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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
v0.23.0 was released in 1/5/2022. Highlights of the new version:
- Support DenseNet, VAN and PoolFormer, and provide pre-trained models.
- Support training on IPU.
- New style API docs, welcome view it.
v0.22.0 was released in 30/3/2022.
Highlights of the new version:
- Support a series of CSP Network, such as CSP-ResNet, CSP-ResNeXt and CSP-DarkNet.
- A new
CustomDatasetclass to help you build dataset of yourself! - Support new backbones - ConvMixer, RepMLP and new dataset - CUB dataset.
Please refer to changelog.md for more details and other release history.
Below are quick steps for installation:
conda create -n open-mmlab python=3.8 pytorch=1.10 cudatoolkit=11.3 torchvision -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 for more detailed 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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- MMSegmentation: OpenMMLab semantic segmentation toolbox and benchmark.
- MMOCR: OpenMMLab text detection, recognition, and understanding toolbox.
- MMPose: OpenMMLab pose estimation toolbox and benchmark.
- MMHuman3D: OpenMMLab 3D human parametric model toolbox and benchmark.
- MMSelfSup: OpenMMLab self-supervised learning toolbox and benchmark.
- MMRazor: OpenMMLab model compression toolbox and benchmark.
- MMFewShot: OpenMMLab fewshot learning toolbox and benchmark.
- MMAction2: OpenMMLab's next-generation action understanding toolbox and benchmark.
- MMTracking: OpenMMLab video perception toolbox and benchmark.
- MMFlow: OpenMMLab optical flow toolbox and benchmark.
- MMEditing: OpenMMLab image and video editing toolbox.
- MMGeneration: OpenMMLab image and video generative models toolbox.
- MMDeploy: OpenMMLab model deployment framework.