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论文实现 - YOLOv7：可训练的免费包为实时物体检测器树立了新标杆

资源文件列表(大概)

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yolov7/
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yolov7/.gitignore
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yolov7/LICENSE.md
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yolov7/README.md
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yolov7/cfg/
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yolov7/cfg/baseline/
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yolov7/cfg/baseline/r50-csp.yaml
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yolov7/cfg/baseline/x50-csp.yaml
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yolov7/cfg/baseline/yolor-csp-x.yaml
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yolov7/cfg/baseline/yolor-csp.yaml
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yolov7/cfg/baseline/yolor-d6.yaml
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yolov7/cfg/baseline/yolor-e6.yaml
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yolov7/cfg/baseline/yolor-p6.yaml
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yolov7/cfg/baseline/yolor-w6.yaml
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yolov7/cfg/baseline/yolov3-spp.yaml
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yolov7/cfg/baseline/yolov3.yaml
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yolov7/cfg/baseline/yolov4-csp.yaml
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yolov7/cfg/deploy/
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yolov7/cfg/deploy/yolov7-d6.yaml
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yolov7/cfg/deploy/yolov7-e6.yaml
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yolov7/cfg/deploy/yolov7-e6e.yaml
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yolov7/cfg/deploy/yolov7-tiny-silu.yaml
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yolov7/cfg/deploy/yolov7-tiny.yaml
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yolov7/cfg/deploy/yolov7-w6.yaml
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yolov7/cfg/deploy/yolov7.yaml
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yolov7/cfg/deploy/yolov7x.yaml
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yolov7/cfg/training/
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yolov7/cfg/training/yolov7-d6.yaml
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yolov7/cfg/training/yolov7-e6.yaml
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yolov7/cfg/training/yolov7-e6e.yaml
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yolov7/cfg/training/yolov7-tiny.yaml
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yolov7/cfg/training/yolov7-w6.yaml
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yolov7/cfg/training/yolov7.yaml
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yolov7/cfg/training/yolov7x.yaml
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yolov7/data/
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yolov7/data/coco.yaml
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yolov7/data/hyp.scratch.custom.yaml
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yolov7/data/hyp.scratch.p5.yaml
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yolov7/data/hyp.scratch.p6.yaml
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yolov7/data/hyp.scratch.tiny.yaml
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yolov7/deploy/
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yolov7/deploy/triton-inference-server/
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yolov7/deploy/triton-inference-server/README.md
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yolov7/deploy/triton-inference-server/boundingbox.py
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yolov7/deploy/triton-inference-server/client.py
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yolov7/deploy/triton-inference-server/data/
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yolov7/deploy/triton-inference-server/data/dog.jpg
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yolov7/deploy/triton-inference-server/data/dog_result.jpg
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yolov7/deploy/triton-inference-server/labels.py
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yolov7/deploy/triton-inference-server/processing.py
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yolov7/deploy/triton-inference-server/render.py
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yolov7/detect.py
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yolov7/export.py
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yolov7/figure/
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yolov7/figure/horses_prediction.jpg
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yolov7/figure/mask.png
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yolov7/figure/performance.png
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yolov7/figure/pose.png
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yolov7/figure/tennis.jpg
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yolov7/figure/tennis_caption.png
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yolov7/figure/tennis_panoptic.png
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yolov7/figure/tennis_semantic.jpg
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yolov7/figure/yolov7_3d.jpg
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yolov7/figure/yolov7_city.jpg
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yolov7/figure/yolov7_lidar.jpg
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yolov7/figure/yolov7_road.jpg
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yolov7/hubconf.py
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yolov7/inference/
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yolov7/inference/images/
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yolov7/inference/images/bus.jpg
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yolov7/inference/images/horses.jpg
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yolov7/inference/images/image1.jpg
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yolov7/inference/images/image3.jpg
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yolov7/inference/images/zidane.jpg
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yolov7/models/
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yolov7/models/__init__.py
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yolov7/models/common.py
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yolov7/models/experimental.py
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yolov7/models/yolo.py
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yolov7/paper/
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yolov7/paper/yolov7.pdf
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yolov7/requirements.txt
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yolov7/scripts/
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yolov7/scripts/get_coco.sh
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yolov7/test.py
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yolov7/tools/
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yolov7/tools/YOLOv7-Dynamic-Batch-ONNXRUNTIME.ipynb
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yolov7/tools/YOLOv7-Dynamic-Batch-TENSORRT.ipynb
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yolov7/tools/YOLOv7CoreML.ipynb
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yolov7/tools/YOLOv7onnx.ipynb
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yolov7/tools/YOLOv7trt.ipynb
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yolov7/tools/compare_YOLOv7_vs_YOLOv5m6.ipynb
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yolov7/tools/compare_YOLOv7_vs_YOLOv5m6_half.ipynb
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yolov7/tools/compare_YOLOv7_vs_YOLOv5s6.ipynb
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yolov7/tools/compare_YOLOv7e6_vs_YOLOv5x6.ipynb
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yolov7/tools/compare_YOLOv7e6_vs_YOLOv5x6_half.ipynb
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yolov7/tools/instance.ipynb
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yolov7/tools/keypoint.ipynb
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yolov7/tools/reparameterization.ipynb
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yolov7/tools/visualization.ipynb
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yolov7/train.py
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yolov7/train_aux.py
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yolov7/utils/
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yolov7/utils/__init__.py
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yolov7/utils/activations.py
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yolov7/utils/add_nms.py
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yolov7/utils/autoanchor.py
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yolov7/utils/aws/
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yolov7/utils/aws/__init__.py
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yolov7/utils/aws/mime.sh
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yolov7/utils/aws/resume.py
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yolov7/utils/aws/userdata.sh
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yolov7/utils/datasets.py
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yolov7/utils/general.py
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yolov7/utils/google_app_engine/
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yolov7/utils/google_app_engine/Dockerfile
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yolov7/utils/google_app_engine/additional_requirements.txt
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yolov7/utils/google_app_engine/app.yaml
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yolov7/utils/google_utils.py
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yolov7/utils/loss.py
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yolov7/utils/metrics.py
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yolov7/utils/plots.py
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yolov7/utils/torch_utils.py
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yolov7/utils/wandb_logging/
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yolov7/utils/wandb_logging/__init__.py
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yolov7/utils/wandb_logging/log_dataset.py
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yolov7/utils/wandb_logging/wandb_utils.py
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资源内容介绍

