\2S9cMlF^2T9bMkF_2U9aMkF_2T9cMjF^2V9bMhF`2W9aMiF_2V9aMjF`2V9SKkFd0OY4V9SKkFc01Y4T9SKlFe0OX4V9RKkFf0OX4V9RKkFc01\\4U9PKjFh0LY4Y9PKkFf00W4R9VKnFc0OX4T9SKnFd0NZ4S9SKoFd0MY4U9RKnFc0O[4T9QKmFd00Z4R9SKnFc00Z4R9SKnFc00Z4Q9SKPGc0N[4R9RKPGd0NY4T9oJPGh0NW4R9nJTGj0JX4Q9mJ[Gg0B^4T9jJ]Gf0^O`4W9hJ]Ge0_Oa4U9iJ\\Gf0\\O_NDP6c9lJ\\Gg0]Oa4V9iJ\\Gf0_Oa4S9kJ^Gd0@`4S9kJ\\Gg0_O_4V9iJZGh0A_4V9hJYGg0G]4P9fJ`Gl0lN]N>T6W9`JcGP1UOa4X9^JeGP1QOd4Z9[JgGP1oNe4Y9ZJlGo0kNg4V9\\JQHl0iNh4X9ZJPHm0jNg4V9\\JRHj0jNi4T9]JSHj0fNk4Y9YJQHl0eNl4Z9XJQHl0gNj4Y9YJPHk0gNn4Y9WJPHl0fNm4Z9WJPHm0eNl4^9TJmGP1dNm4_9RJnGQ1hNh4X9YJPHn0fNl4[9UJnGQ1eNk4\\9UJoGP1dNl4]9TJoGo0cNo4^9QJPHP1bNo4]9QJRHP1cNeNDV6g9UJTHo0bNl4[9UJSHm0bNo4[9TJTHl0]NT5_9QJUHf0SNoN;X6]9SJVHd0SNQO:X6\\9UJUHc0eNV5U9VJWHg0SNlN;Y6\\9PJWHo0\\NR5]9nIVHR1`Nm4X9SJWHP1aNn4Y9QJTHP1WNgN<X6[9oISHR1cNP5X9oIXHo0^NT5Y9nI\\Hk0]NU5X9oI[Hm0ZNV5\\9lIZHn0\\NlNDT6b9VJ^Hj0bNn4Q9WJ]Hi0oMnNb0R6Q9WJ_Hi0cNP5l8YJaHh0`NQ5l8ZJdHc0nMoN?V6n8YJeHb0cNR5h8\\JeH`0nMPOe0f5[OZJ28[98eH>eNf4AdJV97dH>fNf4BdJT98dH=hNd4BgJm8<jH8hNd4BhJn8:hH9hNe4EfJR95aH>jNf4d9eJbGf0jNc4m;]KRDd4n;\\KQDd4Q<\\KoCb4R<^KnCb4S<^KkCc4U<]KlCa4V<^KhCd4X<\\KiCc4X<[KiCd4Z<YKeCi4`<QKcCm4c<kJ_CU5j=Lg0YOg0YOg0YO`0@2N2N2N3N1M3N2O1N200N2N2O1ON2N3L4K4L3j^OfMc`0`2P_O`M27i`0^2X_ObMf`0`2Z_O`Me`0n2O1O1O1O1O1O1O1`0@:F1OO1O1N2N200000000O100002NO1N2O1]OXMl_Oj2S`0YMi_Oi2V`0YMg_Oi2X`0ZMe_Og2Z`0`0N2OkLh_Od2n`0L4L4L4L1O0000O1000jMj^Ob1Va0XNo^Oi1Qa0UNP_Ol1o`0RNT_On1l`0PNU_OQ2\\a0100O10000000000000000O100O100O1O100O1N2MPNa^Oe1]a0WNi^Oi1Ua0SNP_On1m`0PNX_OP2[a0100O1WJPNaIQ2^6XNZIh1f6_NRIb1m6gNkHY1U7nN_HW1a7PORHV1m7SOjDVOX1l1n9UOZDJP1W1e:a1cDeN];f2RD]J1l1l;h3SD[J2m1k;h3ZDXLe;j3ZDVLf;i3\\DVLc;l3\\DTLc;m3^DRLb;m3_DSL`;m3aDSL_;n3_DSL`;i3XD[J9k1_;j3YD[J7k1_;k3gDULX;m3gDSLY;m3hDRLW;P4aDSJOm1^;P4`DSJN0O0012l1_;S4`DRJO42f1[;e4eDTK0^N];]6aDUK4]N\\;S8gDkGZ;Q8iDoGX;o7>KPDXHP<h7QDWHP<i7PDVHQ<i731N10cNmClJS<U5mCgI0l0S<]5nCeI0m0R<_5nCdI0m0R<Q5mCWJ5ILOOo0Q<o4UDQJK10O41NON0Ol0R<T5mDkJR;W5mDiJS;V5nDiJS;i0nCi2n0`LS;g0PDi2l0`LT;f0VDe2f0fLS;e0TDh2i0cLS;e0RDj2k0`LT;b0ZDi2a0fLU;b0XDi2b0gLQ;f0ZDd2e0fLm:k0_D\\2e0jLg:m0eDY2JjK=0C0O0OP1`;P1jDQNFR40mK<0D20T1_;k0oDS2HlKK1O[17jNj:g1PESN=T4DlKN00c16cNl:g1]EW4CRJQ;f1\\EX4BSJR;g1ZE`4f:`K]E]4c:cK]E]4c:cK]E]4c:bK]E_4c:aKYESNHZ6n:cKYEVNGX6n:dK\\Eb4b:bK]EmMF]6l:fK]EmMH^6i:gK^E^4`:dKbEZ4^:eKbE\\4^:cKaE_4^:bKcE]4]:dK`E^4`:aKdE\\4\\:cKfE\\4Z:eKdE\\4[:dKgE[4Y:cKkE[4U:eKjE\\4V:bKmE]4S:dKoEY4n9iKlEnM@[6d:gKnE\\4Q:fKnEZ4R:dKoE]4P:cKPF^4P:aKkEe4T:^KlE`4o9ZIeEU2?`4j9hKVFV4i9kKVFV4e9[IlE10^2?V4d9PL\\FP4e9oK[FQ4d9PLZFR4e9nK]FQ4d9mK`FP4`9PL\\FT4d9mK\\FR4e9lK\\FT4d9kKZFX4f9XImE1OW2?_4j9YIkET2=a4h9[IkES2=c4h9ZIQFl19f4]O^IY:0oEn1;b4@_I`:P2PFW4@jIi:P2fEX4^:fKPEQNc0l5_OVJO3l:k1gEk3GUJE1n:n1eEm3k:TLTEl3P;PLPEn3n:VLSEi3l:YJdDh1`0o3l:WJdDk1`0n3T;QLjDP4U;PLlDP4R;SLmDm3R;WJ]Dk1a0m3W;QLkD_3GhJ^;0ZD6OW1>l3k;nJhCJOX14fNK`5Z<`JgC39;G92YOMb5S=cJnBK0h00m4R=[JSC0N]6j0lIROH0Oe;0gD]65^Jb;b5]D_Jb;`5`D`Ja;a5^D^Jb;c5[DbIBk0U<a5YDaJg;_5YDaJh;^5XDbJh;SOjCZ6>bJk;]5UDcJl;YOfCL2k59SKo;VOgCK1m58RKU<QOfCm54SK`<TOXC`55^Kc<QO]CNJf6j<[I`Cd6a<[I_Ce6l<21O003M1OJ62N3MO1N22N00004LO1", "size": [640, 427]}, "area": 94721.0, "iscrowd": 0, "image_id": 15746, "bbox": [0.0, 298.0, 427.0, 342.0], "category_id": 124, "id": 20000986}, {"segmentation": {"counts": "_1e2\\2`0^9B`F>`9B`F=a9C_F=a9C_F;d9D[F?b9B]F`0b9@^F`0c9_O]F`0d9@\\F?e9A[F?e9AZFa0d9@\\F`0d9@]F?d9BZEYMIU3m:CXE\\MHQ3o:EWEiM]O_2^;GUE]1oNlKg;h2WEc1i:]NWEe1g:[NZEe1d:\\N[Eg1d:XN\\Ei1c:WN]Ej1`:\\KbDj2o0k1]:[KdDW22XNn0U4\\:\\KdDU25YNk0BQO_4];hMjDTNg0FVOY4[;lMhDVNf0GVOV4\\;mMgDWNh0EYOS4X;QNhDnMH1^1o3o9SNlDmMIN^1o3o9UNkDnMHMa1n3l9WNjFi1U9YNkFa1gMXL_;X2iF`1Z9`N^EgMPO6P1`3c:bN^ESNH^3k:^N^EYNAZ3Q;]N^EYN@[3Q;]N_EXN_O]3Q;\\N]EYNBY3S;_NZEXNBY3U;^N[EXNQO_O<j3Y;`NZE[NZOW3[;^NZE\\NYOX3];ZN[E`NWOV3^;TN]DoMP1g0TOV3_;RNfEgNhNZ3a;oMgEhNhNX3`;lM]DVN\\1l4V:lL`DXN]1i4S:oLSFR3l9nLUFR3j9nLWFQ3i9oLWFo2k9PMVFR3h9nLXFR3h9nLYFQ3g9oLYFo2WNXK`;i1YFo2i9QM_EXNDg4m:QM^EZNAV4@WL`;X1XEYN\\O29Y4CSLa;X1WEfNDP4KjKY;a1UEgNH6Lf22jLT;c1TEgNK6Le27fLn:h1bDXNN2:?53Lf28fLm:h1`D^N9=42N^2ImLR<Q2nCfN9MO`2o;mNhChN:I3_2k;ROfChN9F:^2g;1QD`M<\\2b;4QDaM<\\2c;3QD`M>Z2c;6oC`M>Z2c;6oC`M>]2`;2UD_M;^2a;2SDbM;\\2c;3PDbM<[2e;0SDdM8[2f;0RDfM7[2g;NRDjM4W2l;JcC[M<g03U2m;ITDRNOT2o;JQDSN0S2o;JnCVN2o1R<KlCTN5o1P<NkCjMBCg0c2m;0iCjM`0V2g;1gCgMe0W2e;2eChMf0V2e;2eChMf0U2f;3dCfMh0V2e;4cCfMh0V2e;3eCfMf0W2e;3eCgMf0U2e;3hCeMd0n1oNnMe<_2iCeMa0P2POlMf<`2iCcMd0m1oNnMd<c2jC`Md0o1POjMd<h2WCYM?5e0JnNR2n<k0_CXMh06iNZ1P=X1`CWMIMb06UOK0f1m<T1eCYMCNLMh02_Og1d<S1gCfM4@BNHe1l<X1eCdMf0S1f;Y1dCdMg0R1e;Z1eCcMm0k0_;b1cCeMn0e0b;h1]CeMP1a0e;i1\\CfMo0`0e;l1[CcMQ1a0d;l1ZCdMR1`0d;m1YCcMS1`0d;n1XCbMT1`0d;n1XCcMS1?e;o1XCaMS1?f;P2WCbMR1?e;Q2YC\\MU1c0a;R2[CTNDgNc0Q1_<S2[CQMCU17Ll<n1[CkLH\\1J1R=h1dCH\\<8dCF^<:aCF`<:_CFb<:bCkLYO`2X=e0fCTO^<l0`CSOc<m0_CmNe<S1\\CjNf<V1\\CgNe<Y1[CeNg<[1YCdNh<\\1[C`Nf<`1ZC_Ng<a1XC_Nj<`1VC_Nk<a1WC[Nk<e1UCZNl<f1SCZNn<f1nBlLPO]1S>g1mBmLnN\\1V>g1lBmLnNZ1X>i1iBnLoNY1Y>h1hB[NX=f1hBYNY=f1hBZNX=f1iBVMmNg0[>S2hBUMROd0U>X2hBoLnN16MJf0\\>\\2[C^Mj<b2WC\\Mk<j2nBVMQ=k2mBWMR=j2oBTMR=l2nBTMR=l2mBTMT=l2iBWMX=h2gBYMY=h2gBVMZ=i2iBUMW=k2fBXM[=g2dBYM^=f2cBYM]=g2cBYM^=f2aB[M`=d2aB[Mb=b2`B\\Md=Ao@00k2]1dMi=[2WBeMi=[2YBcMh=\\2WBeMi=[2WBeMi=[2WBeMi=[2WBeMi=[2WBeMi=Z2XBfMg=[2YBeMh=Z2XBfMh=Z2XBgMg=Y2XBgMh=Z2\\BbMe=]2`B]Mb=Mg@a2f1cMd=Ji@a2f1`Mh=\\2Q2eM`^O=OA059j0Za0ROf^OOJ15O2o0ka0QO_^OQ1aa0PO_^On0Xb0O4O10OSOS]Of0Zc0J6J0G]\\OO30`c00_h]4", "size": [640, 427]}, "area": 33471.0, "iscrowd": 0, "image_id": 15746, "bbox": [0.0, 35.0, 200.0, 284.0], "category_id": 128, "id": 20000987}, {"segmentation": {"counts": "]==bc03N2L_Oe\\O?]c03O_Oe\\O>^c0NO300OO203MO102M13LNDa\\O=_c0Ca\\O<`c0D`\\O<_c03O100b\\O@\\c0b00O021M10011N00O1O10_Oc\\O`0^c0L411M10O1_\\OF[c0<c\\OD]c0?2M4MO101O101N2L12Ae\\O8ac01O22M01O1O0N31M10N11012MN21O0O5LO1O101N1N20O010O20O00M25K04MO002ON201O0000O20O2N00N3OO3NMd\\OD[c0<31010N01N6L\\Og\\O>_c0O1N20000002M20M2O12L03O1O0O15JN4N1O0O2002\\\\ODac0=O01L1G_\\O;^c0Fb\\O:^c060_Oa\\O;cc0O13L0NDb\\O<_c011N22OM22^\\OD\\c0b0L]Oh\\Ob0Xc031M0^Og\\O?[c06M00N3O2O0O1N2O01OO1O11O00O11ON21O01M22OOhkk2FbTTM1N6KM3NN9LN3M21M102[\\OF`c0>LCc\\O=^c01102N03M000O01Lb\\OH[c07h\\OGYc0:8O\\\\OF_c09a\\OH`c0752M00O210M14L20N02M4NOO302LJl\\O@Vc0b04ON1000K80L310NO1000000N12N4ML50N1ON31O2M002O0001ON^Of\\Oc0Yc011O2O0001O0O02N011O10O20L4F_\\OKfc03Z\\OMjc01Xh80jWG5U\\OJdc0:O0OO21O012L2OJ62N3MO1N13N00004LOUC", "size": [640, 427]}, "area": 3971.0, "iscrowd": 0, "image_id": 15746, "bbox": [0.0, 404.0, 427.0, 43.0], "category_id": 140, "id": 20000988}, {"segmentation": {"counts": "0_1ab0000000001OO1001O000000O1001O00O100O10000O11O00N_]OeNab0[1_]OeNab0[1_]OeNab0[121O0000N20000000000000000O1000000000000000000O10000000000000000000000000000O1000000000000000000000000000000000000O1000000000000000000000000000000O10000000000000000000000O1000000000000O1O100000000000000000000000000000000GT]O^Olb0`0W]O_Oib0`0<M300O10000O10000Iakl0JfcROL]>=[ACdM0Qa0c0SAAo>=l@_OQN023Qa0>l@CQNOUa0<i@LW?3h@NY?1g@OZ?0e@1\\?Nd@2]?Ma@6_?Gd@8]?Dg^OMl1`0]?Bf@?]a0Ac\\O;ec0L1OO20O00O10K]\\OMcc01_\\OOac018OkSl1NVlSN3N1jNLY^O3RO4bb0KU^Ob0ha0^OW^Oe0ga0\\Og]ON8f0Tb0[Oc]O08e0Ub0_O`]ON7d0Zb0Dd]O=]b0Da]O=]b0d0M4O001N1N4O1M2O01UNl]Oh1Zb0L2\\Nd]O00^1^b0bNb]O^1`b001O1O00000001O00000[OeNe^O[1@kN_a0KQ_Oe1o`0[NQ_Od1o`0^Ng^OL0e1Xa0`Ni^OJNf1Za0_Nc^ONN0ON0e1_a0h00Lb^OgM_a0W26O2JX^OoM10ga0P252OO3M02O01O01ObNS^Oi0Pb0SOQ^OBNY1Rb0jNn]O33KMa010Sb0_Oo]O44;L1Yb0_OP^O4HL4?db0Be]O=Yb0Di]O;nb0Db\\OOTP31fcM0PN0V^O04OU10eN0ka02W^O3OMl0NXO0M1M1ka00W^O8l0IUO9ea0HZ^O5n0JRO:ea0HY^O5j0GYO4NL07fa0=P_OE^ONba0=P_ODZOH17ea0<Q_OKXOKea0:R_O8e`0VOi^Od0a05d`0YOf^OL3f0a08d`0VOi^ON0e0b03j`0XOe^O00d0b06f`0WOf^O20d0a03i`08Y_OEh`0<X_ODh`0;Y_ODh`0=W_OBLYO]`0U1h_O@K\\O]`0T1h_O@K_OZ`0T1\\_OiN4d06^O[`0U1Y_OlN6?7BX`0S1X_OlN<?3FV`0T1\\_OdN9;=L`?Hn_Og11YNL;g0L[?Il_O24h1OcNh0KW?0o_Ob1OWN0<j0LW?1P@^10hNh0I[?Om_OS2g0PNZ?Nn_OS2i0mMY?_2f@aM[?_2e@aMYOGo?h2b@XMD6KLn?f2c@[M_O:o?Z2b@gM_OCP`0f2a@hM_OAP`0g2a@iM`?X2`@jM`?T2`@lM`?T2`@lM`?T2`@mM`?R2`@nM`?R2a@lM^?V2b@jMZ<", "size": [640, 427]}, "area": 12505.0, "iscrowd": 0, "image_id": 15746, "bbox": [0.0, 0.0, 427.0, 341.0], "category_id": 157, "id": 20000989}, {"segmentation": {"counts": "WbW723Occ06\\\\ON`c02`\\ON_c03a\\O3Yc0Mg\\O1[c0910000O11O004LL30f\\OBUc0=i\\OCYc0d0ON2M4Mc\\OA\\c00d\\O`0^c0Aa\\O:1G6Omb0>T]OCJOnb0<P]OD39;GQb0S1f]OmNK0ab0Q1_]OTO0N^b0n0b]OZO^b0T1O0111N02N0100Ob]O`N[b0d1OmNf]Oa05nNL2la04T^OKOO030o05oNK1ma01T^OM0031MV1OnNla0N_^ONF30V1Yb0fNg]O51KOU1Yb0kNf]O1022MOV1Zb0jNf]O08V1Tb0jNl]OW1Sb0iNk]OY1Ub0gNm]OW1_b00OO10000000000O100000O1[D", "size": [640, 427]}, "area": 1950.0, "iscrowd": 0, "image_id": 15746, "bbox": [370.0, 321.0, 57.0, 93.0], "category_id": 162, "id": 20000990}, {"segmentation": {"counts": "jde21mc03Z\\OM\\c0<M300O10000O101OOJ7E;000O11O001OO1O10000001O000c@QOi;o0SDWOfLFS?T1VDVOgLFS?U1UDWOk;i0UDXOj;i0UDXOj;i0UDXOj;k0SDVOl;n0QAbN00k2`0T<S1kCmNU<S1kCPOR<Q1mCkNW<U1iCkNW<U1iCkNW<U1iClNV<T1jClNV<T1iClNX<S1iCkNY<V1fCjNZ<V1fCjNZ<V1fCjNZ<U1gClNX<U1gCmNW<T1SA[Na2a0\\<U1QA]Na2a0[<W1aCQO[<P1ZAVN=OA059j0d0h=o0ZA]NOJ15O2o0c0g=b1ZAUNQ19e=b1[AUNn09h=X2WBjMj=W2UBhMl=Y2TBfMl=Z2SBgMm=W2UBiMk=W2UBiMk=Y2jAiL]O30k0i>Y2iARNV>P2gARNX>o1gARNX>o1gAmM]>T2bAhMb>Y2]AdMf>]2YA_Mk>b2RA`Mn>a2n@`LMo0U?b2m@aMS?f30001O00001O000UBVK`<j4^CXKb<h4^C[K_<e4\\CVKQO5c=f4\\BTK;0E0007<]=W5bBjJ^=W5aBiJ_=X5[B[J0`0e=h510000000000O10000000000000000001O000000001O00006J1O1O001O1O1O1O001O1O1O001O1O1O002N1oDRIn8P7mFYIm8h6oFgIe8[6WGhIh8Z6UGgIk8Z6SGgIm8[6PGfIP9[6nFfIR9\\6kFeIU9[6iFgIW9Y6hFhIX9X6cFmI]9S6_FQJa9Q6XFTJh9m5SFWJm9j5QFWJo9j5oEWJQ:k5kEXJT:i5jEXJV:i5gE`JR:g701OO1N2N2O1N2O1000000000000001O00001O00001O00001O00001O1O1O00O100kNkEZH3ROR:d8mESH]:m7cERH^:n7cE`GN8a:Y8`E^G08a:Y8cE\\GN7a:]8gEaG[:_8fE_G[:_8d0M30000O1N21OO1001^DcG_;c8L2eD[G00U;g8kDYGU;i801O1O00000000000000[OoD[HQ;d7PE\\HP;d7PE\\HP;c7RESHL0Q;l7TEUHJNS;m7REPHNN0ON0R;R8i0O100001O00O1002NO1001O000gDnGd:U8XElGBNT;W8oDiG33KMa01m9V8bElG44;Ln9\\8bEmG4HL4]:f8dEcG[:[8fEgGY:P90000000O1`LeE[M[:e2eE[M[:e0eEQL04OU10eN0\\3\\:d0fERL3OMl0NXO0M1M1[3Z:g0fEPL8l0IUO9W3P:f0hERL5n0JRO:W3o9g0iEPL5j0GYO4NL07W3U:h0XFgLE^ONS3U:h0XFgLDZOH17V3U:h0WFhLKXOKV3S:j0WFgL8Y2VOPM]:g3cE^Ld0a05U2m9j0\\E[LL3f0a08V2j9j0\\E]LN0e0b03]2l9e0\\E]L00d0b06Z2i9g0^E[L20d0a03Z2TOUMf:e3^FnLEZ2o9g0]FoLD[2n9f0]FPMDZ2P:f0]FoLBLYOm1j:X1[FPM@K\\Ok1k:Z1YFPM@K_Oh1h:]1\\FaLiN4d06^Oi1i:\\1\\F_LlN6?7Be1h:_1[F\\LlN<?3Fc1d:b1`F]LdN9;=LZ1e:V1SEnLg11YNL;g0LX1f:S1SElL24h1OcNh0KT1f:T1ZEnLb1OWN0<j0LR1GTNo:R3[EoL^10hNh0IT1h:W1WEmLS2g0PNT1f:W1XEmLS2i0mMT1h:U1gGfMaMW1g:S1hGfMaMYOGk1P;V1hG`MXMD6KLj1n:W1hG`M[M_O:j1c:X1gG_MgM_OCk1o:W1gG_MhM_OAj1Q;X1fG_MiM[1a:U1gG`MjMX1`:Z1dG^MlMW1a:[1cG^MlMW1a:[1cG^MmMX1^:[1dG]MnMY1]:Z1eG^MlMW1a:Y1eG`MjMX1`:", "size": [640, 427]}, "area": 59715.0, "iscrowd": 0, "image_id": 15746, "bbox": [137.0, 0.0, 290.0, 317.0], "category_id": 169, "id": 20000991}, {"segmentation": {"counts": "nZ]1>bc00O10000000000O2O000000000O100N2M4M6L3M1O00O100O1O1000J7M4M2N2M4M2O1O2N1O0000O100000O100000000O1O102O0gM_NQBb1m=`NSBa1j=aNUB`1g=dNYBY1e=lNXBT1g=POUBR1h=TOUBk0k=YOSBe0n=^OPB`0P>DnA?n=DoA?n=DPB?l=DRB<m=FRB:m=HRB8l=KSB5l=MRB4m=NRB2m=0RB0m=2RBNl=5SBK5nMf<X2TCJ4QNf<W2UCG5TNd<W2VCE5WNc<U2VCE6XNb<Y2SC_O:ZNb<^2mBXO?\\Nc<d2gBPOg0[N]<Q5cCoJ\\<S5cCmJ]<S5dClJ\\<U5cCkJ\\<V5eCiJ[<X5dChJ[<Z5dCfJ\\<Z5dCgJ[<Z5cCgJ\\<\\5aCeJ_<`5[CbJd<d5TC^Jk<l6@g0YOg0YOg0YO`0@2N2N2N3M2N2N2N2N2N2N2N2O0N01N101L3N3M2N3N101O001O1O001O11O`0@:E10O1N2N2O2O001O1O00000010OO1N2]OmFWFU9h9mFUFU9j9nFSFS9l9oFQFS9n9PGnER9P:`0O1OaFSFm8l9dFhFW9U:L4L4L00000O1000OcGREj7n:PHWEQ8i:mGXET8h:iG[EW8d:hG]EY8c:eG^E\\8S;001O000000001N10006I2O1N101N2O1N2M2N3MXGYE\\8c:aGbEa8[:[GjEf8T:WGQFj8a:2O1N1WJXG:j8D]G4e8gLPGo0c0S2_8iLWG\\NFX2l0X2\\8oL_Gh0=m1Z8YM[Gi0b0gNnNZ1]9SOTGk0j0WN@S1i8InFm0P1gM3m0V8<hFP1e3lMk5Q1aFR1]>kNeAT1\\>kNeAU1[>hNhAW1Y>dNlA\\1U>_NoA`1S>ZNRBf1o=WNSBh1o=VNRBi1P>TNRBl1P>QNRBm1P>PNRBNXNk1g?UNYBj1o?O2OO0O00O2M26K2O1O001O1O1O0001N10001N100O2N100O2O00001O1O1O2N1M4J7J6I5K22O3M3M101O01O001O0O3L5L1N2N2N2O100000000O1000001O0O100Nhi_1", "size": [640, 427]}, "area": 48215.0, "iscrowd": 0, "image_id": 15746, "bbox": [72.0, 198.0, 278.0, 377.0], "category_id": 183, "id": 20000992}, {"segmentation": {"counts": "0\\fb0>^f]O4M3N2M3N2N2O1N101N1O2O001O001O01O01O001O000101N3N1N0010O0100O010O00010O010O01O010O00010O0001O00001O001O0000001O0000010O0000E]OnCb0P<BPD=o;DQD<m;FSD9m;HSD8l;d0NXOVD3j;MVD2j;OVD1i;i0O1L3N3L3O2O1O001O1N1100O01000O10O10O10O02O000O101O0O1cNjDe0V;ROWEi0i:VO]Ef0b:XOdEe0b;N3N1O2M2O2NeLKaI4_62[IMe69VIGj6:TIGl69TIFl6;TIEl6;TIEk6;VIDk6<UIDj6=VICj6=VICa6GoFf0`2Bb6JkFe0c2Ab6LiFc0e2Ab6g0^IXOb6h0_IWOb6i0^IVOc6j0]ISOf6l0m2000O10O1000000O0100000O100000O010000O1000O10O1000000O0100000O1000O10O1000000OGVOSDj0m;WOQDi0Q<VOoCj0Q<WOnCh0S<YOlCg0U<YOjCf0W<ZOiCf0W<[OgCe0[<[OdCe0\\<4101N10QHXO[3g0cL]O]3a0aLB_3>_LE`3:_LIa36^LM`32aLO_30`L2_3MaL5^3KaL7_3GbL;\\3EcL=\\3BdL?]3_OcLc0\\3]ObLe0^3[OaLf0_3YOaLh0_3YO_Li0`3XO^Li0b3XO\\Li0d3XOZLi0f3YOWLi0h3XOVLi0j3XOULh0k3ZOTLe0l3\\OTLc0l3^OTLa0l3@TL?l3CRL=n3DRL;n3FRL9n3IQL7n3JRL5n3KRL5n3LRL3n3MTL1l30TLOl31VLMj33WLLi35WLTOdL2U7l0jLSOV3o0jLoNV3T1hLkNY3V1fLiNZ3Y1fLeNZ3^1dLaN\\3a1cL^N^3c1T43M2O1N3M2N2O1NO1N3L31O2N100O2]E]OZ8k201N1O2N2O0O2O05L6I4\\Mc2G9Fhm]4", "size": [427, 640]}, "area": 261716.0, "iscrowd": 0, "image_id": 15751, "bbox": [0.0, 0.0, 640.0, 427.0], "category_id": 151, "id": 20000993}, {"segmentation": {"counts": "\\fb0>j<4M3N2M3N2N2O0O2O0O2N101O001O0010O01O00001O02O1N2N2OO01O010O10O010O0010O0010O0010O01O01O00001O00001O0010O000001O0000001O0000E^OmCb0P<AQD=o;EPD;n;GRD9m;HSD7m;JSD6k;LVD3j;NUD2j;OVD0j;1VDOi;2XDMd;8[DGc;<]DD_;`0aD@];b0cD]O];d0cD\\O\\;e0dD[O[;f0eDYO[;h0eDXOY;j0gDUOZ;k0fDUOZ;k0fDUOY;^110000O010O10O11N10000O2O0hNbDb0_;SOPEi0P;ROYEj0f:UO`Ef0`:XOeEf0a;O2M2O2N1N3NeLL`I3`63ZIMe69VIFk6:TIGl69TIGk6:UIEl6;TIEk6<UIDk6<UICk6>UIBk6>UIBb6HnFf0`2Bb6JkFd0d2Ab6MhFb0f2Ab6g0^IXOb6i0^IWOb6h0_IUOd6k0\\IROg6n0k200O01000O100000O01000000O1000O10O1000000O0100000O1000O10O1000000O01000O100000FVOSDi0n;XOPDi0Q<VOoCi0R<XOmCh0S<YOlCf0V<ZOiCf0W<ZOiCe0X<\\OfCd0\\<\\OcCd0]<4101N10QHYOZ3f0dL]O]3b0`LB_3=`LF_3:_LIa35_LN_31bL0^3OaL3^3LbL6]3JbL8^3FcL<[3DdL>[3AeL`0\\3_OcLb0]3]ObLf0]3ZObLg0^3YOaLh0_3YO_Lh0a3YO]Li0b3XO\\Li0d3XOZLi0f3YOWLh0i3YOULh0k3YOTLg0l3[OSLe0l3\\OTLc0l3^OTLa0l3@TL?l3CRL=n3DRL;n3FRL9n3IQL6o3KQL4o3LQL4o3LSL2m3OSL0m30TLOl32ULLk34VLKj36VLPOhL6Q7l0jLSOV3o0jLoNV3T1hLkNY3V1fLiNZ3Y1fLeNZ3^1dLaN\\3a1cL^N^3c1T43M2O1N3M2N2O1NM3N3N11O2N100O2]E]OZ8k201N1O2N2O0O22M6K4K3ZMg2Eh0XOXm]4", "size": [427, 640]}, "area": 11336.0, "iscrowd": 0, "image_id": 15751, "bbox": [44.0, 232.0, 255.0, 195.0], "category_id": 183, "id": 20000994}, {"segmentation": {"counts": "fgf77h>2N2O000O2N1O2M3L3L501N2N00001O1O10N11N2K401O000000NXBlNh=T1XBlNh=T1XBlNh=T1XBlNh=S1YBmNg=R140O10LWBQOi=n043N1N1000001N11N0100000M33M0OSOXBc0i=ZOfB9[Zm0", "size": [480, 640]}, "area": 1585.0, "iscrowd": 0, "image_id": 15956, "bbox": [526.0, 160.0, 52.0, 44.0], "category_id": 98, "id": 20000995}, {"segmentation": {"counts": "bej621Ok>9J0000000J6L4001O000000O11O2NL4O1hNBnC>j;KUD5A[Oh;a0gD4[OCl;;fDf0Z;[OeDg0Y;YOfDi0Y;XO^D^OA\\1Q<^1O000000O10000XMQD_2P<]MTDb2V<O001O1O001O10O02N1O010N1001O0O010O02TO]CYOc<f0^C\\O_<a0_CgN4e0[<g0kCWOU<e0QDYOn;h0SDfN@4OKc<]1m00001O2O00001O001O1N110011OM1002\\BjN[=T1fBlN[=W1aBjN_=[1O20001N01O00000N0bNbB_1]=aNcB_1]=aNcB^1^=bNbB`1[=aNeB`1Y=31O1OM`NhB_1X=43M2O0001O010O0ON33MO1ON51NO0O1O1L40001O00000000001O00000001O1^OlBgN0>U=g0oBXOU=CgBl09^OT=a0nB^OR=GcBg0>@R=?PC_OP=e0d0O1NkAYOU>g02010N11O01O0000NjAZOW>e0iA\\OX>b03I:M1O004L1OYS:", "size": [480, 640]}, "area": 7384.0, "iscrowd": 0, "image_id": 15956, "bbox": [466.0, 181.0, 152.0, 95.0], "category_id": 110, "id": 20000996}, {"segmentation": {"counts": "hh63c>f0aAYOl=Y1H2POgNgCa1X<cN]Ce1\\:TNaE4o1T2^8hMcES1Q1V1[9XOdE_N1:4Q2W:W200000000O1000O10000^KdE`4^:1O1O100O1O001O0000O100N200N2000001O0O1010O0000001O01N10000O11OO0010_EdK^:_40001O0O101N1O1000000O10000000000001O01OZNhEcN`;nN`DCOO1h0l0[OTO9n<IiVQ8", "size": [480, 640]}, "area": 10313.0, "iscrowd": 0, "image_id": 15956, "bbox": [14.0, 105.0, 77.0, 153.0], "category_id": 112, "id": 20000997}, {"segmentation": {"counts": "P8P7o711O00001O1OO1001OO10000O100002NK5000000000000000000000000O10000001O1O1O1O1O001O0000O100N200N200000000O1001O0000001O00O10000O11OO1O10mHVHP7m70001O00001O000000000000000000000000001OO1^ORIQIo6n6b000N2O100000000000000000000000000000[HiHa7`7K8H3M00O11O000000000000O10000000000000000004LO1N200000000000000000000O10000000000000000000000000000000000O1000000000000001O3M3M3M4L6[IcG[6d8M5K1O1O001O002N3M1OO10000ZOUJVHl5i7VJVHj5j7WJTHj5m7VJRHj5n7WJfGL6m5U8[JjGf5U8[JkGe5T8\\JlGd5T8\\JkGe5T8]JkGc5T8_JkGa5U8`JiGa5V8aJiG_5W8bJhG^5X8cJgG]5Y8e0000[IgGc6Y8]IgGc6Y8]IgGc6\\8O0[JeGe4[8ZKgGe4Z8YKgGg4X8YKiGg4Y8UKiGk4e8eJ]G[5c8dJ^G\\5c8bJ_G]5a8bJ`G^5_8bJbG^5_8`JbG`5o801O00O1EaJTG`5k8dJoF_5Q98O10000N22N0000001OO11O001ON21O00O10000001O00N2NRJRGn5n820000000000000000O100001O1ON2O1001O00000000O1001O0TJTGd5l8[JUGe5j8[JWGe5i8ZJXGf5R9O0000000000000000O1O1LVJRGj5o83O100000000O11O00001O00O100O100O11O00000000000000000000001O00001OO1000000001OO10000O100000000000000NQJSGo5m820000000000001O2N1O1O1O1O1O1O2N1ON2N2N2O1N2O_KSGQ3m8lLVGT3j8iLYGW3h8gLYGY3f8hLZGX3f8hLZGX3f8hLZGX3f8gL[GY3e8gL[GY3f8fLZGZ3e8gL[GY3f8fLZGZ3f8fLZGZ3e8gL[GY3e8gL[GY3e8gL[GY3e8gL[GY3e8gL[GY3e8dL^G\\3a8aLcG_3^8[LgGe3Y8WLkGi3U8RLPHn3P8mKUHS4k7iKYHW4g7dK^H\\4S900000000000000000000000M3L4K5K5H8@`0B>O10[KXGR3i8jL[GU3f8hL\\GX3e8gL[GY3f8eL[G[3g8cLYG]3h8bLXG^3i8aLWG_3j8_LWGa3j8^LVGb3k8]LUGc3l8\\LTGd3n8ZLRGf3o8SLXGl3i8gKcGY4b900000XMiKgJW4W5UL_Jk3a5^LVJb3k5dLnI\\3R6hLjIX3W6jLfIV3Z6nLbIR3_6QM]Io2i6oLSIQ3j6VMRIj2o6YMmHg2S7\\MkHc2W7_MeHa2Z7dMbH\\2^7hM^HX2`7nM\\HR2h7nMTHR2k7b21O001O001OMQITHP7l72O12N001O00001O2NO1001O1OITIVHP7j73000000000000000000000000000000000\\IUHS6k7gI]HW6V802N1O001O000000O1000000000000000000001O0000001OO1001O00O100O1UOdIRI\\6m6eIUIY6g6eIaH4e0X6j6nITIR6h6TJVIl5j6UJdH@4OKc6]7n0O1O1O1000000000000O11O1O3MO1002cHcHS7\\7nHdHR7`7jHaHU7e701O1O2N00000000N21O00O12N1OO1O1O1O1O1O100001O001O00M33MO1OPIYHi6i7UIXHMOj6j7WI\\Hh6d7WI^Hh6b7XI^Hh6b7WI_Hi6a7WI`Hh6`7XI`Hh6m7O00001O1O001O00001OM]IiGc6U8_IkGa6n7^ISH4N^6l7?N200O1O1L42N1ON21O001O0000001O4L000000003M0000O1N21O00001O00O11O00O11ON200001OO11O001O000000", "size": [480, 640]}, "area": 134004.0, "iscrowd": 0, "image_id": 15956, "bbox": [0.0, 234.0, 640.0, 246.0], "category_id": 114, "id": 20000998}, {"segmentation": {"counts": "eUm18a>JbA<Z>41N20001O000000O10000N20O102NM300O1O1000000000000O1002N00O11N0MfAB[><5GaAMc>1m_m6", "size": [480, 640]}, "area": 614.0, "iscrowd": 0, "image_id": 15956, "bbox": [130.0, 232.0, 37.0, 25.0], "category_id": 132, "id": 20000999}, {"segmentation": {"counts": "jcY74i>4O1O0O2HIeA8[>62OO1N102M2O010L33M00O0011N200O0O2L4O1N2M2O8HTid1", "size": [480, 640]}, "area": 487.0, "iscrowd": 0, "image_id": 15956, "bbox": [498.0, 174.0, 29.0, 26.0], "category_id": 143, "id": 20001000}, {"segmentation": {"counts": "TXZ67e>5^AIT>e0K4K6QOUOcCo0\\<VO[CP1b<n01F31N1O3M001OO24MN11O0O2N101OM_CfMb<Z22O21N001O0000101NO1001O@`CSNO<`<a1jCTNZ<l1=000O1OK6L31011N00001N11O2OK4O1hNiBg0V=ROSCm0m<ROTCn0a=M2O0O2GmAAjRV2", "size": [480, 640]}, "area": 3543.0, "iscrowd": 0, "image_id": 15956, "bbox": [431.0, 177.0, 60.0, 82.0], "category_id": 168, "id": 20001001}, {"segmentation": {"counts": "Xec21al13YbN4K6J6L3M4L4K2O001O2N1O2N00001O0O101O0O2O100001O]BeNa=[1_BeNb=Z1^BfNb=]10O002O2L5N2:EM4M3L4K;G2IVj2A]UMd0WBFY=[1J8iBRNm<S2OO0000010N11O0010M21O00O10000001O00N3MPCTNP=l1200000O1000000000O010001O1ON101001O000O100O01001N100O010O4MO2N4L4L4L3M3M3M3M3K4KdBQOQ=i0SCXOn<8_CHm=000O1N2H7M5[NDaD?^;HXD<h;HPD=n;GlC<S<GgC<Y<GaC=a<CYC`0l<h01N200O001O1O1O001O1O1O1O00100O1O01AZNcCd1X<dNgC[1S<lNmCS1n;TOQDk0m;XOSDg0n;YORDg0o;YOQDd0R<[OnCd0U<ZOkCe0W<YOlCc0W<[OmCa0U<]OPD>R<AQD;Q<CSD9o;EUD7m;GWD5k;JXD2j;MWDOgNIT=8TDNo;2RDLo;6oCIS<8kCGV<<gCCZ<?dC@k:0eEb0Z;BcD`0\\;BaD`0];E^D=`;NTD5i;NTD5f;2VD1f;3XD0c;5YDMd;:VDGh;k1N1N2M3O1N2N2O00001OO1cL_D[3c;0000001OO02N10O10000000O1O01oN`DcN_;[1dDdN\\;Y1gDgNZ;V1iDiNX;T1kDkNV;R1mDmNS;P1QEnNP;P1REkNT;R1oDhNV;U1nD_Na;[1R1N3N3mNS1M3MX\\_3", "size": [480, 640]}, "area": 12419.0, "iscrowd": 0, "image_id": 15956, "bbox": [178.0, 176.0, 224.0, 116.0], "category_id": 173, "id": 20001002}, {"segmentation": {"counts": "0P8P7O11O00001O1OO1001OO10000O1hNSI]Jm6Y5iIfIYO?n6j5[JmIc5V6b1UOdGjJd8U5`G`Jh8Y3QGdL4o1W9[1eFfLS1Q1Y8X2UHgL_N1:4T9T3W200000000O1000000000ZDhLd;Z3000001O0000000000_OeD]M[;`2iD_MW;`2lD^MT;a2oD]MQ;b2PE^MP;b2QE]Mo:b2RE^Mn:b2RE^Mn:b2SE^Ml:b2TE^Ml:b2UE]Mk:b2UE`Mj:`2VE`Mj:`2VE`Mj:`2VE`Mj:`2VE`Mj:a2UE_Mk:a2TEaMk:_2UEaMk:_2UEaMk:`2SEaMm:`2RE`Mn:b2oD_MQ;d2lD[MU;g2gD[MY;V30000000O100O1O1000000O10000000000001O000[GgLc5`4nG`KCOO1h0l0m6Q5`HWK[7W60N2O100000000000000000000000000000[HiHa7`7K8H3M00O11O000000000000O10000000000000000kKnH4Z7\\OgHSM4X3L`L[7V3fHnL7Oo0l1Y6X1bHjLg1m1g5Z1bHhLh1m1f5\\1aJcN_5^1aJbN^5^1cJaN]5^1eJaN[5R1dHVMU2g1W5P1VKoNk4n0YKQOg4l0\\KTOd4k0]KSOe4m0ZKTOf4l0XKVOh4j0WKYOg4g0YKVOj4j0VKVOj4j0mJ]MdMi1`7j0jJ_OW5a0hJ@X5a0fJ@Z5`0fJ@Z5`0eJA[5a0cJ_O]5b0bJ^O^5e0bH^MZ1l1U6Z1jIfNV6[1iIgNU6Z1jIfNV6Z1jIeNW6[1iIfNV6Z1jIeNW6\\1hIeNW6[1iIeNW6[1iIeNW6[1iIeNW6\\1hIdNX6\\1hIdNX6\\1hIeNW6\\1hIdNX6\\1oI]NQ6d1PJZNP6f1RJYNm5h1TJVNl5j1VJSNk5n1iJ]MW5c2kJXMX5i2iJUMW5l2jJoLY5Q3kJgLY5Z3jJ^LZ5c3iJUL[5l3l2001O1O001O1O001O1O1O1O001O1O1O1O1O1O1O1O1O1O1O1O001O1O001O0000001O00000000001O000000001O00001O001O1O001O001O1O001O1O001O001O0000001O0000001O0000001OO1000000000000O1000000000000O1000000000000O10000000000000000000000000000000000000000000000000000001O00000000PO\\FdLd9\\3\\FdLd9X3gFaLY9f2cGWM]8h2dGXM\\8`2WFWM]19\\8_2XFWM]19\\8`2WFWM]19\\8`2WFWM]19\\8`2lG`MT8`2lG`MT8`2lG_MU8a2kG_MU8a2kG_MU8a2kG`MT8`2lG`MT8`2lG`MT8`2lG`MT8`2lG`MT8_2mGaMS8_2mGaMS8_2mGaMS8_2mGaMS8_2mGbMR8^2nGbMR8^2nGcMQ8X2THhMl7X2THiMk7V2VHjMj7W2UHjMj7[2QHfMn7Z2RHfMn7Z2RHgMm7Y2SHiMk7W2UHlMh7T2XHSNa7m1_HYN[7g1eH[NY7e1gH[NY7e1gH]NW7c1iH^NV7b1jH_NU7a1kH_NU7b1jH[NY7e1fH[N[7g1lFUM29T1j0o7l1lF`M7EGM?Q1h8d4100O1N200O1O1000000000cF_J[9c5000000000O1O10000000000O1O10000001O1O1O00001O004LQOlFQLU9k3RGRLn8j3YGTLf8h3`GWL_8f3gGXLX8h3kGULU8k3nGPLT8Q4nGjKT8V4[1000000001O00000000001O00000000001O000000001O2N3M3M3M3YFVKR9m4hFZKT9h4gF^KV9Z5LJSG]Ji8b5;N27I00O1O1O1O1O1001O0000O1O1000000001O00000000001O0000000000000000O1O100O1002N00001O00001O0000O1001O0000000000`NnFRMR9m2PGRMP9l2SGSMm8l2TGTMl8k2VGTMj8l2WGSMi8l2YGTMf8l2[GTMd8l2\\GTMd8l2\\GSMe8l2\\GmL^OXOV9k3[GoLk8R3TGPMj8P3VGPMj8P3VGoLk8R3TGnLl8R3TGmLm8S3RGnLn8S3QGnLn8T3oFjLT9Y3iFjLT9X3hFjLX9^41O11O1O1O001O1O1O1O0000O1O10000O1O1O1N2M3L41O0000000000001O00O1L40000000000000000000000O10000O100O1000000O11OO10000000000OYFRK^9n4_FaKU9_5F00000000000000000000000000000000O100G9O1O10000O1000000000000001O1O1O1O001O00N200O1000000000000O1O1FdFoJ]9P5eFmJ]9Q5fFmJ[9R5:N2O1M3N2N2O1N200", "size": [480, 640]}, "area": 104053.0, "iscrowd": 0, "image_id": 15956, "bbox": [0.0, 0.0, 640.0, 258.0], "category_id": 177, "id": 20001003}, {"segmentation": {"counts": "Qkb4;e>0L;POl0L1HcNmB]1R=dNmB]1S=cNmB]1S=cNmB]1S=8000000000000000000O1000000000000K500O11O5K0000000000000000000000001O0O3WNjB29T1Q=kNUC7EGM?YbS4", "size": [480, 640]}, "area": 2659.0, "iscrowd": 0, "image_id": 15956, "bbox": [313.0, 83.0, 47.0, 67.0], "category_id": 181, "id": 20001004}, {"segmentation": {"counts": "kWd09d>4N3M3M100O2O001O000O10000000O2O00000O2O2M3N2L8IhRm0MllRO1lA4S>MiA7Oo0h<kNVCg1j<YNUCh1k<700O1N2BoMhCT2U<>L3M3O10N2O10000O1FPDcMQ<\\2:O2N10002N1TN_CW1W=N2M101O0000001O0000001O0000UEgNP8Y1mGlNP8[1fGkNX8V1dGoNY8S1cGSOZ8n0bGZORNQOR:g1iG@U8T1TGSOg8o0UGTOk8m0RGUOn8l0mFWOS9m0eFTN@9k9f1]FSNL5h9k1TFQN:3a9f2dF[MY9c2lFZMU9c2PGZMQ9d2SGXMn8g2UGWMl8g2VGWMl8g2VGXMj8g2XGXMi8f2YGXMi8f2YGmLhN7P:j2^GVMc8h2^GWMc8h2^GXMc8e2_G[Mb8c2`G[Mb8c2`G\\Ma8b2aG]M`8`2cG^M_8`2cG_M^8`2R2O1O100O10O010hEdMk7[2THfMl7\\2QHeMP8]2lGeMS8_2gGcMY8a2aGaM_8b2]G_Md8d2WG]Mi8g2RGZMn8j2mFXMR9R3cFoL]9T3[FQMe9U41O0O1DmEiKU:W4:01aNdE^MN8^:f1oFUNQ9j1WGnMk8P2S201O1O010O001O10O01O1O001O010O0000001O0000001O0000001OO0100000000O10O100000O100000O0100O100OTEXNb8h1]GYNc8f1]G[Nb8f1]G[Nc8d1]G]Nc8c1\\G^Nd8d1YG]Ng8f1UG[Nk8h1QGYNo8j1mFWNS9k1jFWNU9k1hFUNY9m1dFTN\\9n1aFRN`9P2[FmMkNJj:Z2VFoMTOCg:`2oEQNW:Q2YE]Ng:m2100O1N2F:[NaDFb;9hD[O[;d0iDUO[;k0iDmN[;R1hDhN\\;W1gDcN];]1eD]N_;e1n03L1O1O001O1O1O001O1O1N101O1O1O1N101O_O`CcN`<V1hCiNW<Q1PDoNo;n0VDQOi;l0[DTOd;o0ZDQOe;Q1[DoNc;S1\\DmNc;V1[DjNd;X1ZDkNd;V1ZDnNb;T1\\DQO`;P1_DSO];o0aDUO\\;l0bDXOZ;j0dDZOY;g0fD\\OW;e0hD\\OV;f0jDYOW;f0jDZOV;e0lDYOW;e0jDYO];`0fD]O];`0eD^O^;?dD_O_;>cD@`;=bDBe;5^DIi;0YDNi;OZDOi;MZD1h;L\\D1h;KXD5P<BQD?Y=M2N2M3M3M3N`12\\N3M3O1000000O100000000000000000000M3L4oM_O^Ef0`:]O[Eg0b:\\OYEi0e:ZOUEk0i:XOREl0l:WOnDn0o:VOlDn0R;TOiDQ1U;ROeDS1X;QO\\DZ1b;S1bMjLoHY3m6nLnHT3n6SMmGo3o7WLmGk3P8[LkGg3U8\\LfGf3Z8]LaGd3a8^LZG_3k8W14K5K5B>_Ob0I6001nFdK^7]4^HfKb7[4[HgKe7Z4ZHfKg7Z4WHgKi7[4UHfKj7[4UHeKk7\\4THdKl7]4RHdKn7]4QHcKP8]4oGcKQ8^4nGbKR8`4lG`KT8a4eGeK[8\\4XGPLi8S53oLoFMT9LYGKj8MdGJ_8YNcFT1[1:T8^NfFP1d19i7aNjFn0f1=b7`NnFj0i1b0Q8WOVHf0n7QO[Hk0h7nN`Hn0m:L4L4L4L3M4L4L4Lkje05oTZO2M2O2N2O1N2O00000O1001O000O2O000O2O3L3N3Hl^j1", "size": [480, 640]}, "area": 26304.0, "iscrowd": 0, "image_id": 15956, "bbox": [43.0, 83.0, 472.0, 254.0], "category_id": 183, "id": 20001005}, {"segmentation": {"counts": "0b3U;00O1O1O10000KiDgLZ;U3gDkL\\;U3300O1001O001O1O1O001O1O001OO11O002N0lDdLm:]3REdLm:c300PE]Ll:f3100001O1XEVLa:j3[EVL13b:Q4N2N2N1O001O0000001O0000O1O1O100@dEeL\\:[3eE]L1L[:h3fEWLb:h3_EXLa:g38001O1O001ON22NHUE_L0O01k:_3:_OlDZMY;g2fDUM^;i2fDTM[;m27MZDUMf;k230000O100000000001O00O12WDSMd;P301O1OO^DoL`;T301O2N00O10bDjL[;Y3000O1002N2N0hDdLU;_303M000mD^LQ;d30001O1O001O001O00O11O00KTE]Ln:d3QE\\Lo:a35LmDeLR;[35001O002N001kD`LQ;c301O1O001O2N00002N1OM32N00N200M3N21OMPE^LQ;b320000001O002N2N0000002N1O1O1OO100K51O1O000000O11O000000O11O00000000O1DSEnLm:Q3TEoLl:Q3TElLo:T3PEiLONR;Y3oDfL30n:Z3oDfLW;W3hDiL\\;R371O1O001O002N2N0000001dDfLY;\\301OO13M0000JjDkLV;o2jDQM2LV;P3kDRM[;o261ON2N200001O00001O0000003MO12N2_DjL];X302NO10000000000N2001O1O00O11OO13MOhDgLT;Z3jDgLV;^3jDaLR;d3N3M000000O1N2002NM3001O001OO100O100O100O11O00002nD[Ln:e3RE\\Lm:g30O1000000O1O10000O11O2NM3N2InDeLO000S;Y3mDhLY;V3jDiLV;X3iDhLW;W351O00O11OO1000bDjL[;V3eDkLZ;U3fDlLY;Z3O0hDdL0OS;d3N003MO1O11O1OO11O1O1OO100M31O002N3M2VESLf:Q4ZEPLa:V4M000000000000O11OO1000000UOaE\\Ma:c2aElLN3f:m2`EnLL1h:o2^EmL0Od:S3f000N21O00O11OO100001O2N2N1OO11O2N000dDhLY;]3NInDiLR;W37001ON20000O1001O00_OdDgM\\;X2cDfMOG^;b2eDeMNI];c2dDcM0K[;b2eDbM2JZ;d2dD_MMN5OZ;e2a0O100O11nC\\Mo;g2O0RDYMl;j2O1fDUMf:l2TEUMGOU;m2lDSM16S;m2kDUMT;m2jDSM0IS;a301O5K000000N200001O1OO1002N2N000VEVL3O_:n3aERLM0`:n3bEWL\\:j3bEWL^:Q4000O1001OO11O001OO1001O00001O1O1O1OO1000000O1000000O10000FdESL11[:k3eESL03[:b3gE[L42V:_3PFeLP:W3cEeL;NE4]:Y3eEcL72H1\\:R3gEkL<OD0\\:R3aEoL30;0W:o2^EPM01T;Q3oDQMP;o2PEnLS;P3;003MO10000ZM_DV2a;iMbDW2\\;gMhDX2W;iMiDW2G`M00<O_:8UEI9e23[MD0g:7ZEH:e2L[MK4e:5gE]2F]ML2f:3jE]2@[M0<0Dd:9lE]2@kMa:IoEP3o9QMQFn2o9SMPFm2P:TMPFl2o9TMRFm2l9SMTFm2l9SMUFn2i9QMXFo2h9QMYFo2f9SMYFl2g9UMYFg2^OmLW:<\\Fd2k9]MUFb2k9_MUFY2AWMZ:b0VFR2S:oMnEo1R:RNnEh1\\OcMh:f0lEe1]OeMg:g0lEb1Y:_NfEa1Z:`NfE^1[:bNfE]1Z:dNfE[1Z:fNgEX1Y:hNhEW1X:kNgEU1X:mNhEQ1X:POgEQ1X:oNiEPOZOn1m:SOlEf0YO[Nj:Q1mEa0ZO^Ni:Q1nE`0YO^Nj:S1nE;[OaNg:U1nE:ZOaNh:X1lE7Z:KdE3^:NcE1\\:1bEKb:7]EIb:8^EGb::]EGb::]EFc:;\\E]N^OV1V;>]ECb:?\\EAd:`0\\E^Oe:b0\\E]O^OlNT;i1^E[Ob:f0^EZOa:f0`EXOa:j0^EVOa:k0_ETOa:l0aEPOa:Q1^EQO`:P1`EPO_:Q1aEnN_:S1aEkN`:W1bEeN^:\\1cEbN]:^1eE`N[:`1fE_NZ:d1eE[NZ:g1eEWN\\:i1fEUNZ:m1fEPN[:R2eEkM\\:U2S11O3M1O1O2N0000O1O1O10000O1O1000000O100O11O002N2N1O1O00001O1]D]MP;S32002N1O1O1O001O1O1O00O100O1O1O10000O11OO1MeDjL[;U3fDlLY;S3hDnLW;R3jDlLW;T36001OO1K`DQM0O`;P3`DPMc;P3200VOcDoMK0h0Kn9n1iESNC0LOk00VO2a:j1XGWNUN03N^:g1ZG[NVN6i:JVES1n1ZOR9e0mF]OUN^On:o0lFDVN^OR;j0gFIVN_OW;b0cF8`9B_F`0f9[OYFg0f9ROcD0e1P1j9mN^FR1c9nN\\FQ1e;0O2UMmNPHS1d7mNmE5[2P1i7mNiE7MHU2W1V8lNfE:a1B_N04\\1W:hNbEL1a0]1\\1P9oNdEUOQ1P2[9RO^FS1b9ROSFT1m9k10000000001O2N000000O12N000000O100000000", "size": [471, 640]}, "area": 70015.0, "iscrowd": 0, "image_id": 16010, "bbox": [0.0, 0.0, 640.0, 145.0], "category_id": 106, "id": 20001006}, {"segmentation": {"counts": "T7g0g0^5]6bJcI^5]6cJbI]5^6aJeI^5[6cJcI^5]6cJoHZO>S6c6cJnHZO`0S6a6dJnHYOa0T6a6gJ_IX5a6hJ`IW5`6iJaIV5_6jJdIS5\\6lJdIU5\\6kJcIV5Y6nJgIR5X6PKkIl4U6TKiIn4W6RKeIR5[6oJ[IQOJR6k6mJSIZO0j5m6b1000000000000O11O00000YHSI00a7n6^HRI10a7Q7^HPIa7S700000000000O100000000O1000000O10000O100001O00O100000000000000001OO100001O001OO100001O00000000O100000gHoHH2m6o6[IoHG5l6l6[IQII4k6k6\\I^Ia6b6_I\\Ic6d6]I^Ia6b6_IaI^6_6bI`I_6`6aI`I_6`6aI`I_6`6aI`I_6`6aI`I_6_6bIaI^6_6bIaI^6_6bIaI^6^6cIbI]6^6cIaI^6`6bI]I`6c6UIPI>7^6i6TIPI`02_6P7bInH_6R7c00000000000001O00000000000000000000000000000000000000O1O1002N00000000N22N000000000000000000O11O00O100001O00O10000000000000000000000000000001O00O11O0000000000000000001O000000000000001O000000000000001O000000001O000000000000000000000000000bHiH\\7W7dHjH[7Y7000O1000dHgHZ7[70000000M300000000001O0000O100000000O100000000O100000000NaHlH_7P7aHRIa7n6`HQI`7o6`HPIa7Q720000N2O]HRI_7n6^HWI`7P700000001O001O00001O0000001oIhHa4X7^KiHb4X7[KiHnNO^5X7cKlHkNNb5V7bKlHkNOd5V7_KQId4o6YKPIPOId5W7XKQISOLe5S7WK[Ij4f6UKYIl4g6TKUIP5k6PKSIXODb5Y7VKRIVOIc5V7RKeHZOf0d5e6QKZIU5f6kJdH[Ob0k5j6mJUIT5k6kJTIW5m6hJRIY5n6gJPI[5P7fJnH[5S7eJiH^5W7bJeHb5[7^JeHb5[7_JdH^OMk5_7dJgH@2e5X7jJdH@8e5T7mJ`H]O9l5W7jJVIk4k6SKVIm4j6SKjHZOHi5^7lJjHZOLh5[7mJSIR5m6nJmH^ODb5_7oJlHDC\\5b7oJjH]5V7cJjH]5V7cJjH]5V7cJjH]5W7bJiH^5W7bJiHEE^5b7mJiHBI`5^7mJiH_OLf5[7kJnH[5S7dJkH^5U7bJlH]5U7bJlH]5T7dJlH[5T7eJlH[5T7fJoHV5Q7lJoHR5Q7mJ[H^Oc0d5R7nJ]H_O>c5U7mJSIR5m6nJcH@3c5[7lJdH@Oe5]7iJPIY5P7gJiHBBj5e7dJfHBHj5b7cJmH^5T7aJjHEAf5e7fJfHDHf5b7fJnH[5R7eJdHCIk5c7bJdHe5\\7\\JbHe5^7\\JbHc5^7^JaHb5_7_J`Ha5`7_JaH`5_7]JeHb5[7]JfHc5Z7^JeHb5[7^JeHb5[7SJXH6=g5[7SJXHM082R6g7hIYHd6g7\\IYHd6g7[IZHU12S4d7hJZHS14U4b7hJZHR18S4^7kJZHQ1>o3^7oKfHo3Z7PLoHh3Q7nKVH]Ol0c4o6nK\\IQ4d6nK^IQ4b6nK`IQ4`6nKbIQ4^6nKcIR4]6mKcIT4]6kKdIU4\\6jK_I\\4a6cK\\Ia4a6UKSH9U1GmNn4j7_KTIk4l6UKRIm4k6VKSIl4o6QKQIP5o6PKQIP5o6PKQIP5o6PKQIP5o6PKSIn4n6QKVIk4j6UKmIT4S6lKmIT4S6lKmIT4S6lKmIT4S6lKmIT4S6lKnIS4R6mKhIY4X6gKQIP5o6PKoHR5Q7nJoHR5Q7nJoH9VOP4k7gKRI4TOV4j7fKVIOQO[4i7fK\\IGmNc4g7fKgIZ4Y6fKgIZ4Y6fKeI\\4[6dKQIH_Oe4a7bKoH3VO\\4k7aKoHR5Q7nJnHS5R7mJnHS5R7mJnHS5R7mJnHS5R7mJnHS5R7mJnHS5Q7nJoHR5Q7nJnHS5R7mJnHS5R7mJnHS5R7mJnHS5R7mJnHS5R7mJnHS5S7lJmHT5S7lJlHU5T7kJlHU5S7lJmHT5S7lJnHS5R7mJPIQ5P7nJRIQ5n6oJTIo4l6QKVI8nNo3l7iKXI5nNQ4j7jKYI2POS4g7kK[INPOW4f7jK\\IHSO^4a7jKmIV4S6jKkIX4U6hKiIZ4W6eKjI[4V6eKiI\\4W6dKiI\\4W6dKoHIIc4X7dKQIJDb4[7dKSIJ@b4]7eKTIo4l6RKUIl4k6UKVIi4j6YKjHACT5c7\\KlHA_OQ5e7]KQIBXOo4g7`KTI@TOn4h7cK^H7GV4k7cK_HX5b7gJ_HX5a7hJ`HW5`7iJ`HW5`7iJ`HW5`7hJbHW5^7hJcHX5]7iJbHW5^7gJRHC`0f5^7gJRHC`0f5]7hJeHX5[7hJeHX5[7jJcHV5]7jJcHV5\\7mJbHS5]7mJdHS5]7mJbHS5^7mJbHS5^7mJcHR5]7lJeHT5[7nJcHR5]7nJcHR5]7lJeHT5[7lJTH_O?e5]7nJcHR5]7lJTH_O?e5]7lJeHT5[7jJgHV5[7hJeHX5\\7fJeHZ5[7fJeHZ5[7eJfH[5Z7fJeHZ5[7gJdHY5\\7hJdHW5\\7kJbHU5^7kJbHU5^7kJbHU5\\7mJdHS5\\7mJdHS5\\7mJSH_O?d5]7PKdHo4]7PKcHP5]7PKcHP5]7PKcHP5]7PKcHP5]7PKcHP5]7PKcHP5]7PKdHo4\\7QKdHo4\\7QKdHo4\\7^JSHa0a0Q5\\7^JSHa0a0Q5\\7^JSHa0a0Q5^7nJcHR5]7nJcHR5\\7oJdHQ5]7nJcHR5]7nJcHR5]7oJbHQ5^7oJbHQ5]7oJdHQ5[7PKeHP5[7PKdHQ5\\7oJeHP5[7PKeHP5[7PKeHP5Z7QKfHo4Z7QKfHo4[7PKeHP5[7PKeHP5[7PKeHP5[7PKeHP5[7PKeHP5[7PKeHP5[7PKeHP5[7PKeHP5[7nJSH@b0b5\\7dJRHOf0]5X7bJTH0e0^5Y7_JSH0g0a5Y8000001OO1001O0000000000000000O11O000000000000000000000bMlFYOT9g0lFYOT9g0lFYOT9g0lFYOT9g0lFYOT9g0mFXOT9g0lFYOT9e0PGYOP9g0RGjM55l8l1RGlM5YOHe0Y9[O`Fn01`0>UO>NP9MYF:JYO22NN0Oh0b09^OPO9S:JWF7[17^OGf;6ZDIk;3UDL^=MN2002NYb87`]G6J4L1O3gBd0YOjN`;d0lDY1n:jNhDb1S;R1kD`LQ;`3oD_L9=Q:V3oEQMj9o2nE]L3g0LCf9Y3[F^L2R10mN11V9f3fFYL1T10mN07l8IYGh3K[MOnN16l8IWGj3LZM0XOm8DVGa5i8`JVGa5i8`JUGb5k85O10000O1000000000000O100O11OO10000O100O1001OO1001O0000", "size": [471, 640]}, "area": 130319.0, "iscrowd": 0, "image_id": 16010, "bbox": [0.0, 221.0, 640.0, 250.0], "category_id": 124, "id": 20001007}, {"segmentation": {"counts": "b3b3U;0O1O1O10000K\\LTEf3i:[LWEh3i:300O1001O0L5N2O1O001O1O001OO11O002N0eLkDS3W;lLjDT3\\;00iLdDS3_;1`DlL00Z;U3eDkL10Z;X3eDiL[;Y31jLdDo2\\;mLdD13P3c;N2N2N1N101O00000O2O0000O001O10OAZMoDf2Q;[MgD1Le2^;\\MaDm2];TMcDl2\\;7101O1O001ON22NHkLiD0O01U3U;;^OaLeEc3]:\\L_Eh3_:\\L^Ee3c:8LoK`EQ4`:4OO10O101O00000001N10O12lK^Eo3e:01O1OOSLZEk3i:01O2N00O10WLUEf3n:00O01002M3N0_LmD_3U;0kD`LV;]3jDcLV;_30aLjD]3X;0001O1O001O010O00O11O00KgLjD[3W;dLiD\\3T;5L`LRE_3n:5001O002N001^LmD^3V;01O1O0O2N3N02N02N1OM32L22L200M3N21OMcLkD^3U;200000O20O02M3N0001O02M2O1O1OO100K51O1O000000O11O000001N11N11O00000O1DfL[EZ3d:gL\\EY3d:gLYE\\3g:cLVEON_3l:bLSE30[3m:bLSEe3i:ZLWEj3d:71O1O001O002O1N0000001ULUEh3m:01OO22M0000JZL[Ef3_:ZLaE2Lg3_:ZLcEl3^:61ON2N200001O00001O000000aEjK`:S4`EnK^:V42N2kK^EP4e:02NYERLd:Q40000000M30N2001O1O01N11OO04MOVLYEf3h:XLYEh3l:XLSEd3R;M4M000000O0O2002NM30NlDaLU;Z3kDhLW;X3jDgLW;X3iDgLW;[320O10N10iDfLS;Z3jDkLS;]31O00002_LjD^3U;bLlD]3W;1N10001O0O1O101O0O20O2NM4M2IYLZEO000h3d:XL]En3a:UL^El3b:SL^Em3a:51O01N11OO1000kKaES4^:mKcER4]:nKdEQ4b:0OoK]E0Ol3k:N003MO2N11O10N11O100OO101L31O002O2M2XLQEd3S;\\LnD_3X;M01O001O00000O11OO1000001TO`L]Fb3b9`LmEN3g3l9_LoEL1i3n9]LnE0Oe3R:g0O0N21O00O11OO100001O2N2N1OO1oE_Km9`4SF`KM0P:_4RFcKn9]4RFcKn9NQF\\40gKT:W4lEiKm92RFU41iKm9^4SFbKm9d41ON201O0O1001O00_OZKQGf4n8YKPGOGh4X9[KoFNIg4Y9ZKmF0Kb4O[KY93lF2J`4^9^KeFMN5O`4_9>23N0O11iJaFT5b9O0mJ^FR5d9O1`K[Fl3f9nK[FGO[4g9fKYF16Y4g9eK[FZ4g9dKYF0IY4[:01O5K000000N200001O1OO1011N2N000oK]E3Of3g:ZLYEM0g3g:[L^Ec3c:[L^Ee3j:O10O1001OO11O010OO10O2O00001O1O1O1OO101O000O1000000O10000F]LZE11b3d:^LZE03b3[:`LbE42]3X:iLlEW3P:\\LlE;NE4d3R:^LjE72H1c3k9`LRF<OD0c3k9ZLVF30;0^3h9WLWF01[4j9hKXFX4g9hKVF[4h9;003MO10000ZMWK^Ki4a4ZK_Kc4`4aK_K^4b4bK^KG`M00<Of3Q7nKPI9e23[MD0n3P7SLoH:e2L[MK4k3o6aLcKF]ML2k3n6eLbK@[M0<0Dj3S7fLcK@kMg3c6iLVLU3k3kLTLU3m3jLSLV3n3jLRLU3n3lLSLR3m3nLSLR3m3oLTLo2k3RMULn2k3SMULl2m3SMRLm2o3SMmK^OmL_3T7TMlKT3T4lLkKT3V4lLbKAWMc3Y7mL[K\\3f4eLXK[3i4eLQK\\OcMQ4]7cLnJ]OeMP4^7cLkJb3V5]LjJc3W5]LgJb3[5_LdJa3]5_LbJa3_5`L_J_3b5bL]J_3d5`L\\J_3f5aLXJ_3i5`LXJ_3h5bLWHZOn1T4l5eLmIYO[NQ4j7fLhIZO^NP4j7gLgIYO^NQ4l7gLbI[OaNn3n7gLaIZOaNo30lKo7i0`Ia3aNfKQ8g0\\Ie3cNdKR8h0ZIe3h6YLTIk3n6TLRIj3P7VLoHk3Q7TLPIk3Q7TLoHl3R7SLfG^OV1_4U7TLlHj3W7TLiHk3Y7ULeHl3[7ULdH^OlN[4b8WLbHi3_7WLaHh3_7YL_Hg3d7XL\\Hg3e7YLZHh3e7ZLWHh3j7WLXHg3i7YLWHf3j7ZLUHf3l7ZLRHg3P8[LlGe3U8\\LiGd3W8^LgGb3Y8_LfGb3\\8]LcGb3_8]L_Gf3_8\\L_Gd3c8\\LZGe3h8[LUGf3k8S1102M1O1O2N0000O2N002OO10O1O1000000O101N11O002N2NnJbJl0]5TOcJm0\\5SOdJm0\\5SOdJm0]5ROcJo0^5mJbJP41S1;QKe4LYK2I3Mm21a2Q5_JeKn2^Oa2h5_MZJjM5_3b5bN\\JlM5YOHP4n5PLkIn01`0>UO>Z3d5aLeI:JYO22NN0Oh0b09^OPOf3f6]LdI7[17^OT3Y8iLgGW3]8eLcG[3o9NM20O3N4K1O1O10000O11OO1MeDjL[;U3fDlLY;S3hDnLW;R3jDlLW;T360VJkL2V3MkL2T3FmLgJ1b5m2ZJQM0O]53dJ3d5j2[JPM`5=4c2LaMbJIR5f2<UN^OQ1ZKoMK0h0K_33fKN1Y1i4d0hKSNC0LOk00VO2m30RLb1h48RMWNUN03Ng3k1mL@YOjN\\2b1_O[NVN6i3e1bL5S2PNaMS1n1ZOl1a1XL>^2fN]O]OUN^Oc3e4[L\\Ka2n0[ODVN^Oc3d4[L[K9=e1>MIVN_Oc3d4eLmKc11O8g1j3_LYK3g0LCd156`0h1h3_LZK2R10mN11Y19<g0h1j3_LUK1T10mN07X1KiN0e1P1j1j3`LWLOnN16Z1Hd0R1c1k3^LXL0XO[1Cc0Q1f1b5F^J;b5C`J=`5hL]IZ2S1P1a5aLaI[2P1U1a5]LcIMHU2W1b1`5ZLfIa1B_N04\\1b3]5WLWI1a0]1\\1[2d5YL`HQ1P2g2f5RM_Jm2g5hL_JX3`80000000001O2NQEZLm:h30O1O012N000000O100000000eK", "size": [471, 640]}, "area": 91919.0, "iscrowd": 0, "image_id": 16010, "bbox": [0.0, 0.0, 640.0, 471.0], "category_id": 169, "id": 20001008}, {"segmentation": {"counts": "gS[641Mf>4il0NUSO2N7H1O1O1O000O2N11ON@mA`0S>@mA`0S>2002N002NO11O0001M22N00N20_OlA?V>0NAlA?T>2N200O100O11O1O101N000000000_OlA?V>1O0000000000001O000000000O1000000001O00O1000O11O00000000000000000000000N^OoAb0h=^O]Bf0a=\\O^Be0a=\\O\\Bg0b=80000010N1001O00000001OO11OO10O11O0000000001O00000000000000001O2POYBl0m=TO]B2I3i=MXa;Na_D1F1iA1V>0gA3X>7M4N3C_OYBN1d0a=]O_Bl0a=500000000000000000G_B\\Oa=d0_BZOc=f0]BYOd=f0]BYOd=f0]BXOe=h05O10O10O2OO1000000000O100O1[OPBd0Q>O01O10O100O1001OO1001O000nG", "size": [471, 640]}, "area": 3894.0, "iscrowd": 0, "image_id": 16010, "bbox": [441.0, 257.0, 199.0, 38.0], "category_id": 178, "id": 20001009}, {"segmentation": {"counts": "f`Q42d>0O2dAOQ>3lANT>1lAOU>610jAKl=6SBMm=>1M3M3NoAFi=;SBHi=LVBb01]Og=0XBb0R>O1N101N2O2M3K4O101OdAMQ>4PB4i=KUB9Q>I8a0@H9LkAIQ>8mALQ>8PBDo=:VBEg=:^BDa==<000000000oAEf=:XBIg=8TBHQ>962N1O10O0103N0M\\ORBa0n=ARB>l=GSB6m=KUB1l=0SBOl=3SBF2No=<oAE11P>:PBHP>?1MQBCk=:TBHm=`01FQBGT>96O1O1O10O1L3O11O000GgA3^k1OPcN105K6I4M9^OWOeBl0X=;N2N2N2NO11ON2L4N2H9L4N2O00100001O2N4Lg0YO00000000MUCZNk<m0l0N2O1001kBUO\\<n0_CTOa<P1ZCQOf<U1RCmNn<^10O1[OmBCV=;UC\\Ok<d0d000000000O1000000000000O10000000000001O2M2O2N2oBnNY<T1dCnN[<S1bCoN^<S1^CPOa<R1XCSOh<\\12N2O0_NQC\\1o<dNQC\\1o<dNRC?33k<^ORCa03Ok<BTC=1Ok<GUC8b=I`B5_=MbB1]=4aBJ_=<]BBb=`0_B^Ob=c0^B[OV>3iALdmh2", "size": [471, 640]}, "area": 4312.0, "iscrowd": 0, "image_id": 16010, "bbox": [281.0, 260.0, 166.0, 65.0], "category_id": 183, "id": 20001010}, {"segmentation": {"counts": "\\h^2270T=2kBNR=6jBNS==O1000O1000001O0O10O100000O101O1OO11BhB006]=IhB2`=Noo58joI8H00000000000O1000000000000L@PC`0n<6G90000000000O11OO1001O0000000001O0:oNTCb0\\=E4L00O1001O000000000000000000001O00001NkXl4", "size": [440, 640]}, "area": 1327.0, "iscrowd": 0, "image_id": 16228, "bbox": [183.0, 101.0, 93.0, 42.0], "category_id": 96, "id": 20001011}, {"segmentation": {"counts": "f^a4a0g1Bf9f1lDaNR;S2O1O13L3N2QN`DG5d1];\\N^D3?[1W;bNTET1l:lNTET1g;oNaCk0^<UOgCg0Y<YOkCb0U<^OmCa0R<_OoCa0Q<^OPDb0o;^OQDc0n;]OSDc0l;]OWD;o;ETD2S<LgRh3", "size": [440, 640]}, "area": 903.0, "iscrowd": 0, "image_id": 16228, "bbox": [338.0, 227.0, 22.0, 79.0], "category_id": 105, "id": 20001012}, {"segmentation": {"counts": "ijd03e=3M2N2N0000000O100O1O2M2N2K14^OoB<W=010O0OJjBLM0U=3>K]P33ZbL3Z=L_j`7", "size": [440, 640]}, "area": 255.0, "iscrowd": 0, "image_id": 16228, "bbox": [48.0, 205.0, 31.0, 31.0], "category_id": 123, "id": 20001013}, {"segmentation": {"counts": "g]T71f=1N2O1N2N202N000003M3K\\o5NhPJ2M100O100NL`B4_=31O0000000000NJbB6]=3001O00002N0000O12NNKaB5_=21O1O00O1O101O0O1O1O1O1O1M300000000O12NN200001O00O12NYDAU:?kEBT:>lEBS:?lEBS:?mEAT:>lEBT:>hEFW:;dEJ\\:E^D?Q11c:O[E3d:NZE4g:KPE=P;EdDf0\\;ZOaDi0_;WO^Dl0b;e0001O[DPNa;Q2_DoMa;T200000004LVNVEc0Q;XOPEg0Q;YOoDg0Q;YOoDf0R;YOoDg0Q;YOoDg0R;WOoDh0S4", "size": [440, 640]}, "area": 2042.0, "iscrowd": 0, "image_id": 16228, "bbox": [531.0, 217.0, 109.0, 75.0], "category_id": 124, "id": 20001014}, {"segmentation": {"counts": "\\_d4115X=KkB?P=BmBi0l<01NVCZOf<c0ZCBd<<\\CHb<5^CMc<0]C1e<MZC4h<IXC7k<FUC8OCn<2SC7V=HjB3]=LaB0ZQ63X\\J3L4L4M3L4L4M3L4K5L4K5L4K4L5N2N0001O0O10001O000LUD[Nl;d1TD\\Nl;d1TD\\Nl;d1TD\\Nl;e1SD[Nn;d1SD[Nm;e1SD[Nm;b1601N1N2O1M4M20000010O000001N1O2M8Hml3WOmSL2J5I7J6K5J6O2N1O100O1O2O0O1O2N1000010O00000001O01O01O0kNXD:R=Bga5j0[]J9F4M3L5a0^O1O100O01O1O0000000O001N3M3O00002N001O0O1O2OO200O001O1O00000000000ODkNWDV1h;jNXDU1i;kNWDU1U<100000O100000O00001O100O1O10000000000000001OfNeCW1^<01O00AjN[DW1R<10000KjNgCW1Y<401N10O1O101O1N101OMgNgC\\1V<eNlCY1S<fNoCY1X<1O001O4L1O1O1O1O1O01O000000000O100010N11MlC`NT<`120000001O0001O0001O1OO1000I_NWDa1i;aNUD_1k;fNSDX1l;dNXD\\1S<O0O01N3N1N2N2000001O01O000000000000000000O10001O000000XNSDe1m;[NSDf1o;O11O0022JO11100O10000O1RG", "size": [440, 640]}, "area": 10992.0, "iscrowd": 0, "image_id": 16228, "bbox": [345.0, 237.0, 295.0, 108.0], "category_id": 125, "id": 20001015}, {"segmentation": {"counts": "YPT72]=900000000002N00bBI[=73M3Lmk00VTO11NYg41dXK0^B1`=40000001O000000000001O000000000001O01OO11O000000000000001O01O000000001O2N[Vb0", "size": [440, 640]}, "area": 367.0, "iscrowd": 0, "image_id": 16228, "bbox": [530.0, 272.0, 67.0, 17.0], "category_id": 126, "id": 20001016}, {"segmentation": {"counts": "glZ52f=000001O01O0M4O4K1N100N2O11N2N2O2M2N2N2NZU20fjM3M2N3N1N3M2N2NAlB>R=CPC;m<DXC;S=000000O2O0O10000100O003N0O1O00101N1ONkBAU=?kBAU=?201O000O1O2O`P6FjoI7I101N1O2O0O1000O00000000O1001M3M2N2N4M101O00Q^31naL1O1O4L6I11OO100000000O1000000O1O10000000000000001O00000001O01O001OO100001O0000010O0O1O10000O101O03M1O001O001O4K2O1O1O6I3N_Un0", "size": [440, 640]}, "area": 1911.0, "iscrowd": 0, "image_id": 16228, "bbox": [397.0, 304.0, 172.0, 44.0], "category_id": 140, "id": 20001017}, {"segmentation": {"counts": "f:d0T=000000000iMHUEFe1c0dN11\\OEO12o8h4L3M2N1O0O2N2O000JoJeGQ5Y8QKgGo4Y8QKgGo4U8=0O2NBdJbHZ5^7hJaHW5_7jJbHT5R8N20O000001O000000000000O1O1@mJYHU5a7d03ZJ`Hn4U8000000000O10000F;WOgJQIb5e6bJWI_5i6cJSI_5m6f0000000001O0000L4O1001O000YISIY6NoI\\7h5a0I5L1bJmGS5]8O000000O1NgJgGV5_8O01O01OO10OGnJnGR5m7lJSH0OT5l7kJXH1LT5V8mJiGT5W8kJhGV5X85JeJoG[5o7hJPHX5Q8hJoGW5S862M2O2O1N2N1N11O009F10000O100O1N2N2O1N2J600000000OPJ`Hj5j7C:1MaJmG]5W801O0XKgGQ4Y8mKiGR4X8lKkGS4U8kKmGV4R8iKPHV4P8hKRHX4n7fKUHY4k7eKWH[4l7`KWH_4a801O001O001O00001O001O001O001O001O001O001O00001O001O001O001O001O001O001O000O20O01N110O001O001O001O0O2O00001O010O001O001O001O001O001O00001O001N1010O01O0O0101OO11OO1001N010000001O000O1000000000000O10000000O1001N01001N1O100O100O100O100O1O100O100O100O010O100O10001O000000000000000000O10O101O0000O11OO10000001OO100000O1<D0000O10000O10000O10000O100O10000O2O00O010000O10000O100O101OO010000O10000O10000O100O10000O10000OQLaFg3i9O000000O10001O000000O10O101OO10000YL]FY3V:K3M2N2N1O2N1O0000O10000O10000002N2N2N2N1O00000000001O0000000000^OZMRFf2n9\\MPFd2P:^MnEb2Q:bMlE^2T:eMjEZ2V:hMhEX2X:kMeEU2[:d000000QMdEb2\\:]MfEb2Z:]MgEc2Y:\\MiEc2W:\\MkEc2U:\\MmEc2W:XMkEg2c:01O1O1OO100O1O1O1N2O1O1N2O1N2N3M2O11O0O2O001O000000O1O1000000O100O100001O10O03M1O1N101O2N1ONSMYEm2g:SMYEm2g:2000000O1O100;E4L3M1O01O01N10000000000000000000001O0001O0AcMcE]2]:fM`EY2a:jM\\EV2d:mMYET2f:?0O1001M1O2N3M2O10O11O0O11N0110OO11O00O3N1O1O4L6J000000000000000000000000000001O00O1000001O00000000001O001OO100001O0000001O0000000000001O0000000001O9G0O1000000GXMaEi2`:XM^Eh2b:70000000000O100001F9OOVMVEj2j:20000001O000000001O1OO1000000001O0000O1001O00000000001O000000000000000000000000001O000O100O2nNnD[OR;f0nD[OR;d0nDWOW;i0iDTO\\;j0dDnNb;P1c02000000000000[E", "size": [440, 640]}, "area": 76181.0, "iscrowd": 0, "image_id": 16228, "bbox": [0.0, 223.0, 640.0, 217.0], "category_id": 149, "id": 20001018}, {"segmentation": {"counts": "^Q3k0_<?H7N3OOO2O10001NMQD_No;`151N3NEQDfN11o;V1SDiNOOo;Y1RDgNS<X17M300O21N00001N4MOnCiNe;W1YDkN];a1[D]N22c;e1\\D]Nc;c1]D^Nb;a1^D_Nc;j11O11O1OO1O11ZDoMc;S20002N2N3M28IM2NO1001O000O1bDhM\\;Q2dDWN\\;h1hDUNW;m1lDoMR;S2nDlMS;k1fDZNd;e17200OK5M34L3L02MWDYNe;m1O1010N1O0010YDUN0N];R2cDPN\\;Q2bDoM_;T2fDlMS;Q2kDnMM20NT;S2mDTNOJj:[2VEoMk:^21O15K0XETMd:R3NO11ON21O0gEoLk9S3QFoLo9Q3QFPMn9P3lEVMT:V310009G1N12`EiL02U:^31N1N2O08J2MM3O1O12N00LmEdLR:\\3oEcLQ:^340O101OM3010N10O11OO2N100O11O1OO1O11OO100O100001O1O2N000000000000M30000O100000hEgLQ:Z3mEhLR:X3mEeLW:]31N2001OO1O12NO1O1O1O1001O000O110O00001O000000001O0O200O00000000001O0000O1000000001O1OO11kE_L00n9e3QF`Lj9h3VFSLg9o3000O100000000O10000000000O1_O^FlLb9T3a0000000000000O1000000000000LeEkL[:S36G90000000000O11OO1001O00000000000Fd0_EdLR:l3E4L00O1001O00000000000000lNaFoM_9h1jFZMCl0c9g1oFYN[9]1eFhM^O41=k9g1SGWNm8i1TGUNm8k1UGoMo8R2[10000001O0000000000KeDkM];U2cDjM^;V22O1002N001O1OO1003M1O1O1O00O1O11OO100O100O100001O1O00O1O100MdDjM\\;U2eDjM\\;V2dDmMY;T2gDjMZ;W2I;O00O1@nDQNO2T;m1PEoMM4Q;l1TEoMM2P;o1XEQNi:o1`0002N0000L4003MoN^D^ONC2NO0O0e;Q1]DQO015NK0g;8WDI0O040L32`0Mc;2UDNL3[=Nmbo3", "size": [440, 640]}, "area": 30501.0, "iscrowd": 0, "image_id": 16228, "bbox": [7.0, 0.0, 336.0, 138.0], "category_id": 157, "id": 20001019}, {"segmentation": {"counts": "S2c4U90O10000O105K]Od0N101K31WGQLi7o3P10001OLQL_FP4_97dFiKT9W4hFlKZ9X4EfKQG11Z4k8hKTGOOZ4n8gKRG_4l88L5N2M03H6O1O2N4MOgKPGl3P9RLRGn3P9jKnF22T4T9kKnFT4R9lKoFS4P9mKPGU4X92N11O1N01O11hKaFU4a9O102O1O2N1N3M2NO1001O00000WLSFh3e9XLcFh3\\9\\LaFe3_9^L]Fc3b9]L]F^3`9[LeFo3KTLZ9Y4100OK4N34J5L01NjKfFQ4a900010N1O2dFiKT9W47kKcF0Ni3f9WL\\Fg3f9WLZFi3j9\\LVFY3k9eLTFM20NZ3m9gLZFOJX3m9hL]FY3P:0015K0kLaEQ3e:NO1bESMR:m2lEPMK2V:V3^FhLS9V3h0TMbE^2`:^MbEb2^:^McEa2]:YMiEg2c:2WE[M]:T3L000O12gLbE02\\2W;O00OM408H4LN3O1O12N00LQN`Dn1`;SN_Dn1a;5O2aDjMX;]1fD\\O];c0cD\\O[;f0gDYOY;g0hDXOY;f0iDYOW;g0iDZOU;f0mDYOT;f0]EjNa:W1o0010O02O1N01O02OO10O0J7M301O1N3N0000000O1000M3000O0100000VOYCc0h<[OZCd0f<[OWCh0l<1N2001OO0012NO1O1O001001O000O101O00001N1000000ERCJQ=3QCLP=0TCLQ=0TWS13mhlN9gBCT=b000mB^O41d<n0N1gCPOh;R1RDSOHKT<b10000001O0000000000K_NQDc1o;]NPDd1P<2O1002N001O1OO10122L3M3MO01N1O11OO100O100O2O0001O1O00O1O2O0MmMaDS2^;nMaDS2_;mMdDP2];PNaDQ2`;310O0O1@lMSEO2V2k:nMQEM4S2j:RNQEM2R2m:VNSEk1m:a0O02N0000L400\\EVM]:g2bEPNg8[OaGNC2NO0O0i25PNh8YOUG015NK0k24mMo7VOnG0O040L32`0M`24iMo7_OmGL3a31UMn7FoGX32TMn7b3QHaLm7_3SHdLj7]3THfLj7Z3VHiLg7j4JO1fNaHZL`7[3kHdLV7\\3kHbLV7]3kHbLV7]3kHbLV7^3kH`LV7_3kH`LV7`3jH_LV7a3kH^LV7a3kH^LV7b3iH_LU7b3lH]LR7f3nHYLP7i3PIWLP7i3QIVLP7j3PIVLP7i3PIWLQ7h3PIWLQ7i3oHVLR7i3nHWLS7i3mHWLS7h3oHVLR7i3oHVLR7j3nHULS7j3nHVLR7j3nHVLR7i3oHWLQ7h3QIWLo6i3QIWLo6i3QIXLn6h3RIXLn6g3SIYLm6g3SIYLm6g3SIYLm6g3SIYLm6f3TI[Lk6e3UI[Lk6e3TI\\Ll6d3TI\\Ll6c3UI]Lk6c3UI]Lk6c3TI_Lk6`3VI`Lj6`3VI`Lj6`3UIaLk6_3UIaLk6^3VIbLj6^3UIcLk6]3UIdLj6\\3VIdLj6[3XIdLh6\\3XIdLh6\\3XIdLh6\\3XIdLh6[3YIfLf6Z3ZIfLf6Z3[IeLe6[3[IeLe6[3[I_Lk6`3YIPLT7P4Z100000000000gG_L[6a3`IeL_6[3aIeL_6[3`IfL`6[3^IfLb6Z3^IfLb6Z3]IgLc6Y3]IgLc6Z3[IgLe6Y3[IfLf6Z3YIgLg6Z3WIgLi6Y3WIfLj6Z3VIfLj6Z3VIeLk6\\3UIcLk6]3UIcLk6]3UIbLl6_3TI`Ll6`3TI`Ll6`3TI_Lm6a3TI^Ll6c3SI]Lm6c3SI\\Ln6d3SI[Lm6f3RIZLn6f3RIYLo6g3PIZLP7f3PIYLQ7h3nHXLR7h3mHXLT7h3lHWLU7i3kHWLU7j3iHVLX7j3hHULY7k3gHULY7l3eHTL\\7l3dHTL\\7l3cHTL^7l3bHSL_7n3`HRL`7n3_HRLb7n3^HQLc7P4\\HPLd7P4[HPLf7P4ZHPLf7P4YHPLh7Q4XHmKi7S4WHmKi7S4XHkKi7V4VHjKj7V4VHjKj7V4WHiKi7W4WHhKj7Y4VHfKj7Z4VHfKj7[4UHeKk7[4SHfKn7[4oGgKQ8Z4lGhKT8Y4iGiKW8W4fGkK[8V4bGlK^8g4000O1000000O1000000O1000000O1000000FbGYK_8f4:O11O1O1O1O1O1O1O1O1O1ON2M3001O001O2N3M3M3M1O1O2N1O1O1O1O1O2N1O2NN2HPHfJ9L^7]5[HdJR8[560aHdJb6[5_IjJ\\6U5dISKU6m4kIYKo5l4WIcJ?g0T6m4kIUKS6m4kIVKR6k4eI^KZ6c4eI^KZ6c4fI\\KZ6e4eI[K[6\\4gI`JMT1\\6[4iIaJJT1]6Z4kIkKU6T4mIkKS6P4RJPLn5n3TJRLl5m3ZJnKf5Q4\\JnKd5Q4^JnKb5Q4`JnK`5R4`JnK`5R4`JnK`5R4`JlKROPO]6T5aJlKb5T4^JkKc5U4]JkKc5U4]JjKd5V4\\JjKd5W4[JgKSOSOb6W5ZJeKi5[4VJgKi5Y4WJgKi5Y4VJhKj5Y4TJhKl5Y4RJhKn5Z4oIgKQ6b4dI^KHnNd6o63001O00002N0000O12NN]IoHc6d5bIbK`6]4bIcK]6\\4eIdKZ6[4hIdKX6\\4iIbKX6^4iI`KX6`4iI_KW6`4kI_KU6`4lI_KU6Z4]IiJ?l0U6[4]IhJ>l0V6[4]IiJ=k0W6\\4PJcKQ6\\4PJaKS6_4mIaKS6^4nIbKR6^4mIcKS6\\4mIeKS6[4mIdKT6[4lIhKR6X4nIfKT6Z4lIfKT6Y4lIhKT6X4kIjKT6V4nIhKR6W4PJgKQ6Y4PJhKn5X4SJgKm5Y4SJgKm5Y4SJfKn5Z4RJeKo5Z4RJgKm5Y4SJgKm5Z4RJeKo5[4QJeKQOZOl6P5TIPKn0h0n5W4UJhKl5X4TJiKk5V4aIRK5h0[6V4`IRK4i0\\6T4aISK2j0]6S4aISK0l0_6P4bITKNm0`6o3ZITKO06o0`6l3\\IUKN14n0c6l3\\IUKL23n0e6j3]IVKK4On0i6g3^IWKJ\\1h6]3^IXKI[1i6\\3_IYKH[1i6]3]I[KFZ1m6_5L000000001O", "size": [440, 640]}, "area": 74266.0, "iscrowd": 0, "image_id": 16228, "bbox": [0.0, 0.0, 640.0, 234.0], "category_id": 169, "id": 20001020}, {"segmentation": {"counts": "`V3d1S<NWD_Na;T1^DlN31[;l0_D_O7GY;i0]DH4B^;e0\\DM4@_;b0^D00_Ob;`0^D>b;]OcDc0_;POkDk0DnNb;2nD5]O?5ZOa;0oD5^O=R=M4N2O000O010O1N1O2N2M4Mbn2c0iPM9I7H4fCSOc;a10000O1001\\NZDT1h;eNaDW1R<D=VOYC<U=M2M4HRb53o]Ja0XO_OVCk0b<;K2jCeNj;e100001O0N3bNWDNL`0J_On;h0RD0l;P1N1O1M3OHZD`Ne;\\1cD`N^;^1\\DdNONd;]1\\DhN0Kd;[1[DmN2Gc;i1]DWNc;e1[D\\Nj;6VDP13jNg;5ZDn00lNg;3VDM0U13jNm;e0nC3V<j0N1O2N2N_NRD83>j;UOZD8O2K1^<FhC9KOo<MQC1U=KjB4W=MhB2Y=NgB0[=LhB4^=00O1Ng\\3OZcL6YCN];7^DOY:GhE3YO=>Kc:EeEn0^O@LCl:1iEi1W:XNhEh1W:YNiEg1OUN`95aFS1ZOWOc0Ca95aFc1L[Nb9c0RFQ1:^Nd9[1mEB>TOd9\\2oE^M;9e9_2YFcMf9_2WFdMh9_2SFcMl9V3O0O2O001N1I8L301O0O101O001O0O2O001O001N101O001O000O2O001O001N101O001O0O2O001O000O2O001O001N101O001O0O2O001O001N10001O001N101O001O0O2O001O001N10001O000O100000O10O1000000000O0100000000000O100000000000000O1000000O100O100O100O1O100O100O100O101N1O100O100O100000000000000O100000000000000000000000000O1000000000000000000<DO10000O103M4K5L3M0O2O0O10000O10000O2O000O10000O101N10000O10000O2O000O10000O101N10000O10000jK[GX3f8gL^Gm0YOS1Z9oM^GT1:DX8WO`GS1c:lN_ER1b:nN_EP1b:oN`EP1`:PObEQ1[:nNiEQ1V:nNmEQ1b;L00M3dNSOhEP1k9[OTFh0j9XOUFk0h9WOVFl0g9UOXFn0f9ROYFQ1d9PO[FS1c9nN[FU1KYN75b7=\\HX1D^NN?l7LaHY1_OdNK`0R8CdH[1XO0Q8eNgHa3W7_LhHc3V7^LiHe3T7[LlHg3S7YLmHf3S7[LlHf3S7ZLmHg3S7YLlHg3T7ZLkHg3U7YLkHg3T7ZLkHf3U7[LjHf3V7ZLiH`2^OmMg7EjH\\2CoMa7ElHZ2GQNZ7FoHX2JRNT7GRIU2NTNn6GTIS22TNj6IUIQ25TNe6LUIn1:TN`6OVIk1>TN\\61VIi1b0TNX63VIf1g0UNS65VId1j0VNo56YI`1m0XNj58YI]1R1YNe5:YI[1V1YNa5;ZIZ1Y1YN\\5>[IZ1Y1WN\\5?[IZ1[1VNY5`0]IY1\\1UNV5b0_IY1^1RNS5e0_IZ1`1nMQ5h0_IZ1b1lMo4i0`I[1d1iMk4m0aIZ1f1gMi4o0aI[1m8eNTGZ1l8fNTGZ1k8gNUGY1^1gMU5P1]IZ1\\1gMW5o0]IZ1[1iMW5n0]IY1[1kMV5m0`IW1Y1nMV5k0aIX1W1oMW5i0bIX1V1PNX5h0bIX1T1SNY5f0\\I]1Z1oMX5h0nHg1h1cMY5g3fJZLZ5f3eJ[L[5f3bJ[L_5e3_J\\Lb5d3]J\\Ld5Q1QIe0Y1ZNh5b0^IU1g0ZNl5`0_IU1c0\\No5?^IU1a0\\NS6>]IV1>]NW6<[IX1<[N[6<ZIZ18ZN`6;YIa1OTNj6;WIR3k6lLVIR3l6nLSIQ3o6nLRIP3Q7oLoHo2S7PMmHo2U7RMjHl2X7TMgHk2\\7TMdHj2^7WMaHg2a7YM^Hf2e7YM[Ha2k7`MTH[2Q8eMnGW2W8iMiGR2]8nMbGn1b8RN]Gn1d8RN\\Gm1f8SNYGm1g8RNYGn1h8RNXGm1i8RNWGn1k8QNUGo1k8QNTGo1m8PNSGP2n8PNRGP2o8oMPGR2P9mMPGT2P9lMPGT2Q9jMoFW2Q9iMoFX2P9hMoFY2Q9fMoF[2R9dMnF\\2R9dMmF^2R9aMnF`2S9_MmFb2R9]MnFd2F`Ma8LiGn3h7RLWHg4R7XKmHm4o6TKPIo4m6QKRIQ5n6oJQIQ5o6oJQIQ5o6oJQIQ5o6PKoHQ5R7nJnHR5R7oJmHQ5T7mJmHS5S7jJoHX5Q7eJQI\\31kMo6fNRI[3b0`M]6ROSIZ3m0[MP6YOTIZ3R1ZM^6b2hI\\MX6a2lI^MT6^2RJ`Mm5\\2YJcMg5X2_JgMa5T2fJjMZ5Q2mJmMR5Q2TKlMl4Q2ZKmMe4P2aKlM`4P2e3O2N2N2N1N3N2N2MIbNYD]1h;8120O100O100O1O100O1000M3L4O100O10200000O0N1O1O2N1O1O1O1O1O2O0OdD^Nd:`1[EaNc:a1\\E`N\\:MSE9MP1c0kN\\:NQEe1c0]N[:g1fE[NY:d0UE0c0]OW:d0\\EG?ET:e0cE_O9MV:NRE<e0A46Y:GQEa0`1He97UF0VOBo98fF?XO\\OS:0eFk0QOYO[:KdFo0kNXOc:HbF^1a9aN_F^1c9bN\\F\\1g9cNYF\\1R;0000000000001O001OMiCeNX<[1hCeNW<[1jCdNV<\\1kCdNT<\\140000000000000000001O0000000000000000000000001O0000000001O0000nMfNfGZ1Z8fNfGZ1Y8hNfGX1Z8hNfGX1Y8iNgGX1X8hNhGX1[NcNb97RHV1XNgNf93RH`1m7aNSH_1m7aNSH^1m7cNSH[1o7dNRHZ1P8fNPHX1Q8iNoGV1R8jNnGV1R8jNnGV1Q8kNoGU1Q8kNoGU1Q8kNoGT1R8lNnGT1R8lNnGT1R8lNnGS1R8nNnGR1R8mNoGm0X8ROhGh0^8XObGe0`8QOcE9m1d0a8_O_G>d8B\\GNS9IPE5n1NU97kFFW9;S2GhB2W=80HiBOV=2jBNV=2kBLU=5kBKT=7kBIU=6mBIR=8nBHaX3", "size": [440, 640]}, "area": 64040.0, "iscrowd": 0, "image_id": 16228, "bbox": [7.0, 130.0, 626.0, 223.0], "category_id": 183, "id": 20001021}, {"segmentation": {"counts": "k7=o:100001N01001N02O01O00000N2M5KjX\\5", "size": [365, 500]}, "area": 185.0, "iscrowd": 0, "image_id": 16249, "bbox": [0.0, 250.0, 16.0, 14.0], "category_id": 125, "id": 20001022}, {"segmentation": {"counts": "0k7=T3100001N01001N02O01O00000N2M5Kkb44S]K2O1N2O0O2O10O0101N3N3L3N3A[OnEh0l9_OPFe0g2TOT4`1eKeNW4b1aKcN[4c1]KcN_4d1YKaNb4f1YK]Nc4k1XKUNe4R2VKnMh4Y2TKgMj4`2PK`MP5e2kJ\\MU5g2hJXMX5l2dJfLFEe5h3cJbLl5g1dIC<fNQ6h1bIE9cNU6i1bIF7`NW6l1bIF3^N[6m1bIH0ZN_6_40aMaI1_60aIO_62aIN_62aIM_64aILb0gMV5^2XJK?kMW5[2ZJI=PNX5W2[JI:TNY5T2]JH7XNZ5Q2_JG4[N\\5o1`JF1^N_5l1`Ja0Z5nLfJh5W56OO10003L5M21O100O1O100O1O100O1O?B1O1O1O100O00O1A`0O0bKhJh2W5WMlJg2T5XMPKf2P5XMSKf2m4YMVKe2j4ZMYKe2g4YM]Kd2c4ZMaKe2^4YMfKW2TOlLV5k0jKV2ROoLT5h0nKV2PORMQ5e0RL5hNl06\\NQ5>SL:fNk08_NQ55SLa0eNi09cNP5MULf0bNi0<eNn4FVLl0aNg0>oNe4XO^LR1_Nf0a0SOT68[Ic09aNEe0d67_Ia05H[6H`I?1M_6E`I<O1b6D_I9O4a6E`I5N8b6CaI3M5OaNd6X1`I0M8h6I[IMM;h6I[IJM?f6A[ImN2Q1Mb0f6]O_IoN36J7NY1f6[O_IPOa0:ZO\\1f6YOaIQO>8\\O`1c6XOdIPO?4YOf1d6UOfIQOd0m1f5ROfIQOb0o1h5oNhIRO:U2m5iNjIRODi2a6UNmIR2S6nMbIPO2R3\\6mMbISO2P3[6lMdIUO1n2Z6nMfIUONm2\\6mMiIVOJl2]6nMjIWOHj2^6nMlIYOEi2^6nMnIZOBh2_6nMRJYO_Oh2_6oMSJXO_Oh2^6oMVJUO^Om2[6nM`JQ2_5oMbJQ2^5nMdJ]1ROUNZ6>eJZ1UOWNU6>gJY1SOmMJ;[6`0iJU1SOQNK8Y6a0jJT1TOSNK6V6d0lJP1UOVNJ5T6e0oJn0TOYNJ1S6h0PK`1nNeMS6k0QK_1Y5aNhJ_1W5aNiJ`1W5^NkJc1T5\\NlJd1T5\\NlJj0bNUOb60mJj0eNSO^61nJl0iNoNY64nJm0POiNR69oJm0XObNh5`0QKn0XOaNh5`0RKl0YOcNe5`0SKl0o5ROSJm0n5QOTJn0n5nNVJP1l5mNVJR1l5jNWJT1l5iNVJV1R8N1O1O1O2N4K4M3M4L3M;Eo\\;4bbD<D<E;aFiN\\8e1aG]NR8P2lGQNh7\\2WHeMb7f2XH[Mj7T3O001O00001O0000001O00000000O1O1O1O100O1O10001N100O1O10000O2O0dNhGBY8>gGBY8=hGCY8;iGCX8=hGCX8<jGCW8;lGCT8=PH_OQ8?SH]On7b0WHZOj7e0YHXOh7f0\\HWOd7h0aHSOY41^Mj0]NROU47\\Me0dNoNk3MnJa0a2b0o3KPL1P47kKGT4;nKBQ4a0oK]Oo3f0TLUOl3n0WLmNh3V1[LeNc3^1aL\\N_3g1RJ[NW8_2N2O2N1O1O100O10000O100O20OO1000000O100O100O2O0FnLXHS3e7<L3M<GElHbLQ7^3nHfLn6Z3PIiLP7V3oHmLo6S3PIoLo6Q3PIQMP7g3N1010O01eKcKd1]4ZNfKe1Z4YNiKf1X4WNkKh1U4nKcK6Om07UO5i3\\4RL[Kj06YO6k3=oKn2O]Lj02[O9l37SLa3b0nK]O=m32WLe3<lK@>m3NYLm0GZ1?]MC`0n3J[Lm0F`1;XMEc0P4D]Lo0Bh18QMIe0Q4@^LQ1_Oo16jLKh0S4WOcLV1YOV22dLOj0Q5X4oJhKR5W4mJjKS5S4mJmJ0Q1T5R4lJoKV5S55K5hJ`JY4f5_K\\Ja4W62N10O01O1N1O2O1N101N2O1N101000O10000O10001O1N2O2N1O1O1O2ZK[J`3ARLn59gJd3YOTLQ62lJi3ROVLY7h3fHZLZ7l33M0001O0000O101N1N2000000000000000001O0O100000000000000000001O000000000000000001O0001O01I6N2N2N3M2N2N2M4L3M3O101N1O1O1O00100O00jNfI[MY6h2jISMU6o2oIkLQ6X3RJcLm5d3PJYLo5o3jIPLV6R4jImKU6T4mIjKR6\\3iIRM7_OQ6a3jIlL7An5f3lIfL8Bk5k3oI_La6d3b03M4L6J5K^LXIg10QNg69ZIe11QNc6;\\Ih1OkMd6>^I]2a6dMbIY2LcL^6U1iIU2IfL]6V1nIP2DkL_6T1PJn1AnL_6T1SJb1@aM]6n0VJ]1_ObM\\6S1UJY1_OdM]6S1TJX1@dM\\6T1UJW1@dM[6n0_ITOg0Y2_OeM[6m0_IVOh0X2\\OeM_6T1VJV1[OfM`6S1VJV1YOhM_6M^IT1j0W1YOhM_6T1YJS1WOjM_6T1[JQ1VOjM_6V1\\Jo0TOlM_6V1^Jn0ROlM`6V1_JT1`5mNaJf0POXN_6S1aJc0ROYN^6S1aJb0ROZN^6T1aJ`0SO[N\\6U1aJ?UO[NZ6V1bJ=VO\\NY6V1bJ<WO]NW6W1bJ<XO[NW6Y1bJ9ZO]NU6Y1aJ9\\O]NS6Z1bJ7R6InI6T6ImI5T6KlI3W6LiI3Y6LgI3Z6MfI2R3", "size": [365, 500]}, "area": 127727.0, "iscrowd": 0, "image_id": 16249, "bbox": [0.0, 0.0, 500.0, 365.0], "category_id": 128, "id": 20001023}, {"segmentation": {"counts": "S`e39R;2001O00001O0O2O0O2O1O2MdRh1", "size": [365, 500]}, "area": 101.0, "iscrowd": 0, "image_id": 16249, "bbox": [329.0, 236.0, 13.0, 11.0], "category_id": 140, "id": 20001024}, {"segmentation": {"counts": "Ua:6V;2O1N101N2O010O101N3N3L3N3A[OnEh0l9_OPFe0g2TOT4`1eKeNW4b1aKcN[4c1]KcN_4d1YKaNb4f1YK]Nc4k1XKTNf4S2UKmMi4Z2SKfMk4a2oJ`MP5e2kJ[MV5h2gJXMX5l2dJeLk5^3SJaLm5h1cIC<eNR6h1bIF8bNV6j1aIF7_NX6l1bIG2]N\\6n1aIH0YN`6o1`I4`6MaI1_60aIO_62aIN_62aIN^63bILb0hMU5^2XJJ`0lMV5[2ZJI=PNX5W2[JI:TNY5T2]JH7XNZ5Q2_JG4ZN]5P2_JF1]N`5m1_J`0[5oLeJf5Y55000O103M4N11O100O1O100O1O101N1O?B1O1O1O100OO1@`001aKfJj2Y5VMiJh2W5WMmJg2R5XMQKf2P5XMSKg2l4XMWKf2i4YM[Ke2e4XM_Kf2a4XMcKY2UOkLW5j0iK1POm02WNU5i0mK1lNn04WNT5g0oK3iNn06YNQ5c0RL6hNl06]NR5;RL=eNj09`NQ53SLc0dNh0:dNQ5JTLi0aNh0=fNo4BVLP1_Nf0?PO[6:VIe0b0TOS67\\Ic09bNDe0d67_I`06H[6I_I>2M_6F_I;02a6D_I804a6E`I5N8b6D`I2N52_N`6[1`ION9g6I[ILN<g6I[IIN?f6_O]IoN0Q1Mb0f6^O^IoN6NL>JZ1e6ZO`IQO`09[O]1e6YOaIQO>7]O`1c6XOdIQO?M^Ol1_6UOfIQOd0n1e5QOgIROa0o1h5nNiISO9T2n5iNjISOCh2b6UNmIS2R6mMcIQO1R3\\6lMcITO1o2\\6mMcIUO1m2[6nMfIVOMm2\\6mMiIVOJl2]6mMkIXOGj2^6mMmIZODh2_6nMoIZO@i2_6mMSJYO_Oi2^6nMTJXO_Oi2]6nM_JR2a5nM`JQ2_5oMbJ_1QOTN\\6=dJ\\1SOVNY6=fJZ1UOWNU6?fJX1TOmMJ;[6`0iJU1SORNJ8Y6`0kJT1TOTNJ6V6c0mJP1UOWNI4U6e0oJo0SOXNK0T6i0oJ`1nNeMS6k0QK_1Y5aNhJ`1V5_NkJb1U5]NlJc1T5\\NlJe1S5[NmJj0cNUO`6OoJk0eNSO\\61oJl0jNnNX65nJm0WObNk5?PKn0WObNh5`0QKm0YOcNf5?SKl0YOcNe5`0SKl0o5QOTJn0m5POUJo0m5mNWJQ1k5lNWJS1k5jNWJT1R8O2N1O1O1O3M3L5L3M3M7Ibh;MVWD<D<E<`FiN\\8e1aG]NR8P2lGQNh7\\2WHeMb7f2XH[Mj7T3O00001O00001O0000001O00O100O1O1O1O100O100O101N100O100O100O2dNfGCZ8=gGBY8=hGBY8=hGCY8<hGCX8<iGDW8;kGCW8<lGAT8>PH_OQ8?TH]Ol7c0WHYOk7e0ZHWOg7g0]HVOc7i0bHROX43]Mi0_NQOT49[Me0dNmNm3OlJa0a2a0P4LoK0Q48jKFU4<mKAR4b0nK[OQ4h0SLSOl3P1WLkNh3W1\\LcNc3a1`LZN_3h1RJ\\NV8_2N2O1O1O1O101N10000O100O11OO10001O0O100O100O100FoLWHR3f7<L2N3[HdLZ7[3nHbLo6^3oHfLn6Z3PIjLo6U3PImLo6S3PIPMn6P3QIRMo6m2PIVMn6e30010O0fKdKc1]4ZNfKe1Z4YNiKf1X4WNkKh1U4nKcK6Oa1<Z2a0nKh02MMeN5L^1<`2;QLd07`0FYN\\1;e26TLb0:R1g0YNj21XL?:\\1`0UNn2MZLb06b1=QNS3H]Le0Oi1<lMY3B_Lh0IQ2:fM`3]O_Lm0DV28aMf3UOeLS1\\O\\24]Mi48QK`22XMn47oJc21WMP56oJd2OXMR54mJf2OWMW50iJn3]5jKeJV4`5dK`J]4f5VKaJj4P61O10O01O1N2N101N2O0O2O1N2O010000O1000000O2O1O1O2N1O1O1O2WK_Jb3]ORLo57iJf3WOULP6OoJk3POWLX7h3fHYL\\7k32OO00000000O101N1iL`H_2b7cM`HY2`7hMaHW2^7jMcHS2^7RN_Hl1a7P1000000001O000`M]HV1c7jN^HU1b7kN_HT1a7lN`HS1`7mNaHR1_7nNaHR1_7oNaHP1_7PO`HP1a7PO_HP1a7PO^HQ1c7nN]HR1c7nN\\HS1d7nN[HR1e7nN[HR1e7[1000001O00000010O00JhMbHm0\\7UOdHl0Y7WOgHh0W7ZOiHf0U7\\OlHc0R7_OnHb0o6@QI`0l6R2M3M4N100O1O1O1O1O010OlNbI[M]6d2gIYMY6j2kIPMT6R3PJiLo5Z3SJaLm5f3QJVLn5R4iIPLV6Q4lIlKT6Z3fIUM8_OQ6^3iIPM8@o5b3kIjL8Am5h3lIdL9Bj5m3RJZL^6i3a03M4LgLmH_2m6bMSIb2h6_MXIg11PNf6:ZIc13RNb6<[Ia16QN^6?]I]18TNY6`0cIW17WND^O^6U1jIQ17[NA_O]6V1nIl06^N^O@`6U1PJg05ZN[O10I`6U1SJc03]N^O1\\6P1UJ=2cN^OO[6S1UJ4[OfNg06]OM]6R1UJOZOmNj06]OH[6V1UJNZOlNi09\\OH\\6m0`IUOf0P1YOlNi0;\\OG\\6j0bIYOe0o02XOZOG^6e0`IAg0j0O[O\\OE_6S1VJMM\\O]OD_6N]IT1j0NL^O]OB`6<]I8k0<I_O^OB`6U1ZJIGA_OA_6V1\\JHDB@A_6V1^JGACB_O`6W1^JF_O@BB02`6W1`JE\\OAEG_6S1aJEYOAIF^6S1aJEWOBKF]6S1bJETOBOE[6T1bJFROA3CZ6V1bJEPOB6BY6V1bJFmNB:AW6W1bJFlNB=@U6Y1bJ8[O^NT6Y1aJ8S6HnI6S6JmI5U6JlI3V6MjI2X6MhI2Z6MfI2[6GlI8l2", "size": [365, 500]}, "area": 53154.0, "iscrowd": 0, "image_id": 16249, "bbox": [29.0, 38.0, 471.0, 312.0], "category_id": 183, "id": 20001025}, {"segmentation": {"counts": "T:V4k:0O1O2N2N2N2N2O1N1O2N2N3M0001O00000000001O000XNnEQML^1V:^1WF_Ni9]1\\FbNd9]1\\FdNd9[1]F[M]OOOU1X:_1cFaN]9^1eFaN[9^1fFbNZ9^1gFaNY9_1gFaNW9`1jF`NU9a1lF^NR9c1oF]NQ9c1oF]NQ9b1PG^NP9a1QG_NP9`1PG`NP9_1RG`Nn8`1RG`Nn8_1SGaNm8_1TG`Nl8_1UGaNl8^1TGbNl8]1UGcNk8\\1VGdNj8\\1WGcNi8\\1XGdNh8[1YGeNh8Y1YGgNg8V1]GiNc8U1_GkNa8U1`GjN`8V1`GjNa8U1_GkNa8U1_GkNa8V1^GjNb8V1_GiNa8W1_GiNa8W1_GiNa8W1`GhNa8W1_GiNa8X1^GhNb8X1^GhNb8X1^G`NZNCX:m1_G_NZNCW:o1^GYNZNF21W:o1]GYNdNFo9Q2^GUNR9l1nFlMZN4i:P2QGmMP9T2n11N1O10000O2O0O1000010N13M0001O00kMiCa1k<00001O0001O0001O00000010OO1O1O1O2MTNPCl1n<40PNRCn1P=00SNPCk1n<VNRCj1n<VNRCj1n<VNRCj1n<VNSCi1m<WNSCj1n<TNRCl1n<TNRCl1Q=00001O000O2O0QCQNj<T201O001O00000000000M303L5L5K4MO2N1^MlCV2Y<eMjCX2`<O00001OO1002N00M32_O?01OO2O01O00001O0001O000000001O000001O01N10000000001O0000001O000000001O000001O01O000000010O0000001O000001O0001O0001O100O00000000000001O0O1000001O000000001O000000001O0001O00O11O01O0001O001O010O2N2N001O0001O0001O0001O00000000001O00000O2O00000000010O000O11O0000010O0010nBSNo<T2K2NO2O00000001ON21OO11ON22N1N10O1M2OUCoMk<Q2201O0001O0000000010O001O1O0010O00000O11O0000O2O000000001O0000000O101O0O100000001O00000O2O0000000EhMnCX2P<lMnCU2P<mMkCKOX2T<`000O1O1O10001O000O100O1O21NoMQMPHP3n7SMQHm2m7UMPHn2o7SMnGP3Q8QMnGQ3P8QMmGQ3S8oLiGHaNZ3f9nLgGW3Y8iLfGX3Z8jLdGV3\\8iLeGW3[8iLdGX3\\8hLdGY3[8gLdGZ3\\8fLcG[3]8eLbG\\3^8dLcG[3]8eLbG\\3^8fL_G\\3`8cL`G^3`8bL_G_3a8aL]Ga3c8_L[Gc3o8SLoF>SOB2O0010Mo2l:aMeE`2Y:aMgE_2V:eMiE[2W:dMjE\\2U:dMlE\\2T:dMlE\\2S:cMoE^2o9bMRF^2l9cMUF]2j9eMUF[2j9cMaE\\Of0Q3h9cM[F]2d9bM^F_2`9aMaF_2]9aMeF_2Z9`MjF^2V9aMlF^2W9^MkFa2lN[Mn91XGS3f8kL[GY3b8eL_G[3a8dL`G\\3a8bL`G^3a8`L`G`3a8`L^G`3c8aL[G_3e8aL[G_3f8aLZG^3g8`LZG`3S:0O10000O1O1O1000000001O00000000000000001O00O1N2000000001O00O10000001O0000000000O1O1@^LaEc3^:_LYEN1d3f:?O11OO11OO10000002NN2O11O1OAjKPFZ4P:fKnE\\4Q:eKlE^4R:dKlE^4T:700O1O11ON2000000000000HTK\\FQ42XLHG04j9MZFn39QLM3`9NWFQ4h0QLP9U4RGhKh81]FZ4a:3ON003M2O01000O100O1LYEoKg:Q4ZEoKe:P47O0O100O@XLjEg3X:YLgEg3W:\\LhEc3W:d01N0100010O0000000hMgEIY:1oEJT:3PFIS:2SFHR:1UFLn9M[FLj91[FIm90WFAZ::hEC\\:7jE]Oa:<fE@^::X2JkjZ1", "size": [480, 640]}, "area": 50166.0, "iscrowd": 0, "image_id": 16439, "bbox": [0.0, 286.0, 548.0, 194.0], "category_id": 110, "id": 20001026}, {"segmentation": {"counts": "f9>a>20O0000000000O2O000000000O101N1K5004L0001N1000000O10001N10000000N3N2M3_OcA70H01_>OaA010O30M00e>0\\A10OYR60b\\J0TA023X4Ok59kEE00S45Q6;jIKS66PF_Oh3>X66hIJW67iIIW66jIJW65jIKU64lILS65mIKS64nIMQ63PJLP63QJMm55SJKn54SJKm54TJMk53UJMW5FUG=e3MU5JSG9i3MR5OPG4o3Mo4i0QKVOP5j0PKUOQ5j0QKROjKIU9U1QKQOS5n0nJPOiKKY9U1PKnNR5R1oJkNfK0[9T1oJmNR5T1]401N10000O10002N1O1NPC@c;?WDGi;8XDHh;8XDHg;8ZDHh;6XDJg;7YDJf;5\\DJd;6\\DJd;4^DLb;3_DMa;2`DN`;2`DN`;OcD2\\;L_C@U1c0];LfD3\\;LdD3_;KbD3`;L^D4iNAi<;^D2n;DTD<gNC_>?00bAA[>>fABZ>>fABZ>>fABZ>>fACY>=gACY>?eAA[>?eAA[>b0000000WD^OS9c0hFBW9>_F\\OnM00;b;:^FNa90bF0[93fFL[93eFM[93fFLZ94fFLZ94fFLY95gFLY93gFM_9MaF3d9H\\F8e9D^F<b9DaF:U8@fF5Z17o7FfF3`12i7LgF2d1Me72eELl06g;K[CO;5GLk07e;J^C01NN026OLm07VOKS<5hCG0419o00POJS<m0lDCS;=mDCQ;?oDAQ;YOPDT10lNm0h0T;>lDAV;?mCjNj0g0Y;?mCkNj0g0W;a0iD_OW;a0iD\\O[;b0fD@X;`0hD@X;?iDBX2_O`5P1XH@X2HY5g0`HAX2GX5g0bHBU2GY5g0bHBU2HY5d0cHDT2HY5d0cHES2G^5?`HJR2Gb5:]HOR2Ga59]H0R2Ga58_H0P2Ha58_H0P2I`56aH1P2H_56bH2o1H_56bH2o1I^54dH3n1I^54dH3n1I]54fH4m1G]54hH4k1HnMLo58XJ3l1IZMLEk0R6]OTK3l1b2`2[MdK3l1c2^2ZMgK3k1c2^2YMhK4j1d2\\2YMjK3k1c2Y2[MmK2j1e2W2YMPL1i1e2Y2XMoK3h1e2Y2WMPL4h1d2X2XMPL4h1d2X2WMQL5g1d2X2WMQL5g1d2X2VMRL6f1d2X2VMSL5f1d2W2VMTL6e1d2W2UMUL7d1d2W2UMUL7d1d2X2SMUL:b1c2Y2SMUL:c1b2X2SMVL;b1b2X2nL[L`0]1d2[4[MfKf2X4ZMhKf2X4[MgKe2Y4[MgKe2Y4[MgKe2Y4[MgKe2Y4[MhKd2Y4[MgKe2Y4[MgKe2Y4[MgKe2Y4\\MgKc2Y4]MgKc2Y4]MgKc2Y4]MgKc2Y4]MhKb2Y4^MfKb2Z4_MeKa2[4`MdK`2\\4`MeK^2\\4bMdK^2\\4bMdK^2\\4bMeK]2\\4bMfK\\2Z4dMhKZ2X4fMiKY2W4gMiKY2W4fMjKZ2V4fMjKZ2V4fMjKZ2V4fMjKZ2W4eMiK[2W4eMiK[2W4eMiK[2W4eMiK[2W4eMiK[2W4eMiKZ2X4fMhKZ2X4fMhKZ2Y4eMgK[2Y4eMgK[2Y4eMgK[2Y4eMgK[2Y4fMfKZ2Z4fMfKZ2Z4eMgK[2Z4eMeK[2[4eMeK[2[4eMeK[2[4eMeK[2[4fMdKZ2\\4fMdKZ2\\4gMcKY2]4gMcKY2^4gMaKX2`4jM^KV2b4lM\\KT2d4lM\\KT2d4mM[KS2e4mM[KS2e4mM[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[NYKe1f4]NYKc1f4^NZKb1d4aN[K_1d4bN\\K]1d4dN\\K\\1c4eN]K[1b4eN_K[1`4gN_KY1`4hN`KX1_4iNaKW1^4iNcKW1\\4jNdKV1[4kNeKU1Z4lNfKT1Y4mNgKS1X4nNhKR1W4oNiKQ1V4POjKP1T4ROlKn0S4SOmKm0R4SOoKm0P4TOPLl0o3UOQLk0n3UOSLk0l3VOTLj0k3WOULi0j3XOVLh0i3YOWLg0h3ZOXLf0V3ZMRJQ2h2e0R3^MUJn1i2d0Q3`MTJl1l2d0o2aMTJl1m2c0o2bMQJm1P3a0o2bMPJn1Q3`0P3bMmIo1S3?P3bMlIe1OYNU3a2o2BPJkMQ3c2Q3_OPJiMKOT3i2S3[O]Me0d2ZO\\Mf0e2XO\\Mh0d2WO]Mi0c2VO^Mj0b2VO^Mj0b2VO^Mj0b2VO^Mj0b2VO^Mk0a2TO`Ml0`2TO`Ml0`2SOaMn0^2QOcMn0^2TO`Mm0_2RObMl0a2SO_Ml0b2QOaMl0SLZM]6g1cMo0_2PObMP1_2mNcMS1]2iNaIaNS4f2\\2gNiMY1W2fNjMZ1X2dNhM^1U2cNkM]1U2bNlM^1T2bNlM^1T2aNmM_1S2`NnMa1Q2^NPNb1P2_NoMa1Q2^NPNd1P2YNQNg1n1YNSNh1l1WNUNi1k1UNWNl1h1RNZNn1f1PNTJSOB2O0010Mf3m2d2oMVJEV3\\2d2nMWJFU3\\2d2kM[JHQ3]2d2kMZJIR3\\2c2kM[JJR3[2c2kM[JJR3Z2c2lMZJLS3X2c2kMZJNS3W2c2iM[J1R3U2d2iM\\J1P3V2d2hMZJ]O\\Of0f3U2e2fMYJ8R3R2e2eMXJ;S3P2d2eMYJ<S3o1m2ZMPJi0S3l1P3XMlIP1R3h1g3ZNXLg1f3[NYLh1c3ZN\\Le1d3\\N\\Lc1d3mMlHE`3^2c3kMQIF\\3_2b3iMUIHY3_2a3gMZIIU3`2b3eMZIKT3`2d3bMZIMR3\\1aLZOS7[O\\IOP3[1bLaNMa0V7B\\I2n2Z1bL\\N6c0n6E]I2m2Y1cL[N8d0l6C^I5k2Y1cL[N9c0l6B^I6k2Z1QMmNh6@]I9j2Z1QMlNj6_O\\I;i2Y1UM]NDOm7:[KZ1j5eNWJ[1i5dNXJ\\1h5dNXJ\\1h5dNXJ\\1h5dNXJ[1h5fNXJZ1h5gNWJY1i5gNWJY1m4cNTH4o2Y1k4hNRH0S3X1j4iNSHOS3X1h4lNTHLT3W1h4nNTHKT3W1f4QOTHIV3V1d4SOVHGV3V1c4UOVHFV3U1b4WOXHDV3U1a4YOWHBY3e0_LZOP8a0WH^O\\3f0gLoND0\\68PKh0kN[O^3d0Y3ZO\\Ji0kNZO`3b0Z3[OYJm0kNVOb3a0Z3_OVJm0kNTOe3`0Z3FnIW1g2SO[3_2eLcMY3\\2hLcMY3]2gLdMW3]2iL_M[3>PIh0e3nNW3:TIi0d3mNX38VIk0b3mNX3KWI]O0k1a3mNX3G\\I@Kl1a3mNX3AcIEDm1a3nNW3_OfIEB01d1`3XOW3_OgIDDM5a1Z3^OU3AjIBO^1S3@S3@lIANo0AYOS4LiK4i67mI@Mo0CQON1_;?hE?g:AYE>h:CXE=g:CXE=i:CWE>h:BXE=i:CWE=i:CWE=i:DSE?m:AoDe0o:[OQEc0Q;]OoDb0R;_OmDb0R;]OkD^OTOV1o;ZOPE@QOg0OAU<4mDEmNO0g01_OV<0SEMfNc02]Oj=f0XBXOj=h0700O1O11ON2000000000hN]OSDc0m;@nCJRO>0Hd4J\\K0Y86dHJSO>1F]46i3NeH8UOA_4:f3NfH6i3O^3KiH0UOGh4>Y3LiH0o35X3KiH0o35W3LjHMQ47U3LeHE]O9h46U3MaHIB4h46U3LfHG\\O6j47T3KfHI[O6k46T3KgHHYO8l45S3LhH3U41S3LhH3U41S3LhH3U41S3LgH4V40S3LgH4V40S3KhH6T4OT3JiH7S4KY3LeH9R4I\\3LcH;S4@a32gH6Y<HiC7a=OcTe1", "size": [480, 640]}, "area": 45156.0, "iscrowd": 0, "image_id": 16439, "bbox": [0.0, 0.0, 526.0, 480.0], "category_id": 123, "id": 20001027}, {"segmentation": {"counts": "\\om52m>3N1L3J6J6L4M3K5J6J6K5O1O10000000000kN_NkDa1T;`NRDMa0c1];_NmC6c0\\1Q1^Nd81fE8d0Y1R1`Nb8OhE9c0X1R1cNa8;\\FR1S1dN`8:\\FT1d0]NSO<HAm9f0TFDO]1U1^NPO=h9Z2WG\\MmN<l9X2WGSNi8m1VGTNj8l1SGUMPOo0m9l1SGWNl8j1TGVNl8j1TGVNl8j1SGWNm8i1SGWNm8i1lF^NT9b1lF^NT9a1nF^NQ9c1oF]NQ9c1mF`NR9`1nF_NS9a1mFkNg8U1YGSO_8m0bGRO^8o0aGPO_8Q1aG_MXOC9R1m8l1bG]MEh0i8k1bG\\MFi0h8k1dGUMVON?S1f8j1^HVNb7j1^HWNa7i1_HWNa7i1_HWNa7i1_HXN`7h1`HXN5iNa5o2ZJXN5iN`5P3\\JVN2lNb5n2\\JVNEmNnN0Q7m2\\JVNAROQOKR7m2\\JVN[OYOVODS7m2\\JVNZO[OVOB01j6l2fJYNWOYOXODM5h6e2lJ_NPOUO^OOf6_OYIi2c1hNlNROANh6]OYIk2b1hNlNSO@Mi6]OYIk2b1hNlNH]6`1fJiNmNG]6`1gJhNmNH[6`1hJhNmNG\\6a1fJiNnNF\\6a1dJkNPOD\\6a1cJlNQOC\\6a1bJVMbNc1`0F\\6a1fJiNoNB^6e1cJiNoN^Ob6i1_JiNoN^Ob6i1_JiNoN^Ob6i1_JiNPO]Oa6j1_JiNoNZO^OTOS7j2`JgNmN^O@QOS7j2`JhNiN_OEmNO0S7\\3lI[NWOCMfNQ7\\3WIeL5X3h68lHcL<U3h66mHhL9R3j6e0UI[Ok6i0QIWOo6h0RIXOn6h0RIXOn6e0UI[Ok6e0UI[Ok6e0UISLDT3W7i0YIWOg6i0ZIVOf6j0ZInMAWOU7l2YImMEROJROY7n3XIoMEPOJSOY7Q4UIkMJPOR7n1kHTNLRO>j1JROQ7n1oHQN8o0HROQ7n1SImM2U1KoNP7o1VIhM1Z1IoNP7o1VIhM1Y1JPOo6o1VIhM1Y1JkNE]OZ7g2VIhM1X1KgNIBU7g2VIhM2W1ImNG\\OX7g2WIiM1W1HmNI[OX7h2VIiM2V1GoNHYOZ7i2TIjM3T1HPOU7R2PIjM3T1HPOU7R2PIjM3T1HPOU7R2PIjM3T1HoNV7S2oHjM3d0]OWO;8V7T2nHiM4c09@f6R2nHkM4b09@e6T2mHjM5a0;@c6U2nHiM3b0>_Oa6V2PIiMNc0b0]O`6W2QIhMLd0d0^O^6V2SIhMId0g0\\O_6X2RIjM\\O]O5V1T9c1\\GZOd8f0]GZOb8f0_GYOa8g0_GYOa8g0_GYOa8g0_GYOa8g0_GYOa8g0_GYOa8g0_GYOa8g0_GZO`8f0`GZO`8f0`GZO`8f0_GYM^Oi1S9n0iGQOW8o0iGYNQOYO1IU9e2jGUNSOZO5Hn8i2jGeMQOK4N^9b2]GcMUOI4L^9h2]G]MSONo9e2nF\\MVOLm9h2mF[MWOLn9h2k1000002N001O1O1N2O1O1O1O4L4L8H1O3M2N2N3M2N3NO05K5J7J3L6J2O3M2G`AMc>1]AOg>LZA3i>12MiSj0", "size": [480, 640]}, "area": 22936.0, "iscrowd": 0, "image_id": 16439, "bbox": [405.0, 49.0, 179.0, 264.0], "category_id": 130, "id": 20001028}, {"segmentation": {"counts": "dUa0190H010^>0aA10O30M001^>3bAM10O2^>>O000O1001OO2O000001OO1010O0]OeA00?_>0OBbA<a>1O0O100O100O101N10000O100O2O0O0100O010O001000O0100O010001N013M00000O100O100O10000O2O0N2O1O100M3NkNYBU1f=30001ON20EoNeBS1[=900O100O11ON2000000O100000O1O001OOO31N1002NN120N01O1O101UObB1_=NcB1\\=OeB0\\=OeB@L:b=D^B;5GL9a=E`B1NN026OL:h=DVB0419:W>O0O100G[A6h>O2KVA2j>NWA0m>0\\`S7", "size": [480, 640]}, "area": 3571.0, "iscrowd": 0, "image_id": 16439, "bbox": [36.0, 268.0, 118.0, 62.0], "category_id": 139, "id": 20001029}, {"segmentation": {"counts": "h\\o01o>1JO[A2c>8K6EAnA`0P>CmA?R>8MUOQBk0n=4NQOSBo0m=2NoNUBQ1k=2O1O10kNXBR1h=nNXBR1k=000O10000001OO10000O10000O10000O1M300O100O100O1000000O1N2O10000O11O1O00000000000000000000000000001O000000001O0000O1M30000000000000000000000006J`0@8UOlA:d>I00001O0000001O00001O0000Q\\k6", "size": [480, 640]}, "area": 3760.0, "iscrowd": 0, "image_id": 16439, "bbox": [66.0, 430.0, 105.0, 50.0], "category_id": 143, "id": 20001030}, {"segmentation": {"counts": "0f9Z5O11O0000000000O10000000000O100O1K5004L0000O10XNeFZLL^1_9U2nFhMR9T2SGkMl8U2TGlMl8S2UGcL]OOOU1_9X2\\GhMc8X2_GgMa8X2`GhM`8X2aGgM_8Y2aGgM_8X2bGhM_8W2bGhM_8V2bGjM_8U2aGkM`8S2aGmM_8R2bGnM^8R2bGnM^8Q2dGnM\\8R2dGnM[8R2fGnMZ8R2gGmMZ8Q2gGoMX8R2hGnMX8Q2iGoMW8P2jGPNV8P2kGoMV8o1kGQNT8o1mGQNl7QOkFm2Z1RNi7QOhF02m2^1TNf7R2YHQNf7P2UHUNi7m1THVNl7j1THVNk7k1UHUNk7l1THTNm7k1THTNl7l1THTNk7m1UHSNk7m1UHSNk7m1VHRNj7o1UHQNi7Q2WHoMj7P2WHoMi7Q2WHoMi7R2VHnMV7kNPHX3j0mMU7oNnGT3n0lMS7UOjGP3S1kMQ7k2oHUMQ7k2oHUMQ7l2oHSMQ7m2oHSMQ7m2oHSMQ7m2QIQMo6o2RIPMn6Q3PIPMo6Q3RInLn6R3QIoLP7P3PIkKhNR1X8S3PIkKhNR1X8T3RIlLn6T3RIlLn6T3QImLo6S3QImLQ7R3nHnLS7Q3mHoLT7P3mHoLT7P3kHQMU7o2kHQMU7P3jHPMU7Q3jHPMX7n2hHRMW7o2iHQMW7o2hHRMX7o2gHQMY7o2gHQMY7o2fHRMZ7n2cHUM]7l2bHTM^7l2aHUM_7k2aHUM_7k2`HVMa7i2_HWMc7h2[HYMf7f2ZHZMf7f2ZHZMg7e2YH[Mg7e2XH\\Mh7e2UH]Mj7d2UH]Mk7c2UH]Mj7d2VH\\Mk7c2TH^Mj7e2VHZMj7f2WHYMi7g2WHYMi7g2WHYMh7h2XHXMh7i2WHWMi7i2UHYMk7g2PH^MP8c2dGgKN00k1]8_2bGjM^8\\4N200O1002NN21OO10000001O00O10000O100N200N20XIjGf6X801O0000001nMZHhLf7X3jHXLV7h3RIPLm6R4RInKn6R4SImKm6S4SImKm6S4SImKn6R4RInKn6R4RInKR7n3nHRLV7k3jHTLW7k3iHULW7k3iHULW7k3iHULW7k3jHTLV7m3iHSLW7m3iHSLW7m3iHSLW7m3iHSLV7n3kHQLU7P4jHPLGLo5T4ZJPLTOLEk0R6Z3VKhNZ4X1fKiNX4X1hKhNX4X1hKiNV4Y1jKfNT4\\1lKfNR4Z1nKeNT4Z1lKfNT4[1lKdNT4\\1lKdNT4\\1lKdNT4\\1lKdNT4\\1lKdNT4]1lKbNT4^1lKbNT4^1lKbNT4^1lKbNU4]1kKcNU4^1kKaNU4_1kKaNU4nLbJV4Y1nN]4S1dKnN[4Q1eKoN[4R1dKnN\\4R1dKnNm3kLnJW4U1nNe3SMWKn3T1oN^3ZM^Kh3T1mNV3dMeK_3U1mNn2lMnKV3T1nNg2SNULo2T1nN_2[N]Lg2U1mNV2dNeL_2U1mNo1kNlLY2T1lNh1TORMQ2V1kN`1R4aNmKW1[4iNeKP1c4oN]Ki0k4WOUKg0m4ZOQKh0n4XORKi0m4WOSKi0m4XORKh0n4ZOPKf0P5\\OnJd0S5\\OlJd0T5\\OlJd0T5\\OlJd0T5\\OlJd0T5\\OlJd0U5[OkJe0U5[OkJe0U5[OkJe0U5[OkJe0U5[OkJe0V5ZOjJf0V5ZOiJg0W5YOiJg0W5YOiJg0W5YOiJg0X5XOhJh0X5XOhJh0X5XOhJh0X5XOhJh0X5XOhJh0X5XOhJh0X5XOhJh0Y5WOgJi0Y5WOgJi0Y5WOgJi0Y5WOgJi0Y5WOgJi0Y5WOgJi0Z5VOfJj0Z5VOeJk0[5UOeJk0[5UOeJk0[5UOeJk0[5UOeJk0[5UOeJk0\\5TOdJl0\\5TOdJl0\\5TOdJl0\\5TOdJl0\\5TOdJl0]5SOcJm0]5SOcJm0]5SOcJm0]5SOcJm0]5SOcJm0]5SOcJm0^5ROaJo0_5QOaJo0_5QOaJo0_5QOaJo0`5PO`JP1`5PO`JP1`5PO`JP1`5PO`JP1`5PO`JP1`5PO`JP1a5oN_JQ1a5oN_JQ1a5oN^JR1b5nN^JR1b5nN^JR1b5nN^JR1b5nN^JR1c5mN]JS1c5mN]JS1c5mN]JS1d5eMaGU1k2V1k5eNUJ[1m5cNSJ]1l5dNTJ\\1l5dNTJ\\1l5dNTJ\\1l5dNTJ\\1l5dNTJ\\1m5cNSJ]1k5eNUJ[1l5dNTJ\\1k5eNUJ[1l5dNTJ\\1j5fNVJZ1l5dNSJ]1n5bNSJ]1m5cNSJ]1l5dNTJ\\1i5gNWJY1g5eM^GR1k2Y1g5iNYJW1g5iNXJX1h5hNYJW1g5iNXJX1i5gNWJY1i5gNWJY1i5gNWJY1i5gNWJY1i5gNWJY1j5fNVJZ1j5fNVJZ1j5fNVJZ1j5fNVJZ1j5fNVJZ1j5fNVJZ1k5eNUJ[1k5eNTJ\\1l5dNTJ\\1l5dNTJ\\1l5dNTJ\\1l5dNTJ\\1l5dNTJ\\1l5dNTJ\\1m5cNSJ]1m5cNSJ]1m5cNSJ]1m5cNSJ]1m5cNSJ]1n5bNRJ^1n5bNRJ^1n5bNRJ^1n5bNRJ^1n5bNRJ^1n5bNRJ^1o5aNQJ_1o5aNQJ_1o5aNQJ_1o5aNQJ_1o5aNQJ_1o5aNQJ_1P6`NPJ`1P6`NPJ`1P6`NPJ`1P6`NPJ`1Q6_NoIa1Q6_NoIa1Q6_NoIa1l0cM^1l0fMa1k0eM^1j0gMa1j0fM_1i0hM`1i0gM`1h0gMa1h0jM_1e0iMa1a0SNd1<kMa18]Nl12lMa1NhNU2GmMa1CSO`2\\OmMa1XO^Ok2QOmMa1mNIV3fNmMa1bN4b3ZNlMb1WN?m3oMlMb1kMk0Y4cMmMa1_MW1d4XMmM_5S2aJmM_5S2aJnM^5S2aJlM`5T2`JmM^5T2bJjM^5X2bJgM]5[2cJbMWOZMV6U5cJaM^5a2aJ_M^5b2bJ^M^5b2bJ^M^5b2bJ^M^5b2bJ^M^5b2bJ`M\\5`2dJ`M\\5`2dJ`M\\5a2cJ_M]5a2cJ_M]5a2cJ`M\\5`2dJ`M\\5`2dJ`M\\5`2dJbMZ5_2eJaM[5_2eJbMZ5^2fJbMZ5^2fJcMY5]2gJcMc5S2]JmMd5S2[JmMe5S2[JmMe5S2[JmMe5S2[JmMd5T2\\JlMd5T2\\JkMd5W2[JiMj0UMZ3R5lKiMi0XMY3o4nKhMj0ZMW3n4oKhMg0]MZ3k4oKhMa0cMa3d4nKiM;iMg3_4mKhM8mMj3\\4nKgM5PNh3^4SLaM1VNg3^4XL\\MK\\Ni3\\4\\LYMDaNk3[4PLbJ4W4h3k1ULUNf3e600O1O1O100O10000QNiD5W;KkD3U;LnD1S;OnD0S;@hDSO<HA^1[;GjDPO=Y1i:FmDmN<]1g:FdE:\\:EeE;[:BfDPOo0^1[:BhE>X:BhE>X:BhE>X:AiE?W:AiE?W:ZOPFf0P:ZOPFf0o9\\OPFd0P:\\OPFd0P:ZOSFe0m9[ORFf0n9ZO^F:b9FfF3Y9NfF2l5WO`Kf0cN5k5_OWHDP3h0^MXOC9R1:m6NoJ7]MEh06k6OPK6\\MFi05j60QK7UMVON?S12j62PKP1VNnNj62PKP1WNmNk61nJR1WNmNj62oJQ1WNmNk61nJR1XNkNg67QKn0XNkNk63mJR1XNkNk62nJT1VNjNl62nJT1VNjNl62nJT1VNjNl62nJT1VNjNm61mJU1VNjNm61mJU1YNgNi65nJT1_N_Nf6<mGBi2c1m5kN]GA4`0GBk2b1m5MXGaNk2b1m51SJNn52RJOm51SJOm51SJNn52RJLP64PJKQ65oIJVMbNh8d1RJNn52RJNn52RJNn52RJNn52RJNn52RJNn51SJOm51SJOm51cJ[Oa5f0_JfNeL5l8T1dJ`NcL<i8T1bJaNhL9f8V1QKiNo4W1UKeNk4[1TKfNl4Z1TKfNl4Z1QKiNo4W1QKiNo4W1QKiNSLDh8c1UKmNk4S1UKnNj4R1VKnNj4R1WKmNn3GdH]1]3lNl3LeHX1b3iNi3OeHW1[2aNTNLRO>j7MeHX1[2eNQN8o6LdHW1\\2iNmM2V7LbHY1[2lNhM1Y7LdHW1[2lNhM1W7NfHU1[2lNhM1U70hHS1[2lNhM1T71iHR1[2lNhM2S70jHR1Z2mNiM1S70jHR1Z2mNiM2Q70lHQ1Z2lNjM3P70lHQ1Z2lNjM3o61mHP1Z2lNjM3k6]OdHd0=P1Z2lNjM3k6^OdHb0=Q1Z2lNjM3d0]Om4i0[Jk0[2kNiM4c0MTO@o4i0VKl0Y2kNkM4b0LTOBn4h0TKo0^2fNjM5a0NSOBo4f0SKQ1`2eNiM3b01QOBo4g0lJU1i2`NiMNc04mNFP5h2gMaMhMLd07kNER5g2gMbMhMId0g0l3o1hMbMjM\\O]O5V1^1[3_1XNcMZOo0\\2_1ZNcMZOl0]2a1YNdMYOk0^2a1YNdMYOk0_2`1XNeMYOl0^2_1YNeMYOl0^2_1YNeMYOl0^2_1YNeMYOl0^2_1YNeMYOl06aNIn2h0eMZOk0OjNJi2m0bMZOk0LnNIh2Q1_MZOk0GTOHg2W1YMYM^Oi1^1H[OLc2Z1`MQOb0BDL_2a1[MYNQOYO1I`1U1II^2g1XMUNSOZO5H]1R1IAh2V2jLeMQOK4NP2f0[OCi2Y2gLcMUOI4LQ2f0\\O]On2e2`L]MSONb2<TO@k2i2\\L\\MVOL`29U4_2UJ[MWOLa24Y4d2o4000002N001O1O1N2O1O1O1O4L4L8H1O3M2N2N3M2N3NO05K5J7J3L6J2O3M2G`AMc>1]AOg>LZA3i>12MiSj0", "size": [480, 640]}, "area": 148979.0, "iscrowd": 0, "image_id": 16439, "bbox": [0.0, 0.0, 640.0, 480.0], "category_id": 172, "id": 20001031}, {"segmentation": {"counts": "Pco17h>6J100O101N1O100O1O100O2O0O1O100O100O1O2O0O100O1O100O101N1O100O100O1O2O0O100O1O100O101N1O100O100O1O2O0O100O100O1O101N100O100O11O0001@\\NbCd1V<dNkC[1n;lNRDT1f;TOZDl0^;\\OcDc0V;DjD<n:LRE5e:4ZEL_:;aEEW:d0gE]OQ:m2H8I7H8N30O1O0000000001O000000000000001O0000000000001O000000000000001O000000000000001O0000000000001O000000000000001O0000000000001O000000000000001O000000000000001O0000000000001O0000000000000000O1O1O1O1O1O1O1O1O1O1O1O1O1O1N2O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1N2O1O1O1O1O1O1O1O1O1O1O1[OfEcL[:\\3fEcL[:\\3gEbLZ:]3gEaL[:_3fE_L[:`3fE_L[:`3gE^LZ:a3gE^LZ:b3b0M3G9H8E;D<E;E;E;E;E;DVjc09hU\\O7J7H8I6I6O0bCbNY;`1cDcN\\;_1`DeN^;]1^DfNb;[1\\DgNb;Z1]DgNc;Z1ZDhNf;Y1XDhNh;Y1VDhNj;Z1RDhNm;Z1QDgNo;[1nCfNR<[1lCfNT<S20000000000000O1UOkCkNU<T1SDeNm;Y1WDeNi;Y1YDfNh;Y1ZDfNf;X1\\DhNd;W1^DhNb;V1`DiNa;V1aDiN`;T1bDlN^;S1dDlN];R1eDlN\\;S1gDkNZ;R1jDlNV;S1mDjNY;P1c1H\\oe2", "size": [480, 640]}, "area": 32222.0, "iscrowd": 0, "image_id": 16439, "bbox": [135.0, 166.0, 321.0, 188.0], "category_id": 183, "id": 20001032}, {"segmentation": {"counts": "XW^31lb00U]O5bb0;M6K200005JM_Oe]Ob0Zb031001OO2Id]OC]b0>c]OA]b0`0b]OA^b0>36LO00001L3F;JmVa7", "size": [612, 612]}, "area": 450.0, "iscrowd": 0, "image_id": 16451, "bbox": [184.0, 243.0, 24.0, 25.0], "category_id": 132, "id": 20001033}, {"segmentation": {"counts": "c6a<c6000000000000000000000000000000000001O00000000000000001OO1000000000000O1001O00O1000000000000000000000000000000001O1O1O00O10000001OO11OO1000000O10000001O001UH]IS3d6kLaI_KJ_7f6oLgIo2Y6nLjIR3U6eLbInK:\\7U6bLXJ^3g5cLYJ]3g5cLYJ]3g5dLXJ\\3h5dLYJ[3g5eLYJ[3g5eLYJ[3h5eLWJ[3h5fLYJY3h5fLXJZ3h5fLXJZ3h5gLXJX3h5hLXJX3i5gLXJX3h5hLXJX3h5iLXJV3i5iLXJV3h5jLZJT3e5nL[JQ3f5nLZJR3f5nL[JQ3e5PM[Jo2e5QM\\Jn2d5SM\\Jl2d5TM\\Jl2d5TM]Jk2c5VM]Ji2c5WM^Jh2b5XMaJe2_5\\M`Jd2`5\\McJa2[5ZIYIU4]1`2Z5[IYIV4]1^2Z5dMhJZ2X5gMhJX2X5hMjJV2V5jMkJU2U5kMlJT2T5lMmJS2S5nMlJR2T5nMmJQ2S5PNmJo1S5RNYIeKa1X6V5VNjJj1W5UNjJj1U5VNnJh1R5XNnJh1S5VNnJj1S5TNQKi1o4WNRKh1o4WNRKh1n4XNSKg1n4XNRKh1l4ZNTKf1l4ZNUKe1l4]NQKc1o4]NRKb1n4^NRKb1n4]NTKb1k4_NVK`1k4^NWKa1i4_NYK_1g4aNYK_1g4aNVIgKQ2g5i4eNWK[1i4eNXKZ1j4cNWK]1j4bNVK^1l4`NUK_1l4_NVK`1k4`NXK\\1j4bNXK\\1h4fNVKZ1j4fNUK[1k4eNUK[1k4dNVK\\1j4dNVK\\1j4cNWK]1i4cNWK]1i4cNWK]1i4cNWK]1i4cNWK]1i4cNXK\\1h4dNWK]1i4cNWK]1h4dNXK\\1h4eNWK[1a4aJYIV4U2Z1c4`JWIU4V2\\1d4_JUIU4U2^1g4]JTIT4U2_1^4nNbKR1i4cNWK]1l4`NTK`1P5\\NPKd1S5YNmJg1U5WNlJh1S5YNlJh1S5YNmJg1S5XNnJh1Q5ZNnJf1Q5[KiHo1V2f2Q5TKiHXO5n2LRMR2d5S5lJZI^2@UMQ2a5U5iJ`Id2Z1c2V5gJbIf2X1c2U5hJcId2Z1c2S5iJcIc2YORMo1a5U5oJ]I_2_ORMP2^5T5mJlHDb0S3_1[2R5oJbIh2[1Y2S5oJbIi2Z1X2T5oJcIh2Y1Y2S5PKcIf2\\1Z2Q5QK\\IACS3R2[2o4QK]IAAQ3U2]2m4QK^I@AP3U2_2n4PK[Id2h1\\2Q5kJ]Ic2c1b2T5hJXIe2e1c2V5eJUIg2g1c2S5gJVIe2g1e2P5jJUIc2m1b2l4mJmHj2W2Z2i4PKjHj2^2V2]4fNdKZ1W4kNjKT1U4nNhKT1X4lNhKT1W4nNhKR1W4POhKP1Y4oNgKQ1X4POhKP1[4mNeKS1]4kNcKU1]4lNaKU1^4lNbKT1`4jN`KV1]4mNdKR1\\4nNcKS1\\4nNdKR1[4oNfKP1Z4POeKQ1[4oNeKQ1[4oNdKR1Y4QOgKo0W4TOgKm0X4TOhKl0R4]JkIg4S2l0R4^JkIe4S2m0S4]JjIf4S2m0S4^JiIe4T2m0T4]JhIf4T2m0T4^JeIO0e4X2n0T4]JdIg4X2l0T4WOkKi0V4VOjKj0V4bMeI[NU2S4W4_MgI]NR2T4X4\\MjI]No1W4W4QKaId1;TOl1W4Y4lJeIf1<SOf1[4X4jJiIg1?oN`1`4X4fJmIi1b0lNZ1d4W4`JbIN?R2`0jNPOUOV2`5Y4`JVJP2<lNX1b4V4bJWJn1=lNW1d4T4bJZJm1<mNU1d4S4aJdI1j0k1;oNS1d4V4aJ^Jj1<POo0e4T4aJeJi19nNP1h4V5XLkInNP1j4V5WLkInNo0k4W5ULmImNn0m4W5TLlIoNk0n4Z5RLlIoNk0n4Z5QLmIPOh0P5]5nKlIQOh0P5]5nKmIPOe0S5_5kKnIROc0R5a4bJRJY1k0QOc0T5^4dJVJU1j0QOc0V5Z4hJYJP1k0QOa0X5[4gJZJo0k0QO?Z5T4oJbJe0l0QO=\\5U4oJbJc0[3_4T2mJbJc0Z3`4T2mJbJd0Y3_4V2kJbJf0X3_4V2kJbJf0X3_4V2jJeIDg0S1^3_4V2hJfI1<h0h3_4W2fJeI>2=R4_4V2eJgIe0L8V4^4W2eJfIo1S4\\3W2iJaIl1X4[3X2RMhMn2X2RMhMn2X2RMhMn2X2SMgMm2Z2RMfMn2Z2`JdINKW2W4[3[2^JhIW2l3[3j2dLVM\\3k2cLUM]3k2dLUM[3l2dLTM\\3l2eLSM[3m2eLSM[3m2eLSM[3m2fLRMZ3n2fLSMY3m2hLQMY3o2gLQMY3o2gLQMY3n2iLQMW3o2iLQMW3o2iLRMV3n2kLPMV3P3jLPMV3P3kLoLU3P3lLPMT3o2mLQMS3n2oLQMQ3o2oLQMQ3o2PMPMP3P3PMoLQ3Q3QMmLo2S3RMlLn2T3TMkLk2U3VMiLk2W3WMgLi2Y3XMfLh2Z3ZMdLf2\\3[MbLf2^3\\MXLl2h3UMjKX3V4jL^K`3b4aL[Ka3e4aLWKa3i4aLXJkLPO00OIO1g6n6cLTJZ4l5hKmIdLWOd7l6kKcIcL@O1c7l6nK]IcLFL1c7k6QLZIhLJW7m6SLTIhLOU7l6iLRIX3o6^4O100001OO11O00O1001ON200000000O1001O000000000000000000001O1O1O00000000001O00000000000PJUIGk6SO_Ji0`5UOfJh0Z5VOkJg0U5WOPKf0P5XOVKd0j4ZO[Kc0e4[O`Kb0`4\\OdKb0\\4[OiKc0U4SKRIY4k2c0S4^OoKa0Q4^OQLa0n3@SL?o3^OSLa0l3_OVL`0k3_OVL`0j3_OXL`0h3_OZL`0f3_O\\L`0d3@]L?c3@_L?a3@aL?_3AbL>^3AdL>\\3BeL=[3CfL<Z3DgL;Y3EhL:X3FjL8V3HkL7U3IlL6T3JmL5S3JoL5Q3KPM4P3LQM3o2MRM2n2NRM2n2NRM2n2NRM2n2NRM2n2NRM2m2NTM2m2MSM3m2MTM2m2MSM3n2LRM4m2MSM3n2MQM3o2MQM3o2NPM2P3NPM2o20QMOo21QMOo22oLOQ31PMNP33nLNR32nLNS32lLNT32kLOT33kLMV32jLNU34jLLU35kLKU35jLLV35iLKW35iLKW36hLJX36hLJX37fLJY37gLIY38fLH[37eLIZ39eLG[39dLH]38bLH^38bLH^39aLG`39_LG`3<\\LFe3;XLFj39SLIn38oKIR48jKJV48gKIZ47cKK\\48aKI_49]KIc48[KIe49XKHg4;UKGk4:SKGm4;oJGQ5;lJFT5<hJFX5;fJF[5;aJG_5;^JFa5=[JEe5k0jIVOU6[1ZIfNe6m600000000000000000000000000000000N2O1O10jLSIUJl6d5\\I\\Jd6`5gIYJX6d5VJRJk5h5bJPJ^5l5iJQJW5k5PKRJP5j5UKUJl4[3WIfLR2Nf4]3\\I_LP24d4]3aIPLChNX2k1d4]3fIkKChNT2o1c4^3kIeKm1m0W4_3PJ`Ki1Q1W4_3UJZKe1W1T4`3]JTK`1[1n3PN]Ib5Z1mJ\\1a1n3d1\\IYN^1]NY1f1k3a1]I_L8h1\\1]NT1k1j3]1nIgMAkNO?d1FV1k1i3]1nITMAH85[1FU1l1i3]1QJjL7JWO;c1GV1m1h3\\1TJgL5?i0@W1n1g3]1SJgL;8d0EX1o1g3\\1SJhL;5e3f1m1^1RJgL=4d3g1m1]1SJeLYO0R18e3f1m1^1WKoLl2c1m1^1PKVMT3[1l1_1TKRMP3_1l1_1TKRMP3_1l1_1TKRMP3_1l1_1SKRMR3_1l1^1QKTMS3^1l1^1QKhLZN6i4d1m1]1SKeLYN9g1GU1n1h3\\1WKPMLFU1n1i3Z1XKQMJHT1m1k3X1WKTMJGT1m1k3S1^KWMCJS1l1n3n0]K^MBIR1k1m3j0ZIYMT2OkM8i1KQ1k1m3j0bKXMjM6f1MQ1k1m3j0bKXMkM5e1OP1j1n3j0jK\\MXO0Q1i1m3j0lK\\MVO1Q1i1l3l0mKYMVO2Q1i1l3k0oKYMTO4P1h1m3j0PLZMSO4P1h1m3j0PLYMTO5o0h1m3j0PLYMTO5o0h1m3j0PLYMTO5o0h1m3i0QLZMSO6n0g1n3j0PLYMTO6n0g1o3h0PL[MSO6n0g1o3h0PL[MSO6n0g1o3g0QL\\MRO7m0f1Q4g0oK\\MSO7m0f1S4e0jMeMSNf1T4UNTI\\2d4iMTNf1V4SNRI^2d4jMSNe1Y4a0dMjMSNe1Y4b0bMjMUNd1Y4a0bMlMUNc1Y4a0`MnMWNa1Y4a0^MPNYN_1Y4a0\\MSNZN\\1[4`0ZMUN[N[1Z4b0WMfMkK7d2a1Z4b0SMhMUL1_2d1X4d0PMhM^LNZ2f1Y4c0PIaMh3;hLJW2g1X4f0cLlMQMHS2f1Y4f0\\LPN\\MCo1g1X4g0nK[NoMVOh1k1Y4f0kM_MgMP2[4d0oM[MbMU2^4a0QNZM[MZ2c4=RN[MTM]2i49SN[MnLa2P53RN]MhLe2U5NTN^MbLh2[5ISNaM[Lk2a5EUNcMQLm2j5ATNgMhKm2T6\\OTNlM_Kl2^6WOSNk1m1UNSNk1l1VNTNj1l1VNUNi1k1WNUNi1k1WNUNi1k1WNUNi1k1XNTNh1m1WNSNi1m1WNTNh1l1XNTNh1l1XNTNh1l1XNTNh1l1XNTNh1m1WNTNh1k1YNUNg1k1ZNSNg1m1YNSNg1m1YNSNg1m1YNRNh1o1WNQNi1n1XNQNi1o1WNQNi1o1WNQNi1o1WNPNj1P2WNoMi1Q2WNnMj1R2VNeMS2[2mM\\M\\2d2dMSMe2m2[MiLo2", "size": [612, 612]}, "area": 187114.0, "iscrowd": 0, "image_id": 16451, "bbox": [0.0, 202.0, 612.0, 410.0], "category_id": 154, "id": 20001034}, {"segmentation": {"counts": "T4_2e`000000000000000000000000000000000000O2O00000000000000001OO1000000000000O1001O00O100000000000000000O11O00O11O000001O1O1O00O10000001OO11OO1000000O10000001O001O1O1O00O11OO100000000001OO11O0000001O00001O00O11O0000000000000000000000Nd_O\\M\\`0d2d_O\\M\\`0d22000000000000000000001OO1001O1O001O001ON2001O000000O11O00000000002N1O2NaMm_On1R`0PNR@o1m?PNU@Q2i?oMX@P2h?PNY@o1g?PNZ@P2f?PNZ@P2f?oM\\@P2d?PN\\@P2d?PN\\@P2d?oM]@Q2c?oM]@Q2c?nM_@Q2a?oM_@Q2a?nM`@R2`?nM`@R2`?mMb@Q2_?oMa@Q2_?oMa@i1]OVNT`00`@4[O^11]NU`00_@3^O`1N\\NU`00`@2@i06_OEFS`01b@1@b0la0^OU^Og0ea0XO\\^Og0ea0YO[^Og0ea0XO\\^Og0ea0YO[^Og0ea0XO\\^Og0ca0[O]^Oe0ca0ZO^^Of0ca0YO]^Og0Pb000000000001BYO]^Og0ca0YO]^Og0ca0YO]^Og0ca0ZO\\^Of0da0ZO\\^Of0da0ZO\\^Of0da0[O[^Oe0ea0[O[^Of0ca0[O]^Oe0ca0[O]^Oe0ca0\\O\\^Od0da0\\O\\^Od0da0\\O\\^Od0da0\\O\\^Od0da0]O[^Oe0ca0[O]^Og0aa0YO_^Oi0_a0WOa^Ok0]a0VOa^Om0]a0SOc^Oo0OnNRa04o^OV1Ra0jNn^OV1Ra0=1O11OO13M2N00O12OL301N0hNk^Ob0Ta0_On^O`0Ra0_OP_O`0Ra0^Oo^Oa0Ta0]Oi^Ob0[a0\\Of^ODKb01Eaa04m^ONCM`a06e^O3OE\\a08f^O10GYa08h^O0OHYa08i^OOOHXa09i^OOOHXa09j^OONEG0aa0<f^O32AZa0<d^O3ga0MX^O4ha0LX^O4ha0MW^O3ia0LX^O4ha0LX^O4ha0LX^O4ha0LX^O4ha0LX^O4ha0LX^O4ga0NX^O2ha0OW^O0ja00V^O0ja00V^O0ja00V^O0ja00R^OB1?la0OT^OA0`0la02T^ONla02T^ONla02T^ONla02T^ONla0e000000000TOlNh_OT1X`0lNa_OOBV1m`0lN__O\\1``0dN`_O_1]`0bNb_OX1d`0iNZ_Of1X`0ZNh_Of1X`0[Ng_Of1X`0[Ng_Oe1Y`0[Nf_Of1Z`0[Ne_Oe1[`0\\Nd_Oe1[`0[Ne_Od1\\`0]Nc_Oc1]`0^Nb_Ob1^`0^Nc_Ob1\\`0_Nc_Oa1]`0`Nb_O`1^`0`Nb_O`1^`0aNa_O`1^`0aNa_O_1OYNNKU`0=m_Oa1N\\NT`0d200O0O2O0000000000000000O10000000000O1O1O1000000000000000000000000000000000000000000O11OO11OO100001OO11O00O1001ON200000000O1001O00000000000O11OO11OO2O1O1M2N2M3N20001O000O1000000O12M3N2N2N3M0000001Ml_OTMT`0l22TMk_Oi2Y`0WMf_Oe2]`02M020M200M3O1O100000000000000000000000000000000000000000O11O001O1OO11O000000O01001OO10001O00O11OO1O1000001O00000O1001OO1001N10001OO11O2N1O1O001OO1000000O100000000001O00O100O1O100000000000000000000000000000000N2O1O100O100O11O0000001OO1000000O100N2Kh_O]MY`0d23N2O11O00aNb_O8^`0Hd_O6\\`0Jf_O5Y`0Kh_O4X`0Li_O3W`0Lk_O3T`0He_OTO8S1T`0HT@6k?KV@4k?LU@3j?NW@1i?OW@2h?NX@2h?OX@1g?OV@4j?MR@7n?`1N111NN110000000O1000000000000001O00001O2N1l_OSMo?o2o_OQMQ`0Q3000000000001OO100O11OO100O1N2M3O1O1O11OO11OO100001O00O100000000001O00000000001OO1000O11O01OO0100001O00O1000000RL", "size": [612, 612]}, "area": 49644.0, "iscrowd": 0, "image_id": 16451, "bbox": [0.0, 118.0, 612.0, 112.0], "category_id": 155, "id": 20001035}, {"segmentation": {"counts": "0T4P?000000000000000000000000000000000000O1000000000000000000000000000000000000000000000000000000000000O11O00O11O0000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001O00O12N00O100O100O100O10000O100000000O1000000O1000000O1000000O1000000O1000000000000000000000000000000000000001O0000000000000000000000000000000000001O001O00001O001O00001O00001O001O0000O1000000000000000000000000000000000000000000001O00O1000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000O11OO11OO10000N2N2M3N200000000O1000000O12N2N2N2N3M0000001O000o@PLn>T4RAkKk>W40O11ON200M3O1O100000000000000000000000000000000000000000000000000000000000000O1001OO1000000000000000000001O0000000000000000O1000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000O11OO11OO100000000001OO11O00O100000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000O11O0000O100001O00O1000000", "size": [612, 612]}, "area": 78785.0, "iscrowd": 0, "image_id": 16451, "bbox": [0.0, 0.0, 612.0, 136.0], "category_id": 157, "id": 20001036}, {"segmentation": {"counts": "Ro^13Pc05Q]OJfb0=K3FGi]O:Sb0=00001O01O0001O01O00010O001O010O1O2O0O00100O100O00100O1O3NO03M100O2O0O2N1O1O10O01000\\Nb^Oa1`a0001N3N0O1O4M1O1O1O000012M01O0O2O1N2O2N00mMR_OQ2Pa0001N10001N203L20N0O10O100O10000000001OO10001O0O001NoJVNlHj1m6`NPI`1Z<000000001OO10O11OiJjM`IV2`6jM`I1kJR2e;mM`I0nJQ2b;oMfIP2Z6PNfIP2f;2NoMR_Oo1m`0QNT_OP2o`010O1O00N2N2N2O1O1O1O1O2N002N001O1010M201O1O000000001N100001OO10WJhMfJY2X5iMgJV2Y5lMfJT2Z5mMeJR2]5kMeJU2Z5lMREKc5W2\\5XN\\Jh1d5PNdJP2\\5QNcJo1]5RNbJn1^5SNaJm1_5SNaJm1_5TN_Jm1a5oMcJQ2]5oMdJP2\\5oMeJQ2[5oMeJQ2\\5nMdJR2[5oMdJR2\\5nMeJQ2[5oMeJQ2[5oMeJQ2[5oMeJR2Z5nMmEJWO1`4O_KV2m:oMmEKVO1d4X2X6kMbELJ1HOd4Z2X6jMbELJ0H1e4X2W6kMbELJ0H0g4X2U6lMbELI1H0i4V2T6iMTE1h02o3V2U6hMSE0h02P4W2X6gMgE3Q4V2X6gMgE2Q4Y2W6eMgE3S4V2W6gMfE2T4X2n6gMSIX2P7fMPI[2l;O20N1O1lJeM_IZ2c6nMaDH[4Y2X7lMPIS2k6SNPIQ2S<O001O1O1O1O001O1O1O1O001O001O00001O0010O0001O000010OO101O000001OO010000O10000O10O10O101OO010000O01H8C<G:N1O0UOS^O100OIO1na06k0MW]OOjb0OW]O1ib0OW]O1ob0O001NhbW5", "size": [612, 612]}, "area": 15900.0, "iscrowd": 0, "image_id": 16451, "bbox": [78.0, 172.0, 253.0, 265.0], "category_id": 167, "id": 20001037}, {"segmentation": {"counts": "\\Zb92mb0OU]O8ab08c^OAkNOXa0a0Y_OAH8n`0:P_O7JWOSa0e0o^O5Xa0Ji^O;Qa0Fo^O;Pa0DP_O=n`0Do^OYO0R1Sa0i03I74L0000O1O10h^OZNRa0i1j^OYNTa0V1U_OROk`0i0__OTO_`0k0f_OQOV`0Q1m_OQOn?P1T@mNi?0[_OR1o0lNg?W1T1N2M2O3L2N2N3N2N2N1O3M1O2N102M2O2N1O1O2OO01O1000001O00000OTcj0", "size": [612, 612]}, "area": 1613.0, "iscrowd": 0, "image_id": 16451, "bbox": [512.0, 302.0, 55.0, 85.0], "category_id": 168, "id": 20001038}, {"segmentation": {"counts": "aoj24nb04M2O1O1N100O10001N100O100O2O0O10000O1j]O\\Ola0d0R^O_Ola0c0Q^O_Ooa0a0Q^O@ma0a0S^O_Oh0e0]?Lj_O@h0h0Z?In_O_Oh0l0V?EQ@@h0o0S?BU@_Oh0P1R?^OX@Ce0o0S?@W@Af0o0S?@V@Bg0m0S?BV@Ag0l0T?0m@OS?1m@NS?GT@_Oi0i0T?HS@_Oi0i0T?HS@_Oi0i0T?HS@_Oi0i0T?HT@^Oh0i0T?JT@]Oh0i0T?IU@^Og0i0T?IV@]Of0j0T?IV@]Of0i0T?KV@\\Of0i0T?KW@[Oe0j0T?KW@[Of0i0S?LW@[Of0h0S?NW@ZOf0h0S?NX@YOe0l0P?K[@YOe0P1l>G_@YOe0T1g>Ce@YOd0W1d>@i@XOe0S1e>Ef@XOg0o0e>Id@XOi0j0e>Ob@WOk0b0i>7]@VOl06R?d0R@VO^a0j0b^OVO^a0j0b^OVO^a0j0>0000O1000000001O001O00O1N2101N3M1V_OnN`?R1_@PO`?P1^@ROb?n0]@TOb?l0^@TOb?l0^@UOa?k0_@UOa?k0`@UO_?k0a@VO^?i0c@WO]?i0c@XO\\?h0d@XOP2IS:o0mCYOm1KU:l0nCYOk1OU:h0PDZO_OEd1;l:f0QDZO[OJe1<k:`0UDYOYONf1?h:9YDZOUO2h1b0e:2^DYORO7k1?e:0^DZOPO:l1>d:N`DYOPO=j1>e:LaDYOPO>i1>f:JaDYOPOb0g1<g:IbDYOPOc0e1>h:EcDZOoNe0e1=h:DdDYOoNi0c1;i:CeDYOoNj0b1;j:AeDYOoNm0`1<j:]OhDZOnNo0^1;k:\\OiDYOnNR1]1:l:ZOiD[OlNS1]1:m:XOjD]OjNS1]19o:VOjDk061n:TOlDl042o:ROmDLlN8W1k0P;POmDKnN<S1j0Q;hNlC@X19lNe0n0k0R;fNoE`0nNk0R;dNRFa0kNl0S;bNSFb0iNm0T;`NSFd0hNl0U;_NTFe0hNl0T;^NSFg0iNl0T;\\NSFh0iNm0S;[NSFJlMc0o0Y1R;YNQFLZN7c0d1S;XNoEMdNO:m1R;WNnENkNJ6Q2Q;VNoEM2n1o9TN\\Hm1c7SN]Hm1d7RN\\Ho1d7PN\\HQ2c7oM^HP2c7oM]HQ2d7nM\\HS2[<O00010O010O00010O0010O00010O00O2O001N10001N101O0O3N1O2M2O2N1N3N2N1N3N1O2M2M5K5K4L5K5K4L5Km[j05jbUOZ1I6K5K5K6J5K5K4K4N3M2N2O1N2N2O1N2N2N2O1N2N2O1N2O1O1O1O1O1O2N1O1N2O1O1O000000001O0O100000000010O001O001O001O0Om@RLS?m3201O001O0O2O00001O0O2O001O001N101O001O0O2O2M2N3L4M2M4M3L3N3L4M3M2M4M3L3N3L4M2M4M3L;Fb0^Ob0]OeP<OVoC8L;E>A=D7I7I5`MSNcCQ2]<SN^Co1a<VNoBChNX2Y>ZNjBChNS2^>_NdBl1\\=XN`Bi1_=\\N[Be1d=aNWB_1i=fNQB[1o=iNlAY1S>lNhAT1X>lNgAU1Y>kNgAU1Y>kNfAW1Q7VM0d1mHW1S7WMNb1nHX1T7XMKa1PIX1U7XMJ`1QIY1T7XMJ`45bKJ^40[KUI8j6]41]KSI8j6[43lKLT45lKJT46mKHU47kKIT1ZIW1m6eMIT1ZIW1n6cMIV1ZIV1m6aMlNJkJ_1\\OV1o6\\MgN9jJU1AU1o<kM]CP1DU1l<nM`Cm0ET1i<QNVCSO8h1JS1f<TNXCRO6i1KQ1e<VNYCRO6g1LQ1c<XN[CRO4e1OP1_<\\N]CQO4c10P1]<^N_CPO3b11P1Z<aNbCnN2a12P1W<oNgC13o0S<SOjCN3o0Q<UOkCM4n0o;WOmCK4n0m;YOoCI4n0k;[OQDG5m0h;_ORDD6m0f;ATDB6m0d;CVD@6m0c;DWD_O7l0`;GYD]O7l0_;e1aD\\N];e1cD[N\\;f1eDYNZ;h1fDXNZ;f1hDZNW;e1kD[NT;e1mD[NS;c1oD]NQ;a1RE^Nn:`1TE`Nl:\\1hDPL4d2T;X1jDZLN^2X;T1kDcLKY2Z;n0oDlLFV2[;h0REUMDR2Z;>ZEdM[Om1\\;4aESNROe1a;W2_DdMf;]2YD^Ml;b2UDYMo;g2SDRMR<n2oClLV<U3jCeLZ<\\3gC_L]<a3fCWL_<i3fCmK`<R4dCfK`<[4dC[Ka<e4o0000000001O01O00000001O00000001O0O100O10000bMfA1[>OeA1[>OfA0[>IlA5U>ERB5S>DTB3U>GRBOW>KPBKY>NWB_O]4", "size": [612, 612]}, "area": 40340.0, "iscrowd": 0, "image_id": 16451, "bbox": [152.0, 123.0, 460.0, 427.0], "category_id": 183, "id": 20001039}, {"segmentation": {"counts": "kfP11^?0d@2\\?3O1NJg@5Y?Kf@6Z?20000001O0000000000001O00O11O000000O100000000001OO11OO1000000000000O1000000O1NGi@9V?3O10000001OOEk@9V?Fj@:V?Fj@:X?0O10000000000000000001O000000000000O10000O11OO10000000000O11OO10000000000000000O100000000O1000000000000O100000000O100O10000O1O10000O100O100000000000000000000O1000000000000000000000000O10000O11O000YOXAb0h>^OXAb0m>00000000000000000O10000000000000000000000O10000O100000000001OO100000000O100000000O11O00000000000000O1000000000000001O000000000000O100000000O11OO100000000O1000000O1001O00O10000000000000000000000O100000000000000000000O11OO100000000O100000000O1000000000000O1O11O1O000000O10000O10000000000001O000000O11OO1000000", "size": [500, 375]}, "area": 6374.0, "iscrowd": 0, "image_id": 16502, "bbox": [66.0, 466.0, 309.0, 34.0], "category_id": 124, "id": 20001040}, {"segmentation": {"counts": "n;f3m;100O10000O1O1N200O1O1O1O1O1O100O100O10000O10000O1000000000000O100O100O100O100O100O1O100O100O1O100O100O100O100O1O100O100O100O1O100O01KVE^Kl:b43O0OQE`Kn:`4RE_Kn:b420O1O1O2O0O1O1O1O1O1O2N1O002N1O1O1O001O1O1O1O101N011N00100O1O100O10O0100O1O01NmEcJR:]52001000001N0oEaJo9`5PF`JP:`5PF`JP:b51N101O00O100O1O1000001O000000000000O2O000O11OO10000000000O11OO101O000000O100001N2O1O0000O0010dJlEX5R:hJPFX5V:000O1N1100000000O100O2O000O1O2O000O100O101O0000001O00001O000O101O0000001O00001O00001O0O101O0O110O00VE[Ke:e4[E[Kf:i4001O001O001O00001N101O1O0000001O000000001N10000O1000001O0001OO10001O000O101O001O1N11N10000000000000O1000000000000001O000000000000O1000001O0O110N101O001O2M101O2N0O2O100O1N100000000000000000O1O10O01O1O1O10000000000000O11OO100000001N100000000O10001O0000000O1O11O10OO1O1O00100O001O10000000001O01O000O11OO2O0000[E", "size": [500, 375]}, "area": 55624.0, "iscrowd": 0, "image_id": 16502, "bbox": [0.0, 309.0, 375.0, 191.0], "category_id": 150, "id": 20001041}, {"segmentation": {"counts": "0n;f3O100O10000O1O1N200O1O1O1O1O1O100O100O10000O10000O1000000000000O100O100O100O100O100O1O100O100O1O100O100O100O100O1O100O100O100O1O100O1000000O100O100O1^MTK[Jm4a5YK\\Jh4b5[K]Je4a5^K]Jc4b5^K]Jc4a5`K]Ja4a5bK]J_4b5bK]J_4a5bK_J_4`5bK_J_4`5aK`J`4^5aKbJ`4^5_KbJb4]5^KcJc4]5\\KcJe4\\5\\KcJe4\\5[KdJf4\\5ZKcJg4^5XKaJi4`5VKnHAm0Z5V6UKfHHR1T5X6iKgIW4Z6hKeIY4[6gKeIY4[6hKcIY4\\6hKcIY4^6fKbIZ4^6fKaI[4_6eK`I\\4`6dK`I\\4a6dK]I]4c6cK]I]4c6cK\\I^4d6cK[I]4f6bKYI_4g6bKWI_4i6aKWI_4i6aKVI`4j6aKTI`4l6`KSIa4m6_KSIa4m6`KRI`4n6`KRI`4n6`KQIa4o6bKnH^4R7hKhHX4X7kK_H[4b7k100000000001O0cIhH^4X7bKhH^4Y7bKfH^4Z7cKeH]4[7dKdH\\4]7cKcH]4]7cKcH]4]7cKcH]4^7bKbH^4^7bKbH^4^7bKbH^4_7aKbH^4^7bKbH^4^7cKaH]4`7bK`H^4`7bK`H^4`7bK`H^4a7aK_H_4a7aK_H_4a7aK_H_4a7aK_H_4a7bK^H^4b7bK^H^4b7bK^H^4b7cK]H]4c7cK^H\\4b7eK]H[4c7eK]H[4d7eK[H[4e7gKXHZ4h7gKWHY4j7iKSHW4m7h10000M300000iIYHcNMh5k7dK^H\\4b7dK\\H^4d7i11O001RJSHS4n7jKTHV4m7fKVHZ4k7cKWH]4j7_KYHa4h7\\KZHd4g7YK[Hg4g7PK`HP5a7iJfHV5[7dJjH\\5\\7ZJhHf5c801O00000000001O0000001O00001O0000001O0000001O00001O00001O00001O00001O00001O001O001O001O00001O001O1O0000001O000000001O0000000000001O00000000001O0000001O001O1O00O1000000000000000000000000000000000000000000000000001O00001O001O001O2N001O2N001O1O1O1O000000000000000000O1O100O1O1O1O1000000000000000000000000001O000000000000001O000000000000001OO1O1O1O100O1O1O10000000000001O000000001O0000", "size": [500, 375]}, "area": 120799.0, "iscrowd": 0, "image_id": 16502, "bbox": [0.0, 0.0, 375.0, 382.0], "category_id": 157, "id": 20001042}, {"segmentation": {"counts": "QlT11_?6L3M2O2M3M2O1N2O000O11O001O01O10O200O_AAn=?kAHT>l010O000O110O000001O0001O010O001O00001O00001O003M6J3UB]N`=k101O000000O2M2B>100O1O01O000010O0001O00001O000010O000000001O00001O00100O2N102M2N1O1L2100E[BfNf=Z1\\B`Ni=_1^BbNZ=^1dBdN\\=j11N1N3M3L4L4M3L4M3K6G8I:H9Hool2", "size": [500, 375]}, "area": 4621.0, "iscrowd": 0, "image_id": 16502, "bbox": [75.0, 238.0, 109.0, 77.0], "category_id": 183, "id": 20001043}, {"segmentation": {"counts": "Wa0Z2ea020N02N2N1O1N2F_MS_Oc2d`0:[_OSMd`0g2c_OYM[`0b2l_OcMn?W2Y@iMP=LYB7S2n1kNoMi<P1VBkN\\1P2LRNe<c1nB7d0VN_<R2]BA[1\\NW<P4PDoKT<g3W2H6J8HP1POo0QOP1POj_f10\\`YN3N2N6J00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000O10000000000006JXTV1HPliN00000000000000000000000O10000COk\\O1Uc0=000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000CBX]O>ib0CU]O=kb0DT]O<mb0EQ]O;nb0GQ]O9Qc0Gm\\O9Rc0Im\\O7Tc0Jj\\O6Wc090000O100004L3M2N3M2N3M3M1OTdS1", "size": [640, 478]}, "area": 5534.0, "iscrowd": 0, "image_id": 16598, "bbox": [0.0, 434.0, 420.0, 206.0], "category_id": 105, "id": 20001044}, {"segmentation": {"counts": "0Wa0Z2>20N02N2N1O1N2F_MS_Oc2d`0a0O1N7J1YMdLYB7S2V3d;gMVBkN\\1\\3a<YNnBj1S=eN]BZ1b=b24L2OmN_JfCb5Q<[1F:G9F:G9F:F:G9L4O100iEeFh9j9O1O1O1O100O1O1O1O1O1O100O1O1O1O1O100O1O1O1O1O1O100O1O1O1O1O100O1O1O1O1O1O100O1O1O1O1O100O1O1O1O1O1O100O1O1O1O1O100O1O1O1O1O1O1K5K5K5L4K5K5K5K5K5L4K5K5K5K5K5L4M3M3L4M3M3M3L4M3M3M3M3L4M3M3M3M3L4M3M3M3L4M3M3M3M3L4M3N2N2M3N2N2M3N2N2N2M3N2N2M3N2N2N2M3GQ^OZNVb0f120000N2N2M3N2N2M3N2N2N2M3N2N2M3N200000000000000000000000000O10000000000000000000000000000O10000000000000000000000000000O10000000000000000000000000000O10000000000000000000000000000O1000000000000000000001O001O1O1O001O1O1O001O1O1O001O1O001O1O1O001O1O1O001O1O1O001O1O001O8H;E;E;E8H4L4L3M4L4L3M4L3M4L4L3M4L3M4L4L3M4L4L:F`0@a0dDSJc7]6aGnI`8c6cFiI]9g6gEdIZ:P8100O1O1O100O1O100O1O100O1O100O1O1O100O1O1mKRGTMn8k2_HhKb7W4`HgKa7Y4^HhKb7W4_HhKb7W4_HhKb7W4_HhKb7X4]HhKd7X4\\HgKe7Y4[HfKf7Z4ZHdKh7\\4WHdKj7\\4VHbKl7^4THaKm7_4VH\\Kl7d4hGZKf8f4YGZKh8f4Y30000000000000000000000000000000000000000000000000000000000000000000000000000000eNhKnBX4]<^LbCb3i;SMWDm2T;iMkDW2h:UNYEk1g:WNWEi1j:UNWEk1i:VNVEj1j:WNUEj1_1]KP7h2aGk1]1_KR7f2aGl1\\1^KS7e2bGn1Z1]KT7e2bGP2X1[KW7d2aGR2W1ZKX7h2]Go1`0SLS8Q2ZGR4g8oKWGQ4i8RLTGn3l8TLRGl3o8VLnFj3R9YLkFg3U9ZLjFf3W9a30000001O00001O00001O0000001O00010O000010O4L4L3N3L3M4M2M4L3N3L3M4M2M4L4L3N3L3M7J9F9G9H9F9G9G8I2M4L4M3L3M4M3L3M4L4M]C", "size": [640, 478]}, "area": 93404.0, "iscrowd": 0, "image_id": 16598, "bbox": [0.0, 0.0, 478.0, 640.0], "category_id": 172, "id": 20001045}, {"segmentation": {"counts": "\\W66cc07I7I7I7I7I7I7I7I7I7]KoMPGX2g8iNZF^1\\9EdEb0S:?nDHh:[1XDlN_;o4F:F: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;E;E;E;E5K4L4L3M4L3M4L4L3M4L3M4L4L3M4L4L3M4L3M`0@a0kDWJQ7Z6RHQJo7`6SGlIn8e6UFgIk9V81O100O1O1O100O00100O1O100O1O100O1O1O100O1O1mKTGRMl8m2aHfK`7Y4aHfK`7Z4`HfK`7Y4aHfKS7J]E_4`3gKT7e4mHZKT7f4lHYKU7g4kHXKV7h4iHXKX7h4hHVKZ7j4fHUK[7k4dHTK^7l4^HWKc7i4ZHXKh7h4VHYKk7g4THXKn7h4RHWKo7i4PHVKR8j4nGUKS8k4mGTKT8l4kGSKW8m4iGRKX8n4gGQK[8o4eGPK\\8P5dGnJ^8R5aGnJ`8R5`GlJb8T5gFPKUNKU;U5eFUKi9k4VFVKj9j4TFYKk9g4TF[Kk9e4UF[Kk9e4UF\\Kj9d4UF^Kj9b4VF\\Kl9d4TF[Km9e4SFYKo9g4QFXKP:h4PFVKQ:k4nEUKS:k4mESKU:m4kERKV:n4jEQKW:o4iEPKX:P5hEPKX:P5gEQKY:o4gEQKY:o4gEQKY:o4gEQKY:o4gEQKY:o4fERKZ:n4fERKZ:n4fERKZ:n4fERKZ:n4gEQKf9ObDP5h1RKP9d0WEZ4i1RK\\8Z1iEd3k1RKg7P2]Fn2l1RKR7g2PGW2n1RKQ7i2PGU2o1RKQ7k2nFS2R2QKP7m2mFR2S2QKQ7U8oHkGQ7V8nHjGR7V8nHjGR7W8mHiGT7W8kHiGU7Y8jHfGV7[8iHeGX7\\8TGVIl8o8001O00001O0000001O00001O00001O0000001O00001O01O01O0003M3N3L3M4M2M4L3N3L3M4M3L3M4L3N3L3M4M3L9G:G8G:F9G:G8G4L4M3L3M4M3L3M4M3L3M4L]C", "size": [640, 478]}, "area": 204768.0, "iscrowd": 0, "image_id": 16598, "bbox": [9.0, 18.0, 469.0, 622.0], "category_id": 183, "id": 20001046}, {"segmentation": {"counts": "]Vh41Z=1O2QJMcN5n0O]ILe56WOd1TKVNd58kNo1`KjMd58`NZ2kK^Me5>mKB\\Ni41XKf5W7YJiHf5X7ZJmHa5T7^JlHb5T7^JlHa5V7]JkHc5^71000O1000O10O10O1000O10O100000O2O00001N11O01O001O000010O01O00001O0010O0001O001O001O01N1O1O100O11O01ZMaJ]M_5c2aJ]M_5c2bJZM`5e2bJYM_5g2bJWM_5i2bJWM]5i2dJUM^5i2dJVM\\5j2dJUM]5k2dJVMZ5j2gJWMW5h2kJWMU5i2lJTMV5l2kJQMBmMc5R5kJPMY5n2iJQMW5o2jJoLW5Q3jJnLV5R3kJmLU5R3oJjLR5V3oJiLR5V3mJkLS5U3mJkLS5T3nJlLR5T3mJlLT5T3lJlLT5T3lJlLT5S3lJoLT5P3lJPMT5P3lJPMT5P3kJPMV5o2kJQMU5o2kJQMU5o2jJQMX5n2hJPMZ5P3fJnL\\5Q3eJmL]5S3bJkLa5U3_JbLk5]3UJbLl5]3TJcLm5]3SJcLm5]3SJbLn5^3QJcLP6\\3PJcLQ6^3nIaLS6_3lIbLT6_3kI`LV6`3jI`LV6a3hI_LY6a3gI^LZ6c3eI\\L]6c3bI]L_6d3`IZLb6f3^IYLc6h3[IXLf6h3ZIVLh6k3WITLj6j2dI_LAg0l6l1cJ\\M`Nf0o6?PLkNQMe0Q7KbLA[Ld0T7HdLDXLb0V7JcLDVLa0X7JdLDTL`0Z7HiLFkKb0]7D`HFY44jKa0_7BmLMcK?b7ClLNaK?d7CkLO`K=f7ClLO^K<h7EjLb0V3^OjLa0X3YOVH1d4f0V3YOVH0f4f0T3ZOnLe0m701O000O2O00001N10001O00001O00001O001GUCOk<1801O0Od]_1", "size": [428, 640]}, "area": 27933.0, "iscrowd": 0, "image_id": 16958, "bbox": [364.0, 51.0, 162.0, 260.0], "category_id": 98, "id": 20001047}, {"segmentation": {"counts": "e;g1e;0001O0000001O00001O0iNZDh0f;POYDO0N1S1g;oNXD05P1c;oNYD06P1a;POZDN5S1b;mNiD1^Oi0b<N1YO^Cb0b<]O^Cd0d<100003M1O00O1000000O10000000000O13MO1O100001ON2O1000000O13M0000O10000O100001OO1000J6M4JQCJ01o<55N2LjB2X=Nii=0ohA12Oo<9M3O100000000000CSC;P=ON22N000000O12NN2M300O100O1001OO100000000001O1O1O000000O100000000O10000O100000000O10000O100O1000000000000000000O10000O1000000O1001O00O1O11O00O1O100O11OO10000O1000000O1O100O1001O00O1000000N2O11OO11O00O10000N2002N000000O100O10000000000O100000000O1000000O100001OO1O10000000000O10000O1O10000MgNRDZ1k;5O1000000O10000000000O1001OO12N1O1O00000000O10000O1000000O1000000O100O1000000O1000000O100\\O\\N`Ed1]:bN`E^1b:aN]E_1`:dN`E\\1`:fN]E[1c:fN\\EZ1c:gN]EY1c:hN[EY1h:eNWE[1j:eNUE[1k:fNTEZ1l:gNREZ1o:eNQE[1o:gNoDY1R;`0001O001O00001O001O00K5TOhMkEQ3S:PMnEP3R:QMmEo2R:6O100000\\NQFKn93QF]N2_1m94VF]NJ030O11m0m9c0_F_NC040Jj0Q:HoE:n0M[9_OmEOJL07P1=R:\\OnD7R1;U:[OiD050o0d0c;O00000000000000001OO11O0000O1hN]OcD0Q1b0Z:DfE<V:^OgD9R19S:MmE3R:NmE3S:MmE3T:LlE4T:LlE4S:MlE4U:KkE5U:LjE4V:LkE3T:NlE2U:MkE3T:NlE2U:KmE5T:HjDFR1a0W:^OgD20NW1b0b;O000000000000O100000000000000PO_OUEa0k:ASE?l:BYDM005ON1MO040>f;J[DHOk0f;e0O10000000000O1001O00000H[DiNh;T1;fNmCR11nNU<0jC627e<DcC0F3Y=L\\^50c`J1VD3JOl;MlC48LH0061M01n;NQDN132NMk0i;[OWDO0L1i0f;h0001O00001O00000XOXDLh;2[DMe;0^DGILi;;`DBGL61g;?`DCl;9e0DRC210\\U4OjWL2iBMU=5O3M1hCNV;2iD1YOGg;9PE8l:HSE9m:GjDLB>c;FkDc0U;]OjDd0S;^OmDc0U;[OkDe0U;ZOlDf0T;ZOkDg0U;XOlDh0T;WOmDi0T;UOmDk0S;TOmDm0T;QOmDo0S;POnDP1g;000O100O100O100O100O10000004L=C6J:F5K0000_d10a[Nb0_O4K8H2OO00001O_OROdDn0\\;SOcDm0];TOcDk0];VObDj0];WOcDi0];XOcDg0];YOcDg0];ZObDf0^;ZOcDe0\\;\\OdDd0\\;\\OdDd0\\;]OdDb0\\;^OdDb0\\;^OeDa0Z;AeD?[;AfD>Z;BfD>V;FjD:U;HkD7T;JlD6S;LmD3R;NnD2R;OnD0Q;1oDOP;3oDMP;4REJm:8TEFj:=WEAf:c0[E[O]:ERET1`0WO]:o0cEQO\\:Q1dEnN[:U1cEkN]:W1aEiN^:Z1`EfN_:]1`EbN_:c1]E]Nb:i1YEWNg:W2O1O1O1O1O1N2N2O1M3M3M3M3O11O00001OO100001O0000000000O1000000O1O1OdMWFW1j9aN^F^1b9[NeFe1[9TNlFl1T9mMTGR2S:0N2L4L4L4L4L4L4L43M1O003M001O00O100000000001O2N1O00001O1O00N200000000001O001O1O001OO1001OO1001O00000000O10000", "size": [428, 640]}, "area": 25528.0, "iscrowd": 0, "image_id": 16958, "bbox": [0.0, 320.0, 640.0, 108.0], "category_id": 101, "id": 20001048}, {"segmentation": {"counts": "`S^4110X=3O00001O00000000001O00000000001O000000001O0000000000001O0000001O000000001O00000000001O00000000001O00000000001O000000001O00000000001O00000000001O000000001O000000001O0000001O00000000001O000000001O000000001O00000000001O000000001O000000001O0000001O000000001O00000000001O000000O100000000O1000000O1000000O1000000O1000000O1000000O100000000O10000O1000000O100000000O100000000O1000000O1000000O100000000O100000000O1000000O10000O100000000O1000000O1000000O1000000O1000000O1000000O10Phj0", "size": [428, 640]}, "area": 3889.0, "iscrowd": 0, "image_id": 16958, "bbox": [340.0, 0.0, 236.0, 29.0], "category_id": 102, "id": 20001049}, {"segmentation": {"counts": "Rmi06S=3KHSC018l<5N200N2O1001O5K00000000O100000000001OO1^ODSD=l;GQD9n;GiCL5=Q<MiCE2?S<NjCD3>R<2mCOR<f0O1O1O10hNPDT1U<O00000000L41GmCQO12T<j0;K4N2O1O001O001OWC_Og<d0M00\\O\\Cf0b<23YO[C00>f<AZC11=h<BYC<l<2NN3L5N2M^h3NbWL4M3L4O0O100O0100000O10O10000O010000000O1000O0100O1000O1000000O100000O10O10O1000O10O1001N10O1O11O0O01O100O11N02O1O1N2O1O2N1N2NiSa0;lk^O10001N01O1000O100000O01000O1O10002KoBJS=25O2OPc42a\\KNVC>e<7O10OO1O12NN1102N0O1000O10O100000O0100O010000O010001O1NO21O3M1O002M2O001N2O1O1O1N102NZ_W4", "size": [428, 640]}, "area": 3105.0, "iscrowd": 0, "image_id": 16958, "bbox": [61.0, 355.0, 254.0, 73.0], "category_id": 114, "id": 20001050}, {"segmentation": {"counts": "eRb18:Ib<?00N200001OO010000000000O0100O100M31O0000000000O10000000000L2MZOaCh0W<:100N2M3M3O6K1O001O0O101O00000@VC>l<001O5LOO11O0000000O2OO1N21O0O2O1O00O1O10O10O1000O1000O10000O1O1L32O00000O5L9D5M1Niic5", "size": [428, 640]}, "area": 1732.0, "iscrowd": 0, "image_id": 16958, "bbox": [119.0, 331.0, 90.0, 41.0], "category_id": 115, "id": 20001051}, {"segmentation": {"counts": "PR3110X=4N0O10001O00001O00000000f0ZOa0_O;E7I6J5K3SEbMN0f:n2ZERMZ:U3K1SFmLHM0110]9d3cFjLR9m3mFhKh8i4RJSKd20bJ[5Z2iJS3S6RLoIn3\\6fKdIZ4f6\\KZId4j6WKWIi4R7nJnHR5h7O2ML40000001O000001O000O1O1O1O100000000000000001O00001OM3L4N2O1000000O100O1N2O1002N001O1OO100M31OO1O1000000ON32N0O100001ON32MM3NnIjHR6V721O00001O00000TOiHcKW7[4lHdK`3^OIj4jLhK]3OXOV4_MjKY3=kNS1bNWNYO;2MW3R1YN?Y1\\N>d1jM0i1[N=W2XM^O[2[N=g2fLPOm2YN=[5CeJ=[5CeJ=\\5BdJ>\\5BdJ>\\5BdJ>\\5BdJ>\\5BdJ=]5CcJ=]5CcJ=]5CcJ=]5CcJ=^5BbJ>^5BbJ>\\5DdJ<[5EeJ:_5CaJ=_5CaJ=_5CaJ=_5D`J<`5D`J<a5C_J=a5C_J=a5C_J=a5C_J<b5D^J<b5D^J<b5D^J<b5D^J<c5C]J=c5C]J=c5C]J=c5C]J=c5C]J=c5C]J=c5C]J=c5C]J<AaLo5S3`J<AaLo5S3`J=@`LP6S3`J=@`LP6S3`J=_OaLR6Q3_J>_OaLR6Q3_Ja0a5_O_Ja0a5_O_J=_OaLT6R3]J=e5C[J=e5C[J<f5DZJ<f5DZJ<g5CYJ=g5CYJ=g5CYJ=g5CYJ=g5CYJ=g5CYJ<h5DXJ<h5DXJ<i5CWJ=i5CWJ=i5CWJ=i5CWJ=i5CWJ=i5CWJ=i5CWJ=i5CWJ<k5CUJ=k5CUJ=k5CUJ=k5CUJ=k5CUJ=k5CUJ=k5CUJ<l5ESJ;m5DTJ<m5CSJ=m5DRJ<n5CSJ=m5CSJ=m5DRJ<n5DRJ;o5EQJ;P6DPJ<P6DPJ<P6DPJ<P6DPJ<P6DPJ<P6DPJ<P6DPJ<Q6CoI<Q6EoI;R6DnI<R6DnI<Q6EoI;R6DnI<R6DnI<R6DnI<R6DnI;T6DlI<T6DlI<T6DlI<T6DlI<T6DlI<T6DlI<U6CjI>V6BhI`0Y6_ObIe0_6[O_Ig0a6ZO]Ig0d6ZOXIh0h6n21O1O3gIWIK9:Ce4]8QKnG[4b801O1O00gGiKZO3k7U4fHjLEjNh6m6SIUIX6_7A_H\\J1MQ7V6O1[IgIU6Z6jIfIV6[6hIfIW6\\6`IcI1O04^6h6O00000O1000000OO200OJ6O13MO21O002O0O2OO0O2M3000O1XIlIW6d601OO1O12NN2002N000000O1000001O0O10000_L^IjL3W3`6NeI1\\6MeI3[6MeI2\\6MfI2Z6NfI2Z6NgI2X6MiI4V6LkI1W6OiI0X6OiINZ6[MPKGH0WO<_Ol0`6POjN;X1DiN=W1@mN?S1AlN?U1AkN?U1@lN=V1FeN8a1GZN9k1GUN4P2LQNNT22c500000001KcS3OalL11OPXQ4", "size": [428, 640]}, "area": 60518.0, "iscrowd": 0, "image_id": 16958, "bbox": [7.0, 16.0, 323.0, 380.0], "category_id": 123, "id": 20001052}, {"segmentation": {"counts": "TUZ15Z=8B4M@XCb0f<300O1O010O001O00N3MZC^Of<b023YC[O00^<f0aCZO11]<h0bCYO]<k0O101O2N2N4LO10O2OO10000000OO2M3N2M300O100O1000000O100000000O10N2O1O100N2M2N3O100000O10O1000O100000001N6K00000O100000O010000000O0100000000O02O1O1O1N2O2N1O1O1OO1001N10O1000O10N@QOdDQ1^;PO_DR1`;nN^DS1c;<O00O10000N3ON\\N\\Df1b;22N000O103L7J2N1O00O10O10000000O0100000OD=O1000O010O1000O0100000O10O1000000O3N1O0I8M31NN12O002N1O2N0O01N30K5BgC]Od<8\\Ri4", "size": [428, 640]}, "area": 6072.0, "iscrowd": 0, "image_id": 16958, "bbox": [100.0, 335.0, 173.0, 77.0], "category_id": 132, "id": 20001053}, {"segmentation": {"counts": "Po[43V=OkB2U;5`FN[NJ030O11a:9oF3_NC040J]:>UFB:n0_9ImE_OOJL07P1n9a0kE_N7R1k9e0kEYN050o0S:T2O0O10000O10000OPMRFd2n9[MUFd2j9\\MVFc2k9\\MWFd2h9\\MXFd2g9]MZFb2f9]M[Fb2f9^M[FY1GaN0Q1m9UO\\FW1MDg9UO]FR1FfN9R1U9oNnF6Oo04LP9oNkF72l04MP9POjF72l04MQ9POhF74l02MR9POhF74l02MS9POeF77k02MT9POdF77l00MV9PObF78l01LU9QObF79k00N^9UOcFl00O]9UOdFl0NO^9UOdFk0O0]9UOeFk0K2Z:OcEoNFR1g:1XEmN20NW1h:\\1O01O001O001O1N101O001O001O001oNQECo:=SEAn:=SEhNM005ON1MO040m:W1[EjNHO[;V1e0O2O001O001O0O2O01O00000TEoN\\9U1aFROX9Q1^EhNR11nN6b:T1^EeN627`0U:o0_EiN0F3a0]:d1_E]Na:W200O1000000O100VOVMRGj2m8WMSGi2l8YMRGh2n8XMRGh2m8YMSGg2n8YMPGc2]O[Mc92PGa1^OhN3JOK`93oF\\1^O^N48LH0061M011_94oFX1@bNN132NM]:8SFT1FdNO0L1Z:9TFP1c:QO]Eo0d:QOZEQ1d:PO\\EP1d:PO\\EP1c:RO\\Eo0e:oNZER1h:mNWES1i:mNWES1g:oNYEQ1g:POWEQ1o0aN]80^F<6S1h0aNoN61N\\9LiF:2T1j0QOZ8AnF7MY1f0SO_8]OPG5K[1I`N210O8k0R9UOQG5Ij1LPOY9RORG4Hn2V9nLSG4Fn2W9nLSG4Eo2X9lLTG6Cn2Y9lLUG5AP3Y9lLVG5@o2Z9lLWG4_OP3Z9kLXGS2_OmM7l0g9m0RF[N<h0b9:SFWO1CM2=j0c98UFWO0G7k0d97UFVO3F4m0e95VFVO`03@Nj9i0VFVO`02CGG2Q:m0VFWO`03m9f0dEVO?3o9e0cEXO=K_OOb:n0cEVO=LZ:l0[EWO`;h0`DUOd;i0^DXO`;g0aDYO`;f0aDYO`;e0aD[O`;c0g0N1N3L3M4KQWT2", "size": [428, 640]}, "area": 10040.0, "iscrowd": 0, "image_id": 16958, "bbox": [334.0, 284.0, 142.0, 144.0], "category_id": 165, "id": 20001054}, {"segmentation": {"counts": "\\__17U=a0_O=Bd0]O?Ab0^O8H3M00N200001OO0100000000000O10O0100M31O000000000O010000000000L4KbEYMa:^2;00000OdMUEY2k:gMUEY2l:fMTE_2g:aMYE`2f:_M[Ea2e:_M[Eb2g:000L^M\\Ee2c:200O1^EXM`:j2000001OO11O0O1000001OO1N21O001O1N10O1O10000O10O100000O10000O1O0M41O0000004L001O0O1000000000001ONdETM[:m222N0000003M6J2N1N10O10000000000O0100000O1\\MTFZO3U2k9\\NTGEjNQ1d<jN^C`0W=_O10QcU5", "size": [428, 640]}, "area": 11739.0, "iscrowd": 0, "image_id": 16958, "bbox": [113.0, 242.0, 130.0, 114.0], "category_id": 171, "id": 20001055}, {"segmentation": {"counts": "0X;T2000000000000TImMeK0g6T2dMPNZ2P2fMPNZ2P2eMQN[2o1eMQN[2o1eMRNZ2n1fMRNY2o1gMQNY2o1gMRNX2n1hMRNX2n1hMRNX2n1hMRNX2n1hMRNX2n1hMhNb1X1^NYOQ1g0oNDV1LjN;T1@mL`NaN21T2a4TOnLfNZN86S2^4nNQMiNVN;:Q2WObMN0Q5Y1ZMjNQN?>\\2VORMe4Y1fMjNoMb0HB<n2Q4UNlMjNoMl03V2KmLHM0110g3Y1cNkNnMl03d2EjL\\3k0nNlNmMl03Z3EhKR3V1YOmNlMl03j3]2]LDnNkMl00lNJ[5Z2oK0POkMl0OQ5[1TKk0POjMd7o0\\IW1QOiMm7f0RIa1ROhMP8b0oHe1SOhMW8:fHn1TOgMo9Y2b0000000000000001O00001]EeMS:[2kEgMU:Y2iEiMW:X2hEhMX:X2gEiMY:W2fEkMY:V2eEkMo0MPOKm8\\2TGnMl02P8P2TGoMi03S8n1TGoMg05U8l1SGQNe05X8k1RGUN?2_8f2YG[Mg8i2TGXMl8d30000000000000000000000000000000000000000000000000000000\\NPGeM6d0j8_1ZGiM0c0i8d0[GhN8NFe0g8b0_HeNmNf0e8e0PIYOQ7g0QIVOP7j0QITOP7m0SInNn6R1UIiNm6W1d2000000001O9G:TFUNc7P2RHWNm7n1SGhM8=d8Q2kFfM?;e8\\2[GdMd8]2[GdMd8]2\\GcMc8NlGNYO6G^O;?i8OPJOmLAn8`0VJ2j5NVJ2j5NVJ2j5NVJ2j5NVJ2j5OUJ1k5OUJ1k5OUJ1k5OUJ1k5OUJ1k5OUJ1k5OUJ1k5OUJ1k5OUJ1k50TJ0l50TJ0l5NVJ2j5MWJ3i50TJ0l50TJ0l50TJ0l50UJOk51UJOk52TJNl52TJNl52TJNl52TJNl52TJNl52TJNl52TJNl52TJNl53SJMm53SJMm53SJMm53SJMm53SJMm53SJMm53SJMm53SJMm53SJMm53SJMm53SJMm53SJMm54RJLn54RJLn54RJLn54RJLn54RJLn54RJLn54RJLn54RJLn54RJLn55QJKo55QJKo55QJKo55QJKo55QJKo55QJKo55QJKo55QJKo56PJJP66PJJP66PJJP66PJJP66PJJP66PJJP66PJJP66PJJP67oIIQ67oIIQ67oIIQ67oIIQ67oIIQ67oIIQ67oIIQ67PJHP68oIIQ68nIHR68oIGQ69nIHR68nIHR68oIGQ69oIGQ69oIGQ6:nIFR6:nIFR6:nIFR6:nIFR6:nIFR6:nIFR6:nIFR6;mIES6:nIFR6;mIES6;mIES6:nIFR6;mIES6;mIES6;mIES6;mIES6<lIDT6<lIDT6<lIDT6<lIDT6<lIDT6<lIDT6=kIBV6>jI@X6a0gIZO^6f0bIXO`6h0aIVO`6k0aIQOa6o0n21O1O3nCPOK9:C^:d2XEgMU:h201O1O001O1O1O1nEmL1Mb9e3O1jFXLf8i3YGWLg8j3WGWLi8j3nFUL1O04P9V4O00000000000000N200O1O1O1O10000001O001O0000O1O1N2N2N2O1N2N2B_FPMb9m2`0@nEeMS:[2nEdMR:\\2oEcMQ:]2QFaMo9_2RF_Mo9a2RF^Mn9b2WFYMi9g2>O10000000XJYMVK3_5d2ZOaMe0_2ZObMf0^2ZObMf0^2YOdMf0\\2ZOdMf0\\2ZOeMe0\\2YOeMg0\\2XOeMg0]2WOcMi0^2cKVM_2=n1_2_KYMIb1]OH0WO<_OW3h2^LTM?<LR5e2iMYMZMMn4k2nMmLYM5m4m2eNSMZ1n2fNRMZ1n2fNQMVNh1fNW1S3TMUNf1dNY1X3PMUNg1RNi1i3`LUNh1QNh1j3`LVNh1oMh1k3`LVNi1nMg1l3`LVNj1mMf1m3`LWNk1iMf1P4_LWNl1fMg1S4]LWNm1`Mk1Z4SL[NS6_401O00001O00001O00001O00001O001O00001O01cHRJZ7Q60O000nKfHS2Z7o10lKfHV2Z7jMfHV2[7m100X1iN0O001O001O01O01O001ONiK\\Gk3d8>001OoH[K[5e4dJ\\K]5c4bJ_K^5`4bJbKX5c4gJ^Kb3X6]LjIV3b6jL_Ij2l6VMZI7B\\N\\9\\1^2O10000O2O00000O10001O0O1000000O2O0000000O101O000O11O01O00000000010O000000000010O00000001O000001O01O0000001O01O00000001O0001O0001O0000000010O00000000010O00000001O0001O0001O0000010O000000010O00000001O01O000000O10001O000O1000000cGDi3=YKc0e4]OlIR2T6oMWIe2i6[MTIh2k6YMUIh2j6XMUIj2j6VMRIQ3k6oLQIiNFY4X7PMoHV3P7jLoHW3Q7iLoHW3Q7iLnHY3P7hLPIX3P7hLPIX3P7iLjHeN1d4U7gLjHeN0f4T7fLlH\\3T7^101O000O2O000O2O00001N10001O000O2O001ZIbI\\6^6dIfIX6Z6hIfIW6\\6hIdIX6\\6gIeIY6g600O1000000O10001O000O1000001O0O1000001N10000O2O00001O0O10001O0O2O00001N101O001N2O1oJ^HV4d7_KeHa4R8010O11OO001OL23K5J6H8L4K5O1O1O1O100O1O1O100000000000000O_LgHQ1Y7mNkHQ1U7oNkHQ1U7oNkHQ1U7oNkHQ1U7nNlHR1T7nNmHQ1S7oNmHQ1S7oNmHQ1S7oNmHQ1S7oNnHP1R7oNoHQ1Q7oNnHR1R7d2O10000000000001O1O1XLQIQ1P7bN\\I^1f6YNaIg1`6QNhIn1Y6kMmIU2T6cMSJ]2o5ZMYJe2h5SM_Jm2b5kLeJU3i701O0000001O0000001O00", "size": [428, 640]}, "area": 90111.0, "iscrowd": 0, "image_id": 16958, "bbox": [0.0, 0.0, 640.0, 386.0], "category_id": 172, "id": 20001056}, {"segmentation": {"counts": "X;=o<00001O00000O2O00001O0bCAo;?iC@O0N1Y<a0iC^O05V<=iC_O06V<;jC@N5Y<<gCO1^Oo;\\1N1SDdNh;\\1WDdNj;^110`0@5N3K0O0O10O1000O100\\O\\C`0i<O1O000O13MO3M4ML401ON2O2O00000O13M0O1O001O10O100001OO10000010O0001OUCAf<d0L4N2O2O5K000O100O0O2O1002N00100N0_OiCNX<1lCLQ<6oCAL5V<8TDBE2W<;VDBD3U<;[DDf;;f0O1O0010RCFj<?N100001O3Ihfg21[YXM001O00K4LVc4`0^\\K10O1O1000O10O1E_CKa<3=O001O1O1Oofj21nXUM4L3M3L5C;J79G8I0O00001O001N101O001N1O1O20O01O001Jb[34[dL5K4O4ON01O00O101O00000010O2MJdk0DoTO1O0PX12`gN>1O01O010O1O010OO10010N1001O01O00000O2O00gE", "size": [428, 640]}, "area": 3506.0, "iscrowd": 0, "image_id": 16958, "bbox": [0.0, 288.0, 640.0, 127.0], "category_id": 177, "id": 20001057}, {"segmentation": {"counts": "\\S:1m02_;N[D8c;IYD<f;EUD`0j;ASDb0HAj;N^Dk0a;VO^Dk0a;VO^Dk0a;VO^Dk0a;VO^Dk0`;WO_Dl0^;UOaDQ1Y;POfDP1Z;QOeDo0Z;SOeDm0[;TOdDl0\\;b000000001O000001O00001]NeDS1[;kNgDU1Y;jNhDV1Y;hNhDX1X;hNhDX1X;gNjDo0IRO^;MjDn00POU;3lDj0b;UO`Dh0b;XO^Df0d;ZO^Dc0f;ZO`D>^<I4Kje;N[mC6T<4hC0V;4iD8NFe0Ed9a1cEmNf0Cg9R2WFoMi9S2TFnMl9S2RFnMo9U2lElMT:W2gEkMY:d2000000004L;WOUE[NR;X1UEgNP;7hD6=BP;1fD:=DS;IdD?=GZ;6iDIX;3lDLU;0oDOW<0bXS2M^glMb0QO^OUDe0i;\\OVDf0h;[OWDg0g;[OWDf0h;[OVDg0i;ZOVDm0c;XOXDh0h;>O1000001O000000001O0001N3N1O1_OXD\\Oj;`0[D]Oo;6i0Im[1f1kXNYNe7h1UH]Nl7c1nGcNQ8^1iGgNX8Y1bGmN]8T1]GQOc8P1WGUOj8l0oFZOP9g0jF^OV9c0dFC\\9V2010O01O01O01O]Od0SOl0H801O0O101O001O0O101O00001N10001O00001N10X1hN1OaC@R<`0mCCR<;oCEQ<;nCGR<8nCHR<8nCIQ<7nCJS<4nCLR<4nCMR<2nCNR<2mC0S<OmC1S<NnC3R<LnC4R<LnC5R<JmC7S<ImC8S<FnC:R<FnC:R<FmC<S<CmC=S<CmC>S<@nC`0R<@mCb0S<]OmCc0S<]OmCd0S<[OmCe0\\<2O001O001N101O0O2O001O0O2O001O010O0010O01O010O010O0001O001O00001N101O0O101O001N101O001N101O0O101O00UM]OnHa0S7_OmHa0S7@lH`0S7BmH<T7EkH;U7FkH9U7HjH8U7IkH6V7KjH4V7MiH3W7NiH1V71iHNDGX5<SKMCIZ5:cJC[N9e1N\\57cJEYN5g11\\57cJFXN1f15_55aJIWNMf17c54_JJVNKf10n5<UJJWNHh9b0nGFZNHh9b0nGF[NFh9d0mGF[NFh9c0mGI[NBh9f0mGH[NBh9f0mGI[86fGJZ86eGL8_OmM6e7`0VJK6Jd5:WJMZO_O@:o6:WJNXO@A8P7:VJOYO@@7P7:XJOWOA@6R7:WJ0VOA@5S7:VJ1VOAA4S79UJ5VO_OA3T79SJ7WO^OB1T7;QJ9XO\\OA1V79oI>XOYOC0V79fIh0@POB0X78eIi0@POCNX7:dIj0_OPOCMZ78eIl0]OPOCL[78dIn0\\OoNDL\\77dIo0ZOPODJ_76cIS1XOmNEKa74aIV1WOmNEJc73aIX1SOmNHIe70`I\\1POnNIFh70_I^1kNCg7nN]IW3c6hL]IY3d6fL[IZ3f6fLYI[3h6cLWI^3j6bLUI^3m6aLRI`3n6_LQIb3Q7]LnHd3R7\\LmHe3T7YLkHi3U7WLjHi3X7VLgHl3X7SLgHS4V7lKiHb4j6^KTIj4g6TKYIW5]6iJbI]5Z6bJdI`5\\6`JcIa5S71N200O10nNfHRLZ7o3QIfKm6[4WIZKYO3`7d4]IYKc6g4Q10O2O0000@_GVL_8k3bGTL^8k3dGTL\\8l3eGTLZ8l3fGTLZ8l3gGSLY8l3hGULW8k3jGTLV8l3jGTLV8l3jGULU8k3kGULU8j3mGVLR8j3nGVLR8j3nGVLR8j3oGVLP8i3QHWLo7e3gGoK:=n7c3WH]Li7c3XH]Lg7b3ZH^Lf7a3\\H^Ld7a3]H`Lb7_3`H`L`7`3`HaL_7^3cHaL^7]3cHeL[7Z3gHgLW7V3mHkLQ7R3SIoLk6j2]IUMc6j2^IVMc6h2`IVM`6i2cIUM]6k2dIUM[6j2hITMX6k2mIQMT6m2PJPMP6o2W200O2N1O1O2N1N2N2N3L3M4L3M3M4L3M4L3L5@`0L4_OnC0j;D`Dd0o;10VO`Ci0^<WObCj0_<2N1O2N1M3K6I6J7J5iE[Na8l1WG[Nh8f1QGbNm8_1lFhNS9Z1eFmNZ9T1_FTO`9X23M3M3M4L4L4K5L4L4L4L3N3O10000000000O1000000000eEnM\\9Q3O1000000000O101E;@`0@`0H9G8H8I7H9G8HPi4", "size": [428, 640]}, "area": 29003.0, "iscrowd": 0, "image_id": 16958, "bbox": [24.0, 44.0, 604.0, 384.0], "category_id": 183, "id": 20001058}, {"segmentation": {"counts": "c8h2Wa002O005LOO100000O101O001OO01O1UO_MU@c2f?cMY@^2b?fM^@Z2_?iMa@X2]?hMX@DJL0g2o?iMS@g2m?XMS@i2m?XMQ@i2n?e0000001OO4M01TOSLcAm3]>ULaAk3_>VL`Ak3^>ULcAj3T>SL]A2NO9l3[>TL^A0I6=g3\\>TL]A:IG6n3a>VL]A;N^ONU4d>RL`A<Lj3_>jKfAo4U>QKmAR5n=oJRBo4o=PKSBo4m=UKPBj4P>XKnAf4T>[KkAd4V>]KiAc4W>d0O00O2OO2O01N0101ON211K3L4N2M4O000O10001O0000O1O0100000IlJbAS5^>nJ`AT5`>7OO1O0002AfJVBZ5k=gJRBZ5_:eJ[E0U33nLX5b:mJ[EJ0Kj25YMZ5a:mJ\\EKi2c5j7mJVHR5j7nJVHR5k7lJVHU5i7kJWHT5k7kJUHU5k7kJUHU5l7kJSHU5m7kJSHT5o7lJQHS5P8kJPHP2YM`0h:^MPHn1\\Mf0d:YMRHl1]Mn0`:UMRHi1bMS1]:TMoGh1eMdNB[O2\\2b:iMWHm1eM`NC[O3`2Z:hMZHn1gM^NLn1P:eM]Ho1kMYNIS2P:`M_HU2hMVNJV2P:_M]HU2hMWNKV2P:_MZHV2iMWNLT2o<EUCUNNV2m<EUCVNMU2o<DTCUNOV2n<ESCUNOV2n<ESCTN0W2m<ESCSN1X2l<EUCQNOZ2l<FTCPN0Z2k<GVChM]OOOOc0c2k<GVChM\\OOf0a2i<HYCfMO_2k<KoC1U<OlCMW<3iCJZ<5hCGZ<9gCE[<:`CM_<2bCM_<2aC0^<0bC0^<OdC0\\<2bCN^<1cCoM^Ni1n=7fCPN]Ni1l=6iCPN[Nj1l=6iCPN[Nj1l=5RDJn;6TBQNi1g1S<8SBTNi1c1T<c0lC\\OZNXNd=]2kCcMaNh11WNb=a2jCaMaNU2OiM]=a2jC`MPOb2P=OoC`MPOa2Q=OoC`MPOa2Q=0oCbMlN]2V=2dC]MVO00i2V=JcC^MWO1Ng2X=JcC_MVO00f2W=KdC`0\\<@cCa0]<_OcCa0]<_OcCc0[<^OeCa0[<_OfC?[<AeC>\\<AeC?[<POVCnM?R3[<oNXDQNaNh2X=VOYDPNaNj2U=UOcDh0^;XObDQNUNe2X=YOeDQNTNf2V=XOgDRNSNf2X=WOdDTNTNe2Y=UOcDVNUNd2X=WOcDUNUNd2W=XOdDTNTNd2X=XOdDTNTNc2Y=ZOfDf0];WOcDi0];XObDh0^;XObDh0];[OaDf0^;[OaDd0`;]O^Dc0b;@[Db0d;^O\\Db0c;_O^Da0a;_O[Dd0e;]O[Dd0d;\\O\\De0c;[O\\Df0d;ZOZDf0h;ZOWDg0i;YOVDh0i;YOXDg0g;YOXDg0i;ZOVDf0j;ZOUDh0k;WOUDh0R<ROnCo0Q<QOQDm0o;TOQDk0o;UOPDk0R<TOnCm0S<QOlCP1U<oNkCQ1U<oNkCUNcNg2b=TOQDk0P<TOPDQ1k;oNUDYN]N`2^=WOXDTN\\Ng2Z=UO`Dk0_;UOaDl0];UOcDm0[;SOdDP1Z;POfDo0[;QOeDn0[;SOeDl0[;UO_DQN^Nk2P=VObDoM\\Nl2T=TOZDoMeNS3o<nN\\DoMeNQ3R=oNYDRNcNP3S=nN[DQNbNR3R=iMnB4]1X2f;\\MmB^O3m0[1X2d;^MoB[O3o0Z1X2e;^MUC:W1V2a;dMYC5V1Z2`;_MZC7Z1W2\\;aMZC8Z1X2];^MYC:Z1Z2[;]MZC[OFl0d1^2\\;ZM`CAA50Ib1Y3`;PMcCHALN2^1Z3e;dLhB002h0NH3T1X3V=hLjBX3V=hLkBW3U=hLlBW3W=gLjBW3P?10N1010OO2O101N3M2M4L3M4M3M2N3M3M2N3M3M2N3L5L4L4L4L4L4K5L3O2M2J7J5JfP[1K^odN5K5J6I7m]OXOf`0o0Y_OTO[`0U1`_OTOT`0T1l_OQOg?X1X@lN]?Y1e@lNV?T1j@ROo>P1QAUOh>l0XA[O`>f0`AAY>?gAEQ>?oAEj=>XBDa=>`BEQ=g0oB]Ol<c0VC@f<a0YCBc<<`CF^<;aCF^<:bCG\\<7fCKY<4hCMW<FaBTNW1W2W<FcBQNW1Z2U<DfBoMU1`2T<AiBmMQ1e2V<]OjBlMQ1f2V<_OiBjMR1e2V<BRD<Q<CmC>T<CkC=V<CiChMfN`2b=HlC7V<IiC7W<JhC4[<LdC3]<McC2_<MaC3_<MbC3^<LbC4^<LaC6S7cMiLW2TL7l6bMjG0X5W2oK9n6fMSMR2nK9k6gMYMo1lK;j6fM[Mn1lK;j6fM[Mn1kK=h6fM]Mn1iK>i6dM_Mm1gK`0k6bM_Mm1fK`0l6cM_Ml1eKQNiNZ2R8jM`Mk1eKPNjN[2R8hM`Mm1iK;g6iM`Ml1iK<f6hMaM_1`JbNY1X2g6eM^M`1gJ`NU1[2f6eM]M_1lJ_NQ1]2f6eM[M`1oJ^NQ1\\2e6fMZM`1eLh0R6hMWMa1hLg0Q6hMVMa1jLg0P6gMUMc1kLg0P6fMTMd1lLf0P6fMRMf1PMc0_5cMgH4i4g1UM=\\5hMeH5h4i1VM<T5fMgH254i4j1VM<S5fMgH154i4j1XM;T5]NcMY1ZM8T5_NaMY1\\M8S5_N`MZ1^M7Q5_N`M\\1^M5R5`N_M[1_M4S5aNZMVOZKU2U2lMjMZ2]7_NYM_1`M5W5\\NYM`1_M4X5\\NYM_1`M5W5\\NYM`1_M5W5\\NXM`1aM6U5ZNYMa1dM3S5\\NYMa1cM3U5\\NWMb1eM1T5]NVMc1fM0T5]NVMb1gM2R5]NVMa1iM0R5_NVM`1iM0Q5`NVMa1hMOR5`NVMa1hMOR5`NVMa1hMOR5`NVMa1hMOR5aNVM_1hMOS5bNUM`1gMOS5aNVM`1gMNT5bNUMa1fMNT5aNVMa1fMNT5aNVM`1gMOS5bNVM^1gM0S5bNVM^1gM0S5bNVM^1^MeMWN[2U7bNVM^1^MfMSN]2Y7_NVM^1^MgMQN_2Y7\\NYM]1]M:\\5ZNVM^1[M:^5XNWM_1ZM:^5WNXM_1ZM9_5YNVM^1ZM9a5YNVM]1YM=^5WNXM\\1ZM:a5ZNTM]1^M5_5_NSMZ1bM4[5bNUMX1`M7Z5bNSMY1_MdMUN`2Z7cNQM[1^McMWN_2Z7dNPMY1`MdMUN`2[7cNPMY1`MeMTN_2\\7cNPMY1`M8`5`NnLX1cM8_5`NnLX1cM8_5aNmLW1dM8_5aNmLW1dM8_5bNlLU1fM9^5bNlLT1fM=\\5`NmLS1fM<_5aNkLS1fM;`5cNiLR1fM<a5bNiLR1fM=`5bNiLR1fM<a5bNiLR1fM<a5cNiLP1fM<b5dNiLo0hM7b5kNeLl0mM7^5mNeLk0nM8]5nNdLj0PN6]5POdLj0hMjMVN\\2^7QOcLj0gMlMVNY2`7QObLl0gM;f5iNcLk0gM=f5iNbLj0hM<g5jNaLj0hMjMXN[2_7RO`Lh0jMjMYN[2]7SOaLf0QN8^5SO`Le0RLjMj1]2f5TO^Le0QLkMk1Z2SNeMc7b1\\Lf0QLjMm1X2TNfMb7b1]Li0mMOi5YO\\Le0kM2i5YO]Le0iMLUNmMg7c1ZLc0lMlMRNk11RNh7d1[La0QNPNkM_1m71XL?QNnMlMa1k72YL>PNoMmM^1M`Nm70SHd1V4>QNoMkM`1m7_NQHd1W4=VN_Oe54TL=WNoMcM]1S87SL=YN\\Od58RL;[N_Ob55SL=kKoM\\2d1c5\\NQHa1U4b0VNBeM[Nm70SHb1T4a0WNFc5HVLb0WNEgMXNi7b1XL?[NEcM]Nj7^1XL`0[NEbM_Nj7\\1XLb0\\NG_5FULb0\\NFb5HRLb0WMoMRO18h1\\7EoKcNRLP2Y1oMQO3NL0m1f7@oKg0TLlMX11QO3NLH14o1i7\\OQLh0\\MoMjNNN35MI04o1i7\\OnKj0aMoMeNL046OLn1i7ZOnKk0bM[NgND1l1h7ZOnKk0`M]NhNC1m1g7YOPLk0^M]NjNk1f7oNSLh0]M_NhNi1j7POQLh0^M^NgNj1k7oNPLh0`M^NeNl1j7nNQLh0`MP1_6XNQLh0`MP1b6UNnKj0bMP1`6VNnKj0\\MkMmNU3i7VNnKj0[MnMlNS3j7UNoKj0[MX1f6nMPLi0XMZ1i6mMnKj0YMZ1h6lMoKj0YMZ1h6lMnKl0XM[1j6gMnKn0XM[1j6gMoKn0VM[1k6gMoKn0VM[1k6gMoKn0VM[1k6gMnKo0UM\\1l6fMPLl0UM^1k6fMoKn0SM_1m6cMQLm0RM`1n6bMQLm0PMb1P7_MRLn0nLmMUOa3m7bMQLo0RM_1m6bMQLn0TM_1l6bMPLo0UM^1n6`MlKS1UM^1n6`MnKQ1UM]1n6bMmKQ1UM]1n6bMmKQ1UM]1c6`L`HT1h3o0VM\\1Z6eLbHN3T1k3m0YMY1S6]NeL9ZMW1R6`NdL9ZMW1R6`NdL9[MW1P6`NfL8[MU1Q6cNcL9\\MT1Q6cNcL:ZMT1S6bNcL9[MU1R6bNbL;WMlMlNW3[7eN\\LTNWLT2V1X1W6dNXLPN[LS2X1X1U6hNULmM^LT2X1U1V6mNQL0jMS1T6POoKMlMT1U6ROlKJnMT1W6VOhKEQNU1W6YOeKCSNU1W6[OYJ[M[N0T1R22Y1U6]OWJ\\MCm17T1o5FSJYMHn15S1P6IPJVMKo14Q1R6MlISMNo14R1Q6OiIQM2m14U1P60gImL6n12W1P60eIkL8o14nMhNNOn2Y7=cIlL6n17mMlNm2S7=^IQM;h1c0nM[NM1e2X7h0WIVMa0b1f0kM_Ne2S7i0PI[Mh0\\1o0`0Y5j0iH`Mo0V1o0?Z5o1gIcMn0?Z5o1gIbMP1>Y5Q2fIaMT1:W5U2eIaMU19V5U2fIcMR19X5T2eIcMT18X5U2dIcMT18X5U2cIdMU18W5T2_IiMZ13W5T2\\ImM[10Y5T2ZInMZ10\\5R2ZInMT1^MZNc2W7Q2[InMT1_MYNb2X7Q2[IoMT1^MWNb2[7Q2ZIoMT14b5m1ZIoMT14b5m1ZInMV15_5m1[InMV13a5P2XImMY11_5R2XInMY1O_5S2XIjM]14Z5R2YIjM]15Y5P2[IlM\\13Y5Q2[IlM\\14X5P2\\IlM\\13Y5Q2[IlM\\13Y5Q2[IlM[14Z5Q2ZIlM\\1OPNaMY7d4[ImM[1NQN`MZ7e4[IlM[1N]5V2WInM\\1J^5X2UIPNZ1Ja5V2UIPNY1Kb5U2UIPNY1Kb5U2UIPNf0?T6a1VIPNg0=T6c1UIQNe0=V6b1SITNf0:W6a1UITNe09W6c1TITNe09W6c1TISNe0:X6c1TISNc0:Y6c1TISNe08W6e1TIRNf09V6e1SISNg08V6e1SISNg08V6f1PITNj06V6f1oHUNm03T6h1nHUNo03S6h1mHVNo03dNRM]7d4PIYNn08n5`1RIYNQ1;h5[1YIYNR1=a5Z1^IXNR1>_5Z1aIVNU1;Z5_1bITNV1=W5`1aIUNX1:W5a1aIUNX1:W5a1aIUNX1:W5b1`ITNY1:W5a1bITNW1:X5c1`ISNY19W5c1aITNW1;W5a1bITNW1;W5a1cISNY18U5f1`ISN\\17S5e1aITN^15R5g1`ITN_15P5g1`IUN`14RNQMl6e4cIUN`17m4", "size": [640, 640]}, "area": 105393.0, "iscrowd": 0, "image_id": 17029, "bbox": [0.0, 0.0, 640.0, 419.0], "category_id": 97, "id": 20001059}, {"segmentation": {"counts": "[6o0f0Di4Q2V4[NQGCj4_ObJ`2d9\\NRGEg4_OdJ`2a9]NSGEm4l1P4aNRGHj4g1T4`NQGJj4h1S4_NRGJk4g1S4_NRGJk4h1R4^NTGIh4k1S4]NUGGi4k1S4^NUGFg4n1dJ\\M^9P1VGFf4R2T4XNVGFg4P2T4ZNUGFg4o1U4[NTGEh4Q2Q4\\NWGBj4o1Q4^NVGAk4R2n3]NWGAl4Q2n3[NXGDk4P2m3]NWGCl4P2m3_NTGVOBJL0]50cJ`2_9aNRGQO]5MQJb2`9aNPGPO`5_2b3aNoFnN_5`2d3aNRM_1m2aNRMb1l2^NTMb1k2_NUMa1k2_NVM_1l2cNQM\\1P3dNoL]1Q3hM]F?^6XOdIa2`9hM`F=\\6n1V3eM_F<[6o1V3dM_F>[6n1W3cM]FNO9]6U2X3dM[FI6=X6DbI^2e9gMfFIG6^6KaI_2g9dMiFN^ONg6`2R3dMjFLV6a2P3dMdM\\2\\2fMaMX2c2hM\\MX2c2jM\\MGhH]2P:iMZMV2h2hMYMV2i2iMWMW2j2hMWMR2^:1O1O0Z^OjMba0[2OO100N20MhM^^OW2ca0300O021N01O1O001N10000O1002NO20OO1M4O0M3M31O00O100001n]OTNoa0n101O02NON31O0O10N200O23K2jIRNXJn1d5VN^Jh1^5[NcJd1Y5`NhJ^1X5cN[EB[O2Q5h1[6fNVEC[O3R5b1^6iNTEL\\4^1]6jNREIb4\\1\\6kNPEJe4[1[6jNQEKd4[1\\6hNRELb4\\1\\6hNPENe4Y1[6iNQEMd4Z1[6iNoDOf4[1X6fNREOf4[1X6fNQE0g4V1oJXNY;b0PE1h4U1\\6lNjDOj4U1]6kNiD0j4T1^6mNaD]OOOOc0T5S1]6nNaD\\OOf0S5P1]6RO_DOS5o0_6KbI2`6O]I3c6M^I3`6O_IOb62_IO^6LhI5hJ\\N[;`1mI7R6IoI7Q6IPJ7n5KQJ5Q6IoI6Q6KkD^Nm4g1V6MmD]Nm4e1U60mD[NP5c1S62mD[NP5c1R6;oICQ6_NnDi1o4IR6]NREi1k4J]66bIHa61_DaNP5_1b6N_DaNP5a1a6E^DPOT5\\1_6C^DPOT5\\1^6D^DPOV5Z1]6F`DlNT5]1]6]O[DVO00a5[1U6^O[DWO1N`5\\1T6_O\\DVO00`5Z1T6AWJ>j5AXJ>h5BXJ>h5BXJ>i5BVJ>j5CUJ<l5DTJ;l5FUJ9k5GUJ9k5HYDSOf5U1Q6HWDUOh5R1R60lI0V6NTDmNc5U1[6MQDoNc5U1\\6KQDPOd5T1]6JPDROb5T1`6HnCUOb5R1a6ImCUOa5R1b6JmCSOa5S1c6IlCTOb5Q1c6O\\I2e6M[I3e6M\\I3d6L\\I3e6M[I3f6L[I3e6L\\I5d6I^I7a6I_I7b6I^I5c6GaI8a6G_IBVJ;]<2]ID\\J2X<9^I^OSJ187Y<7]IB_J7U<5^IB_J7T<6PJC^I2d<;nIB_I2d<:l601N2O0011N10N101N101O[\\OJ`c0<0O1Ob\\OFVc0=e\\OE\\c0`00O001N101O001I`\\OL`c04`\\OJbc00]\\O3hc0LX\\O4hc0LZ\\O2gc0NX\\O2hW1N6003m[OO001NfST2g0_kkM8N1O2N1O1OO0K501ON20010O000000002M100O20OM31O00M201ChNR^OW1oa0jNo]OW1Pb0kNm]OU1Ub0mNi]OQ1Yb0POe]OQ1\\b0oNc]OR1]b080N3O1O^]OfN^b0^11O1O1O000001O00O101M3O0001O01N1O1000]]OiN]b0W1b]OjN^b0[110CgNS^OY1ja0lNS^OU1ka0oNS^OQ1la0POT^OQ1ja0d0N1O1O01002N4MO1OO011N1010O00OL^^OjMda0V24OO11O0002NO11O0O101O10O000000001O001O00O100000G[^OWNea0i1\\^OSNga0m1Z^OQNga0o17M2O000O100003L4M00LS^OUNna0i1S^OWNla0j1T^OUNma0k1T^OTNla0l131000000O1O01O100O101O000003K4M1010IV^OVNka0h1X^OVNia0i1601000o]OXNia0i1V^OYNha0o11PNU^Oj1la0UNU^Ok1la0SNU^Om1oa00001OO2R^ORNha0U2Z^OkM^a0V2_^OlM`a0T2`^OmM_a0T2`^OkMaa0[2O0_^OcM_a0_2O11`Ma^O\\2ba0O1000N3O010N01kNa^ORO18^a0f0a^OQO3NL0]a0Ic^OX11QO3NLH14^a0V1d^OjNNN35MI04]a0X1g^OeNL046OL[a0Y1S_OgND1Ya0V1U_OhNC1Za0U1T_OjNXa0V1i^OhNXa0Y1g^OgNXa0[1g^OeNYa0[1f00O2OJ_]OmNab0R1b]OlN^b0T17N20001N101O00000N111M200O10P]OUOkb0P1O2O1OO11O00001O3M2N001O1O01N011K\\]OlNcb0T14200O1OO1O101OO106KO1O00O01O01_]OhNNOXb0Y1j]OlNVb0_1k]O[NM1ma0f1S^O_Nma0j1001O01O3M10N010000001N2M2JR^OZNna0f1S^OYNna0g1R^OWNoa0i140O2O002O0K40010O000000O200O01000M2O100]Oc010N2O01O0O02O02M100000002M10O2O01O3M1O5J200O000000001OO1003M1N2O1O0O1oG", "size": [640, 640]}, "area": 33538.0, "iscrowd": 0, "image_id": 17029, "bbox": [0.0, 200.0, 640.0, 339.0], "category_id": 111, "id": 20001060}, {"segmentation": {"counts": "Q21`0o0N[1a0[N\\2Q2YOc0OeMkM\\1a0[N[2R2gLZM`2X32dMlM^1>\\N\\2P2hL[M`2V32eMlM]1?\\N\\2V2UO=4cMmM]1`0\\N[2W2TO<7aMjMa1?ZN\\2X2VO;7_MiMc1?ZN\\2Y2UO;8^MhMd1?ZN\\2Y2VO:7^MhMf1?ZN[2V2YO<5^MhMe1`0[NZ2V2XO=6]MgMf1a0[NZ2S2ZOXM\\MT3h2_MhMh1`0YNZ2R2^O>0_MhMh1`0YN[2R2[O?1_MhMh1a0XNZ2S2[O?O`MlMf1`0XNZ2S2\\O>O`MkMe1b0ZNX2T2YO?1_MkMe1b0TNSOAV3h2ZO>0_MlMf1a0oMe2^2mN>2^MkMh1>lMl2a2hN=3]MlMi1>hMo2d2dN>2]MnMi1?eMm2d2SL\\M`2V32\\MoMj1`0cMl2f2RLZMb2W30VMWNP2Y49`Mc00QMZNS2V48_Mf00oL[NT2V48_Md00PMZNU2W46bMc0LSM[NT2W46bMc0LSMZNT2Z45`Md0KTM[NS2Y47_Mc0MSMZNU2Y45_Md0MSM[NT2Z43_Mf0LSM[NT2Z4OnJbMa2W3KUM[NT2[4NbMk0HSM[NT2\\4MaMl0HSM[NT2[4NbMk0GTM\\NS2[4NaMl0HSM\\NR2\\4OnJaM^2Z3LTM\\NR2]4NlJcM_2Y3LTM\\NP2b42VMj0LSM\\NR2g4KRMP1KRM]NS2k4FmLU1KRM\\NT2Q5_OfL^1MnL[NW2Q5^OgL]1NkL\\NZ2o4^OXJoM]2^31iL[N]2o4@dLZ13gL[N_2l4CdLX16dLZNb2k4CeLW1T1WOW2C_L[1X1TOZ2@_L[1V1VO[2_O`LZ1S1YO\\2^OaLiMjM\\3Z1bLb0m2h3\\OgLT1oNcLa0m2k3[OdLV1mNcLiN1i1k2m3[OdLV1lNhLb0i2l3ZOdLW1mNgLc0g2m3[OcLW1mNgLb0j0dN`0[5d0ZJlMW2`3oNfL`0g0iNb0U5g0fLZ1mNPMjNAT1j0^Oc0R5g0eL[1mNUMN2Bi0i4g0jLZ1mNTMNOFk0a4m0oLS1mNVMKLLn0[4o0QMQ1mNVMKH1Q1S4T1SMm0oNVMHD9U1l3U1SMl0QOVMG_O=Z1h3U1SMl0ROUME@`0Y1e3X1RMk0TOSMF@`0Z1d3X1RMk0UOQMF_Oc0\\1`3Y1RMk0VOoLF@c0^1^3X1RMl0XOlL_1o0W2Y1RMl0YOjL`1P1U2Z1TMj0XOjL`1Q1U2[1RMk0ZOgLa1Q1S2_1RMh0ZOhLa1Q1S2_1QMi0\\OfL^OOk0R1Z3`1nLl0@cL[O0m0Q1Z3a1mLk0B_L]O4k0i0UNTOS5f2kLm0FZL\\O6l0h0UNSOT5h2fLP1m0\\LUOi0_3j1`LP1k0^LWOg0]3l1aLo0l0\\LWOi0[3m1aLo0i0_L[Od0[3o1aLn0h0`L\\Ob0]3o1_Lo0g0aL]O3SNKZ5b2`LUMTNf3`2fL@3SNGZ5f2aLj0a0gLA2UNDX5j2bLh0c0[L]M8Q27TNDZ5j2aLh0e0cL]O5h3b2TLf0h0aL]O8f3`2SLj0j0^L]O8f3`2TLi0h0`L^O7g3_2SLj0h0`L^O7f3_2ULj0g0_L_O8e3_2SLl0i0]L_O8e3_2SLl0j0\\L_O8d3`2RLm0k0ZL@9b3b2VLg0T3hLe0`2YLg0R3hLf0a2XLg0R3hLh0a2TLg0U3hLh0_2RLj0W3eLh0a2oKl0Z3aLo0[2fKV1[3^LT1Y2_KZ1^3[LX1W2]K[1\\3]LW1Y2\\KZ1^3[LW1[2[KZ1^3[LX1Z2ZK[1_3YLX1\\2YK[1`3XLW1^2XKZ1T:fNlEZ1S:gNPFV1P:jNPFV1P:jNlEcMXNc3l;jNlEZ1T:fNlEZ1T:fNkE[1U:fNjEZ1U:gNkEY1R:iNnEX1n9mNoEU1n9lNTFT1i9POWFo0f9TOXFn0f9UO[Fi0f9VO[FnL\\Nh3X;[O_Fe0b9ZO^Ff0b9[O^Fd0b9\\O^Fd0b9\\O]Fe0c9[O]Fe0d9[O[Fe0e9]OYFc0g9]OYFc0g9_OWFa0i9_OXF`0h9@XF`0h9\\LfDn2`1h0j9[LeDm2b1g0V:XLXDh2b1P1^:iNcEW1]:jNbEV1^:lN`ET1_:nN`ER1`:oN^ER1b:oN]EQ1c:PO\\EP1d:PO[EQ1e:POZEP1f:POZEP1h:nNXER1h:nNXER1g:oNXER1g:oNXER1h:oNWEQ1i:oNWEQ1h:QOXEn0i:ROVEn0i:SOWEm0j:ROVEn0k:QOVEn0j:SOUEm0S4WL^1k2_Jn0S4YL\\1j2`Jm0T4YL]1i2_Jn0T4YL]1i2_Jn0R4[L^1i2^Jm0Q4]La1e2_Jn0m3cLa1_2bJn0l3dLb1_2bJl0j3gLd1]2aJm0j3fLg1\\2_Jn0h3hLh1\\2_Jl0f3kLj1Z2aJj0f3jLk1\\2_Ji0g3jLj1]2_Ji0h3hLk1_2[Jj0k3fLi1a2[Jj0m3cLj1b2bIQM;k3Y4bLk1a2bIXM2f3b4`Lj1c2[IPM187f3d4^Lg1e2^I\\M7a3d4^Lg1f2\\I]M7`3g4\\Lf1Y3\\I]L2o3m4YLg1[3YI^L2n3n4YLf1d7\\NZHd1f7\\NZHe1e7\\N[Hb1f7^N`H]1_7dN`H\\1`7cNaH]1_7cNaH\\1`7dNaH\\1^7eNcHY1]7iNbHV1^7kNaHU1_7kNaHU1_7mN`HW1[7kNcHU1]7kNcHW1[7jNdHW1[7iNeHX1Z7iNdHZ1Z7gNeH[1Y7eNgHZ1Z7gNeHX1\\7hNcHX1^7jN_HX1`7c512NN21O1OO10000O13M1O1O2N1O1O00000000O1O11OO1001OO1001O0000O1O1O1000000000000000000O10000000^IaCc5_<PJ^D`5a;[JXER5h:kJ^ER5d:jJ`ET5i<M2WLVAX2l>eMXAX2j>fMXAX2j>fMXAX2i>fMZAX2g>gM[AW2g>gMYAY2g>fM[AY2e>gM[AY2f>eM\\AZ2d>fM\\AZ2d>fM]AY2d>eM]A[2c>eM^AZ2b>eM_A[2b>dM_A[2c>cM]A]2f>^M]Aa2f>[M[Ae2g>WM\\Ah2f>UM[Ak2g>RMZAn2h>oLXAR3j?1O1O1M3M3N2O1O1N2O1O1N2O1O1O1N2O1O1N2O1O1O1001O0000000000000000O1000000O1000000O10000O1N2O1O1N2O1N2O1N2F:M3M3L4O1O1O1O1O100O1O1hNhJgCY5W<mJeCS5Z<SK`Cn4_<Z10000001O0000001O000000ZO\\IPDd6o;^IPDb6n;`IRD`6m;bIRD^6n;cIQD]6n;eIPD\\6o;fIoC[6Q<nIeCS6Y<k0O1TH\\H[3e7eL\\HZ3d7fL]HZ3b7eLaHY3_7gLbHY3]7gLdHY3[7eLfH\\3Z7cLfHiKHa7a7eLgHkKI`7_7eLgHlKK^7^7fLeHoKM[7]7fLeHPLN[7\\7eLeHQL0Y7[7fLdHRL1W7\\7gLbHSL2V7\\7WMdHh2\\7XMdHg2]7YMdHg2[7YMeHh2Z7YMeHg2[7YMfHf2Z7ZMgHe2Y7[MhHc2Y7^MgHa2Y7_MiH_2W7aMjH^2V7bMkH]2U7cMmH\\2R7eMoHX2R7hMoHY2o6gMSIY2k6gMVIW2k6jMUI[K_O_6]7VN]Ik1c6UN\\Im1c6SN\\In1d6RN\\Im1e6UNUIQK_Ok6\\7XNPIl1P7WNmHi1S7ZNjHf1V7]NgHd1X7\\NgHg1W7YNhHh1X7YNgHf1Z7ZNeHg1[7_501OO10000000000O11OO11O0000000000002NN21O1OO1O13MM3O1oHdH^1\\7aNgH^1X7aNgH`1Z7_NfHb1Z7]NgHc1Y7]NgHc1Y7\\NhHd1X7[NhHf1X7YNiHg1W7XNjHh1V7XNjHh1V7WNkHi1U7VNlHl1R7SNoHk1S7TNnHk1S7UNmHk1S7TNnHm1Q7RNPIn1P7QNQIo1o6PNSIn1n6RNSIj1P7UNQIk1o6TNRIl1n6SNSIl1n6SNTIl1l6TNTIl1l6SNTIo1k6PNVIP2j6oMWIP2j6oMWIQ2i6oMWIQ2i6nMYIQ2g6nMZIP2h6oMYIo1TO[Ic7e4YIP2UO[Ib7e4ZIo1i6QNZIl1f6TN[Ie1POgIg7d4YIe1POfIh7e4ZIb1j6^NXI`1h6`NYI]1jNnIm7e4ZI\\1h6dNYI[1g6eNYIZ1h6fNXIZ1h6fNXI\\1f6dNZI`1b6`N^I`1hNkIh7e4`Id1^6\\NbIc1jNhIa7e4eIb1iNlIa7b4fIb1hNnIa7`4fIh1\\6XNdIe1_6[NaIg1]6YN_I]KmN]6a7VNaIQ2]6oMdIP2\\6PNaIT2^6lMaIU2_6kMaIV2^6jMbIV2^6jMcIS2_6mMaIS2_6mMaIT2^6lMbIT2^6lMbIT2^6lMbIT2^6lMbIT2^6lMbIU2]6kMcIU2]6kMdIS2]6lMcIV2\\6jMdIV2\\6jMcIY2[6gMeIY2[6gMfIX2Z6hMfIX2Z6hMfIX2Z6hMeIY2[6gMfIX2Z6hMeIZ2Z6fMgIY2Y6gMhIX2X6hMiIW2W6iMjIV2V6jMjIV2V6jMjIV2V6jMiIW2W6iMjIU2W6kMiIU2W6kMiIU2W6kMiIU2W6kMiIU2W6kMjIS2W6mMiIS2W6mMiIT2V6kMlIR2V6nMiIS2W6mMiIS2W6lMjIT2V6lMiIU2W6jMgI_KlNh6]7iMgI_KlNh6]7hMhI`KkNg6^7iMgIZ2X6eMiI[2W6eMiIZ2X6eMjIZ2V6fMjI[2U6fMdHbKT1g6W6kMjIT2V6kMkIU2U6kMkIT2V6lMjIU2U6kMkIV2T6iMmIX2R6gMmIZ2T6hMiIZ2V6gMiIY2W6gMhIZ2X6fMhIZ2X6fMhIY2Y6gMgIZ2X6gMgIY2Y6hMfIW2[6iMeIW2[6hMfIX2Z6hMeIX2\\6hMdIX2\\6hMcIZ2\\6fM_I_2a6aM\\Ib2d6_MZIb2f6^MZIc2e6]M[Ic2e6]M[Ib2f6^MZIb2f6^MZIb2f6^MZIc2e6]M[Ia2g6`MXI`2h6`MXI`2h6`MXIa2g6_MYIb2f6]M[Ic2e6]M[Id2d6\\M\\Ic2e6]M[Ic2e6]M[Ic2e6^MZI_2\\OmHY7d4[I_2\\OlHZ7e4[I^2h6bMWI^2j6bMUI_2k6aMUI_2k6aMUI_2k6aMUI`2j6`MVI_2k6aMUI_2k6aMSIa2m6^MUI`2l6`MTI`2l6`MTI_2m6aMTI^2l6bMTI^2l6bMTI^2l6bMSI_2m6aMSI_2m6bMPI`2P7`MoHa2Q7_MnHb2R7^MmHc2ChH]7d4PIl2l6UMRIQ3i6nLYIU3c6kL^IU3a6kLaIS3_6mLbIS3]6nLaIS3_6mLaIS3_6mLaIS3_6nL`IR3`6mLbIQ3_6PM`IP3`6oLaIR3^6nLbIR3^6nLcIP3^6QM`IQ3_6nLaIR3`6nL`IS3_6mL`IT3BVHl6e4cIW3]6", "size": [640, 640]}, "area": 150762.0, "iscrowd": 0, "image_id": 17029, "bbox": [0.0, 38.0, 640.0, 602.0], "category_id": 124, "id": 20001061}, {"segmentation": {"counts": "\\1e0Zc0]Oc010000M3000000O10JX\\O5kc0O0001O1N5KO1O_\\OH\\c08d\\OH\\c05b\\OJnc00XjR<", "size": [640, 640]}, "area": 149.0, "iscrowd": 0, "image_id": 17029, "bbox": [0.0, 38.0, 21.0, 27.0], "category_id": 132, "id": 20001062}, {"segmentation": {"counts": "b3>bc00O100O100O100O0100O01000O01000OYOAn]O`0la0GS^O9ha0LW^O5fa0MZ^O4_a02a^ON`a01Z^O6fa0JW^O9ia0FW^O:ja0FV^O9la0EU^O;ka0EU^O:la0EU^O;ka0EU^O:la0EU^O;ka0EU^O;ka0DV^O;ka0EU^O;ka0EU^O;ka0DV^O<ja0DV^O<ja0DV^O<ia0EW^O;ca0K\\^O5`a00_^O1aa0O_^O0aa01^^ONda03Y^OOca0WOV^Ok05Nda0ZOV^Oi04Nea0[OV^Oh03Mha0X11N101N3N1O3N0O100O2O;[N_]OT1gb00001N101N20OO01O1U]OoNfb0R1Y]OoNfb0U101O0010O1H`]OPOab0n063O0010O200K5N1O2M3_Oj\\OK05Wc0OQ]OLSc02n\\ONSc00aZh:", "size": [640, 640]}, "area": 3198.0, "iscrowd": 0, "image_id": 17029, "bbox": [0.0, 43.0, 89.0, 85.0], "category_id": 140, "id": 20001063}, {"segmentation": {"counts": "hRl14kc01O1O0101OM30O10L41O2M0100000O3N1O2O1O1OUiV:", "size": [640, 640]}, "area": 128.0, "iscrowd": 0, "image_id": 17029, "bbox": [96.0, 79.0, 21.0, 13.0], "category_id": 142, "id": 20001064}, {"segmentation": {"counts": "dm;251Zc07e\\OJZc07d\\OJ\\c0=00000@d\\O<\\c0Dd\\O=[c0Ce\\O=_c00000000O1O10000N21O0O6Ecnf;", "size": [640, 640]}, "area": 326.0, "iscrowd": 0, "image_id": 17029, "bbox": [19.0, 45.0, 21.0, 19.0], "category_id": 149, "id": 20001065}, {"segmentation": {"counts": "0\\1db0O1O10000M3000000O10W]OkNgb0Y1N1O1O1O000000000000000000000_]OeN]b0[1c]OeN]b0[1c]OeN]b0_10000000O1O10000N21O005K1O00O100Ld]OdN\\b0Z1g]OeNYb0Z1i]OeNWb0[1700O1000000M]]OiNcb0V14O1001O1O00001O001O1O1O001O1O1O1O1O1O001O1O1O1O1O001O1O1O001O1O001O1O1O1O001O001O001O1O1O2N1O1O1O001O1O1O1O3M0000002N1O2N2N2N2N1O1O1O1O1O2N1O1O1O1O1O1O1O1O1O2N1O1O1O1O1O1O1O1O1O2N1O1O1O1O1O1O1O1O1O1O2N1O1O1O1O1O1O1O1O1O2N1O1O1O1O1m@]Kg>d4UA]Km>d4o@^KR?f42M31O1O1O1O1O001O1O001O1O002N001O001OO100001O2N1O002N2N001O001O1O001O002N001O001O000aB_J_<a5YC_J^O3X=]5[CaJ[O3Z=]5[CgJe<U5_ClJ`<V5^CjJb<W5]CiJc<Y5[CgJe<Y5\\CfJ[OFW=e5]CkJAA50T<h5nCmJHALN]<i5cCRJ002h0NH^<P7000002NM3L4M3N2M3N2N2N2N2N2O1N2N2O1O1N2O1O1O1N2O1O1O1O1O1O100O100O100O1O100O100O1O100O100O100O100O100O100kNmA\\LT>b3QBZLP>e3SBXLn=g3UBVLl=j3WBSLi=m3YBPLh=o3[BnKf=R4\\BkKe=T4_BiKa=W4aBfK`=Z4bBcK_=\\4_BfKb=Z4R100O1000000O10000O100O1O1O100O1O1O1O100O1N2J6N2M3N2N2N2Ha_OlLb`0R3b_OkL_`0R39M3N2M3M3N200O10000O100O10000O100O100Gc^OoM]a0P2d^OoM]a0P2d^OPN\\a0o1e^OPN\\a0o1e^OQN[a0n1f^ORNZa0m1h^OQNYa0n1h^ORNXa0m1i^OSNWa0k1k^OUNUa0j1l^OVNTa0i1m^OWNSa0h1n^OXNRa0g1o^OYNQa0f1Q_OYNo`0f1R_OZNn`0e1S_O[Nm`0d1T_O\\Nl`0c1U_O]Nk`0b1V_O^Nj`0a1W_O_Ni`0`1l0N2O1O1O1O1O1O1O1O1O1O1O1O1O1N2O1MQ]OTOPc0f0P]O]OSc0b06M3001OO1001O00O11OO1002N0000000000000000Bf\\O6Zc0Jf\\O5\\c0If\\O5]c0Id\\O5Z_c4", "size": [640, 640]}, "area": 43343.0, "iscrowd": 0, "image_id": 17029, "bbox": [0.0, 0.0, 405.0, 243.0], "category_id": 169, "id": 20001066}, {"segmentation": {"counts": "QPa91oc00O2O00001O1U\\OLhc04X\\OLhc07O001O001O001O2N1O000000O1N2001O00003M000000000000002N00000000O100001O1O2N001O3MO10000Eh\\OLZc0Lj\\ONN3Z_n1", "size": [640, 640]}, "area": 705.0, "iscrowd": 0, "image_id": 17029, "bbox": [488.0, 0.0, 53.0, 24.0], "category_id": 171, "id": 20001067}, {"segmentation": {"counts": "R2`0kb0e00001O003M2N1O0001O1N2O2K6M4LOVOo\\Of0Qc0[OP]O>Vc0Bj\\O;Yc0Eh\\O6]c0If\\O1]c0OjZe0:SeZO4L4K4M4K4L5J5L5N2N1O2O0O2O0O2O0O2O1N101N1O100O1000001N1000000O10001O0O10000O10001O0O2O1O001N2O1O001N2O1O1O0O100001O1O1O1O1O1N2O1O1O2N1O1O1N2O1O1O1O1O1O2N1N2O1O1O1O1O1O1N2O2N1O1O1O1O1O1O1O1O1O1O2N1OW_O]OV>b0iAAV>>iADW>;hAGX>8hAJW>5hANW>1hA1X>NiA3V>LjA6V>HkA8U>GlA8U>DnA=R>_ORB`0o=\\OTBe0l=WOXBh0i=TOZBm0^`0N101N10O001O001O1O001O1O001O10O01O001O010O001O001O1O001O001O1O001O1O00001O001O001O00001O001O00001O001O001O001O001N100L5K4M4M2M4M3M3M4L5K4M4K4L5L4L3L5L4L3M4K5L3M4L5K5K5K5TCQK_:S5PEjK_:[4WE^LV:f3cE`LY:d3bE`L\\:d3]EbL_:b3[EcLi0fMZ8l5gFbLl0fMZ8k5dFdLP1cMY8i8eGYGY8h8eG[GX8g8gG[GV8g8iGYGW8h8gGZGW8g8iGYGW8g8hG[GV8g8iGYGW8g8iGZGU8g8jGZGV8f8jG[GT8g8jGZGV8g8iGZGU8i8iGWGW8l8dGWGV7WOYIf9]OTGX7[OWId9]OTGZ7[OVId9]ORG\\7]OTId9]OoF`7@PIc9]OmFd7BlHc:T7`EiH`:X7bEeH]:\\7gEaHV:b7lE[HS:g7mEXHR:i7lEYHT:h7P12O001N2O1O0O2O1O001N10000010O1O1O1O101N1N2J6N1N3N2N2N1IdCiH_<U7eChH\\<U78N3N2M3M2O20O0O2O1N101VImIET67SJFn55ZJFh56_J^Oi5=_J^Od5<eJ^O]5<mJ]OW5:WK^Oc4eKhHe4Q3BV4jKhH^4^3Bj3QLhHZ4f3_Ob3XLgHY4n3XO[3`LgHW4U4SOS3gLhHU4^4kNj2QMgHT4f4dNc2YMgHR4j4ZMSKA\\7TOgHm3R5YMPKAV7\\OgHf3[5WMnJCo6AhHc3a5TMRK^Oe6LhHW3P6ZMa2@_GV3S6XM\\2CaGT3W6VMW2GbGS3X6UMU2IcGQ3Z6UMR2KdGP3[6TMP2MeGo2\\6TMm1OfGl2_6TMj11gGk2_6TMi12hGi2a6TMf14iGh2b6TMb16lGg2b6RMa18mGf2c6QM_1:nG=0l0c6\\N^1<oG<3i0`6_N]1=PH;6g0]6`N\\1`0PH9:d0[6aN[1c0PH8>a0W6bN[1f0PH7a0>X6`NW1l0PH6b0=`>^On@5c0=]>_OPA3e0=Z>AQA2g0<V>CSA1h0;T>ETA0i0;P>R1RBlNm=U1TBkNj=V1WBiNg=Y1XBiNg=W1XBYMUO\\1b>\\1XBYMWO[1`>\\1XBZMYOZ1^>\\1WB\\M\\OW1]>\\1WB^M\\OW1\\>[1WB`M]OU1[>[1WBaM^OU1Z>Y1XBcM^OT1Z>Y1VBeMAQ1X>Z1WBgM@o0Y>Z1VBhMBm0X>[1UBiMCl0X>Z1TBlMDh0Z>:bAZO?EFe0Z>]1PBPNF`0\\>`0_AWO>JH=\\>b0^AVO>LI9]>d1hAUNM3\\>k0[ARO;3NN]>m0ZARO952H]>Q1YAPO794D]>T1WAoN6;8^O]>X1UAnN5?:XO]>[1UAlN3c0<SO^>_1RAkN2e0?oN^>a1RAiN0i0k?>V@gNNm0l?<X@dNLR1k?;Z@4g?KZ@4f?L\\@4b?M_@5]?Kc@8Z?Hd@=Y?Ce@b0Y?^Od@d0\\?\\Ob@f0^?n10001O01O000001O0001O0001O000001O0001O000001O01O01O001O0O2O0O2N1O2N100O2N1O2N101N1O2N1O2O0O2N1O2N101N1O2N1O2O0O1O2N1O2O0O2N1O2O001O001O001O001O001O001O00001O001O000000000000000O10000000000000000000000000000000000000000000000000O10000000O1000000000000000000O100O2O3L4M4K4M3L4M3M3L4M3L4M3L4M3L4M2M2M3I7I7J6Ifd[1", "size": [640, 640]}, "area": 70816.0, "iscrowd": 0, "image_id": 17029, "bbox": [0.0, 26.0, 570.0, 516.0], "category_id": 183, "id": 20001068}, {"segmentation": {"counts": "Wcj32Z:4cF2U81UG3KF08o82PGd0o8>O1O1O1O1O1O1O1O1M3G\\NfGf1Y88M3N2O100O1N20000O1O1N200M3O1O100O1000000O100O10000001O000000O100O100O100N2O1O1O1N200O1N2O10000001O001O1O00000000O1000000O1M3J600O100O100O100N20000O10000001O002N1O1O001O001O1OO100001OO100O100O1O1N20000001O00001O00001O00", "size": [334, 500]}, "area": 11500.0, "iscrowd": 0, "image_id": 17031, "bbox": [375.0, 219.0, 125.0, 115.0], "category_id": 160, "id": 20001069}, {"segmentation": {"counts": "0gbj02cgUO2O1N3N1O1O2N100O2O0O101N1O101N100O2O0O101N1O100O2O0O101N100O2N100O2O0O101O0O101O0O101O0O1000O01000O^KgN1Y1MiN2W1LlN4T1JoN5P1ISO6n0GUO9k0EWO;h0C[O<f0A]O?b0@@`0`0]OCc0<\\OFc0;ZOH\\LPOV4X1[OOe00ZO2e0OXO4h0KVO8j0HTO:k0GRO<n0CPO`0P1@nNb0Q1^OmNe0S1[OkNg0U1XOiNk0W1UOfNn0Y1SOeNo0[1POcNS1]1mN`NV1_1jN`NX1`1hN]N[1c1eN[N]1d1cNZN`1f1`NXNb1h1]NVNf1i1ZNVNh1j1WNTNl1l1SNSNo1m1QNPNR2o1nMPNT2o1lMoMW2P2iMoMY2P2gMnM\\2Q2dMnM^2Q2cMlM`2S2aMjMb2U2_MiMc2V2^MgMe2X2]MeMe2Z2\\McMg2\\2ZMbMh2^2XM_Mk2`2VM^Ml2a2UM\\Mn2c2SM[Mo2d2SMXMP3g2eMcL]2\\3[2dNUISOm6f0^ITOd6e0gITO\\6e0oIUOS6i0SJPOP6Q1SJiNo5W1d100001O0000001O0000001O0M3M3L5L3M3M3M6J8H7I8H8H5K3M2M3N3M2N2M4M2N3M2N2M4M2N2N3L3O\\O]JaL`5a3dJ[L[5g3hJULW5m3lJPLS5P4QKlKP5T4SKhKm4Y4d0O10O10O10O10O01000O010001N3N2N2M2O2N2M3N2N1N3N2N2N1N3N5K6I5L1O2N2M2O9G1O2N1N2O2N1O1SN^ILd63^IId66^IHc68^IFd68_IEb6;_IAd6>^I]Og6b0[IXOi6g0YIUOj6k0WIPOn6o0SImNP7R1RIiNR7W1oHeNU7Y1mHcNV7]1kH_NX7`1l000O101N3N9G4K01O001O00001O00001O000010O00010O001O01O01O010O00010O0010O00010O01O01O01O1O1N2O1N2O1O1N2O1N2O1O1N2O1N2O001N2O1Noe4KUZK4cF2U81UG3KF08o82PGd0o8>O1O1O1O1O1O1O1O1M3G\\NfGf1Y88M3N2O100O1N20000O1O1N200M3O1O100O1000000O100O10000001O000000O100O100O100N2O1O1O1N200O1N2O10000001O001O1O00000000O1000000O1M3J600O100O100O100N20000O10000001O002N1O1O001O001O1OO100001OO100O100O1O1N20000001O00001O00001O00", "size": [334, 500]}, "area": 124212.0, "iscrowd": 0, "image_id": 17031, "bbox": [0.0, 0.0, 500.0, 332.0], "category_id": 169, "id": 20001070}, {"segmentation": {"counts": "gbj01[:3N1N2O2N1O1O2O0O101N100O2N100O2O0O100O2O0O1O2O0O101N100O2O0O1O2O0O101N10001N10001N10001N01000O0100O^KhN0X1NjN1W1LlN4S1KoN5Q1HRO7n0GUO9k0EWO;h0C[O<f0A]O?c0_O_Oa0`0]ODb0<\\OFc0:[OIe07XOLh04VONi02UO1k0ORO4n0KQO7n0JoN9Q1FmN=S1CkN?T1BiNa0W1^OhNd0X1\\OeNg0[1XOcNl0[1UOcNm0]1SO`NP1`1oN^NT1a1mN]NU1c1jN[NY1e1gNYN[1f1eNXN^1h1bNVN`1j1_NTNd1k1\\NTNf1l1YNRNj1n1UNQNm1o1SNnMP2Q2PNnMS2P2mMnMV2Q2jMnMX2Q2hMmM[2R2eMmM]2R2eMjM^2U2cMhM`2W2aMgMa2X2`MeMc2Z2^MdMd2[2]MbMf2]2[MaMg2_2ZM]Mi2b2XM\\Mj2c2WMZMl2e2UMYMn2e2SMXMP3g2fMbL\\2]3[2dNVIROl6g0_ISOc6f0hISO[6f0PJTOR6j0TJoNo5R1TJhNn5X1d100001O01O0001O0000001M2M3M3M4K4M3M3M8H7I8H8H7I4L3L3N2N3M2N2M4M2N3M2M3N3M2N2N3L3O\\O_J_L^5c3fJYLY5i3jJSLV5n3mJoKS5P4QKlKo4U4TKgKm4Y4c001000O01000O010O10O10O102N1N3N2N2M3N1O2M3N2N1N3N2N2M4M6J5K2M3N1O2N2N8G2O2N1O1O2M2O1TN^IJd65]IId66^IHc68_IEc69_ICd6=]I_Of6`0]IZOh6e0YIVOk6i0WISOl6m0UInNP7P1RIkNR7U1oHgNT7X1nHdNV7[1kHaNX7^1m00000O101N7J9F10O001O001O00001O00001O0001O01O01O010O00010O0010O00010O01O01O01O0010O0001O1O1O1N2O1N2O1N2O1O1N2O1N2O1O1N101N2On_]1", "size": [334, 500]}, "area": 30896.0, "iscrowd": 0, "image_id": 17031, "bbox": [81.0, 35.0, 279.0, 299.0], "category_id": 183, "id": 20001071}, {"segmentation": {"counts": "0Y=g600VKZI[Le6d3`IXL`6g3eITL\\6l3hIPLX6o3mIlKT6S4QJiKo5V4ZJcKe5[4eJ\\K\\5c4mJVKR5e4[KSKe4f4kKQKU4l4VLlJj3Q5aLgJ_3V5gLhJX3U5oLiJo2T5WMiJi2S5^MkJa2R5eMgJkKUN`6P7mMhJV2U5PNfJ[K]Ne6j6TNhJn1U5UNlJj1Q5YNoJg1n4\\NRKd1j4`NWK_1e4dN[K]1b4fN^KZ1a4gN_KY1`4hN`KX1^4lNbKR1]4QO[K\\JdNf6P6QO[KQ1d4RO[Km0c4WO\\Kh0b4\\O[Ke0d4_OYKa0f4BXK>f4FYK9f4JXK6g4MWK3g42WKMh46UKKi4:UKEj4>UKAj4c0RK^Ol4g0QKYOo4i0oJWOP4dIZLX7CTOi3QJaLn6CQOb3^JhLc6DoN]3gJlL]6FkN^3kJiLS=X3nBfLR=Z3PCdLP=\\3QCcLo<^3QC`LP=`3RC^Ln<b3h11O00001O00001O001O001O1O1O1O1O00N2O1N2N2N2O1O1O1O10ZHbLjN]3V1PM^No2a1`MRN`2m1nMfMQ2Z2^NXMb1g2[N]Md1c2YNaMg1^2fLZGc1W6f1`2gL[G`1V6i1f2TNZMk1i2RNXMm1k2SNSMm1P3RNnLl1d2kLgGW1e5m1Y3QNgLn1]3PNbLo1a3nM`LQ2d3lM\\LT2f3jMZLV2h:0000000000O10000000000001O2N2N2N1O2N2N1O2N2N1O2N2N3M2N2N3M2N3M2N2N2N1O001O00001O00001O00000000O1000000O10000OkAgKe<Y4ZCjKd<U4]CmKa<S4^CPL`<o3aCSL]<m3bCWL[<h3eC[LU5@>U4]J]Ln4Dd0n3^JaLg4Fk0h3_JdL`4IP1c3_JgL\\4KS1]3aJkLV4KZ1Z3`JmLQ4HdJWOk6S4`JRMk3Gi1W3\\JTMf3Im1R3]JXMa3JQ2n2]J[M]3HYKjNl6R4^J_MY3G^KgNk6S4]JbMU3H`2e2[JgMP3Fe2b2[JkMk2Fj2_2ZJoMg2Fm2[2ZJSNd2ER3X2YJWN`2BX3W2WJ[N[2@_3U2UJ^NX2@b3R2UJbNS2Af3m1VJeNP2Ai3j1VJiNk1@o3g1UJlNh1_O[MeMc6P4YJPOc1^OaMdMa6n3ZJTO_1CW4Y1YJWO[1C\\4V1XJ[OW1B`4S1XJ^OS1Cd4o0WJCo0Aj4l0UJGc0IX5`0SJL5OWOTLa6Q4QJ0H8FhK`6P4PJg4S6YKlIh4T6XKjIk4U6UKiIl4X6TKfIl4GSGc6Q4dIn4IQGc6Q4bIo4a6QK^IP5b6PK\\IR5d6nJZIT5f6S401OO1N200O10000O1001O0000MWIoBi6Q=WInBj6R=2001OO11VIoBc6Q=[IQCe6U=0000000000000O1002NO11O0000VLbIRJ>_LY5Y9dJRJ4eLX5T9mJSJJiLZ5o8VKSJ@mL[5j8`KUJSORM]5d8iKVJjNVM]5^8TLVJPN[M;1OOS5W8PM^J`M[M:4HLO210]5j7`MfK`2n3nMPLR2l3SNSLm1l3VNQLk1m3YNQLg1n3[NQLe1n3^NPLb1o3aNoK_1o3dNoK]1P4fNnKZ1Q4hNnKX1Q4kNlKV1R4nNbK`KeMd5h6nN]KcKjM`5h6POYK[KkM63_5h6ROYKYKkM83]5g6UOZKVKlM83]5f6WOZK^KPN[5e6YOYK^KQNY5d6\\OZKT1e4nN[KQ1d4QO[Ko0d4SO[Km0c4VO\\Kj0c4XO\\Kh0c4ZOZKh0e4ZOYKg0e4\\OYKe0f4_OWKa0h4ERK<m4IPK6n40lJ2S57fJHY5h0WJYOg5a7O1O1O1N2O1O1O1N2O1O1aNjARMX>m2SBhLn=X3[B^Lf=a3dBUL]=j3mBkKU=U4[1O100O100O100O100O100O100O100O100O100O1O100O11O1O1O1O1O1O2N1O1O1iHmK_NT4`1nK^NS4`1PL^NQ4a1QL]NP4b1RL\\No3b1TL\\Nm3c1UL[Nl3d1VLZNk3d1XLZNi3e1YLYNh3e1[LYNf3f1\\LXNe3g1]LWNd3g1_LWNb3h1_LWNb3h1^LXNc3f1^LZNc3e1\\L\\Ne3c1[L]Nf3a1TLVHZOY6d4_1QLYHVOO0\\6j4Z1nKZHYO3OY6k4Y1mKoNS4Q1mKoNS4P1nKPOR4P1nK\\HWOb6k4R1PLnNQ4Q1nKPOR4R1jKPOV4Q1iKoNW4S1gKmNY4U1eKkN\\4V1bKjN^4\\800001O0000000000000000000000000000000000O100O1O1O100O1O1O1O100O1O1O100O1O1O10UB`KV<_4jCbKV<]4kCdKT<[4lCfKT<Z4lCgKS<X4mCjKR<V4nCkKQ<V4mClKR<T4nCmKQ<S4nCoKQ<Q4nCQLQ<o3oCQLQ<o3nCSLQ<m3nCULR5F5U4hJWLj4M=m3fJYLg4Oc0h3eJ[Lc41h0d3dJ]L_43m0`3cJ`LY45T1[3bJdLS46Z1V3bJhLm35b1S3`JmLe36j1n2_JQM_34S2k2\\JWMX33\\2f2ZJ]MR31d2b2YJbMk20l2^2WJgM_26Z3T2TJlMl1a0Q4j1jIkMk1b0Z4j1bIjMj1b0d4h4RK^Kn4f4hJ`KX5c4_JcKa5a4UJeKk5_4kIhKT6\\4bIjK^6l80O1", "size": [640, 443]}, "area": 97698.0, "iscrowd": 0, "image_id": 17115, "bbox": [0.0, 0.0, 443.0, 464.0], "category_id": 99, "id": 20001072}, {"segmentation": {"counts": "Y=g6Y=01O00O100O1001OO11O000000001iJgBe3[=WLiBiNOe4X=aLlBgNMh4X=UL^Cj3^<ZKhBl0j0j3_<YLaCg3]<XKiBQ1k0f3\\<\\LdCnNUOc4X=]LgCc3Y<]LhCb3X<^LhCb3Y<^LfCb3Y<^LhCb3X<_LhC`3X<`LhC`3X<`LhC`3Z<]LdCROROa4T=_KlBQ1n0oNROa4U=bLiCa3X<bLlBjNNOd0d4c<kL[CU3d<oLZCP3f<PMYCQ3g<oLYCQ3h<nLYCQ3g<oLYCQ3g<oLXCR3i<mLXCR3g<oLYCQ3h<nLXCR3i<mLWCS3h<nLXCR3h<nLXCR3h<kL\\CT3d<kL]CU3c<lL\\CT3d<lL^CR3c<hLgBjNg0^4a<gLhBiNk0`4`<dLeBlNk0`4b<bL`C^3b<`L^C`3d<^L\\Cb3i<YLWCg3S=oKnB;N`0TO1]>fN`Bi047UO:X?]OhA0UOc0T?ZOlAJUOm0X?oNgALVOU1la010000O100O10000O10000O100O10000O100O100hNbNn_O^1^?ZO]@g0P?0l@0f>b0VA^Oj>g0PAZOP?j0l@VOU?n0e@SO[?i20000O11OO100001OO100001O7fK[@k3m?00000002N4L3M3M4UMm_Oa1V`0oMZ@n1Pa0L3M3M4L3M2N1O1O1ORMmNoBS1Q=[ObBd0^=IVB6k=OoA1P>4mAKS>9dAL]>7QA7o>]20000O11O00O1O12N002N00]1cN2N3M2N2N3M2N2N1OO100O10000O100O1fLdMPE\\2b:QN_Eo1Y:YNhC]O`0[2b;^NoCB3P2i;cNTDIGd1U<cNSD7ZOW1d<aNRDd0nNk0P=aNRD\\3o;cLQD]3Q<aLoC_3P<bLPD^3l;^KSCU1P1]3m;gLSDY3n;gLQDhNoN^4Q=iLPDhNPO_4n<aKRCW1T1W3j;cKQCV1U1W3k;kLUDU3k;kLTDV3l;jLSDW3o;gLQDY3o;fLRDZ3m;gLTDX3k;iLUDW3l;hLSDY3o;eLQD[3P<dLPD\\3P<dLQD[3o;cKmBo0P1`3S<aKoBn00PON000O1b0`4i<lLXCT3h<mLWCS3j<lLVCT3k<kLUCU3k<lLTCT3l<lLTCT3j<_KeB^1`0S3k<_KfB]1?T3k<oLUCQ3k<nLVCR3k<mLUCS3j<nLVCR3h<_KgB\\10eN`0a4i<^KgB\\1c0V3e<fLhBmNc0]4e<cLmBnN=`4f<aLnBnN=a4e<aLnBnN=a4e<aLnBnN=a4f<`LmBmN?c4c<aLeC_3Y<cLgC]3Y<bLhC^3W<cLiC]3W<cLiC]3W<cLiC]3V<cLkC]3U<cLkC]3V<aLQCmNa0b4^<aLoBoN0N<b4e<`LoBROOL=b4b<]KoBS11TO0L=`4c<]KnB_11dN?=@@2m3P=dMnBcN`0<D@Mj3Q=gMnBcNa0:F]3l<eMmBdNa0:H[3i<nM_CfNKZ3g<[LoB^1?mNLY3f<ZLQC`1<nNMX3l<jMXClNMZ3k<jMVC`NA<>Z3k<jMVClN0[3j<hMWCnNOY3j<iMXCfN@M=g3k<fMXCfNAM;g3l<fMZCdN1e3d<iMYCcN2e3e<iMYCaN3M\\Of3Z=oMPC_N>a3a<RNgB]N3Li0e3^<ZNdCRNTO_3X=_NdCQNUO`3W=_NiCa1W<TMgBc0S1Y2U<UMhBa0S1[2U<SMhBc0S1Z2U<SMgBd0T1Y2V<QMgBe0T1Z2U<QMgBe0T1Z2T<bLgB;1OO;U1i2Y<_LbB:4HLO210d0T1k2Y=UMgBk2Y=TMjBj2V=VMkBi2T=XMlBh2T=XMlBh2T=XMkB[OhNZ3]>ZMPCf2P=ZMPCf2o<\\MQCc2o<\\MRCd2n<\\MQCe2n<\\MQCe2e<dLTBi0W1c2`<iLXBe0Y1a2\\<eLWB63c0[1b2[<eLVB83a0\\1b2[<eLVB83b0\\1`2[<PMYBa0[1_2[<RMYB>\\1a2[<nMfCR2[<lMeCU2[<hMhCX2X<fMdCQObNZ3j=cMfCb2Z<]MgCc2W<]MjCd2U<\\MkCe2T<\\MmCc2S<]MmCc2R<^MmCc2T<\\MlCd2R<_MnC`2S<_MmCa2R<`MoC_2S<_MnC`2a<QMaCm2m<eLTCZ3i>01O0000O1001O0^M_@e0a?nNl@R1Qa000000000000000000000000000000000000000000O10000O10000O10000O10000O10000O100O10000O10000O1L400mLYNlCh1i;cNWD]1k:UNhBj0]2Q1l:\\OZBfN00j2n1l:[O]BgNf2o1m:YO^BhNf2n1k:ZO[Eg0e:YOZEh0_:PNgBW1l2h0\\:RNcBO0Z1S3c0W:SNgB3OW1S3c0W:DlE:T:EnE:R:FoE9Q:WNfB]1Z3<R:DoE;P:EQF;m9GTF8l9GUF9k9GTF:l9FSF;m9FSF9j>00000O1O1O1004L1O001O001O1O001O001O001O1O001O001O1O001O001O1O001O001O1O001O001O0000oKQOQEo0`:A_E?]:EcE;Y:HhE8U:KkE5Q:NPF2l92TFNh95YFKc99]FG\\9`0dF@]9?cFA^9>XFLh90fBcNc3O]L\\1Z=KfBmN1Md3[1e9KhBjNO0d3\\1e9IiBjNg3]1a9HhBkNh3\\1_9GfBmNQ4\\1Z9FgF;X9FhF:X9FhF:X9FhF:X9FhF:X9FhF:W9GiF9X9FhF:X9EjF:V9FiF;W9EiF;W9EjF:V9GiF9X9FhF:X9FhF:X9FiF9V9GjF:", "size": [640, 443]}, "area": 53639.0, "iscrowd": 0, "image_id": 17115, "bbox": [0.0, 416.0, 443.0, 224.0], "category_id": 124, "id": 20001073}, {"segmentation": {"counts": "jj91mc04X\\OO\\c04a\\OMTc0f0O00001O0n\\OUOnb0o001O01OO110O000ONT]OROlb0n0T]OROlb0n07TOm\\ONOd0\\c0N3NOO1001O00O11O000000000M4N11O02IZOo\\Og0ob0YOo\\Ok0Qc0UOo\\Ok0Qc0200000001NO6M:[Oh\\OMYc00_\\>7]cA9H8XOi0K41OO10O021M3N00010N010001;K`]OaNWb0a16J6J6]O\\]O^OYn9k0[dF4L4L5K3M4L5M21aNg]OU1bb00M27K3L6I7I7L4J5M4JTo9NgPFb0D=D2N1O00O10000000001O001OT]OoNib0Q1V]OPOjb0T1O0000O1O10O101O00O1000011L3ROR]ON000O1b0Zc011O00001O001O0000O10000M_Og\\O`0Zc03I]OQ]Oc0lb0BR]O=nb0DQ]O=ob0CQ]O=ob0CQ]O=ob0Co\\O?Qc0800O1000000O100OWOS]Oa0mb0]OU]O0N<lb0DX]OOL=lb0BZ]O0L=Uc0Bk\\O?Uc0@l\\O`0Tc0@l\\Oa0Sc0_Om\\Oa0Yc00000O11ONc\\OA]c0?1101O002ON204I3F_\\O3jc0MUa70g^H4O1K4]\\OH`c0<O1N3M20O1O2_Ob\\O>^c0Bd\\O=_c0000NFb\\O7[c0He\\ON0:^b0Fe^O8[a0Ie^O7Za0Jf^ONlN5\\b00j^O0Ta02P_OIo`09[12M2O1`\\OC]c0?11O0001O0001O001O0d\\O^O\\c0d04J6J6J`en0V1[YQOK401N011OSNoNP_O0i1Q1V?ROi@n0W?SOi@LYNo0l`0VOn@i0S?XOl@g0T?[Oj@g0U?YOj@j0T?VOl@j0S?XOl@j0Q?WOo@j0P?XOo@h0o>ZOm@i0S?XO]_O@Z1[1W?VO^_O@\\1Y1V?ZOj@g0T?ZOl@f0T?YOl@g0U?ZOj@e0X?ZOh@g0_?QOa@o0a?oN`@P1b?nN_@Q1c?kN`@S1c?kN]@T1f?jNY@V1Za0Oobd0kNe][Oh0]Oc0J6I7H7J5J6K5J6K5K5K;E8D^Li_Oc3O]Lo?0U@1Md3n?]LR@O0d3n?^LQ@g3o?YLQ@h3k?ULU@Q4k?110000000000000O2OO10010O000001N0cA", "size": [640, 443]}, "area": 10984.0, "iscrowd": 0, "image_id": 17115, "bbox": [15.0, 438.0, 428.0, 158.0], "category_id": 136, "id": 20001074}, {"segmentation": {"counts": "mW[4112jc02V\\OMfc0612M3N1O0O100N120HBj\\O=Yc0Dg\\O;Yc07OO11e\\O\\OWc0h0K4O1P]OUOjb0l0U]OUOkb0P1O1O01000O10000O100O3N00L4O1K410O2M1O200001m\\OTOPc0l0P]OVOmb0o0001MU]OSOhb0j0W]OVON0kb0S1O000V]OlNhb0V100001O0O1lNW]OP1lb020O0O1001O010O101NmNU]OR1nb0N01O0003L8I6K5J6J6L[kj0l0]SUO;D;L5OO11O00O10i]OZNSb0j11O1N1O101O002O0L4`Nb]OZ1`b0eNa]O\\1ab0000O02O000001O1M3O00O20aX[1", "size": [640, 443]}, "area": 3809.0, "iscrowd": 0, "image_id": 17115, "bbox": [222.0, 484.0, 151.0, 61.0], "category_id": 150, "id": 20001075}, {"segmentation": {"counts": "_`11nc05K5L4K5K5K9F:G9C=A`0D;E;E6J7I6I7J6I8I6J4L3M2N3L4L4M3O1O1N4M3M3M3L4L4M4L3L4M3M3L5L3L5L3M4K5M2mNkHWEX7\\:UIaEn6R:_IkEc6l9fIQF]6m9fIPF\\6o9eIoE^6n9dIPF^6n9cIQF_6n9aIPFb6n9`IPFe6k9R2^H_FQOTOe3i:kLXFWOTOo3j:cLWFUOTOX4P;UKmDc0T1GTOc4l:PKYE9k0LUOk4X>0O2O0O2O001N2O1O1N2O0OO2O1N1O2N2O001O00aN\\KdCd4k7QKmJS1TMk3n7aKcIg1YNh2T8oKmHf1kN[2W8^LWHe1]Ol1\\8nL_Gd10_1`8n0\\GQOd8V6OiD[GT;e8mD]GQ;a8PEcGl:]8UEeGh:[8YEgGe:Z8YEjGb:Z8\\EhGa:n8M4L3M4M2NX1hN6J6J6J6YH`Db6b<L5L3M3M4TJnBb4U=nJ[Co4o=L3O1N3N1O00O001N2RLbLWG^3d8SMPGn2j8aMkFa2o8gMnFY2m8oMPGS2i8UNgFY2S9oM[Fb2^9T4M34M3L3N3L4L4J6J6K4K6Jb1_N1N3M2O1N3M2N2N0O010000O010O10O^DUIm6;ZJ`6hNXI_6P1]Jh5UOZIo5f1aJP5_O]Ii5T2\\J^4L`Ia5d2UJm39bIZ5S3QJZ3e0fIS5a3kIj2R1hIk4l:UKWEd4m:]KUE^4n:aKUEY4n:hKTES4o:lKTEo3n:QLUEj3n:VLTEd3o:\\LUE_3m:`LVE[3l:eLWEW3k:hLXES3j:lL[Eo2f:QM]Ej2f:UM^Ef2d:YM_Eb2d:]M`E^2b:aMaEZ2b:eMbEV2`:iMdEQ2_:nMdEn1^:QNfEi1]:VNfEf1\\:YNhEa1[:^NhE^1Z:aNjEY1Y:fNjEV1X:hNmER1V:lNoEi0Y:UOkE<b:CbEMk:1YE@T;>X4N2N2N2O1N2N2N2Naef06UZYO;F9G:E;F9F;C=@?@>J5N3L4M2N3M3L3N3M2N3L4M2N3M2M3N2N2M3N2N2N2M3N2N2N2M3N4L6J5J6K9G`0_Oa0@`0@?A8G8I7I7I7H9H7I7`GcFb4QOaKd:ZOhFT5oNWK`:ROnFg5kNmJU<R5UDdJl;[5^DZJd;f5[1O100O100O100O100O100O100O100O10O010O01O10O011O1N2O1O1N2O2N1N2O1_JWJhLk5W3WJgLj5X3XJfLi5X3ZJeLh5Z3ZJdLg5[3[JcLf5[3]JbLe5]3]JaLd5^3^JTJo7j5THiIX8V6jG]Ib8a6aGRIk8m6YGdHS9[7TGSHX9k7`1O1O1N2O1N3N1O1N10000O1010O2N1O2N2O1NX2hM2N2O1N2N2N2Nj2VM001O1O001O001O001O1O001O0O2O1N1O2N101N1O2N2N101N1O2N2O0O2N]CXJP;g5QEYJP;f5oD[JQ;d5PE\\Jj7;[IX5kN_JW7R1gI^4ROaJe6k1QJd3ZObJ\\6Y2SJT3AeJW6a2RJj2GfJS6h2QJc2JgJP6o2QJZ2OhJk5W3QJQ23jJg5^3QJh18kJb5f3QJ_1<lJ^5o3QJU1`0nJV5Y4VJi0b0QKo4P5mIOS1SKi4R;UKPEf4T;YKnDa4W;^KkD]4Y;bKjDX4Z;gKkDP4Z;oKjDj3Z;ULkDc3Z;ZLlD]3Y;bLlDV3X;hLnDP3V;nLPEi2U;UMQEV2d;dMbDl1Q<lMSDk1Z<lMlCj1\\?F:F:F:F:F:F^o0", "size": [640, 443]}, "area": 116554.0, "iscrowd": 0, "image_id": 17115, "bbox": [2.0, 97.0, 439.0, 505.0], "category_id": 183, "id": 20001076}, {"segmentation": {"counts": "o8X2S;MnDmMS;R2mDmMS;T2mDlMS;S2nDmMR;S24N0101OO2O000M3N200O2N2@dDoN\\;n0gDROY;j0`DnN85HK_;S1nDnNW;Q1kDnNU;S1a00N20N20001O00000IiCZOW<c0jCXOO20OW<g0jCXO01Y<c0:1O0110N10O00100O2OO20OO10010O1N21N00O11OO10O011O00N11001OM3N2K5cCTOW<0jCo0T<6kCkNR<W101O0O11N100O10000001OO1O2O001M11O0100O2OO01002NN20001O0000O01O1MeCUO\\<e0dC[O02^<>bC@0120\\<`0eC_O`<a0`C_O`<a05001O0O2O01N1O100000001N12NO010O20O000000O2O00000O10000NN3JB]Ca0c<21N[C_Oe<b021N10O11OO011OO011O00O101O0N1011O0000O1001N01O11O00O11O0OO3OO2N101N2N100001O010M\\C]Od<b0]C_Ob<b0]C^Ob<c0^C]Ob<b0_C]Ob<c03002OO1OZC]Od<e01N2O00OO12OO11OO2N0101L2N3NO10_CZO`<d0`C\\Ob<c04O1000000O100N3O000001OO10O010N2O2O0001N010O20OO100O100001OO2K4K7LoaV5", "size": [427, 640]}, "area": 6242.0, "iscrowd": 0, "image_id": 17178, "bbox": [0.0, 239.0, 240.0, 120.0], "category_id": 111, "id": 20001077}, {"segmentation": {"counts": "Q3n5]7010OO101O01OO2O00010M2K5O2O000M4M2M301O01O010O0001N1O101O00000000O1O2OO10O10001N20N020OO11N001010N02hJoGS5\\8H2N101O000O20O0010O00001O000010O01O00000O110O00001O00001O0001O0O2N0O110M301OO2O00000000010OO10O2O1O:F2N0O101O00001O0O2O01O0001O000O1IkFWLV9b3jF]L3MW9e3;O10OO10000O2O001O000O01O2L^L\\Fd3c9\\L]Fe3b9112N000010N10O1O13L11OO108H2cLRFY3T:LO22MN1011OO1002NO10O2OO11O00004LM22EjE`MU:`2kE_MV:_2lE\\ML1Z:b2mE^MS:a2<001N10O100O100010O01NaEaMT:_2jEcMW:]2fEeM\\:\\2`EfM`:c2O001O10O2M00101O1O10OO3NON7I6K4LN3M33NMkMPEU2P;lMoDT2Q;lMoDT2Q;3000001O01O1O001O0O2O0N10101O01N01001O0O1O100000000013L10N11ONnMkDQ2V;2O002O0N21N2ON20O2O0010O00UOmD^OV;8REGo:\\OgDN000d0<2U;MmD1T;NmD2S;NjD5V;KiD5Y;JfD8Z;GeD9[;HdD8_;F_D;b;E^D;c;C^D=c;B]D>c;B^D=b;C_D;^;IcD6\\;KeD4[;LgD2Y;NiD1X;MfD5Z;KhD3Y;LhD4W;LjD3W;LkD2V;MkD2V;MQEKP;5TEFn:9REFn:;cD1@En;OlD<S<O1O101N1LVSi1LV`VNb0l;_OjCi0U<7O100O11OO11N11OOO21@iCLV<1oCKR<4RDKl;3WDOg;N[D3e;K\\D7b;AoC4a0:c;D_D<b;B`D=_;CbD<W<0000O2O0O1000YOG\\D9\\<N100O100O2O0O100O100O100O10e]U2", "size": [427, 640]}, "area": 33087.0, "iscrowd": 0, "image_id": 17178, "bbox": [0.0, 97.0, 473.0, 191.0], "category_id": 113, "id": 20001078}, {"segmentation": {"counts": "gk42T=5O10000M4M3L3000O2O001O0N20O1O101O000000000001N001001O01N2N011N1001N1O10010N2N2N1O1000000O02O001O0O1001OM3N2K6KlB0njd0OTb[O0RC2f<NYC3M2j<:0O2OO2O000O10001O000O1001O1O1K3JSCLS=14O1OcR<3[mC0N2lBLQ=7O2OO2O00O2O0O2O0001N10OO2N3M5LYY\\20gfcM1O4BO[C;]<95K4L2N8H7K_W1:ThN5L0O2OO2O0100>@Se15fZN4O3L10001N03M3O3L3L4N\\Z82aeG4L5K4L2N00000O20OO6KM5L1N5K6J4M^=Oo_20nbY2", "size": [427, 640]}, "area": 1880.0, "iscrowd": 0, "image_id": 17178, "bbox": [11.0, 210.0, 445.0, 80.0], "category_id": 124, "id": 20001079}, {"segmentation": {"counts": "W;T2T;mMnDS2R;mMmDT2S;lMmDT2R;mMnDS2R;3O1001OO10000M3N200O1O1AfMmEZ2P:iMPFW2l9bMlE85HK^2T:oMmEV2R:lMmET2T:a00000N20000000000IiLZFW3c9jLXFO20OW3g9jLXF01Y3c9;00O11OO100O100001OO11OO1001O00O12N00O11OO100O11O00N200000000000XL_Ff3c901ZL\\Fc3f901O0000O100O10000001OO1O10000N200O100O100O1002NN20000000000O1O1MTLfFm3T9SLlF02o3m8QLQG0120n3n8SLQGR4o8nKQGR4o85001O001O00O1O100000000O12NO100O11O000000O1000000O10000N2O1O1N2M^K\\Gc4d821O00O11OO10000O11O00O10000N2O11O0000O1001OO1O11O00O11O00N2001O001O1O000000001ONYK`Gg4_8ZKbGe4_8ZKaGf4_8ZKaGf4^8[KaGf4_83002N000XK_Gf4c80O10000N21OO11OO1O1001OO100M3NSKfGm4W8SKiGP5U84O1000000O100N20000001OO100O100001O0000O100O11OO100O100001OO1L4K5L\\J_Hf5a7200O100O11O0000O10000O100001O00000000O10000O11OO1O1O100000000O10000001O00001O000000O1O1O1MPJkHP6U73O1O100O1L4N200;E1O1ODSJYIn5d6>1O1O0kIWIh5j6SJYIn5R70O1N2N2FlIXIY6f6lI\\Io5W6kI\\JU6d5lIZJU6h5kIVJU6k5lIRJU6n5nImIT6S6b0O100O100O10000O100O100O1007I2N2N3M2N2N4Lc0]O5K5K3M00D<O100O100O14L3M4L3MoNUKeIh4[6T1O1000000O1003M3M4L6J7I7I<DiNcK`I]4\\6jKaIV4[6QLbIo3Z6WLdIi3[6[LbIe3^6d11cI_In5a6QJ`Io5`6PJdIm5a6mIiIj5l6000000cJhHg4X7YKiHf4Y7WKiHh4m7000001OWORKjHOMP5Y7`KeH`4\\7g0O1001O1OO11O2bJdHi4o700000ZOPKQIP5n6VKnHi4R7\\KiHd4W7h01OO1OjJfH]4[7`KgH`4Y7^KjHa4U7^KmHb4T7[KoHd4R7WKRIi4T7nJPIQ5e700000J6mNhJgId5Z6aJSIl5o672N2N8Hm0SO00O100_ORKdHo4[7VKaHj4_7\\KZHe4f7`000000000FaJgH`5Y7lKXIW2h6_MXJk1h5kMeJR2\\5cMoJ\\2Q5[MXKe2h7O001O1O001O000000000000000000000000000000000000000000000000000000000000000000001O002N1O2N1O2N1O2N2N1O2N1O2N1O2N2N1O2N1O2N2N1O2N1O2N1O2N2N1O2N1O2N1O2N2N1O2N1O2N1O2N2N1O4L7I7IoS_1", "size": [427, 640]}, "area": 76274.0, "iscrowd": 0, "image_id": 17178, "bbox": [0.0, 201.0, 526.0, 226.0], "category_id": 149, "id": 20001080}, {"segmentation": {"counts": "fXj02X=3jBLQ=4oBNo<3PCMP=9N002N000000002N1OO1O1001O000000O1O10000001ONUCDk<<21O1O1O002N00001cC_On;b0kC^OLO11X<b0iCBNMY<b0gCGX<g0O2NM31OO1001OO1001O00O11O4L1nCiNn;X1QDiNn;W1RDjNm;[1\\DcNX;^1_DcN2OO0`;a1aD_NN130X;h1eDWN01[;o1N00O11O0000O1002N002N2N1O000000O11O00000000000000001OOoDjMo:Z2O1O1O001SEcMj:]2VEcMj:_201O2N1O00O10000O13M0[E\\M00a:d2_E_M`:f200aEWM\\:i2cEXM]:l2O1O003M1O1ON21OO11O0000M3001O1O001OO100001OO1N22N001O00000000`MlEj1S:VNiFn0W9POkFo0V9QOjFo0V9QOjFn0W9ROiFm0X9SOhFm0X9SOhFm0X9SOhFl0Y9TOgFk0Z9UOfFm0X9SOhFn0W9ROiFo0V9QOjFP1U9POkFP1U9POkFo0V9POkFP1U9POkFP1U9oNlFGmNO0j00ROY:4iE1b1e0i8VOiE3^1h0j:HeC_O\\<a07NZC_Of<a0200011M12M02OO0100J^O`Ce0[<[OgCh0X<ZOfCf0Z<600N30N100001O0NPOiCP1W<20OlEoNQ8Q1XGXOiNGK011R:Q1QGnNSO8J3Q:>oEWOO4S1OROe0l90RFYOM0R13ROg0l9MSFXOQ15lNk0P:FZG_OfNl0o9E[G`0h8\\O[Gd0e8\\O[Gd0d8]O]Ga0e8@XGa0h8]OmE_OZ1U1j8XO[Gh0d8[OZGd0h8[OXG2dN^OO>U:2XG1hNLP:3XGOjNNo93VGOkNJIIW:=UGOnNEGN00V:OiE9`15POBB0Y:7PG<i9]OmD1Z1a0eN^OV;2oEe0lNYO]<c0cC_O`<=_CB3O^<`0`C@e<a03M20UCBj<?O10O2N0NVCEi<=VCDk<9VCFk<1TC6n<JTC4n<LPC4X1KjN0O3\\;MdD0[1=`;010O00001OO15NLM5O000000O11O0H^OcCe0\\<6N2O11OO2N12NN3O000N2000O30O2jCmNn;X101OfNSDW1Q<0OO210NO22L100O00002N2N1O1O002N2O2N002NO2O000000000000N3M1N2O1O0OOB[C<g<23M30OERC;n<1L400O0L@]C`0c<@\\C10=c<E^C;a<G^C8d<E_C3MKj<0VC9P=KoBNR=0oBNO1Q=OUC0Znl20f^SM5L21O1NN2011NN22N2NN2011N2N2OXg:", "size": [427, 640]}, "area": 13763.0, "iscrowd": 0, "image_id": 17178, "bbox": [63.0, 0.0, 551.0, 190.0], "category_id": 157, "id": 20001081}, {"segmentation": {"counts": "0Q3Z:001O00001O00001O00001O00001O00001O00001O00001O00001O00001O00000000O1O10000O10000O11OO11OO11OO1001OO11mEjLo9`3I1O001O00001O00001O00[L]Fb3h9_LWF]3h9cLZF[3g9dLYF\\3m9OO02OO00000002O0OO1O2O01O01O000O1O01O1N201L1kLnEU3R:2100O1O002N00010WMkEY2W:`MiELO11c2W:^MmENMe2V:[MSFe2Z:9H0K31OO2O01OO2O010O0O11O4M0QMfEk2[:TMfEk2Z:UMgEj2^:_M`EU2a:bM`E2OO0^2c:cM]EN130W2i:fMVE01[2o:N00O11O01O0O2O02N0O3M3KkDoMW;P2iDPNW;P22000O11O01O00O1O13M0000017GhMQEX20gMo:^2MN22MN3dMREX2o:hMQEY2P;O2O20ON10O2OO11N13M0kMlD00U2P;kMSEQ2U;1OoMiDn1W;QNjDo1Z;00O003M1N2ON21OO11O01O0M4O01001N10OO1000101M0O22OO100O002NO14\\MjMlIX2Q6dNSI^1i6dNVIY1n6gNRIU1R7kNmHX1Q7hNnHV1cNjM_8P1nHV1dNiM^8Q1nHV1dNiM^8Q1mHW1V7iNiHX1W7hNkHV1U7jNlHU1T7kNmHT1S7lNnHT1Q7lNoHU1P7kNoHV1P7kNPIV1o6jNQIV1n6kNhGmNO0j00RO[2R8gMSHb1e0j0S7bMSH63^1h0i0S7DeHRN_O[2l7CeHe0Z7[OdHoM_Of2m7[OdHf0]7ZOcHf0]7ZOcHf0]7[OdHc0\\7\\OeHc0\\7_OaHa0`7^ObHa0^7_OaHb0_7]ObHc0^7]ObHc0^7WOPHVNe0c2[7ROPH]Nh0e2T7mNVH\\Nf0i2S7jNSIU1n6kNRIV1m6hNVIU1oNYMk7c1TIU1n6kNRIT1o6lNQIS1P7mNQIR1o6nNQIP1Q7nNoG_NP1d2P7mNRIT1m6lNRIb0oN\\NQ8S1PIJXOiNGK011>P8R1QICnNSO8J3>e7R1iH`NWOO4S1OROe08X7U1hHaNYOM0R13ROg08T7W1iH`NXOQ15lNk0;o6Y1gHF_OfNl0:n6Z1gHF`0UOf6V1fHHd0SOlN@N000c7e1oHHd0[O]6l0PIJa0[O_6l0QIFa0^O^6l0oH[N_OZ1U1^O]6o0kHHh0ZO\\6m0oHGd0^O\\6l0oHF2dN^OO>j0T7m0nHF1hNLg0S7k0PIFOjNNe0S7l0PIDOkNJIIm0]7l0PICOnNEGN00m0]7k0bHWN9`15POBB0m0^7m0iH_O<;k68bHSN1Z1a0eN^Ob1b7>`HUOe0lNYO`1c7g1PIiL_Oc1k0]Nf5W3lIhLB3Ob1a0[NJ2V6U3oIjL@k1?TNa6W3aIgNNRNM1U6W3mIiN`0oMb5X3nIhLBo1`7Z1oHgNP7X1QIiNLoMd6X3_IkNLmMe6Y3]IkNNlMe6X3[IiLET2:kMf6W3]IiLDS2:mMe6Y3ZIhLFT20VNo6n2RIgL6e2h6c0RIjL4e2i6b0SIgL4X1KjN0O3S1k6U2RIhL0[1=F0nNR6j4nIXL0nNR6k4nIVL0ROo5h4QJWL1oNn5j4QJWL`6i3aIVL_6j3`IWL`6i3eIRL[6Q4aIPL_6l3fIRLKUOo5i4VJRLKVOn5h4WJRLLUOm5i4WJRL[6n3dISL\\6m3eIRL[6n3eIRL[6f3SI_Ke0l0X6c3nI]LR6a3PJ_LP6`3QJ`Lo5a3PJ_LP6`3RJaLl5^3UJcLj5_3TJaLl5]3WJbLi5^3WJaLj5_3VJaLj5]3XJbLi5^3WJbLi5^3VJbLk5`3UJ^Lk5c3WJdKmNa0l6k3\\JTLe5l3\\JSLd5m3RIdKW1=h5T4XJkKh5U4WJkKj5V4WJhKi5Z4UJdKm5[4UJ\\KS6g4iIYKX6g4V100O1O11O1O0000O11O1O2N002NO1000000000000001OO1O1O1N2L^G_Kb8a441O1O0\\G[Kb8g40M31O00L\\GaKd8_4\\G`K10`8`4bGaK^8^4dGaK\\8`4aGcK3MK1T8a4lG^K92l7d4oGYKN2T8f4lGYKNO11U8g4jG^K0JW8d5^O8HH8000000O100O10000O1001O1O1OO10000O100O10000O100O10000O100O100001O00001O00001O00001O001O00001O2N1O2N1O001OO1O100O11OO11O0000N21O1O00001O1O0aHVJ]7l501OO11O001O00000000N2J6M3O100O100O10000O1000000003M00000000000000000000000000001O00000000001O0000000000001O00000000001O00O100O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O100O100O100O100O1O100O100O100O100O100O100O100O100O1000000O10000000000O100000000O100000000O100000000O100000000O1000000O1O1O1N2O1O1O1O1O1O1O1IkFWLV9f3oFWLR9g3PGXLQ9i381OM3O11OM31O1OM3O11O1O1O1O00O1O100O100O100O1O100O10000O10000O10000O1000000O1", "size": [427, 640]}, "area": 87008.0, "iscrowd": 0, "image_id": 17178, "bbox": [0.0, 0.0, 640.0, 222.0], "category_id": 169, "id": 20001082}, {"segmentation": {"counts": "enZ33Q=8M2O1O01N2O1O1O1N2000O10001O1O001O100O001N3N2N2O0O7I4LM3C>O001O0000000101N=C1O00BkCCX<<;10001^CDS<?fCEZ<e01N2N2M3G90YCG]<c02N2M3L5K]j50]UJ:M3M4L3M3M5Kd0\\O6J6J4L1OD=00O100O2O04L3M4LQOREDk:<R10N2N1N3N2N2M3N5K5K6M5L5J6J;EeN`FiN`9S1gFjNY9S1mFjNT9R1QGlNo8T1TGiNk8X1c11O2N100O1g0_OOOB<lDkNn9]1jEdNU:c1cE^N]:X2O2OiNVEMi:3gEWOAEh:T1[FjNe9U1X11001O100OOa0@2N2nDXN\\:\\200OnN^EjNI:i:j0aEfN0;_:o0SFlNl9U1[FdNe9[1U110000YEdN^9a1\\FaNd9c1VF`Nh9d1SF^Nm9e1nE^NQ:d1iE`NX:e1_E_N`:V2001HaEbM_:Z2;QOVEPOX;Q1`02N2O1N8Il0SOO1O2O2]O]EYNe:e1`EWNb:g1b0eNjD?X;WO[E>m;L5K5Kk>=^@P1PO=C:G:L4L3kNSMjGP3S8UMgGn2X8VMbGn2[8TMbGo2\\8TMaGn2^8SMbGm2\\8UMdGk2Z8WMeGj2Z8WMfGi2Y8YMeGh2[8XMeGh2[8XMeGh2\\8WMcGj2]8VMcGj2]8WMbGi2^8WMaGj2_8VMaGj2]8aLZGd09k2W8^MhGc2U8`MkG`2T8bMjG_2V8aMjG`2T8jLfG85o2U8iLfG85o2T8jLgG74P3U8aMjG_2V8aMiG`2V8aMjG_2V8aMiG`2W8`MiG`2W8`MhGa2[8[MfGe2Z8jLeGM0Z3[8hLgGMN[3[8hLgGh3Y8WLiGh3W8XLjGg3V8ZLiGf3W8[LhGe3X8\\LgGe3X8[LiGf3U8[LjGf3U8ZLkGh3S8XLmGi3R8XLmGj3Q8VLoGk3P8VLPHk3o7TLQHn3m7RLSHo3l7RLSHP4k7PLUHQ4j7j0N101N2N1OmJ]H`4a7^KaHc4^7\\KbHg4\\7XKcHl4\\7b0O2N1O2N1O2O1N1N3N1N3M2N3M3M2N3M2N3M2N3M3M2N5K8H8H5K1O100O100O100O100O1O100O100O100O100O100O100O100O100O1000000O10000000000O100000000O100000000O100000000O100000000O1000000O1O1O1N2O1O1O1O1O1O1O1O1O1O1O1O1O100O1O1O1O1O100O1O100O100O1O100O100O100O1O100O10000O10000O10000O1000000O1", "size": [427, 640]}, "area": 54706.0, "iscrowd": 0, "image_id": 17178, "bbox": [256.0, 108.0, 384.0, 319.0], "category_id": 183, "id": 20001083}, {"segmentation": {"counts": "Ydf11Y=3_FMg54WJMi54bFKb31l56TJJl5;oIER6<kIEU6;jIFV6:jIFV6:jIFV6;iIEW6;hIFX6;gIEY6;bIJ_67]GBR16a78gFMINR1E\\Oc0R9MdF`08ZODP1`9UObFe0Nm0`9^NaFi2`9a0000000O1N2O100000000N2NQLgFo3Y920000N2000000000000O100000000000000O100O1O1O100O1N21O000000002N3M00000000000000O1000000000000O1000000000000O100000000000000O1000000000000O1000000000000O1000000b0^O00O1O100O1O1O100O1O10000000000001O00000000000000000000000000001O000000000000000000000000000000000000000000000000BVLZGj3P945KO1O10000O10000O1000000000000000000000000O11OO10000000000O1000000O10000000000O100000000O11OO100000000O1000000\\Od000000000000000O10000000000O10000O100001O00000000001O0000000TKbGj4`80N2O100O1000000001O00001O001O00000000000000O1O1O100O11O00000000000000O11O001O1O1O9G000000O100000000000000O1000000000000O100000000000000O1L4001O2N1O000000O100000000000000O100000000000000000000000000000000O10000000000000000O1000000000000001OO100N200OhKdGb3\\8_LfG^3Z8aLgGDN_3[8mLiG]O0g3W8lLRHT3n7lLSHS3m7mLRHT3n7lLRHT3n7kLSHU3m7kLSHU3m7kLUH?^OTO6Jo01ROOi8c0eG4c:MbEO^:]OhD118OIl0?m:AUE<k:EWE2TOLf;0XE3o:LeQ=0doB2N:F001O5K2ONO1K4L8I5K5VDgN\\;_1cDbNW;g1cD[NY;o1gDoMR;X2K4L5K6hFZMc7k2UH[Mg7k2THXMh7o2RHTMk7R3aG`L5c0W8V4N2N2O1O10000O100000000O100000DkGaKU8P4QHlK\\8j3h01N4M001O000O3N00000000O10000000O01000000O10O10O1000000O10O10O1000000O10O10OmG", "size": [428, 640]}, "area": 62577.0, "iscrowd": 0, "image_id": 17182, "bbox": [130.0, 260.0, 510.0, 168.0], "category_id": 110, "id": 20001084}, {"segmentation": {"counts": "b`T715b0U<`0hInNXN2`Lo1X5oMfMm0cL[1f5hMSL;YNQ4c5eKhK`5X4aJ\\Kj5d4`1O0O101N100iNcH^L^7S2`HkL01195O2ILn0\\7T2\\JmLWNa0]7V2[KXMWM0_7d2U300000001ON2J6J6K5K5K5K5L4M3L4L4L4M3L4L4M3M3N2N2O1O10000O100000000O1000000000000O100000000O1000000000000000000O10000000000O1000000000000O1000000000000O100", "size": [428, 640]}, "area": 24766.0, "iscrowd": 0, "image_id": 17182, "bbox": [546.0, 0.0, 94.0, 344.0], "category_id": 112, "id": 20001085}, {"segmentation": {"counts": "dSj6V12ROO;R;f12NNjMnDV2Q;33MO10000N2001O1OO10000001O1O1O2N00000000000000001O0000000000O1O1O100O1O100001O5K2NO1O1N22N000VNgDa1Z;^NgDa1];YNeDg1a;YN]Dc1g;O001O2K5H8K6J6\\O\\C5Wf6J`fI6[C2T<h01O3M001O00002N00000000O100000000O1000000O10000O1000000O10000O1000000O10000O1", "size": [428, 640]}, "area": 5427.0, "iscrowd": 0, "image_id": 17182, "bbox": [521.0, 354.0, 119.0, 74.0], "category_id": 118, "id": 20001086}, {"segmentation": {"counts": "0b8j4O1O1001O000000002N00001OO11OM3M30000NSKeGm4[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[6l630000000000O10000000000000bIUI[6k6dIVI\\6m600000000000O1000000000000000000000000O1000000000000000000009G0000O100001O000nIlHl5T7RJnHn5R7QJoHo5W701O001OO1N2K5N200000000O10000000000000000000000O100000000000000O1000000000000O10000000000000000000000O10000000000O10000000000000000000000O100000000000000O10000000000O100000000cKbIcMl0Y3b5eM`ITO10W2W3j7OO11\\OWFbMX:1oEK@P1>TOX;3aDM:0i;NXD2h;I]D7\\<1O00N2N20an1N]PN=^DJ[;8cDNh:c0VE_Oi:b0TE@l:a0SE_Om:b0PE@P;^10001O0000000000000O100000O1000000000000000000000000000000001O000000000000000000000000000000oNlDmN30Na0S;a0ZE^Oa;F[fW1", "size": [428, 640]}, "area": 117739.0, "iscrowd": 0, "image_id": 17182, "bbox": [0.0, 0.0, 545.0, 276.0], "category_id": 123, "id": 20001087}, {"segmentation": {"counts": "b8j4a81O1001O000000002N00001OO11OM3M30000NSKeGm4[8200001O00O100000000O10000000000O10000000000O10000000000000000000000000000O1000000000000O10000000000O1000000000000000000O100000000000000O10000000000000000O1000000000000000000O100000000000000O10000000000000000O100000000000ULQHY2n7fMUHX2l7hMUHV2l7jMUHaNKb3P8mMWHS2i7mM\\Hn1d7SN]Hj1d7VN\\Hi1e7WN[Hi1e7WN[Hi1d7XN]Hg1c7YN]Hf1d7ZN]He1c7[N]H`1h7aNYH[OBR1T8CZHeNMINR1E\\Oa8T1oGbN`08ZODn8b1WG`Ne0Nj8c1aF^Nf:c1a0000000O1N2O10O100000N2NUDcNk;]120000N20O1000000000O100000000000O10O100O1O1O1nNQDg0o;WOVDe0k;ZOZD`0i;]O]D`0[<L4L4L4LlRT2g0mkkM`000O1000000000O01000000000O10000O0100010O000lNnCn0R<POPDP1P<oNQDQ1U<10O001OO1gCoNU<U1K301100O1000O10001O00001O001O0000000O100000O1O1O100O02O00000000000O10O11O001O1O1O00N2O1N2O010O10000000O10O1O1O10000OoCjNn;Z100000000O101O1O1O1O4L001N3N1O000000O0100000000cKZNkHl0Y3j0]2XN\\J10W2W3@^1i1aNVN`1i1aNWN_1j1mMhLbM_1a4W2`KaLK@P1>TOZ1a4\\3bKSKM:0W1a4P4]KjJ2V1a4P4XKoJ7Q1a4i4`KVK`4j4`KVK`4j4^KXKb4h4\\KZKd4f4\\KZKd4g6000000000dIZKQ4g4kJfK@J_5`4PKhK_ON\\5Z4eJYLM_O]5X4fJZLJ@`5V4fJ[LI_Oa5V4fJ\\LF@d5T4fJZMZ5f2fJZMZ5f2fJ[MX5f2hJZMX5f2hJZMX5f2hJZMX5f2hJZMX5f2hJZMX5f2hJZMX5f2hJZMX5f2gJ[MZ5d2fJ\\MY5e2gJ[MY5e2gJZMX5h2hJXMX5h2hJXMW5`NZJb3?nMW5aN\\J^3=QNW5`N]JDN_3>]NW5`N_J]O0g3:\\NW5`NhJT31\\NW5`NiJS30]NW5`NhJT31\\NW5`NhJT31\\NW5_NiJU30\\NW5_NiJU30\\NW5_NkJ?^OTO6n1:0W5_NnJ4Ma1NLV5`NTKOIc1MNW5lMYJ118OIl0?E_O_OV2`0Nh5nMfJ<Ck1OLf5nMjJ2TOL`0U2LOg5lMkJ3HS2FNg5kMlKW2]NNl72THNj74VHLj74VHLk73UHMl72THNk73UHMk73UHMk73UHMk73UHMl72THNm71SHOn70RH0P8NPHQOAmN30Na0^8a1PHPOO^OQ8b1kHTNX8l1d10bNmM[Fb0`0a1l8oNSGQ1f2mMP12`Lo1J2f2mM>m0cL[18Kg2mMjN;YNQ47fMe2oM_N`5lNaLe2PNSNj5XOVLe2Z5ZMfJf2Z5ZMfJe2[5[MeJe2\\5YMeJf2\\5YMeJg2[5XMfJa1mMSO^7\\OeJ2kM@01195O2ILn0]7\\OeJ3FBWNa0`7GdJJd0MWM0P`P1", "size": [428, 640]}, "area": 51935.0, "iscrowd": 0, "image_id": 17182, "bbox": [0.0, 0.0, 563.0, 428.0], "category_id": 172, "id": 20001088}, {"segmentation": {"counts": "^\\V42Z=1N100MMlB4S=30000000000O1000001O000000HoB5Q=JPC6S=00000000000PCNe<3:1O000000000000001O0000O01000000000O1000000000oV`3", "size": [428, 640]}, "area": 421.0, "iscrowd": 0, "image_id": 17182, "bbox": [321.0, 220.0, 50.0, 20.0], "category_id": 177, "id": 20001089}, {"segmentation": {"counts": "]b]24V=5L5J6K5J6K5J6J5L4M0O10O0100O100O100O010O1O10000O10O100000000000000O10000000O1000O1000000000000O1000000000000\\NaD[1_;81c0]OO1O1O10O01O1O100O1000O1000000000000O10000000001O000001O00000000001O00NnMjDR2V;nMjDR2U;oMkDQ2U;30mMkDP2T;PNlDP2T;40000O1001O00001ONkMmDU2R;lMnDT2Q;nMoDQ2Q;oMoDQ2Q;oMoDQ2Q;VNlEf0U:SOnD?c<b1cN4LlMnDm1S;61N1oMkDk1T;7O01XNoDU1Q;\\ORE0o:U1100001O01O0000000O01002N0000O10O10000000000O100UN[FOf9k10O10000000000O10000iNW1\\ObCa1W;:1O0O10000000000@iTn0lNilQO101N2O100O1000O100000O1O1O1000O0AXC<l<0000000000O2O1O1O2Ldd\\2", "size": [428, 640]}, "area": 10729.0, "iscrowd": 0, "image_id": 17182, "bbox": [185.0, 227.0, 271.0, 79.0], "category_id": 183, "id": 20001090}, {"segmentation": {"counts": "ZYU7d0\\>1O00000000O100000O101O0000O1O1O100O1000O100000000000000000O10000O1O0HgAJ[e42[iK2N1O004K2PBDZ=<[BD11c=<TBC4141d=`0TB_O90d=a0SB_O90d=c0\\B^Od=f0WB[Oi=e0VB[Oj=n000000O1000N21N2OIQO]Bo0c=QO]Bo0c=QO]Bn0c=SO\\Bn0d=RO\\Bn0d=RO\\Bn0d=RO\\Bm0d=TO\\Bl0c=UO[Bm0[=a000O01000000O1000O10O1000000O1000000O100000O0100000O0100000000O0100000O1000000O1000000O1000000O1000000O2O5K1O0O01M3N2M3N101N2O10O1000000O1000000O1o0QO0kN", "size": [480, 640]}, "area": 5003.0, "iscrowd": 0, "image_id": 17207, "bbox": [489.0, 37.0, 151.0, 76.0], "category_id": 96, "id": 20001091}, {"segmentation": {"counts": "f5e1\\=dNmBj0R=VO[C>c<CSDHl;8^100101jAEb=>\\BBd=`0ZBAe=a0YB@f=b0ZB]Oe=g0XBZOf=OUBc06^O8Ok<OhBb07A5Oj<OjB`08A`=OYB?8B^=OZB?9B\\=0[BNN2O30L>O[=1hBO]>0dnQ9", "size": [480, 640]}, "area": 435.0, "iscrowd": 0, "image_id": 17207, "bbox": [0.0, 182.0, 21.0, 56.0], "category_id": 105, "id": 20001092}, {"segmentation": {"counts": "]Z51j>1XA0h>5MJZA7e>200000000O1001O4H4OL6O1O2M2O00O2O000000000000000000000000000000O01000001Ln^c8", "size": [480, 640]}, "area": 372.0, "iscrowd": 0, "image_id": 17207, "bbox": [11.0, 164.0, 41.0, 12.0], "category_id": 132, "id": 20001093}, {"segmentation": {"counts": "_9a5_90000000000000O10000000000kM`JiJa5W5_JfJd5Z5\\JdJf5\\5ZJcJg5]5XJdJh5\\5XJaJk5_5UJ_Jm5a5SJ]Jo5c5QJ\\JP6d5PJ[JQ6e5oIoH0R1R6m5oIQI0n0U6[6lIcIU6]6kI[IN[OW6Z7kI[IOYOW6[7lI[INYOV6\\7lI[IOXOU6]7lIoH<Bi5_7V1O10000000000000000004L4L3M4hKSHU1Q8`NXH^1k7WN`Hf1d7oMeHo1^7\\MWIa2m6TM\\Ij2h6RM[Ik2h6QMZIn2j6mLXIR3l6iLVIV3m6gLTIX3P7cLQI]3Z9O1O1O001O1O00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000N2D<C=000000000000000000001O00000000000000000000001O00000000000000000000001O00000000000000000000001O00000000000000000000001O00000000000000000000001O00000000000000000000001O00000000000000000000001O00000000000000000000001O00000000000000000000001O00000000000000000000001O00000000000000000000001O00000000000000000000001O00000000000000000000001O00000000000000000000001O00000000000000000000001O00000000000000000000001O00000000000000000000001O00000000000000000000001O00000000000000000000001O00000000000000000000001O00000000000000000000001O00000000000000000000001O00000000000000000000001O00000000000000000000001O00000000000000000000001O00000000000000000000001O00000000000000000000001O00N2J6K5K5K5J6K5K5K5K5J6K5K5K5J6K5K5K5K5L4DlIlGT6[8500001O0000000000000000000000001O0000000000000000000000001O00000000000000000000001O2N2N2N3M2N2N2N3M2N2N2N2N3M2N2N2N3M2N2N2N2N00O10000000000O100000000O10000000000O100000000O100000000O1N2N2N2N2N2N2N2N2N2N2N200O1000000O10000O1000000O10000O10000O1000000O10000O1000000O10000O1000000O100000000001O0000001O00001O0000001OZOf0", "size": [480, 640]}, "area": 92436.0, "iscrowd": 0, "image_id": 17207, "bbox": [0.0, 231.0, 640.0, 249.0], "category_id": 149, "id": 20001094}, {"segmentation": {"counts": "0b4^:00O10000000000000aFbK\\8]4eGcK[8]4eGeKY8[4gGeKY8[4gGeKQOMT9^4jGgKQOLT9]4kGgKROLR9]4lGgKROMQ9[4nGhKQOMQ9\\4_GcKI4HMNN2O30^8`4eGjK2MG:b8P4]GPL::Y8Y4gGhKX8Y4gGgKY8]4_GgKa8Y4^GhKb8S4dGkK]8U4dGjK\\8V4fGhKZ8W4hGhKX8X4hGhKX8X4gGiKY8X4fGhKZ8X4fGiKY8W4gGiKY8W4gGiKY8W4gGiKY8W4gGiKY8W4gGiKY8W4gGiKY8W4gGiKY8W4gGiKY8W4gGiKY8W4gGjKX8V4hGjKX8V4hGjKX8V4hGjKX8V4hGeK]8Z4cGaKc8_4]G[Ki8R2PGO7iMo8Y2mFJ4gMU9_2jFHb98bFD^9=eFYOa9g0bFPOd9Q1_FeNg9[1\\F\\Nj9d1XFTNn9m1SFkMS:U2oEcMW:]2V11O001O0000N2L4L4L4L4M3L4L4L4L4L4M3L4L4L4L4L4M3L4L4L4L4M30000000000000000000000000PQ\\50PocJ000000000000000000000000000SA4f>LZA8b>8K4L5Ko0QOa0_O000000000000000000O100000000000000000000O100000000000000000000000000UNPDe0Q<UOSDm0m;SOQDn0P<ROPDe0Y<\\OfCd0Z<B`C>`<J[C3e<V1N8H1O0000000000O100000000000000O10000000000N21O1OIWCWNi<i1WCWNi<i1WCWNi<h1XCWNi<i1WCWNi<i1WCWNi<i1WCWNi<h1XCXNh<g1YCWNi<_1a000O1000000000000O1000000000000000000000000O1000000O10000000000O1000000000000000000000000000000000000001O5K1O00O1M3N2M3N2O1N2O10000000000000000000000", "size": [480, 640]}, "area": 20361.0, "iscrowd": 0, "image_id": 17207, "bbox": [0.0, 0.0, 640.0, 206.0], "category_id": 157, "id": 20001095}, {"segmentation": {"counts": "b4T1l=1N10O10000001OO1KXBROg=o0YBQOg=k0WBVOn=e0SBZO01m=e0XBZOh=d0RB[OW>e020O11KiA_OX>8PBHQ>0oZk6g0ndTIO000O1000O10O1000001N1000O1O00100O10O100000000O1000000O10O1000O01O1G@QBa0g=?01G9000O102N8H1N5L2N1O004K2MgA]OY>8gA1\\>GdA414a>H_A9a>G_A9c>05K1OQ`[1", "size": [480, 640]}, "area": 2013.0, "iscrowd": 0, "image_id": 17207, "bbox": [0.0, 61.0, 547.0, 122.0], "category_id": 169, "id": 20001096}, {"segmentation": {"counts": "[7T2a;UNbEj1^:cNVE[1k:[O`Dd0`;^10TM_DY2b;fM^DZ2d;dM]D[2e;cM[D]2g;aMZD^2h;aMXD^2n;^MQDa2o;`MRD9Nc1Q<UNQD70b1n;YNSD31`1n;^NPD2Oa1S<^NnC0O`1U<aNlCNOa1U<bNkCMO_1X<gNhCI0^1Z<jNfCG0]1\\<VOdCi0]<WOcCh0^<ROcC^OO30T1_<\\OdCZOM61o00kN^<j0bC]O0m0b<0_CNb<1_CHMZOd<n0_CHNYOc<o0`CGNXOc<Q1_CB4\\O]<Q1`CTO[=k0=0000O10000001O0001O04L4M2G:_E\\NX7n1]H[N[7n1ZH]NW4^OdL_2jN\\Na4XO[Le2_NhNS5fNULl2]NgN\\5aNmKR3cN`N^5aNeKX3iNYNa5bN]K_3mNQNf5cNSKf3ROiMj5dNkJl3XO`Mm5fNcJT4[OXMR6eN[Jd6d5^ITJi6k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oH^7=kHV6FcIo6MPJ`6QOhIn7i6L5K4Li1WNR1nN0001N1000000O1000000O1000000O2O00000O100000000O1000000O2O00000O10001O2M3N2N3M2M3N2N3M2M3N2N2N3M2M3N2N3M2M3N2N1O0O1000O10O1000000O0100000O10O1000O100000O10O1000000O0100O1N1O2N2N2M2O2N2N2N1O2O1000O0100000O010000O10O1000O10O10O1000O10O1000O0100000O010000O0100000O01000O1001N1000001N1000001N1000001N1W5iJW7", "size": [480, 640]}, "area": 185256.0, "iscrowd": 0, "image_id": 17207, "bbox": [0.0, 1.0, 639.0, 372.0], "category_id": 183, "id": 20001097}, {"segmentation": {"counts": "niY15^c0Lc\\O00011O;Vc0b0I3M1O100O10000000000000O10000O3]OU]OInb06U]OHPc01U]OLmb02b0N_bb7", "size": [640, 478]}, "area": 666.0, "iscrowd": 0, "image_id": 17379, "bbox": [66.0, 555.0, 23.0, 37.0], "category_id": 132, "id": 20001098}, {"segmentation": {"counts": "W3h;X800O100000001O0O2O000000001O00000O10000000000O2O0000000001O1O010N110N11O00000001O01N100000000000001O0O1O100O10001O0O10000O100000001O0001O1O00O10001O0001OO1001O00000001O000O1O100O100001O00O2O01O0000N2JVGVEj8j:VGUEm8h:TGWEm8i:SGVEn8j:RGUEo8j:4O1O11O0O02OO1001OO11O00000O1000000000000O2O01O00O100000000000000000001N1000000aJjF4V9B[G8d8^OnG:R8\\O_H=b7XOPIa0n6UO^Ik0a6kNiIV1V6aNSJ_1n5`NRJa1m5_NSJa1m5_NSJa1m5_NSJ`1n5`NRJ_1m5cNSJ]1n5bNRJ^1o5aNQJ_1o5bNPJ^1P6bNQJ]1o5cNQJ]1P6bNPJ^1P6bNPJ]1Q6cNoI]1Q6cNoI]1R6bNnI^1Q6cNoI^1Q6aNoI_1Q6aNoI^1R6bNnI^1R6bNnI^1R6bNnI^1R6bNnI^1R6bNnI]1S6cNmI]1T6bNlI^1T6bNlI^1T6bNlI^1T6bNlI^1T6bNlI^1T6bNlI]1U6cNkI]1U6cNkI\\1V6dNjI\\1W6cNiI]1W6dNhI\\1X6dNhI\\1X6dNhI\\1X6dNhI[1Y6eNgI[1Y6eNgI[1Y6eNgI[1Y6eNgIZ1Z6fNfIZ1Z6fNfIZ1Z6fNfIZ1[6eNeI[1[6eNeI[1[6eNeI[1[6eNeI[1[6eNeIY1]6gNcIU1a6kN`IT1`6lN`IS1a6mN_IR1c6mN]IS1c6mN]IS1c6mN]IS1c6mN]IS1c6mN]IS1c6mN]IS1d6lN\\IT1d6mN[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\\7hNcHW1`7hN_HY1a7gN_HY1a7hN]HY1c7gN\\HZ1d7fN\\HZ1d7fN[H\\1d7dN[H]1e7cNZH^1f7bNZH^1f7bNYH_1g7aNXH`1h7`NXHa1g7_NXHc1g7]NRGeM]Oo3a9\\N`FoNWOe2Y:\\NnEm3S:P30O2O0O10000O1O10O12K\\EiFe:V93M300000000001OO2O000000O1001O00O20O00000O10O11O00000000000O101M3NO21N100000000O011O0O3NN20O11N0110O100O00O10O2O0OO3O02M011O001OO101N3M2M3N101O1N02O0O0100O02OO1N12O1N2N101N2O1O1N1OO2O100001O1OO0011OM30O3VGjDf8Y;ZGgDc8[;04L2K3000O2N12OM14M1OO11NO21ON2O1M3M3N2000000O01000N101001aMhDjI2T2W;o3oDQLQ;l3SEdJCh0X;a4ZE\\JFGI02`0MB09[;]5\\EZJHMH:LFO10O70W;0`De40ULf1VOgN051U;OYD]40]Lf>Z3[AlK11Oh0f>Y3ZAoK10Oc0O\\Oi>T4nAQLV>S4i0N1N4M22N1OO0114KO1O0010ZK", "size": [640, 478]}, "area": 133574.0, "iscrowd": 0, "image_id": 17379, "bbox": [0.0, 103.0, 478.0, 377.0], "category_id": 133, "id": 20001099}, {"segmentation": {"counts": "Ra0n2Ra0000000O1N2O1O0O2O1O1O1O1N2O1O001O1O1N2O10O10O10000O2OO010O10O10O010002MO2000O011N10O10O1000O0100O3N000O3N0O10000O10O11N100OO200O01TOS^OEka00Q^OZO00011O;a0aa0_OS^Od0n0FPa0;S_OCl`0=U_OCk`0<V_ODj`0<V_OCk`0<V_ODj`0<V_ODj`0<V_OCk`0=U_OCk`0=U_OCj`0>V_OBj`0>V_OAj`0`0U_OAk`0?U_OAk`0?U_OAk`0?T_OAm`0a0`^O\\OI7fa0=`^O_OH5ha0`0Y^O@L0la0a0V^O0ja01T^O0la0R10O10O10O10O10000000O1000O1000000O100000O1000O100000000O11O0O01001OO11O0RNQ^Ok1Rb00O1001OO10000000001N10000000001O0O1000000O101O0O10000000001O0O10001O001O001N101O0O2O001O00001O001O010M`Md^O`2\\a03N101O001O0O3N3e_OTM\\?n2`@TM_?P3\\@RM`?U3[@mLd?W3V@lLi?X3R@jLn?d30000O100000000000000000000001ON2001ON2001O001O1O000000O11O0000O1000000O100000000O1000000O1000000000000O10000000000O10000O100000000000000O100000000O11O00O1O100O1000000O1O1O11OO1000000M30000001O00N2000000001O00N21OO11O00N_Ke@a4[?200O12NO12N00000000000000O10000O11OO1O100O11O00O1001O0000N20000NZKj@f4V?21O0000O11O0000000000000000O100N21O00O1O11ON2O1000000O1O100O10000000000O100000000O1000000OfLeAQ1[>nNgAQ1Y>oNhAP1X>oNjAP1V>POkAo0U>POmAo0T>POmAo0Q>ROPBn0P>ROQBm0o=ROSBm0m=SOTBl0l=SOVBl0k=ROWBm0i=SOXBl0h=SOZBl0e=UO[Bk0e=TO]Bk0b=VO^Bj0b=VO_Bi0a=WO_Bi0a=WO`Bh0_=YOaBg0_=YObBf0]=[OcBe0]=[OdBd0\\=\\OdBd0[=]OfBb0Z=^OfBb0Z=^OgBa0X=@gBa0Z=^OhB`0X=@gBa0Y=_OgBa0X=@iB?W=AiB?W=AiB?W=AjB>V=BjB>V=BjB>V=BkB=U=CkB=U=CkB=T=DmB;S=EmB;S=EmB;S=EnB:R=FnB:Q=GoB9Q=GPC8P=HPC8P=HPC8Q=GoB9P=HPC8P=HPC8o<IQC7P=HoB9P=HPC8P=HPC8o<IQC7o<IQC7o<IQC7o<HQC9o<GQC9n<HRC8n<HRC8n<HRC8n<HRC8n<HRC8m<ISC7m<IRC8n<HRC8m<HTC8l<HTC8l<HTC8k<IUC7k<IUC7k<IUC7k<IUC7k<ITC8l<ISC7m<ISC7l<JTC6l<JTC6l<JTC6l<JTC6l<KSC5l<LTC4l<LTC4l<LSC5m<LRC4m<MSC3m<MSC3m<NQC3n<NRC2o<MQC3n<0PC0P=2nBNR=2nBN", "size": [640, 478]}, "area": 52404.0, "iscrowd": 0, "image_id": 17379, "bbox": [0.0, 458.0, 478.0, 182.0], "category_id": 165, "id": 20001100}, {"segmentation": {"counts": "0W3h;lNVGT1j8lNVGT1i8mNWGS1i8mNWGS1i8mNWGS1i8mNWGS1i8mNWGT1h8lNXGT1g8lNZGU1e8kN[GU1e8kN[GU1e8kN[GU1e8kN[GU1e8kN[GV1d8jN\\GV1d8jN\\GV1d8iN]GW1b8jN^GV1b8jN^GV1b8jN^GV1b8jN^GV1b8jN^GV1b8iN_GW1`8jN`GW1_8iNaGW1_8iNaGW1_8iNaGW1_8jN`GV1`8iNaGW1`8hN`GX1a8fN`GZ1`8fN`G[1`8dN`G[1a8dN`G]1`8bN`G]1a8cN_G^1`8dN^G\\1b8bN`G^1`8bN`G^1`8bN`G^1a8`N`G`1`8aN_G`1_8aNaG_1_8`NbG`1^8`NbG`1^8`NbG`1^8`NbG`1^8_NcGa1]8_NcGa1]8_NcGb1\\8`NbG`1]8aNcG_1\\8bNdG^1\\8dNbG\\1]8eNcG[1]8eNcG[1]8eNcG\\1\\8dNdG\\1[8eNeG[1[8dNfG\\1Z8eNeG[1Z8fNfGZ1Z8fNfGZ1Z8dNhG\\1X8dNhG\\1X8cNiG^1V8bNjG^1V8`NlG`1U8]NfDTNU3_3V8\\NlGd1T8[NmGe1R8\\NnGd1R8\\NnGd1R8\\NnGe1Q8[NoGe1Q8[NoGe1R8ZNnGf1Q8[NoGe1Q8ZNPHf1Q8YNoGg1Q8XNPHh1P8XNPHh1P8XNPHh1P8XNPHi1o7WNQHi1o7VNRHj1m7WNSHi1l7XNTHh1l7XNTHh1k7YNUHg1k7YNUHg1k7YNUHg1l7WNUHi1k7WNUHi1j7XNVHi1i7VNXHj1i7UNWHk1i7UNWHk1i7UNWHk1g7WNYHi1a7^LVEi1Y3i1a7^LUEj1Z3j1^7]LWEi1\\3j1]7]LVEj1]3i1]7]LUEk1^3h1\\7\\NdHd1[7\\NfHd1Y7]NgHc1Z7\\NfHd1Z7\\NfHc1Z7^NfHc1Y7]NgHb1Z7]NgHc1Y7]NgHd1X7\\NhHc1Y7]NgHd1X7\\NhHd1X7[NiHe1W7\\NhHd1W7]NiHc1W7\\NjHd1V7\\NjHd1V7]NiHc1W7\\NjHd1V7]NiHc1W7]NZEoM\\3d3Y7aNgH`1X7_NiHa1X7^NhHb1X7_NgHa1X7_NiHa1W7_NiHa1W7_NiHa1W7_NiHa1W7_NiHa1W7_NiHa1W7^NjHb1V7^NjHb1V7^NjHb1V7^NjHb1V7^NjHc1T7^NlHb1T7^NlHb1T7]NmHc1S7]NmHc1S7]NmHc1T7\\NlHd1T7[NmHe1S7[NmHf1S7YNmHf1U7XNlHh1U7WNkHi1U7WNkHj1U7UNkHk1U7UNkHk1U7UNkHk1T7VNlHj1S7VNnHh1T7XNlHi1S7WNmHj1R7VNnHj1R7VNnHj1R7VNnHj1R7VNnHk1Q7TNPIl1o6UNQIk1o6UNQIk1o6TNRIm1m6SNSIl1o6SNQIn1n6RNRIn1m6SNSIm1m6SNSIm1m6SNSIm1m6SNSIm1m6SNSIm1l6UNSIk1m6SN_ETNd3j3l6RNVIn1j6QNWIo1i6QNWIo1i6QNWIo1i6QNWIo1h6RNXIn1h6QNYIo1f6RNZIn1f6RNZIo1e6PN\\IP2d6lM`IT2`6kMaIU2_6jMbIV2]6kMcIU2]6kMcIU2]6kMcIU2]6jMdIV2[6kMeIU2[6kMeIU2[6kMeIU2[6kMeIV2Z6jMfIV2Z6jMfIV2Z6jMfIV2Z6jMfIV2X6lMhIT2T6PNlIP2T6PNlIP2S6RNlIn1S6QNoIP2P6PNPJP2P6QNoIo1Q6oMQJQ2o5oMQJQ2o5PNPJP2P6PNPJQ2o5PNPJP2o5RNPJn1P6RNPJn1P6RNPJn1P6RNPJn1P6QNQJo1o5RNPJo1n5RNRJn1n5RNRJn1n5QNSJo1m5QNSJo1m5QNSJo1m5QNSJo1m5PNTJP2l5PNTJQ2k5oMUJQ2k5oMUJQ2k5oMUJQ2j5oMWJQ2i5oMWJQ2i5oMWJR2h5nMXJR2h5mMYJS2g5mMYJS2g5mMYJS2g5mMYJS2g5mMYJS2g5mMYJS2f5nMZJR2f5mM[JS2e5mM[JT2d5lM\\JT2d5lM\\JT2c5mM]JS2c5mM]JS2c5lM^JT2b5lM^JT2b5lM^JT2a5lM`JT2`5lM`JU2_5kMaJU2_5kMaJU2_5kMaJU2_5kMaJU2_5kMaJU2_5kMaJV2^5iMcJV2^5jMbJV2^5jMbJW2]5iMcJW2]5iMcJW2]5hMdJX2[5jMdJV2\\5jMdJV2]5hMdJX2\\5gMeJY2[5gMeJY2\\5eMeJ[2[5eMeJ[2]5cMcJ^2\\5bMdJ^2\\5aMeJ_2[5`MfJ`2Z5_MgJa2Y5`MfJ`2Z5_MgJa2X5`MhJ`2X5`MhJ`2X5`MhJa2W5\\MlJd2T5\\MlJd2T5\\MlJd2T5\\MlJd2T5]MkJc2U5]MkJc2T5\\MnJd2R5\\MnJd2Q5]MoJc2Q5]MoJc2P5^MPKb2n4aMQK`2n4`MRK`2n4^MTKb2l4_MSKa2m4^MTKb2l4_MSKa2n4^MRKb2n4\\MaF]Na4W4n4\\MTKd2l4\\MTKd2l4[MUKe2k4]MSKc2n4[MSKe2n4\\MPKd2P5\\MPKd2P5\\MPKd2o4]MQKc2o4]MQKc2n4^MRKc2m4]MSKc2l4^MTKb2l4]MUKc2k4]MUKc2j4^MVKb2i4^MXKb2h4_MWK`2j4_MWKc2d4gKiFf1d4c2b4`M^K`2_4bMbK^2^4cMaK]2_4cMaK]2_4bMbK^2^4bMbK^2^4cMaK]2`4bM`K^2_4cMaK^2^4`MdK`2\\4`MdK`2\\4`MdK`2\\4^MnFXNf4Y4]4_MeKa2[4`MdKa2[4_MeKa2[4_MeK`2]4^MdKc2[4^MdKb2\\4^MdKb2\\4^MdKb2[4_MeKa2[4_MeK`2\\4`MdKa2[4_MeKa2[4_MeKa2[4_MeKa2[4^MfKb2Z4^MfKb2Z4\\MhKd2W4^MhKb2X4^MhKc2U4^MlKc2R4]MoKa2S4`MlKa2R4^MPLb2P4]MQLc2o3]MQLc2o3]MQLc2o3]MQLc2n3]MSLb2n3]MSLd2l3\\MTLd2k3\\MVLf2h3ZMXLd2j3\\MVLd2i3]MWLc2j3\\MVLc2j3^MVLb2k3\\MVLe2i3[MWLf2i3YMWLh2h3XMXLh2h3XMXLg2i3XMXLh2g3YMYLh2f3XMZLh2e3YM[Le2h3YMYLg2g3YMYLi2d3XM\\Lg2e3YM[Lh2d3XM\\Lh2d3XM\\Li2c3XM\\Lg2e3WM]Li2d3VM\\Li2g3UMYLj2i3UMWLi2l3VMTLi2m3XMRLh2o3WMQLi2P4VMPLi2P4WMQLj2n3VMRLj2m3VMTLi2m3WMSLi2m3WMSLh2m3YMSLh2l3WMULh2l3XMTLf2m3ZMTLg2k3YMULh2i3YMWLh2g3XMZLh2f3XMZLi2d3XM\\Li2c3VM^Lk2a3VM^Lk2`3VM`Lj2_3WMaLg2a3XM`Lg2a3YM_Lg2a3YM_Lg2a3YM_Lh2`3XM`Li2_3WMaLh2_3YMaLf2`3ZM`Lg2_3YMaLd2b3[M_Le2`3\\M`Lf2fJhKf8b1dLi2fJeKc8b1gLk2Y3UMgLo2U3PMlLR3o2QMQMo2o2QMQMo2o2QMQMn2Q3RMnLm2S3RMnLP3Q3oLoLo2R3QMoLR3n2oLQMR3n2mLSMR3n2nLRMS3l2mLUMQ3m2oLSMR3l2nLTMP3n2PMRMo2o2QMQMl2R3SMoLj2T3VMlLh2V3XMjLh2V3XMjLh2V3XMjLh2V3WMkLh2U3YMkLg2U3YMkLg2U3XMlLf2U3[MkLd2V3\\MjLd2V3[MkLf2f0kKjI2T2]1\\3g2c0RLQLW1\\3g2`0VLdJCh0`1T4e2=]L\\JFGI02`0MB09b1U5d28aLZJHMH:LFO10O70Z1a5g2_JfKe40ULf1VOgN051P1`5P3_J^K]40]L]4h48bN\\KlK11Oh0\\4i49`N[KoK10Oc0O\\O`4b55bNRLQLl3]53fNf0Z1ZOdNg0]1XOaNk0_1VO^Nl0b1TO`Nj0`1VOaNh0`1XO_Ni0a1VO`Nj0`1WOdNc0]1]ObNe0]1[ObNe0_1ZOaNg0_1YOaNg0_1", "size": [640, 478]}, "area": 91270.0, "iscrowd": 0, "image_id": 17379, "bbox": [0.0, 0.0, 478.0, 546.0], "category_id": 176, "id": 20001101}, {"segmentation": {"counts": "oS31mc02O1O1N2O1O1O1O1N2O1O1O1O1O1N2O10000O10000O100O10000O100O10000O10000O100O10000O10000O100O10000O100O10000O10000O100O10000O10000O1000000O10000O1000000O1000000O10000O1000000O10000O10000000000O1000000000000000000O1000000000000000000O10000000000000000000000000000001O00000000001O000000[FgNc0Y1SOXOf0h0POJh06nN;l0DjNn0n0ROgNZ1X1fN^Nd1b1\\NUNm1k1SNUNm1k1SNUNm1k1SNUNm1l1RNTNn1l1RNTNn1l1RNTNn1l1RNTNn1m1QNSNo1m1QNTNn1m1QNSNo1m1QNSNo1n1PNRNP2n1PNRNP2o1oMQNQ2o1oMQNQ2P2nMPNR2P2nMPNR2Q2mMoMS2Q2mMoMS2Q2mMoMS2R2lMnMT2R2lMnMT2S2kMmMU2S2kMmMU2T2jMlMV2T2jMlMV2U2iMkMW2U2iMkMW2V2hMjMY2U2gMkMY2V2fMjMZ2V2fMjMZ2Y2bMhM^2[2]MgMc2\\2XMfMh2\\2TMgMk2\\2PMfMP3]2kLeMU3c9000000000000000000000000000000000000000000000001O0000000000000000000000000000000000000000000000000000001O00000000000000000000000000000000000000000000000000001O00000000000O100000000000000001OO1O100O1O1O100O1O100O2N1O100O1O100O1O100O1O1aNc_OZO^`03Q2^O[^[1?gadN2O1N2O1N2O1N101N2O1N2N2O1N2O0O2O001O001O001O001O001O00O^]OeNab0\\110^]OdN`b0\\121O001O00001O00001O00001O0000001O000000000000000000O100000O100000O100000000000O10O1000000000001O00000O10001O0000001N10001O00002N2N2Bh_O", "size": [640, 478]}, "area": 27779.0, "iscrowd": 0, "image_id": 17379, "bbox": [4.0, 214.0, 474.0, 426.0], "category_id": 183, "id": 20001102}, {"segmentation": {"counts": "ha^1116fc03O0O2N1000001N2O001O0O1000002NN2O1O1000O10001O00000N_Oe\\Oa0[c0201O00N201O001a0_OO00100OO0020OO100O100O10000O1000000O01O1O1O2N010N22NN110NR]OROnb0n0R]OROnb0n021ONQ]OUOlb0l0U]OROlb0n0T]OROlb0d0S]OBRc0>n\\O\\O04Rc0`0n\\O\\O10Tc0d0k\\O\\O10Tc0d0k\\O\\OWc0e0201O00O20O03M1ONk\\OYOUc0g021_^OYOg?f0Y@[Oh?e0T@Ed?<Z@Fg?:X@?aNjNZ`0h0UAb0k>jNg_O9OB_1U1Y?YOi@k0S?UOm@g0W?XOj@j0U?iNY_O:b1m0U?iNY_O008b1S1Q?eN]_O007c1S1S?fNZ_O7b1Q1Y?oNh@S1U?lNl@T1T?lNk@U1V?gNW_OOc1U1NkNi=1f@O`1Y1NfNcN1m`0Y1_@oN]?W1]@lNa?S1a@nN\\?S1e@lN\\?X2`_OhM_`0Z2`_OfM_`0m220OO2ON30N11N02lLZ_Ol2h`0oLX_O10Q3n`0PMR_Oj2Ta0N2OO?BO00000LY_OoLf`0R3[_OmLg`0R3Y_OmLg`0T322N0O1000aMY_Oe1g`0TNZ_O_O4\\2c`0hMZ_O51W2f`0aM]_O5NJO0NX2Qa0lM[_OU2e`0jMa_OQ2a`0lM__OU2Wa0N2N101OM30020L021O00002NN2N20O001N20000b^OlMn`0V2S_OjMo`0R2S_OmMm`0S2?10000O400IVN^^Oa1_a0RNa^O0062h1ca0XN]^Og1ba0YN^^Oh1^a0ZN`^OG0R2ba0TN^^OJ0R2ba0TNb^Oj1`a0VN_^Oi1aa0RN]^OO5o1la0M1O3NO0n]OXNna0f1P^O\\Nna0l1O1O12N1O1O0010M31ON23SNo]Od1Qb0[NP^Od1Sb0YNP^Od1Wb0OXF_NT1N]FZ1^8iN\\1V1bNmN_1Q1^NROe1k0\\NUOd1i0[NZOd1f0RNnNQHg0b9?YNoNTHX1MjNj1IR5U1W1mNnGT2OjMX12f5P1h0ROaHm2<SMGLY6P1Q1[O^Hg3NRLa6<_INe7Z4oNXK]I47LH014g7d4nNYKZI0`0OZ7g4oNYKc1h4k6Oi@YKV?i400GWKWAj4g>YKWAh4l>412NO10\\OTKTBl4g=[KXBd4b=eKZB\\4a=lK\\BT4^=UL_Bk3\\=]LaBc3Y=g1YI\\IJg61`IhIOa5f6c0PJVOW6g0kImNb6Q1_IoN_63aJJb55_JId56\\JIe59YJGf5;YJEh5:XJFh5;WJEj5:VJAo5>RJCg5XK\\HU5m1Cg5[KYHP5R2Fc5`0^J@c5?]JBd5?XIUKA\\4V7?XIXKXO@Oj4_7=[I[KRO\\O1U5b73^Ig0`6WOdIh0\\6XOdIj0[OTJX6Q5]JQ1TOnIHLf6U5^JT1ROmI`6o4]Jc1kNYIh6S5\\Jc1j5\\NVJh1f5YNZJi1a5XN`Jk1]5SN^ImJV12jNl6h0UIh4R5hKlJiNQ7`0UIP5k4hKnJhNW77WI4Ce4P5]LoJdN1NQ72_I2Gm4i4]LoJdN1O0OP8R5oLeLPKYNQ8R5oLeLPKZNQ8o4RMZKnJF0X1P8h3UMTKTKP1h7l3UMRKWK0Di0Q8U4SMPKgKd0V7Z4mGRKU6d0n1U4XNlKi1R4YNmKg1S4YNkKi1V4VNiKk1X4TNXKeI=W8\\4WNbKk1V4gGaK`69i11gGI0a30YL21N0_6<k1GnG1H`32\\Le6<b1FPH2Gb3j6eLa1EPH3Ec3m6[LVI1f80XG30W3l6cLZI0d82VG05Z3n?eLm_OO7X3T`0hLm_OX3^`02NO0O22M000;E;RMg^OO0\\2Rb0_O20ON1lNk]O<Wb0Bl]O;Ub0El]O:Ub0Dm]O;Tb0Cn]O9Pc0N2Ef\\OJ]c0Ld\\O5dc0K[\\O022bc0Mb\\O0J0bb40d]K3\\c0;N1N2O0O2O1N1lN\\Ol^Oc0Va0^Oj]O3OK0>56Tb0Dg]O<00[b0Q1ONg]O]NXb0c1g]O]N[b0c11Ie]OgNZb0Z17O03M3ON00O11OO1d]O`NWb0a1i]O_N1ORb0f1m]O[NTb05o]OY1na0dNY^OY1ia0dNZ^OZ1Xb0N0OL61N^OeNP^O03e1ka0`NS^Ob1ka077M0O1YOl]OASb0=P^OCna0=T^O]OLBQb0o0W^O_Oia0a0Z^OTOEMYb0k0U^OUOoa0l0b01O12ON02ON021N1P]OSOmb0o010011NOROR]Oi0Rc0VOo\\Oi0Qc0WOo\\O6OK1;Rc0Eo\\OOkc01N1112Mfj?", "size": [640, 481]}, "area": 32087.0, "iscrowd": 0, "image_id": 17436, "bbox": [74.0, 17.0, 381.0, 488.0], "category_id": 96, "id": 20001103}, {"segmentation": {"counts": "e>`4`?00000000001O000O100001O000000001O1L5N1L4O1VOU@[Ml?]2`@_Ma?^1S@jNf0FW?`1dAaNZ>`1fA`NY>a1gA_NW>b1jA_NV>`1jA`NW>_1jAaNW>]1iAcNW>]1iAdNW>[1iAeNY>Y1gAhNX>X1hAfN]N_Ol?j1gAgN]N_Om?i1gAgN\\NAl?h1hAgN\\N@m?i1gAfN]NAl?i1gAfN]>Y1cAgN^>X1bAiN\\N@P`0f1dAjN\\N@o?g1fAhN[NAo?g1fAhN[NAo?g1nA_NTNJn?g1VBaNd=]1eB_NV=b1d201O^O\\Nh^Od1o`0dNR_O\\1l`0gNi^ODNj1Ua0gNk^O_1Ua0f0000000O11OZ@oM\\=R2dBnM[=S2eBmM[=S2eBmM\\=R2dBnM\\=R2dBnM[=S2eBmM[=R2fBnM[=Q2eBoM[=Q2eBoM\\=P2dBPN\\=P2dBPN^=n1bBRN];_OkD_2HRN[;CkD[2JRNY;GkDW2LRNX;IkDU2LTNW;IlDS2MTNV;KlDQ2NSNW;LkDQ2NSNV;LmDQ2MTNU;KnDQ2MTNU;KnDQ2MTNU;KnDQ2MSNU;MnDP2MSNU;MnDP2MSNU;MnDo1NTNc9ZOnD3MMS1c0`0o1OTNb9HnE4b0P2NTN`9JPF2ODXO]2Y1SN_9LnD^O0310e0?;FoNc2c1kM^9;eE_O;f2b0`M^9;fE^O;f2a0aM^9<fE[O<h2`0aM^9<gEYO<j2?aM^9=hESO>o2<aM_9<UGT2\\O`M_9<UGT2\\O`M_9>TGQ2]O`M`9>SGS2]O`M_9>SGR2^O`M_9?RGQ2_O`M`9>QGR2_O`M`9=oDoNm1T3D_M_9f0oDfN0Og1V3K_M_9n0eFc1L`M^9n0cE`N8Q3h0aM]9n0cEjNOf2Q1bMo9=oD\\O1e2Q1bMo9=nD]O2d2Q1bMo9>mD]O2S2FbM\\1`0P:=kD^O4m1g1XNZ9?\\DPO2KOc0a0i1j1ZNW9a1oD5j1ZNX9`1mD7k1YNX9`1nD5k1[NW9`1mD6l1ZNW9`1lD7m1YNW9`1lD6n1ZNV9`1lD5o1[NU9`1lD5n1\\NV9_1lD4o1]NU9_1mD2o1_NT9_1lD3e1gMTNh0Z;^1mD2e1kMRNe0\\;P4jF]KhMd0];m3mFaKdMb0^;m3oFcKaMa0^;k3SGeK^M?_;m3SG_Ll8a3YG[Lf8f3`GTL`8l3eGnKZ8T4lGfKV8X4PHbKn7a4VHZKj7f4f3VKn@j4R?VKn@j4R?210MTKRAk4m>VKQAl4n>TKRAm4c>RKcAe5k=[JoA04e5m=[JoA10g5Q>XJoA10g5Q>XJoAl5P>20001O101N1UCRJe;l5[DTJe;l5U1PJTBR6j=30aKWBf2g=ZMZBm2`=oLjBl2T=RMnBP3Q=oLhCaNjNe3c1[LT:_1cEU29_LW9MZE19OB_1U1[2?\\LT92fEY1k0U2;lLh8FRFY1g0Y2?iLh8DQF[1j0V2=kLg8FeEG:b1m0U2>mLe8EfEG008b1S1R29QMf8CfEH007m0IGZ1f3E\\LZ9DgEF7m0KDV1Z4YOkKg9GoE>MGV1X4UOmKh9FoE`0MEW1Y4TOmKh9EPF=0GU1\\4QOiKl9HjEBOf0:Jk0Q1kNo16WMS:GhEBOd0>Gk0S1fNcN1]4[:jLTFPOj0U1oNP3R:jLWFnNn0R1lNU3o9kLXFlNl0V1nNR3m9lL[FiNk0Z1lNQ3P:XLdE]O1=[3IPMW4n9VLPF]OG3\\32oLV4Q:VLUJk3k5TLWJk3j5TLUJm3k5QLWJo3i5QLXJn3i5QLVJQ4i5oKXJP4i5PLUJP4l5QLSJo3l5SLTJl3l5VLRJi3o5VLRJj3o5ULRJj3o5UL`J^3^5cLaJ^3]5cLbEBm4k3b5bLaEBn4l3a5eL[J^OXKm3]:dL\\J@VKl3^:dL]J@TKl3_:dL^J]3b5cLaJ[3`5dL`J\\3`5cLaJ]3_5cLaJ]3_5bLbJ^3^5bLbJ^3^5aLcJ^3^5bLbJ^3^5bLbJ^3^5cLaJ]3^5dLbJ\\3^5eL`J\\3^5gL`JZ3b5dL^J\\3\\5ULjE=j4^3^5SLeE00<P5b3Z5SLeE>Q5_3Z5VLeE7T5a3W5VLgE:o4b3Z5RLhE=o4`3Y5TLhE9Q5d3V5RLjE;o4c3W5RLjE:R5b3Z5^LdJd3\\5]LjJ\\3W5cLiJ]3X5bLiJ]3Z5aLeJ^3]5`LeJ_3_5^L`Jb3_5_LcJ_3P;O00001O2N2N2N2N1O000000O10000Z_OhLd`0Z3O2N2M3M11000000001OO11O0001O1O1O1O1N100O1000000000O1000000000000O1015aL]_O1NQ3Qa0L0O00O1001O00000000000000N21O1N1000^Ob000000000000O11OO1001O000000O11nNQMQAo2m>TMi@@O\\3W?VMe@\\O1f3]?g0a@cK`?\\4`@dK`?\\4111OO2N0101O001eK^@W4a?hK`@W4a?hK_@X4g?hKZ@1Nn3h?QLZ@1O0Ok3P`0TLP@m3o?SLR@l3Q`00O2OO10000000N3N011N0110O1N020N020O1XLY@T3Z`0001OO11OO1O11OO1O1000010O0000O0:G8JO0OO100O11O00O10O10O1001O1O001dNXM^Ah2^>]MaAb2]>bMd@\\3[?e000O12NOdK`@Y4b?fK^@Z4d?0JgKe@Y4[?6003M3M000000000gK]@V4b?jK^@1OP4h?oKY@Q4m?N000N21N20N110O1OM40O01YOoK^AP4`>SL`Al3_>VL[ALBo3Q?YL]Ai3a>ZLTAEMV4n>XLRAk30fKm>m431O10OO010N2SAoJl>R50nJUAn4n>10011N0M120]OoJgA1;R5l=a02O11O1O00O02O1OO13M0OO210N2O000O1[JgAb5]>O000O11O0001ON20001O00001_B", "size": [640, 481]}, "area": 65716.0, "iscrowd": 0, "image_id": 17436, "bbox": [0.0, 301.0, 481.0, 320.0], "category_id": 97, "id": 20001104}, {"segmentation": {"counts": "k0a7_<00000]Og0I30L400O10000N2N2M3O1O1001OO100N2O1001O1O00001J6O1O1O1VDkGg;X8OO1001O1O2N001O1O1O1O1O1O002N1O001O2N1OOlDYGQ;m8N5K1O0VEmFg:V90001O1O1O001O1O1O1O4L0000001O3M2N000000O1O1000000O1000000O10000000000000000O10000000000NdE`F\\:`92000000001O001O1O001O1OO1O11OO100O100O10000O1000000O10000001OO1000000O10000000000O1000000000000O1001O00O11O0000000000001OO11OO10000001OO1001O00O1000000001O0000O11O001O001O00001O001O00O100001O1O00O11O00000000O11O0000000000000000O100N22NJfEhFX:Z9fEcF00Z:]9gEbF\\:^9gEbFV:^9hEdFX:\\9fEeF[:[9fEeFY:[9fEfFZ:Z9fEfFZ:Z8gEeHY:Z7hEfHX:X7jEfGL;16X:h7mEfGK:43V:l7oEoGO2S:n7RFmGN1Q:P8UFkGN2n9i7bETH41U1OU9l7d12N00N200O1O10000MlCYHU<d7kC]H0OU<d7nC\\HR<g72N21O2N3M000000O10000O10000O10000001O1O1O1O1O1O1O]LSDTOl;k0UDVOj;h0YDXOf;e0]D\\Ob;e0^D[Oa;c0bDWOEgLh;h3WDYLg0Q3]OmLc;e3^D\\LX1MjNj1I`N5Ha;i3\\DVLT2OjMX12QO3Hb;]3]DiLm2<SMGi;R3^DfLg3Nm7]3b42N2N001O1O1OHf_OhL\\`0U3g_OiL[`0Y35000000\\Oa_OgM_`0T2h_OkMW`0o1R@mMo?n1Y@oMg?k1b@RN^?i1j@TNV?f1e1L4K5J6D<00M3N2O100000000K51O001O001O001O001O002X]O]Ooa0h0j]OXOHL\\b0P1j]OVOVb0X1b]OcN]b0[154L3M3M3`^OZN``0j1Y_OZNf`0j1Q_O]N4CZ`0T2\\_O\\N2Gb`0P30001O00001O00001O000000001O0k_OdLI0e?^3Y@jL1Hf?^3X@kL2Gf?_3V@lL3Eg?n3X@SL30[?n3a@RL05^?j3a@QLO7`?l3`@UL_?W4O1O001O0000;E;PAiJO0_>o5_O2N1O001O001O1O`0@000000001O000000000000000000000000HkBeIU=Y6:O1O10000000000O1002dBeIo<]6kBcIN4T=e6lBVIR=l6000001RCSIg<j6XC\\If<b6YCcIe<m6O00001OO1O12NO1001O001O00001OA]C`Id<`6_C\\Ib<c6_C^I`<b6`C^I`<a6bCVIK2f<i6\\CXIh<h6WC[Ig<e6_CUIa<j6;2N00000000O1O1O1O100O1O1001O000005K0001O0000O1O100O100O11OO1O1N200O100O=HPBnI00o=V6H10000YO", "size": [640, 481]}, "area": 112365.0, "iscrowd": 0, "image_id": 17436, "bbox": [0.0, 0.0, 481.0, 312.0], "category_id": 106, "id": 20001105}, {"segmentation": {"counts": "Uc0k0Uc00000000001O0000000000000000000000M300M300N2O1001O00001O001O001O001ONSOQ]Om0ob0SOQ]Om0ob0SOR]Ol0nb0TOQ]Om0nb0TOR]Ol0nb03001POQ]Om0ob0SOQ]Om0ob0SOQ]Om0ob0SOQ]Om0nb0TOR]Ol0Xc0I4L3M4L1O00001O0000001O0000[Oe00000000000000000000000000000001O00O1001O0000O10000000000000000000000000000O11O000000O1001O00000000000000000000000000000000001O001O001OO1000000O10000001O6J5K2N1O1O1O1O1O3M2N1O0000000000@NR]O2nb0OQ]O1ob00o\\O1Qc00k\\O3Uc0Og\\O3Yc0:000000000000000000O10000O10000O1L[Om\\Oe0Qc0^On\\Ob0Qc071O0000O12NN200000000001O00O10000O100002N1O0000000000O1000000000000O100000000000000001O000000001O000000000000O100000000000000000000000000000000000TOn\\Oj0Tc001O1O1OO1000000001OO11O000000000000O100O1000000000000000000000000001O001O0000000000000000000000000000O10000000000000000O11OO1001O000000O11O0000O1NWOm\\Oi0Sc0WOm\\Oi0Sc0200001OO10000001OO1O1001O1O00001OO1000000001OO11OO11O00O11OO11O00O1001OO11OO1O11OO1O100001O000000O1002N1OO100O11O00O10000O1001O1O001O00O10000000000O100000000000000000000001OO1000000N21O1OO11O00001O001O00000000O1MVOP]Oj0Pc031O1O00O1000TOn\\Oj0Tc00O100002N00N2001ON2O11O1O00O11O1OO13M00N21OO10000O1O10000O11O00000000001O00001O", "size": [640, 481]}, "area": 12751.0, "iscrowd": 0, "image_id": 17436, "bbox": [0.0, 605.0, 481.0, 35.0], "category_id": 140, "id": 20001106}, {"segmentation": {"counts": "\\8Y6g=000004L00L400O10O10N2N3L3O1O10010N2O2L3N2O10N2O000O2N2M30000]IdBb6]=OO2O2OO101OO01O1O1000000N102N1O010N3N10NiI\\Bb51kJi=j4_BWKf=b4]BSKDNU>j4\\BVKj=NeAi4T?0001O1N20OO2N2O100O4K1001O0102M20OeKb@Q2NJi?3[@Ke?3_@Kb?3`@L`?3b@L^?3d@L\\?4d@L]?2c@O^?0b@0^?0b@0_?Oa@1_?Nb@2^?Nb@2^?Nb@2^?]No_O23MMS1c0`0o?_OP@b0P`0^OP@ODXO^`0F\\_O0310e0?;FoNRa0f0V_O;Ua0Fj^O;Va0Eh^O<Xa0Ef^O<[a0E`^O>`a0P1102ON02O1O00OnMY^Om1ma0RNR^O0Og1Wb0O1mNl]O8Tb0HV^OOja0OW^O1ia0NX^O2ia0MX^O2ha0MX^O4ja0]Oj]O2KOc0a0ib000O11OO1O1000000001OO10Tfl0@\\ZSO2L21OM3L5M4N1N2O1N2\\O[OZ]O1=f0Xb0C[]OG3e0bb0b002O0N3ON21O01N20`]OfNYb0[1b]OeN10_b0`1b]O`NWb0g1N2N010OWNm]Oe1Sb0[Nl]Of1Wb00O012N0O100O010O1d^OVNb`0j1W_OWN_O4Xa0g1l^OVN51Sa0j1e^OYN5NJO0NTa0U2P_OWNRa0h1m^O^Nn`0d1o^O\\NSa0Y200O001L030O3OM20N21OO1_NRMPBn2o=SMRBl2l=VMf@L;1a0m2]>XMf@K:43MLk2Q?]Mn@O2MMe2S?cMk@N1LOb2U?hMg@N2MO\\2n>WMPA41U1OSO3]2m>S241M0N110O1O013MLbARKo=0cAo41QK0062R>NhA0OV57lJQ>1hAR57mJU>Q5mAmJG0\\>X5jAhJJ0]>W5iAmJW>S5iAlJY>Q5bAmJO5^>]5M1O3L102NN101O12M2O1O0010M31ON23nATJi=l5VBUJj=m5SBVJk=P602UBnIc=X601O2N0JbIiB]6W=fIhBT6\\=mIdBS6[=mIdBW6\\=70NSC_I]<d6SC]I47LH01f<e6[CZI0`0b<Y6]CnI0[O1NU<m7KSHoCl7S<O1N01O2N12NO1000000000N21O11fNoCaJP<Z5WDhIO=j;g5c1M3NOROP1O2O02M20O010O0OKkJaAU5_>nJ^AP5c>62O21L00O2O0N300O20O01O1O01M11NWAmJg>U54M2O001O00O3]AnJF0[>U5iATKT>m4jAWK0Dn=T5PBgKg=SOYBU6b=602O01O1O^BeI`=^6HbIjB`6R=`IkB21N0_6R=cInBe6S=303nBVIh<j6VCZIh<Q7J5fCkHh;f73M00O13M000000O1002NO1lNWHPFk7n9XHoEi7Q:XHnEh7Q:ZHnEe7R:]HjEo7m9SHQFU8g9mGnEDJb8W:kGeED5h8V:eGeEB022e8Y:hGdEG0J0g8]:j00000000O100lMbEjH3a1[:d5PFZJP:f5QFYJo9f5SFYJm9g5SFYJm9f5UFnHAn0Z:T6VFiHER1m9nNnEV7o0mHo9Y6oDcJZ<_5gCaI3OK0>5l;^6nC]I<0g;d6o01O1O2N1N0111^IkBU6W=eIiBN4Z6_=fI\\BZ6d=00O110jI[Bn5c=QJcBl5\\=SJiBk5g=0TBWJ_=h5^B_J_=c5^B`Ja=Q6M1OL70M_O_BVJ03j=f5[BXJg=f5:4M0O1O01NBUJhBl5X=WJdBh5]=YJdBh5Z=XJfBf5[=\\J\\BK2k5d=WJ]Bl5d=SJ_Bm5_=YJ[Bf5e=<1L200O1000O1O3M0]BgI`=[6_BfI`=Z6`BfI_=[6bBfIO0X=_6iBaIW=]6jBdIW=[6jBdIV=_6gBaIY=c60O10001O000O01O2O1N100O1jBXIU=j6N1O1N200O101NO6QImB00g6^=I000000QI", "size": [640, 481]}, "area": 73082.0, "iscrowd": 0, "image_id": 17436, "bbox": [0.0, 203.0, 481.0, 319.0], "category_id": 169, "id": 20001107}, {"segmentation": {"counts": "WZ>1fc0<E9TOk001O0O10000000001O0000000000001O000000001O8H8G9H8H4L1O000000001O000000VOj0_Oa000000000000O1000000000000000000000000000000000000O100000000000000000000000O10000000000000O1000000@eN[^O[1_a03\\^OjNba0W1`^OhN`a0l100O10000O1O100OEa^OWN^a0j1b^OVN^a0j1d^OTN[a0m1f^ORNRa0U2Q_OiMm`0Y2U_OeMj`0\\2X_ObMf`0`2=0O6K6J5K6J6K4K6J6J5K2O000003M>BO3M2N01O000\\Mk_O]1V`0bNm_O[1T`0dNo_Oi0IcNZ`0b0Q@g0GeNY`0d0o_Og0IdNY`0g0h_Oh00^N[`0i2f_OSM_`0k2c_OQM``0n2;N6J2O2M0ROe^OWO[a0i0g^OUOYa0k0S_OiNm`0W1V_OeNk`0[1V_OdNj`0\\1V_OdNi`0]1X_OaNTa0T1m^OkNha0`0Y^O_Oka0=U^OBna0<S^OCoa0;R^ODPb09Q^OFQb04P^P14lboN0O101O00001O1N2O1O1O2O11O1O0O0N10001O0O10001N1N2O2M2N2NdXZ4", "size": [640, 481]}, "area": 11184.0, "iscrowd": 0, "image_id": 17436, "bbox": [22.0, 481.0, 237.0, 154.0], "category_id": 183, "id": 20001108}, {"segmentation": {"counts": "^i<>`>4NO2OaAA\\>b01OOMeAB^><6M0M4N2MY]`0Oab_O5[A1b>331O011N1O001O00001O0010O01O0000N2000_AC`><`ADS20X:<eCD00Q24V:8iCFR24R::mEJP:7PFKm95SFNj90YFNi93TFMm92UFMk93UFLl93UFLl92PDDU29k93WFLk93a200F^A6f>O3KUh38[iKGo=>gAB21m22X8`0hGBU8?kGBS8?bF[O\\N0W16V<OgC1X<2fCNh;KbC:f0Kc;i0]DWOc;S21O1O00001O2N1O2N1ON2M3eCoMd;T2\\DlMa;W2^DjM`;Z2^DgM^;]2`DdM];a2aD_M\\;i2_DWM_:JVFa3k9j0^EdK`:f2fEjN[:f0\\FROe9m0eFiN[9W1hFfNY9o0^E]N[1b0X9P1_E[N]1b0T9R1cEYN\\1b0Q9U1]GgNc8Z1_GdN]8_1dG`N`8D]Ef1S2fNe8X1ZGgNf8[1YGeNg8\\1XGeNg8\\1YGcNf8_1dEWNj01TO:^:^1aE^Nd0e0k9n0\\EgN?`0T:d1cEZMGU1f:f1ZEUM5T1a:d1fEZNZ:b1kE]NU:b1mE]NS:[1WFcNi9W1^FhNb9R1fFlNZ9S1hFmNW9S1jFlNV9T1lFjNT9V1mFiNS9V1hEUNf0e0a9V1eEYNl0?`9X1bEYNP1;a9k1`FlMh9S2[FfMj9Y2_100H^CQNc<m1`CRN`<m1bCQN_<m1cCSN\\<l1=N3N0OiDZNX9e1hF\\NX9c1gF`NX9`1fFcNY9\\1hFeNW9\\1hFeNW9[1iFeNW9\\1SE]N\\17`9g1_FYNa9g1_FYNa9g1_FYNa9f1`FZN`9e1bFZN]9g1cFYN]9g1cFYN_9e1aF[N_9e1aF[N]9g1cFYN]9g1dFXN\\9h1dFXN\\9=PEd0d1oN\\9;TEd0`1QO\\98PE@5W1_1QO]95QED2OLV1c1RO_94YEj0Y1QO]9LoD5<m0X1RO^9IPE89n0Y1QOc9KZES1S1ROX:n0hEROX:n0hEROX:n0iEROV:n0jEQOV:P1jEQOU:o0kEPOT:R1lEnNS:S1mEmNT:R1mEmNS:S1mEmNS:S1mEmNS:S1nElNR:T1nElNR:T1m111N002NO11O00O100000000001O1O3M3M2O3L100O01N10MmAZOR>d0QB[Oo=c0SB^Ol=a0VB^Oj=>[BBd=<^BD`=>aBB\\=`0`02kABMLc=f0]B@f=o0L3^BkNW=X1hBgNZ=Y1fBfNZ=[1cBeN_=\\13L4M3M2N3M2O2O1O1OL4O1O100N200O11mNTBP1n=0000000O1O11OQOPOSCOh0Q1T<TOlCl0T<SO^CM0Q1c<SO[CMHN6R1g<TOYC00l0c<YOXCN5j0c<F\\C:c<G]C9b<I[C9e<HXC:h<o01O00O10001O000000000000000O10000000000000000000000000000O11O00O10000000H\\CTNd<l1\\CRNf<n1[CQNe<o1[CQNd<P2\\CPNd<P2\\CPN0N`<R2:00000aCnMP<R2?0000000000001iCmM_;T2aDlM^;U2aDlM^;U2bDkM\\;X2cDhM\\;Y2cDhM\\;[2aDfM^;\\2`DfM^;\\2_DfM`;[2_DfM`;[2YDlMf;h2N3N5K0aDcL[;`3gD_LU;f3mDYLm:g3SEZLk:g3UE[Li:e3VE\\Lj:g3SEYLm:m3000000000000000000000000000000000000000000O1000010OO10OITE]Lk:[3ZE_LK5l:j2R1J6N2O1N2N2O2KcCcM^<]221001OO10000N2H800009G0000001O1O2NM3O1000000002NN2O1O1000001N10000O1N2O10000M21O1O00100000000000lCYMo;g2500000000000000000O100000000000O100000000000000001O0000000O100000000000000000001O0O1000000000000000000O1000000`GTM^L0\\8m2RK^Mh4b2XK_Mg4b2XK^Mh4c2VK^Mj4b2VK^Mj4a2XK_MdLCP8n2[KcMe4]2ZKcMg4]2XKcMi4]2VKdMj4\\2UKeMj4\\2VKeMi4[2WKeMi4[2WKeMi4[2WKeMi4[2jJRM^Mc0i7[2iJSMWMN2?S8`2cJXMXM1^8g2ZJ`Mj5a2TJaMk5_2UJ`Ml5a2RJ`Mn5a2PJ`MP6a2oI_MQ6b2nI^MR6c2lI^MT6c2jI^MU6c2kI]MU6c2kI]MU6c2kI]MU6c2kI]MU6c2lI\\MT6d2mI[MS6e2oIYMQ6g2PJkL]6U3g200000dDiLo:W3REiLl:X3UEhLj:X3WEhLh:X3YEhLf:Y3`01O1O1O1O2N1O1O0O100000O11fD_LS;h3O4L3M2N8H0\\EgK30M1`0NU9g4kFYKU9h4jFYKU9h4gFWKE3c9g4bF`K^9R5O00010OO3XN^FnMb9R2lF`MT9_2nF`MR9`2oF_MS9^2nFbMR9]2oFbLZOo0h9]2oFdL@h0a9b2_GYMc8d2aGYM`8d2dGYM\\8g2hGUMY8j2jGTMU8l2lGQMV8P3lGlLV8T3l1O1O001O00REoLm9Q3SFoLm9Q3RFoLP:P3PFQMSO2f:NZEk2m0UMSO3j:g2SF^Mn9`2RFbMm9]2SFdMm9[2SFfMn9X2RFiMn9V2QFkMQ:S2oEmMQ:S2oEmMR:R2nEnMS:Q2mEoMT:P2lEPNV:n1jERNV:n1jERNW:m1iESNW:m1hEUNZ:h1dEYN]:g1cEZN]:e1bE`NP5", "size": [480, 640]}, "area": 44576.0, "iscrowd": 0, "image_id": 17627, "bbox": [27.0, 136.0, 613.0, 226.0], "category_id": 96, "id": 20001109}, {"segmentation": {"counts": "ici5131f>1XA1f>5O1LG`A:`>3O11O00O100O1O100O100O1O1O1O1O1O100JYORBh0n=XOQBi0o=XOoAi0R>2001OO10000000000O100O1N2N2InN_BS1a=mN]B0NS1e=mN\\BV1d=3000000O100O100000000O1MdNbB\\1]=400N2O100O10000O100O1O100O100N2O1IUNXCl1h<TNWCm1i<TNTCO0m1m<40000O100O100O100O13MLnMZCR2d<nMZC02R2c<QN]Co1c<PN\\CR2c<oM\\CR2c<6MeM`C\\2^<400O100001OO1O10000000000001ON2L400M3M3000000N2N2O11O00O100^OoLTER3j:PMVEP3j:RMTEn2j:TMmDJN10Q3T;VMlDT3S;=00001O0000001XLjDd3Y;0O1O100001O000000O1K5G9L41OO100000000000000000000XOgKTF0b0Y4V9hKWF4b0T4T9TLjFl3T9WL^FV4a9e0000000000000000000O100001O00001OO10000000000N22N00N200001O1O001O00001OO1000000000000000000O1O100M3N2O10000000000001OO1O1O1N2N2N2M3O100000000000000001OO11O4L", "size": [480, 640]}, "area": 25902.0, "iscrowd": 0, "image_id": 17627, "bbox": [395.0, 289.0, 245.0, 191.0], "category_id": 140, "id": 20001110}, {"segmentation": {"counts": "nQc0:f>>B=B>C=C>B=C=C>B7I1O2N1O1O1O1N3N1PFkKS9V4hFnKX9S4gFmKY9_4[FaKe9m400000000000000O100000000000000001O3M2N1O1O1O2N00000000002N0000O1000000O100000000O100000000O10000O1M3N2M3O1O1O1O1O100O100O1000000000000O1000000000000O1000000JcJiF]5]90N2001O000000O10aJaF]5a90000000000000001O001OO1001O001O001O00000000000000001O0000001O00M3H8H^JmFg5S930000000000001O0000001O1O1O1O001O000000000000000000000000000000000000000000001OO11OO100000000000000L4001O001O001O000000O100O100O1O100O1O1O1O1N2O10000O1000000O1InIaGQ6k8L2N001O003M2N2N1O1O1O000000O1000000O1M3N2000000000000000000000000000000000000000000000000000000000000000000000000001O000000000000000000000000000000001O1O2N000000O1O1O1O1J600O1000000O10000O10000O10000005K1OO10000O100K5J6GiIdG\\6_8fI`GV6e8lIZGn5f8RJ[Gm5e8SJ]Gk5c8TJ^Gl5e8QJ[Go5k8000000000000000000000000IQGZJ1Nn8g5SGYJQ9f55O1LgF`JZ9`53O11O00O100O1O2OO010HcJnF[5S9eJlF[5X943M1O1O100J\\FoJd9R5\\FmJe9S5\\FkJe9V52001K301O10000000O10O01N2N4GWFVKm9g4SFWK0Nm9k4SFVKP:j43000000O10O0O2O1O1000O1MoEWKQ:h4400N2O100O101O7H100O1O2O0O100N2ONLZESLd:P4\\EoKc:S4^EjKO0c:W44000O0100O100O100O13NKXEPLj:l3VERL02k:j3XEVLj:h3UEWLo:f3REYLP;e36MgD^LZ;`3400O100001OO1O100000O1000001ON2L400M3M3000000N2N2O11O00O100^ORDQNo;m1SDSNm;m1UDQNk;m1WDjMJN10n;W2YDiMQ<V2=GYCRN05g<f1<0000O1001OO1O1O1O1O100000000CiBPOX=P1iBiNN2Y=T1nBjNT=V1;O010O1O10000O1O100000000MVBPOi=m0XBROl=k06N2O1000000000000000K50O100001J500011M4M001O0000OO22M1N02ROQB30M1`0\\>01O1L`AEb>5di2KjeM000n@7H6K3N1N3M2OO02N0O10000O11O00O20WOnAa0S>_OnA?[>N3L3M3M3LRj5", "size": [480, 640]}, "area": 83153.0, "iscrowd": 0, "image_id": 17627, "bbox": [40.0, 271.0, 587.0, 209.0], "category_id": 149, "id": 20001111}, {"segmentation": {"counts": "Pd52n>1O:F9G1O00001O000WC]OU;c0kDKg:5YE8Z:IfEc0m9]OSFP1`9PO`F^1R9bNnF]1S9dNkF[1W9eNiF[1W9eNiF\\1V9dNjF[1W9fNiFZ1V9fNjFY1W9gNiF[1U9eNkF^1R9bNnF^1R9cNnF]1Q9cNoF]1Q9cNoF]1Q9cNoF]1Q9dNnF\\1R9dNnF\\1R9dNoF\\1P9dNPG[1Q9fNnFZ1R9fNnFZ1R9fNnF[1Q9eNPGV1dNnM]:l0oFV1T9kNkFU1U9kNkFW1S9iNmFX1R9hNnFV1T9kNlFT1T9lNlFS1U9mNkFS1U9mNkFS1U9nNjFS1U9mNlFR1T9nNlFR1T9nNlFR1T9nNlFR1T9oNkFQ1U9oNkFQ1U9oNlFP1T9POlFQ1S9POlFP1T9POlFP1T9POlFP1T9POmFn0T9SOkFm0U9SOkFl0V9TOjFl0fNQN^:S1lFn0T9ROlFn0T9SOlFm0S9SOmFm0S9SOmFm0S9SOmFn0R9SOmFm0S9SOnFl0R9TOnFl0R9TOnFl0R9TOnFl0R9UOmFk0S9UOmFk0S9UOnFj0R9VOnFk0Q9VOnFj0R9VOnFj0R9VOnFj0R9VOoFi0Q9XOnFh0R9XOnFh0R9XOnFi0Q9WOoFi0Q9WOoFi0Q9XOoFg0Q9YOoFg0Q9YOoFg0Q9YOoFg0Q9ZOnFg0Q9YOPGf0P9ZOPGf0P9ZOPGf0P9[OoFe0Q9[OoFe0Q9[OoFf0P9ZOQGe0o8[OQGe0hNQNS:[1TGh0l8XOTGj0iNmMk9Y1\\Gn0f8RO[Gm0e8TOZGl0f8TOZGm0e8SO[Gm0e8SO[Gm0e8TOZGHlNIj9?[GFPOIc9a0]GFSOAe9i0XGEWO_Oa9l0XGD\\O^OZ9o0YGBA[OW9S1YG@EXOS9X1XG_Oo9a0QF_Oo9b0PF^OP:b0QF]Oo9c0QF]Oo9c0QF^On9c0RF\\On9d0RF\\On9d0RF\\On9d0SF\\Ol9d0TF\\Ol9e0TFZOl9f0TFZOl9f0TFZOl9f0UFZOj9g0UFYOk9g0UFXOl9l0QFfN\\:a1]ERNP;S2l08H7jE^M`7h2[HXMd7o2S26J7I6`DZMZ:g3O2N6J8H3M00M300GUFXK1NO0k9i4:O1N200001O00O1O100O100O10000O10fE_KU:b4jE^KV:f400000001O0000000000001OO1MkE[KU:f421O0000N2001O00000000N2000000001O00001OcNjEPNV:o1kETMO<W:_2lESMN?U:^2PFPMKb0U:\\2SFnLFM2i0U:^2ZFdMf9]2YFbMh9`2TFaMm9_2SF`Mn9`2RF_Mo9`2TF^Ml9a2UFiL_Oc0\\:b2^FTMh9R3PFRMn9Q3mEQMS:n2oEkLBJ_:[3oEjLCK^:[3mEhLIJ]:^3m01OLaDfL`;Y3aDgL_;U3fDjLZ;V38N21O00008H2NH8N22NFYDUM10g;k2YDUM0Nh;n2WDSMm;m23NPDTMP<l22O10000O1O1M34L0000002NO1001ON20000O1N21O1O000000O10`DYMg:f2[EYMe:f2[E[Me:f2VEZM^O2Z;d2WE`Mj:]2WEeMi:\\2UEgMFDj:j2YEeMLBj:n2TEbM0@m:o2QE_M5Bj:c3TE^Ll:d3QE]Lo:e3oD[L>NR:k3oEXLn9j3nEXLR:h3mEZLR:f3iERLK<[:e3gE\\LZ:T410000001O3M2gEZKT: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`KX:e4O2N1O1O2N1O2N001O1O1O2N001O003M001O", "size": [480, 640]}, "area": 82826.0, "iscrowd": 0, "image_id": 17627, "bbox": [12.0, 0.0, 628.0, 170.0], "category_id": 157, "id": 20001112}, {"segmentation": {"counts": "nX=;d>>CXOjAT1j==C6FZNmBg1S=300M3O1KQNWCQ2i<30000001OO100001OLmMZCT2f<3002N1ON200O100001O000000000000001O000000O100O10iMYCV2h<RNXCd1n<TNXCh1m<31O002N1O001O01O010O010O010O001M5M3MPNRCQ2k<oMUCS2i<mMWC00Q2i<oMYCR2j<O20O0N31M02O0O1NlMXCU2g<208HO10O11ON200001O0000002N5mMZCb1k<TNUC21j1Q=0000HnB\\N04R=`1SC]NU=Z1a0VOVBKT>Ie0O2O1O0ObT<2a\\CN_>OcA1NO09[>901N20O110O00O1O100O100O10000O10YOlAb0U>]OkAc0Y>00001O01O0000000000001N01M^OhAc0X>12O0000N2001O00000000N2000O1002OO0N201OcN^O\\Db0\\O\\O`;3WDO<c0K_O_;1XDN?a0G\\O@5R<3UDKb0b0D]OD2OLn;;UDFM2i0c0CEd;7kD3XO[O5<h;5jD6VO[O89k;2hD`0WODP<LhDV1X;jNgDW1X;lNfDT1Y;mNQD_Oc0d1Z;VO\\Do0k;iNYDU1j;fNXDX1i;jNPDBJc1W<kNnCCKc1V<hNmCIJb1Y<m01OL\\MkCe2T<\\MlCd2P<aMoC_2Q<:L211NO117H2MI8N22NFQM]D10o2c;QM]D0NP3f;oL[DV3d;4MfL^D]3_;5L3N4L1N2N1M25L0000002NO10N3M002N2N1N10300O0001O1N2OTMeDS2Z;oMeDR2Y;nMhDR2Y;iMgD^O2h2V;iMnDY2n:hMTEY2l:eMWEFDV2^;mMQELBU2c;iMmD0@W2e;gMiD5BT2Y<jMhCT2\\<iMeCW2]<gMcC>NY1d<hN_CV1L[Nd<;bC\\1^<cNdC\\1\\<_N\\CK<e1[<]NfCd1j<01O11OQOiBZOOh0Y=LlB4W=IhBIM0^=EbB>4KMHN6a=8eBH00\\=3iBHN5Z=3VCLl<NXC2j<KXC2m=MPoj4", "size": [480, 640]}, "area": 15949.0, "iscrowd": 0, "image_id": 17627, "bbox": [28.0, 85.0, 281.0, 158.0], "category_id": 169, "id": 20001113}, {"segmentation": {"counts": "Q?S6m8f8ZG000001O0O10000000005K9G7I0000001O0\\CROV;n0jD_Oi:a0WEM[:3fE9m9HRFe0a9[O_FR1T9nNlF_1g8aNYGm1Y8TNfGY2m7gMTHe2_7[MaHR3R7nLnH_3e6bLZI^3f6bLZI^3f6bL[I]3e6cL[I]3e6cL[I]3e6dLZI\\3f6dLZI\\3f6dLZI\\3j1eKb1o0eL[3i1SLU1c0QMZ3j1`Lh06^MZ3j1nL:HlMZ3j1[MM[OYNZ3j1hM@oNfNX3j1VNSObNSOX3j1cNfNUN@X3j1QOXNgMNX3j1^OkMZM;X3i1AjMXM<W3j1BiMWM>V3i1EgMUM`0V3i1FfMTMa0V3i1GeMTMa0U3j1HdMSMb0U3j1IbMSMe0S3i1L`MQMg0S3i1M_MQMg0R3j1N^MPMh0R3j11[MmLk0R3j1j1VNVNi1k1XNTNh1m1WNSNi1m1WNSNi1m1WNSNi1m1WNSNi1n1WNQNi1o1WNQNi1o1WNQNi1o1WNQNi1P2VNPNj1P2VNPNj1P2WNPNg1Q2YNoMg1Q2YNoMg1R2XNnMh1R2XNnMh1R2YNmMi1Q2WNoMl1o1SNQNo1m1QNSNP2l1PNTNQ2k1oMVNQ2i1PNVNQ2j1nMWNR2h1nMXNR2h1nMYNQ2g1oMYNQ2h1oMXNP2h1PNXNQ2g1oM[No1e1QN^Nl1c1SN`Nj1`1VNcNg1]1ZNfNa1[1_NhN^1X1bNkN[1V1dNmNY1S1gNPOU1Q1lNPOR1P1nNoNS1R1lNmNU1S1kNmNU1S1kNlNU1U1kNjNV1W1jNhNV1X1jNgNSOSKf1V6WOfNQOWKh1S6WOeNROYKf1R6XOeNROYKc1V6[O`NROcKX1o5F^NROdKT1Q6J[NROdKQ1T6MXNROeKP1T6MWNCUK?e6NVNm0k1TOTNCVK;g62SNj0o1UO]JlNY2j0bL5h65\\JQOV2f0gL1h68[JTOS2g0iLJi6<YJWOR2c0nLHg6>XJ\\Oo1>UMEe6`0VJAm1:XMEe6`0UJFj15\\MEe6`0TJe0m0VOZNDf6a0SJm0e0nNbNDg6a0QJU1>fNjNDg6a0QJ\\17dML?l5b0PJ\\1a1RN_4b0QJ]1^1QNb4a0PJ`1\\1nMe4c0nI`1\\1mMf4c0nIb1Z1kMh4d0nIa1Y1kMi4d0nIc1W1iMl4c0mIe1V1hMm4d0lIf1U1eMP5e0kIf1U1eMP5e0lIb1W1cMmMEQ7V1jI_1[1eMnMCm6Y1iI]1_1gMV5f0mHn1o1\\M[5?YH_2a2oL]5f3gJYL`5]3gJ^L`5W2eGdNg;V1[DSN`<g1b0JaE]Nh7;PF1h1;S9DlFf0l8XOTGk0l8SOiFD]N[1P;jNbFM[N]1P=gNmB\\1P=>M2M10O100O1O1O1YNVCg04UOf<1^C`0P>G9F]AMg>5J7J5N2G:H7I8M2O1O2NlNeBd0Y=XOkBj0T=UOmBm0T=nNmBT1_=1N1O2O0HgNhBZ1V=iNiBW1V=jNiBW1U=lNjBT1T==N2O1N01O1O2O020O0\\NnBZ1]=O1O01O0O1O1000001O00000000001O01O000001O00000000001O00000000001O001O010O001O001O001OO100O100O100O100O1O2N1O2M2O2N10MfNaBY1]=jNbBV1\\=lNeBR1[=POdBP1X=UOhBj0V=XOjBf0X=[OhB87^On<2mBLN94;m<^OTCNK46c0m<WOTC80d0W=WOjBm0U=ROjBP1W=oNgBV1W=jNeB[1]=4L3L4M2N2N2N2O10O1000000J6000000000000000000000000001O0000000000000000000000000001O000000000000000000000000000000000000000000000000000002N1O2NOIRN^Cn1b<RN\\CP2d<PN\\Co1e<QN[Cn1f<SNYCk1i<UNWC0Nc1o<820000`NoBQ1Q=>10000O10000iNmB`0U=@lB>U=BkB=V=CjB;X=EhB:Z=EfB;[=CfBa0W=\\OkBc0W=[OkBc0W=[OjBc0X=YOlBf0U=UOPCj0d=L7FZ[a0Hbd^Og0E7M2O1N2N2O1LeNaB]1^=3000M3M3000000O10000000000O13M3M000000000000O1O1N2N200001O01O00000000000001O7H1O_NdB`1[=aNeB_1Z=bNfB^1Z=bNfB^1Z=401ON2M3cNbBU1a=4100lN^Bj0b=:00000000000O100002N2N1O2M3N0000000000000001O00000000000000000000000000001O0000000000000000000000000000001O0O2O1O00O1000000000000000000000000001O001O1O1O1O1O1N100000000000000000000O1TOYB?g=BYB=g=DYB;g=FYB9g=HYB7h=;6O1O101N1O1I7O0003M3M0O17I2N4L2M4LdT5LakJ=B3N0O1O1O1N2O2M3N1O1O10000000O10000O100000000O10000000000000000O10000000000M301JaH", "size": [480, 640]}, "area": 52731.0, "iscrowd": 0, "image_id": 17627, "bbox": [1.0, 1.0, 639.0, 473.0], "category_id": 183, "id": 20001114}, {"segmentation": {"counts": "0di]11YebN5J7I6K6I6J7I6K6I6J6K6I6J7J5J7I6J7J5J6J7Je1ZN3N2N2O1O2M2O1O1^Ob0[Oe0[Oe0[Oe0ZOf0[Oe0[Oe0[Oe0I700eLfDP3Z;lLmDQ3e;L00000000000000000000000000000O10000000000000000000000000000000000000000O10000000000000000000000000000000000000000O10000000000000000000000000000000000000000O10000000000000000000000000000000000000000O10000000000000000000000000000000000000000O10000000000000000000000000000000000000000O1000FWM]Di2c;XM\\Dh2d;\\MXDd2h;:0000000000000000000000001O00000O10000000000000000000000000000000000000000O10000000000000000000000000000000000000000O10000000000000000000000000000000000000000O1000000000000000000000001O008H00000000000O10000000000000000000000000000000000000000O10000000000000000000000000000000000000000000000000000O100000000000000000000000000000000000000000000000000000000000I^MPDb2P<610O100000000000000000000000000000000000000000000000000000000000O1000000000000000000000O1000000000000000000000000000000000001O1O1O2N1O2N1O1O2N1O1O2N1O2N1O;E>B?A>B>B>B?A>B>B?@?B:F4L4L4L4L4L3K6I7I7J6I7I7I7RMbFOe9_OgG@`8N[cP1", "size": [480, 640]}, "area": 138637.0, "iscrowd": 0, "image_id": 17714, "bbox": [0.0, 0.0, 640.0, 480.0], "category_id": 117, "id": 20001115}, {"segmentation": {"counts": "gVP22j>7H9L0001O00000000000000000000000000000000001O00000000000000000000000000000000001O00000000000000000000000000000000001O000JbAJk>O0000i]c27Pb\\M0000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001O0000000000002NUba2", "size": [480, 640]}, "area": 1648.0, "iscrowd": 0, "image_id": 17714, "bbox": [136.0, 462.0, 329.0, 18.0], "category_id": 165, "id": 20001116}, {"segmentation": {"counts": "di]11m>5J7I6K6I6J7I6K6I6J6K6I6J7J5J7I6J7J5J6J7Je1ZN3N2N2O1O1N2O1O1^OaJaGa5j7SKVHn4U7i1[Oe0ZOg0ZOe0[Oe0[Oe0I70000000000000000000000000001O0000000O100000000000000000000000001O0000000000000O100000000000000000001O0000000000000000000O10000000000000001O00000000000000000000000O10000000001O00000000000000000000000000000O10001O00000000000000000000000000000000001N10000000000000000000000000000000001O00000O1000000000000000000000000000000000000000000O10000000000000000000000000000000000000000O10000000000000000000000000000000001O00000O10000000000000000000000000000000000000000O10000000000000000000000000000000000000000O10000000000000000000000000000000000000000O1000000000000000000000000000001O000000000000000000000O100000000000000000000000000000000000000000000000000000000000000O10000000000000000000000000000000000000O100000000000000000000000O100000000000000000000000000000000000000000000000000000000001N2O2N1O1O2N1O1O2N1O2N1O1O2N5K>B>B?A>B>B>B>B?A>B>B>B5K4L4L4L4L4K5I7I7I7J6I7I6YMoE6X:WOTGIS9EYRQ1", "size": [480, 640]}, "area": 165478.0, "iscrowd": 0, "image_id": 17714, "bbox": [97.0, 82.0, 472.0, 390.0], "category_id": 183, "id": 20001117}, {"segmentation": {"counts": "\\3X8j;aH^J=a5C`J<`5CaJ<`5DaJ<^5CcJ=]5CdJ;]5DdJ<\\5DeJ;[5DfJ<Z5DgJ:Z5EgJ;Y5EhJ:W5FjJ9W5GjJ8V5GkJ9U5GlJ7V5GkJ9Z5BgJ<^5_OcJa0a5[O`Jd0^<01N101O0O2O001N101N101O0O2O001N101N101O0O2O001N10hQ`1OWn_N3M2O2N1N3N`0@0O101O000O13N1N3M2N3N2M2N3M2O2M3O1O1O100O1O1O1O10`jZ6", "size": [640, 480]}, "area": 2035.0, "iscrowd": 0, "image_id": 17899, "bbox": [0.0, 108.0, 155.0, 265.0], "category_id": 105, "id": 20001118}, {"segmentation": {"counts": "0\\3d`02N0000000000000000000000O10000001O5K4L4L2N00O100O10000O100O100O100O100O100O100SNm1O1O1O100O1O100O1O1O100O1O1O100O1O1O100O1O1O100O1O1O100O1O1O100O1O100O1O1O100O1O1O100O1O1O100O1O1O100O1O1O100O1O10000000000000000000000O1000000000000000000000000O10000000000000000000000001O001O001O1O001O001O001O1O001O001O001O1n@[Og:e0XE_Oe:b0XECe:=YEIc:8[ELb:4\\E1a:0]E5_:L^E9_:G_E>^:C`Ea0]:_OaEg0[:ZObEk0[:UOdEo0Y:ROdES1Y:nNeEW1W:iNgE\\1V:eNgE`1V:`NiEd1T:]NiEi1S:WNlEm1Q:UNlEo1Q:TNjER2R:QNjES2S:PNiET2T:oMgEV2V:lMgEY2U:jMgEZ2V:iMeE\\2X:gMdE]2Y:eMdE`2X:aMeEb2Z:_MdEf2X:\\MeEi2W:XMfEm2W:TMgEQ3U:QMhES3U:nLhEX3T:iLjE[3S:gLjE]3S:dLjEa3S:`LkEe3Q:]LlEg3Q:ZLlEk3Q:VLmEn3P:TLmEn3R:SLlEo3S:RLjER4T:PLiER4V:oKhES4W:nKfEV4X:j2O1O1O2N1O1O2N1O1O1OM3L4M3L4M3N200O100O100O100O100O100O100O100O100O100O100O100O100O1O100O100O100O100O100O100O100O100O100O100000000001O000000001O000000001O000000001O000000001O000000001O000000001O000000001O000000001O000000001O000000001O000000001O000000001O000000001O000000001O00hNZESHNP1h:l6`ESHHm0h:P7dEnH\\:Q7eEoH[:P7fEPIZ:P7gEoHY:P7hEPIX:n3aEnM9SNV:h3iEaMLRN5e0V:g3jEVM7^NIe0V:g3kEdLf0ROXOc0W:f3lEQLX1FgNb0U:g3WFdKo03eNb0U:e3YFeKm05fN`0T:5fEa28dLX16fN`0T:HTFm2JeLW16hN`0b;;_DnN_OY1a0hNW12SNNh03YON07W<1iEb0fNTO[11fN2ZOM55g>>iA]O[11eN4FMi>7cAGY11eN1\\?OcC0W`001O_WX2", "size": [640, 480]}, "area": 60213.0, "iscrowd": 0, "image_id": 17899, "bbox": [0.0, 0.0, 364.0, 322.0], "category_id": 112, "id": 20001119}, {"segmentation": {"counts": "SbV91lc03U\\O0dc07GIe\\O;Vc09L5K4L41OPK", "size": [640, 480]}, "area": 198.0, "iscrowd": 0, "image_id": 17899, "bbox": [471.0, 161.0, 9.0, 40.0], "category_id": 115, "id": 20001120}, {"segmentation": {"counts": "d;\\1eb0O00O11eN_]OT1bb0lNi]Oh0Wb0XOU^O=ka0C`^O2`a0NXe00Q\\O0oc00Pd00P\\O0Q\\O0Yi\\69WVcI8d^OLi>5k@7U?JY@f0g?[Oe_OX1\\`0SOW_Oo0g`0SOX_Om0Ua0hNj^OX16RNg`0f0S_OW16TNg`0e0S_OW12SNN3l`0d0S_O[11QNl`0d0S_O[11QNj`0f0U_OY11RNj`0n26Ko^OTMo`0n2010O001O00001Io^O\\MRa0d2n^OZMUa0e2l^OZMTa0f2301O001O01O01O010O00001O010O00010O4Lf^ObMca0n1\\^ORNRb0`1n]OaNRb0^1n]ObNSb0^1j]OdNWb0[1i]OgNOJUb0_1k]OiNVb0V1j]OkNVb0_11O100O1Lg]O_NZb0`14O001O102M002O2M3M10O0O1X]OjNdb0Z100O11O0N\\]OhNdb0X1200000103L01O00WO\\]O7gb0DZ]O>eb0\\O`]Od0nb0100O0N22M10000Mg\\O^OZc0>g\\OCOO\\c0<e\\OEOO\\c0=e\\OCO0\\c0=e\\OC]c0=c\\OC_c0;a\\OFec036N1Nf_b0", "size": [640, 480]}, "area": 6139.0, "iscrowd": 0, "image_id": 17899, "bbox": [0.0, 142.0, 450.0, 274.0], "category_id": 123, "id": 20001121}, {"segmentation": {"counts": "o[e632Ngc08X\\OHdc0<M1O101N1O2O0001OO2O0010O0011h\\O]ON0lb0c0U]O^OM5jb0<[]OJcb05Z]O1bb00]]O1cb0\\O]]O`004fb0MY]O3hb0b00000000000JjNa]OW1_b0iN_]OY1ab0hN^]OX1bb030001O0001O000010O00000001O00010O000jN^]On0cb0QO]]Oo0cb0QO^]On0bb0RO^]Oo0bb0nN`]OR1`b0nNb]OOJo0eb0QOc]Oo0]b0QOd]On0gb000010O0MROT]On0lb030000010O00001O000010O00POR]On0Pc00000001MQOS]Oo0mb0200000010O01O001O2N00100O0000O102N1LPOU]OR1fb0oN[]OOOS1eb0nN\\]OOOT1eb0mN[]OO0T1eb0mN[]OW1cb0iN]]OY1ab0gN`]OX1`b05O00001`]OaN00[b0c11O0O1O11O1O10L201M4M2N2O1O100N200001O01O0000000000000000`G", "size": [640, 480]}, "area": 4988.0, "iscrowd": 0, "image_id": 17899, "bbox": [341.0, 250.0, 139.0, 72.0], "category_id": 132, "id": 20001122}, {"segmentation": {"counts": "hcQ81kc08H9G8H5O0000001O000000001O000000001O000001OO101O000N21O01O00000001O2O2M2N001O0001O0001O00010OO11O001O10O0O13M1O0XOk\\Oe0Yc0ON23M1O0000L_Oj\\O`0Uc06O2Na0jK", "size": [640, 480]}, "area": 2006.0, "iscrowd": 0, "image_id": 17899, "bbox": [412.0, 102.0, 68.0, 59.0], "category_id": 141, "id": 20001123}, {"segmentation": {"counts": "P=U1kb000O1O01O0100O6J2O2M2N3N1N2O2M2N3N1N3M2O2M2OYVm8", "size": [640, 480]}, "area": 513.0, "iscrowd": 0, "image_id": 17899, "bbox": [0.0, 414.0, 23.0, 39.0], "category_id": 143, "id": 20001124}, {"segmentation": {"counts": "ljo72oc0O11O2N2N0O00O0O20O002O0O100O1O101N5L0O1O1n\\OE_b0?\\]OBcb0`0W]OEib0i0O2O0OO13N0O100O1O3N0O100O3M8I2M2n]OSNoa0o1O1O2O0O100O100O2N010O2O0O1O100O100O1O11O2OJeMe^O00V2Va0mMQ_OS2n`0jMV_OV2Ya0100O1O1O1kI", "size": [640, 480]}, "area": 3174.0, "iscrowd": 0, "image_id": 17899, "bbox": [409.0, 185.0, 71.0, 87.0], "category_id": 152, "id": 20001125}, {"segmentation": {"counts": "R?n4j?XOj3VL00000000000000001O00000000000000000000000000000000001O00000000000000000000000000000000001O00000000000000000000MH^\\O8bc0H^\\O8bc0H^\\O8bc0H^\\O8bc0H^\\O8bc0H^\\O8bc0H^\\O8bc0I^\\O6bc0J^\\O6bc0J^\\O6bc0J^\\O6bc0J^\\O6bc0J^\\O6bc0J^\\O6bc0K]\\O5cc0K]\\O5cc0K]\\O5cc0K]\\O5cc0K]\\O5cc0K]\\O5cc0K]\\O5cc0K]\\O5cc040000000000000000000000000000000000000000000000000000000000000000000000000000001O000000000000000000000000000L4O10000LW\\O2hc0NX\\O0jc0030Wd\\40h[cK3K3O10000000000000000000000000000000000000000000H[\\O1R\\f1", "size": [640, 480]}, "area": 1994.0, "iscrowd": 0, "image_id": 17899, "bbox": [0.0, 482.0, 393.0, 158.0], "category_id": 165, "id": 20001126}, {"segmentation": {"counts": "l[d24kc010000LL]\\O3cc0M[\\O5ec0300N20000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001O00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001O000000000000000000000000000000000000000000000000000000000000000L[\\OMec025M\\TT2", "size": [640, 480]}, "area": 2400.0, "iscrowd": 0, "image_id": 17899, "bbox": [134.0, 629.0, 236.0, 11.0], "category_id": 167, "id": 20001127}, {"segmentation": {"counts": "hce7222ec0500000000000000000000001O00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001O00000000H8", "size": [640, 480]}, "area": 716.0, "iscrowd": 0, "image_id": 17899, "bbox": [392.0, 625.0, 88.0, 15.0], "category_id": 168, "id": 20001128}, {"segmentation": {"counts": "h^Z761Lcc0>QOF\\]OK30:?Rb0Ka]OG10:`0Ra02c^O@1N1c050k`0\\OQ_O1Nj0ON1WO10Nd0j`0KV_OT2j`0a0000O1000000O10000O1000000O1ER_OdMn`0[2T_OdMl`0Y2[_ObMf`0]2]_OaMc`0\\2__OeMa`0[2__OdMb`0[2__OeMa`0Z2`_OfM``0X2b_OgM_`0Y2a_OgM_`0o1o^OUNia0c1>E;UO^]OL1Djb04U]O00K29dc0Gd_Z1", "size": [640, 480]}, "area": 2772.0, "iscrowd": 0, "image_id": 17899, "bbox": [375.0, 0.0, 37.0, 101.0], "category_id": 172, "id": 20001129}, {"segmentation": {"counts": "mlm6?Tc0>_Ob0`^OXOd?i0d_ON\\`0k10O100O100O100O10000000000000000C[_OYML3i`0a2`0UOk^OWNK30:Za0W1P_O\\NG10:[a07W_O^O@1N1c05k`00a^OL1Nj0ON1WO10Noa00_Ym1", "size": [640, 480]}, "area": 2218.0, "iscrowd": 0, "image_id": 17899, "bbox": [355.0, 0.0, 27.0, 105.0], "category_id": 174, "id": 20001130}, {"segmentation": {"counts": "fbg71nc02L7K3K210O1O1N200F^OR]Of0fb0>E:VObNh^O1De1Ra0ZNR_O000K29l1j`0eNm^Oc1Sa0e00003M5K4L00000000000000000000000000000000001OO1000000N21O00000000001O2N3M2N001O000000001O00001OO11O001O1O00O13M1O0h_O[LU`0i3ON23M1O0000Lo_OZLP`0e36O1O1", "size": [640, 480]}, "area": 7885.0, "iscrowd": 0, "image_id": 17899, "bbox": [396.0, 0.0, 84.0, 126.0], "category_id": 181, "id": 20001131}, {"segmentation": {"counts": "U>b0`9f5VJ]Kg4d4WKWOn0j0QOWOo0j0cLiIT1]5X2k0dLSJh0S5d2k0bL^J?g4n2l0cLhJ3]4Z3l0bLoJKV4b3l0bLPKKT4c3m0aLQKIS4e3m0bLQKGS4g3m0`LSKGP4h3n0aLSKEP4j3n0_LTKFn3j3o0`LUKCm3m3o0_LUKBm3n3o0_LWKAj3P4P1^LWK@k3P4o0`LXK]Oj3S4o0^LYK^Oh3S4P1_LZK[Og3V4P1]L[K\\Oe3V4Q1^LX1b3iN]LW1b3jN]LW1c3jN\\LV1d3jN\\LV1c3lN[LU1e3kN[LU1d3mNZLT1f3lNZLT1e3nNZLR1f3nNYLS1f3oNYLQ1g3oNYLQ1f3QOXLP1h3POXLQ1f3QOXLP1h3POXLP1k1n0TNROk1o0TNROk1P1TNPOk1Q1UNoNk1R1SNoNl1R1TNnNk1d900O1O1O100O1O1O101N1O1ON^Nh]Ob1Xb0^Nh]Oa1Yb0_Ng]O`1Zb0`Nf]O_1[b0aNe]O_1[b0aNe]O^1\\b0bNd]O]1]b0dNb]O[1_b0eNa]O[1_b0eNa]OZ1`b0fN`]OY1ab0gN_]OX1bb0hN^]OX1bb0hN^]OW1cb02100hN\\]OS1eb0mN[]OR1fb0nNZ]OQ1gb0oNY]OQ1gb0oNY]OP1hb0POX]Oo0ib03100O1O1OcFoNMQ12QOMn04ROLm04UOKj06WOIi06YOIf07\\OHc09]OGb0:_OEa0:@F?;@F?:AG?9@H`07AI?7@J`05@L`04_OMa02@N`01@0`0O@2`0LA5?JB6>HC9<GD:<ED<<BF>:AF`0:^OGc09\\OGe09YOIg07WOLh04WOMi03UO0j00TO2l0NRO5m0KQO<j0CUOc0g0]OWOi0e0WOYOP1eH\\Ne6d0e0U1]HUNVO4h7b0c03hG:c0lNY7g0;3nG::lN]7g094TH;R8AI5YH:j7AL5_H:a7AO5dH<X7_O26kH8R7B27oH4o6E17UI0j6JN7]IJf6OM6aIHb63N3dIF^67O1gIEZ6;0MkIDU6`00JPJBP6d02HoIBP6g02DnIFP6f03BmIHQ6g03^OlIKR6g03]OkIKS6i03YOjINT6j03WOhINV6k04VOeINX6m04TObIO[6m05TO^IN^6o05RO\\IN`6P16QOXIOc6Q1kKnN21_MOe6S1hKQO3Ln5S1mIVO4Gm5T1mIZOX:f0fE_OW:b0fECW:=hEGU::hELT:5iE0T:0kE4R:MkE8R:HmE=o9DnEa0o9_OoEf0n9[OPFj0l9WOQFn0l9ROSFR1j9oNSFV1j9jNTF\\1h9eNVF_1g9aNXFc1e9`NVFe1g9^NUFg1g9\\NUFh1h9[NSFj1j9XNSFl1j9WNRFn1j9UNQFP2l9SNPFQ2m9QNPFS2m9oMoEV2n9kMPFX2n9iMPF\\2l9fMPF`2l9aMRFd2j9]MTFg2i9[MTFi2g1iJR5_2TIn2g1eJR5^2UIQ3f1dJS5\\2TIT3g1_JU5^2QIX3g1ZJX5_2oH\\3f1SJ\\5c2kH^3f1oI_5d2hHb3g1hIb5g2eHd3g1eId5i2bHd3j1aIe5l2^Hf3l1^If5m2\\Hh3j9ZLSFh3l9YLQFj3n9WLPFl3n9T3O1O2N1O1O1O2N1O1O1OM3L4M3L4M3N200O10nGXFl5g9UJZFj5f9VJZFj5e9VJ\\Fj5d9VJ\\Fj5c9WJ\\Fj5d9VJ\\Fj5c9VJ]Fk5c9UJ]Fk5b9VJ]Fk5c9UJ]Fk5b9UJ^Fl5b9TJ^Fl5a9UJ_Fk5a9Z2OkG`Fl5`9Z2O100O100O100O1O100O100O100O100O100O100O100O100O100O100000000001O000000001O000000001O000000001O000000001O000000001O000000001O000001O01O000000001O000000001O000000001O000000001O000000001O000000001O000000001O0000000000000000000000000000000000SJgES2Y:lMUFg1k9YN^F^1b9aNgFW1Y9iNPGn0P9QOXGh0h8XOYGg0g8XOZGh0f8XOZGh0f8WO\\Gh0d8XO\\Gh0d8XO]Gg0c8YO]Gg0c8YO]Gg0c8YO^Gf0b8ZO^Gf0c8YO^Gf0b8ZO^Gf0b8ZO^Gg0a8YO`Gf0`8ZO`Gf0`8ZOaGe0_8[OaGe0_8[OaGe0_8[ObGe0]8[OcGe0]8[OdGd0\\8[OeGe0[8[OeGe0[8[OfGd0Z8\\OfGd0Z8[OgGf0X8ZOiGe0W8[OiGe0W8ZOoGa0Q8_O`H0`70nHBR7>oHBP7=RIBn6>SIAm6?TI@l6?VI@j6`0WI_Oi6a0XI^Oh6a0ZI_Oe6a0\\I^Od6b0]I]Oc6b0^I^Ob6b0_I]Oa6b0aI]O_6c0bI]O]6b0eI]O[6c0fI\\OZ6d0gI[OY6e0hIZOX6f0_FQM[2X2V7g0[FYM\\2o1Y7m0RF\\Mb2g1[7R1jE_Mg2_1_7S2`HnM`7R2_HoMa7Q2_HoMa7Q2^HPNb7Q2]HPNb7P2]HQNc7o1\\HRNd7n1\\HRNd7n1[HSNe7n1ZHRNf7n1YHTNf7l1YHUNg7k1YHUNg7k1XHVNh7k1WHUNi7k1VHVNj7j1VHVNj7j1UHXNj7h1UHYNk7g1UHYNk7h1SHYNm7g1SHYNm7g1RH\\Nl7d1SH^Nl7b1TH_Nk7b1SH`Nl7`1THaNk7_1THcNl7\\1SHgNk7Y1UHhNj7X1UHjNj7W1UHjNj7V1UH_OW7a0hHAW7?iHAW7?hHCW7=iHCW7>gHDX7<hHDX7<gHFX7:gHGY79gHHX79fHHZ78fHHZ78eHJZ76eHK[75eHLZ74eHM[74dHM[73dHN\\72cH0\\70dH0\\70cH2\\7NdH2\\7NeH2Z7NeH3[7M`H9_7G]H=c7CXHc0g7]OTHh0l7XOoGn0P8ROkGS1U8\\5aN`1TKmCkMi0a0Q2", "size": [640, 480]}, "area": 206908.0, "iscrowd": 0, "image_id": 17899, "bbox": [0.0, 12.0, 480.0, 620.0], "category_id": 183, "id": 20001132}, {"segmentation": {"counts": "j;V8j;0O11O00O1001ON22N00O1000000002[HWDo6h;aHXD<0DO]7g;dH]D0M34Y7b;bH^D1O13OJY7m;fHWD081ET7k;lHlDT7Q<ON2O1003M3M1O1OO1O100O1NPIVC;3Q6i<bIUC97S6g<iI^CV6c<hI^CX6U=0O11O0000O100O100O12N00O1000WJ_BS5a=_J_BN0N4e5^=]JaBMNO8d5Z=\\J]B0d00\\O1000Z5[>eJeA11OO[5d>OO100MeJaA[5_>3O13M00O10cJ`AZ5b>10000O11O0iJ]AL0S5l>mJWAm4P?N00O11O001OO100N20SKPAj4R?100O1001O1O000000001ON2N21O00N200001O1OO1N21QKQAk4R?00ZNm@_NT?_1o@_No>a1UA]Nk>b1XA\\Ng>d1[A[Nd>d1^A\\Nk>Z1VAfNZ`0Ig_O7_`0Ac_O?d`0XO^_Oh0h`0POY_OQ1m`0bNZ_O^1da0000000000O10000O1O1O1O1O1oN`Nb_O`1X`0mN^_OV1[`0XO[_Ok0`?ZNQAU1YOd0h?X21OO1O1001O0gLW@[2h?eMY@[2e?gM\\@X2a?hMd@V2\\?nL]@h08Y2[?mL_@l04W2]?mL_@l05V2`?dMZ@^O7l2^?dM]@_OMO7m2_?fM^@[O8o2Y?gMm@Y2S?bM]@_Oa0o2R?bMRA^2l>dMRA^2l>cMWA[2f>fM[A[2e>eMYA]2g>dMZAZ2k>aMUA_2Q?\\Mm@e2X?WMg@i2`?SM^@l2[`0000N21O1O4iMQ_OU1Ra0dNS_O[1ja0O1O001O1O1O1O001O1O1O001OcM^N]Ba1R`0O`NlNU@S1k?]Oe_Oc0\\`0]Od_Ob0\\`0_Oc_Oa0\\`0Ac_O?]`0Ba_O?_`0B`_O>``0C__O=b`0C]_O=``0G^_O:e`0EY_O;i`0DV_O<k`0DT_O<o`0Dm^O=Ua0V10000002N000000001O00O100O100O10000O100001O1OO11O1O1O001O1OO12N1eMc^OJ3Q2Za0SNe^OL2P2Xa0UNn^Oj1Ta0TNl^Ol1Va0QNl^On1ca00000QNQ^Om1oa0SNQ^Om1Qb00NSNQ^Om1oa020QNQ^Om1Qb0000000000O1O10000ATN`^OM4S2Xa0?2N00N`Md^O`2\\a021OO1001O2NO1O1000000O1O100O1O11O00001O001O1OO1001O0000O100001O001O001O1ON2O1001bMb^OX2_a0fMb^OZ2ba001O1iM[^ON1o1ga0PNX^O20n1na001O00O100001OM3N21O00001mMT^OP2na00L41O1O001OO1O1O1O12NO1O11O0000O1000kMW^OS2ha0nMX^OR2ha0mM[^OQ2ia01N22mMT^On1oa001O1O000000000000001O00000000000000O1O1O1O1000000000000001O1ON2001O000VNS^Oa1Wb0[Nh]O`1Yb0_Ng]Oa1\\b00001OMaNf]O^1[b030^Ne]O_1^b00NaNc]O_1]b021O0000004L001O001O00NfN^]OZ1bb0fN^]OZ1bb022NO10000O1NdN`]O\\1`b020000001OO1000000O11O001O001O00N200O100001O00jTg0VOWkXOi01O0000000000000000", "size": [640, 480]}, "area": 45892.0, "iscrowd": 0, "image_id": 17905, "bbox": [0.0, 376.0, 480.0, 264.0], "category_id": 111, "id": 20001133}, {"segmentation": {"counts": "hbd3:`2Ge>e0_@H]NIo`0l0Y@WOkN1g`0X1\\@lN`?a1S@aNk?`1S@aNm?`1Q@aNo?a1n_O`NR`0a1l_OaNS`0`1j_OcNU`0`1g_OaNY`0c1a_O_N^`0b21OO11OO1PMgLYEY3[8cLUG6_NMQ1Z3Z9fLmFNTN6a0NT1W3Y9hLlF4RNOf0LS1Y3k8YMWGNeNEY1T3f8`MXGNZNMc1e2j8aMYGN[NM_1d2l8cMXGk0Kb1m8dMWGl0J`1n8eMhFROUOm1R1\\1P9gMgFZOnNd1Z1[1R8SMPGe0f0^16k0S8XMkFi0IfNc0`2e0i0S8^MgFj0?Q1f0g0T8\\OfFXM6g2o0e0S8c2mG]MS8d2lG\\MT8e2kG[MU8e2kG[MT8g2kGYMT8h2lGXMU8g2kGYMU8h2jGXMU8i2kGWMU8i2kGWMU8i2kGWMU8j2jGVMV8j2jGVMV8j2jGVMV8a3RG`Ln8YMRJn57[IaL3]5e0UOc4kNgJcL2\\5d0VOc4gLfJg09[3>WOS5]KVJd0311:KUO:X5>WOR6]KVI90I0O83HM1b01^O:Z5<XOn6aK^H?=i46WO[7nK_Hk46WO_8]KjFZ5h0XO^8^KjFZ5h0XO`8h0`GXO^8^KjFZ5h0XO^8j0bGVOa0^Kb60UHZ5h0XO`0kKhNC10[7Z5\\IXO2eKVOJ0c0\\7f4\\IXOL^K\\O20o0[7Y4]IXOLfLf6R4^IXOLfLi2hN>Y5nLYOJgLj2gNcL3e3W5TMXOHiLl2XOIh4cMWOE^KB\\1]3KcLiN`2[5YNWOElLo29cLZNo1Z5jNWOElLo2h0gMmMlNM4_5e0SOElLo2h7\\M\\KElLo2g7]M]KDlLo2h7\\M\\KElLo2i7[M[KFlLo2j7ZMZKGlLo2j7ZMYKHmLn2j7ZMYKIlLm2k7ZMYKJkLl2m7YMYKLhLk2o7ZMXKMgLi2T8WMUK2fLf2W8VMSK6dLd2\\8SMPK:cLc2_8QMnJ>fL\\2\\8VMnJ?dL\\2^8UMnJ`0bL\\2_8UMoJ?aL]2`8TMoJ>cL]2^8UMoJ>dLZ2_8XMmJ=dL^2]8UMoJ<dL`2]8TM`FB[4j0gLa2^8XMjJ7gLb2^8XMZI\\OdNi0Fb2\\8ZMXI]OdNi0Fa2^8YMWI_OcNi0G_2_8ZMfG]O33Oh0gNSOn0\\3d8XMdGA120j0E]2g8VMaGEN22h0F\\2j8TM^GJM03e0J]2j8SMZGNK05c0K]2m8QMVG2I07?O^2k8TMQG3KO70UOYO27k0Z3k8WMkF5MM9L]O@h0[3j8[McF71J;J^O[On0_3e8^M\\F:NMc0B@YOQ1^3b8g1gGYNY8g1hGXNX8g1iGYNW8g1hGaK`NQ2h9^2gGbKaNP2h9^2gGaKbNQ2h9\\2fGdKcNYOOX100h9k3gG^KbN_O50In0<Eo9^4VG]KQO>2VOS:4[EU20SN2S1^1hNVO`0X;FjDA\\O>4JMX1[1dN^NLk07YO4n;2aFI`M<MKe2IbM2l;0mCIe23_M=1Fc2JXM175R<IdF1]M93G^21\\M0c?8bCFj<8a3O1K5NhgX3", "size": [640, 480]}, "area": 24437.0, "iscrowd": 0, "image_id": 17905, "bbox": [186.0, 107.0, 126.0, 471.0], "category_id": 132, "id": 20001134}, {"segmentation": {"counts": "U]Y41kc00W\\O1fc0OY\\O5fc04N2OO11O1O1N110N2OO12M20O1N020ON22NT_e4", "size": [640, 480]}, "area": 295.0, "iscrowd": 0, "image_id": 17905, "bbox": [219.0, 538.0, 21.0, 20.0], "category_id": 142, "id": 20001135}, {"segmentation": {"counts": "0j;V800O11O00O1001ON22N00O1000000002[HWDo6h;aHXD<0DO]7g;dH]D0M34Y7b;bH^D1O13OJY7m;fHWD081ET7k;lHlDT7Q<ON2O1003M3M1O1OO1O100O1NPIVC;3Q6i<bIUC97S6g<iI^CV6c<hI^CX6U=0O11O0000O100O100O12N00O1000WJ_BS5a=_J_BN0N4e5^=]JaBMNO8d5Z=\\J]B0d00\\O1000Z5[>eJeA11OO[5d>OO100MeJaA[5_>3O13M00O10cJ`AZ5b>10000O11O0iJ]AL0S5l>mJWAm4P?N00O11O001OO1]KUKQJk4k5ZKRJh4k5[KUJHcKj4T:cKZJ^4c5eK]J[4_5jK_JW4\\5nKdJR4X5SLhJl3T5XLjFi0M]NZ1a4l7]LbFe1_1n1m7@PH`0m7EQH;m7IQH7n7JSH5l7MQH5o7KoG7P8KPH4P8LPH4o7NnG4R8gNQGZMm0o3Q8bNYG^Mf0P4Q8\\NaG\\M_O]Oo0j4P8XNkG]M>Z4g7SNTH_M5_4f7mM_H_MJf4g7gMXJZMiLk4o8mMiGgMV1\\4P7oMaG`Nl0a3c7QNWGPOm0n2k7;oGGQ8=kGCT8b0[GjJJe4k8T6mNUEPIl:j6]ESIc:g6eEVI\\:i6gEUIY:j6jESIW:k6lETIT:k6PFQIQ:n6a1N200O100O100000000000000000[KnH\\LR7b3ZITLf6k3]ISLc6k3`ITL`6k3`IVL`6Y3mHnHd0j3_6R3\\IjH6T4_6k2\\JTMd5f2bJQJYN[2U7^3gJWJUN[2V7kMPIP4i1lKRNX2V7iMVIo3^1PL[NV2R7iMVIW4W1nJZNh08Y2S7fMVIa4o0fJbNl04W2X7aMUIl4g0`JhNl05V2[7]MSIW5b0PKjN^O7l2`8l2XHhJSO_OMO7m2a8Q3THeJXO[O8o2\\8R3RHeJHY2W8P3RHbJWO_Oa0o2V8P3RHbJL^2R8V3jG^J2^2U8\\3^GWJ`0[2R8`3YGVJf0[2R8`3VGUJf0]2T8W4mGgKS8Y4mGgKT8Y4jGhKV8Y4iGgKX8[4fGdKZ8_4cGaK^8f4ZGZKf8i4UGYKk8j4SGUKn8n7001O00001O001O001hJmFAT9>nF@S9?SG[On8c0ZGVOg8i0^GROc8l0cGoN^8P1gGkN]8nLeFj2U11Z8PMcFm2i1^Oh7aM_FQ3S2TOc7eM[FV3S2UOf7`MXF[3S2TOi7]MTFm2LTMW2b2m7XMQFR33kLo1l2R8QMmEX3b2Gm79SHGm7:RHFn7;RHDn7<oGGQ89RHDm7=UHAl7>UHAj7`0YH]Oh7b0ZH\\Oe7e0[H[Oe4\\MTKY37[OY2XMPM6_NMQ1Z3W1[OX2[MhLNTN6a0NT1W3Y1ZOU2^MgL4RNOf0LS1Y3Y1ZOg1oMRMNeNEY1T3Y1ZOb1VNSMNZNMc1e2^1ZOa1WNTMN[NM_1d2a1ZO`1YNSMk0Kb1c1YO_1[NRMl0J`1e1YO^1\\NcLROUOm1R1\\1g1ZO^1]NbLZOnNd1Z1[1h1ZO?iMkLe0f0^16k0i1ZO?nMfLi0IfNc0`2e0i0j1ZO>TNbLj0?Q1f0g0j1[O?1aLXM6g2o0e0k1[O=X3hM]Mk1[O=Y3gM\\Mj1]O?X3fM[Mg1cLVMk2]3W3fM[Md1cLXMo2]3U3fMYMd1Ee0S3gMXMc1Fg0Q3fMYMc1Fg0R3eMXMd1Ff0S3fMWMd1Ff0S3fMWMd1Ff0S3fMWMc1Gg0S3eMVMe1Ff0T3eMVMd1Gg0S3eMVMd1Gg0j3mL`L\\2Fg0c7XO^Hg0c7ZO\\Hf0d7oMYEOR3R2U8eLiDd0311Go2o2S9eLiC90I0O83HM10R3Z3P:iLWFX3X=00000000000SFdLV3\\3iLQM\\IC10Y9\\3lLkLjIJ0c0Z9h2fLdLPJ20o0Y9[2gLlMX3T2hLlM]MhN\\2[37nM]MgNcL3d5X3:PN_MXOi1h2e0eLUJ\\1]3KcLiN`4[3[1SNbM9cLZNo3Z3l1SNbMh06U1X2SNbMi1UO4Y3SNbMk1SO1\\3TNaMk1SO2[3SNbMi1cLiL`2\\3Z3RNcMi1UO6W3QNdMj1TO5X3QNdMi1UO6X3PNcMj1UO6Y3oMbMl1TO6[3lMaMP2RO4_3jM_MQ2SO8]3fM_MQ2UO;\\3bM_MS2UO>Z3^MaMT2UO`0Z3_M\\MQ2ZO`0[3]M\\MR2ZOa0[3[M\\MS2ZOa0[3[M\\MT2YOa0Z3]M\\MQ2[Ob0Y3^MYMQ2_Oa0W3^M]MP2[Ob0W3^M_MP2ZOb0W3]M`MQ2YOb0W3\\MaMS2WO`0X3_M`MQ2XO`0Y3]M`MT2VO?[3\\M_MV2UO>\\3ZLTJn0\\3Y2UO`0]3UM`M[2SOa0]3RMaM^2QOa0[3SMdM\\2QOc0Y3PMgM]2POe0U3PMkMZ2QOf0g2SLgJ27k0Z3Z2QOf0e2YLoJh0[3Y2QOf0e2XLkJn0_3U2POe0f2XLjJQ1^3R2ROe0e2bMYNj1QOd0f2cMXNi1ROd0e2dMYNi1QOc0f2cMaK`NQ2[3Gb0g2bMbKaNP2Y3Id0e2bMaKbNQ2W3Jf0b2aMdKcNYOOX100W3Y1f0b2bM^KbN_O50In0<E^3@VLX2o3f2bM]KQO>2VO`3OmKZ2Q4kMPLU20SN2S1^1hNVO`0Y4b1ATNXLA\\O>4JMX1[1dN^NLk07YO4o4o1@SNPNI`M<MKe2IbM2m4KlJN1o1c4XN\\KIe23_M=1Fc2JXM175Q5IhJ20n1i4PNTN1]M93G^21\\M0\\5n1b2ZNWKFd2o1U2YN[1g1fNXNY1i1hNRN\\1n1eNPN\\1P2[8OM3N21O00001mMT^OP2na00L41O1O001OO1O1O1O12NO1O11O0000O1000kMW^OS2ha0nMX^OR2ha0mM[^OQ2ia01N22mMT^On1oa001O1O000000000000001O00000000000000O1O1O1O1000000000000001O1ON2001O000VNS^Oa1Wb0[Nh]O`1Yb0_Ng]Oa1\\b00001OMaNf]O^1[b030^Ne]O_1^b00NaNc]O_1]b021O0000004L001O001O00NfN^]OZ1bb0fN^]OZ1bb022NO10000O1NdN`]O\\1`b020000001OO1000000O11O001O001O00N200O100001O00K5J6O100O10000O100O100O100O100001O0000000000001O00000000001O001O001O001O1O00;E1O0000000000000000", "size": [640, 480]}, "area": 202133.0, "iscrowd": 0, "image_id": 17905, "bbox": [0.0, 0.0, 480.0, 597.0], "category_id": 163, "id": 20001136}, {"segmentation": {"counts": "anb11kc04M4K5L3L5J5L5K4`]Oi0M]Nd?n0T@e1g?^1J5L4M1O2O0O101NPOk@WMU?c2RA]Ml>^2[AaMe>Y2cA^M^O]Om>P3oA^MZ>[2QB`Mo=[2ZB^Mj=b2j12a_OgMR?[2e@`NR4fNR6l2bEPOQ4WNZ6R5`IRK^6R5]IQK`6T5lEjJm24V7f5fH\\JW6HbGV6m1RJ\\61dG]7?cHg78gG\\7:^Hn79fG`73YHV8:dGd7NTH\\8;eGg7BUHh88bGb8]8`1O200O100O0100O1O1O1O100kNbGgF_8R9oGcFU8g7]GoHj0ROl7n7k2N2O1O1\\OYDmHi;m6i0I6J6]JkBU4]=gKnBj3Y=TLiBP4S=mKoBY4n<cKSCc4j<XKXCn4k=6I6J4L01N1007J8G2O1N2N5L5J5L6I8I7H3M3N2M4iDQHV:Q8cETH\\:i8O10O0100O10aLTEhMm:V2XEgMh:X2_EbMa:\\2eE`M[:_2jE]MV:b2oEYMS:d2SFXMP:aNUEg2[1YNd9kN\\Ek0KYNFm0U2`0Q9ZOgEe0]ObNKj0k2KY8OVF;gNTO2c0k2LZ8NcF<]NDh22[8IdF=aNAa27`8E`Fa0fN\\O\\2=b8A^Fd0U2Jl93VFLk92WFLk91XFNl9LXF2l9IVF6k>N3N3Miac0Y3nZ\\OY1gN1O0000000001O000000000O100000000000000000000000000000000000001O00000000000000000000004L1O0_N^@dNc?Z1_@dNb?L]@33Ob?L^@220a?M`@O31_?Mb@O12^?Nd@L14]?Ne@K05]?Mg@JO7_?If@LO8`?Dg@1K9b?_Oi@3I<jb0^OVVg2_1VhXM6O100O10000O100O100O100O100001O0000000000001O00000000001O001O001O001O1O0]N\\g5", "size": [640, 480]}, "area": 33965.0, "iscrowd": 0, "image_id": 17905, "bbox": [81.0, 133.0, 389.0, 507.0], "category_id": 183, "id": 20001137}, {"segmentation": {"counts": "\\Xl44R=6L211O001O01OO101OO1000000O10000000O100001O00000001OO1O1000O10000O0101O00000001O010N2ON20001O000KRCK0On<57OVSf0Le_YO42Lm<7QCMk<:O2O0000N200O12N00001O1OO1O11O@WC011O32LUZ80RSH2N0000O1001N100jBMT=3lBNS=7N0O100M300N2M2011O10OO2O010N2OTCDi<?01WCDa<b011]CYOa<h0001OJ6N2N2001O1O1O0O2O1O10O10N2ZOaC>g<O0O2O001N2O001O01O10O0O2N20O3NO00O100O100O1O1O1N2N2N2N2O0O1O100NVOeCi0_<OKXOiCh0]<O1001OO100002N001N101ON20000001O00O010002N00N200003L10M30000002N02K302OO001NOaCXO_<h03OO2kF", "size": [427, 640]}, "area": 3503.0, "iscrowd": 0, "image_id": 17959, "bbox": [374.0, 286.0, 266.0, 37.0], "category_id": 96, "id": 20001138}, {"segmentation": {"counts": "c:76T2b:fMbEW2j:YOmDTOT;k0mDTOT;k0mDTOT;j0nDUOS;i0nDWOR;g0oDZOR;=VECR;H[E8g:D[E<f:A[E`0d:_O^Ea0`:_ObEa0\\:@dEa0Z:_OgEb0W:^OhEe0W:ZOhEi0W:VOiEl0W:SOQFf0[;O00000\\O\\Ca0h<O0000O1O100O1RO\\OTEe0g:\\O]Ed0^:]OeEd0Y:ZOjEg0\\;100O1O100O1O100O1O1O1O1O1O1O1O1O1O100O1O1O1O1O1O1O1O1O100L4K5L4O11O00O1000000O1001O0000001O00O100000000O1001O0000001O00O1000000O1001O0000001O00O100000000O1001O0000001O00O1000000O1001O00?A3Mb0^O00L4K5L4L4L4L4LWO]DYO1d0^;6gDJU;8mDHR;8oDHR;6oDJQ;7gDWOOb0Z;?fDAZ;?fDAZ;X100O10000O100000000000000000000O100O11O1O00000000000000O10lNiD4W;LiD4W;LiD4W;LiD4W;LjD3V;MjD3V;NiD2W;NiD2W;InD7Q;JoD6Q;JoD6P;JQE6o:JRE5o:JRE5o:IRE7o:HQE8o:HmD<S;DhD_O0j0X;InD7R;GPE9P;GPE9P;GPE9P;GPE9P;GPE9Q;FnD;S;DmD<S;DmD<S;DmD<S;DmD<S;DmD<S;DmD<S;DmD<S;DmD<S;DmD<S;DmD<S;DmD<S;DmD<S;DmD<S;DmD<S;DmD<S;DmD<S;DmD<S;DmD<S;DmD<S;EkD<U;DkD<U;DkD<U;GhD9X;GhD9X;GgD:Y;FgD:Y;FgD:Y;GfD9Z;GfD9Z;GeD:[;FeD:[;k00000000QOgDLX;2kDNU;0lD1S;MPE3o:LSE4k:KXE5c:N_EBCOP;:`EFCNo:8`EIDMm:6bEKCOm:2aEOCOn:MbE2B1m:JcE4A2m;LUD4k;LUD4k;MUD2k;OTD1l;OTD1k;OVD1j;NXD1h;0WD0i;1VDOj;1VDOj;2VDMj;5SDLl;7RDIn;:oCFQ<=kCDU<?hCAX<a0fC_OZ<i00000O100000000_Oa0001O001O1O001O001O1O001O1O1O1O2N1O1O1O1O2NO1O100O1O1O100O1O1O100O1O1O100O1O100O1O1O100O1O1O1N2O1O1O100001O000000000WO]DJc;NeD2[;FlD;T;]OREe0i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size": [427, 640]}, "area": 34851.0, "iscrowd": 0, "image_id": 17959, "bbox": [0.0, 339.0, 640.0, 88.0], "category_id": 124, "id": 20001139}, {"segmentation": {"counts": "[`d61Z=001O00000000O12N3M2O1O01O001O003M1Nf]^1", "size": [427, 640]}, "area": 84.0, "iscrowd": 0, "image_id": 17959, "bbox": [509.0, 268.0, 19.0, 16.0], "category_id": 143, "id": 20001140}, {"segmentation": {"counts": "0S:X3H8O1JbL]F`3_98N2O1O1I7N2O1N2K5M3N2O1K5M3N2O1O1I7N2N2O1J6N2N2O1K5L4O1N2O1I7NnKYId1f6\\N[Id1d6[N^Ie1\\6_NfIa1W6\\NPJfMAj3^6`NTJa1j5_NWJb1d5aN`J]1]5_NjJa1T5`NmJ`1R5`NoJ`1n4VLQJY2R1b1h4bN[K^1c4_NaKb1^4^NdKa1[4^NgKb1R4cNnK_1P4]NTLe1j3[NULh1j3XNTLk1f3YNYLj1e3QN`LQ2^3PN`LS2_3mM_LV2\\3mMcLV2Z3iMgLZ2W3dMkL^2S3bMlLa2S3_MkLd2o2_MQMd2l2YMXMi2g2WMWMl2h2TMVMo2i2oLWMT3h2gL[M\\3e2cLZM_3e2`LZMc3f2[LYMh3f2RL^MQ4b2nK]MT4c2kK[MX4e2fKZM]4f2`KZMc4f2XK\\Mk4d2TK[Mn4e2PK\\MQ5d2mJ\\MU5e2cJaM^5_2aJaMh1bNh0n3^M`Md1kNl0f3]M_Md1POo0a3VMeMj1mNo0`3TMcMk1POQ1^3RMbMk1SOS1]3nL`Mn1VOT1]3hL_MS2WOT1\\5jNfJW1Y5gNjJY1T5fNoJZ1P5eNSKZ1l4dNWK\\1k4iMmKX2T4UMkI_OX2\\3o3TM[Ll2g3TMWLl2k3SMTLm2n3SMPLm2R4SMhKA`M^3j6PMdKS3^4mL^KU3d4jL[KV3g4jLWKV3k4jLSKV3n4jLQKV3Q5jLmJV3U5jLiJV3Y5iLfJW3\\5iLbJW3`5hL_JX3c5hL[JX3g5gLYJX3i5gLnIHdNa3`7fLkIJcNa3d7dLgIKeNb3d7eLeIIfNc3f7dL^IHjNl3h7]LYIHoNn3h7[LXIi3i6WLUIj3k6WLTIi3l6XLRIi3o6WLPIi3P7XLnHi3R7XLlHi3U7WLkHh3U7ZLiHf3W7[LiHd3X7\\LhHc3X7^LgHb3Y7_LfHa3[7_LdHa3\\7`LdH_3\\7bLcH^3^7bLbH]3^7dL_H^3a7cL]H^3d7bLZH_3f7cLWH^3i7cLVH]3k7cLUH\\3k7eLSH\\3m7eLRH[3o7eLoG\\3Q8o0001O00001O00001O00001O00001O0000iLnJVNQ5i1RKUNn4k1SKTNm4k1UKUNj4j1XKUNh4j1[KTNLRM^4j4gKTNHRMc4i4gKTNCUMg4f4iKZNW4f1jKYNV4f1lKZNS4e1oKZNQ4f1QLXNo3g1SLXNm3g1ULYNj3g1WLXNi3g1ZLWNf3h1\\LXNc3h1^LZN_3e1cL\\N[3c1hL^NU3b1lL_NR3`1PMaNn2^1UM`Nk2`1VM_Nj2`1XM_Nh2`1ZM_Nf2a1\\M]Nd2c1]M\\Nc2e1]MYNd2i1[MVNe2l1[MRN[N\\MW4d4]MRNc2Q2[MmMf2U2ZMiMf2Y2XMfMi2\\2UMdMk2^2RMcMn2_2oLcMP3_2mLaMT3a2iL`MW3b2iL\\MW3f2iLWMX3k2iLRMW3P3jLmLV3U3jLhLW3Z3hLeLX3]3gLbLY3`3fL^L[3d3iKZJb0Q2e3g3cK^Jf0k1g3Q4YLmKh3U4WLjKi3X4VLgKj3Z4VLdKk3^4TLaKl3a4RL_Kn3c4QL[KP4f4PLYKP4i4oKVKQ4l4nKRKS4o4lKQKT4Q5kKoJT4S5kKlJU4U5kKjJU4X5jKgJV4[5iKdJW4^5gKbJY4_5gK`JY4b5fK]JZ4e5eKZJ[4g5eKXJ[4j5dKUJ\\4l5dKSJ\\4n5dKQJ\\4P6dKoI\\4R6dKmI\\4T6dKkI\\4U3aKbN3WN]4W3`KcN3UN]4W3bKdN1SN^4X3bKfN0QN^4X3dKgNNPN^4Y3dKhNNmM_4Z3eKjNKkM`4Z3fKlNJhMa4[3gKmNHgMa4\\3gKnNIcMa4^3hKoNGbMa4^3iKQOF`Ma4^3kKROD^Mb4`3jKSOD\\Mb4`3lKTOBZMc4a3lKVOAXMc4a3nKWO_OWMc4b3nKXO_OTMd4c3oKYO]OSMd4c3PL[O\\OPMe4e3PL[O[OoLe4e3QL\\OZOnLf4e3RL]OXOnLe4d3TL_OWOPMa4a3YL_OVORM^4^3]LAUOSM[4[3bLBSOVMW4X3fLCROXMU4V3iLnMoNJ3UOQ4U3lLgMUOONWOn3T3nLcMZO2JXOl3T3PM]M^O8FTOn3X3mLXMD<AQOP4]3jLRMHa0]OnN=^OU3R4XMmLNe0XOkN`0AQ3S4YMhL2i0TOgNX4i3aLfL4k0SOcNZ4m3_LdL5l0RO`N\\4R4[LaL9m0PO]N^4V4YL_L9n0PO[N`4Y4VL]L<o0nN[N_4Z4WL[L<Q1`3f2RLXL`0R1]3g2SLVLa0o0jN_Na4]4SLTLc0n0lNaN]4^4TLRLd0m0lNdNLN\\4a4VLoKg0l0nNfNIN[4b4WLmKh0j0POhNGOY4c4WLkKk0h0POlNQ4b4TLiKk0h0ROmNn3d4SLgKn0e0TOoN@OW4g4WLdKo0e0UORO^ONV4h4WLcKP1d0WOSO\\ONU4i4XL`KQ1d0YOTOZOOS4j4XL_KR1b0[OWO_3j4SL[KT1b0]OYOTONS4m4WLZKU1a0_OYOSOOQ4n4XLWKV1a0@\\OQONP4Z4mKYL;BW1?C^OnNNo3W4nKiK2c09@X1?E_OlNOm3W4SL\\L7@X1>FAm2U4oK\\L6_OY1<HChNOl3W4UL\\L6_OY1:JFfNNk3W4WL\\L3@\\16LIcNN41[3U4eL]L0A]13NJbNO02_3T4eL\\LOB_1ONO`NNM5a3P4gL^LMAa1L02\\N7_3l3hL]LLB\\3MbL7^3l3iL^LKB\\3NbL5^3m3jL]LKC[3NbL4^3m3lL]LIDX31fL0]3o3kL\\LJDU35hLK_3P4lL[LHDQ3;lLE_3P4mL\\LGDn2>PMA^3P4oL\\LFEh2c0UM[O^3Q4oL\\LFEe2h0WMUO_3S4oLZLGEa2m0o0T3jLZLEEi1NaNT1V3P3kLXLEFj1N\\NX1Y3m2kLWLGFh1NYN^1[3h2kLXLHDS2`1Y1d2oLULFFn1g1[1_2RMRLEHg1OQNj1b3_2PMPLEIg1l1d1[2QMoKEIf1k1f1^2oLmKEJe1k1h1_2nLlKEIe1l1h1`2mLkKFIe1k1i1a2lLkKGIb1k1l1b2iLlKHGc1k1l1a2mLjKDJ`1n1o1^2mLjK[1l1i1\\2jLhKX1Q2n1X2jLgKU1S2Q2V2jLgKU1S2Q2W2jLeKV1S2P2Y2iLeKHJQ1X2^2Z2hLdKJKn0X2_2Y2jLcKIMn0U2`2\\2hLcKJLm0U2a2]2gLbKKNl0S2a2]2iLbKJNk0R2c2_2gLaKK1h0h1m2f2aLaKI2i0_1T3m2[LcKH0i0Y1[3T3TLcKHOk0T1^3Z3PLcKGNk0R1a3d2[KkLa0BGMl0n0e3e2ZKoL=BHKm0k0g3f2ZKSM:@HKn0h0j3g2YKWM6@IIo0e0l3i2XKZM3@IHP1a0P4j2XK]MO@IHo0>T4j2XKaMK_OJHP19W4l2WKeMEALFP15[4l2WKjM@_OOEP12]4m2WKnMYOQOJ?8DQ1M`4o2VKQNVOA4AP1N`4o2VKTNRO@7@Q1M_4P3VKTNRO_O9_Oj6n2kIjNZOYNQ1M^4Q3VKiN[OYNR1M\\4Q3WKjN[OWNR1N\\4R3VK]NTOlN7HS1M[4S3WK[NTOmN8Gi6Q3kI[NUOmN6HS1MZ4T3WKTNUOmN068GS1NX4T3XKTNVOjNON1;6Hh6R3kITNWOhN2:5GS1NW4V3WKSNXOgN4;2Hg6S3kITN^OQO0Hg6T3jISN@QOOHf6U3jIRNAQOOHf6U3jISNAPOOHj2Ng0X3nLRNBPOOHe23l0S3nLRNBPO0G[2=U1k2nLQNBPOOHP2g0a1a2mLPNCPOOHe1R1l1V2mLQNBPO0GV1a1Z2h1mLQNCnN0HV1`1[2i1lLRNBnN1GV1_1[2k1kLQNCnN1GU1`1\\2k1jLQNDmN1GT1`1]2k1jLQNDmN1GT1`1]2l1jLPNCmN2GT1`1]2m1jLoMCmN2GS1`1^2n1jLmMCnN2GS1`1^2n1jLnMBnN3FQ1a1`2n1jLlMBoN3FQ1a1`2o1jLkMBoN2FR1a1`2o1kLjMAPO3FP1`1b2Q2jLiM_OQO5EP1`1b2R2iLhM@RO5DP1_1b2T2iLgM^OSO7CQ1_1`2T2kLYNDSNR1_1_2V2kLWNDUNR1]1_2X2kLVNCUNU1[1]2[2jLUNEUNT1Z1]2\\2kLTNDVNU1Y1\\2^2kLSNDUNV1Y1[2`2kLQNDWNV1W1[2b2kLPNDVNW1W1Z2c2kLPNDVNY1T1Y2g2jLoMCWNZ1R1Y2i2jLmMDWNZ1R1X2k2jLkMDYNW1R1\\2j2jLkMCXNV1T1]2j2iLjMDXNU1V1]2i2jLiMCYNS1W1`2g2kLhMCYNQ1Y1a2g2kLgMBZNo0Z1d2f2jLfMDYNi0`1i2b2jLdMDZNd0e1n2]2kLdMCZN>k1S3X2kLcMDZN9o1Y3U2jLaMD\\N3S2_3Q2iLaME[NMX2e3m1hL`MFl0b3f1gL^MGl0a3g1gL]MHl0a3h1gL[MIl0`3j1fL[MIk0a3k1fLYMIm0`3l1eLWMKQ1\\3i1iLVMKP1\\3k1hLUMLn0^3n1eLUMLn0^3n1fLSMLl0K]Mc3e4eLSMMj0K^Mc3f4eLQMMl0`3T2bLQMMj0b3V2aLoLMk0b3W2`LnLNk0b3X2_LnLOi0b3Z2_LlLOk0a3Y2`LmLNj0b3Z2`LkLNj0c3\\2^LkLOh0c3^2^LiLOi0c3_2]LiLO75WN`3j4\\LgLO76RNe3Q5ULgL05V4U3iKfL14W4W3hKeL0aNOX1X4c3hKdL1`N2V1V4g3gKbL1`N4W1S4g3hKcL1]N7W1P4j3hKaL1\\N;W1l3l3hKbL0ZN>W1j3m3iKaLOZN`0W1h3o3hKaLd0_Od3P4hKaLd0^Oe3R4gK^Lf0_Oc3S4gK]Lg0@b3S4hK[Lh0A`3U4gKXLl0D[3T4jKVLl0FZ3U4iKTLo0GW3U4kKRLo0IV3U4kKPLR1JS3W4jKnKU1JQ3X4kKlKU1LP3X4kKkKW1Ln2Z4kKiKW1Lo2[4jKhKY1Lm2]4jKfKY1Mm2]4jKfKZ1Ll2^4jKfK[1Jl2a4iKdK\\1Jk2b4iKcK^1Ij2e4hKaK_1Ii2f4hKaK`1Hh2g4iK`K`1Hg2i4hK^Kd1M^2e4oK]Kd14V2_4WL\\Kd18Q2]4ZL[Kf17P2^4[LZKf17o1`4ZLXKi17m1a4[LWKi16m1c4[LVKi16l1e4ZLUKj16l1e4[LSKk17j1g4ZLRKm16i1h4[LQKl17i1h4\\LPKl17h1j4[LnJn18g1j4\\LmJn18f1k4\\LmJo17e1m4\\LkJo18e1m4]LjJo18d1o4\\LhJQ29c1o4]LgJQ29b1P5]LgJR28a1R5]LeJR28b1S5]LcJR2:a1T5\\LbJS2:a1U5\\L`JS2;a1V5[L_JS2<b1U5\\L^JR2=b1V5\\L[JS2`0`1V5\\LZJT2?a1W5\\LYJS2`0a1X5[LXJi1KYNd0d3Z5ZLUJj11TN`0h3Z5[LTJi15QN=k3[5ZLSJj1m0l1Q5ZLQJj1n0l1Q5ZLQJk1m0k1S5ZLnIl1o0j1T5]NkJd1U5\\NkJd1V5[NjJe1W5YNjJg1V5[NhJe1Y5]NdJc1]5^NaJb1_5`N_J`1b5`N]J`1d5`N[J`1e5aNYJ`1h5`NWJ`1j5`NUJ`1k5[NfIjM>k3m5[NfIhM=m3n5\\NTJe1l5]NRJc1o5]NmIf1S6SLbIR29k1V6\\NgIf1Z6PL`IU21n1_6nK`I]6a6cI_I50k5b6oI_I32m5_6QJ_IO5o5]6QJ_IM6k1HY2d6oKiIn5W6SJhIg1DX2e6PLhIh1CW2f6QLfIh1DW2f6QLgIl5Z6TJfIk5[6UJdIk5\\6UJeIj5\\6VJcIj5^6UJcIj5^6VJbI[1Hi2f6mKaI[1Fj2j6jK`Ii5a6WJ^Ii5c6VJ]Ij5d6VJ[Ij5f6VJZIi5g6VJ[Ih5f6XJ[If5f6[JYId5g6^JYI`5h6`JYI^5h6cJWI\\5j6eJVIY5k6gJVIW5k6jJUIT5l6mJSIR5m6oJTIo4m6RKSIl4m6VKRIi4o6WKPIi4Q7XKnHg4R7[KmHd4T7\\KkHd4V7]KiHb4W7_KhHa4Y7^KhHa4Y7_KgH`4Y7`KgH`4Z7`KfH_4[7`KfH_4Z7bKeH^4\\7aKeH^4[7bKeH^4\\7bKdH]4]7bKdH]4\\7dKcH\\4^7cKcH\\4^7dKbH[4^7eKbH[4_7eKaHZ4`7eKaHZ4`7eK`H[4b7dK^H[4c7dK]H\\4e7cK[H4Fi3P8RL[H3Fk3P8RLYH1Im3P8PLXH1Io3P8PLWHMLS4o7nKUHMNU4n7mK`HS4b7lK]HT4d7kK\\HU4e7kKZHU4h7jKWHV4j7jKUHV4`80]OjKPHW4P8jKoGV4R8jKmGV4S8iKnGW4R8iKnGW4R8iKnGW4R8iKnGW4R8iKnGW4R8iKnGW4R8iKmGX4T8hKkGX4U8hKkGX4U8hKkGX4U8hKkGX4U8hKkGX4U8hKkGX4U8hKkGX4U8hKjGY4d8000CgKhGY4X8gKhGY4X8gKhGY4e80CgKjGW4V8iKjGW4g8N0000O1AjKiGV4g8OO1", "size": [427, 640]}, "area": 155224.0, "iscrowd": 0, "image_id": 17959, "bbox": [0.0, 0.0, 640.0, 323.0], "category_id": 157, "id": 20001141}, {"segmentation": {"counts": "h^n11W=2M4N1O2L4M2N3M3M3M3N3L3L4N1K6N1N3M3N2M3O1N2O100010N1O10001N10001O3M2N3_OWD[Oj;c0WDXONIo;l0XDVOJNn;l0YDTOJ0m;l0>M3DaCIb<NhCN[<1gCOW<2jCLW<4`0MkBOY=0O10O010O10QgX10lXgN0jB2U=201O001N2O1N101N101N20O02M1O2O010O1000O1N13N10OO100000O001N04M02N10O0100O1O010O1O010O01N100010O1O100O1O100O1M4N1LPCJR=55L2O\\X10dgN001IOSC0m<1PCON1R=0oB2Q=NnB4R=201O010O00010O01O100O0010O01O10O01O0010O01N101NjTU3", "size": [427, 640]}, "area": 2923.0, "iscrowd": 0, "image_id": 17959, "bbox": [149.0, 131.0, 248.0, 207.0], "category_id": 167, "id": 20001142}, {"segmentation": {"counts": "aZ:1Y=2M1I8O1N2I8N2M6K3K3O01000001lNkCP1W<1O21K3L4M3L3M4L3M5L3L_Q:0cnE1O2N2N2N2J6I7002N0010O0000O2OO100001O00O1000001O00OM\\O_Cf0`<3N200000000000001O0000O2OHWOnCi0R<XOlCi0S<WOmCj0S<VOlCk0o;TOoCT1m;mNSDV1m;4O101N10O01O11O001N2O0000010O001ON101O001001OO100O1001O1L4K5M4L3K5L4L4L4L4L3N0O0O11Ri8OQWG1oBNI1j<6XCJN0j<7VCJ0Nk<>M2N3M31OO1001O2N1O1O1O1N2O1O1N2O1O1O1O1O100O1O2Mc[Q10]dnN001O1N2O1O1M3N2N2N3M2O2N11N1000000000O10001O0014K5K5J7JQj03lUO3K5L4M2M4L4M3M0O10O100000010M1J7I8H\\Q:KonE1N2O1J6K5O110N11O001N1000001OO1000001N10000000O100000000000010OO1O100000O1000O1000001O00010OO2O1ON110000000K@]C0Oa0b<600O2OOO21OMXOcCh0]<XOcCg0_<2O10001O01O01N01000_O\\OZDe0e;\\OUDj0k;;O11OO11O0001O0000O01000O1O3M101O0O10000000O1000001N2O1N101O0006I8I4L0SOdC042L?\\<_OkC0M>g<01O0000N201N12N0O101O1OO1O11O@CdC11O32L;Z<b00001O00O101OO100O1000000000000O100001O2N0001N1001N100ROeCl0[<TOfCk0_<N000000000001N010001OO1001OO10WOaCf0b<01ZOaC?d<11_OWC>k<O100000000000000000O2O00O2O1OO10000O1000O02O000001N2O00O1O11O3M000000000O100O100000000000O001O1O1O1O100O1001OO100002N001O001ON20000001O00O100002N00N200003M00M30000002N00M3001O001ONETC<k<3O2O2oE", "size": [427, 640]}, "area": 10387.0, "iscrowd": 0, "image_id": 17959, "bbox": [24.0, 288.0, 616.0, 52.0], "category_id": 169, "id": 20001143}, {"segmentation": {"counts": "[W11Z=1O0O2N1N2H7mNNbD?W;JeD8Y;JeD8Y;HgD9Z;FeD<Z;CgD=Q;ROnD`01>S;QOlD`01>U;QOjDa01=];BcD:b;E_Di0S;VOmDl0Q;UOnDn0o:ROQEP1m:POSEQ1N_Nk:?XEX1g:fNYE\\1f:eNXE\\1i:bNUEa1l:^NTEc1l:[NTEn00^On:BREm00DS;YOmDP12IU;3lD1T;JmD9V;BiDa0Y;[OgDh0Z;SOfDP1];kNbDX1`;dN_D_1c;4M2M3M3M4M2M3M3M3M3N2M3M3O1N3M2N2O1N2N2N2N2O1N3M1OO2M2N2N2O1N30O1O1O100O1O1O1O0101O0J7J6I7J5O2O1N2N1O2O1N2N1O2O1J9gNY100O100O10O10O100O10000O10000O010O100O1O1O1OO101N1O100cEO^72aH5X7LgH5Y7KfH6Y7KcH8]7IaHg0Q7YOmHk0P7VOPI[1^6fNaIX1b6iN\\IR1i6oNWIl0m6UORIg0S7YOoH?V7BiH:[7GeH4`7L_H0iNZOVO1c8f0^IKkNDg7b0]IFnNJf7`0[IBROOc7`0ZI^OUOMmNMe8j0YIVOBOV7k0lJUOT5l0kJTOU5m0e30O1O100O1O100O2N100O1O100ON2N3L3M3NkLXOlId0V6_OhI>[6CdI:^6HbI5_6M`I0b63\\IJg67YIFh6<WIAk6b0n21N2RJ\\OROf0m0ZOSOh0k0XOTOj0k0VOTOl0k0TOUOm0j0SOUOP1j0oNUOS1j0mNVOT1d0QO[OQ1d0oN[OT1c0mN\\OT1b0mN]OU1b0lN\\OW1b0iN^OX1`0jN^OX1a0eNA]1kM]NVO2S3c1\\MeNHFf2f1`MfNCIP3`1\\MhNAJU3]1YMjNAIX3]1TMlNCG[3\\1QMoNBE_3[1nLQOBDb3[1iLSODBe3X1hLWOBAh3V1eL[OB_Ok3S1dL_O@^On3P1cLC^O]OQ4m0aLH]O[OT4j0`LL[OZOW4k0[LL]OYOZ4k0VLN_OVO^4m0PLNAUOa4n0kKNCTOd4n0fK0EROg4m0cK2EQOj4l0`K4EPOm4k0\\K7FnNP5L[Id0o1c0EmN\\5?nJf0DkN`5>jJi0EiNb5>hJj0EhNe5=eJk0FhNg5<aJn0HeNh5<`JP1GdNk5;]JR1GbNo5;XJU1K]Nn5>VJV1K\\NQ6=SJ[1HXNW6<oIc1DQN_6:mIj1_OkMf6;kIn1[OfMl6;hIo1\\OfMn6:eIn1_OhMm6:cIm1AiMn68aIn1g7QNXHm1k7SNTHl1o7SNPHl1S8SNlGk12QNc63ZIk11UNf60XIl1OVNj6NVIn1KWNP7KTIP2HWNU7IRIR2DXN[7FPIS2BXNCD[71_IU2^OYNGDZ4JYN4VNX2\\OXNJDZ4IZN3UNY2YOZNLC[4H\\N2RN\\2WOYNOB[4I]N0QN^2SOZN2A\\4H_NOoM_2QO[N4@]4H_NNmMa3UOlL]4FcNLjMa3WOmL[4HdNJhMb3ZOkLY4KeNHgMb3\\OjLX4LfNIcMb3_OjLV4MhNGbMb3_OkLV4MjNF`Mb3_OlLV4NkND^Mc3BjLU4OlND\\Mb3EkLQ41nNBZMc3GjLP42POAXMc3IiLn35QO_OWMc3LfLm38QO_OTMd31bLi3=RO]OSMd35_Le3a0TO\\OPMe3:[Lb3e0TO[OoLd3>YL_3i0TOZOnLe3f4SM\\NXOPMb3c4WM^NWOQM_3a4[M^NVOSM\\3^4_M`NUOUMX3Z4eMaNROXMV3W4hMbNROYMS3W4jMlLPOJ3VOn2V4nMfLVONNXOk2U4QNaLYO3KWOY2g4bNkK_O7FTO\\2j4_NfKD<@RO9[Oj1b5gNbKI`0]OnN=^Od1c5iN\\KNe0XOkN`0Aa1c5hNYK2i0UOfNh2Y5QNVK4k0SOcNh0HW1e5hNUK7k0RO`Nn2`5iMSK8n0PO]NQ3c5fMRK:m0PO[NS3f5cMPK;P1oNZNR3g5cMoJ=P13ZNh0h5gNlJ`0R11ZNh0h5fNlJa0o0kN^N0OT10i0h5gNjJc0n0kNbNNNT3h5_MjJe0l0lNdNLNT3i5^MgJg0l0nNfNINU3h5]MgJh0j0oNiNHNT3h5\\MfJj0i0QOkNP1N>h5gNeJk0h0ROmNBNY12?g5gNdJm0f0TOoNANV17>b5jNbJn0f0VOQO^ONT1;>^5kNbJo0e0XORO\\ONR1`0;[5PO^JP1e0YOTO[ONn0e0:X5SO\\JQ1c0\\OVOWONn0h09W5TOXJT1b0]OYOTONl0i0:X5TOVJU1a0^OZOTONi0j0;Y5UORJV1a0@\\OQONf0l0=Y5UOPJW1?C^OnNNc0n0`0X5UOmIX1?D@mNN>Q1c0V5VOlIX1>FAjNN=T1c0U5WOjIY1<IBhNO9V1e0T5XOiIZ19JFfNN6X1h0S5WOhI\\16LIcNN3Z1j0S5WOgI]13MKcNNN]1m0Q5WOgI_1OON_NOL^1P1P5VOfIb1K02\\N_1k0o4WOeI\\3McL^1i0Q5YOcI\\3NbL]1h0S5ZOcIZ3OdLZ1h0T5[ObIV33hLV1f0V5\\OaIS37jLR1f0X5\\O_IP3<nLm0d0Y5_O]Il2`0RMi0c0Z5_O^Ig2d0WMc0b0\\5A\\Ic2j0YM=b0^5B[Ih1MdNR1E2;g5DWIi1N_NV1MI6m5EVIj1NZNZ15BOQ6HVIi1MWN`1=[OET6NSIh1NVNd1c0XOZOT65RIh1NRNj1j0S6]OTHg1OoMP2j0n5_OTHf1T2iNg5BTHe1X2gNc5DUHe1[2dNa5FTHe1_2cN\\5ITHd1c2aNX5KVHc1k4SNV1:nIb1e4[N^13mI^1a4eNd1a1ZNfNg1U1VNROl1m0lMYOV2h0bM^O_2b0YMEi2mNcIQ1\\38R3gNdIo0R3a0[3aNcIm0k2h0d3ZNbIn0b2n0m3UNbIk0Z2V1V4nMbIk0P2^1^4hMdIg0g1g1g4aMbIh0`1m1P5[M_Ii0Y1R2Y5UM]Ij0U1V2_5PM[Ik0R1W2n3eL^L8bNl0n0Z2P4eLaL5`Nl0l0]2P4eLdL2_Nn0h0^2S4eLgLN^No0e0`2U4eLiLL\\NP1a0c2W4dLmLH\\NP1=f2X4eLoLF[NP19h2[4eLRMB[NP15k2\\4fLTM@ZNP12m2]4fLXM\\OZNQ1Mo2`4fLYMZOZNQ1Mn2a4hLXMYOZNQ1Mm2b4hLWMZOZNQ1Ml2c4iLWMZOYNQ1Mj2e4jLUM[OYNR1Mh2e4kLVM[OWNR1Ng2g4kLUM[OWNS1M`1n5QNnK]OVNl3n5gLmK\\OVNS1M\\1R6TNkK]OVNT1MY1S6VNkK]OUNS1MX1V6WNhK^OUNT1MS1Z6ZNeK_OTN]3]6UM^K^OUNT1Mg0f6fNYK_OTNT1Ma0l6kNSK@TNm2n6cMoJ_OTNU1L6W7UOiJASNb2Y7mMdJASNU1MDh7EYJBRNj1R8cNlICRNX1d8UO[ICQNU1g8WOXIDQNV1f8VOZICQNV1f8VOYIEPNU1h8VOXIDQNU1g8WOXIEPNT1i8WOVIEQNS1k8XOTIEQNR1l8XOSIFQNR1l8YOSIDRNR1l8ZOQIERNQ1n8ZOPIDRNQ1o8\\OnHDSNo0P9\\OmHESNP1P9[OmHDTNQ1o8[OlHETNQ1o8[OmHCTNS1o8ZOlHDUNR1o8YOmHDTNU1n8WOmHEUNT1m8XOmHDVNU1m8WOmHCVNW1m8VOlHDWNV1m8UOmHDVNX1l8UOmHDVNX1m8TOmHCWNZ1k8SOmHCXNY1d2gNZ3=iKDYNV1_2cN_M:o59kKCXNV1_2SO^34jKDXNS1b2UO]34iKCYNR1c2UO\\37gKCYNP1e2mN^MNn5b0fKBZNm0Z9@\\HCZNh0`9EUHDZNb0f9JQHCZN=k90jGDZN6S:5cGE[N0X:;]GE[NJ^:`0WGFQ99oFFR99nFHR97nFIS97mFIS96mFJU95jFKW94jFKW95hFKY94hFLX94gFLZ93gFLY2Kn28iJMW2MQ36hJMU23o2OlJNT26o2LlJNY22l2OlJN\\2Ll26gJO^91cFN^92aFN`91aFN`91`FOa91_FOa90_F0b90^FOc90]F0d90\\F0aNOg:0hF1`N2g:LiF2_N4g:KjF0^N8g:GkF1\\N<g:DmF0ZN>T3A\\41VJ0YN`0S3@_40UJOXNb0R3Aa4NUJd0W1@e4LRJf0X1^Of4LQJg0X1^Og4KPJi0Y1[Oh4LlIk0X9UOgFm0Y9SOeFo0Z9QOeFP1[9QObFR1^9mNaFT1j1kN`41eIV1c1lNWM2\\7MhIV1e1kNWM3\\7KhIX1d1jNWM4]7KfIY1g1fNVM5_7KdIZ1c9gN\\FZ1c9fN]FZ1d9fN[F\\1c9dN\\F^1d9aN\\F`1c9aN\\F`1c9aN\\F`1d9_N[Fd1c9]N\\Fd1c9\\N]Fe1c9[N\\Ff1c9[N\\Ff1d9YN[Fi1d9XN[Fi1d9WN\\Fj1d9VN[Fj1e9VNZFl1f9TNYFl1g9UNXFl1g9TNYFm1g9SNWFn1i9RNWFo1h9RNWFn1j9RNUFo1j9QNVFP2j9PNTFQ2l9oMTFR2k9oMTFQ2m9oMRFQ2n9oMQFR2P:nMoER2R:mMnES2S:mMlES2T:nMkER2V:mMiET2X:lMgET2Y:lMgET2Z:lMeEh1MYN_:ObEi13SN\\:4aEi1k:WNTEi1m:VNSEj1m:WNREi1o:WNoDj1R;UNnDk1R;71N2O001O1O02O001N101O0O2O001N2OhMRER2m:oMSER2l:70fMTEU2j:lMVET2j:kMWE50d1i:WNWE23f1f:YNWEM7j1b:XNcEh1^:XNbEg1^:YNbEh1Y1VN[72\\Gg1W1aNV7HbGh1W1aNV7GdGg1Y1_NT7JcGg1\\1[NR7MbGh1_:YNaEf1`:ZN_Eg1a:XN`Eg1a:YN_E[1l:eNTE\\1l:dNSE]1m:bNSE^1n:bNQE_1o:aNPE`1P;_NRE`1n:`NRE_1o:aNRE^1n:cNRE\\1m:fNREZ1n:fNSEX1X1iNj70nFV1Q1POR8KlFU1HfNR1:[8KlFT1DiNV17[8MkFX1m0hNX81kFU1m0cNhN1b98iFT1k0cNiN3d97hFQ1k0QOW80mFo0k0nNjNG_9<kFo0Q1iNX89fFn0S1eNcN2b9=gFl0W1eNS8?eFl0S1hN[8=aFk0R1jN\\8<aFj0S1kN\\8:bFk0Q1kN^89bFk0P1hNjNNd9`0aFk0o0eNh8?ZFk0o:UOQEj0o:VOQEj0P;VOPEh0Q;XOoDh0R;XOnDf0T;YOmDf0S;ZOmDd0V;\\OjDa0Y;^OhD?Z;BeD=];BdD;_;EaD8b;G^D7f;HZD5Y1@k8:lE5W10c8IWF4X14a8HWF1\\14`8ITF1^16_8ISFNb18[8ITFMZ<2eCL^<3?O0O101O00TE2h:3aMKfG3Z8NeG0_8OSm00[UO0000000hR20XmM000S`20m_M0id2", "size": [427, 640]}, "area": 46715.0, "iscrowd": 0, "image_id": 17959, "bbox": [2.0, 96.0, 631.0, 325.0], "category_id": 183, "id": 20001144}, {"segmentation": {"counts": "XQc13l>1O10O01O100O1O10O01O1O100O1O100O1O10O02N00101N00100O1O010O1O1O100O1O010O1O100O001O100O1O100O1O10O01O1O100O1O010O0000010O1O2OO000100O1O0001O3N2O1N02N002N0001O00000O100000002N2N2N4L00O11N101O00O010O100O0100000O0100000G91O03N0O10O001O001N1O1N3M2N3O0O2OO10O1000O_OiA;Y>ChA<^>01N010O2O000O10000O1000O10O101OO010O1N11000O10O02N01000000000O2O1O0O01000O10O01000000O0100O1000N20O02O00O10fBJi;7WDJh;6YDJd;9ZDN`;2`DLa;5]DNa;4_DKa;7[DJe;7fC=Y<KaC6W<iNkC\\2U<eMiC]2W<dMfC^2Y<6N0001LhC`MW<]27O100O2O01O00010OL41O2O1N4L5K5L5J7UDiL`;^3M5L3L5K4L003REnKj:U4NN27I3N0O1O1O100O1O1O100O1O100O1O1O100O1OXKlEf4T:YKoEe4P:[KSFc4W:N2N2N2N2N2N2N1O001O1O1O1O1O001O001O001O001O001O001O001O001O1O001O001O001O001O1O001O001O001O001O1L301O001O001O001O001O001O0NUDoLl;?