- 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**](./configs/llava/), [**MiniGPT-4**](./configs/minigpt4), [**Otter**](./configs/otter/), etc.
- 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).
🌟 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.
Please refer to [installation documentation](https://mmpretrain.readthedocs.io/en/latest/get_started.html) for more detailed installation and dataset preparation.
#### 编译环境准备
提供基于fastpt不转码编译：
For multi-modality models support, please install the extra dependencies by:
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:
2. 编译whl包并安装
python3 setup.py -v bdist_wheel
pip install dist/mmpretrain*
```BibTeX
3. 源码编译安装
@misc{2023mmpretrain,
python3 setup.py install
title={OpenMMLab's Pre-training Toolbox and Benchmark},
- 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**](./configs/llava/), [**MiniGPT-4**](./configs/minigpt4), [**Otter**](./configs/otter/), etc.
- 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).
🌟 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.
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)
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},
