<div align="center">
<img src="../resources/mmpt-logo.png" width="600"/>
<div> </div>
<div align="center">
<b><font size="5">OpenMMLab website</font></b>
<sup>
<a href="https://openmmlab.com">
<i><font size="4">HOT</font></i>
</a>
</sup>
<b><font size="5">OpenMMLab platform</font></b>
<sup>
<a href="https://platform.openmmlab.com">
<i><font size="4">TRY IT OUT</font></i>
</a>
</sup>
</div>
<div> </div>
[![PyPI](https://img.shields.io/pypi/v/mmpretrain)](https://pypi.org/project/mmpretrain)
[![Docs](https://img.shields.io/badge/docs-latest-blue)](https://mmpretrain.readthedocs.io/en/latest/)
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[ð Documentation](https://mmpretrain.readthedocs.io/en/latest/) |
[ð ï¸ Installation](https://mmpretrain.readthedocs.io/en/latest/get_started.html#installation) |
[ð Model Zoo](https://mmpretrain.readthedocs.io/en/latest/modelzoo_statistics.html) |
[ð Update News](https://mmpretrain.readthedocs.io/en/latest/notes/changelog.html) |
[ð¤ Reporting Issues](https://github.com/open-mmlab/mmpretrain/issues/new/choose)
<img src="https://user-images.githubusercontent.com/36138628/230307505-4727ad0a-7d71-4069-939d-b499c7e272b7.png" width="400"/>
English | [ç®ä½ä¸æ](/docs/README_zh-CN.mdCN.md)
</div>
</div>
<div align="center">
<a href="https://openmmlab.medium.com/" style="text-decoration:none;">
<img src="https://user-images.githubusercontent.com/25839884/219255827-67c1a27f-f8c5-46a9-811d-5e57448c61d1.png" width="3%" alt="" /></a>
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<a href="https://discord.gg/raweFPmdzG" style="text-decoration:none;">
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</div>
## Introduction
MMPreTrain is an open source pre-training toolbox based on PyTorch. It is a part of the [OpenMMLab](https://openmmlab.com/) 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)
https://github.com/open-mmlab/mmpretrain/assets/26739999/e4dcd3a2-f895-4d1b-a351-fbc74a04e904
## What's new
ð 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](https://github.com/open-mmlab/mmpretrain/tree/main/projects/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](https://mmpretrain.readthedocs.io/en/latest/api/data_process.html#torchvision-transforms)
ð 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](https://mmpretrain.readthedocs.io/en/latest/).
And there are some BC-breaking changes. Please check [the migration tutorial](https://mmpretrain.readthedocs.io/en/latest/migration.html).
Please refer to [changelog](https://mmpretrain.readthedocs.io/en/latest/notes/changelog.html) for more details and other release history.
## Installation
Below are quick steps for installation:
```shell
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](https://mmpretrain.readthedocs.io/en/latest/get_started.html) for more detailed installation and dataset preparation.
For multi-modality models support, please install the extra dependencies by:
```shell
mim install -e ".[multimodal]"
```
## User Guides
We provided a series of tutorials about the basic usage of MMPreTrain for new users:
- [Learn about Configs](https://mmpretrain.readthedocs.io/en/latest/user_guides/config.html)
- [Prepare Dataset](https://mmpretrain.readthedocs.io/en/latest/user_guides/dataset_prepare.html)
- [Inference with existing models](https://mmpretrain.readthedocs.io/en/latest/user_guides/inference.html)
- [Train](https://mmpretrain.readthedocs.io/en/latest/user_guides/train.html)
- [Test](https://mmpretrain.readthedocs.io/en/latest/user_guides/test.html)
- [Downstream tasks](https://mmpretrain.readthedocs.io/en/latest/user_guides/downstream.html)
For more information, please refer to [our documentation](https://mmpretrain.readthedocs.io/en/latest/).
## Model zoo
Resul
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深度学习预训练库,集成各种经典backbone,基于OpenMMLab-MMPretrain库!!! (1642个子文件)
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