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transformers.md
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# PyMatting: A Python Library for Alpha Matting
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[![License: MIT](https://img.shields.io/github/license/pymatting/pymatting?color=brightgreen)](https://opensource.org/licenses/MIT)
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[![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)
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[![PyPI](https://img.shields.io/pypi/v/pymatting)](https://pypi.org/project/PyMatting/)
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[![JOSS](https://joss.theoj.org/papers/9766cab65bfbf07a70c8a835edd3875a/status.svg)](https://joss.theoj.org/papers/9766cab65bfbf07a70c8a835edd3875a)
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[![Gitter](https://img.shields.io/gitter/room/pymatting/pymatting)](https://gitter.im/pymatting/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge)
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We introduce the PyMatting package for Python which implements various methods to solve the alpha matting problem.
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- **Website and Documentation:** [https://pymatting.github.io/](https://pymatting.github.io)
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- **Benchmarks:** [https://pymatting.github.io/benchmarks.html](https://pymatting.github.io/benchmarks.html)
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![Lemur](https://github.com/pymatting/pymatting/raw/master/data/lemur/lemur_at_the_beach.png)
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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).
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PyMatting provides:
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- Alpha matting implementations for:
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- Closed Form Alpha Matting [[1]](#1)
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- Large Kernel Matting [[2]](#2)
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- KNN Matting [[3]](#3)
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- Learning Based Digital Matting [[4]](#4)
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- Random Walk Matting [[5]](#5)
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- Shared Sampling Matting [[6]](#6)
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- Foreground estimation implementations for:
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- Closed Form Foreground Estimation [[1]](#1)
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- Fast Multi-Level Foreground Estimation (CPU, CUDA and OpenCL) [[7]](#7)
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- Fast multithreaded KNN search
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- Preconditioners to accelerate the convergence rate of conjugate gradient descent:
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- The *incomplete thresholded Cholesky decomposition* (*Incomplete* is part of the name. The implementation is quite complete.)
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- The V-Cycle Geometric Multigrid preconditioner
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- Readable code leveraging [NumPy](https://numpy.org/), [SciPy](https://scipy.org/) and [Numba](http://numba.pydata.org/)
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## Getting Started
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### Requirements
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Minimal requirements
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* numpy>=1.16.0
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* pillow>=5.2.0
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* numba>=0.47.0
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* scipy>=1.1.0
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Additional requirements for GPU support
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* cupy-cuda90>=6.5.0 or similar
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* pyopencl>=2019.1.2
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Requirements to run the tests
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* pytest>=5.3.4
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### Installation with PyPI
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```bash
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pip3 install pymatting
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```
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### Installation from Source
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```bash
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git clone https://github.com/pymatting/pymatting
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cd pymatting
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pip3 install .
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```
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## Example
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```python
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from pymatting import cutout
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cutout(
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# input image path
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"data/lemur/lemur.png",
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# input trimap path
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"data/lemur/lemur_trimap.png",
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# output cutout path
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"lemur_cutout.png")
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```
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[More advanced examples](https://pymatting.github.io/examples.html)
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## Trimap Construction
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All implemented methods rely on trimaps which roughly classify the image into foreground, background and unknown regions.
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Trimaps are expected to be `numpy.ndarrays` of type `np.float64` having the same shape as the input image with only one color-channel.
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Trimap values of 0.0 denote pixels which are 100% background.
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Similarly, trimap values of 1.0 denote pixels which are 100% foreground.
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All other values indicate unknown pixels which will be estimated by the algorithm.
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## Testing
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Run the tests from the main directory:
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```
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python3 tests/download_images.py
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pip3 install -r requirements_tests.txt
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pytest
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```
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Currently 89% of the code is covered by tests.
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## Upgrade
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```bash
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pip3 install --upgrade pymatting
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python3 -c "import pymatting"
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```
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## Bug Reports, Questions and Pull-Requests
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Please, see [our community guidelines](https://github.com/pymatting/pymatting/blob/master/CONTRIBUTING.md).
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## Authors
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- **Thomas Germer**
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- **Tobias Uelwer**
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- **Stefan Conrad**
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- **Stefan Harmeling**
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See also the list of [contributors](https://github.com/pymatting/pymatting/contributors) who participated in this project.
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## Projects using PyMatting
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* [Rembg](https://github.com/danielgatis/rembg) - an excellent tool for removing image backgrounds.
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* [PaddleSeg](https://github.com/PaddlePaddle/PaddleSeg) - a library for a wide range of image segmentation tasks.
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* [chaiNNer](https://github.com/chaiNNer-org/chaiNNer) - a node-based image processing GUI.
