nachiiiket/autofish
收藏Official YOLOv7
数据集详情
性能
MS COCO数据集上的性能指标如下：
| 模型 | 测试尺寸 | AP<sup>test</sup> | AP<sub>50</sub><sup>test</sup> | AP<sub>75</sub><sup>test</sup> | 批量1 fps | 批量32平均时间 |
|---|---|---|---|---|---|---|
| YOLOv7 | 640 | 51.4% | 69.7% | 55.9% | 161 fps | 2.8 ms |
| YOLOv7-X | 640 | 53.1% | 71.2% | 57.8% | 114 fps | 4.3 ms |
| YOLOv7-W6 | 1280 | 54.9% | 72.6% | 60.1% | 84 fps | 7.6 ms |
| YOLOv7-E6 | 1280 | 56.0% | 73.5% | 61.2% | 56 fps | 12.3 ms |
| YOLOv7-D6 | 1280 | 56.6% | 74.0% | 61.8% | 44 fps | 15.0 ms |
| YOLOv7-E6E | 1280 | 56.8% | 74.4% | 62.1% | 36 fps | 18.7 ms |
安装
推荐使用Docker环境进行安装。
测试
测试命令和结果如下：
shell 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
测试结果：
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
训练
数据准备：
shell bash scripts/get_coco.sh
单GPU训练：
shell
train p5 models
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
train p6 models
python train_aux.py --workers 8 --device 0 --batch-size 16 --data data/coco.yaml --img 1280 1280 --cfg cfg/training/yolov7-w6.yaml --weights --name yolov7-w6 --hyp data/hyp.scratch.p6.yaml
多GPU训练：
shell
train p5 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-size 128 --data data/coco.yaml --img 640 640 --cfg cfg/training/yolov7.yaml --weights --name yolov7 --hyp data/hyp.scratch.p5.yaml
train p6 models
python -m torch.distributed.launch --nproc_per_node 8 --master_port 9527 train_aux.py --workers 8 --device 0,1,2,3,4,5,6,7 --sync-bn --batch-size 128 --data data/coco.yaml --img 1280 1280 --cfg cfg/training/yolov7-w6.yaml --weights --name yolov7-w6 --hyp data/hyp.scratch.p6.yaml
迁移学习
单GPU微调：
shell
finetune p5 models
python train.py --workers 8 --device 0 --batch-size 32 --data data/custom.yaml --img 640 640 --cfg cfg/training/yolov7-custom.yaml --weights yolov7_training.pt --name yolov7-custom --hyp data/hyp.scratch.custom.yaml
finetune p6 models
python train_aux.py --workers 8 --device 0 --batch-size 16 --data data/custom.yaml --img 1280 1280 --cfg cfg/training/yolov7-w6-custom.yaml --weights yolov7-w6_training.pt --name yolov7-w6-custom --hyp data/hyp.scratch.custom.yaml
推理
视频推理：
shell python detect.py --weights yolov7.pt --conf 0.25 --img-size 640 --source yourvideo.mp4
图像推理：
shell python detect.py --weights yolov7.pt --conf 0.25 --img-size 640 --source inference/images/horses.jpg
导出
Pytorch到CoreML：
shell python export.py --weights yolov7-tiny.pt --grid --end2end --simplify --topk-all 100 --iou-thres 0.65 --conf-thres 0.35 --img-size 640 640 --max-wh 640
Pytorch到ONNX：
shell python export.py --weights yolov7-tiny.pt --grid --end2end --simplify --topk-all 100 --iou-thres 0.65 --conf-thres 0.35 --img-size 640 640 --max-wh 640
Pytorch到TensorRT：
shell wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-tiny.pt python export.py --weights ./yolov7-tiny.pt --grid --end2end --simplify --topk-all 100 --iou-thres 0.65 --conf-thres 0.35 --img-size 640 640 git clone https://github.com/Linaom1214/tensorrt-python.git python ./tensorrt-python/export.py -o yolov7-tiny.onnx -e yolov7-tiny-nms.trt -p fp16
姿态估计
使用YOLOv7进行姿态估计：
shell python detect_pose.py --weights yolov7-w6-pose.pt --conf 0.25 --img-size 640 --source yourvideo.mp4
实例分割
使用YOLOv7进行实例分割：
shell python detect_seg.py --weights yolov7-seg.pt --conf 0.25 --img-size 640 --source yourvideo.mp4
引用
@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} }
