<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>
[](https://pypi.org/project/mmpretrain)
[](https://mmpretrain.readthedocs.io/en/latest/)
[](https://github.com/open-mmlab/mmpretrain/actions)
[](https://codecov.io/gh/open-mmlab/mmpretrain)
[](https://github.com/open-mmlab/mmpretrain/blob/main/LICENSE)
[](https://github.com/open-mmlab/mmpretrain/issues)
[](https://github.com/open-mmlab/mmpretrain/issues)
[đ Documentation](https://mmpretrain.readthedocs.io/en/latest/) |
[đ ī¸ Installation](https://mmpretrain.readthedocs.io/en/latest/get_started.html#installation) |
[đ Model Zoo](https://mmpretrain.readthedocs.io/en/latest/modelzoo_statistics.html) |
[đ Update News](https://mmpretrain.readthedocs.io/en/latest/notes/changelog.html) |
[đ¤ 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 | [įŽäŊä¸æ](/README_zh-CN.md)
</div>
</div>
<div align="center">
<a href="https://openmmlab.medium.com/" 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
đ v1.2.0 was released in 04/01/2023
- Support LLaVA 1.5.
- Implement of RAM with a gradio interface.
đ 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.
đ v1.0.0 was released in 04/07/2023
- Support inference of more **multi-modal** algorithms, such as [**LLaVA**](./configs/llava/), [**MiniGPT-4**](./configs/minigpt4), [**Otter**](./configs/otter/), etc.
- Support around **10 multi-modal** datasets!
- Add [**iTPN**](./configs/itpn/), [**SparK**](./configs/spark/) self-supervised learning algorithms.
- Provide examples of [New Config](./mmpretrain/configs/) and [DeepSpeed/FSDP with FlexibleRunner](./configs/mae/benchmarks/). Here are the documentation links of [New Config](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta) and [DeepSpeed/FSDP with FlexibleRunner](https://mmengine.readthedocs.io/en/latest/api/generated/mmengine.runner.FlexibleRunner.html#mmengine.runner.FlexibleRunner).
đ 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](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
Results and models are available in the [model zoo](https://mmpretrain.readthedocs.io/en/latest/modelzoo_statistics.html).
<div align="center">
<b>Overview</b>
</div>
<table align="center">
<tbody>
<tr align="center" valign="bottom">
<td>
<b>Supported Backbones</b>
</td>
<td>
<b>Self-supervised Learning</b>
</td>
<td>
<b>Multi-Modality Algorithms</b>
</td>
<td>
<b>Others</b>
</td>
</tr>
<tr valign="top">
<td>
<ul>
<li><a href="configs/vgg">VGG</a></li>
<li><a href="configs/resnet">ResNet</a></li>
<li><a href="configs/resnext">ResNeXt</a></li>
<li><a href="configs/seresnet">SE-ResNet</a></li>
<li><a href="configs/seresnet">SE-ResNeXt</a></li>
<li><a href="configs/regnet">RegNet</a></li>
<li><a href="configs/shufflenet_v1">ShuffleNet V1</a></li>
<li><a href="configs/shufflenet_v2">ShuffleNet V2</a></li>
<li><a href="configs/mobilenet_v2">MobileNet V2</a></li>
<li><a href="configs/mobilenet_v3">MobileNet V3</a></li>
<li><a href="configs/swin_transformer">Swin-Transformer</a></li>
<li><a href="configs/swin_transformer_v2">Swin-Transformer V2</a></li>
<li><a href="configs/repvgg">RepVGG</a></li>
<li><a href="configs/vision_transformer">Vision-Transformer</a></li>
<li><a href="configs/tnt">Transformer-in-Transformer</a></li>
<li><a href="configs/res2net">Res2Net</a></li>
<li><a href="configs/mlp_mixer">MLP-Mixer</a></li>
<li><a href="configs/deit">DeiT</a></li>
<li><a href="configs/deit3">DeiT-3</a></li>
<li><a href="configs/conformer">Conformer</a></li>
<li><a href="configs/t2t_vit">T2T-ViT</a></li>
<li><a href="configs/twins">Twins</a></li>
<li><a href="configs/efficientnet">EfficientNet</a></li>
