{"id":24114825,"url":"https://github.com/ml13571/yolov7","last_synced_at":"2026-05-19T10:36:49.921Z","repository":{"id":270884098,"uuid":"891523207","full_name":"ml13571/yolov7","owner":"ml13571","description":"Demonstration for paper - YOLOv7: real-time object detectors","archived":false,"fork":false,"pushed_at":"2024-11-20T14:04:25.000Z","size":75931,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-28T17:02:11.357Z","etag":null,"topics":["darknet","deep-learning","object-detection","pytorch","tracking-algorithm","yolov7"],"latest_commit_sha":null,"homepage":"https://jackiewang13571.wixsite.com/jackie-wang","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ml13571.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.md","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-11-20T13:42:11.000Z","updated_at":"2024-11-20T14:06:50.000Z","dependencies_parsed_at":"2025-01-03T19:48:30.418Z","dependency_job_id":null,"html_url":"https://github.com/ml13571/yolov7","commit_stats":null,"previous_names":["ml13571/yolov7"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/ml13571/yolov7","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ml13571%2Fyolov7","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ml13571%2Fyolov7/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ml13571%2Fyolov7/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ml13571%2Fyolov7/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ml13571","download_url":"https://codeload.github.com/ml13571/yolov7/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ml13571%2Fyolov7/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":266291672,"owners_count":23906310,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["darknet","deep-learning","object-detection","pytorch","tracking-algorithm","yolov7"],"created_at":"2025-01-11T05:35:40.564Z","updated_at":"2026-05-19T10:36:44.889Z","avatar_url":"https://github.com/ml13571.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Official YOLOv7\n\nImplementation of paper - [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://arxiv.org/abs/2207.02696)\n\n[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/yolov7-trainable-bag-of-freebies-sets-new/real-time-object-detection-on-coco)](https://paperswithcode.com/sota/real-time-object-detection-on-coco?p=yolov7-trainable-bag-of-freebies-sets-new)\n[![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/akhaliq/yolov7)\n\u003ca href=\"https://colab.research.google.com/gist/AlexeyAB/b769f5795e65fdab80086f6cb7940dae/yolov7detection.ipynb\"\u003e\u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"\u003e\u003c/a\u003e\n[![arxiv.org](http://img.shields.io/badge/cs.CV-arXiv%3A2207.02696-B31B1B.svg)](https://arxiv.org/abs/2207.02696)\n\n\u003cdiv align=\"center\"\u003e\n    \u003ca href=\"./\"\u003e\n        \u003cimg src=\"./figure/performance.png\" width=\"79%\"/\u003e\n    \u003c/a\u003e\n\u003c/div\u003e\n\n## Web Demo\n\n- Integrated into [Huggingface Spaces 🤗](https://huggingface.co/spaces/akhaliq/yolov7) using Gradio. Try out the Web Demo [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/akhaliq/yolov7)\n\n## Performance \n\nMS COCO\n\n| Model | Test Size | AP\u003csup\u003etest\u003c/sup\u003e | AP\u003csub\u003e50\u003c/sub\u003e\u003csup\u003etest\u003c/sup\u003e | AP\u003csub\u003e75\u003c/sub\u003e\u003csup\u003etest\u003c/sup\u003e | batch 1 fps | batch 32 average time |\n| :-- | :-: | :-: | :-: | :-: | :-: | :-: |\n| [**YOLOv7**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt) | 640 | **51.4%** | **69.7%** | **55.9%** | 161 *fps* | 2.8 *ms* |\n| [**YOLOv7-X**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7x.pt) | 640 | **53.1%** | **71.2%** | **57.8%** | 114 *fps* | 4.3 *ms* |\n|  |  |  |  |  |  |  |\n| [**YOLOv7-W6**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-w6.pt) | 1280 | **54.9%** | **72.6%** | **60.1%** | 84 *fps* | 7.6 *ms* |\n| [**YOLOv7-E6**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6.pt) | 1280 | **56.0%** | **73.5%** | **61.2%** | 56 *fps* | 12.3 *ms* |\n| [**YOLOv7-D6**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-d6.pt) | 1280 | **56.6%** | **74.0%** | **61.8%** | 44 *fps* | 15.0 *ms* |\n| [**YOLOv7-E6E**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6e.pt) | 1280 | **56.8%** | **74.4%** | **62.1%** | 36 *fps* | 18.7 *ms* |\n\n## Installation\n\nDocker environment (recommended)\n\u003cdetails\u003e\u003csummary\u003e \u003cb\u003eExpand\u003c/b\u003e \u003c/summary\u003e\n\n``` shell\n# create the docker container, you can change the share memory size if you have more.