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MMClassification is an open source image classification toolbox based on PyTorch. It is a part of the OpenMMLab project.
The master branch works with PyTorch 1.5+.
- Various backbones and pretrained models
- Bag of training tricks
- Large-scale training configs
- High efficiency and extensibility
- Powerful toolkits
v0.20.0 was released in 30/1/2022.
Highlights of the new version:
- Support K-fold cross-validation. The tutorial will be released later.
- Support HRNet, ConvNeXt, Twins and EfficientNet.
- Support model conversion from PyTorch to Core ML by a tool.
v0.19.0 was released in 31/12/2021.
Highlights of the new version:
- The feature extraction function has been enhanced. See #593 for more details.
- Provide the high-acc ResNet-50 training settings from ResNet strikes back.
- Reproduce the training accuracy of T2T-ViT & RegNetX, and provide self-training checkpoints.
- Support DeiT & Conformer backbone and checkpoints.
- Provide a CAM visualization tool based on pytorch-grad-cam, and detailed user guide!
Please refer to changelog.md for more details and other release history.
Please refer to install.md for installation and dataset preparation.
Please see Getting Started for the basic usage of MMClassification. There are also tutorials:
- Learn about Configs
- Fine-tune Models
- Add New Dataset
- Customizie Data Pipeline
- Add New Modules
- Customizie Schedule
- Customizie Runtime Settings
Colab tutorials are also provided:
- Learn about MMClassification Python API: Preview the notebook or directly run on Colab.
- Learn about MMClassification CLI tools: Preview the notebook or directly run on Colab.
Results and models are available in the model zoo.
Supported backbones
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.
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}
}This project is released under the Apache 2.0 license.
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