<li><a href="configs/edgenext">EdgeNeXt</a></li>
<li><a href="configs/convnext">ConvNeXt</a></li>
<li><a href="configs/hrnet">HRNet</a></li>
<li><a href="configs/van">VAN</a></li>
<li><a href="configs/convmixer">ConvMixer</a></li>
<li><a href="configs/cspnet">CSPNet</a></li>
<li><a href="configs/poolformer">PoolFormer</a></li>
<li><a href="configs/inception_v3">Inception V3</a></li>
<li><a href="configs/mobileone">MobileOne</a></li>
<li><a href="configs/efficientformer">EfficientFormer</a></li>
<li><a href="configs/mvit">MViT</a></li>
<li><a href="configs/hornet">HorNet</a></li>
<li><a href="configs/mobilevit">MobileViT</a></li>
<li><a href="configs/davit">DaViT</a></li>
<li><a href="configs/replknet">RepLKNet</a></li>
<li><a href="configs/beit">BEiT</a></li>
<li><a href="configs/mixmim">MixMIM</a></li>
<li><a href="configs/efficientnet_v2">EfficientNet V2</a></li>
<li><a href="configs/revvit">RevViT</a></li>
<li><a href="configs/convnext_v2">ConvNeXt V2</a></li>
<li><a href="configs/vig">ViG</a></li>
<li><a href="configs/xcit">XCiT</a></li>
<li><a href="configs/levit">LeViT</a></li>
<li><a href="configs/riformer">RIFormer</a></li>
<li><a href="configs/glip">GLIP</a></li>
<li><a href="configs/sam">ViT SAM</a></li>
<li><a href="configs/eva02">EVA02</a></li>
<li><a href="configs/dinov2">DINO V2</a></li>
<li><a href="configs/hivit">HiViT</a></li>
</ul>
</td>
<td>
<ul>
<li><a href="configs/mocov2">MoCo V1 (CVPR'2020)</a></li>
<li><a href="configs/simclr">SimCLR (ICML'2020)</a></li>
<li><a href="configs/mocov2">MoCo V2 (arXiv'2020)</a></li>
<li><a href="configs/byol">BYOL (NeurIPS'2020)</a></li>
<li><a href="configs/swav">SwAV (NeurIPS'2020)</a></li>
<li><a href="configs/densecl">DenseCL (CVPR'2021)</a></li>
<li><a href="configs/simsiam">SimSiam (CVPR'2021)</a></li>
<li><a href="configs/barlowtwins">Barlow Twins (ICML'2021)</a></li>
<li><a href="configs/mocov3">MoCo V3 (ICCV'2021)</a></li>
<li><a href="configs/beit">BEiT (ICLR'2022)</a></li>
<li><a href="configs/mae">MAE (CVPR'2022)</a></li>
<li><a href="configs/simmim">SimMIM (CVPR'2022)</a></li>
<li><a href="configs/maskfeat">MaskFeat (CVPR'2022)</a></li>
<li><a href="configs/cae">CAE (arXiv'2022)</a></li>
<li><a href="configs/milan">MILAN (arXiv'2022)</a></li>
<li><a href="configs/beitv2">BEiT V2 (arXiv'2022)</a></li>
<li><a href="configs/eva">EVA (CVPR'2023)</a></li>
<li><a href="configs/mixmim">MixMIM (arXiv'2022)</a></li>
<li><a href="configs/itpn">iTPN (CVPR'2023)</a></li>
<li><a href="configs/spark">SparK (ICLR'2023)</a></li>
<li><a href="configs/mff">MFF (ICCV'2023)</a></li>
</ul>
</td>
<td>
<ul>
<li><a href="configs/blip">BLIP (arxiv'2022)</a></li>
<li><a href="configs/blip2">BLIP-2 (arxiv'2023)</a></li>
<li><a href="configs/ofa">OFA (CoRR'2022)</a></li>
<li><a href="configs/flamingo">Flamingo (NeurIPS'2022)</a></li>
<li><a href="configs/chinese_clip">Chinese CLIP (arxiv'2022)</a></li>
<li><a href="configs/minigpt4">MiniGPT-4 (arxiv'2023)</a></li>
<li><a href="configs/llava">LLaVA (arxiv'2023)</a></li>
<li><a href="configs/otter">Otter (arxiv'2023)</a></li>
</ul>
</td>
<td>
Image Retrieval Task:
<ul>
<li><a href="configs/arcface">ArcFace (CVPR'2019)</a></li>
</ul>
Training&Test Tips:
<ul>
<li><a href="https://arxiv.org/abs/1909.13719">RandAug</a></li>
<li><a href="https://arxiv.org/abs/1805.09501">AutoAug</a></li>
<li><a href="mmpretrain/datasets/samplers/repeat_aug.py">RepeatAugSampler</a></li>
<li><a href="mmpretrain/models/tta/score_tta.py">TTA</a></li>
<li>...</li>
</ul>
</td>
</tbody>
</table>
## Contributing
We appreciate all contributions to improve MMPreTrain.
