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<div align="center"> |
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<p> |
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<a href="http://www.ultralytics.com/blog/ultralytics-yolov8-turns-one-a-year-of-breakthroughs-and-innovations" target="_blank"> |
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<img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png"></a> |
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<!-- |
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<a align="center" href="https://ultralytics.com/yolov5" target="_blank"> |
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<img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov5/v70/splash.png"></a> |
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--> |
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</p> |
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[中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es/) | [Português](https://docs.ultralytics.com/pt/) | [हिन्दी](https://docs.ultralytics.com/hi/) | [العربية](https://docs.ultralytics.com/ar/) |
|
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<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="YOLOv5 CI"></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://bit.ly/yolov5-paperspace-notebook"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"></a> |
|
<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> |
|
<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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</div> |
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<br> |
|
|
|
YOLOv5 🚀 是世界上最受欢迎的视觉 AI,代表<a href="https://ultralytics.com"> Ultralytics </a>对未来视觉 AI 方法的开源研究,结合在数千小时的研究和开发中积累的经验教训和最佳实践。 |
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|
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我们希望这里的资源能帮助您充分利用 YOLOv5。请浏览 YOLOv5 <a href="https://docs.ultralytics.com/yolov5/">文档</a> 了解详细信息,在 <a href="https://github.com/ultralytics/yolov5/issues/new/choose">GitHub</a> 上提交问题以获得支持,并加入我们的 <a href="https://ultralytics.com/discord">Discord</a> 社区进行问题和讨论! |
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如需申请企业许可,请在 [Ultralytics Licensing](https://ultralytics.com/license) 处填写表格 |
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<div align="center"> |
|
<a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="2%" alt="Ultralytics GitHub"></a> |
|
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%"> |
|
<a href="https://www.linkedin.com/company/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="2%" alt="Ultralytics LinkedIn"></a> |
|
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%"> |
|
<a href="https://twitter.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="2%" alt="Ultralytics Twitter"></a> |
|
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%"> |
|
<a href="https://youtube.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="2%" alt="Ultralytics YouTube"></a> |
|
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%"> |
|
<a href="https://www.tiktok.com/@ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="2%" alt="Ultralytics TikTok"></a> |
|
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%"> |
|
<a href="https://www.instagram.com/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="2%" alt="Ultralytics Instagram"></a> |
|
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%"> |
|
<a href="https://ultralytics.com/discord"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="2%" alt="Ultralytics Discord"></a> |
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</div> |
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</div> |
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|
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## <div align="center">YOLOv8 🚀 新品</div> |
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|
我们很高兴宣布 Ultralytics YOLOv8 🚀 的发布,这是我们新推出的领先水平、最先进的(SOTA)模型,发布于 **[https://github.com/ultralytics/ultralytics](https://github.com/ultralytics/ultralytics)**。 YOLOv8 旨在快速、准确且易于使用,使其成为广泛的物体检测、图像分割和图像分类任务的极佳选择。 |
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请查看 [YOLOv8 文档](https://docs.ultralytics.com)了解详细信息,并开始使用: |
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|
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[![PyPI 版本](https://badge.fury.io/py/ultralytics.svg)](https://badge.fury.io/py/ultralytics) [![下载量](https://static.pepy.tech/badge/ultralytics)](https://pepy.tech/project/ultralytics) |
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|
|
```commandline |
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pip install ultralytics |
