A look at WongKinYiu/yolov7: 14.1k stars on GitHub, written primarily in Jupyter Notebook, tracked under the AI Tools category. Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
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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
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
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
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
codeyolov7-w6-pose.pt
See keypoint.ipynb.
Instance segmentation (with NTU)
codeyolov7-mask.pt
See instance.ipynb.
Instance segmentation
codeyolov7-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
codeyolov7-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}
}
The most recent commit recorded on WongKinYiu/yolov7 was 2.0 years ago, based on the GitHub push timestamp. The repository has 4.4k forks — one of the better signals of community interest.
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What else is in the AI Tools space?
WongKinYiu/yolov7 is tracked by TopGit under the AI Tools category, alongside 7 GitHub-tagged topics. Trending and Topics pages list peer repositories of comparable stars and language.
What language is WongKinYiu/yolov7 written in?
WongKinYiu/yolov7 is written primarily in Jupyter Notebook. GitHub's language field is based on the largest share of bytes in the default branch.
What topics is WongKinYiu/yolov7 associated with?
GitHub's repository topics for WongKinYiu/yolov7: "darknet", "pytorch", "scaled-yolov4", "yolor", "yolov3", "yolov4", "yolov7". TopGit's editorial category is AI Tools.
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