README.md


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    YOLOv3 🚀 is a family of object detection architectures and models pretrained on the COCO dataset, and represents Ultralytics open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development.

    Documentation

    See the YOLOv3 Docs for full documentation on training, testing and deployment.

    Quick Start Examples

    Install

    Python>=3.6.0 is required with all requirements.txt installed including PyTorch>=1.7:

    $ git clone https://github.com/ultralytics/yolov3
    $ cd yolov3
    $ pip install -r requirements.txt
    Inference

    Inference with YOLOv3 and PyTorch Hub. Models automatically download from the latest YOLOv3 release.

    import torch
    
    # Model
    model = torch.hub.load('ultralytics/yolov3', 'yolov3')  # or yolov3-spp, yolov3-tiny, custom
    
    # Images
    img = 'https://ultralytics.com/images/zidane.jpg'  # or file, Path, PIL, OpenCV, numpy, list
    
    # Inference
    results = model(img)
    
    # Results
    results.print()  # or .show(), .save(), .crop(), .pandas(), etc.
    Inference with detect.py

    detect.py runs inference on a variety of sources, downloading models automatically from the latest YOLOv3 release and saving results to runs/detect.

    $ python detect.py --source 0  # webcam
                                img.jpg  # image
                                vid.mp4  # video
                                path/  # directory
                                path/*.jpg  # glob
                                'https://youtu.be/Zgi9g1ksQHc'  # YouTube
                                'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream
    Training
    Tutorials

    Environments

    Get started in seconds with our verified environments. Click each icon below for details.

    Integrations

    Weights and Biases Roboflow NEW
    Automatically track and visualize all your YOLOv3 training runs in the cloud with Weights & Biases Label and export your custom datasets directly to YOLOv3 for training with Roboflow

    Why YOLOv5

    YOLOv3-P5 640 Figure (click to expand)

    Figure Notes (click to expand)
    • COCO AP val denotes mAP@0.5:0.95 metric measured on the 5000-image COCO val2017 dataset over various inference sizes from 256 to 1536.
    • GPU Speed measures average inference time per image on COCO val2017 dataset using a AWS p3.2xlarge V100 instance at batch-size 32.
    • EfficientDet data from google/automl at batch size 8.
    • Reproduce by python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt

    Pretrained Checkpoints

    Model size
    (pixels)
    mAPval
    0.5:0.95
    mAPval
    0.5
    Speed
    CPU b1
    (ms)
    Speed
    V100 b1
    (ms)
    Speed
    V100 b32
    (ms)
    params
    (M)
    FLOPs
    @640 (B)
    YOLOv5n 640 28.4 46.0 45 6.3 0.6 1.9 4.5
    YOLOv5s 640 37.2 56.0 98 6.4 0.9 7.2 16.5
    YOLOv5m 640 45.2 63.9 224 8.2 1.7 21.2 49.0
    YOLOv5l 640 48.8 67.2 430 10.1 2.7 46.5 109.1
    YOLOv5x 640 50.7 68.9 766 12.1 4.8 86.7 205.7
    YOLOv5n6 1280 34.0 50.7 153 8.1 2.1 3.2 4.6
    YOLOv5s6 1280 44.5 63.0 385 8.2 3.6 16.8 12.6
    YOLOv5m6 1280 51.0 69.0 887 11.1 6.8 35.7 50.0
    YOLOv5l6 1280 53.6 71.6 1784 15.8 10.5 76.8 111.4
    YOLOv5x6
    + TTA
    1280
    1536
    54.7
    55.4
    72.4
    72.3
    3136
    -
    26.2
    -
    19.4
    -
    140.7
    -
    209.8
    -
    Table Notes (click to expand)
    • All checkpoints are trained to 300 epochs with default settings and hyperparameters.
    • mAPval values are for single-model single-scale on COCO val2017 dataset.
      Reproduce by python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65
    • Speed averaged over COCO val images using a AWS p3.2xlarge instance. NMS times (~1 ms/img) not included.
      Reproduce by python val.py --data coco.yaml --img 640 --conf 0.25 --iou 0.45
    • TTA Test Time Augmentation includes reflection and scale augmentations.
      Reproduce by python val.py --data coco.yaml --img 1536 --iou 0.7 --augment

    Contribute

    We love your input! We want to make contributing to YOLOv3 as easy and transparent as possible. Please see our Contributing Guide to get started, and fill out the YOLOv3 Survey to send us feedback on your experiences. Thank you to all our contributors!

    Contact

    For YOLOv3 bugs and feature requests please visit GitHub Issues. For business inquiries or professional support requests please visit https://ultralytics.com/contact.


    项目简介

    🚀 Github 镜像仓库 🚀

    源项目地址

    https://github.com/ultralytics/yolov3

    发行版本 12

    v9.6.0 - YOLOv5 v6.0 release compatibility update for YOLOv3

    全部发行版

    贡献者 38

    全部贡献者

    开发语言

    • Python 98.9 %
    • Dockerfile 0.6 %
    • Shell 0.6 %