213 lines
12 KiB
Markdown
213 lines
12 KiB
Markdown
<div align="center">
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<img src="resources/mmcls-logo.png" width="600"/>
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<div> </div>
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<div align="center">
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<b><font size="5">OpenMMLab 官网</font></b>
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<sup>
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<a href="https://openmmlab.com">
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<i><font size="4">HOT</font></i>
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</a>
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</sup>
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<b><font size="5">OpenMMLab 开放平台</font></b>
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<sup>
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<a href="https://platform.openmmlab.com">
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<i><font size="4">TRY IT OUT</font></i>
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</a>
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</sup>
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</div>
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<div> </div>
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[](https://pypi.org/project/mmcls)
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[](https://mmclassification.readthedocs.io/zh_CN/latest/)
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[](https://github.com/open-mmlab/mmclassification/actions)
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[](https://codecov.io/gh/open-mmlab/mmclassification)
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[](https://github.com/open-mmlab/mmclassification/blob/master/LICENSE)
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[](https://github.com/open-mmlab/mmclassification/issues)
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[](https://github.com/open-mmlab/mmclassification/issues)
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[📘 中文文档](https://mmclassification.readthedocs.io/zh_CN/latest/) |
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[🛠️ 安装教程](https://mmclassification.readthedocs.io/zh_CN/latest/install.html) |
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[👀 模型库](https://mmclassification.readthedocs.io/zh_CN/latest/model_zoo.html) |
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[🆕 更新日志](https://mmclassification.readthedocs.io/en/latest/changelog.html) |
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[🤔 报告问题](https://github.com/open-mmlab/mmclassification/issues/new/choose)
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</div>
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## Introduction
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[English](/README.md) | 简体中文
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MMClassification 是一款基于 PyTorch 的开源图像分类工具箱，是 [OpenMMLab](https://openmmlab.com/) 项目的成员之一
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主分支代码目前支持 PyTorch 1.5 以上的版本。
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<div align="center">
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<img src="https://user-images.githubusercontent.com/9102141/87268895-3e0d0780-c4fe-11ea-849e-6140b7e0d4de.gif" width="70%"/>
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</div>
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### 主要特性
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- 支持多样的主干网络与预训练模型
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- 支持配置多种训练技巧
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- 大量的训练配置文件
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- 高效率和高可扩展性
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- 功能强大的工具箱
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## 更新日志
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2022/5/1 发布了 v0.23.0 版本
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新版本亮点：
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- 支持了 **DenseNet**，**VAN** 和 **PoolFormer** 三个网络，并提供了预训练模型。
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- 支持在 IPU 上进行训练。
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- 更新了 API 文档的样式，更方便查阅，[欢迎查阅](https://mmclassification.readthedocs.io/en/master/api/models.html)。
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2022/3/30 发布了 v0.22.0 版本
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新版本亮点：
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- 支持了一系列 **CSP Net**，包括 CSP-ResNet，CSP-ResNeXt 和 CSP-DarkNet。
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- 我们提供了一个新的 `CustomDataset` 类，这个类将帮助你轻松使用**自己的数据集**！
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- 支持了新的主干网络 **ConvMixer**、**RepMLP** 和一个新的数据集 **CUB dataset**。
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发布历史和更新细节请参考 [更新日志](docs/en/changelog.md)
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## 安装
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以下是安装的简要步骤：
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```shell
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conda create -n open-mmlab python=3.8 pytorch=1.10 cudatoolkit=11.3 torchvision -c pytorch -y
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conda activate open-mmlab
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pip3 install openmim
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mim install mmcv-full
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git clone https://github.com/open-mmlab/mmclassification.git
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cd mmclassification
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pip3 install -e .
