292 lines
15 KiB
Markdown
292 lines
15 KiB
Markdown
<div align="center">
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<p>
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<a align="left" href="https://ultralytics.com/yolov5" target="_blank">
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<img width="850" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/splash.jpg"></a>
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</p>
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<br>
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[English](../README.md) | 简体中文
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<div>
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<a href="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml"><img src="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg" alt="CI CPU testing"></a>
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<a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv5 Citation"></a>
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<a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a>
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<br>
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<a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
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<a href="https://www.kaggle.com/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
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<a href="https://join.slack.com/t/ultralytics/shared_invite/zt-w29ei8bp-jczz7QYUmDtgo6r6KcMIAg"><img src="https://img.shields.io/badge/Slack-Join_Forum-blue.svg?logo=slack" alt="Join Forum"></a>
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</div>
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<br>
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<p>
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YOLOv5🚀是一个在COCO数据集上预训练的物体检测架构和模型系列，它代表了<a href="https://ultralytics.com">Ultralytics</a>对未来视觉AI方法的公开研究，其中包含了在数千小时的研究和开发中所获得的经验和最佳实践。
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</p>
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<div align="center">
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<a href="https://github.com/ultralytics">
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<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-github.png" width="2%"/>
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</a>
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<img width="2%" />
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<a href="https://www.linkedin.com/company/ultralytics">
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<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-linkedin.png" width="2%"/>
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</a>
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<img width="2%" />
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<a href="https://twitter.com/ultralytics">
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<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-twitter.png" width="2%"/>
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</a>
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<img width="2%" />
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<a href="https://www.producthunt.com/@glenn_jocher">
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<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-producthunt.png" width="2%"/>
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</a>
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<img width="2%" />
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<a href="https://youtube.com/ultralytics">
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<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-youtube.png" width="2%"/>
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</a>
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<img width="2%" />
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<a href="https://www.facebook.com/ultralytics">
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<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-facebook.png" width="2%"/>
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</a>
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<img width="2%" />
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<a href="https://www.instagram.com/ultralytics/">
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<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-instagram.png" width="2%"/>
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</a>
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</div>
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<!--
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<a align="center" href="https://ultralytics.com/yolov5" target="_blank">
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<img width="800" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/banner-api.png"></a>
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-->
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</div>
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## <div align="center">文件</div>
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请参阅[YOLOv5 Docs](https://docs.ultralytics.com)，了解有关训练、测试和部署的完整文件。
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## <div align="center">快速开始案例</div>
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<details open>
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<summary>安装</summary>
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在[**Python>=3.7.0**](https://www.python.org/) 的环境中克隆版本仓并安装 [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt)，包括[**PyTorch>=1.7**](https://pytorch.org/get-started/locally/)。
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```bash
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git clone https://github.com/ultralytics/yolov5 # 克隆
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cd yolov5
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pip install -r requirements.txt # 安装
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```
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</details>
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<details open>
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<summary>推理</summary>
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YOLOv5 [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) 推理. [模型](https://github.com/ultralytics/yolov5/tree/master/models) 自动从最新YOLOv5 [版本](https://github.com/ultralytics/yolov5/releases)下载。
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```python
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import torch
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# 模型
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model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # or yolov5n - yolov5x6, custom
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# 图像
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img = 'https://ultralytics.com/images/zidane.jpg' # or file, Path, PIL, OpenCV, numpy, list
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# 推理
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results = model(img)
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# 结果
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results.print() # or .show(), .save(), .crop(), .pandas(), etc.