论文实现 - YOLOv7：可训练的免费包为实时物体检测器树立了新标杆
# Official YOLOv7Implementation of paper - [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://arxiv.org/abs/2207.02696)[![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)[![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/akhaliq/yolov7)<a href="https://colab.research.google.com/gist/AlexeyAB/b769f5795e65fdab80086f6cb7940dae/yolov7detection.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>[![arxiv.org](http://img.shields.io/badge/cs.CV-arXiv%3A2207.02696-B31B1B.svg)](https://arxiv.org/abs/2207.02696)<div align="center"> <a href="./"> <img src="./figure/performance.png" width="79%"/> </a></div>## Web Demo- 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)## 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**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt) | 640 | **51.4%** | **69.7%** | **55.9%** | 161 *fps* | 2.8 *ms* || [**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* || | | | | | | || [**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* || [**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* || [**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* || [**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* |## InstallationDocker environment (recommended)<details><summary> <b>Expand</b> </summary>``` shell# 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 packagesapt updateapt install -y zip htop screen libgl1-mesa-glx# pip install required packagespip install seaborn thop# go to code foldercd /yolov7```</details>## Testing[`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)``` shellpython 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](http://images.cocodataset.org/annotations/annotations_trainval2017.zip) to the `./coco/annotations/instances_val2017.json`## TrainingData preparation``` shellbash scripts/get_coco.sh```* 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) Single GPU training``` shell# train p5 modelspython 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 modelspython 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``` shell# train p5 modelspython -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 modelspython -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`](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)Single GPU finetuning for custom dataset``` shell# finetune p5 modelspython 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 modelspython 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-parameterizationSee [reparameterization.ipynb](tools/reparameterization.ipynb)## InferenceOn video:``` shellpython detect.py --weights

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