\nnvidia-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\n\n# apt install required packages\napt update\napt install -y zip htop screen libgl1-mesa-glx\n\n# pip install required packages\npip install seaborn thop\n\n# go to code folder\ncd /yolov7\n```\n\n\u003c/details\u003e\n\n## Testing\n\n[`yolov7.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt) [`yolov7x.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7x.pt) [`yolov7-w6.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-w6.pt) [`yolov7-e6.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6.pt) [`yolov7-d6.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-d6.pt) [`yolov7-e6e.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6e.pt)\n\n``` shell\npython 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\n```\n\nYou will get the results:\n\n```\n Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.51206\n Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.69730\n Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.55521\n Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.35247\n Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.55937\n Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.66693\n Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.38453\n Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.63765\n Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.68772\n Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.53766\n Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.73549\n Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.83868\n```\n\nTo measure accuracy, download [COCO-annotations for Pycocotools](http://images.cocodataset.org/annotations/annotations_trainval2017.zip) to the `./coco/annotations/instances_val2017.json`\n\n## Training\n\nData preparation\n\n``` shell\nbash scripts/get_coco.sh\n```\n\n* Download MS COCO dataset images ([train](http://images.cocodataset.org/zips/train2017.zip), [val](http://images.cocodataset.org/zips/val2017.zip), [test](http://images.cocodataset.org/zips/test2017.zip)) and [labels](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/coco2017labels-segments.zip). 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](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/coco2017labels-segments.zip) \n\nSingle GPU training\n\n``` shell\n# train p5 models\npython 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\n\n# train p6 models\npython 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\n```\n\nMultiple GPU training\n\n``` shell\n# train p5 models\npython -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\n\n# train p6 models\npython -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\n```\n\n## Transfer learning\n\n[`yolov7_training.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7_training.pt) [`yolov7x_training.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7x_training.pt) [`yolov7-w6_training.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-w6_training.pt) [`yolov7-e6_training.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6_training.pt) [`yolov7-d6_training.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-d6_training.pt) [`yolov7-e6e_training.