\\Dh1f;TNkD_1V;\\NSE_1m:^NZE^1W<NOO2O1O1O1O001N2O1O010O010O01O010O01O001O001O001N1N2N3H7K6N1O2N2E[dl0Oo[SO1O1N2O1O1N2O1O1N2O1000000O1000000000000000000000000001O00000000001O000000001O0000001O001O1O001O001O1O001O000000001O00000000001O000000O10000O100O10000O100O11O001O00001O00001O000000000000000000000000O1000000000000000000000000000000O100000000", "size": [480, 640]}, "area": 20533.0, "iscrowd": 0, "image_id": 18150, "bbox": [108.0, 324.0, 532.0, 156.0], "category_id": 93, "id": 20001145}, {"segmentation": {"counts": "e=[1e=000O1000000000000000000000000001O2N2N1O1O001O00000000O1O100O1M3N2O10000O10000000000O100000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000O100O1O1O100O1O100O1O1002N0000O10000000000O1000000000000000000O11OO1001OO1000000O1000000000000O100000000O10000000000000000O10000000000O100O1O1O100001OO1O1000000O1O1O1001O002N002N0000000000O100000000000000000000O1000000O1000000O1000000O100000000003M1O1O000MPCVNP=i1QCWNo<i1QCWNn<i1SCWNl<j1TCVNj<k1WCUNg<m1YCSNe<o1[CQNe<n1\\CRNc<o1]CQNc<n1^CRNb<n1^CRNa<n1`CRN`<n1`CRN`<m1aCSN_<m1aCSN_<m1aCSN_<l1bCTN]<m1cCSN]<l1dCTN[<m1eCSN[<l1gCSNY<m1gCSNX<m1iCSNW<m1iCSNW<l1jCTNU<m1kCSNU<m1kCSNU<l1lCTNT<l1lCTNS<l1nCTNR<l1nCTNR<k1oCUNP<l1PDTNP<l1PDTNP<l1PDTNo;l1RDTNn;l1RDTNn;l1RDTNn;l1RDTNm;m1SDSNn;l1RDTNm;m1SDSNm;m1c0O1000001O1O00O2O1O1O0O2O1O1O1O1N2O1N3N1O1O2M5MO00O10bNnCNR<2oCMQ<4oCKR<3UDGk;9SDIm;5VDJk;6TDJn;2SDOm;\\OgDe0_;UObDj0i<100001OO1O1LQOWBo0e=8N2O1N2O100O1O100K5001O1O3M4L4L5K6POnAf0X>N4L3M4L3MO12GYA5j>OM36JRS]4", "size": [480, 640]}, "area": 18260.0, "iscrowd": 0, "image_id": 18150, "bbox": [0.0, 396.0, 338.0, 84.0], "category_id": 100, "id": 20001146}, {"segmentation": {"counts": "\\4Y9g50000OR1oNS1mNS1mNS1mNS1mNS1mNS1mNS1mN;E0000001O01O2N2O0O1O001O01O00000O2N100O1M3N2O10000O1000000000Ufo12hYPN3N2M1O100O1O1O2O0O11O001O001O02O1N2O3L00O20O010O01N101N101N101O000O2O001OYk9KiTFNXA3g>301N01001O0O1000000O1000O010001OO010O1N20O10O100O00100000O1000O01000O10O10O100O0100000O01000000OO200O0100000O01OO20O000010O0O200N3McVl4", "size": [480, 640]}, "area": 3650.0, "iscrowd": 0, "image_id": 18150, "bbox": [0.0, 140.0, 306.0, 304.0], "category_id": 105, "id": 20001147}, {"segmentation": {"counts": "Poo33m>5K8H8H5K5K;E<VCXNd;R2lCTNT<\\200000O1001O001O001O001O001O001O001O00001kC\\Ml;d2TDRNV;o1iD\\Nl:d1UE_Ng:b1XEbNX1eNP8i2hFeNR1eNV8g2hFgNk0eN]8\\4]GfKd8^4UGeKk8^4nFfKR9R521O00001O00O11OO100000000000000^OgFoJ07OIZ9Q5nFnJK0Z9Q5`0O10000000000000000000000000000000000000000O10000000000O100000000O10000O100000000XOnEbLR:]3PFaLQ:^3RF`Ln9^3UFaLk9_3UFaLk9^3XF_Li9[3_F[Li9[3dEbLe<5`CNoe`1Nhh_N3M4L3aAMk=j0JO10000O100000000000000O10000000000N2N2O100O100000000000000001O000000000000001O1O001O1O001O1O001O1O001O1O001O1O001O001O001O00UO\\BJ;K_>001O001O1O001O06Jc^U1", "size": [480, 640]}, "area": 16566.0, "iscrowd": 0, "image_id": 18150, "bbox": [273.0, 0.0, 287.0, 183.0], "category_id": 109, "id": 20001148}, {"segmentation": {"counts": "Smi4220h>7WAK`>>L10000000000000000000002N001O00O100001OO11N10000000000O011OO100O01000O01000O100000O10000]OZOlBf0T=\\OiBe0h<^ORCOLl0T=POhB08[1Q=eNoB\\1P=dNoB^1Y=1O0002O1N2N>AoNUOPDj0S=01O001GjAFV>9kACZ><5M4I7MPYa3", "size": [480, 640]}, "area": 1708.0, "iscrowd": 0, "image_id": 18150, "bbox": [328.0, 122.0, 70.0, 63.0], "category_id": 112, "id": 20001149}, {"segmentation": {"counts": "cgo32j>6I7J7L0O001O10O0EkAHO1U>8;2O1N1O2N2O1N2N2N2O1N3M3M4WBoN^=0aBT1e=2M2O1N3M3N2M3C>J7L4L4L6I5L1O0N30O01O10O0\\OgDbMW;]2h0M1O2N2N2N2N200O10MmMYCS2g<23N000O10000O100O100O100O3N0O2O0O01000O2OO02O0O100O100O10O02OO010O10O10O0100O010O100O10WE_NS8`1mGaNS8_1mGaNS8_1lGbNT8]1lGdNU8[1kGeNV8Z1iGgNX8W1hGjNY8U1fGkN\\8T1dGlN\\8T1cGmN]8S1bGnN_8Q1aGoN_8Q1`GPO`8P1_GQO`8P1_GQOa8o0_GQOa8n0eFM[93TF>l9BSF?e9YNaEX1i0`0e9YN^E[1k0>g9GXF:e9IZF8`9N_F3_9O`F2_9O`F3a9J_F7b9H]F9d9F[F;f9DXF>i9AVF`0k9_OTFb0m9]OQFf0o9YOPFh0Q:WOmEl0R:TOmEn0R:QOmEQ1S:oNeE_N2d2X:mNdEbN2c2Y:m1O1O1O1OO10002N1O2N1O2N1N3]L]Ee2d:oLgEP3\\:kLhES3Z:hLjEV3Q;01N1O1O001O1O2N2O001VMQDb2W<O1O1O100O7I1D_CVNc<g1dCTN]<j1=O2M2O2N1O3N5J2O0O2O0O2N101N100O2O0O2O0O100O1O100N2O001N2O1O1O1N2N1M4L4M3M3NaY_13]f`N2O1O2N1O2N1N3M2O1N3M2O2M2N2O1J6L4N2O1N3M2O1N2O1N2]O]NfCe1W<WN^C86c1[<dN_C^1`<b0O1O1O1O1O1O001N10000001O010O001O0000011ON20OiJ", "size": [480, 640]}, "area": 17475.0, "iscrowd": 0, "image_id": 18150, "bbox": [272.0, 162.0, 368.0, 162.0], "category_id": 117, "id": 20001150}, {"segmentation": {"counts": "0\\4d:0000CgESLY:l3>N2O1O1O100O100O10000O100O100O100O100O100O100O100O10000O100O100O100O100O100O100O100O100O100O1O100O100O100O100O1O100O100O100O1O100O100O100O100O1O100O100O100O1O100O1O1L4L4L4M3L4L4L4L4M3L4L4M3O1N2O1O1N2O1N2O1O1N2O1N2O1O100O10000O1000000O10000O10000O10000O10000O10000O1000000O10000O10000O10000O10000O10000O1001O1O001O1O001O1O001O1O001O1O001O1O001O1O001O1O001O1O001O1O001O1O001O1O001O1O001O1O001O1O001O1O001O3M2N3M2N3M3M2N3M2N3M2N3M2PDQNk:R2nDQNS;R2eDRN\\;P2^DSNc;P2UDTNl;_2100O10000O100O10000O1O1O1O100O1O1O100000000000000000000000000O10000000000O10000O100000000O100000003M5K8H8G6K5K;E<C;@cbm3HVoQL;_=k00000000000000006ZBdN\\=`101O001O001O001O1O1O001O1O1O001O1O1O001O2N1O2N00O1O1O1O1O1O1O1O11O1O1O001O1YCYNS<[2O1O1O1O1O1O1bDXMb:i2YE\\Mf:e2UEaM8Eh9g3VF]Lg9e3VF^Lh9b3WFbLf9_3WFeLg9\\3WFhLf9Y3XFlLd9U3ZFPMb9Q3\\FoLe9Q3YFoLMROj9P4WFoLk9S4000000000000000O10000001O001O001O0000002NO11O", "size": [480, 640]}, "area": 26070.0, "iscrowd": 0, "image_id": 18150, "bbox": [0.0, 0.0, 640.0, 167.0], "category_id": 172, "id": 20001151}, {"segmentation": {"counts": "`Ya5129OGP;X1VEmNNn1l:k101OO1O100O1O1O1O1C=O1O1O1O100O1O1O1O100O1O100O1O100K5M3N2O1O1O1O1N2O1O100O1O1O100O1O1O100O1O100O100O100O1O100O100O100O100O100O100O1O1O1N2N2M3N2M3M3O1O10000O10000O10000O10000O100001O001O001O00001O00O100O1O4M3L5K4\\ORB0aTX2", "size": [480, 640]}, "area": 7620.0, "iscrowd": 0, "image_id": 18150, "bbox": [378.0, 0.0, 109.0, 128.0], "category_id": 181, "id": 20001152}, {"segmentation": {"counts": "o`1;d>T1kNU1lNT1lNT1lNT1mNS1lNT1mNS1lN;F1O0O100O101N100O101N100O101N100O10001N100O100O100O100O100O100O100O100O100O1O100O100O100O100O1O100O100O100O1O100O100O100O100O1O100O100O100O1O100O1O1L4L4L4M3L4L4L4L4M2M3M4M200O2O000O2O0O10001N100O2O00010O01O1O010O1O10O01O10O01O1O010O1O010O1O010O1O00100O1O010O1O010O1O00100O00100O001O10O01O2O1N1O2O1N1O2N2O0O2N2O0O2N2O0O2N2N101N2N101N2N101N2N1O2O1N1O2O1N10100O00100O010O10O0100O010O10O02N2O2M2O2M3N1N3N1N3N1N3N2UGlJd7V5UHoJk7T5nGoJT8R5eGRKh0B_6_5bHRKP1A]6R6fInIX6Q6kIoIT6P6mIQJQ6i2dH7^1oLn5i2dH8_1PMk5g2gH9`1PMh5f2hH9d1PMb5f2kH:o8EQG:P9FQG9o8FRG:l1PMQ5e2SI:m1SMn4b2VI:m1VMk4`2XI9n1YMh4]2[I9n1\\Md4\\2^I8n1^Mb4Y2aI8o1aM]4W2dI7Q2cMX4W2gI5S2eMT4U2jI6S2fMQ4T2lI6S2hMo3Q2oI7R2jMl3P2RJ6R2lMj3m1UJ7R2mMg3l1WJ7R2oMe3i1ZJ8Q2RNa3g1^J7Q2TN_3d1aJ8P2VN]3b1cJ8P2XN[3`1eJ8P2ZNX3^1iJ8P2[NU3]1kJ8P2]NS3Z1nJ8P2`NP3X1QK7o1dNl2U1VK7n1fNj2S1XK7n1hNh2Q1ZK7n1jNe2P1]K6Q2iN`2P1`K6S2jN[2P1bK6U2jNW2P1dK6X2iNQ2R1gK4[2jNl1R1iK4^2jNf1R1lK4T3VNn0f1nK4Q4[MNb2QL3c8L^G3c8M]G3c8M]G3b8N^G2b8N^G2Y4`MXO^2_L2U4gMXOX2cL1Q4mMZOS2dL0o3RN[Oo1eLNo3TN]Oo1cLMo3SN_OQ2cLKl3UNBQ2aLJk3VNEQ2_LIj3VNIR2\\LHj3jMiKH00Q4h2[LFi3kM9`2mKEh3mM;_2lKDg3oM=_2jKAi3PN=a2iK_Oh3RN?`2hK_Of3SNb0_2gK^Oe3UNd0_2eK]Od3VNf0V5YOkJg0V5VOlJj0T5TOnJk0T5ROoJm0Q5ROQKhLoNl3Q6YO\\Ke0e4YOSJhLR1m3m4XOjISM\\1c3j4DWK:k4DWK:j4EYK8h4]LfIW3e1:f4^LnIo2^1`0e4bLhJQ2g0Y1c4eLjJb1R1e1U4iLlJ\\1V1g1o3mLnJU1Z1i1i3RMPKo0_1i1Y5PNRKg1S5RNPKm1T5lMQKS2S5eMQKZ2l8N1O2O1N1O]O[NnCc1Q<g0N1O1O1O2N1O11O010O1O00100O001O10O01O001O010O001O010O00100O001O010O0010O01O00lCYMQ<g2nC\\Mo;d2QD_Mm;`2RDdMk;k2N2N2M3N2M3N1N101N2O1nLYLhJd2RM_NT8oNiJa2TMaNQ8QOhJ^2XMaNn7SOjJ\\2XMbNm7ROkJ[2YMcNk7SOkJZ2[MdNi7QOmJ[2ZMdNi7QOmJZ2[MfNg7POmJZ2]MfNf7POmJZ2\\MhNf7nNnJY2]MiNd7nNPKX2]MjNb7POPKU2_MlN`7QOoJS2aMlN_7TOnJo1dMnN\\7VOnJk1gMPOY7DbJ\\1UNPOY7EaJV1[NVOe62oJCB;^54oJ_OE>Z54QK]OF?X56QKYOIb0T56SKWOJc0S57RKUOLe0P57TKSOMg0m48UKPOOh0k4:UKmN1j0h4;VKjN4j0f4>SKhN8k0c4?TKeN:l0a4a0TKaN=o0^4a0TK_N?P1\\4c0TK\\Na0R1Y4d0UKXNd0U1V4d0UKVNg0U1o3k0YKoMi0W1j3m0]KjMl0X1k0^N\\Ob2mNgMn0Y1h0^N\\Oc2mNeMQ1Z1f0^N[Od2nN\\M_N2d2`1c0^N[Oe2oNYMcN0c2c1`0_NZOf2R2kNcN_NYOh2U2hNbN`NXOi2W2gN`N`NXOj2Y2eN_NaNXOj2X2gN_N_NXOk2Y2fN_N_NWOm2Y2eN_N^NWOP3X2bNaN^NWOQ3W2bN_N[NoL2Z2T3V2_NPLcNg1L2T3V2^NlKSO_1[O>W3U2[NjK\\O^1ROc0X3S2\\NhKA^1kNf0[3aNcLe2e1cLH`1dNf0^3VNlLP3m2RNjLh0_3QNmLT3l2RNhLh0a3mMPMV3j2TNeLi0Z4S1QOTNeLh0[4S1ROTNcLi0[4R1TOTNaLi0\\4R1UOSN`Lj0\\4R1VOSN^Lk0\\4Q1WOTN]Lj0]4Q1XOUNZLj0_4o0XOWNYLi0`4P1XOWNWLi0b4o0WOXNWLh0d4VNZKb2k3`NVLh0n4g0lNaNVLg0P5g0kNbNTLg0R5f0jNcNTLf0T5g0hNbNTLg0U5f0gNcNTLf0]5`0_NjNTLe0_55bJVNm3o0RLe0a53gJTNf3T1RLc0d53hNYOdKb0f54gNYOdK`0h55gNXOcKa0i55eNXOdK`0j57dNQOiKe0g58aNnNmKi0e57_NnNmKj0j52ZNSOmKk0j51ZNROmKm0i50\\NhNULW1a50\\O0d0O]O1c0N^O1d0N\\O2d0M]O3d0L\\O3e0L\\O4e0K[O5e0J\\O5e0K[O5f0I[O7e0I[O8d0G]O9c0F^O;`0F@:`0DB==BD><AE?;_OGb08]OIc06]OKb06]OKc05[OMd04ZONe03WO1i0OSO5l0LQO7n0IPO:P1FnN<Q1EmN=R1i60000O100000000000000O10000000000N2N2O100O100000000000000001N1O1O1N2O1O1N2O2N2M2O2N2O001O1O001O1O001O1O0O2O1O0010O01O001O010O001O001O10O01O001O010O010O010O01000O10O0100O1O010O1O1O001O2O0O2N00O1O1O1O`GPNR4o1nKTNo3l1RLUNm3j1TLXNi3h1WLZNh3g1WLZNh3g1WL[Nf3g1YLZNf3f1YL]Ne3d1aHmMV3`0W4i1UHQNb38X4W2fKlMX4U2fKmMZ4S2eKoMY4R2eKPN[4P2cKRN\\4o1aKTN^4m1PIUMa1T1_5g1kH[Mc1P1a5f1gH_M:Db0Y1\\6a2PIYLb0W1]6b2nH[La0V1_6_2nH_La0S1`6_2mHbL?Q1c6^2lHfL<n0g6]2kHiL;k0i6]2jHmL8h0m6\\2hHRM6d0Q7Z2hHXM1`0V7Y2gHZOX7X4O1O1O1O1O1O001O0O100000001O00000000001O1O1Ok0fNgG", "size": [480, 640]}, "area": 193614.0, "iscrowd": 0, "image_id": 18150, "bbox": [3.0, 2.0, 637.0, 478.0], "category_id": 183, "id": 20001153}, {"segmentation": {"counts": "o]Q72o>1N00001O4VAIc>;O3K200001O00001O00O11O0000000O11O001N101N1O11N100O101O0O11OO2N10001O2N2ON2O2N1O1O2NO01001OMOdAD\\><dAC^><bAE_>9bAE_>:4O100L4O1Om_]1", "size": [480, 640]}, "area": 839.0, "iscrowd": 0, "image_id": 18193, "bbox": [480.0, 445.0, 62.0, 24.0], "category_id": 94, "id": 20001154}, {"segmentation": {"counts": "hkk233Od>7001O1O00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000O1N2N2N2OMaAG_>2jU`2", "size": [480, 640]}, "area": 1933.0, "iscrowd": 0, "image_id": 18193, "bbox": [195.0, 465.0, 273.0, 15.0], "category_id": 105, "id": 20001155}, {"segmentation": {"counts": "l[f43k>9G:F6J101N101O1N2O001O00100O100O2N1O100O1000O0106J5J101O000O2O000O2O000O2O000O2O001N0100N1N3M3M201000O10000O01000O1dC]N[;c1_DcNa;^1]DcNb;^1^DbNb;_1\\DbNd;_1ZDbNe;_1[DaNe;a1XD`Nh;`1WDbNh;_1WDaNi;`1UDaNk;`1TD`Nl;a1RD`Nn;a1PD`NP<a1oC_NQ<X2O10001O00000000000001O1O9HO00]MhCm0O`0^<@bC`0_<^OcCa0]<]OfCb0Z<]OoC;Q<DPD<Q<BPD>Q<@PD`0R<]OnCe0P<\\OPDd0P<\\OPDd0P<\\OPDd0P<\\OPDd0P<\\OPDd0P<\\OoCd0R<\\OnCd0R<\\OnCc0S<]OmCc0S<]OmCc0R<^OnCa0S<^OnCb0S<nN]C7a0l0R<mN`C3b0l0n;QOcCOd0m0k;ROcD9hN4T>HoA7T>DQB9]>M2O2N2Mcim2", "size": [480, 640]}, "area": 6858.0, "iscrowd": 0, "image_id": 18193, "bbox": [320.0, 341.0, 119.0, 97.0], "category_id": 121, "id": 20001156}, {"segmentation": {"counts": "0e<[200000000KfMcC[2X<9O1M3M3HUMUDQ3k;2N2\\KoLWMS3e2RMbJGQ1Y3[4SMXJDoN0J2`2Z3^4RMRJ3^1n2_4oLSJ4R1^OSNd3g6nLQJ1R1Z3l4cLUJ4b0e3Y5XL]I@H2>b0T1d3X5ZL\\IJ28Z1d3W5TMZIYO_1c3V5PNkJo1U5hKYIV2b1R2X5oMgJQ2[5mMeJS2Z5lMhJT2W5mMiJS2U5nMlJR2T5nMmJQ2n4iK_IW2b1P2a4nKPJMJX2c1n1c4QLkII0V2c1Q2a4aN_K_1`4bN`K^1_4gNoIPMY1Z4i4fNmITMT1X4o4kNoIlLh0[4Y5jNnInLf0X4]5kNkIPMb0X4c5VOYJk0g5]OkIi0U6Q41O00000000001OO1005K1O0K5K4M400000O10000O1000000O1000000O01O1O10O2O002M4M3M1O3L10004ROVG^Kj8b4WG\\Kj8d4^GSKc8i0XGc2<aL]8k0]G^27eL^8l0bGX22jL]8n0aGX23hL]8P1aGW23fL^8S1_GW2Y9iMgFX2X9hMhFX2X9hMgFY2Y9gMgFX2Z9hMgFX2X9hMhFW2Y9iMgFX2X9hMhFX2X9hMiFW2W9iMhFW2Y9iMgFU2[9kMeFV2Z9kMdFV2\\9kMcFT2^9lMcFS2]9oMbFP2^9PNaFQ2_9oMaFo1a9QN_Fn1b9RN^Fn1b9RN_Fl1b9TN]Fl1d9TN]Fj1d9UN]Fk1c9UN]Fk1c9TN^Fk1c9VN\\Fi1e9WN[Fi1e9WN[Fg1g9YNYFg1g9YNYFg1g9YNYFe1i9YNWFi1i9WNXFg1i9YNXFe1i9ZNXFe1i9\\NVFd1j9YNeEnNa0h2k9[NWFd1j9]NUFb1l9^NSFc1m9\\NTFd1l9]NTF`1n9`NSF_1m9aNSF^1n9bNRF]1o9cNQF]1o9cNQF\\1P:dNPF[1Q:gNlEZ1T:gNkEW1W:jNhEV1X:jNiES1Y:kNjES1W:nNhEQ1Y:oNgEP1Z:POfEo0[:QOeEo0[:QOeEn0\\:ROcEo0]:QOcEn0^:RObEn0^:ROcEl0^:TOcEj0^:VOaEl0^:VO`Ei0a:\\OZEd0f:@UE`0l:ARE`0n:APE?Q;h10000PEULk:k3TEWLk:o30O1O100O1O100O100O1O100001OFnDfL`0Io9R3RFSMa:]2RFaMTOMG0e:S2RGmM^N4b:m1YGUNe8j1aGQN_8n1Z2O100O1O1O1\\OnBYOS=f0VCROj<m0WCROj<l0VCUOk<k0XCQOi<n0XCQOi<o0VCQOk<o0UCQOk<o0UCPOl<P1TCoNm<R1QCnNP=Q1`0001O00O100001O1O0000001O001OO10000001O000000N2N2N2@UBIm=7VBDl=;=LaAF_b60\\lI3^AMo=;jAJR>d0N200000000001O001O0000000011N13M:FlNIeBI0Ob0?h<1XC0f<1XC1g<OYC2f<NZC3e<M[C4d<L\\C6b<J^C7a<H`C9_<H_C:`<F`C;_<EaC=]<CcC>\\<BdC?[<AeC`0Z<@fCa0Y<@eCb0Z<T1N1O1O1O1O2XEbNj7_1lFjM2U1P9l1PGVNn8j1RGXNl8h1TGZNj8e1WG]Ng8c1YG_Ne8a1[GaN^3WNQ1X3aKcN\\3ZNn0S3fKeNZ3\\Nl0o2jKgNX3ZNn0o2jKiNV3XNP1P3hKiNX3WNP1R3gKeNY3[No0Q3fKeN[3ZNo0R3eKdN\\3ZNo0T3cK`LEP2i3\\No0U3dK_N]3\\No0V3cK]N^3^No0V3aK]N`3^Nn0U3aK^Na3]Nn0T3cK^N_3^Nn0U3bK]N`3^Nn0U3bK]LGm1i3bNn0T3dKZN^3bNn0T3cK[N_3bNm0T3dKYN_3cNm0T3dKYN_3cNm0S3fKZN\\3cNn0R3fK]NZ3bNP1n2iK`NW3cNo0l2kKbNU3bNP1j2lKeNT3aNP1h2mKiNR3_NQ1h2mKiNQ3`NR1g2lKkNQ3^NS1f2oKjNn2aNR1e2PLkNm2`NS1e2PLkNm2`NS1e2oKmNm2^NT1e2oKnNk2^NV1d2nKQOj2[NX1d2nKSOh2ZNY1c2oKVOe2WN\\1c2oKXOc2UN^1b2PL\\O^2SNb1`2QL_O[2QNd1l1^KoMb0W2Y2nMg1l1_KnMa0Z2S2QNl1[1bKjM5?3@D20k2Q2bNa2g0PLZNFAHU3[1lNW32RL]N]OP3o0jNb3IRLT2]OjKn0R2c31QLX2=gMa31RLl1j0RNT33QLk1k0RNT33QLk1l0QNS34QLl1k0PNT35PLk1m0oMS36PLk1m0PNR35QLk1n0oMQ36QLk1n0oMQ36QLl1m0nMR36QLl1n0mMQ37QLl1n0mMQ37QLl1o0mMo27RL[N_Om2`1aNo28QLYNLd2T1jNo2=cKdM01Nb0c0\\2m0POo2b0aKVNc0X2n0oNn2\\1TLEn0oNn2\\1TLEo0oNl2\\1TLGo0mNm2\\1TLGl2PMP1Y3TLGl2PMP1Y3TLGl2PMP1Y3TLGl2QMo0X3ULGk2RMP1W3ULHf2UMU1S3ULHa2ZMZ1n2TLI^2]M^1j2TLIY2bMc1e2TLIU2gMf1a2TLIP2kMl1\\2TLIk1PNQ2V2ULJf1TNU2R2VLI`1ZNZ2n1VLTM^OR2n1PO^2j1gLWOe0UOc2d1iLVO?[Oh2_1hLWO<^Ol2[1iLVO9An2Y1iLVO7CP3W1iLWO5CR3V1iLWO3FS3S1jLWO1IT3P1lLVONLV3n0lLVOLNX3k0lLYOIN[3j0kLXOH0]3h0lLWOE3_3f0kLXOD4a3d0kLXOB5d3c0jLXO_O8g3g0bLSO\\O`0R4b0]L]2c3eMZL\\2f3eMXL\\2h3dMVL^2j3cMVL\\2j3dMVL\\2j3eMUL[2k3eMUL\\2j3dMVL]2i3cMWLf2`3ZM`Lf2`3YMaLg2A`Ii3j3fLj2Z3VMfLk2Y3TMhLl2X3TMhLl2X3TMhLl2X3UMiLj2V3WMjLi2U3YMiLi2U3WMkLh2V3WMkLi2U3XMjLh2V3XMjLh2V3XMjLh2V3XMjLh2V3WMlLh2T3XMlLh2T3XMlLh2T3YMkLg2U3YMkLf2V3YMkLg2U3YMkLh2@\\I^3l3RMh2@_I[3i3UMg2@cIY3o3nL`2GbIZ3U4gLe2Y3a4000000000000000001O1O1O2N1O1SMVD92d1i;oM[D80g1f;oM]D42l1o;oMUDQ2^<O1O2N1OO1O1O1O100O1O1O1O1O1OMYCoMg<d1YC^NQ=a17O1O1O2N4L5K6J5K6J5K6J5KbALe>1N00001O4VAIc>;O3K200001O00001O00O11O0000000O11O001N101N1O11N100O101O0O11OO2N10001O2N2ON2O2N1O1O2NO01001OMOdAD\\><dACY=5dC7SOEX=6eC3TOEV=9gC1\\<2bCM_<2hBFc09d<2gBGc0:d<1hBDd0<_<BnBe0a0K`<@nBf0a0KR;@hEh0VOHm:EnEa0VOKi:HoE=UOYOAc0Y;KnE;WOOHXOg:`0YF9XOOH[Od:>ZF8[OOD_Oe:;\\F8YO;Z:^O]F9WO9[:_O]F:ROTOKT1e:@]F7oNb0d:YO[Fg1d9]NYFc1g9^NXFb1h9_NXF`1h9`NYF_1g9bNXF^1h9cNSFROYO[2d:eNPF`1o9bNgElNHg2_:`NbEj1^:XN`Eh1_:]10000O1001O00O100O10000001O00001O001O1O1O00001O1O1O0iK[E10m3f:QLZE02o3d:QL\\E1Ol3j:ULUEk3j:61O001O2N2SLoDg3S;WLnD1Od3Y;000001O2NO1O100O100M[LjDf3U;3000000O11O0000000000001O001O001O1O001O00001O1O1O000000", "size": [480, 640]}, "area": 158233.0, "iscrowd": 0, "image_id": 18193, "bbox": [0.0, 0.0, 640.0, 480.0], "category_id": 154, "id": 20001157}, {"segmentation": {"counts": "mfj77i>2L103NOaAFc0On<<]BGc0OA2S=:hBDd01@2o<KnBe0a0_OA3o<InBf0a0KR;@hEh0VOHm:EnEa0VOKi:HoE=UOYOAc0Y;KnE;WOOHXOg:`0YF9XOOH[Od:>ZF8[OOD_Oe:;\\F8YO;Z:^O]F9WO9[:_O]F:ROTOKT1e:@]F7oNb0d:YO[Fg1d9]NYFc1g9^NXFb1h9_NXF`1h9`NYF_1g9bNXF^1h9cNSFROYO[2d:eNPF`1o9bNgElNHg2_:`NbEj1^:XN`Eh1_:]10000O1001O00O100O10000001O00001O001O1O1O00001O1O1O0iK[E10m3f:QLZE02o3d:QL\\E1Ol3j:ULUEk3j:61O001O2N2SLoDg3S;WLnD1Od3Y;000001O2NO1O100O100M[LjDf3U;3000000O11O0000000000001O001O001O1O001O00001O1O1O000000", "size": [480, 640]}, "area": 11965.0, "iscrowd": 0, "image_id": 18193, "bbox": [534.0, 335.0, 106.0, 145.0], "category_id": 162, "id": 20001158}, {"segmentation": {"counts": "e<[2e<0000000KfMcC[2X<9O1M3M3HUMUDQ3k;2N2\\KoLWMS3e2RMbJGQ1Y3[4SMXJDoN0J2`2Z3^4RMRJ3^1n2_4oLSJ4R1^OSNd3g6nLQJ1R1Z3l4cLUJ4b0e3Y5XL]I@H2>b0T1d3X5ZL\\IJ28Z1d3W5TMZIYO_1c3V5PNkJo1U5hKYIV2b1R2X5oMgJQ2[5mMeJS2Z5lMhJT2W5PL[I]1^1c2U5kKiI\\1S1i2T5jKoIX1n0m2n4iK_I1j0V1h0P3a4nKPJMJ0S1Q1`0U3c4QLkII0OX1k0;]3a4YLZK55b3`4YL`K10f3_4YLgK0XN^OY1Z4i4VLlKNQNFT1X4o4SLRL1mMCh0[4Y5PLXLNfMJf0X4]5nK]LM^M0b0X4c5jKcL7fMP4g5hKiL;RMS4U6aKoLQ5R3nJSMm4m2SKYMg4g2XK_Mc4a2]KdM^4\\2bKiMY4W2fKnMV4S2iKQNS4n1nKWNm3i1SL[Ni3j1QL[Nk3a6L5F8H7J5O2N2N2N2N2N2N1O2N2N2N2N2N2N2M2N3M3N2O1N2001N2OO11ON2N22QHWNa2g1_MZNa2e1_McN[2[1`MlN`2R1_MoNb2P1]MROd2l0\\MUOd2j0\\MWOd2h0\\MYOd2f0]MXOf2f0ZMXOi2g0WMXOk2g0TMYOn2f0SMWOQ3g0nLYOT3f0mLXOU3g0kLXOW3g0iLWO[3g0dLYO^3f0`L[Ob3d0_LYOd3f0\\LYOg3e0XL[Oj3d0VLZOm3e0SLZOo3e0QLZOR4d0lK]OV4b0iK]O[4a0eK^O]4a0bK_Oa4?^K@f4>YKCh4<XKCk4;UKEm49RKGP58oJIQ57oJHR58lJJT56lJIU57kJIU57iJJX56hJJW57hJIY57fJJZ56eJJ\\56dJJ\\56cJJ^56aJK_55`JKa55_JKa55_JJb56\\JLd54\\JKe55ZJLf54YJLh54XJLh54WJLj54UJMk53UJLl54RJNn52RJMo53oIOQ61nIOS61lI0T60kI0U61jI0V60jIOW61hI0X60hIOY61fIO[61eIO[61dIO]61bIO_61bIN^62aIN`62`IN`62_INb62^IMc63\\INd62\\IMe63[ILf64ZIIULNa:9ZIHj68o30000000O1000000000000000000000000000000000000001O001G]A1oS`6", "size": [480, 640]}, "area": 13184.0, "iscrowd": 0, "image_id": 18193, "bbox": [0.0, 124.0, 196.0, 356.0], "category_id": 167, "id": 20001159}, {"segmentation": {"counts": "WhY15j>6J7J001O000000O1001O0000001OO10001N2O011OOO10000001OO11N100O11O0000000000N021O1N11OM_OgAa0Z>21O0O0110O1O0000000002M2O1O01M30O000000000O100001O1OO3N5K4K2N2NdQ>O]nA9G1N023L10O1000001N02OO2O0O001O1O2O1O00000010O01OO10000001O000000N2N2N2@GWB;i=HRB:m==LSOTBn0`=?0000000000000000000000000A`B^O`=`0cB]O_==iB\\O\\=`0d0N200000000001O001O000000001O004L;NoNlA0Ob0\\>00N1000000000O11N1000000000001Nil24TSM00000O10000000000001N3O0N2O2N]AEb><1O1N1O020O00[AGc>;0O20O0O2N0N3O1N1O1100O03N0000O100O100000O1O1\\O]OlBb0T=_OkBa0i<AgB5?3@D20W=b0WCFAH\\=d0TC]OU=c0e0O001O00001O00000000000000001O4B^A01Ni>0m`7b0f^H0RB^O\\=S101OO11O000000001O0O02O01OO10007H6K2M2N1N30O01O00000000O110O0O1000011O01N00O11O000000O2O0001O00O10000009G7Hb]P3", "size": [480, 640]}, "area": 5676.0, "iscrowd": 0, "image_id": 18193, "bbox": [89.0, 0.0, 345.0, 146.0], "category_id": 182, "id": 20001160}, {"segmentation": {"counts": "U[=7b><J6J5K6J6J6J5K6J6J6J6J6J6J6J6K5K6I6K5K5J5L4L5K4K5L4L5K3L3N1O2N2M3N2N2M3N1O2N2M3N2N2N2M3N1O2N2M3N2N2M3N1O2N2M3N2N2N2M2N3M3L3N3M2M3N2N3L3N2N2M4M2N2N2M4M2N2M3N3M2M3N2N3M2M4M2N3LWIcJU4[5i2N3N2M100O100O100O100O100O100O100O100ObNSKjHn4V7RKjHm4X7SKgHm47SK^51ZJk47TKa52WJj46UKe50UJj45WKh5ORJj45WKU7h4kHXKV7h4iHXKX7g4hHYKY7g4fHXK\\7g4dHYK]7g4aHZK`7e4`H[Ka7e4^H[Kc7d4]H\\Kd7d4[H[Kg7d4YH\\Kh7c4XH]Ki7c4VH]Kk7b4UH^Kl7a4TH_Km7a4RH^Ko7b4QH^KP8b4oG^KR8a4nG_KS8`4mG`KT8`4kG`KV8_4jGaKW8^4V100O100O1O100O1O100O100O1O10RFkKQ9U4kFnKV9S4fFPLY9g3VFYL=3^9U3fFfLI7b9c2XGTMQO<h9Q2iG`M\\Na0l9m1mGUNS8j1QHRNP8m1SHPNn7o1VHmMk7S2XHjMh7U2\\HgMf7W2b2O2N2O0O2N2N1O1O100O1O1O1O1O100O1O1O1O100000000001O00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000002N1O1O1O1O2N1O1O1O1O2N1O1O1O1O2N1O1O1O1O1O2N1O1O1O1TFhMn6Z2gGQOW8n3N2N2N2N2N2N2QKQJi0Q6UOTJf0n5WOVJf0l5XOTJh0m5WOSJi0m5WOSJi0l5XOUJg0k5YOUJg0k5XOVJh0j5XOVJh0j5XOVJh0i5YOWJg0i5YOXJf0h5YOYJg0g5YOYJg0g5YOYJg0f5ZOZJf0f5ZOZJf0f5ZO[Je0e5ZO\\Jf0d5ZO\\JfMA^1T6k0[JfMIX1m5Q1ZJfM2Q1d5Y1[JcM:m0\\5^1[JdMb0f0S5f1[JcMe0e0Q5g1ZJcMg0e0o4h1ZJbMh0f0o4g1YJaMk0g0l4g1[J`Mk0h0k4g1ZJ`Mm0h0i4h1ZJ_Mo0h0h4h1YJ_MQ1h0g4h1XJ^MT1i0g4f1UJ`MV1i0g4d1UJaMV1j0g4c1SJcMW1i0i4a1PJeMY1i0i4`1nIgMZ1h0k4^1kIjM[1g0l4\\1jIlM\\1g0m4m0UJ[No0g0n49fJPO=f0P5CWKFKf0P5oNhK;YOe0l5oNoI<6d0W5EcJF6e0i43QKWO8e0g45PKVO9e0g45PKVO9e0h45nJUO;f0g45nJUO;f0g46nJSO;g0g46nJRO<h0f47mJQO=h0g46lJRO=h0g47kJPO?i0f47kJPO`0h0e49kJnN`0i0e49kJmNa0j0e49iJmNb0j0e49iJmNb0j0e49iJlNc0k0d4:hJkNd0k0d4:iJjNc0l0e4:gJiNe0m0d4V2kHmLa2m0d4V2kHmLb2l0c4W2kHlLc2m0b4W2lHkLb2n0b4U2nHmL`2n0c4o1RIRM\\2o0b4k1VIVMX2o0b4f1[I[MS2o0b4a1`I_Mo1P1a4]1eIbMj1Q1b4W1iIhMe1Q1b4R1nIlMb1Q1`4o0RJPN^1Q1`4j0WJUNY1Q1`4f0[JXNV1R1`4`0`J]NP1S1`4;eJbNk0S1`49gJcNj0T1_47iJeNh0T1_45kJgNf0T1`42lJiNe0U1_40mMOS2OoM1Q2MQN3o1KSN5n1HTN8l1FVN:j1DXN<h1CYN=g1A[N?e1ZObNf0_1oNlNP1b7000000000000000001O0000000000000000001O0000000000O100O10000O10000O100O10000O10000O100O10000O1O100O1O1O1O1O1O1O100O1O1O1O1O1O100O1O00O1O1O1O2N1O1O1O1O2N5K6J5K6J5K6K4K6JaSZ2", "size": [480, 640]}, "area": 104523.0, "iscrowd": 0, "image_id": 18193, "bbox": [28.0, 45.0, 453.0, 427.0], "category_id": 183, "id": 20001161}, {"segmentation": {"counts": "iPc63V=2N2N2L4K5O2M@iC2i<2L1O1N3M<D1O1O1O1EZOnCi0e;C\\D=b;E^D;c;^O^DENn0c;ZOcDGKo0c;WOhDi0Z;ROiDn0V;POmDP1g;001N2M201N1M3O2N1O2M3K^N]De1a;5L1ONmDWNk:f1>M3M3L4M3M3M3N2O100O4M1N2O0O3M100O100O2N100O100O100O1O01O0010O100O1O100O100O10000000000000000000O11O00000000000000000000O10000000001O2N2N2N2N2N2N2N2N2N2N2N4L5L4K5K4L\\O`DCd;MiD2S;BXEb0g;VOaCl0\\<4MNO34J4M2L`J", "size": [426, 640]}, "area": 3416.0, "iscrowd": 0, "image_id": 18380, "bbox": [507.0, 96.0, 133.0, 106.0], "category_id": 98, "id": 20001162}, {"segmentation": {"counts": "c<g0T=_O0000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000O1000000000000000000000000000000000000000000000000000000000000000000000000O1O10IUCOj<0XC0Q=00000000000000000000000]e73`ZH1N31N1OPC3d<92O00000O10000O10000O1002MJ7H\\l\\22hScMNkB1S=OmB2R=NnB4P=LQC4P=4H8E@aCh0\\<6M3N1N3N20O01O00YOZDJe;2aDM];0hDMZ;1hDL[;1hDMZOOh;2Y]Z3", "size": [426, 640]}, "area": 1521.0, "iscrowd": 0, "image_id": 18380, "bbox": [0.0, 118.0, 384.0, 308.0], "category_id": 105, "id": 20001163}, {"segmentation": {"counts": "n]V41Y=1ROOaD1k:b14KO21OO001O1N1N8J5K4L1O001O001O001O04MN1M4O2N2N2N2N2N1O2N2N2nEVM`9l2^FYM`9U1dFEF[Oe9m0kFFZODf9h0RG8l8FXGiN[OW1\\9MhGOY80iGOcNoNa9P1PHO_NQOa9o0RHO]NROa9n0THO[NTO`9m0UH1m7MVHLo72THEUN@n9a0XHLj73\\HI`7GVF`0^2F\\7JUF?d2CX7?j200O1O100OXFBf6>X3K3L10001OO10000l\\X3", "size": [426, 640]}, "area": 3353.0, "iscrowd": 0, "image_id": 18380, "bbox": [323.0, 10.0, 66.0, 129.0], "category_id": 112, "id": 20001164}, {"segmentation": {"counts": "0l2^:0000000000O10000000000O10000000000O1000000O10000000000000000XO`EfN`:Z1aEdN`:Z1cEeN]:X1hEfNX:W1lEhNT:V1PFhNP:V1SFiNm9W1UFgNP:T1PFkNU:Q1jEPO[:k0eEUOb:LiD?e0En:8REHP;6PEJT;1mDO[;IeD6X<0001O001O5J`U11\\]NONOl<>N1O003M4L2NObCYO[<n0M1O3M2aDQO_:Q1TE^Oh:c0VEFb:;YE0`:`1M000000O1000000000000O10000000000O100000000O106J9G7I8H9G8G;F8H8G9HefV1:SYiN2M0O0O2mNBWE?g:LoD6m:1oDO^9LTG6^OO]9LTG5_OO^9JUG6^O0a9FSG8[O2f9APG=YO4e9AQG;[O3b9DSG9_O0\\9JSG6^O:X9DYGO_Oa0S9EYGKDa0R9DYG2TO[O2U1a9^OYGJVOT1`9ROZGGZOV1\\9SO[GB]O\\1W9RO[G@B]1S9SO\\G\\OE[1T9YOlGb0X8^OdGc0_8]O`Gc0a8]OdG=]8CdG4^N\\Oo9`0cG0cN^Ok9b0bGKiNAl9>[GMlNEm;7f0JO0l;<TYOCi8?RGAl8`0UG@l8b0QG^OP9b0oF^OR9b0k10000001O0000000004LflZ3NmReL?_DUOb:m0TE^Oj:d0nDBR;?fDHZ;U1O0000001O00000000000000000000000000000000000000000000000000000000000000000000000000000000000XFVNP8j1mGZNR8g1lG[NS8e1iG_NW8a1iGaNU8_1kGaNU8_1jGbNV8^1\\GPOd8P1\\GPOd8P1\\GQOc8o0]GQOc8o0]GQOc8o0]GQOc8o0ZGTOf8l0YGUOg8k0YGUOg8k0YGUOg8k0YGTOh8l0WGTOj8l0UGTOl8l0TGTOl8l0SGUOm8j0TGVOl8j0TGVOl8j0TGVOl8j0TGVOl8j0TGVOl8j0TGVOl8j0TGVOl8j0UGUOk8k0VGTOj8l0VGTOj8l0VGTOj8l0VGTOj8k0WGkN[O^O^9h1VGiN_O]O[9j1VGbN\\OA9OU9n1VG_N_9a1dFQNg9o1j000000000000000000000O11O0000000hDQNU;P2jDPNV;R20000000000000O1M3[OiDUOY;g0eDkN86V;j0bDQOS<j09K5J6K5K5LZm2", "size": [426, 640]}, "area": 17440.0, "iscrowd": 0, "image_id": 18380, "bbox": [0.0, 0.0, 633.0, 137.0], "category_id": 123, "id": 20001165}, {"segmentation": {"counts": "dQX33W=3MO012LfU40\\jK00001O0^CNk;1@1TE0k:6oDJQ;9kDKQ;9lDIKKX:i0hE^OOJd:?XE`0]:mN_El0J9g:Y1100000000000000O1O1dMUEW2U;J4J5OOO1O1O10000001O05K1O000O11O00OO2M03]N[D_1j;L3M300O100001O1OO1O1O1O1O10000SOlCd0]oR4", "size": [426, 640]}, "area": 2913.0, "iscrowd": 0, "image_id": 18380, "bbox": [250.0, 10.0, 76.0, 80.0], "category_id": 156, "id": 20001166}, {"segmentation": {"counts": "TRg31X=1N2O2L310N2N2N2O1O2cC@l;c0nC^OU<h03N2M3M3M202O0O010ON3N2M2N30O010O101N2OO@`0O1O1O]WT4", "size": [426, 640]}, "area": 538.0, "iscrowd": 0, "image_id": 18380, "bbox": [286.0, 65.0, 36.0, 47.0], "category_id": 165, "id": 20001167}, {"segmentation": {"counts": "l2e5e7D<H8I7H8I6J7I7J6I7J6J501O1N2O1O1O001O1O1N101O1O1O1O1N2O1O10XO_MgFa2Y9`MeFa2Y9bMfF^2W9gMgFY2V9kMiFT2V9PNhFP2V9SNiFm1W9UNgFk1e7bM_Ic0kNl1f7eM[I>POm1e7jMVI9UOm1e7QNWH]O?e0Em1e7]NcHFHm1e7_NaHDJm1e7cN\\HAOl1e7kNTHYO6m1f7FZH9g7GYH9g7HXH8h7HXH8h7IWH7i7NQH3o7d200000jJRH0ONOk4_8N1O0O4M4L2NOdKWGZ4o8M102M2aLQG`2P9SM_Gi2b8UMgGd2Y8WMRHb2^9NO001O0O2O1O001O001O0O2O001O001O0O2O001O001NIlEZMT:d2oE\\M[71nJa2jM]MX7;eJV2VN_MT7b0_Jl1aNaMP7k0WJb1lNdMk6S1PJY1WOaMj6^1fIQ1Q7YOeHg0[7A]H>d7JSH7m71kGNV8c20O1O100O100O100O1O100O100O100O10000O100000000000000001O1O001O1O1O1O00@fGQLY8j3PHRLP8j3WHTLh7g3`HVL`7f3fHYLY7b3mH]LS7_3RIaLm6]3VIbLj6]3YIbLf6]3\\IcLc6\\3_IeL_6Z3cIgL[6X3gIhLX6W3kIgLU6X3o1O1O1O100000000000000000000000000000000001O1O1O2N1O1O2N1O3M4L4L4L6J4L2NO100O1O100O1O100mNYGbMh8`2ZG\\Mf8e2ZGZMf8f2ZGZMf8e2\\GZMd8d1nFkN?Ac8b1YGcN6Ja8`1`GbNONa8M[Gg06^OOM`8N\\Gf05_OOM`8OZGg06^O0K`85VGd08[O2L`89QGa0=YO4L^89SGa0;[O3K_87UGc09_O0Fb87XGb06^O:BX8>\\Ga0O_Oe9OaF=KDd9OaF<2TO[O2X:>[F<JVOV:>PF<GZOX::QF=B]O]:6QF<@B^:2RF=\\OEa:NSFR1l9nNTFn0P:ROPFm0Q:SOoER1l9nNTFS1k9mNUFS1k9mNUFS1k9SOoEm0Q:V101OJ6G9eN`EHd::^E_Oe:>\\EAg:?[E\\Oh:d0XEYOk:g0UEXOl:h0TEXOl:h0TEWOm:i0SEWOm:i0TEUOm:k0SEUOm:k0SEUOm:k0SETOn:l0RETOn:l0RESO3NT:S1eEoN5OW:^1WEcNc0OV:^1PFbNP:^1PFbNP:]1RFbNn9`1PFaNo9\\1TFdNl9Y1WFhNh9V1[FjNd9T1^FlNb9R1`FnN`9Q1aFQO]9o0eFPOZ9o0kFnNT9R1b100OjD_Oe9a0[FE_9:bFF^9:cFE]9:dFE]9;aFH^90cE]Oo0c0^9OjF2V9MmF1S9MPGNT9HVG7k84P2E\\COe<1:0000000000000000000QCHl<`0K3M2N00000000000000005K1O0000000000N2M30_CYO_<j0O000000000000000000000000000000D<5KO12N000000O1N2000000000000000000004LO1M3000000000000000000003M00M30000000000000000O100N2KVCIk<7UCHl<830000000000005K3M00001OO100006J0000000000000000001O001O1O1O1O1O2N3M`0@001O001O1O1O1O1O2N3M2N000000001O00001O001O001O001O1O1bEmMc9T2ZFoMe9S2oEkML5T:o2J1O2N1O2N1O1O1O2N1O1O1O2N2N3M3M7INQGkKm8U44O1O100O1O100O1O11O000000001O00002NM3N2J6M3O1N2O1O1O1O1O100O100O1000000000000000000000000000000001O00SMaFm1a9dMnFk0UOb0m:TO^Ei0e:oNaEP1a:hNfEU1Z;K400O1M4K4000000O1001O000ZDXOj:P1nDQOQ;n0QEROn:P1kDQOEN`;Q1hDUOGK`;R1fDYOY;j0bDYO];g0aD\\O^;Y1O1O00O11O00N2000000O1MaDZN`;f12O1O10000000000000000000000M3M4N1L40000O1B>0000000000M3O1000000O1O100O0100000000000001O1O00000O1WD[On:d0QE_Om:a0lD\\OA9_;;mDOQ;4aD6`;k0O0000000000000000000O02O0000000YN`Dd1a;[N_De1c;0000000000000O1M3[OZNdEh1X:VNZE86e1[:SN`Eb2[::J5J6K5YGcL\\7k3THbLb7c3RHhLk7^4mGjJP8X501ON25K3M2N", "size": [426, 640]}, "area": 49117.0, "iscrowd": 0, "image_id": 18380, "bbox": [0.0, 0.0, 640.0, 273.0], "category_id": 172, "id": 20001168}, {"segmentation": {"counts": "_e0o4S88I7H8I7I7I7J6I7J6J6O1O1N2O1O1O1O1O1O1N2O1O1O1O1O1N2O1O10000000000O1000000000000000O100000O10000000000000000000000O10000001O001O001O001O001O001O001O1O001O001O0hKXJn0i5SOWJk0i5WOXJf0i5[OXJb0h5@YJ=h5CZJ:f5H[J5f5L[J1e53ZJJg59XJDh5`0WJ]Oj5f0UJWOc5lL^JQ4NPOb5RM`JQ4MjNa5XMaJR4McN`5^McJm6Z5WIeJi6Y5ZIiJc6W5_IiJ_6V5dIkJY6U5iIlJT6S5oImJo5S5SJnJi5R5[JmJc5S5_JnJ^5Q5eJPKX5o4kJQKS5o4d1O10eNjJbJU5a5lJ\\JT5e5oJWJP5l5PKRJo4Q6QKmIo4U6QKiIn4Z6RKdIn4^6QKaIn4b6RK\\In4f6RKXIm4k6RKTIn4n6RKPIn4R7RKkHn4X7RKfHn4\\7QKbH1Jf4f7YK]H0Og4e7ZKYHN4h4e7YKaHg4a7YK]Hg4b7XK`Hh4Q8001O1O001O1O1O1O00@gGPLX8k3QHQLo7k3XHSLg7h3aHUL_7g3gHXLX7c3nH\\LR7`3SI`Ll6^3WIaLi6^3ZIaLe6^3]IcLa6\\3aIeL]6Z3eIfLZ6Y3hIhLV6W3mIiLQ6V3RJjLl5U3XJjLf5U3^JjL`5U3dJRMR5n2PKQMo4o2TKPMj4P3YKnLf4R3\\K[MU4e2nKZMP4f2SLYMk3g2WLXMh3h2ZLXMd3h2^LXM`3h2bLWM]3i2eLWMY3i2iLWMU3i2mLVMLDOV37VMeMCP13T1T39VM_MHo0N[1T39VMZMLNM2L_2U39XMYMHG62Eg2V37PMgMJTOa01_Oo2W36gLgNe0U1e2`2VMbMl2]2QMeMP3[2mLgMT3Y2iLiMY3U2fLlM\\3S2aLoMb3o1[LSNi3e54L4L8H8HK5OPIgKQ5Y4TKhKf4W4^KlK\\4S4gKRLR4n3PLlKT4S4b2O100OdJoKa1Q4_NUL[1k3dNZLX1f3hNXLZ1h3eNWL]1i3cNUL`1j3_NULc1k3]NSLe1m3ZNRLh1n3XNPLj1P4UNoKm1Q4SNmKo1S4PNlKR2T4nMkKS2U4a300001O001N101O00K5L4MTHTL`6k3^IYLa6f3`IZL`6^3VHaL]10\\6[3\\HcL\\10W6X3bHgLU9U3oFjLR9R3QGnLP9m2TGSMm8g2XGYMi8\\2`GeMa8W2bGiM_8T2cGlMa10P4Q2aJoM]8n1RGQNG1X9k1cGXN^8g1c100O100OeGYNU5g1hJ[NY5e1eJ\\N\\5c1\\HXN_16V6b1ZHXN_17X6`1XH[N^16[6_1VH\\N^13_6`1TH]N[10g6b1oG]NX1Nn6d1kG^NT:b1lE]NP10\\7a1fG`Nk00`7[1kGdNd01b7X1mGhN>1f7U1nGkN:0i7S1PHlN61k7Q1QHoNIMVO3Q9P1QHQOC2YOLT9Q1RHPO^O8XOGY9P1VHoNUO`0f8a0XHnNnNb0k8`0cGVO^OKJb0V9<`GHUO4\\94`GGXO0Y98`GHZOMV9;`GHZOOT98bGJZOOS97bGK[O0Q95cGL\\O1o83dGM]O1n84aGMA1l85^GLE1l85[GLH2k84YGLK2k8d1SG_Nk8a1TG_Nm8b1QG]NQ9l22O1N101O10000O10000000000000000BkFgLU9V3oFiLQ9T3RGlLn8R3TGmLm8P3VGPMj8m2YGSMg8j2\\GVMd8i2]GWMc8i2^GVMb8i2_GWMa8i2_GWMa8h2`GXM`8h2`GXM`8h2`GXM`8g2aGYM_8g2bGWM_8h2bGXM^8h2bGXM^8g2cGYM]8g2fGVMZ8i2T100_Oa0N2N2N2N21O1O001O001O1O001O1O3M5K5K2N1O001O001O001O001O002N2N1O2N2N2O1N2N1O2`FfLl8\\3PGiLm8Y3lFnLR9f3O2N1O2N2N1O2N2N1O2N2NcN]GTNb8i1dGVNZ8g1lGXNR8f1SHXNl7f1YHYNe7e1_HZN`7d1cH]N[7`1iHaNU7\\1nHmMXOIi7X2RIkM[OLa7V2XIjM\\OM]7W2ZIjM[OL]7Z2YIgM]OMZOOi7]2aIeMg7[2d1O1000000001O1ZHbM^4^2_KgM_4Z2]KnM^4R2`KRN^4o1_KTN`4l1kKfMX4[2nJaMo5`2lI^MZ6d2U22N2mHXM`3k2kIUMh10[4h2eLXMZ3e2QJ[Mj11S4\\2ZJcMe12o3Y2RJeM^O0d22k3V2XJoMo1Lg3S2^JnMn1Ob3R2cJkMn13^3P2mMQNQ2o1QNPNn1o1TNRNj1l1YNUNe1j1]NVNb1j1`NVN^1j1oKRN:4f3j1oKSN<2e3k1nKTN?O^MNQ6o1RLTN`04\\3h1TLTNa04Z3h1ULTNc04V3h1WLTNd05S3g1XLTNg05P3h1YLRNi06l2h1]LRNf06l2h1`LQNe07i2i1cLPN]N5Y12e3i1fLPNQN?f1Gb3k1fLQNgMg0S2\\O^3l1hLmNLWOZ3m1iLmNNUOX3n1mLZNgMOV2IT3o1QMUNhM2U2IP3Q2\\KlMQ12`N8S2Jo2Q2ZKoM_166Mo2o1ZKPN`146Oo2l2gLUM<Nk2o2gLSM>0i2o2hLRM=0j2o2iLRM;1j2n2kLRM92j2n2lLQM82k2n2lLRM63l2l2mLjL[NKj1a0l2k2oLeLdNK_1f0m2l2nL`LkNNW1i0n2j2PM\\LPO0P1l0n2i2jM[LUOn0P3i2]OYMa0i2kMYLPOR1T3g2iMZLoNS1V3f2fM[LoNT1Y3e2bM[LPOT1]3h2ZMXLTOh0YOQOW4k3jKmK_1b0lNj04bNe3U4oKmKX1e0dNm0l4`2\\KnK?NFP1B:Q5i2SLQLWOe064d4V3RLQLiN?3^On0`0X4a3YLZL0Ik3l3[LSLa5m3^JQLe5n3\\JPLf5o3ZJRLf5o3YJQLg5o3XJRLh5n3XJRLh5n3WJSLi5m3WJSLi5n3UJSLk5m3UJSLk5m3]IkLc6n40O10000000000000000000000000\\MWJgMi5Z2XJdMh5^2WJaMh5b2XJ\\Mh5f2XJXMh5i2XJVMh5l2XJRMh5P3XJnLh5Z500000000001O2N2N2N2N2N2N2N2N7I8H7I8HlK\\Jj0\\5oNnJX1h4dN]K]1a4cN_K_1_4aNaKa1]4_NcKb1\\4]NeKe1Y4[NgKg1W4ZNhKh1V4XNfKm1Y4SNoJ]M5n4j4X3N2N1O2_O`GTLb8l3^GRL`8Q4`GnK_8T4bGjK\\8Z4dGdK[8d0RH`2DjLX8c0WHg2@eLW8h0VHe2DaLU8m0THd2n7`MnGd2n7`MoGb2o7aMmGb2P8]1O1N2G9D<YNSJdKZ6Y4lIbKV6[4PJaKQ6\\4VJ_Kk5^4[J^Kf5^4aJ^K`5_4fJ]K[5`4kJ[KW5b4oJZKR5d4k1O100O100O100O1O100O100O1O100O100O100O1O100O100O100O1O100O100O10000000000000000000000000000000000000000000000000000002N2N2N2N2N2N2N2N2N2N2N2N5K5K4L5K5K4L5K5K4L4L4L3M4L4L3GUImI", "size": [426, 640]}, "area": 180408.0, "iscrowd": 0, "image_id": 18380, "bbox": [1.0, 32.0, 639.0, 394.0], "category_id": 183, "id": 20001169}, {"segmentation": {"counts": "0d2P2^MSNo0^7b1`J^MSNP1]7b1eJ_NZ5a1fJ_NZ5c1dJ\\N]5f1aJZN_5o1PJRMeNo0[7Q2kISMjNl0[7R2hITMmNj0[7S2eIUMPOh0[7T2dITMQOh0[7X2QIkLI5Kh0[7_2eHhL52Kg0[7\\2lHfL06Ih0[7Y2\\IRMYOe0[7V2_IUMVOe0[7S2cIWMROf0[7Q2gIWMnNh0[7o1jIXMkNi0[7m1lIZMiNi0[7k1oI[MfNj0[7i1SJ[MbNl0[7g1WJ[M^Nn0[7e1[J[MZNP1[7c1dJ]N\\5b1eJ_NZ5`1gJ`NY5`1gJ`NY5_1hJaNX5_1hJaNX5_1hJaNX5`1gJ`NY5`1gJ`NY5`1gJ`NY5a1fJ_NZ5d1UJbMaNj0Z7m1kIZMkNi0Z7n1hI[MnNg0Z7n1hI[MnNg0Z7o1fI[MPOf0Z7P2eIZMQOf0Z7P2`I_MVOa0Z7Q2ZIcM\\O;[7S2QIiMD4[7T2lHlMI0[7b3eH^L[7b3eH^L[7a3fH_LZ7a3fH_LZ7a3fH_LZ7a3fH_LZ7a3fH_LZ7a3fH_LZ7a3fH_LZ7a3fH_LZ7a3fH_LZ7a3fH_LZ7a3fH_LZ7a3fH_LZ7_1lHUOJ\\OZ7^1PInMBe04OZ7\\1UIiMCj0N1Z7[1fIcNPO2Z7Z1iIbNmN4Z7Y1lIaNjN6Z7X1nIaNhN7Z7W1oIbNgN7Z7P1UJjNaN6Z7o0UJmN_N5\\7m0UJPO^N3]7l0RJkNdN[OLn0^7l0nIlNSO7o6m0lIlNYO5k6o0kIkN]O5h6P1iIjNC5d6Q1iIhNF7`6R1iIeNI:oNTO[7m1nIZNiNKR1l0X6o0nIVNkNMQ1h0\\6V1QJQNEc0`6d1bIiM1;c6m1jJkM\\5U2kHkMn8V2oFiMT9X2o0001aHfMk3[2PLgMR4\\2hKfMY4`2eH[Mc28i4c2RK`Mo4a2lJcMT5^2gJfMY5\\2cJfM]5[2cJdM]5]2nHoLV1b0l5a2kHoLY1?l5d2gHoL_1;j5h2dHoLc17i5l2`HoLh14h5X3YJfLg5Z3\\JcLd5^3_J_L`5a3dJ]LZ5d3jJZLS5k3T25K6VGmKP8]4cGlKW8T4iGoKT8Q4lGSL4_Ob7^4ZHWL3BY7W4dHZL4_OT7W4hH^L4WOT7Z4iHbL4ROQ7\\4kHeL^7[3bHgL\\7Y3eHiLX7W3hHfL[7Z3eHeL\\7[3dHcL^7]3bHcL^7]3bHcL^7\\2mHRMEb0^7Q2WI^M[Oa0^7f1bIiMPOa0^7a1gInMkNa0^7`1hIoMjNa0^7^1jIQNhNa0^7]1kIRNgNa0^7\\1lISNfNa0^7Z1nIUNdNa0^7Y1oIVNcNa0^7V1cIhNoN2^7R1TI_O^O_O^7m0XIEZO^O^7h0[ILWO\\O^7d0^I1TO[O^7?bI7POZO^7=bI;POXO^7;dI=nNXO^79fI?lNXO^77iI`0iNYO^75kIc0fNXO_73mIf0cNWO`71oIn1Q6PNZJf1g5XNkJX1U5fNoJX1Q5fNRK1^M7`7GTK1\\M8`7HTKO\\M9`7I[KFUMa0`7K[KBUMc0`7L[K@UMd0`7NYK^OWMd0`7OXK]OXMd0`70WK\\OYMd0`78oJTOaMd0`7=jJoNfMd0`7>hJoNhMc0`7>VJAZN1`7?cI2mN_O`7?RIc0^OnN`7`0_HT11\\N`7[3`HeL`7[3`HeL`7[3`HeL`7[3`HeL`7[3`HeL`7[3`HeL`7[3`HeL`7Z3aHfL_7Z3aHfL_7Z3aHfL_7Z3aHfL_7Z3aHfL_7Z3aHfL_7R2aImLPOQ1_7o1lIQNT6k1TJQNl5l1XJRNi5g1^JYNb5`1eJ_N\\5a1dJ_N\\5a1dJ`N[5`1eJ`N[5`1eJ`N[5`1dJbN[5^1eJbN[5^1eJbN[5^1eJbN[5^1eJbN[5^1eJbN[5`1cJ`N]5a1bJ_N^5c1`J]N`5d1^J]Nb5e1\\J[Nd5f1ZJ[Nf5g1XJYNh5g1XJYNh5h1VJYNj5h1UJXNk5h1TJYNl5h1SJXNm5i1RJWNn5j1PJWNP6i1PJWNP6j1gHTNJ2_7X3aHhL_7X3aHhL_7X3aHhL_7X3`HiL`7W3`HiL`7W3`HiL`7W3`HiL`7W3`HiL`7W3`HiL`7W3`HiL`7W3`HiL`7W3`HiL`7W3`HiL`7W3`HiL`7a400000001O00O1001O0000000000000000000000O1000000000000O10000000000001O000000001O000000000000O100O1001O]NaHZM_7d2eHZM[7d2fH]MZ7a2hH_MX7_2jHaMV7]2lHcMT7V2SIjMm6d1eI\\N[6d1dI]N\\6c1]IRMSO[1`7b1]IVMPOi08XO[7i2[InM_7R2`HkMd7a1eGQNf0:j7a1fGRN?;o7b1eGQN`04P8k1cGnMd0Lm7U2bGmMf9R2j000001O00001O00001O0lGjMS5V2\\HjMj17j5P2YHlMj15n5_2VJ\\Mk5e2]2001O001O001O001O0]FSM_O2m8m2cGRM\\O5o8l2cGQMZO6Q9l2cGZM[8i2cGXMJAR8Z3RHVMLKe7Q3dHPME0e7S3jHcLE:`7V3\\IjLc6X3[IhLe6[3XIfLg6\\3VIeLj6]3TIcLl6_3PIbLQ7n3\\HULd7k3n0ROn0N2O1N2O1N2O1N2N2O1@mEfMU:Y2?O1OYHaMd4_2YKdMg4[2WKgMj4X2TKkMl4T2RKoMn4Q2PKQNP5n1nJSNT5l1jJYNT5g1iJ\\NW5c1gJ`NY5_1iJ_NX5`1kJ]NV5c1mJXNU5g1nJWNR5h1QKVNo4l1RKoMP5V2mJgMT5^2j26J5K1O000000001O00000000000SGWMX7i2WHlMe7T2jGaNR8`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", "size": [427, 640]}, "area": 97071.0, "iscrowd": 0, "image_id": 18491, "bbox": [0.0, 0.0, 640.0, 194.0], "category_id": 99, "id": 20001170}, {"segmentation": {"counts": "j5a7j501O00O100000000000000000000000000000000000000001O00000000000000000000000000000000O100000000000000000000000000000000000000000000000000000000000000001VIXJo5i5[IVJ8=[6X601O00001O001OZOeI\\J[6a5mIYJT6e5PJXJQ6f5SJWJn5h5UJVJk5i5l0O100000000000000000000001O2N3M6J9G1O001O001O^O]I\\Jc6d5`IZJ_6h5`IWJ`6i5aIWJ^6l5`ISJ`6Q6]IoIb6c5_I`JNLc6c5cI`JIMc6a5hI_JGOa6b5kI\\JD1b6c5lIYJD1b6f5k000000000000000000000000000000000000000000000O100000000000000000000000000000000000000000000000000000000000000000000O100O1001O00001O0000000000000000000000000000000000000000000000000000000000000000000000000000001O00O1001O000000000000000000000mI]J^4b5^KfJ_4Z5\\KPK_4P5_KWK\\4i4bK^KY4b4fKdKU4\\4iKjKS4V4lKoKP4P4oKSLP4m3nKVLQ4j3nKXLQ4h3mK[LR4e3mK\\LS4d3mK]LR4c3mK_LR4b3lK`LS4`3mKaLR4_3mKcLR4]3kKfLU4Z3fKlLY4U3dKnL[4R3cKQM\\4o2bKTM]4l2bKUM^4k2aKVM_4j2`KWM`4i2_KXMa4g2`KYM`4g2`KYM`4f2aK[M^4e2bK[M^4f2aKZM_4f2aKZM_4f2aKZM_4f2bKYM^4g2bKYM^4g2bKXM_4h2aKZKNl0a4j3aKXK1l0_4l3`KVK3n0]4l3aKSK5P1Z4m3bKPK7FFm0a4]4cKmJL]O9<Ne0[4h4`KgJNC990a0Z4`5\\KhI<50a0X4i5iKeIOb0X4j5jKbINc0Y4k5jK`INe0X4k5lK^IMf0W4m5mKZIMi0V4m5^LSJb3m5^LRJc3o5\\LQJd3o5]LPJc3d5[KnIR1>c3`5hKjI90Ae0_4]5bLRJoN`0_4Z5dLYJlN=`4T5gLbJiN:`4k4oLmJ`N8a4c4UMXKZN4a4d4TMZKZN1c4d4RM_KYNMe4c4RMbKXNKf4a4RMeKYNJe4f4lL`K`NMa4h4iL\\KfNN_4k4eLWKmNO]4o4`LRKTO0[4P5bKVJf0k0^O0Y4Q5`KTJc00Ak041W4R5SLVJ_Og072V4[5iKnIId0:3S4]5gKmILb0<5o3_5eKkI0??7k3`6YL`Ie3_6]LaIb3]6aLdI\\OSOi3V5TLfLk0POUOVOl3V5WL]Lk0^Om2V4YLYLm0@i2X4[LULn0Bg2Y4]LSLl0Dg2Z4_LnKl0Ge2[4bLjKk0Jc2\\4PM[K?8a2\\4WMUK:>_2d4gLVKl05]2f4dLWKo03]2g4aLWKT11[2h4_LXKX1OY2j4\\LXK]1MW2l4YLYKa1JV2m4WL[Kc1HV2n4UL\\Ke1EV2o4VL[Ke1EU2Q5ULZKf1FT2P5WLYKf1FS2R5VLXKg1FS2S5VLVKh1FR2T5VLWKg1DT2o4YL\\JLP1d0_OM6Z3m4^L_K9AN2[3n4_L^K6FNO\\3l4bL_K2INL^3l4bL_K0MOH^3l4dL_KJ31D`3j4fL^KD;4\\Oc3i4hLPMDVNd3l4gLoLCTNg3m4fLQMARNi3m4gLgMZ3X2hLfMY3Y2kLcMV3[2oL`MS3`2mL_MT3a2mL]MT3a2nL^MS3a2oL]MR3b2oL]MR3e2mLYMT3i2jLWMV3l2hLSMX3o2fLQMZ3Q3eLnL[3U3cLjL]3X3aLhL_3[3_LdLa3^3^LaLb3a3]L^Lc3e3ZL[Lf3g3YLXLg3k3WLVLg3l3XL[L`3g3^L^L]3d3bL\\L]3d3dL\\L[3d3eL]LZ3c3gL]LX3c3iL]LV3b3kL^LU3b3lL]LT3j2_LRK>S2S3k2cLnJ:W2S3k2gLjJ7m0ANb3[4jLfJ4o0BO`3\\4nLbJ0R1EN]3^4RM^J0Q1C2[3_4oMZKiN5X3a4oMYKkN4W3d4nMVKmN5U3e4oMTKnN6S3f4PNRKnN8R3f4PNRKoN7Q3g4QNQKnN7R3h4QNoJoN8P3i4RNnJnN9P3i4RNmJPO9n2i4TNmJnN:n2i4UNlJmN:o2j4TNkJoN:m2k4UNjJnN;m2k4VNhJoN<k2l4WN`JVOc0c2m4@RKa0n4@QK`0o4APK?o4CPK=P5DnJ=R5DmJ<S5ElJ;T5FkJ:T5HjJ9V5HiJ8W5JgJ6Y5KfJ5[5KcJ6\\5LcJ4^5LaJ4^5O`J1`5m31O0000000000000000000000000000O10000000000000000000000000000001O00001O001O001O1O00O1O1O10000O10000O1000000000000O100000000001O00000000O1000000000000000000000000000000000000000000000000O1000000O10000000000O100O10000001O00000000000000000000000000000000000000000000000000000000001O0000O10000000000000000000000O1O100O1MSHgJn7Y5RHgJn7X53001O00O1001O00O100000000001O00O1O11O1OO100001OO1001O000000000000O1001O00O100001OO1O11O0000O11O000000000000000000", "size": [427, 640]}, "area": 144551.0, "iscrowd": 0, "image_id": 18491, "bbox": [0.0, 165.0, 640.0, 262.0], "category_id": 145, "id": 20001171}, {"segmentation": {"counts": "d2P22SNS;n1lDSNT;R21O1O2mMmDj1];CaDhN`;U1bDkN_;Q1dDoN];m0fDSO\\;`0oD@W;6mDJZ<0jN7jDIS;?hDAU;e0hD[OU;j0iDVOU;n0iDROU;R1iDnNU;V1iDjNU;Z1iDfNU;^1iDbNU;b1iD^NU;f1iDZNU;o1O1O100O100001O00001AQE`NZ;T1iDiNY;T1iDlNX;R1iDnNW;R1iDnNX;Q1hDoNY;m0jDSOV;h0oDXOR;`0UE@l::YEFj:3ZEMR<000O10000000000000000000000nM7bFI]9;[EBe03n9`0VECj0Mo9Q1PFoNo9T1oElNP:W1nEiNQ:X1oEhNP:Y1PFgNi9_1XFaNg9_1[F`Nd9_1_F`N`9\\1[FgNXOL]:Z1[FUOd9j0[FZOc9d0]F_Ob9a0[FCd9=ZFEo0TOP7V1oGHg0]OY7k0fGjNJQ1m0Eb7b0bGjNNQ1h0Ik7b0\\GHb0MW84WG2:0`8DVGTO0]121h8\\O[G]2e8`MYGd2h8YMUGU2:^Ma8X3dGfL^8V3gGgL[8T3kGiL\\8m2jGQM^8d2W1K5K5K6N2OO[NjDW1V;eNmD[1T;bNmD`1T;[NoDf1Z;101N3N3L4N311N45gMmD^1S;bNQE[1n:eNVE2@n0Z;POYE3Be0T;XO^E2E<m:BaE3E5i:HfE3_O3j:KjE3YO2l:KmE5TOOo:LPF5oNOP;LSF7jNLS;MVF6fNMS;MYF5dNMS;N\\F2bN0Q;N_FOaN0S;1eFM[8>ZFEY;f0gDZOn:Q1REoNi:V1WEjNh:W1XEiNf:Y1ZEgNe:Y1\\EgNc:Z1]EfNa:\\1_EdN`:]1`EcN]:?dFAX9b0iF^OR9e0PG[Ok8i0VGXOe8k0\\GUO_8n0cGRO[8P1eGPOY8R1gGnNW8T1iGlNU8V1kGiNT8Y1mGfNQ8\\1QHbNm7a1l1;Eb0^O3M3WF_Me8c2ZG]Mg8c2XG]Mi8k2nFUMT9k2jFUMW9j2iFVMY9h2gFXMZ9g2fFYM^9c2bF]Mf9[2ZFeMh9Y2XFgMi9o1`FPNa9_1PGaNQ9l0aGTO_8:SHFi:000000000000000O10000000000hNQ1TDoNi;[1L9H4H7I70O100000000000000O102N1O2N2N1N3N1N101O1N101N2O1O0@dDPOl<00000000000000000000O10000000\\P82]oG8H:I7I7J6I6K5J3M3N2M2O2O1N2N2O1N1M4J6M3M3M3N1O1O1O10000000000[NUMjHk2T7XMkHh2S7ZMnHe2P7]MPIc2n6_MRI`2m6aMTI`0bNm0S8jN[I7eNn0^7]OmICgNo0]7WO]HTO`1c0iNQ1Z7WO_HSO`1b0jNHIf0^7LcIZOUO9G_O7<Oc0[7NZIOUOB0D980b0X70WIh0UOaN<50`0e6\\O[Ie0;U1FYNOa0a6B\\I><Z1JRNNd0_6E[I`04[14jMOf0^6HXId0LX1a0dMNf0]6MVIQ3R1RMg5i3XJWLh5i3XJWLh5i3XJVLj5j3VJULj5]3ZIRLl0a0j5Y3]JRLnNe0f6T3]J[LmN`0f6Q3_JbLjN<h6j2cJmLeN9i6_2kJZM[N7j6IPIn1R2UNTN3j6IRIf1W2`NlM1l6^O\\IS2g1aNPNN]7e1^J_NTNL_7g1XJ^NZNK^7l1RJYNaNN[7m1nIVNgNOY7P2jIQNnN0X7S2dImMUO1V7T2aIlMZO1U7T2cHQMc00Aj052S7T2WITM^Of083S76dHR17QNIc0;4Q7KdH^O4o10QNMa0=7n6IfH[O5T2IQN1>`09m6GPIh1BXNd09k6EPI]28nMj6CoHKHj1b0iNZOROW7=WID^O6a0b1<TOTOVO[7`0QIF5\\1>_O^6CeHF`0U1`0@^65UI7?C^64UI7=E`62UI6=G`61[IO7Oa6NfIEK<i6BaIOH>n6ZOYHNNb0m05l6UOfGNOn0?LP13n5V1PIhNT11j5Z1oHfNY10f5\\1nHfN]1Mc5_1nHfN`1Ja5b1lHfNc1H_5d1mHeNe1F]5g1nHbNf1FZ5k1oH_Ng1FX5m1RI\\Ng1FV5P2RI[Ng1Eg4]OjIe2IXNg1Ee4]2eIoLNP1g1De4\\2mIoM;_OM5k5[2PJRN6AN2k5X2UJTN0FNNl5V2ZJVNJINLl5S2_JXNEMOHk5Q2eJZN\\O31Cn5m1iJ\\NSO;4\\Oo5l1mJMSOUNP6l1QK0mNTNR6i1TK5hNQNT6i1WKk0i4XOWKe0i4^OXK=j4FUK7k4LSK3m40RKNS51jJN\\5OcJOc5OZJ0l5MSJ2R6KlI5T6NjI1V61hIOX63gILY67eIH[6:cIF^6<`IC`6?_I@a6b0^I]Ob6f0[IZOe6h0ZIWOf6l0XIUOf6hN[I?NP1_6oMYJo0VOW1\\6YMSK^1`NY1^8bNdG^1_8^NaGc1b8YN_Gg1d8UN]Gj1g8RNYGo1j8mMWGQ2m8lMTGS2P9iMPGW2T9dMnFn0AOe9oNkFP1B0g9lNgFS1ENi9kNhFP1A4c:I`E5`:JbE5_:IcE6^:HdE6^:IbE7^:HdE7]:HcE8^:FdE9]:FcE9^:GcE8^:FcE:^:EbE;^:EcE9_:EbE;_:@fE?[:YOmEf0\\;01N2O1O1O1O1N2O1O1O2M2O1O1O1Ndcj2", "size": [427, 640]}, "area": 30776.0, "iscrowd": 0, "image_id": 18491, "bbox": [0.0, 27.0, 422.0, 286.0], "category_id": 183, "id": 20001172}, {"segmentation": {"counts": "_g`43oc01D:O2I5K1m\\O\\Oh?OV@1T3d0PM\\Od?4YC`0VM\\O_?5ZC>ZM\\O\\?7ZC;\\M^OX?8[C9_M_OV?8YC:bM^OU?9WC8gM_OQ?9[C4eMCo>9[C4\\=LbB5_=JbB4`=L_B4b=L]B3e=L[B4f=LZB2h=MkALlM7Z`0MhA2jMO``0NgA`0[>^OfA`0n`0OnmP5", "size": [640, 515]}, "area": 574.0, "iscrowd": 0, "image_id": 18519, "bbox": [231.0, 240.0, 26.0, 141.0], "category_id": 105, "id": 20001173}, {"segmentation": {"counts": "^3[3j0SNe=k1\\BTNe=k1ZBVNe=k1[BUNe=k1ZBVNe=k1[BUNf=j1YBWNh=h1WBYNh=h1XBXNh=h1YBVNh=j1XBVNg=k1XBVNg=k1YBUNf=l1ZBSNf=m1[BRNg=m1XBTNh=VObAP2e0kNj=SOfAn1?POl=POhA@HZ2f0UOl=oNjAk19WOP>gNkAR21[O_O]Nb>5PBR2N@S>XNVBV2WOjM=i1Q??PA@o>a0QA_Oo>a0QA_Oo>a0PA@P?`0QA_On>b0QA_Oo>a0QA_Oo>a0QA_Oo>a0QA^OP?b0PAWOCcN^?V2m@YOE^Na?Y2h@[OG[Na?[2g@[OZ?e0e@]OZ?d0e@\\O\\?b0f@[OT?[Nj@Z23YOU?^Nf@U2:]Oo>`Nf@P2>@l>`Nd@Q2a0_Ok>f0VAYOk>g0UAYOk>g0UAYOk>g0UAYOk>g0VAXOj>g0WAYOj>f0VAZOi>f0XAYOi>f0XAZOi>d0XA]Og>bNd@h1f0Fn>8[AAf>>eAVO]>i0cAVO`>UOS@_1]1\\Od>d0[A]Ob>f0^AYOc>f0^AYOc>UOP@_1]1[Og>e0YA[Og>d0ZA[Og>e0XA[Oi>e0WAZOk>f0TAZOl>f0TAYOm>h0RAWOo>i0RAUOo>k0QAUOo>k0QATOP?m0n@TOR?m0l@SOU?n0i@SOX?m0Z@_Og?b0X@^Oh?c0V@]Ol?d0R@[Oo?e0P@[OQ`0f0n_OZOS`0f0l_OZOT`0f0k_O[OU`0e0k_O[OU`0e0k_OZOV`0f0j_OYOW`0k0e_OUO[`0n0b_OQO_`0P1`_OoNa`0Q1__OnNa`0T1]_OmNc`0R1^_OnNc`0Q1^_OmNc`0R1^_OmNb`0S1k_OYN[OJo`0m1Q101N11O000O2O1X^O`Nm`0a1R_OfNi`0Y1W_OgNi`0Y1W_OgNj`0X1V_OhNj`0W1W_OiNi`0S1[_OmNe`0n0`_ORO``0l0b_OTO^`0i0e_OWO[`0h0f_OXO[`0g0e_OYO[`0g0e_OYO[`0h0o^OaNc0g0^`0k0a_OUO_`0j0c_OUO]`0k0c_OUO\\`0l0d_OTO\\`0l0d_OTO\\`0l0d_OTO\\`0l0d_OTO\\`0l0d_OTO\\`0l0e_OSO[`0n0d_ORO\\`0n0c_OSO\\`0l0f_OTOZ`0l0f_OTOZ`0m0e_OSO[`0m0e_OSO[`0m0e_OSO[`0m0e_OSO[`0l0f_OTOZ`0k0g_OUOY`0k0g_OUOY`0k0g_OUOY`0j0h_OVOX`0j0h_OVOW`0j0j_OVOT`0l0l_OTOT`0l0l_OSOU`0n0j_OROZ`0j0S@iNn?V1Y1dNf]O]1Yb0dNf]O\\1Zb05O1000O1O1`Ne]O[1ab0O01O0O1000100O010d^OfNP`0Kn^O\\1g0TO^`0m0^_OVOb`0j0]_OWOc`0i0]_OWOc`0i0]_OWOb`0j0^_OVOb`0j0^_OUOd`0j0\\_OVOd`0j0\\_OVOc`0k0]_OUOc`0k0]_OUOb`0l0^_OTOb`0l0^_OTOb`0l0^_OTOc`0k0]_OUOd`0i0]_OWOeb02N4L6K1O1O000oMoNSAQ1i>\\On@a0U?Hb@6`?6T@Go?b0h_O\\OY`0f0f_OWO]`0i0c_OTO``0P1\\_OmNg`0R1Z_OiNk`0Y1S_OfNn`0Z1R_OfNn`0Z1R_OfNn`0Z1R_OfNn`0\\1R_ObNn`0`1g^OYN26Wa0b1e^OZN60Va0g1b^O[Nka0f1S^O[Nla0m1O1000000000O1000N2O100002N1O1N1O1MQ^OTNoa0m12000O1e^ORNc`0o1\\_O[N[`0e1e_O^NX`0b1i_O^NX`0_1h_ObNZ`0\\1f_OdNZ`0\\1e_OfNZ`0Z1d_OhN\\`0V1d_OlN]`0S1b_OoN^`0o0b_ORO_`0l0a_OVO_`0h0b_OXO_`0f0a_OWO^OZOTa0]1__OXOd`0h0\\_OWOe`0j0Z_OVOf`0j0Z_OUOg`0j0Z_OVOf`0j0Z_OVOe`0k0[_OUOe`0k0[_OTOf`0l0Z_OTOe`0m0[_OSOd`0o0[_OQOc`0Q1]_OoNa`0S1__OmN^`0V1b_OjN]`0X1a_OiN^`0X1b_OhN^`0X1b_OhN]`0Y1c_OhNC\\Od`0l1i_OlNX`0S1i_OmNV`0T1j_OkNX`0]OX_O`1`0SOX`0\\OY_Oa1?SOX`0\\OY_Oa1?ROY`0\\OY_Ob1>ROY`0\\OY_Ob1>ROY`0[OZ_O=Lg0`0@Z`0\\O[_O8LH11No0?D\\`0[OY_O:2GKo0?E``0E\\_Oe04F_`0E^_Oe03E``0Ab_Oj0NE``0@c_Ok0MEa`0_Ob_Ol0LEd`0\\Oa_Oo0LEYa0<f^ODZa0<f^ODZa0<f^OC[a0<e^OEZa0<f^ODZa0<f^OC[a0>d^OB\\a0=e^OC[a0=e^OC[a0=e^OC\\a0;e^OE[a09g^OGYa0;e^OE[a0;e^OE[a0<d^OD]a0:c^OG]a09d^OF\\a0:d^OF\\a0:e^OE[a0;f^ODZa0=f^OBYa0>i^OBVa0>j^OBVa0>h^OEWa0;i^OEWa0;i^OFVa09m^OESa0;m^OFSa09k^OIUa07k^OHWa07i^OIWa08h^OHXa08h^OHXa08g^OIZa06g^OH\\a06d^OJ_a03a^ONaa0O_^O1da0L]^O3ba0N^^O1da0O\\^O0ea0O[^O2ca0N^^O2ba0N^^O2aa0O_^O1aa0O_^O0ca00\\^O0da00\\^O0ea0N\\^O2ca0O]^O1ca00\\^O0ea0O[^O1ea0M\\^O4ba0N^^O3`a0N`^O2`a0N`^O2Qa0A_^OOL>d02Pa0?o^O@Pa0b0P_O_Oo`0a0R_O^Om`0c0R_O^On`0b0R_O^On`0b0R_O]Oo`0c0Q_O]Oo`0d0o^O]OPa0c0R_O\\On`0d0R_O\\O_`0^OZ_OX16ZO_`0EU_OP1=[O\\`0KR_Ol0d0VOZ`0Z1U11O100000010N1_^O\\Ng`0d1T_OjNb`0V1^_OiNc`0W1]_OjNc`0U1\\_OlNe`0S1Z_OnNf`0BT_Oo07_Oe`0BT_Oo07_Of`0@T_OQ17^Og`0_OR_OS17^Oi`0]OP_OT19^Oh`0\\OQ_OT18@g`0[OR_OU18^ORa0b0o^O]OQa0b0P_O^OPa0a0R_O^On`0c0Q_O]On`0d0T_OZOk`0g0S_O[Ol`0g0S_OYOm`0g0S_OYOm`0g0S_OYOm`0g0R_OZOn`0f0R_OZOm`0h0R_OXOl`0i0U_OWOj`0k0V_OTOj`0l0V_OTOh`0n0X_OROh`0n0W_OROc`0_OQ_O_1=RO``0AR_O]1?QO_`0W1b_OhN^`0X1d_OfN\\`0Z1b_OhN^`0X1b_OhN^`0Y1a_OgN_`0Y1b_OfN^`0Z1c_OeN]`0[1d_OdN\\`0\\1c_OeN[`0\\1f_OdNZ`0]1d_OdN[`0]1e_OaN]`0_1c_ObN\\`0^1c_ObN^`0^1a_OeN]`0Z1c_OgN]`0Y1b_OhN^`0V1c_OkN]`0T1d_OlN\\`0T1c_OlN^`0T1b_OlN^`0T1c_OlN\\`0T1d_OjN^`0V1b_OhNa`0V1`_OmN]`0S1c_OmN\\`0S1e_OmN[`0S1e_OmN]`0P1f_OoNW`0S1j_OlNV`0S1l_OlNS`0U1n_OiNQ`0Z1n_OfNQ`0[1o_OeNQ`0]1X_OUNa0=P`0D`_Oo1?]NR`0F]_Om1`0^NR`0G\\_Ol1b0]NR`0j1o_OUNQ`0k1o_OVNP`0j1Q@TNP`0l1n_OVNR`0i1P@VNo?k1R@UNl?l1R@VNn?j1R@VNP`0h1P@VNS`0i1n_OWNR`0g1n_O[NP`0e1Q@[No?c1S@]Nm?b1T@^Nl?b1S@_Nl?a1U@_Nk?a1T@`Nl?`1U@_Nk?a1U@_Nk?a1U@_Nk?b1T@^Nl?b1S@_Nl?c1S@]Nm?e1Q@[No?g1o_OYNQ`0g1o_OYNP`0h1P@XNk?@f_OX2?XNj?n1V@RNj?n1V@RNj?n1V@RNj?m1W@SNh?m1Y@SNg?l1Z@SNg?m1Y@SNg?m1Y@SNh?l1X@TNh?l1Y@SNg?m1Y@SNh?k1Y@UNg?k1X@VNf?l1Z@TNe?m1[@SNe?m1[@SNd?o1[@QNe?o1[@QNe?n1\\@RN_?@n_O`2b0PN_?T2b@lM]?V2`@lM`?T2`@lM_?U2b@jM]?V2c@kM]?V2b@jM^?W2a@iM`?U2a@kM_?U2a@kM_?W2_@hMb?X2^@iM`?X2_@iMa?W2_@iMb?V2^@jMb?U2`@jM`?W2_@iMa?W2^@jMb?V2_@iMa?W2_@iM`?X2`@hM`?X2`@hM`?X2`@hM`?X2_@iMa?W2`@hM`?X2`@gM`?Z2a@eM_?[2a@eM_?[2a@fM]?[2e@bM\\?^2c@dM[?\\2e@eM[?[2e@eMZ?\\2f@dM[?[2d@fM\\?Z2d@fM]?Y2c@gM\\?Z2f@cM[?]2d@dM\\?\\2d@dM[?]2e@cM[?]2e@cM[?]2e@cM[?]2e@cM[?]2e@cMZ?^2f@bMY?_2g@aMW?a2i@_MQ<", "size": [640, 515]}, "area": 41058.0, "iscrowd": 0, "image_id": 18519, "bbox": [0.0, 102.0, 515.0, 187.0], "category_id": 124, "id": 20001174}, {"segmentation": {"counts": "W2h0Zc0N1N11OO10000001O00000001O00O1O0001O10L]Ok\\Ob0Sc0^Ok\\Og0Sc040000000000010O01O1N10SOP]Ok0ob0UOQ]Ok0ob0UOQ]Ok0ob0TOR]Ol0Qc01O1V]ORO`b0R1[]OoNeb0V101O010O2Z]OfNcb0[13JO4O1000N111M003N03MNZ]OkNgb0U1201O0O1O01O1000N5K7K3XOg\\Oa0]c0K7D\\\\O30Ogc00fid8", "size": [640, 515]}, "area": 2118.0, "iscrowd": 0, "image_id": 18519, "bbox": [0.0, 62.0, 72.0, 50.0], "category_id": 128, "id": 20001175}, {"segmentation": {"counts": "i6j0^1Jf?7Y@Ig?6Z@Jf?6Z@Ig?6Z@Jf?6Z@Ig?6Z@Jf?5[@Ke?5[@Ke?6Y@Kg?5Y@Kg?4Z@Lf?4Z@Lf?4Y@Mf?4Y@Mg?2Z@Mg?2Z@Nf?1[@0b?2]@1a?N`@3_?Ie@_O]Ng0l`0Ik@9S?WOU_O=i1<T?Ek@<T?Dl@=S?Cm@<T?Cm@=S?Dl@<T?Cm@=S?Cm@=S?Cm@=S?Cl@>T?Bm@=S?Ao@?Q?_OQAa0o>^ORAb0m>]OVAb0j>]OWAc0g>_OXAb0g>_OYAa0b>D^A<_>GaA9^>HbA8]>IcA7]>IcA7]>IcA7]>IcA7]>JbA6]>KcA5]>KcA5\\>LdA4^>JbA6a>G_A8b>H^A7d>OUA1k>:j@FW?9j@EW?;j@DW?:j@FV?:j@GU?9k@FW?9i@GW?9i@GW?9i@HU?8l@HT?8l@HS?9m@HP?:PAFP?:PAFP?;o@EQ?;o@EQ?;j@oNfNf0_`0;i@ROgNd0_`0:i@0V?Oi@3W?@PAf0P?ZOPAf0n>[OTAd0n>ZORAf0n>YOSAg0n>XORAh0o>WOQAi0o>VOSAi0m>WOSAi0m>WOSAi0m>WOSAh0R?TOn@m09hNl<8kBP19hNm<7TBIGX1OhNb00d=7SBJHW1OiNCOM1L1a0NZ>5SBMFV1_OkN8OG?:D]>1UBLF_1FbNF?5Jd>KSBNE]1HI_>mNSBNF]1B^NOZ1e>ZO`A[OJa09T1G\\N1[1e>7bAf1^>n1O11O00001O1QKgAU4Y>jKnAP4R>PLnAP4R>PLnAU20gNR>TOnA[2:QNg=EoAZ2;PNb=JSBR2YOcM[19U=7WBg1R1kMf<`0XBH]OZ1L\\NZ`0e0]@CAZ1S`0TO\\@@D[1P`0UO\\@^OH[1l?WO\\@]OKZ1j?cNd_Oc0g0^ON[1j?VOQAi0n>YORAf0n>ZORAf0n>ZORAf0n>ZORAf0n>ZORAf0n>ZORAf0n>ZORAf0n>[OQAe0P?ZOo@g0Q?XOPAi0m>YORA2lNBS`0<PA2g?MX@3i?MW@3i?MU@5k?KU@5j?LV@4i?MX@2h?NX@2h?NZ@0e?1[@Oe?1Z@0e?1\\@Nd?2\\@Nd?2\\@Od?0]@0b?=Q@Dn?e0j_OZOW`0e0i_O\\OV`0d0k_OdNVOf0n`0f0o_OZOP`0f0P@[Oo?e0Q@[On?f0R@YOn?h0S@WOm?i0S@WOm?i0S@WOn?h0R@XOn?h0S@WOm?i0S@VOn?j0R@VOo?i0Q@WOP`0h0P@XOP`0h0P@XOQ`0`0U@@m?5\\@Kf?1\\@0d?O]@0d?0[@1e?OZ@2f?NY@3g?MY@3g?MX@4h?LX@4h?LY@3g?MY@3g?MZ@1g?O[@Oe?1[@Oe?1\\@Nc?3^@Lb?3`@M[N@Va0>a@3_?Lb@5\\?Le@3[?Me@4Z?Lf@6W?Ki@5W?Kh@6X?Jh@6X?Jh@6U?Mk@3P?2PAMR?2m@OS?1m@OW?Mi@3V?Nj@3W?Kh@5Y?Kf@5[?Kd@6\\?Jc@6`?J\\@8f?]Ok^O2^1a0h?[Ol^O6Y1?l?YOm^O:T1=P`0WOm^O<T1<V`0Dj_O;W`0Ei_O;W`0El_O8T`0Gl_O:T`0Fi_O<X`0Di_O<U`0El_O:T`0Fm_O9S`0Gn_O8R`0Hn_O7S`0Ho_O6R`0Jo_O4R`0Lo_O3Q`0MP@2P`0NQ@2m?OS@1l?OV@0g?Dd^O=e10f?2Z@Nf?2[@Me?5Y@Lf?6Y@Ig?8X@Hh?8Y@Hf?9Y@Gf?9\\@Fd?:\\@Gc?8_@Ga?7a@I]?7e@H\\?7g@EZ?;j@AV??U21O001001O00O100000000001O0000001N10000000O10000000000001O1OO1000000000O011O0000O010001OO10000O1N22N001OO011O001O100N2O00N2000O3N00N20001O000O11O0X^OBn?>R@Ek?;T@Hj?9U@Hj?8U@Kj?5U@Lj?4V@Mh?4W@Nh?2X@0f?0Y@2f?NZ@3f?LY@7e?I[@7d?J[@4eNYOo`0c0]@3fNYOm`0c0^@4f?L[@0bN^OPa0c0`@MbN@m`0c0b@LbNBj`0b0e@KcNBg`0c0g@Jb?6^@Kb?4^@Lb?4]@Mc?4[@Me?2\\@Nd?2\\@Nd?2\\@Nd?2\\@Ne?0\\@0c?2[@Oe?1[@Of?0[_O@9`0T1^OV=c0]ABGO0>e1_OSNOR?e0\\ADD02:i1^OSN01Ol>`1TAmNN7m1^OTN4l>g2iBcMS=f2cB^MZ=a2gBbMV=_2hBfMT=Z2kBhMU=X2iBiMW=BiAj1P1dNX=]OVBd1b0POX=\\OUB`1g0UOT=[OVBf1`0nNZ=[OWBh1?lNZ=[OXBi1?kNX=]OYBMXOa1W1UOX=]OYBH^Of1Q1TOX=_OXBEBg1o0]NhN`0_>GfBZ1<mNm<KhBP1jNbNf1b0g<MiBDROk0e1@b<2RC<`0_O`<6oB8e0@\\<9oB5h0\\OjMM_>d0oB1k0\\OgMO`>c0nB2k0[O\\<e0fB1g?M[@3f?LZ@4f?LZ@4f?LZ@4f?K[@5e?K[@5f?JZ@6e?K[@5f?KY@5g?KY@5g?KY@6f?IZ@AcNc0Ra0M[@_OdNc0Qa0O_@1a?0_@Oa?3]@Mc?1_@Oa?1_@Ob?0^@0b?1]@Oc?2]@Mc?4\\@Ld?3]@Mb?4^@Lc?_Ok^Oa0c10b?_Om^O?a12b?_On^O>`13b?_Oo^O<`15g?JX@7i?HW@8j?GT@G_Nb0aa0CP@`0S`0]Om_Oc0ha00001O001O00001O00ZN@l_O`0R`0Cl_O>n?]Od^O8^1:n?KQ@6n?JS@6k?KV@4j?MU@3k?LV@4i?LX@4h?LX@4g?NX@2h?NX@2g?0X@0f?0\\@0c?2\\@Oc?]Om^Ob0_11d?]On^O?`14b?]On^O?a13f?LZ@4g?LX@4h?KY@5g?KY@5g?_Og^O8a19P`0Dm_O>W`0^Oj_Oa0ka0000000000000000O11O00O101RN[Oe^O8_1<l?HT@8l?HU@8d?\\On^O=^17c?^On^O;_17b?_On^O;`16b?0^@0b?0^@0b?0^@1a?0^@Oc?1]@0a?1_@Oa?0`@0`?0`@0`?0`@0`?0`@0a?O_@1a?O_@1`?0a@O`?0`@0_?1b@N_?Oc@1]?Oc@1]?0b@0]?0c@1^?Nb@2_?Ma@3^?Na@3_?Ma@3a?K_@5a?K_@5a?J`@6`?J`@6`?J`@6_?L_@4c?K]@4d?K\\@5e?LZ@3g?MZ@1g?OX@1i?OW@2h?NX@2h?MY@3g?NX@2h?NX@2h?OW@1i?OW@1i?OW@1i?1U@Ok?0V@0i?0X@0h?0X@0h?0X@1g?NZ@3e?M[@3e?M[@3e?OY@1g?NZ@2f?NZ@2f?NZ@2f?NZ@2f?NZ@2f?NZ@2f?NZ@2f?NZ@2f?N[@1e?O[@1]9", "size": [640, 515]}, "area": 23209.0, "iscrowd": 0, "image_id": 18519, "bbox": [0.0, 195.0, 515.0, 184.0], "category_id": 132, "id": 20001176}, {"segmentation": {"counts": "e9[:e9000O100O1000000000000000000O1001O2N1O2N2N001O1OO1000000000000mNlE]G1f0S:m7lEZG4i0P:m7lEYG5j0o9m7SFSHm9m7TFRHl9m7UFSHk9j7WFWHi9g7YFYHg9g7YFYHg9g7YFYHg9g7YFYHg9f7ZFZHf9f7ZFZHf9f7ZFZHf9f7ZFZHf9f7ZFZHf9f7ZFZHf9f7ZFZHf9e7[F[He9e7[F[He9f7ZFYHg9g7YFXHh9h7XFXHh9g7YFYHg9f7[FXHf9h7[FWHe9i7[FWHe9h7\\FYHc9f7^FYHc9f7^FZHb9f7^FZHb9e7_F\\H`9c7aF]H_9b7bF^H^9b7bF_H]9`7dF`H\\9_7eFaH[9^7fFbHZ9^7fFbHZ9]7bFmG[Of0S:]7_FPH]Od0T:[7^FoHa9Q7]FQIc9n6XFXIh9h6XFXIh9g6ZFXIf9g6[FYIe9f6\\FZId9f6\\FZId9f6\\FZId9f6]FYIc9g6]FYIc9f6^FZIb9e6_FZIb9f6^F[IZ1hNX6l7_H\\IY1hNX6l7`H\\IOhNb00o6l7`H\\IOiNCOM1L1a0Nd7m7_H[I_OkN8OG?:Dh7h7`HdIFbNF?5Jo7c7]HcIHIk7d6]HdIB^NOZ1R8j6VH`IG\\N1[1R8j6UHSKk7a800000000000000000UNcHSG]7S9mH]FS7c9nH\\FR7`9lGoE[19j6b9dIWF]6l7nGfIL\\NT:l7TFfIn9X6UFgIk9W6YFgIg9X6\\FfId9X6_FgIa9W6bFhI^9X6cFgI]9Y6cFgI]9Y6cFgI]9Y6cFgI]9Y6cFgI]9Y6cFgI]9Y6cFgI]9Y6cFgI3VOl7S7PHhI4VOk7R7QHiI2VOm7Q7PHTICBa09l7Q7oGTIk0KV7Q7nGTIm0KU7Q7nGTIl0LV7P7lGVIn0JV7P7lGVIn0JV7P7lGVIn0IW7Q7lGUIl0KX7P7lGUIl0KX7P7nGSIj0MX7P7nGSIj0LY7Q7lGTIk0KY7Q7mGSIk0KX7R7mGSIk0JY7R7mGUIi0IZ7R7nGUIg0I[7R7nGVIf0H\\7R7oGUIe0I\\7R7oGVId0H]7R7PH^HVOf0\\1J^7R7SHTI>J_7R7SHUI=I`7R7SHUI=I`7R7SHTI>J_7R7THSI>J^7S7THSI=J`7S7SHSI=J`7S7SHSI=J`7S7THRI<K`7S7THQI<M`7R7THQI<M`7R7THQI<M`7R7THQI<M`7R7THQI;Na7Q7SHQI=N`7R7QHPI`0N_7R7PHQIa0M_7R7PHPIa0O_7Q7oGQIb0N_7Q7nGRIc0M_7Q7mGSId0L_7P7nGTIb0M`7o6mGUIc0L`7o6mGUIc0L`7o6nGTIb0M`7o6nGTIa0Mb7o6nGRIa0Oa7o6PHPI?1a7o6PHPI?1a7o6QHoH=3b7n6RHnH<4b7o6RHmHRO@W1d0e7R7PHkH;3e7W7kGgH>3g7W7kGeH>4g7X7jGeH>3h7X7jGgH;2k7W7jGgH;2k7Y7gGfH>1k7Y7gGfH=2l7Y7fGeH>2l7Z7eGdH?2l7Z7eGcH?4l7Z7cGcHa03l7Z7cGcH`04m7Z7bGbHa03n7\\7`GbHa02o7\\7_GbHb03o7\\7]GaHe03n7\\7\\GbHf02n7]7ZGaHh03n7]7WGbHk01n7]7WGbHk01n7^7VGaHk02o7]7VGaHk01P8_7UG_Hj03Q8_7TG]Hk04R8_7SG]Hj05S8_7UGYHg08U8_7SGZHg08V8W8kGhGV8Y8hGhGX8X8gGhGZ8X8eGiG[8W8dGiG]8V8dGjG\\8U8dGkG]8T8cGmG]8S8bGmG_8S8`GnG`8S8^GmGc8S8\\GnGd8R8[GnGf8S8XGnGh8R8VGoGk8Q8TGPHl8Q8RGoGo8Q8PGPHP9P8oFPHR9Q8kFQHU9o7jFQHW9o7hFRHX9o7fFQH[9o7dFQH]9Y9_GaEa7^:bHcE[7\\:]HnEb7Q:_HQF_7o9\\HVFd7i9XH]Fg7b9YH`Ff7_9YHdFf7\\9YHeFg7Z9[HfFd7Y9\\HhFd7W9[HkFe7T9[HnFd7Q9`HlF`7S9_HoFa7P9^HRGb7m8_HSGa7l8_HVG`7i8`HXG`7g8`HZG`7e8aH\\G^7c8UHTF4Z1g7a8TH[F1U1j7b8SHmGm7T8RHlGn7U8QHlGn7S:0001O001O00001O00001O001O00001O00002N2N2N2N1O000000O100000000O100000000O100N2O1O1O1O1N2O1]KVHoLk7Q3UHoLk7Q3VHmLk7S3lHULU7k3nHQLS7n3RImKo6S4PImKQ7T4nHkKS7U4lHkKU7U4kHjKV7U4jHkKW7U4iHkKW7V4hHiKY7W4fHiK[7W4eHhK\\7X4cHgK_7X4bHgK_7Y4`HgKa7Y4_HfKb7Z4]HYIWOb1]8U5]HXIXO`1\\8X5]HXIWO^1]8Z5]HTITO^O2R2^8\\5]HRIUO@0o1`8`5[HPIVOBMm1c8a5[HoHWOBKl1d8c5[HnH[O^1Z8d5[HoHZO\\1\\8d5[HPIZO[1[8f5YHPI]OX1[8h5WHQI^OV1\\8i5VHQI_OU1[8j5VHQI@S1[8k5VHRI_OR1\\8l5UHRI@Q1[8m5UHRIBn0Z8P6SHSIFj0W8S6SHSIJC]Of0g8g6oGPI99h7j6]G_H0>m07kNWOh8i7ZG`H2:Q16kNXO1Ob8j7^GcHN7V15kN]Ob8Q9^HnG^7[8YHhGd7W8]HlG`7U8_HoG]7Q8cHPH\\7Q8cHoG]7Q8cHoG]7P8eHoG[7l7lHRHT7U8hHeFUOi0S8c8jHaFTOk0R8d8lH^FTOm0P8j6lGQJW1UOm6e6RHVJQ1TOm6d6UHWJo0]NhN`0T8j6YHWJT1mNc6l6[HoIAbNf1b0^6m6[HdHIk0e1@Y6P7fH^IU1_OW6S7dH[IY1@T6U7cHYI\\1\\OjMMX8^7cHWI^1\\OgMOX8^7cHWI^1[OT6^7]HXIP:h6PFXIP:h6PFXIP:h6PFXIP:h6PFXIP:h6PFXIP:h6PFXIP:h6PFXIP:h6PFXIP:h6PFXIP:h6PFXIP:h6PFYIo9f6QFdHVOc0h:j6RFbHWOc0g:k6WFUIi9k6XFTIh9l6XFTIh9l6XFTIh9l6XFTIh9l6XFTIh9l6XFTIh9l6YFSIg9m6YFSIg9m6YFSIg9m6YFSIg9m6YFSIg9k6[FUIe9l6ZFTIf9k6[FUIe9m6WFTIj9l6UFTIl9l6QFbHSOb0m:l6oEWIQ:i6oEWIQ:h6PFXIP:h6PFXIP:i6PFVIP:h6RFXIn9f6UFYIk9j6RFVIn9j6RFVIn9j6SFUIm9k6SFUIm9l6RFTIn9l6QFUIo9k6RFTIn9k6SFVIl9j6UFVIj9j6WFUIi9k6WFUIi9j6XFVIh9j6XFVIh9j6XFVIh9j6XFVIh9k6WFUIi9j6XFVIh9j6XFVIh9j6XFWIg9j6XFVIh9j6XFVIh9j6YFUIg9i6[FWIe9j6ZFVIf9k6YFUIg9k6YFUIg9k6XFUIi9k6TFWIm9i6SFVIn9j6RFVIn9j6RFVIn9j6RFVIn9j6RFVIn9j6RFVIn9j6RFVIn9j6RFVIn9j6RFVIn9j6QFWIo9i6RFVIn9j6RFVIn9j6QFWIo9i6QFXIn9h6RFWIo9i6QFWIo9i6RFWIm9i6SFWIm9i6SFWIm9h6TFXIl9h6TFXIl9h6TFXIl9h6TFYIk9g6UFXIl9h6TFYIk9g6UFYIk9g6UFYIk9g6UFYIk9g6UFYIk9g6UFYIk9g6UFYIk9g6UFYIk9g6VFXIj9h6VFXIj9h6WFWIi9i6WFWIi9i6WFWIi9i6WFWIi9i6VFXIj9h6VFXIj9h6VFXIj9h6UFYIk9g6UFYIk9f6VFZIj9g6UFYIk9g6UFYIk9g6UFYIk9g6UFYIk9g6TFYIm9g6SFXIn9h6QFXIP:h6PFWIQ:i6PFUIQ:k6nEUIS:k6mEVIR:j6nEVIR:j6nEVIR:j6nEVIR:i6oEWIQ:i6oEWIQ:i6oEWIQ:j6nEVIR:i6oEWIQ:j6nEVIR:j6nEVIR:j6nEVIR:j6nEWIQ:i6oEXIP:h6PFXIP:h6PFXIP:g6QFYIo9g6QFYIo9g6QFYIo9g6QFYIo9g6QFYIo9g6QFYIo9g6QFYIo9g6QFYIo9g6QFYIo9g6RFXIn9h6RFXIn9", "size": [640, 515]}, "area": 172206.0, "iscrowd": 0, "image_id": 18519, "bbox": [0.0, 235.0, 515.0, 405.0], "category_id": 140, "id": 20001177}, {"segmentation": {"counts": "WRP13lc04L1N3O001O001O0O101O0O10O101N1N3O0000001N2N2O1O1OS_^4No`aK0O2N3M4L4L4MO02O1OO010O10O2O0O2OO10105JOO102O4G^\\OLb\\W10YWiN2N1O1N1O100N5L4M2N001O000O100O100O01N1Ke\\OE]c0;5O0O3H[\\O1jc000O100O100OQX10PhN1O0O2O1N1O1N2\\\\OH_c0`0M3L4MO11O0001O0O2O2N2N0000000Dc\\OLfc03400O2O0OfmU1", "size": [640, 515]}, "area": 1412.0, "iscrowd": 0, "image_id": 18519, "bbox": [51.0, 162.0, 403.0, 59.0], "category_id": 162, "id": 20001178}, {"segmentation": {"counts": "0W2h0XNj?j1T@XNj?i1T@YNk?g1V@WNk?i1T@XNl?h1T@WNm?i1S@XNl?h1T@YNk?g1V@WNk?i1U@WNk?i1U@WNk?i1U@VNl?j1T@UNm?l1R@SNo?m1Q@RNP`0m1Q@TNn?k1R@VN0BX?W2h@YNNEV?R2k@[NMF@Hd?W2o@\\NKIU?g1]@oMb0e0EI\\?`1]@PNg0g0_OK\\?\\1YAjNUO1`?U1[AEe>;[ADf><ZADf><ZADf><ZADf><[ABf>?YAAg>?ZA@f>`0[A_Oe>`0\\A@d>`0_@RNk0]1f>a0_@RNk0]1f>a0_@RNk0\\1g>b0]@SNl0Z1h>f0YAXOh>h0YAVNROh0e?R1]AQNoNd0O@f?k1aAeNjN^Of?m1aAcNkN_Od?n1aAcNkN]Of?Q2_AaNg>_1[AnMfN?o?d1\\A\\Nd>g1VA\\Nj>`1ZA`Nf>_1[AaNe>_1[AbNd>^1ZAcNg>\\1ZAdNf>]1WAfNh>X1XAjNmNZOc?m1^AROb>m0aARO^>m0`ARNkNS1d?k0aAYOmNiN\\?n1gA]OY>d0fAYO]>g0bAZO^>f0aA[OoNfN]?n1eA_O[>`0fA@Z>`0fA@Z>`0dAB\\>a0_AB`>c0[A]Oe>f0c@hMa0d1l>f0o@_OQ?c0c@dM30Oj1[?d0e@A[?b2000001O00001O00001O0000000000000000O1001O00O100001O000000O100O11O001O0000000000001O0000000000O100000000000000O10000000000000000000000O1N200004L1OKi@aKW?_4j@`KV?`45O10000000d@aKW?e4O0000O100001O001O0i@[KS?i40000000O1001O00O100O100001O1O^1bN4L00O10000O1_N]BeLc=W3jB`LV=`3SCWLm<i3_ChKd<X4b1O1000000000000000000000000O1O100000000000000N2O100002N1O1O0000O10000O1O100002N2N0000001O1O1O1O1O1O0000000000O10000O1O1N2N2M3O1O100O10000O11b@dKV?\\4i@eKW?[4i@eKW?[4h@fKX?Z4h@fKX?Z4g@gK=L\\>\\4WAiK8LH11Nc>]4[AgK:2GKd>a4eAjKZ>U4fAlKZ>T4aAQL_>o3`ARL`>o3_AQLa>P4\\AQLe>e40000000O100000000001O000000001O0000000000O10000000000001O001O00O100O1O1O1O1O100O10000O10000O100O100001O6JO1O11OO1O1N200O100000000O100ASAjKn>U4YAeKg>Y4_AbKb>^4`0O1O10000001OO11O00001O1O0l@dKd>\\4\\AdKd>]4ZAdKf>^4XAbKh>`4VA`Kj>a4TAaKk>_4TAbKl>i40000000O1O1O100000000O1N2O100N200Ik@cKU?\\4m@cKS?]480000000000000000N200O10000000000000000000000001O00O1002NN200O1N2O100I\\@RLd?n3^@PLb?o3`@oKa?Q4700000000O1O1002N1O1OO1000000O1000000000000O1000000O1KR@XLn?g36000000O10000001O00001O00N2O100O10000Kh_OaLY`0^35O100O1O100001O000000O1001O0000000000O10000000000O10000O100O100O11O001OO10000O100000000O1O1N2", "size": [640, 515]}, "area": 72020.0, "iscrowd": 0, "image_id": 18519, "bbox": [0.0, 0.0, 515.0, 205.0], "category_id": 169, "id": 20001179}, {"segmentation": {"counts": "fj]2a0_c00O1000000O1000000O01000000O2O000O1000000000000000001OO1000000O1000000O0100000O1000000O1000000O1000000O1000000O10O10O100bM\\O`Ad0]>_ObAb0\\>AcA?Z>DfA<X>GfA:W>JhA6U>MjA4S>0lA0o=5QBKn=7PBJP>7oAIQ>7oAIQ>8mAIS>7mAIS>8lAHS>:kAFV>:jAFV>;hAEY>;fAFZ>;dAE]><aAE^><aAD`>=_ACa>>]ABd>?ZABf>?XAAi>?VABj>?UA@k>b0SA_Om>b0QA^OP?c0n@^OR?c0l@]OU?c0j@^OV?c0h@]OX?d0g@]OY?d0e@\\O\\?d0b@^O^?c0`@]Od?`0[@Ag?lNd_O\\1d0HW`00g_O1Y`0Oe_O2]`0Na_O3_`0M`_O3a`0N]_O2e`0MZ_O3g`0NW_O3i`0MV_O3l`0NP_O3Qa00k^O0Wa0\\10Om_O^Mj>a2VAaMj>]2UAfMj>Z2TAiMl>U2TAlMl>S2SAPNl>o1TARNm>l1RAWNm>h1SAYNl>g1SA[Nm>d1SA^Nk>b1TA`Nl>_1TAbNl>]1SAfNk>Z1UAgNk>X1TAkNj>U1VAlNj>T1TAnNk>U1RAmNm>T1PAnNo>T1o@mNQ?R3N101O0O101O0O101O001N10001N10001O0O3N2N2M3N1OO100000000O10000000000O10001M2O2N1O2N1N3N1O2N1O2Oh0WO4L5K5K4L2N2N3M2N2O1N3L3N2N3M1O2N2N1O2M3N1O2O1N1O2O1N1O2O1N101N4L4nN_]O?eb0VOb]Oi0kb0O2N100O2N1O100O1O1O102M3M4L4M\\eS3", "size": [640, 515]}, "area": 16792.0, "iscrowd": 0, "image_id": 18519, "bbox": [124.0, 225.0, 231.0, 175.0], "category_id": 183, "id": 20001180}, {"segmentation": {"counts": "0[4a1S9;FORD0m80RG4l8KTG8i8HVG<h8BXGa0f8_OZGd0d8ZO\\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5jA2V><0000000000000000000000000000000000000000000000001O00000000000000000001O000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001O0000000000000000000000000000000000000000000000000000000000O10000O10000O100O10000O10000O1N3O0O1O1O1O1O1O1O1001O000000000000000000001OO100001O00000000000000000000000000000000001O0000000000000000000000000000000000001O0000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001O0000000000000000000000000000000000000000000000000000D6", "size": [480, 640]}, "area": 15670.0, "iscrowd": 0, "image_id": 18575, "bbox": [0.0, 0.0, 640.0, 480.0], "category_id": 165, "id": 20001181}, {"segmentation": {"counts": "[4a1^=:GORD1l8OSG5k8JUG9h8FXG>f8AYGb0e8^O[Ge0c8WO_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8n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dFV9", "size": [480, 640]}, "area": 290866.0, "iscrowd": 0, "image_id": 18575, "bbox": [0.0, 0.0, 640.0, 472.0], "category_id": 183, "id": 20001182}, {"segmentation": {"counts": "`eh51Z=4L000O100000001O000000000O100000O6JmXZ2", "size": [427, 640]}, "area": 114.0, "iscrowd": 0, "image_id": 18737, "bbox": [442.0, 368.0, 19.0, 8.0], "category_id": 149, "id": 20001183}, {"segmentation": {"counts": "h6c6i6O00001O001OM30000O11O00003MNaIXI_6h623M001O000000O1001O00N2MaIZI_6d6500N2O1NYI`Ig6`620000001O1ON21O000000001O000000O11O1O1OO1001O00O100O10000001O00O12ZI\\Ia6i6^IXI]6k60O1N20000000000001OO12NO1O12N1OO100N2O100000000001O000000O12N0000O10000O100O1000000O11O0000O11O00N25K2N0cITI[6P7N2N2NO1O1N2O10fIQIX6Q70O1O1O11O0000O11OO11O1O00N2O1001OO1O11OO1000000O1N22N0000O12N000000O11O1ON21O1O001O00O1O11OO11O000000O1001O1O1OM32N1O00O1O100O11OO11O0000O11O1OJeIYI^6g6bIYI^6f6cIZI]6f64001OO100001O000000O11OO10000000000O1001ON20000O10[I]Ib6f600000000fIYIP6g6oIZIQ6f6lI]ILMU6S704L1O00000000000000O11O1O00O11O1O>B4L4L4L4L4L4L4L4L4L1O00000000000000000000000UNRGYOn8@VGG4b0f8DYGJ2a0e8A]GNO`0d8_O`G1L`0d8]ObG3K?b8_OcG1L`0a8_OcG1M?`8@dG0L`0`8AcGOM`0`8AcGNNa0_8AcGNNa0_8AcGNNa0_8BlGCFj0^8C[HTOWOY1^8CPI=P7CPI=P7DoH<Q7DoH<Q7DPI;P7DQI<o6YO\\Ig0d6YO\\Ig0d6XO]Ih0c6XO]Ih0c6XO^Ig0d6VO]Ij0e6TO[Il0g6ROYIn0i6oNXIQ1k6lNUIT1m6jNTIU1n6iNRIW1P7fNQIZ1`9000001O000000001O001O1O1O001O1O1O001O1O1O001O1O1O1O001O1O1O001O1O1O001O00001O00000000000000UKGdL9\\30[L0d31\\LOc32]LNc31^LOa32_LNa31`LO_32aLN^33cLL]33dLM[34eLLZ34gLLd2^OdIf0h3Le2^O`Ih0k3Je2@]If0o3Je2@ZIg0Q4Ie2BWIe0U4Ie2BSIg0X4Gf2CoHg0[4Fh2BkHh0^4Fh2?XMAj2^OhH02h0]4Ji2^OmHg0Y4LP34PMMQ32nLOR31mL0R30nL1S3NmL2S3MmL4S3LlL5T3KkL6U3IkL8T3IlL7T3HlL9T3GkL:U3FjL;V3DjL=W3BhL?X3@iL`0V3AiL`0X3@fLa0Z3@eL`0[3AcL`0\\3BbL?^3B`L?`3B_L>a3C]L>c3B\\L?d3B[L>e3CYL>f3DXL=i3CUL>k3CTL=m3CQL>o3CoK>Q4BnK?R4BmK>R4CmK>U4@jKa0U4AjK?V4AiK`0W4@hKa0W4AhK?Y4@fKa0Y4@gK`0Y4@fKa0X4BgK>Y4BfK?V4EjK;V4FhK;X4EhK;X4EgK<X4EhK;Y4EeK<Z4EfK;]4BcK>^4AbK?_4_ObKa0^4^ObKc0^4\\OcKd0]4[OdKe0\\4bNgH9n2U1[4^NkH<k2V1Z4\\NmH=j2W1Z4ZNmH>j2X1Z4XNmH?k2X1V4ZNPI=o2U1a4iN`KW1`4hNaKX1_4gNbKY1^4fNcKZ1]4eNdK[1\\4dNeK\\1[4cNgK\\1Y4cNhK]1X4bNiK^1W4aNjK_1W4_NjKa1V4^NkKb1o700000O1IaNaD`1_;60000000000O1000000000000000000O100000000000000000000O1000000000000000000O1000000O100O100O1O100O100O1O100O100O1O100O100O1O100O100O1O100O100O1O100O100O1O100O100O1O12N1O2N2N1O2N2N1O2N1O2N001OXLTNRLk1n3VNQLj1P4VNTIGX2R2c4YNSIHY2n1c4]NQIHZ2k1d4_NPII[2g1f4SNdH8:O\\2e1o4YNcH5\\2b1R5ZN_H7^2^1S5\\N]H9_2Z1n4dNaH5_2W1U5`N[H;_2T1W5[OiJd0W5^OgJb0R5GmJ8T5IkJ6Z5GdJ9Z5JfJ5Z5MeJ2Y52eJNb5M]J2c50\\JOd53ZJMb59]JFh57WJHi5:UJFl5:SJFj5?SJBP6=lIET6=iIDX6=eIDW6b0fI_OZ6c0bI_Ob6?[IBf6?WIBh6a0TIAh6d0ZH9k7a21O00MdJVH]5g756J1O00M3N26J0000L4005K0000J6N28HO100I7N27I0000001O3M000000O11O1O0000000000O1001O2N00", "size": [427, 640]}, "area": 85908.0, "iscrowd": 0, "image_id": 18737, "bbox": [0.0, 209.0, 640.0, 218.0], "category_id": 154, "id": 20001184}, {"segmentation": {"counts": "0T6W7000000000000000000000000000000O100000000O1HkHYJV7f58O100O1N2O100000000001O5K3bHTJW7P601O0000O100O1000000002N000000000000000000O1000000O1000000000000000000000000000000000000O1O1N2000000001O001O00000000000000O10000O1O1O1O1N2O1000000O1001OO100001OO11OO1001[H\\Ja7i5N0aHYJZ7g5fHYJZ7m5O0000000000000000O11OO11O0000oNgHQK:d0n6Y4VIgKj6Y4VIfKk6Y4YIeKf6[4YIfKg6Z4ZIdKg6[4YIfKg6Z4YIfKg6X4[IhKe6X4[IiKd6V4\\IkKd6U4\\IjKe6V4\\IiKd6W4[IjKe6V4[IiKf6W4[IhKe6X4_IdKa6\\4cI`K]6`4dI_K\\6c4cI[K^6d4eIWK^6j4Q1001O001O1O1ON2000jGTKm7l4lG[KT8n400000000000001ON200O[HQKP7o4PISKn6m4oHXKo6i4oHYKP7g4PIZKo6g4fHkJ024<V7a500000001O000000000000001ON200O100O10000000000000000000000001O0000O10000000000000000000000O1N200000000000000000000001O00O1000000001O1O00000000O1000000O11OO1000000O11O001OO1O100O100001O0000001O00001O001OPNfHVLNd1\\7Q2cHYL<e1Q7R2QI]MB[O]7X3RITMCYON:^7Y3PISMFVOL11=]7Y3QIPMGYOJ04<Z7[3QInL0Ho6Y3RIkL@@`0<n6X3TIhLAC`0;k6[3SIgL9Ld6[3VIfL;K`6`3TI]L]O4o0O`6_3[I_L53`6^3ZI]LWOOS11^6c3XI\\L=0\\6`3^H`Lg9`32JWFiLj9T3VFjL0Om9W35O1000000000000N21OO1000nGoLj5Q3VJoLj5Q3UJoLl5R3RJPMm5P3oIRMTNIm7U3PJQMR6P3lIQMT6o2lIQMT6n2jIUMV6i2QHmLh1:X6i2gIZMY6e2hInLXN;P8h2iIXMW6h2hIXMY6f2gI\\MY6d2fI^MY6d2eI]MZ6b2gIoLZNO062Jn7P3iIPMWN00111S8k2hISMTN00021S8h2[2M3000000000000O1000000000000O11O1O2N1iGWMj5l2nIUMoM1R8j2lI^MS6d2kI\\MU6e2kI[MT6e2jI\\MW6e2hI\\MW6f2dInL\\N>n7c2fIPM[N?n7d2dI`M[6_2hI`MW6a2hI`MW6_2jIcMT6\\2mIeMR6[2nIkMl5T2VJlMi5T2VJnMi5R2WJnMi5R2XJmMh5T2WJQM[Nd0^7[2VJPM`Nc0Z7^2_JbMa5_2]JbMc5^2\\JcMd5^2^JlLRNa0_7c2eJZM[5e2dJ]M\\5d2dJ[M\\5e2eJYM\\5i2cJWM\\5P3QIbLi0?V6o2mHbLF4T19Z6S3`H`L80I5S1:\\6S3eH^LL1O5U12`6[3_HhLR1Ic6P4\\12N00O11O1O001VHmKZ6R4aIUL^6i3`I\\L_6c3_I`La6`3]IbLc6_3oHRLBa0`7\\3jHTMU7l2hHWMX7k2aHZM_7Z4OO10000000000000000000000000000000000002NM31O00O11OO1001OO1000000O100000000000000000000O100O1O1O1O1O1O1O1O1O1O1O1O1O1O100O100O100O10000O100O100O10000O100O100O100O1001O1O001O001O1O001O001O1O001O1O001O001O1O001O1O001O001O1O001O1O001O1O1O1O001O1O1O001O00L4K50000O10000O10000O10000O100O1000XHfJ_7c501O001O001O00001O1O1OO1O100N2000000O1000000O10000O10000000000000000000000000000000000001O00001O00O1O100000000000000O1O1M3J600000000000000000000000000N2001OM34L1O0000O11O00", "size": [427, 640]}, "area": 108857.0, "iscrowd": 0, "image_id": 18737, "bbox": [0.0, 0.0, 640.0, 196.0], "category_id": 157, "id": 20001185}, {"segmentation": {"counts": "T6d0g<1O00001O001OM30000O11O00003NM\\C]Oe<b023NO01O000001N1001O01M2MXCCh<;6O0N01O0SCFl<;110000O2N2ON12O0O1000001O02N000O11O1O1OO1001O0O0100O10O10001O00O12ZC\\Oa<i0^CXO]<k00O1N20000000000001OO12M000O4N1OO100N3N101O00000001O00000O012N0O13ROcCg0a<ZO`Ca0e<0O02O0O1000000O111N00O020M2OO16I[O^Cg0`<2`CWO]<n0M3M3ON1O1N2O10eCROY<P10O1O1O020OO11N11OO20O1N11M2O0101OO1O20OO010000O01N22N000O003OO1ONTOhCi0X<ZOeCe0]<5M3NO301O0OM40OO0O1NoNmCQ1R<34N0O1OO1N1102O1O000L32N1O00O1O2O0O11OO11O0000O20M3OJeCYO]<h0cCXO]<g0dCYO[<h04001OO100001O000000O11OO2O0000O1000O1001ON20000O10]C[O`<h000O1N200kCTOk;l0TDUOl;k0QDXOLMP<X104L1O0000000001O00O011O1O00O200EZDPOe;P1;00000O100000O011OO1000000O11N101OO1O100O1000100O1O1O100O1O01O0O2O000O10O10O10000O1000O10O10000O1000O02O3\\OgC0Y<`0MEjCIV<7kCHV<7jCHV<9kCFR<=PDAn;a0TD\\On;c0UDZOl;f0UDXOk;h0;12NO11O0YO`Cc0b<23NN1001O0000001ON31NO1N23MO1O12N00N3O01O00001OM3002OO000001OO11O0010N1O10010M30100N1O2M2N2N2O0O2M20100O2N1O2SDbN02e;\\1[DgNd;_111O01N11O000O11O0O012N2N00M4N10O5ML3O1M22O1O1O0000O11N11O010N100000O12NO10000O11OO100N200L4000000O3OL222M1N20H]DfNb;Z1801N01000OZDgN\\;Y1dDhNZ;X1=0ZDgNZ;X1=00002N1O2N2N00N1O5K5K6J5L4K5K5K5KkiV1OiUiN?N3M100TOZOSEg0g;3cCVOV<T1J3[OkNPEW1o:mNmDn0Y;VOaDQ1X;c0O00I71N6KN200N18I000OM45K001OM33M001OL4004L1OO1L45K1O00MoDkMR;R265J1O01L3N17I1000L4005K0000J5O19E2K60I7N27I0000001O3M000000O11M3O01OM34L1O0O10O201N0eJ", "size": [427, 640]}, "area": 20472.0, "iscrowd": 0, "image_id": 18737, "bbox": [0.0, 166.0, 640.0, 95.0], "category_id": 164, "id": 20001186}, {"segmentation": {"counts": "Y^61Y=2O001O000O1HOUC2j<9N100O1N3N10001O00N2N3N6J4ImB0W=01N_Xj03YZUONR=6oBKk<;N102O00000O1001OO3N00O20NO3N001NVCCk<0VC9Q=M1JPC0Q=0_W60][I:k<400O4MO11N010N200O01001OO1001O4L4L11ON3eCQOW<R1O10MjCROS<n0PDoNQ<Q15M3NO3O2NVOhCb0W<WOPDf0\\<1OO1N2M[CAd<?43M100M11N1L500M4N101EQC024[_Z1Oe`eNNj<OTC<k<2YDBa:?VECYONa;>UEFVOL11d;>REGYOJ04c;;RE0n:1nD@@`0a;2kDAC`0a;NlD9P;JmD;Q;DgD]O4o0T;KiD5X;JfDWOOS11nNS;h0lD=0lNP;NPEW2P;2JgMYEY2e:gMYE0O]2g:8L10L4001OO1M3N0300N10O1CZEWNd:i1\\EVNe:i1[EYNe:e1]EZNKHd:j1cETNI<f:a1`EdN^:Z1cEeN^:[1bEeN]:Y1fEhNW:_O_Eh1:jNW:U1lEkNS:V1`EXN;b0V:W1jEjNU:U1kEjNU:V1mElNQ:S1QFkNR:S1oEjNR:W1_EZNO062Ja0]:V1cEWN00111e0Y:V1eETN00021d0W:]2K30N2001O00001NN3002N0000001N0200O2O0UOkE]NY:[1hEoM1d0X:Z1PFeNR:Y1nEhNR:X1nEeNT:Y1mEiNS:V1nEkNR:P1bE\\N>b0o9R1dE[N?b0P:P1TFnNl9U1SFiNo9V1RFhNn9Y1TFeNk9\\1VFbNk9^1[FZNf9h1n0O10010OaD[NX;d1gD`NX;i11N1O22cDRNW;T2LO30O11O6]NcDi0^;SObDF4T1[;jNaD80I5S1:mNb:4oDL1O5U12SOh:L[ER1IVO];Y15M10O5ML4N1M4J4K4N3N3N3BjCBX<:?M5IPio2", "size": [427, 640]}, "area": 10016.0, "iscrowd": 0, "image_id": 18737, "bbox": [15.0, 85.0, 395.0, 117.0], "category_id": 169, "id": 20001187}, {"segmentation": {"counts": "[TZ37T=b0^O4L4L4L4L4L4K5L4L4L1O0000000O10000000O100000VGRNX6m1aHVNG5b7d1dHZNJ3b7b1`H_NNOc7a1\\HdN0Ld7_1ZHgN2Je7^1YHhN1Lf7[1YHiN1Lf7[1YHiN1Lf7[1ZHhN0Me7\\1[HgNOOe7Z1\\HhNNNf7Z1\\HhNNNe7[1^HPOCEo7[1^H_OTOVO^8[1^H3a7M`H4_7LbH3^7MbH3]7NcH2]7NbH3^7MWH>i7BWH?g7BXH?h7AXH?h7BWH>h7EUH<k7FSH:m7HQH9m7JPH7P8LmG4b7cMfH[2F2T81jGOg7cMcH`2CNj7cMbHP5]7PKbHQ5^7PKaHP5_7RK_Hn4a7TK\\Hm4d7VKYHj4h7WKVHj4i7<0001O1O1O00100O1O001O1O1O00100O1O001O1O1O1O00100O1O001O1O000001O00000000000VKlH]3T7bLWIU3i6jL\\IQ3d6nL]IR3c6nL]IR3c6mL]IT3d6jL]IV3d6iL[IX3g6eLZI[3h6bLYI^3h6aLWI`3k6]LVIc3k6[LUIf3m6WLTIi3m6ULTIk3n6RLRIo3o6nKSIR4o6kKQIW4o6gKRIY4P7dKQI\\4n710O010O001O1O010O001O1O0010O01O1O0010O01O2N2O1N2N2O1N2N1O2O1N2N2O1N2N2N101N1O1O2O0O101N100O101N100O2O0O100O2O0O1000O1bNWMYHj2f7XMXHi2h7WMWHj2h7WM_GK9o2X8VM[G0;k2Z8UMYG2<i2[8VMXG2<i2\\8UMWG3<i2\\8UMWG3<i2]8TMVG4<i2]8TMVG4<i2^8cMaG^2_8bM`G_2b8`M\\Ga2f8]MYGd2h8[MWGe2l8YMSGh2m8XMRGi2l8YMSGh2k8ZMTGg2_7cLfIh0jNf2_7fLbIf0nNe2_7jL]IV4b6nKZIS4e6QLVIQ4i6SLRIo3m6ULnHl3R7XLiHj3V7V1O1O1O1O1O1O100O010O100O10000O100O10O010000O100O100O100O0101O1O001O001O1O001N101N2N101N2O0O2N101N2O0O2N2O0O2O0O2N2O0O2O1N1O2O1N2O1N1O2O1N2O0O2N10L3L50O1O010O10O3N1O2M3N1O2M3N1N3N1O[JcH]5e7100OgKnHY2S7bMRI^2m6aMUIBQOY2j7RNXICQOY2g7RN\\IBPO[2d7PN_ICPO\\2`7PNbICPO[2_7oMdIDoN]2]7lMhIDnN_2\\7iMhIFoN_2]7eMgIJoN`2\\7aMbJ^2W8N101O1N2O1O1O1N101O1N2O1O1N2M3L4M3M2N3L4M3M3L4^Oefc0", "size": [427, 640]}, "area": 47582.0, "iscrowd": 0, "image_id": 18737, "bbox": [254.0, 159.0, 338.0, 249.0], "category_id": 183, "id": 20001188}, {"segmentation": {"counts": "a^W2c0i<000000HYCKg<4ZCLf<3[CMe<2\\CNd<1]COc<0^C0b<O_C1a<N`C2`<MaC3_<LbC4^<KcC5j<000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001O00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000", "size": [428, 640]}, "area": 2714.0, "iscrowd": 0, "image_id": 18770, "bbox": [170.0, 409.0, 470.0, 19.0], "category_id": 105, "id": 20001189}, {"segmentation": {"counts": "XT[61[=1O000000000000000000O1000000000000001O1O001O1O001O1O001O001O1O001O1O001O1O001O001O1O1O1O1O001O1O1O1O1O1O1O1O001O1O1O1O1O1O1O1O1O1O1O2N1O1O1O1O1O1O1O1O1O1O1O1O1O1O2N1O1O1O1O1O2N1O1O1O1O2N1O2N1O2N1O2N1O1O2N1O2N1O2N001O1O001O1O001O1O001O1O001O1O001O1O001O1O001O1O001O001O00001O00001O00001O00001O00001O00001O3M", "size": [428, 640]}, "area": 8292.0, "iscrowd": 0, "image_id": 18770, "bbox": [486.0, 0.0, 154.0, 116.0], "category_id": 172, "id": 20001190}, {"segmentation": {"counts": "0a^W2c0XnhM00000000000000000000000000000O1O1O1O1N2O1O1O1O1N2O1O1O1N2O1O1O1N2O1O1O1N2M3N2M3N2N2M3N2M3N2N2N2N2M3N2N2N2N2N2L4L4K5L4L4K5L4M3M3M3M3lFaLW8c3iGdLm7_3SHgLd7\\3[HlL[7W3eHPMP7T3PIQMh6R3XIRMa6Q3_ISMZ6P3eIUMU6m2kIWMo5k2QJZMh5h2XJ\\Ma5g2^J]M]5e2cJ]MY5e2gJ\\MV5f2jJ\\MR5f2mJ\\MP5f2PK\\Ml4f2TK[Mi4g2WK[Md4h2[K[Ma4g2_KZM^4h2bKZMZ4h2fKYMW4i2iKWMU4k2kKVMR4l2nKTMQ4m2oKTMn3n2RLRMl3P3TLQMi3Q3WLoLh3R3XLoLe3S3ZLnLd3T3\\LlLb3V3^LkL`3V3`LjL^3X3bLiL[3Y3eLgLY3[3gLfLW3[3iLeLU3]3kLdLR3^3nLbLP3`3PMaLn2`3RM`Ll2b3TM_Li2c3WM]Lg2e3YM\\Le2e3[M[Lc2g3]MZL`2h3`MXL^2j3bMWL\\2j3dMVLZ2l3fMTLX2n3hMSLV2n3jMRLT2P4lMQLQ2Q4oMoKo1S4QNnKm1S4SNmKk1U4UNlKh1V4XNjKg1W4YNjKe1W4[NiKc1Y4]NhKa1Y4_NgK`1Z4`NgK^1Z4bNfK]1[4cNfK[1[4eNeKZ1\\4fNeKX1\\4hNdKW1]4iNdKU1]4kNdKS1]4mNcKR1^4nNcKo0^8O2N1O2N1O2N1O2N1O2N1O2M2O3L4L4MfXR4", "size": [428, 640]}, "area": 107085.0, "iscrowd": 0, "image_id": 18770, "bbox": [0.0, 0.0, 328.0, 409.0], "category_id": 173, "id": 20001191}, {"segmentation": {"counts": "\\TY21Z=1O1O1O1O1O1O1O1O1O1O1O1O1O1O1N2O1O1O1O1N2O1O1O1N2O1O1O1N2O1O1O1N2M3N2M3N2N2M3N2M3N2N2N2N2M3N2N2N2N2N2L4L4K5L4L4K5L4M3M3M3M3lFhLP8\\3PHkLf7X3ZHnL]7U3bHSMT7P3lHWMi6m2WIXMa6k2_IYMZ6j2fIZMS6i2lI\\Mn5f2RJ^Mh5d2XJaMa5a2_JcMZ5`2eJdMV5^2jJdMR5^2nJcMo4_2QKcMk4_2TKcMi4_2WKcMe4_2[KbMb4`2^KbM]4a2bKbMZ4`2fKaMW4a2iKaMS4a2mK_MQ4c2oK^Mn3d2RL\\Ml3f2TL[Mj3f2VLZMh3h2XLYMe3i2[LWMc3k2]LVMa3k2_LUM_3m2`LUM]3m2cLSM[3o2eLRMY3o2gLQMW3Q3iLPMT3R3lLnLR3T3nLmLP3T3PMlLn2V3RMjLl2X3TMiLi2Y3WMgLh2Z3XMgLe2[3[MeLc2]3]MdL`2^3`MbL_2_3aMbL\\2`3dM`LZ2b3fM_LW2c3iM]LV2d3jM]LS2e3mM[LQ2g3oMZLo1g3QNYLm1j3RNWLk1k3UNULi1m3WNTLg1m3YNSLe1o3[NRLb1P4^NPLa1Q4_NPL_1Q4aNoK]1S4cNnK[1S4eNmKZ1T4fNmKX1T4hNlKW1U4iNlKU1U4kNkKT1V4lNkKR1V4nNjKQ1W4oNjKo0W4QOiKn0X4ROiKl0X4TOhKj0Z4VOgKh0Z4XOfKg0[4YOfKe0[4[OeKd0\\4\\OeKb0\\4^OdKa0]4_OdK?]4AcK>^4BcK<^4DbK;_4EbK9_4GaK7a4I`K5a4KaK1a4OaKMa43aKJ`46[40000000000001O00000000000000001O000000000000001O00000000000000001O000000000000001O00000000001O000000001O000000001O000000001O000000001O000000001O000000000000000000O10000000000O10000000000O100000000O10000000000O1000000000000O100000000000000000000000000O100000000000000000000000000O1000000000000000000O10000000000000000O1000000000000000000O1000000000000001O1O001O1O001O1O001O001O1O001O1O001O1O010O001O1O1O1O1O001O1O1O1O1O1O1O1O001O1O1O1O1O1O1O1O1O1O1O2N1O1O1O1O1O1O1O1O1O1O1O1O1O2N1O1O1O1O1O1O2N1O1O1O1O2N1O2N1O2N1O2N1O2N1O2N1O1O2N001O1O001O1O001O1O001O1O001O1O001O1O001O1O001O1O001O001O00001O00001O00001O00001O00001O00001O>]IlN2UM", "size": [428, 640]}, "area": 154465.0, "iscrowd": 0, "image_id": 18770, "bbox": [174.0, 1.0, 466.0, 422.0], "category_id": 183, "id": 20001192}, {"segmentation": {"counts": "`6j6`6000O100001OO100000000O1O1001O1O1O001O1O1OO10000O1O100O1O1O10000O1O100001OMTIiIk6W6UIiIk6X63001OO100N200LPImIQ7S6300M3O100N2O100001O0000O1O100000000O1000000000000M3000000001OO100O1J^HcJc7\\5_HbJb7\\5`HdJ`7[58O100000000O10000O1000000000000O100O100000000O100000000000000O100001O000000000000000000O10000000000O100000000000000001O000000001O00MoGmJQ8P56M3O1N20000O100000000O1001O00O100N2O1O100000000001O000000000000000jG[KC000P8e4[HaKe7`4THZKL0087I_7f4ZHiK7B]7V5dHlJZ7T5fHnJX7R5hHPKV7P5jHRKT7n4lHTKR7l4nHVKP7k4oHWKo6i4QIZKl6f4TI\\Kj6d4VI^Kh6b4XI`Kf6`4ZIbKd6^4\\IdKb6\\4^IfK`6Z4`IhK^6\\4^IfK`6]4]IeKa6d5N2N2N2N2N1O1O000000000000001O00000000000000001O00000000001O1O1O1O1O1O1O1O1O1O1O1O1hL_He0b7YO^Hh0c7VO]Hk0d7SO\\Hn0e7PO[HQ1f7mN[HS1f7kNZHV1g7hNYHY1h7eNXH\\1i7bNWH_1j7_NWHa1j7]NVHd1l7YNTHh1m7VNSHk1n7SNRHn1o7PNRHP2o7nMQHS2o7lMQHU2n7kMRHV2n7iMRHX2n7gMSHY2m7fMSH[2l7eMTH\\2l7cMTH^2l7aMTH`2l7_MUHa2j7_MVHb2j7]MVHd2j7[MVHf2k7XMUHi2k7VMVHj2j7UMVHl2k7RMUHo2k7PMUHQ3l7mLTHT3l7kLUHU3k7jLUHW3l7gLTHZ3l7eLTH\\3m7bLSH_3m7`LSHa3n7]LSHc3m7\\LSHe3m7ZLSHg3n7WLRHj3n7ULRHl3o7RLRHn3n7QLRHP4o7nKQHS4o7lKQHU4P8iKPHX4P8gKQHW4Q8hKoGY4R8eKnG[4S8dKmG]4_801N11O000l0TOQ2PNN1J6K5J5K5L6I6J7J5J6J7L32O1N3N1N3M2M3O2L3N2O2M2O1O2M2O2NPOcMTG[2m8hMQGV2Q9lMnFR2S9PNkFn1W9UNfFi1\\9YNcFe1^9]N`Fa1a9bN]F]1d9eN[FX1g9jNWFT1k9oNSFo0n9SOPFk0Q:XOmEg0T:[OkEb0W:@gE>[:DcE;^:H`E5b:X1101NG^MmE`2S:bMmE\\2T:eMlEY2U:iMiEV2X:kMhET2X:mMhEQ2Y:QNfEm1[:TNeEj1\\:WNcEi1]:YNbEe1_:\\NaEb1`:_N`E_1a:cN^E[1b:gN^EW1c:jN\\EV1d:lN[ES1e:nN[ER1d:oN\\EQ1c:PO]EP1b:l0000O1O01O00000000O0K]EeM`1Na7]2eHdMY7]2hHbMW7[2mHgMP7T2VGjMm12TNNb8U2_IUN^6j1dIVN[6i1gIWNX6h1kIWNT6h1oIWNP6i1QJWNn5h1SJYNl5f1UJ[Nj5c1XJ^Nl5\\1SJgNP6T1nIPOU6k0gI[O]6a0WIKl6LVGPOe1_1Y7XOWGYOX1g1d7gNWHAUOl1h8[NWHIoNn1m8VNSHMmNP2S9oMQH1iNS2e:mMXEV2\\9iMhG0iN^2`9bMbGd2_8\\M^Gf2b9100000000O10000\\OXMfFh2U9^MgFe2X9_MaFe2]9^M^Ff2a9b0N2O1N2O1N2O1M3N2N2N2N2N2N2N2M3N2N2N2N2N200N2N2N2N2M3N2N2N2M3N2N2N2M3N2N2N2M3N2N2M3M3L4M3M3M3L4M3M3M3M3L4M3M3M3L4M3M3M3L4M3M3M3M3L4M3M3M3L4M3M3M3L4M3M3M3L4M3M3M3M3N21O1O1OHgEaMW:Z2SFbMl9[2c0FYEmMk:S2600000000O100O100O11eH[No2d1oLcNm2]1oLgNQ3X1nLkNQ3T1nLnNR3Q1mLSOQ3l0oLVOP3i0oLZOP3e0PM^On2a0RM@n2?RMCm2<SMFl29UMGk28UMJj26VMJj26UML_NbN[N0W3a10MYNkNUN5[3R18OTNG_1:=ORNIa17>0QNHb17>2nMHd16>2mMIe14?3kMJf13?3kMJf12`04iMJh12`03gMLi11`03gMLi11`02gMNi10a01fMOi1Ob02dM0j1Nc01cM2i1Md01bM3j1Kf00aM5i1Kf00`M6j1Ih0ZOQLL]1Q1j1Ii0VOULMY1U1h1Hl0QOXLNV1Y1f1Gn0nN[LKV1`1a1GQ1hNYN`1f0Hh39WLGi39WLGj38VLHj38VLHj38VLHj38VLHk36VLJj36VLJk35ULKl34TLLl35SLKn33SLMo31QLNQ41oKOV4FPL:S4]OSLb0S4hN^LX1j700000000001O1O001O00001O000000001O00001O001O00001O00O100001O0000002NO1O11O1O1O1O2N5K1O1O00000000001O00000000000000", "size": [426, 640]}, "area": 140868.0, "iscrowd": 0, "image_id": 18833, "bbox": [0.0, 44.0, 640.0, 382.0], "category_id": 105, "id": 20001193}, {"segmentation": {"counts": "\\\\l32X=0O100O1N200O1O1O100O1O1O1N2N200O100O1O100O100O100O1O1N200N2O100O10000O10000O1O100O1O100O100O1O100O100O1O100O1O100O1O1O1O1O100O1O1O100O1O1O1O1O100001O1O1O0000O1O100O1O1O1O1N2IUNmD`1NjNT;\\1mDcNS;^1lDbNP;b1RE\\Nh:JVEm12TNNOh:R2:2N2N3M3N1N1O1N2OO1N2L6D6RNnDe1X;`NhDX1Z;KeDUO[;o0eDoN];P1dDmN^;T1bDiNb;V1^DgNg;T1XDiNP<R17M2O101O1O1O1N2O1O0\\O_C?g<L6I5Kmfh2", "size": [426, 640]}, "area": 4998.0, "iscrowd": 0, "image_id": 18833, "bbox": [298.0, 302.0, 128.0, 124.0], "category_id": 118, "id": 20001194}, {"segmentation": {"counts": "\\Pk6?h<7H4N3M2N4M2M3N3M1O2N2O0O2O0O2ZFbN[N0a8^1oHkNUN5e8Q1RIGi69UIIk67UIHl69RIHn68QIIo67PIJP76PIJP76oHJS75lHLT74lHLT73lHNU71kHOU71jH0W7OiH2V7NiH3X7KiH5W7KhH6Y7SOZGL]1Q1Z7oN^GMY1U1Z7iNbGNV1Y1Y7eNfGKV1`1W7_NdI`1S91O01O0000001N101O1O010N3N1O5E9G;ZOckg0", "size": [426, 640]}, "area": 3635.0, "iscrowd": 0, "image_id": 18833, "bbox": [526.0, 170.0, 56.0, 154.0], "category_id": 132, "id": 20001195}, {"segmentation": {"counts": "0`6j60000O100001OO100000000O1O1001O1O1O001O1O1OO10000O1O100O1O1O10000O1O100001OMTIiIk6W6UIiIk6X63001OO100N200LPImIQ7S6300M3O100N2O100001O0000O1O100000000O1000000000000M3000000001OO100O1J^HcJc7\\5_HbJb7\\5`HdJ`7[58O100000000O10000O1000000000000O100O100000000O100000000000000O100001O000000000000000000O10000000000O100000000000000001O000000001O00MoGmJQ8P56M3O1N20000O100000000O1001O00O100N2O1O100000000001O000000000000000jG[KC000P8e4[HaKe7`4THZKL008P8_4PHiKo7h4100000000001O00000000000000004L3M1O0000000000000000000000000000O1O1O1O1O1O1O1O100O1O1O1O1O1O1O1O1O100O1O1O1O1O1O1O1O1O1O100O1O1O1O1O1O1O1O1O100O1O1O1O1O1O1O1O1O100N2O1N2O1N2O1N2O1N2O1N2O1N2O1L4G9F:G9F:G9G9K5001O000000001O0000001O001O2N2N2N1O2N2N2N1O2N2N2N1O2N2N2N2N1O2N2N2N1O2N2N2N1O2N2N2N1O2N2N2N1O1O1O2N1O1O1O1O2N00001O001O00001O00001O001O00001O00001O001O00002N1O2N1O2N1O2N2N1O2N1O2N001O001O00001O000000O10000O10000O10000O100O10000O10000O1O1O1O1N2O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1N2O1O1O1O1O1O1O10000O100000000O1000000O100000000O100000000O100000000O1000000O1000000O10000O10000O10000O100O10000O10000O10000O10000O10000O10000O100O10000O10000001O1O1OHgEaMW:Z2SFbMl9[2c0FYEmMk:S2600000000O100O100O11OO100O1O1O1O1O1O1O1O1O1O1O10000O1O100O1O100O100O100000000O10000O100O10000O10000000000000000000000000000O10000O10000000000001O1O001O00001O000000001O00001O001O00001O00O100001O0000002NO1O11O1O1O1O2N5K1O1O00000000001O00000000000000", "size": [426, 640]}, "area": 80357.0, "iscrowd": 0, "image_id": 18833, "bbox": [0.0, 0.0, 640.0, 211.0], "category_id": 172, "id": 20001196}, {"segmentation": {"counts": "on]22Y=1N2N2N2N2N2N2N2N3M2N2N2N2N2N2N2N2N2N2N2N2N2N2N1O000000001O0000000O1O1O1O2N1O1O1O100O1O1O2N1O1O2N2N2N2O1N2N2N2N2N2N2N2N2oGWM^5k2`JUM`5m2^JSMc5n2[JRMe5P3YJPMg5R3WJoLh5S3VJmLj5U3TJkLl5W3RJiLn5Y3PJgLP6[3nIfLQ6\\3mIdLS6^3kIbLV6`3gI`LY6b3eI^L[6d3cI]L\\6d3cI\\L]6e3bI[L^6f3aIZL_6g3`IYL`6g3`IZL_6g3`IYL`6h3_IXLb6h3]IXLb6i3^IWLb6j3]IWLa6k3^IULb6l3]ITLb6n3]IRLc6o3\\IQLc6R4[IoKd6R4[InKd6T4[IlKe6V4YIjKf6X4YIhKg6Z4WIgKg6[4XIeKh6]4VIcKg6a4XI_K`6j4_IVKX6U5fIlJQ6]5nIcJi5h5UJXJc5Q6\\JoI\\5Z6cJfIY5`6eJaI[5_6dJaI^5_6`JaIa5_6^JaIc5_6\\JaIe5`6YJaIg5_6XJaIj5_6TJaIm5_6RJaIo5`6oI`IR6k62O0O3M2N3j0VOn1RNH7H9H8G9G8I8G9G9H8G8I8M3O1O010O1O10O0100O1O010O1O10O01oNXI^Kh6a4ZI]Kf6b4]I\\Kc6d4_I[K`6e4bIYK_6f4dIWK\\6i4fIVKY6i4jIUKV6k4lISKT6m4nIRKR6m4PJQKo5P5TJnJj5R5YJlJg5T5[JjJd5W5^JhJa5X5aJfJ^5[5dJcJ[5^5gJaJX5^5Z11O10DYHTKf7l4[HTKe7k4\\HTKd7l4^HSKa7m4`HSK`7l4aHTK^7l4dHSK[7l4gHSKZ7l4gHTKX7l4jHSKV7l4kHTKT7l4mHTKR7l4PIRKR7l4oHTKQ7k4PIUKQ7i4QIVKP7h4QIXKo6g4RIYKo6d4SI[Kn6d4n0N2N1O2M2N1N3N1O2N1O1O2N1O1O1O001O1O001O1O001004K4M3L5L3M4K4M4L3L4L3M2N2M3N1O2N2N2N2N2N1O2N1O00O100O100O010O100O1O1O1O1O2M3N1O2N2N2N2N1O2M3O10OO2M3N2N2N1N3N2N2N2M2O2N2N2M3N1O2N2M3N1N3L4M3M2N3M3L3N3M3M2M4M3M2N3L3N3M3M2N3L4M2N3M3L3N3M3M2M4M3M2N3L4M2N3M2N3L4M2N3MhWg1", "size": [426, 640]}, "area": 42418.0, "iscrowd": 0, "image_id": 18833, "bbox": [187.0, 47.0, 320.0, 321.0], "category_id": 183, "id": 20001197}, {"segmentation": {"counts": "k_h02m>5I3XON^B6a=c030L4ON21N011O000000000O10000O200O2N0000ONiN]BW1c=30O100O100O101N100OJ7I610000O001O1O001N2O2N1O1O1O1O2M011O1O002N0OoMUORFi0k9BmE>T:AmE>T:BlE=U:CkE<V:DjE;W:EjE8X:HgE8Z:HfE7[:IeE7[:IeE7[:IeE6\\:K^DL88Z;KSDC28HGj0c0Y;LRDA49:GCd0m;KnCA108:2GD20f0S<1nCK0a0R<=mCCS<e1000O100000000000000O100000000000000O10000000000000000O100000000000000O100000000000000O10000000000000000O100000000000000O10000000000000000O100000000000000O100000000000000O10000000000000000ROdCWO]<h0dCXO\\<h0fCVOZ<j0eCkNLN_<W1gCiNKO^<X1gCiNKO^<X1fCjNNL\\<Z1gChNNN[<W1YDhNh;W1[DgNe;Y1]DeNc;[1]DeNc;Y1`DfN`;T1Y14LO10000001O001OE\\BSO03d=i0]BSO0OO2e=l0\\BSO3Ob=a0ZBJ1El=a0TB@00O12Nk=a0XB_OL12Oj=`0YB@K1Q>9nAF01_>9aAF02a>2_AM0O02m>OQ]o0OabPO0mA1DO]>0jA0I6]>JjA8V>IfA:Z>7N100000002N00O11OO100O10000000000001O000000001O0000000000E;1OO17IO1O1O100O11OO1001O000000000000001O007I1O6J5KO1M33M4L1O1O000000001O001O^O[BIe=7[BIe=6]BIc=7^BHb=7`B]OK7e=<_B^OM5d==bB[OJ7e=>eB@\\=?dBB\\=<e0O1005K00LhA@X>`0iA_OW>b03O11O1O0000M300O11O000000NcAB\\>>dAB\\>>eAA[>>4O11O001O0000O100N200O100001O001O0000001O001O1O000000001OO11OOUB@X=`0ZBBO1NNi=a0XB@O;e=;[BDf=<YBEg=k0O001ON2001OO1O11ON21O00O10000001O0000000ZBjNb=W1]BiNc=[1\\BfN`=^1O00001O000000001O1O1O001O001oC[Ne:c1YEeNPOIY;b1gEgNnNIY;`1iEhNmNG[;`1hEjNiNJ_;\\1hEjNiNL];Z1jETOV:m0jESOU:m0jETOV:l0jEVOjNYOV;a1QFXOP:h0PFXOP:P1iDUN94GN8k0o:P1REFj::UEIi:7VEIk:7UEIk:8UEFK]Nl:m1ZEFI]Nm:R2VE@N]Nl:P4_EPLR:T4hElKI10O\\:U4iEQLMJV:U4lETLLG00V:W4lERLNIV:f40001O1O001O1O1O1O1O002N00I77I0000000000000000^OTFPLl9Q4SFoKm9Q4TFnKl9T3TF^LO0002b00Ho9l2RFlL<=GFn96oEX24^M96@Na:NkE[2c0dMRONZ;f0QE;DROS1M]:?SE=`=BmAF[>1e^U1", "size": [480, 640]}, "area": 24486.0, "iscrowd": 0, "image_id": 18837, "bbox": [52.0, 0.0, 508.0, 162.0], "category_id": 96, "id": 20001198}, {"segmentation": {"counts": "n9_2a<0000O01000O1O1001O001O00N12O001O002N0000001O0O10IiCfMV<U2=K5K5J7J5K5K5K4K7J5G9FUPo72noPH>C5J5L3L5L5K3O23M9F10000000000O1000000001OO1O10001O1O0N3O010O001O1O1N101O1N2@fAL0113Y>NRB1n=LTB3]>000SX6", "size": [480, 640]}, "area": 3883.0, "iscrowd": 0, "image_id": 18837, "bbox": [0.0, 278.0, 626.0, 122.0], "category_id": 97, "id": 20001199}, {"segmentation": {"counts": "]<e0[>O1MeAAZ>`0fA_O[>a021NN210100O01O01M0fA^O[>a0eA_O[>a0eA_O\\>`0202N0O12N010O0001OO12N2N2N1O2M3O1N2NRVk8", "size": [480, 640]}, "area": 537.0, "iscrowd": 0, "image_id": 18837, "bbox": [0.0, 394.0, 35.0, 24.0], "category_id": 111, "id": 20001200}, {"segmentation": {"counts": "mhl83m>0000010O2N1O2N2N2N2N2N2N2N2M3N1O2N5K00000O100000O10000000000000TH", "size": [480, 640]}, "area": 723.0, "iscrowd": 0, "image_id": 18837, "bbox": [607.0, 252.0, 33.0, 34.0], "category_id": 113, "id": 20001201}, {"segmentation": {"counts": "\\ma032Og><G101aAA]>a00O01VN_OUE`0l:@TE`0l:@TEb0i:_OXD02b0_;]OjC1c0O[O0j0e0[;[OmC19OD=a0a0h:ROgD0O:080=a0N_:`0PEAc0Ja:e1_EZNb:g1]EVNf:f1cEQNa:o1X11OO10000SN\\CC10a0;IEH7b<5QD1DCI8b<3PDO[OF00:0H8d<2QDNZOH`07U<OZCLg0NYO61J`07V<M[CLh06J2S<IZD2D6S<G[D1D5P>MYAMj>2VANfS[8", "size": [480, 640]}, "area": 1343.0, "iscrowd": 0, "image_id": 18837, "bbox": [38.0, 0.0, 33.0, 127.0], "category_id": 132, "id": 20001202}, {"segmentation": {"counts": "R=n1Q=1MRNTCn1l<RNSCo1m<21OM3001O1O00001ONPNTCP2l<PNTCP2l<PNTCP2l<20000O12N001O0000000000000000O11O00001O2N2N1O2N2N2N2N2N2N1O2N2N2N2N2N2N2N1N3LmAWOU>f05M3N1O1O0O10000O10000000000O10O1000O1001OO10000O12NO1002NO100N200O1O10000O8HhaU76S^jHo0PO2O01OO10000O01N200O100O01000001OO2N02N100000O10N0201O0O10000O0101OO01001H6O2O1MO13L4M3001OO100000000000000OTF", "size": [480, 640]}, "area": 5483.0, "iscrowd": 0, "image_id": 18837, "bbox": [0.0, 317.0, 640.0, 163.0], "category_id": 140, "id": 20001203}, {"segmentation": {"counts": "oYi01m>0TA0k>3O1000000O10000O10000000000O1000000O1001OO10000O12NO1002NO100N200O1O10000O1GJgA7Y>80000000000O10000000000000000000000001O1O1O1O00000000O100000000001O00001OO100O1000000O10000O10000O100O1000000000000000000000000O100000000000000000000000000000000000000000000000000000000O100000000000000000000000000000000000000000000000000000000O1000000000000000000000000000000000000000000000000000000O100000000000000000000000000000000000000000000000000000000O100000000000000000000000000000000000000000000000000000000O100000000001O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1EcA1^>MfZi00eeVO0K2XANf>2[AOc>1_AO_>2aAO]>2dAN[>2fANY>1iAOW>0jA0V>OjA2U>NlA2T>LnA4_>0O1000000O100000000O1000000O1000000O10000O100O1002N5K4Lge39PZL>A9H8G8I7I7H8I6I100M3N2O100K5J6I7N2M33M00003M00001O000000000000001O000000000000001O00000000006J8H7I8H8H7I5K1O00001O00000000001O000000001O00000000001O00000000001O00000000001O0000000000000000O100000000O1000000O1000000O1000000O1000000O100000000O1000000O1000000O1000000N2N2N2N2N2N2N2O1N2N2N2N2N2N2O1N2N2N2N2N2N2O1N2\\Od01O0000000000O1N200O100O10000001OO1O11O000000O100N20000O10000O1001OO100]NVL`F011W1i3V8bLaGe3]8]LaGe3_8[L`Gf3P8jLmGY3S8gLjG\\3V8dLfG`3Z8`LcGc3]8]LcGc3]8]LdGb3\\8^LcGc3\\8^LdGb3\\8^LdGb3\\8^LdGb3\\8^LdGb3\\8^LdGb3\\8^LdGb3\\8^LdGb3\\8^LcGc3", "size": [480, 640]}, "area": 23902.0, "iscrowd": 0, "image_id": 18837, "bbox": [53.0, 285.0, 587.0, 195.0], "category_id": 149, "id": 20001204}, {"segmentation": {"counts": "WZS84Q<0cF4T95gF1S94UEK;DROa0];1oD1=7d:m0nDYNFQ1\\;g0eDG[;o1000002NO11O001O00000000G9M3002N3YDfLb;^3N0000O1001O000000001OHaDoL_;P3dDnL\\;P3fDPMZ;Q3hDmLW;R3jDnLV;S3iDlLX;T3:1ODZD^Mf;_2XDZM27f;_2YDYM40L1h;f2XDZM20NOi;g2XDYMQ<h23001O1O001O001O00HnC\\M31o;W2d000000000000000000000O10000O10000000000000000000000000000", "size": [480, 640]}, "area": 7758.0, "iscrowd": 0, "image_id": 18837, "bbox": [553.0, 0.0, 87.0, 114.0], "category_id": 157, "id": 20001205}, {"segmentation": {"counts": "0V6j800000000000000000000000000000000000000AWGgJi8Y5WGgJi8Y5XGfJh8Z5XGfJh8Z5XG]J0MO4i8a5dG[J]8b5b00000000000000000000000iMfFfLOn1[9\\1QG]No8a1RGaNm8^1TGhLAj1Y9^1XGcNg8]1XGfNf8Y1[GgNe8_OjF;`07f8^OjF;`07f8^OjF;b05d8_OkF]O02b0R1c8XOjFoN1c0O[O0j0e0n0a8[OjFoN19OD=a0a0W1X8`NjFH0O:080=a0NX1g9WNZFc0LV1l:jNXES1h:lNXET1d:mN_EP1d:oN[Eo0j:QOVEj0m:TOWEg0m:YOQEe0S;[OnD`0Y9POdE10a0;IEH7<l0>o8SOZFf01DCI8;l0:R9YOUFd0O[OF00:0H8=l05V9]OPFf0NZOH`07Nm01Z9AhEOLg0NYO61J`07Nm0M^9FbE0Lh06J2Jl0Jb9K[En02D6Ik0Ic9MYEo01D5Go0Hc9l1[EnMM`0U1Fc9m1ZEmMN`0U1Ed9c2^F\\Mb9e2^FYMc9g2_FWMa9i2[10000MlCZMT<f230000000000001O0000I7I7000000O10000O100001O000000001ON20000O11OO1mM\\Ck1S=H0100000001OO100000001jNSC8k<]OkB28HGj0W=[OiB49:GCX=KhB108:2GD20e=1mB0Y>OoQ\\12nmcN102NOcALP>6nAKQ>5oAKQ>4PBNn=3PBNm=d0O3N2N0N3I82LO1000O101O001OEnNaB03R1[=oNaB0OO2S1^=nNaB3OP1S=lNXC1EY1T=gNmB00O12NX1T=kNlBL12OW1S=lNmBK1^1l<aNSC01l1l<TNSC02k1`<WNbCNM0O02k1`<cN_C]1a<`010000000000000000O100000000000000O11O1O1O001O1O1O1O1O1O1O1O1O001O1O1O1O1O1O1OO1N2VNmCe0U<YOPDb0R<]ORD?o;ASD<n;DSD9o;GRD6P<JQD3Q<MPD0R<0nCNT<3lCJV<6kCFX<:jCBX<8PDDR<;QDAQ<?QD_Oo;`0SD^On;b0RD]Oo;c0QD\\OP<d0oC\\OR<d0mC\\OT<e0S100001O0000HiAGW>JiA<1DO5V>KjA70I65P>KjA78Om=JlA3:3j=J]B6V>007YBD\\<=`CDb<=[CCi<<TCDn<=oBCR=?kBAX=?dBB]=`0\\B^OK2k=a0XBBi=`0VB^Ol=c0SB[Oo=f0PBYOQ>j01O1N2O2N1O1O1O1N3N1O1N2N2N2OF93M1O9G10000O2O00Q?0PA0Yf40TkJ8o=<6K4LN2L42M4M000O01O1O1O2O3L4M<QOnNVCS1i<mNWCS1h<oNWCR1h<oNWCY1`<iNTCK7]1d<gNVCM5^1c<hNUCJ7`1c<jN[CX1c<hN^CY1_<f0N2O14LO1KfMbC[2]<fMbC[2^<4N20000O1O1L4O1N200O1O1O1MRMSDn2l;RMTDo2k;RMTDo2j;5N20O0100O1O0O2O1M3O0O2O00O2O001O00000O2O001O1O0002N2N2O`0^O200NaLTEm2k:eLWEO1NN`3j:aLWEO;]3c:cL\\E_3c:`L^Ea3Q;0O101L3O01OO1O11ON21O00O100000010O000000ZLjDb3W;]LiDc3[;\\LfD`3^;O00001O000000001O1O1O001O001oM[De0c;YOeDPOIY1b;GgDnNIY1`;IhDmNG[1`;HjDiNJ_1\\;HjDiNL]1Z;JTE7l:ITE6l:IUE7k:IWEjNYOW1`;0YE1g:OYE1o:hNVD94GN8k0P1o:QOgEk0Y:TOjEj0V:UOjEl0V:TOjEl0W:TOgEK]Nm0l;YOgEI]Nn0Q<UOaEN]Nm0o=^OQB3S>GmAI10O=T>HRBMJ7T>KUBLG007V>KSBNI7e>0001O1O001O1O1O100O00km05eX3KaiKO01QO0QBO0002b00H1Q=<TCGF0V=:lB@Nc0V=NgB@Nj0\\=FdB>\\=c000000001O02NO11O001O00000000G9M3002N3VNiBe1[=N0000O1001O000000001OH^NRCc1l<`NRC`1l<bNTC^1m<dNQC[1n<fNRCZ1o<eNPC\\1P=:1ODVNbCj1[<TN^C27j1[<UN]C40L1l1b<TN^C20NOm1c<TN]CU2d<303Nd0\\Od0[Of0[Od0[O10O00HQKYF31l4Z9e0O0000000000000000000O01000O10000000O10000000000000000000iM", "size": [480, 640]}, "area": 42988.0, "iscrowd": 0, "image_id": 18837, "bbox": [0.0, 0.0, 640.0, 254.0], "category_id": 169, "id": 20001206}, {"segmentation": {"counts": "[V8l0T>00000000000000^OmA7P4Dm54UF7i3LT6EWF?`33`6L[I;c6EWIc0h6\\OSIj0l6VOoHQ1P7mNlHZ1T7aNjHf1V7TNhHT2l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;7nDDT;;PEAQ;?QE^OP;b0QE[OQ;e0PEXOR;h0oDUOS;k0nDROT;n0lDPOV;Q1jDlNX;T1iDhNZ;X1hDdN[;U1mDgNV;W1mDeNV;Z1lDdNU;Z1mDdNU;[1jDeNX;Z1hDeNZ;Z1eDfN];Y1bDgN`;Y1T1O1O100O1O1O100O100O100O^CoNV;W1_DkNd;U1YDkNDLe;Y1dDkNKL^;[1dDiN2LX;\\1cDhN9LR;]1bDgN]<Z1ZCbNM5k<Z1WCdNK0P=]1TCfNn<[1PCdNR=]1mBaNU=`1jB_NW=d1`DaN`9_1cF_N]9_1fF_N[9`1hF^NX9a1jF^NV9a1kF_NU9`1V2O1N2O1O0O2N2N2O1N2N2N2NfDSOe8l0YGWOg8h0XGZOg8e0[G[Oe8d0\\G\\Od8c0]G]Oc8c0]G\\Od8d0\\G\\Oc8e0]G[Oc8e0\\G\\Od8d0\\G\\Oc8e0]GZOd8f0\\GZOd8f0\\GZOc8g0]GYOc8g0]GYOb8h0^GXOb8i0]GVOd8k0`ESOP12U76RIb1HXNT77UIa1FYNT76WIb1DXNS76ZId1BVNR77]IOmMY1c1`NS78]INoM[1`1_NR78aIMnM\\1_1_NP78eILmM^1]1^No68hILlM`1]1[Nn67lILkMb1[1ZNm68oIKjMd1Y1YNl68RJKjMe1X1WNk68UJKhMh1W1UNj68PJHmM24m1Q1QNl67kJk1^6SNeIQ2U6nMmI_2e5`M^Ja2`5]McJc2[5\\MhJe2i88G2O2N1O1N2O1O1O1N2O1O1O1N2O1O1N2N2O12N4L3M00O1O1O1O1O1\\K^LhMc3W2]LiMd3U2]L`K>DV3j4]LbKj0WOi2W5\\LcKS1nNb2]5\\LdKZ1hN[2c5[LeKa1aNU2h5[LfKh1[Nm1o5[LfKn1UNh1S6[LhKT2nMb1X6[LiKT6V4kIkKU6T4lIkKV6T4jIhKZ6W4fIfK]6d2dInLO9b6j2VJTLYOn0e6n2dIZN`6g1_IUNe6l1ZIgLEJU7`3UIaL5@j6Q4PIYLe0YO_6_4kHSLe8n3ZGnKj8R4[GhKf8Y4]GbKa1Fn4h4dI]K\\1NR5d4dIZKX15V5`4dIXKS1;j51[H^3`2eLV5LZH`3\\2gL[5HYHa3Y2jL_5DXHc3U2mLc5_OXHe3Q2oLi5ZOVHh3l1RMo5UOUHj3h1TMT6QOTHl3=]N`7fMSHi6n7VIRHj6P8TIPHl6R81O0000000000001O00006J8H7I8I7H7I5K1O00000000001O000000001O00000000001O000000000010O0000000001O000000001O00000000O1000000O1000000O1000001O0O1000000O1000000O1000000O1000000O1000000O100000000O2N1N2N2O1N2N2N2N2N2N2N2O1N2N2N2N2N2N2N2O1`MWHgMk7U2aHaMa7Z2lH\\MV7`2WIUMk6g2i2K5L5J5N2N2OcDcLY;[3gDPMP;o2PERMP;m2oDUMQ;i2oDYMQ;f2oD[MQ;Z30XOoDaMQ;V31000>Be0[Oe0[Oe0[O?BO001O0010O2O1O1N2O1O0O2O1O1O1N2O1O?@hg6", "size": [480, 640]}, "area": 187877.0, "iscrowd": 0, "image_id": 18837, "bbox": [17.0, 15.0, 608.0, 465.0], "category_id": 183, "id": 20001207}, {"segmentation": {"counts": "`0X1[:1O000O10000000001O00000O10N3L300O100N2N3O0000000O12M3O0M20001O2N1O00001O000O1001N1O100N3N2O0O101O01O1N3N01N13M000O000000O2O10O1001N100000000000RFlN[90VFT1=oN\\9S1bFoN^9Q1XFkN38d9Q1ZFQOf9^100000000000004M<C1O0O02O0O4M10O000000000000000O0101N00101O2N0O1O01000O101O00O0010O2N2N11OO_Ob00O100O1000000O100O100000000N200O11O000000000000O1O100O>C3L1O100O101O0O2O0M22N12J220EhEYOY:h07200000O10000000L43M3NOOO22N0000000000000000O02O00O10100O0O2O2N01O01NO20000001OLbERO_:m04000000000000000O10000NRO_En0a:22M2OO11O3M001O00000000000O02O0000O100001O3YETOa:l0_ETOa:o001OO10OO04OO2M100000000O1K50O10O100000000000000000000002N00N2000000000WEXOg:h0YEXO00e:h0[EZOe:j0OM\\EWOf:i0ZEWOf:i0110KZE\\Og:b0[E^Oe:b0\\E]Od:b0]E^Oc:?:O100O2O00O11N1M31N20IjDOZR_1OTYaN1O00001bDMZ;8N1O1O1O00O100O11ON21O1OO1M3K5Nko30idK3R;9N2N2000001O0000O10000O1001O1OO6J:F]Wi1", "size": [371, 640]}, "area": 12937.0, "iscrowd": 0, "image_id": 19042, "bbox": [0.0, 0.0, 482.0, 87.0], "category_id": 95, "id": 20001208}, {"segmentation": {"counts": "`Yf23_;1J601N2OOO31N01O0N20O100O20O1O0001FlD2U;MmD1U;OkDMX;4hcY4", "size": [371, 640]}, "area": 259.0, "iscrowd": 0, "image_id": 19042, "bbox": [238.0, 60.0, 22.0, 16.0], "category_id": 96, "id": 20001209}, {"segmentation": {"counts": "hje05\\;4N0JHnD1O7Q;712M100001O00000000000000000000000O10100O1O00O1O1N2O10000N200000000000000O100O1O100000O01O101O0O0100000O100O1O1O11OO10N13N0O1000000O100O100000000N200O11O0000000O11O0O1O10O0100O1O100O10O1O02O1L31O00L5O0E`NaFb1_99000000O10O11O00000001OO100000O1001OO100O1000O10001O01O1O001O2N00001ON20000001OLYN[Fh1d940000000000000000000000001O1O001O3M001YNUFd1m9000000O2OO100001O00000mNSF`0n9^OSFb0o9VOWFj0j9UOWFj0j9TOXFk0W:11O001O0000O100O11O1O0O10^OXOPFM2l0m9XOPFN1k0n9YOQFLMn0Q:WOSFl0l9>O100000000000O10N200000000O1O11O000000002N0_NTFONY1X:O001N02N01000O1010O1iNiEP1]:03M1O00O10TOaEd0_:ZO^EO4f0^:[OeEd0Z:\\OfEe0d:01O00001O000O020N1]OREa0P;0001O00O100O1000000O11OO1O10010N00100O1O100O1O11OO100O11OO100O1000O11OO01000000000UOZEi0h:0O10000000000O100O100001OZEUOb:o000001OO0110N11O000O10ONRO`Eo0[:QOeET1[:2O1M300O100M300O1O1O100L_NUFb1k930000000O02O00O011O0000N2000000010OO0010000000001O001O0O1000000N2001O00O100000000O10000000000O10000O10000O1O10000001ONoMbFQ2^921O1OO100O1KmMjFT2U9mMiFT2W9kMiFW26iMg80TG_2`8aM^G10O41Ma2^8eMdGJMc2^8?001O0000O10000O1dGlLY8T3gGlLZ8S3eGnL\\8T3dGkLY8V3gGjLh74bHU3^7TMXHn2g7?00000000000000000000000O1O1000000001XHWLd7h3\\HYLd7g3\\HXLe7h3[HXLe7h3[HXLe7h3[HXLe7h3[HXLe7h3[HXLe7l3OK[H\\Le7d3\\H[Ld7f3400O1000000001O000000N21O001O1ONXHYLh7f33001OO10UH[Lh7i3O0000001OI7001O000UH[Lh7f3WH\\Lg7d3ZH[Lf7e3ZH[Lf7e3ZH[Lf7e3ZH[Lf7f3YHZLg7f3YHZLg7i300000000000O1O1002N00000000O100002N00O10000MZHYLf7h32NWHZLi7f3WHZLi7f320000001O1O001O1OO1001OO10000O10000O1O100O1O100O12NM31O0000001O00O11O1O001O2N1O2N00002N00000000001O0000000000000]HRLa7n3_HRLa7n3_HRLa7n3_HRLa7Q4O000000", "size": [371, 640]}, "area": 37729.0, "iscrowd": 0, "image_id": 19042, "bbox": [60.0, 0.0, 580.0, 130.0], "category_id": 97, "id": 20001210}, {"segmentation": {"counts": "j2a0R;0000O1001OO1001OO1O11O00000O1O011O000O01001O001N2O00O1O2O001O00000O10oDAn:?REAn:?REAn:b0O10O2M3OO10001O_OPE>1BP;=PECP;>oDBR;?00O0100O1O2O10O00002NHQEKP;3900MdDO_n`6", "size": [371, 640]}, "area": 1053.0, "iscrowd": 0, "image_id": 19042, "bbox": [0.0, 86.0, 64.0, 22.0], "category_id": 125, "id": 20001211}, {"segmentation": {"counts": "Q[d04_;3L1O1O2M2O10001O0O10O11O00000000N2N2FkD3\\;Nem[6", "size": [371, 640]}, "area": 257.0, "iscrowd": 0, "image_id": 19042, "bbox": [56.0, 50.0, 21.0, 16.0], "category_id": 127, "id": 20001212}, {"segmentation": {"counts": "[3Q4b7000O10100N3N01OO1O20O00000O11N11N1001N1010O001O1N11N1O10000000O1O010jKfHR4[7nKeHR4[7nKeHR4^71O0O3L11O1O1O2OOnKcHo3^7QLbHo3_7PLaHP4a7O10O101N1O1001O0O1O3N]HSL_7R4O00001N21N01O00MdHPL[7P4400000000O100001O2N000O10010O0000000000001O001O1O00N20000N2O1O1O1000000002M100010O00cLlKZOT4>lK^L1T3S4>lK^L1T3S4>mK]L0U3S4=nK]L0V3S4<lK^L2V3P4<PLZLM23X3P4<PLZL4Z3l3:mK\\L304Z3m39ZLGg3:WLFj39VLEl3<SLFk3:ULFj3<ULDk3<ULDj3=VLCj3=VLCj3=VLCj3=VLCj3<WLDh3>WLBi3>WLBi3>PLYL1Y3o3>oKZL2X3n3`0oKXL3X3n3d0SL\\Om3c0TL]Ok3e0TL[Ol3e0TL[Ok3f0ULZOj3g0VLYOj3g0VLXOk3h0ULYOj3g0VLYOj3f0WLZOi3f0WLZOi3f0WLYOj3c0ZL]Of3c0ZL]Og3>oKYL<Y3e3kLoKL0_30aL<Y3e3kLQLY3>Lb3iLPL^3<Ij3:SLEn37gKbL<V3n38fKbL<W3m37gKbL<X3m35VLKj36ULJk36ULJk36ULKi36lK`L6Z3n36kKaL7Y3n36kK`L8Z3m3:SLFo38QLHP4nLfKP392R4mLeKQ392S4lLdKR391T4mLcKR392S4gLdK02Y350Y4fLbKZ350]4OdK1]4MdK3\\4MdK3Z4fL]KW393Z4fL]KW393Y40gK0X41hKNY42gKNY43fKNY42gKMZ43fKMZ43fKL[44eKL[40]KjL8V3[4M]KlL009W3\\4jN[KmN0QO109X3\\4iNZKlN2SOO09Y3[4hN[KPN0j04UO6Y3[4hN\\KoMOj0a0_2U4^N[K_N0JOk0`0^2V4^N\\K^N1e0>_2T4_N]KXN011i0>_2T4_NgKSO4^2U4_NgKSONWOHW3c4]NiKUOKXOIV3d4^NgKROMZOHV3d4\\NZK[N=i0JWOI42U3d4\\NZK[N=i0DWOO4001V3e4[NiKWOBUO2_3c4UNiKWOBUO2_3c4TNiKZOATO3]3c4VNiK\\OD^2c4VNhK^OB^2g4SNhK\\NG]4_4YM]KZNb0\\4Q4ZM\\K[Nc0[4P4_MQLfN[OW3c4TNRLeN[OW3Y4XLoKl1<\\N\\O00`3T4YLlK26l1=XN]O11_3R4dLnKf1a0VN@OOa3R4YLoK7Nk1b0UNAMOc3U4eN[LgMEMI03g3T4eN[LfMMe3g3eN]LfML0Aa3V4gNjKfMd01KODb3R4iNkKeMe00Nc3c3gNjKfMY1d3n2eNSM\\1l2eNTM[1Y1\\LmNY2`NfMX1e3Y1_LoNU2`NgMX1e3X1`LPOV2HZ1X1aLoNV2HY1V1dLROS2cNdMQ1e3Z1dLROW2aNaMP1c3]1fLSOZ2^OP1_1fLSO[2]Oo0`1fLSO[2]Oo0`1fLSO[2]Oo0a1eLRO\\2VO[MZO04c3Z2fLSO5_Ne1g0PN\\O`3Z2gLUO3^Nf1e0RN\\O`3\\2eLVO2]Ng1e0j1h1]LWON\\NH1S2a0mM^Od00d2]2RMYOLZNJ1S2a0mM^O;2JNS3]2hLcM8f1NZNJ1S2a0ZNAGMT3_2oLXOO\\Nm1?ZNGk2W2lLZO2XNm1`0ZNGk2Y2jLYO2XNo1?[NFj2Z4jLcKP2=\\NFj2d4iNgK[NFl2c4iNgKZNGm2b4hNiKWN[OI;X3a4]NTK[N05h01ER3^4^NUKZN05i01CR3^4_NYK]Ng01BS3\\4aN\\KYNg03AS3\\4aN\\KXNi04WOK1X3b4bN]KWNi08SOG5W3c4dN\\L]NlNH5W3c4dN]L^NPOn2c4eNYL`NoND3W3e4eNZLaNQOj2e4eNYLbNROi2o3iLbLl1<bNTOi2m3jLcLk1;cNUOh2k3mLdLh1<cNUOh2k3mLdLh1<cNUOh2j3nLeLh1;bNVOh2i3oLgLf1;bNUOi2i3oLgLf1;aNVOj2h3oLgLf1;`NWOj2h3PMfLf1<^NWOl2f3QMgLc1?_NTOm2f3QMgL0\\OW10bNY13oNS3e3QMhLO\\OW1Z1fNmNS3e3QMhLO]OR1OhN[13lNS3e3PMiLO]OS1NhN[13lNS3e3PMiLO^OR1[1lNiNS3e3PMiLO^OR1[1lNiNS3e3PMiLO]OT1[1jNjNS3e3oLjLO]OU1Y1kNkNR3e3oLkLM]OW1Y1iNkNT3d3oLkLL]OW1[1iNjNU3c3oLkLL]OW1h1n1P2oLSMS1=jNdNN8V3e3nLRMT1<kNeNN7U3f3nLQMU1;QOmNl2g3nLPMV1;ROmNj2j3lLhL0YOZ1W1POnNi2e4kLVK01Z1W1SOmNi2d4kLVK00Z1Y1ROmNh2f4nLTKY1Y1POmNi2f4[N^LkNmNj2e4TNYKgNS10iN:6k2e4TNbLiNbN00O<U3_4SNdLlNaNN:S3a4SNdLhNfN06U3`4nMVKoN\\1OhNO6U3`4nMWKnN[10hNO6U3`4nMYKlN01<120CO6U3_4PN_KkN7120CN7V3^4PN_KlN506OOU3U4RNaKjN53i0Q3a3TN^KiN82i0Q3]3jL]K[14iN5LG7OIT1V3_3YNaKfN4MI84He0T3h3ZNbKeN2c0f0^2e3[NcKdN1e0f0\\2e3]NdKaNOh0h0Z2e3]NdKaNNh0j0Z2c3bNaK[N2j0j0Y2b3cNbKZN1k0k0X2_1SM]Of1XNoNl0W2^1XM[Oc2WO6\\1YM[Ob2YO4\\1]MYO_2[O4[1_MYO]2\\O4Z1aMXO\\2^O3Z1cMPOYOnNP3h04Z1cMhNXOVO422K_2k0`0Z1dMfNXOWO62NL`2k0`0[1eMcNYOVO57KIc2l0?[1hM`N@ADIe2k0?[1hM`N@EY2`0?T1PMPNi0e0@GY2?>S1SNfNVOIX2>?U1oMgNYOEZ2?>V1kMhN]ODZ2>>V1UMoM6n0ARO8=^2>>W1SMTN0j0JPO7<_2?=X1QM_OK^N103<c2?=X1oL@NaN39a2?`0W1lLBO_N49a2??X3PMYLa2??X3oLZLb2>?X3oLYLc2?>X3oLZLb2>?Y3nLYLc2>?Y3mLZLd2=?m3@SL`0n3^OSLb0m3_ORLa0n3_ORLa0n3_OVK\\Mj0U3Q4gMUKk0j0^1Q4fMVKl0i0^1Q4eMXKk0h0`1P4cM[Kl0e0a1P4bM\\K4IWO000O0<j0h2S4_M[KDH94_OL0100;j0i2S4^M\\K_OG20>8_OI:i0k2R4]M]K_OHON01a06AK7j0k2R4]MdK_O@`06@J:j0j2Q4_McK^OB`09Ia0j2Q4_MgKOHG`0k2Q4_MfK0IEa0l2P4_MfK1HDb0l2o3`MgK0HCc0m2n3`MgKOIDb0m2n3aMgKKKFa0n2m3`MhKLKDa0P3l3`MiKJQ1f2U3aMkKHP1g2U3aMkKGQ1h2T3aMkKFR1i2T3`MfK\\O@:g1j2S3\\MTKAc00^O9h1j2S3[MkKBZO8i1k2R3[MkKBZO6k1m2n2]MlKA[O5k1m2n2]MlKA[O6j1l2UNlLg4a0PLAZO6j1n2n2YMnKNT1i2m2ZMnKMV1i2l2ZMnKMV1i2l2ZMmK0U1e2n2\\MmK1S1d2m2]MPL0R1b2l2aMRLMR1c2i2aMVLMP1b2h2ZMbKDi0?n0c2e2[MeL1f0d2e2[MeL1f0c2g2[MdL1e0d2f2\\MeL0e0d2f2\\MfLNe0f2e2\\MiLKb0i2e2[MkLK`0j2e2WMeKDU1;b0j2d2WMeKDU10iN9i1l2d2WMnLDfN0O69LS1S3Q3XMPMCcN30M92T1S3Q3VMRMGnNNo0U3T3RMPMIoN0m0U3V3oLaMNWOS3Y3lL`M3lMLY1U3a5RM_Jn2`5SM`Jm2`5SM`Jm2`5SM`Jm2`5SM`Jm2`5SM`Jm2`5SM`Jl2e3lLlL8_Om2b3mLVK0d16VOI@T3^4oLWK0d14WOJ_OR3_4QMWK0c13XOJ_OR3]4SMZKNa12YOL_OQ3]4]MjLEZOM_OQ3\\4^MkLCZOO_OP3\\4^MlLAZO1^OP3\\4^MmL@YO2^OP3\\4^MnL^OYO3^OQ3Z4_MlMCiMn2[4^MlMFhMm2\\4\\MlMFiMn2[4lL[K=a2IiMm2\\4lL[K=a2JhMm2^4ZMjMIhMm2_4YMiMJhMm2_4XMjMKgMm2`4WMjMKfMn2b4mLUK3b24gMm2e4nLfM3fMo2c4oLgM2fMP3b4nLhM2fMP3c4lLgM5fMo2c4jLiM8cMn2^7QMbHo2_7PMaHP3`7oL`HQ3a7kLbHU3n70000000SLjLbK10Of3V3b0YMZOg2g0XMYOh2h0XMWOh2i0XMWOh2i0XMWOh2h0YMXOg2g0YMZOg2f0YMZOg2g0XMYOh2g0XMYOh2g0XMYOh2h0WMXOi2h0VMYOj2h0UMXOk2i0TMWOl2j0SMVOm2k0RMUOn2k0PMWOP3j0nLWOR3k0lLUOT3l0jLUOV3d40000000000001O001mGhLi7a302N1O00000000000000000TH\\Li7d3WH\\Li7d3VH]Lj7f3VHYLh7g3XHYLh7i301O000000000000000000000000O1O1000000001XHWLd7h3\\HYLd7g3\\HXLe7h3[HXLe7h3[HXLe7h3[HXLe7h3[HXLe7h3[HXLe7l3OK[H\\Le7d3\\H[Ld7f3400O1000000001O000000N21O001O1ONXHYLh7f33001OO10UH[Lh7i3O0000001OI7001O000UH[Lh7f3WH\\Lg7d3ZH[Lf7e3ZH[Lf7e3ZH[Lf7e3ZH[Lf7f3YHZLg7f3YHZLg7i300000000000O1O1002N00000000O100002N00O10000MZHYLf7h32NWHZLi7f3WHZLi7f320000001O1O001O1OO1001OO10000O10000O1O100O1O100O12NM31O0000001eM\\LeLd3Z3_LdL`3]3aLbL`3\\3aLdL`3Z3aLfL_3Z3aLfL`3Y3`LgLb3V3_LjLb3U3^LkLd3S3\\LmLd3S3\\LmLd3S3\\LmLf3Q3ZLoLf3Q3ZLoLf3Q3ZLoLf3Q3ZLoLf3R3YLnLg3R3YLnLh3Q3XLoLh3R3WLnLi3R3WLnLi3R3VLoLj3Q3VLoLj3Q3VLoLj3Q3ULPMk3iMRLU53RMk3iMRLU54QMj3jMRLV52QMl3iMRLX50oLQ4P3oKPMQ4Y50000", "size": [371, 640]}, "area": 127558.0, "iscrowd": 0, "image_id": 19042, "bbox": [0.0, 101.0, 640.0, 270.0], "category_id": 148, "id": 20001213}, {"segmentation": {"counts": "nZa22_;3N101L300O10000001O00O10000000FiD8Y;O2M4N\\_5Ne`J1N3O0O1O1000000O10000000O1000000O100000001O00002NViY1OkVfN1O0O100O100000OO3N2OO10000000001ON21N1000O1010O1O1O1Ll^\\2", "size": [371, 640]}, "area": 505.0, "iscrowd": 0, "image_id": 19042, "bbox": [224.0, 183.0, 204.0, 178.0], "category_id": 150, "id": 20001214}, {"segmentation": {"counts": "\\7W4\\7000001O1O2N0000001O000000O11O00O1001O001O000000O11O0000000000O1O100O100001O002NO1O1O1O100O10000O100001O00000000O1O10iKgHT4]7O00001O1O001O00MPLdHo3\\7400000000O100001O2N000000001O0000000000001O001O1O00N20000N2O1O1O1000000002N00001O000O0H`H^La7b3_H^La7b3`H]L`7b3aH]La7b3^H^La7b3aHZLM2b7d3aHZLc7d3ZH\\L30d7c381OO01O0O2O0O10000000O02O000ZHYL`7g3_HZL`7h3_HXLa7l31O02O0O1O10000000O10000L401KZHYLh74XHL0_30aLh74ZHY3k7eLUH^3o73MLRHbLo7^3QHbLo7^3QHbLP8]331O000O1RH`Li7`3VHaLj7_3VH`Lk7d32N1fLPHP3P8oLPHQ3Q8nLoGR3Q8nLoGR3Q8iLoG02Y3Q8fLoGZ3U8O2N10NiLhGW3X8iLhGW3W83O10001O00000000LkGjLU8S3kGlL00X8Q2hGmN0QO10Y8Q2fGlN2SOO0Y8Q2fGPN0j04UOV8Q2gGoMOj0b8m0_G_N0JOk0a8l0aG^N1e0^8n0aGXN011i0^8n0kGSOT8m0lGSONWOh7d1\\HUOKXOj7d1ZHROMZOi7b1mG[N=i0JWOI4S8a1mG[N=i0DWOO4001Lo7e1_HWOBUO25m7_1_HWOBUO25m7^1_HZOATO33m7`1_H\\ODTOm7`1^H^OBTOQ8]1^H\\NGS1i7c0SHZNb0R1[7d0RH[Nc0Q1Z7i0gHfN[OMm7^1hHeN[OMc7BeHl1<\\N\\O006^7CbH26l1=XN]O115\\7NdHf1a0VN@OO7\\7CeH7Nk1b0UNAMO9_7o1QIgMEMI03=^7o1QIfMM;Q7o1SIfML0A7`7Q2`HfMd01KOD8\\7S2aHeMe00N9m6Q2`HfMY1:X6o1iIRNV6o1jIQNV6o1`HfMX1;Y6n1_HgMX1;Y6P2gIPNY6Q2fIoMZ6Q2aHdMQ1;^6U2_HaMP1:b6Y2\\IgMd6Z2[IfMe6Z2[IfMe6Z2[IfMe6Z2TI[MZO04:_74]He1g0PN\\O7a73\\Hf1e0RN\\O7d70[Hg1e0aNQ7DZHH1S2a0mM^Od0i7BWHJ1S2a0mM^O;2Jg71WHJ1S2a0ZNAGf72ZHm1?ZNa7IoGm1`0ZNb7GoGo1?[Nb7DQHP2=\\Nl7c1UH[Nl7e1THZNm7e1UHWN[OIa8e1_G[N05h01j7`1^GZN05i01g7a1cG]Ng01d7d1fGYNg03c7d1fGXNi04WOKR8j1nGWNi08SOGV8k1nH]NlNHV8k1oH^NQ7c1kH`NoNDT8l1nHaNR7_1mHbNS7^1lHbNU7^1jHcNV7]1jHcNV7]1jHcNV7^1iHbNX7]1iHbNW7^1iHaNX7_1hH`NY7`1hH^NY7`1jH_NV7NgGW10bNY13R73fGW1Z1fNP73gGR1OhN[13o62hGS1NhN[13o62iGR1[1lNl62iGR1[1lNl62hGT1[1jNm61iGU1Y1kNo6NiGW1Y1iNo6OhGW1[1iNn60gGW1T:0TGjNdNNd8X1gHkNeNNb8X1gHQOW7P1hHROQ7FgGZ1W1POP7DiG01Z1W1SOo6DhG00Z1Y1ROP7FgGY1Y1POP7T1QIkNP7n0kGgNS10iN:Y8o0TIiNbN00O_8X1PIlNaNN]8V1RIhNfN0Y8S1cGoN\\1OhNOY8S1dGnN[10hNOY8S1fGlN01<120COX8U1lGkN7120CNY8V1kGlN506Oo7W1fGjN53i8U1oFiN82e8KnF[14iN5LG7OIo8_1UGfN4MI84H_8^1`GeN2c0_8i0_GdN1e0^8i0aGaNOh0`8g0aGaNNh0a8l0]G[N2j0`8l0^GZN1k0a8Q1^GoNa8R2O10000O1JoLkGP3m7oLSH422K_2o7\\MSH62NL`2o7^MPH57KIc2P8gMXHDIe2o7gM\\HY2c7hM]HY2b7gM_HX2b7gM]HZ2b7fM_HZ2b7ZMQH8=^2a7[MRH7<_2c7YMQH103<c2b7ZMVH39a2c7[MTH49a2j7_MVHa2j7^MWHb2i7^MVHc2j7]MWHb2j7]MVHc2j7\\MWHd2]8O1N11O000SG\\Ml8l0TGk0l8TOUGl0k8SOWGk0j8SOYGl0g8SOZG4IWO000O0<n87ZGDH94_OL0100;n87[G_OG20>8_OI:m88[G_OHON01a06AK7n88bG_O@`06@J:m89aG^OB`09Id89eGOHGc8:dG0IEd8;cG1HDd8<dG0HCe8=cGOIDd8>cGKKFc8>cGLKDc8`0cGJR97oFHQ98oFGR99nFFT99hF\\O@:i96UFAc00^O9j95lFBZO8k96kFBZO6k9:jFA[O5k9:jFA[O6i99mFAZO6k97kFNT93kFMV93jFMV93iF0T91lF1P91PG0l83TGMj84WGMf8LdFDi0?b8NfG1Z8OfG1[8NfG1Y80gG0Y80hGNY82jGKV84lGKT81fFDU1;V81eFDU10iN9]93nGDfN0O69Lg8;]HCcN30M92i89^HGnNNg87\\HIoN0g84mHNR7OnH3lMLZ;7O10000000000TNH]H8a7JfF0d16d7LgF0d14d7MhF0c13c7OkFNa12e7:ZHEe7<[HCf7=[HAf7?[H@e7`0\\H^Od7c0f1O2O0@PE=P;BQE=R;11O0O2O2FiD3\\;O01001N1NZl20WhL10OV;?MO10O000O1O0101O00001O0O2O1O1O1O0N3N3N1N[_g10e`XN3N1N1O10000O100000000000000001O00001O000O10000O10010N3N00_i0", "size": [371, 640]}, "area": 38965.0, "iscrowd": 0, "image_id": 19042, "bbox": [0.0, 227.0, 637.0, 144.0], "category_id": 162, "id": 20001215}, {"segmentation": {"counts": "0`0X1b0a7^O`Ha0`7_O`Ha0`7_O`Ha0`7^OaHb0_7^OaHb0_7^OaHb0_7^OaHb0_7^OaHb0_7^ObHa0^7_ObHa0^7_ObHa0^7_ObHa0^7^OcHb0]7^OaHd0_7]O]He0d7[O\\Hd0e7\\OZHe0f7[OZHe0f7[OXHg0h7YOVHh0k7YOTHg0l7YOTHg0l7YOTHg0l7YOTHg0l7YOSHg0n7YOTHe0l7ZOWHd0i7]OWHb0i7\\OYHe0f7[OZHe0f7[O[He0d7[O^Hc0b7]O_Hb0a7^O_Hb0a7^O_Hb0a7^O`Ha0`7_O`Ha0`7_O`Ha0`7^OaHa0`7_O`Ha0`7@^Hb0a7^O^Hd0a7\\O_Hc0b7]O\\He0d7\\OZHe0f7\\OYHd0mNmNh8?ZHh0e7XO[Hh0e7YOZHh0e7XO\\Hg0d7YO]He0d7ZO_Hd0a7\\O_Hd0a7]O]Hd0c7\\O`Hb0_7^OeH?Z7AiH<W7CjH=V7BkH>U7AmH>S7AnH?R7AnH?R7AnH>S7AoH<S7DmH<S7ElH:U7FjH;V7EkH:U7FkH;T7ElH;T7ElH;T7EmH:S7DXGFf1e0R7EPI;P7EQI:o6FQI:o6FQI:o6FQI:o6FQI:o6FQI:o6FQI:o6FQI:o6FQI;n6EVI8i6IcIK\\65eIJ[66eII\\66dIJ]67bIG`69_IGb6<[IDe6=[IBe6>X200000000000000O1001OO1001O2N0000O10000001O00O1O1001O1O000dT:KakE3M000000001O001O00O1O12NN200O1N200000000000000L43M3M00N22N0000000000000000O11O00O1001O00O100001O00O10000000000000000000000000000O10000NkDFU;:kEFU9:jFHU98kFHU98kFIT9:iFIT94QFIh03W92SFKf04WOIm98VFLe05U98kFHU90UFKf05U9OVFLe05U9OVFLe06T9NWFLe05U9NWFMc06V9MWFLe06T9OVFKf07S9NWFKf07S9NWFKe08T9MVFLf07T9MVFLf08S9LWFLg09P9KYFLj0MTO7h91YFKk0MTO8g91YFJo08e8L\\FNP13f8O[FMn07e8M]FKn08e83ZGOf8O^GMd84[GLe86XGKh85XGKh85XGLg84YGLg84YGLg84XGMh83SGOP91PGNQ91PGNQ92nFNS92mFMT93lFLU94kFLU94kFLU94kFLU94kFLU94kFLU94kFLU94kFLU94kFJW96iFJW96kFHU98kFHU98iFJW96iFIX97hFHY98gFIX97hFIX97hFAXO6P:9hFAXO004P:;hFAZO4n9;lFGR98kFBWO81KONe9=TGBWOb0d9LUG6k8IVG8i8HRGD\\Od0c9HoFF^Oa0d9InFG]O`0e9ImFH^O?e9IjF`0W9@hFa0X9_OhFb0W9^OhFd0EQO\\9<nFi0R9WOnFl0o8SORGn0m8SOQGn0o8ROnFP1S9POnFo0JfNP9:XGo0FdNO4R9:RGCO[1NkNP9n1PGRNQ9X201O00001O0000O11O0PG_Mn8c20001O00O100O1000000O11OO1O1001OO1O100O1O100O1O11OO100O11OO100O1PNhFe1X9ZNjFe1V9ZNkFf1U9YNlFg1T9XNmFg1T9XNmFh1S9WNnFi1R9VNoFj1Q9UNPGk1P9UNPGk1GTNW91RGm1n8SNRGl1o8TNQGl1o8TNQGl1o8TNQGl1o8TNQGl1o8TNQGl1o8TNQGk1P9TNQGl1o8TNQGk1P9UNoFl1Q9TNoFl1Q9TNoFm1P9SNPGk1R9UNnFk1R9UNnFk1R9UNnFl1Q9TNoFk1R9UNmFl1S9TNmFm1R9SNnFm1R9SNnFm1R9SNnFm1R9SNnFl1S9TNmFl1S9TNmFi1HWN\\90lFd1H\\Na90fFe1\\9[NdFd1]9\\NcFa1`9_N`Fa1`9_N`F`1a9`N_F`1a9`N_F]1d9dN[F\\1e9gNXFX1i9jNTFV1m97100LRFbNo9^1300000000O10000O11O0000N2000000001OO1O10000000000000000O1000000N2001O03L2O00001lNcEP1b:M2O1O1O00O10O011ON12O1OO0N2L5N2O101ONcEnN]:R121O1OO100O1KaEVO_:j0bETO_:6`EL39MEa:2kE9DF`:OnEOB010O41MY;2gDMXPo1", "size": [371, 640]}, "area": 17234.0, "iscrowd": 0, "image_id": 19042, "bbox": [0.0, 0.0, 467.0, 93.0], "category_id": 169, "id": 20001216}, {"segmentation": {"counts": "bR`22a;0Vg00hXO20Nf;0[D10000O1001O0000000000001O1ON02100_Xf08VgYO4M2N3M2N2O1O1O012M3M0IXOcEi0Z:92K4L3[O`E3e:KcEKa:2a0O2Mad50`[J2N1O1O1O1O1O1O0100000000000000000O1000000000O10000000000000000O100000000000000O1000002N2N2NQki2", "size": [371, 640]}, "area": 876.0, "iscrowd": 0, "image_id": 19042, "bbox": [221.0, 1.0, 171.0, 204.0], "category_id": 183, "id": 20001217}, {"segmentation": {"counts": "U3W4U90O011O2N2M01O10VGfKb8Y4_GjK^8V4eGjK30b7V4kHPLn6GZHY4f0VLk6B_HW4h0YLd6AeHV4g0\\L`6_OhHo3NRLk0c0[6]OlHi35ULd0h0X6ZOoHh3U1QMh5XORIf3X1RNf4WNSJg3W1RNf4WNSJg3W1eMgNkNo5IRJh3X1bMjNlNk5KSJg3X1`MmNnNg5KTJg3X1_MoNmNf5MRJh3Y1^MPOlNe5MSJi3i0XL^OV12lNc5MTJi3<XL9U1GjN`5R4\\JkKc0k0VO_O\\6R4cIdKm0h0XOAX6U4cJWLWODW6IaIT4S1^LWODX6G^IV4T1_LVOEZ6i3bJbLSOF]6f3aJcLROH_6a3`JfLROI_6`3_JgLROJ^6_3`JgLQOK_6^3bJeLoNM^6_3eJbLmN0]6^3gJaLkN2^6]3hJ`LjN3^6^3hJ^LjN4^6_3hJ\\LkN5[6a3jJYLkN5\\6b3jJTLbNL94d6m3PJQLEMI3d6Z4hJeKY5^4eJaK[5a4cJ^K`5_4h1_OTG_Lm8`3ZGYLh8e3b0M2N010RObFfMe9W2m0I3O2O010O2NN21O3NDhMeEV2Z:mMbEU2_:jMbEU2_1gMY74WGV2a1hMm40`K4nMJOZ2h1jMe45aKNSNU2g1nM_41cK^2NbM@Eb3=nL^2OcM^OIa37mL_22dMUO_O^O5`48lL`20fMRO^OD3]49mLc2MUNbNnN71Y4=PMb2LZNlNfNZ4=mLj2ETNRObNa4`0_LDPOZ3c0QN]OcN\\4a0dLBoNh35bMLcNV4OeKb0R1]OjN66X5LlJ7HW4g0eL@POU52hJ2LGNY41iKh0T1@oNS53jJ1NQ4i0iLAQOl45mJOMo3l0iKPO06i09ZOj48mJMNo3X1_Lc4DWJNNo3X1^Ld4EVJNNo3X1iKdNb0Q6HUJNNo3[1ZLb4JTJMOo3\\1ZL`4JUJMOo3^1XL^4MSJZ4`1gK]4CQJ;0\\4c1]K^NOo5U5eKjJ]NOn5X5eKjJ]NNm5X5Q20O10000O100O10000O1000000000000000000000000O100001O2N1O1O1O2N1O1O3M2N3M1O001O001O001O001O001O001O1O3M1OAdKmG]4S8iKgGW4Y8>00000000000000000000000000000000000000O1N2N2OQKkGi4T8XKoGe4P8\\KSHa4l7`KWH]4g7eK\\HX4e7gK^HV4b7iKbHT4^7lKeHQ4[7oKhHn3X7RLkHk3U7TLnHj3R7VLoHi3Q7VLPIj3P7ULRIj3o6RLQI5mNd3U9;M3N2M3M3O100O10000000000O1000000001O2N1O2N1O2N1O2N1OGoKXGP4g8RLYGm3f8ULYGk3f8WLYGi3g8WLYGi3f8XLZGh3e8YL[Gg3d8[L[Ge3d8b0O1O10000O1000000000000O1O1O10000000000001O00000000001O1O00000000000dKaGk3_8TLaGm3_8QLaGQ4_8lKbGV4^8kK_GW4a8:00O100000000000000O100000000000000O10000O1^OTKdHl4f6XKZI1Jh4j6`KUIJNg4l6gKnHE4d4m6Z1N2GgI_IY6_6;00000000001O2lJ]I[3e6`L_I_3c6XKWIl0:l3h6mK^IR4e6fK`IZ4f6[KbIb4j7J1O1O1O001O2N1O00\\OfKUH[4k7fKSH[4m7eKRH\\4n7dKQH]4o7dKPH\\4P8dKPH\\4P8dKPH\\4d7dKXH12\\4e7fKWHN3]4f7PLYHQ4f7QLXHP4g7QLVHR4j7nKTHT4k7nKTHR4k7oKUHQ4k7oKUHQ4k7h0000000000000O1O1O100000000000000000000O1N2N2O1N2K5M3N2O10000O11fJkH[4U7eKmHXOOj4T7SLnHn3S7RLlHn3V7S1LQJlHn5S76O[JnHR5R7\\KnHV4S7Q10000000SKmHa3T7_LmH_3U7_LPI\\3R7eLmHY3U7hLiHW3X7kLfHT3Y7mLgHdNO\\4Z7jLmHY3T7eLmH[3T7bLoH]3S7`LnH`3S7^LnHb3S7iKaH8=o3S7gKiH16W4R7eKcI[4g701O0000O10000O10000O100O1O100O1XO^K\\H1Ka4h7aK[HNLb4i7aKZHMMb4i7bKYHMLb4k7bKWHMMb4k7bKWHMMb4l7bKUHg4j7>O100O1CeJhH\\5W7fJhHZ5W7hJgHY5X7iJgHW5Y7iJgHW5Y7jJfHV5Z7>00000000001O1O2N2N0hJ`Hd4_7e0O10000000000001O1O2N8H00I7O100O1001O001O1O1ZJ]Ha5g70000000001O1O2N6JK5O1O1O10000000000NaJWH_5g7cJYH]5f75000000002N2N000000001O1O2N00001O0000N2N21O2N1O1O1O1O2NF:O1O1O10000000XKZHj3f7WL\\Hf3d7Q11O1O2N8H000000001O1O2N6JRO_KQIa4l6cKSI]4l6gKQIY4n6iKQIW4n6lKPIT4P7mKoHS4Q7nKnHR4R7SLgHo3Y7R1000000000000001OO100O10000001ZJgHW5Y7iJhHV5Y7iJhHV5X7?1WJeH_5c71O1O1O1O100N2N2J6O1O1000000001O1O4nIgHi5_7O001O1O003M4\\JXH\\5i732N3M002N1O2N2N1O2N2N2N001RKcGk4]8WKcGg4^8300`NZKfI3Ec4d6\\K_I8N[4c6^KXI=O[4i6XKSIi5m6`JfId4Z6\\KfId4[6ZKgII\\O`4n6fKfIK\\O^4Q7dKcIO[O]4R7dKeINYO]4U7bKaI2ZO\\4V7`KaI5YOZ4V7aKaIm4a6QK_Io4b6oJ^Ih0ZO\\3T8cLoG[3Q8\\LZHb3f7_LYHa3f7`L[H_3e7bL[H]3e7dLZH\\3f7eLZHZ3g7fLXHZ3h7fLZHX3g7hL^HR3b7oL]HQ3d7oL[HQ3e7PMUHU3m7iLSHW3n7iLPHX3R8gLlGZ3U8fLiG[3Y8cLhG\\3Y8dL`G]O000Q4a8h01O1O1O1O1O1O1O1O", "size": [428, 640]}, "area": 155718.0, "iscrowd": 0, "image_id": 19109, "bbox": [0.0, 0.0, 640.0, 325.0], "category_id": 96, "id": 20001218}, {"segmentation": {"counts": "`lW71X=31O00L4TCJc<6[CKb<OXC701g<<O00OL500000000000O100000ON^C[Ob<c05N20O1OM22L5DZOlCk0Y<32K3N2O0O1O00000001OO10O1O1E;00001O00001O001O001O001O1O00O1L31000O1O1O1O1O1N20000K500O1O1O11OJcNYD00_1g;41O001O001O0000O1hF", "size": [428, 640]}, "area": 2519.0, "iscrowd": 0, "image_id": 19109, "bbox": [554.0, 294.0, 86.0, 63.0], "category_id": 97, "id": 20001219}, {"segmentation": {"counts": "^Qk421OU=7J8G3fC^Ol;d0kC\\OM00003Z<d0fC@[<h000N2N200000000O1N2O1O1O1MhNRDX1n;hNRDX1n;hNRDX1n;300O10000000000O10000000000O1000000000000O1000000000000O100000000O1000000000000O10000000000000000O100O1O100002N001O0000000^NYD_1U<E2N1O001O1OO11OO1O100000000NnNjCR1V<nNjCR1V<nNjCR1U<oNkCQ1U<3O11kNkCQ1U<oNkCQ1V<nNkCQ1X<00000000000000000000000000000002N00001OO1000000001OO10000O10000N2001O00O1000000O10000000000000000O11O00O1000000001OO11O00000000O11O1O2N0000000000002NO1O10000000000O1O1O10000000000000000001O0000N200N2O100O1O11O001OO1001OO100000000O1O1N2M33MN200O1002NO1O100000aNVD\\1m;01O0000001O0000O1000000000000000000000000O10000000000000000000000", "size": [428, 640]}, "area": 10849.0, "iscrowd": 0, "image_id": 19109, "bbox": [370.0, 373.0, 270.0, 55.0], "category_id": 140, "id": 20001220}, {"segmentation": {"counts": "j;b1j;00000000000000000000000000000O10000003M2N1O000000001O000000000000001O1O2N1O0000FnNZDR1e;;O100O100O100O1000[N]Da1i;N1O1O2N1O2N001O1O001O001O1O001O1O001O001OO1N2N2O1O100O10000O100bNjNeFW1]9hNbFX1_9hN_FY1b9gN]FY1d9fN[F[1k:0O1oNdNQF]1P:cNWE1?]1[:bNUE3=\\1^:bNSE69[1d:`NQE86[1i:f01O00000000002N2N2N2N3M2N4L3M2N1O2N3M1O1O001O001O0000001O00000000000000O100000000O100O1O1O100O1O100O1O1O100O1N2QOZNUFi1j9YNSFi1m9XNoEk1Q:VNkEm1U:e000000000002N3M4L3M4L2N2N2N2N2N1O1O2N1O1O1O1O1O0000O1O100O1B[NUEe1k:>BlMcEU2]:oM]ES2e:92N2N2N1O2N2N1OO100O10000O100O100O100O100O100O1O1O1O10000000000000000000000000000a0_O1O00O100000000000000O100IQNQEo1n:TNPEl1P;VNnDj1Q;900O100O100O100O1O100O100O100O100O1O1O1O1O1O1O1O1O1O1O1O1O1O14L4L5K4L4L1O1O1O1O1O1O1O1O1O1O1O1O1OO100O100O100O100O100O10000O100O100O100O100O1O1K500000000000000000000O100O1O100O1O100O100M]M]Ec2a:^MaEa2f:O1O1O001O1O1O1O001O1O1OO100O100O1O100O1O100O11O1O1O00000ZOSEZNOa0n:S1YEkNg:Q1aEkN_:P1dEQO]:m0eEjN\\OM0000R;[1eEeN_:\\1h000N2N11O1N200O00O102N3M2LlCnNU<Q1kCoNU<Q1kCoNT<R1300O010000O100O001O1O1O1I72M100000001O000O100000001O001N2O001O001N101O001O00001N100000O1000000000O100O1O100002N010O00000000000O0O2NO03NKkCTOU<k0jCTOQ<V16L3N2N7I1O1O0O2O0O2O0LYCCg<8^CHb<8810O100000TOHdD8W<0WOH`D8Y<1XOG\\D:e;FZD:g;FWD;i;GSD:\\<4M3O11O2N1O2M3N1O1O001O0O2O001O0000000O1000000000O1000O0100O100O100O10O2O1O000000000O10O1O10000O1O1O0010000000000000O1000000000001O0000001N1000000000V^o0", "size": [428, 640]}, "area": 27891.0, "iscrowd": 0, "image_id": 19109, "bbox": [0.0, 310.0, 564.0, 118.0], "category_id": 149, "id": 20001221}, {"segmentation": {"counts": "0U3W:00O1000000O100000000O1LgEUMY:j2hEVMX:h2iEYMW:f2jEYMW:f2jEZMV:f2jEZMV:e2kEZMV:f2iE[MW:e2iE[MW:e2iEZMX:e2iE[MW:e2iE[MW:e2iEZMX:f2gE[MY:e2gE[MY:e27O1001^EZM\\:i2aEWM_:l2O2N3M1O0000O100000000O100001O00002N1O001O2N1OO100O13MO100O1001OM3002NDoE^MP:`2SF\\MP:d2oE]MQ:c2oE\\MR:<lEb14nMJOW:>RF`1NSNR:=RF\\1Z:cNhE[1Y:hNhES1Y:nNfES1Y:lNgET1Z:hNjEW1W:kNhET1X:lNhEk0DPO^:2QFn0BoN]:M_EOb0R1]OjN66]:0VFo0@PO[:L]E1h0T1@oNZ:MZFQ1AQOU;L]D06i09ZO`;c0e0O1[O[Cb0i<O101N1N2GTCOo<ORCOo<2QCNkYS7", "size": [428, 640]}, "area": 8085.0, "iscrowd": 0, "image_id": 19109, "bbox": [0.0, 0.0, 97.0, 106.0], "category_id": 169, "id": 20001222}, {"segmentation": {"counts": "ffm51Y==E2M2O0M4O1H[OdCg0Y<XOfCo0X<200100000000NgCQOX<P1hCPOX<P1hCPOW<P1jCPOV<P1OROjCm0R<XOnCi01POm;Y1SDgNm;Z1RDgNl;]100000000O10000000000O10000000002N0O101OO100000O101OO10000O10O10N2001O00O1000O10O100000000000O1000O11O00O10O1000001OO11O000O1000O11O1O2N000O100000002NO1O1]N_D[1a;bNbD^1^;cNaD]1_;cNaD]1^;`NfD7Jo0`;kNfD4KP1a;jNeD01T1o;N10O1000000000000O101O000MkNnCT1Q<5N02O10L31O200O2ON01010OO100000001N001N2M33MN200O1002NO1O100000fCQOW<R101O0000001N1000O1000000000000000000000000O1000000000000000000000VE", "size": [428, 640]}, "area": 8043.0, "iscrowd": 0, "image_id": 19109, "bbox": [453.0, 336.0, 187.0, 61.0], "category_id": 172, "id": 20001223}, {"segmentation": {"counts": "\\7W2U;R1mNP1ROO2N2NO1O10QLlFf3S9[LPG8Mh2S9SMoF13i2m8dMZGX2f8hM]GV2a8kMbGR2^8nMeGo1T8XMZGj0e0k1l7bM]Gc0j0h1h7dN[H\\Of0Cn6S1eJPOX5P1hJROV5n0SIhN3>g6j0TIkN4=f6h0TInN5:g6i0RIoN5:h6g0SIPOBHBa0i7LiGG>Ml08_ON^O<P8GoG:T1HZO0^O9Y8_OgGg0i0VO_O?3i0V8DUHWOBV2W8eNUHXOCS2V8gNVHWODR2U8iNVHUOFQ2R8lNXHSOFQ2R8lNXHROHQ2o7nNXHROIQ2n7lNZHSOIQ2l7nNYHQOKR2k7oNXHnNNU2h7oNXHlN1U2f7POXHkN2U2f7QOWHjN3j1@VNX8V1THjN4j1S8\\OhGkN5g1U8^OeGkN5g1W8nN[G_O5DK94o1l8ZNTGOKI4n1Q9TNmF:NF2m1V9ZOjFf0X9YOfFg0Y9g1@oKdGQ4\\8VL]Gk3KPLb8g4M2NlLbG]1]8cNcG_1]7oLRIj1AX1X7BhH?S7P3M3N101N2N1O2N101N2N1OnJ_I\\3`6eLbIY3\\6hLgIU3Y6lLkIOFX1_6hNnI_OInNLi2`6iNmIVOA[O=0EY2c6TOmITO@@:1FS2f6WOoInN\\OH93Em1j6WOcJiNoN4Di1n6WO_JlNoN7Cd1Y7nNVJWOnN9C`1e7dNjICnN;C[1W8UNYI5mN=CW1_9UNkF74>BB4R1[9lNgF28d0DWO0V1\\9nNhF18m0D3[9POjFO7P1CO\\9SOjFN7Q1CL^9UOgFM9S1BJ^9VOgFM:S1@I`9WOfFM:X2P9kMfFM:W2Q9mMdFL:X1A^Ob9_OaF52P1KcN2?`9]O_F:1;No00_N5;_9^2]FUM78^9d2g0000O10001N100O3N2N2M3N3M2N4L3M2N1O2N3M1O1O000O2O0010O2N1O1O1O2N1O2N2N3M2N1O0O2O001N101N1O2O0[MoEn1S:VNlEe1U:PNkEN0P2W:oMjE30l1X:lMgE=128e0T:]OeEK4ALU1\\:AcEF6FKR1m9oNYFe0JA4KMn0m9WORF`00^O30Ni0n98RFlN160a0P:>oEiN0:2;R:d0kEdN1`025T:i0kETO1OW:o0dETO6IY:j1d000000000O14L3M4L3M4L2N2N2N2M3L3M3NlLQFo2l9RMWFl2g9UM\\Fi2b9WMbFg2[9[MhFc2X9\\MkFb2T9^MoF^2R9bMQG[2o8eMTGW2m8hMWGU2i8jMYGk0SO3e9ROYGi0VO2a9TOZGi0UOSOI`08Ba91[Gh0QOZOL7];IfD=NG1OY;6eDOO25BW;d0dD9a;l0K5K5M2O2M3N0000O100O010O100O101O2M2O2M2N3N1N3M2N3M1000000000000001O000000000000a0AN1O1O1O1O2M2O1O1O1O2N1O1G[DiNf;V1]DgNd;W1TOgNXFX1h9hNYFV1g9kNZFS1g9mN[FQ1e9oN\\Fo0e9QO\\Fn0d9SO\\Fk0e9UO\\Fj0d9VOcEN6j0X:XO`E18f0W:ZO_E49`0Y:]O[E6<;Z:_OYE9<7[:AVE;?2\\:EQE=b0M]:GnD?e0H^:d0`E\\O`:e0`EYO`:j0^EUOc:l0ZEUOg:m0TEUOm:l0oDVOR;a11O1O1O001O1O1N2005K4L5J5L4L1N2]O\\M\\Fe2m8_MSG1Jb2P9fMoFJNb2Q9lMiFE4`2Q9[1M3FiK]GX4`8<O1O1O00O1PM]GW1d8g13jL_G]1c8^NaG`1b8WMXGk0;n1f8kM`GT2f8bM^G]2i8XMcGa2f9M2O1N2XNPER1P;oNQEn0Q;ROoDm0T;QOmDl0U;TOlDj0S;dNjDb03KM>c:UOnE2_O?5KL=b:WOmE4@=5MK9c:ZOkE5B;6c0^:mNiE7C:5a0_:nNiE8D85<b:UOdE8E847f:YOaE9FU1]:bNiE03<FR1\\:eNiEN3=Ho0\\:QOkE3Ij0[:TOkE4Kf0Y:XOhE7OXOOR1Z:_OfE:57T:@gE;44T:CgE;31W:DfE<3OW:EfE=3MW:l0hESOY:n0gEPOZ:R1dEnN\\:S1dEkN]:V1cEhN^:Y1cEdN^:]1dE`N]:a1cE\\N_:e1bEXN`:h1`EQNOKa:U2hEmMY:S2bEQN^:_21N101OF]MjEc2T:^MnEa2n9bMSF]2i9gMYFX2d9jM]FV2`9lMbFS2\\9nMeFQ2Y9QNiFm1R9XNoFf1o8]NSG`1l8bNUG]1j8dNVG[1k8fNTGY1m8gNTGX1k8iNiFTOOh0J<`9HeF146W9HfFJ;=P9O^F[OMLf0h0Q91[FHb06U9GXF?b0HS9VOZFk1c0mNS9@]F[1?TOS9H[FR1c0SOS9OdF\\OFR1b0BU9f1iFXNX9k1UGdMl8_2o03MeN[E0f:N]E0d:O_ENb:3]ELd:5ZEMc:6[EdNOU1c:4bEhNKV1`:OhEmNHS1`:MjEQOFS1_:JmESOER1_:HmEXOCR1^:DPF[OCZ1S:fNRF49NBW1S:dN\\FN17AW1Q:aNTG9lNU1Y;mNgDQ1Z;oNgDo0Y;SOgDk0Y;XOfDDMj0];EfD_OMl0];HgD9Y;l0010O1O0XObDC_;>bD_O^;c0bD[O^;g0cDVO^;k0cDiNf;c1TO\\N^E1Ke1d:]N_ENLf1e:]N^EMMf1e:^N]ELMg1f:^N[EMMf1g:^N[EMMf1h:^NYEk1f:>O101N1O101N101O001O0101O0O100O1O2OL[M`Ed2_:_M`E`2_:cM`E\\2b:62^M\\EX2g:gMZEV2h:iMZET2g:kMZET2f:mM[EP2g:PNYEo1h:PNYEo1g:RNVEP2k:PNRER2n:70SNREZ1a:XNgEa0HU1W:fNnE8KP1V:bNkEN1c0Nk0V:eNjEl01<T:iNkEm018T:kNkEo014U:mNkEn013T:oNnEm0N2U:POPFm0K1V:oNTFo0F0m:OSENk:6WEDj:`0WE[Oi:i0WEROj:KQEi05WOl:1PEj03POo:7nDl0\\;UOeDl0X;UOhDl0W;TOjDl0T;UOlDQ1m:POSEP1l:QOTEn0m:ROREo0m:SOREm0m:UOREj0n:WOVEd0k:n000DeMgE[2W:gMiEY2V:hMjEX2V:hMjEX2V:hMjEW2W:iMiEW2W:iMgE^1HXOl:j0TEVOk:l0UEROj:h11YOUElNl:U1TEjNl:V1UEhNl:Y1UEaNo:j14M32N101N1O2L4L3001N100O100O2O1N3M2N2N2M3N3M2N3hN[Dc0Q<nNSDQ1U<N2O2N1N2O1N3M2O1N2N2O5Ihh5I_WJ3O1N2O2M2O1O1O01O0O20OMQCOj<MYC5m<2N2N1O2N101_C@R<b0mCAo;b0oCAi;c0XD_Of;b0YDAd;`0[DBd;>[DEc;;\\DGc;S1N2O0017IO2M3N1O2N21VN_Db1b;]N^Dc1c;31O1OO6L2M1000O1N1O2N2N0YN]De1d;[N]Dc1c;_N\\D`1e;aN[D\\1f;fNZDl0b:UORF2FP1W:POkE8Nh0V:POfE>Nh0\\:kN`E02`0N>`:ZOTFS1k9mNUFS1l9lNUFI\\Oo0a:TOTFDZO81o0b:TOSFEZO90o0b:SOVFNYOn0d:QORF3YOm0e:oNRF5YOk0e:PORF_1o9^NRF9XON0?c;0`DO_;GlD0\\;1eD0X;1gDNY;3hDKY;6gDIY;8fDG[;:eDD];;cDD^;=i00O100O02O001O001N1000000O100O100O1Nd4", "size": [428, 640]}, "area": 50867.0, "iscrowd": 0, "image_id": 19109, "bbox": [0.0, 175.0, 639.0, 221.0], "category_id": 183, "id": 20001224}, {"segmentation": {"counts": "]6a8]600000O100000000000000O1000000000000000000O100000000000000000000O1000000000000000000O100000000000000000000O1000000000000000000000000000000O10000O1000000000000O100O1O1O10lIQJY3n5\\LcJ_3]5VLSKe3l4WL_Kc3`4]LfK^3Z4`LmK[3S4cLULW3k3hL[LS3d3lLdLn2\\3QMlLh2T3VMTMd2l2ZM\\M`2d2_McM[2]2cMhMZ2X2eMkMY2V2dMnMZ2R2eMQNY2o1eMTNZ2l1eMWNY2j1eMYNY2g1eM]NY2c1fM_NY2a1fMbNX2^1fMfNX2Z1gMiNW2V1iMlNV2T1hMPOV2P1iMROV2m0jMUOU2k0iMXOV2h0iMZOV2f0iM]OU2b0kM@T2?kMDT2<kMFT29lMIS27kMLT24kMNT22kMOU21iM2V2NiM3W2MhM5W2LfM6Z2JeM7[2JcM8\\2J`M8`2J]M8b2R601O00b0^O1^NVCn0k<PO`Cf0a<YO_Cg0c<VO^Cj0c<UO]Ck0e<RO\\Cn0e<PO\\CP1Z=O00000000006J8H1O001O1OclV3@kSiL2N2N2N3HJdA8[>7N101O001N101PB\\Od=P1O1O0O2O0O100O1000000O100000001O002N3M3M5K1N1000000001O0000000001N100O3N5J7J2M100O101N10000O100000001O2N5K8H00000000000000001O0000000ZEYMT9h2iF\\MNES8^3`GnL:HU8Z3_GPMJA09d8W3aGQMEE37e8T3ZGiMMVOh8g4UG\\Kj8\\500O100000000O1000000000000000000010OO101O0000001N011O0_GjIZ8W6eGiI[8W6dGiI]8X6bGhI^8X6bGhI^8\\600TKeIU1[6kNeIU1[6kNeIU1[6kNeIU1[6kNeIU1[6jNfIV1Z6jNfIV1Z6iNgIV1Z6iNgIW1Y6iNgIW1Y6hNhIX1X6gNiIY1W6gNiIY1V6gNkIY1U6fNlIZ1T6fNlIZ1T6eNmI\\1R6cNoI]1Q6cNoI]1Q6bNPJ]1Q6bNPJ^1P6bNPJ]1Q6bNPJ^1P6aNQJ_1o5`NRJ`1n5^NTJb1l5]NUJc1k5[NWJe1i5ZNXJf1h5XNZJh1g5VNZJi1h5TNZJl1f5SN[Jm1f5PN\\JP2e5nM\\JR2f5jM\\JV2f5gM[JY2g5dMZJ\\2h5`MZJ`2h5]MYJc2i5YMYJf2j5WMWJi2k5SMWJm2k5PMVJP3l5lLVJT3k5jLVJV3l5fLVJY3l5eLUJ[3l5bLVJ^3l5_LUJa3l5]LUJc3m5YLUJg3l5ULWJj3k5QLYJo3i5lKZJT4g5hK\\JX4f5bK^J^4c5^K`Jc4m70000O100001OO100000000O100000000000000O10000O10000O100000000O1000000000000O1001O000000O1001OO100000000000000O100000000000000000000000000O1001O00000000000000O100000000000000", "size": [478, 640]}, "area": 87361.0, "iscrowd": 0, "image_id": 19221, "bbox": [0.0, 139.0, 640.0, 339.0], "category_id": 123, "id": 20001225}, {"segmentation": {"counts": "]Xn33k>1\\ENl4ObG6K0c3Ok4KSG20O=91Ma30n40]G64K^32P52VG5:J]30R53UG5;I[32R5g0bGZOX31U5g0ZGA^3LU5`1iJbNV5`1fJbNZ5_1eJaN[5a1bJ`N^5a1aJ_N^5c1aJ]N_5d1`J\\Nb5b1]J_Nb5c1]J]Nc5d1\\J[Nf5d1ZJYNj5g1TJXNo5i0VGcNNg0l2LR6f0[G]Oc2MS6e0[G^Oa2MU6d0[G_O^2NX6c0ZG^O^20W6c0[G]O]20Y6`0\\GAZ2O[6?[GBY2O^6?YGBX2Mb6`0WGCV2Kf6`0UGEU2Ii6`0TGGQ2Hn6?QGKP2EP7?PGMP2BS7?lF2P2]OW7`0hF5o1[O[7`0eFOV2@W7b0`FNZ2@W7a0_FOZ2_OY7`0_F0W2A[7`0\\F0X2@]7?ZF3X2]O_7a0XF2Z2\\O_7a0XF3X2[Ob7b0UF3Z2ZOb7c0SF4Z2YOd7d0oE4^2VOe7g0iE5b2TOf7i1ZHUNh7j1YHUNi7i1WHWNj7i1f2O100O1O1O1O1O1O2M2N2N2O1N2N2N2Nj_7oNYaH1OO1O^mR4", "size": [478, 640]}, "area": 4637.0, "iscrowd": 0, "image_id": 19221, "bbox": [270.0, 178.0, 89.0, 203.0], "category_id": 170, "id": 20001226}, {"segmentation": {"counts": "0]6a8000000O100000000000000O1000000000000000000O100000000000000000000O1000000000000000000O100000000000000000000O1000000000000000000000000000000O10000O1000000000000O100O1O1O100O100O1O1000000O10000000000001O0000001O0000000000O10000O1000000O1O100O10000000000001O001O2N2N00O1O1N2O1O1N2O1O1N2O1O1O100O1O1O1O100O1O1O1O100O1O1O1O100O1O1O100O1O1O1O100O100O100O100O100O100O100O100O100O100O100O100O100O100O100O100O100O100O100O100O100O100O100O10000O1000jEkKc9T4]FnKb9R4\\FQLc9o3\\FQLe9o3ZFRLf9m3YFTLh9l3WFTLj9l3UFULk9j3TFYLk9g3TFZLl9f3SF]Lk9c3TF^Ll9a3TFaLk9_3TFaLm9_3RFcLm9]3RFdLn9[3RFfLn9Z3QFhLn9X3QFTL2<m9_3QFUL4:l9d3oEPL7:j9e3QFoK6;j9f3ZFQLE4Q:k3aFSL_9m3aFSL_9m3bFQL_9n3bFRL^9n3i0000000001O2VEmKe:V401O1O3M001O1O00O10000O11O1O00O1O100N2L400O11O001O1O001O00001O00001O0eEfKR: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", "size": [478, 640]}, "area": 102050.0, "iscrowd": 0, "image_id": 19221, "bbox": [0.0, 0.0, 640.0, 205.0], "category_id": 172, "id": 20001227}, {"segmentation": {"counts": "`nW12`>a0@`0F;I6I7I8I6I8I8G8H8I7H5L3L4M3L3N3M3L4M2N3L4M3M2M4M2N2M3N2N3M2M3N2N2M3N2N1N2O2N1N3N1O2M2O2M2O1O2M2O2N1N3N1O1N3N1O2N101N1O1O3M2O2M2N2N3M2O2M2N2N3M2O2M2N2N3N1N3M2N2N2O1N101N101N101N101N101N2O1N2O1N2O1N2O1N2O1N2O1N100O100O100O100O100O101N100O100O100O100O100OnFoJY8Q5fGRKX8n4gGUKX8i4gG\\KV8d4iG_KU8a4jGbKT8]4kGgKS8Y4lGiKR8X4mGkKQ8U4mGnKR8Q4nGQLQ8o3nGTLP8l3nGWLQ8h3oG[Ln7f3QH\\Ln7d3QH_Lm7a3RHaLm7^3SHeLk7[3THgLk7Y3THjLj7V3UHlLi7T3WHoLg7Q3XHQMg7o2XHTMf7k2ZHWMe7R5O1N2O1O1O1O1O[IeHi5X7SJmHn5R7QJoHP6P7nIRIS6m6kIUIV6j6hIXIY6g6eI[I\\6d6cI]I^6a6aIaI`6^6^IdIc6[6[IgIf6X6XIjIi6U6VIlIk6S6SIoIn6o5QISJP7m61O1O1O1O2N1O2N1kLfHdN[7Y1hHfNZ7W1gHiNZ7T1hHlNZ7P1iHoNX7o0iHQOX7l0kHSOW7i0kHWOV7f0mHYOU7d0lH\\OU7a0mH_OU7=nHBS7<nHDS7;nHDT7:lHFU79lHFU78lHHU77lHHT78oHER7:RIB^4_MkLm2hNDZ4cMmLi2gNFZ4dMoLe2eNIY4fMRM`2cNLX4hMTM[2cNOV4jMWMV2bN1T4lM[MR2_N4S4nM]Mn1^N6R4PN`Mh1]N:P4RNcMc1[N=m3VNgM]1[N>j3ZNkMW1YNa0h3]NoMP1XNe0e3`NRNk0WNg0g3_NRNi0UNj0i3]NSNh0SNl0j3]NRNf0SNo0k3\\NRNd0QNR1m3[NRNb0PNT1n3[NQNa0PNU1o3[NPN?RNV1o3[NnM?RNW1P4[NmM>SNW1P4\\NlM=TNW1P4\\NlM=SNX1Q4\\NkM=SNW1R4]NjM<SNX1S4]NiM<SNW1T4^NhM;TNW1T4_NgM;SNW1V4_NfM:TNW1V4`NeM:SNW1X4`NdM9TNW1Y4aNaM9UNV1Z4bN`MR4`2oK_MQ4a2PL^MP4b2QL]Mo3c2RL\\Mn3d2SL[Mm3f2SLYMm3g2TLXMl3i2TLVMl3j2VLSMk3m2VLQMk3P3ULnLl3R3ULmLk3T3ULjLl3V3ULhLl3Y3TLeLm3[3TLcLm3]3j31O001O00001O0QH^Kb5c4\\JbK`5^4^JhK^5X4`JoK[5R4cJSLY5n3dJWLY5i3fJ\\LV5e3gJaLU5`3iJeLS5[3mJhLP5Y3oJhLP5X3oJiLQ5X3nJhLR5Y3mJgLS5Y3mJhLR5Y3mJgLS5Z3lJfLl0^Nf1l4]MgLm0]Nf1m4\\MgLm0\\Ng1n4[MfLm0^Ng1l4\\MfLm0^Ng1m4YMgLo0]Nh1m4UMjLR1YNi1m4SMlLT1WNi1n4PMmLV1VNj1n4mLnLY1TNj1n4jLRM[1PNk1o4gLSM]1PNk1n4eLUM_1mMl1n4cLWMa1kMl1o4`LYMd1gMl1Q5]LZMg1eMl1S5YL[Mj1bMm1V5TLZMo1`Mm1Z5oKXMS2^Mn1V8RNkGm1V8RNjGn1V8SNiGm1X8RNhGn1Y8QNgGo1Y8QNgGo1Z8PNfGP2Z8PNfGP2[8oMfGP2Z8PNfGP2[8PNdGP2\\8PNdGP2]8oMcGQ2^8nMbGR2^8nMbGR2^8nMbGR2_8mMbGR2^8nMbGR2^8oMaGQ2_8oMaGQ2_8PN`GP2a8oM_GQ2a8PN^GP2b8PN^GP2b8QN^Gn1b8RN^Gn1c8QN]Go1c8RN\\Gn1d8RN\\Gn1d8SN[Gm1e8SN[Gm1f8SNZGl1f8TNZGl1f8UNYGk1g8UNYGk1g8V21O000000001O00000000001iK]GQ2c8kMaGU2_8iMcGW2]8gMeGY2[8dMhG\\2X8bMjG^2V8`MlG`2U8\\MnGd2R8XMSHg2m7VMVHj2j7RMZHn2f7oL]HQ3c7lL`HT3`7hLdHX3]7dLfH\\3Z7aLjH^3V7`LlH`3T7]LoHc3Q7ZLRIf3n6XLTIh3l6ULXIj3n8000001O000000001O000000001O0000001O000000001O000000001O000000001O0000001O0000000000O1000000000000000O1O100O1O100O1O10O01O100O1O100O1O100O1O1N2O1N2O1N2O2M3N1N3N3M3L4M3L4M3L4M3L4M3L3N2M4M2M3N3M2M4K4L4L5K4K6KTfX1", "size": [478, 640]}, "area": 93904.0, "iscrowd": 0, "image_id": 19221, "bbox": [85.0, 128.0, 467.0, 347.0], "category_id": 183, "id": 20001228}, {"segmentation": {"counts": "o3X6g8VMmjg4cL[[XK3M000000O10000000000000000000O1000000O1000000O1000000O1000000O1000000O10000002N00000000000000000000000000000O10000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000O10000000000000000000000000000000000000000000000000000000000000000O10000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000lMT2H800", "size": [480, 640]}, "area": 2016.0, "iscrowd": 0, "image_id": 19402, "bbox": [0.0, 0.0, 640.0, 480.0], "category_id": 105, "id": 20001229}, {"segmentation": {"counts": "0k31XLP;k31L4O1O1O1O100O1O1O1O1O100O1O1O1O1O100O1O1O1O1O100O1O1O1O1O1O100O1O1O1O1O100O1O1O1O1O100O1O1O100O10000O10000O100O10000O10000O10000O100O10000O10000O10000O100O10000O10000O10000O10000O100O10000O10000O10000O100O10000O10000O10000O100O10000O10000O10000O100O10000O10000O100001O00001O001O001O001O001O001O001O001O001O00001O001O001O001O001O001O001O001O00001O001O001O001O001O001O001O001O00001O001O001O001O001O001O001O001O001O00001O001O001O001O001O001O001O001O001O1O001O1O1O1O1O001O1O1O1O1O001O1O1O1O001O1O1O1O1O001O1O1O1O1O001O1O1O00N2M3N2N2N2M3N2N2M3N2N2N2M3N2N2N2M3N2N2N2M3N2N2M3N2N2N2N2M3N2N2N2N2M3N2N2N2M3N2N2N2N2M3N2N2N2M3N2N2N2N2M3N2N200000000000000000001OonS2NSQlM0000000000001O1O2N1O1O1O1O1O1O1O1O1O1O2N1O1O1O1O1O1O1O1O1O2N1O1O1O1O1O1O1O1O1O1O2N1O1O1O1O1O1O1O1O1O2N1O1O1O1O1O1O1O1O1O2N001O0000001O0000001QHQNk2o1TMSNk2m1TMTNl2l1SMUNm2l1QMVNn2j1PMXNP3h1oLZNP3f1oL[NQ3f1mL[NS3e1kL^NT3b1kL_NU3a1jLaNU3`1iLaNW3_1hLbNX3^1fLeNY3[1fLfNZ3[1cLhN\\3X1cLiN]3W1aLkN_3U1`LmN_3T1^LnNb3R1]LoNc3Q1[LROd3n0ZLTOf3m0XLUOg3l0VLVOj3l0SLUOm3n0nKUOQ4m0lKTOT4n0hKUOW4m0fKTOZ4n0bKTO^4n0_KTO`4o0[KSOe4o0WKSOi4o0TKSOk4o0QKSOo4o0nJSOQ5o0kJSOU5P1gJQOY5Q1cJRO\\5P1^JTOb5n0XJWOg5k0SJYOm5i0mI[OS6h0fI]OY6e0aI_O_6c0[IBd6`0VIDj6>PIFP7<kHHT7R40]NmHYKS7e4SIXKl6i4TIVKl6j4TIWKk6i4UIWKk6i4VIVKj6j4VIWKi6i4XIVKh6i4_IRK`6n4aIQK_6U4mHaKb0;a6T4mHaKb0<`6S4nHaKb0<`6R4oHbK`0>`6j3QIPK1g0>?`6j3RIoJO4O>?f0a6h3WITKJ=>h0`6g3YIRKK3LL2ON0b0Y1^6g3YIQKL00Oc0Y1X6g3YIQK4O:[1X6e3ZIPKn0[1h5d3dJ]L[5c3eJ]L[5c3hJZLX5f3hJ[LW5e3lJYLS5g3QKVLn4j3UKULi4l3XKSLg4k3[KVLd4g3_K[L_4e3aK\\L^4a3eK`LZ4_3hKaLW4]3kKeLS4[3mKfLR4Y3oKhLP4X3PLjLn3U3SLlLl3T3TLmLk3S3ULnLj3R3VLPMh3P3YLPMf3P3ZLg0OYO1g0O", "size": [480, 640]}, "area": 44335.0, "iscrowd": 0, "image_id": 19402, "bbox": [0.0, 0.0, 640.0, 400.0], "category_id": 173, "id": 20001230}, {"segmentation": {"counts": "k31b8`2kNR5^Lb3O1O1O1O100O1O1O1O1O100O1O1O1O1O100O1O1O1O1O100O1O1O1O1O1O100O1O1O1O1O100O1O1O1O1O100O1O1O100O10000O10000O100O10000O10000O10000O100O10000O10000O10000O100O10000O10000O10000O10000O100O10000O10000O10000O100O10000O10000O10000O100O10000O10000O10000O100O10000O10000O100001O00001O001O001O001O001O001O001O001O001O00001O001O001O001O001O001O010O001O00001O001O001O001O001O001O001O001O00001O001O001O001O001O001O001O001O001O00001O001O001O001O001O001O001O001O001O1O001O1O1O1O1O001O1O1O1O1O001O1O1O1O001O1O1O1O1O001O1O1O1O1O001O1O1O00N2M3N2N2N2M3N2N2M3N2N2N2M3N2N2N2M3N2N2N2M3N2N2M3N2N2N2N2M3N2N2N2N2M3N2N2N2M3N2N2N2N2M3N2N2N2M3N2N2N2N2M3N2N2000000000000000000000000000000O10000000000000O1000000O1000000O1000000O1000000O1000000O1000000O100002N000000000000TIIoN7R1ImN7U1IiN7X1IgN7Z1JdN6^1J`N6a1J^N6d1JZN6g1JXN5j1LTN4n1LPN4Q2LnM4T2LjM4W2LfLNlK6_7MbLMPL6`7M\\LMVL6_7MXLNZL5_7NTLM^L5`7NnKMdL5_7NkKMgL5`7NeKNlL4`7b0mJ[OdM3`78TKD^M4`7JeH7m0LoN3W94`GJZO2Z98SGEE3]9<cFB12`9`0UF_O<1d9d0fEZOh02g9g0WEXOS11j9k0iDUO^10n9k0]FUOg<000000000000000000000000000000000000000000000000000000O1000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001O1O2N1O1O1O1O1O1O1O1O1O2N1O1O1O1O1O1O1O1O1O1O2N1O1O1O1O1O1O1O1O1O2N1O1O1O1O1O1O1O1O1O1O2N1O1O1O1O1O1O1O1O1O2N001O0000001O00000WHmMe2T2YMmMg2S2XMoMg2Q2XMPNh2P2WMQNi2P2TMSNk2m1TMTNl2l1SMVNl2j1SMWNm2j1QMWNo2i1oLZNP3f1oL[NQ3e1nL]NQ3d1mL]NS3c1kL_NU3a1jLaNU3_1iLcNW3^1gLdNX3\\1fLfNZ3Z1eLgN[3Y1cLjN\\3W1bLjN^3V1`LlN`3T1_LnN`3R1^LPOb3Q1\\LQOc3Q1YLQOg3Q1VLPOj3R1RLQOm3Q1oKQOQ4Q1lKQOS4Q1iKQOW4R1eKoN[4S1aKPO^4R1_KoNa4S1[KoNe4S1XKoNg4S1UKoNk4T1QKnNn4T1nJnNR5T1kJmNU5U1eJPOZ5R1`JRO`5P1[JTOd5o0UJUOk5m0oIWOQ6k0iIZOV6h0dI\\O\\6f0^I_Oa6c0YIAg6b0RIBn6Y4O00001O001O00001O001O00001O001O00001O00001O001O00001O1O1O2N1O1O2N1O1O1O2N1O1O2N1O1O2N1O1^Khi0", "size": [480, 640]}, "area": 257604.0, "iscrowd": 0, "image_id": 19402, "bbox": [0.0, 1.0, 638.0, 479.0], "category_id": 183, "id": 20001231}, {"segmentation": {"counts": "0g4Y:bK]F?D]OKW2T:mM]F>04@?S:oN]F>02D?o9QO]F=12E`0l9QO^F=12E`0l9RO]F<2NJc0g9TO\\F;3LLe0e9TO[F<4JMf0d9TO[F<4JMe0e9UOZFNCH00b0>Mg0d9UOZFLMJ7>Ne0f9WO]FFa0>\\Oe0\\OmNX:;hG3cNh0e9VOgG2cN=C[OR:7gG1cN<HZOn98hG2aN<KWOn9;fG2aN<W:BXG2aN;MXOk9;gG2aN;OWOh9<gG4aN9Y:CVG4aN91WOf9<RGD\\O`0M7W:F^FD^O?O5U:ImED^O001a0=04T:JlE40N32P:LkE62M4NP:OiE73L5Mo9OjE82L8Im93iE82L:Dn97gE91L<@n9;eE91L];KbD91L];KbDGB8>7^;IcDFJ08a0[;IcDEd0b0i:IcDEd0b0i:ImEFd::Q200000000000QBEY=;gBFX=:hBGW=9iBI_OLa=;PC0_OH0MR=;_C=a<C_C=a<C_C=a<C_C>`<B`C>`<B`C>`<B`C=a<C_C=a<C_C=a<C`C;a<ElCNT<2nCLR<3PDLP<4RDJn;6SDIm;7^100000000000000001RBEU=;iBNP=3oBOo<2oBKU=6iBD^=>a03dBAnNMP=c0SD\\OR<b0W1N2O1O100O100O1000000O10000001O000000000000000000000000000000000mADb=<]BEc=;]BDd=<`00lADd=<[BDf=<ZBDf=<?000jADh=<>0iADj=<WBDh=<YBDf==>001O1O001O1PF@^5?_JEa5;\\JHd57YJLh53UJ1k5NSJ5m5KoI9Q6FlI>T6BiIa0W6_OfId0Z6\\OhIb0X6^OjI`0V6@lI>T6BnI<R6DoI;Q6EQJ9o5FSJ9m5GUJ7k5HWJ7i5IYJ5g5K[J3e5M\\JNh52ZJHj58WJHh58ZJIc57_JFb5:bJGY59fJHZ58hJFX5:g400O1O10000000000000000000000000000001O001O1O001O1O1O2N5K00000000MhA_OW>a0WB^O\\=b0dB^O\\=b0a0O10000000000000000O1000000000hB@P<`0PD@P<`0PD@P<`0PD@P<`0PD@P<?QDAo;?QDAo;?QDAo;?QDAo;?QDAo;?RDAm;?SDAm;?SDBl;>TDBl;>TDCk;>TDBl;>TDBl;>UDAk;?UDCi;=WDEg1Mm6>\\GI_1MU7:\\GI[11Y76SG5^1H_73jF5P2HV73bFNg2Og64XF1V3Kb6j0`IVO`6g0cIYO]6=YFXO[3;\\6<nIDR6;oIEQ66TJIm56TJJl55UJKk54VJKk54VJLj53\\GB_1;U72RGNj1OWMFj9<TG1l1KWMHi9;RG5n1HUMJk99QG6o1KP7OoF8R2Ho60mF:T2Fo60mF:T2Fo60mF:U2En61lF;W2Cm62lF;W2Cm62lF;W2Cm62lF;W2Cm62lF;X2Bl64kF:Z2Ak65kF:Z2Ak65kF:[2@j66kF:[2@j66kF:[2@j66kF:\\2_Oi67kF:\\2_Oi67kF:^<FbC:^<FbC:^<FbC:^<FbC:^<FbC:^<FaC;_<EaC;_<EaC;_<EaC;_<EaC;_<EaC;_<EaC;_<EaC;_<EaC;_<EaC<^<DcC;]<EcC<\\<DdC<\\<DdC=[<CeC=[<CeC=[<CeC>Z<CdCEU=<g000001O1O2N1ON2O100000000000000000000000000000000000000O100000000000000000000000000000000000000001QFCY5>VJIWLGc9a0c4001O1O1O0RF^O^5c0]JAc5f0QJ_Oo5j0gIWOY6R1aFaNa2=n6^1RIbNn6^1RIbNn6Z1VIfNj6S1]ImNc6m0cISOkLCY9T1RJBn58XJHh55[JKe51_JOa5NbJ2^5McJ3]5LdJ4\\5KeJ5[5KeJ6Z5IgJ7Y5HhJ7Y5HhJ9W5GiJ9W5GiJ9W5GiJ9W5GiJ9W5GiJ9W5GiJ:V5FjJ8X5HhJ8X5HhJ8X5HhJ8X5HhJ8X5HhJ9W5GiJ9W5GiJ9W5GiJ9W5GiJ9W5GiJ9W5GiJ9W5GiJ9W5GiJ9W5GiJ9W5GiJ9W5GiJ9W5GiJ9W5GiJ9W5GiJ9W5GiJ9W5GiJ:V5FlJ7U5InJ5Q5KRK2n4OTKNl42XKKg45VKMk43lJ6T5JeJ>Z5BbJa0_5_O`JM[LNU95fJA[L:o85dJC]L9n84eJC^L8m85fJB]L8n87WKIi48_K_Oa4b0eGFnNIj<a0oCKY<5]C5c<KSC?m<j00DVCbNj<S1aCmN_<c0QD]Oo;>VDBj;>VDBj;>VDBj;=WDCi;=WDCi;=WDCi;=WDCi;=WDCi;<YDCg;=YDCg;=YDCg;=YDCg;=YDCg;=YDCg;=YDCg;=YDCg;=YDCg;=YDCg;=YDCg;=YDCg;=YDCg;=YDCg;=YDCg;=YDCg;=^100000000000000000000000000000000001]DG]89RF=[OVOc:=RF<j:EUE:l:FUE8l:HTEH\\;9cDF^;:bDE_;<`DCa;=_DBb;>]DCc;?YDBh;`0SDAo;b0jC]O[<]1UDhN\\:Q1jEPOV:h0PFZOP:?oETO^N`0c;7SFOm9OUF1k9MWF3i9KYF5g9I[F7e9H\\F8d9H\\F8d9I[F7e9I[F7e9I[F7e9I[F7e9I[F7e9I[F7e9I[F7e9I[F7e9I[F7e9I[F7e9I[F7e9I[F7e9I[F7e9I[F7e9I[F7e9I[F7e9I\\F6d9J\\F6d9J\\F6d9J\\F6d9J\\F6d9J\\F6d9K[F5e9K[F5e9KZF6f9JYF7g9IYF7g9IYF7g9IYF7g9IYFOo91QFHV:8Y200000000000000000000001O00003M2kE[OmK1h9g0XJZOoKNi9k0XF[Ob3NX6h0RJWOo5?[JAe5>\\JAe5?[JAe5?[J_Og5a0YJ^Oh5b0XJ^Oh5b0[40000000000000WF_OS5a0lJ@T5`0lJ@T5`0kJAU5>lJBT5>jJDV5<iJEW5;iJEW5;hJFX5:hJGW59gJIY57fJJZ56fJJZ56fJJZ56eJK[55eJK[55fJJZ56fJJZ56fJJZ56fJJZ56fJJZ56fJJZ56gJIY57kJEU5;oJAQ5?h40000000000000000000000001O2mCAa9c0RFFn95QF1o9NSF1m9NUF1UO_O_9?]G2oNEc99_G1lNJc95aG1lNO^90fG1lNO^9OgG2kNO^9OaFE4=MO^9OaFF3<N0]9NbFKN720_9N`FLN73O_9N_FMO63O_9N]FO143OB@\\9=mF22230_OB^9:nF22230_OD\\98PG22230_OD\\98PG22221@C\\98PG22230_OB^9:nF22230_OB^9:nF22230_O_OE0i9=nF22221@]OF1h9=nF22221C^O[9=nF22212D\\O\\9=nF31221C]O\\9=nF31221C]O\\9=nF31220D^O[9=nF3122Oa9L\\F31210b9K\\F31210b9K]F20310b9K^F1O410b9K_F0N56K]90_F0N57J\\91_F0N58I[92_F0N59HZ93_FN077HZ94^FL286HZ95]FJ495HZ95\\FJ694HZ95[FK784HZ96XFL:64HZ97TFN>33I[97SFM?32J\\9", "size": [480, 640]}, "area": 19711.0, "iscrowd": 0, "image_id": 19432, "bbox": [0.0, 0.0, 640.0, 178.0], "category_id": 123, "id": 20001232}, {"segmentation": {"counts": "[6e8[6000000000000000000000000000001O0000O10000001OO1N21O00000000002N0000000000000000000000000000O100N2O1N2M300000PNnIRKR6k4RJTKn5h4XJVKh5g4\\JXKd5e4`JZK`5c4dJ\\K`5]4eJaK`5W4dJhKb5o3bJPLd5g3`JXLe5`3^J`Lg5X3\\JhLi5P3ZJPMj5j2XJVMm5b2WJ]Mn5[2UJeMm5V2VJjMl5Q2WJoMk5l1XJTNj5g1YJYNi5b1ZJ^Ni5]1YJcNi5X1ZJhNh5S1[JmNg5n0\\JROf5i0]JWOe5d0^J\\Oe5>^JBd59_JGc55_JKc50`J0b5KbJ4`5GcJ9_5BdJ>_5\\OdJd0b5SOaJm0i5gNYJY1g900000000000000000001O00B`N\\C`1c<bN\\C^1d<cN[C]1e<dNZC\\1e<fNZCZ1f<gNYCY1f<iNYCW1g<jNXCV1g<lNXCT1h<lNXCT1g<nNXCR1g<POXCP1c<VO\\Cj0_<n0001O000000001O000000001O1O001O001OjNUNXEj1f:[NWEe1g:^NYEa1e:bN[E]1b:fN^EZ1`:iN`EV1^:mNaES1[5nMoNQ1gKP1[5RNUNK\\LV13m0\\5UN]JJP3>fLi0`0j0^5\\NSJGR3l1]M1]5lNoLX1cML^5nNiL[1hMG_5QOcL[1nMD_5SO]L^1SN_O`5VOWL_1YNZO_5ZORL`1_NVO_5]OlKa1dNRO`5_OhKb1gNoNa5AcKc1lNlNa5AaKf1mNiNb5A_Ki1nNfNc5AXJaNg0[3^OcNe5@SJiNc0W3D`Nh5^OPJPO=V3J\\Nj5]OnIWO7R30[Nm59lI_16XNP6W4oIiKS6U4mIkKU6T4jIlKW6T4gImKZ6R4fInKZ6S4eImK[6S4eImK[6T4dIlK\\6T4cImK]6T4bIlK^6[6000000000000001O3M3M3M3M0000000000O100000000000000O1000000000000O1000000N2000000000000N2O1NeG_I[8a6eG_I[8a6200O1O100000000000000001O001O001O1O000000NeG_I[8a6eG_I[8a6eG_I[8a620000001O000000N21O00O100000000001O1O1O0000001O1fG[IW8g601O000000001O00KkG^IV8a6lG_IS8a6mG]IU8c6kG]IU8a6iG_I[8a62000000000000O100000000O1001O001O000000O1NbGbI^8^62O1N2N2N20000O1001O00O11O1O2N1O1fIjIh3Z6QLlIl3U6QLnIn3R6QLPJn3P6RLPJn3P6QLQJo3P6PLPJP4Q6nKPJR4P6nKPJR4P6nKQJQ4o5oKQJQ4o5oKPJR4P6nKPJR4P6nKPJR4P6nKoIS4Q6nKnIR4R6nKnIR4Q6PLnIP4R6PLmIQ4S6PLlIP4T6RLiIo3X6RLeIo3[6SLaIo3^6Z20000001OO100000000000000000000001O00O1001O00O100000000000000001OO10000000000001O0000001OO100000000001O001O0000001O1O0000001O1O001O00001O1O0000001O1O001O0000001O1O0000001OAUHjIj7W6UHiIk7X6UHhIj7W6XHhIh7X6XHhIh7X6XHiIg7W6YHjIf7W6ZHhIf7W6\\HhId7Y6[HgIe7Y6[HhId7X6]HhIb7X6^HhIb7W6`HiI_7X6aHgIOIT7`6mHgINKT7]6oHhILMT7[6PIiIJNU7Y6RIVJn6j5RIVJn6j5SIUJm6o600000O1O1O100N2O100O100001O001O0000001O1O0000001OBhHUIW7k6iHUIW7k6jHUIU7k6lHTIT7l6lHTIT7l6lHUIS7k6mHVIR7j6oHUIQ7k6PITIP7l6PITIP7l6PIUIo6_7O00001O00001O1O0000001O1O001O00001O1O0000001O1O00001O00001OBZIcHe6]7[IcHe6]7\\IbHd6^7\\IbHd6^7\\IcHc6]7^IcHa6n701O00001O1O0000001O1O00001O001O001O0000001O1O001O00001O1O0000001O1O00001O001O001O0000001O1O00001O001O001O00001O1O00001O001O001O0000001O1O0000RO\\J`Hd5P7jJRIV5n6eJWI[5i6dJXI\\5h6_J\\Ib5d6[JUHOW1g5n72K5N2L4N2K5O1K5N2L4`NYIYI0Z1i6g64UNYIkILX1P7l4XIUKi6k4SITKR7l4lHTKV7W64N2L4N2K5N2L4KoGXIT8g63K5O1LfGcIY8]6dGiIY8_601O1O000000000000000000", "size": [480, 640]}, "area": 146721.0, "iscrowd": 0, "image_id": 19432, "bbox": [0.0, 192.0, 640.0, 288.0], "category_id": 145, "id": 20001233}, {"segmentation": {"counts": "g4d1\\=000001O00000000O100N20[NgBc1[=01O00N3N12N1N101O0aNmBQ1c=O0N1N30O00000000002M1O1O1N2L40000000000O11O00000O010N100O2M30003M7I7I5K6J6J[ZP15\\eoN7J6K4N3M3L4M3M3M3M3M2N^OcBDZ==iBBT=>oB_OQ=b0VCUOk<j0f000000000O1000L4L41O3MO3OmNVBP1l=1O2O10000O01O1N2N2N1O2N2N4L3M3M3M4L3M3M3N[N3gBMg>8O1O1O1N1O2N2N2N2N2O0O2N2O1N2N2N2O0O2N2N2N2N2N2N2O2M2N1O00000001O000N5L1N_BeN`=\\1`BdN^=^111N1O101UOiNlCEG]1Y<UOTDj0l;PO[Do0e;iNdDV1e<N00L401O001O001O1O000000NbBbN^=^1bBbN^=^1bBbN]=_12000O101O00`NcB]1\\=dNdB\\1\\=bNfB\\1`=1O00O100000000001O1O1O0000001O1bB_N[=c1K6O0001O1O01O0CXNcCb1D`Nj<NbCa1FaNg<NhC\\1AdNi<0fC\\1AdNi<0fCZ1_OfNb=Z111000000001O0O101O001O0O10100O01O01O001N1NUBoNl=P12O1N2N3M3O000O2O010O0O20O1O2O000O4L1O01O1O0100O000001O1O0000001O1N101OB[OaBe0_=[OaBf0^=ZOcBe0]=[OdBc0]=^ObBb0^=_OaBa0_=_OaBa0_=@aB`0^=@cB>^=BbB>^=CbB<^=DbB<^=EaB;_=EaB;_=EbB:^=GbB8^=HbB8^=IaB7_=IaB7_=JaB5V>0O01N10010O1N1000001O1O001O0001OO2O1O0000BLPB4Q>LoA3Q>MPB3o=MQB3o=NQB1o=0PB0P>0PB1n=0SBNo=1RBNm=4RBLo=4PBLP>4PBLP>4>1O01OVjS3MXgkLO[>;0000N200MCcA=\\>DdA<[>EeA;[>EdA<\\>DdA<\\>DdA<\\>DdA8`>12M4N2LaBNjN0Y=0mC2gN4`>8N3K5M2K5N2L4K5O1K5O1K5mJ", "size": [480, 640]}, "area": 11606.0, "iscrowd": 0, "image_id": 19432, "bbox": [0.0, 126.0, 640.0, 92.0], "category_id": 172, "id": 20001234}, {"segmentation": {"counts": "nmi04n7MjN?l0EnNg0g0_OoMFkKT1n5ZOTNBkK_1h5SOYN@kKh1c5lN_N\\OkKS2]5fNdNXOkK\\2Y5`N`NAPLZ2W5YN`NIPLY2W5RNhNU3P1nLoNX3i0kLVO[3b0hL]O]3=eLBa36bLId3O_L0d3K_L4d3G_L9c3B`LKQNUMb5k2`LJSN[M`5f2`LHWNaM]5b2^LF\\NgMY5^2_LD]NnMW5Y2_LD^NSNV5T2_LG[NVNY5n1_LJYNXN[5i1_LMVN[N_5b1^L2SN[Nc5^1]L6QNZNf5\\1[L:nMZNj5W1[L>lMYNm5T1ZLc0hMXNR6P1YLg0fMXNT6l0YLl0cMVNY6h0WLR1_MUNb6`0RL[1\\MTNm65iKf1ZMUNm:k1REUNo:k1QESNQ;m1oDRNS;n1kDRNV;n1n00000000000000BSNiCm1W<TNhCl1W<VNhCj19RN[;5[Di19SN[;6\\Df19SN\\;8ZDe19TN\\;9ZDc19TN^;:XDb1:TN];<XD`1:TN_;<WD`19UN_;=VD_1;TN\\;a0XD[1<SNX;h0[DU1=SNV;j2jDUMW;k2iDUMW;k2hDVMX;j2hDUMY;k2fDVM[;i2eDWM[;i2eDXMZ;h2fDXMZ;h2gDWMZ;i2gDVMX;j2?1YDUMQ43d2h2gM]MW2c2eMdMY2]2]ITMg3f0j2V2\\IVMg3j0j2]2QMjMg1XOXKm2m2RNf1VOZKh2m2XNd1TO^Kd2i2_Nd1POcK`2e2gNc1lNgK^2b2lNb1hNmK[2]2TOa1dNQLX2Z2ZOa1aNULU2Y2]O\\1aN[LR2[2^Ob0cMhLP1;P2[2^O;ZOYM\\1Z2[O6]O`M]1W2WO2AfMR2_1^Ne0EkMW2X1UNf0HRN]2o0lMi0LWN\\2n0iMd00]N[2l0fMc01aN]2j0cM`03eN^2i0_M=7iN^2g0[M=9lN^2g0WM:?nN\\2g0TMTOVMk0]3ZOZ2g0QMRO_Me0Y3BY2g0nLPOeMa0W3FY2i0iLoNmM;T3LX2i0fLnNUN5P34W2i0`1VOaNk0^1SOeNm0Y1SOiNm0V1ROlNm0T1ROcNY1[1gN_Na1`1]NZNl1d1TNjM`2U2_MiMd2V2\\MgMi2X2VMeMo2e6d0I6J6K5L5L3L4M3L\\NmEaNo9`1RFaNj9a1WF`Nf9a1[F`Nb9`1`F]Na9c1aF[N_9e1cFYN]9h1cFWN]9i1eFUN[9k1gFSNS9jN\\FS3c0QNn8oN_FP3e0oMj8SOaFo2f0kMh8YOaFl2h0jMn7ROkG;_Oi2j0hMl7YOdG8Fg2l0fMj7DXG22e2m0cMi79ZGT2o0aMf7<[GS2Q1_Md7>[GS2S1]Mb7`0[GT2S1ZMc7b0ZGT2U1XMa7d0ZGT2W1VM_7f0ZGT2Y1TM]7i0YGS2\\1RM[7k0YGS2_1oLX7n0YGS2a1mLU7Q1ZGR2d1iLS7U1YGR2^:nMbER2^:nMbER2^:nMbES2]:mMcES2]:mMcES2]:mMcES2]:mMdES2[:nMdEU2X:lMhES2Y:mMgEQ2[:oMeEP2\\:PNdEn1^:RNcEl1^:TNbEj1`:VN`ES1OYNb:d0`Ej0=YNBJ[:T1fEHb1^Og8j0gEGZ1Io8?hEGQ13V96iEEj0`0[9KlEZOk0P1\\9FjEQOi0_1`9AfEiNi0j1e9]OZFi0i9ROTFR1P:fNoEb1R:UNnET2R:cMSFa2V;00000000000000000001O00000000000O10000000000000000000000000000000000000000000000000VD\\MX;d2bDbM^;^2\\DhMd;j200001O0O2E:]OQDTNQ<Z1\\DjNd;e0hD_OY;0TE3n:[OUES1R:Y2J7K400O2O001O1N2O100000000000000000000000000000001O000VISLa2l3]MZL_2g3_M\\L`2d3_M]La2c3_M]La2c3^M_La2a3_M_La2a3^M`Lb2_3_MaLa2_3_MaLa2_3_MaLa2_3_MaLa2_3_M`Lb2`3^M`Lb2`3^M`Lb2_3`M`L`2Q4oLnKR3R4oLmKQ3T4nLkKS3U4oLiKQ3X4PMfKP3Z4RMaKQ3`4h3D<[OoEbLi9R3VFZMj9`2PFmMP:m1hE`NY:X1bEUO^:d22K7J>B=G:eLXDj2o;000L4I7J6J6J6M3L4M3O1O1O100O1O1O1000000000000000000000000000000000000000000000TMYE_1g:aNYE_1g:]10000000UMYE`1d:a1M3N2M4LH8G9G9G9G9G9G9GVDnMX;i1gDkNP;k0oDHi:DPEmN5T2b:eNgEn1P:iMQFf2h9PMbFP3^9gLkFY3^:0DmLkDS3j:YMUEg2[:iMeEW2V:nMjER2V:nMjER2V:nMjER2U:oMkEQ2U:oMkEQ2U:oMkEQ2U:oMkEQ2U:oMkEQ2T:PNlEP2T:PNlEP2T:PNlEP2T:PNlEP2T:PNlEP2T:PNlEP2T:PNlEP2T:PNlEP2T:QNkEo1U:QNkEo1U:QNkEo1U:QNkEo1U:QNkEo1U:QNkEo1U:QNkEo1U:QNkEo1U:QNkEo1U:QNkEo1U:QNkEo1U:QNkEo1U:QNkEo1U:QNkEo1U:QNkEo1U:QNkEo1U:^100000000000000000mNT1_N`1XOh001O00001O001O01OM5L5J6_O^BGR>6J7H7J6L5N2N2N2N2O1001O0000000000000000000000000000000000000000010O000000000000000000dEiNo6W1hHROX7n0^H\\Ob7d0THFl7:jG0V80aG9_8HVGb0j8_OkFk0U9UObFn0d9SOQFQ1U:oNaEY1a:c11O001O3fL]EQ2e:dMdE\\2_:XM`ER3m::F:F:O10000000000000001O0000000000000O1000000000000000000000000000000000002N4L4L3M1O1O001OI7G9H8G9H8G9H8G:gNiC_Oc<I^CJ12m<HQDO]od0", "size": [480, 640]}, "area": 80385.0, "iscrowd": 0, "image_id": 19432, "bbox": [55.0, 12.0, 541.0, 419.0], "category_id": 183, "id": 20001235}, {"segmentation": {"counts": "0[W31fhL80M0KQ;d0O4POYOhFh0U9]OiFc0V9^OTFIb0k0X9UOVF9a0b0Z9AdF`0n8\\OfF6:b0m8LhFTON21R1W9Y1O001N101O0O112M1O00000000000000000000000000001O02N000O100000000000000O0101AdMbG\\2^8fM_G]2_8eM_G[2a8=00000O1001OOO22N00001O0UM^Ge2h82N3M001O00O2O001ZM_GNLIOR2f8oMhGOB08Q2^8PNVHn1k8N2N3L8ZNlEO0[1\\:M0O5L4K100BZEKh:4aEAa:;XEES;:5O101N1M3Mbgi08VXVOR1oNQ1oN<D4L4L4L4K5L4L4L4L4L4K5L4N2M3M3N2M3M3N2M3N2M3M3N2M3M3N2N2M3N2N2N2M3N2N2N2M3N2N2N2M3N2N2Mj0WO6J6J5J7J5K1O0O1000000O1000000O10000O100001O000000000001O00000000000000000000000000000000000001O0000000000000000000000000000001O01O000000L4K6XNeLmJa3R5cLeJc3[5`L]Je3b5`LTJf3l5]LkIi3T6m001N1O100O100O1O2O0O100O1O2L4M2N3L4M2M4M3M2M4M3M3L5L4K5L4L4K5L4L4K5L4K5L4L4K5L4K5L4L4Knej1", "size": [374, 500]}, "area": 145804.0, "iscrowd": 0, "image_id": 19742, "bbox": [0.0, 0.0, 500.0, 374.0], "category_id": 177, "id": 20001236}, {"segmentation": {"counts": "Tnn1:Y;R1oNQ1oN<D4L4L4L4K5L4L4L4L4L4K5L4N2M3M3N2M3M3N2M3N2M3M3N2M3M3N2N2M3N2N2N2M3N2N2N2M3N2N2N2M3N2N2Mj0WO6J6J5J7J5K000O101O000O1000000O10000O100001O00000000000000000000000000000000000000000001O000001O0000000000000000000000000000001O000000000J6WNeLQKb3m4bLkJc3U5aLaJe3^5_LYJg3f5^LQJg3o5\\LhIj3W6k001N1O100O100O101N1O100O1N3M3L3N3L4M2N3L4M2N3L4M4L4K5L4K5L4L4K5L4L4K5L4K5L4L4K5L4L4K5Llej1", "size": [374, 500]}, "area": 34436.0, "iscrowd": 0, "image_id": 19742, "bbox": [172.0, 72.0, 167.0, 275.0], "category_id": 183, "id": 20001237}, {"segmentation": {"counts": "\\3`0W;000000000000000000000aEF`9;nEC1ML8T:f0kEAR:R1ZO_NQGi1l8[NiFn1U9;O00N31O1N3M2N3N1N3RG[Me8n2N4K2O2N5KO13MN20kGaLR8b30O10000O1N2N2O1O1N2N2N101O12N0O3N001O1O1O0O3N2M2O00O1O02O2N1RH]Lf7l3N1O2N3MO1M30000000001O00001O001O002N1O1O1O000O2M3M4M3L5L4L4L3M4L4M3L4L3M4L4L4K5I7J7H8I7Imn]4", "size": [375, 500]}, "area": 10330.0, "iscrowd": 0, "image_id": 19786, "bbox": [0.0, 64.0, 112.0, 133.0], "category_id": 104, "id": 20001238}, {"segmentation": {"counts": "WXn1?W;01M3F:00000000000000000000O10000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000fj05SUO401O0O10O1O10dM4kHKS73RILl62YIMc63bIL[63hIOU6OoI1n5NVJ2h5M[J3b5N`J2]50dJ1V52lJNS51oJOP51PK0o4OSK1l4NVK1l4LVK4j4KWK5j4HXK7U8000O010000O1000O011O2TKHSN:k1FVN9i1JUNN^K2[62bIL`4OeK2Y69XNDj1=f42N22N1O1O2IfDM[;0gD0`;000000000YOMjE3m:000000000O1000000000000000000000000000000O100000000000000000000000000000000O10N2O1Ndig01]VXO8F30O00000KhDKX;1lDO^;00000000000000000000000000000000000O1000000000000000000000000000000000000000000000000000000000000O100E;Q^8", "size": [375, 500]}, "area": 1312.0, "iscrowd": 0, "image_id": 19786, "bbox": [170.0, 0.0, 306.0, 375.0], "category_id": 105, "id": 20001239}, {"segmentation": {"counts": "0e2R900O100000000O100000000O1001ONmF`MS9^24N2O1O10000O1O1O100O1O100O1000000000000000000000000000000000000O1000000O10000000000O100O1000000O1000000000000000000000000001O00001O001O002N1O1O1O00001O1OO1000000001O2N2N1O00O100O100O100O10000O100O100O100O100O10000O100O100O100O10000O100O100O100O100O10000O100000g0XOb0_Ob0^OR]16hbNS1mNb0^O2NO100001O000000001O00G9F:eNTF`0f:GcSh3", "size": [375, 500]}, "area": 11335.0, "iscrowd": 0, "image_id": 19786, "bbox": [0.0, 0.0, 172.0, 85.0], "category_id": 109, "id": 20001240}, {"segmentation": {"counts": "Z8U1c:L3N3O0O101O0O100O2N1CRE2n:MRE3o:KRE1OJe;NW\\41dcK4M3O100N3O02N001O0000010O000010hDKl:5SEMl:3SENn:1QE0o:1PEOP;1oD1P;;00O1O10001O000000GUEFl:6REEW;<3O01O00000002N1O10O00001O0000000O2O0000000O11Ohh4E`WK4M2O1000001O01O000001O00010OO`f30bXLI]F8N1^8IbG80O\\8KUGL2:=OW8FPG98K27`0NV82WGNJ5i0MV80WG?`0DZ8LUGa0b0BY8NTG`0c0BY8NSGa0d0AY8NSGa0d0AY80PG`0g0@Z80nFc0e0]O]8R1cGnN]8R1cGnN]8S1cGlN]8V1aGjN_8X1SG]NMb0Q9o1000[NnFe0R9YOPGg0P9XOQGh0o8XORGg0o8XOQGh0o8XOUGd0k8\\OUGd0k8]OTG9HUOT9b0SG:JSOS9c0SG9OPOo87lF26g01nNm85QG51g0[9nNjF:Lg0h9YOYFe0j9XOXFg0^:N2M6K3L7I5KV]16kWNL`0J_89PG084b8LUGX1k8hNUGX1k8hNUGX1k8iNTGW1l8iNSGX1m8hNSGX1n8gNQGZ1o8fNQGZ1o8fNPG[1P9eNPG[1P9dNQG\\1o8dNQG\\1P9bNPG_1P9aNPG_1P9aNoF`1Q9aNnF_1R9aNnF_1R9aNnF_1S9`NmF`1S9aNlF_1T9aNlF_1T9aNkF`1U9aNjF_1V9bNhF_1X9aNhF_1Y9`NfFa1Z9_NeFb1[9]NfFc1g9000000000001O2N3MM\\NVFa1h980000001OO1N2G90000O100Q2oM1O0000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000O1L4M3M3M3N2N2O1N2O1N2N2O1N2N2O1N2O1NGdE[OZ:g0fEYOY:g0jEWOT:j0nEPOL1U:P1;1OK`EUOa:k041O100O10O101O20O1O1O01M2M3M3L5L3M3M3MT];KScD1N2N200000000J6H80000O10000O1000000O10000O10000O10000O1000000O10000O10000O10000O1000000O10000O10000O1O1O1O1O1O1O100O1O1O1O1O1O1O100O1OEmETOR:m0VFkNi9T1>1N100O100O10L3L5L3M4L3M4K4M4L3Mo_g0", "size": [375, 500]}, "area": 10525.0, "iscrowd": 0, "image_id": 19786, "bbox": [0.0, 266.0, 435.0, 109.0], "category_id": 118, "id": 20001241}, {"segmentation": {"counts": "e2g0`0g3b5YL^Jh3`5YL`Jg3`5YL`Jh3_5XLaJh3_5XLaJh3_5XLaJi3]5XLcJh3]5XLcJh3]5XLcJh3]5XLcJi3\\5WLdJOFl25]LT5h0RK]OC1ML8b31\\LU5g0`KZOAc3H]L1Om4d0\\Lg3b3RLfLn3Z3hKoLY4W51N2L31O101N1O2N101dKiHR4a7]O\\HnLi7j2[HVMf7f2]HYMf7d2[HTMR8g2nGUMU8j2`0MN4NVMVGg2m81N10000O2M2N2O2N1N2N3M2O1O12N0O3M101O1O1O002N2O0N11N1O11O2N1UMZGc2o80O01O2MOaMTGU2_9H8H5K4L4L4K6K4L4L4L4K5MSECc:f04L5J5M0O01O0001O000001O00MeDJ[;37MnUV22jiiM1iD1S;1lDOR;3kD0V;9O000000010O000001O00017M4L4Kc`[1L\\_dN2O1M200011N01O001O00001O010O05I]D0odc0", "size": [375, 500]}, "area": 9589.0, "iscrowd": 0, "image_id": 19786, "bbox": [0.0, 82.0, 447.0, 218.0], "category_id": 123, "id": 20001242}, {"segmentation": {"counts": "VeY1:\\;2N1O1O1NEkD8T;HnD8P;HQE8n:HSE8l:HVE7h:JYE6f:J\\E4d:L]E2d:N]E0d:0^ELc:5?0000000O100000000O100000000O1aFI_77`HK_75aHL^74cHMDa0_5AnJNCR1n4oN`K3^O`1`4\\NSL4]OQ2P4iMeL5QOHUOj2e4ZMTM3SOJSOZ3U4iLeM3ROOPOd4j2ZKeNMjN6g01UOR6n0jI82mN1g047Y6M\\IjN<:Lg03j0IVNg5U1cJTOKe03n0FjNS5?[KQOKg01n0HAh6_O`IP1I6S6gNUJS1Il0`5lMgJY1Ha1n4RM[K]1HW2Z1hLd0]O[Nd1Hl2c0eLl0fNjNi1H_3NRLV2?mM_3MQLV2a0lM_3NoKV2b0eMg35fKV2\\6iMoI<`N`0JTOh70mI;dN8n7]O]I:h7FWH:k2B>4fL:n2C94iL8Q3B66hL7o7IPH7Q8InG6T8JkG6V8IjG6X8JgG6Z8JdG7]8IbG7^8J`G6b8J]G6d8J[G5g8KYG4h8LWG4j8LUG4l8LSG4n8KSG4n8LQG4o8MPG2R9NnF1S9OmFOU91jFOW91hFNZ92eFM]93aFN`92WF6j9IVF7j9JUF6l9JTF5m9KRF5o9KPF5Q:KnE6R:JmE8R:HmE;Q:EoE7BG\\:3QF4X:KhE5Y:KfE5[:KdE5]:KbE6^:JbE4_:M^E3a::63M3M2D=QYV3", "size": [375, 500]}, "area": 4520.0, "iscrowd": 0, "image_id": 19786, "bbox": [113.0, 0.0, 107.0, 375.0], "category_id": 132, "id": 20001243}, {"segmentation": {"counts": "gTR32e;3N3L3N4K01O001O02O001N01N101N1O101N1O2O0O2O000O2O1N10_Zi02kdVOd0O200000O10O1O010H]OZEd0[:a0O2O1O001O10O01O1O010O1O001O100O001O1N1O2N2N101N1O2N101N1O2N1\\OXNXGj1g8WNWGj1i8WNVGi1j8c0N101O001O00001O0010O100O100O1O01N1O2N10O\\GPMb8P330@RMVHm2l7WMoGj2Q8YMlGg2T8`0N00000000000000YOmGkMR8U2VHcMj7]2^H`M^OG00k7i2oH\\MQ7d2Q1000000O100hMWGb1i8^NYG`1g8`N[G^1e8aN^G]1b8cN`G[1`8eNbGY1^8gNeGV1[8iNhGU1X8mNhGQ1X8POhGn0Y8UOdGk0\\8XOaGh0_8YO`Gg0`8ZO`Ge0`8\\O`Gc0`8]OaGb0_8^ObGa0^8_OcG>_8BaG;b8E^G1l8OTGNo8OTGOn8IZG6g8G\\G:c8E^G;b8E^G;b8E^G<a8D^G>a8B_Gc0\\8^OdGb0[8^OfGa0Z8^OhGa0X8_OiG?X8AkG:W8EoG5S8IQH3Q8KXHLVa3", "size": [375, 500]}, "area": 8882.0, "iscrowd": 0, "image_id": 19786, "bbox": [268.0, 0.0, 223.0, 123.0], "category_id": 172, "id": 20001244}, {"segmentation": {"counts": "YVj07Y;?H8H5K4L4L4L5K4L4L4L4L4L5K4L5K4L001N1N2N2]LSM[Nn2e1TMWNn2i1UMSNm2l1VMPNk2P2XMlMi2V2WMgMj2Z2XMbMi2`2XM]Mh2d2ZMXMh2g2[MUMf2j2]MSMd2m2_MoLb2P3bMlL_2T3dMgL^2X3fMaL_2^3dM\\L_2c3fMUL^2k3fMnK]2R4gMgK\\2X4b20O001O001N10PK`Ik4_6UKbIj4^6VKdIi4[6WKfIi4X6XKjIf4V6ZKkIf4T6ZKmIe4S6[KnIe4Q6[KQJc4n5^KSJb4l5^KUJa4k5_KVJa4i5_KXJa4g5_KZJ`4e5aK]J^4b5bK_J]4a5cK`J]4_5cKbJ\\4^5dKcJ\\4[5eKfJZ4Z5fKhJY4W5gKjJX4V5hKkJX4T5hKmJX4Q5iKPKV4P5jKQKV4n4jKTKU4k4kKVKU4j4jKWKU4j4jKWKV4i4iKXK_4`4`KaKQ5n3nJTLa5\\3`JcL]6`2cI`MT8i0lGWOe9XO[Fh0[:ORJlNY1U1fNA^6f0ZOe0[Of0YOf0[Of0XIhLd3m3fKdLj3o3bKQL_4o3aKPL_4Q4aKoKW4Y4iKfKl3f4SLZKc3Q5]LnJX3^5hLaJn2j0mLk25ZLc2X1ZMZ24^LW2d1eMm14^LW2h1cMj16]LW2e6iMZIW2g6iMXIW2i6iMVIW2k6hMVIW2k6iMTIW2m6iMRIW2n6jMQIV2P7jMoHV2R7jMmHU2U7kMjHU2W7kMiHT2X7lMgHT2\\2iM`13SLT2]2PNZ1LXLT2e2PNm0K^LU2o2mM<OdLT2Z3jML2jLS2d3hM\\O5oLS2c7mM\\HS2e7mMZHS2g7mMXHS2i7mMVHS2k7mMUHR2l7nMSHR2m7nMSHR2n7nMQHR2P8nMoGR2R8nMmGR2T8nMlGQ2U8oMjGQ2W8oMhGQ2Y8oMfGQ2[8oMdGQ2\\8PNcGP2^8oMbGQ2_8oMaGP2`8PN_GP2b8PN]GP2d8PN[GP2c8SNTGU2i8>fNVMdIm2]6VM\\Im2e6WMSIl2^7>^3bL0000000000000000000000000000000000000000000000000000000000000000hGMl43nJ5P5KRK5l4KVK5h4KZK5d4K]K6a4JaK5^4KdK5Z4K^I0n04c5L]I:j0Ih5M]I8P1Jb5N\\I7W1J\\5O\\I5]1LU5O\\I4b1NQ5OZI2i1Ol43VILR21g46RIIZ21c4:nHEa22_4<kHDg20]4k0eKUOX4l0iKUOR4o0oKQOm3Q1ULoNf3T1\\LkNb3V1`LjN^3W1cLiNZ3X1hLgNV3Z1lLfNR3Z1oLgNn2[1SMdNk2]1WMcNg2]1[McNb2_1_M`N`2`1bM`N[2a1gM_NV2c1jM]NU2c1mM^No1d1RN^NiMG\\2l1K`NgMJU2g15bN_1`1aNbN`L@^4n1TOaNX1a1hN_NW1b1iN_NT1=PK4m3_OQ1>TK2l3_Oo0?XK1h3@n0`0\\KOf3Al0a0`KNc3Bk0`0eKUOC?n3Ki0`0iKSOBc0k3Jj0?QLGU3Jj0>RLIS3Ik0?QLHU3Hk0`0nKJV3Ii0>PLI_3F<b0TLIh3@0d0[LMm3[ODd0bL1U4UOTOg0jL5V8GnG9R8DQH=o7_OTHa0l7\\OWHe0h7XO[Hh0Z91O1O001O0O2O001O001O001O1O001O001O001O001N2O001O001O001O001O001O1O001O001N101B=01N101O1N101O0O2mFUNX8e201O1N2O001N2O001N2O001N2O001O1N101O1N101O1N101O1N2O001N2O001O1N101N1O2N1O2N1O2N101N1O1O2N1O2N1O2O0O1O2N1O2O0O2O0O\\LaIS2^6iMfIW2Y6fMkIZ2U6bMnI`2P6\\MUJd2k5XMYJi2f5SM^Jm2a5PMcJQ3\\5kLhJU3W5gLnJZ3Q5bLSK^3l4_LXKa3h4[L\\Kf3b4WLbKi3V61O001O00001O00QOYHoMf7Q2bHgM^7Y2jH`MU7`2SIXMm6h2Q1000000O100000000000O10000000O1000aHTMS6l2mITMS6l2mIUMR6k2nIVMQ6j2oIWMP6i2PJXMo5h2QJYMn5g2RJZMm5f2SJ[Ml5d2UJ]Me5h2[JYMW4T4iKlKj2a5VM`J\\1m6dNTINY82f1000<Dh0XOeg5", "size": [375, 500]}, "area": 106416.0, "iscrowd": 0, "image_id": 19786, "bbox": [71.0, 1.0, 413.0, 371.0], "category_id": 183, "id": 20001245}, {"segmentation": {"counts": "TQX12b?00001O00000000000000001O0N2000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000WQX1OjngN2N0000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000O10000000000000000000000000000004Llci1", "size": [500, 458]}, "area": 1116.0, "iscrowd": 0, "image_id": 19924, "bbox": [81.0, 492.0, 258.0, 8.0], "category_id": 105, "id": 20001246}, {"segmentation": {"counts": "0Pg7k2geH2N2N7I2N2N2N2N2N2N2N2N2N2N2N2N2N2N2N2N2N2N2N2N2N200QJlM]NU2a1hNdMX1[2jNdMV1[2kNdMV1Z2mNeMS1Z2nNeMS1Y2POfMP1Y2QOgMo0X2SOfMn0X2TOhMl0W2VOhMj0V2XOiMi0V2YOiMg0V2ZOjMf0T2]OjMd0U2]OkMc0T2_OkMa0S2AlM`0S2BlM>R2DmM=R2EmM;R2FnM:P2InM8Q2IoM7o1LPN4o1MPN4o1NPN2n10RN0m12QNOn12RNNn13QNMn14QNMo14PNLP24oMMP25oMKQ25oMKP27nMJR26nMJR27mMIR29lMHT28lMHS2:lMFT2:kMGU2:jMFU2;kMEU2<iMEV2<jMDV2=hMDX2<hMDW2>hMBX2>gMCX2?gMAY2?gMAY2`0eMAZ2`0fM@Z2a0eM_OZ2b0eM_O[2b0dM^O\\2b0dM^O[2d0cM]O]2c0cM]O\\2e0cM[O]2e0bM\\O^2e0aM[O^2f0aM[O_2f0`MZO_2g0aMYO_2h0_MYOa2g0_MYO`2i0_MWOa2i0^MXOa2j0^MVOb2j0^MVOa2l0]MUOc2k0]MUOc2l0\\MTOc2m0\\MTOd2m0[MSOd2n0[MSOe2n0ZMROf2n0ZMROe2P1YMQOg2o0YMQOf2Q1YMoNg2R1WMoNi2Q1WMoNh2S1WMmNh2T1WMmNe2X1ZMhNc2[1]MeN`2_1^MbN^2b1bM^N\\2e1bM\\N]2e1cM[N\\2g1cMYN\\2h1cMYN\\2i1cMWN\\2j1dMVN[2b7O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1N2O1O1O1O100000000O100000000000000000000000000O100000000000000000000000000O100000000000000000000000000O100000000000000000000000000O1000000001O00001O001O00001O00001O001O00001O00001O001O00001O001O00001O00001O001O00001O00001O1O2N2N2N2N2N2N2N2N2N2N2N2N2N2N2N2N1O0000001O0000001O000000001O000000001O0000001O000000001O000000001O0000001O000000001O0SH`Mf2`2YMjM^2V2aMTNV2m1iM\\Nn1d1QNfNf1Z1YNPO^1P1aNYOW1g0hNZOX1g0fN[OY1e0gN[OY1e0fN]OY1c0fN^OZ1b0eN@Z1a0dN@\\1`0cNB\\1>dNB\\1>cND\\1=bND^1<aNF^1:aNG_19`NI_17aNI_18_NJ`16_NKa15^NMa13^NNb12^NOa12]NOc11\\N1c1O\\N2d1N[N3e1NYN4f1LZN4f1LYN6f1JYN7g1IXN9g1HWN9i1HUN:j1FVN:j1GTN;k1FSN;m1FQN<n1EPN<P2EnM=Q2DnM<R2DmM>R2ClM>T2CjM?U2BiM?W2BgM`0X2AgM?Y2AfMa0Y2@eMa0[2@cMb0\\2_ObMd0\\2]OcMf0Z2[OdMi0Y2XOeMl0X2TOgMo0W2ROgMR1V2oNhMT1V2mNiMV1T2kNjMY1S2hNkM\\1R2dNmM`1P2aNlMe1Q2\\NjMk1S2VNiMo1U2RNfMU2W2lMdM[2Y2eMcMb2Z2_MaMh2\\2YM_Mm2_2TM\\MS3a2nL[MX3b2iLYM^3d2cLWMc3g2]LUMj3h2WLSMP4j2QLQMV4l2\\4M2N3EVCaM02m<Z2YCcMj<Z2:N3M3M3M3M2N3M3M3M4L6J6J6J6J7I6J6Jg]3", "size": [500, 458]}, "area": 76015.0, "iscrowd": 0, "image_id": 19924, "bbox": [0.0, 0.0, 458.0, 500.0], "category_id": 173, "id": 20001247}, {"segmentation": {"counts": "Pg7j2h<2N2N2N2N2N2N2N2N2N2N2N2N2N2N2N2N2N2N2N2N2N2N2N2N200QJRNWNo1g1nN^MR1a2oN_MQ1`2QO^MP1`2RO`Mn0_2TO_Mm0_2UOaMk0^2WOaMi0^2XOaMi0]2ZObMf0]2[OcMe0[2^OcMc0\\2^OdMb0[2@dM`0Z2BeM?Z2CeM=Z2DfM<X2GfM:Y2GgM9W2JgM7X2JhM6W2LhM4V2NiM3V2OiM1U21kMOT23jMNU24jMLT26lMJS28kMIT28lMHT29kMGT2:kMGU2:jMFV2:iMGV2;iMEW2;iMEV2=hMDX2<hMDX2=gMCX2>gMCY2>fMBY2?gMAY2`0eMA[2?eMAZ2a0eM_O[2a0dM@[2b0dM^O\\2b0cM_O]2b0bM^O]2c0cM]O]2d0aM]O^2d0bM\\O^2e0aM[O_2e0`M\\O_2f0`MZO`2f0`MZO_2h0_MYOa2g0_MYOa2h0^MXOa2i0^MXOb2i0]MWOb2j0^MVOb2k0\\MVOd2j0\\MVOc2l0[MUOe2k0[MUOd2m0[MSOe2n0YMSOg2m0YMSOf2o0YMQOg2o0XMROg2P1XMPOh2P1XMPOg2R1WMoNi2Q1WMoNi2R1VMnNi2S1VMnNj2S1UMmNj2T1UMmNk2T1TMlNl2T1TMlNk2V1SMkNm2U1SMkNl2W1SMiNm2W1RMjNn2W1QMiNn2X1RMhNm2Z1QMgNk2]1UMbNi2b1VM^Ng2e1XM\\Nd2i1[MWNc2k1\\MVNc2l1\\MTNc2m1]MSNb2o1\\MRNc2o1]MQNb2Q2]MoMb2R2]MoMb2b7O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1O1N2O1O1O1O100000000O10000000000000000kNfAP1Z>nNhAR1]>000000O100000000000000000001O00000O100000000000000000000000000O1000000000001O0000000000000O10000000I8O00001O001O00001O0O101O001O00001O00001O001O00001O001O00001O00001O001O00001O00001O1O2N2N2N2N2N2N2N2N2N2N2N2N2N2N2N2N1O0000001O0000001O000000001O000000001O0000001O000000001O000000001O0000001O000000001ZHSMc2m2\\M]M[2c2dMgMS2Y2lMQNk1P2TNXNd1h1[NbN\\1^1cNlNT1T1kNROP1n0oNSOQ1n0mNTOR1l0nNTOR1l0mNVOR1j0mNWOS1i0lNYOS1h0kNYOU1g0jN[OU1e0kN[OU1e0jN]OU1d0iN]OW1c0hN_OW1a0hN@X1`0gNBX1>hNBX1?fNCY1=fNDZ1<eNFZ1:eNG[19eNHZ19dNH\\18cNJ\\16cNK]15bNL^15`NM_13aNM_13`NO_11`N0`10_N2`1O^N2b1O\\N3b1N^N2b1O\\N3c1N[N3e1NYN4f1MXN4h1MVN5i1LVN4j1LUN6j1KTN6l1KRN7m1JQN7o1JoM8P2IoM7Q2InM9Q2HmM9S2HkM:T2GjM=S2DkM`0R2AmMa0Q2@mMd0P2\\OoMh0n1YOPNk0m1VOQNn0l1SOSNo0k1ROSNR1j1oNTNU1i1kNVNY1g1hNTN_1i1bNRNd1l1]NoMj1n1WNmMP2P2QNkMV2R2jMjM\\2T2eMhMa2U2`MfMg2W2ZMdMm2Y2TMbMS3[2nL`MX3^2iL]M^3`2bL\\Me3a2\\LZMk3c2VLXMP4f2b4M3M3M3M2N3M3M3M3M2N3M3M3M2N6J6J7I6J6J6J6J_m3", "size": [500, 458]}, "area": 150056.0, "iscrowd": 0, "image_id": 19924, "bbox": [15.0, 29.0, 434.0, 471.0], "category_id": 183, "id": 20001248}, {"segmentation": {"counts": "d]8<m<2N20000O2O001OO10000O10WC@\\<1eC00d0Y<:0O1O1O1001O1O1O00001O001O0O1O2N000011N1O10O2O20GkCVOT<l06211L2N002NO001O2OO014NM12ONO05NON20N2O22K12O01N0VOjCb0V<ZOkCi0[<O1O016I1O1O1O2O4LO10M8HbmQ7", "size": [427, 640]}, "area": 2234.0, "iscrowd": 0, "image_id": 20059, "bbox": [20.0, 64.0, 78.0, 43.0], "category_id": 96, "id": 20001249}, {"segmentation": {"counts": "S7X6R710000001OO1O11O00000000N2O1NSKUI^3h6aLWIcN3<NQ4f6mLZIfN6:KT4g6eLXImN;:GS4d6]LYIVO16?3CT4d6XLYI\\O0O44=L_O3002Z4d6WL\\I\\O50I1d02]OO2Z4b6WLhJ_OeNNOY4d6ZLhJ^OjNW4^6[LRKe3n4[LRK^OYNT4e6^LRK^OZNS4d6_LSK]OYNT4d6`LRK\\OYNU4d6^LXKa3h4`LkJ^OiNR4\\6aLeJ@iNL3S4_6\\L\\ICU1<oNe3`6mK^I:NEg04ZOL6`0Md3`6mK^I:f0g0[OR3a6mK_I;b0g0\\OS3c6kK_I;a0i0\\OQ3e6jK^I7d0X5S6dJgI3@Z5\\7<00000000O11OO100NSJfHm5V7TJjHo5U7400LlIPIU6o6lIRIS6o6mIPIS6P7nIoHR6Q74000000000000001OO100001O00000000KiIVIW6j6iIVIW6j6jIUIV6k6jIUIV6l6jISIV6m6jITIU6l6lISIT6m6lISIT6m650000000000O10000000000000000000000001O00000000001OO10000`0@3M3M3M2NT1lN>B5K001O00TOZLSHf3d7oLQHP3R8YMbGg2b8o0k0UO001O1O1OeN_LXH0N`3`7kMVHU2k7e11O1O1O001O2N3Mb0^O3M3M2N3M2N3M3M2N1O001O001O001O]OfLUGZ3h8f0M3M3M3M3N2O1O1O1D<00O10`KWH^3h7`L\\H_3d7]LaHb3_7ZLfHe3Y7XLlHg3T7VLPIi3o6RLWIn3i7mK\\GS4`8SL^Gm3_8XL^Gi3^8d0M3L4M3N2N2N2N2N2;E3M2N2N2N2N2N1O2N1OoM^K^I0g1`4k4aK[I4h1Z4m4bK[I6h1V4n4cKYI:h1R4o4dKYI=f1n3Q5VLkJl3U5VLfJm3Z5VLaJl3_5ULRJ\\O^Oa4`6TLmIC]O[4e6SLjI_4V6aKeId4[6\\KaIh4`6WK_Ij4a6UK_Il4a6TK^Im4b6SK\\Io4d6QK[IP5e6PKZIQ5f6oJYIR5g6i01O003M00M3000000O1O1001O1O0000L_I\\I01`6c6bI]I^6c6bI]I^6c65000000000000001OO10000000000000[I\\Ic6f6000O1O1001O0000000000O1001O000000002N00O10000002NN21O0000O11O000000000000O11OO10000O100001O0000O1O1N21O000000M3000000001O1O0000O100000000000000000000000000000000000000O11O000QKaIR3_6SL^Jk3b5RLcJl3]5QLiJl3W5\\KaI`0^1R4U5hKSKV4S7N2N1O1O1O001O001O1O2N1OQOUL^Hk3`7ZL]Hf3a7_L\\Ha3a7eLTHc3k7o0O1O1O1N2O1O1O1O1N2O1O11O1O1O001O0000000000001O0000000000000000001O000000000000000000001O000000000000000jK]Hd2d7PMhHo2X7gLQIZ3o6_LXIa3h6]LYId3g6YL\\Ig3d6VL^Ik3b6SL`Im3`6PLbIQ4l70O100O1O100L4H8I7I7M3N2O10000000000003M;E4L4L3M1O1O2N1O1O001O00gNcLeH\\3[7fLdHY3[7jLdHU3\\7nLbHQ3^7QM`Ho2`7SM_Hl2`7XMYHl2g7\\10000O1N21O1O1OcNbJnJ]5Q5dJPK[5P5eJRKY5m4hJTKW5l4iJeIN_1V5l4oJhIKY1S5o4RKgIL]1o4l4VKeILa1l4j4]KfID`1l4j4dKXKY4h4gK[KV4e4kK]KR4d4nK]KP4b4RL`Kk3`4ULcKh3]4YLdKe3\\4\\LdKc3\\4^LfK_3Z4bLfK]3Z4cLgK\\3Y4eLgKZ3Y4gLgKX3Z4hLeKX3\\4hLbKY3^4gLaKZ3^4hLaKX3_4iL_KX3a4iL]KX3c4hL]KX3b4jL\\KW3d4iL[KX3f4hLYKX3g4hLXKY3h4hLfJi3Z5WL_JP4`5RLYJT4g5h11O0000002N0000000000000000O100001OO1O100001O001OO100000000001O00001OO11O0000O11O0000O1001OO12N00O1001OO100O10000000000000000001O00O1001O00O11O1OO11O0000000000001O0000000000O100O1O1000000KUIjIk6V6UIgI01k6X6UIgIn6Y6RIgIn6Y6200001O00N22N00000000O11O00O1001O1O2N1O0000O1001O0000N21OO1001OO10000", "size": [427, 640]}, "area": 121041.0, "iscrowd": 0, "image_id": 20059, "bbox": [0.0, 201.0, 640.0, 226.0], "category_id": 124, "id": 20001250}, {"segmentation": {"counts": "0i3b900O10000001O000000O1001O0000ORKZL9f3CYLaK3<NQ4e3N[LeK6:KT4d3H[LjK;:GS4c3^OZLUL16?3CT4d2nKlMZ3]O\\L0O44=L_O3002Z4c2PLmMW3@\\L50I1d02]OO2Z4`2SLoMS3k0`LeNNOY4b2VLPNR3j0`LjNW4\\2WLoMS3U1f0k0YLoMQ3V1_LYNT4b2\\LoMQ3V1_LZNS4a2^LnMP3X1^LYNT4a2]LoMR3V1]LYNU4b2\\LoMP3[1c0f0]LoMQ3n0`LiNR4Z2\\LQNj1@iNV1ROiNL3S4]2\\L^M@`0V2JfNFWOU1<oNe3S2mKjM00d0?_1NWND0NEg04ZOL6`0Md3Q2kL\\NS1NTNCK0:f0g0[OR3R2jL]NS1OoMF1L;b0g0\\OS3T2gL\\Nf0E]Nl01QO;a0i0\\OQ3U2fL\\Nh0j0\\NhN7d0X5^1]K\\Nh0o0_NQO3@Z5T2]K[Ng0c1X42RK[Ne0c1Y42SKXNf0f1W42SKYNe0e1X42SKYNe0e1X42TKWNe0f1X43SKWNe0g1W42UKVNd0g1X43TKWNCF8P2Q52UKYNAG]OOe0m1[OPNm5S2WKXN_O2O_1_OTNo5R2TK[N]O2OP1DhNP6j1PK^N[O21MCC:5CK84l5k1QKcNkN_O0O3b08N:AIOB0U6P2nJlNmNH2JNC`10d4o1nJ5QOkM^10c4P2nJ8c0gM_4P2PK7h6IZI5f6MQInMVOW2i7HSI>m6BUI;l6HUI5j6KXI2i6NWI2i6NYI0g60XI1h6NXIlNkNC03m7^1WIlNoNZONI3c0i7_1VIeNmNZO724>c7`1UIdN[O]ON?a7`1ZI]NMNk6g1UI\\N5Gg6m1VIZN4Ei6R2QIZN]8d1cG^N]8e1aGYN`8g1^G\\Na8d1_G\\Na8b1aG^N_8a1cG]N^8f1]G\\Nc8c1_G\\Na8d1_G\\Na8e1]G\\Nc8c1cFoMb0=k8e1_FoMi0:i8n1VGRNk8n1TGUNj8j1WGTNk8R2nFoMR9Q2mFPNS9P2lFRNS9n1lFSNT9n1kFRNU9R2gFnMY9Q2hFQNV9o1gFSNZ9R2^FRNa9n2NN2O11OO100O1N21O001ON]F\\Lc9d320[F\\Lc9d3]F\\Lc9d3]F\\Lc9f30O10000O100O1O1001O001OO1O1O10000003M000[F\\Lc9f303M00001O00001O00O1001OO10000O100O100O100003MM3O1001OO10000001OO1000000O1000000000000000000000\\F\\La9f31O100002N2N0000O1001OO12N00O1O100O1000000O1O1M300001OO100O100O100001O1O2N002N1O2N00O1001O00O11OO10000000000M3O10000001O00O11O00001OO1O11OO100001O1OO11O001O00O1N21O001OO100O12N002NO1002NN2000000000000O100001OO10000N^F[Lb9e3200O1000000O11O001O1O1O1O1O1O1OO10000O100000jH\\Le4d3[K`LfMHh6h3aKeL^4[3aKfL_4Z3aKfL_4Z3nJYLjN<Y6\\3hI\\LAKf00HOA>h6\\3gIeL1FELOa0d6W3gIfL21@2g6[3bIaL0BF01Y1g6g2^IaLLS1f6m4000000000000000^OXIaJh6^5_IUJI2h6h5cISJE5h6h5hIUJX6g5h000^NeHfL2XOY7c3aHcLf0OGFR7d3oI]LaNOQ8d3d100000000000000000000000000000fF\\Lm8W4J0000000000000000000000000000000000000000000000000000000000000000O1@`0000000000000000000000000000000000000000000000000000000000000000000`0@00000000001O0000000000000000000000000000000000000000000000001O0000O100000000001O0000O1001O00O1001O00O1001OO100000000001O00O11OO11O001O2N001O1OO11O001ONUGdKk8[4VGeKj8\\4UGdKk8\\42001OO1O1O1N2O100000000000000000000000000O1001O00O1000000O10000000000O100O1K5M3K5N200O100O14L1OO11OO11O0000O100000000O1000000O1001OO1001OO1O11O000000O11O00O1O1O1001O001O0000O100001O1O1O001OO1O11O1O000000O100O1001O1O00000000000000000000001O1O1O0000001OO11O00000000O11O0000", "size": [427, 640]}, "area": 81246.0, "iscrowd": 0, "image_id": 20059, "bbox": [0.0, 0.0, 640.0, 294.0], "category_id": 169, "id": 20001251}, {"segmentation": {"counts": "i3Z3Q:O010000010N1O11O0O1001O0N2O0O2M2O22NN3OO01O2OO1000000000O101]OfESNY:i1nEUNR:i1RFUNo9i1RFTNQ:k1QFoMR:c1cE]NW;d180001ON21O00O100001oN]D8d;G^D]OOe0b;NhDOY;OiDOP1Dd8;^F1MCC:5CK8T:HmE0O3b08N:AI\\:BWF2JNC^1c;eN\\D\\1f;3O1000000O11OO10001OOjN\\NRF03d1j9_NjENI3c0`1d9\\NjE724>[1b9hNoEN?Y1_9_O\\Fc0d9BVF?j9BRFa0n9Y1PFoLP:Q3PFoLo9R3RFmLo9R3QFnLo9S3QFlLo9T3QFmLm9T3TFkLl9U3TFkLl9U3500000O1O12MO200001O0000002NO11O2NN2O20OO100O1N210OO2ONiLPFW3P:K7nLiEo2X:QMhEn2Y:RMgEm2\\:O001O1O001O00001O2N1O2OO1O0010O10003M000^MYE`2i:03M00001O00001O00O1001OO10000O100O100O100003MM3O1001OO10000001OO1000000O1000000000001O00000000^MZE`2g:1O100011N2N01O2M3N2O1M300N2M3MLhEXMW:h2jEUMW:l2iETMW:m2hERMY:n2gERMY:n211O1M3000O2OO101N100O100001O1O2OO021NO2K30O1001N1O0110N2O0000LeEVM01Z:i2hEWMX:i2hEWMU:l2410000001O00O110OO101OO1O11OO100eERMY:P31O1N002O010O00O1N21O0O2OO2O0O12N0020M10O3NN20002NN21O000O011O01OO10000NQMhEo2X:111N010000O011O010O1O1N2N2M300OO1000M2100000DbEkM^:U2fEfM[:Y2<O100]OQEjNP;1TEAKf00HOAR;?]E1FELOT;;ZE21@i:9UE0BF01S<5^DLdh3NSdL7kBIi<;TCEl<`0I7M0^N^OmE2XO?]:[OiEf0OGF9]:h0dEaNOX1]:d100000000cEVMY:n203MF:N2N2N2N2N2N2N2PNREb1b;J0O1O100O100O1O100O1O1001O0000001O0000001O00001O0000001O0000001O0O1@a0O0000001O000000000000000000000000000000000000000000000000000000000`0@00000000001O00000001O000000001O00000000001O0000001O1O001O1O100O001N2O1O1O001O100O001N2O100OISEPNl:P2UEQNj:o1VERNj:l1WEUNh:k1XEnMN5h:n1]EPNe:P2[EoMLNg:R2^EnML1d:R2eEnM[:R2?00001OO2O00O11OO11O001O2OO10O1N011O001NOTNeDl1Z;UNfDl1Z;SNfDm1Z;2001N01O2N1N2O102N0000000000000000O100001N01O2O00O2O001OO010000000001N100O2J4N4J5N20O02O0O14K2OO20N03OO0O10O2OO100O10O1000000O1001OO1010OO00110O00O11N20N11N1O1O1001O0010O000O100001N2O1N1O2OO1O11J_E`Mb:_2^E^M01b:a2^E^Me:b2[E^Me:b21100O2O01M31N00000000O11O00O1001O101O00O000O1010N11O0N21OO10O20N1000UL", "size": [427, 640]}, "area": 50770.0, "iscrowd": 0, "image_id": 20059, "bbox": [0.0, 114.0, 640.0, 114.0], "category_id": 175, "id": 20001252}, {"segmentation": {"counts": "bR^1g0c<4L4L4L3MU1kN?A6J1O1O2NPOXF_Ng9\\1jFYNU9m1Q15Lk0UO0O2O1N2O`N_F^N00a9]1e11O1O1O001O1O2N3Mb0^O3M3M2N3M2N3M3M2N001O001O001O^OYF`Mg9]2bF^M]9`2h0M3M3M3M3O1O1O1O1E;O100OYE`Ne9`1XFdNg9[1VFjNi9V1SFoNl9P1QFTOo9l0nEXOQ:g0jE_OV:_1gEfMZ:V2kEhMU:T2QFjMo9S2f0L4M4M2N2N2N3M2N2<F1O0O10000000O100O2O0O2O0O1M4K4L4K6_OPD_OQ<=a0L4J6O1O001O1N2O1Oagc12cW\\Nm0K5K6I6H8F:D<L3M3M2N3M3M2N3N2O2M2NPOZF\\Ng9b1^F[Na9d1dFUN`9i1Q100O2O00001O2N1O1N2O2N1001O010O1O01O01O0001O0000001O000010O000001O000000000001O0000000000000000001O0000000iE^Ne8b1PGiNP9W1fFRO[9n0^FYOb9g0\\FZOe9f0XF]Oh9d0TF^Om9b0QF@o9`0nEBS:k10O100O100N2I7I7I7J6N3N1O1001O01O000003M;E4M3L3M1O101O0O010000fNXFoNi9o0ZFPOe9P1^FnNb9Q1`FnN`9Q1bFmN_9Q1eFmN[9R1gFkNZ9U1iFdN\\9[1[1N2O0N3O100000O1000001O2M3N2N1O2N2M3N2N1O2M3N1O002N0O1000O1O1N1O2O1N2N101N1O2O0B?H7J7H_Wg1", "size": [427, 640]}, "area": 17664.0, "iscrowd": 0, "image_id": 20059, "bbox": [110.0, 185.0, 397.0, 121.0], "category_id": 183, "id": 20001253}, {"segmentation": {"counts": "YZh41c?000O100L1d@N24i>LTAP1Z>9M2N2O1N2_IbNkN_1U1_N_H1Z6a1Z1_NdNb1R1^NmH2n5a1R1kNnNV1P1lNPOT1m0oNSOQ1k0QOUOo0h0TOXOl0e0WOZOj0", "size": [500, 333]}, "area": 956.0, "iscrowd": 0, "image_id": 20107, "bbox": [311.0, 219.0, 22.0, 281.0], "category_id": 111, "id": 20001254}, {"segmentation": {"counts": "i9k5i90O1000000000000000000O1O100O100O10000000000O10000O100O1O1N2000000O10000001OO100000000000000001O001O4L1O4L1ON21O1O001OO1O100001O0000N21O0000000000OdJ[Fk4d9UK_Fi4b9UKbFh4_9VKgFe4Z9YKgFg4Z9PKWF0e0P5l91000000000000O10000004L5K2N1O1O00001O00000000005K3M3M1O000000001O0000003M7I2N1O00001O000000001O3M2N2N1O2N1OO1001O00001O2N3M1O5K00000000000iNhCROX<1fDN`=01O001O1O001O001O1O001O001O1O001O001O1O001O0000001O0000001O1Fj@4ZgY2", "size": [500, 333]}, "area": 25139.0, "iscrowd": 0, "image_id": 20107, "bbox": [0.0, 301.0, 182.0, 199.0], "category_id": 118, "id": 20001255}, {"segmentation": {"counts": "0l1h=00O1ZO]B[Oc=d0^B[Oc=f0\\BZOd=f0\\BYOe=g0[B[OMC`=R1dBA[=8_BWO7a0Z=7mBKQ=5nB_OGMZ=d0oB]OI0W=b0QC^OH0W=a0RC^OI0U=b0RCXOO6o<b0cC^O^<d0`C\\O`<e0_C[Oa<e0_CZOb<f0_CWOc<j0]CSOe<k0S1O1O1O1O1G9O1O1NbBH]NOo<9cDJ\\NNQ=8bD1_;O`D2`;O_D1a;N`D1a;O_D1a;O_D1a;O_D2`;N_D2b;N`DOa;2^DMc;3]DIg;7ZDGg;9YDFh;;h1001O0000O1O1O6J1MfgW4", "size": [500, 333]}, "area": 1706.0, "iscrowd": 0, "image_id": 20107, "bbox": [0.0, 0.0, 55.0, 80.0], "category_id": 119, "id": 20001256}, {"segmentation": {"counts": "RVg49S?7H9H9F;F9F:G9F9M3N1O100OZOaCPNG7f<LZCZ1m0hNg;0ZCK1X1R1kNd;6ZCl0U1lN`;0[C62e0Q>TO[Bc0`>00O1O1O10XH", "size": [500, 333]}, "area": 1130.0, "iscrowd": 0, "image_id": 20107, "bbox": [309.0, 231.0, 24.0, 101.0], "category_id": 132, "id": 20001257}, {"segmentation": {"counts": "Yk`031NT?2QA5l>NQA9d>NZA4b>`0M4M1101O00000003M4eAeNU>f1G0HZNbBf15ZNk<1QCe11`Nj<OQCa14bNh<l1TCZNg<[2ON30O1O001OO1O1000O2O0O10N120O00001O1O1NcC\\Ml;c2TD`Mi;b2UDbMh;_2VDgMd;[2ZDfMf;[2QDVM0e0n;n210O1000000001N01000004L5K2N1O1N10001N12N1OO100O6K3M3L2O0000000O2O0001O03L8I2N1O00mLaEQ1_:iNlES1S:hNWFS1i9gNcFS1]9hNmFS1T9fNTGX1m8eNUG\\1k8bNWG`1f8^N]Gc1`8]NbGd1\\8ZNgGf1X8XNkGi1S8VNnGk1Q8SNRHk1n7UNTHj1l7TNWHl1h7SNZHl1f7RN]Hm1c7RN^Ho1d7lM_HU2`:2N004LO002N2N3M2N2N3M2QOo0PO][=NSVB1[?;I01O0000001O0000001O0000001O0000001O0000001O00000000000000001O00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000O1N2O1N2O1N2N2O1N2N2O1O1O1O1O1O100O1O1O1O1O100O1O1O1O1O1O100O1O1O1L4H8G9H8G9H8G9H8G9H8G9H8G9H8G9H800O10YOkEoKV:P4jEPLX:n3hERLY:l3hETLY:k3gEULZ:f3gEfKN249W:g3eEmKP10[9m3Y1O1O10YO`LQEG7g3\\:VMbEi2[:[McEf2\\:]MbEc2X:_1G910O1O1O10OhG", "size": [500, 333]}, "area": 18659.0, "iscrowd": 0, "image_id": 20107, "bbox": [34.0, 218.0, 299.0, 282.0], "category_id": 140, "id": 20001258}, {"segmentation": {"counts": "l1`1T>O00[OgNQCX1o<iNPCW1R=hNnBX1R=hNmBX1T=hNnBMCR1`=ROSCk0h<oNgB7a0j0g<]O[Ca0e<^OoBGMh0V=AkBI0d0U=DkBH0c0U=FjBI0`0W=GcBO69X=9hBG[=7eBI\\=6dBI]=7bBI_=8^BId=7YBJi=P11M4M2N1O2N3M1O1CjAXOZ>g0hAWOY>h0:O100000002N2M30OO4L01M2010N0010oN_OoB`0Q=FRBKb0`0[=L`B7`=J^B7W=_OaB;OA2e0_=_O]B?O_O4c0a=;_BDb=>ZBCg=T11N200O1O100N2000000O1O100N2O100O1O1O1O1O1O1N200O100O10000O1000000O1O1O1O1N2N2O1O1O100O1O100O1O1O1O1M300O10000O1O100O100O1N2O1Oa`W3", "size": [500, 333]}, "area": 4379.0, "iscrowd": 0, "image_id": 20107, "bbox": [0.0, 0.0, 121.0, 108.0], "category_id": 142, "id": 20001259}, {"segmentation": {"counts": "_Sl31c?001O0_@O^?4O1N2nMIkC1D8`<HkC1B9c<FjC4@7e<GjC4]O7i<EiC8XO7n<AhC=VO3P=CoBJc0[1\\<SOoBD>Z1b<YO\\Ci0_<@[Ca0_<]1O101O0000001O0000001O00003M4L5K5K2N2N1O2N2N1O2N1O2N2N1O2N2N1O2N1O2N2N1O2N1O2N2N001O0000001O00001O00001O0000001O00001O00001O", "size": [500, 333]}, "area": 8611.0, "iscrowd": 0, "image_id": 20107, "bbox": [254.0, 0.0, 79.0, 159.0], "category_id": 172, "id": 20001260}, {"segmentation": {"counts": "\\3]6V91O001O100O1O1N20000N2O00001O0010O01O1O1O1TNXIlJh6S5ZIjJg6V5\\IgJe6X5]IfJd6Y5]IfJc6R5gIlJZ6S5iIjJW6V5jIiJV6V5lIiJS6Y5mIgJS6Y5mIfJT6Z5lIfJT6[5kIeJh4kN_JNV1b6DdJ^4lNmJ5o0[6FdJ]4oNkJ9l0T6LdJX4VOnJ4l0R60cJS4KnKa51aJo32nK^55]Jl36PL^54ZJm38oK^58RJn3?jK_58QJo3`0iK_56RJR4?hK`55oIU4a0fK`56mIU4f0bK^57lIX4j0hJeNc0e6=lIX4U1SKo4d0kIZ4V1RKT5GWIb0`0]4_OUKf15Z54dIb4]OVKe11\\55cIe4@QKa14]5MmH2e0k4_1RKR5NlH4c0l4P9TKoFm4S9oJnFS5h91N200O1O100N20O100O01O011M2O101N2N2N1O001O0O20O010O010000O101OO10O1O1O1O1N2N2O001O10O03M2OO01O001O1M210O10000O00100O2O0O0O2O1O1I70000000001O1O1O00O100000000O1oNaGmJ_8o4lGjJT8R5VHhJj7T5aHeJ_7W5a1L4L4O100O1O100O1O100O1O1O100O1O100O1O100N2N2O1N2O1N2N2O1N2O1N2N2O1N2O1N2O1N2O1O1N2O1O1N2O1O1N2O1O1N2O100O10000O10000O10000O10000O10000O10000O10000O10000O10000O10000O10000O10000O10000O10000O1000000O10000O100001O0000001O0000001OMWC[Mi<e2WC[Mi<e2XCZMh<f2XCZMh<e24O1N2nMPCd01DP=HPCc01BQ=KnBb04@o<MoBb04]Oo<1mBa08XOn<7jB?=VOj<9lBFJc0R>E\\BFD>Q>KbB3_=IjB1W=Iajm0", "size": [500, 333]}, "area": 42646.0, "iscrowd": 0, "image_id": 20107, "bbox": [0.0, 0.0, 272.0, 313.0], "category_id": 173, "id": 20001261}, {"segmentation": {"counts": "Z[k16X?;F:E<E:E7M3N2M3N2M2N3N2M3N2]NlMbEW2Y:TN_En1\\:\\N\\Ef1`:eNXE]1c:mNUEV1f:o1K5N2O0O2N3N2M4L3jIlJh1V5YMgKf2]4YLbLe3a3\\K\\Mc4g2]JXNb5n5N1O2O0O2N2O0N3M2O2M3N1N3M2O2M3N1N3M2O1N3N1N2O1N3N1O1N2O2N1N2O1O2M2O1O1N3N100O101O0O10001N10000O2O000O101O0O10000O10000O10000O10000O10000O10000O10000O10000O101O0O1000000O10000O100001O0000001O0000001O0000001O000O1O2M2N2O1N3N1N2N2O2M2O1O1O2N1O1O101N1O1O1O2N1O105J5K6J5K2N3N2M2N3M2G:F:E:G:E:G:E;F9F;F9F;F:E:F;F:E:G:E9H9F9H8G9H9F9J6N3M2O1N3M2O1N2N3N1N2N3L3N2M4LTK", "size": [500, 333]}, "area": 63020.0, "iscrowd": 0, "image_id": 20107, "bbox": [121.0, 87.0, 212.0, 407.0], "category_id": 183, "id": 20001262}, {"segmentation": {"counts": "^6k7R8\\N1O00001O00000000001O00000000001O00000000001O00000000001O0000000000001O000000gN[JmIf5k5dJRJ]5f5mJWJT5a5WKZJk4^5_K_Jb4\\5eKaJ\\4[5kKaJV4Z5QLbJQ4Y5VLdJk3W5f2L4K5K5K5L4K5M3N2M3N2N2N2N2N2N2N2M3N2N2N2N2N2N2M3L4L4L4L4L4L4L4L4L4L4M3M3]OgCSO\\<m0`0000000000000O10000O1O1000000000000000000001O1O00001O00000000001O000000O10000000000O100000000000000O100O100000000001OO100O11OO10000000000000000O100000000O100O100O100O1O100O100O1O100O100N]B^Oc=a03O1001O001O1O1O000000000000000000000000001OO1O100O1000000000000000000000000000000001O001O000000000000O10000001O00O10000O1000000O11O00O1000000001OO1000000O100000000000000001OO1001OO11OO100O11OO11OO1O10000O100000000001O00001O000000O100001OO100O100000000000000O10000000000001O00000000001O0000000000000000O10dH]1ROcNl0c1PO]Nn0i1nNWNP1o1lNQNR1U2jNkMT1[2hNeMV1a2fN_MX1i2bNWM\\1f3iMYLV2l3gMTLW2P4gMPLW2T4gMlKW2Y4fMfKZ2]4dMcK\\2`4aM`K_2b4_M^Ka2d4]M]Ka2g4\\MXKe2j4YMWKf2l4WMTKh2o4VMQKj2Q5TMnJm2U5PMkJo2X5oLgJR3\\5kLbJW3`5gL`JX3c5fL\\J[3g5bLUJb3m5\\LRJd3R6X22N3L3N3M2M4M2N3L3N3M2M4M2N3L3N1N2O0O2O0O2O0O2O0O101N101N101N100O2O0O2O0010O0001O001O0010O0000000\\NmIQKT6o4PJmJQ6R5RJjJo5U5UJhJk5X5YJcJh5]5[J`Je5`5^J\\Jc5c5aJZJ_5f5eJUJ]5j5fJSJZ5m5iJoIX5P6mJlIS5T6PKhIQ5[6]17J>A<D1O001O0010O01O1O001O0100O010O0100O0100O010O0100O01O1N1O2M2O2N3L4M3M3L4K5K5K5J6K5K5M210O0010O01O010O001O0O2O2M3M2N3M3N2M3M2N3N3L4L4L4M3L4L7H:F9G9G9G9F;F9G8H:F:F9G:E:GlUi1", "size": [457, 640]}, "area": 234700.0, "iscrowd": 0, "image_id": 20247, "bbox": [0.0, 3.0, 640.0, 454.0], "category_id": 111, "id": 20001263}, {"segmentation": {"counts": "0j1_<ROn01O1O2N1O1O2NN2J6I7J600000000000000001O00000000000000000000001O00001O00001O00001O00001O00001O0000001O00001O00001O00001O0000000000001O00000000001O0000000000001O000000001O5K00000000000000O10000O1O1000000000000000000001O1O00001O00000000001O000000O10000000000O100000000000000O100O100000000001OO100O11OO10000000000000000O100000000O100O100O100O1O100O100O1O100O100N]B^Oc=a03O1001O001O1O1O000000000000000000000000001OO1O100O1000000000000000000000000000000001O001O000000000000O10000001O00O10000O1000000O11O00O1000000001OO1000000O100000000000000001OO1001OO11OO100O11OO11OO1O10000O100000000001O00001O000000O100001OO100O100000000000000O10000000000001O00000000001O0000000000000000O1000000000000000000O1000000O10000001OO11O0000O100O1N200O1L4Onck3", "size": [457, 640]}, "area": 8006.0, "iscrowd": 0, "image_id": 20247, "bbox": [0.0, 0.0, 363.0, 58.0], "category_id": 150, "id": 20001264}, {"segmentation": {"counts": "U?V7T700O2N1O1O2NN3I6I7J6000001O00000000010O00000000001O00000000010O0001O000010OkNbI^J_6Z5kIcJW6T5TJgJn5Q5\\JlJe5l4eJQK]5g4mJUKT5g4RKVKo4e4XKWKk4c4]KYKd4b4d2K5L5J5K5K6K4K5N3M2N2N3M2M3N2N2N2N2N3M2N2N2N2M3N3L3L4L4L4L4L4L5K4L4L4L4M9G\\]^3Z1[aaL6J6J6J6J6J6J8Hm0SO5K4L4L5L3N3M2N2M4M2N3L3N2N3L3N3M2M3N3M2M4M2N3L3N3M2M4M2N3L3N3M2M4M2N3L3N1N2O0O101N101N101N100O2O0O2O0O2O0O101N101O010O001O00001O010O0000O1\\NnIQKR6o4RJlJP6S5SJjJm5V5VJfJk5Y5ZJbJg5^5\\J_Jd5a5_J[Jb5e5bJWJ^5i5eJSJ]5k5gJRJY5n5kJmIV5S6mJjIS5V6PKfIQ5]6\\17J>A<D001O001O10O01O001O001000O010O010O010O10O10O010O010N2N1N3N1O2N3L4M3M3L4J6K5K5J6K5K5O010O0010O01O010O001O0O3M2N3N2M3M3M2O2M3M3M4L4M3L4L4L6J8G9G9G9G:E:G9G9G9G9G:F:E:G:FhUi1", "size": [457, 640]}, "area": 49310.0, "iscrowd": 0, "image_id": 20247, "bbox": [1.0, 15.0, 510.0, 424.0], "category_id": 183, "id": 20001265}, {"segmentation": {"counts": "Q9o:Q9]NVGPHj8i6^HVIb7]6mHaIS7^6PI`IP7`6QI_Io6`6TI^Il6a6WI]Ii6b6ZI\\If6c6\\I\\Id6c6_I[Ia6d6bIZI^6e6dIZI\\6e6gIYIY6f6jIXIV6h6lIVIT6i6nIVIc6Y6_IeIa6Z6bIdI^6\\6fI`IZ6_6hI`IX6`6jIZIeLOa9f6nIXIbL0a9h6d300O100O100O10000O100O100O100O10000O100O100O2O000O100jIbBi5`=TJcBk5k=M10000O2O0O100O2O0O1O2N1O1O^OcJcB\\5]=gJ]BJI_5j=b03N2NROSJkCl5V<^J`Ca5a<aJ]C^5c<eJ\\CY5e<hJ[CU5f<mJXCS5i<mJVCS5k<mJTCS5m<nJQCR5P=oJoBP5S=oJmBP5T=QKkBV1Hl1_=mLiBV1Ki1_=VM_BQ15]1c=fMWBm09o0k=NXBET>9oAZO[>g0hAkNc>T1`A_Nk>`1XASNR?m1RAeMY?[2U101O1O010OXO_MS@`2n?cMn_O]2R`0fMe_Oa2Z`0dMb_O[2]`0d0O20L3M4L4K5L3M4K6K5K4L3L4M5K3L4N2O1O1O1O1O3M2O2M3M3M201N2N3N1N3M3N2M2N3M3N1N3M3NSW_11kh`N0X\\OOac0:c\\OFH050kb0=S]O1ib0d0O1O1N2O1N2O1N2O1O1N2O1N2O1N2O1N2O1O1N2O1N2O1N2O1O1N2O1N2O1N2O1O1N2O1N21O1O1O1O1O1O1O1O1O1O1O001O000000000000000000000000O1000000000000000000000000000000000000000000000000O1O1O100O1O100O1O1O100O1O1O100O1O100O1O1O100O1O100O1N2O1O1O1N2RM^M]Dc2b;lMoCU2P<UN]BQOc0l2o<POiBQ1V=ROgBo0];kLhEZ2fNm0b;mLbEZ2iNj0d;RM]EX2kNg0h;UMWEX2nNd0l;XMPEf5n:dIUDk0f0c5U;bIUDl0c0d5W;aIVDj0<UOIa6e;`IWDi0;n5^;YIWDj09n5_;YIXDe0O[O8i6a;VIYD417NE8j6_;WIZD237LF013i6d;VI]D056GI210k6\\<YIbCK32Mj6`<VIfCP7[<oHcCS7^<lHcCS7f<O1O0000001O001O1O00N2O1O1N200ImH_CU7a<5KeHcC]7R<cHTDb7l;9002N1O001O00jNbHjE^7U:dHjE\\7T:hHjEX7T:kHkEU7U:mHiES7Y:kHgEU7[:jHdEV7]:iHbEX7_:gHaEY7_:hH`EX7`:hH`EX7`:hH_EY7a:gH_EY7`:hH`EX7`:hH`EX7[:XHVEa0>W7]:mHcES7^:lHcES7_:lH`ET7a:lH^ET7d:kH[EU7f:lHWEU7i:mHTET7l:PIoDQ7Q;o00000001O001O00001O000000001O", "size": [640, 427]}, "area": 52652.0, "iscrowd": 0, "image_id": 20333, "bbox": [0.0, 231.0, 427.0, 409.0], "category_id": 111, "id": 20001266}, {"segmentation": {"counts": "0Q9o:00000000000000000000000000000000a0_O000000000000000000000000000000000000000000000000000000001O0000000jEbFj9_9SFdFl9j9N0000001O0000001O00001O0000]OYFmFg9R9\\FhFJIk9^9d01O1OQOcE[H]:e7mEQHS:o7oEoGQ:P8RFoGm9Q8TFoGk9P8WFnGj9R8VFmGk9S8UFlGl9T8UFjGl9V8UFiGk9X8TFhGl9X8UFgGk9Z8TFfGl9[8YF^Gh9_8\\F`Gd9`8n01OO10000O100O10000O100eMaD\\KNYOa;X5iDZKIZO_;Y5PEWKDWOc;`5nDUKDWO^;a5REUKc;h4aDUKa;i4bDVK^;g4fDWK[;f4iDYKW;e4lDZKT;c4PE\\KP;a4SE^Kn:`4UE_Kk:^4YEbKf:[4^EeKa:W4dEhK\\:U4hEiKY:T4kEjKV:S4nEmKQ:P4SFnKn9o3VFoKk9m3ZFQLg9l3]FRLd9l3_FRLb9n3^FQLc9n3X3O1O100O1O1O100O1O100O1O1O100O1O1O100O100O10000O10000O10000O10000O10000O10000O10000000000000000001O0000000000001O000000000000001O000000000000001O000000001O001O001O001O001O001O001O1O001O001O001O001O001O001O001O001O001O1O1O1O2N1O2N1O1O2N1O1O2N1O1O1O1O2N1O1O1O1O1O1[BeKc;]4YDkKa;V4VDTKoNj0j<S4UDTLj;m3TDTLl;m3RDULm;l3QDVLn;k3oCWLQ<j3mCXLR<j3kCWLU<j3iCXLV<i3hCXLX<i5O001O001O001O001O001O001O001O001O001O001O00001O001O001O001O1O001O001O1O001O001O001O1O001O001O1O001O001O001O1O00001O001O001O00001O001O001O001O001O001O001O001O001O1O2kG[Gk4g8SKYGm4j8PKVGkM5R6h8PLSGmM<m5d8SLQGoM;n5f8QLoFRN9n5k8mKlFQNO[O8i6P9gKjFcM17NE8j6R9dKgFeM37LF013i6Y9aKhFeM56GI210k6]9ZKWGoMZOK32Mj6b9VKWGPN]OP7_9lJVGSNYOS7d9fJUGVNXOS7e9dJ]G[5f8`J]G_5f8]J[Gc5h8YJYGg5i8VJXGj5T9iInFV6^;01O1O00N2O1O1N200ImH_CU7a<5KeHcC]7R<cHTDb7l;9002N1O001O00jNbHjE^7U:dHjE\\7T:hHjEX7T:kHkEU7U:mHiES7Y:kHgEU7[:jHdEV7]:iHbEX7_:gHaEY7_:hH`EX7`:hH`EX7`:hH_EY7a:gH_EY7`:hH`EX7`:hH`EX7[:XHVEa0>W7]:mHcES7^:lHcES7_:lH`ET7a:lH^ET7d:kH[EU7f:lHWEU7i:mHTET7l:PIoDQ7Q;o00000001O001O00001O000000001O", "size": [640, 427]}, "area": 101797.0, "iscrowd": 0, "image_id": 20333, "bbox": [0.0, 0.0, 427.0, 423.0], "category_id": 129, "id": 20001267}, {"segmentation": {"counts": "b_<3oc0O10T\\OOfc03W\\ON0Oec090000000000000000000000000000000000000000000000000000000000000000O100000000000000000000000000000000000000000000000000000000000000O10000000000000000000000000000000000000000000000000000000000000000O100000000000000000000000000000000000000000000000000000000000000O10000000000000000000000000000000000000000000000000000000000000000O100000000000000000000000000000000000000000000O1O1N2O1N2O1O1N2O100OZOm\\Oa0Zc0Cf\\OH050L\\c02Rao3", "size": [640, 427]}, "area": 2586.0, "iscrowd": 0, "image_id": 20333, "bbox": [19.0, 612.0, 203.0, 28.0], "category_id": 132, "id": 20001268}, {"segmentation": {"counts": "]V17bb0X1B?M3N1N3M3M3M2N3M3M2N3M3N2M2O2M3N1N3N2M10000O100O100O10000O100O100O100O01000O100O100O10000O100O100ORMk_OS2U`0jMP@T2P`0iMT@V2k?kMW@S2i?PNV@n1i?VNV@h1j?[NU@c1j?aNT@^1l?eNS@Y1l?iNU@U1j?lNX@R1g?POY@o0f?RO\\@l0c?VO^@h0a?YO`@f0_?[Oa@e0]?^Ob@b0]?_Ob@b0]?@b@?^?_Oe@a0Z?^Og@c0X?\\Oj@d0U?ZOn@f0P?ZORAf0m>ZOSAg0l>Gg@9X?Ld@4[?Md@4[?Nd@2[?Oe@1b;kMPHU2]L1b;mMmGU2`LNb;PNbG\\2lLDb;SNTGd2YMZOb;UNhFT2fMVNOa1b;XN\\F]2aN[MAP2b;[NnEf2WOoNj:^NbEo2BdNk:`NVEX3NXNk:dNiD`3;lMl:[3REfLm:\\3REdLm:^3REbLm:`3QEaLo:_3QEaLn:a3QE_LR6hMZNk5bK^LQ6oMWNe5gK\\Lo5WNUN]5lK\\Lm5\\NSNY5oK\\Lk5_NTNV5QLZLi5dNUNR5RLZLg5gNRNS5WLVLd5kNPNZ4^LmJMo1b5mNoMZ4dLiJKP2`5POkM[4kLeJJP2]5TOiMZ4RMaJGR2[5WOgMZ4XM\\JFS2Y5ZOcM[4`MUJFV2T5^OaMZ4gMPJDY2Q5A_MY4nMjID\\2k4F^MW4TNeIE^2f4J[MW4hNPLj3MYMV4nNmKf31WMV4SOiKc35TMX4`NWI6\\2S49SMY4]NXI;W2Q4=TMX4ZNYI`0R2o3a0TMk2bNiK:k0n3c0TMh2kNgK1o0P4c0QMf2UOeKIR1P4e0oLc2_O`KDX1m3g0mL_2J\\K]O_1k3g0mL\\22XKXOe1i3h0jLZ2:UKUOi1f3j0hLW2a0SKkNT2k3c0gLU2h0QK_N_2Q4=gLP2n0\\N[2EeLm1T1]NV2HdLn1R1\\NY2IcLl1l0bNa2CaLn1l0_Nb2F_Lm1n0\\Nc2J]Lk1o0[Nc2M[Lj1Q1YNd2NYLl1Q1VNe21WL\\2c0bMU35VLZ2d0aMU37ULZ2e0_MV39RLZ2g0]MV3<QLX2h0\\MW3U7gLkHX3W7gLiHY3X7gLgHY3Z7fLfHY3\\7fLdHZ3]7eLcH[3^7dLbH[3`7dL`H\\3a7cL_H]3a7cL_H\\3c7dL\\H\\3e7cL[H]3e7cL[H\\3g7cLYH]3g7cLYH]3h7bLXH]3i7cLWH]3j7cLUH]3l7bLTH]3m7cLSH]3n7bLRH^3n7bLRH^3o7aLQH_3P8`LPH`3P8`LoGa3R8_LmGa3S8_LmGa3T8^LlGb3V8\\LjGe3V8ZLjGf3W8YLiGg3W8YLiGg3W8YLiGg3W8ZLhGf3X8ZLhGf3Y8YLgGh3X8XLhGh3X8XLhGh3X8XLhGh3X8XLhGh3X8YLgGg3Y8YLgGg3Y8YLgGg3Y8YLgGh3X8XLhGh3X8XLhGh3X8XLhGh3X8XLhGh3Y8XLfGh3Z8XLfGh3[8WLeGi3[8WLeGj3Z8VLfGj3[8ULeGk3[8ULeGk3[8VLdGj3]8ULbGm3]8SLcGm3]8SLcGn3]8QLcGo3]8QLcGP4\\8PLdGP4]8oKcGR4\\8oKcGQ4]8oKcGR4]8mKcGS4]8mKbGU4]8kKaGW4S<2N2]LeK]G]4R<2M2O2M2O2M2O2N1N3N1RMPKjFS5V9mJhFT5X9mJeFV5Z9kJdFV5]9iJbFY5Y9lJdFV5Q9VKmFl4f8aKWGb4]8jKaGX4S8TLjGP4P8TLoGn3P8SLmGP4R8QLlGR4R8oKlGS4R8oKkGT4T8mKjGV4T8kKiGX4V8hKiG[4U8fKhG]4V8eKhG]4W8dKgG^4X8cKeG`4Z8aKdGb4Z8^KdGe4[8\\KcGf4]8ZK`Gi4`8WK^Gk4b8UK\\Gm4d8SKYGP5h8nJWGT5d0ZIQ6b1XIX5=aHQOk0W7\\1YIZ5=bHoNj0\\7Z1UI]5>`I]6Q1VI`5;`I`6m0XIc56bIb6i0ZIe51eIe6d0\\Ig5MfIh6a0]Ii5IhIj6=_Il5DhIn6:`In5@jIP76bIP8^6oGdIQ8\\6mGeIS8[6lGgIT8Y6jGiIU8W6jGkIV8U6YGfHZOV1]9U6UGlHYOP1c9T6oFaJQ9m5^FVJc9S8001O001O001O001O001O001O00001O001O001O0O2O1O001O001O1O001O001O001O1N1O2N101N2N101N1O2N101N2N100O2N1O2O0O2N100O2N1O2O0O2M2O2N1N3N1RMUEfLm:X3`ETKoNb0b;Y4iEfJTOf0V;b4_FZKb9j2WD\\N[2fN`9l2[DXNX2hN_9P3]DRNY2jN\\9S3`DlMX2nNZ9T3cDiM]>U2hAdM]>X2jAaMZ>\\2lA\\MY>a2nAWMU>f2f1M3L4M2N3L4L4L3M4K5L4L3M4L7I?AXaT1", "size": [640, 427]}, "area": 110540.0, "iscrowd": 0, "image_id": 20333, "bbox": [1.0, 107.0, 367.0, 523.0], "category_id": 183, "id": 20001269}]}
\ No newline at end of file
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010005.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010005.jpg
deleted file mode 100644
index 0d01d232c81b26c9aa248aa57c7efa0a99df3174..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010005.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010008.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010008.jpg
deleted file mode 100644
index 008094db744379178cd6c926280f3e8d7b1c8c23..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010008.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010012.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010012.jpg
deleted file mode 100644
index 67387d63b8049d1cb2c92061922e5caf2ddbe801..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010012.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010014.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010014.jpg
deleted file mode 100644
index 10fc29c73a293ff8f285097ee22647ac4120ce3c..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010014.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010015.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010015.jpg
deleted file mode 100644
index 3f65ed88daf660f12ab4a517b5d94700a5fafbc8..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010015.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010023.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010023.jpg
deleted file mode 100644
index fcac786c55af86715c1e3952b3af603cefc68ba4..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010023.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010024.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010024.jpg
deleted file mode 100644
index 99300eccfc423be6022bf506c3072e892c165d3f..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010024.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010037.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010037.jpg
deleted file mode 100644
index 689e9f544084c9c40eebd03c598a43fdb854e906..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010037.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010039.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010039.jpg
deleted file mode 100644
index e1f866df32bb473d2b634fc0c1c05590a26d86ef..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010039.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010040.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010040.jpg
deleted file mode 100644
index 4a1f1cea8ee8534102acf12a12f4ae1c78c48cbc..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010040.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010041.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010041.jpg
deleted file mode 100644
index eb3443116ee4e5962690f64cdb782e22a2674f86..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010041.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010046.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010046.jpg
deleted file mode 100644
index 7aa89ea79433c3217e5db6c18301a2c865601c2c..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010046.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010056.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010056.jpg
deleted file mode 100644
index e6445455ff5a84fa8b50bb03813799ed854a65a1..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010056.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010058.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010058.jpg
deleted file mode 100644
index c5950601f0fb68c31419565dd3e94b26e47a6c9f..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010058.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010069.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010069.jpg
deleted file mode 100644
index 9c0d6a0a9ec6c9ff7050e0c9892d3f922d438f85..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010069.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010073.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010073.jpg
deleted file mode 100644
index de4b4a372a09f5523e51aa4ec68991ed850e6b3e..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010073.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010077.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010077.jpg
deleted file mode 100644
index e28cadef3d056117246cfc32eb856e9323ef1db3..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010077.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010082.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010082.jpg
deleted file mode 100644
index 359d1b70e5ac7c198c8b7194251e65aa3a1d9005..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010082.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010083.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010083.jpg
deleted file mode 100644
index b2e653dba8b3e0e6e1e3fe06839fdc4c1ee534e5..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010083.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010084.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010084.jpg
deleted file mode 100644
index dba1752e7996493f25c29efd4edeb317e47d69fe..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010084.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010094.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010094.jpg
deleted file mode 100644
index a7e9e1a8e4a4814a84bf4fafd223f1d411c79ef4..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010094.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010097.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010097.jpg
deleted file mode 100644
index f7dd764632d057170fb433a5af7d8c90aa4edd36..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010097.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010104.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010104.jpg
deleted file mode 100644
index 6e57d05be3d920af265d9949f0d05fae2ee2fa7b..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010104.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010107.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010107.jpg
deleted file mode 100644
index 92fe1dfe8c81eac02013081fc6efb0a5340c5280..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010107.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010108.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010108.jpg
deleted file mode 100644
index 98d5e91ea96b2d018d7e6cb650169ff5b8023d01..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010108.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010114.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010114.jpg
deleted file mode 100644
index af644b98c1aee7d176c30a720657beafbcca3bbd..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010114.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010115.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010115.jpg
deleted file mode 100644
index fc53bea050e5e6f019d6d2f2b5fbbf89abe43885..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010115.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010123.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010123.jpg
deleted file mode 100644
index 8eeba46f0882d234be75f2c954f750438563f3d6..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010123.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010125.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010125.jpg
deleted file mode 100644
index 54a2d88e7e5ce36f1de86726593f1743e327d0e0..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010125.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010130.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010130.jpg
deleted file mode 100644
index 812165fd9a14e7dfeb89c1791741de382e2e68b0..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010130.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010136.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010136.jpg
deleted file mode 100644
index 63cad9040f944094d59532a535e4d53aa8f947ac..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010136.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010138.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010138.jpg
deleted file mode 100644
index 95d92a7a65365f79035749daa63f837390af6165..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010138.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010142.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010142.jpg
deleted file mode 100644
index 8e33d5d5f26d2ea9793d3f545456cb9ccd6854de..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010142.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010145.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010145.jpg
deleted file mode 100644
index f97492871a92bb37a6ca5680515d04a71bcf5077..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010145.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010149.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010149.jpg
deleted file mode 100644
index 521b9aefa2b48f28ca501d7deaa6689ad17c341f..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010149.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010161.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010161.jpg
deleted file mode 100644
index 63b016eeca2823bcbe638839815627714c06f875..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010161.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010166.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010166.jpg
deleted file mode 100644
index 04ac245e2292d3f5218098a87c988b205a1e92f3..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010166.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010175.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010175.jpg
deleted file mode 100644
index a243e6d6dbc8563930f29f69382608ae72588857..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010175.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010176.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010176.jpg
deleted file mode 100644
index 6a01a7b41cff3f986cf93bbcfce4ee726bab326f..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010176.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010179.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010179.jpg
deleted file mode 100644
index d54cff8341fe5a4d3438171874f96a982133e263..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010179.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010192.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010192.jpg
deleted file mode 100644
index 4b147369ad45172bf535a9712e4df8bf90e89a93..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010192.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010196.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010196.jpg
deleted file mode 100644
index ca2428c2531d24c3cc949bc931d394f76e60c610..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010196.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010205.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010205.jpg
deleted file mode 100644
index c0a37eaf8802cbc0985b9454119d1f24a60d7b68..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010205.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010211.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010211.jpg
deleted file mode 100644
index 4e36020afd10bd08616ef326e52d25868bb7410b..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010211.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010216.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010216.jpg
deleted file mode 100644
index e3fa8130f5fa58b24d6265d3cf4643f1ed716f0a..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010216.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010217.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010217.jpg
deleted file mode 100644
index c286e654c7a98da75a71a6f107722d9586bf072e..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010217.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010219.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010219.jpg
deleted file mode 100644
index b5fc7dae66b6378deeceb62c454e2e0aab90290e..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010219.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010222.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010222.jpg
deleted file mode 100644
index 21fb0b19f04aa6f026b29b573aaa484772cd3984..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010222.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010229.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010229.jpg
deleted file mode 100644
index 6d240643a59949d4587d66c98a5b2de9b71f01ad..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010229.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010230.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010230.jpg
deleted file mode 100644
index cc015805511907ff7268f1e8bc3b92e4ac4f123f..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010230.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010232.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010232.jpg
deleted file mode 100644
index 9f2b4bb8c500433ae27cbec4ea020dfc1374b78b..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010232.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010239.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010239.jpg
deleted file mode 100644
index 89ab497d180f2e0652f2cf0a27ee4195a33d2afb..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010239.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010241.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010241.jpg
deleted file mode 100644
index ade270b2af4061e1d151d8f42411ed1647e0d7ee..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010241.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010243.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010243.jpg
deleted file mode 100644
index e1de0ab8fecfd1ee7ef7eebc3aea1718bbb94e6c..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010243.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010244.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010244.jpg
deleted file mode 100644
index dd63205f6f70eac8b81c9a0ce8e65cb4ea25ec34..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010244.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010245.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010245.jpg
deleted file mode 100644
index 66c44a1e95ae77401c1f894b37417d74e71e4d18..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010245.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010248.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010248.jpg
deleted file mode 100644
index beb9a8e3374b94e5d028135f856483a0dd180a74..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010248.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010249.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010249.jpg
deleted file mode 100644
index 538c32912eb2c35bc8ff14623103021811c72909..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010249.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010256.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010256.jpg
deleted file mode 100644
index d20e4bfb259d5167256083f20ca952018c02900d..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010256.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010263.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010263.jpg
deleted file mode 100644
index 14f8ea3baa01b1b7adb22660264f13182138488f..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010263.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010265.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010265.jpg
deleted file mode 100644
index ac6c8309c7bb805a706c2cc4609264d6bdc1dc3c..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010265.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010275.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010275.jpg
deleted file mode 100644
index 3103a3adefc2d7e2f7d0227a44ef13c1cf9c9fb6..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010275.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010276.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010276.jpg
deleted file mode 100644
index 3565e1c6e48fe235cdc1086d94a62cd9329c56cd..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010276.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010281.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010281.jpg
deleted file mode 100644
index 25185cba897ecbf7205696f229c2001b48a1ab97..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010281.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010290.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010290.jpg
deleted file mode 100644
index 67eb933911f2e5f6ec802f7084d4833b527701ed..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010290.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010303.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010303.jpg
deleted file mode 100644
index e17cb7157c4914050b32c0db202552d4eebad998..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010303.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010309.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010309.jpg
deleted file mode 100644
index 2e16e69a939cf383a676a8ca83ac608b421da816..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010309.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010313.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010313.jpg
deleted file mode 100644
index ebd1b1da721c38a59d1ba033206ca6cc43657ca6..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010313.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010318.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010318.jpg
deleted file mode 100644
index b93f06d19d20e1bb1824a98a793e5d2b5a26ca9a..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010318.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010319.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010319.jpg
deleted file mode 100644
index 5004052107bee859fdea69d5ce90d61411598df0..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010319.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010321.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010321.jpg
deleted file mode 100644
index c06ca6d4ddd349e621e700db9556132602ac964a..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010321.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010324.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010324.jpg
deleted file mode 100644
index e8e7a69c4405582646b72b7551aea77ed0dbc0c0..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010324.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010327.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010327.jpg
deleted file mode 100644
index 96f34d95e22b9b4b51c44f7639571b0ce645b2ae..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010327.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010337.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010337.jpg
deleted file mode 100644
index ff3094913ec7f40989247191b884f817f127fd32..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010337.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010342.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010342.jpg
deleted file mode 100644
index 9242ff36e5f2cdadb826c6b28201fa2a2041515b..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010342.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010343.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010343.jpg
deleted file mode 100644
index 49bec9bf618e8eb1c8799c0bf7cb9f9914a06b43..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010343.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010346.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010346.jpg
deleted file mode 100644
index ad3318a388c5c92d7da25821d7db37984a39357b..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010346.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010358.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010358.jpg
deleted file mode 100644
index 294af8cc94a39920ab8c5faabcbf82a0df6d57fa..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010358.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010369.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010369.jpg
deleted file mode 100644
index b83a0bcf3bce46dd169d7e1488bf2eabe909ded8..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010369.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010386.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010386.jpg
deleted file mode 100644
index 9a7efcb5bb1a8a26ab133e419b3e65f3750d0e47..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010386.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010388.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010388.jpg
deleted file mode 100644
index aab8c87a31c42305e8f42ad56165c529d26ef048..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010388.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010393.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010393.jpg
deleted file mode 100644
index 98485b9ca79f6f7fe92bb8520907628035d07511..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010393.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010395.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010395.jpg
deleted file mode 100644
index b9b393aeba4bc975598cfe07cf0692e847819a53..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010395.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010400.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010400.jpg
deleted file mode 100644
index 000394595fa46b1e3ca6e112eb5369695af42cda..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010400.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010403.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010403.jpg
deleted file mode 100644
index 15651186e1de44ea6ab710fdb09d7f42a95e7165..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010403.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010405.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010405.jpg
deleted file mode 100644
index 26cf47b485f66c5ec5526dc5b01e3e4deb4f0830..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010405.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010407.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010407.jpg
deleted file mode 100644
index 69f1861a2613d2d52645ebca6c752689877dbf71..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010407.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010414.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010414.jpg
deleted file mode 100644
index 955dd0cd6174579495170111de6efb41d6e7c12e..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010414.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010420.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010420.jpg
deleted file mode 100644
index 928302c30fef1b76a5cfc9d5511f8db86da401a2..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010420.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010421.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010421.jpg
deleted file mode 100644
index 45cbe7767672356caa9d5e0f8eff71bdeea0a872..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010421.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010428.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010428.jpg
deleted file mode 100644
index 6f55f2d1a4f549ad4ebf42b278be45cb7cc0b972..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010428.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010430.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010430.jpg
deleted file mode 100644
index 69de3a1c04e4ddce054762fd4c9f4bee8967894b..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010430.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010432.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010432.jpg
deleted file mode 100644
index aa3af3238f92b5dd762f75ed14c974a3ec204d5b..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010432.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010434.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010434.jpg
deleted file mode 100644
index 41e5f3f55c63ac002052923a03ce5e21d9127100..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010434.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010440.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010440.jpg
deleted file mode 100644
index bb2949c47f074877924270d53ced182f1ebca372..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010440.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010442.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010442.jpg
deleted file mode 100644
index b77845478799b0f56f6101f661f5e956e94fc667..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010442.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010444.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010444.jpg
deleted file mode 100644
index f5de15163f43e3e15339fa38d262ef9d5f6084b2..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010444.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010445.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010445.jpg
deleted file mode 100644
index 89d64af18dee567da468d76180f2d15b77c32a50..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010445.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010449.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010449.jpg
deleted file mode 100644
index ff9f4b25060b7197c09995be8cccd07f65f2b65b..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010449.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010463.jpg b/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010463.jpg
deleted file mode 100644
index 8111ec0ffae2246d512353ebb24b1903e8a03907..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/train2017/000000010463.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000010092.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000010092.jpg
deleted file mode 100644
index 54b98d5a019517fbf152416297690acebb310729..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000010092.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000010363.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000010363.jpg
deleted file mode 100644
index 99016f07a5354b8818d7d8bf59a2ad02be5b3e5f..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000010363.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000010583.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000010583.jpg
deleted file mode 100644
index aac4bf20f29ffdf5a19e2f26847229c3f3f17d52..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000010583.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000010707.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000010707.jpg
deleted file mode 100644
index f7c74e04d327843d02c514aad3a9e4b6ce0e0d54..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000010707.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000010764.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000010764.jpg
deleted file mode 100644
index 39327f0ece01500863c596aae58a7c69ed5630ca..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000010764.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000010977.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000010977.jpg
deleted file mode 100644
index e88481e26df33eb9fb807b563efccd5a9017c2dd..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000010977.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000010995.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000010995.jpg
deleted file mode 100644
index ef1db3e31f659fc08b915b3c8b768d57e0d99016..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000010995.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000011051.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000011051.jpg
deleted file mode 100644
index 71a14e1b6d823eb34d8051d3dd1b7226c7834386..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000011051.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000011122.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000011122.jpg
deleted file mode 100644
index 9541405d88ae703a4bc013a0dfdc973576aa852f..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000011122.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000011149.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000011149.jpg
deleted file mode 100644
index 3de5f53a2d09ccb139c9c789cca5666cb83d15a8..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000011149.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000011197.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000011197.jpg
deleted file mode 100644
index 3843d44902b0fc485d82956f2a7d5e0fe1ac6378..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000011197.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000011511.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000011511.jpg
deleted file mode 100644
index bbaf351478612d1724fbc0adb1c1dd11cb169e8a..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000011511.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000011615.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000011615.jpg
deleted file mode 100644
index 66ec4adf86de0db93dd6f297fa33ec181de3fe65..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000011615.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000011699.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000011699.jpg
deleted file mode 100644
index e65936fb180daaf152b432ed867c3c71b35da3c0..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000011699.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000011760.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000011760.jpg
deleted file mode 100644
index b16a5edf837d317f41da591af22be52efc684c06..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000011760.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000011813.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000011813.jpg
deleted file mode 100644
index 987783aa0d15e367663ffb6f4568d464367b62fb..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000011813.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000012062.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000012062.jpg
deleted file mode 100644
index 3c9de73569c928fc4937df914ea99ab5c45de0cc..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000012062.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000012120.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000012120.jpg
deleted file mode 100644
index a938ab298d0daf9c774c23b9ef96dc788981b153..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000012120.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000012280.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000012280.jpg
deleted file mode 100644
index 48127a519bdad9c8273f7b33c2a734fa3ed9069a..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000012280.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000012576.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000012576.jpg
deleted file mode 100644
index d4b1a5b32e7eeaeb973fe7b3640c18699371abd5..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000012576.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000012639.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000012639.jpg
deleted file mode 100644
index b2ed15cf8ae0de8fee4af45155f16c54d16fc21a..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000012639.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000012667.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000012667.jpg
deleted file mode 100644
index 0cf4956c5de3dc464c05be5f2a6617c7fedd89c6..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000012667.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000012670.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000012670.jpg
deleted file mode 100644
index b3e2e423bda5d9a4253008ba76ef6f28bdf8fd91..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000012670.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000012748.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000012748.jpg
deleted file mode 100644
index 8b5f4c5805f8d09b9e0f7d64fad5a4b938b6c5ea..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000012748.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000013004.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000013004.jpg
deleted file mode 100644
index 6cb5573cd4041c13a94d3002bbf3acc5f314eea8..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000013004.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000013177.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000013177.jpg
deleted file mode 100644
index 5b0b54aae527c605bac5a871ce0080af9cbf2fa6..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000013177.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000013201.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000013201.jpg
deleted file mode 100644
index b5ff17f37f01c14626504eecf1212177f12699c4..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000013201.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000013291.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000013291.jpg
deleted file mode 100644
index 19c4fffa6f0d45d1e2e15cc9b5e07c4fe6b5461c..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000013291.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000013348.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000013348.jpg
deleted file mode 100644
index 03bbb9404dcc1a2d900cd7f17447da3ca0e00999..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000013348.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000013546.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000013546.jpg
deleted file mode 100644
index 389684fac4ad79ea89ad8a22227e7c2816d449b5..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000013546.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000013597.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000013597.jpg
deleted file mode 100644
index 9a2687cfecd4c10db9008441ba70b0c194342ca0..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000013597.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000013659.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000013659.jpg
deleted file mode 100644
index 123d95f88c816368b3bc15473ffbe963a4f3b6cc..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000013659.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000013729.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000013729.jpg
deleted file mode 100644
index dd2bc4d99425885b19cd8b523c72f4c2d628249d..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000013729.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000013774.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000013774.jpg
deleted file mode 100644
index de140d83b64e10d0d36debc7babf5c32284abba3..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000013774.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000013923.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000013923.jpg
deleted file mode 100644
index 05e3f7cb0d95d30b50354566e509773f27a997c0..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000013923.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000014007.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000014007.jpg
deleted file mode 100644
index 86961634015e26e5b973f99622c8f67b51d26cc4..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000014007.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000014038.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000014038.jpg
deleted file mode 100644
index eb0cc770c4d2d712b00c05c4311d5103f806a0db..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000014038.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000014226.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000014226.jpg
deleted file mode 100644
index 4ae7f4274ae00565d6f690481ced3d69108cc695..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000014226.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000014380.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000014380.jpg
deleted file mode 100644
index a85ece8fb086dbaf6c23955d4464a2584180ace9..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000014380.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000014439.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000014439.jpg
deleted file mode 100644
index 0abbdab06eb5950b93908cc91adfa640e8a3ac78..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000014439.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000014473.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000014473.jpg
deleted file mode 100644
index b7192f3ffa82a95b621f28f8f8e7907f8a3bfd42..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000014473.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000014831.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000014831.jpg
deleted file mode 100644
index 27eda37bbc9458a05c116665f46520066edaaa18..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000014831.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000014888.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000014888.jpg
deleted file mode 100644
index a609cdf68a8a96a14e3f789fe8aff5e2435ef69d..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000014888.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000015079.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000015079.jpg
deleted file mode 100644
index 24be72cb39b6fb3cf87e44290b17fd1c06dadb97..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000015079.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000015254.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000015254.jpg
deleted file mode 100644
index 717ba8a59e510c173ef8204bf39a3f3bda2d51fc..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000015254.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000015272.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000015272.jpg
deleted file mode 100644
index 6cc3919689a4a1a0b6d0b2072462cdf10c0a6275..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000015272.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000015278.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000015278.jpg
deleted file mode 100644
index 1e69377f5405635ecec17ad38965af01a46cd125..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000015278.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000015335.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000015335.jpg
deleted file mode 100644
index 1231ff8d13cd839bccb4f22ac4873905ce1ea6fc..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000015335.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000015338.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000015338.jpg
deleted file mode 100644
index 765870ebd5be099ecb72987d789b5f09b953e62d..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000015338.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000015440.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000015440.jpg
deleted file mode 100644
index 4362ec1868889f76db5170bb63c359cf35b212cf..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000015440.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000015497.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000015497.jpg
deleted file mode 100644
index c2ab1aa89a8de9466ef10d3505aef0d3f47cf93f..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000015497.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000015517.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000015517.jpg
deleted file mode 100644
index 7b6751b450ac3c2c61b16c14e8932bf5403976da..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000015517.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000015597.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000015597.jpg
deleted file mode 100644
index a23272707413a3e14c85d45ca479b7b583ee763f..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000015597.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000015660.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000015660.jpg
deleted file mode 100644
index 69fc83c0ec773b0f0a1b7fce53e4fa995d4d93d1..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000015660.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000015746.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000015746.jpg
deleted file mode 100644
index 0fca66897b583029f4575579eb6e7e958abec02a..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000015746.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000015751.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000015751.jpg
deleted file mode 100644
index 5c629a8f6fd0c2efe51cf397b19fd704ae3b642b..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000015751.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000015956.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000015956.jpg
deleted file mode 100644
index f4cb09c4b01a3633e7fd1ea680a7112b41b28abf..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000015956.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000016010.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000016010.jpg
deleted file mode 100644
index aa3f083f485b4d927ee46d6e334d9a1201417c29..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000016010.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000016228.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000016228.jpg
deleted file mode 100644
index d5a5a3fb41c8954f8e7446907bee94a92688263b..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000016228.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000016249.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000016249.jpg
deleted file mode 100644
index 021626a393f4f9c559d8ed3962bfa63610a28211..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000016249.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000016439.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000016439.jpg
deleted file mode 100644
index 01c59d78539e5655c104bbbe262c2fb2ea885826..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000016439.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000016451.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000016451.jpg
deleted file mode 100644
index 806a9f64cd14df8e06d04591ef61efc964244064..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000016451.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000016502.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000016502.jpg
deleted file mode 100644
index ef4f855a3f2f2e5c837a27809c1683a5f0fb72f4..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000016502.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000016598.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000016598.jpg
deleted file mode 100644
index e401f9ab0b53ec29014f2d0021e0e30485b0ee4f..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000016598.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000016958.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000016958.jpg
deleted file mode 100644
index e637589a07ee4ae6f6b8a272c27ab37dafcf63fc..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000016958.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000017029.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000017029.jpg
deleted file mode 100644
index 3e2351f293cbf6d4a0fc525f00869728e623a460..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000017029.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000017031.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000017031.jpg
deleted file mode 100644
index 7d091e99ba05b6337b23f74748ad065f933a3a90..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000017031.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000017115.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000017115.jpg
deleted file mode 100644
index 3d6ec777845dcbb040dcaef38bb3f2a0a662c71b..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000017115.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000017178.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000017178.jpg
deleted file mode 100644
index 50016b522cd4a7c2ee7a623a1b5f45c740923f64..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000017178.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000017182.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000017182.jpg
deleted file mode 100644
index 28949d2c6fd22d26f20bfda1be582dba639dc15d..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000017182.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000017207.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000017207.jpg
deleted file mode 100644
index 494b48c99e0ceb33a28e9f99d566525eaed310ea..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000017207.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000017379.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000017379.jpg
deleted file mode 100644
index 04ee678f82c95de44eb73f02679ef30b6cdd9b37..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000017379.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000017436.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000017436.jpg
deleted file mode 100644
index 12dca6f0a16afea687045ae1cb5a00d1ff42e5d3..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000017436.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000017627.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000017627.jpg
deleted file mode 100644
index 3e11df1e5c49f05ce98fc9edd1b676478c3926d6..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000017627.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000017714.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000017714.jpg
deleted file mode 100644
index b1ebfff93d709153d1fb1070b685e57aa37db6a1..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000017714.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000017899.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000017899.jpg
deleted file mode 100644
index 618db3bd347e76aaddbbef0d4e5d0a2b3e53e48e..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000017899.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000017905.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000017905.jpg
deleted file mode 100644
index 6e87a525486a5f075875b3ebaf6d5d843f35cbe8..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000017905.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000017959.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000017959.jpg
deleted file mode 100644
index fba6776922c689b538e424495987aaa7e9dbf4ee..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000017959.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000018150.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000018150.jpg
deleted file mode 100644
index 547b5561169bcb86931c6e40577583deabd6e6dc..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000018150.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000018193.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000018193.jpg
deleted file mode 100644
index 4446aefee75836676db3e7cf2a8ae0035b17b5bf..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000018193.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000018380.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000018380.jpg
deleted file mode 100644
index b349df1b1320f95115176401eab6bcd16df9bb4e..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000018380.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000018491.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000018491.jpg
deleted file mode 100644
index 40bb5d796159b0310b313ea1610c53b2082394d2..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000018491.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000018519.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000018519.jpg
deleted file mode 100644
index e6a485a9bd2f0e246d839f7a2d320a990b44fe06..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000018519.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000018575.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000018575.jpg
deleted file mode 100644
index 618984af87601ec3a6bbf5bced7746ccc8de4e62..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000018575.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000018737.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000018737.jpg
deleted file mode 100644
index 340c394ff1398a1496c81855ff1128bbf8071842..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000018737.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000018770.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000018770.jpg
deleted file mode 100644
index d2472e57ae9e388fbb488c93d2e9deb1233f4ccc..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000018770.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000018833.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000018833.jpg
deleted file mode 100644
index 6408447eb6cfaa8bcb2aea7885b5f6f52aa1f106..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000018833.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000018837.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000018837.jpg
deleted file mode 100644
index 51b7b34992cb507620356020b070f61ad5a685c1..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000018837.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000019042.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000019042.jpg
deleted file mode 100644
index 440d74b0cda8a8c5a2badf2c4f10d8615d0edbaf..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000019042.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000019109.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000019109.jpg
deleted file mode 100644
index 1e20cdde8aeb8d81354488f4687e899a02fb6d11..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000019109.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000019221.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000019221.jpg
deleted file mode 100644
index 64afe2e38c4904f56c6d0a8ea2906ea37cabae4f..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000019221.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000019402.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000019402.jpg
deleted file mode 100644
index 82010ce5c61a061f02c41246aef5a4e8a0b7d12f..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000019402.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000019432.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000019432.jpg
deleted file mode 100644
index efdcc7fcda44e16d19cd4efbcbc7a7a8f73dcd70..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000019432.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000019742.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000019742.jpg
deleted file mode 100644
index 62ef3c4ca3fae28dd50cb8a854b71484451b6453..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000019742.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000019786.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000019786.jpg
deleted file mode 100644
index 5e174abcec3729cf870dbd6f8256c955ed7005e7..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000019786.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000019924.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000019924.jpg
deleted file mode 100644
index 1675bd91f592a61444018a8567f78acc2f5b5880..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000019924.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000020059.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000020059.jpg
deleted file mode 100644
index 32f2ab6412b14c646c02cbd341b1babc51d1300d..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000020059.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000020107.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000020107.jpg
deleted file mode 100644
index ced9d7c1f0e4e629ca9076cc9c0eeefb766b6026..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000020107.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000020247.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000020247.jpg
deleted file mode 100644
index 6e0e39f8955b28df574159bfc1cac1e9c1ec9425..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000020247.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000020333.jpg b/repositories/taming-transformers/data/coco_annotations_100/val2017/000000020333.jpg
deleted file mode 100644
index 6b7a73cca502f3ab4a6367a29feb131fbce939b3..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_annotations_100/val2017/000000020333.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_examples.txt b/repositories/taming-transformers/data/coco_examples.txt
deleted file mode 100644
index 030bd0f6c1d11fbd18f065f1748a34145dbfa881..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/data/coco_examples.txt
+++ /dev/null
@@ -1,35 +0,0 @@
-000000064898.jpg
-000000166563.jpg
-000000231169.jpg
-000000098392.jpg
-000000154358.jpg
-000000452122.jpg
-000000323895.jpg
-000000175387.jpg
-000000491464.jpg
-000000403385.jpg
-000000303653.jpg
-000000205834.jpg
-000000299355.jpg
-000000569273.jpg
-000000185599.jpg
-000000119445.jpg
-000000057672.jpg
-000000110638.jpg
-000000517069.jpg
-000000052507.jpg
-000000255824.jpg
-000000348045.jpg
-000000361180.jpg
-000000335529.jpg
-000000356347.jpg
-000000237928.jpg
-000000348481.jpg
-000000406997.jpg
-000000128658.jpg
-000000018380.jpg
-000000166259.jpg
-000000256775.jpg
-000000350405.jpg
-000000299720.jpg
-000000522393.jpg
