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<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/1.x/)
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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/1.x/LICENSE)
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2022-06-02 15:22:01 +08:00
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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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2022-08-31 23:57:51 +08:00
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[📘 中文文档](https://mmclassification.readthedocs.io/zh_CN/1.x/) |
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[🛠️ 安装教程](https://mmclassification.readthedocs.io/zh_CN/1.x/get_started.html) |
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[👀 模型库](https://mmclassification.readthedocs.io/zh_CN/1.x/modelzoo_statistics.html) |
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[🆕 更新日志](https://mmclassification.readthedocs.io/en/1.x/notes/changelog.html) |
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2022-06-02 15:22:01 +08:00
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[🤔 报告问题](https://github.com/open-mmlab/mmclassification/issues/new/choose)
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2021-04-26 13:58:18 +08:00
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2022-01-22 10:36:39 +08:00
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</div>
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2021-04-26 13:58:18 +08:00
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## Introduction
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[English](/README.md) | 简体中文
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2021-06-03 16:49:53 +08:00
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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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2022-01-22 10:36:39 +08:00
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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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2022-10-12 16:52:31 +08:00
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2022/10/12 发布了 v1.0.0rc2 版本
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- 支持了 Deit-3 主干网络
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- 修复了 MMEngine 版本依赖问题
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2022-09-30 17:35:44 +08:00
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2022/9/30 发布了 v1.0.0rc1 版本
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- 支持了 MViT，EdgeNeXt，Swin-Transformer V2，EfficientFormer，MobileOne 等主干网络。
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- 支持了 BEiT 风格的 transformer 层。
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2022/8/31 发布了 v1.0.0rc0 版本
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这个版本引入一个全新的，可扩展性强的训练和测试引擎，但目前仍在开发中。欢迎根据[文档](https://mmclassification.readthedocs.io/zh_CN/1.x/)进行试用。
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同时，新版本中存在一些与旧版本不兼容的修改。请查看[迁移文档](https://mmclassification.readthedocs.io/zh_CN/1.x/migration.html)来详细了解这些变动。
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新版本的公测将持续到 2022 年末，在此期间，我们将基于 `1.x` 分支进行更新，不会合入到 `master` 分支。另外，至少
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到 2023 年末，我们会保持对 0.x 版本的维护。
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发布历史和更新细节请参考 [更新日志](https://mmclassification.readthedocs.io/zh_CN/1.x/notes/changelog.html)
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2022-01-22 10:36:39 +08:00
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## 安装
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2022-03-30 19:16:45 +08:00
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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==1.10.1 torchvision==0.11.2 cudatoolkit=11.3 -c pytorch -y
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conda activate open-mmlab
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pip3 install openmim
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git clone -b 1.x https://github.com/open-mmlab/mmclassification.git
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cd mmclassification
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mim install -e .
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```
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2022-08-31 23:57:51 +08:00
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更详细的步骤请参考 [安装指南](https://mmclassification.readthedocs.io/zh_CN/1.x/get_started.html) 进行安装。
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2022-01-22 10:36:39 +08:00
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## 基础教程
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2022-08-31 23:57:51 +08:00
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我们为新用户提供了一系列基础教程：
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2022-08-31 23:57:51 +08:00
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- [使用现有模型推理](https://mmclassification.readthedocs.io/zh_CN/1.x/user_guides/inference.html)
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- [准备数据集](https://mmclassification.readthedocs.io/zh_CN/1.x/user_guides/dataset_prepare.html)
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- [训练与测试](https://mmclassification.readthedocs.io/zh_CN/1.x/user_guides/train_test.html)
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- [学习配置文件](https://mmclassification.readthedocs.io/zh_CN/1.x/user_guides/config.html)
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- [如何微调模型](https://mmclassification.readthedocs.io/zh_CN/1.x/user_guides/finetune.html)
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- [分析工具](https://mmclassification.readthedocs.io/zh_CN/1.x/user_guides/analysis.html)
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- [可视化工具](https://mmclassification.readthedocs.io/zh_CN/1.x/user_guides/visualization.html)
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- [其他工具](https://mmclassification.readthedocs.io/zh_CN/1.x/user_guides/useful_tools.html)
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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/zh_CN/1.x/modelzoo_statistics.html) 中获得
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2021-12-31 12:55:47 +08:00
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<details open>
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<summary>支持的主干网络</summary>
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2022-08-31 23:57:51 +08:00
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- [x] [VGG](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/vgg)
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- [x] [ResNet](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/resnet)
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- [x] [ResNeXt](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/resnext)
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- [x] [SE-ResNet](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/seresnet)
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- [x] [SE-ResNeXt](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/seresnet)
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- [x] [RegNet](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/regnet)
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- [x] [ShuffleNetV1](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/shufflenet_v1)
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- [x] [ShuffleNetV2](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/shufflenet_v2)
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- [x] [MobileNetV2](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/mobilenet_v2)
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- [x] [MobileNetV3](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/mobilenet_v3)
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- [x] [Swin-Transformer](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/swin_transformer)