Please refer to [CONTRUBUTING](https://mmpretrain.readthedocs.io/en/latest/notes/contribution_guide.html) 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:
```BibTeX
@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](LICENSE).
## Projects in OpenMMLab
- [MMEngine](https://github.com/open-mmlab/mmengine): OpenMMLab foundational library for training deep learning models.
- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision.
- [MIM](https://github.com/open-mmlab/mim): MIM installs OpenMMLab packages.
- [MMEval](https://github.com/open-mmlab/mmeval): A unified evaluation library for multiple machine learning libraries.
- [MMPreTrain](https://github.com/open-mmlab/mmpretrain): OpenMMLab pre-training toolbox and benchmark.
- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab detection toolbox and benchmark.
- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection.
- [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab rotated object detection toolbox and benchmark.
- [MMYOLO](https://github.com/open-mmlab/mmyolo): OpenMMLab YOLO series toolbox and benchmark.
- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab semantic segmentation toolbox and benchmark.
- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab text detection, recognition, and understanding toolbox.
- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark.
- [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 3D human parametric model toolbox and benchmark.
- [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab self-supervised learning toolbox and benchmark.
- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab model compression toolbox and benchmark.
- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab fewshot learning toolbox and benchmark.
- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab's next-generation action understanding toolbox and benchmark.
- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab video perception toolbox and benchmark.
- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab optical flow toolbox and benchmark.
- [MMagic](https://github.com/open-mmlab/mmagic): Open**MM**Lab **A**dvanced, **G**enerative and **I**ntelligent **C**reation toolbox.
- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab image and video generative models toolbox.
- [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab model deployment framework.
- [Playground](https://github.com/open-mmlab/playground): A central hub for gathering and showcasing amazing projects built upon OpenMMLab.
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"description": "<div align=\"center\">\n\n<img src=\"resources/mmpt-logo.png\" width=\"600\"/>\n <div> </div>\n <div align=\"center\">\n <b><font size=\"5\">OpenMMLab website</font></b>\n <sup>\n <a href=\"https://openmmlab.com\">\n <i><font size=\"4\">HOT</font></i>\n </a>\n </sup>\n \n <b><font size=\"5\">OpenMMLab platform</font></b>\n <sup>\n <a href=\"https://platform.openmmlab.com\">\n <i><font size=\"4\">TRY IT OUT</font></i>\n </a>\n </sup>\n </div>\n <div> </div>\n\n[](https://pypi.org/project/mmpretrain)\n[](https://mmpretrain.readthedocs.io/en/latest/)\n[](https://github.com/open-mmlab/mmpretrain/actions)\n[](https://codecov.io/gh/open-mmlab/mmpretrain)\n[](https://github.com/open-mmlab/mmpretrain/blob/main/LICENSE)\n[](https://github.com/open-mmlab/mmpretrain/issues)\n[](https://github.com/open-mmlab/mmpretrain/issues)\n\n[\ud83d\udcd8 Documentation](https://mmpretrain.readthedocs.io/en/latest/) |\n[\ud83d\udee0\ufe0f Installation](https://mmpretrain.readthedocs.io/en/latest/get_started.html#installation) |\n[\ud83d\udc40 Model Zoo](https://mmpretrain.readthedocs.io/en/latest/modelzoo_statistics.html) |\n[\ud83c\udd95 Update News](https://mmpretrain.readthedocs.io/en/latest/notes/changelog.html) |\n[\ud83e\udd14 