Metadata-Version: 2.1
Name: mmcls
Version: 0.25.0
Summary: OpenMMLab Image Classification Toolbox and Benchmark
Home-page: https://github.com/open-mmlab/mmclassification
Author: MMClassification Contributors
Author-email: openmmlab@gmail.com
License: Apache License 2.0
Description: <div align="center">
        
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            <b><font size="5">OpenMMLab website</font></b>
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        [📘 Documentation](https://mmclassification.readthedocs.io/en/latest/) |
        [🛠️ Installation](https://mmclassification.readthedocs.io/en/latest/install.html) |
        [👀 Model Zoo](https://mmclassification.readthedocs.io/en/latest/model_zoo.html) |
        [🆕 Update News](https://mmclassification.readthedocs.io/en/latest/changelog.html) |
        [🤔 Reporting Issues](https://github.com/open-mmlab/mmclassification/issues/new/choose)
        
        :point_right: **MMClassification 1.0 branch is in trial, welcome every to [try it](https://github.com/open-mmlab/mmclassification/tree/1.x) and [discuss with us](https://github.com/open-mmlab/mmclassification/discussions)!** :point_left:
        
        </div>
        
        ## Introduction
        
        English | [简体中文](/README_zh-CN.md)
        
        MMClassification is an open source image classification toolbox based on PyTorch. It is
        a part of the [OpenMMLab](https://openmmlab.com/) project.
        
        The master branch works with **PyTorch 1.5+**.
        
        <div align="center">
          <img src="https://user-images.githubusercontent.com/9102141/87268895-3e0d0780-c4fe-11ea-849e-6140b7e0d4de.gif" width="70%"/>
        </div>
        
        ### Major features
        
        - Various backbones and pretrained models
        - Bag of training tricks
        - Large-scale training configs
        - High efficiency and extensibility
        - Powerful toolkits
        
        ## What's new
        
        The MMClassification 1.0 has released! It's still unstable and in release candidate. If you want to try it, go
        to [the 1.x branch](https://github.com/open-mmlab/mmclassification/tree/1.x) and discuss it with us in
        [the discussion](https://github.com/open-mmlab/mmclassification/discussions).
        
        v0.25.0 was released in 06/12/2022.
        Highlights of the new version:
        
        - Support MLU backend.
        - Add `dist_train_arm.sh` for ARM device.
        
        v0.24.1 was released in 31/10/2022.
        Highlights of the new version:
        
        - Support HUAWEI Ascend device.
        
        v0.24.0 was released in 30/9/2022.
        Highlights of the new version:
        
        - Support **HorNet**, **EfficientFormerm**, **SwinTransformer V2** and **MViT** backbones.
        - Support Standford Cars dataset.
        
        Please refer to [changelog.md](docs/en/changelog.md) for more details and other release history.
        
        ## Installation
        
        Below are quick steps for installation:
        
        ```shell
        conda create -n open-mmlab python=3.8 pytorch=1.10 cudatoolkit=11.3 torchvision==0.11.0 -c pytorch -y
        conda activate open-mmlab
        pip3 install openmim
        mim install mmcv-full
        git clone https://github.com/open-mmlab/mmclassification.git
        cd mmclassification
        pip3 install -e .
        ```
        
        Please refer to [install.md](https://mmclassification.readthedocs.io/en/latest/install.html) for more detailed installation and dataset preparation.
        
        ## Getting Started
        
        Please see [Getting Started](https://mmclassification.readthedocs.io/en/latest/getting_started.html) for the basic usage of MMClassification. There are also tutorials:
        
        - [Learn about Configs](https://mmclassification.readthedocs.io/en/latest/tutorials/config.html)
        - [Fine-tune Models](https://mmclassification.readthedocs.io/en/latest/tutorials/finetune.html)
        - [Add New Dataset](https://mmclassification.readthedocs.io/en/latest/tutorials/new_dataset.html)
        - [Customizie Data Pipeline](https://mmclassification.readthedocs.io/en/latest/tutorials/data_pipeline.html)
        - [Add New Modules](https://mmclassification.readthedocs.io/en/latest/tutorials/new_modules.html)
        - [Customizie Schedule](https://mmclassification.readthedocs.io/en/latest/tutorials/schedule.html)
        - [Customizie Runtime Settings](https://mmclassification.readthedocs.io/en/latest/tutorials/runtime.html)
        
        Colab tutorials are also provided:
        
        - Learn about MMClassification **Python API**: [Preview the notebook](https://github.com/open-mmlab/mmclassification/blob/master/docs/en/tutorials/MMClassification_python.ipynb) or directly [run on Colab](https://colab.research.google.com/github/open-mmlab/mmclassification/blob/master/docs/en/tutorials/MMClassification_python.ipynb).
        - Learn about MMClassification **CLI tools**: [Preview the notebook](https://github.com/open-mmlab/mmclassification/blob/master/docs/en/tutorials/MMClassification_tools.ipynb) or directly [run on Colab](https://colab.research.google.com/github/open-mmlab/mmclassification/blob/master/docs/en/tutorials/MMClassification_tools.ipynb).
        
        ## Model zoo
        
        Results and models are available in the [model zoo](https://mmclassification.readthedocs.io/en/latest/model_zoo.html).
        
