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<p>
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<img width="850" src="https://github.com/ultralytics/assets/raw/master/yolov5/v62/splash_readme.png"></a>
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[English](../README.md) | 简体中文
<br>
<div>
<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>
<a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv5 Citation"></a>
<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>
<br>
<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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<br>
<p>
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。
</p>
<div align="center">
<a href="https://github.com/ultralytics" style="text-decoration:none;">
<img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-github.png" width="2%" alt="" /></a>
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</div>
</div>
## <div align="center">文件</div>
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。
## <div align="center">快速开始案例</div>
<details open>
<summary>安装</summary>
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/)。
```bash
git clone https://github.com/ultralytics/yolov5 # 克隆
cd yolov5
pip install -r requirements.txt # 安装
```
</details>
<details open>
<summary>推理</summary>
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。
```python
import torch
# 模型
model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # or yolov5n - yolov5x6, custom
# 图像
img = 'https://ultralytics.com/images/zidane.jpg' # or file, Path, PIL, OpenCV, numpy, list
# 推理
results = model(img)
# 结果
results.print() # or .show(), .save(), .crop(), .pandas(), etc.
```
</details>
<details>
<summary>用 detect.py 进行推理</summary>
`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。
```bash
python detect.py --source 0 # 网络摄像头
img.jpg # 图像
vid.mp4 # 视频
path/ # 文件夹
'path/*.jpg' # glob
'https://youtu.be/Zgi9g1ksQHc' # YouTube
'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP 流
```
</details>
<details>
<summary>训练</summary>
以下指令再现了 YOLOv5 [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh)
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。
```bash
python train.py --data coco.yaml --cfg yolov5n.yaml --weights '' --batch-size 128
yolov5s 64
yolov5m 40
yolov5l 24
yolov5x 16
```
<img width="800" src="https://user-images.githubusercontent.com/26833433/90222759-949d8800-ddc1-11ea-9fa1-1c97eed2b963.png">
</details>
<details open>
<summary>教程</summary>
- [训练自定义数据集](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data) 🚀 推荐
- [获得最佳训练效果的技巧](https://github.com/ultralytics/yolov5/wiki/Tips-for-Best-Training-Results) ☘️
推荐
- [多GPU训练](https://github.com/ultralytics/yolov5/issues/475)
- [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) 🌟 新
- [TFLite, ONNX, CoreML, TensorRT 输出](https://github.com/ultralytics/yolov5/issues/251) 🚀
- [测试时数据增强 (TTA)](https://github.com/ultralytics/yolov5/issues/303)
- [模型集成](https://github.com/ultralytics/yolov5/issues/318)
- [模型剪枝/稀疏性](https://github.com/ultralytics/yolov5/issues/304)
- [超参数进化](https://github.com/ultralytics/yolov5/issues/607)
- [带有冻结层的迁移学习](https://github.com/ultralytics/yolov5/issues/1314)
- [架构概要](https://github.com/ultralytics/yolov5/issues/6998) 🌟 新
- [使用Weights & Biases 记录实验](https://github.com/ultralytics/yolov5/issues/1289)
- [Roboflow：数据集，标签和主动学习](https://github.com/ultralytics/yolov5/issues/4975) 🌟 新
- [使用ClearML 记录实验](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/clearml) 🌟 新
- [Deci 平台](https://github.com/ultralytics/yolov5/wiki/Deci-Platform) 🌟 新
</details>
## <div align="center">环境</div>
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。
<div align="center">
<a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb">
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-colab-small.png" width="15%"/>
</a>
<a href="https://www.kaggle.com/ultralytics/yolov5">
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-kaggle-small.png" width="15%"/>
</a>
<a href="https://hub.docker.com/r/ultralytics/yolov5">
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-docker-small.png" width="15%"/>
</a>
<a href="https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart">
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-aws-small.png" width="15%"/>
</a>
<a href="https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart">
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-gcp-small.png" width="15%"/>
</a>
</div>
## <div align="center">如何与第三方集成</div>
<div align="center">
<a href="https://bit.ly/yolov5-deci-platform">
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-deci.png" width="10%" /></a>