Reporting Issues](https://github.com/open-mmlab/mmpretrain/issues/new/choose)\n\n<img src=\"https://user-images.githubusercontent.com/36138628/230307505-4727ad0a-7d71-4069-939d-b499c7e272b7.png\" width=\"400\"/>\n\nEnglish | [\u7b80\u4f53\u4e2d\u6587](/README_zh-CN.md)\n\n</div>\n\n</div>\n\n<div align=\"center\">\n <a href=\"https://openmmlab.medium.com/\" style=\"text-decoration:none;\">\n <img src=\"https://user-images.githubusercontent.com/25839884/219255827-67c1a27f-f8c5-46a9-811d-5e57448c61d1.png\" width=\"3%\" alt=\"\" /></a>\n <img src=\"https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png\" width=\"3%\" alt=\"\" />\n <a href=\"https://discord.gg/raweFPmdzG\" style=\"text-decoration:none;\">\n <img src=\"https://user-images.githubusercontent.com/25839884/218347213-c080267f-cbb6-443e-8532-8e1ed9a58ea9.png\" width=\"3%\" alt=\"\" /></a>\n <img src=\"https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png\" width=\"3%\" alt=\"\" />\n <a href=\"https://twitter.com/OpenMMLab\" style=\"text-decoration:none;\">\n <img src=\"https://user-images.githubusercontent.com/25839884/218346637-d30c8a0f-3eba-4699-8131-512fb06d46db.png\" width=\"3%\" alt=\"\" /></a>\n <img src=\"https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png\" width=\"3%\" alt=\"\" />\n <a href=\"https://www.youtube.com/openmmlab\" style=\"text-decoration:none;\">\n <img src=\"https://user-images.githubusercontent.com/25839884/218346691-ceb2116a-465a-40af-8424-9f30d2348ca9.png\" width=\"3%\" alt=\"\" /></a>\n <img src=\"https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png\" width=\"3%\" alt=\"\" />\n <a href=\"https://space.bilibili.com/1293512903\" style=\"text-decoration:none;\">\n <img src=\"https://user-images.githubusercontent.com/25839884/219026751-d7d14cce-a7c9-4e82-9942-8375fca65b99.png\" width=\"3%\" alt=\"\" /></a>\n <img src=\"https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png\" width=\"3%\" alt=\"\" />\n <a href=\"https://www.zhihu.com/people/openmmlab\" style=\"text-decoration:none;\">\n <img src=\"https://user-images.githubusercontent.com/25839884/219026120-ba71e48b-6e94-4bd4-b4e9-b7d175b5e362.png\" width=\"3%\" alt=\"\" /></a>\n</div>\n\n## Introduction\n\nMMPreTrain is an open source pre-training toolbox based on PyTorch. It is a part of the [OpenMMLab](https://openmmlab.com/) project.\n\nThe `main` branch works with **PyTorch 1.8+**.\n\n### Major features\n\n- Various backbones and pretrained models\n- Rich training strategies (supervised learning, self-supervised learning, multi-modality learning etc.)\n- Bag of training tricks\n- Large-scale training configs\n- High efficiency and extensibility\n- Powerful toolkits for model analysis and experiments\n- Various out-of-box inference tasks.\n - Image Classification\n - Image Caption\n - Visual Question Answering\n - Visual Grounding\n - Retrieval (Image-To-Image, Text-To-Image, Image-To-Text)\n\nhttps://github.com/open-mmlab/mmpretrain/assets/26739999/e4dcd3a2-f895-4d1b-a351-fbc74a04e904\n\n## What's new\n\n\ud83c\udf1f v1.2.0 was released in 04/01/2023\n\n- Support LLaVA 1.5.\n- Implement of RAM with a gradio interface.\n\n\ud83c\udf1f v1.1.0 was released in 12/10/2023\n\n- Support Mini-GPT4 training and provide a Chinese model (based on Baichuan-7B)\n- Support zero-shot classification based on CLIP.\n\n\ud83c\udf1f v1.0.0 was released in 04/07/2023\n\n- Support inference of more **multi-modal** algorithms, such as [**LLaVA**](./configs/llava/), [**MiniGPT-4**](./configs/minigpt4), [**Otter**](./configs/otter/), etc.\n- Support around **10 multi-modal** datasets!\n- Add [**iTPN**](./configs/itpn/), [**SparK**](./configs/spark/) self-supervised learning algorithms.