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yolov7: 在美团 YOLOv6 推出后不到两个星期，YOLOv4 团队就发布了更新一代的YOLOv7版本 YOLOv7 在 5 FPS 到 160 FPS 范围内，速度和精度都超过了所有已知

 您所在的位置：网站首页 › d6下载网 › yolov7: 在美团 YOLOv6 推出后不到两个星期，YOLOv4 团队就发布了更新一代的YOLOv7版本 YOLOv7 在 5 FPS 到 160 FPS 范围内，速度和精度都超过了所有已知

yolov7: 在美团 YOLOv6 推出后不到两个星期，YOLOv4 团队就发布了更新一代的YOLOv7版本 YOLOv7 在 5 FPS 到 160 FPS 范围内，速度和精度都超过了所有已知

2024-07-13 11:37| 来源: 网络整理| 查看: 265

Official YOLOv7

Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors

PWC Hugging Face Spaces Open In Colab arxiv.org

Web Demo Integrated into Huggingface Spaces 🤗 using Gradio. Try out the Web Demo Hugging Face Spaces Performance

MS COCO

Model Test Size APtest AP50test AP75test batch 1 fps batch 32 average time 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 Installation

Docker environment (recommended)

Expand # 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 Testing

yolov7.pt yolov7x.pt yolov7-w6.pt yolov7-e6.pt yolov7-d6.pt 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

To measure accuracy, download COCO-annotations for Pycocotools to the ./coco/annotations/instances_val2017.json

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 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

Multiple GPU training

# 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 Transfer learning

yolov7_training.pt yolov7x_training.pt yolov7-w6_training.pt yolov7-e6_training.pt yolov7-d6_training.pt yolov7-e6e_training.pt

Single GPU finetuning for custom dataset

# 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 Re-parameterization

See reparameterization.ipynb

Inference

On video:

python detect.py --weights yolov7.pt --conf 0.25 --img-size 640 --source yourvideo.mp4

On image:

python detect.py --weights yolov7.pt --conf 0.25 --img-size 640 --source inference/images/horses.jpg Export

Pytorch to CoreML (and inference on MacOS/iOS) Open In Colab

Pytorch to ONNX with NMS (and inference) Open In Colab

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 to TensorRT with NMS (and inference) Open In Colab

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

Pytorch to TensorRT another way Open In Colab Expand

wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-tiny.pt python export.py --weights yolov7-tiny.pt --grid --include-nms 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 # Or use trtexec to convert ONNX to TensorRT engine /usr/src/tensorrt/bin/trtexec --onnx=yolov7-tiny.onnx --saveEngine=yolov7-tiny-nms.trt --fp16

Tested with: Python 3.7.13, Pytorch 1.12.0+cu113

Pose estimation

code yolov7-w6-pose.pt

See keypoint.ipynb.

Instance segmentation (with NTU)

code yolov7-mask.pt

See instance.ipynb.

Instance segmentation

code yolov7-seg.pt

YOLOv7 for instance segmentation (YOLOR + YOLOv5 + YOLACT)

Model Test Size APbox AP50box AP75box APmask AP50mask AP75mask YOLOv7-seg 640 51.4% 69.4% 55.8% 41.5% 65.5% 43.7% Anchor free detection head

code yolov7-u6.pt

YOLOv7 with decoupled TAL head (YOLOR + YOLOv5 + YOLOv6)

Model Test Size APval AP50val AP75val YOLOv7-u6 640 52.6% 69.7% 57.3% Citation @inproceedings{wang2023yolov7, 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}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, year={2023} } @article{wang2023designing, title={Designing Network Design Strategies Through Gradient Path Analysis}, author={Wang, Chien-Yao and Liao, Hong-Yuan Mark and Yeh, I-Hau}, journal={Journal of Information Science and Engineering}, year={2023} } Teaser

YOLOv7-semantic & YOLOv7-panoptic & YOLOv7-caption

YOLOv7-semantic & YOLOv7-detection & YOLOv7-depth (with NTUT)

YOLOv7-3d-detection & YOLOv7-lidar & YOLOv7-road (with NTUT)

Acknowledgements Expand https://github.com/AlexeyAB/darknet https://github.com/WongKinYiu/yolor https://github.com/WongKinYiu/PyTorch_YOLOv4 https://github.com/WongKinYiu/ScaledYOLOv4 https://github.com/Megvii-BaseDetection/YOLOX https://github.com/ultralytics/yolov3 https://github.com/ultralytics/yolov5 https://github.com/DingXiaoH/RepVGG https://github.com/JUGGHM/OREPA_CVPR2022 https://github.com/TexasInstruments/edgeai-yolov5/tree/yolo-pose


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