[Docs] update readme (#474)
* update readme * update readme zh-cn * fix lint * refine * update simmim and mocov3pull/490/head
Yixiao Fang
GitHub
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README.md
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README.md
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@ -65,6 +65,8 @@ The master branch works with **PyTorch 1.5** or higher.
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## What's New
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### Stable version
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MMSelfSup **v0.9.2** was released in 28/07/2022.
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Highlights of the new version:
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@ -75,17 +77,33 @@ Please refer to [changelog.md](docs/en/changelog.md) for details and release his
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Differences between MMSelfSup and OpenSelfSup codebases can be found in [compatibility.md](docs/en/compatibility.md).
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### Preview of 1.x version
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A brand new version of **MMSelfSup v1.0.0rc1** was released in 01/09/2022:
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Highlights of the new version:
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- Based on [MMEngine](https://github.com/open-mmlab/mmengine) and [MMCV](https://github.com/open-mmlab/mmcv/tree/2.x).
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- Released with refactor.
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- Refine all [documents](https://mmselfsup.readthedocs.io/en/1.x/).
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- Support `MAE`, `SimMIM`, `MoCoV3` with different pre-training epochs and backbones of different scales.
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- More concise API.
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- More powerful data pipeline.
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- Higher accurcy for some algorithms.
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Find more new features in [1.x branch](https://github.com/open-mmlab/mmselfsup/tree/1.x). Issues and PRs are welcome!
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## Installation
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MMSelfSup depends on [PyTorch](https://pytorch.org/), [MMCV](https://github.com/open-mmlab/mmcv) and [MMClassification](https://github.com/open-mmlab/mmclassification).
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MMSelfSup relies on [PyTorch](https://pytorch.org/), [MMCV](https://github.com/open-mmlab/mmcv) and [MMClassification](https://github.com/open-mmlab/mmclassification).
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Please refer to [install.md](docs/en/install.md) for more detailed instruction.
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## Get Started
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Please refer to [prepare_data.md](docs/en/prepare_data.md) for dataset preparation and [get_started.md](docs/en/get_started.md) for the basic usage of MMSelfSup.
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Please refer to [prepare_data.md](docs/en/prepare_data.md) for dataset preparation, [get_started.md](docs/en/get_started.md) for the basic usage and [benchmarks.md](docs/en/tutorials/6_benchmarks.md) for running benchmarks.
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We also provides tutorials for more details:
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We also provides more detailed tutorials:
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- [config](docs/en/tutorials/0_config.md)
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- [add new dataset](docs/en/tutorials/1_new_dataset.md)
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@ -93,7 +111,6 @@ We also provides tutorials for more details:
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- [add new module](docs/en/tutorials/3_new_module.md)
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- [customize schedules](docs/en/tutorials/4_schedule.md)
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- [customize runtime](docs/en/tutorials/5_runtime.md)
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- [benchmarks](docs/en/tutorials/6_benchmarks.md)
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Besides, we provide [colab tutorial](https://github.com/open-mmlab/mmselfsup/blob/master/demo/mmselfsup_colab_tutorial.ipynb) for basic usage.
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@ -105,23 +122,23 @@ Please refer to [model_zoo.md](docs/en/model_zoo.md) for a comprehensive set of
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Supported algorithms:
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- [x] [Relative Location (ICCV'2015)](https://arxiv.org/abs/1505.05192)
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- [x] [Rotation Prediction (ICLR'2018)](https://arxiv.org/abs/1803.07728)
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- [x] [DeepCluster (ECCV'2018)](https://arxiv.org/abs/1807.05520)
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- [x] [NPID (CVPR'2018)](https://arxiv.org/abs/1805.01978)
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- [x] [ODC (CVPR'2020)](https://arxiv.org/abs/2006.10645)
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- [x] [MoCo v1 (CVPR'2020)](https://arxiv.org/abs/1911.05722)
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- [x] [SimCLR (ICML'2020)](https://arxiv.org/abs/2002.05709)
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- [x] [MoCo v2 (ArXiv'2020)](https://arxiv.org/abs/2003.04297)