\n- Provide examples of [New Config](./mmpretrain/configs/) and [DeepSpeed/FSDP with FlexibleRunner](./configs/mae/benchmarks/). Here are the documentation links of [New Config](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta) and [DeepSpeed/FSDP with FlexibleRunner](https://mmengine.readthedocs.io/en/latest/api/generated/mmengine.runner.FlexibleRunner.html#mmengine.runner.FlexibleRunner).\n\n\ud83c\udf1f Upgrade from MMClassification to MMPreTrain\n\n- Integrated Self-supervised learning algorithms from **MMSelfSup**, such as **MAE**, **BEiT**, etc.\n- Support **RIFormer**, a simple but effective vision backbone by removing token mixer.\n- Refactor dataset pipeline visualization.\n- Support **LeViT**, **XCiT**, **ViG**, **ConvNeXt-V2**, **EVA**, **RevViT**, **EfficientnetV2**, **CLIP**, **TinyViT** and **MixMIM** backbones.\n\nThis release introduced a brand new and flexible training & test engine, but it's still in progress. Welcome\nto try according to [the documentation](https://mmpretrain.readthedocs.io/en/latest/).\n\nAnd there are some BC-breaking changes. Please check [the migration tutorial](https://mmpretrain.readthedocs.io/en/latest/migration.html).\n\nPlease refer to [changelog](https://mmpretrain.readthedocs.io/en/latest/notes/changelog.html) for more details and other release history.\n\n## Installation\n\nBelow are quick steps for installation:\n\n```shell\nconda create -n open-mmlab python=3.8 pytorch==1.10.1 torchvision==0.11.2 cudatoolkit=11.3 -c pytorch -y\nconda activate open-mmlab\npip install openmim\ngit clone https://github.com/open-mmlab/mmpretrain.git\ncd mmpretrain\nmim install -e .\n```\n\nPlease refer to [installation documentation](https://mmpretrain.readthedocs.io/en/latest/get_started.html) for more detailed installation and dataset preparation.\n\nFor multi-modality models support, please install the extra dependencies by:\n\n```shell\nmim install -e \".[multimodal]\"\n```\n\n## User Guides\n\nWe provided a series of tutorials about the basic usage of MMPreTrain for new users:\n\n- [Learn about Configs](https://mmpretrain.readthedocs.io/en/latest/user_guides/config.html)\n- [Prepare Dataset](https://mmpretrain.readthedocs.io/en/latest/user_guides/dataset_prepare.html)\n- [Inference with existing models](https://mmpretrain.readthedocs.io/en/latest/user_guides/inference.html)\n- [Train](https://mmpretrain.readthedocs.io/en/latest/user_guides/train.html)\n- [Test](https://mmpretrain.readthedocs.io/en/latest/user_guides/test.html)\n- [Downstream tasks](https://mmpretrain.readthedocs.io/en/latest/user_guides/downstream.html)\n\nFor more information, please refer to [our documentation](https://mmpretrain.readthedocs.io/en/latest/).\n\n## Model zoo\n\nResults and models are available in the [model zoo](https://mmpretrain.readthedocs.io/en/latest/modelzoo_statistics.html).\n\n<div align=\"center\">\n <b>Overview</b>\n</div>\n<table align=\"center\">\n <tbody>\n <tr align=\"center\" valign=\"bottom\">\n <td>\n <b>Supported Backbones</b>\n </td>\n <td>\n <b>Self-supervised Learning</b>\n </td>\n <td>\n <b>Multi-Modality Algorithms</b>\n </td>\n <td>\n <b>Others</b>\n </td>\n </tr>\n <tr valign=\"top\">\n <td>\n <ul>\n <li><a href=\"configs/vgg\">VGG</a></li>\n <li><a href=\"configs/resnet\">ResNet</a></li>\n <li><a href=\"configs/resnext\">ResNeXt</a></li>\n <li><a href=\"configs/seresnet\">SE-ResNet</a></li>\n <li><a href=\"configs/seresnet\">SE-ResNeXt</a></li>\n <li><a href=\"configs/regnet\">RegNet</a></li>\n <li><a href=\"configs/shufflenet_v1\">ShuffleNet V1</a></li>\n <li><a href=\"configs/shufflenet_v2\">ShuffleNet V2</a></li>\n <li><a href=\"configs/mobilenet_v2\">MobileNet V2</a></li>\n <li><a href=\"configs/mobilenet_v3\">MobileNet V3</a></li>\n <li><a href=\"configs/swin_transformer\">Swin-Transformer</a></li>\n <li><a href=\"configs/swin_transformer_v2\">Swin-Transformer