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transformers.md

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+# PyMatting: A Python Library for Alpha Matting
+[![License: MIT](https://img.shields.io/github/license/pymatting/pymatting?color=brightgreen)](https://opensource.org/licenses/MIT)
+[![CI](https://img.shields.io/github/actions/workflow/status/pymatting/pymatting/.github/workflows/tests.yml?branch=master)](https://github.com/pymatting/pymatting/actions?query=workflow%3Atests)
+[![PyPI](https://img.shields.io/pypi/v/pymatting)](https://pypi.org/project/PyMatting/)
+[![JOSS](https://joss.theoj.org/papers/9766cab65bfbf07a70c8a835edd3875a/status.svg)](https://joss.theoj.org/papers/9766cab65bfbf07a70c8a835edd3875a)
+[![Gitter](https://img.shields.io/gitter/room/pymatting/pymatting)](https://gitter.im/pymatting/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge)
+
+We introduce the PyMatting package for Python which implements various methods to solve the alpha matting problem.
+
+- **Website and Documentation:** [https://pymatting.github.io/](https://pymatting.github.io)
+- **Benchmarks:**  [https://pymatting.github.io/benchmarks.html](https://pymatting.github.io/benchmarks.html)
+
+![Lemur](https://github.com/pymatting/pymatting/raw/master/data/lemur/lemur_at_the_beach.png)
+
+Given an input image and a hand-drawn trimap (top row), alpha matting estimates the alpha channel of a foreground object which can then be composed onto a different background (bottom row).
+
+PyMatting provides:
+- Alpha matting implementations for:
+  - Closed Form Alpha Matting [[1]](#1)
+  - Large Kernel Matting [[2]](#2)
+  - KNN Matting [[3]](#3)
+  - Learning Based Digital Matting [[4]](#4)
+  - Random Walk Matting [[5]](#5)
+  - Shared Sampling Matting [[6]](#6)
+- Foreground estimation implementations for:
+  - Closed Form Foreground Estimation [[1]](#1)
+  - Fast Multi-Level Foreground Estimation (CPU, CUDA and OpenCL) [[7]](#7)
+- Fast multithreaded KNN search
+- Preconditioners to accelerate the convergence rate of conjugate gradient descent:
+  - The *incomplete thresholded Cholesky decomposition* (*Incomplete* is part of the name. The implementation is quite complete.)
+  - The V-Cycle Geometric Multigrid preconditioner
+- Readable code leveraging [NumPy](https://numpy.org/), [SciPy](https://scipy.org/) and [Numba](http://numba.pydata.org/)
+
+## Getting Started
+
+### Requirements
+
+Minimal requirements
+* numpy>=1.16.0
+* pillow>=5.2.0
+* numba>=0.47.0
+* scipy>=1.1.0
+
+Additional requirements for GPU support
+* cupy-cuda90>=6.5.0 or similar
+* pyopencl>=2019.1.2
+
+Requirements to run the tests
+* pytest>=5.3.4
+
+### Installation with PyPI
+
+```bash
+pip3 install pymatting
+```
+
+### Installation from Source
+
+```bash
+git clone https://github.com/pymatting/pymatting
+cd pymatting
+pip3 install .
+```
+
+## Example
+```python
+from pymatting import cutout
+
+cutout(
+    # input image path
+    "data/lemur/lemur.png",
+    # input trimap path
+    "data/lemur/lemur_trimap.png",
+    # output cutout path
+    "lemur_cutout.png")
+```
+
+[More advanced examples](https://pymatting.github.io/examples.html)
+
+## Trimap Construction
+
+All implemented methods rely on trimaps which roughly classify the image into foreground, background and unknown regions.
+Trimaps are expected to be `numpy.ndarrays` of type `np.float64`  having the same shape as the input image with only one color-channel.
+Trimap values of 0.0 denote pixels which are 100% background.
+Similarly, trimap values of 1.0 denote pixels which are 100% foreground.
+All other values indicate unknown pixels which will be estimated by the algorithm.
+
+
+## Testing
+
+Run the tests from the main directory:
+```
+ python3 tests/download_images.py
+ pip3 install -r requirements_tests.txt
+ pytest
+```
+
+Currently 89% of the code is covered by tests.