        <details open>
        <summary>Supported backbones</summary>
        
        - [x] [VGG](https://github.com/open-mmlab/mmclassification/tree/master/configs/vgg)
        - [x] [ResNet](https://github.com/open-mmlab/mmclassification/tree/master/configs/resnet)
        - [x] [ResNeXt](https://github.com/open-mmlab/mmclassification/tree/master/configs/resnext)
        - [x] [SE-ResNet](https://github.com/open-mmlab/mmclassification/tree/master/configs/seresnet)
        - [x] [SE-ResNeXt](https://github.com/open-mmlab/mmclassification/tree/master/configs/seresnet)
        - [x] [RegNet](https://github.com/open-mmlab/mmclassification/tree/master/configs/regnet)
        - [x] [ShuffleNetV1](https://github.com/open-mmlab/mmclassification/tree/master/configs/shufflenet_v1)
        - [x] [ShuffleNetV2](https://github.com/open-mmlab/mmclassification/tree/master/configs/shufflenet_v2)
        - [x] [MobileNetV2](https://github.com/open-mmlab/mmclassification/tree/master/configs/mobilenet_v2)
        - [x] [MobileNetV3](https://github.com/open-mmlab/mmclassification/tree/master/configs/mobilenet_v3)
        - [x] [Swin-Transformer](https://github.com/open-mmlab/mmclassification/tree/master/configs/swin_transformer)
        - [x] [RepVGG](https://github.com/open-mmlab/mmclassification/tree/master/configs/repvgg)
        - [x] [Vision-Transformer](https://github.com/open-mmlab/mmclassification/tree/master/configs/vision_transformer)
        - [x] [Transformer-in-Transformer](https://github.com/open-mmlab/mmclassification/tree/master/configs/tnt)
        - [x] [Res2Net](https://github.com/open-mmlab/mmclassification/tree/master/configs/res2net)
        - [x] [MLP-Mixer](https://github.com/open-mmlab/mmclassification/tree/master/configs/mlp_mixer)
        - [x] [DeiT](https://github.com/open-mmlab/mmclassification/tree/master/configs/deit)
        - [x] [Conformer](https://github.com/open-mmlab/mmclassification/tree/master/configs/conformer)
        - [x] [T2T-ViT](https://github.com/open-mmlab/mmclassification/tree/master/configs/t2t_vit)
        - [x] [Twins](https://github.com/open-mmlab/mmclassification/tree/master/configs/twins)
        - [x] [EfficientNet](https://github.com/open-mmlab/mmclassification/tree/master/configs/efficientnet)
        - [x] [ConvNeXt](https://github.com/open-mmlab/mmclassification/tree/master/configs/convnext)
        - [x] [HRNet](https://github.com/open-mmlab/mmclassification/tree/master/configs/hrnet)
        - [x] [VAN](https://github.com/open-mmlab/mmclassification/tree/master/configs/van)
        - [x] [ConvMixer](https://github.com/open-mmlab/mmclassification/tree/master/configs/convmixer)
        - [x] [CSPNet](https://github.com/open-mmlab/mmclassification/tree/master/configs/cspnet)
        - [x] [PoolFormer](https://github.com/open-mmlab/mmclassification/tree/master/configs/poolformer)
        - [x] [MViT](https://github.com/open-mmlab/mmclassification/tree/master/configs/mvit)
        - [x] [EfficientFormer](https://github.com/open-mmlab/mmclassification/tree/master/configs/efficientformer)
        - [x] [HorNet](https://github.com/open-mmlab/mmclassification/tree/master/configs/hornet)
        
        </details>
        
        ## Contributing
        
        We appreciate all contributions to improve MMClassification.
        Please refer to [CONTRUBUTING.md](https://mmclassification.readthedocs.io/en/latest/community/CONTRIBUTING.html) for the contributing guideline.
        
        ## Acknowledgement
        
        MMClassification is an open source project that is contributed by researchers and engineers from various colleges and companies. We appreciate all the contributors who implement their methods or add new features, as well as users who give valuable feedbacks.
        We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to reimplement existing methods and develop their own new classifiers.
        
        ## Citation
        
        If you find this project useful in your research, please consider cite:
        
        ```BibTeX
        @misc{2020mmclassification,
            title={OpenMMLab's Image Classification Toolbox and Benchmark},
            author={MMClassification Contributors},
            howpublished = {\url{https://github.com/open-mmlab/mmclassification}},
            year={2020}
        }
        ```
        
        ## License
        
        This project is released under the [Apache 2.0 license](LICENSE).
        
        ## Projects in OpenMMLab
        
        - [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision.
        - [MIM](https://github.com/open-mmlab/mim): MIM installs OpenMMLab packages.
        - [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab image classification toolbox and benchmark.
        - [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab detection toolbox and benchmark.
        - [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection.
        - [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab rotated object detection toolbox and benchmark.
        - [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab semantic segmentation toolbox and benchmark.
        - [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab text detection, recognition, and understanding toolbox.
        - [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark.
        - [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 3D human parametric model toolbox and benchmark.
        - [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab self-supervised learning toolbox and benchmark.
        - [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab model compression toolbox and benchmark.
        - [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab fewshot learning toolbox and benchmark.
        - [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab's next-generation action understanding toolbox and benchmark.
        - [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab video perception toolbox and benchmark.
        - [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab optical flow toolbox and benchmark.
        - [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab image and video editing toolbox.
        - [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab image and video generative models toolbox.
        - [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab model deployment framework.
        
Keywords: computer vision,image classification
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Description-Content-Type: text/markdown
Provides-Extra: all
Provides-Extra: tests
Provides-Extra: optional
Provides-Extra: mim