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* [LSA-Matting](https://github.com/kfeng123/LSA-Matting) - improving deep image matting via local smoothness assumption.
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## License
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This project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details
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## Citing
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If you found PyMatting to be useful for your work, please consider citing our [paper](https://doi.org/10.21105/joss.02481):
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```
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@article{Germer2020,
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doi = {10.21105/joss.02481},
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url = {https://doi.org/10.21105/joss.02481},
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year = {2020},
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publisher = {The Open Journal},
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volume = {5},
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number = {54},
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pages = {2481},
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author = {Thomas Germer and Tobias Uelwer and Stefan Conrad and Stefan Harmeling},
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title = {PyMatting: A Python Library for Alpha Matting},
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journal = {Journal of Open Source Software}
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}
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```
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## References
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<a id="1">[1]</a>
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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.
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<a id="2">[2]</a>
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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.
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<a id="3">[3]</a>
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Qifeng Chen, Dingzeyu Li, and Chi-Keung Tang. Knn matting. IEEE transactions on pattern analysis and machine intelligence, 35(9):2175–2188, 2013.
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<a id="4">[4]</a>
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Yuanjie Zheng and Chandra Kambhamettu. Learning based digital matting. In 2009 IEEE 12th international conference on computer vision, 889–896. IEEE, 2009.
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<a id="5">[5]</a>
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Leo Grady, Thomas Schiwietz, Shmuel Aharon, and Rüdiger Westermann. Random walks for interactive alpha-matting. In Proceedings of VIIP, volume 2005, 423–429. 2005.
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<a id="6">[6]</a>
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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.
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<a id="7">[7]</a>
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Germer, T., Uelwer, T., Conrad, S., & Harmeling, S. (2020). Fast Multi-Level Foreground Estimation. arXiv preprint arXiv:2006.14970.
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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/).
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# YOLOv9
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Implementation of paper - [YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information](https://arxiv.org/abs/2402.13616)
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[![arxiv.org](http://img.shields.io/badge/cs.CV-arXiv%3A2402.13616-B31B1B.svg)](https://arxiv.org/abs/2402.13616)
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[![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/kadirnar/Yolov9)
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[![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/merve/yolov9)
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[![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)
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[![OpenCV](https://img.shields.io/badge/OpenCV-BlogPost-black?logo=opencv&labelColor=blue&color=black)](https://learnopencv.com/yolov9-advancing-the-yolo-legacy/)
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<div align="center">
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<a href="./">
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<img src="./figure/performance.png" width="79%"/>
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</a>
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</div>
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## Performance
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19 |
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MS COCO
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| Model | Test Size | AP<sup>val</sup> | AP<sub>50</sub><sup>val</sup> | AP<sub>75</sub><sup>val</sup> | Param. | FLOPs |
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| :-- | :-: | :-: | :-: | :-: | :-: | :-: |
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| [**YOLOv9-T**]() | 640 | **38.3%** | **53.1%** | **41.3%** | **2.0M** | **7.7G** |
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| [**YOLOv9-S**]() | 640 | **46.8%** | **63.4%** | **50.7%** | **7.1M** | **26.4G** |
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| [**YOLOv9-M**]() | 640 | **51.4%** | **68.1%** | **56.1%** | **20.0M** | **76.3G** |
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27 |
+
| [**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** |
|
28 |
+