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```
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</details>
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<details>
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<summary>用 detect.py 进行推理</summary>
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`detect.py` 在各种数据源上运行推理, 其会从最新的 YOLOv5 [版本](https://github.com/ultralytics/yolov5/releases) 中自动下载 [模型](https://github.com/ultralytics/yolov5/tree/master/models) 并将检测结果保存到 `runs/detect` 目录。
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```bash
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python detect.py --source 0 # 网络摄像头
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img.jpg # 图像
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vid.mp4 # 视频
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path/ # 文件夹
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path/*.jpg # glob
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'https://youtu.be/Zgi9g1ksQHc' # YouTube
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'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP 流
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```
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</details>
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<details>
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<summary>训练</summary>
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以下指令再现了 YOLOv5 [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh)
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数据集结果. [模型](https://github.com/ultralytics/yolov5/tree/master/models) 和 [数据集](https://github.com/ultralytics/yolov5/tree/master/data) 自动从最新的YOLOv5 [版本](https://github.com/ultralytics/yolov5/releases) 中下载。YOLOv5n/s/m/l/x的训练时间在V100 GPU上是 1/2/4/6/8天（多GPU倍速）. 尽可能使用最大的 `--batch-size`, 或通过 `--batch-size -1` 来实现 YOLOv5 [自动批处理](https://github.com/ultralytics/yolov5/pull/5092). 批量大小显示为 V100-16GB。
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```bash
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python train.py --data coco.yaml --cfg yolov5n.yaml --weights '' --batch-size 128
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yolov5s 64
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yolov5m 40
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yolov5l 24
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yolov5x 16
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```
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<img width="800" src="https://user-images.githubusercontent.com/26833433/90222759-949d8800-ddc1-11ea-9fa1-1c97eed2b963.png">
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</details>
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<details open>
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<summary>教程</summary>
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- [训练自定义数据](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data) 🚀 推荐
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- [获得最佳训练效果的技巧](https://github.com/ultralytics/yolov5/wiki/Tips-for-Best-Training-Results) ☘️ 推荐
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- [使用 Weights & Biases 记录实验](https://github.com/ultralytics/yolov5/issues/1289) 🌟 新
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- [Roboflow：数据集、标签和主动学习](https://github.com/ultralytics/yolov5/issues/4975) 🌟 新
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- [多GPU训练](https://github.com/ultralytics/yolov5/issues/475)
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- [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) ⭐ 新
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- [TFLite, ONNX, CoreML, TensorRT 导出](https://github.com/ultralytics/yolov5/issues/251) 🚀
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- [测试时数据增强 (TTA)](https://github.com/ultralytics/yolov5/issues/303)
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- [模型集成](https://github.com/ultralytics/yolov5/issues/318)
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- [模型剪枝/稀疏性](https://github.com/ultralytics/yolov5/issues/304)
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- [超参数进化](https://github.com/ultralytics/yolov5/issues/607)
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- [带有冻结层的迁移学习](https://github.com/ultralytics/yolov5/issues/1314) ⭐ 新
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- [架构概要](https://github.com/ultralytics/yolov5/issues/6998) ⭐ 新
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</details>
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## <div align="center">环境</div>
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使用经过我们验证的环境，几秒钟就可以开始。点击下面的每个图标了解详情。
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<div align="center">
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<a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb">
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<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-colab-small.png" width="15%"/>
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</a>
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<a href="https://www.kaggle.com/ultralytics/yolov5">
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<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-kaggle-small.png" width="15%"/>
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</a>
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<a href="https://hub.docker.com/r/ultralytics/yolov5">
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<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-docker-small.png" width="15%"/>
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</a>
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<a href="https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart">
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<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-aws-small.png" width="15%"/>
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</a>
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<a href="https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart">
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<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-gcp-small.png" width="15%"/>
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</a>
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</div>
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## <div align="center">如何与第三方集成</div>
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<div align="center">
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<a href="https://wandb.ai/site?utm_campaign=repo_yolo_readme">
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<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-wb-long.png" width="49%"/>
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</a>
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<a href="https://roboflow.com/?ref=ultralytics">
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<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-roboflow-long.png" width="49%"/>
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</a>
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</div>
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|Weights and Biases|Roboflow ⭐ 新|
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|:-:|:-:|
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|通过 [Weights & Biases](https://wandb.ai/site?utm_campaign=repo_yolo_readme) 自动跟踪和可视化你在云端的所有YOLOv5训练运行状态。|标记并将您的自定义数据集直接导出到YOLOv5，以便用 [Roboflow](https://roboflow.com/?ref=ultralytics) 进行训练。 |
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<!-- ## <div align="center">Compete and Win</div>
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We are super excited about our first-ever Ultralytics YOLOv5 🚀 EXPORT Competition with **$10,000** in cash prizes!
