全新的SOTA模型YOLOv9

# YOLOv9 Implementation of paper - [YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information](https://arxiv.org/abs/2402.13616) <div align="center"> <a href="./"> <img src="./figure/performance.png" width="79%"/> </a> </div> ## Performance MS COCO | Model | Test Size | AP^val | AP₅₀^val | AP₇₅^val | 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**](https://github.com/WongKinYiu/yolov9/releases/download/v0.1/yolov9-c.pt) | 640 | **53.0%** | **70.2%** | **57.8%** | **25.5M** | **102.8G** | | [**YOLOv9-E**](https://github.com/WongKinYiu/yolov9/releases/download/v0.1/yolov9-e.pt) | 640 | **55.6%** | **72.8%** | **60.6%** | **58.1M** | **192.5G** | <!-- small and medium models will be released after the paper be accepted and published. --> ## 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.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 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 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](https://arxiv.org/abs/2309.16921) are released in the repository. ## 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>