diff --git a/repositories/taming-transformers/data/coco_images/000000018380.jpg b/repositories/taming-transformers/data/coco_images/000000018380.jpg
deleted file mode 100644
index b349df1b1320f95115176401eab6bcd16df9bb4e..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_images/000000018380.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_images/000000052507.jpg b/repositories/taming-transformers/data/coco_images/000000052507.jpg
deleted file mode 100644
index 882e148214a920408bba45f6a34234c2c84ab2b9..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_images/000000052507.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_images/000000057672.jpg b/repositories/taming-transformers/data/coco_images/000000057672.jpg
deleted file mode 100644
index d94f20de16e23e372eb4d622424fe6c5e23290ab..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_images/000000057672.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_images/000000064898.jpg b/repositories/taming-transformers/data/coco_images/000000064898.jpg
deleted file mode 100644
index c11b4c8f0ccaea43a19ca6d67d6f5d6b1e971cf2..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_images/000000064898.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_images/000000098392.jpg b/repositories/taming-transformers/data/coco_images/000000098392.jpg
deleted file mode 100644
index 6a6f23d06a01f0a2757bd8105c3bdfe51ec2908e..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_images/000000098392.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_images/000000110638.jpg b/repositories/taming-transformers/data/coco_images/000000110638.jpg
deleted file mode 100644
index 785492c1cba1c4870900f476b915a33603e882df..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_images/000000110638.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_images/000000119445.jpg b/repositories/taming-transformers/data/coco_images/000000119445.jpg
deleted file mode 100644
index d0ce218223773ed16ad7643ee42397c6a7793970..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_images/000000119445.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_images/000000128658.jpg b/repositories/taming-transformers/data/coco_images/000000128658.jpg
deleted file mode 100644
index 359d7accd382281ba93e3f979c62eb04c08fa771..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_images/000000128658.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_images/000000154358.jpg b/repositories/taming-transformers/data/coco_images/000000154358.jpg
deleted file mode 100644
index e8b00fa50fee12a90d82c5f0d67d1df7c76c3fc9..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_images/000000154358.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_images/000000166259.jpg b/repositories/taming-transformers/data/coco_images/000000166259.jpg
deleted file mode 100644
index fff26119bb2e2aa79daa51a04ffe794f834941fc..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_images/000000166259.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_images/000000166563.jpg b/repositories/taming-transformers/data/coco_images/000000166563.jpg
deleted file mode 100644
index 3248c20732dfefb32b106714197fc3f5a0e95d85..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_images/000000166563.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_images/000000175387.jpg b/repositories/taming-transformers/data/coco_images/000000175387.jpg
deleted file mode 100644
index cf579f03cbb257e38fa619daf11d5ad739dd02aa..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_images/000000175387.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_images/000000185599.jpg b/repositories/taming-transformers/data/coco_images/000000185599.jpg
deleted file mode 100644
index f5ceab0d6c5560a80821478e0ae77259d7e752d8..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_images/000000185599.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_images/000000205834.jpg b/repositories/taming-transformers/data/coco_images/000000205834.jpg
deleted file mode 100644
index f8e688831e1569bc986a378f10a9866f6d0ca2a8..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_images/000000205834.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_images/000000231169.jpg b/repositories/taming-transformers/data/coco_images/000000231169.jpg
deleted file mode 100644
index b67352bdec35320aa6567898ee5199fc4c4a305e..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_images/000000231169.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_images/000000237928.jpg b/repositories/taming-transformers/data/coco_images/000000237928.jpg
deleted file mode 100644
index 9a49e5364b0afc102cb5b530f6f1f0c314ff1bdc..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_images/000000237928.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_images/000000255824.jpg b/repositories/taming-transformers/data/coco_images/000000255824.jpg
deleted file mode 100644
index e201a7b08d0bc9190e9836219ed4f1cf490c02cd..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_images/000000255824.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_images/000000256775.jpg b/repositories/taming-transformers/data/coco_images/000000256775.jpg
deleted file mode 100644
index d5f2aaf05c4f3ce1415682f7ae2e36e1aa5a4881..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_images/000000256775.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_images/000000299355.jpg b/repositories/taming-transformers/data/coco_images/000000299355.jpg
deleted file mode 100644
index 1f4cb23a4f413ef4da992220130356c5fa44d1d2..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_images/000000299355.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_images/000000299720.jpg b/repositories/taming-transformers/data/coco_images/000000299720.jpg
deleted file mode 100644
index 60f20b9a5c030508d3e29ee622aef3bd26f49fa0..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_images/000000299720.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_images/000000303653.jpg b/repositories/taming-transformers/data/coco_images/000000303653.jpg
deleted file mode 100644
index 31ea042c0107f91f5978510c726d0f91c9ec5c85..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_images/000000303653.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_images/000000323895.jpg b/repositories/taming-transformers/data/coco_images/000000323895.jpg
deleted file mode 100644
index 1b6819075412c0b2262a60c7f0a70da29804aeca..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_images/000000323895.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_images/000000335529.jpg b/repositories/taming-transformers/data/coco_images/000000335529.jpg
deleted file mode 100644
index 8347b065277be324040c5f3391c1dc43220ce096..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_images/000000335529.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_images/000000348045.jpg b/repositories/taming-transformers/data/coco_images/000000348045.jpg
deleted file mode 100644
index ff993b67a98591ea0e09beeb1ab85847c492caac..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_images/000000348045.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_images/000000348481.jpg b/repositories/taming-transformers/data/coco_images/000000348481.jpg
deleted file mode 100644
index a2cb75cc0c0629480e1fdfdfe3d56a92f260e47f..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_images/000000348481.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_images/000000350405.jpg b/repositories/taming-transformers/data/coco_images/000000350405.jpg
deleted file mode 100644
index cc70b638edbaa99d573f33882382e538dbb79c66..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_images/000000350405.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_images/000000356347.jpg b/repositories/taming-transformers/data/coco_images/000000356347.jpg
deleted file mode 100644
index 400fbfc928af368a176a6cbce993493738e8af94..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_images/000000356347.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_images/000000361180.jpg b/repositories/taming-transformers/data/coco_images/000000361180.jpg
deleted file mode 100644
index c50d671a72c770bdb56c18c3568bc0eaf55b58c4..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_images/000000361180.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_images/000000403385.jpg b/repositories/taming-transformers/data/coco_images/000000403385.jpg
deleted file mode 100644
index 39573bfa168ce1ba05c8d3fa6d541b310211fcf7..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_images/000000403385.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_images/000000406997.jpg b/repositories/taming-transformers/data/coco_images/000000406997.jpg
deleted file mode 100644
index 4ea81fbaa2cce050a248f5327ba68c0973c5cbec..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_images/000000406997.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_images/000000452122.jpg b/repositories/taming-transformers/data/coco_images/000000452122.jpg
deleted file mode 100644
index 2e2271f5326f4172e3c58993b4e2f31c6c1d4f4e..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_images/000000452122.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_images/000000491464.jpg b/repositories/taming-transformers/data/coco_images/000000491464.jpg
deleted file mode 100644
index 1d7d455607ace3d584fa1f62d7c5a4d6337801b9..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_images/000000491464.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_images/000000517069.jpg b/repositories/taming-transformers/data/coco_images/000000517069.jpg
deleted file mode 100644
index a00451f49820132c366142019974bc174a5a43b9..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_images/000000517069.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_images/000000522393.jpg b/repositories/taming-transformers/data/coco_images/000000522393.jpg
deleted file mode 100644
index 1a5875b5d660b82711f25215476bf4fff549b716..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_images/000000522393.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_images/000000569273.jpg b/repositories/taming-transformers/data/coco_images/000000569273.jpg
deleted file mode 100644
index 00327f1cd0b06c41ef0ad0b66efe8df2ba6ea25f..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_images/000000569273.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_segmentations/000000018380.png b/repositories/taming-transformers/data/coco_segmentations/000000018380.png
deleted file mode 100644
index 4a49387c7eb4c7706862e736381f2445a6ab62ca..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_segmentations/000000018380.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_segmentations/000000052507.png b/repositories/taming-transformers/data/coco_segmentations/000000052507.png
deleted file mode 100644
index 7062352ec59e4417be73521ec4978b542dac234d..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_segmentations/000000052507.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_segmentations/000000057672.png b/repositories/taming-transformers/data/coco_segmentations/000000057672.png
deleted file mode 100644
index 4ae78b24ca42cb72fa250d3a77c5358cc6730f4d..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_segmentations/000000057672.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_segmentations/000000064898.png b/repositories/taming-transformers/data/coco_segmentations/000000064898.png
deleted file mode 100644
index 654462abd6d0006d95ad9a9e595973dc119c0a6a..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_segmentations/000000064898.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_segmentations/000000098392.png b/repositories/taming-transformers/data/coco_segmentations/000000098392.png
deleted file mode 100644
index c4f7e1ef28990e51d4ac6884f875fc325b046529..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_segmentations/000000098392.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_segmentations/000000110638.png b/repositories/taming-transformers/data/coco_segmentations/000000110638.png
deleted file mode 100644
index 4f4144b8f5909e6d7dd4c052c2cb026ceaa6101a..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_segmentations/000000110638.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_segmentations/000000119445.png b/repositories/taming-transformers/data/coco_segmentations/000000119445.png
deleted file mode 100644
index ab63053b4e6884424bf83208fabf438ae63d1f60..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_segmentations/000000119445.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_segmentations/000000128658.png b/repositories/taming-transformers/data/coco_segmentations/000000128658.png
deleted file mode 100644
index 8b9cd9690ecb50e90eda455c6b8be04b66563b98..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_segmentations/000000128658.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_segmentations/000000154358.png b/repositories/taming-transformers/data/coco_segmentations/000000154358.png
deleted file mode 100644
index e97dd364d4d44cbed993c7e69a0eb92a216fe14e..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_segmentations/000000154358.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_segmentations/000000166259.png b/repositories/taming-transformers/data/coco_segmentations/000000166259.png
deleted file mode 100644
index eae767fc5a6aabf4e3adbfb0d21993da5fda4b97..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_segmentations/000000166259.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_segmentations/000000166563.png b/repositories/taming-transformers/data/coco_segmentations/000000166563.png
deleted file mode 100644
index 23cb9aa0971cc9365bc8802793ab9558328d3728..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_segmentations/000000166563.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_segmentations/000000175387.png b/repositories/taming-transformers/data/coco_segmentations/000000175387.png
deleted file mode 100644
index 5084cf28fb0094960011174e98094e4c2970e517..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_segmentations/000000175387.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_segmentations/000000185599.png b/repositories/taming-transformers/data/coco_segmentations/000000185599.png
deleted file mode 100644
index cedf7656ff5020b7725036eebefb6b21c8dcfcc7..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_segmentations/000000185599.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_segmentations/000000205834.png b/repositories/taming-transformers/data/coco_segmentations/000000205834.png
deleted file mode 100644
index 511a7ec0ed28bb424224b9609debcdb4c33da4f4..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_segmentations/000000205834.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_segmentations/000000231169.png b/repositories/taming-transformers/data/coco_segmentations/000000231169.png
deleted file mode 100644
index 4754ce883f16720cc7854b6ec769ab48decda33c..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_segmentations/000000231169.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_segmentations/000000237928.png b/repositories/taming-transformers/data/coco_segmentations/000000237928.png
deleted file mode 100644
index 44bc25768927db32a9dc65cae1730adc2bc48e73..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_segmentations/000000237928.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_segmentations/000000255824.png b/repositories/taming-transformers/data/coco_segmentations/000000255824.png
deleted file mode 100644
index 1a304f5b844c2ba8ff2948bd7679c7472610e2e9..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_segmentations/000000255824.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_segmentations/000000256775.png b/repositories/taming-transformers/data/coco_segmentations/000000256775.png
deleted file mode 100644
index a04c7d159dd8f34d0a5e2c02ae0a40e3f2df0b88..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_segmentations/000000256775.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_segmentations/000000299355.png b/repositories/taming-transformers/data/coco_segmentations/000000299355.png
deleted file mode 100644
index 34721a5fc18f893a74e4a5a739d3b63067eec812..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_segmentations/000000299355.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_segmentations/000000299720.png b/repositories/taming-transformers/data/coco_segmentations/000000299720.png
deleted file mode 100644
index bf19c93a62d818521247036d3689bd04ef8c4f41..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_segmentations/000000299720.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_segmentations/000000303653.png b/repositories/taming-transformers/data/coco_segmentations/000000303653.png
deleted file mode 100644
index 91ea8823ec01f82e6b7092fac3f95b34d07103df..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_segmentations/000000303653.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_segmentations/000000323895.png b/repositories/taming-transformers/data/coco_segmentations/000000323895.png
deleted file mode 100644
index 925e3aca5344b0bc4cd2ac576e898bc18db9104f..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_segmentations/000000323895.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_segmentations/000000335529.png b/repositories/taming-transformers/data/coco_segmentations/000000335529.png
deleted file mode 100644
index c8097938938379a507fbb8b77a059ed40295e91e..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_segmentations/000000335529.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_segmentations/000000348045.png b/repositories/taming-transformers/data/coco_segmentations/000000348045.png
deleted file mode 100644
index 0ee1fe9cabf72256ac3642e835b4812bde5b5fde..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_segmentations/000000348045.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_segmentations/000000348481.png b/repositories/taming-transformers/data/coco_segmentations/000000348481.png
deleted file mode 100644
index c98372b490fdb3e780b6da1c3949af1449320b61..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_segmentations/000000348481.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_segmentations/000000350405.png b/repositories/taming-transformers/data/coco_segmentations/000000350405.png
deleted file mode 100644
index 86194603110e4fa381ac0f9b8edfbd6944dc86f9..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_segmentations/000000350405.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_segmentations/000000356347.png b/repositories/taming-transformers/data/coco_segmentations/000000356347.png
deleted file mode 100644
index d1bebe56be4cce5571d175a5a587fdaeffa17b30..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_segmentations/000000356347.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_segmentations/000000361180.png b/repositories/taming-transformers/data/coco_segmentations/000000361180.png
deleted file mode 100644
index 6287aba7667e416ac52c8278fb569e9df5f4502a..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_segmentations/000000361180.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_segmentations/000000403385.png b/repositories/taming-transformers/data/coco_segmentations/000000403385.png
deleted file mode 100644
index 2e3aaddf6c6c52dcb80e3bc1ff4d856c3e156b3f..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_segmentations/000000403385.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_segmentations/000000406997.png b/repositories/taming-transformers/data/coco_segmentations/000000406997.png
deleted file mode 100644
index 2af15a30992d3101ff81b2a11d1dc7b3066bde5a..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_segmentations/000000406997.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_segmentations/000000452122.png b/repositories/taming-transformers/data/coco_segmentations/000000452122.png
deleted file mode 100644
index 511c0cafde9cc244e98d4681e05bb6b4917f27f3..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_segmentations/000000452122.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_segmentations/000000491464.png b/repositories/taming-transformers/data/coco_segmentations/000000491464.png
deleted file mode 100644
index b9423744a767acc760103481eae048c3f7511cff..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_segmentations/000000491464.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_segmentations/000000517069.png b/repositories/taming-transformers/data/coco_segmentations/000000517069.png
deleted file mode 100644
index 0659c86904b53d12d5952895cc4b42f95d48e7d6..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_segmentations/000000517069.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_segmentations/000000522393.png b/repositories/taming-transformers/data/coco_segmentations/000000522393.png
deleted file mode 100644
index 02c0ceb748020ff95680b8d58f82723ec474798b..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_segmentations/000000522393.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/coco_segmentations/000000569273.png b/repositories/taming-transformers/data/coco_segmentations/000000569273.png
deleted file mode 100644
index ba0a48151c3c75bcde7a62257347f9e2e481f615..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/coco_segmentations/000000569273.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/drin_depth/n01795545/ILSVRC2012_val_00023344.png b/repositories/taming-transformers/data/drin_depth/n01795545/ILSVRC2012_val_00023344.png
deleted file mode 100644
index 16850a3143d997a6f7df1e10ce6c98862ffb3cfd..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/drin_depth/n01795545/ILSVRC2012_val_00023344.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/drin_depth/n01819313/ILSVRC2012_val_00003068.png b/repositories/taming-transformers/data/drin_depth/n01819313/ILSVRC2012_val_00003068.png
deleted file mode 100644
index 7da56a672753ac01ab941ede9a8df65cffcb1b98..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/drin_depth/n01819313/ILSVRC2012_val_00003068.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/drin_depth/n01820546/ILSVRC2012_val_00034784.png b/repositories/taming-transformers/data/drin_depth/n01820546/ILSVRC2012_val_00034784.png
deleted file mode 100644
index c4fe9aef2553974fefaa2e4a3474625aec10f3c9..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/drin_depth/n01820546/ILSVRC2012_val_00034784.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/drin_depth/n01820546/ILSVRC2012_val_00047491.png b/repositories/taming-transformers/data/drin_depth/n01820546/ILSVRC2012_val_00047491.png
deleted file mode 100644
index 20e83c3a52985206b9bdaa190d7f2fbd20725de9..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/drin_depth/n01820546/ILSVRC2012_val_00047491.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/drin_depth/n01828970/ILSVRC2012_val_00001336.png b/repositories/taming-transformers/data/drin_depth/n01828970/ILSVRC2012_val_00001336.png
deleted file mode 100644
index df10b308fa4a61a16ff343613e1e285c52c49de6..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/drin_depth/n01828970/ILSVRC2012_val_00001336.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/drin_depth/n01828970/ILSVRC2012_val_00008236.png b/repositories/taming-transformers/data/drin_depth/n01828970/ILSVRC2012_val_00008236.png
deleted file mode 100644
index 6287a49b92a1c5ef3e51eb1a755fb87388183c43..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/drin_depth/n01828970/ILSVRC2012_val_00008236.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/drin_depth/n01828970/ILSVRC2012_val_00046802.png b/repositories/taming-transformers/data/drin_depth/n01828970/ILSVRC2012_val_00046802.png
deleted file mode 100644
index c4b046001aec2bc84cc5e42a427216d64488af92..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/drin_depth/n01828970/ILSVRC2012_val_00046802.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/drin_depth/n01843065/ILSVRC2012_val_00022439.png b/repositories/taming-transformers/data/drin_depth/n01843065/ILSVRC2012_val_00022439.png
deleted file mode 100644
index 633185b69a7391cd30a78f1863dfec7b1219cd64..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/drin_depth/n01843065/ILSVRC2012_val_00022439.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/drin_depth/n01847000/ILSVRC2012_val_00022364.png b/repositories/taming-transformers/data/drin_depth/n01847000/ILSVRC2012_val_00022364.png
deleted file mode 100644
index 22f95bd68d203955f754623e1c73973d4dea5e38..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/drin_depth/n01847000/ILSVRC2012_val_00022364.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/drin_depth/n02085782/ILSVRC2012_val_00012298.png b/repositories/taming-transformers/data/drin_depth/n02085782/ILSVRC2012_val_00012298.png
deleted file mode 100644
index e96a7cf5b096f5a1a8ee6d3a7bdbddfe041dd9f9..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/drin_depth/n02085782/ILSVRC2012_val_00012298.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/drin_depth/n02086646/ILSVRC2012_val_00011473.png b/repositories/taming-transformers/data/drin_depth/n02086646/ILSVRC2012_val_00011473.png
deleted file mode 100644
index 6811ae8000fdda1a84e2703e9ff74f897e4cdce4..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/drin_depth/n02086646/ILSVRC2012_val_00011473.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/drin_depth/n02088466/ILSVRC2012_val_00013651.png b/repositories/taming-transformers/data/drin_depth/n02088466/ILSVRC2012_val_00013651.png
deleted file mode 100644
index dcfdfdfa1eb5bb6383307e887df90b306c4ef100..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/drin_depth/n02088466/ILSVRC2012_val_00013651.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/drin_depth/n02089973/ILSVRC2012_val_00000028.png b/repositories/taming-transformers/data/drin_depth/n02089973/ILSVRC2012_val_00000028.png
deleted file mode 100644
index 2e2aea97296bc551bae39a526481d6e17e4ed6de..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/drin_depth/n02089973/ILSVRC2012_val_00000028.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/drin_depth/n02093256/ILSVRC2012_val_00046547.png b/repositories/taming-transformers/data/drin_depth/n02093256/ILSVRC2012_val_00046547.png
deleted file mode 100644
index 0eca5fa4412dd36bc3c20e0cd2b548938fd123d7..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/drin_depth/n02093256/ILSVRC2012_val_00046547.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/drin_depth/n02096294/ILSVRC2012_val_00042133.png b/repositories/taming-transformers/data/drin_depth/n02096294/ILSVRC2012_val_00042133.png
deleted file mode 100644
index 38aedd8449b7a7b1538528955d3b0d958702b221..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/drin_depth/n02096294/ILSVRC2012_val_00042133.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/drin_depth/n02099601/ILSVRC2012_val_00005697.png b/repositories/taming-transformers/data/drin_depth/n02099601/ILSVRC2012_val_00005697.png
deleted file mode 100644
index 7a7dfb117e269c48b8da278dd2a55ace374fcce9..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/drin_depth/n02099601/ILSVRC2012_val_00005697.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/drin_depth/n02099712/ILSVRC2012_val_00023471.png b/repositories/taming-transformers/data/drin_depth/n02099712/ILSVRC2012_val_00023471.png
deleted file mode 100644
index 5169c340902711f7957b977bbdd6da25f1aa9f15..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/drin_depth/n02099712/ILSVRC2012_val_00023471.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/drin_depth/n02100877/ILSVRC2012_val_00039863.png b/repositories/taming-transformers/data/drin_depth/n02100877/ILSVRC2012_val_00039863.png
deleted file mode 100644
index 9ae67afaea45980e2c1f3c69920999a53b6b0a1a..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/drin_depth/n02100877/ILSVRC2012_val_00039863.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/drin_depth/n02101006/ILSVRC2012_val_00032333.png b/repositories/taming-transformers/data/drin_depth/n02101006/ILSVRC2012_val_00032333.png
deleted file mode 100644
index 0dfb99ace6cbb3751022ed8504bdab22334d95a8..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/drin_depth/n02101006/ILSVRC2012_val_00032333.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/drin_depth/n02101006/ILSVRC2012_val_00047325.png b/repositories/taming-transformers/data/drin_depth/n02101006/ILSVRC2012_val_00047325.png
deleted file mode 100644
index 6d2c611d49b16196512b6c9b63ecab4c60433f75..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/drin_depth/n02101006/ILSVRC2012_val_00047325.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/drin_depth/n02101556/ILSVRC2012_val_00030540.png b/repositories/taming-transformers/data/drin_depth/n02101556/ILSVRC2012_val_00030540.png
deleted file mode 100644
index ca4df4b7bb29ef32dd0077eae410d0a3d2007474..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/drin_depth/n02101556/ILSVRC2012_val_00030540.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/drin_depth/n02102318/ILSVRC2012_val_00024691.png b/repositories/taming-transformers/data/drin_depth/n02102318/ILSVRC2012_val_00024691.png
deleted file mode 100644
index 334f3045af2683774dc66c1fcb28b04367abc83c..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/drin_depth/n02102318/ILSVRC2012_val_00024691.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/drin_depth/n02105505/ILSVRC2012_val_00031252.png b/repositories/taming-transformers/data/drin_depth/n02105505/ILSVRC2012_val_00031252.png
deleted file mode 100644
index f526968ec1cce1019775f8b8974c1594d496d857..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/drin_depth/n02105505/ILSVRC2012_val_00031252.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/drin_depth/n02110627/ILSVRC2012_val_00008310.png b/repositories/taming-transformers/data/drin_depth/n02110627/ILSVRC2012_val_00008310.png
deleted file mode 100644
index 290ca590a02022d5c25543350049aebf8a0c8dcd..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/drin_depth/n02110627/ILSVRC2012_val_00008310.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/drin_depth/n02111889/ILSVRC2012_val_00042625.png b/repositories/taming-transformers/data/drin_depth/n02111889/ILSVRC2012_val_00042625.png
deleted file mode 100644
index 4859c0645d37b31673686be89d2511e8db8504fc..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/drin_depth/n02111889/ILSVRC2012_val_00042625.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/drin_examples.txt b/repositories/taming-transformers/data/drin_examples.txt
deleted file mode 100644
index aee7b4267abe081b44d503e1854e083147b41772..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/data/drin_examples.txt
+++ /dev/null
@@ -1,25 +0,0 @@
-n02100877/ILSVRC2012_val_00039863.JPEG
-n02085782/ILSVRC2012_val_00012298.JPEG
-n02102318/ILSVRC2012_val_00024691.JPEG
-n02105505/ILSVRC2012_val_00031252.JPEG
-n01819313/ILSVRC2012_val_00003068.JPEG
-n02101006/ILSVRC2012_val_00047325.JPEG
-n01828970/ILSVRC2012_val_00046802.JPEG
-n01843065/ILSVRC2012_val_00022439.JPEG
-n01847000/ILSVRC2012_val_00022364.JPEG
-n02089973/ILSVRC2012_val_00000028.JPEG
-n01828970/ILSVRC2012_val_00001336.JPEG
-n02101006/ILSVRC2012_val_00032333.JPEG
-n02101556/ILSVRC2012_val_00030540.JPEG
-n02093256/ILSVRC2012_val_00046547.JPEG
-n02086646/ILSVRC2012_val_00011473.JPEG
-n02099601/ILSVRC2012_val_00005697.JPEG
-n01820546/ILSVRC2012_val_00047491.JPEG
-n02088466/ILSVRC2012_val_00013651.JPEG
-n02111889/ILSVRC2012_val_00042625.JPEG
-n02099712/ILSVRC2012_val_00023471.JPEG
-n01820546/ILSVRC2012_val_00034784.JPEG
-n02110627/ILSVRC2012_val_00008310.JPEG
-n02096294/ILSVRC2012_val_00042133.JPEG
-n01828970/ILSVRC2012_val_00008236.JPEG
-n01795545/ILSVRC2012_val_00023344.JPEG
diff --git a/repositories/taming-transformers/data/drin_images/n01795545/ILSVRC2012_val_00023344.JPEG b/repositories/taming-transformers/data/drin_images/n01795545/ILSVRC2012_val_00023344.JPEG
deleted file mode 100644
index 8be40cd994ef269be64f6ab366f8eebbf0604189..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/drin_images/n01795545/ILSVRC2012_val_00023344.JPEG and /dev/null differ
diff --git a/repositories/taming-transformers/data/drin_images/n01819313/ILSVRC2012_val_00003068.JPEG b/repositories/taming-transformers/data/drin_images/n01819313/ILSVRC2012_val_00003068.JPEG
deleted file mode 100644
index 93aa80ce279c01f794abf7e605186b4d2f1a9493..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/drin_images/n01819313/ILSVRC2012_val_00003068.JPEG and /dev/null differ
diff --git a/repositories/taming-transformers/data/drin_images/n01820546/ILSVRC2012_val_00034784.JPEG b/repositories/taming-transformers/data/drin_images/n01820546/ILSVRC2012_val_00034784.JPEG
deleted file mode 100644
index ee2475bfc6c6a433af0570c1586b47b3e1c2096b..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/drin_images/n01820546/ILSVRC2012_val_00034784.JPEG and /dev/null differ
diff --git a/repositories/taming-transformers/data/drin_images/n01820546/ILSVRC2012_val_00047491.JPEG b/repositories/taming-transformers/data/drin_images/n01820546/ILSVRC2012_val_00047491.JPEG
deleted file mode 100644
index 52b088cad04871d592cf8f301999b80c5ce2a781..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/drin_images/n01820546/ILSVRC2012_val_00047491.JPEG and /dev/null differ
diff --git a/repositories/taming-transformers/data/drin_images/n01828970/ILSVRC2012_val_00001336.JPEG b/repositories/taming-transformers/data/drin_images/n01828970/ILSVRC2012_val_00001336.JPEG
deleted file mode 100644
index 0545b6ca34444386a2b44564b67783484b4dcfe7..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/drin_images/n01828970/ILSVRC2012_val_00001336.JPEG and /dev/null differ
diff --git a/repositories/taming-transformers/data/drin_images/n01828970/ILSVRC2012_val_00008236.JPEG b/repositories/taming-transformers/data/drin_images/n01828970/ILSVRC2012_val_00008236.JPEG
deleted file mode 100644
index 9245278802ec124690ba8048cd32b9ea3256cbc5..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/drin_images/n01828970/ILSVRC2012_val_00008236.JPEG and /dev/null differ
diff --git a/repositories/taming-transformers/data/drin_images/n01828970/ILSVRC2012_val_00046802.JPEG b/repositories/taming-transformers/data/drin_images/n01828970/ILSVRC2012_val_00046802.JPEG
deleted file mode 100644
index 40fc0b7ca438775a18c4498d0abb0c048648e0c7..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/drin_images/n01828970/ILSVRC2012_val_00046802.JPEG and /dev/null differ
diff --git a/repositories/taming-transformers/data/drin_images/n01843065/ILSVRC2012_val_00022439.JPEG b/repositories/taming-transformers/data/drin_images/n01843065/ILSVRC2012_val_00022439.JPEG
deleted file mode 100644
index a986cbe422aa448bcf360d6dc6b8ffb60d527db5..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/drin_images/n01843065/ILSVRC2012_val_00022439.JPEG and /dev/null differ
diff --git a/repositories/taming-transformers/data/drin_images/n01847000/ILSVRC2012_val_00022364.JPEG b/repositories/taming-transformers/data/drin_images/n01847000/ILSVRC2012_val_00022364.JPEG
deleted file mode 100644
index 8bfd129a98d33c3c8b84b1b2f6eee3a5d53e00f3..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/drin_images/n01847000/ILSVRC2012_val_00022364.JPEG and /dev/null differ
diff --git a/repositories/taming-transformers/data/drin_images/n02085782/ILSVRC2012_val_00012298.JPEG b/repositories/taming-transformers/data/drin_images/n02085782/ILSVRC2012_val_00012298.JPEG
deleted file mode 100644
index 2d6cc8b9b58081fd8dde9a13eb0b2e3fe427e0c1..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/drin_images/n02085782/ILSVRC2012_val_00012298.JPEG and /dev/null differ
diff --git a/repositories/taming-transformers/data/drin_images/n02086646/ILSVRC2012_val_00011473.JPEG b/repositories/taming-transformers/data/drin_images/n02086646/ILSVRC2012_val_00011473.JPEG
deleted file mode 100644
index bc304d73c7fa93f1905acfe6bf9ede5bb378f6dd..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/drin_images/n02086646/ILSVRC2012_val_00011473.JPEG and /dev/null differ
diff --git a/repositories/taming-transformers/data/drin_images/n02088466/ILSVRC2012_val_00013651.JPEG b/repositories/taming-transformers/data/drin_images/n02088466/ILSVRC2012_val_00013651.JPEG
deleted file mode 100644
index 0a5006e5ea2fc30817f59b9a9c8ca510249d6de5..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/drin_images/n02088466/ILSVRC2012_val_00013651.JPEG and /dev/null differ
diff --git a/repositories/taming-transformers/data/drin_images/n02089973/ILSVRC2012_val_00000028.JPEG b/repositories/taming-transformers/data/drin_images/n02089973/ILSVRC2012_val_00000028.JPEG
deleted file mode 100644
index 1fbcd80a848d7b3ef9395a1e755b8a56ce8eefe0..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/drin_images/n02089973/ILSVRC2012_val_00000028.JPEG and /dev/null differ
diff --git a/repositories/taming-transformers/data/drin_images/n02093256/ILSVRC2012_val_00046547.JPEG b/repositories/taming-transformers/data/drin_images/n02093256/ILSVRC2012_val_00046547.JPEG
deleted file mode 100644
index a0282b5afc31f8909ed6757e47bd537de6082dfd..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/drin_images/n02093256/ILSVRC2012_val_00046547.JPEG and /dev/null differ
diff --git a/repositories/taming-transformers/data/drin_images/n02096294/ILSVRC2012_val_00042133.JPEG b/repositories/taming-transformers/data/drin_images/n02096294/ILSVRC2012_val_00042133.JPEG
deleted file mode 100644
index ccb669dc28e6558fb7017984d8b7cf9d833e6c17..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/drin_images/n02096294/ILSVRC2012_val_00042133.JPEG and /dev/null differ
diff --git a/repositories/taming-transformers/data/drin_images/n02099601/ILSVRC2012_val_00005697.JPEG b/repositories/taming-transformers/data/drin_images/n02099601/ILSVRC2012_val_00005697.JPEG
deleted file mode 100644
index 44d202e4dbe2b7b3d3382377c12f3eca02cd3569..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/drin_images/n02099601/ILSVRC2012_val_00005697.JPEG and /dev/null differ
diff --git a/repositories/taming-transformers/data/drin_images/n02099712/ILSVRC2012_val_00023471.JPEG b/repositories/taming-transformers/data/drin_images/n02099712/ILSVRC2012_val_00023471.JPEG
deleted file mode 100644
index f64b46bdf6c3f99f93de7d5f0faf4cd57dc5d690..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/drin_images/n02099712/ILSVRC2012_val_00023471.JPEG and /dev/null differ
diff --git a/repositories/taming-transformers/data/drin_images/n02100877/ILSVRC2012_val_00039863.JPEG b/repositories/taming-transformers/data/drin_images/n02100877/ILSVRC2012_val_00039863.JPEG
deleted file mode 100644
index 31893dac4ca74f731cb35e4cccd538f7593596e0..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/drin_images/n02100877/ILSVRC2012_val_00039863.JPEG and /dev/null differ
diff --git a/repositories/taming-transformers/data/drin_images/n02101006/ILSVRC2012_val_00032333.JPEG b/repositories/taming-transformers/data/drin_images/n02101006/ILSVRC2012_val_00032333.JPEG
deleted file mode 100644
index 0fbb7d7cdefc03002fd1816ac62f3c8ea7c4c980..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/drin_images/n02101006/ILSVRC2012_val_00032333.JPEG and /dev/null differ
diff --git a/repositories/taming-transformers/data/drin_images/n02101006/ILSVRC2012_val_00047325.JPEG b/repositories/taming-transformers/data/drin_images/n02101006/ILSVRC2012_val_00047325.JPEG
deleted file mode 100644
index 515b8060385b3636662d622287166f093be21068..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/drin_images/n02101006/ILSVRC2012_val_00047325.JPEG and /dev/null differ
diff --git a/repositories/taming-transformers/data/drin_images/n02101556/ILSVRC2012_val_00030540.JPEG b/repositories/taming-transformers/data/drin_images/n02101556/ILSVRC2012_val_00030540.JPEG
deleted file mode 100644
index 898b0192dc3aead1e20fb8a9ad3fbf3fe1fef37f..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/drin_images/n02101556/ILSVRC2012_val_00030540.JPEG and /dev/null differ
diff --git a/repositories/taming-transformers/data/drin_images/n02102318/ILSVRC2012_val_00024691.JPEG b/repositories/taming-transformers/data/drin_images/n02102318/ILSVRC2012_val_00024691.JPEG
deleted file mode 100644
index 6c059a394019ee54f766e66fb943d328a4957b20..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/drin_images/n02102318/ILSVRC2012_val_00024691.JPEG and /dev/null differ
diff --git a/repositories/taming-transformers/data/drin_images/n02105505/ILSVRC2012_val_00031252.JPEG b/repositories/taming-transformers/data/drin_images/n02105505/ILSVRC2012_val_00031252.JPEG
deleted file mode 100644
index 8c84d30ae56f3810c491ac6ac90c2be1d3f5fb91..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/drin_images/n02105505/ILSVRC2012_val_00031252.JPEG and /dev/null differ
diff --git a/repositories/taming-transformers/data/drin_images/n02110627/ILSVRC2012_val_00008310.JPEG b/repositories/taming-transformers/data/drin_images/n02110627/ILSVRC2012_val_00008310.JPEG
deleted file mode 100644
index 1a0752a1650636d647f58454e4a5e67cbf81bcb3..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/drin_images/n02110627/ILSVRC2012_val_00008310.JPEG and /dev/null differ
diff --git a/repositories/taming-transformers/data/drin_images/n02111889/ILSVRC2012_val_00042625.JPEG b/repositories/taming-transformers/data/drin_images/n02111889/ILSVRC2012_val_00042625.JPEG
deleted file mode 100644
index 1d006eb9f065490ccc4895fba33fc5f9e9c2bc63..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/drin_images/n02111889/ILSVRC2012_val_00042625.JPEG and /dev/null differ
diff --git a/repositories/taming-transformers/data/ffhqtrain.txt b/repositories/taming-transformers/data/ffhqtrain.txt
deleted file mode 100644
index 59729b1e904b7d1ad22699049feb33029661d82f..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/data/ffhqtrain.txt
+++ /dev/null
@@ -1,60000 +0,0 @@
-52535.png
-59777.png
-34697.png
-02469.png
-14829.png
-30422.png
-67376.png
-33750.png
-53892.png
-28195.png
-29092.png
-52892.png
-44199.png
-58925.png
-64331.png
-34247.png
-19821.png
-51546.png
-54387.png
-09355.png
-40638.png
-47508.png
-37287.png
-37815.png
-37586.png
-44148.png
-30275.png
-49542.png
-12843.png
-53820.png
-40411.png
-51095.png
-55788.png
-33199.png
-04083.png
-03557.png
-36146.png
-36803.png
-49914.png
-09730.png
-60284.png
-18145.png
-59598.png
-28986.png
-16960.png
-41184.png
-06653.png
-45879.png
-26625.png
-20460.png
-37634.png
-25341.png
-61836.png
-60843.png
-04638.png
-09380.png
-03584.png
-21989.png
-49175.png
-55568.png
-14356.png
-16415.png
-21576.png
-25995.png
-52520.png
-10997.png
-50881.png
-16764.png
-60844.png
-53804.png
-63305.png
-25562.png
-29774.png
-65947.png
-36260.png
-33847.png
-26168.png
-31586.png
-25423.png
-27633.png
-13473.png
-30098.png
-04337.png
-46297.png
-08041.png
-45153.png
-17463.png
-06097.png
-19116.png
-44313.png
-69680.png
-53047.png
-51170.png
-12952.png
-04056.png
-46668.png
-69890.png
-57889.png
-22130.png
-35083.png
-28697.png
-57492.png
-38531.png
-03872.png
-22232.png
-18883.png
-21357.png
-21687.png
-60244.png
-22987.png
-55705.png
-59117.png
-15646.png
-67692.png
-52587.png
-52456.png
-08461.png
-34961.png
-02420.png
-36689.png
-16341.png
-66524.png
-32318.png
-57991.png
-67679.png
-01911.png
-20660.png
-48625.png
-21900.png
-47505.png
-63299.png
-34464.png
-26388.png
-51854.png
-31311.png
-69621.png
-20803.png
-21391.png
-56788.png
-00250.png
-61010.png
-27029.png
-30810.png
-24078.png
-18905.png
-49148.png
-19461.png
-35706.png
-63627.png
-27947.png
-21652.png
-16875.png
-20534.png
-04474.png
-20535.png
-57561.png
-46448.png
-38624.png
-67007.png
-25358.png
-28305.png
-23822.png
-43910.png
-20700.png
-38890.png
-31623.png
-53657.png
-60124.png
-64627.png
-32896.png
-69387.png
-17486.png
-48711.png
-19970.png
-29882.png
-13363.png
-36552.png
-31589.png
-04040.png
-05536.png
-65326.png
-29699.png
-04884.png
-64998.png
-59344.png
-24038.png
-38203.png
-48152.png
-48885.png
-09349.png
-39783.png
-05636.png
-34402.png
-29625.png
-66760.png
-66597.png
-33160.png
-52762.png
-00151.png
-67803.png
-01408.png
-37447.png
-29529.png
-53809.png
-68260.png
-37944.png
-61284.png
-32497.png
-23103.png
-65183.png
-60290.png
-54304.png
-60080.png
-54616.png
-23360.png
-29525.png
-41568.png
-05711.png
-08848.png
-44903.png
-41145.png
-67348.png
-57224.png
-41609.png
-18507.png
-21496.png
-59036.png
-40188.png
-29577.png
-05212.png
-61058.png
-24684.png
-53059.png
-56698.png
-37483.png
-19772.png
-39757.png
-13897.png
-41177.png
-01392.png
-26986.png
-64915.png
-43519.png
-03478.png
-54628.png
-39744.png
-51364.png
-61183.png
-09559.png
-42797.png
-15951.png
-56550.png
-05707.png
-67448.png
-37380.png
-55386.png
-59176.png
-58044.png
-56013.png
-37122.png
-08195.png
-45014.png
-05745.png
-60382.png
-68319.png
-56560.png
-20394.png
-03237.png
-39178.png
-62465.png
-01866.png
-08513.png
-31446.png
-36118.png
-69921.png
-55445.png
-10281.png
-49258.png
-29044.png
-02639.png
-15494.png
-51642.png
-38274.png
-62907.png
-24182.png
-55150.png
-22675.png
-09207.png
-34312.png
-57009.png
-06598.png
-11338.png
-46147.png
-02113.png
-46091.png
-69552.png
-09117.png
-68434.png
-20289.png
-28363.png
-55877.png
-43784.png
-68390.png
-22917.png
-16518.png
-68357.png
-67361.png
-25367.png
-55525.png
-33872.png
-47747.png
-18206.png
-50114.png
-02224.png
-66247.png
-13615.png
-12836.png
-21375.png
-30518.png
-04615.png
-12601.png
-17517.png
-69197.png
-65074.png
-69247.png
-36326.png
-69800.png
-02315.png
-28701.png
-01488.png
-30097.png
-39918.png
-44528.png
-04117.png
-67057.png
-59975.png
-20094.png
-39576.png
-22919.png
-68018.png
-25651.png
-59933.png
-39812.png
-62214.png
-42890.png
-38571.png
-68547.png
-17625.png
-50038.png
-39970.png
-25791.png
-01376.png
-49956.png
-15521.png
-06905.png
-55981.png
-07131.png
-45746.png
-33458.png
-03599.png
-65493.png
-00690.png
-21791.png
-66942.png
-03697.png
-19474.png
-51181.png
-33464.png
-63350.png
-58489.png
-11401.png
-63991.png
-59545.png
-49954.png
-37778.png
-69814.png
-14821.png
-41111.png
-14842.png
-04154.png
-30027.png
-58166.png
-38674.png
-54540.png
-58827.png
-21237.png
-16017.png
-21575.png
-43057.png
-36496.png
-38730.png
-61489.png
-39388.png
-51820.png
-57870.png
-31710.png
-02204.png
-07506.png
-68322.png
-12939.png
-61819.png
-36695.png
-15207.png
-52122.png
-60910.png
-57109.png
-12335.png
-42726.png
-69080.png
-32217.png
-23655.png
-15776.png
-34654.png
-09870.png
-01797.png
-51123.png
-24970.png
-55676.png
-61881.png
-61913.png
-69054.png
-45782.png
-65845.png
-35948.png
-51916.png
-65622.png
-38710.png
-01739.png
-47967.png
-49530.png
-58540.png
-33041.png
-27157.png
-62696.png
-38765.png
-35405.png
-09630.png
-51187.png
-05913.png
-41858.png
-24787.png
-25963.png
-10858.png
-14846.png
-00569.png
-63066.png
-10804.png
-34469.png
-26896.png
-25745.png
-59121.png
-52505.png
-43458.png
-60233.png
-15817.png
-23651.png
-37496.png
-17720.png
-00664.png
-54237.png
-62535.png
-57006.png
-13916.png
-43619.png
-14234.png
-16622.png
-62399.png
-40589.png
-49857.png
-13040.png
-63737.png
-11968.png
-04759.png
-28935.png
-37636.png
-65276.png
-08155.png
-02762.png
-62241.png
-06288.png
-31416.png
-55158.png
-69039.png
-67438.png
-28354.png
-13847.png
-62631.png
-56467.png
-38277.png
-49958.png
-51205.png
-14012.png
-40015.png
-69162.png
-04881.png
-24323.png
-47427.png
-18361.png
-18048.png
-24539.png
-32357.png
-58329.png
-02141.png
-12409.png
-21263.png
-47606.png
-29924.png
-36259.png
-37388.png
-27922.png
-68179.png
-59746.png
-42173.png
-60496.png
-03616.png
-56110.png
-12905.png
-18619.png
-07837.png
-25646.png
-17930.png
-20791.png
-14636.png
-51143.png
-35119.png
-42341.png
-27863.png
-63988.png
-44129.png
-50692.png
-58535.png
-07871.png
-44726.png
-37861.png
-63112.png
-12539.png
-34537.png
-25236.png
-10510.png
-55833.png
-68722.png
-43381.png
-03983.png
-39042.png
-49437.png
-16636.png
-48522.png
-62247.png
-68840.png
-62226.png
-67945.png
-24335.png
-38414.png
-11412.png
-38145.png
-53530.png
-21998.png
-07144.png
-57498.png
-14985.png
-44117.png
-29966.png
-53020.png
-61650.png
-10991.png
-04972.png
-50475.png
-64583.png
-23009.png
-06915.png
-03564.png
-46684.png
-32645.png
-14693.png
-47882.png
-21243.png
-19867.png
-31042.png
-60756.png
-54190.png
-38544.png
-28665.png
-22516.png
-29808.png
-14789.png
-59747.png
-13046.png
-26967.png
-43720.png
-63718.png
-16985.png
-05593.png
-64688.png
-29551.png
-60678.png
-52039.png
-47921.png
-38578.png
-22269.png
-35779.png
-50458.png
-07388.png
-29185.png
-28231.png
-48307.png
-67286.png
-29450.png
-59076.png
-49565.png
-12152.png
-08537.png
-11211.png
-34243.png
-11895.png
-03378.png
-65091.png
-33662.png
-52250.png
-27172.png
-67324.png
-27659.png
-37458.png
-29850.png
-43514.png
-11844.png
-07071.png
-14893.png
-58967.png
-13459.png
-12822.png
-67632.png
-29941.png
-24196.png
-01326.png
-44229.png
-03986.png
-64141.png
-52983.png
-67175.png
-04200.png
-64571.png
-30674.png
-02925.png
-68564.png
-64516.png
-25443.png
-05321.png
-36734.png
-52378.png
-69083.png
-23813.png
-69539.png
-24159.png
-00799.png
-46830.png
-56568.png
-25022.png
-19350.png
-37888.png
-36575.png
-07358.png
-38413.png
-08944.png
-07737.png
-02386.png
-25489.png
-56364.png
-05808.png
-41755.png
-64326.png
-30454.png
-45647.png
-13690.png
-46817.png
-08280.png
-04028.png
-01466.png
-60206.png
-00079.png
-53104.png
-35322.png
-41265.png
-66929.png
-68653.png
-60625.png
-29917.png
-00257.png
-48405.png
-54038.png
-04381.png
-36663.png
-61800.png
-32455.png
-12398.png
-41585.png
-69440.png
-20767.png
-69323.png
-59114.png
-26503.png
-57149.png
-56885.png
-63495.png
-33816.png
-20667.png
-11273.png
-55526.png
-06243.png
-58174.png
-53988.png
-05122.png
-27661.png
-46420.png
-45525.png
-41277.png
-05726.png
-31205.png
-00272.png
-49387.png
-66806.png
-56082.png
-44666.png
-27081.png
-68343.png
-39925.png
-60740.png
-24795.png
-64250.png
-63217.png
-01980.png
-67349.png
-61998.png
-64373.png
-07341.png
-35103.png
-20788.png
-38140.png
-60672.png
-24997.png
-03474.png
-22690.png
-12691.png
-39707.png
-57537.png
-23174.png
-20341.png
-62332.png
-32185.png
-16318.png
-15101.png
-50423.png
-18054.png
-61885.png
-57769.png
-23200.png
-31391.png
-38359.png
-36568.png
-26979.png
-16581.png
-35708.png
-53080.png
-67554.png
-24072.png
-62183.png
-68068.png
-23532.png
-52857.png
-20264.png
-04784.png
-32350.png
-64254.png
-57461.png
-36137.png
-23121.png
-28327.png
-29694.png
-57614.png
-33645.png
-62710.png
-58543.png
-01619.png
-33622.png
-45025.png
-22039.png
-35925.png
-51389.png
-65537.png
-22555.png
-66117.png
-09130.png
-44532.png
-23406.png
-49091.png
-42585.png
-43838.png
-11351.png
-19750.png
-63165.png
-26385.png
-63158.png
-32569.png
-47242.png
-07033.png
-26962.png
-14272.png
-06274.png
-29649.png
-41514.png
-14257.png
-24256.png
-34722.png
-13436.png
-42091.png
-22864.png
-48986.png
-04493.png
-33682.png
-22393.png
-42344.png
-59115.png
-28872.png
-46465.png
-25501.png
-17223.png
-47500.png
-15780.png
-01953.png
-18786.png
-13240.png
-05368.png
-35360.png
-31905.png
-27353.png
-19420.png
-33573.png
-18453.png
-50581.png
-62599.png
-54408.png
-21220.png
-54600.png
-21542.png
-25667.png
-17044.png
-02061.png
-63464.png
-17595.png
-63086.png
-05652.png
-32984.png
-23691.png
-09464.png
-66673.png
-65548.png
-25973.png
-51135.png
-68984.png
-20979.png
-55609.png
-68268.png
-05320.png
-62074.png
-42746.png
-05688.png
-45569.png
-22477.png
-28623.png
-44151.png
-62110.png
-54213.png
-23932.png
-28265.png
-54451.png
-04661.png
-46288.png
-15413.png
-34094.png
-63989.png
-26238.png
-33452.png
-30492.png
-53076.png
-27728.png
-12312.png
-32483.png
-14928.png
-02632.png
-21400.png
-68493.png
-27206.png
-27784.png
-66594.png
-20391.png
-07462.png
-52886.png
-51796.png
-25414.png
-59738.png
-41691.png
-13851.png
-47818.png
-37852.png
-02130.png
-66926.png
-15275.png
-36347.png
-07806.png
-34566.png
-21663.png
-47859.png
-46024.png
-14292.png
-44704.png
-65265.png
-62275.png
-65255.png
-63871.png
-65875.png
-04037.png
-04304.png
-17378.png
-02584.png
-32273.png
-66311.png
-43725.png
-56249.png
-01060.png
-09034.png
-12961.png
-68816.png
-24248.png
-61797.png
-53254.png
-14161.png
-23699.png
-24632.png
-53141.png
-18679.png
-52021.png
-24546.png
-21194.png
-48887.png
-26335.png
-47726.png
-54380.png
-27407.png
-47377.png
-30217.png
-30980.png
-24617.png
-33087.png
-48052.png
-07019.png
-25559.png
-39281.png
-09556.png
-20245.png
-26598.png
-17246.png
-20590.png
-56047.png
-14568.png
-53882.png
-25688.png
-33569.png
-53932.png
-61577.png
-44644.png
-35491.png
-33202.png
-23410.png
-18079.png
-40681.png
-57207.png
-31522.png
-44472.png
-33132.png
-61048.png
-63251.png
-34465.png
-65270.png
-10414.png
-51571.png
-01350.png
-48902.png
-00875.png
-66700.png
-12589.png
-13376.png
-33281.png
-68378.png
-63788.png
-60911.png
-43567.png
-28225.png
-64180.png
-08156.png
-16766.png
-48977.png
-33249.png
-42166.png
-52729.png
-54113.png
-29438.png
-32624.png
-51379.png
-07485.png
-49870.png
-46259.png
-14205.png
-20397.png
-35817.png
-27424.png
-17759.png
-65651.png
-69123.png
-13079.png
-43104.png
-60814.png
-38547.png
-43913.png
-08026.png
-06674.png
-48189.png
-62877.png
-17676.png
-25820.png
-40052.png
-27936.png
-13969.png
-36667.png
-69188.png
-50562.png
-61380.png
-20336.png
-48210.png
-27938.png
-62501.png
-02793.png
-52922.png
-16251.png
-13877.png
-47225.png
-45188.png
-15684.png
-17578.png
-08262.png
-07869.png
-33463.png
-01711.png
-59974.png
-27650.png
-34966.png
-16704.png
-04570.png
-31578.png
-25105.png
-46867.png
-00883.png
-51589.png
-21137.png
-33865.png
-69079.png
-14874.png
-43938.png
-39882.png
-27351.png
-40469.png
-50266.png
-61084.png
-15625.png
-69435.png
-69215.png
-58218.png
-36678.png
-29856.png
-03250.png
-47375.png
-58228.png
-30678.png
-27592.png
-03969.png
-41381.png
-66994.png
-29904.png
-26824.png
-40921.png
-17449.png
-27952.png
-16859.png
-45322.png
-06474.png
-34717.png
-09172.png
-27613.png
-54547.png
-47353.png
-21562.png
-20891.png
-56892.png
-41174.png
-51232.png
-64102.png
-47153.png
-32899.png
-35977.png
-43125.png
-56655.png
-15862.png
-54845.png
-52696.png
-55280.png
-41241.png
-45251.png
-36987.png
-63505.png
-58420.png
-32822.png
-20549.png
-21673.png
-07991.png
-33438.png
-20634.png
-20481.png
-16290.png
-46945.png
-29653.png
-35204.png
-23232.png
-44145.png
-39136.png
-49378.png
-52407.png
-05966.png
-50224.png
-45270.png
-22196.png
-27709.png
-37305.png
-31316.png
-48895.png
-63153.png
-11963.png
-33124.png
-04839.png
-69731.png
-56379.png
-18108.png
-54187.png
-34905.png
-67831.png
-02637.png
-68645.png
-57014.png
-14223.png
-21466.png
-14343.png
-43691.png
-64875.png
-51704.png
-61632.png
-15322.png
-37121.png
-05089.png
-03472.png
-61011.png
-13486.png
-14555.png
-21743.png
-43248.png
-31685.png
-29477.png
-11840.png
-07650.png
-60811.png
-52326.png
-34642.png
-52575.png
-05416.png
-50162.png
-64142.png
-55587.png
-57246.png
-62904.png
-29693.png
-45941.png
-04088.png
-11699.png
-39870.png
-65817.png
-67078.png
-69149.png
-08907.png
-38079.png
-19615.png
-69932.png
-04866.png
-55549.png
-48606.png
-38223.png
-55013.png
-27093.png
-39335.png
-45687.png
-61267.png
-25421.png
-63145.png
-28524.png
-11198.png
-65531.png
-45413.png
-37607.png
-48528.png
-30498.png
-43009.png
-08089.png
-21255.png
-11891.png
-33454.png
-52441.png
-58295.png
-15148.png
-12554.png
-67809.png
-22632.png
-42938.png
-61485.png
-45109.png
-11225.png
-29892.png
-40394.png
-34494.png
-64536.png
-46510.png
-04956.png
-01188.png
-61990.png
-32219.png
-26788.png
-59662.png
-23033.png
-47271.png
-16174.png
-29532.png
-35289.png
-57047.png
-46784.png
-48555.png
-54312.png
-46791.png
-60692.png
-14000.png
-65724.png
-60241.png
-03954.png
-56145.png
-23476.png
-00732.png
-61796.png
-04614.png
-10145.png
-25118.png
-36672.png
-59208.png
-55236.png
-16767.png
-56418.png
-62772.png
-53562.png
-45098.png
-35835.png
-17491.png
-12447.png
-66646.png
-09628.png
-53810.png
-61687.png
-68950.png
-17310.png
-66356.png
-61961.png
-09518.png
-14800.png
-59141.png
-49887.png
-68141.png
-04656.png
-63181.png
-08594.png
-47658.png
-64283.png
-19370.png
-03391.png
-00745.png
-69712.png
-24204.png
-36302.png
-66236.png
-13411.png
-20193.png
-37544.png
-58327.png
-00849.png
-48389.png
-16548.png
-06234.png
-53035.png
-36057.png
-16984.png
-64224.png
-44361.png
-25363.png
-31936.png
-29526.png
-61401.png
-15612.png
-64454.png
-50711.png
-69829.png
-23125.png
-27942.png
-12352.png
-69924.png
-51408.png
-30742.png
-27128.png
-36589.png
-02162.png
-58214.png
-12234.png
-57708.png
-22028.png
-69096.png
-03913.png
-19477.png
-56524.png
-13675.png
-03385.png
-02010.png
-04164.png
-63460.png
-40179.png
-50961.png
-63248.png
-26673.png
-36722.png
-56024.png
-37582.png
-00947.png
-66497.png
-02440.png
-43305.png
-69394.png
-55057.png
-24655.png
-35385.png
-44742.png
-64033.png
-36240.png
-42345.png
-49049.png
-19113.png
-42702.png
-18025.png
-68027.png
-00467.png
-15495.png
-08271.png
-29690.png
-63529.png
-44663.png
-67755.png
-68031.png
-03311.png
-59489.png
-67493.png
-00180.png
-41626.png
-35062.png
-60333.png
-48968.png
-26468.png
-54115.png
-44748.png
-49119.png
-38207.png
-12640.png
-49473.png
-63603.png
-20182.png
-54838.png
-04467.png
-61880.png
-45575.png
-28843.png
-02717.png
-02783.png
-62702.png
-42920.png
-55507.png
-69885.png
-17257.png
-27546.png
-08475.png
-42131.png
-00857.png
-26084.png
-36524.png
-28128.png
-45107.png
-39348.png
-68424.png
-39567.png
-47131.png
-29890.png
-40235.png
-34359.png
-03309.png
-53647.png
-07848.png
-33195.png
-16218.png
-60004.png
-13955.png
-15296.png
-34549.png
-07773.png
-33535.png
-36701.png
-05455.png
-03410.png
-63085.png
-20425.png
-44625.png
-38107.png
-16674.png
-32932.png
-39353.png
-01042.png
-22837.png
-15093.png
-02778.png
-38180.png
-52082.png
-44863.png
-15436.png
-08171.png
-22397.png
-22115.png
-32618.png
-08885.png
-44196.png
-19401.png
-09538.png
-14979.png
-16640.png
-27143.png
-15025.png
-68792.png
-23210.png
-09204.png
-28998.png
-41753.png
-53031.png
-50613.png
-31903.png
-66391.png
-14677.png
-67173.png
-16949.png
-52476.png
-16916.png
-50968.png
-67056.png
-07416.png
-49219.png
-15604.png
-62194.png
-38293.png
-30619.png
-12646.png
-16715.png
-16181.png
-59223.png
-16201.png
-38516.png
-24980.png
-67412.png
-24180.png
-52188.png
-41909.png
-02322.png
-33337.png
-34329.png
-17116.png
-26476.png
-54076.png
-11700.png
-41120.png
-34811.png
-12388.png
-47238.png
-61745.png
-52192.png
-03086.png
-24070.png
-44207.png
-40392.png
-30135.png
-66696.png
-21323.png
-02569.png
-34784.png
-58597.png
-28082.png
-45094.png
-23190.png
-02692.png
-32127.png
-47721.png
-22248.png
-59454.png
-44394.png
-14148.png
-01954.png
-67953.png
-03488.png
-39526.png
-17459.png
-10536.png
-46883.png
-55942.png
-12408.png
-35666.png
-54449.png
-53149.png
-11375.png
-14828.png
-64182.png
-15595.png
-42717.png
-50972.png
-07977.png
-39449.png
-16992.png
-20023.png
-59513.png
-66682.png
-64028.png
-36717.png
-47644.png
-17622.png
-28137.png
-49103.png
-19236.png
-00192.png
-63203.png
-33839.png
-65359.png
-06379.png
-52636.png
-58536.png
-18641.png
-42533.png
-36217.png
-45611.png
-22939.png
-63545.png
-45173.png
-11829.png
-36415.png
-54215.png
-24255.png
-47535.png
-12558.png
-52325.png
-41577.png
-11107.png
-19483.png
-52264.png
-14509.png
-42913.png
-12605.png
-03319.png
-12384.png
-37291.png
-28499.png
-16131.png
-46369.png
-69900.png
-35644.png
-11939.png
-69366.png
-23653.png
-02848.png
-08578.png
-27612.png
-50750.png
-56510.png
-57327.png
-19264.png
-02203.png
-34143.png
-13537.png
-54080.png
-00626.png
-23246.png
-25768.png
-19408.png
-66848.png
-45397.png
-15414.png
-31053.png
-11816.png
-03197.png
-69589.png
-21965.png
-25074.png
-23140.png
-38916.png
-20086.png
-19330.png
-10118.png
-24713.png
-23151.png
-66578.png
-06218.png
-59233.png
-29988.png
-69133.png
-48604.png
-02851.png
-46438.png
-61900.png
-12347.png
-43153.png
-43621.png
-66121.png
-19597.png
-46457.png
-43260.png
-62831.png
-37118.png
-38941.png
-11186.png
-37505.png
-50006.png
-53726.png
-67409.png
-23114.png
-56567.png
-68105.png
-68581.png
-46975.png
-66353.png
-52446.png
-53410.png
-16608.png
-04423.png
-02314.png
-43279.png
-48792.png
-65137.png
-02877.png
-69248.png
-13719.png
-26139.png
-35074.png
-53681.png
-48705.png
-10458.png
-11594.png
-14155.png
-64655.png
-11978.png
-30427.png
-00390.png
-17129.png
-15540.png
-19673.png
-31804.png
-63246.png
-39340.png
-57055.png
-52177.png
-00265.png
-63583.png
-53054.png
-44340.png
-58144.png
-21611.png
-06424.png
-46761.png
-34442.png
-65149.png
-60732.png
-41431.png
-19674.png
-12089.png
-33549.png
-11094.png
-38295.png
-40059.png
-20556.png
-36413.png
-23572.png
-26067.png
-36452.png
-57934.png
-14349.png
-00197.png
-61879.png
-38428.png
-48816.png
-07574.png
-07325.png
-14092.png
-38045.png
-03702.png
-17657.png
-03103.png
-37179.png
-42947.png
-55740.png
-07092.png
-45641.png
-35280.png
-13791.png
-35585.png
-23618.png
-51371.png
-33321.png
-56135.png
-43192.png
-23693.png
-56158.png
-63666.png
-26360.png
-60396.png
-48524.png
-18015.png
-16849.png
-44111.png
-13996.png
-01729.png
-03337.png
-45718.png
-05856.png
-42256.png
-24354.png
-50760.png
-30104.png
-31062.png
-14224.png
-34688.png
-13578.png
-42246.png
-25978.png
-61480.png
-53592.png
-31537.png
-50090.png
-10553.png
-32100.png
-07064.png
-50963.png
-20177.png
-63135.png
-68292.png
-01274.png
-49714.png
-34145.png
-31756.png
-55615.png
-50550.png
-55044.png
-33129.png
-26571.png
-34217.png
-28234.png
-21893.png
-08523.png
-03985.png
-50461.png
-60759.png
-35007.png
-59015.png
-46659.png
-53007.png
-28722.png
-39410.png
-66438.png
-57308.png
-61748.png
-03647.png
-14803.png
-03165.png
-45756.png
-07648.png
-09672.png
-32309.png
-00519.png
-68375.png
-44450.png
-02488.png
-54362.png
-19390.png
-29720.png
-15525.png
-19604.png
-43345.png
-58273.png
-58865.png
-30835.png
-57592.png
-29249.png
-55841.png
-59484.png
-56897.png
-38057.png
-15917.png
-56204.png
-08077.png
-25181.png
-46460.png
-40560.png
-08664.png
-42135.png
-03809.png
-59471.png
-19195.png
-45918.png
-03300.png
-65361.png
-20809.png
-42996.png
-25289.png
-01859.png
-29278.png
-17734.png
-61450.png
-34271.png
-45139.png
-20902.png
-00522.png
-33389.png
-02089.png
-58891.png
-28272.png
-33455.png
-21208.png
-37332.png
-27570.png
-29151.png
-37921.png
-11897.png
-58257.png
-16156.png
-35559.png
-02890.png
-67577.png
-38068.png
-33225.png
-34927.png
-11029.png
-04452.png
-02524.png
-37409.png
-02867.png
-36080.png
-01691.png
-10213.png
-61685.png
-03181.png
-02693.png
-66685.png
-46760.png
-25822.png
-41030.png
-43158.png
-25910.png
-65347.png
-56083.png
-16157.png
-21042.png
-38628.png
-67161.png
-56325.png
-67653.png
-16077.png
-45854.png
-56780.png
-29507.png
-18799.png
-58424.png
-51010.png
-35222.png
-42128.png
-36752.png
-64644.png
-40122.png
-56884.png
-23565.png
-41764.png
-67367.png
-48249.png
-00006.png
-03579.png
-15319.png
-31459.png
-02379.png
-39643.png
-02937.png
-42468.png
-02365.png
-68833.png
-54428.png
-12683.png
-37068.png
-68284.png
-31796.png
-53262.png
-35960.png
-00084.png
-58631.png
-12202.png
-22104.png
-19124.png
-50761.png
-38777.png
-50478.png
-69869.png
-49052.png
-05356.png
-65065.png
-27105.png
-42780.png
-56766.png
-22213.png
-67516.png
-66264.png
-31665.png
-03409.png
-10748.png
-04907.png
-61396.png
-61021.png
-47175.png
-10492.png
-51398.png
-68463.png
-33509.png
-38268.png
-51278.png
-18254.png
-63469.png
-12302.png
-24542.png
-49551.png
-12697.png
-09885.png
-29261.png
-61059.png
-16793.png
-12547.png
-46758.png
-17406.png
-49723.png
-51006.png
-33883.png
-08374.png
-36485.png
-51631.png
-63348.png
-66621.png
-14341.png
-54166.png
-04322.png
-64341.png
-57031.png
-61484.png
-33603.png
-24218.png
-05242.png
-63687.png
-39039.png
-54199.png
-41981.png
-59340.png
-42601.png
-60812.png
-40085.png
-37470.png
-24127.png
-05430.png
-50735.png
-53305.png
-41233.png
-22743.png
-21997.png
-25557.png
-31946.png
-57355.png
-38564.png
-17829.png
-12536.png
-21764.png
-53292.png
-06735.png
-05200.png
-45927.png
-67875.png
-63906.png
-25214.png
-60014.png
-51137.png
-66201.png
-63275.png
-34433.png
-30396.png
-52103.png
-01227.png
-67805.png
-25967.png
-58694.png
-24562.png
-15213.png
-22252.png
-41978.png
-33808.png
-68300.png
-01433.png
-23743.png
-45533.png
-12912.png
-04130.png
-42315.png
-37664.png
-17344.png
-21643.png
-13342.png
-00177.png
-49776.png
-47545.png
-40983.png
-60736.png
-52879.png
-14256.png
-20893.png
-32933.png
-45466.png
-16385.png
-31081.png
-31204.png
-53033.png
-39871.png
-38729.png
-54720.png
-09604.png
-19283.png
-26913.png
-21806.png
-38308.png
-19521.png
-09723.png
-39484.png
-45531.png
-50467.png
-50167.png
-20085.png
-64654.png
-59253.png
-27110.png
-54929.png
-51238.png
-12967.png
-58019.png
-18993.png
-15133.png
-09437.png
-00539.png
-45930.png
-61775.png
-63683.png
-42408.png
-43745.png
-67335.png
-46932.png
-58190.png
-62761.png
-59901.png
-46154.png
-17114.png
-27338.png
-11177.png
-21360.png
-10824.png
-52295.png
-52546.png
-09925.png
-43695.png
-26024.png
-26998.png
-07202.png
-01303.png
-51217.png
-08222.png
-35120.png
-51930.png
-05990.png
-03326.png
-42397.png
-36439.png
-53233.png
-21422.png
-37166.png
-48158.png
-22309.png
-68210.png
-09798.png
-24544.png
-33205.png
-19939.png
-13329.png
-19817.png
-43157.png
-45729.png
-46858.png
-69764.png
-07082.png
-00313.png
-36443.png
-40368.png
-33724.png
-54990.png
-26078.png
-55200.png
-03884.png
-66011.png
-34478.png
-64952.png
-18937.png
-45281.png
-24352.png
-59863.png
-19100.png
-63370.png
-53706.png
-24211.png
-45568.png
-37726.png
-26577.png
-63402.png
-14587.png
-34394.png
-20706.png
-21012.png
-29312.png
-50559.png
-68165.png
-12783.png
-05529.png
-08495.png
-65715.png
-14956.png
-18246.png
-26006.png
-40035.png
-52022.png
-64491.png
-41552.png
-02738.png
-13529.png
-42045.png
-04796.png
-65051.png
-69537.png
-30485.png
-33725.png
-47282.png
-54723.png
-64166.png
-06245.png
-59942.png
-11334.png
-56161.png
-37889.png
-09435.png
-10196.png
-18576.png
-43121.png
-37123.png
-27964.png
-19657.png
-16956.png
-62056.png
-22736.png
-43932.png
-06367.png
-52178.png
-67587.png
-62523.png
-55014.png
-46116.png
-07418.png
-41463.png
-16814.png
-30905.png
-37916.png
-36074.png
-00853.png
-55620.png
-25180.png
-55258.png
-52621.png
-37090.png
-10671.png
-31944.png
-68981.png
-13485.png
-18382.png
-28648.png
-25516.png
-10372.png
-06145.png
-45600.png
-07448.png
-52251.png
-37142.png
-69584.png
-57044.png
-64305.png
-20139.png
-27203.png
-55290.png
-57281.png
-61569.png
-11087.png
-39014.png
-42811.png
-54711.png
-35244.png
-45421.png
-23540.png
-36941.png
-37196.png
-37576.png
-01520.png
-38641.png
-36569.png
-30645.png
-35427.png
-17933.png
-04184.png
-65466.png
-65690.png
-09439.png
-20806.png
-37862.png
-42198.png
-58459.png
-09208.png
-26663.png
-20802.png
-38247.png
-30434.png
-09442.png
-49030.png
-36395.png
-08770.png
-40755.png
-44619.png
-38302.png
-48131.png
-30838.png
-42139.png
-32908.png
-52872.png
-22780.png
-06453.png
-37937.png
-45502.png
-22176.png
-22823.png
-46660.png
-01032.png
-10594.png
-26729.png
-41281.png
-37720.png
-68050.png
-12321.png
-64550.png
-10001.png
-43575.png
-31832.png
-06806.png
-25710.png
-45242.png
-20485.png
-11865.png
-06103.png
-33767.png
-59193.png
-11850.png
-00262.png
-00149.png
-00840.png
-03170.png
-35318.png
-46935.png
-63826.png
-36307.png
-35725.png
-48669.png
-08247.png
-15270.png
-65723.png
-34031.png
-04616.png
-10881.png
-27638.png
-35651.png
-22202.png
-39756.png
-05676.png
-51279.png
-47112.png
-63750.png
-13630.png
-55074.png
-07223.png
-03211.png
-30588.png
-25234.png
-68560.png
-29215.png
-07288.png
-51291.png
-45399.png
-34944.png
-59845.png
-33489.png
-52867.png
-38894.png
-17955.png
-25295.png
-08223.png
-29341.png
-06800.png
-59459.png
-33384.png
-57419.png
-61504.png
-66591.png
-20604.png
-56691.png
-42813.png
-63537.png
-10150.png
-11649.png
-44276.png
-26730.png
-05783.png
-41019.png
-58149.png
-42870.png
-05732.png
-63173.png
-16657.png
-20017.png
-06870.png
-31350.png
-24484.png
-69457.png
-02416.png
-53432.png
-04030.png
-31880.png
-63944.png
-35057.png
-62575.png
-69071.png
-67084.png
-03073.png
-04660.png
-22020.png
-42202.png
-65285.png
-34545.png
-40344.png
-53198.png
-55288.png
-11678.png
-61012.png
-58595.png
-36310.png
-00418.png
-08328.png
-59576.png
-34522.png
-47826.png
-16140.png
-02878.png
-18609.png
-43993.png
-63998.png
-10583.png
-65180.png
-22089.png
-29543.png
-55467.png
-30708.png
-52365.png
-37575.png
-28733.png
-24077.png
-66305.png
-30804.png
-22893.png
-38420.png
-47995.png
-11654.png
-13987.png
-16289.png
-59447.png
-36535.png
-41655.png
-50913.png
-16159.png
-11837.png
-56737.png
-03671.png
-47372.png
-33575.png
-30975.png
-63059.png
-39062.png
-24482.png
-13361.png
-35141.png
-03425.png
-09886.png
-09599.png
-66985.png
-06745.png
-44473.png
-55315.png
-23518.png
-36044.png
-42248.png
-00430.png
-66783.png
-65784.png
-41262.png
-26731.png
-60076.png
-59895.png
-13285.png
-63585.png
-31613.png
-00475.png
-08653.png
-48896.png
-55252.png
-58821.png
-30504.png
-50914.png
-04676.png
-12915.png
-57789.png
-03657.png
-46087.png
-68015.png
-63720.png
-44092.png
-10844.png
-00763.png
-36018.png
-48700.png
-41354.png
-29251.png
-20898.png
-36446.png
-02648.png
-37912.png
-12426.png
-52989.png
-30631.png
-17179.png
-25433.png
-51064.png
-17050.png
-23351.png
-10012.png
-09560.png
-03822.png
-10695.png
-12250.png
-14763.png
-54515.png
-37672.png
-27615.png
-25991.png
-08957.png
-38598.png
-67329.png
-20943.png
-31401.png
-60912.png
-16090.png
-49620.png
-59719.png
-46486.png
-64408.png
-56569.png
-44306.png
-43255.png
-29083.png
-06731.png
-10504.png
-33686.png
-60893.png
-66417.png
-06216.png
-46754.png
-52237.png
-06924.png
-43642.png
-09142.png
-27989.png
-58129.png
-55448.png
-40705.png
-38757.png
-39071.png
-56678.png
-48747.png
-18549.png
-41352.png
-31244.png
-50210.png
-50082.png
-69343.png
-26232.png
-58537.png
-32150.png
-48245.png
-52096.png
-28403.png
-59049.png
-37180.png
-44786.png
-31157.png
-62422.png
-51296.png
-62817.png
-08719.png
-06289.png
-41515.png
-01432.png
-67571.png
-36596.png
-09191.png
-40861.png
-22392.png
-15486.png
-59677.png
-53340.png
-54092.png
-28908.png
-16046.png
-08959.png
-65711.png
-18034.png
-25258.png
-45945.png
-30347.png
-67700.png
-61140.png
-19752.png
-48053.png
-40870.png
-53066.png
-42823.png
-00245.png
-18913.png
-39369.png
-63910.png
-39795.png
-60568.png
-30846.png
-39540.png
-41340.png
-16725.png
-27941.png
-54988.png
-64119.png
-34155.png
-67992.png
-20011.png
-13683.png
-08308.png
-07476.png
-04288.png
-26696.png
-19452.png
-52716.png
-18039.png
-31358.png
-26077.png
-63978.png
-55217.png
-04507.png
-39302.png
-60904.png
-01962.png
-44485.png
-47617.png
-05373.png
-25941.png
-09381.png
-55926.png
-60494.png
-47327.png
-26646.png
-44343.png
-05102.png
-50132.png
-01513.png
-62694.png
-40671.png
-31901.png
-29500.png
-69121.png
-45905.png
-20389.png
-63602.png
-60151.png
-61107.png
-58511.png
-03459.png
-50762.png
-30141.png
-61027.png
-14168.png
-67699.png
-65150.png
-00148.png
-53461.png
-39574.png
-18969.png
-08970.png
-27419.png
-58944.png
-12200.png
-55243.png
-11129.png
-31681.png
-04458.png
-40470.png
-17227.png
-30798.png
-56085.png
-44592.png
-18459.png
-07731.png
-47098.png
-09120.png
-41203.png
-10207.png
-29754.png
-17281.png
-37111.png
-09719.png
-62986.png
-14289.png
-57451.png
-66944.png
-57446.png
-59461.png
-27146.png
-56183.png
-65032.png
-28004.png
-23979.png
-47063.png
-55310.png
-52824.png
-02640.png
-20575.png
-07193.png
-60817.png
-33780.png
-23598.png
-27996.png
-06634.png
-31421.png
-54160.png
-49094.png
-61817.png
-63236.png
-06970.png
-11534.png
-25226.png
-24053.png
-60842.png
-32942.png
-39958.png
-56080.png
-36911.png
-09635.png
-18172.png
-08947.png
-13409.png
-49978.png
-07239.png
-11380.png
-02184.png
-68902.png
-27125.png
-19329.png
-11893.png
-52452.png
-34598.png
-47652.png
-29459.png
-45650.png
-15703.png
-16125.png
-41077.png
-42146.png
-39154.png
-02438.png
-53808.png
-50483.png
-04914.png
-26544.png
-37733.png
-61627.png
-34970.png
-29088.png
-40290.png
-20542.png
-45638.png
-13873.png
-39058.png
-58588.png
-66030.png
-69352.png
-19813.png
-67214.png
-52223.png
-06358.png
-06623.png
-32677.png
-18849.png
-11973.png
-56544.png
-21587.png
-10472.png
-49607.png
-28961.png
-24832.png
-17699.png
-14331.png
-46229.png
-02332.png
-15811.png
-63890.png
-13552.png
-03428.png
-62149.png
-29552.png
-16542.png
-53746.png
-56702.png
-39634.png
-19616.png
-41715.png
-42109.png
-39900.png
-35396.png
-29239.png
-22515.png
-07548.png
-40161.png
-53774.png
-33451.png
-27859.png
-60925.png
-33862.png
-19331.png
-29320.png
-38260.png
-46458.png
-02247.png
-25512.png
-14716.png
-20004.png
-00807.png
-34400.png
-68211.png
-42475.png
-44597.png
-48102.png
-67427.png
-36581.png
-13565.png
-41856.png
-53250.png
-36519.png
-64764.png
-47612.png
-10589.png
-31826.png
-48838.png
-28659.png
-59320.png
-31907.png
-46149.png
-24469.png
-40387.png
-41907.png
-31378.png
-02374.png
-50546.png
-64398.png
-09461.png
-03846.png
-48927.png
-55250.png
-05076.png
-55067.png
-33185.png
-05502.png
-19275.png
-55778.png
-35870.png
-54043.png
-47898.png
-62350.png
-08186.png
-45627.png
-31800.png
-00913.png
-24712.png
-01896.png
-17292.png
-43474.png
-36202.png
-54371.png
-60614.png
-36383.png
-48293.png
-42473.png
-63347.png
-48707.png
-69515.png
-55784.png
-33825.png
-21177.png
-32369.png
-30211.png
-10777.png
-29020.png
-39289.png
-49678.png
-16836.png
-57488.png
-55129.png
-50484.png
-59186.png
-00373.png
-45912.png
-49117.png
-67529.png
-07017.png
-34255.png
-40840.png
-36269.png
-50926.png
-26834.png
-41425.png
-22878.png
-36882.png
-46718.png
-01531.png
-58186.png
-11615.png
-48689.png
-43641.png
-39234.png
-26556.png
-42954.png
-00743.png
-40430.png
-45304.png
-58226.png
-04617.png
-50299.png
-14703.png
-49294.png
-68212.png
-67440.png
-13063.png
-68585.png
-46074.png
-05734.png
-52396.png
-24206.png
-21390.png
-36772.png
-64589.png
-07271.png
-30867.png
-51085.png
-51799.png
-49455.png
-10043.png
-11161.png
-29035.png
-63415.png
-61230.png
-64403.png
-06411.png
-46933.png
-16072.png
-27161.png
-36801.png
-07461.png
-02228.png
-47913.png
-12230.png
-23953.png
-69276.png
-18395.png
-37200.png
-31556.png
-63073.png
-13326.png
-45100.png
-42279.png
-43452.png
-20527.png
-47931.png
-63779.png
-03650.png
-43869.png
-08125.png
-10679.png
-43264.png
-44198.png
-06238.png
-36287.png
-14375.png
-12575.png
-29822.png
-43716.png
-19592.png
-34265.png
-39383.png
-66826.png
-18570.png
-29854.png
-37163.png
-00964.png
-45010.png
-36048.png
-22628.png
-10385.png
-32071.png
-62800.png
-35992.png
-42501.png
-17051.png
-23556.png
-02910.png
-03667.png
-31858.png
-00594.png
-57558.png
-33588.png
-68690.png
-47391.png
-22009.png
-51326.png
-23193.png
-69088.png
-03262.png
-68047.png
-50813.png
-31093.png
-19885.png
-20884.png
-20461.png
-57296.png
-28987.png
-59667.png
-63303.png
-68672.png
-42956.png
-03060.png
-28298.png
-21766.png
-32171.png
-34509.png
-48670.png
-02235.png
-42311.png
-41576.png
-39217.png
-17017.png
-35658.png
-59097.png
-08799.png
-21519.png
-10072.png
-17339.png
-62307.png
-23705.png
-48339.png
-17716.png
-22890.png
-45341.png
-42694.png
-37078.png
-46915.png
-69362.png
-03442.png
-22918.png
-42558.png
-43361.png
-60561.png
-22585.png
-36467.png
-06765.png
-65291.png
-60300.png
-55058.png
-02618.png
-36190.png
-37175.png
-15237.png
-66600.png
-23713.png
-41396.png
-60304.png
-26243.png
-07520.png
-17348.png
-51576.png
-15036.png
-32117.png
-31893.png
-01013.png
-34967.png
-20249.png
-34352.png
-18007.png
-28710.png
-11579.png
-44074.png
-39028.png
-53028.png
-43493.png
-38174.png
-56468.png
-00168.png
-68132.png
-64953.png
-11983.png
-04819.png
-58401.png
-63512.png
-20276.png
-65950.png
-44030.png
-28553.png
-41493.png
-47574.png
-51881.png
-38769.png
-22223.png
-20184.png
-33506.png
-44346.png
-03029.png
-61051.png
-03441.png
-33440.png
-11147.png
-46603.png
-28118.png
-07342.png
-28488.png
-65182.png
-36448.png
-24223.png
-13204.png
-54917.png
-06205.png
-38690.png
-65620.png
-39364.png
-65929.png
-02385.png
-63684.png
-50883.png
-68975.png
-24962.png
-52940.png
-31620.png
-61115.png
-19954.png
-36629.png
-61712.png
-44711.png
-19561.png
-41844.png
-67356.png
-66991.png
-04988.png
-05592.png
-11395.png
-46188.png
-54872.png
-41163.png
-01545.png
-57974.png
-10064.png
-28471.png
-05620.png
-44301.png
-67982.png
-05902.png
-29735.png
-13035.png
-22774.png
-68374.png
-36392.png
-57556.png
-25318.png
-55885.png
-02016.png
-62001.png
-38719.png
-24985.png
-26642.png
-03192.png
-55416.png
-67734.png
-08398.png
-39792.png
-18131.png
-67240.png
-47055.png
-10217.png
-41332.png
-33978.png
-09131.png
-57626.png
-54681.png
-28216.png
-07423.png
-24480.png
-35116.png
-16432.png
-24029.png
-54979.png
-66807.png
-16237.png
-36144.png
-69681.png
-51147.png
-28664.png
-56238.png
-68568.png
-08281.png
-57057.png
-36012.png
-40809.png
-39603.png
-64945.png
-67793.png
-65507.png
-14020.png
-63955.png
-44667.png
-26412.png
-14706.png
-48934.png
-38365.png
-31748.png
-20459.png
-69669.png
-08977.png
-11804.png
-31814.png
-30339.png
-57155.png
-08676.png
-23826.png
-24293.png
-52413.png
-15140.png
-68694.png
-08906.png
-46642.png
-42284.png
-38209.png
-64880.png
-36743.png
-41207.png
-60343.png
-50253.png
-46274.png
-08151.png
-68836.png
-37659.png
-46382.png
-30443.png
-58468.png
-58336.png
-53369.png
-08296.png
-23021.png
-69629.png
-56840.png
-55050.png
-05178.png
-69830.png
-37355.png
-62065.png
-44045.png
-09537.png
-36014.png
-52508.png
-34650.png
-44247.png
-68439.png
-30473.png
-66162.png
-30256.png
-46250.png
-03645.png
-43132.png
-33288.png
-59952.png
-15758.png
-05507.png
-21399.png
-32615.png
-39747.png
-09861.png
-26366.png
-60010.png
-42331.png
-40471.png
-03638.png
-21290.png
-34331.png
-55844.png
-25803.png
-60194.png
-00532.png
-23682.png
-35520.png
-31935.png
-61378.png
-60924.png
-46052.png
-15945.png
-33023.png
-63408.png
-18293.png
-30613.png
-27005.png
-23137.png
-34833.png
-51786.png
-36254.png
-50984.png
-69556.png
-62334.png
-10193.png
-45475.png
-41097.png
-52293.png
-45247.png
-05691.png
-27773.png
-21310.png
-29002.png
-16908.png
-20698.png
-45194.png
-14801.png
-23746.png
-12541.png
-16666.png
-40247.png
-63982.png
-16488.png
-02698.png
-49255.png
-07243.png
-02381.png
-35433.png
-37834.png
-07006.png
-44516.png
-10029.png
-04629.png
-61676.png
-12676.png
-06181.png
-21480.png
-34534.png
-13367.png
-32635.png
-36962.png
-42155.png
-02978.png
-60509.png
-38621.png
-27107.png
-41581.png
-15735.png
-12616.png
-64115.png
-17302.png
-20520.png
-02568.png
-07382.png
-38526.png
-56233.png
-53415.png
-17743.png
-30144.png
-43521.png
-57135.png
-24898.png
-37207.png
-06468.png
-29894.png
-10680.png
-57415.png
-47212.png
-06549.png
-39729.png
-10587.png
-29590.png
-10922.png
-61155.png
-68991.png
-51656.png
-22912.png
-27109.png
-27458.png
-18406.png
-03644.png
-61190.png
-50187.png
-04455.png
-36246.png
-49521.png
-32613.png
-65200.png
-35089.png
-23238.png
-58436.png
-57225.png
-56954.png
-60338.png
-22040.png
-60532.png
-44381.png
-18278.png
-45583.png
-13815.png
-20477.png
-44115.png
-36157.png
-63274.png
-43312.png
-68046.png
-11626.png
-55720.png
-17255.png
-21392.png
-60995.png
-46881.png
-05806.png
-08836.png
-14646.png
-24251.png
-04147.png
-62266.png
-30882.png
-63431.png
-47935.png
-22343.png
-47700.png
-23674.png
-50011.png
-65750.png
-53434.png
-08044.png
-63521.png
-69285.png
-04494.png
-09847.png
-06666.png
-17874.png
-14744.png
-45106.png
-60439.png
-53308.png
-43569.png
-28671.png
-33085.png
-50358.png
-43912.png
-26873.png
-68965.png
-25384.png
-58173.png
-03375.png
-53175.png
-19865.png
-35680.png
-04389.png
-46053.png
-25150.png
-01547.png
-39533.png
-21746.png
-64461.png
-10040.png
-03324.png
-25968.png
-64961.png
-02223.png
-35781.png
-54154.png
-50249.png
-19706.png
-14557.png
-63987.png
-06049.png
-44016.png
-48630.png
-08651.png
-63536.png
-62249.png
-05114.png
-37392.png
-16279.png
-19361.png
-44204.png
-38745.png
-10077.png
-28442.png
-61245.png
-46950.png
-25958.png
-44003.png
-68571.png
-09360.png
-02628.png
-14663.png
-59448.png
-46130.png
-45965.png
-43970.png
-38018.png
-23999.png
-65623.png
-08945.png
-44589.png
-63543.png
-11690.png
-65939.png
-59445.png
-29104.png
-11218.png
-37997.png
-28673.png
-62533.png
-51594.png
-51627.png
-04234.png
-52379.png
-17709.png
-18445.png
-37783.png
-49766.png
-13408.png
-58596.png
-57701.png
-04905.png
-04777.png
-14478.png
-47857.png
-38649.png
-52372.png
-39200.png
-47286.png
-30346.png
-50804.png
-35679.png
-10412.png
-13580.png
-23467.png
-07990.png
-07772.png
-10027.png
-48060.png
-13062.png
-23430.png
-51486.png
-11149.png
-38144.png
-42840.png
-49851.png
-30807.png
-17445.png
-64018.png
-07499.png
-67714.png
-42222.png
-28688.png
-30394.png
-11229.png
-50953.png
-31441.png
-13939.png
-34986.png
-39140.png
-40190.png
-46135.png
-16990.png
-14625.png
-46096.png
-37769.png
-44456.png
-21599.png
-00693.png
-08385.png
-43524.png
-33164.png
-06686.png
-39652.png
-32375.png
-34061.png
-65635.png
-24443.png
-45375.png
-28211.png
-01289.png
-57848.png
-06144.png
-48588.png
-65898.png
-59533.png
-22849.png
-26469.png
-34387.png
-13041.png
-01261.png
-40767.png
-37514.png
-44816.png
-12660.png
-58672.png
-05310.png
-60360.png
-26976.png
-09546.png
-60680.png
-68359.png
-16931.png
-31169.png
-64176.png
-49311.png
-40920.png
-50740.png
-22615.png
-46773.png
-35988.png
-39297.png
-67881.png
-27516.png
-62428.png
-47757.png
-62305.png
-32534.png
-29096.png
-51300.png
-18358.png
-35531.png
-05849.png
-58246.png
-40359.png
-14773.png
-32983.png
-16194.png
-31508.png
-12040.png
-32731.png
-29397.png
-42361.png
-00628.png
-40296.png
-66246.png
-41541.png
-45692.png
-08318.png
-20130.png
-28989.png
-21650.png
-29502.png
-34212.png
-49487.png
-00815.png
-50646.png
-48127.png
-49864.png
-21398.png
-34404.png
-64671.png
-43076.png
-35575.png
-18936.png
-13926.png
-60610.png
-26623.png
-34894.png
-44392.png
-31876.png
-67029.png
-06950.png
-13286.png
-64844.png
-56896.png
-00595.png
-54417.png
-24177.png
-30841.png
-26603.png
-53407.png
-64759.png
-44636.png
-27970.png
-68420.png
-45743.png
-41591.png
-19897.png
-26115.png
-49975.png
-53571.png
-33278.png
-28990.png
-66841.png
-53423.png
-53762.png
-16516.png
-34292.png
-52063.png
-53338.png
-46675.png
-65931.png
-31210.png
-54309.png
-26517.png
-07350.png
-27327.png
-66111.png
-41327.png
-54338.png
-19269.png
-10447.png
-22138.png
-63405.png
-31669.png
-58275.png
-09138.png
-04255.png
-56576.png
-00571.png
-02221.png
-30371.png
-59983.png
-42334.png
-47297.png
-25411.png
-10082.png
-48964.png
-43543.png
-51249.png
-37899.png
-12023.png
-03972.png
-65181.png
-03527.png
-61003.png
-47007.png
-50529.png
-54915.png
-08024.png
-69410.png
-18352.png
-02801.png
-67922.png
-09379.png
-62652.png
-68468.png
-59082.png
-15715.png
-10570.png
-50411.png
-01856.png
-35758.png
-10203.png
-48507.png
-14337.png
-06436.png
-58806.png
-19438.png
-14909.png
-01389.png
-22570.png
-15907.png
-61026.png
-36193.png
-52074.png
-28361.png
-67369.png
-16872.png
-16771.png
-27756.png
-35686.png
-27284.png
-48971.png
-43707.png
-11671.png
-18846.png
-55659.png
-29652.png
-09550.png
-16513.png
-33936.png
-52209.png
-05372.png
-30735.png
-58922.png
-28928.png
-35635.png
-15739.png
-53603.png
-51889.png
-11894.png
-01648.png
-62365.png
-36813.png
-09638.png
-08706.png
-09364.png
-66873.png
-08554.png
-47073.png
-05949.png
-54046.png
-64947.png
-05897.png
-66256.png
-49441.png
-64394.png
-14132.png
-07125.png
-44304.png
-06520.png
-17589.png
-38066.png
-20282.png
-22657.png
-49502.png
-48230.png
-61842.png
-49302.png
-21005.png
-24715.png
-07570.png
-26894.png
-25031.png
-47788.png
-39460.png
-01391.png
-56838.png
-37532.png
-54340.png
-38165.png
-17634.png
-41529.png
-04581.png
-59821.png
-10334.png
-66570.png
-48073.png
-36345.png
-21884.png
-29271.png
-59908.png
-52461.png
-63466.png
-54971.png
-26009.png
-38613.png
-34493.png
-23548.png
-49260.png
-17876.png
-56692.png
-37686.png
-10713.png
-57894.png
-01934.png
-09310.png
-57937.png
-51049.png
-67373.png
-63685.png
-22418.png
-19929.png
-26718.png
-30342.png
-33175.png
-56320.png
-64818.png
-20989.png
-59183.png
-61862.png
-39808.png
-61049.png
-37750.png
-41408.png
-63912.png
-29648.png
-08567.png
-33029.png
-50512.png
-64081.png
-39920.png
-37645.png
-29617.png
-36546.png
-51180.png
-02326.png
-40652.png
-14537.png
-51494.png
-32289.png
-67179.png
-09103.png
-20696.png
-20597.png
-56721.png
-52398.png
-53016.png
-21212.png
-03027.png
-13670.png
-66487.png
-20415.png
-57548.png
-65138.png
-09949.png
-60898.png
-04742.png
-38748.png
-38055.png
-03876.png
-67754.png
-55274.png
-68181.png
-25096.png
-37425.png
-26318.png
-35196.png
-48765.png
-46996.png
-37150.png
-35509.png
-64098.png
-18856.png
-18851.png
-31663.png
-06416.png
-10809.png
-48501.png
-06858.png
-59441.png
-20188.png
-26447.png
-44522.png
-29220.png
-51243.png
-55903.png
-10503.png
-36975.png
-60041.png
-00126.png
-13259.png
-50322.png
-17244.png
-59312.png
-03929.png
-13269.png
-49197.png
-52548.png
-07985.png
-55686.png
-23599.png
-62807.png
-56951.png
-23250.png
-54941.png
-00859.png
-19898.png
-46548.png
-16331.png
-38162.png
-32768.png
-64894.png
-55420.png
-07381.png
-51822.png
-48719.png
-10546.png
-36233.png
-36550.png
-28285.png
-12773.png
-07472.png
-51711.png
-50469.png
-15741.png
-56718.png
-04719.png
-65590.png
-04006.png
-32916.png
-48878.png
-57527.png
-62722.png
-31570.png
-63317.png
-55485.png
-36620.png
-18964.png
-64825.png
-09023.png
-52920.png
-57375.png
-51800.png
-45678.png
-36177.png
-15433.png
-07970.png
-19154.png
-18862.png
-23747.png
-48903.png
-67115.png
-65986.png
-29297.png
-28010.png
-38492.png
-08587.png
-19968.png
-36683.png
-55515.png
-21859.png
-57964.png
-21441.png
-20762.png
-37176.png
-37049.png
-04415.png
-24518.png
-66050.png
-44775.png
-11263.png
-02707.png
-55953.png
-68592.png
-54008.png
-49381.png
-41848.png
-20981.png
-39544.png
-14735.png
-59957.png
-52530.png
-31745.png
-54506.png
-35161.png
-31457.png
-66197.png
-13845.png
-68784.png
-61661.png
-59379.png
-68497.png
-44448.png
-33479.png
-18157.png
-02913.png
-13800.png
-54310.png
-28511.png
-29679.png
-45210.png
-28651.png
-16431.png
-01211.png
-16972.png
-42528.png
-16506.png
-25551.png
-47438.png
-64044.png
-66737.png
-43475.png
-06738.png
-44878.png
-32716.png
-03706.png
-45089.png
-05943.png
-08993.png
-36191.png
-39558.png
-09539.png
-67518.png
-61127.png
-39487.png
-62242.png
-62764.png
-28175.png
-53767.png
-40934.png
-29547.png
-47139.png
-04481.png
-44767.png
-12188.png
-13737.png
-20874.png
-47738.png
-17110.png
-08172.png
-40976.png
-54007.png
-01938.png
-54361.png
-38859.png
-15984.png
-57443.png
-62438.png
-25221.png
-57042.png
-20975.png
-67532.png
-65099.png
-33259.png
-22532.png
-55745.png
-22804.png
-31529.png
-34190.png
-61895.png
-34741.png
-10554.png
-02808.png
-01714.png
-08471.png
-37713.png
-46263.png
-45514.png
-41263.png
-65878.png
-11230.png
-18701.png
-41973.png
-69202.png
-69735.png
-09736.png
-16169.png
-63380.png
-27827.png
-17267.png
-06729.png
-20906.png
-00078.png
-58725.png
-21574.png
-23874.png
-04718.png
-44420.png
-39589.png
-53223.png
-53477.png
-61088.png
-07353.png
-65309.png
-27792.png
-69818.png
-59190.png
-07305.png
-41256.png
-44380.png
-00770.png
-06388.png
-22303.png
-32606.png
-22254.png
-64872.png
-43717.png
-51455.png
-60582.png
-46618.png
-54625.png
-43175.png
-32370.png
-52420.png
-33466.png
-68508.png
-67208.png
-14246.png
-07098.png
-28130.png
-01333.png
-39501.png
-08288.png
-50597.png
-19844.png
-31951.png
-36111.png
-41761.png
-32858.png
-09787.png
-08871.png
-51189.png
-29730.png
-29533.png
-30088.png
-68565.png
-11452.png
-36297.png
-60886.png
-05909.png
-20729.png
-17814.png
-50022.png
-04112.png
-55889.png
-22810.png
-01448.png
-57112.png
-60965.png
-39577.png
-45358.png
-63605.png
-20429.png
-60864.png
-13960.png
-48757.png
-06072.png
-49471.png
-29221.png
-39974.png
-53190.png
-64993.png
-36348.png
-57567.png
-54052.png
-10011.png
-46704.png
-01750.png
-11343.png
-61395.png
-65553.png
-44432.png
-62540.png
-67045.png
-66471.png
-48929.png
-67396.png
-25872.png
-35989.png
-42161.png
-58344.png
-24034.png
-02030.png
-33711.png
-67904.png
-51000.png
-21671.png
-03924.png
-43739.png
-14047.png
-30879.png
-54698.png
-15779.png
-63141.png
-40034.png
-45123.png
-33432.png
-29775.png
-29545.png
-35888.png
-19430.png
-65098.png
-51639.png
-05476.png
-59805.png
-33191.png
-61740.png
-45903.png
-65777.png
-27708.png
-37265.png
-14377.png
-36156.png
-09977.png
-58462.png
-11207.png
-13437.png
-45362.png
-47288.png
-68791.png
-10264.png
-31787.png
-34773.png
-01601.png
-06657.png
-36009.png
-25616.png
-14044.png
-51001.png
-60989.png
-23570.png
-05924.png
-05166.png
-46586.png
-44542.png
-07680.png
-04227.png
-28261.png
-31879.png
-66627.png
-07486.png
-15769.png
-60735.png
-63772.png
-69195.png
-23373.png
-53840.png
-05656.png
-21364.png
-32789.png
-12995.png
-10976.png
-39026.png
-44405.png
-48140.png
-02323.png
-09797.png
-54483.png
-46157.png
-26057.png
-34228.png
-29203.png
-37831.png
-56907.png
-37936.png
-54056.png
-36416.png
-68538.png
-33209.png
-63205.png
-10360.png
-09383.png
-35299.png
-28030.png
-12436.png
-45219.png
-26778.png
-00769.png
-62815.png
-62929.png
-63487.png
-26224.png
-24449.png
-63919.png
-45586.png
-12543.png
-29707.png
-28491.png
-07274.png
-29032.png
-41326.png
-63859.png
-02430.png
-32649.png
-31638.png
-06768.png
-05559.png
-43843.png
-66495.png
-57289.png
-35384.png
-00426.png
-27373.png
-36610.png
-31353.png
-39170.png
-01055.png
-30758.png
-66936.png
-14446.png
-08737.png
-10218.png
-24410.png
-61542.png
-26863.png
-11470.png
-64518.png
-52926.png
-65908.png
-49579.png
-64904.png
-16354.png
-51646.png
-26943.png
-42110.png
-16388.png
-55326.png
-52377.png
-63480.png
-57021.png
-45521.png
-00420.png
-30828.png
-08167.png
-12286.png
-69820.png
-04770.png
-63819.png
-67807.png
-12658.png
-28493.png
-05188.png
-32139.png
-60058.png
-48654.png
-49681.png
-08334.png
-33355.png
-54862.png
-21920.png
-46331.png
-53943.png
-62186.png
-00432.png
-40406.png
-21988.png
-68125.png
-65791.png
-25742.png
-25328.png
-18765.png
-00424.png
-06797.png
-10194.png
-31562.png
-11744.png
-41494.png
-46299.png
-47626.png
-52281.png
-68002.png
-09216.png
-35302.png
-28774.png
-52955.png
-17813.png
-40117.png
-56931.png
-55356.png
-08237.png
-36106.png
-17566.png
-19287.png
-49906.png
-55696.png
-11653.png
-27700.png
-48815.png
-33119.png
-34693.png
-36231.png
-34019.png
-35381.png
-35723.png
-58232.png
-08745.png
-37398.png
-12625.png
-04278.png
-59207.png
-15327.png
-19102.png
-37223.png
-16452.png
-68085.png
-67230.png
-32335.png
-28387.png
-36483.png
-50495.png
-13905.png
-09095.png
-05443.png
-41330.png
-58544.png
-38563.png
-47349.png
-22295.png
-01052.png
-26997.png
-66222.png
-39212.png
-45199.png
-09363.png
-62123.png
-09177.png
-18214.png
-54145.png
-33880.png
-29192.png
-37226.png
-62142.png
-38968.png
-36252.png
-28690.png
-01576.png
-26188.png
-62992.png
-01388.png
-19746.png
-45222.png
-43109.png
-39805.png
-25080.png
-14750.png
-25203.png
-49040.png
-39458.png
-46402.png
-42333.png
-57716.png
-37089.png
-05624.png
-55022.png
-67639.png
-32411.png
-26928.png
-26676.png
-07354.png
-49365.png
-46849.png
-58009.png
-07525.png
-13157.png
-32680.png
-45117.png
-54400.png
-56566.png
-41756.png
-18979.png
-13471.png
-48709.png
-31837.png
-59456.png
-25193.png
-24956.png
-43385.png
-50857.png
-38883.png
-13870.png
-09924.png
-46102.png
-26212.png
-43090.png
-64276.png
-23836.png
-49350.png
-49269.png
-00419.png
-00282.png
-45079.png
-00312.png
-14815.png
-51310.png
-48304.png
-60311.png
-52549.png
-09677.png
-63424.png
-19050.png
-50402.png
-54610.png
-53749.png
-27648.png
-67154.png
-41686.png
-08101.png
-53848.png
-41379.png
-48392.png
-38797.png
-51873.png
-48043.png
-67857.png
-50977.png
-44968.png
-66532.png
-51811.png
-05127.png
-47502.png
-10235.png
-23069.png
-42076.png
-52354.png
-66516.png
-21312.png
-39719.png
-44037.png
-22909.png
-17422.png
-10386.png
-32077.png
-65825.png
-69178.png
-66734.png
-16283.png
-13888.png
-16111.png
-21134.png
-01337.png
-25276.png
-17080.png
-66788.png
-65029.png
-67884.png
-19769.png
-50810.png
-58917.png
-26995.png
-61589.png
-51580.png
-11701.png
-05486.png
-66394.png
-48021.png
-00380.png
-68878.png
-36808.png
-55832.png
-41289.png
-03238.png
-27983.png
-26497.png
-68624.png
-64645.png
-06462.png
-37093.png
-41841.png
-34002.png
-44386.png
-11329.png
-00182.png
-10995.png
-28226.png
-52247.png
-50217.png
-61781.png
-12382.png
-00637.png
-66893.png
-50878.png
-28389.png
-54431.png
-02371.png
-17068.png
-01436.png
-53587.png
-46318.png
-42384.png
-54687.png
-39907.png
-57608.png
-43469.png
-59856.png
-43453.png
-03252.png
-11000.png
-06298.png
-64335.png
-21588.png
-47785.png
-15577.png
-28937.png
-51491.png
-16315.png
-07561.png
-35950.png
-01010.png
-25858.png
-10865.png
-05843.png
-27042.png
-07800.png
-60355.png
-60492.png
-13341.png
-26400.png
-24661.png
-12578.png
-14972.png
-29764.png
-22331.png
-30085.png
-24041.png
-65810.png
-37126.png
-06406.png
-11378.png
-26950.png
-08728.png
-20864.png
-23924.png
-09952.png
-38392.png
-33821.png
-21329.png
-63080.png
-39923.png
-60800.png
-01319.png
-64776.png
-34511.png
-11063.png
-27026.png
-60964.png
-07135.png
-56271.png
-18286.png
-52331.png
-69379.png
-07757.png
-36782.png
-16825.png
-26798.png
-21664.png
-25471.png
-29394.png
-17170.png
-68324.png
-49799.png
-44890.png
-49525.png
-25569.png
-05809.png
-18059.png
-21361.png
-50455.png
-20881.png
-35181.png
-62626.png
-33167.png
-32735.png
-15865.png
-67415.png
-38109.png
-36632.png
-67072.png
-56853.png
-62180.png
-34957.png
-40764.png
-33320.png
-50879.png
-29325.png
-66297.png
-27130.png
-55793.png
-50108.png
-57342.png
-65787.png
-67137.png
-69205.png
-41010.png
-00090.png
-46284.png
-45157.png
-11764.png
-48572.png
-46383.png
-38438.png
-50734.png
-20834.png
-13633.png
-11555.png
-16113.png
-25897.png
-13833.png
-29722.png
-61860.png
-38292.png
-60166.png
-59682.png
-44249.png
-30590.png
-54295.png
-66763.png
-18398.png
-69442.png
-56531.png
-52394.png
-04297.png
-55795.png
-13223.png
-68438.png
-65781.png
-50076.png
-61789.png
-25331.png
-13994.png
-36553.png
-04924.png
-67528.png
-49347.png
-64581.png
-32277.png
-13861.png
-33146.png
-40990.png
-49240.png
-17855.png
-50988.png
-04806.png
-67655.png
-48226.png
-04822.png
-01793.png
-67097.png
-07198.png
-18057.png
-34520.png
-19728.png
-26102.png
-52882.png
-39307.png
-44507.png
-40056.png
-35802.png
-36774.png
-37698.png
-16713.png
-12993.png
-61221.png
-65147.png
-68067.png
-64358.png
-63593.png
-23819.png
-40689.png
-16932.png
-23358.png
-22070.png
-15030.png
-69293.png
-39669.png
-36369.png
-24064.png
-56573.png
-52207.png
-57221.png
-40360.png
-57595.png
-52531.png
-21024.png
-40715.png
-57766.png
-66332.png
-53049.png
-14368.png
-10446.png
-54543.png
-01020.png
-15215.png
-22386.png
-57173.png
-68063.png
-55174.png
-41664.png
-47554.png
-55226.png
-06071.png
-51935.png
-28494.png
-24355.png
-19185.png
-22578.png
-46121.png
-65749.png
-54823.png
-04138.png
-15085.png
-26143.png
-43928.png
-33702.png
-23997.png
-60337.png
-37627.png
-51052.png
-36350.png
-28220.png
-63306.png
-36930.png
-28458.png
-48493.png
-03007.png
-58619.png
-18041.png
-00686.png
-29702.png
-08053.png
-25553.png
-24331.png
-49753.png
-20953.png
-61984.png
-50093.png
-12586.png
-05524.png
-02974.png
-09063.png
-16740.png
-04606.png
-31251.png
-54570.png
-48549.png
-57755.png
-48089.png
-13318.png
-02964.png
-64243.png
-67746.png
-68842.png
-52537.png
-10608.png
-18554.png
-11491.png
-26837.png
-41739.png
-24380.png
-18022.png
-32123.png
-54830.png
-03055.png
-42452.png
-17167.png
-18210.png
-20135.png
-21651.png
-45513.png
-04264.png
-23108.png
-47163.png
-68133.png
-61981.png
-49467.png
-57741.png
-27977.png
-32894.png
-31142.png
-57172.png
-18282.png
-68593.png
-35476.png
-37506.png
-09525.png
-22797.png
-45870.png
-27323.png
-63641.png
-50833.png
-54535.png
-11718.png
-12498.png
-46584.png
-22933.png
-11727.png
-54564.png
-32181.png
-58753.png
-22638.png
-13402.png
-33035.png
-41826.png
-50535.png
-23329.png
-61271.png
-41590.png
-10421.png
-14041.png
-57245.png
-65481.png
-34745.png
-05765.png
-16845.png
-51966.png
-65087.png
-36584.png
-31592.png
-24209.png
-13917.png
-58225.png
-57182.png
-04497.png
-18291.png
-51366.png
-54335.png
-25829.png
-17262.png
-26344.png
-23335.png
-38805.png
-33433.png
-29975.png
-36783.png
-65829.png
-18261.png
-43800.png
-36199.png
-35148.png
-37806.png
-60078.png
-41732.png
-52543.png
-47619.png
-14045.png
-60676.png
-55514.png
-50263.png
-31389.png
-21125.png
-44067.png
-32167.png
-09283.png
-33617.png
-38937.png
-27924.png
-36293.png
-42644.png
-50403.png
-49855.png
-52847.png
-17538.png
-19666.png
-60695.png
-51717.png
-13488.png
-32105.png
-56355.png
-53532.png
-44523.png
-25884.png
-46968.png
-56222.png
-01768.png
-54168.png
-21843.png
-39612.png
-07814.png
-07756.png
-14308.png
-30830.png
-62295.png
-66672.png
-14983.png
-14732.png
-60916.png
-46790.png
-50270.png
-63271.png
-13684.png
-38636.png
-21019.png
-43658.png
-03489.png
-02138.png
-37976.png
-33487.png
-27750.png
-27760.png
-04321.png
-00553.png
-18768.png
-59424.png
-62614.png
-23755.png
-62645.png
-28973.png
-03160.png
-68161.png
-52551.png
-68552.png
-01360.png
-15112.png
-60642.png
-45476.png
-13986.png
-12849.png
-08790.png
-56711.png
-65712.png
-60216.png
-40211.png
-61274.png
-39730.png
-47312.png
-30892.png
-13704.png
-26303.png
-58127.png
-07273.png
-55393.png
-33097.png
-32189.png
-28969.png
-02510.png
-45752.png
-52875.png
-08477.png
-02579.png
-16923.png
-59058.png
-22051.png
-01351.png
-48981.png
-65295.png
-40773.png
-68947.png
-52282.png
-57944.png
-52445.png
-09521.png
-44210.png
-25356.png
-22788.png
-06132.png
-01664.png
-02064.png
-43307.png
-49775.png
-69415.png
-02831.png
-68510.png
-62960.png
-30471.png
-11528.png
-11497.png
-26397.png
-20684.png
-32266.png
-22696.png
-14429.png
-18663.png
-48521.png
-03121.png
-15038.png
-13497.png
-12046.png
-11226.png
-27147.png
-65152.png
-23969.png
-68147.png
-04243.png
-38011.png
-50493.png
-62442.png
-66633.png
-63726.png
-36560.png
-36994.png
-23407.png
-19800.png
-49134.png
-63920.png
-69328.png
-53799.png
-12688.png
-14951.png
-60130.png
-65072.png
-06596.png
-54480.png
-05232.png
-50065.png
-19953.png
-26012.png
-36970.png
-03856.png
-64262.png
-07362.png
-46223.png
-13094.png
-33457.png
-17588.png
-15449.png
-38939.png
-49585.png
-24371.png
-56139.png
-46860.png
-59366.png
-57640.png
-53553.png
-11786.png
-34651.png
-18613.png
-18671.png
-09233.png
-18492.png
-68935.png
-18454.png
-52801.png
-03859.png
-38990.png
-03843.png
-64099.png
-67469.png
-31916.png
-21301.png
-11703.png
-69420.png
-05478.png
-21023.png
-67868.png
-66871.png
-45974.png
-53466.png
-24809.png
-33856.png
-56659.png
-19626.png
-05150.png
-25472.png
-64165.png
-00412.png
-12634.png
-45338.png
-29093.png
-00001.png
-22456.png
-35819.png
-33772.png
-62366.png
-03267.png
-22523.png
-10021.png
-50860.png
-36746.png
-48694.png
-15053.png
-58458.png
-16589.png
-66941.png
-24464.png
-29573.png
-32938.png
-28607.png
-12707.png
-60006.png
-19302.png
-53521.png
-58302.png
-64666.png
-30829.png
-34755.png
-33246.png
-32430.png
-68304.png
-13356.png
-24228.png
-01949.png
-11873.png
-01944.png
-39653.png
-60880.png
-25702.png
-21252.png
-44216.png
-40254.png
-64743.png
-38349.png
-11242.png
-59944.png
-54716.png
-39054.png
-05983.png
-58939.png
-10953.png
-37138.png
-60521.png
-01444.png
-48593.png
-57206.png
-31765.png
-42035.png
-46407.png
-56652.png
-44679.png
-20897.png
-38319.png
-51008.png
-34290.png
-08278.png
-18813.png
-38670.png
-30786.png
-40102.png
-11100.png
-42986.png
-32092.png
-18072.png
-31294.png
-30470.png
-59481.png
-10481.png
-59179.png
-59535.png
-48949.png
-37924.png
-47914.png
-28747.png
-51653.png
-42874.png
-53160.png
-44888.png
-09567.png
-40539.png
-46925.png
-43922.png
-12113.png
-07958.png
-12927.png
-34480.png
-63597.png
-53728.png
-62302.png
-27372.png
-29100.png
-69540.png
-47351.png
-65019.png
-46666.png
-39005.png
-15790.png
-34643.png
-27366.png
-00284.png
-09367.png
-35485.png
-01021.png
-22648.png
-21182.png
-51999.png
-29079.png
-08755.png
-64456.png
-66623.png
-27418.png
-43201.png
-19132.png
-26887.png
-24621.png
-26897.png
-34086.png
-53956.png
-31713.png
-37552.png
-11118.png
-35717.png
-62664.png
-66526.png
-61653.png
-43426.png
-48376.png
-67538.png
-22239.png
-61358.png
-28366.png
-14003.png
-35506.png
-42873.png
-63199.png
-06102.png
-29371.png
-02740.png
-24700.png
-15245.png
-65654.png
-27871.png
-01723.png
-38545.png
-33932.png
-34280.png
-46296.png
-54350.png
-68815.png
-55477.png
-07810.png
-16197.png
-66735.png
-48155.png
-52816.png
-21138.png
-68099.png
-10956.png
-59268.png
-25391.png
-10098.png
-56758.png
-17643.png
-25432.png
-55905.png
-30218.png
-44118.png
-25993.png
-19149.png
-27968.png
-00550.png
-28215.png
-32835.png
-19317.png
-15499.png
-54101.png
-38258.png
-51850.png
-49399.png
-57621.png
-23220.png
-39879.png
-65874.png
-29067.png
-15634.png
-22711.png
-37816.png
-20147.png
-42423.png
-57542.png
-60266.png
-51489.png
-66899.png
-36984.png
-64806.png
-36949.png
-61420.png
-61449.png
-12170.png
-38163.png
-44922.png
-68054.png
-37116.png
-08143.png
-55261.png
-64307.png
-67817.png
-46421.png
-42969.png
-14779.png
-50885.png
-20595.png
-61564.png
-28208.png
-46897.png
-44022.png
-56182.png
-33077.png
-18228.png
-65088.png
-69984.png
-59612.png
-30537.png
-33279.png
-32073.png
-44384.png
-35801.png
-42740.png
-63816.png
-43196.png
-14912.png
-48340.png
-68782.png
-11869.png
-02387.png
-35282.png
-64910.png
-57054.png
-01094.png
-39433.png
-36586.png
-42946.png
-33350.png
-27196.png
-18248.png
-44076.png
-14742.png
-07788.png
-02275.png
-11929.png
-31304.png
-03707.png
-68329.png
-03898.png
-16233.png
-23139.png
-08483.png
-36370.png
-68277.png
-61844.png
-45353.png
-20136.png
-25924.png
-65846.png
-22679.png
-42190.png
-52991.png
-45034.png
-56813.png
-59887.png
-15987.png
-17604.png
-23107.png
-38435.png
-48951.png
-48214.png
-03583.png
-19889.png
-07998.png
-17966.png
-50871.png
-57286.png
-68534.png
-44424.png
-60542.png
-66068.png
-04812.png
-54857.png
-34195.png
-17764.png
-62516.png
-32682.png
-15147.png
-64377.png
-61680.png
-66027.png
-40954.png
-38816.png
-00780.png
-41589.png
-14787.png
-56179.png
-68009.png
-08336.png
-18265.png
-23763.png
-59227.png
-61335.png
-05525.png
-49725.png
-54643.png
-36463.png
-60050.png
-31704.png
-04522.png
-42778.png
-36686.png
-56953.png
-49946.png
-51878.png
-62790.png
-32650.png
-15429.png
-26407.png
-25756.png
-00827.png
-17328.png
-39000.png
-56431.png
-00781.png
-30766.png
-49358.png
-33253.png
-49199.png
-65789.png
-10291.png
-40045.png
-60138.png
-00821.png
-38129.png
-02766.png
-58502.png
-42939.png
-60731.png
-33442.png
-23343.png
-37543.png
-64381.png
-40629.png
-60949.png
-15250.png
-35502.png
-69949.png
-29050.png
-20852.png
-13938.png
-42041.png
-13550.png
-28222.png
-12569.png
-19640.png
-10306.png
-42867.png
-39055.png
-69241.png
-37055.png
-25435.png
-21675.png
-67694.png
-36899.png
-24836.png
-27100.png
-69213.png
-37260.png
-25969.png
-29990.png
-18122.png
-16662.png
-13543.png
-59920.png
-27671.png
-56976.png
-37714.png
-58326.png
-05929.png
-35990.png
-04708.png
-30724.png
-19238.png
-28036.png
-05743.png
-68183.png
-26518.png
-01997.png
-41497.png
-63194.png
-24246.png
-11996.png
-54343.png
-12712.png
-36167.png
-19407.png
-61367.png
-19893.png
-60577.png
-32653.png
-45255.png
-00935.png
-65478.png
-35043.png
-18637.png
-19070.png
-44506.png
-57853.png
-46647.png
-18019.png
-69019.png
-18232.png
-69189.png
-52375.png
-43876.png
-62726.png
-02928.png
-15468.png
-60314.png
-49010.png
-18386.png
-53693.png
-28103.png
-27213.png
-31114.png
-66969.png
-02014.png
-50708.png
-14522.png
-65433.png
-31691.png
-65379.png
-23106.png
-41521.png
-12268.png
-52525.png
-36920.png
-16534.png
-47493.png
-27515.png
-66706.png
-30501.png
-34576.png
-45406.png
-13892.png
-06801.png
-61711.png
-16212.png
-31143.png
-31887.png
-19894.png
-34632.png
-05800.png
-29448.png
-38270.png
-34816.png
-52563.png
-45978.png
-43562.png
-55697.png
-64985.png
-29589.png
-31237.png
-35125.png
-53101.png
-25532.png
-36061.png
-59750.png
-00387.png
-48284.png
-11985.png
-47476.png
-10732.png
-07470.png
-42187.png
-17159.png
-60347.png
-12130.png
-33245.png
-35344.png
-15054.png
-09014.png
-18520.png
-42963.png
-35890.png
-39997.png
-28052.png
-57686.png
-51938.png
-57675.png
-37508.png
-48505.png
-61303.png
-54966.png
-67851.png
-69483.png
-04730.png
-07153.png
-55267.png
-00142.png
-14387.png
-05217.png
-19784.png
-66098.png
-26411.png
-66101.png
-52967.png
-41847.png
-15291.png
-60155.png
-62695.png
-25054.png
-44591.png
-36212.png
-29294.png
-37368.png
-14976.png
-21440.png
-18721.png
-43823.png
-01591.png
-68632.png
-24590.png
-27336.png
-18209.png
-30719.png
-14727.png
-51101.png
-37188.png
-47150.png
-15049.png
-59310.png
-09740.png
-48858.png
-50382.png
-56195.png
-28650.png
-53092.png
-42715.png
-20664.png
-07393.png
-24178.png
-58608.png
-33420.png
-15401.png
-06207.png
-00638.png
-39032.png
-31288.png
-65177.png
-39977.png
-08826.png
-69778.png
-23933.png
-05978.png
-28138.png
-14067.png
-04648.png
-61090.png
-36931.png
-63509.png
-62900.png
-25246.png
-65921.png
-13866.png
-53073.png
-21112.png
-22433.png
-32347.png
-50207.png
-03138.png
-49290.png
-12469.png
-33541.png
-26969.png
-05994.png
-66158.png
-18985.png
-48098.png
-17662.png
-67139.png
-35818.png
-42269.png
-62795.png
-62692.png
-59101.png
-53123.png
-45534.png
-35267.png
-67912.png
-17111.png
-56986.png
-46049.png
-37032.png
-44055.png
-31351.png
-43114.png
-07169.png
-45904.png
-54856.png
-30579.png
-58647.png
-52254.png
-42724.png
-44054.png
-34132.png
-21530.png
-64158.png
-42905.png
-40134.png
-30447.png
-40446.png
-54139.png
-14174.png
-01764.png
-49055.png
-03783.png
-07000.png
-68910.png
-60988.png
-42485.png
-41839.png
-17142.png
-55960.png
-08097.png
-59037.png
-01439.png
-51655.png
-69895.png
-20412.png
-60781.png
-64220.png
-56851.png
-45734.png
-25759.png
-03944.png
-69047.png
-46422.png
-00496.png
-20665.png
-42250.png
-24887.png
-34071.png
-20129.png
-56406.png
-59242.png
-14297.png
-13869.png
-63785.png
-27730.png
-27881.png
-05319.png
-41086.png
-38581.png
-37950.png
-15783.png
-35469.png
-38124.png
-42763.png
-18058.png
-58655.png
-38985.png
-46115.png
-39188.png
-04282.png
-40298.png
-45245.png
-58785.png
-62303.png
-21596.png
-16446.png
-66662.png
-58388.png
-52037.png
-40895.png
-57082.png
-30635.png
-34349.png
-23911.png
-51414.png
-29449.png
-65284.png
-03838.png
-44963.png
-04331.png
-55893.png
-22136.png
-32742.png
-24456.png
-21934.png
-52901.png
-23697.png
-28269.png
-20299.png
-49794.png
-39045.png
-20038.png
-40772.png
-33277.png
-40630.png
-40749.png
-33289.png
-64502.png
-68920.png
-46898.png
-00654.png
-60697.png
-60234.png
-57035.png
-58483.png
-19751.png
-32298.png
-42436.png
-51616.png
-47809.png
-05930.png
-24867.png
-49580.png
-42744.png
-12192.png
-24459.png
-10564.png
-26357.png
-09454.png
-30113.png
-06387.png
-26911.png
-21463.png
-13608.png
-65443.png
-48213.png
-04751.png
-22267.png
-32342.png
-26599.png
-20796.png
-63584.png
-54877.png
-19870.png
-53403.png
-08786.png
-58066.png
-59884.png
-60349.png
-13652.png
-29133.png
-25807.png
-09057.png
-03963.png
-18972.png
-07419.png
-42982.png
-11312.png
-06849.png
-69685.png
-32130.png
-06275.png
-25268.png
-66767.png
-36707.png
-33987.png
-43563.png
-67813.png
-25757.png
-23334.png
-00264.png
-33963.png
-16665.png
-03314.png
-59608.png
-22793.png
-69221.png
-00337.png
-19947.png
-41988.png
-04860.png
-53116.png
-29879.png
-17149.png
-33971.png
-27879.png
-16930.png
-37577.png
-15969.png
-20250.png
-40858.png
-21831.png
-13668.png
-31845.png
-05354.png
-69852.png
-33859.png
-54650.png
-56411.png
-34313.png
-04710.png
-48270.png
-57452.png
-17576.png
-19621.png
-13347.png
-65323.png
-31393.png
-17465.png
-47547.png
-64069.png
-39743.png
-17575.png
-32467.png
-40228.png
-60042.png
-58580.png
-11479.png
-07162.png
-63609.png
-02068.png
-10627.png
-54654.png
-56236.png
-31084.png
-05799.png
-36531.png
-47708.png
-24885.png
-08767.png
-00381.png
-03413.png
-24864.png
-64799.png
-13738.png
-27247.png
-24442.png
-37675.png
-22337.png
-20800.png
-35338.png
-42129.png
-21415.png
-39406.png
-44809.png
-64877.png
-68186.png
-66094.png
-65545.png
-07925.png
-07844.png
-67406.png
-09825.png
-53721.png
-66492.png
-19273.png
-41318.png
-04025.png
-10036.png
-07808.png
-11088.png
-01566.png
-30712.png
-22870.png
-13867.png
-41006.png
-06446.png
-42571.png
-15236.png
-34421.png
-11630.png
-16034.png
-32326.png
-28807.png
-06995.png
-06708.png
-61846.png
-68240.png
-24888.png
-37751.png
-13660.png
-08445.png
-02189.png
-04931.png
-19866.png
-28178.png
-00222.png
-25746.png
-00951.png
-49843.png
-51892.png
-42240.png
-52411.png
-61543.png
-36248.png
-34524.png
-45897.png
-12764.png
-20643.png
-65228.png
-51907.png
-38126.png
-04599.png
-54044.png
-37160.png
-61186.png
-37676.png
-07880.png
-65792.png
-21497.png
-40354.png
-52156.png
-50201.png
-09937.png
-33836.png
-66017.png
-30272.png
-11125.png
-31771.png
-43263.png
-21526.png
-04626.png
-25568.png
-31981.png
-46166.png
-20204.png
-18661.png
-41928.png
-44026.png
-42503.png
-38911.png
-46798.png
-24267.png
-45566.png
-69624.png
-41995.png
-03445.png
-55808.png
-48583.png
-30578.png
-13491.png
-17349.png
-33581.png
-18727.png
-15930.png
-20332.png
-28279.png
-28342.png
-15803.png
-18748.png
-61833.png
-13296.png
-06368.png
-46545.png
-06580.png
-27949.png
-05805.png
-07807.png
-39770.png
-30477.png
-28831.png
-40936.png
-68725.png
-08769.png
-48110.png
-64460.png
-15541.png
-44144.png
-61592.png
-00511.png
-16816.png
-44939.png
-37555.png
-49220.png
-61720.png
-39947.png
-03893.png
-08161.png
-62660.png
-05344.png
-61374.png
-06655.png
-36423.png
-50875.png
-06346.png
-44107.png
-32841.png
-33515.png
-12595.png
-42594.png
-64178.png
-20058.png
-30212.png
-45791.png
-58902.png
-63633.png
-18790.png
-65887.png
-20923.png
-43428.png
-00259.png
-58698.png
-12116.png
-61158.png
-28095.png
-64107.png
-65304.png
-20110.png
-43348.png
-02098.png
-54791.png
-25849.png
-04070.png
-04582.png
-65121.png
-22149.png
-12367.png
-22644.png
-12151.png
-48097.png
-17959.png
-53605.png
-15661.png
-18854.png
-47615.png
-68963.png
-52443.png
-63033.png
-21999.png
-37383.png
-25016.png
-22300.png
-02852.png
-30681.png
-06544.png
-68379.png
-34779.png
-45909.png
-38867.png
-55948.png
-54105.png
-49777.png
-46923.png
-03695.png
-61247.png
-26355.png
-09030.png
-43964.png
-19194.png
-64817.png
-22952.png
-08866.png
-03862.png
-56537.png
-69993.png
-44942.png
-47797.png
-54896.png
-64147.png
-40101.png
-17527.png
-69665.png
-43871.png
-58058.png
-21088.png
-67678.png
-49936.png
-38487.png
-46772.png
-10836.png
-63414.png
-06631.png
-03264.png
-22348.png
-56498.png
-40626.png
-56502.png
-69422.png
-01440.png
-60139.png
-48175.png
-43195.png
-68908.png
-20115.png
-12199.png
-31965.png
-45993.png
-34496.png
-01197.png
-45208.png
-17496.png
-17698.png
-03744.png
-11031.png
-17485.png
-32364.png
-15759.png
-67234.png
-12306.png
-54298.png
-43516.png
-37240.png
-67713.png
-33010.png
-45924.png
-09162.png
-37893.png
-31433.png
-15797.png
-20468.png
-66558.png
-51503.png
-55253.png
-13180.png
-62063.png
-64383.png
-57699.png
-53829.png
-09002.png
-35565.png
-55216.png
-69590.png
-03129.png
-38440.png
-29303.png
-24166.png
-59970.png
-01836.png
-24854.png
-05626.png
-03960.png
-05298.png
-57176.png
-65316.png
-35347.png
-33895.png
-16143.png
-39371.png
-61755.png
-08211.png
-25319.png
-67456.png
-33153.png
-42120.png
-68709.png
-28139.png
-59818.png
-21356.png
-53873.png
-05860.png
-03649.png
-37600.png
-69277.png
-31063.png
-43304.png
-62808.png
-64008.png
-49566.png
-68198.png
-19341.png
-64014.png
-20342.png
-66076.png
-21401.png
-40184.png
-39877.png
-29346.png
-22722.png
-43671.png
-48685.png
-30045.png
-12545.png
-51921.png
-27361.png
-34164.png
-12860.png
-44363.png
-46167.png
-09405.png
-01156.png
-47219.png
-51784.png
-46868.png
-23886.png
-18321.png
-00705.png
-59130.png
-30642.png
-13029.png
-68983.png
-43819.png
-49985.png
-15683.png
-62968.png
-69011.png
-56879.png
-04071.png
-10241.png
-15640.png
-17028.png
-18946.png
-15183.png
-27119.png
-55137.png
-51248.png
-02849.png
-26113.png
-27305.png
-56707.png
-62078.png
-19172.png
-42332.png
-55974.png
-35336.png
-10827.png
-06235.png
-13983.png
-17536.png
-41190.png
-40023.png
-47772.png
-35728.png
-47744.png
-04155.png
-43363.png
-24531.png
-64430.png
-49925.png
-02300.png
-05438.png
-12626.png
-65348.png
-00640.png
-29138.png
-11628.png
-47419.png
-63332.png
-50418.png
-07077.png
-47969.png
-50153.png
-35603.png
-31835.png
-55349.png
-10243.png
-54911.png
-06077.png
-05030.png
-18040.png
-42770.png
-58265.png
-22762.png
-05413.png
-08874.png
-40633.png
-68423.png
-58860.png
-65549.png
-21946.png
-69097.png
-05709.png
-42225.png
-35993.png
-44457.png
-59136.png
-24896.png
-13072.png
-24241.png
-13631.png
-29779.png
-08913.png
-05467.png
-08685.png
-60217.png
-58529.png
-01628.png
-12523.png
-42326.png
-35711.png
-61708.png
-19137.png
-24006.png
-65202.png
-16298.png
-11047.png
-52230.png
-68697.png
-65574.png
-69368.png
-20116.png
-11853.png
-53204.png
-61295.png
-51226.png
-47497.png
-67916.png
-19681.png
-14723.png
-25431.png
-08750.png
-24874.png
-59014.png
-37954.png
-55837.png
-33667.png
-05196.png
-09109.png
-02961.png
-57287.png
-61025.png
-49763.png
-13627.png
-66436.png
-10370.png
-16303.png
-54250.png
-60730.png
-38141.png
-24572.png
-04959.png
-60077.png
-02603.png
-12494.png
-44064.png
-00901.png
-43116.png
-40107.png
-04129.png
-00919.png
-19115.png
-13093.png
-62612.png
-48615.png
-68862.png
-32084.png
-14837.png
-45380.png
-37601.png
-68857.png
-43072.png
-10812.png
-48029.png
-37923.png
-15465.png
-06348.png
-60190.png
-66681.png
-15377.png
-46871.png
-01708.png
-63722.png
-39209.png
-33737.png
-47947.png
-67715.png
-29907.png
-20172.png
-30195.png
-61756.png
-44112.png
-14181.png
-17701.png
-41820.png
-18176.png
-39230.png
-38565.png
-56809.png
-43172.png
-46662.png
-24405.png
-46705.png
-16213.png
-29323.png
-68382.png
-17457.png
-02759.png
-36426.png
-62346.png
-12039.png
-30888.png
-29767.png
-25292.png
-00818.png
-46406.png
-51277.png
-01209.png
-01229.png
-21459.png
-69208.png
-03791.png
-04385.png
-53436.png
-28297.png
-01177.png
-15738.png
-41270.png
-17475.png
-49861.png
-43847.png
-11746.png
-20132.png
-25202.png
-33763.png
-54086.png
-13198.png
-29071.png
-40067.png
-25476.png
-08381.png
-46393.png
-27762.png
-19599.png
-30633.png
-49962.png
-40564.png
-30286.png
-13256.png
-02542.png
-69345.png
-34609.png
-51199.png
-11527.png
-05775.png
-40959.png
-16049.png
-21915.png
-42551.png
-64489.png
-35796.png
-41524.png
-15544.png
-30995.png
-56661.png
-38032.png
-04074.png
-15944.png
-06626.png
-34041.png
-69064.png
-57586.png
-46042.png
-06159.png
-01009.png
-22361.png
-34527.png
-62339.png
-63794.png
-20823.png
-64122.png
-59358.png
-63861.png
-02520.png
-36019.png
-48740.png
-08259.png
-27183.png
-22019.png
-37043.png
-08640.png
-43777.png
-37237.png
-42399.png
-02498.png
-02481.png
-03845.png
-29673.png
-59797.png
-57241.png
-28675.png
-12176.png
-66751.png
-69704.png
-48646.png
-42486.png
-60969.png
-42496.png
-32836.png
-07625.png
-65204.png
-39359.png
-14448.png
-56545.png
-66130.png
-00314.png
-11788.png
-60503.png
-09323.png
-47489.png
-42082.png
-20217.png
-58231.png
-09743.png
-28934.png
-14974.png
-34729.png
-62505.png
-62979.png
-25775.png
-47293.png
-34984.png
-53394.png
-58716.png
-42147.png
-48264.png
-19567.png
-66408.png
-24741.png
-27561.png
-31409.png
-62154.png
-08484.png
-52220.png
-45168.png
-39166.png
-16202.png
-61909.png
-41022.png
-38948.png
-03894.png
-34877.png
-42388.png
-02185.png
-37276.png
-00825.png
-00289.png
-31993.png
-18018.png
-51950.png
-22268.png
-50707.png
-64088.png
-40292.png
-40874.png
-63949.png
-13718.png
-01282.png
-25448.png
-65444.png
-15062.png
-28180.png
-01755.png
-46731.png
-09154.png
-66099.png
-51640.png
-64211.png
-55460.png
-45651.png
-52533.png
-64963.png
-42907.png
-02881.png
-24775.png
-48279.png
-36047.png
-24574.png
-11946.png
-56620.png
-53517.png
-50342.png
-34358.png
-65905.png
-64325.png
-52388.png
-07103.png
-48149.png
-06678.png
-63576.png
-21014.png
-66160.png
-15826.png
-52029.png
-49380.png
-56019.png
-62324.png
-24947.png
-13975.png
-13432.png
-68960.png
-37721.png
-45859.png
-34535.png
-47065.png
-37784.png
-32094.png
-34895.png
-69425.png
-44899.png
-43704.png
-66611.png
-62739.png
-63284.png
-17558.png
-14668.png
-31033.png
-54439.png
-67895.png
-34092.png
-53474.png
-49801.png
-20797.png
-11538.png
-58441.png
-48893.png
-32909.png
-28960.png
-31175.png
-41618.png
-38251.png
-53981.png
-35263.png
-54466.png
-64846.png
-38903.png
-40116.png
-42854.png
-54529.png
-34662.png
-67379.png
-02812.png
-19083.png
-18180.png
-44514.png
-28306.png
-40501.png
-16602.png
-49760.png
-36090.png
-46997.png
-09081.png
-36453.png
-52947.png
-58020.png
-29001.png
-46796.png
-34299.png
-44147.png
-55422.png
-47299.png
-33088.png
-26562.png
-19286.png
-03807.png
-49400.png
-46536.png
-66066.png
-04489.png
-37252.png
-42001.png
-35733.png
-42560.png
-11509.png
-57412.png
-18627.png
-41504.png
-64893.png
-47356.png
-62134.png
-31579.png
-01190.png
-41274.png
-61430.png
-66453.png
-58061.png
-12886.png
-47670.png
-67720.png
-47335.png
-24798.png
-48579.png
-51368.png
-08163.png
-15956.png
-19995.png
-44203.png
-34605.png
-46717.png
-63382.png
-21567.png
-61283.png
-22096.png
-64197.png
-32777.png
-50463.png
-67806.png
-63999.png
-12239.png
-56797.png
-56365.png
-25886.png
-24803.png
-50337.png
-21612.png
-43822.png
-12997.png
-05842.png
-41328.png
-22368.png
-68863.png
-11612.png
-61518.png
-46082.png
-38255.png
-24136.png
-30314.png
-56398.png
-04244.png
-62313.png
-04709.png
-39749.png
-13564.png
-18177.png
-00848.png
-16493.png
-66590.png
-48017.png
-33390.png
-12493.png
-06965.png
-13358.png
-47108.png
-22123.png
-63392.png
-20744.png
-26922.png
-31399.png
-14659.png
-61130.png
-48672.png
-48921.png
-37693.png
-55385.png
-55041.png
-40673.png
-30241.png
-24244.png
-06621.png
-44773.png
-31186.png
-04236.png
-69645.png
-28706.png
-40941.png
-05289.png
-16093.png
-17317.png
-13435.png
-64735.png
-56546.png
-18804.png
-25361.png
-39425.png
-26487.png
-57500.png
-66216.png
-12525.png
-22505.png
-53075.png
-60551.png
-39057.png
-02509.png
-46927.png
-05935.png
-37070.png
-56622.png
-35939.png
-54633.png
-56309.png
-02503.png
-11909.png
-11811.png
-48529.png
-45796.png
-15672.png
-27530.png
-34129.png
-08286.png
-44977.png
-60257.png
-67815.png
-43406.png
-49750.png
-00942.png
-07293.png
-65450.png
-28759.png
-12651.png
-48809.png
-15788.png
-32883.png
-45239.png
-32378.png
-28465.png
-63209.png
-06509.png
-37801.png
-39311.png
-09573.png
-23721.png
-40328.png
-12780.png
-52076.png
-66913.png
-66105.png
-55436.png
-04402.png
-38393.png
-38589.png
-46673.png
-42051.png
-10066.png
-10184.png
-41105.png
-41822.png
-28435.png
-28958.png
-35609.png
-02370.png
-39984.png
-30714.png
-63315.png
-63262.png
-56552.png
-17189.png
-13450.png
-36757.png
-48016.png
-17875.png
-26378.png
-55962.png
-30287.png
-05494.png
-69609.png
-16003.png
-56744.png
-60558.png
-02062.png
-24825.png
-68749.png
-57709.png
-44293.png
-32275.png
-45621.png
-38210.png
-48603.png
-15947.png
-13412.png
-11307.png
-55850.png
-17095.png
-69640.png
-28035.png
-47571.png
-55495.png
-27131.png
-46323.png
-23076.png
-01812.png
-25259.png
-37006.png
-31255.png
-67513.png
-51029.png
-69954.png
-31203.png
-64971.png
-57985.png
-48034.png
-15445.png
-46198.png
-16726.png
-46989.png
-62567.png
-05021.png
-64043.png
-60846.png
-66325.png
-40597.png
-59753.png
-47543.png
-11966.png
-32538.png
-61471.png
-01127.png
-45919.png
-55602.png
-44205.png
-46690.png
-43551.png
-13857.png
-11427.png
-45607.png
-57774.png
-52571.png
-69948.png
-30426.png
-01246.png
-03241.png
-32532.png
-46672.png
-48545.png
-63937.png
-28797.png
-60237.png
-19333.png
-61268.png
-07616.png
-22256.png
-51390.png
-49528.png
-16834.png
-63712.png
-31793.png
-23269.png
-61343.png
-18292.png
-12945.png
-11122.png
-43771.png
-34550.png
-58793.png
-67085.png
-55465.png
-52491.png
-64981.png
-03226.png
-26114.png
-45373.png
-36224.png
-01101.png
-47773.png
-02059.png
-40997.png
-65853.png
-18485.png
-32222.png
-31629.png
-24525.png
-69723.png
-39859.png
-33028.png
-63450.png
-64950.png
-61624.png
-45956.png
-52013.png
-12555.png
-48388.png
-55112.png
-22749.png
-48786.png
-44810.png
-63864.png
-17151.png
-22789.png
-67271.png
-60029.png
-31250.png
-55921.png
-42857.png
-03617.png
-41697.png
-18097.png
-30239.png
-03439.png
-36238.png
-16638.png
-00789.png
-00113.png
-51367.png
-50787.png
-57918.png
-37275.png
-12769.png
-67740.png
-59401.png
-68770.png
-69287.png
-24876.png
-44372.png
-25017.png
-58031.png
-25792.png
-57320.png
-34295.png
-28835.png
-61535.png
-35894.png
-21279.png
-39462.png
-53831.png
-61255.png
-64063.png
-11428.png
-08383.png
-58387.png
-33647.png
-33403.png
-49520.png
-66129.png
-23054.png
-57303.png
-30711.png
-40499.png
-34305.png
-02929.png
-60966.png
-33089.png
-62687.png
-10944.png
-08502.png
-43386.png
-27591.png
-27589.png
-38653.png
-18416.png
-29973.png
-51490.png
-12619.png
-32144.png
-47041.png
-32426.png
-05755.png
-20740.png
-45555.png
-14572.png
-56773.png
-37168.png
-63632.png
-07876.png
-53494.png
-21585.png
-26361.png
-29021.png
-45163.png
-07994.png
-14454.png
-22388.png
-51758.png
-43560.png
-57462.png
-36924.png
-15791.png
-17241.png
-28725.png
-28765.png
-16585.png
-35582.png
-19832.png
-43990.png
-20716.png
-18493.png
-18322.png
-34615.png
-25024.png
-10598.png
-22474.png
-56553.png
-24409.png
-28554.png
-09347.png
-64685.png
-07438.png
-05814.png
-52073.png
-00133.png
-53925.png
-05390.png
-24578.png
-21160.png
-12681.png
-58545.png
-33877.png
-16914.png
-18734.png
-45554.png
-32257.png
-11171.png
-39046.png
-51115.png
-42602.png
-19804.png
-04682.png
-31693.png
-10579.png
-31192.png
-52235.png
-59177.png
-10046.png
-51744.png
-50616.png
-22229.png
-32374.png
-65857.png
-53449.png
-15980.png
-32199.png
-09145.png
-05651.png
-26460.png
-62176.png
-05134.png
-44750.png
-58829.png
-00955.png
-65333.png
-40546.png
-12879.png
-00845.png
-58195.png
-69225.png
-13812.png
-22375.png
-02803.png
-60270.png
-04079.png
-62588.png
-29668.png
-54367.png
-18446.png
-23356.png
-36338.png
-11405.png
-51614.png
-16705.png
-46353.png
-30262.png
-36638.png
-14623.png
-59494.png
-11964.png
-67991.png
-16368.png
-12334.png
-22360.png
-47792.png
-02855.png
-51526.png
-10006.png
-50099.png
-46472.png
-38216.png
-31448.png
-61960.png
-23943.png
-38127.png
-41555.png
-03820.png
-38214.png
-54045.png
-49687.png
-55193.png
-18104.png
-63399.png
-64513.png
-29350.png
-21000.png
-29954.png
-53655.png
-13536.png
-04683.png
-69396.png
-43261.png
-63976.png
-29503.png
-23670.png
-00841.png
-25905.png
-59917.png
-33829.png
-66186.png
-07667.png
-39556.png
-49642.png
-69607.png
-15045.png
-11986.png
-10590.png
-50884.png
-08133.png
-26686.png
-16478.png
-23382.png
-63860.png
-03844.png
-24742.png
-51021.png
-60402.png
-63658.png
-40918.png
-11345.png
-29272.png
-68282.png
-34057.png
-12867.png
-45712.png
-07936.png
-24149.png
-39688.png
-02911.png
-13116.png
-25008.png
-11608.png
-68919.png
-00742.png
-08901.png
-01284.png
-21711.png
-16808.png
-10854.png
-68428.png
-28129.png
-19689.png
-27283.png
-20826.png
-37829.png
-61293.png
-02482.png
-64487.png
-35375.png
-17466.png
-42733.png
-67531.png
-35911.png
-21311.png
-27707.png
-31281.png
-17616.png
-45701.png
-20653.png
-64717.png
-40960.png
-60094.png
-21976.png
-38974.png
-06399.png
-69332.png
-32796.png
-11632.png
-29863.png
-33809.png
-30881.png
-59568.png
-11135.png
-46934.png
-62919.png
-12866.png
-30335.png
-16217.png
-05578.png
-31046.png
-35701.png
-23187.png
-14359.png
-30968.png
-13756.png
-60792.png
-69859.png
-49594.png
-45604.png
-65448.png
-03883.png
-33483.png
-00105.png
-31299.png
-15981.png
-50702.png
-67003.png
-04761.png
-33692.png
-36358.png
-67873.png
-57703.png
-36455.png
-02932.png
-28637.png
-47725.png
-68036.png
-59302.png
-28358.png
-67436.png
-09038.png
-53742.png
-34456.png
-55079.png
-62016.png
-67588.png
-16841.png
-39139.png
-07911.png
-46239.png
-66548.png
-49797.png
-40498.png
-07906.png
-40118.png
-05939.png
-13416.png
-09705.png
-35264.png
-44266.png
-62125.png
-42174.png
-25589.png
-04830.png
-10721.png
-48954.png
-66690.png
-23298.png
-26993.png
-16882.png
-09721.png
-57845.png
-02194.png
-57712.png
-54816.png
-44156.png
-14486.png
-48002.png
-00707.png
-33937.png
-48610.png
-01184.png
-28156.png
-25075.png
-42058.png
-13019.png
-01517.png
-07734.png
-06973.png
-38945.png
-68496.png
-52840.png
-02786.png
-32888.png
-10502.png
-36141.png
-29987.png
-12289.png
-57083.png
-17392.png
-63105.png
-36365.png
-69474.png
-55545.png
-13539.png
-60822.png
-54662.png
-26329.png
-43672.png
-34360.png
-18676.png
-26198.png
-27769.png
-37306.png
-30909.png
-07173.png
-68194.png
-65428.png
-21911.png
-35053.png
-52358.png
-21559.png
-10896.png
-39804.png
-69475.png
-68928.png
-08253.png
-56695.png
-21774.png
-07233.png
-32898.png
-20812.png
-58986.png
-64727.png
-45504.png
-49454.png
-40729.png
-28422.png
-65834.png
-18149.png
-06064.png
-00805.png
-42012.png
-33893.png
-53509.png
-66427.png
-20233.png
-13707.png
-28836.png
-06692.png
-19830.png
-62615.png
-26682.png
-46598.png
-68578.png
-47053.png
-03930.png
-30940.png
-55654.png
-52936.png
-30372.png
-14004.png
-05018.png
-23658.png
-22882.png
-66517.png
-08793.png
-47217.png
-24096.png
-21604.png
-22651.png
-17866.png
-67067.png
-42784.png
-43419.png
-44917.png
-25045.png
-58832.png
-20876.png
-44227.png
-66856.png
-34814.png
-46118.png
-17105.png
-34532.png
-28728.png
-41371.png
-54421.png
-22140.png
-20190.png
-42007.png
-57304.png
-55039.png
-37334.png
-05883.png
-64984.png
-45817.png
-26617.png
-03971.png
-34252.png
-09822.png
-17133.png
-68757.png
-12674.png
-32939.png
-39138.png
-37061.png
-36102.png
-15649.png
-23122.png
-60146.png
-55004.png
-49385.png
-21605.png
-12340.png
-05145.png
-03755.png
-21193.png
-58997.png
-19680.png
-33325.png
-39621.png
-06018.png
-40487.png
-18645.png
-05569.png
-07372.png
-28802.png
-38430.png
-65027.png
-28218.png
-14051.png
-19713.png
-14201.png
-43324.png
-03374.png
-26726.png
-50717.png
-66818.png
-31011.png
-01474.png
-41368.png
-44580.png
-35941.png
-36786.png
-63108.png
-05258.png
-07199.png
-53417.png
-60112.png
-44428.png
-42677.png
-33891.png
-39403.png
-29845.png
-13903.png
-67985.png
-20201.png
-60161.png
-07280.png
-16083.png
-12344.png
-28420.png
-35382.png
-11071.png
-37204.png
-58552.png
-49160.png
-11564.png
-46994.png
-34356.png
-66575.png
-41156.png
-62385.png
-07934.png
-12208.png
-50449.png
-14615.png
-46139.png
-49798.png
-64681.png
-27753.png
-59678.png
-68013.png
-18330.png
-14189.png
-66753.png
-42456.png
-09818.png
-65523.png
-68831.png
-20931.png
-00819.png
-49533.png
-69625.png
-02936.png
-51893.png
-22235.png
-27586.png
-28888.png
-50575.png
-20821.png
-23393.png
-64348.png
-55504.png
-55570.png
-67983.png
-44582.png
-03034.png
-65899.png
-20265.png
-54587.png
-41501.png
-49035.png
-00851.png
-10212.png
-29057.png
-54488.png
-26569.png
-04910.png
-62390.png
-42628.png
-03242.png
-48309.png
-40143.png
-38965.png
-17132.png
-17773.png
-49433.png
-27299.png
-28597.png
-08142.png
-63134.png
-43006.png
-02496.png
-56839.png
-23487.png
-59396.png
-57134.png
-18741.png
-23981.png
-65609.png
-27473.png
-31021.png
-35770.png
-15762.png
-60634.png
-60544.png
-55946.png
-20934.png
-22352.png
-66637.png
-50798.png
-24138.png
-02682.png
-30160.png
-64419.png
-31461.png
-55613.png
-57062.png
-19282.png
-50937.png
-09286.png
-07949.png
-00416.png
-52988.png
-49976.png
-25739.png
-02779.png
-57893.png
-57972.png
-31037.png
-55273.png
-61168.png
-21499.png
-54200.png
-52766.png
-45078.png
-05329.png
-50074.png
-16751.png
-35432.png
-09419.png
-69479.png
-57581.png
-54957.png
-26752.png
-36168.png
-30128.png
-23386.png
-22396.png
-00920.png
-16660.png
-69450.png
-30378.png
-53719.png
-60327.png
-59275.png
-31092.png
-69182.png
-09733.png
-11033.png
-51237.png
-24497.png
-33785.png
-35463.png
-68822.png
-39245.png
-56540.png
-43339.png
-52615.png
-37387.png
-59565.png
-52652.png
-38858.png
-32154.png
-13338.png
-37220.png
-45279.png
-59967.png
-45884.png
-22998.png
-24699.png
-15807.png
-01738.png
-00280.png
-59399.png
-19763.png
-12243.png
-01030.png
-03687.png
-66664.png
-08612.png
-39710.png
-62583.png
-25438.png
-10160.png
-32320.png
-48199.png
-33137.png
-39267.png
-09699.png
-55311.png
-30789.png
-18381.png
-36633.png
-52279.png
-20286.png
-27329.png
-67059.png
-32366.png
-64843.png
-47061.png
-34343.png
-52727.png
-32082.png
-56293.png
-53703.png
-19950.png
-59521.png
-54813.png
-34192.png
-65500.png
-17224.png
-36800.png
-31821.png
-28994.png
-28951.png
-52817.png
-32973.png
-44300.png
-47014.png
-59946.png
-19032.png
-43810.png
-11757.png
-17736.png
-12579.png
-40911.png
-33635.png
-41861.png
-26187.png
-65889.png
-64392.png
-61216.png
-51047.png
-11736.png
-44657.png
-67589.png
-50376.png
-35807.png
-40594.png
-17878.png
-68244.png
-37621.png
-25811.png
-67288.png
-64544.png
-07904.png
-47777.png
-27027.png
-56163.png
-50189.png
-30993.png
-62037.png
-45921.png
-60883.png
-64808.png
-59472.png
-31568.png
-02806.png
-08127.png
-69792.png
-55996.png
-05801.png
-53978.png
-31954.png
-68302.png
-30120.png
-48580.png
-31767.png
-52628.png
-19416.png
-10979.png
-12908.png
-21654.png
-00676.png
-13918.png
-53480.png
-45183.png
-15887.png
-54058.png
-20348.png
-65245.png
-06360.png
-04717.png
-69487.png
-49599.png
-56996.png
-11925.png
-48530.png
-61441.png
-35477.png
-20049.png
-42327.png
-47258.png
-38700.png
-52059.png
-69164.png
-35259.png
-45542.png
-01379.png
-63500.png
-07947.png
-22851.png
-50332.png
-47492.png
-11856.png
-62895.png
-04299.png
-31178.png
-14591.png
-06955.png
-24711.png
-46516.png
-34067.png
-42942.png
-37590.png
-42945.png
-37318.png
-07852.png
-30510.png
-46333.png
-00996.png
-39387.png
-21538.png
-19406.png
-40651.png
-62740.png
-58162.png
-50236.png
-60985.png
-11802.png
-55028.png
-48067.png
-06060.png
-29412.png
-35265.png
-03916.png
-39731.png
-39331.png
-41878.png
-27667.png
-27208.png
-25147.png
-18435.png
-25758.png
-59374.png
-28325.png
-04693.png
-42688.png
-25979.png
-63230.png
-52222.png
-10002.png
-03651.png
-00379.png
-31697.png
-00554.png
-40521.png
-42661.png
-47180.png
-37041.png
-44854.png
-27783.png
-64708.png
-15388.png
-29435.png
-04698.png
-36195.png
-01205.png
-08556.png
-49788.png
-63267.png
-27540.png
-38604.png
-19638.png
-06899.png
-62747.png
-12761.png
-46971.png
-47608.png
-66970.png
-16087.png
-33977.png
-45021.png
-51281.png
-31190.png
-27607.png
-17460.png
-61428.png
-34211.png
-06183.png
-47237.png
-18848.png
-45825.png
-22246.png
-64913.png
-67101.png
-46005.png
-20632.png
-69972.png
-42758.png
-24964.png
-17849.png
-04374.png
-14113.png
-44440.png
-53500.png
-64082.png
-25023.png
-34671.png
-10569.png
-31434.png
-10798.png
-31631.png
-03952.png
-42801.png
-20861.png
-23737.png
-40841.png
-08040.png
-28922.png
-03698.png
-35628.png
-08898.png
-54126.png
-15330.png
-55409.png
-69434.png
-07465.png
-47881.png
-16148.png
-49277.png
-35534.png
-62419.png
-36135.png
-31502.png
-40550.png
-57557.png
-31768.png
-37373.png
-58073.png
-40869.png
-59632.png
-56354.png
-44671.png
-14882.png
-61138.png
-04700.png
-60500.png
-05307.png
-42521.png
-45090.png
-22541.png
-58333.png
-59569.png
-51162.png
-21406.png
-64871.png
-13010.png
-54514.png
-68474.png
-24416.png
-22732.png
-47043.png
-35474.png
-14718.png
-02841.png
-20717.png
-50368.png
-05027.png
-05704.png
-25093.png
-54805.png
-60605.png
-60847.png
-24273.png
-37488.png
-49093.png
-40318.png
-18990.png
-08063.png
-62441.png
-66043.png
-14426.png
-51083.png
-66894.png
-08478.png
-17018.png
-30848.png
-69572.png
-66415.png
-53951.png
-48575.png
-57373.png
-25789.png
-42071.png
-42104.png
-37595.png
-14988.png
-64555.png
-49587.png
-04928.png
-68952.png
-33046.png
-07584.png
-16236.png
-55645.png
-07216.png
-43916.png
-43449.png
-42847.png
-62571.png
-69111.png
-33953.png
-43626.png
-53452.png
-48448.png
-16732.png
-31349.png
-28970.png
-43252.png
-51360.png
-45864.png
-19901.png
-18247.png
-48558.png
-53674.png
-29216.png
-36791.png
-66528.png
-16567.png
-43129.png
-02997.png
-00089.png
-69361.png
-15662.png
-27340.png
-19402.png
-08236.png
-02180.png
-51319.png
-23980.png
-12414.png
-03495.png
-00895.png
-24627.png
-52962.png
-02163.png
-11330.png
-01759.png
-00795.png
-24786.png
-03772.png
-60292.png
-32665.png
-41088.png
-47978.png
-45071.png
-01489.png
-09262.png
-29666.png
-10925.png
-63900.png
-12277.png
-56442.png
-55943.png
-60018.png
-53271.png
-41642.png
-26105.png
-49154.png
-46722.png
-12179.png
-45175.png
-06524.png
-08119.png
-07705.png
-11857.png
-00087.png
-21330.png
-63420.png
-42235.png
-52830.png
-29255.png
-50758.png
-25004.png
-47478.png
-08961.png
-08916.png
-50935.png
-16590.png
-36361.png
-35311.png
-66139.png
-20171.png
-58111.png
-18327.png
-41275.png
-01186.png
-68660.png
-06916.png
-27729.png
-52088.png
-31428.png
-67757.png
-03132.png
-59361.png
-60990.png
-01643.png
-65961.png
-27653.png
-47987.png
-50572.png
-01195.png
-63711.png
-36832.png
-69093.png
-09203.png
-28741.png
-20418.png
-19328.png
-25227.png
-54170.png
-30792.png
-52803.png
-44571.png
-20109.png
-29505.png
-65128.png
-66135.png
-15558.png
-24848.png
-49650.png
-60457.png
-40308.png
-06897.png
-19799.png
-55865.png
-25579.png
-04054.png
-31039.png
-61161.png
-29375.png
-29427.png
-56607.png
-49083.png
-45811.png
-60414.png
-65080.png
-04026.png
-48819.png
-66555.png
-07893.png
-18796.png
-14313.png
-26433.png
-11062.png
-14866.png
-07009.png
-12546.png
-39850.png
-11969.png
-40687.png
-17957.png
-27502.png
-42002.png
-60613.png
-21722.png
-46511.png
-37120.png
-18920.png
-42018.png
-12035.png
-57941.png
-64600.png
-40756.png
-03254.png
-31335.png
-02154.png
-26451.png
-07044.png
-14405.png
-64634.png
-26044.png
-38158.png
-14777.png
-59648.png
-55702.png
-37538.png
-02308.png
-24847.png
-55034.png
-01232.png
-32689.png
-19654.png
-48590.png
-19876.png
-43438.png
-04519.png
-58652.png
-23926.png
-43163.png
-62285.png
-24194.png
-18308.png
-41495.png
-60213.png
-25464.png
-03887.png
-31479.png
-04046.png
-42781.png
-46086.png
-28119.png
-43143.png
-02229.png
-52262.png
-16474.png
-48364.png
-23638.png
-17815.png
-39857.png
-31863.png
-05891.png
-43259.png
-16424.png
-19296.png
-11918.png
-51953.png
-56267.png
-06351.png
-18582.png
-35113.png
-49818.png
-69962.png
-48278.png
-30585.png
-35356.png
-14243.png
-62457.png
-38503.png
-30615.png
-41940.png
-35608.png
-16782.png
-28957.png
-68276.png
-59842.png
-53225.png
-14824.png
-32935.png
-49106.png
-20243.png
-27239.png
-28696.png
-49129.png
-49970.png
-00411.png
-05318.png
-48995.png
-22056.png
-61599.png
-61510.png
-59440.png
-31520.png
-68386.png
-14831.png
-69268.png
-23931.png
-67691.png
-08568.png
-59841.png
-42435.png
-03700.png
-07663.png
-16734.png
-00065.png
-34556.png
-68937.png
-46626.png
-11682.png
-49756.png
-03373.png
-40986.png
-20396.png
-12213.png
-26222.png
-32488.png
-66156.png
-04291.png
-50258.png
-40862.png
-46841.png
-24680.png
-28336.png
-22904.png
-26352.png
-07117.png
-19189.png
-44246.png
-17839.png
-40385.png
-53743.png
-09948.png
-04258.png
-53788.png
-43842.png
-24857.png
-35270.png
-52751.png
-03866.png
-07639.png
-47494.png
-69023.png
-21653.png
-28863.png
-42799.png
-40264.png
-32895.png
-62240.png
-64598.png
-12032.png
-04182.png
-22853.png
-13020.png
-57475.png
-40380.png
-26214.png
-19744.png
-57753.png
-63924.png
-16720.png
-56155.png
-36982.png
-52062.png
-35727.png
-47542.png
-65976.png
-25845.png
-33121.png
-23560.png
-63994.png
-28480.png
-02127.png
-36490.png
-36091.png
-47860.png
-53618.png
-37710.png
-27201.png
-15638.png
-43398.png
-05226.png
-59792.png
-42142.png
-68824.png
-02236.png
-05414.png
-09834.png
-13728.png
-14714.png
-11355.png
-04280.png
-12976.png
-55084.png
-07203.png
-23957.png
-02196.png
-37754.png
-48795.png
-36063.png
-27969.png
-58384.png
-59463.png
-50789.png
-67461.png
-69594.png
-52523.png
-59455.png
-64960.png
-53518.png
-15342.png
-17642.png
-56610.png
-61356.png
-21851.png
-59629.png
-30965.png
-62417.png
-36754.png
-14681.png
-34555.png
-04653.png
-17058.png
-15753.png
-42548.png
-59748.png
-19878.png
-19347.png
-03736.png
-52695.png
-16175.png
-65106.png
-06074.png
-36635.png
-36291.png
-39155.png
-01090.png
-61409.png
-52199.png
-20366.png
-32887.png
-17015.png
-03507.png
-55524.png
-06007.png
-20571.png
-26522.png
-49614.png
-41046.png
-49135.png
-41971.png
-59625.png
-09132.png
-31531.png
-33871.png
-52017.png
-01031.png
-14333.png
-41056.png
-26708.png
-24910.png
-48166.png
-25558.png
-63809.png
-21338.png
-64819.png
-03380.png
-22652.png
-22091.png
-56878.png
-64339.png
-41370.png
-56821.png
-55439.png
-07318.png
-44435.png
-09211.png
-35219.png
-00850.png
-43366.png
-62163.png
-02282.png
-51330.png
-43094.png
-44430.png
-46150.png
-14776.png
-02390.png
-03030.png
-06196.png
-32101.png
-57104.png
-53117.png
-66220.png
-07886.png
-01560.png
-58869.png
-08070.png
-23263.png
-21296.png
-52931.png
-24516.png
-00102.png
-46714.png
-50784.png
-62222.png
-56202.png
-27569.png
-12746.png
-53830.png
-24257.png
-69752.png
-44121.png
-45294.png
-05678.png
-63481.png
-41331.png
-23680.png
-17786.png
-31170.png
-30059.png
-00921.png
-20961.png
-54548.png
-09004.png
-53514.png
-01335.png
-34679.png
-20128.png
-05874.png
-43019.png
-53303.png
-25188.png
-56562.png
-47583.png
-16018.png
-15610.png
-67549.png
-17280.png
-51739.png
-59883.png
-62779.png
-68257.png
-47197.png
-51015.png
-67320.png
-67662.png
-48250.png
-44723.png
-63625.png
-44234.png
-20021.png
-38612.png
-65460.png
-27943.png
-01679.png
-04837.png
-41875.png
-21768.png
-62413.png
-03024.png
-45795.png
-17135.png
-00634.png
-14228.png
-07348.png
-28629.png
-35780.png
-19187.png
-07191.png
-21836.png
-60119.png
-14433.png
-24521.png
-25274.png
-08607.png
-18674.png
-44817.png
-34704.png
-14932.png
-63639.png
-01310.png
-27761.png
-06560.png
-20343.png
-42431.png
-03789.png
-45295.png
-51070.png
-60293.png
-46227.png
-54719.png
-23686.png
-62492.png
-61887.png
-58516.png
-27663.png
-26548.png
-59313.png
-38228.png
-07323.png
-14552.png
-67105.png
-51708.png
-06279.png
-68110.png
-15761.png
-36424.png
-64005.png
-60100.png
-52599.png
-36679.png
-20065.png
-05915.png
-32124.png
-65131.png
-06058.png
-32035.png
-64547.png
-68659.png
-04644.png
-05937.png
-24579.png
-68967.png
-55108.png
-42834.png
-43706.png
-00627.png
-58650.png
-50588.png
-18739.png
-17927.png
-23709.png
-10582.png
-09224.png
-02947.png
-01796.png
-17434.png
-47306.png
-59605.png
-06760.png
-30460.png
-64284.png
-17004.png
-44083.png
-60467.png
-07833.png
-27812.png
-25346.png
-64111.png
-49276.png
-40832.png
-21358.png
-67169.png
-15960.png
-24296.png
-08408.png
-57923.png
-27696.png
-38905.png
-08338.png
-38952.png
-47216.png
-00423.png
-15337.png
-59475.png
-07820.png
-26453.png
-22481.png
-39924.png
-61921.png
-36205.png
-57351.png
-08785.png
-56327.png
-64431.png
-34892.png
-55634.png
-37314.png
-52583.png
-60022.png
-33556.png
-14566.png
-09983.png
-39950.png
-05459.png
-48618.png
-16042.png
-64405.png
-50116.png
-15678.png
-18991.png
-53145.png
-37109.png
-15402.png
-05264.png
-45213.png
-38172.png
-24021.png
-54291.png
-67954.png
-36024.png
-07777.png
-55441.png
-61734.png
-19545.png
-22503.png
-56071.png
-00404.png
-04875.png
-62355.png
-48169.png
-14875.png
-10350.png
-18178.png
-67675.png
-50012.png
-16245.png
-45140.png
-08136.png
-64485.png
-44133.png
-26255.png
-27032.png
-15309.png
-12735.png
-33376.png
-35392.png
-54144.png
-50323.png
-06638.png
-57037.png
-39089.png
-26436.png
-03823.png
-17182.png
-52811.png
-24080.png
-06186.png
-00551.png
-06518.png
-09245.png
-20528.png
-23436.png
-56943.png
-26509.png
-01454.png
-37689.png
-59020.png
-00714.png
-21914.png
-28091.png
-66437.png
-04566.png
-51853.png
-15575.png
-40090.png
-48476.png
-00304.png
-64793.png
-62397.png
-43882.png
-49310.png
-26177.png
-07675.png
-41201.png
-61886.png
-66183.png
-06099.png
-54835.png
-10882.png
-30516.png
-36405.png
-14171.png
-03784.png
-08648.png
-22494.png
-26331.png
-67118.png
-24723.png
-03591.png
-44783.png
-30400.png
-43048.png
-53662.png
-28355.png
-66434.png
-25079.png
-14622.png
-08558.png
-04217.png
-06888.png
-12485.png
-59945.png
-18466.png
-48663.png
-07916.png
-18583.png
-55388.png
-46432.png
-40383.png
-08145.png
-51955.png
-30453.png
-09806.png
-54577.png
-57499.png
-52410.png
-19555.png
-54128.png
-17325.png
-15701.png
-07964.png
-24063.png
-57421.png
-25949.png
-44489.png
-28281.png
-60246.png
-61683.png
-07700.png
-16763.png
-39786.png
-38459.png
-68794.png
-53392.png
-68309.png
-53589.png
-19738.png
-39502.png
-30242.png
-06402.png
-62938.png
-21423.png
-31315.png
-16467.png
-26322.png
-42696.png
-20480.png
-34660.png
-15204.png
-04886.png
-19605.png
-34769.png
-10114.png
-35720.png
-28280.png
-50184.png
-32609.png
-48192.png
-14724.png
-43701.png
-34223.png
-42391.png
-43028.png
-69762.png
-31377.png
-28399.png
-65431.png
-25407.png
-32572.png
-38470.png
-55985.png
-10272.png
-46707.png
-29544.png
-12444.png
-14675.png
-45536.png
-01853.png
-31516.png
-05439.png
-12818.png
-19084.png
-26906.png
-49673.png
-01172.png
-11461.png
-49016.png
-50096.png
-32571.png
-27705.png
-56516.png
-32115.png
-69622.png
-27513.png
-28079.png
-33313.png
-16684.png
-15379.png
-43303.png
-27045.png
-40746.png
-48367.png
-27073.png
-51087.png
-60015.png
-31323.png
-23608.png
-00614.png
-15685.png
-14039.png
-44568.png
-31442.png
-11247.png
-54691.png
-06610.png
-51422.png
-06073.png
-30647.png
-47199.png
-14525.png
-08010.png
-51186.png
-13581.png
-44613.png
-50339.png
-66775.png
-57593.png
-37904.png
-37545.png
-21029.png
-49967.png
-36559.png
-27557.png
-61509.png
-25499.png
-07266.png
-39064.png
-68801.png
-31123.png
-56441.png
-21916.png
-61226.png
-65630.png
-67213.png
-11619.png
-29635.png
-59199.png
-18375.png
-69216.png
-33516.png
-30071.png
-47971.png
-54685.png
-31422.png
-68617.png
-28245.png
-03015.png
-20883.png
-43164.png
-13060.png
-42176.png
-21536.png
-47275.png
-26284.png
-12100.png
-66385.png
-32892.png
-30939.png
-48488.png
-12322.png
-18697.png
-06873.png
-29419.png
-66032.png
-30180.png
-69087.png
-66982.png
-68146.png
-48068.png
-26104.png
-45979.png
-46134.png
-36643.png
-57602.png
-04790.png
-41416.png
-35395.png
-13970.png
-69341.png
-16011.png
-59547.png
-67519.png
-48378.png
-58895.png
-47662.png
-55598.png
-17362.png
-13112.png
-69044.png
-29728.png
-66195.png
-39802.png
-03119.png
-38257.png
-60381.png
-00668.png
-18625.png
-34910.png
-02772.png
-11086.png
-68832.png
-01822.png
-60429.png
-14712.png
-45548.png
-64820.png
-63244.png
-64470.png
-37986.png
-32523.png
-21968.png
-32928.png
-57387.png
-10975.png
-69893.png
-33030.png
-59763.png
-44185.png
-48993.png
-19323.png
-45810.png
-39750.png
-69605.png
-42536.png
-12873.png
-47236.png
-55578.png
-41720.png
-43374.png
-43036.png
-60116.png
-38803.png
-08707.png
-34210.png
-26285.png
-28141.png
-55798.png
-30818.png
-17682.png
-60089.png
-49874.png
-57683.png
-30122.png
-37592.png
-56169.png
-43393.png
-23460.png
-58481.png
-21566.png
-28374.png
-31228.png
-24395.png
-38556.png
-58241.png
-13832.png
-64968.png
-60682.png
-44265.png
-44208.png
-35741.png
-41533.png
-50416.png
-60951.png
-57164.png
-68540.png
-48785.png
-45935.png
-43901.png
-55882.png
-00408.png
-36437.png
-65212.png
-61202.png
-12492.png
-05698.png
-48107.png
-06747.png
-18222.png
-17653.png
-14252.png
-15367.png
-06563.png
-27786.png
-26227.png
-60059.png
-22796.png
-30411.png
-61063.png
-44087.png
-55404.png
-38677.png
-43347.png
-62500.png
-36461.png
-45426.png
-60141.png
-19181.png
-68004.png
-38137.png
-06587.png
-39290.png
-22506.png
-49180.png
-15197.png
-42273.png
-38042.png
-32499.png
-12309.png
-65111.png
-49079.png
-45032.png
-51285.png
-26650.png
-18896.png
-65005.png
-16149.png
-03401.png
-02220.png
-43608.png
-03365.png
-67076.png
-54163.png
-41818.png
-51141.png
-06794.png
-67899.png
-32440.png
-50948.png
-57514.png
-40902.png
-68135.png
-39671.png
-33204.png
-34260.png
-24299.png
-61805.png
-61780.png
-49668.png
-36406.png
-35537.png
-28870.png
-00629.png
-66303.png
-13937.png
-05951.png
-35232.png
-36069.png
-60711.png
-15622.png
-51684.png
-24760.png
-04253.png
-57121.png
-10146.png
-42876.png
-64897.png
-15369.png
-05936.png
-36658.png
-35668.png
-64881.png
-31786.png
-68261.png
-24363.png
-16040.png
-60147.png
-59426.png
-32604.png
-09528.png
-47125.png
-27404.png
-46844.png
-17778.png
-62293.png
-24364.png
-51393.png
-69963.png
-41724.png
-12449.png
-15642.png
-55614.png
-24171.png
-38812.png
-27412.png
-57954.png
-47986.png
-13071.png
-55463.png
-09231.png
-05016.png
-38794.png
-49451.png
-66908.png
-09047.png
-28842.png
-20379.png
-23044.png
-69535.png
-21771.png
-30936.png
-21093.png
-07661.png
-26180.png
-00560.png
-46142.png
-16451.png
-52509.png
-42244.png
-62776.png
-51943.png
-52823.png
-51778.png
-42323.png
-47070.png
-51706.png
-45223.png
-42568.png
-48428.png
-29524.png
-07043.png
-62587.png
-35287.png
-24060.png
-00488.png
-03044.png
-55904.png
-04929.png
-57346.png
-32391.png
-23485.png
-67119.png
-48605.png
-63497.png
-17333.png
-53865.png
-48260.png
-00699.png
-54860.png
-28303.png
-42648.png
-16647.png
-30038.png
-08160.png
-10529.png
-14673.png
-00521.png
-07836.png
-30228.png
-33555.png
-37294.png
-33075.png
-65335.png
-02256.png
-33918.png
-23442.png
-29022.png
-64874.png
-22925.png
-58325.png
-57177.png
-56144.png
-05451.png
-10849.png
-65691.png
-31111.png
-15288.png
-02661.png
-33578.png
-17522.png
-32442.png
-48855.png
-40250.png
-04357.png
-20601.png
-41919.png
-04121.png
-69260.png
-59430.png
-12387.png
-21629.png
-66931.png
-53289.png
-55308.png
-20083.png
-09529.png
-69250.png
-53550.png
-58081.png
-13236.png
-69942.png
-62735.png
-61029.png
-47557.png
-03443.png
-36236.png
-04815.png
-06024.png
-28087.png
-08876.png
-10900.png
-12906.png
-64995.png
-58756.png
-16464.png
-59022.png
-15487.png
-28967.png
-53924.png
-30439.png
-17400.png
-58497.png
-03614.png
-61042.png
-47787.png
-31507.png
-53760.png
-05898.png
-00566.png
-38513.png
-44972.png
-34985.png
-49070.png
-46875.png
-26206.png
-23516.png
-14218.png
-02518.png
-67869.png
-13200.png
-23811.png
-37796.png
-47307.png
-07374.png
-16322.png
-37747.png
-50457.png
-47840.png
-32487.png
-07920.png
-03588.png
-16559.png
-42603.png
-02689.png
-63887.png
-23952.png
-51142.png
-03074.png
-02945.png
-05378.png
-67309.png
-53568.png
-30544.png
-62849.png
-31855.png
-62080.png
-32386.png
-17615.png
-31126.png
-32792.png
-28277.png
-69763.png
-00817.png
-55792.png
-56054.png
-40741.png
-48113.png
-43998.png
-47746.png
-08335.png
-17021.png
-13074.png
-67974.png
-07212.png
-09690.png
-15216.png
-55320.png
-26391.png
-59363.png
-50716.png
-66429.png
-38961.png
-61639.png
-68091.png
-59244.png
-68266.png
-20903.png
-05917.png
-27609.png
-23613.png
-14144.png
-69844.png
-41956.png
-24092.png
-09317.png
-46057.png
-18713.png
-00290.png
-20954.png
-26814.png
-30393.png
-42529.png
-18412.png
-28318.png
-25770.png
-15549.png
-00513.png
-55440.png
-65235.png
-66291.png
-53492.png
-08228.png
-32801.png
-41357.png
-68861.png
-50590.png
-69623.png
-18986.png
-41944.png
-20991.png
-63607.png
-54116.png
-54217.png
-26958.png
-64245.png
-37337.png
-21963.png
-33626.png
-57256.png
-23967.png
-38143.png
-13242.png
-24023.png
-37231.png
-50979.png
-40547.png
-32623.png
-68254.png
-16454.png
-06543.png
-67413.png
-66825.png
-41294.png
-38092.png
-41966.png
-42124.png
-22190.png
-61180.png
-60142.png
-18314.png
-68858.png
-27282.png
-21569.png
-67799.png
-61112.png
-23216.png
-14480.png
-13154.png
-26848.png
-18895.png
-43654.png
-56712.png
-26226.png
-33641.png
-41821.png
-55328.png
-69300.png
-49232.png
-20288.png
-55268.png
-03317.png
-60118.png
-68415.png
-25153.png
-34617.png
-50282.png
-00291.png
-36318.png
-34306.png
-61066.png
-44855.png
-39609.png
-57657.png
-36793.png
-18568.png
-26343.png
-21926.png
-05442.png
-66669.png
-56910.png
-19409.png
-21758.png
-60108.png
-21222.png
-57405.png
-25911.png
-02654.png
-20368.png
-62796.png
-05360.png
-02790.png
-69098.png
-17700.png
-18921.png
-37988.png
-45029.png
-68637.png
-14270.png
-49880.png
-44824.png
-28254.png
-20860.png
-55548.png
-58915.png
-21840.png
-56419.png
-00790.png
-60189.png
-65877.png
-32978.png
-02691.png
-35776.png
-60594.png
-13687.png
-68381.png
-57369.png
-40855.png
-18343.png
-52389.png
-48640.png
-56981.png
-22922.png
-21795.png
-57091.png
-65660.png
-45576.png
-02403.png
-21157.png
-01996.png
-03239.png
-17601.png
-10560.png
-68551.png
-63091.png
-04477.png
-27328.png
-11169.png
-69611.png
-04169.png
-19478.png
-22874.png
-58678.png
-58022.png
-28235.png
-23326.png
-58283.png
-53061.png
-24727.png
-57476.png
-28034.png
-34173.png
-62158.png
-27362.png
-61576.png
-47650.png
-36501.png
-05480.png
-57639.png
-52485.png
-51972.png
-33098.png
-03896.png
-13045.png
-53960.png
-51569.png
-30693.png
-39239.png
-08695.png
-30917.png
-06999.png
-02273.png
-54203.png
-54942.png
-51257.png
-67867.png
-23221.png
-24792.png
-25089.png
-53896.png
-01077.png
-03547.png
-23421.png
-12742.png
-05780.png
-45229.png
-63662.png
-34648.png
-56682.png
-24756.png
-48409.png
-41737.png
-20298.png
-42290.png
-47174.png
-07482.png
-43718.png
-40294.png
-29045.png
-23767.png
-10613.png
-06315.png
-59653.png
-26900.png
-36545.png
-08969.png
-18830.png
-42632.png
-26542.png
-20327.png
-05059.png
-11416.png
-43879.png
-68484.png
-01720.png
-22812.png
-16754.png
-57084.png
-66901.png
-36884.png
-46164.png
-01897.png
-03330.png
-11843.png
-21805.png
-64435.png
-68281.png
-56130.png
-28853.png
-19978.png
-32987.png
-41140.png
-14762.png
-65498.png
-01113.png
-50343.png
-31560.png
-18822.png
-49237.png
-11935.png
-50606.png
-22429.png
-17351.png
-35117.png
-15135.png
-61117.png
-61172.png
-66530.png
-30390.png
-15507.png
-60863.png
-69200.png
-55166.png
-24802.png
-02915.png
-04293.png
-51898.png
-36599.png
-08598.png
-51672.png
-02528.png
-51197.png
-48556.png
-59779.png
-50728.png
-35394.png
-37867.png
-25350.png
-29370.png
-07975.png
-53177.png
-65248.png
-03099.png
-26135.png
-11819.png
-39327.png
-27155.png
-40971.png
-07300.png
-50058.png
-48632.png
-04584.png
-27625.png
-46904.png
-00321.png
-28426.png
-00471.png
-65765.png
-69218.png
-53875.png
-33400.png
-59173.png
-22453.png
-44796.png
-48179.png
-68130.png
-50874.png
-57727.png
-54879.png
-36625.png
-25900.png
-37995.png
-52064.png
-44787.png
-04749.png
-38004.png
-51071.png
-30999.png
-27988.png
-12458.png
-44014.png
-47451.png
-15005.png
-07343.png
-26244.png
-34012.png
-63698.png
-53356.png
-33982.png
-16033.png
-63156.png
-49929.png
-44658.png
-56972.png
-25132.png
-30514.png
-30896.png
-63813.png
-24397.png
-41817.png
-03902.png
-46863.png
-19629.png
-42025.png
-38390.png
-06332.png
-66089.png
-40569.png
-13653.png
-57888.png
-14293.png
-44844.png
-11262.png
-59924.png
-01950.png
-57844.png
-26845.png
-33638.png
-45220.png
-36363.png
-26060.png
-37209.png
-33252.png
-12427.png
-36176.png
-07331.png
-26249.png
-35568.png
-28787.png
-28622.png
-37407.png
-17242.png
-02044.png
-64835.png
-31555.png
-26753.png
-04969.png
-51926.png
-01490.png
-16903.png
-53253.png
-40575.png
-19144.png
-56658.png
-33994.png
-62671.png
-65903.png
-67036.png
-27395.png
-59487.png
-51579.png
-23188.png
-10270.png
-38062.png
-44052.png
-55916.png
-22049.png
-69675.png
-01557.png
-16569.png
-46491.png
-34472.png
-56654.png
-23302.png
-02789.png
-26589.png
-49274.png
-65664.png
-37614.png
-56923.png
-43841.png
-53536.png
-51136.png
-34364.png
-04498.png
-35397.png
-44197.png
-37466.png
-14510.png
-40919.png
-15037.png
-20277.png
-29592.png
-22723.png
-66333.png
-10228.png
-35689.png
-36295.png
-24730.png
-07296.png
-25197.png
-55195.png
-35481.png
-59087.png
-65709.png
-21973.png
-27884.png
-61974.png
-47588.png
-65990.png
-66884.png
-07738.png
-08216.png
-37567.png
-23564.png
-45799.png
-67483.png
-31293.png
-43384.png
-52974.png
-06109.png
-00223.png
-47081.png
-13645.png
-12073.png
-04960.png
-16543.png
-02347.png
-18711.png
-61749.png
-04239.png
-60806.png
-10276.png
-64493.png
-33701.png
-48328.png
-57349.png
-32666.png
-33776.png
-20313.png
-47970.png
-60851.png
-04593.png
-65871.png
-21983.png
-06954.png
-49853.png
-60390.png
-54307.png
-01373.png
-14452.png
-36171.png
-06045.png
-54918.png
-56877.png
-14755.png
-39201.png
-49243.png
-64209.png
-52761.png
-11309.png
-35793.png
-24817.png
-59997.png
-56417.png
-19165.png
-61590.png
-56152.png
-63400.png
-46225.png
-45161.png
-66589.png
-32118.png
-35588.png
-28338.png
-38573.png
-47215.png
-39665.png
-60635.png
-05173.png
-08638.png
-58199.png
-18731.png
-09219.png
-43678.png
-12162.png
-10478.png
-17387.png
-07046.png
-66758.png
-04798.png
-06876.png
-55073.png
-61394.png
-01500.png
-15282.png
-24118.png
-34587.png
-09449.png
-19828.png
-05322.png
-67772.png
-21796.png
-38029.png
-06994.png
-44046.png
-25863.png
-05170.png
-14187.png
-38077.png
-40039.png
-67497.png
-56000.png
-30336.png
-28050.png
-58796.png
-64333.png
-39514.png
-55848.png
-61249.png
-16500.png
-68472.png
-22574.png
-52923.png
-54201.png
-19786.png
-30979.png
-55093.png
-30215.png
-11413.png
-56103.png
-60019.png
-06469.png
-39668.png
-61949.png
-16039.png
-34324.png
-69618.png
-46338.png
-52428.png
-06048.png
-30797.png
-34896.png
-69376.png
-46328.png
-32141.png
-60970.png
-45425.png
-33436.png
-39218.png
-46396.png
-42666.png
-46148.png
-50580.png
-64966.png
-52440.png
-54948.png
-64459.png
-39450.png
-09675.png
-46507.png
-66355.png
-30543.png
-56916.png
-16167.png
-23423.png
-31761.png
-51619.png
-06011.png
-53063.png
-51909.png
-50851.png
-33120.png
-45012.png
-08521.png
-69171.png
-33411.png
-44530.png
-05563.png
-42973.png
-35371.png
-19239.png
-22410.png
-40479.png
-14852.png
-21095.png
-69918.png
-64223.png
-32761.png
-05501.png
-60592.png
-30785.png
-26107.png
-29562.png
-16628.png
-01877.png
-62803.png
-12015.png
-48218.png
-53497.png
-16598.png
-60037.png
-62155.png
-43055.png
-58711.png
-55480.png
-13509.png
-04171.png
-45892.png
-43984.png
-54733.png
-65923.png
-08980.png
-45042.png
-39273.png
-04553.png
-63266.png
-37324.png
-09246.png
-22160.png
-44684.png
-55344.png
-53638.png
-15838.png
-57841.png
-13929.png
-22333.png
-18478.png
-63516.png
-24914.png
-53290.png
-17654.png
-19823.png
-07197.png
-35472.png
-21510.png
-69759.png
-05088.png
-03728.png
-51060.png
-67914.png
-02797.png
-42653.png
-19073.png
-40317.png
-22510.png
-52997.png
-57445.png
-05970.png
-28991.png
-25334.png
-39180.png
-02333.png
-53524.png
-37730.png
-41164.png
-47361.png
-41116.png
-45395.png
-46587.png
-26987.png
-16141.png
-45624.png
-05649.png
-21846.png
-69494.png
-37570.png
-06998.png
-14960.png
-52502.png
-18236.png
-18167.png
-67844.png
-50674.png
-10067.png
-45256.png
-06881.png
-69819.png
-44402.png
-02111.png
-30654.png
-56143.png
-65557.png
-52718.png
-35363.png
-11952.png
-36894.png
-27961.png
-27248.png
-59814.png
-44195.png
-69384.png
-45898.png
-03839.png
-67796.png
-24033.png
-54353.png
-67399.png
-66310.png
-51308.png
-21145.png
-69717.png
-38520.png
-23294.png
-06688.png
-46764.png
-48105.png
-36947.png
-37824.png
-05973.png
-48316.png
-17989.png
-39328.png
-15201.png
-23771.png
-47235.png
-00109.png
-26842.png
-48710.png
-36669.png
-39068.png
-45816.png
-27002.png
-62560.png
-29290.png
-01527.png
-16928.png
-57395.png
-52853.png
-62276.png
-20600.png
-44244.png
-49546.png
-05526.png
-32112.png
-21355.png
-22276.png
-50826.png
-51875.png
-23311.png
-20508.png
-00092.png
-49593.png
-38889.png
-28988.png
-55859.png
-41854.png
-10049.png
-13393.png
-56095.png
-16486.png
-03010.png
-64865.png
-26299.png
-33995.png
-11481.png
-06265.png
-40194.png
-63114.png
-53390.png
-60570.png
-31601.png
-38686.png
-31320.png
-16353.png
-20076.png
-29234.png
-52019.png
-65108.png
-21660.png
-19924.png
-06650.png
-68189.png
-60356.png
-47851.png
-27986.png
-47529.png
-26704.png
-61054.png
-43515.png
-37026.png
-54841.png
-16653.png
-40807.png
-35133.png
-60854.png
-13924.png
-50714.png
-31406.png
-11097.png
-46352.png
-49085.png
-50170.png
-63820.png
-03455.png
-48295.png
-46127.png
-50866.png
-35853.png
-21950.png
-14674.png
-15847.png
-26148.png
-35003.png
-61174.png
-56361.png
-04799.png
-04469.png
-54659.png
-39211.png
-45132.png
-40554.png
-01106.png
-20772.png
-45550.png
-27150.png
-48480.png
-55376.png
-27390.png
-62893.png
-27803.png
-09453.png
-61493.png
-68369.png
-56924.png
-11933.png
-09866.png
-12174.png
-54501.png
-48763.png
-04893.png
-23396.png
-29522.png
-14887.png
-35681.png
-53894.png
-45944.png
-65844.png
-45065.png
-12142.png
-66328.png
-37462.png
-41546.png
-41665.png
-42295.png
-41870.png
-07727.png
-05811.png
-13962.png
-66927.png
-07933.png
-09841.png
-69055.png
-36691.png
-35953.png
-57649.png
-06478.png
-36607.png
-00224.png
-50554.png
-60543.png
-67628.png
-22119.png
-26800.png
-01293.png
-60531.png
-36628.png
-23039.png
-24258.png
-41867.png
-65663.png
-33765.png
-16239.png
-07618.png
-59716.png
-13484.png
-17647.png
-01595.png
-03950.png
-58991.png
-55345.png
-13862.png
-68641.png
-57262.png
-66830.png
-68078.png
-15571.png
-68177.png
-31146.png
-08951.png
-06280.png
-23507.png
-27279.png
-06400.png
-36646.png
-46048.png
-14536.png
-42888.png
-66506.png
-18275.png
-36971.png
-69692.png
-39185.png
-31749.png
-59607.png
-00544.png
-06117.png
-18873.png
-52201.png
-34459.png
-60861.png
-02643.png
-17411.png
-29555.png
-56793.png
-39043.png
-04242.png
-65832.png
-19186.png
-56605.png
-44730.png
-05778.png
-03092.png
-59803.png
-46304.png
-28194.png
-16235.png
-22041.png
-46276.png
-16738.png
-61423.png
-31937.png
-48265.png
-63823.png
-17973.png
-03661.png
-38743.png
-29344.png
-21759.png
-24921.png
-31476.png
-44320.png
-21531.png
-33418.png
-69428.png
-46113.png
-01346.png
-15265.png
-07491.png
-68021.png
-00642.png
-62079.png
-59791.png
-18351.png
-55054.png
-28433.png
-02729.png
-09808.png
-19446.png
-12429.png
-60526.png
-62379.png
-55247.png
-38935.png
-10389.png
-18877.png
-24298.png
-61205.png
-29565.png
-03392.png
-61305.png
-44264.png
-35017.png
-20016.png
-31427.png
-43134.png
-50769.png
-15937.png
-41536.png
-13898.png
-29980.png
-10282.png
-48516.png
-23218.png
-17156.png
-14036.png
-38635.png
-68677.png
-36096.png
-27821.png
-58284.png
-63229.png
-14417.png
-21525.png
-49150.png
-29117.png
-49304.png
-20404.png
-45967.png
-45693.png
-01880.png
-62987.png
-07089.png
-21047.png
-63406.png
-23533.png
-06150.png
-18464.png
-56230.png
-20646.png
-31318.png
-45954.png
-60497.png
-27141.png
-09436.png
-69372.png
-20496.png
-46779.png
-15324.png
-27632.png
-39456.png
-56539.png
-65302.png
-17066.png
-20027.png
-06920.png
-64177.png
-24750.png
-29008.png
-41994.png
-11454.png
-54062.png
-08407.png
-09640.png
-50499.png
-36515.png
-31162.png
-23028.png
-44188.png
-36220.png
-62586.png
-02931.png
-46065.png
-24524.png
-37071.png
-60288.png
-41169.png
-27855.png
-32713.png
-36877.png
-39636.png
-01100.png
-41960.png
-26020.png
-53574.png
-31147.png
-00174.png
-56225.png
-25878.png
-25942.png
-34059.png
-62020.png
-65600.png
-26956.png
-39591.png
-00023.png
-17288.png
-59160.png
-00491.png
-10724.png
-69867.png
-54602.png
-30100.png
-45017.png
-08293.png
-64021.png
-14647.png
-51736.png
-60250.png
-40327.png
-01308.png
-05774.png
-36107.png
-51072.png
-29358.png
-03798.png
-55102.png
-29723.png
-29107.png
-13643.png
-48531.png
-09458.png
-40267.png
-17620.png
-44109.png
-45996.png
-18773.png
-48462.png
-23942.png
-37255.png
-55674.png
-02809.png
-61649.png
-58145.png
-39886.png
-09778.png
-26756.png
-12143.png
-06306.png
-64738.png
-39976.png
-02494.png
-05953.png
-20070.png
-58372.png
-03710.png
-04733.png
-29245.png
-01359.png
-59673.png
-35310.png
-57145.png
-47576.png
-26751.png
-00301.png
-58517.png
-62949.png
-07682.png
-30031.png
-50259.png
-57854.png
-12570.png
-59757.png
-36221.png
-42086.png
-68074.png
-35753.png
-45126.png
-06487.png
-38810.png
-02585.png
-39482.png
-09526.png
-39179.png
-36279.png
-04011.png
-69769.png
-26564.png
-23786.png
-03776.png
-58065.png
-59325.png
-34088.png
-17404.png
-29363.png
-69793.png
-21083.png
-64840.png
-53272.png
-52179.png
-29444.png
-37295.png
-13574.png
-17982.png
-03155.png
-51792.png
-33697.png
-56214.png
-51851.png
-64135.png
-11770.png
-35847.png
-54207.png
-11154.png
-30671.png
-55499.png
-02388.png
-34028.png
-12965.png
-43161.png
-62541.png
-17077.png
-48170.png
-08628.png
-00999.png
-06422.png
-23247.png
-38166.png
-24987.png
-58742.png
-04752.png
-11531.png
-47099.png
-28850.png
-59609.png
-45821.png
-09478.png
-58219.png
-17713.png
-33985.png
-55419.png
-41456.png
-08463.png
-53154.png
-14307.png
-36966.png
-56160.png
-27564.png
-68131.png
-43086.png
-08001.png
-59522.png
-40938.png
-21383.png
-65417.png
-64090.png
-09178.png
-08960.png
-13111.png
-20352.png
-29819.png
-30367.png
-19069.png
-42720.png
-41588.png
-55716.png
-11624.png
-39966.png
-45487.png
-50919.png
-18861.png
-37925.png
-28259.png
-03432.png
-32814.png
-69278.png
-28562.png
-66619.png
-31949.png
-65483.png
-48928.png
-18017.png
-29513.png
-27403.png
-32856.png
-04203.png
-64308.png
-22338.png
-19301.png
-43391.png
-14099.png
-03043.png
-19814.png
-67000.png
-36225.png
-27724.png
-33689.png
-56213.png
-27731.png
-21256.png
-57722.png
-59743.png
-59804.png
-51034.png
-19053.png
-44595.png
-65354.png
-10871.png
-02829.png
-13067.png
-44497.png
-05644.png
-44745.png
-08362.png
-59460.png
-17035.png
-38232.png
-34316.png
-63269.png
-05992.png
-32399.png
-56917.png
-32580.png
-44851.png
-21075.png
-26989.png
-60289.png
-12726.png
-26035.png
-17279.png
-04842.png
-52655.png
-10230.png
-58050.png
-46072.png
-09090.png
-19502.png
-22271.png
-06546.png
-31494.png
-58887.png
-44849.png
-09148.png
-36630.png
-29259.png
-37914.png
-17284.png
-30558.png
-26160.png
-39953.png
-21378.png
-18396.png
-65030.png
-66603.png
-47936.png
-64498.png
-09336.png
-57038.png
-69771.png
-07480.png
-68603.png
-00062.png
-39892.png
-61452.png
-15616.png
-49488.png
-03949.png
-54744.png
-39735.png
-67416.png
-36690.png
-28923.png
-38928.png
-17441.png
-00190.png
-37961.png
-25235.png
-52610.png
-38835.png
-32626.png
-14008.png
-15408.png
-66288.png
-02103.png
-45843.png
-55952.png
-51676.png
-55601.png
-28230.png
-60549.png
-57659.png
-47784.png
-26513.png
-21842.png
-06421.png
-47756.png
-15339.png
-53686.png
-64815.png
-28409.png
-30568.png
-64982.png
-43034.png
-03440.png
-49366.png
-04716.png
-20702.png
-19560.png
-26267.png
-15396.png
-51951.png
-65702.png
-28007.png
-61243.png
-52187.png
-30983.png
-19724.png
-22727.png
-40503.png
-02472.png
-28559.png
-01731.png
-44459.png
-66040.png
-60103.png
-56395.png
-26512.png
-63708.png
-16183.png
-01687.png
-30274.png
-64734.png
-15118.png
-49029.png
-16330.png
-44001.png
-31639.png
-22084.png
-10890.png
-10390.png
-28882.png
-51416.png
-36711.png
-29599.png
-24157.png
-18281.png
-69264.png
-68574.png
-64526.png
-44923.png
-62098.png
-57299.png
-15637.png
-50757.png
-14567.png
-17835.png
-16948.png
-21829.png
-24302.png
-10775.png
-16654.png
-30271.png
-44469.png
-36286.png
-11038.png
-38929.png
-10192.png
-02540.png
-46688.png
-54027.png
-62033.png
-24677.png
-05071.png
-61191.png
-15232.png
-23380.png
-08617.png
-05954.png
-65151.png
-22702.png
-01862.png
-02053.png
-39813.png
-01727.png
-06142.png
-51045.png
-35223.png
-37393.png
-41090.png
-30138.png
-33769.png
-66643.png
-60416.png
-16867.png
-25502.png
-48248.png
-04976.png
-63967.png
-25158.png
-08566.png
-24412.png
-54413.png
-50268.png
-07301.png
-28855.png
-55206.png
-57235.png
-43518.png
-17248.png
-02677.png
-64324.png
-11798.png
-15774.png
-67680.png
-60329.png
-03574.png
-35974.png
-45048.png
-45000.png
-19305.png
-67684.png
-46911.png
-56787.png
-63316.png
-20925.png
-60344.png
-50612.png
-60835.png
-61560.png
-25441.png
-56570.png
-06204.png
-17296.png
-13657.png
-39255.png
-13822.png
-67931.png
-48354.png
-53193.png
-08369.png
-66882.png
-25709.png
-05812.png
-27162.png
-00211.png
-56501.png
-34733.png
-47456.png
-25207.png
-36017.png
-20101.png
-55381.png
-50318.png
-39883.png
-25352.png
-64519.png
-50889.png
-28022.png
-50980.png
-17904.png
-52651.png
-04531.png
-67128.png
-50346.png
-28887.png
-30249.png
-64315.png
-55187.png
-05967.png
-43764.png
-04287.png
-66444.png
-32002.png
-64769.png
-03253.png
-69295.png
-13966.png
-36603.png
-13470.png
-17842.png
-40195.png
-32242.png
-08543.png
-06175.png
-68918.png
-13456.png
-13234.png
-41144.png
-22920.png
-62281.png
-10253.png
-28878.png
-48777.png
-57685.png
-39420.png
-49900.png
-05469.png
-42818.png
-15366.png
-27643.png
-26272.png
-57615.png
-36088.png
-23376.png
-50611.png
-50468.png
-03569.png
-07790.png
-20999.png
-41610.png
-56705.png
-67906.png
-56280.png
-13752.png
-43257.png
-44007.png
-66957.png
-01123.png
-06270.png
-64965.png
-23677.png
-39467.png
-56735.png
-15150.png
-69270.png
-34690.png
-01210.png
-46820.png
-10038.png
-33545.png
-36776.png
-19888.png
-46483.png
-65661.png
-17083.png
-09346.png
-36492.png
-69125.png
-36731.png
-65254.png
-13165.png
-06715.png
-53207.png
-38774.png
-49770.png
-66018.png
-17943.png
-54970.png
-47941.png
-63789.png
-02907.png
-05991.png
-20295.png
-66996.png
-51761.png
-54890.png
-09852.png
-25774.png
-53115.png
-28571.png
-38014.png
-29594.png
-46918.png
-58526.png
-40926.png
-49997.png
-59768.png
-51607.png
-42137.png
-54395.png
-00185.png
-66918.png
-49809.png
-55379.png
-44897.png
-69813.png
-54432.png
-39914.png
-09810.png
-40231.png
-25339.png
-29115.png
-06465.png
-16021.png
-06583.png
-38225.png
-15550.png
-07065.png
-51504.png
-08079.png
-12000.png
-18220.png
-61566.png
-10773.png
-51188.png
-51020.png
-46831.png
-37781.png
-57926.png
-20896.png
-11115.png
-58315.png
-60088.png
-60626.png
-47385.png
-24132.png
-67455.png
-32980.png
-12998.png
-63694.png
-10845.png
-17363.png
-28775.png
-06004.png
-21996.png
-02153.png
-39296.png
-45635.png
-13378.png
-05349.png
-43923.png
-66013.png
-63592.png
-27673.png
-60701.png
-15476.png
-38204.png
-45713.png
-48191.png
-40421.png
-64449.png
-43723.png
-54260.png
-22473.png
-55289.png
-22751.png
-38907.png
-11732.png
-51611.png
-05297.png
-41490.png
-22500.png
-18480.png
-26171.png
-48802.png
-25027.png
-35262.png
-66459.png
-28795.png
-60755.png
-40508.png
-03231.png
-50314.png
-66343.png
-42992.png
-57959.png
-65353.png
-16502.png
-37653.png
-35734.png
-05039.png
-38291.png
-62504.png
-50650.png
-19630.png
-02514.png
-05247.png
-18532.png
-21945.png
-12602.png
-21397.png
-27115.png
-62585.png
-12001.png
-41573.png
-00091.png
-23068.png
-29165.png
-59465.png
-03925.png
-13310.png
-32533.png
-66122.png
-11698.png
-49619.png
-17343.png
-09377.png
-56891.png
-50390.png
-25724.png
-58905.png
-12930.png
-57244.png
-20749.png
-58139.png
-62556.png
-52135.png
-38243.png
-15199.png
-06372.png
-29814.png
-08008.png
-53176.png
-69651.png
-36785.png
-44005.png
-18143.png
-32419.png
-30754.png
-19422.png
-23736.png
-49934.png
-08014.png
-13997.png
-36725.png
-07978.png
-39344.png
-53741.png
-02742.png
-46885.png
-35650.png
-63103.png
-25522.png
-61043.png
-26830.png
-41397.png
-08309.png
-49156.png
-39573.png
-42689.png
-36432.png
-02317.png
-03037.png
-03058.png
-56522.png
-16961.png
-19801.png
-60488.png
-36525.png
-61893.png
-56215.png
-60442.png
-26790.png
-67645.png
-20588.png
-04286.png
-15222.png
-32915.png
-48377.png
-29081.png
-40171.png
-04424.png
-29610.png
-60555.png
-00236.png
-35839.png
-28724.png
-49253.png
-08647.png
-07573.png
-04407.png
-68851.png
-40314.png
-45755.png
-12351.png
-16591.png
-56649.png
-05000.png
-47933.png
-51925.png
-50226.png
-63123.png
-31398.png
-06236.png
-63474.png
-51828.png
-19882.png
-49345.png
-50474.png
-14438.png
-30048.png
-10521.png
-17195.png
-24424.png
-16815.png
-06092.png
-41155.png
-64921.png
-05710.png
-45615.png
-10051.png
-60427.png
-37468.png
-06528.png
-03721.png
-65469.png
-33181.png
-11109.png
-67769.png
-27967.png
-59021.png
-17694.png
-63730.png
-26523.png
-16523.png
-04004.png
-44967.png
-46306.png
-69529.png
-64664.png
-47631.png
-24897.png
-28999.png
-48066.png
-20683.png
-08966.png
-30521.png
-68352.png
-50136.png
-24679.png
-58332.png
-51597.png
-04979.png
-28512.png
-45160.png
-61625.png
-51303.png
-11157.png
-08476.png
-20220.png
-52684.png
-43757.png
-40603.png
-27775.png
-47817.png
-06774.png
-12920.png
-55703.png
-17539.png
-69755.png
-18134.png
-63936.png
-43222.png
-36760.png
-59479.png
-53723.png
-64042.png
-26937.png
-38156.png
-50000.png
-49403.png
-00240.png
-52954.png
-02899.png
-40658.png
-38658.png
-16953.png
-45840.png
-20315.png
-41578.png
-30914.png
-08462.png
-43530.png
-43405.png
-03636.png
-18891.png
-34289.png
-31287.png
-31500.png
-67163.png
-47989.png
-24523.png
-66689.png
-06047.png
-40680.png
-30288.png
-46600.png
-64145.png
-58296.png
-28074.png
-60305.png
-60389.png
-17924.png
-13570.png
-25661.png
-58567.png
-65897.png
-47011.png
-36200.png
-10965.png
-50426.png
-50331.png
-27917.png
-11754.png
-20522.png
-55096.png
-62314.png
-65607.png
-13942.png
-22800.png
-59745.png
-57950.png
-19727.png
-27913.png
-31678.png
-39196.png
-32403.png
-42605.png
-34853.png
-02508.png
-02233.png
-42282.png
-66535.png
-57284.png
-58507.png
-57254.png
-62237.png
-41008.png
-26338.png
-23441.png
-26426.png
-12324.png
-33263.png
-58059.png
-13747.png
-60033.png
-41411.png
-53318.png
-03812.png
-68180.png
-05875.png
-38012.png
-04050.png
-40381.png
-55624.png
-52975.png
-50651.png
-14728.png
-17070.png
-32710.png
-23902.png
-40532.png
-51477.png
-65676.png
-57428.png
-45211.png
-44762.png
-12596.png
-31607.png
-57363.png
-28343.png
-36517.png
-54325.png
-64334.png
-14021.png
-29716.png
-23909.png
-16898.png
-16108.png
-59294.png
-53058.png
-34466.png
-32226.png
-09065.png
-37821.png
-56136.png
-48344.png
-09176.png
-41929.png
-13022.png
-67839.png
-31875.png
-13017.png
-49757.png
-52874.png
-40506.png
-44314.png
-28057.png
-33323.png
-48623.png
-07770.png
-38699.png
-06172.png
-37732.png
-28251.png
-32911.png
-31595.png
-47359.png
-66257.png
-48699.png
-12512.png
-62039.png
-50209.png
-47365.png
-06573.png
-49940.png
-13051.png
-08200.png
-44654.png
-61297.png
-19155.png
-44909.png
-31072.png
-11041.png
-51612.png
-46391.png
-13379.png
-12330.png
-04495.png
-63079.png
-08522.png
-60212.png
-54427.png
-18518.png
-12885.png
-68953.png
-23554.png
-57921.png
-38453.png
-36060.png
-04568.png
-60923.png
-37624.png
-26484.png
-33296.png
-02096.png
-11367.png
-57214.png
-16314.png
-29527.png
-12180.png
-57632.png
-21259.png
-69826.png
-06923.png
-69181.png
-30595.png
-11575.png
-14043.png
-03056.png
-39355.png
-09909.png
-11472.png
-21931.png
-66502.png
-42614.png
-11489.png
-55046.png
-59878.png
-34272.png
-29369.png
-63090.png
-34513.png
-58193.png
-49993.png
-17290.png
-66577.png
-35788.png
-37361.png
-53242.png
-61709.png
-67638.png
-51680.png
-18197.png
-29536.png
-21630.png
-51995.png
-37485.png
-18083.png
-14235.png
-48629.png
-42851.png
-37984.png
-51110.png
-03835.png
-38732.png
-57160.png
-17582.png
-60420.png
-08662.png
-18531.png
-41791.png
-13219.png
-02837.png
-02565.png
-56342.png
-55561.png
-42612.png
-63457.png
-45574.png
-68398.png
-43204.png
-24968.png
-10942.png
-38167.png
-50309.png
-18709.png
-48725.png
-37186.png
-68631.png
-69784.png
-12817.png
-60268.png
-62182.png
-41708.png
-61204.png
-42242.png
-44519.png
-00030.png
-06751.png
-51700.png
-13475.png
-68612.png
-48916.png
-40256.png
-14209.png
-30517.png
-05708.png
-20108.png
-49872.png
-45839.png
-57955.png
-43657.png
-66022.png
-26252.png
-34038.png
-42810.png
-21035.png
-20209.png
-23970.png
-43400.png
-16449.png
-61636.png
-56453.png
-42764.png
-34583.png
-47540.png
-31853.png
-04404.png
-21174.png
-35549.png
-27420.png
-56383.png
-26606.png
-35184.png
-15220.png
-16031.png
-59380.png
-66221.png
-19161.png
-19431.png
-20446.png
-19136.png
-09111.png
-15603.png
-05005.png
-06900.png
-16203.png
-19453.png
-35460.png
-47669.png
-68188.png
-51512.png
-32081.png
-32746.png
-13374.png
-00561.png
-64765.png
-45327.png
-36051.png
-54718.png
-18126.png
-62372.png
-52118.png
-49576.png
-43066.png
-07881.png
-18129.png
-41797.png
-61468.png
-36513.png
-08164.png
-61963.png
-04300.png
-34103.png
-17719.png
-37005.png
-37298.png
-06828.png
-51148.png
-58374.png
-21238.png
-01239.png
-52740.png
-24983.png
-49039.png
-62007.png
-51654.png
-34864.png
-57915.png
-22501.png
-33716.png
-64713.png
-64301.png
-43362.png
-04018.png
-57642.png
-07087.png
-32312.png
-48955.png
-29992.png
-04215.png
-12030.png
-27348.png
-52890.png
-19118.png
-32743.png
-14864.png
-11108.png
-65866.png
-39407.png
-66966.png
-69892.png
-19989.png
-68985.png
-35882.png
-57904.png
-22631.png
-56902.png
-31152.png
-01929.png
-52966.png
-05570.png
-53446.png
-21152.png
-26190.png
-15983.png
-10326.png
-38834.png
-21822.png
-41614.png
-39110.png
-17902.png
-44907.png
-33341.png
-49376.png
-05340.png
-00371.png
-49995.png
-18156.png
-30911.png
-51038.png
-22027.png
-32183.png
-42488.png
-11532.png
-50542.png
-66364.png
-09182.png
-44804.png
-25843.png
-27144.png
-10880.png
-29564.png
-12647.png
-00911.png
-14933.png
-60748.png
-54181.png
-50422.png
-06493.png
-58357.png
-29228.png
-21879.png
-32212.png
-52489.png
-22391.png
-23331.png
-56687.png
-28936.png
-05276.png
-15619.png
-02058.png
-67542.png
-25885.png
-27234.png
-00333.png
-57264.png
-26459.png
-28372.png
-31888.png
-43558.png
-29311.png
-65751.png
-54075.png
-65627.png
-23319.png
-44884.png
-10084.png
-21418.png
-08810.png
-05352.png
-47672.png
-21876.png
-44934.png
-59672.png
-39965.png
-29144.png
-26539.png
-42172.png
-05747.png
-65164.png
-55218.png
-50923.png
-53678.png
-30959.png
-34517.png
-05663.png
-35881.png
-32293.png
-27842.png
-58729.png
-00027.png
-04975.png
-42259.png
-30259.png
-25970.png
-14458.png
-44073.png
-41036.png
-21429.png
-04327.png
-51265.png
-52340.png
-57945.png
-33292.png
-35884.png
-03998.png
-14760.png
-20692.png
-03542.png
-38782.png
-10034.png
-45268.png
-20359.png
-27993.png
-68837.png
-12034.png
-34388.png
-55127.png
-35316.png
-00828.png
-09209.png
-31254.png
-26489.png
-13274.png
-09734.png
-69693.png
-09664.png
-53620.png
-03774.png
-62326.png
-22594.png
-48599.png
-56694.png
-25196.png
-54348.png
-40353.png
-06456.png
-24202.png
-27287.png
-11455.png
-60136.png
-00315.png
-40126.png
-50508.png
-54737.png
-21254.png
-48074.png
-10542.png
-61654.png
-49510.png
-44601.png
-43631.png
-54106.png
-36342.png
-54131.png
-15196.png
-26320.png
-52643.png
-31069.png
-37167.png
-33576.png
-53629.png
-21750.png
-64642.png
-31248.png
-61815.png
-16810.png
-20790.png
-07001.png
-30178.png
-18798.png
-57301.png
-25111.png
-07355.png
-46484.png
-69413.png
-59650.png
-13872.png
-03630.png
-35760.png
-52075.png
-07677.png
-38339.png
-44990.png
-06893.png
-46808.png
-28346.png
-34954.png
-17158.png
-54183.png
-05534.png
-19327.png
-27171.png
-42200.png
-07028.png
-44211.png
-66006.png
-06301.png
-54709.png
-52603.png
-37082.png
-04571.png
-03724.png
-37092.png
-41860.png
-56207.png
-44066.png
-03335.png
-43748.png
-14867.png
-67194.png
-30854.png
-49089.png
-43482.png
-66832.png
-04146.png
-13972.png
-05650.png
-32022.png
-67557.png
-63334.png
-52347.png
-06136.png
-60548.png
-02492.png
-59562.png
-14361.png
-46441.png
-01170.png
-43463.png
-53606.png
-69443.png
-61307.png
-64805.png
-68820.png
-39413.png
-32568.png
-07049.png
-18839.png
-16044.png
-49733.png
-13577.png
-60350.png
-40583.png
-61000.png
-13479.png
-56424.png
-52941.png
-55265.png
-58063.png
-67996.png
-61892.png
-10768.png
-44652.png
-51720.png
-30661.png
-27994.png
-34822.png
-52593.png
-06872.png
-22058.png
-19354.png
-44869.png
-38150.png
-32025.png
-05313.png
-03734.png
-17414.png
-66625.png
-63094.png
-10177.png
-38193.png
-47686.png
-55758.png
-55343.png
-40459.png
-35343.png
-17472.png
-22163.png
-50990.png
-09894.png
-42863.png
-20732.png
-22442.png
-03537.png
-38943.png
-22102.png
-50135.png
-05296.png
-61866.png
-47661.png
-51745.png
-59161.png
-42750.png
-06766.png
-41884.png
-03273.png
-37722.png
-20742.png
-35440.png
-28424.png
-30043.png
-66177.png
-29026.png
-08430.png
-03118.png
-50982.png
-33990.png
-18239.png
-18754.png
-02983.png
-26316.png
-25409.png
-39637.png
-58368.png
-32936.png
-37657.png
-42773.png
-35359.png
-29631.png
-30356.png
-25082.png
-21903.png
-22323.png
-45319.png
-51712.png
-29058.png
-41271.png
-27382.png
-69141.png
-60145.png
-12391.png
-60207.png
-00533.png
-10280.png
-21235.png
-26399.png
-64962.png
-14139.png
-11024.png
-02463.png
-54686.png
-15724.png
-59382.png
-50901.png
-57820.png
-17827.png
-14149.png
-61317.png
-45769.png
-22906.png
-09596.png
-61125.png
-18588.png
-52600.png
-56733.png
-54669.png
-20837.png
-69604.png
-41951.png
-25249.png
-63485.png
-05728.png
-32408.png
-63812.png
-48050.png
-41977.png
-12420.png
-31825.png
-69878.png
-13676.png
-26660.png
-31856.png
-20096.png
-55739.png
-56210.png
-55025.png
-34934.png
-66448.png
-19248.png
-24153.png
-28310.png
-22717.png
-32834.png
-26498.png
-15557.png
-20500.png
-69917.png
-10928.png
-01659.png
-06027.png
-55956.png
-21801.png
-21100.png
-10729.png
-06441.png
-42445.png
-44114.png
-58210.png
-69235.png
-42163.png
-28588.png
-20187.png
-00954.png
-12703.png
-59132.png
-24278.png
-60996.png
-29149.png
-19291.png
-32080.png
-63725.png
-13635.png
-56117.png
-07658.png
-64012.png
-50667.png
-25275.png
-22363.png
-28405.png
-39939.png
-56604.png
-05154.png
-58602.png
-55790.png
-08030.png
-58428.png
-13689.png
-33456.png
-23462.png
-69412.png
-44461.png
-34657.png
-53130.png
-14431.png
-65225.png
-33055.png
-64369.png
-05064.png
-19585.png
-37395.png
-46073.png
-46110.png
-37302.png
-14998.png
-31762.png
-54504.png
-61675.png
-65726.png
-49768.png
-35182.png
-23785.png
-39260.png
-09874.png
-44341.png
-68648.png
-64089.png
-30710.png
-67181.png
-14597.png
-54293.png
-54891.png
-09032.png
-45013.png
-69453.png
-52743.png
-14730.png
-27791.png
-64248.png
-05053.png
-45246.png
-51572.png
-60575.png
-51311.png
-42544.png
-52641.png
-59588.png
-04533.png
-58479.png
-30162.png
-39259.png
-22210.png
-59473.png
-57072.png
-41502.png
-59876.png
-67270.png
-03626.png
-07254.png
-31628.png
-67948.png
-04364.png
-09824.png
-32388.png
-17693.png
-40794.png
-67046.png
-26795.png
-45793.png
-00031.png
-13152.png
-09012.png
-48871.png
-48353.png
-13569.png
-05045.png
-46262.png
-37303.png
-29825.png
-00829.png
-25812.png
-51813.png
-46411.png
-69790.png
-46937.png
-27972.png
-63878.png
-40618.png
-29784.png
-68798.png
-05274.png
-34314.png
-41489.png
-21416.png
-16917.png
-18191.png
-39727.png
-30046.png
-04292.png
-35230.png
-51299.png
-24238.png
-11201.png
-11248.png
-31852.png
-36045.png
-18764.png
-23623.png
-03567.png
-01137.png
-46535.png
-11719.png
-22124.png
-53669.png
-16258.png
-62754.png
-27297.png
-26935.png
-64653.png
-53683.png
-30061.png
-67617.png
-67419.png
-22362.png
-50355.png
-39381.png
-16676.png
-21777.png
-61713.png
-40012.png
-33810.png
-68020.png
-41286.png
-28061.png
-66937.png
-67171.png
-51320.png
-49386.png
-58831.png
-07784.png
-20235.png
-53690.png
-37708.png
-45037.png
-57801.png
-62231.png
-66670.png
-66238.png
-07642.png
-67514.png
-63731.png
-25801.png
-35729.png
-50504.png
-34168.png
-54578.png
-21603.png
-13390.png
-41394.png
-67551.png
-51958.png
-41954.png
-58840.png
-19704.png
-50244.png
-24637.png
-54499.png
-58928.png
-14736.png
-29116.png
-25136.png
-04176.png
-11962.png
-16296.png
-07766.png
-14402.png
-62496.png
-11803.png
-47727.png
-17745.png
-12441.png
-69503.png
-41535.png
-17412.png
-48982.png
-38324.png
-49041.png
-42967.png
-08447.png
-40808.png
-27308.png
-50853.png
-00586.png
-40220.png
-25457.png
-14937.png
-04937.png
-67635.png
-05535.png
-64928.png
-20104.png
-64198.png
-15586.png
-24600.png
-69068.png
-61433.png
-15828.png
-13421.png
-44867.png
-37028.png
-13895.png
-62273.png
-01707.png
-11516.png
-65367.png
-57263.png
-16099.png
-08244.png
-26387.png
-23550.png
-04589.png
-22790.png
-35767.png
-67555.png
-54741.png
-17199.png
-63959.png
-54627.png
-62617.png
-35127.png
-66085.png
-32265.png
-65762.png
-46079.png
-45346.png
-20006.png
-44651.png
-20334.png
-18783.png
-62369.png
-36003.png
-22372.png
-15276.png
-40493.png
-41745.png
-69155.png
-42117.png
-12782.png
-39022.png
-34937.png
-46569.png
-49823.png
-17075.png
-43607.png
-68154.png
-57591.png
-01571.png
-00768.png
-00438.png
-22655.png
-65849.png
-24584.png
-57838.png
-18778.png
-16022.png
-28860.png
-46742.png
-40379.png
-55605.png
-10728.png
-13621.png
-65297.png
-57738.png
-58713.png
-12249.png
-15047.png
-59583.png
-59773.png
-46292.png
-47320.png
-26395.png
-37434.png
-20427.png
-69385.png
-67013.png
-63285.png
-08673.png
-36280.png
-69099.png
-45519.png
-07269.png
-52705.png
-35104.png
-38353.png
-22103.png
-00541.png
-57539.png
-32586.png
-57016.png
-32379.png
-23644.png
-50586.png
-08869.png
-18640.png
-27212.png
-18403.png
-21269.png
-05561.png
-31080.png
-65474.png
-67129.png
-43350.png
-45732.png
-68625.png
-50044.png
-61612.png
-65729.png
-00442.png
-69134.png
-27689.png
-16615.png
-20707.png
-13214.png
-32302.png
-45878.png
-52757.png
-57147.png
-19321.png
-05377.png
-56643.png
-67565.png
-60868.png
-69021.png
-69816.png
-10319.png
-20263.png
-52700.png
-40266.png
-42169.png
-40110.png
-19131.png
-33003.png
-68604.png
-28785.png
-18302.png
-38993.png
-62538.png
-48728.png
-16699.png
-23603.png
-47173.png
-13954.png
-60666.png
-08357.png
-05798.png
-01889.png
-30926.png
-45646.png
-04224.png
-64246.png
-38500.png
-69364.png
-37513.png
-40172.png
-41414.png
-15307.png
-60101.png
-12395.png
-02312.png
-37446.png
-53941.png
-57706.png
-28160.png
-05035.png
-24237.png
-03423.png
-63747.png
-11871.png
-22562.png
-19974.png
-63434.png
-68914.png
-44518.png
-24948.png
-51763.png
-64429.png
-51791.png
-49575.png
-26882.png
-58342.png
-21461.png
-49006.png
-15179.png
-09502.png
-28595.png
-14705.png
-18477.png
-65124.png
-20908.png
-46788.png
-49460.png
-16395.png
-02605.png
-55281.png
-28171.png
-30012.png
-27099.png
-01891.png
-16976.png
-24259.png
-12974.png
-24450.png
-30876.png
-08779.png
-14881.png
-10828.png
-02002.png
-68654.png
-29791.png
-35407.png
-01821.png
-42879.png
-63341.png
-04316.png
-49802.png
-53897.png
-69147.png
-57802.png
-05198.png
-66831.png
-54302.png
-68526.png
-30406.png
-62475.png
-22617.png
-60968.png
-57111.png
-40214.png
-18706.png
-11762.png
-14421.png
-16800.png
-61159.png
-28814.png
-04803.png
-37038.png
-17155.png
-21228.png
-13391.png
-00776.png
-48008.png
-54998.png
-42924.png
-01294.png
-65595.png
-36208.png
-30851.png
-31153.png
-13836.png
-09481.png
-52787.png
-54585.png
-37285.png
-30266.png
-06519.png
-31989.png
-15373.png
-47266.png
-31929.png
-04456.png
-48045.png
-22734.png
-21620.png
-20251.png
-61124.png
-50473.png
-40961.png
-44178.png
-07076.png
-34510.png
-09544.png
-44347.png
-48837.png
-03102.png
-61067.png
-31462.png
-16527.png
-18632.png
-29967.png
-30626.png
-65336.png
-22192.png
-09308.png
-08111.png
-66412.png
-31057.png
-27853.png
-61448.png
-43015.png
-07541.png
-01554.png
-18087.png
-56850.png
-55594.png
-33377.png
-31264.png
-59525.png
-12847.png
-17072.png
-69007.png
-57827.png
-24330.png
-29853.png
-21074.png
-00465.png
-02253.png
-23571.png
-67207.png
-01152.png
-42414.png
-33334.png
-37773.png
-50344.png
-67781.png
-12730.png
-21793.png
-33037.png
-66466.png
-20356.png
-30937.png
-36041.png
-41644.png
-41465.png
-03601.png
-65206.png
-03251.png
-61699.png
-63096.png
-62758.png
-02470.png
-67476.png
-62109.png
-06505.png
-50465.png
-53966.png
-57580.png
-57449.png
-53815.png
-32443.png
-24341.png
-60833.png
-30234.png
-39912.png
-00252.png
-27990.png
-04783.png
-18789.png
-10887.png
-33983.png
-13270.png
-01530.png
-02197.png
-52477.png
-20808.png
-03381.png
-31885.png
-16630.png
-22044.png
-62601.png
-54397.png
-49406.png
-37763.png
-41754.png
-10496.png
-68003.png
-37758.png
-13333.png
-44784.png
-40417.png
-15912.png
-55142.png
-49081.png
-56999.png
-34505.png
-57851.png
-24819.png
-61950.png
-24545.png
-21640.png
-22961.png
-37130.png
-03084.png
-24945.png
-68106.png
-40024.png
-47348.png
-14488.png
-67562.png
-20372.png
-20633.png
-50020.png
-07403.png
-02564.png
-41057.png
-05465.png
-19984.png
-34962.png
-25637.png
-62883.png
-54594.png
-25134.png
-49529.png
-33200.png
-65262.png
-11134.png
-67146.png
-60959.png
-67421.png
-69757.png
-14853.png
-37455.png
-53826.png
-15378.png
-24104.png
-17811.png
-51845.png
-11916.png
-18460.png
-26838.png
-18572.png
-27216.png
-19613.png
-13289.png
-45690.png
-04512.png
-05733.png
-05767.png
-44281.png
-46046.png
-58457.png
-18981.png
-37876.png
-57729.png
-55168.png
-25394.png
-64091.png
-17500.png
-55891.png
-62621.png
-18784.png
-69491.png
-59085.png
-10703.png
-28749.png
-29492.png
-37602.png
-43152.png
-26354.png
-28396.png
-58028.png
-39622.png
-64593.png
-64707.png
-13757.png
-08330.png
-49146.png
-02999.png
-30799.png
-40215.png
-37172.png
-55366.png
-10588.png
-34342.png
-56472.png
-24589.png
-12667.png
-38276.png
-04665.png
-37868.png
-23807.png
-54248.png
-58165.png
-07660.png
-04994.png
-28844.png
-21103.png
-51781.png
-08564.png
-50998.png
-34439.png
-25194.png
-38073.png
-28811.png
-15159.png
-09812.png
-31512.png
-33892.png
-39595.png
-44634.png
-14611.png
-47024.png
-16970.png
-65358.png
-30408.png
-50086.png
-06031.png
-44218.png
-03490.png
-52921.png
-58518.png
-43610.png
-38808.png
-52868.png
-00069.png
-05171.png
-48341.png
-52302.png
-16457.png
-57510.png
-41608.png
-10966.png
-64130.png
-48393.png
-57790.png
-69179.png
-17714.png
-68835.png
-58180.png
-45479.png
-15353.png
-44181.png
-16355.png
-02561.png
-33551.png
-00896.png
-15079.png
-46119.png
-32233.png
-03646.png
-50072.png
-59098.png
-59787.png
-41911.png
-42830.png
-14447.png
-01850.png
-24570.png
-42819.png
-39177.png
-48803.png
-33116.png
-59559.png
-24651.png
-52002.png
-16946.png
-33228.png
-43244.png
-64481.png
-10164.png
-42774.png
-68363.png
-61171.png
-18580.png
-63963.png
-27887.png
-25120.png
-20089.png
-46177.png
-13250.png
-67282.png
-02651.png
-38909.png
-49609.png
-47640.png
-07557.png
-17174.png
-66125.png
-63839.png
-42606.png
-47123.png
-50009.png
-47381.png
-41166.png
-33080.png
-05855.png
-00176.png
-58720.png
-37297.png
-64564.png
-08708.png
-13829.png
-26567.png
-44924.png
-30185.png
-14317.png
-32221.png
-24233.png
-22829.png
-52327.png
-55836.png
-00460.png
-69882.png
-39204.png
-44493.png
-55371.png
-31470.png
-41687.png
-24649.png
-08644.png
-42458.png
-11408.png
-52812.png
-66551.png
-03723.png
-34355.png
-11288.png
-14163.png
-40468.png
-15451.png
-63732.png
-58430.png
-11989.png
-04420.png
-11552.png
-42607.png
-64537.png
-45182.png
-39985.png
-19274.png
-37851.png
-17506.png
-21797.png
-60105.png
-43337.png
-07093.png
-53158.png
-02102.png
-10843.png
-19796.png
-61692.png
-35570.png
-43682.png
-09701.png
-43598.png
-27715.png
-57479.png
-53097.png
-57011.png
-17016.png
-57768.png
-47763.png
-03339.png
-41114.png
-41631.png
-25994.png
-67932.png
-09343.png
-36592.png
-42817.png
-32683.png
-66755.png
-16597.png
-10578.png
-10363.png
-18450.png
-53838.png
-53634.png
-44982.png
-56523.png
-25663.png
-62876.png
-39823.png
-37546.png
-28753.png
-13401.png
-50356.png
-39469.png
-65310.png
-37857.png
-61799.png
-68872.png
-39067.png
-05834.png
-07858.png
-69416.png
-67840.png
-43669.png
-30381.png
-51906.png
-50119.png
-17329.png
-23968.png
-03545.png
-61545.png
-00885.png
-27478.png
-41551.png
-13914.png
-58973.png
-57715.png
-69504.png
-01143.png
-30052.png
-25300.png
-14474.png
-48613.png
-03518.png
-61131.png
-38240.png
-18853.png
-50334.png
-16080.png
-12813.png
-18875.png
-20456.png
-03075.png
-22559.png
-25805.png
-02578.png
-41291.png
-13128.png
-05517.png
-39755.png
-43883.png
-65893.png
-41276.png
-41516.png
-11981.png
-19299.png
-05336.png
-65677.png
-41219.png
-52174.png
-60401.png
-22540.png
-14335.png
-67296.png
-37428.png
-66834.png
-54788.png
-42679.png
-29898.png
-19148.png
-69251.png
-53208.png
-34531.png
-04844.png
-32722.png
-23962.png
-18954.png
-63568.png
-20824.png
-47499.png
-69931.png
-20596.png
-46979.png
-64440.png
-46588.png
-62212.png
-68490.png
-08844.png
-37144.png
-51385.png
-48352.png
-15898.png
-02631.png
-22708.png
-28959.png
-40439.png
-39375.png
-56198.png
-27582.png
-20839.png
-23391.png
-12609.png
-13658.png
-26301.png
-67752.png
-43424.png
-37264.png
-07656.png
-11285.png
-05589.png
-63001.png
-67380.png
-61251.png
-18566.png
-05477.png
-24567.png
-41443.png
-50377.png
-06336.png
-00431.png
-10935.png
-04460.png
-39548.png
-02858.png
-63069.png
-10984.png
-07467.png
-19874.png
-55437.png
-52710.png
-63475.png
-28048.png
-04163.png
-54997.png
-09805.png
-06483.png
-41278.png
-52338.png
-15050.png
-08997.png
-42188.png
-61724.png
-24151.png
-53152.png
-31528.png
-01788.png
-60082.png
-32660.png
-28856.png
-39667.png
-21731.png
-55970.png
-04149.png
-28948.png
-56301.png
-48520.png
-42348.png
-48544.png
-54894.png
-24236.png
-02018.png
-63551.png
-61975.png
-40192.png
-37066.png
-52496.png
-31861.png
-46341.png
-56074.png
-33316.png
-65387.png
-18274.png
-23984.png
-59776.png
-38335.png
-66226.png
-67574.png
-24931.png
-32484.png
-33184.png
-51829.png
-07496.png
-14192.png
-12459.png
-10214.png
-69154.png
-01299.png
-34377.png
-57957.png
-33799.png
-23684.png
-60709.png
-20929.png
-58159.png
-49752.png
-37891.png
-37749.png
-19802.png
-12168.png
-08187.png
-17777.png
-33819.png
-28812.png
-32639.png
-56640.png
-55796.png
-47405.png
-56669.png
-68731.png
-09603.png
-23610.png
-11898.png
-23368.png
-12785.png
-49691.png
-09881.png
-02132.png
-55636.png
-55083.png
-56347.png
-26738.png
-54844.png
-49690.png
-64320.png
-52068.png
-15531.png
-57437.png
-49484.png
-65266.png
-17611.png
-58383.png
-63774.png
-52147.png
-59826.png
-34791.png
-52136.png
-41226.png
-07218.png
-47564.png
-02965.png
-56066.png
-31328.png
-20950.png
-29121.png
-52565.png
-07746.png
-43855.png
-25152.png
-59290.png
-58164.png
-44255.png
-04545.png
-23308.png
-34770.png
-22973.png
-08443.png
-46839.png
-08242.png
-15489.png
-46596.png
-64587.png
-33234.png
-20385.png
-12080.png
-19962.png
-30803.png
-14306.png
-02556.png
-59381.png
-23851.png
-36748.png
-10562.png
-30556.png
-33192.png
-48202.png
-28314.png
-53200.png
-07702.png
-65282.png
-34341.png
-05080.png
-09183.png
-23411.png
-09620.png
-52359.png
-67832.png
-44385.png
-52298.png
-01886.png
-62436.png
-41165.png
-08428.png
-26144.png
-20606.png
-15422.png
-42057.png
-64382.png
-32709.png
-44095.png
-07745.png
-53484.png
-10088.png
-15798.png
-61126.png
-18945.png
-48779.png
-11266.png
-16178.png
-20406.png
-01630.png
-65026.png
-24271.png
-35140.png
-13697.png
-39480.png
-57429.png
-34194.png
-20820.png
-63133.png
-19771.png
-67174.png
-65785.png
-40909.png
-34222.png
-21388.png
-37198.png
-07175.png
-15280.png
-23352.png
-64093.png
-26536.png
-68148.png
-39504.png
-42640.png
-47884.png
-43411.png
-25417.png
-55335.png
-47393.png
-47082.png
-43266.png
-02435.png
-66108.png
-63411.png
-49128.png
-45250.png
-57311.png
-23748.png
-55464.png
-11602.png
-14965.png
-05616.png
-38370.png
-24152.png
-51191.png
-61068.png
-16698.png
-36055.png
-29238.png
-54573.png
-49073.png
-23971.png
-62101.png
-23439.png
-65368.png
-54094.png
-04225.png
-48635.png
-52795.png
-34602.png
-03604.png
-58707.png
-04416.png
-21874.png
-51651.png
-67030.png
-47398.png
-49852.png
-52616.png
-68014.png
-01398.png
-04895.png
-51214.png
-47142.png
-68734.png
-20566.png
-64292.png
-57778.png
-43060.png
-58686.png
-62720.png
-42116.png
-51111.png
-05288.png
-18245.png
-12119.png
-20794.png
-31289.png
-24882.png
-04260.png
-38691.png
-60195.png
-25937.png
-35785.png
-34435.png
-13419.png
-09767.png
-17023.png
-14840.png
-02357.png
-22569.png
-12846.png
-51915.png
-55762.png
-45345.png
-29176.png
-66287.png
-53235.png
-21022.png
-16062.png
-15249.png
-61034.png
-06472.png
-69899.png
-18235.png
-54470.png
-34382.png
-50333.png
-64857.png
-46026.png
-18224.png
-64622.png
-60313.png
-29164.png
-04597.png
-15488.png
-08752.png
-39810.png
-13523.png
-44360.png
-05781.png
-59982.png
-09976.png
-37633.png
-22298.png
-59409.png
-02118.png
-32172.png
-54424.png
-52484.png
-27660.png
-61633.png
-50097.png
-12835.png
-54964.png
-18729.png
-66132.png
-50113.png
-34808.png
-16521.png
-35834.png
-51167.png
-22768.png
-11386.png
-61035.png
-01098.png
-65568.png
-17597.png
-02126.png
-04142.png
-28857.png
-56983.png
-54982.png
-01371.png
-07834.png
-09195.png
-00498.png
-56900.png
-30689.png
-53306.png
-54559.png
-24662.png
-50328.png
-00103.png
-24346.png
-49871.png
-24925.png
-14526.png
-57762.png
-24245.png
-28174.png
-32448.png
-00619.png
-61945.png
-69175.png
-09096.png
-28479.png
-01582.png
-61098.png
-26793.png
-01039.png
-09722.png
-62635.png
-25316.png
-25484.png
-60644.png
-21341.png
-45190.png
-35471.png
-67193.png
-02570.png
-49211.png
-11523.png
-19182.png
-54468.png
-49984.png
-09887.png
-64041.png
-65468.png
-60205.png
-19643.png
-67737.png
-17572.png
-20351.png
-11442.png
-56425.png
-62224.png
-62717.png
-68253.png
-18332.png
-64347.png
-21231.png
-47910.png
-11876.png
-48205.png
-64139.png
-12996.png
-42565.png
-09165.png
-63787.png
-48662.png
-12031.png
-10128.png
-08100.png
-06201.png
-17357.png
-34659.png
-52402.png
-59885.png
-24676.png
-18112.png
-68462.png
-42877.png
-05560.png
-03766.png
-30624.png
-32884.png
-57913.png
-67663.png
-47813.png
-04443.png
-67804.png
-04294.png
-62595.png
-41780.png
-66565.png
-04528.png
-10348.png
-59474.png
-69766.png
-45535.png
-56636.png
-00931.png
-09766.png
-09490.png
-24902.png
-06338.png
-36489.png
-31120.png
-56975.png
-32550.png
-41675.png
-51091.png
-49224.png
-31648.png
-02259.png
-56281.png
-58976.png
-30354.png
-51545.png
-59230.png
-42154.png
-66860.png
-25133.png
-02258.png
-60432.png
-00251.png
-58426.png
-20904.png
-24491.png
-63143.png
-39606.png
-41137.png
-02337.png
-23323.png
-54469.png
-38548.png
-02008.png
-01837.png
-49844.png
-06457.png
-02117.png
-12698.png
-45284.png
-11321.png
-00283.png
-18669.png
-42926.png
-33147.png
-38363.png
-06991.png
-57425.png
-05109.png
-29799.png
-12731.png
-64473.png
-36684.png
-13600.png
-46829.png
-45760.png
-52429.png
-22430.png
-33423.png
-66602.png
-61162.png
-61604.png
-20353.png
-36988.png
-28794.png
-14794.png
-52009.png
-66584.png
-42703.png
-36204.png
-47847.png
-23167.png
-37524.png
-60878.png
-24440.png
-25480.png
-51030.png
-16547.png
-24950.png
-30179.png
-36124.png
-38316.png
-26090.png
-60533.png
-51247.png
-51581.png
-04771.png
-63052.png
-60435.png
-49209.png
-29478.png
-65434.png
-05183.png
-26082.png
-50870.png
-56493.png
-44202.png
-55406.png
-58681.png
-17415.png
-00786.png
-68351.png
-58910.png
-08702.png
-67052.png
-00797.png
-47807.png
-53174.png
-64701.png
-32904.png
-24602.png
-32018.png
-55861.png
-21319.png
-22856.png
-56898.png
-56494.png
-09922.png
-01732.png
-27325.png
-01663.png
-66632.png
-17478.png
-21741.png
-55071.png
-29062.png
-21937.png
-22705.png
-69722.png
-35527.png
-12904.png
-46266.png
-10136.png
-45722.png
-20899.png
-34805.png
-17090.png
-41512.png
-18738.png
-66074.png
-05280.png
-60102.png
-41506.png
-65964.png
-33732.png
-19087.png
-37129.png
-40258.png
-57968.png
-44238.png
-61512.png
-36741.png
-32216.png
-21841.png
-26825.png
-06681.png
-24648.png
-55325.png
-12898.png
-11815.png
-63215.png
-42252.png
-19753.png
-68116.png
-01623.png
-07402.png
-20185.png
-41967.png
-17040.png
-13314.png
-42762.png
-47455.png
-24406.png
-53577.png
-19910.png
-18056.png
-67364.png
-50942.png
-33940.png
-05470.png
-38352.png
-58669.png
-16656.png
-39825.png
-61415.png
-44886.png
-58249.png
-25525.png
-44630.png
-49241.png
-46208.png
-48129.png
-45155.png
-20955.png
-62284.png
-44357.png
-64916.png
-15211.png
-33057.png
-62383.png
-67022.png
-66113.png
-62622.png
-09123.png
-12393.png
-17355.png
-55296.png
-32862.png
-59298.png
-16868.png
-09962.png
-10251.png
-51391.png
-16160.png
-67608.png
-49659.png
-25595.png
-63941.png
-38151.png
-46612.png
-12610.png
-42247.png
-44071.png
-33841.png
-20969.png
-19577.png
-27508.png
-32457.png
-67789.png
-56401.png
-07516.png
-28669.png
-15357.png
-18513.png
-21124.png
-33790.png
-60248.png
-04408.png
-32679.png
-45087.png
-35744.png
-01311.png
-65628.png
-68786.png
-11867.png
-02843.png
-54619.png
-19975.png
-18021.png
-57726.png
-36627.png
-60434.png
-45081.png
-38759.png
-52486.png
-29724.png
-67745.png
-44599.png
-56713.png
-08339.png
-61052.png
-27267.png
-17918.png
-16759.png
-51740.png
-04820.png
-68353.png
-44043.png
-47345.png
-28319.png
-01833.png
-62752.png
-13230.png
-69718.png
-08645.png
-32191.png
-42583.png
-52482.png
-60258.png
-66606.png
-63917.png
-51513.png
-38910.png
-35599.png
-31896.png
-61739.png
-61870.png
-21326.png
-29928.png
-46575.png
-30176.png
-12636.png
-67866.png
-31553.png
-46040.png
-65674.png
-47616.png
-52124.png
-05735.png
-62282.png
-24399.png
-06330.png
-35480.png
-28021.png
-12421.png
-57168.png
-11734.png
-27232.png
-12781.png
-50049.png
-33408.png
-22226.png
-46023.png
-43246.png
-46785.png
-14185.png
-26213.png
-10301.png
-49315.png
-45207.png
-64249.png
-30952.png
-41283.png
-32687.png
-33422.png
-24274.png
-57416.png
-65993.png
-46474.png
-13169.png
-46184.png
-38463.png
-35665.png
-37491.png
-13027.png
-50480.png
-24643.png
-42406.png
-56339.png
-19261.png
-44724.png
-64746.png
-68461.png
-23642.png
-53710.png
-28570.png
-50976.png
-05537.png
-60688.png
-58068.png
-18527.png
-21176.png
-28603.png
-09128.png
-17630.png
-19005.png
-43523.png
-02079.png
-22013.png
-05007.png
-54328.png
-03384.png
-31324.png
-51172.png
-64658.png
-66390.png
-20638.png
-37374.png
-33984.png
-65050.png
-49485.png
-40229.png
-09388.png
-44329.png
-48872.png
-63856.png
-33771.png
-01063.png
-67278.png
-37004.png
-12133.png
-12399.png
-42929.png
-48813.png
-50445.png
-48396.png
-26196.png
-24601.png
-04238.png
-33744.png
-52927.png
-25779.png
-44544.png
-09764.png
-30526.png
-33592.png
-26021.png
-05057.png
-36274.png
-32381.png
-44371.png
-57167.png
-12968.png
-05201.png
-09807.png
-12363.png
-66581.png
-04978.png
-11366.png
-58620.png
-60559.png
-54391.png
-12065.png
-20911.png
-24220.png
-65965.png
-21760.png
-45588.png
-64551.png
-50388.png
-34234.png
-02166.png
-36075.png
-42871.png
-25691.png
-06696.png
-03531.png
-32617.png
-40011.png
-63814.png
-48686.png
-62373.png
-61228.png
-22778.png
-29263.png
-69350.png
-56337.png
-26290.png
-62381.png
-15286.png
-59934.png
-52224.png
-27121.png
-62469.png
-57998.png
-19175.png
-58389.png
-04538.png
-39863.png
-49508.png
-23135.png
-09832.png
-31307.png
-42575.png
-69880.png
-16831.png
-49675.png
-58264.png
-50775.png
-47539.png
-43016.png
-02458.png
-16280.png
-31195.png
-42027.png
-15164.png
-32966.png
-49440.png
-19760.png
-01713.png
-65650.png
-67511.png
-41441.png
-01987.png
-42591.png
-01404.png
-46437.png
-60391.png
-17002.png
-13057.png
-07240.png
-03408.png
-54861.png
-15618.png
-22620.png
-18347.png
-51540.png
-69561.png
-27697.png
-17757.png
-09043.png
-08405.png
-05116.png
-53280.png
-41242.png
-24620.png
-17263.png
-49095.png
-21198.png
-29398.png
-02446.png
-58340.png
-44449.png
-48624.png
-18152.png
-49142.png
-49372.png
-49459.png
-23726.png
-60084.png
-61779.png
-53582.png
-13979.png
-12894.png
-06091.png
-53552.png
-45686.png
-01124.png
-62345.png
-19075.png
-47835.png
-46234.png
-45818.png
-60143.png
-65306.png
-56742.png
-28008.png
-06241.png
-68222.png
-20915.png
-39598.png
-09193.png
-55764.png
-31923.png
-29139.png
-34308.png
-04757.png
-34705.png
-37248.png
-18372.png
-33818.png
-45959.png
-53345.png
-49425.png
-29884.png
-07572.png
-67739.png
-39593.png
-55684.png
-42618.png
-08805.png
-16520.png
-49120.png
-38056.png
-47121.png
-14858.png
-33695.png
-42897.png
-34440.png
-54269.png
-45916.png
-63706.png
-60629.png
-18906.png
-31283.png
-07566.png
-20639.png
-67124.png
-35854.png
-63385.png
-20111.png
-32013.png
-40588.png
-20181.png
-57298.png
-43421.png
-13042.png
-68242.png
-21016.png
-00454.png
-15970.png
-11750.png
-21709.png
-51823.png
-29314.png
-20557.png
-19424.png
-19167.png
-37024.png
-17107.png
-26590.png
-16152.png
-47498.png
-62090.png
-06259.png
-19104.png
-65815.png
-32238.png
-19601.png
-56901.png
-31218.png
-17665.png
-47269.png
-21619.png
-36343.png
-03605.png
-66496.png
-44318.png
-47001.png
-10008.png
-51764.png
-18542.png
-47388.png
-58651.png
-35999.png
-45231.png
-65170.png
-01673.png
-31232.png
-39693.png
-52854.png
-46271.png
-01181.png
-50681.png
-24879.png
-63719.png
-61994.png
-35504.png
-50326.png
-49100.png
-42471.png
-68368.png
-40350.png
-06516.png
-01277.png
-57906.png
-51229.png
-14460.png
-60047.png
-53212.png
-13740.png
-09472.png
-50005.png
-45128.png
-07447.png
-41427.png
-18227.png
-26462.png
-16334.png
-20977.png
-22294.png
-56554.png
-47176.png
-52197.png
-14621.png
-45080.png
-12081.png
-55165.png
-34001.png
-68527.png
-08479.png
-20862.png
-61385.png
-35078.png
-03212.png
-43645.png
-24086.png
-35794.png
-34422.png
-55546.png
-41018.png
-13273.png
-43378.png
-17712.png
-63733.png
-36889.png
-38171.png
-67544.png
-32563.png
-37629.png
-55099.png
-38932.png
-14670.png
-19043.png
-24530.png
-10418.png
-37040.png
-37074.png
-42317.png
-51336.png
-46774.png
-43767.png
-56101.png
-57002.png
-12588.png
-49452.png
-40431.png
-20424.png
-61703.png
-23445.png
-48005.png
-66397.png
-33497.png
-52011.png
-40442.png
-65948.png
-34027.png
-55088.png
-10646.png
-54476.png
-25948.png
-64617.png
-60351.png
-12495.png
-57939.png
-25415.png
-17192.png
-42233.png
-52405.png
-33746.png
-52631.png
-65341.png
-51748.png
-27801.png
-25091.png
-51054.png
-03308.png
-37027.png
-38389.png
-08210.png
-61417.png
-17660.png
-52310.png
-08735.png
-16595.png
-39919.png
-67231.png
-44741.png
-46111.png
-33396.png
-21436.png
-37348.png
-08261.png
-56070.png
-45724.png
-26745.png
-09297.png
-08875.png
-64675.png
-40864.png
-14098.png
-18938.png
-66867.png
-20620.png
-49957.png
-35496.png
-50276.png
-68658.png
-20748.png
-32187.png
-16005.png
-09601.png
-14836.png
-64613.png
-29219.png
-66173.png
-57813.png
-69118.png
-43228.png
-49227.png
-35924.png
-69254.png
-60564.png
-60098.png
-50805.png
-60150.png
-23575.png
-14492.png
-59849.png
-60428.png
-20935.png
-64415.png
-31431.png
-48317.png
-20310.png
-18173.png
-62315.png
-61169.png
-03889.png
-44395.png
-60512.png
-13739.png
-29159.png
-09151.png
-38226.png
-34580.png
-33543.png
-14699.png
-19443.png
-27236.png
-36685.png
-22761.png
-60879.png
-30090.png
-43905.png
-64590.png
-48425.png
-67390.png
-49812.png
-10356.png
-28767.png
-46928.png
-68531.png
-50246.png
-56895.png
-30184.png
-54683.png
-32481.png
-13309.png
-55690.png
-55106.png
-17260.png
-04942.png
-26784.png
-39872.png
-43529.png
-29414.png
-24991.png
-31127.png
-59360.png
-31696.png
-13455.png
-40252.png
-16967.png
-36740.png
-05144.png
-09856.png
-01230.png
-34389.png
-52797.png
-29905.png
-34471.png
-31369.png
-14575.png
-34718.png
-30705.png
-58656.png
-42806.png
-54832.png
-20845.png
-16975.png
-55303.png
-28954.png
-19486.png
-07236.png
-69671.png
-37261.png
-60446.png
-66386.png
-69286.png
-40931.png
-67601.png
-05595.png
-59134.png
-49623.png
-65502.png
-00101.png
-57555.png
-32756.png
-26944.png
-23541.png
-18436.png
-44081.png
-19033.png
-67609.png
-59852.png
-57306.png
-65644.png
-47642.png
-07610.png
-53350.png
-46667.png
-57524.png
-15708.png
-66864.png
-53352.png
-11469.png
-36473.png
-39915.png
-33648.png
-23465.png
-30283.png
-05013.png
-45709.png
-52750.png
-58843.png
-41941.png
-25211.png
-05683.png
-33670.png
-51002.png
-67314.png
-47420.png
-21446.png
-66069.png
-30819.png
-34200.png
-61437.png
-07094.png
-25456.png
-13162.png
-65966.png
-53483.png
-39844.png
-08351.png
-19059.png
-49641.png
-31124.png
-34920.png
-02407.png
-22413.png
-20910.png
-25737.png
-30779.png
-24866.png
-22703.png
-32174.png
-39832.png
-60905.png
-26045.png
-19528.png
-55024.png
-69914.png
-39477.png
-55241.png
-56123.png
-25286.png
-08323.png
-43356.png
-68098.png
-00749.png
-50397.png
-21498.png
-37263.png
-45990.png
-05590.png
-12007.png
-17740.png
-18243.png
-41359.png
-03370.png
-09489.png
-20381.png
-51382.png
-68323.png
-39304.png
-50941.png
-02341.png
-31920.png
-27377.png
-08493.png
-56679.png
-27046.png
-05876.png
-06690.png
-43573.png
-12196.png
-46728.png
-60341.png
-23727.png
-49560.png
-47871.png
-48211.png
-67644.png
-43722.png
-22902.png
-09979.png
-06757.png
-23890.png
-00388.png
-55228.png
-21794.png
-57297.png
-45857.png
-39642.png
-45356.png
-23527.png
-42729.png
-38895.png
-37372.png
-07262.png
-32259.png
-08347.png
-14015.png
-45549.png
-45092.png
-64674.png
-55191.png
-30599.png
-28801.png
-60954.png
-53265.png
-40666.png
-68361.png
-27824.png
-55209.png
-10449.png
-34903.png
-23948.png
-29607.png
-58286.png
-14925.png
-15873.png
-46076.png
-40825.png
-07112.png
-34778.png
-59092.png
-15132.png
-21685.png
-64452.png
-46344.png
-01661.png
-20501.png
-66657.png
-06082.png
-46541.png
-15259.png
-65882.png
-35015.png
-28177.png
-36065.png
-44359.png
-04311.png
-08306.png
-37335.png
-36869.png
-00243.png
-49121.png
-25847.png
-30663.png
-17326.png
-65833.png
-37828.png
-24644.png
-44011.png
-59554.png
-09692.png
-65827.png
-67393.png
-21475.png
-61890.png
-69701.png
-59232.png
-33158.png
-34732.png
-34890.png
-43343.png
-42767.png
-45906.png
-49341.png
-63417.png
-67546.png
-41134.png
-36583.png
-65865.png
-30866.png
-31345.png
-16819.png
-04188.png
-12056.png
-69012.png
-45655.png
-63851.png
-42223.png
-45274.png
-30210.png
-60746.png
-10884.png
-47761.png
-67492.png
-35875.png
-38451.png
-55283.png
-54738.png
-19260.png
-64625.png
-28454.png
-49737.png
-51497.png
-61100.png
-08940.png
-61495.png
-27583.png
-45911.png
-48525.png
-09950.png
-11899.png
-29380.png
-53279.png
-14485.png
-43544.png
-65891.png
-59860.png
-58585.png
-12505.png
-22514.png
-09562.png
-53167.png
-24582.png
-02538.png
-05789.png
-45661.png
-08742.png
-43663.png
-16702.png
-29345.png
-57238.png
-36312.png
-63329.png
-25468.png
-35774.png
-38620.png
-13612.png
-16642.png
-57582.png
-34420.png
-45484.png
-26972.png
-03915.png
-25508.png
-25011.png
-58106.png
-08623.png
-34682.png
-55554.png
-67223.png
-37935.png
-23012.png
-18137.png
-57714.png
-48229.png
-60245.png
-37402.png
-19811.png
-01919.png
-30853.png
-62770.png
-38497.png
-56416.png
-65371.png
-41904.png
-65925.png
-58802.png
-01717.png
-03402.png
-45923.png
-27020.png
-07078.png
-50306.png
-69092.png
-34563.png
-69059.png
-02633.png
-47581.png
-06615.png
-13840.png
-09813.png
-14834.png
-56968.png
-04672.png
-35649.png
-50801.png
-43832.png
-01414.png
-15554.png
-29659.png
-50350.png
-68084.png
-34249.png
-55821.png
-55556.png
-35846.png
-53646.png
-18999.png
-32482.png
-66919.png
-00944.png
-38853.png
-22156.png
-07821.png
-16736.png
-15846.png
-65516.png
-24494.png
-32781.png
-35444.png
-37327.png
-42467.png
-02663.png
-09744.png
-14364.png
-31841.png
-51518.png
-16459.png
-06570.png
-41542.png
-58446.png
-56581.png
-16275.png
-52210.png
-52054.png
-53055.png
-47130.png
-04281.png
-52142.png
-48677.png
-52597.png
-15202.png
-13708.png
-67485.png
-67788.png
-42493.png
-17628.png
-69485.png
-63147.png
-47086.png
-66075.png
-48086.png
-18666.png
-47673.png
-06062.png
-14994.png
-45917.png
-03224.png
-09986.png
-60978.png
-61020.png
-55018.png
-57365.png
-25684.png
-29675.png
-07357.png
-53191.png
-47208.png
-51449.png
-41419.png
-42004.png
-56684.png
-56125.png
-11927.png
-33802.png
-41192.png
-62128.png
-14743.png
-49464.png
-36927.png
-46232.png
-68665.png
-26516.png
-34807.png
-10349.png
-19366.png
-64232.png
-58416.png
-34847.png
-11132.png
-41730.png
-41089.png
-19622.png
-23573.png
-38697.png
-14602.png
-55557.png
-29493.png
-54661.png
-22349.png
-12574.png
-30998.png
-19200.png
-09382.png
-13831.png
-33745.png
-30002.png
-11879.png
-57772.png
-55146.png
-20765.png
-58818.png
-18349.png
-18747.png
-61398.png
-29222.png
-58321.png
-17590.png
-55679.png
-20495.png
-11241.png
-48735.png
-60282.png
-09516.png
-48861.png
-19806.png
-08813.png
-11808.png
-58217.png
-40732.png
-07196.png
-55387.png
-54562.png
-14396.png
-64559.png
-27198.png
-41450.png
-30437.png
-07681.png
-66859.png
-00233.png
-53585.png
-69946.png
-65543.png
-47329.png
-20388.png
-01397.png
-58671.png
-50436.png
-18147.png
-27916.png
-38179.png
-15513.png
-19829.png
-45448.png
-57181.png
-13081.png
-26456.png
-21056.png
-07303.png
-16551.png
-46903.png
-41200.png
-26313.png
-10113.png
-39520.png
-28906.png
-45176.png
-38085.png
-25167.png
-07440.png
-67771.png
-37376.png
-66879.png
-50850.png
-45830.png
-06020.png
-33948.png
-44725.png
-62251.png
-35086.png
-63098.png
-44397.png
-00624.png
-46605.png
-47531.png
-53328.png
-55518.png
-50447.png
-10340.png
-58765.png
-06098.png
-56039.png
-48084.png
-53183.png
-48263.png
-02903.png
-44760.png
-22991.png
-27618.png
-68494.png
-67402.png
-66820.png
-63024.png
-16842.png
-02140.png
-12924.png
-20452.png
-11468.png
-06113.png
-16309.png
-57230.png
-56826.png
-31802.png
-25088.png
-10628.png
-10789.png
-35722.png
-64174.png
-19610.png
-00916.png
-37343.png
-64969.png
-41711.png
-54646.png
-53903.png
-26038.png
-63601.png
-00846.png
-41175.png
-24188.png
-11065.png
-37288.png
-17277.png
-62878.png
-17832.png
-35459.png
-34151.png
-55735.png
-16607.png
-23762.png
-12613.png
-14629.png
-24466.png
-24807.png
-63186.png
-34630.png
-24170.png
-60789.png
-18929.png
-59688.png
-40835.png
-47596.png
-58788.png
-01598.png
-09300.png
-08902.png
-50141.png
-13153.png
-38213.png
-38395.png
-33760.png
-00728.png
-40063.png
-55648.png
-09838.png
-32048.png
-24612.png
-41250.png
-56800.png
-00923.png
-31632.png
-55729.png
-55976.png
-43586.png
-16256.png
-14105.png
-49665.png
-36066.png
-29264.png
-45824.png
-37018.png
-33081.png
-55804.png
-31490.png
-46853.png
-42527.png
-53971.png
-14642.png
-68969.png
-46982.png
-36213.png
-56059.png
-41202.png
-53656.png
-27856.png
-16051.png
-31285.png
-25864.png
-29998.png
-67885.png
-66601.png
-27889.png
-42336.png
-43798.png
-39127.png
-02572.png
-34056.png
-12055.png
-54977.png
-13092.png
-26655.png
-59836.png
-33804.png
-28186.png
-32095.png
-67439.png
-25108.png
-30013.png
-53752.png
-48019.png
-69843.png
-24688.png
-64711.png
-10293.png
-52963.png
-45172.png
-65807.png
-27909.png
-07853.png
-09384.png
-36530.png
-52215.png
-47316.png
-19365.png
-04852.png
-37680.png
-35018.png
-03080.png
-65828.png
-41992.png
-45736.png
-50380.png
-56736.png
-68083.png
-34901.png
-14766.png
-20652.png
-42685.png
-64986.png
-59928.png
-60510.png
-15195.png
-10410.png
-16495.png
-14282.png
-44681.png
-05955.png
-23837.png
-67035.png
-61288.png
-23701.png
-46702.png
-06600.png
-32149.png
-35251.png
-21951.png
-39261.png
-15405.png
-12118.png
-29846.png
-09124.png
-37354.png
-62341.png
-11870.png
-06689.png
-63939.png
-02173.png
-43325.png
-43099.png
-40538.png
-53551.png
-60823.png
-68477.png
-42274.png
-69373.png
-48379.png
-08739.png
-20537.png
-08394.png
-27494.png
-22577.png
-26861.png
-54382.png
-16081.png
-24318.png
-17687.png
-57086.png
-12907.png
-11475.png
-12295.png
-42107.png
-38536.png
-58551.png
-05510.png
-13584.png
-44020.png
-61514.png
-13469.png
-45577.png
-66491.png
-19574.png
-68113.png
-16344.png
-35631.png
-59328.png
-08319.png
-07776.png
-48187.png
-43105.png
-43593.png
-26039.png
-35986.png
-02591.png
-27980.png
-33563.png
-49136.png
-11185.png
-56209.png
-49195.png
-01722.png
-38332.png
-60476.png
-32714.png
-07012.png
-68482.png
-19315.png
-68783.png
-36996.png
-35115.png
-59252.png
-46032.png
-03293.png
-57136.png
-30476.png
-14600.png
-29560.png
-15680.png
-41232.png
-62122.png
-29145.png
-01159.png
-15424.png
-24133.png
-63383.png
-63951.png
-57063.png
-49250.png
-45801.png
-10689.png
-68753.png
-39692.png
-19288.png
-14871.png
-17540.png
-42094.png
-64643.png
-20647.png
-03607.png
-53708.png
-15919.png
-49544.png
-56998.png
-39848.png
-69347.png
-56112.png
-63089.png
-36952.png
-65073.png
-34574.png
-42783.png
-13426.png
-44056.png
-14676.png
-23205.png
-63282.png
-52891.png
-35195.png
-41021.png
-49207.png
-51978.png
-18705.png
-19153.png
-15829.png
-06373.png
-35871.png
-67215.png
-67530.png
-50193.png
-13530.png
-41180.png
-41864.png
-21860.png
-49046.png
-25648.png
-21857.png
-31521.png
-03104.png
-33720.png
-35449.png
-06153.png
-19586.png
-50819.png
-25548.png
-49069.png
-56495.png
-01898.png
-26610.png
-53877.png
-47880.png
-34428.png
-45244.png
-52492.png
-25649.png
-52180.png
-39675.png
-02880.png
-16512.png
-59784.png
-08038.png
-22731.png
-33005.png
-40528.png
-56317.png
-45774.png
-61997.png
-53508.png
-41936.png
-43568.png
-49980.png
-01838.png
-41893.png
-47408.png
-35454.png
-63452.png
-62836.png
-00852.png
-45445.png
-14781.png
-20482.png
-54924.png
-23078.png
-35825.png
-45952.png
-01061.png
-32707.png
-56561.png
-54060.png
-09849.png
-12796.png
-30000.png
-15074.png
-55300.png
-24419.png
-21609.png
-12122.png
-07061.png
-68845.png
-29616.png
-08081.png
-26656.png
-31913.png
-18760.png
-44694.png
-07439.png
-21207.png
-08667.png
-58011.png
-36935.png
-51865.png
-55715.png
-02796.png
-27551.png
-24208.png
-03265.png
-30831.png
-45243.png
-26690.png
-31650.png
-33550.png
-52943.png
-38869.png
-54359.png
-27777.png
-24862.png
-11457.png
-65038.png
-47406.png
-32126.png
-18161.png
-43342.png
-23369.png
-63942.png
-65831.png
-09146.png
-69738.png
-04190.png
-45790.png
-40830.png
-36762.png
-16790.png
-54265.png
-55045.png
-14477.png
-69721.png
-40114.png
-03817.png
-61419.png
-06890.png
-51714.png
-01615.png
-69999.png
-43477.png
-59349.png
-28978.png
-12950.png
-04075.png
-39298.png
-38325.png
-51843.png
-67908.png
-66501.png
-47589.png
-55361.png
-52637.png
-37777.png
-66677.png
-42133.png
-56302.png
-42098.png
-42787.png
-19508.png
-17488.png
-47379.png
-24998.png
-40204.png
-55264.png
-07865.png
-32725.png
-38374.png
-15273.png
-41121.png
-12774.png
-62188.png
-48718.png
-00364.png
-42775.png
-65219.png
-02104.png
-32867.png
-37856.png
-67808.png
-17979.png
-46362.png
-40127.png
-12194.png
-67826.png
-59724.png
-47431.png
-34463.png
-56034.png
-53146.png
-22599.png
-26532.png
-33342.png
-34980.png
-68225.png
-26486.png
-23611.png
-19042.png
-36585.png
-16366.png
-52306.png
-53957.png
-63473.png
-26162.png
-46195.png
-16578.png
-34315.png
-69472.png
-24594.png
-61908.png
-61106.png
-17909.png
-35867.png
-07685.png
-47132.png
-24656.png
-58949.png
-13976.png
-06693.png
-02715.png
-28309.png
-33593.png
-04781.png
-58062.png
-68410.png
-43012.png
-69901.png
-54301.png
-04167.png
-18884.png
-34097.png
-20560.png
-52162.png
-34713.png
-48716.png
-11868.png
-64958.png
-01597.png
-38981.png
-02615.png
-28240.png
-47965.png
-65453.png
-48482.png
-29076.png
-01369.png
-61355.png
-68853.png
-02923.png
-48000.png
-07652.png
-46613.png
-16802.png
-48267.png
-47181.png
-28452.png
-16606.png
-31950.png
-34304.png
-20098.png
-10047.png
-23226.png
-24287.png
-53805.png
-66844.png
-22633.png
-54015.png
-40276.png
-34702.png
-07725.png
-10099.png
-60513.png
-45265.png
-24112.png
-34658.png
-26719.png
-57140.png
-57231.png
-20471.png
-55462.png
-24018.png
-48652.png
-48244.png
-15675.png
-49138.png
-00666.png
-58870.png
-59986.png
-47635.png
-69608.png
-38454.png
-54871.png
-13741.png
-17147.png
-34929.png
-41422.png
-08234.png
-59843.png
-49499.png
-33149.png
-24716.png
-67672.png
-03613.png
-28163.png
-15027.png
-40644.png
-39996.png
-56086.png
-01452.png
-61316.png
-13344.png
-51394.png
-69168.png
-19152.png
-50865.png
-26792.png
-43582.png
-09486.png
-00025.png
-41272.png
-04334.png
-24217.png
-54009.png
-16004.png
-05756.png
-05611.png
-55917.png
-58237.png
-22966.png
-35124.png
-66330.png
-61507.png
-35346.png
-42962.png
-11140.png
-31615.png
-33962.png
-10996.png
-52153.png
-50949.png
-24040.png
-38419.png
-23992.png
-24294.png
-19993.png
-20413.png
-39797.png
-53321.png
-15310.png
-35036.png
-69719.png
-65332.png
-27275.png
-13375.png
-29805.png
-07750.png
-00187.png
-32528.png
-10591.png
-30362.png
-07451.png
-53391.png
-62697.png
-32308.png
-12720.png
-47982.png
-11162.png
-07518.png
-68315.png
-54792.png
-47618.png
-40208.png
-65327.png
-12366.png
-69004.png
-25575.png
-10344.png
-11188.png
-31735.png
-50016.png
-57746.png
-31109.png
-17059.png
-18469.png
-03447.png
-17221.png
-52878.png
-33437.png
-67418.png
-26551.png
-54282.png
-51255.png
-50978.png
-08965.png
-55908.png
-13864.png
-57505.png
-36149.png
-16329.png
-20010.png
-63877.png
-62219.png
-33542.png
-18283.png
-28939.png
-59402.png
-50084.png
-19127.png
-60040.png
-00191.png
-00152.png
-59044.png
-66163.png
-68370.png
-38010.png
-21786.png
-20052.png
-48063.png
-59739.png
-51775.png
-36341.png
-08659.png
-24974.png
-04628.png
-39959.png
-23162.png
-54185.png
-68288.png
-64321.png
-46317.png
-15641.png
-51347.png
-46275.png
-25939.png
-32001.png
-29852.png
-43051.png
-14931.png
-02366.png
-33959.png
-24054.png
-46322.png
-60658.png
-30278.png
-52280.png
-47141.png
-66411.png
-11234.png
-45922.png
-47875.png
-54085.png
-47027.png
-47017.png
-32160.png
-26159.png
-32803.png
-65242.png
-32850.png
-50429.png
-67690.png
-25205.png
-34801.png
-53905.png
-53179.png
-41225.png
-11611.png
-22992.png
-45097.png
-54306.png
-02124.png
-56781.png
-05120.png
-37232.png
-22795.png
-60953.png
-34529.png
-06561.png
-16938.png
-53995.png
-46783.png
-27587.png
-64549.png
-26822.png
-45273.png
-22311.png
-32452.png
-51431.png
-25483.png
-69667.png
-59091.png
-30777.png
-19071.png
-06707.png
-19085.png
-04714.png
-34899.png
-33114.png
-46525.png
-68143.png
-06133.png
-45510.png
-65967.png
-50527.png
-13808.png
-35350.png
-07735.png
-14807.png
-04271.png
-63896.png
-08432.png
-62882.png
-60973.png
-28463.png
-67357.png
-48385.png
-46700.png
-16144.png
-16465.png
-12412.png
-24654.png
-01493.png
-38311.png
-19372.png
-68676.png
-14825.png
-17868.png
-37941.png
-09815.png
-26936.png
-59951.png
-37673.png
-62912.png
-17069.png
-03298.png
-18204.png
-65167.png
-40668.png
-12067.png
-38701.png
-16884.png
-05530.png
-18388.png
-22047.png
-30895.png
-34486.png
-40449.png
-57380.png
-45297.png
-13028.png
-25477.png
-49249.png
-17707.png
-15262.png
-05664.png
-32153.png
-56247.png
-31301.png
-50364.png
-41983.png
-29861.png
-31870.png
-66599.png
-23310.png
-33354.png
-54512.png
-68646.png
-27098.png
-68591.png
-67055.png
-62458.png
-54903.png
-08771.png
-26914.png
-17212.png
-45855.png
-28419.png
-62677.png
-33272.png
-02889.png
-00813.png
-36787.png
-34782.png
-44747.png
-46888.png
-08674.png
-48078.png
-13304.png
-05637.png
-65403.png
-54091.png
-14778.png
-31362.png
-11774.png
-08621.png
-50564.png
-33244.png
-19842.png
-20804.png
-00698.png
-43820.png
-61256.png
-52589.png
-01033.png
-43664.png
-35869.png
-01087.png
-66855.png
-59795.png
-45636.png
-69338.png
-06703.png
-24934.png
-61177.png
-33668.png
-65830.png
-49644.png
-45706.png
-47240.png
-19266.png
-24232.png
-23300.png
-36038.png
-25984.png
-69371.png
-35566.png
-04635.png
-21942.png
-45438.png
-37817.png
-58823.png
-35180.png
-28621.png
-35059.png
-49064.png
-48912.png
-05906.png
-68507.png
-35738.png
-65076.png
-48660.png
-17758.png
-44965.png
-30108.png
-55794.png
-30130.png
-58037.png
-33254.png
-21067.png
-65278.png
-17014.png
-36430.png
-05886.png
-01164.png
-21561.png
-30519.png
-16185.png
-47767.png
-13998.png
-49469.png
-60799.png
-31978.png
-23209.png
-29036.png
-15727.png
-07267.png
-68580.png
-58673.png
-30740.png
-05162.png
-22700.png
-52789.png
-22317.png
-04387.png
-63499.png
-62061.png
-02703.png
-50261.png
-08214.png
-18828.png
-40580.png
-67354.png
-29807.png
-31341.png
-24498.png
-38800.png
-09228.png
-06300.png
-58356.png
-27892.png
-35173.png
-61300.png
-03938.png
-69199.png
-35227.png
-06299.png
-62791.png
-28793.png
-19459.png
-61760.png
-18417.png
-14687.png
-53546.png
-38740.png
-10473.png
-38980.png
-32140.png
-58893.png
-29262.png
-55038.png
-54936.png
-61464.png
-26626.png
-49466.png
-67797.png
-14284.png
-52730.png
-23694.png
-13659.png
-06593.png
-20956.png
-10761.png
-41484.png
-21960.png
-36249.png
-68314.png
-08135.png
-03360.png
-06791.png
-33122.png
-29847.png
-03524.png
-68647.png
-45427.png
-67191.png
-37512.png
-06162.png
-44182.png
-48013.png
-58691.png
-56356.png
-64800.png
-03040.png
-57925.png
-28977.png
-11707.png
-31933.png
-18918.png
-28792.png
-31005.png
-60229.png
-51284.png
-21698.png
-59874.png
-15000.png
-35852.png
-39774.png
-01450.png
-58299.png
-43506.png
-52427.png
-02151.png
-22980.png
-36010.png
-26337.png
-42590.png
-33476.png
-38838.png
-07128.png
-06661.png
-19764.png
-60425.png
-25953.png
-68178.png
-20476.png
-00287.png
-02752.png
-28000.png
-53591.png
-18000.png
-25925.png
-23908.png
-37206.png
-53660.png
-04782.png
-25959.png
-68595.png
-23146.png
-41157.png
-34193.png
-43811.png
-69313.png
-06853.png
-38708.png
-17409.png
-27421.png
-41038.png
-55176.png
-13380.png
-22752.png
-14070.png
-15188.png
-47227.png
-68964.png
-21116.png
-09010.png
-17287.png
-29442.png
-23841.png
-49027.png
-24979.png
-43786.png
-36207.png
-08231.png
-45228.png
-57383.png
-21990.png
-53512.png
-28655.png
-01264.png
-58805.png
-18431.png
-02566.png
-28373.png
-07161.png
-67435.png
-55009.png
-02257.png
-36555.png
-37827.png
-20445.png
-15497.png
-65052.png
-06191.png
-26279.png
-05986.png
-54390.png
-24624.png
-00402.png
-25980.png
-32874.png
-69664.png
-68911.png
-53203.png
-29940.png
-10982.png
-47567.png
-67786.png
-20507.png
-27455.png
-14159.png
-18678.png
-09359.png
-55639.png
-46364.png
-21209.png
-61235.png
-49457.png
-17613.png
-43926.png
-61386.png
-20462.png
-12711.png
-32462.png
-58366.png
-13189.png
-64478.png
-05098.png
-55994.png
-26505.png
-59615.png
-66379.png
-24965.png
-49009.png
-26442.png
-68576.png
-61465.png
-03492.png
-12797.png
-63117.png
-47888.png
-42145.png
-00022.png
-19171.png
-31590.png
-61644.png
-36064.png
-17787.png
-15060.png
-55677.png
-20681.png
-65140.png
-00854.png
-15069.png
-60643.png
-25677.png
-36417.png
-36056.png
-08988.png
-49082.png
-40585.png
-38950.png
-02355.png
-13141.png
-43818.png
-29768.png
-40828.png
-39998.png
-31014.png
-69713.png
-02940.png
-24422.png
-58665.png
-35253.png
-28294.png
-43049.png
-21601.png
-24312.png
-56908.png
-29676.png
-34113.png
-13122.png
-15106.png
-16082.png
-41464.png
-68650.png
-41362.png
-67226.png
-28301.png
-58506.png
-44078.png
-42312.png
-04664.png
-14987.png
-54829.png
-19904.png
-54672.png
-07583.png
-06481.png
-33125.png
-64173.png
-17646.png
-39538.png
-32086.png
-19841.png
-57724.png
-27269.png
-04532.png
-15114.png
-59740.png
-44108.png
-46381.png
-42519.png
-66057.png
-07143.png
-60598.png
-15203.png
-61942.png
-66967.png
-65173.png
-03368.png
-08274.png
-07165.png
-28495.png
-64541.png
-30187.png
-33032.png
-08858.png
-54670.png
-50393.png
-23508.png
-13388.png
-62005.png
-50077.png
-51927.png
-63513.png
-29777.png
-42995.png
-36654.png
-23647.png
-51273.png
-14376.png
-14371.png
-04296.png
-52045.png
-23808.png
-46453.png
-00615.png
-35762.png
-00117.png
-52248.png
-53300.png
-59497.png
-16639.png
-39338.png
-28859.png
-26286.png
-68927.png
-38668.png
-52221.png
-62414.png
-33806.png
-47018.png
-58178.png
-52702.png
-56529.png
-45146.png
-10873.png
-57056.png
-01251.png
-69035.png
-28824.png
-25139.png
-67080.png
-45623.png
-63845.png
-42125.png
-27484.png
-42460.png
-14573.png
-11551.png
-08693.png
-12563.png
-22660.png
-24158.png
-48687.png
-45285.png
-17304.png
-62861.png
-44777.png
-12292.png
-49498.png
-47200.png
-48591.png
-41773.png
-04734.png
-50476.png
-37635.png
-33270.png
-66522.png
-60536.png
-07344.png
-29434.png
-40245.png
-56422.png
-01884.png
-59315.png
-55827.png
-04237.png
-04980.png
-37499.png
-65146.png
-61457.png
-55233.png
-19001.png
-44755.png
-63109.png
-47148.png
-61389.png
-32628.png
-41912.png
-18415.png
-38705.png
-38448.png
-45581.png
-55446.png
-25511.png
-60981.png
-00071.png
-02930.png
-23176.png
-28191.png
-50156.png
-17997.png
-26693.png
-48196.png
-43528.png
-32300.png
-62119.png
-46429.png
-55651.png
-68012.png
-43437.png
-55190.png
-56586.png
-21173.png
-22513.png
-01049.png
-12245.png
-04539.png
-59900.png
-36965.png
-18712.png
-19887.png
-36507.png
-34369.png
-60454.png
-56926.png
-04047.png
-09484.png
-57344.png
-09339.png
-37517.png
-40883.png
-53842.png
-32460.png
-07707.png
-36089.png
-29664.png
-37036.png
-22222.png
-21442.png
-39077.png
-39640.png
-18692.png
-03045.png
-59634.png
-02635.png
-04480.png
-38052.png
-39174.png
-31912.png
-69391.png
-47783.png
-00042.png
-11203.png
-13786.png
-02598.png
-15617.png
-44417.png
-48471.png
-24830.png
-20959.png
-67854.png
-32967.png
-24120.png
-41146.png
-66168.png
-18519.png
-36011.png
-12141.png
-19488.png
-46058.png
-68764.png
-19276.png
-18770.png
-15563.png
-14657.png
-10070.png
-44140.png
-17509.png
-34992.png
-64303.png
-42514.png
-65044.png
-62736.png
-27869.png
-19960.png
-57890.png
-16601.png
-06607.png
-58646.png
-24493.png
-44883.png
-06808.png
-05181.png
-69702.png
-55930.png
-47340.png
-00227.png
-68478.png
-49706.png
-20951.png
-68996.png
-26785.png
-03274.png
-66960.png
-25682.png
-07003.png
-20518.png
-26474.png
-34370.png
-36127.png
-38447.png
-48780.png
-22891.png
-07477.png
-15418.png
-49921.png
-00246.png
-62518.png
-21236.png
-18716.png
-00019.png
-48810.png
-38154.png
-52125.png
-36372.png
-45764.png
-51122.png
-19856.png
-00938.png
-06197.png
-03515.png
-52004.png
-40685.png
-11438.png
-33268.png
-14886.png
-21143.png
-39453.png
-49538.png
-66014.png
-63566.png
-38206.png
-01656.png
-51939.png
-51410.png
-55163.png
-17303.png
-59017.png
-35365.png
-46698.png
-27103.png
-44914.png
-21191.png
-64650.png
-02177.png
-12224.png
-49062.png
-57651.png
-64906.png
-62910.png
-36539.png
-01086.png
-57377.png
-25495.png
-11735.png
-68224.png
-00968.png
-53664.png
-14378.png
-68843.png
-42649.png
-58003.png
-09890.png
-06305.png
-03088.png
-02835.png
-25906.png
-61308.png
-44607.png
-26189.png
-64496.png
-01607.png
-28257.png
-31117.png
-16664.png
-32361.png
-00744.png
-20093.png
-63677.png
-00131.png
-20144.png
-03717.png
-58736.png
-28641.png
-68256.png
-33898.png
-26647.png
-38848.png
-09017.png
-21297.png
-03081.png
-54697.png
-04435.png
-29059.png
-40523.png
-39638.png
-40943.png
-23914.png
-66659.png
-36263.png
-02191.png
-06370.png
-13629.png
-09386.png
-61613.png
-09608.png
-66732.png
-22533.png
-53201.png
-22709.png
-29786.png
-10919.png
-56341.png
-65575.png
-47624.png
-30755.png
-32808.png
-42422.png
-58474.png
-35860.png
-19316.png
-45831.png
-35114.png
-48585.png
-11988.png
-62199.png
-37971.png
-42901.png
-56240.png
-20165.png
-64739.png
-66241.png
-03632.png
-52046.png
-18860.png
-22897.png
-09495.png
-16377.png
-04642.png
-21187.png
-61076.png
-57629.png
-37723.png
-27023.png
-27677.png
-48359.png
-06376.png
-06739.png
-13859.png
-51560.png
-47537.png
-62667.png
-42559.png
-03110.png
-24353.png
-42673.png
-67718.png
-34908.png
-51519.png
-20478.png
-32097.png
-43902.png
-15559.png
-00279.png
-26921.png
-44309.png
-12345.png
-16637.png
-13985.png
-03841.png
-26701.png
-39354.png
-13669.png
-43476.png
-49298.png
-49284.png
-68101.png
-09816.png
-51158.png
-36020.png
-35576.png
-28779.png
-51295.png
-09468.png
-21353.png
-45987.png
-62940.png
-12128.png
-30826.png
-56940.png
-55107.png
-40873.png
-12238.png
-52115.png
-69367.png
-14167.png
-45526.png
-68905.png
-03769.png
-01161.png
-60994.png
-50027.png
-60346.png
-14809.png
-57333.png
-50657.png
-32821.png
-08203.png
-30932.png
-06918.png
-00573.png
-56191.png
-31833.png
-19176.png
-19936.png
-41922.png
-18142.png
-24852.png
-08917.png
-17089.png
-48943.png
-64357.png
-36039.png
-06622.png
-58412.png
-00804.png
-53695.png
-57148.png
-05290.png
-17544.png
-60928.png
-53534.png
-61923.png
-52827.png
-31518.png
-60801.png
-06706.png
-24615.png
-14029.png
-65008.png
-03701.png
-22121.png
-58727.png
-13201.png
-49728.png
-58015.png
-21923.png
-66223.png
-04765.png
-24981.png
-23612.png
-67525.png
-06815.png
-42609.png
-21421.png
-58359.png
-47549.png
-69480.png
-05610.png
-05206.png
-05790.png
-44836.png
-59222.png
-07479.png
-08535.png
-35308.png
-36503.png
-33417.png
-29424.png
-01540.png
-18592.png
-65161.png
-66461.png
-13695.png
-50720.png
-53241.png
-09883.png
-07151.png
-25830.png
-42272.png
-35602.png
-43023.png
-32920.png
-02645.png
-37117.png
-19931.png
-36298.png
-58636.png
-10633.png
-38734.png
-10585.png
-42596.png
-60404.png
-06015.png
-43911.png
-56660.png
-67227.png
-03183.png
-43415.png
-47047.png
-30252.png
-22306.png
-03347.png
-65645.png
-07414.png
-29468.png
-48156.png
-05012.png
-67332.png
-26927.png
-65221.png
-23595.png
-07522.png
-24930.png
-46068.png
-64424.png
-67566.png
-52998.png
-26122.png
-01012.png
-05594.png
-65946.png
-41205.png
-24824.png
-26988.png
-00971.png
-39784.png
-49577.png
-10201.png
-11363.png
-01586.png
-49783.png
-14500.png
-18479.png
-69025.png
-58743.png
-30887.png
-02454.png
-34340.png
-12262.png
-52006.png
-62619.png
-02741.png
-22627.png
-58612.png
-47042.png
-35183.png
-68673.png
-02678.png