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<p align="center">
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<a href="https://github.com/ultralytics/yolov5/discussions/3213">
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<img width="850" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/banner-export-competition.png"></a>
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</p> -->
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## <div align="center">为什么选择 YOLOv5</div>
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<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/155040763-93c22a27-347c-4e3c-847a-8094621d3f4e.png"></p>
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<details>
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<summary>YOLOv5-P5 640 图像 (点击扩展)</summary>
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<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/155040757-ce0934a3-06a6-43dc-a979-2edbbd69ea0e.png"></p>
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</details>
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<details>
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<summary>图片注释 (点击扩展)</summary>
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- **COCO AP val** 表示 mAP@0.5:0.95 在5000张图像的[COCO val2017](http://cocodataset.org)数据集上，在256到1536的不同推理大小上测量的指标。
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- **GPU Speed** 衡量的是在 [COCO val2017](http://cocodataset.org) 数据集上使用 [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) V100实例在批量大小为32时每张图像的平均推理时间。
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- **EfficientDet** 数据来自 [google/automl](https://github.com/google/automl) ，批量大小设置为 8。
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- 复现 mAP 方法: `python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt`
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</details>
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### 预训练检查点
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|Model |size<br><sup>(pixels) |mAP<sup>val<br>0.5:0.95 |mAP<sup>val<br>0.5 |Speed<br><sup>CPU b1<br>(ms) |Speed<br><sup>V100 b1<br>(ms) |Speed<br><sup>V100 b32<br>(ms) |params<br><sup>(M) |FLOPs<br><sup>@640 (B)
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|--- |--- |--- |--- |--- |--- |--- |--- |---
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|[YOLOv5n][assets] |640 |28.0 |45.7 |**45** |**6.3**|**0.6**|**1.9**|**4.5**
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|[YOLOv5s][assets] |640 |37.4 |56.8 |98 |6.4 |0.9 |7.2 |16.5
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|[YOLOv5m][assets] |640 |45.4 |64.1 |224 |8.2 |1.7 |21.2 |49.0
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|[YOLOv5l][assets] |640 |49.0 |67.3 |430 |10.1 |2.7 |46.5 |109.1
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|[YOLOv5x][assets] |640 |50.7 |68.9 |766 |12.1 |4.8 |86.7 |205.7
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| | | | | | | | |
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|[YOLOv5n6][assets] |1280 |36.0 |54.4 |153 |8.1 |2.1 |3.2 |4.6
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|[YOLOv5s6][assets] |1280 |44.8 |63.7 |385 |8.2 |3.6 |12.6 |16.8
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|[YOLOv5m6][assets] |1280 |51.3 |69.3 |887 |11.1 |6.8 |35.7 |50.0
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|[YOLOv5l6][assets] |1280 |53.7 |71.3 |1784 |15.8 |10.5 |76.8 |111.4
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|[YOLOv5x6][assets]<br>+ [TTA][TTA]|1280<br>1536 |55.0<br>**55.8** |72.7<br>**72.7** |3136<br>- |26.2<br>- |19.4<br>- |140.7<br>- |209.8<br>-
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<details>
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<summary>表格注释 (点击扩展)</summary>
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- 所有检查点都以默认设置训练到300个时期. Nano和Small模型用 [hyp.scratch-low.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-low.yaml) hyps, 其他模型使用 [hyp.scratch-high.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-high.yaml).
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- **mAP<sup>val</sup>** 值是 [COCO val2017](http://cocodataset.org) 数据集上的单模型单尺度的值。
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<br>复现方法: `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65`
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- 使用 [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) 实例对COCO val图像的平均速度。不包括NMS时间（~1 ms/img)
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<br>复现方法: `python val.py --data coco.yaml --img 640 --task speed --batch 1`
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- **TTA** [测试时数据增强](https://github.com/ultralytics/yolov5/issues/303) 包括反射和比例增强.
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<br>复现方法: `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment`
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</details>
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## <div align="center">贡献</div>
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我们重视您的意见! 我们希望给大家提供尽可能的简单和透明的方式对 YOLOv5 做出贡献。开始之前请先点击并查看我们的 [贡献指南](CONTRIBUTING.md)，填写[YOLOv5调查问卷](https://ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey) 来向我们发送您的经验反馈。真诚感谢我们所有的贡献者!
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<a href="https://github.com/ultralytics/yolov5/graphs/contributors"><img src="https://opencollective.com/ultralytics/contributors.svg?width=990" /></a>
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## <div align="center">联系</div>
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关于YOLOv5的漏洞和功能问题，请访问 [GitHub Issues](https://github.com/ultralytics/yolov5/issues)。商业咨询或技术支持服务请访问[https://ultralytics.com/contact](https://ultralytics.com/contact)。
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<br>
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<div align="center">
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<a href="https://github.com/ultralytics">
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<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-github.png" width="3%"/>
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</a>
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<img width="3%" />
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<a href="https://www.linkedin.com/company/ultralytics">
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<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-linkedin.png" width="3%"/>
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</a>
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<img width="3%" />
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<a href="https://twitter.com/ultralytics">
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<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-twitter.png" width="3%"/>
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</a>
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<img width="3%" />
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<a href="https://www.producthunt.com/@glenn_jocher">
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<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-producthunt.png" width="3%"/>
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</a>
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<img width="3%" />
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<a href="https://youtube.com/ultralytics">
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<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-youtube.png" width="3%"/>
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</a>
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<img width="3%" />
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<a href="https://www.facebook.com/ultralytics">
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<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-facebook.png" width="3%"/>
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</a>
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<img width="3%" />
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<a href="https://www.instagram.com/ultralytics/">
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<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-instagram.png" width="3%"/>
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</a>
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</div>
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[assets]: https://github.com/ultralytics/yolov5/releases
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[tta]: https://github.com/ultralytics/yolov5/issues/303
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