[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-72768":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":17,"stars7d":18,"stars30d":19,"stars90d":16,"forks30d":16,"starsTrendScore":20,"compositeScore":21,"rankGlobal":10,"rankLanguage":10,"license":22,"archived":23,"fork":23,"defaultBranch":24,"hasWiki":25,"hasPages":23,"topics":26,"createdAt":10,"pushedAt":10,"updatedAt":33,"readmeContent":34,"aiSummary":35,"trendingCount":16,"starSnapshotCount":16,"syncStatus":36,"lastSyncTime":37,"discoverSource":38},72768,"yolov7","WongKinYiu\u002Fyolov7","WongKinYiu","Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors","",null,"Jupyter Notebook",14104,4363,112,1464,0,6,7,16,18,87.1,"GNU General Public License v3.0",false,"main",true,[27,28,29,30,31,32,5],"darknet","pytorch","scaled-yolov4","yolor","yolov3","yolov4","2026-06-12 04:01:07","# Official YOLOv7\n\nImplementation of paper - [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https:\u002F\u002Farxiv.org\u002Fabs\u002F2207.02696)\n\n[![PWC](https:\u002F\u002Fimg.shields.io\u002Fendpoint.svg?url=https:\u002F\u002Fpaperswithcode.com\u002Fbadge\u002Fyolov7-trainable-bag-of-freebies-sets-new\u002Freal-time-object-detection-on-coco)](https:\u002F\u002Fpaperswithcode.com\u002Fsota\u002Freal-time-object-detection-on-coco?p=yolov7-trainable-bag-of-freebies-sets-new)\n[![Hugging Face Spaces](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https:\u002F\u002Fhuggingface.co\u002Fspaces\u002Fakhaliq\u002Fyolov7)\n\u003Ca href=\"https:\u002F\u002Fcolab.research.google.com\u002Fgist\u002FAlexeyAB\u002Fb769f5795e65fdab80086f6cb7940dae\u002Fyolov7detection.ipynb\">\u003Cimg src=\"https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg\" alt=\"Open In Colab\">\u003C\u002Fa>\n[![arxiv.org](http:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fcs.CV-arXiv%3A2207.02696-B31B1B.svg)](https:\u002F\u002Farxiv.org\u002Fabs\u002F2207.02696)\n\n\u003Cdiv align=\"center\">\n    \u003Ca href=\".\u002F\">\n        \u003Cimg src=\".\u002Ffigure\u002Fperformance.png\" width=\"79%\"\u002F>\n    \u003C\u002Fa>\n\u003C\u002Fdiv>\n\n## Web Demo\n\n- Integrated into [Huggingface Spaces 🤗](https:\u002F\u002Fhuggingface.co\u002Fspaces\u002Fakhaliq\u002Fyolov7) using Gradio. Try out the Web Demo [![Hugging Face Spaces](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https:\u002F\u002Fhuggingface.co\u002Fspaces\u002Fakhaliq\u002Fyolov7)\n\n## Performance \n\nMS COCO\n\n| Model | Test Size | AP\u003Csup>test\u003C\u002Fsup> | AP\u003Csub>50\u003C\u002Fsub>\u003Csup>test\u003C\u002Fsup> | AP\u003Csub>75\u003C\u002Fsub>\u003Csup>test\u003C\u002Fsup> | batch 1 fps | batch 32 average time |\n| :-- | :-: | :-: | :-: | :-: | :-: | :-: |\n| [**YOLOv7**](https:\u002F\u002Fgithub.com\u002FWongKinYiu\u002Fyolov7\u002Freleases\u002Fdownload\u002Fv0.1\u002Fyolov7.pt) | 