-08426.png
-45871.png
-51649.png
-14973.png
-24510.png
-31934.png
-63313.png
-51887.png
-37628.png
-23006.png
-07928.png
-26594.png
-69467.png
-56596.png
-05397.png
-02680.png
-09532.png
-09782.png
-57029.png
-55713.png
-62374.png
-13644.png
-02335.png
-28250.png
-39539.png
-32791.png
-45596.png
-37809.png
-69726.png
-09446.png
-02122.png
-00518.png
-07312.png
-23452.png
-68915.png
-29315.png
-52208.png
-05850.png
-63263.png
-31475.png
-34231.png
-68422.png
-49061.png
-01417.png
-08553.png
-28992.png
-12146.png
-26959.png
-67042.png
-05542.png
-31481.png
-24037.png
-58946.png
-29515.png
-38983.png
-59923.png
-42711.png
-24301.png
-48817.png
-30096.png
-60745.png
-46750.png
-21961.png
-40860.png
-46623.png
-54885.png
-17432.png
-07285.png
-12913.png
-43553.png
-38368.png
-20974.png
-61500.png
-65953.png
-28501.png
-66731.png
-31918.png
-01549.png
-36300.png
-28143.png
-56436.png
-23117.png
-38304.png
-47768.png
-08677.png
-00662.png
-43364.png
-04145.png
-12858.png
-21776.png
-40543.png
-02045.png
-13088.png
-16058.png
-07729.png
-27932.png
-62485.png
-04592.png
-56343.png
-59116.png
-23673.png
-60848.png
-66542.png
-24198.png
-67362.png
-28284.png
-60188.png
-18336.png
-55489.png
-15180.png
-64327.png
-32371.png
-20850.png
-28950.png
-66048.png
-61231.png
-57110.png
-16307.png
-39745.png
-55608.png
-22498.png
-39851.png
-42564.png
-12722.png
-56469.png
-27704.png
-16372.png
-52723.png
-16098.png
-47037.png
-43899.png
-69119.png
-22650.png
-30299.png
-12553.png
-24785.png
-21508.png
-29839.png
-48417.png
-35080.png
-59714.png
-65797.png
-26884.png
-08686.png
-04680.png
-44785.png
-10945.png
-41024.png
-05768.png
-14210.png
-16316.png
-61093.png
-43635.png
-68489.png
-10246.png
-15384.png
-22094.png
-45749.png
-26852.png
-53950.png
-68586.png
-66640.png
-09673.png
-58408.png
-47165.png
-43731.png
-24849.png
-09682.png
-26999.png
-04485.png
-54099.png
-08115.png
-05488.png
-33036.png
-49731.png
-52426.png
-41713.png
-17708.png
-07120.png
-62510.png
-21468.png
-02306.png
-27468.png
-61962.png
-54972.png
-13166.png
-19281.png
-20449.png
-35868.png
-24187.png
-32587.png
-06948.png
-51682.png
-62173.png
-56508.png
-33212.png
-10483.png
-40648.png
-40163.png
-67477.png
-05249.png
-60223.png
-24929.png
-14857.png
-43765.png
-34783.png
-27772.png
-31846.png
-06846.png
-19081.png
-58301.png
-24604.png
-16869.png
-48683.png
-09432.png
-66176.png
-68075.png
-68532.png
-24122.png
-21853.png
-40910.png
-32729.png
-10153.png
-08682.png
-66583.png
-59507.png
-45717.png
-39224.png
-05104.png
-11526.png
-18189.png
-00456.png
-16624.png
-36824.png
-22670.png
-64816.png
-62955.png
-12156.png
-52899.png
-35627.png
-52664.png
-66699.png
-51695.png
-23219.png
-62028.png
-57116.png
-18989.png
-54892.png
-07456.png
-20377.png
-23367.png
-25485.png
-06087.png
-52312.png
-65796.png
-64505.png
-60765.png
-28569.png
-66692.png
-44835.png
-19485.png
-68152.png
-16196.png
-44012.png
-04492.png
-47503.png
-41540.png
-59492.png
-27058.png
-43872.png
-63197.png
-14850.png
-34724.png
-30564.png
-21882.png
-40766.png
-19435.png
-45960.png
-59812.png
-28478.png
-60600.png
-05444.png
-66995.png
-39471.png
-15537.png
-24543.png
-13716.png
-22402.png
-54937.png
-41302.png
-58104.png
-53919.png
-62389.png
-41054.png
-12470.png
-46200.png
-48419.png
-41805.png
-64004.png
-45842.png
-15814.png
-04490.png
-43240.png
-26266.png
-21818.png
-25647.png
-07859.png
-17603.png
-08856.png
-62906.png
-53042.png
-29078.png
-34501.png
-04827.png
-09783.png
-14982.png
-08287.png
-56316.png
-14134.png
-31540.png
-26420.png
-27356.png
-37339.png
-59664.png
-31959.png
-28382.png
-64501.png
-27253.png
-42159.png
-37417.png
-50622.png
-15206.png
-08048.png
-39090.png
-46665.png
-28369.png
-29602.png
-49163.png
-15492.png
-38230.png
-22307.png
-06440.png
-63219.png
-16380.png
-46405.png
-45723.png
-41398.png
-09688.png
-15066.png
-56457.png
-12778.png
-37459.png
-04741.png
-26203.png
-26432.png
-39856.png
-04731.png
-37140.png
-68884.png
-38116.png
-25674.png
-67225.png
-49727.png
-23045.png
-41081.png
-61261.png
-41023.png
-00159.png
-19501.png
-39956.png
-21133.png
-33338.png
-11618.png
-09530.png
-07629.png
-55123.png
-31886.png
-26155.png
-27498.png
-49868.png
-42099.png
-41492.png
-09121.png
-66519.png
-34333.png
-17706.png
-05454.png
-32875.png
-00892.png
-54893.png
-59555.png
-19349.png
-42928.png
-60035.png
-27931.png
-37964.png
-56313.png
-12733.png
-29860.png
-64755.png
-65558.png
-00203.png
-30871.png
-56294.png
-67980.png
-39717.png
-66365.png
-29495.png
-59623.png
-36928.png
-09961.png
-37617.png
-33115.png
-05468.png
-20555.png
-28804.png
-01977.png
-17188.png
-05853.png
-60563.png
-06393.png
-22956.png
-24779.png
-14900.png
-10807.png
-32708.png
-40440.png
-11239.png
-09016.png
-48559.png
-46559.png
-43501.png
-11114.png
-00681.png
-59282.png
-68144.png
-52030.png
-47874.png
-09027.png
-61444.png
-25062.png
-26176.png
-54151.png
-42504.png
-46088.png
-46590.png
-18320.png
-57458.png
-52756.png
-16794.png
-60794.png
-65056.png
-10079.png
-46047.png
-37272.png
-55016.png
-49924.png
-35533.png
-03680.png
-15517.png
-35765.png
-66410.png
-27691.png
-02077.png
-43548.png
-43774.png
-41470.png
-06269.png
-49337.png
-43499.png
-47056.png
-47603.png
-12559.png
-06968.png
-51166.png
-16879.png
-46563.png
-40115.png
-30764.png
-23795.png
-54018.png
-53346.png
-36977.png
-46105.png
-17608.png
-19225.png
-39689.png
-27573.png
-43075.png
-10450.png
-28982.png
-05680.png
-45503.png
-49769.png
-44931.png
-36192.png
-05641.png
-65324.png
-19235.png
-68760.png
-03753.png
-67900.png
-43064.png
-08147.png
-11984.png
-59697.png
-23253.png
-65217.png
-66340.png
-59560.png
-39101.png
-54907.png
-13433.png
-44126.png
-37853.png
-35820.png
-59585.png
-34990.png
-43742.png
-12339.png
-28789.png
-30823.png
-40524.png
-03357.png
-54827.png
-05095.png
-58269.png
-29717.png
-68145.png
-43775.png
-63378.png
-60053.png
-46445.png
-59350.png
-32132.png
-10116.png
-54316.png
-20992.png
-40955.png
-35710.png
-63272.png
-37670.png
-64841.png
-52841.png
-49654.png
-60027.png
-01406.png
-02934.png
-01057.png
-32169.png
-14626.png
-09792.png
-30743.png
-49326.png
-24272.png
-54787.png
-55978.png
-33849.png
-58320.png
-19213.png
-35660.png
-62042.png
-14849.png
-69941.png
-62181.png
-51493.png
-38159.png
-35339.png
-21464.png
-14826.png
-08487.png
-40301.png
-36502.png
-47806.png
-21242.png
-60547.png
-61189.png
-04794.png
-50701.png
-54978.png
-19934.png
-20285.png
-12252.png
-64596.png
-00528.png
-22948.png
-44490.png
-47770.png
-69091.png
-01182.png
-30918.png
-00608.png
-63554.png
-68233.png
-12063.png
-51462.png
-42017.png
-29974.png
-60334.png
-36214.png
-59875.png
-27934.png
-43919.png
-12471.png
-11323.png
-08051.png
-63600.png
-67437.png
-02688.png
-02447.png
-58570.png
-05867.png
-56619.png
-65459.png
-31608.png
-51638.png
-14822.png
-20281.png
-55454.png
-56241.png
-06295.png
-54072.png
-56228.png
-69143.png
-56830.png
-08688.png
-49916.png
-38908.png
-55173.png
-54177.png
-06912.png
-54828.png
-31414.png
-14855.png
-19198.png
-45883.png
-45708.png
-47158.png
-15193.png
-65847.png
-48004.png
-09493.png
-61936.png
-56860.png
-07611.png
-05119.png
-31151.png
-52977.png
-54138.png
-39563.png
-15962.png
-19789.png
-69108.png
-01497.png
-29657.png
-37516.png
-18509.png
-48383.png
-49190.png
-38415.png
-10277.png
-36082.png
-58076.png
-05899.png
-02240.png
-35551.png
-24863.png
-20044.png
-36305.png
-67063.png
-56668.png
-38261.png
-50204.png
-12740.png
-00715.png
-60645.png
-55691.png
-04208.png
-53635.png
-39875.png
-57585.png
-23700.png
-01609.png
-66174.png
-28531.png
-42114.png
-66564.png
-58757.png
-24051.png
-27757.png
-05964.png
-16199.png
-23477.png
-25782.png
-48796.png
-12018.png
-21419.png
-00567.png
-27975.png
-30483.png
-52680.png
-08520.png
-13776.png
-09519.png
-48257.png
-12328.png
-19398.png
-51444.png
-62004.png
-58445.png
-00055.png
-21972.png
-40180.png
-12135.png
-20658.png
-59279.png
-16724.png
-47749.png
-08179.png
-12029.png
-53954.png
-63875.png
-47203.png
-09667.png
-22257.png
-62257.png
-29441.png
-58501.png
-01631.png
-64548.png
-65528.png
-00298.png
-42020.png
-34500.png
-49866.png
-64792.png
-06680.png
-40120.png
-20789.png
-05481.png
-50329.png
-46090.png
-20141.png
-47092.png
-14164.png
-35422.png
-11388.png
-44423.png
-42281.png
-12551.png
-59851.png
-33138.png
-42028.png
-48177.png
-23606.png
-64351.png
-57997.png
-10019.png
-09072.png
-68671.png
-11577.png
-60222.png
-16667.png
-37719.png
-25877.png
-67510.png
-67966.png
-40667.png
-56706.png
-39318.png
-29750.png
-35234.png
-39583.png
-47209.png
-63216.png
-17796.png
-00605.png
-67386.png
-09395.png
-11655.png
-01553.png
-12587.png
-39860.png
-00689.png
-14879.png
-60441.png
-52654.png
-26637.png
-58259.png
-62920.png
-37681.png
-54993.png
-48037.png
-40632.png
-22997.png
-16796.png
-45186.png
-24698.png
-42239.png
-10401.png
-22191.png
-45483.png
-67382.png
-02214.png
-07580.png
-11709.png
-29160.png
-17315.png
-30333.png
-12618.png
-40153.png
-57867.png
-53902.png
-59954.png
-35841.png
-44175.png
-35766.png
-16477.png
-01412.png
-69010.png
-20013.png
-38181.png
-26370.png
-31012.png
-24056.png
-27184.png
-08146.png
-23080.png
-01384.png
-24776.png
-24768.png
-49966.png
-51735.png
-24067.png
-49087.png
-00225.png
-36745.png
-53854.png
-04095.png
-01267.png
-56444.png
-09302.png
-67220.png
-65576.png
-11142.png
-55070.png
-36605.png
-55287.png
-08819.png
-26424.png
-19026.png
-64269.png
-56506.png
-66940.png
-59783.png
-10399.png
-21230.png
-29932.png
-43112.png
-63863.png
-00452.png
-58328.png
-51327.png
-42186.png
-64561.png
-30154.png
-02047.png
-01235.png
-51270.png
-25975.png
-32633.png
-64934.png
-57105.png
-47096.png
-46367.png
-34902.png
-04303.png
-03135.png
-10913.png
-49048.png
-29053.png
-11851.png
-09578.png
-31058.png
-37910.png
-65006.png
-02764.png
-12895.png
-69574.png
-39840.png
-62208.png
-05267.png
-62165.png
-00701.png
-67073.png
-31076.png
-29099.png
-23088.png
-37874.png
-22884.png
-01523.png
-01249.png
-59060.png
-59486.png
-44720.png
-57028.png
-21982.png
-22848.png
-40083.png
-11411.png
-21142.png
-35661.png
-52973.png
-17366.png
-26446.png
-63673.png
-67462.png
-65308.png
-53739.png
-36013.png
-20487.png
-65861.png
-07164.png
-54175.png
-31695.png
-45986.png
-02972.png
-14601.png
-65570.png
-58407.png
-05262.png
-51485.png
-39001.png
-57501.png
-61522.png
-61143.png
-22357.png
-18508.png
-35883.png
-41838.png
-06361.png
-13826.png
-00706.png
-40634.png
-07490.png
-30386.png
-60808.png
-66490.png
-62794.png
-63786.png
-63744.png
-32832.png
-04344.png
-62311.png
-28546.png
-61327.png
-18857.png
-16719.png
-57455.png
-33166.png
-40775.png
-67504.png
-50931.png
-20980.png
-53457.png
-23463.png
-03594.png
-67422.png
-00260.png
-43637.png
-50827.png
-32521.png
-23998.png
-66308.png
-05066.png
-10792.png
-68127.png
-00602.png
-01647.png
-05404.png
-36925.png
-46502.png
-56722.png
-11361.png
-36942.png
-61345.png
-39674.png
-15705.png
-53081.png
-55743.png
-48946.png
-68961.png
-57736.png
-40378.png
-56994.png
-04874.png
-05504.png
-59914.png
-05961.png
-48173.png
-36203.png
-36378.png
-35163.png
-03178.png
-48880.png
-52191.png
-53648.png
-49961.png
-54049.png
-04954.png
-39732.png
-54333.png
-33358.png
-35613.png
-47516.png
-35754.png
-10351.png
-08460.png
-41545.png
-35893.png
-16356.png
-60170.png
-12690.png
-50785.png
-44598.png
-64264.png
-43796.png
-13992.png
-11058.png
-48174.png
-63169.png
-67108.png
-13679.png
-55845.png
-17901.png
-62576.png
-15800.png
-06085.png
-00235.png
-14475.png
-05824.png
-18652.png
-10154.png
-69920.png
-32136.png
-27229.png
-05395.png
-31224.png
-64855.png
-65572.png
-55807.png
-67707.png
-66829.png
-52483.png
-48333.png
-37529.png
-18521.png
-53753.png
-14052.png
-28920.png
-61434.png
-38069.png
-20723.png
-48822.png
-46475.png
-65479.png
-61830.png
-23362.png
-30524.png
-69283.png
-02101.png
-43802.png
-48410.png
-20546.png
-63840.png
-06879.png
-38355.png
-38665.png
-66656.png
-28968.png
-21324.png
-05512.png
-03297.png
-40922.png
-14530.png
-69631.png
-69344.png
-15463.png
-15254.png
-05522.png
-16260.png
-11733.png
-42307.png
-33050.png
-40542.png
-45699.png
-00793.png
-54549.png
-38995.png
-45045.png
-18287.png
-63523.png
-33406.png
-15717.png
-62375.png
-40426.png
-10267.png
-02192.png
-14238.png
-06476.png
-62781.png
-61926.png
-39037.png
-08238.png
-29319.png
-10433.png
-44123.png
-46887.png
-36895.png
-61167.png
-07915.png
-44781.png
-58880.png
-09731.png
-08833.png
-65022.png
-17525.png
-19928.png
-34809.png
-38347.png
-25046.png
-10643.png
-39257.png
-28288.png
-53688.png
-16092.png
-25533.png
-55231.png
-41239.png
-18638.png
-12515.png
-48289.png
-64436.png
-49078.png
-38584.png
-62130.png
-48616.png
-46219.png
-38191.png
-12293.png
-19346.png
-22802.png
-16925.png
-29670.png
-27379.png
-45200.png
-17074.png
-53628.png
-33395.png
-54640.png
-68269.png
-51910.png
-26764.png
-26806.png
-62337.png
-05337.png
-28919.png
-02614.png
-03483.png
-58792.png
-36494.png
-29628.png
-05895.png
-65344.png
-57487.png
-35832.png
-15789.png
-41831.png
-13434.png
-16231.png
-56756.png
-62713.png
-64905.png
-62669.png
-30987.png
-48186.png
-42866.png
-02205.png
-23118.png
-25064.png
-17956.png
-58084.png
-58553.png
-53698.png
-50778.png
-54318.png
-15223.png
-46098.png
-61376.png
-66850.png
-04128.png
-09770.png
-47621.png
-35691.png
-15251.png
-06906.png
-29535.png
-04210.png
-21445.png
-15691.png
-28788.png
-30375.png
-17976.png
-47204.png
-52693.png
-29074.png
-23978.png
-22592.png
-23547.png
-53286.png
-21720.png
-31904.png
-28626.png
-12319.png
-18888.png
-21886.png
-13004.png
-47272.png
-22387.png
-05815.png
-55421.png
-48568.png
-12821.png
-16057.png
-41674.png
-20963.png
-06883.png
-40604.png
-41962.png
-17684.png
-66319.png
-33851.png
-00234.png
-04298.png
-24889.png
-22436.png
-57360.png
-59433.png
-15666.png
-20113.png
-04137.png
-46697.png
-21455.png
-48768.png
-55666.png
-62470.png
-53275.png
-60939.png
-52837.png
-33031.png
-17019.png
-50910.png
-12298.png
-53859.png
-29029.png
-15656.png
-64007.png
-52151.png
-08254.png
-29650.png
-33921.png
-06714.png
-23290.png
-20805.png
-25036.png
-40275.png
-18268.png
-00725.png
-68891.png
-11561.png
-19385.png
-21276.png
-46650.png
-00992.png
-53910.png
-05153.png
-01760.png
-51011.png
-15419.png
-24486.png
-48049.png
-43805.png
-52229.png
-13003.png
-05613.png
-32106.png
-11200.png
-11633.png
-41390.png
-25492.png
-51593.png
-35462.png
-08206.png
-01800.png
-57033.png
-16430.png
-05596.png
-22965.png
-34886.png
-28644.png
-67855.png
-66180.png
-50722.png
-53654.png
-36284.png
-15504.png
-47136.png
-17067.png
-57993.png
-64328.png
-08488.png
-67069.png
-08547.png
-34855.png
-06442.png
-19899.png
-09426.png
-62643.png
-61135.png
-37886.png
-54824.png
-03078.png
-34965.png
-21949.png
-63938.png
-66171.png
-10790.png
-30183.png
-55785.png
-05331.png
-49584.png
-11093.png
-32940.png
-55843.png
-47611.png
-06148.png
-08840.png
-52578.png
-26671.png
-28572.png
-56248.png
-33885.png
-13118.png
-64407.png
-03940.png
-44978.png
-52640.png
-47273.png
-40138.png
-51547.png
-14811.png
-48950.png
-35968.png
-45142.png
-44901.png
-40330.png
-00991.png
-59062.png
-02901.png
-14050.png
-35594.png
-20721.png
-43797.png
-26154.png
-18517.png
-52858.png
-38875.png
-38561.png
-27385.png
-57385.png
-09158.png
-10147.png
-36733.png
-48527.png
-47988.png
-22097.png
-06198.png
-06597.png
-14782.png
-06526.png
-15111.png
-48495.png
-27622.png
-58490.png
-45318.png
-58782.png
-66911.png
-20322.png
-47850.png
-12499.png
-45374.png
-54255.png
-35683.png
-69531.png
-34085.png
-00972.png
-13889.png
-43875.png
-03655.png
-01244.png
-56227.png
-62649.png
-35203.png
-43826.png
-36151.png
-20918.png
-54259.png
-13096.png
-13131.png
-57869.png
-66191.png
-32367.png
-41178.png
-60790.png
-41211.png
-04245.png
-12693.png
-48778.png
-44230.png
-34035.png
-46007.png
-50008.png
-57951.png
-04705.png
-12418.png
-23061.png
-61821.png
-01066.png
-38505.png
-32389.png
-51441.png
-22302.png
-30706.png
-64274.png
-68702.png
-65227.png
-18067.png
-69697.png
-00782.png
-29692.png
-38822.png
-17781.png
-64680.png
-13603.png
-45324.png
-26553.png
-40390.png
-49088.png
-46114.png
-23379.png
-11502.png
-40509.png
-47411.png
-28735.png
-57045.png
-30044.png
-51250.png
-10517.png
-05993.png
-13083.png
-62272.png
-26898.png
-21875.png
-60715.png
-65705.png
-48794.png
-67392.png
-38309.png
-49172.png
-48123.png
-66766.png
-29667.png
-03873.png
-62963.png
-06318.png
-58603.png
-61466.png
-38888.png
-15458.png
-59834.png
-60866.png
-39325.png
-36713.png
-21649.png
-62957.png
-35829.png
-38231.png
-19855.png
-68140.png
-17165.png
-56475.png
-45875.png
-01814.png
-05351.png
-44794.png
-41426.png
-44557.png
-10086.png
-27533.png
-49602.png
-16394.png
-32043.png
-44971.png
-42665.png
-31689.png
-59033.png
-31577.png
-26138.png
-67087.png
-31881.png
-15936.png
-08294.png
-42149.png
-56129.png
-12763.png
-10031.png
-16257.png
-62896.png
-33659.png
-11298.png
-46415.png
-31466.png
-04872.png
-39893.png
-11777.png
-30364.png
-06675.png
-29331.png
-11166.png
-23365.png
-45797.png
-28592.png
-42727.png
-45050.png
-27055.png
-24868.png
-44184.png
-04966.png
-20622.png
-16939.png
-40932.png
-39223.png
-18141.png
-65602.png
-38182.png
-08671.png
-43317.png
-17131.png
-08152.png
-48589.png
-41363.png
-23448.png
-10362.png
-20005.png
-12088.png
-08393.png
-53362.png
-21697.png
-29162.png
-28065.png
-41833.png
-65267.png
-66210.png
-34146.png
-33048.png
-48161.png
-53939.png
-42368.png
-66878.png
-23500.png
-69657.png
-40578.png
-04024.png
-01847.png
-64078.png
-50944.png
-33929.png
-07640.png
-23993.png
-61488.png
-04150.png
-30496.png
-19854.png
-56952.png
-53495.png
-40654.png
-43131.png
-57220.png
-38718.png
-01709.png
-08626.png
-59106.png
-22195.png
-50623.png
-27751.png
-43851.png
-21119.png
-12933.png
-14861.png
-57133.png
-67275.png
-24533.png
-69052.png
-18401.png
-29226.png
-16626.png
-38469.png
-43080.png
-49923.png
-69683.png
-46913.png
-53111.png
-15383.png
-68112.png
-37969.png
-21977.png
-23444.png
-37894.png
-51817.png
-39445.png
-34476.png
-34942.png
-49482.png
-18776.png
-18153.png
-36601.png
-11446.png
-38382.png
-30576.png
-20248.png
-47945.png
-56602.png
-49541.png
-25390.png
-63210.png
-05231.png
-02485.png
-47684.png
-59225.png
-35755.png
-02871.png
-68455.png
-59741.png
-35071.png
-36756.png
-54029.png
-33265.png
-00470.png
-68614.png
-04901.png
-65879.png
-54262.png
-36608.png
-69521.png
-19513.png
-13405.png
-68250.png
-07234.png
-30258.png
-08470.png
-00754.png
-18110.png
-17505.png
-33941.png
-05854.png
-26193.png
-56476.png
-29418.png
-49021.png
-07363.png
-25025.png
-11143.png
-51022.png
-06190.png
-04148.png
-50587.png
-17101.png
-30320.png
-07230.png
-00020.png
-03017.png
-40065.png
-62356.png
-25777.png
-21300.png
-16050.png
-46336.png
-26289.png
-27687.png
-53747.png
-05760.png
-15017.png
-38533.png
-46567.png
-09448.png
-02784.png
-29472.png
-49674.png
-27142.png
-14741.png
-47378.png
-40369.png
-10424.png
-45511.png
-09500.png
-45103.png
-23927.png
-45443.png
-35370.png
-31375.png
-05631.png
-13481.png
-07413.png
-09312.png
-32730.png
-44270.png
-08744.png
-28055.png
-46278.png
-45275.png
-39473.png
-16056.png
-19812.png
-69554.png
-67802.png
-51517.png
-50790.png
-52316.png
-08973.png
-20306.png
-11931.png
-58906.png
-51340.png
-23126.png
-02125.png
-41104.png
-04780.png
-34707.png
-24630.png
-21725.png
-64010.png
-65491.png
-26621.png
-02432.png
-32218.png
-62289.png
-12725.png
-13224.png
-53526.png
-34947.png
-55625.png
-14908.png
-65163.png
-52152.png
-42647.png
-02078.png
-34845.png
-01613.png
-07874.png
-68444.png
-49335.png
-43853.png
-06584.png
-44708.png
-33382.png
-41537.png
-10190.png
-23292.png
-62881.png
-34798.png
-27767.png
-25517.png
-07586.png
-48204.png
-40365.png
-14605.png
-04977.png
-19038.png
-24585.png
-00538.png
-24390.png
-52794.png
-47026.png
-60255.png
-27876.png
-68870.png
-40609.png
-38003.png
-61004.png
-62320.png
-57097.png
-35702.png
-07391.png
-26572.png
-41668.png
-26916.png
-21192.png
-34790.png
-55742.png
-52268.png
-38806.png
-69940.png
-23404.png
-11301.png
-31668.png
-15989.png
-01000.png
-11976.png
-57619.png
-06362.png
-28404.png
-56905.png
-65906.png
-03581.png
-42359.png
-22835.png
-59630.png
-16396.png
-18646.png
-66443.png
-60650.png
-66107.png
-28862.png
-69103.png
-09174.png
-52627.png
-29361.png
-34269.png
-12145.png
-63865.png
-16153.png
-38281.png
-34834.png
-53219.png
-45753.png
-29439.png
-59619.png
-52139.png
-00497.png
-24634.png
-19278.png
-66039.png
-38050.png
-40515.png
-56870.png
-54793.png
-62657.png
-03243.png
-36356.png
-40692.png
-07387.png
-23637.png
-61321.png
-09932.png
-56776.png
-13782.png
-61865.png
-69354.png
-58781.png
-65714.png
-28333.png
-33063.png
-21004.png
-01959.png
-39229.png
-48059.png
-22231.png
-34612.png
-64944.png
-38666.png
-33931.png
-65467.png
-04600.png
-45868.png
-60714.png
-17386.png
-33751.png
-28822.png
-65398.png
-49165.png
-58908.png
-33508.png
-48645.png
-39559.png
-51056.png
-57574.png
-05218.png
-68686.png
-23668.png
-39088.png
-22241.png
-59949.png
-41804.png
-67727.png
-52839.png
-64295.png
-30093.png
-11317.png
-27838.png
-20230.png
-66254.png
-06996.png
-04923.png
-26578.png
-58143.png
-67943.png
-28049.png
-35488.png
-48404.png
-50713.png
-66663.png
-39549.png
-09062.png
-07768.png
-59756.png
-30273.png
-12361.png
-38343.png
-27823.png
-13538.png
-02966.png
-31606.png
-38452.png
-31099.png
-04684.png
-24692.png
-05155.png
-17210.png
-24550.png
-21196.png
-60283.png
-48998.png
-59643.png
-47004.png
-39822.png
-24975.png
-45310.png
-40342.png
-02815.png
-52304.png
-50419.png
-14355.png
-11032.png
-38679.png
-26982.png
-55061.png
-18424.png
-23043.png
-19492.png
-44521.png
-01813.png
-22408.png
-62156.png
-38489.png
-30304.png
-21962.png
-23824.png
-16514.png
-59893.png
-36121.png
-59796.png
-19455.png
-01737.png
-34215.png
-16539.png
-56440.png
-37908.png
-69690.png
-69335.png
-05040.png
-46734.png
-55210.png
-32207.png
-37411.png
-61863.png
-35468.png
-54011.png
-65061.png
-61978.png
-42038.png
-03263.png
-63137.png
-26314.png
-05614.png
-65625.png
-27764.png
-39645.png
-13171.png
-69138.png
-20831.png
-06949.png
-45874.png
-31940.png
-68821.png
-01671.png
-66774.png
-41026.png
-46940.png
-23416.png
-53846.png
-01651.png
-40070.png
-59888.png
-09376.png
-56132.png
-05744.png
-28755.png
-08130.png
-59154.png
-32494.png
-08139.png
-61332.png
-47569.png
-57749.png
-49819.png
-46506.png
-15764.png
-62089.png
-05521.png
-39780.png
-39339.png
-46794.png
-61014.png
-40995.png
-30313.png
-38215.png
-22972.png
-59303.png
-07441.png
-15437.png
-19798.png
-25490.png
-31622.png
-52935.png
-48500.png
-38384.png
-40844.png
-01863.png
-48655.png
-09535.png
-38250.png
-24918.png
-39975.png
-38652.png
-59418.png
-12299.png
-40490.png
-41946.png
-40434.png
-47298.png
-22653.png
-35623.png
-67190.png
-05926.png
-34483.png
-49419.png
-63060.png
-56042.png
-66865.png
-12407.png
-43265.png
-32387.png
-25650.png
-51267.png
-06249.png
-06100.png
-35051.png
-32767.png
-07781.png
-17041.png
-36727.png
-37711.png
-38400.png
-23853.png
-33307.png
-15631.png
-42427.png
-22215.png
-15155.png
-00740.png
-15115.png
-23354.png
-15129.png
-54710.png
-47473.png
-60051.png
-15508.png
-52190.png
-14654.png
-24358.png
-23437.png
-52318.png
-59840.png
-34652.png
-39775.png
-11332.png
-14076.png
-19074.png
-39599.png
-44504.png
-02969.png
-01175.png
-56984.png
-54647.png
-57466.png
-51919.png
-17922.png
-03943.png
-56608.png
-52964.png
-67782.png
-51228.png
-50666.png
-47832.png
-34943.png
-18865.png
-60192.png
-46143.png
-21641.png
-27264.png
-58135.png
-41279.png
-61482.png
-65215.png
-23585.png
-68951.png
-64751.png
-45872.png
-11377.png
-26014.png
-12670.png
-56697.png
-16997.png
-02042.png
-23357.png
-59079.png
-13036.png
-15344.png
-19562.png
-30190.png
-61896.png
-66336.png
-31176.png
-16866.png
-66009.png
-27544.png
-66845.png
-43659.png
-30237.png
-17671.png
-27447.png
-11783.png
-52451.png
-38987.png
-11456.png
-21340.png
-30699.png
-31199.png
-05773.png
-57931.png
-36033.png
-62363.png
-22151.png
-40534.png
-64167.png
-62031.png
-26375.png
-44819.png
-41554.png
-43442.png
-25772.png
-65652.png
-29430.png
-68895.png
-29172.png
-57275.png
-27452.png
-02217.png
-34911.png
-34233.png
-52569.png
-57397.png
-01948.png
-38979.png
-16535.png
-25587.png
-45076.png
-66447.png
-55596.png
-07124.png
-67488.png
-51175.png
-07405.png
-50432.png
-61841.png
-33215.png
-22529.png
-01255.png
-29041.png
-08759.png
-58920.png
-10455.png
-04010.png
-22449.png
-55230.png
-63220.png
-27734.png
-56093.png
-54153.png
-69109.png
-65694.png
-66252.png
-04689.png
-46349.png
-16273.png
-37975.png
-62148.png
-65416.png
-35943.png
-58006.png
-58610.png
-08649.png
-61214.png
-50158.png
-51139.png
-53985.png
-26747.png
-64060.png
-08764.png
-03570.png
-05692.png
-24370.png
-09780.png
-63643.png
-54070.png
-22373.png
-31333.png
-03840.png
-54821.png
-39815.png
-67623.png
-48944.png
-50240.png
-27149.png
-49278.png
-10552.png
-39992.png
-33606.png
-15285.png
-48898.png
-03203.png
-25383.png
-43917.png
-45765.png
-42974.png
-21729.png
-37103.png
-15474.png
-07057.png
-11403.png
-04174.png
-50101.png
-02630.png
-10202.png
-03342.png
-50327.png
-04926.png
-17772.png
-21018.png
-03332.png
-48715.png
-50024.png
-26674.png
-23704.png
-14559.png
-01735.png
-43486.png
-07317.png
-28770.png
-08329.png
-48395.png
-05826.png
-15560.png
-10465.png
-22024.png
-09990.png
-42318.png
-69595.png
-59586.png
-49948.png
-59090.png
-15446.png
-20472.png
-01423.png
-21701.png
-07031.png
-32396.png
-65706.png
-03869.png
-01093.png
-59950.png
-15509.png
-32211.png
-48288.png
-68207.png
-28821.png
-20378.png
-38966.png
-13209.png
-05971.png
-36567.png
-69981.png
-54774.png
-43845.png
-27763.png
-45360.png
-55122.png
-32589.png
-37906.png
-44463.png
-63980.png
-60183.png
-19091.png
-05996.png
-63289.png
-38902.png
-50996.png
-49972.png
-34655.png
-37187.png
-24249.png
-41740.png
-28914.png
-21045.png
-60211.png
-54197.png
-06359.png
-41392.png
-28073.png
-04013.png
-18127.png
-46924.png
-45608.png
-09558.png
-03167.png
-66754.png
-19981.png
-22445.png
-18357.png
-48460.png
-08506.png
-37039.png
-01434.png
-31714.png
-42409.png
-03670.png
-37597.png
-20968.png
-64698.png
-26692.png
-60585.png
-68787.png
-07697.png
-34497.png
-10663.png
-02479.png
-19095.png
-00958.png
-29129.png
-17106.png
-53600.png
-06138.png
-56846.png
-51511.png
-00670.png
-14143.png
-57022.png
-22486.png
-61122.png
-13512.png
-63975.png
-45984.png
-22407.png
-24365.png
-41965.png
-60366.png
-25850.png
-06741.png
-25540.png
-31552.png
-21924.png
-31514.png
-19405.png
-12923.png
-59599.png
-11801.png
-02977.png
-27200.png
-13634.png
-44130.png
-47865.png
-33302.png
-00021.png
-50412.png
-15113.png
-21354.png
-34337.png
-05341.png
-19742.png
-60648.png
-54016.png
-67020.png
-66368.png
-24001.png
-27436.png
-60358.png
-25815.png
-12454.png
-57832.png
-08162.png
-15653.png
-36794.png
-05499.png
-40285.png
-10672.png
-40119.png
-38314.png
-51783.png
-26787.png
-42093.png
-47333.png
-02755.png
-34756.png
-03168.png
-49617.png
-23082.png
-00359.png
-41776.png
-43078.png
-19451.png
-55278.png
-64749.png
-25882.png
-54317.png
-37378.png
-46059.png
-66119.png
-21534.png
-32816.png
-20358.png
-18961.png
-38080.png
-36085.png
-26358.png
-68421.png
-25149.png
-20463.png
-44953.png
-35587.png
-23440.png
-43100.png
-29762.png
-37855.png
-22006.png
-43376.png
-12592.png
-57323.png
-15593.png
-36154.png
-53906.png
-54622.png
-40259.png
-43962.png
-09765.png
-07710.png
-13253.png
-47115.png
-06308.png
-67245.png
-64057.png
-14138.png
-23672.png
-27525.png
-64121.png
-25935.png
-56463.png
-07539.png
-23780.png
-12922.png
-48456.png
-06405.png
-65436.png
-03957.png
-55121.png
-47981.png
-48302.png
-14519.png
-64391.png
-39342.png
-65733.png
-04981.png
-20687.png
-35656.png
-59048.png
-67411.png
-53418.png
-22381.png
-31731.png
-36092.png
-18805.png
-18217.png
-67705.png
-33150.png
-38486.png
-58632.png
-45823.png
-10950.png
-57459.png
-29926.png
-00598.png
-03201.png
-41627.png
-27538.png
-49716.png
-69519.png
-18005.png
-01285.png
-35837.png
-19048.png
-52767.png
-47838.png
-43509.png
-04009.png
-64355.png
-13149.png
-08384.png
-12979.png
-48139.png
-20092.png
-44527.png
-19872.png
-14515.png
-32428.png
-11562.png
-30116.png
-63760.png
-44139.png
-66112.png
-29969.png
-64484.png
-45004.png
-33518.png
-44097.png
-22528.png
-48350.png
-17393.png
-54846.png
-07715.png
-14225.png
-13417.png
-67572.png
-29586.png
-12171.png
-21387.png
-46283.png
-05639.png
-45442.png
-39336.png
-58090.png
-46320.png
-10531.png
-18063.png
-36828.png
-08112.png
-64306.png
-61651.png
-08605.png
-47679.png
-18090.png
-30298.png
-42935.png
-42141.png
-67001.png
-24640.png
-41684.png
-24378.png
-12793.png
-09621.png
-29118.png
-42795.png
-58307.png
-18735.png
-25200.png
-04488.png
-50067.png
-67222.png
-00124.png
-10947.png
-49164.png
-21583.png
-21804.png
-18378.png
-47551.png
-00993.png
-68232.png
-38815.png
-23016.png
-20180.png
-12825.png
-07309.png
-50391.png
-67114.png
-67170.png
-58126.png
-66489.png
-38473.png
-20033.png
-15109.png
-04903.png
-50557.png
-65517.png
-53987.png
-54783.png
-31432.png
-63947.png
-46929.png
-64531.png
-07507.png
-10709.png
-52374.png
-17185.png
-32784.png
-61608.png
-38479.png
-19121.png
-40150.png
-45030.png
-52701.png
-43167.png
-23259.png
-67470.png
-29979.png
-44698.png
-52351.png
-02712.png
-59520.png
-21223.png
-56686.png
-24717.png
-52144.png
-62596.png
-27721.png
-08528.png
-35276.png
-28904.png
-57900.png
-24433.png
-67280.png
-00677.png
-23040.png
-01427.png
-68346.png
-34552.png
-01486.png
-42047.png
-55544.png
-54065.png
-41110.png
-45598.png
-31121.png
-68335.png
-01930.png
-49176.png
-05197.png
-58464.png
-61370.png
-11535.png
-02660.png
-30146.png
-45006.png
-04484.png
-01861.png
-40166.png
-08071.png
-44587.png
-58531.png
-53565.png
-19563.png
-56963.png
-41645.png
-40956.png
-64967.png
-04785.png
-16348.png
-47685.png
-52722.png
-37580.png
-30469.png
-65046.png
-06562.png
-19645.png
-35445.png
-16996.png
-68766.png
-26223.png
-62146.png
-35714.png
-00318.png
-17833.png
-29143.png
-10094.png
-46730.png
-68600.png
-34889.png
-56944.png
-37953.png
-06704.png
-37203.png
-03435.png
-28428.png
-50992.png
-10906.png
-11269.png
-30739.png
-52448.png
-64059.png
-62377.png
-30843.png
-16259.png
-46902.png
-47062.png
-57658.png
-39153.png
-37668.png
-03276.png
-35917.png
-25539.png
-63879.png
-00082.png
-32917.png
-12754.png
-50511.png
-22784.png
-24032.png
-24924.png
-52904.png
-09447.png
-11864.png
-06684.png
-21948.png
-02737.png
-65107.png
-31930.png
-33925.png
-50479.png
-12584.png
-52104.png
-35027.png
-36330.png
-47077.png
-11556.png
-50175.png
-30220.png
-25107.png
-31900.png
-44814.png
-03942.png
-41309.png
-60002.png
-64336.png
-43614.png
-49996.png
-60472.png
-41334.png
-27009.png
-66795.png
-10855.png
-61957.png
-24917.png
-46442.png
-48868.png
-36328.png
-26777.png
-46736.png
-68271.png
-15711.png
-37849.png
-02131.png
-23038.png
-59830.png
-13982.png
-13235.png
-40021.png
-51292.png
-17112.png
-34592.png
-19482.png
-46433.png
-56681.png
-24452.png
-34077.png
-66363.png
-35975.png
-66096.png
-01701.png
-10938.png
-41811.png
-04213.png
-52339.png
-23053.png
-48534.png
-40820.png
-40760.png
-33906.png
-38495.png
-28170.png
-53422.png
-27231.png
-25285.png
-41770.png
-67267.png
-03967.png
-20503.png
-41705.png
-64523.png
-27954.png
-27718.png
-01509.png
-16906.png
-44353.png
-24923.png
-59517.png
-19228.png
-06490.png
-35334.png
-13949.png
-56762.png
-51988.png
-56109.png
-37731.png
-67259.png
-58607.png
-14539.png
-54859.png
-26807.png
-00295.png
-69212.png
-37683.png
-46357.png
-09882.png
-05817.png
-48753.png
-18418.png
-44981.png
-35235.png
-11494.png
-09594.png
-45827.png
-48195.png
-06028.png
-11308.png
-67322.png
-49395.png
-08467.png
-31801.png
-55501.png
-59977.png
-61410.png
-33882.png
-38575.png
-47480.png
-01198.png
-11960.png
-60705.png
-58450.png
-55488.png
-42556.png
-54393.png
-21832.png
-44086.png
-15390.png
-64371.png
-53598.png
-33025.png
-19780.png
-13805.png
-26234.png
-40947.png
-60456.png
-58117.png
-59660.png
-40345.png
-38283.png
-20609.png
-20149.png
-06003.png
-42325.png
-52056.png
-02619.png
-45714.png
-46080.png
-23706.png
-61520.png
-09328.png
-21181.png
-60768.png
-16284.png
-61384.png
-17587.png
-35315.png
-29324.png
-67111.png
-54165.png
-45643.png
-39131.png
-37313.png
-56390.png
-55390.png
-60621.png
-45882.png
-53006.png
-02893.png
-01478.png
-28892.png
-41123.png
-46503.png
-60095.png
-16774.png
-49215.png
-26892.png
-53442.png
-55627.png
-14844.png
-28567.png
-25696.png
-28037.png
-39264.png
-52231.png
-16292.png
-27220.png
-06022.png
-07294.png
-17605.png
-57720.png
-18707.png
-38524.png
-22767.png
-41639.png
-54372.png
-20602.png
-27169.png
-56739.png
-56991.png
-07802.png
-68644.png
-20778.png
-47924.png
-41119.png
-12633.png
-08942.png
-18146.png
-54241.png
-61737.png
-55931.png
-46206.png
-60571.png
-36833.png
-35764.png
-05539.png
-65779.png
-69363.png
-47952.png
-09992.png
-15895.png
-45557.png
-06244.png
-20988.png
-69522.png
-45496.png
-40022.png
-31642.png
-39766.png
-06017.png
-58017.png
-43566.png
-31262.png
-43759.png
-54801.png
-66232.png
-41147.png
-66809.png
-28858.png
-45605.png
-06782.png
-30082.png
-63550.png
-03322.png
-63657.png
-14320.png
-14330.png
-51539.png
-37035.png
-51290.png
-37331.png
-23535.png
-14968.png
-59307.png
-17807.png
-69261.png
-57783.png
-57259.png
-53342.png
-20986.png
-16497.png
-05946.png
-29909.png
-63319.png
-62060.png
-23678.png
-19357.png
-20708.png
-32322.png
-20509.png
-20562.png
-15948.png
-41471.png
-06588.png
-32811.png
-01035.png
-16121.png
-45453.png
-11053.png
-43189.png
-15364.png
-32548.png
-08270.png
-29407.png
-50532.png
-54047.png
-26605.png
-56011.png
-30479.png
-27698.png
-54358.png
-01121.png
-15840.png
-26888.png
-33248.png
-10788.png
-39447.png
-40857.png
-66263.png
-58616.png
-55197.png
-48935.png
-17805.png
-29680.png
-03512.png
-29335.png
-32028.png
-26996.png
-57040.png
-41945.png
-20450.png
-09788.png
-27837.png
-33092.png
-08371.png
-69282.png
-66145.png
-63868.png
-30076.png
-19921.png
-40599.png
-35374.png
-16703.png
-62850.png
-25228.png
-48058.png
-52274.png
-59055.png
-01604.png
-35499.png
-65752.png
-57917.png
-18202.png
-01487.png
-65933.png
-27310.png
-64782.png
-34736.png
-57748.png
-37715.png
-68970.png
-63498.png
-00563.png
-02004.png
-22661.png
-60637.png
-27710.png
-25521.png
-53852.png
-06517.png
-24387.png
-26305.png
-46960.png
-62312.png
-54671.png
-16425.png
-18994.png
-42893.png
-49704.png
-43994.png
-40025.png
-38320.png
-62256.png
-47733.png
-67276.png
-50413.png
-29109.png
-55175.png
-27092.png
-66350.png
-25215.png
-07817.png
-69146.png
-10910.png
-15928.png
-50981.png
-21268.png
-21669.png
-30916.png
-20030.png
-63835.png
-30087.png
-65195.png
-54077.png
-41722.png
-45311.png
-55983.png
-52910.png
-66896.png
-42102.png
-49953.png
-29726.png
-00534.png
-65766.png
-08194.png
-15932.png
-46469.png
-37267.png
-43814.png
-61108.png
-36337.png
-01138.png
-47852.png
-46630.png
-43456.png
-23035.png
-13468.png
-26241.png
-23896.png
-41455.png
-06283.png
-25806.png
-55336.png
-44248.png
-44164.png
-31776.png
-37838.png
-64169.png
-16028.png
-37046.png
-48980.png
-49340.png
-63901.png
-11631.png
-62708.png
-57474.png
-22279.png
-17666.png
-58907.png
-01064.png
-00070.png
-38445.png
-51316.png
-35092.png
-08696.png
-02548.png
-43298.png
-02995.png
-66486.png
-08039.png
-52758.png
-02305.png
-02234.png
-56915.png
-02060.png
-16281.png
-62813.png
-46185.png
-48708.png
-25271.png
-22399.png
-14939.png
-10604.png
-46568.png
-45349.png
-60219.png
-62412.png
-60651.png
-11915.png
-10933.png
-05862.png
-53245.png
-30191.png
-69196.png
-20411.png
-06312.png
-01688.png
-23047.png
-15961.png
-02112.png
-03420.png
-59243.png
-29769.png
-53607.png
-54822.png
-54236.png
-41364.png
-04560.png
-13345.png
-40363.png
-34748.png
-62511.png
-19117.png
-08083.png
-18413.png
-49479.png
-39299.png
-43755.png
-06324.png
-67561.png
-52092.png
-26158.png
-62719.png
-35377.png
-06023.png
-19658.png
-34843.png
-65084.png
-46035.png
-09548.png
-06541.png
-68485.png
-51768.png
-28317.png
-26130.png
-60784.png
-01348.png
-64079.png
-53438.png
-16035.png
-61588.png
-60369.png
-32889.png
-03847.png
-19041.png
-49149.png
-35572.png
-00760.png
-66922.png
-20603.png
-01639.png
-41075.png
-43591.png
-64184.png
-44267.png
-65688.png
-52423.png
-64539.png
-65251.png
-36387.png
-56616.png
-27140.png
-19199.png
-54303.png
-04250.png
-51215.png
-37445.png
-39332.png
-32995.png
-57829.png
-46631.png
-50970.png
-61616.png
-68491.png
-18421.png
-61659.png
-38035.png
-58645.png
-58274.png
-47873.png
-38263.png
-69896.png
-67509.png
-59478.png
-38387.png
-54386.png
-20733.png
-20916.png
-44310.png
-57568.png
-00684.png
-04089.png
-29148.png
-40978.png
-57684.png
-18868.png
-27069.png
-25216.png
-00348.png
-58306.png
-06702.png
-58833.png
-38023.png
-28640.png
-56412.png
-06211.png
-11695.png
-53572.png
-60881.png
-06769.png
-50179.png
-13505.png
-49658.png
-41921.png
-30677.png
-47950.png
-39250.png
-11271.png
-32696.png
-45263.png
-65837.png
-22913.png
-57887.png
-60462.png
-05253.png
-56481.png
-02129.png
-37588.png
-18252.png
-63353.png
-03979.png
-08516.png
-55100.png
-61571.png
-52957.png
-56635.png
-41007.png
-13317.png
-67098.png
-51529.png
-59231.png
-42453.png
-07282.png
-64482.png
-26680.png
-58921.png
-31866.png
-59960.png
-59652.png
-63410.png
-42260.png
-10172.png
-08373.png
-60484.png
-56372.png
-35964.png
-38108.png
-00505.png
-35742.png
-51980.png
-14658.png
-11666.png
-37052.png
-45357.png
-04315.png
-46866.png
-43138.png
-03467.png
-53406.png
-20548.png
-10526.png
-48729.png
-05460.png
-49735.png
-24801.png
-43352.png
-69689.png
-40323.png
-15299.png
-09957.png
-09006.png
-69459.png
-35861.png
-58263.png
-34322.png
-08854.png
-00218.png
-57188.png
-48537.png
-65927.png
-04900.png
-32007.png
-09872.png
-08863.png
-26401.png
-67795.png
-65918.png
-11326.png
-68149.png
-05689.png
-28731.png
-55869.png
-16494.png
-12207.png
-67180.png
-68740.png
-40533.png
-51806.png
-20853.png
-58221.png
-18125.png
-04472.png
-59626.png
-14952.png
-58138.png
-40713.png
-47984.png
-68457.png
-30651.png
-32669.png
-69027.png
-45858.png
-57873.png
-04249.png
-60340.png
-29915.png
-47461.png
-67559.png
-30117.png
-19584.png
-06711.png
-49876.png
-07896.png
-04996.png
-31059.png
-35149.png
-34923.png
-67249.png
-61642.png
-37002.png
-00441.png
-25998.png
-39547.png
-37277.png
-50206.png
-57470.png
-11846.png
-46566.png
-26416.png
-58141.png
-58330.png
-09555.png
-63623.png
-62444.png
-28126.png
-06114.png
-58052.png
-11335.png
-06624.png
-27428.png
-15462.png
-15516.png
-28726.png
-13061.png
-15988.png
-59668.png
-45386.png
-17360.png
-29212.png
-54448.png
-22035.png
-40167.png
-58475.png
-25466.png
-04637.png
-04064.png
-40341.png
-47429.png
-65273.png
-45848.png
-25697.png
-22030.png
-36554.png
-24285.png
-65697.png
-35123.png
-56064.png
-16381.png
-07080.png
-00589.png
-51662.png
-13288.png
-28165.png
-60169.png
-53711.png
-54968.png
-62604.png
-31927.png
-62234.png
-25726.png
-42730.png
-28796.png
-50788.png
-58125.png
-24566.png
-09895.png
-62844.png
-46356.png
-21628.png
-21427.png
-66244.png
-45083.png
-13070.png
-11610.png
-25868.png
-38221.png
-42541.png
-56593.png
-59295.png
-35873.png
-00579.png
-29734.png
-22281.png
-13170.png
-38054.png
-47762.png
-23761.png
-23164.png
-51457.png
-65144.png
-25654.png
-36134.png
-50227.png
-15442.png
-03306.png
-12068.png
-46896.png
-66082.png
-24175.png
-10149.png
-28840.png
-43168.png
-06156.png
-21136.png
-26961.png
-11688.png
-15387.png
-41517.png
-31145.png
-42751.png
-19785.png
-19722.png
-12098.png
-22943.png
-48126.png
-64483.png
-28875.png
-28740.png
-32748.png
-35928.png
-50363.png
-63130.png
-34824.png
-31506.png
-15153.png
-33450.png
-62866.png
-33271.png
-58726.png
-09553.png
-44828.png
-63697.png
-52611.png
-46400.png
-29429.png
-64856.png
-38103.png
-12954.png
-11901.png
-03926.png
-20148.png
-68307.png
-32946.png
-14920.png
-24706.png
-62081.png
-18494.png
-63113.png
-24446.png
-31758.png
-06391.png
-04034.png
-30594.png
-62265.png
-47593.png
-58582.png
-57483.png
-12381.png
-13241.png
-05866.png
-68383.png
-09354.png
-47284.png
-44563.png
-69418.png
-08474.png
-52052.png
-29548.png
-49299.png
-07974.png
-12105.png
-01869.png
-59684.png
-11665.png
-65191.png
-25766.png
-08718.png
-20166.png
-49741.png
-22820.png
-44805.png
-23954.png
-34165.png
-29798.png
-45630.png
-43783.png
-33958.png
-12573.png
-64810.png
-27454.png
-08165.png
-36823.png
-18051.png
-26239.png
-45834.png
-39957.png
-59168.png
-04473.png
-09064.png
-65802.png
-14117.png
-02248.png
-15523.png
-31818.png
-62395.png
-39483.png
-31809.png
-09107.png
-17094.png
-32732.png
-07292.png
-26276.png
-63155.png
-62550.png
-55152.png
-69198.png
-48644.png
-46249.png
-20121.png
-28323.png
-46403.png
-32999.png
-55037.png
-66079.png
-52919.png
-59654.png
-47450.png
-48136.png
-60221.png
-35756.png
-01563.png
-62202.png
-43027.png
-28144.png
-10078.png
-67363.png
-36676.png
-49084.png
-58509.png
-15301.png
-13532.png
-18983.png
-46160.png
-52098.png
-25100.png
-46778.png
-10169.png
-24304.png
-13284.png
-52169.png
-10912.png
-17947.png
-18077.png
-14624.png
-44065.png
-34670.png
-29518.png
-33967.png
-07971.png
-34264.png
-33227.png
-02250.png
-06397.png
-33135.png
-29855.png
-51128.png
-56371.png
-22693.png
-14493.png
-17953.png
-49682.png
-07503.png
-00453.png
-55251.png
-15157.png
-62637.png
-41602.png
-39323.png
-47856.png
-19421.png
-46193.png
-49429.png
-01690.png
-63252.png
-18489.png
-42328.png
-44315.png
-68607.png
-51298.png
-05447.png
-00051.png
-43921.png
-42216.png
-05293.png
-56107.png
-52321.png
-13606.png
-34209.png
-15077.png
-61145.png
-45257.png
-26209.png
-14365.png
-02232.png
-31614.png
-20792.png
-14129.png
-42789.png
-19803.png
-08591.png
-66916.png
-45020.png
-36308.png
-51596.png
-07955.png
-53396.png
-34978.png
-10736.png
-54478.png
-33482.png
-61337.png
-65257.png
-14175.png
-11972.png
-41557.png
-33447.png
-18128.png
-04861.png
-41738.png
-49286.png
-33123.png
-42490.png
-20705.png
-10787.png
-50192.png
-04399.png
-47632.png
-13803.png
-14582.png
-68879.png
-42005.png
-67729.png
-27192.png
-55911.png
-37615.png
-08342.png
-48596.png
-15657.png
-62816.png
-44339.png
-27578.png
-62107.png
-28930.png
-06417.png
-12038.png
-67658.png
-59549.png
-10476.png
-48365.png
-19467.png
-58262.png
-42461.png
-10711.png
-21405.png
-38785.png
-44808.png
-28122.png
-54243.png
-12253.png
-02777.png
-29600.png
-29310.png
-62656.png
-02396.png
-28995.png
-42394.png
-60452.png
-06477.png
-12438.png
-07950.png
-46680.png
-39145.png
-46379.png
-29612.png
-55125.png
-64332.png
-12909.png
-46155.png
-54364.png
-33496.png
-32524.png
-60546.png
-36314.png
-41798.png
-54969.png
-29576.png
-34819.png
-17607.png
-60317.png
-39990.png
-01256.png
-61792.png
-10371.png
-05724.png
-67579.png
-00427.png
-28127.png
-43606.png
-13879.png
-08191.png
-42393.png
-12557.png
-15312.png
-64195.png
-64731.png
-06120.png
-22664.png
-06317.png
-27492.png
-60597.png
-07181.png
-01135.png
-16195.png
-11404.png
-23202.png
-48846.png
-61702.png
-46795.png
-23402.png
-56409.png
-25498.png
-07369.png
-62168.png
-45049.png
-53083.png
-30148.png
-35909.png
-29266.png
-52361.png
-00669.png
-48548.png
-57371.png
-23145.png
-33419.png
-50018.png
-63075.png
-17705.png
-21414.png
-23359.png
-65307.png
-01694.png
-12154.png
-06616.png
-26140.png
-27024.png
-68799.png
-50122.png
-09098.png
-40710.png
-14373.png
-51713.png
-28773.png
-50225.png
-47891.png
-27678.png
-09259.png
-36037.png
-61366.png
-09296.png
-43652.png
-62716.png
-09551.png
-45560.png
-15899.png
-56789.png
-04126.png
-69305.png
-08975.png
-32292.png
-48464.png
-45260.png
-35213.png
-31985.png
-40272.png
-27554.png
-22875.png
-53029.png
-20061.png
-25755.png
-39917.png
-10599.png
-32076.png
-00591.png
-26461.png
-43217.png
-30541.png
-06922.png
-35081.png
-50140.png
-11567.png
-28149.png
-28670.png
-52016.png
-35063.png
-58354.png
-05261.png
-11192.png
-50319.png
-47985.png
-31269.png
-18720.png
-32673.png
-17848.png
-51634.png
-44948.png
-21257.png
-30571.png
-62547.png
-53641.png
-13562.png
-10324.png
-35358.png
-51685.png
-46988.png
-15321.png
-29231.png
-46958.png
-04005.png
-12788.png
-23839.png
-46174.png
-13359.png
-31138.png
-60767.png
-04547.png
-56564.png
-36816.png
-20843.png
-52709.png
-61299.png
-13283.png
-03765.png
-13322.png
-36276.png
-11444.png
-40536.png
-35424.png
-05111.png
-50912.png
-33131.png
-64492.png
-04419.png
-23975.png
-19748.png
-48252.png
-64579.png
-19173.png
-21346.png
-48514.png
-49814.png
-53675.png
-18579.png
-50709.png
-15971.png
-64900.png
-11813.png
-32522.png
-31819.png
-06944.png
-46385.png
-52435.png
-09781.png
-18842.png
-11111.png
-26278.png
-47006.png
-62830.png
-12845.png
-16232.png
-30589.png
-35786.png
-62407.png
-69560.png
-57178.png
-44010.png
-48843.png
-14059.png
-64570.png
-41903.png
-11274.png
-25034.png
-50275.png
-00107.png
-18500.png
-48966.png
-08815.png
-34955.png
-18987.png
-32927.png
-21864.png
-58435.png
-31110.png
-47192.png
-40486.png
-12520.png
-11441.png
-60670.png
-33370.png
-25881.png
-31118.png
-46310.png
-07037.png
-42089.png
-19387.png
-55417.png
-46309.png
-16282.png
-04405.png
-07186.png
-02754.png
-03745.png
-62620.png
-42118.png
-52227.png
-38638.png
-63484.png
-15174.png
-58626.png
-05240.png
-55910.png
-54233.png
-68898.png
-29611.png
-56184.png
-69050.png
-56465.png
-28408.png
-15166.png
-24657.png
-38837.png
-09210.png
-54418.png
-13780.png
-13340.png
-52101.png
-41310.png
-64992.png
-48692.png
-51194.png
-28509.png
-56068.png
-21314.png
-11407.png
-27094.png
-13264.png
-33393.png
-56154.png
-21321.png
-62832.png
-51212.png
-33754.png
-55430.png
-01157.png
-20559.png
-46386.png
-33768.png
-21203.png
-46974.png
-54707.png
-20363.png
-13525.png
-24460.png
-58429.png
-58957.png
-69034.png
-15271.png
-63074.png
-46301.png
-67602.png
-07716.png
-31617.png
-37156.png
-21078.png
-25050.png
-45053.png
-19709.png
-62775.png
-15771.png
-10219.png
-47958.png
-31003.png
-36570.png
-55473.png
-33345.png
-07421.png
-62215.png
-14489.png
-26236.png
-43270.png
-08705.png
-64179.png
-54984.png
-45454.png
-55626.png
-21899.png
-15757.png
-42369.png
-18099.png
-23724.png
-10907.png
-60474.png
-53666.png
-57571.png
-58478.png
-41356.png
-26065.png
-29837.png
-20399.png
-37610.png
-34950.png
-57107.png
-01823.png
-13489.png
-37321.png
-44531.png
-53472.png
-19064.png
-39492.png
-27423.png
-66512.png
-21248.png
-43043.png
-31541.png
-54597.png
-66874.png
-21736.png
-60263.png
-62384.png
-24307.png
-63821.png
-03781.png
-66167.png
-46768.png
-07528.png
-16706.png
-09127.png
-58347.png
-09498.png
-39522.png
-62915.png
-39718.png
-48134.png
-50847.png
-00044.png
-65362.png
-50290.png
-27040.png
-07002.png
-54944.png
-18373.png
-35337.png
-54054.png
-27134.png
-57436.png
-43435.png
-59241.png
-48565.png
-64105.png
-49524.png
-14812.png
-53564.png
-65757.png
-04920.png
-31074.png
-62082.png
-57787.png
-17871.png
-42699.png
-36209.png
-26640.png
-22355.png
-03513.png
-06309.png
-00647.png
-43108.png
-09648.png
-08390.png
-12987.png
-66369.png
-24940.png
-62565.png
-02567.png
-07519.png
-09290.png
-14768.png
-20728.png
-52981.png
-59519.png
-49409.png
-55327.png
-18096.png
-63734.png
-54552.png
-45288.png
-60449.png
-34081.png
-49371.png
-13323.png
-57019.png
-00873.png
-54806.png
-02905.png
-45041.png
-21001.png
-33655.png
-27902.png
-19913.png
-05406.png
-36528.png
-28768.png
-06444.png
-44085.png
-65589.png
-13249.png
-16166.png
-41000.png
-37128.png
-22695.png
-38771.png
-21225.png
-03532.png
-04858.png
-15200.png
-64527.png
-69191.png
-55219.png
-22366.png
-03312.png
-59733.png
-53918.png
-22131.png
-62473.png
-25954.png
-67244.png
-62584.png
-22173.png
-59877.png
-12814.png
-66042.png
-00900.png
-16215.png
-47520.png
-05330.png
-32269.png
-19961.png
-63676.png
-49779.png
-17494.png
-67877.png
-14123.png
-57313.png
-62765.png
-07840.png
-10459.png
-25711.png
-46440.png
-58527.png
-37558.png
-06554.png
-59592.png
-14393.png
-17180.png
-51538.png
-65489.png
-68800.png
-23825.png
-35769.png
-44103.png
-25638.png
-22674.png
-62798.png
-20365.png
-09159.png
-15128.png
-16712.png
-53316.png
-09473.png
-44596.png
-45835.png
-30584.png
-25909.png
-27699.png
-31132.png
-67429.png
-08784.png
-55961.png
-05628.png
-12668.png
-01194.png
-01612.png
-43956.png
-42755.png
-20398.png
-11739.png
-27177.png
-07220.png
-17641.png
-31988.png
-23947.png
-12062.png
-15552.png
-50173.png
-02147.png
-66446.png
-56167.png
-17803.png
-16862.png
-54286.png
-58924.png
-41765.png
-12622.png
-22822.png
-32104.png
-00171.png
-11379.png
-61083.png
-47290.png
-61446.png
-15359.png
-33477.png
-45266.png
-65856.png
-15210.png
-56703.png
-23949.png
-30808.png
-37141.png
-68533.png
-04855.png
-54812.png
-04397.png
-67307.png
-03338.png
-30475.png
-05787.png
-51263.png
-21033.png
-65455.png
-25083.png
-25112.png
-47035.png
-01923.png
-18779.png
-42828.png
-34760.png
-68064.png
-46962.png
-14380.png
-47102.png
-38817.png
-23346.png
-63749.png
-42433.png
-27068.png
-07882.png
-67915.png
-68986.png
-32527.png
-04151.png
-03235.png
-02246.png
-68076.png
-42951.png
-29087.png
-27321.png
-45261.png
-37841.png
-40683.png
-62105.png
-57747.png
-26098.png
-02328.png
-44263.png
-07395.png
-32459.png
-40697.png
-39490.png
-18262.png
-17439.png
-35030.png
-39507.png
-48327.png
-02891.png
-33902.png
-11559.png
-22036.png
-59291.png
-64445.png
-51451.png
-11607.png
-56921.png
-52671.png
-61298.png
-14263.png
-35540.png
-55203.png
-53268.png
-65505.png
-40916.png
-03868.png
-13354.png
-33992.png
-13920.png
-21347.png
-20492.png
-61810.png
-57717.png
-12872.png
-58659.png
-54231.png
-07961.png
-60226.png
-57877.png
-23530.png
-13545.png
-44401.png
-63635.png
-64704.png
-48633.png
-55791.png
-36327.png
-23387.png
-66344.png
-44106.png
-52728.png
-06531.png
-60932.png
-34846.png
-18561.png
-30653.png
-65935.png
-44611.png
-00726.png
-16045.png
-43218.png
-69523.png
-40708.png
-64400.png
-30036.png
-40723.png
-54692.png
-30554.png
-29490.png
-32225.png
-00683.png
-32578.png
-05172.png
-58575.png
-66215.png
-05418.png
-50237.png
-37651.png
-26831.png
-28536.png
-32690.png
-12362.png
-67403.png
-08946.png
-28893.png
-29553.png
-34590.png
-15933.png
-58599.png
-36034.png
-60353.png
-65648.png
-14606.png
-05479.png
-28183.png
-30229.png
-10435.png
-34047.png
-48621.png
-00977.png
-02231.png
-60780.png
-60383.png
-59683.png
-40356.png
-35139.png
-00329.png
-22522.png
-47903.png
-63193.png
-66959.png
-16788.png
-51914.png
-39663.png
-22384.png
-17504.png
-12953.png
-46540.png
-64696.png
-61693.png
-34996.png
-35615.png
-10102.png
-28337.png
-22673.png
-60804.png
-40241.png
-41449.png
-21727.png
-04019.png
-67712.png
-58813.png
-05353.png
-37208.png
-48477.png
-17483.png
-59224.png
-17792.png
-28786.png
-38186.png
-44317.png
-66307.png
-49203.png
-25802.png
-43738.png
-59905.png
-36331.png
-27944.png
-60445.png
-08725.png
-13561.png
-69233.png
-48639.png
-67812.png
-59527.png
-37798.png
-05387.png
-12946.png
-31260.png
-12011.png
-20097.png
-18115.png
-42675.png
-16206.png
-23900.png
-18353.png
-03481.png
-31747.png
-05230.png
-63646.png
-19177.png
-42550.png
-64861.png
-69805.png
-49568.png
-63309.png
-35300.png
-22623.png
-22052.png
-53071.png
-62918.png
-32256.png
-46089.png
-21837.png
-29655.png
-67018.png
-17510.png
-21226.png
-62435.png
-48305.png
-04964.png
-44702.png
-63588.png
-63454.png
-47651.png
-49952.png
-26703.png
-03232.png
-51722.png
-66230.png
-52784.png
-04325.png
-18011.png
-57406.png
-60522.png
-08282.png
-19977.png
-32574.png
-04515.png
-66508.png
-69656.png
-24333.png
-33536.png
-39754.png
-60209.png
-47693.png
-00274.png
-28776.png
-30592.png
-66756.png
-24745.png
-23102.png
-67508.png
-44653.png
-41488.png
-64054.png
-44100.png
-04496.png
-42674.png
-69365.png
-06002.png
-69430.png
-48823.png
-66451.png
-62046.png
-34334.png
-29302.png
-22314.png
-33846.png
-19847.png
-59734.png
-51707.png
-29137.png
-53476.png
-36558.png
-52686.png
-21552.png
-65753.png
-67976.png
-48508.png
-42970.png
-48025.png
-51411.png
-45862.png
-22183.png
-51234.png
-54209.png
-01680.png
-41819.png
-30957.png
-47410.png
-48653.png
-04246.png
-25529.png
-07508.png
-42419.png
-02647.png
-64530.png
-46978.png
-26902.png
-03200.png
-11635.png
-25323.png
-61673.png
-27295.png
-20532.png
-03622.png
-19833.png
-57444.png
-10859.png
-22187.png
-24858.png
-49423.png
-59620.png
-10357.png
-32807.png
-69389.png
-22692.png
-18082.png
-30430.png
-25698.png
-26563.png
-15725.png
-14352.png
-57816.png
-43035.png
-66362.png
-21544.png
-19834.png
-16717.png
-24423.png
-36538.png
-43171.png
-29758.png
-41637.png
-34481.png
-04319.png
-40925.png
-28287.png
-22590.png
-66846.png
-08909.png
-38446.png
-47279.png
-65259.png
-03348.png
-02015.png
-19396.png
-00891.png
-06033.png
-41366.png
-07483.png
-55922.png
-08028.png
-33741.png
-05725.png
-67878.png
-11831.png
-23023.png
-21135.png
-53375.png
-13649.png
-65184.png
-32431.png
-44827.png
-48422.png
-52706.png
-43644.png
-29367.png
-69811.png
-40074.png
-63328.png
-59250.png
-59921.png
-61057.png
-51338.png
-62049.png
-26582.png
-02664.png
-48112.png
-61777.png
-64104.png
-10166.png
-42262.png
-11123.png
-57215.png
-33564.png
-31955.png
-03643.png
-52320.png
-11039.png
-54899.png
-39936.png
-52965.png
-08096.png
-00837.png
-58836.png
-36594.png
-19511.png
-19292.png
-36838.png
-45669.png
-47979.png
-15351.png
-02595.png
-48690.png
-14541.png
-42306.png
-28608.png
-65207.png
-69586.png
-09545.png
-17305.png
-24928.png
-54083.png
-01189.png
-31230.png
-42525.png
-03685.png
-14820.png
-56079.png
-63754.png
-51816.png
-41597.png
-56614.png
-25144.png
-59762.png
-07987.png
-33348.png
-58409.png
-32422.png
-46251.png
-60487.png
-68723.png
-14258.png
-28558.png
-49567.png
-15589.png
-32678.png
-49904.png
-28848.png
-00285.png
-01733.png
-69299.png
-69469.png
-24571.png
-24283.png
-66073.png
-64009.png
-14140.png
-46172.png
-65713.png
-14114.png
-44821.png
-42213.png
-04631.png
-08190.png
-08118.png
-11090.png
-33143.png
-42417.png
-45428.png
-42595.png
-57279.png
-36878.png
-28154.png
-46414.png
-20275.png
-00414.png
-03831.png
-11645.png
-66776.png
-27037.png
-56880.png
-54696.png
-69930.png
-04464.png
-44718.png
-01407.png
-02537.png
-64888.png
-19009.png
-15491.png
-50284.png
-60180.png
-06371.png
-50434.png
-02290.png
-14265.png
-63389.png
-32478.png
-68045.png
-46370.png
-09056.png
-09654.png
-15925.png
-30348.png
-05327.png
-36393.png
-20174.png
-50254.png
-30478.png
-54345.png
-33572.png
-48148.png
-32912.png
-07692.png
-66513.png
-16955.png
-14038.png
-28292.png
-66561.png
-06908.png
-06869.png
-54636.png
-29897.png
-54268.png
-62610.png
-50638.png
-58814.png
-26440.png
-33000.png
-35695.png
-11043.png
-44513.png
-29447.png
-02861.png
-54146.png
-16411.png
-67048.png
-58438.png
-30515.png
-62088.png
-20645.png
-40914.png
-55925.png
-66559.png
-17321.png
-17820.png
-59820.png
-06550.png
-14506.png
-07302.png
-07579.png
-61192.png
-24343.png
-62871.png
-51365.png
-59681.png
-66971.png
-34353.png
-15227.png
-61086.png
-04132.png
-44312.png
-57530.png
-66431.png
-66810.png
-69569.png
-49111.png
-12791.png
-60589.png
-07953.png
-04114.png
-54935.png
-62094.png
-25104.png
-18296.png
-24506.png
-08248.png
-13427.png
-05093.png
-44036.png
-17612.png
-57902.png
-58709.png
-37213.png
-09785.png
-35129.png
-09011.png
-63668.png
-63591.png
-13400.png
-65048.png
-68875.png
-16728.png
-42347.png
-04630.png
-08184.png
-20564.png
-65524.png
-21889.png
-31489.png
-34320.png
-26746.png
-25895.png
-23160.png
-36431.png
-50756.png
-28617.png
-66400.png
-19739.png
-50071.png
-07599.png
-33190.png
-68372.png
-10563.png
-30024.png
-63997.png
-01118.png
-20473.png
-40881.png
-38923.png
-34040.png
-35019.png
-45400.png
-62552.png
-23622.png
-03895.png
-30632.png
-31970.png
-50897.png
-52835.png
-39249.png
-24922.png
-16797.png
-21661.png
-44431.png
-17886.png
-31040.png
-65560.png
-30194.png
-33568.png
-60857.png
-28949.png
-05506.png
-22083.png
-52732.png
-62966.png
-59980.png
-37917.png
-27453.png
-29559.png
-04618.png
-56747.png
-34735.png
-48985.png
-32954.png
-49773.png
-02410.png
-44536.png
-29268.png
-43952.png
-09549.png
-32325.png
-48571.png
-04655.png
-20047.png
-23715.png
-44627.png
-05090.png
-57907.png
-14434.png
-49581.png
-59343.png
-69386.png
-64667.png
-66779.png
-61312.png
-61022.png
-27535.png
-29809.png
-39949.png
-29732.png
-27153.png
-12084.png
-39317.png
-03686.png
-67599.png
-08113.png
-52159.png
-00876.png
-61793.png
-14603.png
-68808.png
-09326.png
-38766.png
-59202.png
-52807.png
-50766.png
-61069.png
-13610.png
-63368.png
-66624.png
-17975.png
-07654.png
-50379.png
-07287.png
-15787.png
-12517.png
-03266.png
-22924.png
-18949.png
-25946.png
-27083.png
-22945.png
-41969.png
-52822.png
-16560.png
-58482.png
-18211.png
-49789.png
-54781.png
-01961.png
-55992.png
-43200.png
-59907.png
-19905.png
-55617.png
-10454.png
-25155.png
-15410.png
-22166.png
-32965.png
-32648.png
-03223.png
-62581.png
-14588.png
-20099.png
-14692.png
-23567.png
-51687.png
-52540.png
-11592.png
-40891.png
-19650.png
-10948.png
-13007.png
-16189.png
-35070.png
-55403.png
-26030.png
-14347.png
-10682.png
-19423.png
-49364.png
-68417.png
-41295.png
-54103.png
-18699.png
-38422.png
-03909.png
-32565.png
-64601.png
-08900.png
-54315.png
-08239.png
-06344.png
-19890.png
-01301.png
-67756.png
-14711.png
-17823.png
-52315.png
-62616.png
-51968.png
-28535.png
-56600.png
-60407.png
-06940.png
-43001.png
-07736.png
-37352.png
-57502.png
-46949.png
-04348.png
-46416.png
-31834.png
-60273.png
-52290.png
-58808.png
-33973.png
-01592.png
-59052.png
-45457.png
-19106.png
-17207.png
-14798.png
-69320.png
-02168.png
-16863.png
-62858.png
-02701.png
-13839.png
-56929.png
-25528.png
-07157.png
-18620.png
-28757.png
-38955.png
-39263.png
-29443.png
-16184.png
-19649.png
-45890.png
-58128.png
-54444.png
-37502.png
-40324.png
-18114.png
-16103.png
-12882.png
-26225.png
-00753.png
-48323.png
-45682.png
-36261.png
-56946.png
-50262.png
-20878.png
-44433.png
-64720.png
-24134.png
-18429.png
-48656.png
-11930.png
-04918.png
-09817.png
-07091.png
-23295.png
-47366.png
-67205.png
-58130.png
-39009.png
-48355.png
-64061.png
-17451.png
-36521.png
-59040.png
-50216.png
-36805.png
-01858.png
-01742.png
-01767.png
-46890.png
-13428.png
-27309.png
-59663.png
-64647.png
-12700.png
-38279.png
-18345.png
-43011.png
-37811.png
-02730.png
-61328.png
-66888.png
-44673.png
-04085.png
-17266.png
-04529.png
-35388.png
-29685.png
-33008.png
-05846.png
-55089.png
-59606.png
-48337.png
-07360.png
-36790.png
-40269.png
-56405.png
-25725.png
-45212.png
-21020.png
-30366.png
-32810.png
-02056.png
-64546.png
-52234.png
-35790.png
-37017.png
-54254.png
-61782.png
-68077.png
-16393.png
-43334.png
-59024.png
-15535.png
-32594.png
-23313.png
-34075.png
-06917.png
-40742.png
-44446.png
-19166.png
-46211.png
-34744.png
-10396.png
-52742.png
-31711.png
-28472.png
-69557.png
-24160.png
-52241.png
-09775.png
-59912.png
-52198.png
-09073.png
-49259.png
-36169.png
-09762.png
-06185.png
-44793.png
-35048.png
-19926.png
-01778.png
-50146.png
-22055.png
-20079.png
-15808.png
-43710.png
-61635.png
-60214.png
-38047.png
-28084.png
-10092.png
-63962.png
-12648.png
-12840.png
-61707.png
-20212.png
-51317.png
-60535.png
-45203.png
-65901.png
-22470.png
-59810.png
-19568.png
-13726.png
-48584.png
-23450.png
-16503.png
-52834.png
-57665.png
-41153.png
-03593.png
-24547.png
-43448.png
-44445.png
-29643.png
-05238.png
-19034.png
-68312.png
-55547.png
-62548.png
-36433.png
-55610.png
-30301.png
-65070.png
-02818.png
-65488.png
-34958.png
-30767.png
-53156.png
-12049.png
-54909.png
-63409.png
-39995.png
-01091.png
-21139.png
-07900.png
-34089.png
-20539.png
-61715.png
-24310.png
-08811.png
-64000.png
-09901.png
-61469.png
-06513.png
-62445.png
-29908.png
-52239.png
-10659.png
-68874.png
-08865.png
-53818.png
-54740.png
-18882.png
-30680.png
-16921.png
-65405.png
-12263.png
-67028.png
-50278.png
-43412.png
-54851.png
-03553.png
-68341.png
-42136.png
-65280.png
-69144.png
-01413.png
-35111.png
-29923.png
-18909.png
-45807.png
-30374.png
-31573.png
-14540.png
-24147.png
-61315.png
-07759.png
-69243.png
-61477.png
-19512.png
-50689.png
-00682.png
-32066.png
-23017.png
-39656.png
-05350.png
-26475.png
-27056.png
-17768.png
-64261.png
-23223.png
-68987.png
-15648.png
-35664.png
-08221.png
-29390.png
-01196.png
-54946.png
-44253.png
-44534.png
-69617.png
-45786.png
-35899.png
-59600.png
-57191.png
-05987.png
-06210.png
-33791.png
-23222.png
-63456.png
-50315.png
-04409.png
-52896.png
-44655.png
-55857.png
-07270.png
-15615.png
-62751.png
-39319.png
-20703.png
-20319.png
-22273.png
-17661.png
-67152.png
-18053.png
-04588.png
-06232.png
-19332.png
-04032.png
-02171.png
-57187.png
-35583.png
-07954.png
-03112.png
-35987.png
-58161.png
-34258.png
-43565.png
-08675.png
-28918.png
-00847.png
-09936.png
-17711.png
-51996.png
-18687.png
-40994.png
-00697.png
-51963.png
-63834.png
-43681.png
-41380.png
-19720.png
-54886.png
-45342.png
-40577.png
-08485.png
-22301.png
-05852.png
-29613.png
-26204.png
-56783.png
-35530.png
-42421.png
-64110.png
-54344.png
-53182.png
-00902.png
-28687.png
-37836.png
-09054.png
-40079.png
-12869.png
-33934.png
-44158.png
-40845.png
-63249.png
-62169.png
-47594.png
-62059.png
-69750.png
-31721.png
-44125.png
-59025.png
-53816.png
-10545.png
-56254.png
-50442.png
-43522.png
-31302.png
-19822.png
-30832.png
-46056.png
-34467.png
-46133.png
-10847.png
-08967.png
-63990.png
-40817.png
-02610.png
-69780.png
-68049.png
-20928.png
-08332.png
-53440.png
-63805.png
-51691.png
-50117.png
-38587.png
-10619.png
-03994.png
-45437.png
-10305.png
-12747.png
-51465.png
-09851.png
-44326.png
-51222.png
-11769.png
-40497.png
-04870.png
-63968.png
-29849.png
-63042.png
-02513.png
-31854.png
-39182.png
-61912.png
-66587.png
-43947.png
-51877.png
-43180.png
-36995.png
-69058.png
-33585.png
-47074.png
-30925.png
-07555.png
-26170.png
-28645.png
-06720.png
-48847.png
-15011.png
-26619.png
-39498.png
-66462.png
-13254.png
-10917.png
-38256.png
-34759.png
-17145.png
-45120.png
-07550.png
-07935.png
-15938.png
-39512.png
-15908.png
-60525.png
-43029.png
-22967.png
-68326.png
-58968.png
-22611.png
-30822.png
-56676.png
-61330.png
-41217.png
-34239.png
-34820.png
-27441.png
-62420.png
-20210.png
-19045.png
-26054.png
-27396.png
-18792.png
-19707.png
-20504.png
-63206.png
-09113.png
-35088.png
-63483.png
-12014.png
-58137.png
-26019.png
-46236.png
-30817.png
-31548.png
-21762.png
-08801.png
-66580.png
-65763.png
-50015.png
-27865.png
-00559.png
-09748.png
-25067.png
-00779.png
-17253.png
-45350.png
-47022.png
-43884.png
-55496.png
-03509.png
-33653.png
-32014.png
-60115.png
-19337.png
-15001.png
-55178.png
-48649.png
-67615.png
-64652.png
-51084.png
-34616.png
-02289.png
-58577.png
-31539.png
-62418.png
-59720.png
-15172.png
-50041.png
-52553.png
-15754.png
-66380.png
-69691.png
-33755.png
-46847.png
-69676.png
-11796.png
-23689.png
-69803.png
-06817.png
-31276.png
-16422.png
-24004.png
-64230.png
-13193.png
-18188.png
-68286.png
-10406.png
-08918.png
-63061.png
-43505.png
-25722.png
-51601.png
-39246.png
-38539.png
-49369.png
-09462.png
-36374.png
-51525.png
-03535.png
-21058.png
-21578.png
-63441.png
-49800.png
-11437.png
-43445.png
-28378.png
-15805.png
-43945.png
-27480.png
-59410.png
-55177.png
-42987.png
-45147.png
-57855.png
-44427.png
-09227.png
-16461.png
-40293.png
-03853.png
-22322.png
-57857.png
-09391.png
-07636.png
-18026.png
-40813.png
-46342.png
-40917.png
-01193.png
-60971.png
-08983.png
-23846.png
-31543.png
-28283.png
-12124.png
-02751.png
-04607.png
-28107.png
-12102.png
-04791.png
-60545.png
-43433.png
-60850.png
-46977.png
-48904.png
-34837.png
-55145.png
-39142.png
-08989.png
-33297.png
-68173.png
-68736.png
-28367.png
-14363.png
-60339.png
-37389.png
-00613.png
-36244.png
-28411.png
-50048.png
-34263.png
-24608.png
-60760.png
-03005.png
-58018.png
-61902.png
-20073.png
-30302.png
-63295.png
-16839.png
-17385.png
-57695.png
-26428.png
-40905.png
-44520.png
-27460.png
-01921.png
-46727.png
-28682.png
-24088.png
-33421.png
-67081.png
-64025.png
-15553.png
-38132.png
-62386.png
-02725.png
-05873.png
-57382.png
-24417.png
-42153.png
-11209.png
-45240.png
-31737.png
-49077.png
-64545.png
-52283.png
-41048.png
-19777.png
-22984.png
-00620.png
-67722.png
-54429.png
-69173.png
-39084.png
-59181.png
-68871.png
-18962.png
-06396.png
-12956.png
-47143.png
-18387.png
-04014.png
-63003.png
-53361.png
-19223.png
-34235.png
-49257.png
-36721.png
-32632.png
-51349.png
-20191.png
-52799.png
-05985.png
-12581.png
-33864.png
-06220.png
-46716.png
-32621.png
-39876.png
-27950.png
-67731.png
-25460.png
-51325.png
-64891.png
-63189.png
-04521.png
-64423.png
-28984.png
-54581.png
-21541.png
-53921.png
-16584.png
-01787.png
-10439.png
-11146.png
-46039.png
-44235.png
-10129.png
-61129.png
-42201.png
-14570.png
-29326.png
-37183.png
-31465.png
-14583.png
-56584.png
-28407.png
-01605.png
-19082.png
-62225.png
-59159.png
-31546.png
-05148.png
-49346.png
-66292.png
-44852.png
-43584.png
-23052.png
-22791.png
-62451.png
-66424.png
-19659.png
-69505.png
-39787.png
-48915.png
-31456.png
-19479.png
-04179.png
-40253.png
-30534.png
-10799.png
-45402.png
-25989.png
-23903.png
-36136.png
-36000.png
-38280.png
-50371.png
-61951.png
-24718.png
-43835.png
-16877.png
-57491.png
-25797.png
-66021.png
-44217.png
-65736.png
-34487.png
-35721.png
-62753.png
-62057.png
-46724.png
-03914.png
-68696.png
-69563.png
-68209.png
-53118.png
-55375.png
-29686.png
-15716.png
-02659.png
-23418.png
-61379.png
-22719.png
-49004.png
-10665.png
-54048.png
-12884.png
-51647.png
-01270.png
-60261.png
-16697.png
-44292.png
-53933.png
-39933.png
-04284.png
-54897.png
-27495.png
-62783.png
-58288.png
-69550.png
-17031.png
-33201.png
-35947.png
-52969.png
-27641.png
-26990.png
-00129.png
-17908.png
-40892.png
-20693.png
-00778.png
-68674.png
-17254.png
-13032.png
-23810.png
-15923.png
-32921.png
-43737.png
-61137.png
-01097.png
-00956.png
-11347.png
-14239.png
-34687.png
-58801.png
-68616.png
-12948.png
-30355.png
-63985.png
-54757.png
-34198.png
-40762.png
-18195.png
-22953.png
-65318.png
-07495.png
-54981.png
-53304.png
-03888.png
-39491.png
-22844.png
-44806.png
-15395.png
-38814.png
-22618.png
-33434.png
-38236.png
-49571.png
-40660.png
-60785.png
-15809.png
-28443.png
-35275.png
-25063.png
-09289.png
-12110.png
-68589.png
-57971.png
-53982.png
-44840.png
-03571.png
-02466.png
-46826.png
-48678.png
-58204.png
-50079.png
-07856.png
-05096.png
-36450.png
-22716.png
-50040.png
-64577.png
-52225.png
-48642.png
-29669.png
-40806.png
-15723.png
-54574.png
-50547.png
-46509.png
-41061.png
-44842.png
-53677.png
-05163.png
-61630.png
-21929.png
-18164.png
-40612.png
-65300.png
-13090.png
-19297.png
-00009.png
-06484.png
-69573.png
-31891.png
-54555.png
-29488.png
-55748.png
-24142.png
-43936.png
-50361.png
-44316.png
-05623.png
-62843.png
-54923.png
-65012.png
-65395.png
-41078.png
-50066.png
-07560.png
-43071.png
-56835.png
-18346.png
-37229.png
-07079.png
-30243.png
-40716.png
-63595.png
-46029.png
-45599.png
-39004.png
-41079.png
-31348.png
-44970.png
-54205.png
-34454.png
-35286.png
-12816.png
-56812.png
-46653.png
-34448.png
-02176.png
-51556.png
-60079.png
-68729.png
-22971.png
-63213.png
-22589.png
-52330.png
-54922.png
-21742.png
-33312.png
-69398.png
-04042.png
-18329.png
-57153.png
-56098.png
-62826.png
-67145.png
-09240.png
-29426.png
-52141.png
-48682.png
-42731.png
-16860.png
-59408.png
-38543.png
-68387.png
-18659.png
-32479.png
-21090.png
-50642.png
-51468.png
-24653.png
-08348.png
-69633.png
-61656.png
-14655.png
-45947.png
-09061.png
-09878.png
-59311.png
-16594.png
-34114.png
-52959.png
-43787.png
-16646.png
-54551.png
-19609.png
-26895.png
-51913.png
-21625.png
-18475.png
-39279.png
-69077.png
-67016.png
-08359.png
-34373.png
-10464.png
-17118.png
-07918.png
-66645.png
-03106.png
-10813.png
-41260.png
-37419.png
-68164.png
-50387.png
-27603.png
-15645.png
-08625.png
-66997.png
-26270.png
-28642.png
-65411.png
-35411.png
-21008.png
-09471.png
-25461.png
-65211.png
-37737.png
-41604.png
-43483.png
-00729.png
-14145.png
-47221.png
-68976.png
-52150.png
-54732.png
-12862.png
-41636.png
-29111.png
-50375.png
-69339.png
-56190.png
-07769.png
-55027.png
-01510.png
-41767.png
-25799.png
-61491.png
-19196.png
-10441.png
-02589.png
-24875.png
-52406.png
-36626.png
-50243.png
-19881.png
-08846.png
-34274.png
-35010.png
-39554.png
-52760.png
-12460.png
-28206.png
-17481.png
-59105.png
-63636.png
-51555.png
-01402.png
-42820.png
-38298.png
-41130.png
-30486.png
-69626.png
-42668.png
-13312.png
-54563.png
-51917.png
-42704.png
-13919.png
-32659.png
-45719.png
-48825.png
-54653.png
-47712.png
-05017.png
-64781.png
-62891.png
-23929.png
-68408.png
-24628.png
-09372.png
-27850.png
-40591.png
-23191.png
-69786.png
-55143.png
-36641.png
-22537.png
-13771.png
-32859.png
-64164.png
-44429.png
-41629.png
-41259.png
-00392.png
-20724.png
-59375.png
-65082.png
-56229.png
-45731.png
-68765.png
-21175.png
-16799.png
-40695.png
-37587.png
-46240.png
-27117.png
-41650.png
-47533.png
-54505.png
-02142.png
-66261.png
-56893.png
-20919.png
-14386.png
-34983.png
-27402.png
-47758.png
-65216.png
-20015.png
-25450.png
-01734.png
-50369.png
-02082.png
-38693.png
-02419.png
-04817.png
-16900.png
-43220.png
-61418.png
-51835.png
-59503.png
-09789.png
-57413.png
-57260.png
-44838.png
-62358.png
-44082.png
-63634.png
-20020.png
-17790.png
-29745.png
-58057.png
-04831.png
-24116.png
-00155.png
-17929.png
-01747.png
-18667.png
-35321.png
-10499.png
-60839.png
-29581.png
-36876.png
-44856.png
-44194.png
-57512.png
-46880.png
-04135.png
-12808.png
-47020.png
-00731.png
-45771.png
-08847.png
-50470.png
-27303.png
-25531.png
-33276.png
-47560.png
-42192.png
-38403.png
-25913.png
-12149.png
-20241.png
-01314.png
-16496.png
-40571.png
-41195.png
-43772.png
-46136.png
-27692.png
-30929.png
-63314.png
-30538.png
-69217.png
-07641.png
-02760.png
-00791.png
-30387.png
-21307.png
-50618.png
-48956.png
-62854.png
-39748.png
-43183.png
-58095.png
-17346.png
-50700.png
-41963.png
-06194.png
-28486.png
-60127.png
-65578.png
-54865.png
-66945.png
-54545.png
-10821.png
-26372.png
-33309.png
-66921.png
-14030.png
-22162.png
-62207.png
-00012.png
-50466.png
-00111.png
-54425.png
-07943.png
-19587.png
-12288.png
-36613.png
-59129.png
-29199.png
-64097.png
-29935.png
-38826.png
-39493.png
-46719.png
-07410.png
-04429.png
-58255.png
-14022.png
-08266.png
-44706.png
-04428.png
-01614.png
-11923.png
-20561.png
-05239.png
-19271.png
-23056.png
-66880.png
-19017.png
-22962.png
-29277.png
-35560.png
-64890.png
-41033.png
-50032.png
-01341.png
-22794.png
-51075.png
-18495.png
-38112.png
-31878.png
-46589.png
-57895.png
-07030.png
-51419.png
-35320.png
-57000.png
-63822.png
-62069.png
-29843.png
-51595.png
-33610.png
-43594.png
-20526.png
-38441.png
-17375.png
-27415.png
-19684.png
-35295.png
-14253.png
-66124.png
-28239.png
-60777.png
-42822.png
-63885.png
-69876.png
-51934.png
-14462.png
-02560.png
-14946.png
-59028.png
-18298.png
-51100.png
-52259.png
-08636.png
-66217.png
-37991.png
-55581.png
-43277.png
-05161.png
-28271.png
-46889.png
-05617.png
-21979.png
-66418.png
-59705.png
-67964.png
-32108.png
-21367.png
-26557.png
-00083.png
-21462.png
-31952.png
-12169.png
-08465.png
-26064.png
-52308.png
-55692.png
-43779.png
-69228.png
-12489.png
-48856.png
-64525.png
-49724.png
-09092.png
-48784.png
-19294.png
-41041.png
-14093.png
-50888.png
-62522.png
-02914.png
-21694.png
-14793.png
-42850.png
-50729.png
-06609.png
-42130.png
-10877.png
-40551.png
-23385.png
-64032.png
-35466.png
-38218.png
-00376.png
-62067.png
-39673.png
-51218.png
-59075.png
-42497.png
-23792.png
-67933.png
-20361.png
-05544.png
-44505.png
-25182.png
-06005.png
-28332.png
-64665.png
-26867.png
-09187.png
-15281.png
-59624.png
-21800.png
-27896.png
-61139.png
-02046.png
-01273.png
-59167.png
-13967.png
-09403.png
-63768.png
-06385.png
-02336.png
-28151.png
-40584.png
-47088.png
-51324.png
-36905.png
-06851.png
-49840.png
-02313.png
-25581.png
-14262.png
-31599.png
-51827.png
-29230.png
-15601.png
-26728.png
-24522.png
-55415.png
-22068.png
-56886.png
-17166.png
-47729.png
-68121.png
-03801.png
-34376.png
-26145.png
-27393.png
-58470.png
-11342.png
-06443.png
-20885.png
-32138.png
-33293.png
-42403.png
-59816.png
-22535.png
-53764.png
-07867.png
-63969.png
-01056.png
-15393.png
-19815.png
-35778.png
-32415.png
-19437.png
-28606.png
-28672.png
-45181.png
-34586.png
-34841.png
-32247.png
-62325.png
-24189.png
-48612.png
-69230.png
-62073.png
-65451.png
-32596.png
-10864.png
-35773.png
-06753.png
-61738.png
-62335.png
-28548.png
-51124.png
-44579.png
-56283.png
-41131.png
-22270.png
-61262.png
-02948.png
-39705.png
-57070.png
-63780.png
-37430.png
-14040.png
-21656.png
-52928.png
-53067.png
-33952.png
-43601.png
-53835.png
-62962.png
-12101.png
-49076.png
-22472.png
-09631.png
-67272.png
-50485.png
-16614.png
-42927.png
-21272.png
-26568.png
-05407.png
-08054.png
-64039.png
-54093.png
-31744.png
-49758.png
-68557.png
-46838.png
-03072.png
-22758.png
-62651.png
-27219.png
-38680.png
-33117.png
-20654.png
-23383.png
-45673.png
-09352.png
-39363.png
-33607.png
-29334.png
-47458.png
-20573.png
-66327.png
-62961.png
-21488.png
-25177.png
-09404.png
-58451.png
-58695.png
-30265.png
-63756.png
-40465.png
-30685.png
-26081.png
-60398.png
-27271.png
-19627.png
-01895.png
-39489.png
-48286.png
-68700.png
-57482.png
-26609.png
-03286.png
-48638.png
-09427.png
-10500.png
-57519.png
-21420.png
-03674.png
-67449.png
-27170.png
-67643.png
-37254.png
-65256.png
-51671.png
-09400.png
-61544.png
-36514.png
-66371.png
-55015.png
-67089.png
-07365.png
-18027.png
-65949.png
-30010.png
-48666.png
-10697.png
-63152.png
-62043.png
-61991.png
-31347.png
-02149.png
-63574.png
-58752.png
-21025.png
-69116.png
-32180.png
-65422.png
-36388.png
-21434.png
-49507.png
-55077.png
-24658.png
-49246.png
-49206.png
-09712.png
-25841.png
-67920.png
-08704.png
-68573.png
-01362.png
-68460.png
-12888.png
-58790.png
-32998.png
-02617.png
-40203.png
-10014.png
-09587.png
-04991.png
-49933.png
-01849.png
-08822.png
-39716.png
-65824.png
-16555.png
-02066.png
-62922.png
-00517.png
-11451.png
-22999.png
-52995.png
-49886.png
-66953.png
-04891.png
-22147.png
-27350.png
-48848.png
-58662.png
-28289.png
-06135.png
-57981.png
-33407.png
-60668.png
-05545.png
-31652.png
-35836.png
-36763.png
-07497.png
-40233.png
-46201.png
-67505.png
-52256.png
-23990.png
-31471.png
-59640.png
-03603.png
-07749.png
-23661.png
-57563.png
-07775.png
-54747.png
-36891.png
-43948.png
-06523.png
-38074.png
-58867.png
-11773.png
-41418.png
-44958.png
-55372.png
-05771.png
-26691.png
-12496.png
-63776.png
-47525.png
-69567.png
-16328.png
-33631.png
-05601.png
-55810.png
-03658.png
-45385.png
-21867.png
-55023.png
-56674.png
-67138.png
-21317.png
-56174.png
-24382.png
-51426.png
-42759.png
-62561.png
-50186.png
-06935.png
-11942.png
-49312.png
-26127.png
-50956.png
-33229.png
-17027.png
-22616.png
-33301.png
-62189.png
-39991.png
-14053.png
-56287.png
-27226.png
-17492.png
-35497.png
-48601.png
-09774.png
-46244.png
-29304.png
-19356.png
-41580.png
-19445.png
-21944.png
-28248.png
-52944.png
-02573.png
-58410.png
-67313.png
-56105.png
-61591.png
-54736.png
-02665.png
-53431.png
-39677.png
-52297.png
-28320.png
-15475.png
-01367.png
-33795.png
-05575.png
-16776.png
-47698.png
-47012.png
-39056.png
-14749.png
-35804.png
-24961.png
-63979.png
-38773.png
-39343.png
-69124.png
-36821.png
-54036.png
-14207.png
-50133.png
-12734.png
-22624.png
-29031.png
-27735.png
-41900.png
-23707.png
-48181.png
-64282.png
-33726.png
-25137.png
-42950.png
-02427.png
-50280.png
-43276.png
-35685.png
-24721.png
-31436.png
-02553.png
-41742.png
-51256.png
-01154.png
-50089.png
-59556.png
-62304.png
-29332.png
-55944.png
-29630.png
-40415.png
-10333.png
-45891.png
-22995.png
-32067.png
-12329.png
-54751.png
-07127.png
-14269.png
-19377.png
-54989.png
-22327.png
-29953.png
-37250.png
-10970.png
-14688.png
-56837.png
-39151.png
-23988.png
-24020.png
-42014.png
-19768.png
-01007.png
-67321.png
-55097.png
-02735.png
-19123.png
-66750.png
-31469.png
-02310.png
-07244.png
-19782.png
-39534.png
-58277.png
-57547.png
-39158.png
-09659.png
-50056.png
-65185.png
-09527.png
-08681.png
-50947.png
-28702.png
-40044.png
-49225.png
-58004.png
-21275.png
-22641.png
-42364.png
-50232.png
-49496.png
-04856.png
-13239.png
-39590.png
-09051.png
-62361.png
-01700.png
-67451.png
-52933.png
-03327.png
-08166.png
-06773.png
-33410.png
-52741.png
-31134.png
-03907.png
-53471.png
-21141.png
-32924.png
-08352.png
-33051.png
-45551.png
-27489.png
-43778.png
-18916.png
-46942.png
-35441.png
-38334.png
-50724.png
-38778.png
-39167.png
-32155.png
-12254.png
-53855.png
-50748.png
-09868.png
-06863.png
-37233.png
-22786.png
-66811.png
-47775.png
-65213.png
-32971.png
-17926.png
-39315.png
-59337.png
-00903.png
-31805.png
-68938.png
-34470.png
-04470.png
-53984.png
-60618.png
-34825.png
-44241.png
-00278.png
-25264.png
-67265.png
-50047.png
-38436.png
-25419.png
-46084.png
-32750.png
-14970.png
-26240.png
-33189.png
-29598.png
-32783.png
-11458.png
-27086.png
-52506.png
-40781.png
-64884.png
-04596.png
-66741.png
-35084.png
-64233.png
-31177.png
-61626.png
-45665.png
-31000.png
-13299.png
-65592.png
-56612.png
-57825.png
-35626.png
-32202.png
-36979.png
-10758.png
-26253.png
-65556.png
-16737.png
-26694.png
-45214.png
-50574.png
-19490.png
-60167.png
-60849.png
-24703.png
-23488.png
-60656.png
-63375.png
-57292.png
-58179.png
-52978.png
-56651.png
-45787.png
-53967.png
-20091.png
-11560.png
-57285.png
-32228.png
-65884.png
-56856.png
-55132.png
-28278.png
-15349.png
-69486.png
-04044.png
-25537.png
-02906.png
-11324.png
-43694.png
-36968.png
-66733.png
-51405.png
-39511.png
-04559.png
-24771.png
-36234.png
-16682.png
-20888.png
-47452.png
-10581.png
-40073.png
-41246.png
-38664.png
-02644.png
-55292.png
-65890.png
-20947.png
-20865.png
-39898.png
-02942.png
-14430.png
-02158.png
-62129.png
-45148.png
-45688.png
-30573.png
-47190.png
-33339.png
-53887.png
-19035.png
-21335.png
-03344.png
-12360.png
-27835.png
-61322.png
-58587.png
-42693.png
-40567.png
-61674.png
-33907.png
-58185.png
-41126.png
-38212.png
-53426.png
-65162.png
-32157.png
-10487.png
-03038.png
-59595.png
-67827.png
-63106.png
-13452.png
-36588.png
-43021.png
-56791.png
-03551.png
-63129.png
-32110.png
-63445.png
-68946.png
-54155.png
-63312.png
-63057.png
-02810.png
-55269.png
-37221.png
-05046.png
-27596.png
-28100.png
-57850.png
-39169.png
-24108.png
-00066.png
-64080.png
-00231.png
-01443.png
-59642.png
-69737.png
-07824.png
-39485.png
-62780.png
-26332.png
-23242.png
-30385.png
-24348.png
-34351.png
-35505.png
-51024.png
-19304.png
-08986.png
-46846.png
-48771.png
-65547.png
-43359.png
-61691.png
-49891.png
-26325.png
-55552.png
-64798.png
-35191.png
-36843.png
-27483.png
-62679.png
-32692.png
-28418.png
-65655.png
-68606.png
-49238.png
-05316.png
-18683.png
-56448.png
-55188.png
-54743.png
-60286.png
-56205.png
-41539.png
-59915.png
-28869.png
-16297.png
-40284.png
-51541.png
-39579.png
-50644.png
-49671.png
-66231.png
-19098.png
-08879.png
-66063.png
-49562.png
-58363.png
-12709.png
-66338.png
-23284.png
-05011.png
-13535.png
-15042.png
-54734.png
-10432.png
-44782.png
-00643.png
-35028.png
-55450.png
-01650.png
-25115.png
-34599.png
-65812.png
-59579.png
-39098.png
-01974.png
-41859.png
-63015.png
-63472.png
-45296.png
-30794.png
-02302.png
-48754.png
-15399.png
-51669.png
-39764.png
-08754.png
-05877.png
-12487.png
-04868.png
-27163.png
-10413.png
-51674.png
-35688.png
-68026.png
-01899.png
-52077.png
-44604.png
-33101.png
-25614.png
-37056.png
-24148.png
-37107.png
-68849.png
-42916.png
-65325.png
-05625.png
-33778.png
-57677.png
-41603.png
-47996.png
-36440.png
-61505.png
-50975.png
-10143.png
-10332.png
-14319.png
-30290.png
-23491.png
-46215.png
-25867.png
-62460.png
-16264.png
-35946.png
-11056.png
-39228.png
-34436.png
-10616.png
-19498.png
-01083.png
-36875.png
-64700.png
-61581.png
-44616.png
-03209.png
-58538.png
-67051.png
-01644.png
-50098.png
-48007.png
-26043.png
-46878.png
-69768.png
-29180.png
-67800.png
-52821.png
-08765.png
-10710.png
-31569.png
-48931.png
-44586.png
-40763.png
-68732.png
-24111.png
-09923.png
-47187.png
-29902.png
-06051.png
-62941.png
-30933.png
-53617.png
-49550.png
-41989.png
-54843.png
-40491.png
-16468.png
-01283.png
-06647.png
-15692.png
-48224.png
-28043.png
-65692.png
-48083.png
-69865.png
-11009.png
-41680.png
-31325.png
-42183.png
-36239.png
-24788.png
-67479.png
-65419.png
-17862.png
-56671.png
-27900.png
-20871.png
-44801.png
-19179.png
-16710.png
-38585.png
-26047.png
-25750.png
-11133.png
-17836.png
-46009.png
-27074.png
-22310.png
-01199.png
-37165.png
-30692.png
-19743.png
-60264.png
-55769.png
-23582.png
-22283.png
-52645.png
-47075.png
-23417.png
-54267.png
-30042.png
-14550.png
-53416.png
-17785.png
-44603.png
-39426.png
-54864.png
-06839.png
-42144.png
-04084.png
-39782.png
-18636.png
-67725.png
-62408.png
-03234.png
-23381.png
-62909.png
-45522.png
-04619.png
-01698.png
-13944.png
-29070.png
-68488.png
-33264.png
-63403.png
-29550.png
-14136.png
-45617.png
-18930.png
-34614.png
-68217.png
-46564.png
-31635.png
-04441.png
-13663.png
-58993.png
-30778.png
-59864.png
-49183.png
-26345.png
-67742.png
-04119.png
-48611.png
-12274.png
-52024.png
-44881.png
-28654.png
-44408.png
-11999.png
-55396.png
-03790.png
-50585.png
-07083.png
-44647.png
-43272.png
-12852.png
-01749.png
-38375.png
-63357.png
-29921.png
-53630.png
-21657.png
-24101.png
-62977.png
-45844.png
-34450.png
-23136.png
-68771.png
-23426.png
-11305.png
-35331.png
-21688.png
-11311.png
-00175.png
-17741.png
-02879.png
-01973.png
-40878.png
-51609.png
-51312.png
-13798.png
-32849.png
-63908.png
-63442.png
-08862.png
-54955.png
-08851.png
-54797.png
-46006.png
-29085.png
-07551.png
-42040.png
-58109.png
-53214.png
-45813.png
-28545.png
-12304.png
-69967.png
-56173.png
-46183.png
-58239.png
-27204.png
-34727.png
-44124.png
-39013.png
-00720.png
-49494.png
-62999.png
-17523.png
-15581.png
-22550.png
-18973.png
-22489.png
-41676.png
-21117.png
-30315.png
-32658.png
-14495.png
-34666.png
-55164.png
-43756.png
-63288.png
-46534.png
-37412.png
-17710.png
-55213.png
-12535.png
-63490.png
-32156.png
-23116.png
-05528.png
-35200.png
-00536.png
-00045.png
-11020.png
-58525.png
-52226.png
-50347.png
-66694.png
-49074.png
-66666.png
-07329.png
-45410.png
-62880.png
-19773.png
-37375.png
-68657.png
-34544.png
-33597.png
-31729.png
-10819.png
-09076.png
-07306.png
-45865.png
-13052.png
-12594.png
-07601.png
-02453.png
-01144.png
-38356.png
-62403.png
-30870.png
-63255.png
-19668.png
-54826.png
-29084.png
-16509.png
-13575.png
-60328.png
-04525.png
-25527.png
-15886.png
-38282.png
-50062.png
-47246.png
-48938.png
-03684.png
-64554.png
-65840.png
-39937.png
-09025.png
-29516.png
-63228.png
-38572.png
-47481.png
-10310.png
-01247.png
-45812.png
-11141.png
-64935.png
-61996.png
-44678.png
-26109.png
-41258.png
-36133.png
-10095.png
-26593.png
-38264.png
-10974.png
-26146.png
-53639.png
-05933.png
-04841.png
-60306.png
-65686.png
-28614.png
-08243.png
-03011.png
-60181.png
-41887.png
-59501.png
-04241.png
-39063.png
-28188.png
-24797.png
-22220.png
-29931.png
-61939.png
-46436.png
-12659.png
-29848.png
-24551.png
-64533.png
-12275.png
-41734.png
-13266.png
-28328.png
-46951.png
-01363.png
-18719.png
-34721.png
-31183.png
-66407.png
-62867.png
-48681.png
-21128.png
-32804.png
-49700.png
-54870.png
-35654.png
-43130.png
-40559.png
-27429.png
-09671.png
-34880.png
-39181.png
-41530.png
-47578.png
-35557.png
-37972.png
-38918.png
-62609.png
-39240.png
-25620.png
-63465.png
-19355.png
-56801.png
-03094.png
-02063.png
-33047.png
-40125.png
-42100.png
-51559.png
-37222.png
-19715.png
-13294.png
-57988.png
-10645.png
-35976.png
-18811.png
-39440.png
-50822.png
-60173.png
-12057.png
-14795.png
-28376.png
-41034.png
-05037.png
-66198.png
-05453.png
-52143.png
-00270.png
-34936.png
-36131.png
-08401.png
-36497.png
-20274.png
-14276.png
-21082.png
-26304.png
-16091.png
-41905.png
-17231.png
-60584.png
-04990.png
-11409.png
-60560.png
-19933.png
-15331.png
-60506.png
-38831.png
-25554.png
-22718.png
-31370.png
-04323.png
-52930.png
-30772.png
-61519.png
-28800.png
-15221.png
-05168.png
-18664.png
-36956.png
-27684.png
-53215.png
-33766.png
-46488.png
-20247.png
-27521.png
-48026.png
-67219.png
-61154.png
-69961.png
-45527.png
-55712.png
-31246.png
-06262.png
-15538.png
-56588.png
-39955.png
-26037.png
-41595.png
-21839.png
-28390.png
-69246.png
-24477.png
-16095.png
-35669.png
-18440.png
-17865.png
-69904.png
-31164.png
-07559.png
-02996.png
-60581.png
-13320.png
-65013.png
-63564.png
-21332.png
-60254.png
-32279.png
-52072.png
-17390.png
-12240.png
-29993.png
-14936.png
-47566.png
-41403.png
-50221.png
-53099.png
-29322.png
-17193.png
-45459.png
-29521.png
-28932.png
-22647.png
-36062.png
-08434.png
-29698.png
-31478.png
-63510.png
-07257.png
-10841.png
-63451.png
-28341.png
-41503.png
-59567.png
-12005.png
-51750.png
-17034.png
-60609.png
-45593.png
-59713.png
-40414.png
-20538.png
-54079.png
-60299.png
-50723.png
-26414.png
-03760.png
-53851.png
-54769.png
-03228.png
-59692.png
-26027.png
-65565.png
-16346.png
-13959.png
-44141.png
-13743.png
-58316.png
-00748.png
-11640.png
-04970.png
-29504.png
-36615.png
-48412.png
-63427.png
-46451.png
-34363.png
-45138.png
-09136.png
-57664.png
-44941.png
-59853.png
-56148.png
-12750.png
-31724.png
-60882.png
-14394.png
-27907.png
-30899.png
-06822.png
-16359.png
-22746.png
-41692.png
-63425.png
-27878.png
-30318.png
-25523.png
-45971.png
-21382.png
-59174.png
-39186.png
-09003.png
-32352.png
-40900.png
-47895.png
-27819.png
-05204.png
-11881.png
-04561.png
-12789.png
-63352.png
-18120.png
-05123.png
-48478.png
-65754.png
-27488.png
-60867.png
-15732.png
-12951.png
-05968.png
-03050.png
-23211.png
-66761.png
-30395.png
-21473.png
-19908.png
-21881.png
-35612.png
-06564.png
-22680.png
-14315.png
-21221.png
-16420.png
-57671.png
-54849.png
-62041.png
-34777.png
-67819.png
-22606.png
-02895.png
-32788.png
-59855.png
-60135.png
-64199.png
-36618.png
-59603.png
-23639.png
-39441.png
-04219.png
-44659.png
-34381.png
-41009.png
-07647.png
-39503.png
-62269.png
-07523.png
-13161.png
-13030.png
-49443.png
-04175.png
-50837.png
-42742.png
-59170.png
-68195.png
-66849.png
-18071.png
-33924.png
-30248.png
-08388.png
-62885.png
-32390.png
-38618.png
-05311.png
-53501.png
-15328.png
-49543.png
-15317.png
-18324.png
-14911.png
-28868.png
-60196.png
-51573.png
-21938.png
-54299.png
-33022.png
-08402.png
-03633.png
-57146.png
-21634.png
-43854.png
-42072.png
-03572.png
-42073.png
-55401.png
-62319.png
-06532.png
-61377.png
-61744.png
-43026.png
-08154.png
-02992.png
-42998.png
-24383.png
-38830.png
-09018.png
-13693.png
-25965.png
-26221.png
-00877.png
-01894.png
-22968.png
-11406.png
-60712.png
-34800.png
-32839.png
-51361.png
-49732.png
-37091.png
-38224.png
-25311.png
-48061.png
-35221.png
-16070.png
-05878.png
-63014.png
-22521.png
-17431.png
-51994.png
-25507.png
-15561.png
-39144.png
-31813.png
-48807.png
-51551.png
-22345.png
-27645.png
-67779.png
-17307.png
-08748.png
-02976.png
-11264.png
-62884.png
-56601.png
-58131.png
-04099.png
-15483.png
-59297.png
-69653.png
-42151.png
-22423.png
-24560.png
-50050.png
-60013.png
-60831.png
-53792.png
-46560.png
-10175.png
-21271.png
-25206.png
-11511.png
-31773.png
-25988.png
-03639.png
-09097.png
-36665.png
-38534.png
-53469.png
-13278.png
-19531.png
-23163.png
-10795.png
-12656.png
-57174.png
-63110.png
-65911.png
-06167.png
-10693.png
-63631.png
-47572.png
-10654.png
-20242.png
-16610.png
-18893.png
-08087.png
-69249.png
-40545.png
-38650.png
-35879.png
-47403.png
-41746.png
-01492.png
-17970.png
-20948.png
-24720.png
-33070.png
-01791.png
-33838.png
-33471.png
-20770.png
-12464.png
-52653.png
-55407.png
-09804.png
-41406.png
-35659.png
-08606.png
-08183.png
-12220.png
-46011.png
-03484.png
-05100.png
-50524.png
-31455.png
-33178.png
-12052.png
-19967.png
-00355.png
-54949.png
-55466.png
-05822.png
-32227.png
-14468.png
-55076.png
-61451.png
-43202.png
-47334.png
-66961.png
-52384.png
-01730.png
-15439.png
-63372.png
-63253.png
-67166.png
-16482.png
-27890.png
-39635.png
-29456.png
-05133.png
-06878.png
-13598.png
-68103.png
-52668.png
-51760.png
-29747.png
-59264.png
-36035.png
-12419.png
-00997.png
-40262.png
-44051.png
-62698.png
-53128.png
-66458.png
-36077.png
-57123.png
-61759.png
-27392.png
-64155.png
-29782.png
-12491.png
-25114.png
-45348.png
-55631.png
-67039.png
-22108.png
-26111.png
-32477.png
-10057.png
-59708.png
-44957.png
-64651.png
-31995.png
-01242.png
-20024.png
-13013.png
-41613.png
-47068.png
-50659.png
-02245.png
-06408.png
-60430.png
-17436.png
-01994.png
-33694.png
-63869.png
-63769.png
-41594.png
-16978.png
-63542.png
-14318.png
-64862.png
-62405.png
-11855.png
-47032.png
-65951.png
-16926.png
-02681.png
-16345.png
-17964.png
-34179.png
-36505.png
-25655.png
-64686.png
-64801.png
-47595.png
-59616.png
-22959.png
-20613.png
-66695.png
-33715.png
-16253.png
-41654.png
-24623.png
-13226.png
-55299.png
-57952.png
-01756.png
-35663.png
-53295.png
-28312.png
-66388.png
-69520.png
-17060.png
-02005.png
-06188.png
-22037.png
-66989.png
-34637.png
-29888.png
-66769.png
-23546.png
-06141.png
-15046.png
-37699.png
-47303.png
-55947.png
-18457.png
-28526.png
-06677.png
-42983.png
-24606.png
-50417.png
-19386.png
-44061.png
-37772.png
-46330.png
-69775.png
-00604.png
-31850.png
-08069.png
-18193.png
-41661.png
-45517.png
-31523.png
-38887.png
-23899.png
-50396.png
-67116.png
-66092.png
-54323.png
-54194.png
-10081.png
-16922.png
-34763.png
-56511.png
-16300.png
-43396.png
-29126.png
-32065.png
-51657.png
-64803.png
-46192.png
-20223.png
-44938.png
-31189.png
-27529.png
-24505.png
-28727.png
-28964.png
-37460.png
-44764.png
-26083.png
-11423.png
-11374.png
-03959.png
-08680.png
-47844.png
-22787.png
-11790.png
-25736.png
-33474.png
-19676.png
-31079.png
-50265.png
-17949.png
-24243.png
-14071.png
-45685.png
-17899.png
-44574.png
-00473.png
-32610.png
-54082.png
-55510.png
-63356.png
-14332.png
-11340.png
-16499.png
-10738.png
-00696.png
-56040.png
-58684.png
-26007.png
-58710.png
-07995.png
-02714.png
-47598.png
-29961.png
-54996.png
-55575.png
-25675.png
-15609.png
-12003.png
-58379.png
-06902.png
-48130.png
-34207.png
-04992.png
-66895.png
-44709.png
-50731.png
-52866.png
-63667.png
-38025.png
-46171.png
-11486.png
-40824.png
-50460.png
-32197.png
-23522.png
-30660.png
-27948.png
-05048.png
-45416.png
-04889.png
-32468.png
-09480.png
-27363.png
-52216.png
-27841.png
-50509.png
-07712.png
-09164.png
-28829.png
-34033.png
-14406.png
-04505.png
-15306.png
-23055.png
-51523.png
-38557.png
-19250.png
-64883.png
-64672.png
-39570.png
-42573.png
-16798.png
-67984.png
-06865.png
-49439.png
-50028.png
-01183.png
-24047.png
-15728.png
-53034.png
-11953.png
-40315.png
-67450.png
-19158.png
-29094.png
-61089.png
-47241.png
-14851.png
-23681.png
-19758.png
-30026.png
-66376.png
-66605.png
-13834.png
-62192.png
-41423.png
-12373.png
-21991.png
-12456.png
-60087.png
-43414.png
-69051.png
-38466.png
-21872.png
-45632.png
-04022.png
-63477.png
-28758.png
-04134.png
-50633.png
-00221.png
-07885.png
-47876.png
-08060.png
-35677.png
-11105.png
-35194.png
-19138.png
-11314.png
-26588.png
-27078.png
-13770.png
-44919.png
-52829.png
-17052.png
-61422.png
-15673.png
-54889.png
-51358.png
-33084.png
-07747.png
-11267.png
-26126.png
-29942.png
-38996.png
-60021.png
-12980.png
-36125.png
-36408.png
-03090.png
-02933.png
-53315.png
-13646.png
-58196.png
-04329.png
-59552.png
-59935.png
-53934.png
-44857.png
-35188.png
-11510.png
-03352.png
-26909.png
-62161.png
-59574.png
-66349.png
-59993.png
-22458.png
-54297.png
-53251.png
-61233.png
-14554.png
-31122.png
-02207.png
-61212.png
-34806.png
-37069.png
-44921.png
-12285.png
-14100.png
-18942.png
-14917.png
-25151.png
-66868.png
-16136.png
-20078.png
-32091.png
-15014.png
-34762.png
-67467.png
-42296.png
-04685.png
-15786.png
-23124.png
-63170.png
-40810.png
-02213.png
-08098.png
-14589.png
-55257.png
-65230.png
-25571.png
-21265.png
-43111.png
-49187.png
-04160.png
-21683.png
-58439.png
-18558.png
-40949.png
-68464.png
-03400.png
-49214.png
-28300.png
-29751.png
-12815.png
-17732.png
-08829.png
-13806.png
-00945.png
-00121.png
-11943.png
-00053.png
-40401.png
-54178.png
-54811.png
-26438.png
-26953.png
-03026.png
-26110.png
-09899.png
-57439.png
-31706.png
-48453.png
-11600.png
-40319.png
-45929.png
-14107.png
-06070.png
-23490.png
-31510.png
-67736.png
-61714.png
-13750.png
-11596.png
-51992.png
-02864.png
-69060.png
-69951.png
-36936.png
-27598.png
-34991.png
-07489.png
-38259.png
-66240.png
-46350.png
-53127.png
-08132.png
-68790.png
-48144.png
-10097.png
-47184.png
-14422.png
-11001.png
-49058.png
-46326.png
-66439.png
-03066.png
-57113.png
-23934.png
-61541.png
-43122.png
-03958.png
-50892.png
-44275.png
-44453.png
-26415.png
-10365.png
-64607.png
-36464.png
-41268.png
-65055.png
-21606.png
-00543.png
-30235.png
-45486.png
-12396.png
-57068.png
-06975.png
-03851.png
-28500.png
-60553.png
-64112.png
-04275.png
-60860.png
-05107.png
-24050.png
-26583.png
-06426.png
-02847.png
-05546.png
-56298.png
-02968.png
-58112.png
-55777.png
-58845.png
-08551.png
-45910.png
-32359.png
-54764.png
-13107.png
-26347.png
-55873.png
-61809.png
-59323.png
-35593.png
-26247.png
-62164.png
-40480.png
-60514.png
-30095.png
-64619.png
-13262.png
-61733.png
-57795.png
-59356.png
-53964.png
-34852.png
-25908.png
-25026.png
-53976.png
-29336.png
-25338.png
-25578.png
-10751.png
-44995.png
-26514.png
-14268.png
-05323.png
-21424.png
-06353.png
-44155.png
-06767.png
-64885.png
-12641.png
-69936.png
-33332.png
-07534.png
-22496.png
-48988.png
-03461.png
-20407.png
-49011.png
-22597.png
-35296.png
-46941.png
-60238.png
-46966.png
-20349.png
-62095.png
-69673.png
-64730.png
-32355.png
-21306.png
-31645.png
-15953.png
-08224.png
-29327.png
-15572.png
-49832.png
-68492.png
-56716.png
-67273.png
-37436.png
-65818.png
-49683.png
-60578.png
-08831.png
-47373.png
-60820.png
-64410.png
-68896.png
-06303.png
-54365.png
-08065.png
-43162.png
-32143.png
-69582.png
-57788.png
-45656.png
-04534.png
-07742.png
-19255.png
-51007.png
-05425.png
-01110.png
-66582.png
-03108.png
-29886.png
-26797.png
-27655.png
-54426.png
-68695.png
-69496.png
-66642.png
-14885.png
-27015.png
-16722.png
-29922.png
-00963.png
-21294.png
-39648.png
-47677.png
-39165.png
-21190.png
-48801.png
-12777.png
-08563.png
-68596.png
-61150.png
-57485.png
-04122.png
-17614.png
-15350.png
-35552.png
-44006.png
-40069.png
-37649.png
-20857.png
-02657.png
-16779.png
-54638.png
-20131.png
-22742.png
-55103.png
-18257.png
-16391.png
-54141.png
-68092.png
-49316.png
-42243.png
-17638.png
-11955.png
-44350.png
-26520.png
-01653.png
-12477.png
-59185.png
-25636.png
-68893.png
-61593.png
-56010.png
-30780.png
-69417.png
-43592.png
-38418.png
-42572.png
-30741.png
-02178.png
-22663.png
-61721.png
-50007.png
-53414.png
-54794.png
-38481.png
-12513.png
-13490.png
-42197.png
-11019.png
-68781.png
-62352.png
-23607.png
-60057.png
-44985.png
-69990.png
-57007.png
-20594.png
-54991.png
-48789.png
-22561.png
-48331.png
-03369.png
-21132.png
-00407.png
-64133.png
-65435.png
-63848.png
-36130.png
-15032.png
-65618.png
-65372.png
-12311.png
-46669.png
-54556.png
-39772.png
-07609.png
-03832.png
-23110.png
-45546.png
-26465.png
-38953.png
-24308.png
-67586.png
-59832.png
-56942.png
-52606.png
-57637.png
-46708.png
-21787.png
-39106.png
-54119.png
-05410.png
-55442.png
-59348.png
-18953.png
-60332.png
-55304.png
-30161.png
-10556.png
-07868.png
-27158.png
-09564.png
-29224.png
-64163.png
-36042.png
-36582.png
-58370.png
-19440.png
-05995.png
-25699.png
-51041.png
-63575.png
-42739.png
-23835.png
-07514.png
-16649.png
-58150.png
-44564.png
-08656.png
-44334.png
-29356.png
-67264.png
-01038.png
-08317.png
-28926.png
-27904.png
-48438.png
-12762.png
-03093.png
-05721.png
-22289.png
-47509.png
-51962.png
-08690.png
-68221.png
-00880.png
-59931.png
-49808.png
-67107.png
-01475.png
-55829.png
-34354.png
-02311.png
-23260.png
-19413.png
-24886.png
-48581.png
-58661.png
-55092.png
-45928.png
-34926.png
-39846.png
-38685.png
-39963.png
-66978.png
-51313.png
-13336.png
-61528.png
-06793.png
-10475.png
-53285.png
-51089.png
-38842.png
-29609.png
-26700.png
-36682.png
-46884.png
-68456.png
-38522.png
-59369.png
-39895.png
-45435.png
-43857.png
-63349.png
-43395.png
-39683.png
-42209.png
-00397.png
-55964.png
-44757.png
-10801.png
-55736.png
-01914.png
-00336.png
-14086.png
-12938.png
-22365.png
-00835.png
-25732.png
-54410.png
-66286.png
-54566.png
-53085.png
-49450.png
-29701.png
-14950.png
-60776.png
-08377.png
-13773.png
-46819.png
-60239.png
-65155.png
-21833.png
-60243.png
-52015.png
-65384.png
-32655.png
-03329.png
-61826.png
-45587.png
-23936.png
-54771.png
-53264.png
-28981.png
-51401.png
-22667.png
-50384.png
-56558.png
-51225.png
-06254.png
-13915.png
-26636.png
-43531.png
-56757.png
-34610.png
-00908.png
-43766.png
-21214.png
-29528.png
-12233.png
-35937.png
-00600.png
-50902.png
-25844.png
-17761.png
-09811.png
-22935.png
-53283.png
-12673.png
-24254.png
-55732.png
-25481.png
-59578.png
-21693.png
-30942.png
-69464.png
-18031.png
-34345.png
-61823.png
-29486.png
-11686.png
-14953.png
-17756.png
-16940.png
-05394.png
-17508.png
-43296.png
-62064.png
-41182.png
-23725.png
-00366.png
-68247.png
-37830.png
-41378.png
-68058.png
-60406.png
-29405.png
-45657.png
-06941.png
-48991.png
-23524.png
-08439.png
-66616.png
-38672.png
-14178.png
-21327.png
-55880.png
-57423.png
-02939.png
-00238.png
-47016.png
-37509.png
-03984.png
-67523.png
-10557.png
-13571.png
-03739.png
-34848.png
-45545.png
-59071.png
-22116.png
-56062.png
-47278.png
-68317.png
-22199.png
-45851.png
-46289.png
-25396.png
-14978.png
-49858.png
-00612.png
-07764.png
-06268.png
-61742.png
-39189.png
-40659.png
-62835.png
-35806.png
-29033.png
-19368.png
-14770.png
-29910.png
-22259.png
-00396.png
-28155.png
-54272.png
-14287.png
-01134.png
-59273.png
-50645.png
-54292.png
-20668.png
-08050.png
-53645.png
-15748.png
-35979.png
-28132.png
-64035.png
-41063.png
-46936.png
-29725.png
-03227.png
-36142.png
-51553.png
-59254.png
-10784.png
-56565.png
-38702.png
-22688.png
-30238.png
-39951.png
-06451.png
-09889.png
-01936.png
-64724.png
-43234.png
-61461.png
-28313.png
-46816.png
-23455.png
-64557.png
-48114.png
-43166.png
-04059.png
-35896.png
-68959.png
-07563.png
-37461.png
-51356.png
-31068.png
-06286.png
-58176.png
-68025.png
-35376.png
-36619.png
-61103.png
-67761.png
-67913.png
-04051.png
-10733.png
-23143.png
-46944.png
-06594.png
-03217.png
-27039.png
-67113.png
-28679.png
-47732.png
-12638.png
-49817.png
-50198.png
-30009.png
-67968.png
-42581.png
-28610.png
-15158.png
-00059.png
-67347.png
-51933.png
-43301.png
-62623.png
-69753.png
-23800.png
-50145.png
-45679.png
-63650.png
-52624.png
-61704.png
-22346.png
-25761.png
-42542.png
-28805.png
-37973.png
-32152.png
-27427.png
-25549.png
-69426.png
-30795.png
-20426.png
-04797.png
-38570.png
-19352.png
-12261.png
-03210.png
-09619.png
-44956.png
-25504.png
-54920.png
-46797.png
-28898.png
-69209.png
-22534.png
-62785.png
-53065.png
-44365.png
-30906.png
-36737.png
-30855.png
-36946.png
-57467.png
-53511.png
-41635.png
-36818.png
-26003.png
-02415.png
-23214.png
-43332.png
-12267.png
-40904.png
-36477.png
-37328.png
-49351.png
-60275.png
-52334.png
-08614.png
-00481.png
-02539.png
-52120.png
-53022.png
-58542.png
-05190.png
-36597.png
-67428.png
-40929.png
-01957.png
-45853.png
-12066.png
-22186.png
-18086.png
-42087.png
-44902.png
-25094.png
-01222.png
-01438.png
-34296.png
-32539.png
-34391.png
-47741.png
-54497.png
-46067.png
-40619.png
-02070.png
-58455.png
-04867.png
-64126.png
-12521.png
-09915.png
-41296.png
-63830.png
-43829.png
-14802.png
-30977.png
-24603.png
-42993.png
-21885.png
-21049.png
-36606.png
-62290.png
-16407.png
-31038.png
-13167.png
-36841.png
-42507.png
-41894.png
-43600.png
-54528.png
-10888.png
-32907.png
-40410.png
-36849.png
-62368.png
-18610.png
-02711.png
-54246.png
-51535.png
-23782.png
-65135.png
-17640.png
-04107.png
-50552.png
-56786.png
-01317.png
-22395.png
-54169.png
-42626.png
-65378.png
-35064.png
-11731.png
-54836.png
-61578.png
-00413.png
-23790.png
-16681.png
-28624.png
-58097.png
-24757.png
-15668.png
-68530.png
-23977.png
-64020.png
-50462.png
-42077.png
-10949.png
-08301.png
-21232.png
-01453.png
-14034.png
-28551.png
-36108.png
-32397.png
-49589.png
-28517.png
-07011.png
-17629.png
-13133.png
-42245.png
-08412.png
-48093.png
-35067.png
-09253.png
-10753.png
-67582.png
-56987.png
-28846.png
-03682.png
-29512.png
-41624.png
-14865.png
-26651.png
-17046.png
-61077.png
-09917.png
-03928.png
-53909.png
-69131.png
-50055.png
-31023.png
-19979.png
-51078.png
-17553.png
-53777.png
-55650.png
-12669.png
-36500.png
-63752.png
-19044.png
-00034.png
-23898.png
-09026.png
-38720.png
-64656.png
-17797.png
-37007.png
-41559.png
-62864.png
-20621.png
-54731.png
-05972.png
-09938.png
-11213.png
-31386.png
-59858.png
-22672.png
-48137.png
-29810.png
-32956.png
-42721.png
-31071.png
-03577.png
-07138.png
-11362.png
-50017.png
-41959.png
-51348.png
-16826.png
-38854.png
-69566.png
-48338.png
-25561.png
-65868.png
-63195.png
-01322.png
-69507.png
-22988.png
-20439.png
-61736.png
-18781.png
-69506.png
-47896.png
-51376.png
-15815.png
-51443.png
-15731.png
-53170.png
-33873.png
-23272.png
-32038.png
-57454.png
-41842.png
-11545.png
-37473.png
-63771.png
-62429.png
-58314.png
-18730.png
-24939.png
-40563.png
-39655.png
-02641.png
-34021.png
-29415.png
-34541.png
-58965.png
-58594.png
-36855.png
-51603.png
-12075.png
-62787.png
-24454.png
-21813.png
-21856.png
-53997.png
-69614.png
-14065.png
-64606.png
-36445.png
-18109.png
-25240.png
-46549.png
-43173.png
-32747.png
-55087.png
-03529.png
-68267.png
-15303.png
-28668.png
-50081.png
-66781.png
-05962.png
-56462.png
-26376.png
-04247.png
-35348.png
-24633.png
-22697.png
-16241.png
-66987.png
-19319.png
-44761.png
-31505.png
-39597.png
-59807.png
-52859.png
-20985.png
-52474.png
-26890.png
-47519.png
-13440.png
-24660.png
-55149.png
-27088.png
-11448.png
-49686.png
-17586.png
-35361.png
-29829.png
-56645.png
-53138.png
-02724.png
-63831.png
-42079.png
-66843.png
-57278.png
-04091.png
-36046.png
-12041.png
-22612.png
-62025.png
-15750.png
-16036.png
-54951.png
-49344.png
-10541.png
-14388.png
-67759.png
-12776.png
-37519.png
-52644.png
-23024.png
-21659.png
-22691.png
-07879.png
-39368.png
-53355.png
-33823.png
-22113.png
-08768.png
-30350.png
-06278.png
-29950.png
-27014.png
-03761.png
-25633.png
-57600.png
-06392.png
-67716.png
-31007.png
-38582.png
-03665.png
-06836.png
-16097.png
-17808.png
-07606.png
-09982.png
-28985.png
-22531.png
-15916.png
-39069.png
-59385.png
-35439.png
-28561.png
-11208.png
-11173.png
-64774.png
-25962.png
-07730.png
-13337.png
-06901.png
-29073.png
-06734.png
-48090.png
-12677.png
-66120.png
-31240.png
-47692.png
-06250.png
-33054.png
-32876.png
-01558.png
-25933.png
-26504.png
-52065.png
-01620.png
-32721.png
-06576.png
-18556.png
-56044.png
-01204.png
-47789.png
-53791.png
-41656.png
-66483.png
-67981.png
-63045.png
-29519.png
-38770.png
-23901.png
-58414.png
-30456.png
-41486.png
-01872.png
-39928.png
-40348.png
-59886.png
-59083.png
-11280.png
-15904.png
-51678.png
-38008.png
-56765.png
-46981.png
-33064.png
-20441.png
-53314.png
-60933.png
-34490.png
-17690.png
-46282.png
-13257.png
-51701.png
-41148.png
-66951.png
-34696.png
-06969.png
-42736.png
-14179.png
-41031.png
-02025.png
-62100.png
-31054.png
-33105.png
-18897.png
-24035.png
-45430.png
-61443.png
-52934.png
-42177.png
-23494.png
-05912.png
-18658.png
-22461.png
-67164.png
-21568.png
-05741.png
-61533.png
-35522.png
-49071.png
-28242.png
-15566.png
-12271.png
-65133.png
-50087.png
-03398.png
-02875.png
-18132.png
-44416.png
-37087.png
-48782.png
-42140.png
-51885.png
-51254.png
-46248.png
-57946.png
-37286.png
-34924.png
-60935.png
-24141.png
-15274.png
-60410.png
-02527.png
-11478.png
-25102.png
-49322.png
-33139.png
-14805.png
-49748.png
-03067.png
-55128.png
-12272.png
-49392.png
-47591.png
-32670.png
-11055.png
-58056.png
-05538.png
-44277.png
-02134.png
-12757.png
-06907.png
-16679.png
-67612.png
-62862.png
-53544.png
-14561.png
-09313.png
-09188.png
-03773.png
-09582.png
-26508.png
-63011.png
-11003.png
-02434.png
-24137.png
-51461.png
-13993.png
-34238.png
-44023.png
-69977.png
-34861.png
-24185.png
-39733.png
-61783.png
-24201.png
-45082.png
-04936.png
-22986.png
-60495.png
-22779.png
-19259.png
-62757.png
-53046.png
-04623.png
-56464.png
-34912.png
-15823.png
-43115.png
-51904.png
-21589.png
-56290.png
-15766.png
-47867.png
-30643.png
-63522.png
-38399.png
-64219.png
-37729.png
-01811.png
-57422.png
-19891.png
-33966.png
-29744.png
-62396.png
-43697.png
-37900.png
-36487.png
-24441.png
-19197.png
-58935.png
-51437.png
-63496.png
-57868.png
-53632.png
-01171.png
-07595.png
-10780.png
-11069.png
-08899.png
-33629.png
-01525.png
-47710.png
-03677.png
-50807.png
-38391.png
-09288.png
-44994.png
-50672.png
-39801.png
-35639.png
-32990.png
-42418.png
-08539.png
-31275.png
-39632.png
-46674.png
-12911.png
-29186.png
-48101.png
-43097.png
-09945.png
-23892.png
-32400.png
-31564.png
-21621.png
-44539.png
-07984.png
-43639.png
-41901.png
-10037.png
-48631.png
-02093.png
-01004.png
-61206.png
-46081.png
-00086.png
-41777.png
-10284.png
-45431.png
-65767.png
-66604.png
-14648.png
-35971.png
-18374.png
-21993.png
-43969.png
-42722.png
-44974.png
-10863.png
-47360.png
-08029.png
-56366.png
-54498.png
-26558.png
-47417.png
-58086.png
-14451.png
-02959.png
-62859.png
-22468.png
-68243.png
-29156.png
-47428.png
-40761.png
-13500.png
-08268.png
-12172.png
-42342.png
-31794.png
-50973.png
-20598.png
-12371.png
-09460.png
-68962.png
-25388.png
-50582.png
-15755.png
-67120.png
-44594.png
-01541.png
-08344.png
-16701.png
-29241.png
-21140.png
-46020.png
-20199.png
-10981.png
-46682.png
-52619.png
-24580.png
-35815.png
-29797.png
-01682.png
-08800.png
-35910.png
-59074.png
-09799.png
-61242.png
-55711.png
-35357.png
-17697.png
-10954.png
-06852.png
-29959.png
-49945.png
-17679.png
-35973.png
-33912.png
-04707.png
-49715.png
-65809.png
-63221.png
-36990.png
-05892.png
-34455.png
-42367.png
-59210.png
-48432.png
-68682.png
-63395.png
-56087.png
-12717.png
-09494.png
-29574.png
-05609.png
-16623.png
-15687.png
-49563.png
-52323.png
-59704.png
-24099.png
-10158.png
-51625.png
-06972.png
-62246.png
-39986.png
-67218.png
-20623.png
-44547.png
-21469.png
-00123.png
-67686.png
-14122.png
-69887.png
-17765.png
-34547.png
-43005.png
-61017.png
-11278.png
-16069.png
-55876.png
-61260.png
-22545.png
-53543.png
-32704.png
-49481.png
-68134.png
-29018.png
-63918.png
-05679.png
-44502.png
-01377.png
-65801.png
-40128.png
-44612.png
-59722.png
-57219.png
-66988.png
-19915.png
-22463.png
-37956.png
-08971.png
-36181.png
-46332.png
-06582.png
-23065.png
-59906.png
-32385.png
-06884.png
-43552.png
-25266.png
-28514.png
-62517.png
-26382.png
-62887.png
-65456.png
-18628.png
-59042.png
-02170.png
-61811.png
-49755.png
-02378.png
-24324.png
-25608.png
-06251.png
-19339.png
-44008.png
-06933.png
-42686.png
-46334.png
-34646.png
-51599.png
-05466.png
-60761.png
-69871.png
-11006.png
-07521.png
-35176.png
-36634.png
-67123.png
-40008.png
-06122.png
-36186.png
-21457.png
-49994.png
-13327.png
-63023.png
-35962.png
-13507.png
-54041.png
-20739.png
-26527.png
-56096.png
-21106.png
-34178.png
-09857.png
-68995.png
-65269.png
-03229.png
-52579.png
-04764.png
-20236.png
-54572.png
-64375.png
-50459.png
-53096.png
-45177.png
-41550.png
-51155.png
-24394.png
-33061.png
-36123.png
-52592.png
-50174.png
-10629.png
-10985.png
-61567.png
-21563.png
-38034.png
-25368.png
-41209.png
-18168.png
-03071.png
-59789.png
-18422.png
-33453.png
-30377.png
-24933.png
-00995.png
-15311.png
-56456.png
-35079.png
-16593.png
-46723.png
-47862.png
-48141.png
-53716.png
-50846.png
-63200.png
-41567.png
-17746.png
-08670.png
-58812.png
-29773.png
-16030.png
-15651.png
-24697.png
-28012.png
-65885.png
-27091.png
-25612.png
-26327.png
-06413.png
-20909.png
-04668.png
-18864.png
-22682.png
-01506.png
-02155.png
-02799.png
-52792.png
-14769.png
-37907.png
-63654.png
-17190.png
-25106.png
-42162.png
-50660.png
-21956.png
-04027.png
-08639.png
-16537.png
-33387.png
-54232.png
-51969.png
-03247.png
-42143.png
-27740.png
-03813.png
-12227.png
-42672.png
-50211.png
-64503.png
-14142.png
-45940.png
-08926.png
-31922.png
-48743.png
-34325.png
-22801.png
-36397.png
-36498.png
-27679.png
-64629.png
-25280.png
-52051.png
-00286.png
-60950.png
-39944.png
-56323.png
-01306.png
-52467.png
-13031.png
-66525.png
-32255.png
-60528.png
-63502.png
-48598.png
-19480.png
-31136.png
-22127.png
-14353.png
-33704.png
-03003.png
-16538.png
-61354.png
-49637.png
-62811.png
-30466.png
-15392.png
-53922.png
-67556.png
-26949.png
-27175.png
-06248.png
-55339.png
-47930.png
-05766.png
-19675.png
-13566.png
-44319.png
-09042.png
-24123.png
-54591.png
-08509.png
-20686.png
-41032.png
-15156.png
-69315.png
-40049.png
-19918.png
-36848.png
-07050.png
-17232.png
-23687.png
-16741.png
-69232.png
-35703.png
-13276.png
-35969.png
-58901.png
-54910.png
-02108.png
-12257.png
-38602.png
-61064.png
-06021.png
-33854.png
-60955.png
-46202.png
-03190.png
-26891.png
-29731.png
-24052.png
-52119.png
-08055.png
-29253.png
-18098.png
-25066.png
-45291.png
-45584.png
-49288.png
-64990.png
-20168.png
-57504.png
-60020.png
-01669.png
-05588.png
-15879.png
-03575.png
-25119.png
-22277.png
-06029.png
-27244.png
-13364.png
-26128.png
-61006.png
-28380.png
-66392.png
-56861.png
-60707.png
-61753.png
-32133.png
-32056.png
-25690.png
-24515.png
-50635.png
-38254.png
-30035.png
-36334.png
-46706.png
-21554.png
-15105.png
-48960.png
-03836.png
-36587.png
-62402.png
-09241.png
-14838.png
-34876.png
-32720.png
-54320.png
-60737.png
-50305.png
-32552.png
-24568.png
-50773.png
-56206.png
-66402.png
-63008.png
-05959.png
-57612.png
-08586.png
-46636.png
-63653.png
-36030.png
-26934.png
-68205.png
-03947.png
-23371.png
-36160.png
-13144.png
-33416.png
-28436.png
-63440.png
-14689.png
-46295.png
-03182.png
-05157.png
-50767.png
-38084.png
-40527.png
-65260.png
-13880.png
-55781.png
-26491.png
-09337.png
-22069.png
-57384.png
-58630.png
-67823.png
-51719.png
-47687.png
-17784.png
-37169.png
-37113.png
-47604.png
-61431.png
-55932.png
-01692.png
-18463.png
-57702.png
-06859.png
-02668.png
-54927.png
-14695.png
-09566.png
-47706.png
-33964.png
-51849.png
-60464.png
-25934.png
-00582.png
-15580.png
-41596.png
-39486.png
-23506.png
-46834.png
-26425.png
-32053.png
-07809.png
-48910.png
-53782.png
-03117.png
-50301.png
-31049.png
-17751.png
-14757.png
-23935.png
-16453.png
-24009.png
-08624.png
-15258.png
-12337.png
-38521.png
-51397.png
-17265.png
-62047.png
-30410.png
-19745.png
-64256.png
-68107.png
-69806.png
-53789.png
-25987.png
-26545.png
-17471.png
-36097.png
-39519.png
-29498.png
-62898.png
-46210.png
-63154.png
-60274.png
-10767.png
-47918.png
-37059.png
-11275.png
-08170.png
-07540.png
-38709.png
-23248.png
-01803.png
-22154.png
-55159.png
-67304.png
-27849.png
-61585.png
-14326.png
-04947.png
-50880.png
-27469.png
-12701.png
-47350.png
-05629.png
-21185.png
-63407.png
-23640.png
-23515.png
-36313.png
-01022.png
-00568.png
-06319.png
-15279.png
-55803.png
-01626.png
-37574.png
-55091.png
-49313.png
-02195.png
-49902.png
-45997.png
-14729.png
-12016.png
-39890.png
-64615.png
-51501.png
-28176.png
-10959.png
-33840.png
-57092.png
-35518.png
-44877.png
-53879.png
-65463.png
-26697.png
-46019.png
-34909.png
-08830.png
-41198.png
-63095.png
-52726.png
-61686.png
-34248.png
-13705.png
-21057.png
-11206.png
-63717.png
-28552.png
-25852.png
-25583.png
-67767.png
-12768.png
-00917.png
-65349.png
-05417.png
-20123.png
-63765.png
-60251.png
-64925.png
-66805.png
-17567.png
-63381.png
-21068.png
-45167.png
-50043.png
-67112.png
-44259.png
-52707.png
-61859.png
-46593.png
-12713.png
-44570.png
-69861.png
-27097.png
-08555.png
-34885.png
-55951.png
-00160.png
-20143.png
-19853.png
-45783.png
-46574.png
-09786.png
-39016.png
-65298.png
-57737.png
-53086.png
-40876.png
-44034.png
-27590.png
-56348.png
-54327.png
-14516.png
-02620.png
-07324.png
-33627.png
-32229.png
-08579.png
-16963.png
-34678.png
-25611.png
-66242.png
-62947.png
-51042.png
-24663.png
-25938.png
-13664.png
-28497.png
-45369.png
-64034.png
-52133.png
-51635.png
-00796.png
-44305.png
-52745.png
-49171.png
-03554.png
-19338.png
-41620.png
-53325.png
-15590.png
-67389.png
-05847.png
-32818.png
-33053.png
-17834.png
-61031.png
-04774.png
-45936.png
-22112.png
-47506.png
-19634.png
-39008.png
-60603.png
-19441.png
-39654.png
-69995.png
-14203.png
-67898.png
-44992.png
-67006.png
-20725.png
-66531.png
-28750.png
-13626.png
-25138.png
-20497.png
-25816.png
-02297.png
-58586.png
-53545.png
-22321.png
-21292.png
-17907.png
-07989.png
-15409.png
-23939.png
-39436.png
-11091.png
-64970.png
-02687.png
-61237.png
-27639.png
-37012.png
-34792.png
-03246.png
-06697.png
-29132.png
-54727.png
-40149.png
-50699.png
-63656.png
-37467.png
-17843.png
-20624.png
-49722.png
-58466.png
-25382.png
-22122.png
-27019.png
-59351.png
-45328.png
-60652.png
-48543.png
-28542.png
-25355.png
-05848.png
-21598.png
-05332.png
-59829.png
-40748.png
-08763.png
-69677.png
-41696.png
-22635.png
-42756.png
-35838.png
-43535.png
-29342.png
-60630.png
-04543.png
-56297.png
-25617.png
-14116.png
-18467.png
-02085.png
-52590.png
-26450.png
-64144.png
-41888.png
-11812.png
-14088.png
-19719.png
-66990.png
-49141.png
-21040.png
-07897.png
-19637.png
-61605.png
-26644.png
-53188.png
-60200.png
-38049.png
-43859.png
-03343.png
-19625.png
-33194.png
-37631.png
-40154.png
-47607.png
-26554.png
-07311.png
-33705.png
-37403.png
-42472.png
-16375.png
-05165.png
-53993.png
-03885.png
-27106.png
-39384.png
-51400.png
-09474.png
-38119.png
-11202.png
-57523.png
-52559.png
-55871.png
-35991.png
-66271.png
-11095.png
-50202.png
-19548.png
-29184.png
-39011.png
-30337.png
-59557.png
-48772.png
-28125.png
-12928.png
-41154.png
-41666.png
-07219.png
-35431.png
-58309.png
-01840.png
-67762.png
-39358.png
-22383.png
-58948.png
-18782.png
-00674.png
-30682.png
-27666.png
-39601.png
-38098.png
-45707.png
-03130.png
-58766.png
-59442.png
-40744.png
-68458.png
-43427.png
-32948.png
-16564.png
-33242.png
-58718.png
-46607.png
-41138.png
-03219.png
-27738.png
-65579.png
-11199.png
-49285.png
-59096.png
-11182.png
-10903.png
-61105.png
-56693.png
-21966.png
-59491.png
-64291.png
-03611.png
-36153.png
-17176.png
-33652.png
-09085.png
-50900.png
-68701.png
-35731.png
-03694.png
-55517.png
-07014.png
-63401.png
-35494.png
-53358.png
-07235.png
-33510.png
-15455.png
-53240.png
-32759.png
-54538.png
-26919.png
-04370.png
-36243.png
-03970.png
-25261.png
-15443.png
-08519.png
-43747.png
-40785.png
-02767.png
-02853.png
-09657.png
-56326.png
-43677.png
-29491.png
-50374.png
-12639.png
-44075.png
-47031.png
-41261.png
-16558.png
-54546.png
-44510.png
-33091.png
-61400.png
-11318.png
-15355.png
-68430.png
-29240.png
-36201.png
-06737.png
-02733.png
-40064.png
-31185.png
-53009.png
-09465.png
-66839.png
-21028.png
-57976.png
-17537.png
-25322.png
-36835.png
-30868.png
-69906.png
-55342.png
-34913.png
-37184.png
-60588.png
-05225.png
-27332.png
-22930.png
-04367.png
-56520.png
-07902.png
-43827.png
-12672.png
-48569.png
-50895.png
-64524.png
-54357.png
-68313.png
-52470.png
-31791.png
-45249.png
-49291.png
-59531.png
-67941.png
-30946.png
-09307.png
-39960.png
-07979.png
-10640.png
-01593.png
-50815.png
-53383.png
-45899.png
-60199.png
-61540.png
-55721.png
-02087.png
-37013.png
-34379.png
-08453.png
-41643.png
-24049.png
-15096.png
-18537.png
-57473.png
-36706.png
-10730.png
-11085.png
-50335.png
-30938.png
-59209.png
-18525.png
-09609.png
-18522.png
-64638.png
-66890.png
-18344.png
-60252.png
-36268.png
-45689.png
-18803.png
-36867.png
-16067.png
-22427.png
-02157.png
-20112.png
-49446.png
-36435.png
-03922.png
-53247.png
-62544.png
-14271.png
-69073.png
-35616.png
-53596.png
-09341.png
-34774.png
-54833.png
-66743.png
-04033.png
-16384.png
-14323.png
-69421.png
-30788.png
-38562.png
-31869.png
-24366.png
-58136.png
-30863.png
-62391.png
-18966.png
-05738.png
-40230.png
-36636.png
-23821.png
-54798.png
-30173.png
-67892.png
-33511.png
-04919.png
-26351.png
-31624.png
-07767.png
-36043.png
-55939.png
-18950.png
-48499.png
-63710.png
-61562.png
-20773.png
-31812.png
-42589.png
-39107.png
-08148.png
-50908.png
-36909.png
-01910.png
-47791.png
-12504.png
-33817.png
-63088.png
-08213.png
-64432.png
-51420.png
-54135.png
-45629.png
-17723.png
-30948.png
-54759.png
-61790.png
-61598.png
-41442.png
-33002.png
-08850.png
-16571.png
-27577.png
-31827.png
-12104.png
-15031.png
-13301.png
-25763.png
-32566.png
-61843.png
-65092.png
-64070.png
-23287.png
-30300.png
-27254.png
-58667.png
-43351.png
-64067.png
-15999.png
-62342.png
-32591.png
-19747.png
-55563.png
-51424.png
-00361.png
-68454.png
-36389.png
-50920.png
-45847.png
-18819.png
-68436.png
-10140.png
-16933.png
-46570.png
-05042.png
-45282.png
-25305.png
-03673.png
-45180.png
-48884.png
-11859.png
-12164.png
-62993.png
-20059.png
-63082.png
-46661.png
-13991.png
-30053.png
-63628.png
-26885.png
-44675.png
-00398.png
-12522.png
-29217.png
-58679.png
-54834.png
-57984.png
-61481.png
-57362.png
-42712.png
-26773.png
-27815.png
-12218.png
-59988.png
-37215.png
-65186.png
-48470.png
-05571.png
-50751.png
-43683.png
-38975.png
-62387.png
-04002.png
-51375.png
-02364.png
-58260.png
-05136.png
-22032.png
-26185.png
-41043.png
-01309.png
-19240.png
-40790.png
-55539.png
-22093.png
-20438.png
-59112.png
-22105.png
-08009.png
-23501.png
-13135.png
-29579.png
-11465.png
-22519.png
-64695.png
-27179.png
-62376.png
-04353.png
-20942.png
-26001.png
-50267.png
-37457.png
-11103.png
-10505.png
-30751.png
-56805.png
-65096.png
-23456.png
-08226.png
-42293.png
-56666.png
-09292.png
-57886.png
-26449.png
-05152.png
-09974.png
-15145.png
-21528.png
-22783.png
-45485.png
-11376.png
-18835.png
-13648.png
-04938.png
-61752.png
-48182.png
-23091.png
-31982.png
-66749.png
-44577.png
-51732.png
-17448.png
-52299.png
-08511.png
-64251.png
-46864.png
-54370.png
-64931.png
-22212.png
-64712.png
-45705.png
-01699.png
-37125.png
-65958.png
-43144.png
-62526.png
-51534.png
-35052.png
-43547.png
-18305.png
-26819.png
-50297.png
-14002.png
-52395.png
-34221.png
-51605.png
-16442.png
-09898.png
-06068.png
-62939.png
-42776.png
-49629.png
-28492.png
-46879.png
-34540.png
-39982.png
-58876.png
-12934.png
-57874.png
-13515.png
-63714.png
-18199.png
-53516.png
-11563.png
-55750.png
-19618.png
-65220.png
-11590.png
-40720.png
-67845.png
-23243.png
-22634.png
-35850.png
-47414.png
-56843.png
-32612.png
-48888.png
-64171.png
-29755.png
-55590.png
-22118.png
-00017.png
-66723.png
-42777.png
-42074.png
-44799.png
-48373.png
-25174.png
-29826.png
-39711.png
-68818.png
-21318.png
-10712.png
-09879.png
-50118.png
-13964.png
-64103.png
-18216.png
-15730.png
-14767.png
-11064.png
-42083.png
-12077.png
-54802.png
-28666.png
-23027.png
-16272.png
-69312.png
-04131.png
-16563.png
-63007.png
-40719.png
-33275.png
-25790.png
-22687.png
-43804.png
-40854.png
-07097.png
-69263.png
-37319.png
-66047.png
-52160.png
-16208.png
-16854.png
-32637.png
-61313.png
-22831.png
-27872.png
-40889.png
-45248.png
-11119.png
-01250.png
-08821.png
-61940.png
-62507.png
-42055.png
-13146.png
-33226.png
-50401.png
-06010.png
-39523.png
-50925.png
-19484.png
-09126.png
-31066.png
-29112.png
-26131.png
-06501.png
-67484.png
-65616.png
-45114.png
-11740.png
-32237.png
-33140.png
-55405.png
-67605.png
-20877.png
-02376.png
-13100.png
-01494.png
-58693.png
-26903.png
-36617.png
-43712.png
-29052.png
-54051.png
-69281.png
-28646.png
-65721.png
-15860.png
-19010.png
-24777.png
-07708.png
-31258.png
-01263.png
-30842.png
-23895.png
-12004.png
-20178.png
-05519.png
-20795.png
-60460.png
-23745.png
-15365.png
-04595.png
-11830.png
-00655.png
-54541.png
-41076.png
-07025.png
-47703.png
-37112.png
-27397.png
-22157.png
-24740.png
-34743.png
-05490.png
-25376.png
-26942.png
-64087.png
-37424.png
-13365.png
-22464.png
-16439.png
-54693.png
-37155.png
-31757.png
-22460.png
-12889.png
-59913.png
-47821.png
-53502.png
-64747.png
-11027.png
-62318.png
-23374.png
-28173.png
-65666.png
-68688.png
-25267.png
-60259.png
-39807.png
-26029.png
-42552.png
-47019.png
-48608.png
-51979.png
-44104.png
-18044.png
-57758.png
-11170.png
-53763.png
-48120.png
-14959.png
-59865.png
-05440.png
-63664.png
-52463.png
-44382.png
-12242.png
-69183.png
-20331.png
-40178.png
-36671.png
-48724.png
-24495.png
-09046.png
-48056.png
-49578.png
-50506.png
-69297.png
-54366.png
-01258.png
-54253.png
-56598.png
-28902.png
-27261.png
-61254.png
-04549.png
-65461.png
-58133.png
-47400.png
-33490.png
-34716.png
-19648.png
-39306.png
-31649.png
-04384.png
-30806.png
-11875.png
-11255.png
-23237.png
-59149.png
-42639.png
-20625.png
-05881.png
-61218.png
-20170.png
-06627.png
-63517.png
-42868.png
-05423.png
-39078.png
-50104.png
-62688.png
-20875.png
-69063.png
-22604.png
-40722.png
-20913.png
-08985.png
-03083.png
-22297.png
-57350.png
-25168.png
-41997.png
-49712.png
-33900.png
-42053.png
-57329.png
-69279.png
-63587.png
-24179.png
-60524.png
-13595.png
-58918.png
-47151.png
-58717.png
-67594.png
-46518.png
-03911.png
-41827.png
-40462.png
-20090.png
-67853.png
-25992.png
-36612.png
-15965.png
-30974.png
-07694.png
-02744.png
-47962.png
-01672.png
-61286.png
-20373.png
-59966.png
-19061.png
-53548.png
-64285.png
-12853.png
-40199.png
-38801.png
-11860.png
-27242.png
-68248.png
-62472.png
-15524.png
-28090.png
-12632.png
-02270.png
-31550.png
-10261.png
-31600.png
-37661.png
-45359.png
-42230.png
-61408.png
-53496.png
-38659.png
-19820.png
-31839.png
-61778.png
-34045.png
-33174.png
-28440.png
-54637.png
-62851.png
-09520.png
-38405.png
-36697.png
-62711.png
-30147.png
-19375.png
-26396.png
-69975.png
-26886.png
-30729.png
-37224.png
-17364.png
-01828.png
-18941.png
-34562.png
-16171.png
-53707.png
-12516.png
-26607.png
-27013.png
-46521.png
-20769.png
-34430.png
-40561.png
-33580.png
-45955.png
-24362.png
-59649.png
-69788.png
-47816.png
-25154.png
-55656.png
-17735.png
-04259.png
-58167.png
-10367.png
-14177.png
-51098.png
-67864.png
-31187.png
-65748.png
-24066.png
-01062.png
-59904.png
-05004.png
-48617.png
-62235.png
-52033.png
-48472.png
-19647.png
-18540.png
-06534.png
-65888.png
-12767.png
-60320.png
-13906.png
-45675.png
-11277.png
-60870.png
-15056.png
-08220.png
-14084.png
-62553.png
-62998.png
-17635.png
-06014.png
-38128.png
-11453.png
-24951.png
-13588.png
-65059.png
-38712.png
-07908.png
-68299.png
-50755.png
-28094.png
-54006.png
-24598.png
-51438.png
-56084.png
-15992.png
-17722.png
-61411.png
-42011.png
-24986.png
-58783.png
-41885.png
-60384.png
-51673.png
-35304.png
-06527.png
-19917.png
-14007.png
-34293.png
-35629.png
-51009.png
-60719.png
-67677.png
-16650.png
-58272.png
-40777.png
-65376.png
-30945.png
-01451.png
-47382.png
-55051.png
-25832.png
-28152.png
-01514.png
-21121.png
-54958.png
-38735.png
-14563.png
-51050.png
-05627.png
-60696.png
-44672.png
-27774.png
-36304.png
-23772.png
-52953.png
-19671.png
-04569.png
-16299.png
-68096.png
-25804.png
-65992.png
-53060.png
-57326.png
-01937.png
-01946.png
-23662.png
-66586.png
-16238.png
-02842.png
-30079.png
-65165.png
-08660.png
-18872.png
-28179.png
-47548.png
-39333.png
-10294.png
-67820.png
-32406.png
-15447.png
-04932.png
-23173.png
-08150.png
-40678.png
-43174.png
-34994.png
-40726.png
-33513.png
-08387.png
-03233.png
-62903.png
-13494.png
-52027.png
-30877.png
-64411.png
-54571.png
-61607.png
-04958.png
-18175.png
-06945.png
-56164.png
-38789.png
-08672.png
-38795.png
-11549.png
-48937.png
-60062.png
-56006.png
-07364.png
-45720.png
-26804.png
-47402.png
-29794.png
-10240.png
-44926.png
-41109.png
-48842.png
-00545.png
-53561.png
-30539.png
-40425.png
-37359.png
-41890.png
-43736.png
-48863.png
-03113.png
-68903.png
-38046.png
-43749.png
-35625.png
-42054.png
-36980.png
-66181.png
-10766.png
-19631.png
-17968.png
-64974.png
-46354.png
-09482.png
-07493.png
-48411.png
-02284.png
-28192.png
-38431.png
-42489.png
-50075.png
-14907.png
-28516.png
-38412.png
-22320.png
-34286.png
-13369.png
-45198.png
-30963.png
-27893.png
-49869.png
-42862.png
-28886.png
-44812.png
-14403.png
-49118.png
-28738.png
-68698.png
-15858.png
-42006.png
-02601.png
-40093.png
-36346.png
-11672.png
-40371.png
-35876.png
-64499.png
-34485.png
-17416.png
-26500.png
-37001.png
-23719.png
-42557.png
-39981.png
-66403.png
-53663.png
-20510.png
-43861.png
-14512.png
-25851.png
-02393.png
-29796.png
-39708.png
-35862.png
-05081.png
-30451.png
-54037.png
-48866.png
-67394.png
-41029.png
-04326.png
-22782.png
-56977.png
-40279.png
-39428.png
-25769.png
-30648.png
-16991.png
-49833.png
-17810.png
-43726.png
-37542.png
-66478.png
-59862.png
-10987.png
-08246.png
-42957.png
-05839.png
-10083.png
-62484.png
-18258.png
-45401.png
-37309.png
-58775.png
-02011.png
-01503.png
-55475.png
-25113.png
-15229.png
-22601.png
-47262.png
-28123.png
-69570.png
-38146.png
-32423.png
-56446.png
-05292.png
-25635.png
-50421.png
-29097.png
-22299.png
-63954.png
-48042.png
-32843.png
-09041.png
-16688.png
-06827.png
-62981.png
-40447.png
-22928.png
-25773.png
-25491.png
-52573.png
-31363.png
-58896.png
-53912.png
-52764.png
-54569.png
-51131.png
-34317.png
-65429.png
-29793.png
-54208.png
-23596.png
-34641.png
-21101.png
-65703.png
-37905.png
-22050.png
-14910.png
-11158.png
-33717.png
-08198.png
-26181.png
-36222.png
-43991.png
-10288.png
-45869.png
-68945.png
-03349.png
-53725.png
-66796.png
-13476.png
-54765.png
-45556.png
-26735.png
-26250.png
-63320.png
-34891.png
-24689.png
-49660.png
-06539.png
-60729.png
-49349.png
-26766.png
-13745.png
-44868.png
-56858.png
-19252.png
-00378.png
-62539.png
-15343.png
-29970.png
-25446.png
-03754.png
-28321.png
-69831.png
-46435.png
-55155.png
-18009.png
-59822.png
-63232.png
-65394.png
-59364.png
-64529.png
-40850.png
-20873.png
-41084.png
-34046.png
-00727.png
-68184.png
-08230.png
-24977.png
-05513.png
-41707.png
-41188.png
-47214.png
-37236.png
-00273.png
-60154.png
-59558.png
-43713.png
-37031.png
-36964.png
-02670.png
-18285.png
-35035.png
-63716.png
-15144.png
-59039.png
-69490.png
-40249.png
-52134.png
-50214.png
-24292.png
-65959.png
-27611.png
-13623.png
-41196.png
-30322.png
-09277.png
-58113.png
-01617.png
-19578.png
-53087.png
-22484.png
-19242.png
-46531.png
-03180.png
-45867.png
-69706.png
-30639.png
-27806.png
-06926.png
-63626.png
-13784.png
-12417.png
-44336.png
-00036.png
-29208.png
-42080.png
-11885.png
-41141.png
-00603.png
-36027.png
-69267.png
-67157.png
-48457.png
-57345.png
-40747.png
-10010.png
-01396.png
-17426.png
-60336.png
-45495.png
-69872.png
-63870.png
-35021.png
-55094.png
-62330.png
-59524.png
-33067.png
-48976.png
-05335.png
-12655.png
-26706.png
-33232.png
-39393.png
-49198.png
-06214.png
-28942.png
-48497.png
-18980.png
-33822.png
-60030.png
-38198.png
-66004.png
-63291.png
-32880.png
-28351.png
-28832.png
-69381.png
-06841.png
-23510.png
-59247.png
-66159.png
-13594.png
-68888.png
-64453.png
-62053.png
-16911.png
-14310.png
-33503.png
-44212.png
-01947.png
-27057.png
-61351.png
-58883.png
-50507.png
-48563.png
-49807.png
-34582.png
-46097.png
-55892.png
-10445.png
-13480.png
-14667.png
-13510.png
-31473.png
-30270.png
-27868.png
-69610.png
-40466.png
-17473.png
-34874.png
-34347.png
-32691.png
-29563.png
-66842.png
-17946.png
-17049.png
-06137.png
-62524.png
-20662.png
-03255.png
-17215.png
-66138.png
-40610.png
-20228.png
-10497.png
-33523.png
-20663.png
-17545.png
-14558.png
-02074.png
-30330.png
-33529.png
-22247.png
-29948.png
-28348.png
-20568.png
-15340.png
-37638.png
-47541.png
-64071.png
-40104.png
-10797.png
-63928.png
-55021.png
-13396.png
-16319.png
-52277.png
-27067.png
-28080.png
-21060.png
-48035.png
-53038.png
-68005.png
-17993.png
-41387.png
-56197.png
-12716.png
-51514.png
-22188.png
-06418.png
-56242.png
-10566.png
-21734.png
-40664.png
-19376.png
-32045.png
-20914.png
-18751.png
-09906.png
-16897.png
-43517.png
-38474.png
-25385.png
-07406.png
-35486.png
-43294.png
-08095.png
-20292.png
-26079.png
-07444.png
-20811.png
-18672.png
-58485.png
-54995.png
-08424.png
-26231.png
-18229.png
-48745.png
-34865.png
-38736.png
-49113.png
-36333.png
-57872.png
-49265.png
-01753.png
-51289.png
-44738.png
-58123.png
-00644.png
-10584.png
-42374.png
-66300.png
-63180.png
-53234.png
-25918.png
-28962.png
-39308.png
-25873.png
-48523.png
-38318.png
-68968.png
-02133.png
-32271.png
-08646.png
-50602.png
-44590.png
-21216.png
-32420.png
-50155.png
-13655.png
-53914.png
-42442.png
-47760.png
-62140.png
-48667.png
-16274.png
-15139.png
-62147.png
-02148.png
-66235.png
-20308.png
-63162.png
-37942.png
-67302.png
-10655.png
-34785.png
-21682.png
-36180.png
-07649.png
-23916.png
-12167.png
-38323.png
-52286.png
-11017.png
-37818.png
-29817.png
-14905.png
-26186.png
-20035.png
-13255.png
-12139.png
-53294.png
-46638.png
-53336.png
-67032.png
-08066.png
-35833.png
-58980.png
-45134.png
-45451.png
-34122.png
-61219.png
-49935.png
-32267.png
-52568.png
-42682.png
-16714.png
-19046.png
-60586.png
-12827.png
-08733.png
-69429.png
-47104.png
-45703.png
-42972.png
-14726.png
-26659.png
-57920.png
-31966.png
-54164.png
-11806.png
-61073.png
-04922.png
-00449.png
-31070.png
-31925.png
-51794.png
-08300.png
-48361.png
-37550.png
-61164.png
-06032.png
-15269.png
-14889.png
-49293.png
-40313.png
-17991.png
-19313.png
-35920.png
-13980.png
-58198.png
-25440.png
-20283.png
-62733.png
-35574.png
-17278.png
-08798.png
-17806.png
-11625.png
-22117.png
-42793.png
-60627.png
-28352.png
-22434.png
-67858.png
-26076.png
-42444.png
-50053.png
-01271.png
-37230.png
-37440.png
-30796.png
-24607.png
-13353.png
-37258.png
-52982.png
-01297.png
-03008.png
-63177.png
-54580.png
-27090.png
-35058.png
-43785.png
-57846.png
-43460.png
-04995.png
-13483.png
-06335.png
-37057.png
-21936.png
-64833.png
-66714.png
-66768.png
-53326.png
-65870.png
-15963.png
-39838.png
-52363.png
-65685.png
-08068.png
-02291.png
-56128.png
-01067.png
-55642.png
-32951.png
-49705.png
-15143.png
-57358.png
-04985.png
-26652.png
-68259.png
-42695.png
-39505.png
-53786.png
-32601.png
-14106.png
-62525.png
-24994.png
-38269.png
-34159.png
-69989.png
-24229.png
-26749.png
-29811.png
-18622.png
-30089.png
-23156.png
-41772.png
-48737.png
-31454.png
-62050.png
-40682.png
-51221.png
-42782.png
-66298.png
-58811.png
-67998.png
-09247.png
-39203.png
-39377.png
-27509.png
-61690.png
-33526.png
-24552.png
-46494.png
-58564.png
-69139.png
-45861.png
-69392.png
-55169.png
-67657.png
-49709.png
-62846.png
-37688.png
-56344.png
-34663.png
-47764.png
-65671.png
-51802.png
-19580.png
-46374.png
-08110.png
-65346.png
-33947.png
-05863.png
-60919.png
-63136.png
-59539.png
-23738.png
-22728.png
-40398.png
-62392.png
-49601.png
-28088.png
-25255.png
-51227.png
-54931.png
-46953.png
-56276.png
-27120.png
-15959.png
-50031.png
-04199.png
-12401.png
-47961.png
-10298.png
-61634.png
-25780.png
-39213.png
-22754.png
-03797.png
-64201.png
-09855.png
-31857.png
-50693.png
-50295.png
-63421.png
-63570.png
-33187.png
-51423.png
-44739.png
-07505.png
-51380.png
-45894.png
-08936.png
-14325.png
-05009.png
-50131.png
-59674.png
-53672.png
-05285.png
-63925.png
-39826.png
-20685.png
-24270.png
-04221.png
-02036.png
-66289.png
-09298.png
-30324.png
-68769.png
-52335.png
-57541.png
-25955.png
-11101.png
-14198.png
-38755.png
-33216.png
-07072.png
-25749.png
-36138.png
-59026.png
-15218.png
-03558.png
-20253.png
-57520.png
-57564.png
-54133.png
-04504.png
-42340.png
-28388.png
-37014.png
-67378.png
-53627.png
-14641.png
-63524.png
-44308.png
-54351.png
-47599.png
-50882.png
-62632.png
-36616.png
-43661.png
-08129.png
-47530.png
-44226.png
-23489.png
-07657.png
-43054.png
-08723.png
-03526.png
-69174.png
-22139.png
-03505.png
-23141.png
-55432.png
-47929.png
-19556.png
-25889.png
-37884.png
-60316.png
-44584.png
-22558.png
-35162.png
-53547.png
-61689.png
-50706.png
-01401.png
-14990.png
-61061.png
-43556.png
-07748.png
-69670.png
-16864.png
-05243.png
-05044.png
-25320.png
-09119.png
-46034.png
-63783.png
-07481.png
-19692.png
-19973.png
-45447.png
-09269.png
-62359.png
-51615.png
-63556.png
-48889.png
-10486.png
-65670.png
-34452.png
-68466.png
-42832.png
-15034.png
-32943.png
-32472.png
-05445.png
-62299.png
-18279.png
-28953.png
-69964.png
-00038.png
-34254.png
-65599.png
-16294.png
-34935.png
-01535.png
-08730.png
-50229.png
-56451.png
-67100.png
-46473.png
-53032.png
-00157.png
-15911.png
-68334.png
-20659.png
-38113.png
-58881.png
-61565.png
-68719.png
-33598.png
-59073.png
-06656.png
-60303.png
-31392.png
-37034.png
-61785.png
-10050.png
-53094.png
-62985.png
-42191.png
-58816.png
-69837.png
-45590.png
-02607.png
-63107.png
-10715.png
-63672.png
-19403.png
-62253.png
-61677.png
-47544.png
-27933.png
-58874.png
-31364.png
-56559.png
-25013.png
-17391.png
-39076.png
-44753.png
-28062.png
-44491.png
-18812.png
-35856.png
-68298.png
-21539.png
-40475.png
-16389.png
-07632.png
-08423.png
-45305.png
-46124.png
-48201.png
-12437.png
-65343.png
-43580.png
-63318.png
-05736.png
-56920.png
-41412.png
-08525.png
-40734.png
-43439.png
-33707.png
-11519.png
-09756.png
-62856.png
-19665.png
-22369.png
-00436.png
-40094.png
-56909.png
-53689.png
-67209.png
-51690.png
-20886.png
-61338.png
-00679.png
-45634.png
-13199.png
-62944.png
-47364.png
-36272.png
-05857.png
-58969.png
-46532.png
-60674.png
-36614.png
-29260.png
-58092.png
-36903.png
-12561.png
-29541.png
-51886.png
-45423.png
-09450.png
-62019.png
-32432.png
-57754.png
-07152.png
-28166.png
-00056.png
-30016.png
-22181.png
-20736.png
-68759.png
-43960.png
-35098.png
-58261.png
-15665.png
-11159.png
-65761.png
-32252.png
-47894.png
-16001.png
-39737.png
-19824.png
-29645.png
-41850.png
-03678.png
-58771.png
-30240.png
-59613.png
-11900.png
-25580.png
-47491.png
-56857.png
-10080.png
-34664.png
-58751.png
-28077.png
-51564.png
-66964.png
-41345.png
-01328.png
-01201.png
-31699.png
-47753.png
-00026.png
-31229.png
-11414.png
-13195.png
-07454.png
-07024.png
-01718.png
-40882.png
-03642.png
-50250.png
-68007.png
-07589.png
-21013.png
-38731.png
-23729.png
-51053.png
-64773.png
-47034.png
-53090.png
-44122.png
-28715.png
-62127.png
-50103.png
-49679.png
-37842.png
-01051.png
-40062.png
-67971.png
-65510.png
-29352.png
-23563.png
-55611.png
-27472.png
-02175.png
-50444.png
-08817.png
-55770.png
-20531.png
-24107.png
-31792.png
-19412.png
-69161.png
-23861.png
-38807.png
-16643.png
-40593.png
-20921.png
-33762.png
-23291.png
-13101.png
-43103.png
-42769.png
-21167.png
-37202.png
-10103.png
-11517.png
-24163.png
-54831.png
-21163.png
-35652.png
-33475.png
-39739.png
-35913.png
-37863.png
-22625.png
-25510.png
-39694.png
-60400.png
-64395.png
-58491.png
-53433.png
-00584.png
-17382.png
-13084.png
-38480.png
-27499.png
-64465.png
-00617.png
-61575.png
-63539.png
-65742.png
-34044.png
-22938.png
-46196.png
-30467.png
-01017.png
-15998.png
-67939.png
-36155.png
-09265.png
-17342.png
-28945.png
-15007.png
-02702.png
-04859.png
-06266.png
-25348.png
-38033.png
-50010.png
-35844.png
-41027.png
-50921.png
-28400.png
-41509.png
-68611.png
-03157.png
-52584.png
-34084.png
-02325.png
-69856.png
-69601.png
-29561.png
-36781.png
-07373.png
-68575.png
-32078.png
-36556.png
-58714.png
-27464.png
-26061.png
-64310.png
-22344.png
-57606.png
-62663.png
-30820.png
-54289.png
-61931.png
-02719.png
-08694.png
-31777.png
-66421.png
-24061.png
-46689.png
-66947.png
-60215.png
-52552.png
-55223.png
-35437.png
-08589.png
-16048.png
-27749.png
-15545.png
-23447.png
-17728.png
-08229.png
-54458.png
-08827.png
-39379.png
-41574.png
-58289.png
-57681.png
-57169.png
-00487.png
-34675.png
-16352.png
-55997.png
-67838.png
-49998.png
-52181.png
-16566.png
-24851.png
-52560.png
-60744.png
-20517.png
-36322.png
-32285.png
-12662.png
-28498.png
-04944.png
-29823.png
-33546.png
-21282.png
-16409.png
-34629.png
-25914.png
-53427.png
-69082.png
-49389.png
-39278.png
-41759.png
-03462.png
-58298.png
-01652.png
-15431.png
-69460.png
-26208.png
-17609.png
-24845.png
-46204.png
-30728.png
-13211.png
-07585.png
-57251.png
-06722.png
-46965.png
-29721.png
-52507.png
-62793.png
-17073.png
-50394.png
-44908.png
-59016.png
-48085.png
-56256.png
-56020.png
-03740.png
-44722.png
-27251.png
-04478.png
-66709.png
-05033.png
-46752.png
-52669.png
-43675.png
-62868.png
-37993.png
-65957.png
-56060.png
-49229.png
-45881.png
-38327.png
-57132.png
-31883.png
-26210.png
-69447.png
-16822.png
-69827.png
-35465.png
-21728.png
-34039.png
-17882.png
-68175.png
-27702.png
-52114.png
-52085.png
-48701.png
-03202.png
-19395.png
-31609.png
-28745.png
-38850.png
-10692.png
-28866.png
-09590.png
-20570.png
-43050.png
-07889.png
-36114.png
-42357.png
-54863.png
-67038.png
-06090.png
-25497.png
-41712.png
-02417.png
-02039.png
-42032.png
-01846.png
-59341.png
-65739.png
-58243.png
-37289.png
-10489.png
-34577.png
-50400.png
-14720.png
-30840.png
-39280.png
-24017.png
-20232.png
-17581.png
-05139.png
-01203.png
-42150.png
-48275.png
-68073.png
-04882.png
-29868.png
-32280.png
-68071.png
-09443.png
-41775.png
-32113.png
-31171.png
-16180.png
-33803.png
-57821.png
-69085.png
-66366.png
-52632.png
-40148.png
-29876.png
-67569.png
-50924.png
-25932.png
-66858.png
-64917.png
-34964.png
-35951.png
-50964.png
-18207.png
-57293.png
-46165.png
-08884.png
-63081.png
-58038.png
-42779.png
-68804.png
-56530.png
-56273.png
-38494.png
-57150.png
-33269.png
-64318.png
-46583.png
-36955.png
-01257.png
-02638.png
-48658.png
-51328.png
-07613.png
-30003.png
-18898.png
-39329.png
-31245.png
-21183.png
-27506.png
-64128.png
-60957.png
-43464.png
-09842.png
-59038.png
-48391.png
-09972.png
-69148.png
-02296.png
-62092.png
-34973.png
-40717.png
-32698.png
-51952.png
-68094.png
-68229.png
-44110.png
-44174.png
-01460.png
-67248.png
-49015.png
-10688.png
-33198.png
-38651.png
-39434.png
-51807.png
-23192.png
-18267.png
-13511.png
-04067.png
-34136.png
-24507.png
-65854.png
-31217.png
-22757.png
-07487.png
-41199.png
-06723.png
-01372.png
-41011.png
-10320.png
-51471.png
-48958.png
-12048.png
-60363.png
-54721.png
-58119.png
-21217.png
-49875.png
-14410.png
-65034.png
-00986.png
-19582.png
-12971.png
-43957.png
-10061.png
-65612.png
-05928.png
-46269.png
-12959.png
-53412.png
-60269.png
-64186.png
-42848.png
-12175.png
-67144.png
-39618.png
-55398.png
-31997.png
-13620.png
-51307.png
-56882.png
-51428.png
-17388.png
-58988.png
-33590.png
-18184.png
-23322.png
-47168.png
-64977.png
-31967.png
-49416.png
-06460.png
-36551.png
-00866.png
-12147.png
-66295.png
-21038.png
-30428.png
-17365.png
-56025.png
-00990.png
-00934.png
-06819.png
-23688.png
-35814.png
-57183.png
-35418.png
-01136.png
-48570.png
-08419.png
-55205.png
-36099.png
-44262.png
-36642.png
-58581.png
-25343.png
-19493.png
-68758.png
-35750.png
-17493.png
-20760.png
-30156.png
-11382.png
-26063.png
-44069.png
-64787.png
-09716.png
-37556.png
-64376.png
-48027.png
-51059.png
-59451.png
-12983.png
-20300.png
-50092.png
-50528.png
-23973.png
-44765.png
-59873.png
-13346.png
-64565.png
-02451.png
-38329.png
-05199.png
-01331.png
-36385.png
-49191.png
-33894.png
-45472.png
-10952.png
-42254.png
-36384.png
-26985.png
-03355.png
-38633.png
-18462.png
-60308.png
-02013.png
-01782.png
-16371.png
-17487.png
-28510.png
-41070.png
-44019.png
-47638.png
-59332.png
-38358.png
-33152.png
-55459.png
-09488.png
-41001.png
-55047.png
-66622.png
-14513.png
-52889.png
-48200.png
-07160.png
-23303.png
-57554.png
-60176.png
-18752.png
-18912.png
-58444.png
-46622.png
-05558.png
-51837.png
-37901.png
-56259.png
-19393.png
-52539.png
-09536.png
-35584.png
-07666.png
-53811.png
-05686.png
-39268.png
-07545.png
-46837.png
-23229.png
-35673.png
-27003.png
-44789.png
-65916.png
-19532.png
-15635.png
-52906.png
-48164.png
-01233.png
-07842.png
-37642.png
-07617.png
-69211.png
-47128.png
-01779.png
-47386.png
-53996.png
-09406.png
-05086.png
-57199.png
-11238.png
-11705.png
-47848.png
-09876.png
-44846.png
-23183.png
-07420.png
-52043.png
-55239.png
-18878.png
-38776.png
-68638.png
-66455.png
-47522.png
-35640.png
-35579.png
-09299.png
-42979.png
-38245.png
-42825.png
-16227.png
-12310.png
-55672.png
-44676.png
-19384.png
-51219.png
-32651.png
-09331.png
-26908.png
-18523.png
-60025.png
-14791.png
-64076.png
-17311.png
-07644.png
-19529.png
-11450.png
-64160.png
-68608.png
-67126.png
-28880.png
-55237.png
-63907.png
-10278.png
-22026.png
-38455.png
-64532.png
-23742.png
-54152.png
-62411.png
-55012.png
-45565.png
-05893.png
-41886.png
-50806.png
-65109.png
-59125.png
-05802.png
-06037.png
-45721.png
-17651.png
-28133.png
-16661.png
-32629.png
-59483.png
-00120.png
-40157.png
-67794.png
-60516.png
-68955.png
-07529.png
-54445.png
-66837.png
-31388.png
-18376.png
-66299.png
-13282.png
-03076.png
-31596.png
-48258.png
-49942.png
-21922.png
-57186.png
-00869.png
-41793.png
-18249.png
-31179.png
-42554.png
-31430.png
-09658.png
-41193.png
-40557.png
-15599.png
-38006.png
-53787.png
-68000.png
-27500.png
-18834.png
-30008.png
-43950.png
-15346.png
-12009.png
-17524.png
-30750.png
-21614.png
-18340.png
-61906.png
-36901.png
-02626.png
-32010.png
-68989.png
-60072.png
-44191.png
-16071.png
-02749.png
-36219.png
-33329.png
-57163.png
-68805.png
-53961.png
-11322.png
-59696.png
-07981.png
-45420.png
-46651.png
-17468.png
-49375.png
-32712.png
-15659.png
-49937.png
-45277.png
-35324.png
-03277.png
-34284.png
-31751.png
-03610.png
-01254.png
-30670.png
-13126.png
-22450.png
-04894.png
-27290.png
-14594.png
-37846.png
-15935.png
-37766.png
-50656.png
-13348.png
-56948.png
-00912.png
-16907.png
-51528.png
-44451.png
-53771.png
-22934.png
-04669.png
-35145.png
-48776.png
-30284.png
-47357.png
-46287.png
-07116.png
-31273.png
-10198.png
-11995.png
-42900.png
-25599.png
-33860.png
-08712.png
-11541.png
-52876.png
-10382.png
-63128.png
-44206.png
-36815.png
-65247.png
-52071.png
-59502.png
-10093.png
-28532.png
-18201.png
-39561.png
-51876.png
-40420.png
-57958.png
-63438.png
-02694.png
-59027.png
-01826.png
-68306.png
-60901.png
-56410.png
-36185.png
-40312.png
-02020.png
-68639.png
-17912.png
-28244.png
-69934.png
-02468.png
-43967.png
-32495.png
-62762.png
-54878.png
-57969.png
-07386.png
-11296.png
-54311.png
-07864.png
-55153.png
-36152.png
-33492.png
-24008.png
-25662.png
-15333.png
-32474.png
-13164.png
-31503.png
-43951.png
-03291.png
-56637.png
-25257.png
-15225.png
-67102.png
-41693.png
-20302.png
-18502.png
-46377.png
-04036.png
-25546.png
-14077.png
-32319.png
-52719.png
-03814.png
-60611.png
-28335.png
-08137.png
-48503.png
-09215.png
-52112.png
-14219.png
-59009.png
-16349.png
-63956.png
-19790.png
-00472.png
-11175.png
-13248.png
-59943.png
-29318.png
-56473.png
-57550.png
-28185.png
-51618.png
-35495.png
-10964.png
-01602.png
-50640.png
-25977.png
-45023.png
-62029.png
-24336.png
-63321.png
-08057.png
-65203.png
-13855.png
-08188.png
-28513.png
-48280.png
-15515.png
-45759.png
-22806.png
-40325.png
-16968.png
-31098.png
-07314.png
-51106.png
-69929.png
-23940.png
-56862.png
-60773.png
-50069.png
-00946.png
-24514.png
-35107.png
-26059.png
-56369.png
-40818.png
-60133.png
-22207.png
-63327.png
-28397.png
-40928.png
-42709.png
-53658.png
-05514.png
-59362.png
-31969.png
-09960.png
-09746.png
-37029.png
-69787.png
-44496.png
-47998.png
-34528.png
-25535.png
-14962.png
-05797.png
-46993.png
-58706.png
-41080.png
-53002.png
-10329.png
-11023.png
-22081.png
-64851.png
-03089.png
-51012.png
-56992.png
-41230.png
-28827.png
-17054.png
-06192.png
-37930.png
-43089.png
-07596.png
-51637.png
-69014.png
-58992.png
-37959.png
-51350.png
-48080.png
-46504.png
-61291.png
-30697.png
-35011.png
-37759.png
-39256.png
-13766.png
-58589.png
-47655.png
-40482.png
-32663.png
-66507.png
-17883.png
-60096.png
-09225.png
-44688.png
-48573.png
-37652.png
-15224.png
-55126.png
-66903.png
-58191.png
-59118.png
-61239.png
-50898.png
-42050.png
-55540.png
-06410.png
-69159.png
-00410.png
-16223.png
-53125.png
-69980.png
-25020.png
-38938.png
-55080.png
-33663.png
-39762.png
-03738.png
-29030.png
-59212.png
-53017.png
-03516.png
-38546.png
-01504.png
-25172.png
-05540.png
-64911.png
-03256.png
-07801.png
-26585.png
-64488.png
-12540.png
-18276.png
-17819.png
-29729.png
-43170.png
-42026.png
-57599.png
-44945.png
-25692.png
-02455.png
-05306.png
-17526.png
-30890.png
-42687.png
-12370.png
-26951.png
-20305.png
-10237.png
-55694.png
-59197.png
-21564.png
-26157.png
-63433.png
-30297.png
-34667.png
-13172.png
-18461.png
-21830.png
-46740.png
-11759.png
-49405.png
-60896.png
-57627.png
-13038.png
-65926.png
-46272.png
-17841.png
-24334.png
-00269.png
-18306.png
-14584.png
-44221.png
-03150.png
-23960.png
-30208.png
-58282.png
-08078.png
-10967.png
-17767.png
-40248.png
-55451.png
-34064.png
-69356.png
-50034.png
-34862.png
-41096.png
-09759.png
-25279.png
-49883.png
-60874.png
-57450.png
-37139.png
-61611.png
-05441.png
-51073.png
-17274.png
-15633.png
-09320.png
-44468.png
-16809.png
-67833.png
-45031.png
-48399.png
-67187.png
-56332.png
-26853.png
-03199.png
-17139.png
-15857.png
-12964.png
-02612.png
-64902.png
-32033.png
-54281.png
-57013.png
-48841.png
-11068.png
-05291.png
-36866.png
-65319.png
-58966.png
-55774.png
-65624.png
-47233.png
-07184.png
-25400.png
-46022.png
-02032.png
-69810.png
-13392.png
-56583.png
-41282.png
-43961.png
-38881.png
-68774.png
-69242.png
-55868.png
-24668.png
-62568.png
-04729.png
-19701.png
-33758.png
-17650.png
-16408.png
-09994.png
-21737.png
-42499.png
-04611.png
-63238.png
-62706.png
-24242.png
-24436.png
-03987.png
-47779.png
-03973.png
-18841.png
-17692.png
-16749.png
-32060.png
-28649.png
-55580.png
-37526.png
-04038.png
-03941.png
-10152.png
-29654.png
-23244.png
-32041.png
-32772.png
-15134.png
-21229.png
-58350.png
-24043.png
-04318.png
-36278.png
-49825.png
-33206.png
-64206.png
-10853.png
-47376.png
-49428.png
-09747.png
-22141.png
-39347.png
-43852.png
-19679.png
-66493.png
-37214.png
-22455.png
-14037.png
-57338.png
-13789.png
-27257.png
-03321.png
-04077.png
-48731.png
-11431.png
-61822.png
-43878.png
-18586.png
-21080.png
-63781.png
-39868.png
-49332.png
-66847.png
-19911.png
-21633.png
-37835.png
-17593.png
-69733.png
-37623.png
-68481.png
-62741.png
-35657.png
-54699.png
-45376.png
-52724.png
-01798.png
-58579.png
-40804.png
-26008.png
-06997.png
-65998.png
-20720.png
-05548.png
-54539.png
-60685.png
-40657.png
-65224.png
-59147.png
-21821.png
-62354.png
-60649.png
-07623.png
-68405.png
-30900.png
-25308.png
-49772.png
-61435.png
-37880.png
-03919.png
-15425.png
-32717.png
-45230.png
-69069.png
-25945.png
-16120.png
-03870.png
-59717.png
-22681.png
-54762.png
-59687.png
-15696.png
-02763.png
-11402.png
-43140.png
-69953.png
-63027.png
-20515.png
-31701.png
-35743.png
-25148.png
-50727.png
-09645.png
-29862.png
-47822.png
-42599.png
-10183.png
-12679.png
-38244.png
-39114.png
-58010.png
-56740.png
-63426.png
-37246.png
-09637.png
-59089.png
-37225.png
-34175.png
-68598.png
-08273.png
-11249.png
-00502.png
-68062.png
-45024.png
-60164.png
-34694.png
-00606.png
-54717.png
-59314.png
-49131.png
-11693.png
-14497.png
-28089.png
-33998.png
-32906.png
-66279.png
-63444.png
-33459.png
-24915.png
-02265.png
-14089.png
-18892.png
-52778.png
-61340.png
-00948.png
-25317.png
-14016.png
-23087.png
-62700.png
-13048.png
-32079.png
-45189.png
-53467.png
-21707.png
-43285.png
-56774.png
-67232.png
-69072.png
-67526.png
-67634.png
-28674.png
-54645.png
-59765.png
-04475.png
-54022.png
-12225.png
-23955.png
-28956.png
-68776.png
-00204.png
-25186.png
-02981.png
-55826.png
-07655.png
-21030.png
-42400.png
-53862.png
-52541.png
-09104.png
-41751.png
-38262.png
-31417.png
-15362.png
-31871.png
-54753.png
-68475.png
-24003.png
-43311.png
-11580.png
-09373.png
-10522.png
-33961.png
-56455.png
-07315.png
-32838.png
-44136.png
-18379.png
-42884.png
-25730.png
-59127.png
-43024.png
-44173.png
-45492.png
-03747.png
-43760.png
-49426.png
-03468.png
-69785.png
-38714.png
-00558.png
-41399.png
-54755.png
-06786.png
-63766.png
-50441.png
-66015.png
-39620.png
-38856.png
-59094.png
-63330.png
-60677.png
-67425.png
-54914.png
-68206.png
-49630.png
-39532.png
-68633.png
-63577.png
-44462.png
-60377.png
-05673.png
-31077.png
-06065.png
-53269.png
-04898.png
-17038.png
-37656.png
-43323.png
-46905.png
-39814.png
-67776.png
-41161.png
-10735.png
-22004.png
-22340.png
-61304.png
-01024.png
-21303.png
-60796.png
-39380.png
-13733.png
-03310.png
-07628.png
-15313.png
-53837.png
-24381.png
-18943.png
-59695.png
-29487.png
-01640.png
-49210.png
-41045.png
-27209.png
-36802.png
-65024.png
-62837.png
-47094.png
-12177.png
-00950.png
-10117.png
-03257.png
-17877.png
-59109.png
-60708.png
-11553.png
-11650.png
-68289.png
-49472.png
-47253.png
-69549.png
-20984.png
-62280.png
-58151.png
-47928.png
-40027.png
-45950.png
-65544.png
-47983.png
-26750.png
-33251.png
-47483.png
-26165.png
-06799.png
-61203.png
-46156.png
-64056.png
-31674.png
-00663.png
-13935.png
-60307.png
-62213.png
-44937.png
-64633.png
-13893.png
-34408.png
-06323.png
-56603.png
-51274.png
-54992.png
-02261.png
-38338.png
-18791.png
-17554.png
-55208.png
-32261.png
-21368.png
-34294.png
-25519.png
-64940.png
-29582.png
-28148.png
-03354.png
-35307.png
-22738.png
-20718.png
-00485.png
-24408.png
-30433.png
-66046.png
-33615.png
-38205.png
-06394.png
-32492.png
-25615.png
-00368.png
-44157.png
-17440.png
-29437.png
-22351.png
-53448.png
-33926.png
-36478.png
-02515.png
-13736.png
-26938.png
-55198.png
-66250.png
-42377.png
-37293.png
-58476.png
-17204.png
-13371.png
-32769.png
-28394.png
-28723.png
-19551.png
-60900.png
-46610.png
-27860.png
-39569.png
-10831.png
-46285.png
-07170.png
-20287.png
-33723.png
-56701.png
-24357.png
-30294.png
-10346.png
-59659.png
-69017.png
-53604.png
-00514.png
-67058.png
-18859.png
-33386.png
-04809.png
-16186.png
-10681.png
-13863.png
-51198.png
-45307.png
-48104.png
-52344.png
-02748.png
-30694.png
-07782.png
-16242.png
-16604.png
-45344.png
-53523.png
-53425.png
-28438.png
-04483.png
-32997.png
-47171.png
-10932.png
-51659.png
-18186.png
-54848.png
-38376.png
-40443.png
-44015.png
-37367.png
-41448.png
-50196.png
-30126.png
-04692.png
-61977.png
-32017.png
-18571.png
-52556.png
-68599.png
-35025.png
-29433.png
-37050.png
-20628.png
-51894.png
-17618.png
-41582.png
-50001.png
-59911.png
-34374.png
-16951.png
-51036.png
-05600.png
-60179.png
-19415.png
-48751.png
-35110.png
-67685.png
-22645.png
-40013.png
-08984.png
-26175.png
-65499.png
-50115.png
-56157.png
-47415.png
-60778.png
-65471.png
-52861.png
-09470.png
-44733.png
-40875.png
-28496.png
-00169.png
-67501.png
-21646.png
-58690.png
-66209.png
-08414.png
-32162.png
-26439.png
-21372.png
-43866.png
-31387.png
-57710.png
-47630.png
-00870.png
-64132.png
-23617.png
-67534.png
-37137.png
-29061.png
-37076.png
-34032.png
-15360.png
-27077.png
-05060.png
-58892.png
-32510.png
-16795.png
-16775.png
-69884.png
-32062.png
-26629.png
-19134.png
-17099.png
-10375.png
-49194.png
-18762.png
-23832.png
-65413.png
-60197.png
-06967.png
-27360.png
-63365.png
-59882.png
-43211.png
-03095.png
-13271.png
-69700.png
-16959.png
-01538.png
-46948.png
-32741.png
-58565.png
-19230.png
-20939.png
-22282.png
-32447.png
-21848.png
-45976.png
-59221.png
-20229.png
-67563.png
-12326.png
-33928.png
-51403.png
-15040.png
-69269.png
-29937.png
-02521.png
-61511.png
-23549.png
-43373.png
-23336.png
-64117.png
-05234.png
-55373.png
-01330.png
-18473.png
-66196.png
-62058.png
-19987.png
-22880.png
-30206.png
-05896.png
-30232.png
-55340.png
-20607.png
-17573.png
-08315.png
-52433.png
-36237.png
-29040.png
-58471.png
-34142.png
-25473.png
-61914.png
-34795.png
-65415.png
-28894.png
-33889.png
-28219.png
-37561.png
-32485.png
-30168.png
-11828.png
-10924.png
-30676.png
-32929.png
-25961.png
-60726.png
-62646.png
-41750.png
-36840.png
-04194.png
-30990.png
-30575.png
-30749.png
-37379.png
-67457.png
-45112.png
-60424.png
-17022.png
-03120.png
-16619.png
-23840.png
-31026.png
-43539.png
-29520.png
-41786.png
-51846.png
-26479.png
-44187.png
-61579.png
-19436.png
-68163.png
-01226.png
-60815.png
-57152.png
-34367.png
-01943.png
-17079.png
-62070.png
-39809.png
-65629.png
-08896.png
-19685.png
-65158.png
-65731.png
-55285.png
-50783.png
-64740.png
-23473.png
-67673.png
-03679.png
-03523.png
-53783.png
-58945.png
-26725.png
-65432.png
-20636.png
-57253.png
-27471.png
-61142.png
-28013.png
-35143.png
-17822.png
-39083.png
-46749.png
-66322.png
-61110.png
-06966.png
-36637.png
-15882.png
-20294.png
-17733.png
-46107.png
-67547.png
-27984.png
-02188.png
-30050.png
-60641.png
-04767.png
-00281.png
-27640.png
-23878.png
-25788.png
-17177.png
-58305.png
-34640.png
-37310.png
-46776.png
-12739.png
-04598.png
-64942.png
-36779.png
-56399.png
-55653.png
-20469.png
-38632.png
-42580.png
-22086.png
-67458.png
-04762.png
-21433.png
-31865.png
-64510.png
-00646.png
-62508.png
-63534.png
-41587.png
-07450.png
-19094.png
-36809.png
-08032.png
-00375.png
-58639.png
-26678.png
-05920.png
-56730.png
-64849.png
-53014.png
-48783.png
-17249.png
-69727.png
-13451.png
-05871.png
-02164.png
-14685.png
-35724.png
-39488.png
-59384.png
-47205.png
-37521.png
-46973.png
-43418.png
-05584.png
-35532.png
-31587.png
-67201.png
-21071.png
-10889.png
-02121.png
-25574.png
-12451.png
-34218.png
-48216.png
-61728.png
-44803.png
-12416.png
-39874.png
-02830.png
-26093.png
-66188.png
-66574.png
-02909.png
-05101.png
-09068.png
-07492.png
-40908.png
-42482.png
-35404.png
-65880.png
-28712.png
-11174.png
-33672.png
-58292.png
-50842.png
-37257.png
-41706.png
-51103.png
-34751.png
-21543.png
-28264.png
-62834.png
-63849.png
-00194.png
-54460.png
-50903.png
-06325.png
-01703.png
-59266.png
-52725.png
-65356.png
-23578.png
-42843.png
-62347.png
-53946.png
-41346.png
-21402.png
-02397.png
-32758.png
-39398.png
-46930.png
-12580.png
-58091.png
-61114.png
-01150.png
-01163.png
-14832.png
-40281.png
-31342.png
-30874.png
-39837.png
-65160.png
-47925.png
-29436.png
-65252.png
-37881.png
-05374.png
-35178.png
-59869.png
-00722.png
-66626.png
-30966.png
-16974.png
-51883.png
-13856.png
-32957.png
-69714.png
-68321.png
-41320.png
-55641.png
-42861.png
-20375.png
-32964.png
-26237.png
-60769.png
-55683.png
-27742.png
-23306.png
-09614.png
-02167.png
-16024.png
-08353.png
-15624.png
-56284.png
-31078.png
-41339.png
-39466.png
-29913.png
-03292.png
-63373.png
-24374.png
-10028.png
-05207.png
-53575.png
-05282.png
-03664.png
-41663.png
-46616.png
-19617.png
-31583.png
-18686.png
-03663.png
-13086.png
-41702.png
-18476.png
-22509.png
-54928.png
-33605.png
-04434.png
-15837.png
-41873.png
-50023.png
-09589.png
-61534.png
-69432.png
-33052.png
-45985.png
-25685.png
-48394.png
-53779.png
-69839.png
-15073.png
-47863.png
-36674.png
-42046.png
-66259.png
-20704.png
-11883.png
-66309.png
-44272.png
-59030.png
-39324.png
-51251.png
-26560.png
-27534.png
-19092.png
-23405.png
-32254.png
-48607.png
-50719.png
-41101.png
-07600.png
-23111.png
-08877.png
-25670.png
-36708.png
-42164.png
-23757.png
-39238.png
-39049.png
-25747.png
-50835.png
-36129.png
-59054.png
-68778.png
-21273.png
-25947.png
-64520.png
-16744.png
-30490.png
-19875.png
-26123.png
-24744.png
-10105.png
-29150.png
-41690.png
-42437.png
-39095.png
-64802.png
-11588.png
-01089.png
-09037.png
-44354.png
-38991.png
-08939.png
-57277.png
-10271.png
-18259.png
-00962.png
-26492.png
-02980.png
-58825.png
-15110.png
-34573.png
-03429.png
-24349.png
-22438.png
-41803.png
-15689.png
-45994.png
-42807.png
-60750.png
-09708.png
-59802.png
-15472.png
-14014.png
-69868.png
-19831.png
-25689.png
-29956.png
-24592.png
-65730.png
-03303.png
-49986.png
-48492.png
-48374.png
-08585.png
-62914.png
-06055.png
-53468.png
-68401.png
-06043.png
-59100.png
-05164.png
-63276.png
-57973.png
-62166.png
-29748.png
-58207.png
-44285.png
-55572.png
-06855.png
-41634.png
-15975.png
-33783.png
-47437.png
-18055.png
-02100.png
-27523.png
-03313.png
-32016.png
-50563.png
-17117.png
-03977.png
-20553.png
-61123.png
-08159.png
-58561.png
-15863.png
-04193.png
-13692.png
-01286.png
-08169.png
-27543.png
-34365.png
-22216.png
-26094.png
-34972.png
-28386.png
-51469.png
-33001.png
-41014.png
-57970.png
-06577.png
-35001.png
-13039.png
-39366.png
-34396.png
-36959.png
-18552.png
-52078.png
-41042.png
-69636.png
-33261.png
-00145.png
-09826.png
-35172.png
-28817.png
-68887.png
-12502.png
-67053.png
-37454.png
-39374.png
-27768.png
-30628.png
-61109.png
-07919.png
-37009.png
-62143.png
-00029.png
-30462.png
-56258.png
-42923.png
-13923.png
-12921.png
-21783.png
-37420.png
-36609.png
-60070.png
-14079.png
-32854.png
-62916.png
-67133.png
-09394.png
-68445.png
-17649.png
-27826.png
-65209.png
-16338.png
-17238.png
-35354.png
-25224.png
-46428.png
-31987.png
-38893.png
-55874.png
-22031.png
-07662.png
-00421.png
-40123.png
-60044.png
-15149.png
-32694.png
-58978.png
-26281.png
-36784.png
-50497.png
-31484.png
-40033.png
-16146.png
-16718.png
-08534.png
-46696.png
-24000.png
-59701.png
-42208.png
-03825.png
-45221.png
-50080.png
-47892.png
-22841.png
-39641.png
-46424.png
-06101.png
-54912.png
-00444.png
-57402.png
-13751.png
-56804.png
-53642.png
-25731.png
-32959.png
-14370.png
-11002.png
-41987.png
-23064.png
-37253.png
-44729.png
-26261.png
-08903.png
-57088.png
-40277.png
-46842.png
-08808.png
-07779.png
-42829.png
-53904.png
-68635.png
-68680.png
-67975.png
-25862.png
-57005.png
-66730.png
-42892.png
-19037.png
-65613.png
-29741.png
-27352.png
-50989.png
-59047.png
-23964.png
-24181.png
-46470.png
-17844.png
-32515.png
-34554.png
-18242.png
-64022.png
-24200.png
-21054.png
-63792.png
-00061.png
-50579.png
-26702.png
-01665.png
-63486.png
-33383.png
-58774.png
-55698.png
-58364.png
-14880.png
-41273.png
-63201.png
-68048.png
-14463.png
-69571.png
-26964.png
-37563.png
-66541.png
-23337.png
-03109.png
-40525.png
-14153.png
-30988.png
-68620.png
-15629.png
-61905.png
-06437.png
-35670.png
-61438.png
-22683.png
-20318.png
-17088.png
-07231.png
-59718.png
-16893.png
-46578.png
-07397.png
-29977.png
-53608.png
-56067.png
-35020.png
-60840.png
-57076.png
-52106.png
-63459.png
-44910.png
-36886.png
-18497.png
-39969.png
-63294.png
-60413.png
-37695.png
-08365.png
-47291.png
-39422.png
-03573.png
-49442.png
-02832.png
-56985.png
-06963.png
-17828.png
-61289.png
-50172.png
-30416.png
-07571.png
-23542.png
-35026.png
-53424.png
-35931.png
-04658.png
-39831.png
-30368.png
-58395.png
-00370.png
-63847.png
-44862.png
-69370.png
-46591.png
-50490.png
-14414.png
-03143.png
-55574.png
-27563.png
-12877.png
-57374.png
-10573.png
-29291.png
-14449.png
-22359.png
-16248.png
-35205.png
-13313.png
-41098.png
-43596.png
-51797.png
-08417.png
-15706.png
-34484.png
-43903.png
-47836.png
-23534.png
-11913.png
-29936.png
-45166.png
-57975.png
-20640.png
-04302.png
-43233.png
-35732.png
-28104.png
-48793.png
-45631.png
-12203.png
-25230.png
-46312.png
-58094.png
-53157.png
-01365.png
-11004.png
-69319.png
-29615.png
-54748.png
-48038.png
-69160.png
-15892.png
-68412.png
-45539.png
-13103.png
-05036.png
-56331.png
-17596.png
-52831.png
-02761.png
-24492.png
-61555.png
-46579.png
-69222.png
-33986.png
-41186.png
-31268.png
-52158.png
-45480.png
-67570.png
-53205.png
-56306.png
-36182.png
-58761.png
-61372.png
-33380.png
-48106.png
-40311.png
-17961.png
-08809.png
-08691.png
-32975.png
-36267.png
-44059.png
-32567.png
-51583.png
-06840.png
-30812.png
-06654.png
-28732.png
-65205.png
-38725.png
-40333.png
-68997.png
-06713.png
-55055.png
-42203.png
-39225.png
-08058.png
-27552.png
-07208.png
-55220.png
-32536.png
-32602.png
-22142.png
-40307.png
-23521.png
-03301.png
-34273.png
-67648.png
-04012.png
-67903.png
-04144.png
-41937.png
-60962.png
-31971.png
-43983.png
-07036.png
-14085.png
-35187.png
-45035.png
-40412.png
-22478.png
-10311.png
-13343.png
-19463.png
-00557.png
-39821.png
-47475.png
-23099.png
-44969.png
-41429.png
-22471.png
-59577.png
-32674.png
-13628.png
-23288.png
-29842.png
-09905.png
-20784.png
-32711.png
-51798.png
-26421.png
-42613.png
-34416.png
-64156.png
-30616.png
-10848.png
-46526.png
-43297.png
-54218.png
-57503.png
-68376.png
-04984.png
-65806.png
-37157.png
-49028.png
-64722.png
-25405.png
-43326.png
-59759.png
-19342.png
-66346.png
-65210.png
-07428.png
-14848.png
-28839.png
-13591.png
-47736.png
-03666.png
-22262.png
-16029.png
-24969.png
-46735.png
-66984.png
-34997.png
-14327.png
-50443.png
-14334.png
-52253.png
-05112.png
-00980.png
-33855.png
-33954.png
-26970.png
-18600.png
-43390.png
-35216.png
-22082.png
-45287.png
-62639.png
-45477.png
-34000.png
-01358.png
-03294.png
-43699.png
-31702.png
-40672.png
-17000.png
-57118.png
-18724.png
-07582.png
-64753.png
-54012.png
-21516.png
-67631.png
-25281.png
-41799.png
-59936.png
-43093.png
-61341.png
-63185.png
-21775.png
-19024.png
-61601.png
-46815.png
-64271.png
-55314.png
-41284.png
-38178.png
-12878.png
-35151.png
-47878.png
-16043.png
-29872.png
-04544.png
-12221.png
-47556.png
-44337.png
-42457.png
-43410.png
-59045.png
-53813.png
-02334.png
-40570.png
-33896.png
-38754.png
-21755.png
-37700.png
-66234.png
-07392.png
-65672.png
-45422.png
-51562.png
-05158.png
-38715.png
-51253.png
-47374.png
-65985.png
-63166.png
-10461.png
-30340.png
-06585.png
-06804.png
-49404.png
-47490.png
-32195.png
-56368.png
-10452.png
-25135.png
-11259.png
-38220.png
-41016.png
-21579.png
-61838.png
-13968.png
-11303.png
-46894.png
-56349.png
-64275.png
-69588.png
-64811.png
-09390.png
-11622.png
-64205.png
-53114.png
-17373.png
-24926.png
-39003.png
-53450.png
-25835.png
-39272.png
-23469.png
-28602.png
-47834.png
-45436.png
-67009.png
-13601.png
-31816.png
-00546.png
-60975.png
-03871.png
-48824.png
-47659.png
-16269.png
-56328.png
-69684.png
-61005.png
-04183.png
-07455.png
-51903.png
-30224.png
-53926.png
-35409.png
-45262.png
-53650.png
-07733.png
-38373.png
-33878.png
-22807.png
-15894.png
-37547.png
-22048.png
-25680.png
-54766.png
-21939.png
-23559.png
-10597.png
-04410.png
-37965.png
-35314.png
-10176.png
-03091.png
-52929.png
-34005.png
-17202.png
-68613.png
-19635.png
-41908.png
-05191.png
-57982.png
-49738.png
-41862.png
-18831.png
-58443.png
-34177.png
-51387.png
-35932.png
-08178.png
-41787.png
-38510.png
-47265.png
-19624.png
-47849.png
-33462.png
-55855.png
-52712.png
-50431.png
-25871.png
-37060.png
-40647.png
-04950.png
-65454.png
-43935.png
-08524.png
-51149.png
-32417.png
-54275.png
-19877.png
-52670.png
-55380.png
-60386.png
-13613.png
-33718.png
-16447.png
-60771.png
-45772.png
-49514.png
-27139.png
-34181.png
-42316.png
-04437.png
-47169.png
-09627.png
-64632.png
-03696.png
-14948.png
-07619.png
-40198.png
-43444.png
-07475.png
-02927.png
-02822.png
-16188.png
-25720.png
-55364.png
-60805.png
-65017.png
-06710.png
-01906.png
-56363.png
-22582.png
-15137.png
-43793.png
-65352.png
-67132.png
-22694.png
-62670.png
-41836.png
-68117.png
-42586.png
-41930.png
-68636.png
-14906.png
-17568.png
-32857.png
-31047.png
-31698.png
-61771.png
-23461.png
-65662.png
-11795.png
-22871.png
-68272.png
-66585.png
-21740.png
-30809.png
-43289.png
-24172.png
-15835.png
-15587.png
-37045.png
-68332.png
-60123.png
-44302.png
-45622.png
-55576.png
-20894.png
-29051.png
-40361.png
-51948.png
-68772.png
-65741.png
-07466.png
-18524.png
-23863.png
-05006.png
-38827.png
-31666.png
-43833.png
-32765.png
-57130.png
-46713.png
-50753.png
-67604.png
-20060.png
-54589.png
-62170.png
-19020.png
-17945.png
-55291.png
-01743.png
-67355.png
-49162.png
-33868.png
-07645.png
-62654.png
-61281.png
-57294.png
-10215.png
-19472.png
-09561.png
-16064.png
-69922.png
-31655.png
-31968.png
-57090.png
-07967.png
-32496.png
-49983.png
-69832.png
-06660.png
-02083.png
-49989.png
-60675.png
-24773.png
-54206.png
-42934.png
-09823.png
-22265.png
-33082.png
-25673.png
-22759.png
-10302.png
-48442.png
-00941.png
-07815.png
-65972.png
-64614.png
-00347.png
-54503.png
-52913.png
-38828.png
-50872.png
-44002.png
-67033.png
-17962.png
-10660.png
-42069.png
-56414.png
-39614.png
-17864.png
-56824.png
-00422.png
-40601.png
-67620.png
-56648.png
-60607.png
-59523.png
-16937.png
-58184.png
-68365.png
-49270.png
-66948.png
-18397.png
-12937.png
-41914.png
-43267.png
-54383.png
-29047.png
-46854.png
-29200.png
-48732.png
-03289.png
-55384.png
-63948.png
-39839.png
-48487.png
-00499.png
-27992.png
-15354.png
-02593.png
-11316.png
-18881.png
-06193.png
-17138.png
-18639.png
-48292.png
-57713.png
-26846.png
-40507.png
-22698.png
-14379.png
-58930.png
-49254.png
-22133.png
-55531.png
-48054.png
-68543.png
-22915.png
-66038.png
-37890.png
-49477.png
-16302.png
-64694.png
-68810.png
-63674.png
-22725.png
-37386.png
-50818.png
-00409.png
-06294.png
-41633.png
-10963.png
-26952.png
-61176.png
-47470.png
-05487.png
-58794.png
-27347.png
-08632.png
-63047.png
-37872.png
-25263.png
-15029.png
-34202.png
-61119.png
-65646.png
-26166.png
-54474.png
-62132.png
-06284.png
-37557.png
-18574.png
-63491.png
-64623.png
-43209.png
-17160.png
-10611.png
-63811.png
-35470.png
-25658.png
-23577.png
-21186.png
-07605.png
-28375.png
-21293.png
-42955.png
-56461.png
-66248.png
-08386.png
-05615.png
-46843.png
-34003.png
-31300.png
-41641.png
-02029.png
-61925.png
-69997.png
-28630.png
-69229.png
-21034.png
-16479.png
-38333.png
-69959.png
-63396.png
-21869.png
-17944.png
-48498.png
-29252.png
-06789.png
-50555.png
-29446.png
-32090.png
-57763.png
-62673.png
-00184.png
-57839.png
-48256.png
-10825.png
-48168.png
-65737.png
-50573.png
-61776.png
-05566.png
-33015.png
-69256.png
-42263.png
-01999.png
-67106.png
-69916.png
-52639.png
-68899.png
-01047.png
-05604.png
-14737.png
-21501.png
-36640.png
-27034.png
-37897.png
-03980.png
-40653.png
-10523.png
-08608.png
-07375.png
-11070.png
-18629.png
-62810.png
-60210.png
-17345.png
-01160.png
-33294.png
-09409.png
-53287.png
-44943.png
-55215.png
-40998.png
-67064.png
-05055.png
-48076.png
-55374.png
-05684.png
-15466.png
-64258.png
-17850.png
-18767.png
-41507.png
-11104.png
-15819.png
-64187.png
-20998.png
-01981.png
-35591.png
-44606.png
-35147.png
-28739.png
-49123.png
-34712.png
-55338.png
-30502.png
-15434.png
-14765.png
-40364.png
-35595.png
-27844.png
-00675.png
-28877.png
-07398.png
-24389.png
-65732.png
-24473.png
-49059.png
-40700.png
-23479.png
-69646.png
-12178.png
-62032.png
-56102.png
-61152.png
-49532.png
-13281.png
-35878.png
-68651.png
-33781.png
-08593.png
-45652.png
-37317.png
-32296.png
-45044.png
-02602.png
-12528.png
-35799.png
-10108.png
-10383.png
-67237.png
-44443.png
-02505.png
-27930.png
-02616.png
-37479.png
-24726.png
-08382.png
-49287.png
-59633.png
-28737.png
-34948.png
-53074.png
-42660.png
-39644.png
-56533.png
-13711.png
-43292.png
-28005.png
-47313.png
-23478.png
-59846.png
-13387.png
-50091.png
-31082.png
-43360.png
-41638.png
-66773.png
-63233.png
-03065.png
-13642.png
-42206.png
-34553.png
-10745.png
-01356.png
-68566.png
-32554.png
-15582.png
-65988.png
-44094.png
-52687.png
-00166.png
-53360.png
-67682.png
-39818.png
-08349.png
-31559.png
-64287.png
-35250.png
-42766.png
-00867.png
-38411.png
-47597.png
-44900.png
-36566.png
-10708.png
-43849.png
-49126.png
-34591.png
-18512.png
-12185.png
-43455.png
-09275.png
-25990.png
-59644.png
-00305.png
-05402.png
-32853.png
-65426.png
-17753.png
-60236.png
-02327.png
-67285.png
-33642.png
-35414.png
-20987.png
-46358.png
-03115.png
-29914.png
-08019.png
-42084.png
-61852.png
-48486.png
-55202.png
-48294.png
-52287.png
-06699.png
-21586.png
-36723.png
-25028.png
-15130.png
-25269.png
-14299.png
-01864.png
-19434.png
-54973.png
-66654.png
-11488.png
-49583.png
-43215.png
-04632.png
-01206.png
-37622.png
-09921.png
-69466.png
-61695.png
-03032.png
-01721.png
-49244.png
-14416.png
-32992.png
-25793.png
-69638.png
-23744.png
-56578.png
-68230.png
-14464.png
-55752.png
-17851.png
-47025.png
-54956.png
-03787.png
-31139.png
-19404.png
-09541.png
-12826.png
-08440.png
-14101.png
-29986.png
-32681.png
-60359.png
-00048.png
-40151.png
-19487.png
-22960.png
-02998.png
-03096.png
-05219.png
-55541.png
-69498.png
-28707.png
-35391.png
-31365.png
-58496.png
-41254.png
-40988.png
-47040.png
-16265.png
-10274.png
-64592.png
-60834.png
-31453.png
-57935.png
-36937.png
-69433.png
-19432.png
-59263.png
-30897.png
-67299.png
-27496.png
-46041.png
-26816.png
-22860.png
-26854.png
-64853.png
-02150.png
-05175.png
-42218.png
-31086.png
-59596.png
-56828.png
-19879.png
-63648.png
-06783.png
-50063.png
-32328.png
-23309.png
-11954.png
-52166.png
-61930.png
-26849.png
-62965.png
-55870.png
-30886.png
-15903.png
-55816.png
-63049.png
-11928.png
-54880.png
-00095.png
-26010.png
-40302.png
-07785.png
-21684.png
-31603.png
-37667.png
-05010.png
-18869.png
-35842.png
-12255.png
-41243.png
-34489.png
-35154.png
-05025.png
-40512.png
-29183.png
-01574.png
-43617.png
-05493.png
-34698.png
-15151.png
-40924.png
-30214.png
-08580.png
-49008.png
-26957.png
-56340.png
-60309.png
-46695.png
-24675.png
-40010.png
-23277.png
-00656.png
-20304.png
-53098.png
-22520.png
-49066.png
-19380.png
-39464.png
-32547.png
-56460.png
-16417.png
-32958.png
-65780.png
-15720.png
-51031.png
-25019.png
-43574.png
-61751.png
-30321.png
-66576.png
-04917.png
-49893.png
-29095.png
-64011.png
-06787.png
-58417.png
-10466.png
-45488.png
-35256.png
-09385.png
-60301.png
-24872.png
-50124.png
-28823.png
-26666.png
-01841.png
-32846.png
-51777.png
-51488.png
-15070.png
-30985.png
-34851.png
-23339.png
-51495.png
-03850.png
-57992.png
-46770.png
-61397.png
-29763.png
-09393.png
-63803.png
-41350.png
-59059.png
-32365.png
-16485.png
-07608.png
-29329.png
-09554.png
-66772.png
-68583.png
-54191.png
-45187.png
-32840.png
-08341.png
-05424.png
-19660.png
-53010.png
-28557.png
-57026.png
-27070.png
-63817.png
-54852.png
-40851.png
-57108.png
-46246.png
-01970.png
-50891.png
-39472.png
-53691.png
-30233.png
-30025.png
-32805.png
-46946.png
-26112.png
-37047.png
-62071.png
-43127.png
-09082.png
-68449.png
-45691.png
-24764.png
-40103.png
-62789.png
-61044.png
-36235.png
-63333.png
-51119.png
-30666.png
-23922.png
-06529.png
-63582.png
-47766.png
-23702.png
-53100.png
-29204.png
-59155.png
-20267.png
-25781.png
-06154.png
-32098.png
-44224.png
-12824.png
-28238.png
-39806.png
-03619.png
-28213.png
-55115.png
-12819.png
-41044.png
-26000.png
-12969.png
-33562.png
-14891.png
-23090.png
-16147.png
-17919.png
-15845.png
-32032.png
-34625.png
-08177.png
-52129.png
-47193.png
-52566.png
-15831.png
-01029.png
-21912.png
-46261.png
-21219.png
-57989.png
-66323.png
-06163.png
-01059.png
-48262.png
-39352.png
-00680.png
-10273.png
-31310.png
-35218.png
-65386.png
-17201.png
-61587.png
-52245.png
-69642.png
-19006.png
-08601.png
-43053.png
-00459.png
-62274.png
-14524.png
-35601.png
-65682.png
-46380.png
-43459.png
-61888.png
-03668.png
-69406.png
-57331.png
-52902.png
-06676.png
-08929.png
-29317.png
-54211.png
-63802.png
-02884.png
-61725.png
-52737.png
-52937.png
-42360.png
-59010.png
-56120.png
-04952.png
-11148.png
-39942.png
-13838.png
-42836.png
-66277.png
-57020.png
-57734.png
-16491.png
-50679.png
-60937.png
-05079.png
-36425.png
-39423.png
-38402.png
-12901.png
-49283.png
-13702.png
-24685.png
-55521.png
-14055.png
-04911.png
-23251.png
-58227.png
-58397.png
-20043.png
-48914.png
-68435.png
-49912.png
-15240.png
-27207.png
-35042.png
-34474.png
-19835.png
-49125.png
-49324.png
-52547.png
-47695.png
-20069.png
-25839.png
-20483.png
-13182.png
-41625.png
-17809.png
-29396.png
-00575.png
-03927.png
-63629.png
-23558.png
-30637.png
-51754.png
-54511.png
-57882.png
-42977.png
-54882.png
-08641.png
-43489.png
-47206.png
-29101.png
-10308.png
-33093.png
-42538.png
-63458.png
-48602.png
-10065.png
-30943.png
-04647.png
-38024.png
-02389.png
-17656.png
-05905.png
-04270.png
-39867.png
-62503.png
-49143.png
-41229.png
-45064.png
-33748.png
-21523.png
-17574.png
-65698.png
-10699.png
-01939.png
-60699.png
-65490.png
-02028.png
-47839.png
-13936.png
-29054.png
-10818.png
-22398.png
-65286.png
-15146.png
-20746.png
-51301.png
-67818.png
-10946.png
-46151.png
-28207.png
-46329.png
-23212.png
-34601.png
-52356.png
-05082.png
-23581.png
-40495.png
-39631.png
-38115.png
-25282.png
-53569.png
-38988.png
-61078.png
-35372.png
-58954.png
-55819.png
-01302.png
-62048.png
-00657.png
-32662.png
-55775.png
-28955.png
-30920.png
-57723.png
-45084.png
-12246.png
-39927.png
-10374.png
-16399.png
-13461.png
-66923.png
-32333.png
-10559.png
-27713.png
-60468.png
-65261.png
-15743.png
-17789.png
-20563.png
-47049.png
-13260.png
-18420.png
-42301.png
-66993.png
-45414.png
-49139.png
-63557.png
-10260.png
-18458.png
-54100.png
-23390.png
-64294.png
-21051.png
-18648.png
-15647.png
-02286.png
-18139.png
-39650.png
-59806.png
-41478.png
-19535.png
-13065.png
-30119.png
-32099.png
-38779.png
-52877.png
-44273.png
-06927.png
-17006.png
-01696.png
-51688.png
-69881.png
-47315.png
-55709.png
-57696.png
-23630.png
-48850.png
-29202.png
-55052.png
-12663.png
-50887.png
-26371.png
-53143.png
-05748.png
-23917.png
-41563.png
-67149.png
-36380.png
-01343.png
-67433.png
-24949.png
-38724.png
-02254.png
-64247.png
-52999.png
-62613.png
-52130.png
-39400.png
-68634.png
-17925.png
-64560.png
-54954.png
-48770.png
-24743.png
-26864.png
-16529.png
-19779.png
-32362.png
-14694.png
-23912.png
-66115.png
-65404.png
-21967.png
-69635.png
-37658.png
-08409.png
-69730.png
-05357.png
-42978.png
-54688.png
-02599.png
-53171.png
-65009.png
-41460.png
-06157.png
-39195.png
-58740.png
-43999.png
-54013.png
-69913.png
-08094.png
-39286.png
-32891.png
-10565.png
-67094.png
-11524.png
-00466.png
-43609.png
-62956.png
-30370.png
-66814.png
-62629.png
-35508.png
-01583.png
-52734.png
-30106.png
-16207.png
-15562.png
-68129.png
-46066.png
-63763.png
-37243.png
-32380.png
-39421.png
-36059.png
-30913.png
-53671.png
-35135.png
-03675.png
-20730.png
-66146.png
-53759.png
-68303.png
-34728.png
-25985.png
-30459.png
-17621.png
-68017.png
-40688.png
-69454.png
-43981.png
-40047.png
-31374.png
-20945.png
-56959.png
-55369.png
-29357.png
-68330.png
-24199.png
-10112.png
-07268.png
-18655.png
-19322.png
-67999.png
-54496.png
-60772.png
-20225.png
-02372.png
-42095.png
-18795.png
-14691.png
-37979.png
-52774.png
-14127.png
-06742.png
-66468.png
-16909.png
-47182.png
-55509.png
-24474.png
-63494.png
-67923.png
-45934.png
-66511.png
-18503.png
-53462.png
-45540.png
-14569.png
-08444.png
-04440.png
-65585.png
-42268.png
-05422.png
-00641.png
-54850.png
-33736.png
-65264.png
-38253.png
-03818.png
-38884.png
-06830.png
-09333.png
-33219.png
-11291.png
-11371.png
-21918.png
-12931.png
-58493.png
-03383.png
-40821.png
-08375.png
-38105.png
-54994.png
-45850.png
-32294.png
-29140.png
-53399.png
-44252.png
-56335.png
-37793.png
-21097.png
-66280.png
-14916.png
-02218.png
-38793.png
-49115.png
-53685.png
-62221.png
-11439.png
-43711.png
-45516.png
-21066.png
-58989.png
-59458.png
-04094.png
-58764.png
-68037.png
-31519.png
-57490.png
-64086.png
-58958.png
-45491.png
-16746.png
-67339.png
-46542.png
-68433.png
-10388.png
-11926.png
-64385.png
-26769.png
-58682.png
-05130.png
-16886.png
-57704.png
-18538.png
-24575.png
-31862.png
-47423.png
-15875.png
-28946.png
-59178.png
-48619.png
-36110.png
-12600.png
-24313.png
-38568.png
-49907.png
-10941.png
-18951.png
-08541.png
-46550.png
-39803.png
-19000.png
-31249.png
-49548.png
-24815.png
-54678.png
-00005.png
-48300.png
-27717.png
-58477.png
-65587.png
-64618.png
-26058.png
-01561.png
-38097.png
-01928.png
-39605.png
-53323.png
-61920.png
-59771.png
-41673.png
-48994.png
-17427.png
-56159.png
-61201.png
-16672.png
-59397.png
-43988.png
-09610.png
-17750.png
-08724.png
-12620.png
-16441.png
-26519.png
-16400.png
-04678.png
-24431.png
-09217.png
-51537.png
-24842.png
-13774.png
-51352.png
-61425.png
-30245.png
-36536.png
-14401.png
-58093.png
-41298.png
-30495.png
-15574.png
-65120.png
-19976.png
-12519.png
-28584.png
-16220.png
-50218.png
-22161.png
-66212.png
-58216.png
-09793.png
-32582.png
-06432.png
-25370.png
-27737.png
-05888.png
-50126.png
-31738.png
-08756.png
-34184.png
-49539.png
-38874.png
-12047.png
-05555.png
-62006.png
-39943.png
-33083.png
-34013.png
-01327.png
-49020.png
-02186.png
-65272.png
-27324.png
-04202.png
-61314.png
-04864.png
-57983.png
-13424.png
-49402.png
-06862.png
-29387.png
-66721.png
-57410.png
-42510.png
-24487.png
-63183.png
-07937.png
-06334.png
-14955.png
-45562.png
-54463.png
-35168.png
-69488.png
-20854.png
-53722.png
-21749.png
-03146.png
-56299.png
-12201.png
-51174.png
-46821.png
-21199.png
-51165.png
-37792.png
-35478.png
-01126.png
-65449.png
-43461.png
-01815.png
-02954.png
-40097.png
-05737.png
-17178.png
-07445.png
-59269.png
-21613.png
-02288.png
-19031.png
-20196.png
-09411.png
-21880.png
-13526.png
-53936.png
-04605.png
-59526.png
-42349.png
-22640.png
-09330.png
-01349.png
-69945.png
-31385.png
-03448.png
-10448.png
-14157.png
-13894.png
-37755.png
-43008.png
-07088.png
-64788.png
-12397.png
-24044.png
-30857.png
-32855.png
-19725.png
-47071.png
-49053.png
-57247.png
-32026.png
-06377.png
-43119.png
-10782.png
-20270.png
-58744.png
-30801.png
-62778.png
-28092.png
-24451.png
-47083.png
-32520.png
-13267.png
-49067.png
-48814.png
-64013.png
-05685.png
-18200.png
-47322.png
-17319.png
-23395.png
-33584.png
-17293.png
-01378.png
-34134.png
-58550.png
-19932.png
-10181.png
-46633.png
-48423.png
-30358.png
-68483.png
-08429.png
-13291.png
-50271.png
-11662.png
-00165.png
-11743.png
-21403.png
-53737.png
-31626.png
-01670.png
-65086.png
-05265.png
-68892.png
-21438.png
-30317.png
-39232.png
-54308.png
-10221.png
-03756.png
-22781.png
-67852.png
-17562.png
-33071.png
-43042.png
-33104.png
-56579.png
-08650.png
-34332.png
-01366.png
-03411.png
-12308.png
-29715.png
-12848.png
-11054.png
-68204.png
-32088.png
-07982.png
-58280.png
-32991.png
-02613.png
-23769.png
-42039.png
-56585.png
-03627.png
-35787.png
-69066.png
-13373.png
-12859.png
-68226.png
-68522.png
-06536.png
-33975.png
-11571.png
-55727.png
-64949.png
-03500.png
-53972.png
-26632.png
-18615.png
-38597.png
-10039.png
-66912.png
-30688.png
-07553.png
-06423.png
-33308.png
-22010.png
-51734.png
-16332.png
-24212.png
-22792.png
-62727.png
-33012.png
-49116.png
-60717.png
-39243.png
-22073.png
-35716.png
-19997.png
-19418.png
-66552.png
-47005.png
-05052.png
-29620.png
-52018.png
-35653.png
-43689.png
-19838.png
-64585.png
-39776.png
-36215.png
-53163.png
-63127.png
-46267.png
-36845.png
-00515.png
-31088.png
-17113.png
-45886.png
-26687.png
-48082.png
-56378.png
-02395.png
-15454.png
-29816.png
-15768.png
-63546.png
-44998.png
-35451.png
-54407.png
-40771.png
-52292.png
-64741.png
-02634.png
-03206.png
-54576.png
-48071.png
-43401.png
-26871.png
-03361.png
-67810.png
-65054.png
-04747.png
-00405.png
-19388.png
-47159.png
-54492.png
-66029.png
-35313.png
-10222.png
-15990.png
-49448.png
-20498.png
-17371.png
-52008.png
-11196.png
-24329.png
-05248.png
-29006.png
-14385.png
-17309.png
-61621.png
-21503.png
-43268.png
-19770.png
-42725.png
-08368.png
-03171.png
-38219.png
-02263.png
-35698.png
-61516.png
-09869.png
-44752.png
-69877.png
-53886.png
-21465.png
-04105.png
-55322.png
-15529.png
-53644.png
-05434.png
-28273.png
-18870.png
-54136.png
-02768.png
-06434.png
-16277.png
-26535.png
-14126.png
-19227.png
-23013.png
-01139.png
-24919.png
-23268.png
-26340.png
-58012.png
-01483.png
-17675.png
-58683.png
-43422.png
-52714.png
-07755.png
-51565.png
-39443.png
-64873.png
-53368.png
-60869.png
-68059.png
-15974.png
-52369.png
-06664.png
-04093.png
-39811.png
-03606.png
-59008.png
-49811.png
-12259.png
-63429.png
-06810.png
-51120.png
-58153.png
-11339.png
-39901.png
-13662.png
-51483.png
-12374.png
-31889.png
-42350.png
-43898.png
-34668.png
-09441.png
-55297.png
-39793.png
-41305.png
-03708.png
-59287.png
-17845.png
-29945.png
-24990.png
-51014.png
-47179.png
-29736.png
-21686.png
-03534.png
-25124.png
-38574.png
-57486.png
-41947.png
-31884.png
-07861.png
-04273.png
-45141.png
-37712.png
-53866.png
-67172.png
-28522.png
-12645.png
-59450.png
-60698.png
-01521.png
-21010.png
-48351.png
-56014.png
-65942.png
-00838.png
-41975.png
-14195.png
-19690.png
-62952.png
-68199.png
-05941.png
-50234.png
-05485.png
-49806.png
-27744.png
-28783.png
-47369.png
-29742.png
-62699.png
-07576.png
-25204.png
-59111.png
-53715.png
-22159.png
-65562.png
-01001.png
-00297.png
-14411.png
-63351.png
-12383.png
-17992.png
-50540.png
-42872.png
-65041.png
-02791.png
-57916.png
-18589.png
-50021.png
-44170.png
-62421.png
-65816.png
-07130.png
-43576.png
-23169.png
-18810.png
-60693.png
-07502.png
-16323.png
-52305.png
-36893.png
-10875.png
-34882.png
-28322.png
-62676.png
-52595.png
-25060.png
-32902.png
-49427.png
-47036.png
-38507.png
-51104.png
-61429.png
-04090.png
-23389.png
-58899.png
-32775.png
-47344.png
-69860.png
-64094.png
-11151.png
-65835.png
-07513.png
-36474.png
-53343.png
-62640.png
-00752.png
-41744.png
-38471.png
-64494.png
-47330.png
-66324.png
-60508.png
-52468.png
-64455.png
-18654.png
-62950.png
-34025.png
-32338.png
-35170.png
-30614.png
-45808.png
-56471.png
-37357.png
-17996.png
-33165.png
-14048.png
-68542.png
-29196.png
-52450.png
-10567.png
-05677.png
-48313.png
-35398.png
-58852.png
-41311.png
-50765.png
-58797.png
-60322.png
-05054.png
-63051.png
-21044.png
-07993.png
-04517.png
-43685.png
-28897.png
-33827.png
-07862.png
-43526.png
-00330.png
-65710.png
-06063.png
-58373.png
-57058.png
-11245.png
-36164.png
-37201.png
-59217.png
-23092.png
-33727.png
-36247.png
-11397.png
-67552.png
-22636.png
-62102.png
-48206.png
-65994.png
-35590.png
-53364.png
-15573.png
-57590.png
-18965.png
-46265.png
-12689.png
-28678.png
-45726.png
-07052.png
-29280.png
-59602.png
-53301.png
-28544.png
-53714.png
-22174.png
-47387.png
-47178.png
-36604.png
-29171.png
-17384.png
-31137.png
-41100.png
-21426.png
-01129.png
-09192.png
-18612.png
-48738.png
-43686.png
-46471.png
-41532.png
-22286.png
-07578.png
-69240.png
-28110.png
-36401.png
-16404.png
-17376.png
-36418.png
-22319.png
-35333.png
-34310.png
-47739.png
-20610.png
-38313.png
-42981.png
-49383.png
-58611.png
-47239.png
-19359.png
-60733.png
-54030.png
-46241.png
-45461.png
-37227.png
-24681.png
-21679.png
-02181.png
-67186.png
-03592.png
-35895.png
-05338.png
-67567.png
-41572.png
-19391.png
-46804.png
-46409.png
-01572.png
-50696.png
-03865.png
-61353.png
-55131.png
-57095.png
-32473.png
-58047.png
-52408.png
-48541.png
-67366.png
-48326.png
-33266.png
-03719.png
-44823.png
-41758.png
-07972.png
-35447.png
-61746.png
-07627.png
-17585.png
-59277.png
-59496.png
-43667.png
-38654.png
-44554.png
-60664.png
-26975.png
-53129.png
-64908.png
-59102.png
-28384.png
-62602.png
-34495.png
-17374.png
-35645.png
-24583.png
-43850.png
-41236.png
-10485.png
-40856.png
-51814.png
-30169.png
-49273.png
-51268.png
-43250.png
-65776.png
-41377.png
-29506.png
-65305.png
-20436.png
-64744.png
-04187.png
-49932.png
-67905.png
-26435.png
-16304.png
-55275.png
-60092.png
-40907.png
-27435.png
-28996.png
-61455.png
-65424.png
-28364.png
-37058.png
-17548.png
-05576.png
-07426.png
-45563.png
-44441.png
-59963.png
-62573.png
-43110.png
-57611.png
-65472.png
-06223.png
-32243.png
-56367.png
-21291.png
-13102.png
-14934.png
-46144.png
-66793.png
-01789.png
-08033.png
-64242.png
-45564.png
-34338.png
-64214.png
-48320.png
-62117.png
-14109.png
-36571.png
-41122.png
-58985.png
-56945.png
-69334.png
-47823.png
-36262.png
-11753.png
-44555.png
-12132.png
-08637.png
-00489.png
-22178.png
-62703.png
-18501.png
-24036.png
-39849.png
-45337.png
-40576.png
-59309.png
-50742.png
-49701.png
-25783.png
-54825.png
-02782.png
-40957.png
-25494.png
-65002.png
-18900.png
-09504.png
-68746.png
-51261.png
-17295.png
-40646.png
-62462.png
-31201.png
-58600.png
-62506.png
-32585.png
-55984.png
-52703.png
-23161.png
-69136.png
-36227.png
-49910.png
-27695.png
-47790.png
-34656.png
-00556.png
-38804.png
-67491.png
-06579.png
-60576.png
-19992.png
-42420.png
-66321.png
-16631.png
-62174.png
-20072.png
-04888.png
-52203.png
-50195.png
-14465.png
-40776.png
-44179.png
-12798.png
-40579.png
-03085.png
-06780.png
-02621.png
-10664.png
-25786.png
-48176.png
-37690.png
-27065.png
-65494.png
-51460.png
-02119.png
-17247.png
-52646.png
-50649.png
-18253.png
-25335.png
-60032.png
-32074.png
-62661.png
-09522.png
-28883.png
-00311.png
-06313.png
-08076.png
-33385.png
-05382.png
-06329.png
-66977.png
-04392.png
-53898.png
-51474.png
-39611.png
-22448.png
-66892.png
-05380.png
-46230.png
-19184.png
-47915.png
-12542.png
-45975.png
-37075.png
-23989.png
-25727.png
-38747.png
-12549.png
-49158.png
-10515.png
-13429.png
-41210.png
-32517.png
-08284.png
-57871.png
-43457.png
-00927.png
-50743.png
-03399.png
-23893.png
-38799.png
-63311.png
-56775.png
-25277.png
-43262.png
-57597.png
-16416.png
-29193.png
-40413.png
-32360.png
-07930.png
-69113.png
-10686.png
-42855.png
-37456.png
-06645.png
-54319.png
-54068.png
-39175.png
-12883.png
-15506.png
-56806.png
-54667.png
-22834.png
-01908.png
-37832.png
-28885.png
-11272.png
-22339.png
-67749.png
-16572.png
-24686.png
-44331.png
-05147.png
-48299.png
-38535.png
-21472.png
-00158.png
-07923.png
-49901.png
-17430.png
-45007.png
-28439.png
-13973.png
-36602.png
-24479.png
-16267.png
-52127.png
-37911.png
-08343.png
-45845.png
-67824.png
-62755.png
-28504.png
-23539.png
-01099.png
-14704.png
-63093.png
-18423.png
-29010.png
-07562.png
-55134.png
-14312.png
-24437.png
-51245.png
-00758.png
-28746.png
-52783.png
-30634.png
-30421.png
-20490.png
-39940.png
-54750.png
-64597.png
-59953.png
-46920.png
-24102.png
-16221.png
-68912.png
-42505.png
-40217.png
-48600.png
-15467.png
-43954.png
-48984.png
-18290.png
-02452.png
-49676.png
-54443.png
-20369.png
-16651.png
-42587.png
-07286.png
-19850.png
-47709.png
-43306.png
-24048.png
-54360.png
-68754.png
-14275.png
-49919.png
-42425.png
-67150.png
-16806.png
-30412.png
-05083.png
-63709.png
-57863.png
-32262.png
-56065.png
-68852.png
-52594.png
-05492.png
-33845.png
-11694.png
-44541.png
-15162.png
-37101.png
-55987.png
-31464.png
-33624.png
-19244.png
-24082.png
-35851.png
-20155.png
-08999.png
-50064.png
-43169.png
-42449.png
-24687.png
-16896.png
-18102.png
-34866.png
-24058.png
-15585.png
-28291.png
-49949.png
-61195.png
-16123.png
-27720.png
-67837.png
-43319.png
-46533.png
-63280.png
-58835.png
-56854.png
-09646.png
-00363.png
-09309.png
-01752.png
-38005.png
-25330.png
-40544.png
-30205.png
-33409.png
-06146.png
-32469.png
-02908.png
-09392.png
-45794.png
-25536.png
-03214.png
-63379.png
-17230.png
-49263.png
-60871.png
-43828.png
-66084.png
-27754.png
-24610.png
-41667.png
-48415.png
-17047.png
-23384.png
-10074.png
-64745.png
-62230.png
-02581.png
-10978.png
-47683.png
-12687.png
-32311.png
-36873.png
-42597.png
-01976.png
-32475.png
-47435.png
-00803.png
-28356.png
-14120.png
-36939.png
-68043.png
-38788.png
-56575.png
-32303.png
-17985.png
-44284.png
-53332.png
-27894.png
-35831.png
-01638.png
-22120.png
-54950.png
-53557.png
-15226.png
-61582.png
-07983.png
-20420.png
-29558.png
-48767.png
-14420.png
-44474.png
-35423.png
-65934.png
-29466.png
-68934.png
-33372.png
-69257.png
-60770.png
-56615.png
-42194.png
-58513.png
-28568.png
-30102.png
-37677.png
-69330.png
-55934.png
-23142.png
-19284.png
-03580.png
-12624.png
-15123.png
-68337.png
-20105.png
-44288.png
-65803.png
-48167.png
-51838.png
-52513.png
-28683.png
-30947.png
-13677.png
-51912.png
-07855.png
-05056.png
-01435.png
-52353.png
-10313.png
-29566.png
-58220.png
-35984.png
-00041.png
-65064.png
-49018.png
-36945.png
-47325.png
-48970.png
-60374.png
-66416.png
-19055.png
-10957.png
-57589.png
-18317.png
-10339.png
-43583.png
-41430.png
-60956.png
-53344.png
-34344.png
-29795.png
-49019.png
-00516.png
-65229.png
-39596.png
-27461.png
-31867.png
-21313.png
-13651.png
-13882.png
-54735.png
-05695.png
-11944.png
-41287.png
-68556.png
-07898.png
-29637.png
-48024.png
-28193.png
-39391.png
-66097.png
-53836.png
-54613.png
-18486.png
-38070.png
-02377.png
-23020.png
-30281.png
-60450.png
-36390.png
-52524.png
-11254.png
-67397.png
-11365.png
-28751.png
-64181.png
-21345.png
-63903.png
-50105.png
-56547.png
-15102.png
-18844.png
-35562.png
-52802.png
-28903.png
-03945.png
-12887.png
-66786.png
-56384.png
-45559.png
-69325.png
-14577.png
-18927.png
-04229.png
-24509.png
-56185.png
-40001.png
-34764.png
-31860.png
-16109.png
-46922.png
-63873.png
-62839.png
-03498.png
-03879.png
-57229.png
-16444.png
-13244.png
-65611.png
-30423.png
-37177.png
-54712.png
-11393.png
-06845.png
-62897.png
-06988.png
-11419.png
-24895.png
-63547.png
-19093.png
-19025.png
-67990.png
-30586.png
-27817.png
-62116.png
-60852.png
-33431.png
-62756.png
-11102.png
-03127.png
-68520.png
-05531.png
-01069.png
-66213.png
-66266.png
-53519.png
-27033.png
-30158.png
-00574.png
-06147.png
-32763.png
-59785.png
-59172.png
-15189.png
-39148.png
-57192.png
-21680.png
-16139.png
-21521.png
-14461.png
-14346.png
-19916.png
-46303.png
-15314.png
-32323.png
-48346.png
-29445.png
-59095.png
-33186.png
-15722.png
-33989.png
-22903.png
-39402.png
-38836.png
-21363.png
-07909.png
-16924.png
-23905.png
-25248.png
-56439.png
-29421.png
-35634.png
-14725.png
-01790.png
-42960.png
-62734.png
-62593.png
-57418.png
-13846.png
-52415.png
-66441.png
-62077.png
-17632.png
-31755.png
-04336.png
-48502.png
-69765.png
-24528.png
-25904.png
-36954.png
-16904.png
-15555.png
-40797.png
-20755.png
-66470.png
-02576.png
-63691.png
-12165.png
-06331.png
-03236.png
-19051.png
-07693.png
-02026.png
-49189.png
-02109.png
-13185.png
-26966.png
-59477.png
-27011.png
-39550.png
-17744.png
-39572.png
-00486.png
-43729.png
-36165.png
-60899.png
-06515.png
-01935.png
-47713.png
-59145.png
-56919.png
-37242.png
-21309.png
-34789.png
-64457.png
-36825.png
-12718.png
-13732.png
-59434.png
-42948.png
-48142.png
-58025.png
-55761.png
-33714.png
-28085.png
-60144.png
-12301.png
-59838.png
-22008.png
-66095.png
-33996.png
-32341.png
-19892.png
-17433.png
-06427.png
-44529.png
-52260.png
-05266.png
-59929.png
-20932.png
-11420.png
-69258.png
-30606.png
-10263.png
-14455.png
-61414.png
-53353.png
-25449.png
-43052.png
-37654.png
-58974.png
-24513.png
-43420.png
-56817.png
-38663.png
-12483.png
-65321.png
-04516.png
-57233.png
-30091.png
-56577.png
-06542.png
-17903.png
-27406.png
-21026.png
-44426.png
-04430.png
-37369.png
-36529.png
-64189.png
-03623.png
-50404.png
-35085.png
-34797.png
-47337.png
-66245.png
-37599.png
-02622.png
-32049.png
-58512.png
-16633.png
-15480.png
-30268.png
-27733.png
-57987.png
-10915.png
-27006.png
-08310.png
-53847.png
-63470.png
-65021.png
-65794.png
-04073.png
-64216.png
-38781.png
-17655.png
-61872.png
-21204.png
-15514.png
-58312.png
-21180.png
-49500.png
-54090.png
-02531.png
-21814.png
-31243.png
-42952.png
-30196.png
-62954.png
-60595.png
-10507.png
-09511.png
-61933.png
-38832.png
-13137.png
-16135.png
-08455.png
-21706.png
-30707.png
-28748.png
-01296.png
-50965.png
-00862.png
-51305.png
-04940.png
-58233.png
-17013.png
-59561.png
-01470.png
-25037.png
-64783.png
-37397.png
-47932.png
-40752.png
-56334.png
-36670.png
-56046.png
-66618.png
-63084.png
-00492.png
-17606.png
-40260.png
-51723.png
-46404.png
-35415.png
-14119.png
-42463.png
-06059.png
-35199.png
-42508.png
-05475.png
-14896.png
-46678.png
-18198.png
-55878.png
-07500.png
-55317.png
-32500.png
-60345.png
-07442.png
-69333.png
-37641.png
-13850.png
-10802.png
-09137.png
-47195.png
-38388.png
-67695.png
-22654.png
-29385.png
-32316.png
-43498.png
-53120.png
-06877.png
-54184.png
-33378.png
-44058.png
-14717.png
-21850.png
-43092.png
-06601.png
-43230.png
-69142.png
-00346.png
-06750.png
-15972.png
-26963.png
-36402.png
-37572.png
-49881.png
-40831.png
-13673.png
-59265.png
-08249.png
-60807.png
-29288.png
-16246.png
-47664.png
-67874.png
-00054.png
-58897.png
-48246.png
-63617.png
-40602.png
-51499.png
-17318.png
-48538.png
-20133.png
-65962.png
-47920.png
-37752.png
-21039.png
-30833.png
-55714.png
-33875.png
-55101.png
-47803.png
-19569.png
-04267.png
-33262.png
-29646.png
-05974.png
-31873.png
-69643.png
-11722.png
-05271.png
-31277.png
-38351.png
-18205.png
-54629.png
-20355.png
-05580.png
-17216.png
-63119.png
-48877.png
-39192.png
-01295.png
-59698.png
-33684.png
-67585.png
-28286.png
-08669.png
-10109.png
-22958.png
-08011.png
-03543.png
-65408.png
-42925.png
-32575.png
-42794.png
-22370.png
-16807.png
-01754.png
-01922.png
-51235.png
-64954.png
-47754.png
-13593.png
-39602.png
-32581.png
-57883.png
-13759.png
-66224.png
-36067.png
-68705.png
-52918.png
-52776.png
-30198.png
-08990.png
-05126.png
-28316.png
-66352.png
-13778.png
-04807.png
-31662.png
-66211.png
-22926.png
-05634.png
-47527.png
-63455.png
-15041.png
-25260.png
-47105.png
-57223.png
-12868.png
-10676.png
-31591.png
-49711.png
-41806.png
-62491.png
-29778.png
-08635.png
-40232.png
-52383.png
-17084.png
-68153.png
-31646.png
-13824.png
-53324.png
-03900.png
-39066.png
-52960.png
-40485.png
-69355.png
-37164.png
-00350.png
-14551.png
-58419.png
-03114.png
-30494.png
-56381.png
-47358.png
-67666.png
-19397.png
-57184.png
-53682.png
-09955.png
-61532.png
-49478.png
-43404.png
-51409.png
-23030.png
-59353.png
-13921.png
-61525.png
-07383.png
-24007.png
-63818.png
-16629.png
-56423.png
-29485.png
-60388.png
-60889.png
-05435.png
-21213.png
-16901.png
-06818.png
-68001.png
-59800.png
-20759.png
-00802.png
-51568.png
-53744.png
-19732.png
-54446.png
-15942.png
-00214.png
-42033.png
-17275.png
-41160.png
-50763.png
-54675.png
-02511.png
-49355.png
-03002.png
-64120.png
-69933.png
-14384.png
-38656.png
-42772.png
-27594.png
-08314.png
-47610.png
-45237.png
-30548.png
-01998.png
-61829.png
-63777.png
-03810.png
-52035.png
-39258.png
-26269.png
-33643.png
-34011.png
-22167.png
-54096.png
-56708.png
-01162.png
-04654.png
-59922.png
-38461.png
-18221.png
-56055.png
-65527.png
-16966.png
-18383.png
-32424.png
-24769.png
-50427.png
-44180.png
-37535.png
-30200.png
-12134.png
-23225.png
-40244.png
-53601.png
-57878.png
-38307.png
-67279.png
-64066.png
-26576.png
-47469.png
-26829.png
-04383.png
-30950.png
-61730.png
-55668.png
-11570.png
-42339.png
-10550.png
-38657.png
-53621.png
-00730.png
-53108.png
-45105.png
-16700.png
-35240.png
-44964.png
-16343.png
-11938.png
-39580.png
-51369.png
-21460.png
-63443.png
-00691.png
-31050.png
-45741.png
-32753.png
-05131.png
-16466.png
-51626.png
-51224.png
-66817.png
-24121.png
-31272.png
-50212.png
-53968.png
-11284.png
-29209.png
-01748.png
-24453.png
-36179.png
-39050.png
-26684.png
-51093.png
-06835.png
-27399.png
-04361.png
-23363.png
-04116.png
-33837.png
-23944.png
-32327.png
-57101.png
-63072.png
-42358.png
-33721.png
-09156.png
-10914.png
-13487.png
-66141.png
-38527.png
-58317.png
-01992.png
-33314.png
-58692.png
-18037.png
-22524.png
-18809.png
-58975.png
-52601.png
-51433.png
-21691.png
-48900.png
-20409.png
-03159.png
-37030.png
-51190.png
-22564.png
-30487.png
-26543.png
-19433.png
-09751.png
-10972.png
-14895.png
-30672.png
-21295.png
-65401.png
-64299.png
-63946.png
-39759.png
-11621.png
-32102.png
-62333.png
-34413.png
-40769.png
-36332.png
-49719.png
-61499.png
-36414.png
-38768.png
-26709.png
-13228.png
-06603.png
-28711.png
-59894.png
-35258.png
-27926.png
-01924.png
-27562.png
-18761.png
-26714.png
-24439.png
-26833.png
-32240.png
-52154.png
-52860.png
-42232.png
-07252.png
-65855.png
-47459.png
-15592.png
-11258.png
-56617.png
-66720.png
-68555.png
-66481.png
-18802.png
-54457.png
-56784.png
-30981.png
-19233.png
-33425.png
-16540.png
-07944.png
-01120.png
-36206.png
-25029.png
-50927.png
-12779.png
-14748.png
-40106.png
-24758.png
-28027.png
-12043.png
-04641.png
-09242.png
-63211.png
-45798.png
-62557.png
-38643.png
-32384.png
-45116.png
-38017.png
-01006.png
-18369.png
-55560.png
-11092.png
-00868.png
-49726.png
-06189.png
-07679.png
-26759.png
-42734.png
-54283.png
-42251.png
-62590.png
-41910.png
-05087.png
-17892.png
-33472.png
-32128.png
-36376.png
-29393.png
-16625.png
-66312.png
-02383.png
-20901.png
-56526.png
-25668.png
-51751.png
-02283.png
-23267.png
-69150.png
-61082.png
-47955.png
-33315.png
-24129.png
-31381.png
-19496.png
-14949.png
-29480.png
-40819.png
-07567.png
-02484.png
-12389.png
-24100.png
-52263.png
-09408.png
-59412.png
-41374.png
-15613.png
-13044.png
-00833.png
-43590.png
-53850.png
-08473.png
-28421.png
-00093.png
-15606.png
-31718.png
-58039.png
-15320.png
-57257.png
-51613.png
-19862.png
-10939.png
-19966.png
-43943.png
-09463.png
-43082.png
-62211.png
-33402.png
-47110.png
-10156.png
-25626.png
-14013.png
-65241.png
-13952.png
-05759.png
-00186.png
-61877.png
-20307.png
-11525.png
-67494.png
-63798.png
-15889.png
-47257.png
-41466.png
-26710.png
-33897.png
-49647.png
-69465.png
-69858.png
-53870.png
-07887.png
-02072.png
-53819.png
-31173.png
-11387.png
-16426.png
-57283.png
-13971.png
-04096.png
-28896.png
-03796.png
-03965.png
-36479.png
-24483.png
-32504.png
-02642.png
-15477.png
-63986.png
-10973.png
-01882.png
-55678.png
-43640.png
-54369.png
-42314.png
-49717.png
-20626.png
-23696.png
-22809.png
-11197.png
-20856.png
-57307.png
-28819.png
-56663.png
-13311.png
-01096.png
-39221.png
-44220.png
-18907.png
-44047.png
-36860.png
-20118.png
-43128.png
-62220.png
-56965.png
-57098.png
-50381.png
-31703.png
-01526.png
-35065.png
-49105.png
-22189.png
-62424.png
-01018.png
-09267.png
-16369.png
-21211.png
-01585.png
-52000.png
-20127.png
-33310.png
-31064.png
-45552.png
-36335.png
-58229.png
-34007.png
-34368.png
-18121.png
-45143.png
-45197.png
-16172.png
-22274.png
-07701.png
-06548.png
-29463.png
-18885.png
-58776.png
-66728.png
-09963.png
-45393.png
-40142.png
-55240.png
-24810.png
-10304.png
-60486.png
-69705.png
-66998.png
-45498.png
-05914.png
-50630.png
-48162.png
-39651.png
-29098.png
-64374.png
-66934.png
-66398.png
-44608.png
-43210.png
-14505.png
-30295.png
-69358.png
-56165.png
-68193.png
-50428.png
-25056.png
-56430.png
-25518.png
-65591.png
-13268.png
-45309.png
-55665.png
-14281.png
-23868.png
-06342.png
-42351.png
-05796.png
-44057.png
-37464.png
-62306.png
-17951.png
-10612.png
-59601.png
-68656.png
-06552.png
-04394.png
-55330.png
-07921.png
-17467.png
-34128.png
-64901.png
-67210.png
-43107.png
-20156.png
-64312.png
-13413.png
-23718.png
-50095.png
-15338.png
-67683.png
-20499.png
-18746.png
-09681.png
-23179.png
-23172.png
-07304.png
-05429.png
-41482.png
-28713.png
-13265.png
-36999.png
-23760.png
-05950.png
-33392.png
-53131.png
-03596.png
-44578.png
-47397.png
-59078.png
-11434.png
-56925.png
-46599.png
-30065.png
-01345.png
-67301.png
-36317.png
-13555.png
-28229.png
-09508.png
-61071.png
-56621.png
-43473.png
-22266.png
-04577.png
-04939.png
-59216.png
-03204.png
-09028.png
-12856.png
-69686.png
-63713.png
-14873.png
-63751.png
-57309.png
-13474.png
-11752.png
-16367.png
-62296.png
-37435.png
-33531.png
-56069.png
-19348.png
-28815.png
-53277.png
-37234.png
-10395.png
-43698.png
-67689.png
-69965.png
-50570.png
-20192.png
-53520.png
-14521.png
-31149.png
-33398.png
-32184.png
-01464.png
-69078.png
-04052.png
-23498.png
-64148.png
-65529.png
-67199.png
-43545.png
-05749.png
-11755.png
-63225.png
-03049.png
-46986.png
-47152.png
-59249.png
-09803.png
-02609.png
-06089.png
-57604.png
-43587.png
-17579.png
-31373.png
-02350.png
-38567.png
-30068.png
-57310.png
-29024.png
-15061.png
-12442.png
-67871.png
-35554.png
-21788.png
-45728.png
-65357.png
-00143.png
-62009.png
-23398.png
-52219.png
-32926.png
-45022.png
-45316.png
-36086.png
-02502.png
-62157.png
-14733.png
-32438.png
-10359.png
-55412.png
-51322.png
-46640.png
-08279.png
-41358.png
-68245.png
-56551.png
-53699.png
-52409.png
-35329.png
-10366.png
-03559.png
-30618.png
-64074.png
-33828.png
-62666.png
-55583.png
-17533.png
-08435.png
-10244.png
-45571.png
-40592.png
-33161.png
-39922.png
-02846.png
-43365.png
-40146.png
-00578.png
-50298.png
-11276.png
-28766.png
-34835.png
-49393.png
-69724.png
-28921.png
-51109.png
-53229.png
-54959.png
-45625.png
-37081.png
-33944.png
-64368.png
-26056.png
-39118.png
-06013.png
-64678.png
-45121.png
-51185.png
-03434.png
-58786.png
-40257.png
-09101.png
-50817.png
-33623.png
-58871.png
-66200.png
-15885.png
-04735.png
-06230.png
-56126.png
-40963.png
-53444.png
-68040.png
-12097.png
-20221.png
-31213.png
-42616.png
-30353.png
-64239.png
-67420.png
-51345.png
-16437.png
-37022.png
-53679.png
-31330.png
-57204.png
-69031.png
-06173.png
-52167.png
-03101.png
-47137.png
-52527.png
-32949.png
-08583.png
-18605.png
-13463.png
-56778.png
-16336.png
-55182.png
-44928.png
-57707.png
-69756.png
-31536.png
-30970.png
-59590.png
-63146.png
-61458.png
-09189.png
-52715.png
-10561.png
-25678.png
-40887.png
-44538.png
-37571.png
-06784.png
-67744.png
-54263.png
-18616.png
-24391.png
-05305.png
-03733.png
-01044.png
-58201.png
-37021.png
-03382.png
-34443.png
-34133.png
-11430.png
-09916.png
-64946.png
-20734.png
-52196.png
-36329.png
-49362.png
-35526.png
-62461.png
-49267.png
-30997.png
-48698.png
-11772.png
-66701.png
-45602.png
-17754.png
-01765.png
-10606.png
-18903.png
-13818.png
-02353.png
-00823.png
-26169.png
-36422.png
-05554.png
-26205.png
-33208.png
-18042.png
-31271.png
-52391.png
-61383.png
-11766.png
-58132.png
-66500.png
-05785.png
-40827.png
-03471.png
-56016.png
-07349.png
-39555.png
-34979.png
-05845.png
-51440.png
-15891.png
-57949.png
-57757.png
-47260.png
-40649.png
-24682.png
-60403.png
-65913.png
-30507.png
-36897.png
-69378.png
-16168.png
-40164.png
-15178.png
-58367.png
-37083.png
-38456.png
-06277.png
-35369.png
-43750.png
-53244.png
-00319.png
-50839.png
-56810.png
-24303.png
-25853.png
-13814.png
-54767.png
-11515.png
-42008.png
-67760.png
-64218.png
-58304.png
-54212.png
-06646.png
-47956.png
-63721.png
-31291.png
-46580.png
-17219.png
-51459.png
-28066.png
-22485.png
-14170.png
-10781.png
-36228.png
-68108.png
-58884.png
-22230.png
-64188.png
-10513.png
-64574.png
-47002.png
-40221.png
-27270.png
-17300.png
-06425.png
-39789.png
-26591.png
-36749.png
-55765.png
-04041.png
-07721.png
-09978.png
-39241.png
-29349.png
-27898.png
-42495.png
-19428.png
-37205.png
-11061.png
-39699.png
-25728.png
-52364.png
-54934.png
-42578.png
-15745.png
-19711.png
-63827.png
-25618.png
-21890.png
-14947.png
-09710.png
-29818.png
-53823.png
-49033.png
-10834.png
-39796.png
-44670.png
-31545.png
-61104.png
-38038.png
-30303.png
-17136.png
-40859.png
-36694.png
-39244.png
-53023.png
-29141.png
-56038.png
-57361.png
-67512.png
-12348.png
-40641.png
-14324.png
-30413.png
-57065.png
-20234.png
-34004.png
-44875.png
-12509.png
-01174.png
-53224.png
-07546.png
-66072.png
-67958.png
-02870.png
-26046.png
-03431.png
-26634.png
-62658.png
-07190.png
-17424.png
-13713.png
-38364.png
-63265.png
-54542.png
-18060.png
-63670.png
-06728.png
-57995.png
-50873.png
-23812.png
-41913.png
-65512.png
-14604.png
-16673.png
-38111.png
-61953.png
-19145.png
-12729.png
-49388.png
-57544.png
-24859.png
-14202.png
-39767.png
-35617.png
-53609.png
-10122.png
-67447.png
-03860.png
-55737.png
-03141.png
-34916.png
-30062.png
-07996.png
-57508.png
-36470.png
-03826.png
-02462.png
-41317.png
-50764.png
-39320.png
-00200.png
-61002.png
-62820.png
-59239.png
-28076.png
-45704.png
-60690.png
-57267.png
-40407.png
-11637.png
-41500.png
-27795.png
-62537.png
-55508.png
-21593.png
-15707.png
-00458.png
-62847.png
-27901.png
-47052.png
-52173.png
-47407.png
-62084.png
-19008.png
-08006.png
-43184.png
-11716.png
-65601.png
-34399.png
-57963.png
-67331.png
-31337.png
-38615.png
-29113.png
-43159.png
-28537.png
-66872.png
-25307.png
-45306.png
-59110.png
-67543.png
-04110.png
-04851.png
-04152.png
-46093.png
-16612.png
-09776.png
-23801.png
-18411.png
-57566.png
-54157.png
-40987.png
-25602.png
-04674.png
-24361.png
-07713.png
-25834.png
-59808.png
-51505.png
-59187.png
-47723.png
-40075.png
-18510.png
-24448.png
-15583.png
-49971.png
-50868.png
-21146.png
-66401.png
-31774.png
-22293.png
-39691.png
-61339.png
-64552.png
-62682.png
-07432.png
-63338.png
-35198.png
-08893.png
-42562.png
-65912.png
-38760.png
-44904.png
-27553.png
-06454.png
-23649.png
-08325.png
-62380.png
-58508.png
-53940.png
-26443.png
-20694.png
-15567.png
-11878.png
-64761.png
-16617.png
-36198.png
-67521.png
-54680.png
-17521.png
-65597.png
-55486.png
-47602.png
-01675.png
-64867.png
-42958.png
-41531.png
-18043.png
-06057.png
-21895.png
-33657.png
-68627.png
-23127.png
-08235.png
-07698.png
-44526.png
-60859.png
-12988.png
-32780.png
-48121.png
-46037.png
-29206.png
-57335.png
-51702.png
-20479.png
-02362.png
-66149.png
-66464.png
-41389.png
-65883.png
-32905.png
-62364.png
-48990.png
-01740.png
-42220.png
-50438.png
-19190.png
-14469.png
-66653.png
-09840.png
-17960.png
-15952.png
-61263.png
-21352.png
-14301.png
-45418.png
-64464.png
-51918.png
-35757.png
-58758.png
-62707.png
-00506.png
-49570.png
-25284.png
-53828.png
-36516.png
-19364.png
-61290.png
-11421.png
-39768.png
-66828.png
-49667.png
-09742.png
-34101.png
-37611.png
-41437.png
-43893.png
-44944.png
-13136.png
-00406.png
-06428.png
-33612.png
-37290.png
-28636.png
-36410.png
-13546.png
-36881.png
-61679.png
-60694.png
-04834.png
-61156.png
-30360.png
-52511.png
-05804.png
-69650.png
-27999.png
-29997.png
-65970.png
-30329.png
-33255.png
-50078.png
-43014.png
-36973.png
-69296.png
-44323.png
-32405.png
-46544.png
-56377.png
-44499.png
-13688.png
-19309.png
-01904.png
-06820.png
-47331.png
-18650.png
-23770.png
-08751.png
-25185.png
-02413.png
-39463.png
-12982.png
-52418.png
-45570.png
-11874.png
-60201.png
-66182.png
-68270.png
-00884.png
-45055.png
-36611.png
-08517.png
-39285.png
-26342.png
-34867.png
-52403.png
-29408.png
-09110.png
-53349.png
-04878.png
-38305.png
-68060.png
-68594.png
-34686.png
-13206.png
-64567.png
-52813.png
-39571.png
-56872.png
-31241.png
-45015.png
-48235.png
-22963.png
-22828.png
-49045.png
-11712.png
-25506.png
-68295.png
-14094.png
-37770.png
-07020.png
-54626.png
-24311.png
-65772.png
-67791.png
-41553.png
-00501.png
-58578.png
-03923.png
-05557.png
-61786.png
-02342.png
-27976.png
-08603.png
-30213.png
-62784.png
-32029.png
-21877.png
-22063.png
-53134.png
-35945.png
-47586.png
-17994.png
-17269.png
-10899.png
-31513.png
-55800.png
-37764.png
-23281.png
-47927.png
-29043.png
-62542.png
-32449.png
-08277.png
-55148.png
-23951.png
-21328.png
-07795.png
-09717.png
-16224.png
-44952.png
-13183.png
-66249.png
-02201.png
-42319.png
-12544.png
-45336.png
-59570.png
-67607.png
-46832.png
-24327.png
-04261.png
-42265.png
-65608.png
-58894.png
-12217.png
-24031.png
-16133.png
-21692.png
-42356.png
-65047.png
-51475.png
-09053.png
-46252.png
-08092.png
-46298.png
-67151.png
-01832.png
-39778.png
-30738.png
-32020.png
-03540.png
-63056.png
-07463.png
-28692.png
-32785.png
-41461.png
-10022.png
-07109.png
-45962.png
-62984.png
-08027.png
-23945.png
-05816.png
-47095.png
-21863.png
-55263.png
-48857.png
-50669.png
-42305.png
-27833.png
-44573.png
-05830.png
-62087.png
-68733.png
-29275.png
-27921.png
-64756.png
-55090.png
-59752.png
-58000.png
-65859.png
-55181.png
-68605.png
-55707.png
-58952.png
-02208.png
-69438.png
-24995.png
-39704.png
-42466.png
-19633.png
-15231.png
-11080.png
-36853.png
-24465.png
-02037.png
-02713.png
-04003.png
-51472.png
-29296.png
-19290.png
-61320.png
-42042.png
-16434.png
-63514.png
-03648.png
-35483.png
-30958.png
-53322.png
-06125.png
-10540.png
-39738.png
-43242.png
-10644.png
-15444.png
-28262.png
-61229.png
-53013.png
-16735.png
-34219.png
-38465.png
-21995.png
-11496.png
-19449.png
-05870.png
-31051.png
-07351.png
-25128.png
-58853.png
-68501.png
-12794.png
-42940.png
-18910.png
-11490.png
-08321.png
-24106.png
-20649.png
-10568.png
-17087.png
-59978.png
-23734.png
-25362.png
-50513.png
-62103.png
-37067.png
-43703.png
-53915.png
-19312.png
-36015.png
-65639.png
-63855.png
-40088.png
-10840.png
-53535.png
-28694.png
-21337.png
-46524.png
-43355.png
-51767.png
-00712.png
-19272.png
-07626.png
-31602.png
-10015.png
-47317.png
-39546.png
-33582.png
-01867.png
-59004.png
-47577.png
-35803.png
-00985.png
-38596.png
-62277.png
-67952.png
-27925.png
-35997.png
-20262.png
-28464.png
-13775.png
-38506.png
-01654.png
-19505.png
-09459.png
-03444.png
-44135.png
-02894.png
-21873.png
-38752.png
-23891.png
-35675.png
-32847.png
-09513.png
-68479.png
-53490.png
-32502.png
-07115.png
-10961.png
-03473.png
-40300.png
-21172.png
-01269.png
-68514.png
-12095.png
-50607.png
-46827.png
-33740.png
-47973.png
-19125.png
-01655.png
-17096.png
-05186.png
-57940.png
-43649.png
-42412.png
-66033.png
-12158.png
-35167.png
-51306.png
-65596.png
-20056.png
-37072.png
-54330.png
-65189.png
-14903.png
-20567.png
-59063.png
-17128.png
-63032.png
-32603.png
-02467.png
-03358.png
-36796.png
-13220.png
-66649.png
-21161.png
-32340.png
-47222.png
-48926.png
-69770.png
-43446.png
-55918.png
-34982.png
-53110.png
-55505.png
-16895.png
-32158.png
-40400.png
-03654.png
-63883.png
-19699.png
-38170.png
-00167.png
-54601.png
-09401.png
-05837.png
-01014.png
-18614.png
-47536.png
-35034.png
-16165.png
-39397.png
-02629.png
-67452.png
-65226.png
-39135.png
-41985.png
-19456.png
-59500.png
-07069.png
-66518.png
-15702.png
-08665.png
-37790.png
-52057.png
-49759.png
-36767.png
-37823.png
-26496.png
-31957.png
-48343.png
-68399.png
-40600.png
-54668.png
-10205.png
-31525.png
-34052.png
-67233.png
-29161.png
-55687.png
-13843.png
-54221.png
-64124.png
-36871.png
-56295.png
-55493.png
-58403.png
-35032.png
-54087.png
-53026.png
-08458.png
-14229.png
-17663.png
-14612.png
-59693.png
-25534.png
-15370.png
-52622.png
-50389.png
-20952.png
-05868.png
-47154.png
-02671.png
-35961.png
-12635.png
-21616.png
-10394.png
-35097.png
-06959.png
-15065.png
-26968.png
-18528.png
-24110.png
-41300.png
-50939.png
-60064.png
-09902.png
-57515.png
-08518.png
-11138.png
-12350.png
-34600.png
-65014.png
-60056.png
-08250.png
-67674.png
-27041.png
-33824.png
-43982.png
-24097.png
-49024.png
-25893.png
-52284.png
-65156.png
-40774.png
-31356.png
-22377.png
-68028.png
-55660.png
-39019.png
-13809.png
-08355.png
-03296.png
-04510.png
-25387.png
-62203.png
-12071.png
-51418.png
-53613.png
-04365.png
-47177.png
-63829.png
-31817.png
-30373.png
-54326.png
-54482.png
-28716.png
-05214.png
-25101.png
-53720.png
-16179.png
-27852.png
-64344.png
-38401.png
-06556.png
-00928.png
-43020.png
-37593.png
-15430.png
-11096.png
-07265.png
-69955.png
-08397.png
-33467.png
-32851.png
-36644.png
-50652.png
-22168.png
-10519.png
-63034.png
-08760.png
-07114.png
-68109.png
-68517.png
-34022.png
-66726.png
-58181.png
-03367.png
-47117.png
-42404.png
-26639.png
-33960.png
-34187.png
-28359.png
-01146.png
-40197.png
-20944.png
-55348.png
-68936.png
-20655.png
-44024.png
-34386.png
-48297.png
-10229.png
-24026.png
-38959.png
-09161.png
-66658.png
-17854.png
-49261.png
-35016.png
-34110.png
-58745.png
-10717.png
-53704.png
-44864.png
-15450.png
-44712.png
-50197.png
-13192.png
-68940.png
-27531.png
-18704.png
-50321.png
-29479.png
-64217.png
-40676.png
-20224.png
-47657.png
-51464.png
-30964.png
-15326.png
-40727.png
-57249.png
-13247.png
-01092.png
-27880.png
-54593.png
-65154.png
-65678.png
-43643.png
-09946.png
-41108.png
-31022.png
-21812.png
-60944.png
-06834.png
-52113.png
-57457.png
-24176.png
-69958.png
-01988.png
-22772.png
-59391.png
-02774.png
-52667.png
-62689.png
-53470.png
-33538.png
-28039.png
-38460.png
-44233.png
-00307.png
-19710.png
-03699.png
-58393.png
-34949.png
-16484.png
-00248.png
-04965.png
-25156.png
-03704.png
-17316.png
-14056.png
-44920.png
-68467.png
-23483.png
-27047.png
-19653.png
-48957.png
-10377.png
-17565.png
-42765.png
-24240.png
-43198.png
-53529.png
-55603.png
-40537.png
-07074.png
-42625.png
-28879.png
-21287.png
-65658.png
-13519.png
-33236.png
-27168.png
-34581.png
-17122.png
-06482.png
-69575.png
-04754.png
-01757.png
-46062.png
-66563.png
-40872.png
-50260.png
-68629.png
-66520.png
-23783.png
-64899.png
-53570.png
-44407.png
-09251.png
-67696.png
-58492.png
-35901.png
-67216.png
-04276.png
-28620.png
-36366.png
-09934.png
-45156.png
-66313.png
-48890.png
-55478.png
-43223.png
-61405.png
-11459.png
-57359.png
-46699.png
-34174.png
-63026.png
-58937.png
-13765.png
-06578.png
-15964.png
-33013.png
-56734.png
-09009.png
-56382.png
-38064.png
-12823.png
-48509.png
-22504.png
-62167.png
-44063.png
-03530.png
-01879.png
-27113.png
-38790.png
-60063.png
-39588.png
-19237.png
-00661.png
-57569.png
-49909.png
-35390.png
-62115.png
-57138.png
-12801.png
-05043.png
-18439.png
-01618.png
-61956.png
-06866.png
-67698.png
-69715.png
-47553.png
-37501.png
-37385.png
-63973.png
-61646.png
-37913.png
-67160.png
-01645.png
-01456.png
-29934.png
-22316.png
-43068.png
-54819.png
-23093.png
-42882.png
-28379.png
-01394.png
-20464.png
-10770.png
-67706.png
-46485.png
-58362.png
-11768.png
-02199.png
-10171.png
-16405.png
-43318.png
-15205.png
-13352.png
-61553.png
-45750.png
-65388.png
-53665.png
-14018.png
-66866.png
-19306.png
-67822.png
-49838.png
-42236.png
-54454.png
-55036.png
-33016.png
-03164.png
-54778.png
-15737.png
-07621.png
-40623.png
-06929.png
-09860.png
-05718.png
-56913.png
-05958.png
-10275.png
-31633.png
-05516.png
-44705.png
-42641.png
-16240.png
-26085.png
-03046.png
-00341.png
-19591.png
-40169.png
-38501.png
-12358.png
-15697.png
-16772.png
-06538.png
-59383.png
-13187.png
-56749.png
-50940.png
-63846.png
-63364.png
-29746.png
-15272.png
-08448.png
-57399.png
-41355.png
-06448.png
-41749.png
-16678.png
-07435.png
-43890.png
-64386.png
-01913.png
-55723.png
-17770.png
-41238.png
-56714.png
-06155.png
-30891.png
-44495.png
-15906.png
-02709.png
-33238.png
-38061.png
-50966.png
-06575.png
-34063.png
-25901.png
-51945.png
-07695.png
-58303.png
-02653.png
-12772.png
-24834.png
-58365.png
-40422.png
-46371.png
-44621.png
-31094.png
-16727.png
-40222.png
-68185.png
-20271.png
-30166.png
-47769.png
-19632.png
-24813.png
-18618.png
-53962.png
-25590.png
-18505.png
-13696.png
-20080.png
-16786.png
-36699.png
-60357.png
-22684.png
-31727.png
-62582.png
-64648.png
-67474.png
-00965.png
-65689.png
-43597.png
-22175.png
-04279.png
-40625.png
-11418.png
-30327.png
-07340.png
-62021.png
-36880.png
-31840.png
-67846.png
-12654.png
-59867.png
-20444.png
-63366.png
-27135.png
-34099.png
-42885.png
-63138.png
-67711.png
-16295.png
-27466.png
-54768.png
-67050.png
-42003.png
-51208.png
-63872.png
-66920.png
-00864.png
-25856.png
-16075.png
-38377.png
-10687.png
-08883.png
-55347.png
-20926.png
-11642.png
-19326.png
-13700.png
-18597.png
-63838.png
-09940.png
-13989.png
-03779.png
-59638.png
-66420.png
-00178.png
-66739.png
-39435.png
-48634.png
-04058.png
-60458.png
-67079.png
-30149.png
-13731.png
-12550.png
-55211.png
-19399.png
-12012.png
-16565.png
-31973.png
-30931.png
-24884.png
-67262.png
-06825.png
-40031.png
-01511.png
-21883.png
-55483.png
-29642.png
-23752.png
-59964.png
-15546.png
-07194.png
-07105.png
-65136.png
-28081.png
-33233.png
-04793.png
-01080.png
-26550.png
-35260.png
-05980.png
-14232.png
-50120.png
-32377.png
-12916.png
-22261.png
-11974.png
-14860.png
-39524.png
-47093.png
-30125.png
-56638.png
-33317.png
-50045.png
-20825.png
-36652.png
-46464.png
-53528.png
-13553.png
-03752.png
-33712.png
-38579.png
-15836.png
-27148.png
-50338.png
-44626.png
-32561.png
-26965.png
-21086.png
-63964.png
-67088.png
-24860.png
-28432.png
-52366.png
-30520.png
-02736.png
-60417.png
-58473.png
-60505.png
-66070.png
-05375.png
-13360.png
-12474.png
-44000.png
-55749.png
-39479.png
-02343.png
-33365.png
-68626.png
-68011.png
-29338.png
-22354.png
-10069.png
-48380.png
-53992.png
-35257.png
-09280.png
-16382.png
-05670.png
-28825.png
-57274.png
-40834.png
-20650.png
-18535.png
-45961.png
-14785.png
-61901.png
-62578.png
-10107.png
-38130.png
-56315.png
-17444.png
-20491.png
-08727.png
-36506.png
-25950.png
-14180.png
-68813.png
-48015.png
-42526.png
-50320.png
-24790.png
-62416.png
-04724.png
-07401.png
-40611.png
-50435.png
-51252.png
-33938.png
-31956.png
-32705.png
-68038.png
-08438.png
-04846.png
-18585.png
-26150.png
-51487.png
-22733.png
-61524.png
-23409.png
-69158.png
-36323.png
-39112.png
-00457.png
-45314.png
-36703.png
-24823.png
-38982.png
-28884.png
-09891.png
-11789.png
-38892.png
-22043.png
-54363.png
-41508.png
-24384.png
-16351.png
-44325.png
-50190.png
-48051.png
-53795.png
-58322.png
-05739.png
-17100.png
-52240.png
-15718.png
-11546.png
-11144.png
-57234.png
-34109.png
-41251.png
-54679.png
-25894.png
-68846.png
-68409.png
-40224.png
-61402.png
-27575.png
-26874.png
-31939.png
-63679.png
-16745.png
-42278.png
-67553.png
-36025.png
-30021.png
-02952.png
-30690.png
-65968.png
-64568.png
-50203.png
-50946.png
-52314.png
-42835.png
-57805.png
-56480.png
-47974.png
-42965.png
-15020.png
-54114.png
-64757.png
-63053.png
-43602.png
-53489.png
-38514.png
-56235.png
-64582.png
-38882.png
-16957.png
-03457.png
-43953.png
-19971.png
-32107.png
-31252.png
-05448.png
-68745.png
-28680.png
-18681.png
-49014.png
-15080.png
-39506.png
-64241.png
-25127.png
-49742.png
-39628.png
-16455.png
-53293.png
-11235.png
-00308.png
-44333.png
-58021.png
-63355.png
-52480.png
-50205.png
-66598.png
-09680.png
-17752.png
-11848.png
-61972.png
-23667.png
-07058.png
-13609.png
-32057.png
-24639.png
-33494.png
-59580.png
-58674.png
-41682.png
-56324.png
-54919.png
-66968.png
-51982.png
-61561.png
-30533.png
-56345.png
-53942.png
-05751.png
-12749.png
-46325.png
-30581.png
-12019.png
-61732.png
-35093.png
-51476.png
-42582.png
-56869.png
-59910.png
-53907.png
-18565.png
-03589.png
-44299.png
-51386.png
-36846.png
-08762.png
-33042.png
-20002.png
-02105.png
-23011.png
-18303.png
-68044.png
-42036.png
-40815.png
-30944.png
-08392.png
-26291.png
-20771.png
-22745.png
-11565.png
-46845.png
-01539.png
-23764.png
-08276.png
-68762.png
-29292.png
-24305.png
-41950.png
-07021.png
-53538.png
-69273.png
-03177.png
-20474.png
-31234.png
-27719.png
-47740.png
-67951.png
-39543.png
-33514.png
-08345.png
-04001.png
-11084.png
-07849.png
-55358.png
-32313.png
-63178.png
-56750.png
-20930.png
-42930.png
-55266.png
-15255.png
-60168.png
-67894.png
-56875.png
-24941.png
-50736.png
-30244.png
-51741.png
-68285.png
-41365.png
-63630.png
-63461.png
-47294.png
-60986.png
-51729.png
-52050.png
-11227.png
-16530.png
-61663.png
-36932.png
-28302.png
-20743.png
-19941.png
-51156.png
-10141.png
-66515.png
-39376.png
-40061.png
-33999.png
-11359.png
-56497.png
-43880.png
-21935.png
-13454.png
-06850.png
-39082.png
-05829.png
-28980.png
-32404.png
-17009.png
-33583.png
-37665.png
-24942.png
-38496.png
-54658.png
-19843.png
-52089.png
-27501.png
-38168.png
-52419.png
-64202.png
-08548.png
-02958.png
-43557.png
-56252.png
-30209.png
-25359.png
-03562.png
-27602.png
-55255.png
-04120.png
-23400.png
-01844.png
-64200.png
-28790.png
-10100.png
-42914.png
-65464.png
-03070.png
-64957.png
-21448.png
-53274.png
-36468.png
-67669.png
-22208.png
-10393.png
-60122.png
-23459.png
-61857.png
-06256.png
-66044.png
-66426.png
-07676.png
-11793.png
-45068.png
-66747.png
-21109.png
-38577.png
-33935.png
-07018.png
-40418.png
-38208.png
-45043.png
-60574.png
-26075.png
-24960.png
-31343.png
-62923.png
-43273.png
-12567.png
-69703.png
-14194.png
-22777.png
-11346.png
-30092.png
-24155.png
-13410.png
-66387.png
-69791.png
-02216.png
-39199.png
-63038.png
-59261.png
-61965.png
-28467.png
-57494.png
-68414.png
-04621.png
-30041.png
-42288.png
-54690.png
-18808.png
-26881.png
-67726.png
-51863.png
-66373.png
-27408.png
-36081.png
-40297.png
-63198.png
-43636.png
-59372.png
-16476.png
-37996.png
-40139.png
-63898.png
-58719.png
-38917.png
-23816.png
-27920.png
-49963.png
-26712.png
-64864.png
-22565.png
-14487.png
-54517.png
-15091.png
-59370.png
-22978.png
-02361.png
-41240.png
-13682.png
-18328.png
-50863.png
-62022.png
-27043.png
-32845.png
-08604.png
-65369.png
-52704.png
-11896.png
-63701.png
-31460.png
-14024.png
-57143.png
-65519.png
-35541.png
-58817.png
-18506.png
-64024.png
-69016.png
-61763.png
-38529.png
-47484.png
-23484.png
-10852.png
-28596.png
-13875.png
-01816.png
-44800.png
-60265.png
-28255.png
-68400.png
-11713.png
-01982.png
-36294.png
-29308.png
-50538.png
-60411.png
-27908.png
-58188.png
-27536.png
-03353.png
-46273.png
-45493.png
-20671.png
-48281.png
-07711.png
-10322.png
-25467.png
-14764.png
-58254.png
-49632.png
-17942.png
-18482.png
-57804.png
-58036.png
-06450.png
-02115.png
-04445.png
-50670.png
-55341.png
-50410.png
-05715.png
-09201.png
-41766.png
-24855.png
-16905.png
-51272.png
-32553.png
-25040.png
-39036.png
-69172.png
-58352.png
-33532.png
-05370.png
-60231.png
-17053.png
-57745.png
-65843.png
-11877.png
-19682.png
-06837.png
-57364.png
-27796.png
-23925.png
-49318.png
-43430.png
-01848.png
-20431.png
-28972.png
-01744.png
-56078.png
-44617.png
-00429.png
-59143.png
-21540.png
-62338.png
-26049.png
-07345.png
-66792.png
-03962.png
-24010.png
-41788.png
-02985.png
-57865.png
-46237.png
-08457.png
-12566.png
-39842.png
-16690.png
-09344.png
-62563.png
-10429.png
-32503.png
-39913.png
-50439.png
-27272.png
-28096.png
-04459.png
-02090.png
-29557.png
-31060.png
-13758.png
-12854.png
-14322.png
-07952.png
-38897.png
-69937.png
-07085.png
-45389.png
-27080.png
-66556.png
-13619.png
-26220.png
-24115.png
-57697.png
-44269.png
-64543.png
-11593.png
-55753.png
-45888.png
-02917.png
-31572.png
-18003.png
-66482.png
-65168.png
-18689.png
-58172.png
-48095.png
-51159.png
-08114.png
-27315.png
-39362.png
-39115.png
-16742.png
-60365.png
-69094.png
-33256.png
-58323.png
-37837.png
-56548.png
-43278.png
-15816.png
-23880.png
-39349.png
-50273.png
-12737.png
-26294.png
-19846.png
-47671.png
-64676.png
-63606.png
-61496.png
-55906.png
-67297.png
-16708.png
-59104.png
-22889.png
-06889.png
-23593.png
-15024.png
-60518.png
-30982.png
-11646.png
-44585.png
-19543.png
-25053.png
-66567.png
-12526.png
-66060.png
-28720.png
-27630.png
-14542.png
-11081.png
-24225.png
-49201.png
-63002.png
-42608.png
-39124.png
-15022.png
-55160.png
-53927.png
-58343.png
-51294.png
-23008.png
-52211.png
-11042.png
-19959.png
-29462.png
-23789.png
-62404.png
-47656.png
-33909.png
-27703.png
-16760.png
-29131.png
-08825.png
-61319.png
-47774.png
-53443.png
-47781.png
-26404.png
-10764.png
-18498.png
-20630.png
-05925.png
-56664.png
-49653.png
-67828.png
-69744.png
-51809.png
-10666.png
-64311.png
-08425.png
-46264.png
-40003.png
-07443.png
-43511.png
-24461.png
-31125.png
-60297.png
-59120.png
-38515.png
-63544.png
-02345.png
-03269.png
-02919.png
-33616.png
-51993.png
-52288.png
-01686.png
-47155.png
-52102.png
-57666.png
-21305.png
-05361.png
-42381.png
-20254.png
-62243.png
-32170.png
-57809.png
-11730.png
-12838.png
-49272.png
-00479.png
-36486.png
-30107.png
-46992.png
-61558.png
-07602.png
-25762.png
-25201.png
-51013.png
-41415.png
-04045.png
-42545.png
-27405.png
-47674.png
-33879.png
-45069.png
-28020.png
-63030.png
-50002.png
-06648.png
-24069.png
-23983.png
-05918.png
-61570.png
-01280.png
-08378.png
-65000.png
-35959.png
-64994.png
-19142.png
-55350.png
-35693.png
-35293.png
-58664.png
-59764.png
-22605.png
-49694.png
-47252.png
-42363.png
-51133.png
-41304.png
-06356.png
-04081.png
-63897.png
-35641.png
-22579.png
-54491.png
-33305.png
-17688.png
-63376.png
-35317.png
-54799.png
-10381.png
-01005.png
-09724.png
-19662.png
-60673.png
-39658.png
-69802.png
-63889.png
-62519.png
-64980.png
-29428.png
-40669.png
-09318.png
-31278.png
-38273.png
-42802.png
-29167.png
-48283.png
-53256.png
-12770.png
-25278.png
-01416.png
-58668.png
-18174.png
-28676.png
-16658.png
-06107.png
-53015.png
-57670.png
-44498.png
-42127.png
-65126.png
-25645.png
-44562.png
-19602.png
-32205.png
-63965.png
-16376.png
-16752.png
-68610.png
-31597.png
-49296.png
-61803.png
-32556.png
-43572.png
-65759.png
-08668.png
-11708.png
-54842.png
-63100.png
-07835.png
-66883.png
-40234.png
-21533.png
-00767.png
-38426.png
-39141.png
-64973.png
-25117.png
-08868.png
-54644.png
-29366.png
-50123.png
-35813.png
-56644.png
-57588.png
-46737.png
-52826.png
-55162.png
-18001.png
-18338.png
-69734.png
-36664.png
-55806.png
-41424.png
-47649.png
-28915.png
-50492.png
-24724.png
-57411.png
-55726.png
-48011.png
-44861.png
-00787.png
-65035.png
-18852.png
-06470.png
-58267.png
-38660.png
-17323.png
-67486.png
-23698.png
-38946.png
-27987.png
-69708.png
-29949.png
-65130.png
-40535.png
-54544.png
-69822.png
-00874.png
-56785.png
-29321.png
-12074.png
-68055.png
-00249.png
-58699.png
-53329.png
-11121.png
-67765.png
-65860.png
-58054.png
-22707.png
-41782.png
-61726.png
-40040.png
-25170.png
-18148.png
-67517.png
-38978.png
-60034.png
-18313.png
-58638.png
-45388.png
-27787.png
-05642.png
-21622.png
-36778.png
-07207.png
-11641.png
-49051.png
-21052.png
-02845.png
-14734.png
-41828.png
-04946.png
-25392.png
-45283.png
-66384.png
-08923.png
-06115.png
-15848.png
-67770.png
-40404.png
-56993.png
-21545.png
-51216.png
-00179.png
-27339.png
-53419.png
-40112.png
-56231.png
-52086.png
-27581.png
-04205.png
-63913.png
-05008.png
-23695.png
-23325.png
-09729.png
-60126.png
-20153.png
-28161.png
-55377.png
-23100.png
-12490.png
-27485.png
-33427.png
-56073.png
-64829.png
-02041.png
-67994.png
-12282.png
-69237.png
-25679.png
-59532.png
-26565.png
-68761.png
-63992.png
-28135.png
-62946.png
-24145.png
-51196.png
-61113.png
-56832.png
-03351.png
-27425.png
-58563.png
-68713.png
-10895.png
-31561.png
-22982.png
-11982.png
-23112.png
-08391.png
-50191.png
-26384.png
-64401.png
-29622.png
-63882.png
-40458.png
-25141.png
-63436.png
-53221.png
-49363.png
-36421.png
-03158.png
-68667.png
-02123.png
-42624.png
-01807.png
-27741.png
-07828.png
-02775.png
-28196.png
-04913.png
-44847.png
-56318.png
-12732.png
-28813.png
-07758.png
-27189.png
-12187.png
-39517.png
-35273.png
-14165.png
-49537.png
-58886.png
-12228.png
-60039.png
-38102.png
-38117.png
-54728.png
-01873.png
-03506.png
-66881.png
-48458.png
-67388.png
-11834.png
-29108.png
-10727.png
-19279.png
-13325.png
-10916.png
-55535.png
-59064.png
-15386.png
-25983.png
-11604.png
-39002.png
-69380.png
-28111.png
-57879.png
-63981.png
-21827.png
-05189.png
-56264.png
-55487.png
-34371.png
-05697.png
-48766.png
-16357.png
-40242.png
-59171.png
-36381.png
-69517.png
-53540.png
-26529.png
-69206.png
-57367.png
-63240.png
-29198.png
-63307.png
-64706.png
-49661.png
-54034.png
-20321.png
-62659.png
-63565.png
-36856.png
-64427.png
-15818.png
-46478.png
-55658.png
-24642.png
-55140.png
-63692.png
-60593.png
-04916.png
-00288.png
-12810.png
-24455.png
-43789.png
-18922.png
-50349.png
-61091.png
-57321.png
-31404.png
-35255.png
-32638.png
-11503.png
-35797.png
-31028.png
-46957.png
-60580.png
-29878.png
-30753.png
-06111.png
-10182.png
-27450.png
-31397.png
-43897.png
-42630.png
-06755.png
-02950.png
-48185.png
-35784.png
-46055.png
-10607.png
-05129.png
-29783.png
-64350.png
-58521.png
-45639.png
-25146.png
-07298.png
-04425.png
-44746.png
-51991.png
-50138.png
-44979.png
-01054.png
-12181.png
-32641.png
-14862.png
-42619.png
-44703.png
-47255.png
-25386.png
-27072.png
-52146.png
-26501.png
-44355.png
-52242.png
-17025.png
-02271.png
-46597.png
-23454.png
-62018.png
-41592.png
-46339.png
-15059.png
-37228.png
-37756.png
-67238.png
-07205.png
-55577.png
-42513.png
-66537.png
-05065.png
-12266.png
-06340.png
-13097.png
-00504.png
-05997.png
-55635.png
-59927.png
-43958.png
-44618.png
-29265.png
-43909.png
-45016.png
-03245.png
-30448.png
-61416.png
-16418.png
-25550.png
-62184.png
-07739.png
-32051.png
-12411.png
-64260.png
-29065.png
-03544.png
-32301.png
-39102.png
-45278.png
-37416.png
-07263.png
-56822.png
-47623.png
-65996.png
-44930.png
-45067.png
-17535.png
-27899.png
-43423.png
-53680.png
-23847.png
-13884.png
-15263.png
-46762.png
-31309.png
-56798.png
-06165.png
-46972.png
-30904.png
-56719.png
-51510.png
-11658.png
-32089.png
-37727.png
-16817.png
-01918.png
-18389.png
-63013.png
-60073.png
-44916.png
-23257.png
-34189.png
-06763.png
-62298.png
-25420.png
-63257.png
-40778.png
-32213.png
-06398.png
-17061.png
-01268.png
-23856.png
-10400.png
-06943.png
-66169.png
-29395.png
-64058.png
-63261.png
-16074.png
-65584.png
-44322.png
-01920.png
-50795.png
-55262.png
-14503.png
-17534.png
-50037.png
-18546.png
-05304.png
-55032.png
-09735.png
-02507.png
-46224.png
-25262.png
-67017.png
-56091.png
-45302.png
-33500.png
-21708.png
-44766.png
-68366.png
-12533.png
-39023.png
-08803.png
-16986.png
-40890.png
-17895.png
-10810.png
-42902.png
-58618.png
-46363.png
-12108.png
-21253.png
-15570.png
-48209.png
-25187.png
-59061.png
-52382.png
-28579.png
-62153.png
-25479.png
-31314.png
-29709.png
-04651.png
-30461.png
-55068.png
-17884.png
-58027.png
-21781.png
-69324.png
-25921.png
-62769.png
-68495.png
-69321.png
-19030.png
-07041.png
-29772.png
-16561.png
-34104.png
-19289.png
-04802.png
-41630.png
-07211.png
-41957.png
-34432.png
-40635.png
-04833.png
-47072.png
-51399.png
-13764.png
-58205.png
-38605.png
-00737.png
-59276.png
-28703.png
-49894.png
-66088.png
-27873.png
-13804.png
-66003.png
-52720.png
-02301.png
-54905.png
-02993.png
-46756.png
-49536.png
-47605.png
-12064.png
-03653.png
-56768.png
-67591.png
-67135.png
-07171.png
-43416.png
-64322.png
-30307.png
-56949.png
-46828.png
-21205.png
-10886.png
-08018.png
-21626.png
-59005.png
-15530.png
-59621.png
-42592.png
-54510.png
-10820.png
-64938.png
-26506.png
-60590.png
-23265.png
-39163.png
-05142.png
-59215.png
-13066.png
-56394.png
-08620.png
-22038.png
-30034.png
-42103.png
-58121.png
-43148.png
-28929.png
-13974.png
-63552.png
-42214.png
-09912.png
-42915.png
-63836.png
-04971.png
-09758.png
-12628.png
-20259.png
-68487.png
-34166.png
-07963.png