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6e_training.pt)\n\nSingle GPU finetuning for custom dataset\n\n``` shell\n# finetune p5 models\npython 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\n\n# finetune p6 models\npython 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\n```\n\n## Re-parameterization\n\nSee [reparameterization.ipynb](tools/reparameterization.ipynb)\n\n## Inference\n\nOn video:\n``` shell\npython detect.py --weights yolov7.pt --conf 0.25 --img-size 640 --source yourvideo.mp4\n```\n\nOn image:\n``` shell\npython detect.py --weights yolov7.pt --conf 0.25 --img-size 640 --source inference/images/horses.jpg\n```\n\n\u003cdiv align=\"center\"\u003e\n    \u003ca href=\"./\"\u003e\n        \u003cimg src=\"./figure/horses_prediction.jpg\" width=\"59%\"/\u003e\n    \u003c/a\u003e\n\u003c/div\u003e\n\n\n## Export\n\n**Pytorch to CoreML (and inference on MacOS/iOS)** \u003ca href=\"https://colab.research.google.com/github/WongKinYiu/yolov7/blob/main/tools/YOLOv7CoreML.ipynb\"\u003e\u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"\u003e\u003c/a\u003e\n\n**Pytorch to ONNX with NMS (and inference)** \u003ca href=\"https://colab.research.google.com/github/WongKinYiu/yolov7/blob/main/tools/YOLOv7onnx.ipynb\"\u003e\u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"\u003e\u003c/a\u003e\n```shell\npython export.py --weights yolov7-tiny.pt --grid --end2end --simplify \\\n        --topk-all 100 --iou-thres 0.65 --conf-thres 0.35 --img-size 640 640 --max-wh 640\n```\n\n**Pytorch to TensorRT with NMS (and inference)** \u003ca href=\"https://colab.research.google.com/github/WongKinYiu/yolov7/blob/main/tools/YOLOv7trt.ipynb\"\u003e\u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"\u003e\u003c/a\u003e\n\n```shell\nwget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-tiny.pt\npython export.py --weights ./yolov7-tiny.pt --grid --end2end --simplify --topk-all 100 --iou-thres 0.65 --conf-thres 0.35 --img-size 640 640\ngit clone https://github.com/Linaom1214/tensorrt-python.git\npython ./tensorrt-python/export.py -o yolov7-tiny.onnx -e yolov7-tiny-nms.trt -p fp16\n```\n\n**Pytorch to TensorRT another way** \u003ca href=\"https://colab.research.google.com/gist/AlexeyAB/fcb47ae544cf284eb24d8ad8e880d45c/yolov7trtlinaom.ipynb\"\u003e\u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"\u003e\u003c/a\u003e \u003cdetails\u003e\u003csummary\u003e \u003cb\u003eExpand\u003c/b\u003e \u003c/summary\u003e\n\n\n```shell\nwget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-tiny.pt\npython export.py --weights yolov7-tiny.pt --grid --include-nms\ngit clone https://github.com/Linaom1214/tensorrt-python.git\npython ./tensorrt-python/export.py -o yolov7-tiny.onnx -e yolov7-tiny-nms.trt -p fp16\n\n# Or use trtexec to convert ONNX to TensorRT engine\n/usr/src/tensorrt/bin/trtexec --onnx=yolov7-tiny.onnx --saveEngine=yolov7-tiny-nms.trt --fp16\n```\n\n\u003c/details\u003e\n\nTested with: Python 3.7.13, Pytorch 1.12.0+cu113\n\n## Pose estimation\n\n[`code`](https://github.com/WongKinYiu/yolov7/tree/pose) [`yolov7-w6-pose.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-w6-pose.pt)\n\nSee [keypoint.ipynb](https://github.com/WongKinYiu/yolov7/blob/main/tools/keypoint.ipynb).\n\n\u003cdiv align=\"center\"\u003e\n    \u003ca href=\"./\"\u003e\n        \u003cimg src=\"./figure/pose.png\" width=\"39%\"/\u003e\n    \u003c/a\u003e\n\u003c/div\u003e\n\n\n## Instance segmentation (with NTU)\n\n[`code`](https://github.com/WongKinYiu/yolov7/tree/mask) [`yolov7-mask.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-mask.pt)\n\nSee [instance.ipynb](https://github.com/WongKinYiu/yolov7/blob/main/tools/instance.ipynb).\n\n\u003cdiv align=\"center\"\u003e\n    \u003ca href=\"./\"\u003e\n        \u003cimg src=\"./figure/mask.png\" width=\"59%\"/\u003e\n    \u003c/a\u003e\n\u003c/div\u003e\n\n## Instance segmentation\n\n[`code`](https://github.com/WongKinYiu/yolov7/tree/u7/seg) [`yolov7-seg.