<img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="14%" height="0" alt="" />
<a href="https://cutt.ly/yolov5-readme-clearml">
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-clearml.png" width="10%" /></a>
<img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="14%" height="0" alt="" />
<a href="https://roboflow.com/?ref=ultralytics">
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-roboflow.png" width="10%" /></a>
<img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="14%" height="0" alt="" />
<a href="https://wandb.ai/site?utm_campaign=repo_yolo_readme">
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-wb.png" width="10%" /></a>
</div>
|Deci ⭐ NEW|ClearML ⭐ NEW|Roboflow|Weights & Biases
|:-:|:-:|:-:|:-:|
|在[Deci](https://bit.ly/yolov5-deci-platform)一键自动编译和量化YOLOv5以提高推理性能|使用[ClearML](https://cutt.ly/yolov5-readme-clearml) (开源!)自动追踪，可视化，以及远程训练YOLOv5|标记并将您的自定义数据直接导出到YOLOv5后，用[Roboflow](https://roboflow.com/?ref=ultralytics)进行训练 |通过[Weights & Biases](https://wandb.ai/site?utm_campaign=repo_yolo_readme)自动跟踪以及可视化你在云端所有的YOLOv5训练运行情况
## <div align="center">为什么选择 YOLOv5</div>
<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/155040763-93c22a27-347c-4e3c-847a-8094621d3f4e.png"></p>
<details>
<summary>YOLOv5-P5 640 图像 (点击扩展)</summary>
<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/155040757-ce0934a3-06a6-43dc-a979-2edbbd69ea0e.png"></p>
</details>
<details>
<summary>图片注释 (点击扩展)</summary>
- **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。
- **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。
- **EfficientDet** 数据来自 [google/automl](https://github.com/google/automl) ，批量大小设置为 8。
- 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`
</details>
### 预训练检查点
| 模型 | 规模<br><sup>(像素) | mAP<sup>验证<br>0.5:0.95 | mAP<sup>验证<br>0.5 | 速度<br><sup>CPU b1<br>(ms) | 速度<br><sup>V100 b1<br>(ms) | 速度<br><sup>V100 b32<br>(ms) | 参数<br><sup>(M) | 浮点运算<br><sup>@640 (B) |
|------------------------------------------------------------------------------------------------------|-----------------------|-------------------------|--------------------|------------------------------|-------------------------------|--------------------------------|--------------------|------------------------|
| [YOLOv5n](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5n.pt) | 640 | 28.0 | 45.7 | **45** | **6.3** | **0.6** | **1.9** | **4.5** |
| [YOLOv5s](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5s.pt) | 640 | 37.4 | 56.8 | 98 | 6.4 | 0.9 | 7.2 | 16.5 |
| [YOLOv5m](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5m.pt) | 640 | 45.4 | 64.1 | 224 | 8.2 | 1.7 | 21.2 | 49.0 |
| [YOLOv5l](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5l.pt) | 640 | 49.0 | 67.3 | 430 | 10.1 | 2.7 | 46.5 | 109.1 |
| [YOLOv5x](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5x.pt) | 640 | 50.7 | 68.9 | 766 | 12.1 | 4.8 | 86.7 | 205.7 |
| | | | | | | | | |
| [YOLOv5n6](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5n6.pt) | 1280 | 36.0 | 54.4 | 153 | 8.1 | 2.1 | 3.2 | 4.6 |
| [YOLOv5s6](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5s6.pt) | 1280 | 44.8 | 63.7 | 385 | 8.2 | 3.6 | 12.6 | 16.8 |
| [YOLOv5m6](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5m6.pt) | 1280 | 51.3 | 69.3 | 887 | 11.1 | 6.8 | 35.7 | 50.0 |
| [YOLOv5l6](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5l6.pt) | 1280 | 53.7 | 71.3 | 1784 | 15.8 | 10.5 | 76.8 | 111.4 |
| [YOLOv5x6](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5x6.pt)<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>- |
<details>
<summary>表格注释 (点击扩展)</summary>
- 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).
- **mAP<sup>val</sup>** 值是 [COCO val2017](http://cocodataset.org) 数据集上的单模型单尺度的值。
<br>复现方法: `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65`
- 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)
<br>复现方法: `python val.py --data coco.yaml --img 640 --task speed --batch 1`
- **TTA** [测试时数据增强](https://github.com/ultralytics/yolov5/issues/303) 包括反射和比例增强.
<br>复现方法: `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment`
</details>
## <div align="center">分类 ⭐ 新</div>
YOLOv5发布的[v6.2版本](https://github.com/ultralytics/yolov5/releases) 支持训练，验证，预测和输出分类模型！这使得训练分类器模型非常简单。点击下面开始尝试！
<details>
<summary>分类检查点 (点击展开)</summary>
<br>
我们在ImageNet上使用了4xA100的实例训练YOLOv5-cls分类模型90个epochs，并以相同的默认设置同时训练了ResNet和EfficientNet模型来进行比较。我们将所有的模型导出到ONNX FP32进行CPU速度测试，又导出到TensorRT FP16进行GPU速度测试。最后，为了方便重现，我们在[Google Colab Pro](https://colab.research.google.com/signup)上进行了所有的速度测试。