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``` |
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<div align="center"> |
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<a href="https://ultralytics.com/yolov8" target="_blank"> |
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<img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/yolo-comparison-plots.png"></a> |
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</div> |
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|
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## <div align="center">文档</div> |
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有关训练、测试和部署的完整文档见[YOLOv5 文档](https://docs.ultralytics.com/yolov5/)。请参阅下面的快速入门示例。 |
|
|
|
<details open> |
|
<summary>安装</summary> |
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|
|
克隆 repo,并要求在 [**Python>=3.8.0**](https://www.python.org/) 环境中安装 [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) ,且要求 [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/) 。 |
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|
|
```bash |
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git clone https://github.com/ultralytics/yolov5 # clone |
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cd yolov5 |
|
pip install -r requirements.txt # install |
|
``` |
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|
|
</details> |
|
|
|
<details> |
|
<summary>推理</summary> |
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|
使用 YOLOv5 [PyTorch Hub](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading) 推理。最新 [模型](https://github.com/ultralytics/yolov5/tree/master/models) 将自动的从 YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) 中下载。 |
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|
|
```python |
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import torch |
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# Model |
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model = torch.hub.load("ultralytics/yolov5", "yolov5s") # or yolov5n - yolov5x6, custom |
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|
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# Images |
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img = "https://ultralytics.com/images/zidane.jpg" # or file, Path, PIL, OpenCV, numpy, list |
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|
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# Inference |
|
results = model(img) |
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|
|
# Results |
|
results.print() # or .show(), .save(), .crop(), .pandas(), etc. |
|
``` |
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|
|
</details> |
|
|
|
<details> |
|
<summary>使用 detect.py 推理</summary> |
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|
|
`detect.py` 在各种来源上运行推理, [模型](https://github.com/ultralytics/yolov5/tree/master/models) 自动从 最新的YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) 中下载,并将结果保存到 `runs/detect` 。 |
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|
|
```bash |
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python detect.py --weights yolov5s.pt --source 0 # webcam |
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img.jpg # image |
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vid.mp4 # video |
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screen # screenshot |
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path/ # directory |
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list.txt # list of images |
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list.streams # list of streams |
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'path/*.jpg' # glob |
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'https://youtu.be/LNwODJXcvt4' # YouTube |
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'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream |
|
``` |
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|
|
</details> |
|
|
|
<details> |
|
<summary>训练</summary> |
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|
|
下面的命令重现 YOLOv5 在 [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh) 数据集上的结果。 最新的 [模型](https://github.com/ultralytics/yolov5/tree/master/models) 和 [数据集](https://github.com/ultralytics/yolov5/tree/master/data) |
|
将自动的从 YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) 中下载。 YOLOv5n/s/m/l/x 在 V100 GPU 的训练时间为 1/2/4/6/8 天( [多GPU](https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training) 训练速度更快)。 尽可能使用更大的 `--batch-size` ,或通过 `--batch-size -1` 实现 YOLOv5 [自动批处理](https://github.com/ultralytics/yolov5/pull/5092) 。下方显示的 batchsize 适用于 V100-16GB。 |
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|
