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📘 Documentation |
🛠️ Installation |
👀 Model Zoo |
🆕 Update News |
🤔 Reporting Issues
English | 简体中文
MMPreTrain is an open source pre-training toolbox based on PyTorch. It is a part of the OpenMMLab project.
The main
branch works with PyTorch 1.8+.
https://github.com/open-mmlab/mmpretrain/assets/26739999/e4dcd3a2-f895-4d1b-a351-fbc74a04e904
🌟 v1.1.0 was released in 12/10/2023
🌟 v1.0.0 was released in 04/07/2023
🌟 Upgrade from MMClassification to MMPreTrain
This release introduced a brand new and flexible training & test engine, but it's still in progress. Welcome
to try according to the documentation.
And there are some BC-breaking changes. Please check the migration tutorial.
Please refer to changelog for more details and other release history.
Below are quick steps for installation:
conda create -n open-mmlab python=3.8 pytorch==1.10.1 torchvision==0.11.2 cudatoolkit=11.3 -c pytorch -y
conda activate open-mmlab
pip install openmim
git clone https://github.com/open-mmlab/mmpretrain.git
cd mmpretrain
mim install -e .
Please refer to installation documentation for more detailed installation and dataset preparation.
For multi-modality models support, please install the extra dependencies by:
mim install -e ".[multimodal]"
We provided a series of tutorials about the basic usage of MMPreTrain for new users:
For more information, please refer to our documentation.
Results and models are available in the model zoo.
We appreciate all contributions to improve MMPreTrain.
Please refer to CONTRUBUTING for the contributing guideline.
MMPreTrain 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 supporting their own academic research.
If you find this project useful in your research, please consider cite:
@misc{2023mmpretrain,
title={OpenMMLab's Pre-training Toolbox and Benchmark},
author={MMPreTrain Contributors},
howpublished = {\url{https://github.com/open-mmlab/mmpretrain}},
year={2023}
}
This project is released under the Apache 2.0 license.
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