+
+## Upgrade
+
+```bash
+pip3 install --upgrade pymatting
+python3 -c "import pymatting"
+```
+
+## Bug Reports, Questions and Pull-Requests
+
+Please, see [our community guidelines](https://github.com/pymatting/pymatting/blob/master/CONTRIBUTING.md).
+
+## Authors
+
+- **Thomas Germer**
+- **Tobias Uelwer**
+- **Stefan Conrad**
+- **Stefan Harmeling**
+
+See also the list of [contributors](https://github.com/pymatting/pymatting/contributors) who participated in this project.
+
+## Projects using PyMatting
+
+* [Rembg](https://github.com/danielgatis/rembg) - an excellent tool for removing image backgrounds.
+* [PaddleSeg](https://github.com/PaddlePaddle/PaddleSeg) - a library for a wide range of image segmentation tasks.
+* [chaiNNer](https://github.com/chaiNNer-org/chaiNNer) - a node-based image processing GUI.
+* [LSA-Matting](https://github.com/kfeng123/LSA-Matting) - improving deep image matting via local smoothness assumption.
+
+## License
+
+This project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details
+
+## Citing
+
+If you found PyMatting to be useful for your work, please consider citing our [paper](https://doi.org/10.21105/joss.02481):
+
+```
+@article{Germer2020,
+  doi = {10.21105/joss.02481},
+  url = {https://doi.org/10.21105/joss.02481},
+  year = {2020},
+  publisher = {The Open Journal},
+  volume = {5},
+  number = {54},
+  pages = {2481},
+  author = {Thomas Germer and Tobias Uelwer and Stefan Conrad and Stefan Harmeling},
+  title = {PyMatting: A Python Library for Alpha Matting},
+  journal = {Journal of Open Source Software}
+}
+```
+
+## References
+
+<a id="1">[1]</a>
+Anat Levin, Dani Lischinski, and Yair Weiss. A closed-form solution to natural image matting. IEEE transactions on pattern analysis and machine intelligence, 30(2):228–242, 2007.
+
+<a id="2">[2]</a>
+Kaiming He, Jian Sun, and Xiaoou Tang. Fast matting using large kernel matting laplacian matrices. In 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2165–2172. IEEE, 2010.
+
+<a id="3">[3]</a>
+Qifeng Chen, Dingzeyu Li, and Chi-Keung Tang. Knn matting. IEEE transactions on pattern analysis and machine intelligence, 35(9):2175–2188, 2013.
+
+<a id="4">[4]</a>
+Yuanjie Zheng and Chandra Kambhamettu. Learning based digital matting. In 2009 IEEE 12th international conference on computer vision, 889–896. IEEE, 2009.
+
+<a id="5">[5]</a>
+Leo Grady, Thomas Schiwietz, Shmuel Aharon, and Rüdiger Westermann. Random walks for interactive alpha-matting. In Proceedings of VIIP, volume 2005, 423–429. 2005.
+
+<a id="6">[6]</a>
+Eduardo S. L. Gastal and Manuel M. Oliveira. "Shared Sampling for Real-Time Alpha Matting". Computer Graphics Forum. Volume 29 (2010), Number 2, Proceedings of Eurographics 2010, pp. 575-584.
+
+<a id="7">[7]</a>
+Germer, T., Uelwer, T., Conrad, S., & Harmeling, S. (2020). Fast Multi-Level Foreground Estimation. arXiv preprint arXiv:2006.14970.
+
+Lemur image by Mathias Appel from https://www.flickr.com/photos/mathiasappel/25419442300/ licensed under [CC0 1.0 Universal (CC0 1.0) Public Domain License](https://creativecommons.org/publicdomain/zero/1.0/).