|
```bash |
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python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml --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> |
|
|
|
<details open> |
|
<summary>教程</summary> |
|
|
|
- [训练自定义数据](https://docs.ultralytics.com/yolov5/tutorials/train_custom_data) 🚀 推荐 |
|
- [获得最佳训练结果的技巧](https://docs.ultralytics.com/yolov5/tutorials/tips_for_best_training_results) ☘️ |
|
- [多GPU训练](https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training) |
|
- [PyTorch Hub](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading) 🌟 新 |
|
- [TFLite,ONNX,CoreML,TensorRT导出](https://docs.ultralytics.com/yolov5/tutorials/model_export) 🚀 |
|
- [NVIDIA Jetson平台部署](https://docs.ultralytics.com/yolov5/tutorials/running_on_jetson_nano) 🌟 新 |
|
- [测试时增强 (TTA)](https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation) |
|
- [模型集成](https://docs.ultralytics.com/yolov5/tutorials/model_ensembling) |
|
- [模型剪枝/稀疏](https://docs.ultralytics.com/yolov5/tutorials/model_pruning_and_sparsity) |
|
- [超参数进化](https://docs.ultralytics.com/yolov5/tutorials/hyperparameter_evolution) |
|
- [冻结层的迁移学习](https://docs.ultralytics.com/yolov5/tutorials/transfer_learning_with_frozen_layers) |
|
- [架构概述](https://docs.ultralytics.com/yolov5/tutorials/architecture_description) 🌟 新 |
|
- [Roboflow用于数据集、标注和主动学习](https://docs.ultralytics.com/yolov5/tutorials/roboflow_datasets_integration) |
|
- [ClearML日志记录](https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration) 🌟 新 |
|
- [使用Neural Magic的Deepsparse的YOLOv5](https://docs.ultralytics.com/yolov5/tutorials/neural_magic_pruning_quantization) 🌟 新 |
|
- [Comet日志记录](https://docs.ultralytics.com/yolov5/tutorials/comet_logging_integration) 🌟 新 |
|
|
|
</details> |
|
|
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## <div align="center">模块集成</div> |
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|
|
<br> |
|
<a align="center" href="https://bit.ly/ultralytics_hub" target="_blank"> |
|
<img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/integrations-loop.png"></a> |
|
<br> |
|
<br> |
|
|
|
<div align="center"> |
|
<a href="https://roboflow.com/?ref=ultralytics"> |
|
<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-roboflow.png" width="10%" /></a> |
|
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="" /> |
|
<a href="https://cutt.ly/yolov5-readme-clearml"> |
|
<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-clearml.png" width="10%" /></a> |
|
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="" /> |
|
<a href="https://bit.ly/yolov5-readme-comet2"> |
|
<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-comet.png" width="10%" /></a> |
|
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="" /> |
|
<a href="https://bit.ly/yolov5-neuralmagic"> |
|
<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-neuralmagic.png" width="10%" /></a> |
|
</div> |
|
|
|
| Roboflow | ClearML ⭐ 新 | Comet ⭐ 新 | Neural Magic ⭐ 新 | |
|
| :--------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: | |
|
| 将您的自定义数据集进行标注并直接导出到 YOLOv5 以进行训练 [Roboflow](https://roboflow.com/?ref=ultralytics) | 自动跟踪、可视化甚至远程训练 YOLOv5 [ClearML](https://cutt.ly/yolov5-readme-clearml)(开源!) | 永远免费,[Comet](https://bit.ly/yolov5-readme-comet2)可让您保存 YOLOv5 模型、恢复训练以及交互式可视化和调试预测 | 使用 [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic),运行 YOLOv5 推理的速度最高可提高6倍 | |
|
|
|
## <div align="center">Ultralytics HUB</div> |
|
|
|
[Ultralytics HUB](https://bit.ly/ultralytics_hub) 是我们的⭐**新的**用于可视化数据集、训练 YOLOv5 🚀 模型并以无缝体验部署到现实世界的无代码解决方案。现在开始 **免费** 使用他! |
|
|
|
<a align="center" href="https://bit.ly/ultralytics_hub" target="_blank"> |
|
<img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png"></a> |
|
|
|
## <div align="center">为什么选择 YOLOv5</div> |
|
|
|
YOLOv5 超级容易上手,简单易学。我们优先考虑现实世界的结果。 |
|
|
|
<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</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>图表笔记</summary> |
|
|
|
- **COCO AP val** 表示 [email protected]:0.95 指标,在 [COCO val2017](http://cocodataset.org) 数据集的 5000 张图像上测得, 图像包含 256 到 1536 各种推理大小。 |
|
- **显卡推理速度** 为在 [COCO val2017](http://cocodataset.org) 数据集上的平均推理时间,使用 [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) V100实例,batchsize 为 32 。 |
|
- **EfficientDet** 数据来自 [google/automl](https://github.com/google/automl) , batchsize 为32。 |
|
- **复现命令** 为 `python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt` |
|
|
|
</details> |
|
|
|
### 预训练模型 |
|
|
|
| 模型 | 尺寸<br><sup>(像素) | mAP<sup>val<br>50-95 | mAP<sup>val<br>50 | 推理速度<br><sup>CPU b1<br>(ms) | 推理速度<br><sup>V100 b1<br>(ms) | 速度<br><sup>V100 b32<br>(ms) | 参数量<br><sup>(M) | FLOPs<br><sup>@640 (B) | |
|
