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📘 Documentation | 🛠️ Installation | 👀 Model Zoo | 🆕 Update News | 🤔 Reporting Issues

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Introduction

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+.

Major features

• Various backbones and pretrained models
• Rich training strategies (supervised learning, self-supervised learning, multi-modality learning etc.)
• Bag of training tricks
• Large-scale training configs
• High efficiency and extensibility
• Powerful toolkits for model analysis and experiments
• Various out-of-box inference tasks.
  • Image Classification
  • Image Caption
  • Visual Question Answering
  • Visual Grounding
  • Retrieval (Image-To-Image, Text-To-Image, Image-To-Text)

mmpretrain.mp4

What's new

🌟 v1.0.0rc8 was released in 22/05/2023

• Support multiple multi-modal algorithms and inferencers. You can explore these features by the gradio demo!
• Add EVA-02, Dino-V2, ViT-SAM and GLIP backbones.
• Register torchvision transforms into MMPretrain, you can now easily integrate torchvision's data augmentations in MMPretrain. See the doc

🌟 v1.0.0rc7 was released in 07/04/2023

• Integrated Self-supervised learning algorithms from MMSelfSup, such as MAE, BEiT, etc.
• Support RIFormer, a simple but effective vision backbone by removing token mixer.
• Add t-SNE visualization.
• Refactor dataset pipeline visualization.

Update of previous versions

• Support LeViT, XCiT, ViG, ConvNeXt-V2, EVA, RevViT, EfficientnetV2, CLIP, TinyViT and MixMIM backbones.
• Reproduce the training accuracy of ConvNeXt and RepVGG.
• Support confusion matrix calculation and plot.
• Support multi-task training and testing.
• Support Test-time Augmentation.
• Upgrade API to get pre-defined models of MMPreTrain.
• Refactor BEiT backbone and support v1/v2 inference.

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.

Installation

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]"

User Guides

We provided a series of tutorials about the basic usage of MMPreTrain for new users:

• Learn about Configs
• Prepare Dataset
• Inference with existing models
• Train
• Test
• Downstream tasks

For more information, please refer to our documentation.

Model zoo

Results and models are available in the model zoo.

Overview

Supported Backbones	Self-supervised Learning	Multi-Modality Algorithms	Others
VGG ResNet ResNeXt SE-ResNet SE-ResNeXt RegNet ShuffleNet V1 ShuffleNet V2 MobileNet V2 MobileNet V3 Swin-Transformer Swin-Transformer V2 RepVGG Vision-Transformer Transformer-in-Transformer Res2Net MLP-Mixer DeiT DeiT-3 Conformer T2T-ViT Twins EfficientNet EdgeNeXt ConvNeXt HRNet VAN ConvMixer CSPNet PoolFormer Inception V3 MobileOne EfficientFormer MViT HorNet MobileViT DaViT RepLKNet BEiT MixMIM EfficientNet V2 RevViT ConvNeXt V2 ViG XCiT LeViT RIFormer GLIP	MoCo V1 (CVPR'2020) SimCLR (ICML'2020) MoCo V2 (arXiv'2020) BYOL (NeurIPS'2020) SwAV (NeurIPS'2020) DenseCL (CVPR'2021) SimSiam (CVPR'2021) Barlow Twins (ICML'2021) MoCo V3 (ICCV'2021) BEiT (ICLR'2022) MAE (CVPR'2022) SimMIM (CVPR'2022) MaskFeat (CVPR'2022) CAE (arXiv'2022) MILAN (arXiv'2022) BEiT V2 (arXiv'2022) EVA (CVPR'2023) MixMIM (arXiv'2022)	BLIP (arxiv'2022) BLIP-2 (arxiv'2023) OFA (CoRR'2022) Flamingo (NeurIPS'2022) Chinese CLIP (arxiv'2022)	Image Retrieval Task: ArcFace (CVPR'2019) Training&Test Tips: RandAug AutoAug RepeatAugSampler TTA ...

Contributing

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

Acknowledgement

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.

Citation

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}
}

License

This project is released under the Apache 2.0 license.

Projects in OpenMMLab

• MMEngine: OpenMMLab foundational library for training deep learning models.
• MMCV: OpenMMLab foundational library for computer vision.
• MIM: MIM installs OpenMMLab packages.
• MMEval: A unified evaluation library for multiple machine learning libraries.
• MMPreTrain: OpenMMLab pre-training toolbox and benchmark.
• MMDetection: OpenMMLab detection toolbox and benchmark.
• MMDetection3D: OpenMMLab's next-generation platform for general 3D object detection.
• MMRotate: OpenMMLab rotated object detection toolbox and benchmark.
• MMYOLO: OpenMMLab YOLO series toolbox and benchmark.
• 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.
• MMagic: OpenMMLab Advanced, Generative and Intelligent Creation toolbox.
• MMGeneration: OpenMMLab image and video generative models toolbox.
• MMDeploy: OpenMMLab model deployment framework.
• Playground: A central hub for gathering and showcasing amazing projects built upon OpenMMLab.