-30442.png
-50294.png
-40765.png
-49612.png
-56400.png
-62536.png
-63741.png
-64779.png
-21379.png
-13205.png
-40944.png
-52522.png
-41872.png
-63335.png
-65293.png
-06158.png
-33343.png
-59387.png
-56634.png
-48287.png
-28252.png
-51633.png
-57928.png
-28003.png
-26711.png
-47474.png
-38287.png
-01642.png
-35604.png
-53503.png
-25738.png
-19340.png
-59330.png
-25071.png
-19550.png
-39586.png
-18890.png
-69204.png
-53339.png
-03852.png
-14532.png
-33118.png
-20976.png
-30051.png
-65366.png
-43479.png
-01519.png
-28895.png
-65496.png
-20738.png
-04579.png
-07638.png
-67182.png
-13908.png
-45582.png
-11638.png
-24105.png
-10188.png
-51154.png
-32498.png
-04106.png
-62066.png
-61955.png
-22685.png
-23130.png
-41881.png
-25453.png
-32526.png
-10539.png
-27682.png
-11826.png
-20167.png
-66770.png
-54820.png
-60987.png
-21636.png
-56609.png
-18241.png
-46681.png
-50770.png
-66686.png
-11232.png
-42105.png
-53812.png
-09248.png
-57400.png
-58308.png
-45326.png
-25087.png
-47270.png
-49922.png
-21123.png
-36940.png
-27831.png
-33507.png
-16151.png
-17680.png
-15897.png
-60001.png
-47383.png
-06694.png
-17361.png
-40289.png
-05890.png
-69115.png
-11327.png
-48208.png
-01512.png
-11022.png
-64363.png
-23353.png
-22799.png
-30737.png
-11817.png
-52956.png
-34461.png
-14082.png
-43799.png
-46985.png
-42796.png
-63360.png
-06823.png
-59516.png
-26901.png
-50698.png
-01385.png
-69274.png
-20779.png
-49497.png
-00510.png
-60107.png
-69301.png
-23282.png
-27722.png
-45133.png
-20197.png
-53667.png
-18487.png
-43867.png
-57081.png
-64649.png
-36404.png
-42553.png
-53599.png
-56099.png
-60230.png
-47625.png
-28145.png
-44330.png
-24641.png
-18367.png
-02627.png
-26875.png
-39061.png
-36917.png
-21385.png
-12465.png
-28044.png
-67358.png
-03485.png
-18297.png
-00302.png
-50624.png
-63993.png
-50959.png
-35319.png
-34765.png
-36957.png
-09800.png
-31909.png
-43313.png
-01300.png
-69910.png
-34458.png
-59405.png
-33930.png
-42498.png
-50834.png
-01508.png
-60480.png
-60178.png
-68419.png
-03405.png
-03379.png
-56485.png
-02672.png
-49684.png
-31440.png
-30880.png
-65738.png
-68877.png
-45991.png
-20697.png
-20827.png
-19334.png
-27867.png
-46634.png
-24109.png
-19016.png
-49056.png
-15652.png
-11904.png
-23451.png
-41561.png
-12232.png
-44999.png
-50951.png
-37757.png
-61407.png
-52900.png
-44484.png
-36885.png
-34281.png
-03808.png
-63337.png
-37312.png
-51160.png
-08333.png
-04704.png
-41607.png
-35131.png
-68174.png
-59782.png
-54598.png
-17109.png
-43933.png
-44222.png
-66704.png
-52246.png
-26477.png
-20189.png
-63412.png
-51287.png
-33554.png
-55331.png
-22249.png
-04590.png
-13203.png
-48153.png
-32306.png
-60633.png
-48969.png
-20215.png
-35408.png
-19518.png
-60845.png
-63323.png
-13186.png
-10208.png
-47730.png
-47645.png
-63290.png
-45999.png
-47129.png
-26310.png
-18010.png
-05916.png
-12957.png
-06947.png
-58573.png
-56402.png
-26032.png
-12284.png
-53581.png
-67843.png
-48504.png
-63446.png
-25916.png
-23347.png
-45500.png
-19324.png
-40665.png
-61609.png
-05255.png
-04390.png
-07299.png
-47842.png
-11872.png
-60224.png
-65521.png
-68748.png
-04222.png
-09515.png
-23446.png
-52711.png
-35325.png
-13295.png
-66194.png
-34438.png
-59731.png
-59881.png
-36647.png
-56421.png
-00809.png
-09469.png
-37108.png
-39979.png
-24695.png
-25297.png
-39197.png
-00189.png
-67168.png
-24755.png
-21753.png
-64752.png
-32868.png
-42048.png
-36573.png
-24984.png
-59003.png
-50272.png
-65904.png
-26274.png
-15234.png
-23482.png
-61462.png
-25930.png
-25329.png
-46070.png
-53126.png
-28415.png
-43605.png
-09877.png
-10017.png
-30449.png
-50682.png
-33561.png
-54247.png
-12630.png
-62300.png
-60473.png
-34220.png
-39444.png
-08726.png
-01141.png
-68239.png
-08046.png
-63519.png
-22378.png
-52880.png
-25324.png
-45653.png
-61904.png
-29812.png
-56053.png
-67825.png
-30255.png
-58613.png
-27493.png
-45424.png
-47742.png
-65143.png
-39105.png
-14876.png
-16383.png
-48473.png
-07320.png
-11782.png
-35830.png
-28691.png
-38593.png
-12079.png
-14450.png
-32239.png
-46916.png
-02920.png
-50950.png
-24195.png
-53291.png
-36864.png
-37620.png
-28032.png
-60838.png
-51975.png
-22892.png
-40286.png
-20586.png
-11648.png
-62644.png
-65171.png
-33713.png
-39252.png
-10600.png
-69100.png
-32166.png
-02892.png
-31581.png
-00724.png
-63171.png
-14562.png
-33512.png
-35099.png
-60661.png
-68338.png
-02206.png
-66062.png
-34043.png
-41487.png
-02331.png
-65198.png
-03019.png
-19293.png
-01567.png
-05810.png
-17413.png
-32739.png
-27654.png
-61662.png
-06683.png
-34073.png
-22350.png
-27507.png
-26016.png
-49417.png
-09059.png
-69401.png
-18763.png
-06435.png
-43808.png
-18908.png
-03486.png
-54524.png
-07458.png
-67371.png
-19718.png
-34689.png
-23645.png
-68391.png
-17541.png
-43490.png
-27851.png
-31980.png
-06242.png
-21149.png
-17726.png
-12812.png
-03406.png
-65892.png
-44250.png
-65094.png
-41106.png
-18365.png
-61941.png
-68088.png
-04640.png
-49804.png
-24674.png
-00237.png
-19642.png
-13125.png
-44537.png
-40617.png
-66440.png
-51140.png
-14827.png
-63661.png
-65745.png
-39399.png
-15163.png
-22548.png
-20781.png
-57892.png
-52393.png
-27758.png
-68622.png
-31402.png
-36104.png
-18899.png
-59679.png
-40362.png
-11426.png
-37920.png
-11729.png
-48345.png
-64280.png
-13573.png
-63302.png
-37016.png
-29389.png
-21169.png
-33006.png
-46030.png
-31799.png
-64553.png
-29128.png
-50771.png
-69482.png
-69478.png
-26611.png
-66206.png
-03974.png
-09387.png
-44565.png
-26805.png
-56012.png
-64495.png
-14407.png
-42474.png
-02359.png
-16935.png
-56763.png
-44161.png
-56624.png
-60837.png
-47449.png
-59728.png
-04961.png
-45366.png
-12283.png
-18736.png
-37463.png
-57417.png
-31360.png
-06726.png
-16268.png
-18698.png
-03307.png
-31784.png
-31594.png
-37077.png
-54507.png
-52786.png
-11800.png
-23703.png
-34093.png
-12989.png
-14897.png
-23004.png
-39661.png
-45645.png
-34597.png
-49461.png
-48875.png
-07592.png
-49390.png
-57024.png
-16105.png
-51211.png
-16857.png
-49905.png
-42800.png
-39945.png
-56474.png
-23641.png
-39853.png
-58903.png
-45937.png
-36631.png
-17749.png
-09368.png
-08153.png
-66485.png
-05314.png
-65910.png
-25298.png
-04703.png
-18530.png
-08482.png
-34813.png
-37578.png
-57575.png
-22225.png
-02499.png
-68562.png
-59730.png
-08193.png
-41248.png
-41964.png
-20814.png
-22490.png
-56121.png
-23574.png
-40201.png
-48203.png
-29247.png
-61539.png
-60152.png
-34098.png
-62111.png
-64719.png
-62340.png
-68561.png
-19381.png
-35009.png
-27333.png
-06903.png
-69437.png
-05270.png
-23264.png
-47905.png
-65581.png
-64450.png
-05273.png
-30930.png
-03271.png
-59444.png
-00599.png
-37238.png
-02926.png
-08652.png
-56156.png
-20159.png
-28453.png
-63604.png
-27342.png
-23029.png
-51482.png
-22011.png
-22067.png
-19848.png
-54921.png
-56279.png
-52275.png
-00161.png
-22145.png
-68197.png
-33335.png
-32584.png
-32486.png
-46291.png
-08185.png
-50235.png
-49645.png
-31657.png
-06719.png
-57613.png
-68728.png
-06447.png
-36916.png
-14941.png
-64758.png
-24404.png
-66778.png
-50836.png
-22054.png
-06934.png
-37530.png
-15452.png
-42297.png
-01941.png
-17403.png
-25351.png
-20009.png
-43934.png
-01016.png
-67310.png
-09079.png
-19210.png
-04687.png
-63369.png
-38322.png
-09761.png
-33939.png
-69687.png
-34138.png
-43825.png
-68942.png
-20842.png
-57625.png
-67306.png
-00597.png
-40089.png
-67444.png
-21105.png
-41949.png
-06788.png
-25006.png
-12806.png
-65768.png
-13596.png
-02882.png
-21894.png
-28769.png
-04100.png
-49890.png
-33371.png
-02865.png
-46069.png
-27440.png
-41474.png
-05505.png
-04849.png
-02262.png
-16858.png
-26485.png
-28695.png
-22275.png
-15729.png
-60706.png
-63533.png
-46664.png
-40519.png
-66956.png
-26661.png
-35866.png
-01115.png
-54590.png
-06054.png
-06662.png
-10163.png
-63308.png
-47283.png
-44955.png
-69346.png
-34453.png
-55937.png
-23520.png
-01989.png
-57731.png
-31155.png
-05474.png
-40624.png
-42013.png
-49527.png
-36273.png
-33107.png
-66915.png
-35768.png
-35571.png
-53597.png
-30962.png
-10605.png
-07999.png
-62121.png
-42891.png
-04335.png
-63037.png
-47814.png
-29227.png
-08205.png
-39416.png
-46823.png
-41685.png
-69326.png
-59540.png
-61643.png
-67989.png
-62852.png
-48452.png
-51377.png
-11228.png
-46984.png
-54182.png
-34506.png
-10892.png
-39065.png
-51039.png
-36053.png
-49746.png
-04350.png
-21458.png
-67539.png
-40037.png
-50816.png
-58156.png
-23123.png
-57493.png
-57690.png
-45241.png
-50671.png
-25865.png
-09703.png
-59794.png
-37401.png
-06957.png
-42440.png
-67960.png
-26216.png
-27416.png
-00136.png
-56656.png
-36396.png
-67336.png
-45085.png
-31526.png
-41700.png
-68685.png
-53320.png
-54702.png
-19086.png
-46064.png
-64637.png
-28460.png
-69846.png
-66887.png
-24837.png
-13654.png
-58245.png
-53890.png
-30955.png
-09476.png
-54196.png
-29542.png
-25032.png
-45091.png
-01315.png
-29743.png
-28153.png
-01917.png
-24345.png
-01418.png
-09036.png
-55066.png
-27256.png
-55065.png
-20753.png
-19935.png
-47210.png
-29880.png
-63287.png
-21492.png
-50151.png
-23754.png
-14154.png
-10199.png
-17144.png
-31131.png
-65470.png
-12446.png
-37345.png
-12804.png
-51566.png
-50345.png
-05742.png
-42396.png
-60813.png
-53005.png
-06352.png
-40030.png
-54639.png
-45439.png
-25347.png
-04136.png
-31582.png
-59001.png
-48381.png
-32947.png
-07122.png
-47289.png
-28736.png
-32817.png
-31924.png
-12803.png
-06035.png
-22126.png
-67221.png
-57237.png
-13640.png
-02519.png
-24870.png
-32036.png
-09433.png
-02239.png
-61023.png
-57543.png
-00108.png
-44442.png
-20036.png
-24502.png
-06824.png
-37426.png
-15168.png
-11072.png
-05125.png
-40451.png
-40174.png
-12943.png
-28585.png
-26717.png
-50503.png
-03890.png
-67315.png
-01260.png
-49297.png
-69061.png
-11887.png
-16378.png
-45963.png
-42875.png
-21932.png
-16586.png
-46789.png
-07673.png
-00096.png
-40838.png
-29759.png
-39864.png
-36661.png
-41321.png
-60008.png
-08285.png
-39494.png
-45681.png
-29056.png
-23753.png
-05532.png
-57673.png
-59301.png
-03023.png
-13438.png
-58888.png
-69661.png
-23576.png
-36830.png
-02780.png
-33681.png
-06258.png
-34963.png
-69502.png
-38271.png
-28459.png
-23262.png
-36352.png
-43003.png
-04574.png
-13398.png
-12977.png
-29964.png
-37860.png
-41168.png
-49960.png
-51582.png
-00704.png
-52346.png
-10989.png
-36540.png
-31778.png
-09947.png
-57208.png
-50685.png
-47750.png
-11566.png
-52100.png
-60700.png
-68393.png
-59192.png
-68577.png
-21638.png
-66898.png
-27765.png
-22403.png
-21224.png
-41525.png
-57189.png
-43896.png
-09197.png
-38139.png
-62606.png
-58534.png
-11657.png
-44639.png
-20973.png
-23060.png
-53144.png
-57079.png
-27971.png
-56956.png
-59639.png
-36693.png
-17987.png
-59415.png
-12994.png
-48636.png
-60075.png
-53713.png
-56927.png
-17598.png
-41306.png
-32037.png
-64786.png
-37820.png
-53623.png
-19590.png
-56433.png
-45833.png
-30875.png
-60279.png
-30019.png
-53173.png
-64517.png
-03933.png
-07337.png
-69592.png
-48012.png
-06854.png
-59283.png
-68097.png
-52434.png
-69340.png
-22750.png
-48420.png
-32414.png
-40081.png
-33685.png
-68814.png
-24434.png
-27178.png
-05952.png
-48790.png
-35805.png
-60622.png
-35075.png
-62431.png
-04251.png
-39536.png
-29106.png
-31902.png
-42623.png
-29049.png
-15630.png
-54817.png
-21079.png
-08469.png
-10743.png
-19861.png
-58294.png
-07598.png
-20278.png
-37644.png
-50085.png
-18181.png
-68347.png
-26579.png
-32145.png
-34049.png
-19845.png
-48769.png
-38383.png
-63929.png
-51453.png
-09019.png
-07774.png
-36705.png
-35192.png
-60783.png
-32960.png
-10857.png
-05224.png
-43190.png
-24388.png
-40935.png
-36738.png
-62301.png
-45460.png
-10462.png
-02522.png
-58773.png
-02814.png
-08464.png
-34111.png
-28938.png
-46840.png
-28591.png
-50304.png
-21092.png
-31395.png
-19852.png
-39271.png
-65895.png
-46537.png
-45392.png
-01915.png
-65075.png
-41982.png
-28818.png
-39623.png
-22432.png
-51623.png
-38184.png
-21713.png
-46476.png
-53267.png
-68294.png
-03725.png
-33424.png
-64691.png
-40711.png
-34546.png
-00232.png
-23883.png
-57434.png
-14898.png
-56641.png
-55053.png
-61682.png
-36804.png
-07634.png
-11399.png
-22932.png
-66676.png
-57396.png
-06075.png
-11756.png
-29382.png
-42718.png
-52605.png
-38148.png
-07838.png
-65804.png
-45471.png
-53413.png
-57634.png
-03709.png
-16327.png
-68169.png
-22499.png
-62937.png
-08886.png
-59518.png
-26743.png
-26161.png
-14147.png
-20154.png
-20940.png
-21732.png
-64416.png
-05491.png
-37761.png
-22240.png
-60052.png
-64628.png
-03912.png
-14199.png
-00672.png
-57562.png
-28917.png
-16086.png
-48296.png
-07249.png
-37364.png
-31383.png
-26472.png
-20126.png
-21432.png
-62648.png
-62738.png
-30713.png
-55042.png
-02340.png
-04998.png
-25110.png
-65406.png
-17454.png
-37360.png
-23915.png
-20280.png
-28613.png
-04195.png
-48647.png
-38525.png
-68241.png
-64387.png
-67702.png
-26716.png
-11683.png
-60681.png
-58932.png
-43309.png
-10493.png
-22487.png
-19670.png
-51815.png
-34029.png
-37955.png
-30312.png
-48413.png
-61133.png
-29276.png
-22221.png
-59657.png
-47887.png
-48862.png
-38886.png
-39087.png
-13478.png
-61079.png
-13614.png
-38611.png
-31977.png
-13913.png
-34715.png
-64661.png
-33738.png
-13279.png
-16162.png
-59611.png
-43665.png
-13130.png
-47776.png
-17470.png
-36780.png
-05303.png
-48727.png
-01245.png
-15417.png
-53387.png
-51974.png
-25880.png
-18984.png
-54256.png
-65303.png
-43585.png
-08892.png
-07646.png
-20054.png
-08302.png
-58394.png
-42258.png
-34206.png
-50687.png
-49271.png
-60109.png
-08812.png
-41725.png
-11165.png
-65234.png
-61118.png
-27311.png
-41733.png
-40095.png
-11888.png
-42976.png
-32116.png
-65552.png
-38537.png
-51590.png
-09618.png
-14632.png
-01662.png
-57066.png
-04511.png
-53439.png
-34536.png
-30099.png
-24536.png
-08064.png
-30143.png
-49354.png
-27292.png
-50619.png
-39356.png
-56811.png
-02745.png
-33794.png
-24665.png
-67385.png
-69864.png
-16770.png
-67140.png
-48009.png
-32272.png
-46639.png
-41342.png
-35923.png
-50548.png
-21676.png
-05582.png
-21921.png
-55063.png
-47342.png
-06316.png
-25059.png
-08919.png
-11139.png
-02854.png
-65819.png
-66454.png
-04369.png
-11500.png
-51057.png
-23350.png
-42387.png
-25237.png
-32646.png
-30551.png
-17418.png
-24573.png
-61594.png
-06636.png
-13625.png
-54641.png
-51108.png
-47734.png
-19664.png
-45762.png
-19530.png
-13441.png
-63850.png
-15924.png
-11028.png
-36537.png
-69524.png
-14696.png
-24871.png
-29440.png
-46963.png
-19667.png
-42506.png
-48402.png
-12439.png
-45676.png
-05381.png
-33797.png
-51126.png
-64677.png
-10800.png
-47707.png
-47815.png
-07870.png
-33683.png
-02338.png
-65525.png
-68035.png
-23910.png
-23976.png
-69952.png
-29223.png
-00721.png
-68628.png
-23273.png
-31214.png
-29864.png
-45355.png
-01905.png
-68385.png
-26423.png
-60945.png
-45866.png
-03590.png
-32177.png
-36101.png
-63479.png
-47220.png
-10920.png
-05099.png
-49965.png
-10538.png
-27341.png
-04342.png
-13605.png
-25451.png
-67346.png
-42160.png
-22836.png
-06053.png
-39627.png
-15175.png
-58100.png
-24481.png
-25891.png
-57288.png
-44689.png
-06880.png
-01102.png
-29027.png
-67068.png
-55256.png
-10468.png
-29972.png
-52200.png
-42411.png
-18472.png
-53784.png
-12369.png
-42804.png
-51636.png
-43069.png
-62083.png
-69020.png
-26050.png
-01361.png
-41417.png
-19107.png
-51681.png
-15667.png
-26983.png
-64462.png
-55693.png
-35044.png
-07753.png
-32047.png
-50604.png
-65123.png
-44740.png
-58768.png
-40663.png
-21113.png
-18052.png
-61479.png
-51018.png
-11179.png
-05606.png
-50539.png
-30081.png
-13117.png
-46565.png
-63886.png
-04127.png
-01668.png
-25169.png
-66270.png
-31424.png
-00966.png
-57067.png
-69140.png
-62855.png
-06395.png
-65603.png
-45467.png
-66641.png
-13661.png
-65982.png
-03464.png
-58934.png
-20513.png
-38131.png
-54631.png
-51660.png
-67784.png
-09814.png
-45368.png
-42638.png
-67503.png
-00969.png
-10287.png
-30617.png
-01481.png
-27089.png
-38684.png
-20844.png
-60208.png
-27597.png
-04541.png
-49167.png
-31820.png
-36210.png
-00930.png
-02022.png
-32636.png
-47277.png
-64887.png
-30919.png
-29815.png
-59700.png
-12728.png
-35632.png
-10233.png
-18449.png
-59889.png
-36256.png
-63071.png
-55550.png
-66954.png
-52090.png
-56865.png
-41376.png
-60875.png
-44143.png
-23842.png
-13540.png
-17032.png
-21430.png
-33444.png
-43821.png
-57213.png
-09428.png
-10245.png
-35872.png
-55731.png
-58803.png
-63652.png
-37531.png
-14660.png
-18277.png
-21662.png
-59088.png
-43508.png
-17399.png
-01712.png
-03713.png
-38722.png
-18504.png
-59587.png
-35055.png
-49908.png
-36100.png
-68773.png
-10704.png
-43388.png
-40650.png
-07204.png
-03609.png
-26026.png
-11466.png
-07102.png
-63168.png
-08269.png
-38717.png
-44873.png
-51271.png
-47127.png
-00154.png
-57864.png
-06260.png
-56226.png
-47722.png
-36720.png
-50365.png
-62453.png
-31960.png
-62328.png
-25665.png
-50993.png
-17085.png
-62805.png
-55151.png
-29866.png
-24247.png
-44163.png
-37143.png
-31753.png
-69634.png
-06946.png
-67664.png
-57414.png
-26119.png
-04016.png
-15078.png
-08934.png
-41301.png
-35332.png
-58035.png
-60380.png
-30923.png
-40707.png
-56310.png
-54607.png
-19499.png
-01419.png
-13037.png
-40020.png
-09750.png
-67499.png
-69024.png
-07307.png
-05921.png
-65775.png
-57248.png
-56815.png
-48894.png
-45553.png
-01575.png
-25622.png
-12021.png
-40596.png
-68559.png
-09084.png
-03466.png
-08743.png
-23860.png
-08354.png
-52781.png
-42123.png
-11506.png
-62608.png
-64409.png
-24534.png
-41513.png
-53563.png
-47477.png
-44475.png
-63531.png
-18006.png
-07631.png
-34154.png
-49453.png
-48595.png
-65253.png
-22861.png
-27974.png
-37370.png
-28311.png
-36021.png
-40009.png
-09212.png
-41129.png
-52081.png
-39906.png
-59985.png
-16052.png
-51163.png
-66852.png
-30497.png
-31024.png
-15658.png
-07328.png
-31182.png
-30602.png
-22587.png
-45838.png
-01528.png
-19998.png
-48760.png
-46496.png
-64323.png
-66067.png
-46004.png
-47029.png
-57094.png
-18928.png
-41984.png
-51516.png
-17847.png
-57018.png
-48924.png
-33159.png
-62448.png
-29364.png
-10862.png
-42108.png
-19717.png
-44837.png
-12828.png
-11691.png
-48714.png
-57370.png
-27318.png
-01473.png
-46027.png
-52422.png
-12173.png
-25129.png
-14211.png
-61935.png
-21645.png
-10548.png
-12212.png
-00716.png
-50383.png
-68404.png
-41698.png
-62675.png
-34977.png
-47534.png
-08045.png
-42395.png
-56137.png
-59537.png
-43744.png
-26939.png
-69804.png
-10653.png
-25920.png
-47991.png
-21365.png
-05345.png
-23275.png
-45086.png
-44813.png
-26645.png
-51506.png
-34875.png
-68443.png
-46645.png
-13243.png
-13305.png
-22658.png
-55270.png
-00872.png
-07013.png
-53983.png
-43407.png
-67590.png
-61834.png
-47668.png
-44160.png
-38780.png
-38031.png
-68061.png
-21773.png
-37718.png
-51107.png
-53216.png
-41499.png
-65465.png
-29287.png
-27895.png
-67239.png
-09133.png
-31361.png
-11634.png
-28719.png
-36761.png
-61985.png
-68691.png
-55295.png
-32206.png
-38721.png
-40202.png
-07237.png
-18526.png
-03908.png
-14984.png
-16577.png
-22769.png
-13142.png
-00806.png
-40702.png
-49170.png
-16568.png
-13881.png
-28523.png
-60091.png
-26723.png
-32433.png
-00494.png
-15568.png
-14366.png
-62894.png
-38627.png
-48065.png
-00673.png
-17631.png
-22996.png
-13558.png
-43634.png
-56245.png
-07860.png
-30557.png
-47263.png
-17168.png
-26324.png
-41434.png
-14247.png
-07026.png
-14913.png
-52721.png
-68082.png
-19019.png
-52498.png
-18419.png
-27123.png
-01827.png
-53958.png
-10544.png
-37598.png
-04836.png
-37919.png
-49462.png
-63125.png
-41853.png
-08304.png
-16988.png
-50578.png
-32068.png
-55352.png
-39021.png
-38153.png
-19930.png
-55259.png
-19110.png
-06502.png
-26698.png
-09202.png
-64342.png
-49805.png
-43684.png
-21819.png
-21637.png
-47113.png
-21267.png
-27813.png
-20163.png
-20644.png
-66979.png
-54943.png
-20252.png
-39386.png
-36428.png
-67034.png
-27085.png
-23594.png
-67687.png
-32727.png
-56534.png
-44032.png
-17479.png
-50223.png
-59335.png
-30015.png
-17464.png
-07313.png
-36714.png
-44811.png
-17196.png
-13431.png
-02788.png
-57551.png
-00181.png
-17286.png
-24943.png
-46644.png
-33916.png
-68191.png
-64231.png
-61287.png
-43537.png
-02986.png
-64595.png
-61968.png
-19159.png
-41393.png
-49831.png
-23996.png
-44254.png
-00144.png
-07588.png
-06492.png
-54516.png
-21769.png
-50109.png
-41059.png
-43976.png
-38922.png
-08928.png
-42684.png
-55600.png
-41547.png
-17936.png
-64137.png
-23171.png
-08787.png
-05696.png
-20880.png
-55835.png
-27914.png
-17762.png
-30431.png
-30105.png
-52436.png
-60111.png
-54377.png
-68235.png
-52053.png
-33574.png
-65340.png
-53525.png
-04675.png
-06705.png
-53893.png
-06898.png
-66933.png
-08496.png
-48451.png
-07857.png
-26767.png
-49830.png
-04526.png
-26826.png
-57601.png
-14804.png
-19923.png
-25743.png
-18354.png
-48223.png
-02732.png
-15394.png
-07159.png
-55872.png
-07310.png
-53164.png
-54854.png
-12759.png
-09222.png
-29523.png
-23717.png
-03482.png
-12771.png
-51967.png
-42478.png
-28377.png
-39721.png
-11073.png
-20173.png
-66902.png
-34078.png
-05836.png
-36359.png
-18826.png
-38488.png
-53817.png
-31266.png
-16827.png
-26359.png
-14470.png
-63161.png
-61460.png
-01395.png
-12045.png
-54001.png
-34869.png
-08910.png
-53309.png
-54666.png
-45529.png
-14236.png
-03939.png
-03768.png
-23057.png
-55461.png
-18611.png
-32786.png
-07811.png
-21761.png
-59645.png
-28944.png
-43226.png
-44374.png
-54226.png
-65214.png
-35417.png
-10073.png
-37219.png
-18192.png
-49622.png
-43314.png
-00335.png
-66693.png
-61037.png
-44399.png
-27481.png
-34118.png
-63824.png
-60679.png
-24565.png
-03000.png
-03331.png
-66955.png
-60466.png
-56657.png
-39150.png
-03620.png
-60684.png
-65783.png
-45482.png
-20362.png
-24616.png
-02728.png
-50596.png
-65571.png
-65446.png
-02839.png
-20970.png
-21170.png
-01458.png
-00810.png
-35206.png
-55551.png
-52810.png
-39880.png
-45543.png
-06595.png
-45901.png
-33144.png
-66452.png
-54611.png
-12153.png
-64573.png
-61788.png
-02027.png
-23429.png
-63966.png
-22927.png
-06034.png
-53139.png
-20651.png
-21270.png
-22467.png
-67592.png
-26547.png
-14814.png
-54121.png
-06217.png
-59723.png
-50351.png
-36797.png
-02636.png
-01411.png
-25078.png
-16792.png
-26437.png
-15438.png
-40206.png
-57840.png
-67693.png
-00529.png
-19209.png
-66467.png
-42255.png
-54475.png
-13957.png
-29939.png
-13025.png
-18821.png
-28243.png
-12121.png
-56181.png
-50821.png
-46016.png
-66650.png
-64604.png
-54005.png
-40912.png
-04021.png
-19072.png
-68619.png
-45742.png
-66110.png
-52249.png
-64784.png
-62261.png
-30695.png
-41405.png
-67401.png
-57806.png
-62106.png
-15415.png
-42309.png
-57085.png
-23963.png
-66713.png
-66476.png
-24230.png
-25848.png
-67925.png
-46205.png
-53908.png
-30073.png
-25184.png
-23818.png
-28913.png
-17652.png
-57573.png
-08569.png
-08267.png
-62002.png
-44364.png
-07155.png
-52850.png
-01336.png
-10725.png
-21767.png
-36759.png
-25454.png
-11784.png
-43907.png
-25603.png
-33079.png
-21582.png
-19951.png
-18140.png
-55503.png
-08120.png
-53121.png
-15663.png
-25857.png
-07890.png
-18234.png
-25369.png
-23458.png
-48907.png
-02143.png
-48942.png
-26794.png
-48233.png
-34266.png
-60583.png
-28482.png
-59973.png
-23768.png
-28461.png
-20142.png
-00771.png
-09351.png
-00746.png
-60920.png
-58078.png
-53868.png
-66635.png
-67464.png
-17804.png
-28019.png
-64490.png
-41125.png
-61222.png
-52449.png
-24982.png
-28053.png
-04594.png
-24457.png
-49504.png
-63647.png
-32120.png
-06535.png
-69580.png
-16041.png
-62515.png
-49434.png
-41942.png
-11668.png
-40572.png
-08891.png
-07366.png
-38135.png
-59443.png
-46512.png
-07290.png
-17626.png
-35949.png
-32435.png
-31242.png
-68389.png
-09314.png
-49359.png
-40843.png
-44850.png
-52755.png
-24650.png
-22923.png
-59685.png
-56288.png
-23517.png
-58339.png
-29629.png
-59712.png
-53697.png
-16193.png
-09836.png
-30912.png
-24631.png
-21852.png
-20045.png
-20756.png
-17552.png
-02476.png
-26493.png
-38583.png
-18690.png
-27369.png
-04514.png
-11163.png
-45614.png
-12342.png
-20284.png
-40701.png
-57650.png
-13287.png
-03147.png
-41717.png
-38321.png
-29606.png
-43344.png
-52349.png
-12463.png
-29656.png
-64540.png
-14057.png
-39120.png
-16374.png
-68297.png
-37262.png
-65653.png
-16317.png
-68057.png
-69988.png
-47392.png
-11049.png
-08104.png
-31104.png
-12106.png
-12562.png
-29376.png
-18847.png
-49771.png
-52588.png
-45002.png
-04576.png
-48197.png
-11920.png
-53385.png
-09965.png
-02145.png
-21452.png
-25437.png
-07167.png
-51864.png
-43322.png
-52950.png
-05365.png
-58087.png
-09809.png
-30077.png
-45387.png
-02497.png
-27008.png
-08732.png
-69026.png
-34714.png
-58931.png
-42212.png
-21964.png
-37300.png
-08897.png
-06736.png
-29197.png
-18402.png
-00924.png
-29651.png
-59536.png
-61995.png
-60421.png
-69548.png
-58748.png
-67835.png
-57525.png
-53228.png
-50316.png
-66049.png
-04509.png
-60149.png
-40441.png
-21690.png
-28172.png
-01155.png
-50057.png
-42289.png
-36512.png
-01588.png
-61436.png
-61758.png
-59879.png
-68550.png
-48657.png
-12455.png
-45932.png
-34297.png
-32842.png
-31368.png
-25402.png
-27217.png
-60433.png
-63983.png
-15518.png
-13827.png
-32363.png
-06046.png
-05020.png
-45191.png
-43653.png
-68437.png
-58987.png
-15121.png
-23714.png
-18111.png
-65329.png
-05347.png
-10130.png
-19465.png
-39294.png
-00372.png
-02080.png
-00936.png
-31658.png
-63764.png
-27126.png
-65805.png
-37096.png
-56166.png
-48918.png
-46077.png
-42261.png
-21444.png
-45192.png
-22771.png
-28033.png
-04712.png
-47681.png
-68662.png
-03188.png
-55425.png
-50300.png
-50814.png
-67665.png
-10714.png
-14233.png
-68785.png
-68548.png
-24881.png
-35792.png
-14924.png
-35159.png
-04753.png
-30765.png
-01344.png
-25212.png
-08932.png
-67536.png
-48440.png
-41083.png
-12059.png
-67558.png
-30903.png
-20269.png
-65462.png
-07554.png
-08948.png
-33761.png
-31107.png
-12380.png
-51129.png
-60689.png
-41012.png
-30746.png
-63563.png
-62353.png
-57962.png
-40132.png
-51040.png
-51355.png
-51413.png
-65932.png
-08772.png
-28662.png
-28124.png
-38078.png
-54874.png
-57835.png
-64972.png
-48668.png
-11617.png
-52020.png
-12497.png
-32444.png
-59512.png
-17394.png
-51337.png
-43491.png
-45154.png
-11597.png
-69481.png
-35479.png
-48564.png
-42426.png
-20095.png
-08571.png
-28484.png
-68263.png
-54987.png
-17141.png
-66746.png
-08687.png
-39465.png
-03652.png
-28434.png
-45702.png
-67919.png
-22272.png
-42895.png
-30559.png
-55894.png
-00509.png
-36117.png
-66777.png
-44166.png
-45896.png
-59043.png
-24367.png
-57166.png
-59529.png
-34117.png
-40111.png
-20207.png
-61278.png
-03194.png
-19840.png
-59811.png
-68587.png
-28911.png
-26641.png
-27306.png
-21443.png
-59146.png
-23881.png
-60579.png
-44398.png
-24119.png
-48382.png
-27736.png
-47510.png
-25190.png
-22415.png
-40992.png
-12486.png
-69219.png
-31504.png
-55863.png
-33747.png
-53393.png
-11112.png
-60907.png
-30761.png
-03849.png
-25321.png
-15209.png
-57645.png
-41395.png
-13251.png
-56755.png
-09397.png
-32461.png
-09416.png
-15482.png
-49127.png
-69193.png
-46983.png
-20631.png
-08404.png
-18511.png
-46787.png
-28045.png
-54913.png
-02227.png
-38640.png
-49233.png
-54388.png
-44600.png
-65534.png
-47628.png
-62737.png
-01820.png
-00772.png
-49618.png
-43734.png
-32918.png
-52430.png
-30577.png
-38063.png
-42831.png
-14533.png
-10479.png
-16927.png
-50522.png
-42241.png
-28150.png
-65178.png
-35012.png
-13910.png
-17967.png
-16012.png
-10832.png
-21835.png
-55746.png
-59131.png
-45019.png
-67640.png
-18196.png
-19066.png
-11370.png
-57781.png
-46679.png
-26393.png
-67668.png
-58877.png
-64635.png
-69987.png
-22290.png
-02241.png
-42304.png
-26326.png
-22619.png
-61946.png
-52986.png
-60370.png
-38642.png
-61072.png
-43165.png
-21985.png
-03726.png
-29813.png
-56725.png
-08839.png
-23378.png
-30023.png
-51984.png
-09652.png
-21409.png
-38169.png
-08197.png
-36591.png
-61595.png
-19612.png
-39282.png
-11300.png
-18468.png
-16837.png
-01818.png
-33481.png
-55006.png
-27409.png
-58533.png
-25653.png
-55896.png
-25741.png
-69318.png
-46782.png
-22954.png
-53043.png
-31113.png
-00552.png
-18894.png
-22557.png
-14919.png
-49023.png
-42019.png
-52976.png
-67921.png
-23233.png
-25879.png
-32464.png
-10765.png
-68355.png
-56217.png
-26390.png
-68167.png
-02461.png
-19533.png
-64679.png
-55323.png
-29688.png
-19481.png
-52157.png
-26454.png
-00898.png
-07593.png
-64976.png
-16471.png
-23731.png
-24577.png
-64051.png
-50112.png
-67987.png
-05835.png
-49845.png
-62131.png
-26429.png
-46529.png
-13231.png
-38647.png
-01685.png
-68675.png
-64293.png
-07484.png
-29497.png
-53386.png
-38449.png
-34988.png
-44820.png
-32549.png
-37655.png
-09415.png
-34008.png
-47276.png
-05969.png
-07158.png
-47010.png
-06795.png
-19729.png
-36548.png
-59293.png
-47368.png
-54025.png
-67801.png
-30005.png
-58140.png
-45202.png
-45347.png
-20430.png
-57282.png
-57372.png
-52863.png
-64964.png
-03396.png
-53380.png
-12743.png
-42880.png
-18273.png
-32542.png
-07430.png
-39345.png
-34788.png
-15747.png
-56088.png
-45201.png
-02827.png
-48512.png
-31346.png
-51480.png
-39337.png
-30033.png
-34034.png
-44546.png
-39987.png
-51374.png
-30611.png
-13871.png
-65475.png
-25097.png
-61602.png
-45754.png
-57203.png
-38121.png
-07957.png
-14839.png
-57053.png
-00002.png
-24591.png
-33649.png
-53180.png
-37422.png
-61681.png
-45329.png
-17599.png
-26765.png
-34975.png
-08418.png
-19594.png
-41025.png
-29874.png
-60281.png
-25332.png
-21234.png
-39137.png
-58209.png
-68642.png
-43743.png
-38021.png
-22400.png
-55316.png
-24269.png
-06868.png
-20872.png
-12756.png
-49616.png
-08017.png
-39508.png
-50653.png
-54713.png
-57819.png
-25069.png
-46063.png
-33600.png
-03100.png
-43316.png
-21151.png
-09540.png
-69289.png
-07228.png
-06307.png
-33706.png
-29388.png
-37292.png
-26389.png
-18708.png
-14830.png
-28268.png
-15877.png
-24903.png
-26073.png
-41255.png
-68392.png
-57391.png
-46255.png
-35239.png
-43616.png
-63022.png
-40006.png
-00447.png
-19119.png
-34323.png
-16068.png
-31150.png
-16065.png
-29901.png
-46855.png
-35705.png
-54210.png
-02611.png
-57903.png
-31775.png
-36948.png
-14901.png
-28401.png
-42275.png
-54198.png
-63748.png
-55359.png
-53659.png
-16261.png
-03087.png
-49634.png
-45195.png
-14075.png
-24496.png
-63437.png
-13464.png
-15681.png
-41938.png
-29534.png
-18634.png
-15810.png
-27723.png
-20867.png
-43150.png
-47430.png
-67880.png
-30656.png
-34525.png
-34922.png
-58177.png
-00028.png
-68706.png
-02358.png
-24826.png
-20219.png
-16934.png
-00926.png
-21344.png
-25199.png
-44132.png
-67959.png
-24191.png
-69448.png
-19414.png
-55591.png
-31610.png
-14915.png
-42540.png
-32866.png
-03556.png
-19097.png
-58189.png
-57003.png
-12044.png
-21102.png
-09959.png
-17043.png
-29665.png
-27939.png
-40357.png
-39454.png
-50004.png
-17689.png
-47245.png
-38478.png
-63202.png
-33368.png
-37371.png
-04556.png
-56296.png
-28997.png
-48308.png
-20540.png
-48821.png
-26635.png
-22776.png
-16247.png
-39698.png
-52066.png
-06182.png
-53371.png
-21495.png
-05302.png
-03692.png
-11861.png
-08356.png
-53113.png
-54967.png
-05085.png
-22060.png
-43546.png
-35458.png
-39888.png
-60721.png
-47716.png
-09593.png
-14244.png
-25296.png
-55033.png
-59056.png
-69641.png
-02675.png
-13493.png
-35298.png
-23536.png
-48697.png
-27874.png
-27111.png
-52438.png
-07436.png
-09880.png
-24937.png
-56061.png
-00700.png
-59126.png
-44512.png
-10902.png
-22385.png
-21251.png
-66764.png
-40427.png
-45312.png
-40287.png
-53376.png
-03777.png
-36729.png
-60319.png
-53876.png
-06838.png
-66572.png
-24429.png
-44393.png
-35874.png
-18119.png
-31763.png
-25130.png
-11368.png
-09234.png
-48773.png
-20678.png
-43588.png
-38549.png
-12226.png
-43959.png
-48911.png
-32559.png
-10123.png
-61916.png
-32421.png
-61175.png
-18213.png
-04307.png
-08978.png
-12338.png
-43540.png
-61824.png
-48987.png
-41617.png
-04380.png
-67701.png
-54111.png
-62730.png
-29423.png
-20040.png
-37100.png
-25717.png
-50945.png
-23584.png
-36742.png
-59000.png
-52793.png
-46152.png
-17130.png
-41428.png
-53748.png
-37974.png
-49959.png
-18371.png
-19126.png
-19912.png
-55701.png
-62641.png
-66822.png
-26133.png
-17683.png
-53487.png
-56428.png
-66688.png
-47421.png
-26980.png
-52678.png
-51842.png
-12870.png
-61097.png
-46539.png
-11742.png
-30530.png
-29168.png
-05940.png
-25232.png
-65400.png
-37612.png
-13414.png
-30063.png
-09702.png
-08828.png
-48088.png
-04743.png
-66205.png
-53181.png
-55244.png
-36429.png
-63250.png
-00300.png
-53281.png
-24203.png
-64002.png
-41410.png
-11975.png
-24856.png
-25057.png
-00037.png
-34685.png
-11484.png
-32631.png
-23873.png
-45532.png
-60387.png
-34752.png
-41640.png
-57538.png
-40754.png
-03281.png
-63612.png
-28410.png
-54527.png
-41807.png
-13362.png
-47485.png
-36862.png
-02558.png
-29368.png
-07295.png
-06369.png
-19996.png
-22437.png
-01023.png
-46609.png
-59220.png
-49104.png
-68679.png
-07511.png
-05740.png
-39461.png
-30219.png
-06671.png
-07771.png
-17674.png
-07149.png
-20390.png
-02069.png
-22206.png
-62838.png
-39346.png
-67049.png
-64848.png
-40696.png
-23777.png
-03288.png
-33486.png
-43207.png
-21454.png
-68708.png
-21380.png
-10647.png
-22170.png
-45591.png
-16756.png
-12484.png
-05403.png
-50744.png
-23739.png
-18534.png
-34137.png
-10392.png
-52550.png
-14305.png
-60368.png
-06778.png
-20237.png
-55355.png
-66269.png
-59715.png
-09801.png
-33696.png
-01368.png
-14467.png
-09718.png
-48722.png
-24438.png
-56028.png
-51286.png
-58770.png
-36032.png
-31726.png
-09579.png
-12423.png
-22756.png
-10625.png
-51552.png
-11660.png
-02375.png
-18591.png
-69739.png
-18569.png
-50182.png
-28474.png
-06252.png
-65487.png
-49444.png
-60819.png
-61531.png
-63041.png
-38296.png
-07038.png
-61716.png
-63503.png
-26549.png
-45462.png
-54676.png
-54180.png
-26004.png
-65355.png
-22581.png
-61476.png
-34287.png
-55734.png
-29583.png
-34010.png
-26602.png
-35367.png
-58067.png
-11725.png
-58187.png
-62827.png
-06433.png
-60372.png
-42786.png
-15620.png
-28576.png
-06322.png
-00565.png
-58557.png
-49506.png
-27825.png
-63700.png
-20392.png
-50083.png
-01951.png
-69875.png
-36888.png
-46182.png
-09821.png
-05317.png
-60198.png
-05355.png
-54140.png
-39629.png
-69619.png
-31571.png
-07720.png
-25915.png
-22416.png
-37694.png
-51578.png
-13761.png
-13785.png
-64345.png
-48467.png
-15957.png
-08600.png
-54729.png
-04814.png
-60628.png
-01888.png
-10574.png
-34519.png
-40735.png
-50164.png
-50222.png
-23050.png
-27946.png
-47396.png
-33488.png
-23740.png
-23314.png
-09491.png
-63614.png
-38878.png
-30072.png
-64272.png
-07424.png
-54419.png
-30834.png
-49491.png
-33870.png
-28445.png
-09563.png
-14538.png
-03999.png
-03891.png
-18837.png
-50626.png
-06225.png
-13909.png
-62437.png
-17727.png
-10223.png
-08531.png
-53769.png
-20022.png
-41892.png
-64879.png
-28852.png
-14216.png
-57830.png
-41299.png
-31895.png
-61696.png
-51870.png
-41611.png
-69298.png
-61359.png
-55969.png
-24999.png
-55529.png
-63876.png
-43193.png
-06178.png
-39752.png
-03996.png
-67625.png
-44889.png
-56270.png
-14665.png
-39121.png
-57824.png
-11156.png
-42698.png
-34881.png
-12642.png
-52971.png
-51354.png
-13795.png
-16605.png
-24268.png
-54378.png
-31872.png
-49973.png
-01551.png
-66545.png
-49837.png
-11127.png
-27646.png
-34335.png
-56056.png
-53773.png
-08794.png
-15536.png
-63115.png
-56513.png
-26364.png
-58398.png
-66789.png
-03134.png
-11244.png
-52060.png
-21792.png
-25002.png
-04462.png
-44041.png
-48717.png
-20614.png
-34261.png
-55867.png
-45036.png
-69873.png
-28781.png
-17939.png
-26799.png
-30894.png
-42061.png
-12571.png
-10498.png
-02544.png
-41955.png
-41215.png
-32418.png
-54234.png
-06606.png
-61280.png
-22596.png
-35927.png
-25972.png
-17226.png
-50830.png
-53643.png
-14996.png
-66593.png
-40347.png
-47858.png
-69823.png
-51304.png
-19927.png
-46461.png
-30572.png
-24729.png
-69801.png
-35898.png
-27159.png
-20529.png
-64955.png
-31705.png
-27313.png
-68922.png
-09999.png
-29102.png
-42757.png
-52437.png
-54974.png
-22441.png
-24838.png
-69451.png
-69292.png
-29995.png
-57672.png
-33558.png
-26427.png
-14033.png
-68453.png
-30827.png
-25173.png
-51998.png
-13465.png
-30529.png
-58034.png
-61390.png
-67197.png
-69559.png
-09571.png
-07615.png
-09184.png
-36574.png
-10656.png
-33571.png
-16829.png
-20937.png
-59506.png
-31221.png
-45836.png
-52661.png
-40699.png
-15308.png
-45124.png
-36427.png
-10444.png
-61705.png
-16110.png
-20158.png
-59205.png
-04564.png
-24704.png
-04368.png
-03477.png
-32075.png
-32974.png
-60007.png
-24275.png
-02606.png
-50014.png
-19298.png
-64556.png
-54238.png
-22964.png
-31340.png
-66282.png
-18642.png
-08227.png
-25192.png
-43648.png
-26994.png
-53192.png
-60375.png
-51280.png
-12841.png
-48917.png
-65533.png
-22071.png
-27381.png
-34069.png
-54761.png
-38616.png
-67014.png
-39834.png
-41293.png
-33014.png
-28889.png
-18050.png
-13854.png
-66348.png
-16270.png
-31740.png
-35667.png
-25475.png
-69388.png
-65615.png
-31795.png
-50312.png
-49245.png
-64975.png
-55982.png
-24011.png
-64591.png
-60999.png
-19062.png
-26309.png
-43239.png
-15976.png
-42643.png
-58700.png
-38036.png
-45060.png
-26840.png
-05779.png
-65296.png
-58089.png
-63435.png
-42838.png
-37553.png
-21041.png
-26763.png
-57001.png
-37616.png
-43688.png
-21285.png
-32358.png
-68265.png
-06333.png
-68215.png
-60876.png
-36659.png
-57211.png
-12766.png
-64641.png
-10867.png
-23276.png
-26457.png
-19265.png
-64170.png
-38267.png
-65669.png
-07804.png
-56005.png
-42922.png
-45778.png
-18270.png
-63346.png
-43824.png
-38306.png
-19565.png
-20326.png
-40160.png
-52772.png
-63270.png
-58994.png
-67882.png
-16250.png
-50281.png
-44183.png
-69761.png
-28189.png
-61016.png
-59417.png
-21546.png
-30547.png
-69375.png
-64839.png
-68187.png
-20025.png
-05309.png
-64131.png
-09115.png
-44843.png
-32686.png
-46498.png
-67578.png
-18845.png
-23155.png
-57027.png
-21274.png
-35718.png
-65334.png
-53142.png
-66521.png
-21089.png
-37583.png
-10838.png
-59237.png
-57061.png
-57587.png
-21240.png
-26410.png
-21202.png
-35297.png
-40377.png
-43478.png
-67246.png
-24512.png
-21110.png
-20330.png
-04503.png
-45902.png
-24207.png
-20268.png
-07222.png
-14709.png
-51591.png
-11539.png
-43031.png
-37235.png
-67967.png
-55646.png
-48128.png
-54561.png
-04604.png
-58290.png
-08264.png
-58955.png
-05671.png
-20924.png
-26761.png
-18092.png
-65693.png
-51987.png
-41544.png
-40738.png
-65049.png
-29284.png
-69102.png
-20380.png
-25819.png
-28700.png
-26612.png
-21631.png
-24173.png
-12837.png
-45110.png
-61618.png
-38491.png
-68228.png
-69541.png
-16362.png
-27214.png
-45995.png
-05828.png
-54906.png
-27919.png
-38087.png
-27966.png
-48459.png
-10609.png
-64690.png
-69748.png
-33060.png
-45102.png
-44732.png
-05825.png
-41694.png
-40721.png
-33007.png
-57339.png
-55363.png
-26108.png
-42598.png
-05887.png
-06921.png
-31018.png
-26707.png
-48236.png
-33033.png
-02055.png
-03207.png
-06233.png
-17935.png
-44261.png
-67184.png
-54782.png
-05259.png
-17917.png
-37189.png
-29753.png
-09398.png
-02563.png
-38671.png
-43613.png
-30032.png
-48851.png
-21280.png
-64116.png
-48468.png
-64438.png
-43550.png
-16473.png
-20345.png
-12527.png
-69255.png
-48979.png
-43149.png
-65187.png
-41599.png
-67753.png
-08721.png
-23414.png
-25412.png
-55301.png
-39448.png
-56970.png
-07201.png
-65063.png
-55302.png
-64729.png
-35152.png
-02165.png
-38190.png
-69672.png
-15298.png
-09196.png
-54902.png
-48932.png
-30787.png
-16313.png
-66283.png
-53554.png
-15086.png
-04649.png
-16573.png
-01290.png
-55293.png
-35674.png
-62108.png
-25565.png
-16768.png
-30768.png
-15994.png
-41952.png
-02211.png
-46131.png
-58627.png
-43155.png
-00172.png
-20019.png
-46315.png
-10283.png
-49815.png
-42859.png
-07830.png
-20360.png
-12086.png
-28241.png
-20966.png
-28365.png
-48992.png
-19607.png
-40906.png
-02792.png
-15564.png
-69431.png
-39659.png
-64296.png
-36216.png
-48587.png
-15856.png
-49981.png
-21571.png
-01399.png
-16350.png
-58382.png
-48620.png
-30060.png
-64314.png
-27834.png
-27647.png
-30404.png
-40937.png
-10988.png
-64987.png
-52796.png
-35694.png
-32200.png
-20971.png
-17397.png
-41974.png
-66056.png
-08272.png
-37451.png
-26455.png
-28709.png
-08602.png
-16721.png
-14771.png
-39701.png
-51641.png
-09271.png
-29661.png
-07321.png
-49860.png
-37810.png
-47911.png
-35242.png
-03612.png
-01068.png
-68980.png
-62774.png
-48135.png
-47021.png
-07408.png
-68873.png
-57115.png
-18833.png
-08561.png
-28296.png
-59819.png
-46908.png
-57680.png
-51169.png
-25161.png
-15012.png
-08510.png
-11945.png
-33344.png
-20695.png
-66661.png
-44213.png
-21437.png
-69678.png
-40540.png
-58970.png
-50960.png
-34839.png
-56199.png
-60983.png
-04082.png
-22566.png
-66729.png
-06326.png
-16659.png
-11083.png
-60927.png
-29672.png
-50061.png
-50169.png
-40522.png
-55030.png
-49635.png
-23095.png
-46126.png
-32288.png
-12422.png
-13377.png
-14546.png
-01318.png
-28508.png
-45125.png
-44368.png
-07778.png
-05586.png
-53062.png
-33699.png
-61055.png
-17978.png
-62534.png
-69501.png
-13777.png
-14104.png
-15057.png
-65679.png
-36850.png
-05287.png
-44860.png
-45289.png
-30058.png
-41710.png
-45659.png
-38341.png
-27888.png
-64827.png
-40187.png
-26870.png
-44680.png
-41197.png
-55928.png
-58033.png
-26287.png
-60877.png
-54952.png
-32501.png
-40615.png
-65271.png
-64683.png
-00978.png
-13722.png
-04308.png
-18575.png
-37182.png
-35301.png
-44853.png
-51138.png
-05880.png
-17591.png
-24216.png
-61490.png
-29267.png
-32661.png
-25818.png
-17530.png
-57216.png
-65515.png
-64150.png
-10091.png
-53155.png
-11889.png
-61368.png
-57046.png
-44687.png
-57348.png
-00775.png
-23492.png
-27490.png
-27614.png
-06458.png
-64309.png
-44053.png
-08552.png
-28026.png
-37903.png
-37946.png
-57239.png
-52258.png
-52581.png
-29958.png
-25055.png
-51262.png
-31463.png
-63016.png
-06803.png
-33910.png
-15478.png
-12900.png
-30103.png
-15921.png
-39430.png
-53990.png
-27477.png
-14884.png
-44439.png
-64236.png
-27716.png
-47752.png
-63212.png
-63387.png
-51079.png
-17474.png
-53479.png
-07453.png
-29235.png
-06894.png
-69187.png
-60086.png
-36639.png
-63504.png
-65858.png
-59324.png
-27843.png
-30075.png
-04158.png
-31788.png
-68649.png
-49695.png
-15490.png
-50219.png
-37406.png
-00374.png
-32882.png
-44048.png
-39205.png
-34557.png
-67103.png
-04508.png
-15169.png
-27809.png
-69598.png
-65756.png
-38843.png
-22808.png
-24705.png
-67814.png
-42480.png
-64812.png
-61554.png
-22542.png
-49512.png
-62782.png
-22379.png
-27064.png
-25505.png
-00832.png
-56941.png
-17950.png
-59727.png
-50430.png
-23591.png
-62823.png
-39236.png
-49730.png
-65016.png
-06076.png
-54399.png
-45508.png
-55474.png
-31928.png
-28762.png
-03414.png
-05228.png
-46955.png
-00018.png
-56141.png
-11994.png
-27049.png
-40455.png
-25463.png
-56505.png
-25931.png
-26924.png
-31882.png
-03284.png
-60703.png
-61445.png
-60758.png
-69513.png
-37274.png
-30409.png
-05463.png
-41998.png
-09706.png
-22508.png
-58447.png
-28704.png
-22854.png
-43595.png
-68115.png
-52979.png
-08841.png
-58411.png
-59292.png
-53288.png
-44421.png
-64154.png
-68377.png
-17519.png
-13208.png
-32599.png
-49621.png
-38973.png
-56350.png
-11987.png
-62973.png
-58634.png
-21700.png
-34017.png
-35419.png
-52567.png
-48301.png
-43176.png
-18774.png
-59754.png
-37774.png
-28413.png
-42405.png
-10993.png
-09058.png
-41351.png
-65036.png
-03528.png
-47831.png
-39226.png
-51549.png
-63476.png
-51496.png
-53901.png
-32299.png
-20828.png
-07888.png
-36398.png
-54117.png
-52194.png
-24555.png
-57131.png
-46869.png
-55537.png
-04968.png
-43666.png
-56764.png
-02156.png
-15726.png
-63669.png
-65440.png
-00914.png
-40042.png
-21555.png
-45323.png
-10856.png
-57831.png
-06151.png
-37481.png
-23394.png
-04310.png
-10157.png
-35510.png
-06016.png
-44609.png
-58014.png
-45298.png
-44040.png
-48741.png
-14751.png
-25290.png
-34771.png
-36289.png
-24377.png
-37581.png
-27456.png
-17078.png
-18325.png
-53935.png
-40958.png
-17429.png
-46242.png
-09281.png
-07279.png
-24812.png
-21278.png
-64912.png
-00766.png
-39408.png
-46628.png
-33621.png
-16855.png
-64365.png
-61141.png
-64534.png
-16739.png
-48303.png
-52542.png
-29420.png
-35998.png
-60240.png
-56599.png
-00209.png
-58637.png
-47087.png
-09913.png
-35211.png
-47922.png
-50188.png
-11432.png
-56979.png
-18064.png
-36465.png
-59200.png
-17033.png
-39092.png
-69649.png
-59175.png
-40367.png
-19636.png
-69606.png
-22169.png
-43181.png
-00015.png
-19216.png
-34672.png
-20741.png
-66950.png
-12327.png
-38603.png
-21681.png
-40055.png
-45254.png
-37674.png
-53754.png
-15782.png
-56049.png
-67090.png
-68554.png
-40273.png
-35041.png
-01202.png
-10126.png
-43987.png
-65848.png
-34607.png
-14066.png
-14845.png
-29857.png
-34758.png
-39962.png
-55232.png
-48845.png
-10425.png
-21099.png
-22553.png
-54588.png
-34393.png
-09760.png
-66473.png
-13503.png
-08855.png
-07316.png
-06486.png
-54222.png
-62870.png
-06844.png
-04290.png
-56239.png
-31486.png
-65001.png
-02422.png
-49377.png
-11827.png
-07371.png
-69628.png
-32579.png
-42430.png
-39133.png
-55789.png
-33843.png
-59218.png
-66477.png
-15700.png
-39385.png
-47412.png
-39125.png
-36093.png
-33495.png
-12364.png
-33151.png
-30223.png
-51694.png
-61223.png
-40218.png
-36116.png
-27668.png
-20029.png
-48791.png
-33812.png
-32249.png
-43996.png
-15473.png
-53089.png
-02820.png
-53189.png
-43044.png
-12406.png
-24614.png
-33640.png
-37725.png
-22029.png
-00339.png
-10908.png
-48706.png
-29660.png
-22404.png
-15824.png
-19169.png
-21201.png
-27258.png
-09911.png
-32176.png
-28777.png
-62141.png
-12627.png
-07797.png
-64476.png
-10749.png
-35023.png
-10135.png
-48881.png
-17812.png
-02159.png
-51699.png
-18171.png
-02487.png
-05657.png
-16524.png
-33674.png
-32270.png
-21667.png
-14409.png
-17623.png
-56729.png
-61922.png
-14902.png
-05379.png
-34508.png
-30170.png
-14418.png
-06951.png
-13188.png
-10335.png
-69807.png
-09099.png
-23001.png
-14620.png
-06221.png
-34516.png
-45933.png
-11902.png
-60031.png
-17513.png
-24581.png
-57243.png
-48347.png
-32031.png
-17837.png
-06861.png
-59462.png
-29381.png
-38238.png
-35121.png
-33280.png
-49132.png
-12925.png
-29568.png
-45119.png
-11440.png
-09140.png
-12506.png
-54652.png
-22813.png
-45800.png
-45642.png
-25222.png
-52735.png
-63297.png
-30078.png
-00623.png
-00800.png
-07938.png
-03116.png
-56304.png
-22911.png
-05103.png
-02477.png
-68549.png
-62889.png
-21159.png
-13788.png
-39284.png
-08881.png
-63853.png
-57334.png
-50627.png
-16170.png
-52416.png
-10675.png
-21913.png
-35498.png
-68237.png
-06383.png
-06419.png
-10042.png
-30523.png
-65003.png
-10911.png
-52439.png
-34357.png
-62233.png
-14596.png
-21817.png
-44894.png
-04879.png
-28826.png
-57594.png
-10138.png
-06716.png
-30813.png
-40842.png
-44543.png
-10534.png
-10456.png
-21892.png
-52659.png
-33435.png
-49680.png
-05556.png
-06364.png
-32314.png
-61943.png
-14571.png
-38880.png
-20018.png
-41208.png
-38134.png
-35553.png
-27978.png
-62449.png
-33974.png
-59326.png
-07968.png
-34960.png
-16944.png
-50749.png
-27617.png
-51643.png
-32543.png
-54147.png
-19606.png
-30776.png
-06131.png
-29644.png
-38999.png
-39367.png
-09967.png
-59968.png
-00610.png
-56380.png
-13568.png
-64943.png
-20164.png
-20677.png
-11164.png
-63618.png
-52047.png
-33465.png
-31509.png
-19112.png
-06467.png
-26052.png
-64040.png
-00801.png
-08016.png
-41252.png
-33553.png
-57760.png
-67176.png
-45144.png
-03995.png
-49896.png
-09533.png
-10142.png
-00138.png
-67862.png
-54962.png
-03195.png
-53880.png
-01995.png
-22944.png
-43382.png
-46865.png
-29572.png
-30508.png
-25178.png
-61801.png
-21572.png
-00808.png
-43022.png
-64210.png
-39269.png
-17270.png
-09157.png
-51666.png
-08454.png
-16305.png
-01758.png
-54706.png
-48427.png
-15973.png
-15888.png
-67074.png
-20619.png
-47590.png
-25721.png
-41386.png
-24059.png
-11283.png
-19672.png
-10257.png
-59229.png
-55048.png
-49317.png
-11543.png
-14631.png
-14354.png
-28873.png
-59542.png
-02825.png
-39926.png
-54595.png
-54657.png
-60572.png
-45616.png
-43561.png
-22457.png
-46552.png
-26878.png
-68200.png
-04080.png
-46641.png
-24955.png
-31754.png
-48269.png
-09844.png
-04439.png
-14064.png
-20669.png
-09357.png
-58115.png
-16440.png
-02428.png
-60418.png
-41388.png
-32161.png
-09964.png
-49353.png
-49252.png
-69796.png
-37241.png
-58963.png
-04732.png
-34618.png
-22292.png
-40867.png
-63538.png
-69395.png
-35519.png
-12881.png
-20618.png
-27958.png
-49652.png
-26724.png
-11353.png
-07145.png
-48465.png
-20211.png
-22863.png
-15519.png
-64631.png
-57522.png
-41102.png
-16428.png
-45594.png
-13181.png
-02360.png
-09714.png
-42785.png
-65520.png
-67856.png
-55530.png
-18560.png
-45603.png
-10878.png
-26889.png
-30770.png
-42484.png
-52666.png
-36049.png
-28109.png
-18304.png
-50577.png
-58208.png
-69288.png
-00981.png
-50026.png
-13453.png
-21953.png
-19469.png
-05840.png
-69658.png
-03453.png
-42371.png
-19023.png
-68541.png
-26681.png
-61193.png
-08399.png
-59742.png
-49097.png
-53310.png
-08920.png
-10772.png
-16365.png
-49193.png
-69399.png
-56260.png
-55389.png
-14032.png
-15108.png
-10516.png
-61250.png
-02001.png
-35412.png
-30591.png
-38728.png
-11281.png
-29233.png
-26295.png
-27912.png
-66045.png
-10670.png
-53003.png
-35528.png
-48726.png
-48146.png
-61764.png
-37951.png
-00570.png
-13820.png
-02512.png
-47966.png
-06669.png
-09871.png
-10829.png
-15076.png
-66051.png
-51068.png
-49321.png
-00183.png
-36364.png
-57032.png
-67400.png
-02896.png
-61373.png
-51558.png
-23037.png
-01465.png
-35045.png
-61403.png
-66705.png
-05383.png
-03921.png
-19678.png
-49072.png
-64703.png
-35752.png
-68710.png
-67004.png
-08976.png
-23152.png
-06637.png
-22150.png
-54242.png
-09170.png
-65563.png
-43946.png
-50551.png
-56543.png
-62245.png
-52493.png
-42052.png
-52681.png
-28164.png
-25198.png
-22412.png
-43369.png
-50283.png
-03278.png
-23796.png
-34291.png
-18820.png
-08835.png
-52255.png
-23531.png
-12548.png
-33441.png
-06152.png
-57728.png
-68157.png
-14204.png
-10980.png
-15955.png
-62360.png
-54523.png
-38994.png
-63807.png
-51810.png
-18203.png
-58158.png
-65636.png
-11706.png
-31128.png
-57528.png
-41266.png
-34298.png
-62162.png
-02464.png
-57978.png
-11980.png
-56312.png
-49545.png
-13671.png
-16228.png
-69015.png
-35031.png
-32168.png
-45530.png
-09420.png
-20761.png
-26495.png
-36598.png
-19458.png
-29282.png
-21565.png
-22435.png
-52694.png
-60330.png
-61580.png
-54606.png
-48886.png
-41117.png
-44610.png
-28430.png
-41071.png
-57943.png
-24253.png
-36992.png
-35450.png
-08316.png
-42788.png
-54810.png
-09295.png
-34042.png
-45149.png
-11499.png
-25288.png
-13632.png
-61864.png
-49330.png
-19595.png
-04068.png
-42064.png
-48742.png
-43371.png
-30152.png
-35190.png
-62262.png
-58311.png
-65610.png
-49282.png
-09935.png
-15127.png
-39210.png
-25253.png
-37762.png
-45738.png
-54376.png
-35782.png
-13724.png
-11036.png
-21059.png
-06231.png
-42096.png
-52412.png
-08297.png
-13790.png
-03041.png
-48923.png
-01304.png
-32451.png
-39785.png
-60557.png
-62017.png
-55941.png
-49096.png
-48414.png
-08864.png
-33913.png
-53856.png
-31405.png
-43010.png
-62908.png
-67575.png
-61603.png
-52414.png
-64822.png
-30202.png
-57908.png
-26480.png
-43187.png
-60702.png
-25336.png
-34708.png
-14169.png
-38509.png
-47714.png
-44887.png
-01375.png
-60892.png
-40076.png
-41176.png
-09958.png
-59959.png
-04330.png
-25140.png
-66342.png
-16182.png
-65699.png
-01808.png
-58964.png
-38608.png
-53977.png
-61873.png
-22061.png
-69944.png
-61244.png
-23199.png
-34523.png
-15052.png
-59732.png
-21412.png
-60074.png
-47538.png
-27431.png
-24214.png
-41459.png
-50171.png
-01969.png
-13407.png
-37015.png
-15002.png
-23081.png
-12060.png
-21744.png
-09739.png
-61992.png
-40156.png
-49762.png
-31270.png
-44466.png
-23804.png
-18491.png
-69743.png
-14627.png
-04449.png
-34945.png
-67979.png
-69207.png
-10106.png
-58721.png
-34492.png
-22419.png
-43370.png
-22530.png
-23207.png
-24005.png
-47923.png
-55901.png
-56894.png
-36288.png
-22177.png
-10508.png
-25643.png
-18725.png
-44635.png
-06779.png
-46038.png
-33066.png
-39525.png
-00013.png
-43577.png
-20213.png
-60066.png
-53333.png
-41360.png
-17986.png
-49795.png
-40899.png
-34024.png
-20037.png
-55979.png
-07400.png
-55909.png
-16803.png
-46765.png
-18756.png
-69644.png
-38586.png
-14108.png
-54522.png
-33413.png
-67524.png
-04076.png
-29386.png
-47843.png
-44193.png
-66480.png
-19600.png
-03359.png
-05693.png
-45104.png
-13159.png
-05608.png
-00146.png
-40109.png
-06790.png
-18957.png
-56866.png
-62642.png
-63745.png
-38559.png
-45964.png
-03287.png
-63394.png
-01424.png
-54776.png
-01745.png
-64978.png
-42157.png
-11508.png
-23159.png
-60423.png
-31425.png
-49531.png
-25601.png
-60455.png
-59815.png
-10641.png
-22100.png
-36140.png
-48609.png
-08207.png
-29671.png
-47501.png
-60515.png
-09843.png
-51937.png
-12209.png
-18673.png
-57273.png
-23625.png
-68797.png
-06039.png
-51624.png
-19464.png
-12992.png
-51824.png
-39476.png
-62024.png
-31972.png
-45893.png
-09479.png
-25143.png
-02023.png
-65363.png
-54749.png
-16445.png
-42805.png
-58820.png
-48874.png
-28140.png
-19797.png
-33247.png
-18559.png
-10494.png
-28227.png
-00310.png
-25520.png
-05437.png
-43013.png
-69613.png
-16562.png
-62630.png
-34479.png
-25462.png
-58737.png
-30349.png
-27225.png
-27550.png
-38733.png
-54026.png
-55853.png
-02734.png
-36509.png
-21130.png
-58213.png
-02182.png
-48020.png
-11390.png
-50341.png
-24869.png
-58732.png
-34253.png
-66475.png
-24146.png
-10178.png
-48641.png
-67023.png
-34831.png
-60431.png
-58609.png
-17691.png
-59156.png
-40191.png
-47926.png
-23986.png
-30482.png
-12903.png
-47442.png
-53622.png
-67758.png
-13567.png
-34068.png
-27474.png
-00998.png
-35047.png
-29187.png
-47886.png
-21642.png
-16421.png
-46247.png
-16211.png
-68258.png
-39909.png
-24016.png
-63296.png
-06630.png
-47084.png
-43095.png
-29636.png
-25887.png
-67541.png
-26537.png
-57222.png
-37559.png
-52763.png
-27608.png
-62777.png
-20670.png
-46990.png
-37044.png
-16370.png
-50392.png
-53947.png
-36172.png
-11627.png
-15304.png
-53653.png
-21986.png
-69850.png
-54624.png
-22443.png
-45739.png
-09457.png
-27555.png
-52252.png
-62015.png
-42683.png
-27885.png
-56186.png
-22305.png
-43333.png
-19553.png
-01569.png
-49556.png
-60131.png
-28475.png
-37490.png
-25486.png
-54586.png
-57332.png
-45464.png
-13145.png
-47990.png
-51664.png
-49624.png
-45434.png
-69716.png
-47810.png
-59416.png
-06651.png
-64193.png
-58064.png
-20475.png
-12296.png
-69252.png
-13319.png
-48975.png
-22900.png
-34125.png
-52140.png
-03452.png
-06826.png
-47575.png
-27228.png
-61861.png
-58291.png
-10707.png
-20838.png
-68519.png
-50779.png
-39862.png
-09421.png
-60055.png
-17872.png
-01857.png
-55312.png
-55597.png
-20965.png
-13961.png
-50877.png
-59165.png
-67650.png
-56507.png
-11609.png
-55940.png
-59300.png
-39884.png
-05061.png
-30380.png
-42710.png
-05551.png
-17209.png
-35982.png
-18062.png
-52673.png
-61215.png
-35514.png
-45524.png
-57435.png
-42291.png
-48133.png
-04359.png
-49749.png
-43914.png
-05619.png
-11310.png
-52744.png
-46497.png
-58400.png
-27503.png
-68238.png
-51981.png
-60249.png
-46919.png
-14619.png
-13335.png
-61258.png
-37278.png
-48336.png
-35995.png
-10558.png
-02277.png
-60716.png
-29069.png
-42182.png
-47146.png
-40718.png
-21594.png
-54883.png
-59828.png
-39521.png
-38554.png
-62139.png
-49882.png
-40770.png
-05281.png
-04823.png
-09365.png
-30389.png
-56665.png
-25306.png
-21154.png
-06977.png
-04865.png
-27394.png
-29601.png
-41925.png
-29286.png
-67576.png
-54095.png
-27075.png
-55536.png
-48445.png
-19519.png
-22280.png
-50425.png
-23104.png
-07877.png
-10630.png
-58670.png
-20737.png
-61672.png
-34588.png
-01761.png
-62362.png
-53112.png
-69462.png
-21322.png
-31451.png
-52528.png
-52182.png
-43680.png
-30171.png
-58192.png
-00555.png
-64297.png
-64621.png
-33858.png
-44707.png
-40811.png
-04125.png
-12932.png
-29091.png
-26192.png
-22495.png
-42655.png
-36109.png
-23966.png
-32372.png
-00495.png
-45301.png
-33319.png
-52300.png
-06261.png
-18437.png
-12210.png
-15763.png
-04231.png
-23648.png
-22090.png
-58708.png
-31636.png
-15900.png
-57129.png
-46031.png
-06990.png
-46658.png
-24467.png
-32353.png
-09641.png
-04124.png
-36673.png
-34015.png
-44351.png
-05523.png
-55802.png
-50456.png
-34673.png
-23031.png
-66064.png
-33789.png
-55434.png
-55588.png
-28385.png
-62309.png
-20103.png
-00468.png
-44035.png
-23802.png
-52838.png
-37410.png
-54003.png
-54617.png
-37080.png
-32034.png
-64609.png
-17879.png
-53701.png
-35900.png
-41891.png
-17898.png
-04355.png
-40489.png
-22109.png
-36700.png
-21790.png
-06176.png
-02765.png
-47443.png
-62223.png
-09819.png
-58936.png
-54772.png
-39292.png
-63867.png
-32182.png
-20776.png
-06909.png
-21283.png
-48576.png
-20077.png
-43615.png
-29604.png
-64989.png
-54490.png
-28864.png
-35944.png
-14799.png
-52894.png
-52311.png
-35800.png
-57408.png
-49553.png
-54401.png
-57125.png
-52791.png
-34127.png
-36590.png
-54042.png
-51787.png
-11059.png
-25883.png
-04667.png
-01637.png
-40226.png
-19392.png
-49625.png
-47188.png
-25061.png
-51259.png
-48384.png
-35929.png
-62172.png
-10234.png
-25817.png
-27636.png
-22584.png
-43972.png
-48372.png
-09850.png
-01095.png
-44237.png
-58863.png
-39373.png
-22921.png
-15902.png
-01008.png
-46231.png
-17681.png
-68904.png
-44409.png
-23781.png
-26860.png
-51017.png
-42646.png
-30644.png
-66272.png
-29510.png
-01065.png
-35500.png
-34502.png
-53493.png
-12970.png
-64780.png
-02139.png
-44661.png
-23372.png
-45419.png
-28329.png
-55957.png
-48360.png
-22440.png
-24306.png
-40000.png
-54063.png
-09268.png
-31720.png
-64417.png
-51762.png
-50629.png
-29841.png
-25888.png
-16632.png
-29475.png
-10931.png
-62989.png
-46025.png
-28560.png
-44780.png
-09998.png
-69979.png
-30652.png
-62975.png
-53631.png
-16818.png
-50354.png
-56113.png
-06378.png
-63242.png
-22686.png
-50777.png
-62638.png
-02504.png
-03716.png
-45446.png
-25874.png
-51574.png
-18617.png
-27940.png
-46712.png
-42844.png
-23258.png
-65511.png
-64072.png
-28056.png
-10442.png
-14046.png
-37859.png
-25659.png
-69853.png
-08209.png
-46619.png
-41548.png
-57226.png
-52747.png
-28067.png
-19920.png
-01983.png
-54341.png
-47827.png
-57994.png
-34236.png
-34611.png
-28974.png
-42021.png
-56904.png
-31598.png
-49465.png
-26638.png
-52572.png
-47439.png
-25929.png
-41768.png
-67773.png
-40975.png
-61424.png
-17423.png
-18793.png
-01040.png
-13656.png
-31844.png
-29624.png
-26575.png
-56518.png
-12706.png
-62824.png
-57796.png
-49468.png
-62327.png
-46061.png
-55754.png
-00989.png
-30293.png
-27302.png
-07415.png
-15556.png
-05716.png
-30379.png
-16693.png
-68155.png
-10189.png
-24667.png
-51765.png
-28406.png
-60392.png
-09713.png
-31637.png
-55718.png
-12323.png
-62378.png
-30029.png
-14966.png
-08994.png
-64253.png
-37377.png
-66118.png
-00960.png
-58346.png
-09319.png
-67988.png
-22662.png
-54216.png
-68602.png
-09709.png
-00046.png
-14580.png
-48496.png
-34423.png
-43541.png
-65793.png
-27194.png
-38252.png
-38337.png
-27665.png
-44458.png
-08789.png
-30186.png
-68128.png
-01817.png
-68929.png
-69032.png
-06126.png
-43782.png
-56928.png
-12623.png
-54553.png
-67496.png
-50738.png
-58355.png
-18481.png
-44936.png
-05999.png
-19400.png
-01985.png
-60373.png
-20458.png
-22204.png
-69500.png
-59690.png
-25831.png
-24781.png
-61194.png
-57137.png
-17206.png
-12197.png
-46907.png
-25513.png
-58313.png
-63905.png
-41060.png
-35535.png
-61467.png
-55271.png
-26242.png
-62270.png
-16750.png
-30488.png
-65172.png
-49834.png
-12585.png
-35972.png
-43113.png
-37744.png
-59240.png
-37742.png
-02294.png
-31524.png
-07531.png
-18515.png
-46321.png
-40091.png
-61292.png
-10327.png
-44486.png
-21824.png
-36145.png
-69968.png
-23923.png
-53165.png
-23184.png
-04301.png
-20799.png
-55619.png
-28209.png
-59707.png
-40058.png
-30111.png
-27830.png
-04061.png
-15187.png
-05753.png
-14069.png
-31261.png
-00816.png
-51836.png
-05661.png
-13712.png
-49865.png
-49361.png
-67593.png
-40982.png
-06887.png
-43037.png
-39736.png
-35563.png
-68308.png
-63218.png
-21584.png
-64073.png
-31480.png
-13420.png
-63833.png
-14345.png
-09738.png
-42611.png
-66034.png
-09791.png
-59339.png
-43992.png
-03460.png
-25944.png
-53558.png
-01472.png
-66317.png
-13011.png
-62917.png
-28756.png
-48479.png
-48840.png
-13449.png
-60897.png
-64480.png
-40655.png
-52518.png
-15539.png
-52345.png
-24428.png
-17045.png
-34578.png
-19735.png
-34418.png
-55623.png
-63097.png
-15081.png
-01288.png
-09607.png
-11576.png
-55199.png
-58013.png
-64190.png
-68367.png
-47384.png
-31327.png
-42894.png
-28425.png
-63715.png
-52907.png
-27745.png
-23844.png
-08655.png
-46744.png
-11487.png
-33197.png
-27215.png
-19795.png
-21704.png
-34558.png
-38622.png
-24666.png
-42138.png
-03855.png
-11841.png
-37216.png
-43647.png
-27963.png
-63164.png
-60984.png
-55831.png
-40219.png
-12020.png
-12276.png
-09543.png
-39326.png
-33752.png
-19963.png
-02747.png
-33634.png
-63322.png
-61812.png
-49552.png
-65389.png
-06389.png
-34915.png
-02726.png
-26362.png
-67434.png
-00592.png
-65244.png
-41247.png
-01386.png
-66253.png
-08761.png
-20120.png
-50603.png
-18997.png
-39276.png
-33614.png
-50287.png
-41863.png
-41879.png
-48879.png
-00188.png
-55862.png
-10224.png
-59151.png
-28237.png
-34407.png
-65551.png
-14062.png
-43714.png
-65190.png
-01291.png
-01078.png
-04935.png
-38648.png
-63638.png
-44874.png
-10885.png
-06776.png
-34776.png
-07182.png
-38266.png
-57529.png
-35177.png
-30084.png
-01507.png
-38767.png
-44480.png
-34737.png
-18758.png
-57849.png
-04957.png
-30163.png
-01281.png
-43497.png
-29077.png
-27050.png
-10003.png
-36315.png
-01978.png
-50869.png
-33250.png
-26330.png
-29481.png
-41752.png
-62175.png
-15185.png
-18843.png
-41522.png
-23652.png
-06530.png
-33363.png
-22214.png
-32188.png
-32700.png
-36974.png
-23474.png
-67710.png
-30569.png
-63615.png
-01624.png
-62655.png
-54442.png
-17637.png
-46851.png
-65045.png
-21164.png
-17377.png
-21298.png
-58524.png
-31445.png
-03039.png
-31105.png
-20810.png
-00671.png
-33687.png
-46508.png
-14607.png
-22607.png
-00685.png
-16392.png
-17867.png
-48899.png
-23434.png
-27995.png
-68971.png
-43660.png
-29120.png
-07330.png
-10032.png
-31643.png
-42846.png
-67821.png
-40967.png
-49031.png
-01312.png
-63620.png
-69311.png
-23195.png
-68279.png
-61032.png
-64222.png
-00747.png
-14316.png
-19583.png
-51803.png
-32573.png
-58256.png
-01706.png
-20423.png
-03098.png
-39231.png
-23132.png
-21716.png
-65125.png
-30566.png
-68331.png
-01809.png
-20689.png
-49247.png
-60218.png
-04143.png
-23865.png
-02431.png
-52497.png
-16310.png
-62879.png
-62268.png
-18621.png
-63120.png
-34706.png
-67147.png
-54379.png
-18995.png
-33945.png
-42659.png
-00688.png
-56484.png
-47172.png
-64300.png
-36526.png
-28282.png
-39722.png
-41051.png
-46812.png
-65122.png
-59284.png
-10632.png
-22721.png
-33480.png
-11652.png
-50353.png
-55433.png
-63258.png
-57656.png
-54980.png
-23756.png
-53824.png
-01176.png
-36863.png
-68720.png
-03446.png
-69971.png
-39855.png
-42944.png
-00367.png
-10266.png
-35864.png
-34197.png
-09745.png
-48860.png
-20691.png
-04324.png
-00254.png
-29537.png
-28505.png
-45343.png
-69224.png
-13034.png
-59572.png
-06464.png
-48291.png
-04433.png
-22747.png
-61837.png
-68718.png
-08075.png
-59466.png
-13315.png
-65118.png
-34050.png
-30425.png
-31676.png
-17742.png
-06952.png
-68310.png
-06314.png
-16582.png
-02346.png
-56362.png
-00982.png
-06503.png
-21520.png
-10007.png
-27218.png
-10909.png
-54465.png
-40159.png
-26616.png
-35529.png
-31236.png
-18190.png
-09077.png
-25687.png
-53857.png
-67203.png
-26720.png
-54513.png
-34203.png
-48444.png
-05813.png
-07726.png
-46805.png
-37054.png
-39999.png
-03968.png
-41895.png
-45233.png
-31419.png
-57770.png
-62927.png
-16200.png
-68172.png
-26739.png
-66103.png
-62833.png
-06668.png
-53357.png
-25406.png
-67847.png
-54477.png
-69551.png
-49640.png
-29038.png
-26828.png
-57210.png
-27101.png
-20530.png
-40637.png
-68615.png
-12503.png
-30269.png
-14023.png
-67291.png
-50641.png
-15184.png
-35957.png
-62718.png
-39582.png
-07132.png
-27221.png
-49721.png
-34157.png
-49396.png
-08559.png
-64442.png
-23036.png
-28006.png
-13425.png
-12357.png
-34883.png
-24511.png
-12704.png
-64083.png
-32215.png
-50525.png
-09075.png
-55766.png
-62829.png
-22905.png
-08489.png
-15551.png
-44039.png
-32755.png
-59689.png
-49216.png
-19256.png
-67522.png
-60646.png
-57481.png
-05717.png
-26905.png
-55771.png
-16981.png
-10299.png
-39836.png
-50142.png
-69982.png
-60444.png
-29242.png
-54715.png
-68246.png
-12919.png
-24057.png
-06939.png
-34169.png
-13127.png
-53397.png
-57577.png
-52307.png
-69591.png
-57171.png
-36336.png
-40893.png
-02836.png
-30382.png
-40516.png
-12985.png
-54032.png
-69471.png
-26219.png
-52961.png
-51509.png
-47515.png
-00967.png
-00322.png
-13422.png
-08358.png
-17831.png
-26801.png
-50039.png
-57682.png
-00710.png
-38090.png
-30745.png
-27370.png
-05279.png
-62250.png
-60312.png
-60552.png
-10774.png
-24025.png
-16142.png
-26273.png
-17091.png
-66367.png
-14240.png
-69062.png
-03323.png
-64616.png
-51965.png
-21017.png
-01589.png
-31864.png
-52895.png
-35918.png
-67750.png
-36379.png
-27903.png
-67936.png
-42068.png
-18085.png
-69308.png
-46514.png
-62971.png
-67011.png
-62988.png
-19054.png
-41085.png
-01909.png
-59195.png
-43888.png
-20855.png
-59169.png
-51548.png
-49248.png
-02658.png
-66543.png
-49329.png
-46814.png
-69666.png
-22242.png
-01719.png
-04157.png
-38914.png
-34589.png
-19374.png
-13514.png
-61938.png
-56094.png
-69647.png
-34680.png
-23877.png
-12851.png
-58734.png
-56587.png
-45953.png
-41206.png
-07789.png
-63743.png
-01446.png
-60470.png
-35429.png
-32331.png
-34998.png
-68442.png
-19506.png
-59347.png
-18827.png
-14616.png
-11917.png
-46010.png
-47194.png
-40017.png
-60271.png
-11265.png
-63163.png
-59866.png
-69070.png
-28661.png
-18923.png
-46931.png
-53041.png
-46376.png
-17150.png
-52517.png
-00217.png
-14945.png
-16360.png
-20255.png
-04690.png
-02517.png
-58556.png
-43331.png
-28353.png
-36593.png
-09492.png
-44189.png
-67850.png
-37522.png
-34326.png
-45788.png
-01228.png
-25422.png
-50228.png
-56934.png
-04500.png
-45377.png
-61080.png
-06847.png
-22497.png
-33102.png
-08921.png
-24638.png
-25631.png
-63843.png
-31161.png
-66630.png
-12511.png
-60742.png
-63931.png
-08609.png
-32931.png
-41653.png
-58148.png
-68093.png
-01238.png
-33469.png
-55075.png
-34844.png
-27160.png
-24332.png
-24224.png
-11429.png
-67962.png
-46324.png
-51890.png
-56990.png
-68838.png
-62255.png
-33349.png
-15695.png
-22770.png
-47689.png
-33157.png
-09981.png
-64584.png
-06121.png
-27680.png
-51099.png
-22426.png
-61762.png
-33109.png
-35294.png
-05630.png
-22012.png
-57407.png
-46759.png
-69974.png
-46180.png
-28162.png
-32019.png
-51897.png
-51421.png
-63021.png
-66479.png
-20688.png
-29205.png
-29911.png
-34167.png
-30839.png
-69390.png
-14517.png
-33537.png
-24535.png
-22017.png
-63746.png
-56216.png
-48241.png
-26627.png
-15244.png
-42226.png
-42270.png
-22643.png
-08084.png
-53717.png
-66294.png
-42621.png
-49186.png
-19003.png
-53150.png
-02450.png
-03729.png
-67198.png
-61743.png
-46197.png
-63525.png
-46260.png
-28446.png
-59971.png
-60157.png
-48892.png
-02844.png
-13043.png
-31567.png
-19687.png
-50360.png
-27133.png
-67471.png
-58001.png
-47855.png
-49112.png
-62773.png
-23048.png
-56966.png
-59166.png
-12448.png
-48948.png
-68957.png
-45748.png
-58704.png
-26402.png
-10726.png
-35292.png
-35207.png
-08499.png
-09342.png
-53929.png
-36006.png
-19859.png
-10618.png
-08955.png
-65594.png
-05550.png
-28070.png
-35699.png
-11807.png
-18823.png
-37515.png
-63101.png
-02956.png
-05194.png
-39113.png
-26832.png
-68664.png
-40086.png
-35517.png
-00788.png
-15051.png
-03899.png
-16761.png
-00530.png
-40078.png
-38595.png
-37482.png
-02354.png
-09651.png
-01405.png
-16652.png
-66592.png
-31575.png
-14255.png
-15015.png
-06310.png
-10958.png
-12405.png
-00961.png
-37480.png
-52746.png
-53733.png
-14514.png
-61794.png
-13945.png
-05763.png
-49848.png
-66544.png
-53649.png
-17776.png
-27312.png
-51069.png
-40372.png
-09634.png
-07577.png
-46592.png
-30825.png
-61513.png
-66134.png
-58376.png
-51125.png
-19991.png
-51686.png
-58043.png
-10791.png
-44588.png
-20339.png
-38631.png
-03336.png
-10811.png
-13849.png
-39894.png
-57142.png
-59355.png
-31334.png
-56351.png
-61847.png
-60655.png
-23413.png
-22197.png
-18598.png
-08395.png
-04986.png
-68521.png
-12748.png
-23034.png
-26398.png
-36890.png
-20920.png
-55431.png
-00010.png
-65402.png
-15977.png
-49699.png
-65345.png
-34512.png
-10026.png
-15843.png
-33604.png
-66284.png
-69114.png
-40531.png
-50661.png
-43025.png
-17203.png
-68956.png
-33443.png
-44098.png
-03981.png
-39321.png
-55564.png
-40048.png
-13858.png
-14133.png
-30802.png
-02052.png
-22858.png
-55897.png
-54150.png
-07699.png
-69562.png
-05547.png
-51862.png
-49151.png
-63548.png
-69627.png
-42085.png
-13890.png
-27197.png
-67263.png
-42465.png
-36811.png
-53482.png
-39097.png
-57388.png
-31576.png
-31677.png
-00439.png
-23871.png
-50928.png
-64364.png
-41017.png
-04141.png
-13927.png
-39824.png
-20003.png
-61614.png
-11391.png
-27470.png
-10167.png
-04854.png
-03917.png
-04446.png
-22583.png
-01804.png
-29483.png
-30489.png
-36963.png
-12666.png
-32163.png
-35130.png
-41158.png
-43674.png
-67860.png
-34884.png
-48622.png
-30436.png
-01140.png
-25145.png
-46573.png
-01213.png
-04610.png
-69194.png
-37442.png
-30182.png
-01772.png
-38106.png
-61217.png
-16994.png
-41068.png
-11520.png
-23857.png
-49918.png
-58642.png
-19206.png
-69907.png
-42761.png
-65377.png
-38606.png
-17673.png
-19191.png
-69468.png
-66938.png
-01495.png
-03731.png
-27276.png
-03280.png
-02970.png
-09005.png
-40288.png
-69018.png
-04876.png
-14006.png
-54536.png
-43599.png
-58617.png
-22251.png
-29163.png
-27165.png
-31312.png
-19571.png
-31253.png
-41037.png
-46300.png
-65540.png
-44991.png
-51037.png
-29749.png
-11726.png
-42964.png
-44279.png
-08912.png
-09975.png
-50454.png
-03268.png
-59280.png
-05772.png
-44413.png
-52034.png
-68223.png
-31803.png
-22609.png
-53206.png
-18832.png
-63344.png
-44525.png
-39541.png
-12926.png
-40849.png
-02264.png
-51246.png
-22603.png
-41337.png
-35596.png
-56426.png
-06407.png
-48282.png
-20832.png
-22367.png
-59011.png
-55313.png
-43330.png
-14278.png
-63655.png
-47424.png
-06292.png
-40520.png
-20194.png
-12643.png
-44391.png
-37702.png
-40517.png
-33591.png
-03770.png
-54532.png
-07251.png
-21845.png
-41099.png
-28686.png
-66409.png
-62825.png
-45184.png
-45403.png
-09213.png
-66595.png
-17984.png
-41916.png
-58635.png
-49649.png
-51293.png
-37735.png
-05019.png
-62888.png
-18182.png
-50893.png
-43622.png
-17337.png
-54642.png
-34375.png
-01854.png
-04351.png
-05786.png
-24174.png
-16730.png
-34058.png
-47465.png
-56396.png
-10716.png
-36719.png
-67636.png
-23475.png
-60325.png
-53404.png
-59926.png
-24372.png
-33888.png
-27672.png
-16762.png
-08168.png
-63397.png
-30127.png
-22845.png
-38378.png
-23828.png
-60872.png
-21373.png
-24401.png
-66697.png
-38614.png
-55472.png
-42723.png
-69956.png
-39411.png
-19792.png
-61213.png
-53451.png
-60379.png
-58480.png
-57353.png
-26271.png
-33177.png
-39111.png
-22910.png
-53825.png
-32910.png
-25936.png
-64932.png
-57471.png
-17172.png
-16517.png
-09523.png
-09220.png
-48018.png
-59429.png
-48115.png
-12313.png
-53018.png
-51738.png
-48111.png
-24351.png
-12155.png
-11315.png
-34738.png
-23312.png
-55532.png
-58268.png
-67131.png
-41815.png
-45066.png
-62766.png
-20231.png
-53616.png
-31627.png
-58879.png
-08783.png
-06960.png
-12792.png
-11360.png
-49781.png
-60097.png
-52204.png
-35428.png
-29614.png
-53970.png
-17547.png
-50102.png
-18162.png
-02404.png
-02272.png
-13482.png
-03107.png
-18255.png
-00147.png
-59628.png
-08595.png
-26092.png
-21825.png
-31782.png
-19318.png
-24691.png
-45766.png
-03191.png
-65457.png
-67317.png
-69040.png
-01842.png
-45528.png
-12478.png
-36309.png
-49826.png
-27326.png
-21233.png
-00523.png
-18717.png
-44471.png
-55606.png
-08678.png
-62297.png
-22593.png
-59421.png
-17990.png
-56447.png
-30197.png
-58116.png
-23189.png
-16009.png
-40581.png
-09237.png
-18651.png
-12051.png
-37870.png
-42543.png
-27524.png
-58979.png
-00970.png
-54415.png
-12576.png
-35647.png
-43425.png
-03905.png
-50654.png
-06572.png
-17205.png
-24894.png
-52193.png
-43662.png
-36409.png
-30928.png
-63259.png
-55305.png
-15412.png
-57576.png
-23730.png
-40795.png
-16226.png
-21928.png
-59084.png
-33322.png
-25183.png
-14969.png
-56842.png
-34276.png
-28708.png
-59854.png
-28590.png
-28743.png
-59437.png
-37270.png
-60081.png
-54251.png
-48983.png
-03778.png
-14296.png
-35224.png
-17097.png
-10404.png
-55534.png
-23499.png
-31859.png
-52613.png
-10937.png
-09660.png
-32802.png
-19757.png
-45773.png
-29865.png
-57201.png
-12786.png
-09371.png
-00932.png
-58940.png
-51527.png
-69439.png
-57860.png
-42106.png
-18123.png
-37153.png
-28086.png
-59107.png
-36281.png
-67626.png
-68909.png
-38396.png
-42791.png
-48145.png
-29623.png
-58804.png
-33540.png
-36232.png
-58250.png
-62792.png
-42300.png
-51884.png
-17001.png
-08421.png
-42056.png
-27897.png
-07147.png
-37666.png
-36444.png
-03325.png
-43402.png
-18105.png
-62160.png
-57578.png
-49829.png
-22947.png
-45441.png
-53168.png
-48238.png
-35539.png
-25943.png
-06496.png
-31227.png
-30014.png
-15173.png
-66114.png
-18684.png
-26654.png
-64448.png
-32324.png
-05503.png
-30540.png
-30231.png
-00635.png
-30251.png
-24342.png
-26392.png
-40548.png
-62288.png
-18124.png
-55643.png
-64268.png
-09163.png
-09907.png
-42219.png
-31605.png
-18368.png
-56690.png
-29190.png
-55671.png
-12042.png
-02594.png
-59070.png
-55196.png
-18408.png
-62624.png
-39929.png
-39676.png
-16088.png
-68293.png
-42680.png
-05075.png
-24844.png
-61858.png
-05821.png
-18647.png
-44641.png
-64215.png
-54240.png
-68291.png
-19226.png
-33024.png
-23280.png
-31743.png
-53124.png
-46558.png
-47161.png
-23631.png
-57635.png
-43335.png
-01462.png
-20835.png
-07016.png
-47968.png
-66838.png
-05286.png
-19389.png
-45579.png
-43145.png
-48688.png
-08295.png
-43231.png
-53409.png
-17546.png
-52887.png
-23366.png
-69903.png
-61181.png
-08629.png
-38787.png
-52749.png
-52591.png
-37564.png
-28136.png
-61832.png
-61257.png
-05244.png
-28634.png
-55418.png
-38931.png
-09425.png
-32738.png
-35076.png
-16263.png
-41789.png
-46757.png
-62995.png
-23032.png
-29130.png
-64221.png
-04069.png
-18581.png
-54271.png
-40885.png
-45370.png
-59124.png
-42714.png
-47013.png
-38386.png
-32955.png
-51620.png
-66910.png
-63686.png
-33148.png
-58982.png
-09243.png
-30080.png
-03660.png
-58722.png
-09926.png
-60129.png
-08736.png
-26042.png
-02951.png
-17264.png
-20419.png
-32798.png
-02697.png
-65680.png
-14534.png
-19457.png
-33633.png
-63254.png
-35704.png
-37943.png
-49137.png
-66745.png
-08252.png
-51105.png
-64237.png
-22591.png
-08964.png
-14816.png
-46044.png
-25605.png
-36399.png
-51731.png
-28828.png
-41699.png
-47156.png
-37279.png
-42741.png
-10144.png
-66644.png
-69009.png
-49913.png
-62388.png
-37518.png
-45895.png
-20354.png
-53948.png
-61101.png
-58240.png
-33646.png
-60942.png
-32297.png
-08126.png
-64227.png
-25459.png
-42229.png
-40675.png
-40338.png
-10754.png
-28847.png
-44807.png
-20145.png
-67211.png
-62748.png
-26040.png
-41367.png
-65484.png
-67125.png
-10592.png
-30280.png
-46158.png
-25809.png
-12138.png
-66136.png
-65506.png
-06633.png
-45733.png
-39254.png
-42483.png
-29082.png
-20900.png
-43865.png
-47189.png
-50165.png
-40894.png
-25628.png
-01442.png
-49266.png
-44717.png
-61306.png
-60110.png
-04209.png
-41361.png
-23182.png
-55618.png
-35013.png
-06036.png
-13095.png
-41107.png
-61828.png
-44373.png
-62195.png
-14097.png
-35903.png
-54055.png
-44376.png
-69294.png
-26230.png
-01499.png
-34384.png
-44481.png
-01028.png
-21573.png
-61153.png
-05105.png
-62279.png
-02169.png
-68901.png
-40416.png
-50130.png
-14103.png
-57765.png
-09276.png
-69089.png
-06667.png
-46045.png
-01830.png
-00040.png
-46624.png
-03936.png
-58702.png
-18256.png
-30608.png
-30506.png
-32282.png
-33401.png
-37308.png
-12820.png
-07367.png
-40304.png
-67728.png
-39500.png
-28412.png
-39219.png
-46777.png
-61259.png
-44171.png
-22876.png
-60573.png
-07684.png
-28698.png
-48547.png
-35512.png
-33331.png
-20437.png
-09287.png
-27017.png
-18596.png
-61442.png
-34259.png
-32425.png
-09118.png
-13744.png
-42432.png
-01596.png
-02506.png
-23286.png
-48087.png
-47256.png
-07674.png
-04102.png
-18004.png
-41648.png
-08452.png
-51753.png
-09725.png
-05215.png
-37494.png
-15985.png
-30673.png
-20780.png
-12403.png
-25952.png
-23131.png
-68019.png
-68086.png
-30522.png
-16096.png
-51882.png
-57480.png
-00500.png
-00394.png
-64959.png
-46467.png
-68446.png
-49859.png
-33034.png
-37471.png
-07980.png
-63971.png
-40186.png
-56667.png
-01785.png
-06212.png
-11130.png
-32083.png
-53030.png
-26483.png
-37871.png
-63832.png
-33949.png
-49513.png
-04465.png
-47100.png
-09668.png
-62151.png
-51492.png
-36449.png
-27965.png
-44072.png
-30419.png
-32790.png
-40216.png
-62210.png
-34451.png
-15982.png
-34403.png
-61684.png
-50831.png
-04263.png
-69104.png
-01556.png
-52444.png
-13232.png
-55621.png
-10816.png
-45805.png
-58046.png
-04828.png
-38530.png
-52025.png
-28344.png
-15290.png
-65312.png
-60992.png
-28120.png
-01621.png
-12991.png
-35245.png
-42821.png
-32667.png
-46468.png
-67325.png
-44756.png
-58048.png
-30151.png
-21726.png
-67777.png
-19791.png
-30172.png
-32109.png
-12214.png
-05665.png
-56237.png
-42518.png
-08895.png
-03934.png
-21184.png
-06105.png
-03419.png
-32545.png
-40613.png
-62351.png
-50598.png
-44425.png
-02384.png
-35536.png
-41373.png
-35169.png
-42615.png
-51521.png
-57818.png
-39551.png
-45204.png
-17338.png
-40953.png
-05844.png
-38857.png
-54188.png
-12629.png
-57779.png
-30474.png
-16276.png
-34126.png
-42771.png
-65023.png
-36696.png
-69106.png
-52536.png
-58875.png
-21527.png
-50256.png
-41579.png
-32343.png
-31565.png
-02081.png
-16843.png
-19524.png
-67659.png
-37626.png
-48474.png
-13418.png
-32046.png
-07119.png
-43070.png
-23666.png
-68850.png
-56250.png
-11738.png
-31682.png
-49173.png
-26298.png
-40556.png
-13946.png
-38775.png
-61839.png
-49778.png
-58369.png
-15600.png
-64983.png
-17794.png
-44743.png
-24125.png
-06807.png
-11035.png
-12443.png
-27457.png
-32852.png
-46515.png
-59918.png
-54630.png
-31684.png
-16820.png
-08255.png
-52341.png
-19470.png
-18548.png
-66869.png
-69725.png
-39610.png
-32799.png
-48551.png
-17213.png
-39080.png
-64096.png
-68925.png
-38809.png
-22551.png
-38161.png
-45863.png
-03469.png
-55569.png
-22544.png
-20416.png
-50665.png
-50502.png
-16023.png
-59735.png
-45354.png
-67345.png
-50677.png
-58947.png
-51146.png
-00508.png
-45398.png
-61802.png
-19588.png
-52790.png
-27574.png
-30361.png
-49373.png
-56218.png
-52780.png
-30910.png
-61629.png
-38176.png
-40433.png
-56292.png
-19972.png
-43578.png
-58463.png
-63828.png
-28729.png
-26899.png
-50264.png
-40295.png
-01447.png
-63345.png
-07973.png
-23708.png
-31429.png
-09643.png
-67117.png
-02921.png
-46995.png
-47828.png
-35771.png
-32159.png
-68429.png
-56021.png
-26349.png
-34674.png
-07308.png
-04792.png
-60537.png
-14125.png
-52914.png
-64338.png
-25377.png
-56574.png
-17211.png
-04422.png
-02459.png
-63340.png
-23075.png
-19857.png
-45452.png
-52554.png
-59144.png
-30700.png
-25243.png
-23987.png
-29066.png
-46140.png
-37453.png
-24946.png
-60465.png
-62945.png
-20798.png
-40555.png
-53334.png
-20311.png
-07113.png
-13899.png
-50544.png
-61474.png
-62456.png
-59510.png
-15191.png
-42738.png
-30123.png
-44905.png
-50812.png
-15181.png
-51567.png
-04466.png
-27759.png
-63222.png
-19068.png
-23660.png
-19520.png
-40071.png
-18409.png
-57767.png
-50859.png
-49782.png
-67010.png
-03328.png
-18407.png
-28157.png
-32779.png
-44988.png
-00268.png
-61070.png
-51164.png
-25247.png
-10258.png
-10180.png
-41562.png
-17551.png
-01036.png
-53172.png
-49222.png
-49511.png
-23109.png
-47666.png
-21969.png
-06463.png
-09052.png
-18595.png
-26779.png
-25116.png
-27559.png
-10255.png
-54479.png
-51373.png
-14847.png
-08312.png
-66835.png
-34149.png
-14746.png
-35236.png
-48030.png
-47620.png
-18573.png
-39660.png
-38601.png
-23397.png
-43655.png
-16026.png
-20612.png
-65863.png
-37132.png
-50969.png
-63935.png
-52924.png
-23958.png
-43985.png
-25447.png
-18978.png
-13001.png
-15548.png
-65497.png
-64226.png
-04180.png
-39030.png
-14810.png
-28018.png
-69470.png
-38912.png
-34969.png
-66347.png
-34477.png
-47126.png
-14935.png
-15246.png
-69812.png
-48940.png
-37949.png
-50823.png
-58422.png
-66620.png
-64699.png
-40923.png
-22077.png
-67580.png
-51586.png
-52170.png
-65447.png
-15979.png
-39682.png
-18631.png
-01469.png
-67475.png
-04850.png
-19576.png
-58649.png
-26847.png
-68881.png
-27743.png
-58431.png
-58293.png
-45716.png
-67141.png
-45727.png
-69585.png
-58576.png
-51447.png
-54663.png
-36481.png
-27606.png
-33756.png
-67330.png
-67893.png
-23240.png
-08998.png
-29015.png
-34746.png
-27675.png
-15849.png
-50110.png
-24488.png
-37244.png
-26946.png
-27301.png
-13807.png
-50317.png
-30491.png
-31443.png
-49001.png
-45009.png
-08199.png
-37452.png
-05746.png
-69084.png
-63659.png
-13178.png
-51479.png
-41189.png
-28593.png
-06498.png
-17592.png
-39191.png
-63810.png
-61056.png
-42451.png
-27891.png
-68897.png
-62310.png
-66320.png
-45039.png
-11599.png
-64878.png
-17780.png
-41621.png
-23178.png
-38512.png
-24920.png
-40175.png
-69957.png
-67298.png
-68689.png
-41050.png
-28784.png
-18889.png
-17921.png
-55604.png
-06056.png
-55458.png
-54285.png
-57678.png
-39994.png
-36739.png
-11537.png
-35109.png
-10162.png
-17824.png
-68972.png
-29343.png
-45325.png
-29401.png
-59257.png
-55138.png
-53780.png
-44289.png
-48041.png
-07141.png
-08542.png
-44044.png
-56438.png
-45290.png
-16214.png
-57190.png
-54609.png
-21878.png
-15261.png
-33719.png
-13794.png
-55938.png
-17098.png
-10520.png
-14938.png
-43065.png
-19277.png
-15397.png
-02886.png
-27227.png
-47944.png
-51275.png
-32103.png
-19203.png
-68371.png
-30756.png
-08634.png
-68473.png
-14863.png
-45609.png
-17830.png
-25021.png
-03097.png
-61718.png
-68278.png
-12123.png
-30625.png
-39439.png
-07261.png
-33601.png
-30094.png
-32773.png
-41191.png
-58222.png
-57328.png
-32813.png
-47050.png
-13430.png
-52131.png
-21705.png
-06551.png
-25767.png
-32450.png
-01871.png
-05449.png
-23005.png
-56001.png
-53185.png
-33374.png
-53963.png
-32085.png
-00345.png
-17273.png
-36447.png
-03786.png
-56808.png
-66090.png
-37533.png
-00526.png
-42837.png
-57017.png
-46125.png
-25712.png
-64852.png
-39171.png
-31148.png
-33311.png
-08597.png
-67342.png
-53082.png
-37106.png
-03077.png
-64466.png
-59157.png
-05527.png
-67414.png
-50595.png
-27726.png
-59248.png
-11333.png
-11336.png
-44535.png
-41316.png
-50251.png
-14212.png
-00976.png
-50936.png
-14899.png
-19538.png
-01942.png
-27389.png
-43039.png
-65924.png
-13300.png
-15021.png
-46389.png
-58990.png
-14560.png
-27459.png
-13423.png
-44779.png
-53800.png
-42918.png
-12415.png
-00929.png
-47937.png
-09683.png
-34156.png
-01019.png
-03978.png
-43221.png
-60323.png
-16205.png
-33770.png
-34594.png
-21155.png
-62933.png
-59013.png
-42121.png
-40549.png
-36621.png
-01149.png
-58504.png
-04233.png
-16588.png
-24368.png
-16229.png
-39451.png
-43139.png
-56914.png
-68926.png
-58749.png
-41784.png
-02669.png
-52313.png
-23304.png
-15493.png
-49166.png
-26134.png
-69492.png
-53186.png
-17127.png
-08878.png
-32537.png
-01955.png
-44089.png
-67681.png
-64370.png
-24400.png
-11776.png
-04832.png
-47587.png
-67654.png
-42953.png
-62749.png
-11792.png
-27829.png
-08497.png
-67255.png
-46343.png
-30811.png
-39051.png
-56825.png
-47601.png
-27223.png
-31322.png
-68465.png
-57800.png
-44540.png
-32828.png
-51381.png
-51570.png
-15131.png
-14707.png
-23067.png
-25364.png
-05573.png
-68418.png
-56262.png
-18657.png
-37539.png
-22380.png
-46926.png
-27788.png
-57100.png
-07668.png
-35005.png
-18694.png
-66965.png
-68034.png
-24953.png
-07793.png
-68880.png
-44631.png
-05723.png
-68823.png
-20758.png
-13650.png
-18562.png
-15644.png
-20949.png
-55825.png
-64175.png
-01874.png
-13965.png
-68889.png
-57430.png
-29827.png
-04788.png
-36367.png
-50373.png
-33364.png
-23150.png
-45506.png
-09221.png
-64151.png
-07166.png
-60884.png
-05648.png
-25836.png
-54373.png
-50987.png
-17668.png
-57356.png
-14437.png
-45072.png
-15389.png
-53814.png
-48239.png
-46886.png
-55242.png
-08758.png
-65963.png
-35958.png
-53874.png
-24751.png
-36480.png
-58560.png
-41308.png
-23820.png
-00357.png
-37775.png
-30655.png
-29009.png
-56115.png
-37450.png
-51789.png
-39085.png
-11443.png
-13399.png
-25389.png
-63970.png
-61196.png
-39291.png
-29404.png
-54406.png
-32925.png
-31979.png
-30580.png
-66178.png
-15092.png
-17461.png
-12736.png
-02674.png
-15297.png
-56114.png
-39961.png
-17667.png
-27021.png
-33127.png
-31894.png
-35146.png
-58769.png
-48806.png
-14001.png
-25035.png
-68425.png
-09989.png
-08088.png
-46493.png
-52205.png
-27004.png
-48839.png
-14083.png
-06547.png
-65239.png
-09749.png
-12880.png
-59308.png
-45757.png
-43450.png
-37477.png
-61871.png
-10295.png
-21450.png
-02586.png
-57826.png
-27631.png
-50565.png
-16973.png
-58760.png
-24501.png
-18130.png
-05294.png
-33501.png
-61547.png
-26677.png
-37797.png
-55664.png
-50523.png
-44298.png
-61279.png
-25313.png
-11959.png
-12944.png
-39903.png
-67748.png
-24794.png
-22637.png
-18780.png
-04908.png
-29351.png
-64152.png
-50747.png
-51334.png
-48489.png
-30399.png
-10951.png
-37804.png
-48752.png
-25753.png
-07336.png
-12702.png
-27426.png
-27304.png
-40280.png
-64837.png
-12694.png
-27634.png
-43955.png
-47949.png
-00063.png
-54604.png
-02982.png
-35586.png
-69369.png
-47486.png
-53106.png
-51484.png
-36735.png
-06380.png
-49338.png
-68779.png
-35775.png
-18047.png
-07378.png
-00032.png
-35952.png
-60362.png
-27114.png
-69122.png
-15605.png
-20869.png
-10833.png
-34256.png
-48989.png
-69998.png
-24748.png
-22567.png
-58856.png
-30446.png
-18623.png
-60909.png
-34952.png
-36098.png
-48048.png
-43417.png
-37266.png
-27151.png
-21600.png
-32436.png
-03751.png
-30030.png
-41457.png
-56715.png
-67595.png
-52012.png
-32754.png
-39372.png
-28190.png
-57570.png
-33282.png
-52562.png
-65617.png
-54579.png
-51737.png
-53363.png
-05984.png
-47274.png
-37933.png
-40970.png
-38241.png
-34216.png
-51520.png
-23203.png
-66091.png
-45391.png
-54960.png
-41584.png
-69847.png
-32000.png
-11540.png
-02800.png
-09755.png
-16198.png
-45943.png
-20157.png
-13984.png
-58998.png
-18550.png
-28275.png
-51670.png
-10678.png
-14227.png
-44754.png
-03732.png
-52717.png
-03169.png
-35538.png
-23138.png
-63961.png
-54120.png
-30420.png
-43496.png
-61878.png
-31108.png
-64830.png
-25912.png
-65279.png
-33644.png
-37740.png
-42170.png
-19002.png
-58083.png
-15841.png
-69863.png
-58120.png
-19270.png
-57078.png
-26022.png
-69407.png
-58236.png
-22896.png
-44731.png
-36923.png
-49841.png
-55069.png
-48755.png
-25744.png
-37127.png
-08327.png
-51895.png
-16675.png
-32723.png
-00632.png
-66116.png
-37174.png
-55049.png
-34710.png
-20257.png
-56723.png
-37336.png
-27870.png
-01130.png
-61546.png
-26494.png
-35907.png
-08514.png
-65684.png
-06203.png
-29068.png
-18919.png
-58842.png
-36656.png
-67889.png
-38192.png
-61907.png
-27935.png
-62126.png
-44404.png
-48533.png
-24186.png
-66152.png
-36170.png
-30553.png
-56232.png
-31908.png
-14156.png
-33599.png
-32248.png
-35547.png
-22827.png
-47942.png
-54620.png
-58203.png
-69411.png
-35261.png
-30664.png
-21517.png
-45169.png
-15756.png
-10131.png
-46825.png
-29123.png
-16347.png
-35402.png
-17971.png
-12148.png
-64352.png
-27883.png
-53730.png
-52036.png
-54244.png
-59546.png
-62514.png
-35040.png
-61350.png
-07530.png
-58075.png
-47285.png
-15859.png
-17725.png
-35513.png
-05067.png
-19566.png
-43944.png
-04580.png
-22203.png
-39396.png
-60993.png
-37486.png
-00226.png
-60440.png
-40454.png
-51478.png
-56023.png
-18414.png
-00130.png
-22014.png
-30850.png
-20140.png
-54608.png
-14339.png
-57352.png
-21195.png
-14248.png
-35142.png
-68978.png
-20066.png
-65907.png
-58415.png
-02439.png
-07206.png
-38502.png
-18434.png
-61671.png
-67978.png
-06415.png
-40530.png
-16209.png
-25917.png
-32872.png
-02398.png
-09639.png
-63293.png
-09694.png
-42706.png
-34526.png
-11530.png
-63586.png
-37734.png
-32740.png
-13885.png
-68166.png
-57521.png
-21844.png
-45063.png
-35826.png
-55667.png
-03153.png
-55135.png
-69476.png
-31382.png
-22000.png
-46311.png
-33968.png
-15746.png
-50825.png
-16124.png
-67774.png
-14060.png
-14579.png
-38970.png
-07291.png
-17437.png
-05633.png
-11493.png
-56077.png
-61065.png
-61273.png
-52814.png
-43135.png
-13706.png
-58663.png
-35822.png
-16358.png
-45908.png
-56738.png
-69797.png
-58461.png
-67970.png
-68201.png
-14796.png
-18825.png
-16847.png
-30115.png
-61470.png
-37299.png
-46571.png
-66377.png
-60023.png
-06589.png
-10617.png
-42635.png
-43900.png
-08442.png
-11184.png
-47416.png
-21009.png
-37568.png
-33675.png
-27102.png
-59390.png
-09007.png
-36028.png
-14160.png
-42286.png
-06041.png
-61185.png
-60906.png
-55484.png
-62232.png
-33203.png
-41269.png
-49174.png
-26097.png
-55975.png
-33043.png
-18179.png
-40836.png
-33303.png
-57071.png
-14425.png
-11356.png
-07653.png
-14997.png
-45145.png
-32760.png
-48526.png
-31295.png
-68358.png
-41556.png
-32194.png
-15485.png
-41020.png
-47370.png
-34564.png
-05182.png
-27828.png
-09837.png
-54565.png
-21919.png
-58404.png
-00633.png
-24739.png
-54313.png
-68855.png
-62744.png
-18438.png
-63124.png
-20102.png
-35268.png
-07664.png
-18363.png
-08036.png
-14638.png
-19943.png
-18668.png
-22883.png
-43338.png
-17972.png
-69495.png
-41980.png
-68498.png
-62570.png
-09669.png
-57859.png
-56779.png
-18251.png
-52189.png
-26096.png
-32402.png
-37507.png
-55830.png
-38879.png
-55559.png
-41049.png
-63423.png
-08427.png
-29619.png
-53883.png
-35828.png
-60184.png
-57750.png
-66719.png
-03387.png
-07023.png
-25771.png
-39578.png
-00639.png
-06730.png
-51058.png
-31574.png
-37738.png
-67790.png
-64238.png
-02219.png
-57828.png
-17906.png
-57759.png
-55254.png
-01305.png
-28339.png
-47409.png
-17636.png
-30552.png
-59464.png
-60632.png
-29627.png
-61277.png
-37197.png
-28798.png
-56632.png
-56967.png
-11578.png
-43939.png
-29869.png
-57565.png
-67316.png
-62555.png
-45828.png
-68218.png
-46419.png
-18858.png
-41035.png
-22865.png
-53037.png
-05674.png
-53370.png
-61087.png
-56389.png
-16066.png
-28447.png
-33972.png
-31736.png
-45628.png
-15699.png
-00199.png
-19325.png
-30865.png
-33876.png
-60159.png
-60276.png
-60662.png
-69992.png
-15058.png
-11425.png
-26991.png
-34871.png
-04256.png
-48072.png
-29055.png
-41341.png
-47162.png
-04072.png
-33922.png
-44286.png
-13556.png
-50938.png
-50772.png
-07956.png
-11685.png
-21286.png
-25765.png
-40100.png
-29733.png
-30752.png
-30757.png
-42199.png
-68944.png
-08956.png
-50378.png
-43904.png
-05632.png
-08804.png
-37844.png
-53057.png
-62814.png
-01340.png
-54704.png
-62370.png
-64979.png
-34818.png
-00783.png
-07099.png
-48285.png
-63796.png
-24678.png
-57464.png
-66314.png
-28983.png
-45478.png
-53863.png
-02587.png
-68426.png
-44818.png
-58539.png
-58467.png
-08807.png
-10706.png
-32642.png
-48962.png
-13638.png
-58739.png
-09273.png
-34692.png
-12896.png
-48835.png
-19734.png
-27422.png
-04633.png
-28466.png
-38157.png
-40991.png
-19108.png
-54416.png
-39365.png
-41067.png
-42385.png
-54171.png
-33887.png
-60520.png
-56319.png
-59286.png
-23566.png
-28187.png
-18594.png
-41385.png
-34634.png
-42662.png
-37181.png
-16993.png
-13121.png
-68041.png
-44876.png
-02174.png
-27446.png
-65281.png
-52683.png
-02017.png
-60045.png
-59817.png
-17512.png
-27400.png
-36836.png
-27911.png
-22005.png
-14599.png
-50560.png
-10669.png
-61406.png
-38149.png
-25855.png
-12784.png
-22741.png
-57719.png
-12140.png
-54809.png
-58469.png
-50915.png
-60530.png
-15880.png
-40826.png
-30532.png
-63176.png
-02526.png
-22217.png
-17799.png
-35272.png
-07743.png
-49688.png
-54887.png
-02281.png
-68072.png
-00011.png
-68016.png
-35810.png
-13787.png
-32055.png
-22798.png
-04906.png
-38222.png
-04873.png
-29359.png
-33620.png
-00482.png
-26615.png
-56029.png
-56958.png
-14819.png
-02552.png
-66757.png
-55520.png
-57655.png
-57051.png
-12914.png
-00630.png
-07544.png
-19836.png
-28628.png
-22287.png
-48234.png
-10052.png
-60175.png
-09517.png
-29014.png
-50325.png
-56329.png
-49523.png
-59902.png
-65773.png
-52987.png
-47321.png
-47251.png
-19895.png
-26153.png
-32470.png
-37768.png
-60408.png
-58858.png
-68525.png
-11886.png
-64302.png
-21227.png
-20067.png
-44845.png
-28830.png
-59246.png
-33221.png
-06976.png
-24055.png
-42276.png
-10595.png
-38823.png
-09873.png
-31759.png
-25956.png
-40901.png
-50608.png
-20524.png
-66031.png
-30384.png
-59485.png
-20516.png
-51632.png
-45286.png
-39176.png
-04636.png
-11260.png
-54665.png
-04354.png
-61932.png
-66375.png
-41845.png
-11787.png
-31332.png
-62499.png
-44225.png
-27294.png
-54057.png
-12219.png
-48812.png
-07004.png
-56873.png
-42362.png
-27882.png
-29013.png
-49718.png
-20435.png
-03438.png
-63227.png
-52465.png
-43870.png
-54603.png
-33533.png
-43858.png
-13069.png
-29158.png
-44375.png
-26930.png
-11832.png
-14442.png
-08797.png
-15356.png
-03218.png
-41375.png
-52808.png
-02402.png
-05211.png
-05681.png
-10428.png
-38751.png
-05752.png
-43973.png
-38942.png
-57127.png
-09910.png
-10894.png
-25073.png
-59449.png
-16838.png
-09166.png
-58780.png
-04612.png
-07015.png
-29153.png
-24558.png
-17857.png
-57409.png
-69473.png
-35687.png
-31154.png
-63462.png
-32718.png
-69928.png
-43120.png
-63067.png
-04987.png
-24816.png
-14756.png
-61610.png
-48178.png
-56052.png
-22135.png
-37500.png
-00294.png
-61128.png
-46459.png
-44622.png
-30960.png
-31563.png
-65917.png
-16910.png
-18958.png
-40477.png
-37064.png
-34828.png
-40686.png
-44915.png
-51463.png
-65760.png
-28540.png
-06892.png
-52885.png
-48273.png
-49279.png
-04403.png
-63279.png
-65287.png
-56912.png
-02038.png
-23327.png
-07671.png
-56178.png
-50157.png
-17187.png
-55003.png
-29781.png
-26802.png
-65142.png
-14208.png
-45585.png
-59322.png
-19245.png
-09044.png
-03711.png
-24139.png
-22846.png
-52478.png
-46726.png
-16592.png
-58523.png
-66144.png
-45518.png
-24611.png
-24719.png
-42148.png
-00736.png
-06670.png
-67228.png
-69583.png
-08560.png
-66372.png
-20895.png
-15423.png
-00741.png
-58060.png
-34863.png
-01103.png
-24738.png
-58072.png
-42266.png
-31238.png
-53458.png
-38682.png
-37218.png
-30623.png
-27062.png
-35745.png
-15289.png
-55923.png
-61200.png
-06857.png
-05269.png
-11677.png
-09194.png
-36196.png
-63004.png
-04295.png
-38142.png
-41483.png
-45216.png
-47904.png
-50367.png
-58331.png
-32398.png
-24782.png
-69081.png
-50994.png
-48897.png
-28854.png
-46694.png
-27906.png
-32198.png
-58961.png
-02255.png
-23342.png
-05879.png
-64077.png
-45969.png
-62120.png
-05128.png
-17677.png
-07384.png
-67995.png
-16546.png
-10155.png
-62409.png
-67200.png
-41312.png
-31165.png
-44303.png
-16061.png
-41558.png
-31379.png
-60534.png
-38044.png
-09129.png
-38468.png
-15033.png
-06772.png
-23676.png
-28370.png
-45915.png
-37791.png
-65425.png
-25126.png
-03470.png
-52031.png
-47310.png
-51016.png
-06430.png
-11016.png
-67256.png
-15825.png
-10242.png
-11529.png
-69512.png
-69695.png
-19077.png
-61765.png
-41222.png
-63298.png
-64204.png
-62680.png
-00652.png
-15484.png
-33478.png
-14358.png
-67025.png
-18714.png
-18088.png
-09325.png
-22411.png
-41002.png
-49065.png
-43465.png
-65790.png
-03511.png
-05094.png
-34406.png
-22023.png
-66833.png
-12161.png
-42747.png
-15026.png
-20448.png
-08538.png
-08774.png
-61573.png
-03261.png
-64775.png
-41132.png
-35970.png
-32393.png
-02071.png
-12353.png
-43579.png
-23938.png
-67041.png
-35241.png
-22018.png
-49666.png
-16541.png
-13140.png
-04601.png
-31998.png
-02009.png
-14213.png
-03389.png
-06374.png
-09499.png
-54933.png
-17154.png
-52698.png
-38865.png
-58377.png
-62558.png
-06177.png
-04847.png
-40291.png
-24540.png
-69538.png
-08738.png
-44798.png
-56890.png
-45664.png
-43525.png
-37812.png
-35886.png
-09572.png
-38336.png
-53541.png
-48039.png
-12910.png
-12607.png
-50904.png
-26984.png
-26596.png
-45640.png
-11462.png
-49955.png
-48661.png
-68882.png
-53420.png
-52925.png
-42984.png
-02795.png
-12094.png
-52371.png
-66557.png
-14118.png
-66999.png
-43096.png
-18635.png
-11447.png
-61151.png
-69533.png
-52849.png
-38630.png
-29348.png
-55852.png
-62316.png
-07517.png
-04825.png
-38310.png
-27549.png
-38847.png
-52206.png
-17580.png
-60182.png
-16490.png
-09155.png
-16119.png
-57122.png
-50710.png
-62218.png
-03875.png
-53776.png
-64349.png
-53566.png
-02225.png
-27445.png
-19212.png
-66771.png
-41505.png
-50293.png
-56512.png
-28146.png
-04133.png
-34431.png
-29770.png
-54224.png
-65642.png
-23198.png
-07068.png
-04377.png
-38763.png
-32490.png
-03780.png
-41518.png
-29540.png
-37119.png
-09569.png
-05900.png
-26103.png
-19344.png
-44060.png
-53513.png
-08363.png
-38039.png
-36985.png
-56127.png
-63063.png
-28290.png
-25162.png
-34928.png
-27937.png
-27260.png
-60036.png
-12082.png
-34739.png
-62023.png
-69652.png
-50239.png
-64421.png
-61768.png
-50025.png
-68839.png
-19965.png
-08176.png
-59468.png
-41150.png
-05495.png
-45440.png
-39006.png
-25567.png
-06553.png
-33594.png
-01050.png
-46959.png
-03449.png
-16038.png
-35185.png
-56187.png
-42438.png
-16936.png
-27012.png
-14294.png
-05564.png
-01736.png
-67431.png
-34879.png
-42221.png
-52367.png
-33833.png
-46335.png
-17218.png
-03767.png
-66184.png
-38591.png
-08370.png
-10111.png
-61622.png
-61197.png
-38152.png
-34395.png
-08870.png
-45267.png
-08720.png
-06139.png
-25357.png
-49873.png
-17163.png
-52558.png
-59998.png
-09642.png
-57633.png
-43769.png
-42185.png
-62357.png
-24696.png
-59850.png
-20203.png
-61439.png
-10739.png
-36183.png
-01468.png
-50781.png
-55156.png
-22015.png
-19267.png
-51096.png
-59984.png
-13901.png
-15822.png
-27779.png
-05793.png
-11060.png
-69815.png
-64951.png
-42839.png
-13332.png
-11751.png
-51983.png
-51177.png
-32914.png
-09859.png
-20938.png
-14435.png
-56353.png
-52618.png
-50796.png
-53801.png
-53475.png
-30109.png
-15284.png
-31191.png
-12028.png
-06485.png
-28705.png
-24880.png
-55272.png
-17873.png
-40636.png
-49348.png
-33473.png
-48764.png
-06390.png
-55919.png
-51563.png
-23432.png
-08710.png
-44774.png
-34191.png
-09966.png
-60687.png
-48368.png
-10197.png
-22976.png
-47565.png
-17594.png
-41915.png
-08508.png
-17121.png
-42577.png
-11050.png
-38921.png
-46258.png
-52682.png
-53162.png
-29965.png
-13225.png
-33735.png
-08174.png
-33656.png
-19150.png
-56876.png
-53282.png
-23946.png
-22312.png
-03216.png
-55782.png
-05904.png
-01991.png
-54945.png
-19525.png
-47434.png
-04417.png
-38746.png
-41743.png
-06605.png
-10460.png
-40026.png
-31823.png
-67243.png
-49708.png
-54347.png
-44640.png
-09106.png
-11294.png
-60936.png
-39828.png
-52648.png
-19677.png
-01544.png
-25794.png
-54509.png
-08411.png
-36122.png
-34241.png
-20470.png
-25838.png
-22146.png
-41421.png
-00377.png
-36362.png
-23041.png
-48554.png
-30509.png
-25664.png
-25530.png
-62027.png
-07564.png
-57484.png
-07385.png
-04896.png
-31828.png
-59833.png
-24415.png
-60903.png
-36831.png
-07195.png
-57161.png
-08991.png
-50481.png
-54473.png
-45779.png
-39794.png
-33267.png
-31267.png
-32513.png
-56767.png
-26630.png
-28506.png
-51961.png
-42112.png
-13749.png
-65351.png
-57898.png
-31890.png
-12755.png
-62574.png
-30859.png
-37967.png
-01715.png
-37685.png
-29593.png
-60929.png
-00703.png
-09970.png
-22209.png
-26179.png
-56477.png
-50583.png
-04191.png
-34226.png
-59354.png
-11991.png
-52099.png
-61184.png
-12892.png
-46015.png
-50292.png
-57977.png
-49729.png
-38742.png
-55260.png
-28437.png
-12844.png
-48696.png
-31496.png
-12087.png
-17549.png
-16287.png
-06805.png
-25070.png
-67375.png
-34761.png
-01218.png
-25641.png
-20735.png
-38840.png
-54701.png
-19022.png
-37320.png
-46792.png
-07146.png
-61774.png
-10648.png
-46466.png
-06127.png
-37938.png
-61209.png
-38924.png
-61664.png
-36355.png
-01046.png
-42515.png
-14679.png
-65522.png
-07107.png
-38963.png
-64109.png
-58747.png
-03621.png
-01629.png
-16511.png
-52843.png
-42676.png
-22649.png
-08529.png
-49319.png
-24340.png
-09152.png
-37161.png
-41689.png
-09969.png
-03824.png
-55029.png
-56246.png
-45744.png
-36542.png
-36230.png
-02989.png
-63055.png
-36119.png
-32930.png
-48251.png
-06273.png
-20583.png
-21677.png
-62191.png
-38443.png
-45299.png
-52213.png
-37443.png
-53832.png
-16616.png
-66026.png
-53473.png
-02287.png
-06980.png
-04558.png
-11921.png
-27274.png
-31822.png
-66787.png
-08914.png
-27262.png
-21532.png
-51112.png
-06698.png
-46595.png
-69310.png
-03320.png
-00822.png
-37789.png
-39024.png
-44825.png
-27802.png
-11168.png
-65328.png
-41565.png
-60601.png
-63996.png
-66932.png
-38866.png
-07056.png
-47854.png
-16958.png
-14183.png
-60616.png
-30134.png
-00860.png
-30230.png
-21288.png
-24850.png
-26510.png
-66638.png
-36758.png
-46643.png
-68640.png
-66396.png
-22898.png
-58730.png
-11212.png
-32530.png
-50399.png
-33798.png
-06746.png
-56289.png
-20754.png
-58598.png
-46094.png
-07264.png
-06725.png
-43118.png
-50180.png
-19558.png
-40445.png
-69331.png
-42302.png
-52656.png
-28609.png
-63304.png
-00987.png
-60060.png
-50510.png
-32395.png
-64999.png
-07390.png
-46835.png
-31517.png
-14251.png
-11187.png
-27714.png
-25899.png
-01480.png
-38290.png
-22763.png
-32146.png
-38360.png
-36532.png
-36161.png
-11396.png
-31501.png
-04214.png
-40605.png
-25861.png
-01636.png
-46656.png
-23897.png
-39932.png
-38202.png
-64907.png
-04927.png
-66359.png
-08265.png
-56027.png
-05549.png
-68251.png
-64939.png
-04527.png
-57733.png
-31198.png
-15055.png
-61915.png
-41047.png
-11025.png
-22957.png
-55823.png
-61120.png
-64477.png
-26770.png
-18970.png
-28867.png
-04448.png
-36499.png
-07510.png
-57280.png
-10634.png
-16873.png
-55523.png
-66986.png
-12264.png
-14745.png
-63118.png
-11582.png
-02904.png
-48400.png
-57442.png
-01409.png
-31010.png
-12440.png
-20554.png
-08491.png
-20279.png
-31420.png
-62450.png
-60519.png
-55817.png
-21798.png
-69227.png
-53255.png
-36662.png
-51113.png
-59544.png
-36926.png
-68394.png
-58040.png
-21376.png
-38523.png
-47890.png
-06832.png
-60048.png
-58572.png
-43413.png
-05579.png
-60504.png
-24489.png
-53844.png
-33933.png
-37149.png
-26622.png
-53796.png
-23728.png
-38072.png
-45572.png
-35060.png
-35353.png
-38315.png
-60749.png
-15522.png
-64353.png
-06328.png
-43752.png
-10620.png
-61767.png
-35006.png
-31806.png
-41619.png
-54220.png
-57843.png
-17928.png
-62271.png
-17715.png
-05047.png
-04829.png
-07319.png
-34627.png
-10533.png
-68114.png
-42010.png
-21888.png
-06913.png
-01883.png
-41783.png
-32148.png
-60093.png
-01324.png
-47314.png
-15688.png
-60890.png
-47441.png
-51067.png
-34397.png
-39552.png
-31752.png
-25337.png
-36918.png
-00665.png
-44713.png
-66331.png
-18101.png
-41244.png
-58731.png
-28563.png
-28423.png
-12652.png
-32192.png
-32250.png
-54437.png
-43117.png
-35985.png
-60604.png
-19958.png
-42204.png
-19036.png
-45926.png
-08123.png
-12829.png
-54273.png
-10769.png
-44548.png
-20333.png
-05002.png
-02984.png
-41462.png
-17518.png
-59589.png
-44511.png
-25509.png
-12565.png
-41353.png
-41409.png
-40165.png
-18963.png
-46054.png
-63693.png
-29360.png
-21930.png
-49424.png
-67935.png
-32676.png
-57272.png
-65503.png
-09332.png
-69996.png
-35690.png
-24553.png
-54745.png
-66876.png
-46346.png
-24694.png
-52014.png
-19539.png
-22137.png
-15986.png
-28114.png
-63000.png
-64046.png
-45054.png
-12963.png
-06080.png
-31211.png
-14273.png
-34147.png
-63121.png
-66010.png
-35171.png
-06429.png
-38048.png
-31415.png
-05333.png
-62874.png
-56360.png
-03795.png
-03815.png
-23656.png
-63610.png
-55763.png
-16222.png
-68630.png
-56482.png
-25660.png
-22818.png
-49185.png
-42443.png
-26147.png
-58413.png
-33273.png
-33993.png
-20552.png
-18100.png
-11014.png
-53091.png
-42899.png
-55786.png
-04177.png
-15913.png
-43245.png
-37962.png
-26191.png
-64845.png
-52911.png
-60810.png
-69037.png
-35368.png
-29410.png
-59333.png
-66339.png
-42980.png
-41181.png
-45407.png
-27623.png
-07549.png
-00792.png
-35573.png
-58810.png
-31660.png
-55117.png
-31911.png
-20715.png
-11849.png
-16545.png
-44700.png
-12678.png
-54467.png
-04786.png
-09833.png
-49557.png
-42588.png
-35128.png
-41322.png
-21150.png
-13516.png
-53999.png
-28470.png
-36648.png
-55760.png
-38457.png
-20608.png
-64842.png
-28657.png
-01590.png
-34699.png
-58432.png
-67303.png
-65365.png
-31144.png
-19079.png
-54264.png
-02887.png
-59798.png
-34016.png
-22764.png
-39020.png
-04476.png
-44256.png
-34224.png
-18231.png
-40141.png
-37520.png
-39208.png
-49002.png
-54098.png
-09644.png
-63857.png
-69013.png
-35105.png
-32334.png
-27604.png
-04066.png
-11045.png
-35456.png
-31184.png
-47248.png
-23153.png
-20433.png
-53670.png
-08778.png
-66203.png
-54375.png
-25560.png
-22085.png
-01649.png
-07926.png
-62913.png
-33291.png
-00446.png
-19471.png
-33172.png
-39053.png
-04254.png
-45170.png
-41066.png
-04962.png
-38906.png
-30066.png
-22193.png
-33904.png
-09423.png
-23328.png
-07907.png
-06475.png
-49300.png
-34023.png
-16073.png
-14958.png
-32134.png
-06930.png
-27355.png
-11572.png
-05032.png
-52833.png
-28947.png
-39390.png
-00162.png
-00709.png
-33020.png
-69201.png
-47614.png
-11614.png
-67082.png
-05788.png
-24403.png
-03430.png
-07683.png
-27649.png
-63660.png
-69151.png
-27048.png
-33901.png
-60376.png
-43033.png
-16435.png
-40698.png
-69353.png
-28924.png
-38200.png
-11373.png
-55854.png
-64732.png
-11384.png
-19900.png
-46413.png
-25570.png
-58055.png
-51230.png
-15547.png
-06339.png
-51832.png
-68966.png
-43133.png
-50398.png
-55394.png
-28101.png
-69525.png
-25122.png
-43753.png
-00547.png
-54452.png
-06404.png
-08643.png
-24309.png
-15035.png
-16252.png
-13012.png
-37724.png
-53740.png
-52266.png
-15214.png
-57162.png
-68601.png
-59278.png
-68471.png
-19603.png
-62205.png
-20592.png
-08196.png
-20386.png
-31766.png
-34515.png
-34854.png
-08532.png
-66639.png
-66906.png
-20722.png
-02555.png
-65289.png
-49911.png
-55056.png
-34603.png
-61146.png
-38246.png
-03961.png
-09953.png
-50489.png
-02210.png
-67236.png
-58005.png
-15152.png
-38558.png
-25072.png
-53781.png
-64558.png
-14444.png
-18914.png
-54033.png
-41069.png
-61207.png
-09670.png
-29295.png
-65744.png
-52466.png
-56799.png
-43856.png
-25754.png
-15752.png
-35569.png
-30862.png
-02276.png
-69489.png
-00572.png
-58520.png
-29103.png
-03283.png
-54614.png
-15671.png
-61645.png
-43225.png
-12013.png
-41329.png
-49938.png
-59896.png
-57559.png
-64804.png
-13466.png
-04499.png
-41600.png
-53891.png
-62145.png
-30402.png
-53839.png
-50482.png
-06215.png
-33857.png
-27322.png
-67641.png
-56133.png
-00761.png
-27789.png
-14813.png
-57598.png
-25713.png
-47613.png
-62179.png
-57114.png
-09612.png
-67766.png
-24549.png
-04362.png
-30659.png
-32466.png
-58621.png
-08366.png
-07139.png
-15500.png
-32458.png
-45620.png
-26101.png
-27662.png
-02530.png
-26471.png
-59647.png
-47957.png
-62681.png
-61148.png
-19178.png
-12386.png
-51033.png
-66875.png
-29530.png
-13592.png
-25919.png
-10484.png
-62502.png
-32797.png
-48675.png
-66510.png
-56521.png
-59182.png
-61486.png
-69527.png
-69694.png
-51395.png
-64754.png
-57775.png
-68939.png
-07434.png
-21828.png
-51689.png
-45989.png
-27335.png
-29042.png
-47472.png
-67024.png
-39334.png
-16106.png
-25764.png
-45763.png
-19549.png
-24300.png
-18455.png
-54148.png
-37709.png
-29378.png
-15820.png
-04779.png
-33890.png
-18874.png
-06123.png
-40448.png
-55998.png
-21553.png
-42455.png
-62921.png
-47751.png
-23906.png
-49517.png
-65997.png
-35279.png
-10480.png
-25265.png
-24978.png
-65299.png
-56590.png
-57752.png
-48070.png
-18563.png
-26879.png
-58099.png
-50703.png
-63950.png
-43091.png
-14137.png
-27712.png
-44290.png
-02279.png
-68124.png
-39537.png
-34832.png
-03819.png
-52494.png
-28635.png
-14484.png
-59499.png
-26856.png
-09662.png
-02491.png
-36472.png
-51725.png
-15095.png
-45505.png
-31785.png
-30986.png
-43980.png
-04373.png
-60324.png
-18750.png
-62712.png
-30582.png
-64580.png
-08005.png
-26055.png
-48913.png
-52319.png
-14472.png
-20536.png
-09928.png
-38829.png
-59511.png
-27384.png
-68528.png
-34240.png
-18587.png
-21712.png
-66799.png
-06856.png
-10762.png
-00003.png
-16913.png
-48582.png
-47254.png
-24161.png
-25748.png
-38155.png
-07110.png
-12758.png
-04953.png
-39270.png
-00228.png
-42373.png
-20100.png
-39817.png
-40805.png
-02955.png
-50498.png
-56415.png
-51856.png
-38120.png
-26732.png
-58927.png
-27657.png
-33826.png
-25798.png
-30657.png
-58859.png
-53284.png
-42910.png
-29501.png
-26406.png
-44494.png
-31056.png
-19439.png
-14961.png
-35024.png
-00618.png
-05074.png
-16401.png
-02423.png
-47309.png
-54471.png
-07905.png
-65057.png
-24911.png
-05301.png
-45852.png
-57495.png
-43281.png
-50106.png
-25951.png
-09374.png
-50725.png
-42470.png
-07229.png
-15241.png
-64684.png
-47157.png
-49628.png
-11711.png
-60478.png
-38346.png
-26265.png
-21108.png
-02086.png
-47033.png
-43837.png
-32236.png
-14190.png
-15247.png
-46635.png
-67473.png
-11606.png
-04017.png
-68943.png
-60615.png
-38342.png
-16337.png
-66419.png
-08000.png
-44383.png
-64771.png
-31194.png
-58160.png
-37780.png
-47841.png
-69284.png
-69101.png
-60461.png
-40408.png
-66219.png
-52108.png
-16791.png
-51557.png
-32812.png
-12314.png
-38904.png
-14553.png
-03299.png
-11220.png
-04393.png
-16225.png
-28538.png
-16055.png
-11586.png
-29313.png
-13709.png
-59955.png
-35243.png
-39027.png
-57897.png
-10292.png
-18902.png
-52968.png
-49007.png
-21015.png
-52149.png
-14397.png
-00415.png
-46095.png
-24375.png
-35136.png
-09951.png
-26164.png
-65071.png
-22586.png
-62618.png
-21577.png
-51094.png
-63933.png
-63384.png
-07394.png
-44154.png
-58830.png
-29994.png
-32514.png
-17184.png
-35963.png
-32830.png
-08943.png
-12136.png
-44282.png
-54858.png
-02373.png
-01248.png
-39079.png
-39452.png
-21955.png
-31670.png
-46220.png
-56490.png
-30457.png
-52938.png
-11293.png
-05754.png
-07630.png
-67733.png
-09304.png
-21752.png
-22866.png
-23751.png
-17405.png
-03602.png
-04773.png
-05820.png
-69576.png
-20712.png
-14381.png
-31715.png
-31554.png
-54334.png
-66668.png
-13168.png
-27998.png
-09931.png
-39798.png
-59194.png
-50905.png
-43740.png
-05944.png
-00777.png
-16379.png
-31439.png
-16014.png
-26528.png
-43147.png
-44792.png
-05393.png
-23794.png
-48040.png
-42060.png
-56790.png
-14298.png
-55078.png
-63682.png
-04603.png
-62187.png
-01192.png
-69357.png
-26071.png
-67616.png
-66836.png
-47900.png
-69883.png
-29776.png
-23466.png
-13445.png
-35598.png
-68137.png
-22867.png
-27560.png
-13058.png
-31222.png
-24890.png
-10402.png
-04486.png
-60393.png
-02278.png
-58943.png
-42654.png
-37663.png
-62890.png
-14494.png
-56175.png
-65392.png
-58996.png
-42754.png
-23270.png
-57008.png
-28350.png
-09067.png
-45122.png
-38122.png
-69886.png
-14833.png
-28782.png
-36904.png
-48003.png
-18925.png
-47528.png
-07174.png
-40046.png
-07334.png
-03063.png
-69271.png
-29760.png
-30308.png
-08791.png
-24369.png
-25824.png
-15107.png
-08596.png
-00067.png
-00540.png
-50060.png
-52381.png
-30924.png
-45558.png
-00391.png
-33448.png
-64388.png
-24280.png
-21807.png
-65864.png
-00973.png
-43906.png
-25452.png
-63974.png
-39031.png
-52650.png
-29597.png
-63562.png
-07556.png
-33176.png
-40320.png
-33196.png
-62136.png
-43392.png
-12741.png
-00141.png
-47341.png
-25043.png
-37940.png
-65626.png
-28249.png
-49555.png
-21003.png
-02019.png
-62886.png
-25378.png
-57305.png
-34930.png
-62804.png
-25009.png
-36718.png
-18593.png
-09496.png
-53729.png
-16016.png
-20990.png
-50820.png
-49999.png
-36351.png
-23632.png
-53676.png
-55875.png
-13014.png
-18326.png
-04591.png
-53712.png
-05362.png
-37939.png
-40144.png
-46209.png
-50415.png
-34503.png
-42207.png
-06293.png
-52362.png
-47135.png
-65373.png
-55717.png
-53428.png
-68214.png
-34897.png
-34684.png
-05668.png
-03563.png
-31180.png
-26260.png
-26695.png
-12424.png
-26156.png
-03757.png
-02412.png
-41877.png
-52373.png
-25103.png
-34720.png
-28031.png
-26207.png
-27681.png
-13990.png
-22639.png
-38957.png
-55020.png
-37472.png
-39891.png
-01824.png
-63017.png
-03937.png
-19723.png
-43881.png
-25541.png
-24821.png
-03525.png
-53166.png
-51065.png
-16982.png
-30760.png
-63343.png
-26689.png
-16510.png
-33903.png
-01958.png
-57531.png
-32268.png
-07622.png
-57240.png
-08729.png
-13446.png
-46746.png
-58641.png
-34288.png
-38472.png
-55435.png
-28097.png
-54803.png
-18215.png
-04438.png
-02625.png
-08588.png
-13160.png
-07965.png
-01610.png
-62447.png
-45873.png
-10451.png
-66952.png
-51026.png
-68703.png
-40373.png
-21602.png
-13053.png
-66679.png
-59835.png
-56376.png
-58738.png
-21053.png
-45115.png
-04309.png
-27063.png
-00085.png
-29626.png
-54000.png
-26858.png
-46608.png
-50614.png
-01420.png
-46489.png
-54612.png
-64127.png
-04720.png
-65668.png
-55479.png
-17245.png
-17795.png
-34065.png
-33287.png
-49264.png
-23627.png
-43295.png
-10999.png
-41452.png
-59593.png
-48225.png
-33784.png
-38114.png
-49169.png
-01529.png
-13762.png
-08182.png
-53735.png
-52424.png
-08823.png
-63854.png
-04371.png
-54073.png
-54674.png
-06489.png
-08049.png
-57996.png
-11838.png
-34823.png
-16308.png
-05910.png
-26409.png
-08020.png
-10268.png
-16856.png
-42728.png
-66140.png
-00587.png
-45334.png
-30762.png
-14476.png
-54158.png
-61986.png
-37307.png
-68683.png
-19737.png
-49692.png
-42298.png
-57513.png
-66752.png
-39132.png
-59769.png
-68958.png
-42959.png
-12890.png
-55481.png
-35144.png
-56009.png
-31298.png
-20064.png
-17297.png
-22859.png
-35442.png
-51561.png
-15527.png
-04491.png
-27031.png
-17093.png
-06343.png
-14671.png
-47401.png
-15044.png
-37037.png
-45920.png
-30627.png
-19174.png
-21239.png
-21665.png
-00247.png
-61329.png
-07248.png
-00043.png
-06732.png
-04123.png
-11410.png
-05348.png
-29912.png
-38704.png
-11741.png
-43536.png
-41762.png
-02160.png
-50248.png
-65364.png
-51920.png
-37878.png
-20978.png
-00988.png
-53050.png
-20521.png
-08067.png
-66815.png
-46187.png
-29752.png
-55382.png
-36580.png
-57648.png
-23918.png
-47422.png
-00625.png
-64588.png
-53070.png
-53945.png
-55858.png
-61772.png
-69192.png
-20962.png
-27413.png
-43256.png
-30429.png
-56978.png
-05364.png
-40789.png
-64602.png
-23815.png
-41560.png
-38783.png
-50746.png
-58912.png
-43137.png
-10658.png
-48567.png
-26365.png
-25239.png
-52473.png
-45470.png
-50932.png
-47879.png
-68349.png
-66151.png
-34135.png
-57448.png
-17217.png
-38164.png
-19783.png
-25971.png
-33813.png
-23850.png
-34993.png
-06169.png
-47318.png
-65839.png
-38851.png
-37825.png
-25545.png
-62935.png
-57693.png
-26809.png
-54481.png
-05757.png
-49813.png
-57196.png
-03185.png
-13227.png
-48762.png
-48870.png
-28616.png
-42989.png
-37350.png
-03126.png
-49217.png
-04376.png
-10062.png
-03475.png
-45411.png
-63916.png
-49982.png
-44925.png
-41253.png
-05257.png
-05603.png
-57646.png
-23403.png
-32453.png
-62286.png
-41383.png
-15770.png
-20830.png
-47696.png
-24835.png
-28450.png
-44116.png
-40322.png
-40450.png
-05024.png
-47066.png
-53132.png
-36868.png
-16781.png
-34538.png
-08256.png
-17981.png
-51051.png
-63239.png
-69002.png
-14078.png
-67370.png
-14286.png
-36922.png
-49862.png
-26618.png
-34768.png
-21085.png
-41185.png
-24407.png
-37739.png
-55456.png
-11852.png
-48901.png
-30790.png
-40436.png
-29406.png
-59228.png
-22328.png
-29957.png
-61038.png
-13521.png
-33525.png
-63025.png
-64304.png
-57782.png
-21715.png
-36022.png
-19135.png
-29301.png
-52195.png
-53522.png
-05861.png
-03057.png
-19759.png
-10416.png
-59495.png
-35772.png
-06691.png
-46686.png
-38372.png
-21104.png
-25229.png
-38432.png
-28527.png
-36887.png
-05252.png
-07176.png
-46313.png
-52768.png
-14700.png
-02483.png
-41235.png
-18348.png
-04542.png
-56076.png
-07744.png
-33848.png
-12385.png
-51074.png
-67787.png
-38299.png
-18743.png
-49161.png
-34593.png
-41721.png
-67841.png
-52107.png
-02321.png
-29048.png
-35210.png
-11512.png
-57381.png
-62343.png
-20933.png
-26124.png
-27449.png
-44533.png
-04941.png
-58864.png
-36466.png
-29293.png
-65223.png
-59490.png
-67972.png
-14130.png
-51726.png
-61980.png
-17664.png
-32245.png
-41095.png
-46235.png
-42152.png
-16583.png
-01053.png
-59386.png
-57154.png
-40209.png
-12115.png
-10718.png
-17327.png
-34620.png
-20401.png
-40898.png
-41931.png
-06272.png
-59709.png
-55822.png
-56352.png
-58170.png
-19204.png
-37926.png
-38511.png
-54605.png
-01932.png
-04699.png
-21710.png
-62742.png
-23614.png
-22382.png
-55161.png
-14883.png
-33324.png
-26353.png
-40639.png
-33886.png
-47600.png
-34482.png
-05553.png
-05156.png
-52800.png
-58442.png
-22250.png
-57469.png
-14499.png
-59564.png
-42491.png
-57489.png
-56936.png
-43056.png
-02457.png
-39222.png
-30267.png
-14633.png
-49648.png
-54162.png
-12404.png
-17923.png
-00135.png
-64882.png
-67535.png
-38553.png
-29584.png
-43073.png
-09078.png
-15456.png
-26341.png
-61361.png
-56370.png
-14073.png
-33793.png
-05339.png
-48648.png
-49877.png
-44642.png
-08581.png
-39309.png
-27794.png
-13472.png
-17214.png
-00384.png
-32196.png
-53806.png
-26053.png
-29178.png
-66274.png
-41391.png
-52457.png
-04843.png
-30782.png
-34116.png
-69894.png
-28462.png
-16574.png
-07217.png
-04840.png
-27755.png
-65508.png
-48679.png
-52665.png
-65647.png
-07872.png
-33722.png
-15315.png
-53079.png
-20422.png
-40227.png
-54368.png
-11128.png
-28481.png
-46203.png
-65554.png
-46128.png
-14277.png
-40310.png
-17056.png
-51942.png
-19527.png
-53610.png
-44099.png
-48031.png
-54618.png
-37989.png
-61849.png
-32064.png
-34434.png
-58851.png
-08080.png
-67498.png
-46670.png
-34115.png
-56041.png
-32317.png
-38133.png
-65778.png
-08931.png
-21359.png
-52864.png
-06461.png
-46366.png
-66628.png
-07377.png
-04108.png
-52691.png
-19540.png
-65193.png
-14708.png
-02580.png
-60751.png
-08436.png
-12936.png
-00139.png
-33567.png
-41073.png
-02534.png
-41979.png
-52898.png
-31999.png
-48047.png
-33068.png
-39529.png
-43487.png
-51066.png
-10262.png
-53019.png
-17125.png
-54440.png
-29257.png
-34898.png
-20785.png
-55318.png
-65640.png
-12376.png
-31974.png
-55397.png
-60120.png
-60827.png
-22621.png
-55840.png
-10551.png
-53248.png
-18066.png
-51209.png
-58348.png
-26033.png
-26876.png
-67328.png
-02116.png
-67142.png
-53624.png
-62991.png
-51772.png
-29166.png
-42593.png
-12204.png
-38629.png
-48371.png
-42569.png
-02655.png
-60352.png
-29003.png
-34183.png
-61252.png
-60426.png
-04662.png
-68301.png
-05084.png
-47868.png
-47301.png
-26664.png
-26587.png
-51858.png
-43208.png
-65764.png
-40812.png
-02097.png
-25644.png
-22873.png
-45515.png
-00882.png
-05409.png
-56726.png
-31914.png
-54705.png
-42961.png
-66983.png
-34361.png
-08853.png
-52350.png
-54186.png
-59809.png
-04899.png
-19314.png
-23675.png
-44356.png
-67294.png
-63589.png
-46207.png
-34303.png
-09830.png
-43538.png
-38147.png
-00049.png
-19028.png
-24554.png
-28580.png
-53761.png
-65031.png
-12125.png
-39360.png
-14237.png
-37932.png
-65458.png
-55801.png
-14640.png
-68803.png
-04363.png
-25131.png
-05325.png
-40207.png
-44149.png
-64277.png
-03576.png
-36103.png
-19103.png
-31493.png
-00879.png
-21122.png
-06643.png
-39048.png
-46581.png
-68712.png
-37918.png
-56153.png
-05342.png
-28633.png
-13114.png
-20301.png
-30150.png
-53001.png
-40453.png
-65975.png
-61347.png
-39455.png
-39149.png
-23830.png
-01471.png
-41729.png
-33919.png
-17368.png
-56311.png
-33391.png
-48650.png
-60395.png
-63018.png
-22128.png
-51130.png
-65375.png
-40621.png
-19258.png
-17896.png
-09625.png
-68869.png
-09605.png
-67092.png
-66889.png
-16522.png
-23364.png
-56606.png
-38484.png
-30192.png
-24281.png
-17082.png
-38949.png
-66128.png
-21560.png
-21506.png
-52555.png
-31449.png
-14527.png
-51260.png
-14639.png
-64809.png
-27298.png
-30781.png
-46163.png
-13467.png
-61631.png
-67965.png
-43136.png
-25176.png
-23965.png
-56151.png
-61773.png
-33884.png
-12297.png
-07740.png
-59669.png
-24508.png
-41779.png
-05675.png
-21084.png
-09306.png
-32770.png
-42092.png
-18150.png
-14230.png
-07637.png
-23134.png
-34030.png
-10815.png
-55555.png
-08466.png
-53202.png
-23601.png
-27781.png
-61876.png
-34810.png
-49754.png
-20547.png
-08550.png
-58643.png
-65944.png
-22843.png
-03275.png
-14152.png
-00039.png
-32782.png
-43327.png
-37333.png
-21114.png
-15335.png
-01774.png
-64267.png
-14586.png
-54124.png
-54464.png
-43862.png
-24418.png
-50134.png
-32576.png
-21751.png
-58923.png
-22536.png
-40750.png
-47218.png
-36844.png
-17146.png
-64709.png
-65179.png
-29691.png
-52948.png
-21908.png
-48455.png
-61182.png
-03174.png
-54722.png
-17737.png
-26872.png
-55157.png
-01677.png
-41348.png
-06084.png
-25354.png
-37423.png
-53944.png
-25927.png
-56176.png
-15511.png
-48403.png
-05278.png
-20801.png
-41726.png
-01548.png
-07409.png
-05509.png
-06403.png
-49889.png
-50636.png
-23066.png
-66943.png
-21556.png
-40283.png
-01354.png
-46771.png
-37469.png
-55194.png
-24447.png
-40640.png
-03162.png
-15821.png
-67798.png
-16128.png
-05699.png
-51746.png
-58815.png
-36753.png
-41257.png
-43328.png
-30365.png
-52820.png
-15896.png
-15839.png
-41999.png
-50799.png
-12069.png
-60104.png
-52458.png
-61883.png
-64794.png
-54664.png
-01599.png
-50231.png
-46117.png
-44348.png
-28258.png
-58498.png
-55221.png
-24164.png
-09232.png
-10535.png
-69530.png
-48022.png
-18840.png
-06984.png
-55346.png
-60747.png
-69555.png
-45931.png
-06821.png
-21581.png
-57691.png
-10408.png
-48906.png
-12682.png
-02135.png
-34829.png
-57518.png
-26262.png
-23299.png
-48941.png
-52514.png
-20393.png
-04756.png
-39566.png
-22428.png
-63532.png
-66071.png
-54724.png
-39220.png
-56939.png
-32024.png
-67997.png
-48566.png
-32511.png
-09100.png
-29703.png
-37363.png
-08437.png
-02298.png
-63028.png
-45856.png
-04727.png
-69374.png
-21847.png
-24672.png
-41172.png
-66025.png
-68395.png
-69349.png
-19218.png
-41369.png
-30452.png
-07468.png
-54730.png
-27243.png
-38920.png
-53119.png
-06357.png
-49430.png
-45379.png
-57119.png
-23774.png
-19495.png
-07703.png
-26018.png
-12114.png
-05299.png
-54763.png
-02699.png
-05792.png
-62953.png
-10968.png
-02324.png
-28134.png
-23175.png
-28838.png
-18514.png
-61791.png
-62076.png
-35567.png
-33230.png
-00631.png
-50916.png
-14112.png
-07536.png
-66055.png
-44070.png
-69244.png
-65862.png
-37301.png
-13190.png
-12235.png
-11659.png
-56266.png
-46528.png
-44550.png
-41511.png
-65320.png
-01825.png
-27432.png
-52942.png
-20836.png
-04915.png
-10752.png
-61412.png
-44321.png
-35748.png
-40352.png
-43836.png
-66199.png
-22974.png
-10983.png
-50828.png
-24996.png
-61085.png
-30629.png
-03295.png
-66539.png
-19886.png
-10586.png
-39971.png
-06592.png
-30227.png
-03688.png
-61331.png
-00933.png
-50999.png
-10776.png
-59306.png
-10373.png
-03240.png
-58341.png
-23504.png
-44146.png
-42253.png
-47885.png
-26920.png
-53955.png
-28573.png
-09466.png
-29871.png
-24783.png
-20175.png
-17793.png
-56696.png
-25543.png
-09340.png
-51536.png
-00450.png
-04645.png
-60539.png
-12241.png
-23589.png
-29706.png
-15461.png
-10577.png
-05023.png
-66000.png
-40798.png
-09294.png
-22233.png
-49212.png
-05831.png
-53772.png
-23907.png
-49034.png
-43502.png
-05533.png
-00899.png
-25821.png
-39633.png
-32407.png
-44444.png
-42477.png
-30956.png
-28063.png
-09235.png
-33318.png
-09086.png
-07813.png
-42745.png
-64991.png
-57472.png
-48356.png
-29023.png
-53430.png
-07081.png
-38727.png
-43908.png
-60394.png
-55378.png
-53807.png
-34730.png
-26817.png
-59077.png
-53952.png
-40800.png
-19869.png
-53313.png
-14445.png
-58799.png
-18028.png
-29927.png
-39686.png
-17610.png
-07272.png
-03416.png
-25418.png
-50238.png
-27566.png
-00905.png
-53928.png
-37095.png
-56075.png
-24957.png
-00230.png
-44142.png
-63773.png
-17011.png
-15171.png
-23797.png
-64380.png
-29681.png
-37136.png
-52303.png
-51302.png
-04989.png
-55818.png
-69888.png
-02866.png
-48490.png
-59710.png
-08307.png
-05923.png
-44960.png
-40823.png
-64515.png
-34622.png
-04536.png
-12127.png
-25353.png
-26422.png
-13792.png
-41476.png
-39904.png
-19688.png
-36810.png
-68819.png
-45998.png
-18218.png
-10137.png
-56277.png
-26940.png
-29248.png
-17453.png
-51648.png
-60378.png
-56611.png
-20088.png
-33628.png
-18787.png
-60743.png
-25922.png
-31808.png
-10673.png
-62494.png
-49846.png
-51771.png
-46008.png
-58900.png
-35340.png
-22614.png
-55284.png
-04463.png
-19060.png
-44976.png
-35912.png
-57156.png
-16154.png
-37283.png
-11684.png
-01125.png
-09020.png
-45509.png
-10009.png
-63690.png
-24314.png
-20293.png
-13701.png
-48930.png
-35808.png
-65380.png
-00601.png
-15018.png
-03821.png
-02319.png
-40510.png
-26595.png
-49822.png
-62566.png
-42650.png
-05426.png
-01868.png
-11193.png
-21153.png
-55719.png
-29105.png
-05947.png
-66442.png
-10005.png
-12984.png
-20316.png
-59041.png
-41344.png
-18075.png
-32764.png
-05427.png
-63724.png
-14501.png
-59990.png
-45468.png
-13819.png
-08015.png
-12377.png
-57536.png
-32757.png
-60225.png
-52186.png
-39409.png
-43618.png
-42549.png
-16620.png
-30207.png
-59972.png
-56847.png
-57990.png
-55286.png
-59981.png
-06365.png
-47048.png
-28476.png
-63453.png
-10511.png
-58486.png
-31611.png
-40476.png
-63775.png
-26971.png
-07179.png
-35229.png
-69745.png
-09600.png
-32529.png
-48001.png
-15867.png
-25344.png
-05919.png
-69711.png
-38417.png
-04396.png
-10238.png
-60774.png
-52269.png
-32039.png
-30484.png
-19957.png
-33899.png
-57015.png
-65978.png
-36716.png
-27670.png
-05343.png
-11908.png
-61285.png
-16780.png
-61952.png
-62860.png
-49204.png
-12653.png
-62464.png
-15072.png
-09570.png
-08777.png
-61572.png
-39146.png
-65536.png
-62228.png
-13618.png
-51959.png
-27572.png
-56955.png
-51315.png
-54703.png
-19012.png
-00208.png
-58826.png
-27595.png
-05907.png
-66187.png
-15293.png
-22931.png
-39202.png
-42512.png
-61769.png
-38340.png
-53213.png
-52908.png
-63611.png
-22007.png
-44815.png
-16364.png
-63560.png
-15371.png
-52285.png
-69842.png
-41814.png
-21802.png
-23096.png
-14774.png
-16150.png
-35789.png
-11501.png
-21815.png
-19925.png
-27982.png
-04967.png
-27463.png
-22975.png
-08837.png
-49470.png
-66133.png
-55497.png
-68932.png
-10427.png
-30683.png
-39684.png
-24636.png
-21188.png
-35809.png
-24645.png
-50673.png
-36311.png
-22970.png
-40336.png
-62477.png
-08749.png
-08562.png
-27960.png
-68867.png
-05653.png
-49646.png
-52425.png
-11845.png
-42896.png
-24772.png
-11951.png
-52510.png
-28574.png
-00050.png
-54266.png
-27576.png
-31830.png
-04113.png
-17801.png
-53488.png
-26803.png
-01185.png
-02161.png
-12126.png
-44219.png
-65514.png
-35134.png
-15608.png
-63207.png
-59411.png
-46162.png
-58854.png
-48232.png
-26721.png
-02516.png
-47872.png
-17937.png
-22748.png
-22936.png
-08700.png
-23051.png
-08102.png
-56492.png
-69003.png
-23888.png
-24973.png
-57509.png
-11067.png
-09973.png
-62200.png
-34661.png
-13948.png
-10419.png
-12112.png
-55136.png
-46233.png
-26386.png
-15632.png
-14362.png
-63902.png
-54789.png
-49767.png
-45264.png
-57268.png
-42335.png
-65631.png
-01792.png
-14993.png
-02860.png
-35700.png
-07187.png
-15660.png
-24563.png
-04813.png
-49505.png
-46999.png
-62104.png
-58519.png
-31831.png
-10336.png
-29714.png
-59305.png
-39412.png
-27320.png
-12467.png
-63301.png
-50242.png
-11771.png
-52243.png
-57075.png
-50496.png
-09378.png
-23022.png
-56720.png
-00512.png
-22552.png
-11350.png
-54239.png
-64402.png
-17258.png
-36857.png
-33548.png
-33911.png
-35692.png
-18073.png
-46103.png
-21055.png
-39075.png
-45364.png
-62062.png
-34750.png
-28393.png
-57236.png
-67703.png
-61979.png
-58960.png
-12686.png
-25538.png
-55914.png
-40168.png
-03270.png
-05690.png
-54112.png
-66974.png
-49503.png
-50366.png
-35501.png
-51759.png
-29458.png
-31313.png
-66472.png
-31357.png
-04414.png
-34062.png
-55779.png
-51192.png
-09920.png
-12434.png
-37366.png
-27285.png
-04643.png
-56549.png
-67816.png
-32815.png
-19444.png
-21834.png
-64512.png
-46345.png
-01990.png
-60125.png
-57197.png
-59256.png
-53881.png
-42132.png
-14576.png
-01860.png
-62267.png
-21655.png
-15028.png
-33988.png
-67610.png
-01191.png
-00719.png
-15719.png
-57336.png
-67398.png
-47977.png
-18863.png
-52028.png
-05115.png
-41606.png
-15690.png
-02382.png
-68123.png
-18996.png
-08570.png
-62198.png
-62008.png
-58505.png
-52501.png
-06479.png
-09700.png
-56219.png
-31279.png
-53337.png
-09094.png
-48426.png
-44501.png
-28556.png
-33214.png
-38569.png
-30261.png
-69397.png
-01323.png
-14609.png
-63651.png
-10269.png
-56106.png
-50591.png
-33757.png
-53454.png
-00451.png
-32310.png
-48933.png
-03918.png
-69076.png
-27199.png
-50867.png
-19163.png
-44167.png
-36650.png
-34787.png
-19909.png
-35274.png
-10025.png
-27388.png
-23636.png
-08952.png
-29392.png
-18118.png
-25734.png
-39251.png
-37560.png
-11172.png
-38194.png
-10227.png
-66341.png
-39530.png
-29269.png
-66712.png
-34415.png
-14921.png
-27953.png
-64790.png
-18030.png
-37537.png
-55675.png
-48867.png
-38749.png
-68868.png
-57636.png
-58846.png
-26149.png
-65909.png
-14249.png
-29891.png
-15427.png
-51161.png
-36859.png
-30503.png
-07783.png
-51665.png
-68812.png
-66715.png
-12802.png
-52596.png
-34409.png
-49559.png
-14025.png
-31612.png
-48953.png
-24458.png
-01374.png
-63256.png
-43047.png
-11205.png
-24124.png
-48240.png
-11505.png
-21304.png
-68234.png
-23149.png
-64507.png
-58728.png
-18539.png
-28605.png
-12222.png
-13886.png
-67929.png
-60885.png
-00035.png
-56962.png
-66304.png
-36282.png
-57157.png
-31733.png
-28677.png
-47295.png
-50732.png
-01881.png
-02120.png
-38067.png
-32114.png
-29588.png
-44018.png
-24673.png
-23241.png
-32186.png
-69456.png
-17395.png
-39096.png
-51388.png
-29064.png
-48594.png
-02975.png
-62030.png
-31257.png
-38967.png
-47183.png
-17143.png
-06200.png
-57232.png
-20747.png
-49025.png
-06224.png
-27265.png
-29432.png
-04622.png
-21826.png
-21703.png
-25588.png
-34799.png
-16020.png
-35981.png
-05585.png
-68742.png
-15874.png
-46286.png
-09485.png
-09914.png
-54519.png
-50054.png
-21374.png
-58385.png
-32410.png
-69891.png
-44524.png
-26100.png
-50808.png
-67829.png
-06567.png
-01963.png
-07913.png
-56973.png
-19851.png
-21896.png
-46766.png
-30989.png
-01660.png
-27417.png
-46987.png
-33133.png
-42705.png
-51309.png
-03718.png
-09715.png
-68356.png
-23733.png
-02900.png
-60683.png
-10760.png
-39109.png
-39481.png
-49192.png
-58584.png
-51841.png
-64266.png
-14457.png
-23320.png
-02721.png
-62801.png
-18685.png
-10901.png
-49422.png
-41005.png
-50983.png
-57389.png
-18430.png
-12665.png
-03230.png
-47138.png
-02545.png
-53775.png
-47453.png
-13384.png
-34449.png
-06813.png
-45751.png
-61160.png
-34020.png
-60926.png
-49333.png
-34953.png
-27746.png
-40541.png
-08746.png
-06320.png
-69925.png
-13383.png
-12833.png
-55005.png
-56592.png
-54999.png
-66727.png
-29981.png
-59404.png
-69112.png
-35942.png
-67886.png
-09790.png
-63058.png
-04388.png
-26530.png
-23798.png
-40460.png
-31200.png
-67507.png
-57041.png
-16995.png
-25810.png
-28598.png
-47305.png
-24992.png
-06604.png
-29683.png
-48193.png
-67340.png
-41970.png
-18696.png
-10378.png
-57170.png
-19345.png
-34802.png
-14999.png
-12318.png
-63439.png
-46384.png
-19251.png
-08303.png
-29896.png
-10200.png
-07597.png
-12231.png
-09035.png
-33283.png
-08996.png
-57756.png
-42287.png
-47959.png
-08337.png
-66744.png
-64428.png
-16804.png
-22977.png
-43466.png
-20574.png
-69564.png
-50648.png
-11585.png
-47324.png
-31135.png
-31690.png
-18312.png
-04602.png
-65632.png
-69493.png
-26013.png
-10041.png
-67778.png
-17285.png
-65208.png
-40349.png
-14214.png
-29175.png
-24210.png
-22817.png
-41728.png
-06104.png
-37365.png
-47994.png
-46137.png
-27875.png
-17438.png
-51905.png
-32794.png
-09904.png
-51132.png
-35827.png
-02718.png
-15421.png
-12775.png
-61276.png
-34372.png
-59916.png
-55438.png
-50712.png
-51283.png
-17026.png
-11616.png
-39104.png
-29976.png
-60930.png
-33062.png
-14424.png
-68190.png
-31654.png
-08459.png
-17186.png
-32190.png
-48859.png
-06724.png
-63927.png
-25403.png
-02824.png
-06050.png
-67830.png
-07008.png
-40014.png
-40108.png
-62714.png
-00702.png
-51458.png
-38762.png
-16085.png
-15855.png
-56727.png
-23872.png
-33086.png
-26981.png
-55019.png
-43214.png
-39873.png
-34971.png
-05927.png
-31671.png
-07427.png
-12501.png
-46527.png
-42390.png
-37858.png
-09505.png
-28656.png
-33524.png
-12053.png
-58154.png
-63596.png
-65940.png
-61615.png
-01081.png
-53447.png
-62264.png
-68432.png
-31102.png
-41402.png
-67545.png
-19712.png
-66698.png
-33844.png
-61294.png
-31947.png
-43974.png
-57705.png
-47521.png
-68156.png
-61897.png
-32456.png
-41335.png
-36023.png
-68192.png
-21261.png
-13542.png
-51448.png
-36572.png
-10170.png
-66260.png
-27233.png
-00865.png
-32011.png
-46693.png
-29213.png
-17289.png
-13016.png
-25273.png
-46270.png
-47436.png
-02979.png
-05436.png
-31498.png
-16965.png
-56997.png
-67836.png
-13699.png
-10702.png
-43542.png
-35746.png
-06961.png
-32962.png
-59438.png
-55395.png
-03123.png
-16161.png
-01916.png
-20317.png
-04657.png
-06679.png
-17331.png
-35824.png
-43816.png
-25474.png
-62446.png
-02957.png
-14651.png
-28519.png
-49109.png
-34279.png
-07594.png
-09045.png
-13730.png
-39908.png
-24320.png
-05412.png
-36822.png
-48820.png
-51857.png
-31447.png
-52662.png
-67015.png
-28046.png
-00277.png
-08494.png
-69008.png
-12871.png
-66061.png
-02215.png
-40989.png
-21987.png
-30341.png
-23664.png
-37212.png
-46132.png
-58266.png
-59999.png
-57667.png
-53794.png
-64026.png
-03215.png
-34447.png
-06086.png
-08788.png
-61344.png
-40391.png
-13665.png
-04170.png
-02758.png
-05029.png
-04172.png
-46492.png
-01903.png
-53025.png
-59939.png
-62579.png
-30953.png
-26293.png
-57424.png
-30007.png
-01875.png
-13308.png
-43299.png
-51157.png
-69848.png
-13143.png
-58824.png
-51867.png
-36998.png
-00836.png
-45626.png
-60663.png
-37404.png
-51351.png
-19096.png
-63800.png
-21129.png
-60348.png
-17153.png
-08441.png
-66562.png
-58898.png
-62478.png
-42663.png
-12355.png
-42337.png
-02031.png
-12072.png
-18788.png
-06495.png
-48298.png
-24805.png
-09149.png
-10893.png
-02739.png
-25676.png
-62554.png
-55334.png
-03427.png
-46646.png
-19810.png
-01705.png
-59304.png
-14481.png
-20657.png
-00328.png
-54402.png
-60639.png
-22665.png
-57258.png
-13891.png
-06611.png
-43203.png
-05415.png
-63608.png
-19761.png
-36953.png
-02525.png
-13681.png
-15248.png
-19489.png
-26074.png
-40213.png
-59965.png
-08149.png
-40513.png
-68342.png
-65258.png
-26552.png
-49582.png
-12368.png
-13303.png
-26005.png
-52915.png
-56478.png
-67957.png
-47389.png
-05976.png
-15832.png
-17175.png
-08557.png
-49445.png
-51346.png
-67465.png
-20152.png
-26245.png
-12320.png
-13585.png
-06931.png
-18070.png
-08843.png
-68924.png
-64710.png
-21570.png
-31013.png
-16419.png
-14404.png
-58819.png
-65501.png
-52184.png
-03025.png
-33327.png
-63881.png
-18880.png
-33257.png
-49005.png
-62287.png
-39405.png
-47976.png
-26336.png
-37585.png
-50803.png
-67047.png
-59766.png
-29494.png
-42812.png
-24761.png
-05851.png
-05118.png
-22715.png
-24135.png
-05667.png
-11924.png
-00881.png
-14471.png
-41526.png
-29496.png
-06701.png
-67573.png
-56918.png
-16851.png
-03395.png
-45613.png
-04266.png
-31474.png
-14102.png
-59238.png
-41990.png
-37800.png
-05827.png
-14274.png
-63862.png
-03476.png
-57858.png
-26877.png
-14877.png
-06341.png
-47802.png
-31306.png
-63759.png
-67942.png
-66808.png
-62611.png
-25301.png
-13779.png
-03422.png
-62331.png
-55124.png
-31319.png
-30720.png
-26197.png
-48322.png
-67600.png
-45829.png
-63224.png
-36223.png
-65583.png
-69637.png
-49407.png
-38639.png
-61606.png
-68539.png
-05108.png
-46387.png
-32015.png
-31750.png
-52564.png
-30464.png
-34055.png
-50286.png
-64420.png
-02991.png
-09552.png
-02316.png
-38075.png
-53766.png
-47122.png
-39330.png
-56769.png
-43924.png
-56582.png
-37033.png
-67872.png
-18356.png
-63339.png
-61697.png
-19969.png
-64404.png
-43403.png
-19088.png
-37162.png
-52380.png
-25785.png
-01429.png
-42090.png
-60858.png
-56208.png
-33586.png
-39931.png
-52401.png
-30424.png
-27079.png
-38930.png
-65722.png
-16773.png
-02449.png
-24012.png
-52023.png
-09483.png
-01279.png
-17617.png
-68380.png
-65886.png
-56104.png
-17840.png
-17499.png
-37947.png
-13907.png
-38600.png
-19579.png
-25090.png
-15457.png
-24714.png
-36050.png
-13477.png
-02577.png
-12534.png
-31160.png
-02000.png
-17932.png
-17963.png
-17747.png
-30472.png
-55881.png
-64726.png
-47829.png
-33357.png
-06628.png
-22983.png
-51826.png
-33632.png
-03811.png
-11993.png
-34362.png
-03341.png
-00207.png
-67901.png
-01658.png
-47467.png
-05177.png
-05151.png
-11372.png
-51727.png
-64748.png
-37618.png
-36900.png
-07547.png
-16526.png
-66498.png
-63144.png
-62901.png
-39726.png
-12661.png
-09666.png
-21384.png
-00842.png
-44710.png
-47639.png
-63688.png
-30621.png
-03363.png
-64378.png
-31336.png
-38285.png
-60667.png
-27018.png
-19335.png
-60787.png
-47724.png
-44778.png
-03499.png
-30527.png
-08002.png
-61694.png
-53555.png
-69835.png
-33788.png
-23950.png
-61478.png
-05655.png
-09284.png
-48920.png
-19537.png
-23217.png
-55370.png
-65292.png
-24319.png
-67008.png
-64372.png
-04118.png
-23885.png
-43081.png
-53276.png
-19426.png
-56434.png
-17282.png
-61381.png
-07180.png
-57478.png
-54866.png
-51871.png
-48217.png
-51452.png
-01533.png
-56728.png
-01148.png
-54790.png
-48418.png
-55811.png
-40993.png
-22989.png
-49926.png
-50414.png
-25163.png
-41924.png
-39305.png
-20587.png
-43527.png
-12129.png
-62410.png
-36812.png
-16677.png
-20581.png
-60295.png
-21481.png
-40751.png
-38187.png
-69736.png
-05401.png
-48397.png
-41834.png
-50855.png
-49026.png
-01771.png
-66748.png
-27259.png
-61459.png
-45694.png
-09711.png
-58762.png
-49486.png
-11971.png
-57938.png
-62709.png
-13150.png
-49168.png
-18867.png
-13174.png
-02400.png
-22814.png
-56483.png
-06639.png
-60483.png
-19015.png
-43321.png
-10926.png
-18749.png
-56057.png
-67274.png
-19130.png
-61157.png
-20421.png
-22465.png
-17703.png
-33527.png
-03857.png
-61121.png
-15170.png
-48435.png
-49042.png
-07224.png
-04230.png
-62958.png
-42380.png
-23985.png
-59844.png
-43668.png
-48826.png
-32509.png
-35719.png
-02299.png
-05369.png
-58746.png
-62467.png
-66840.png
-47191.png
-45293.png
-52322.png
-20200.png
-38997.png
-14466.png
-26748.png
-56177.png
-33636.png
-10232.png
-32953.png
-30638.png
-37957.png
-57266.png
-26734.png
-40019.png
-46186.png
-42343.png
-49850.png
-42134.png
-54002.png
-31435.png
-57012.png
-68545.png
-56911.png
-32778.png
-54156.png
-62495.png
-15087.png
-41857.png
-38177.png
-32392.png
-25555.png
-22525.png
-22706.png
-11153.png
-04201.png
-01927.png
-24190.png
-51425.png
-47462.png
-52521.png
-46800.png
-27001.png
-46444.png
-67606.png
-28489.png
-58622.png
-37249.png
-39123.png
-26742.png
-46245.png
-28414.png
-13091.png
-55214.png
-19183.png
-57730.png
-50553.png
-54035.png
-07665.png
-39314.png
-16962.png
-65110.png
-06874.png
-09022.png
-52048.png
-11048.png
-11331.png
-11661.png
-17007.png
-29903.png
-32622.png
-22755.png
-00435.png
-63909.png
-00033.png
-27644.png
-63541.png
-17817.png
-24075.png
-01037.png
-31961.png
-53745.png
-60894.png
-03450.png
-61503.png
-26499.png
-22336.png
-47504.png
-17760.png
-65746.png
-07798.png
-34507.png
-33729.png
-59234.png
-68249.png
-40553.png
-41159.png
-55225.png
-56017.png
-23333.png
-08716.png
-19076.png
-06025.png
-67946.png
-40631.png
-38331.png
-26955.png
-08820.png
-03064.png
-29214.png
-28023.png
-45185.png
-60631.png
-37970.png
-15278.png
-18183.png
-10805.png
-31411.png
-36163.png
-33361.png
-57128.png
-50541.png
-16683.png
-34460.png
-11348.png
-12317.png
-56282.png
-02922.png
-43679.png
-68590.png
-15693.png
-35373.png
-08950.png
-13218.png
-14854.png
-48535.png
-01071.png
-16733.png
-49964.png
-14456.png
-01940.png
-44686.png
-04386.png
-51743.png
-44214.png
-51507.png
-16325.png
-04625.png
-05208.png
-27371.png
-13215.png
-63616.png
-49739.png
-08389.png
-42547.png
-22554.png
-55846.png
-15520.png
-14643.png
-58032.png
-14943.png
-07922.png
-50372.png
-24444.png
-65418.png
-54088.png
-52945.png
-21591.png
-21974.png
-51202.png
-15510.png
-11319.png
-16114.png
-53249.png
-12475.png
-42988.png
-13772.png
-42285.png
-05392.png
-40758.png
-06327.png
-62943.png
-00382.png
-16078.png
-10897.png
-01693.png
-69220.png
-00072.png
-53673.png
-14222.png
-62857.png
-59139.png
-05184.png
-16680.png
-56623.png
-52972.png
-50289.png
-66081.png
-05764.png
-69280.png
-25430.png
-21315.png
-22951.png
-50274.png
-43692.png
-14372.png
-09112.png
-69166.png
-31035.png
-46447.png
-49421.png
-04885.png
-33654.png
-02716.png
-00474.png
-04857.png
-53348.png
-49885.png
-54983.png
-63104.png
-48614.png
-02773.png
-36834.png
-64235.png
-45196.png
-30405.png
-30236.png
-26713.png
-26525.png
-06652.png
-48557.png
-07741.png
-51946.png
-29595.png
-06809.png
-09279.png
-01476.png
-54409.png
-22353.png
-02833.png
-21147.png
-10139.png
-34074.png
-33534.png
-06494.png
-16397.png
-16480.png
-58276.png
-54342.png
-28699.png
-03727.png
-31983.png
-04701.png
-33373.png
-41869.png
-32715.png
-66083.png
-57175.png
-53103.png
-23692.png
-46895.png
-40948.png
-09389.png
-23388.png
-01085.png
-39697.png
-47718.png
-43235.png
-39073.png
-45715.png
-56212.png
-24076.png
-19109.png
-64418.png
-11392.png
-64896.png
-21091.png
-01219.png
-15834.png
-29157.png
-60451.png
-27537.png
-40743.png
-66540.png
-14713.png
-23848.png
-28547.png
-24144.png
-08935.png
-10035.png
-38626.png
-39099.png
-22580.png
-26178.png
-62463.png
-35611.png
-65605.png
-24276.png
-33375.png
-62238.png
-27122.png
-49590.png
-12850.png
-40733.png
-38951.png
-57376.png
-54064.png
-11805.png
-65852.png
-45633.png
-40505.png
-46980.png
-01180.png
-30709.png
-66290.png
-69740.png
-56450.png
-58334.png
-41469.png
-45127.png
-17477.png
-54760.png
-22914.png
-14783.png
-67930.png
-09695.png
-59775.png
-61165.png
-10323.png
-11195.png
-08861.png
-69746.png
-48151.png
-09358.png
-48626.png
-27981.png
-66153.png
-28663.png
-65268.png
-12198.png
-54004.png
-02743.png
-28345.png
-29461.png
-46305.png
-23858.png
-20260.png
-69185.png
-57638.png
-07878.png
-56188.png
-19241.png
-45887.png
-37020.png
-51793.png
-51004.png
-23882.png
-50615.png
-54422.png
-17489.png
-56752.png
-27528.png
-61868.png
-17619.png
-44492.png
-37746.png
-45501.png
-34836.png
-54274.png
-23266.png
-07866.png
-31403.png
-62853.png
-20726.png
-65097.png
-62662.png
-00134.png
-33485.png
-52534.png
-51621.png
-22994.png
-07247.png
-32119.png
-32344.png
-23271.png
-27375.png
-58705.png
-66345.png
-56647.png
-17915.png
-02650.png
-68763.png
-23252.png
-45465.png
-36321.png
-67360.png
-43730.png
-13796.png
-52392.png
-53485.png
-09305.png
-56732.png
-00428.png
-57817.png
-05384.png
-33193.png
-39703.png
-31746.png
-59528.png
-08074.png
-45966.png
-63590.png
-25223.png
-06882.png
-46372.png
-38619.png
-43354.png
-33290.png
-31712.png
-40370.png
-43101.png
-63019.png
-50774.png
-44628.png
-33231.png
-41785.png
-66546.png
-54900.png
-12960.png
-32337.png
-26142.png
-12265.png
-38464.png
-11137.png
-50324.png
-14598.png
-62159.png
-48014.png
-00609.png
-60493.png
-50930.png
-02596.png
-09542.png
-19427.png
-22488.png
-61369.png
-68327.png
-11911.png
-09665.png
-32516.png
-49669.png
-25349.png
-52649.png
-43397.png
-06304.png
-34726.png
-35214.png
-13956.png
-25996.png
-64191.png
-20454.png
-13544.png
-39854.png
-59749.png
-27522.png
-42566.png
-14740.png
-05607.png
-47667.png
-67343.png
-24701.png
-51808.png
-36915.png
-61508.png
-67254.png
-54726.png
-08904.png
-64659.png
-35542.png
-39790.png
-20912.png
-45056.png
-59857.png
-38059.png
-55730.png
-30535.png
-05300.png
-10705.png
-50752.png
-23133.png
-50768.png
-46452.png
-20055.png
-16127.png
-58449.png
-38927.png
-32121.png
-00537.png
-60686.png
-62842.png
-04366.png
-52692.png
-12552.png
-51473.png
-58798.png
-60242.png
-13953.png
-02007.png
-58155.png
-31207.png
-50800.png
-38369.png
-66529.png
-42215.png
-07651.png
-17169.png
-17577.png
-01166.png
-49631.png
-39746.png
-16116.png
-00354.png
-29182.png
-11749.png
-28121.png
-45352.png
-43687.png
-68913.png
-23538.png
-65700.png
-61392.png
-24548.png
-39010.png
-54071.png
-01073.png
-02107.png
-07214.png
-44984.png
-33207.png
-37311.png
-19013.png
-01370.png
-61856.png
-60186.png
-59427.png
-33915.png
-29906.png
-64796.png
-07356.png
-07192.png
-55669.png
-13119.png
-61628.png
-35672.png
-23716.png
-35326.png
-62970.png
-60855.png
-13988.png
-36001.png
-13123.png
-60054.png
-28639.png
-28315.png
-08204.png
-67093.png
-61301.png
-58942.png
-09892.png
-32021.png
-68730.png
-06521.png
-06240.png
-25632.png
-45846.png
-06796.png
-41435.png
-50686.png
-36174.png
-45698.png
-69509.png
-29298.png
-18136.png
-63837.png
-17048.png
-39378.png
-55513.png
-12354.png
-43275.png
-10297.png
-33786.png
-68262.png
-54336.png
-29828.png
-46859.png
-26167.png
-68992.png
-02868.png
-17380.png
-60527.png
-68362.png
-47231.png
-14754.png
-56051.png
-58568.png
-42637.png
-54847.png
-57034.png
-37086.png
-55001.png
-60318.png
-65960.png
-56597.png
-50864.png
-01975.png
-48165.png
-00734.png
-54438.png
-56111.png
-42864.png
-10495.png
-28577.png
-25591.png
-50033.png
-57357.png
-45597.png
-06001.png
-27527.png
-03794.png
-61246.png
-08043.png
-30260.png
-56563.png
-63087.png
-14196.png
-59726.png
-37323.png
-27711.png
-43790.png
-15968.png
-31338.png
-24846.png
-56845.png
-44358.png
-45040.png
-39714.png
-03976.png
-23206.png
-56871.png
-05591.png
-39017.png
-03997.png
-43258.png
-47627.png
-66802.png
-29944.png
-19614.png
-28304.png
-29989.png
-67155.png
-09584.png
-40306.png
-24904.png
-12163.png
-61850.png
-06066.png
-30583.png
-34205.png
-23480.png
-36344.png
-18766.png
-41598.png
-04097.png
-23829.png
-18723.png
-59737.png
-16552.png
-22832.png
-24093.png
-47280.png
-29803.png
-31722.png
-66684.png
-04479.png
-43480.png
-15643.png
-14752.png
-46522.png
-46170.png
-43868.png
-55104.png
-35475.png
-24971.png
-66143.png
-07106.png
-49331.png
-21477.png
-07892.png
-03020.png
-39646.png
-50336.png
-40496.png
-48553.png
-32961.png
-07672.png
-47147.png
-41058.png
-34628.png
-52276.png
-06932.png
-28806.png
-68168.png
-24734.png
-47332.png
-12514.png
-56630.png
-56829.png
-57533.png
-56100.png
-33224.png
-36471.png
-12091.png
-14267.png
-29063.png
-51821.png
-61371.png
-26121.png
-04974.png
-19253.png
-51941.png
-16301.png
-20046.png
-25718.png
-42063.png
-23073.png
-59103.png
-31089.png
-44822.png
-11046.png
-10364.png
-09763.png
-17920.png
-17870.png
-34856.png
-23148.png
-63188.png
-06771.png
-63581.png
-61225.png
-55227.png
-25596.png
-19491.png
-06044.png
-57036.png
-02474.png
-50505.png
-34457.png
-10998.png
-22098.png
-09274.png
-47514.png
-37639.png
-50127.png
-12157.png
-61019.png
-60617.png
-32881.png
-14309.png
-52236.png
-09199.png
-00261.png
-28876.png
-51696.png
-05063.png
-29236.png
-31133.png
-60541.png
-02817.png
-06829.png
-50852.png
-49145.png
-55579.png
-01467.png
-44239.png
-09900.png
-20637.png
-01426.png
-37929.png
-57052.png
-23261.png
-60267.png
-07501.png
-23982.png
-18074.png
-56224.png
-19542.png
-47487.png
-53044.png
-64572.png
-29330.png
-02608.png
-29476.png
-56532.png
-39562.png
-26705.png
-64038.png
-21513.png
-60132.png
-52951.png
-60941.png
-40210.png
-58677.png
-12144.png
-06166.png
-55612.png
-49720.png
-02425.png
-52095.png
-35905.png
-13710.png
-19078.png
-69707.png
-14399.png
-15242.png
-37414.png
-02073.png
-25563.png
-34124.png
-48198.png
-29402.png
-36704.png
-41319.png
-32945.png
-00616.png
-48999.png
-45948.png
-43495.png
-16321.png
-60723.png
-14666.png
-19295.png
-00723.png
-06663.png
-39972.png
-23420.png
-35857.png
-24221.png
-30679.png
-03173.png
-67071.png
-09510.png
-44692.png
-02924.png
-29274.png
-17435.png
-02666.png
-32069.png
-51989.png
-30250.png
-69452.png
-57096.png
-50163.png
-41657.png
-12159.png
-57317.png
-63940.png
-56192.png
-05471.png
-61637.png
-07075.png
-22765.png
-01364.png
-48828.png
-38086.png
-57535.png
-45361.png
-64129.png
-49022.png
-28060.png
-22451.png
-67391.png
-52123.png
-48721.png
-27601.png
-23659.png
-06247.png
-22335.png
-20106.png
-17402.png
-07460.png
-19040.png
-14226.png
-17800.png
-49080.png
-26722.png
-11250.png
-01109.png
-07620.png
-09303.png
-03315.png
-22571.png
-07941.png
-51117.png
-15160.png
-54434.png
-16286.png
-64834.png
-38673.png
-44162.png
-63324.png
-31897.png
-18516.png
-41040.png
-11286.png
-48332.png
-38235.png
-54868.png
-49603.png
-41703.png
-62969.png
-55783.png
-49944.png
-36837.png
-56819.png
-58614.png
-34595.png
-65240.png
-41669.png
-53069.png
-66798.png
-05001.png
-62600.png
-11124.png
-59781.png
-41173.png
-59890.png
-51023.png
-07407.png
-15252.png
-59940.png
-28503.png
-36799.png
-21370.png
-57679.png
-13120.png
-18307.png
-15794.png
-40796.png
-23119.png
-49596.png
-59067.png
-11780.png
-44128.png
-19300.png
-67312.png
-10700.png
-09784.png
-38919.png
-30545.png
-28575.png
-16492.png
-56265.png
-64474.png
-15501.png
-01400.png
-59158.png
-29210.png
-19156.png
-25573.png
-60826.png
-40502.png
-43788.png
-08806.png
-63139.png
-68427.png
-55062.png
-21717.png
-68788.png
-64029.png
-25840.png
-69597.png
-48147.png
-64624.png
-57721.png
-28761.png
-30784.png
-19429.png
-09577.png
-09444.png
-58134.png
-01128.png
-06375.png
-49790.png
-60277.png
-50013.png
-16101.png
-53243.png
-38900.png
-60017.png
-45415.png
-52105.png
-32593.png
-34676.png
-36469.png
-44682.png
-33502.png
-40082.png
-18380.png
-17584.png
-05859.png
-48959.png
-07847.png
-32052.png
-47326.png
-40080.png
-62724.png
-05960.png
-46138.png
-51502.png
-64316.png
-36736.png
-55114.png
-25179.png
-54245.png
-35487.png
-69239.png
-41932.png
-31728.png
-23493.png
-51404.png
-20868.png
-28734.png
-48207.png
-13113.png
-03386.png
-02330.png
-28581.png
-52238.png
-64037.png
-33560.png
-61236.png
-17516.png
-32672.png
-57540.png
-34753.png
-41678.png
-64125.png
-33869.png
-59813.png
-60026.png
-37902.png
-43611.png
-35730.png
-01045.png
-62545.png
-56081.png
-46410.png
-26945.png
-16463.png
-56689.png
-59786.png
-49475.png
-69444.png
-10652.png
-24065.png
-47120.png
-21907.png
-54837.png
-42415.png
-52905.png
-26201.png
-16748.png
-32588.png
-42179.png
-56150.png
-44716.png
-13368.png
-63508.png
-67515.png
-20731.png
-39283.png
-09141.png
-65246.png
-35558.png
-48787.png
-03454.png
-30665.png
-04341.png
-28009.png
-49068.png
-26068.png
-35434.png
-47101.png
-28718.png
-02137.png
-49436.png
-14250.png
-37854.png
-00858.png
-09173.png
-30612.png
-21904.png
-04078.png
-40140.png
-58169.png
-23562.png
-26028.png
-49182.png
-45227.png
-31488.png
-04168.png
-39897.png
-04578.png
-64393.png
-41152.png
-63116.png
-10354.png
-30397.png
-54031.png
-30992.png
-59543.png
-39910.png
-67533.png
-52132.png
-55533.png
-52512.png
-48325.png
-65980.png
-50111.png
-38458.png
-07177.png
-67887.png
-10482.png
-64471.png
-23961.png
-17450.png
-25455.png
-52218.png
-63432.png
-02034.png
-54174.png
-41074.png
-53122.png
-27374.png
-62934.png
-40562.png
-48550.png
-30418.png
-43007.png
-57641.png
-18777.png
-49747.png
-03061.png
-34102.png
-10060.png
-03538.png
-14758.png
-23249.png
-34907.png
-25704.png
-57700.png
-10808.png
-36007.png
-52846.png
-53931.png
-37885.png
-58971.png
-35215.png
-27166.png
-08661.png
-22821.png
-59979.png
-41658.png
-49101.png
-07826.png
-68203.png
-16989.png
-37322.png
-49054.png
-28780.png
-27799.png
-64161.png
-54102.png
-55637.png
-41170.png
-34208.png
-69782.png
-35545.png
-50811.png
-02683.png
-18726.png
-37978.png
-24767.png
-45841.png
-39227.png
-67506.png
-00455.png
-10045.png
-23227.png
-18675.png
-61929.png
-40574.png
-07509.png
-26258.png
-34775.png
-11591.png
-26604.png
-45280.png
-48952.png
-16508.png
-50168.png
-02475.png
-55990.png
-67480.png
-31400.png
-24732.png
-31115.png
-42237.png
-28371.png
-22483.png
-19575.png
-37848.png
-69302.png
-50628.png
-46281.png
-01058.png
-48424.png
-02575.png
-56269.png
-00975.png
-22078.png
-56631.png
-24445.png
-01900.png
-60204.png
-14548.png
-61432.png
-32879.png
-09254.png
-05411.png
-12093.png
-08003.png
-02885.png
-05552.png
-58754.png
-66301.png
-15104.png
-38576.png
-40964.png
-35935.png
-54337.png
-04557.png
-55895.png
-50907.png
-12450.png
-49535.png
-26755.png
-03775.png
-30747.png
-53460.png
-38404.png
-26458.png
-50494.png
-20007.png
-60335.png
-61983.png
-34567.png
-37192.png
-32137.png
-05399.png
-59575.png
-37895.png
-67730.png
-55747.png
-12205.png
-66504.png
-61937.png
-10491.png
-17194.png
-37843.png
-10934.png
-53899.png
-09588.png
-26715.png
-38100.png
-36188.png
-14390.png
-17379.png
-68806.png
-02686.png
-42115.png
-02352.png
-41846.png
-43431.png
-44795.png
-25751.png
-23330.png
-61348.png
-57456.png
-39715.png
-09586.png
-59658.png
-27805.png
-61814.png
-44335.png
-29509.png
-26182.png
-35548.png
-26675.png
-38528.png
-08486.png
-03491.png
-28555.png
-05942.png
-10691.png
-09574.png
-02501.png
-24193.png
-63572.png
-45252.png
-09737.png
-22244.png
-27770.png
-24226.png
-52116.png
-10668.png
-68516.png
-19611.png
-49600.png
-41961.png
-03641.png
-37882.png
-02040.png
-53755.png
-45768.png
-24595.png
-57252.png
-68339.png
-49793.png
-69758.png
-25380.png
-52044.png
-68860.png
-06885.png
-66123.png
-27249.png
-19192.png
-22729.png
-11145.png
-50658.png
-64414.png
-49810.png
-49867.png
-65196.png
-07259.png
-49325.png
-13940.png
-12216.png
-18960.png
-52663.png
-29422.png
-48974.png
-54294.png
-23741.png
-51220.png
-12070.png
-00400.png
-13835.png
-50094.png
-39206.png
-63527.png
-51855.png
-16743.png
-52785.png
-23341.png
-44176.png
-39116.png
-17779.png
-66907.png
-46368.png
-40607.png
-19536.png
-64356.png
-22568.png
-59392.png
-55689.png
-36564.png
-36451.png
-64361.png
-15929.png
-08175.png
-00621.png
-46319.png
-54017.png
-49990.png
-39242.png
-65987.png
-25500.png
-15954.png
-64766.png
-03735.png
-06264.png
-28631.png
-41934.png
-41477.png
-00583.png
-10796.png
-00708.png
-42210.png
-24183.png
-49977.png
-13572.png
-39313.png
-66607.png
-43963.png
-02456.png
-04921.png
-05167.png
-04343.png
-59198.png
-17181.png
-22182.png
-48387.png
-19837.png
-65104.png
-27286.png
-39475.png
-19052.png
-12260.png
-05433.png
-24426.png
-27278.png
-31770.png
-69833.png
-39288.png
-62348.png
-65420.png
-31116.png
-21046.png
-24702.png
-63915.png
-20550.png
-22324.png
-50409.png
-25231.png
-02973.png
-01347.png
-12765.png
-64646.png
-23766.png
-26125.png
-34870.png
-38845.png
-08324.png
-52881.png
-12273.png
-61917.png
-45271.png
-45785.png
-07361.png
-15909.png
-49697.png
-46159.png
-07215.png
-42016.png
-45820.png
-23569.png
-37630.png
-01702.png
-16544.png
-24619.png
-08360.png
-54487.png
-56751.png
-05070.png
-36266.png
-54346.png
-15082.png
-42030.png
-25778.png
-51171.png
-69976.png
-39143.png
-39510.png
-04686.png
-44344.png
-46748.png
-51044.png
-07048.png
-43650.png
-07111.png
-33211.png
-30292.png
-12899.png
-64212.png
-38434.png
-52585.png
-19522.png
-30587.png
-67768.png
-09270.png
-17863.png
-15479.png
-20506.png
-05645.png
-55408.png
-44549.png
-36730.png
-00581.png
-58007.png
-15712.png
-67075.png
-55447.png
-46463.png
-53372.png
-65735.png
-39869.png
-33169.png
-16942.png
-28891.png
-09853.png
-04652.png
-01278.png
-57814.png
-22125.png
-23424.png
-21809.png
-21076.png
-12695.png
-43673.png
-16015.png
-46378.png
-58828.png
-27263.png
-35189.png
-61807.png
-04198.png
-25733.png
-46546.png
-02785.png
-13217.png
-49947.png
-49414.png
-59446.png
-21007.png
-24736.png
-57316.png
-33094.png
-25393.png
-38312.png
-66275.png
-11903.png
-21887.png
-12918.png
-01320.png
-62452.png
-40888.png
-03635.png
-37418.png
-62902.png
-55180.png
-22421.png
-03016.png
-30994.png
-58390.png
-47940.png
-67441.png
-24791.png
-07283.png
-67973.png
-13439.png
-50593.png
-25299.png
-26306.png
-35709.png
-68264.png
-17934.png
-58724.png
-16669.png
-24989.png
-51168.png
-17312.png
-68360.png
-45331.png
-34060.png
-21331.png
-52163.png
-33991.png
-07659.png
-01845.png
-45206.png
-40316.png
-62512.png
-00052.png
-50125.png
-32837.png
-55040.png
-04111.png
-21954.png
-14220.png
-59962.png
-59636.png
-02307.png
-32535.png
-67247.png
-24765.png
-69518.png
-66671.png
-15584.png
-20227.png
-36143.png
-39690.png
-67783.png
-24234.png
-20905.png
-20001.png
-48390.png
-36456.png
-56322.png
-53159.png
-05117.png
-48160.png
-41898.png
-15742.png
-11821.png
-34182.png
-53455.png
-06297.png
-57300.png
-54708.png
-50035.png
-54276.png
-35106.png
-49696.png
-17126.png
-48485.png
-17755.png
-05567.png
-65823.png
-40212.png
-28612.png
-26868.png
-18263.png
-23002.png
-40472.png
-56628.png
-00830.png
-19702.png
-56947.png
-61919.png
-36675.png
-04740.png
-25708.png
-20350.png
-20320.png
-37094.png
-33800.png
-58380.png
-59163.png
-57126.png
-23285.png
-65580.png
-11507.png
-57905.png
-17490.png
-57792.png
-39159.png
-26570.png
-07709.png
-55757.png
-39168.png
-45612.png
-20041.png
-55599.png
-01580.png
-67206.png
-23235.png
-63043.png
-05605.png
-53008.png
-67550.png
-23254.png
-15016.png
-47426.png
-48996.png
-67977.png
-65561.png
-24749.png
-61036.png
-32307.png
-40429.png
-64850.png
-40753.png
-62976.png
-37566.png
-69045.png
-10315.png
-11013.png
-26011.png
-52400.png
-47448.png
-40939.png
-56458.png
-43815.png
-52994.png
-41183.png
-68747.png
-58098.png
-54840.png
-58399.png
-61050.png
-55064.png
-04871.png
-22171.png
-18225.png
-61970.png
-23551.png
-40866.png
-13767.png
-12875.png
-13078.png
-01220.png
-49098.png
-61517.png
-58615.png
-31394.png
-66711.png
-58763.png
-00212.png
-27246.png
-00202.png
-66816.png
-43083.png
-58981.png
-47993.png
-26841.png
-19596.png
-67292.png
-45372.png
-62636.png
-56613.png
-09547.png
-20272.png
-17826.png
-69754.png
-04948.png
-10555.png
-19503.png
-07136.png
-69620.png
-63723.png
-07380.png
-51435.png
-42990.png
-29482.png
-28214.png
-46606.png
-32208.png
-61556.png
-34152.png
-48939.png
-32640.png
-59203.png
-27307.png
-01852.png
-38617.png
-44102.png
-48370.png
-45450.png
-33731.png
-29859.png
-69927.png
-38960.png
-20701.png
-38852.png
-51055.png
-66267.png
-59711.png
-18801.png
-13158.png
-32441.png
-24350.png
-42652.png
-19628.png
-21838.png
-30622.png
-44996.png
-10353.png
-63644.png
-64439.png
-53612.png
-42228.png
-08526.png
-26839.png
-34302.png
-49327.png
-25752.png
-33866.png
-20611.png
-27811.png
-01133.png
-06649.png
-63448.png
-62394.png
-26813.png
-29787.png
-41709.png
-15664.png
-49698.png
-43040.png
-64813.png
-65920.png
-18967.png
-19656.png
-08380.png
-25686.png
-59646.png
-37743.png
-39301.png
-61530.png
-60000.png
-39843.png
-01003.png
-13404.png
-13370.png
-25252.png
-65004.png
-38555.png
-42271.png
-15940.png
-13357.png
-03213.png
-20216.png
-13748.png
-25693.png
-07986.png
-52825.png
-61784.png
-44500.png
-30263.png
-37098.png
-54770.png
-61272.png
-53913.png
-49320.png
-05221.png
-07154.png
-21449.png
-17542.png
-03830.png
-59318.png
-66273.png
-28520.png
-40375.png
-56863.png
-48844.png
-18426.png
-18911.png
-10474.png
-36961.png
-45469.png
-05777.png
-33957.png
-09316.png
-22969.png
-39758.png
-11584.png
-26649.png
-13106.png
-52270.png
-30736.png
-63471.png
-64898.png
-42943.png
-51860.png
-30164.png
-13009.png
-15827.png
-34636.png
-61583.png
-28028.png
-18608.png
-49418.png
-08108.png
-46755.png
-30296.png
-55357.png
-24117.png
-41433.png
-15796.png
-64511.png
-35137.png
-14028.png
-26445.png
-66293.png
-48484.png
-28047.png
-48099.png
-40877.png
-56180.png
-26120.png
-65439.png
-16335.png
-29918.png
-38160.png
-19988.png
-58676.png
-62840.png
-29982.png
-17556.png
-37983.png
-23224.png
-65197.png
-25429.png
-35763.png
-63580.png
-58023.png
-57653.png
-29704.png
-54107.png
-34072.png
-26533.png
-05833.png
-00104.png
-09698.png
-08698.png
-00525.png
-22815.png
-53576.png
-47953.png
-09263.png
-48125.png
-66785.png
-16063.png
-61302.png
-12721.png
-22855.png
-61898.png
-10746.png
-37992.png
-60134.png
-17669.png
-40147.png
-22507.png
-33237.png
-26631.png
-68213.png
-19669.png
-49569.png
-60367.png
-32986.png
-64728.png
-38444.png
-33491.png
-00957.png
-35823.png
-35252.png
-13978.png
-25715.png
-15993.png
-58977.png
-30967.png
-65695.png
-62948.png
-18333.png
-32244.png
-37413.png
-43727.png
-25218.png
-47363.png
-18433.png
-62797.png
-49412.png
-52738.png
-38972.png
-11907.png
-16230.png
-00503.png
-43844.png
-25427.png
-60943.png
-63680.png
-15669.png
-44295.png
-51363.png
-37282.png
-49515.png
-59389.png
-11605.png
-24084.png
-20512.png
-31495.png
-40183.png
-48358.png
-21870.png
-64422.png
-04955.png
-28653.png
-18310.png
-68160.png
-11415.png
-36218.png
-24504.png
-55118.png
-34817.png
-19447.png
-46169.png
-49463.png
-42111.png
-34275.png
-09678.png
-16254.png
-12359.png
-60620.png
-33801.png
-02571.png
-14918.png
-01131.png
-60302.png
-38984.png
-21615.png
-24126.png
-16060.png
-51176.png
-01431.png
-56903.png
-43633.png
-18488.png
-14797.png
-45099.png
-42044.png
-44885.png
-07437.png
-22888.png
-68216.png
-10870.png
-60191.png
-47853.png
-51062.png
-16433.png
-61574.png
-46307.png
-58712.png
-66251.png
-18829.png
-06798.png
-13686.png
-17442.png
-21959.png
-45939.png
-11810.png
-13667.png
-41481.png
-07643.png
-20580.png
-60762.png
-20273.png
-39625.png
-39616.png
-61463.png
-16941.png
-14926.png
-07558.png
-36522.png
-46104.png
-67446.png
-23094.png
-53351.png
-38758.png
-34749.png
-62382.png
-00244.png
-34781.png
-48253.png
-24427.png
-15607.png
-52806.png
-54214.png
-03906.png
-69516.png
-44478.png
-02656.png
-23408.png
-44068.png
-19039.png
-04092.png
-04778.png
-51456.png
-61363.png
-69845.png
-37753.png
-39528.png
-25426.png
-64772.png
-20314.png
-57322.png
-04413.png
-61652.png
-13324.png
-65243.png
-39509.png
-18700.png
-31680.png
-14892.png
-03437.png
-44605.png
-14989.png
-24266.png
-15995.png
-21823.png
-61178.png
-09795.png
-19564.png
-32664.png
-49063.png
-05701.png
-18814.png
-46721.png
-57777.png
-64263.png
-59321.png
-57688.png
-24967.png
-10737.png
-26334.png
-68768.png
-03833.png
-44870.png
-13050.png
-10477.png
-65952.png
-03705.png
-29110.png
-37269.png
-42122.png
-03897.png
-09674.png
-18824.png
-14391.png
-40340.png
-68162.png
-22228.png
-26844.png
-02547.png
-66087.png
-06987.png
-36242.png
-67229.png
-44940.png
-31838.png
-22940.png
-35132.png
-25610.png
-02876.png
-43791.png
-15569.png
-07819.png
-51986.png
-47394.png
-62124.png
-47552.png
-38234.png
-62674.png
-42413.png
-44993.png
-52342.png
-44696.png
-20576.png
-09943.png
-12518.png
-23301.png
-00667.png
-35328.png
-10120.png
-53261.png
-08225.png
-23788.png
-16580.png
-38123.png
-33608.png
-44338.png
-13841.png
-42375.png
-25360.png
-13517.png
-26464.png
-58433.png
-20075.png
-56969.png
-56138.png
-09591.png
-33602.png
-16609.png
-27486.png
-63914.png
-19122.png
-08974.png
-43177.png
-25434.png
-19454.png
-08692.png
-28752.png
-65007.png
-37124.png
-35277.png
-10376.png
-14683.png
-05911.png
-67969.png
-44771.png
-69854.png
-00349.png
-68704.png
-67597.png
-42690.png
-68655.png
-10723.png
-34336.png
-59128.png
-05421.png
-42217.png
-43377.png
-67283.png
-59188.png
-08832.png
-24027.png
-54202.png
-13703.png
-58197.png
-61365.png
-48510.png
-06366.png
-63728.png
-14374.png
-69970.png
-51830.png
-33346.png
-15565.png
-63398.png
-19369.png
-45977.png
-24490.png
-34772.png
-14967.png
-27997.png
-32864.png
-65656.png
-66456.png
-68033.png
-52404.png
-19232.png
-30814.png
-05245.png
-63567.png
-14817.png
-07226.png
-27096.png
-40036.png
-06187.png
-22376.png
-01187.png
-25986.png
-15235.png
-67907.png
-28456.png
-13811.png
-19755.png
-13212.png
-42277.png
-11473.png
-23361.png
-28601.png
-53027.png
-25707.png
-21260.png
-08689.png
-16758.png
-11760.png
-13599.png
-55249.png
-22318.png
-06848.png
-43571.png
-45077.png
-29603.png
-02500.png
-09514.png
-21668.png
-39935.png
-13129.png
-51733.png
-47346.png
-28874.png
-49490.png
-06208.png
-62096.png
-30546.png
-60940.png
-29465.png
-45051.png
-35849.png
-62317.png
-55011.png
-02582.png
-37980.png
-06871.png
-47311.png
-50631.png
-29154.png
-36534.png
-58559.png
-34919.png
-66930.png
-11681.png
-51427.png
-40969.png
-04739.png
-46906.png
-60443.png
-36184.png
-05602.png
-63428.png
-21070.png
-54078.png
-08490.png
-67165.png
-58319.png
-51833.png
-37342.png
-47069.png
-21404.png
-53102.png
-06867.png
-01259.png
-16670.png
-29708.png
-42355.png
-15003.png
-65873.png
-42097.png
-56435.png
-06618.png
-33171.png
-57899.png
-57605.png
-35662.png
-57932.png
-64153.png
-39531.png
-63326.png
-64736.png
-37427.png
-32164.png
-07678.png
-23165.png
-17222.png
-06290.png
-02418.png
-21120.png
-40282.png
-42231.png
-03758.png
-51588.png
-10161.png
-55967.png
-03137.png
-59824.png
-69935.png
-37475.png
-29004.png
-01568.png
-30114.png
-66797.png
-23453.png
-65066.png
-16501.png
-14656.png
-23615.png
-32332.png
-62471.png
-24625.png
-62480.png
-37922.png
-47558.png
-62701.png
-12433.png
-14423.png
-12978.png
-35327.png
-64586.png
-13460.png
-03196.png
-63131.png
-61018.png
-53802.png
-55915.png
-19303.png
-26434.png
-67442.png
-08202.png
-22785.png
-48172.png
-12615.png
-37171.png
-14856.png
-17119.png
-39779.png
-37819.png
-67614.png
-08481.png
-32555.png
-16536.png
-25333.png
-35228.png
-62690.png
-38138.png
-48734.png
-15588.png
-20840.png
-07762.png
-16621.png
-29152.png
-46861.png
-24227.png
-39035.png
-28890.png
-52949.png
-12290.png
-39921.png
-08109.png
-14197.png
-14672.png
-57630.png
-30403.png
-56251.png
-69741.png
-50487.png
-40428.png
-41774.png
-50137.png
-51213.png
-57242.png
-36826.png
-66886.png
-31437.png
-27785.png
-18091.png
-65740.png
-59281.png
-36692.png
-55879.png
-50634.png
-09125.png
-34079.png
-55468.png
-02802.png
-67077.png
-31492.png
-15329.png
-32147.png
-42878.png
-39253.png
-29194.png
-31990.png
-15006.png
-43715.png
-49374.png
-42492.png
-00493.png
-41564.png
-48431.png
-68829.png
-05998.png
-28905.png
-00253.png
-28014.png
-59371.png
-61275.png
-35605.png
-18272.png
-67268.png
-69795.png
-33111.png
-56515.png
-50357.png
-23838.png
-19111.png
-14382.png
-48665.png
-38467.png
-30376.png
-24766.png
-27145.png
-16079.png
-31943.png
-61795.png
-41968.png
-32413.png
-18294.png
-32557.png
-52842.png
-42748.png
-11781.png
-20994.png
-49824.png
-47038.png
-67423.png
-66316.png
-48695.png
-19090.png
-63488.png
-54784.png
-55516.png
-57910.png
-69749.png
-26653.png
-24822.png
-65414.png
-57986.png
-42883.png
-65060.png
-38844.png
-30458.png
-65541.png
-41336.png
-01241.png
-62014.png
-45052.png
-29127.png
-28131.png
-46050.png
-17911.png
-33240.png
-02114.png
-25833.png
-17350.png
-02873.png
-31239.png
-46212.png
-60475.png
-37346.png
-52337.png
-09741.png
-26658.png
-30759.png
-30264.png
-49307.png
-13155.png
-06613.png
-48408.png
-25808.png
-23481.png
-37358.png
-62559.png
-39952.png
-55813.png
-21535.png
-04723.png
-21509.png
-48091.png
-17497.png
-22480.png
-50241.png
-16076.png
-66233.png
-45131.png
-60298.png
-17421.png
-50534.png
-15949.png
-39438.png
-05143.png
-11394.png
-68448.png
-45458.png
-18393.png
-47864.png
-48577.png
-32749.png
-51818.png
-65518.png
-45658.png
-29947.png
-20432.png
-32833.png
-41855.png
-38820.png
-29469.png
-06296.png
-68111.png
-22198.png
-05254.png
-38350.png
-63036.png
-36958.png
-10210.png
-48096.png
-42494.png
-48259.png
-28202.png
-02209.png
-10087.png
-54396.png
-33260.png
-66225.png
-31085.png
-66549.png
-22185.png
-56849.png
-31101.png
-19952.png
-39680.png
-46513.png
-54279.png
-02685.png
-25077.png
-37102.png
-48124.png
-04670.png
-34411.png
-29801.png
-16390.png
-08093.png
-14061.png
-15176.png
-41769.png
-30017.png
-58741.png
-14788.png
-26282.png
-26418.png
-49038.png
-35175.png
-27973.png
-41623.png
-41285.png
-58171.png
-63535.png
-50958.png
-40950.png
-60285.png
-28398.png
-57025.png
-23823.png
-63029.png
-14135.png
-69808.png
-08326.png
-65374.png
-49803.png
-59767.png
-20676.png
-28108.png
-65331.png
-01104.png
-02392.png
-67318.png
-16177.png
-44447.png
-58909.png
-56045.png
-10110.png
-61248.png
-46893.png
-15068.png
-25241.png
-46033.png
-29785.png
-58653.png
-24252.png
-39835.png
-26538.png
-16696.png
-52884.png
-25109.png
-10990.png
-23712.png
-00477.png
-35655.png
-69660.png
-36166.png
-31444.png
-25250.png
-51434.png
-43151.png
-17234.png
-01522.png
-39702.png
-14283.png
-03541.png
-41315.png
-55607.png
-22158.png
-11656.png
-40839.png
-23015.png
-17659.png
-51880.png
-41473.png
-09754.png
-18710.png
-58779.png
-48398.png
-55354.png
-10543.png
-45075.png
-61557.png
-31390.png
-55321.png
-69423.png
-12280.png
-08217.png
-61770.png
-05185.png
-54651.png
-25398.png
-35049.png
-55172.png
-59245.png
-44558.png
-43925.png
-51606.png
-61494.png
-19411.png
-27124.png
-32728.png
-56974.png
-17897.png
-45152.png
-11181.png
-07717.png
-30512.png
-00863.png
-36058.png
-20887.png
-56852.png
-54682.png
-25326.png
-22088.png
-65915.png
-24708.png
-69001.png
-42294.png
-01865.png
-07751.png
-03151.png
-09663.png
-51872.png
-60738.png
-42949.png
-18544.png
-55640.png
-02590.png
-23344.png
-58452.png
-43274.png
-47304.png
-56777.png
-00434.png
-37330.png
-46872.png
-19367.png
-60775.png
-18238.png
-34608.png
-35618.png
-58574.png
-14964.png
-26757.png
-64948.png
-32627.png
-66538.png
-33155.png
-08468.png
-24222.png
-30282.png
-04673.png
-10173.png
-15403.png
-42365.png
-02967.png
-11785.png
-07322.png
-07895.png
-21992.png
-40965.png
-01933.png
-48805.png
-20050.png
-17748.png
-28652.png
-56470.png
-06756.png
-61670.png
-50793.png
-36159.png
-19063.png
-41220.png
-16692.png
-29135.png
-66851.png
-51206.png
-31640.png
-15614.png
-46359.png
-05975.png
-12811.png
-09650.png
-38450.png
-35069.png
-51718.png
-32744.png
-04065.png
-46425.png
-36910.png
-25621.png
-04378.png
-15946.png
-24392.png
-62911.png
-21816.png
-16117.png
-03714.png
-14957.png
-16326.png
-22092.png
-21739.png
-59566.png
-33130.png
-50370.png
-14664.png
-01287.png
-63553.png
-07029.png
-23470.png
-29761.png
-07042.png
-02369.png
-48122.png
-06253.png
-62980.png
-42999.png
-31716.png
-49384.png
-37170.png
-40690.png
-21810.png
-47550.png
-58995.png
-15136.png
-60437.png
-64050.png
-50545.png
-10846.png
-48818.png
-05233.png
-02406.png
-41179.png
-01907.png
-07335.png
-16823.png
-32210.png
-32630.png
-49133.png
-48450.png
-03161.png
-35816.png
-52625.png
-36677.png
-03800.png
-23315.png
-58571.png
-41741.png
-01415.png
-16249.png
-43353.png
-58069.png
-24379.png
-51498.png
-11825.png
-66568.png
-65900.png
-27766.png
-60364.png
-40409.png
-44400.png
-07032.png
-18932.png
-67110.png
-39930.png
-43874.png
-64824.png
-26977.png
-63678.png
-25379.png
-08781.png
-15740.png
-24878.png
-16755.png
-09512.png
-61521.png
-22588.png
-24557.png
-23778.png
-12715.png
-56391.png
-23433.png
-01410.png
-57643.png
-37875.png
-19224.png
-31824.png
-13134.png
-49305.png
-57250.png
-12990.png
-06480.png
-32600.png
-04450.png
-58926.png
-11521.png
-13837.png
-11089.png
-67261.png
-29920.png
-60158.png
-54433.png
-50683.png
-62723.png
-24693.png
-37813.png
-00125.png
-59296.png
-51179.png
-55988.png
-28971.png
-28565.png
-19608.png
-07431.png
-05750.png
-26791.png
-49572.png
-14507.png
-54526.png
-10226.png
-53567.png
-15943.png
-46611.png
-37982.png
-49495.png
-37147.png
-19160.png
-04436.png
-40833.png
-07090.png
-00940.png
-38610.png
-43930.png
-10512.png
-30852.png
-63009.png
-44932.png
-68231.png
-67381.png
-56454.png
-44788.png
-19285.png
-39091.png
-14971.png
-33381.png
-21302.png
-35455.png
-18917.png
-27846.png
-26311.png
-62994.png
-55738.png
-26163.png
-03600.png
-57440.png
-65409.png
-65166.png
-29984.png
-33285.png
-43142.png
-18484.png
-38508.png
-31976.png
-50070.png
-67217.png
-26202.png
-23077.png
-06620.png
-61757.png
-42743.png
-46293.png
-41569.png
-08753.png
-03750.png
-44648.png
-53884.png
-65747.png
-02679.png
-46455.png
-01200.png
-52352.png
-44615.png
-32463.png
-40358.png
-16285.png
-42469.png
-41906.png
-29929.png
-27664.png
-67454.png
-09771.png
-43965.png
-52026.png
-64996.png
-64146.png
-04192.png
-58107.png
-02226.png
-65614.png
-02003.png
-57010.png
-40041.png
-36120.png
-64252.png
-32070.png
-62254.png
-23215.png
-28912.png
-41065.png
-50609.png
-19948.png
-31188.png
-48832.png
-48075.png
-34161.png
-63893.png
-22291.png
-37703.png
-41583.png
-50571.png
-33056.png
-10168.png
-46691.png
-07946.png
-08995.png
-34392.png
-51372.png
-21072.png
-13911.png
-27430.png
-23239.png
-16504.png
-46857.png
-69128.png
-21861.png
-13698.png
-68931.png
-53137.png
-45538.png
-59859.png
-55491.png
-38588.png
-52042.png
-35157.png
-44307.png
-35164.png
-62349.png
-04862.png
-58938.png
-38173.png
-60922.png
-25584.png
-57616.png
-16528.png
-53045.png
-02562.png
-49591.png
-36255.png
-53584.png
-30641.png
-37510.png
-42376.png
-47780.png
-26116.png
-33367.png
-41028.png
-24317.png
-55000.png
-29114.png
-69304.png
-56330.png
-25208.png
-35378.png
-60873.png
-01972.png
-34405.png
-13023.png
-26194.png
-13213.png
-56528.png
-23072.png
-56321.png
-53583.png
-21595.png
-50046.png
-35174.png
-67086.png
-09794.png
-09576.png
-44951.png
-12699.png
-05682.png
-68954.png
-08515.png
-63960.png
-19781.png
-18250.png
-20151.png
-17039.png
-58152.png
-63192.png
-49854.png
-58085.png
-03681.png
-30687.png
-32994.png
-37704.png
-56675.png
-25902.png
-38371.png
-69508.png
-36951.png
-27836.png
-58838.png
-17055.png
-13263.png
-33356.png
-00255.png
-62490.png
-36944.png
-62036.png
-36908.png
-08987.png
-66052.png
-59184.png
-39262.png
-44989.png
-15124.png
-20787.png
-40724.png
-43000.png
-28051.png
-09445.png
-51407.png
-38862.png
-67242.png
-21178.png
-65222.png
-30505.png
-32416.png
-15679.png
-48081.png
-36459.png
-24652.png
-63740.png
-42627.png
-59666.png
-30610.png
-27548.png
-67917.png
-21366.png
-16244.png
-13147.png
-20605.png
-11892.png
-32246.png
-01635.png
-21871.png
-44119.png
-20615.png
-33180.png
-57180.png
-00784.png
-16979.png
-55706.png
-63363.png
-07221.png
-31027.png
-56509.png
-59702.png
-63637.png
-65317.png
-21866.png
-46547.png
-27268.png
-24184.png
-01676.png
-13763.png
-08679.png
-20879.png
-58453.png
-11636.png
-32454.png
-32008.png
-47362.png
-38095.png
-36622.png
-35678.png
-28040.png
-27193.png
-64497.png
-28619.png
-42678.png
-21943.png
-39233.png
-00580.png
-47319.png
-13586.png
-61717.png
-40270.png
-49205.png
-31644.png
-00433.png
-40278.png
-32058.png
-68202.png
-42257.png
-31692.png
-54725.png
-15088.png
-22773.png
-46976.png
-02912.png
-23279.png
-05498.png
-50310.png
-18607.png
-44278.png
-40725.png
-59498.png
-65174.png
-01482.png
-19774.png
-04098.png
-53556.png
-51756.png
-11217.png
-12598.png
-27084.png
-56140.png
-18968.png
-49382.png
-17171.png
-25787.png
-26574.png
-34172.png
-53147.png
-49564.png
-69449.png
-40181.png
-03503.png
-58889.png
-47232.png
-33609.png
-44090.png
-40622.png
-51652.png
-56710.png
-05358.png
-65476.png
-15064.png
-62052.png
-17042.png
-21244.png
-67738.png
-34498.png
-25547.png
-40913.png
-64620.png
-55222.png
-15048.png
-20218.png
-20026.png
-07452.png
-22573.png
-23355.png
-55629.png
-58687.png
-12564.png
-04766.png
-37256.png
-31259.png
-66423.png
-18567.png
-14543.png
-39565.png
-10185.png
-38197.png
-32751.png
-43389.png
-26932.png
-65814.png
-45108.png
-59357.png
-50954.png
-64143.png
-39215.png
-04345.png
-00952.png
-47399.png
-35715.png
-14241.png
-31233.png
-29283.png
-43408.png
-56274.png
-60172.png
-25714.png
-48693.png
-25827.png
-69043.png
-16950.png
-04552.png
-20028.png
-19018.png
-32165.png
-31953.png
-17774.png
-62054.png
-37053.png
-14005.png
-16852.png
-34321.png
-37814.png
-09399.png
-33730.png
-65100.png
-34938.png
-44906.png
-38361.png
-53986.png
-38096.png
-00585.png
-48416.png
-46141.png
-51834.png
-26184.png
-22003.png
-03205.png
-64920.png
-25085.png
-65513.png
-46901.png
-64714.png
-56680.png
-05137.png
-21515.png
-22576.png
-63696.png
-56308.png
-59142.png
-46663.png
-55812.png
-16954.png
-52257.png
-45473.png
-00309.png
-41917.png
-52602.png
-13087.png
-29306.png
-45313.png
-33445.png
-49849.png
-38002.png
-03493.png
-25225.png
-52084.png
-10063.png
-41763.png
-37097.png
-36126.png
-53953.png
-54024.png
-52871.png
-12279.png
-18316.png
-17369.png
-49670.png
-11664.png
-25310.png
-50985.png
-56557.png
-36518.png
-40566.png
-54534.png
-63260.png
-26954.png
-30357.png
-22885.png
-31045.png
-39557.png
-06095.png
-43087.png
-46423.png
-41612.png
-16469.png
-03176.png
-46811.png
-26683.png
-47795.png
-63092.png
-44649.png
-07339.png
-45982.png
-20666.png
-10636.png
-46751.png
-28993.png
-24588.png
-35978.png
-36386.png
-16784.png
-68336.png
-34014.png
-18362.png
-61187.png
-52324.png
-50568.png
-20376.png
-05385.png
-51092.png
-02705.png
-14091.png
-03804.png
-22072.png
-32434.png
-14288.png
-31938.png
-06586.png
-01459.png
-43735.png
-13194.png
-44833.png
-68316.png
-26780.png
-22194.png
-61045.png
-18624.png
-60278.png
-37431.png
-05706.png
-57603.png
-02856.png
-33353.png
-28299.png
-49398.png
-08504.png
-58194.png
-33528.png
-22356.png
-58223.png
-47134.png
-29309.png
-55890.png
-03048.png
-19231.png
-25465.png
-27957.png
-46810.png
-01236.png
-50558.png
-43809.png
-10720.png
-00593.png
-03022.png
-68941.png
-43481.png
-35416.png
-13930.png
-54053.png
-29830.png
-02722.png
-66863.png
-44017.png
-31168.png
-45671.png
-69510.png
-60361.png
-03854.png
-05260.png
-55886.png
-23879.png
-19863.png
-59398.png
-03882.png
-06608.png
-45988.png
-54384.png
-15217.png
-50567.png
-53636.png
-01984.png
-13947.png
-59848.png
-32437.png
-24683.png
-30902.png
-11136.png
-26917.png
-35237.png
-44470.png
-57668.png
-60710.png
-66675.png
-12955.png
-55502.png
-64064.png
-21590.png
-29780.png
-07066.png
-60752.png
-18366.png
-01810.png
-38028.png
-35955.png
-56243.png
-02941.png
-60739.png
-06979.png
-35996.png
-15626.png
-56429.png
-08711.png
-12972.png
-18384.png
-05518.png
-12917.png
-68396.png
-00855.png
-64514.png
-68118.png
-04909.png
-08963.png
-67719.png
-58592.png
-58528.png
-49323.png
-69923.png
-12195.png
-32860.png
-04755.png
-15177.png
-65033.png
-65665.png
-32004.png
-37010.png
-15469.png
-26211.png
-53056.png
-37985.png
-22838.png
-55883.png
-43280.png
-54599.png
-64721.png
-04235.png
-07786.png
-59488.png
-00081.png
-46964.png
-31097.png
-24140.png
-37883.png
-19703.png
-08292.png
-69477.png
-37562.png
-04060.png
-69317.png
-46120.png
-03749.png
-27038.png
-16668.png
-10785.png
-25470.png
-64737.png
-18194.png
-22950.png
-42059.png
-43533.png
-37736.png
-04546.png
-65509.png
-23683.png
-68817.png
-22420.png
-14115.png
-64909.png
-11237.png
-52041.png
-20119.png
-24958.png
-68182.png
-53151.png
-43702.png
-12024.png
-41234.png
-32675.png
-61007.png
-69265.png
-55035.png
-01578.png
-49239.png
-40848.png
-38229.png
-24286.png
-18577.png
-65526.png
-04305.png
-02448.png
-32427.png
-21115.png
-46302.png
-20387.png
-15407.png
-18855.png
-30847.png
-55095.png
-48335.png
-02700.png
-03634.png
-09285.png
-38713.png
-53330.png
-41632.png
-61669.png
-28360.png
-51288.png
-03587.png
-23443.png
-40274.png
-02106.png
-29060.png
-23428.png
-56211.png
-35008.png
-28349.png
-17856.png
-67383.png
-66422.png
-51134.png
-55113.png
-08106.png
-30112.png
-16448.png
-56823.png
-04008.png
-21901.png
-66703.png
-11542.png
-61456.png
-40586.png
-53923.png
-25825.png
-62459.png
-42299.png
-11723.png
-13602.png
-24114.png
-50662.png
-45159.png
-36148.png
-48749.png
-03014.png
-54677.png
-21770.png
-22830.png
-24763.png
-02091.png
-24820.png
-05869.png
-28444.png
-06540.png
-14010.png
-57798.png
-49633.png
-08416.png
-41479.png
-16293.png
-66791.png
-40595.png
-13842.png
-19442.png
-48271.png
-48254.png
-68553.png
-05988.png
-22955.png
-20183.png
-06754.png
-11214.png
-35383.png
-07816.png
-50161.png
-17240.png
-64610.png
-04086.png
-64988.png
-10328.png
-40679.png
-30763.png
-24891.png
-57195.png
-49662.png
-10755.png
-47861.png
-18391.png
-44866.png
-43892.png
-51785.png
-52582.png
-01622.png
-63132.png
-19990.png
-43625.png
-09074.png
-62760.png
-23293.png
-29470.png
-26257.png
-34054.png
-48433.png
-29122.png
-44283.png
-01893.png
-49613.png
-33011.png
-25997.png
-07108.png
-14128.png
-13691.png
-11863.png
-51698.png
-66652.png
-35401.png
-12111.png
-34703.png
-22979.png
-30225.png
-29353.png
-63738.png
-40177.png
-35249.png
-31482.png
-36688.png
-30222.png
-49262.png
-44997.png
-50362.png
-22057.png
-51207.png
-26667.png
-51554.png
-60227.png
-18319.png
-39706.png
-18427.png
-10055.png
-11098.png
-22080.png
-41267.png
-22394.png
-33976.png
-54179.png
-07992.png
-22469.png
-63707.png
-62439.png
-14279.png
-65922.png
-48761.png
-56646.png
-54818.png
-43194.png
-09361.png
-47390.png
-26662.png
-16489.png
-05203.png
-25166.png
-09278.png
-10615.png
-65954.png
-43284.png
-39316.png
-52161.png
-69169.png
-23412.png
-20150.png
-07034.png
-64367.png
-20125.png
-68567.png
-42692.png
-40645.png
-01646.png
-41314.png
-17965.png
-23855.png
-42459.png
-01498.png
-15804.png
-57711.png
-47425.png
-28901.png
-67863.png
-41923.png
-43375.png
-50911.png
-17550.png
-52946.png
-46238.png
-07851.png
-54423.png
-55824.png
-15230.png
-64599.png
-67460.png
-36373.png
-25213.png
-59108.png
-16707.png
-31075.png
-44727.png
-45101.png
-44296.png
-21868.png
-38977.png
-47106.png
-00762.png
-64203.png
-48329.png
-30216.png
-67520.png
-50181.png
-64886.png
-19262.png
-55105.png
-47202.png
-69402.png
-30011.png
-39799.png
-40182.png
-04274.png
-14389.png
-26672.png
-08713.png
-66914.png
-15363.png
-62091.png
-30140.png
-52500.png
-33009.png
-37341.png
-07148.png
-61311.png
-02429.png
-57866.png
-34568.png
-41808.png
-19128.png
-08615.png
-27442.png
-45968.png
-16848.png
-34681.png
-52736.png
-63825.png
-56980.png
-48100.png
-27918.png
-08792.png
-17250.png
-49480.png
-67667.png
-43488.png
-11477.png
-00332.png
-07670.png
-29258.png
-69654.png
-54814.png
-40432.png
-10741.png
-49884.png
-03693.png
-14342.png
-39457.png
-10151.png
-26490.png
-43705.png
-39791.png
-17191.png
-12893.png
-37887.png
-36950.png
-38442.png
-57764.png
-16853.png
-18425.png
-26172.png
-58183.png
-07543.png
-02490.png
-30285.png
-15374.png
-38272.png
-29632.png
-17332.png
-22033.png
-66020.png
-62321.png
-10756.png
-44650.png
-21730.png
-45070.png
-08400.png
-04053.png
-56864.png
-23317.png
-38009.png
-02243.png
-00695.png
-46075.png
-08546.png
-65995.png
-54489.png
-52672.png
-13865.png
-54855.png
-37785.png
-60185.png
-04554.png
-25503.png
-35621.png
-57674.png
-22414.png
-59162.png
-57228.png
-52980.png
-38706.png
-36488.png
-25652.png
-61550.png
-10415.png
-01763.png
-15411.png
-20766.png
-36340.png
-33557.png
-10601.png
-69939.png
-45308.png
-13928.png
-38297.png
-22260.png
-17134.png
-19215.png
-68119.png
-48562.png
-05312.png
-22929.png
-04444.png
-39899.png
-51844.png
-19593.png
-69303.png
-34850.png
-62580.png
-25012.png
-34398.png
-01292.png
-23722.png
-29585.png
-36071.png
-16027.png
-34518.png
-28075.png
-29256.png
-31129.png
-57652.png
-18289.png
-65079.png
-20465.png
-17838.png
-66179.png
-00576.png
-09944.png
-16812.png
-01084.png
-52390.png
-66204.png
-20982.png
-35342.png
-08618.png
-68523.png
-24564.png
-60798.png
-55828.png
-59476.png
-60405.png
-39553.png
-30927.png
-16983.png
-58962.png
-63755.png
-46899.png
-42313.png
-25038.png
-19766.png
-01461.png
-31096.png
-22538.png
-11692.png
-62821.png
-68384.png
-13887.png
-34082.png
-21610.png
-36458.png
-23513.png
-30323.png
-60691.png
-32787.png
-48628.png
-11721.png
-38499.png
-30369.png
-58472.png
-18089.png
-58701.png
-21854.png
-59573.png
-15991.png
-02094.png
-53652.png
-64458.png
-29835.png
-24532.png
-11965.png
-39081.png
-54961.png
-30976.png
-64797.png
-19581.png
-15736.png
-08415.png
-03221.png
-37259.png
-51090.png
-01697.png
-68544.png
-27505.png
-54926.png
-58392.png
-56427.png
-06953.png
-13876.png
-62865.png
-40880.png
-16824.png
-33182.png
-65586.png
-03012.png
-15542.png
-26808.png
-22546.png
-02493.png
-28197.png
-04332.png
-57689.png
-51063.png
-13817.png
-50858.png
-35966.png
-42657.png
-29467.png
-28533.png
-29250.png
-65141.png
-45637.png
-48363.png
-09997.png
-24669.png
-21941.png
-42636.png
-03864.png
-17010.png
-51703.png
-62806.png
-59736.png
-02179.png
-23425.png
-20408.png
-13021.png
-29727.png
-59548.png
-64742.png
-52577.png
-51231.png
-60491.png
-25695.png
-30973.png
-31836.png
-38060.png
-63784.png
-13448.png
-52469.png
-11337.png
-27816.png
-43554.png
-42716.png
-38976.png
-63467.png
-62075.png
-69414.png
-31667.png
-47067.png
-21094.png
-68350.png
-31263.png
-68721.png
-16340.png
-53694.png
-55980.png
-21377.png
-15927.png
-63493.png
-67697.png
-58703.png
-69424.png
-52397.png
-29270.png
-29875.png
-03393.png
-20186.png
-22218.png
-31112.png
-68988.png
-09626.png
-62398.png
-05432.png
-08962.png
-68669.png
-28647.png
-10839.png
-07284.png
-17447.png
-21624.png
-56938.png
-04212.png
-03903.png
-46701.png
-63888.png
-64854.png
-19049.png
-36710.png
-05456.png
-25566.png
-43432.png
-13583.png
-30359.png
-59029.png
-48334.png
-36750.png
-09375.png
-62678.png
-29978.png
-62455.png
-40962.png
-18169.png
-63083.png
-13415.png
-24154.png
-51533.png
-34036.png
-50451.png
-17313.png
-29682.png
-62869.png
-63858.png
-02268.png
-53107.png
-16556.png
-00365.png
-56827.png
-31604.png
-24596.png
-06171.png
-42670.png
-64777.png
-64084.png
-43085.png
-37084.png
-50521.png
-65956.png
-23318.png
-47562.png
-00256.png
-62038.png
-63065.png
-16980.png
-24784.png
-13848.png
-53273.png
-54227.png
-57785.png
-09272.png
-25219.png
-34426.png
-20346.png
-44791.png
-65593.png
-67598.png
-64956.png
-41790.png
-04400.png
-38462.png
-46099.png
-52271.png
-09689.png
-41004.png
-40136.png
-36147.png
-06466.png
-21648.png
-00326.png
-03259.png
-06008.png
-07185.png
-22937.png
-58499.png
-06052.png
-22730.png
-61081.png
-02144.png
-57030.png
-50386.png
-22990.png
-51705.png
-12753.png
-19756.png
-03458.png
-59267.png
-04821.png
-66175.png
-54689.png
-58625.png
-37608.png
-67481.png
-57927.png
-54039.png
-37449.png
-30291.png
-36991.png
-52069.png
-54300.png
-68755.png
-44387.png
-17408.png
-45668.png
-47259.png
-11839.png
-04646.png
-20367.png
-55571.png
-62802.png
-20849.png
-39946.png
-11358.png
-55589.png
-55119.png
-64693.png
-30435.png
-30549.png
-05396.png
-07277.png
-54853.png
-15453.png
-52990.png
-67351.png
-08257.png
-26419.png
-30334.png
-30344.png
-17894.png
-63922.png
-52883.png
-31810.png
-50886.png
-26566.png
-38695.png
-26229.png
-68171.png
-02727.png
-54472.png
-15775.png
-17367.png
-39293.png
-67688.png
-63559.png
-42919.png
-59299.png
-62951.png
-61421.png
-29136.png
-48978.png
-63422.png
-21266.png
-20719.png
-01207.png
-47980.png
-19546.png
-03788.png
-13611.png
-28564.png
-37554.png
-08258.png
-68741.png
-36258.png
-33044.png
-04744.png
-28841.png
-32766.png
-48220.png
-02863.png
-64030.png
-32793.png
-39103.png
-38662.png
-21721.png
-45860.png
-20927.png
-22626.png
-41407.png
-06206.png
-46106.png
-46085.png
-46129.png
-17476.png
-13006.png
-67911.png
-13444.png
-46373.png
-14544.png
-05714.png
-30253.png
-20405.png
-38926.png
-10642.png
-63844.png
-35004.png
-04550.png
-02486.png
-20627.png
-62873.png
-43243.png
-26328.png
-68717.png
-39424.png
-29952.png
-39157.png
-59992.png
-25796.png
-27345.png
-62728.png
-15332.png
-58548.png
-31996.png
-36113.png
-17062.png
-36482.png
-14408.png
-56286.png
-48808.png
-47116.png
-34083.png
-10096.png
-01799.png
-26688.png
-42178.png
-18815.png
-27186.png
-30889.png
-23496.png
-64023.png
-09704.png
-55498.png
-07007.png
-44797.png
-12356.png
-37650.png
-36820.png
-17030.png
-66827.png
-65294.png
-25304.png
-59329.png
-62647.png
-60567.png
-08144.png
-59377.png
-21043.png
-60647.png
-14260.png
-24587.png
-31344.png
-60865.png
-66674.png
-59066.png
-65062.png
-48637.png
-32944.png
-18745.png
-34873.png
-31874.png
-66973.png
-31355.png
-03316.png
-02953.png
-38669.png
-26080.png
-34638.png
-49950.png
-27185.png
-29471.png
-33691.png
-12941.png
-03948.png
-25195.png
-66972.png
-36150.png
-18016.png
-37476.png
-57073.png
-26051.png
-29229.png
-46439.png
-33728.png
-19509.png
-39040.png
-45405.png
-54385.png
-18626.png
-41324.png
-09524.png
-12215.png
-18133.png
-37682.png
-52168.png
-18974.png
-12468.png
-34180.png
-32373.png
-48723.png
-40133.png
-55929.png
-59594.png
-16406.png
-09089.png
-68847.png
-65604.png
-15910.png
-39820.png
-33360.png
-17910.png
-27621.png
-34719.png
-10469.png
-64003.png
-42428.png
-30563.png
-54795.png
-01352.png
-15655.png
-26628.png
-35082.png
-23158.png
-43604.png
-01689.png
-66762.png
-32699.png
-34285.png
-13082.png
-23750.png
-36016.png
-16549.png
-06209.png
-58279.png
-62138.png
-09611.png
-54807.png
-26947.png
-00206.png
-65582.png
-25826.png
-02099.png
-65720.png
-61028.png
-14859.png
-13768.png
-53949.png
-06459.png
-00119.png
-29820.png
-07133.png
-17256.png
-42320.png
-18955.png
-53959.png
-12832.png
-01313.png
-06497.png
-27376.png
-66227.png
-59211.png
-59259.png
-20813.png
-27599.png
-28330.png
-14035.png
-17529.png
-33923.png
-28834.png
-36132.png
-24002.png
-10317.png
-36978.png
-07988.png
-00116.png
-55519.png
-40335.png
-10719.png
-18441.png
-63336.png
-50472.png
-44646.png
-24128.png
-24559.png
-18116.png
-05450.png
-43394.png
-24046.png
-53930.png
-63797.png
-18106.png
-21505.png
-10352.png
-38860.png
-30703.png
-20370.png
-25095.png
-14412.png
-58484.png
-06116.png
-15698.png
-64228.png
-27087.png
-48436.png
-21087.png
-60511.png
-62474.png
-64914.png
-44388.png
-55973.png
-01357.png
-40627.png
-53989.png
-61387.png
-03028.png
-56796.png
-62925.png
-18836.png
-27862.png
-30193.png
-57102.png
-24586.png
-33351.png
-18901.png
-07494.png
-46450.png
-67127.png
-55482.png
-24833.png
-04573.png
-02280.png
-00925.png
-19410.png
-44201.png
-59018.png
-19268.png
-25634.png
-46921.png
-63598.png
-15674.png
-51153.png
-07010.png
-27044.png
-37073.png
-68982.png
-29824.png
-15398.png
-32445.png
-18237.png
-67466.png
-40784.png
-27929.png
-36913.png
-31044.png
-27319.png
-23806.png
-24095.png
-10622.png
-03834.png
-47568.png
-64279.png
-15876.png
-36241.png
-05391.png
-34319.png
-15812.png
-55708.png
-41523.png
-44685.png
-51795.png
-47468.png
-06179.png
-38288.png
-64313.png
-35179.png
-13528.png
-67352.png
-18818.png
-38696.png
-22549.png
-29383.png
-06420.png
-40694.png
-26546.png
-31798.png
-63791.png
-66258.png
-13933.png
-44645.png
-37701.png
-40704.png
-27154.png
-59258.png
-34346.png
-23590.png
-40376.png
-65288.png
-51342.png
-41224.png
-16423.png
-60608.png
-17627.png
-29285.png
-39147.png
-14653.png
-24282.png
-62248.png
-62400.png
-37211.png
-48518.png
-62818.png
-52273.png
-08260.png
-36095.png
-22543.png
-56097.png
-39902.png
-06525.png
-43288.png
-37637.png
-54939.png
-17670.png
-17003.png
-04506.png
-43676.png
-05818.png
-42062.png
-24709.png
-50680.png
-30363.png
-58456.png
-19057.png
-46482.png
-27747.png
-09345.png
-69266.png
-58657.png
-40326.png
-20997.png
-61220.png
-32505.png
-62113.png
-66102.png
-66488.png
-28927.png
-38026.png
-46655.png
-42037.png
-60436.png
-11476.png
-55898.png
-29925.png
-02684.png
-02533.png
-27108.png
-33852.png
-65977.png
-54381.png
-12300.png
-31962.png
-05482.png
-32305.png
-45390.png
-00774.png
-20656.png
-22847.png
-14590.png
-39100.png
-03640.png
-56989.png
-38265.png
-53785.png
-00344.png
-07238.png
-47343.png
-61552.png
-32644.png
-40514.png
-44665.png
-60562.png
-00649.png
-42389.png
-50486.png
-36072.png
-11932.png
-23283.png
-03975.png
-18662.png
-21849.png
-34199.png
-34188.png
-14121.png
-13714.png
-02212.png
-15292.png
-35739.png
-38483.png
-31535.png
-50519.png
-55184.png
-15161.png
-52171.png
-20428.png
-34158.png
-13860.png
-31533.png
-45913.png
-56677.png
-29870.png
-21118.png
-21417.png
-56514.png
-13331.png
-52633.png
-36777.png
-16210.png
-48430.png
-52818.png
-60982.png
-25482.png
-64240.png
-66716.png
-35736.png
-62549.png
-44138.png
-23415.png
-34237.png
-24014.png
-20062.png
-50199.png
-62625.png
-56855.png
-56359.png
-49930.png
-20447.png
-20162.png
-07565.png
-05192.png
-56026.png
-43741.png
-08403.png
-61334.png
-60888.png
-22646.png
-01387.png
-22946.png
-29713.png
-31899.png
-29289.png
-17938.png
-13721.png
-51747.png
-66001.png
-27580.png
-55616.png
-18838.png
-12744.png
-01072.png
-49920.png
-00812.png
-16635.png
-00831.png
-59135.png
-45205.png
-48159.png
-24646.png
-56906.png
-14261.png
-28477.png
-57743.png
-38637.png
-29858.png
-51940.png
-68663.png
-23759.png
-43462.png
-69120.png
-14841.png
-05511.png
-47517.png
-11862.png
-23887.png
-19473.png
-34070.png
-47622.png
-59467.png
-09679.png
-30001.png
-52080.png
-63489.png
-34105.png
-59235.png
-56043.png
-31764.png
-38344.png
-13506.png
-34711.png
-40598.png
-28115.png
-19994.png
-51752.png
-50042.png
-50446.png
-23758.png
-42238.png
-23049.png
-44452.png
-19883.png
-12002.png
-50754.png
-60220.png
-27237.png
-09732.png
-32976.png
-48272.png
-69309.png
-35254.png
-64027.png
-22144.png
-49821.png
-23834.png
-64869.png
-07213.png
-59452.png
-18065.png
-24062.png
-06911.png
-01355.png
-11252.png
-33566.png
-48691.png
-25907.png
-18584.png
-21062.png
-34037.png
-35840.png
-21714.png
-45957.png
-59665.png
-07917.png
-32795.png
-13002.png
-59206.png
-64639.png
-66243.png
-64521.png
-64789.png
-37023.png
-57862.png
-41679.png
-61845.png
-63891.png
-04513.png
-21898.png
-13306.png
-51774.png
-05461.png
-06816.png
-01562.png
-10929.png
-65423.png
-66667.png
-45490.png
-23502.png
-48221.png
-11809.png
-65153.png
-00843.png
-06978.png
-42119.png
-13589.png
-04039.png
-08549.png
-14131.png
-07473.png
-22284.png
-48231.png
-40246.png
-69383.png
-57773.png
-29539.png
-25175.png
-11947.png
-14618.png
-35158.png
-37579.png
-49223.png
-53366.png
-31843.png
-33168.png
-21524.png
-20661.png
-53048.png
-69821.png
-22842.png
-53237.png
-39672.png
-16641.png
-56496.png
-26099.png
-52832.png
-53252.png
-42181.png
-51118.png
-13124.png
-02444.png
-46408.png
-18444.png
-08450.png
-14722.png
-37705.png
-12400.png
-05965.png
-50632.png
-42530.png
-60113.png
-40933.png
-18753.png
-09772.png
-65275.png
-18448.png
-23113.png
-01783.png
-68070.png
-32800.png
-24843.png
-29737.png
-19234.png
-07022.png
-62767.png
-34153.png
-06088.png
-15100.png
-27060.png
-50664.png
-37934.png
-44029.png
-49256.png
-41731.png
-07134.png
-32409.png
-33108.png
-34974.png
-36993.png
-53938.png
-16094.png
-40236.png
-02050.png
-01634.png
-04314.png
-06134.png
-19868.png
-56479.png
-55682.png
-08263.png
-03054.png
-30951.png
-14340.png
-45455.png
-08449.png
-57117.png
-04713.png
-60419.png
-14991.png
-57165.png
-54084.png
-39575.png
-13008.png
-54714.png
-01775.png
-46939.png
-53105.png
-31055.png
-52515.png
-38362.png
-47555.png
-56743.png
-25001.png
-67861.png
-47908.png
-14166.png
-40162.png
-06081.png
-62841.png
-23147.png
-17890.png
-21855.png
-04140.png
-53429.png
-54403.png
-02033.png
-00694.png
-17571.png
-65971.png
-49765.png
-12435.png
-35610.png
-65573.png
-42671.png
-21719.png
-17299.png
-08922.png
-26280.png
-66335.png
-47629.png
-28871.png
-63241.png
-48834.png
-68563.png
-68529.png
-64937.png
-01606.png
-11349.png
-51929.png
-01142.png
-47582.png
-09820.png
-58855.png
-37548.png
-66759.png
-46676.png
-62597.png
-18551.png
-68609.png
-55776.png
-50424.png
-15773.png
-00156.png
-05326.png
-58434.png
-48775.png
-20138.png
-48461.png
-20870.png
-69710.png
-46692.png
-47745.png
-39560.png
-38607.png
-34268.png
-41778.png
-02193.png
-32368.png
-43581.png
-48276.png
-08007.png
-51668.png
-09647.png
-47433.png
-07787.png
-42633.png
-20764.png
-36197.png
-44683.png
-23197.png
-18931.png
-14491.png
-61325.png
-53400.png
-11778.png
-68579.png
-63681.png
-13624.png
-43728.png
-47091.png
-29844.png
-00478.png
-25609.png
-58689.png
-21342.png
-29893.png
-67879.png
-42860.png
-39845.png
-34108.png
-68807.png
-18977.png
-20309.png
-05577.png
-19573.png
-31527.png
-63190.png
-41496.png
-31339.png
-15670.png
-02411.png
-03128.png
-56420.png
-27364.png
-55511.png
-46316.png
-32476.png
-12531.png
-41372.png
-53209.png
-06026.png
-33258.png
-54374.png
-25896.png
-62486.png
-47830.png
-03741.png
-06833.png
-27025.png
-01741.png
-25552.png
-54484.png
-09244.png
-47778.png
-68413.png
-39980.png
-36296.png
-24936.png
-39216.png
-57217.png
-18244.png
-07704.png
-63006.png
-15391.png
-65480.png
-19919.png
-29181.png
-09757.png
-37497.png
-50655.png
-06718.png
-54020.png
-31807.png
-10740.png
-55147.png
-10443.png
-09769.png
-27053.png
-62072.png
-64138.png
-67848.png
-28803.png
-65112.png
-45026.png
-69107.png
-02436.png
-64486.png
-25209.png
-67368.png
-04333.png
-64508.png
-03356.png
-22164.png
-14206.png
-38947.png
-48831.png
-57901.png
-50592.png
-34606.png
-07845.png
-67763.png
-69074.png
-48830.png
-65914.png
-60725.png
-27138.png
-59755.png
-62201.png
-06401.png
-16915.png
-09181.png
-42629.png
-27300.png
-17881.png
-13395.png
-45958.png
-19466.png
-07232.png
-30901.png
-52660.png
-36981.png
-42523.png
-41343.png
-31286.png
-65093.png
-27028.png
-29474.png
-13587.png
-69985.png
-26256.png
-15576.png
-55695.png
-00323.png
-56753.png
-24430.png
-59338.png
-34932.png
-43077.png
-25314.png
-19839.png
-03225.png
-65068.png
-54127.png
-64669.png
-39584.png
-31783.png
-30598.png
-15426.png
-63753.png
-65821.png
-17076.png
-53510.png
-55725.png
-59406.png
-25604.png
-43507.png
-35712.png
-49177.png
-14652.png
-03568.png
-43206.png
-13723.png
-69342.png
-07027.png
-09257.png
-53533.png
-61346.png
-01956.png
-22622.png
-13447.png
-27627.png
-49090.png
-14678.png
-14529.png
-45648.png
-51329.png
-66389.png
-00535.png
-19552.png
-31043.png
-20599.png
-40396.png
-24081.png
-61867.png
-61899.png
-11990.png
-52343.png
-49268.png
-55866.png
-69898.png
-56333.png
-41701.png
-67918.png
-29499.png
-35186.png
-50257.png
-40974.png
-09636.png
-28427.png
-66357.png
-01704.png
-54986.png
-26796.png
-17243.png
-37963.png
-17428.png
-50705.png
-16531.png
-34148.png
-51353.png
-19572.png
-44556.png
-41662.png
-37960.png
-13499.png
-43831.png
-23654.png
-08616.png
-09802.png
-37987.png
-53311.png
-20357.png
-29201.png
-04982.png
-26608.png
-55362.png
-56449.png
-69789.png
-38791.png
-43763.png
-09410.png
-19873.png
-44483.png
-28681.png
-58082.png
-03302.png
-43929.png
-53611.png
-46648.png
-42067.png
-51184.png
-09114.png
-56036.png
-48103.png
-23120.png
-67251.png
-68066.png
-64114.png
-66058.png
-63852.png
-66678.png
-01966.png
-63571.png
-10453.png
-24265.png
-30561.png
-34941.png
-55991.png
-69969.png
-34747.png
-12211.png
-26869.png
-56162.png
-06228.png
-57340.png
-60321.png
-59758.png
-15958.png
-21476.png
-47561.png
-24737.png
-04951.png
-03578.png
-68744.png
-19168.png
-45171.png
-59428.png
-44436.png
-01834.png
-05758.png
-49509.png
-19419.png
-00106.png
-44366.png
-08544.png
-37304.png
-55449.png
-66405.png
-31772.png
-09424.png
-57881.png
-23799.png
-30908.png
-01132.png
-02731.png
-37065.png
-23255.png
-01968.png
-26851.png
-04468.png
-21947.png
-43213.png
-42909.png
-40511.png
-07347.png
-38104.png
-67956.png
-48064.png
-62055.png
-54508.png
-43801.png
-68999.png
-00325.png
-28416.png
-67870.png
-37604.png
-05113.png
-16787.png
-09693.png
-01986.png
-05497.png
-08714.png
-57319.png
-49152.png
-26865.png
-42645.png
-18944.png
-04748.png
-30039.png
-02874.png
-56841.png
-41204.png
-17137.png
-15621.png
-51542.png
-48945.png
-52996.png
-55167.png
-49897.png
-32923.png
-44674.png
-13639.png
-63540.png
-07210.png
-56116.png
-32608.png
-54554.png
-39470.png
-61741.png
-54447.png
-23859.png
-21808.png
-62434.png
-17482.png
-54290.png
-56007.png
-47471.png
-13210.png
-23097.png
-24814.png
-16876.png
-08505.png
-69030.png
-52529.png
-69534.png
-50233.png
-50051.png
-35364.png
-04347.png
-06925.png
-00331.png
-60291.png
-64346.png
-40393.png
-11498.png
-57534.png
-03618.png
-55245.png
-62768.png
-61666.png
-41920.png
-64319.png
-56385.png
-56650.png
-10250.png
-35934.png
-53549.png
-11936.png
-27187.png
-11799.png
-48586.png
-65412.png
-12524.png
-56257.png
-17528.png
-00362.png
-42126.png
-21695.png
-37011.png
-08576.png
-04218.png
-05819.png
-60856.png
-19982.png
-57911.png
-07884.png
-34123.png
-00564.png
-55842.png
-53803.png
-36969.png
-65734.png
-52731.png
-37405.png
-46226.png
-50517.png
-43229.png
-08116.png
-62144.png
-30199.png
-59469.png
-69926.png
-16840.png
-23545.png
-33110.png
-01964.png
-67253.png
-46892.png
-61804.png
-49147.png
-55959.png
-61053.png
-34677.png
-10501.png
-44093.png
-56124.png
-59582.png
-06302.png
-13958.png
-10488.png
-65989.png
-24197.png
-23348.png
-67021.png
-35795.png
-66190.png
-38873.png
-02592.png
-38551.png
-09557.png
-60003.png
-62684.png
-45740.png
-60653.png
-59251.png
-68666.png
-40483.png
-33782.png
-02348.png
-15533.png
-50100.png
-24993.png
-43253.png
-20890.png
-10054.png
-66053.png
-61750.png
-69978.png
-35066.png
-50594.png
-14710.png
-00385.png
-11078.png
-17123.png
-39287.png
-55179.png
-65755.png
-31159.png
-18547.png
-61668.png
-40016.png
-50802.png
-27465.png
-60931.png
-33835.png
-16747.png
-47432.png
-53614.png
-40131.png
-13559.png
-47755.png
-36986.png
-14914.png
-06672.png
-53088.png
-54257.png
-41918.png
-55673.png
-06281.png
-30121.png
-21485.png
-09417.png
-55565.png
-64468.png
-02356.png
-00751.png
-01390.png
-24068.png
-39029.png
-38083.png
-06658.png
-46538.png
-41221.png
-18284.png
-11583.png
-50450.png
-44245.png
-36270.png
-21439.png
-18630.png
-43320.png
-60071.png
-41447.png
-08107.png
-23025.png
-49974.png
-26463.png
-39941.png
-07469.png
-32286.png
-14439.png
-65015.png
-33188.png
-47003.png
-68570.png
-42234.png
-30040.png
-47997.png
-24752.png
-01746.png
-07172.png
-44515.png
-52329.png
-53227.png
-03548.png
-60825.png
-45946.png
-42509.png
-16850.png
-16429.png
-25058.png
-42853.png
-05473.png
-28975.png
-35523.png
-68506.png
-66019.png
-55399.png
-47793.png
-17484.png
-24932.png
-38971.png
-63070.png
-30783.png
-47078.png
-18024.png
-66651.png
-13293.png
-19047.png
-14087.png
-18219.png
-40492.png
-17354.png
-38821.png
-50185.png
-16519.png
-22760.png
-64434.png
-30481.png
-02043.png
-08004.png
-23869.png
-30836.png
-67323.png
-08090.png
-18299.png
-13179.png
-07829.png
-15372.png
-11495.png
-18578.png
-03858.png
-23557.png
-39478.png
-23497.png
-53084.png
-44091.png
-10380.png
-12010.png
-12614.png
-53078.png
-64045.png
-36026.png
-64006.png
-11589.png
-13406.png
-19360.png
-45179.png
-49043.png
-01546.png
-40423.png
-06781.png
-35676.png
-33559.png
-34647.png
-05400.png
-04745.png
-22409.png
-06149.png
-52873.png
-29887.png
-42113.png
-38242.png
-47167.png
-18023.png
-09080.png
-30769.png
-43803.png
-58628.png
-01252.png
-31656.png
-42732.png
-27778.png
-44345.png
-30800.png
-06096.png
-14792.png
-51899.png
-00548.png
-28210.png
-66550.png
-08346.png
-45174.png
-49153.png
-09315.png
-36094.png
-57956.png
-32044.png
-04207.png
-19644.png
-67637.png
-48369.png
-36400.png
-61735.png
-50501.png
-46454.png
-21111.png
-02819.png
-08717.png
-66318.png
-17563.png
-18081.png
-13941.png
-16648.png
-21393.png
-13508.png
-53317.png
-34596.png
-30401.png
-66536.png
-56253.png
-13931.png
-52815.png
-27979.png
-44769.png
-02769.png
-56745.png
-53218.png
-31413.png
-21551.png
-60961.png
-42827.png
-11010.png
-27674.png
-39889.png
-40865.png
-13813.png
-35811.png
-25828.png
-26034.png
-61874.png
-21210.png
-57324.png
-00464.png
-61440.png
-37625.png
-46092.png
-36029.png
-06665.png
-11189.png
-44935.png
-27877.png
-37273.png
-06473.png
-40662.png
-36277.png
-16694.png
-09846.png
-00369.png
-20146.png
-12244.png
-00316.png
-24840.png
-64716.png
-57212.png
-45367.png
-22263.png
-43124.png
-12453.png
-55360.png
-64889.png
-00097.png
-60448.png
-16363.png
-14617.png
-54930.png
-29142.png
-49702.png
-45217.png
-28566.png
-23422.png
-28861.png
-18722.png
-25981.png
-14888.png
-23278.png
-24707.png
-03585.png
-57089.png
-64390.png
-39495.png
-43061.png
-36747.png
-56307.png
-09179.png
-50408.png
-13089.png
-43520.png
-35697.png
-11680.png
-12248.png
-56193.png
-43848.png
-28068.png
-09168.png
-49651.png
-50247.png
-27082.png
-65115.png
-22406.png
-33589.png
-49685.png
-13527.png
-18886.png
-55298.png
-09091.png
-18715.png
-04411.png
-67426.png
-03910.png
-44768.png
-13999.png
-08313.png
-41795.png
-64663.png
-00713.png
-08582.png
-59584.png
-20713.png
-32214.png
-26783.png
-04432.png
-58487.png
-45417.png
-19164.png
-15348.png
-54173.png
-28015.png
-44049.png
-23921.png
-35638.png
-30849.png
-47919.png
-55543.png
-66782.png
-27487.png
-69836.png
-51341.png
-53872.png
-60517.png
-05865.png
-33298.png
-48560.png
-03142.png
-28586.png
-20457.png
-35597.png
-60409.png
-31219.png
-01266.png
-69400.png
-00953.png
-14095.png
-15294.png
-03720.png
-64876.png
-62193.png
-07526.png
-65902.png
-38094.png
-56300.png
-54450.png
-29372.png
-55246.png
-46014.png
-39404.png
-36839.png
-38625.png
-22511.png
-38870.png
-56048.png
-66708.png
-48919.png
-55954.png
-24818.png
-05574.png
-19985.png
-06831.png
-11717.png
-03285.png
-62799.png
-69306.png
-03340.png
-17358.png
-46836.png
-26031.png
-09083.png
-32251.png
-58371.png
-26333.png
-63884.png
-21984.png
-18212.png
-27504.png
-32564.png
-12025.png
-41142.png
-23690.png
-24827.png
-65383.png
-51276.png
-12332.png
-58913.png
-02426.png
-34066.png
-52278.png
-51679.png
-10115.png
-41549.png
-06537.png
-13727.png
-21281.png
-45129.png
-55662.png
-16129.png
-39742.png
-09434.png
-29017.png
-26408.png
-11328.png
-00317.png
-28054.png
-68476.png
-64047.png
-30721.png
-64463.png
-11120.png
-54109.png
-57607.png
-33970.png
-19211.png
-28168.png
-32061.png
-23634.png
-59969.png
-05672.png
-03861.png
-41187.png
-46365.png
-15336.png
-43624.png
-45001.png
-31065.png
-06229.png
-31468.png
-54461.png
-32260.png
-45670.png
-61831.png
-69053.png
-25469.png
-65157.png
-47308.png
-59099.png
-19146.png
-21316.png
-64433.png
-19902.png
-49044.png
-34917.png
-13064.png
-67548.png
-38580.png
-01479.png
-63068.png
-03988.png
-43286.png
-33021.png
-14441.png
-11480.png
-53064.png
-61492.png
-04892.png
-27000.png
-52903.png
-32590.png
-66722.png
-16731.png
-07696.png
-01683.png
-62809.png
-69291.png
-51901.png
-17887.png
-19320.png
-51241.png
-05367.png
-23543.png
-55522.png
-36115.png
-13302.png
-13176.png
-56408.png
-02781.png
-29605.png
-39934.png
-51151.png
-54322.png
-03964.png
-17452.png
-09848.png
-01584.png
-15686.png
-66374.png
-22816.png
-42579.png
-25233.png
-31932.png
-56261.png
-32382.png
-58361.png
-65037.png
-06740.png
-18975.png
-68716.png
-27547.png
-68780.png
-59790.png
-13184.png
-50302.png
-25121.png
-65941.png
-63762.png
-27289.png
-65619.png
-32745.png
-60606.png
-34551.png
-49951.png
-31418.png
-24090.png
-56358.png
-47445.png
-55026.png
-42622.png
-36651.png
-23579.png
-11044.png
-28810.png
-37717.png
-42849.png
-67468.png
-60114.png
-63160.png
-48044.png
-01116.png
-65728.png
-53692.png
-29279.png
-68500.png
-19733.png
-11385.png
-64760.png
-41628.png
-22881.png
-11544.png
-67091.png
-31630.png
-46582.png
-55426.png
-30499.png
-69382.png
-19514.png
-47526.png
-23144.png
-04165.png
-09311.png
-31235.png
-68333.png
-41162.png
-40529.png
-64612.png
-03956.png
-47665.png
-68811.png
-55655.png
-58750.png
-49604.png
-53382.png
-56595.png
-46256.png
-54780.png
-64362.png
-16472.png
-25837.png
-15654.png
-45038.png
-64924.png
-28449.png
-05069.png
-66193.png
-02024.png
-62097.png
-44455.png
-60502.png
-60202.png
-53278.png
-42196.png
-40946.png
-47328.png
-63292.png
-12223.png
-46801.png
-38940.png
-39193.png
-41485.png
-57669.png
-59691.png
-15598.png
-19229.png
-09829.png
-69436.png
-36769.png
-63640.png
-23196.png
-50605.png
-25242.png
-26348.png
-51321.png
-64140.png
-34318.png
-16342.png
-41723.png
-15039.png
-27281.png
-59847.png
-56988.png
-65315.png
-38896.png
-51445.png
-12336.png
-23071.png
-41704.png
-31919.png
-61853.png
-40028.png
-12120.png
-25303.png
-51429.png
-67704.png
-32726.png
-04173.png
-03282.png
-15884.png
-14290.png
-53965.png
-48643.png
-13547.png
-13534.png
-58929.png
-38217.png
-62650.png
-42088.png
-06958.png
-59317.png
-63799.png
-40481.png
-69728.png
-30872.png
-43471.png
-25294.png
-47454.png
-68283.png
-61136.png
-21218.png
-38040.png
-09335.png
-33217.png
-67353.png
-20822.png
-06514.png
-62498.png
-20907.png
-36814.png
-37648.png
-13443.png
-39392.png
-30969.png
-31290.png
-00750.png
-20641.png
-52932.png
-34499.png
-54459.png
-63358.png
-63413.png
-13769.png
-57441.png
-43693.png
-05140.png
-39615.png
-55907.png
-58759.png
-44324.png
-30696.png
-10722.png
-26072.png
-51417.png
-59991.png
-05659.png
-20882.png
-41413.png
-01967.png
-33693.png
-13109.png
-48348.png
-54331.png
-05703.png
-28940.png
-28024.png
-45096.png
-32941.png
-02990.png
-35209.png
-58494.png
-53975.png
-24780.png
-68756.png
-42886.png
-14191.png
-30345.png
-30153.png
-27593.png
-40788.png
-36921.png
-47796.png
-44127.png
-52909.png
-33300.png
-15806.png
-57930.png
-39938.png
-28760.png
-36657.png
-21550.png
-63354.png
-42713.png
-40984.png
-30791.png
-07126.png
-30392.png
-02897.png
-07791.png
-11822.png
-61391.png
-28221.png
-07142.png
-00596.png
-62721.png
-57654.png
-48069.png
-39094.png
-15709.png
-52233.png
-02092.png
-00075.png
-53750.png
-27780.png
-38000.png
-15154.png
-42310.png
-41748.png
-09169.png
-02391.png
-18470.png
-55989.png
-12675.png
-47720.png
-21754.png
-23128.png
-32493.png
-12331.png
-46228.png
-17889.png
-61665.png
-10662.png
-63594.png
-30383.png
-60803.png
-22180.png
-26820.png
-06812.png
-43315.png
-18465.png
-27690.png
-38385.png
-51114.png
-46277.png
-03260.png
-48712.png
-64636.png
-05251.png
-66268.png
-01542.png
-26417.png
-26507.png
-61688.png
-67241.png
-65930.png
-32693.png
-62118.png
-15219.png
-31215.png
-06682.png
-38833.png
-62443.png
-06559.png
-25270.png
-29916.png
-50440.png
-64017.png
-55685.png
-47869.png
-19793.png
-14747.png
-49835.png
-03397.png
-62672.png
-15404.png
-56488.png
-61798.png
-01430.png
-23070.png
-57687.png
-06617.png
-61648.png
-17455.png
-63842.png
-69919.png
-31694.png
-38764.png
-16054.png
-46703.png
-02971.png
-41727.png
-27104.png
-31534.png
-23587.png
-09956.png
-16373.png
-10220.png
-41651.png
-58847.png
-33579.png
-49595.png
-25671.png
-00352.png
-59046.png
-20012.png
-06843.png
-19362.png
-46194.png
-42189.png
-08305.png
-28837.png
-68743.png
-50894.png
-15744.png
-55235.png
-58358.png
-03549.png
-37079.png
-19864.png
-37104.png
-60887.png
-07178.png
-20818.png
-65550.png
-03519.png
-33850.png
-35843.png
-12760.png
-29933.png
-24094.png
-58252.png
-46254.png
-27482.png
-01491.png
-13799.png
-26259.png
-24359.png
-41972.png
-16155.png
-49558.png
-10814.png
-33956.png
-02604.png
-27467.png
-07581.png
-08882.png
-15870.png
-37191.png
-04904.png
-54261.png
-68796.png
-09440.png
-01819.png
-36715.png
-27387.png
-14868.png
-42267.png
-48150.png
-61698.png
-42043.png
-18094.png
-31407.png
-38433.png
-43018.png
-61584.png
-30188.png
-27520.png
-30276.png
-44879.png
-47559.png
-04204.png
-30159.png
-01158.png
-09167.png
-12751.png
-53515.png
-07333.png
-19139.png
-04401.png
-34339.png
-63515.png
-54040.png
-32897.png
-11261.png
-33328.png
-39978.png
-65786.png
-46083.png
-35855.png
-35619.png
-06078.png
-16412.png
-36527.png
-25735.png
-09896.png
-02649.png
-66861.png
-65081.png
-53384.png
-25010.png
-04565.png
-57596.png
-62591.png
-17501.png
-62035.png
-40196.png
-14929.png
-56221.png
-04769.png
-67568.png
-58872.png
-32006.png
-13139.png
-14502.png
-32346.png
-00461.png
-63300.png
-18160.png
-19741.png
-47948.png
-48132.png
-05894.png
-34858.png
-64168.png
-27240.png
-55968.png
-38381.png
-20545.png
-36112.png
-39129.png
-35592.png
-30593.png
-06267.png
-52570.png
-48680.png
-41809.png
-07688.png
-57553.png
-24167.png
-17401.png
-12058.png
-01167.png
-42994.png
-15967.png
-60947.png
-16919.png
-43454.png
-51949.png
-10923.png
-38416.png
-61667.png
-16037.png
-34076.png
-31732.png
-34575.png
-27955.png
-43237.png
-24039.png
-46856.png
-47688.png
-20859.png
-17511.png
-50477.png
-63904.png
-06042.png
-39350.png
-43758.png
-64750.png
-69851.png
-20757.png
-31083.png
-58809.png
-55834.png
-11302.png
-13394.png
-19468.png
-69307.png
-07140.png
-27588.png
-05315.png
-49122.png
-17340.png
-53178.png
-63361.png
-05838.png
-19956.png
-63187.png
-10321.png
-13197.png
-53539.png
-05308.png
-64860.png
-04775.png
-68296.png
-07096.png
-17353.png
-00122.png
-64767.png
-09858.png
-55751.png
-11257.png
-07275.png
-54193.png
-43500.png
-38954.png
-48961.png
-23177.png
-15142.png
-17228.png
-60177.png
-55772.png
-00811.png
-03920.png
-40473.png
-66794.png
-47917.png
-50291.png
-38284.png
-61487.png
-25640.png
-65194.png
-20442.png
-57179.png
-67741.png
-06030.png
-05389.png
-17092.png
-68290.png
-09015.png
-56803.png
-38741.png
-39973.png
-18315.png
-00242.png
-67926.png
-04161.png
-24478.png
-40757.png
-12413.png
-00128.png
-64922.png
-37133.png
-16002.png
-43834.png
-35965.png
-37062.png
-30844.png
-28246.png
-43291.png
-55116.png
-26296.png
-66804.png
-31359.png
-00907.png
-66509.png
-28038.png
-69336.png
-36788.png
-00094.png
-65231.png
-69259.png
-42366.png
-23528.png
-58335.png
-23166.png
-51412.png
-53594.png
-11160.png
-23889.png
-60596.png
-53700.png
-00588.png
-08361.png
-68142.png
-64053.png
-39866.png
-36744.png
-27600.png
-33429.png
-34378.png
-22642.png
-35511.png
-14158.png
-22285.png
-67624.png
-46657.png
-43141.png
-24396.png
-10524.png
-41796.png
-09001.png
-47145.png
-14995.png
-64467.png
-54235.png
-56782.png
-34246.png
-15854.png
-45885.png
-47447.png
-62759.png
-63649.png
-57337.png
-16386.png
-46355.png
-62520.png
-28448.png
-29454.png
-30686.png
-22720.png
-54932.png
-51035.png
-05646.png
-62292.png
-01208.png
-55329.png
-46850.png
-26452.png
-09624.png
-41227.png
-19661.png
-23427.png
-48330.png
-00293.png
-50311.png
-04055.png
-58866.png
-28714.png
-48788.png
-60713.png
-45663.png
-57545.png
-09353.png
-53000.png
-46361.png
-58108.png
-09422.png
-44419.png
-65390.png
-01272.png
-35355.png
-67459.png
-42441.png
-07200.png
-08173.png
-10361.png
-14427.png
-26088.png
-13462.png
-45674.png
-69879.png
-56538.png
-48108.png
-51193.png
-27510.png
-41245.png
-35580.png
-23186.png
-19141.png
-49586.png
-48227.png
-51997.png
-65820.png
-35281.png
-58247.png
-01960.png
-04442.png
-29305.png
-64329.png
-28778.png
-36054.png
-68441.png
-39829.png
-23204.png
-45162.png
-39414.png
-34053.png
-68546.png
-11854.png
-31371.png
-53095.png
-31975.png
-04057.png
-41953.png
-55007.png
-49615.png
-14011.png
-10211.png
-64286.png
-01555.png
-01831.png
-16010.png
-12117.png
-42634.png
-38518.png
-17389.png
-06602.png
-53758.png
-59419.png
-01111.png
-60117.png
-21674.png
-05658.png
-49501.png
-65657.png
-16255.png
-62483.png
-00195.png
-58253.png
-52490.png
-25760.png
-30289.png
-52111.png
-62848.png
-10148.png
-38348.png
-36299.png
-55856.png
-38019.png
-12027.png
-20578.png
-56172.png
-36301.png
-02222.png
-34725.png
-11573.png
-02049.png
-05160.png
-25582.png
-46028.png
-64317.png
-06619.png
-43513.png
-66012.png
-52001.png
-21027.png
-25487.png
-04115.png
-26307.png
-20752.png
-38541.png
-61240.png
-14338.png
-59627.png
-57663.png
-68069.png
-24966.png
-61015.png
-16890.png
-46257.png
-32827.png
-31367.png
-11099.png
-16964.png
-69046.png
-36698.png
-43302.png
-25795.png
-67940.png
-65634.png
-43429.png
-39312.png
-59958.png
-32063.png
-07722.png
-27624.png
-63739.png
-06271.png
-53702.png
-07039.png
-18904.png
-63430.png
-22236.png
-51908.png
-30873.png
-57694.png
-53077.png
-06569.png
-56960.png
-37503.png
-00112.png
-00219.png
-33749.png
-28528.png
-18226.png
-33764.png
-40337.png
-30432.png
-13533.png
-10259.png
-11724.png
-58890.png
-31842.png
-37795.png
-48536.png
-66877.png
-66228.png
-35283.png
-30716.png
-60619.png
-42168.png
-45832.png
-19151.png
-65707.png
-63191.png
-06452.png
-32518.png
-30570.png
-67134.png
-03799.png
-56033.png
-14528.png
-54656.png
-17161.png
-59007.png
-15117.png
-38864.png
-57506.png
-10345.png
-36976.png
-36827.png
-18107.png
-26383.png
-20226.png
-11474.png
-43247.png
-30181.png
-36563.png
-39183.png
-48592.png
-19740.png
-13237.png
-56625.png
-25999.png
-41760.png
-29019.png
-50909.png
-39018.png
-31467.png
-18233.png
-46652.png
-27840.png
-65704.png
-13047.png
-53858.png
-55238.png
-22833.png
-11191.png
-28717.png
-32607.png
-16089.png
-19646.png
-00448.png
-20505.png
-29451.png
-63736.png
-14242.png
-35636.png
-27567.png
-09160.png
-19914.png
-57609.png
-38538.png
-61234.png
-54324.png
-46443.png
-47877.png
-55809.png
-69360.png
-27383.png
-54332.png
-14254.png
-56933.png
-67405.png
-48277.png
-08592.png
-21670.png
-41757.png
-13720.png
-01993.png
-69137.png
-67924.png
-08802.png
-30935.png
-33306.png
-08037.png
-13077.png
-69783.png
-46956.png
-03376.png
-03546.png
-23604.png
-66553.png
-50933.png
-23723.png
-37799.png
-52459.png
-08915.png
-15926.png
-24789.png
-13518.png
-02269.png
-46216.png
-44403.png
-31052.png
-00692.png
-09507.png
-53296.png
-10231.png
-57209.png
-24219.png
-28910.png
-22155.png
-25436.png
-43768.png
-23735.png
-68775.png
-64528.png
-45234.png
-58807.png
-66296.png
-49413.png
-47211.png
-25371.png
-66885.png
-12560.png
-47076.png
-18311.png
-35865.png
-33405.png
-46739.png
-06069.png
-58002.png
-05366.png
-41347.png
-08834.png
-25272.png
-39993.png
-35305.png
-11702.png
-14383.png
-23062.png
-40366.png
-34826.png
-47149.png
-26931.png
-02706.png
-27962.png
-32598.png
-69067.png
-34429.png
-50537.png
-03149.png
-56314.png
-07425.png
-65838.png
-32175.png
-13403.png
-10657.png
-47580.png
-26810.png
-50516.png
-31282.png
-49740.png
-68345.png
-27637.png
-23665.png
-38792.png
-47643.png
-62497.png
-67404.png
-58547.png
-11052.png
-49303.png
-26374.png
-45396.png
-50308.png
-50576.png
-14986.png
-61393.png
-46431.png
-21308.png
-38380.png
-40478.png
-09617.png
-17913.png
-51266.png
-30319.png
-53036.png
-18742.png
-11919.png
-58206.png
-62543.png
-04746.png
-27022.png
-10731.png
-00395.png
-21906.png
-10823.png
-31719.png
-39887.png
-43251.png
-18601.png
-36508.png
-66016.png
-47906.png
-54773.png
-49102.png
-07912.png
-57912.png
-25238.png
-21258.png
-22668.png
-43549.png
-06414.png
-55457.png
-36377.png
-00660.png
-37284.png
-12719.png
-00393.png
-67721.png
-31029.png
-48748.png
-60757.png
-47243.png
-10470.png
-05791.png
-62532.png
-62705.png
-67629.png
-24873.png
-60636.png
-50285.png
-18816.png
-08540.png
-28083.png
-39607.png
-63020.png
-47907.png
-32173.png
-24597.png
-60952.png
-14413.png
-60809.png
-12050.png
-19363.png
-18499.png
-20107.png
-20577.png
-13830.png
-33239.png
-53374.png
-56466.png
-04666.png
-56673.png
-64196.png
-22278.png
-03487.png
-56089.png
-36419.png
-69408.png
-11958.png
-61199.png
-22201.png
-47728.png
-42936.png
-06985.png
-59788.png
-69912.png
-32970.png
-15194.png
-17775.png
-49221.png
-02960.png
-10437.png
-64234.png
-22079.png
-05472.png
-14111.png
-54808.png
-19896.png
-30971.png
-16685.png
-17334.png
-08201.png
-42165.png
-58861.png
-50663.png
-26151.png
-37745.png
-49057.png
-48849.png
-53684.png
-65437.png
-03762.png
-27007.png
-48036.png
-11352.png
-63157.png
-66172.png
-18490.png
-02441.png
-18230.png
-37492.png
-27059.png
-35904.png
-67671.png
-04000.png
-69086.png
-59571.png
-62521.png
-35954.png
-60660.png
-68039.png
-47244.png
-68518.png
-46961.png
-38898.png
-16243.png
-67167.png
-41958.png
-01559.png
-21862.png
-04562.png
-02710.png
-02944.png
-36173.png
-27359.png
-42402.png
-16130.png
-66503.png
-59031.png
-19308.png
-37898.png
-30279.png
-60482.png
-69960.png
-33520.png
-36052.png
-42167.png
-35507.png
-11866.png
-28865.png
-28539.png
-57218.png
-21981.png
-40791.png
-38868.png
-51780.png
-33669.png
-52697.png
-43762.png
-10369.png
-60187.png
-62693.png
-54635.png
-06802.png
-66790.png
-00198.png
-03047.png
-04220.png
-21514.png
-67890.png
-54756.png
-59153.png
-39681.png
-09338.png
-21325.png
-29575.png
-55144.png
-31193.png
-42681.png
-56859.png
-66718.png
-56131.png
-07607.png
-52754.png
-10639.png
-60540.png
-69838.png
-27176.png
-41349.png
-17407.png
-13280.png
-42000.png
-25672.png
-27620.png
-15103.png
-04548.png
-06983.png
-38737.png
-12349.png
-48744.png
-35578.png
-62872.png
-52040.png
-29373.png
-62974.png
-50600.png
-14574.png
-15710.png
-24475.png
-51931.png
-46803.png
-17480.png
-26774.png
-53367.png
-48215.png
-09753.png
-50617.png
-14731.png
-40740.png
-61224.png
-17702.png
-04999.png
-01403.png
-62481.png
-15375.png
-06143.png
-32348.png
-36765.png
-47820.png
-37549.png
-05263.png
-53052.png
-26502.png
-00653.png
-20782.png
-18394.png
-56771.png
-40237.png
-56037.png
-32684.png
-33069.png
-44328.png
-67949.png
-45815.png
-62564.png
-27658.png
-21064.png
-44460.png
-10342.png
-12999.png
-57516.png
-62928.png
-43470.png
-33805.png
-44839.png
-51936.png
-58919.png
-51082.png
-56393.png
-45300.png
-16902.png
-66780.png
-47846.png
-10817.png
-09684.png
-01516.png
-69943.png
-11667.png
-42065.png
-54430.png
-06129.png
-64640.png
-08507.png
-21431.png
-08545.png
-21608.png
-33426.png
-06431.png
-27238.png
-69419.png
-64108.png
-45667.png
-05097.png
-12863.png
-55688.png
-01777.png
-19450.png
-59285.png
-00047.png
-04997.png
-42707.png
-37968.png
-60280.png
-11400.png
-04973.png
-15212.png
-59994.png
-03867.png
-57618.png
-61039.png
-23305.png
-67723.png
-04789.png
-52916.png
-03184.png
-48519.png
-41082.png
-15931.png
-31095.png
-01215.png
-57050.png
-51359.png
-29299.png
-15922.png
-40092.png
-37042.png
-49334.png
-68930.png
-04659.png
-26915.png
-11180.png
-32893.png
-46294.png
-31452.png
-13582.png
-11289.png
-66928.png
-22949.png
-41118.png
-64562.png
-44690.png
-65393.png
-12621.png
-22106.png
-27541.png
-13196.png
-69545.png
-39012.png
-02380.png
-03345.png
-03759.png
-43224.png
-46078.png
-20206.png
-66281.png
-20768.png
-23377.png
-53560.png
-44895.png
-28451.png
-27314.png
-50556.png
-20323.png
-13349.png
-32519.png
-65842.png
-58555.png
-53889.png
-61536.png
-07940.png
-00338.png
-67060.png
-48671.png
-37344.png
-03208.png
-14264.png
-45330.png
-05026.png
-12745.png
-33547.png
-31898.png
-48119.png
-06895.png
-52855.png
-27927.png
-23775.png
-44872.png
-09039.png
-24402.png
-29740.png
-54010.png
-32354.png
-19507.png
-64469.png
-36158.png
-06511.png
-66361.png
-53860.png
-28112.png
-08775.png
-48876.png
-39437.png
-08590.png
-44438.png
-66404.png
-10518.png
-37569.png
-37671.png
-11295.png
-22179.png
-14395.png
-39162.png
-19460.png
-20680.png
-64257.png
-04627.png
-15071.png
-10068.png
-36002.png
-30600.png
-68618.png
-12606.png
-20525.png
-57099.png
-31172.png
-29674.png
-09623.png
-07714.png
-15918.png
-04426.png
-25000.png
-09728.png
-47198.png
-64795.png
-03136.png
-03417.png
-12099.png
-33412.png
-51406.png
-59453.png
-43627.png
-34277.png
-11292.png
-00425.png
-18670.png
-66534.png
-30542.png
-03131.png
-00164.png
-58285.png
-30550.png
-07137.png
-40779.png
-46998.png
-17239.png
-27782.png
-39626.png
-54621.png
-30898.png
-12425.png
-03552.png
-19639.png
-16765.png
-49836.png
-38825.png
-65169.png
-04835.png
-56200.png
-60821.png
-59509.png
-69262.png
-14221.png
-51683.png
-07411.png
-47103.png
-58230.png
-14146.png
-47015.png
-48032.png
-61761.png
-15228.png
-48947.png
-11167.png
-17456.png
-34786.png
-69214.png
-22985.png
-40130.png
-48759.png
-41297.png
-67938.png
-65127.png
-08842.png
-29766.png
-27280.png
-36902.png
-49251.png
-24796.png
-01002.png
-04804.png
-38623.png
-59367.png
-27628.png
-51801.png
-47938.png
-15267.png
-15734.png
-37850.png
-19425.png
-38409.png
-45136.png
-66208.png
-38849.png
-56022.png
-26597.png
-49655.png
-35430.png
-57948.png
-51629.png
-57583.png
-21247.png
-68280.png
-53668.png
-02110.png
-49342.png
-42481.png
-59837.png
-06974.png
-03062.png
-54694.png
-12631.png
-02304.png
-55139.png
-24963.png
-55469.png
-27683.png
-00266.png
-09839.png
-11152.png
-09929.png
-32703.png
-59947.png
-59289.png
-63911.png
-05386.png
-58723.png
-22452.png
-10850.png
-32376.png
-32570.png
-65566.png
-03539.png
-15464.png
-42931.png
-07728.png
-52769.png
-02888.png
-00360.png
-68883.png
-64389.png
-66824.png
-47513.png
-41167.png
-06083.png
-43931.png
-49979.png
-61806.png
-46175.png
-34723.png
-15914.png
-33352.png
-63264.png
-01780.png
-52399.png
-35000.png
-37779.png
-06507.png
-44830.png
-56255.png
-11240.png
-58488.png
-32774.png
-01225.png
-28742.png
-08061.png
-51524.png
-61454.png
-04877.png
-34307.png
-16998.png
-26992.png
-60228.png
-41112.png
-44152.png
-46555.png
-32508.png
-39861.png
-67083.png
-27886.png
-38689.png
-37315.png
-15420.png
-53640.png
-69599.png
-08498.png
-47064.png
-59376.png
-18850.png
-39108.png
-19570.png
-61660.png
-38233.png
-42656.png
-34366.png
-49411.png
-67341.png
-01581.png
-25515.png
-26620.png
-49751.png
-22245.png
-43503.png
-46551.png
-31709.png
-13015.png
-49677.png
-30006.png
-26941.png
-10195.png
-47975.png
-19716.png
-61009.png
-69698.png
-45735.png
-59413.png
-13202.png
-57511.png
-21470.png
-59262.png
-31284.png
-15893.png
-58583.png
-26152.png
-61808.png
-68572.png
-37594.png
-03042.png
-52454.png
-43918.png
-24393.png
-30949.png
-49791.png
-26062.png
-39160.png
-58375.png
-56015.png
-20972.png
-10420.png
-15098.png
-46427.png
-41659.png
-20543.png
-31212.png
-61453.png
-38876.png
-04063.png
-55492.png
-16398.png
-05776.png
-54777.png
-54050.png
-52003.png
-04848.png
-67002.png
-51944.png
-53578.png
-30455.png
-35269.png
-04431.png
-27991.png
-00275.png
-01781.png
-35037.png
-44770.png
-56452.png
-54404.png
-07575.png
-47647.png
-08572.png
-19641.png
-52809.png
-43216.png
-60043.png
-25657.png
-66337.png
-16977.png
-52503.png
-17162.png
-31941.png
-29712.png
-23345.png
-18982.png
-60069.png
-38081.png
-30630.png
-01445.png
-00417.png
-69855.png
-19310.png
-18223.png
-41194.png
-05073.png
-59937.png
-48730.png
-29641.png
-02546.png
-36084.png
-05106.png
-40739.png
-06508.png
-02943.png
-43968.png
-59133.png
-10826.png
-10593.png
-27066.png
-34888.png
-45907.png
-37619.png
-25047.png
-60915.png
-65991.png
-40656.png
-09477.png
-68711.png
-67121.png
-60720.png
-04934.png
-65274.png
-38196.png
-21933.png
-28764.png
-46585.png
-19129.png
-10058.png
-59050.png
-23230.png
-42379.png
-67395.png
-42075.png
-35848.png
-26321.png
-63174.png
-57271.png
-62427.png
-59610.png
-59541.png
-27437.png
-35073.png
-67122.png
-69105.png
-30155.png
-11178.png
-50733.png
-35622.png
-64923.png
-44415.png
-25160.png
-62425.png
-41566.png
-42852.png
-47404.png
-44545.png
-27491.png
-01234.png
-40643.png
-40829.png
-16969.png
-23074.png
-48290.png
-32776.png
-61040.png
-49432.png
-61404.png
-64396.png
-65813.png
-33650.png
-52462.png
-57074.png
-62905.png
-02349.png
-20208.png
-42534.png
-28357.png
-44192.png
-19417.png
-66100.png
-67387.png
-52798.png
-49314.png
-42224.png
-35101.png
-07533.png
-27676.png
-20417.png
-17381.png
-47646.png
-20648.png
-05660.png
-64868.png
-05015.png
-35967.png
-39847.png
-07604.png
-64289.png
-07474.png
-55567.png
-64576.png
-07863.png
-41802.png
-63416.png
-15767.png
-14614.png
-58212.png
-69127.png
-03803.png
-07067.png
-25256.png
-22826.png
-09200.png
-61548.png
-41468.png
-22735.png
-46523.png
-48242.png
-53969.png
-64475.png
-57460.png
-59637.png
-26263.png
-33905.png
-44668.png
-46733.png
-25210.png
-24988.png
-07227.png
-56008.png
-42378.png
-44633.png
-53297.png
-07873.png
-24853.png
-09971.png
-07976.png
-26540.png
-10793.png
-50678.png
-25123.png
-25898.png
-11449.png
-37697.png
-22134.png
-32752.png
-02523.png
-07624.png
-31781.png
-45215.png
-16483.png
-43975.png
-17704.png
-38427.png
-39769.png
-00520.png
-02249.png
-58406.png
-56932.png
-57404.png
-26754.png
-67054.png
-03407.png
-33570.png
-61375.png
-09369.png
-00356.png
-06355.png
-22153.png
-05500.png
-63150.png
-40737.png
-49226.png
-25890.png
-37909.png
-62294.png
-12109.png
-02076.png
-23349.png
-37396.png
-62415.png
-33703.png
-44553.png
-53347.png
-16450.png
-28106.png
-54888.png
-34584.png
-12986.png
-57341.png
-26117.png
-56814.png
-16753.png
-48673.png
-10927.png
-14182.png
-63277.png
-29840.png
-25365.png
-64379.png
-68509.png
-43620.png
-05901.png
-36523.png
-12966.png
-15597.png
-32901.png
-59987.png
-13853.png
-32446.png
-28223.png
-29833.png
-57318.png
-63770.png
-28820.png
-41677.png
-41812.png
-04339.png
-17738.png
-14682.png
-16778.png
-14184.png
-01577.png
-66905.png
-19193.png
-46687.png
-25397.png
-31497.png
-31661.png
-68830.png
-06942.png
-49368.png
-57623.png
-09252.png
-49181.png
-22941.png
-37536.png
-01119.png
-15939.png
-16487.png
-24861.png
-30658.png
-23018.png
-53878.png
-50307.png
-61619.png
-37977.png
-20134.png
-13821.png
-36645.png
-29658.png
-29039.png
-00140.png
-39881.png
-21396.png
-39152.png
-09773.png
-37931.png
-20783.png
-47223.png
-42584.png
-30351.png
-68752.png
-49275.png
-69441.png
-47079.png
-48439.png
-19598.png
-56194.png
-26531.png
-34462.png
-10089.png
-27610.png
-10930.png
-43447.png
-01912.png
-66237.png
-64828.png
-60669.png
-25526.png
-32861.png
-06108.png
-54081.png
-39070.png
-22064.png
-67269.png
-67642.png
-36819.png
-02172.png
-45303.png
-25577.png
-06904.png
-16865.png
-01428.png
-64113.png
-41446.png
-30845.png
-52368.png
-46807.png
-00229.png
-48664.png
-59113.png
-64354.png
-55787.png
-08099.png
-28899.png
-36660.png
-52557.png
-32030.png
-38915.png
-41292.png
-10683.png
-40886.png
-14761.png
-50797.png
-36083.png
-50862.png
-12452.png
-34633.png
-66148.png
-15795.png
-40271.png
-59236.png
-18155.png
-59508.png
-06687.png
-06124.png
-20936.png
-14739.png
-33460.png
-55400.png
-02667.png
-27752.png
-55008.png
-31073.png
-09418.png
-47059.png
-49213.png
-66636.png
-41830.png
-40240.png
-08372.png
-32950.png
-03122.png
-06276.png
-67027.png
-47675.png
-55949.png
-38698.png
-29757.png
-37151.png
-69170.png
-54352.png
-10307.png
-66054.png
-59825.png
-31017.png
-50437.png
-08630.png
-58772.png
-00088.png
-02857.png
-60554.png
-36454.png
-61910.png
-35544.png
-39700.png
-47354.png
-45463.png
-19964.png
-50621.png
-38030.png
-59368.png
-36912.png
-28228.png
-37584.png
-59164.png
-46268.png
-27619.png
-52385.png
-34300.png
-13275.png
-47794.png
-31103.png
-02696.png
-44637.png
-07404.png
-67747.png
-54531.png
-51127.png
-61210.png
-07591.png
-44933.png
-14026.png
-25544.png
-28169.png
-18405.png
-55471.png
-53732.png
-34887.png
-22234.png
-46308.png
-47609.png
-17672.png
-28615.png
-68029.png
-32583.png
-61013.png
-10694.png
-38423.png
-15385.png
-68681.png
-17888.png
-17555.png
-15380.png
-66958.png
-34842.png
-11155.png
-09467.png
-33842.png
-35126.png
-21168.png
-33136.png
-29996.png
-66024.png
-60902.png
-16059.png
-29391.png
-06311.png
-38439.png
-28274.png
-04226.png
-08766.png
-31931.png
-33220.png
-27845.png
-37565.png
-21362.png
-61617.png
-11745.png
-25444.png
-23710.png
-09598.png
-21482.png
-01153.png
-38099.png
-40060.png
-40334.png
-50433.png
-37927.png
-68894.png
-42845.png
-04166.png
-62260.png
-46848.png
-24735.png
-41444.png
-36898.png
-40105.png
-27414.png
-45537.png
-06712.png
-16462.png
-28267.png
-12432.png
-37474.png
-32209.png
-51848.png
-53900.png
-65811.png
-62177.png
-28849.png
-34278.png
-38992.png
-18806.png
-20340.png
-56957.png
-33666.png
-52087.png
-38101.png
-21061.png
-59140.png
-56688.png
-32544.png
-06642.png
-01536.png
-37573.png