yolo.md

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+# YOLOv9
+
+Implementation of paper - [YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information](https://arxiv.org/abs/2402.13616)
+
+[![arxiv.org](http://img.shields.io/badge/cs.CV-arXiv%3A2402.13616-B31B1B.svg)](https://arxiv.org/abs/2402.13616)
+[![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/kadirnar/Yolov9)
+[![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/merve/yolov9)
+[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/train-yolov9-object-detection-on-custom-dataset.ipynb)
+[![OpenCV](https://img.shields.io/badge/OpenCV-BlogPost-black?logo=opencv&labelColor=blue&color=black)](https://learnopencv.com/yolov9-advancing-the-yolo-legacy/)
+
+<div align="center">
+    <a href="./">
+        <img src="./figure/performance.png" width="79%"/>
+    </a>
+</div>
+
+
+## Performance 
+
+MS COCO
+
+| Model | Test Size | AP<sup>val</sup> | AP<sub>50</sub><sup>val</sup> | AP<sub>75</sub><sup>val</sup> | Param. | FLOPs |
+| :-- | :-: | :-: | :-: | :-: | :-: | :-: |
+| [**YOLOv9-T**]() | 640 | **38.3%** | **53.1%** | **41.3%** | **2.0M** | **7.7G** |
+| [**YOLOv9-S**]() | 640 | **46.8%** | **63.4%** | **50.7%** | **7.1M** | **26.4G** |
+| [**YOLOv9-M**]() | 640 | **51.4%** | **68.1%** | **56.1%** | **20.0M** | **76.3G** |
+| [**YOLOv9-C**](https://github.com/WongKinYiu/yolov9/releases/download/v0.1/yolov9-c-converted.pt) | 640 | **53.0%** | **70.2%** | **57.8%** | **25.3M** | **102.1G** |
+| [**YOLOv9-E**](https://github.com/WongKinYiu/yolov9/releases/download/v0.1/yolov9-e-converted.pt) | 640 | **55.6%** | **72.8%** | **60.6%** | **57.3M** | **189.0G** |
+<!-- | [**YOLOv9 (ReLU)**]() | 640 | **51.9%** | **69.1%** | **56.5%** | **25.3M** | **102.1G** | -->
+
+<!-- tiny, small, and medium models will be released after the paper be accepted and published. -->
+
+## Useful Links
+
+<details><summary> <b>Expand</b> </summary>
+
+Custom training: https://github.com/WongKinYiu/yolov9/issues/30#issuecomment-1960955297
+    
+ONNX export: https://github.com/WongKinYiu/yolov9/issues/2#issuecomment-1960519506 https://github.com/WongKinYiu/yolov9/issues/40#issue-2150697688 https://github.com/WongKinYiu/yolov9/issues/130#issue-2162045461
+
+TensorRT inference: https://github.com/WongKinYiu/yolov9/issues/143#issuecomment-1975049660 https://github.com/WongKinYiu/yolov9/issues/34#issue-2150393690 https://github.com/WongKinYiu/yolov9/issues/79#issue-2153547004 https://github.com/WongKinYiu/yolov9/issues/143#issue-2164002309
+
+QAT TensirRT: https://github.com/WongKinYiu/yolov9/issues/253#issue-2189520073
+
+OpenVINO: https://github.com/WongKinYiu/yolov9/issues/164#issue-2168540003
+
+C# ONNX inference: https://github.com/WongKinYiu/yolov9/issues/95#issue-2155974619
+
+C# OpenVINO inference: https://github.com/WongKinYiu/yolov9/issues/95#issuecomment-1968131244
+
+OpenCV: https://github.com/WongKinYiu/yolov9/issues/113#issuecomment-1971327672
+
+Hugging Face demo: https://github.com/WongKinYiu/yolov9/issues/45#issuecomment-1961496943
+
+CoLab demo: https://github.com/WongKinYiu/yolov9/pull/18
+
+ONNXSlim export: https://github.com/WongKinYiu/yolov9/pull/37
+
+YOLOv9 ROS: https://github.com/WongKinYiu/yolov9/issues/144#issue-2164210644
+
+YOLOv9 ROS TensorRT: https://github.com/WongKinYiu/yolov9/issues/145#issue-2164218595
+
+YOLOv9 Julia: https://github.com/WongKinYiu/yolov9/issues/141#issuecomment-1973710107
+
+YOLOv9 MLX: https://github.com/WongKinYiu/yolov9/issues/258#issue-2190586540
+
+YOLOv9 ByteTrack: https://github.com/WongKinYiu/yolov9/issues/78#issue-2153512879
+
+YOLOv9 DeepSORT: https://github.com/WongKinYiu/yolov9/issues/98#issue-2156172319
+
+YOLOv9 counting: https://github.com/WongKinYiu/yolov9/issues/84#issue-2153904804
+
+YOLOv9 face detection: https://github.com/WongKinYiu/yolov9/issues/121#issue-2160218766
+
+YOLOv9 segmentation onnxruntime: https://github.com/WongKinYiu/yolov9/issues/151#issue-2165667350
+
+Comet logging: https://github.com/WongKinYiu/yolov9/pull/110
+
+MLflow logging: https://github.com/WongKinYiu/yolov9/pull/87
+
+AnyLabeling tool: https://github.com/WongKinYiu/yolov9/issues/48#issue-2152139662
+
+AX650N deploy: https://github.com/WongKinYiu/yolov9/issues/96#issue-2156115760
+
+Conda environment: https://github.com/WongKinYiu/yolov9/pull/93
+
+AutoDL docker environment: https://github.com/WongKinYiu/yolov9/issues/112#issue-2158203480
+
+</details>
+
+
+## Installation
+
+Docker environment (recommended)
+<details><summary> <b>Expand</b> </summary>
+
+``` shell
+# create the docker container, you can change the share memory size if you have more.