| ---------------------------------------------------------------------------------------------- | --------------------- | -------------------- | ----------------- | --------------------------------- | ---------------------------------- | ------------------------------- | ------------------ | ---------------------- | |
|
| [YOLOv5n](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n.pt) | 640 | 28.0 | 45.7 | **45** | **6.3** | **0.6** | **1.9** | **4.5** | |
|
| [YOLOv5s](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt) | 640 | 37.4 | 56.8 | 98 | 6.4 | 0.9 | 7.2 | 16.5 | |
|
| [YOLOv5m](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m.pt) | 640 | 45.4 | 64.1 | 224 | 8.2 | 1.7 | 21.2 | 49.0 | |
|
| [YOLOv5l](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l.pt) | 640 | 49.0 | 67.3 | 430 | 10.1 | 2.7 | 46.5 | 109.1 | |
|
| [YOLOv5x](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x.pt) | 640 | 50.7 | 68.9 | 766 | 12.1 | 4.8 | 86.7 | 205.7 | |
|
| | | | | | | | | | |
|
| [YOLOv5n6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n6.pt) | 1280 | 36.0 | 54.4 | 153 | 8.1 | 2.1 | 3.2 | 4.6 | |
|
| [YOLOv5s6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s6.pt) | 1280 | 44.8 | 63.7 | 385 | 8.2 | 3.6 | 12.6 | 16.8 | |
|
| [YOLOv5m6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m6.pt) | 1280 | 51.3 | 69.3 | 887 | 11.1 | 6.8 | 35.7 | 50.0 | |
|
| [YOLOv5l6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l6.pt) | 1280 | 53.7 | 71.3 | 1784 | 15.8 | 10.5 | 76.8 | 111.4 | |
|
| [YOLOv5x6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x6.pt)<br>+[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>笔记</summary> |
|
|
|
- 所有模型都使用默认配置,训练 300 epochs。n和s模型使用 [hyp.scratch-low.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-low.yaml) ,其他模型都使用 [hyp.scratch-high.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-high.yaml) 。 |
|
- \*\*mAP<sup>val</sup>\*\*在单模型单尺度上计算,数据集使用 [COCO val2017](http://cocodataset.org) 。<br>复现命令 `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65` |
|
- **推理速度**在 COCO val 图像总体时间上进行平均得到,测试环境使用[AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/)实例。 NMS 时间 (大约 1 ms/img) 不包括在内。<br>复现命令 `python val.py --data coco.yaml --img 640 --task speed --batch 1` |
|
- **TTA** [测试时数据增强](https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation) 包括反射和尺度变换。<br>复现命令 `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment` |
|
|
|
</details> |
|
|
|
## <div align="center">实例分割模型 ⭐ 新</div> |
|
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我们新的 YOLOv5 [release v7.0](https://github.com/ultralytics/yolov5/releases/v7.0) 实例分割模型是世界上最快和最准确的模型,击败所有当前 [SOTA 基准](https://paperswithcode.com/sota/real-time-instance-segmentation-on-mscoco)。我们使它非常易于训练、验证和部署。更多细节请查看 [发行说明](https://github.com/ultralytics/yolov5/releases/v7.0) 或访问我们的 [YOLOv5 分割 Colab 笔记本](https://github.com/ultralytics/yolov5/blob/master/segment/tutorial.ipynb) 以快速入门。 |
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<details> |
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<summary>实例分割模型列表</summary> |
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<br> |
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<div align="center"> |
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<a align="center" href="https://ultralytics.com/yolov5" target="_blank"> |
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<img width="800" src="https://user-images.githubusercontent.com/61612323/204180385-84f3aca9-a5e9-43d8-a617-dda7ca12e54a.png"></a> |
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</div> |
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我们使用 A100 GPU 在 COCO 上以 640 图像大小训练了 300 epochs 得到 YOLOv5 分割模型。我们将所有模型导出到 ONNX FP32 以进行 CPU 速度测试,并导出到 TensorRT FP16 以进行 GPU 速度测试。为了便于再现,我们在 Google [Colab Pro](https://colab.research.google.com/signup) 上进行了所有速度测试。 |
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| 模型 | 尺寸<br><sup>(像素) | mAP<sup>box<br>50-95 | mAP<sup>mask<br>50-95 | 训练时长<br><sup>300 epochs<br>A100 GPU(小时) | 推理速度<br><sup>ONNX CPU<br>(ms) | 推理速度<br><sup>TRT A100<br>(ms) | 参数量<br><sup>(M) | FLOPs<br><sup>@640 (B) | |
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| ------------------------------------------------------------------------------------------ | --------------------- | -------------------- | --------------------- | ----------------------------------------------- | ----------------------------------- | ----------------------------------- | ------------------ | ---------------------- | |
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| [YOLOv5n-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n-seg.pt) | 640 | 27.6 | 23.4 | 80:17 | **62.7** | **1.2** | **2.0** | **7.1** | |