-47963.png
-35747.png
-30601.png
-30047.png
-24089.png
-16785.png
-31220.png
-29938.png
-66801.png
-50036.png
-42971.png
-07488.png
-06764.png
-27224.png
-33113.png
-43937.png
-23827.png
-21394.png
-08052.png
-12476.png
-48552.png
-21627.png
-38125.png
-16723.png
-19307.png
-19379.png
-56268.png
-46390.png
-14511.png
-69632.png
-44913.png
-43630.png
-34170.png
-06625.png
-17417.png
-04587.png
-52010.png
-69558.png
-47660.png
-10786.png
-10104.png
-29788.png
-27651.png
-67811.png
-00077.png
-01806.png
-23552.png
-05705.png
-30907.png
-44859.png
-26297.png
-63478.png
-24261.png
-31708.png
-15871.png
-58984.png
-62562.png
-03035.png
-43436.png
-03333.png
-22740.png
-61818.png
-01892.png
-02190.png
-44258.png
-44242.png
-62569.png
-22107.png
-40484.png
-41454.png
-01393.png
-15623.png
-29546.png
-68789.png
-09186.png
-69049.png
-18335.png
-10547.png
-68504.png
-09334.png
-19353.png
-08433.png
-04883.png
-04695.png
-06599.png
-45684.png
-47715.png
-49638.png
-04418.png
-50794.png
-56618.png
-00648.png
-06161.png
-17644.png
-62178.png
-14400.png
-01666.png
-36079.png
-30596.png
-18392.png
-21456.png
-30528.png
-21940.png
-57999.png
-19705.png
-30603.png
-31686.png
-54122.png
-09301.png
-58648.png
-08086.png
-32230.png
-13977.png
-35290.png
-52109.png
-41933.png
-16047.png
-46360.png
-62479.png
-10286.png
-62026.png
-51343.png
-13950.png
-44918.png
-17788.png
-30118.png
-69639.png
-19280.png
-65482.png
-42610.png
-07352.png
-06842.png
-14753.png
-61388.png
-40565.png
-47496.png
-28502.png
-34956.png
-44465.png
-46221.png
-36579.png
-54901.png
-24103.png
-03362.png
-25630.png
-35791.png
-14520.png
-36412.png
-69663.png
-35421.png
-52164.png
-28965.png
-60371.png
-07156.png
-66819.png
-52172.png
-16878.png
-61102.png
-08982.png
-27438.png
-19101.png
-56831.png
-58848.png
-21511.png
-01243.png
-66813.png
-40424.png
-07504.png
-18300.png
-15013.png
-65043.png
-58396.png
-10872.png
-38317.png
-36316.png
-56589.png
-69720.png
-21617.png
-53239.png
-35150.png
-53196.png
-47663.png
-17958.png
-23495.png
-32284.png
-25157.png
-59316.png
-12291.png
-43656.png
-54815.png
-44719.png
-33404.png
-38594.png
-21658.png
-65058.png
-09033.png
-38683.png
-12705.png
-20699.png
-35226.png
-54219.png
-14110.png
-22288.png
-07242.png
-34521.png
-66449.png
-01265.png
-11398.png
-38956.png
-30954.png
-47287.png
-33076.png
-14398.png
-51102.png
-61840.png
-33027.png
-19819.png
-24906.png
-65025.png
-16118.png
-69000.png
-28256.png
-53354.png
-50896.png
-28518.png
-68735.png
-66523.png
-64118.png
-30201.png
-18035.png
-47899.png
-13637.png
-47207.png
-03051.png
-22512.png
-50121.png
-46218.png
-01043.png
-15502.png
-40329.png
-50269.png
-41571.png
-46290.png
-24799.png
-13904.png
-42803.png
-59123.png
-44699.png
-51544.png
-52765.png
-08121.png
-44352.png
-66023.png
-18359.png
-31001.png
-11066.png
-61364.png
-16888.png
-43004.png
-36068.png
-32982.png
-56386.png
-09102.png
-44966.png
-48401.png
-35345.png
-23289.png
-33461.png
-09676.png
-27273.png
-18682.png
-60786.png
-53405.png
-32977.png
-30132.png
-23624.png
-14690.png
-58045.png
-10904.png
-34301.png
-48310.png
-56500.png
-02949.png
-59989.png
-52526.png
-05572.png
-26264.png
-39564.png
-59365.png
-08085.png
-04049.png
-23505.png
-50854.png
-24954.png
-63621.png
-55924.png
-33759.png
-50566.png
-14150.png
-32614.png
-45666.png
-11775.png
-28002.png
-00834.png
-44691.png
-17520.png
-10994.png
-23635.png
-32541.png
-35090.png
-29374.png
-25342.png
-39459.png
-00757.png
-22744.png
-03951.png
-53359.png
-03946.png
-10397.png
-52912.png
-44243.png
-06345.png
-19907.png
-51027.png
-58338.png
-46178.png
-14870.png
-61971.png
-14645.png
-01496.png
-50643.png
-12612.png
-03394.png
-29457.png
-50088.png
-17283.png
-65139.png
-60171.png
-16889.png
-37794.png
-69866.png
-33096.png
-47546.png
-37822.png
-03624.png
-67453.png
-62099.png
-05014.png
-55002.png
-28833.png
-22111.png
-29119.png
-34006.png
-30325.png
-14808.png
-11569.png
-42570.png
-42651.png
-66207.png
-29804.png
-31032.png
-26948.png
-68973.png
-18769.png
-65176.png
-06641.png
-21529.png
-67109.png
-38082.png
-51710.png
-46108.png
-41214.png
-15578.png
-36541.png
-17064.png
-58510.png
-47676.png
-26002.png
-26132.png
-43638.png
-14531.png
-45618.png
-60137.png
-66474.png
-25976.png
-02559.png
-25245.png
-29453.png
-03672.png
-58697.png
-26978.png
-53687.png
-39604.png
-50313.png
-09350.png
-58182.png
-62171.png
-69463.png
-55971.png
-07841.png
-00755.png
-55851.png
-14080.png
-22466.png
-55309.png
-58593.png
-02554.png
-68678.png
-27558.png
-47446.png
-52487.png
-00607.png
-68825.png
-59996.png
-52007.png
-13746.png
-47444.png
-22205.png
-10580.png
-26789.png
-37660.png
-23059.png
-56760.png
-11670.png
-41816.png
-29328.png
-56699.png
-42555.png
-31551.png
-16320.png
-61041.png
-07761.png
-64106.png
-51439.png
-63549.png
-33596.png
-11820.png
-08320.png
-39187.png
-57953.png
-11937.png
-54143.png
-69776.png
-48925.png
-22547.png
-44172.png
-07188.png
-10090.png
-50453.png
-17977.png
-64330.png
-13576.png
-57432.png
-44271.png
-51971.png
-07568.png
-58658.png
-67407.png
-33304.png
-58175.png
-16122.png
-25251.png
-52848.png
-03561.png
-51048.png
-02200.png
-14392.png
-06409.png
-50697.png
-47250.png
-33173.png
-48799.png
-11977.png
-51819.png
-63932.png
-35897.png
-49573.png
-27293.png
-33831.png
-35393.png
-05227.png
-54758.png
-42156.png
-42814.png
-28324.png
-42029.png
-20846.png
-32886.png
-20455.png
-54059.png
-03793.png
-55553.png
-25408.png
-61324.png
-34931.png
-64608.png
-34731.png
-54125.png
-63035.png
-12247.png
-25003.png
-15448.png
-15792.png
-16163.png
-19860.png
-07084.png
-66533.png
-47731.png
-51805.png
-31760.png
-44228.png
-48116.png
-28041.png
-06700.png
-61111.png
-58562.png
-61399.png
-40787.png
-05803.png
-26679.png
-61211.png
-33223.png
-51335.png
-00542.png
-28963.png
-27380.png
-18318.png
-58016.png
-32706.png
-13261.png
-11435.png
-59760.png
-00004.png
-34754.png
-25869.png
-51831.png
-33552.png
-39666.png
-69033.png
-07723.png
-53195.png
-00756.png
-13110.png
-26880.png
-43349.png
-22110.png
-13298.png
-51500.png
-12430.png
-08245.png
-61999.png
-01611.png
-01477.png
-05246.png
-20841.png
-36105.png
-52376.png
-67735.png
-08208.png
-14074.png
-09691.png
-23370.png
-53153.png
-40608.png
-18163.png
-14565.png
-62003.png
-58904.png
-09348.png
-26069.png
-11715.png
-00915.png
-44559.png
-55815.png
-59948.png
-55972.png
-02421.png
-46375.png
-53259.png
-62489.png
-35858.png
-49643.png
-34160.png
-60068.png
-22895.png
-31041.png
-13793.png
-06545.png
-35443.png
-47786.png
-15300.png
-02274.png
-59035.png
-61529.png
-19775.png
-21479.png
-35100.png
-52083.png
-35038.png
-65774.png
-33943.png
-68305.png
-16138.png
-18185.png
-63569.png
-58310.png
-38750.png
-38675.png
-44575.png
-29473.png
-47464.png
-03837.png
-60447.png
-12752.png
-49352.png
-03451.png
-09249.png
-35436.png
-16470.png
-26381.png
-44240.png
-19067.png
-44669.png
-14058.png
-00443.png
-37115.png
-31811.png
-08322.png
-10685.png
-43367.png
-50906.png
-65283.png
-63943.png
-68751.png
-14535.png
-63235.png
-18553.png
-54521.png
-13085.png
-51923.png
-00611.png
-21386.png
-40399.png
-40980.png
-32870.png
-30650.png
-37134.png
-46477.png
-49435.png
-37678.png
-43290.png
-61266.png
-31216.png
-67308.png
-09854.png
-35288.png
-21978.png
-24599.png
-49786.png
-43368.png
-17825.png
-35435.png
-44929.png
-42966.png
-33613.png
-57823.png
-24071.png
-57496.png
-14610.png
-39608.png
-47648.png
-48833.png
-47114.png
-39277.png
-31584.png
-31366.png
-51415.png
-00080.png
-05568.png
-56234.png
-15345.png
-15470.png
-60538.png
-50952.png
-47636.png
-36666.png
-46543.png
-04691.png
-61095.png
-40395.png
-14635.png
-53542.png
-35138.png
-66093.png
-60485.png
-35933.png
-02285.png
-04805.png
-04738.png
-65683.png
-32809.png
-28772.png
-55506.png
-14786.png
-01165.png
-50194.png
-61296.png
-57093.png
-57325.png
-65758.png
-39033.png
-27518.png
-48261.png
-27565.png
-34900.png
-65314.png
-47573.png
-00008.png
-40457.png
-42370.png
-04963.png
-17791.png
-35891.png
-31292.png
-21032.png
-47833.png
-07590.png
-36565.png
-29244.png
-37808.png
-41622.png
-64273.png
-57676.png
-64134.png
-69130.png
-37551.png
-24908.png
-36919.png
-65850.png
-20958.png
-48437.png
-20488.png
-16278.png
-01179.png
-58440.png
-09264.png
-35072.png
-69446.png
-07281.png
-51888.png
-68081.png
-40945.png
-10626.png
-26288.png
-50464.png
-25860.png
-49050.png
-08683.png
-39865.png
-16333.png
-52417.png
-08251.png
-63196.png
-35821.png
-35108.png
-41935.png
-56555.png
-19652.png
-04555.png
-45384.png
-62012.png
-60972.png
-44848.png
-28685.png
-39172.png
-04650.png
-24907.png
-59671.png
-33708.png
-46621.png
-56741.png
-67044.png
-14151.png
-57546.png
-69630.png
-03816.png
-56818.png
-37865.png
-57227.png
-42708.png
-28943.png
-23519.png
-38330.png
-45695.png
-67277.png
-20960.png
-29012.png
-27686.png
-65782.png
-02329.png
-69911.png
-38183.png
-54456.png
-57398.png
-13350.png
-52453.png
-29179.png
-01455.png
-02962.png
-25039.png
-32595.png
-28016.png
-07752.png
-60262.png
-63528.png
-35386.png
-12166.png
-08013.png
-33336.png
-23228.png
-30018.png
-63374.png
-69709.png
-11748.png
-10191.png
-11460.png
-04317.png
-21784.png
-18447.png
-17696.png
-68104.png
-42023.png
-20511.png
-66717.png
-27016.png
-43504.png
-23003.png
-54097.png
-20672.png
-56203.png
-36870.png
-03350.png
-00150.png
-28441.png
-07799.png
-57426.png
-32736.png
-52176.png
-52635.png
-00073.png
-58777.png
-44858.png
-67897.png
-66647.png
-32394.png
-61426.png
-35091.png
-60727.png
-48546.png
-15119.png
-34282.png
-25428.png
-57447.png
-08887.png
-12269.png
-08816.png
-21474.png
-32223.png
-13816.png
-48366.png
-54149.png
-09835.png
-51543.png
-27439.png
-11485.png
-26699.png
-28331.png
-66588.png
-69648.png
-04285.png
-50561.png
-53633.png
-55410.png
-66870.png
-05092.png
-16438.png
-48574.png
-07794.png
-50128.png
-35643.png
-50129.png
-40004.png
-64522.png
-60830.png
-09930.png
-64919.png
-43873.png
-15872.png
-24156.png
-44084.png
-09696.png
-67040.png
-04726.png
-03426.png
-19726.png
-26573.png
-46417.png
-35484.png
-65337.png
-69777.png
-34107.png
-63247.png
-58675.png
-20087.png
-37394.png
-28167.png
-35033.png
-05328.png
-60591.png
-59400.png
-68022.png
-62771.png
-28604.png
-53040.png
-47186.png
-63214.png
-13068.png
-03001.png
-57852.png
-60090.png
-25723.png
-12834.png
-40972.png
-34700.png
-59435.png
-28627.png
-32551.png
-31036.png
-30725.png
-24470.png
-55402.png
-37771.png
-09144.png
-44105.png
-21580.png
-45111.png
-59196.png
-27448.png
-65637.png
-22341.png
-43949.png
-24083.png
-44893.png
-61075.png
-16611.png
-35118.png
-29832.png
-13735.png
-19946.png
-40403.png
-13607.png
-61754.png
-58049.png
-27136.png
-58423.png
-19818.png
-38397.png
-17164.png
-17057.png
-40951.png
-17952.png
-17233.png
-55799.png
-58448.png
-49879.png
-22304.png
-15120.png
-60481.png
-01678.png
-05462.png
-38550.png
-08857.png
-50059.png
-34270.png
-60005.png
-21970.png
-22714.png
-18797.png
-53021.png
-05807.png
-20246.png
-66127.png
-20068.png
-43846.png
-20711.png
-05938.png
-36929.png
-25624.png
-16613.png
-42329.png
-41670.png
-42700.png
-51976.png
-15760.png
-39630.png
-35581.png
-31025.png
-29411.png
-34569.png
-57023.png
-55444.png
-45802.png
-36997.png
-08141.png
-62546.png
-62513.png
-43629.png
-07346.png
-14428.png
-61497.png
-20034.png
-54455.png
-36933.png
-46729.png
-59072.png
-43887.png
-69322.png
-27129.png
-11957.png
-10265.png
-42066.png
-11381.png
-05049.png
-65577.png
-56387.png
-60724.png
-29416.png
-59631.png
-45777.png
-57438.png
-35783.png
-39093.png
-42249.png
-55759.png
-49483.png
-12190.png
-36817.png
-05769.png
-16894.png
-52629.png
-53827.png
-29570.png
-27367.png
-55966.png
-40802.png
-11040.png
-17306.png
-08021.png
-27266.png
-49898.png
-64859.png
-17396.png
-51896.png
-58844.png
-31226.png
-43690.png
-16689.png
-53864.png
-44776.png
-35425.png
-54296.png
-33484.png
-48539.png
-17420.png
-67527.png
-54500.png
-42517.png
-07718.png
-51201.png
-31992.png
-42576.png
-39207.png
-09595.png
-29080.png
-05622.png
-04930.png
-55839.png
-03082.png
-29355.png
-47352.png
-55085.png
-50491.png
-68802.png
-32096.png
-11884.png
-23471.png
-39214.png
-23605.png
-13597.png
-04677.png
-21021.png
-41771.png
-61967.png
-42330.png
-13616.png
-57049.png
-65981.png
-47901.png
-27250.png
-59641.png
-05268.png
-29339.png
-00717.png
-21738.png
-25593.png
-39761.png
-34989.png
-10309.png
-62190.png
-40268.png
-30878.png
-08574.png
-15347.png
-26859.png
-41290.png
-22062.png
-24291.png
-40158.png
-44662.png
-32616.png
-44566.png
-60046.png
-58353.png
-54537.png
-04482.png
-54118.png
-46418.png
-37146.png
-29211.png
-29705.png
-21493.png
-44891.png
-34565.png
-32203.png
-46327.png
-59346.png
-11941.png
-10127.png
-64443.png
-29696.png
-03412.png
-38110.png
-46480.png
-49144.png
-37833.png
-31538.png
-55814.png
-24091.png
-36872.png
-38027.png
-16312.png
-41649.png
-23296.png
-23876.png
-39059.png
-55538.png
-44927.png
-27854.png
-49988.png
-44113.png
-25189.png
-25542.png
-62607.png
-34283.png
-26973.png
-56542.png
-30257.png
-17086.png
-11518.png
-35589.png
-47097.png
-40814.png
-45236.png
-01178.png
-04379.png
-61357.png
-63012.png
-67350.png
-49792.png
-68100.png
-59675.png
-16655.png
-61208.png
-66935.png
-11304.png
-67775.png
-18564.png
-65445.png
-14054.png
-58715.png
-42283.png
-46122.png
-42968.png
-06635.png
-41543.png
-28791.png
-58983.png
-47281.png
-59706.png
-60507.png
-69908.png
-16870.png
-68220.png
-22475.png
-56263.png
-12831.png
-24938.png
-15498.png
-04801.png
-37782.png
-07045.png
-56971.png
-16554.png
-61132.png
-38944.png
-14348.png
-26827.png
-21558.png
-35220.png
-33241.png
-66106.png
-54389.png
-62217.png
-44389.png
-18702.png
-02994.png
-52336.png
-66306.png
-58258.png
-30124.png
-53459.png
-47511.png
-30137.png
-36476.png
-61964.png
-40847.png
-30020.png
-11110.png
-59938.png
-13647.png
-46517.png
-49047.png
-42338.png
-22238.png
-06640.png
-03418.png
-34987.png
-56683.png
-52121.png
-38498.png
-52032.png
-38855.png
-28632.png
-56517.png
-40057.png
-26910.png
-40464.png
-55423.png
-25439.png
-47735.png
-10514.png
-28205.png
-48180.png
-37145.png
-24162.png
-00839.png
-67374.png
-39816.png
-11912.png
-31915.png
-40903.png
-43191.png
-25068.png
-27343.png
-08189.png
-51924.png
-58789.png
-53053.png
-64278.png
-31225.png
-69156.png
-05722.png
-52094.png
-57718.png
-20374.png
-63231.png
-07792.png
-40050.png
-47837.png
-42697.png
-27629.png
-48447.png
-54494.png
-65546.png
-45737.png
-10004.png
-29765.png
-10187.png
-31679.png
-18557.png
-34561.png
-17645.png
-65716.png
-49394.png
-62426.png
-48055.png
-04320.png
-49431.png
-29034.png
-59425.png
-61163.png
-54615.png
-62572.png
-34695.png
-01515.png
-09356.png
-15997.png
-32619.png
-24435.png
-11644.png
-62093.png
-01437.png
-22143.png
-08533.png
-20000.png
-30493.png
-04257.png
-04306.png
-08117.png
-63325.png
-08937.png
-67424.png
-23449.png
-34653.png
-59148.png
-53231.png
-08134.png
-16883.png
-46071.png
-42537.png
-68328.png
-49863.png
-05669.png
-60587.png
-69029.png
-35620.png
-18020.png
-38694.png
-67290.png
-32540.png
-43434.png
-24322.png
-26248.png
-45837.png
-27526.png
-44396.png
-66285.png
-48314.png
-21411.png
-12076.png
-58244.png
-12365.png
-45408.png
-49208.png
-32652.png
-03886.png
-23154.png
-06761.png
-31140.png
-07910.png
-63934.png
-50176.png
-31020.png
-48511.png
-34504.png
-18341.png
-48854.png
-05932.png
-44362.png
-51076.png
-21447.png
-07255.png
-14285.png
-03069.png
-42941.png
-26733.png
-68052.png
-69427.png
-35713.png
-55319.png
-58427.png
-55593.png
-06864.png
-26933.png
-27539.png
-50515.png
-69729.png
-32009.png
-13878.png
-22811.png
-18170.png
-22148.png
-46112.png
-03318.png
-23307.png
-69581.png
-56633.png
-15334.png
-10341.png
-59892.png
-58733.png
-64870.png
-15459.png
-17298.png
-06438.png
-58666.png
-34427.png
-15323.png
-28099.png
-24725.png
-01570.png
-32620.png
-23956.png
-43074.png
-26624.png
-00098.png
-65370.png
-65011.png
-47413.png
-69543.png
-00798.png
-10649.png
-26173.png
-23919.png
-45761.png
-49456.png
-21957.png
-05687.png
-38599.png
-25496.png
-62528.png
-19938.png
-68219.png
-46000.png
-10256.png
-63808.png
-43346.png
-51804.png
-42158.png
-29300.png
-57644.png
-35068.png
-15869.png
-17916.png
-60816.png
-66354.png
-04197.png
-34051.png
-29771.png
-31511.png
-13925.png
-37687.png
-58953.png
-51868.png
-18703.png
-60016.png
-29806.png
-00403.png
-12375.png
-67955.png
-19180.png
-27205.png
-22852.png
-00201.png
-31647.png
-54916.png
-21772.png
-47039.png
-20057.png
-36712.png
-25425.png
-35303.png
-58386.png
-15063.png
-07399.png
-59480.png
-11077.png
-41792.png
-20786.png
-30309.png
-48386.png
-29569.png
-04567.png
-46947.png
-62323.png
-38609.png
-09050.png
-60602.png
-67195.png
-36798.png
-33521.png
-48057.png
-48321.png
-53378.png
-66812.png
-01501.png
-20922.png
-14518.png
-67751.png
-41498.png
-33863.png
-57144.png
-35561.png
-63995.png
-68809.png
-40007.png
-16627.png
-26275.png
-35525.png
-38294.png
-54575.png
-38560.png
-42768.png
-46910.png
-44865.png
-44987.png
-11835.png
-32657.png
-26592.png
-54435.png
-06337.png
-19805.png
-47916.png
-17081.png
-02574.png
-05209.png
-10076.png
-11910.png
-22439.png
-59868.png
-61944.png
-63175.png
-01502.png
-49764.png
-40402.png
-27410.png
-51585.png
-28658.png
-67337.png
-23646.png
-33709.png
-30824.png
-21126.png
-28266.png
-52481.png
-00216.png
-54069.png
diff --git a/repositories/taming-transformers/data/ffhqvalidation.txt b/repositories/taming-transformers/data/ffhqvalidation.txt
deleted file mode 100644
index e749083689505a437a770dfcc565a6bc7e68c15e..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/data/ffhqvalidation.txt
+++ /dev/null
@@ -1,10000 +0,0 @@
-47234.png
-19351.png
-35914.png
-69674.png
-56874.png
-42520.png
-46003.png
-17268.png
-04356.png
-27798.png
-00215.png
-52296.png
-07831.png
-60203.png
-17370.png
-60917.png
-28976.png
-21349.png
-55110.png
-39685.png
-32890.png
-01725.png
-68917.png
-33295.png
-35482.png
-14049.png
-65090.png
-57924.png
-04228.png
-04706.png
-53727.png
-58418.png
-36441.png
-45449.png
-38796.png
-21789.png
-24747.png
-30136.png
-14578.png
-46833.png
-02309.png
-38885.png
-40590.png
-01041.png
-40981.png
-18772.png
-00711.png
-29177.png
-60499.png
-58850.png
-08701.png
-23208.png
-02318.png
-69798.png
-65569.png
-03510.png
-52677.png
-54655.png
-12710.png
-15295.png
-62010.png
-10024.png
-48676.png
-63984.png
-19089.png
-05794.png
-68558.png
-67235.png
-20815.png
-23026.png
-25627.png
-04993.png
-45970.png
-22492.png
-28417.png
-59622.png
-04523.png
-04679.png
-43920.png
-46430.png
-03560.png
-40731.png
-24277.png
-10337.png
-65250.png
-64185.png
-60498.png
-15358.png
-33660.png
-10992.png
-47089.png
-46109.png
-03304.png
-26129.png
-50029.png
-54132.png
-09531.png
-35266.png
-67031.png
-60991.png
-65504.png
-38367.png
-10013.png
-24778.png
-07850.png
-29016.png
-34227.png
-51622.png
-13725.png
-68176.png
-40837.png
-14669.png
-22981.png
-36407.png
-08654.png
-26118.png
-26821.png
-25859.png
-23773.png
-24022.png
-09497.png
-16007.png
-20074.png
-65795.png
-56168.png
-18876.png
-23974.png
-04048.png
-64016.png
-07959.png
-69553.png
-56724.png
-12727.png
-50876.png
-51902.png
-34904.png
-23862.png
-35438.png
-22600.png
-24347.png
-66469.png
-63005.png
-07614.png
-53298.png
-66218.png
-52175.png
-68459.png
-38490.png
-42737.png
-67489.png
-28308.png
-33664.png
-25572.png
-06989.png
-69056.png
-49292.png
-21799.png
-31547.png
-45523.png
-29662.png
-38756.png
-23392.png
-12599.png
-08622.png
-29756.png
-23526.png
-36906.png
-48362.png
-65788.png
-36360.png
-17569.png
-65407.png
-53757.png
-00076.png
-18915.png
-20383.png
-26893.png
-17560.png
-10312.png
-22726.png
-66036.png
-41843.png
-18545.png
-03806.png
-20833.png
-11728.png
-28368.png
-46769.png
-12462.png
-38969.png
-38354.png
-19382.png
-32934.png
-06566.png
-10684.png
-42429.png
-47808.png
-24762.png
-50166.png
-32697.png
-08420.png
-07396.png
-61551.png
-27291.png
-69774.png
-65342.png
-64563.png
-49968.png
-59065.png
-60024.png
-45494.png
-03555.png
-66463.png
-54228.png
-24325.png
-28529.png
-14823.png
-46912.png
-63507.png
-62258.png
-48972.png
-27804.png
-41829.png
-46747.png
-22364.png
-03013.png
-58956.png
-59334.png
-51210.png
-37803.png
-32072.png
-40977.png
-38482.png
-56171.png
-39773.png
-32831.png
-19731.png
-10422.png
-35306.png
-09239.png
-42833.png
-07763.png
-04983.png
-60753.png
-09918.png
-69129.png
-43088.png
-39819.png
-56189.png
-06282.png
-05931.png
-20179.png
-57276.png
-20995.png
-17398.png
-11836.png
-16835.png
-27164.png
-49856.png
-65042.png
-13810.png
-68139.png
-28730.png
-42462.png
-14686.png
-66214.png
-44215.png
-48513.png
-07924.png
-22656.png
-26317.png
-65876.png
-24411.png
-07040.png
-51575.png
-13018.png
-27191.png
-58755.png
-12472.png
-37528.png
-03009.png
-69461.png
-05284.png
-55681.png
-51195.png
-11117.png
-69825.png
-60162.png
-57403.png
-65360.png
-38301.png
-04427.png
-50625.png
-43510.png
-58791.png
-66334.png
-10860.png
-19765.png
-41719.png
-08219.png
-03533.png
-06471.png
-26511.png
-03848.png
-12160.png
-07379.png
-18377.png
-14473.png
-33397.png
-51600.png
-52067.png
-45983.png
-32988.png
-53270.png
-14231.png
-15714.png
-36681.png
-36649.png
-29173.png
-35812.png
-46799.png
-67859.png
-39038.png
-45409.png
-13173.png
-58345.png
-35503.png
-60976.png
-31181.png
-16112.png
-12685.png
-28966.png
-27532.png
-61976.png
-50844.png
-43063.png
-21189.png
-09655.png
-45710.png
-28941.png
-53834.png
-68151.png
-27959.png
-52455.png
-38802.png
-59006.png
-41991.png
-44975.png
-41223.png
-63159.png
-54568.png
-33917.png
-26668.png
-34559.png
-11674.png
-40072.png
-10289.png
-05210.png
-19937.png
-31549.png
-13551.png
-69210.png
-52952.png
-01383.png
-58238.png
-24414.png
-24622.png
-07005.png
-32611.png
-12962.png
-17980.png
-34150.png
-61640.png
-33468.png
-48092.png
-16216.png
-59827.png
-54873.png
-56142.png
-05003.png
-24800.png
-25784.png
-28181.png
-34560.png
-16671.png
-36755.png
-16145.png
-04702.png
-46938.png
-42669.png
-53849.png
-08619.png
-13216.png
-12008.png
-63179.png
-46556.png
-35014.png
-39497.png
-07966.png
-23884.png
-44287.png
-12649.png
-52970.png
-15428.png
-03346.png
-19494.png
-48237.png
-28490.png
-03763.png
-51318.png
-10783.png
-60287.png
-24239.png
-32131.png
-17005.png
-34542.png
-09867.png
-03248.png
-14329.png
-29460.png
-30860.png
-30748.png
-30438.png
-03828.png
-68470.png
-37199.png
-17225.png
-56336.png
-43084.png
-29007.png
-19027.png
-60061.png
-05520.png
-46561.png
-50288.png
-01552.png
-67365.png
-41866.png
-35399.png
-69135.png
-59407.png
-50776.png
-41338.png
-05457.png
-13229.png
-07948.png
-01843.png
-00904.png
-37158.png
-25514.png
-09185.png
-00622.png
-56627.png
-45339.png
-52856.png
-14072.png
-64836.png
-02409.png
-39720.png
-03689.png
-11034.png
-22358.png
-65974.png
-07732.png
-18733.png
-14684.png
-04609.png
-09475.png
-19955.png
-44488.png
-63278.png
-28001.png
-46456.png
-10075.png
-06744.png
-13548.png
-30067.png
-32560.png
-56375.png
-30254.png
-57141.png
-68255.png
-04289.png
-36510.png
-48243.png
-13912.png
-06982.png
-35002.png
-24909.png
-42292.png
-14980.png
-34009.png
-55638.png
-18785.png
-50584.png
-08565.png
-21206.png
-19697.png
-28925.png
-58118.png
-09942.png
-07960.png
-20198.png
-18740.png
-18410.png
-09707.png
-13522.png
-56018.png
-09431.png
-63223.png
-67596.png
-44190.png
-07706.png
-64447.png
-07927.png
-17816.png
-49159.png
-31530.png
-57366.png
-51990.png
-61903.png
-24526.png
-53595.png
-15400.png
-56761.png
-01329.png
-49627.png
-00937.png
-24279.png
-07689.png
-15325.png
-29697.png
-31031.png
-08796.png
-17029.png
-42826.png
-66302.png
-25576.png
-29877.png
-14790.png
-16219.png
-12457.png
-02048.png
-19940.png
-59870.png
-56816.png
-17425.png
-25065.png
-10434.png
-47992.png
-15799.png
-64826.png
-01802.png
-40884.png
-42600.png
-56118.png
-24538.png
-24321.png
-20551.png
-35406.png
-45258.png
-04043.png
-64425.png
-15116.png
-05222.png
-44624.png
-45497.png
-25373.png
-65800.png
-65175.png
-38692.png
-33680.png
-35956.png
-03730.png
-40930.png
-32125.png
-46253.png
-21699.png
-43915.png
-47457.png
-59761.png
-33965.png
-10667.png
-51239.png
-28078.png
-63899.png
-33673.png
-16691.png
-27245.png
-13694.png
-68890.png
-09260.png
-08472.png
-31002.png
-17729.png
-29273.png
-50832.png
-10016.png
-06067.png
-67156.png
-44735.png
-69862.png
-24912.png
-65188.png
-15801.png
-27822.png
-37408.png
-07121.png
-26727.png
-14369.png
-02424.png
-32349.png
-63449.png
-43942.png
-02787.png
-27127.png
-38477.png
-37493.png
-18948.png
-22053.png
-31683.png
-66975.png
-39382.png
-61820.png
-41053.png
-08140.png
-35696.png
-49184.png
-42932.png
-22916.png
-45062.png
-05428.png
-64444.png
-67560.png
-25030.png
-58168.png
-03953.png
-44412.png
-57526.png
-39989.png
-29831.png
-53299.png
-18775.png
-06038.png
-09066.png
-45803.png
-12897.png
-01550.png
-02363.png
-08925.png
-37990.png
-06886.png
-57922.png
-27652.png
-62732.png
-16687.png
-20240.png
-58297.png
-14453.png
-04639.png
-22002.png
-51019.png
-19147.png
-12183.png
-02433.png
-64997.png
-18036.png
-49518.png
-67963.png
-41823.png
-51608.png
-35600.png
-02816.png
-12325.png
-57876.png
-51331.png
-69226.png
-57330.png
-30837.png
-08232.png
-44025.png
-55805.png
-02826.png
-56050.png
-18159.png
-15920.png
-37251.png
-02624.png
-05638.png
-47951.png
-02529.png
-32824.png
-47678.png
-57043.png
-14432.png
-45677.png
-60569.png
-28198.png
-12287.png
-04352.png
-49356.png
-52612.png
-02136.png
-03565.png
-11761.png
-15233.png
-40343.png
-20493.png
-10403.png
-55154.png
-29377.png
-02237.png
-27669.png
-69186.png
-17152.png
-58378.png
-39417.png
-18158.png
-10621.png
-04925.png
-25681.png
-32465.png
-68979.png
-11880.png
-12800.png
-16000.png
-10056.png
-39841.png
-40351.png
-15627.png
-08889.png
-37805.png
-41115.png
-43380.png
-38406.png
-48219.png
-18633.png
-06119.png
-57048.png
-65945.png
-29425.png
-06614.png
-50704.png
-54869.png
-38899.png
-65105.png
-47264.png
-12617.png
-14523.png
-42792.png
-60160.png
-38753.png
-30388.png
-18271.png
-26319.png
-05371.png
-66724.png
-34267.png
-56373.png
-36934.png
-33369.png
-45135.png
-29174.png
-22868.png
-46434.png
-23115.png
-56031.png
-48079.png
-59941.png
-06160.png
-01338.png
-59504.png
-00389.png
-00306.png
-45619.png
-56868.png
-67250.png
-25283.png
-57875.png
-15182.png
-61326.png
-17198.png
-16828.png
-14581.png
-69870.png
-56092.png
-55912.png
-31984.png
-50330.png
-02583.png
-41695.png
-56653.png
-62683.png
-00959.png
-00718.png
-59551.png
-09175.png
-44802.png
-36892.png
-05702.png
-63579.png
-08972.png
-60471.png
-35521.png
-68684.png
-40587.png
-25076.png
-03595.png
-17559.png
-01627.png
-58911.png
-05346.png
-57628.png
-29640.png
-21410.png
-39662.png
-09622.png
-14027.png
-50840.png
-56030.png
-19475.png
-47771.png
-04826.png
-05283.png
-24087.png
-50759.png
-29556.png
-24297.png
-29836.png
-12480.png
-38661.png
-42912.png
-44551.png
-69145.png
-00267.png
-19714.png
-33142.png
-44027.png
-04760.png
-28307.png
-38716.png
-61989.png
-37437.png
-64065.png
-67909.png
-07118.png
-58029.png
-17495.png
-51812.png
-43860.png
-06777.png
-49888.png
-42985.png
-61559.png
-45394.png
-56887.png
-42904.png
-12975.png
-40706.png
-26488.png
-63804.png
-30915.png
-60012.png
-22255.png
-39274.png
-24626.png
-54560.png
-20014.png
-00099.png
-35061.png
-22724.png
-08747.png
-22517.png
-31948.png
-60399.png
-25086.png
-45789.png
-12510.png
-28881.png
-02465.png
-16271.png
-57378.png
-02344.png
-61731.png
-21778.png
-18350.png
-57354.png
-18404.png
-57735.png
-16892.png
-10837.png
-07245.png
-32877.png
-47140.png
-26048.png
-21858.png
-35936.png
-18095.png
-01855.png
-25033.png
-20451.png
-23941.png
-44209.png
-41103.png
-40205.png
-68502.png
-59051.png
-63526.png
-45972.png
-32826.png
-46882.png
-30204.png
-18474.png
-64446.png
-09865.png
-02405.png
-04697.png
-02368.png
-44327.png
-69600.png
-08501.png
-45981.png
-47045.png
-55592.png
-36128.png
-62863.png
-57497.png
-29789.png
-55888.png
-45046.png
-20258.png
-50899.png
-62997.png
-04694.png
-34803.png
-34838.png
-37439.png
-23457.png
-67196.png
-69973.png
-69578.png
-10734.png
-21165.png
-07250.png
-45512.png
-54550.png
-04248.png
-14311.png
-31815.png
-13579.png
-45093.png
-10506.png
-32969.png
-40728.png
-32253.png
-68767.png
-50601.png
-47909.png
-55950.png
-09954.png
-68535.png
-21487.png
-37448.png
-12805.png
-54695.png
-46952.png
-17115.png
-07433.png
-31352.png
-51900.png
-00137.png
-12131.png
-16475.png
-09049.png
-10624.png
-05363.png
-65649.png
-24398.png
-46741.png
-48702.png
-66900.png
-56063.png
-08930.png
-08406.png
-30525.png
-11679.png
-50974.png
-14977.png
-62135.png
-61360.png
-21037.png
-48733.png
-37948.png
-65867.png
-43102.png
-08697.png
-29170.png
-67432.png
-46877.png
-08924.png
-52291.png
-20402.png
-24671.png
-31848.png
-57811.png
-65606.png
-13902.png
-35921.png
-25375.png
-49474.png
-04530.png
-57620.png
-04696.png
-17065.png
-68388.png
-47380.png
-60795.png
-15264.png
-52679.png
-26041.png
-29340.png
-48891.png
-44422.png
-51481.png
-31158.png
-26283.png
-04763.png
-65872.png
-28069.png
-23419.png
-33544.png
-36382.png
-51645.png
-17769.png
-22347.png
-01441.png
-30961.png
-46501.png
-13163.png
-11424.png
-69897.png
-02339.png
-36653.png
-24722.png
-35550.png
-48315.png
-51947.png
-25642.png
-44656.png
-29991.png
-07332.png
-01380.png
-44077.png
-18049.png
-37788.png
-26431.png
-54441.png
-04015.png
-66005.png
-13233.png
-51081.png
-63371.png
-10239.png
-60754.png
-29867.png
-04853.png
-40239.png
-15864.png
-35614.png
-29025.png
-24733.png
-62336.png
-63793.png
-66351.png
-21148.png
-53465.png
-34933.png
-37271.png
-03955.png
-28247.png
-11514.png
-54649.png
-32054.png
-64831.png
-53696.png
-61641.png
-16550.png
-42997.png
-67260.png
-05770.png
-17998.png
-44896.png
-39760.png
-51650.png
-23663.png
-27316.png
-67891.png
-63599.png
-59482.png
-68236.png
-07724.png
-31742.png
-03172.png
-34100.png
-44050.png
-34921.png
-41128.png
-13685.png
-52604.png
-62685.png
-53441.png
-67463.png
-12949.png
-59219.png
-23007.png
-57463.png
-27793.png
-22308.png
-25142.png
-09414.png
-30129.png
-56685.png
-63310.png
-53068.png
-17782.png
-07389.png
-64858.png
-49492.png
-56794.png
-54195.png
-10179.png
-46146.png
-26918.png
-68866.png
-11758.png
-49230.png
-66126.png
-47697.png
-31100.png
-47748.png
-61948.png
-69126.png
-19827.png
-69005.png
-20244.png
-46615.png
-22908.png
-32274.png
-11256.png
-46943.png
-44297.png
-66803.png
-48829.png
-44701.png
-45226.png
-09413.png
-11467.png
-46043.png
-09863.png
-41280.png
-09048.png
-59423.png
-60550.png
-15596.png
-30922.png
-08283.png
-34380.png
-35516.png
-46743.png
-39341.png
-54557.png
-53845.png
-66192.png
-52748.png
-07901.png
-59563.png
-42354.png
-09565.png
-69153.png
-54493.png
-25458.png
-34414.png
-01579.png
-34951.png
-43977.png
-49915.png
-01457.png
-24463.png
-41087.png
-05599.png
-10690.png
-56122.png
-19219.png
-11961.png
-53148.png
-30607.png
-61030.png
-07635.png
-52117.png
-09616.png
-01931.png
-54014.png
-60397.png
-31851.png
-14193.png
-65441.png
-59703.png
-27334.png
-19120.png
-58554.png
-11190.png
-66823.png
-54975.png
-18002.png
-06721.png
-10651.png
-09768.png
-33739.png
-02690.png
-10285.png
-66925.png
-11287.png
-08663.png
-24316.png
-16887.png
-23972.png
-64075.png
-45573.png
-20967.png
-23529.png
-18323.png
-50520.png
-33853.png
-40225.png
-18649.png
-57885.png
-10575.png
-47579.png
-69327.png
-43341.png
-61144.png
-49784.png
-39753.png
-30310.png
-03139.png
-66358.png
-57453.png
-29232.png
-30996.png
-22001.png
-60938.png
-10650.png
-33218.png
-12103.png
-47170.png
-09888.png
-35448.png
-54485.png
-09139.png
-60501.png
-59591.png
-37866.png
-52917.png
-09996.png
-39429.png
-57834.png
-06629.png
-69165.png
-36076.png
-51121.png
-32087.png
-37465.png
-61563.png
-02723.png
-33522.png
-10247.png
-03520.png
-26581.png
-67096.png
-29800.png
-26070.png
-31910.png
-08500.png
-09988.png
-29985.png
-28468.png
-07150.png
-20917.png
-01769.png
-47845.png
-49796.png
-62308.png
-65983.png
-02292.png
-51869.png
-03156.png
-48703.png
-63359.png
-41216.png
-55663.png
-56833.png
-04178.png
-58624.png
-35751.png
-51840.png
-17969.png
-68865.png
-04087.png
-07051.png
-04360.png
-08908.png
-42024.png
-44223.png
-00024.png
-03187.png
-53341.png
-09412.png
-69902.png
-01518.png
-11306.png
-22046.png
-28916.png
-66765.png
-07260.png
-63530.png
-17259.png
-08911.png
-34860.png
-37915.png
-59891.png
-61894.png
-14818.png
-14697.png
-32059.png
-57302.png
-41136.png
-05072.png
-58591.png
-45949.png
-30731.png
-07929.png
-05452.png
-41897.png
-66617.png
-11882.png
-37329.png
-08431.png
-07515.png
-14661.png
-23875.png
-05176.png
-40121.png
-42911.png
-30056.png
-44251.png
-33773.png
-06512.png
-03631.png
-37478.png
-15639.png
-35054.png
-42447.png
-06195.png
-51730.png
-17183.png
-43700.png
-13104.png
-21733.png
-40135.png
-15141.png
-39911.png
-02946.png
-39968.png
-67293.png
-20957.png
-29963.png
-48800.png
-43219.png
-53445.png
-23511.png
-27035.png
-35740.png
-31231.png
-50177.png
-24831.png
-65442.png
-02963.png
-32771.png
-12107.png
-60294.png
-00890.png
-13513.png
-23803.png
-68287.png
-16053.png
-64715.png
-45481.png
-57368.png
-41926.png
-53790.png
-22237.png
-65743.png
-09697.png
-21200.png
-09452.png
-37613.png
-30328.png
-20946.png
-25823.png
-23817.png
-23749.png
-33814.png
-10209.png
-63806.png
-48811.png
-66554.png
-62433.png
-22074.png
-40096.png
-64281.png
-19143.png
-38303.png
-12790.png
-28643.png
-10314.png
-37433.png
-35231.png
-47512.png
-31557.png
-60963.png
-03415.png
-19014.png
-08658.png
-65114.png
-27173.png
-10603.png
-09031.png
-41616.png
-16266.png
-36484.png
-15097.png
-51269.png
-29689.png
-68844.png
-45780.png
-46397.png
-24647.png
-69688.png
-59614.png
-27475.png
-11018.png
-14188.png
-54875.png
-62509.png
-30450.png
-31487.png
-37647.png
-63894.png
-25493.png
-41528.png
-03508.png
-16603.png
-36874.png
-03676.png
-38326.png
-10411.png
-55927.png
-56642.png
-21747.png
-24143.png
-46637.png
-59138.png
-50526.png
-57004.png
-33449.png
-03479.png
-25048.png
-19462.png
-24839.png
-33039.png
-32291.png
-64770.png
-56201.png
-33019.png
-27928.png
-63702.png
-66655.png
-24635.png
-24015.png
-46767.png
-53453.png
-29834.png
-23779.png
-45027.png
-34914.png
-34475.png
-12529.png
-25606.png
-57312.png
-13541.png
-30861.png
-44038.png
-40979.png
-53625.png
-11445.png
-19114.png
-59081.png
-27479.png
-16811.png
-58660.png
-63031.png
-62845.png
-42386.png
-40661.png
-54884.png
-41091.png
-09198.png
-35389.png
-15772.png
-65643.png
-16115.png
-34570.png
-02021.png
-52217.png
-51928.png
-11057.png
-42816.png
-35096.png
-04272.png
-24916.png
-47902.png
-33874.png
-65083.png
-09069.png
-14496.png
-24462.png
-23401.png
-52770.png
-65641.png
-57202.png
-44737.png
-67656.png
-68848.png
-65485.png
-49397.png
-50277.png
-03433.png
-35859.png
-50848.png
-05597.png
-65311.png
-00577.png
-24205.png
-60326.png
-62196.png
-38429.png
-32701.png
-68090.png
-42546.png
-17294.png
-21050.png
-05621.png
-68080.png
-60153.png
-08512.png
-63148.png
-48190.png
-65053.png
-14630.png
-36411.png
-11005.png
-43106.png
-51602.png
-44759.png
-45489.png
-43199.png
-56392.png
-10940.png
-18871.png
-15833.png
-07183.png
-32304.png
-24900.png
-58071.png
-36375.png
-21623.png
-37382.png
-50845.png
-48311.png
-11779.png
-40863.png
-47825.png
-26430.png
-00193.png
-28611.png
-01674.png
-66963.png
-41003.png
-66514.png
-51782.png
-28638.png
-62634.png
-50569.png
-12650.png
-06184.png
-24520.png
-40029.png
-23297.png
-21215.png
-49743.png
-25870.png
-49611.png
-05908.png
-11675.png
-02708.png
-64660.png
-55710.png
-19188.png
-59617.png
-15777.png
-54947.png
-02489.png
-42308.png
-15852.png
-01926.png
-54067.png
-34473.png
-40568.png
-17462.png
-21607.png
-02443.png
-05496.png
-36457.png
-02242.png
-51985.png
-06347.png
-10835.png
-49032.png
-05581.png
-54533.png
-62263.png
-65659.png
-12316.png
-10759.png
-34087.png
-19949.png
-31265.png
-22824.png
-19778.png
-25014.png
-21428.png
-59774.png
-54786.png
-46738.png
-14719.png
-09223.png
-32695.png
-17108.png
-69659.png
-62406.png
-07818.png
-60296.png
-44294.png
-53705.png
-39184.png
-06255.png
-05235.png
-05903.png
-12929.png
-17291.png
-00210.png
-27358.png
-47765.png
-23784.png
-09908.png
-49827.png
-65955.png
-50599.png
-41660.png
-42101.png
-44980.png
-49547.png
-24618.png
-35684.png
-38175.png
-03966.png
-50348.png
-49236.png
-40129.png
-31196.png
-06128.png
-55072.png
-27512.png
-00476.png
-05489.png
-30311.png
-15094.png
-60310.png
-25629.png
-03691.png
-17685.png
-64866.png
-57891.png
-01240.png
-49391.png
-65984.png
-52828.png
-30221.png
-09875.png
-35467.png
-67070.png
-61851.png
-42869.png
-38037.png
-06937.png
-53408.png
-34514.png
-47479.png
-25701.png
-07512.png
-39581.png
-05654.png
-48266.png
-50690.png
-13222.png
-30480.png
-27739.png
-17766.png
-16436.png
-29124.png
-53222.png
-65938.png
-66691.png
-51592.png
-53615.png
-00324.png
-31034.png
-45973.png
-20889.png
-26228.png
-59359.png
-62964.png
-05762.png
-52939.png
-04890.png
-18483.png
-33881.png
-52893.png
-53531.png
-11224.png
-37326.png
-01231.png
-01214.png
-12582.png
-09568.png
-14698.png
-02351.png
-16777.png
-55294.png
-68859.png
-07459.png
-30054.png
-66262.png
-00220.png
-38726.png
-67950.png
-03683.png
-59457.png
-55913.png
-67649.png
-16427.png
-35233.png
-68948.png
-21927.png
-19058.png
-00906.png
-41436.png
-33733.png
-56935.png
-68750.png
-07035.png
-12597.png
-49489.png
-13754.png
-62763.png
-28900.png
-35777.png
-55780.png
-38552.png
-11464.png
-36194.png
-35637.png
-38239.png
-05662.png
-44260.png
-03152.png
-43940.png
-39649.png
-40038.png
-43058.png
-67651.png
-49713.png
-36549.png
-18078.png
-62691.png
-36680.png
-41671.png
-69874.png
-10290.png
-45880.png
-48781.png
-15496.png
-58077.png
-38278.png
-66430.png
-63761.png
-48171.png
-65020.png
-01076.png
-49339.png
-06094.png
-10020.png
-51446.png
-50154.png
-50943.png
-03764.png
-63286.png
-02367.png
-23181.png
-69499.png
-52788.png
-30277.png
-66612.png
-19343.png
-28600.png
-46100.png
-47090.png
-13080.png
-28589.png
-63419.png
-36285.png
-48542.png
-39781.png
-18496.png
-27076.png
-16813.png
-67289.png
-61934.png
-43807.png
-53004.png
-22857.png
-50647.png
-53724.png
-09633.png
-20296.png
-44274.png
-13316.png
-52387.png
-53867.png
-48268.png
-68354.png
-24804.png
-08703.png
-34859.png
-62743.png
-51604.png
-55476.png
-21891.png
-67708.png
-02057.png
-45432.png
-32276.png
-45371.png
-27951.png
-27054.png
-11221.png
-47633.png
-24215.png
-37981.png
-01600.png
-57295.png
-63613.png
-14351.png
-53778.png
-59436.png
-45193.png
-69834.png
-31197.png
-24376.png
-52865.png
-00897.png
-64504.png
-62034.png
-06246.png
-04375.png
-50721.png
-52214.png
-61527.png
-31426.png
-01298.png
-65598.png
-01766.png
-48491.png
-09939.png
-65117.png
-24500.png
-14608.png
-57386.png
-27156.png
-34626.png
-14304.png
-33179.png
-46179.png
-69602.png
-45003.png
-51755.png
-30536.png
-44028.png
-35761.png
-43045.png
-11522.png
-38345.png
-50838.png
-21246.png
-34925.png
-07276.png
-02183.png
-61596.png
-23089.png
-00480.png
-31741.png
-11204.png
-06591.png
-23079.png
-51046.png
-65881.png
-50406.png
-15890.png
-22701.png
-42476.png
-32179.png
-19708.png
-68126.png
-46753.png
-32963.png
-07123.png
-31797.png
-08367.png
-12958.png
-52499.png
-58644.png
-56437.png
-64159.png
-51530.png
-41605.png
-61884.png
-26836.png
-37194.png
-17695.png
-36562.png
-32525.png
-60788.png
-34186.png
-36275.png
-34579.png
-44728.png
-11116.png
-50149.png
-16164.png
-43212.png
-13557.png
-00213.png
-33362.png
-38998.png
-18068.png
-07914.png
-19504.png
-32283.png
-05041.png
-38051.png
-51314.png
-05376.png
-16402.png
-15602.png
-42416.png
-46909.png
-30305.png
-51264.png
-40261.png
-07209.png
-58604.png
-39418.png
-33811.png
-23867.png
-63923.png
-31016.png
-60028.png
-40780.png
-24213.png
-58224.png
-07338.png
-48827.png
-35543.png
-15083.png
-53580.png
-61597.png
-04453.png
-45317.png
-01945.png
-37185.png
-41124.png
-07839.png
-12942.png
-45877.png
-35606.png
-04620.png
-02757.png
-62589.png
-66738.png
-03782.png
-29739.png
-12708.png
-59597.png
-55192.png
-47201.png
-08072.png
-42991.png
-61238.png
-26760.png
-47691.png
-67204.png
-53238.png
-11914.png
-39695.png
-45770.png
-52759.png
-69190.png
-22462.png
-57401.png
-47694.png
-67258.png
-50160.png
-22630.png
-11279.png
-48421.png
-45130.png
-19257.png
-03222.png
-11320.png
-40614.png
-64412.png
-20579.png
-19220.png
-67487.png
-00263.png
-23995.png
-59871.png
-13307.png
-25165.png
-20484.png
-67334.png
-21500.png
-36271.png
-19517.png
-52862.png
-48804.png
-02266.png
-22840.png
-27605.png
-25410.png
-62899.png
-40897.png
-42809.png
-44062.png
-57059.png
-02916.png
-05124.png
-03006.png
-28907.png
-24338.png
-44137.png
-67581.png
-09236.png
-61911.png
-11131.png
-62828.png
-36031.png
-22342.png
-21811.png
-34412.png
-23586.png
-04277.png
-07532.png
-69528.png
-21453.png
-41865.png
-12809.png
-22526.png
-37444.png
-04634.png
-46401.png
-30815.png
-23236.png
-43532.png
-05713.png
-19794.png
-48407.png
-24356.png
-40966.png
-17173.png
-01168.png
-41438.png
-12723.png
-57194.png
-33950.png
-09501.png
-30157.png
-59213.png
-64183.png
-19871.png
-06110.png
-55333.png
-66164.png
-65028.png
-67928.png
-00463.png
-13604.png
-34683.png
-57124.png
-18643.png
-15941.png
-34445.png
-42691.png
-43399.png
-15238.png
-45507.png
-24263.png
-47261.png
-67849.png
-19808.png
-17914.png
-31223.png
-08492.png
-30306.png
-52070.png
-32241.png
-37135.png
-34804.png
-10296.png
-45232.png
-49447.png
-17272.png
-20714.png
-50967.png
-20195.png
-08954.png
-10891.png
-31308.png
-07603.png
-23523.png
-50929.png
-33134.png
-60640.png
-43589.png
-51236.png
-05884.png
-32903.png
-35039.png
-26926.png
-12839.png
-04457.png
-01173.png
-58558.png
-23468.png
-33565.png
-48449.png
-39542.png
-40852.png
-16413.png
-63151.png
-55351.png
-50340.png
-37131.png
-29409.png
-43886.png
-43340.png
-34868.png
-33284.png
-59676.png
-58163.png
-36306.png
-32985.png
-09456.png
-49306.png
-36883.png
-16971.png
-25669.png
-09884.png
-24728.png
-23620.png
-38566.png
-38986.png
-14702.png
-32885.png
-37896.png
-21765.png
-21975.png
-30513.png
-54648.png
-19541.png
-24629.png
-07875.png
-16460.png
-67202.png
-42382.png
-36251.png
-13397.png
-54405.png
-44677.png
-65134.png
-65391.png
-25191.png
-15676.png
-06140.png
-55500.png
-01216.png
-18656.png
-43646.png
-65277.png
-53651.png
-14585.png
-38532.png
-27190.png
-42070.png
-29337.png
-23321.png
-01724.png
-55424.png
-49588.png
-43372.png
-55185.png
-54305.png
-19056.png
-59032.png
-50841.png
-63418.png
-19825.png
-66740.png
-01422.png
-30028.png
-35564.png
-32023.png
-58110.png
-56489.png
-56881.png
-53217.png
-37190.png
-00445.png
-41681.png
-00759.png
-64149.png
-40355.png
-61515.png
-26444.png
-32873.png
-04328.png
-38366.png
-22476.png
-44186.png
-49308.png
-42353.png
-30605.png
-67647.png
-18033.png
-02532.png
-50531.png
-28909.png
-35362.png
-21647.png
-51223.png
-42790.png
-18260.png
-21779.png
-23711.png
-19500.png
-05241.png
-37088.png
-10085.png
-29968.png
-37589.png
-58074.png
-05464.png
-00469.png
-34571.png
-41039.png
-37640.png
-51203.png
-36858.png
-69163.png
-25401.png
-67311.png
-50780.png
-68524.png
-04586.png
-01524.png
-13492.png
-12864.png
-64048.png
-29821.png
-68569.png
-62530.png
-22527.png
-27693.png
-27701.png
-49420.png
-30884.png
-12343.png
-07095.png
-65410.png
-48406.png
-40915.png
-68318.png
-60836.png
-39419.png
-58200.png
-34213.png
-40251.png
-66631.png
-37504.png
-36491.png
-01537.png
-49991.png
-53994.png
-67961.png
-65896.png
-32005.png
-10883.png
-47160.png
-67478.png
-30649.png
-24901.png
-05963.png
-44379.png
-18165.png
-62209.png
-58024.png
-59080.png
-16158.png
-13026.png
-00351.png
-47460.png
-07587.png
-08181.png
-05922.png
-43178.png
-05388.png
-38687.png
-12294.png
-09226.png
-21369.png
-01878.png
-02828.png
-16599.png
-59793.png
-31673.png
-00735.png
-09534.png
-24288.png
-65095.png
-24527.png
-40389.png
-28530.png
-32844.png
-66615.png
-27337.png
-69409.png
-57624.png
-42392.png
-11436.png
-13922.png
-69275.png
-37362.png
-40374.png
-69028.png
-22894.png
-09897.png
-13496.png
-44961.png
-01710.png
-11950.png
-26665.png
-46852.png
-43709.png
-39248.png
-26613.png
-54895.png
-04104.png
-02807.png
-07104.png
-11574.png
-43179.png
-50684.png
-18866.png
-21407.png
-53736.png
-28845.png
-21006.png
-17731.png
-50715.png
-16234.png
-43154.png
-04808.png
-01835.png
-41849.png
-23685.png
-63281.png
-26091.png
-57786.png
-52699.png
-29452.png
-47164.png
-29146.png
-06382.png
-11082.png
-04728.png
-03364.png
-32507.png
-23791.png
-23101.png
-53527.png
-49636.png
-60659.png
-44349.png
-35413.png
-09685.png
-60148.png
-06875.png
-42567.png
-01608.png
-56802.png
-62259.png
-21069.png
-18339.png
-10409.png
-64668.png
-04023.png
-49903.png
-40113.png
-08480.png
-38519.png
-26268.png
-06792.png
-07843.png
-14592.png
-68588.png
-16871.png
-06006.png
-28933.png
-57532.png
-16575.png
-44576.png
-22253.png
-32979.png
-49003.png
-45578.png
-22129.png
-03901.png
-19695.png
-27174.png
-10303.png
-48713.png
-14215.png
-10053.png
-24169.png
-02006.png
-45900.png
-67327.png
-43308.png
-56746.png
-26405.png
-05237.png
-52777.png
-24295.png
-22296.png
-38811.png
-03154.png
-40853.png
-24561.png
-47964.png
-45335.png
-11858.png
-55585.png
-38022.png
-42205.png
-07891.png
-21865.png
-11533.png
-29508.png
-25666.png
-01625.png
-34139.png
-17983.png
-30972.png
-13191.png
-18390.png
-36807.png
-23046.png
-62476.png
-68512.png
-66800.png
-44561.png
-63492.png
-45474.png
-67384.png
-46487.png
-04551.png
-48704.png
-01112.png
-58147.png
-49281.png
-63874.png
-30667.png
-44660.png
-45150.png
-62227.png
-53765.png
-32937.png
-44569.png
-69075.png
-31167.png
-16947.png
-55628.png
-17802.png
-58360.png
-06226.png
-50957.png
-15253.png
-28515.png
-21389.png
-03802.png
-11767.png
-32135.png
-59772.png
-12237.png
-06237.png
-27344.png
-33665.png
-08451.png
-49526.png
-66077.png
-33676.png
-44946.png
-17931.png
-62013.png
-39687.png
-54739.png
-21512.png
-55031.png
-23568.png
-51515.png
-00678.png
-40628.png
-58532.png
-39237.png
-42561.png
-52504.png
-65233.png
-65238.png
-17261.png
-09487.png
-54804.png
-30189.png
-12830.png
-21490.png
-58868.png
-24541.png
-37845.png
-34383.png
-39389.png
-69983.png
-11504.png
-25081.png
-10132.png
-01657.png
-10874.png
-07780.png
-50730.png
-51384.png
-48736.png
-58878.png
-41852.png
-44614.png
-45444.png
-29086.png
-37438.png
-06213.png
-17320.png
-00462.png
-11673.png
-01784.png
-16929.png
-37999.png
-19311.png
-48651.png
-21408.png
-46802.png
-54700.png
-07326.png
-26744.png
-65799.png
-61318.png
-30609.png
-36861.png
-46481.png
-06673.png
-58688.png
-54938.png
-49408.png
-11601.png
-55733.png
-47699.png
-00531.png
-41439.png
-56888.png
-30567.png
-35492.png
-06533.png
-53797.png
-68834.png
-50791.png
-48434.png
-22021.png
-19157.png
-39122.png
-41882.png
-16880.png
-45218.png
-57392.png
-34468.png
-59780.png
-43997.png
-11720.png
-48046.png
-40032.png
-29237.png
-37381.png
-46786.png
-20344.png
-42401.png
-51610.png
-03521.png
-25395.png
-51402.png
-11492.png
-31769.png
-31472.png
-01534.png
-26736.png
-33073.png
-17880.png
-03785.png
-23514.png
-32339.png
-54898.png
-53586.png
-13680.png
-51970.png
-29307.png
-57933.png
-58605.png
-68670.png
-38772.png
-54754.png
-57552.png
-69781.png
-67500.png
-66629.png
-30864.png
-04787.png
-01485.png
-13874.png
-60038.png
-56571.png
-01870.png
-24913.png
-06363.png
-29738.png
-08814.png
-55098.png
-13381.png
-52165.png
-16695.png
-16176.png
-57884.png
-47912.png
-57433.png
-56397.png
-46479.png
-55201.png
-29379.png
-36896.png
-65430.png
-07894.png
-14628.png
-16805.png
-22942.png
-35642.png
-39370.png
-24952.png
-30921.png
-37839.png
-09329.png
-04869.png
-35490.png
-10405.png
-13852.png
-60623.png
-22329.png
-52155.png
-12530.png
-26781.png
-27797.png
-32719.png
-18442.png
-14350.png
-08872.png
-43724.png
-15009.png
-06118.png
-56504.png
-49196.png
-25964.png
-59204.png
-59514.png
-10236.png
-20400.png
-03496.png
-29169.png
-31634.png
-29881.png
-07903.png
-40299.png
-11233.png
-26857.png
-58606.png
-09021.png
-29531.png
-32871.png
-38475.png
-52609.png
-25683.png
-64194.png
-51003.png
-55111.png
-66202.png
-10358.png
-15368.png
-02095.png
-50745.png
-64603.png
-46348.png
-58460.png
-51144.png
-45595.png
-53917.png
-34130.png
-41652.png
-62244.png
-53187.png
-19011.png
-66710.png
-33141.png
-43754.png
-43067.png
-17271.png
-14954.png
-43236.png
-23633.png
-52657.png
-16132.png
-12305.png
-55582.png
-10822.png
-11389.png
-44714.png
-08818.png
-35546.png
-67944.png
-01667.png
-19693.png
-61766.png
-12608.png
-19686.png
-66904.png
-16481.png
-34701.png
-67947.png
-00320.png
-28293.png
-27378.png
-28042.png
-47634.png
-60065.png
-22775.png
-17893.png
-20523.png
-57517.png
-39401.png
-29225.png
-43358.png
-67660.png
-28391.png
-63558.png
-13760.png
-25220.png
-15966.png
-36854.png
-21425.png
-64936.png
-29677.png
-10779.png
-19208.png
-06499.png
-34185.png
-37527.png
-17948.png
-24664.png
-37245.png
-32733.png
-44826.png
-00342.png
-32968.png
-25049.png
-53312.png
-06504.png
-30640.png
-00058.png
-23831.png
-03456.png
-37281.png
-24421.png
-63729.png
-15901.png
-44841.png
-18543.png
-59823.png
-66432.png
-08981.png
-57159.png
-44434.png
-34840.png
-02495.png
-63972.png
-07612.png
-45118.png
-11075.png
-16019.png
-00878.png
-15043.png
-03615.png
-57106.png
-57077.png
-42324.png
-20996.png
-50352.png
-52148.png
-14009.png
-30049.png
-38195.png
-52958.png
-63077.png
-09903.png
-59470.png
-05132.png
-33677.png
-40185.png
-55528.png
-57837.png
-51392.png
-47801.png
-40397.png
-55744.png
-10059.png
-02535.png
-04189.png
-06168.png
-13995.png
-50208.png
-37669.png
-69616.png
-65103.png
-00824.png
-29638.png
-43271.png
-03249.png
-35403.png
-51776.png
-03166.png
-66547.png
-65717.png
-33333.png
-59023.png
-49656.png
-09597.png
-18939.png
-37643.png
-25814.png
-50955.png
-15905.png
-28203.png
-07047.png
-61170.png
-55899.png
-51721.png
-62822.png
-01114.png
-48758.png
-14173.png
-42898.png
-52431.png
-65564.png
-07883.png
-06814.png
-57507.png
-17658.png
-46018.png
-34821.png
-20169.png
-08838.png
-53478.png
-44898.png
-00068.png
-09993.png
-13457.png
-18400.png
-40674.png
-15868.png
-36533.png
-26786.png
-58541.png
-28525.png
-69849.png
-65237.png
-00887.png
-45806.png
-53402.png
-12379.png
-41149.png
-23332.png
-38861.png
-45499.png
-69245.png
-12473.png
-36577.png
-39468.png
-26685.png
-43794.png
-41527.png
-53136.png
-18471.png
-13715.png
-33026.png
-06743.png
-37495.png
-34348.png
-38703.png
-69840.png
-52620.png
-51891.png
-26373.png
-06759.png
-48375.png
-09968.png
-04535.png
-07542.png
-53661.png
-00271.png
-22712.png
-58114.png
-57947.png
-34530.png
-56305.png
-54061.png
-47946.png
-00299.png
-07163.png
-23732.png
-16553.png
-43840.png
-62665.png
-50488.png
-62892.png
-60566.png
-42303.png
-57622.png
-67177.png
-36623.png
-43885.png
-42975.png
-61855.png
-41716.png
-27545.png
-04340.png
-28473.png
-62551.png
-09282.png
-67281.png
-09727.png
-32848.png
-22825.png
-54167.png
-15084.png
-06928.png
-52232.png
-30086.png
-26855.png
-51577.png
-51258.png
-46012.png
-54904.png
-17859.png
-25404.png
-45849.png
-19201.png
-62531.png
-47226.png
-67627.png
-24944.png
-06106.png
-13734.png
-44080.png
-28455.png
-54277.png
-66315.png
-40176.png
-29549.png
-64941.png
-39885.png
-19663.png
-15138.png
-43512.png
-01887.png
-29647.png
-24373.png
-68826.png
-39126.png
-21336.png
-31048.png
-37399.png
-32383.png
-02872.png
-22659.png
-39696.png
-01564.png
-47230.png
-01505.png
-55722.png
-49336.png
-26346.png
-17410.png
-20338.png
-47057.png
-56536.png
-09941.png
-36847.png
-24472.png
-38076.png
-17071.png
-16769.png
-57771.png
-43570.png
-39777.png
-41055.png
-52309.png
-25719.png
-46347.png
-26555.png
-60997.png
-10876.png
-64406.png
-01107.png
-03629.png
-03052.png
-35735.png
-43030.png
-48883.png
-11992.png
-27748.png
-52733.png
-67502.png
-46763.png
-13742.png
-29464.png
-02798.png
-66185.png
-58972.png
-15257.png
-43041.png
-64689.png
-09258.png
-51061.png
-48274.png
-48118.png
-67344.png
-34488.png
-58242.png
-40868.png
-06986.png
-29384.png
-05982.png
-03989.png
-56147.png
-43812.png
-38227.png
-21264.png
-48746.png
-08290.png
-13328.png
-55429.png
-23805.png
-14321.png
-69679.png
-40018.png
-23019.png
-67037.png
-57966.png
-55887.png
-15318.png
-61336.png
-33775.png
-09143.png
-45412.png
-62493.png
-15778.png
-07063.png
-08023.png
-01876.png
-51332.png
-35877.png
-43628.png
-28771.png
-44772.png
-28072.png
-50890.png
-10549.png
-09407.png
-26775.png
-42535.png
-09013.png
-53379.png
-22556.png
-04845.png
-18924.png
-05180.png
-09321.png
-06381.png
-44629.png
-20486.png
-37400.png
-62812.png
-45683.png
-30352.png
-23464.png
-24013.png
-19170.png
-05795.png
-61413.png
-10121.png
-03536.png
-33183.png
-57980.png
-28276.png
-31963.png
-50139.png
-27945.png
-42372.png
-45005.png
-65836.png
-45601.png
-58391.png
-08953.png
-00949.png
-22313.png
-21241.png
-46813.png
-39741.png
-08536.png
-34893.png
-27434.png
-34131.png
-27814.png
-61973.png
-33156.png
-31829.png
-50694.png
-67888.png
-20673.png
-15440.png
-10174.png
-45224.png
-24754.png
-49438.png
-28754.png
-54963.png
-20256.png
-37105.png
-33979.png
-59137.png
-19922.png
-15023.png
-43995.png
-52328.png
-34976.png
-69841.png
-68023.png
-03371.png
-31423.png
-29663.png
-52294.png
-07686.png
-10325.png
-20858.png
-45178.png
-66008.png
-39060.png
-49370.png
-06170.png
-56223.png
-18364.png
-35285.png
-22608.png
-48873.png
-39827.png
-49231.png
-42604.png
-35916.png
-44695.png
-07538.png
-16532.png
-39678.png
-09180.png
-65018.png
-26300.png
-53319.png
-24284.png
-33326.png
-29028.png
-28029.png
-40573.png
-01217.png
-08031.png
-01484.png
-48306.png
-13221.png
-34330.png
-20114.png
-60704.png
-38738.png
-31202.png
-41835.png
-20239.png
-27095.png
-46161.png
-67927.png
-26925.png
-17561.png
-33619.png
-57205.png
-67618.png
-29889.png
-33017.png
-48183.png
-06574.png
-05483.png
-19243.png
-68621.png
-25586.png
-16576.png
-22629.png
-24899.png
-09919.png
-12637.png
-32093.png
-60354.png
-00524.png
-60841.png
-39751.png
-01902.png
-49360.png
-07055.png
-04223.png
-38913.png
-36960.png
-09218.png
-64838.png
-23588.png
-06717.png
-68511.png
-08289.png
-06962.png
-47268.png
-27241.png
-65538.png
-65427.png
-44411.png
-43197.png
-05977.png
-17724.png
-62229.png
-23503.png
-61727.png
-52301.png
-31789.png
-47213.png
-05220.png
-68344.png
-39357.png
-67646.png
-39496.png
-20031.png
-51839.png
-25125.png
-59744.png
-18069.png
-23472.png
-64337.png
-18113.png
-37632.png
-22334.png
-17336.png
-61537.png
-17120.png
-26315.png
-03059.png
-33920.png
-53388.png
-43002.png
-18309.png
-63377.png
-26350.png
-35493.png
-67482.png
-06000.png
-07687.png
-14440.png
-26218.png
-07796.png
-63841.png
-49295.png
-18355.png
-45992.png
-19476.png
-07932.png
-64244.png
-56244.png
-49108.png
-66614.png
-30511.png
-18208.png
-37296.png
-07669.png
-36005.png
-64832.png
-10778.png
-33927.png
-55936.png
-60891.png
-37019.png
-69587.png
-05229.png
-07951.png
-25974.png
-34096.png
-63699.png
-52185.png
-63342.png
-15125.png
-22872.png
-23509.png
-19247.png
-65301.png
-68340.png
-33299.png
-08795.png
-63126.png
-13049.png
-48774.png
-29571.png
-41384.png
-04563.png
-57961.png
-47118.png
-28534.png
-27725.png
-28117.png
-62930.png
-65350.png
-49554.png
-44378.png
-55010.png
-19858.png
-15481.png
-68994.png
-52544.png
-15302.png
-49626.png
-61024.png
-31926.png
-03932.png
-28979.png
-47654.png
-25524.png
-00764.png
-61620.png
-01925.png
-37217.png
-51677.png
-52992.png
-59872.png
-54252.png
-21484.png
-53579.png
-48684.png
-06860.png
-55307.png
-05841.png
-52432.png
-03737.png
-29955.png
-07690.png
-56134.png
-23512.png
-44832.png
-68990.png
-14042.png
-34543.png
-41851.png
-27800.png
-65675.png
-02776.png
-29281.png
-50003.png
-00437.png
-63665.png
-60469.png
-69234.png
-67676.png
-03748.png
-10300.png
-14944.png
-20816.png
-15526.png
-08958.png
-40243.png
-45332.png
-27277.png
-15192.png
-13272.png
-53718.png
-43443.png
-60779.png
-16324.png
-37316.png
-31868.png
-27626.png
-30775.png
-44134.png
-20565.png
-14634.png
-61253.png
-54953.png
-42346.png
-49664.png
-19065.png
-31209.png
-10614.png
-55945.png
-69526.png
-26251.png
-66165.png
-30662.png
-15287.png
-00276.png
-66949.png
-01801.png
-51874.png
-50144.png
-29347.png
-00340.png
-16757.png
-04196.png
-55768.png
-52574.png
-28158.png
-22016.png
-55977.png
-59925.png
-33038.png
-60463.png
-43978.png
-15243.png
-59655.png
-63518.png
-57468.png
-10338.png
-24344.png
-09987.png
-13054.png
-50215.png
-18744.png
-15844.png
-05141.png
-11433.png
-22211.png
-14504.png
-00922.png
-53381.png
-48798.png
-34709.png
-56374.png
-21592.png
-08734.png
-22689.png
-14806.png
-15950.png
-01262.png
-52386.png
-59670.png
-10457.png
-09927.png
-19544.png
-21678.png
-03904.png
-19378.png
-14894.png
-12684.png
-25600.png
-47296.png
-24315.png
-62875.png
-05179.png
-16458.png
-45363.png
-67717.png
-05135.png
-15733.png
-57139.png
-35577.png
-46394.png
-07527.png
-66239.png
-20266.png
-34176.png
-55133.png
-13717.png
-53979.png
-32922.png
-53853.png
-59086.png
-63078.png
-09105.png
-04031.png
-41133.png
-39034.png
-25366.png
-44177.png
-60657.png
-62236.png
-68469.png
-39724.png
-47999.png
-29400.png
-41401.png
-44414.png
-25866.png
-30246.png
-26633.png
-52689.png
-08379.png
-04795.png
-69351.png
-55383.png
-15677.png
-51977.png
-45649.png
-09581.png
-60479.png
-00894.png
-30069.png
-11354.png
-44418.png
-44623.png
-67537.png
-02238.png
-23014.png
-06632.png
-47300.png
-29365.png
-37487.png
-64068.png
-15432.png
-11007.png
-42620.png
-38784.png
-14090.png
-02746.png
-38863.png
-37210.png
-12865.png
-61001.png
-45876.png
-36967.png
-56403.png
-55455.png
-18360.png
-25044.png
-60489.png
-46449.png
-01885.png
-59932.png
-68856.png
-67012.png
-59721.png
-07803.png
-38936.png
-57967.png
-48319.png
-63184.png
-53885.png
-36768.png
-55060.png
-14415.png
-55204.png
-67633.png
-55958.png
-40879.png
-61134.png
-16288.png
-20238.png
-46495.png
-11623.png
-37268.png
-49140.png
-10771.png
-37609.png
-09150.png
-61227.png
-33049.png
-12270.png
-20291.png
-04810.png
-19736.png
-53398.png
-15416.png
-51470.png
-64673.png
-00334.png
-22132.png
-09777.png
-55427.png
-62283.png
-15067.png
-38437.png
-10631.png
-32491.png
-44437.png
-48627.png
-08124.png
-67319.png
-65322.png
-55699.png
-57833.png
-58041.png
-10431.png
-38964.png
-03690.png
-51466.png
-22431.png
-31297.png
-10905.png
-16899.png
-67148.png
-37840.png
-35352.png
-29147.png
-16339.png
-50200.png
-29089.png
-61173.png
-55965.png
-44079.png
-36245.png
-44567.png
-45792.png
-05731.png
-66413.png
-66702.png
-29851.png
-53331.png
-21702.png
-58088.png
-59378.png
-38723.png
-46214.png
-44721.png
-56770.png
-49761.png
-32737.png
-08709.png
-06009.png
-11823.png
-22405.png
-16498.png
-18771.png
-30991.png
-59270.png
-55332.png
-59656.png
-49663.png
-49493.png
-51152.png
-58914.png
-03163.png
-56670.png
-58654.png
-28064.png
-46051.png
-62931.png
-10921.png
-52145.png
-66687.png
-59150.png
-10525.png
-64437.png
-54159.png
-04945.png
-43383.png
-60948.png
-01851.png
-19906.png
-50252.png
-63140.png
-58767.png
-42479.png
-50668.png
-69824.png
-25381.png
-48429.png
-62967.png
-33658.png
-50220.png
-04185.png
-36728.png
-49114.png
-37534.png
-47702.png
-51773.png
-11696.png
-13934.png
-19371.png
-48228.png
-24793.png
-57087.png
-44096.png
-68998.png
-38421.png
-57732.png
-66329.png
-55955.png
-06222.png
-53072.png
-19762.png
-47954.png
-58287.png
-21467.png
-12402.png
-65495.png
-38088.png
-34171.png
-46822.png
-32671.png
-57347.png
-14593.png
-49943.png
-24905.png
-17891.png
-37154.png
-02794.png
-13781.png
-17359.png
-17197.png
-00939.png
-69184.png
-32734.png
-67910.png
-23338.png
-60921.png
-40801.png
-33639.png
-33815.png
-32336.png
-35707.png
-12092.png
-50829.png
-55081.png
-46745.png
-17443.png
-41781.png
-23602.png
-17678.png
-07446.png
-45383.png
-47943.png
-07457.png
-66104.png
-60763.png
-00170.png
-64343.png
-05275.png
-19623.png
-10638.png
-57698.png
-15532.png
-49234.png
-62239.png
-15441.png
-61825.png
-30646.png
-68724.png
-63102.png
-49736.png
-33942.png
-69794.png
-52532.png
-54288.png
-23274.png
-54414.png
-06733.png
-28625.png
-14162.png
-54518.png
-42511.png
-42617.png
-50148.png
-47682.png
-59505.png
-45253.png
-55902.png
-44602.png
-56867.png
-12644.png
-32287.png
-41213.png
-60824.png
-52202.png
-26614.png
-11270.png
-05149.png
-46671.png
-63149.png
-12556.png
-59002.png
-03186.png
-08599.png
-60490.png
-55680.png
-20205.png
-59432.png
-28159.png
-33499.png
-48194.png
-29962.png
-01316.png
-37421.png
-61309.png
-02012.png
-22042.png
-68008.png
-21518.png
-64657.png
-11194.png
-55170.png
-02938.png
-32281.png
-17900.png
-02988.png
-26561.png
-32919.png
-59260.png
-05419.png
-43484.png
-68051.png
-68693.png
-26356.png
-63958.png
-40223.png
-43126.png
-02536.png
-18428.png
-68042.png
-43941.png
-50471.png
-31653.png
-07942.png
-47267.png
-39568.png
-00893.png
-29621.png
-54684.png
-59431.png
-69377.png
-21723.png
-31544.png
-20829.png
-33112.png
-46036.png
-51847.png
-38091.png
-15099.png
-03742.png
-04863.png
-04262.png
-34204.png
-02600.png
-46557.png
-36394.png
-06919.png
-52690.png
-33779.png
-11890.png
-00196.png
-34635.png
-61147.png
-57822.png
-49458.png
-15305.png
-69167.png
-64479.png
-41976.png
-21435.png
-61188.png
-33170.png
-33807.png
-03004.png
-45580.png
-26086.png
-65851.png
-12428.png
-37351.png
-27514.png
-44882.png
-26448.png
-33787.png
-17372.png
-59899.png
-30055.png
-49235.png
-58569.png
-56572.png
-25099.png
-58680.png
-10747.png
-17446.png
-33498.png
-65979.png
-57936.png
-28184.png
-55337.png
-40444.png
-51861.png
-27861.png
-02252.png
-47030.png
-53871.png
-53421.png
-20709.png
-15512.png
-55657.png
-10030.png
-64509.png
-02804.png
-04613.png
-32295.png
-39303.png
-13245.png
-69157.png
-21171.png
-56002.png
-25800.png
-08610.png
-55542.png
-62137.png
-43612.png
-56937.png
-40386.png
-65687.png
-40145.png
-40952.png
-00738.png
-16587.png
-64692.png
-64821.png
-54908.png
-17235.png
-41491.png
-00814.png
-36460.png
-01795.png
-58950.png
-01776.png
-14068.png
-49357.png
-11015.png
-38237.png
-41445.png
-16709.png
-46554.png
-10623.png
-13099.png
-18187.png
-35798.png
-01342.png
-66147.png
-37280.png
-67472.png
-01681.png
-43670.png
-69596.png
-59493.png
-30684.png
-22417.png
-58381.png
-24079.png
-50918.png
-49689.png
-02075.png
-49820.png
-00060.png
-16711.png
-23431.png
-17102.png
-29431.png
-26741.png
-26772.png
-63234.png
-46870.png
-63122.png
-65542.png
-01794.png
-67490.png
-69329.png
-57390.png
-59068.png
-60049.png
-39830.png
-54839.png
-51922.png
-44088.png
-22444.png
-57797.png
-00358.png
-52332.png
-56541.png
-17469.png
-26482.png
-42563.png
-22101.png
-35122.png
-22739.png
-29838.png
-14975.png
-02260.png
-43310.png
-29567.png
-65129.png
-66736.png
-46576.png
-25585.png
-35919.png
-38676.png
-44517.png
-32489.png
-03272.png
-63705.png
-29189.png
-22805.png
-37356.png
-27132.png
-10225.png
-42227.png
-60791.png
-28105.png
-29792.png
-67865.png
-43232.png
-15628.png
-45137.png
-61149.png
-19826.png
-61889.png
-30247.png
-10960.png
-62252.png
-39072.png
-64055.png
-11558.png
-69484.png
-39044.png
-28541.png
-09362.png
-34533.png
-20866.png
-36162.png
-38872.png
-02230.png
-52836.png
-51973.png
-48869.png
-20160.png
-33611.png
-44892.png
-49639.png
-26559.png
-49592.png
-37994.png
-45660.png
-15704.png
-40642.png
-09396.png
-10576.png
-15682.png
-64441.png
-24774.png
-69203.png
-48255.png
-66981.png
-05250.png
-61958.png
-15122.png
-46123.png
-68854.png
-21958.png
-47701.png
-48506.png
-03829.png
-33004.png
-22258.png
-25005.png
-45700.png
-39592.png
-25597.png
-23010.png
-07297.png
-67066.png
-32506.png
-31725.png
-16885.png
-07256.png
-48720.png
-65638.png
-68032.png
-34080.png
-08158.png
-28270.png
-40494.png
-05062.png
-27357.png
-49516.png
-55512.png
-52005.png
-69905.png
-57269.png
-18998.png
-08824.png
-20851.png
-17503.png
-10830.png
-16107.png
-05640.png
-21048.png
-38425.png
-60331.png
-35457.png
-38408.png
-20124.png
-39878.png
-31156.png
-25345.png
-49534.png
-13678.png
-55622.png
-62653.png
-17229.png
-66378.png
-45767.png
-22447.png
-36175.png
-51658.png
-44487.png
-05761.png
-41593.png
-69038.png
-27195.png
-17686.png
-34780.png
-31958.png
-43336.png
-12085.png
-29790.png
-53266.png
-69547.png
-44947.png
-28457.png
-05981.png
-45592.png
-33753.png
-11639.png
-11550.png
-04252.png
-49013.png
-52058.png
-29511.png
-55365.png
-16084.png
-48312.png
-68739.png
-08422.png
-50991.png
-44715.png
-22539.png
-15341.png
-01751.png
-06910.png
-04139.png
-55082.png
-56772.png
-14545.png
-12431.png
-62686.png
-42735.png
-18988.png
-46572.png
-45238.png
-19903.png
-46991.png
-55595.png
-67661.png
-37869.png
-35607.png
-55306.png
-68065.png
-51661.png
-04156.png
-00645.png
-28116.png
-48797.png
-54355.png
-53980.png
-63273.png
-21065.png
-39446.png
-12855.png
-17251.png
-61655.png
-15505.png
-41480.png
-49037.png
-35682.png
-29517.png
-02293.png
-48852.png
-66382.png
-18038.png
-01034.png
-64791.png
-30597.png
-59515.png
-51080.png
-62529.png
-26524.png
-31780.png
-61813.png
-47812.png
-56404.png
-64085.png
-09322.png
-51178.png
-64605.png
-61362.png
-39613.png
-31019.png
-53377.png
-61506.png
-41825.png
-00064.png
-36036.png
-28684.png
-05121.png
-17543.png
-38761.png
-56883.png
-69767.png
-18014.png
-65067.png
-05110.png
-01901.png
-27905.png
-56580.png
-02551.png
-42184.png
-34163.png
-64015.png
-31376.png
-65113.png
-05022.png
-29711.png
-03608.png
-13073.png
-68906.png
-05028.png
-63237.png
-55652.png
-37591.png
-68505.png
-30465.png
-17763.png
-36702.png
-67670.png
-25729.png
-30717.png
-58234.png
-54204.png
-37178.png
-34631.png
-26762.png
-13797.png
-69751.png
-04007.png
-52471.png
-35749.png
-31061.png
-02035.png
-27188.png
-03125.png
-49410.png
-12604.png
-64192.png
-19004.png
-09602.png
-31790.png
-20591.png
-18971.png
-55860.png
-36226.png
-57647.png
-06749.png
-21724.png
-12078.png
-53888.png
-22862.png
-37603.png
-53895.png
-28487.png
-58096.png
-36789.png
-04838.png
-22613.png
-69057.png
-67333.png
-09029.png
-05583.png
-41440.png
-18032.png
-64288.png
-29333.png
-06354.png
-04103.png
-23679.png
-48469.png
-10637.png
-45059.png
-16191.png
-41113.png
-34906.png
-49280.png
-20793.png
-02771.png
-69458.png
-00114.png
-03514.png
-51692.png
-39535.png
-61074.png
-23435.png
-09580.png
-40054.png
-31030.png
-10033.png
-65077.png
-31945.png
-12017.png
-40053.png
-05091.png
-44973.png
-20807.png
-62628.png
-37353.png
-65069.png
-66862.png
-15594.png
-49124.png
-57744.png
-13729.png
-13252.png
-68515.png
-38185.png
-44369.png
-08845.png
-37193.png
-63622.png
-45292.png
-66393.png
-24841.png
-11341.png
-19162.png
-23105.png
-23544.png
-63895.png
-45158.png
-23098.png
-59331.png
-05236.png
-35420.png
-58834.png
-61988.png
-59214.png
-46970.png
-57761.png
-08992.png
-46711.png
-47144.png
-66505.png
-68916.png
-29634.png
-25656.png
-67443.png
-33951.png
-27137.png
-50691.png
-21909.png
-38681.png
-08867.png
-44311.png
-60260.png
-27330.png
-52897.png
-24290.png
-47870.png
-36595.png
-54176.png
-38394.png
-57803.png
-06896.png
-17905.png
-10101.png
-21031.png
-14081.png
-61062.png
-14302.png
-14784.png
-64594.png
-45061.png
-58270.png
-35473.png
-01027.png
-59530.png
-40474.png
-52888.png
-37384.png
-68451.png
-08364.png
-53593.png
-44464.png
-42921.png
-31675.png
-12935.png
-57661.png
-10530.png
-46876.png
-26183.png
-36250.png
-32512.png
-67410.png
-35922.png
-32439.png
-36438.png
-09088.png
-43283.png
-57919.png
-43468.png
-41467.png
-29207.png
-24413.png
-64360.png
-02695.png
-09632.png
-19945.png
-49941.png
-21262.png
-09229.png
-30984.png
-20324.png
-11587.png
-09135.png
-64778.png
-58933.png
-24503.png
-66154.png
-59699.png
-02244.png
-50786.png
-10048.png
-55282.png
-50279.png
-16920.png
-57255.png
-02811.png
-53226.png
-31067.png
-25287.png
-22677.png
-52676.png
-23083.png
-35930.png
-41688.png
-09206.png
-42631.png
-06971.png
-26473.png
-34424.png
-12787.png
-13056.png
-32900.png
-63054.png
-61706.png
-22819.png
-11765.png
-32122.png
-48905.png
-66150.png
-68403.png
-47466.png
-31991.png
-65725.png
-15435.png
-14547.png
-11581.png
-36989.png
-04313.png
-12680.png
-29885.png
-25623.png
-61269.png
-56072.png
-09583.png
-09190.png
-48466.png
-41993.png
-36865.png
-18144.png
-34604.png
-52852.png
-19358.png
-19655.png
-36004.png
-57807.png
-43708.png
-09980.png
-55130.png
-61719.png
-36943.png
-52421.png
-43813.png
-51769.png
-42749.png
-53210.png
-20337.png
-60009.png
-59053.png
-50147.png
-24192.png
-30883.png
-38744.png
-15579.png
-19983.png
-25564.png
-59930.png
-22886.png
-08291.png
-37484.png
-54746.png
-54223.png
-29195.png
-28809.png
-45711.png
-25703.png
-64229.png
-39025.png
-61959.png
-61427.png
-49202.png
-41015.png
-20617.png
-45819.png
-54161.png
-51550.png
-46954.png
-32351.png
-56672.png
-05187.png
-02146.png
-03031.png
-17301.png
-60272.png
-17600.png
-06936.png
-67359.png
-13115.png
-41996.png
-30744.png
-41288.png
-45381.png
-69359.png
-47185.png
-31688.png
-00562.png
-34441.png
-61282.png
-47028.png
-23000.png
-15277.png
-50739.png
-33777.png
-55724.png
-22610.png
-45747.png
-34095.png
-32981.png
-55767.png
-27317.png
-43780.png
-46499.png
-23852.png
-17157.png
-42903.png
-54592.png
-51378.png
-52586.png
-05782.png
-62633.png
-11710.png
-06491.png
-57776.png
-49842.png
-31354.png
-17771.png
-55141.png
-15019.png
-11906.png
-16013.png
-32321.png
-62750.png
-22502.png
-33734.png
-26648.png
-06219.png
-23643.png
-24485.png
-26584.png
-46625.png
-15089.png
-21635.png
-03220.png
-34120.png
-53920.png
-52675.png
-63362.png
-12500.png
-38989.png
-22753.png
-03036.png
-40526.png
-47044.png
-65159.png
-18807.png
-18817.png
-62924.png
-50843.png
-02840.png
-13672.png
-31621.png
-40124.png
-03659.png
-68582.png
-68010.png
-09649.png
-26600.png
-46426.png
-59880.png
-00549.png
-37787.png
-61523.png
-32819.png
-66433.png
-58503.png
-13075.png
-65039.png
-24113.png
-19754.png
-28543.png
-49899.png
-62051.png
-22114.png
-39518.png
-01353.png
-25315.png
-36178.png
-53051.png
-03480.png
-02478.png
-36403.png
-33213.png
-12036.png
-12981.png
-45259.png
-53498.png
-39788.png
-44101.png
-27616.png
-22075.png
-28093.png
-19807.png
-33243.png
-13292.png
-37247.png
-43287.png
-59069.png
-33774.png
-25217.png
-44962.png
-14721.png
-08722.png
-09575.png
-05694.png
-13098.png
-53973.png
-49242.png
-06555.png
-34665.png
-07376.png
-15543.png
-37000.png
-29687.png
-52623.png
-30316.png
-22454.png
-09070.png
-12508.png
-09893.png
-02859.png
-31483.png
-50610.png
-40488.png
-55086.png
-18759.png
-07241.png
-56639.png
-26883.png
-45776.png
-42464.png
-00483.png
-05647.png
-23671.png
-26670.png
-09250.png
-46505.png
-45520.png
-33388.png
-35845.png
-53335.png
-66853.png
-43467.png
-68030.png
-35489.png
-59403.png
-37099.png
-63758.png
-69577.png
-00386.png
-50782.png
-01728.png
-22425.png
-44734.png
-11557.png
-21483.png
-30722.png
-47811.png
-32989.png
-15186.png
-25607.png
-01237.png
-27732.png
-03279.png
-63795.png
-07945.png
-56626.png
-31408.png
-50856.png
-36434.png
-52055.png
-49987.png
-04029.png
-20682.png
-15381.png
-15842.png
-51628.png
-02295.png
-46793.png
-53199.png
-34437.png
-18976.png
-66634.png
-61270.png
-66395.png
-68949.png
-35452.png
-17324.png
-02557.png
-60459.png
-64575.png
-21494.png
-13290.png
-20039.png
-54229.png
-35892.png
-69909.png
-33090.png
-67902.png
-43238.png
-20589.png
-42658.png
-59288.png
-42439.png
-08666.png
-36806.png
-30793.png
-63243.png
-51716.png
-13753.png
-37063.png
-08215.png
-61179.png
-59976.png
-03501.png
-37003.png
-39624.png
-32762.png
-28618.png
-63945.png
-38871.png
-29802.png
-31651.png
-52128.png
-49744.png
-41943.png
-55573.png
-66041.png
-30167.png
-63245.png
-34225.png
-28931.png
-23084.png
-41052.png
-39723.png
-22059.png
-59345.png
-02770.png
-45544.png
-53626.png
-09238.png
-63663.png
-04358.png
-57465.png
-40942.png
-43773.png
-19516.png
-09214.png
-26368.png
-62000.png
-42842.png
-47570.png
-19021.png
-43146.png
-22598.png
-55630.png
-15008.png
-27051.png
-02704.png
-39295.png
-65869.png
-52183.png
-17995.png
-34619.png
-53751.png
-21284.png
-02918.png
-51173.png
-35940.png
-59839.png
-12083.png
-32003.png
-11643.png
-12842.png
-64340.png
-20443.png
-38328.png
-62715.png
-61310.png
-46806.png
-57896.png
-24131.png
-13108.png
-23316.png
-54349.png
-43492.png
-48006.png
-25706.png
-64001.png
-12902.png
-18933.png
-51956.png
-34999.png
-47737.png
-30343.png
-13321.png
-32345.png
-23845.png
-33867.png
-68158.png
-52093.png
-11463.png
-66435.png
-33100.png
-54752.png
-25842.png
-25254.png
-25340.png
-21557.png
-57799.png
-49785.png
-12572.png
-00844.png
-41736.png
-08938.png
-10869.png
-02823.png
-49597.png
-12307.png
-14981.png
-22850.png
-67540.png
-01594.png
-58101.png
-06199.png
-64092.png
-09366.png
-42171.png
-30074.png
-41683.png
-42448.png
-07969.png
-32913.png
-22422.png
-38901.png
-48010.png
-16533.png
-36253.png
-01979.png
-09071.png
-22602.png
-31008.png
-69655.png
-07258.png
-64814.png
-62594.png
-63064.png
-02883.png
-48922.png
-53709.png
-69546.png
-60156.png
-01449.png
-69090.png
-34201.png
-40896.png
-37338.png
-25399.png
-46862.png
-14835.png
-18693.png
-49657.png
-63511.png
-64630.png
-40437.png
-03662.png
-01334.png
-19246.png
-46917.png
-13504.png
-58281.png
-67764.png
-68513.png
-55566.png
-50549.png
-54486.png
-38824.png
-33493.png
-07939.png
-03566.png
-18947.png
-47196.png
-51825.png
-31410.png
-38275.png
-43534.png
-03550.png
-00856.png
-68411.png
-51960.png
-33679.png
-24659.png
-04447.png
-25325.png
-46602.png
-29538.png
-02187.png
-08979.png
-18166.png
-56487.png
-54287.png
-33820.png
-08631.png
-28224.png
-52674.png
-64506.png
-38540.png
-62440.png
-21371.png
-56275.png
-03390.png
-15611.png
-11325.png
-33651.png
-04800.png
-14031.png
-46818.png
-61166.png
-68006.png
-64785.png
-68699.png
-05174.png
-29710.png
-37195.png
-10955.png
-33519.png
-55367.png
-09661.png
-59180.png
-40189.png
-32820.png
-09827.png
-47054.png
-42937.png
-43839.png
-07073.png
-51587.png
-24326.png
-47524.png
-04153.png
-46153.png
-34410.png
-55207.png
-26394.png
-63619.png
-04750.png
-21696.png
-55920.png
-13442.png
-24593.png
-40996.png
-54985.png
-05077.png
-53133.png
-51790.png
-07569.png
-47717.png
-13355.png
-43017.png
-31450.png
-43889.png
-29000.png
-21245.png
-01726.png
-08331.png
-45804.png
-59119.png
-67257.png
-31280.png
-10430.png
-34048.png
-65621.png
-11568.png
-31618.png
-29618.png
-27476.png
-44758.png
-04887.png
-65116.png
-09291.png
-20892.png
-40152.png
-01603.png
-14613.png
-29700.png
-51145.png
-63952.png
-60671.png
-53619.png
-23833.png
-24605.png
-04062.png
-42487.png
-10527.png
-19698.png
-27642.png
-48443.png
-40077.png
-27398.png
-23994.png
-66059.png
-17458.png
-15878.png
-31964.png
-08212.png
-10133.png
-21502.png
-52752.png
-48143.png
-30414.png
-50533.png
-14508.png
-11216.png
-19373.png
-26235.png
-25018.png
-00258.png
-12278.png
-01224.png
-14927.png
-17885.png
-49598.png
-52779.png
-26929.png
-37048.png
-28587.png
-28295.png
-21537.png
-27685.png
-23809.png
-03111.png
-28347.png
-57791.png
-01463.png
-33439.png
-51911.png
-47939.png
-58337.png
-16952.png
-24098.png
-18529.png
-51088.png
-34827.png
-47824.png
-14419.png
-42574.png
-31499.png
-50230.png
-62393.png
-57740.png
-10407.png
-02869.png
-54134.png
-43781.png
-02813.png
-53211.png
-36775.png
-40346.png
-58053.png
-57660.png
-13258.png
-58454.png
-37692.png
-01145.png
-30177.png
-13524.png
-67652.png
-00014.png
-48138.png
-55120.png
-60722.png
-20161.png
-32531.png
-06386.png
-27848.png
-46462.png
-63048.png
-15165.png
-44638.png
-23561.png
-57291.png
-06981.png
-68876.png
-58999.png
-24746.png
-37776.png
-62430.png
-66028.png
-56792.png
-19217.png
-35994.png
-66137.png
-51077.png
-22025.png
-33742.png
-50536.png
-15881.png
-13005.png
-39190.png
-35759.png
-59801.png
-48222.png
-38592.png
-54742.png
-26923.png
-10537.png
-58157.png
-55558.png
-21504.png
-21179.png
-68828.png
-37696.png
-49086.png
-04398.png
-10119.png
-48561.png
-58278.png
-23042.png
-43927.png
-31906.png
-04232.png
-51442.png
-00794.png
-18536.png
-04391.png
-20616.png
-27584.png
-31588.png
-00343.png
-43472.png
-66370.png
-60734.png
-28599.png
-55392.png
-34621.png
-51522.png
-46562.png
-12891.png
-11422.png
-12603.png
-15090.png
-45662.png
-23486.png
-51150.png
-60163.png
-58685.png
-52819.png
-29218.png
-21351.png
-08740.png
-10701.png
-54019.png
-63695.png
-39015.png
-66665.png
-69132.png
-39858.png
-26835.png
-49289.png
-23597.png
-17730.png
-49703.png
-65201.png
-07289.png
-12507.png
-59909.png
-33078.png
-62603.png
-21011.png
-03993.png
-57420.png
-15793.png
-59725.png
-63111.png
-05784.png
-16204.png
-02473.png
-65770.png
-64863.png
-45272.png
-58211.png
-52805.png
-47085.png
-69593.png
-00241.png
-42383.png
-07422.png
-55773.png
-22575.png
-20775.png
-41832.png
-25478.png
-43863.png
-44871.png
-41800.png
-01829.png
-50448.png
-45164.png
-57379.png
-47463.png
-10423.png
-33700.png
-39725.png
-24468.png
-03366.png
-60934.png
-10969.png
-23619.png
-29719.png
-40461.png
-22165.png
-22572.png
-10986.png
-01070.png
-28582.png
-26960.png
-65937.png
-55017.png
-00739.png
-48463.png
-04585.png
-19694.png
-18541.png
-50183.png
-68974.png
-67178.png
-53161.png
-05934.png
-55234.png
-47482.png
-08047.png
-16783.png
-31477.png
-33980.png
-38517.png
-52538.png
-36493.png
-33128.png
-24074.png
-28429.png
-11357.png
-55413.png
-04406.png
-04583.png
-28799.png
-68827.png
-36773.png
-33637.png
-54314.png
-34830.png
-32668.png
-36292.png
-02834.png
-55704.png
-68122.png
-50107.png
-66939.png
-12054.png
-23129.png
-05587.png
-36504.png
-22482.png
-63778.png
-47637.png
-35029.png
-28763.png
-21333.png
-51183.png
-49707.png
-41093.png
-37239.png
-21785.png
-01221.png
-41510.png
-36792.png
-04471.png
-65727.png
-22172.png
-29243.png
-61827.png
-11021.png
-10248.png
-55644.png
-00871.png
-26246.png
-44581.png
-19619.png
-55584.png
-11979.png
-31130.png
-14680.png
-22737.png
-29011.png
-36078.png
-42524.png
-34140.png
-18879.png
-25312.png
-29072.png
-52598.png
-47934.png
-42031.png
-13277.png
-48212.png
-46614.png
-67005.png
-63463.png
-30730.png
-69177.png
-34639.png
-68079.png
-10971.png
-67192.png
-60764.png
-64031.png
-39235.png
-03377.png
-40677.png
-11313.png
-36687.png
-63957.png
-13151.png
-64687.png
-18432.png
-34214.png
-37429.png
-09985.png
-60828.png
-39647.png
-21644.png
-39516.png
-00651.png
-63801.png
-52753.png
-57784.png
-09087.png
-49931.png
-18728.png
-01074.png
-01616.png
-47339.png
-01641.png
-39395.png
-39156.png
-04035.png
-13674.png
-20048.png
-15650.png
-05989.png
-37523.png
-45333.png
-42667.png
-23904.png
-39432.png
-45008.png
-31532.png
-62725.png
-36139.png
-50245.png
-12193.png
-36842.png
-08530.png
-46720.png
-23063.png
-58633.png
-22459.png
-40783.png
-53734.png
-49927.png
-26862.png
-21666.png
-54023.png
-39617.png
-05051.png
-27181.png
-65199.png
-42009.png
-43732.png
-41986.png
-67934.png
-20084.png
-39265.png
-32480.png
-58318.png
-56413.png
-52360.png
-00826.png
-12445.png
-12537.png
-26541.png
-30574.png
-06061.png
-45433.png
-17341.png
-58515.png
-22678.png
-06811.png
-60862.png
-21782.png
-62045.png
-65973.png
-03712.png
-19221.png
-20642.png
-11956.png
-30858.png
-18660.png
-66579.png
-60913.png
-21451.png
-45225.png
-61447.png
-28263.png
-27036.png
-15167.png
-42454.png
-38188.png
-20993.png
-54660.png
-61549.png
-41143.png
-14280.png
-52626.png
-65841.png
-34121.png
-23085.png
-68196.png
-45351.png
-64019.png
-35387.png
-52617.png
-07812.png
-13138.png
-05712.png
-38020.png
-39198.png
-57692.png
-64697.png
-62085.png
-33618.png
-49672.png
-20593.png
-32577.png
-51028.png
-37807.png
-17507.png
-39948.png
-30203.png
-02067.png
-49017.png
-26669.png
-38007.png
-66613.png
-34018.png
-64538.png
-12229.png
-14878.png
-68150.png
-41475.png
-48517.png
-11737.png
-01011.png
-05034.png
-38644.png
-02054.png
-27116.png
-15850.png
-58215.png
-13000.png
-05050.png
-50589.png
-05169.png
-21491.png
-51598.png
-17860.png
-16644.png
-43623.png
-35271.png
-31723.png
-12236.png
-14498.png
-57200.png
-28660.png
-55849.png
-23180.png
-16100.png
-08611.png
-18800.png
-30326.png
-56090.png
-55229.png
-60011.png
-45606.png
-08138.png
-60247.png
-23185.png
-50303.png
-66610.png
-63208.png
-20544.png
-10661.png
-35737.png
-34419.png
-40419.png
-20817.png
-54089.png
-27847.png
-37684.png
-20727.png
-38934.png
-29999.png
-43632.png
-65477.png
-51859.png
-36732.png
-30463.png
-62704.png
-04159.png
-36008.png
-31166.png
-07168.png
-03991.png
-68056.png
-59152.png
-43182.png
-17602.png
-33914.png
-55741.png
-09261.png
-46530.png
-30332.png
-54965.png
-48997.png
-28113.png
-04454.png
-41672.png
-67300.png
-26015.png
-30165.png
-53770.png
-67621.png
-04811.png
-52495.png
-44042.png
-19547.png
-53246.png
-11290.png
-48446.png
-08157.png
-19776.png
-33946.png
-66360.png
-04725.png
-48157.png
-08396.png
-06439.png
-57158.png
-06412.png
-45378.png
-28689.png
-03105.png
-33359.png
-34995.png
-30941.png
-32235.png
-55986.png
-29983.png
-51693.png
-44790.png
-47704.png
-27368.png
-55995.png
-30715.png
-10159.png
-01026.png
-64723.png
-44200.png
-43156.png
-59995.png
-45018.png
-59534.png
-49075.png
-29134.png
-21820.png
-37347.png
-54142.png
-60818.png
-60477.png
-41072.png
-39442.png
-48853.png
-50741.png
-08941.png
-43485.png
-33095.png
-17940.png
-66857.png
-41519.png
-14063.png
-69947.png
-00239.png
-40736.png
-14300.png
-09779.png
-64413.png
-31438.png
-26866.png
-53768.png
-58026.png
-58351.png
-63393.png
-01971.png
-16821.png
-21081.png
-52138.png
-64095.png
-18606.png
-57103.png
-33415.png
-20494.png
-64426.png
-60853.png
-01633.png
-65539.png
-45047.png
-68993.png
-59388.png
-31659.png
-12947.png
-42701.png
-06287.png
-27810.png
-25015.png
-10318.png
-35202.png
-69857.png
-60099.png
-20410.png
-43185.png
-64136.png
-47166.png
-61198.png
-39515.png
-67026.png
-51697.png
-65798.png
-63930.png
-49969.png
-55276.png
-66484.png
-62086.png
-50150.png
-16716.png
-05823.png
-33072.png
-08122.png
-69760.png
-14649.png
-14490.png
-37415.png
-61869.png
-14992.png
-50405.png
-08776.png
-29580.png
-33274.png
-45382.png
-06384.png
-25416.png
-46601.png
-51005.png
-47759.png
-44169.png
-04020.png
-46781.png
-17352.png
-38819.png
-25966.png
-06709.png
-33163.png
-35906.png
-13351.png
-51323.png
-61382.png
-66166.png
-01573.png
-55391.png
-56388.png
-69236.png
-53435.png
-03079.png
-23600.png
-49561.png
-27771.png
-68274.png
-37877.png
-50452.png
-11669.png
-36972.png
-01421.png
-37802.png
-34767.png
-12795.png
-44736.png
-11663.png
-41538.png
-10803.png
-04540.png
-08503.png
-40582.png
-03628.png
-43329.png
-64762.png
-30774.png
-08012.png
-51779.png
-39499.png
-34328.png
-35160.png
-34244.png
-32562.png
-47228.png
-39594.png
-24165.png
-19944.png
-28233.png
-33428.png
-57794.png
-22227.png
-57343.png
-52228.png
-37873.png
-09585.png
-23256.png
-46824.png
-05146.png
-10936.png
-03494.png
-55700.png
-44986.png
-27519.png
-03148.png
-57751.png
-09687.png
-56357.png
-69915.png
-69152.png
-63283.png
-30816.png
-53499.png
-64172.png
-32401.png
-61094.png
-36354.png
-57427.png
-59635.png
-09653.png
-49992.png
-10379.png
-09116.png
-45610.png
-12590.png
-11997.png
-36290.png
-27255.png
-00110.png
-56278.png
-36264.png
-40786.png
-50159.png
-47584.png
-54270.png
-48865.png
-26023.png
-18084.png
-45095.png
-53507.png
-34327.png
-68397.png
-57069.png
-40388.png
-31329.png
-09093.png
-45561.png
-11949.png
-61729.png
-19497.png
-30869.png
-31542.png
-20325.png
-37498.png
-47367.png
-64036.png
-04768.png
-54028.png
-62959.png
-62627.png
-41868.png
-31942.png
-37716.png
-19202.png
-34548.png
-33162.png
-03842.png
-66278.png
-46191.png
-50934.png
-32471.png
-13801.png
-39041.png
-10471.png
-41840.png
-44476.png
-36283.png
-10044.png
-24808.png
-20489.png
-21980.png
-20364.png
-59012.png
-46145.png
-20635.png
-08241.png
-29873.png
-42908.png
-31485.png
-62978.png
-08888.png
-03935.png
-20467.png
-08684.png
-04349.png
-68406.png
-08073.png
-16187.png
-07832.png
-05730.png
-63506.png
-47960.png
-25098.png
-22493.png
-24235.png
-11513.png
-38136.png
-54596.png
-66917.png
-56950.png
-30500.png
-58800.png
-30560.png
-64207.png
-45057.png
-53793.png
-56748.png
-17063.png
-69272.png
-40068.png
-38933.png
-56700.png
-59373.png
-24806.png
-27071.png
-02051.png
-16729.png
-25052.png
-17717.png
-08240.png
-51866.png
-49301.png
-55171.png
-28232.png
-64100.png
-13385.png
-62204.png
-16134.png
-09055.png
-22595.png
-15376.png
-69732.png
-13366.png
-43451.png
-32972.png
-29971.png
-53307.png
-69662.png
-43494.png
-27296.png
-61498.png
-26254.png
-22676.png
-18959.png
-43895.png
-27839.png
-30604.png
-69603.png
-38476.png
-17200.png
-24959.png
-59414.png
-00733.png
-61924.png
-61483.png
-17515.png
-29362.png
-14172.png
-64725.png
-44165.png
-12692.png
-67104.png
-39964.png
-12206.png
-46775.png
-29254.png
-18266.png
-60174.png
-10438.png
-35094.png
-35351.png
-64298.png
-24420.png
-58102.png
-59661.png
-38634.png
-05068.png
-10943.png
-30004.png
-16291.png
-04722.png
-48578.png
-49130.png
-51182.png
-19749.png
-40792.png
-35515.png
-11012.png
-62011.png
-43249.png
-68416.png
-68886.png
-42889.png
-47347.png
-40173.png
-25488.png
-55189.png
-58051.png
-35156.png
-11011.png
-62329.png
-00658.png
-07754.png
-10602.png
-28521.png
-40693.png
-12479.png
-53024.png
-36187.png
-51957.png
-36907.png
-28098.png
-63331.png
-09438.png
-01716.png
-45088.png
-60960.png
-03931.png
-05405.png
-23580.png
-18135.png
-32278.png
-47798.png
-17852.png
-68864.png
-03179.png
-35887.png
-05885.png
-31616.png
-52771.png
-45822.png
-17236.png
-56443.png
-11037.png
-31119.png
-20629.png
-49092.png
-51116.png
-37432.png
-28667.png
-03436.png
-65339.png
-64101.png
-19254.png
-15256.png
-01839.png
-19105.png
-45113.png
-64892.png
-46519.png
-05858.png
-12191.png
-58079.png
-13755.png
-40321.png
-16311.png
-69048.png
-05138.png
-20414.png
-12807.png
-11030.png
-52685.png
-54392.png
-00440.png
-66527.png
-39954.png
-00889.png
-53869.png
-45429.png
-24289.png
-54520.png
-11842.png
-53327.png
-62982.png
-63468.png
-25625.png
-58784.png
-03637.png
-34245.png
-03656.png
-26441.png
-30415.png
-65397.png
-11818.png
-36462.png
-34425.png
-23843.png
-52607.png
-11998.png
-17721.png
-19510.png
-17103.png
-41586.png
-23849.png
-06263.png
-24337.png
-42193.png
-18045.png
-56527.png
-63367.png
-15853.png
-39130.png
-60980.png
-03033.png
-04520.png
-20440.png
-12006.png
-27985.png
-56491.png
-63880.png
-02202.png
-49608.png
-32290.png
-00527.png
-19620.png
-02987.png
-25445.png
-41323.png
-00659.png
-12857.png
-31707.png
-01952.png
-42760.png
-57572.png
-26174.png
-21002.png
-52634.png
-29188.png
-13502.png
-03625.png
-45033.png
-10384.png
-68707.png
-04395.png
-61232.png
-20941.png
-30704.png
-26811.png
-20261.png
-12022.png
-14662.png
-56003.png
-06445.png
-65769.png
-10018.png
-34939.png
-42539.png
-26277.png
-61927.png
-50675.png
-56964.png
-24710.png
-58882.png
-41794.png
-28816.png
-24028.png
-26657.png
-41249.png
-57393.png
-04338.png
-51043.png
-22424.png
-50019.png
-52061.png
-15785.png
-15075.png
-13382.png
-34232.png
-43795.png
-68450.png
-47800.png
-46632.png
-65330.png
-36520.png
-58437.png
-35524.png
-33210.png
-51630.png
-25694.png
-52442.png
-03771.png
-13981.png
-20777.png
-07827.png
-49157.png
-24609.png
-46500.png
-66086.png
-33106.png
-44332.png
-07765.png
-22326.png
-09629.png
-67986.png
-12186.png
-39800.png
-21897.png
-50073.png
-55279.png
-05159.png
-00399.png
-09327.png
-12061.png
-63642.png
-35197.png
-51770.png
-67130.png
-01117.png
-40002.png
-31009.png
-41880.png
-42752.png
-11794.png
-66078.png
-04816.png
-69497.png
-58696.png
-22671.png
-23340.png
-51508.png
-28485.png
-17988.png
-69348.png
-40087.png
-12372.png
-44572.png
-14843.png
-12303.png
-57185.png
-20585.png
-41451.png
-56594.png
-16008.png
-69065.png
-29413.png
-54130.png
-17531.png
-07823.png
-63172.png
-42434.png
-56346.png
-12461.png
-51788.png
-67019.png
-61947.png
-43123.png
-19696.png
-69176.png
-49179.png
-13520.png
-48908.png
-27451.png
-23991.png
-67613.png
-58514.png
-04933.png
-24972.png
-12150.png
-22371.png
-04736.png
-07822.png
-61966.png
-56272.png
-06993.png
-35380.png
-52317.png
-43188.png
-01786.png
-46710.png
-30531.png
-58142.png
-35400.png
-04487.png
-12696.png
-00074.png
-07327.png
-49574.png
-16987.png
-20983.png
-47224.png
-52079.png
-09933.png
-59093.png
-42500.png
-62466.png
-44236.png
-16443.png
-42034.png
-42280.png
-66571.png
-60438.png
-58349.png
-43894.png
-28142.png
-28392.png
-34757.png
-20371.png
-26308.png
-67993.png
-58324.png
-51242.png
-40940.png
-21549.png
-02821.png
-28594.png
-17633.png
-06239.png
-47133.png
-31593.png
-57584.png
-32151.png
-21905.png
-26974.png
-68089.png
-03175.png
-56503.png
-55453.png
-66229.png
-40405.png
-25598.png
-36561.png
-38818.png
-26217.png
-25302.png
-04524.png
-15802.png
-08860.png
-24759.png
-68597.png
-21350.png
-68499.png
-33997.png
-56119.png
-45404.png
-41062.png
-35330.png
-46217.png
-31619.png
-19884.png
-03403.png
-33690.png
-31091.png
-08218.png
-24360.png
-59439.png
-37085.png
-10750.png
-68727.png
-52870.png
-63482.png
-39988.png
-05515.png
-28808.png
-67495.png
-35648.png
-26481.png
-30037.png
-43254.png
-14942.png
-47395.png
-23937.png
-17861.png
-44912.png
-36724.png
-11268.png
-39600.png
-12341.png
-31380.png
-14890.png
-42906.png
-04421.png
-67780.png
-21131.png
-19080.png
-57914.png
-46732.png
-07449.png
-07997.png
-49847.png
-49107.png
-62367.png
-63520.png
-59770.png
-07429.png
-23913.png
-38249.png
-40263.png
-20750.png
-06727.png
-11282.png
-11647.png
-25092.png
-21144.png
-00886.png
-23920.png
-50688.png
-20774.png
-15784.png
-36320.png
-19559.png
-19515.png
-67212.png
-02805.png
-57942.png
-66460.png
-50068.png
-34815.png
-46176.png
-12256.png
-39161.png
-37114.png
-13563.png
-24231.png
-46001.png
-09122.png
-24024.png
-53798.png
-43770.png
-18590.png
-22993.png
-43696.png
-39896.png
-36353.png
-33347.png
-42078.png
-29591.png
-55470.png
-28550.png
-66326.png
-67876.png
-22901.png
-12033.png
-03880.png
-54785.png
-65085.png
-64670.png
-26025.png
-35556.png
-11243.png
-46388.png
-65010.png
-40504.png
-24519.png
-31208.png
-34849.png
-37786.png
-56004.png
-14336.png
-30133.png
-02543.png
-09991.png
-00773.png
-64500.png
-53504.png
-12315.png
-03805.png
-47883.png
-68537.png
-58873.png
-61538.png
-66821.png
-00115.png
-37541.png
-34250.png
-63892.png
-37966.png
-69231.png
-06174.png
-61472.png
-35671.png
-03517.png
-38398.png
-11106.png
-29554.png
-05543.png
-54412.png
-68053.png
-14459.png
-01015.png
-30727.png
-08340.png
-38071.png
-12724.png
-27118.png
-24264.png
-50514.png
-65696.png
-09147.png
-44503.png
-09503.png
-21757.png
-68687.png
-32329.png
-66680.png
-31877.png
-27288.png
-14922.png
-04737.png
-41874.png
-07962.png
-38043.png
-04186.png
-22045.png
-60654.png
-60529.png
-18718.png
-30691.png
-24811.png
-11847.png
-18604.png
-66450.png
-39128.png
-34872.png
-35624.png
-12664.png
-15190.png
-69779.png
-04502.png
-41472.png
-18695.png
-46021.png
-69006.png
-32597.png
-18240.png
-05882.png
-20222.png
-15268.png
-40871.png
-18737.png
-16881.png
-40500.png
-64702.png
-58885.png
-39905.png
-55993.png
-16600.png
-46594.png
-05334.png
-53401.png
-61657.png
-13148.png
-17570.png
-40816.png
-30726.png
-14124.png
-27727.png
-14930.png
-17941.png
-58421.png
-63578.png
-37540.png
-45925.png
-52370.png
-33065.png
-68325.png
-53263.png
-16891.png
-03421.png
-08456.png
-48033.png
-54172.png
-31986.png
-11364.png
-69747.png
-68407.png
-11536.png
-30856.png
-42522.png
-04772.png
-10347.png
-64847.png
-60140.png
-28851.png
-38407.png
-29608.png
-56032.png
-56149.png
-51200.png
-42753.png
-42446.png
-32724.png
-20176.png
-21166.png
-08275.png
-46351.png
-60998.png
-64927.png
-11150.png
-04912.png
-38093.png
-13590.png
-01325.png
-36578.png
-25926.png
-00401.png
-08699.png
-01147.png
-60523.png
-19788.png
-08022.png
-67417.png
-24556.png
-16006.png
-47119.png
-39474.png
-21780.png
-51663.png
-14266.png
-18940.png
-13386.png
-52545.png
-55443.png
-17036.png
-23866.png
-24085.png
-13207.png
-29005.png
-23086.png
-65438.png
-40238.png
-63010.png
-23609.png
-49745.png
-28204.png
-27365.png
-60728.png
-09606.png
-52049.png
-39916.png
-05700.png
-50500.png
-68364.png
-49519.png
-13823.png
-53491.png
-30398.png
-47440.png
-06770.png
-66897.png
-06012.png
-20863.png
-41404.png
-08627.png
-43817.png
-67619.png
-65399.png
-11482.png
-25041.png
-43776.png
-15883.png
-34691.png
-15316.png
-45942.png
-57265.png
-33059.png
-58124.png
-49012.png
-26843.png
-32825.png
-57960.png
-24935.png
-42015.png
-36475.png
-16663.png
-13177.png
-69772.png
-08298.png
-68668.png
-42841.png
-23854.png
-09108.png
-25171.png
-45456.png
-05889.png
-11297.png
-41876.png
-35863.png
-06079.png
-51357.png
-56995.png
-06500.png
-06891.png
-52212.png
-47229.png
-00888.png
-38485.png
-60422.png
-33504.png
-52630.png
-57549.png
-08413.png
-03258.png
-45567.png
-24386.png
-51749.png
-25957.png
-28011.png
-00100.png
-32050.png
-14186.png
-57261.png
-43409.png
-25424.png
-30565.png
-44593.png
-55661.png
-43877.png
-38289.png
-44831.png
-69238.png
-48481.png
-04537.png
-31779.png
-31296.png
-59336.png
-10330.png
-05078.png
-53602.png
-41883.png
-44643.png
-07129.png
-47124.png
-01223.png
-35323.png
-26818.png
-05956.png
-41303.png
-44632.png
-25776.png
-22152.png
-05359.png
-36189.png
-21077.png
-56535.png
-31515.png
-04101.png
-48023.png
-25007.png
-07062.png
-40999.png
-66131.png
-26403.png
-54329.png
-08633.png
-10165.png
-39119.png
-40709.png
-17008.png
-08849.png
-65263.png
-09726.png
-36914.png
-68536.png
-38504.png
-60718.png
-61710.png
-14259.png
-53590.png
-31303.png
-41735.png
-25813.png
-22518.png
-42917.png
-30110.png
-52097.png
-26066.png
-25716.png
-69290.png
-68120.png
-36442.png
-40456.png
-03892.png
-44583.png
-18644.png
-02267.png
-62291.png
-36391.png
-62114.png
-42352.png
-09256.png
-18301.png
-54582.png
-02898.png
-36265.png
-53822.png
-34740.png
-33861.png
-10206.png
-13617.png
-56731.png
-40309.png
-05979.png
-46914.png
-03881.png
-54673.png
-67785.png
-36771.png
-56717.png
-47518.png
-44454.png
-23814.png
-47592.png
-67062.png
-55109.png
-57610.png
-44911.png
-57725.png
-55847.png
-11026.png
-46654.png
-57060.png
-56445.png
-64259.png
-51715.png
-34572.png
-32012.png
-22224.png
-41432.png
-08577.png
-52333.png
-28147.png
-61502.png
-60256.png
-27790.png
-29399.png
-05635.png
-57836.png
-16456.png
-60128.png
-60741.png
-63573.png
-23058.png
-06510.png
-58495.png
-46314.png
-69615.png
-43046.png
-03982.png
-49099.png
-10124.png
-47109.png
-41801.png
-44257.png
-65089.png
-29639.png
-69022.png
-08757.png
-35153.png
-01079.png
-62527.png
-06938.png
-61568.png
-13372.png
-54278.png
-69568.png
-26339.png
-56220.png
-45151.png
-02088.png
-15361.png
-37679.png
-16102.png
-32412.png
-23194.png
-58405.png
-41013.png
-22330.png
-12026.png
-04572.png
-60782.png
-36851.png
-39639.png
-14295.png
-07691.png
-04181.png
-64626.png
-62746.png
-36349.png
-02414.png
-03424.png
-21971.png
-53537.png
-22713.png
-10806.png
-51932.png
-45758.png
-34242.png
-67153.png
-14595.png
-27688.png
-03874.png
-22390.png
-40670.png
-05223.png
-25327.png
-09171.png
-11687.png
-14344.png
-37110.png
-35983.png
-49415.png
-40305.png
-39833.png
-36709.png
-59034.png
-60193.png
-43387.png
-37748.png
-43746.png
-09255.png
-32993.png
-27857.png
-16830.png
-45209.png
-26302.png
-62112.png
-14637.png
-38786.png
-19263.png
-30131.png
-66445.png
-20071.png
-23592.png
-48062.png
-64225.png
-12940.png
-67284.png
-59778.png
-40985.png
-12184.png
-49605.png
-28217.png
-53841.png
-57064.png
-30139.png
-42424.png
-67185.png
-61060.png
-26379.png
-68623.png
-00000.png
-59122.png
-20572.png
-20395.png
-09509.png
-40084.png
-06291.png
-48597.png
-56982.png
-43032.png
-05195.png
-00979.png
-24432.png
-37525.png
-58042.png
-67937.png
-14303.png
-61987.png
-16789.png
-30734.png
-41139.png
-66742.png
-09230.png
-31106.png
-34649.png
-41896.png
-16262.png
-13389.png
-58549.png
-52137.png
-40793.png
-54411.png
-18757.png
-68793.png
-50296.png
-47418.png
-50637.png
-20514.png
-30885.png
-27222.png
-46199.png
-21632.png
-19448.png
-08128.png
-45672.png
-24529.png
-57617.png
-69544.png
-63782.png
-09720.png
-41939.png
-53012.png
-53257.png
-11629.png
-62598.png
-63182.png
-50030.png
-49060.png
-23537.png
-47292.png
-02673.png
-42815.png
-38925.png
-53135.png
-62322.png
-17783.png
-01048.png
-48540.png
-52472.png
-31141.png
-39266.png
-25928.png
-47023.png
-31163.png
-06130.png
-30723.png
-42933.png
-54495.png
-23375.png
-55756.png
-46715.png
-26466.png
-01339.png
-12392.png
-10316.png
-43751.png
-57560.png
-06914.png
-26812.png
-44552.png
-32878.png
-65473.png
-56303.png
-05729.png
-18288.png
-54867.png
-48342.png
-15460.png
-51370.png
-52460.png
-36938.png
-05957.png
-17858.png
-63039.png
-39664.png
-48963.png
-15591.png
-40745.png
-67896.png
-65078.png
-08780.png
-53258.png
-20081.png
-43186.png
-11548.png
-07253.png
-10696.png
-68692.png
-65535.png
-34385.png
-51432.png
-63757.png
-06785.png
-15765.png
-34857.png
-26195.png
-66381.png
-31585.png
-19651.png
-25705.png
-06449.png
-41534.png
-29946.png
-38424.png
-05720.png
-05256.png
-21748.png
-46969.png
-22022.png
-18061.png
-24385.png
-35379.png
-03746.png
-03598.png
-28102.png
-15266.png
-49178.png
-47000.png
-05431.png
-26768.png
-05618.png
-20964.png
-35056.png
-12090.png
-31717.png
-01587.png
-12394.png
-07412.png
-33671.png
-40438.png
-29930.png
-64397.png
-17148.png
-69041.png
-06285.png
-41813.png
-02750.png
-36420.png
-34144.png
-66035.png
-21547.png
-27585.png
-13825.png
-34491.png
-60083.png
-03018.png
-02401.png
-12714.png
-11299.png
-32263.png
-39134.png
-33366.png
-41615.png
-43603.png
-20122.png
-21689.png
-61647.png
-55820.png
-14715.png
-36325.png
-67326.png
-11651.png
-25613.png
-36371.png
-38962.png
-36576.png
-14019.png
-52516.png
-43062.png
-52357.png
-21952.png
-53637.png
-56108.png
-17974.png
-55043.png
-20042.png
-32111.png
-56285.png
-10962.png
-43300.png
-52447.png
-00127.png
-15636.png
-06040.png
-35225.png
-65040.png
-60832.png
-64062.png
-39427.png
-44150.png
-48659.png
-18935.png
-68921.png
-42407.png
-53395.png
-44744.png
-50809.png
-02676.png
-39052.png
-47507.png
-12657.png
-32042.png
-68024.png
-58629.png
-62454.png
-47897.png
-68402.png
-61875.png
-46490.png
-31849.png
-13641.png
-47336.png
-27858.png
-33969.png
-26643.png
-46446.png
-57815.png
-01632.png
-21395.png
-09752.png
-10000.png
-25051.png
-51450.png
-25413.png
-59651.png
-54881.png
-51362.png
-47972.png
-51383.png
-17498.png
-65822.png
-33340.png
-64807.png
-30698.png
-18680.png
-12182.png
-39007.png
-53821.png
-05643.png
-45781.png
-04382.png
-30174.png
-63953.png
-08299.png
-10186.png
-31664.png
-21735.png
-27252.png
-13156.png
-64611.png
-56629.png
-67842.png
-35349.png
-18264.png
-51097.png
-52267.png
-08034.png
-45073.png
-22184.png
-21277.png
-29417.png
-68095.png
-65148.png
-32592.png
-09370.png
-42022.png
-48965.png
-52580.png
-36879.png
-67564.png
-66428.png
-51240.png
-67583.png
-63062.png
-13951.png
-25293.png
-11595.png
-02065.png
-09451.png
-45697.png
-65492.png
-28017.png
-01684.png
-03703.png
-69404.png
-06488.png
-19849.png
-66609.png
-37349.png
-08880.png
-54876.png
-31641.png
-01382.png
-54356.png
-44031.png
-24731.png
-54021.png
-53937.png
-41400.png
-53464.png
-22879.png
-55649.png
-20051.png
-67445.png
-35247.png
-26413.png
-69994.png
-58105.png
-68320.png
-64049.png
-67095.png
-42398.png
-15934.png
-23583.png
-39763.png
-36368.png
-44829.png
-49449.png
-36764.png
-52464.png
-09615.png
-64472.png
-44950.png
-20751.png
-23399.png
-16846.png
-01169.png
-22563.png
-47051.png
-55368.png
-45654.png
-41520.png
-49036.png
-37998.png
-44231.png
-53373.png
-11008.png
-60895.png
-47323.png
-45696.png
-10977.png
-65588.png
-53998.png
-59255.png
-08859.png
-67305.png
-53481.png
-17383.png
-56035.png
-40684.png
-26815.png
-11598.png
-04715.png
-02408.png
-67065.png
-28431.png
-22699.png
-62745.png
-31734.png
-35050.png
-55864.png
-68715.png
-45826.png
-29919.png
-58500.png
-25854.png
-51233.png
-26036.png
-03388.png
-50995.png
-67188.png
-05193.png
-52638.png
-46392.png
-12568.png
-34251.png
-59956.png
-26233.png
-60385.png
-31090.png
-11126.png
-51766.png
-27354.png
-67061.png
-35284.png
-66399.png
-66425.png
-28402.png
-17564.png
-47046.png
-08410.png
-23234.png
-34162.png
-49710.png
-04671.png
-06565.png
-33446.png
-64052.png
-08782.png
-50178.png
-08852.png
-02550.png
-32996.png
-12577.png
-20763.png
-65232.png
-32356.png
-24690.png
-19721.png
-43989.png
-11471.png
-00132.png
-53236.png
-60121.png
-03669.png
-30675.png
-57290.png
-19700.png
-38015.png
-50695.png
-44954.png
-63742.png
-54632.png
-65936.png
-55900.png
-33630.png
-32224.png
-18952.png
-44168.png
-60665.png
-22315.png
-65567.png
-13622.png
-43241.png
-36324.png
-38357.png
-16861.png
-02437.png
-35461.png
-57394.png
-04943.png
-26478.png
-47111.png
-04711.png
-32634.png
-32129.png
-02902.png
-49549.png
-33698.png
-38645.png
-48028.png
-49476.png
-51742.png
-04902.png
-15528.png
-10596.png
-51532.png
-39771.png
-15751.png
-61475.png
-26907.png
-62401.png
-33235.png
-68907.png
-01082.png
-17356.png
-61333.png
-18154.png
-36339.png
-10490.png
-16645.png
-26601.png
-32142.png
-08715.png
-13828.png
-39619.png
-30733.png
-59201.png
-39086.png
-10387.png
-06019.png
-56407.png
-68373.png
-08103.png
-65943.png
-56795.png
-68138.png
-61046.png
-38410.png
-59618.png
-35980.png
-26323.png
-51724.png
-31274.png
-52851.png
-15004.png
-46620.png
-59919.png
-41064.png
-10426.png
-23629.png
-62150.png
-46222.png
-21158.png
-52272.png
-39513.png
-07053.png
-26199.png
-07060.png
-59553.png
-69511.png
-40452.png
-34230.png
-40730.png
-32258.png
-14923.png
-56704.png
-10417.png
-49328.png
-24471.png
-69889.png
-28212.png
-65530.png
-22325.png
-68777.png
-22560.png
-69565.png
-51757.png
-36557.png
-24576.png
-12488.png
-62972.png
-53220.png
-54137.png
-50255.png
-21902.png
-14872.png
-05038.png
-00327.png
-01025.png
-43719.png
-04624.png
-40968.png
-03715.png
-06775.png
-46243.png
-43564.png
-64682.png
-08642.png
-46900.png
-31372.png
-06758.png
-31921.png
-36544.png
-46617.png
-21597.png
-27391.png
-15694.png
-50861.png
-26095.png
-21098.png
-40714.png
-68841.png
-06321.png
-10023.png
-04346.png
-69445.png
-51396.png
-10763.png
-62068.png
-18280.png
-24045.png
-09153.png
-17853.png
-25619.png
-53194.png
-52775.png
-16634.png
-44009.png
-20290.png
-34401.png
-61241.png
-62216.png
-15996.png
-11967.png
-51436.png
-27542.png
-61928.png
-13339.png
-42865.png
-26089.png
-42321.png
-42808.png
-46213.png
-27956.png
-51964.png
-64366.png
-35908.png
-13554.png
-03497.png
-65667.png
-19589.png
-29246.png
-27182.png
-14483.png
-39765.png
-49367.png
-09266.png
-14217.png
-09293.png
-61092.png
-06522.png
-48188.png
-66142.png
-04776.png
-66980.png
-20453.png
-47705.png
-66784.png
-09831.png
-47009.png
-59394.png
-38891.png
-56459.png
-67158.png
-27694.png
-30821.png
-19816.png
-41264.png
-39587.png
-33955.png
-50518.png
-61954.png
-24130.png
-64918.png
-01108.png
-40703.png
-27635.png
-36495.png
-52091.png
-12037.png
-15406.png
-08446.png
-35248.png
-37952.png
-29695.png
-44370.png
-58008.png
-22389.png
-14775.png
-45745.png
-61047.png
-51339.png
-09060.png
-60067.png
-38041.png
-43806.png
-45644.png
-26904.png
-63767.png
-48494.png
-64569.png
-32702.png
-35726.png
-69393.png
-43357.png
-06202.png
-21299.png
-36303.png
-39415.png
-60415.png
-22887.png
-09324.png
-17322.png
-40463.png
-13246.png
-27910.png
-63391.png
-34946.png
-38053.png
-48184.png
-42532.png
-17252.png
-51584.png
-10879.png
-54394.png
-22899.png
-35217.png
-41458.png
-58640.png
-09828.png
-08233.png
-24893.png
-58271.png
-36795.png
-65145.png
-55224.png
-12410.png
-14759.png
-34734.png
-30391.png
-09506.png
-15382.png
-03504.png
-35453.png
-55414.png
-21756.png
-13175.png
-11231.png
-57039.png
-64384.png
-49928.png
-41092.png
-61600.png
-41382.png
-10851.png
-34090.png
-18138.png
-58202.png
-04412.png
-12973.png
-13297.png
-03877.png
-10571.png
-29191.png
-34091.png
-49878.png
-39074.png
-20569.png
-55248.png
-32656.png
-20690.png
-60946.png
-14443.png
-20584.png
-53559.png
-54530.png
-32315.png
-57810.png
-37691.png
-08056.png
-69042.png
-10572.png
-02445.png
-32201.png
-37489.png
-05719.png
-26737.png
-45547.png
-67611.png
-36070.png
-26380.png
-69536.png
-58590.png
-37707.png
-27808.png
-07278.png
-37728.png
-68440.png
-00983.png
-46725.png
-24339.png
-23650.png
-02198.png
-49343.png
-20303.png
-41151.png
-68087.png
-28578.png
-08042.png
-35087.png
-40099.png
-24168.png
-52244.png
-47371.png
-07899.png
-00918.png
-64705.png
-34390.png
-24150.png
-41218.png
-58735.png
-09429.png
-68431.png
-18934.png
-02251.png
-33379.png
-36770.png
-27010.png
-54192.png
-62932.png
-67743.png
-33099.png
-62197.png
-49606.png
-10279.png
-61623.png
-49892.png
-54925.png
-17237.png
-64566.png
-46168.png
-48094.png
-26776.png
-67287.png
-30145.png
-53260.png
-05872.png
-32429.png
-45809.png
-31625.png
-21250.png
-36624.png
-47711.png
-40265.png
-11833.png
-45914.png
-65681.png
-25846.png
-68738.png
-38707.png
-02152.png
-33595.png
-58566.png
-20202.png
-12874.png
-44751.png
-63727.png
-39173.png
-33399.png
-65381.png
-25374.png
-33688.png
-20403.png
-14940.png
-22803.png
-55183.png
-43891.png
-64542.png
-41927.png
-25875.png
-65559.png
-40332.png
-56196.png
-03305.png
-61722.png
-14436.png
-33045.png
-68795.png
-64933.png
-35155.png
-58248.png
-09613.png
-13501.png
-64270.png
-10898.png
-02588.png
-29484.png
-47866.png
-69938.png
-08933.png
-64763.png
-30771.png
-66660.png
-34624.png
-69403.png
-19140.png
-64213.png
-02480.png
-00007.png
-12258.png
-19029.png
-50971.png
-40005.png
-31006.png
-34311.png
-33040.png
-03198.png
-00057.png
-35165.png
-62423.png
-13896.png
-53230.png
-13802.png
-28744.png
-23870.png
-58070.png
-62206.png
-40255.png
-56338.png
-69699.png
-34350.png
-49540.png
-27866.png
-63675.png
-20434.png
-29125.png
-07417.png
-22666.png
-08584.png
-42410.png
-46395.png
-43440.png
-55186.png
-62133.png
-66992.png
-05565.png
-21073.png
-13334.png
-54567.png
-06659.png
-07368.png
-21639.png
-14738.png
-25244.png
-06558.png
-63561.png
-01105.png
-60565.png
-27568.png
-22374.png
-63447.png
-58857.png
-08527.png
-18555.png
-44508.png
-65555.png
-45321.png
-00650.png
-39164.png
-18732.png
-08905.png
-59327.png
-45775.png
-58841.png
-30444.png
-16032.png
-68933.png
-44664.png
-09845.png
-37760.png
-28693.png
-49734.png
-09686.png
-68170.png
-01122.png
-13932.png
-16833.png
-59581.png
-29883.png
-27112.png
-53011.png
-37741.png
-38199.png
-40339.png
-46891.png
-67372.png
-19691.png
-14482.png
-07464.png
-37008.png
-53140.png
-55411.png
-11704.png
-26521.png
-32625.png
-09984.png
-06964.png
-22095.png
-57631.png
-10343.png
-63735.png
-11253.png
-11417.png
-30070.png
-21522.png
-22704.png
-54623.png
-38688.png
-02320.png
-57198.png
-41646.png
-54321.png
-69117.png
-21478.png
-15851.png
-18103.png
-30718.png
-29403.png
-65673.png
-48756.png
-59189.png
-53365.png
-24569.png
-16525.png
-35902.png
-16190.png
-35077.png
-44880.png
-57314.png
-17954.png
-41307.png
-00974.png
-03053.png
-57842.png
-61349.png
-31700.png
-45320.png
-42798.png
-33154.png
-48163.png
-07760.png
-26136.png
-60232.png
-22243.png
-60085.png
-21618.png
-24883.png
-05202.png
-20745.png
-14479.png
-08773.png
-64399.png
-69799.png
-46709.png
-66109.png
-06644.png
-60235.png
-02471.png
-59538.png
-45814.png
-57780.png
-40384.png
-25903.png
-65218.png
-09040.png
-40051.png
-24425.png
-62592.png
-28326.png
-54502.png
-05398.png
-17012.png
-10861.png
-45028.png
-20117.png
-16192.png
-40973.png
-26823.png
-39670.png
-56556.png
-08613.png
-44749.png
-49774.png
-31331.png
-23776.png
-52126.png
-64157.png
-44467.png
-43079.png
-57861.png
-30064.png
-50620.png
-01965.png
-52355.png
-44477.png
-22401.png
-61033.png
-20466.png
-43160.png
-30620.png
-69809.png
-03290.png
-64895.png
-37892.png
-65718.png
-57880.png
-52642.png
-44410.png
-19383.png
-27915.png
-27706.png
-68480.png
-25084.png
-10436.png
-12281.png
-03863.png
-46279.png
-28182.png
-18926.png
-66566.png
-48109.png
-45589.png
-17624.png
-07086.png
-05458.png
-08131.png
-21107.png
-19554.png
-63226.png
-46683.png
-05598.png
-69316.png
-68643.png
-11689.png
-44949.png
-11676.png
-46190.png
-61526.png
-14701.png
-36436.png
-25372.png
-16505.png
-39585.png
-20347.png
-31892.png
-09000.png
-54129.png
-45340.png
-59898.png
-12481.png
-24753.png
-23626.png
-23525.png
-29960.png
-20541.png
-47249.png
-04265.png
-37025.png
-66265.png
-68486.png
-45889.png
-02303.png
-24019.png
-42856.png
-67408.png
-22076.png
-00687.png
-33834.png
-56146.png
-23168.png
-69542.png
-53093.png
-42642.png
-35464.png
-08105.png
-56834.png
-18602.png
-67622.png
-16410.png
-12583.png
-32232.png
-37391.png
-23787.png
-67724.png
-49917.png
-47355.png
-62577.png
-41313.png
-06180.png
-34106.png
-33908.png
-61993.png
-51204.png
-23765.png
-04206.png
-16025.png
-56889.png
-42264.png
-18012.png
-38678.png
-58778.png
-43966.png
-18653.png
-24892.png
-20710.png
-59799.png
-57909.png
-02460.png
-35446.png
-37325.png
-57477.png
-58530.png
-53463.png
-22877.png
-43559.png
-31847.png
-25592.png
-34417.png
-62668.png
-07498.png
-13868.png
-41747.png
-41718.png
-49379.png
-55277.png
-21925.png
-06350.png
-16104.png
-64123.png
-25309.png
-62044.png
-05420.png
-16832.png
-64162.png
-63921.png
-39322.png
-08575.png
-54779.png
-65808.png
-32605.png
-23170.png
-60967.png
-06748.png
-55884.png
-47893.png
-40043.png
-35335.png
-17999.png
-09024.png
-52845.png
-38201.png
-08082.png
-42719.png
-35630.png
-29354.png
-45541.png
-65894.png
-30893.png
-14328.png
-18533.png
-25159.png
-35193.png
-40066.png
-57965.png
-16943.png
-40331.png
-22066.png
-59903.png
-31396.png
-36547.png
-38286.png
-10509.png
-50052.png
-19394.png
-19942.png
-66457.png
-18794.png
-11714.png
-25740.png
-64662.png
-44291.png
-63671.png
-54453.png
-21910.png
-39117.png
-28362.png
-60958.png
-04283.png
-06762.png
-52688.png
-30805.png
-38013.png
-11079.png
-10528.png
-37340.png
-07471.png
-35555.png
-58849.png
-09008.png
-46189.png
-43059.png
-36829.png
-69966.png
-11697.png
-06093.png
-56836.png
-64578.png
-55353.png
-03992.png
-13033.png
-49401.png
-38065.png
-51954.png
-50407.png
-14963.png
-62185.png
-00353.png
-44697.png
-41094.png
-52576.png
-44131.png
-25700.png
-21343.png
-26580.png
-57793.png
-61982.png
-49188.png
-29684.png
-24537.png
-34793.png
-07805.png
-23669.png
-07246.png
-44367.png
-53437.png
-41871.png
-32264.png
-65701.png
-54800.png
-31305.png
-34585.png
-31015.png
-04663.png
-60624.png
-16686.png
-42531.png
-32040.png
-58122.png
-04269.png
-49787.png
-48247.png
-65249.png
-61835.png
-06455.png
-63555.png
-08657.png
-65313.png
-25042.png
-59751.png
-06557.png
-17648.png
-39275.png
-31321.png
-37051.png
-62936.png
-30022.png
-11246.png
-50543.png
-59271.png
-38839.png
-59393.png
-58839.png
-61854.png
-18269.png
-66189.png
-10674.png
-10391.png
-35238.png
-29633.png
-27433.png
-35366.png
-18151.png
-43098.png
-00994.png
-09402.png
-27052.png
-16570.png
-61701.png
-30934.png
-32027.png
-53039.png
-46412.png
-30226.png
-36766.png
-68737.png
-16137.png
-20675.png
-20847.png
-23555.png
-19986.png
-45235.png
-46685.png
-59550.png
-59897.png
-67430.png
-36751.png
-02128.png
-46649.png
-41810.png
-04312.png
-37511.png
-55728.png
-14096.png
-60638.png
-22839.png
-48077.png
-51282.png
-21763.png
-25940.png
-19526.png
-00590.png
-54074.png
-47585.png
-26771.png
-08025.png
-43038.png
-54775.png
-47060.png
-59274.png
-00861.png
-31491.png
-18008.png
-56486.png
-11051.png
-29455.png
-30142.png
-17739.png
-55647.png
-45011.png
-47782.png
-46780.png
-56519.png
-05058.png
-53169.png
-59019.png
-16173.png
-59057.png
-43651.png
-61264.png
-35278.png
-02399.png
-57856.png
-19099.png
-56922.png
-32558.png
-04897.png
-55933.png
-14017.png
-00173.png
-50726.png
-18080.png
-39728.png
-07054.png
-38300.png
-17104.png
-19249.png
-27235.png
-36229.png
-44479.png
-42211.png
-22200.png
-47107.png
-32829.png
-63404.png
-21917.png
-45680.png
-30978.png
-63390.png
-19787.png
-46340.png
-40782.png
-01276.png
-58146.png
-59342.png
-69668.png
-47680.png
-37706.png
-11747.png
-36983.png
-31917.png
-66007.png
-11940.png
-38711.png
-17639.png
-20008.png
-03021.png
-47495.png
-15915.png
-66854.png
-14644.png
-09430.png
-28340.png
-56662.png
-06112.png
-07846.png
-12591.png
-03792.png
-16918.png
-19523.png
-21745.png
-45315.png
-22710.png
-36511.png
-03193.png
-53756.png
-04818.png
-37606.png
-12799.png
-44232.png
-08890.png
-00118.png
-53232.png
-44560.png
-15866.png
-04518.png
-42881.png
-29895.png
-47743.png
-26740.png
-17208.png
-15126.png
-43979.png
-46873.png
-65928.png
-04575.png
-58822.png
-00163.png
-13132.png
-07524.png
-06695.png
-02720.png
-34444.png
-51344.png
-03878.png
-55999.png
-34794.png
-48739.png
-30732.png
-63624.png
-17024.png
-40768.png
-22907.png
-37958.png
-04240.png
-15978.png
-24476.png
-14556.png
-40759.png
-69950.png
-10252.png
-44482.png
-66414.png
-44004.png
-54462.png
-17502.png
-23894.png
-47889.png
-57431.png
-00292.png
-69314.png
-56432.png
-21471.png
-52708.png
-56961.png
-52658.png
-37662.png
-28253.png
-18331.png
-19336.png
-13531.png
-48532.png
-08035.png
-56899.png
-52993.png
-14200.png
-34141.png
-39734.png
-56759.png
-27556.png
-45725.png
-62152.png
-65919.png
-38118.png
-29587.png
-54189.png
-66157.png
-65969.png
-27832.png
-64359.png
-11483.png
-40803.png
-26312.png
-65385.png
-60797.png
-11222.png
-21486.png
-15749.png
-03827.png
-66976.png
-34796.png
-32865.png
-18992.png
-51675.png
-10125.png
-69612.png
-35022.png
-25639.png
-19767.png
-49816.png
-00943.png
-43555.png
-38211.png
-27571.png
-53505.png
-30175.png
-35112.png
-11934.png
-12137.png
-18117.png
-30701.png
-67183.png
-32220.png
-63815.png
-63689.png
-41212.png
-17846.png
-42049.png
-23864.png
-48483.png
-60253.png
-37159.png
-67136.png
-41333.png
-30057.png
-29037.png
-30668.png
-07854.png
-05295.png
-08311.png
-43733.png
-11763.png
-05945.png
-12671.png
-34940.png
-62990.png
-14141.png
-11547.png
-51333.png
-36668.png
-42180.png
-55324.png
-41824.png
-06752.png
-62819.png
-25164.png
-03465.png
-26137.png
-52038.png
-35046.png
-43864.png
-34669.png
-53302.png
-11797.png
-02623.png
-24328.png
-57579.png
-46629.png
-13238.png
-46337.png
-49228.png
-48973.png
-62983.png
-50420.png
-19133.png
-49218.png
-09134.png
-04824.png
-65421.png
-27923.png
-33222.png
-62926.png
-51879.png
-55494.png
-29316.png
-45365.png
-51667.png
-55755.png
-27497.png
-58795.png
-53506.png
-57315.png
-60979.png
-03068.png
-27061.png
-03743.png
-69579.png
-32863.png
-23720.png
-29155.png
-19007.png
-55797.png
-37864.png
-16874.png
-18603.png
-06956.png
-40620.png
-22479.png
-16945.png
-12538.png
-00785.png
-43269.png
-14549.png
-01773.png
-15721.png
-24829.png
-10355.png
-62468.png
-29090.png
-29514.png
-47058.png
-54436.png
-34196.png
-13783.png
-27462.png
-42887.png
-14357.png
-03722.png
-41575.png
-61116.png
-40616.png
-42502.png
-37605.png
-66707.png
-36357.png
-07059.png
-04162.png
-11383.png
-67252.png
-24877.png
-21803.png
-33074.png
-33661.png
-66255.png
-58787.png
-08192.png
-10868.png
-20297.png
-63044.png
-69223.png
-11613.png
-37148.png
-18029.png
-41171.png
-54420.png
-02442.png
-35212.png
-62432.png
-45165.png
-30441.png
-01212.png
-12738.png
-15260.png
-53731.png
-56709.png
-22869.png
-13498.png
-26367.png
-33018.png
-34119.png
-52475.png
-27511.png
-51617.png
-54583.png
-50986.png
-48674.png
-34112.png
-28952.png
-46677.png
-26292.png
-68584.png
-05446.png
-22766.png
-17314.png
-15283.png
-20082.png
-43282.png
-40552.png
-68227.png
-45980.png
-36211.png
-40155.png
-61816.png
-36726.png
-41902.png
-61473.png
-39431.png
-17557.png
-65452.png
-25960.png
-16126.png
-21289.png
-60165.png
-21994.png
-59226.png
-68726.png
-18665.png
-50676.png
-05948.png
-44021.png
-00016.png
-39983.png
-18452.png
-04721.png
-10204.png
-57739.png
-48475.png
-42322.png
-22491.png
-04681.png
-05864.png
-34262.png
-08949.png
-02646.png
-59272.png
-33126.png
-17308.png
-30468.png
-40382.png
-62996.png
-57929.png
-64903.png
-01088.png
-50359.png
-27401.png
-00984.png
-39351.png
-58522.png
-56754.png
-03145.png
-51531.png
-68900.png
-54354.png
-04461.png
-65382.png
-11905.png
-60829.png
-40518.png
-62786.png
-13330.png
-42195.png
-14869.png
-47080.png
-25442.png
-37173.png
-33625.png
-12378.png
-41228.png
-06349.png
-24262.png
-68503.png
-18677.png
-53184.png
-46809.png
-30440.png
-34539.png
-61638.png
-66080.png
-44268.png
-41453.png
-24670.png
-56848.png
-22065.png
-58951.png
-21156.png
-64930.png
-36543.png
-34309.png
-67189.png
-44377.png
-69110.png
-64535.png
-45058.png
-15010.png
-19880.png
-27776.png
-26363.png
-10866.png
-69036.png
-24499.png
-34026.png
-10071.png
-17514.png
-54525.png
-29578.png
-32204.png
-21507.png
-03144.png
-03334.png
-08741.png
-40927.png
-13900.png
-18887.png
-44033.png
-08350.png
-26467.png
-14176.png
-44280.png
-16403.png
-19214.png
-60599.png
-37152.png
-27864.png
-35410.png
-44620.png
-59680.png
-41647.png
-27152.png
-61678.png
-28381.png
-12346.png
-54634.png
-35095.png
-09455.png
-43830.png
-39545.png
-05324.png
-20674.png
-10610.png
-51728.png
-49155.png
-05031.png
-49828.png
-31412.png
-19222.png
-40558.png
-38542.png
-12532.png
-69986.png
-20335.png
-29943.png
-53911.png
-01332.png
-33517.png
-55586.png
-49780.png
-59729.png
-46013.png
-00205.png
-05408.png
-67792.png
-68311.png
-30445.png
-11970.png
-64290.png
-48515.png
-53573.png
-26782.png
-50962.png
-34959.png
-51467.png
-22034.png
-42664.png
-16579.png
-55632.png
-20582.png
-05213.png
-69455.png
-20502.png
-46627.png
-50718.png
-54258.png
-03597.png
-43379.png
-61342.png
-47804.png
-13059.png
-18456.png
-08894.png
-63388.png
-48936.png
-03372.png
-45938.png
-58601.png
-09862.png
-28260.png
-03124.png
-08873.png
-69514.png
-69180.png
-18334.png
-52110.png
-01075.png
-54339.png
-21718.png
-46060.png
-42450.png
-37390.png
-30702.png
-61700.png
-55670.png
-29951.png
-55935.png
-30555.png
-27820.png
-17818.png
-17335.png
-03990.png
-59961.png
-47247.png
-10677.png
-37596.png
-36257.png
-60918.png
-39394.png
-22669.png
-18013.png
-23616.png
-14360.png
-33430.png
-53109.png
-20053.png
-38798.png
-66946.png
-54940.png
-26377.png
-34645.png
-11603.png
-61882.png
-38667.png
-58080.png
-28058.png
-38248.png
-51454.png
-58103.png
-60977.png
-36319.png
-03586.png
-67883.png
-59694.png
-17020.png
-26369.png
-15713.png
-07719.png
-33832.png
-32330.png
-56820.png
-31730.png
-08731.png
-66962.png
-16557.png
-66276.png
-05272.png
-40303.png
-41601.png
-58959.png
-39967.png
-59352.png
-67338.png
-16618.png
-40467.png
-33587.png
-63142.png
-13844.png
-37767.png
-11223.png
-54066.png
-65102.png
-28507.png
-15198.png
-30338.png
-15239.png
-52869.png
-64926.png
-48441.png
-48117.png
-41899.png
-60412.png
-48454.png
-39247.png
-00507.png
-39194.png
-62788.png
-06612.png
-23553.png
-15830.png
-66170.png
-50997.png
-56591.png
-48967.png
-21672.png
-28071.png
-35246.png
-65101.png
-64768.png
-65826.png
-58425.png
-35633.png
-01762.png
-39527.png
-15503.png
-00909.png
-39740.png
-08091.png
-52261.png
-41570.png
-47690.png
-67834.png
-24073.png
-45074.png
-35208.png
-10398.png
-01307.png
-52348.png
-60974.png
-66465.png
-58300.png
-65236.png
-61008.png
-10757.png
-00383.png
-16515.png
-38958.png
-57120.png
-05205.png
-17583.png
-28201.png
-06257.png
-06568.png
-43441.png
-51852.png
-43205.png
-55562.png
-31687.png
-03404.png
-39047.png
-21162.png
-67630.png
-37826.png
-60766.png
-60802.png
-21096.png
-21063.png
-10698.png
-46002.png
-52289.png
-36852.png
-65192.png
-55527.png
-01425.png
-14772.png
-23928.png
-48836.png
-14564.png
-62729.png
-18295.png
-54584.png
-18755.png
-30562.png
-07825.png
-63703.png
-52488.png
-15534.png
-11236.png
-22219.png
-07478.png
-63040.png
-11219.png
-30331.png
-14904.png
-53197.png
-41948.png
-50395.png
-20679.png
-33577.png
-68275.png
-40691.png
-66924.png
-64208.png
-34446.png
-51709.png
-52614.png
-43721.png
-68136.png
-58465.png
-48882.png
-64451.png
-04216.png
-52984.png
-21320.png
-69817.png
-11113.png
-35880.png
-48750.png
-39361.png
-36655.png
-53456.png
-59319.png
-09592.png
-07535.png
-69773.png
-16387.png
-01805.png
-66909.png
-20519.png
-10254.png
-09656.png
-49309.png
-23245.png
-57812.png
-03189.png
-31672.png
-40200.png
-21339.png
-14367.png
-68452.png
-10467.png
-20382.png
-06571.png
-35646.png
-35309.png
-40712.png
-64733.png
-16306.png
-38846.png
-11791.png
-17798.png
-62040.png
-32806.png
-28236.png
-01565.png
-11076.png
-09864.png
-09205.png
-26515.png
-58546.png
-21334.png
-62488.png
-69095.png
-07931.png
-03140.png
-44959.png
-46604.png
-10842.png
-51086.png
-54558.png
-38001.png
-05216.png
-53588.png
-32647.png
-44342.png
-07189.png
-31326.png
-32193.png
-16999.png
-13076.png
-05666.png
-33058.png
-25982.png
-07537.png
-22087.png
-38058.png
-24260.png
-41231.png
-66573.png
-50385.png
-53833.png
-24030.png
-56499.png
-27443.png
-12096.png
-02652.png
-40606.png
-18385.png
-07359.png
-21548.png
-17140.png
-21381.png
-27180.png
-31580.png
-44406.png
-03133.png
-01770.png
-69828.png
-35291.png
-36087.png
-46280.png
-26912.png
-63645.png
-50922.png
-11210.png
-11251.png
-54104.png
-33330.png
-52773.png
-68885.png
-27807.png
-04949.png
-28059.png
-68661.png
-10918.png
-57742.png
-29678.png
-09796.png
-61352.png
-54230.png
-53974.png
-30101.png
-12482.png
-41135.png
-01253.png
-44159.png
-01890.png
-10744.png
-62942.png
-43792.png
-31458.png
-04880.png
-20384.png
-55490.png
-32234.png
-10440.png
-27211.png
-40435.png
-25556.png
-13549.png
-13943.png
-18342.png
-68252.png
-61265.png
-04451.png
-64255.png
-00484.png
-49610.png
-15861.png
-34613.png
-34257.png
-13495.png
-24927.png
-40799.png
-54249.png
-22332.png
-67584.png
-05562.png
-45269.png
-06506.png
-35341.png
-46553.png
-39709.png
-31384.png
-00765.png
-48154.png
-24250.png
-27346.png
-55428.png
-52713.png
-17821.png
-02756.png
-32952.png
-02549.png
-50530.png
-56844.png
-02541.png
-28025.png
-35885.png
-65338.png
-18046.png
-35915.png
-25594.png
-02862.png
-63099.png
-37646.png
-43761.png
-67143.png
-64718.png
-12251.png
-69696.png
-18443.png
-69742.png
-67377.png
-29046.png
-66608.png
-63386.png
-18076.png
-00636.png
-26141.png
-18599.png
-63050.png
-20032.png
-51025.png
-66499.png
-18691.png
-64823.png
-64265.png
-49895.png
-61969.png
-46577.png
-60793.png
-43971.png
-49839.png
-61918.png
-65486.png
-29075.png
-45730.png
-62731.png
-66683.png
-04501.png
-23930.png
-29489.png
-24865.png
-45784.png
-66596.png
-55838.png
-14245.png
-68208.png
-38590.png
-61787.png
-16596.png
-11369.png
-51244.png
-32869.png
-68447.png
-32643.png
-46181.png
-36600.png
-29596.png
-28483.png
-61658.png
-60453.png
-40098.png
-46874.png
-12861.png
-38089.png
-41420.png
-19980.png
-18688.png
-35201.png
-02084.png
-56291.png
-07225.png
-51032.png
-25876.png
-50737.png
-66155.png
-39679.png
-02662.png
-16507.png
-26470.png
-04372.png
-49000.png
-31739.png
-33145.png
-44763.png
-56170.png
-31317.png
-23213.png
-68102.png
-26017.png
-69532.png
-13666.png
-07370.png
-14780.png
-43293.png
-34878.png
-16912.png
-10249.png
-52519.png
-68348.png
-41237.png
-61501.png
-62487.png
-28549.png
-28334.png
-31087.png
-33796.png
-47532.png
-52985.png
-60556.png
-33470.png
-53389.png
-44983.png
-52608.png
-54123.png
-30407.png
-50639.png
-07552.png
-21413.png
-08180.png
-00490.png
-21249.png
-17124.png
-11824.png
-38877.png
-05484.png
-16844.png
-26087.png
-44693.png
-66037.png
-04758.png
-07100.png
-04109.png
-04688.png
-60315.png
-05832.png
-62344.png
-66383.png
-02394.png
-63046.png
-33414.png
-30083.png
-32823.png
-55633.png
-31206.png
-59420.png
-10134.png
-26200.png
-54398.png
-68923.png
-52561.png
-00153.png
-20848.png
-01543.png
-27030.png
-50792.png
-25923.png
-26586.png
-59686.png
-59422.png
-65633.png
-28469.png
-14291.png
-59604.png
-52844.png
-11215.png
-17220.png
-32231.png
-27210.png
-21127.png
-11176.png
-69682.png
-23438.png
-65719.png
-01532.png
-40822.png
-47641.png
-11554.png
-20329.png
-33286.png
-20137.png
-56058.png
-17869.png
-67224.png
-35926.png
-31994.png
-11074.png
-48864.png
-22264.png
-20558.png
-04211.png
-27202.png
-50143.png
-05612.png
-53738.png
-35166.png
-32178.png
-31174.png
-47653.png
-39657.png
-22446.png
-08927.png
-65132.png
-12611.png
-34644.png
-16361.png
-35312.png
-47523.png
-28199.png
-12390.png
-63704.png
-46173.png
-54110.png
-69991.png
-03582.png
-66891.png
-21197.png
-29900.png
-11620.png
-06685.png
-10463.png
-24613.png
-12466.png
-13963.png
-55212.png
-47488.png
-66002.png
-13560.png
-53916.png
-10794.png
-30669.png
-67295.png
-34968.png
-67043.png
-46520.png
-13105.png
-44120.png
-36073.png
-19557.png
-02935.png
-40846.png
-42516.png
-33743.png
-61586.png
-03244.png
-06590.png
-19730.png
-24770.png
-31004.png
-27818.png
-37928.png
-13458.png
-29899.png
-02753.png
-49522.png
-20214.png
-37945.png
-60908.png
-40137.png
-13024.png
-34742.png
-03502.png
-62278.png
-58030.png
-30773.png
-28200.png
-00296.png
-11183.png
-68273.png
-48324.png
-57270.png
-17532.png
-46017.png
-23959.png
-65119.png
-57662.png
-34981.png
-58402.png
-40193.png
-40170.png
-10368.png
-46967.png
-08968.png
-35938.png
-63977.png
-33710.png
-24976.png
-54976.png
-27444.png
-58623.png
-52739.png
-16801.png
-47563.png
-38655.png
-63204.png
-04608.png
-01695.png
-50213.png
-59191.png
-00303.png
-53486.png
-50152.png
-48318.png
-14314.png
-39852.png
-52479.png
-12876.png
-47302.png
-23201.png
-60106.png
-05508.png
-41714.png
-43227.png
-59861.png
-49110.png
-38841.png
-27656.png
-10635.png
-66648.png
-38813.png
-28395.png
-54280.png
-11344.png
-32688.png
-63076.png
-55059.png
-27349.png
-48909.png
-63790.png
-61848.png
-33981.png
-02838.png
-15813.png
-03522.png
-13055.png
-46399.png
-57847.png
-33394.png
-27167.png
-18399.png
-68714.png
-27230.png
-14650.png
-04268.png
-25892.png
-67709.png
-55963.png
-34766.png
-26106.png
-08573.png
-43986.png
-32546.png
-44013.png
-11948.png
-15781.png
-63167.png
-23793.png
-63501.png
-00820.png
-32654.png
-65290.png
-33792.png
-69337.png
-19534.png
-42858.png
-68652.png
-00910.png
-47008.png
-33103.png
-58941.png
-18956.png
-24517.png
-53991.png
-33530.png
-58916.png
-39300.png
-67603.png
-01151.png
-27386.png
-10742.png
-62605.png
-20312.png
-44390.png
-68159.png
-57808.png
-26526.png
-17419.png
-61723.png
-10216.png
-24042.png
-48349.png
-47805.png
-20533.png
-27411.png
-57979.png
-53861.png
-67099.png
-67162.png
-65532.png
-58251.png
-17276.png
-18451.png
-12333.png
-61891.png
-09995.png
-62482.png
-19205.png
-57151.png
-20819.png
-36040.png
-65708.png
-62371.png
-30636.png
-08376.png
-17037.png
-45276.png
-07070.png
-26850.png
-37441.png
-57080.png
-02597.png
-18370.png
-37879.png
-64929.png
-39713.png
-05541.png
-63866.png
-19683.png
-56807.png
-07633.png
-31256.png
-42824.png
-20328.png
-21036.png
-31558.png
-66560.png
-61323.png
-66065.png
-17330.png
-23157.png
-15352.png
-42081.png
-31566.png
-26758.png
-41325.png
-51644.png
-47819.png
-08062.png
-28383.png
-33539.png
-08059.png
-63926.png
-38493.png
-50917.png
-26215.png
-39712.png
-38739.png
-57193.png
-03463.png
-35426.png
-13636.png
-67732.png
-65396.png
-49939.png
-12593.png
-06227.png
-34623.png
-66725.png
-52265.png
-41889.png
-44834.png
-52782.png
-65999.png
-58235.png
-52804.png
-53411.png
-32685.png
-63268.png
-47799.png
-58837.png
-34812.png
-16414.png
-35889.png
-37765.png
-13883.png
-49200.png
-11814.png
-39310.png
-10532.png
-34229.png
-51430.png
-59395.png
-30417.png
-24828.png
-31247.png
-23621.png
-33830.png
-06992.png
-50824.png
-55452.png
-11922.png
-15471.png
-27517.png
-53843.png
-23657.png
-66569.png
-38016.png
-19207.png
-23628.png
-61747.png
-17718.png
-48357.png
-45951.png
-10331.png
-01321.png
-60914.png
-47719.png
-42175.png
-38379.png
-44153.png
-20063.png
-38646.png
-54108.png
-15208.png
-47338.png
-28583.png
-18337.png
-26534.png
-23324.png
-21348.png
-61099.png
-46398.png
-44509.png
-19809.png
-25291.png
-37847.png
-38189.png
-58862.png
-65771.png
-27331.png
-33678.png
-50849.png
-66406.png
-41837.png
-29718.png
-54796.png
-34918.png
-07101.png
-22099.png
-69253.png
-02850.png
-41127.png
-35102.png
-18093.png
-49693.png
-51297.png
-33505.png
-23231.png
-17347.png
-56930.png
-54225.png
-12189.png
-60342.png
-66161.png
-46101.png
-19999.png
-54284.png
-52647.png
-60612.png
-01381.png
-01275.png
-66494.png
-67266.png
-67159.png
-27579.png
-56525.png
-69405.png
-05727.png
-05277.png
-06164.png
-21489.png
-28721.png
-06581.png
-59831.png
-68977.png
-61096.png
diff --git a/repositories/taming-transformers/data/flickr_tags.txt b/repositories/taming-transformers/data/flickr_tags.txt
deleted file mode 100644
index 24c7af9acf9e4b7264855488238d520c8960a46c..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/data/flickr_tags.txt
+++ /dev/null
@@ -1,40 +0,0 @@
-alaska_lakes
-alaska_mountains
-antarctica_landscape
-australia
-black_forest
-british_columbia_landscape
-canada
-carribean
-cliff_ocean
-desert
-dramatic_landscape
-geysir
-glacier_national_park
-glacier_sunset
-glacier_waterton_national_park
-gobi_landscape
-greenland_landscape
-iceland_landscape
-ireland
-jungle
-kamchatka_landscape
-lakes
-meadow
-mongolia
-mountain_inversion
-mountains_autumn
-mountain_sunset
-newzealand_np
-norway
-prairie_landscape
-rondane_national_park
-scotland
-sunset
-swiss_landscape
-swiss_mountains
-taiga_landscape
-tibet_landscape
-tundra_landscape
-utah_landscape
-volcano
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/class-descriptions-boxable.csv b/repositories/taming-transformers/data/open_images_annotations_100/class-descriptions-boxable.csv
deleted file mode 100644
index 9f634a5a56350db062cd8ed9601bd146f9a61e84..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/data/open_images_annotations_100/class-descriptions-boxable.csv
+++ /dev/null
@@ -1,601 +0,0 @@
-/m/011k07,Tortoise
-/m/011q46kg,Container
-/m/012074,Magpie
-/m/0120dh,Sea turtle
-/m/01226z,Football
-/m/012n7d,Ambulance
-/m/012w5l,Ladder
-/m/012xff,Toothbrush
-/m/012ysf,Syringe
-/m/0130jx,Sink
-/m/0138tl,Toy
-/m/013y1f,Organ (Musical Instrument)
-/m/01432t,Cassette deck
-/m/014j1m,Apple
-/m/014sv8,Human eye
-/m/014trl,Cosmetics
-/m/014y4n,Paddle
-/m/0152hh,Snowman
-/m/01599,Beer
-/m/01_5g,Chopsticks
-/m/015h_t,Human beard
-/m/015p6,Bird
-/m/015qbp,Parking meter
-/m/015qff,Traffic light
-/m/015wgc,Croissant
-/m/015x4r,Cucumber
-/m/015x5n,Radish
-/m/0162_1,Towel
-/m/0167gd,Doll
-/m/016m2d,Skull
-/m/0174k2,Washing machine
-/m/0174n1,Glove
-/m/0175cv,Tick
-/m/0176mf,Belt
-/m/017ftj,Sunglasses
-/m/018j2,Banjo
-/m/018p4k,Cart
-/m/018xm,Ball
-/m/01940j,Backpack
-/m/0199g,Bicycle
-/m/019dx1,Home appliance
-/m/019h78,Centipede
-/m/019jd,Boat
-/m/019w40,Surfboard
-/m/01b638,Boot
-/m/01b7fy,Headphones
-/m/01b9xk,Hot dog
-/m/01bfm9,Shorts
-/m/01_bhs,Fast food
-/m/01bjv,Bus
-/m/01bl7v,Boy
-/m/01bms0,Screwdriver
-/m/01bqk0,Bicycle wheel
-/m/01btn,Barge
-/m/01c648,Laptop
-/m/01cmb2,Miniskirt
-/m/01d380,Drill (Tool)
-/m/01d40f,Dress
-/m/01dws,Bear
-/m/01dwsz,Waffle
-/m/01dwwc,Pancake
-/m/01dxs,Brown bear
-/m/01dy8n,Woodpecker
-/m/01f8m5,Blue jay
-/m/01f91_,Pretzel
-/m/01fb_0,Bagel
-/m/01fdzj,Tower
-/m/01fh4r,Teapot
-/m/01g317,Person
-/m/01g3x7,Bow and arrow
-/m/01gkx_,Swimwear
-/m/01gllr,Beehive
-/m/01gmv2,Brassiere
-/m/01h3n,Bee
-/m/01h44,Bat (Animal)
-/m/01h8tj,Starfish
-/m/01hrv5,Popcorn
-/m/01j3zr,Burrito
-/m/01j4z9,Chainsaw
-/m/01j51,Balloon
-/m/01j5ks,Wrench
-/m/01j61q,Tent
-/m/01jfm_,Vehicle registration plate
-/m/01jfsr,Lantern
-/m/01k6s3,Toaster
-/m/01kb5b,Flashlight
-/m/01knjb,Billboard
-/m/01krhy,Tiara
-/m/01lcw4,Limousine
-/m/01llwg,Necklace
-/m/01lrl,Carnivore
-/m/01lsmm,Scissors
-/m/01lynh,Stairs
-/m/01m2v,Computer keyboard
-/m/01m4t,Printer
-/m/01mqdt,Traffic sign
-/m/01mzpv,Chair
-/m/01n4qj,Shirt
-/m/01n5jq,Poster
-/m/01nkt,Cheese
-/m/01nq26,Sock
-/m/01pns0,Fire hydrant
-/m/01prls,Land vehicle
-/m/01r546,Earrings
-/m/01rkbr,Tie
-/m/01rzcn,Watercraft
-/m/01s105,Cabinetry
-/m/01s55n,Suitcase
-/m/01tcjp,Muffin
-/m/01vbnl,Bidet
-/m/01ww8y,Snack
-/m/01x3jk,Snowmobile
-/m/01x3z,Clock
-/m/01xgg_,Medical equipment
-/m/01xq0k1,Cattle
-/m/01xqw,Cello
-/m/01xs3r,Jet ski
-/m/01x_v,Camel
-/m/01xygc,Coat
-/m/01xyhv,Suit
-/m/01y9k5,Desk
-/m/01yrx,Cat
-/m/01yx86,Bronze sculpture
-/m/01z1kdw,Juice
-/m/02068x,Gondola
-/m/020jm,Beetle
-/m/020kz,Cannon
-/m/020lf,Computer mouse
-/m/021mn,Cookie
-/m/021sj1,Office building
-/m/0220r2,Fountain
-/m/0242l,Coin
-/m/024d2,Calculator
-/m/024g6,Cocktail
-/m/02522,Computer monitor
-/m/025dyy,Box
-/m/025fsf,Stapler
-/m/025nd,Christmas tree
-/m/025rp__,Cowboy hat
-/m/0268lbt,Hiking equipment
-/m/026qbn5,Studio couch
-/m/026t6,Drum
-/m/0270h,Dessert
-/m/0271qf7,Wine rack
-/m/0271t,Drink
-/m/027pcv,Zucchini
-/m/027rl48,Ladle
-/m/0283dt1,Human mouth
-/m/0284d,Dairy Product
-/m/029b3,Dice
-/m/029bxz,Oven
-/m/029tx,Dinosaur
-/m/02bm9n,Ratchet (Device)
-/m/02crq1,Couch
-/m/02ctlc,Cricket ball
-/m/02cvgx,Winter melon
-/m/02d1br,Spatula
-/m/02d9qx,Whiteboard
-/m/02ddwp,Pencil sharpener
-/m/02dgv,Door
-/m/02dl1y,Hat
-/m/02f9f_,Shower
-/m/02fh7f,Eraser
-/m/02fq_6,Fedora
-/m/02g30s,Guacamole
-/m/02gzp,Dagger
-/m/02h19r,Scarf
-/m/02hj4,Dolphin
-/m/02jfl0,Sombrero
-/m/02jnhm,Tin can
-/m/02jvh9,Mug
-/m/02jz0l,Tap
-/m/02l8p9,Harbor seal
-/m/02lbcq,Stretcher
-/m/02mqfb,Can opener
-/m/02_n6y,Goggles
-/m/02p0tk3,Human body
-/m/02p3w7d,Roller skates
-/m/02p5f1q,Coffee cup
-/m/02pdsw,Cutting board
-/m/02pjr4,Blender
-/m/02pkr5,Plumbing fixture
-/m/02pv19,Stop sign
-/m/02rdsp,Office supplies
-/m/02rgn06,Volleyball (Ball)
-/m/02s195,Vase
-/m/02tsc9,Slow cooker
-/m/02vkqh8,Wardrobe
-/m/02vqfm,Coffee
-/m/02vwcm,Whisk
-/m/02w3r3,Paper towel
-/m/02w3_ws,Personal care
-/m/02wbm,Food
-/m/02wbtzl,Sun hat
-/m/02wg_p,Tree house
-/m/02wmf,Flying disc
-/m/02wv6h6,Skirt
-/m/02wv84t,Gas stove
-/m/02x8cch,Salt and pepper shakers
-/m/02x984l,Mechanical fan
-/m/02xb7qb,Face powder
-/m/02xqq,Fax
-/m/02xwb,Fruit
-/m/02y6n,French fries
-/m/02z51p,Nightstand
-/m/02zn6n,Barrel
-/m/02zt3,Kite
-/m/02zvsm,Tart
-/m/030610,Treadmill
-/m/0306r,Fox
-/m/03120,Flag
-/m/0319l,French horn
-/m/031b6r,Window blind
-/m/031n1,Human foot
-/m/0323sq,Golf cart
-/m/032b3c,Jacket
-/m/033cnk,Egg (Food)
-/m/033rq4,Street light
-/m/0342h,Guitar
-/m/034c16,Pillow
-/m/035r7c,Human leg
-/m/035vxb,Isopod
-/m/0388q,Grape
-/m/039xj_,Human ear
-/m/03bbps,Power plugs and sockets
-/m/03bj1,Panda
-/m/03bk1,Giraffe
-/m/03bt1vf,Woman
-/m/03c7gz,Door handle
-/m/03d443,Rhinoceros
-/m/03dnzn,Bathtub
-/m/03fj2,Goldfish
-/m/03fp41,Houseplant
-/m/03fwl,Goat
-/m/03g8mr,Baseball bat
-/m/03grzl,Baseball glove
-/m/03hj559,Mixing bowl
-/m/03hl4l9,Marine invertebrates
-/m/03hlz0c,Kitchen utensil
-/m/03jbxj,Light switch
-/m/03jm5,House
-/m/03k3r,Horse
-/m/03kt2w,Stationary bicycle
-/m/03l9g,Hammer
-/m/03ldnb,Ceiling fan
-/m/03m3pdh,Sofa bed
-/m/03m3vtv,Adhesive tape
-/m/03m5k,Harp
-/m/03nfch,Sandal
-/m/03p3bw,Bicycle helmet
-/m/03q5c7,Saucer
-/m/03q5t,Harpsichord
-/m/03q69,Human hair
-/m/03qhv5,Heater
-/m/03qjg,Harmonica
-/m/03qrc,Hamster
-/m/03rszm,Curtain
-/m/03ssj5,Bed
-/m/03s_tn,Kettle
-/m/03tw93,Fireplace
-/m/03txqz,Scale
-/m/03v5tg,Drinking straw
-/m/03vt0,Insect
-/m/03wvsk,Hair dryer
-/m/03_wxk,Kitchenware
-/m/03wym,Indoor rower
-/m/03xxp,Invertebrate
-/m/03y6mg,Food processor
-/m/03__z0,Bookcase
-/m/040b_t,Refrigerator
-/m/04169hn,Wood-burning stove
-/m/0420v5,Punching bag
-/m/043nyj,Common fig
-/m/0440zs,Cocktail shaker
-/m/0449p,Jaguar (Animal)
-/m/044r5d,Golf ball
-/m/0463sg,Fashion accessory
-/m/046dlr,Alarm clock
-/m/047j0r,Filing cabinet
-/m/047v4b,Artichoke
-/m/04bcr3,Table
-/m/04brg2,Tableware
-/m/04c0y,Kangaroo
-/m/04cp_,Koala
-/m/04ctx,Knife
-/m/04dr76w,Bottle
-/m/04f5ws,Bottle opener
-/m/04g2r,Lynx
-/m/04gth,Lavender (Plant)
-/m/04h7h,Lighthouse
-/m/04h8sr,Dumbbell
-/m/04hgtk,Human head
-/m/04kkgm,Bowl
-/m/04lvq_,Humidifier
-/m/04m6gz,Porch
-/m/04m9y,Lizard
-/m/04p0qw,Billiard table
-/m/04rky,Mammal
-/m/04rmv,Mouse
-/m/04_sv,Motorcycle
-/m/04szw,Musical instrument
-/m/04tn4x,Swim cap
-/m/04v6l4,Frying pan
-/m/04vv5k,Snowplow
-/m/04y4h8h,Bathroom cabinet
-/m/04ylt,Missile
-/m/04yqq2,Bust
-/m/04yx4,Man
-/m/04z4wx,Waffle iron
-/m/04zpv,Milk
-/m/04zwwv,Ring binder
-/m/050gv4,Plate
-/m/050k8,Mobile phone
-/m/052lwg6,Baked goods
-/m/052sf,Mushroom
-/m/05441v,Crutch
-/m/054fyh,Pitcher (Container)
-/m/054_l,Mirror
-/m/054xkw,Personal flotation device
-/m/05_5p_0,Table tennis racket
-/m/05676x,Pencil case
-/m/057cc,Musical keyboard
-/m/057p5t,Scoreboard
-/m/0584n8,Briefcase
-/m/058qzx,Kitchen knife
-/m/05bm6,Nail (Construction)
-/m/05ctyq,Tennis ball
-/m/05gqfk,Plastic bag
-/m/05kms,Oboe
-/m/05kyg_,Chest of drawers
-/m/05n4y,Ostrich
-/m/05r5c,Piano
-/m/05r655,Girl
-/m/05s2s,Plant
-/m/05vtc,Potato
-/m/05w9t9,Hair spray
-/m/05y5lj,Sports equipment
-/m/05z55,Pasta
-/m/05z6w,Penguin
-/m/05zsy,Pumpkin
-/m/061_f,Pear
-/m/061hd_,Infant bed
-/m/0633h,Polar bear
-/m/063rgb,Mixer
-/m/0642b4,Cupboard
-/m/065h6l,Jacuzzi
-/m/0663v,Pizza
-/m/06_72j,Digital clock
-/m/068zj,Pig
-/m/06bt6,Reptile
-/m/06c54,Rifle
-/m/06c7f7,Lipstick
-/m/06_fw,Skateboard
-/m/06j2d,Raven
-/m/06k2mb,High heels
-/m/06l9r,Red panda
-/m/06m11,Rose
-/m/06mf6,Rabbit
-/m/06msq,Sculpture
-/m/06ncr,Saxophone
-/m/06nrc,Shotgun
-/m/06nwz,Seafood
-/m/06pcq,Submarine sandwich
-/m/06__v,Snowboard
-/m/06y5r,Sword
-/m/06z37_,Picture frame
-/m/07030,Sushi
-/m/0703r8,Loveseat
-/m/071p9,Ski
-/m/071qp,Squirrel
-/m/073bxn,Tripod
-/m/073g6,Stethoscope
-/m/074d1,Submarine
-/m/0755b,Scorpion
-/m/076bq,Segway
-/m/076lb9,Training bench
-/m/078jl,Snake
-/m/078n6m,Coffee table
-/m/079cl,Skyscraper
-/m/07bgp,Sheep
-/m/07c52,Television
-/m/07c6l,Trombone
-/m/07clx,Tea
-/m/07cmd,Tank
-/m/07crc,Taco
-/m/07cx4,Telephone
-/m/07dd4,Torch
-/m/07dm6,Tiger
-/m/07fbm7,Strawberry
-/m/07gql,Trumpet
-/m/07j7r,Tree
-/m/07j87,Tomato
-/m/07jdr,Train
-/m/07k1x,Tool
-/m/07kng9,Picnic basket
-/m/07mcwg,Cooking spray
-/m/07mhn,Trousers
-/m/07pj7bq,Bowling equipment
-/m/07qxg_,Football helmet
-/m/07r04,Truck
-/m/07v9_z,Measuring cup
-/m/07xyvk,Coffeemaker
-/m/07y_7,Violin
-/m/07yv9,Vehicle
-/m/080hkjn,Handbag
-/m/080n7g,Paper cutter
-/m/081qc,Wine
-/m/083kb,Weapon
-/m/083wq,Wheel
-/m/084hf,Worm
-/m/084rd,Wok
-/m/084zz,Whale
-/m/0898b,Zebra
-/m/08dz3q,Auto part
-/m/08hvt4,Jug
-/m/08ks85,Pizza cutter
-/m/08p92x,Cream
-/m/08pbxl,Monkey
-/m/096mb,Lion
-/m/09728,Bread
-/m/099ssp,Platter
-/m/09b5t,Chicken
-/m/09csl,Eagle
-/m/09ct_,Helicopter
-/m/09d5_,Owl
-/m/09ddx,Duck
-/m/09dzg,Turtle
-/m/09f20,Hippopotamus
-/m/09f_2,Crocodile
-/m/09g1w,Toilet
-/m/09gtd,Toilet paper
-/m/09gys,Squid
-/m/09j2d,Clothing
-/m/09j5n,Footwear
-/m/09k_b,Lemon
-/m/09kmb,Spider
-/m/09kx5,Deer
-/m/09ld4,Frog
-/m/09qck,Banana
-/m/09rvcxw,Rocket
-/m/09tvcd,Wine glass
-/m/0b3fp9,Countertop
-/m/0bh9flk,Tablet computer
-/m/0bjyj5,Waste container
-/m/0b_rs,Swimming pool
-/m/0bt9lr,Dog
-/m/0bt_c3,Book
-/m/0bwd_0j,Elephant
-/m/0by6g,Shark
-/m/0c06p,Candle
-/m/0c29q,Leopard
-/m/0c2jj,Axe
-/m/0c3m8g,Hand dryer
-/m/0c3mkw,Soap dispenser
-/m/0c568,Porcupine
-/m/0c9ph5,Flower
-/m/0ccs93,Canary
-/m/0cd4d,Cheetah
-/m/0cdl1,Palm tree
-/m/0cdn1,Hamburger
-/m/0cffdh,Maple
-/m/0cgh4,Building
-/m/0ch_cf,Fish
-/m/0cjq5,Lobster
-/m/0cjs7,Garden Asparagus
-/m/0c_jw,Furniture
-/m/0cl4p,Hedgehog
-/m/0cmf2,Airplane
-/m/0cmx8,Spoon
-/m/0cn6p,Otter
-/m/0cnyhnx,Bull
-/m/0_cp5,Oyster
-/m/0cqn2,Horizontal bar
-/m/0crjs,Convenience store
-/m/0ct4f,Bomb
-/m/0cvnqh,Bench
-/m/0cxn2,Ice cream
-/m/0cydv,Caterpillar
-/m/0cyf8,Butterfly
-/m/0cyfs,Parachute
-/m/0cyhj_,Orange
-/m/0czz2,Antelope
-/m/0d20w4,Beaker
-/m/0d_2m,Moths and butterflies
-/m/0d4v4,Window
-/m/0d4w1,Closet
-/m/0d5gx,Castle
-/m/0d8zb,Jellyfish
-/m/0dbvp,Goose
-/m/0dbzx,Mule
-/m/0dftk,Swan
-/m/0dj6p,Peach
-/m/0djtd,Coconut
-/m/0dkzw,Seat belt
-/m/0dq75,Raccoon
-/m/0_dqb,Chisel
-/m/0dt3t,Fork
-/m/0dtln,Lamp
-/m/0dv5r,Camera
-/m/0dv77,Squash (Plant)
-/m/0dv9c,Racket
-/m/0dzct,Human face
-/m/0dzf4,Human arm
-/m/0f4s2w,Vegetable
-/m/0f571,Diaper
-/m/0f6nr,Unicycle
-/m/0f6wt,Falcon
-/m/0f8s22,Chime
-/m/0f9_l,Snail
-/m/0fbdv,Shellfish
-/m/0fbw6,Cabbage
-/m/0fj52s,Carrot
-/m/0fldg,Mango
-/m/0fly7,Jeans
-/m/0fm3zh,Flowerpot
-/m/0fp6w,Pineapple
-/m/0fqfqc,Drawer
-/m/0fqt361,Stool
-/m/0frqm,Envelope
-/m/0fszt,Cake
-/m/0ft9s,Dragonfly
-/m/0ftb8,Common sunflower
-/m/0fx9l,Microwave oven
-/m/0fz0h,Honeycomb
-/m/0gd2v,Marine mammal
-/m/0gd36,Sea lion
-/m/0gj37,Ladybug
-/m/0gjbg72,Shelf
-/m/0gjkl,Watch
-/m/0gm28,Candy
-/m/0grw1,Salad
-/m/0gv1x,Parrot
-/m/0gxl3,Handgun
-/m/0h23m,Sparrow
-/m/0h2r6,Van
-/m/0h8jyh6,Grinder
-/m/0h8kx63,Spice rack
-/m/0h8l4fh,Light bulb
-/m/0h8lkj8,Corded phone
-/m/0h8mhzd,Sports uniform
-/m/0h8my_4,Tennis racket
-/m/0h8mzrc,Wall clock
-/m/0h8n27j,Serving tray
-/m/0h8n5zk,Kitchen & dining room table
-/m/0h8n6f9,Dog bed
-/m/0h8n6ft,Cake stand
-/m/0h8nm9j,Cat furniture
-/m/0h8nr_l,Bathroom accessory
-/m/0h8nsvg,Facial tissue holder
-/m/0h8ntjv,Pressure cooker
-/m/0h99cwc,Kitchen appliance
-/m/0h9mv,Tire
-/m/0hdln,Ruler
-/m/0hf58v5,Luggage and bags
-/m/0hg7b,Microphone
-/m/0hkxq,Broccoli
-/m/0hnnb,Umbrella
-/m/0hnyx,Pastry
-/m/0hqkz,Grapefruit
-/m/0j496,Band-aid
-/m/0jbk,Animal
-/m/0jg57,Bell pepper
-/m/0jly1,Turkey
-/m/0jqgx,Lily
-/m/0jwn_,Pomegranate
-/m/0jy4k,Doughnut
-/m/0jyfg,Glasses
-/m/0k0pj,Human nose
-/m/0k1tl,Pen
-/m/0_k2,Ant
-/m/0k4j,Car
-/m/0k5j,Aircraft
-/m/0k65p,Human hand
-/m/0km7z,Skunk
-/m/0kmg4,Teddy bear
-/m/0kpqd,Watermelon
-/m/0kpt_,Cantaloupe
-/m/0ky7b,Dishwasher
-/m/0l14j_,Flute
-/m/0l3ms,Balance beam
-/m/0l515,Sandwich
-/m/0ll1f78,Shrimp
-/m/0llzx,Sewing machine
-/m/0lt4_,Binoculars
-/m/0m53l,Rays and skates
-/m/0mcx2,Ipod
-/m/0mkg,Accordion
-/m/0mw_6,Willow
-/m/0n28_,Crab
-/m/0nl46,Crown
-/m/0nybt,Seahorse
-/m/0p833,Perfume
-/m/0pcr,Alpaca
-/m/0pg52,Taxi
-/m/0ph39,Canoe
-/m/0qjjc,Remote control
-/m/0qmmr,Wheelchair
-/m/0wdt60w,Rugby ball
-/m/0xfy,Armadillo
-/m/0xzly,Maracas
-/m/0zvk5,Helmet
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/oidv6-train-annotations-bbox.csv b/repositories/taming-transformers/data/open_images_annotations_100/oidv6-train-annotations-bbox.csv
deleted file mode 100644
index c027a074d6dae374936b94d6f594bfc4c081acb6..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/data/open_images_annotations_100/oidv6-train-annotations-bbox.csv
+++ /dev/null
@@ -1,949 +0,0 @@
-ImageID,Source,LabelName,Confidence,XMin,XMax,YMin,YMax,IsOccluded,IsTruncated,IsGroupOf,IsDepiction,IsInside,XClick1X,XClick2X,XClick3X,XClick4X,XClick1Y,XClick2Y,XClick3Y,XClick4Y
-000ab31e6be35fed,activemil,/m/02fq_6,1,0.013125,0.303750,0.288333,0.734167,-1,-1,-1,-1,-1,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000
-000ab31e6be35fed,xclick,/m/02jfl0,1,0.000000,0.297500,0.310000,0.733333,0,1,0,0,0,0.173750,0.297500,0.146250,0.000000,0.733333,0.603333,0.310000,0.464167
-000ab31e6be35fed,xclick,/m/02wbtzl,1,0.000000,0.295625,0.310833,0.730833,0,1,0,0,0,0.139375,0.000000,0.172500,0.295625,0.310833,0.458333,0.730833,0.601667
-000ab31e6be35fed,xclick,/m/03bt1vf,1,0.420625,0.627500,0.434167,0.999167,1,1,0,0,0,0.475000,0.627500,0.420625,0.511250,0.434167,0.766667,0.542500,0.999167
-000ab31e6be35fed,xclick,/m/03bt1vf,1,0.553750,0.637500,0.428333,0.571667,1,0,0,0,0,0.590000,0.637500,0.553750,0.588750,0.428333,0.485833,0.499167,0.571667
-000ab31e6be35fed,xclick,/m/03bt1vf,1,0.590625,0.721875,0.392500,0.721667,1,0,0,0,0,0.673750,0.721875,0.642500,0.590625,0.392500,0.522500,0.721667,0.581667
-000ab31e6be35fed,xclick,/m/03bt1vf,1,0.632500,0.843125,0.355000,0.999167,1,1,0,0,0,0.786250,0.843125,0.632500,0.755000,0.355000,0.489167,0.823333,0.999167
-000ab31e6be35fed,xclick,/m/03bt1vf,1,0.796875,0.926875,0.430833,0.999167,1,1,0,0,0,0.855000,0.926875,0.796875,0.850625,0.430833,0.600833,0.604167,0.999167
-000ab31e6be35fed,xclick,/m/03bt1vf,1,0.867500,0.999375,0.490833,0.999167,1,1,0,0,0,0.999375,0.999375,0.867500,0.880625,0.490833,0.633333,0.947500,0.999167
-000ab31e6be35fed,xclick,/m/0crjs,1,0.000000,0.999375,0.000000,0.999167,1,1,0,0,1,0.000000,0.423125,0.999375,0.575000,0.449167,0.000000,0.540833,0.999167
-000ab7bec71cc50a,activemil,/m/0284d,1,0.397000,0.668000,0.203733,0.572317,0,0,0,0,0,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000
-000ab7bec71cc50a,activemil,/m/0cxn2,1,0.379000,0.678000,0.192846,0.606532,0,0,0,0,0,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000
-000ab7bec71cc50a,xclick,/m/0284d,1,0.655000,0.824000,0.562986,0.772939,0,0,0,0,0,0.655000,0.724000,0.720000,0.824000,0.751166,0.562986,0.772939,0.609642
-000ab7bec71cc50a,xclick,/m/09728,1,0.068000,0.750000,0.393468,0.931571,0,0,0,0,0,0.308000,0.750000,0.068000,0.399000,0.393468,0.536547,0.766719,0.931571
-000ab7bec71cc50a,xclick,/m/09728,1,0.487000,0.959000,0.447900,0.920684,1,0,1,0,0,0.789000,0.487000,0.959000,0.758000,0.447900,0.920684,0.765163,0.920684
-000ab8c20b3e5b58,xclick,/m/0jqgx,1,0.251250,0.423125,0.148889,0.420000,1,0,0,0,0,0.251250,0.279375,0.423125,0.319375,0.201111,0.148889,0.244444,0.420000
-000ab8c20b3e5b58,xclick,/m/0jqgx,1,0.275625,0.528125,0.196667,0.705556,0,0,0,0,0,0.275625,0.469375,0.528125,0.471875,0.393333,0.196667,0.484444,0.705556
-000abc075d659122,xclick,/m/01g317,1,0.000000,0.033125,0.460000,0.820000,0,1,0,1,0,0.008750,0.000000,0.006250,0.033125,0.460000,0.595000,0.820000,0.543333
-000abc075d659122,xclick,/m/01g317,1,0.115625,0.175000,0.458333,0.751667,0,0,0,1,0,0.145625,0.115625,0.144375,0.175000,0.458333,0.563333,0.751667,0.548333
-000abc075d659122,xclick,/m/01g317,1,0.223125,0.258125,0.469167,0.711667,0,0,0,1,0,0.240000,0.223125,0.229375,0.258125,0.469167,0.680833,0.711667,0.692500
-000abc075d659122,xclick,/m/01g317,1,0.290000,0.326250,0.467500,0.682500,0,0,0,1,0,0.306875,0.290000,0.300625,0.326250,0.467500,0.550000,0.682500,0.530833
-000abc075d659122,xclick,/m/01g317,1,0.323750,0.400000,0.467500,0.803333,1,0,0,0,0,0.366250,0.323750,0.360000,0.400000,0.467500,0.577500,0.803333,0.634167
-000abc075d659122,xclick,/m/01g317,1,0.458125,0.515000,0.474167,0.672500,1,0,0,0,0,0.483750,0.458125,0.482500,0.515000,0.474167,0.522500,0.672500,0.665000
-000abc075d659122,xclick,/m/01g317,1,0.562500,0.606875,0.475833,0.650000,0,0,0,1,0,0.562500,0.576875,0.606875,0.576875,0.530833,0.475833,0.532500,0.650000
-000abc075d659122,xclick,/m/01g317,1,0.648125,0.696875,0.484167,0.658333,0,0,0,1,0,0.648125,0.680000,0.696875,0.681250,0.569167,0.484167,0.529167,0.658333
-000abc075d659122,xclick,/m/01g317,1,0.765625,0.813125,0.482500,0.665000,0,0,0,1,0,0.783750,0.765625,0.790000,0.813125,0.482500,0.515833,0.665000,0.579167
-000abc075d659122,xclick,/m/01g317,1,0.923125,0.945000,0.484167,0.701667,0,0,0,1,0,0.924375,0.923125,0.929375,0.945000,0.701667,0.700000,0.484167,0.558333
-000abc075d659122,xclick,/m/01g317,1,0.956875,0.977500,0.480833,0.748333,0,0,0,1,0,0.969375,0.956875,0.968125,0.977500,0.480833,0.551667,0.748333,0.535833
-000abc075d659122,xclick,/m/01g317,1,0.968125,0.999375,0.534167,0.880833,0,1,0,0,0,0.999375,0.968125,0.990000,0.999375,0.534167,0.608333,0.880833,0.815000
-000abc075d659122,xclick,/m/09j5n,1,0.334375,0.370625,0.776667,0.802500,0,0,0,0,0,0.352500,0.334375,0.348750,0.370625,0.802500,0.795833,0.776667,0.791667
-000abc075d659122,xclick,/m/09j5n,1,0.363750,0.390000,0.761667,0.787500,1,0,0,0,0,0.378125,0.374375,0.363750,0.390000,0.761667,0.787500,0.772500,0.777500
-000abc075d659122,xclick,/m/09j5n,1,0.466250,0.491875,0.663333,0.675833,1,0,0,0,0,0.473750,0.480000,0.491875,0.466250,0.663333,0.675833,0.674167,0.672500
-000abc075d659122,xclick,/m/09j5n,1,0.495625,0.510000,0.661667,0.670833,1,0,0,0,0,0.502500,0.502500,0.495625,0.510000,0.661667,0.670833,0.670833,0.666667
-000abc075d659122,xclick,/m/09j5n,1,0.981250,0.999375,0.844167,0.875833,0,1,0,0,0,0.991875,0.999375,0.995625,0.981250,0.844167,0.856667,0.875833,0.865000
-000abc821f66a892,xclick,/m/033rq4,1,0.615625,0.660937,0.299550,0.522523,1,0,0,0,0,0.646875,0.615625,0.645312,0.660937,0.299550,0.331081,0.522523,0.313063
-000abc821f66a892,xclick,/m/033rq4,1,0.657812,0.726562,0.000000,0.518018,1,0,0,0,0,0.657812,0.726562,0.690625,0.703125,0.117117,0.103604,0.000000,0.518018
-000abe5eddc5b303,activemil,/m/01g317,1,0.755000,0.803125,0.421111,0.605556,-1,-1,-1,-1,-1,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000
-000abe5eddc5b303,xclick,/m/01g317,1,0.147500,0.217500,0.686667,0.846667,0,0,0,0,0,0.165625,0.147500,0.194375,0.217500,0.686667,0.844444,0.846667,0.755556
-000abe5eddc5b303,xclick,/m/01g317,1,0.339375,0.421250,0.578889,0.758889,0,0,0,0,0,0.372500,0.339375,0.421250,0.365000,0.578889,0.672222,0.733333,0.758889
-000abe5eddc5b303,xclick,/m/07yv9,1,0.953750,0.999375,0.387778,0.538889,0,0,0,0,0,0.999375,0.953750,0.999375,0.999375,0.387778,0.491111,0.538889,0.470000
-000ac34008b0ba4c,xclick,/m/01g317,1,0.070000,0.243333,0.773585,0.998742,0,0,0,0,0,0.172500,0.070000,0.243333,0.230000,0.773585,0.923270,0.949686,0.998742
-000ac34008b0ba4c,xclick,/m/033rq4,1,0.445833,0.620000,0.056604,0.998742,0,1,0,0,0,0.475000,0.525833,0.620000,0.445833,0.056604,0.998742,0.147170,0.103145
-000ac8c676b6077a,xclick,/m/01n5jq,1,0.046667,0.956667,0.031667,0.975000,0,0,0,0,0,0.463333,0.488333,0.046667,0.956667,0.031667,0.975000,0.596667,0.611667
-000ac8c676b6077a,xclick,/m/07j7r,1,0.043333,0.275000,0.041667,0.868333,1,1,1,1,0,0.053333,0.275000,0.066667,0.043333,0.041667,0.490000,0.868333,0.868333
-000ac8c676b6077a,xclick,/m/07j7r,1,0.366667,0.963333,0.038333,0.966667,1,1,1,1,0,0.723333,0.366667,0.791667,0.963333,0.038333,0.046667,0.966667,0.376667
-000ac8c676b6077a,xclick,/m/0c9ph5,1,0.528333,0.965000,0.038333,0.963333,1,1,1,1,0,0.675000,0.965000,0.808333,0.528333,0.038333,0.605000,0.963333,0.101667
-000ac95750ac7399,xclick,/m/01n4qj,1,0.224375,0.457500,0.367978,0.795880,1,0,0,0,0,0.228750,0.224375,0.453750,0.457500,0.367978,0.382959,0.722846,0.795880
-000ac95750ac7399,xclick,/m/01n4qj,1,0.497500,0.598750,0.670412,0.893258,1,0,0,0,0,0.497500,0.497500,0.598750,0.574375,0.670412,0.706929,0.893258,0.766854
-000ac95750ac7399,xclick,/m/01rkbr,1,0.305625,0.466250,0.464419,0.892322,1,0,0,0,0,0.330000,0.305625,0.466250,0.460000,0.464419,0.506554,0.892322,0.813670
-000ac95750ac7399,xclick,/m/01rkbr,1,0.570625,0.633125,0.768727,0.958802,1,0,0,0,0,0.581875,0.570625,0.628125,0.633125,0.768727,0.778090,0.951311,0.958802
-000ac95750ac7399,xclick,/m/01xygc,1,0.056250,0.515000,0.392322,0.999064,0,1,0,0,0,0.221250,0.056250,0.194375,0.515000,0.392322,0.776217,0.999064,0.818352
-000ac95750ac7399,xclick,/m/01xygc,1,0.651875,0.823750,0.852996,0.999064,0,1,0,0,0,0.706875,0.651875,0.708125,0.823750,0.852996,0.997191,0.999064,0.999064
-000ac95750ac7399,xclick,/m/01xyhv,1,0.049375,0.563125,0.384831,0.999064,0,1,0,0,0,0.220625,0.049375,0.563125,0.524375,0.384831,0.740637,0.999064,0.997191
-000ac95750ac7399,xclick,/m/01xyhv,1,0.495625,0.661875,0.670412,0.999064,1,1,0,0,0,0.495625,0.505625,0.660625,0.661875,0.670412,0.937266,0.991573,0.999064
-000ac95750ac7399,xclick,/m/02dl1y,1,0.466875,0.646250,0.500000,0.605805,1,0,0,0,0,0.553125,0.646250,0.466875,0.492500,0.500000,0.538390,0.556180,0.605805
-000ac95750ac7399,xclick,/m/02dl1y,1,0.663750,0.808125,0.637640,0.789326,0,0,0,0,0,0.773750,0.808125,0.691875,0.663750,0.637640,0.676030,0.789326,0.752809
-000ac95750ac7399,xclick,/m/04yx4,1,0.049375,0.560625,0.084270,0.999064,0,1,0,0,0,0.319375,0.049375,0.287500,0.560625,0.084270,0.717228,0.999064,0.993446
-000ac95750ac7399,xclick,/m/04yx4,1,0.485625,0.658750,0.496255,0.999064,1,0,0,0,0,0.552500,0.485625,0.658750,0.596250,0.496255,0.682584,0.999064,0.999064
-000acf666d991c39,xclick,/m/0bt9lr,1,0.177500,0.749375,0.461251,0.999066,1,1,0,0,0,0.423750,0.177500,0.608125,0.749375,0.461251,0.601307,0.999066,0.989729
-000acf666d991c39,xclick,/m/0bt9lr,1,0.449375,0.999375,0.038282,0.999066,0,1,0,0,0,0.753125,0.449375,0.818125,0.999375,0.038282,0.729225,0.999066,0.794585
-000ad0ecfb21ee63,xclick,/m/01d40f,1,0.643646,0.999309,0.412551,0.998971,1,1,0,0,0,0.856354,0.643646,0.799033,0.999309,0.412551,0.856996,0.998971,0.769547
-000ad0ecfb21ee63,xclick,/m/0dzf4,1,0.116713,0.219613,0.516461,0.998971,1,1,0,0,0,0.173343,0.219613,0.116713,0.131906,0.516461,0.598765,0.944444,0.998971
-000ad0ecfb21ee63,xclick,/m/0dzf4,1,0.342541,0.422652,0.522634,0.826132,1,0,0,0,0,0.368785,0.422652,0.405387,0.342541,0.522634,0.792181,0.826132,0.770576
-000ad0ecfb21ee63,xclick,/m/0dzf4,1,0.636740,0.709945,0.491770,0.998971,1,1,0,0,0,0.678177,0.636740,0.657459,0.709945,0.491770,0.861111,0.998971,0.856996
-000ad0ecfb21ee63,xclick,/m/0dzf4,1,0.866713,0.999309,0.447531,0.849794,1,1,0,0,0,0.932320,0.866713,0.974448,0.999309,0.447531,0.776749,0.849794,0.696502
-000ad0ecfb21ee63,xclick,/m/0k65p,1,0.341851,0.411602,0.736625,0.829218,1,0,0,0,0,0.399171,0.411602,0.405387,0.341851,0.736625,0.786008,0.829218,0.745885
-000ad0ecfb21ee63,xclick,/m/0k65p,1,0.860497,0.960635,0.735597,0.836420,1,0,0,0,0,0.942680,0.960635,0.914365,0.860497,0.836420,0.796296,0.735597,0.782922
-000ad20b5e452b24,xclick,/m/050k8,1,0.000000,0.603125,0.192188,0.917188,0,1,0,0,0,0.170313,0.340625,0.603125,0.000000,0.192188,0.917188,0.642188,0.446875
-000ad20b5e452b24,xclick,/m/050k8,1,0.407813,0.998438,0.134375,0.918750,1,1,0,0,0,0.679688,0.798438,0.407813,0.998438,0.134375,0.918750,0.289062,0.701562
-000ad3d42653f5f6,xclick,/m/019jd,1,0.000000,0.665556,0.485000,0.778333,0,1,0,0,0,0.032222,0.143333,0.665556,0.000000,0.778333,0.485000,0.585000,0.641667
-000ad3d42653f5f6,xclick,/m/019jd,1,0.612222,0.860000,0.416667,0.708333,1,0,0,0,0,0.761111,0.860000,0.818889,0.612222,0.708333,0.660000,0.416667,0.695000
-000ad3d42653f5f6,xclick,/m/019jd,1,0.781111,0.953333,0.431667,0.703333,1,0,0,0,0,0.923333,0.867778,0.953333,0.781111,0.703333,0.431667,0.645000,0.546667
-000ad3d42653f5f6,xclick,/m/019jd,1,0.912222,0.998889,0.523333,0.671667,1,1,0,0,0,0.983333,0.964444,0.998889,0.912222,0.671667,0.523333,0.580000,0.548333
-000ad3d42653f5f6,xclick,/m/07j7r,1,0.360000,0.453333,0.236667,0.498333,0,0,0,0,0,0.416667,0.440000,0.360000,0.453333,0.236667,0.498333,0.325000,0.265000
-000ad6c520be9ec5,xclick,/m/03fwl,1,0.093750,0.668125,0.192500,0.940833,0,0,0,1,0,0.204375,0.282500,0.668125,0.093750,0.192500,0.940833,0.391667,0.396667
-000ad6c520be9ec5,xclick,/m/06msq,1,0.000000,0.094375,0.720833,0.824167,0,1,0,1,0,0.000000,0.094375,0.000000,0.000000,0.720833,0.777500,0.790833,0.824167
-000ad6c520be9ec5,xclick,/m/06msq,1,0.086875,0.663750,0.210833,0.940833,0,0,0,1,0,0.431250,0.663750,0.086875,0.279375,0.210833,0.441667,0.401667,0.940833
-000ad6c520be9ec5,xclick,/m/06msq,1,0.711875,0.932500,0.115833,0.445000,0,0,0,1,0,0.857500,0.932500,0.711875,0.741250,0.115833,0.375833,0.415833,0.445000
-000ad6fa67b5ad96,xclick,/m/06msq,1,0.067542,0.999062,0.353750,0.999375,1,1,0,0,0,0.999062,0.693246,0.151970,0.067542,0.873750,0.999375,0.353750,0.370625
-000ad6fa67b5ad96,xclick,/m/0jbk,1,0.075047,0.999062,0.363125,0.998750,0,1,0,0,0,0.195122,0.075047,0.470919,0.999062,0.363125,0.388750,0.998750,0.711875
-000adcdd7244ce4a,xclick,/m/01g317,1,0.119176,0.415771,0.091610,0.941176,0,0,0,1,0,0.174731,0.119176,0.244624,0.415771,0.941176,0.309547,0.091610,0.629701
-000adcdd7244ce4a,xclick,/m/01g317,1,0.551971,0.933692,0.304725,0.945034,0,0,0,1,0,0.674731,0.551971,0.836918,0.933692,0.304725,0.433944,0.945034,0.522662
-000adcdd7244ce4a,xclick,/m/04hgtk,1,0.145161,0.277778,0.085824,0.264224,0,0,0,1,0,0.277778,0.229391,0.145161,0.225806,0.134041,0.085824,0.138862,0.264224
-000adcdd7244ce4a,xclick,/m/04hgtk,1,0.554659,0.853047,0.315333,0.686596,0,0,0,1,0,0.654122,0.554659,0.853047,0.729391,0.315333,0.523626,0.469624,0.686596
-000adcdd7244ce4a,xclick,/m/04rky,1,0.114695,0.413082,0.093539,0.942141,0,1,0,1,0,0.218638,0.413082,0.114695,0.188172,0.093539,0.624879,0.314368,0.942141
-000adcdd7244ce4a,xclick,/m/04rky,1,0.553763,0.935484,0.310511,0.942141,1,1,0,1,0,0.553763,0.670251,0.935484,0.850358,0.456123,0.310511,0.617165,0.942141
-000adcdd7244ce4a,xclick,/m/0dzf4,1,0.116487,0.259857,0.224687,0.479267,0,0,0,1,0,0.259857,0.116487,0.170251,0.259857,0.224687,0.305689,0.479267,0.259402
-000adcdd7244ce4a,xclick,/m/0dzf4,1,0.299283,0.413082,0.290260,0.671167,0,0,0,1,0,0.299283,0.299283,0.387097,0.413082,0.290260,0.465767,0.671167,0.648987
-000adef7197e3118,xclick,/m/07j7r,1,0.000000,0.999375,0.000000,0.886064,1,1,1,0,0,0.331250,0.000000,0.517500,0.999375,0.000000,0.299435,0.886064,0.303202
-000adef7197e3118,xclick,/m/07j7r,1,0.790625,0.950000,0.000000,0.306968,1,1,0,0,0,0.857500,0.790625,0.950000,0.866250,0.000000,0.301318,0.008475,0.306968
-000adef7197e3118,xclick,/m/0cvnqh,1,0.380000,0.481875,0.314501,0.409605,1,0,0,0,0,0.381250,0.380000,0.481875,0.420000,0.314501,0.409605,0.402072,0.320151
-000adef7197e3118,xclick,/m/0cvnqh,1,0.423750,0.562500,0.580038,0.831450,1,0,0,0,0,0.455625,0.455625,0.423750,0.562500,0.580038,0.831450,0.813559,0.592279
-000adef7197e3118,xclick,/m/0cvnqh,1,0.648125,0.791875,0.568738,0.842750,1,0,0,0,0,0.722500,0.791875,0.778750,0.648125,0.568738,0.807910,0.842750,0.568738
-000adfe5b817011c,activemil,/m/0cgh4,1,0.065833,1.000000,0.106250,0.843125,-1,-1,-1,-1,-1,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000
-000adfe5b817011c,xclick,/m/0cgh4,1,0.008333,0.260833,0.000000,0.195625,1,1,0,0,0,0.183333,0.260833,0.008333,0.015000,0.000000,0.118750,0.195625,0.132500
-000ae235808cc1e8,xclick,/m/019jd,1,0.000741,0.040741,0.644444,0.683333,1,1,0,0,0,0.040741,0.000741,0.029630,0.033333,0.644444,0.644444,0.683333,0.683333
-000ae235808cc1e8,xclick,/m/019jd,1,0.037037,0.108148,0.634444,0.696667,1,0,0,0,0,0.083704,0.037037,0.061481,0.108148,0.634444,0.646667,0.696667,0.644444
-000ae235808cc1e8,xclick,/m/019jd,1,0.061481,0.183704,0.636667,0.733333,1,0,0,0,0,0.122222,0.061481,0.137778,0.183704,0.636667,0.675556,0.733333,0.673333
-000ae235808cc1e8,xclick,/m/019jd,1,0.154074,0.395556,0.636667,0.810000,0,0,0,0,0,0.212593,0.154074,0.373333,0.395556,0.636667,0.714444,0.810000,0.722222
-000ae235808cc1e8,xclick,/m/019jd,1,0.442222,0.712593,0.694444,0.783333,1,0,0,0,0,0.590370,0.442222,0.443704,0.712593,0.694444,0.741111,0.783333,0.770000
-000ae235808cc1e8,xclick,/m/019jd,1,0.692593,0.815556,0.702222,0.751111,1,0,0,0,0,0.745926,0.692593,0.814815,0.815556,0.702222,0.741111,0.743333,0.751111
-000ae235808cc1e8,xclick,/m/019jd,1,0.721482,0.994815,0.633333,0.766667,1,1,0,0,0,0.956296,0.721482,0.828889,0.994815,0.633333,0.673333,0.766667,0.715556
-000ae235808cc1e8,xclick,/m/019jd,1,0.927407,0.999259,0.612222,0.710000,1,1,0,0,0,0.996296,0.927407,0.998519,0.999259,0.612222,0.627778,0.685556,0.710000
-000ae235808cc1e8,xclick,/m/03jm5,1,0.055556,0.080000,0.488889,0.631111,0,0,0,0,0,0.071852,0.055556,0.065185,0.080000,0.488889,0.588889,0.631111,0.617778
-000ae235808cc1e8,xclick,/m/03jm5,1,0.080000,0.106667,0.497778,0.660000,0,0,0,0,0,0.080000,0.096296,0.106667,0.094815,0.617778,0.497778,0.565556,0.660000
-000ae235808cc1e8,xclick,/m/03jm5,1,0.104444,0.146667,0.494444,0.644444,0,0,0,0,0,0.104444,0.120741,0.146667,0.142963,0.644444,0.494444,0.621111,0.641111
-000ae235808cc1e8,xclick,/m/03jm5,1,0.141481,0.190370,0.507778,0.688889,0,0,0,0,0,0.141481,0.153333,0.178519,0.190370,0.643333,0.507778,0.688889,0.635556
-000ae235808cc1e8,xclick,/m/03jm5,1,0.190370,0.242222,0.403333,0.635556,0,0,0,0,0,0.190370,0.199259,0.239259,0.242222,0.635556,0.442222,0.403333,0.575556
-000ae235808cc1e8,xclick,/m/03jm5,1,0.234074,0.297037,0.371111,0.614444,0,0,0,0,0,0.234074,0.237778,0.297037,0.296296,0.614444,0.490000,0.371111,0.546667
-000ae235808cc1e8,xclick,/m/03jm5,1,0.296296,0.380741,0.345556,0.670000,0,0,0,0,0,0.296296,0.328148,0.380741,0.360000,0.610000,0.345556,0.424444,0.670000
-000ae235808cc1e8,xclick,/m/03jm5,1,0.388889,0.588889,0.205556,0.710000,0,0,0,0,0,0.502222,0.588889,0.388889,0.528889,0.205556,0.273333,0.647778,0.710000
-000ae235808cc1e8,xclick,/m/03jm5,1,0.600000,0.661481,0.283333,0.586667,0,0,0,0,0,0.600000,0.629630,0.661481,0.660000,0.571111,0.283333,0.432222,0.586667
-000ae235808cc1e8,xclick,/m/03jm5,1,0.654815,0.714074,0.335556,0.660000,0,0,0,0,0,0.654815,0.670370,0.714074,0.714074,0.623333,0.335556,0.442222,0.660000
-000ae235808cc1e8,xclick,/m/03jm5,1,0.715556,0.769630,0.418889,0.623333,0,0,0,0,0,0.715556,0.728148,0.766667,0.769630,0.623333,0.421111,0.418889,0.610000
-000ae235808cc1e8,xclick,/m/03jm5,1,0.769630,0.808148,0.416667,0.670000,0,0,0,0,0,0.769630,0.808148,0.796296,0.796296,0.573333,0.670000,0.416667,0.416667
-000ae235808cc1e8,xclick,/m/03jm5,1,0.796296,0.850370,0.393333,0.644444,0,0,0,0,0,0.796296,0.822222,0.850370,0.808148,0.416667,0.393333,0.643333,0.644444
-000ae235808cc1e8,xclick,/m/03jm5,1,0.822222,0.888148,0.403333,0.646667,0,0,0,0,0,0.822222,0.846667,0.888148,0.857037,0.546667,0.403333,0.646667,0.646667
-000ae235808cc1e8,xclick,/m/03jm5,1,0.857037,0.957778,0.353333,0.646667,0,0,0,0,0,0.857037,0.888148,0.944444,0.957778,0.646667,0.442222,0.353333,0.617778
-000ae235808cc1e8,xclick,/m/03jm5,1,0.960000,0.999259,0.364444,0.646667,0,0,0,0,0,0.999259,0.994815,0.960000,0.991852,0.496667,0.364444,0.536667,0.646667
-000ae28755d2d20e,xclick,/m/04yx4,1,0.245000,0.538750,0.221667,0.999167,1,1,0,0,0,0.312500,0.538750,0.368750,0.245000,0.221667,0.632500,0.999167,0.842500
-000ae28755d2d20e,xclick,/m/07j7r,1,0.162500,0.533125,0.043333,0.505833,1,1,1,0,0,0.206250,0.220625,0.162500,0.533125,0.505833,0.043333,0.090833,0.390833
-000ae28755d2d20e,xclick,/m/07mhn,1,0.243750,0.446875,0.790000,0.999167,1,1,0,0,0,0.256875,0.243750,0.446875,0.427500,0.790000,0.838333,0.995833,0.999167
-000ae28755d2d20e,xclick,/m/07mhn,1,0.667500,0.707500,0.775833,0.913333,1,0,0,0,0,0.699375,0.667500,0.681250,0.707500,0.775833,0.884167,0.913333,0.784167
-000ae28755d2d20e,xclick,/m/0dzf4,1,0.216250,0.490625,0.475833,0.716667,0,0,0,0,0,0.241875,0.490625,0.216250,0.313750,0.475833,0.681667,0.558333,0.716667
-000ae28755d2d20e,xclick,/m/0dzf4,1,0.394375,0.528125,0.476667,0.657500,0,0,0,0,0,0.394375,0.396875,0.425000,0.528125,0.476667,0.554167,0.657500,0.657500
-000ae28755d2d20e,xclick,/m/0jyfg,1,0.286250,0.380000,0.320000,0.350833,1,0,0,0,0,0.374375,0.343750,0.286250,0.380000,0.320000,0.350833,0.320000,0.334167
-000aecd78b230135,xclick,/m/01n4qj,1,0.043750,0.402500,0.274254,0.761194,1,0,0,0,0,0.233750,0.402500,0.043750,0.261250,0.274254,0.535448,0.542910,0.761194
-000aecd78b230135,xclick,/m/01n4qj,1,0.636250,0.998750,0.177239,0.798507,1,1,0,0,0,0.786250,0.998750,0.915000,0.636250,0.177239,0.458955,0.798507,0.389925
-000aecd78b230135,xclick,/m/035r7c,1,0.105000,0.267500,0.742537,0.998134,1,1,0,0,0,0.140000,0.267500,0.242500,0.105000,0.742537,0.794776,0.998134,0.938433
-000aecd78b230135,xclick,/m/035r7c,1,0.242500,0.478750,0.727612,0.998134,1,1,0,0,0,0.302500,0.478750,0.428750,0.242500,0.727612,0.972015,0.998134,0.753731
-000aecd78b230135,xclick,/m/035r7c,1,0.371250,0.535000,0.718284,0.947761,1,1,0,0,0,0.408750,0.371250,0.535000,0.477500,0.718284,0.781716,0.800373,0.947761
-000aecd78b230135,xclick,/m/035r7c,1,0.478750,0.667500,0.654851,0.990672,1,1,0,0,0,0.566250,0.667500,0.641250,0.478750,0.654851,0.805970,0.990672,0.979478
-000aecd78b230135,xclick,/m/035r7c,1,0.668750,0.865000,0.727612,0.998134,1,1,0,0,0,0.830000,0.668750,0.865000,0.800000,0.727612,0.820896,0.819030,0.998134
-000aecd78b230135,xclick,/m/035r7c,1,0.838750,0.998750,0.722015,0.893657,1,1,0,0,0,0.838750,0.998750,0.998750,0.858750,0.722015,0.802239,0.893657,0.804104
-000aecd78b230135,xclick,/m/0fly7,1,0.082500,0.468750,0.716418,0.998134,0,1,0,0,0,0.326250,0.468750,0.206250,0.082500,0.716418,0.968284,0.998134,0.899254
-000aecd78b230135,xclick,/m/0fly7,1,0.380000,0.653750,0.666045,0.998134,0,1,0,0,0,0.563750,0.545000,0.380000,0.653750,0.666045,0.998134,0.744403,0.811567
-000aecd78b230135,xclick,/m/0fly7,1,0.660000,0.993750,0.697761,0.985075,1,1,0,0,0,0.993750,0.660000,0.661250,0.697500,0.707090,0.985075,0.809701,0.697761
-000aee0af66d4237,xclick,/m/011k07,1,0.088750,0.908750,0.000000,0.808333,1,0,0,0,0,0.468125,0.908750,0.798750,0.088750,0.000000,0.763333,0.808333,0.437500
-000aee0af66d4237,xclick,/m/0120dh,1,0.095000,0.916875,0.005000,0.830000,1,0,0,0,0,0.478750,0.916875,0.095000,0.848125,0.005000,0.762500,0.458333,0.830000
-000aee0af66d4237,xclick,/m/05s2s,1,0.000000,0.999375,0.000000,0.999167,1,1,1,0,0,0.413125,0.000000,0.486875,0.999375,0.000000,0.388333,0.999167,0.809167
-000aee0af66d4237,xclick,/m/0f4s2w,1,0.000000,0.546875,0.504167,0.999167,1,1,0,0,0,0.088125,0.546875,0.276250,0.000000,0.504167,0.999167,0.999167,0.915000
-000aee0af66d4237,xclick,/m/0kpqd,1,0.247500,0.793750,0.574167,0.900833,1,0,0,0,0,0.300625,0.247500,0.553750,0.793750,0.574167,0.582500,0.900833,0.606667
-000af180a3163f17,activemil,/m/01bl7v,1,0.593750,0.998438,0.000000,0.997642,-1,-1,-1,-1,-1,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000
-000af180a3163f17,xclick,/m/014sv8,1,0.592969,0.642969,0.383255,0.457547,0,0,0,0,0,0.609375,0.592969,0.625000,0.642969,0.383255,0.451651,0.457547,0.400943
-000af180a3163f17,xclick,/m/014sv8,1,0.702344,0.775781,0.320755,0.375000,0,0,0,0,0,0.734375,0.775781,0.746094,0.702344,0.320755,0.331368,0.366745,0.375000
-000af180a3163f17,xclick,/m/0283dt1,1,0.660937,0.776563,0.541274,0.640330,0,0,0,0,0,0.776563,0.703125,0.660937,0.757031,0.547170,0.640330,0.579009,0.541274
-000af180a3163f17,xclick,/m/03q69,1,0.553906,0.943750,0.031840,0.387972,1,0,0,0,0,0.743750,0.553906,0.869531,0.943750,0.031840,0.251179,0.387972,0.318396
-000af180a3163f17,xclick,/m/04hgtk,1,0.612500,0.917969,0.093160,0.745283,1,0,0,0,0,0.612500,0.658594,0.747656,0.917969,0.503538,0.093160,0.745283,0.304245
-000af180a3163f17,xclick,/m/0dzct,1,0.575000,0.896094,0.142689,0.771226,1,0,0,0,0,0.575000,0.671875,0.896094,0.735938,0.380896,0.142689,0.419811,0.771226
-000af631fb329557,xclick,/m/01g317,1,0.056000,0.258000,0.658000,0.949000,0,1,0,0,0,0.056000,0.056000,0.173000,0.258000,0.949000,0.949000,0.658000,0.698000
-000af631fb329557,xclick,/m/01g317,1,0.056000,0.332000,0.344000,0.629000,0,0,0,0,0,0.197000,0.332000,0.140000,0.056000,0.344000,0.465000,0.629000,0.453000
-000af631fb329557,xclick,/m/01g317,1,0.244000,0.343000,0.647000,0.945000,0,1,0,1,0,0.244000,0.319000,0.326000,0.343000,0.722000,0.647000,0.945000,0.933000
-000af631fb329557,xclick,/m/01g317,1,0.260000,0.357000,0.143000,0.337000,0,1,0,1,0,0.350000,0.260000,0.327000,0.357000,0.143000,0.243000,0.337000,0.299000
-000af631fb329557,xclick,/m/01g317,1,0.379000,0.656000,0.051000,0.334000,0,1,0,0,0,0.379000,0.485000,0.656000,0.622000,0.219000,0.051000,0.216000,0.334000
-000af631fb329557,xclick,/m/01g317,1,0.420000,0.654000,0.354000,0.646000,0,1,0,0,0,0.420000,0.535000,0.654000,0.654000,0.505000,0.354000,0.646000,0.646000
-000af631fb329557,xclick,/m/01g317,1,0.490000,0.658000,0.665000,0.958000,0,1,0,1,0,0.561000,0.490000,0.582000,0.658000,0.665000,0.805000,0.958000,0.819000
-000af631fb329557,xclick,/m/01g317,1,0.686000,0.859000,0.812000,0.958000,0,1,0,1,0,0.721000,0.859000,0.780000,0.686000,0.812000,0.904000,0.958000,0.932000
-000af631fb329557,xclick,/m/01g317,1,0.743000,0.967000,0.394000,0.646000,0,1,0,1,0,0.950000,0.967000,0.743000,0.800000,0.394000,0.509000,0.642000,0.646000
-000af631fb329557,xclick,/m/0dzct,1,0.047000,0.168000,0.767000,0.947000,0,1,0,1,0,0.078000,0.106000,0.047000,0.168000,0.767000,0.947000,0.902000,0.853000
-000af631fb329557,xclick,/m/0dzct,1,0.114000,0.251000,0.347000,0.562000,0,0,0,1,0,0.189000,0.197000,0.114000,0.251000,0.347000,0.562000,0.443000,0.444000
-000af631fb329557,xclick,/m/0dzct,1,0.310000,0.347000,0.791000,0.937000,0,1,0,1,0,0.343000,0.338000,0.310000,0.347000,0.791000,0.937000,0.867000,0.855000
-000af631fb329557,xclick,/m/0dzct,1,0.525000,0.659000,0.351000,0.533000,0,1,0,1,0,0.627000,0.631000,0.525000,0.659000,0.351000,0.533000,0.443000,0.410000
-000af631fb329557,xclick,/m/0dzct,1,0.641000,0.660000,0.822000,0.926000,0,1,0,1,0,0.648000,0.653000,0.660000,0.641000,0.822000,0.926000,0.864000,0.878000
-000afe7726e121ea,xclick,/m/02p0tk3,1,0.213125,0.407500,0.162289,0.993433,0,1,0,0,0,0.311875,0.213125,0.274375,0.407500,0.162289,0.448405,0.993433,0.351782
-000afe7726e121ea,xclick,/m/04yx4,1,0.215625,0.413750,0.169794,0.999062,1,1,0,0,0,0.215625,0.327500,0.413750,0.353125,0.451220,0.169794,0.753283,0.999062
-000afe7726e121ea,xclick,/m/0dzf4,1,0.241250,0.411875,0.250469,0.512195,1,0,0,0,0,0.343125,0.241250,0.411875,0.357500,0.250469,0.423077,0.357411,0.512195
-000afe7726e121ea,xclick,/m/0k65p,1,0.339375,0.388750,0.244841,0.321764,0,0,0,0,0,0.339375,0.340625,0.380625,0.388750,0.244841,0.257974,0.321764,0.299250
-000b06c0eed42a4c,xclick,/m/01n4qj,1,0.139167,0.462500,0.161875,0.395625,1,0,0,0,0,0.214167,0.139167,0.305000,0.462500,0.161875,0.240000,0.395625,0.211250
-000b06c0eed42a4c,xclick,/m/01n4qj,1,0.155000,0.421667,0.612500,0.913125,1,0,0,0,0,0.248333,0.155000,0.255000,0.421667,0.612500,0.770000,0.913125,0.728750
-000b06c0eed42a4c,xclick,/m/01n4qj,1,0.617500,0.873333,0.140625,0.424375,1,0,0,0,0,0.712500,0.873333,0.617500,0.742500,0.140625,0.235625,0.259375,0.424375
-000b06c0eed42a4c,xclick,/m/01n4qj,1,0.617500,0.888333,0.619375,0.898125,1,0,0,0,0,0.708333,0.617500,0.740000,0.888333,0.619375,0.773125,0.898125,0.759375
-000b06c0eed42a4c,xclick,/m/03q69,1,0.195000,0.390000,0.051250,0.213125,0,0,0,0,0,0.292500,0.195000,0.390000,0.335833,0.051250,0.133125,0.133125,0.213125
-000b06c0eed42a4c,xclick,/m/03q69,1,0.219167,0.360000,0.499375,0.618750,0,0,0,0,0,0.291667,0.219167,0.332500,0.360000,0.499375,0.540625,0.618750,0.560625
-000b06c0eed42a4c,xclick,/m/03q69,1,0.667500,0.820000,0.495625,0.618750,0,0,0,0,0,0.742500,0.667500,0.704167,0.820000,0.495625,0.548750,0.618750,0.553750
-000b06c0eed42a4c,xclick,/m/03q69,1,0.681667,0.818333,0.021250,0.147500,0,0,0,0,0,0.759167,0.681667,0.790833,0.818333,0.021250,0.085625,0.147500,0.123750
-000b06c0eed42a4c,xclick,/m/0dzf4,1,0.089167,0.241667,0.674375,0.918125,1,0,0,0,0,0.241667,0.089167,0.139167,0.221667,0.918125,0.913125,0.691250,0.674375
-000b06c0eed42a4c,xclick,/m/0dzf4,1,0.135000,0.232500,0.206250,0.443750,1,0,0,0,0,0.185000,0.135000,0.157500,0.232500,0.206250,0.218125,0.443750,0.426250
-000b06c0eed42a4c,xclick,/m/0dzf4,1,0.296667,0.428333,0.656875,0.931875,1,0,0,0,0,0.360000,0.428333,0.296667,0.365000,0.656875,0.682500,0.918125,0.931875
-000b06c0eed42a4c,xclick,/m/0dzf4,1,0.326667,0.458333,0.172500,0.354375,1,0,0,0,0,0.408333,0.326667,0.458333,0.345000,0.172500,0.349375,0.237500,0.354375
-000b06c0eed42a4c,xclick,/m/0dzf4,1,0.620000,0.700000,0.195625,0.428125,0,0,0,0,0,0.620000,0.681667,0.629167,0.700000,0.195625,0.211250,0.428125,0.419375
-000b06c0eed42a4c,xclick,/m/0dzf4,1,0.624167,0.695000,0.661875,0.918125,1,0,0,0,0,0.695000,0.658333,0.624167,0.667500,0.894375,0.918125,0.675625,0.661875
-000b06c0eed42a4c,xclick,/m/0dzf4,1,0.775000,0.865833,0.233125,0.416250,1,0,0,0,0,0.775000,0.865833,0.856667,0.813333,0.416250,0.407500,0.233125,0.233125
-000b06c0eed42a4c,xclick,/m/0dzf4,1,0.795000,0.881667,0.710000,0.903125,1,0,0,0,0,0.820000,0.881667,0.795000,0.868333,0.710000,0.711875,0.887500,0.903125
-000b06c0eed42a4c,xclick,/m/0fly7,1,0.162500,0.388333,0.900625,0.998125,1,1,0,0,0,0.322500,0.162500,0.367500,0.388333,0.900625,0.998125,0.990000,0.916250
-000b06c0eed42a4c,xclick,/m/0fly7,1,0.176667,0.381667,0.349375,0.499375,1,0,0,0,0,0.276667,0.176667,0.381667,0.381667,0.349375,0.499375,0.499375,0.499375
-000b06c0eed42a4c,xclick,/m/0fly7,1,0.638333,0.858333,0.398750,0.497500,1,0,0,0,0,0.735833,0.697500,0.638333,0.858333,0.398750,0.497500,0.461875,0.439375
-000b06c0eed42a4c,xclick,/m/0fly7,1,0.650000,0.860000,0.888750,0.996250,1,1,0,0,0,0.650000,0.650000,0.735000,0.860000,0.907500,0.926250,0.888750,0.996250
-000b093da01e5bfe,xclick,/m/07j7r,1,0.000000,0.291875,0.439183,0.726091,1,1,1,0,0,0.006250,0.000000,0.291875,0.051875,0.439183,0.535747,0.608171,0.726091
-000b093da01e5bfe,xclick,/m/07j7r,1,0.000000,0.999375,0.199629,0.544104,1,1,1,0,0,0.880625,0.999375,0.741875,0.000000,0.199629,0.239554,0.544104,0.415970
-000b093da01e5bfe,xclick,/m/07j7r,1,0.034375,0.363750,0.722377,0.999071,0,1,0,0,0,0.168750,0.034375,0.362500,0.363750,0.722377,0.972145,0.992572,0.999071
-000b093da01e5bfe,xclick,/m/07j7r,1,0.107500,0.362500,0.305478,0.705664,1,0,1,0,0,0.241875,0.107500,0.362500,0.121250,0.305478,0.703807,0.587744,0.705664
-000b093da01e5bfe,xclick,/m/07j7r,1,0.116875,0.196250,0.490251,0.713092,1,0,0,0,0,0.168125,0.196250,0.170625,0.116875,0.490251,0.616527,0.713092,0.698236
-000b093da01e5bfe,xclick,/m/07j7r,1,0.206875,0.226875,0.553389,0.669452,1,0,0,0,0,0.206875,0.226875,0.220625,0.206875,0.553389,0.629526,0.669452,0.669452
-000b093da01e5bfe,xclick,/m/07j7r,1,0.232500,0.273125,0.319406,0.663881,1,0,0,0,0,0.232500,0.273125,0.245000,0.232500,0.319406,0.598886,0.658310,0.663881
-000b093da01e5bfe,xclick,/m/07j7r,1,0.251250,0.276875,0.393686,0.636954,1,0,0,0,0,0.259375,0.276875,0.270625,0.251250,0.393686,0.580316,0.635097,0.636954
-000b093da01e5bfe,xclick,/m/07j7r,1,0.274375,0.313125,0.475395,0.636954,1,0,0,0,0,0.287500,0.313125,0.302500,0.274375,0.475395,0.557103,0.616527,0.636954
-000b093da01e5bfe,xclick,/m/07j7r,1,0.313125,0.363125,0.417827,0.646240,1,0,0,0,0,0.326875,0.363125,0.336250,0.313125,0.417827,0.600743,0.646240,0.623955
-000b093da01e5bfe,xclick,/m/07j7r,1,0.323125,0.469375,0.035283,0.635098,1,0,0,0,0,0.421875,0.469375,0.415625,0.323125,0.035283,0.513463,0.635098,0.618384
-000b093da01e5bfe,xclick,/m/07j7r,1,0.441250,0.496250,0.272052,0.553389,1,0,0,0,0,0.464375,0.496250,0.469375,0.441250,0.272052,0.492108,0.553389,0.542247
-000b093da01e5bfe,xclick,/m/07j7r,1,0.468750,0.723750,0.162488,0.549675,1,0,1,0,0,0.582500,0.468750,0.723750,0.627500,0.162488,0.435469,0.490251,0.549675
-000b093da01e5bfe,xclick,/m/07j7r,1,0.475625,0.578125,0.221913,0.538533,1,0,0,0,0,0.538750,0.578125,0.575625,0.475625,0.221913,0.509749,0.538533,0.515320
-000b093da01e5bfe,xclick,/m/07j7r,1,0.561875,0.598125,0.178273,0.545961,1,0,0,0,0,0.578750,0.598125,0.583750,0.561875,0.178273,0.479109,0.545961,0.298979
-000b093da01e5bfe,xclick,/m/07j7r,1,0.591250,0.636875,0.364902,0.551532,1,0,0,0,0,0.625625,0.636875,0.620625,0.591250,0.364902,0.493965,0.547818,0.551532
-000b093da01e5bfe,xclick,/m/07j7r,1,0.602500,0.683125,0.269266,0.547818,1,0,0,0,0,0.646250,0.683125,0.663750,0.602500,0.269266,0.471681,0.540390,0.547818
-000b093da01e5bfe,xclick,/m/07j7r,1,0.696250,0.723125,0.345404,0.547818,1,0,0,0,0,0.716250,0.723125,0.710000,0.696250,0.345404,0.467967,0.547818,0.542247
-000b093da01e5bfe,xclick,/m/07j7r,1,0.729375,0.813750,0.448468,0.549675,1,0,1,0,0,0.803750,0.813750,0.800000,0.729375,0.448468,0.488394,0.519034,0.549675
-000b093da01e5bfe,xclick,/m/07j7r,1,0.738750,0.999375,0.183844,0.537604,1,1,1,0,0,0.981250,0.738750,0.999375,0.748125,0.183844,0.528319,0.427112,0.537604
-000b093da01e5bfe,xclick,/m/07j7r,1,0.863750,0.886875,0.391829,0.500464,1,0,0,0,0,0.874375,0.886875,0.875625,0.863750,0.391829,0.435469,0.497679,0.500464
-000b093da01e5bfe,xclick,/m/07j7r,1,0.885625,0.908750,0.357474,0.493965,1,0,0,0,0,0.900000,0.908750,0.894375,0.885625,0.357474,0.454039,0.493965,0.493965
-000b093da01e5bfe,xclick,/m/07j7r,1,0.897500,0.926875,0.337976,0.492108,1,0,0,0,0,0.912500,0.926875,0.905000,0.897500,0.337976,0.439183,0.492108,0.488394
-000b093da01e5bfe,xclick,/m/07j7r,1,0.916250,0.952500,0.296193,0.484680,1,0,0,0,0,0.931875,0.952500,0.934375,0.916250,0.296193,0.446611,0.484680,0.482823
-000b093da01e5bfe,xclick,/m/07j7r,1,0.946875,0.999375,0.189415,0.475395,1,1,0,0,0,0.983125,0.999375,0.991875,0.946875,0.189415,0.328691,0.466110,0.475395
-000b093da01e5bfe,xclick,/m/0c9ph5,1,0.141250,0.200625,0.718663,0.824513,0,0,0,0,0,0.170625,0.141250,0.200625,0.173125,0.718663,0.783658,0.774373,0.824513
-000b093da01e5bfe,xclick,/m/0c9ph5,1,0.147500,0.183750,0.730734,0.790158,0,0,1,0,0,0.160625,0.170625,0.147500,0.183750,0.730734,0.790158,0.765088,0.754875
-000b09d5d3fc821f,xclick,/m/0167gd,1,0.426667,0.815238,0.060000,0.680000,0,0,0,0,0,0.754286,0.426667,0.815238,0.565714,0.060000,0.497143,0.165714,0.680000
-000b09d5d3fc821f,xclick,/m/01d40f,1,0.432381,0.725714,0.205714,0.598571,1,0,0,0,0,0.666667,0.432381,0.725714,0.657143,0.205714,0.502857,0.447143,0.598571
-000b09d5d3fc821f,xclick,/m/07bgp,1,0.000000,0.215238,0.588571,0.851429,0,1,0,1,0,0.076190,0.000000,0.215238,0.076190,0.588571,0.648571,0.777143,0.851429
-000b09d5d3fc821f,xclick,/m/07bgp,1,0.211429,0.489524,0.370000,0.647143,1,0,0,1,0,0.411429,0.489524,0.384762,0.211429,0.370000,0.414286,0.647143,0.534286
-000b0f235dcf2caa,xclick,/m/01g317,1,0.589375,0.611250,0.864615,0.933846,1,0,0,0,0,0.601875,0.589375,0.611250,0.601875,0.864615,0.889231,0.895385,0.933846
-000b0f235dcf2caa,xclick,/m/01g317,1,0.640625,0.683125,0.861538,0.998462,0,1,0,0,0,0.667500,0.683125,0.640625,0.672500,0.861538,0.998462,0.998462,0.998462
-000b0f235dcf2caa,xclick,/m/01g317,1,0.688750,0.738750,0.850769,0.998462,0,1,0,0,0,0.708750,0.688750,0.738750,0.726875,0.850769,0.992308,0.995385,0.998462
-000b0f235dcf2caa,xclick,/m/01g317,1,0.725000,0.737500,0.858462,0.909231,1,0,0,0,0,0.731250,0.725000,0.737500,0.727500,0.858462,0.892308,0.880000,0.909231
-000b0f235dcf2caa,xclick,/m/01g317,1,0.882500,0.999375,0.840000,0.998462,1,1,1,0,0,0.914375,0.882500,0.999375,0.998125,0.840000,0.889231,0.921538,0.998462
-000b0f235dcf2caa,xclick,/m/01knjb,1,0.606875,0.756250,0.133846,0.292308,0,0,0,0,0,0.672500,0.756250,0.676250,0.606875,0.133846,0.267692,0.292308,0.283077
-000b0f235dcf2caa,xclick,/m/0cgh4,1,0.000000,0.349375,0.389231,0.856923,1,1,0,0,0,0.349375,0.334375,0.000000,0.000000,0.403077,0.824615,0.389231,0.856923
-000b0f235dcf2caa,xclick,/m/0cgh4,1,0.000000,0.999375,0.247692,0.998462,1,1,1,0,0,0.121875,0.000000,0.757500,0.999375,0.998462,0.566154,0.247692,0.373846
-000b0f235dcf2caa,xclick,/m/0cgh4,1,0.073750,0.354375,0.383077,0.655385,1,0,0,0,0,0.237500,0.073750,0.354375,0.210625,0.383077,0.512308,0.484615,0.655385
-000b0f235dcf2caa,xclick,/m/0cgh4,1,0.358125,0.860625,0.266154,0.824615,1,0,0,0,0,0.603125,0.358125,0.626875,0.860625,0.266154,0.418462,0.824615,0.587692
-000b0f235dcf2caa,xclick,/m/0cgh4,1,0.914375,0.995000,0.361538,0.881538,1,1,0,0,0,0.995000,0.993750,0.928750,0.914375,0.361538,0.881538,0.515385,0.836923
-000b0f235dcf2caa,xclick,/m/0d4v4,1,0.076875,0.168750,0.536923,0.624615,1,0,1,0,0,0.090625,0.076875,0.166250,0.168750,0.536923,0.587692,0.624615,0.552308
-000b0f235dcf2caa,xclick,/m/0d4v4,1,0.245625,0.343125,0.404615,0.636923,1,0,1,0,0,0.245625,0.248125,0.286875,0.343125,0.404615,0.423077,0.636923,0.432308
-000b0f235dcf2caa,xclick,/m/0d4v4,1,0.253750,0.265625,0.533846,0.561538,0,0,0,0,0,0.255000,0.265625,0.264375,0.253750,0.561538,0.561538,0.540000,0.533846
-000b0f235dcf2caa,xclick,/m/0d4v4,1,0.355000,0.598125,0.287692,0.643077,1,0,1,0,0,0.582500,0.598125,0.567500,0.355000,0.287692,0.461538,0.643077,0.533846
-000b0f235dcf2caa,xclick,/m/0k4j,1,0.361875,0.459375,0.850769,0.938462,1,0,0,0,0,0.361875,0.421875,0.459375,0.459375,0.923077,0.850769,0.938462,0.938462
-000b0f235dcf2caa,xclick,/m/0k4j,1,0.445000,0.537500,0.833846,0.929231,1,0,0,0,0,0.445000,0.490000,0.537500,0.518125,0.863077,0.833846,0.920000,0.929231
-000b0f235dcf2caa,xclick,/m/0k4j,1,0.527500,0.586250,0.847692,0.950769,1,0,0,0,0,0.527500,0.561875,0.586250,0.554375,0.910769,0.847692,0.913846,0.950769
-000b0f235dcf2caa,xclick,/m/0k4j,1,0.729375,0.774375,0.856923,0.907692,1,0,0,0,0,0.729375,0.747500,0.774375,0.761875,0.875385,0.856923,0.890769,0.907692
-000b0f235dcf2caa,xclick,/m/0k4j,1,0.801250,0.883750,0.844615,0.998462,0,0,0,0,0,0.851875,0.883750,0.814375,0.801250,0.844615,0.923077,0.998462,0.932308
-000b0f5159f54105,xclick,/m/01g317,1,0.378750,0.405000,0.518889,0.617778,1,0,0,0,0,0.388125,0.378750,0.405000,0.384375,0.518889,0.553333,0.555556,0.617778
-000b0f5159f54105,xclick,/m/07j7r,1,0.000000,0.992500,0.000000,0.563333,1,1,1,0,0,0.000000,0.184375,0.992500,0.942500,0.236667,0.000000,0.316667,0.563333
-000b0f5159f54105,xclick,/m/07j7r,1,0.496875,0.593125,0.297778,0.526667,0,0,0,0,0,0.556875,0.496875,0.545625,0.593125,0.297778,0.412222,0.526667,0.415556
-000b168e791f591d,xclick,/m/09j5n,1,0.154784,0.274859,0.618750,0.658125,1,0,0,0,0,0.233583,0.154784,0.274859,0.261726,0.618750,0.656250,0.630625,0.658125
-000b168e791f591d,xclick,/m/09j5n,1,0.257974,0.321764,0.586250,0.632500,1,0,0,0,0,0.309568,0.278612,0.257974,0.321764,0.586250,0.632500,0.622500,0.595000
-000b168e791f591d,xclick,/m/09j5n,1,0.755159,0.801126,0.364375,0.391875,0,0,0,0,0,0.796435,0.788931,0.755159,0.801126,0.364375,0.390000,0.391875,0.376250
-000b168e791f591d,xclick,/m/09j5n,1,0.842402,0.907129,0.378125,0.398750,0,0,0,0,0,0.878049,0.873358,0.842402,0.907129,0.378125,0.398750,0.396875,0.391875
-000b168e791f591d,xclick,/m/09j5n,1,0.957786,0.978424,0.359375,0.381250,0,0,0,0,0,0.978424,0.975610,0.957786,0.978424,0.359375,0.381250,0.376250,0.374375
-000b1971d8daaeef,xclick,/m/01fdzj,1,0.155625,0.160625,0.389464,0.527752,0,0,0,0,0,0.160625,0.156875,0.160625,0.155625,0.389464,0.518344,0.506115,0.527752
-000b1971d8daaeef,xclick,/m/01fdzj,1,0.390625,0.441875,0.411101,0.515522,0,0,0,0,0,0.413750,0.441875,0.422500,0.390625,0.411101,0.493885,0.515522,0.471308
-000b1971d8daaeef,xclick,/m/01fdzj,1,0.443125,0.466250,0.476952,0.550329,0,0,0,0,0,0.463750,0.466250,0.456875,0.443125,0.476952,0.532455,0.550329,0.517404
-000b1971d8daaeef,xclick,/m/01fdzj,1,0.470000,0.500625,0.495767,0.569144,0,0,0,0,0,0.490625,0.500625,0.493125,0.470000,0.495767,0.539040,0.569144,0.546566
-000b1971d8daaeef,xclick,/m/01fdzj,1,0.493125,0.521250,0.495767,0.572907,0,0,0,0,0,0.499375,0.521250,0.506875,0.493125,0.495767,0.544685,0.572907,0.548448
-000b1971d8daaeef,xclick,/m/01fdzj,1,0.515000,0.537500,0.436500,0.548448,0,0,0,0,0,0.522500,0.515000,0.526250,0.537500,0.436500,0.490122,0.548448,0.523048
-000b1971d8daaeef,xclick,/m/01fdzj,1,0.532500,0.577500,0.346190,0.536218,0,0,0,0,0,0.551875,0.577500,0.560625,0.532500,0.346190,0.461900,0.536218,0.425212
-000b1971d8daaeef,xclick,/m/01fdzj,1,0.593125,0.617500,0.444026,0.567262,0,0,0,0,0,0.598125,0.617500,0.609375,0.593125,0.444026,0.530574,0.567262,0.532455
-000b1971d8daaeef,xclick,/m/01fdzj,1,0.611875,0.704375,0.423330,0.569144,0,0,0,0,0,0.655625,0.704375,0.655625,0.611875,0.423330,0.478833,0.569144,0.495767
-000b1971d8daaeef,xclick,/m/01fdzj,1,0.704375,0.771250,0.399812,0.493885,0,0,0,0,0,0.735000,0.704375,0.728750,0.771250,0.399812,0.436500,0.493885,0.463782
-000b1971d8daaeef,xclick,/m/01fdzj,1,0.771250,0.845000,0.409219,0.542803,0,0,0,0,0,0.771250,0.843750,0.845000,0.796875,0.409219,0.414864,0.542803,0.542803
-000b1971d8daaeef,xclick,/m/01fdzj,1,0.814375,0.826250,0.381938,0.528692,0,0,0,0,0,0.814375,0.825000,0.826250,0.814375,0.381938,0.471308,0.528692,0.471308
-000b1971d8daaeef,xclick,/m/01fdzj,1,0.845000,0.887500,0.376294,0.539040,0,0,0,0,0,0.861875,0.887500,0.880000,0.845000,0.376294,0.471308,0.539040,0.480715
-000b1971d8daaeef,xclick,/m/033rq4,1,0.219375,0.245000,0.479774,0.642521,1,0,0,0,0,0.241250,0.219375,0.233750,0.245000,0.642521,0.493885,0.479774,0.488241
-000b1971d8daaeef,xclick,/m/033rq4,1,0.478750,0.498125,0.419567,0.659454,1,0,0,0,0,0.490625,0.498125,0.478750,0.486250,0.419567,0.425212,0.432738,0.659454
-000b1971d8daaeef,xclick,/m/033rq4,1,0.925625,0.958750,0.310442,0.653810,1,0,0,0,0,0.950000,0.925625,0.940625,0.958750,0.653810,0.341486,0.310442,0.341486
-000b1971d8daaeef,xclick,/m/079cl,1,0.390625,0.438125,0.429915,0.508937,1,0,0,0,0,0.416250,0.390625,0.438125,0.438125,0.429915,0.457197,0.508937,0.508937
-000b1971d8daaeef,xclick,/m/079cl,1,0.535000,0.565000,0.359360,0.558796,0,0,0,0,0,0.555625,0.545625,0.535000,0.565000,0.359360,0.558796,0.458137,0.463782
-000b1971d8daaeef,xclick,/m/079cl,1,0.840000,0.882500,0.387582,0.549389,1,0,0,0,0,0.860625,0.840000,0.850625,0.882500,0.387582,0.497648,0.549389,0.491063
-000b1b3b85edd850,xclick,/m/032b3c,1,0.153750,0.368750,0.439962,0.876173,1,0,0,0,0,0.258750,0.153750,0.273750,0.368750,0.439962,0.691370,0.876173,0.722326
-000b1b3b85edd850,xclick,/m/04yx4,1,0.315625,0.561250,0.129456,0.998124,1,1,0,0,0,0.433750,0.315625,0.373125,0.561250,0.129456,0.469981,0.998124,0.553471
-000b1b3b85edd850,xclick,/m/07j7r,1,0.000000,0.746250,0.000000,0.962477,1,1,1,0,0,0.055625,0.000000,0.183125,0.746250,0.962477,0.672608,0.000000,0.034709
-000b1b3b85edd850,xclick,/m/07j7r,1,0.740625,0.999375,0.314259,0.397749,1,1,1,0,0,0.789375,0.779375,0.999375,0.740625,0.397749,0.314259,0.352720,0.341463
-000b1b3b85edd850,xclick,/m/07j7r,1,0.930625,0.999375,0.000000,0.166979,1,1,0,0,0,0.930625,0.938125,0.999375,0.993125,0.022514,0.000000,0.037523,0.166979
-000b1b92f0800e94,xclick,/m/01bfm9,1,0.352500,0.468750,0.611667,0.781667,1,0,0,0,0,0.461875,0.362500,0.352500,0.468750,0.611667,0.781667,0.781667,0.665000
-000b1b92f0800e94,xclick,/m/01bfm9,1,0.569375,0.696875,0.569167,0.755000,1,0,0,0,0,0.588125,0.569375,0.696875,0.663125,0.569167,0.735833,0.595833,0.755000
-000b1b92f0800e94,xclick,/m/01n4qj,1,0.770000,0.897500,0.480000,0.627500,1,0,0,0,0,0.826250,0.897500,0.770000,0.786875,0.480000,0.539167,0.575000,0.627500
-000b1b92f0800e94,xclick,/m/02p0tk3,1,0.328125,0.585625,0.232500,0.999167,0,0,0,0,0,0.430000,0.585625,0.328125,0.381875,0.232500,0.433333,0.655000,0.999167
-000b1b92f0800e94,xclick,/m/02p0tk3,1,0.460625,0.592500,0.312500,0.995833,1,0,0,0,0,0.531875,0.460625,0.540625,0.592500,0.312500,0.574167,0.995833,0.462500
-000b1b92f0800e94,xclick,/m/02p0tk3,1,0.562500,0.724375,0.224167,0.995833,1,0,0,0,0,0.620625,0.571875,0.562500,0.724375,0.224167,0.725833,0.995833,0.534167
-000b1b92f0800e94,xclick,/m/02p0tk3,1,0.769375,0.902500,0.408333,0.787500,1,0,0,0,0,0.841875,0.769375,0.790000,0.902500,0.408333,0.565833,0.787500,0.543333
-000b1b92f0800e94,xclick,/m/02p0tk3,1,0.928125,0.998125,0.441667,0.572500,1,0,0,0,0,0.963125,0.928125,0.943750,0.998125,0.441667,0.501667,0.572500,0.520833
-000b1b92f0800e94,xclick,/m/035r7c,1,0.336875,0.414375,0.593333,0.995833,1,1,0,0,0,0.355000,0.414375,0.380625,0.336875,0.593333,0.670833,0.995833,0.956667
-000b1b92f0800e94,xclick,/m/035r7c,1,0.385625,0.456875,0.629167,0.995833,1,1,0,0,0,0.456875,0.385625,0.443750,0.442500,0.629167,0.935833,0.965000,0.995833
-000b1b92f0800e94,xclick,/m/035r7c,1,0.470000,0.533125,0.680833,0.958333,1,1,0,0,0,0.501875,0.470000,0.533125,0.516250,0.680833,0.729167,0.892500,0.958333
-000b1b92f0800e94,xclick,/m/035r7c,1,0.511250,0.575625,0.685833,0.999167,1,1,0,0,0,0.547500,0.511250,0.575625,0.550000,0.685833,0.763333,0.732500,0.999167
-000b1b92f0800e94,xclick,/m/035r7c,1,0.559375,0.630625,0.573333,0.999167,1,1,0,0,0,0.585000,0.630625,0.559375,0.583750,0.573333,0.676667,0.978333,0.999167
-000b1b92f0800e94,xclick,/m/035r7c,1,0.637500,0.709375,0.585833,0.991667,1,1,0,0,0,0.637500,0.693750,0.646875,0.709375,0.655000,0.585833,0.991667,0.632500
-000b1b92f0800e94,xclick,/m/035r7c,1,0.775625,0.832500,0.603333,0.774167,1,1,0,0,0,0.804375,0.775625,0.832500,0.792500,0.603333,0.635833,0.627500,0.774167
-000b1b92f0800e94,xclick,/m/04yx4,1,0.332500,0.592500,0.235000,0.999167,1,1,0,0,0,0.332500,0.441250,0.379375,0.592500,0.445000,0.235000,0.999167,0.419167
-000b1b92f0800e94,xclick,/m/04yx4,1,0.566875,0.724375,0.230833,0.999167,1,1,0,0,0,0.623125,0.724375,0.573750,0.566875,0.230833,0.630000,0.999167,0.421667
-000b1b92f0800e94,xclick,/m/04yx4,1,0.770625,0.908750,0.411667,0.795833,1,0,0,0,0,0.770625,0.851875,0.786250,0.908750,0.565000,0.411667,0.795833,0.539167
-000b1b92f0800e94,xclick,/m/04yx4,1,0.786250,0.841875,0.440000,0.500000,1,0,0,0,0,0.786250,0.815625,0.804375,0.841875,0.493333,0.440000,0.500000,0.482500
-000b1b92f0800e94,xclick,/m/07mhn,1,0.466250,0.568750,0.692500,0.928333,1,0,0,0,0,0.515625,0.466250,0.568750,0.549375,0.692500,0.708333,0.819167,0.928333
-000b1b92f0800e94,xclick,/m/07mhn,1,0.786250,0.851875,0.616667,0.761667,1,0,0,0,0,0.851875,0.816875,0.786250,0.801250,0.650000,0.616667,0.670000,0.761667
-000b1b92f0800e94,xclick,/m/09j5n,1,0.334375,0.390000,0.909167,0.999167,1,0,0,0,0,0.373125,0.334375,0.360000,0.390000,0.909167,0.999167,0.999167,0.995000
-000b1b92f0800e94,xclick,/m/09j5n,1,0.380625,0.446250,0.902500,0.999167,1,0,0,0,0,0.409375,0.380625,0.426875,0.446250,0.902500,0.933333,0.999167,0.986667
-000b1b92f0800e94,xclick,/m/09j5n,1,0.491875,0.530625,0.880000,0.954167,1,0,0,0,0,0.521250,0.491875,0.515000,0.530625,0.880000,0.940000,0.954167,0.900833
-000b1b92f0800e94,xclick,/m/09j5n,1,0.516250,0.560000,0.923333,0.999167,1,0,0,0,0,0.558750,0.516250,0.538125,0.560000,0.923333,0.995000,0.999167,0.981667
-000b1b92f0800e94,xclick,/m/09j5n,1,0.558750,0.618125,0.912500,0.999167,1,0,0,0,0,0.593750,0.558750,0.573125,0.618125,0.912500,0.991667,0.999167,0.933333
-000b1b92f0800e94,xclick,/m/09j5n,1,0.635625,0.676250,0.883333,0.990833,1,0,0,0,0,0.651250,0.648750,0.676250,0.635625,0.990833,0.883333,0.954167,0.926667
-000b1b92f0800e94,xclick,/m/09j5n,1,0.773125,0.818750,0.747500,0.788333,1,0,0,0,0,0.793750,0.773125,0.796875,0.818750,0.747500,0.775000,0.788333,0.750833
-000b1b92f0800e94,xclick,/m/09j5n,1,0.828750,0.885625,0.720000,0.786667,1,0,0,0,0,0.858750,0.828750,0.863750,0.885625,0.720000,0.747500,0.786667,0.773333
-000b21663becc68e,xclick,/m/01g317,1,0.180625,0.884375,0.428983,0.854362,1,1,0,1,0,0.884375,0.180625,0.233125,0.671250,0.666907,0.428983,0.854362,0.808219
-000b21663becc68e,xclick,/m/025dyy,1,0.030000,0.966875,0.084355,0.957462,0,0,0,0,0,0.733750,0.030000,0.079375,0.966875,0.084355,0.868061,0.957462,0.849315
-000b21663becc68e,xclick,/m/0k65p,1,0.381250,0.481875,0.615717,0.749820,1,0,0,0,0,0.422500,0.481875,0.381250,0.409375,0.615717,0.691420,0.696467,0.749820
-000b260e1f08a32a,xclick,/m/01fdzj,1,0.064375,0.873750,0.025000,0.999167,0,1,0,0,0,0.064375,0.676250,0.860000,0.873750,0.999167,0.025000,0.999167,0.987500
-000b260e1f08a32a,xclick,/m/0d4v4,1,0.150000,0.183750,0.955000,0.999167,0,1,0,0,0,0.179375,0.150000,0.163125,0.183750,0.955000,0.999167,0.999167,0.958333
-000b260e1f08a32a,xclick,/m/0d4v4,1,0.303750,0.361875,0.906667,0.999167,0,1,0,0,0,0.361875,0.303750,0.358125,0.361875,0.906667,0.999167,0.999167,0.999167
-000b260e1f08a32a,xclick,/m/0d4v4,1,0.342500,0.381250,0.459167,0.559167,0,0,0,0,0,0.380000,0.342500,0.343750,0.381250,0.459167,0.541667,0.559167,0.462500
-000b260e1f08a32a,xclick,/m/0d4v4,1,0.443125,0.501250,0.835000,0.999167,0,1,0,0,0,0.500000,0.443125,0.500000,0.501250,0.835000,0.999167,0.999167,0.999167
-000b260e1f08a32a,xclick,/m/0d4v4,1,0.541250,0.600625,0.796667,0.999167,0,1,0,0,0,0.600625,0.541250,0.596875,0.596875,0.796667,0.999167,0.998333,0.998333
-000b260e1f08a32a,xclick,/m/0d4v4,1,0.564375,0.598125,0.520833,0.690000,0,0,0,0,0,0.591875,0.564375,0.568750,0.598125,0.520833,0.596667,0.690000,0.641667
-000b260e1f08a32a,xclick,/m/0d4v4,1,0.600625,0.625000,0.321667,0.431667,0,0,0,0,0,0.621250,0.600625,0.625000,0.607500,0.321667,0.355833,0.421667,0.431667
-000b29496f75c8e5,xclick,/m/01g317,1,0.335648,0.756944,0.109694,0.964286,0,0,0,1,0,0.354167,0.756944,0.625000,0.335648,0.109694,0.301020,0.964286,0.109694
-000b29496f75c8e5,xclick,/m/06msq,1,0.349537,0.759259,0.119898,0.938776,1,0,0,0,0,0.349537,0.349537,0.759259,0.622685,0.119898,0.122449,0.306122,0.938776
-000b299b5f5ed902,xclick,/m/01g317,1,0.000000,0.999280,0.255319,0.999149,1,1,1,0,0,0.455331,0.999280,0.872478,0.000000,0.255319,0.628936,0.999149,0.665532
-000b299b5f5ed902,xclick,/m/01g317,1,0.514409,0.928674,0.508936,0.999149,0,0,0,0,0,0.596542,0.514409,0.883285,0.928674,0.508936,0.609362,0.999149,0.757447
-000b299b5f5ed902,xclick,/m/04rky,1,0.000000,0.051873,0.363404,0.828085,1,1,0,0,0,0.006484,0.051873,0.007925,0.000000,0.363404,0.434043,0.828085,0.713191
-000b299b5f5ed902,xclick,/m/04rky,1,0.000000,0.152017,0.296170,0.561702,1,1,0,0,0,0.120317,0.152017,0.049712,0.000000,0.296170,0.337021,0.561702,0.430638
-000b299b5f5ed902,xclick,/m/04rky,1,0.000000,0.216138,0.797447,0.995745,1,1,0,0,0,0.096542,0.000000,0.032421,0.216138,0.797447,0.891915,0.995745,0.967660
-000b299b5f5ed902,xclick,/m/04rky,1,0.123199,0.312680,0.309787,0.628936,1,0,0,0,0,0.213256,0.123199,0.304035,0.312680,0.309787,0.538723,0.628936,0.578723
-000b299b5f5ed902,xclick,/m/04rky,1,0.197406,0.245677,0.908085,0.999149,1,1,0,0,0,0.217579,0.197406,0.217579,0.245677,0.908085,0.940426,0.999149,0.999149
-000b299b5f5ed902,xclick,/m/04rky,1,0.291066,0.623199,0.295319,0.999149,1,0,0,0,0,0.435879,0.291066,0.364553,0.623199,0.295319,0.514043,0.999149,0.517447
-000b299b5f5ed902,xclick,/m/04rky,1,0.298271,0.396974,0.402553,0.630638,1,0,0,0,0,0.332853,0.396974,0.346542,0.298271,0.402553,0.494468,0.630638,0.492766
-000b299b5f5ed902,xclick,/m/04rky,1,0.392651,0.652738,0.602553,0.926809,1,0,0,0,0,0.608790,0.652738,0.452450,0.392651,0.602553,0.635745,0.926809,0.859574
-000b299b5f5ed902,xclick,/m/04rky,1,0.516571,0.922190,0.501277,0.999149,1,1,0,0,0,0.799712,0.922190,0.871758,0.516571,0.501277,0.783830,0.999149,0.575319
-000b299b5f5ed902,xclick,/m/04rky,1,0.701009,0.930115,0.375319,0.673191,1,0,0,0,0,0.839337,0.930115,0.894813,0.701009,0.375319,0.560000,0.673191,0.590638
-000b299b5f5ed902,xclick,/m/04rky,1,0.896254,0.999280,0.308085,0.866383,1,1,0,0,0,0.998559,0.896254,0.979107,0.999280,0.308085,0.669787,0.866383,0.787234
-000b2a982a903d0d,xclick,/m/01lrl,1,0.000000,0.717188,0.000000,0.998438,1,1,0,0,0,0.717188,0.434375,0.000000,0.337500,0.589063,0.998438,0.317188,0.000000
-000b2a982a903d0d,xclick,/m/04hgtk,1,0.362500,0.632812,0.717188,0.970312,0,0,0,1,0,0.393750,0.362500,0.554688,0.632812,0.770312,0.845313,0.970312,0.717188
-000b2a982a903d0d,xclick,/m/0f6wt,1,0.075000,0.709375,0.114063,0.996875,0,1,0,1,0,0.075000,0.332813,0.709375,0.532813,0.226562,0.114063,0.757812,0.996875
-000b2b00065e564a,xclick,/m/01n4qj,1,0.210173,0.349328,0.316875,0.469375,1,0,0,0,0,0.225528,0.210173,0.262956,0.349328,0.316875,0.328750,0.469375,0.423125
-000b2b00065e564a,xclick,/m/01n4qj,1,0.789827,0.904031,0.339375,0.531250,1,0,0,0,0,0.899232,0.904031,0.789827,0.789827,0.339375,0.344375,0.531250,0.531250
-000b2b00065e564a,xclick,/m/01rkbr,1,0.274472,0.342610,0.390625,0.618125,1,0,0,0,0,0.274472,0.310940,0.310940,0.342610,0.418125,0.390625,0.618125,0.486250
-000b2b00065e564a,xclick,/m/01rkbr,1,0.778311,0.840691,0.397500,0.647500,1,0,0,0,0,0.824376,0.787908,0.840691,0.778311,0.397500,0.542500,0.518125,0.647500
-000b2b00065e564a,xclick,/m/01xyhv,1,0.000960,0.421305,0.313750,0.694375,1,1,0,0,0,0.207294,0.304223,0.000960,0.421305,0.313750,0.694375,0.676250,0.538125
-000b2b00065e564a,xclick,/m/01xyhv,1,0.679463,0.999040,0.341250,0.653750,1,1,0,0,0,0.900192,0.679463,0.999040,0.695777,0.341250,0.624375,0.595625,0.653750
-000b2b00065e564a,xclick,/m/04yx4,1,0.000000,0.466411,0.188125,0.694375,0,1,0,0,0,0.301344,0.466411,0.014395,0.000000,0.188125,0.679375,0.694375,0.541875
-000b2b00065e564a,xclick,/m/04yx4,1,0.686180,0.996161,0.202500,0.658750,1,1,0,0,0,0.822457,0.686180,0.996161,0.917466,0.202500,0.628125,0.539375,0.658750
-000b2d1789d5f80d,xclick,/m/01fdzj,1,0.111250,0.168750,0.714958,0.936971,0,0,0,0,0,0.138125,0.111250,0.111250,0.168750,0.714958,0.936971,0.936971,0.761994
-000b2d1789d5f80d,xclick,/m/01fdzj,1,0.573750,0.712500,0.000000,0.290687,0,0,0,0,0,0.644375,0.573750,0.573750,0.712500,0.000000,0.290687,0.290687,0.281279
-000b2d1789d5f80d,xclick,/m/0d4v4,1,0.506875,0.526250,0.400753,0.437441,0,0,0,0,0,0.506875,0.506875,0.523750,0.526250,0.406397,0.437441,0.431797,0.400753
-000b2d1789d5f80d,xclick,/m/0d4v4,1,0.510000,0.528750,0.457197,0.510818,0,0,0,0,0,0.516250,0.510000,0.528750,0.511250,0.457197,0.508937,0.510818,0.466604
-000b2d1789d5f80d,xclick,/m/0d4v4,1,0.595625,0.615000,0.347131,0.398871,0,0,0,0,0,0.595625,0.595625,0.612500,0.615000,0.352775,0.395108,0.398871,0.347131
-000b2d1789d5f80d,xclick,/m/0d4v4,1,0.598125,0.615000,0.452493,0.501411,0,0,0,0,0,0.598125,0.599375,0.615000,0.615000,0.452493,0.501411,0.499530,0.452493
-000b2d1789d5f80d,xclick,/m/0d4v4,1,0.622500,0.636875,0.111947,0.136406,0,0,0,0,0,0.622500,0.623750,0.635000,0.636875,0.111947,0.134525,0.136406,0.111947
-000b2d1789d5f80d,xclick,/m/0d4v4,1,0.645625,0.667500,0.345249,0.395108,0,0,0,0,0,0.645625,0.645625,0.667500,0.666250,0.349012,0.395108,0.393227,0.345249
-000b2d1789d5f80d,xclick,/m/0d4v4,1,0.645625,0.667500,0.443086,0.497648,0,0,0,0,0,0.646875,0.645625,0.667500,0.666250,0.443086,0.497648,0.495767,0.448730
-000b2d1789d5f80d,xclick,/m/0d4v4,1,0.696875,0.722500,0.336783,0.393227,0,0,0,0,0,0.700625,0.696875,0.722500,0.711875,0.343368,0.393227,0.391345,0.336783
-000b2d1789d5f80d,xclick,/m/0d4v4,1,0.701875,0.717500,0.437441,0.491063,0,0,0,0,0,0.701875,0.701875,0.717500,0.717500,0.441204,0.491063,0.491063,0.437441
-000b2d1789d5f80d,xclick,/m/0d4v4,1,0.756875,0.776250,0.433678,0.489182,0,0,0,0,0,0.756875,0.756875,0.776250,0.773750,0.439323,0.489182,0.489182,0.433678
-000b2d1789d5f80d,xclick,/m/0d4v4,1,0.756875,0.777500,0.339605,0.385701,0,0,0,0,0,0.758125,0.756875,0.777500,0.771250,0.341486,0.385701,0.381938,0.339605
-000b2d1789d5f80d,xclick,/m/0d4v4,1,0.803125,0.823750,0.334901,0.383819,0,0,0,0,0,0.803125,0.803125,0.823750,0.823750,0.334901,0.383819,0.381938,0.336783
-000b2d1789d5f80d,xclick,/m/0d4v4,1,0.805625,0.823750,0.433678,0.483537,0,0,0,0,0,0.806875,0.805625,0.823750,0.823750,0.433678,0.483537,0.483537,0.433678
-000b2d1789d5f80d,xclick,/m/0d4v4,1,0.858125,0.880000,0.428034,0.483537,0,0,0,0,0,0.858125,0.859375,0.877500,0.880000,0.431797,0.483537,0.481656,0.428034
-000b2d1789d5f80d,xclick,/m/0d4v4,1,0.860000,0.875000,0.335842,0.381938,0,0,0,0,0,0.860000,0.873750,0.875000,0.860000,0.339605,0.335842,0.378175,0.381938
-000b2d1789d5f80d,xclick,/m/0d5gx,1,0.101250,0.979375,0.002822,0.997178,0,0,0,0,0,0.645000,0.101250,0.979375,0.710625,0.002822,0.857949,0.745061,0.997178
-000b38d9f2f664fe,xclick,/m/05s2s,1,0.103750,0.790000,0.000000,0.999167,1,1,0,0,0,0.103750,0.498750,0.790000,0.680625,0.672500,0.000000,0.251667,0.999167
-000b38d9f2f664fe,xclick,/m/05s2s,1,0.215625,0.781250,0.000000,0.527500,1,1,0,0,0,0.518750,0.215625,0.399375,0.781250,0.000000,0.260833,0.527500,0.249167
-000b393437134262,xclick,/m/0k4j,1,0.111250,0.901250,0.090056,0.939024,0,0,0,0,0,0.111250,0.494375,0.901250,0.708125,0.640713,0.939024,0.428705,0.090056
-000b393437134262,xclick,/m/0k4j,1,0.898750,0.927500,0.232645,0.271107,0,0,0,0,0,0.909375,0.927500,0.898750,0.918125,0.232645,0.247655,0.269231,0.271107
-000b393437134262,xclick,/m/0k4j,1,0.904375,0.973125,0.188555,0.382739,0,0,0,0,0,0.968125,0.904375,0.930000,0.973125,0.190432,0.342402,0.382739,0.188555
-000b3940e7d25c03,xclick,/m/020jm,1,0.228277,0.600884,0.182711,0.646365,0,0,0,0,0,0.228277,0.469809,0.600884,0.244477,0.182711,0.646365,0.565815,0.624754
-000b3940e7d25c03,xclick,/m/020jm,1,0.480118,0.880707,0.000000,0.695481,1,1,0,0,0,0.680412,0.480118,0.880707,0.620029,0.000000,0.245580,0.204322,0.695481
-000b3940e7d25c03,xclick,/m/04rky,1,0.223859,0.642121,0.135560,0.638507,1,0,0,0,0,0.229750,0.223859,0.642121,0.539028,0.135560,0.638507,0.632613,0.286837
-000b3940e7d25c03,xclick,/m/0c9ph5,1,0.379971,0.556701,0.208251,0.459725,0,0,0,0,0,0.421208,0.379971,0.556701,0.536082,0.208251,0.326130,0.312377,0.459725
-000b3940e7d25c03,xclick,/m/0c9ph5,1,0.440353,0.553756,0.090373,0.265226,1,0,0,0,0,0.477172,0.440353,0.509573,0.553756,0.090373,0.125737,0.265226,0.186640
-000b397382b2464a,xclick,/m/05s2s,1,0.000000,0.894375,0.474874,0.937186,1,1,1,0,0,0.000000,0.643750,0.894375,0.816250,0.823283,0.474874,0.773032,0.937186
-000b397382b2464a,xclick,/m/05s2s,1,0.261875,0.298125,0.952261,0.999162,1,0,0,0,0,0.293125,0.261875,0.298125,0.287500,0.952261,0.969849,0.994975,0.999162
-000b397382b2464a,xclick,/m/05s2s,1,0.301875,0.326250,0.909548,0.940536,1,0,0,0,0,0.322500,0.301875,0.326250,0.323750,0.909548,0.929648,0.935511,0.940536
-000b397382b2464a,xclick,/m/05s2s,1,0.373750,0.405000,0.906198,0.966499,1,0,0,0,0,0.396875,0.373750,0.405000,0.393125,0.906198,0.926298,0.940536,0.966499
-000b42cae15622e0,xclick,/m/02dgv,1,0.643125,0.846250,0.455000,0.875833,1,0,0,0,0,0.643125,0.846250,0.820625,0.650000,0.455000,0.875833,0.520833,0.629167
-000b42cae15622e0,xclick,/m/03jm5,1,0.061250,0.928125,0.000000,0.999167,0,0,0,0,0,0.696875,0.061250,0.211250,0.928125,0.000000,0.629167,0.999167,0.195000
-000b42cae15622e0,xclick,/m/07j7r,1,0.000000,0.114375,0.229167,0.954167,1,1,0,0,0,0.021875,0.114375,0.000000,0.000000,0.229167,0.815833,0.928333,0.954167
-000b42cae15622e0,xclick,/m/07j7r,1,0.000000,0.184375,0.000000,0.260000,0,1,0,0,0,0.011250,0.000000,0.184375,0.184375,0.260000,0.000000,0.000000,0.000000
-000b42cae15622e0,xclick,/m/07j7r,1,0.148125,0.785000,0.394167,0.999167,0,1,0,0,0,0.148125,0.456250,0.785000,0.463750,0.968333,0.394167,0.817500,0.999167
-000b42cae15622e0,xclick,/m/07j7r,1,0.189375,0.580625,0.000000,0.318333,0,1,0,0,0,0.269375,0.580625,0.190625,0.189375,0.318333,0.000000,0.000000,0.000000
-000b42cae15622e0,xclick,/m/07j7r,1,0.740000,0.999375,0.308333,0.999167,0,1,0,0,0,0.966875,0.740000,0.999375,0.999375,0.308333,0.699167,0.999167,0.999167
-000b42cae15622e0,xclick,/m/07j7r,1,0.853125,0.999375,0.000000,0.394167,0,1,0,0,0,0.853125,0.998125,0.999375,0.999375,0.194167,0.000000,0.368333,0.394167
-000b42cae15622e0,xclick,/m/0d4v4,1,0.110000,0.147500,0.734167,0.897500,1,0,0,0,0,0.126875,0.147500,0.134375,0.110000,0.748333,0.734167,0.885833,0.897500
-000b42cae15622e0,xclick,/m/0d4v4,1,0.643125,0.839375,0.443333,0.794167,1,0,0,0,0,0.643125,0.816250,0.839375,0.644375,0.443333,0.534167,0.794167,0.579167
-000b42cae15622e0,xclick,/m/0d4v4,1,0.757500,0.827500,0.162500,0.287500,0,0,0,0,0,0.757500,0.820000,0.769375,0.827500,0.162500,0.213333,0.256667,0.287500
-000b432ae644b679,xclick,/m/017ftj,1,0.440263,0.552211,0.230552,0.304102,1,0,0,0,0,0.533396,0.552211,0.440263,0.463782,0.304102,0.256011,0.241867,0.230552
-000b432ae644b679,xclick,/m/01xygc,1,0.014111,0.143932,0.380481,0.700141,0,0,0,0,0,0.062088,0.014111,0.062088,0.143932,0.380481,0.489392,0.700141,0.553041
-000b432ae644b679,xclick,/m/01xygc,1,0.273754,0.387582,0.321075,0.678925,1,0,0,0,0,0.329257,0.273754,0.352775,0.387582,0.321075,0.408769,0.678925,0.322489
-000b432ae644b679,xclick,/m/032b3c,1,0.016933,0.140169,0.374823,0.697313,0,0,0,0,0,0.049859,0.016933,0.061148,0.140169,0.374823,0.480905,0.697313,0.540311
-000b432ae644b679,xclick,/m/032b3c,1,0.216369,0.307620,0.330976,0.462518,1,0,0,0,0,0.242709,0.216369,0.262465,0.307620,0.330976,0.356436,0.462518,0.333805
-000b432ae644b679,xclick,/m/032b3c,1,0.338664,0.602070,0.343706,0.998586,1,1,0,0,0,0.406397,0.338664,0.483537,0.602070,0.343706,0.557284,0.998586,0.966054
-000b432ae644b679,xclick,/m/032b3c,1,0.552211,0.841016,0.294201,0.710042,1,0,0,0,0,0.603010,0.552211,0.841016,0.705550,0.294201,0.519095,0.482320,0.710042
-000b432ae644b679,xclick,/m/0fly7,1,0.060207,0.171214,0.666195,0.981612,0,0,0,0,0,0.132643,0.140169,0.171214,0.060207,0.666195,0.981612,0.895332,0.701556
-000b432ae644b679,xclick,/m/0fly7,1,0.126058,0.218250,0.374823,0.507779,0,0,0,0,0,0.150517,0.218250,0.154280,0.126058,0.374823,0.374823,0.507779,0.408769
-000b432ae644b679,xclick,/m/0jyfg,1,0.499530,0.550329,0.244696,0.304102,1,0,0,0,0,0.507056,0.538100,0.499530,0.550329,0.250354,0.304102,0.244696,0.270156
-000b4671075914cd,xclick,/m/01xyhv,1,0.331075,0.779848,0.396442,0.768742,1,0,0,0,0,0.538527,0.757832,0.779848,0.331075,0.396442,0.768742,0.743329,0.612452
-000b4671075914cd,xclick,/m/01xyhv,1,0.753599,0.780694,0.327827,0.438374,1,0,0,0,0,0.762913,0.753599,0.780694,0.760373,0.327827,0.329098,0.351970,0.438374
-000b4671075914cd,xclick,/m/01xyhv,1,0.810330,0.838273,0.336722,0.420584,1,0,0,0,0,0.816257,0.810330,0.838273,0.818798,0.336722,0.343075,0.355781,0.420584
-000b4671075914cd,xclick,/m/021sj1,1,0.000847,0.999153,0.000000,0.998729,1,1,0,0,1,0.522439,0.000847,0.321761,0.999153,0.998729,0.471410,0.000000,0.569250
-000b485cedacbf97,xclick,/m/01yx86,1,0.123827,0.981238,0.066875,0.915000,0,0,0,0,0,0.981238,0.123827,0.455910,0.616323,0.655000,0.622500,0.066875,0.915000
-000b485cedacbf97,xclick,/m/04hgtk,1,0.445591,0.593809,0.188125,0.305625,0,0,0,1,0,0.516886,0.445591,0.593809,0.466229,0.188125,0.288125,0.238750,0.305625
-000b485cedacbf97,xclick,/m/04hgtk,1,0.812383,0.963415,0.609375,0.691250,0,0,0,1,0,0.914634,0.812383,0.901501,0.963415,0.609375,0.656875,0.691250,0.650000
-000b4935979bf4b5,xclick,/m/03g8mr,1,0.425781,0.498698,0.221790,0.430934,1,0,0,0,0,0.436849,0.492188,0.498698,0.425781,0.221790,0.430934,0.415370,0.233463
-000b4935979bf4b5,xclick,/m/03grzl,1,0.580078,0.598958,0.158560,0.193580,1,0,0,0,0,0.586589,0.580078,0.586589,0.598958,0.158560,0.176070,0.193580,0.181907
-000b4935979bf4b5,xclick,/m/04yx4,1,0.205729,0.460938,0.428016,0.959144,1,0,0,0,0,0.381510,0.205729,0.414714,0.460938,0.428016,0.903696,0.959144,0.939689
-000b4935979bf4b5,xclick,/m/04yx4,1,0.414714,0.641927,0.530156,0.943580,1,0,0,0,0,0.529948,0.641927,0.573568,0.414714,0.530156,0.897860,0.943580,0.850195
-000b4935979bf4b5,xclick,/m/04yx4,1,0.460938,0.660807,0.382296,0.813230,1,0,0,0,0,0.490234,0.460938,0.634115,0.660807,0.382296,0.603113,0.813230,0.483463
-000b4935979bf4b5,xclick,/m/04yx4,1,0.485026,0.590495,0.045720,0.213035,1,0,0,0,0,0.554036,0.485026,0.590495,0.493490,0.045720,0.187743,0.138132,0.213035
-000b4935979bf4b5,xclick,/m/09j5n,1,0.203125,0.239583,0.903696,0.922179,1,0,0,0,0,0.209635,0.218750,0.203125,0.239583,0.903696,0.922179,0.920233,0.920233
-000b4935979bf4b5,xclick,/m/09j5n,1,0.367839,0.452474,0.911479,0.959144,1,0,0,0,0,0.423828,0.432943,0.367839,0.452474,0.911479,0.959144,0.951362,0.945525
-000b4935979bf4b5,xclick,/m/09j5n,1,0.548177,0.591797,0.855058,0.949416,0,0,0,0,0,0.562500,0.548177,0.591797,0.580078,0.855058,0.884241,0.932879,0.949416
-000b4935979bf4b5,xclick,/m/09j5n,1,0.600911,0.641927,0.781128,0.815175,1,0,0,0,0,0.610026,0.615234,0.600911,0.641927,0.781128,0.815175,0.811284,0.797665
-000b4935979bf4b5,xclick,/m/0h8mhzd,1,0.201823,0.451823,0.472763,0.950389,1,0,0,0,0,0.367188,0.201823,0.451823,0.354167,0.472763,0.881323,0.894942,0.950389
-000b4935979bf4b5,xclick,/m/0h8mhzd,1,0.413411,0.643229,0.423152,0.852140,0,0,0,0,0,0.639323,0.643229,0.449870,0.413411,0.423152,0.577821,0.852140,0.781128
-000b4935979bf4b5,xclick,/m/0h8mhzd,1,0.427734,0.641927,0.601167,0.932879,1,0,0,0,0,0.541667,0.427734,0.595703,0.641927,0.601167,0.739300,0.932879,0.885214
-000b4935979bf4b5,xclick,/m/0h8mhzd,1,0.495443,0.595703,0.071012,0.203307,1,0,0,0,0,0.545573,0.495443,0.595703,0.585286,0.071012,0.201362,0.114786,0.203307
-000b4fcdf1af3361,xclick,/m/07j7r,1,0.000000,0.999167,0.307500,0.998750,1,1,1,0,0,0.548333,0.700833,0.999167,0.000000,0.998750,0.307500,0.472500,0.510000
-000b4fcdf1af3361,xclick,/m/07j7r,1,0.391667,0.468333,0.467500,0.628750,0,0,0,0,0,0.424167,0.468333,0.409167,0.391667,0.467500,0.555000,0.628750,0.537500
-000b50bdd1933a36,xclick,/m/06nwz,1,0.179375,0.989375,0.023333,0.866667,1,0,1,0,0,0.179375,0.399375,0.989375,0.893750,0.148333,0.023333,0.587500,0.866667
-000b50bdd1933a36,xclick,/m/0grw1,1,0.000000,0.999375,0.006667,0.999167,0,1,0,0,0,0.518125,0.000000,0.489375,0.999375,0.006667,0.465833,0.999167,0.492500
-000b55559b0244d7,xclick,/m/015h_t,1,0.727500,0.828125,0.256795,0.384255,0,0,0,0,0,0.818750,0.828125,0.768750,0.727500,0.256795,0.283974,0.384255,0.331771
-000b55559b0244d7,xclick,/m/02wbtzl,1,0.121250,0.260000,0.375820,0.491097,0,0,0,0,0,0.181875,0.121250,0.260000,0.240625,0.375820,0.487348,0.466729,0.491097
-000b55559b0244d7,xclick,/m/02wbtzl,1,0.338125,0.466250,0.253046,0.384255,0,0,0,0,0,0.388125,0.338125,0.466250,0.446875,0.253046,0.349578,0.357076,0.384255
-000b55559b0244d7,xclick,/m/02wbtzl,1,0.500625,0.670625,0.103093,0.243674,0,0,0,0,0,0.570000,0.670625,0.500625,0.523750,0.103093,0.214620,0.230553,0.243674
-000b55559b0244d7,xclick,/m/02wbtzl,1,0.700625,0.828750,0.113402,0.254920,0,0,0,0,0,0.776250,0.700625,0.828750,0.795625,0.113402,0.254920,0.192127,0.232427
-000b55559b0244d7,xclick,/m/03bt1vf,1,0.005000,0.281250,0.376757,0.999063,1,1,0,0,0,0.189375,0.005000,0.050625,0.281250,0.376757,0.999063,0.999063,0.753515
-000b55559b0244d7,xclick,/m/03bt1vf,1,0.045625,0.130000,0.386129,0.609185,1,0,0,0,0,0.078750,0.045625,0.077500,0.130000,0.386129,0.536082,0.609185,0.521087
-000b55559b0244d7,xclick,/m/03bt1vf,1,0.245625,0.518750,0.257732,0.999063,1,1,0,0,0,0.245625,0.372500,0.518750,0.403125,0.521087,0.999063,0.749766,0.257732
-000b55559b0244d7,xclick,/m/03bt1vf,1,0.465000,0.527500,0.328960,0.426429,1,0,0,0,0,0.488750,0.465000,0.478750,0.527500,0.328960,0.389878,0.426429,0.358013
-000b55559b0244d7,xclick,/m/0jyfg,1,0.071875,0.104375,0.442362,0.459231,0,0,0,0,0,0.082500,0.104375,0.071875,0.077500,0.442362,0.446111,0.446111,0.459231
-000b55559b0244d7,xclick,/m/0jyfg,1,0.715000,0.816875,0.231490,0.282099,1,0,0,0,0,0.787500,0.816875,0.715000,0.733125,0.237113,0.231490,0.249297,0.282099
-000b55e339f0b131,xclick,/m/019jd,1,0.549167,0.686667,0.650000,0.685556,0,0,0,0,0,0.615833,0.549167,0.685000,0.686667,0.685556,0.670000,0.667778,0.650000
-000b55e339f0b131,xclick,/m/0cdl1,1,0.000000,0.295000,0.000000,0.985556,0,1,0,0,0,0.117500,0.000000,0.295000,0.067500,0.000000,0.358889,0.407778,0.985556
-000b55e339f0b131,xclick,/m/0cdl1,1,0.302500,0.746667,0.000000,0.998889,0,1,0,0,0,0.451667,0.302500,0.746667,0.407500,0.000000,0.313333,0.376667,0.998889
-000b55e339f0b131,xclick,/m/0cdl1,1,0.755000,0.999167,0.000000,0.998889,0,1,0,0,0,0.872500,0.999167,0.755000,0.806667,0.000000,0.285556,0.288889,0.998889
-000b567c26dd4e5d,xclick,/m/01jfsr,1,0.345625,0.732500,0.076582,0.795782,0,0,0,0,0,0.345625,0.450625,0.732500,0.581250,0.429523,0.076582,0.397336,0.795782
-000b567c26dd4e5d,xclick,/m/05zsy,1,0.338750,0.772500,0.032186,0.806881,0,0,0,0,0,0.579375,0.772500,0.574375,0.338750,0.032186,0.534961,0.806881,0.386238
-000b59a7822679e6,xclick,/m/01g317,1,0.000000,0.999219,0.301042,0.618750,1,1,1,1,0,0.564844,0.000000,0.999219,0.999219,0.301042,0.472917,0.386458,0.618750
-000b59a7822679e6,xclick,/m/01g317,1,0.085156,0.180469,0.402083,0.646875,1,0,0,0,0,0.117969,0.085156,0.180469,0.172656,0.402083,0.612500,0.633333,0.646875
-000b59a7822679e6,xclick,/m/01g317,1,0.156250,0.651563,0.327083,0.952083,1,0,0,0,0,0.430469,0.651563,0.496875,0.156250,0.327083,0.616667,0.952083,0.682292
-000b59a7822679e6,xclick,/m/01g317,1,0.168750,0.313281,0.443750,0.941667,1,0,0,0,0,0.235156,0.168750,0.313281,0.267969,0.443750,0.797917,0.608333,0.941667
-000b59a7822679e6,xclick,/m/01g317,1,0.702344,0.835156,0.396875,0.837500,1,0,0,0,0,0.800000,0.702344,0.835156,0.744531,0.396875,0.498958,0.422917,0.837500
-000b59a7822679e6,xclick,/m/01g317,1,0.830469,0.975000,0.394792,0.846875,1,0,0,0,0,0.916406,0.830469,0.975000,0.882031,0.394792,0.607292,0.582292,0.846875
-000b59a7822679e6,xclick,/m/07c52,1,0.581250,0.650781,0.234375,0.327083,1,0,0,0,0,0.637500,0.650781,0.623437,0.581250,0.234375,0.283333,0.327083,0.264583
-000b59a7822679e6,xclick,/m/0dzf4,1,0.114063,0.189844,0.492708,0.652083,1,0,0,1,0,0.125781,0.114063,0.176563,0.189844,0.492708,0.596875,0.652083,0.626042
-000b59a7822679e6,xclick,/m/0dzf4,1,0.157813,0.365625,0.364583,0.854167,1,0,0,1,0,0.358594,0.157813,0.246094,0.365625,0.364583,0.702083,0.854167,0.382292
-000b59a7822679e6,xclick,/m/0dzf4,1,0.193750,0.217969,0.523958,0.577083,1,0,0,1,0,0.211719,0.193750,0.207813,0.217969,0.523958,0.555208,0.577083,0.553125
-000b59a7822679e6,xclick,/m/0dzf4,1,0.492969,0.653125,0.469792,0.742708,1,0,0,1,0,0.522656,0.653125,0.629687,0.492969,0.469792,0.617708,0.742708,0.545833
-000b59a7822679e6,xclick,/m/0dzf4,1,0.697656,0.760938,0.480208,0.565625,1,0,0,1,0,0.721094,0.697656,0.737500,0.760938,0.480208,0.498958,0.565625,0.544792
-000b59a7822679e6,xclick,/m/0dzf4,1,0.736719,0.809375,0.501042,0.631250,1,0,0,1,0,0.783594,0.736719,0.741406,0.809375,0.501042,0.545833,0.631250,0.563542
-000b59a7822679e6,xclick,/m/0dzf4,1,0.818750,0.914844,0.518750,0.655208,1,0,0,1,0,0.903125,0.818750,0.877344,0.914844,0.518750,0.565625,0.655208,0.633333
-000b5bc07c0c5df7,xclick,/m/014sv8,1,0.511250,0.564375,0.253333,0.306667,0,0,0,0,0,0.535625,0.530625,0.511250,0.564375,0.253333,0.306667,0.271667,0.265000
-000b5bc07c0c5df7,xclick,/m/014sv8,1,0.599375,0.659375,0.249167,0.305833,0,0,0,0,0,0.621250,0.599375,0.659375,0.631875,0.249167,0.271667,0.281667,0.305833
-000b5bc07c0c5df7,xclick,/m/015h_t,1,0.521250,0.740625,0.327500,0.544167,0,0,0,0,0,0.710000,0.740625,0.582500,0.521250,0.327500,0.362500,0.544167,0.425000
-000b5bc07c0c5df7,xclick,/m/01n4qj,1,0.423125,0.999375,0.450833,0.996667,1,1,0,0,0,0.751875,0.999375,0.423125,0.681875,0.450833,0.775000,0.969167,0.996667
-000b5bc07c0c5df7,xclick,/m/03q69,1,0.525625,0.811250,0.056667,0.474167,0,0,0,0,0,0.650000,0.525625,0.811250,0.733750,0.056667,0.146667,0.220833,0.474167
-000b5bc07c0c5df7,xclick,/m/04hgtk,1,0.513750,0.806250,0.060000,0.530000,0,0,0,0,0,0.658125,0.806250,0.598125,0.513750,0.060000,0.226667,0.530000,0.260833
-000b5bc07c0c5df7,xclick,/m/04yx4,1,0.422500,0.999375,0.055000,0.999167,0,1,0,0,0,0.648750,0.999375,0.422500,0.633125,0.055000,0.820000,0.996667,0.999167
-000b5bc07c0c5df7,xclick,/m/0dzct,1,0.516250,0.730625,0.119167,0.540833,1,0,0,0,0,0.603125,0.730625,0.596875,0.516250,0.119167,0.340000,0.540833,0.242500
-000b5bc07c0c5df7,xclick,/m/0hg7b,1,0.340625,0.548750,0.465000,0.731667,0,0,0,0,0,0.511250,0.340625,0.360000,0.548750,0.465000,0.712500,0.731667,0.504167
-000b5bc07c0c5df7,xclick,/m/0k0pj,1,0.545625,0.608125,0.260000,0.365833,0,0,0,0,0,0.571250,0.565000,0.608125,0.545625,0.260000,0.365833,0.346667,0.339167
-000b606e130bdf5e,xclick,/m/01g317,1,0.000000,0.350625,0.186667,0.999167,1,1,0,0,0,0.008750,0.350625,0.000000,0.000625,0.999167,0.926667,0.346667,0.186667
-000b606e130bdf5e,xclick,/m/01g317,1,0.106875,0.318750,0.417500,0.999167,1,1,0,0,0,0.159375,0.106875,0.194375,0.318750,0.417500,0.460000,0.999167,0.744167
-000b606e130bdf5e,xclick,/m/01g317,1,0.268125,0.902500,0.063333,0.999167,1,1,0,0,0,0.465625,0.268125,0.517500,0.902500,0.063333,0.994167,0.999167,0.860833
-000b606e130bdf5e,xclick,/m/01g317,1,0.509375,0.779375,0.245000,0.999167,1,1,0,0,0,0.509375,0.693125,0.700625,0.779375,0.403333,0.245000,0.999167,0.529167
-000b606e130bdf5e,xclick,/m/01g317,1,0.844375,0.905000,0.900833,0.999167,1,1,0,0,0,0.867500,0.844375,0.870000,0.905000,0.900833,0.994167,0.999167,0.935833
-000b606e130bdf5e,xclick,/m/01g317,1,0.848125,0.926875,0.427500,0.849167,1,0,0,0,0,0.848125,0.910000,0.888125,0.926875,0.747500,0.427500,0.849167,0.829167
-000b606e130bdf5e,xclick,/m/01g317,1,0.893125,0.999375,0.212500,0.999167,1,1,0,0,0,0.893125,0.999375,0.936875,0.999375,0.477500,0.212500,0.999167,0.873333
-000b606e130bdf5e,xclick,/m/02fq_6,1,0.316875,0.633750,0.059167,0.224167,0,0,0,0,0,0.468125,0.389375,0.316875,0.633750,0.059167,0.224167,0.217500,0.207500
-000b606e130bdf5e,xclick,/m/04rky,1,0.000000,0.363750,0.124167,0.997500,1,1,0,0,0,0.000000,0.363750,0.265625,0.001875,0.124167,0.918333,0.997500,0.585833
-000b606e130bdf5e,xclick,/m/04rky,1,0.000000,0.390625,0.019167,0.999167,1,1,0,0,0,0.036875,0.390625,0.136250,0.000000,0.019167,0.456667,0.999167,0.410000
-000b606e130bdf5e,xclick,/m/04rky,1,0.111875,0.308125,0.411667,0.995833,1,0,0,0,0,0.168750,0.111875,0.308125,0.188125,0.411667,0.461667,0.737500,0.995833
-000b606e130bdf5e,xclick,/m/04rky,1,0.270625,0.905000,0.052500,0.999167,0,1,0,0,0,0.468125,0.270625,0.905000,0.461875,0.052500,0.992500,0.853333,0.999167
-000b606e130bdf5e,xclick,/m/04rky,1,0.518750,0.780000,0.254167,0.999167,1,1,0,0,0,0.693125,0.518750,0.724375,0.780000,0.254167,0.440833,0.999167,0.527500
-000b606e130bdf5e,xclick,/m/04rky,1,0.845625,0.901250,0.899167,0.999167,1,0,0,0,0,0.870000,0.863750,0.901250,0.845625,0.899167,0.999167,0.921667,0.992500
-000b606e130bdf5e,xclick,/m/04rky,1,0.848125,0.917500,0.443333,0.830833,1,0,0,0,0,0.910000,0.848125,0.917500,0.861250,0.443333,0.830833,0.825000,0.829167
-000b606e130bdf5e,xclick,/m/04rky,1,0.876250,0.999375,0.207500,0.999167,0,1,0,0,0,0.999375,0.876250,0.968125,0.999375,0.207500,0.995833,0.999167,0.975000
-000b63a1445f53c8,xclick,/m/03jm5,1,0.000000,0.966250,0.000000,0.885833,0,0,0,0,0,0.081250,0.000000,0.015000,0.966250,0.000000,0.182500,0.885833,0.586667
-000b63a1445f53c8,xclick,/m/03jm5,1,0.941250,0.999375,0.438333,0.729167,1,1,0,0,0,0.941250,0.986250,0.999375,0.947500,0.467500,0.438333,0.500000,0.729167
-000b63a1445f53c8,xclick,/m/0d4v4,1,0.283125,0.337500,0.555000,0.651667,0,0,0,0,0,0.285625,0.283125,0.334375,0.337500,0.555000,0.651667,0.650000,0.563333
-000b63a1445f53c8,xclick,/m/0d4v4,1,0.340625,0.493125,0.189167,0.406667,1,0,0,0,0,0.416875,0.340625,0.493125,0.435000,0.189167,0.292500,0.323333,0.406667
-000b63a1445f53c8,xclick,/m/0d4v4,1,0.373125,0.458750,0.283333,0.386667,0,0,0,0,0,0.380000,0.373125,0.453750,0.458750,0.283333,0.351667,0.386667,0.315833
-000b63a1445f53c8,xclick,/m/0d4v4,1,0.455625,0.503125,0.568333,0.650833,0,0,0,0,0,0.465625,0.493125,0.503125,0.455625,0.575000,0.568333,0.637500,0.650833
-000b63a1445f53c8,xclick,/m/0d4v4,1,0.533125,0.620625,0.292500,0.430833,1,0,0,0,0,0.585000,0.533125,0.620625,0.601250,0.292500,0.340833,0.358333,0.430833
-000b63a1445f53c8,xclick,/m/0d4v4,1,0.570000,0.604375,0.336667,0.425833,0,0,0,0,0,0.570000,0.570000,0.598125,0.604375,0.336667,0.393333,0.425833,0.355000
-000b63a1445f53c8,xclick,/m/0d4v4,1,0.689375,0.766875,0.382500,0.520000,1,0,0,0,0,0.738750,0.689375,0.766875,0.736875,0.382500,0.435833,0.445833,0.520000
-000b63a1445f53c8,xclick,/m/0d4v4,1,0.719375,0.749375,0.434167,0.505000,0,0,0,0,0,0.719375,0.719375,0.749375,0.748125,0.434167,0.462500,0.505000,0.445000
-000b63a1445f53c8,xclick,/m/0d4v4,1,0.727500,0.768750,0.627500,0.696667,0,0,0,0,0,0.733750,0.727500,0.765000,0.768750,0.627500,0.685833,0.696667,0.634167
-000b63a1445f53c8,xclick,/m/0d4v4,1,0.793750,0.853125,0.437500,0.547500,1,0,0,0,0,0.821250,0.793750,0.853125,0.832500,0.437500,0.497500,0.502500,0.547500
-000b63a1445f53c8,xclick,/m/0d4v4,1,0.807500,0.838125,0.482500,0.530833,0,0,0,0,0,0.812500,0.807500,0.833125,0.838125,0.482500,0.501667,0.530833,0.496667
-000b63a1445f53c8,xclick,/m/0d4v4,1,0.821875,0.846250,0.646667,0.692500,0,0,0,0,0,0.821875,0.825625,0.846250,0.846250,0.648333,0.689167,0.692500,0.646667
-000b63a1445f53c8,xclick,/m/0d4v4,1,0.904375,0.926250,0.561667,0.620833,0,0,0,0,0,0.904375,0.905625,0.926250,0.921250,0.561667,0.613333,0.620833,0.569167
-000b63a1445f53c8,xclick,/m/0d4v4,1,0.917500,0.944375,0.674167,0.740833,0,0,0,0,0,0.917500,0.920000,0.944375,0.939375,0.679167,0.737500,0.740833,0.674167
-000b63a1445f53c8,xclick,/m/0d5gx,1,0.000000,0.999375,0.000000,0.895000,1,1,0,0,0,0.165625,0.000000,0.999375,0.000000,0.000000,0.366667,0.515833,0.895000
-000b65a36ad46f9e,xclick,/m/01g317,1,0.461250,0.909375,0.148333,0.999167,1,1,0,0,0,0.653125,0.461250,0.909375,0.637500,0.148333,0.830833,0.687500,0.999167
-000b65a36ad46f9e,xclick,/m/04szw,1,0.644375,0.951250,0.003333,0.999167,1,1,0,0,0,0.883750,0.644375,0.951250,0.839375,0.003333,0.999167,0.998333,0.999167
-000b65a36ad46f9e,xclick,/m/04szw,1,0.850625,0.999375,0.506667,0.905000,1,1,0,0,0,0.920625,0.850625,0.936875,0.999375,0.506667,0.537500,0.905000,0.865000
-000b65a36ad46f9e,xclick,/m/04szw,1,0.880625,0.986250,0.236667,0.327500,1,0,0,0,0,0.905000,0.880625,0.973125,0.986250,0.236667,0.256667,0.325833,0.327500
-000b65a36ad46f9e,xclick,/m/04szw,1,0.906250,0.998125,0.146667,0.193333,1,1,0,0,0,0.931875,0.906250,0.970625,0.998125,0.146667,0.170833,0.193333,0.193333
-000b65a36ad46f9e,xclick,/m/04szw,1,0.945000,0.999375,0.275833,0.535833,1,1,0,0,0,0.999375,0.945000,0.999375,0.999375,0.275833,0.446667,0.535833,0.534167
-000b65a36ad46f9e,xclick,/m/0hg7b,1,0.461875,0.576875,0.334167,0.397500,0,0,0,0,0,0.461875,0.547500,0.576875,0.568125,0.397500,0.334167,0.358333,0.389167
-000b65a36ad46f9e,xclick,/m/0hg7b,1,0.896250,0.948750,0.410000,0.439167,1,0,0,0,0,0.896250,0.948750,0.941250,0.919375,0.410000,0.418333,0.439167,0.421667
-000b70a84aab664b,xclick,/m/026t6,1,0.473750,0.533750,0.686679,0.899625,1,0,0,0,0,0.482500,0.508125,0.473750,0.533750,0.686679,0.899625,0.772983,0.727017
-000b70a84aab664b,xclick,/m/026t6,1,0.485000,0.533750,0.558161,0.661351,1,0,0,0,0,0.508125,0.503125,0.485000,0.533750,0.558161,0.661351,0.646341,0.575047
-000b70a84aab664b,xclick,/m/026t6,1,0.523125,0.566875,0.557223,0.665103,1,0,0,0,0,0.536250,0.523125,0.559375,0.566875,0.557223,0.646341,0.665103,0.559099
-000b70a84aab664b,xclick,/m/0342h,1,0.180000,0.231250,0.315197,0.697936,1,0,0,0,0,0.225000,0.180000,0.186875,0.231250,0.315197,0.590056,0.697936,0.532833
-000b70a84aab664b,xclick,/m/0342h,1,0.361875,0.457500,0.388368,0.639775,1,0,0,0,0,0.457500,0.370000,0.361875,0.455000,0.388368,0.639775,0.632270,0.398687
-000b72e1446f8849,xclick,/m/02crq1,1,0.000000,0.640000,0.561090,0.999060,0,0,0,0,0,0.000000,0.376875,0.640000,0.220000,0.812970,0.561090,0.805451,0.999060
-000b72e1446f8849,xclick,/m/02crq1,1,0.648750,0.999375,0.564850,0.992481,1,1,0,0,0,0.648750,0.999375,0.978125,0.978125,0.618421,0.564850,0.992481,0.992481
-000b72e1446f8849,xclick,/m/035r7c,1,0.361875,0.600625,0.646617,0.999060,1,1,0,0,0,0.446875,0.600625,0.593750,0.361875,0.646617,0.992481,0.999060,0.705827
-000b72e1446f8849,xclick,/m/035r7c,1,0.440625,0.499375,0.733083,0.999060,1,1,0,0,0,0.440625,0.470000,0.499375,0.441875,0.756579,0.733083,0.806391,0.999060
-000b72e1446f8849,xclick,/m/035r7c,1,0.655625,0.732500,0.718045,0.999060,1,1,0,0,0,0.655625,0.706875,0.732500,0.732500,0.718045,0.999060,0.958647,0.958647
-000b72e1446f8849,xclick,/m/035r7c,1,0.667500,0.780000,0.734962,0.999060,1,1,0,0,0,0.667500,0.735000,0.780000,0.780000,0.734962,0.999060,0.999060,0.999060
-000b72e1446f8849,xclick,/m/03bt1vf,1,0.348125,0.608125,0.315789,0.999060,1,1,0,0,0,0.466250,0.348125,0.595625,0.608125,0.315789,0.715226,0.999060,0.996241
-000b72e1446f8849,xclick,/m/03bt1vf,1,0.648125,0.906875,0.291353,0.999060,1,1,0,0,0,0.756250,0.736875,0.906875,0.648125,0.291353,0.999060,0.749060,0.708647
-000b72e1446f8849,xclick,/m/03q69,1,0.420000,0.512500,0.310150,0.451128,1,0,0,0,0,0.460000,0.445625,0.420000,0.512500,0.310150,0.451128,0.381579,0.375940
-000b72e1446f8849,xclick,/m/03q69,1,0.707500,0.809375,0.291353,0.402256,1,0,0,0,0,0.739375,0.780625,0.809375,0.707500,0.291353,0.402256,0.367481,0.345865
-000b76a9b80ba43a,xclick,/m/079cl,1,0.000000,0.064375,0.324444,0.677778,1,1,0,0,0,0.009375,0.064375,0.033750,0.000000,0.324444,0.486667,0.677778,0.623333
-000b76a9b80ba43a,xclick,/m/079cl,1,0.041250,0.243750,0.347778,0.660000,0,0,0,0,0,0.079375,0.041250,0.133125,0.243750,0.347778,0.484444,0.660000,0.621111
-000b76a9b80ba43a,xclick,/m/079cl,1,0.133750,0.269375,0.313333,0.452222,1,0,0,0,0,0.247500,0.133750,0.133750,0.269375,0.313333,0.331111,0.452222,0.426667
-000b76a9b80ba43a,xclick,/m/079cl,1,0.237500,0.408750,0.404444,0.653333,0,0,0,0,0,0.325625,0.237500,0.334375,0.408750,0.404444,0.466667,0.653333,0.603333
-000b76a9b80ba43a,xclick,/m/079cl,1,0.370625,0.476875,0.423333,0.638889,0,0,0,0,0,0.411250,0.370625,0.447500,0.476875,0.423333,0.486667,0.638889,0.616667
-000b76a9b80ba43a,xclick,/m/079cl,1,0.447500,0.620000,0.417778,0.643333,0,0,0,0,0,0.506250,0.447500,0.500000,0.620000,0.417778,0.468889,0.643333,0.575556
-000b76a9b80ba43a,xclick,/m/079cl,1,0.496250,0.613750,0.400000,0.516667,0,0,0,0,0,0.548750,0.496250,0.558750,0.613750,0.400000,0.402222,0.516667,0.491111
-000b76a9b80ba43a,xclick,/m/079cl,1,0.591875,0.750000,0.441111,0.630000,0,0,0,0,0,0.638125,0.591875,0.667500,0.750000,0.441111,0.495556,0.630000,0.614444
-000b76a9b80ba43a,xclick,/m/079cl,1,0.678125,0.771250,0.406667,0.536667,0,0,0,0,0,0.748125,0.678125,0.744375,0.771250,0.406667,0.427778,0.536667,0.536667
-000b76a9b80ba43a,xclick,/m/079cl,1,0.735625,0.766250,0.408889,0.506667,1,0,0,0,0,0.746250,0.735625,0.751250,0.766250,0.408889,0.452222,0.504444,0.506667
-000b76a9b80ba43a,xclick,/m/079cl,1,0.765000,0.856250,0.495556,0.651111,1,0,0,0,0,0.820000,0.765000,0.794375,0.856250,0.495556,0.521111,0.651111,0.621111
-000b76a9b80ba43a,xclick,/m/079cl,1,0.841875,0.920000,0.495556,0.616667,0,0,0,0,0,0.855000,0.841875,0.920000,0.918750,0.495556,0.508889,0.616667,0.538889
-000b76a9b80ba43a,xclick,/m/079cl,1,0.916250,0.999375,0.514444,0.645556,1,1,0,0,0,0.956250,0.916250,0.956250,0.999375,0.514444,0.554444,0.645556,0.638889
-000b76a9b80ba43a,xclick,/m/07yv9,1,0.771875,0.873750,0.818889,0.896667,1,0,0,0,0,0.847500,0.873750,0.784375,0.771875,0.818889,0.885556,0.896667,0.867778
-000b7dfaa1810a83,xclick,/m/01g317,1,0.745625,0.801250,0.600753,0.720339,0,0,1,0,0,0.745625,0.766250,0.801250,0.788125,0.646893,0.600753,0.669492,0.720339
-000b7dfaa1810a83,xclick,/m/01g317,1,0.819375,0.858125,0.592279,0.731638,0,0,0,0,0,0.823750,0.833750,0.858125,0.819375,0.641243,0.592279,0.645009,0.731638
-000b7dfaa1810a83,xclick,/m/07j7r,1,0.000000,0.094375,0.267420,0.599812,1,1,0,0,0,0.043125,0.000000,0.030625,0.094375,0.267420,0.298493,0.599812,0.358757
-000b7dfaa1810a83,xclick,/m/07j7r,1,0.035625,0.105000,0.390772,0.452919,1,0,0,0,0,0.070000,0.035625,0.067500,0.105000,0.390772,0.414313,0.452919,0.433145
-000b7dfaa1810a83,xclick,/m/07j7r,1,0.130625,0.399375,0.348399,0.587571,1,0,1,0,0,0.355000,0.130625,0.163750,0.399375,0.348399,0.449153,0.587571,0.506591
-000b7dfaa1810a83,xclick,/m/07j7r,1,0.150000,0.194375,0.292844,0.513183,1,0,0,0,0,0.165000,0.150000,0.165000,0.194375,0.292844,0.317326,0.513183,0.323917
-000b7dfaa1810a83,xclick,/m/07j7r,1,0.186875,0.278125,0.323917,0.502825,1,0,0,0,0,0.223750,0.186875,0.235625,0.278125,0.323917,0.392655,0.502825,0.381356
-000b7dfaa1810a83,xclick,/m/07j7r,1,0.203750,0.324375,0.117702,0.477401,1,0,0,0,0,0.324375,0.274375,0.203750,0.253750,0.234463,0.117702,0.226930,0.477401
-000b7dfaa1810a83,xclick,/m/07j7r,1,0.306250,0.341875,0.226930,0.576271,1,0,0,0,0,0.333125,0.306250,0.329375,0.341875,0.226930,0.371940,0.576271,0.290960
-000b7dfaa1810a83,xclick,/m/07j7r,1,0.338125,0.863125,0.151601,0.418079,1,0,1,0,0,0.841875,0.338125,0.475000,0.863125,0.151601,0.261770,0.418079,0.300377
-000b7dfaa1810a83,xclick,/m/07j7r,1,0.394375,0.435625,0.223164,0.530132,1,0,0,0,0,0.406250,0.394375,0.401875,0.435625,0.223164,0.392655,0.530132,0.515066
-000b7dfaa1810a83,xclick,/m/07j7r,1,0.421250,0.470000,0.206215,0.572505,0,0,0,0,0,0.443125,0.421250,0.450625,0.470000,0.206215,0.561205,0.572505,0.485876
-000b7dfaa1810a83,xclick,/m/07j7r,1,0.496875,0.543125,0.188324,0.518832,1,0,0,0,0,0.506875,0.496875,0.512500,0.543125,0.188324,0.489642,0.518832,0.469868
-000b7dfaa1810a83,xclick,/m/07j7r,1,0.656875,0.704375,0.142185,0.576271,1,0,0,0,0,0.671250,0.656875,0.686250,0.704375,0.142185,0.520716,0.576271,0.506591
-000b7dfaa1810a83,xclick,/m/07j7r,1,0.711875,0.755625,0.144068,0.506591,1,0,0,0,0,0.722500,0.711875,0.731250,0.755625,0.144068,0.485876,0.506591,0.419962
-000b7dfaa1810a83,xclick,/m/07j7r,1,0.850625,0.903125,0.002825,0.344633,1,1,0,0,0,0.856875,0.850625,0.850625,0.903125,0.002825,0.258004,0.344633,0.248588
-000b7dfaa1810a83,xclick,/m/07j7r,1,0.871250,0.905625,0.063089,0.234463,1,0,0,0,0,0.891250,0.871250,0.894375,0.905625,0.063089,0.080038,0.234463,0.090395
-000b7dfaa1810a83,xclick,/m/07j7r,1,0.887500,0.921250,0.120527,0.254237,1,0,0,0,0,0.899375,0.887500,0.899375,0.921250,0.120527,0.149718,0.254237,0.246704
-000b7dfaa1810a83,xclick,/m/07j7r,1,0.940000,0.986250,0.028249,0.190207,1,0,0,0,0,0.963125,0.940000,0.963125,0.986250,0.028249,0.057439,0.190207,0.047081
-000b81b5757963e0,xclick,/m/01xyhv,1,0.361875,0.641250,0.411820,0.967167,1,0,0,0,0,0.571875,0.361875,0.641250,0.469375,0.411820,0.786116,0.532833,0.967167
-000b81b5757963e0,xclick,/m/01xyhv,1,0.478125,0.914375,0.495310,0.999062,1,1,0,0,0,0.478125,0.802500,0.914375,0.809375,0.947467,0.495310,0.668856,0.999062
-000b825dea3016eb,activemil,/m/0jyfg,1,0.254687,0.745313,0.073437,0.253125,-1,-1,-1,-1,-1,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000
-000b825dea3016eb,xclick,/m/01g317,1,0.221875,0.309375,0.556250,0.829687,0,0,0,0,0,0.265625,0.221875,0.309375,0.235937,0.556250,0.635938,0.698438,0.829687
-000b825dea3016eb,xclick,/m/01g317,1,0.395312,0.606250,0.615625,0.764063,0,0,0,0,0,0.451562,0.606250,0.395312,0.509375,0.615625,0.729688,0.750000,0.764063
-000b87119cc301cf,xclick,/m/01xygc,1,0.195312,0.731250,0.622917,0.997917,0,1,0,0,0,0.446875,0.195312,0.321875,0.731250,0.622917,0.997917,0.997917,0.997917
-000b87119cc301cf,xclick,/m/03bt1vf,1,0.184375,0.707812,0.283333,0.997917,0,1,0,0,0,0.385937,0.370312,0.707812,0.184375,0.283333,0.997917,0.943750,0.895833
-000b87119cc301cf,xclick,/m/04yx4,1,0.635938,0.859375,0.068750,0.993750,1,1,0,0,0,0.659375,0.635938,0.859375,0.739062,0.068750,0.716667,0.683333,0.993750
-000b8d80f7386698,activemil,/m/03q69,1,0.345313,0.520312,0.163146,0.415493,1,0,0,0,0,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000
-000b8d80f7386698,activemil,/m/04hgtk,1,0.345313,0.520312,0.163146,0.415493,1,0,0,0,0,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000
-000b8d80f7386698,activemil,/m/05s2s,1,0.564844,0.989062,0.451878,0.951878,1,1,0,0,0,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000
-000b8d80f7386698,activemil,/m/0dzf4,1,0.095312,0.367969,0.333333,0.477700,1,0,0,0,0,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000
-000b8d80f7386698,xclick,/m/01d40f,1,0.399219,0.579687,0.623239,0.998826,1,1,0,0,0,0.503125,0.399219,0.443750,0.579687,0.998826,0.853286,0.623239,0.995305
-000b8d80f7386698,xclick,/m/01xyhv,1,0.000000,0.128906,0.267606,0.998826,0,0,0,0,0,0.039062,0.128906,0.007031,0.000000,0.267606,0.375587,0.998826,0.471831
-000b8d80f7386698,xclick,/m/01xyhv,1,0.028906,0.639844,0.392019,0.998826,1,1,0,0,0,0.398438,0.639844,0.249219,0.028906,0.392019,0.530516,0.998826,0.901408
-000b8d80f7386698,xclick,/m/03bt1vf,1,0.095312,0.582812,0.178404,0.998826,1,1,0,0,0,0.413281,0.095312,0.507031,0.582812,0.178404,0.426056,0.998826,0.997653
-000b8d80f7386698,xclick,/m/03bt1vf,1,0.609375,0.666406,0.430751,0.612676,1,0,0,0,0,0.634375,0.609375,0.636719,0.666406,0.430751,0.473005,0.612676,0.508216
-000b8d80f7386698,xclick,/m/03bt1vf,1,0.684375,0.726562,0.442488,0.575117,1,0,0,0,0,0.703125,0.684375,0.700781,0.726562,0.575117,0.510563,0.442488,0.514085