| 模型 | 规模<br><sup>(像素) | 准确度<br><sup>第一 | 准确度<br><sup>前五 | 训练<br><sup>90 epochs<br>4xA100 (小时) | 速度<br><sup>ONNX CPU<br>(ms) | 速度<br><sup>TensorRT V100<br>(ms) | 参数<br><sup>(M) | 浮点运算<br><sup>@224 (B) |
|----------------------------------------------------------------------------------------------------|-----------------------|------------------|------------------|----------------------------------------------|--------------------------------|-------------------------------------|--------------------|------------------------|
| [YOLOv5n-cls](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5n-cls.pt) | 224 | 64.6 | 85.4 | 7:59 | **3.3** | **0.5** | **2.5** | **0.5** |
| [YOLOv5s-cls](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5s-cls.pt) | 224 | 71.5 | 90.2 | 8:09 | 6.6 | 0.6 | 5.4 | 1.4 |
| [YOLOv5m-cls](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5m-cls.pt) | 224 | 75.9 | 92.9 | 10:06 | 15.5 | 0.9 | 12.9 | 3.9 |
| [YOLOv5l-cls](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5l-cls.pt) | 224 | 78.0 | 94.0 | 11:56 | 26.9 | 1.4 | 26.5 | 8.5 |
| [YOLOv5x-cls](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5x-cls.pt) | 224 | **79.0** | **94.4** | 15:04 | 54.3 | 1.8 | 48.1 | 15.9 |
| |
| [ResNet18](https://github.com/ultralytics/yolov5/releases/download/v6.2/resnet18.pt) | 224 | 70.3 | 89.5 | **6:47** | 11.2 | 0.5 | 11.7 | 3.7 |
| [ResNet34](https://github.com/ultralytics/yolov5/releases/download/v6.2/resnet34.pt) | 224 | 73.9 | 91.8 | 8:33 | 20.6 | 0.9 | 21.8 | 7.4 |
| [ResNet50](https://github.com/ultralytics/yolov5/releases/download/v6.2/resnet50.pt) | 224 | 76.8 | 93.4 | 11:10 | 23.4 | 1.0 | 25.6 | 8.5 |
| [ResNet101](https://github.com/ultralytics/yolov5/releases/download/v6.2/resnet101.pt) | 224 | 78.5 | 94.3 | 17:10 | 42.1 | 1.9 | 44.5 | 15.9 |
| |
| [EfficientNet_b0](https://github.com/ultralytics/yolov5/releases/download/v6.2/efficientnet_b0.pt) | 224 | 75.1 | 92.4 | 13:03 | 12.5 | 1.3 | 5.3 | 1.0 |
| [EfficientNet_b1](https://github.com/ultralytics/yolov5/releases/download/v6.2/efficientnet_b1.pt) | 224 | 76.4 | 93.2 | 17:04 | 14.9 | 1.6 | 7.8 | 1.5 |
| [EfficientNet_b2](https://github.com/ultralytics/yolov5/releases/download/v6.2/efficientnet_b2.pt) | 224 | 76.6 | 93.4 | 17:10 | 15.9 | 1.6 | 9.1 | 1.7 |
| [EfficientNet_b3](https://github.com/ultralytics/yolov5/releases/download/v6.2/efficientnet_b3.pt) | 224 | 77.7 | 94.0 | 19:19 | 18.9 | 1.9 | 12.2 | 2.4 |
<details>
<summary>表格注释 (点击扩展)</summary>
- 所有检查点都被SGD优化器训练到90 epochs, `lr0=0.001` 和 `weight_decay=5e-5`， 图像大小为224，全为默认设置。<br>运行数据记录于 https://wandb.ai/glenn-jocher/YOLOv5-Classifier-v6-2。
- **准确度** 值为[ImageNet-1k](https://www.image-net.org/index.php)数据集上的单模型单尺度。<br>通过`python classify/val.py --data ../datasets/imagenet --img 224`进行复制。
- 使用Google [Colab Pro](https://colab.research.google.com/signup) V100 High-RAM实例得出的100张推理图像的平均**速度**。<br>通过 `python classify/val.py --data ../datasets/imagenet --img 224 --batch 1`进行复制。
- 用`export.py`**导出**到FP32的ONNX和FP16的TensorRT。<br>通过 `python export.py --weights yolov5s-cls.pt --include engine onnx --imgsz 224`进行复制。
</details>
</details>
<details>
<summary>分类使用实例 (点击展开)</summary>
### 训练
YOLOv5分类训练支持自动下载MNIST, Fashion-MNIST, CIFAR10, CIFAR100, Imagenette, Imagewoof和ImageNet数据集，并使用`--data` 参数. 打个比方，在MNIST上使用`--data mnist`开始训练。
```bash
# 单GPU
python classify/train.py --model yolov5s-cls.pt --data cifar100 --epochs 5 --img 224 --batch 128
# 多-GPU DDP
python -m torch.distributed.run --nproc_per_node 4 --master_port 1 classify/train.py --model yolov5s-cls.pt --data imagenet --epochs 5 --img 224 --device 0,1,2,3
```
### 验证
在ImageNet-1k数据集上验证YOLOv5m-cl的准确性:
```bash
bash data/scripts/get_imagenet.sh --val # download ImageNet val split (6.3G, 50000 images)
python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224 # validate
```
### 预测
用提前训练好的YOLOv5s-cls.pt去预测bus.jpg:
```bash
python classify/predict.py --weights yolov5s-cls.pt --data data/images/bus.jpg
```
```python
model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s-cls.pt') # load from PyTorch Hub
```
### 导出
导出一组训练好的YOLOv5s-cls, ResNet和EfficientNet模型到ONNX和TensorRT:
```bash
python export.py --weights yolov5s-cls.pt resnet50.pt efficientnet_b0.pt --include onnx engine --img 224
```
</details>
## <div align="center">贡献</div>
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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## <div align="center">联系</div>
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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[assets]: https://github.com/ultralytics/yolov5/releases
[tta]: https://github.com/ultralytics/yolov5/issues/303