640 | **51.4%** | **69.7%** | **55.9%** | 161 *fps* | 2.8 *ms* |\n| [**YOLOv7-X**](https:\u002F\u002Fgithub.com\u002FWongKinYiu\u002Fyolov7\u002Freleases\u002Fdownload\u002Fv0.1\u002Fyolov7x.pt) | 640 | **53.1%** | **71.2%** | **57.8%** | 114 *fps* | 4.3 *ms* |\n|  |  |  |  |  |  |  |\n| [**YOLOv7-W6**](https:\u002F\u002Fgithub.com\u002FWongKinYiu\u002Fyolov7\u002Freleases\u002Fdownload\u002Fv0.1\u002Fyolov7-w6.pt) | 1280 | **54.9%** | **72.6%** | **60.1%** | 84 *fps* | 7.6 *ms* |\n| [**YOLOv7-E6**](https:\u002F\u002Fgithub.com\u002FWongKinYiu\u002Fyolov7\u002Freleases\u002Fdownload\u002Fv0.1\u002Fyolov7-e6.pt) | 1280 | **56.0%** | **73.5%** | **61.2%** | 56 *fps* | 12.3 *ms* |\n| [**YOLOv7-D6**](https:\u002F\u002Fgithub.com\u002FWongKinYiu\u002Fyolov7\u002Freleases\u002Fdownload\u002Fv0.1\u002Fyolov7-d6.pt) | 1280 | **56.6%** | **74.0%** | **61.8%** | 44 *fps* | 15.0 *ms* |\n| [**YOLOv7-E6E**](https:\u002F\u002Fgithub.com\u002FWongKinYiu\u002Fyolov7\u002Freleases\u002Fdownload\u002Fv0.1\u002Fyolov7-e6e.pt) | 1280 | **56.8%** | **74.4%** | **62.1%** | 36 *fps* | 18.7 *ms* |\n\n## Installation\n\nDocker environment (recommended)\n\u003Cdetails>\u003Csummary> \u003Cb>Expand\u003C\u002Fb> \u003C\u002Fsummary>\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\u002F:\u002Fcoco\u002F -v your_code_path\u002F:\u002Fyolov7 --shm-size=64g nvcr.io\u002Fnvidia\u002Fpytorch: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 \u002Fyolov7\n```\n\n\u003C\u002Fdetails>\n\n## Testing\n\n[`yolov7.pt`](https:\u002F\u002Fgithub.com\u002FWongKinYiu\u002Fyolov7\u002Freleases\u002Fdownload\u002Fv0.1\u002Fyolov7.pt) [`yolov7x.pt`](https:\u002F\u002Fgithub.com\u002FWongKinYiu\u002Fyolov7\u002Freleases\u002Fdownload\u002Fv0.1\u002Fyolov7x.pt) [`yolov7-w6.pt`](https:\u002F\u002Fgithub.com\u002FWongKinYiu\u002Fyolov7\u002Freleases\u002Fdownload\u002Fv0.1\u002Fyolov7-w6.pt) [`yolov7-e6.pt`](https:\u002F\u002Fgithub.com\u002FWongKinYiu\u002Fyolov7\u002Freleases\u002Fdownload\u002Fv0.1\u002Fyolov7-e6.pt) [`yolov7-d6.pt`](https:\u002F\u002Fgithub.com\u002FWongKinYiu\u002Fyolov7\u002Freleases\u002Fdownload\u002Fv0.1\u002Fyolov7-d6.pt) [`yolov7-e6e.pt`](https:\u002F\u002Fgithub.com\u002FWongKinYiu\u002Fyolov7\u002Freleases\u002Fdownload\u002Fv0.1\u002Fyolov7-e6e.pt)\n\n``` shell\npython test.py --data data\u002Fcoco.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:\u002F\u002Fimages.cocodataset.org\u002Fannotations\u002Fannotations_trainval2017.zip) to the `.