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yolov7_2
dictvv/yolov7_2
yolov7_2 is a machine learning model from dictvv. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
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Updated Nov 27, 2025
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- Access
- Public
- Created
- Nov 27, 2025
- Updated
- Nov 27, 2025
- SHA
- 5a0cb1f2
Model card
From the Hugging Face model README
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<sup>test</sup> | AP<sub>50</sub><sup>test</sup> | AP<sub>75</sub><sup>test</sup> | 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)
<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
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.cacheandval2017.cachefiles, 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
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) <a href="https://colab.research.google.com/github/WongKinYiu/yolov7/blob/main/tools/YOLOv7CoreML.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
Pytorch to ONNX with NMS (and inference) <a href="https://colab.research.google.com/github/WongKinYiu/yolov7/blob/main/tools/YOLOv7onnx.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
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) <a href="https://colab.research.google.com/github/WongKinYiu/yolov7/blob/main/tools/YOLOv7trt.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
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 <a href="https://colab.research.google.com/gist/AlexeyAB/fcb47ae544cf284eb24d8ad8e880d45c/yolov7trtlinaom.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> <details><summary> <b>Expand</b> </summary>
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
See keypoint.ipynb.
<div align="center"> <a href="./"> <img src="./figure/pose.png" width="39%"/> </a> </div>Instance segmentation (with NTU)
See instance.ipynb.
<div align="center"> <a href="./"> <img src="./figure/mask.png" width="59%"/> </a> </div>Instance segmentation
YOLOv7 for instance segmentation (YOLOR + YOLOv5 + YOLACT)
| Model | Test Size | AP<sup>box</sup> | AP<sub>50</sub><sup>box</sup> | AP<sub>75</sub><sup>box</sup> | AP<sup>mask</sup> | AP<sub>50</sub><sup>mask</sup> | AP<sub>75</sub><sup>mask</sup> |
|---|---|---|---|---|---|---|---|
| YOLOv7-seg | 640 | 51.4% | 69.4% | 55.8% | 41.5% | 65.5% | 43.7% |
Anchor free detection head
YOLOv7 with decoupled TAL head (YOLOR + YOLOv5 + YOLOv6)
| Model | Test Size | AP<sup>val</sup> | AP<sub>50</sub><sup>val</sup> | AP<sub>75</sub><sup>val</sup> |
|---|---|---|---|---|
| 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
<div align="center"> <a href="./"> <img src="./figure/tennis.jpg" width="24%"/> </a> <a href="./"> <img src="./figure/tennis_semantic.jpg" width="24%"/> </a> <a href="./"> <img src="./figure/tennis_panoptic.png" width="24%"/> </a> <a href="./"> <img src="./figure/tennis_caption.png" width="24%"/> </a> </div>YOLOv7-semantic & YOLOv7-detection & YOLOv7-depth (with NTUT)
<div align="center"> <a href="./"> <img src="./figure/yolov7_city.jpg" width="80%"/> </a> </div>YOLOv7-3d-detection & YOLOv7-lidar & YOLOv7-road (with NTUT)
<div align="center"> <a href="./"> <img src="./figure/yolov7_3d.jpg" width="30%"/> </a> <a href="./"> <img src="./figure/yolov7_lidar.jpg" width="30%"/> </a> <a href="./"> <img src="./figure/yolov7_road.jpg" width="30%"/> </a> </div>Acknowledgements
<details><summary> <b>Expand</b> </summary>- 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
- Created
- Nov 27, 2025
- Updated
- Nov 27, 2025