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| [YOLOv5s-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-seg.pt) | 640 | 37.6 | 31.7 | 88:16 | 173.3 | 1.4 | 7.6 | 26.4 | |
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| [YOLOv5m-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m-seg.pt) | 640 | 45.0 | 37.1 | 108:36 | 427.0 | 2.2 | 22.0 | 70.8 | |
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| [YOLOv5l-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l-seg.pt) | 640 | 49.0 | 39.9 | 66:43 (2x) | 857.4 | 2.9 | 47.9 | 147.7 | |
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| [YOLOv5x-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x-seg.pt) | 640 | **50.7** | **41.4** | 62:56 (3x) | 1579.2 | 4.5 | 88.8 | 265.7 | |
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- 所有模型使用 SGD 优化器训练, 都使用 `lr0=0.01` 和 `weight_decay=5e-5` 参数, 图像大小为 640 。<br>训练 log 可以查看 https://wandb.ai/glenn-jocher/YOLOv5_v70_official |
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- **准确性**结果都在 COCO 数据集上,使用单模型单尺度测试得到。<br>复现命令 `python segment/val.py --data coco.yaml --weights yolov5s-seg.pt` |
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- **推理速度**是使用 100 张图像推理时间进行平均得到,测试环境使用 [Colab Pro](https://colab.research.google.com/signup) 上 A100 高 RAM 实例。结果仅表示推理速度(NMS 每张图像增加约 1 毫秒)。<br>复现命令 `python segment/val.py --data coco.yaml --weights yolov5s-seg.pt --batch 1` |
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- **模型转换**到 FP32 的 ONNX 和 FP16 的 TensorRT 脚本为 `export.py`.<br>运行命令 `python export.py --weights yolov5s-seg.pt --include engine --device 0 --half` |
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</details> |
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<details> |
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<summary>分割模型使用示例 <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/segment/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></summary> |
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### 训练 |
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YOLOv5分割训练支持自动下载 COCO128-seg 分割数据集,用户仅需在启动指令中包含 `--data coco128-seg.yaml` 参数。 若要手动下载,使用命令 `bash data/scripts/get_coco.sh --train --val --segments`, 在下载完毕后,使用命令 `python train.py --data coco.yaml` 开启训练。 |
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```bash |
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# 单 GPU |
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python segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640 |
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# 多 GPU, DDP 模式 |
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python -m torch.distributed.run --nproc_per_node 4 --master_port 1 segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640 --device 0,1,2,3 |
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``` |
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### 验证 |
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在 COCO 数据集上验证 YOLOv5s-seg mask mAP: |
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```bash |
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bash data/scripts/get_coco.sh --val --segments # 下载 COCO val segments 数据集 (780MB, 5000 images) |
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python segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640 # 验证 |
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``` |
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### 预测 |
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使用预训练的 YOLOv5m-seg.pt 来预测 bus.jpg: |
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```bash |
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python segment/predict.py --weights yolov5m-seg.pt --source data/images/bus.jpg |
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``` |
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```python |
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model = torch.hub.load( |
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"ultralytics/yolov5", "custom", "yolov5m-seg.pt" |
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) # 从load from PyTorch Hub 加载模型 (WARNING: 推理暂未支持) |
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``` |
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| ![zidane](https://user-images.githubusercontent.com/26833433/203113421-decef4c4-183d-4a0a-a6c2-6435b33bc5d3.jpg) | ![bus](https://user-images.githubusercontent.com/26833433/203113416-11fe0025-69f7-4874-a0a6-65d0bfe2999a.jpg) | |
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| ---------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------- | |