| [**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** |
|
29 |
+
<!-- | [**YOLOv9 (ReLU)**]() | 640 | **51.9%** | **69.1%** | **56.5%** | **25.3M** | **102.1G** | -->
|
30 |
+
|
31 |
+
<!-- tiny, small, and medium models will be released after the paper be accepted and published. -->
|
32 |
+
|
33 |
+
## Useful Links
|
34 |
+
|
35 |
+
<details><summary> <b>Expand</b> </summary>
|
36 |
+
|
37 |
+
Custom training: https://github.com/WongKinYiu/yolov9/issues/30#issuecomment-1960955297
|
38 |
+
|
39 |
+
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
|
40 |
+
|
41 |
+
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
|
42 |
+
|
43 |
+
QAT TensirRT: https://github.com/WongKinYiu/yolov9/issues/253#issue-2189520073
|
44 |
+
|
45 |
+
OpenVINO: https://github.com/WongKinYiu/yolov9/issues/164#issue-2168540003
|
46 |
+
|
47 |
+
C# ONNX inference: https://github.com/WongKinYiu/yolov9/issues/95#issue-2155974619
|
48 |
+
|
49 |
+
C# OpenVINO inference: https://github.com/WongKinYiu/yolov9/issues/95#issuecomment-1968131244
|
50 |
+
|
51 |
+
OpenCV: https://github.com/WongKinYiu/yolov9/issues/113#issuecomment-1971327672
|
52 |
+
|
53 |
+
Hugging Face demo: https://github.com/WongKinYiu/yolov9/issues/45#issuecomment-1961496943
|
54 |
+
|
55 |
+
CoLab demo: https://github.com/WongKinYiu/yolov9/pull/18
|
56 |
+
|
57 |
+
ONNXSlim export: https://github.com/WongKinYiu/yolov9/pull/37
|
58 |
+
|
59 |
+
YOLOv9 ROS: https://github.com/WongKinYiu/yolov9/issues/144#issue-2164210644
|
60 |
+
|
61 |
+
YOLOv9 ROS TensorRT: https://github.com/WongKinYiu/yolov9/issues/145#issue-2164218595
|
62 |
+
|
63 |
+
YOLOv9 Julia: https://github.com/WongKinYiu/yolov9/issues/141#issuecomment-1973710107
|
64 |
+
|
65 |
+
YOLOv9 MLX: https://github.com/WongKinYiu/yolov9/issues/258#issue-2190586540
|
66 |
+
|
67 |
+
YOLOv9 ByteTrack: https://github.com/WongKinYiu/yolov9/issues/78#issue-2153512879
|
68 |
+
|
69 |
+
YOLOv9 DeepSORT: https://github.com/WongKinYiu/yolov9/issues/98#issue-2156172319
|
70 |
+
|
71 |
+
YOLOv9 counting: https://github.com/WongKinYiu/yolov9/issues/84#issue-2153904804
|
72 |
+
|
73 |
+
YOLOv9 face detection: https://github.com/WongKinYiu/yolov9/issues/121#issue-2160218766
|
74 |
+
|
75 |
+
YOLOv9 segmentation onnxruntime: https://github.com/WongKinYiu/yolov9/issues/151#issue-2165667350
|
76 |
+
|
77 |
+
Comet logging: https://github.com/WongKinYiu/yolov9/pull/110
|
78 |
+
|
79 |
+
MLflow logging: https://github.com/WongKinYiu/yolov9/pull/87
|
80 |
+
|
81 |
+
AnyLabeling tool: https://github.com/WongKinYiu/yolov9/issues/48#issue-2152139662
|
82 |
+
|
83 |
+
AX650N deploy: https://github.com/WongKinYiu/yolov9/issues/96#issue-2156115760
|
84 |
+
|
85 |
+
Conda environment: https://github.com/WongKinYiu/yolov9/pull/93
|
86 |
+
|
87 |
+
AutoDL docker environment: https://github.com/WongKinYiu/yolov9/issues/112#issue-2158203480
|
88 |
+
|
89 |
+
</details>
|
90 |
+
|
91 |
+
|
92 |
+
## Installation
|
93 |
+
|
94 |
+
Docker environment (recommended)
|
95 |
+
<details><summary> <b>Expand</b> </summary>
|
96 |
+
|
97 |
+
``` shell
|
98 |
+
# create the docker container, you can change the share memory size if you have more.
|
99 |
+
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
|
100 |
+
|
101 |
+
# apt install required packages
|
102 |
+
apt update
|
103 |
+
apt install -y zip htop screen libgl1-mesa-glx
|
104 |
+
|
105 |
+
# pip install required packages
|
106 |
+
pip install seaborn thop
|
107 |
+
|
108 |
+
# go to code folder
|
109 |
+
cd /yolov9
|
110 |
+
```
|
111 |
+
|
112 |
+
</details>
|
113 |
+
|
114 |
+
|
115 |
+
## Evaluation
|
116 |
+
|
117 |
+
[`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)
|
118 |
+
|
119 |
+
``` shell
|
120 |
+
# evaluate converted yolov9 models
|
121 |
+
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
|
122 |
+
|
123 |
+
# evaluate yolov9 models
|
124 |
+
# 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
|
125 |
+
|
126 |
+
# evaluate gelan models
|
127 |
+
# 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
|
128 |
+
```
|
129 |
+
|
130 |
+
You will get the results:
|
131 |
+
|
132 |
+
```
|
133 |
+
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.530
|
134 |
+
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.702
|
135 |
+
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.578
|
136 |
+
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.362
|
137 |
+
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.585
|
138 |
+
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.693
|
139 |
+
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.392
|
140 |
+
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.652
|
141 |
+
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.702
|
142 |
+
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.541
|
143 |
+
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.760
|
144 |
+
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.844
|
145 |
+
```
|
146 |
+
|
147 |
+
|
148 |
+
## Training
|
149 |
+
|
150 |
+
Data preparation
|
151 |
+
|
152 |
+
``` shell
|
153 |
+
bash scripts/get_coco.sh
|
154 |
+
```
|
155 |
+
|
156 |
+
* 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)
|
157 |
+
|
158 |
+
Single GPU training
|
159 |
+
|
160 |
+
``` shell
|
161 |
+
# train yolov9 models
|
162 |
+
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
|
163 |
+
|
164 |
+
# train gelan models
|
165 |
+
# 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
|
166 |
+
```
|
167 |
+
|
168 |
+
Multiple GPU training
|
169 |
+
|
170 |
+
``` shell
|
171 |
+
# train yolov9 models
|
172 |
+
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
|
173 |
+
|
174 |
+
# train gelan models
|
175 |
+
# 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
|
176 |
+
```
|
177 |
+
|
178 |
+
|
179 |
+
## Re-parameterization
|
180 |
+
|
181 |
+
See [reparameterization.ipynb](https://github.com/WongKinYiu/yolov9/blob/main/tools/reparameterization.ipynb).
