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代码版本:0714 commit #4000 $ git clone https://github.com/ultralytics/yolov5 $ cd yolov5 $ git checkout 720aaa65c8873c0d87df09e3c1c14f3581d4ea61这个代码只是注释版本哈,不是很建议直接运行。 考虑到可能会有人直接运行,可能之后有时间会出一个新的6.0版本的注释emmmm. yolov5-6.0最近发现了一个更详细的yolov5注释版yolov5-5.x-annotations. Yolov5网络结构图(2.0-5.0) 下面的文件均有注释,有些没有用到的函数,以及网络结构模块等没有注释 yolov5_annotations ├── data ├── models │ ├── common.py │ ├── experimental.py │ ├── yolo.py ├── utils │ ├── augmentations.py │ ├── autoanchor.py │ ├── datasets.py │ ├── general.py │ ├── loss.py │ ├── metrics.py │ ├── plots.py │ ├── torch_utils.py │ ├── google_utils.py ├── export.py ├── hubconf.py ├── train.py ├── val.py ├── detect.py原README分割线
![]() ![]() ![]() ![]() ![]() ![]() YOLOv5 🚀 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. DocumentationSee the YOLOv5 Docs for full documentation on training, testing and deployment. Quick Start Examples InstallPython >= 3.6.0 required with all requirements.txt dependencies installed: $ git clone https://github.com/ultralytics/yolov5 $ cd yolov5 $ pip install -r requirements.txt InferenceInference with YOLOv5 and PyTorch Hub. Models automatically download from the latest YOLOv5 release. import torch # Model model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # or yolov5m, yolov5x, custom # Images img = 'https://ultralytics.com/images/zidane.jpg' # or file, PIL, OpenCV, numpy, multiple # Inference results = model(img) # Results results.print() # or .show(), .save(), .crop(), .pandas(), etc. Inference with detect.pydetect.py runs inference on a variety of sources, downloading models automatically from the latest YOLOv5 release and saving results to runs/detect. $ python detect.py --source 0 # webcam file.jpg # image file.mp4 # video path/ # directory path/*.jpg # glob 'https://youtu.be/NUsoVlDFqZg' # YouTube video 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream TrainingRun commands below to reproduce results on COCO dataset (dataset auto-downloads on first use). Training times for YOLOv5s/m/l/x are 2/4/6/8 days on a single V100 (multi-GPU times faster). Use the largest --batch-size your GPU allows (batch sizes shown for 16 GB devices). $ python train.py --data coco.yaml --cfg yolov5s.yaml --weights '' --batch-size 64 yolov5m 40 yolov5l 24 yolov5x 16![]() Get started in seconds with our verified environments and integrations, including Weights & Biases for automatic YOLOv5 experiment logging. Click each icon below for details. ![]() ![]() ![]() ![]() ![]() ![]() We are super excited about our first-ever Ultralytics YOLOv5 🚀 EXPORT Competition with $10,000 in cash prizes!
We love your input! We want to make contributing to YOLOv5 as easy and transparent as possible. Please see our Contributing Guide to get started. ContactFor issues running YOLOv5 please visit GitHub Issues. For business or professional support requests please visit https://ultralytics.com/contact. ![]() ![]() ![]() ![]() ![]() ![]() |
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