mmpretrain - Comprehensive Pre-Training Toolset for Diverse Learning Modalities

Project Introduction: MMPreTrain

MMPreTrain is an innovative open-source project designed to provide a comprehensive pre-training toolbox, built on the robust framework of PyTorch. It forms an integral part of the larger OpenMMLab project, which is dedicated to advancing machine learning technologies. MMPreTrain specifically caters to those interested in developing sophisticated models through pre-training methods, making it an essential resource for both researchers and developers in the field of artificial intelligence.

Major Features

MMPreTrain is a versatile tool offering a host of features to enhance the model pre-training process:

Variety of Models and Backbones: It supports numerous backbones along with pretrained models. This provides users with the flexibility to choose models that best suit their specific needs.
Diverse Training Strategies: The toolbox includes a range of training strategies such as supervised learning, self-supervised learning, and multi-modality learning. This diversity empowers users to experiment with different approaches for better outcomes.
Training Enhancements: MMPreTrain leverages a bag of tricks for improved training performance as well as supports large-scale training configurations, emphasizing efficiency and extensibility.
Tools for Analysis and Experimentation: Users benefit from powerful tools designed for in-depth model analysis and experimentation, which are crucial for refining models.
Out-of-the-Box Inference Tasks: MMPreTrain covers various inference tasks readily available to users, including image classification, image captioning, visual question answering, and more.

What's New in MMPreTrain

The toolbox is consistently updated to meet the evolving needs of its users:

Version 1.2.0 (April 2023): Introduced support for LLaVA 1.5 and RAM features with a Gradio interface.
Version 1.1.0 (December 2023): Added support for training with Mini-GPT4 and zero-shot classification via CLIP, including a Chinese model.
Version 1.0.0 (April 2023): Expanded inference capabilities for multiple multi-modal algorithms and supported around ten multi-modal datasets. It also introduced new self-supervised learning algorithms.

Installation

Installing MMPreTrain is straightforward. It requires Python, PyTorch, and some additional packages. Below is a quick installation guide:

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 .

For additional support of multi-modality models, one can install extra dependencies:

mim install -e ".[multimodal]"

User Guides and Model Zoo

The project documentation offers detailed user guides covering configuration, dataset preparation, training, and testing. Additionally, the model zoo provides a variety of pretrained models and results, which can easily be accessed and used by the community.

Contributing

The MMPreTrain project thrives on contributions from researchers and developers. Whether it’s adding new features or refining existing ones, contributions help to make the toolbox more robust. Guidelines for contributing are available, encouraging more individuals to join in enhancing this open-source project.

Community Support and Acknowledgement

MMPreTrain is supported by a community of contributors from diverse backgrounds. Their effort in providing feedback, implementing methods, and developing new features is greatly appreciated. The project aims to support ongoing research by offering a flexible toolkit for experimentation and innovation.

License

The project is licensed under the Apache 2.0 license, ensuring it remains freely accessible and modifiable while protecting the integrity of the work and its contributors.