|
182 |
+
|
183 |
+
|
184 |
+
## Inference
|
185 |
+
|
186 |
+
<div align="center">
|
187 |
+
<a href="./">
|
188 |
+
<img src="./figure/horses_prediction.jpg" width="49%"/>
|
189 |
+
</a>
|
190 |
+
</div>
|
191 |
+
|
192 |
+
``` shell
|
193 |
+
# inference converted yolov9 models
|
194 |
+
python detect.py --source './data/images/horses.jpg' --img 640 --device 0 --weights './yolov9-c-converted.pt' --name yolov9_c_c_640_detect
|
195 |
+
|
196 |
+
# inference yolov9 models
|
197 |
+
# python detect_dual.py --source './data/images/horses.jpg' --img 640 --device 0 --weights './yolov9-c.pt' --name yolov9_c_640_detect
|
198 |
+
|
199 |
+
# inference gelan models
|
200 |
+
# python detect.py --source './data/images/horses.jpg' --img 640 --device 0 --weights './gelan-c.pt' --name gelan_c_c_640_detect
|
201 |
+
```
|
202 |
+
|
203 |
+
|
204 |
+
## Citation
|
205 |
+
|
206 |
+
```
|
207 |
+
@article{wang2024yolov9,
|
208 |
+
title={{YOLOv9}: Learning What You Want to Learn Using Programmable Gradient Information},
|
209 |
+
author={Wang, Chien-Yao and Liao, Hong-Yuan Mark},
|
210 |
+
booktitle={arXiv preprint arXiv:2402.13616},
|
211 |
+
year={2024}
|
212 |
+
}
|
213 |
+
```
|
214 |
+
|
215 |
+
```
|
216 |
+
@article{chang2023yolor,
|
217 |
+
title={{YOLOR}-Based Multi-Task Learning},
|
218 |
+
author={Chang, Hung-Shuo and Wang, Chien-Yao and Wang, Richard Robert and Chou, Gene and Liao, Hong-Yuan Mark},
|
219 |
+
journal={arXiv preprint arXiv:2309.16921},
|
220 |
+
year={2023}
|
221 |
+
}
|
222 |
+
```
|
223 |
+
|
224 |
+
|
225 |
+
## Teaser
|
226 |
+
|
227 |
+
Parts of code of [YOLOR-Based Multi-Task Learning](https://arxiv.org/abs/2309.16921) are released in the repository.
