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README.md

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-## BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation
-
-## Announcement: BLIP is now officially integrated into [LAVIS](https://github.com/salesforce/LAVIS) - a one-stop library for language-and-vision research and applications!
-
-<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.
+---
+title: SCRIPture
+emoji: emoji
+colorFrom: Blue
+colorTo: Green
+sdk: gradio
+sdk_version: 3.9.0
+app_file: app.py
+pinned: false
+---