\u002Fcoco\u002Fannotations\u002Finstances_val2017.json`\n\n## Training\n\nData preparation\n\n``` shell\nbash scripts\u002Fget_coco.sh\n```\n\n* Download MS COCO dataset images ([train](http:\u002F\u002Fimages.cocodataset.org\u002Fzips\u002Ftrain2017.zip), [val](http:\u002F\u002Fimages.cocodataset.org\u002Fzips\u002Fval2017.zip), [test](http:\u002F\u002Fimages.cocodataset.org\u002Fzips\u002Ftest2017.zip)) and [labels](https:\u002F\u002Fgithub.com\u002FWongKinYiu\u002Fyolov7\u002Freleases\u002Fdownload\u002Fv0.1\u002Fcoco2017labels-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:\u002F\u002Fgithub.com\u002FWongKinYiu\u002Fyolov7\u002Freleases\u002Fdownload\u002Fv0.1\u002Fcoco2017labels-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\u002Fcoco.yaml --img 640 640 --cfg cfg\u002Ftraining\u002Fyolov7.yaml --weights '' --name yolov7 --hyp data\u002Fhyp.scratch.p5.yaml\n\n# train p6 models\npython train_aux.py --workers 8 --device 0 --batch-size 16 --data data\u002Fcoco.yaml --img 1280 1280 --cfg cfg\u002Ftraining\u002Fyolov7-w6.yaml --weights '' --name yolov7-w6 --hyp data\u002Fhyp.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\u002Fcoco.yaml --img 640 640 --cfg cfg\u002Ftraining\u002Fyolov7.yaml --weights '' --name yolov7 --hyp data\u002Fhyp.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\u002Fcoco.yaml --img 1280 1280 --cfg cfg\u002Ftraining\u002Fyolov7-w6.yaml --weights '' --name yolov7-w6 --hyp data\u002Fhyp.scratch.p6.yaml\n```\n\n## Transfer learning\n\n[`yolov7_training.pt`](https:\u002F\u002Fgithub.com\u002FWongKinYiu\u002Fyolov7\u002Freleases\u002Fdownload\u002Fv0.1\u002Fyolov7_training.pt) [`yolov7x_training.pt`](https:\u002F\u002Fgithub.com\u002FWongKinYiu\u002Fyolov7\u002Freleases\u002Fdownload\u002Fv0.1\u002Fyolov7x_training.pt) [`yolov7-w6_training.pt`](https:\u002F\u002Fgithub.com\u002FWongKinYiu\u002Fyolov7\u002Freleases\u002Fdownload\u002Fv0.1\u002Fyolov7-w6_training.pt) [`yolov7-e6_training.pt`](https:\u002F\u002Fgithub.com\u002FWongKinYiu\u002Fyolov7\u002Freleases\u002Fdownload\u002Fv0.1\u002Fyolov7-e6_training.pt) [`yolov7-d6_training.pt`](https:\u002F\u002Fgithub.com\u002FWongKinYiu\u002Fyolov7\u002Freleases\u002Fdownload\u002Fv0.1\u002Fyolov7-d6_training.pt) [`yolov7-e6e_training.pt`](https:\u002F\u002Fgithub.com\u002FWongKinYiu\u002Fyolov7\u002Freleases\u002Fdownload\u002Fv0.1\u002Fyolov7-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\u002Fcustom.yaml --img 640 640 --cfg cfg\u002Ftraining\u002Fyolov7-custom.yaml --weights 'yolov7_training.pt' --name yolov7-custom --hyp data\u002Fhyp.scratch.custom.yaml\n\n# finetune p6 models\npython train_aux.py --workers 8 --device 0 --batch-size 16 --data data\u002Fcustom.yaml --img 1280 1280 --cfg cfg\u002Ftraining\u002Fyolov7-w6-custom.yaml --weights 'yolov7-w6_training.pt' --name yolov7-w6-custom --hyp data\u002Fhyp.scratch.custom.yaml\n```\n\n## Re-parameterization\n\nSee [reparameterization.ipynb](tools\u002Freparameterization.