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### 模型导出 |
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将 YOLOv5s-seg 模型导出到 ONNX 和 TensorRT: |
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```bash |
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python export.py --weights yolov5s-seg.pt --include onnx engine --img 640 --device 0 |
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``` |
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</details> |
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## <div align="center">分类网络 ⭐ 新</div> |
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YOLOv5 [release v6.2](https://github.com/ultralytics/yolov5/releases) 带来对分类模型训练、验证和部署的支持!详情请查看 [发行说明](https://github.com/ultralytics/yolov5/releases/v6.2) 或访问我们的 [YOLOv5 分类 Colab 笔记本](https://github.com/ultralytics/yolov5/blob/master/classify/tutorial.ipynb) 以快速入门。 |
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<details> |
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<summary>分类网络模型</summary> |
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<br> |
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我们使用 4xA100 实例在 ImageNet 上训练了 90 个 epochs 得到 YOLOv5-cls 分类模型,我们训练了 ResNet 和 EfficientNet 模型以及相同的默认训练设置以进行比较。我们将所有模型导出到 ONNX FP32 以进行 CPU 速度测试,并导出到 TensorRT FP16 以进行 GPU 速度测试。为了便于重现,我们在 Google 上进行了所有速度测试 [Colab Pro](https://colab.research.google.com/signup) 。 |
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| 模型 | 尺寸<br><sup>(像素) | acc<br><sup>top1 | acc<br><sup>top5 | 训练时长<br><sup>90 epochs<br>4xA100(小时) | 推理速度<br><sup>ONNX CPU<br>(ms) | 推理速度<br><sup>TensorRT V100<br>(ms) | 参数<br><sup>(M) | FLOPs<br><sup>@640 (B) | |
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| -------------------------------------------------------------------------------------------------- | --------------------- | ---------------- | ---------------- | -------------------------------------------- | ----------------------------------- | ---------------------------------------- | ---------------- | ---------------------- | |
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| [YOLOv5n-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n-cls.pt) | 224 | 64.6 | 85.4 | 7:59 | **3.3** | **0.5** | **2.5** | **0.5** | |
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| [YOLOv5s-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-cls.pt) | 224 | 71.5 | 90.2 | 8:09 | 6.6 | 0.6 | 5.4 | 1.4 | |
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| [YOLOv5m-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m-cls.pt) | 224 | 75.9 | 92.9 | 10:06 | 15.5 | 0.9 | 12.9 | 3.9 | |
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| [YOLOv5l-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l-cls.pt) | 224 | 78.0 | 94.0 | 11:56 | 26.9 | 1.4 | 26.5 | 8.5 | |
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| [YOLOv5x-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x-cls.pt) | 224 | **79.0** | **94.4** | 15:04 | 54.3 | 1.8 | 48.1 | 15.9 | |
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| | | | | | | | | | |
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| [ResNet18](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet18.pt) | 224 | 70.3 | 89.5 | **6:47** | 11.2 | 0.5 | 11.7 | 3.7 | |
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| [Resnetzch](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet34.pt) | 224 | 73.9 | 91.8 | 8:33 | 20.6 | 0.9 | 21.8 | 7.4 | |
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| [ResNet50](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet50.pt) | 224 | 76.8 | 93.4 | 11:10 | 23.4 | 1.0 | 25.6 | 8.5 | |
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| [ResNet101](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet101.pt) | 224 | 78.5 | 94.3 | 17:10 | 42.1 | 1.9 | 44.5 | 15.9 | |
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| [EfficientNet_b0](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b0.pt) | 224 | 75.1 | 92.4 | 13:03 | 12.5 | 1.3 | 5.3 | 1.0 | |
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| [EfficientNet_b1](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b1.pt) | 224 | 76.4 | 93.2 | 17:04 | 14.9 | 1.6 | 7.8 | 1.5 | |
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| [EfficientNet_b2](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b2.pt) | 224 | 76.6 | 93.4 | 17:10 | 15.9 | 1.6 | 9.1 | 1.7 | |
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| [EfficientNet_b3](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b3.pt) | 224 | 77.7 | 94.0 | 19:19 | 18.9 | 1.9 | 12.2 | 2.4 | |
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<details> |
