YOLOV7源码内含有训练模型

# Official YOLOv7 Implementation of paper - [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://arxiv.org/abs/2207.02696) <img src="./figure/performance.png" height="480"> ## Performance MS COCO | Model | Test Size | AP^test | AP₅₀^test | AP₇₅^test | batch 1 fps | batch 32 average time | | :-- | :-: | :-: | :-: | :-: | :-: | :-: | | [**YOLOv7**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt) | 640 | **51.4%** | **69.7%** | **55.9%** | 161 *fps* | 2.8 *ms* | | [**YOLOv7-X**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7x.pt) | 640 | **53.1%** | **71.2%** | **57.8%** | 114 *fps* | 4.3 *ms* | | | | | | | | | | [**YOLOv7-W6**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-w6.pt) | 1280 | **54.9%** | **72.6%** | **60.1%** | 84 *fps* | 7.6 *ms* | | [**YOLOv7-E6**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6.pt) | 1280 | **56.0%** | **73.5%** | **61.2%** | 56 *fps* | 12.3 *ms* | | [**YOLOv7-D6**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-d6.pt) | 1280 | **56.6%** | **74.0%** | **61.8%** | 44 *fps* | 15.0 *ms* | | [**YOLOv7-E6E**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6e.pt) | 1280 | **56.8%** | **74.4%** | **62.1%** | 36 *fps* | 18.7 *ms* | ## Installation Docker environment (recommended) <details><summary> <b>Expand</b> </summary> ``` # create the docker container, you can change the share memory size if you have more. nvidia-docker run --name yolov7 -it -v your_coco_path/:/coco/ -v your_code_path/:/yolov7 --shm-size=64g nvcr.io/nvidia/pytorch:21.08-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 /yolov7 ``` </details> ## Testing [`yolov7.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt) [`yolov7x.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7x.pt) [`yolov7-w6.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-w6.pt) [`yolov7-e6.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6.pt) [`yolov7-d6.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-d6.pt) [`yolov7-e6e.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6e.pt) ``` python test.py --data data/coco.yaml --img 640 --batch 32 --conf 0.001 --iou 0.65 --device 0 --weights yolov7.pt --name yolov7_640_val ``` You will get the results: ``` Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.51206 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.69730 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.55521 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.35247 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.55937 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.66693 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.38453 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.63765 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.68772 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.53766 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.73549 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.83868 ``` ## Training ``` python train.py --workers 8 --device 0 --batch-size 32 --data data/coco.yaml --img 640 640 --cfg cfg/training/yolov7.yaml --weights '' --name yolov7 --hyp data/hyp.scratch.p5.yaml python train.py --workers 8 --device 0 --batch-size 32 --data data/coco.yaml --img 640 640 --cfg cfg/training/yolov7x.yaml --weights '' --name yolov7x --hyp data/hyp.scratch.p5.yaml ``` ``` python -m torch.distributed.launch --nproc_per_node 4 --master_port 9527 train.py --workers 8 --device 0,1,2,3 --sync-bn --batch-size 128 --data data/coco.yaml --img 640 640 --cfg cfg/training/yolov7.yaml --weights '' --name yolov7 --hyp data/hyp.scratch.p5.yaml python -m torch.distributed.launch --nproc_per_node 4 --master_port 9527 train.py --workers 8 --device 0,1,2,3 --sync-bn --batch-size 128 --data data/coco.yaml --img 640 640 --cfg cfg/training/yolov7x.yaml --weights '' --name yolov7x --hyp data/hyp.scratch.p5.yaml ``` The training code and instruction of p6 models will release soon. ## Re-parameterization The re-parameterization code and instruction will release soon. ## Citation ``` @article{wang2022yolov7, title={{YOLOv7}: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors}, author={Wang, Chien-Yao and Bochkovskiy, Alexey and Liao, Hong-Yuan Mark}, journal={arXiv preprint arXiv:2207.02696}, year={2022} } ``` ## 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/PyTorch_YOLOv4](https://github.com/WongKinYiu/PyTorch_YOLOv4) * [https://github.com/WongKinYiu/ScaledYOLOv4](https://github.com/WongKinYiu/ScaledYOLOv4) * [https://github.com/Megvii-BaseDetection/YOLOX](https://github.com/Megvii-BaseDetection/YOLOX) * [https://github.com/ultralytics/yolov3](https://github.com/ultralytics/yolov3) * [https://github.com/ultralytics/yolov5](https://github.com/ultralytics/yolov5) * [https://github.com/DingXiaoH/RepVGG](https://github.com/DingXiaoH/RepVGG) * [https://github.com/JUGGHM/OREPA_CVPR2022](https://github.com/JUGGHM/OREPA_CVPR2022) </details>