mmpretrain

OpenMMLab Pre-training Toolbox and Benchmark

beit clip constrastive-learning convnext deep-learning image-classification mae masked-image-modeling mobilenet moco multimodal pretrained-models pytorch resnet self-supervised-learning swin-transformer vision-transformer

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README.md

Introduction

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/

Major features

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

License

This project is released under the Apache 2.0 license.

Changelog

v0.7.0 was released in 31/12/2020. Please refer to changelog.md for details and release history.

Benchmark and model zoo

Results and models are available in the model zoo.

Supported backbones:

ResNet
ResNeXt
SE-ResNet
SE-ResNeXt
RegNet
ShuffleNetV1
ShuffleNetV2
MobileNetV2
MobileNetV3

Installation

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

Getting Started

Please see getting_started.md for the basic usage of MMClassification. There are also tutorials for finetuning models, adding new dataset, designing data pipeline, and adding new modules.

Contributing

We appreciate all contributions to improve MMClassification. Please refer to CONTRUBUTING.md 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.