+nvidia-docker run --name yolov9 -it -v your_coco_path/:/coco/ -v your_code_path/:/yolov9 --shm-size=64g nvcr.io/nvidia/pytorch:21.11-py3
+
+# apt install required packages
+apt update
+apt install -y zip htop screen libgl1-mesa-glx
+
+# pip install required packages
+pip install seaborn thop
+
+# go to code folder
+cd /yolov9
+```
+
+</details>
+
+
+## Evaluation
+
+[`yolov9-c-converted.pt`](https://github.com/WongKinYiu/yolov9/releases/download/v0.1/yolov9-c-converted.pt) [`yolov9-e-converted.pt`](https://github.com/WongKinYiu/yolov9/releases/download/v0.1/yolov9-e-converted.pt) [`yolov9-c.pt`](https://github.com/WongKinYiu/yolov9/releases/download/v0.1/yolov9-c.pt) [`yolov9-e.pt`](https://github.com/WongKinYiu/yolov9/releases/download/v0.1/yolov9-e.pt) [`gelan-c.pt`](https://github.com/WongKinYiu/yolov9/releases/download/v0.1/gelan-c.pt) [`gelan-e.pt`](https://github.com/WongKinYiu/yolov9/releases/download/v0.1/gelan-e.pt)
+
+``` shell
+# evaluate converted yolov9 models
+python val.py --data data/coco.yaml --img 640 --batch 32 --conf 0.001 --iou 0.7 --device 0 --weights './yolov9-c-converted.pt' --save-json --name yolov9_c_c_640_val
+
+# evaluate yolov9 models
+# python val_dual.py --data data/coco.yaml --img 640 --batch 32 --conf 0.001 --iou 0.7 --device 0 --weights './yolov9-c.pt' --save-json --name yolov9_c_640_val
+
+# evaluate gelan models
+# python val.py --data data/coco.yaml --img 640 --batch 32 --conf 0.001 --iou 0.7 --device 0 --weights './gelan-c.pt' --save-json --name gelan_c_640_val
+```
+
+You will get the results:
+
+```
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.530
+ Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.702
+ Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.578
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.362
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.585
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.693
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.392
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.652
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.702
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.541
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.760
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.844
+```
+
+
+## Training
+
+Data preparation
+
+``` shell
+bash scripts/get_coco.sh
+```
+
+* Download MS COCO dataset images ([train](http://images.cocodataset.org/zips/train2017.zip), [val](http://images.cocodataset.org/zips/val2017.zip), [test](http://images.cocodataset.org/zips/test2017.zip)) and [labels](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/coco2017labels-segments.zip). If you have previously used a different version of YOLO, we strongly recommend that you delete `train2017.cache` and `val2017.cache` files, and redownload [labels](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/coco2017labels-segments.zip) 
+
+Single GPU training
+
+``` shell
+# train yolov9 models
+python train_dual.py --workers 8 --device 0 --batch 16 --data data/coco.yaml --img 640 --cfg models/detect/yolov9-c.yaml --weights '' --name yolov9-c --hyp hyp.scratch-high.yaml --min-items 0 --epochs 500 --close-mosaic 15
+
+# train gelan models
+# python train.py --workers 8 --device 0 --batch 32 --data data/coco.yaml --img 640 --cfg models/detect/gelan-c.yaml --weights '' --name gelan-c --hyp hyp.scratch-high.yaml --min-items 0 --epochs 500 --close-mosaic 15
+```
+
+Multiple GPU training
+
+``` shell
+# train yolov9 models
+python -m torch.distributed.launch --nproc_per_node 8 --master_port 9527 train_dual.py --workers 8 --device 0,1,2,3,4,5,6,7 --sync-bn --batch 128 --data data/coco.yaml --img 640 --cfg models/detect/yolov9-c.yaml --weights '' --name yolov9-c --hyp hyp.scratch-high.yaml --min-items 0 --epochs 500 --close-mosaic 15
+
+# train gelan models
+# python -m torch.distributed.launch --nproc_per_node 4 --master_port 9527 train.py --workers 8 --device 0,1,2,3 --sync-bn --batch 128 --data data/coco.yaml --img 640 --cfg models/detect/gelan-c.yaml --weights '' --name gelan-c --hyp hyp.scratch-high.yaml --min-items 0 --epochs 500 --close-mosaic 15
+```
+
+
+## Re-parameterization
+
+See [reparameterization.ipynb](https://github.com/WongKinYiu/yolov9/blob/main/tools/reparameterization.ipynb).
