YOLOv9

Programmable Gradient Information (PGI) YOLOv9 minimizes the loss of spatial data during transformations within the neural network by employing a brand-new technique called PGI. PGI can handle various adjustments required by deep neural networks to reach multiple objectives.

Implementation of paper - YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information

Installation

pip install yolov9pip

Performance

MS COCO

Model	Test Size	APval	AP50val	AP75val	Param.	FLOPs
YOLOv9-S	640	46.8%	63.4%	50.7%	7.2M	26.7G
YOLOv9-M	640	51.4%	68.1%	56.1%	20.1M	76.8G
YOLOv9-C	640	53.0%	70.2%	57.8%	25.5M	102.8G
YOLOv9-E	640	55.6%	72.8%	60.6%	58.1M	192.5G

Useful Links

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Custom training: WongKinYiu/yolov9#30 (comment)

ONNX export: WongKinYiu/yolov9#2 (comment) WongKinYiu/yolov9#40 (comment)

TensorRT inference: WongKinYiu/yolov9#34 (comment)

Hugging Face demo: WongKinYiu/yolov9#45 (comment)

CoLab demo: WongKinYiu/yolov9#18

ONNXSlim export: WongKinYiu/yolov9#37

AnyLabeling tool: WongKinYiu/yolov9#48 (comment)

Installation

Docker environment (recommended)

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# 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

Evaluation

yolov9-c.pt yolov9-e.pt gelan-c.pt gelan-e.pt

# 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

bash scripts/get_coco.sh

• Download MS COCO dataset images (train, val, test) and labels. 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

Single GPU training

# 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

# 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

Under construction.

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 are released in the repository.

Acknowledgements

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• https://github.com/AlexeyAB/darknet
• https://github.com/WongKinYiu/yolor
• https://github.com/WongKinYiu/yolov7
• https://github.com/VDIGPKU/DynamicDet
• https://github.com/DingXiaoH/RepVGG
• https://github.com/ultralytics/yolov5
• https://github.com/meituan/YOLOv6