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\u002Fimages\u002Fhorses.jpg\n```\n\n\u003Cdiv align=\"center\">\n    \u003Ca href=\".\u002F\">\n        \u003Cimg src=\".\u002Ffigure\u002Fhorses_prediction.jpg\" width=\"59%\"\u002F>\n    \u003C\u002Fa>\n\u003C\u002Fdiv>\n\n\n## Export\n\n**Pytorch to CoreML (and inference on MacOS\u002FiOS)** \u003Ca href=\"https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002FWongKinYiu\u002Fyolov7\u002Fblob\u002Fmain\u002Ftools\u002FYOLOv7CoreML.ipynb\">\u003Cimg src=\"https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg\" alt=\"Open In Colab\">\u003C\u002Fa>\n\n**Pytorch to ONNX with NMS (and inference)** \u003Ca href=\"https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002FWongKinYiu\u002Fyolov7\u002Fblob\u002Fmain\u002Ftools\u002FYOLOv7onnx.ipynb\">\u003Cimg src=\"https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg\" alt=\"Open In Colab\">\u003C\u002Fa>\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:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002FWongKinYiu\u002Fyolov7\u002Fblob\u002Fmain\u002Ftools\u002FYOLOv7trt.ipynb\">\u003Cimg src=\"https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg\" alt=\"Open In Colab\">\u003C\u002Fa>\n\n```shell\nwget https:\u002F\u002Fgithub.com\u002FWongKinYiu\u002Fyolov7\u002Freleases\u002Fdownload\u002Fv0.1\u002Fyolov7-tiny.pt\npython export.py --weights .\u002Fyolov7-tiny.pt --grid --end2end --simplify --topk-all 100 --iou-thres 0.65 --conf-thres 0.35 --img-size 640 640\ngit clone https:\u002F\u002Fgithub.com\u002FLinaom1214\u002Ftensorrt-python.git\npython .\u002Ftensorrt-python\u002Fexport.py -o yolov7-tiny.onnx -e yolov7-tiny-nms.trt -p fp16\n```\n\n**Pytorch to TensorRT another way** \u003Ca href=\"https:\u002F\u002Fcolab.research.google.com\u002Fgist\u002FAlexeyAB\u002Ffcb47ae544cf284eb24d8ad8e880d45c\u002Fyolov7trtlinaom.ipynb\">\u003Cimg src=\"https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg\" alt=\"Open In Colab\">\u003C\u002Fa> \u003Cdetails>\u003Csummary> \u003Cb>Expand\u003C\u002Fb> \u003C\u002Fsummary>\n\n\n```shell\nwget https:\u002F\u002Fgithub.com\u002FWongKinYiu\u002Fyolov7\u002Freleases\u002Fdownload\u002Fv0.1\u002Fyolov7-tiny.pt\npython export.py --weights yolov7-tiny.pt --grid --include-nms\ngit clone https:\u002F\u002Fgithub.com\u002FLinaom1214\u002Ftensorrt-python.git\npython .\u002Ftensorrt-python\u002Fexport.py -o yolov7-tiny.onnx -e yolov7-tiny-nms.trt -p fp16\n\n# Or use trtexec to convert ONNX to TensorRT engine\n\u002Fusr\u002Fsrc\u002Ftensorrt\u002Fbin\u002Ftrtexec --onnx=yolov7-tiny.onnx --saveEngine=yolov7-tiny-nms.trt --fp16\n```\n\n\u003C\u002Fdetails>\n\nTested with: Python 3.7.13, Pytorch 1.12.0+cu113\n\n## Pose estimation\n\n[`code`](https:\u002F\u002Fgithub.com\u002FWongKinYiu\u002Fyolov7\u002Ftree\u002Fpose) [`yolov7-w6-pose.pt`](https:\u002F\u002Fgithub.com\u002FWongKinYiu\u002Fyolov7\u002Freleases\u002Fdownload\u002Fv0.1\u002Fyolov7-w6-pose.pt)\n\nSee [keypoint.ipynb](https:\u002F\u002Fgithub.com\u002FWongKinYiu\u002Fyolov7\u002Fblob\u002Fmain\u002Ftools\u002Fkeypoint.ipynb).