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MMClassification is an open source image classification toolbox based on PyTorch. It is a part of the OpenMMLab project.

Documentation: https://mmclassification.readthedocs.io/en/latest/

• Various backbones and pretrained models
• Bag of training tricks
• Large-scale training configs
• High efficiency and extensibility

This project is released under the Apache 2.0 license.

v0.18.0 was released in 30/11/2021.

Highlights of the new version:

• Support MLP-Mixer backbone and provide pre-trained checkpoints.
• Add a tool to visualize the learning rate curve of the training phase. Welcome to use with the tutorial!

v0.17.0 was released in 29/10/2021.

Highlights of this version:

• Support Tokens-to-Token ViT backbone and Res2Net backbone. Welcome to use!
• Support ImageNet21k dataset.
• Add a pipeline visualization tool. Try it with the tutorials!

Please refer to changelog.md for more details and other release history.

Results and models are available in the model zoo.

Supported backbones:

• VGG
• ResNet
• ResNeXt
• SE-ResNet
• SE-ResNeXt
• RegNet
• ShuffleNetV1
• ShuffleNetV2
• MobileNetV2
• MobileNetV3
• Swin-Transformer
• RepVGG
• Vision-Transformer
• Transformer-in-Transformer
• Res2Net
• MLP-Mixer
• DeiT
• Conformer
• EfficientNet

Please refer to install.md for installation and dataset preparation.

Please see getting_started.md for the basic usage of MMClassification. There are also tutorials:

• learn about configs
• finetuning models
• adding new dataset
• designing data pipeline
• adding new modules
• customizing schedule
• customizing runtime settings

Colab tutorials are also provided. To learn about MMClassification Python API, you may preview the notebook here or directly run on Colab. To learn about MMClassification shell tools, you may preview the notebook here or directly run on Colab.

If you find this project useful in your research, please consider cite:

@misc{2020mmclassification,
    title={OpenMMLab's Image Classification Toolbox and Benchmark},
    author={MMClassification Contributors},
    howpublished = {\url{https://github.com/open-mmlab/mmclassification}},
    year={2020}
}

We appreciate all contributions to improve MMClassification. Please refer to CONTRUBUTING.md for the contributing guideline.

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.

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• MMDetection: OpenMMLab detection toolbox and benchmark.
• MMDetection3D: OpenMMLab's next-generation platform for general 3D object detection.
• MMSegmentation: OpenMMLab semantic segmentation toolbox and benchmark.
• MMAction2: OpenMMLab's next-generation action understanding toolbox and benchmark.
• MMTracking: OpenMMLab video perception toolbox and benchmark.
• MMPose: OpenMMLab pose estimation toolbox and benchmark.
• MMEditing: OpenMMLab image and video editing toolbox.
• MMOCR: OpenMMLab toolbox for text detection, recognition and understanding.
• MMGeneration: OpenMMlab toolkit for generative models.
• MMFlow OpenMMLab optical flow toolbox and benchmark.
• MMFewShot: OpenMMLab FewShot Learning Toolbox and Benchmark.
• MMHuman3D: OpenMMLab 3D Human Parametric Model Toolbox and Benchmark.