-000b8d80f7386698,xclick,/m/03q69,1,0.000000,0.048437,0.112676,0.210094,1,1,0,0,0,0.034375,0.048437,0.000000,0.000000,0.210094,0.190141,0.115023,0.112676
-000b8d80f7386698,xclick,/m/03q69,1,0.195312,0.343750,0.188967,0.350939,1,0,0,0,0,0.324219,0.343750,0.263281,0.195312,0.350939,0.284038,0.188967,0.308685
-000b8d80f7386698,xclick,/m/04hgtk,1,0.000000,0.050781,0.104460,0.278169,0,1,0,0,0,0.000000,0.000000,0.000000,0.050781,0.104460,0.278169,0.227700,0.205399
-000b8d80f7386698,xclick,/m/04hgtk,1,0.199219,0.348437,0.188967,0.352113,1,0,0,0,0,0.267188,0.199219,0.348437,0.329688,0.188967,0.321596,0.322770,0.352113
-000b8d80f7386698,xclick,/m/04yx4,1,0.000000,0.125781,0.110329,0.998826,1,1,0,0,0,0.000000,0.125781,0.064062,0.000000,0.110329,0.373239,0.998826,0.865023
-000b8d80f7386698,xclick,/m/04yx4,1,0.025781,0.638281,0.180751,0.998826,1,1,0,0,0,0.275781,0.638281,0.253906,0.025781,0.180751,0.534038,0.998826,0.908451
-000b8d80f7386698,xclick,/m/04yx4,1,0.609375,0.657031,0.423709,0.611502,1,0,0,0,0,0.639844,0.609375,0.657031,0.642188,0.423709,0.474178,0.507042,0.611502
-000b8d80f7386698,xclick,/m/04yx4,1,0.660937,0.689062,0.464789,0.563380,1,0,0,0,0,0.671094,0.660937,0.677344,0.689062,0.464789,0.518779,0.563380,0.512911
-000b8d80f7386698,xclick,/m/04yx4,1,0.716406,0.759375,0.443662,0.585681,1,0,0,0,0,0.750000,0.716406,0.752344,0.759375,0.443662,0.475352,0.585681,0.523474
-000b8d80f7386698,xclick,/m/0dzct,1,0.000000,0.043750,0.126761,0.279343,0,1,0,0,0,0.000000,0.000000,0.043750,0.002344,0.217136,0.126761,0.213615,0.279343
-000b8d80f7386698,xclick,/m/0dzct,1,0.346875,0.452344,0.204225,0.399061,1,0,0,0,0,0.346875,0.391406,0.452344,0.444531,0.300469,0.204225,0.339202,0.399061
-000b8d80f7386698,xclick,/m/0dzf4,1,0.085156,0.131250,0.354460,0.553991,1,0,0,0,0,0.085156,0.131250,0.093750,0.115625,0.431925,0.403756,0.354460,0.553991
-000b8d80f7386698,xclick,/m/0dzf4,1,0.367188,0.575781,0.620892,0.998826,0,1,0,0,0,0.476562,0.575781,0.410156,0.367188,0.631455,0.620892,0.998826,0.962441
-000b8d80f7386698,xclick,/m/0dzf4,1,0.394531,0.664062,0.454225,0.615023,1,0,0,0,0,0.493750,0.492188,0.394531,0.664062,0.615023,0.454225,0.465962,0.530516
-000b8d80f7386698,xclick,/m/0dzf4,1,0.626562,0.656250,0.460094,0.501174,1,0,0,0,0,0.656250,0.626562,0.642188,0.652344,0.495305,0.501174,0.460094,0.498826
-000b8d80f7386698,xclick,/m/0dzf4,1,0.692187,0.735937,0.504695,0.549296,1,0,0,0,0,0.735937,0.692187,0.700000,0.717969,0.543427,0.504695,0.549296,0.510563
-000b8d80f7386698,xclick,/m/0dzf4,1,0.730469,0.748438,0.476526,0.539906,0,0,0,0,0,0.730469,0.746094,0.743750,0.748438,0.507042,0.492958,0.476526,0.539906
-000b8d80f7386698,xclick,/m/0k0pj,1,0.362500,0.393750,0.255869,0.322770,0,0,0,0,0,0.389844,0.381250,0.362500,0.393750,0.255869,0.322770,0.288732,0.272300
-000b9007a01f7405,xclick,/m/01g317,1,0.001876,0.813321,0.086875,0.999375,1,1,0,0,0,0.486867,0.387430,0.001876,0.813321,0.086875,0.999375,0.528750,0.411250
-000b9007a01f7405,xclick,/m/02p0tk3,1,0.000000,0.823640,0.079375,0.999375,0,1,0,0,0,0.507505,0.823640,0.000000,0.145403,0.079375,0.516250,0.533125,0.999375
-000b9007a01f7405,xclick,/m/0dzf4,1,0.001876,0.314259,0.084375,0.586250,1,1,0,0,0,0.268293,0.001876,0.135084,0.314259,0.084375,0.536875,0.586250,0.376250
-000b9007a01f7405,xclick,/m/0dzf4,1,0.279550,0.804878,0.081250,0.582500,1,0,0,0,0,0.279550,0.297373,0.804878,0.524390,0.081250,0.521250,0.481875,0.582500
-000b9007a01f7405,xclick,/m/0hg7b,1,0.196060,0.423077,0.051250,0.213125,1,0,0,0,0,0.198874,0.196060,0.423077,0.400563,0.051250,0.083750,0.173750,0.213125
-000b9007a01f7405,xclick,/m/0k65p,1,0.204503,0.296435,0.116250,0.260000,1,0,0,0,0,0.238274,0.204503,0.273921,0.296435,0.116250,0.143750,0.155625,0.260000
-000b9007a01f7405,xclick,/m/0k65p,1,0.258912,0.409944,0.078750,0.241875,1,0,0,0,0,0.312383,0.258912,0.297373,0.409944,0.078750,0.090625,0.241875,0.141250
-000b93644609911f,xclick,/m/01d40f,1,0.240625,0.416250,0.474672,0.998124,1,1,0,0,0,0.416250,0.366250,0.240625,0.275000,0.990619,0.998124,0.901501,0.474672
-000b93644609911f,xclick,/m/01d40f,1,0.639375,0.900625,0.310507,0.999062,1,1,0,0,0,0.740625,0.639375,0.900625,0.680000,0.310507,0.494371,0.988743,0.999062
-000b93644609911f,xclick,/m/01rkbr,1,0.516250,0.552500,0.423077,0.512195,1,0,0,0,0,0.516250,0.536875,0.552500,0.516250,0.512195,0.423077,0.505629,0.506567
-000b93644609911f,xclick,/m/01xyhv,1,0.394375,0.655625,0.363977,0.999062,1,1,0,0,0,0.556875,0.655625,0.592500,0.394375,0.363977,0.914634,0.999062,0.643527
-000b93644609911f,xclick,/m/03bt1vf,1,0.210000,0.415000,0.295497,0.999062,1,1,0,0,0,0.307500,0.210000,0.317500,0.415000,0.295497,0.678236,0.999062,0.526266
-000b93644609911f,xclick,/m/03bt1vf,1,0.616875,0.885000,0.171670,0.996248,1,1,0,0,0,0.663125,0.616875,0.773750,0.885000,0.171670,0.264540,0.996248,0.946529
-000b93644609911f,xclick,/m/03rszm,1,0.080625,0.703125,0.106942,0.997186,1,1,0,0,0,0.153750,0.080625,0.703125,0.677500,0.202627,0.830206,0.106942,0.997186
-000b93644609911f,xclick,/m/03rszm,1,0.716250,0.999375,0.062852,0.999062,1,1,0,0,0,0.965000,0.841250,0.716250,0.999375,0.999062,0.062852,0.134146,0.197936
-000b9814a07fd974,xclick,/m/01jfsr,1,0.105973,0.233141,0.107417,0.602302,0,0,0,0,0,0.105973,0.180154,0.158960,0.233141,0.500000,0.107417,0.602302,0.280051
-000b9814a07fd974,xclick,/m/01jfsr,1,0.754335,0.875723,0.182864,0.630435,0,0,0,0,0,0.754335,0.803468,0.819846,0.875723,0.530691,0.182864,0.630435,0.519182
-000b9a97776b3634,xclick,/m/04hgtk,1,0.002500,0.102500,0.135338,0.337406,1,1,0,0,0,0.043125,0.002500,0.067500,0.102500,0.135338,0.204887,0.337406,0.216165
-000b9a97776b3634,xclick,/m/04hgtk,1,0.244375,0.361250,0.150376,0.344925,1,0,0,0,0,0.298125,0.244375,0.361250,0.275000,0.150376,0.250940,0.245301,0.344925
-000b9a97776b3634,xclick,/m/04hgtk,1,0.330625,0.430000,0.121241,0.275376,1,0,0,0,0,0.377500,0.330625,0.430000,0.395625,0.121241,0.171992,0.190789,0.275376
-000b9a97776b3634,xclick,/m/04hgtk,1,0.476875,0.660000,0.033835,0.388158,0,0,0,0,0,0.577500,0.563125,0.660000,0.476875,0.033835,0.388158,0.161654,0.188910
-000b9a97776b3634,xclick,/m/04hgtk,1,0.655625,0.796875,0.044173,0.271617,1,0,0,0,0,0.726250,0.655625,0.796875,0.722500,0.044173,0.161654,0.137218,0.271617
-000b9a97776b3634,xclick,/m/04yx4,1,0.012500,0.291875,0.127820,0.930451,1,1,0,0,0,0.012500,0.040625,0.156250,0.291875,0.188910,0.127820,0.930451,0.545113
-000b9a97776b3634,xclick,/m/04yx4,1,0.107500,0.528750,0.154135,0.902256,1,0,0,0,0,0.291875,0.107500,0.239375,0.528750,0.154135,0.561090,0.902256,0.537594
-000b9a97776b3634,xclick,/m/04yx4,1,0.236875,0.425000,0.121241,0.397556,1,0,0,0,0,0.376250,0.271250,0.236875,0.425000,0.121241,0.397556,0.337406,0.202068
-000b9a97776b3634,xclick,/m/04yx4,1,0.316875,0.941250,0.033835,0.999060,1,1,0,0,0,0.573125,0.941250,0.316875,0.593750,0.033835,0.679511,0.796992,0.999060
-000b9a97776b3634,xclick,/m/04yx4,1,0.662500,0.999375,0.046053,0.607143,1,1,0,0,0,0.727500,0.932500,0.999375,0.662500,0.046053,0.607143,0.545113,0.109962
-000b9a97776b3634,xclick,/m/083kb,1,0.440625,0.513750,0.513158,0.680451,1,0,0,0,0,0.486875,0.440625,0.513750,0.451875,0.513158,0.618421,0.620301,0.680451
-000b9a97776b3634,xclick,/m/09j2d,1,0.017500,0.998750,0.243421,0.999060,1,1,1,0,0,0.115000,0.017500,0.737500,0.998750,0.243421,0.569549,0.999060,0.513158
-000b9b00d7aef8f5,xclick,/m/09tvcd,1,0.055625,0.104375,0.509461,0.786026,1,0,0,0,0,0.055625,0.104375,0.077500,0.095000,0.515284,0.509461,0.759825,0.786026
-000b9b00d7aef8f5,xclick,/m/09tvcd,1,0.088750,0.145000,0.553130,0.834061,1,0,0,0,0,0.088750,0.142500,0.104375,0.145000,0.563319,0.553130,0.834061,0.816594
-000b9b00d7aef8f5,xclick,/m/09tvcd,1,0.260625,0.313750,0.410480,0.892285,0,1,0,0,0,0.310000,0.260625,0.305000,0.313750,0.410480,0.496361,0.892285,0.529840
-000b9b00d7aef8f5,xclick,/m/09tvcd,1,0.392500,0.451250,0.532751,0.807860,1,0,0,0,0,0.392500,0.451250,0.397500,0.443750,0.532751,0.532751,0.786026,0.807860
-000b9b00d7aef8f5,xclick,/m/09tvcd,1,0.423125,0.480625,0.574964,0.854440,1,0,0,0,0,0.423125,0.470625,0.452500,0.480625,0.592431,0.574964,0.854440,0.834061
-000b9b00d7aef8f5,xclick,/m/09tvcd,1,0.601250,0.653750,0.427948,0.911208,0,1,0,0,0,0.601250,0.651250,0.611250,0.653750,0.443959,0.427948,0.893741,0.911208
-000b9b00d7aef8f5,xclick,/m/09tvcd,1,0.752500,0.810000,0.532751,0.804949,1,0,0,0,0,0.752500,0.810000,0.773125,0.793125,0.538574,0.532751,0.788937,0.804949
-000b9b00d7aef8f5,xclick,/m/09tvcd,1,0.790625,0.838125,0.566230,0.876274,1,0,0,0,0,0.790625,0.831875,0.816250,0.838125,0.592431,0.566230,0.876274,0.854440
-000b9b00d7aef8f5,xclick,/m/09tvcd,1,0.961250,0.999375,0.430859,0.911208,0,1,0,0,0,0.961250,0.999375,0.967500,0.998125,0.452693,0.430859,0.902475,0.911208
-000b9b00d7aef8f5,xclick,/m/0dzf4,1,0.005000,0.094375,0.346434,0.815138,1,0,0,0,0,0.091875,0.005000,0.094375,0.015000,0.346434,0.713246,0.695779,0.815138
-000b9b00d7aef8f5,xclick,/m/0dzf4,1,0.055000,0.295625,0.391557,0.835517,0,0,0,0,0,0.118750,0.055000,0.295625,0.220000,0.391557,0.510917,0.678311,0.835517
-000b9b00d7aef8f5,xclick,/m/0dzf4,1,0.312500,0.451875,0.084425,0.612809,1,0,0,0,0,0.449375,0.312500,0.389375,0.451875,0.084425,0.564774,0.612809,0.366812
-000b9b00d7aef8f5,xclick,/m/0dzf4,1,0.363750,0.639375,0.433770,0.820961,1,0,0,0,0,0.616250,0.363750,0.639375,0.530000,0.433770,0.480349,0.713246,0.820961
-000b9b00d7aef8f5,xclick,/m/0dzf4,1,0.659375,0.804375,0.331878,0.809316,1,0,0,0,0,0.680000,0.659375,0.804375,0.718750,0.331878,0.379913,0.531295,0.809316
-000b9b00d7aef8f5,xclick,/m/0dzf4,1,0.822500,0.979375,0.270742,0.816594,0,0,0,0,0,0.870000,0.822500,0.916250,0.979375,0.270742,0.393013,0.816594,0.602620
-000b9b00d7aef8f5,xclick,/m/0jyfg,1,0.155000,0.230625,0.141194,0.197962,1,0,0,0,0,0.165000,0.155000,0.230625,0.215000,0.141194,0.164483,0.187773,0.197962
-000b9b00d7aef8f5,xclick,/m/0jyfg,1,0.473125,0.539375,0.145560,0.222707,1,0,0,0,0,0.535000,0.473125,0.539375,0.510625,0.145560,0.202329,0.180495,0.222707
-000b9b00d7aef8f5,xclick,/m/0jyfg,1,0.826250,0.901875,0.167394,0.218341,1,0,0,0,0,0.845625,0.826250,0.901875,0.880000,0.167394,0.184862,0.197962,0.218341
-000b9b00d7aef8f5,xclick,/m/0k65p,1,0.044375,0.117500,0.368268,0.544396,1,0,0,0,0,0.065000,0.044375,0.094375,0.117500,0.544396,0.502183,0.368268,0.395924
-000b9b00d7aef8f5,xclick,/m/0k65p,1,0.070000,0.146875,0.393013,0.544396,1,0,0,0,0,0.116250,0.146875,0.116250,0.070000,0.544396,0.478894,0.393013,0.499272
-000b9b00d7aef8f5,xclick,/m/0k65p,1,0.365000,0.480000,0.425036,0.574964,0,0,0,0,0,0.454375,0.480000,0.428750,0.365000,0.574964,0.481805,0.425036,0.496361
-000b9b00d7aef8f5,xclick,/m/0k65p,1,0.398125,0.474375,0.077147,0.317322,0,0,0,0,0,0.451250,0.474375,0.415000,0.398125,0.077147,0.209607,0.317322,0.266376
-000b9b00d7aef8f5,xclick,/m/0k65p,1,0.656875,0.735000,0.333333,0.574964,0,0,0,0,0,0.709375,0.656875,0.676250,0.735000,0.574964,0.388646,0.333333,0.490539
-000b9b00d7aef8f5,xclick,/m/0k65p,1,0.818125,0.900625,0.257642,0.526929,0,0,0,0,0,0.864375,0.818125,0.866875,0.900625,0.526929,0.393013,0.257642,0.475983
-000b9b61afea2cd4,xclick,/m/01g317,1,0.055823,0.222329,0.787772,0.935348,1,1,0,1,0,0.055823,0.222329,0.104909,0.057748,0.825720,0.935348,0.787772,0.829234
-000b9b61afea2cd4,xclick,/m/01g317,1,0.056785,0.260828,0.298665,0.589599,1,1,0,1,0,0.141482,0.260828,0.056785,0.146295,0.298665,0.367533,0.475053,0.589599
-000b9b61afea2cd4,xclick,/m/01g317,1,0.107796,0.262753,0.702038,0.851019,1,1,0,1,0,0.107796,0.187680,0.262753,0.194418,0.735067,0.851019,0.815179,0.702038
-000b9b61afea2cd4,xclick,/m/01g317,1,0.217517,0.337825,0.640197,0.851019,1,1,0,1,0,0.217517,0.323388,0.337825,0.337825,0.674631,0.851019,0.640197,0.655657
-000b9b61afea2cd4,xclick,/m/01g317,1,0.362849,0.485082,0.662684,0.808152,1,1,0,1,0,0.375361,0.362849,0.447546,0.485082,0.662684,0.808152,0.748419,0.664793
-000b9b61afea2cd4,xclick,/m/01g317,1,0.388835,0.642926,0.748419,0.940267,0,1,0,1,0,0.388835,0.480269,0.642926,0.485082,0.904427,0.940267,0.794800,0.748419
-000b9b61afea2cd4,xclick,/m/01g317,1,0.450433,0.689124,0.685172,0.837667,0,1,0,1,0,0.530318,0.689124,0.564966,0.450433,0.685172,0.743500,0.837667,0.784259
-000b9b61afea2cd4,xclick,/m/01g317,1,0.476420,0.856593,0.193254,0.680253,1,0,0,1,0,0.589028,0.476420,0.595765,0.856593,0.193254,0.277583,0.680253,0.630358
-000b9b61afea2cd4,xclick,/m/01g317,1,0.650626,0.969201,0.325369,0.674631,1,1,0,1,0,0.828681,0.969201,0.908566,0.650626,0.325369,0.392129,0.674631,0.513703
-000b9b61afea2cd4,xclick,/m/01n5jq,1,0.000000,0.999038,0.000000,0.990162,0,0,0,0,0,0.000000,0.999038,0.674687,0.689124,0.751933,0.581869,0.000000,0.990162
-000b9c365c9e307a,activemil,/m/0kmg4,1,0.012500,0.596875,0.017874,0.954845,-1,-1,-1,-1,-1,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000
-000b9c365c9e307a,xclick,/m/0167gd,1,0.000000,0.591250,0.007526,0.948260,0,0,0,0,0,0.346875,0.000000,0.591250,0.361875,0.007526,0.133584,0.585136,0.948260
-000b9d6c0f7d794d,xclick,/m/01g317,1,0.365957,0.689362,0.496815,0.971338,1,0,0,1,0,0.510638,0.365957,0.582979,0.689362,0.496815,0.866242,0.971338,0.697452
-000b9d6c0f7d794d,xclick,/m/01g317,1,0.468085,0.995745,0.079618,0.484076,1,1,1,1,0,0.957447,0.995745,0.995745,0.468085,0.079618,0.146497,0.484076,0.286624
-000b9d6c0f7d794d,xclick,/m/01g317,1,0.634043,0.995745,0.550955,0.996815,0,1,0,1,0,0.995745,0.995745,0.795745,0.634043,0.550955,0.748408,0.996815,0.993631
-000b9d6c0f7d794d,xclick,/m/0bt_c3,1,0.000000,0.995745,0.006369,0.996815,1,1,1,0,0,0.204255,0.000000,0.995745,0.191489,0.006369,0.340764,0.308917,0.996815
-000b9d6c0f7d794d,xclick,/m/0bt_c3,1,0.582979,0.995745,0.433121,0.996815,0,1,0,0,0,0.582979,0.893617,0.995745,0.791489,0.996815,0.996815,0.923567,0.433121
-000b9f3ba4891c11,xclick,/m/025rp__,1,0.189189,0.735736,0.084000,0.352000,0,0,0,0,0,0.456456,0.189189,0.735736,0.297297,0.084000,0.280000,0.220000,0.352000
-000b9f3ba4891c11,xclick,/m/02jfl0,1,0.183183,0.759760,0.094000,0.364000,1,0,0,0,0,0.183183,0.318318,0.759760,0.501502,0.286000,0.364000,0.176000,0.094000
-000b9f3ba4891c11,xclick,/m/02wbtzl,1,0.183183,0.747748,0.092000,0.360000,0,0,0,0,0,0.501502,0.747748,0.183183,0.300300,0.092000,0.186000,0.290000,0.360000
-000b9f3ba4891c11,xclick,/m/03fp41,1,0.006006,0.450450,0.612000,0.998000,0,0,0,0,0,0.063063,0.450450,0.006006,0.006006,0.612000,0.822000,0.998000,0.998000
-000b9f3ba4891c11,xclick,/m/04yx4,1,0.153153,0.996997,0.082000,0.998000,1,1,0,0,0,0.153153,0.483483,0.996997,0.708709,0.288000,0.082000,0.496000,0.998000
-000b9f3ba4891c11,xclick,/m/0c9ph5,1,0.000000,0.078078,0.856000,0.932000,0,1,0,0,0,0.012012,0.006006,0.000000,0.078078,0.856000,0.932000,0.896000,0.884000
-000b9f3ba4891c11,xclick,/m/0c9ph5,1,0.000000,0.240240,0.204000,0.686000,1,0,1,0,0,0.066066,0.027027,0.000000,0.240240,0.204000,0.686000,0.298000,0.376000
-000b9f3ba4891c11,xclick,/m/0c9ph5,1,0.012012,0.438438,0.642000,0.886000,1,0,1,0,0,0.153153,0.147147,0.012012,0.438438,0.642000,0.886000,0.750000,0.774000
-000b9f3ba4891c11,xclick,/m/0c9ph5,1,0.102102,0.174174,0.076000,0.120000,0,0,0,0,0,0.147147,0.132132,0.102102,0.174174,0.076000,0.120000,0.094000,0.090000
-000b9f3ba4891c11,xclick,/m/0c9ph5,1,0.225225,0.426426,0.768000,0.878000,0,0,0,0,0,0.336336,0.312312,0.426426,0.225225,0.878000,0.768000,0.804000,0.844000
-000b9f3ba4891c11,xclick,/m/0c9ph5,1,0.249249,0.402402,0.094000,0.158000,1,0,1,0,0,0.354354,0.321321,0.249249,0.402402,0.096000,0.158000,0.136000,0.094000
-000b9f3ba4891c11,xclick,/m/0c9ph5,1,0.654655,0.834835,0.106000,0.200000,1,0,0,0,0,0.747748,0.795796,0.654655,0.834835,0.106000,0.200000,0.156000,0.150000
-000b9f3ba4891c11,xclick,/m/0c9ph5,1,0.876877,0.909910,0.154000,0.176000,0,0,0,0,0,0.891892,0.891892,0.876877,0.909910,0.154000,0.176000,0.168000,0.158000
-000b9f3ba4891c11,xclick,/m/0c9ph5,1,0.978979,0.996997,0.252000,0.318000,0,1,0,0,0,0.990991,0.984985,0.978979,0.996997,0.252000,0.318000,0.280000,0.276000
-000b9f3ba4891c11,xclick,/m/0fm3zh,1,0.000000,0.081081,0.116000,0.212000,1,1,0,0,0,0.033033,0.000000,0.060060,0.081081,0.116000,0.150000,0.212000,0.132000
-000b9f3ba4891c11,xclick,/m/0fm3zh,1,0.000000,0.321321,0.864000,0.998000,1,1,0,0,0,0.270270,0.000000,0.093093,0.321321,0.864000,0.992000,0.998000,0.868000
-000b9f3ba4891c11,xclick,/m/0fm3zh,1,0.150150,0.285285,0.416000,0.502000,1,0,0,0,0,0.258258,0.150150,0.189189,0.285285,0.416000,0.438000,0.502000,0.468000
-000b9f3ba4891c11,xclick,/m/0fm3zh,1,0.660661,0.732733,0.358000,0.398000,1,0,0,0,0,0.687688,0.660661,0.690691,0.732733,0.358000,0.386000,0.398000,0.374000
-000b9f3ba4891c11,xclick,/m/0fm3zh,1,0.702703,0.780781,0.210000,0.272000,1,0,0,0,0,0.771772,0.702703,0.756757,0.780781,0.210000,0.264000,0.272000,0.222000
-000b9f3ba4891c11,xclick,/m/0fm3zh,1,0.852853,0.906907,0.216000,0.276000,1,0,0,0,0,0.891892,0.852853,0.873874,0.906907,0.216000,0.226000,0.276000,0.250000
-000b9f3ba4891c11,xclick,/m/0jyfg,1,0.330330,0.663664,0.232000,0.330000,1,0,0,0,0,0.615616,0.330330,0.663664,0.456456,0.232000,0.314000,0.254000,0.330000
-000ba221f70676c6,xclick,/m/01226z,1,0.660625,0.746875,0.818949,0.944653,0,0,0,0,0,0.704375,0.660625,0.746875,0.711875,0.818949,0.890244,0.890244,0.944653
-000ba221f70676c6,xclick,/m/01bl7v,1,0.000000,0.083750,0.000000,0.284240,1,1,0,0,0,0.000000,0.040625,0.083750,0.028750,0.258912,0.000000,0.149156,0.284240
-000ba221f70676c6,xclick,/m/01bl7v,1,0.000000,0.216875,0.095685,0.832083,0,0,0,0,0,0.000000,0.216875,0.128750,0.001875,0.405253,0.498124,0.095685,0.832083
-000ba221f70676c6,xclick,/m/01bl7v,1,0.819375,0.890000,0.105066,0.545966,1,0,0,0,0,0.819375,0.855625,0.890000,0.865625,0.183865,0.105066,0.507505,0.545966
-000ba221f70676c6,xclick,/m/05s2s,1,0.000000,0.999375,0.000000,0.999062,1,1,1,0,0,0.334375,0.999375,0.804375,0.000000,0.000000,0.451220,0.999062,0.416510
-000ba28d70b1a999,xclick,/m/01g317,1,0.000000,0.960600,0.000000,0.999375,0,1,0,0,0,0.540338,0.000000,0.300188,0.960600,0.000000,0.597500,0.999375,0.759375
-000ba28d70b1a999,xclick,/m/035r7c,1,0.000000,0.382739,0.174375,0.999375,0,0,0,0,0,0.112570,0.000000,0.176360,0.382739,0.174375,0.528125,0.999375,0.946250
-000ba28d70b1a999,xclick,/m/09j5n,1,0.249531,0.617261,0.585625,0.830625,1,0,0,0,0,0.545966,0.443715,0.249531,0.617261,0.585625,0.830625,0.725000,0.616250
-000ba28d70b1a999,xclick,/m/0fly7,1,0.004690,0.999062,0.396875,0.999375,0,0,0,0,0,0.124765,0.213884,0.004690,0.999062,0.396875,0.999375,0.781250,0.774375
-000ba28d70b1a999,xclick,/m/0k65p,1,0.086304,0.565666,0.233125,0.546875,1,0,0,0,0,0.383677,0.086304,0.565666,0.373358,0.233125,0.406875,0.357500,0.546875
-000ba28d70b1a999,xclick,/m/0k65p,1,0.275797,0.999062,0.207500,0.605000,1,0,0,0,0,0.772983,0.999062,0.275797,0.489681,0.207500,0.394375,0.440000,0.605000
-000ba3ca8a2ca955,xclick,/m/02dgv,1,0.000000,0.058161,0.613750,0.965625,0,1,0,0,0,0.058161,0.016886,0.058161,0.000000,0.618750,0.613750,0.960625,0.965625
-000ba3ca8a2ca955,xclick,/m/02dgv,1,0.880863,0.906191,0.678750,0.828750,1,0,0,0,0,0.898687,0.888368,0.906191,0.880863,0.678750,0.828750,0.823750,0.806875
-000ba3ca8a2ca955,xclick,/m/03jm5,1,0.000000,0.939963,0.003125,0.996875,0,1,0,0,0,0.000000,0.939963,0.895872,0.018762,0.785625,0.858750,0.003125,0.996875
-000ba3ca8a2ca955,xclick,/m/03jm5,1,0.959662,0.999062,0.131250,0.848750,1,1,0,0,0,0.959662,0.993433,0.999062,0.999062,0.716875,0.131250,0.848750,0.665625
-000ba3ca8a2ca955,xclick,/m/0d4v4,1,0.000000,0.090994,0.000000,0.246250,1,1,0,0,0,0.000000,0.068480,0.090994,0.083490,0.000000,0.000000,0.243125,0.246250
-000ba3ca8a2ca955,xclick,/m/0d4v4,1,0.237336,0.332083,0.007500,0.292500,1,0,0,0,0,0.237336,0.332083,0.330206,0.278612,0.007500,0.046875,0.277500,0.292500
-000ba3ca8a2ca955,xclick,/m/0d4v4,1,0.442777,0.531895,0.103125,0.315000,1,0,0,0,0,0.442777,0.514071,0.531895,0.496248,0.103125,0.125000,0.315000,0.315000
-000ba3ca8a2ca955,xclick,/m/0d4v4,1,0.706379,0.760788,0.238125,0.405625,1,0,0,0,0,0.709193,0.740150,0.760788,0.706379,0.238125,0.241250,0.383125,0.405625
-000ba3ca8a2ca955,xclick,/m/0d4v4,1,0.841463,0.897749,0.259375,0.463750,1,0,0,0,0,0.847092,0.897749,0.882739,0.841463,0.259375,0.375000,0.463750,0.363125
-000ba3ca8a2ca955,xclick,/m/0d4v4,1,0.851782,0.939024,0.245625,0.506250,1,0,0,0,0,0.872420,0.939024,0.910882,0.851782,0.245625,0.398750,0.506250,0.391875
-000ba3ca8a2ca955,xclick,/m/0d4v4,1,0.965291,0.999062,0.333750,0.413750,1,0,0,0,0,0.965291,0.999062,0.996248,0.965291,0.335625,0.333750,0.403750,0.413750
-000ba40bf7a2b458,xclick,/m/03jm5,1,0.000000,0.978333,0.000000,0.957286,0,0,0,0,0,0.763333,0.978333,0.000000,0.710000,0.000000,0.301508,0.500000,0.957286
-000ba40bf7a2b458,xclick,/m/03jm5,1,0.898333,0.998333,0.520100,0.859297,0,1,0,0,0,0.998333,0.898333,0.918333,0.998333,0.520100,0.638191,0.831658,0.859297
-000ba40bf7a2b458,xclick,/m/05s2s,1,0.000000,0.998333,0.836683,0.997487,1,1,0,0,0,0.000000,0.998333,0.983333,0.768333,0.861809,0.836683,0.997487,0.841709
-000ba40bf7a2b458,xclick,/m/05s2s,1,0.066667,0.111667,0.798995,0.864322,0,0,0,0,0,0.066667,0.080000,0.111667,0.098333,0.839196,0.798995,0.831658,0.864322
-000ba40bf7a2b458,xclick,/m/0d4v4,1,0.906667,0.955000,0.660804,0.726131,1,0,0,0,0,0.906667,0.955000,0.953333,0.908333,0.665829,0.660804,0.721106,0.726131
-000ba40bf7a2b458,xclick,/m/0d4v4,1,0.970000,0.998333,0.658291,0.721106,0,1,0,0,0,0.970000,0.971667,0.998333,0.998333,0.660804,0.721106,0.721106,0.658291
-000ba940f8cfc9bf,xclick,/m/014sv8,1,0.329167,0.357500,0.180258,0.261803,1,0,0,0,0,0.329167,0.355000,0.338333,0.357500,0.180258,0.193133,0.244635,0.261803
-000ba940f8cfc9bf,xclick,/m/014sv8,1,0.459167,0.490833,0.503577,0.543634,0,0,0,0,0,0.490833,0.459167,0.475833,0.475833,0.535050,0.515021,0.503577,0.543634
-000ba940f8cfc9bf,xclick,/m/0283dt1,1,0.289167,0.330833,0.307582,0.386266,0,0,0,0,0,0.289167,0.320833,0.291667,0.330833,0.307582,0.313305,0.386266,0.341917
-000ba940f8cfc9bf,xclick,/m/0283dt1,1,0.408333,0.434167,0.537911,0.579399,0,0,0,0,0,0.434167,0.427500,0.408333,0.415833,0.573677,0.537911,0.579399,0.546495
-000ba940f8cfc9bf,xclick,/m/039xj_,1,0.179167,0.236667,0.035765,0.177396,1,0,0,0,0,0.236667,0.199167,0.179167,0.207500,0.037196,0.035765,0.137339,0.177396
-000ba940f8cfc9bf,xclick,/m/03bt1vf,1,0.000000,0.650833,0.000000,0.998569,1,1,0,0,0,0.226667,0.650833,0.000000,0.118333,0.000000,0.958512,0.735336,0.998569
-000ba940f8cfc9bf,xclick,/m/04hgtk,1,0.000000,0.417500,0.000000,0.450644,1,1,0,0,0,0.313333,0.417500,0.274167,0.000000,0.000000,0.067239,0.450644,0.264664
-000ba940f8cfc9bf,xclick,/m/04hgtk,1,0.400833,0.587500,0.457797,0.646638,1,0,0,0,0,0.484167,0.587500,0.400833,0.440833,0.457797,0.557940,0.592275,0.646638
-000ba940f8cfc9bf,xclick,/m/0dzct,1,0.203333,0.384167,0.070100,0.450644,1,0,0,0,0,0.355000,0.384167,0.203333,0.275000,0.070100,0.164521,0.224607,0.450644
-000ba940f8cfc9bf,xclick,/m/0dzct,1,0.408333,0.507500,0.473534,0.638054,1,0,0,0,0,0.479167,0.408333,0.439167,0.507500,0.473534,0.559371,0.638054,0.519313
-000baa6f7dae9b79,activemil,/m/01g317,1,0.773125,0.959375,0.245833,0.941667,1,0,0,1,0,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000
-000baa6f7dae9b79,xclick,/m/01g317,1,0.000000,0.999375,0.205000,0.921667,1,1,1,1,0,0.161875,0.000000,0.394375,0.999375,0.205000,0.369167,0.921667,0.590833
-000baa6f7dae9b79,xclick,/m/01g317,1,0.017500,0.044375,0.198333,0.235000,1,0,0,0,0,0.034375,0.017500,0.036875,0.044375,0.198333,0.220833,0.235000,0.220833
-000baa6f7dae9b79,xclick,/m/01g317,1,0.062500,0.097500,0.203333,0.250000,1,0,0,0,0,0.083125,0.097500,0.065000,0.062500,0.203333,0.225833,0.250000,0.245000
-000baa6f7dae9b79,xclick,/m/01g317,1,0.332500,0.362500,0.188333,0.235000,1,0,0,0,0,0.346875,0.362500,0.344375,0.332500,0.188333,0.222500,0.235000,0.230833
-000baa6f7dae9b79,xclick,/m/01g317,1,0.486250,0.561250,0.162500,0.241667,1,0,0,0,0,0.528750,0.561250,0.520000,0.486250,0.162500,0.225833,0.241667,0.225833
-000baa6f7dae9b79,xclick,/m/01j51,1,0.000000,0.999375,0.206667,0.497500,1,1,1,0,0,0.166250,0.122500,0.000000,0.999375,0.206667,0.497500,0.368333,0.282500
-000baa6f7dae9b79,xclick,/m/07j7r,1,0.000000,0.161250,0.000000,0.217500,1,1,1,0,0,0.056250,0.000000,0.058750,0.161250,0.000000,0.067500,0.217500,0.174167
-000baa6f7dae9b79,xclick,/m/07j7r,1,0.113125,0.205000,0.000000,0.218333,1,1,0,0,0,0.205000,0.165000,0.146875,0.113125,0.218333,0.000000,0.000000,0.020833
-000baa6f7dae9b79,xclick,/m/07j7r,1,0.305625,0.800625,0.000000,0.203333,1,1,1,0,0,0.305625,0.486875,0.800625,0.471250,0.127500,0.000000,0.072500,0.203333
-000baa6f7dae9b79,xclick,/m/07j7r,1,0.317500,0.482500,0.004167,0.207500,1,1,1,0,0,0.389375,0.398750,0.317500,0.482500,0.004167,0.170833,0.119167,0.207500
-000baa6f7dae9b79,xclick,/m/07j7r,1,0.328750,0.515000,0.000000,0.055833,0,1,0,0,0,0.432500,0.450000,0.515000,0.328750,0.000000,0.055833,0.015833,0.009167
-000baa6f7dae9b79,xclick,/m/07j7r,1,0.494375,0.695625,0.000000,0.232500,1,1,0,0,0,0.600625,0.695625,0.586250,0.494375,0.000000,0.114167,0.232500,0.105833
-000baa6f7dae9b79,xclick,/m/07j7r,1,0.706250,0.781250,0.005833,0.167500,1,1,0,0,0,0.770625,0.734375,0.781250,0.706250,0.005833,0.167500,0.130000,0.086667
-000baa6f7dae9b79,xclick,/m/07j7r,1,0.799375,0.921875,0.000000,0.235833,1,1,0,0,0,0.833750,0.921875,0.843125,0.799375,0.235833,0.172500,0.000000,0.000000
-000baa6f7dae9b79,xclick,/m/09j2d,1,0.334375,0.361250,0.195833,0.235833,1,0,0,0,0,0.334375,0.352500,0.361250,0.336875,0.220000,0.195833,0.220000,0.235833
-000baa6f7dae9b79,xclick,/m/09j2d,1,0.495000,0.561250,0.200833,0.239167,1,0,0,0,0,0.521875,0.495000,0.561250,0.553125,0.200833,0.223333,0.221667,0.239167
-000bab5b1a67844e,xclick,/m/03jm5,1,0.000000,0.235625,0.078889,0.853333,0,0,0,0,0,0.033125,0.000000,0.235625,0.135625,0.078889,0.258889,0.395556,0.853333
-000bab5b1a67844e,xclick,/m/03jm5,1,0.284375,0.548125,0.181111,0.843333,0,0,0,0,0,0.548125,0.298125,0.284375,0.356250,0.366667,0.181111,0.208889,0.843333
-000bab5b1a67844e,xclick,/m/03jm5,1,0.540625,0.944375,0.131111,0.840000,0,0,0,0,0,0.828750,0.540625,0.944375,0.738125,0.131111,0.517778,0.370000,0.840000
-000bab5b1a67844e,xclick,/m/0d4v4,1,0.000000,0.015000,0.316667,0.448889,1,1,0,0,0,0.000000,0.015000,0.010000,0.000000,0.323333,0.316667,0.412222,0.448889
-000bab5b1a67844e,xclick,/m/0d4v4,1,0.007500,0.063750,0.145556,0.246667,1,1,0,0,0,0.007500,0.063750,0.062500,0.058750,0.145556,0.171111,0.234444,0.246667
-000bab5b1a67844e,xclick,/m/0d4v4,1,0.040625,0.101250,0.327778,0.440000,1,0,0,0,0,0.040625,0.101250,0.098750,0.045625,0.327778,0.337778,0.440000,0.428889
-000bab5b1a67844e,xclick,/m/0d4v4,1,0.185625,0.231875,0.360000,0.483333,1,0,0,0,0,0.189375,0.221250,0.231875,0.185625,0.360000,0.362222,0.483333,0.478889
-000bab5b1a67844e,xclick,/m/0d4v4,1,0.205000,0.236875,0.227778,0.312222,1,0,0,0,0,0.205000,0.222500,0.236875,0.213750,0.227778,0.238889,0.303333,0.312222
-000bab5b1a67844e,xclick,/m/0d4v4,1,0.308750,0.336875,0.271111,0.351111,1,0,0,0,0,0.308750,0.323750,0.336875,0.312500,0.271111,0.282222,0.340000,0.351111
-000bab5b1a67844e,xclick,/m/0d4v4,1,0.462500,0.495625,0.357778,0.426667,1,0,0,0,0,0.462500,0.491875,0.495625,0.494375,0.357778,0.360000,0.421111,0.426667
-000bab5b1a67844e,xclick,/m/0d4v4,1,0.576250,0.606875,0.318889,0.421111,0,0,0,0,0,0.576250,0.606875,0.605625,0.578750,0.335556,0.318889,0.398889,0.421111
-000bab5b1a67844e,xclick,/m/0d4v4,1,0.650625,0.680000,0.290000,0.390000,0,0,0,0,0,0.650625,0.672500,0.680000,0.655625,0.301111,0.290000,0.371111,0.390000
-000bab5b1a67844e,xclick,/m/0d4v4,1,0.729375,0.784375,0.224444,0.343333,1,0,0,0,0,0.781875,0.784375,0.735625,0.729375,0.224444,0.326667,0.343333,0.254444
-000bb0ae453283b0,xclick,/m/015qff,1,0.081875,0.086875,0.463303,0.477982,0,0,0,0,0,0.081875,0.086875,0.085625,0.081875,0.465138,0.463303,0.477982,0.477982
-000bb0ae453283b0,xclick,/m/015qff,1,0.106250,0.111250,0.463303,0.477982,0,0,0,0,0,0.106250,0.111250,0.110000,0.106250,0.465138,0.463303,0.477982,0.477982
-000bb0ae453283b0,xclick,/m/015qff,1,0.191875,0.196875,0.474312,0.497248,0,0,0,0,0,0.194375,0.191875,0.194375,0.196875,0.474312,0.485321,0.497248,0.489908
-000bb0ae453283b0,xclick,/m/015qff,1,0.309375,0.318125,0.395413,0.423853,0,0,0,0,0,0.311250,0.316875,0.309375,0.318125,0.395413,0.396330,0.423853,0.420183
-000bb0ae453283b0,xclick,/m/015qff,1,0.348750,0.361875,0.404587,0.438532,0,0,0,0,0,0.357500,0.348750,0.356250,0.361875,0.438532,0.422018,0.404587,0.417431
-000bb0ae453283b0,xclick,/m/015qff,1,0.440625,0.448125,0.472477,0.511927,0,0,0,0,0,0.445625,0.440625,0.443125,0.448125,0.472477,0.489908,0.511927,0.491743
-000bb0ae453283b0,xclick,/m/015qff,1,0.523750,0.530000,0.485321,0.502752,0,0,0,0,0,0.527500,0.530000,0.526250,0.523750,0.485321,0.493578,0.502752,0.493578
-000bb0ae453283b0,xclick,/m/015qff,1,0.536875,0.541875,0.493578,0.504587,0,0,0,0,0,0.539375,0.541875,0.540625,0.536875,0.493578,0.502752,0.504587,0.500917
-000bb0ae453283b0,xclick,/m/015qff,1,0.715000,0.721250,0.466972,0.495413,1,0,0,0,0,0.720000,0.715000,0.717500,0.721250,0.466972,0.479817,0.495413,0.485321
-000bb0ae453283b0,xclick,/m/015qff,1,0.893125,0.901875,0.468807,0.502752,0,0,0,0,0,0.899375,0.901875,0.898125,0.893125,0.468807,0.489908,0.502752,0.485321
-000bb0ae453283b0,xclick,/m/033rq4,1,0.086875,0.140625,0.427523,0.523853,1,0,0,0,0,0.115000,0.086875,0.089375,0.140625,0.523853,0.438532,0.427523,0.429358
-000bb0ae453283b0,xclick,/m/033rq4,1,0.120625,0.211250,0.145872,0.540367,1,0,0,0,0,0.168125,0.120625,0.147500,0.211250,0.540367,0.171560,0.145872,0.169725
-000bb0ae453283b0,xclick,/m/033rq4,1,0.130625,0.190625,0.291743,0.522018,1,0,0,0,0,0.165000,0.130625,0.140625,0.190625,0.522018,0.307339,0.291743,0.305505
-000bb0ae453283b0,xclick,/m/033rq4,1,0.206250,0.386875,0.121101,0.570642,1,0,0,0,0,0.386875,0.206250,0.231875,0.305625,0.164220,0.164220,0.121101,0.570642
-000bb0ae453283b0,xclick,/m/033rq4,1,0.375625,0.445625,0.203670,0.540367,1,0,0,0,0,0.406250,0.375625,0.386875,0.445625,0.540367,0.212844,0.203670,0.226606
-000bb0ae453283b0,xclick,/m/033rq4,1,0.841875,0.868750,0.296330,0.540367,1,0,0,0,0,0.861250,0.841875,0.863750,0.868750,0.540367,0.332110,0.296330,0.312844
-000bb0ae453283b0,xclick,/m/033rq4,1,0.945625,0.962500,0.343119,0.525688,1,0,0,0,0,0.951875,0.945625,0.958125,0.962500,0.525688,0.371560,0.343119,0.348624
-000bb0ae453283b0,xclick,/m/079cl,1,0.263125,0.399375,0.138532,0.512844,0,0,0,0,0,0.263125,0.340000,0.399375,0.348125,0.498165,0.138532,0.199083,0.512844
-000bb200fc78fc30,xclick,/m/05s2s,1,0.000000,0.995313,0.651563,0.998438,1,1,0,0,0,0.000000,0.000000,0.445312,0.995313,0.656250,0.942187,0.998438,0.651563
-000bb200fc78fc30,xclick,/m/09ld4,1,0.325000,0.950000,0.314063,0.942187,0,0,0,0,0,0.765625,0.950000,0.531250,0.325000,0.942187,0.609375,0.314063,0.517188
-000bb2f7132013dc,activemil,/m/0d5gx,1,0.013021,0.924479,0.231250,0.970312,-1,-1,-1,-1,-1,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000
-000bb2f7132013dc,xclick,/m/01fdzj,1,0.562500,0.798177,0.212500,0.693750,0,0,0,0,0,0.677083,0.562500,0.798177,0.798177,0.212500,0.367969,0.693750,0.693750
-000bb2f7132013dc,xclick,/m/01x3z,1,0.621094,0.682292,0.368750,0.411719,0,0,0,0,0,0.649740,0.682292,0.653646,0.621094,0.368750,0.391406,0.411719,0.394531
-000bb2f7132013dc,xclick,/m/07j7r,1,0.000000,0.298177,0.619531,0.999219,0,1,0,0,0,0.062500,0.000000,0.298177,0.053385,0.619531,0.704688,0.835156,0.999219
-000bb2f7132013dc,xclick,/m/07j7r,1,0.269531,0.839844,0.787500,0.999219,0,1,0,0,0,0.621094,0.516927,0.269531,0.839844,0.787500,0.999219,0.994531,0.996094
-000bb2f7132013dc,xclick,/m/07j7r,1,0.782552,0.998698,0.619531,0.877344,1,1,1,0,0,0.942708,0.782552,0.997396,0.998698,0.636719,0.619531,0.877344,0.857031
-000bb2f7132013dc,xclick,/m/0d4v4,1,0.320312,0.360677,0.703125,0.776563,0,0,0,0,0,0.320312,0.334635,0.355469,0.360677,0.776563,0.714844,0.703125,0.706250
-000bb2f7132013dc,xclick,/m/0d4v4,1,0.385417,0.430990,0.871094,0.912500,0,0,0,0,0,0.385417,0.421875,0.430990,0.393229,0.876563,0.871094,0.912500,0.910937
-000bb2f7132013dc,xclick,/m/0d4v4,1,0.424479,0.446615,0.800781,0.833594,0,0,0,0,0,0.424479,0.424479,0.446615,0.446615,0.833594,0.800781,0.800781,0.833594
-000bb2f7132013dc,xclick,/m/0d4v4,1,0.460938,0.486979,0.802344,0.833594,0,0,0,0,0,0.460938,0.472656,0.481771,0.486979,0.833594,0.803906,0.802344,0.832031
-000bb2f7132013dc,xclick,/m/0d4v4,1,0.486979,0.529948,0.866406,0.922656,1,0,0,0,0,0.498698,0.489583,0.486979,0.529948,0.872656,0.903906,0.922656,0.866406
-000bb2f7132013dc,xclick,/m/0d4v4,1,0.606771,0.641927,0.431250,0.489844,0,0,0,0,0,0.615885,0.606771,0.632812,0.641927,0.489844,0.471094,0.432812,0.431250
-000bb2f7132013dc,xclick,/m/0d4v4,1,0.658854,0.692708,0.428125,0.492969,0,0,0,0,0,0.661458,0.658854,0.675781,0.692708,0.489844,0.465625,0.428125,0.492969
-000bb2f7132013dc,xclick,/m/0d4v4,1,0.678385,0.710938,0.835156,0.879687,0,0,0,0,0,0.678385,0.682292,0.690104,0.710938,0.879687,0.842187,0.835156,0.871094
-000bb81adefe7332,xclick,/m/01g317,1,0.290000,0.393125,0.081667,0.296667,1,0,0,0,0,0.325625,0.290000,0.393125,0.393125,0.081667,0.130833,0.211667,0.296667
-000bb81adefe7332,xclick,/m/01n5jq,1,0.385625,0.980000,0.005833,0.995833,0,1,0,0,0,0.385625,0.980000,0.959375,0.397500,0.007500,0.005833,0.995833,0.989167
-000bb846e2629e83,xclick,/m/01mzpv,1,0.000000,0.998145,0.437500,0.998958,1,1,0,0,0,0.927644,0.220779,0.000000,0.998145,0.437500,0.998958,0.780208,0.691667
-000bb846e2629e83,xclick,/m/035r7c,1,0.000000,0.226345,0.723958,0.994792,1,0,0,0,0,0.152134,0.000000,0.011132,0.226345,0.723958,0.848958,0.994792,0.784375
-000bb846e2629e83,xclick,/m/0dzf4,1,0.217069,0.955473,0.460417,0.704167,1,0,0,0,0,0.873840,0.955473,0.333952,0.217069,0.460417,0.508333,0.704167,0.666667
-000bb846e2629e83,xclick,/m/0dzf4,1,0.419295,0.669759,0.366667,0.479167,1,0,0,0,0,0.575139,0.419295,0.512059,0.669759,0.366667,0.458333,0.479167,0.372917
-000bb846e2629e83,xclick,/m/0k65p,1,0.207792,0.417440,0.625000,0.706250,1,0,0,0,0,0.307978,0.417440,0.207792,0.304267,0.625000,0.658333,0.665625,0.706250
-000bb846e2629e83,xclick,/m/0k65p,1,0.519481,0.649351,0.382292,0.439583,1,0,0,0,0,0.552876,0.519481,0.614100,0.649351,0.439583,0.423958,0.382292,0.403125
-000bb8bd9b1bca65,xclick,/m/0138tl,1,0.440000,0.646875,0.790000,0.999167,0,1,0,0,0,0.465625,0.440000,0.561250,0.646875,0.790000,0.928333,0.999167,0.828333
-000bb8bd9b1bca65,xclick,/m/0220r2,1,0.211250,0.566250,0.515000,0.805833,0,0,0,0,0,0.385625,0.397500,0.211250,0.566250,0.515000,0.805833,0.722500,0.709167
-000bb8bd9b1bca65,xclick,/m/07j7r,1,0.000000,0.099375,0.355000,0.583333,0,1,0,0,0,0.024375,0.000000,0.099375,0.055000,0.355000,0.463333,0.495833,0.583333
-000bb8bd9b1bca65,xclick,/m/07j7r,1,0.000000,0.254375,0.034167,0.580000,0,1,0,0,0,0.213750,0.000000,0.254375,0.125000,0.034167,0.294167,0.255000,0.580000
-000bb8bd9b1bca65,xclick,/m/07j7r,1,0.000000,0.294375,0.000000,0.195833,0,1,0,0,0,0.181875,0.000000,0.294375,0.293125,0.000000,0.195833,0.065000,0.065000
-000bb8bd9b1bca65,xclick,/m/07j7r,1,0.254375,0.424375,0.000000,0.320000,0,1,0,0,0,0.361250,0.254375,0.424375,0.390000,0.000000,0.305000,0.299167,0.320000
-000bb8bd9b1bca65,xclick,/m/07j7r,1,0.317500,0.470000,0.330833,0.535833,1,0,0,0,0,0.392500,0.317500,0.470000,0.397500,0.330833,0.423333,0.400833,0.535833
-000bb8bd9b1bca65,xclick,/m/07j7r,1,0.426875,0.999375,0.000000,0.484167,0,1,0,0,0,0.426875,0.999375,0.999375,0.801875,0.000000,0.000000,0.150000,0.484167
-000bb8bd9b1bca65,xclick,/m/07j7r,1,0.460625,0.550000,0.315000,0.568333,0,0,0,0,0,0.511250,0.460625,0.550000,0.517500,0.315000,0.499167,0.463333,0.568333
-000bb8bd9b1bca65,xclick,/m/07j7r,1,0.551250,0.630000,0.306667,0.545833,0,0,0,0,0,0.586250,0.551250,0.626250,0.630000,0.306667,0.444167,0.492500,0.545833
-000bb8bd9b1bca65,xclick,/m/07j7r,1,0.626250,0.707500,0.256667,0.585833,0,0,0,0,0,0.662500,0.626250,0.707500,0.680625,0.256667,0.439167,0.485833,0.585833
-000bbdf0dc8099d8,xclick,/m/01g317,1,0.000000,0.346516,0.421250,0.903750,0,1,0,0,0,0.248588,0.346516,0.000000,0.080038,0.421250,0.798125,0.660625,0.903750
-000bbdf0dc8099d8,xclick,/m/01g317,1,0.403955,0.632768,0.087500,0.964375,1,0,0,1,0,0.632768,0.506591,0.403955,0.604520,0.941875,0.087500,0.240625,0.964375
-000bbdf0dc8099d8,xclick,/m/01g317,1,0.416196,0.745763,0.010625,0.319375,1,0,0,1,0,0.478343,0.416196,0.745763,0.689266,0.010625,0.201250,0.290000,0.319375
-000bbdf0dc8099d8,xclick,/m/01yx86,1,0.378531,0.749529,0.012500,0.978125,0,0,0,0,0,0.598870,0.475518,0.749529,0.378531,0.978125,0.012500,0.279375,0.851875
-000bbdf0dc8099d8,xclick,/m/02p0tk3,1,0.000000,0.310734,0.424375,0.903750,0,1,0,0,0,0.243879,0.310734,0.000000,0.087571,0.424375,0.540000,0.638750,0.903750
-000bbdf0dc8099d8,xclick,/m/02p0tk3,1,0.398305,0.607345,0.086875,0.968125,0,0,0,1,0,0.514124,0.398305,0.586629,0.607345,0.086875,0.516875,0.243750,0.968125
-000bbdf0dc8099d8,xclick,/m/02p0tk3,1,0.439736,0.741055,0.010000,0.327500,1,0,0,1,0,0.475518,0.439736,0.741055,0.699623,0.010000,0.057500,0.285000,0.327500
-000bbdf0dc8099d8,xclick,/m/035r7c,1,0.000000,0.125235,0.596875,0.905000,-1,-1,-1,-1,-1,0.001883,0.000000,0.081921,0.125235,0.596875,0.627500,0.905000,0.894375
-000bbdf0dc8099d8,xclick,/m/035r7c,1,0.145009,0.340866,0.588125,0.811875,1,0,0,0,0,0.145009,0.340866,0.309793,0.183616,0.600000,0.790000,0.811875,0.588125
-000bbdf0dc8099d8,xclick,/m/0dzf4,1,0.000000,0.236347,0.512500,0.747500,0,0,0,0,0,0.023540,0.000000,0.236347,0.218456,0.512500,0.603125,0.698125,0.747500
-000bbdf0dc8099d8,xclick,/m/0dzf4,1,0.177024,0.246704,0.479375,0.737500,1,0,0,0,0,0.228814,0.177024,0.228814,0.246704,0.479375,0.583750,0.737500,0.598750
-000bbdf0dc8099d8,xclick,/m/0dzf4,1,0.387947,0.496234,0.129375,0.341250,0,0,0,1,0,0.387947,0.450094,0.472693,0.496234,0.341250,0.328750,0.129375,0.141250
-000bbdf0dc8099d8,xclick,/m/0dzf4,1,0.500942,0.576271,0.176875,0.360000,0,0,0,1,0,0.545198,0.576271,0.500942,0.508475,0.176875,0.195625,0.341250,0.360000
-000bc1eb7f74adae,xclick,/m/01xyhv,1,0.000000,0.391875,0.254206,0.994393,0,1,0,0,0,0.140625,0.391875,0.358125,0.000000,0.254206,0.872897,0.994393,0.388785
-000bc1eb7f74adae,xclick,/m/01xyhv,1,0.655625,0.999375,0.298131,0.999065,1,1,0,0,0,0.891250,0.999375,0.799375,0.655625,0.298131,0.591589,0.999065,0.752336
-000bc1eb7f74adae,xclick,/m/04yx4,1,0.000000,0.422500,0.000000,0.992523,1,1,0,0,0,0.242500,0.000000,0.422500,0.283750,0.000000,0.435514,0.805607,0.992523
-000bc1eb7f74adae,xclick,/m/04yx4,1,0.603750,0.999375,0.040187,0.999065,1,1,0,0,0,0.603750,0.817500,0.789375,0.999375,0.842991,0.040187,0.999065,0.760748
-000bc33717a6371f,xclick,/m/01mzpv,1,0.014375,0.300625,0.422500,0.796667,1,0,0,0,0,0.091250,0.014375,0.275625,0.300625,0.422500,0.493333,0.796667,0.597500
-000bc33717a6371f,xclick,/m/01mzpv,1,0.258125,0.363750,0.320000,0.520000,1,0,0,0,0,0.260625,0.363750,0.357500,0.258125,0.320000,0.413333,0.520000,0.327500
-000bc33717a6371f,xclick,/m/01mzpv,1,0.755000,0.918125,0.410000,0.794167,1,0,0,0,0,0.856250,0.918125,0.818750,0.755000,0.410000,0.616667,0.794167,0.430833
-000bc33717a6371f,xclick,/m/01s105,1,0.505625,0.798125,0.463333,0.711667,0,0,0,0,0,0.583750,0.505625,0.730625,0.798125,0.463333,0.486667,0.711667,0.550000
-000bc33717a6371f,xclick,/m/01y9k5,1,0.738750,0.999375,0.458333,0.857500,1,1,0,0,0,0.738750,0.999375,0.879375,0.884375,0.519167,0.554167,0.857500,0.458333
-000bc33717a6371f,xclick,/m/03m3pdh,1,0.000000,0.446875,0.573333,0.999167,0,1,0,0,0,0.107500,0.000000,0.123125,0.446875,0.573333,0.719167,0.999167,0.980833
-000bc33717a6371f,xclick,/m/03rszm,1,0.131875,0.288125,0.015000,0.332500,1,0,0,0,0,0.221875,0.184375,0.131875,0.288125,0.332500,0.015000,0.157500,0.136667
-000bc33717a6371f,xclick,/m/04bcr3,1,0.247500,0.455625,0.334167,0.554167,1,0,0,0,0,0.247500,0.455625,0.367500,0.343125,0.339167,0.356667,0.554167,0.334167
-000bc33717a6371f,xclick,/m/04bcr3,1,0.739375,0.999375,0.474167,0.853333,1,1,0,0,0,0.739375,0.876875,0.969375,0.999375,0.503333,0.853333,0.474167,0.575000
-000bc33717a6371f,xclick,/m/07c52,1,0.308125,0.381875,0.146667,0.255000,0,0,0,0,0,0.376250,0.381875,0.365000,0.308125,0.255000,0.255000,0.146667,0.153333
-000bc33717a6371f,xclick,/m/07c52,1,0.564375,0.703750,0.152500,0.325000,0,0,0,0,0,0.689375,0.638750,0.703750,0.564375,0.152500,0.325000,0.157500,0.162500
-000bc33717a6371f,xclick,/m/0dtln,1,0.375000,0.446875,0.199167,0.358333,0,0,0,0,0,0.440000,0.375000,0.411875,0.446875,0.358333,0.268333,0.199167,0.303333
-000bc33717a6371f,xclick,/m/0gjbg72,1,0.325000,0.445000,0.268333,0.313333,1,0,0,0,0,0.352500,0.325000,0.445000,0.416875,0.268333,0.292500,0.299167,0.313333
-000bc33717a6371f,xclick,/m/0gjbg72,1,0.510000,0.804375,0.492500,0.702500,1,0,0,0,0,0.518750,0.510000,0.783750,0.804375,0.492500,0.602500,0.557500,0.702500
-000bc33717a6371f,xclick,/m/0gjbg72,1,0.591250,0.774375,0.330833,0.434167,1,0,0,0,0,0.603125,0.591250,0.773125,0.774375,0.330833,0.370000,0.382500,0.434167
-000bc387c731dd97,activemil,/m/04yx4,1,0.785000,0.986875,0.091111,0.965556,1,1,0,0,0,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000,-1.000000
-000bc387c731dd97,xclick,/m/03bt1vf,1,0.323750,0.393125,0.290000,0.522222,1,0,0,0,0,0.345625,0.323750,0.375000,0.393125,0.290000,0.322222,0.522222,0.427778
-000bc387c731dd97,xclick,/m/03bt1vf,1,0.575000,0.645625,0.210000,0.998889,1,1,0,0,0,0.635625,0.645625,0.575000,0.622500,0.210000,0.214444,0.503333,0.998889
-000bc387c731dd97,xclick,/m/03bt1vf,1,0.651875,0.767500,0.275556,0.998889,1,1,0,0,0,0.736875,0.651875,0.710000,0.767500,0.275556,0.548889,0.998889,0.867778
-000bc387c731dd97,xclick,/m/03bt1vf,1,0.916250,0.982500,0.181111,0.407778,1,0,0,0,0,0.923750,0.982500,0.975000,0.916250,0.181111,0.322222,0.407778,0.255556
-000bc387c731dd97,xclick,/m/04rky,1,0.000000,0.017500,0.244444,0.368889,1,1,0,0,0,0.005000,0.000000,0.000000,0.017500,0.244444,0.314444,0.368889,0.316667
-000bc387c731dd97,xclick,/m/04rky,1,0.000000,0.101250,0.268889,0.594444,1,1,0,0,0,0.020000,0.000000,0.101250,0.101250,0.268889,0.457778,0.594444,0.594444
-000bc387c731dd97,xclick,/m/04rky,1,0.111250,0.231875,0.227778,0.590000,1,0,0,0,0,0.181875,0.111250,0.231875,0.216250,0.227778,0.390000,0.431111,0.590000
-000bc387c731dd97,xclick,/m/04rky,1,0.273750,0.339375,0.260000,0.592222,1,0,0,0,0,0.301875,0.273750,0.288125,0.339375,0.260000,0.476667,0.592222,0.462222
-000bc387c731dd97,xclick,/m/04rky,1,0.322500,0.400625,0.294444,0.565556,1,0,0,0,0,0.348125,0.322500,0.400625,0.366250,0.294444,0.316667,0.481111,0.565556
-000bc387c731dd97,xclick,/m/04rky,1,0.375000,0.468750,0.236667,0.603333,1,0,0,0,0,0.440625,0.375000,0.418750,0.468750,0.236667,0.553333,0.603333,0.327778
-000bc387c731dd97,xclick,/m/04rky,1,0.412500,0.586875,0.160000,0.988889,1,0,0,0,0,0.548125,0.412500,0.465000,0.586875,0.160000,0.834444,0.988889,0.470000
-000bc387c731dd97,xclick,/m/04rky,1,0.486875,0.609375,0.186667,0.867778,1,0,0,0,0,0.589375,0.486875,0.538125,0.609375,0.186667,0.583333,0.867778,0.253333
-000bc387c731dd97,xclick,/m/04rky,1,0.562500,0.643125,0.157778,0.431111,1,0,0,0,0,0.615000,0.562500,0.568750,0.643125,0.157778,0.394444,0.431111,0.193333
-000bc387c731dd97,xclick,/m/04rky,1,0.585625,0.655625,0.207778,0.492222,1,0,0,0,0,0.625000,0.586875,0.655625,0.585625,0.207778,0.492222,0.207778,0.492222
-000bc387c731dd97,xclick,/m/04rky,1,0.585625,0.696875,0.205556,0.542222,1,0,0,0,0,0.650625,0.585625,0.596875,0.696875,0.205556,0.503333,0.542222,0.318889
-000bc387c731dd97,xclick,/m/04rky,1,0.586875,0.743125,0.207778,0.998889,1,0,0,0,0,0.725000,0.586875,0.650625,0.743125,0.207778,0.626667,0.998889,0.218889
-000bc387c731dd97,xclick,/m/04rky,1,0.650625,0.759375,0.271111,0.998889,1,0,0,0,0,0.732500,0.650625,0.682500,0.759375,0.271111,0.553333,0.998889,0.861111
-000bc387c731dd97,xclick,/m/04rky,1,0.672500,0.748125,0.088889,0.327778,1,0,0,0,0,0.717500,0.672500,0.686250,0.748125,0.088889,0.268889,0.327778,0.173333
-000bc387c731dd97,xclick,/m/04rky,1,0.694375,0.825000,0.160000,0.998889,1,1,0,0,0,0.768750,0.694375,0.825000,0.790625,0.160000,0.517778,0.346667,0.998889
-000bc387c731dd97,xclick,/m/04rky,1,0.776250,0.867500,0.188889,0.998889,1,1,0,0,0,0.841875,0.776250,0.809375,0.867500,0.188889,0.628889,0.998889,0.292222
-000bc387c731dd97,xclick,/m/04rky,1,0.792500,0.995625,0.111111,0.998889,1,1,0,0,0,0.883750,0.792500,0.868125,0.995625,0.111111,0.817778,0.998889,0.983333
-000bc387c731dd97,xclick,/m/04rky,1,0.910625,0.954375,0.177778,0.310000,1,0,0,0,0,0.928750,0.910625,0.954375,0.949375,0.177778,0.264444,0.298889,0.310000
-000bc387c731dd97,xclick,/m/04yx4,1,0.000000,0.103750,0.268889,0.596667,1,1,0,0,0,0.024375,0.103750,0.000000,0.005000,0.268889,0.576667,0.520000,0.596667
-000bc387c731dd97,xclick,/m/04yx4,1,0.115000,0.235625,0.234444,0.590000,1,0,0,0,0,0.188125,0.235625,0.115000,0.217500,0.234444,0.421111,0.396667,0.590000
-000bc387c731dd97,xclick,/m/04yx4,1,0.271250,0.335625,0.271111,0.592222,1,0,0,0,0,0.300625,0.335625,0.271250,0.301875,0.271111,0.421111,0.478889,0.592222
-000bc387c731dd97,xclick,/m/04yx4,1,0.366250,0.476250,0.257778,0.598889,1,0,0,0,0,0.453125,0.476250,0.366250,0.411250,0.257778,0.373333,0.548889,0.598889
-000bc387c731dd97,xclick,/m/04yx4,1,0.406250,0.594375,0.134444,0.986667,1,0,0,0,0,0.556875,0.594375,0.406250,0.463750,0.134444,0.248889,0.954444,0.986667
-000bc387c731dd97,xclick,/m/04yx4,1,0.499375,0.612500,0.193333,0.836667,1,0,0,0,0,0.589375,0.612500,0.499375,0.515000,0.193333,0.280000,0.737778,0.836667
-000bc387c731dd97,xclick,/m/04yx4,1,0.589375,0.698125,0.207778,0.544444,1,0,0,0,0,0.650625,0.698125,0.589375,0.605625,0.207778,0.318889,0.472222,0.544444
-000bc387c731dd97,xclick,/m/04yx4,1,0.673750,0.758125,0.097778,0.330000,1,0,0,0,0,0.728750,0.758125,0.673750,0.689375,0.097778,0.191111,0.280000,0.330000
-000bc387c731dd97,xclick,/m/04yx4,1,0.696875,0.827500,0.188889,0.998889,1,1,0,0,0,0.788750,0.827500,0.696875,0.722500,0.188889,0.351111,0.485556,0.998889
-000bc387c731dd97,xclick,/m/05r655,1,0.323125,0.396250,0.282222,0.564444,1,0,0,0,0,0.340000,0.396250,0.323125,0.373125,0.282222,0.426667,0.323333,0.564444
-000bc387c731dd97,xclick,/m/09j5n,1,0.450625,0.499375,0.850000,0.971111,1,0,0,0,0,0.461250,0.450625,0.485625,0.499375,0.850000,0.908889,0.971111,0.961111
-000bc387c731dd97,xclick,/m/0fly7,1,0.150000,0.225625,0.501111,0.583333,1,0,0,0,0,0.193750,0.184375,0.225625,0.150000,0.501111,0.583333,0.561111,0.540000
-000bc5006eb7fd98,xclick,/m/01bl7v,1,0.000000,0.422139,0.000000,0.452500,0,0,0,0,0,0.228893,0.000000,0.422139,0.095685,0.000000,0.245000,0.221250,0.452500
-000bc5006eb7fd98,xclick,/m/035r7c,1,0.000000,0.193246,0.248750,0.457500,0,1,0,0,0,0.193246,0.000938,0.000000,0.101313,0.303750,0.248750,0.341250,0.457500
-000bc5006eb7fd98,xclick,/m/035r7c,1,0.000000,0.421201,0.713750,0.999375,1,1,0,0,0,0.000000,0.170732,0.421201,0.144465,0.732500,0.713750,0.921875,0.999375
-000bc5006eb7fd98,xclick,/m/035r7c,1,0.106942,0.839587,0.730625,0.986875,0,1,0,0,0,0.106942,0.354597,0.839587,0.759850,0.839375,0.730625,0.917500,0.986875
-000bc5006eb7fd98,xclick,/m/03q69,1,0.099437,0.339587,0.000000,0.096250,0,1,0,0,0,0.232645,0.099437,0.306754,0.339587,0.000000,0.046875,0.096250,0.028125
-000bc5006eb7fd98,xclick,/m/03q69,1,0.329268,0.803940,0.118125,0.563750,0,0,0,0,0,0.614447,0.500000,0.803940,0.329268,0.118125,0.551875,0.563750,0.543125
-000bc5006eb7fd98,xclick,/m/03q69,1,0.380863,0.596623,0.000000,0.201875,1,1,0,0,0,0.424953,0.380863,0.414634,0.596623,0.000000,0.106250,0.201875,0.015625
-000bc5006eb7fd98,xclick,/m/03q69,1,0.596623,0.814259,0.000625,0.150625,0,0,0,0,0,0.732645,0.814259,0.744841,0.596623,0.000625,0.061875,0.150625,0.096250
-000bc5006eb7fd98,xclick,/m/03q69,1,0.898687,0.999062,0.000000,0.116875,0,1,0,0,0,0.965291,0.999062,0.975610,0.898687,0.000000,0.022500,0.116875,0.050000
-000bc5006eb7fd98,xclick,/m/05r655,1,0.000000,0.836773,0.113750,0.999375,1,1,0,0,0,0.606004,0.000000,0.836773,0.775797,0.113750,0.679375,0.937500,0.999375
-000bc5006eb7fd98,xclick,/m/05r655,1,0.794559,0.999062,0.000000,0.390000,1,1,0,0,0,0.794559,0.936210,0.999062,0.848968,0.175000,0.000000,0.045000,0.390000
-000bc5006eb7fd98,xclick,/m/09j5n,1,0.031895,0.255159,0.358125,0.445000,1,0,0,0,0,0.075985,0.123827,0.031895,0.255159,0.358125,0.445000,0.404375,0.393750
-000bc5006eb7fd98,xclick,/m/09j5n,1,0.062852,0.375235,0.879375,0.999375,0,1,0,0,0,0.231707,0.203565,0.062852,0.375235,0.999375,0.879375,0.942500,0.950625
-000bc5006eb7fd98,xclick,/m/09j5n,1,0.626642,0.833021,0.892500,0.991250,0,1,0,0,0,0.690432,0.626642,0.833021,0.757036,0.892500,0.956875,0.958750,0.991250
-000bc5006eb7fd98,xclick,/m/09j5n,1,0.957786,0.999062,0.523750,0.633125,0,1,0,0,0,0.999062,0.957786,0.999062,0.996248,0.578750,0.590625,0.523750,0.633125
-000bc5006eb7fd98,xclick,/m/0dzf4,1,0.000000,0.145403,0.108125,0.258750,1,1,0,0,0,0.142589,0.000000,0.009381,0.145403,0.108125,0.195000,0.258750,0.227500
-000bc5006eb7fd98,xclick,/m/0dzf4,1,0.142589,0.416510,0.164375,0.348750,1,0,0,0,0,0.370544,0.416510,0.237336,0.142589,0.164375,0.282500,0.348750,0.263750
-000bc5006eb7fd98,xclick,/m/0dzf4,1,0.144465,0.739212,0.453125,0.740625,1,0,0,0,0,0.708255,0.144465,0.739212,0.396811,0.453125,0.728750,0.516250,0.740625
-000bc5006eb7fd98,xclick,/m/0dzf4,1,0.555347,0.627580,0.017500,0.133750,1,1,0,0,0,0.590994,0.627580,0.558161,0.555347,0.017500,0.063750,0.133750,0.096250
-000bc5006eb7fd98,xclick,/m/0dzf4,1,0.742964,0.870544,0.203750,0.410625,1,0,0,0,0,0.842402,0.870544,0.809568,0.742964,0.203750,0.306250,0.410625,0.338750
-000bc5006eb7fd98,xclick,/m/0dzf4,1,0.799250,0.922139,0.033125,0.233125,1,0,0,0,0,0.876173,0.799250,0.914634,0.922139,0.033125,0.173125,0.233125,0.231250
-000bc5006eb7fd98,xclick,/m/0dzf4,1,0.893996,0.993433,0.220625,0.282500,1,1,0,0,0,0.993433,0.893996,0.924953,0.993433,0.220625,0.248125,0.280625,0.282500
-000bc5ad4cc3ae73,xclick,/m/014sv8,1,0.109375,0.129883,0.313324,0.335286,0,0,0,0,0,0.122070,0.109375,0.129883,0.129883,0.313324,0.329429,0.335286,0.322108
-000bc5ad4cc3ae73,xclick,/m/014sv8,1,0.681641,0.702148,0.084919,0.109810,0,0,0,0,0,0.687500,0.694336,0.702148,0.681641,0.084919,0.109810,0.093704,0.098097
-000bc5ad4cc3ae73,xclick,/m/02p0tk3,1,0.000000,0.539062,0.209370,0.998536,1,1,0,0,0,0.085938,0.000000,0.539062,0.252930,0.209370,0.351391,0.588580,0.998536
-000bc5ad4cc3ae73,xclick,/m/02p0tk3,1,0.413086,0.933594,0.000000,0.998536,0,0,0,0,0,0.706055,0.933594,0.413086,0.732422,0.000000,0.452416,0.612006,0.998536
-000bc5ad4cc3ae73,xclick,/m/039xj_,1,0.032227,0.065430,0.327965,0.389458,0,0,0,0,0,0.037109,0.032227,0.051758,0.065430,0.327965,0.346999,0.389458,0.389458
-000bc5ad4cc3ae73,xclick,/m/039xj_,1,0.756836,0.777344,0.071742,0.131772,0,0,0,0,0,0.764648,0.756836,0.763672,0.777344,0.071742,0.102489,0.131772,0.101025
-000bc5ad4cc3ae73,xclick,/m/04yx4,1,0.000000,0.543945,0.206442,0.998536,1,1,0,0,0,0.061523,0.543945,0.109375,0.000000,0.206442,0.575403,0.998536,0.919473
-000bc5ad4cc3ae73,xclick,/m/04yx4,1,0.412109,0.932617,0.000000,0.998536,1,1,0,0,0,0.778320,0.932617,0.412109,0.730469,0.000000,0.487555,0.613470,0.998536
-000bc5ad4cc3ae73,xclick,/m/0dzf4,1,0.064453,0.541992,0.513909,0.840410,1,0,0,0,0,0.109375,0.064453,0.322266,0.541992,0.513909,0.749634,0.840410,0.515373
-000bc5ad4cc3ae73,xclick,/m/0dzf4,1,0.416992,0.664062,0.462665,0.737921,1,0,0,0,0,0.664062,0.652344,0.502930,0.416992,0.462665,0.737921,0.705710,0.556369
-000bc5ad4cc3ae73,xclick,/m/0dzf4,1,0.450195,0.921875,0.303075,0.726208,1,0,0,0,0,0.867188,0.450195,0.775391,0.921875,0.303075,0.458272,0.726208,0.458272
-000bc5ad4cc3ae73,xclick,/m/0k65p,1,0.410156,0.506836,0.503660,0.667643,1,0,0,0,0,0.485352,0.506836,0.435547,0.410156,0.667643,0.628111,0.503660,0.620791
-000bc5ad4cc3ae73,xclick,/m/0k65p,1,0.427734,0.541992,0.484627,0.657394,1,0,0,0,0,0.454102,0.427734,0.458984,0.541992,0.657394,0.622255,0.484627,0.575403
-000bc5ad4cc3ae73,xclick,/m/0k65p,1,0.449219,0.585938,0.351391,0.560761,0,0,0,0,0,0.536133,0.585938,0.449219,0.544922,0.351391,0.458272,0.468521,0.560761
-000bc75d38907c78,xclick,/m/01bfm9,1,0.600224,0.753639,0.552426,0.737089,1,0,0,0,0,0.615901,0.600224,0.753639,0.690929,0.552426,0.633803,0.636933,0.737089
-000bc75d38907c78,xclick,/m/01bl7v,1,0.142217,0.273236,0.133020,0.871674,1,0,0,0,0,0.228443,0.273236,0.180291,0.142217,0.133020,0.547731,0.871674,0.543036
-000bc75d38907c78,xclick,/m/01bl7v,1,0.319149,0.477044,0.048513,0.935837,1,0,0,0,0,0.402016,0.319149,0.477044,0.403135,0.048513,0.802817,0.665102,0.935837
-000bc75d38907c78,xclick,/m/01bl7v,1,0.578947,0.795073,0.156495,0.918623,1,0,0,0,0,0.695409,0.578947,0.795073,0.617021,0.156495,0.881064,0.912363,0.918623
-000bc75d38907c78,xclick,/m/0220r2,1,0.000000,0.998880,0.000000,0.851330,0,1,0,0,0,0.617021,0.426652,0.000000,0.998880,0.000000,0.851330,0.469484,0.524257
-000bc75d38907c78,xclick,/m/02p0tk3,1,0.137738,0.246361,0.123631,0.826291,0,0,0,0,0,0.220605,0.137738,0.246361,0.189250,0.123631,0.461659,0.641628,0.826291
-000bc75d38907c78,xclick,/m/02p0tk3,1,0.315789,0.472564,0.050078,0.884194,0,0,0,0,0,0.382979,0.315789,0.472564,0.409854,0.050078,0.378717,0.663537,0.884194
-000bc75d38907c78,xclick,/m/02p0tk3,1,0.583427,0.799552,0.158059,0.924883,0,0,0,0,0,0.698768,0.583427,0.764838,0.799552,0.158059,0.887324,0.924883,0.907668
-000bc75d38907c78,xclick,/m/031n1,1,0.155655,0.202688,0.813772,0.879499,1,0,0,0,0,0.179171,0.155655,0.185890,0.202688,0.813772,0.837246,0.879499,0.863850
-000bc75d38907c78,xclick,/m/031n1,1,0.190370,0.265398,0.805947,0.868545,1,0,0,0,0,0.220605,0.190370,0.258679,0.265398,0.805947,0.846635,0.868545,0.860720
-000bc75d38907c78,xclick,/m/031n1,1,0.322508,0.384099,0.765258,0.907668,1,0,0,0,0,0.356103,0.322508,0.368421,0.384099,0.765258,0.796557,0.901408,0.907668
-000bc75d38907c78,xclick,/m/031n1,1,0.370661,0.436730,0.841941,0.928012,1,0,0,0,0,0.399776,0.370661,0.413214,0.436730,0.841941,0.912363,0.928012,0.924883
-000bc75d38907c78,xclick,/m/031n1,1,0.591265,0.637178,0.835681,0.907668,1,0,0,0,0,0.591265,0.597984,0.637178,0.622620,0.862285,0.835681,0.892019,0.907668
-000bc75d38907c78,xclick,/m/031n1,1,0.723404,0.779395,0.868545,0.920188,1,0,0,0,0,0.746920,0.723404,0.763718,0.779395,0.868545,0.909233,0.920188,0.898279
-000bc75d38907c78,xclick,/m/04yx4,1,0.000000,0.082867,0.359937,0.583725,1,1,0,0,0,0.055991,0.000000,0.014558,0.082867,0.359937,0.488263,0.583725,0.399061
-000bc75d38907c78,xclick,/m/04yx4,1,0.141097,0.277716,0.133020,0.879499,1,0,0,0,0,0.218365,0.277716,0.179171,0.141097,0.133020,0.535211,0.879499,0.347418
-000bc75d38907c78,xclick,/m/04yx4,1,0.319149,0.472564,0.054773,0.928012,1,0,0,0,0,0.385218,0.319149,0.438970,0.472564,0.054773,0.804382,0.928012,0.657277
-000bc75d38907c78,xclick,/m/04yx4,1,0.585666,0.797312,0.162754,0.917058,0,0,0,0,0,0.695409,0.751400,0.797312,0.585666,0.162754,0.917058,0.912363,0.885759
-000bc75d38907c78,xclick,/m/06_fw,1,0.315789,0.541993,0.477308,0.566510,1,0,0,0,0,0.315789,0.319149,0.541993,0.501680,0.477308,0.489828,0.511737,0.566510
-000bc7b0a1889bcb,xclick,/m/07j7r,1,0.000000,0.998889,0.000000,0.908898,1,1,1,0,0,0.000000,0.000000,0.998889,0.998889,0.000000,0.908898,0.699153,0.699153
-000bc7b0a1889bcb,xclick,/m/07j7r,1,0.801111,0.998889,0.000000,0.449153,1,1,1,0,0,0.801111,0.924444,0.998889,0.935556,0.148305,0.000000,0.141949,0.449153
-000bc7b0a1889bcb,xclick,/m/0c9ph5,1,0.000000,0.012222,0.349576,0.379237,1,1,0,0,0,0.004444,0.003333,0.000000,0.012222,0.349576,0.379237,0.355932,0.370763
-000bc7b0a1889bcb,xclick,/m/0c9ph5,1,0.000000,0.151111,0.101695,0.531780,1,1,1,0,0,0.098889,0.151111,0.010000,0.000000,0.101695,0.250000,0.531780,0.493644
-000bc7b0a1889bcb,xclick,/m/0c9ph5,1,0.238889,0.502222,0.067797,0.345339,1,0,1,0,0,0.238889,0.272222,0.367778,0.502222,0.110169,0.067797,0.345339,0.188559
-000bc7b0a1889bcb,xclick,/m/0c9ph5,1,0.752222,0.914444,0.122881,0.572034,1,0,1,0,0,0.752222,0.816667,0.847778,0.914444,0.347458,0.122881,0.572034,0.430085
-000bcd3bcd95cbb3,xclick,/m/02p0tk3,1,0.251250,0.383125,0.232645,0.720450,0,0,0,0,0,0.350625,0.383125,0.371250,0.251250,0.232645,0.288931,0.720450,0.265478
-000bcd3bcd95cbb3,xclick,/m/02p0tk3,1,0.383750,0.465000,0.296435,0.575047,0,0,0,0,0,0.383750,0.432500,0.465000,0.417500,0.538462,0.296435,0.406191,0.575047
-000bcd3bcd95cbb3,xclick,/m/02p0tk3,1,0.420000,0.548125,0.481238,0.707317,0,0,0,0,0,0.420000,0.485000,0.548125,0.537500,0.569418,0.481238,0.635084,0.707317
-000bcd3bcd95cbb3,xclick,/m/035r7c,1,0.316250,0.385000,0.469981,0.723265,0,0,0,0,0,0.348750,0.316250,0.385000,0.376875,0.469981,0.493433,0.510319,0.723265
-000bcd3bcd95cbb3,xclick,/m/035r7c,1,0.381250,0.427500,0.430582,0.536585,1,0,0,0,0,0.422500,0.381250,0.391875,0.427500,0.430582,0.532833,0.536585,0.438086
-000bcd3bcd95cbb3,xclick,/m/035r7c,1,0.413750,0.428750,0.439962,0.573171,1,0,0,0,0,0.427500,0.413750,0.428750,0.423750,0.439962,0.573171,0.496248,0.488743
-000bcd3bcd95cbb3,xclick,/m/035r7c,1,0.441875,0.494375,0.582552,0.670732,1,0,0,0,0,0.441875,0.470000,0.494375,0.468750,0.582552,0.670732,0.609756,0.582552
-000bcd3bcd95cbb3,xclick,/m/035r7c,1,0.504375,0.540000,0.576923,0.682927,1,0,0,0,0,0.504375,0.540000,0.514375,0.528750,0.632270,0.576923,0.682927,0.670732
-000bcd3bcd95cbb3,xclick,/m/09j5n,1,0.381875,0.407500,0.503752,0.547842,1,0,0,0,0,0.396875,0.407500,0.396875,0.381875,0.503752,0.529081,0.547842,0.536585
-000bcd3bcd95cbb3,xclick,/m/09j5n,1,0.411250,0.426250,0.548781,0.572233,1,0,0,0,0,0.411250,0.420000,0.426250,0.425000,0.568480,0.548781,0.570356,0.572233
-000bcd3bcd95cbb3,xclick,/m/0dzf4,1,0.253125,0.310000,0.255159,0.384615,1,0,0,0,0,0.267500,0.310000,0.253125,0.278125,0.255159,0.367730,0.271107,0.384615
-000bcd3bcd95cbb3,xclick,/m/0dzf4,1,0.341250,0.390000,0.236398,0.348030,1,0,0,0,0,0.353750,0.341250,0.369375,0.390000,0.236398,0.324578,0.348030,0.290807
-000bcd3bcd95cbb3,xclick,/m/0dzf4,1,0.405000,0.455000,0.359287,0.436210,1,0,0,0,0,0.407500,0.405000,0.425625,0.455000,0.359287,0.424953,0.436210,0.404315
-000bcd3bcd95cbb3,xclick,/m/0dzf4,1,0.418125,0.456250,0.529081,0.668856,1,0,0,0,0,0.453750,0.418125,0.442500,0.456250,0.529081,0.590056,0.661351,0.668856
-000bcd3bcd95cbb3,xclick,/m/0dzf4,1,0.448750,0.531875,0.525328,0.707317,1,0,0,0,0,0.516875,0.448750,0.480625,0.531875,0.525328,0.694184,0.707317,0.555347
-000bcee5bed5446b,xclick,/m/03jm5,1,0.000000,0.051643,0.364375,0.603750,0,0,0,0,0,0.041315,0.000000,0.008451,0.051643,0.364375,0.408125,0.603750,0.593750
-000bcee5bed5446b,xclick,/m/03jm5,1,0.000000,0.221596,0.182500,0.483125,0,0,0,0,0,0.000000,0.079812,0.221596,0.113615,0.308750,0.182500,0.421250,0.483125
-000bcee5bed5446b,xclick,/m/03jm5,1,0.116432,0.999061,0.209375,0.381875,0,0,0,0,0,0.116432,0.131455,0.999061,0.704225,0.218125,0.209375,0.301875,0.381875
-000bcee5bed5446b,xclick,/m/03jm5,1,0.742723,0.999061,0.348125,0.698125,0,0,0,0,0,0.742723,0.831925,0.999061,0.999061,0.441875,0.348125,0.523750,0.698125
-000bcee5bed5446b,xclick,/m/0k4j,1,0.165258,0.249765,0.508125,0.548750,0,0,0,0,0,0.175587,0.216901,0.165258,0.249765,0.508125,0.548750,0.533750,0.521250
-000bcee5bed5446b,xclick,/m/0k4j,1,0.170892,0.260094,0.478750,0.508125,1,0,0,0,0,0.209390,0.216901,0.170892,0.260094,0.478750,0.506250,0.495625,0.508125
-000bcee5bed5446b,xclick,/m/0k4j,1,0.295775,0.347418,0.406875,0.436250,1,0,0,0,0,0.329577,0.295775,0.295775,0.347418,0.406875,0.436250,0.424375,0.424375
-000bcee5bed5446b,xclick,/m/0k4j,1,0.606573,0.661033,0.492500,0.530000,0,0,0,0,0,0.627230,0.642254,0.606573,0.661033,0.492500,0.530000,0.513125,0.509375
-000bcee5bed5446b,xclick,/m/0k4j,1,0.607512,0.683568,0.618125,0.670625,0,0,0,0,0,0.643192,0.607512,0.622535,0.683568,0.618125,0.626875,0.670625,0.653750
-000bcee5bed5446b,xclick,/m/0k4j,1,0.624413,0.661033,0.480625,0.494375,1,0,0,0,0,0.642254,0.650704,0.624413,0.661033,0.480625,0.494375,0.485625,0.489375
-000bcee5bed5446b,xclick,/m/0k4j,1,0.673239,0.822535,0.499375,0.533750,0,0,0,0,0,0.735211,0.693897,0.673239,0.822535,0.499375,0.533750,0.520000,0.520000
-000bcee5bed5446b,xclick,/m/0k4j,1,0.739906,0.883568,0.608750,0.636250,1,0,0,0,0,0.812207,0.812207,0.739906,0.883568,0.608750,0.636250,0.622500,0.608750
-000bcee5bed5446b,xclick,/m/0k4j,1,0.799061,0.999061,0.668750,0.726250,0,0,0,0,0,0.865728,0.832864,0.799061,0.999061,0.668750,0.726250,0.702500,0.702500
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train-images-boxable.csv b/repositories/taming-transformers/data/open_images_annotations_100/train-images-boxable.csv
deleted file mode 100644
index 7dfaa769b0632e7944650e2cdd2fb659d0d55565..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/data/open_images_annotations_100/train-images-boxable.csv
+++ /dev/null
@@ -1,101 +0,0 @@
-image_name,image_url
-000abe5eddc5b303.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000abe5eddc5b303.jpg
-000b567c26dd4e5d.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000b567c26dd4e5d.jpg
-000b9814a07fd974.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000b9814a07fd974.jpg
-000b63a1445f53c8.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000b63a1445f53c8.jpg
-000b59a7822679e6.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000b59a7822679e6.jpg
-000ab31e6be35fed.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000ab31e6be35fed.jpg
-000bc387c731dd97.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000bc387c731dd97.jpg
-000afe7726e121ea.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000afe7726e121ea.jpg
-000b4671075914cd.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000b4671075914cd.jpg
-000bb200fc78fc30.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000bb200fc78fc30.jpg
-000bc75d38907c78.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000bc75d38907c78.jpg
-000b5bc07c0c5df7.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000b5bc07c0c5df7.jpg
-000ba221f70676c6.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000ba221f70676c6.jpg
-000b9007a01f7405.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000b9007a01f7405.jpg
-000ab8c20b3e5b58.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000ab8c20b3e5b58.jpg
-000b260e1f08a32a.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000b260e1f08a32a.jpg
-000ac34008b0ba4c.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000ac34008b0ba4c.jpg
-000baa6f7dae9b79.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000baa6f7dae9b79.jpg
-000b87119cc301cf.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000b87119cc301cf.jpg
-000b1971d8daaeef.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000b1971d8daaeef.jpg
-000b21663becc68e.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000b21663becc68e.jpg
-000ad6c520be9ec5.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000ad6c520be9ec5.jpg
-000bc1eb7f74adae.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000bc1eb7f74adae.jpg
-000b9d6c0f7d794d.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000b9d6c0f7d794d.jpg
-000ac8c676b6077a.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000ac8c676b6077a.jpg
-000ab7bec71cc50a.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000ab7bec71cc50a.jpg
-000b09d5d3fc821f.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000b09d5d3fc821f.jpg
-000ae235808cc1e8.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000ae235808cc1e8.jpg
-000b8d80f7386698.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000b8d80f7386698.jpg
-000bbdf0dc8099d8.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000bbdf0dc8099d8.jpg
-000b93644609911f.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000b93644609911f.jpg
-000b4fcdf1af3361.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000b4fcdf1af3361.jpg
-000b168e791f591d.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000b168e791f591d.jpg
-000b393437134262.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000b393437134262.jpg
-000aecd78b230135.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000aecd78b230135.jpg
-000b76a9b80ba43a.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000b76a9b80ba43a.jpg
-000b55559b0244d7.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000b55559b0244d7.jpg
-000b72e1446f8849.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000b72e1446f8849.jpg
-000bcee5bed5446b.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000bcee5bed5446b.jpg
-000b9f3ba4891c11.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000b9f3ba4891c11.jpg
-000b9b61afea2cd4.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000b9b61afea2cd4.jpg
-000b1b92f0800e94.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000b1b92f0800e94.jpg
-000b29496f75c8e5.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000b29496f75c8e5.jpg
-000bc33717a6371f.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000bc33717a6371f.jpg
-000b70a84aab664b.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000b70a84aab664b.jpg
-000acf666d991c39.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000acf666d991c39.jpg
-000bc5ad4cc3ae73.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000bc5ad4cc3ae73.jpg
-000b4935979bf4b5.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000b4935979bf4b5.jpg
-000ac95750ac7399.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000ac95750ac7399.jpg
-000bc7b0a1889bcb.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000bc7b0a1889bcb.jpg
-000b0f235dcf2caa.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000b0f235dcf2caa.jpg
-000b55e339f0b131.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000b55e339f0b131.jpg
-000b06c0eed42a4c.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000b06c0eed42a4c.jpg
-000af631fb329557.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000af631fb329557.jpg
-000b9a97776b3634.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000b9a97776b3634.jpg
-000abc821f66a892.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000abc821f66a892.jpg
-000adef7197e3118.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000adef7197e3118.jpg
-000adcdd7244ce4a.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000adcdd7244ce4a.jpg
-000b606e130bdf5e.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000b606e130bdf5e.jpg
-000b50bdd1933a36.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000b50bdd1933a36.jpg
-000ad6fa67b5ad96.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000ad6fa67b5ad96.jpg
-000b299b5f5ed902.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000b299b5f5ed902.jpg
-000adfe5b817011c.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000adfe5b817011c.jpg
-000bcd3bcd95cbb3.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000bcd3bcd95cbb3.jpg
-000b2d1789d5f80d.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000b2d1789d5f80d.jpg
-000bb8bd9b1bca65.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000bb8bd9b1bca65.jpg
-000b9c365c9e307a.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000b9c365c9e307a.jpg
-000ba3ca8a2ca955.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000ba3ca8a2ca955.jpg
-000b42cae15622e0.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000b42cae15622e0.jpg
-000b9b00d7aef8f5.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000b9b00d7aef8f5.jpg
-000ad3d42653f5f6.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000ad3d42653f5f6.jpg
-000bc5006eb7fd98.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000bc5006eb7fd98.jpg
-000b2b00065e564a.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000b2b00065e564a.jpg
-000b2a982a903d0d.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000b2a982a903d0d.jpg
-000ae28755d2d20e.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000ae28755d2d20e.jpg
-000bab5b1a67844e.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000bab5b1a67844e.jpg
-000b38d9f2f664fe.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000b38d9f2f664fe.jpg
-000bb2f7132013dc.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000bb2f7132013dc.jpg
-000bb0ae453283b0.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000bb0ae453283b0.jpg
-000b093da01e5bfe.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000b093da01e5bfe.jpg
-000b825dea3016eb.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000b825dea3016eb.jpg
-000abc075d659122.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000abc075d659122.jpg
-000b1b3b85edd850.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000b1b3b85edd850.jpg
-000b7dfaa1810a83.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000b7dfaa1810a83.jpg
-000ad20b5e452b24.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000ad20b5e452b24.jpg
-000b485cedacbf97.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000b485cedacbf97.jpg
-000b81b5757963e0.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000b81b5757963e0.jpg
-000ba940f8cfc9bf.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000ba940f8cfc9bf.jpg
-000bb81adefe7332.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000bb81adefe7332.jpg
-000b397382b2464a.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000b397382b2464a.jpg
-000ba28d70b1a999.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000ba28d70b1a999.jpg
-000b0f5159f54105.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000b0f5159f54105.jpg
-000b3940e7d25c03.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000b3940e7d25c03.jpg
-000ad0ecfb21ee63.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000ad0ecfb21ee63.jpg
-000bb846e2629e83.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000bb846e2629e83.jpg
-000aee0af66d4237.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000aee0af66d4237.jpg
-000af180a3163f17.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000af180a3163f17.jpg
-000b65a36ad46f9e.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000b65a36ad46f9e.jpg
-000b432ae644b679.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000b432ae644b679.jpg
-000ba40bf7a2b458.jpg,https://requestor-proxy.figure-eight.com/figure_eight_datasets/open-images/train/000ba40bf7a2b458.jpg
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000ab31e6be35fed.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000ab31e6be35fed.jpg
deleted file mode 100644
index 8f9ee0bdf8140eed5ccddfe7f9ef0d1d1aa2d8d6..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000ab31e6be35fed.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000ab7bec71cc50a.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000ab7bec71cc50a.jpg
deleted file mode 100644
index 6190d85e7db67938a314bfea524568fc9a14e61b..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000ab7bec71cc50a.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000ab8c20b3e5b58.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000ab8c20b3e5b58.jpg
deleted file mode 100644
index 35c297421ad934b14aa4534d66a17f92edae0b02..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000ab8c20b3e5b58.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000abc075d659122.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000abc075d659122.jpg
deleted file mode 100644
index 00f720e93c652de1e8cd532d0981e7c150ff889d..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000abc075d659122.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000abc821f66a892.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000abc821f66a892.jpg
deleted file mode 100644
index a7d35a5845f16141f141a27736b806fd622d830c..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000abc821f66a892.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000abe5eddc5b303.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000abe5eddc5b303.jpg
deleted file mode 100644
index f22b56793b74ab27883c536a0d069d912442dbdd..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000abe5eddc5b303.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000ac34008b0ba4c.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000ac34008b0ba4c.jpg
deleted file mode 100644
index 479828bbb67e2cb50a4dd6ac685f59ff8d7f079b..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000ac34008b0ba4c.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000ac8c676b6077a.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000ac8c676b6077a.jpg
deleted file mode 100644
index 2a6ddf7cb86b5c83f1a557c8a4c6b06d49bfe7ef..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000ac8c676b6077a.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000ac95750ac7399.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000ac95750ac7399.jpg
deleted file mode 100644
index 2f21d7b6e8e3729cb872fbb8b713dd966a7ba94f..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000ac95750ac7399.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000acf666d991c39.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000acf666d991c39.jpg
deleted file mode 100644
index 125a6388cbca5b8cd257b4d26b9f855c8b6c1e8a..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000acf666d991c39.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000ad0ecfb21ee63.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000ad0ecfb21ee63.jpg
deleted file mode 100644
index 2c641326a0b74914d2dcb6f4fc321d5805d624b9..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000ad0ecfb21ee63.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000ad20b5e452b24.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000ad20b5e452b24.jpg
deleted file mode 100644
index 30f07632c94100bc05ce4a7b6635b62a3d67e2fa..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000ad20b5e452b24.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000ad3d42653f5f6.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000ad3d42653f5f6.jpg
deleted file mode 100644
index ac3c86072508b7f53232cb4cd64ffaab4b9331af..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000ad3d42653f5f6.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000ad6c520be9ec5.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000ad6c520be9ec5.jpg
deleted file mode 100644
index d093fa6caded27a72cf446a37c93c550ffc2670c..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000ad6c520be9ec5.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000ad6fa67b5ad96.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000ad6fa67b5ad96.jpg
deleted file mode 100644
index d4e7d5cd161ebeb5fd8a61b0469b4681b02837b3..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000ad6fa67b5ad96.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000adcdd7244ce4a.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000adcdd7244ce4a.jpg
deleted file mode 100644
index ffa766c24cb6e79d09d6e6ef212bd6c51b5c1636..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000adcdd7244ce4a.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000adef7197e3118.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000adef7197e3118.jpg
deleted file mode 100644
index 2e78ca86c2ee54fd66f5315dd86f775c52d4f6d2..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000adef7197e3118.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000adfe5b817011c.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000adfe5b817011c.jpg
deleted file mode 100644
index c813703dc17323b5f93de522bc7b90b0432687b1..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000adfe5b817011c.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000ae235808cc1e8.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000ae235808cc1e8.jpg
deleted file mode 100644
index a9a8c395a9d3de92af57033904a4b461084fa360..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000ae235808cc1e8.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000ae28755d2d20e.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000ae28755d2d20e.jpg
deleted file mode 100644
index 50f69656372b5d2eeffb9ba82d8f0c6f03cebd37..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000ae28755d2d20e.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000aecd78b230135.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000aecd78b230135.jpg
deleted file mode 100644
index 840105ae16b78d3ee7ec1bfcbe3cedbfb11c6e3f..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000aecd78b230135.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000aee0af66d4237.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000aee0af66d4237.jpg
deleted file mode 100644
index c72432184a09505724c8df2632aff0ead89c45e0..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000aee0af66d4237.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000af180a3163f17.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000af180a3163f17.jpg
deleted file mode 100644
index 6634316e8ae2d71cb54bb8fa56a71c85ba123092..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000af180a3163f17.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000af631fb329557.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000af631fb329557.jpg
deleted file mode 100644
index cf4fa6e8dc9337ae6fdd54a953aa48736ceb4195..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000af631fb329557.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000afe7726e121ea.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000afe7726e121ea.jpg
deleted file mode 100644
index 39bff8eb0d463d5a38fc8e2711c8e71723d5417c..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000afe7726e121ea.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000b06c0eed42a4c.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000b06c0eed42a4c.jpg
deleted file mode 100644
index 972b7d7f0b09ef77a8824026bbd9479408b28d4b..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000b06c0eed42a4c.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000b093da01e5bfe.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000b093da01e5bfe.jpg
deleted file mode 100644
index a02b3ec7d09dd1e809c910642bacec28c0e73be9..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000b093da01e5bfe.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000b09d5d3fc821f.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000b09d5d3fc821f.jpg
deleted file mode 100644
index 1e5eb3cdf698cc8c994992320cb5c1324b67ab14..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000b09d5d3fc821f.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000b0f235dcf2caa.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000b0f235dcf2caa.jpg
deleted file mode 100644
index a90b85e6b4324a7657e38799863fb90490583b73..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000b0f235dcf2caa.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000b0f5159f54105.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000b0f5159f54105.jpg
deleted file mode 100644
index 7fdbbab55e8e534d85fdd3b79fb420868352e82a..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000b0f5159f54105.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000b168e791f591d.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000b168e791f591d.jpg
deleted file mode 100644
index a238b116b1d2f0c29ff5b7cb60f443c0ddfa22a0..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000b168e791f591d.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000b1971d8daaeef.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000b1971d8daaeef.jpg
deleted file mode 100644
index 46e0277b80ab153377e962a1e25f1aacd31369f5..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000b1971d8daaeef.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000b1b3b85edd850.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000b1b3b85edd850.jpg
deleted file mode 100644
index 72419a8b818a104561ed1fd0b17e5ac567a7f442..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/data/open_images_annotations_100/train/000b1b3b85edd850.jpg
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:78c5af25af6c0cc17892bacae70b86c2177d71f20c5f945bdf6d1b316fcd48d1
-size 1848832
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000b1b92f0800e94.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000b1b92f0800e94.jpg
deleted file mode 100644
index 4198cc100e13e4f16d6c1415eed6edfa5f5e6fe4..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000b1b92f0800e94.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000b21663becc68e.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000b21663becc68e.jpg
deleted file mode 100644
index e5a1bb12aa807925285e634c9c3787235ef11729..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000b21663becc68e.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000b260e1f08a32a.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000b260e1f08a32a.jpg
deleted file mode 100644
index 4d421904e3612790a2d4f346da25c4654353cc97..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000b260e1f08a32a.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000b29496f75c8e5.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000b29496f75c8e5.jpg
deleted file mode 100644
index b406a2babd8f6871b4c3484b3cc114d2e815e808..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000b29496f75c8e5.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000b299b5f5ed902.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000b299b5f5ed902.jpg
deleted file mode 100644
index 58c076f52bdaaddcd411845f71c1d3ab3abfc100..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000b299b5f5ed902.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000b2a982a903d0d.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000b2a982a903d0d.jpg
deleted file mode 100644
index e669b5593af1060f9b1c98998ab6fdf6a6ad41cb..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000b2a982a903d0d.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000b2b00065e564a.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000b2b00065e564a.jpg
deleted file mode 100644
index 50db9688692e84ba156e3cb376960d6ed6a1db6b..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000b2b00065e564a.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000b2d1789d5f80d.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000b2d1789d5f80d.jpg
deleted file mode 100644
index adc14fdeda11c18f26c39905a5f0d59105e5175b..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000b2d1789d5f80d.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000b38d9f2f664fe.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000b38d9f2f664fe.jpg
deleted file mode 100644
index bf18ce1497b23fc4a4c95836673e97b94f0a3075..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000b38d9f2f664fe.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000b393437134262.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000b393437134262.jpg
deleted file mode 100644
index 6771a9a4e4e0131a51853166b22b5e8b4bea8351..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000b393437134262.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000b3940e7d25c03.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000b3940e7d25c03.jpg
deleted file mode 100644
index b150e88f51bae66fe7a0a83fc7afb64476872a21..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000b3940e7d25c03.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000b397382b2464a.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000b397382b2464a.jpg
deleted file mode 100644
index f5fd92cef39665e9339fe9c9e464e0aa9d3d1c5a..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000b397382b2464a.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000b42cae15622e0.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000b42cae15622e0.jpg
deleted file mode 100644
index fa1c943ea02566eac0b47e5175df66bbf497d749..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000b42cae15622e0.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000b432ae644b679.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000b432ae644b679.jpg
deleted file mode 100644
index 32aa12d50d97e84056d76260f13ca32bc494f43c..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000b432ae644b679.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000b4671075914cd.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000b4671075914cd.jpg
deleted file mode 100644
index b66b1bbce44059fcc5b6400b1a9bf609f6ba3b8a..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000b4671075914cd.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000b485cedacbf97.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000b485cedacbf97.jpg
deleted file mode 100644
index 2e684e0b9d2a408fd5c409c6ec4506c10fcc06de..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000b485cedacbf97.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000b4935979bf4b5.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000b4935979bf4b5.jpg
deleted file mode 100644
index 1ee7299271d8941d723dc2957694d2307d0cd344..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000b4935979bf4b5.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000b4fcdf1af3361.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000b4fcdf1af3361.jpg
deleted file mode 100644
index bce290fd6ace18b725a8c2526c35b291efcf5143..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000b4fcdf1af3361.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000b50bdd1933a36.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000b50bdd1933a36.jpg
deleted file mode 100644
index ad5a54cfd1f3ddca512daf11818442f340530cdf..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000b50bdd1933a36.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000b55559b0244d7.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000b55559b0244d7.jpg
deleted file mode 100644
index 35ec466bc9beaabe27b5d5497aadbf7e593a3b75..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000b55559b0244d7.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000b55e339f0b131.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000b55e339f0b131.jpg
deleted file mode 100644
index 5fdbe1f88747abd17abaecbbae2a3cad4e09a3aa..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000b55e339f0b131.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000b567c26dd4e5d.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000b567c26dd4e5d.jpg
deleted file mode 100644
index 52d6301674428d85157886b8288a9f37766eeb9c..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000b567c26dd4e5d.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000b59a7822679e6.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000b59a7822679e6.jpg
deleted file mode 100644
index 540cc8206cc723f23ea5d675b6ac7e8113d52438..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000b59a7822679e6.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000b5bc07c0c5df7.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000b5bc07c0c5df7.jpg
deleted file mode 100644
index a54b8882af26d56fca8465c482aa00752852c23a..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000b5bc07c0c5df7.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000b606e130bdf5e.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000b606e130bdf5e.jpg
deleted file mode 100644
index fbd92a53934e03704f38e984b3614172f2bc5aec..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000b606e130bdf5e.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000b63a1445f53c8.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000b63a1445f53c8.jpg
deleted file mode 100644
index 73b6387b413204297a9dd1c8f4db1c00da7194e2..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000b63a1445f53c8.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000b65a36ad46f9e.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000b65a36ad46f9e.jpg
deleted file mode 100644
index 5685cbe48ec5b54c603bc625e886a2d06b56f65c..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000b65a36ad46f9e.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000b70a84aab664b.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000b70a84aab664b.jpg
deleted file mode 100644
index ff21b53f2980c71d35cc00ae659ceb7c6cc9c913..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000b70a84aab664b.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000b72e1446f8849.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000b72e1446f8849.jpg
deleted file mode 100644
index b2247932547a67b8a76352ef4dda4e6b01187b84..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000b72e1446f8849.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000b76a9b80ba43a.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000b76a9b80ba43a.jpg
deleted file mode 100644
index f8fb655a381cbe1ae1f988bce772e855afe4cef4..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000b76a9b80ba43a.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000b7dfaa1810a83.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000b7dfaa1810a83.jpg
deleted file mode 100644
index 2d4315169cddfd44d3ac71e707abff47d8dacb3f..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000b7dfaa1810a83.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000b81b5757963e0.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000b81b5757963e0.jpg
deleted file mode 100644
index 94888211441256a2f5d360b947976eacab633e83..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000b81b5757963e0.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000b825dea3016eb.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000b825dea3016eb.jpg
deleted file mode 100644
index 48a7cc1b32470b259d909229a6eb695c2c7891a5..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000b825dea3016eb.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000b87119cc301cf.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000b87119cc301cf.jpg
deleted file mode 100644
index 957979e9dd8d7049748bad20b00f526e551054e3..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000b87119cc301cf.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000b8d80f7386698.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000b8d80f7386698.jpg
deleted file mode 100644
index b39e0eeabf8f701771e5cba5ca54bb4f14a84e3d..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000b8d80f7386698.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000b9007a01f7405.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000b9007a01f7405.jpg
deleted file mode 100644
index 3049b8cb4b4213250e2a7d5c4f9e09df8a79d6eb..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000b9007a01f7405.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000b93644609911f.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000b93644609911f.jpg
deleted file mode 100644
index 0923a8843921e5dc53842c6461b940c7cb96c4bf..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000b93644609911f.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000b9814a07fd974.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000b9814a07fd974.jpg
deleted file mode 100644
index 1e9b44a761731c9cdfb191068f8ebfbce6f06bdf..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000b9814a07fd974.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000b9a97776b3634.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000b9a97776b3634.jpg
deleted file mode 100644
index eef935950d77b4a6b38827d753fc8f7b5613f1ec..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000b9a97776b3634.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000b9b00d7aef8f5.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000b9b00d7aef8f5.jpg
deleted file mode 100644
index 99ae6ee9165c376734173a9de21ee63a9ca4f548..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000b9b00d7aef8f5.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000b9b61afea2cd4.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000b9b61afea2cd4.jpg
deleted file mode 100644
index 7a4481a9b4ccf106f39adb1190360b6b10cf8408..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000b9b61afea2cd4.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000b9c365c9e307a.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000b9c365c9e307a.jpg
deleted file mode 100644
index 0346db79852ef17d9391cc098421679295626fa9..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000b9c365c9e307a.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000b9d6c0f7d794d.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000b9d6c0f7d794d.jpg
deleted file mode 100644
index 2f49300dffa6c8acc5226be92b0e07f42466285a..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000b9d6c0f7d794d.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000b9f3ba4891c11.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000b9f3ba4891c11.jpg
deleted file mode 100644
index 712fd7b7393e694025d2b8486150a474ed4deb04..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000b9f3ba4891c11.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000ba221f70676c6.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000ba221f70676c6.jpg
deleted file mode 100644
index ad7c336f0a49ba2306e723170cfd242c87c61d3e..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000ba221f70676c6.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000ba28d70b1a999.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000ba28d70b1a999.jpg
deleted file mode 100644
index b21c3d2ddac8bd94beba0663022639b553794a1f..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000ba28d70b1a999.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000ba3ca8a2ca955.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000ba3ca8a2ca955.jpg
deleted file mode 100644
index 69e7641ab79b9a625a76ea1591ff30332c8102ce..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000ba3ca8a2ca955.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000ba40bf7a2b458.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000ba40bf7a2b458.jpg
deleted file mode 100644
index 9388acbf762a366cf012717f866f80e7594871a8..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000ba40bf7a2b458.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000ba940f8cfc9bf.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000ba940f8cfc9bf.jpg
deleted file mode 100644
index 8de230d4b8019735bdd36fcf2b98d615f5c71cd9..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000ba940f8cfc9bf.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000baa6f7dae9b79.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000baa6f7dae9b79.jpg
deleted file mode 100644
index ddde6d90922c1b3f3ddcf87740396aa6d8c026b4..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000baa6f7dae9b79.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000bab5b1a67844e.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000bab5b1a67844e.jpg
deleted file mode 100644
index 618593811196e391d4ed214c3ad6384202df1e9f..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000bab5b1a67844e.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000bb0ae453283b0.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000bb0ae453283b0.jpg
deleted file mode 100644
index 68546f9c90923ec69d17c915f706a72e9780efba..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000bb0ae453283b0.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000bb200fc78fc30.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000bb200fc78fc30.jpg
deleted file mode 100644
index e2f9c1634edf7af40040aad5e84b98cc32c29106..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000bb200fc78fc30.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000bb2f7132013dc.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000bb2f7132013dc.jpg
deleted file mode 100644
index 87cdc39303199bc83cc3b58f02c9ffe40aea9bfd..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000bb2f7132013dc.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000bb81adefe7332.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000bb81adefe7332.jpg
deleted file mode 100644
index 9eb5d7f6e6b8dfb622179f2a6a12d4c178b4d0d7..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000bb81adefe7332.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000bb846e2629e83.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000bb846e2629e83.jpg
deleted file mode 100644
index 5c2d6554780496f8dc94a1ccca134901db9c72e2..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000bb846e2629e83.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000bb8bd9b1bca65.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000bb8bd9b1bca65.jpg
deleted file mode 100644
index 6b807c88b42da1b0b3da3f176d7f9b8b34af065c..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000bb8bd9b1bca65.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000bbdf0dc8099d8.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000bbdf0dc8099d8.jpg
deleted file mode 100644
index e0f0462c5cf347dc39aad25d0656e7ea5ec8bb36..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000bbdf0dc8099d8.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000bc1eb7f74adae.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000bc1eb7f74adae.jpg
deleted file mode 100644
index 360c25a641046217cbddfa3c27197db4bad59ad6..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000bc1eb7f74adae.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000bc33717a6371f.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000bc33717a6371f.jpg
deleted file mode 100644
index 98ebb775231569fbed32a7dc553c7da4933df6a7..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000bc33717a6371f.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000bc387c731dd97.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000bc387c731dd97.jpg
deleted file mode 100644
index 42becfc435c7b8cb6c979bc05b362215f71ce2d1..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000bc387c731dd97.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000bc5006eb7fd98.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000bc5006eb7fd98.jpg
deleted file mode 100644
index 0b9e840ec8f6f3db6f89d594650b6761694573f9..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000bc5006eb7fd98.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000bc5ad4cc3ae73.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000bc5ad4cc3ae73.jpg
deleted file mode 100644
index 0a93dadca5fd54e7ff81accae42d9a9b9b9c9a0c..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000bc5ad4cc3ae73.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000bc75d38907c78.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000bc75d38907c78.jpg
deleted file mode 100644
index 5b5a3a38acae0e7e9c04567a022ec6a57e25203f..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000bc75d38907c78.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000bc7b0a1889bcb.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000bc7b0a1889bcb.jpg
deleted file mode 100644
index 97b64d59e1f373d50e8f7df73c50ea8a273a01a4..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000bc7b0a1889bcb.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000bcd3bcd95cbb3.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000bcd3bcd95cbb3.jpg
deleted file mode 100644
index 53ec8aff3143463d5dac290595989ae66b1ec9cc..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000bcd3bcd95cbb3.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/train/000bcee5bed5446b.jpg b/repositories/taming-transformers/data/open_images_annotations_100/train/000bcee5bed5446b.jpg
deleted file mode 100644
index f9ce8ebb9d2cd1016e4eced5bf3da948436d876b..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/train/000bcee5bed5446b.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation-annotations-bbox.csv b/repositories/taming-transformers/data/open_images_annotations_100/validation-annotations-bbox.csv
deleted file mode 100644
index 62f1780b4bc34ab6760dc0e5db3c888e6028b955..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/data/open_images_annotations_100/validation-annotations-bbox.csv
+++ /dev/null
@@ -1,895 +0,0 @@
-ImageID,Source,LabelName,Confidence,XMin,XMax,YMin,YMax,IsOccluded,IsTruncated,IsGroupOf,IsDepiction,IsInside
-09c5b4d6bc25788d,xclick,/m/0dzct,1,0.43651927,0.51640517,0.35159817,0.51826483,0,0,0,0,0
-09c5b4d6bc25788d,xclick,/m/0dzct,1,0.5977175,0.6091298,0.8538813,0.8767123,0,0,0,0,0
-09c5b4d6bc25788d,xclick,/m/0dzct,1,0.66333807,0.6761769,0.8584475,0.8812785,0,0,0,0,0
-09c5b4d6bc25788d,xclick,/m/03q69,1,0.4293866,0.52639085,0.3127854,0.4155251,0,0,0,1,0
-09c5b4d6bc25788d,xclick,/m/04hgtk,1,0.43223965,0.53067046,0.3127854,0.5525114,0,0,0,0,0
-09c5b4d6bc25788d,xclick,/m/0k65p,1,0.553495,0.7089872,0.3584475,0.55707765,0,0,0,0,0
-09c5b4d6bc25788d,xclick,/m/0dzf4,1,0.3951498,0.43937233,0.60730594,0.7648402,1,0,0,0,0
-09c5b4d6bc25788d,xclick,/m/0dzf4,1,0.5520685,0.7089872,0.3561644,0.6438356,0,0,0,1,0
-09c5b4d6bc25788d,xclick,/m/0dzf4,1,0.5920114,0.5977175,0.88812786,0.9041096,1,0,0,0,0
-09c5b4d6bc25788d,xclick,/m/0dzf4,1,0.6119829,0.6319544,0.8538813,0.89269406,1,0,0,0,0
-09c5b4d6bc25788d,xclick,/m/0dzf4,1,0.6504993,0.66333807,0.8789954,0.9041096,1,0,0,0,0
-09c5b4d6bc25788d,xclick,/m/0dzf4,1,0.6804565,0.6918688,0.8812785,0.9041096,1,0,0,0,0
-09c5b4d6bc25788d,xclick,/m/04yx4,1,0.39657632,0.7104137,0.30821916,0.7648402,0,1,0,0,0
-09c5b4d6bc25788d,xclick,/m/04yx4,1,0.58773184,0.617689,0.84474885,0.9063927,0,0,0,0,0
-09c5b4d6bc25788d,xclick,/m/04yx4,1,0.6519258,0.6904422,0.84703195,0.9041096,0,1,0,1,0
-09c67960e389e4df,xclick,/m/0bt9lr,1,0.8082596,0.9587021,0.46460176,0.63495576,1,0,0,0,0
-09c67960e389e4df,xclick,/m/0k4j,1,0,0.29056048,0.3340708,0.5154867,1,1,0,0,0
-09c67960e389e4df,xclick,/m/0k4j,1,0.0014749262,0.072271384,0.33628318,0.38053098,1,1,0,0,0
-09c67960e389e4df,xclick,/m/0k4j,1,0.0029498525,0.26548672,0.32964602,0.40486726,1,0,0,0,0
-09c67960e389e4df,xclick,/m/0k4j,1,0.17551622,0.8318584,0.23451327,0.7632743,0,0,0,0,0
-09c67960e389e4df,xclick,/m/0k4j,1,0.23746313,0.5722714,0.3340708,0.44911504,1,0,0,0,0
-09c67960e389e4df,xclick,/m/01prls,1,0.0014749262,0.8303835,0.2300885,0.7654867,1,1,1,0,0
-09c67960e389e4df,xclick,/m/01prls,1,0.7787611,0.8982301,0.2721239,0.3539823,1,0,0,0,0
-09c67960e389e4df,xclick,/m/01prls,1,0.8746313,0.9852507,0.23230088,0.3141593,1,1,0,0,0
-09c67960e389e4df,xclick,/m/04rky,1,0.06342183,1,0.21017699,0.67477876,0,1,1,0,0
-09c6ddd2c210450e,xclick,/m/0dzct,1,0.5077605,0.60310423,0.101620026,0.18114875,0,0,0,0,0
-09c6ddd2c210450e,xclick,/m/01yx86,1,0.28381374,0.75388026,0.060382918,0.95287186,0,0,0,0,0
-09c6ddd2c210450e,xclick,/m/03q69,1,0.4767184,0.6186253,0.06332842,0.19734904,0,0,0,1,0
-09c6ddd2c210450e,xclick,/m/035r7c,1,0.33702883,0.5631929,0.46980855,0.83505154,0,0,0,1,0
-09c6ddd2c210450e,xclick,/m/035r7c,1,0.4745011,0.54545456,0.47717232,0.65537554,1,0,0,0,0
-09c6ddd2c210450e,xclick,/m/05r655,1,0.28381374,0.76053214,0.06480118,0.9484536,0,0,0,1,0
-09c6ddd2c210450e,xclick,/m/0k0pj,1,0.54323727,0.5698448,0.13696612,0.15905744,0,0,0,1,0
-09c6ddd2c210450e,xclick,/m/04hgtk,1,0.48337027,0.6053215,0.06332842,0.18114875,0,0,0,1,0
-09c6ddd2c210450e,xclick,/m/04rky,1,0.28381374,0.72505546,0.0544919,0.8453608,0,0,0,1,0
-09c6ddd2c210450e,xclick,/m/0dzf4,1,0.28824833,0.50554323,0.072164945,0.23858616,0,0,0,1,0
-09c6ddd2c210450e,xclick,/m/0dzf4,1,0.6097561,0.7339246,0.21207659,0.40795287,0,0,0,1,0
-09c6ddd2c210450e,xclick,/m/03bt1vf,1,0.28824833,0.75166297,0.06332842,0.83505154,0,0,0,1,0
-09c7f89055cf399b,xclick,/m/05s2s,1,0,1,0,1,0,0,1,0,0
-09c7f89055cf399b,xclick,/m/0c9ph5,1,0.05277402,0.19079837,0.43855423,0.63373494,1,0,0,0,0
-09c7f89055cf399b,xclick,/m/0c9ph5,1,0.17456022,0.7510149,0,0.87710845,0,0,0,0,0
-09c7f89055cf399b,xclick,/m/0c9ph5,1,0.6752368,0.932341,0.5253012,0.913253,1,0,0,0,0
-09c863d76bcf6b00,xclick,/m/0dzct,1,0.1875,0.84166664,0.040625,0.678125,0,0,0,1,0
-09c863d76bcf6b00,xclick,/m/01yx86,1,0,0.96666664,0,1,0,0,0,0,0
-09c863d76bcf6b00,xclick,/m/01g317,1,0,0.96875,0,0.9984375,1,0,0,1,0
-09c863d76bcf6b00,xclick,/m/0k0pj,1,0.46458334,0.7125,0.3203125,0.465625,0,0,0,1,0
-09c863d76bcf6b00,xclick,/m/09j2d,1,0.025,0.95625,0.5203125,0.9984375,0,1,0,1,0
-09c863d76bcf6b00,xclick,/m/04hgtk,1,0.00625,0.8541667,0,0.675,0,0,0,1,0
-09c863d76bcf6b00,xclick,/m/04yqq2,1,0,0.96666664,0,1,0,0,0,0,0
-09c993afacd01547,xclick,/m/015p6,1,0.109375,0.496875,0.16666667,0.6958333,1,0,0,0,0
-09c993afacd01547,xclick,/m/015p6,1,0.4515625,0.8859375,0.3875,0.83958334,0,0,0,0,0
-09cbba9f5e097a19,xclick,/m/0k4j,1,0.015625,0.51875,0.295,0.575,1,0,0,0,0
-09cbba9f5e097a19,xclick,/m/0k4j,1,0.1171875,0.934375,0.2825,0.6875,1,0,0,0,0
-09cbba9f5e097a19,xclick,/m/0k4j,1,0.3234375,1,0.23,1,0,1,0,0,0
-09cbba9f5e097a19,xclick,/m/07yv9,1,0.0171875,1,0.23,1,0,1,1,0,0
-09d2112596d9155b,xclick,/m/0k4j,1,0,0.25516224,0.33849558,0.6924779,1,1,0,0,0
-09d2112596d9155b,xclick,/m/0k4j,1,0.06342183,0.96460176,0.03539823,0.9358407,0,0,0,0,0
-09d2112596d9155b,xclick,/m/0k4j,1,0.71091443,0.9896755,0.34070796,0.6880531,1,0,0,0,0
-09d2112596d9155b,xclick,/m/08dz3q,1,0.39823008,0.83923304,0.5221239,0.6216814,0,0,0,0,0
-09d2112596d9155b,xclick,/m/083wq,1,0.072271384,0.109144546,0.5641593,0.829646,0,0,0,0,0
-09d2112596d9155b,xclick,/m/083wq,1,0.26253688,0.37905604,0.59070796,0.9380531,0,0,0,0,0
-09d2112596d9155b,xclick,/m/083wq,1,0.53687316,0.57669616,0.460177,0.48451328,1,0,0,0,0
-09d2112596d9155b,xclick,/m/083wq,1,0.9380531,0.95132744,0.65929204,0.6880531,1,0,0,0,0
-09d354dbd3dcc857,xclick,/m/0dzct,1,0.64254063,0.6706056,0.42384106,0.47019866,1,0,0,0,0
-09d354dbd3dcc857,xclick,/m/03q69,1,0.64254063,0.67799115,0.40397352,0.44591612,0,0,0,0,0
-09d354dbd3dcc857,xclick,/m/0k4j,1,0.23338257,0.89660263,0.3112583,0.79911697,0,0,0,0,0
-09d354dbd3dcc857,xclick,/m/01g317,1,0.64254063,0.7666174,0.401766,0.49668875,1,0,0,0,0
-09d354dbd3dcc857,xclick,/m/04hgtk,1,0.64254063,0.68242246,0.40397352,0.46578366,0,0,0,0,0
-09d354dbd3dcc857,xclick,/m/083wq,1,0.4549483,0.5731167,0.61147904,0.79911697,1,0,0,0,0
-09d354dbd3dcc857,xclick,/m/083wq,1,0.7917282,0.8537666,0.58278143,0.69757175,1,0,0,0,0
-09d45c49c4adbae4,xclick,/m/07r04,1,0.09066306,0.9201624,0.09638554,0.9518072,0,0,0,0,0
-09d45c49c4adbae4,xclick,/m/01g317,1,0.41407308,0.45737484,0.23614457,0.34698796,1,0,0,0,0
-09d45c49c4adbae4,xclick,/m/01prls,1,0.8173207,1,0.48192772,1,0,1,0,0,0
-09d45c49c4adbae4,xclick,/m/0h2r6,1,0,0.34506088,0.15421687,0.66024095,1,1,0,0,0
-09d45c49c4adbae4,xclick,/m/0h9mv,1,0.37483084,0.5277402,0.6698795,0.9518072,0,0,0,0,0
-09d45c49c4adbae4,xclick,/m/0h9mv,1,0.74695534,0.849797,0.59518075,0.80963856,1,0,0,0,0
-09d45c49c4adbae4,xclick,/m/0h9mv,1,0.83897156,0.97158325,0.7638554,1,1,1,0,0,0
-09d45c49c4adbae4,xclick,/m/083wq,1,0.37483084,0.5277402,0.6578313,0.95421684,0,0,0,0,0
-09d45c49c4adbae4,xclick,/m/083wq,1,0.74695534,0.8376184,0.5975904,0.81445783,1,0,0,0,0
-09d64f43c7111879,xclick,/m/0dzct,1,0.058035713,0.79464287,0.27192983,0.9298246,0,0,0,0,0
-09d64f43c7111879,xclick,/m/01xyhv,1,0,0.99776787,0.7719298,0.99707603,0,1,0,0,0
-09d64f43c7111879,xclick,/m/03q69,1,0,0.90848213,0.05263158,0.94152045,0,0,0,0,0
-09d64f43c7111879,xclick,/m/0k0pj,1,0.2767857,0.46205357,0.5204678,0.69298244,0,0,0,0,0
-09d64f43c7111879,xclick,/m/02p0tk3,1,0,0.99776787,0.05116959,0.99707603,0,1,0,0,0
-09d64f43c7111879,xclick,/m/04hgtk,1,0,0.91741073,0.05263158,0.9356725,0,0,0,0,0
-09d64f43c7111879,xclick,/m/014sv8,1,0.13392857,0.28794643,0.5233918,0.5628655,0,0,0,0,0
-09d64f43c7111879,xclick,/m/014sv8,1,0.4330357,0.6183036,0.50438595,0.5409357,0,0,0,0,0
-09d64f43c7111879,xclick,/m/039xj_,1,0.81473213,0.9129464,0.42251462,0.5994152,1,0,0,0,0
-09d64f43c7111879,xclick,/m/0283dt1,1,0.28348213,0.56026787,0.73391813,0.80994153,0,0,0,0,0
-09d64f43c7111879,xclick,/m/015h_t,1,0.27901787,0.625,0.83625734,0.9356725,0,0,0,0,0
-09d64f43c7111879,xclick,/m/04yx4,1,0,1,0.05263158,1,0,1,0,0,0
-09d8aa2d19ff724d,xclick,/m/0c_jw,1,0,0.99826086,0.59737825,0.99812734,1,1,0,0,0
-09d8aa2d19ff724d,xclick,/m/04hgtk,1,0.31304348,0.43130434,0.24531835,0.41011235,0,0,0,0,0
-09d8aa2d19ff724d,xclick,/m/04rky,1,0.18086956,0.6,0.2340824,0.6310862,1,0,0,0,0
-09d8aa2d19ff724d,xclick,/m/04rky,1,0.32173914,0.43826088,0.87265915,0.99812734,0,1,0,1,0
-09d8aa2d19ff724d,xclick,/m/04rky,1,0.7026087,0.8069565,0.8857678,0.99812734,0,1,0,1,0
-09d8aa2d19ff724d,xclick,/m/04rky,1,0.7547826,0.8382609,0.43820226,0.6573034,0,0,0,1,0
-09d8aa2d19ff724d,xclick,/m/04yx4,1,0.18086956,0.6017391,0.24531835,0.6310862,1,0,0,0,0
-09dcb9b52055d40f,xclick,/m/0k4j,1,0.10932945,0.74489796,0.37583894,0.95302016,0,0,0,0,0
-09dcb9b52055d40f,xclick,/m/01prls,1,0.07580175,0.12244898,0.5525727,0.57941836,0,0,0,0,0
-09dcb9b52055d40f,xclick,/m/083wq,1,0.07725947,0.08309038,0.5704698,0.57941836,0,0,0,0,0
-09dd0671cd633432,xclick,/m/05s2s,1,0,1,0.0022123894,0.84513277,0,0,1,0,0
-09dd0671cd633432,xclick,/m/01g317,1,0.072271384,0.90707964,0.06415929,0.96460176,0,0,1,0,0
-09dd0671cd633432,xclick,/m/09j2d,1,0.07079646,0.24631268,0.15707965,0.9314159,0,0,0,0,0
-09dd0671cd633432,xclick,/m/09j2d,1,0.23303835,0.4218289,0.1659292,0.8982301,0,0,0,0,0
-09dd0671cd633432,xclick,/m/09j2d,1,0.39823008,0.6061947,0.2079646,0.8783186,0,0,0,0,0
-09dd0671cd633432,xclick,/m/09j2d,1,0.5678466,0.7463127,0.29867256,0.8584071,0,0,0,0,0
-09dd0671cd633432,xclick,/m/09j2d,1,0.72861356,0.9056047,0.24115044,0.8893805,0,0,0,0,0
-09dd0671cd633432,xclick,/m/02p0tk3,1,0.072271384,0.24041298,0.061946902,0.9623894,1,0,0,0,0
-09dd0671cd633432,xclick,/m/02p0tk3,1,0.23303835,0.42035398,0.07743363,0.95132744,1,0,0,0,0
-09dd0671cd633432,xclick,/m/02p0tk3,1,0.39675516,0.60471976,0.119469024,0.9314159,1,0,0,0,0
-09dd0671cd633432,xclick,/m/02p0tk3,1,0.5678466,0.74336284,0.19690265,0.90707964,1,0,0,0,0
-09dd0671cd633432,xclick,/m/02p0tk3,1,0.71533924,0.90855455,0.13938053,0.9358407,1,0,0,0,0
-09dd0671cd633432,xclick,/m/07j7r,1,0,0.9985251,0,0.5287611,0,1,1,0,0
-09dd0671cd633432,xclick,/m/04yx4,1,0.07079646,0.24926254,0.06858407,0.9756637,1,0,0,0,0
-09dd0671cd633432,xclick,/m/04yx4,1,0.2359882,0.42477876,0.086283185,0.9557522,1,0,0,0,0
-09dd0671cd633432,xclick,/m/04yx4,1,0.39675516,0.60471976,0.123893805,0.9314159,0,0,0,0,0
-09df63bd01367ca3,xclick,/m/01bms0,1,0.14040561,0.51326054,0.44885176,0.6263048,0,0,0,0,0
-09df63bd01367ca3,xclick,/m/07k1x,1,0.4274571,0.9984399,0.0960334,0.2901879,0,1,0,0,0
-09df63bd01367ca3,xclick,/m/07k1x,1,0.5039002,0.9984399,0.38204592,0.57202506,1,1,0,0,0
-09e094375efab7fe,xclick,/m/01mzpv,1,0.565625,0.734375,0.2875,0.45833334,1,0,1,0,0
-09e094375efab7fe,xclick,/m/01mzpv,1,0.8390625,0.9984375,0.6125,0.74375,1,1,0,0,0
-09e094375efab7fe,xclick,/m/03q69,1,0.3421875,0.4,0.016666668,0.08541667,1,0,0,0,0
-09e094375efab7fe,xclick,/m/0qmmr,1,0.1609375,0.6078125,0.15833333,0.9166667,1,0,0,0,0
-09e094375efab7fe,xclick,/m/0c_jw,1,0.6578125,1,0.725,1,0,1,0,0,0
-09e094375efab7fe,xclick,/m/0c_jw,1,0.8421875,1,0.64166665,0.75,1,1,0,0,0
-09e094375efab7fe,xclick,/m/035r7c,1,0.265625,0.5875,0.40416667,0.95,1,0,0,0,0
-09e094375efab7fe,xclick,/m/035r7c,1,0.4171875,0.5453125,0.39375,0.94375,1,0,0,0,0
-09e094375efab7fe,xclick,/m/035r7c,1,0.5890625,0.8515625,0.35208333,0.77708334,1,0,0,0,0
-09e094375efab7fe,xclick,/m/035r7c,1,0.6859375,0.8546875,0.4875,0.8520833,1,0,0,0,0
-09e094375efab7fe,xclick,/m/01g317,1,0.590625,1,0,0.85,0,1,0,0,0
-09e094375efab7fe,xclick,/m/01g317,1,0.60625,0.640625,0.25833333,0.35416666,1,0,0,0,0
-09e094375efab7fe,xclick,/m/01g317,1,0.6390625,0.6796875,0.24583334,0.34166667,1,0,0,0,0
-09e094375efab7fe,xclick,/m/01g317,1,0.6984375,0.7390625,0.275,0.33541667,1,0,0,0,0
-09e094375efab7fe,xclick,/m/09j2d,1,0.19375,0.5390625,0.014583333,0.77916664,1,0,0,0,0
-09e094375efab7fe,xclick,/m/09j2d,1,0.609375,0.6390625,0.27916667,0.33958334,1,0,0,0,0
-09e094375efab7fe,xclick,/m/09j2d,1,0.6390625,0.68125,0.26666668,0.34375,1,0,0,0,0
-09e094375efab7fe,xclick,/m/09j2d,1,0.7015625,0.740625,0.275,0.34375,1,0,0,0,0
-09e094375efab7fe,xclick,/m/09j2d,1,0.7203125,0.9984375,0.072916664,0.73541665,1,1,0,0,0
-09e094375efab7fe,xclick,/m/02p0tk3,1,0.1875,0.56875,0,0.95,0,0,0,0,0
-09e094375efab7fe,xclick,/m/02p0tk3,1,0.5859375,1,0,0.8375,0,1,0,0,0
-09e094375efab7fe,xclick,/m/04rky,1,0.1953125,0.5875,0.014583333,0.9458333,1,0,0,0,0
-09e094375efab7fe,xclick,/m/04rky,1,0.59375,0.9984375,0,0.83958334,1,0,0,0,0
-09e094375efab7fe,xclick,/m/09j5n,1,0.4609375,0.5359375,0.83958334,0.9479167,1,0,0,0,0
-09e094375efab7fe,xclick,/m/09j5n,1,0.590625,0.7296875,0.68541664,0.77708334,0,0,0,0,0
-09e094375efab7fe,xclick,/m/09j5n,1,0.6890625,0.746875,0.77708334,0.85,0,1,0,0,0
-09e094375efab7fe,xclick,/m/0dzf4,1,0.1953125,0.396875,0.1625,0.36458334,0,0,0,0,0
-09e094375efab7fe,xclick,/m/0dzf4,1,0.4109375,0.5140625,0.12916666,0.34375,1,0,0,0,0
-09e094375efab7fe,xclick,/m/0dzf4,1,0.7625,0.96875,0.20416667,0.4125,1,0,0,0,0
-09e094375efab7fe,xclick,/m/0dzf4,1,0.8109375,1,0.1625,0.66875,1,1,0,0,0
-09e094375efab7fe,xclick,/m/03bt1vf,1,0.1921875,0.5859375,0.0125,0.95,1,0,0,0,0
-09e617d9d3120b32,xclick,/m/01mzpv,1,0.23946784,0.827051,0.35882354,0.99852943,1,1,0,0,0
-09e617d9d3120b32,xclick,/m/03q69,1,0.29268292,0.6518847,0.16617647,0.51911765,0,0,0,0,0
-09e617d9d3120b32,xclick,/m/035r7c,1,0.079822615,0.3991131,0.6485294,1,0,0,0,0,0
-09e617d9d3120b32,xclick,/m/035r7c,1,0.5609756,1,0.62647057,1,0,1,0,0,0
-09e617d9d3120b32,xclick,/m/05r655,1,0.2572062,0.72283816,0.31323528,0.7352941,0,0,0,0,0
-09e617d9d3120b32,xclick,/m/09j2d,1,0.24168514,0.72505546,0.30294117,0.725,0,0,0,0,0
-09e617d9d3120b32,xclick,/m/02p0tk3,1,0.07538803,1,0.16617647,1,0,0,0,0,0
-09e617d9d3120b32,xclick,/m/04hgtk,1,0.37694013,0.52549887,0.16617647,0.28529412,0,0,0,0,0
-09e617d9d3120b32,xclick,/m/014sv8,1,0.38137472,0.41241685,0.20588236,0.225,0,0,0,0,0
-09e617d9d3120b32,xclick,/m/014sv8,1,0.4368071,0.4789357,0.1882353,0.20441176,0,0,0,0,0
-09e617d9d3120b32,xclick,/m/0k65p,1,0.3924612,0.5121951,0.64411765,0.7705882,0,0,0,0,0
-09e617d9d3120b32,xclick,/m/0k65p,1,0.4878049,0.6097561,0.62647057,0.7058824,0,0,0,0,0
-09e617d9d3120b32,xclick,/m/0dzf4,1,0.29268292,0.50554323,0.3779412,0.7735294,1,0,0,0,0
-09e617d9d3120b32,xclick,/m/0dzf4,1,0.4789357,0.72283816,0.34264705,0.70735294,0,0,0,0,0
-09e617d9d3120b32,xclick,/m/03bt1vf,1,0.07760532,1,0.16617647,1,0,0,0,0,0
-09ea349ee555b61d,xclick,/m/0dzct,1,0.34264705,0.4617647,0.36807096,0.6474501,0,0,0,0,0
-09ea349ee555b61d,xclick,/m/0dzct,1,0.6573529,0.7470588,0.4345898,0.5898004,1,0,0,0,0
-09ea349ee555b61d,xclick,/m/02522,1,0,0.85588235,0,0.9977827,0,1,0,0,0
-09ea349ee555b61d,xclick,/m/01m2v,1,0.6073529,0.99852943,0.85365856,0.9977827,0,1,0,0,0
-09ea349ee555b61d,xclick,/m/01g317,1,0.14558823,0.80588233,0.32150775,1,0,0,1,1,0
-09ea349ee555b61d,xclick,/m/09j2d,1,0.18235295,0.5779412,0.6141907,0.9756098,1,1,0,1,0
-09ea349ee555b61d,xclick,/m/09j2d,1,0.57941175,0.67058825,0.54545456,0.84035474,1,0,0,1,0
-09ea349ee555b61d,xclick,/m/09j2d,1,0.67058825,0.69264704,0.73170733,0.789357,0,1,0,1,0
-09ea349ee555b61d,xclick,/m/09j2d,1,0.6897059,0.7235294,0.6851441,0.7849224,1,1,0,1,0
-09ea349ee555b61d,xclick,/m/09j2d,1,0.7823529,0.7985294,0.63414633,0.6984479,1,0,0,1,0
-09ea349ee555b61d,xclick,/m/04hgtk,1,0.32058823,0.475,0.32372504,0.6518847,0,0,0,1,0
-09ea349ee555b61d,xclick,/m/04hgtk,1,0.6426471,0.76029414,0.37028825,0.58314854,0,0,0,0,0
-09ea349ee555b61d,xclick,/m/04hgtk,1,0.6985294,0.71617645,0.6385809,0.6851441,0,0,0,0,0
-09ea349ee555b61d,xclick,/m/01c648,1,0,1,0,1,0,1,0,0,0
-09ea349ee555b61d,xclick,/m/0dzf4,1,0.15294118,0.34558824,0.7006652,1,0,0,0,1,0
-09ea349ee555b61d,xclick,/m/0dzf4,1,0.5029412,0.6,0.6518847,0.88913524,1,1,0,0,0
-09ea349ee555b61d,xclick,/m/0dzf4,1,0.5926471,0.7647059,0.62971175,0.8226164,1,1,0,0,0
-09ea349ee555b61d,xclick,/m/0dzf4,1,0.6720588,0.69411767,0.67184037,0.73170733,0,1,0,0,0
-09ea349ee555b61d,xclick,/m/0dzf4,1,0.6867647,0.7132353,0.70288247,0.75609756,1,0,0,0,0
-09ea349ee555b61d,xclick,/m/0dzf4,1,0.72205883,0.7867647,0.6430155,0.71175164,0,0,0,0,0
-09ea349ee555b61d,xclick,/m/04yx4,1,0.15147059,0.58235294,0.32372504,1,0,1,0,1,0
-09ea349ee555b61d,xclick,/m/04yx4,1,0.57941175,0.8117647,0.36807096,0.8470067,1,0,0,1,0
-09ebcee57699eb98,xclick,/m/014trl,1,0.0031201248,0.12480499,0.28392485,0.7494781,0,0,0,0,0
-09ebcee57699eb98,xclick,/m/014trl,1,0.63026524,0.73322934,0.24843423,0.6868476,0,0,0,1,0
-09f531fe4f6d95f3,xclick,/m/05s2s,1,0,0.996875,0.58958334,0.99791664,1,0,1,0,0
-09f531fe4f6d95f3,xclick,/m/01fdzj,1,0.0421875,0.1390625,0.37708333,0.5854167,1,0,0,0,0
-09f531fe4f6d95f3,xclick,/m/01fdzj,1,0.128125,0.2296875,0.36458334,0.5875,1,0,0,0,0
-09f531fe4f6d95f3,xclick,/m/01fdzj,1,0.2125,0.278125,0.38125,0.55625,1,0,0,0,0
-09f531fe4f6d95f3,xclick,/m/01fdzj,1,0.253125,0.3125,0.42291668,0.56458336,1,0,0,0,0
-09f531fe4f6d95f3,xclick,/m/01fdzj,1,0.2796875,0.3765625,0.37083334,0.5708333,1,0,0,0,0
-09f531fe4f6d95f3,xclick,/m/01fdzj,1,0.31875,0.375,0.34791666,0.59583336,1,0,0,0,0
-09f531fe4f6d95f3,xclick,/m/01fdzj,1,0.3546875,0.41875,0.3875,0.5708333,1,0,0,0,0
-09f531fe4f6d95f3,xclick,/m/01fdzj,1,0.4171875,0.515625,0.375,0.5708333,1,0,0,0,0
-09f531fe4f6d95f3,xclick,/m/01fdzj,1,0.44375,0.478125,0.45833334,0.5875,1,0,0,0,0
-09f531fe4f6d95f3,xclick,/m/01fdzj,1,0.49375,0.5390625,0.38958332,0.57708335,1,0,0,0,0
-09f531fe4f6d95f3,xclick,/m/01fdzj,1,0.5390625,0.6421875,0.38333333,0.58125,1,0,0,0,0
-09f531fe4f6d95f3,xclick,/m/01fdzj,1,0.5609375,0.690625,0.09583333,0.6125,0,0,0,0,0
-09f531fe4f6d95f3,xclick,/m/01fdzj,1,0.5609375,0.5875,0.475,0.5833333,1,0,0,0,0
-09f531fe4f6d95f3,xclick,/m/01fdzj,1,0.66875,0.68125,0.50416666,0.5833333,1,0,0,0,0
-09f531fe4f6d95f3,xclick,/m/01fdzj,1,0.678125,0.6984375,0.48541668,0.5875,1,0,0,0,0
-09f531fe4f6d95f3,xclick,/m/01fdzj,1,0.690625,0.7171875,0.49166667,0.58125,1,0,0,0,0
-09f8b77a88f224d9,xclick,/m/0k4j,1,0.085798815,0.8816568,0.26211452,0.66519827,0,0,0,0,0
-09f8b77a88f224d9,xclick,/m/01prls,1,0.9674556,1,0.33920705,0.5396476,0,1,0,0,0
-09f8b77a88f224d9,xclick,/m/0h9mv,1,0.21745563,0.33284023,0.49779737,0.66519827,0,0,0,0,0
-09f8b77a88f224d9,xclick,/m/0h9mv,1,0.7026627,0.8076923,0.5,0.6629956,1,0,0,0,0
-09f8b77a88f224d9,xclick,/m/0d4v4,1,0.44674557,0.5325444,0,0.06387665,0,1,0,0,0
-09f8b77a88f224d9,xclick,/m/0d4v4,1,0.59319526,0.69822484,0.002202643,0.06828194,0,1,0,0,0
-09f8b77a88f224d9,xclick,/m/0d4v4,1,0.78846157,0.841716,0,0.06828194,1,1,0,0,0
-09f8b77a88f224d9,xclick,/m/083wq,1,0.21597633,0.33431953,0.5,0.6674009,0,0,0,0,0
-09f8b77a88f224d9,xclick,/m/083wq,1,0.6997042,0.8091716,0.49779737,0.6585903,1,0,0,0,0
-09f8e760f60df0da,xclick,/m/01yrx,1,0.21091445,0.56489676,0.21902655,0.6792035,1,0,0,0,0
-09f8e760f60df0da,xclick,/m/01yrx,1,0.37168142,0.8377581,0.21902655,1,0,0,0,0,0
-09fa093bcd300c1a,xclick,/m/01g317,1,0.42774567,0.50578034,0.30248308,0.3792325,1,0,0,0,0
-09fa093bcd300c1a,xclick,/m/01g317,1,0.61416185,0.63728327,0.34311512,0.40632054,1,0,0,0,0
-09fa093bcd300c1a,xclick,/m/04rky,1,0.43641618,0.49710983,0.30022573,0.3792325,1,0,0,0,0
-09fa093bcd300c1a,xclick,/m/04rky,1,0.59393066,0.63728327,0.34988713,0.4288939,1,0,0,0,0
-09fa093bcd300c1a,xclick,/m/019jd,1,0.23121387,0.90751445,0,0.69074494,1,0,0,0,0
-0a02c648d24f39fb,xclick,/m/0dzct,1,0.3888889,0.57905984,0.10534351,0.259542,0,0,0,0,0
-0a02c648d24f39fb,xclick,/m/03q69,1,0.3696581,0.6559829,0.045801528,0.25496182,0,0,0,0,0
-0a02c648d24f39fb,xclick,/m/0fly7,1,0.17521368,1,0.68854964,1,0,1,0,0,0
-0a02c648d24f39fb,xclick,/m/0k0pj,1,0.44230768,0.4957265,0.15267175,0.1908397,0,0,0,0,0
-0a02c648d24f39fb,xclick,/m/09j2d,1,0.05982906,1,0.23816794,1,0,0,0,0,0
-0a02c648d24f39fb,xclick,/m/04hgtk,1,0.36752138,0.6559829,0.047328245,0.26259542,0,0,0,0,0
-0a02c648d24f39fb,xclick,/m/014sv8,1,0.40811965,0.4636752,0.13587786,0.15877862,0,0,0,0,0
-0a02c648d24f39fb,xclick,/m/014sv8,1,0.491453,0.5405983,0.14198473,0.16183206,0,0,0,0,0
-0a02c648d24f39fb,xclick,/m/04rky,1,0.0042735045,0.99786323,0.05496183,0.9984733,1,1,0,0,0
-0a02c648d24f39fb,xclick,/m/0283dt1,1,0.42521366,0.5106838,0.19541985,0.22290076,0,0,0,0,0
-0a02c648d24f39fb,xclick,/m/0mkg,1,0.23290598,1,0.2687023,0.75114506,0,0,0,0,0
-0a02c648d24f39fb,xclick,/m/04yx4,1,0.0021367522,1,0.047328245,1,0,0,0,0,0
-0a02e8b6820064f5,xclick,/m/0k4j,1,0.06636501,0.9366516,0.2857143,0.76623374,0,0,0,0,0
-0a02e8b6820064f5,xclick,/m/04hgtk,1,0.47209653,0.4856712,0.36796537,0.3917749,1,0,0,0,0
-0a02e8b6820064f5,xclick,/m/08dz3q,1,0.57315236,0.5882353,0.4047619,0.44588745,1,0,0,0,0
-0a02e8b6820064f5,xclick,/m/083wq,1,0.13574661,0.2820513,0.5714286,0.7619048,0,0,0,0,0
-0a02e8b6820064f5,xclick,/m/083wq,1,0.71342385,0.8567119,0.54545456,0.7467533,0,0,0,0,0
-0a03326036647703,xclick,/m/0f4s2w,1,0.044313148,0.6573117,0.1942605,0.44591612,0,0,0,0,1
-0a03326036647703,xclick,/m/0f4s2w,1,0.20088626,0.96750367,0.4437086,0.7682119,0,0,0,0,1
-0a08a4711c728078,xclick,/m/05s2s,1,0,0.45060894,0.426506,0.78313255,1,0,1,0,0
-0a08a4711c728078,xclick,/m/05s2s,1,0.37077132,0.6725305,0.44096386,0.6915663,1,0,1,0,0
-0a08a4711c728078,xclick,/m/05s2s,1,0.66441137,0.8254398,0.5036145,0.70843375,0,0,0,0,0
-0a08a4711c728078,xclick,/m/05s2s,1,0.80108255,1,0.25301206,0.70843375,1,1,1,0,0
-0a08a4711c728078,xclick,/m/05s2s,1,0.9093369,1,0.46506023,0.7783133,0,1,0,0,0
-0a08a4711c728078,xclick,/m/07j7r,1,0,1,0,0.6096386,1,1,1,0,0
-0a120822d362dddf,xclick,/m/01g317,1,0,0.023564065,0.63716817,0.88495576,0,1,0,0,0
-0a120822d362dddf,xclick,/m/01g317,1,0.0044182623,0.02945508,0.65707964,0.8163717,1,1,0,0,0
-0a120822d362dddf,xclick,/m/01g317,1,0.095729016,0.15758468,0.63053095,0.9181416,1,0,0,0,0
-0a120822d362dddf,xclick,/m/01g317,1,0.25184095,0.3107511,0.63716817,0.9314159,0,0,0,0,0
-0a120822d362dddf,xclick,/m/01g317,1,0.41678938,0.5154639,0.7787611,0.94911504,0,0,0,0,0
-0a120822d362dddf,xclick,/m/01g317,1,0.5022091,0.5640648,0.829646,0.94911504,1,0,0,0,0
-0a120822d362dddf,xclick,/m/01g317,1,0.5699558,0.6141384,0.6946903,0.95353985,1,0,0,0,0
-0a120822d362dddf,xclick,/m/01g317,1,0.6892489,0.73048604,0.6946903,0.96902657,0,0,0,0,0
-0a120822d362dddf,xclick,/m/01g317,1,0.73048604,0.7540501,0.83849555,0.9756637,0,0,0,0,0
-0a120822d362dddf,xclick,/m/01g317,1,0.7378498,0.7790869,0.7323009,0.9778761,1,0,0,0,0
-0a120822d362dddf,xclick,/m/01g317,1,0.7761414,0.79675996,0.7256637,0.8893805,1,0,0,0,0
-0a120822d362dddf,xclick,/m/01g317,1,0.86008835,0.89838,0.86283183,0.9712389,0,0,0,0,0
-0a120822d362dddf,xclick,/m/01g317,1,0.89838,0.9425626,0.8738938,0.9778761,0,0,0,0,0
-0a120822d362dddf,xclick,/m/09ct_,1,0.0353461,1,0.14823009,0.78539824,0,0,1,0,0
-0a120822d362dddf,xclick,/m/07yv9,1,0,1,0.1460177,0.92699116,0,0,1,0,0
-0a13dcaaab9a35e0,xclick,/m/0dzct,1,0.38,0.5282353,0.20498616,0.7063712,0,0,0,1,0
-0a13dcaaab9a35e0,xclick,/m/03q69,1,0.35764706,0.5411765,0.09972299,0.4570637,0,0,0,0,0
-0a13dcaaab9a35e0,xclick,/m/01g317,1,0.30588236,0.5423529,0.09695291,0.9196676,0,1,1,0,0
-0a13dcaaab9a35e0,xclick,/m/0k0pj,1,0.45764706,0.5035294,0.43213296,0.5207756,0,0,0,1,0
-0a13dcaaab9a35e0,xclick,/m/04hgtk,1,0.35058823,0.5411765,0.09418283,0.70914125,0,0,0,1,0
-0a13dcaaab9a35e0,xclick,/m/0283dt1,1,0.43529412,0.5035294,0.54293627,0.6066482,0,0,0,1,0
-0a1b11867383b13e,xclick,/m/0c9ph5,1,0.18436578,0.7418879,0,0.8938053,0,0,0,0,0
-0a1b11867383b13e,xclick,/m/07j7r,1,0,0.9985251,0,0.99557525,1,1,1,0,0
-0a1bd356f90aaab6,xclick,/m/03q69,1,0.6645833,1,0,0.2234375,0,1,0,0,0
-0a1bd356f90aaab6,xclick,/m/035r7c,1,0.0125,0.86875,0.46875,0.784375,1,0,0,0,0
-0a1bd356f90aaab6,xclick,/m/035r7c,1,0.13125,0.65,0.3125,0.96875,0,0,0,0,0
-0a1bd356f90aaab6,xclick,/m/01g317,1,0.008333334,1,0,0.96875,0,0,0,0,0
-0a1bd356f90aaab6,xclick,/m/09j2d,1,0.20833333,0.825,0.0828125,0.75625,0,0,0,0,0
-0a1bd356f90aaab6,xclick,/m/02p0tk3,1,0.039583333,1,0,0.9625,0,0,0,0,0
-0a1bd356f90aaab6,xclick,/m/04hgtk,1,0.7291667,1,0,0.1828125,0,0,0,0,0
-0a1bd356f90aaab6,xclick,/m/014sv8,1,0.8354167,0.86041665,0.053125,0.08125,0,0,0,0,0
-0a1bd356f90aaab6,xclick,/m/014sv8,1,0.87708336,0.90208334,0.1046875,0.134375,0,0,0,0,0
-0a1bd356f90aaab6,xclick,/m/0k65p,1,0.45,0.65208334,0.2671875,0.3609375,1,0,0,0,0
-0a1bd356f90aaab6,xclick,/m/0283dt1,1,0.7729167,0.82916665,0.0921875,0.1640625,0,0,0,0,0
-0a1bd356f90aaab6,xclick,/m/0dzf4,1,0.40208334,0.62916666,0.0578125,0.3125,0,1,0,0,0
-0a1bd356f90aaab6,xclick,/m/0dzf4,1,0.46458334,0.8666667,0.2203125,0.5015625,1,0,0,0,0
-0a1bd356f90aaab6,xclick,/m/03nfch,1,0.010416667,0.27291667,0.5734375,0.7828125,0,0,0,0,0
-0a1bd356f90aaab6,xclick,/m/03nfch,1,0.13541667,0.40625,0.7484375,0.96875,0,0,0,0,0
-0a1bd356f90aaab6,xclick,/m/031n1,1,0.010416667,0.32291666,0.571875,0.78125,1,0,0,0,0
-0a1bd356f90aaab6,xclick,/m/031n1,1,0.13125,0.41666666,0.7015625,0.96875,1,0,0,0,0
-0a1f4761dc7fe1eb,xclick,/m/015p6,1,0.29082775,0.78970915,0.3842795,0.9796215,1,0,0,0,0
-0a1f4761dc7fe1eb,xclick,/m/07j7r,1,0,1,0,1,0,1,0,0,0
-0a23d3f0e7d850f4,xclick,/m/0284d,1,0.2796875,0.7015625,0.28333333,0.8208333,0,0,0,0,0
-0a23d3f0e7d850f4,xclick,/m/04brg2,1,0.0109375,0.9375,0,0.99791664,1,1,0,0,0
-0a23d3f0e7d850f4,xclick,/m/04brg2,1,0.440625,0.9984375,0.46666667,0.7729167,1,1,0,0,0
-0a23d3f0e7d850f4,xclick,/m/04brg2,1,0.7765625,0.9984375,0,0.225,1,1,0,0,0
-0a278d979b63fc72,xclick,/m/0k4j,1,0,0.92330384,0.17920354,0.96681416,0,1,0,0,0
-0a278d979b63fc72,xclick,/m/0k4j,1,0.8053097,0.95132744,0.36725664,0.5132743,1,0,0,0,0
-0a278d979b63fc72,xclick,/m/0k4j,1,0.920354,1,0.3761062,0.59070796,1,1,0,0,0
-0a278d979b63fc72,xclick,/m/01prls,1,0.8067847,0.94690263,0.36725664,0.44247788,1,0,0,0,0
-0a278d979b63fc72,xclick,/m/0h9mv,1,0.6563422,0.75516224,0.829646,0.9159292,1,0,0,0,0
-0a278d979b63fc72,xclick,/m/083wq,1,0.6666667,0.7477876,0.8318584,0.90707964,1,0,0,0,0
-0a2c6ef66896fb92,xclick,/m/0463sg,1,0.10279001,0.68281937,0.10421286,0.93791574,0,0,0,0,0
-0a2c6ef66896fb92,xclick,/m/0463sg,1,0.4375918,0.80469894,0.09534368,0.7427938,1,0,0,0,0
-0a2c6ef66896fb92,xclick,/m/06k2mb,1,0.09985316,0.68281937,0.101995565,0.9467849,1,0,0,0,0
-0a2c6ef66896fb92,xclick,/m/06k2mb,1,0.43612334,0.8061674,0.09756097,0.7405765,1,0,0,0,0
-0a2c6ef66896fb92,xclick,/m/01b638,1,0.09985316,0.68135095,0.10421286,0.94013304,0,0,0,0,0
-0a2c6ef66896fb92,xclick,/m/01b638,1,0.43612334,0.80469894,0.09312639,0.7427938,1,0,0,0,0
-0a34d80ee1db201e,xclick,/m/03vt0,1,0.14306785,0.63126844,0.19247788,0.83849555,0,0,0,0,0
-0a34d80ee1db201e,xclick,/m/03vt0,1,0.36135694,0.85545725,0.22345133,0.9823009,0,0,0,0,0
-0a37aa0734ac8016,xclick,/m/0k4j,1,0,1,0.1460177,0.84513277,0,1,1,0,0
-0a37aa0734ac8016,xclick,/m/01prls,1,0,0.49484536,0.14380531,0.46238938,0,1,1,0,0
-0a37aa0734ac8016,xclick,/m/01prls,1,0.22238587,0.9587629,0.14823009,0.8473451,0,0,1,0,0
-0a37aa0734ac8016,xclick,/m/01prls,1,0.9513991,1,0.19690265,0.30973452,0,1,1,0,0
-0a37aa0734ac8016,xclick,/m/083wq,1,0.026509574,0.08541974,0.37168142,0.40044248,0,0,0,0,0
-0a37aa0734ac8016,xclick,/m/083wq,1,0.48159057,0.49189985,0.19247788,0.21902655,0,0,0,0,0
-0a37aa0734ac8016,xclick,/m/083wq,1,0.5154639,0.52577317,0.21460177,0.22345133,0,1,0,0,0
-0a37aa0734ac8016,xclick,/m/083wq,1,0.54050076,0.5552283,0.2300885,0.24336283,0,0,0,0,0
-0a37aa0734ac8016,xclick,/m/083wq,1,0.5581738,0.6126657,0.32964602,0.39823008,1,0,0,0,0
-0a37aa0734ac8016,xclick,/m/083wq,1,0.5832106,0.59204715,0.21681416,0.24336283,0,0,0,0,0
-0a37aa0734ac8016,xclick,/m/083wq,1,0.6303387,0.72312224,0.57743365,0.84292036,1,0,0,0,0
-0a37aa0734ac8016,xclick,/m/083wq,1,0.8026509,0.8468336,0.42920354,0.5818584,1,0,0,0,0
-0a37aa0734ac8016,xclick,/m/083wq,1,0.8645066,0.89985275,0.3539823,0.47566372,1,0,0,0,0
-0a37aa0734ac8016,xclick,/m/083wq,1,0.90279824,0.9234168,0.32079646,0.40707964,1,0,0,0,0
-0a37aa0734ac8016,xclick,/m/083wq,1,0.93519884,0.9455081,0.2721239,0.289823,0,0,0,0,0
-0a37aa0734ac8016,xclick,/m/083wq,1,0.9837997,0.9911635,0.26769912,0.29646018,1,0,0,0,0
-0a37aa0734ac8016,xclick,/m/083wq,1,0.98821795,1,0.27433628,0.31637168,1,1,0,0,0
-0a37aa0734ac8016,xclick,/m/01jfm_,1,0.020618556,0.04712813,0.25442478,0.28318584,0,0,0,0,0
-0a37aa0734ac8016,xclick,/m/01jfm_,1,0.16936672,0.20618556,0.34734514,0.38716814,0,0,0,0,0
-0a37aa0734ac8016,xclick,/m/01jfm_,1,0.31222385,0.39027983,0.6017699,0.69911504,0,0,0,0,0
-0a3873442ad329c2,xclick,/m/09j2d,1,0.20353982,0.53244835,0.31637168,0.95132744,1,0,0,0,0
-0a3873442ad329c2,xclick,/m/02p0tk3,1,0.20058997,0.5825959,0.18584071,0.960177,1,0,0,0,0
-0a3873442ad329c2,xclick,/m/013y1f,1,0.45132744,1,0.35840708,1,0,1,0,0,0
-0a3873442ad329c2,xclick,/m/05r5c,1,0.45280236,1,0.35840708,1,0,0,0,0,0
-0a3873442ad329c2,xclick,/m/0bt_c3,1,0.4498525,1,0.38716814,1,0,1,0,0,0
-0a3873442ad329c2,xclick,/m/04yx4,1,0.20353982,0.5899705,0.19026549,0.96460176,1,0,0,0,0
-0a392d80c905a9df,xclick,/m/02wbm,1,0.012635379,0.900722,0.2202166,0.55595666,0,0,1,0,0
-0a392d80c905a9df,xclick,/m/02wbm,1,0.039711192,0.9909747,0.5740072,1,0,0,1,0,0
-0a392d80c905a9df,xclick,/m/02wbm,1,0.2833935,0.8790614,0.0018050541,0.23104693,0,0,1,0,0
-0a39325e5ad7f5a0,xclick,/m/0dzct,1,0.4734375,0.8546875,0.29166666,0.86875,0,0,0,0,0
-0a39325e5ad7f5a0,xclick,/m/03q69,1,0,1,0,1,0,0,0,0,0
-0a39325e5ad7f5a0,xclick,/m/0463sg,1,0.4890625,0.7375,0.58125,0.8958333,0,0,0,0,0
-0a39325e5ad7f5a0,xclick,/m/01g317,1,0,0.9734375,0,1,0,1,0,1,0
-0a39325e5ad7f5a0,xclick,/m/0k0pj,1,0.71875,0.8328125,0.45416668,0.62291664,0,0,0,1,0
-0a39325e5ad7f5a0,xclick,/m/02p0tk3,1,0,1,0,1,0,1,0,0,0
-0a39325e5ad7f5a0,xclick,/m/014sv8,1,0.621875,0.7140625,0.44375,0.5104167,1,0,0,0,0
-0a39325e5ad7f5a0,xclick,/m/014sv8,1,0.7859375,0.8375,0.4375,0.50208336,0,0,0,0,0
-0a39325e5ad7f5a0,xclick,/m/04rky,1,0,0.9546875,0,0.99791664,1,1,0,0,0
-0a39325e5ad7f5a0,xclick,/m/0283dt1,1,0.73125,0.8625,0.65833336,0.77708334,0,0,0,0,0
-0a3c01759e77a02d,xclick,/m/03q69,1,0.009375,0.996875,0,0.077083334,0,0,1,0,0
-0a3c01759e77a02d,xclick,/m/0k4j,1,0,0.0296875,0.039583333,0.110416666,1,1,0,0,0
-0a3c01759e77a02d,xclick,/m/0k4j,1,0,0.028125,0.110416666,0.18958333,0,1,0,0,0
-0a3c01759e77a02d,xclick,/m/0k4j,1,0.0859375,0.8546875,0.04375,0.7916667,0,0,0,0,0
-0a3c01759e77a02d,xclick,/m/0k4j,1,0.209375,0.7796875,0,0.10208333,1,1,1,0,0
-0a3c01759e77a02d,xclick,/m/0k4j,1,0.559375,1,0.047916666,0.46041667,1,1,0,0,0
-0a3c01759e77a02d,xclick,/m/01g317,1,0.0078125,0.9984375,0,0.18333334,0,1,1,0,0
-0a3c01759e77a02d,xclick,/m/09j2d,1,0.0140625,1,0,0.17291667,0,0,1,0,0
-0a3c01759e77a02d,xclick,/m/01prls,1,0,1,0,0.7895833,0,0,1,0,0
-0a3c01759e77a02d,xclick,/m/04rky,1,0.0078125,0.996875,0,0.17916666,1,0,1,0,0
-0a3c01759e77a02d,xclick,/m/083wq,1,0.09375,0.1234375,0.24375,0.39583334,1,0,0,0,0
-0a3c01759e77a02d,xclick,/m/083wq,1,0.23125,0.321875,0.40625,0.70208335,0,0,0,0,0
-0a3c01759e77a02d,xclick,/m/083wq,1,0.9125,1,0.28958333,0.46458334,0,0,0,0,0
-0a3c01759e77a02d,xclick,/m/04yx4,1,0.009375,0.9453125,0,0.14166667,0,1,1,0,0
-0a3f577a327ca7cc,xclick,/m/0cgh4,1,0,1,0,1,0,1,0,0,0
-0a3f577a327ca7cc,xclick,/m/0d4v4,1,0,0.99791664,0.0015625,1,0,0,1,0,0
-0a3f9b3d57ef354a,xclick,/m/0k4j,1,0.052336447,0.9364486,0.16928446,0.92670155,0,0,0,0,0
-0a3f9b3d57ef354a,xclick,/m/0k4j,1,0.72149533,1,0.19546248,0.37696335,0,0,0,0,0
-0a3f9b3d57ef354a,xclick,/m/07yv9,1,0,1,0.03141361,0.9249564,0,0,0,0,0
-0a41cda5f44baaf6,xclick,/m/0k4j,1,0,0.22566372,0,0.3960177,1,1,0,0,0
-0a41cda5f44baaf6,xclick,/m/0k4j,1,0.013274336,0.37758112,0.03318584,0.095132746,1,0,1,0,0
-0a41cda5f44baaf6,xclick,/m/0k4j,1,0.032448377,0.9734513,0.14380531,0.8716814,0,0,0,0,0
-0a41cda5f44baaf6,xclick,/m/0k4j,1,0.28466076,0.71681416,0,0.30309734,0,0,0,0,0
-0a41cda5f44baaf6,xclick,/m/0k4j,1,0.82300884,1,0.015486726,0.49336284,1,1,0,0,0
-0a41cda5f44baaf6,xclick,/m/01prls,1,0,1,0,0.8738938,0,0,1,0,0
-0a41cda5f44baaf6,xclick,/m/083wq,1,0.056047197,0.12831858,0.38053098,0.59513277,1,0,0,0,0
-0a41cda5f44baaf6,xclick,/m/083wq,1,0.2581121,0.38053098,0.5575221,0.86725664,1,0,0,0,0
-0a41cda5f44baaf6,xclick,/m/083wq,1,0.6342183,0.6946903,0.25663716,0.29867256,1,0,0,0,0
-0a41cda5f44baaf6,xclick,/m/083wq,1,0.82448375,0.859882,0.17256637,0.21681416,1,0,0,0,0
-0a47e7d602855f93,xclick,/m/0k4j,1,0,1,0.08483755,0.8935018,0,1,0,0,0
-0a47e7d602855f93,xclick,/m/0h9mv,1,0.59386283,0.7833935,0.5740072,0.8953069,0,0,0,0,0
-0a47e7d602855f93,xclick,/m/0h9mv,1,0.8880866,0.99638987,0.42238268,0.6028881,0,0,0,0,0
-0a47e7d602855f93,xclick,/m/0d4v4,1,0.6805054,0.7021661,0.06498195,0.10108303,1,0,0,0,0
-0a47e7d602855f93,xclick,/m/0d4v4,1,0.71119136,0.7400722,0.066787004,0.102888085,1,0,0,0,0
-0a47e7d602855f93,xclick,/m/0d4v4,1,0.7454874,0.77075815,0.066787004,0.1064982,1,0,0,0,0
-0a47e7d602855f93,xclick,/m/0d4v4,1,0.77797836,0.799639,0.06317689,0.1064982,1,0,0,0,0
-0a47e7d602855f93,xclick,/m/0d4v4,1,0.8194946,0.96570396,0.16245487,0.32129964,0,0,0,0,0
-0a47e7d602855f93,xclick,/m/0d4v4,1,0.92057765,0.9476534,0.0866426,0.11732852,1,0,0,0,0
-0a47e7d602855f93,xclick,/m/0d4v4,1,0.96570396,1,0.18050541,0.3032491,0,1,0,0,0
-0a47e7d602855f93,xclick,/m/07j7r,1,0.86823106,1,0.023465704,0.13537906,1,0,0,0,0
-0a4abf0a8071b917,xclick,/m/0cmf2,1,0.04733728,0.9585799,0.24229075,0.7995595,0,0,0,0,0
-0a4abf0a8071b917,xclick,/m/01g317,1,0.5192308,0.5473373,0.42731276,0.4735683,1,0,0,0,0
-0a4abf0a8071b917,xclick,/m/04rky,1,0.5192308,0.556213,0.42951542,0.47797358,1,0,0,0,0
-0a4db5693da70448,xclick,/m/03q69,1,0.49791667,0.7125,0,0.1140625,1,1,0,0,0
-0a4db5693da70448,xclick,/m/05s2s,1,0.08125,0.48541668,0.240625,0.5390625,1,0,1,0,0
-0a4db5693da70448,xclick,/m/03fp41,1,0,1,0.2,1,0,0,1,0,0
-0a4db5693da70448,xclick,/m/09j2d,1,0.23125,0.98541665,0.1140625,1,1,1,0,0,0
-0a4db5693da70448,xclick,/m/04rky,1,0.22291666,0.98541665,0,0.9984375,1,1,0,0,0
-0a4db5693da70448,xclick,/m/0dzf4,1,0.23125,0.52708334,0.0609375,0.4015625,0,0,0,0,0
-0a4db5693da70448,xclick,/m/0dzf4,1,0.68958336,0.96875,0.1578125,0.4828125,0,0,0,0,0
-0a4db5693da70448,xclick,/m/07j7r,1,0,1,0,0.6671875,1,1,0,0,0
-0a4db5693da70448,xclick,/m/07j7r,1,0.0020833334,0.18333334,0.565625,1,0,1,0,0,0
-0a4db5693da70448,xclick,/m/07j7r,1,0.0875,0.13541667,0.309375,0.4796875,1,0,0,0,0
-0a4db5693da70448,xclick,/m/07j7r,1,0.15625,1,0.2,0.925,1,1,0,0,0
-0a4db5693da70448,xclick,/m/04yx4,1,0.22916667,0.98541665,0,0.9984375,1,1,0,0,0
-0a50911d08250183,xclick,/m/03q69,1,0.72083336,0.9791667,0.2359375,0.453125,0,0,0,0,0
-0a50911d08250183,xclick,/m/01g317,1,0.93333334,1,0.4328125,0.5625,0,1,0,0,0
-0a50911d08250183,xclick,/m/09j2d,1,0.6166667,1,0.484375,1,0,1,0,0,0
-0a50911d08250183,xclick,/m/02p0tk3,1,0.32083333,1,0.234375,0.9984375,0,1,0,0,0
-0a50911d08250183,xclick,/m/02p0tk3,1,0.9375,1,0.4328125,0.5625,1,1,0,0,0
-0a50911d08250183,xclick,/m/04hgtk,1,0.6645833,0.98125,0.240625,0.4921875,0,0,0,0,0
-0a50911d08250183,xclick,/m/03jm5,1,0,0.99791664,0,0.9984375,1,1,0,0,0
-0a50911d08250183,xclick,/m/0k65p,1,0.32083333,0.46875,0.3515625,0.4421875,0,0,0,0,0
-0a50911d08250183,xclick,/m/0k65p,1,0.42083332,0.6166667,0.6734375,0.8421875,0,0,0,0,0
-0a50911d08250183,xclick,/m/04rky,1,0.3125,0.99791664,0.240625,0.9984375,1,1,0,0,0
-0a50911d08250183,xclick,/m/0283dt1,1,0.68125,0.6979167,0.4140625,0.4453125,1,0,0,0,0
-0a50911d08250183,xclick,/m/0dzf4,1,0.31458333,0.725,0.3484375,0.5796875,0,0,0,0,0
-0a50911d08250183,xclick,/m/0dzf4,1,0.425,1,0.6703125,1,0,0,0,0,0
-0a50911d08250183,xclick,/m/04yx4,1,0.31458333,0.99791664,0.234375,0.9984375,0,1,0,0,0
-0a556c8163b58fae,xclick,/m/0c_jw,1,0.0125,0.4703125,0.4125,0.6875,0,0,0,0,0
-0a556c8163b58fae,xclick,/m/0cgh4,1,0,1,0,1,0,0,0,0,1
-0a556c8163b58fae,xclick,/m/04bcr3,1,0.953125,0.9984375,0.56041664,0.60833335,1,1,0,0,0
-0a556c8163b58fae,xclick,/m/0d4v4,1,0.0078125,0.4078125,0.11875,0.45208332,0,0,0,0,0
-0a563d05ebab4fe3,xclick,/m/05s2s,1,0,1,0,1,0,0,1,0,0
-0a563d05ebab4fe3,xclick,/m/01yrx,1,0.415625,1,0.21041666,0.76666665,0,0,0,0,0
-0a5972c68b6bb265,xclick,/m/01g317,1,0.29228687,0.36806494,0.57349396,0.84819275,1,0,0,0,0
-0a5972c68b6bb265,xclick,/m/01g317,1,0.37483084,0.43301758,0.5518072,0.8385542,1,0,0,0,0
-0a5972c68b6bb265,xclick,/m/01g317,1,0.4939107,0.5277402,0.5975904,0.84819275,1,0,0,0,0
-0a5972c68b6bb265,xclick,/m/01g317,1,0.5805142,0.65629226,0.5710843,0.853012,1,0,0,0,0
-0a5972c68b6bb265,xclick,/m/01g317,1,0.699594,0.7388363,0.57831323,0.8457831,1,0,0,0,0
-0a599940d33b6b2b,xclick,/m/0c9ph5,1,0.196468,0.89183223,0.062038403,0.9527326,0,0,0,0,0
-0a599940d33b6b2b,xclick,/m/0c9ph5,1,0.5629139,0.9602649,0.15214181,0.2791728,0,0,0,0,0
-0a600f1148d1023c,xclick,/m/0c9ph5,1,0.08681672,0.1221865,0.29817444,0.326572,0,0,0,0,0
-0a600f1148d1023c,xclick,/m/07j7r,1,0,1,0,1,0,0,1,0,0
-0a6a03c8f23ee744,xclick,/m/01g317,1,0.04992658,0.5198238,0.3503326,0.8980044,0,0,0,0,0
-0a6a03c8f23ee744,xclick,/m/01g317,1,0.7048458,0.99412626,0.28603104,0.85809314,0,0,0,0,0
-0a6a03c8f23ee744,xclick,/m/083kb,1,0.47723934,0.7283407,0.28159645,0.48337027,0,0,0,0,0
-0a6a03c8f23ee744,xclick,/m/083kb,1,0.6020558,0.7283407,0.031042129,0.383592,0,0,0,0,0
-0a6a03c8f23ee744,xclick,/m/09j2d,1,0.086637296,0.49779737,0.41685143,0.8780488,0,0,0,0,0
-0a6a03c8f23ee744,xclick,/m/09j2d,1,0.7107195,0.9809104,0.31929046,0.80266076,0,0,0,0,0
-0a6a03c8f23ee744,xclick,/m/02p0tk3,1,0.04992658,0.5227606,0.3503326,0.9002217,0,0,0,0,0
-0a6a03c8f23ee744,xclick,/m/02p0tk3,1,0.7048458,0.99412626,0.28603104,0.85809314,0,0,0,0,0
-0a6a03c8f23ee744,xclick,/m/05y5lj,1,0.04845815,0.105726875,0.8470067,0.9002217,0,0,0,0,0
-0a6a03c8f23ee744,xclick,/m/05y5lj,1,0.29809105,0.37151247,0.345898,0.46119735,1,0,0,0,0
-0a6a03c8f23ee744,xclick,/m/05y5lj,1,0.30983847,0.36857563,0.35254988,0.45454547,0,0,0,0,0
-0a6a03c8f23ee744,xclick,/m/05y5lj,1,0.40234947,0.4684288,0.827051,0.8780488,0,0,0,0,0
-0a6a03c8f23ee744,xclick,/m/05y5lj,1,0.5095448,0.72246695,0.28603104,0.48337027,0,0,0,0,0
-0a6a03c8f23ee744,xclick,/m/05y5lj,1,0.6079295,0.72393537,0.05321508,0.383592,0,0,0,0,0
-0a6a03c8f23ee744,xclick,/m/05y5lj,1,0.74596184,0.8120411,0.28603104,0.40133038,0,0,0,0,0
-0a6a03c8f23ee744,xclick,/m/05y5lj,1,0.7753304,0.83994126,0.7716186,0.85365856,0,0,0,0,0
-0a6a03c8f23ee744,xclick,/m/05y5lj,1,0.88105726,0.9985316,0.49667406,0.6696231,0,0,0,0,0
-0a6a03c8f23ee744,xclick,/m/05y5lj,1,0.9676946,0.99265784,0.7849224,0.849224,0,0,0,0,0
-0a6bc386b28f2aac,xclick,/m/01mzpv,1,0.282,0.41,0.40533334,0.632,1,0,0,0,0
-0a6bc386b28f2aac,xclick,/m/0c_jw,1,0.184,0.998,0.49866667,1,0,0,0,0,0
-0a6bc386b28f2aac,xclick,/m/02z51p,1,0.422,0.538,0.53333336,0.59466666,1,0,0,0,0
-0a6bc386b28f2aac,xclick,/m/03jm5,1,0,0.998,0,0.99733335,1,1,0,0,1
-0a6bc386b28f2aac,xclick,/m/04bcr3,1,0.036,0.284,0.528,0.6906667,0,0,0,0,0
-0a6bc386b28f2aac,xclick,/m/03ssj5,1,0.182,0.716,0.536,0.928,0,0,0,0,0
-0a6bc386b28f2aac,xclick,/m/03ssj5,1,0.376,0.99,0.59466666,1,0,0,0,0,0
-0a7074a2a5515531,xclick,/m/01g317,1,0.4078125,0.625,0.18541667,0.7875,0,0,0,1,0
-0a7074a2a5515531,xclick,/m/09j2d,1,0.3875,0.759375,0.35833332,0.8375,1,0,0,1,0
-0a7074a2a5515531,xclick,/m/04hgtk,1,0.49375,0.5796875,0.24166666,0.35208333,0,0,0,0,0
-0a7074a2a5515531,xclick,/m/04rky,1,0.3875,0.6734375,0.1875,0.78333336,1,0,0,1,0
-0a7074a2a5515531,xclick,/m/06msq,1,0.334375,0.7640625,0.1875,0.84583336,1,0,0,0,0
-0a72fef43a51c479,xclick,/m/0gd2v,1,0,1,0.0072202166,0.78880864,0,1,1,0,0
-0a72fef43a51c479,xclick,/m/0by6g,1,0,0.70397115,0.11732852,0.64440435,0,1,0,0,0
-0a72fef43a51c479,xclick,/m/0by6g,1,0.092057765,0.2599278,0.0054151625,0.13537906,0,1,0,0,0
-0a72fef43a51c479,xclick,/m/0by6g,1,0.25812274,0.6805054,0.59386283,0.7833935,0,0,0,0,0
-0a72fef43a51c479,xclick,/m/0by6g,1,0.33574006,0.99819493,0.13718411,0.37184116,0,1,0,0,0
-0a72fef43a51c479,xclick,/m/0by6g,1,0.80144405,0.99819493,0.3736462,0.54873645,0,1,0,0,0
-0a72fef43a51c479,xclick,/m/02hj4,1,0.0018050541,0.70577615,0.11732852,0.64440435,0,1,0,0,0
-0a72fef43a51c479,xclick,/m/02hj4,1,0.08483755,1,0,0.2202166,1,1,0,0,0
-0a72fef43a51c479,xclick,/m/02hj4,1,0.25451264,0.67509025,0.5956679,0.7978339,0,0,0,0,0
-0a72fef43a51c479,xclick,/m/02hj4,1,0.3393502,1,0.13537906,0.3772563,0,0,0,0,0
-0a72fef43a51c479,xclick,/m/02hj4,1,0.7906137,1,0.37545127,0.55595666,0,1,0,0,0
-0a73064c82730ff5,xclick,/m/02wbm,1,0.25625,0.8625,0.0125,0.76666665,0,0,0,0,0
-0a73064c82730ff5,xclick,/m/0cgh4,1,0,0.9984375,0,0.99791664,0,1,0,0,1
-0a73064c82730ff5,xclick,/m/01g317,1,0,0.5140625,0.3,1,0,1,0,0,0
-0a73064c82730ff5,xclick,/m/09j2d,1,0,0.371875,0.51666665,0.99791664,0,1,0,0,0
-0a73064c82730ff5,xclick,/m/02p0tk3,1,0,0.5125,0.30208334,1,0,1,0,0,0
-0a73064c82730ff5,xclick,/m/0k65p,1,0.45,0.5140625,0.82708335,0.92291665,0,0,0,0,0
-0a73064c82730ff5,xclick,/m/04rky,1,0,0.5140625,0.30625,0.99791664,0,1,0,0,0
-0a78374f2d3949ae,xclick,/m/052lwg6,1,0.055646483,0.3747954,0.187251,0.5557769,1,0,0,0,0
-0a78374f2d3949ae,xclick,/m/052lwg6,1,0.15711948,0.5581015,0.40239045,0.8306773,0,0,0,0,0
-0a78374f2d3949ae,xclick,/m/052lwg6,1,0.36988544,0.700491,0.119521916,0.49402392,1,0,0,0,0
-0a78374f2d3949ae,xclick,/m/052lwg6,1,0.5450082,0.9230769,0.32868525,0.7768924,0,0,0,0,0
-0a78374f2d3949ae,xclick,/m/0cdn1,1,0.058919802,0.37315875,0.18924303,0.55378485,1,0,0,0,0
-0a78374f2d3949ae,xclick,/m/0cdn1,1,0.14729951,0.5581015,0.41035858,0.8386454,0,0,0,0,0
-0a78374f2d3949ae,xclick,/m/0cdn1,1,0.36988544,0.705401,0.12948208,0.47808766,1,0,0,0,0
-0a78374f2d3949ae,xclick,/m/0cdn1,1,0.5531915,0.9230769,0.32868525,0.77091634,0,0,0,0,0
-0a7be0b883a12966,xclick,/m/0k4j,1,0,0.41948578,0,0.19036144,0,1,0,0,0
-0a7be0b883a12966,xclick,/m/0k4j,1,0.01894452,1,0.106024094,0.98313254,0,0,0,0,0
-0a7be0b883a12966,xclick,/m/0k4j,1,0.38430312,0.47496617,0,0.072289154,0,1,0,0,0
-0a7be0b883a12966,xclick,/m/083wq,1,0,0.07848444,0,0.10120482,1,0,0,0,0
-0a7be0b883a12966,xclick,/m/083wq,1,0.29228687,0.3585927,0.0939759,0.18554217,1,0,0,0,0
-0a7be0b883a12966,xclick,/m/083wq,1,0.3991881,0.41542625,0.01686747,0.040963855,0,0,0,0,0
-0a7be0b883a12966,xclick,/m/083wq,1,0.44790256,0.46414074,0.045783132,0.06746988,1,0,0,0,0
-0a7be0b883a12966,xclick,/m/083wq,1,0.4668471,0.58457375,0.67228913,0.97831327,1,0,0,0,0
-0a7be0b883a12966,xclick,/m/083wq,1,0.84167796,0.9039242,0.6313253,0.74698794,1,0,0,0,0
-0a7c597abf1e90d4,xclick,/m/01mzpv,1,0,0.35625,0.44583333,0.99791664,0,1,0,0,0
-0a7c597abf1e90d4,xclick,/m/01mzpv,1,0.378125,0.771875,0.45208332,0.99791664,1,1,0,0,0
-0a7c597abf1e90d4,xclick,/m/05s2s,1,0.2234375,0.44375,0.31041667,0.49166667,0,0,0,0,0
-0a7c597abf1e90d4,xclick,/m/07j7r,1,0,0.5375,0,0.45,1,1,0,0,0
-0a7c597abf1e90d4,xclick,/m/07j7r,1,0.4984375,0.9984375,0,0.38125,1,1,0,0,0
-0a7c597abf1e90d4,xclick,/m/07j7r,1,0.71875,0.9984375,0.3875,0.93125,1,1,0,0,0
-0a7d56b2fb989fe8,xclick,/m/01g317,1,0.4734375,0.615625,0.25416666,0.56041664,0,0,0,0,0
-0a7d56b2fb989fe8,xclick,/m/09j2d,1,0.5,0.5765625,0.29375,0.5229167,1,0,0,0,0
-0a7d56b2fb989fe8,xclick,/m/02p0tk3,1,0.471875,0.615625,0.25416666,0.55833334,0,0,0,1,0
-0a7d56b2fb989fe8,xclick,/m/04hgtk,1,0.471875,0.509375,0.25416666,0.32291666,0,0,0,0,0
-0a7f13330a5d0023,xclick,/m/0k4j,1,0,1,0.17870036,0.8104693,0,0,1,0,0
-0a7f13330a5d0023,xclick,/m/01g317,1,0.6137184,0.87725633,0.15703972,0.90613717,0,0,0,0,0
-0a7f13330a5d0023,xclick,/m/0h9mv,1,0.62815887,0.732852,0.6191336,0.78519857,1,0,0,0,0
-0a7f13330a5d0023,xclick,/m/0h9mv,1,0.84476537,0.89169675,0.42238268,0.56498194,1,0,0,0,0
-0a7f13330a5d0023,xclick,/m/0d4v4,1,0.23646209,0.2761733,0.037906136,0.08122744,1,0,0,0,0
-0a7f13330a5d0023,xclick,/m/083wq,1,0.62274367,0.73465705,0.62454873,0.79422385,1,0,0,0,0
-0a7f13330a5d0023,xclick,/m/083wq,1,0.8574007,0.8953069,0.42057762,0.55595666,0,0,0,0,0
-0a7f13330a5d0023,xclick,/m/01jfm_,1,0.8935018,0.92779785,0.2870036,0.3068592,0,0,0,0,0
-0a7f4d9a0ccb9afe,xclick,/m/0bjyj5,1,0.1515625,0.421875,0.22916667,0.70416665,1,0,0,0,0
-0a7f4d9a0ccb9afe,xclick,/m/0bjyj5,1,0.565625,0.8265625,0.2125,0.6979167,1,0,0,0,0
-0a7f4d9a0ccb9afe,xclick,/m/07j7r,1,0.4828125,0.7265625,0.110416666,0.40625,1,0,0,0,0
-0a7f4d9a0ccb9afe,xclick,/m/07j7r,1,0.5578125,0.9984375,0,0.21875,1,1,0,0,0
-0a7fbc1d68e4e5ae,xclick,/m/0k4j,1,0.0947226,0.8755074,0.06746988,0.92771083,0,0,0,0,0
-0a7fbc1d68e4e5ae,xclick,/m/0k4j,1,0.13667117,0.202977,0.01686747,0.12048193,1,0,0,1,0
-0a7fbc1d68e4e5ae,xclick,/m/0k4j,1,0.42083898,0.45737484,0,0.045783132,1,0,0,1,0
-0a7fbc1d68e4e5ae,xclick,/m/0k4j,1,0.4451962,0.47767252,0,0.040963855,1,0,0,1,0
-0a7fbc1d68e4e5ae,xclick,/m/0k4j,1,0.8430311,1,0.13493976,0.33493975,1,1,0,0,0
-0a7fbc1d68e4e5ae,xclick,/m/0k4j,1,0.864682,1,0.2433735,0.50843376,1,1,0,0,0
-0a7fbc1d68e4e5ae,xclick,/m/083wq,1,0.4668471,0.6008119,0.5710843,0.90361446,0,0,0,0,0
-0a7fbc1d68e4e5ae,xclick,/m/083wq,1,0.7916103,0.8633288,0.42168674,0.63373494,0,0,0,0,0
-0a7fbc1d68e4e5ae,xclick,/m/083wq,1,0.8930988,0.97023004,0.3759036,0.5036145,0,0,0,0,0
-0a7ffd65766a4741,xclick,/m/03jm5,1,0.06197655,0.93467337,0.074074075,0.9649123,0,0,0,0,0
-0a7ffd65766a4741,xclick,/m/0d4v4,1,0.11725293,0.8358459,0.56530213,0.9298246,0,0,0,0,0
-0a7ffd65766a4741,xclick,/m/0d4v4,1,0.11725293,0.2562814,0.76413256,0.9259259,1,0,0,0,0
-0a7ffd65766a4741,xclick,/m/0d4v4,1,0.16582915,0.26130652,0.582846,0.69590646,0,0,0,0,0
-0a7ffd65766a4741,xclick,/m/0d4v4,1,0.18927974,0.24288107,0.4269006,0.53216374,0,0,0,0,0
-0a7ffd65766a4741,xclick,/m/0d4v4,1,0.3400335,0.39363486,0.57894737,0.69005847,0,0,0,0,0
-0a7ffd65766a4741,xclick,/m/0d4v4,1,0.3400335,0.3919598,0.7738792,0.9278752,0,0,0,0,0
-0a7ffd65766a4741,xclick,/m/0d4v4,1,0.49078727,0.6515913,0.56725144,0.69005847,0,0,0,0,0
-0a7ffd65766a4741,xclick,/m/0d4v4,1,0.49078727,0.6532663,0.76413256,0.9298246,0,0,0,0,0
-0a7ffd65766a4741,xclick,/m/0d4v4,1,0.5410385,0.59463984,0.4093567,0.5126706,0,0,0,0,0
-0a7ffd65766a4741,xclick,/m/0d4v4,1,0.7403685,0.8726968,0.37037036,0.49317738,0,0,0,0,0
-0a7ffd65766a4741,xclick,/m/0d4v4,1,0.74539363,0.8559464,0.3957115,0.49317738,0,0,0,0,0
-0a7ffd65766a4741,xclick,/m/0d4v4,1,0.7470687,0.8743719,0.7465887,0.9220273,0,0,0,0,0
-0a7ffd65766a4741,xclick,/m/0d4v4,1,0.7788945,0.8291457,0.57894737,0.69590646,0,0,0,0,0
-0a7ffd65766a4741,xclick,/m/07j7r,1,0.9480737,0.99832493,0.65497077,0.9590643,0,1,0,0,0
-0a82f0443c940816,xclick,/m/02wbm,1,0.076696165,1,0,0.82522124,0,1,0,0,0
-0a82f0443c940816,xclick,/m/01g317,1,0.8303835,0.9985251,0,0.19026549,1,1,0,0,0
-0a8657e8b5c9d7bb,xclick,/m/0dzct,1,0.38053098,0.49705014,0.1460177,0.34513274,0,0,0,0,0
-0a8657e8b5c9d7bb,xclick,/m/03q69,1,0.15044248,0.16371681,0.670354,0.6924779,0,0,0,0,0
-0a8657e8b5c9d7bb,xclick,/m/03q69,1,0.37758112,0.5117994,0.13938053,0.22787611,0,1,0,1,0
-0a8657e8b5c9d7bb,xclick,/m/0jyfg,1,0.3761062,0.5,0.16150442,0.27654868,0,0,0,1,0
-0a8657e8b5c9d7bb,xclick,/m/017ftj,1,0.37758112,0.5,0.16150442,0.2721239,0,0,0,0,0
-0a8657e8b5c9d7bb,xclick,/m/0k0pj,1,0.41887906,0.44837758,0.23672566,0.2920354,0,0,0,1,0
-0a8657e8b5c9d7bb,xclick,/m/02p0tk3,1,0.120943956,0.6106195,0.13716814,0.76106197,0,0,0,1,0
-0a8657e8b5c9d7bb,xclick,/m/04hgtk,1,0.36873156,0.5162242,0.13938053,0.35176992,0,0,0,1,0
-0a8657e8b5c9d7bb,xclick,/m/0k65p,1,0.1519174,0.28023598,0.36946902,0.6084071,0,0,0,1,0
-0a8657e8b5c9d7bb,xclick,/m/0k65p,1,0.46755162,0.6106195,0.53539824,0.7234513,1,1,0,1,0
-0a8657e8b5c9d7bb,xclick,/m/0283dt1,1,0.40560472,0.44690266,0.29646018,0.31858408,0,0,0,1,0
-0a877314ca2039d9,xclick,/m/01d40f,1,0.4692082,0.9354839,0.4318182,0.99545455,0,1,0,0,0
-0a877314ca2039d9,xclick,/m/03q69,1,0.55131966,0.8914956,0,0.7590909,0,1,0,0,0
-0a877314ca2039d9,xclick,/m/0463sg,1,0.53958946,0.60117304,0.35454544,0.55,1,0,0,0,0
-0a877314ca2039d9,xclick,/m/0463sg,1,0.78299123,0.8504399,0.37272727,0.5681818,1,0,0,0,0
-0a877314ca2039d9,xclick,/m/05r655,1,0.4398827,1,0,1,0,1,0,0,0
-0a877314ca2039d9,xclick,/m/0k0pj,1,0.6451613,0.7096774,0.32272726,0.45454547,0,0,0,0,0
-0a877314ca2039d9,xclick,/m/02p0tk3,1,0.4340176,1,0,1,0,1,0,0,0
-0a877314ca2039d9,xclick,/m/04hgtk,1,0.542522,0.856305,0,0.6045455,0,0,0,0,0
-0a877314ca2039d9,xclick,/m/014sv8,1,0.5894428,0.6568915,0.29090908,0.35454544,0,0,0,0,0
-0a877314ca2039d9,xclick,/m/014sv8,1,0.7038123,0.78299123,0.3,0.36363637,0,0,0,0,0
-0a877314ca2039d9,xclick,/m/039xj_,1,0.54545456,0.56891495,0.18636364,0.31363636,0,0,0,0,0
-0a877314ca2039d9,xclick,/m/039xj_,1,0.80938417,0.85337245,0.2090909,0.3909091,0,0,0,0,0
-0a877314ca2039d9,xclick,/m/0283dt1,1,0.6363636,0.72140765,0.48181817,0.5318182,0,0,0,0,0
-0a877314ca2039d9,xclick,/m/0dzf4,1,0.4222874,0.55718476,0.48636365,1,0,1,0,0,0
-0a877314ca2039d9,xclick,/m/0dzf4,1,0.83870965,1,0.55,1,0,1,0,0,0
-0a877314ca2039d9,xclick,/m/03bt1vf,1,0.44281524,1,0,1,0,1,0,0,0
-0a917bbca24cf75d,xclick,/m/02wbm,1,0.018050542,0.98194945,0.018050542,0.98194945,0,0,0,0,0
-0a917bbca24cf75d,xclick,/m/0f4s2w,1,0.08303249,0.1101083,0.52527076,0.5505415,0,0,0,0,0
-0a917bbca24cf75d,xclick,/m/0f4s2w,1,0.092057765,0.12815884,0.32129964,0.34837544,0,0,0,0,0
-0a917bbca24cf75d,xclick,/m/0f4s2w,1,0.09386282,0.12815884,0.4963899,0.5361011,0,0,0,0,0
-0a917bbca24cf75d,xclick,/m/0f4s2w,1,0.18953069,0.23826715,0.3501805,0.37906137,0,0,0,0,0
-0a917bbca24cf75d,xclick,/m/0f4s2w,1,0.25631768,0.27797833,0.47833934,0.51263535,0,0,0,0,0
-0a917bbca24cf75d,xclick,/m/0f4s2w,1,0.2635379,0.32129964,0.42057762,0.44223827,0,0,0,0,0
-0a917bbca24cf75d,xclick,/m/0f4s2w,1,0.29422382,0.32851985,0.1696751,0.20397112,0,0,0,0,0
-0a917bbca24cf75d,xclick,/m/0f4s2w,1,0.31949458,0.7454874,0.08483755,0.63898915,1,0,1,0,0
-0a917bbca24cf75d,xclick,/m/0f4s2w,1,0.44765344,0.4765343,0.7599278,0.8267148,0,0,0,0,0
-0a917bbca24cf75d,xclick,/m/0f4s2w,1,0.5830325,0.599278,0.057761732,0.08844765,0,0,0,0,0
-0a917bbca24cf75d,xclick,/m/0f4s2w,1,0.62274367,0.66064984,0.7527076,0.78880864,0,0,0,0,0
-0a917bbca24cf75d,xclick,/m/0f4s2w,1,0.68231046,0.7238267,0.7833935,0.8375451,0,0,0,0,0
-0a917bbca24cf75d,xclick,/m/0f4s2w,1,0.6859206,0.72202164,0.48375452,0.53429604,0,0,0,0,0
-0a917bbca24cf75d,xclick,/m/0f4s2w,1,0.7075812,0.84115523,0.58844763,0.6768953,1,0,1,0,0
-0a917bbca24cf75d,xclick,/m/0f4s2w,1,0.71299636,0.7599278,0.78519857,0.8303249,1,0,0,0,0
-0a917bbca24cf75d,xclick,/m/0f4s2w,1,0.732852,0.767148,0.3736462,0.4097473,0,0,0,0,0
-0a917bbca24cf75d,xclick,/m/0f4s2w,1,0.7978339,0.84837544,0.70397115,0.77978337,1,0,0,0,0
-0a917bbca24cf75d,xclick,/m/0f4s2w,1,0.83212996,0.86281586,0.7617329,0.8032491,0,0,0,0,0
-0a917bbca24cf75d,xclick,/m/0f4s2w,1,0.8429603,0.9025271,0.37184116,0.41877255,0,0,0,0,0
-0a917bbca24cf75d,xclick,/m/0f4s2w,1,0.87545127,0.8935018,0.45306858,0.4765343,1,0,0,0,0
-0a917bbca24cf75d,xclick,/m/0f4s2w,1,0.88447654,0.9169675,0.49097472,0.5,1,0,0,0,0
-0a917bbca24cf75d,xclick,/m/0f4s2w,1,0.900722,0.92057765,0.5379061,0.566787,1,0,0,0,0
-0a917bbca24cf75d,xclick,/m/0grw1,1,0.07400722,0.9801444,0.054151624,0.85559565,0,0,0,0,0
-0a94296ff543a1dc,xclick,/m/0k4j,1,0,1,0,1,0,0,0,0,1
-0a94296ff543a1dc,xclick,/m/08dz3q,1,0,1,0,1,0,0,0,0,0
-0a9f183e46c76019,xclick,/m/0dzct,1,0.3734375,0.6296875,0.28541666,0.7416667,0,0,0,0,0
-0a9f183e46c76019,xclick,/m/03q69,1,0.3015625,0.79375,0.035416666,1,0,1,0,0,0
-0a9f183e46c76019,xclick,/m/0463sg,1,0.45625,0.6734375,0.8229167,0.99791664,1,1,0,0,0
-0a9f183e46c76019,xclick,/m/05r655,1,0.0453125,1,0.06875,1,0,1,0,0,0
-0a9f183e46c76019,xclick,/m/0k0pj,1,0.4,0.4671875,0.44166666,0.5729167,0,0,0,0,0
-0a9f183e46c76019,xclick,/m/09j2d,1,0.6296875,0.83125,0.7625,0.99791664,1,1,0,0,0
-0a9f183e46c76019,xclick,/m/02p0tk3,1,0.046875,1,0.052083332,1,0,1,0,0,0
-0a9f183e46c76019,xclick,/m/04hgtk,1,0.303125,0.80625,0.05,0.7416667,0,0,0,0,0
-0a9f183e46c76019,xclick,/m/014sv8,1,0.365625,0.4234375,0.42916667,0.48333332,0,0,0,0,0
-0a9f183e46c76019,xclick,/m/014sv8,1,0.471875,0.546875,0.41875,0.475,0,0,0,0,0
-0a9f183e46c76019,xclick,/m/0283dt1,1,0.4109375,0.5,0.59583336,0.6791667,0,0,0,0,0
-0a9f183e46c76019,xclick,/m/0dzf4,1,0.046875,0.3109375,0.725,1,0,1,0,0,0
-0a9f183e46c76019,xclick,/m/0dzf4,1,0.7953125,1,0.76458335,1,0,1,0,0,0
-0a9f183e46c76019,xclick,/m/03bt1vf,1,0.046875,1,0.052083332,1,0,1,0,0,0
-0a9f73b3c2557150,xclick,/m/01yx86,1,0,0.70208335,0,0.9984375,0,1,0,0,0
-0a9f73b3c2557150,xclick,/m/06msq,1,0.4125,1,0.09375,0.615625,1,0,0,0,0
-0a9ff75a7897e757,xclick,/m/01ww8y,1,0.3515625,0.5515625,0.57916665,0.78125,1,0,0,0,0
-0a9ff75a7897e757,xclick,/m/01ww8y,1,0.3578125,0.5453125,0.42083332,0.61875,1,0,0,0,0
-0a9ff75a7897e757,xclick,/m/01ww8y,1,0.4828125,0.6296875,0.325,0.5229167,1,0,0,0,0
-0a9ff75a7897e757,xclick,/m/01ww8y,1,0.509375,0.73125,0.7,0.83125,1,0,0,0,0
-0a9ff75a7897e757,xclick,/m/01ww8y,1,0.5609375,0.765625,0.48541668,0.7125,1,0,0,0,0
-0a9ff75a7897e757,xclick,/m/01ww8y,1,0.6703125,0.8453125,0.33333334,0.54375,1,0,0,0,0
-0a9ff75a7897e757,xclick,/m/01ww8y,1,0.7171875,0.9671875,0.6625,0.83125,1,0,0,0,0
-0a9ff75a7897e757,xclick,/m/01ww8y,1,0.7890625,0.9859375,0.48125,0.70625,1,0,0,0,0
-0a9ff75a7897e757,xclick,/m/0271t,1,0.0265625,0.25,0.44583333,0.59166664,0,0,0,0,0
-0a9ff75a7897e757,xclick,/m/07clx,1,0.0015625,0.3453125,0.29375,0.66875,0,0,0,0,0
-0aa206fa7ea80036,xclick,/m/07j7r,1,0.019125683,0.11065574,0.7947494,0.8353222,0,0,1,0,0
-0aa206fa7ea80036,xclick,/m/07j7r,1,0.11065574,0.21994536,0.7732697,0.8281623,0,0,1,0,0
-0aa206fa7ea80036,xclick,/m/07j7r,1,0.22404371,0.28961748,0.7040573,0.83054894,0,0,1,0,0
-0aa206fa7ea80036,xclick,/m/07j7r,1,0.3852459,0.53688526,0.6300716,0.8162291,0,0,1,0,0
-0aa206fa7ea80036,xclick,/m/07j7r,1,0.534153,0.62568307,0.59188545,0.7899761,1,0,0,0,0
-0aa206fa7ea80036,xclick,/m/07j7r,1,0.61885244,0.6953552,0.58472556,0.8019093,1,0,0,0,0
-0aa206fa7ea80036,xclick,/m/07j7r,1,0.67349726,1,0.60143197,0.7875895,0,0,1,0,0
-0aa3a6c33fca122b,xclick,/m/01n5jq,1,0,0.99638987,0,0.99819493,0,0,0,0,0
-0aa3a6c33fca122b,xclick,/m/01g317,1,0.23646209,0.9187726,0.3393502,1,0,0,0,1,0
-0aa3a6c33fca122b,xclick,/m/09j2d,1,0.29602888,0.8574007,0.3393502,1,1,1,0,1,0
-0aaad833ac61ac9d,xclick,/m/05s2s,1,0,0.1472754,0.518847,0.8780488,1,1,1,0,0
-0aaad833ac61ac9d,xclick,/m/05s2s,1,0.101620026,0.41237113,0.0022172949,0.4878049,1,0,1,0,0
-0aaad833ac61ac9d,xclick,/m/05s2s,1,0.8173785,0.9926362,0,0.68292683,1,1,1,0,0
-0aaad833ac61ac9d,xclick,/m/052sf,1,0.0736377,0.3431517,0.44345897,0.7649667,0,0,1,0,0
-0aaad833ac61ac9d,xclick,/m/052sf,1,0.35051546,0.91163474,0.40798226,0.84257203,0,0,1,0,0
-0aaad833ac61ac9d,xclick,/m/07j7r,1,0,1,0,0.6962306,0,1,0,0,0
-0aacbdb54e853a0a,xclick,/m/0k4j,1,0,0.11875,0.2375,0.37083334,1,1,0,0,0
-0aacbdb54e853a0a,xclick,/m/0k4j,1,0.0578125,0.1171875,0.28541666,0.41875,1,0,0,0,0
-0aacbdb54e853a0a,xclick,/m/0k4j,1,0.096875,0.95625,0.15416667,0.95,0,0,0,0,0
-0aacbdb54e853a0a,xclick,/m/01g317,1,0.0015625,1,0.19166666,0.41666666,1,0,1,0,0
-0aacbdb54e853a0a,xclick,/m/01prls,1,0.046875,0.1125,0.28125,0.41041666,1,0,0,0,0
-0aacbdb54e853a0a,xclick,/m/0h9mv,1,0.1,0.2046875,0.43333334,0.6458333,0,0,0,0,0
-0aacbdb54e853a0a,xclick,/m/0h9mv,1,0.35,0.58125,0.56666666,0.9479167,0,0,0,0,0
-0aacbdb54e853a0a,xclick,/m/0h9mv,1,0.78125,0.90625,0.68958336,0.7708333,0,0,0,0,0
-0aacbdb54e853a0a,xclick,/m/083wq,1,0.1,0.2046875,0.43333334,0.6479167,0,0,0,0,0
-0aacbdb54e853a0a,xclick,/m/083wq,1,0.35,0.58125,0.56666666,0.9458333,0,0,0,0,0
-0aacbdb54e853a0a,xclick,/m/083wq,1,0.5234375,0.5921875,0.28541666,0.31666666,1,0,0,0,0
-0aacbdb54e853a0a,xclick,/m/083wq,1,0.7859375,0.9015625,0.68958336,0.76666665,1,0,0,0,0
-0aad9fc79a35bd53,xclick,/m/0k4j,1,0.07060334,0.9268293,0.17258883,0.83502537,0,0,0,0,0
-0aad9fc79a35bd53,xclick,/m/083wq,1,0.14249037,0.29268292,0.5786802,0.84010154,0,0,0,0,0
-0aad9fc79a35bd53,xclick,/m/083wq,1,0.70731705,0.84852374,0.5329949,0.8096447,0,0,0,0,0
-0aae34863935e33a,xclick,/m/05s2s,1,0,1,0,1,0,0,1,0,0
-0aae34863935e33a,xclick,/m/0c9ph5,1,0.03539823,0.11504425,0.19247788,0.32079646,0,0,0,0,0
-0aae34863935e33a,xclick,/m/0c9ph5,1,0.16961652,0.27286136,0.1039823,0.3318584,0,0,0,0,0
-0aae34863935e33a,xclick,/m/0c9ph5,1,0.35840708,0.44247788,0.22123894,0.3761062,0,0,0,0,0
-0aae34863935e33a,xclick,/m/0c9ph5,1,0.41445428,0.58112097,0.4380531,0.7079646,0,0,0,0,0
-0aae34863935e33a,xclick,/m/0c9ph5,1,0.5014749,0.6460177,0.048672568,0.28318584,0,0,0,0,0
-0aae34863935e33a,xclick,/m/0c9ph5,1,0.5147493,0.59882003,0.42256638,0.5707965,0,0,0,0,0
-0aae34863935e33a,xclick,/m/0c9ph5,1,0.5516224,0.60324484,0.29646018,0.38938054,0,0,0,0,0
-0aae34863935e33a,xclick,/m/0c9ph5,1,0.5973451,0.6342183,0.43141592,0.47123894,0,0,0,0,0
-0aae34863935e33a,xclick,/m/0c9ph5,1,0.7743363,0.81120944,0.17035398,0.25,0,0,0,0,0
-0aae34863935e33a,xclick,/m/0c9ph5,1,0.8466077,0.87905604,0.18362832,0.25,0,0,0,0,0
-0aae34863935e33a,xclick,/m/0c9ph5,1,0.8480826,0.899705,0.090707965,0.19469027,0,0,0,0,0
-0aae34863935e33a,xclick,/m/0c9ph5,1,0.85103244,0.8982301,0.21681416,0.28539824,0,0,0,0,0
-0aae34863935e33a,xclick,/m/0c9ph5,1,0.89528024,0.9734513,0.26769912,0.38274336,0,0,0,0,0
-0aae34863935e33a,xclick,/m/0c9ph5,1,0.98082596,1,0.5221239,0.5862832,0,1,0,0,0
-0ab050b51e78acdb,xclick,/m/07fbm7,1,0.16144201,0.70376176,0.10602911,0.7567568,0,0,1,0,0
-0ab050b51e78acdb,xclick,/m/0284d,1,0.15360501,0.70846397,0.06652807,0.93970895,0,0,0,0,0
-0ab050b51e78acdb,xclick,/m/01ww8y,1,0.17554858,0.70062697,0.1018711,0.7546778,1,0,0,0,0
-0ab050b51e78acdb,xclick,/m/0270h,1,0.15360501,0.70689654,0.064449064,0.93970895,0,0,0,0,0
-0ab10a6417ef2301,xclick,/m/02zt3,1,0.065625,0.1,0.5520833,0.57916665,0,0,0,0,0
-0ab10a6417ef2301,xclick,/m/02zt3,1,0.3125,0.3546875,0.4125,0.44375,0,0,0,0,0
-0ab10a6417ef2301,xclick,/m/02zt3,1,0.421875,1,0.28333333,0.5729167,0,0,0,0,0
-0ab10a6417ef2301,xclick,/m/02zt3,1,0.6375,0.75,0.29375,0.37083334,0,0,0,0,0
-0ab10a6417ef2301,xclick,/m/01g317,1,0.403125,0.571875,0.46875,0.7291667,1,0,0,1,0
-0ab10a6417ef2301,xclick,/m/04rky,1,0.0375,0.05,0.71666664,0.76458335,0,0,0,0,0
-0ab10a6417ef2301,xclick,/m/04rky,1,0.0609375,0.078125,0.72291666,0.7604167,0,0,0,0,0
-0ab10a6417ef2301,xclick,/m/04rky,1,0.128125,0.14375,0.71458334,0.775,0,0,0,0,0
-0ab10a6417ef2301,xclick,/m/04rky,1,0.20625,0.2234375,0.71875,0.7583333,1,0,0,0,0
-0ab10a6417ef2301,xclick,/m/04rky,1,0.2359375,0.25625,0.71458334,0.7604167,0,0,0,0,0
-0ab10a6417ef2301,xclick,/m/04rky,1,0.2546875,0.2734375,0.71458334,0.7583333,1,0,0,0,0
-0ab10a6417ef2301,xclick,/m/04rky,1,0.284375,0.721875,0.71875,0.775,1,0,1,0,0
-0ab10a6417ef2301,xclick,/m/04rky,1,0.40625,0.575,0.46458334,0.72083336,1,0,1,0,0
-0ab10a6417ef2301,xclick,/m/04rky,1,0.76875,0.8546875,0.71875,0.76666665,1,0,1,0,0
-0ab10a6417ef2301,xclick,/m/04rky,1,0.796875,0.8203125,0.72083336,0.78333336,1,0,0,0,0
-0ab10a6417ef2301,xclick,/m/04rky,1,0.9265625,0.9796875,0.72291666,0.76666665,1,0,1,0,0
-0ab10a6417ef2301,xclick,/m/04yx4,1,0,0.98125,0.71666664,0.79791665,0,0,1,0,0
-0ab2b64f27f8baca,xclick,/m/01g317,1,0.084684685,0.9279279,0.02173913,0.7862319,0,0,0,0,0
-0ab2b64f27f8baca,xclick,/m/02p0tk3,1,0.084684685,0.9279279,0.019927537,0.7826087,0,0,0,1,0
-0ab2b64f27f8baca,xclick,/m/0138tl,1,0.08108108,0.93153155,0.02173913,0.7880435,0,0,0,1,0
-0ab5c690eebfad95,xclick,/m/01ww8y,1,0.052083332,0.25625,0.4640625,0.5390625,0,0,0,0,0
-0ab5c690eebfad95,xclick,/m/01ww8y,1,0.052083332,0.26041666,0.5890625,0.646875,0,0,0,0,0
-0ab5c690eebfad95,xclick,/m/01ww8y,1,0.16041666,0.45833334,0.1546875,0.2109375,0,0,0,0,0
-0ab5c690eebfad95,xclick,/m/01ww8y,1,0.20416667,0.46666667,0.8125,0.9203125,0,0,0,0,0
-0ab5c690eebfad95,xclick,/m/01ww8y,1,0.31666666,0.5375,0.396875,0.4578125,0,0,0,0,0
-0ab5c690eebfad95,xclick,/m/01ww8y,1,0.38125,0.65625,0.71875,0.784375,0,0,0,0,0
-0ab5c690eebfad95,xclick,/m/01ww8y,1,0.59375,0.81666666,0.2875,0.371875,0,0,0,0,0
-0ab5c690eebfad95,xclick,/m/01ww8y,1,0.64166665,0.8833333,0.196875,0.2546875,0,0,0,0,0
-0ab5c690eebfad95,xclick,/m/01ww8y,1,0.6625,0.89375,0.75625,0.83125,0,0,0,0,0
-0ab5c690eebfad95,xclick,/m/01ww8y,1,0.6666667,0.92291665,0.628125,0.696875,0,0,0,0,0
-0ab5c690eebfad95,xclick,/m/01ww8y,1,0.68541664,0.94166666,0.7203125,0.76875,0,0,0,0,0
-0ac166d12e401a98,xclick,/m/03q69,1,0.221875,0.2859375,0.73333335,0.77916664,1,0,0,0,0
-0ac166d12e401a98,xclick,/m/03q69,1,0.3765625,0.409375,0.7125,0.77708334,1,0,0,0,0
-0ac166d12e401a98,xclick,/m/03q69,1,0.64375,0.665625,0.68958336,0.73333335,1,0,0,0,0
-0ac166d12e401a98,xclick,/m/03q69,1,0.9,1,0.49166667,0.72291666,1,1,0,0,0
-0ac166d12e401a98,xclick,/m/01g317,1,0.121875,1,0.40625,1,0,1,1,0,0
-0ac166d12e401a98,xclick,/m/0k0pj,1,0.7328125,0.7515625,0.59375,0.61875,0,0,0,0,0
-0ac166d12e401a98,xclick,/m/09j2d,1,0.1046875,1,0.6125,1,0,1,1,0,0
-0ac166d12e401a98,xclick,/m/04rky,1,0.128125,0.346875,0.65833336,0.99791664,1,1,0,0,0
-0ac166d12e401a98,xclick,/m/04rky,1,0.29375,0.521875,0.64166665,0.99791664,1,1,0,0,0
-0ac166d12e401a98,xclick,/m/04rky,1,0.453125,0.5125,0.58125,0.68333334,1,0,0,0,0
-0ac166d12e401a98,xclick,/m/04rky,1,0.5078125,0.584375,0.59166664,0.7395833,1,0,0,0,0
-0ac166d12e401a98,xclick,/m/04rky,1,0.596875,0.6765625,0.65,0.99791664,1,0,0,0,0
-0ac166d12e401a98,xclick,/m/04rky,1,0.640625,0.853125,0.47916666,0.99791664,1,1,0,0,0
-0ac166d12e401a98,xclick,/m/04rky,1,0.69375,0.725,0.6041667,0.65,1,0,0,0,0
-0ac166d12e401a98,xclick,/m/04rky,1,0.834375,0.9984375,0.40833333,0.99791664,1,1,0,0,0
-0ac2f91a7995aa8b,xclick,/m/08pbxl,1,0.0068965517,0.78965515,0.04914934,0.96219283,0,0,0,0,0
-0ac2f91a7995aa8b,xclick,/m/08pbxl,1,0.2551724,0.99482757,0.0018903592,0.9584121,1,0,0,0,0
-0ac2f91a7995aa8b,xclick,/m/07j7r,1,0,0.9982759,0,0.9962193,1,1,0,0,0
-0ac3c1db1b3645f2,xclick,/m/0463sg,1,0.40384614,0.7795858,0.24669604,0.68281937,0,0,0,0,0
-0ac3c1db1b3645f2,xclick,/m/0463sg,1,0.60650885,0.9408284,0.2290749,0.69162995,0,0,0,0,0
-0ac51477636a6933,xclick,/m/0bt9lr,1,0.23485968,0.5450517,0.22516556,0.73289186,0,0,0,0,0
-0ac51477636a6933,xclick,/m/0bt9lr,1,0.65140325,0.8183161,0.22737306,0.73730683,0,0,0,0,0
-0ac52440f73b5c80,xclick,/m/03hl4l9,1,0,1,0.2625,1,0,0,0,1,0
-0ac52440f73b5c80,xclick,/m/0ch_cf,1,0,1,0,0.3765625,0,0,0,1,0
-0ac52440f73b5c80,xclick,/m/0ch_cf,1,0,1,0.2578125,1,0,0,0,1,0
-0acfa779589204bf,xclick,/m/0dzct,1,0.35920578,0.8104693,0.27797833,0.8375451,1,0,0,0,0
-0acfa779589204bf,xclick,/m/03q69,1,0.32310468,0.84837544,0.05234657,0.46570396,0,0,0,0,0
-0acfa779589204bf,xclick,/m/04dr76w,1,0,0.22202165,0.066787004,0.9476534,0,0,0,0,0
-0acfa779589204bf,xclick,/m/01599,1,0,0.22202165,0.066787004,0.9476534,0,0,0,0,0
-0acfa779589204bf,xclick,/m/01g317,1,0,1,0.054151624,1,0,1,0,0,0
-0acfa779589204bf,xclick,/m/0k0pj,1,0.59205776,0.71480143,0.6101083,0.7454874,1,0,0,0,0
-0acfa779589204bf,xclick,/m/09j2d,1,0.20397112,0.99819493,0.48194945,0.99819493,1,1,0,0,0
-0acfa779589204bf,xclick,/m/09j2d,1,0.83935016,0.86101085,0.23104693,0.31949458,1,0,0,0,0
-0acfa779589204bf,xclick,/m/02p0tk3,1,0,0.99819493,0.05956679,0.99819493,1,1,0,0,0
-0acfa779589204bf,xclick,/m/04hgtk,1,0.299639,0.85559565,0.050541516,0.8357401,1,0,0,0,0
-0acfa779589204bf,xclick,/m/014sv8,1,0.44945848,0.57942235,0.54151624,0.62274367,0,0,0,0,0
-0acfa779589204bf,xclick,/m/014sv8,1,0.6624549,0.7689531,0.51805055,0.59205776,0,0,0,0,0
-0acfa779589204bf,xclick,/m/0k65p,1,0,0.38267148,0.6173285,1,0,1,0,0,0
-0acfa779589204bf,xclick,/m/0k65p,1,0.63718414,1,0.7833935,1,0,1,0,0,0
-0acfa779589204bf,xclick,/m/081qc,1,0,0.22382672,0.06498195,0.9494585,0,0,0,0,0
-0acfa779589204bf,xclick,/m/081qc,1,0.5270758,0.77075815,0.7454874,1,1,0,0,0,0
-0acfa779589204bf,xclick,/m/0dzf4,1,0,0.39350182,0.6119134,0.99819493,1,1,0,0,0
-0acfa779589204bf,xclick,/m/0dzf4,1,0.65162456,0.99819493,0.51805055,0.99819493,1,1,0,0,0
-0acfa779589204bf,xclick,/m/04yx4,1,0.20758122,1,0.050541516,1,0,1,0,0,0
-0ad602c943c9f568,xclick,/m/03q69,1,0.76957166,1,0.09713025,0.8278146,0,1,0,0,0
-0ad602c943c9f568,xclick,/m/0c_jw,1,0,0.31905466,0.7571744,0.9977925,1,1,0,0,0
-0ad602c943c9f568,xclick,/m/0c_jw,1,0.019202363,0.38552436,0.66225165,0.9977925,1,1,0,0,0
-0ad602c943c9f568,xclick,/m/01g317,1,0.49039882,1,0.09713025,1,0,1,0,0,0
-0ad602c943c9f568,xclick,/m/083kb,1,0.5731167,0.6026588,0.6578366,0.7240618,1,0,0,0,0
-0ad602c943c9f568,xclick,/m/09j2d,1,0.6144756,0.9985229,0.607064,0.9977925,1,1,0,0,0
-0ad602c943c9f568,xclick,/m/0dzf4,1,0.48153618,0.6883309,0.598234,0.9977925,1,1,0,0,0
-0ad602c943c9f568,xclick,/m/0dzf4,1,0.65435743,0.7843427,0.5452539,0.6467991,1,0,0,0,0
-0ad7884032419621,xclick,/m/03q69,1,0.34375,0.409375,0.40208334,0.58125,1,0,0,0,0
-0ad7884032419621,xclick,/m/035r7c,1,0.7828125,1,0.5520833,0.76458335,0,1,0,0,0
-0ad7884032419621,xclick,/m/035r7c,1,0.9265625,1,0.70208335,0.73125,0,0,0,0,0
-0ad7884032419621,xclick,/m/01g317,1,0.26875,1,0.15,0.7729167,0,0,0,0,0
-0ad7884032419621,xclick,/m/09j2d,1,0.2703125,1,0.31666666,0.7708333,0,1,0,0,0
-0ad7884032419621,xclick,/m/02p0tk3,1,0.2703125,1,0.15208334,0.76458335,0,0,0,0,0
-0ad7884032419621,xclick,/m/04hgtk,1,0.3375,0.45625,0.375,0.51458335,1,0,0,0,0
-0ad7884032419621,xclick,/m/0k65p,1,0.7984375,0.8984375,0.15208334,0.32291666,0,0,0,0,0
-0ad7884032419621,xclick,/m/04rky,1,0.26875,0.9984375,0.15,0.7729167,0,1,0,0,0
-0ad7884032419621,xclick,/m/039xj_,1,0.340625,0.3875,0.48333332,0.51666665,0,0,0,0,0
-0ad7884032419621,xclick,/m/0dzf4,1,0.4859375,0.896875,0.15208334,0.5854167,0,0,0,0,0
-0ad7bad30cd432df,xclick,/m/01g317,1,0,1,0.19469027,0.9314159,0,0,1,0,0
-0ad7bad30cd432df,xclick,/m/09j2d,1,0,1,0.28318584,0.90929204,0,0,1,0,0
-0ad7bad30cd432df,xclick,/m/04rky,1,0,0.0899705,0.22566372,0.46460176,1,0,0,0,0
-0ad7bad30cd432df,xclick,/m/04rky,1,0.07522124,0.18141593,0.22123894,0.4778761,1,0,0,0,0
-0ad7bad30cd432df,xclick,/m/04rky,1,0.13864307,0.20058997,0.2920354,0.43362832,1,0,0,0,0
-0ad7bad30cd432df,xclick,/m/04rky,1,0.14011799,0.39823008,0.34513274,0.8584071,1,0,0,0,0
-0ad7bad30cd432df,xclick,/m/04rky,1,0.18141593,0.21828909,0.39159292,0.45132744,1,0,0,0,0
-0ad7bad30cd432df,xclick,/m/04rky,1,0.20501475,0.2640118,0.2920354,0.45575222,1,0,0,0,0
-0ad7bad30cd432df,xclick,/m/04rky,1,0.27876106,0.32153392,0.29424778,0.36725664,1,0,0,0,0
-0ad7bad30cd432df,xclick,/m/04rky,1,0.34070796,0.39823008,0.34513274,0.47123894,1,0,0,0,0
-0ad7bad30cd432df,xclick,/m/04rky,1,0.36873156,0.44247788,0.2699115,0.46238938,1,0,0,0,0
-0ad7bad30cd432df,xclick,/m/04rky,1,0.42330384,0.4719764,0.24778761,0.45132744,1,0,0,0,0
-0ad7bad30cd432df,xclick,/m/04rky,1,0.4660767,0.52507377,0.22787611,0.3318584,1,0,0,0,0
-0ad7bad30cd432df,xclick,/m/04rky,1,0.4719764,0.5265487,0.33849558,0.45353982,1,0,0,0,0
-0ad7bad30cd432df,xclick,/m/04rky,1,0.48820058,0.55309737,0.29867256,0.45353982,1,0,0,0,0
-0ad7bad30cd432df,xclick,/m/04rky,1,0.5103245,0.55309737,0.28097346,0.38495576,1,0,0,0,0
-0ad7bad30cd432df,xclick,/m/04rky,1,0.54867256,0.6061947,0.30530974,0.44911504,1,0,0,0,0
-0ad7bad30cd432df,xclick,/m/04rky,1,0.59144545,0.63716817,0.28097346,0.42035398,1,0,0,0,0
-0ad7bad30cd432df,xclick,/m/04rky,1,0.6135693,0.7300885,0.30752212,0.6836283,1,0,0,0,0
-0ad7bad30cd432df,xclick,/m/04rky,1,0.63716817,0.6696165,0.30088496,0.36946902,1,0,0,0,0
-0ad7bad30cd432df,xclick,/m/04rky,1,0.64306784,0.8023599,0.33628318,0.69911504,1,0,0,0,0
-0ad7bad30cd432df,xclick,/m/04rky,1,0.78318584,0.8318584,0.3119469,0.4181416,1,0,0,0,0
-0ad7bad30cd432df,xclick,/m/04rky,1,0.8023599,0.82890856,0.2721239,0.32522124,1,0,0,0,0
-0ad7bad30cd432df,xclick,/m/04rky,1,0.8303835,0.86725664,0.28761062,0.40265486,1,0,0,0,0
-0ad7bad30cd432df,xclick,/m/04rky,1,0.8480826,0.8923304,0.32079646,0.41371682,1,0,0,0,0
-0ad7bad30cd432df,xclick,/m/04rky,1,0.8480826,0.9985251,0.53318584,0.8119469,1,0,0,0,0
-0ad7bad30cd432df,xclick,/m/04rky,1,0.86725664,0.89085543,0.28097346,0.31858408,1,0,0,0,0
-0ad7bad30cd432df,xclick,/m/04rky,1,0.9056047,0.96312684,0.29867256,0.42256638,1,0,0,0,0
-0ad7bad30cd432df,xclick,/m/04rky,1,0.9306785,0.9557522,0.38938054,0.41371682,1,0,0,0,0
-0ad7bad30cd432df,xclick,/m/09j5n,1,0.14011799,0.19026549,0.7920354,0.86504424,1,0,0,0,0
-0ad7bad30cd432df,xclick,/m/09j5n,1,0.37168142,0.40265486,0.789823,0.86946905,1,0,0,0,0
-0ad7bad30cd432df,xclick,/m/09j5n,1,0.6563422,0.6858407,0.63716817,0.6902655,1,0,0,0,0
-0ad7bad30cd432df,xclick,/m/05y5lj,1,0.14159292,0.19174041,0.7920354,0.86725664,0,0,0,0,0
-0ad7bad30cd432df,xclick,/m/05y5lj,1,0.21386431,0.3318584,0.579646,0.88053095,1,0,0,0,0
-0ad7bad30cd432df,xclick,/m/05y5lj,1,0.26106194,0.320059,0.34955752,0.46681416,0,0,0,0,0
-0ad7bad30cd432df,xclick,/m/05y5lj,1,0.2861357,0.31858408,0.6216814,0.6637168,1,0,0,0,0
-0ad7bad30cd432df,xclick,/m/05y5lj,1,0.31120944,0.359882,0.5199115,0.59070796,1,0,0,0,0
-0ad7bad30cd432df,xclick,/m/05y5lj,1,0.3761062,0.40117994,0.79867256,0.8761062,0,0,0,0,0
-0ad7bad30cd432df,xclick,/m/05y5lj,1,0.60471976,0.66519177,0.18141593,0.4181416,1,0,0,0,0
-0ad7bad30cd432df,xclick,/m/05y5lj,1,0.65929204,0.6902655,0.6261062,0.6814159,0,0,0,0,0
-0ad7bad30cd432df,xclick,/m/05y5lj,1,0.6814159,0.71091443,0.3119469,0.37168142,0,0,0,0,0
-0ad7bad30cd432df,xclick,/m/05y5lj,1,0.71533924,0.9985251,0.8053097,0.9336283,1,0,0,0,0
-0ad7bad30cd432df,xclick,/m/05y5lj,1,0.71681416,0.7300885,0.579646,0.63716817,1,0,0,0,0
-0ad7bad30cd432df,xclick,/m/05y5lj,1,0.7418879,0.7758112,0.34292036,0.42035398,0,0,0,0,0
-0ad7bad30cd432df,xclick,/m/05y5lj,1,0.75663716,0.9985251,0.88495576,0.9314159,1,0,0,0,0
-0ad7bad30cd432df,xclick,/m/05y5lj,1,0.88053095,0.9292035,0.539823,0.6858407,0,0,0,0,0
-0ad7bad30cd432df,xclick,/m/04yx4,1,0,0.10176991,0.21681416,0.46681416,1,1,0,0,0
-0ad7bad30cd432df,xclick,/m/04yx4,1,0.072271384,0.18141593,0.21017699,0.46902654,1,0,0,0,0
-0ad7bad30cd432df,xclick,/m/04yx4,1,0.4218289,0.46460176,0.24778761,0.45575222,1,0,0,0,0
-0ad7bad30cd432df,xclick,/m/04yx4,1,0.46312684,0.52359885,0.2300885,0.34292036,1,0,0,0,0
-0ad7bad30cd432df,xclick,/m/04yx4,1,0.51917404,0.5501475,0.2721239,0.38274336,1,0,0,0,0
-0ad7bad30cd432df,xclick,/m/04yx4,1,0.59144545,0.6283186,0.27433628,0.42035398,1,0,0,0,0
-0ad7bad30cd432df,xclick,/m/04yx4,1,0.63716817,0.6740413,0.28761062,0.36283186,1,0,0,0,0
-0ad7bad30cd432df,xclick,/m/04yx4,1,0.7787611,0.960177,0.28097346,0.4181416,1,0,1,0,0
-0ad96a2881998657,xclick,/m/0k4j,1,0.028485756,0.14392804,0.1764706,0.47930282,0,1,0,0,0
-0ad96a2881998657,xclick,/m/0k4j,1,0.046476763,0.91454273,0.18300654,0.73638344,0,0,0,0,0
-0ad96a2881998657,xclick,/m/0k4j,1,0.20539731,0.47826087,0.11328976,0.3224401,0,1,0,0,0
-0ad96a2881998657,xclick,/m/083wq,1,0.049475264,0.12593703,0.32026145,0.47058824,1,0,0,0,0
-0ad96a2881998657,xclick,/m/083wq,1,0.6041979,0.7196402,0.39651415,0.7298475,1,0,0,0,0
-0ad96a2881998657,xclick,/m/083wq,1,0.8305847,0.90854573,0.37908497,0.5795207,1,0,0,0,0
-0ad99d610a9092e6,xclick,/m/04bcr3,1,0,1,0,1,0,0,0,0,0
-0ad99d610a9092e6,xclick,/m/04kkgm,1,0,1,0,1,0,0,1,0,0
-0ad99d610a9092e6,xclick,/m/03hj559,1,0,0.998645,0,0.99759036,1,1,1,0,0
-0ada35baba28134b,xclick,/m/0k4j,1,0.030674847,0.96779144,0.036093418,0.9872612,0,0,0,0,0
-0ada35baba28134b,xclick,/m/07yv9,1,0,0.30981594,0.11677282,0.4564756,0,1,0,0,0
-0ada35baba28134b,xclick,/m/083wq,1,0.17638037,0.21932516,0.35881105,0.44798303,1,0,0,0,0
-0ada35baba28134b,xclick,/m/083wq,1,0.2101227,0.25460124,0.36305732,0.44585988,0,0,0,0,0
-0ada35baba28134b,xclick,/m/083wq,1,0.61809814,0.7791411,0.6348195,0.9723992,0,0,0,1,0
-0ada35baba28134b,xclick,/m/083wq,1,0.8803681,0.9570552,0.462845,0.6390658,0,0,0,0,0
-0adc1330287b2e66,xclick,/m/03q69,1,0.24678111,0.32618025,0.2336874,0.3141123,0,0,0,1,0
-0adc1330287b2e66,xclick,/m/01g317,1,0.21244635,0.32832617,0.24279211,0.5007587,0,0,0,1,0
-0adc1330287b2e66,xclick,/m/0k0pj,1,0.24892704,0.2832618,0.26251897,0.28983307,0,0,0,0,0
-0adc1330287b2e66,xclick,/m/04hgtk,1,0.24248926,0.31759655,0.23520486,0.3141123,0,0,0,1,0
-0adc1330287b2e66,xclick,/m/04hgtk,1,0.77253217,0.85193133,0.5037936,0.54172987,0,0,0,1,0
-0adc1330287b2e66,xclick,/m/09j5n,1,0.20171674,0.2532189,0.46130502,0.491654,0,0,0,1,0
-0adc1330287b2e66,xclick,/m/09j5n,1,0.27682403,0.3025751,0.4157815,0.46130502,0,0,0,1,0
-0adc1330287b2e66,xclick,/m/0dzf4,1,0.2167382,0.31759655,0.3080425,0.39453718,0,0,0,1,0
-0adc373e996aadc2,xclick,/m/0cmf2,1,0.1109375,0.8125,0.43958333,0.71458334,0,0,0,0,0
-0adc373e996aadc2,xclick,/m/01g317,1,0.415625,0.4765625,0.4625,0.5083333,1,0,0,0,0
-0adc373e996aadc2,xclick,/m/083wq,1,0.3078125,0.3390625,0.6645833,0.7104167,0,0,0,0,0
-0adc373e996aadc2,xclick,/m/083wq,1,0.4859375,0.5203125,0.65208334,0.7083333,0,0,0,0,0
-0add91a2efb3f33d,xclick,/m/035r7c,1,0,1,0.26548672,0.8477876,0,0,1,0,0
-0add91a2efb3f33d,xclick,/m/05r655,1,0,0.20626152,0.1840708,0.52743363,1,1,0,0,0
-0add91a2efb3f33d,xclick,/m/05r655,1,0.08471455,0.4217311,0.10619469,0.7575221,1,0,0,0,0
-0add91a2efb3f33d,xclick,/m/05r655,1,0.10128913,0.86924493,0.35044247,0.87256634,1,0,0,0,0
-0add91a2efb3f33d,xclick,/m/05r655,1,0.6335175,0.76243097,0.10973451,0.42477876,0,1,0,0,0
-0add91a2efb3f33d,xclick,/m/01g317,1,0,0.10865562,0.07079646,0.22123894,1,1,0,0,0
-0add91a2efb3f33d,xclick,/m/01g317,1,0.05893186,0.20626152,0.19115044,0.39292035,1,0,0,0,0
-0add91a2efb3f33d,xclick,/m/01g317,1,0.06445672,0.18416207,0.17168142,0.3699115,1,0,0,0,0
-0add91a2efb3f33d,xclick,/m/01g317,1,0.24309392,0.3756906,0.10619469,0.32743362,1,0,0,0,0
-0add91a2efb3f33d,xclick,/m/01g317,1,0.89134437,1,0,0.64247787,0,1,0,0,0
-0add91a2efb3f33d,xclick,/m/0h8mhzd,1,0.08839779,0.42541435,0.099115044,0.7840708,1,0,0,0,0
-0add91a2efb3f33d,xclick,/m/0h8mhzd,1,0.15285452,0.8674033,0.3539823,0.8513274,1,0,0,0,0
-0add91a2efb3f33d,xclick,/m/02p0tk3,1,0,1,0,1,1,0,1,0,0
-0add91a2efb3f33d,xclick,/m/04rky,1,0,0.08655617,0.0761062,0.2141593,1,0,0,0,0
-0add91a2efb3f33d,xclick,/m/04rky,1,0,0.21731123,0.18938053,0.5168142,1,1,0,0,0
-0add91a2efb3f33d,xclick,/m/04rky,1,0.06629834,0.18416207,0.18938053,0.3681416,1,0,0,0,0
-0add91a2efb3f33d,xclick,/m/04rky,1,0.08839779,0.4198895,0.107964605,0.7823009,1,0,0,0,0
-0add91a2efb3f33d,xclick,/m/04rky,1,0.14917128,0.8655617,0.3539823,0.87256634,1,0,0,0,0
-0add91a2efb3f33d,xclick,/m/04rky,1,0.2320442,0.3664825,0.10442478,0.31681415,1,0,0,0,0
-0add91a2efb3f33d,xclick,/m/04rky,1,0.62983423,0.7642726,0.107964605,0.41061947,1,0,0,0,0
-0add91a2efb3f33d,xclick,/m/04rky,1,0.76979744,0.97974217,0.21769911,0.49557522,1,0,0,0,0
-0add91a2efb3f33d,xclick,/m/04rky,1,0.8802947,0.9981584,0,0.660177,1,1,0,0,0
-0add91a2efb3f33d,xclick,/m/09j5n,1,0.027624309,0.07550645,0.4920354,0.52566373,0,0,0,0,0
-0add91a2efb3f33d,xclick,/m/09j5n,1,0.17127071,0.24309392,0.71327436,0.7504425,0,0,0,0,0
-0add91a2efb3f33d,xclick,/m/09j5n,1,0.17679559,0.22467771,0.76637167,0.8070797,1,0,0,0,0
-0add91a2efb3f33d,xclick,/m/09j5n,1,0.6869245,0.72007364,0.3876106,0.42477876,0,0,0,0,0
-0add91a2efb3f33d,xclick,/m/0dzf4,1,0,0.34254143,0.16283186,0.46725664,1,1,1,0,0
-0add91a2efb3f33d,xclick,/m/0dzf4,1,0.19152854,0.41436464,0.2920354,0.52389383,1,0,0,0,0
-0add91a2efb3f33d,xclick,/m/0dzf4,1,0.3941068,0.65377533,0.4460177,0.7044248,1,0,0,0,0
-0add91a2efb3f33d,xclick,/m/0dzf4,1,0.62246776,0.8379374,0.57699114,0.87079644,1,0,0,0,0
-0add91a2efb3f33d,xclick,/m/0dzf4,1,0.6353591,0.7053407,0.17699115,0.3061947,1,0,0,0,0
-0add91a2efb3f33d,xclick,/m/0dzf4,1,0.73664826,0.7642726,0.1840708,0.29557523,1,0,0,0,0
-0add91a2efb3f33d,xclick,/m/0dzf4,1,0.78268874,0.92449355,0.30265486,0.40884957,1,0,0,0,0
-0add91a2efb3f33d,xclick,/m/0dzf4,1,0.88766116,0.9742173,0,0.22123894,1,0,0,0,0
-0add91a2efb3f33d,xclick,/m/0dzf4,1,0.9097606,0.946593,0.31150442,0.43362832,1,0,0,0,0
-0add91a2efb3f33d,xclick,/m/03bt1vf,1,0,0.22467771,0.18584071,0.52920353,1,1,0,0,0
-0add91a2efb3f33d,xclick,/m/03bt1vf,1,0.08839779,0.42541435,0.107964605,0.7840708,1,0,0,0,0
-0add91a2efb3f33d,xclick,/m/03bt1vf,1,0.15285452,0.86924493,0.35929203,0.87079644,0,0,0,0,0
-0add91a2efb3f33d,xclick,/m/04yx4,1,0.76979744,0.9815838,0.21769911,0.49911505,0,0,0,0,0
-0ade7aef439e2102,xclick,/m/0k4j,1,0,0.0796875,0.22083333,0.48125,1,1,0,0,0
-0ade7aef439e2102,xclick,/m/0k4j,1,0.028125,0.9765625,0.175,0.9895833,0,0,0,0,0
-0ade7aef439e2102,xclick,/m/0d4v4,1,0.06875,0.240625,0.21875,0.35625,0,0,0,0,0
-0ade7aef439e2102,xclick,/m/0d4v4,1,0.075,0.184375,0.21666667,0.35625,0,0,0,0,0
-0ade7aef439e2102,xclick,/m/083wq,1,0.034375,0.103125,0.4125,0.59375,0,0,0,0,0
-0ade7aef439e2102,xclick,/m/083wq,1,0.3046875,0.5125,0.5875,0.98333335,0,0,0,0,0
-0ade7aef439e2102,xclick,/m/083wq,1,0.4140625,0.4765625,0.2625,0.29166666,1,0,0,0,0
-0ade7aef439e2102,xclick,/m/07j7r,1,0.9671875,1,0.0375,0.17916666,0,0,0,0,0
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation-images.csv b/repositories/taming-transformers/data/open_images_annotations_100/validation-images.csv
deleted file mode 100644
index ef3475cb8e1fa186f30a5a1a7b6fd13048612fed..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/data/open_images_annotations_100/validation-images.csv
+++ /dev/null
@@ -1 +0,0 @@
-image_name,image_url
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/09c5b4d6bc25788d.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/09c5b4d6bc25788d.jpg
deleted file mode 100644
index 471cb350fa6039bb3b258e5348b98508e42c5c3e..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/09c5b4d6bc25788d.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/09c67960e389e4df.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/09c67960e389e4df.jpg
deleted file mode 100644
index e29bfed9ffbd1ca52da6cbed37c94985cc0a2dbe..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/09c67960e389e4df.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/09c6ddd2c210450e.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/09c6ddd2c210450e.jpg
deleted file mode 100644
index 14b8363faada5ef01127fada0d9244f8c605c47f..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/09c6ddd2c210450e.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/09c7f89055cf399b.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/09c7f89055cf399b.jpg
deleted file mode 100644
index cafd7c904507c2299259a7a76625462b7c8d0a84..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/09c7f89055cf399b.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/09c863d76bcf6b00.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/09c863d76bcf6b00.jpg
deleted file mode 100644
index efcda7fe38ac58fc4ba84021f0a080c18caf8c7c..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/09c863d76bcf6b00.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/09c993afacd01547.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/09c993afacd01547.jpg
deleted file mode 100644
index 603d471b542169156f7e30fc3d651d602aad9b31..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/09c993afacd01547.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/09cbba9f5e097a19.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/09cbba9f5e097a19.jpg
deleted file mode 100644
index d030f672e0db6443a3caa187b16ad2e84f6c5f76..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/09cbba9f5e097a19.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/09d2112596d9155b.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/09d2112596d9155b.jpg
deleted file mode 100644
index b605302b4b748fb62a6475e043a2585028f5b803..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/09d2112596d9155b.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/09d354dbd3dcc857.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/09d354dbd3dcc857.jpg
deleted file mode 100644
index 4fed27e553d6ed2dd7e8fe71b5dcff410964d0d8..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/09d354dbd3dcc857.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/09d45c49c4adbae4.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/09d45c49c4adbae4.jpg
deleted file mode 100644
index 6e6093fe69ead3a413b57d8225eec01dac2a4f87..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/09d45c49c4adbae4.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/09d64f43c7111879.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/09d64f43c7111879.jpg
deleted file mode 100644
index 3330af1d748a315aeb4a34e02f1034de12eccc38..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/09d64f43c7111879.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/09d8aa2d19ff724d.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/09d8aa2d19ff724d.jpg
deleted file mode 100644
index f0f5cec96319f96cf34fbe6ae2d2bcb036bd66c4..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/09d8aa2d19ff724d.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/09dcb9b52055d40f.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/09dcb9b52055d40f.jpg
deleted file mode 100644
index 4db52d24f50bd646e12eb1ec205d6a26982c77f6..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/09dcb9b52055d40f.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/09dd0671cd633432.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/09dd0671cd633432.jpg
deleted file mode 100644
index 8c2cd660381e4e88de0c938f04f26875b1ba928e..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/09dd0671cd633432.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/09df63bd01367ca3.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/09df63bd01367ca3.jpg
deleted file mode 100644
index 3bffb062119e736db771577ca0922be5e8d6550f..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/09df63bd01367ca3.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/09e094375efab7fe.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/09e094375efab7fe.jpg
deleted file mode 100644
index ba8a78bf25e760b152ed44d5e95006638f284cff..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/09e094375efab7fe.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/09e617d9d3120b32.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/09e617d9d3120b32.jpg
deleted file mode 100644
index bf400bd74b40f9a170fcdfb7f4a72aa644e30be2..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/09e617d9d3120b32.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/09ea349ee555b61d.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/09ea349ee555b61d.jpg
deleted file mode 100644
index b58795cd245bf0b3237d9c63a4cac0b7ad5b8c4b..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/09ea349ee555b61d.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/09ebcee57699eb98.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/09ebcee57699eb98.jpg
deleted file mode 100644
index 5564c57a5f1f3c6ec5a31e78d571f084aafc85b1..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/09ebcee57699eb98.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/09f531fe4f6d95f3.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/09f531fe4f6d95f3.jpg
deleted file mode 100644
index a2d8f8082cc07d8428eb7d0e3a731432ffd53b1a..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/09f531fe4f6d95f3.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/09f8b77a88f224d9.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/09f8b77a88f224d9.jpg
deleted file mode 100644
index 5bb7b2ebf0e27e3070318aaa142362de3d000a9c..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/09f8b77a88f224d9.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/09f8e760f60df0da.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/09f8e760f60df0da.jpg
deleted file mode 100644
index f5796ef07a752d1aa3733ebf5e50653f012ee9db..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/09f8e760f60df0da.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/09fa093bcd300c1a.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/09fa093bcd300c1a.jpg
deleted file mode 100644
index 0f2370d36af1831c47acb87fd57d19ac7b2c28b4..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/09fa093bcd300c1a.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a02c648d24f39fb.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0a02c648d24f39fb.jpg
deleted file mode 100644
index 1336750ad8fbd404a10cd65b932835bdfae40abf..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a02c648d24f39fb.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a02e8b6820064f5.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0a02e8b6820064f5.jpg
deleted file mode 100644
index 8acc3cabcb9d986462c2b5b52265eb509f10134c..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a02e8b6820064f5.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a03326036647703.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0a03326036647703.jpg
deleted file mode 100644
index b2336f13eae949dd9f06e3a4fb818f8947329b4e..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a03326036647703.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a08a4711c728078.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0a08a4711c728078.jpg
deleted file mode 100644
index 213cb61b0750ddbe38606e1589bf794d098860b5..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a08a4711c728078.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a120822d362dddf.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0a120822d362dddf.jpg
deleted file mode 100644
index e78d1c5695ec5fcd8d5707be2b4e0a72d546cf49..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a120822d362dddf.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a13dcaaab9a35e0.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0a13dcaaab9a35e0.jpg
deleted file mode 100644
index 9cd5094e8d08719c602b47c59b5a8f5ada1386fa..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a13dcaaab9a35e0.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a1b11867383b13e.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0a1b11867383b13e.jpg
deleted file mode 100644
index 92ee63d48d35583db6e5da19a32a0fef89d06505..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a1b11867383b13e.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a1bd356f90aaab6.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0a1bd356f90aaab6.jpg
deleted file mode 100644
index 7c2c4d25fa860036ca94d49216d5ee55badfae14..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a1bd356f90aaab6.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a1f4761dc7fe1eb.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0a1f4761dc7fe1eb.jpg
deleted file mode 100644
index 22ec6ca038a995595d04f04dc08d1bc5f22bf57e..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a1f4761dc7fe1eb.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a23d3f0e7d850f4.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0a23d3f0e7d850f4.jpg
deleted file mode 100644
index 364c3e74f628f1acc7f8fb3762006f1781f013e5..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a23d3f0e7d850f4.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a278d979b63fc72.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0a278d979b63fc72.jpg
deleted file mode 100644
index 953af129f05b208ab5f5bc4c0a394db1d07202cc..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a278d979b63fc72.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a2c6ef66896fb92.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0a2c6ef66896fb92.jpg
deleted file mode 100644
index aba0ca5ab4440401e2e2699e9a6031d9fadfd8fd..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a2c6ef66896fb92.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a34d80ee1db201e.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0a34d80ee1db201e.jpg
deleted file mode 100644
index e1cc00adc6f4e15627bf50e6f4cb980d80839035..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a34d80ee1db201e.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a37aa0734ac8016.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0a37aa0734ac8016.jpg
deleted file mode 100644
index 36f866b20d8f95619d216d01681c282c0e928119..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a37aa0734ac8016.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a3873442ad329c2.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0a3873442ad329c2.jpg
deleted file mode 100644
index 39ed3591f628a0befefabecb6fa2820a258b820f..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a3873442ad329c2.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a392d80c905a9df.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0a392d80c905a9df.jpg
deleted file mode 100644
index b6c0382a91a6e38f482ae7cfac0a3e9e50b84462..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a392d80c905a9df.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a39325e5ad7f5a0.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0a39325e5ad7f5a0.jpg
deleted file mode 100644
index 3285932559a2c29e104d1cd7ec3e52cc78ad59d3..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a39325e5ad7f5a0.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a3c01759e77a02d.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0a3c01759e77a02d.jpg
deleted file mode 100644
index d5af7563ea809c996861ab6202b6dc803294635f..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a3c01759e77a02d.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a3f577a327ca7cc.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0a3f577a327ca7cc.jpg
deleted file mode 100644
index aa9ce199be5563147e090772548bb108aeffba05..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a3f577a327ca7cc.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a3f9b3d57ef354a.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0a3f9b3d57ef354a.jpg
deleted file mode 100644
index 92067bb765839840bddfc98415ae880baa818fb0..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a3f9b3d57ef354a.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a41cda5f44baaf6.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0a41cda5f44baaf6.jpg
deleted file mode 100644
index 2aad348475a7699cd044e2c17345d90a133e4788..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a41cda5f44baaf6.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a47e7d602855f93.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0a47e7d602855f93.jpg
deleted file mode 100644
index ed07ef915bad7807e8f372b5fc81d91ea012bf5a..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a47e7d602855f93.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a4abf0a8071b917.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0a4abf0a8071b917.jpg
deleted file mode 100644
index 3cc1c237a4959dd88a22f6ac301bf0044ab8e3a8..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a4abf0a8071b917.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a4db5693da70448.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0a4db5693da70448.jpg
deleted file mode 100644
index 64b717c26f5f1040ac4995ce903b80cbd767ce54..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a4db5693da70448.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a50911d08250183.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0a50911d08250183.jpg
deleted file mode 100644
index 6f546628abcc887384370d52e43659cfc3ddca9b..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a50911d08250183.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a556c8163b58fae.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0a556c8163b58fae.jpg
deleted file mode 100644
index a5e9932ee91666edf28645ce6ed107ddfe542b8d..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a556c8163b58fae.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a563d05ebab4fe3.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0a563d05ebab4fe3.jpg
deleted file mode 100644
index e172cf5f3d96e96e269f119d03ad856181b6fd0a..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a563d05ebab4fe3.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a5972c68b6bb265.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0a5972c68b6bb265.jpg
deleted file mode 100644
index 87115b6b6789828ae29d6994ae8188de4b33bcb6..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a5972c68b6bb265.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a599940d33b6b2b.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0a599940d33b6b2b.jpg
deleted file mode 100644
index 2ac203e2999b1f2878344cde69dae57612dbaed1..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a599940d33b6b2b.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a600f1148d1023c.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0a600f1148d1023c.jpg
deleted file mode 100644
index adf0ea9b5b5a26dc246e31414ef00a20e2c63ca6..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a600f1148d1023c.jpg
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:bcfb917af6632ed456d0a7ca09a940b313cf563cd0149985fe9812b1f7ebf8f8
-size 1347699
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a6a03c8f23ee744.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0a6a03c8f23ee744.jpg
deleted file mode 100644
index d424fd4fae2c61e0aea0c67f4540a57343e163dc..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a6a03c8f23ee744.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a6bc386b28f2aac.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0a6bc386b28f2aac.jpg
deleted file mode 100644
index f6a6d72b93ce61558662a86cf020279c19320f7b..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a6bc386b28f2aac.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a7074a2a5515531.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0a7074a2a5515531.jpg
deleted file mode 100644
index 021e5bb3f69c0d3db5b05b37c41b3f076ac144d9..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a7074a2a5515531.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a72fef43a51c479.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0a72fef43a51c479.jpg
deleted file mode 100644
index e2b8f9dd36cf01616b8dce412e6893a01346839e..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a72fef43a51c479.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a73064c82730ff5.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0a73064c82730ff5.jpg
deleted file mode 100644
index 09b552ebe2b2ec9c8c424d411c9f178d197b6778..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a73064c82730ff5.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a78374f2d3949ae.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0a78374f2d3949ae.jpg
deleted file mode 100644
index 9999c93a0e9195af4c634d97db0cada4f1f3be83..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a78374f2d3949ae.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a7be0b883a12966.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0a7be0b883a12966.jpg
deleted file mode 100644
index 7e596263f0904cc8eda6704c38431faa49e1cc25..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a7be0b883a12966.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a7c597abf1e90d4.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0a7c597abf1e90d4.jpg
deleted file mode 100644
index e5f2e3fa9cb9e3be6d22f3031167645f0e7936da..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a7c597abf1e90d4.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a7d56b2fb989fe8.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0a7d56b2fb989fe8.jpg
deleted file mode 100644
index e6e664356966efe77772a7a54724f5c0038d5922..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a7d56b2fb989fe8.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a7f13330a5d0023.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0a7f13330a5d0023.jpg
deleted file mode 100644
index 0bbb8e6beef26f648418acf69fa06d23b5dc8a09..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a7f13330a5d0023.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a7f4d9a0ccb9afe.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0a7f4d9a0ccb9afe.jpg
deleted file mode 100644
index 00aec11478603ae73b2330238dfe5c46eceb0696..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a7f4d9a0ccb9afe.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a7fbc1d68e4e5ae.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0a7fbc1d68e4e5ae.jpg
deleted file mode 100644
index ab1a384b9b575a3e306395c276424e3e988ad895..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a7fbc1d68e4e5ae.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a7ffd65766a4741.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0a7ffd65766a4741.jpg
deleted file mode 100644
index a5cf1be2b1a05f42b3c99b25708b9cfcef8d82ff..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a7ffd65766a4741.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a82f0443c940816.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0a82f0443c940816.jpg
deleted file mode 100644
index e6278ff6bea0cdc27f0895b1a767a77ae8c3722d..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a82f0443c940816.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a8657e8b5c9d7bb.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0a8657e8b5c9d7bb.jpg
deleted file mode 100644
index 232dd6e724c9376408853ec69ee78bb508eeee75..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a8657e8b5c9d7bb.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a877314ca2039d9.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0a877314ca2039d9.jpg
deleted file mode 100644
index 5483a07e904bffe986421a9381afecf9376ef9f4..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a877314ca2039d9.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a917bbca24cf75d.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0a917bbca24cf75d.jpg
deleted file mode 100644
index b976be17c473575a5eccbd5d604740369f9e2987..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a917bbca24cf75d.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a94296ff543a1dc.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0a94296ff543a1dc.jpg
deleted file mode 100644
index c91fb319395dbef76295f689cb9f3d9a64d52d80..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a94296ff543a1dc.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a9f183e46c76019.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0a9f183e46c76019.jpg
deleted file mode 100644
index 37f68db633a57a81bdc8f01ba8013a49a02529a2..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a9f183e46c76019.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a9f73b3c2557150.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0a9f73b3c2557150.jpg
deleted file mode 100644
index 59b96baf4713eb039d78f872362a5e1f571f6243..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a9f73b3c2557150.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a9ff75a7897e757.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0a9ff75a7897e757.jpg
deleted file mode 100644
index 32457a6a82e794b1d86af6d52e46ed0aea984d04..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0a9ff75a7897e757.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0aa206fa7ea80036.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0aa206fa7ea80036.jpg
deleted file mode 100644
index fab1344a1addaa32cb23d1fc1494b822184fe3ce..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0aa206fa7ea80036.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0aa3a6c33fca122b.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0aa3a6c33fca122b.jpg
deleted file mode 100644
index deabad442164c4fb306ae71f836c62f67d56f996..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0aa3a6c33fca122b.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0aaad833ac61ac9d.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0aaad833ac61ac9d.jpg
deleted file mode 100644
index e21a748e592aae8cc73016b62adc0d222a29b93b..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0aaad833ac61ac9d.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0aacbdb54e853a0a.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0aacbdb54e853a0a.jpg
deleted file mode 100644
index 303f98f33c6345e210726e181be1295fb42e6a19..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0aacbdb54e853a0a.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0aad9fc79a35bd53.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0aad9fc79a35bd53.jpg
deleted file mode 100644
index b4f4d52a3064141a83293d73a83b467372f200fb..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0aad9fc79a35bd53.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0aae34863935e33a.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0aae34863935e33a.jpg
deleted file mode 100644
index e6d011b2912574ec7fb44bdd1f49df310fa1c670..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0aae34863935e33a.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0ab050b51e78acdb.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0ab050b51e78acdb.jpg
deleted file mode 100644
index a6a27771f0f187916f8ec16bd3f6483887d9290f..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0ab050b51e78acdb.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0ab10a6417ef2301.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0ab10a6417ef2301.jpg
deleted file mode 100644
index bcc10330f20f78df26e987ee71453cebc59b4531..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0ab10a6417ef2301.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0ab2b64f27f8baca.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0ab2b64f27f8baca.jpg
deleted file mode 100644
index 50fc9e090415e85fed471067745626160c0936f2..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0ab2b64f27f8baca.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0ab5c690eebfad95.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0ab5c690eebfad95.jpg
deleted file mode 100644
index 9bbcc7267d57b036e15d5d205b39860cb0e7f9ce..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0ab5c690eebfad95.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0ac166d12e401a98.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0ac166d12e401a98.jpg
deleted file mode 100644
index c461b7734437e88aa0ceede7bef2551e51859f4d..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0ac166d12e401a98.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0ac2f91a7995aa8b.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0ac2f91a7995aa8b.jpg
deleted file mode 100644
index 0cb2c79c850b475d1bdd4f2f334b5c5c2b5c2afc..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0ac2f91a7995aa8b.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0ac3c1db1b3645f2.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0ac3c1db1b3645f2.jpg
deleted file mode 100644
index 23b2e91593bf1759f1567999636a71401a6b9767..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0ac3c1db1b3645f2.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0ac51477636a6933.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0ac51477636a6933.jpg
deleted file mode 100644
index 60fb7feeb54d013903eb64cfdb98171b005708d8..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0ac51477636a6933.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0ac52440f73b5c80.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0ac52440f73b5c80.jpg
deleted file mode 100644
index b1898dc9449812573bffbc92cac49d0c1bf946da..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0ac52440f73b5c80.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0acfa779589204bf.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0acfa779589204bf.jpg
deleted file mode 100644
index 21ea2a7fd6a03f1c9347d5234f65cade291e1824..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0acfa779589204bf.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0ad602c943c9f568.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0ad602c943c9f568.jpg
deleted file mode 100644
index 84f838cd442e5e38f6119f8ac5c7c988338da70a..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0ad602c943c9f568.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0ad7884032419621.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0ad7884032419621.jpg
deleted file mode 100644
index c98d361492c8e69ad5ba08815244220bfa87b2dc..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0ad7884032419621.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0ad7bad30cd432df.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0ad7bad30cd432df.jpg
deleted file mode 100644
index 1355ddb8efe73b695f27d24e8c40ec2197460125..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0ad7bad30cd432df.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0ad96a2881998657.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0ad96a2881998657.jpg
deleted file mode 100644
index 9f48beffddceb92619d5c0f574c1d72173b7ba6e..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0ad96a2881998657.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0ad99d610a9092e6.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0ad99d610a9092e6.jpg
deleted file mode 100644
index 530660e8f9c922c6ca00ba48823a6410b2765d43..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0ad99d610a9092e6.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0ada35baba28134b.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0ada35baba28134b.jpg
deleted file mode 100644
index eb3a7e7a8b8017a72dcb979468b5477eef0b39ca..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0ada35baba28134b.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0adc1330287b2e66.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0adc1330287b2e66.jpg
deleted file mode 100644
index b4ecedaff192b7641a7db902ad1a6e9aed3aae01..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0adc1330287b2e66.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0adc373e996aadc2.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0adc373e996aadc2.jpg
deleted file mode 100644
index 77c47c168ffe983fc2e41cf7b0ebe4f9ed488c28..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0adc373e996aadc2.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0add91a2efb3f33d.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0add91a2efb3f33d.jpg
deleted file mode 100644
index d00e99e95568d930d7167e32aa08cc10c1a44dca..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0add91a2efb3f33d.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/open_images_annotations_100/validation/0ade7aef439e2102.jpg b/repositories/taming-transformers/data/open_images_annotations_100/validation/0ade7aef439e2102.jpg
deleted file mode 100644
index be75e772365d2524dfca24afcdaf1e01a829a4e1..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/open_images_annotations_100/validation/0ade7aef439e2102.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/sflckr_examples.txt b/repositories/taming-transformers/data/sflckr_examples.txt
deleted file mode 100644
index cd3f45eeb43b61bfecab6ea1e779b79bfe24e445..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/data/sflckr_examples.txt
+++ /dev/null
@@ -1,25 +0,0 @@
-canada/256743165_9f130ba95b_b.jpg
-swiss_landscape/4079319632_0133685b2c_b.jpg
-black_forest/44974691685_8e7372e2b1_b.jpg
-swiss_mountains/33509672006_bf4c416afd_b.jpg
-desert/4534149722_3cc4f92891_b.jpg
-geysir/3542389801_a2cbfee1e1_b.jpg
-geysir/14996762478_a9bdbf959a_b.jpg
-ireland/15570753471_74db396d14_b.jpg
-norway/25735082181_999927fe5a_b.jpg
-norway/20099378793_cc2df820af_b.jpg
-newzealand_np/7942812194_9348729b93_b.jpg
-geysir/26320755536_7c769b6218_b.jpg
-australia/8720651218_ca82a6608e_b.jpg
-black_forest/8364557382_c6c9ee2fd6_b.jpg
-australia/12822389285_a7723081b5_b.jpg
-canada/2883773_881c197107_c.jpg
-geysir/4748115806_7219c2b3be_b.jpg
-meadow/18864473291_844325caab_b.jpg
-carribean/18176301_c9d27557cf_b.jpg
-cliff_ocean/36142796444_45d452f567_b.jpg
-volcano/50254383883_27ed6ea93a_b.jpg
-alaska_lakes/43259216952_59352d7204_b.jpg
-carribean/14351041152_ef77484a1f_b.jpg
-mongolia/6076373946_e9ea2aee32_b.jpg
-lakes/39933489595_f0e5d85b6d_b.jpg
diff --git a/repositories/taming-transformers/data/sflckr_images/alaska_lakes/43259216952_59352d7204_b.jpg b/repositories/taming-transformers/data/sflckr_images/alaska_lakes/43259216952_59352d7204_b.jpg
deleted file mode 100644
index 033e17b9ed294be65910c03301ea3b04a148328c..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/sflckr_images/alaska_lakes/43259216952_59352d7204_b.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/sflckr_images/australia/12822389285_a7723081b5_b.jpg b/repositories/taming-transformers/data/sflckr_images/australia/12822389285_a7723081b5_b.jpg
deleted file mode 100644
index 38353398b2f5d07e3c2361d46e32094f88a97428..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/sflckr_images/australia/12822389285_a7723081b5_b.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/sflckr_images/australia/8720651218_ca82a6608e_b.jpg b/repositories/taming-transformers/data/sflckr_images/australia/8720651218_ca82a6608e_b.jpg
deleted file mode 100644
index 15e5491561c13220d3155e030bce9cf7383cdeaf..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/sflckr_images/australia/8720651218_ca82a6608e_b.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/sflckr_images/black_forest/44974691685_8e7372e2b1_b.jpg b/repositories/taming-transformers/data/sflckr_images/black_forest/44974691685_8e7372e2b1_b.jpg
deleted file mode 100644
index 9aaff142509c5b5712f9aa94da5fef213caf3aa4..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/sflckr_images/black_forest/44974691685_8e7372e2b1_b.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/sflckr_images/black_forest/8364557382_c6c9ee2fd6_b.jpg b/repositories/taming-transformers/data/sflckr_images/black_forest/8364557382_c6c9ee2fd6_b.jpg
deleted file mode 100644
index c063db013414c10a6b565776a091ab7e225e4f47..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/sflckr_images/black_forest/8364557382_c6c9ee2fd6_b.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/sflckr_images/canada/256743165_9f130ba95b_b.jpg b/repositories/taming-transformers/data/sflckr_images/canada/256743165_9f130ba95b_b.jpg
deleted file mode 100644
index ab0e7e2c82329dc0c73b1200c659d7cff065ced5..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/sflckr_images/canada/256743165_9f130ba95b_b.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/sflckr_images/canada/2883773_881c197107_c.jpg b/repositories/taming-transformers/data/sflckr_images/canada/2883773_881c197107_c.jpg
deleted file mode 100644
index 2e65e6563d8815aea2c646d0d796c901bfd42a91..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/sflckr_images/canada/2883773_881c197107_c.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/sflckr_images/carribean/14351041152_ef77484a1f_b.jpg b/repositories/taming-transformers/data/sflckr_images/carribean/14351041152_ef77484a1f_b.jpg
deleted file mode 100644
index b3afab018da8e1625ebf0322997303bc2131a96b..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/sflckr_images/carribean/14351041152_ef77484a1f_b.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/sflckr_images/carribean/18176301_c9d27557cf_b.jpg b/repositories/taming-transformers/data/sflckr_images/carribean/18176301_c9d27557cf_b.jpg
deleted file mode 100644
index 25589cbea1824741ab8cc7f5b683ea2258e56377..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/sflckr_images/carribean/18176301_c9d27557cf_b.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/sflckr_images/cliff_ocean/36142796444_45d452f567_b.jpg b/repositories/taming-transformers/data/sflckr_images/cliff_ocean/36142796444_45d452f567_b.jpg
deleted file mode 100644
index 99fe980a0bd29f75cc2e7205a7661387b07dd75c..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/sflckr_images/cliff_ocean/36142796444_45d452f567_b.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/sflckr_images/desert/4534149722_3cc4f92891_b.jpg b/repositories/taming-transformers/data/sflckr_images/desert/4534149722_3cc4f92891_b.jpg
deleted file mode 100644
index 48171e0a98562422f396d3c43fe883ebce7cf85a..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/sflckr_images/desert/4534149722_3cc4f92891_b.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/sflckr_images/geysir/14996762478_a9bdbf959a_b.jpg b/repositories/taming-transformers/data/sflckr_images/geysir/14996762478_a9bdbf959a_b.jpg
deleted file mode 100644
index 9baeb5a8ef4b8118dcd4eb2baf6b0a9132fc7772..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/sflckr_images/geysir/14996762478_a9bdbf959a_b.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/sflckr_images/geysir/26320755536_7c769b6218_b.jpg b/repositories/taming-transformers/data/sflckr_images/geysir/26320755536_7c769b6218_b.jpg
deleted file mode 100644
index 491772dcfd83c1b8344b01c712f034569ab7d23a..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/sflckr_images/geysir/26320755536_7c769b6218_b.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/sflckr_images/geysir/3542389801_a2cbfee1e1_b.jpg b/repositories/taming-transformers/data/sflckr_images/geysir/3542389801_a2cbfee1e1_b.jpg
deleted file mode 100644
index fc0d950bc3d1a81ec8a821299e6cd222423ed2f9..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/sflckr_images/geysir/3542389801_a2cbfee1e1_b.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/sflckr_images/geysir/4748115806_7219c2b3be_b.jpg b/repositories/taming-transformers/data/sflckr_images/geysir/4748115806_7219c2b3be_b.jpg
deleted file mode 100644
index 37b81897d864818a8052825a1a528185d5da925e..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/sflckr_images/geysir/4748115806_7219c2b3be_b.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/sflckr_images/ireland/15570753471_74db396d14_b.jpg b/repositories/taming-transformers/data/sflckr_images/ireland/15570753471_74db396d14_b.jpg
deleted file mode 100644
index 94763908099204b90d4e06e9a84e615030c3c0ea..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/sflckr_images/ireland/15570753471_74db396d14_b.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/sflckr_images/lakes/39933489595_f0e5d85b6d_b.jpg b/repositories/taming-transformers/data/sflckr_images/lakes/39933489595_f0e5d85b6d_b.jpg
deleted file mode 100644
index 576da0db19a49516abe3b3c7c64e41ee0d4b09ac..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/sflckr_images/lakes/39933489595_f0e5d85b6d_b.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/sflckr_images/meadow/18864473291_844325caab_b.jpg b/repositories/taming-transformers/data/sflckr_images/meadow/18864473291_844325caab_b.jpg
deleted file mode 100644
index 917eebc065dd13b7bc791c8b5781e7abf1031769..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/sflckr_images/meadow/18864473291_844325caab_b.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/sflckr_images/mongolia/6076373946_e9ea2aee32_b.jpg b/repositories/taming-transformers/data/sflckr_images/mongolia/6076373946_e9ea2aee32_b.jpg
deleted file mode 100644
index ba600ec426bf714bc8180d53f13da09ae8780052..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/sflckr_images/mongolia/6076373946_e9ea2aee32_b.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/sflckr_images/newzealand_np/7942812194_9348729b93_b.jpg b/repositories/taming-transformers/data/sflckr_images/newzealand_np/7942812194_9348729b93_b.jpg
deleted file mode 100644
index 227725c1ac5598af8c534766330b3d43ca604107..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/sflckr_images/newzealand_np/7942812194_9348729b93_b.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/sflckr_images/norway/20099378793_cc2df820af_b.jpg b/repositories/taming-transformers/data/sflckr_images/norway/20099378793_cc2df820af_b.jpg
deleted file mode 100644
index d5ea9aee07f1206f5443a066a16c755eddba4a43..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/sflckr_images/norway/20099378793_cc2df820af_b.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/sflckr_images/norway/25735082181_999927fe5a_b.jpg b/repositories/taming-transformers/data/sflckr_images/norway/25735082181_999927fe5a_b.jpg
deleted file mode 100644
index f2cf643bc54068a53669365849e2be90ab0a9777..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/sflckr_images/norway/25735082181_999927fe5a_b.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/sflckr_images/swiss_landscape/4079319632_0133685b2c_b.jpg b/repositories/taming-transformers/data/sflckr_images/swiss_landscape/4079319632_0133685b2c_b.jpg
deleted file mode 100644
index 62d2ced2c5009d4724c85ec2b40d403e0276b4a6..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/sflckr_images/swiss_landscape/4079319632_0133685b2c_b.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/sflckr_images/swiss_mountains/33509672006_bf4c416afd_b.jpg b/repositories/taming-transformers/data/sflckr_images/swiss_mountains/33509672006_bf4c416afd_b.jpg
deleted file mode 100644
index a35fe578e46d1bcd1fb6c31ff1b178e6dbd62bad..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/sflckr_images/swiss_mountains/33509672006_bf4c416afd_b.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/sflckr_images/volcano/50254383883_27ed6ea93a_b.jpg b/repositories/taming-transformers/data/sflckr_images/volcano/50254383883_27ed6ea93a_b.jpg
deleted file mode 100644
index 09f086287e0b6e864b6e57f0ec689f2c472b327c..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/sflckr_images/volcano/50254383883_27ed6ea93a_b.jpg and /dev/null differ
diff --git a/repositories/taming-transformers/data/sflckr_segmentations/alaska_lakes/43259216952_59352d7204_b.png b/repositories/taming-transformers/data/sflckr_segmentations/alaska_lakes/43259216952_59352d7204_b.png
deleted file mode 100644
index 9e117848ac1988895f5e0253c4730c32ad76523b..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/sflckr_segmentations/alaska_lakes/43259216952_59352d7204_b.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/sflckr_segmentations/australia/12822389285_a7723081b5_b.png b/repositories/taming-transformers/data/sflckr_segmentations/australia/12822389285_a7723081b5_b.png
deleted file mode 100644
index c1e963480795bfe76e26ea7ff37e74efb22763cf..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/sflckr_segmentations/australia/12822389285_a7723081b5_b.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/sflckr_segmentations/australia/8720651218_ca82a6608e_b.png b/repositories/taming-transformers/data/sflckr_segmentations/australia/8720651218_ca82a6608e_b.png
deleted file mode 100644
index 42bfee79c3deab8317d9f8387941b2f961a78f55..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/sflckr_segmentations/australia/8720651218_ca82a6608e_b.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/sflckr_segmentations/black_forest/44974691685_8e7372e2b1_b.png b/repositories/taming-transformers/data/sflckr_segmentations/black_forest/44974691685_8e7372e2b1_b.png
deleted file mode 100644
index 6a66beecc360eb07a1d1fe4bcf1c863c24b5a8d0..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/sflckr_segmentations/black_forest/44974691685_8e7372e2b1_b.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/sflckr_segmentations/black_forest/8364557382_c6c9ee2fd6_b.png b/repositories/taming-transformers/data/sflckr_segmentations/black_forest/8364557382_c6c9ee2fd6_b.png
deleted file mode 100644
index d703b2bd478e4599518aef1e6ce6c9d12246c9ad..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/sflckr_segmentations/black_forest/8364557382_c6c9ee2fd6_b.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/sflckr_segmentations/canada/256743165_9f130ba95b_b.png b/repositories/taming-transformers/data/sflckr_segmentations/canada/256743165_9f130ba95b_b.png
deleted file mode 100644
index cedcc521650ebd3c2f5f51c24d6b0478ee3fa04f..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/sflckr_segmentations/canada/256743165_9f130ba95b_b.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/sflckr_segmentations/canada/2883773_881c197107_c.png b/repositories/taming-transformers/data/sflckr_segmentations/canada/2883773_881c197107_c.png
deleted file mode 100644
index a48bc81b95f1bf662ad249a49280696490b23044..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/sflckr_segmentations/canada/2883773_881c197107_c.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/sflckr_segmentations/carribean/14351041152_ef77484a1f_b.png b/repositories/taming-transformers/data/sflckr_segmentations/carribean/14351041152_ef77484a1f_b.png
deleted file mode 100644
index 7b304253b44f3bb4ca43211cbd8e1ac3b1a405d7..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/sflckr_segmentations/carribean/14351041152_ef77484a1f_b.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/sflckr_segmentations/carribean/18176301_c9d27557cf_b.png b/repositories/taming-transformers/data/sflckr_segmentations/carribean/18176301_c9d27557cf_b.png
deleted file mode 100644
index 7c126357ded408afcd093463a8f5e1df9c0f1dc3..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/sflckr_segmentations/carribean/18176301_c9d27557cf_b.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/sflckr_segmentations/cliff_ocean/36142796444_45d452f567_b.png b/repositories/taming-transformers/data/sflckr_segmentations/cliff_ocean/36142796444_45d452f567_b.png
deleted file mode 100644
index cc12c39622fa1941d4427229a8fbfc2cb5cecfbe..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/sflckr_segmentations/cliff_ocean/36142796444_45d452f567_b.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/sflckr_segmentations/desert/4534149722_3cc4f92891_b.png b/repositories/taming-transformers/data/sflckr_segmentations/desert/4534149722_3cc4f92891_b.png
deleted file mode 100644
index d31f4ffd816caed8852d1166f5e07c2a9c58c84e..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/sflckr_segmentations/desert/4534149722_3cc4f92891_b.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/sflckr_segmentations/geysir/14996762478_a9bdbf959a_b.png b/repositories/taming-transformers/data/sflckr_segmentations/geysir/14996762478_a9bdbf959a_b.png
deleted file mode 100644
index 8ccb797ce2030e3ae7dac3e71aaeafe03fb40b6e..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/sflckr_segmentations/geysir/14996762478_a9bdbf959a_b.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/sflckr_segmentations/geysir/26320755536_7c769b6218_b.png b/repositories/taming-transformers/data/sflckr_segmentations/geysir/26320755536_7c769b6218_b.png
deleted file mode 100644
index 34966fe42fba78fc86af480a1385b36d41e70a62..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/sflckr_segmentations/geysir/26320755536_7c769b6218_b.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/sflckr_segmentations/geysir/3542389801_a2cbfee1e1_b.png b/repositories/taming-transformers/data/sflckr_segmentations/geysir/3542389801_a2cbfee1e1_b.png
deleted file mode 100644
index 4465c34a933b90c9cd54b3c597270b2357991b42..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/sflckr_segmentations/geysir/3542389801_a2cbfee1e1_b.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/sflckr_segmentations/geysir/4748115806_7219c2b3be_b.png b/repositories/taming-transformers/data/sflckr_segmentations/geysir/4748115806_7219c2b3be_b.png
deleted file mode 100644
index fff9b09e9b6e9a2adff744a2cc6905362a8da866..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/sflckr_segmentations/geysir/4748115806_7219c2b3be_b.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/sflckr_segmentations/ireland/15570753471_74db396d14_b.png b/repositories/taming-transformers/data/sflckr_segmentations/ireland/15570753471_74db396d14_b.png
deleted file mode 100644
index a5dd6c7935abb4000018a8781aa506789df0fa2f..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/sflckr_segmentations/ireland/15570753471_74db396d14_b.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/sflckr_segmentations/lakes/39933489595_f0e5d85b6d_b.png b/repositories/taming-transformers/data/sflckr_segmentations/lakes/39933489595_f0e5d85b6d_b.png
deleted file mode 100644
index 79031a1443e34f1f5e5791c17ecc167235d61308..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/sflckr_segmentations/lakes/39933489595_f0e5d85b6d_b.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/sflckr_segmentations/meadow/18864473291_844325caab_b.png b/repositories/taming-transformers/data/sflckr_segmentations/meadow/18864473291_844325caab_b.png
deleted file mode 100644
index f3774ed7e7c34ba9800403583dc60aa4fccadd81..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/sflckr_segmentations/meadow/18864473291_844325caab_b.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/sflckr_segmentations/mongolia/6076373946_e9ea2aee32_b.png b/repositories/taming-transformers/data/sflckr_segmentations/mongolia/6076373946_e9ea2aee32_b.png
deleted file mode 100644
index 46a70b9aed5f692bc4b92545f8b6fdcba997760f..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/sflckr_segmentations/mongolia/6076373946_e9ea2aee32_b.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/sflckr_segmentations/newzealand_np/7942812194_9348729b93_b.png b/repositories/taming-transformers/data/sflckr_segmentations/newzealand_np/7942812194_9348729b93_b.png
deleted file mode 100644
index 0c5feb09dd7df4f878facbe8a1577a212713eceb..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/sflckr_segmentations/newzealand_np/7942812194_9348729b93_b.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/sflckr_segmentations/norway/20099378793_cc2df820af_b.png b/repositories/taming-transformers/data/sflckr_segmentations/norway/20099378793_cc2df820af_b.png
deleted file mode 100644
index 6f2022224c0ca02eb99c9c1be57f3f1605bd13ce..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/sflckr_segmentations/norway/20099378793_cc2df820af_b.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/sflckr_segmentations/norway/25735082181_999927fe5a_b.png b/repositories/taming-transformers/data/sflckr_segmentations/norway/25735082181_999927fe5a_b.png
deleted file mode 100644
index 56d7e166413d3c4d27372335b905ab9e852e0f41..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/sflckr_segmentations/norway/25735082181_999927fe5a_b.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/sflckr_segmentations/swiss_landscape/4079319632_0133685b2c_b.png b/repositories/taming-transformers/data/sflckr_segmentations/swiss_landscape/4079319632_0133685b2c_b.png
deleted file mode 100644
index 46c30af6f8d353ef3c4d2be4b44be9455622fd20..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/sflckr_segmentations/swiss_landscape/4079319632_0133685b2c_b.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/sflckr_segmentations/swiss_mountains/33509672006_bf4c416afd_b.png b/repositories/taming-transformers/data/sflckr_segmentations/swiss_mountains/33509672006_bf4c416afd_b.png
deleted file mode 100644
index 66cb0ccf209d2c3d0df8c21a0fa40835663c91ae..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/sflckr_segmentations/swiss_mountains/33509672006_bf4c416afd_b.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/sflckr_segmentations/volcano/50254383883_27ed6ea93a_b.png b/repositories/taming-transformers/data/sflckr_segmentations/volcano/50254383883_27ed6ea93a_b.png
deleted file mode 100644
index 61abb205ec9514233d7e7637c0e62c8cf425550c..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/data/sflckr_segmentations/volcano/50254383883_27ed6ea93a_b.png and /dev/null differ
diff --git a/repositories/taming-transformers/data/subreddits.txt b/repositories/taming-transformers/data/subreddits.txt
deleted file mode 100644
index 564c2990a4aad40bdcd25bc716cfa26f7b76ad57..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/data/subreddits.txt
+++ /dev/null
@@ -1,12 +0,0 @@
-AutumnPorn
-BeachPorn
-DesertPorn
-EarthPorn
-LakePorn
-LavaPorn
-MushroomPorn
-SkyPorn
-SpacePorn
-SpringPorn
-WeatherPorn
-WinterPorn
diff --git a/repositories/taming-transformers/environment.yaml b/repositories/taming-transformers/environment.yaml
deleted file mode 100644
index 3fbba586e55dbe64184d006319fad969805ef16f..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/environment.yaml
+++ /dev/null
@@ -1,25 +0,0 @@
-name: taming
-channels:
-  - pytorch
-  - defaults
-dependencies:
-  - python=3.8.5
-  - pip=20.3
-  - cudatoolkit=10.2
-  - pytorch=1.7.0
-  - torchvision=0.8.1
-  - numpy=1.19.2
-  - pip:
-    - albumentations==0.4.3
-    - opencv-python==4.1.2.30
-    - pudb==2019.2
-    - imageio==2.9.0
-    - imageio-ffmpeg==0.4.2
-    - pytorch-lightning==1.0.8
-    - omegaconf==2.0.0
-    - test-tube>=0.7.5
-    - streamlit>=0.73.1
-    - einops==0.3.0
-    - more-itertools>=8.0.0
-    - transformers==4.3.1
-    - -e .
diff --git a/repositories/taming-transformers/main.py b/repositories/taming-transformers/main.py
deleted file mode 100644
index 3d83cb21c1dc5d0f5d7f396479c74e64691ec364..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/main.py
+++ /dev/null
@@ -1,585 +0,0 @@
-import argparse, os, sys, datetime, glob, importlib
-from omegaconf import OmegaConf
-import numpy as np
-from PIL import Image
-import torch
-import torchvision
-from torch.utils.data import random_split, DataLoader, Dataset
-import pytorch_lightning as pl
-from pytorch_lightning import seed_everything
-from pytorch_lightning.trainer import Trainer
-from pytorch_lightning.callbacks import ModelCheckpoint, Callback, LearningRateMonitor
-from pytorch_lightning.utilities.distributed import rank_zero_only
-
-from taming.data.utils import custom_collate
-
-
-def get_obj_from_str(string, reload=False):
-    module, cls = string.rsplit(".", 1)
-    if reload:
-        module_imp = importlib.import_module(module)
-        importlib.reload(module_imp)
-    return getattr(importlib.import_module(module, package=None), cls)
-
-
-def get_parser(**parser_kwargs):
-    def str2bool(v):
-        if isinstance(v, bool):
-            return v
-        if v.lower() in ("yes", "true", "t", "y", "1"):
-            return True
-        elif v.lower() in ("no", "false", "f", "n", "0"):
-            return False
-        else:
-            raise argparse.ArgumentTypeError("Boolean value expected.")
-
-    parser = argparse.ArgumentParser(**parser_kwargs)
-    parser.add_argument(
-        "-n",
-        "--name",
-        type=str,
-        const=True,
-        default="",
-        nargs="?",
-        help="postfix for logdir",
-    )
-    parser.add_argument(
-        "-r",
-        "--resume",
-        type=str,
-        const=True,
-        default="",
-        nargs="?",
-        help="resume from logdir or checkpoint in logdir",
-    )
-    parser.add_argument(
-        "-b",
-        "--base",
-        nargs="*",
-        metavar="base_config.yaml",
-        help="paths to base configs. Loaded from left-to-right. "
-        "Parameters can be overwritten or added with command-line options of the form `--key value`.",
-        default=list(),
-    )
-    parser.add_argument(
-        "-t",
-        "--train",
-        type=str2bool,
-        const=True,
-        default=False,
-        nargs="?",
-        help="train",
-    )
-    parser.add_argument(
-        "--no-test",
-        type=str2bool,
-        const=True,
-        default=False,
-        nargs="?",
-        help="disable test",
-    )
-    parser.add_argument("-p", "--project", help="name of new or path to existing project")
-    parser.add_argument(
-        "-d",
-        "--debug",
-        type=str2bool,
-        nargs="?",
-        const=True,
-        default=False,
-        help="enable post-mortem debugging",
-    )
-    parser.add_argument(
-        "-s",
-        "--seed",
-        type=int,
-        default=23,
-        help="seed for seed_everything",
-    )
-    parser.add_argument(
-        "-f",
-        "--postfix",
-        type=str,
-        default="",
-        help="post-postfix for default name",
-    )
-
-    return parser
-
-
-def nondefault_trainer_args(opt):
-    parser = argparse.ArgumentParser()
-    parser = Trainer.add_argparse_args(parser)
-    args = parser.parse_args([])
-    return sorted(k for k in vars(args) if getattr(opt, k) != getattr(args, k))
-
-
-def instantiate_from_config(config):
-    if not "target" in config:
-        raise KeyError("Expected key `target` to instantiate.")