\n\n\u003Cdiv align=\"center\">\n    \u003Ca href=\".\u002F\">\n        \u003Cimg src=\".\u002Ffigure\u002Fpose.png\" width=\"39%\"\u002F>\n    \u003C\u002Fa>\n\u003C\u002Fdiv>\n\n\n## Instance segmentation (with NTU)\n\n[`code`](https:\u002F\u002Fgithub.com\u002FWongKinYiu\u002Fyolov7\u002Ftree\u002Fmask) [`yolov7-mask.pt`](https:\u002F\u002Fgithub.com\u002FWongKinYiu\u002Fyolov7\u002Freleases\u002Fdownload\u002Fv0.1\u002Fyolov7-mask.pt)\n\nSee [instance.ipynb](https:\u002F\u002Fgithub.com\u002FWongKinYiu\u002Fyolov7\u002Fblob\u002Fmain\u002Ftools\u002Finstance.ipynb).\n\n\u003Cdiv align=\"center\">\n    \u003Ca href=\".\u002F\">\n        \u003Cimg src=\".\u002Ffigure\u002Fmask.png\" width=\"59%\"\u002F>\n    \u003C\u002Fa>\n\u003C\u002Fdiv>\n\n## Instance segmentation\n\n[`code`](https:\u002F\u002Fgithub.com\u002FWongKinYiu\u002Fyolov7\u002Ftree\u002Fu7\u002Fseg) [`yolov7-seg.pt`](https:\u002F\u002Fgithub.com\u002FWongKinYiu\u002Fyolov7\u002Freleases\u002Fdownload\u002Fv0.1\u002Fyolov7-seg.pt)\n\nYOLOv7 for instance segmentation (YOLOR + YOLOv5 + YOLACT)\n\n| Model | Test Size | AP\u003Csup>box\u003C\u002Fsup> | AP\u003Csub>50\u003C\u002Fsub>\u003Csup>box\u003C\u002Fsup> | AP\u003Csub>75\u003C\u002Fsub>\u003Csup>box\u003C\u002Fsup> | AP\u003Csup>mask\u003C\u002Fsup> | AP\u003Csub>50\u003C\u002Fsub>\u003Csup>mask\u003C\u002Fsup> | AP\u003Csub>75\u003C\u002Fsub>\u003Csup>mask\u003C\u002Fsup> |\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:\u002F\u002Fgithub.com\u002FWongKinYiu\u002Fyolov7\u002Ftree\u002Fu6) [`yolov7-u6.pt`](https:\u002F\u002Fgithub.com\u002FWongKinYiu\u002Fyolov7\u002Freleases\u002Fdownload\u002Fv0.1\u002Fyolov7-u6.pt)\n\nYOLOv7 with decoupled TAL head (YOLOR + YOLOv5 + YOLOv6)\n\n| Model | Test Size | AP\u003Csup>val\u003C\u002Fsup> | AP\u003Csub>50\u003C\u002Fsub>\u003Csup>val\u003C\u002Fsup> | AP\u003Csub>75\u003C\u002Fsub>\u003Csup>val\u003C\u002Fsup> |\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\u002FCVF 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 & YOLOv7-panoptic & YOLOv7-caption\n\n\u003Cdiv align=\"center\">\n    \u003Ca href=\".\u002F\">\n        \u003Cimg src=\".\u002Ffigure\u002Ftennis.jpg\" width=\"24%\"\u002F>\n    \u003C\u002Fa>\n    \u003Ca href=\".\u002F\">\n        \u003Cimg src=\".\u002Ffigure\u002Ftennis_semantic.jpg\" width=\"24%\"\u002F>\n    \u003C\u002Fa>\n    \u003Ca href=\".\u002F\">\n        \u003Cimg src=\".\u002Ffigure\u002Ftennis_panoptic.png\" width=\"24%\"\u002F>\n    \u003C\u002Fa>\n    \u003Ca href=\".