yolov7 README

The project's README from github.com/WongKinYiu/yolov7. As one markdown file: README.md.

Official YOLOv7

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

Web Demo

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

Performance

MS COCO

Model	Test Size	AP^test	AP₅₀^test	AP₇₅^test	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)

Pytorch to ONNX with NMS (and inference)

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)

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

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	AP^box	AP₅₀^box	AP₇₅^box	AP^mask	AP₅₀^mask	AP₇₅^mask
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	AP^val	AP₅₀^val	AP₇₅^val
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

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14.2K ·GitHub

这是什么？

这是 YOLOv7 的官方 PyTorch 实现。YOLOv7 是一个实时目标检测模型，作者通过一种他们称为“可训练的免费技巧包”（trainable bag-of-freebies）的技术，在速度与精度的权衡上达到了新的最先进水平。

这是一个开箱即用的应用，还是需要我自己训练和运行的研究代码？

这是一个研究代码库，包含训练、测试、推理和导出脚本以及预训练权重，而不是打包好的应用程序。

我该如何让它跑起来？

配置运行环境（推荐使用 Docker），下载其中一个预训练权重（例如 yolov7.pt），然后运行测试或检测脚本，或者从头训练，或在自己的数据集上微调。

我需要特定的 Python/PyTorch 版本和 GPU 吗？

它基于 PyTorch 构建，并在 Python 3.7.13 和 PyTorch 1.12.0+cu113 上测试过；训练需要使用 CUDA GPU（支持单卡或多卡）。

它只能做边界框检测，还是还有其他功能？

基础仓库支持边界框目标检测，此外还附带了用于姿态估计、实例分割以及无锚框（anchor-free）检测头的独立变体和脚本。

我能在 PyTorch 之外导出并运行它吗，例如在移动端或生产环境中？

可以，该仓库提供了导出到 CoreML（用于 macOS/iOS 推理）的途径，以及带 NMS 的 ONNX 和带 NMS 的 TensorRT，便于用于生产环境部署。

YOLOv7 与其他模型相比，速度和精度如何？

基础版 YOLOv7 在 MS COCO test 上达到 51.4% AP，批次大小为 1 时速度为 161 fps；而更大的变体如 YOLOv7-E6E 达到 56.8% AP，速度为 36 fps，即以速度换取精度。

我能不安装任何东西就试用吗？

可以，它在 Hugging Face Spaces 上提供了一个使用 Gradio 构建的在线网页演示。

“重参数化”（reparameterization）这一步是做什么用的？

训练完成后，运行重参数化笔记本（reparameterization.ipynb），合并或转换训练好的模型结构，使推理更快、更简洁。

我能用自己的数据对它进行微调吗？

可以，该仓库包含单 GPU 微调说明，以及单独的“-training”权重文件（例如 yolov7_training.pt），用于在自定义数据集上进行迁移学习。

What are similar projects?

ultralytics, YOLOX, PaddleDetection, yolov3, darknet, deep-learning-for-image-processing, High-Angle_Robust_Fast_FaceAlignment, X-AnyLabeling

Which topics is it in?

darknet, instance-segmentation, semantic-segmentation, pose-estimation, cnn, object-detection, onnx, neural-network, jupyter notebook, inference, computer-vision, cuda

Creator: WongKinYiu
Size: 74.2 MB
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Updated: CommitNov 03
Age: 4y
Commits · 90 Days