-    return get_obj_from_str(config["target"])(**config.get("params", dict()))
-
-
-class WrappedDataset(Dataset):
-    """Wraps an arbitrary object with __len__ and __getitem__ into a pytorch dataset"""
-    def __init__(self, dataset):
-        self.data = dataset
-
-    def __len__(self):
-        return len(self.data)
-
-    def __getitem__(self, idx):
-        return self.data[idx]
-
-
-class DataModuleFromConfig(pl.LightningDataModule):
-    def __init__(self, batch_size, train=None, validation=None, test=None,
-                 wrap=False, num_workers=None):
-        super().__init__()
-        self.batch_size = batch_size
-        self.dataset_configs = dict()
-        self.num_workers = num_workers if num_workers is not None else batch_size*2
-        if train is not None:
-            self.dataset_configs["train"] = train
-            self.train_dataloader = self._train_dataloader
-        if validation is not None:
-            self.dataset_configs["validation"] = validation
-            self.val_dataloader = self._val_dataloader
-        if test is not None:
-            self.dataset_configs["test"] = test
-            self.test_dataloader = self._test_dataloader
-        self.wrap = wrap
-
-    def prepare_data(self):
-        for data_cfg in self.dataset_configs.values():
-            instantiate_from_config(data_cfg)
-
-    def setup(self, stage=None):
-        self.datasets = dict(
-            (k, instantiate_from_config(self.dataset_configs[k]))
-            for k in self.dataset_configs)
-        if self.wrap:
-            for k in self.datasets:
-                self.datasets[k] = WrappedDataset(self.datasets[k])
-
-    def _train_dataloader(self):
-        return DataLoader(self.datasets["train"], batch_size=self.batch_size,
-                          num_workers=self.num_workers, shuffle=True, collate_fn=custom_collate)
-
-    def _val_dataloader(self):
-        return DataLoader(self.datasets["validation"],
-                          batch_size=self.batch_size,
-                          num_workers=self.num_workers, collate_fn=custom_collate)
-
-    def _test_dataloader(self):
-        return DataLoader(self.datasets["test"], batch_size=self.batch_size,
-                          num_workers=self.num_workers, collate_fn=custom_collate)
-
-
-class SetupCallback(Callback):
-    def __init__(self, resume, now, logdir, ckptdir, cfgdir, config, lightning_config):
-        super().__init__()
-        self.resume = resume
-        self.now = now
-        self.logdir = logdir
-        self.ckptdir = ckptdir
-        self.cfgdir = cfgdir
-        self.config = config
-        self.lightning_config = lightning_config
-
-    def on_pretrain_routine_start(self, trainer, pl_module):
-        if trainer.global_rank == 0:
-            # Create logdirs and save configs
-            os.makedirs(self.logdir, exist_ok=True)
-            os.makedirs(self.ckptdir, exist_ok=True)
-            os.makedirs(self.cfgdir, exist_ok=True)
-
-            print("Project config")
-            print(self.config.pretty())
-            OmegaConf.save(self.config,
-                           os.path.join(self.cfgdir, "{}-project.yaml".format(self.now)))
-
-            print("Lightning config")
-            print(self.lightning_config.pretty())
-            OmegaConf.save(OmegaConf.create({"lightning": self.lightning_config}),
-                           os.path.join(self.cfgdir, "{}-lightning.yaml".format(self.now)))
-
-        else:
-            # ModelCheckpoint callback created log directory --- remove it
-            if not self.resume and os.path.exists(self.logdir):
-                dst, name = os.path.split(self.logdir)
-                dst = os.path.join(dst, "child_runs", name)
-                os.makedirs(os.path.split(dst)[0], exist_ok=True)
-                try:
-                    os.rename(self.logdir, dst)
-                except FileNotFoundError:
-                    pass
-
-
-class ImageLogger(Callback):
-    def __init__(self, batch_frequency, max_images, clamp=True, increase_log_steps=True):
-        super().__init__()
-        self.batch_freq = batch_frequency
-        self.max_images = max_images
-        self.logger_log_images = {
-            pl.loggers.WandbLogger: self._wandb,
-            pl.loggers.TestTubeLogger: self._testtube,
-        }
-        self.log_steps = [2 ** n for n in range(int(np.log2(self.batch_freq)) + 1)]
-        if not increase_log_steps:
-            self.log_steps = [self.batch_freq]
-        self.clamp = clamp
-
-    @rank_zero_only
-    def _wandb(self, pl_module, images, batch_idx, split):
-        raise ValueError("No way wandb")
-        grids = dict()
-        for k in images:
-            grid = torchvision.utils.make_grid(images[k])
-            grids[f"{split}/{k}"] = wandb.Image(grid)
-        pl_module.logger.experiment.log(grids)
-
-    @rank_zero_only
-    def _testtube(self, pl_module, images, batch_idx, split):
-        for k in images:
-            grid = torchvision.utils.make_grid(images[k])
-            grid = (grid+1.0)/2.0 # -1,1 -> 0,1; c,h,w
-
-            tag = f"{split}/{k}"
-            pl_module.logger.experiment.add_image(
-                tag, grid,
-                global_step=pl_module.global_step)
-
-    @rank_zero_only
-    def log_local(self, save_dir, split, images,
-                  global_step, current_epoch, batch_idx):
-        root = os.path.join(save_dir, "images", split)
-        for k in images:
-            grid = torchvision.utils.make_grid(images[k], nrow=4)
-
-            grid = (grid+1.0)/2.0 # -1,1 -> 0,1; c,h,w
-            grid = grid.transpose(0,1).transpose(1,2).squeeze(-1)
-            grid = grid.numpy()
-            grid = (grid*255).astype(np.uint8)
-            filename = "{}_gs-{:06}_e-{:06}_b-{:06}.png".format(
-                k,
-                global_step,
-                current_epoch,
-                batch_idx)
-            path = os.path.join(root, filename)
-            os.makedirs(os.path.split(path)[0], exist_ok=True)
-            Image.fromarray(grid).save(path)
-
-    def log_img(self, pl_module, batch, batch_idx, split="train"):
-        if (self.check_frequency(batch_idx) and  # batch_idx % self.batch_freq == 0
-                hasattr(pl_module, "log_images") and
-                callable(pl_module.log_images) and
-                self.max_images > 0):
-            logger = type(pl_module.logger)
-
-            is_train = pl_module.training
-            if is_train:
-                pl_module.eval()
-
-            with torch.no_grad():
-                images = pl_module.log_images(batch, split=split, pl_module=pl_module)
-
-            for k in images:
-                N = min(images[k].shape[0], self.max_images)
-                images[k] = images[k][:N]
-                if isinstance(images[k], torch.Tensor):
-                    images[k] = images[k].detach().cpu()
-                    if self.clamp:
-                        images[k] = torch.clamp(images[k], -1., 1.)
-
-            self.log_local(pl_module.logger.save_dir, split, images,
-                           pl_module.global_step, pl_module.current_epoch, batch_idx)
-
-            logger_log_images = self.logger_log_images.get(logger, lambda *args, **kwargs: None)
-            logger_log_images(pl_module, images, pl_module.global_step, split)
-
-            if is_train:
-                pl_module.train()
-
-    def check_frequency(self, batch_idx):
-        if (batch_idx % self.batch_freq) == 0 or (batch_idx in self.log_steps):
-            try:
-                self.log_steps.pop(0)
-            except IndexError:
-                pass
-            return True
-        return False
-
-    def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
-        self.log_img(pl_module, batch, batch_idx, split="train")
-
-    def on_validation_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
-        self.log_img(pl_module, batch, batch_idx, split="val")
-
-
-
-if __name__ == "__main__":
-    # custom parser to specify config files, train, test and debug mode,
-    # postfix, resume.
-    # `--key value` arguments are interpreted as arguments to the trainer.
-    # `nested.key=value` arguments are interpreted as config parameters.
-    # configs are merged from left-to-right followed by command line parameters.
-
-    # model:
-    #   base_learning_rate: float
-    #   target: path to lightning module
-    #   params:
-    #       key: value
-    # data:
-    #   target: main.DataModuleFromConfig
-    #   params:
-    #      batch_size: int
-    #      wrap: bool
-    #      train:
-    #          target: path to train dataset
-    #          params:
-    #              key: value
-    #      validation:
-    #          target: path to validation dataset
-    #          params:
-    #              key: value
-    #      test:
-    #          target: path to test dataset
-    #          params:
-    #              key: value
-    # lightning: (optional, has sane defaults and can be specified on cmdline)
-    #   trainer:
-    #       additional arguments to trainer
-    #   logger:
-    #       logger to instantiate
-    #   modelcheckpoint:
-    #       modelcheckpoint to instantiate
-    #   callbacks:
-    #       callback1:
-    #           target: importpath
-    #           params:
-    #               key: value
-
-    now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S")
-
-    # add cwd for convenience and to make classes in this file available when
-    # running as `python main.py`
-    # (in particular `main.DataModuleFromConfig`)
-    sys.path.append(os.getcwd())
-
-    parser = get_parser()
-    parser = Trainer.add_argparse_args(parser)
-
-    opt, unknown = parser.parse_known_args()
-    if opt.name and opt.resume:
-        raise ValueError(
-            "-n/--name and -r/--resume cannot be specified both."
-            "If you want to resume training in a new log folder, "
-            "use -n/--name in combination with --resume_from_checkpoint"
-        )
-    if opt.resume:
-        if not os.path.exists(opt.resume):
-            raise ValueError("Cannot find {}".format(opt.resume))
-        if os.path.isfile(opt.resume):
-            paths = opt.resume.split("/")
-            idx = len(paths)-paths[::-1].index("logs")+1
-            logdir = "/".join(paths[:idx])
-            ckpt = opt.resume
-        else:
-            assert os.path.isdir(opt.resume), opt.resume
-            logdir = opt.resume.rstrip("/")
-            ckpt = os.path.join(logdir, "checkpoints", "last.ckpt")
-
-        opt.resume_from_checkpoint = ckpt
-        base_configs = sorted(glob.glob(os.path.join(logdir, "configs/*.yaml")))
-        opt.base = base_configs+opt.base
-        _tmp = logdir.split("/")
-        nowname = _tmp[_tmp.index("logs")+1]
-    else:
-        if opt.name:
-            name = "_"+opt.name
-        elif opt.base:
-            cfg_fname = os.path.split(opt.base[0])[-1]
-            cfg_name = os.path.splitext(cfg_fname)[0]
-            name = "_"+cfg_name
-        else:
-            name = ""
-        nowname = now+name+opt.postfix
-        logdir = os.path.join("logs", nowname)
-
-    ckptdir = os.path.join(logdir, "checkpoints")
-    cfgdir = os.path.join(logdir, "configs")
-    seed_everything(opt.seed)
-
-    try:
-        # init and save configs
-        configs = [OmegaConf.load(cfg) for cfg in opt.base]
-        cli = OmegaConf.from_dotlist(unknown)
-        config = OmegaConf.merge(*configs, cli)
-        lightning_config = config.pop("lightning", OmegaConf.create())
-        # merge trainer cli with config
-        trainer_config = lightning_config.get("trainer", OmegaConf.create())
-        # default to ddp
-        trainer_config["distributed_backend"] = "ddp"
-        for k in nondefault_trainer_args(opt):
-            trainer_config[k] = getattr(opt, k)
-        if not "gpus" in trainer_config:
-            del trainer_config["distributed_backend"]
-            cpu = True
-        else:
-            gpuinfo = trainer_config["gpus"]
-            print(f"Running on GPUs {gpuinfo}")
-            cpu = False
-        trainer_opt = argparse.Namespace(**trainer_config)
-        lightning_config.trainer = trainer_config
-
-        # model
-        model = instantiate_from_config(config.model)
-
-        # trainer and callbacks
-        trainer_kwargs = dict()
-
-        # default logger configs
-        # NOTE wandb < 0.10.0 interferes with shutdown
-        # wandb >= 0.10.0 seems to fix it but still interferes with pudb
-        # debugging (wrongly sized pudb ui)
-        # thus prefer testtube for now
-        default_logger_cfgs = {
-            "wandb": {
-                "target": "pytorch_lightning.loggers.WandbLogger",
-                "params": {
-                    "name": nowname,
-                    "save_dir": logdir,
-                    "offline": opt.debug,
-                    "id": nowname,
-                }
-            },
-            "testtube": {
-                "target": "pytorch_lightning.loggers.TestTubeLogger",
-                "params": {
-                    "name": "testtube",
-                    "save_dir": logdir,
-                }
-            },
-        }
-        default_logger_cfg = default_logger_cfgs["testtube"]
-        logger_cfg = lightning_config.logger or OmegaConf.create()
-        logger_cfg = OmegaConf.merge(default_logger_cfg, logger_cfg)
-        trainer_kwargs["logger"] = instantiate_from_config(logger_cfg)
-
-        # modelcheckpoint - use TrainResult/EvalResult(checkpoint_on=metric) to
-        # specify which metric is used to determine best models
-        default_modelckpt_cfg = {
-            "target": "pytorch_lightning.callbacks.ModelCheckpoint",
-            "params": {
-                "dirpath": ckptdir,
-                "filename": "{epoch:06}",
-                "verbose": True,
-                "save_last": True,
-            }
-        }
-        if hasattr(model, "monitor"):
-            print(f"Monitoring {model.monitor} as checkpoint metric.")
-            default_modelckpt_cfg["params"]["monitor"] = model.monitor
-            default_modelckpt_cfg["params"]["save_top_k"] = 3
-
-        modelckpt_cfg = lightning_config.modelcheckpoint or OmegaConf.create()
-        modelckpt_cfg = OmegaConf.merge(default_modelckpt_cfg, modelckpt_cfg)
-        trainer_kwargs["checkpoint_callback"] = instantiate_from_config(modelckpt_cfg)
-
-        # add callback which sets up log directory
-        default_callbacks_cfg = {
-            "setup_callback": {
-                "target": "main.SetupCallback",
-                "params": {
-                    "resume": opt.resume,
-                    "now": now,
-                    "logdir": logdir,
-                    "ckptdir": ckptdir,
-                    "cfgdir": cfgdir,
-                    "config": config,
-                    "lightning_config": lightning_config,
-                }
-            },
-            "image_logger": {
-                "target": "main.ImageLogger",
-                "params": {
-                    "batch_frequency": 750,
-                    "max_images": 4,
-                    "clamp": True
-                }
-            },
-            "learning_rate_logger": {
-                "target": "main.LearningRateMonitor",
-                "params": {
-                    "logging_interval": "step",
-                    #"log_momentum": True
-                }
-            },
-        }
-        callbacks_cfg = lightning_config.callbacks or OmegaConf.create()
-        callbacks_cfg = OmegaConf.merge(default_callbacks_cfg, callbacks_cfg)
-        trainer_kwargs["callbacks"] = [instantiate_from_config(callbacks_cfg[k]) for k in callbacks_cfg]
-
-        trainer = Trainer.from_argparse_args(trainer_opt, **trainer_kwargs)
-
-        # data
-        data = instantiate_from_config(config.data)
-        # NOTE according to https://pytorch-lightning.readthedocs.io/en/latest/datamodules.html
-        # calling these ourselves should not be necessary but it is.
-        # lightning still takes care of proper multiprocessing though
-        data.prepare_data()
-        data.setup()
-
-        # configure learning rate
-        bs, base_lr = config.data.params.batch_size, config.model.base_learning_rate
-        if not cpu:
-            ngpu = len(lightning_config.trainer.gpus.strip(",").split(','))
-        else:
-            ngpu = 1
-        accumulate_grad_batches = lightning_config.trainer.accumulate_grad_batches or 1
-        print(f"accumulate_grad_batches = {accumulate_grad_batches}")
-        lightning_config.trainer.accumulate_grad_batches = accumulate_grad_batches
-        model.learning_rate = accumulate_grad_batches * ngpu * bs * base_lr
-        print("Setting learning rate to {:.2e} = {} (accumulate_grad_batches) * {} (num_gpus) * {} (batchsize) * {:.2e} (base_lr)".format(
-            model.learning_rate, accumulate_grad_batches, ngpu, bs, base_lr))
-
-        # allow checkpointing via USR1
-        def melk(*args, **kwargs):
-            # run all checkpoint hooks
-            if trainer.global_rank == 0:
-                print("Summoning checkpoint.")
-                ckpt_path = os.path.join(ckptdir, "last.ckpt")
-                trainer.save_checkpoint(ckpt_path)
-
-        def divein(*args, **kwargs):
-            if trainer.global_rank == 0:
-                import pudb; pudb.set_trace()
-
-        import signal
-        signal.signal(signal.SIGUSR1, melk)
-        signal.signal(signal.SIGUSR2, divein)
-
-        # run
-        if opt.train:
-            try:
-                trainer.fit(model, data)
-            except Exception:
-                melk()
-                raise
-        if not opt.no_test and not trainer.interrupted:
-            trainer.test(model, data)
-    except Exception:
-        if opt.debug and trainer.global_rank==0:
-            try:
-                import pudb as debugger
-            except ImportError:
-                import pdb as debugger
-            debugger.post_mortem()
-        raise
-    finally:
-        # move newly created debug project to debug_runs
-        if opt.debug and not opt.resume and trainer.global_rank==0:
-            dst, name = os.path.split(logdir)
-            dst = os.path.join(dst, "debug_runs", name)
-            os.makedirs(os.path.split(dst)[0], exist_ok=True)
-            os.rename(logdir, dst)
diff --git a/repositories/taming-transformers/scripts/extract_depth.py b/repositories/taming-transformers/scripts/extract_depth.py
deleted file mode 100644
index d6aa0d80c63a3e580fa28e0f2c7af4e9ae003b64..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/scripts/extract_depth.py
+++ /dev/null
@@ -1,112 +0,0 @@
-import os
-import torch
-import numpy as np
-from tqdm import trange
-from PIL import Image
-
-
-def get_state(gpu):
-    import torch
-    midas = torch.hub.load("intel-isl/MiDaS", "MiDaS")
-    if gpu:
-        midas.cuda()
-    midas.eval()
-
-    midas_transforms = torch.hub.load("intel-isl/MiDaS", "transforms")
-    transform = midas_transforms.default_transform
-
-    state = {"model": midas,
-             "transform": transform}
-    return state
-
-
-def depth_to_rgba(x):
-    assert x.dtype == np.float32
-    assert len(x.shape) == 2
-    y = x.copy()
-    y.dtype = np.uint8
-    y = y.reshape(x.shape+(4,))
-    return np.ascontiguousarray(y)
-
-
-def rgba_to_depth(x):
-    assert x.dtype == np.uint8
-    assert len(x.shape) == 3 and x.shape[2] == 4
-    y = x.copy()
-    y.dtype = np.float32
-    y = y.reshape(x.shape[:2])
-    return np.ascontiguousarray(y)
-
-
-def run(x, state):
-    model = state["model"]
-    transform = state["transform"]
-    hw = x.shape[:2]
-    with torch.no_grad():
-        prediction = model(transform((x + 1.0) * 127.5).cuda())
-        prediction = torch.nn.functional.interpolate(
-            prediction.unsqueeze(1),
-            size=hw,
-            mode="bicubic",
-            align_corners=False,
-        ).squeeze()
-        output = prediction.cpu().numpy()
-    return output
-
-
-def get_filename(relpath, level=-2):
-    # save class folder structure and filename:
-    fn = relpath.split(os.sep)[level:]
-    folder = fn[-2]
-    file   = fn[-1].split('.')[0]
-    return folder, file
-
-
-def save_depth(dataset, path, debug=False):
-    os.makedirs(path)
-    N = len(dset)
-    if debug:
-        N = 10
-    state = get_state(gpu=True)
-    for idx in trange(N, desc="Data"):
-        ex = dataset[idx]
-        image, relpath = ex["image"], ex["relpath"]
-        folder, filename = get_filename(relpath)
-        # prepare
-        folderabspath = os.path.join(path, folder)
-        os.makedirs(folderabspath, exist_ok=True)
-        savepath = os.path.join(folderabspath, filename)
-        # run model
-        xout = run(image, state)
-        I = depth_to_rgba(xout)
-        Image.fromarray(I).save("{}.png".format(savepath))
-
-
-if __name__ == "__main__":
-    from taming.data.imagenet import ImageNetTrain, ImageNetValidation
-    out = "data/imagenet_depth"
-    if not os.path.exists(out):
-        print("Please create a folder or symlink '{}' to extract depth data ".format(out) +
-              "(be prepared that the output size will be larger than ImageNet itself).")
-        exit(1)
-
-    # go
-    dset = ImageNetValidation()
-    abspath = os.path.join(out, "val")
-    if os.path.exists(abspath):
-        print("{} exists - not doing anything.".format(abspath))
-    else:
-        print("preparing {}".format(abspath))
-        save_depth(dset, abspath)
-        print("done with validation split")
-
-    dset = ImageNetTrain()
-    abspath = os.path.join(out, "train")
-    if os.path.exists(abspath):
-        print("{} exists - not doing anything.".format(abspath))
-    else:
-        print("preparing {}".format(abspath))
-        save_depth(dset, abspath)
-        print("done with train split")
-
-    print("done done.")
diff --git a/repositories/taming-transformers/scripts/extract_segmentation.py b/repositories/taming-transformers/scripts/extract_segmentation.py
deleted file mode 100644
index 235b3c4b4575981b7533ce18bceaff97e05b55f9..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/scripts/extract_segmentation.py
+++ /dev/null
@@ -1,130 +0,0 @@
-import sys, os
-import numpy as np
-import scipy
-import torch
-import torch.nn as nn
-from scipy import ndimage
-from tqdm import tqdm, trange
-from PIL import Image
-import torch.hub
-import torchvision
-import torch.nn.functional as F
-
-# download deeplabv2_resnet101_msc-cocostuff164k-100000.pth from
-# https://github.com/kazuto1011/deeplab-pytorch/releases/download/v1.0/deeplabv2_resnet101_msc-cocostuff164k-100000.pth
-# and put the path here
-CKPT_PATH = "TODO"
-
-rescale = lambda x: (x + 1.) / 2.
-
-def rescale_bgr(x):
-    x = (x+1)*127.5
-    x = torch.flip(x, dims=[0])
-    return x
-
-
-class COCOStuffSegmenter(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.config = config
-        self.n_labels = 182
-        model = torch.hub.load("kazuto1011/deeplab-pytorch", "deeplabv2_resnet101", n_classes=self.n_labels)
-        ckpt_path = CKPT_PATH
-        model.load_state_dict(torch.load(ckpt_path))
-        self.model = model
-
-        normalize = torchvision.transforms.Normalize(mean=self.mean, std=self.std)
-        self.image_transform = torchvision.transforms.Compose([
-            torchvision.transforms.Lambda(lambda image: torch.stack(
-                [normalize(rescale_bgr(x)) for x in image]))
-        ])
-
-    def forward(self, x, upsample=None):
-        x = self._pre_process(x)
-        x = self.model(x)
-        if upsample is not None:
-            x = torch.nn.functional.upsample_bilinear(x, size=upsample)
-        return x
-
-    def _pre_process(self, x):
-        x = self.image_transform(x)
-        return x
-
-    @property
-    def mean(self):
-        # bgr
-        return [104.008, 116.669, 122.675]
-
-    @property
-    def std(self):
-        return [1.0, 1.0, 1.0]
-
-    @property
-    def input_size(self):
-        return [3, 224, 224]
-
-
-def run_model(img, model):
-    model = model.eval()
-    with torch.no_grad():
-        segmentation = model(img, upsample=(img.shape[2], img.shape[3]))
-        segmentation = torch.argmax(segmentation, dim=1, keepdim=True)
-    return segmentation.detach().cpu()
-
-
-def get_input(batch, k):
-    x = batch[k]
-    if len(x.shape) == 3:
-        x = x[..., None]
-    x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format)
-    return x.float()
-
-
-def save_segmentation(segmentation, path):
-    # --> class label to uint8, save as png
-    os.makedirs(os.path.dirname(path), exist_ok=True)
-    assert len(segmentation.shape)==4
-    assert segmentation.shape[0]==1
-    for seg in segmentation:
-        seg = seg.permute(1,2,0).numpy().squeeze().astype(np.uint8)
-        seg = Image.fromarray(seg)
-        seg.save(path)
-
-
-def iterate_dataset(dataloader, destpath, model):
-    os.makedirs(destpath, exist_ok=True)
-    num_processed = 0
-    for i, batch in tqdm(enumerate(dataloader), desc="Data"):
-        try:
-            img = get_input(batch, "image")
-            img = img.cuda()
-            seg = run_model(img, model)
-
-            path = batch["relative_file_path_"][0]
-            path = os.path.splitext(path)[0]
-
-            path = os.path.join(destpath, path + ".png")
-            save_segmentation(seg, path)
-            num_processed += 1
-        except Exception as e:
-            print(e)
-            print("but anyhow..")
-
-    print("Processed {} files. Bye.".format(num_processed))
-
-
-from taming.data.sflckr import Examples
-from torch.utils.data import DataLoader
-
-if __name__ == "__main__":
-    dest = sys.argv[1]
-    batchsize = 1
-    print("Running with batch-size {}, saving to {}...".format(batchsize, dest))
-
-    model = COCOStuffSegmenter({}).cuda()
-    print("Instantiated model.")
-
-    dataset = Examples()
-    dloader = DataLoader(dataset, batch_size=batchsize)
-    iterate_dataset(dataloader=dloader, destpath=dest, model=model)
-    print("done.")
diff --git a/repositories/taming-transformers/scripts/extract_submodel.py b/repositories/taming-transformers/scripts/extract_submodel.py
deleted file mode 100644
index 559bc5e04281a7cf833a82e3cd48627b20f1a76d..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/scripts/extract_submodel.py
+++ /dev/null
@@ -1,17 +0,0 @@
-import torch
-import sys
-
-if __name__ == "__main__":
-    inpath = sys.argv[1]
-    outpath = sys.argv[2]
-    submodel = "cond_stage_model"
-    if len(sys.argv) > 3:
-        submodel = sys.argv[3]
-
-    print("Extracting {} from {} to {}.".format(submodel, inpath, outpath))
-
-    sd = torch.load(inpath, map_location="cpu")
-    new_sd = {"state_dict": dict((k.split(".", 1)[-1],v)
-                                 for k,v in sd["state_dict"].items()
-                                 if k.startswith("cond_stage_model"))}
-    torch.save(new_sd, outpath)
diff --git a/repositories/taming-transformers/scripts/make_samples.py b/repositories/taming-transformers/scripts/make_samples.py
deleted file mode 100644
index 5e4d6995cd41cc07b4e8861cb941c6052b0f5517..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/scripts/make_samples.py
+++ /dev/null
@@ -1,292 +0,0 @@
-import argparse, os, sys, glob, math, time
-import torch
-import numpy as np
-from omegaconf import OmegaConf
-from PIL import Image
-from main import instantiate_from_config, DataModuleFromConfig
-from torch.utils.data import DataLoader
-from torch.utils.data.dataloader import default_collate
-from tqdm import trange
-
-
-def save_image(x, path):
-    c,h,w = x.shape
-    assert c==3
-    x = ((x.detach().cpu().numpy().transpose(1,2,0)+1.0)*127.5).clip(0,255).astype(np.uint8)
-    Image.fromarray(x).save(path)
-
-
-@torch.no_grad()
-def run_conditional(model, dsets, outdir, top_k, temperature, batch_size=1):
-    if len(dsets.datasets) > 1:
-        split = sorted(dsets.datasets.keys())[0]
-        dset = dsets.datasets[split]
-    else:
-        dset = next(iter(dsets.datasets.values()))
-    print("Dataset: ", dset.__class__.__name__)
-    for start_idx in trange(0,len(dset)-batch_size+1,batch_size):
-        indices = list(range(start_idx, start_idx+batch_size))
-        example = default_collate([dset[i] for i in indices])
-
-        x = model.get_input("image", example).to(model.device)
-        for i in range(x.shape[0]):
-            save_image(x[i], os.path.join(outdir, "originals",
-                                          "{:06}.png".format(indices[i])))
-
-        cond_key = model.cond_stage_key
-        c = model.get_input(cond_key, example).to(model.device)
-
-        scale_factor = 1.0
-        quant_z, z_indices = model.encode_to_z(x)
-        quant_c, c_indices = model.encode_to_c(c)
-
-        cshape = quant_z.shape
-
-        xrec = model.first_stage_model.decode(quant_z)
-        for i in range(xrec.shape[0]):
-            save_image(xrec[i], os.path.join(outdir, "reconstructions",
-                                             "{:06}.png".format(indices[i])))
-
-        if cond_key == "segmentation":
-            # get image from segmentation mask
-            num_classes = c.shape[1]
-            c = torch.argmax(c, dim=1, keepdim=True)
-            c = torch.nn.functional.one_hot(c, num_classes=num_classes)
-            c = c.squeeze(1).permute(0, 3, 1, 2).float()
-            c = model.cond_stage_model.to_rgb(c)
-
-        idx = z_indices
-
-        half_sample = False
-        if half_sample:
-            start = idx.shape[1]//2
-        else:
-            start = 0
-
-        idx[:,start:] = 0
-        idx = idx.reshape(cshape[0],cshape[2],cshape[3])
-        start_i = start//cshape[3]
-        start_j = start %cshape[3]
-
-        cidx = c_indices
-        cidx = cidx.reshape(quant_c.shape[0],quant_c.shape[2],quant_c.shape[3])
-
-        sample = True
-
-        for i in range(start_i,cshape[2]-0):
-            if i <= 8:
-                local_i = i
-            elif cshape[2]-i < 8:
-                local_i = 16-(cshape[2]-i)
-            else:
-                local_i = 8
-            for j in range(start_j,cshape[3]-0):
-                if j <= 8:
-                    local_j = j
-                elif cshape[3]-j < 8:
-                    local_j = 16-(cshape[3]-j)
-                else:
-                    local_j = 8
-
-                i_start = i-local_i
-                i_end = i_start+16
-                j_start = j-local_j
-                j_end = j_start+16
-                patch = idx[:,i_start:i_end,j_start:j_end]
-                patch = patch.reshape(patch.shape[0],-1)
-                cpatch = cidx[:, i_start:i_end, j_start:j_end]
-                cpatch = cpatch.reshape(cpatch.shape[0], -1)
-                patch = torch.cat((cpatch, patch), dim=1)
-                logits,_ = model.transformer(patch[:,:-1])
-                logits = logits[:, -256:, :]
-                logits = logits.reshape(cshape[0],16,16,-1)
-                logits = logits[:,local_i,local_j,:]
-
-                logits = logits/temperature
-
-                if top_k is not None:
-                    logits = model.top_k_logits(logits, top_k)
-                # apply softmax to convert to probabilities
-                probs = torch.nn.functional.softmax(logits, dim=-1)
-                # sample from the distribution or take the most likely
-                if sample:
-                    ix = torch.multinomial(probs, num_samples=1)
-                else:
-                    _, ix = torch.topk(probs, k=1, dim=-1)
-                idx[:,i,j] = ix
-
-        xsample = model.decode_to_img(idx[:,:cshape[2],:cshape[3]], cshape)
-        for i in range(xsample.shape[0]):
-            save_image(xsample[i], os.path.join(outdir, "samples",
-                                                "{:06}.png".format(indices[i])))
-
-
-def get_parser():
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "-r",
-        "--resume",
-        type=str,
-        nargs="?",
-        help="load from logdir or checkpoint in logdir",
-    )
-    parser.add_argument(
-        "-b",
-        "--base",
-        nargs="*",
-        metavar="base_config.yaml",
-        help="paths to base configs. Loaded from left-to-right. "
-        "Parameters can be overwritten or added with command-line options of the form `--key value`.",
-        default=list(),
-    )
-    parser.add_argument(
-        "-c",
-        "--config",
-        nargs="?",
-        metavar="single_config.yaml",
-        help="path to single config. If specified, base configs will be ignored "
-        "(except for the last one if left unspecified).",
-        const=True,
-        default="",
-    )
-    parser.add_argument(
-        "--ignore_base_data",
-        action="store_true",
-        help="Ignore data specification from base configs. Useful if you want "
-        "to specify a custom datasets on the command line.",
-    )
-    parser.add_argument(
-        "--outdir",
-        required=True,
-        type=str,
-        help="Where to write outputs to.",
-    )
-    parser.add_argument(
-        "--top_k",
-        type=int,
-        default=100,
-        help="Sample from among top-k predictions.",
-    )
-    parser.add_argument(
-        "--temperature",
-        type=float,
-        default=1.0,
-        help="Sampling temperature.",
-    )
-    return parser
-
-
-def load_model_from_config(config, sd, gpu=True, eval_mode=True):
-    if "ckpt_path" in config.params:
-        print("Deleting the restore-ckpt path from the config...")
-        config.params.ckpt_path = None
-    if "downsample_cond_size" in config.params:
-        print("Deleting downsample-cond-size from the config and setting factor=0.5 instead...")
-        config.params.downsample_cond_size = -1
-        config.params["downsample_cond_factor"] = 0.5
-    try:
-        if "ckpt_path" in config.params.first_stage_config.params:
-            config.params.first_stage_config.params.ckpt_path = None
-            print("Deleting the first-stage restore-ckpt path from the config...")
-        if "ckpt_path" in config.params.cond_stage_config.params:
-            config.params.cond_stage_config.params.ckpt_path = None
-            print("Deleting the cond-stage restore-ckpt path from the config...")
-    except:
-        pass
-
-    model = instantiate_from_config(config)
-    if sd is not None:
-        missing, unexpected = model.load_state_dict(sd, strict=False)
-        print(f"Missing Keys in State Dict: {missing}")
-        print(f"Unexpected Keys in State Dict: {unexpected}")
-    if gpu:
-        model.cuda()
-    if eval_mode:
-        model.eval()
-    return {"model": model}
-
-
-def get_data(config):
-    # get data
-    data = instantiate_from_config(config.data)
-    data.prepare_data()
-    data.setup()
-    return data
-
-
-def load_model_and_dset(config, ckpt, gpu, eval_mode):
-    # get data
-    dsets = get_data(config)   # calls data.config ...
-
-    # now load the specified checkpoint
-    if ckpt:
-        pl_sd = torch.load(ckpt, map_location="cpu")
-        global_step = pl_sd["global_step"]
-    else:
-        pl_sd = {"state_dict": None}
-        global_step = None
-    model = load_model_from_config(config.model,
-                                   pl_sd["state_dict"],
-                                   gpu=gpu,
-                                   eval_mode=eval_mode)["model"]
-    return dsets, model, global_step
-
-
-if __name__ == "__main__":
-    sys.path.append(os.getcwd())
-
-    parser = get_parser()
-
-    opt, unknown = parser.parse_known_args()
-
-    ckpt = None
-    if opt.resume:
-        if not os.path.exists(opt.resume):
-            raise ValueError("Cannot find {}".format(opt.resume))
-        if os.path.isfile(opt.resume):
-            paths = opt.resume.split("/")
-            try:
-                idx = len(paths)-paths[::-1].index("logs")+1
-            except ValueError:
-                idx = -2 # take a guess: path/to/logdir/checkpoints/model.ckpt
-            logdir = "/".join(paths[:idx])
-            ckpt = opt.resume
-        else:
-            assert os.path.isdir(opt.resume), opt.resume
-            logdir = opt.resume.rstrip("/")
-            ckpt = os.path.join(logdir, "checkpoints", "last.ckpt")
-        print(f"logdir:{logdir}")
-        base_configs = sorted(glob.glob(os.path.join(logdir, "configs/*-project.yaml")))
-        opt.base = base_configs+opt.base
-
-    if opt.config:
-        if type(opt.config) == str:
-            opt.base = [opt.config]
-        else:
-            opt.base = [opt.base[-1]]
-
-    configs = [OmegaConf.load(cfg) for cfg in opt.base]
-    cli = OmegaConf.from_dotlist(unknown)
-    if opt.ignore_base_data:
-        for config in configs:
-            if hasattr(config, "data"): del config["data"]
-    config = OmegaConf.merge(*configs, cli)
-
-    print(ckpt)
-    gpu = True
-    eval_mode = True
-    show_config = False
-    if show_config:
-        print(OmegaConf.to_container(config))
-
-    dsets, model, global_step = load_model_and_dset(config, ckpt, gpu, eval_mode)
-    print(f"Global step: {global_step}")
-
-    outdir = os.path.join(opt.outdir, "{:06}_{}_{}".format(global_step,
-                                                           opt.top_k,
-                                                           opt.temperature))
-    os.makedirs(outdir, exist_ok=True)
-    print("Writing samples to ", outdir)
-    for k in ["originals", "reconstructions", "samples"]:
-        os.makedirs(os.path.join(outdir, k), exist_ok=True)
-    run_conditional(model, dsets, outdir, opt.top_k, opt.temperature)
diff --git a/repositories/taming-transformers/scripts/make_scene_samples.py b/repositories/taming-transformers/scripts/make_scene_samples.py
deleted file mode 100644
index c096b98460874be0acbe5b85464593fbad4bedd0..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/scripts/make_scene_samples.py
+++ /dev/null
@@ -1,198 +0,0 @@
-import glob
-import os
-import sys
-from itertools import product
-from pathlib import Path
-from typing import Literal, List, Optional, Tuple
-
-import numpy as np
-import torch
-from omegaconf import OmegaConf
-from pytorch_lightning import seed_everything
-from torch import Tensor
-from torchvision.utils import save_image
-from tqdm import tqdm
-
-from scripts.make_samples import get_parser, load_model_and_dset
-from taming.data.conditional_builder.objects_center_points import ObjectsCenterPointsConditionalBuilder
-from taming.data.helper_types import BoundingBox, Annotation
-from taming.data.annotated_objects_dataset import AnnotatedObjectsDataset
-from taming.models.cond_transformer import Net2NetTransformer
-
-seed_everything(42424242)
-device: Literal['cuda', 'cpu'] = 'cuda'
-first_stage_factor = 16
-trained_on_res = 256
-
-
-def _helper(coord: int, coord_max: int, coord_window: int) -> (int, int):
-    assert 0 <= coord < coord_max
-    coord_desired_center = (coord_window - 1) // 2
-    return np.clip(coord - coord_desired_center, 0, coord_max - coord_window)
-
-
-def get_crop_coordinates(x: int, y: int) -> BoundingBox:
-    WIDTH, HEIGHT = desired_z_shape[1], desired_z_shape[0]
-    x0 = _helper(x, WIDTH, first_stage_factor) / WIDTH
-    y0 = _helper(y, HEIGHT, first_stage_factor) / HEIGHT
-    w = first_stage_factor / WIDTH
-    h = first_stage_factor / HEIGHT
-    return x0, y0, w, h
-
-
-def get_z_indices_crop_out(z_indices: Tensor, predict_x: int, predict_y: int) -> Tensor:
-    WIDTH, HEIGHT = desired_z_shape[1], desired_z_shape[0]
-    x0 = _helper(predict_x, WIDTH, first_stage_factor)
-    y0 = _helper(predict_y, HEIGHT, first_stage_factor)
-    no_images = z_indices.shape[0]
-    cut_out_1 = z_indices[:, y0:predict_y, x0:x0+first_stage_factor].reshape((no_images, -1))
-    cut_out_2 = z_indices[:, predict_y, x0:predict_x]
-    return torch.cat((cut_out_1, cut_out_2), dim=1)
-
-
-@torch.no_grad()
-def sample(model: Net2NetTransformer, annotations: List[Annotation], dataset: AnnotatedObjectsDataset,
-           conditional_builder: ObjectsCenterPointsConditionalBuilder, no_samples: int,
-           temperature: float, top_k: int) -> Tensor:
-    x_max, y_max = desired_z_shape[1], desired_z_shape[0]
-
-    annotations = [a._replace(category_no=dataset.get_category_number(a.category_id)) for a in annotations]
-
-    recompute_conditional = any((desired_resolution[0] > trained_on_res, desired_resolution[1] > trained_on_res))
-    if not recompute_conditional:
-        crop_coordinates = get_crop_coordinates(0, 0)
-        conditional_indices = conditional_builder.build(annotations, crop_coordinates)
-        c_indices = conditional_indices.to(device).repeat(no_samples, 1)
-        z_indices = torch.zeros((no_samples, 0), device=device).long()
-        output_indices = model.sample(z_indices, c_indices, steps=x_max*y_max, temperature=temperature,
-                                      sample=True, top_k=top_k)
-    else:
-        output_indices = torch.zeros((no_samples, y_max, x_max), device=device).long()
-        for predict_y, predict_x in tqdm(product(range(y_max), range(x_max)), desc='sampling_image', total=x_max*y_max):
-            crop_coordinates = get_crop_coordinates(predict_x, predict_y)
-            z_indices = get_z_indices_crop_out(output_indices, predict_x, predict_y)
-            conditional_indices = conditional_builder.build(annotations, crop_coordinates)
-            c_indices = conditional_indices.to(device).repeat(no_samples, 1)
-            new_index = model.sample(z_indices, c_indices, steps=1, temperature=temperature, sample=True, top_k=top_k)
-            output_indices[:, predict_y, predict_x] = new_index[:, -1]
-    z_shape = (
-        no_samples,
-        model.first_stage_model.quantize.e_dim,  # codebook embed_dim
-        desired_z_shape[0],  # z_height
-        desired_z_shape[1]  # z_width
-    )
-    x_sample = model.decode_to_img(output_indices, z_shape) * 0.5 + 0.5
-    x_sample = x_sample.to('cpu')
-
-    plotter = conditional_builder.plot
-    figure_size = (x_sample.shape[2], x_sample.shape[3])
-    scene_graph = conditional_builder.build(annotations, (0., 0., 1., 1.))
-    plot = plotter(scene_graph, dataset.get_textual_label_for_category_no, figure_size)
-    return torch.cat((x_sample, plot.unsqueeze(0)))
-
-
-def get_resolution(resolution_str: str) -> (Tuple[int, int], Tuple[int, int]):
-    if not resolution_str.count(',') == 1:
-        raise ValueError("Give resolution as in 'height,width'")
-    res_h, res_w = resolution_str.split(',')
-    res_h = max(int(res_h), trained_on_res)
-    res_w = max(int(res_w), trained_on_res)
-    z_h = int(round(res_h/first_stage_factor))
-    z_w = int(round(res_w/first_stage_factor))
-    return (z_h, z_w), (z_h*first_stage_factor, z_w*first_stage_factor)
-
-
-def add_arg_to_parser(parser):
-    parser.add_argument(
-        "-R",
-        "--resolution",
-        type=str,
-        default='256,256',
-        help=f"give resolution in multiples of {first_stage_factor}, default is '256,256'",
-    )
-    parser.add_argument(
-        "-C",
-        "--conditional",
-        type=str,
-        default='objects_bbox',
-        help=f"objects_bbox or objects_center_points",
-    )
-    parser.add_argument(
-        "-N",
-        "--n_samples_per_layout",
-        type=int,
-        default=4,
-        help=f"how many samples to generate per layout",
-    )
-    return parser
-
-
-if __name__ == "__main__":
-    sys.path.append(os.getcwd())
-
-    parser = get_parser()
-    parser = add_arg_to_parser(parser)
-
-    opt, unknown = parser.parse_known_args()
-
-    ckpt = None
-    if opt.resume:
-        if not os.path.exists(opt.resume):
-            raise ValueError("Cannot find {}".format(opt.resume))
-        if os.path.isfile(opt.resume):
-            paths = opt.resume.split("/")
-            try:
-                idx = len(paths)-paths[::-1].index("logs")+1
-            except ValueError:
-                idx = -2  # take a guess: path/to/logdir/checkpoints/model.ckpt
-            logdir = "/".join(paths[:idx])
-            ckpt = opt.resume
-        else:
-            assert os.path.isdir(opt.resume), opt.resume
-            logdir = opt.resume.rstrip("/")
-            ckpt = os.path.join(logdir, "checkpoints", "last.ckpt")
-        print(f"logdir:{logdir}")
-        base_configs = sorted(glob.glob(os.path.join(logdir, "configs/*-project.yaml")))
-        opt.base = base_configs+opt.base
-
-    if opt.config:
-        if type(opt.config) == str:
-            opt.base = [opt.config]
-        else:
-            opt.base = [opt.base[-1]]
-
-    configs = [OmegaConf.load(cfg) for cfg in opt.base]
-    cli = OmegaConf.from_dotlist(unknown)
-    if opt.ignore_base_data:
-        for config in configs:
-            if hasattr(config, "data"):
-                del config["data"]
-    config = OmegaConf.merge(*configs, cli)
-    desired_z_shape, desired_resolution = get_resolution(opt.resolution)
-    conditional = opt.conditional
-
-    print(ckpt)
-    gpu = True
-    eval_mode = True
-    show_config = False
-    if show_config:
-        print(OmegaConf.to_container(config))
-
-    dsets, model, global_step = load_model_and_dset(config, ckpt, gpu, eval_mode)
-    print(f"Global step: {global_step}")
-
-    data_loader = dsets.val_dataloader()
-    print(dsets.datasets["validation"].conditional_builders)
-    conditional_builder = dsets.datasets["validation"].conditional_builders[conditional]
-
-    outdir = Path(opt.outdir).joinpath(f"{global_step:06}_{opt.top_k}_{opt.temperature}")
-    outdir.mkdir(exist_ok=True, parents=True)
-    print("Writing samples to ", outdir)
-
-    p_bar_1 = tqdm(enumerate(iter(data_loader)), desc='batch', total=len(data_loader))
-    for batch_no, batch in p_bar_1:
-        save_img: Optional[Tensor] = None
-        for i, annotations in tqdm(enumerate(batch['annotations']), desc='within_batch', total=data_loader.batch_size):
-            imgs = sample(model, annotations, dsets.datasets["validation"], conditional_builder,
-                          opt.n_samples_per_layout, opt.temperature, opt.top_k)
-            save_image(imgs, outdir.joinpath(f'{batch_no:04}_{i:02}.png'), n_row=opt.n_samples_per_layout+1)
diff --git a/repositories/taming-transformers/scripts/reconstruction_usage.ipynb b/repositories/taming-transformers/scripts/reconstruction_usage.ipynb
deleted file mode 100644
index 35ca2a43cfaa05c59b82f4f7b181799882dc6998..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/scripts/reconstruction_usage.ipynb
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:389aa20c5070678317cbf562755277833feec6d85a2e7f26ee92138241cc9197
-size 13700393
diff --git a/repositories/taming-transformers/scripts/sample_conditional.py b/repositories/taming-transformers/scripts/sample_conditional.py
deleted file mode 100644
index 174cf2af07c1a1ca4e6c35fc0e4f8d6e53591b56..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/scripts/sample_conditional.py
+++ /dev/null
@@ -1,355 +0,0 @@
-import argparse, os, sys, glob, math, time
-import torch
-import numpy as np
-from omegaconf import OmegaConf
-import streamlit as st
-from streamlit import caching
-from PIL import Image
-from main import instantiate_from_config, DataModuleFromConfig
-from torch.utils.data import DataLoader
-from torch.utils.data.dataloader import default_collate
-
-
-rescale = lambda x: (x + 1.) / 2.
-
-
-def bchw_to_st(x):
-    return rescale(x.detach().cpu().numpy().transpose(0,2,3,1))
-
-def save_img(xstart, fname):
-    I = (xstart.clip(0,1)[0]*255).astype(np.uint8)
-    Image.fromarray(I).save(fname)
-
-
-
-def get_interactive_image(resize=False):
-    image = st.file_uploader("Input", type=["jpg", "JPEG", "png"])
-    if image is not None:
-        image = Image.open(image)
-        if not image.mode == "RGB":
-            image = image.convert("RGB")
-        image = np.array(image).astype(np.uint8)
-        print("upload image shape: {}".format(image.shape))
-        img = Image.fromarray(image)
-        if resize:
-            img = img.resize((256, 256))
-        image = np.array(img)
-        return image
-
-
-def single_image_to_torch(x, permute=True):
-    assert x is not None, "Please provide an image through the upload function"
-    x = np.array(x)
-    x = torch.FloatTensor(x/255.*2. - 1.)[None,...]
-    if permute:
-        x = x.permute(0, 3, 1, 2)
-    return x
-
-
-def pad_to_M(x, M):
-    hp = math.ceil(x.shape[2]/M)*M-x.shape[2]
-    wp = math.ceil(x.shape[3]/M)*M-x.shape[3]
-    x = torch.nn.functional.pad(x, (0,wp,0,hp,0,0,0,0))
-    return x
-
-@torch.no_grad()
-def run_conditional(model, dsets):
-    if len(dsets.datasets) > 1:
-        split = st.sidebar.radio("Split", sorted(dsets.datasets.keys()))
-        dset = dsets.datasets[split]
-    else:
-        dset = next(iter(dsets.datasets.values()))
-    batch_size = 1
-    start_index = st.sidebar.number_input("Example Index (Size: {})".format(len(dset)), value=0,
-                                          min_value=0,
-                                          max_value=len(dset)-batch_size)
-    indices = list(range(start_index, start_index+batch_size))
-
-    example = default_collate([dset[i] for i in indices])
-
-    x = model.get_input("image", example).to(model.device)
-
-    cond_key = model.cond_stage_key
-    c = model.get_input(cond_key, example).to(model.device)
-
-    scale_factor = st.sidebar.slider("Scale Factor", min_value=0.5, max_value=4.0, step=0.25, value=1.00)
-    if scale_factor != 1.0:
-        x = torch.nn.functional.interpolate(x, scale_factor=scale_factor, mode="bicubic")
-        c = torch.nn.functional.interpolate(c, scale_factor=scale_factor, mode="bicubic")
-
-    quant_z, z_indices = model.encode_to_z(x)
-    quant_c, c_indices = model.encode_to_c(c)
-
-    cshape = quant_z.shape
-
-    xrec = model.first_stage_model.decode(quant_z)
-    st.write("image: {}".format(x.shape))
-    st.image(bchw_to_st(x), clamp=True, output_format="PNG")
-    st.write("image reconstruction: {}".format(xrec.shape))
-    st.image(bchw_to_st(xrec), clamp=True, output_format="PNG")
-
-    if cond_key == "segmentation":
-        # get image from segmentation mask
-        num_classes = c.shape[1]
-        c = torch.argmax(c, dim=1, keepdim=True)
-        c = torch.nn.functional.one_hot(c, num_classes=num_classes)
-        c = c.squeeze(1).permute(0, 3, 1, 2).float()
-        c = model.cond_stage_model.to_rgb(c)
-
-    st.write(f"{cond_key}: {tuple(c.shape)}")
-    st.image(bchw_to_st(c), clamp=True, output_format="PNG")
-
-    idx = z_indices
-
-    half_sample = st.sidebar.checkbox("Image Completion", value=False)
-    if half_sample:
-        start = idx.shape[1]//2
-    else:
-        start = 0
-
-    idx[:,start:] = 0
-    idx = idx.reshape(cshape[0],cshape[2],cshape[3])
-    start_i = start//cshape[3]
-    start_j = start %cshape[3]
-
-    if not half_sample and quant_z.shape == quant_c.shape:
-        st.info("Setting idx to c_indices")
-        idx = c_indices.clone().reshape(cshape[0],cshape[2],cshape[3])
-
-    cidx = c_indices
-    cidx = cidx.reshape(quant_c.shape[0],quant_c.shape[2],quant_c.shape[3])
-
-    xstart = model.decode_to_img(idx[:,:cshape[2],:cshape[3]], cshape)
-    st.image(bchw_to_st(xstart), clamp=True, output_format="PNG")
-
-    temperature = st.number_input("Temperature", value=1.0)
-    top_k = st.number_input("Top k", value=100)
-    sample = st.checkbox("Sample", value=True)
-    update_every = st.number_input("Update every", value=75)
-
-    st.text(f"Sampling shape ({cshape[2]},{cshape[3]})")
-
-    animate = st.checkbox("animate")
-    if animate:
-        import imageio
-        outvid = "sampling.mp4"
-        writer = imageio.get_writer(outvid, fps=25)
-    elapsed_t = st.empty()
-    info = st.empty()
-    st.text("Sampled")
-    if st.button("Sample"):
-        output = st.empty()
-        start_t = time.time()
-        for i in range(start_i,cshape[2]-0):
-            if i <= 8:
-                local_i = i
-            elif cshape[2]-i < 8:
-                local_i = 16-(cshape[2]-i)
-            else:
-                local_i = 8
-            for j in range(start_j,cshape[3]-0):
-                if j <= 8:
-                    local_j = j
-                elif cshape[3]-j < 8:
-                    local_j = 16-(cshape[3]-j)
-                else:
-                    local_j = 8
-
-                i_start = i-local_i
-                i_end = i_start+16
-                j_start = j-local_j
-                j_end = j_start+16
-                elapsed_t.text(f"Time: {time.time() - start_t} seconds")
-                info.text(f"Step: ({i},{j}) | Local: ({local_i},{local_j}) | Crop: ({i_start}:{i_end},{j_start}:{j_end})")
-                patch = idx[:,i_start:i_end,j_start:j_end]
-                patch = patch.reshape(patch.shape[0],-1)
-                cpatch = cidx[:, i_start:i_end, j_start:j_end]
-                cpatch = cpatch.reshape(cpatch.shape[0], -1)
-                patch = torch.cat((cpatch, patch), dim=1)
-                logits,_ = model.transformer(patch[:,:-1])
-                logits = logits[:, -256:, :]
-                logits = logits.reshape(cshape[0],16,16,-1)
-                logits = logits[:,local_i,local_j,:]
-
-                logits = logits/temperature
-
-                if top_k is not None:
-                    logits = model.top_k_logits(logits, top_k)
-                # apply softmax to convert to probabilities
-                probs = torch.nn.functional.softmax(logits, dim=-1)
-                # sample from the distribution or take the most likely
-                if sample:
-                    ix = torch.multinomial(probs, num_samples=1)
-                else:
-                    _, ix = torch.topk(probs, k=1, dim=-1)
-                idx[:,i,j] = ix
-
-                if (i*cshape[3]+j)%update_every==0:
-                    xstart = model.decode_to_img(idx[:, :cshape[2], :cshape[3]], cshape,)
-
-                    xstart = bchw_to_st(xstart)
-                    output.image(xstart, clamp=True, output_format="PNG")
-
-                    if animate:
-                        writer.append_data((xstart[0]*255).clip(0, 255).astype(np.uint8))
-
-        xstart = model.decode_to_img(idx[:,:cshape[2],:cshape[3]], cshape)
-        xstart = bchw_to_st(xstart)
-        output.image(xstart, clamp=True, output_format="PNG")
-        #save_img(xstart, "full_res_sample.png")
-        if animate:
-            writer.close()
-            st.video(outvid)
-
-
-def get_parser():
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "-r",
-        "--resume",
-        type=str,
-        nargs="?",
-        help="load from logdir or checkpoint in logdir",
-    )
-    parser.add_argument(
-        "-b",
-        "--base",
-        nargs="*",
-        metavar="base_config.yaml",
-        help="paths to base configs. Loaded from left-to-right. "
-        "Parameters can be overwritten or added with command-line options of the form `--key value`.",
-        default=list(),
-    )
-    parser.add_argument(
-        "-c",
-        "--config",
-        nargs="?",
-        metavar="single_config.yaml",
-        help="path to single config. If specified, base configs will be ignored "
-        "(except for the last one if left unspecified).",
-        const=True,
-        default="",
-    )
-    parser.add_argument(
-        "--ignore_base_data",
-        action="store_true",
-        help="Ignore data specification from base configs. Useful if you want "
-        "to specify a custom datasets on the command line.",
-    )
-    return parser
-
-
-def load_model_from_config(config, sd, gpu=True, eval_mode=True):
-    if "ckpt_path" in config.params:
-        st.warning("Deleting the restore-ckpt path from the config...")
-        config.params.ckpt_path = None
-    if "downsample_cond_size" in config.params:
-        st.warning("Deleting downsample-cond-size from the config and setting factor=0.5 instead...")
-        config.params.downsample_cond_size = -1
-        config.params["downsample_cond_factor"] = 0.5
-    try:
-        if "ckpt_path" in config.params.first_stage_config.params:
-            config.params.first_stage_config.params.ckpt_path = None
-            st.warning("Deleting the first-stage restore-ckpt path from the config...")
-        if "ckpt_path" in config.params.cond_stage_config.params:
-            config.params.cond_stage_config.params.ckpt_path = None
-            st.warning("Deleting the cond-stage restore-ckpt path from the config...")
-    except:
-        pass
-
-    model = instantiate_from_config(config)
-    if sd is not None:
-        missing, unexpected = model.load_state_dict(sd, strict=False)
-        st.info(f"Missing Keys in State Dict: {missing}")
-        st.info(f"Unexpected Keys in State Dict: {unexpected}")
-    if gpu:
-        model.cuda()
-    if eval_mode:
-        model.eval()
-    return {"model": model}
-
-
-def get_data(config):
-    # get data
-    data = instantiate_from_config(config.data)
-    data.prepare_data()
-    data.setup()
-    return data
-
-
-@st.cache(allow_output_mutation=True, suppress_st_warning=True)
-def load_model_and_dset(config, ckpt, gpu, eval_mode):
-    # get data
-    dsets = get_data(config)   # calls data.config ...
-
-    # now load the specified checkpoint
-    if ckpt:
-        pl_sd = torch.load(ckpt, map_location="cpu")
-        global_step = pl_sd["global_step"]
-    else:
-        pl_sd = {"state_dict": None}
-        global_step = None
-    model = load_model_from_config(config.model,
-                                   pl_sd["state_dict"],
-                                   gpu=gpu,
-                                   eval_mode=eval_mode)["model"]
-    return dsets, model, global_step
-
-
-if __name__ == "__main__":
-    sys.path.append(os.getcwd())
-
-    parser = get_parser()
-
-    opt, unknown = parser.parse_known_args()
-
-    ckpt = None
-    if opt.resume:
-        if not os.path.exists(opt.resume):
-            raise ValueError("Cannot find {}".format(opt.resume))
-        if os.path.isfile(opt.resume):
-            paths = opt.resume.split("/")
-            try:
-                idx = len(paths)-paths[::-1].index("logs")+1
-            except ValueError:
-                idx = -2 # take a guess: path/to/logdir/checkpoints/model.ckpt
-            logdir = "/".join(paths[:idx])
-            ckpt = opt.resume
-        else:
-            assert os.path.isdir(opt.resume), opt.resume
-            logdir = opt.resume.rstrip("/")
-            ckpt = os.path.join(logdir, "checkpoints", "last.ckpt")
-        print(f"logdir:{logdir}")
-        base_configs = sorted(glob.glob(os.path.join(logdir, "configs/*-project.yaml")))
-        opt.base = base_configs+opt.base
-
-    if opt.config:
-        if type(opt.config) == str:
-            opt.base = [opt.config]
-        else:
-            opt.base = [opt.base[-1]]
-
-    configs = [OmegaConf.load(cfg) for cfg in opt.base]
-    cli = OmegaConf.from_dotlist(unknown)
-    if opt.ignore_base_data:
-        for config in configs:
-            if hasattr(config, "data"): del config["data"]
-    config = OmegaConf.merge(*configs, cli)
-
-    st.sidebar.text(ckpt)
-    gs = st.sidebar.empty()
-    gs.text(f"Global step: ?")
-    st.sidebar.text("Options")
-    #gpu = st.sidebar.checkbox("GPU", value=True)
-    gpu = True
-    #eval_mode = st.sidebar.checkbox("Eval Mode", value=True)
-    eval_mode = True
-    #show_config = st.sidebar.checkbox("Show Config", value=False)
-    show_config = False
-    if show_config:
-        st.info("Checkpoint: {}".format(ckpt))
-        st.json(OmegaConf.to_container(config))
-
-    dsets, model, global_step = load_model_and_dset(config, ckpt, gpu, eval_mode)
-    gs.text(f"Global step: {global_step}")
-    run_conditional(model, dsets)
diff --git a/repositories/taming-transformers/scripts/sample_fast.py b/repositories/taming-transformers/scripts/sample_fast.py
deleted file mode 100644
index ff546c7dcbe459807ac3b70f834ccc1082fe8b4e..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/scripts/sample_fast.py
+++ /dev/null
@@ -1,260 +0,0 @@
-import argparse, os, sys, glob
-import torch
-import time
-import numpy as np
-from omegaconf import OmegaConf
-from PIL import Image
-from tqdm import tqdm, trange
-from einops import repeat
-
-from main import instantiate_from_config
-from taming.modules.transformer.mingpt import sample_with_past
-
-
-rescale = lambda x: (x + 1.) / 2.
-
-
-def chw_to_pillow(x):
-    return Image.fromarray((255*rescale(x.detach().cpu().numpy().transpose(1,2,0))).clip(0,255).astype(np.uint8))
-
-
-@torch.no_grad()
-def sample_classconditional(model, batch_size, class_label, steps=256, temperature=None, top_k=None, callback=None,
-                            dim_z=256, h=16, w=16, verbose_time=False, top_p=None):
-    log = dict()
-    assert type(class_label) == int, f'expecting type int but type is {type(class_label)}'
-    qzshape = [batch_size, dim_z, h, w]
-    assert not model.be_unconditional, 'Expecting a class-conditional Net2NetTransformer.'
-    c_indices = repeat(torch.tensor([class_label]), '1 -> b 1', b=batch_size).to(model.device)  # class token
-    t1 = time.time()
-    index_sample = sample_with_past(c_indices, model.transformer, steps=steps,
-                                    sample_logits=True, top_k=top_k, callback=callback,
-                                    temperature=temperature, top_p=top_p)
-    if verbose_time:
-        sampling_time = time.time() - t1
-        print(f"Full sampling takes about {sampling_time:.2f} seconds.")
-    x_sample = model.decode_to_img(index_sample, qzshape)
-    log["samples"] = x_sample
-    log["class_label"] = c_indices
-    return log
-
-
-@torch.no_grad()
-def sample_unconditional(model, batch_size, steps=256, temperature=None, top_k=None, top_p=None, callback=None,
-                         dim_z=256, h=16, w=16, verbose_time=False):
-    log = dict()
-    qzshape = [batch_size, dim_z, h, w]
-    assert model.be_unconditional, 'Expecting an unconditional model.'
-    c_indices = repeat(torch.tensor([model.sos_token]), '1 -> b 1', b=batch_size).to(model.device)  # sos token
-    t1 = time.time()
-    index_sample = sample_with_past(c_indices, model.transformer, steps=steps,
-                                    sample_logits=True, top_k=top_k, callback=callback,
-                                    temperature=temperature, top_p=top_p)
-    if verbose_time:
-        sampling_time = time.time() - t1
-        print(f"Full sampling takes about {sampling_time:.2f} seconds.")
-    x_sample = model.decode_to_img(index_sample, qzshape)
-    log["samples"] = x_sample
-    return log
-
-
-@torch.no_grad()
-def run(logdir, model, batch_size, temperature, top_k, unconditional=True, num_samples=50000,
-        given_classes=None, top_p=None):
-    batches = [batch_size for _ in range(num_samples//batch_size)] + [num_samples % batch_size]
-    if not unconditional:
-        assert given_classes is not None
-        print("Running in pure class-conditional sampling mode. I will produce "
-              f"{num_samples} samples for each of the {len(given_classes)} classes, "
-              f"i.e. {num_samples*len(given_classes)} in total.")
-        for class_label in tqdm(given_classes, desc="Classes"):
-            for n, bs in tqdm(enumerate(batches), desc="Sampling Class"):
-                if bs == 0: break
-                logs = sample_classconditional(model, batch_size=bs, class_label=class_label,
-                                               temperature=temperature, top_k=top_k, top_p=top_p)
-                save_from_logs(logs, logdir, base_count=n * batch_size, cond_key=logs["class_label"])
-    else:
-        print(f"Running in unconditional sampling mode, producing {num_samples} samples.")
-        for n, bs in tqdm(enumerate(batches), desc="Sampling"):
-            if bs == 0: break
-            logs = sample_unconditional(model, batch_size=bs, temperature=temperature, top_k=top_k, top_p=top_p)
-            save_from_logs(logs, logdir, base_count=n * batch_size)
-
-
-def save_from_logs(logs, logdir, base_count, key="samples", cond_key=None):
-    xx = logs[key]
-    for i, x in enumerate(xx):
-        x = chw_to_pillow(x)
-        count = base_count + i
-        if cond_key is None:
-            x.save(os.path.join(logdir, f"{count:06}.png"))
-        else:
-            condlabel = cond_key[i]
-            if type(condlabel) == torch.Tensor: condlabel = condlabel.item()
-            os.makedirs(os.path.join(logdir, str(condlabel)), exist_ok=True)
-            x.save(os.path.join(logdir, str(condlabel), f"{count:06}.png"))
-
-
-def get_parser():
-    def str2bool(v):
-        if isinstance(v, bool):
-            return v
-        if v.lower() in ("yes", "true", "t", "y", "1"):
-            return True
-        elif v.lower() in ("no", "false", "f", "n", "0"):
-            return False
-        else:
-            raise argparse.ArgumentTypeError("Boolean value expected.")
-
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "-r",
-        "--resume",
-        type=str,
-        nargs="?",
-        help="load from logdir or checkpoint in logdir",
-    )
-    parser.add_argument(
-        "-o",
-        "--outdir",
-        type=str,
-        nargs="?",
-        help="path where the samples will be logged to.",
-        default=""
-    )
-    parser.add_argument(
-        "-b",
-        "--base",
-        nargs="*",
-        metavar="base_config.yaml",
-        help="paths to base configs. Loaded from left-to-right. "
-        "Parameters can be overwritten or added with command-line options of the form `--key value`.",
-        default=list(),
-    )
-    parser.add_argument(
-        "-n",
-        "--num_samples",
-        type=int,
-        nargs="?",
-        help="num_samples to draw",
-        default=50000
-    )
-    parser.add_argument(
-        "--batch_size",
-        type=int,
-        nargs="?",
-        help="the batch size",
-        default=25
-    )
-    parser.add_argument(
-        "-k",
-        "--top_k",
-        type=int,
-        nargs="?",
-        help="top-k value to sample with",
-        default=250,
-    )
-    parser.add_argument(
-        "-t",
-        "--temperature",
-        type=float,
-        nargs="?",
-        help="temperature value to sample with",
-        default=1.0
-    )
-    parser.add_argument(
-        "-p",
-        "--top_p",
-        type=float,
-        nargs="?",
-        help="top-p value to sample with",
-        default=1.0
-    )
-    parser.add_argument(
-        "--classes",
-        type=str,
-        nargs="?",
-        help="specify comma-separated classes to sample from. Uses 1000 classes per default.",
-        default="imagenet"
-    )
-    return parser
-
-
-def load_model_from_config(config, sd, gpu=True, eval_mode=True):
-    model = instantiate_from_config(config)
-    if sd is not None:
-        model.load_state_dict(sd)
-    if gpu:
-        model.cuda()
-    if eval_mode:
-        model.eval()
-    return {"model": model}
-
-
-def load_model(config, ckpt, gpu, eval_mode):
-    # load the specified checkpoint
-    if ckpt:
-        pl_sd = torch.load(ckpt, map_location="cpu")
-        global_step = pl_sd["global_step"]
-        print(f"loaded model from global step {global_step}.")
-    else:
-        pl_sd = {"state_dict": None}
-        global_step = None
-    model = load_model_from_config(config.model, pl_sd["state_dict"], gpu=gpu, eval_mode=eval_mode)["model"]
-    return model, global_step
-
-
-if __name__ == "__main__":
-    sys.path.append(os.getcwd())
-    parser = get_parser()
-
-    opt, unknown = parser.parse_known_args()
-    assert opt.resume
-
-    ckpt = None
-
-    if not os.path.exists(opt.resume):
-        raise ValueError("Cannot find {}".format(opt.resume))
-    if os.path.isfile(opt.resume):
-        paths = opt.resume.split("/")
-        try:
-            idx = len(paths)-paths[::-1].index("logs")+1
-        except ValueError:
-            idx = -2 # take a guess: path/to/logdir/checkpoints/model.ckpt
-        logdir = "/".join(paths[:idx])
-        ckpt = opt.resume
-    else:
-        assert os.path.isdir(opt.resume), opt.resume
-        logdir = opt.resume.rstrip("/")
-        ckpt = os.path.join(logdir, "checkpoints", "last.ckpt")
-
-    base_configs = sorted(glob.glob(os.path.join(logdir, "configs/*-project.yaml")))
-    opt.base = base_configs+opt.base
-
-    configs = [OmegaConf.load(cfg) for cfg in opt.base]
-    cli = OmegaConf.from_dotlist(unknown)
-    config = OmegaConf.merge(*configs, cli)
-
-    model, global_step = load_model(config, ckpt, gpu=True, eval_mode=True)
-
-    if opt.outdir:
-        print(f"Switching logdir from '{logdir}' to '{opt.outdir}'")
-        logdir = opt.outdir
-
-    if opt.classes == "imagenet":
-        given_classes = [i for i in range(1000)]
-    else:
-        cls_str = opt.classes
-        assert not cls_str.endswith(","), 'class string should not end with a ","'
-        given_classes = [int(c) for c in cls_str.split(",")]
-
-    logdir = os.path.join(logdir, "samples", f"top_k_{opt.top_k}_temp_{opt.temperature:.2f}_top_p_{opt.top_p}",
-                          f"{global_step}")
-
-    print(f"Logging to {logdir}")
-    os.makedirs(logdir, exist_ok=True)
-
-    run(logdir, model, opt.batch_size, opt.temperature, opt.top_k, unconditional=model.be_unconditional,
-        given_classes=given_classes, num_samples=opt.num_samples, top_p=opt.top_p)
-
-    print("done.")
diff --git a/repositories/taming-transformers/scripts/taming-transformers.ipynb b/repositories/taming-transformers/scripts/taming-transformers.ipynb
deleted file mode 100644
index 05f096c600cad909fed42368c9c03658b8a0489b..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/scripts/taming-transformers.ipynb
+++ /dev/null
@@ -1,659 +0,0 @@
-{
- "nbformat": 4,
- "nbformat_minor": 0,
- "metadata": {
-  "colab": {
-   "name": "taming-transformers.ipynb",
-   "provenance": [],
-   "collapsed_sections": [],
-   "toc_visible": true
-  },
-  "kernelspec": {
-   "name": "python3",
-   "display_name": "Python 3"
-  },
-  "accelerator": "GPU"
- },
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "2U0NA9HrrZey"
-   },
-   "source": [
-    "# Taming Transformers\n",
-    "\n",
-    "This notebook is a minimal working example to generate landscape images as in [Taming Transformers for High-Resolution Image Synthesis](https://github.com/CompVis/taming-transformers). **tl;dr** We combine the efficiancy of convolutional approaches with the expressivity of transformers by introducing a convolutional VQGAN, which learns a codebook of context-rich visual parts, whose composition is modeled with an autoregressive transformer."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "4hdc6YonrvoC"
-   },
-   "source": [
-    "## Setup\n",
-    "The setup code in this section was written to be [run in a Colab environment](https://colab.research.google.com/github/CompVis/taming-transformers/blob/master/scripts/taming-transformers.ipynb). For a full, local setup, we recommend the provided [conda environment](https://github.com/CompVis/taming-transformers/blob/master/environment.yaml), as [described in the readme](https://github.com/CompVis/taming-transformers#requirements). This will also allow you to run a streamlit based demo.\n",
-    "\n",
-    "Here, we first clone the repository and download a model checkpoint and config."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "Wwj8j_l201aF",
-    "outputId": "833f54a6-0620-4dc1-dd8c-69ab01433d17"
-   },
-   "source": [
-    "!git clone https://github.com/CompVis/taming-transformers\n",
-    "%cd taming-transformers\n",
-    "!mkdir -p logs/2020-11-09T13-31-51_sflckr/checkpoints\n",
-    "!wget 'https://heibox.uni-heidelberg.de/d/73487ab6e5314cb5adba/files/?p=%2Fcheckpoints%2Flast.ckpt&dl=1' -O 'logs/2020-11-09T13-31-51_sflckr/checkpoints/last.ckpt'\n",
-    "!mkdir logs/2020-11-09T13-31-51_sflckr/configs\n",
-    "!wget 'https://heibox.uni-heidelberg.de/d/73487ab6e5314cb5adba/files/?p=%2Fconfigs%2F2020-11-09T13-31-51-project.yaml&dl=1' -O 'logs/2020-11-09T13-31-51_sflckr/configs/2020-11-09T13-31-51-project.yaml'"
-   ],
-   "execution_count": 2,
-   "outputs": [
-    {
-     "output_type": "stream",
-     "text": [
-      "Cloning into 'taming-transformers'...\n",
-      "remote: Enumerating objects: 287, done.\u001B[K\n",
-      "remote: Counting objects: 100% (287/287), done.\u001B[K\n",
-      "remote: Compressing objects: 100% (232/232), done.\u001B[K\n",
-      "remote: Total 287 (delta 47), reused 285 (delta 45), pack-reused 0\u001B[K\n",
-      "Receiving objects: 100% (287/287), 50.50 MiB | 47.84 MiB/s, done.\n",
-      "Resolving deltas: 100% (47/47), done.\n",
-      "/content/taming-transformers\n",
-      "--2021-01-03 21:17:37--  https://heibox.uni-heidelberg.de/d/73487ab6e5314cb5adba/files/?p=%2Fcheckpoints%2Flast.ckpt&dl=1\n",
-      "Resolving heibox.uni-heidelberg.de (heibox.uni-heidelberg.de)... 129.206.7.113\n",
-      "Connecting to heibox.uni-heidelberg.de (heibox.uni-heidelberg.de)|129.206.7.113|:443... connected.\n",
-      "HTTP request sent, awaiting response... 302 Found\n",
-      "Location: https://heibox.uni-heidelberg.de/seafhttp/files/0fa273f3-83b9-41a5-a7b6-49d825c6a892/last.ckpt [following]\n",
-      "--2021-01-03 21:17:38--  https://heibox.uni-heidelberg.de/seafhttp/files/0fa273f3-83b9-41a5-a7b6-49d825c6a892/last.ckpt\n",
-      "Reusing existing connection to heibox.uni-heidelberg.de:443.\n",
-      "HTTP request sent, awaiting response... 200 OK\n",
-      "Length: 4263525412 (4.0G) [application/octet-stream]\n",
-      "Saving to: ‘logs/2020-11-09T13-31-51_sflckr/checkpoints/last.ckpt’\n",
-      "\n",
-      "logs/2020-11-09T13- 100%[===================>]   3.97G  10.0MB/s    in 6m 50s  \n",
-      "\n",
-      "2021-01-03 21:24:28 (9.91 MB/s) - ‘logs/2020-11-09T13-31-51_sflckr/checkpoints/last.ckpt’ saved [4263525412/4263525412]\n",
-      "\n",
-      "--2021-01-03 21:24:28--  https://heibox.uni-heidelberg.de/d/73487ab6e5314cb5adba/files/?p=%2Fconfigs%2F2020-11-09T13-31-51-project.yaml&dl=1\n",
-      "Resolving heibox.uni-heidelberg.de (heibox.uni-heidelberg.de)... 129.206.7.113\n",
-      "Connecting to heibox.uni-heidelberg.de (heibox.uni-heidelberg.de)|129.206.7.113|:443... connected.\n",
-      "HTTP request sent, awaiting response... 302 Found\n",
-      "Location: https://heibox.uni-heidelberg.de/seafhttp/files/7f8d75ed-f754-4341-8027-f5e2c8eb906a/2020-11-09T13-31-51-project.yaml [following]\n",
-      "--2021-01-03 21:24:29--  https://heibox.uni-heidelberg.de/seafhttp/files/7f8d75ed-f754-4341-8027-f5e2c8eb906a/2020-11-09T13-31-51-project.yaml\n",
-      "Reusing existing connection to heibox.uni-heidelberg.de:443.\n",
-      "HTTP request sent, awaiting response... 200 OK\n",
-      "Length: 1603 (1.6K) [application/octet-stream]\n",
-      "Saving to: ‘logs/2020-11-09T13-31-51_sflckr/configs/2020-11-09T13-31-51-project.yaml’\n",
-      "\n",
-      "logs/2020-11-09T13- 100%[===================>]   1.57K  --.-KB/s    in 0.1s    \n",
-      "\n",
-      "2021-01-03 21:24:29 (12.8 KB/s) - ‘logs/2020-11-09T13-31-51_sflckr/configs/2020-11-09T13-31-51-project.yaml’ saved [1603/1603]\n",
-      "\n"
-     ],
-     "name": "stdout"
-    }
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "eeBqWgMQDjZb"
-   },
-   "source": [
-    "Next, we install minimal required dependencies."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "hzQNmIuT_0uF",
-    "outputId": "e6e1ce0f-bce9-4ebe-a852-95003ca7b630"
-   },
-   "source": [
-    "%pip install omegaconf>=2.0.0 pytorch-lightning>=1.0.8 einops transformers\n",
-    "import sys\n",
-    "sys.path.append(\".\")"
-   ],
-   "execution_count": 3,
-   "outputs": [
-    {
-     "output_type": "stream",
-     "text": [
-      "Collecting omegaconf==2.0.0\n",
-      "  Downloading https://files.pythonhosted.org/packages/3d/95/ebd73361f9c6e94bd0f3b19ffe31c24e833834c022f1c0328ac71b2d6c90/omegaconf-2.0.0-py3-none-any.whl\n",
-      "Collecting pytorch-lightning==1.0.8\n",
-      "\u001B[?25l  Downloading https://files.pythonhosted.org/packages/2e/92/078c5524c875c274ded8a0317ef31a2bb86d02c5c74089ab754d0f12b29c/pytorch_lightning-1.0.8-py3-none-any.whl (561kB)\n",
-      "\u001B[K     |████████████████████████████████| 563kB 17.7MB/s \n",
-      "\u001B[?25hRequirement already satisfied: typing-extensions in /usr/local/lib/python3.6/dist-packages (from omegaconf==2.0.0) (3.7.4.3)\n",
-      "Requirement already satisfied: dataclasses; python_version == \"3.6\" in /usr/local/lib/python3.6/dist-packages (from omegaconf==2.0.0) (0.8)\n",
-      "Requirement already satisfied: PyYAML in /usr/local/lib/python3.6/dist-packages (from omegaconf==2.0.0) (3.13)\n",
-      "Requirement already satisfied: tensorboard>=2.2.0 in /usr/local/lib/python3.6/dist-packages (from pytorch-lightning==1.0.8) (2.4.0)\n",
-      "Requirement already satisfied: torch>=1.3 in /usr/local/lib/python3.6/dist-packages (from pytorch-lightning==1.0.8) (1.7.0+cu101)\n",
-      "Requirement already satisfied: tqdm>=4.41.0 in /usr/local/lib/python3.6/dist-packages (from pytorch-lightning==1.0.8) (4.41.1)\n",
-      "Requirement already satisfied: numpy>=1.16.4 in /usr/local/lib/python3.6/dist-packages (from pytorch-lightning==1.0.8) (1.19.4)\n",
-      "Collecting future>=0.17.1\n",
-      "\u001B[?25l  Downloading https://files.pythonhosted.org/packages/45/0b/38b06fd9b92dc2b68d58b75f900e97884c45bedd2ff83203d933cf5851c9/future-0.18.2.tar.gz (829kB)\n",
-      "\u001B[K     |████████████████████████████████| 829kB 51.0MB/s \n",
-      "\u001B[?25hCollecting fsspec>=0.8.0\n",
-      "\u001B[?25l  Downloading https://files.pythonhosted.org/packages/ec/80/72ac0982cc833945fada4b76c52f0f65435ba4d53bc9317d1c70b5f7e7d5/fsspec-0.8.5-py3-none-any.whl (98kB)\n",
-      "\u001B[K     |████████████████████████████████| 102kB 15.7MB/s \n",
-      "\u001B[?25hRequirement already satisfied: absl-py>=0.4 in /usr/local/lib/python3.6/dist-packages (from tensorboard>=2.2.0->pytorch-lightning==1.0.8) (0.10.0)\n",
-      "Requirement already satisfied: grpcio>=1.24.3 in /usr/local/lib/python3.6/dist-packages (from tensorboard>=2.2.0->pytorch-lightning==1.0.8) (1.32.0)\n",
-      "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard>=2.2.0->pytorch-lightning==1.0.8) (51.0.0)\n",
-      "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard>=2.2.0->pytorch-lightning==1.0.8) (1.7.0)\n",
-      "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard>=2.2.0->pytorch-lightning==1.0.8) (2.23.0)\n",
-      "Requirement already satisfied: wheel>=0.26; python_version >= \"3\" in /usr/local/lib/python3.6/dist-packages (from tensorboard>=2.2.0->pytorch-lightning==1.0.8) (0.36.2)\n",
-      "Requirement already satisfied: google-auth<2,>=1.6.3 in /usr/local/lib/python3.6/dist-packages (from tensorboard>=2.2.0->pytorch-lightning==1.0.8) (1.17.2)\n",
-      "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.6/dist-packages (from tensorboard>=2.2.0->pytorch-lightning==1.0.8) (0.4.2)\n",
-      "Requirement already satisfied: protobuf>=3.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard>=2.2.0->pytorch-lightning==1.0.8) (3.12.4)\n",
-      "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard>=2.2.0->pytorch-lightning==1.0.8) (1.15.0)\n",
-      "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard>=2.2.0->pytorch-lightning==1.0.8) (3.3.3)\n",
-      "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard>=2.2.0->pytorch-lightning==1.0.8) (1.0.1)\n",
-      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard>=2.2.0->pytorch-lightning==1.0.8) (2020.12.5)\n",
-      "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard>=2.2.0->pytorch-lightning==1.0.8) (3.0.4)\n",
-      "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard>=2.2.0->pytorch-lightning==1.0.8) (1.24.3)\n",
-      "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard>=2.2.0->pytorch-lightning==1.0.8) (2.10)\n",
-      "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard>=2.2.0->pytorch-lightning==1.0.8) (4.2.0)\n",
-      "Requirement already satisfied: rsa<5,>=3.1.4; python_version >= \"3\" in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard>=2.2.0->pytorch-lightning==1.0.8) (4.6)\n",
-      "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard>=2.2.0->pytorch-lightning==1.0.8) (0.2.8)\n",
-      "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard>=2.2.0->pytorch-lightning==1.0.8) (1.3.0)\n",
-      "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from markdown>=2.6.8->tensorboard>=2.2.0->pytorch-lightning==1.0.8) (3.3.0)\n",
-      "Requirement already satisfied: pyasn1>=0.1.3 in /usr/local/lib/python3.6/dist-packages (from rsa<5,>=3.1.4; python_version >= \"3\"->google-auth<2,>=1.6.3->tensorboard>=2.2.0->pytorch-lightning==1.0.8) (0.4.8)\n",
-      "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard>=2.2.0->pytorch-lightning==1.0.8) (3.1.0)\n",
-      "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard>=2.2.0->pytorch-lightning==1.0.8) (3.4.0)\n",
-      "Building wheels for collected packages: future\n",
-      "  Building wheel for future (setup.py) ... \u001B[?25l\u001B[?25hdone\n",
-      "  Created wheel for future: filename=future-0.18.2-cp36-none-any.whl size=491057 sha256=77114a99b4b6e1a15924fdf57670f539d17f33b42f8ff0e951f27d6712e10848\n",
-      "  Stored in directory: /root/.cache/pip/wheels/8b/99/a0/81daf51dcd359a9377b110a8a886b3895921802d2fc1b2397e\n",
-      "Successfully built future\n",
-      "\u001B[31mERROR: pytorch-lightning 1.0.8 has requirement PyYAML>=5.1, but you'll have pyyaml 3.13 which is incompatible.\u001B[0m\n",
-      "Installing collected packages: omegaconf, future, fsspec, pytorch-lightning\n",
-      "  Found existing installation: future 0.16.0\n",
-      "    Uninstalling future-0.16.0:\n",
-      "      Successfully uninstalled future-0.16.0\n",
-      "Successfully installed fsspec-0.8.5 future-0.18.2 omegaconf-2.0.0 pytorch-lightning-1.0.8\n"
-     ],
-     "name": "stdout"
-    }
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "5gaAQZXTxFxD"
-   },
-   "source": [
-    "## Loading the model\n",
-    "\n",
-    "We load and print the config."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "hUOUJaTj02Bq",
-    "outputId": "6671562d-f7f6-4407-ee40-22e1b83421e3"
-   },
-   "source": [
-    "from omegaconf import OmegaConf\n",
-    "config_path = \"logs/2020-11-09T13-31-51_sflckr/configs/2020-11-09T13-31-51-project.yaml\"\n",
-    "config = OmegaConf.load(config_path)\n",
-    "import yaml\n",
-    "print(yaml.dump(OmegaConf.to_container(config)))"
-   ],
-   "execution_count": 2,
-   "outputs": [
-    {
-     "output_type": "stream",
-     "text": [
-      "data:\n",
-      "  params:\n",
-      "    batch_size: 1\n",
-      "    validation: {target: taming.data.sflckr.Examples}\n",
-      "  target: main.DataModuleFromConfig\n",
-      "model:\n",
-      "  base_learning_rate: 4.5e-06\n",
-      "  params:\n",
-      "    cond_stage_config:\n",
-      "      params:\n",
-      "        ddconfig:\n",
-      "          attn_resolutions: [16]\n",
-      "          ch: 128\n",
-      "          ch_mult: [1, 1, 2, 2, 4]\n",
-      "          double_z: false\n",
-      "          dropout: 0.0\n",
-      "          in_channels: 182\n",
-      "          num_res_blocks: 2\n",
-      "          out_ch: 182\n",
-      "          resolution: 256\n",
-      "          z_channels: 256\n",
-      "        embed_dim: 256\n",
-      "        image_key: segmentation\n",
-      "        lossconfig: {target: taming.modules.losses.DummyLoss}\n",
-      "        n_embed: 1024\n",
-      "      target: taming.models.vqgan.VQModel\n",
-      "    cond_stage_key: segmentation\n",
-      "    first_stage_config:\n",
-      "      params:\n",
-      "        ddconfig:\n",
-      "          attn_resolutions: [16]\n",
-      "          ch: 128\n",
-      "          ch_mult: [1, 1, 2, 2, 4]\n",
-      "          double_z: false\n",
-      "          dropout: 0.0\n",
-      "          in_channels: 3\n",
-      "          num_res_blocks: 2\n",
-      "          out_ch: 3\n",
-      "          resolution: 256\n",
-      "          z_channels: 256\n",
-      "        embed_dim: 256\n",
-      "        lossconfig: {target: taming.modules.losses.DummyLoss}\n",
-      "        n_embed: 1024\n",
-      "      target: taming.models.vqgan.VQModel\n",
-      "    first_stage_key: image\n",
-      "    transformer_config:\n",
-      "      params: {block_size: 512, n_embd: 1024, n_head: 16, n_layer: 24, vocab_size: 1024}\n",
-      "      target: taming.modules.transformer.mingpt.GPT\n",
-      "  target: taming.models.cond_transformer.Net2NetTransformer\n",
-      "\n"
-     ],
-     "name": "stdout"
-    }
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "FzQqNgiLEJ9J"
-   },
-   "source": [
-    "Instantiate the model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "sWvDDVoz3RB4",
-    "outputId": "08462e1d-77da-4f9d-d3e9-43bdd2be74b8"
-   },
-   "source": [
-    "from taming.models.cond_transformer import Net2NetTransformer\n",
-    "model = Net2NetTransformer(**config.model.params)"
-   ],
-   "execution_count": 3,
-   "outputs": [
-    {
-     "output_type": "stream",
-     "text": [
-      "Working with z of shape (1, 256, 16, 16) = 65536 dimensions.\n",
-      "Working with z of shape (1, 256, 16, 16) = 65536 dimensions.\n"
-     ],
-     "name": "stdout"
-    }
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "njAiY_aqENwV"
-   },
-   "source": [
-    "Load the checkpoint."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {
-    "id": "QABLRpVsDhba"
-   },
-   "source": [
-    "import torch\n",
-    "ckpt_path = \"logs/2020-11-09T13-31-51_sflckr/checkpoints/last.ckpt\"\n",
-    "sd = torch.load(ckpt_path, map_location=\"cpu\")[\"state_dict\"]\n",
-    "missing, unexpected = model.load_state_dict(sd, strict=False)"
-   ],
-   "execution_count": 4,
-   "outputs": []
-  },
-  {
-   "cell_type": "code",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "iYN9cREY-r3N",
-    "outputId": "0954af07-f4b9-474a-d6bf-45355f2b360d"
-   },
-   "source": [
-    "model.cuda().eval()\n",
-    "torch.set_grad_enabled(False)"
-   ],
-   "execution_count": 5,
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "<torch.autograd.grad_mode.set_grad_enabled at 0x7f8ad5a112e8>"
-      ]
-     },
-     "metadata": {
-      "tags": []
-     },
-     "execution_count": 5
-    }
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "tTusbqk2y0u3"
-   },
-   "source": [
-    "## Load example data\n",
-    "\n",
-    "Load an example segmentation and visualize."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {
-    "id": "8LiAOU6C-vTP"
-   },
-   "source": [
-    "from PIL import Image\n",
-    "import numpy as np\n",
-    "segmentation_path = \"data/sflckr_segmentations/norway/25735082181_999927fe5a_b.png\"\n",
-    "segmentation = Image.open(segmentation_path)\n",
-    "segmentation = np.array(segmentation)\n",
-    "segmentation = np.eye(182)[segmentation]\n",
-    "segmentation = torch.tensor(segmentation.transpose(2,0,1)[None]).to(dtype=torch.float32, device=model.device)"
-   ],
-   "execution_count": 27,
-   "outputs": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "oCNy13FGPMy6"
-   },
-   "source": [
-    "Visualize"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 700
-    },
-    "id": "eZFxCkQ5McKG",
-    "outputId": "367a3774-3fcc-405c-c643-4477aad91a2d"
-   },
-   "source": [
-    "def show_segmentation(s):\n",
-    "  s = s.detach().cpu().numpy().transpose(0,2,3,1)[0,:,:,None,:]\n",
-    "  colorize = np.random.RandomState(1).randn(1,1,s.shape[-1],3)\n",
-    "  colorize = colorize / colorize.sum(axis=2, keepdims=True)\n",
-    "  s = s@colorize\n",
-    "  s = s[...,0,:]\n",
-    "  s = ((s+1.0)*127.5).clip(0,255).astype(np.uint8)\n",
-    "  s = Image.fromarray(s)\n",
-    "  display(s)\n",
-    "\n",
-    "show_segmentation(segmentation)"
-   ],
-   "execution_count": 28,
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<PIL.Image.Image image mode=RGB size=1024x683 at 0x7F8AD58F94E0>"
-      ]
-     },
-     "metadata": {
-      "tags": []
-     }
-    }
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "hNBdHGbNTrfM"
-   },
-   "source": [
-    "Our model also employs a VQGAN for the conditioning information, i.e. the segmentation in this example. Let's autoencode the segmentation map. Encoding returns both the quantized code and its representation in terms of indices of a learned codebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 724
-    },
-    "id": "y_HS2hCCORLr",
-    "outputId": "770227a3-533e-4e77-a017-8f6a9b919940"
-   },
-   "source": [
-    "c_code, c_indices = model.encode_to_c(segmentation)\n",
-    "print(\"c_code\", c_code.shape, c_code.dtype)\n",
-    "print(\"c_indices\", c_indices.shape, c_indices.dtype)\n",
-    "assert c_code.shape[2]*c_code.shape[3] == c_indices.shape[0]\n",
-    "segmentation_rec = model.cond_stage_model.decode(c_code)\n",
-    "show_segmentation(torch.softmax(segmentation_rec, dim=1))"
-   ],
-   "execution_count": 29,
-   "outputs": [
-    {
-     "output_type": "stream",
-     "text": [
-      "c_code torch.Size([1, 256, 42, 64]) torch.float32\n",
-      "c_indices torch.Size([1, 2688]) torch.int64\n"
-     ],
-     "name": "stdout"
-    },
-    {
-     "output_type": "display_data",
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<PIL.Image.Image image mode=RGB size=1024x672 at 0x7F8AD5998390>"
-      ]
-     },
-     "metadata": {
-      "tags": []
-     }
-    }
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "pp-OsC1RXAQ9"
-   },
-   "source": [
-    "Let's sample indices corresponding to codes from the image VQGAN given the segmentation code. We init randomly and take a look."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 689
-    },
-    "id": "VTfao3jJSCfW",
-    "outputId": "1a3320ff-a389-4759-ced3-5dee58bf7a29"
-   },
-   "source": [
-    "def show_image(s):\n",
-    "  s = s.detach().cpu().numpy().transpose(0,2,3,1)[0]\n",
-    "  s = ((s+1.0)*127.5).clip(0,255).astype(np.uint8)\n",
-    "  s = Image.fromarray(s)\n",
-    "  display(s)\n",
-    "\n",
-    "codebook_size = config.model.params.first_stage_config.params.embed_dim\n",
-    "z_indices_shape = c_indices.shape\n",
-    "z_code_shape = c_code.shape\n",
-    "z_indices = torch.randint(codebook_size, z_indices_shape, device=model.device)\n",
-    "x_sample = model.decode_to_img(z_indices, z_code_shape)\n",
-    "show_image(x_sample)"
-   ],
-   "execution_count": 30,
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<PIL.Image.Image image mode=RGB size=1024x672 at 0x7F8AD5998828>"
-      ]
-     },
-     "metadata": {
-      "tags": []
-     }
-    }
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "ftDWneY3zJZ5"
-   },
-   "source": [
-    "## Sample an image\n",
-    "\n",
-    "We use the transformer in a sliding window manner to sample all code entries sequentially. The code below assumes a window size of $16\\times 16$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 724
-    },
-    "id": "5rVRrUOwbEH0",
-    "outputId": "f5f860e0-6a3c-44ac-9361-bd4d1be8318e"
-   },
-   "source": [
-    "from IPython.display import clear_output\n",
-    "import time\n",
-    "\n",
-    "idx = z_indices\n",
-    "idx = idx.reshape(z_code_shape[0],z_code_shape[2],z_code_shape[3])\n",
-    "\n",
-    "cidx = c_indices\n",
-    "cidx = cidx.reshape(c_code.shape[0],c_code.shape[2],c_code.shape[3])\n",
-    "\n",
-    "temperature = 1.0\n",
-    "top_k = 100\n",
-    "update_every = 50\n",
-    "\n",
-    "start_t = time.time()\n",
-    "for i in range(0, z_code_shape[2]-0):\n",
-    "  if i <= 8:\n",
-    "    local_i = i\n",
-    "  elif z_code_shape[2]-i < 8:\n",
-    "    local_i = 16-(z_code_shape[2]-i)\n",
-    "  else:\n",
-    "    local_i = 8\n",
-    "  for j in range(0,z_code_shape[3]-0):\n",
-    "    if j <= 8:\n",
-    "      local_j = j\n",
-    "    elif z_code_shape[3]-j < 8:\n",
-    "      local_j = 16-(z_code_shape[3]-j)\n",
-    "    else:\n",
-    "      local_j = 8\n",
-    "\n",
-    "    i_start = i-local_i\n",
-    "    i_end = i_start+16\n",
-    "    j_start = j-local_j\n",
-    "    j_end = j_start+16\n",
-    "    \n",
-    "    patch = idx[:,i_start:i_end,j_start:j_end]\n",
-    "    patch = patch.reshape(patch.shape[0],-1)\n",
-    "    cpatch = cidx[:, i_start:i_end, j_start:j_end]\n",
-    "    cpatch = cpatch.reshape(cpatch.shape[0], -1)\n",
-    "    patch = torch.cat((cpatch, patch), dim=1)\n",
-    "    logits,_ = model.transformer(patch[:,:-1])\n",
-    "    logits = logits[:, -256:, :]\n",
-    "    logits = logits.reshape(z_code_shape[0],16,16,-1)\n",
-    "    logits = logits[:,local_i,local_j,:]\n",
-    "\n",
-    "    logits = logits/temperature\n",
-    "\n",
-    "    if top_k is not None:\n",
-    "      logits = model.top_k_logits(logits, top_k)\n",
-    "\n",
-    "    probs = torch.nn.functional.softmax(logits, dim=-1)\n",
-    "    idx[:,i,j] = torch.multinomial(probs, num_samples=1)\n",
-    "\n",
-    "    step = i*z_code_shape[3]+j\n",
-    "    if step%update_every==0 or step==z_code_shape[2]*z_code_shape[3]-1:\n",
-    "      x_sample = model.decode_to_img(idx, z_code_shape)\n",
-    "      clear_output()\n",
-    "      print(f\"Time: {time.time() - start_t} seconds\")\n",
-    "      print(f\"Step: ({i},{j}) | Local: ({local_i},{local_j}) | Crop: ({i_start}:{i_end},{j_start}:{j_end})\")\n",
-    "      show_image(x_sample)"
-   ],
-   "execution_count": 31,
-   "outputs": [
-    {
-     "output_type": "stream",
-     "text": [
-      "Time: 378.9832909107208 seconds\n",
-      "Step: (41,63) | Local: (15,15) | Crop: (26:42,48:64)\n"
-     ],
-     "name": "stdout"
-    },
-    {
-     "output_type": "display_data",
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<PIL.Image.Image image mode=RGB size=1024x672 at 0x7F8AD592B978>"
-      ]
-     },
-     "metadata": {
-      "tags": []
-     }
-    }
-   ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {
-    "id": "3Pu-VjGHnFta"
-   },
-   "source": [
-    ""
-   ],
-   "execution_count": 31,
-   "outputs": []
-  }
- ]
-}
\ No newline at end of file
diff --git a/repositories/taming-transformers/setup.py b/repositories/taming-transformers/setup.py
deleted file mode 100644
index a220d12b21d96c5093a218c406cf47f1e7c8761a..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/setup.py
+++ /dev/null
@@ -1,13 +0,0 @@
-from setuptools import setup, find_packages
-
-setup(
-    name='taming-transformers',
-    version='0.0.1',
-    description='Taming Transformers for High-Resolution Image Synthesis',
-    packages=find_packages(),
-    install_requires=[
-        'torch',
-        'numpy',
-        'tqdm',
-    ],
-)
diff --git a/repositories/taming-transformers/taming/data/ade20k.py b/repositories/taming-transformers/taming/data/ade20k.py
deleted file mode 100644
index 366dae97207dbb8356598d636e14ad084d45bc76..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/taming/data/ade20k.py
+++ /dev/null
@@ -1,124 +0,0 @@
-import os
-import numpy as np
-import cv2
-import albumentations
-from PIL import Image
-from torch.utils.data import Dataset
-
-from taming.data.sflckr import SegmentationBase # for examples included in repo
-
-
-class Examples(SegmentationBase):
-    def __init__(self, size=256, random_crop=False, interpolation="bicubic"):
-        super().__init__(data_csv="data/ade20k_examples.txt",
-                         data_root="data/ade20k_images",
-                         segmentation_root="data/ade20k_segmentations",
-                         size=size, random_crop=random_crop,
-                         interpolation=interpolation,
-                         n_labels=151, shift_segmentation=False)
-
-
-# With semantic map and scene label
-class ADE20kBase(Dataset):
-    def __init__(self, config=None, size=None, random_crop=False, interpolation="bicubic", crop_size=None):
-        self.split = self.get_split()
-        self.n_labels = 151 # unknown + 150
-        self.data_csv = {"train": "data/ade20k_train.txt",
-                         "validation": "data/ade20k_test.txt"}[self.split]
-        self.data_root = "data/ade20k_root"
-        with open(os.path.join(self.data_root, "sceneCategories.txt"), "r") as f:
-            self.scene_categories = f.read().splitlines()
-        self.scene_categories = dict(line.split() for line in self.scene_categories)
-        with open(self.data_csv, "r") as f:
-            self.image_paths = f.read().splitlines()
-        self._length = len(self.image_paths)
-        self.labels = {
-            "relative_file_path_": [l for l in self.image_paths],
-            "file_path_": [os.path.join(self.data_root, "images", l)
-                           for l in self.image_paths],
-            "relative_segmentation_path_": [l.replace(".jpg", ".png")
-                                            for l in self.image_paths],
-            "segmentation_path_": [os.path.join(self.data_root, "annotations",
-                                                l.replace(".jpg", ".png"))
-                                   for l in self.image_paths],
-            "scene_category": [self.scene_categories[l.split("/")[1].replace(".jpg", "")]
-                               for l in self.image_paths],
-        }
-
-        size = None if size is not None and size<=0 else size
-        self.size = size
-        if crop_size is None:
-            self.crop_size = size if size is not None else None
-        else:
-            self.crop_size = crop_size
-        if self.size is not None:
-            self.interpolation = interpolation
-            self.interpolation = {
-                "nearest": cv2.INTER_NEAREST,
-                "bilinear": cv2.INTER_LINEAR,
-                "bicubic": cv2.INTER_CUBIC,
-                "area": cv2.INTER_AREA,
-                "lanczos": cv2.INTER_LANCZOS4}[self.interpolation]
-            self.image_rescaler = albumentations.SmallestMaxSize(max_size=self.size,
-                                                                 interpolation=self.interpolation)
-            self.segmentation_rescaler = albumentations.SmallestMaxSize(max_size=self.size,
-                                                                        interpolation=cv2.INTER_NEAREST)
-
-        if crop_size is not None:
-            self.center_crop = not random_crop
-            if self.center_crop:
-                self.cropper = albumentations.CenterCrop(height=self.crop_size, width=self.crop_size)
-            else:
-                self.cropper = albumentations.RandomCrop(height=self.crop_size, width=self.crop_size)
-            self.preprocessor = self.cropper
-
-    def __len__(self):
-        return self._length
-
-    def __getitem__(self, i):
-        example = dict((k, self.labels[k][i]) for k in self.labels)
-        image = Image.open(example["file_path_"])
-        if not image.mode == "RGB":
-            image = image.convert("RGB")
-        image = np.array(image).astype(np.uint8)
-        if self.size is not None:
-            image = self.image_rescaler(image=image)["image"]
-        segmentation = Image.open(example["segmentation_path_"])
-        segmentation = np.array(segmentation).astype(np.uint8)
-        if self.size is not None:
-            segmentation = self.segmentation_rescaler(image=segmentation)["image"]
-        if self.size is not None:
-            processed = self.preprocessor(image=image, mask=segmentation)
-        else:
-            processed = {"image": image, "mask": segmentation}
-        example["image"] = (processed["image"]/127.5 - 1.0).astype(np.float32)
-        segmentation = processed["mask"]
-        onehot = np.eye(self.n_labels)[segmentation]
-        example["segmentation"] = onehot
-        return example
-
-
-class ADE20kTrain(ADE20kBase):
-    # default to random_crop=True
-    def __init__(self, config=None, size=None, random_crop=True, interpolation="bicubic", crop_size=None):
-        super().__init__(config=config, size=size, random_crop=random_crop,
-                          interpolation=interpolation, crop_size=crop_size)
-
-    def get_split(self):
-        return "train"
-
-
-class ADE20kValidation(ADE20kBase):
-    def get_split(self):
-        return "validation"
-
-
-if __name__ == "__main__":
-    dset = ADE20kValidation()
-    ex = dset[0]
-    for k in ["image", "scene_category", "segmentation"]:
-        print(type(ex[k]))
-        try:
-            print(ex[k].shape)
-        except:
-            print(ex[k])
diff --git a/repositories/taming-transformers/taming/data/annotated_objects_coco.py b/repositories/taming-transformers/taming/data/annotated_objects_coco.py
deleted file mode 100644
index af000ecd943d7b8a85d7eb70195c9ecd10ab5edc..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/taming/data/annotated_objects_coco.py
+++ /dev/null
@@ -1,139 +0,0 @@
-import json
-from itertools import chain
-from pathlib import Path
-from typing import Iterable, Dict, List, Callable, Any
-from collections import defaultdict
-
-from tqdm import tqdm
-
-from taming.data.annotated_objects_dataset import AnnotatedObjectsDataset
-from taming.data.helper_types import Annotation, ImageDescription, Category
-
-COCO_PATH_STRUCTURE = {
-    'train': {
-        'top_level': '',
-        'instances_annotations': 'annotations/instances_train2017.json',
-        'stuff_annotations': 'annotations/stuff_train2017.json',
-        'files': 'train2017'
-    },
-    'validation': {
-        'top_level': '',
-        'instances_annotations': 'annotations/instances_val2017.json',
-        'stuff_annotations': 'annotations/stuff_val2017.json',
-        'files': 'val2017'
-    }
-}
-
-
-def load_image_descriptions(description_json: List[Dict]) -> Dict[str, ImageDescription]:
-    return {
-        str(img['id']): ImageDescription(
-            id=img['id'],
-            license=img.get('license'),
-            file_name=img['file_name'],
-            coco_url=img['coco_url'],
-            original_size=(img['width'], img['height']),
-            date_captured=img.get('date_captured'),
-            flickr_url=img.get('flickr_url')
-        )
-        for img in description_json
-    }
-
-
-def load_categories(category_json: Iterable) -> Dict[str, Category]:
-    return {str(cat['id']): Category(id=str(cat['id']), super_category=cat['supercategory'], name=cat['name'])
-            for cat in category_json if cat['name'] != 'other'}
-
-
-def load_annotations(annotations_json: List[Dict], image_descriptions: Dict[str, ImageDescription],
-                     category_no_for_id: Callable[[str], int], split: str) -> Dict[str, List[Annotation]]:
-    annotations = defaultdict(list)
-    total = sum(len(a) for a in annotations_json)
-    for ann in tqdm(chain(*annotations_json), f'Loading {split} annotations', total=total):
-        image_id = str(ann['image_id'])
-        if image_id not in image_descriptions:
-            raise ValueError(f'image_id [{image_id}] has no image description.')
-        category_id = ann['category_id']
-        try:
-            category_no = category_no_for_id(str(category_id))
-        except KeyError:
-            continue
-
-        width, height = image_descriptions[image_id].original_size
-        bbox = (ann['bbox'][0] / width, ann['bbox'][1] / height, ann['bbox'][2] / width, ann['bbox'][3] / height)
-
-        annotations[image_id].append(
-            Annotation(
-                id=ann['id'],
-                area=bbox[2]*bbox[3],  # use bbox area
-                is_group_of=ann['iscrowd'],
-                image_id=ann['image_id'],
-                bbox=bbox,
-                category_id=str(category_id),
-                category_no=category_no
-            )
-        )
-    return dict(annotations)
-
-
-class AnnotatedObjectsCoco(AnnotatedObjectsDataset):
-    def __init__(self, use_things: bool = True, use_stuff: bool = True, **kwargs):
-        """
-        @param data_path: is the path to the following folder structure:
-                          coco/
-                          ├── annotations
-                          │   ├── instances_train2017.json
-                          │   ├── instances_val2017.json
-                          │   ├── stuff_train2017.json
-                          │   └── stuff_val2017.json
-                          ├── train2017
-                          │   ├── 000000000009.jpg
-                          │   ├── 000000000025.jpg
-                          │   └── ...
-                          ├── val2017
-                          │   ├── 000000000139.jpg
-                          │   ├── 000000000285.jpg
-                          │   └── ...
-        @param: split: one of 'train' or 'validation'
-        @param: desired image size (give square images)
-        """
-        super().__init__(**kwargs)
-        self.use_things = use_things
-        self.use_stuff = use_stuff
-
-        with open(self.paths['instances_annotations']) as f:
-            inst_data_json = json.load(f)
-        with open(self.paths['stuff_annotations']) as f:
-            stuff_data_json = json.load(f)
-
-        category_jsons = []
-        annotation_jsons = []
-        if self.use_things:
-            category_jsons.append(inst_data_json['categories'])
-            annotation_jsons.append(inst_data_json['annotations'])
-        if self.use_stuff:
-            category_jsons.append(stuff_data_json['categories'])
-            annotation_jsons.append(stuff_data_json['annotations'])
-
-        self.categories = load_categories(chain(*category_jsons))
-        self.filter_categories()
-        self.setup_category_id_and_number()
-
-        self.image_descriptions = load_image_descriptions(inst_data_json['images'])
-        annotations = load_annotations(annotation_jsons, self.image_descriptions, self.get_category_number, self.split)
-        self.annotations = self.filter_object_number(annotations, self.min_object_area,
-                                                     self.min_objects_per_image, self.max_objects_per_image)
-        self.image_ids = list(self.annotations.keys())
-        self.clean_up_annotations_and_image_descriptions()
-
-    def get_path_structure(self) -> Dict[str, str]:
-        if self.split not in COCO_PATH_STRUCTURE:
-            raise ValueError(f'Split [{self.split} does not exist for COCO data.]')
-        return COCO_PATH_STRUCTURE[self.split]
-
-    def get_image_path(self, image_id: str) -> Path:
-        return self.paths['files'].joinpath(self.image_descriptions[str(image_id)].file_name)
-
-    def get_image_description(self, image_id: str) -> Dict[str, Any]:
-        # noinspection PyProtectedMember
-        return self.image_descriptions[image_id]._asdict()
diff --git a/repositories/taming-transformers/taming/data/annotated_objects_dataset.py b/repositories/taming-transformers/taming/data/annotated_objects_dataset.py
deleted file mode 100644
index 53cc346a1c76289a4964d7dc8a29582172f33dc0..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/taming/data/annotated_objects_dataset.py
+++ /dev/null
@@ -1,218 +0,0 @@
-from pathlib import Path
-from typing import Optional, List, Callable, Dict, Any, Union
-import warnings
-
-import PIL.Image as pil_image
-from torch import Tensor
-from torch.utils.data import Dataset
-from torchvision import transforms
-
-from taming.data.conditional_builder.objects_bbox import ObjectsBoundingBoxConditionalBuilder
-from taming.data.conditional_builder.objects_center_points import ObjectsCenterPointsConditionalBuilder
-from taming.data.conditional_builder.utils import load_object_from_string
-from taming.data.helper_types import BoundingBox, CropMethodType, Image, Annotation, SplitType
-from taming.data.image_transforms import CenterCropReturnCoordinates, RandomCrop1dReturnCoordinates, \
-    Random2dCropReturnCoordinates, RandomHorizontalFlipReturn, convert_pil_to_tensor
-
-
-class AnnotatedObjectsDataset(Dataset):
-    def __init__(self, data_path: Union[str, Path], split: SplitType, keys: List[str], target_image_size: int,
-                 min_object_area: float, min_objects_per_image: int, max_objects_per_image: int,
-                 crop_method: CropMethodType, random_flip: bool, no_tokens: int, use_group_parameter: bool,
-                 encode_crop: bool, category_allow_list_target: str = "", category_mapping_target: str = "",
-                 no_object_classes: Optional[int] = None):
-        self.data_path = data_path
-        self.split = split
-        self.keys = keys
-        self.target_image_size = target_image_size
-        self.min_object_area = min_object_area
-        self.min_objects_per_image = min_objects_per_image
-        self.max_objects_per_image = max_objects_per_image
-        self.crop_method = crop_method
-        self.random_flip = random_flip
-        self.no_tokens = no_tokens
-        self.use_group_parameter = use_group_parameter
-        self.encode_crop = encode_crop
-
-        self.annotations = None
-        self.image_descriptions = None
-        self.categories = None
-        self.category_ids = None
-        self.category_number = None
-        self.image_ids = None
-        self.transform_functions: List[Callable] = self.setup_transform(target_image_size, crop_method, random_flip)
-        self.paths = self.build_paths(self.data_path)
-        self._conditional_builders = None
-        self.category_allow_list = None
-        if category_allow_list_target:
-            allow_list = load_object_from_string(category_allow_list_target)
-            self.category_allow_list = {name for name, _ in allow_list}
-        self.category_mapping = {}
-        if category_mapping_target:
-            self.category_mapping = load_object_from_string(category_mapping_target)
-        self.no_object_classes = no_object_classes
-
-    def build_paths(self, top_level: Union[str, Path]) -> Dict[str, Path]:
-        top_level = Path(top_level)
-        sub_paths = {name: top_level.joinpath(sub_path) for name, sub_path in self.get_path_structure().items()}
-        for path in sub_paths.values():
-            if not path.exists():
-                raise FileNotFoundError(f'{type(self).__name__} data structure error: [{path}] does not exist.')
-        return sub_paths
-
-    @staticmethod
-    def load_image_from_disk(path: Path) -> Image:
-        return pil_image.open(path).convert('RGB')
-
-    @staticmethod
-    def setup_transform(target_image_size: int, crop_method: CropMethodType, random_flip: bool):
-        transform_functions = []
-        if crop_method == 'none':
-            transform_functions.append(transforms.Resize((target_image_size, target_image_size)))
-        elif crop_method == 'center':
-            transform_functions.extend([
-                transforms.Resize(target_image_size),
-                CenterCropReturnCoordinates(target_image_size)
-            ])
-        elif crop_method == 'random-1d':
-            transform_functions.extend([
-                transforms.Resize(target_image_size),
-                RandomCrop1dReturnCoordinates(target_image_size)
-            ])
-        elif crop_method == 'random-2d':
-            transform_functions.extend([
-                Random2dCropReturnCoordinates(target_image_size),
-                transforms.Resize(target_image_size)
-            ])
-        elif crop_method is None:
-            return None
-        else:
-            raise ValueError(f'Received invalid crop method [{crop_method}].')
-        if random_flip:
-            transform_functions.append(RandomHorizontalFlipReturn())
-        transform_functions.append(transforms.Lambda(lambda x: x / 127.5 - 1.))
-        return transform_functions
-
-    def image_transform(self, x: Tensor) -> (Optional[BoundingBox], Optional[bool], Tensor):
-        crop_bbox = None
-        flipped = None
-        for t in self.transform_functions:
-            if isinstance(t, (RandomCrop1dReturnCoordinates, CenterCropReturnCoordinates, Random2dCropReturnCoordinates)):
-                crop_bbox, x = t(x)
-            elif isinstance(t, RandomHorizontalFlipReturn):
-                flipped, x = t(x)
-            else:
-                x = t(x)
-        return crop_bbox, flipped, x
-
-    @property
-    def no_classes(self) -> int:
-        return self.no_object_classes if self.no_object_classes else len(self.categories)
-
-    @property
-    def conditional_builders(self) -> ObjectsCenterPointsConditionalBuilder:
-        # cannot set this up in init because no_classes is only known after loading data in init of superclass
-        if self._conditional_builders is None:
-            self._conditional_builders = {
-                'objects_center_points': ObjectsCenterPointsConditionalBuilder(
-                    self.no_classes,
-                    self.max_objects_per_image,
-                    self.no_tokens,
-                    self.encode_crop,
-                    self.use_group_parameter,
-                    getattr(self, 'use_additional_parameters', False)
-                ),
-                'objects_bbox': ObjectsBoundingBoxConditionalBuilder(
-                    self.no_classes,
-                    self.max_objects_per_image,
-                    self.no_tokens,
-                    self.encode_crop,
-                    self.use_group_parameter,
-                    getattr(self, 'use_additional_parameters', False)
-                )
-            }
-        return self._conditional_builders
-
-    def filter_categories(self) -> None:
-        if self.category_allow_list:
-            self.categories = {id_: cat for id_, cat in self.categories.items() if cat.name in self.category_allow_list}
-        if self.category_mapping:
-            self.categories = {id_: cat for id_, cat in self.categories.items() if cat.id not in self.category_mapping}
-
-    def setup_category_id_and_number(self) -> None:
-        self.category_ids = list(self.categories.keys())
-        self.category_ids.sort()
-        if '/m/01s55n' in self.category_ids:
-            self.category_ids.remove('/m/01s55n')
-            self.category_ids.append('/m/01s55n')
-        self.category_number = {category_id: i for i, category_id in enumerate(self.category_ids)}
-        if self.category_allow_list is not None and self.category_mapping is None \
-                and len(self.category_ids) != len(self.category_allow_list):
-            warnings.warn('Unexpected number of categories: Mismatch with category_allow_list. '
-                          'Make sure all names in category_allow_list exist.')
-
-    def clean_up_annotations_and_image_descriptions(self) -> None:
-        image_id_set = set(self.image_ids)
-        self.annotations = {k: v for k, v in self.annotations.items() if k in image_id_set}
-        self.image_descriptions = {k: v for k, v in self.image_descriptions.items() if k in image_id_set}
-
-    @staticmethod
-    def filter_object_number(all_annotations: Dict[str, List[Annotation]], min_object_area: float,
-                             min_objects_per_image: int, max_objects_per_image: int) -> Dict[str, List[Annotation]]:
-        filtered = {}
-        for image_id, annotations in all_annotations.items():
-            annotations_with_min_area = [a for a in annotations if a.area > min_object_area]
-            if min_objects_per_image <= len(annotations_with_min_area) <= max_objects_per_image:
-                filtered[image_id] = annotations_with_min_area
-        return filtered
-
-    def __len__(self):
-        return len(self.image_ids)
-
-    def __getitem__(self, n: int) -> Dict[str, Any]:
-        image_id = self.get_image_id(n)
-        sample = self.get_image_description(image_id)
-        sample['annotations'] = self.get_annotation(image_id)
-
-        if 'image' in self.keys:
-            sample['image_path'] = str(self.get_image_path(image_id))
-            sample['image'] = self.load_image_from_disk(sample['image_path'])
-            sample['image'] = convert_pil_to_tensor(sample['image'])
-            sample['crop_bbox'], sample['flipped'], sample['image'] = self.image_transform(sample['image'])
-            sample['image'] = sample['image'].permute(1, 2, 0)
-
-        for conditional, builder in self.conditional_builders.items():
-            if conditional in self.keys:
-                sample[conditional] = builder.build(sample['annotations'], sample['crop_bbox'], sample['flipped'])
-
-        if self.keys:
-            # only return specified keys
-            sample = {key: sample[key] for key in self.keys}
-        return sample
-
-    def get_image_id(self, no: int) -> str:
-        return self.image_ids[no]
-
-    def get_annotation(self, image_id: str) -> str:
-        return self.annotations[image_id]
-
-    def get_textual_label_for_category_id(self, category_id: str) -> str:
-        return self.categories[category_id].name
-
-    def get_textual_label_for_category_no(self, category_no: int) -> str:
-        return self.categories[self.get_category_id(category_no)].name
-
-    def get_category_number(self, category_id: str) -> int:
-        return self.category_number[category_id]
-
-    def get_category_id(self, category_no: int) -> str:
-        return self.category_ids[category_no]
-
-    def get_image_description(self, image_id: str) -> Dict[str, Any]:
-        raise NotImplementedError()
-
-    def get_path_structure(self):
-        raise NotImplementedError
-
-    def get_image_path(self, image_id: str) -> Path:
-        raise NotImplementedError
diff --git a/repositories/taming-transformers/taming/data/annotated_objects_open_images.py b/repositories/taming-transformers/taming/data/annotated_objects_open_images.py
deleted file mode 100644
index aede6803d2cef7a74ca784e7907d35fba6c71239..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/taming/data/annotated_objects_open_images.py
+++ /dev/null
@@ -1,137 +0,0 @@
-from collections import defaultdict
-from csv import DictReader, reader as TupleReader
-from pathlib import Path
-from typing import Dict, List, Any
-import warnings
-
-from taming.data.annotated_objects_dataset import AnnotatedObjectsDataset
-from taming.data.helper_types import Annotation, Category
-from tqdm import tqdm
-
-OPEN_IMAGES_STRUCTURE = {
-    'train': {
-        'top_level': '',
-        'class_descriptions': 'class-descriptions-boxable.csv',
-        'annotations': 'oidv6-train-annotations-bbox.csv',
-        'file_list': 'train-images-boxable.csv',
-        'files': 'train'
-    },
-    'validation': {
-        'top_level': '',
-        'class_descriptions': 'class-descriptions-boxable.csv',
-        'annotations': 'validation-annotations-bbox.csv',
-        'file_list': 'validation-images.csv',
-        'files': 'validation'
-    },
-    'test': {
-        'top_level': '',
-        'class_descriptions': 'class-descriptions-boxable.csv',
-        'annotations': 'test-annotations-bbox.csv',
-        'file_list': 'test-images.csv',
-        'files': 'test'
-    }
-}
-
-
-def load_annotations(descriptor_path: Path, min_object_area: float, category_mapping: Dict[str, str],
-                     category_no_for_id: Dict[str, int]) -> Dict[str, List[Annotation]]:
-    annotations: Dict[str, List[Annotation]] = defaultdict(list)
-    with open(descriptor_path) as file:
-        reader = DictReader(file)
-        for i, row in tqdm(enumerate(reader), total=14620000, desc='Loading OpenImages annotations'):
-            width = float(row['XMax']) - float(row['XMin'])
-            height = float(row['YMax']) - float(row['YMin'])
-            area = width * height
-            category_id = row['LabelName']
-            if category_id in category_mapping:
-                category_id = category_mapping[category_id]
-            if area >= min_object_area and category_id in category_no_for_id:
-                annotations[row['ImageID']].append(
-                    Annotation(
-                        id=i,
-                        image_id=row['ImageID'],
-                        source=row['Source'],
-                        category_id=category_id,
-                        category_no=category_no_for_id[category_id],
-                        confidence=float(row['Confidence']),
-                        bbox=(float(row['XMin']), float(row['YMin']), width, height),
-                        area=area,
-                        is_occluded=bool(int(row['IsOccluded'])),
-                        is_truncated=bool(int(row['IsTruncated'])),
-                        is_group_of=bool(int(row['IsGroupOf'])),
-                        is_depiction=bool(int(row['IsDepiction'])),
-                        is_inside=bool(int(row['IsInside']))
-                    )
-                )
-        if 'train' in str(descriptor_path) and i < 14000000:
-            warnings.warn(f'Running with subset of Open Images. Train dataset has length [{len(annotations)}].')
-        return dict(annotations)
-
-
-def load_image_ids(csv_path: Path) -> List[str]:
-    with open(csv_path) as file:
-        reader = DictReader(file)
-        return [row['image_name'] for row in reader]
-
-
-def load_categories(csv_path: Path) -> Dict[str, Category]:
-    with open(csv_path) as file:
-        reader = TupleReader(file)
-        return {row[0]: Category(id=row[0], name=row[1], super_category=None) for row in reader}
-
-
-class AnnotatedObjectsOpenImages(AnnotatedObjectsDataset):
-    def __init__(self, use_additional_parameters: bool, **kwargs):
-        """
-        @param data_path: is the path to the following folder structure:
-                          open_images/
-                          │   oidv6-train-annotations-bbox.csv
-                          ├── class-descriptions-boxable.csv
-                          ├── oidv6-train-annotations-bbox.csv
-                          ├── test
-                          │   ├── 000026e7ee790996.jpg
-                          │   ├── 000062a39995e348.jpg
-                          │   └── ...
-                          ├── test-annotations-bbox.csv
-                          ├── test-images.csv
-                          ├── train
-                          │   ├── 000002b66c9c498e.jpg
-                          │   ├── 000002b97e5471a0.jpg
-                          │   └── ...
-                          ├── train-images-boxable.csv
-                          ├── validation
-                          │   ├── 0001eeaf4aed83f9.jpg
-                          │   ├── 0004886b7d043cfd.jpg
-                          │   └── ...
-                          ├── validation-annotations-bbox.csv
-                          └── validation-images.csv
-        @param: split: one of 'train', 'validation' or 'test'
-        @param: desired image size (returns square images)
-        """
-
-        super().__init__(**kwargs)
-        self.use_additional_parameters = use_additional_parameters
-
-        self.categories = load_categories(self.paths['class_descriptions'])
-        self.filter_categories()
-        self.setup_category_id_and_number()
-
-        self.image_descriptions = {}
-        annotations = load_annotations(self.paths['annotations'], self.min_object_area, self.category_mapping,
-                                       self.category_number)
-        self.annotations = self.filter_object_number(annotations, self.min_object_area, self.min_objects_per_image,
-                                                     self.max_objects_per_image)
-        self.image_ids = list(self.annotations.keys())
-        self.clean_up_annotations_and_image_descriptions()
-
-    def get_path_structure(self) -> Dict[str, str]:
-        if self.split not in OPEN_IMAGES_STRUCTURE:
-            raise ValueError(f'Split [{self.split} does not exist for Open Images data.]')
-        return OPEN_IMAGES_STRUCTURE[self.split]
-
-    def get_image_path(self, image_id: str) -> Path:
-        return self.paths['files'].joinpath(f'{image_id:0>16}.jpg')
-
-    def get_image_description(self, image_id: str) -> Dict[str, Any]:
-        image_path = self.get_image_path(image_id)
-        return {'file_path': str(image_path), 'file_name': image_path.name}
diff --git a/repositories/taming-transformers/taming/data/base.py b/repositories/taming-transformers/taming/data/base.py
deleted file mode 100644
index e21667df4ce4baa6bb6aad9f8679bd756e2ffdb7..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/taming/data/base.py
+++ /dev/null
@@ -1,70 +0,0 @@
-import bisect
-import numpy as np
-import albumentations
-from PIL import Image
-from torch.utils.data import Dataset, ConcatDataset
-
-
-class ConcatDatasetWithIndex(ConcatDataset):
-    """Modified from original pytorch code to return dataset idx"""
-    def __getitem__(self, idx):
-        if idx < 0:
-            if -idx > len(self):
-                raise ValueError("absolute value of index should not exceed dataset length")
-            idx = len(self) + idx
-        dataset_idx = bisect.bisect_right(self.cumulative_sizes, idx)
-        if dataset_idx == 0:
-            sample_idx = idx
-        else:
-            sample_idx = idx - self.cumulative_sizes[dataset_idx - 1]
-        return self.datasets[dataset_idx][sample_idx], dataset_idx
-
-
-class ImagePaths(Dataset):
-    def __init__(self, paths, size=None, random_crop=False, labels=None):
-        self.size = size
-        self.random_crop = random_crop
-
-        self.labels = dict() if labels is None else labels
-        self.labels["file_path_"] = paths
-        self._length = len(paths)
-
-        if self.size is not None and self.size > 0:
-            self.rescaler = albumentations.SmallestMaxSize(max_size = self.size)
-            if not self.random_crop:
-                self.cropper = albumentations.CenterCrop(height=self.size,width=self.size)
-            else:
-                self.cropper = albumentations.RandomCrop(height=self.size,width=self.size)
-            self.preprocessor = albumentations.Compose([self.rescaler, self.cropper])
-        else:
-            self.preprocessor = lambda **kwargs: kwargs
-
-    def __len__(self):
-        return self._length
-
-    def preprocess_image(self, image_path):
-        image = Image.open(image_path)
-        if not image.mode == "RGB":
-            image = image.convert("RGB")
-        image = np.array(image).astype(np.uint8)
-        image = self.preprocessor(image=image)["image"]
-        image = (image/127.5 - 1.0).astype(np.float32)
-        return image
-
-    def __getitem__(self, i):
-        example = dict()
-        example["image"] = self.preprocess_image(self.labels["file_path_"][i])
-        for k in self.labels:
-            example[k] = self.labels[k][i]
-        return example
-
-
-class NumpyPaths(ImagePaths):
-    def preprocess_image(self, image_path):
-        image = np.load(image_path).squeeze(0)  # 3 x 1024 x 1024
-        image = np.transpose(image, (1,2,0))
-        image = Image.fromarray(image, mode="RGB")
-        image = np.array(image).astype(np.uint8)
-        image = self.preprocessor(image=image)["image"]
-        image = (image/127.5 - 1.0).astype(np.float32)
-        return image
diff --git a/repositories/taming-transformers/taming/data/coco.py b/repositories/taming-transformers/taming/data/coco.py
deleted file mode 100644
index 2b2f7838448cb63dcf96daffe9470d58566d975a..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/taming/data/coco.py
+++ /dev/null
@@ -1,176 +0,0 @@
-import os
-import json
-import albumentations
-import numpy as np
-from PIL import Image
-from tqdm import tqdm
-from torch.utils.data import Dataset
-
-from taming.data.sflckr import SegmentationBase # for examples included in repo
-
-
-class Examples(SegmentationBase):
-    def __init__(self, size=256, random_crop=False, interpolation="bicubic"):
-        super().__init__(data_csv="data/coco_examples.txt",
-                         data_root="data/coco_images",
-                         segmentation_root="data/coco_segmentations",
-                         size=size, random_crop=random_crop,
-                         interpolation=interpolation,
-                         n_labels=183, shift_segmentation=True)
-
-
-class CocoBase(Dataset):
-    """needed for (image, caption, segmentation) pairs"""
-    def __init__(self, size=None, dataroot="", datajson="", onehot_segmentation=False, use_stuffthing=False,
-                 crop_size=None, force_no_crop=False, given_files=None):
-        self.split = self.get_split()
-        self.size = size
-        if crop_size is None:
-            self.crop_size = size
-        else:
-            self.crop_size = crop_size
-
-        self.onehot = onehot_segmentation       # return segmentation as rgb or one hot
-        self.stuffthing = use_stuffthing        # include thing in segmentation
-        if self.onehot and not self.stuffthing:
-            raise NotImplemented("One hot mode is only supported for the "
-                                 "stuffthings version because labels are stored "
-                                 "a bit different.")
-
-        data_json = datajson
-        with open(data_json) as json_file:
-            self.json_data = json.load(json_file)
-            self.img_id_to_captions = dict()
-            self.img_id_to_filepath = dict()
-            self.img_id_to_segmentation_filepath = dict()
-
-        assert data_json.split("/")[-1] in ["captions_train2017.json",
-                                            "captions_val2017.json"]
-        if self.stuffthing:
-            self.segmentation_prefix = (
-                "data/cocostuffthings/val2017" if
-                data_json.endswith("captions_val2017.json") else
-                "data/cocostuffthings/train2017")
-        else:
-            self.segmentation_prefix = (
-                "data/coco/annotations/stuff_val2017_pixelmaps" if
-                data_json.endswith("captions_val2017.json") else
-                "data/coco/annotations/stuff_train2017_pixelmaps")
-
-        imagedirs = self.json_data["images"]
-        self.labels = {"image_ids": list()}
-        for imgdir in tqdm(imagedirs, desc="ImgToPath"):
-            self.img_id_to_filepath[imgdir["id"]] = os.path.join(dataroot, imgdir["file_name"])
-            self.img_id_to_captions[imgdir["id"]] = list()
-            pngfilename = imgdir["file_name"].replace("jpg", "png")
-            self.img_id_to_segmentation_filepath[imgdir["id"]] = os.path.join(
-                self.segmentation_prefix, pngfilename)
-            if given_files is not None:
-                if pngfilename in given_files:
-                    self.labels["image_ids"].append(imgdir["id"])
-            else:
-                self.labels["image_ids"].append(imgdir["id"])
-
-        capdirs = self.json_data["annotations"]
-        for capdir in tqdm(capdirs, desc="ImgToCaptions"):
-            # there are in average 5 captions per image
-            self.img_id_to_captions[capdir["image_id"]].append(np.array([capdir["caption"]]))
-
-        self.rescaler = albumentations.SmallestMaxSize(max_size=self.size)
-        if self.split=="validation":
-            self.cropper = albumentations.CenterCrop(height=self.crop_size, width=self.crop_size)
-        else:
-            self.cropper = albumentations.RandomCrop(height=self.crop_size, width=self.crop_size)
-        self.preprocessor = albumentations.Compose(
-            [self.rescaler, self.cropper],
-            additional_targets={"segmentation": "image"})
-        if force_no_crop:
-            self.rescaler = albumentations.Resize(height=self.size, width=self.size)
-            self.preprocessor = albumentations.Compose(
-                [self.rescaler],
-                additional_targets={"segmentation": "image"})
-
-    def __len__(self):
-        return len(self.labels["image_ids"])
-
-    def preprocess_image(self, image_path, segmentation_path):
-        image = Image.open(image_path)
-        if not image.mode == "RGB":
-            image = image.convert("RGB")
-        image = np.array(image).astype(np.uint8)
-
-        segmentation = Image.open(segmentation_path)
-        if not self.onehot and not segmentation.mode == "RGB":
-            segmentation = segmentation.convert("RGB")
-        segmentation = np.array(segmentation).astype(np.uint8)
-        if self.onehot:
-            assert self.stuffthing
-            # stored in caffe format: unlabeled==255. stuff and thing from
-            # 0-181. to be compatible with the labels in
-            # https://github.com/nightrome/cocostuff/blob/master/labels.txt
-            # we shift stuffthing one to the right and put unlabeled in zero
-            # as long as segmentation is uint8 shifting to right handles the
-            # latter too
-            assert segmentation.dtype == np.uint8
-            segmentation = segmentation + 1
-
-        processed = self.preprocessor(image=image, segmentation=segmentation)
-        image, segmentation = processed["image"], processed["segmentation"]
-        image = (image / 127.5 - 1.0).astype(np.float32)
-
-        if self.onehot:
-            assert segmentation.dtype == np.uint8
-            # make it one hot
-            n_labels = 183
-            flatseg = np.ravel(segmentation)
-            onehot = np.zeros((flatseg.size, n_labels), dtype=np.bool)
-            onehot[np.arange(flatseg.size), flatseg] = True
-            onehot = onehot.reshape(segmentation.shape + (n_labels,)).astype(int)
-            segmentation = onehot
-        else:
-            segmentation = (segmentation / 127.5 - 1.0).astype(np.float32)
-        return image, segmentation
-
-    def __getitem__(self, i):
-        img_path = self.img_id_to_filepath[self.labels["image_ids"][i]]
-        seg_path = self.img_id_to_segmentation_filepath[self.labels["image_ids"][i]]
-        image, segmentation = self.preprocess_image(img_path, seg_path)
-        captions = self.img_id_to_captions[self.labels["image_ids"][i]]
-        # randomly draw one of all available captions per image
-        caption = captions[np.random.randint(0, len(captions))]
-        example = {"image": image,
-                   "caption": [str(caption[0])],
-                   "segmentation": segmentation,
-                   "img_path": img_path,
-                   "seg_path": seg_path,
-                   "filename_": img_path.split(os.sep)[-1]
-                    }
-        return example
-
-
-class CocoImagesAndCaptionsTrain(CocoBase):
-    """returns a pair of (image, caption)"""
-    def __init__(self, size, onehot_segmentation=False, use_stuffthing=False, crop_size=None, force_no_crop=False):
-        super().__init__(size=size,
-                         dataroot="data/coco/train2017",
-                         datajson="data/coco/annotations/captions_train2017.json",
-                         onehot_segmentation=onehot_segmentation,
-                         use_stuffthing=use_stuffthing, crop_size=crop_size, force_no_crop=force_no_crop)
-
-    def get_split(self):
-        return "train"
-
-
-class CocoImagesAndCaptionsValidation(CocoBase):
-    """returns a pair of (image, caption)"""
-    def __init__(self, size, onehot_segmentation=False, use_stuffthing=False, crop_size=None, force_no_crop=False,
-                 given_files=None):
-        super().__init__(size=size,
-                         dataroot="data/coco/val2017",
-                         datajson="data/coco/annotations/captions_val2017.json",
-                         onehot_segmentation=onehot_segmentation,
-                         use_stuffthing=use_stuffthing, crop_size=crop_size, force_no_crop=force_no_crop,
-                         given_files=given_files)
-
-    def get_split(self):
-        return "validation"
diff --git a/repositories/taming-transformers/taming/data/conditional_builder/objects_bbox.py b/repositories/taming-transformers/taming/data/conditional_builder/objects_bbox.py
deleted file mode 100644
index 15881e76b7ab2a914df8f2dfe08ae4f0c6c511b5..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/taming/data/conditional_builder/objects_bbox.py
+++ /dev/null
@@ -1,60 +0,0 @@
-from itertools import cycle
-from typing import List, Tuple, Callable, Optional
-
-from PIL import Image as pil_image, ImageDraw as pil_img_draw, ImageFont
-from more_itertools.recipes import grouper
-from taming.data.image_transforms import convert_pil_to_tensor
-from torch import LongTensor, Tensor
-
-from taming.data.helper_types import BoundingBox, Annotation
-from taming.data.conditional_builder.objects_center_points import ObjectsCenterPointsConditionalBuilder
-from taming.data.conditional_builder.utils import COLOR_PALETTE, WHITE, GRAY_75, BLACK, additional_parameters_string, \
-    pad_list, get_plot_font_size, absolute_bbox
-
-
-class ObjectsBoundingBoxConditionalBuilder(ObjectsCenterPointsConditionalBuilder):
-    @property
-    def object_descriptor_length(self) -> int:
-        return 3
-
-    def _make_object_descriptors(self, annotations: List[Annotation]) -> List[Tuple[int, ...]]:
-        object_triples = [
-            (self.object_representation(ann), *self.token_pair_from_bbox(ann.bbox))
-            for ann in annotations
-        ]
-        empty_triple = (self.none, self.none, self.none)
-        object_triples = pad_list(object_triples, empty_triple, self.no_max_objects)
-        return object_triples
-
-    def inverse_build(self, conditional: LongTensor) -> Tuple[List[Tuple[int, BoundingBox]], Optional[BoundingBox]]:
-        conditional_list = conditional.tolist()
-        crop_coordinates = None
-        if self.encode_crop:
-            crop_coordinates = self.bbox_from_token_pair(conditional_list[-2], conditional_list[-1])
-            conditional_list = conditional_list[:-2]
-        object_triples = grouper(conditional_list, 3)
-        assert conditional.shape[0] == self.embedding_dim
-        return [
-            (object_triple[0], self.bbox_from_token_pair(object_triple[1], object_triple[2]))
-            for object_triple in object_triples if object_triple[0] != self.none
-        ], crop_coordinates
-
-    def plot(self, conditional: LongTensor, label_for_category_no: Callable[[int], str], figure_size: Tuple[int, int],
-             line_width: int = 3, font_size: Optional[int] = None) -> Tensor:
-        plot = pil_image.new('RGB', figure_size, WHITE)
-        draw = pil_img_draw.Draw(plot)
-        font = ImageFont.truetype(
-            "/usr/share/fonts/truetype/lato/Lato-Regular.ttf",
-            size=get_plot_font_size(font_size, figure_size)
-        )
-        width, height = plot.size
-        description, crop_coordinates = self.inverse_build(conditional)
-        for (representation, bbox), color in zip(description, cycle(COLOR_PALETTE)):
-            annotation = self.representation_to_annotation(representation)
-            class_label = label_for_category_no(annotation.category_no) + ' ' + additional_parameters_string(annotation)
-            bbox = absolute_bbox(bbox, width, height)
-            draw.rectangle(bbox, outline=color, width=line_width)
-            draw.text((bbox[0] + line_width, bbox[1] + line_width), class_label, anchor='la', fill=BLACK, font=font)
-        if crop_coordinates is not None:
-            draw.rectangle(absolute_bbox(crop_coordinates, width, height), outline=GRAY_75, width=line_width)
-        return convert_pil_to_tensor(plot) / 127.5 - 1.
diff --git a/repositories/taming-transformers/taming/data/conditional_builder/objects_center_points.py b/repositories/taming-transformers/taming/data/conditional_builder/objects_center_points.py
deleted file mode 100644
index 9a480329cc47fb38a7b8729d424e092b77d40749..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/taming/data/conditional_builder/objects_center_points.py
+++ /dev/null
@@ -1,168 +0,0 @@
-import math
-import random
-import warnings
-from itertools import cycle
-from typing import List, Optional, Tuple, Callable
-
-from PIL import Image as pil_image, ImageDraw as pil_img_draw, ImageFont
-from more_itertools.recipes import grouper
-from taming.data.conditional_builder.utils import COLOR_PALETTE, WHITE, GRAY_75, BLACK, FULL_CROP, filter_annotations, \
-    additional_parameters_string, horizontally_flip_bbox, pad_list, get_circle_size, get_plot_font_size, \
-    absolute_bbox, rescale_annotations
-from taming.data.helper_types import BoundingBox, Annotation
-from taming.data.image_transforms import convert_pil_to_tensor
-from torch import LongTensor, Tensor
-
-
-class ObjectsCenterPointsConditionalBuilder:
-    def __init__(self, no_object_classes: int, no_max_objects: int, no_tokens: int, encode_crop: bool,
-                 use_group_parameter: bool, use_additional_parameters: bool):
-        self.no_object_classes = no_object_classes
-        self.no_max_objects = no_max_objects
-        self.no_tokens = no_tokens
-        self.encode_crop = encode_crop
-        self.no_sections = int(math.sqrt(self.no_tokens))
-        self.use_group_parameter = use_group_parameter
-        self.use_additional_parameters = use_additional_parameters
-
-    @property
-    def none(self) -> int:
-        return self.no_tokens - 1
-
-    @property
-    def object_descriptor_length(self) -> int:
-        return 2
-
-    @property
-    def embedding_dim(self) -> int:
-        extra_length = 2 if self.encode_crop else 0
-        return self.no_max_objects * self.object_descriptor_length + extra_length
-
-    def tokenize_coordinates(self, x: float, y: float) -> int:
-        """
-        Express 2d coordinates with one number.
-        Example: assume self.no_tokens = 16, then no_sections = 4:
-        0  0  0  0
-        0  0  #  0
-        0  0  0  0
-        0  0  0  x
-        Then the # position corresponds to token 6, the x position to token 15.
-        @param x: float in [0, 1]
-        @param y: float in [0, 1]
-        @return: discrete tokenized coordinate
-        """
-        x_discrete = int(round(x * (self.no_sections - 1)))
-        y_discrete = int(round(y * (self.no_sections - 1)))
-        return y_discrete * self.no_sections + x_discrete
-
-    def coordinates_from_token(self, token: int) -> (float, float):
-        x = token % self.no_sections
-        y = token // self.no_sections
-        return x / (self.no_sections - 1), y / (self.no_sections - 1)
-
-    def bbox_from_token_pair(self, token1: int, token2: int) -> BoundingBox:
-        x0, y0 = self.coordinates_from_token(token1)
-        x1, y1 = self.coordinates_from_token(token2)
-        return x0, y0, x1 - x0, y1 - y0
-
-    def token_pair_from_bbox(self, bbox: BoundingBox) -> Tuple[int, int]:
-        return self.tokenize_coordinates(bbox[0], bbox[1]), \
-               self.tokenize_coordinates(bbox[0] + bbox[2], bbox[1] + bbox[3])
-
-    def inverse_build(self, conditional: LongTensor) \
-            -> Tuple[List[Tuple[int, Tuple[float, float]]], Optional[BoundingBox]]:
-        conditional_list = conditional.tolist()
-        crop_coordinates = None
-        if self.encode_crop:
-            crop_coordinates = self.bbox_from_token_pair(conditional_list[-2], conditional_list[-1])
-            conditional_list = conditional_list[:-2]
-        table_of_content = grouper(conditional_list, self.object_descriptor_length)
-        assert conditional.shape[0] == self.embedding_dim
-        return [
-            (object_tuple[0], self.coordinates_from_token(object_tuple[1]))
-            for object_tuple in table_of_content if object_tuple[0] != self.none
-        ], crop_coordinates
-
-    def plot(self, conditional: LongTensor, label_for_category_no: Callable[[int], str], figure_size: Tuple[int, int],
-             line_width: int = 3, font_size: Optional[int] = None) -> Tensor:
-        plot = pil_image.new('RGB', figure_size, WHITE)
-        draw = pil_img_draw.Draw(plot)
-        circle_size = get_circle_size(figure_size)
-        font = ImageFont.truetype('/usr/share/fonts/truetype/lato/Lato-Regular.ttf',
-                                  size=get_plot_font_size(font_size, figure_size))
-        width, height = plot.size
-        description, crop_coordinates = self.inverse_build(conditional)
-        for (representation, (x, y)), color in zip(description, cycle(COLOR_PALETTE)):
-            x_abs, y_abs = x * width, y * height
-            ann = self.representation_to_annotation(representation)
-            label = label_for_category_no(ann.category_no) + ' ' + additional_parameters_string(ann)
-            ellipse_bbox = [x_abs - circle_size, y_abs - circle_size, x_abs + circle_size, y_abs + circle_size]
-            draw.ellipse(ellipse_bbox, fill=color, width=0)
-            draw.text((x_abs, y_abs), label, anchor='md', fill=BLACK, font=font)
-        if crop_coordinates is not None:
-            draw.rectangle(absolute_bbox(crop_coordinates, width, height), outline=GRAY_75, width=line_width)
-        return convert_pil_to_tensor(plot) / 127.5 - 1.
-
-    def object_representation(self, annotation: Annotation) -> int:
-        modifier = 0
-        if self.use_group_parameter:
-            modifier |= 1 * (annotation.is_group_of is True)
-        if self.use_additional_parameters:
-            modifier |= 2 * (annotation.is_occluded is True)
-            modifier |= 4 * (annotation.is_depiction is True)
-            modifier |= 8 * (annotation.is_inside is True)
-        return annotation.category_no + self.no_object_classes * modifier
-
-    def representation_to_annotation(self, representation: int) -> Annotation:
-        category_no = representation % self.no_object_classes
-        modifier = representation // self.no_object_classes
-        # noinspection PyTypeChecker
-        return Annotation(
-            area=None, image_id=None, bbox=None, category_id=None, id=None, source=None, confidence=None,
-            category_no=category_no,
-            is_group_of=bool((modifier & 1) * self.use_group_parameter),
-            is_occluded=bool((modifier & 2) * self.use_additional_parameters),
-            is_depiction=bool((modifier & 4) * self.use_additional_parameters),
-            is_inside=bool((modifier & 8) * self.use_additional_parameters)
-        )
-
-    def _crop_encoder(self, crop_coordinates: BoundingBox) -> List[int]:
-        return list(self.token_pair_from_bbox(crop_coordinates))
-
-    def _make_object_descriptors(self, annotations: List[Annotation]) -> List[Tuple[int, ...]]:
-        object_tuples = [
-            (self.object_representation(a),
-             self.tokenize_coordinates(a.bbox[0] + a.bbox[2] / 2, a.bbox[1] + a.bbox[3] / 2))
-            for a in annotations
-        ]
-        empty_tuple = (self.none, self.none)
-        object_tuples = pad_list(object_tuples, empty_tuple, self.no_max_objects)
-        return object_tuples
-
-    def build(self, annotations: List, crop_coordinates: Optional[BoundingBox] = None, horizontal_flip: bool = False) \
-            -> LongTensor:
-        if len(annotations) == 0:
-            warnings.warn('Did not receive any annotations.')
-        if len(annotations) > self.no_max_objects:
-            warnings.warn('Received more annotations than allowed.')
-            annotations = annotations[:self.no_max_objects]
-
-        if not crop_coordinates:
-            crop_coordinates = FULL_CROP
-
-        random.shuffle(annotations)
-        annotations = filter_annotations(annotations, crop_coordinates)
-        if self.encode_crop:
-            annotations = rescale_annotations(annotations, FULL_CROP, horizontal_flip)
-            if horizontal_flip:
-                crop_coordinates = horizontally_flip_bbox(crop_coordinates)
-            extra = self._crop_encoder(crop_coordinates)
-        else:
-            annotations = rescale_annotations(annotations, crop_coordinates, horizontal_flip)
-            extra = []
-
-        object_tuples = self._make_object_descriptors(annotations)
-        flattened = [token for tuple_ in object_tuples for token in tuple_] + extra
-        assert len(flattened) == self.embedding_dim
-        assert all(0 <= value < self.no_tokens for value in flattened)
-        return LongTensor(flattened)
diff --git a/repositories/taming-transformers/taming/data/conditional_builder/utils.py b/repositories/taming-transformers/taming/data/conditional_builder/utils.py
deleted file mode 100644
index d0ee175f2e05a80dbc71c22acbecb22dddadbb42..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/taming/data/conditional_builder/utils.py
+++ /dev/null
@@ -1,105 +0,0 @@
-import importlib
-from typing import List, Any, Tuple, Optional
-
-from taming.data.helper_types import BoundingBox, Annotation
-
-# source: seaborn, color palette tab10
-COLOR_PALETTE = [(30, 118, 179), (255, 126, 13), (43, 159, 43), (213, 38, 39), (147, 102, 188),
-                 (139, 85, 74), (226, 118, 193), (126, 126, 126), (187, 188, 33), (22, 189, 206)]
-BLACK = (0, 0, 0)
-GRAY_75 = (63, 63, 63)
-GRAY_50 = (127, 127, 127)
-GRAY_25 = (191, 191, 191)
-WHITE = (255, 255, 255)
-FULL_CROP = (0., 0., 1., 1.)
-
-
-def intersection_area(rectangle1: BoundingBox, rectangle2: BoundingBox) -> float:
-    """
-    Give intersection area of two rectangles.
-    @param rectangle1: (x0, y0, w, h) of first rectangle
-    @param rectangle2: (x0, y0, w, h) of second rectangle
-    """
-    rectangle1 = rectangle1[0], rectangle1[1], rectangle1[0] + rectangle1[2], rectangle1[1] + rectangle1[3]
-    rectangle2 = rectangle2[0], rectangle2[1], rectangle2[0] + rectangle2[2], rectangle2[1] + rectangle2[3]
-    x_overlap = max(0., min(rectangle1[2], rectangle2[2]) - max(rectangle1[0], rectangle2[0]))
-    y_overlap = max(0., min(rectangle1[3], rectangle2[3]) - max(rectangle1[1], rectangle2[1]))
-    return x_overlap * y_overlap
-
-
-def horizontally_flip_bbox(bbox: BoundingBox) -> BoundingBox:
-    return 1 - (bbox[0] + bbox[2]), bbox[1], bbox[2], bbox[3]
-
-
-def absolute_bbox(relative_bbox: BoundingBox, width: int, height: int) -> Tuple[int, int, int, int]:
-    bbox = relative_bbox
-    bbox = bbox[0] * width, bbox[1] * height, (bbox[0] + bbox[2]) * width, (bbox[1] + bbox[3]) * height
-    return int(bbox[0]), int(bbox[1]), int(bbox[2]), int(bbox[3])
-
-
-def pad_list(list_: List, pad_element: Any, pad_to_length: int) -> List:
-    return list_ + [pad_element for _ in range(pad_to_length - len(list_))]
-
-
-def rescale_annotations(annotations: List[Annotation], crop_coordinates: BoundingBox, flip: bool) -> \
-        List[Annotation]:
-    def clamp(x: float):
-        return max(min(x, 1.), 0.)
-
-    def rescale_bbox(bbox: BoundingBox) -> BoundingBox:
-        x0 = clamp((bbox[0] - crop_coordinates[0]) / crop_coordinates[2])
-        y0 = clamp((bbox[1] - crop_coordinates[1]) / crop_coordinates[3])
-        w = min(bbox[2] / crop_coordinates[2], 1 - x0)
-        h = min(bbox[3] / crop_coordinates[3], 1 - y0)
-        if flip:
-            x0 = 1 - (x0 + w)
-        return x0, y0, w, h
-
-    return [a._replace(bbox=rescale_bbox(a.bbox)) for a in annotations]
-
-
-def filter_annotations(annotations: List[Annotation], crop_coordinates: BoundingBox) -> List:
-    return [a for a in annotations if intersection_area(a.bbox, crop_coordinates) > 0.0]
-
-
-def additional_parameters_string(annotation: Annotation, short: bool = True) -> str:
-    sl = slice(1) if short else slice(None)
-    string = ''
-    if not (annotation.is_group_of or annotation.is_occluded or annotation.is_depiction or annotation.is_inside):
-        return string
-    if annotation.is_group_of:
-        string += 'group'[sl] + ','
-    if annotation.is_occluded:
-        string += 'occluded'[sl] + ','
-    if annotation.is_depiction:
-        string += 'depiction'[sl] + ','
-    if annotation.is_inside:
-        string += 'inside'[sl]
-    return '(' + string.strip(",") + ')'
-
-
-def get_plot_font_size(font_size: Optional[int], figure_size: Tuple[int, int]) -> int:
-    if font_size is None:
-        font_size = 10
-        if max(figure_size) >= 256:
-            font_size = 12
-        if max(figure_size) >= 512:
-            font_size = 15
-    return font_size
-
-
-def get_circle_size(figure_size: Tuple[int, int]) -> int:
-    circle_size = 2
-    if max(figure_size) >= 256:
-        circle_size = 3
-    if max(figure_size) >= 512:
-        circle_size = 4
-    return circle_size
-
-
-def load_object_from_string(object_string: str) -> Any:
-    """
-    Source: https://stackoverflow.com/a/10773699
-    """
-    module_name, class_name = object_string.rsplit(".", 1)
-    return getattr(importlib.import_module(module_name), class_name)
diff --git a/repositories/taming-transformers/taming/data/custom.py b/repositories/taming-transformers/taming/data/custom.py
deleted file mode 100644
index 33f302a4b55ba1e8ec282ec3292b6263c06dfb91..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/taming/data/custom.py
+++ /dev/null
@@ -1,38 +0,0 @@
-import os
-import numpy as np
-import albumentations
-from torch.utils.data import Dataset
-
-from taming.data.base import ImagePaths, NumpyPaths, ConcatDatasetWithIndex
-
-
-class CustomBase(Dataset):
-    def __init__(self, *args, **kwargs):
-        super().__init__()
-        self.data = None
-
-    def __len__(self):
-        return len(self.data)
-
-    def __getitem__(self, i):
-        example = self.data[i]
-        return example
-
-
-
-class CustomTrain(CustomBase):
-    def __init__(self, size, training_images_list_file):
-        super().__init__()
-        with open(training_images_list_file, "r") as f:
-            paths = f.read().splitlines()
-        self.data = ImagePaths(paths=paths, size=size, random_crop=False)
-
-
-class CustomTest(CustomBase):
-    def __init__(self, size, test_images_list_file):
-        super().__init__()
-        with open(test_images_list_file, "r") as f:
-            paths = f.read().splitlines()
-        self.data = ImagePaths(paths=paths, size=size, random_crop=False)
-
-
diff --git a/repositories/taming-transformers/taming/data/faceshq.py b/repositories/taming-transformers/taming/data/faceshq.py
deleted file mode 100644
index 6912d04b66a6d464c1078e4b51d5da290f5e767e..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/taming/data/faceshq.py
+++ /dev/null
@@ -1,134 +0,0 @@
-import os
-import numpy as np
-import albumentations
-from torch.utils.data import Dataset
-
-from taming.data.base import ImagePaths, NumpyPaths, ConcatDatasetWithIndex
-
-
-class FacesBase(Dataset):
-    def __init__(self, *args, **kwargs):
-        super().__init__()
-        self.data = None
-        self.keys = None
-
-    def __len__(self):
-        return len(self.data)
-
-    def __getitem__(self, i):
-        example = self.data[i]
-        ex = {}
-        if self.keys is not None:
-            for k in self.keys:
-                ex[k] = example[k]
-        else:
-            ex = example
-        return ex
-
-
-class CelebAHQTrain(FacesBase):
-    def __init__(self, size, keys=None):
-        super().__init__()
-        root = "data/celebahq"
-        with open("data/celebahqtrain.txt", "r") as f:
-            relpaths = f.read().splitlines()
-        paths = [os.path.join(root, relpath) for relpath in relpaths]
-        self.data = NumpyPaths(paths=paths, size=size, random_crop=False)
-        self.keys = keys
-
-
-class CelebAHQValidation(FacesBase):
-    def __init__(self, size, keys=None):
-        super().__init__()
-        root = "data/celebahq"
-        with open("data/celebahqvalidation.txt", "r") as f:
-            relpaths = f.read().splitlines()
-        paths = [os.path.join(root, relpath) for relpath in relpaths]
-        self.data = NumpyPaths(paths=paths, size=size, random_crop=False)
-        self.keys = keys
-
-
-class FFHQTrain(FacesBase):
-    def __init__(self, size, keys=None):
-        super().__init__()
-        root = "data/ffhq"
-        with open("data/ffhqtrain.txt", "r") as f:
-            relpaths = f.read().splitlines()
-        paths = [os.path.join(root, relpath) for relpath in relpaths]
-        self.data = ImagePaths(paths=paths, size=size, random_crop=False)
-        self.keys = keys
-
-
-class FFHQValidation(FacesBase):
-    def __init__(self, size, keys=None):
-        super().__init__()
-        root = "data/ffhq"
-        with open("data/ffhqvalidation.txt", "r") as f:
-            relpaths = f.read().splitlines()
-        paths = [os.path.join(root, relpath) for relpath in relpaths]
-        self.data = ImagePaths(paths=paths, size=size, random_crop=False)
-        self.keys = keys
-
-
-class FacesHQTrain(Dataset):
-    # CelebAHQ [0] + FFHQ [1]
-    def __init__(self, size, keys=None, crop_size=None, coord=False):
-        d1 = CelebAHQTrain(size=size, keys=keys)
-        d2 = FFHQTrain(size=size, keys=keys)
-        self.data = ConcatDatasetWithIndex([d1, d2])
-        self.coord = coord
-        if crop_size is not None:
-            self.cropper = albumentations.RandomCrop(height=crop_size,width=crop_size)
-            if self.coord:
-                self.cropper = albumentations.Compose([self.cropper],
-                                                      additional_targets={"coord": "image"})
-
-    def __len__(self):
-        return len(self.data)
-
-    def __getitem__(self, i):
-        ex, y = self.data[i]
-        if hasattr(self, "cropper"):
-            if not self.coord:
-                out = self.cropper(image=ex["image"])
-                ex["image"] = out["image"]
-            else:
-                h,w,_ = ex["image"].shape
-                coord = np.arange(h*w).reshape(h,w,1)/(h*w)
-                out = self.cropper(image=ex["image"], coord=coord)
-                ex["image"] = out["image"]
-                ex["coord"] = out["coord"]
-        ex["class"] = y
-        return ex
-
-
-class FacesHQValidation(Dataset):
-    # CelebAHQ [0] + FFHQ [1]
-    def __init__(self, size, keys=None, crop_size=None, coord=False):
-        d1 = CelebAHQValidation(size=size, keys=keys)
-        d2 = FFHQValidation(size=size, keys=keys)
-        self.data = ConcatDatasetWithIndex([d1, d2])
-        self.coord = coord
-        if crop_size is not None:
-            self.cropper = albumentations.CenterCrop(height=crop_size,width=crop_size)
-            if self.coord:
-                self.cropper = albumentations.Compose([self.cropper],
-                                                      additional_targets={"coord": "image"})
-
-    def __len__(self):
-        return len(self.data)
-
-    def __getitem__(self, i):
-        ex, y = self.data[i]
-        if hasattr(self, "cropper"):
-            if not self.coord:
-                out = self.cropper(image=ex["image"])
-                ex["image"] = out["image"]
-            else:
-                h,w,_ = ex["image"].shape
-                coord = np.arange(h*w).reshape(h,w,1)/(h*w)
-                out = self.cropper(image=ex["image"], coord=coord)
-                ex["image"] = out["image"]
-                ex["coord"] = out["coord"]
-        ex["class"] = y
-        return ex
diff --git a/repositories/taming-transformers/taming/data/helper_types.py b/repositories/taming-transformers/taming/data/helper_types.py
deleted file mode 100644
index fb51e301da08602cfead5961c4f7e1d89f6aba79..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/taming/data/helper_types.py
+++ /dev/null
@@ -1,49 +0,0 @@
-from typing import Dict, Tuple, Optional, NamedTuple, Union
-from PIL.Image import Image as pil_image
-from torch import Tensor
-
-try:
-  from typing import Literal
-except ImportError:
-  from typing_extensions import Literal
-
-Image = Union[Tensor, pil_image]
-BoundingBox = Tuple[float, float, float, float]  # x0, y0, w, h
-CropMethodType = Literal['none', 'random', 'center', 'random-2d']
-SplitType = Literal['train', 'validation', 'test']
-
-
-class ImageDescription(NamedTuple):
-    id: int
-    file_name: str
-    original_size: Tuple[int, int]  # w, h
-    url: Optional[str] = None
-    license: Optional[int] = None
-    coco_url: Optional[str] = None
-    date_captured: Optional[str] = None
-    flickr_url: Optional[str] = None
-    flickr_id: Optional[str] = None
-    coco_id: Optional[str] = None
-
-
-class Category(NamedTuple):
-    id: str
-    super_category: Optional[str]
-    name: str
-
-
-class Annotation(NamedTuple):
-    area: float
-    image_id: str
-    bbox: BoundingBox
-    category_no: int
-    category_id: str
-    id: Optional[int] = None
-    source: Optional[str] = None
-    confidence: Optional[float] = None
-    is_group_of: Optional[bool] = None
-    is_truncated: Optional[bool] = None
-    is_occluded: Optional[bool] = None
-    is_depiction: Optional[bool] = None
-    is_inside: Optional[bool] = None
-    segmentation: Optional[Dict] = None
diff --git a/repositories/taming-transformers/taming/data/image_transforms.py b/repositories/taming-transformers/taming/data/image_transforms.py
deleted file mode 100644
index 657ac332174e0ac72f68315271ffbd757b771a0f..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/taming/data/image_transforms.py
+++ /dev/null
@@ -1,132 +0,0 @@
-import random
-import warnings
-from typing import Union
-
-import torch
-from torch import Tensor
-from torchvision.transforms import RandomCrop, functional as F, CenterCrop, RandomHorizontalFlip, PILToTensor
-from torchvision.transforms.functional import _get_image_size as get_image_size
-
-from taming.data.helper_types import BoundingBox, Image
-
-pil_to_tensor = PILToTensor()
-
-
-def convert_pil_to_tensor(image: Image) -> Tensor:
-    with warnings.catch_warnings():
-        # to filter PyTorch UserWarning as described here: https://github.com/pytorch/vision/issues/2194
-        warnings.simplefilter("ignore")
-        return pil_to_tensor(image)
-
-
-class RandomCrop1dReturnCoordinates(RandomCrop):
-    def forward(self, img: Image) -> (BoundingBox, Image):
-        """
-        Additionally to cropping, returns the relative coordinates of the crop bounding box.
-        Args:
-            img (PIL Image or Tensor): Image to be cropped.
-
-        Returns:
-            Bounding box: x0, y0, w, h
-            PIL Image or Tensor: Cropped image.
-
-        Based on:
-            torchvision.transforms.RandomCrop, torchvision 1.7.0
-        """
-        if self.padding is not None:
-            img = F.pad(img, self.padding, self.fill, self.padding_mode)
-
-        width, height = get_image_size(img)
-        # pad the width if needed
-        if self.pad_if_needed and width < self.size[1]:
-            padding = [self.size[1] - width, 0]
-            img = F.pad(img, padding, self.fill, self.padding_mode)
-        # pad the height if needed
-        if self.pad_if_needed and height < self.size[0]:
-            padding = [0, self.size[0] - height]
-            img = F.pad(img, padding, self.fill, self.padding_mode)
-
-        i, j, h, w = self.get_params(img, self.size)
-        bbox = (j / width, i / height, w / width, h / height)  # x0, y0, w, h
-        return bbox, F.crop(img, i, j, h, w)
-
-
-class Random2dCropReturnCoordinates(torch.nn.Module):
-    """
-    Additionally to cropping, returns the relative coordinates of the crop bounding box.
-    Args:
-        img (PIL Image or Tensor): Image to be cropped.
-
-    Returns:
-        Bounding box: x0, y0, w, h
-        PIL Image or Tensor: Cropped image.
-
-    Based on:
-        torchvision.transforms.RandomCrop, torchvision 1.7.0
-    """
-
-    def __init__(self, min_size: int):
-        super().__init__()
-        self.min_size = min_size
-
-    def forward(self, img: Image) -> (BoundingBox, Image):
-        width, height = get_image_size(img)
-        max_size = min(width, height)
-        if max_size <= self.min_size:
-            size = max_size
-        else:
-            size = random.randint(self.min_size, max_size)
-        top = random.randint(0, height - size)
-        left = random.randint(0, width - size)
-        bbox = left / width, top / height, size / width, size / height
-        return bbox, F.crop(img, top, left, size, size)
-
-
-class CenterCropReturnCoordinates(CenterCrop):
-    @staticmethod
-    def get_bbox_of_center_crop(width: int, height: int) -> BoundingBox:
-        if width > height:
-            w = height / width
-            h = 1.0
-            x0 = 0.5 - w / 2
-            y0 = 0.
-        else:
-            w = 1.0
-            h = width / height
-            x0 = 0.
-            y0 = 0.5 - h / 2
-        return x0, y0, w, h
-
-    def forward(self, img: Union[Image, Tensor]) -> (BoundingBox, Union[Image, Tensor]):
-        """
-        Additionally to cropping, returns the relative coordinates of the crop bounding box.
-        Args:
-            img (PIL Image or Tensor): Image to be cropped.
-
-        Returns:
-            Bounding box: x0, y0, w, h
-            PIL Image or Tensor: Cropped image.
-        Based on:
-            torchvision.transforms.RandomHorizontalFlip (version 1.7.0)
-        """
-        width, height = get_image_size(img)
-        return self.get_bbox_of_center_crop(width, height),  F.center_crop(img, self.size)
-
-
-class RandomHorizontalFlipReturn(RandomHorizontalFlip):
-    def forward(self, img: Image) -> (bool, Image):
-        """
-        Additionally to flipping, returns a boolean whether it was flipped or not.
-        Args:
-            img (PIL Image or Tensor): Image to be flipped.
-
-        Returns:
-            flipped: whether the image was flipped or not
-            PIL Image or Tensor: Randomly flipped image.
-
-        Based on:
-            torchvision.transforms.RandomHorizontalFlip (version 1.7.0)
-        """
-        if torch.rand(1) < self.p:
-            return True, F.hflip(img)
-        return False, img
diff --git a/repositories/taming-transformers/taming/data/imagenet.py b/repositories/taming-transformers/taming/data/imagenet.py
deleted file mode 100644
index 9a02ec44ba4af9e993f58c91fa43482a4ecbe54c..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/taming/data/imagenet.py
+++ /dev/null
@@ -1,558 +0,0 @@
-import os, tarfile, glob, shutil
-import yaml
-import numpy as np
-from tqdm import tqdm
-from PIL import Image
-import albumentations
-from omegaconf import OmegaConf
-from torch.utils.data import Dataset
-
-from taming.data.base import ImagePaths
-from taming.util import download, retrieve
-import taming.data.utils as bdu
-
-
-def give_synsets_from_indices(indices, path_to_yaml="data/imagenet_idx_to_synset.yaml"):
-    synsets = []
-    with open(path_to_yaml) as f:
-        di2s = yaml.load(f)
-    for idx in indices:
-        synsets.append(str(di2s[idx]))
-    print("Using {} different synsets for construction of Restriced Imagenet.".format(len(synsets)))
-    return synsets
-
-
-def str_to_indices(string):
-    """Expects a string in the format '32-123, 256, 280-321'"""
-    assert not string.endswith(","), "provided string '{}' ends with a comma, pls remove it".format(string)
-    subs = string.split(",")
-    indices = []
-    for sub in subs:
-        subsubs = sub.split("-")
-        assert len(subsubs) > 0
-        if len(subsubs) == 1:
-            indices.append(int(subsubs[0]))
-        else:
-            rang = [j for j in range(int(subsubs[0]), int(subsubs[1]))]
-            indices.extend(rang)
-    return sorted(indices)
-
-
-class ImageNetBase(Dataset):
-    def __init__(self, config=None):
-        self.config = config or OmegaConf.create()
-        if not type(self.config)==dict:
-            self.config = OmegaConf.to_container(self.config)
-        self._prepare()
-        self._prepare_synset_to_human()
-        self._prepare_idx_to_synset()
-        self._load()
-
-    def __len__(self):
-        return len(self.data)
-
-    def __getitem__(self, i):
-        return self.data[i]
-
-    def _prepare(self):
-        raise NotImplementedError()
-
-    def _filter_relpaths(self, relpaths):
-        ignore = set([
-            "n06596364_9591.JPEG",
-        ])
-        relpaths = [rpath for rpath in relpaths if not rpath.split("/")[-1] in ignore]
-        if "sub_indices" in self.config:
-            indices = str_to_indices(self.config["sub_indices"])
-            synsets = give_synsets_from_indices(indices, path_to_yaml=self.idx2syn)  # returns a list of strings
-            files = []
-            for rpath in relpaths:
-                syn = rpath.split("/")[0]
-                if syn in synsets:
-                    files.append(rpath)
-            return files
-        else:
-            return relpaths
-
-    def _prepare_synset_to_human(self):
-        SIZE = 2655750
-        URL = "https://heibox.uni-heidelberg.de/f/9f28e956cd304264bb82/?dl=1"
-        self.human_dict = os.path.join(self.root, "synset_human.txt")
-        if (not os.path.exists(self.human_dict) or
-                not os.path.getsize(self.human_dict)==SIZE):
-            download(URL, self.human_dict)
-
-    def _prepare_idx_to_synset(self):
-        URL = "https://heibox.uni-heidelberg.de/f/d835d5b6ceda4d3aa910/?dl=1"
-        self.idx2syn = os.path.join(self.root, "index_synset.yaml")
-        if (not os.path.exists(self.idx2syn)):
-            download(URL, self.idx2syn)
-
-    def _load(self):
-        with open(self.txt_filelist, "r") as f:
-            self.relpaths = f.read().splitlines()
-            l1 = len(self.relpaths)
-            self.relpaths = self._filter_relpaths(self.relpaths)
-            print("Removed {} files from filelist during filtering.".format(l1 - len(self.relpaths)))
-
-        self.synsets = [p.split("/")[0] for p in self.relpaths]
-        self.abspaths = [os.path.join(self.datadir, p) for p in self.relpaths]
-
-        unique_synsets = np.unique(self.synsets)
-        class_dict = dict((synset, i) for i, synset in enumerate(unique_synsets))
-        self.class_labels = [class_dict[s] for s in self.synsets]
-
-        with open(self.human_dict, "r") as f:
-            human_dict = f.read().splitlines()
-            human_dict = dict(line.split(maxsplit=1) for line in human_dict)
-
-        self.human_labels = [human_dict[s] for s in self.synsets]
-
-        labels = {
-            "relpath": np.array(self.relpaths),
-            "synsets": np.array(self.synsets),
-            "class_label": np.array(self.class_labels),
-            "human_label": np.array(self.human_labels),
-        }
-        self.data = ImagePaths(self.abspaths,
-                               labels=labels,
-                               size=retrieve(self.config, "size", default=0),
-                               random_crop=self.random_crop)
-
-
-class ImageNetTrain(ImageNetBase):
-    NAME = "ILSVRC2012_train"
-    URL = "http://www.image-net.org/challenges/LSVRC/2012/"
-    AT_HASH = "a306397ccf9c2ead27155983c254227c0fd938e2"
-    FILES = [
-        "ILSVRC2012_img_train.tar",
-    ]
-    SIZES = [
-        147897477120,
-    ]
-
-    def _prepare(self):
-        self.random_crop = retrieve(self.config, "ImageNetTrain/random_crop",
-                                    default=True)
-        cachedir = os.environ.get("XDG_CACHE_HOME", os.path.expanduser("~/.cache"))
-        self.root = os.path.join(cachedir, "autoencoders/data", self.NAME)
-        self.datadir = os.path.join(self.root, "data")
-        self.txt_filelist = os.path.join(self.root, "filelist.txt")
-        self.expected_length = 1281167
-        if not bdu.is_prepared(self.root):
-            # prep
-            print("Preparing dataset {} in {}".format(self.NAME, self.root))
-
-            datadir = self.datadir
-            if not os.path.exists(datadir):
-                path = os.path.join(self.root, self.FILES[0])
-                if not os.path.exists(path) or not os.path.getsize(path)==self.SIZES[0]:
-                    import academictorrents as at
-                    atpath = at.get(self.AT_HASH, datastore=self.root)
-                    assert atpath == path
-
-                print("Extracting {} to {}".format(path, datadir))
-                os.makedirs(datadir, exist_ok=True)
-                with tarfile.open(path, "r:") as tar:
-                    tar.extractall(path=datadir)
-
-                print("Extracting sub-tars.")
-                subpaths = sorted(glob.glob(os.path.join(datadir, "*.tar")))
-                for subpath in tqdm(subpaths):
-                    subdir = subpath[:-len(".tar")]
-                    os.makedirs(subdir, exist_ok=True)
-                    with tarfile.open(subpath, "r:") as tar:
-                        tar.extractall(path=subdir)
-
-
-            filelist = glob.glob(os.path.join(datadir, "**", "*.JPEG"))
-            filelist = [os.path.relpath(p, start=datadir) for p in filelist]
-            filelist = sorted(filelist)
-            filelist = "\n".join(filelist)+"\n"
-            with open(self.txt_filelist, "w") as f:
-                f.write(filelist)
-
-            bdu.mark_prepared(self.root)
-
-
-class ImageNetValidation(ImageNetBase):
-    NAME = "ILSVRC2012_validation"
-    URL = "http://www.image-net.org/challenges/LSVRC/2012/"
-    AT_HASH = "5d6d0df7ed81efd49ca99ea4737e0ae5e3a5f2e5"
-    VS_URL = "https://heibox.uni-heidelberg.de/f/3e0f6e9c624e45f2bd73/?dl=1"
-    FILES = [
-        "ILSVRC2012_img_val.tar",
-        "validation_synset.txt",
-    ]
-    SIZES = [
-        6744924160,
-        1950000,
-    ]
-
-    def _prepare(self):
-        self.random_crop = retrieve(self.config, "ImageNetValidation/random_crop",
-                                    default=False)
-        cachedir = os.environ.get("XDG_CACHE_HOME", os.path.expanduser("~/.cache"))
-        self.root = os.path.join(cachedir, "autoencoders/data", self.NAME)
-        self.datadir = os.path.join(self.root, "data")
-        self.txt_filelist = os.path.join(self.root, "filelist.txt")
-        self.expected_length = 50000
-        if not bdu.is_prepared(self.root):
-            # prep
-            print("Preparing dataset {} in {}".format(self.NAME, self.root))
-
-            datadir = self.datadir
-            if not os.path.exists(datadir):
-                path = os.path.join(self.root, self.FILES[0])
-                if not os.path.exists(path) or not os.path.getsize(path)==self.SIZES[0]:
-                    import academictorrents as at
-                    atpath = at.get(self.AT_HASH, datastore=self.root)
-                    assert atpath == path
-
-                print("Extracting {} to {}".format(path, datadir))
-                os.makedirs(datadir, exist_ok=True)
-                with tarfile.open(path, "r:") as tar:
-                    tar.extractall(path=datadir)
-
-                vspath = os.path.join(self.root, self.FILES[1])
-                if not os.path.exists(vspath) or not os.path.getsize(vspath)==self.SIZES[1]:
-                    download(self.VS_URL, vspath)
-
-                with open(vspath, "r") as f:
-                    synset_dict = f.read().splitlines()
-                    synset_dict = dict(line.split() for line in synset_dict)
-
-                print("Reorganizing into synset folders")
-                synsets = np.unique(list(synset_dict.values()))
-                for s in synsets:
-                    os.makedirs(os.path.join(datadir, s), exist_ok=True)
-                for k, v in synset_dict.items():
-                    src = os.path.join(datadir, k)
-                    dst = os.path.join(datadir, v)
-                    shutil.move(src, dst)
-
-            filelist = glob.glob(os.path.join(datadir, "**", "*.JPEG"))
-            filelist = [os.path.relpath(p, start=datadir) for p in filelist]
-            filelist = sorted(filelist)
-            filelist = "\n".join(filelist)+"\n"
-            with open(self.txt_filelist, "w") as f:
-                f.write(filelist)
-
-            bdu.mark_prepared(self.root)
-
-
-def get_preprocessor(size=None, random_crop=False, additional_targets=None,
-                     crop_size=None):
-    if size is not None and size > 0:
-        transforms = list()
-        rescaler = albumentations.SmallestMaxSize(max_size = size)
-        transforms.append(rescaler)
-        if not random_crop:
-            cropper = albumentations.CenterCrop(height=size,width=size)
-            transforms.append(cropper)
-        else:
-            cropper = albumentations.RandomCrop(height=size,width=size)
-            transforms.append(cropper)
-            flipper = albumentations.HorizontalFlip()
-            transforms.append(flipper)
-        preprocessor = albumentations.Compose(transforms,
-                                              additional_targets=additional_targets)
-    elif crop_size is not None and crop_size > 0:
-        if not random_crop:
-            cropper = albumentations.CenterCrop(height=crop_size,width=crop_size)
-        else:
-            cropper = albumentations.RandomCrop(height=crop_size,width=crop_size)
-        transforms = [cropper]
-        preprocessor = albumentations.Compose(transforms,
-                                              additional_targets=additional_targets)
-    else:
-        preprocessor = lambda **kwargs: kwargs
-    return preprocessor
-
-
-def rgba_to_depth(x):
-    assert x.dtype == np.uint8
-    assert len(x.shape) == 3 and x.shape[2] == 4
-    y = x.copy()
-    y.dtype = np.float32
-    y = y.reshape(x.shape[:2])
-    return np.ascontiguousarray(y)
-
-
-class BaseWithDepth(Dataset):
-    DEFAULT_DEPTH_ROOT="data/imagenet_depth"
-
-    def __init__(self, config=None, size=None, random_crop=False,
-                 crop_size=None, root=None):
-        self.config = config
-        self.base_dset = self.get_base_dset()
-        self.preprocessor = get_preprocessor(
-            size=size,
-            crop_size=crop_size,
-            random_crop=random_crop,
-            additional_targets={"depth": "image"})
-        self.crop_size = crop_size
-        if self.crop_size is not None:
-            self.rescaler = albumentations.Compose(
-                [albumentations.SmallestMaxSize(max_size = self.crop_size)],
-                additional_targets={"depth": "image"})
-        if root is not None:
-            self.DEFAULT_DEPTH_ROOT = root
-
-    def __len__(self):
-        return len(self.base_dset)
-
-    def preprocess_depth(self, path):
-        rgba = np.array(Image.open(path))
-        depth = rgba_to_depth(rgba)
-        depth = (depth - depth.min())/max(1e-8, depth.max()-depth.min())
-        depth = 2.0*depth-1.0
-        return depth
-
-    def __getitem__(self, i):
-        e = self.base_dset[i]
-        e["depth"] = self.preprocess_depth(self.get_depth_path(e))
-        # up if necessary
-        h,w,c = e["image"].shape
-        if self.crop_size and min(h,w) < self.crop_size:
-            # have to upscale to be able to crop - this just uses bilinear
-            out = self.rescaler(image=e["image"], depth=e["depth"])
-            e["image"] = out["image"]
-            e["depth"] = out["depth"]
-        transformed = self.preprocessor(image=e["image"], depth=e["depth"])
-        e["image"] = transformed["image"]
-        e["depth"] = transformed["depth"]
-        return e
-
-
-class ImageNetTrainWithDepth(BaseWithDepth):
-    # default to random_crop=True
-    def __init__(self, random_crop=True, sub_indices=None, **kwargs):
-        self.sub_indices = sub_indices
-        super().__init__(random_crop=random_crop, **kwargs)
-
-    def get_base_dset(self):
-        if self.sub_indices is None:
-            return ImageNetTrain()
-        else:
-            return ImageNetTrain({"sub_indices": self.sub_indices})
-
-    def get_depth_path(self, e):
-        fid = os.path.splitext(e["relpath"])[0]+".png"
-        fid = os.path.join(self.DEFAULT_DEPTH_ROOT, "train", fid)
-        return fid
-
-
-class ImageNetValidationWithDepth(BaseWithDepth):
-    def __init__(self, sub_indices=None, **kwargs):
-        self.sub_indices = sub_indices
-        super().__init__(**kwargs)
-
-    def get_base_dset(self):
-        if self.sub_indices is None:
-            return ImageNetValidation()
-        else:
-            return ImageNetValidation({"sub_indices": self.sub_indices})
-
-    def get_depth_path(self, e):
-        fid = os.path.splitext(e["relpath"])[0]+".png"
-        fid = os.path.join(self.DEFAULT_DEPTH_ROOT, "val", fid)
-        return fid
-
-
-class RINTrainWithDepth(ImageNetTrainWithDepth):
-    def __init__(self, config=None, size=None, random_crop=True, crop_size=None):
-        sub_indices = "30-32, 33-37, 151-268, 281-285, 80-100, 365-382, 389-397, 118-121, 300-319"
-        super().__init__(config=config, size=size, random_crop=random_crop,
-                         sub_indices=sub_indices, crop_size=crop_size)
-
-
-class RINValidationWithDepth(ImageNetValidationWithDepth):
-    def __init__(self, config=None, size=None, random_crop=False, crop_size=None):
-        sub_indices = "30-32, 33-37, 151-268, 281-285, 80-100, 365-382, 389-397, 118-121, 300-319"
-        super().__init__(config=config, size=size, random_crop=random_crop,
-                         sub_indices=sub_indices, crop_size=crop_size)
-
-
-class DRINExamples(Dataset):
-    def __init__(self):
-        self.preprocessor = get_preprocessor(size=256, additional_targets={"depth": "image"})
-        with open("data/drin_examples.txt", "r") as f:
-            relpaths = f.read().splitlines()
-        self.image_paths = [os.path.join("data/drin_images",
-                                         relpath) for relpath in relpaths]
-        self.depth_paths = [os.path.join("data/drin_depth",
-                                         relpath.replace(".JPEG", ".png")) for relpath in relpaths]
-
-    def __len__(self):
-        return len(self.image_paths)
-
-    def preprocess_image(self, image_path):
-        image = Image.open(image_path)
-        if not image.mode == "RGB":
-            image = image.convert("RGB")
-        image = np.array(image).astype(np.uint8)
-        image = self.preprocessor(image=image)["image"]
-        image = (image/127.5 - 1.0).astype(np.float32)
-        return image
-
-    def preprocess_depth(self, path):
-        rgba = np.array(Image.open(path))
-        depth = rgba_to_depth(rgba)
-        depth = (depth - depth.min())/max(1e-8, depth.max()-depth.min())
-        depth = 2.0*depth-1.0
-        return depth
-
-    def __getitem__(self, i):
-        e = dict()
-        e["image"] = self.preprocess_image(self.image_paths[i])
-        e["depth"] = self.preprocess_depth(self.depth_paths[i])
-        transformed = self.preprocessor(image=e["image"], depth=e["depth"])
-        e["image"] = transformed["image"]
-        e["depth"] = transformed["depth"]
-        return e
-
-
-def imscale(x, factor, keepshapes=False, keepmode="bicubic"):
-    if factor is None or factor==1:
-        return x
-
-    dtype = x.dtype
-    assert dtype in [np.float32, np.float64]
-    assert x.min() >= -1
-    assert x.max() <= 1
-
-    keepmode = {"nearest": Image.NEAREST, "bilinear": Image.BILINEAR,
-                "bicubic": Image.BICUBIC}[keepmode]
-
-    lr = (x+1.0)*127.5
-    lr = lr.clip(0,255).astype(np.uint8)
-    lr = Image.fromarray(lr)
-
-    h, w, _ = x.shape
-    nh = h//factor
-    nw = w//factor
-    assert nh > 0 and nw > 0, (nh, nw)
-
-    lr = lr.resize((nw,nh), Image.BICUBIC)
-    if keepshapes:
-        lr = lr.resize((w,h), keepmode)
-    lr = np.array(lr)/127.5-1.0
-    lr = lr.astype(dtype)
-
-    return lr
-
-
-class ImageNetScale(Dataset):
-    def __init__(self, size=None, crop_size=None, random_crop=False,
-                 up_factor=None, hr_factor=None, keep_mode="bicubic"):
-        self.base = self.get_base()
-
-        self.size = size
-        self.crop_size = crop_size if crop_size is not None else self.size
-        self.random_crop = random_crop
-        self.up_factor = up_factor
-        self.hr_factor = hr_factor
-        self.keep_mode = keep_mode
-
-        transforms = list()
-
-        if self.size is not None and self.size > 0:
-            rescaler = albumentations.SmallestMaxSize(max_size = self.size)
-            self.rescaler = rescaler
-            transforms.append(rescaler)
-
-        if self.crop_size is not None and self.crop_size > 0:
-            if len(transforms) == 0:
-                self.rescaler = albumentations.SmallestMaxSize(max_size = self.crop_size)
-
-            if not self.random_crop:
-                cropper = albumentations.CenterCrop(height=self.crop_size,width=self.crop_size)
-            else:
-                cropper = albumentations.RandomCrop(height=self.crop_size,width=self.crop_size)
-            transforms.append(cropper)
-
-        if len(transforms) > 0:
-            if self.up_factor is not None:
-                additional_targets = {"lr": "image"}
-            else:
-                additional_targets = None
-            self.preprocessor = albumentations.Compose(transforms,
-                                                       additional_targets=additional_targets)
-        else:
-            self.preprocessor = lambda **kwargs: kwargs
-
-    def __len__(self):
-        return len(self.base)
-
-    def __getitem__(self, i):
-        example = self.base[i]
-        image = example["image"]
-        # adjust resolution
-        image = imscale(image, self.hr_factor, keepshapes=False)
-        h,w,c = image.shape
-        if self.crop_size and min(h,w) < self.crop_size:
-            # have to upscale to be able to crop - this just uses bilinear
-            image = self.rescaler(image=image)["image"]
-        if self.up_factor is None:
-            image = self.preprocessor(image=image)["image"]
-            example["image"] = image
-        else:
-            lr = imscale(image, self.up_factor, keepshapes=True,
-                         keepmode=self.keep_mode)
-
-            out = self.preprocessor(image=image, lr=lr)
-            example["image"] = out["image"]
-            example["lr"] = out["lr"]
-
-        return example
-
-class ImageNetScaleTrain(ImageNetScale):
-    def __init__(self, random_crop=True, **kwargs):
-        super().__init__(random_crop=random_crop, **kwargs)
-
-    def get_base(self):
-        return ImageNetTrain()
-
-class ImageNetScaleValidation(ImageNetScale):
-    def get_base(self):
-        return ImageNetValidation()
-
-
-from skimage.feature import canny
-from skimage.color import rgb2gray
-
-
-class ImageNetEdges(ImageNetScale):
-    def __init__(self, up_factor=1, **kwargs):
-        super().__init__(up_factor=1, **kwargs)
-
-    def __getitem__(self, i):
-        example = self.base[i]
-        image = example["image"]
-        h,w,c = image.shape
-        if self.crop_size and min(h,w) < self.crop_size:
-            # have to upscale to be able to crop - this just uses bilinear
-            image = self.rescaler(image=image)["image"]
-
-        lr = canny(rgb2gray(image), sigma=2)
-        lr = lr.astype(np.float32)
-        lr = lr[:,:,None][:,:,[0,0,0]]
-
-        out = self.preprocessor(image=image, lr=lr)
-        example["image"] = out["image"]
-        example["lr"] = out["lr"]
-
-        return example
-
-
-class ImageNetEdgesTrain(ImageNetEdges):
-    def __init__(self, random_crop=True, **kwargs):
-        super().__init__(random_crop=random_crop, **kwargs)
-
-    def get_base(self):
-        return ImageNetTrain()
-
-class ImageNetEdgesValidation(ImageNetEdges):
-    def get_base(self):
-        return ImageNetValidation()
diff --git a/repositories/taming-transformers/taming/data/open_images_helper.py b/repositories/taming-transformers/taming/data/open_images_helper.py
deleted file mode 100644
index 8feb7c6e705fc165d2983303192aaa88f579b243..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/taming/data/open_images_helper.py
+++ /dev/null
@@ -1,379 +0,0 @@
-open_images_unify_categories_for_coco = {
-    '/m/03bt1vf': '/m/01g317',
-    '/m/04yx4': '/m/01g317',
-    '/m/05r655': '/m/01g317',
-    '/m/01bl7v': '/m/01g317',
-    '/m/0cnyhnx': '/m/01xq0k1',
-    '/m/01226z': '/m/018xm',
-    '/m/05ctyq': '/m/018xm',
-    '/m/058qzx': '/m/04ctx',
-    '/m/06pcq': '/m/0l515',
-    '/m/03m3pdh': '/m/02crq1',
-    '/m/046dlr': '/m/01x3z',
-    '/m/0h8mzrc': '/m/01x3z',
-}
-
-
-top_300_classes_plus_coco_compatibility = [
-    ('Man', 1060962),
-    ('Clothing', 986610),
-    ('Tree', 748162),
-    ('Woman', 611896),
-    ('Person', 610294),
-    ('Human face', 442948),
-    ('Girl', 175399),
-    ('Building', 162147),
-    ('Car', 159135),
-    ('Plant', 155704),
-    ('Human body', 137073),
-    ('Flower', 133128),
-    ('Window', 127485),
-    ('Human arm', 118380),
-    ('House', 114365),
-    ('Wheel', 111684),
-    ('Suit', 99054),
-    ('Human hair', 98089),
-    ('Human head', 92763),
-    ('Chair', 88624),
-    ('Boy', 79849),
-    ('Table', 73699),
-    ('Jeans', 57200),
-    ('Tire', 55725),
-    ('Skyscraper', 53321),
-    ('Food', 52400),
-    ('Footwear', 50335),
-    ('Dress', 50236),
-    ('Human leg', 47124),
-    ('Toy', 46636),
-    ('Tower', 45605),
-    ('Boat', 43486),
-    ('Land vehicle', 40541),
-    ('Bicycle wheel', 34646),
-    ('Palm tree', 33729),
-    ('Fashion accessory', 32914),
-    ('Glasses', 31940),
-    ('Bicycle', 31409),
-    ('Furniture', 30656),
-    ('Sculpture', 29643),
-    ('Bottle', 27558),
-    ('Dog', 26980),
-    ('Snack', 26796),
-    ('Human hand', 26664),
-    ('Bird', 25791),
-    ('Book', 25415),
-    ('Guitar', 24386),
-    ('Jacket', 23998),
-    ('Poster', 22192),
-    ('Dessert', 21284),
-    ('Baked goods', 20657),
-    ('Drink', 19754),
-    ('Flag', 18588),
-    ('Houseplant', 18205),
-    ('Tableware', 17613),
-    ('Airplane', 17218),
-    ('Door', 17195),
-    ('Sports uniform', 17068),
-    ('Shelf', 16865),
-    ('Drum', 16612),
-    ('Vehicle', 16542),
-    ('Microphone', 15269),
-    ('Street light', 14957),
-    ('Cat', 14879),
-    ('Fruit', 13684),
-    ('Fast food', 13536),
-    ('Animal', 12932),
-    ('Vegetable', 12534),
-    ('Train', 12358),
-    ('Horse', 11948),
-    ('Flowerpot', 11728),
-    ('Motorcycle', 11621),
-    ('Fish', 11517),
-    ('Desk', 11405),
-    ('Helmet', 10996),
-    ('Truck', 10915),
-    ('Bus', 10695),
-    ('Hat', 10532),
-    ('Auto part', 10488),
-    ('Musical instrument', 10303),
-    ('Sunglasses', 10207),
-    ('Picture frame', 10096),
-    ('Sports equipment', 10015),
-    ('Shorts', 9999),
-    ('Wine glass', 9632),
-    ('Duck', 9242),
-    ('Wine', 9032),
-    ('Rose', 8781),
-    ('Tie', 8693),
-    ('Butterfly', 8436),
-    ('Beer', 7978),
-    ('Cabinetry', 7956),
-    ('Laptop', 7907),
-    ('Insect', 7497),
-    ('Goggles', 7363),
-    ('Shirt', 7098),
-    ('Dairy Product', 7021),
-    ('Marine invertebrates', 7014),
-    ('Cattle', 7006),
-    ('Trousers', 6903),
-    ('Van', 6843),
-    ('Billboard', 6777),
-    ('Balloon', 6367),
-    ('Human nose', 6103),
-    ('Tent', 6073),
-    ('Camera', 6014),
-    ('Doll', 6002),
-    ('Coat', 5951),
-    ('Mobile phone', 5758),
-    ('Swimwear', 5729),
-    ('Strawberry', 5691),
-    ('Stairs', 5643),
-    ('Goose', 5599),
-    ('Umbrella', 5536),
-    ('Cake', 5508),
-    ('Sun hat', 5475),
-    ('Bench', 5310),
-    ('Bookcase', 5163),
-    ('Bee', 5140),
-    ('Computer monitor', 5078),
-    ('Hiking equipment', 4983),
-    ('Office building', 4981),
-    ('Coffee cup', 4748),
-    ('Curtain', 4685),
-    ('Plate', 4651),
-    ('Box', 4621),
-    ('Tomato', 4595),
-    ('Coffee table', 4529),
-    ('Office supplies', 4473),
-    ('Maple', 4416),
-    ('Muffin', 4365),
-    ('Cocktail', 4234),
-    ('Castle', 4197),
-    ('Couch', 4134),
-    ('Pumpkin', 3983),
-    ('Computer keyboard', 3960),
-    ('Human mouth', 3926),
-    ('Christmas tree', 3893),
-    ('Mushroom', 3883),
-    ('Swimming pool', 3809),
-    ('Pastry', 3799),
-    ('Lavender (Plant)', 3769),
-    ('Football helmet', 3732),
-    ('Bread', 3648),
-    ('Traffic sign', 3628),
-    ('Common sunflower', 3597),
-    ('Television', 3550),
-    ('Bed', 3525),
-    ('Cookie', 3485),
-    ('Fountain', 3484),
-    ('Paddle', 3447),
-    ('Bicycle helmet', 3429),
-    ('Porch', 3420),
-    ('Deer', 3387),
-    ('Fedora', 3339),
-    ('Canoe', 3338),
-    ('Carnivore', 3266),
-    ('Bowl', 3202),
-    ('Human eye', 3166),
-    ('Ball', 3118),
-    ('Pillow', 3077),
-    ('Salad', 3061),
-    ('Beetle', 3060),
-    ('Orange', 3050),
-    ('Drawer', 2958),
-    ('Platter', 2937),
-    ('Elephant', 2921),
-    ('Seafood', 2921),
-    ('Monkey', 2915),
-    ('Countertop', 2879),
-    ('Watercraft', 2831),
-    ('Helicopter', 2805),
-    ('Kitchen appliance', 2797),
-    ('Personal flotation device', 2781),
-    ('Swan', 2739),
-    ('Lamp', 2711),
-    ('Boot', 2695),
-    ('Bronze sculpture', 2693),
-    ('Chicken', 2677),
-    ('Taxi', 2643),
-    ('Juice', 2615),
-    ('Cowboy hat', 2604),
-    ('Apple', 2600),
-    ('Tin can', 2590),
-    ('Necklace', 2564),
-    ('Ice cream', 2560),
-    ('Human beard', 2539),
-    ('Coin', 2536),
-    ('Candle', 2515),
-    ('Cart', 2512),
-    ('High heels', 2441),
-    ('Weapon', 2433),
-    ('Handbag', 2406),
-    ('Penguin', 2396),
-    ('Rifle', 2352),
-    ('Violin', 2336),
-    ('Skull', 2304),
-    ('Lantern', 2285),
-    ('Scarf', 2269),
-    ('Saucer', 2225),
-    ('Sheep', 2215),
-    ('Vase', 2189),
-    ('Lily', 2180),
-    ('Mug', 2154),
-    ('Parrot', 2140),
-    ('Human ear', 2137),
-    ('Sandal', 2115),
-    ('Lizard', 2100),
-    ('Kitchen & dining room table', 2063),
-    ('Spider', 1977),
-    ('Coffee', 1974),
-    ('Goat', 1926),
-    ('Squirrel', 1922),
-    ('Cello', 1913),
-    ('Sushi', 1881),
-    ('Tortoise', 1876),
-    ('Pizza', 1870),
-    ('Studio couch', 1864),
-    ('Barrel', 1862),
-    ('Cosmetics', 1841),
-    ('Moths and butterflies', 1841),
-    ('Convenience store', 1817),
-    ('Watch', 1792),
-    ('Home appliance', 1786),
-    ('Harbor seal', 1780),
-    ('Luggage and bags', 1756),
-    ('Vehicle registration plate', 1754),
-    ('Shrimp', 1751),
-    ('Jellyfish', 1730),
-    ('French fries', 1723),
-    ('Egg (Food)', 1698),
-    ('Football', 1697),
-    ('Musical keyboard', 1683),
-    ('Falcon', 1674),
-    ('Candy', 1660),
-    ('Medical equipment', 1654),
-    ('Eagle', 1651),
-    ('Dinosaur', 1634),
-    ('Surfboard', 1630),
-    ('Tank', 1628),
-    ('Grape', 1624),
-    ('Lion', 1624),
-    ('Owl', 1622),
-    ('Ski', 1613),
-    ('Waste container', 1606),
-    ('Frog', 1591),
-    ('Sparrow', 1585),
-    ('Rabbit', 1581),
-    ('Pen', 1546),
-    ('Sea lion', 1537),
-    ('Spoon', 1521),
-    ('Sink', 1512),
-    ('Teddy bear', 1507),
-    ('Bull', 1495),
-    ('Sofa bed', 1490),
-    ('Dragonfly', 1479),
-    ('Brassiere', 1478),
-    ('Chest of drawers', 1472),
-    ('Aircraft', 1466),
-    ('Human foot', 1463),
-    ('Pig', 1455),
-    ('Fork', 1454),
-    ('Antelope', 1438),
-    ('Tripod', 1427),
-    ('Tool', 1424),
-    ('Cheese', 1422),
-    ('Lemon', 1397),
-    ('Hamburger', 1393),
-    ('Dolphin', 1390),
-    ('Mirror', 1390),
-    ('Marine mammal', 1387),
-    ('Giraffe', 1385),
-    ('Snake', 1368),
-    ('Gondola', 1364),
-    ('Wheelchair', 1360),
-    ('Piano', 1358),
-    ('Cupboard', 1348),
-    ('Banana', 1345),
-    ('Trumpet', 1335),
-    ('Lighthouse', 1333),
-    ('Invertebrate', 1317),
-    ('Carrot', 1268),
-    ('Sock', 1260),
-    ('Tiger', 1241),
-    ('Camel', 1224),
-    ('Parachute', 1224),
-    ('Bathroom accessory', 1223),
-    ('Earrings', 1221),
-    ('Headphones', 1218),
-    ('Skirt', 1198),
-    ('Skateboard', 1190),
-    ('Sandwich', 1148),
-    ('Saxophone', 1141),
-    ('Goldfish', 1136),
-    ('Stool', 1104),
-    ('Traffic light', 1097),
-    ('Shellfish', 1081),
-    ('Backpack', 1079),
-    ('Sea turtle', 1078),
-    ('Cucumber', 1075),
-    ('Tea', 1051),
-    ('Toilet', 1047),
-    ('Roller skates', 1040),
-    ('Mule', 1039),
-    ('Bust', 1031),
-    ('Broccoli', 1030),
-    ('Crab', 1020),
-    ('Oyster', 1019),
-    ('Cannon', 1012),
-    ('Zebra', 1012),
-    ('French horn', 1008),
-    ('Grapefruit', 998),
-    ('Whiteboard', 997),
-    ('Zucchini', 997),
-    ('Crocodile', 992),
-
-    ('Clock', 960),
-    ('Wall clock', 958),
-
-    ('Doughnut', 869),
-    ('Snail', 868),
-
-    ('Baseball glove', 859),
-
-    ('Panda', 830),
-    ('Tennis racket', 830),
-
-    ('Pear', 652),
-
-    ('Bagel', 617),
-    ('Oven', 616),
-    ('Ladybug', 615),
-    ('Shark', 615),
-    ('Polar bear', 614),
-    ('Ostrich', 609),
-
-    ('Hot dog', 473),
-    ('Microwave oven', 467),
-    ('Fire hydrant', 20),
-    ('Stop sign', 20),
-    ('Parking meter', 20),
-    ('Bear', 20),
-    ('Flying disc', 20),
-    ('Snowboard', 20),
-    ('Tennis ball', 20),
-    ('Kite', 20),
-    ('Baseball bat', 20),
-    ('Kitchen knife', 20),
-    ('Knife', 20),
-    ('Submarine sandwich', 20),
-    ('Computer mouse', 20),
-    ('Remote control', 20),
-    ('Toaster', 20),
-    ('Sink', 20),
-    ('Refrigerator', 20),
-    ('Alarm clock', 20),
-    ('Wall clock', 20),
-    ('Scissors', 20),
-    ('Hair dryer', 20),
-    ('Toothbrush', 20),
-    ('Suitcase', 20)
-]
diff --git a/repositories/taming-transformers/taming/data/sflckr.py b/repositories/taming-transformers/taming/data/sflckr.py
deleted file mode 100644
index 91101be5953b113f1e58376af637e43f366b3dee..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/taming/data/sflckr.py
+++ /dev/null
@@ -1,91 +0,0 @@
-import os
-import numpy as np
-import cv2
-import albumentations
-from PIL import Image
-from torch.utils.data import Dataset
-
-
-class SegmentationBase(Dataset):
-    def __init__(self,
-                 data_csv, data_root, segmentation_root,
-                 size=None, random_crop=False, interpolation="bicubic",
-                 n_labels=182, shift_segmentation=False,
-                 ):
-        self.n_labels = n_labels
-        self.shift_segmentation = shift_segmentation
-        self.data_csv = data_csv
-        self.data_root = data_root
-        self.segmentation_root = segmentation_root
-        with open(self.data_csv, "r") as f:
-            self.image_paths = f.read().splitlines()
-        self._length = len(self.image_paths)
-        self.labels = {
-            "relative_file_path_": [l for l in self.image_paths],
-            "file_path_": [os.path.join(self.data_root, l)
-                           for l in self.image_paths],
-            "segmentation_path_": [os.path.join(self.segmentation_root, l.replace(".jpg", ".png"))
-                                   for l in self.image_paths]
-        }
-
-        size = None if size is not None and size<=0 else size
-        self.size = size
-        if self.size is not None:
-            self.interpolation = interpolation
-            self.interpolation = {
-                "nearest": cv2.INTER_NEAREST,
-                "bilinear": cv2.INTER_LINEAR,
-                "bicubic": cv2.INTER_CUBIC,
-                "area": cv2.INTER_AREA,
-                "lanczos": cv2.INTER_LANCZOS4}[self.interpolation]
-            self.image_rescaler = albumentations.SmallestMaxSize(max_size=self.size,
-                                                                 interpolation=self.interpolation)
-            self.segmentation_rescaler = albumentations.SmallestMaxSize(max_size=self.size,
-                                                                        interpolation=cv2.INTER_NEAREST)
-            self.center_crop = not random_crop
-            if self.center_crop:
-                self.cropper = albumentations.CenterCrop(height=self.size, width=self.size)
-            else:
-                self.cropper = albumentations.RandomCrop(height=self.size, width=self.size)
-            self.preprocessor = self.cropper
-
-    def __len__(self):
-        return self._length
-
-    def __getitem__(self, i):
-        example = dict((k, self.labels[k][i]) for k in self.labels)
-        image = Image.open(example["file_path_"])
-        if not image.mode == "RGB":
-            image = image.convert("RGB")
-        image = np.array(image).astype(np.uint8)
-        if self.size is not None:
-            image = self.image_rescaler(image=image)["image"]
-        segmentation = Image.open(example["segmentation_path_"])
-        assert segmentation.mode == "L", segmentation.mode
-        segmentation = np.array(segmentation).astype(np.uint8)
-        if self.shift_segmentation:
-            # used to support segmentations containing unlabeled==255 label
-            segmentation = segmentation+1
-        if self.size is not None:
-            segmentation = self.segmentation_rescaler(image=segmentation)["image"]
-        if self.size is not None:
-            processed = self.preprocessor(image=image,
-                                          mask=segmentation
-                                          )
-        else:
-            processed = {"image": image,
-                         "mask": segmentation
-                         }
-        example["image"] = (processed["image"]/127.5 - 1.0).astype(np.float32)
-        segmentation = processed["mask"]
-        onehot = np.eye(self.n_labels)[segmentation]
-        example["segmentation"] = onehot
-        return example
-
-
-class Examples(SegmentationBase):
-    def __init__(self, size=None, random_crop=False, interpolation="bicubic"):
-        super().__init__(data_csv="data/sflckr_examples.txt",
-                         data_root="data/sflckr_images",
-                         segmentation_root="data/sflckr_segmentations",
-                         size=size, random_crop=random_crop, interpolation=interpolation)
diff --git a/repositories/taming-transformers/taming/data/utils.py b/repositories/taming-transformers/taming/data/utils.py
deleted file mode 100644
index 2b3c3d53cd2b6c72b481b59834cf809d3735b394..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/taming/data/utils.py
+++ /dev/null
@@ -1,169 +0,0 @@
-import collections
-import os
-import tarfile
-import urllib
-import zipfile
-from pathlib import Path
-
-import numpy as np
-import torch
-from taming.data.helper_types import Annotation
-from torch._six import string_classes
-from torch.utils.data._utils.collate import np_str_obj_array_pattern, default_collate_err_msg_format
-from tqdm import tqdm
-
-
-def unpack(path):
-    if path.endswith("tar.gz"):
-        with tarfile.open(path, "r:gz") as tar:
-            tar.extractall(path=os.path.split(path)[0])
-    elif path.endswith("tar"):
-        with tarfile.open(path, "r:") as tar:
-            tar.extractall(path=os.path.split(path)[0])
-    elif path.endswith("zip"):
-        with zipfile.ZipFile(path, "r") as f:
-            f.extractall(path=os.path.split(path)[0])
-    else:
-        raise NotImplementedError(
-            "Unknown file extension: {}".format(os.path.splitext(path)[1])
-        )
-
-
-def reporthook(bar):
-    """tqdm progress bar for downloads."""
-
-    def hook(b=1, bsize=1, tsize=None):
-        if tsize is not None:
-            bar.total = tsize
-        bar.update(b * bsize - bar.n)
-
-    return hook
-
-
-def get_root(name):
-    base = "data/"
-    root = os.path.join(base, name)
-    os.makedirs(root, exist_ok=True)
-    return root
-
-
-def is_prepared(root):
-    return Path(root).joinpath(".ready").exists()
-
-
-def mark_prepared(root):
-    Path(root).joinpath(".ready").touch()
-
-
-def prompt_download(file_, source, target_dir, content_dir=None):
-    targetpath = os.path.join(target_dir, file_)
-    while not os.path.exists(targetpath):
-        if content_dir is not None and os.path.exists(
-            os.path.join(target_dir, content_dir)
-        ):
-            break
-        print(
-            "Please download '{}' from '{}' to '{}'.".format(file_, source, targetpath)
-        )
-        if content_dir is not None:
-            print(
-                "Or place its content into '{}'.".format(
-                    os.path.join(target_dir, content_dir)
-                )
-            )
-        input("Press Enter when done...")
-    return targetpath
-
-
-def download_url(file_, url, target_dir):
-    targetpath = os.path.join(target_dir, file_)
-    os.makedirs(target_dir, exist_ok=True)
-    with tqdm(
-        unit="B", unit_scale=True, unit_divisor=1024, miniters=1, desc=file_
-    ) as bar:
-        urllib.request.urlretrieve(url, targetpath, reporthook=reporthook(bar))
-    return targetpath
-
-
-def download_urls(urls, target_dir):
-    paths = dict()
-    for fname, url in urls.items():
-        outpath = download_url(fname, url, target_dir)
-        paths[fname] = outpath
-    return paths
-
-
-def quadratic_crop(x, bbox, alpha=1.0):
-    """bbox is xmin, ymin, xmax, ymax"""
-    im_h, im_w = x.shape[:2]
-    bbox = np.array(bbox, dtype=np.float32)
-    bbox = np.clip(bbox, 0, max(im_h, im_w))
-    center = 0.5 * (bbox[0] + bbox[2]), 0.5 * (bbox[1] + bbox[3])
-    w = bbox[2] - bbox[0]
-    h = bbox[3] - bbox[1]
-    l = int(alpha * max(w, h))
-    l = max(l, 2)
-
-    required_padding = -1 * min(
-        center[0] - l, center[1] - l, im_w - (center[0] + l), im_h - (center[1] + l)
-    )
-    required_padding = int(np.ceil(required_padding))
-    if required_padding > 0:
-        padding = [
-            [required_padding, required_padding],
-            [required_padding, required_padding],
-        ]
-        padding += [[0, 0]] * (len(x.shape) - 2)
-        x = np.pad(x, padding, "reflect")
-        center = center[0] + required_padding, center[1] + required_padding
-    xmin = int(center[0] - l / 2)
-    ymin = int(center[1] - l / 2)
-    return np.array(x[ymin : ymin + l, xmin : xmin + l, ...])
-
-
-def custom_collate(batch):
-    r"""source: pytorch 1.9.0, only one modification to original code """
-
-    elem = batch[0]
-    elem_type = type(elem)
-    if isinstance(elem, torch.Tensor):
-        out = None
-        if torch.utils.data.get_worker_info() is not None:
-            # If we're in a background process, concatenate directly into a
-            # shared memory tensor to avoid an extra copy
-            numel = sum([x.numel() for x in batch])
-            storage = elem.storage()._new_shared(numel)
-            out = elem.new(storage)
-        return torch.stack(batch, 0, out=out)
-    elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \
-            and elem_type.__name__ != 'string_':
-        if elem_type.__name__ == 'ndarray' or elem_type.__name__ == 'memmap':
-            # array of string classes and object
-            if np_str_obj_array_pattern.search(elem.dtype.str) is not None:
-                raise TypeError(default_collate_err_msg_format.format(elem.dtype))
-
-            return custom_collate([torch.as_tensor(b) for b in batch])
-        elif elem.shape == ():  # scalars
-            return torch.as_tensor(batch)
-    elif isinstance(elem, float):
-        return torch.tensor(batch, dtype=torch.float64)
-    elif isinstance(elem, int):
-        return torch.tensor(batch)
-    elif isinstance(elem, string_classes):
-        return batch
-    elif isinstance(elem, collections.abc.Mapping):
-        return {key: custom_collate([d[key] for d in batch]) for key in elem}
-    elif isinstance(elem, tuple) and hasattr(elem, '_fields'):  # namedtuple
-        return elem_type(*(custom_collate(samples) for samples in zip(*batch)))
-    if isinstance(elem, collections.abc.Sequence) and isinstance(elem[0], Annotation):  # added
-        return batch  # added
-    elif isinstance(elem, collections.abc.Sequence):
-        # check to make sure that the elements in batch have consistent size
-        it = iter(batch)
-        elem_size = len(next(it))
-        if not all(len(elem) == elem_size for elem in it):
-            raise RuntimeError('each element in list of batch should be of equal size')
-        transposed = zip(*batch)
-        return [custom_collate(samples) for samples in transposed]
-
-    raise TypeError(default_collate_err_msg_format.format(elem_type))
diff --git a/repositories/taming-transformers/taming/lr_scheduler.py b/repositories/taming-transformers/taming/lr_scheduler.py
deleted file mode 100644
index e598ed120159c53da6820a55ad86b89f5c70c82d..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/taming/lr_scheduler.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import numpy as np
-
-
-class LambdaWarmUpCosineScheduler:
-    """
-    note: use with a base_lr of 1.0
-    """
-    def __init__(self, warm_up_steps, lr_min, lr_max, lr_start, max_decay_steps, verbosity_interval=0):
-        self.lr_warm_up_steps = warm_up_steps
-        self.lr_start = lr_start
-        self.lr_min = lr_min
-        self.lr_max = lr_max
-        self.lr_max_decay_steps = max_decay_steps
-        self.last_lr = 0.
-        self.verbosity_interval = verbosity_interval
-
-    def schedule(self, n):
-        if self.verbosity_interval > 0:
-            if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_lr}")
-        if n < self.lr_warm_up_steps:
-            lr = (self.lr_max - self.lr_start) / self.lr_warm_up_steps * n + self.lr_start
-            self.last_lr = lr
-            return lr
-        else:
-            t = (n - self.lr_warm_up_steps) / (self.lr_max_decay_steps - self.lr_warm_up_steps)
-            t = min(t, 1.0)
-            lr = self.lr_min + 0.5 * (self.lr_max - self.lr_min) * (
-                    1 + np.cos(t * np.pi))
-            self.last_lr = lr
-            return lr
-
-    def __call__(self, n):
-        return self.schedule(n)
-
diff --git a/repositories/taming-transformers/taming/models/cond_transformer.py b/repositories/taming-transformers/taming/models/cond_transformer.py
deleted file mode 100644
index e4c63730fa86ac1b92b37af14c14fb696595b1ab..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/taming/models/cond_transformer.py
+++ /dev/null
@@ -1,352 +0,0 @@
-import os, math
-import torch
-import torch.nn.functional as F
-import pytorch_lightning as pl
-
-from main import instantiate_from_config
-from taming.modules.util import SOSProvider
-
-
-def disabled_train(self, mode=True):
-    """Overwrite model.train with this function to make sure train/eval mode
-    does not change anymore."""
-    return self
-
-
-class Net2NetTransformer(pl.LightningModule):
-    def __init__(self,
-                 transformer_config,
-                 first_stage_config,
-                 cond_stage_config,
-                 permuter_config=None,
-                 ckpt_path=None,
-                 ignore_keys=[],
-                 first_stage_key="image",
-                 cond_stage_key="depth",
-                 downsample_cond_size=-1,
-                 pkeep=1.0,
-                 sos_token=0,
-                 unconditional=False,
-                 ):
-        super().__init__()
-        self.be_unconditional = unconditional
-        self.sos_token = sos_token
-        self.first_stage_key = first_stage_key
-        self.cond_stage_key = cond_stage_key
-        self.init_first_stage_from_ckpt(first_stage_config)
-        self.init_cond_stage_from_ckpt(cond_stage_config)
-        if permuter_config is None:
-            permuter_config = {"target": "taming.modules.transformer.permuter.Identity"}
-        self.permuter = instantiate_from_config(config=permuter_config)
-        self.transformer = instantiate_from_config(config=transformer_config)
-
-        if ckpt_path is not None:
-            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
-        self.downsample_cond_size = downsample_cond_size
-        self.pkeep = pkeep
-
-    def init_from_ckpt(self, path, ignore_keys=list()):
-        sd = torch.load(path, map_location="cpu")["state_dict"]
-        for k in sd.keys():
-            for ik in ignore_keys:
-                if k.startswith(ik):
-                    self.print("Deleting key {} from state_dict.".format(k))
-                    del sd[k]
-        self.load_state_dict(sd, strict=False)
-        print(f"Restored from {path}")
-
-    def init_first_stage_from_ckpt(self, config):
-        model = instantiate_from_config(config)
-        model = model.eval()
-        model.train = disabled_train
-        self.first_stage_model = model
-
-    def init_cond_stage_from_ckpt(self, config):
-        if config == "__is_first_stage__":
-            print("Using first stage also as cond stage.")
-            self.cond_stage_model = self.first_stage_model
-        elif config == "__is_unconditional__" or self.be_unconditional:
-            print(f"Using no cond stage. Assuming the training is intended to be unconditional. "
-                  f"Prepending {self.sos_token} as a sos token.")
-            self.be_unconditional = True
-            self.cond_stage_key = self.first_stage_key
-            self.cond_stage_model = SOSProvider(self.sos_token)
-        else:
-            model = instantiate_from_config(config)
-            model = model.eval()
-            model.train = disabled_train
-            self.cond_stage_model = model
-
-    def forward(self, x, c):
-        # one step to produce the logits
-        _, z_indices = self.encode_to_z(x)
-        _, c_indices = self.encode_to_c(c)
-
-        if self.training and self.pkeep < 1.0:
-            mask = torch.bernoulli(self.pkeep*torch.ones(z_indices.shape,
-                                                         device=z_indices.device))
-            mask = mask.round().to(dtype=torch.int64)
-            r_indices = torch.randint_like(z_indices, self.transformer.config.vocab_size)
-            a_indices = mask*z_indices+(1-mask)*r_indices
-        else:
-            a_indices = z_indices
-
-        cz_indices = torch.cat((c_indices, a_indices), dim=1)
-
-        # target includes all sequence elements (no need to handle first one
-        # differently because we are conditioning)
-        target = z_indices
-        # make the prediction
-        logits, _ = self.transformer(cz_indices[:, :-1])
-        # cut off conditioning outputs - output i corresponds to p(z_i | z_{<i}, c)
-        logits = logits[:, c_indices.shape[1]-1:]
-
-        return logits, target
-
-    def top_k_logits(self, logits, k):
-        v, ix = torch.topk(logits, k)
-        out = logits.clone()
-        out[out < v[..., [-1]]] = -float('Inf')
-        return out
-
-    @torch.no_grad()
-    def sample(self, x, c, steps, temperature=1.0, sample=False, top_k=None,
-               callback=lambda k: None):
-        x = torch.cat((c,x),dim=1)
-        block_size = self.transformer.get_block_size()
-        assert not self.transformer.training
-        if self.pkeep <= 0.0:
-            # one pass suffices since input is pure noise anyway
-            assert len(x.shape)==2
-            noise_shape = (x.shape[0], steps-1)
-            #noise = torch.randint(self.transformer.config.vocab_size, noise_shape).to(x)
-            noise = c.clone()[:,x.shape[1]-c.shape[1]:-1]
-            x = torch.cat((x,noise),dim=1)
-            logits, _ = self.transformer(x)
-            # take all logits for now and scale by temp
-            logits = logits / temperature
-            # optionally crop probabilities to only the top k options
-            if top_k is not None:
-                logits = self.top_k_logits(logits, top_k)
-            # apply softmax to convert to probabilities
-            probs = F.softmax(logits, dim=-1)
-            # sample from the distribution or take the most likely
-            if sample:
-                shape = probs.shape
-                probs = probs.reshape(shape[0]*shape[1],shape[2])
-                ix = torch.multinomial(probs, num_samples=1)
-                probs = probs.reshape(shape[0],shape[1],shape[2])
-                ix = ix.reshape(shape[0],shape[1])
-            else:
-                _, ix = torch.topk(probs, k=1, dim=-1)
-            # cut off conditioning
-            x = ix[:, c.shape[1]-1:]
-        else:
-            for k in range(steps):
-                callback(k)
-                assert x.size(1) <= block_size # make sure model can see conditioning
-                x_cond = x if x.size(1) <= block_size else x[:, -block_size:]  # crop context if needed
-                logits, _ = self.transformer(x_cond)
-                # pluck the logits at the final step and scale by temperature
-                logits = logits[:, -1, :] / temperature
-                # optionally crop probabilities to only the top k options
-                if top_k is not None:
-                    logits = self.top_k_logits(logits, top_k)
-                # apply softmax to convert to probabilities
-                probs = F.softmax(logits, dim=-1)
-                # sample from the distribution or take the most likely
-                if sample:
-                    ix = torch.multinomial(probs, num_samples=1)
-                else:
-                    _, ix = torch.topk(probs, k=1, dim=-1)
-                # append to the sequence and continue
-                x = torch.cat((x, ix), dim=1)
-            # cut off conditioning
-            x = x[:, c.shape[1]:]
-        return x
-
-    @torch.no_grad()
-    def encode_to_z(self, x):
-        quant_z, _, info = self.first_stage_model.encode(x)
-        indices = info[2].view(quant_z.shape[0], -1)
-        indices = self.permuter(indices)
-        return quant_z, indices
-
-    @torch.no_grad()
-    def encode_to_c(self, c):
-        if self.downsample_cond_size > -1:
-            c = F.interpolate(c, size=(self.downsample_cond_size, self.downsample_cond_size))
-        quant_c, _, [_,_,indices] = self.cond_stage_model.encode(c)
-        if len(indices.shape) > 2:
-            indices = indices.view(c.shape[0], -1)
-        return quant_c, indices
-
-    @torch.no_grad()
-    def decode_to_img(self, index, zshape):
-        index = self.permuter(index, reverse=True)
-        bhwc = (zshape[0],zshape[2],zshape[3],zshape[1])
-        quant_z = self.first_stage_model.quantize.get_codebook_entry(
-            index.reshape(-1), shape=bhwc)
-        x = self.first_stage_model.decode(quant_z)
-        return x
-
-    @torch.no_grad()
-    def log_images(self, batch, temperature=None, top_k=None, callback=None, lr_interface=False, **kwargs):
-        log = dict()
-
-        N = 4
-        if lr_interface:
-            x, c = self.get_xc(batch, N, diffuse=False, upsample_factor=8)
-        else:
-            x, c = self.get_xc(batch, N)
-        x = x.to(device=self.device)
-        c = c.to(device=self.device)
-
-        quant_z, z_indices = self.encode_to_z(x)
-        quant_c, c_indices = self.encode_to_c(c)
-
-        # create a "half"" sample
-        z_start_indices = z_indices[:,:z_indices.shape[1]//2]
-        index_sample = self.sample(z_start_indices, c_indices,
-                                   steps=z_indices.shape[1]-z_start_indices.shape[1],
-                                   temperature=temperature if temperature is not None else 1.0,
-                                   sample=True,
-                                   top_k=top_k if top_k is not None else 100,
-                                   callback=callback if callback is not None else lambda k: None)
-        x_sample = self.decode_to_img(index_sample, quant_z.shape)
-
-        # sample
-        z_start_indices = z_indices[:, :0]
-        index_sample = self.sample(z_start_indices, c_indices,
-                                   steps=z_indices.shape[1],
-                                   temperature=temperature if temperature is not None else 1.0,
-                                   sample=True,
-                                   top_k=top_k if top_k is not None else 100,
-                                   callback=callback if callback is not None else lambda k: None)
-        x_sample_nopix = self.decode_to_img(index_sample, quant_z.shape)
-
-        # det sample
-        z_start_indices = z_indices[:, :0]
-        index_sample = self.sample(z_start_indices, c_indices,
-                                   steps=z_indices.shape[1],
-                                   sample=False,
-                                   callback=callback if callback is not None else lambda k: None)
-        x_sample_det = self.decode_to_img(index_sample, quant_z.shape)
-
-        # reconstruction
-        x_rec = self.decode_to_img(z_indices, quant_z.shape)
-
-        log["inputs"] = x
-        log["reconstructions"] = x_rec
-
-        if self.cond_stage_key in ["objects_bbox", "objects_center_points"]:
-            figure_size = (x_rec.shape[2], x_rec.shape[3])
-            dataset = kwargs["pl_module"].trainer.datamodule.datasets["validation"]
-            label_for_category_no = dataset.get_textual_label_for_category_no
-            plotter = dataset.conditional_builders[self.cond_stage_key].plot
-            log["conditioning"] = torch.zeros_like(log["reconstructions"])
-            for i in range(quant_c.shape[0]):
-                log["conditioning"][i] = plotter(quant_c[i], label_for_category_no, figure_size)
-            log["conditioning_rec"] = log["conditioning"]
-        elif self.cond_stage_key != "image":
-            cond_rec = self.cond_stage_model.decode(quant_c)
-            if self.cond_stage_key == "segmentation":
-                # get image from segmentation mask
-                num_classes = cond_rec.shape[1]
-
-                c = torch.argmax(c, dim=1, keepdim=True)
-                c = F.one_hot(c, num_classes=num_classes)
-                c = c.squeeze(1).permute(0, 3, 1, 2).float()
-                c = self.cond_stage_model.to_rgb(c)
-
-                cond_rec = torch.argmax(cond_rec, dim=1, keepdim=True)
-                cond_rec = F.one_hot(cond_rec, num_classes=num_classes)
-                cond_rec = cond_rec.squeeze(1).permute(0, 3, 1, 2).float()
-                cond_rec = self.cond_stage_model.to_rgb(cond_rec)
-            log["conditioning_rec"] = cond_rec
-            log["conditioning"] = c
-
-        log["samples_half"] = x_sample
-        log["samples_nopix"] = x_sample_nopix
-        log["samples_det"] = x_sample_det
-        return log
-
-    def get_input(self, key, batch):
-        x = batch[key]
-        if len(x.shape) == 3:
-            x = x[..., None]
-        if len(x.shape) == 4:
-            x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format)
-        if x.dtype == torch.double:
-            x = x.float()
-        return x
-
-    def get_xc(self, batch, N=None):
-        x = self.get_input(self.first_stage_key, batch)
-        c = self.get_input(self.cond_stage_key, batch)
-        if N is not None:
-            x = x[:N]
-            c = c[:N]
-        return x, c
-
-    def shared_step(self, batch, batch_idx):
-        x, c = self.get_xc(batch)
-        logits, target = self(x, c)
-        loss = F.cross_entropy(logits.reshape(-1, logits.size(-1)), target.reshape(-1))
-        return loss
-
-    def training_step(self, batch, batch_idx):
-        loss = self.shared_step(batch, batch_idx)
-        self.log("train/loss", loss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
-        return loss
-
-    def validation_step(self, batch, batch_idx):
-        loss = self.shared_step(batch, batch_idx)
-        self.log("val/loss", loss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
-        return loss
-
-    def configure_optimizers(self):
-        """
-        Following minGPT:
-        This long function is unfortunately doing something very simple and is being very defensive:
-        We are separating out all parameters of the model into two buckets: those that will experience
-        weight decay for regularization and those that won't (biases, and layernorm/embedding weights).
-        We are then returning the PyTorch optimizer object.
-        """
-        # separate out all parameters to those that will and won't experience regularizing weight decay
-        decay = set()
-        no_decay = set()
-        whitelist_weight_modules = (torch.nn.Linear, )
-        blacklist_weight_modules = (torch.nn.LayerNorm, torch.nn.Embedding)
-        for mn, m in self.transformer.named_modules():
-            for pn, p in m.named_parameters():
-                fpn = '%s.%s' % (mn, pn) if mn else pn # full param name
-
-                if pn.endswith('bias'):
-                    # all biases will not be decayed
-                    no_decay.add(fpn)
-                elif pn.endswith('weight') and isinstance(m, whitelist_weight_modules):
-                    # weights of whitelist modules will be weight decayed
-                    decay.add(fpn)
-                elif pn.endswith('weight') and isinstance(m, blacklist_weight_modules):
-                    # weights of blacklist modules will NOT be weight decayed
-                    no_decay.add(fpn)
-
-        # special case the position embedding parameter in the root GPT module as not decayed
-        no_decay.add('pos_emb')
-
-        # validate that we considered every parameter
-        param_dict = {pn: p for pn, p in self.transformer.named_parameters()}
-        inter_params = decay & no_decay
-        union_params = decay | no_decay
-        assert len(inter_params) == 0, "parameters %s made it into both decay/no_decay sets!" % (str(inter_params), )
-        assert len(param_dict.keys() - union_params) == 0, "parameters %s were not separated into either decay/no_decay set!" \
-                                                    % (str(param_dict.keys() - union_params), )
-
-        # create the pytorch optimizer object
-        optim_groups = [
-            {"params": [param_dict[pn] for pn in sorted(list(decay))], "weight_decay": 0.01},
-            {"params": [param_dict[pn] for pn in sorted(list(no_decay))], "weight_decay": 0.0},
-        ]
-        optimizer = torch.optim.AdamW(optim_groups, lr=self.learning_rate, betas=(0.9, 0.95))
-        return optimizer
diff --git a/repositories/taming-transformers/taming/models/dummy_cond_stage.py b/repositories/taming-transformers/taming/models/dummy_cond_stage.py
deleted file mode 100644
index 6e19938078752e09b926a3e749907ee99a258ca0..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/taming/models/dummy_cond_stage.py
+++ /dev/null
@@ -1,22 +0,0 @@
-from torch import Tensor
-
-
-class DummyCondStage:
-    def __init__(self, conditional_key):
-        self.conditional_key = conditional_key
-        self.train = None
-
-    def eval(self):
-        return self
-
-    @staticmethod
-    def encode(c: Tensor):
-        return c, None, (None, None, c)
-
-    @staticmethod
-    def decode(c: Tensor):
-        return c
-
-    @staticmethod
-    def to_rgb(c: Tensor):
-        return c
diff --git a/repositories/taming-transformers/taming/models/vqgan.py b/repositories/taming-transformers/taming/models/vqgan.py
deleted file mode 100644
index a6950baa5f739111cd64c17235dca8be3a5f8037..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/taming/models/vqgan.py
+++ /dev/null
@@ -1,404 +0,0 @@
-import torch
-import torch.nn.functional as F
-import pytorch_lightning as pl
-
-from main import instantiate_from_config
-
-from taming.modules.diffusionmodules.model import Encoder, Decoder
-from taming.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer
-from taming.modules.vqvae.quantize import GumbelQuantize
-from taming.modules.vqvae.quantize import EMAVectorQuantizer
-
-class VQModel(pl.LightningModule):
-    def __init__(self,
-                 ddconfig,
-                 lossconfig,
-                 n_embed,
-                 embed_dim,
-                 ckpt_path=None,
-                 ignore_keys=[],
-                 image_key="image",
-                 colorize_nlabels=None,
-                 monitor=None,
-                 remap=None,
-                 sane_index_shape=False,  # tell vector quantizer to return indices as bhw
-                 ):
-        super().__init__()
-        self.image_key = image_key
-        self.encoder = Encoder(**ddconfig)
-        self.decoder = Decoder(**ddconfig)
-        self.loss = instantiate_from_config(lossconfig)
-        self.quantize = VectorQuantizer(n_embed, embed_dim, beta=0.25,
-                                        remap=remap, sane_index_shape=sane_index_shape)
-        self.quant_conv = torch.nn.Conv2d(ddconfig["z_channels"], embed_dim, 1)
-        self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
-        if ckpt_path is not None:
-            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
-        self.image_key = image_key
-        if colorize_nlabels is not None:
-            assert type(colorize_nlabels)==int
-            self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
-        if monitor is not None:
-            self.monitor = monitor
-
-    def init_from_ckpt(self, path, ignore_keys=list()):
-        sd = torch.load(path, map_location="cpu")["state_dict"]
-        keys = list(sd.keys())
-        for k in keys:
-            for ik in ignore_keys:
-                if k.startswith(ik):
-                    print("Deleting key {} from state_dict.".format(k))
-                    del sd[k]
-        self.load_state_dict(sd, strict=False)
-        print(f"Restored from {path}")
-
-    def encode(self, x):
-        h = self.encoder(x)
-        h = self.quant_conv(h)
-        quant, emb_loss, info = self.quantize(h)
-        return quant, emb_loss, info
-
-    def decode(self, quant):
-        quant = self.post_quant_conv(quant)
-        dec = self.decoder(quant)
-        return dec
-
-    def decode_code(self, code_b):
-        quant_b = self.quantize.embed_code(code_b)
-        dec = self.decode(quant_b)
-        return dec
-
-    def forward(self, input):
-        quant, diff, _ = self.encode(input)
-        dec = self.decode(quant)
-        return dec, diff
-
-    def get_input(self, batch, k):
-        x = batch[k]
-        if len(x.shape) == 3:
-            x = x[..., None]
-        x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format)
-        return x.float()
-
-    def training_step(self, batch, batch_idx, optimizer_idx):
-        x = self.get_input(batch, self.image_key)
-        xrec, qloss = self(x)
-
-        if optimizer_idx == 0:
-            # autoencode
-            aeloss, log_dict_ae = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
-                                            last_layer=self.get_last_layer(), split="train")
-
-            self.log("train/aeloss", aeloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
-            self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True)
-            return aeloss
-
-        if optimizer_idx == 1:
-            # discriminator
-            discloss, log_dict_disc = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
-                                            last_layer=self.get_last_layer(), split="train")
-            self.log("train/discloss", discloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
-            self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=True)
-            return discloss
-
-    def validation_step(self, batch, batch_idx):
-        x = self.get_input(batch, self.image_key)
-        xrec, qloss = self(x)
-        aeloss, log_dict_ae = self.loss(qloss, x, xrec, 0, self.global_step,
-                                            last_layer=self.get_last_layer(), split="val")
-
-        discloss, log_dict_disc = self.loss(qloss, x, xrec, 1, self.global_step,
-                                            last_layer=self.get_last_layer(), split="val")
-        rec_loss = log_dict_ae["val/rec_loss"]
-        self.log("val/rec_loss", rec_loss,
-                   prog_bar=True, logger=True, on_step=True, on_epoch=True, sync_dist=True)
-        self.log("val/aeloss", aeloss,
-                   prog_bar=True, logger=True, on_step=True, on_epoch=True, sync_dist=True)
-        self.log_dict(log_dict_ae)
-        self.log_dict(log_dict_disc)
-        return self.log_dict
-
-    def configure_optimizers(self):
-        lr = self.learning_rate
-        opt_ae = torch.optim.Adam(list(self.encoder.parameters())+
-                                  list(self.decoder.parameters())+
-                                  list(self.quantize.parameters())+
-                                  list(self.quant_conv.parameters())+
-                                  list(self.post_quant_conv.parameters()),
-                                  lr=lr, betas=(0.5, 0.9))
-        opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
-                                    lr=lr, betas=(0.5, 0.9))
-        return [opt_ae, opt_disc], []
-
-    def get_last_layer(self):
-        return self.decoder.conv_out.weight
-
-    def log_images(self, batch, **kwargs):
-        log = dict()
-        x = self.get_input(batch, self.image_key)
-        x = x.to(self.device)
-        xrec, _ = self(x)
-        if x.shape[1] > 3:
-            # colorize with random projection
-            assert xrec.shape[1] > 3
-            x = self.to_rgb(x)
-            xrec = self.to_rgb(xrec)
-        log["inputs"] = x
-        log["reconstructions"] = xrec
-        return log
-
-    def to_rgb(self, x):
-        assert self.image_key == "segmentation"
-        if not hasattr(self, "colorize"):
-            self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
-        x = F.conv2d(x, weight=self.colorize)
-        x = 2.*(x-x.min())/(x.max()-x.min()) - 1.
-        return x
-
-
-class VQSegmentationModel(VQModel):
-    def __init__(self, n_labels, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.register_buffer("colorize", torch.randn(3, n_labels, 1, 1))
-
-    def configure_optimizers(self):
-        lr = self.learning_rate
-        opt_ae = torch.optim.Adam(list(self.encoder.parameters())+
-                                  list(self.decoder.parameters())+
-                                  list(self.quantize.parameters())+
-                                  list(self.quant_conv.parameters())+
-                                  list(self.post_quant_conv.parameters()),
-                                  lr=lr, betas=(0.5, 0.9))
-        return opt_ae
-
-    def training_step(self, batch, batch_idx):
-        x = self.get_input(batch, self.image_key)
-        xrec, qloss = self(x)
-        aeloss, log_dict_ae = self.loss(qloss, x, xrec, split="train")
-        self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True)
-        return aeloss
-
-    def validation_step(self, batch, batch_idx):
-        x = self.get_input(batch, self.image_key)
-        xrec, qloss = self(x)
-        aeloss, log_dict_ae = self.loss(qloss, x, xrec, split="val")
-        self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True)
-        total_loss = log_dict_ae["val/total_loss"]
-        self.log("val/total_loss", total_loss,
-                 prog_bar=True, logger=True, on_step=True, on_epoch=True, sync_dist=True)
-        return aeloss
-
-    @torch.no_grad()
-    def log_images(self, batch, **kwargs):
-        log = dict()
-        x = self.get_input(batch, self.image_key)
-        x = x.to(self.device)
-        xrec, _ = self(x)
-        if x.shape[1] > 3:
-            # colorize with random projection
-            assert xrec.shape[1] > 3
-            # convert logits to indices
-            xrec = torch.argmax(xrec, dim=1, keepdim=True)
-            xrec = F.one_hot(xrec, num_classes=x.shape[1])
-            xrec = xrec.squeeze(1).permute(0, 3, 1, 2).float()
-            x = self.to_rgb(x)
-            xrec = self.to_rgb(xrec)
-        log["inputs"] = x
-        log["reconstructions"] = xrec
-        return log
-
-
-class VQNoDiscModel(VQModel):
-    def __init__(self,
-                 ddconfig,
-                 lossconfig,
-                 n_embed,
-                 embed_dim,
-                 ckpt_path=None,
-                 ignore_keys=[],
-                 image_key="image",
-                 colorize_nlabels=None
-                 ):
-        super().__init__(ddconfig=ddconfig, lossconfig=lossconfig, n_embed=n_embed, embed_dim=embed_dim,
-                         ckpt_path=ckpt_path, ignore_keys=ignore_keys, image_key=image_key,
-                         colorize_nlabels=colorize_nlabels)
-
-    def training_step(self, batch, batch_idx):
-        x = self.get_input(batch, self.image_key)
-        xrec, qloss = self(x)
-        # autoencode
-        aeloss, log_dict_ae = self.loss(qloss, x, xrec, self.global_step, split="train")
-        output = pl.TrainResult(minimize=aeloss)
-        output.log("train/aeloss", aeloss,
-                   prog_bar=True, logger=True, on_step=True, on_epoch=True)
-        output.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True)
-        return output
-
-    def validation_step(self, batch, batch_idx):
-        x = self.get_input(batch, self.image_key)
-        xrec, qloss = self(x)
-        aeloss, log_dict_ae = self.loss(qloss, x, xrec, self.global_step, split="val")
-        rec_loss = log_dict_ae["val/rec_loss"]
-        output = pl.EvalResult(checkpoint_on=rec_loss)
-        output.log("val/rec_loss", rec_loss,
-                   prog_bar=True, logger=True, on_step=True, on_epoch=True)
-        output.log("val/aeloss", aeloss,
-                   prog_bar=True, logger=True, on_step=True, on_epoch=True)
-        output.log_dict(log_dict_ae)
-
-        return output
-
-    def configure_optimizers(self):
-        optimizer = torch.optim.Adam(list(self.encoder.parameters())+
-                                  list(self.decoder.parameters())+
-                                  list(self.quantize.parameters())+
-                                  list(self.quant_conv.parameters())+
-                                  list(self.post_quant_conv.parameters()),
-                                  lr=self.learning_rate, betas=(0.5, 0.9))
-        return optimizer
-
-
-class GumbelVQ(VQModel):
-    def __init__(self,
-                 ddconfig,
-                 lossconfig,
-                 n_embed,
-                 embed_dim,
-                 temperature_scheduler_config,
-                 ckpt_path=None,
-                 ignore_keys=[],
-                 image_key="image",
-                 colorize_nlabels=None,
-                 monitor=None,
-                 kl_weight=1e-8,
-                 remap=None,
-                 ):
-
-        z_channels = ddconfig["z_channels"]
-        super().__init__(ddconfig,
-                         lossconfig,
-                         n_embed,
-                         embed_dim,
-                         ckpt_path=None,
-                         ignore_keys=ignore_keys,
-                         image_key=image_key,
-                         colorize_nlabels=colorize_nlabels,
-                         monitor=monitor,
-                         )
-
-        self.loss.n_classes = n_embed
-        self.vocab_size = n_embed
-
-        self.quantize = GumbelQuantize(z_channels, embed_dim,
-                                       n_embed=n_embed,
-                                       kl_weight=kl_weight, temp_init=1.0,
-                                       remap=remap)
-
-        self.temperature_scheduler = instantiate_from_config(temperature_scheduler_config)   # annealing of temp
-
-        if ckpt_path is not None:
-            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
-
-    def temperature_scheduling(self):
-        self.quantize.temperature = self.temperature_scheduler(self.global_step)
-
-    def encode_to_prequant(self, x):
-        h = self.encoder(x)
-        h = self.quant_conv(h)
-        return h
-
-    def decode_code(self, code_b):
-        raise NotImplementedError
-
-    def training_step(self, batch, batch_idx, optimizer_idx):
-        self.temperature_scheduling()
-        x = self.get_input(batch, self.image_key)
-        xrec, qloss = self(x)
-
-        if optimizer_idx == 0:
-            # autoencode
-            aeloss, log_dict_ae = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
-                                            last_layer=self.get_last_layer(), split="train")
-
-            self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True)
-            self.log("temperature", self.quantize.temperature, prog_bar=False, logger=True, on_step=True, on_epoch=True)
-            return aeloss
-
-        if optimizer_idx == 1:
-            # discriminator
-            discloss, log_dict_disc = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
-                                            last_layer=self.get_last_layer(), split="train")
-            self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=True)
-            return discloss
-
-    def validation_step(self, batch, batch_idx):
-        x = self.get_input(batch, self.image_key)
-        xrec, qloss = self(x, return_pred_indices=True)
-        aeloss, log_dict_ae = self.loss(qloss, x, xrec, 0, self.global_step,
-                                        last_layer=self.get_last_layer(), split="val")
-
-        discloss, log_dict_disc = self.loss(qloss, x, xrec, 1, self.global_step,
-                                            last_layer=self.get_last_layer(), split="val")
-        rec_loss = log_dict_ae["val/rec_loss"]
-        self.log("val/rec_loss", rec_loss,
-                 prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True)
-        self.log("val/aeloss", aeloss,
-                 prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True)
-        self.log_dict(log_dict_ae)
-        self.log_dict(log_dict_disc)
-        return self.log_dict
-
-    def log_images(self, batch, **kwargs):
-        log = dict()
-        x = self.get_input(batch, self.image_key)
-        x = x.to(self.device)
-        # encode
-        h = self.encoder(x)
-        h = self.quant_conv(h)
-        quant, _, _ = self.quantize(h)
-        # decode
-        x_rec = self.decode(quant)
-        log["inputs"] = x
-        log["reconstructions"] = x_rec
-        return log
-
-
-class EMAVQ(VQModel):
-    def __init__(self,
-                 ddconfig,
-                 lossconfig,
-                 n_embed,
-                 embed_dim,
-                 ckpt_path=None,
-                 ignore_keys=[],
-                 image_key="image",
-                 colorize_nlabels=None,
-                 monitor=None,
-                 remap=None,
-                 sane_index_shape=False,  # tell vector quantizer to return indices as bhw
-                 ):
-        super().__init__(ddconfig,
-                         lossconfig,
-                         n_embed,
-                         embed_dim,
-                         ckpt_path=None,
-                         ignore_keys=ignore_keys,
-                         image_key=image_key,
-                         colorize_nlabels=colorize_nlabels,
-                         monitor=monitor,
-                         )
-        self.quantize = EMAVectorQuantizer(n_embed=n_embed,
-                                           embedding_dim=embed_dim,
-                                           beta=0.25,
-                                           remap=remap)
-    def configure_optimizers(self):
-        lr = self.learning_rate
-        #Remove self.quantize from parameter list since it is updated via EMA
-        opt_ae = torch.optim.Adam(list(self.encoder.parameters())+
-                                  list(self.decoder.parameters())+
-                                  list(self.quant_conv.parameters())+
-                                  list(self.post_quant_conv.parameters()),
-                                  lr=lr, betas=(0.5, 0.9))
-        opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
-                                    lr=lr, betas=(0.5, 0.9))
-        return [opt_ae, opt_disc], []                                           
\ No newline at end of file
diff --git a/repositories/taming-transformers/taming/modules/diffusionmodules/model.py b/repositories/taming-transformers/taming/modules/diffusionmodules/model.py
deleted file mode 100644
index d3a5db6aa2ef915e270f1ae135e4a9918fdd884c..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/taming/modules/diffusionmodules/model.py
+++ /dev/null
@@ -1,776 +0,0 @@
-# pytorch_diffusion + derived encoder decoder
-import math
-import torch
-import torch.nn as nn
-import numpy as np
-
-
-def get_timestep_embedding(timesteps, embedding_dim):
-    """
-    This matches the implementation in Denoising Diffusion Probabilistic Models:
-    From Fairseq.
-    Build sinusoidal embeddings.
-    This matches the implementation in tensor2tensor, but differs slightly
-    from the description in Section 3.5 of "Attention Is All You Need".
-    """
-    assert len(timesteps.shape) == 1
-
-    half_dim = embedding_dim // 2
-    emb = math.log(10000) / (half_dim - 1)
-    emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb)
-    emb = emb.to(device=timesteps.device)
-    emb = timesteps.float()[:, None] * emb[None, :]
-    emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
-    if embedding_dim % 2 == 1:  # zero pad
-        emb = torch.nn.functional.pad(emb, (0,1,0,0))
-    return emb
-
-
-def nonlinearity(x):
-    # swish
-    return x*torch.sigmoid(x)
-
-
-def Normalize(in_channels):
-    return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
-
-
-class Upsample(nn.Module):
-    def __init__(self, in_channels, with_conv):
-        super().__init__()
-        self.with_conv = with_conv
-        if self.with_conv:
-            self.conv = torch.nn.Conv2d(in_channels,
-                                        in_channels,
-                                        kernel_size=3,
-                                        stride=1,
-                                        padding=1)
-
-    def forward(self, x):
-        x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
-        if self.with_conv:
-            x = self.conv(x)
-        return x
-
-
-class Downsample(nn.Module):
-    def __init__(self, in_channels, with_conv):
-        super().__init__()
-        self.with_conv = with_conv
-        if self.with_conv:
-            # no asymmetric padding in torch conv, must do it ourselves
-            self.conv = torch.nn.Conv2d(in_channels,
-                                        in_channels,
-                                        kernel_size=3,
-                                        stride=2,
-                                        padding=0)
-
-    def forward(self, x):
-        if self.with_conv:
-            pad = (0,1,0,1)
-            x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
-            x = self.conv(x)
-        else:
-            x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)
-        return x
-
-
-class ResnetBlock(nn.Module):
-    def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False,
-                 dropout, temb_channels=512):
-        super().__init__()
-        self.in_channels = in_channels
-        out_channels = in_channels if out_channels is None else out_channels
-        self.out_channels = out_channels
-        self.use_conv_shortcut = conv_shortcut
-
-        self.norm1 = Normalize(in_channels)
-        self.conv1 = torch.nn.Conv2d(in_channels,
-                                     out_channels,
-                                     kernel_size=3,
-                                     stride=1,
-                                     padding=1)
-        if temb_channels > 0:
-            self.temb_proj = torch.nn.Linear(temb_channels,
-                                             out_channels)
-        self.norm2 = Normalize(out_channels)
-        self.dropout = torch.nn.Dropout(dropout)
-        self.conv2 = torch.nn.Conv2d(out_channels,
-                                     out_channels,
-                                     kernel_size=3,
-                                     stride=1,
-                                     padding=1)
-        if self.in_channels != self.out_channels:
-            if self.use_conv_shortcut:
-                self.conv_shortcut = torch.nn.Conv2d(in_channels,
-                                                     out_channels,
-                                                     kernel_size=3,
-                                                     stride=1,
-                                                     padding=1)
-            else:
-                self.nin_shortcut = torch.nn.Conv2d(in_channels,
-                                                    out_channels,
-                                                    kernel_size=1,
-                                                    stride=1,
-                                                    padding=0)
-
-    def forward(self, x, temb):
-        h = x
-        h = self.norm1(h)
-        h = nonlinearity(h)
-        h = self.conv1(h)
-
-        if temb is not None:
-            h = h + self.temb_proj(nonlinearity(temb))[:,:,None,None]
-
-        h = self.norm2(h)
-        h = nonlinearity(h)
-        h = self.dropout(h)
-        h = self.conv2(h)
-
-        if self.in_channels != self.out_channels:
-            if self.use_conv_shortcut:
-                x = self.conv_shortcut(x)
-            else:
-                x = self.nin_shortcut(x)
-
-        return x+h
-
-
-class AttnBlock(nn.Module):
-    def __init__(self, in_channels):
-        super().__init__()
-        self.in_channels = in_channels
-
-        self.norm = Normalize(in_channels)
-        self.q = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.k = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.v = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.proj_out = torch.nn.Conv2d(in_channels,
-                                        in_channels,
-                                        kernel_size=1,
-                                        stride=1,
-                                        padding=0)
-
-
-    def forward(self, x):
-        h_ = x
-        h_ = self.norm(h_)
-        q = self.q(h_)
-        k = self.k(h_)
-        v = self.v(h_)
-
-        # compute attention
-        b,c,h,w = q.shape
-        q = q.reshape(b,c,h*w)
-        q = q.permute(0,2,1)   # b,hw,c
-        k = k.reshape(b,c,h*w) # b,c,hw
-        w_ = torch.bmm(q,k)     # b,hw,hw    w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
-        w_ = w_ * (int(c)**(-0.5))
-        w_ = torch.nn.functional.softmax(w_, dim=2)
-
-        # attend to values
-        v = v.reshape(b,c,h*w)
-        w_ = w_.permute(0,2,1)   # b,hw,hw (first hw of k, second of q)
-        h_ = torch.bmm(v,w_)     # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
-        h_ = h_.reshape(b,c,h,w)
-
-        h_ = self.proj_out(h_)
-
-        return x+h_
-
-
-class Model(nn.Module):
-    def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
-                 attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
-                 resolution, use_timestep=True):
-        super().__init__()
-        self.ch = ch
-        self.temb_ch = self.ch*4
-        self.num_resolutions = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        self.resolution = resolution
-        self.in_channels = in_channels
-
-        self.use_timestep = use_timestep
-        if self.use_timestep:
-            # timestep embedding
-            self.temb = nn.Module()
-            self.temb.dense = nn.ModuleList([
-                torch.nn.Linear(self.ch,
-                                self.temb_ch),
-                torch.nn.Linear(self.temb_ch,
-                                self.temb_ch),
-            ])
-
-        # downsampling
-        self.conv_in = torch.nn.Conv2d(in_channels,
-                                       self.ch,
-                                       kernel_size=3,
-                                       stride=1,
-                                       padding=1)
-
-        curr_res = resolution
-        in_ch_mult = (1,)+tuple(ch_mult)
-        self.down = nn.ModuleList()
-        for i_level in range(self.num_resolutions):
-            block = nn.ModuleList()
-            attn = nn.ModuleList()
-            block_in = ch*in_ch_mult[i_level]
-            block_out = ch*ch_mult[i_level]
-            for i_block in range(self.num_res_blocks):
-                block.append(ResnetBlock(in_channels=block_in,
-                                         out_channels=block_out,
-                                         temb_channels=self.temb_ch,
-                                         dropout=dropout))
-                block_in = block_out
-                if curr_res in attn_resolutions:
-                    attn.append(AttnBlock(block_in))
-            down = nn.Module()
-            down.block = block
-            down.attn = attn
-            if i_level != self.num_resolutions-1:
-                down.downsample = Downsample(block_in, resamp_with_conv)
-                curr_res = curr_res // 2
-            self.down.append(down)
-
-        # middle
-        self.mid = nn.Module()
-        self.mid.block_1 = ResnetBlock(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=dropout)
-        self.mid.attn_1 = AttnBlock(block_in)
-        self.mid.block_2 = ResnetBlock(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=dropout)
-
-        # upsampling
-        self.up = nn.ModuleList()
-        for i_level in reversed(range(self.num_resolutions)):
-            block = nn.ModuleList()
-            attn = nn.ModuleList()
-            block_out = ch*ch_mult[i_level]
-            skip_in = ch*ch_mult[i_level]
-            for i_block in range(self.num_res_blocks+1):
-                if i_block == self.num_res_blocks:
-                    skip_in = ch*in_ch_mult[i_level]
-                block.append(ResnetBlock(in_channels=block_in+skip_in,
-                                         out_channels=block_out,
-                                         temb_channels=self.temb_ch,
-                                         dropout=dropout))
-                block_in = block_out
-                if curr_res in attn_resolutions:
-                    attn.append(AttnBlock(block_in))
-            up = nn.Module()
-            up.block = block
-            up.attn = attn
-            if i_level != 0:
-                up.upsample = Upsample(block_in, resamp_with_conv)
-                curr_res = curr_res * 2
-            self.up.insert(0, up) # prepend to get consistent order
-
-        # end
-        self.norm_out = Normalize(block_in)
-        self.conv_out = torch.nn.Conv2d(block_in,
-                                        out_ch,
-                                        kernel_size=3,
-                                        stride=1,
-                                        padding=1)
-
-
-    def forward(self, x, t=None):
-        #assert x.shape[2] == x.shape[3] == self.resolution
-
-        if self.use_timestep:
-            # timestep embedding
-            assert t is not None
-            temb = get_timestep_embedding(t, self.ch)
-            temb = self.temb.dense[0](temb)
-            temb = nonlinearity(temb)
-            temb = self.temb.dense[1](temb)
-        else:
-            temb = None
-
-        # downsampling
-        hs = [self.conv_in(x)]
-        for i_level in range(self.num_resolutions):
-            for i_block in range(self.num_res_blocks):
-                h = self.down[i_level].block[i_block](hs[-1], temb)
-                if len(self.down[i_level].attn) > 0:
-                    h = self.down[i_level].attn[i_block](h)
-                hs.append(h)
-            if i_level != self.num_resolutions-1:
-                hs.append(self.down[i_level].downsample(hs[-1]))
-
-        # middle
-        h = hs[-1]
-        h = self.mid.block_1(h, temb)
-        h = self.mid.attn_1(h)
-        h = self.mid.block_2(h, temb)
-
-        # upsampling
-        for i_level in reversed(range(self.num_resolutions)):
-            for i_block in range(self.num_res_blocks+1):
-                h = self.up[i_level].block[i_block](
-                    torch.cat([h, hs.pop()], dim=1), temb)
-                if len(self.up[i_level].attn) > 0:
-                    h = self.up[i_level].attn[i_block](h)
-            if i_level != 0:
-                h = self.up[i_level].upsample(h)
-
-        # end
-        h = self.norm_out(h)
-        h = nonlinearity(h)
-        h = self.conv_out(h)
-        return h
-
-
-class Encoder(nn.Module):
-    def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
-                 attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
-                 resolution, z_channels, double_z=True, **ignore_kwargs):
-        super().__init__()
-        self.ch = ch
-        self.temb_ch = 0
-        self.num_resolutions = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        self.resolution = resolution
-        self.in_channels = in_channels
-
-        # downsampling
-        self.conv_in = torch.nn.Conv2d(in_channels,
-                                       self.ch,
-                                       kernel_size=3,
-                                       stride=1,
-                                       padding=1)
-
-        curr_res = resolution
-        in_ch_mult = (1,)+tuple(ch_mult)
-        self.down = nn.ModuleList()
-        for i_level in range(self.num_resolutions):
-            block = nn.ModuleList()
-            attn = nn.ModuleList()
-            block_in = ch*in_ch_mult[i_level]
-            block_out = ch*ch_mult[i_level]
-            for i_block in range(self.num_res_blocks):
-                block.append(ResnetBlock(in_channels=block_in,
-                                         out_channels=block_out,
-                                         temb_channels=self.temb_ch,
-                                         dropout=dropout))
-                block_in = block_out
-                if curr_res in attn_resolutions:
-                    attn.append(AttnBlock(block_in))
-            down = nn.Module()
-            down.block = block
-            down.attn = attn
-            if i_level != self.num_resolutions-1:
-                down.downsample = Downsample(block_in, resamp_with_conv)
-                curr_res = curr_res // 2
-            self.down.append(down)
-
-        # middle
-        self.mid = nn.Module()
-        self.mid.block_1 = ResnetBlock(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=dropout)
-        self.mid.attn_1 = AttnBlock(block_in)
-        self.mid.block_2 = ResnetBlock(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=dropout)
-
-        # end
-        self.norm_out = Normalize(block_in)
-        self.conv_out = torch.nn.Conv2d(block_in,
-                                        2*z_channels if double_z else z_channels,
-                                        kernel_size=3,
-                                        stride=1,
-                                        padding=1)
-
-
-    def forward(self, x):
-        #assert x.shape[2] == x.shape[3] == self.resolution, "{}, {}, {}".format(x.shape[2], x.shape[3], self.resolution)
-
-        # timestep embedding
-        temb = None
-
-        # downsampling
-        hs = [self.conv_in(x)]
-        for i_level in range(self.num_resolutions):
-            for i_block in range(self.num_res_blocks):
-                h = self.down[i_level].block[i_block](hs[-1], temb)
-                if len(self.down[i_level].attn) > 0:
-                    h = self.down[i_level].attn[i_block](h)
-                hs.append(h)
-            if i_level != self.num_resolutions-1:
-                hs.append(self.down[i_level].downsample(hs[-1]))
-
-        # middle
-        h = hs[-1]
-        h = self.mid.block_1(h, temb)
-        h = self.mid.attn_1(h)
-        h = self.mid.block_2(h, temb)
-
-        # end
-        h = self.norm_out(h)
-        h = nonlinearity(h)
-        h = self.conv_out(h)
-        return h
-
-
-class Decoder(nn.Module):
-    def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
-                 attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
-                 resolution, z_channels, give_pre_end=False, **ignorekwargs):
-        super().__init__()
-        self.ch = ch
-        self.temb_ch = 0
-        self.num_resolutions = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        self.resolution = resolution
-        self.in_channels = in_channels
-        self.give_pre_end = give_pre_end
-
-        # compute in_ch_mult, block_in and curr_res at lowest res
-        in_ch_mult = (1,)+tuple(ch_mult)
-        block_in = ch*ch_mult[self.num_resolutions-1]
-        curr_res = resolution // 2**(self.num_resolutions-1)
-        self.z_shape = (1,z_channels,curr_res,curr_res)
-        print("Working with z of shape {} = {} dimensions.".format(
-            self.z_shape, np.prod(self.z_shape)))
-
-        # z to block_in
-        self.conv_in = torch.nn.Conv2d(z_channels,
-                                       block_in,
-                                       kernel_size=3,
-                                       stride=1,
-                                       padding=1)
-
-        # middle
-        self.mid = nn.Module()
-        self.mid.block_1 = ResnetBlock(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=dropout)
-        self.mid.attn_1 = AttnBlock(block_in)
-        self.mid.block_2 = ResnetBlock(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=dropout)
-
-        # upsampling
-        self.up = nn.ModuleList()
-        for i_level in reversed(range(self.num_resolutions)):
-            block = nn.ModuleList()
-            attn = nn.ModuleList()
-            block_out = ch*ch_mult[i_level]
-            for i_block in range(self.num_res_blocks+1):
-                block.append(ResnetBlock(in_channels=block_in,
-                                         out_channels=block_out,
-                                         temb_channels=self.temb_ch,
-                                         dropout=dropout))
-                block_in = block_out
-                if curr_res in attn_resolutions:
-                    attn.append(AttnBlock(block_in))
-            up = nn.Module()
-            up.block = block
-            up.attn = attn
-            if i_level != 0:
-                up.upsample = Upsample(block_in, resamp_with_conv)
-                curr_res = curr_res * 2
-            self.up.insert(0, up) # prepend to get consistent order
-
-        # end
-        self.norm_out = Normalize(block_in)
-        self.conv_out = torch.nn.Conv2d(block_in,
-                                        out_ch,
-                                        kernel_size=3,
-                                        stride=1,
-                                        padding=1)
-
-    def forward(self, z):
-        #assert z.shape[1:] == self.z_shape[1:]
-        self.last_z_shape = z.shape
-
-        # timestep embedding
-        temb = None
-
-        # z to block_in
-        h = self.conv_in(z)
-
-        # middle
-        h = self.mid.block_1(h, temb)
-        h = self.mid.attn_1(h)
-        h = self.mid.block_2(h, temb)
-
-        # upsampling
-        for i_level in reversed(range(self.num_resolutions)):
-            for i_block in range(self.num_res_blocks+1):
-                h = self.up[i_level].block[i_block](h, temb)
-                if len(self.up[i_level].attn) > 0:
-                    h = self.up[i_level].attn[i_block](h)
-            if i_level != 0:
-                h = self.up[i_level].upsample(h)
-
-        # end
-        if self.give_pre_end:
-            return h
-
-        h = self.norm_out(h)
-        h = nonlinearity(h)
-        h = self.conv_out(h)
-        return h
-
-
-class VUNet(nn.Module):
-    def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
-                 attn_resolutions, dropout=0.0, resamp_with_conv=True,
-                 in_channels, c_channels,
-                 resolution, z_channels, use_timestep=False, **ignore_kwargs):
-        super().__init__()
-        self.ch = ch
-        self.temb_ch = self.ch*4
-        self.num_resolutions = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        self.resolution = resolution
-
-        self.use_timestep = use_timestep
-        if self.use_timestep:
-            # timestep embedding
-            self.temb = nn.Module()
-            self.temb.dense = nn.ModuleList([
-                torch.nn.Linear(self.ch,
-                                self.temb_ch),
-                torch.nn.Linear(self.temb_ch,
-                                self.temb_ch),
-            ])
-
-        # downsampling
-        self.conv_in = torch.nn.Conv2d(c_channels,
-                                       self.ch,
-                                       kernel_size=3,
-                                       stride=1,
-                                       padding=1)
-
-        curr_res = resolution
-        in_ch_mult = (1,)+tuple(ch_mult)
-        self.down = nn.ModuleList()
-        for i_level in range(self.num_resolutions):
-            block = nn.ModuleList()
-            attn = nn.ModuleList()
-            block_in = ch*in_ch_mult[i_level]
-            block_out = ch*ch_mult[i_level]
-            for i_block in range(self.num_res_blocks):
-                block.append(ResnetBlock(in_channels=block_in,
-                                         out_channels=block_out,
-                                         temb_channels=self.temb_ch,
-                                         dropout=dropout))
-                block_in = block_out
-                if curr_res in attn_resolutions:
-                    attn.append(AttnBlock(block_in))
-            down = nn.Module()
-            down.block = block
-            down.attn = attn
-            if i_level != self.num_resolutions-1:
-                down.downsample = Downsample(block_in, resamp_with_conv)
-                curr_res = curr_res // 2
-            self.down.append(down)
-
-        self.z_in = torch.nn.Conv2d(z_channels,
-                                    block_in,
-                                    kernel_size=1,
-                                    stride=1,
-                                    padding=0)
-        # middle
-        self.mid = nn.Module()
-        self.mid.block_1 = ResnetBlock(in_channels=2*block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=dropout)
-        self.mid.attn_1 = AttnBlock(block_in)
-        self.mid.block_2 = ResnetBlock(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=dropout)
-
-        # upsampling
-        self.up = nn.ModuleList()
-        for i_level in reversed(range(self.num_resolutions)):
-            block = nn.ModuleList()
-            attn = nn.ModuleList()
-            block_out = ch*ch_mult[i_level]
-            skip_in = ch*ch_mult[i_level]
-            for i_block in range(self.num_res_blocks+1):
-                if i_block == self.num_res_blocks:
-                    skip_in = ch*in_ch_mult[i_level]
-                block.append(ResnetBlock(in_channels=block_in+skip_in,
-                                         out_channels=block_out,
-                                         temb_channels=self.temb_ch,
-                                         dropout=dropout))
-                block_in = block_out
-                if curr_res in attn_resolutions:
-                    attn.append(AttnBlock(block_in))
-            up = nn.Module()
-            up.block = block
-            up.attn = attn
-            if i_level != 0:
-                up.upsample = Upsample(block_in, resamp_with_conv)
-                curr_res = curr_res * 2
-            self.up.insert(0, up) # prepend to get consistent order
-
-        # end
-        self.norm_out = Normalize(block_in)
-        self.conv_out = torch.nn.Conv2d(block_in,
-                                        out_ch,
-                                        kernel_size=3,
-                                        stride=1,
-                                        padding=1)
-
-
-    def forward(self, x, z):
-        #assert x.shape[2] == x.shape[3] == self.resolution
-
-        if self.use_timestep:
-            # timestep embedding
-            assert t is not None
-            temb = get_timestep_embedding(t, self.ch)
-            temb = self.temb.dense[0](temb)
-            temb = nonlinearity(temb)
-            temb = self.temb.dense[1](temb)
-        else:
-            temb = None
-
-        # downsampling
-        hs = [self.conv_in(x)]
-        for i_level in range(self.num_resolutions):
-            for i_block in range(self.num_res_blocks):
-                h = self.down[i_level].block[i_block](hs[-1], temb)
-                if len(self.down[i_level].attn) > 0:
-                    h = self.down[i_level].attn[i_block](h)
-                hs.append(h)
-            if i_level != self.num_resolutions-1:
-                hs.append(self.down[i_level].downsample(hs[-1]))
-
-        # middle
-        h = hs[-1]
-        z = self.z_in(z)
-        h = torch.cat((h,z),dim=1)
-        h = self.mid.block_1(h, temb)
-        h = self.mid.attn_1(h)
-        h = self.mid.block_2(h, temb)
-
-        # upsampling
-        for i_level in reversed(range(self.num_resolutions)):
-            for i_block in range(self.num_res_blocks+1):
-                h = self.up[i_level].block[i_block](
-                    torch.cat([h, hs.pop()], dim=1), temb)
-                if len(self.up[i_level].attn) > 0:
-                    h = self.up[i_level].attn[i_block](h)
-            if i_level != 0:
-                h = self.up[i_level].upsample(h)
-
-        # end
-        h = self.norm_out(h)
-        h = nonlinearity(h)
-        h = self.conv_out(h)
-        return h
-
-
-class SimpleDecoder(nn.Module):
-    def __init__(self, in_channels, out_channels, *args, **kwargs):
-        super().__init__()
-        self.model = nn.ModuleList([nn.Conv2d(in_channels, in_channels, 1),
-                                     ResnetBlock(in_channels=in_channels,
-                                                 out_channels=2 * in_channels,
-                                                 temb_channels=0, dropout=0.0),
-                                     ResnetBlock(in_channels=2 * in_channels,
-                                                out_channels=4 * in_channels,
-                                                temb_channels=0, dropout=0.0),
-                                     ResnetBlock(in_channels=4 * in_channels,
-                                                out_channels=2 * in_channels,
-                                                temb_channels=0, dropout=0.0),
-                                     nn.Conv2d(2*in_channels, in_channels, 1),
-                                     Upsample(in_channels, with_conv=True)])
-        # end
-        self.norm_out = Normalize(in_channels)
-        self.conv_out = torch.nn.Conv2d(in_channels,
-                                        out_channels,
-                                        kernel_size=3,
-                                        stride=1,
-                                        padding=1)
-
-    def forward(self, x):
-        for i, layer in enumerate(self.model):
-            if i in [1,2,3]:
-                x = layer(x, None)
-            else:
-                x = layer(x)
-
-        h = self.norm_out(x)
-        h = nonlinearity(h)
-        x = self.conv_out(h)
-        return x
-
-
-class UpsampleDecoder(nn.Module):
-    def __init__(self, in_channels, out_channels, ch, num_res_blocks, resolution,
-                 ch_mult=(2,2), dropout=0.0):
-        super().__init__()
-        # upsampling
-        self.temb_ch = 0
-        self.num_resolutions = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        block_in = in_channels
-        curr_res = resolution // 2 ** (self.num_resolutions - 1)
-        self.res_blocks = nn.ModuleList()
-        self.upsample_blocks = nn.ModuleList()
-        for i_level in range(self.num_resolutions):
-            res_block = []
-            block_out = ch * ch_mult[i_level]
-            for i_block in range(self.num_res_blocks + 1):
-                res_block.append(ResnetBlock(in_channels=block_in,
-                                         out_channels=block_out,
-                                         temb_channels=self.temb_ch,
-                                         dropout=dropout))
-                block_in = block_out
-            self.res_blocks.append(nn.ModuleList(res_block))
-            if i_level != self.num_resolutions - 1:
-                self.upsample_blocks.append(Upsample(block_in, True))
-                curr_res = curr_res * 2
-
-        # end
-        self.norm_out = Normalize(block_in)
-        self.conv_out = torch.nn.Conv2d(block_in,
-                                        out_channels,
-                                        kernel_size=3,
-                                        stride=1,
-                                        padding=1)
-
-    def forward(self, x):
-        # upsampling
-        h = x
-        for k, i_level in enumerate(range(self.num_resolutions)):
-            for i_block in range(self.num_res_blocks + 1):
-                h = self.res_blocks[i_level][i_block](h, None)
-            if i_level != self.num_resolutions - 1:
-                h = self.upsample_blocks[k](h)
-        h = self.norm_out(h)
-        h = nonlinearity(h)
-        h = self.conv_out(h)
-        return h
-
diff --git a/repositories/taming-transformers/taming/modules/discriminator/model.py b/repositories/taming-transformers/taming/modules/discriminator/model.py
deleted file mode 100644
index 2aaa3110d0a7bcd05de7eca1e45101589ca5af05..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/taming/modules/discriminator/model.py
+++ /dev/null
@@ -1,67 +0,0 @@
-import functools
-import torch.nn as nn
-
-
-from taming.modules.util import ActNorm
-
-
-def weights_init(m):
-    classname = m.__class__.__name__
-    if classname.find('Conv') != -1:
-        nn.init.normal_(m.weight.data, 0.0, 0.02)
-    elif classname.find('BatchNorm') != -1:
-        nn.init.normal_(m.weight.data, 1.0, 0.02)
-        nn.init.constant_(m.bias.data, 0)
-
-
-class NLayerDiscriminator(nn.Module):
-    """Defines a PatchGAN discriminator as in Pix2Pix
-        --> see https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/networks.py
-    """
-    def __init__(self, input_nc=3, ndf=64, n_layers=3, use_actnorm=False):
-        """Construct a PatchGAN discriminator
-        Parameters:
-            input_nc (int)  -- the number of channels in input images
-            ndf (int)       -- the number of filters in the last conv layer
-            n_layers (int)  -- the number of conv layers in the discriminator
-            norm_layer      -- normalization layer
-        """
-        super(NLayerDiscriminator, self).__init__()
-        if not use_actnorm:
-            norm_layer = nn.BatchNorm2d
-        else:
-            norm_layer = ActNorm
-        if type(norm_layer) == functools.partial:  # no need to use bias as BatchNorm2d has affine parameters
-            use_bias = norm_layer.func != nn.BatchNorm2d
-        else:
-            use_bias = norm_layer != nn.BatchNorm2d
-
-        kw = 4
-        padw = 1
-        sequence = [nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)]
-        nf_mult = 1
-        nf_mult_prev = 1
-        for n in range(1, n_layers):  # gradually increase the number of filters
-            nf_mult_prev = nf_mult
-            nf_mult = min(2 ** n, 8)
-            sequence += [
-                nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias),
-                norm_layer(ndf * nf_mult),
-                nn.LeakyReLU(0.2, True)
-            ]
-
-        nf_mult_prev = nf_mult
-        nf_mult = min(2 ** n_layers, 8)
-        sequence += [
-            nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias),
-            norm_layer(ndf * nf_mult),
-            nn.LeakyReLU(0.2, True)
-        ]
-
-        sequence += [
-            nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)]  # output 1 channel prediction map
-        self.main = nn.Sequential(*sequence)
-
-    def forward(self, input):
-        """Standard forward."""
-        return self.main(input)
diff --git a/repositories/taming-transformers/taming/modules/losses/__init__.py b/repositories/taming-transformers/taming/modules/losses/__init__.py
deleted file mode 100644
index d09caf9eb805f849a517f1b23503e1a4d6ea1ec5..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/taming/modules/losses/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from taming.modules.losses.vqperceptual import DummyLoss
-
diff --git a/repositories/taming-transformers/taming/modules/losses/lpips.py b/repositories/taming-transformers/taming/modules/losses/lpips.py
deleted file mode 100644
index a7280447694ffc302a7636e7e4d6183408e0aa95..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/taming/modules/losses/lpips.py
+++ /dev/null
@@ -1,123 +0,0 @@
-"""Stripped version of https://github.com/richzhang/PerceptualSimilarity/tree/master/models"""
-
-import torch
-import torch.nn as nn
-from torchvision import models
-from collections import namedtuple
-
-from taming.util import get_ckpt_path
-
-
-class LPIPS(nn.Module):
-    # Learned perceptual metric
-    def __init__(self, use_dropout=True):
-        super().__init__()
-        self.scaling_layer = ScalingLayer()
-        self.chns = [64, 128, 256, 512, 512]  # vg16 features
-        self.net = vgg16(pretrained=True, requires_grad=False)
-        self.lin0 = NetLinLayer(self.chns[0], use_dropout=use_dropout)
-        self.lin1 = NetLinLayer(self.chns[1], use_dropout=use_dropout)
-        self.lin2 = NetLinLayer(self.chns[2], use_dropout=use_dropout)
-        self.lin3 = NetLinLayer(self.chns[3], use_dropout=use_dropout)
-        self.lin4 = NetLinLayer(self.chns[4], use_dropout=use_dropout)
-        self.load_from_pretrained()
-        for param in self.parameters():
-            param.requires_grad = False
-
-    def load_from_pretrained(self, name="vgg_lpips"):
-        ckpt = get_ckpt_path(name, "taming/modules/autoencoder/lpips")
-        self.load_state_dict(torch.load(ckpt, map_location=torch.device("cpu")), strict=False)
-        print("loaded pretrained LPIPS loss from {}".format(ckpt))
-
-    @classmethod
-    def from_pretrained(cls, name="vgg_lpips"):
-        if name != "vgg_lpips":
-            raise NotImplementedError
-        model = cls()
-        ckpt = get_ckpt_path(name)
-        model.load_state_dict(torch.load(ckpt, map_location=torch.device("cpu")), strict=False)
-        return model
-
-    def forward(self, input, target):
-        in0_input, in1_input = (self.scaling_layer(input), self.scaling_layer(target))
-        outs0, outs1 = self.net(in0_input), self.net(in1_input)
-        feats0, feats1, diffs = {}, {}, {}
-        lins = [self.lin0, self.lin1, self.lin2, self.lin3, self.lin4]
-        for kk in range(len(self.chns)):
-            feats0[kk], feats1[kk] = normalize_tensor(outs0[kk]), normalize_tensor(outs1[kk])
-            diffs[kk] = (feats0[kk] - feats1[kk]) ** 2
-
-        res = [spatial_average(lins[kk].model(diffs[kk]), keepdim=True) for kk in range(len(self.chns))]
-        val = res[0]
-        for l in range(1, len(self.chns)):
-            val += res[l]
-        return val
-
-
-class ScalingLayer(nn.Module):
-    def __init__(self):
-        super(ScalingLayer, self).__init__()
-        self.register_buffer('shift', torch.Tensor([-.030, -.088, -.188])[None, :, None, None])
-        self.register_buffer('scale', torch.Tensor([.458, .448, .450])[None, :, None, None])
-
-    def forward(self, inp):
-        return (inp - self.shift) / self.scale
-
-
-class NetLinLayer(nn.Module):
-    """ A single linear layer which does a 1x1 conv """
-    def __init__(self, chn_in, chn_out=1, use_dropout=False):
-        super(NetLinLayer, self).__init__()
-        layers = [nn.Dropout(), ] if (use_dropout) else []
-        layers += [nn.Conv2d(chn_in, chn_out, 1, stride=1, padding=0, bias=False), ]
-        self.model = nn.Sequential(*layers)
-
-
-class vgg16(torch.nn.Module):
-    def __init__(self, requires_grad=False, pretrained=True):
-        super(vgg16, self).__init__()
-        vgg_pretrained_features = models.vgg16(pretrained=pretrained).features
-        self.slice1 = torch.nn.Sequential()
-        self.slice2 = torch.nn.Sequential()
-        self.slice3 = torch.nn.Sequential()
-        self.slice4 = torch.nn.Sequential()
-        self.slice5 = torch.nn.Sequential()
-        self.N_slices = 5
-        for x in range(4):
-            self.slice1.add_module(str(x), vgg_pretrained_features[x])
-        for x in range(4, 9):
-            self.slice2.add_module(str(x), vgg_pretrained_features[x])
-        for x in range(9, 16):
-            self.slice3.add_module(str(x), vgg_pretrained_features[x])
-        for x in range(16, 23):
-            self.slice4.add_module(str(x), vgg_pretrained_features[x])
-        for x in range(23, 30):
-            self.slice5.add_module(str(x), vgg_pretrained_features[x])
-        if not requires_grad:
-            for param in self.parameters():
-                param.requires_grad = False
-
-    def forward(self, X):
-        h = self.slice1(X)
-        h_relu1_2 = h
-        h = self.slice2(h)
-        h_relu2_2 = h
-        h = self.slice3(h)
-        h_relu3_3 = h
-        h = self.slice4(h)
-        h_relu4_3 = h
-        h = self.slice5(h)
-        h_relu5_3 = h
-        vgg_outputs = namedtuple("VggOutputs", ['relu1_2', 'relu2_2', 'relu3_3', 'relu4_3', 'relu5_3'])
-        out = vgg_outputs(h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3, h_relu5_3)
-        return out
-
-
-def normalize_tensor(x,eps=1e-10):
-    norm_factor = torch.sqrt(torch.sum(x**2,dim=1,keepdim=True))
-    return x/(norm_factor+eps)
-
-
-def spatial_average(x, keepdim=True):
-    return x.mean([2,3],keepdim=keepdim)
-
diff --git a/repositories/taming-transformers/taming/modules/losses/segmentation.py b/repositories/taming-transformers/taming/modules/losses/segmentation.py
deleted file mode 100644
index 4ba77deb5159a6307ed2acba9945e4764a4ff0a5..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/taming/modules/losses/segmentation.py
+++ /dev/null
@@ -1,22 +0,0 @@
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-class BCELoss(nn.Module):
-    def forward(self, prediction, target):
-        loss = F.binary_cross_entropy_with_logits(prediction,target)
-        return loss, {}
-
-
-class BCELossWithQuant(nn.Module):
-    def __init__(self, codebook_weight=1.):
-        super().__init__()
-        self.codebook_weight = codebook_weight
-
-    def forward(self, qloss, target, prediction, split):
-        bce_loss = F.binary_cross_entropy_with_logits(prediction,target)
-        loss = bce_loss + self.codebook_weight*qloss
-        return loss, {"{}/total_loss".format(split): loss.clone().detach().mean(),
-                      "{}/bce_loss".format(split): bce_loss.detach().mean(),
-                      "{}/quant_loss".format(split): qloss.detach().mean()
-                      }
diff --git a/repositories/taming-transformers/taming/modules/losses/vqperceptual.py b/repositories/taming-transformers/taming/modules/losses/vqperceptual.py
deleted file mode 100644
index c2febd445728479d4cd9aacdb2572cb1f1af04db..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/taming/modules/losses/vqperceptual.py
+++ /dev/null
@@ -1,136 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from taming.modules.losses.lpips import LPIPS
-from taming.modules.discriminator.model import NLayerDiscriminator, weights_init
-
-
-class DummyLoss(nn.Module):
-    def __init__(self):
-        super().__init__()
-
-
-def adopt_weight(weight, global_step, threshold=0, value=0.):
-    if global_step < threshold:
-        weight = value
-    return weight
-
-
-def hinge_d_loss(logits_real, logits_fake):
-    loss_real = torch.mean(F.relu(1. - logits_real))
-    loss_fake = torch.mean(F.relu(1. + logits_fake))
-    d_loss = 0.5 * (loss_real + loss_fake)
-    return d_loss
-
-
-def vanilla_d_loss(logits_real, logits_fake):
-    d_loss = 0.5 * (
-        torch.mean(torch.nn.functional.softplus(-logits_real)) +
-        torch.mean(torch.nn.functional.softplus(logits_fake)))
-    return d_loss
-
-
-class VQLPIPSWithDiscriminator(nn.Module):
-    def __init__(self, disc_start, codebook_weight=1.0, pixelloss_weight=1.0,
-                 disc_num_layers=3, disc_in_channels=3, disc_factor=1.0, disc_weight=1.0,
-                 perceptual_weight=1.0, use_actnorm=False, disc_conditional=False,
-                 disc_ndf=64, disc_loss="hinge"):
-        super().__init__()
-        assert disc_loss in ["hinge", "vanilla"]
-        self.codebook_weight = codebook_weight
-        self.pixel_weight = pixelloss_weight
-        self.perceptual_loss = LPIPS().eval()
-        self.perceptual_weight = perceptual_weight
-
-        self.discriminator = NLayerDiscriminator(input_nc=disc_in_channels,
-                                                 n_layers=disc_num_layers,
-                                                 use_actnorm=use_actnorm,
-                                                 ndf=disc_ndf
-                                                 ).apply(weights_init)
-        self.discriminator_iter_start = disc_start
-        if disc_loss == "hinge":
-            self.disc_loss = hinge_d_loss
-        elif disc_loss == "vanilla":
-            self.disc_loss = vanilla_d_loss
-        else:
-            raise ValueError(f"Unknown GAN loss '{disc_loss}'.")
-        print(f"VQLPIPSWithDiscriminator running with {disc_loss} loss.")
-        self.disc_factor = disc_factor
-        self.discriminator_weight = disc_weight
-        self.disc_conditional = disc_conditional
-
-    def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None):
-        if last_layer is not None:
-            nll_grads = torch.autograd.grad(nll_loss, last_layer, retain_graph=True)[0]
-            g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0]
-        else:
-            nll_grads = torch.autograd.grad(nll_loss, self.last_layer[0], retain_graph=True)[0]
-            g_grads = torch.autograd.grad(g_loss, self.last_layer[0], retain_graph=True)[0]
-
-        d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4)
-        d_weight = torch.clamp(d_weight, 0.0, 1e4).detach()
-        d_weight = d_weight * self.discriminator_weight
-        return d_weight
-
-    def forward(self, codebook_loss, inputs, reconstructions, optimizer_idx,
-                global_step, last_layer=None, cond=None, split="train"):
-        rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous())
-        if self.perceptual_weight > 0:
-            p_loss = self.perceptual_loss(inputs.contiguous(), reconstructions.contiguous())
-            rec_loss = rec_loss + self.perceptual_weight * p_loss
-        else:
-            p_loss = torch.tensor([0.0])
-
-        nll_loss = rec_loss
-        #nll_loss = torch.sum(nll_loss) / nll_loss.shape[0]
-        nll_loss = torch.mean(nll_loss)
-
-        # now the GAN part
-        if optimizer_idx == 0:
-            # generator update
-            if cond is None:
-                assert not self.disc_conditional
-                logits_fake = self.discriminator(reconstructions.contiguous())
-            else:
-                assert self.disc_conditional
-                logits_fake = self.discriminator(torch.cat((reconstructions.contiguous(), cond), dim=1))
-            g_loss = -torch.mean(logits_fake)
-
-            try:
-                d_weight = self.calculate_adaptive_weight(nll_loss, g_loss, last_layer=last_layer)
-            except RuntimeError:
-                assert not self.training
-                d_weight = torch.tensor(0.0)
-
-            disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start)
-            loss = nll_loss + d_weight * disc_factor * g_loss + self.codebook_weight * codebook_loss.mean()
-
-            log = {"{}/total_loss".format(split): loss.clone().detach().mean(),
-                   "{}/quant_loss".format(split): codebook_loss.detach().mean(),
-                   "{}/nll_loss".format(split): nll_loss.detach().mean(),
-                   "{}/rec_loss".format(split): rec_loss.detach().mean(),
-                   "{}/p_loss".format(split): p_loss.detach().mean(),
-                   "{}/d_weight".format(split): d_weight.detach(),
-                   "{}/disc_factor".format(split): torch.tensor(disc_factor),
-                   "{}/g_loss".format(split): g_loss.detach().mean(),
-                   }
-            return loss, log
-
-        if optimizer_idx == 1:
-            # second pass for discriminator update
-            if cond is None:
-                logits_real = self.discriminator(inputs.contiguous().detach())
-                logits_fake = self.discriminator(reconstructions.contiguous().detach())
-            else:
-                logits_real = self.discriminator(torch.cat((inputs.contiguous().detach(), cond), dim=1))
-                logits_fake = self.discriminator(torch.cat((reconstructions.contiguous().detach(), cond), dim=1))
-
-            disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start)
-            d_loss = disc_factor * self.disc_loss(logits_real, logits_fake)
-
-            log = {"{}/disc_loss".format(split): d_loss.clone().detach().mean(),
-                   "{}/logits_real".format(split): logits_real.detach().mean(),
-                   "{}/logits_fake".format(split): logits_fake.detach().mean()
-                   }
-            return d_loss, log
diff --git a/repositories/taming-transformers/taming/modules/misc/coord.py b/repositories/taming-transformers/taming/modules/misc/coord.py
deleted file mode 100644
index ee69b0c897b6b382ae673622e420f55e494f5b09..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/taming/modules/misc/coord.py
+++ /dev/null
@@ -1,31 +0,0 @@
-import torch
-
-class CoordStage(object):
-    def __init__(self, n_embed, down_factor):
-        self.n_embed = n_embed
-        self.down_factor = down_factor
-
-    def eval(self):
-        return self
-
-    def encode(self, c):
-        """fake vqmodel interface"""
-        assert 0.0 <= c.min() and c.max() <= 1.0
-        b,ch,h,w = c.shape
-        assert ch == 1
-
-        c = torch.nn.functional.interpolate(c, scale_factor=1/self.down_factor,
-                                            mode="area")
-        c = c.clamp(0.0, 1.0)
-        c = self.n_embed*c
-        c_quant = c.round()
-        c_ind = c_quant.to(dtype=torch.long)
-
-        info = None, None, c_ind
-        return c_quant, None, info
-
-    def decode(self, c):
-        c = c/self.n_embed
-        c = torch.nn.functional.interpolate(c, scale_factor=self.down_factor,
-                                            mode="nearest")
-        return c
diff --git a/repositories/taming-transformers/taming/modules/transformer/mingpt.py b/repositories/taming-transformers/taming/modules/transformer/mingpt.py
deleted file mode 100644
index d14b7b68117f4b9f297b2929397cd4f55089334c..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/taming/modules/transformer/mingpt.py
+++ /dev/null
@@ -1,415 +0,0 @@
-"""
-taken from: https://github.com/karpathy/minGPT/
-GPT model:
-- the initial stem consists of a combination of token encoding and a positional encoding
-- the meat of it is a uniform sequence of Transformer blocks
-    - each Transformer is a sequential combination of a 1-hidden-layer MLP block and a self-attention block
-    - all blocks feed into a central residual pathway similar to resnets
-- the final decoder is a linear projection into a vanilla Softmax classifier
-"""
-
-import math
-import logging
-
-import torch
-import torch.nn as nn
-from torch.nn import functional as F
-from transformers import top_k_top_p_filtering
-
-logger = logging.getLogger(__name__)
-
-
-class GPTConfig:
-    """ base GPT config, params common to all GPT versions """
-    embd_pdrop = 0.1
-    resid_pdrop = 0.1
-    attn_pdrop = 0.1
-
-    def __init__(self, vocab_size, block_size, **kwargs):
-        self.vocab_size = vocab_size
-        self.block_size = block_size
-        for k,v in kwargs.items():
-            setattr(self, k, v)
-
-
-class GPT1Config(GPTConfig):
-    """ GPT-1 like network roughly 125M params """
-    n_layer = 12
-    n_head = 12
-    n_embd = 768
-
-
-class CausalSelfAttention(nn.Module):
-    """
-    A vanilla multi-head masked self-attention layer with a projection at the end.
-    It is possible to use torch.nn.MultiheadAttention here but I am including an
-    explicit implementation here to show that there is nothing too scary here.
-    """
-
-    def __init__(self, config):
-        super().__init__()
-        assert config.n_embd % config.n_head == 0
-        # key, query, value projections for all heads
-        self.key = nn.Linear(config.n_embd, config.n_embd)
-        self.query = nn.Linear(config.n_embd, config.n_embd)
-        self.value = nn.Linear(config.n_embd, config.n_embd)
-        # regularization
-        self.attn_drop = nn.Dropout(config.attn_pdrop)
-        self.resid_drop = nn.Dropout(config.resid_pdrop)
-        # output projection
-        self.proj = nn.Linear(config.n_embd, config.n_embd)
-        # causal mask to ensure that attention is only applied to the left in the input sequence
-        mask = torch.tril(torch.ones(config.block_size,
-                                     config.block_size))
-        if hasattr(config, "n_unmasked"):
-            mask[:config.n_unmasked, :config.n_unmasked] = 1
-        self.register_buffer("mask", mask.view(1, 1, config.block_size, config.block_size))
-        self.n_head = config.n_head
-
-    def forward(self, x, layer_past=None):
-        B, T, C = x.size()
-
-        # calculate query, key, values for all heads in batch and move head forward to be the batch dim
-        k = self.key(x).view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
-        q = self.query(x).view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
-        v = self.value(x).view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
-
-        present = torch.stack((k, v))
-        if layer_past is not None:
-            past_key, past_value = layer_past
-            k = torch.cat((past_key, k), dim=-2)
-            v = torch.cat((past_value, v), dim=-2)
-
-        # causal self-attention; Self-attend: (B, nh, T, hs) x (B, nh, hs, T) -> (B, nh, T, T)
-        att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
-        if layer_past is None:
-            att = att.masked_fill(self.mask[:,:,:T,:T] == 0, float('-inf'))
-
-        att = F.softmax(att, dim=-1)
-        att = self.attn_drop(att)
-        y = att @ v # (B, nh, T, T) x (B, nh, T, hs) -> (B, nh, T, hs)
-        y = y.transpose(1, 2).contiguous().view(B, T, C) # re-assemble all head outputs side by side
-
-        # output projection
-        y = self.resid_drop(self.proj(y))
-        return y, present   # TODO: check that this does not break anything
-
-
-class Block(nn.Module):
-    """ an unassuming Transformer block """
-    def __init__(self, config):
-        super().__init__()
-        self.ln1 = nn.LayerNorm(config.n_embd)
-        self.ln2 = nn.LayerNorm(config.n_embd)
-        self.attn = CausalSelfAttention(config)
-        self.mlp = nn.Sequential(
-            nn.Linear(config.n_embd, 4 * config.n_embd),
-            nn.GELU(),  # nice
-            nn.Linear(4 * config.n_embd, config.n_embd),
-            nn.Dropout(config.resid_pdrop),
-        )
-
-    def forward(self, x, layer_past=None, return_present=False):
-        # TODO: check that training still works
-        if return_present: assert not self.training
-        # layer past: tuple of length two with B, nh, T, hs
-        attn, present = self.attn(self.ln1(x), layer_past=layer_past)
-
-        x = x + attn
-        x = x + self.mlp(self.ln2(x))
-        if layer_past is not None or return_present:
-            return x, present
-        return x
-
-
-class GPT(nn.Module):
-    """  the full GPT language model, with a context size of block_size """
-    def __init__(self, vocab_size, block_size, n_layer=12, n_head=8, n_embd=256,
-                 embd_pdrop=0., resid_pdrop=0., attn_pdrop=0., n_unmasked=0):
-        super().__init__()
-        config = GPTConfig(vocab_size=vocab_size, block_size=block_size,
-                           embd_pdrop=embd_pdrop, resid_pdrop=resid_pdrop, attn_pdrop=attn_pdrop,
-                           n_layer=n_layer, n_head=n_head, n_embd=n_embd,
-                           n_unmasked=n_unmasked)
-        # input embedding stem
-        self.tok_emb = nn.Embedding(config.vocab_size, config.n_embd)
-        self.pos_emb = nn.Parameter(torch.zeros(1, config.block_size, config.n_embd))
-        self.drop = nn.Dropout(config.embd_pdrop)
-        # transformer
-        self.blocks = nn.Sequential(*[Block(config) for _ in range(config.n_layer)])
-        # decoder head
-        self.ln_f = nn.LayerNorm(config.n_embd)
-        self.head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
-        self.block_size = config.block_size
-        self.apply(self._init_weights)
-        self.config = config
-        logger.info("number of parameters: %e", sum(p.numel() for p in self.parameters()))
-
-    def get_block_size(self):
-        return self.block_size
-
-    def _init_weights(self, module):
-        if isinstance(module, (nn.Linear, nn.Embedding)):
-            module.weight.data.normal_(mean=0.0, std=0.02)
-            if isinstance(module, nn.Linear) and module.bias is not None:
-                module.bias.data.zero_()
-        elif isinstance(module, nn.LayerNorm):
-            module.bias.data.zero_()
-            module.weight.data.fill_(1.0)
-
-    def forward(self, idx, embeddings=None, targets=None):
-        # forward the GPT model
-        token_embeddings = self.tok_emb(idx) # each index maps to a (learnable) vector
-
-        if embeddings is not None: # prepend explicit embeddings
-            token_embeddings = torch.cat((embeddings, token_embeddings), dim=1)
-
-        t = token_embeddings.shape[1]
-        assert t <= self.block_size, "Cannot forward, model block size is exhausted."
-        position_embeddings = self.pos_emb[:, :t, :] # each position maps to a (learnable) vector
-        x = self.drop(token_embeddings + position_embeddings)
-        x = self.blocks(x)
-        x = self.ln_f(x)
-        logits = self.head(x)
-
-        # if we are given some desired targets also calculate the loss
-        loss = None
-        if targets is not None:
-            loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1))
-
-        return logits, loss
-
-    def forward_with_past(self, idx, embeddings=None, targets=None, past=None, past_length=None):
-        # inference only
-        assert not self.training
-        token_embeddings = self.tok_emb(idx)    # each index maps to a (learnable) vector
-        if embeddings is not None:              # prepend explicit embeddings
-            token_embeddings = torch.cat((embeddings, token_embeddings), dim=1)
-
-        if past is not None:
-            assert past_length is not None
-            past = torch.cat(past, dim=-2)   # n_layer, 2, b, nh, len_past, dim_head
-            past_shape = list(past.shape)
-            expected_shape = [self.config.n_layer, 2, idx.shape[0], self.config.n_head, past_length, self.config.n_embd//self.config.n_head]
-            assert past_shape == expected_shape, f"{past_shape} =/= {expected_shape}"
-            position_embeddings = self.pos_emb[:, past_length, :]  # each position maps to a (learnable) vector
-        else:
-            position_embeddings = self.pos_emb[:, :token_embeddings.shape[1], :]
-
-        x = self.drop(token_embeddings + position_embeddings)
-        presents = []  # accumulate over layers
-        for i, block in enumerate(self.blocks):
-            x, present = block(x, layer_past=past[i, ...] if past is not None else None, return_present=True)
-            presents.append(present)
-
-        x = self.ln_f(x)
-        logits = self.head(x)
-        # if we are given some desired targets also calculate the loss
-        loss = None
-        if targets is not None:
-            loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1))
-
-        return logits, loss, torch.stack(presents)  # _, _, n_layer, 2, b, nh, 1, dim_head
-
-
-class DummyGPT(nn.Module):
-    # for debugging
-    def __init__(self, add_value=1):
-        super().__init__()
-        self.add_value = add_value
-
-    def forward(self, idx):
-        return idx + self.add_value, None
-
-
-class CodeGPT(nn.Module):
-    """Takes in semi-embeddings"""
-    def __init__(self, vocab_size, block_size, in_channels, n_layer=12, n_head=8, n_embd=256,
-                 embd_pdrop=0., resid_pdrop=0., attn_pdrop=0., n_unmasked=0):
-        super().__init__()
-        config = GPTConfig(vocab_size=vocab_size, block_size=block_size,
-                           embd_pdrop=embd_pdrop, resid_pdrop=resid_pdrop, attn_pdrop=attn_pdrop,
-                           n_layer=n_layer, n_head=n_head, n_embd=n_embd,
-                           n_unmasked=n_unmasked)
-        # input embedding stem
-        self.tok_emb = nn.Linear(in_channels, config.n_embd)
-        self.pos_emb = nn.Parameter(torch.zeros(1, config.block_size, config.n_embd))
-        self.drop = nn.Dropout(config.embd_pdrop)
-        # transformer
-        self.blocks = nn.Sequential(*[Block(config) for _ in range(config.n_layer)])
-        # decoder head
-        self.ln_f = nn.LayerNorm(config.n_embd)
-        self.head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
-        self.block_size = config.block_size
-        self.apply(self._init_weights)
-        self.config = config
-        logger.info("number of parameters: %e", sum(p.numel() for p in self.parameters()))
-
-    def get_block_size(self):
-        return self.block_size
-
-    def _init_weights(self, module):
-        if isinstance(module, (nn.Linear, nn.Embedding)):
-            module.weight.data.normal_(mean=0.0, std=0.02)
-            if isinstance(module, nn.Linear) and module.bias is not None:
-                module.bias.data.zero_()
-        elif isinstance(module, nn.LayerNorm):
-            module.bias.data.zero_()
-            module.weight.data.fill_(1.0)
-
-    def forward(self, idx, embeddings=None, targets=None):
-        # forward the GPT model
-        token_embeddings = self.tok_emb(idx) # each index maps to a (learnable) vector
-
-        if embeddings is not None: # prepend explicit embeddings
-            token_embeddings = torch.cat((embeddings, token_embeddings), dim=1)
-
-        t = token_embeddings.shape[1]
-        assert t <= self.block_size, "Cannot forward, model block size is exhausted."
-        position_embeddings = self.pos_emb[:, :t, :] # each position maps to a (learnable) vector
-        x = self.drop(token_embeddings + position_embeddings)
-        x = self.blocks(x)
-        x = self.taming_cinln_f(x)
-        logits = self.head(x)
-
-        # if we are given some desired targets also calculate the loss
-        loss = None
-        if targets is not None:
-            loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1))
-
-        return logits, loss
-
-
-
-#### sampling utils
-
-def top_k_logits(logits, k):
-    v, ix = torch.topk(logits, k)
-    out = logits.clone()
-    out[out < v[:, [-1]]] = -float('Inf')
-    return out
-
-@torch.no_grad()
-def sample(model, x, steps, temperature=1.0, sample=False, top_k=None):
-    """
-    take a conditioning sequence of indices in x (of shape (b,t)) and predict the next token in
-    the sequence, feeding the predictions back into the model each time. Clearly the sampling
-    has quadratic complexity unlike an RNN that is only linear, and has a finite context window
-    of block_size, unlike an RNN that has an infinite context window.
-    """
-    block_size = model.get_block_size()
-    model.eval()
-    for k in range(steps):
-        x_cond = x if x.size(1) <= block_size else x[:, -block_size:]  # crop context if needed
-        logits, _ = model(x_cond)
-        # pluck the logits at the final step and scale by temperature
-        logits = logits[:, -1, :] / temperature
-        # optionally crop probabilities to only the top k options
-        if top_k is not None:
-            logits = top_k_logits(logits, top_k)
-        # apply softmax to convert to probabilities
-        probs = F.softmax(logits, dim=-1)
-        # sample from the distribution or take the most likely
-        if sample:
-            ix = torch.multinomial(probs, num_samples=1)
-        else:
-            _, ix = torch.topk(probs, k=1, dim=-1)
-        # append to the sequence and continue
-        x = torch.cat((x, ix), dim=1)
-
-    return x
-
-
-@torch.no_grad()
-def sample_with_past(x, model, steps, temperature=1., sample_logits=True,
-                     top_k=None, top_p=None, callback=None):
-    # x is conditioning
-    sample = x
-    cond_len = x.shape[1]
-    past = None
-    for n in range(steps):
-        if callback is not None:
-            callback(n)
-        logits, _, present = model.forward_with_past(x, past=past, past_length=(n+cond_len-1))
-        if past is None:
-            past = [present]
-        else:
-            past.append(present)
-        logits = logits[:, -1, :] / temperature
-        if top_k is not None:
-            logits = top_k_top_p_filtering(logits, top_k=top_k, top_p=top_p)
-
-        probs = F.softmax(logits, dim=-1)
-        if not sample_logits:
-            _, x = torch.topk(probs, k=1, dim=-1)
-        else:
-            x = torch.multinomial(probs, num_samples=1)
-        # append to the sequence and continue
-        sample = torch.cat((sample, x), dim=1)
-    del past
-    sample = sample[:, cond_len:]  # cut conditioning off
-    return sample
-
-
-#### clustering utils
-
-class KMeans(nn.Module):
-    def __init__(self, ncluster=512, nc=3, niter=10):
-        super().__init__()
-        self.ncluster = ncluster
-        self.nc = nc
-        self.niter = niter
-        self.shape = (3,32,32)
-        self.register_buffer("C", torch.zeros(self.ncluster,nc))
-        self.register_buffer('initialized', torch.tensor(0, dtype=torch.uint8))
-
-    def is_initialized(self):
-        return self.initialized.item() == 1
-
-    @torch.no_grad()
-    def initialize(self, x):
-        N, D = x.shape
-        assert D == self.nc, D
-        c = x[torch.randperm(N)[:self.ncluster]] # init clusters at random
-        for i in range(self.niter):
-            # assign all pixels to the closest codebook element
-            a = ((x[:, None, :] - c[None, :, :])**2).sum(-1).argmin(1)
-            # move each codebook element to be the mean of the pixels that assigned to it
-            c = torch.stack([x[a==k].mean(0) for k in range(self.ncluster)])
-            # re-assign any poorly positioned codebook elements
-            nanix = torch.any(torch.isnan(c), dim=1)
-            ndead = nanix.sum().item()
-            print('done step %d/%d, re-initialized %d dead clusters' % (i+1, self.niter, ndead))
-            c[nanix] = x[torch.randperm(N)[:ndead]] # re-init dead clusters
-
-        self.C.copy_(c)
-        self.initialized.fill_(1)
-
-
-    def forward(self, x, reverse=False, shape=None):
-        if not reverse:
-            # flatten
-            bs,c,h,w = x.shape
-            assert c == self.nc
-            x = x.reshape(bs,c,h*w,1)
-            C = self.C.permute(1,0)
-            C = C.reshape(1,c,1,self.ncluster)
-            a = ((x-C)**2).sum(1).argmin(-1) # bs, h*w indices
-            return a
-        else:
-            # flatten
-            bs, HW = x.shape
-            """
-            c = self.C.reshape( 1, self.nc,  1, self.ncluster)
-            c = c[bs*[0],:,:,:]
-            c = c[:,:,HW*[0],:]
-            x =      x.reshape(bs,       1, HW,             1)
-            x = x[:,3*[0],:,:]
-            x = torch.gather(c, dim=3, index=x)
-            """
-            x = self.C[x]
-            x = x.permute(0,2,1)
-            shape = shape if shape is not None else self.shape
-            x = x.reshape(bs, *shape)
-
-            return x
diff --git a/repositories/taming-transformers/taming/modules/transformer/permuter.py b/repositories/taming-transformers/taming/modules/transformer/permuter.py
deleted file mode 100644
index 0d43bb135adde38d94bf18a7e5edaa4523cd95cf..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/taming/modules/transformer/permuter.py
+++ /dev/null
@@ -1,248 +0,0 @@
-import torch
-import torch.nn as nn
-import numpy as np
-
-
-class AbstractPermuter(nn.Module):
-    def __init__(self, *args, **kwargs):
-        super().__init__()
-    def forward(self, x, reverse=False):
-        raise NotImplementedError
-
-
-class Identity(AbstractPermuter):
-    def __init__(self):
-        super().__init__()
-
-    def forward(self, x, reverse=False):
-        return x
-
-
-class Subsample(AbstractPermuter):
-    def __init__(self, H, W):
-        super().__init__()
-        C = 1
-        indices = np.arange(H*W).reshape(C,H,W)
-        while min(H, W) > 1:
-            indices = indices.reshape(C,H//2,2,W//2,2)
-            indices = indices.transpose(0,2,4,1,3)
-            indices = indices.reshape(C*4,H//2, W//2)
-            H = H//2
-            W = W//2
-            C = C*4
-        assert H == W == 1
-        idx = torch.tensor(indices.ravel())
-        self.register_buffer('forward_shuffle_idx',
-                             nn.Parameter(idx, requires_grad=False))
-        self.register_buffer('backward_shuffle_idx',
-                             nn.Parameter(torch.argsort(idx), requires_grad=False))
-
-    def forward(self, x, reverse=False):
-        if not reverse:
-            return x[:, self.forward_shuffle_idx]
-        else:
-            return x[:, self.backward_shuffle_idx]
-
-
-def mortonify(i, j):
-    """(i,j) index to linear morton code"""
-    i = np.uint64(i)
-    j = np.uint64(j)
-
-    z = np.uint(0)
-
-    for pos in range(32):
-        z = (z |
-             ((j & (np.uint64(1) << np.uint64(pos))) << np.uint64(pos)) |
-             ((i & (np.uint64(1) << np.uint64(pos))) << np.uint64(pos+1))
-             )
-    return z
-
-
-class ZCurve(AbstractPermuter):
-    def __init__(self, H, W):
-        super().__init__()
-        reverseidx = [np.int64(mortonify(i,j)) for i in range(H) for j in range(W)]
-        idx = np.argsort(reverseidx)
-        idx = torch.tensor(idx)
-        reverseidx = torch.tensor(reverseidx)
-        self.register_buffer('forward_shuffle_idx',
-                             idx)
-        self.register_buffer('backward_shuffle_idx',
-                             reverseidx)
-
-    def forward(self, x, reverse=False):
-        if not reverse:
-            return x[:, self.forward_shuffle_idx]
-        else:
-            return x[:, self.backward_shuffle_idx]
-
-
-class SpiralOut(AbstractPermuter):
-    def __init__(self, H, W):
-        super().__init__()
-        assert H == W
-        size = W
-        indices = np.arange(size*size).reshape(size,size)
-
-        i0 = size//2
-        j0 = size//2-1
-
-        i = i0
-        j = j0
-
-        idx = [indices[i0, j0]]
-        step_mult = 0
-        for c in range(1, size//2+1):
-            step_mult += 1
-            # steps left
-            for k in range(step_mult):
-                i = i - 1
-                j = j
-                idx.append(indices[i, j])
-
-            # step down
-            for k in range(step_mult):
-                i = i
-                j = j + 1
-                idx.append(indices[i, j])
-
-            step_mult += 1
-            if c < size//2:
-                # step right
-                for k in range(step_mult):
-                    i = i + 1
-                    j = j
-                    idx.append(indices[i, j])
-
-                # step up
-                for k in range(step_mult):
-                    i = i
-                    j = j - 1
-                    idx.append(indices[i, j])
-            else:
-                # end reached
-                for k in range(step_mult-1):
-                    i = i + 1
-                    idx.append(indices[i, j])
-
-        assert len(idx) == size*size
-        idx = torch.tensor(idx)
-        self.register_buffer('forward_shuffle_idx', idx)
-        self.register_buffer('backward_shuffle_idx', torch.argsort(idx))
-
-    def forward(self, x, reverse=False):
-        if not reverse:
-            return x[:, self.forward_shuffle_idx]
-        else:
-            return x[:, self.backward_shuffle_idx]
-
-
-class SpiralIn(AbstractPermuter):
-    def __init__(self, H, W):
-        super().__init__()
-        assert H == W
-        size = W
-        indices = np.arange(size*size).reshape(size,size)
-
-        i0 = size//2
-        j0 = size//2-1
-
-        i = i0
-        j = j0
-
-        idx = [indices[i0, j0]]
-        step_mult = 0
-        for c in range(1, size//2+1):
-            step_mult += 1
-            # steps left
-            for k in range(step_mult):
-                i = i - 1
-                j = j
-                idx.append(indices[i, j])
-
-            # step down
-            for k in range(step_mult):
-                i = i
-                j = j + 1
-                idx.append(indices[i, j])
-
-            step_mult += 1
-            if c < size//2:
-                # step right
-                for k in range(step_mult):
-                    i = i + 1
-                    j = j
-                    idx.append(indices[i, j])
-
-                # step up
-                for k in range(step_mult):
-                    i = i
-                    j = j - 1
-                    idx.append(indices[i, j])
-            else:
-                # end reached
-                for k in range(step_mult-1):
-                    i = i + 1
-                    idx.append(indices[i, j])
-
-        assert len(idx) == size*size
-        idx = idx[::-1]
-        idx = torch.tensor(idx)
-        self.register_buffer('forward_shuffle_idx', idx)
-        self.register_buffer('backward_shuffle_idx', torch.argsort(idx))
-
-    def forward(self, x, reverse=False):
-        if not reverse:
-            return x[:, self.forward_shuffle_idx]
-        else:
-            return x[:, self.backward_shuffle_idx]
-
-
-class Random(nn.Module):
-    def __init__(self, H, W):
-        super().__init__()
-        indices = np.random.RandomState(1).permutation(H*W)
-        idx = torch.tensor(indices.ravel())
-        self.register_buffer('forward_shuffle_idx', idx)
-        self.register_buffer('backward_shuffle_idx', torch.argsort(idx))
-
-    def forward(self, x, reverse=False):
-        if not reverse:
-            return x[:, self.forward_shuffle_idx]
-        else:
-            return x[:, self.backward_shuffle_idx]
-
-
-class AlternateParsing(AbstractPermuter):
-    def __init__(self, H, W):
-        super().__init__()
-        indices = np.arange(W*H).reshape(H,W)
-        for i in range(1, H, 2):
-            indices[i, :] = indices[i, ::-1]
-        idx = indices.flatten()
-        assert len(idx) == H*W
-        idx = torch.tensor(idx)
-        self.register_buffer('forward_shuffle_idx', idx)
-        self.register_buffer('backward_shuffle_idx', torch.argsort(idx))
-
-    def forward(self, x, reverse=False):
-        if not reverse:
-            return x[:, self.forward_shuffle_idx]
-        else:
-            return x[:, self.backward_shuffle_idx]
-
-
-if __name__ == "__main__":
-    p0 = AlternateParsing(16, 16)
-    print(p0.forward_shuffle_idx)
-    print(p0.backward_shuffle_idx)
-
-    x = torch.randint(0, 768, size=(11, 256))
-    y = p0(x)
-    xre = p0(y, reverse=True)
-    assert torch.equal(x, xre)
-
-    p1 = SpiralOut(2, 2)
-    print(p1.forward_shuffle_idx)
-    print(p1.backward_shuffle_idx)
diff --git a/repositories/taming-transformers/taming/modules/util.py b/repositories/taming-transformers/taming/modules/util.py
deleted file mode 100644
index 9ee16385d8b1342a2d60a5f1aa5cadcfbe934bd8..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/taming/modules/util.py
+++ /dev/null
@@ -1,130 +0,0 @@
-import torch
-import torch.nn as nn
-
-
-def count_params(model):
-    total_params = sum(p.numel() for p in model.parameters())
-    return total_params
-
-
-class ActNorm(nn.Module):
-    def __init__(self, num_features, logdet=False, affine=True,
-                 allow_reverse_init=False):
-        assert affine
-        super().__init__()
-        self.logdet = logdet
-        self.loc = nn.Parameter(torch.zeros(1, num_features, 1, 1))
-        self.scale = nn.Parameter(torch.ones(1, num_features, 1, 1))
-        self.allow_reverse_init = allow_reverse_init
-
-        self.register_buffer('initialized', torch.tensor(0, dtype=torch.uint8))
-
-    def initialize(self, input):
-        with torch.no_grad():
-            flatten = input.permute(1, 0, 2, 3).contiguous().view(input.shape[1], -1)
-            mean = (
-                flatten.mean(1)
-                .unsqueeze(1)
-                .unsqueeze(2)
-                .unsqueeze(3)
-                .permute(1, 0, 2, 3)
-            )
-            std = (
-                flatten.std(1)
-                .unsqueeze(1)
-                .unsqueeze(2)
-                .unsqueeze(3)
-                .permute(1, 0, 2, 3)
-            )
-
-            self.loc.data.copy_(-mean)
-            self.scale.data.copy_(1 / (std + 1e-6))
-
-    def forward(self, input, reverse=False):
-        if reverse:
-            return self.reverse(input)
-        if len(input.shape) == 2:
-            input = input[:,:,None,None]
-            squeeze = True
-        else:
-            squeeze = False
-
-        _, _, height, width = input.shape
-
-        if self.training and self.initialized.item() == 0:
-            self.initialize(input)
-            self.initialized.fill_(1)
-
-        h = self.scale * (input + self.loc)
-
-        if squeeze:
-            h = h.squeeze(-1).squeeze(-1)
-
-        if self.logdet:
-            log_abs = torch.log(torch.abs(self.scale))
-            logdet = height*width*torch.sum(log_abs)
-            logdet = logdet * torch.ones(input.shape[0]).to(input)
-            return h, logdet
-
-        return h
-
-    def reverse(self, output):
-        if self.training and self.initialized.item() == 0:
-            if not self.allow_reverse_init:
-                raise RuntimeError(
-                    "Initializing ActNorm in reverse direction is "
-                    "disabled by default. Use allow_reverse_init=True to enable."
-                )
-            else:
-                self.initialize(output)
-                self.initialized.fill_(1)
-
-        if len(output.shape) == 2:
-            output = output[:,:,None,None]
-            squeeze = True
-        else:
-            squeeze = False
-
-        h = output / self.scale - self.loc
-
-        if squeeze:
-            h = h.squeeze(-1).squeeze(-1)
-        return h
-
-
-class AbstractEncoder(nn.Module):
-    def __init__(self):
-        super().__init__()
-
-    def encode(self, *args, **kwargs):
-        raise NotImplementedError
-
-
-class Labelator(AbstractEncoder):
-    """Net2Net Interface for Class-Conditional Model"""
-    def __init__(self, n_classes, quantize_interface=True):
-        super().__init__()
-        self.n_classes = n_classes
-        self.quantize_interface = quantize_interface
-
-    def encode(self, c):
-        c = c[:,None]
-        if self.quantize_interface:
-            return c, None, [None, None, c.long()]
-        return c
-
-
-class SOSProvider(AbstractEncoder):
-    # for unconditional training
-    def __init__(self, sos_token, quantize_interface=True):
-        super().__init__()
-        self.sos_token = sos_token
-        self.quantize_interface = quantize_interface
-
-    def encode(self, x):
-        # get batch size from data and replicate sos_token
-        c = torch.ones(x.shape[0], 1)*self.sos_token
-        c = c.long().to(x.device)
-        if self.quantize_interface:
-            return c, None, [None, None, c]
-        return c
diff --git a/repositories/taming-transformers/taming/modules/vqvae/__pycache__/quantize.cpython-310.pyc b/repositories/taming-transformers/taming/modules/vqvae/__pycache__/quantize.cpython-310.pyc
deleted file mode 100644
index 0f402b629adb59eca9e4348760e5c3712a444f59..0000000000000000000000000000000000000000
Binary files a/repositories/taming-transformers/taming/modules/vqvae/__pycache__/quantize.cpython-310.pyc and /dev/null differ
diff --git a/repositories/taming-transformers/taming/modules/vqvae/quantize.py b/repositories/taming-transformers/taming/modules/vqvae/quantize.py
deleted file mode 100644
index d75544e41fa01bce49dd822b1037963d62f79b51..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/taming/modules/vqvae/quantize.py
+++ /dev/null
@@ -1,445 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import numpy as np
-from torch import einsum
-from einops import rearrange
-
-
-class VectorQuantizer(nn.Module):
-    """
-    see https://github.com/MishaLaskin/vqvae/blob/d761a999e2267766400dc646d82d3ac3657771d4/models/quantizer.py
-    ____________________________________________
-    Discretization bottleneck part of the VQ-VAE.
-    Inputs:
-    - n_e : number of embeddings
-    - e_dim : dimension of embedding
-    - beta : commitment cost used in loss term, beta * ||z_e(x)-sg[e]||^2
-    _____________________________________________
-    """
-
-    # NOTE: this class contains a bug regarding beta; see VectorQuantizer2 for
-    # a fix and use legacy=False to apply that fix. VectorQuantizer2 can be
-    # used wherever VectorQuantizer has been used before and is additionally
-    # more efficient.
-    def __init__(self, n_e, e_dim, beta):
-        super(VectorQuantizer, self).__init__()
-        self.n_e = n_e
-        self.e_dim = e_dim
-        self.beta = beta
-
-        self.embedding = nn.Embedding(self.n_e, self.e_dim)
-        self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e)
-
-    def forward(self, z):
-        """
-        Inputs the output of the encoder network z and maps it to a discrete
-        one-hot vector that is the index of the closest embedding vector e_j
-        z (continuous) -> z_q (discrete)
-        z.shape = (batch, channel, height, width)
-        quantization pipeline:
-            1. get encoder input (B,C,H,W)
-            2. flatten input to (B*H*W,C)
-        """
-        # reshape z -> (batch, height, width, channel) and flatten
-        z = z.permute(0, 2, 3, 1).contiguous()
-        z_flattened = z.view(-1, self.e_dim)
-        # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
-
-        d = torch.sum(z_flattened ** 2, dim=1, keepdim=True) + \
-            torch.sum(self.embedding.weight**2, dim=1) - 2 * \
-            torch.matmul(z_flattened, self.embedding.weight.t())
-
-        ## could possible replace this here
-        # #\start...
-        # find closest encodings
-        min_encoding_indices = torch.argmin(d, dim=1).unsqueeze(1)
-
-        min_encodings = torch.zeros(
-            min_encoding_indices.shape[0], self.n_e).to(z)
-        min_encodings.scatter_(1, min_encoding_indices, 1)
-
-        # dtype min encodings: torch.float32
-        # min_encodings shape: torch.Size([2048, 512])
-        # min_encoding_indices.shape: torch.Size([2048, 1])
-
-        # get quantized latent vectors
-        z_q = torch.matmul(min_encodings, self.embedding.weight).view(z.shape)
-        #.........\end
-
-        # with:
-        # .........\start
-        #min_encoding_indices = torch.argmin(d, dim=1)
-        #z_q = self.embedding(min_encoding_indices)
-        # ......\end......... (TODO)
-
-        # compute loss for embedding
-        loss = torch.mean((z_q.detach()-z)**2) + self.beta * \
-            torch.mean((z_q - z.detach()) ** 2)
-
-        # preserve gradients
-        z_q = z + (z_q - z).detach()
-
-        # perplexity
-        e_mean = torch.mean(min_encodings, dim=0)
-        perplexity = torch.exp(-torch.sum(e_mean * torch.log(e_mean + 1e-10)))
-
-        # reshape back to match original input shape
-        z_q = z_q.permute(0, 3, 1, 2).contiguous()
-
-        return z_q, loss, (perplexity, min_encodings, min_encoding_indices)
-
-    def get_codebook_entry(self, indices, shape):
-        # shape specifying (batch, height, width, channel)
-        # TODO: check for more easy handling with nn.Embedding
-        min_encodings = torch.zeros(indices.shape[0], self.n_e).to(indices)
-        min_encodings.scatter_(1, indices[:,None], 1)
-
-        # get quantized latent vectors
-        z_q = torch.matmul(min_encodings.float(), self.embedding.weight)
-
-        if shape is not None:
-            z_q = z_q.view(shape)
-
-            # reshape back to match original input shape
-            z_q = z_q.permute(0, 3, 1, 2).contiguous()
-
-        return z_q
-
-
-class GumbelQuantize(nn.Module):
-    """
-    credit to @karpathy: https://github.com/karpathy/deep-vector-quantization/blob/main/model.py (thanks!)
-    Gumbel Softmax trick quantizer
-    Categorical Reparameterization with Gumbel-Softmax, Jang et al. 2016
-    https://arxiv.org/abs/1611.01144
-    """
-    def __init__(self, num_hiddens, embedding_dim, n_embed, straight_through=True,
-                 kl_weight=5e-4, temp_init=1.0, use_vqinterface=True,
-                 remap=None, unknown_index="random"):
-        super().__init__()
-
-        self.embedding_dim = embedding_dim
-        self.n_embed = n_embed
-
-        self.straight_through = straight_through
-        self.temperature = temp_init
-        self.kl_weight = kl_weight
-
-        self.proj = nn.Conv2d(num_hiddens, n_embed, 1)
-        self.embed = nn.Embedding(n_embed, embedding_dim)
-
-        self.use_vqinterface = use_vqinterface
-
-        self.remap = remap
-        if self.remap is not None:
-            self.register_buffer("used", torch.tensor(np.load(self.remap)))
-            self.re_embed = self.used.shape[0]
-            self.unknown_index = unknown_index # "random" or "extra" or integer
-            if self.unknown_index == "extra":
-                self.unknown_index = self.re_embed
-                self.re_embed = self.re_embed+1
-            print(f"Remapping {self.n_embed} indices to {self.re_embed} indices. "
-                  f"Using {self.unknown_index} for unknown indices.")
-        else:
-            self.re_embed = n_embed
-
-    def remap_to_used(self, inds):
-        ishape = inds.shape
-        assert len(ishape)>1
-        inds = inds.reshape(ishape[0],-1)
-        used = self.used.to(inds)
-        match = (inds[:,:,None]==used[None,None,...]).long()
-        new = match.argmax(-1)
-        unknown = match.sum(2)<1
-        if self.unknown_index == "random":
-            new[unknown]=torch.randint(0,self.re_embed,size=new[unknown].shape).to(device=new.device)
-        else:
-            new[unknown] = self.unknown_index
-        return new.reshape(ishape)
-
-    def unmap_to_all(self, inds):
-        ishape = inds.shape
-        assert len(ishape)>1
-        inds = inds.reshape(ishape[0],-1)
-        used = self.used.to(inds)
-        if self.re_embed > self.used.shape[0]: # extra token
-            inds[inds>=self.used.shape[0]] = 0 # simply set to zero
-        back=torch.gather(used[None,:][inds.shape[0]*[0],:], 1, inds)
-        return back.reshape(ishape)
-
-    def forward(self, z, temp=None, return_logits=False):
-        # force hard = True when we are in eval mode, as we must quantize. actually, always true seems to work
-        hard = self.straight_through if self.training else True
-        temp = self.temperature if temp is None else temp
-
-        logits = self.proj(z)
-        if self.remap is not None:
-            # continue only with used logits
-            full_zeros = torch.zeros_like(logits)
-            logits = logits[:,self.used,...]
-
-        soft_one_hot = F.gumbel_softmax(logits, tau=temp, dim=1, hard=hard)
-        if self.remap is not None:
-            # go back to all entries but unused set to zero
-            full_zeros[:,self.used,...] = soft_one_hot
-            soft_one_hot = full_zeros
-        z_q = einsum('b n h w, n d -> b d h w', soft_one_hot, self.embed.weight)
-
-        # + kl divergence to the prior loss
-        qy = F.softmax(logits, dim=1)
-        diff = self.kl_weight * torch.sum(qy * torch.log(qy * self.n_embed + 1e-10), dim=1).mean()
-
-        ind = soft_one_hot.argmax(dim=1)
-        if self.remap is not None:
-            ind = self.remap_to_used(ind)
-        if self.use_vqinterface:
-            if return_logits:
-                return z_q, diff, (None, None, ind), logits
-            return z_q, diff, (None, None, ind)
-        return z_q, diff, ind
-
-    def get_codebook_entry(self, indices, shape):
-        b, h, w, c = shape
-        assert b*h*w == indices.shape[0]
-        indices = rearrange(indices, '(b h w) -> b h w', b=b, h=h, w=w)
-        if self.remap is not None:
-            indices = self.unmap_to_all(indices)
-        one_hot = F.one_hot(indices, num_classes=self.n_embed).permute(0, 3, 1, 2).float()
-        z_q = einsum('b n h w, n d -> b d h w', one_hot, self.embed.weight)
-        return z_q
-
-
-class VectorQuantizer2(nn.Module):
-    """
-    Improved version over VectorQuantizer, can be used as a drop-in replacement. Mostly
-    avoids costly matrix multiplications and allows for post-hoc remapping of indices.
-    """
-    # NOTE: due to a bug the beta term was applied to the wrong term. for
-    # backwards compatibility we use the buggy version by default, but you can
-    # specify legacy=False to fix it.
-    def __init__(self, n_e, e_dim, beta, remap=None, unknown_index="random",
-                 sane_index_shape=False, legacy=True):
-        super().__init__()
-        self.n_e = n_e
-        self.e_dim = e_dim
-        self.beta = beta
-        self.legacy = legacy
-
-        self.embedding = nn.Embedding(self.n_e, self.e_dim)
-        self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e)
-
-        self.remap = remap
-        if self.remap is not None:
-            self.register_buffer("used", torch.tensor(np.load(self.remap)))
-            self.re_embed = self.used.shape[0]
-            self.unknown_index = unknown_index # "random" or "extra" or integer
-            if self.unknown_index == "extra":
-                self.unknown_index = self.re_embed
-                self.re_embed = self.re_embed+1
-            print(f"Remapping {self.n_e} indices to {self.re_embed} indices. "
-                  f"Using {self.unknown_index} for unknown indices.")
-        else:
-            self.re_embed = n_e
-
-        self.sane_index_shape = sane_index_shape
-
-    def remap_to_used(self, inds):
-        ishape = inds.shape
-        assert len(ishape)>1
-        inds = inds.reshape(ishape[0],-1)
-        used = self.used.to(inds)
-        match = (inds[:,:,None]==used[None,None,...]).long()
-        new = match.argmax(-1)
-        unknown = match.sum(2)<1
-        if self.unknown_index == "random":
-            new[unknown]=torch.randint(0,self.re_embed,size=new[unknown].shape).to(device=new.device)
-        else:
-            new[unknown] = self.unknown_index
-        return new.reshape(ishape)
-
-    def unmap_to_all(self, inds):
-        ishape = inds.shape
-        assert len(ishape)>1
-        inds = inds.reshape(ishape[0],-1)
-        used = self.used.to(inds)
-        if self.re_embed > self.used.shape[0]: # extra token
-            inds[inds>=self.used.shape[0]] = 0 # simply set to zero
-        back=torch.gather(used[None,:][inds.shape[0]*[0],:], 1, inds)
-        return back.reshape(ishape)
-
-    def forward(self, z, temp=None, rescale_logits=False, return_logits=False):
-        assert temp is None or temp==1.0, "Only for interface compatible with Gumbel"
-        assert rescale_logits==False, "Only for interface compatible with Gumbel"
-        assert return_logits==False, "Only for interface compatible with Gumbel"
-        # reshape z -> (batch, height, width, channel) and flatten
-        z = rearrange(z, 'b c h w -> b h w c').contiguous()
-        z_flattened = z.view(-1, self.e_dim)
-        # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
-
-        d = torch.sum(z_flattened ** 2, dim=1, keepdim=True) + \
-            torch.sum(self.embedding.weight**2, dim=1) - 2 * \
-            torch.einsum('bd,dn->bn', z_flattened, rearrange(self.embedding.weight, 'n d -> d n'))
-
-        min_encoding_indices = torch.argmin(d, dim=1)
-        z_q = self.embedding(min_encoding_indices).view(z.shape)
-        perplexity = None
-        min_encodings = None
-
-        # compute loss for embedding
-        if not self.legacy:
-            loss = self.beta * torch.mean((z_q.detach()-z)**2) + \
-                   torch.mean((z_q - z.detach()) ** 2)
-        else:
-            loss = torch.mean((z_q.detach()-z)**2) + self.beta * \
-                   torch.mean((z_q - z.detach()) ** 2)
-
-        # preserve gradients
-        z_q = z + (z_q - z).detach()
-
-        # reshape back to match original input shape
-        z_q = rearrange(z_q, 'b h w c -> b c h w').contiguous()
-
-        if self.remap is not None:
-            min_encoding_indices = min_encoding_indices.reshape(z.shape[0],-1) # add batch axis
-            min_encoding_indices = self.remap_to_used(min_encoding_indices)
-            min_encoding_indices = min_encoding_indices.reshape(-1,1) # flatten
-
-        if self.sane_index_shape:
-            min_encoding_indices = min_encoding_indices.reshape(
-                z_q.shape[0], z_q.shape[2], z_q.shape[3])
-
-        return z_q, loss, (perplexity, min_encodings, min_encoding_indices)
-
-    def get_codebook_entry(self, indices, shape):
-        # shape specifying (batch, height, width, channel)
-        if self.remap is not None:
-            indices = indices.reshape(shape[0],-1) # add batch axis
-            indices = self.unmap_to_all(indices)
-            indices = indices.reshape(-1) # flatten again
-
-        # get quantized latent vectors
-        z_q = self.embedding(indices)
-
-        if shape is not None:
-            z_q = z_q.view(shape)
-            # reshape back to match original input shape
-            z_q = z_q.permute(0, 3, 1, 2).contiguous()
-
-        return z_q
-
-class EmbeddingEMA(nn.Module):
-    def __init__(self, num_tokens, codebook_dim, decay=0.99, eps=1e-5):
-        super().__init__()
-        self.decay = decay
-        self.eps = eps        
-        weight = torch.randn(num_tokens, codebook_dim)
-        self.weight = nn.Parameter(weight, requires_grad = False)
-        self.cluster_size = nn.Parameter(torch.zeros(num_tokens), requires_grad = False)
-        self.embed_avg = nn.Parameter(weight.clone(), requires_grad = False)
-        self.update = True
-
-    def forward(self, embed_id):
-        return F.embedding(embed_id, self.weight)
-
-    def cluster_size_ema_update(self, new_cluster_size):
-        self.cluster_size.data.mul_(self.decay).add_(new_cluster_size, alpha=1 - self.decay)
-
-    def embed_avg_ema_update(self, new_embed_avg): 
-        self.embed_avg.data.mul_(self.decay).add_(new_embed_avg, alpha=1 - self.decay)
-
-    def weight_update(self, num_tokens):
-        n = self.cluster_size.sum()
-        smoothed_cluster_size = (
-                (self.cluster_size + self.eps) / (n + num_tokens * self.eps) * n
-            )
-        #normalize embedding average with smoothed cluster size
-        embed_normalized = self.embed_avg / smoothed_cluster_size.unsqueeze(1)
-        self.weight.data.copy_(embed_normalized)   
-
-
-class EMAVectorQuantizer(nn.Module):
-    def __init__(self, n_embed, embedding_dim, beta, decay=0.99, eps=1e-5,
-                remap=None, unknown_index="random"):
-        super().__init__()
-        self.codebook_dim = codebook_dim
-        self.num_tokens = num_tokens
-        self.beta = beta
-        self.embedding = EmbeddingEMA(self.num_tokens, self.codebook_dim, decay, eps)
-
-        self.remap = remap
-        if self.remap is not None:
-            self.register_buffer("used", torch.tensor(np.load(self.remap)))
-            self.re_embed = self.used.shape[0]
-            self.unknown_index = unknown_index # "random" or "extra" or integer
-            if self.unknown_index == "extra":
-                self.unknown_index = self.re_embed
-                self.re_embed = self.re_embed+1
-            print(f"Remapping {self.n_embed} indices to {self.re_embed} indices. "
-                  f"Using {self.unknown_index} for unknown indices.")
-        else:
-            self.re_embed = n_embed
-
-    def remap_to_used(self, inds):
-        ishape = inds.shape
-        assert len(ishape)>1
-        inds = inds.reshape(ishape[0],-1)
-        used = self.used.to(inds)
-        match = (inds[:,:,None]==used[None,None,...]).long()
-        new = match.argmax(-1)
-        unknown = match.sum(2)<1
-        if self.unknown_index == "random":
-            new[unknown]=torch.randint(0,self.re_embed,size=new[unknown].shape).to(device=new.device)
-        else:
-            new[unknown] = self.unknown_index
-        return new.reshape(ishape)
-
-    def unmap_to_all(self, inds):
-        ishape = inds.shape
-        assert len(ishape)>1
-        inds = inds.reshape(ishape[0],-1)
-        used = self.used.to(inds)
-        if self.re_embed > self.used.shape[0]: # extra token
-            inds[inds>=self.used.shape[0]] = 0 # simply set to zero
-        back=torch.gather(used[None,:][inds.shape[0]*[0],:], 1, inds)
-        return back.reshape(ishape)
-
-    def forward(self, z):
-        # reshape z -> (batch, height, width, channel) and flatten
-        #z, 'b c h w -> b h w c'
-        z = rearrange(z, 'b c h w -> b h w c')
-        z_flattened = z.reshape(-1, self.codebook_dim)
-        
-        # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
-        d = z_flattened.pow(2).sum(dim=1, keepdim=True) + \
-            self.embedding.weight.pow(2).sum(dim=1) - 2 * \
-            torch.einsum('bd,nd->bn', z_flattened, self.embedding.weight) # 'n d -> d n'
-
-
-        encoding_indices = torch.argmin(d, dim=1)
-
-        z_q = self.embedding(encoding_indices).view(z.shape)
-        encodings = F.one_hot(encoding_indices, self.num_tokens).type(z.dtype)     
-        avg_probs = torch.mean(encodings, dim=0)
-        perplexity = torch.exp(-torch.sum(avg_probs * torch.log(avg_probs + 1e-10)))
-
-        if self.training and self.embedding.update:
-            #EMA cluster size
-            encodings_sum = encodings.sum(0)            
-            self.embedding.cluster_size_ema_update(encodings_sum)
-            #EMA embedding average
-            embed_sum = encodings.transpose(0,1) @ z_flattened            
-            self.embedding.embed_avg_ema_update(embed_sum)
-            #normalize embed_avg and update weight
-            self.embedding.weight_update(self.num_tokens)
-
-        # compute loss for embedding
-        loss = self.beta * F.mse_loss(z_q.detach(), z) 
-
-        # preserve gradients
-        z_q = z + (z_q - z).detach()
-
-        # reshape back to match original input shape
-        #z_q, 'b h w c -> b c h w'
-        z_q = rearrange(z_q, 'b h w c -> b c h w')
-        return z_q, loss, (perplexity, encodings, encoding_indices)
diff --git a/repositories/taming-transformers/taming/util.py b/repositories/taming-transformers/taming/util.py
deleted file mode 100644
index 06053e5defb87977f9ab07e69bf4da12201de9b7..0000000000000000000000000000000000000000
--- a/repositories/taming-transformers/taming/util.py
+++ /dev/null
@@ -1,157 +0,0 @@
-import os, hashlib
-import requests
-from tqdm import tqdm
-
-URL_MAP = {
-    "vgg_lpips": "https://heibox.uni-heidelberg.de/f/607503859c864bc1b30b/?dl=1"
-}
-
-CKPT_MAP = {
-    "vgg_lpips": "vgg.pth"
-}
-
-MD5_MAP = {
-    "vgg_lpips": "d507d7349b931f0638a25a48a722f98a"
-}
-
-
-def download(url, local_path, chunk_size=1024):
-    os.makedirs(os.path.split(local_path)[0], exist_ok=True)
-    with requests.get(url, stream=True) as r:
-        total_size = int(r.headers.get("content-length", 0))
-        with tqdm(total=total_size, unit="B", unit_scale=True) as pbar:
-            with open(local_path, "wb") as f:
-                for data in r.iter_content(chunk_size=chunk_size):
-                    if data:
-                        f.write(data)
-                        pbar.update(chunk_size)
-
-
-def md5_hash(path):
-    with open(path, "rb") as f:
-        content = f.read()
-    return hashlib.md5(content).hexdigest()
-
-
-def get_ckpt_path(name, root, check=False):
-    assert name in URL_MAP
-    path = os.path.join(root, CKPT_MAP[name])
-    if not os.path.exists(path) or (check and not md5_hash(path) == MD5_MAP[name]):
-        print("Downloading {} model from {} to {}".format(name, URL_MAP[name], path))
-        download(URL_MAP[name], path)
-        md5 = md5_hash(path)
-        assert md5 == MD5_MAP[name], md5
-    return path
-
-
-class KeyNotFoundError(Exception):
-    def __init__(self, cause, keys=None, visited=None):
-        self.cause = cause
-        self.keys = keys
-        self.visited = visited
-        messages = list()
-        if keys is not None:
-            messages.append("Key not found: {}".format(keys))
-        if visited is not None:
-            messages.append("Visited: {}".format(visited))
-        messages.append("Cause:\n{}".format(cause))
-        message = "\n".join(messages)
-        super().__init__(message)
-
-
-def retrieve(
-    list_or_dict, key, splitval="/", default=None, expand=True, pass_success=False
-):
-    """Given a nested list or dict return the desired value at key expanding
-    callable nodes if necessary and :attr:`expand` is ``True``. The expansion
-    is done in-place.
-
-    Parameters
-    ----------
-        list_or_dict : list or dict
-            Possibly nested list or dictionary.
-        key : str
-            key/to/value, path like string describing all keys necessary to
-            consider to get to the desired value. List indices can also be
-            passed here.
-        splitval : str
-            String that defines the delimiter between keys of the
-            different depth levels in `key`.
-        default : obj
-            Value returned if :attr:`key` is not found.
-        expand : bool
-            Whether to expand callable nodes on the path or not.
-
-    Returns
-    -------
-        The desired value or if :attr:`default` is not ``None`` and the
-        :attr:`key` is not found returns ``default``.
-
-    Raises
-    ------
-        Exception if ``key`` not in ``list_or_dict`` and :attr:`default` is
-        ``None``.
-    """
-
-    keys = key.split(splitval)
-
-    success = True
-    try:
-        visited = []
-        parent = None
-        last_key = None
-        for key in keys:
-            if callable(list_or_dict):
-                if not expand:
-                    raise KeyNotFoundError(
-                        ValueError(
-                            "Trying to get past callable node with expand=False."
-                        ),
-                        keys=keys,
-                        visited=visited,
-                    )
-                list_or_dict = list_or_dict()
-                parent[last_key] = list_or_dict
-
-            last_key = key
-            parent = list_or_dict
-
-            try:
-                if isinstance(list_or_dict, dict):
-                    list_or_dict = list_or_dict[key]
-                else:
-                    list_or_dict = list_or_dict[int(key)]
-            except (KeyError, IndexError, ValueError) as e:
-                raise KeyNotFoundError(e, keys=keys, visited=visited)
-
-            visited += [key]
-        # final expansion of retrieved value
-        if expand and callable(list_or_dict):
-            list_or_dict = list_or_dict()
-            parent[last_key] = list_or_dict
-    except KeyNotFoundError as e:
-        if default is None:
-            raise e
-        else:
-            list_or_dict = default
-            success = False
-
-    if not pass_success:
-        return list_or_dict
-    else:
-        return list_or_dict, success
-
-
-if __name__ == "__main__":
-    config = {"keya": "a",
-              "keyb": "b",
-              "keyc":
-                  {"cc1": 1,
-                   "cc2": 2,
-                   }
-              }
-    from omegaconf import OmegaConf
-    config = OmegaConf.create(config)
-    print(config)
-    retrieve(config, "keya")
-