+
+
+## Inference
+
+<div align="center">
+    <a href="./">
+        <img src="./figure/horses_prediction.jpg" width="49%"/>
+    </a>
+</div>
+
+``` shell
+# inference converted yolov9 models
+python detect.py --source './data/images/horses.jpg' --img 640 --device 0 --weights './yolov9-c-converted.pt' --name yolov9_c_c_640_detect
+
+# inference yolov9 models
+# python detect_dual.py --source './data/images/horses.jpg' --img 640 --device 0 --weights './yolov9-c.pt' --name yolov9_c_640_detect
+
+# inference gelan models
+# python detect.py --source './data/images/horses.jpg' --img 640 --device 0 --weights './gelan-c.pt' --name gelan_c_c_640_detect
+```
+
+
+## Citation
+
+```
+@article{wang2024yolov9,
+  title={{YOLOv9}: Learning What You Want to Learn Using Programmable Gradient Information},
+  author={Wang, Chien-Yao  and Liao, Hong-Yuan Mark},
+  booktitle={arXiv preprint arXiv:2402.13616},
+  year={2024}
+}
+```
+
+```
+@article{chang2023yolor,
+  title={{YOLOR}-Based Multi-Task Learning},
+  author={Chang, Hung-Shuo and Wang, Chien-Yao and Wang, Richard Robert and Chou, Gene and Liao, Hong-Yuan Mark},
+  journal={arXiv preprint arXiv:2309.16921},
+  year={2023}
+}
+```
+
+
+## Teaser
+
+Parts of code of [YOLOR-Based Multi-Task Learning](https://arxiv.org/abs/2309.16921) are released in the repository.
+
+<div align="center">
+    <a href="./">
+        <img src="./figure/multitask.png" width="99%"/>
+    </a>
+</div>
+
+#### Object Detection
+
+[`gelan-c-det.pt`](https://github.com/WongKinYiu/yolov9/releases/download/v0.1/gelan-c-det.pt)
+
+`object detection`
+
+``` shell
+# coco/labels/{split}/*.txt
+# bbox or polygon (1 instance 1 line)
+python train.py --workers 8 --device 0 --batch 32 --data data/coco.yaml --img 640 --cfg models/detect/gelan-c.yaml --weights '' --name gelan-c-det --hyp hyp.scratch-high.yaml --min-items 0 --epochs 300 --close-mosaic 10
+```
+
+| Model | Test Size | Param. | FLOPs | AP<sup>box</sup> |
+| :-- | :-: | :-: | :-: | :-: |
+| [**GELAN-C-DET**](https://github.com/WongKinYiu/yolov9/releases/download/v0.1/gelan-c-det.pt) | 640 | 25.3M | 102.1G |**52.3%** |
+| [**YOLOv9-C-DET**]() | 640 | 25.3M | 102.1G | **53.0%** |
+
+#### Instance Segmentation
+
+[`gelan-c-seg.pt`](https://github.com/WongKinYiu/yolov9/releases/download/v0.1/gelan-c-seg.pt)
+
+`object detection` `instance segmentation`
+
+``` shell
+# coco/labels/{split}/*.txt
+# polygon (1 instance 1 line)
+python segment/train.py --workers 8 --device 0 --batch 32  --data coco.yaml --img 640 --cfg models/segment/gelan-c-seg.yaml --weights '' --name gelan-c-seg --hyp hyp.scratch-high.yaml --no-overlap --epochs 300 --close-mosaic 10
+```
+
+| Model | Test Size | Param. | FLOPs | AP<sup>box</sup> | AP<sup>mask</sup>  |
+| :-- | :-: | :-: | :-: | :-: | :-: |
+| [**GELAN-C-SEG**](https://github.com/WongKinYiu/yolov9/releases/download/v0.1/gelan-c-seg.pt) | 640 | 27.4M | 144.6G | **52.3%** | **42.4%** |
+| [**YOLOv9-C-SEG**]() | 640 | 27.4M | 145.5G | **53.3%** | **43.5%** |
