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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

	ONNX export for segmentation: https://github.com/WongKinYiu/yolov9/issues/260#issue-2191162150

	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 TensorRT: https://github.com/WongKinYiu/yolov9/issues/327#issue-2229284136 https://github.com/WongKinYiu/yolov9/issues/253#issue-2189520073

	TFLite: https://github.com/WongKinYiu/yolov9/issues/374#issuecomment-2065751706

	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 StrongSORT with OSNet: https://github.com/WongKinYiu/yolov9/issues/299#issue-2212093340

	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 \| 52.7% \| 43.0% \| 39.8%/- \| 52.2% \| 40.5% \|

	#### 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 \| 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> \| BLEU@4<sup>caption</sup> \| CIDEr<sup>caption</sup> \|
	\| :-- \| :-: \| :-: \| :-: \| :-: \| :-: \| :-: \| :-: \| :-: \| :-: \| :-: \|
	\| [GELAN-C-CAP]() \| 640 \| 47.5M \| - \| 51.9% \| 42.6% \| 42.5%/- \| 56.5% \| 41.7% \| 38.8 \| 122.3 \|
	\| [YOLOv9-C-CAP]() \| 640 \| 47.5M \| - \| 52.1% \| 42.6% \| 43.0%/- \| 56.4% \| 42.1% \| 39.1 \| 122.0 \|
	<!--\| [YOLOR-MT]() \| 640 \| 79.3M \| - \| 51.0% \| 41.7% \| -/49.6% \| 55.9% \| 40.5% \| 35.7 \| 112.7 \|-->


	## 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>