\u002F\">\n        \u003Cimg src=\".\u002Ffigure\u002Ftennis_caption.png\" width=\"24%\"\u002F>\n    \u003C\u002Fa>\n\u003C\u002Fdiv>\n\nYOLOv7-semantic & YOLOv7-detection & YOLOv7-depth (with NTUT)\n\n\u003Cdiv align=\"center\">\n    \u003Ca href=\".\u002F\">\n        \u003Cimg src=\".\u002Ffigure\u002Fyolov7_city.jpg\" width=\"80%\"\u002F>\n    \u003C\u002Fa>\n\u003C\u002Fdiv>\n\nYOLOv7-3d-detection & YOLOv7-lidar & YOLOv7-road (with NTUT)\n\n\u003Cdiv align=\"center\">\n    \u003Ca href=\".\u002F\">\n        \u003Cimg src=\".\u002Ffigure\u002Fyolov7_3d.jpg\" width=\"30%\"\u002F>\n    \u003C\u002Fa>\n    \u003Ca href=\".\u002F\">\n        \u003Cimg src=\".\u002Ffigure\u002Fyolov7_lidar.jpg\" width=\"30%\"\u002F>\n    \u003C\u002Fa>\n    \u003Ca href=\".\u002F\">\n        \u003Cimg src=\".\u002Ffigure\u002Fyolov7_road.jpg\" width=\"30%\"\u002F>\n    \u003C\u002Fa>\n\u003C\u002Fdiv>\n\n\n## Acknowledgements\n\n\u003Cdetails>\u003Csummary> \u003Cb>Expand\u003C\u002Fb> \u003C\u002Fsummary>\n\n* [https:\u002F\u002Fgithub.com\u002FAlexeyAB\u002Fdarknet](https:\u002F\u002Fgithub.com\u002FAlexeyAB\u002Fdarknet)\n* [https:\u002F\u002Fgithub.com\u002FWongKinYiu\u002Fyolor](https:\u002F\u002Fgithub.com\u002FWongKinYiu\u002Fyolor)\n* [https:\u002F\u002Fgithub.com\u002FWongKinYiu\u002FPyTorch_YOLOv4](https:\u002F\u002Fgithub.com\u002FWongKinYiu\u002FPyTorch_YOLOv4)\n* [https:\u002F\u002Fgithub.com\u002FWongKinYiu\u002FScaledYOLOv4](https:\u002F\u002Fgithub.com\u002FWongKinYiu\u002FScaledYOLOv4)\n* [https:\u002F\u002Fgithub.com\u002FMegvii-BaseDetection\u002FYOLOX](https:\u002F\u002Fgithub.com\u002FMegvii-BaseDetection\u002FYOLOX)\n* [https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fyolov3](https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fyolov3)\n* [https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fyolov5](https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fyolov5)\n* [https:\u002F\u002Fgithub.com\u002FDingXiaoH\u002FRepVGG](https:\u002F\u002Fgithub.com\u002FDingXiaoH\u002FRepVGG)\n* [https:\u002F\u002Fgithub.com\u002FJUGGHM\u002FOREPA_CVPR2022](https:\u002F\u002Fgithub.com\u002FJUGGHM\u002FOREPA_CVPR2022)\n* [https:\u002F\u002Fgithub.com\u002FTexasInstruments\u002Fedgeai-yolov5\u002Ftree\u002Fyolo-pose](https:\u002F\u002Fgithub.com\u002FTexasInstruments\u002Fedgeai-yolov5\u002Ftree\u002Fyolo-pose)\n\n\u003C\u002Fdetails>\n","YOLOv7是一个实时物体检测器的实现，基于论文《YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors》。该项目通过引入可训练的bag-of-freebies技术，在保持高精度的同时大幅提升了检测速度，适用于需要高性能实时物体检测的应用场景，如自动驾驶、视频监控等。它支持多种模型版本（如YOLOv7, YOLOv7-X, YOLOv7-W6等），以满足不同硬件条件下的需求。在COCO数据集上的测试表明，这些模型不仅在准确率上表现优异（例如，YOLOv7在640x640分辨率下达到了51.4%的AP），而且处理速度也非常快（例如，YOLOv7可以达到每秒161帧）。项目使用PyTorch框架开发，并提供了详细的安装指南和在线演示环境，便于开发者快速上手。",2,"2026-06-11 03:43:32","high_star"]