+
+#### Panoptic Segmentation
+
+[`gelan-c-pan.pt`](https://github.com/WongKinYiu/yolov9/releases/download/v0.1/gelan-c-pan.pt)
+
+`object detection` `instance segmentation` `semantic segmentation` `stuff segmentation` `panoptic segmentation`
+
+``` shell
+# coco/labels/{split}/*.txt
+# polygon (1 instance 1 line)
+# coco/stuff/{split}/*.txt
+# polygon (1 semantic 1 line)
+python panoptic/train.py --workers 8 --device 0 --batch 32  --data coco.yaml --img 640 --cfg models/panoptic/gelan-c-pan.yaml --weights '' --name gelan-c-pan --hyp hyp.scratch-high.yaml --no-overlap --epochs 300 --close-mosaic 10
+```
+
+| Model | Test Size | Param. | FLOPs | AP<sup>box</sup> | AP<sup>mask</sup>  | mIoU<sub>164k/10k</sub><sup>semantic</sup> | mIoU<sup>stuff</sup> | PQ<sup>panoptic</sup> |
+| :-- | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: |
+| [**GELAN-C-PAN**](https://github.com/WongKinYiu/yolov9/releases/download/v0.1/gelan-c-pan.pt) | 640 | 27.6M | 146.7G | **52.6%** | **42.5%** | **39.0%/48.3%** | **52.7%** | **39.4%** |
+<!--| [**YOLOv9-C-PAN**]() | 640 | 28.8M | 187.0G | **%** | **%** | **** | **%** | **%** |-->
+
+#### Image Captioning (not yet released)
+
+<!--[`gelan-c-cap.pt`]()-->
+
+`object detection` `instance segmentation` `semantic segmentation` `stuff segmentation` `panoptic segmentation` `image captioning`
+
+``` shell
+# coco/labels/{split}/*.txt
+# polygon (1 instance 1 line)
+# coco/stuff/{split}/*.txt
+# polygon (1 semantic 1 line)
+# coco/annotations/*.json
+# json (1 split 1 file)
+python caption/train.py --workers 8 --device 0 --batch 32  --data coco.yaml --img 640 --cfg models/caption/gelan-c-cap.yaml --weights '' --name gelan-c-cap --hyp hyp.scratch-high.yaml --no-overlap --epochs 300 --close-mosaic 10
+```
+
+| Model | Test Size | AP<sup>box</sup> | AP<sup>mask</sup>  | mIoU<sup>semantic</sup>  | mIoU<sup>stuff</sup> | PQ<sup>panoptic</sup> | BLEU@4<sup>caption</sup> | CIDEr<sup>caption</sup> |
+| :-- | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: |
+| [**YOLOR-MT**]() | 640 | **51.0%** | **41.7%** | **49.6%** | **55.9%** | **40.5%** | **35.7** | **112.7** |
+<!--| [**GELAN-C-CAP**]() | 640 | **-** | **-** | **-** | **-** | **-** | **-** | **-** |
+| [**YOLOv9-C-CAP**]() | 640 | **-** | **-** | **-** | **-** | **-** | **-** | **-** |-->
+
+
+## Acknowledgements
+
+<details><summary> <b>Expand</b> </summary>
+
+* [https://github.com/AlexeyAB/darknet](https://github.com/AlexeyAB/darknet)
+* [https://github.com/WongKinYiu/yolor](https://github.com/WongKinYiu/yolor)
+* [https://github.com/WongKinYiu/yolov7](https://github.com/WongKinYiu/yolov7)
+* [https://github.com/VDIGPKU/DynamicDet](https://github.com/VDIGPKU/DynamicDet)
+* [https://github.com/DingXiaoH/RepVGG](https://github.com/DingXiaoH/RepVGG)
+* [https://github.com/ultralytics/yolov5](https://github.com/ultralytics/yolov5)
+* [https://github.com/meituan/YOLOv6](https://github.com/meituan/YOLOv6)
+
+</details>