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2022-09-20 15:45:27 +08:00
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- [x] [Swin-Transformer V2](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/swin_transformer_v2)
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2022-08-31 23:57:51 +08:00
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- [x] [RepVGG](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/repvgg)
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- [x] [Vision-Transformer](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/vision_transformer)
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- [x] [Transformer-in-Transformer](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/tnt)
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- [x] [Res2Net](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/res2net)
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- [x] [MLP-Mixer](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/mlp_mixer)
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- [x] [DeiT](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/deit)
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2022-10-10 14:54:20 +08:00
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- [x] [DeiT-3](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/deit3)
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2022-08-31 23:57:51 +08:00
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- [x] [Conformer](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/conformer)
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- [x] [T2T-ViT](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/t2t_vit)
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- [x] [Twins](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/twins)
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- [x] [EfficientNet](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/efficientnet)
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2022-09-21 13:27:04 +08:00
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- [x] [EdgeNeXt](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/edgenext)
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2022-08-31 23:57:51 +08:00
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- [x] [ConvNeXt](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/convnext)
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- [x] [HRNet](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/hrnet)
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- [x] [VAN](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/van)
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- [x] [ConvMixer](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/convmixer)
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- [x] [CSPNet](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/cspnet)
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- [x] [PoolFormer](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/poolformer)
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- [x] [Inception V3](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/inception_v3)
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2022-09-16 11:55:10 +08:00
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- [x] [MobileOne](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/mobileone)
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2022-09-22 09:23:49 +08:00
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- [x] [EfficientFormer](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/efficientformer)
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- [x] [MViT](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/mvit)
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2022-10-18 17:05:59 +08:00
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- [x] [MobileViT](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/mobilevit)
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2021-12-31 12:55:47 +08:00
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</details>
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2021-04-26 13:58:18 +08:00
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## 参与贡献
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2022-08-31 23:57:51 +08:00
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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/1.x/notes/contribution_guide.html) 来了解如何参与贡献。
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2021-04-26 13:58:18 +08:00
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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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2022-01-22 10:36:39 +08:00
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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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2021-04-26 13:58:18 +08:00
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## OpenMMLab 的其他项目
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2022-08-31 23:57:51 +08:00
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- [MMEngine](https://github.com/open-mmlab/mmengine): OpenMMLab 深度学习模型训练基础库
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2021-04-26 13:58:18 +08:00
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- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab 计算机视觉基础库
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2021-07-27 17:58:04 +08:00
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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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2022-03-02 18:57:12 +08:00
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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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2021-06-03 16:49:53 +08:00
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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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2022-03-02 18:57:12 +08:00
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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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2021-04-26 13:58:18 +08:00
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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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2022-03-02 18:57:12 +08:00
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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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2022-01-04 11:38:05 +08:00
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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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2022-03-02 18:57:12 +08:00
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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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2022-01-04 11:38:05 +08:00
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- [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab 模型部署框架
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2021-04-26 13:58:18 +08:00
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## 欢迎加入 OpenMMLab 社区
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2022-01-22 10:36:39 +08:00
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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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2021-04-26 13:58:18 +08:00
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<div align="center">
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2022-01-22 10:36:39 +08:00
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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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2021-04-26 13:58:18 +08:00
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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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