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```
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更详细的步骤请参考 [安装指南](https://mmclassification.readthedocs.io/zh_CN/latest/install.html) 进行安装。
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## 基础教程
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请参考 [基础教程](https://mmclassification.readthedocs.io/zh_CN/latest/getting_started.html) 来了解 MMClassification 的基本使用。MMClassification 也提供了其他更详细的教程：
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- [如何编写配置文件](https://mmclassification.readthedocs.io/zh_CN/latest/tutorials/config.html)
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- [如何微调模型](https://mmclassification.readthedocs.io/zh_CN/latest/tutorials/finetune.html)
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- [如何增加新数据集](https://mmclassification.readthedocs.io/zh_CN/latest/tutorials/new_dataset.html)
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- [如何设计数据处理流程](https://mmclassification.readthedocs.io/zh_CN/latest/tutorials/data_pipeline.html)
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- [如何增加新模块](https://mmclassification.readthedocs.io/zh_CN/latest/tutorials/new_modules.html)
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- [如何自定义优化策略](https://mmclassification.readthedocs.io/zh_CN/latest/tutorials/schedule.html)
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- [如何自定义运行参数](https://mmclassification.readthedocs.io/zh_CN/latest/tutorials/runtime.html)
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我们也提供了相应的中文 Colab 教程：
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- 了解 MMClassification **Python API**：[预览 Notebook](https://github.com/open-mmlab/mmclassification/blob/master/docs/zh_CN/tutorials/MMClassification_python_cn.ipynb) 或者直接[在 Colab 上运行](https://colab.research.google.com/github/open-mmlab/mmclassification/blob/master/docs/zh_CN/tutorials/MMClassification_python_cn.ipynb)。
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- 了解 MMClassification **命令行工具**：[预览 Notebook](https://github.com/open-mmlab/mmclassification/blob/master/docs/zh_CN/tutorials/MMClassification_tools_cn.ipynb) 或者直接[在 Colab 上运行](https://colab.research.google.com/github/open-mmlab/mmclassification/blob/master/docs/zh_CN/tutorials/MMClassification_tools_cn.ipynb)。
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## 模型库
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相关结果和模型可在 [model zoo](https://mmclassification.readthedocs.io/en/latest/model_zoo.html) 中获得
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<details open>
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<summary>支持的主干网络</summary>
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- [x] [VGG](https://github.com/open-mmlab/mmclassification/tree/master/configs/vgg)
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- [x] [ResNet](https://github.com/open-mmlab/mmclassification/tree/master/configs/resnet)
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- [x] [ResNeXt](https://github.com/open-mmlab/mmclassification/tree/master/configs/resnext)
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- [x] [SE-ResNet](https://github.com/open-mmlab/mmclassification/tree/master/configs/seresnet)
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- [x] [SE-ResNeXt](https://github.com/open-mmlab/mmclassification/tree/master/configs/seresnet)
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- [x] [RegNet](https://github.com/open-mmlab/mmclassification/tree/master/configs/repvgg)
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- [x] [ShuffleNetV1](https://github.com/open-mmlab/mmclassification/tree/master/configs/shufflenet_v1)
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- [x] [ShuffleNetV2](https://github.com/open-mmlab/mmclassification/tree/master/configs/shufflenet_v2)
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- [x] [MobileNetV2](https://github.com/open-mmlab/mmclassification/tree/master/configs/mobilenet_v2)
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- [x] [MobileNetV3](https://github.com/open-mmlab/mmclassification/tree/master/configs/mobilenet_v3)
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- [x] [Swin-Transformer](https://github.com/open-mmlab/mmclassification/tree/master/configs/swin_transformer)
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- [x] [RepVGG](https://github.com/open-mmlab/mmclassification/tree/master/configs/repvgg)
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- [x] [Vision-Transformer](https://github.com/open-mmlab/mmclassification/tree/master/configs/vision_transformer)
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- [x] [Transformer-in-Transformer](https://github.com/open-mmlab/mmclassification/tree/master/configs/tnt)
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- [x] [Res2Net](https://github.com/open-mmlab/mmclassification/tree/master/configs/res2net)
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- [x] [MLP-Mixer](https://github.com/open-mmlab/mmclassification/tree/master/configs/mlp_mixer)
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- [x] [DeiT](https://github.com/open-mmlab/mmclassification/tree/master/configs/deit)
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- [x] [Conformer](https://github.com/open-mmlab/mmclassification/tree/master/configs/conformer)
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- [x] [T2T-ViT](https://github.com/open-mmlab/mmclassification/tree/master/configs/t2t_vit)
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- [x] [Twins](https://github.com/open-mmlab/mmclassification/tree/master/configs/twins)
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- [x] [EfficientNet](https://github.com/open-mmlab/mmclassification/tree/master/configs/efficientnet)
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- [x] [ConvNeXt](https://github.com/open-mmlab/mmclassification/tree/master/configs/convnext)
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- [x] [HRNet](https://github.com/open-mmlab/mmclassification/tree/master/configs/hrnet)