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<summary>Table Notes (点击以展开)</summary> |
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- 所有模型都使用 SGD 优化器训练 90 个 epochs,都使用 `lr0=0.001` 和 `weight_decay=5e-5` 参数, 图像大小为 224 ,且都使用默认设置。<br>训练 log 可以查看 https://wandb.ai/glenn-jocher/YOLOv5-Classifier-v6-2 |
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- **准确性**都在单模型单尺度上计算,数据集使用 [ImageNet-1k](https://www.image-net.org/index.php) 。<br>复现命令 `python classify/val.py --data ../datasets/imagenet --img 224` |
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- **推理速度**是使用 100 个推理图像进行平均得到,测试环境使用谷歌 [Colab Pro](https://colab.research.google.com/signup) V100 高 RAM 实例。<br>复现命令 `python classify/val.py --data ../datasets/imagenet --img 224 --batch 1` |
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- **模型导出**到 FP32 的 ONNX 和 FP16 的 TensorRT 使用 `export.py` 。<br>复现命令 `python export.py --weights yolov5s-cls.pt --include engine onnx --imgsz 224` |
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</details> |
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</details> |
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<details> |
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<summary>分类训练示例 <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/classify/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></summary> |
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### 训练 |
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YOLOv5 分类训练支持自动下载 MNIST、Fashion-MNIST、CIFAR10、CIFAR100、Imagenette、Imagewoof 和 ImageNet 数据集,命令中使用 `--data` 即可。 MNIST 示例 `--data mnist` 。 |
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```bash |
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# 单 GPU |
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python classify/train.py --model yolov5s-cls.pt --data cifar100 --epochs 5 --img 224 --batch 128 |
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# 多 GPU, DDP 模式 |
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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 |
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``` |
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### 验证 |
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在 ImageNet-1k 数据集上验证 YOLOv5m-cls 的准确性: |
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```bash |
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bash data/scripts/get_imagenet.sh --val # download ImageNet val split (6.3G, 50000 images) |
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python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224 # validate |
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``` |
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### 预测 |
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使用预训练的 YOLOv5s-cls.pt 来预测 bus.jpg: |
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|
|
```bash |
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python classify/predict.py --weights yolov5s-cls.pt --source data/images/bus.jpg |
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``` |
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|
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```python |
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model = torch.hub.load( |
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"ultralytics/yolov5", "custom", "yolov5s-cls.pt" |
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) # load from PyTorch Hub |
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``` |
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|
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### 模型导出 |
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将一组经过训练的 YOLOv5s-cls、ResNet 和 EfficientNet 模型导出到 ONNX 和 TensorRT: |
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```bash |
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python export.py --weights yolov5s-cls.pt resnet50.pt efficientnet_b0.pt --include onnx engine --img 224 |
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``` |
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</details> |
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## <div align="center">环境</div> |
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使用下面我们经过验证的环境,在几秒钟内开始使用 YOLOv5 。单击下面的图标了解详细信息。 |
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<div align="center"> |
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<a href="https://bit.ly/yolov5-paperspace-notebook"> |
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<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-gradient.png" width="10%" /></a> |
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="5%" alt="" /> |