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-seg.pt)\n\nYOLOv7 for instance segmentation (YOLOR + YOLOv5 + YOLACT)\n\n| Model | Test Size | AP\u003csup\u003ebox\u003c/sup\u003e | AP\u003csub\u003e50\u003c/sub\u003e\u003csup\u003ebox\u003c/sup\u003e | AP\u003csub\u003e75\u003c/sub\u003e\u003csup\u003ebox\u003c/sup\u003e | AP\u003csup\u003emask\u003c/sup\u003e | AP\u003csub\u003e50\u003c/sub\u003e\u003csup\u003emask\u003c/sup\u003e | AP\u003csub\u003e75\u003c/sub\u003e\u003csup\u003emask\u003c/sup\u003e |\n| :-- | :-: | :-: | :-: | :-: | :-: | :-: | :-: |\n| **YOLOv7-seg** | 640 | **51.4%** | **69.4%** | **55.8%** | **41.5%** | **65.5%** | **43.7%** |\n\n## Anchor free detection head\n\n[`code`](https://github.com/WongKinYiu/yolov7/tree/u6) [`yolov7-u6.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-u6.pt)\n\nYOLOv7 with decoupled TAL head (YOLOR + YOLOv5 + YOLOv6)\n\n| Model | Test Size | AP\u003csup\u003eval\u003c/sup\u003e | AP\u003csub\u003e50\u003c/sub\u003e\u003csup\u003eval\u003c/sup\u003e | AP\u003csub\u003e75\u003c/sub\u003e\u003csup\u003eval\u003c/sup\u003e |\n| :-- | :-: | :-: | :-: | :-: |\n| **YOLOv7-u6** | 640 | **52.6%** | **69.7%** | **57.3%** |\n\n\n## Citation\n\n```\n@inproceedings{wang2023yolov7,\n  title={{YOLOv7}: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors},\n  author={Wang, Chien-Yao and Bochkovskiy, Alexey and Liao, Hong-Yuan Mark},\n  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},\n  year={2023}\n}\n```\n\n```\n@article{wang2023designing,\n  title={Designing Network Design Strategies Through Gradient Path Analysis},\n  author={Wang, Chien-Yao and Liao, Hong-Yuan Mark and Yeh, I-Hau},\n  journal={Journal of Information Science and Engineering},\n  year={2023}\n}\n```\n\n\n## Teaser\n\nYOLOv7-semantic \u0026 YOLOv7-panoptic \u0026 YOLOv7-caption\n\n\u003cdiv align=\"center\"\u003e\n    \u003ca href=\"./\"\u003e\n        \u003cimg src=\"./figure/tennis.jpg\" width=\"24%\"/\u003e\n    \u003c/a\u003e\n    \u003ca href=\"./\"\u003e\n        \u003cimg src=\"./figure/tennis_semantic.jpg\" width=\"24%\"/\u003e\n    \u003c/a\u003e\n    \u003ca href=\"./\"\u003e\n        \u003cimg src=\"./figure/tennis_panoptic.png\" width=\"24%\"/\u003e\n    \u003c/a\u003e\n    \u003ca href=\"./\"\u003e\n        \u003cimg src=\"./figure/tennis_caption.png\" width=\"24%\"/\u003e\n    \u003c/a\u003e\n\u003c/div\u003e\n\nYOLOv7-semantic \u0026 YOLOv7-detection \u0026 YOLOv7-depth (with NTUT)\n\n\u003cdiv align=\"center\"\u003e\n    \u003ca href=\"./\"\u003e\n        \u003cimg src=\"./figure/yolov7_city.jpg\" width=\"80%\"/\u003e\n    \u003c/a\u003e\n\u003c/div\u003e\n\nYOLOv7-3d-detection \u0026 YOLOv7-lidar \u0026 YOLOv7-road (with NTUT)\n\n\u003cdiv align=\"center\"\u003e\n    \u003ca href=\"./\"\u003e\n        \u003cimg src=\"./figure/yolov7_3d.jpg\" width=\"30%\"/\u003e\n    \u003c/a\u003e\n    \u003ca href=\"./\"\u003e\n        \u003cimg src=\"./figure/yolov7_lidar.jpg\" width=\"30%\"/\u003e\n    \u003c/a\u003e\n    \u003ca href=\"./\"\u003e\n        \u003cimg src=\"./figure/yolov7_road.jpg\" width=\"30%\"/\u003e\n    \u003c/a\u003e\n\u003c/div\u003e\n\n\n## Acknowledgements\n\n\u003cdetails\u003e\u003csummary\u003e \u003cb\u003eExpand\u003c/b\u003e \u003c/summary\u003e\n\n* [https://github.com/AlexeyAB/darknet](https://github.com/AlexeyAB/darknet)\n* [https://github.com/WongKinYiu/yolor](https://github.com/WongKinYiu/yolor)\n* [https://github.com/WongKinYiu/PyTorch_YOLOv4](https://github.com/WongKinYiu/PyTorch_YOLOv4)\n* [https://github.com/WongKinYiu/ScaledYOLOv4](https://github.com/WongKinYiu/ScaledYOLOv4)\n* [https://github.com/Megvii-BaseDetection/YOLOX](https://github.com/Megvii-BaseDetection/YOLOX)\n* [https://github.com/ultralytics/yolov3](https://github.com/ultralytics/yolov3)\n* [https://github.com/ultralytics/yolov5](https://github.com/ultralytics/yolov5)\n* [https://github.com/DingXiaoH/RepVGG](https://github.com/DingXiaoH/RepVGG)\n* [https://github.com/JUGGHM/OREPA_CVPR2022](https://github.com/JUGGHM/OREPA_CVPR2022)\n* [https://github.com/TexasInstruments/edgeai-yolov5/tree/yolo-pose](https://github.com/TexasInstruments/edgeai-yolov5/tree/yolo-pose)\n\n\u003c/details\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fml13571%2Fyolov7","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fml13571%2Fyolov7","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fml13571%2Fyolov7/lists"}