OpenMMLab website ^HOT      OpenMMLab platform ^{TRY IT OUT}

 

📘 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+.

• 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)

🌟 v1.2.0 was released in 04/01/2023

• Support LLaVA 1.5.
• Implement of RAM with a gradio interface.

🌟 v1.1.0 was released in 12/10/2023

• Support Mini-GPT4 training and provide a Chinese model (based on Baichuan-7B)
• Support zero-shot classification based on CLIP.

🌟 v1.0.0 was released in 04/07/2023

• Support inference of more multi-modal algorithms, such as LLaVA, MiniGPT-4, Otter, etc.
• Support around 10 multi-modal datasets!
• Add iTPN, SparK self-supervised learning algorithms.
• Provide examples of New Config and DeepSpeed/FSDP with FlexibleRunner. Here are the documentation links of New Config and DeepSpeed/FSDP with FlexibleRunner.

🌟 Upgrade from MMClassification to MMPreTrain

• Integrated Self-supervised learning algorithms from MMSelfSup, such as MAE, BEiT, etc.
• Support RIFormer, a simple but effective vision backbone by removing token mixer.
• Refactor dataset pipeline visualization.
• Support LeViT, XCiT, ViG, ConvNeXt-V2, EVA, RevViT, EfficientnetV2, CLIP, TinyViT and MixMIM backbones.

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:

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

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.

• 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.