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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="10%" /></a> |
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="5%" alt="" /> |
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<a href="https://www.kaggle.com/ultralytics/yolov5"> |
|
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-kaggle-small.png" width="10%" /></a> |
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="5%" alt="" /> |
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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="10%" /></a> |
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="5%" alt="" /> |
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<a href="https://docs.ultralytics.com/yolov5/environments/aws_quickstart_tutorial/"> |
|
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-aws-small.png" width="10%" /></a> |
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="5%" alt="" /> |
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<a href="https://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial/"> |
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<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-gcp-small.png" width="10%" /></a> |
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</div> |
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## <div align="center">贡献</div> |
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我们喜欢您的意见或建议!我们希望尽可能简单和透明地为 YOLOv5 做出贡献。请看我们的 [投稿指南](https://docs.ultralytics.com/help/contributing/),并填写 [YOLOv5调查](https://ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey) 向我们发送您的体验反馈。感谢我们所有的贡献者! |
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<!-- SVG image from https://opencollective.com/ultralytics/contributors.svg?width=990 --> |
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|
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<a href="https://github.com/ultralytics/yolov5/graphs/contributors"> |
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<img src="https://github.com/ultralytics/assets/raw/main/im/image-contributors.png" /></a> |
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|
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## <div align="center">许可证</div> |
|
|
|
Ultralytics 提供两种许可证选项以适应各种使用场景: |
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|
|
- **AGPL-3.0 许可证**:这个[OSI 批准](https://opensource.org/licenses/)的开源许可证非常适合学生和爱好者,可以推动开放的协作和知识分享。请查看[LICENSE](https://github.com/ultralytics/yolov5/blob/master/LICENSE) 文件以了解更多细节。 |
|
- **企业许可证**:专为商业用途设计,该许可证允许将 Ultralytics 的软件和 AI 模型无缝集成到商业产品和服务中,从而绕过 AGPL-3.0 的开源要求。如果您的场景涉及将我们的解决方案嵌入到商业产品中,请通过 [Ultralytics Licensing](https://ultralytics.com/license)与我们联系。 |
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## <div align="center">联系方式</div> |
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|
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对于 Ultralytics 的错误报告和功能请求,请访问 [GitHub Issues](https://github.com/ultralytics/yolov5/issues),并加入我们的 [Discord](https://ultralytics.com/discord) 社区进行问题和讨论! |
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<br> |
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<div align="center"> |
|
<a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="3%" alt="Ultralytics GitHub"></a> |
|
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%"> |
|
<a href="https://www.linkedin.com/company/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="3%" alt="Ultralytics LinkedIn"></a> |
|
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%"> |
|
<a href="https://twitter.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="3%" alt="Ultralytics Twitter"></a> |
|
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%"> |
|
<a href="https://youtube.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="3%" alt="Ultralytics YouTube"></a> |
|
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%"> |
|
<a href="https://www.tiktok.com/@ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="3%" alt="Ultralytics TikTok"></a> |
|
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%"> |
|
<a href="https://www.instagram.com/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="3%" alt="Ultralytics Instagram"></a> |
|
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%"> |
|
<a href="https://ultralytics.com/discord"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="3%" alt="Ultralytics Discord"></a> |
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</div> |
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[tta]: https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation |
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