|
228 |
+
|
229 |
+
<div align="center">
|
230 |
+
<a href="./">
|
231 |
+
<img src="./figure/multitask.png" width="99%"/>
|
232 |
+
</a>
|
233 |
+
</div>
|
234 |
+
|
235 |
+
#### Object Detection
|
236 |
+
|
237 |
+
[`gelan-c-det.pt`](https://github.com/WongKinYiu/yolov9/releases/download/v0.1/gelan-c-det.pt)
|
238 |
+
|
239 |
+
`object detection`
|
240 |
+
|
241 |
+
``` shell
|
242 |
+
# coco/labels/{split}/*.txt
|
243 |
+
# bbox or polygon (1 instance 1 line)
|
244 |
+
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
|
245 |
+
```
|
246 |
+
|
247 |
+
| Model | Test Size | Param. | FLOPs | AP<sup>box</sup> |
|
248 |
+
| :-- | :-: | :-: | :-: | :-: |
|
249 |
+
| [**GELAN-C-DET**](https://github.com/WongKinYiu/yolov9/releases/download/v0.1/gelan-c-det.pt) | 640 | 25.3M | 102.1G |**52.3%** |
|
250 |
+
| [**YOLOv9-C-DET**]() | 640 | 25.3M | 102.1G | **53.0%** |
|
251 |
+
|
252 |
+
#### Instance Segmentation
|
253 |
+
|
254 |
+
[`gelan-c-seg.pt`](https://github.com/WongKinYiu/yolov9/releases/download/v0.1/gelan-c-seg.pt)
|
255 |
+
|
256 |
+
`object detection` `instance segmentation`
|
257 |
+
|
258 |
+
``` shell
|
259 |
+
# coco/labels/{split}/*.txt
|
260 |
+
# polygon (1 instance 1 line)
|
261 |
+
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
|
262 |
+
```
|
263 |
+
|
264 |
+
| Model | Test Size | Param. | FLOPs | AP<sup>box</sup> | AP<sup>mask</sup> |
|
265 |
+
| :-- | :-: | :-: | :-: | :-: | :-: |
|
266 |
+
| [**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%** |
|
267 |
+
| [**YOLOv9-C-SEG**]() | 640 | 27.4M | 145.5G | **53.3%** | **43.5%** |
|
268 |
+
|
269 |
+
#### Panoptic Segmentation
|
270 |
+
|
271 |
+
[`gelan-c-pan.pt`](https://github.com/WongKinYiu/yolov9/releases/download/v0.1/gelan-c-pan.pt)
|
272 |
+
|
273 |
+
`object detection` `instance segmentation` `semantic segmentation` `stuff segmentation` `panoptic segmentation`
|
274 |
+
|
275 |
+
``` shell
|
276 |
+
# coco/labels/{split}/*.txt
|
277 |
+
# polygon (1 instance 1 line)
|
278 |
+
# coco/stuff/{split}/*.txt
|
279 |
+
# polygon (1 semantic 1 line)
|
280 |
+
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
|
281 |
+
```
|
282 |
+
|
283 |
+
| 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> |
|
284 |
+
| :-- | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: |
|
285 |
+
| [**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%** |
|
286 |
+
<!--| [**YOLOv9-C-PAN**]() | 640 | 28.8M | 187.0G | **%** | **%** | **** | **%** | **%** |-->
|
287 |
+
|
288 |
+
#### Image Captioning (not yet released)
|
289 |
+
|
290 |
+
<!--[`gelan-c-cap.pt`]()-->
|
291 |
+
|
292 |
+
`object detection` `instance segmentation` `semantic segmentation` `stuff segmentation` `panoptic segmentation` `image captioning`
|
293 |
+
|
294 |
+
``` shell
|
295 |
+
# coco/labels/{split}/*.txt
|
296 |
+
# polygon (1 instance 1 line)
|
297 |
+
# coco/stuff/{split}/*.txt
|
298 |
+
# polygon (1 semantic 1 line)
|
299 |
+
# coco/annotations/*.json
|
300 |
+
# json (1 split 1 file)
|
301 |
+
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
|
302 |
+
```
|
303 |
+
|
304 |
+
| 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> |
|
305 |
+
| :-- | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: |
|
306 |
+
| [**YOLOR-MT**]() | 640 | **51.0%** | **41.7%** | **49.6%** | **55.9%** | **40.5%** | **35.7** | **112.7** |
|
307 |
+
<!--| [**GELAN-C-CAP**]() | 640 | **-** | **-** | **-** | **-** | **-** | **-** | **-** |
|
308 |
+
| [**YOLOv9-C-CAP**]() | 640 | **-** | **-** | **-** | **-** | **-** | **-** | **-** |-->
|
309 |
+
|
310 |
+
|
311 |
+
## Acknowledgements
|
312 |
+
|
313 |
+
<details><summary> <b>Expand</b> </summary>
|
314 |
+
|
315 |
+
* [https://github.com/AlexeyAB/darknet](https://github.com/AlexeyAB/darknet)
|
316 |
+
* [https://github.com/WongKinYiu/yolor](https://github.com/WongKinYiu/yolor)
|
317 |
+
* [https://github.com/WongKinYiu/yolov7](https://github.com/WongKinYiu/yolov7)
|
318 |
+
* [https://github.com/VDIGPKU/DynamicDet](https://github.com/VDIGPKU/DynamicDet)
|
319 |
+
* [https://github.com/DingXiaoH/RepVGG](https://github.com/DingXiaoH/RepVGG)
|
320 |
+
* [https://github.com/ultralytics/yolov5](https://github.com/ultralytics/yolov5)
|
321 |
+
* [https://github.com/meituan/YOLOv6](https://github.com/meituan/YOLOv6)
|
322 |
+
|
323 |
+
</details>
|