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- [x] [VAN](https://github.com/open-mmlab/mmclassification/tree/master/configs/van)
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- [x] [ConvMixer](https://github.com/open-mmlab/mmclassification/tree/master/configs/convmixer)
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- [x] [CSPNet](https://github.com/open-mmlab/mmclassification/tree/master/configs/cspnet)
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- [x] [PoolFormer](https://github.com/open-mmlab/mmclassification/tree/master/configs/poolformer)
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</details>
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## 参与贡献
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我们非常欢迎任何有助于提升 MMClassification 的贡献，请参考 [贡献指南](https://mmclassification.readthedocs.io/zh_CN/latest/community/CONTRIBUTING.html) 来了解如何参与贡献。
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## 致谢
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MMClassification 是一款由不同学校和公司共同贡献的开源项目。我们感谢所有为项目提供算法复现和新功能支持的贡献者，以及提供宝贵反馈的用户。
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我们希望该工具箱和基准测试可以为社区提供灵活的代码工具，供用户复现现有算法并开发自己的新模型，从而不断为开源社区提供贡献。
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## 引用
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如果你在研究中使用了本项目的代码或者性能基准，请参考如下 bibtex 引用 MMClassification。
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```BibTeX
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@misc{2020mmclassification,
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title={OpenMMLab's Image Classification Toolbox and Benchmark},
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author={MMClassification Contributors},
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howpublished = {\url{https://github.com/open-mmlab/mmclassification}},
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year={2020}
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}
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```
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## 许可证
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该项目开源自 [Apache 2.0 license](LICENSE).
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## OpenMMLab 的其他项目
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- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab 计算机视觉基础库
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- [MIM](https://github.com/open-mmlab/mim): MIM 是 OpenMMlab 项目、算法、模型的统一入口
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- [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab 图像分类工具箱
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- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab 目标检测工具箱
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- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab 新一代通用 3D 目标检测平台
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- [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab 旋转框检测工具箱与测试基准
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- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab 语义分割工具箱
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- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab 全流程文字检测识别理解工具包
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- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab 姿态估计工具箱
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- [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 人体参数化模型工具箱与测试基准
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- [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab 自监督学习工具箱与测试基准
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- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab 模型压缩工具箱与测试基准
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- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab 少样本学习工具箱与测试基准
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- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab 新一代视频理解工具箱
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- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab 一体化视频目标感知平台
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- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab 光流估计工具箱与测试基准
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- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab 图像视频编辑工具箱
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- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab 图片视频生成模型工具箱
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- [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab 模型部署框架
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## 欢迎加入 OpenMMLab 社区
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扫描下方的二维码可关注 OpenMMLab 团队的 [知乎官方账号](https://www.zhihu.com/people/openmmlab)，加入 OpenMMLab 团队的 [官方交流 QQ 群](https://jq.qq.com/?_wv=1027&k=aCvMxdr3) 或联络 OpenMMLab 官方微信小助手
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<div align="center">
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<img src="https://github.com/open-mmlab/mmcv/raw/master/docs/en/_static/zhihu_qrcode.jpg" height="400" /> <img src="https://github.com/open-mmlab/mmcv/raw/master/docs/en/_static/qq_group_qrcode.jpg" height="400" /> <img src="https://github.com/open-mmlab/mmcv/raw/master/docs/en/_static/wechat_qrcode.jpg" height="400" />
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</div>
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我们会在 OpenMMLab 社区为大家
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- 📢 分享 AI 框架的前沿核心技术
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- 💻 解读 PyTorch 常用模块源码
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- 📰 发布 OpenMMLab 的相关新闻
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- 🚀 介绍 OpenMMLab 开发的前沿算法
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- 🏃 获取更高效的问题答疑和意见反馈
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- 🔥 提供与各行各业开发者充分交流的平台
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干货满满 📘，等你来撩 💗，OpenMMLab 社区期待您的加入 👬
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