V2</a></li>\n <li><a href=\"configs/repvgg\">RepVGG</a></li>\n <li><a href=\"configs/vision_transformer\">Vision-Transformer</a></li>\n <li><a href=\"configs/tnt\">Transformer-in-Transformer</a></li>\n <li><a href=\"configs/res2net\">Res2Net</a></li>\n <li><a href=\"configs/mlp_mixer\">MLP-Mixer</a></li>\n <li><a href=\"configs/deit\">DeiT</a></li>\n <li><a href=\"configs/deit3\">DeiT-3</a></li>\n <li><a href=\"configs/conformer\">Conformer</a></li>\n <li><a href=\"configs/t2t_vit\">T2T-ViT</a></li>\n <li><a href=\"configs/twins\">Twins</a></li>\n <li><a href=\"configs/efficientnet\">EfficientNet</a></li>\n <li><a href=\"configs/edgenext\">EdgeNeXt</a></li>\n <li><a href=\"configs/convnext\">ConvNeXt</a></li>\n <li><a href=\"configs/hrnet\">HRNet</a></li>\n <li><a href=\"configs/van\">VAN</a></li>\n <li><a href=\"configs/convmixer\">ConvMixer</a></li>\n <li><a href=\"configs/cspnet\">CSPNet</a></li>\n <li><a href=\"configs/poolformer\">PoolFormer</a></li>\n <li><a href=\"configs/inception_v3\">Inception V3</a></li>\n <li><a href=\"configs/mobileone\">MobileOne</a></li>\n <li><a href=\"configs/efficientformer\">EfficientFormer</a></li>\n <li><a href=\"configs/mvit\">MViT</a></li>\n <li><a href=\"configs/hornet\">HorNet</a></li>\n <li><a href=\"configs/mobilevit\">MobileViT</a></li>\n <li><a href=\"configs/davit\">DaViT</a></li>\n <li><a href=\"configs/replknet\">RepLKNet</a></li>\n <li><a href=\"configs/beit\">BEiT</a></li>\n <li><a href=\"configs/mixmim\">MixMIM</a></li>\n <li><a href=\"configs/efficientnet_v2\">EfficientNet V2</a></li>\n <li><a href=\"configs/revvit\">RevViT</a></li>\n <li><a href=\"configs/convnext_v2\">ConvNeXt V2</a></li>\n <li><a href=\"configs/vig\">ViG</a></li>\n <li><a href=\"configs/xcit\">XCiT</a></li>\n <li><a href=\"configs/levit\">LeViT</a></li>\n <li><a href=\"configs/riformer\">RIFormer</a></li>\n <li><a href=\"configs/glip\">GLIP</a></li>\n <li><a href=\"configs/sam\">ViT SAM</a></li>\n <li><a href=\"configs/eva02\">EVA02</a></li>\n <li><a href=\"configs/dinov2\">DINO V2</a></li>\n <li><a href=\"configs/hivit\">HiViT</a></li>\n </ul>\n </td>\n <td>\n <ul>\n <li><a href=\"configs/mocov2\">MoCo V1 (CVPR'2020)</a></li>\n <li><a href=\"configs/simclr\">SimCLR (ICML'2020)</a></li>\n <li><a href=\"configs/mocov2\">MoCo V2 (arXiv'2020)</a></li>\n <li><a href=\"configs/byol\">BYOL (NeurIPS'2020)</a></li>\n <li><a href=\"configs/swav\">SwAV (NeurIPS'2020)</a></li>\n <li><a href=\"configs/densecl\">DenseCL (CVPR'2021)</a></li>\n <li><a href=\"configs/simsiam\">SimSiam (CVPR'2021)</a></li>\n <li><a href=\"configs/barlowtwins\">Barlow Twins (ICML'2021)</a></li>\n <li><a href=\"configs/mocov3\">MoCo V3 (ICCV'2021)</a></li>\n <li><a href=\"configs/beit\">BEiT (ICLR'2022)</a></li>\n <li><a href=\"configs/mae\">MAE (CVPR'2022)</a></li>\n <li><a href=\"configs/simmim\">SimMIM (CVPR'2022)</a></li>\n <li><a href=\"configs/maskfeat\">MaskFeat (CVPR'2022)</a></li>\n <li><a href=\"configs/cae\">CAE (arXiv'2022)</a></li>\n <li><a href=\"configs/milan\">MILAN (arXiv'2022)</a></li>\n <li><a href=\"configs/beitv2\">BEiT V2 (arXiv'2022)</a></li>\n <li><a href=\"configs/eva\">EVA (CVPR'2023)</a></li>\n <li><a href=\"configs/mixmim\">MixMIM (arXiv'2022)</a></li>\n <li><a href=\"configs/itpn\">iTPN (CVPR'2023)</a></li>\n <li><a href=\"configs/spark\">SparK (ICLR'2023)</a></li>\n <li><a href=\"configs/mff\">MFF (ICCV'2023)</a></li>\n </ul>\n </td>\n <td>\n <ul>\n <li><a href=\"configs/blip\">BLIP (arxiv'2022)</a></li>\n <li><a href=\"configs/blip2\">BLIP-2 (arxiv'2023)</a></li>\n <li><a href=\"configs/ofa\">OFA (CoRR'2022)</a></li>\n <li><a href=\"configs/flamingo\">Flamingo (NeurIPS'2022)</a></li>\n <li><a href=\"configs/chinese_clip\">Chinese CLIP (arxiv'2022)</a></li>\n <li><a href=\"configs/minigpt4\">MiniGPT-4 (arxiv'2023)</a></li>\n <li><a href=\"configs/llava\">LLaVA (arxiv'2023)</a></li>\n <li><a href=\"configs/otter\">Otter (arxiv'2023)</a></li>\n </ul>\n </td>\n <td>\n Image Retrieval Task:\n <ul>\n <li><a href=\"configs/arcface\">ArcFace (CVPR'2019)</a></li>\n </ul>\n Training&Test Tips:\n <ul>\n <li><a href=\"https://arxiv.org/abs/1909.13719\">RandAug</a></li>\n <li><a href=\"https://arxiv.org/abs/1805.09501\">AutoAug</a></li>\n <li><a href=\"mmpretrain/datasets/samplers/repeat_aug.py\">RepeatAugSampler</a></li>\n <li><a href=\"mmpretrain/models/tta/score_tta.py\">TTA</a></li>\n <li>...</li>\n </ul>\n </td>\n </tbody>\n</table>\n\n## Contributing\n\nWe appreciate all contributions to improve MMPreTrain.\nPlease refer to [CONTRUBUTING](https://mmpretrain.readthedocs.io/en/latest/notes/contribution_guide.html) for the contributing guideline.\n\n## Acknowledgement\n\nMMPreTrain 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.\nWe 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.\n\n## Citation\n\nIf you find this project useful in your research, please consider cite:\n\n```BibTeX\n@misc{2023mmpretrain,\n title={OpenMMLab's Pre-training Toolbox and Benchmark},\n author={MMPreTrain Contributors},\n howpublished = {\\url{https://github.com/open-mmlab/mmpretrain}},\n year={2023}\n}\n```\n\n## License\n\nThis project is released under the [Apache 2.0 license](LICENSE).\n\n## Projects in OpenMMLab\n\n- [MMEngine](https://github.com/open-mmlab/mmengine): OpenMMLab foundational library for training deep learning models.\n- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision.\n- [MIM](https://github.com/open-mmlab/mim): MIM installs OpenMMLab packages.\n- [MMEval](https://github.com/open-mmlab/mmeval): A unified evaluation library for multiple machine learning libraries.\n- [MMPreTrain](https://github.com/open-mmlab/mmpretrain): OpenMMLab pre-training toolbox and benchmark.\n- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab detection toolbox and benchmark.\n- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection.\n- [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab rotated object detection toolbox and benchmark.\n- [MMYOLO](https://github.com/open-mmlab/mmyolo): OpenMMLab YOLO series toolbox and benchmark.\n- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab semantic segmentation toolbox and benchmark.\n- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab text detection, recognition, and understanding toolbox.\n- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark.\n- [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 3D human parametric model toolbox and benchmark.\n- [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab self-supervised learning toolbox and benchmark.\n- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab model compression toolbox and benchmark.\n- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab fewshot learning toolbox and benchmark.\n- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab's next-generation action understanding toolbox and benchmark.\n- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab video perception toolbox and benchmark.\n- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab optical flow toolbox and benchmark.\n- [MMagic](https://github.com/open-mmlab/mmagic): Open**MM**Lab **A**dvanced, **G**enerative and **I**ntelligent **C**reation toolbox.\n- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab image and video generative models toolbox.\n- [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab model deployment framework.\n- [Playground](https://github.com/open-mmlab/playground): A central hub for gathering and showcasing amazing projects built upon OpenMMLab.\n\n\n",
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