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- [x] [BYOL (NeurIPS'2020)](https://arxiv.org/abs/2006.07733)
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- [x] [SwAV (NeurIPS'2020)](https://arxiv.org/abs/2006.09882)
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- [x] [DenseCL (CVPR'2021)](https://arxiv.org/abs/2011.09157)
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- [x] [SimSiam (CVPR'2021)](https://arxiv.org/abs/2011.10566)
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- [x] [Barlow Twins (ICML'2021)](https://arxiv.org/abs/2103.03230)
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- [x] [MoCo v3 (ICCV'2021)](https://arxiv.org/abs/2104.02057)
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- [x] [MAE (CVPR'2022)](https://arxiv.org/abs/2111.06377)
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- [x] [SimMIM (CVPR'2022)](https://arxiv.org/abs/2111.09886)
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- [x] [CAE (ArXiv'2022)](https://arxiv.org/abs/2202.03026)
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- [x] [Relative Location (ICCV'2015)](https://github.com/open-mmlab/mmselfsup/tree/master/configs/selfsup/relative_loc)
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- [x] [Rotation Prediction (ICLR'2018)](https://github.com/open-mmlab/mmselfsup/tree/master/configs/selfsup/rotation_pred)
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- [x] [DeepCluster (ECCV'2018)](https://github.com/open-mmlab/mmselfsup/tree/master/configs/selfsup/deepcluster)
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- [x] [NPID (CVPR'2018)](https://github.com/open-mmlab/mmselfsup/tree/master/configs/selfsup/npid)
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- [x] [ODC (CVPR'2020)](https://github.com/open-mmlab/mmselfsup/tree/master/configs/selfsup/odc)
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- [x] [MoCo v1 (CVPR'2020)](https://github.com/open-mmlab/mmselfsup/tree/master/configs/selfsup/mocov1)
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- [x] [SimCLR (ICML'2020)](https://github.com/open-mmlab/mmselfsup/tree/master/configs/selfsup/simclr)
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- [x] [MoCo v2 (ArXiv'2020)](https://github.com/open-mmlab/mmselfsup/tree/master/configs/selfsup/byol)
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- [x] [BYOL (NeurIPS'2020)](https://github.com/open-mmlab/mmselfsup/tree/master/configs/selfsup/mocov2)
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- [x] [SwAV (NeurIPS'2020)](https://github.com/open-mmlab/mmselfsup/tree/master/configs/selfsup/swav)
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- [x] [DenseCL (CVPR'2021)](https://github.com/open-mmlab/mmselfsup/tree/master/configs/selfsup/densecl)
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- [x] [SimSiam (CVPR'2021)](https://github.com/open-mmlab/mmselfsup/tree/master/configs/selfsup/simsiam)
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- [x] [Barlow Twins (ICML'2021)](https://github.com/open-mmlab/mmselfsup/tree/master/configs/selfsup/barlowtwins)
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- [x] [MoCo v3 (ICCV'2021)](https://github.com/open-mmlab/mmselfsup/tree/master/configs/selfsup/mocov3)
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- [x] [MAE (CVPR'2022)](https://github.com/open-mmlab/mmselfsup/tree/master/configs/selfsup/mae)
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- [x] [SimMIM (CVPR'2022)](https://github.com/open-mmlab/mmselfsup/tree/master/configs/selfsup/simmim)
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- [x] [CAE (ArXiv'2022)](https://github.com/open-mmlab/mmselfsup/tree/master/configs/selfsup/cae)
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More algorithms are in our plan.
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@ -65,6 +65,8 @@ MMSelfSup 是一个基于 PyTorch 实现的开源自监督表征学习工具箱
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## 更新
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### 稳定版本
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最新的 **v0.9.2** 版本已经在 2022.07.28 发布。
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新版本亮点：
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@ -75,6 +77,22 @@ MMSelfSup 是一个基于 PyTorch 实现的开源自监督表征学习工具箱
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MMSelfSup 和 OpenSelfSup 的不同点写在 [对比文档](docs/en/compatibility.md) 中。
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### 1.x 预览版本
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全新的 **MMSelfSup v1.0.0rc1** 版本已在 2022.09.01 发布。
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新版本亮点：
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- 基于全新的 [MMEngine](https://github.com/open-mmlab/mmengine) 和 [MMCV](https://github.com/open-mmlab/mmcv/tree/2.x)。
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- 代码库重构，统一接口。
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- 完善了新版本 [文档](https://mmselfsup.readthedocs.io/en/1.x/)。
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- 支持了不同训练时间、不同尺寸的 `MAE`, `SimMIM`, `MoCoV3` 的预训练模型。
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- 更加简洁的 API。
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- 更加强大的数据管道。
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- 部分模型具有更高的准确率。
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在 [1.x 分支](https://github.com/open-mmlab/mmselfsup/tree/1.x) 查看更多新特性。 欢迎大家提 Issues 和 PRs!
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## 安装
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MMSelfSup 依赖 [PyTorch](https://pytorch.org/), [MMCV](https://github.com/open-mmlab/mmcv) 和 [MMClassification](https://github.com/open-mmlab/mmclassification).
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@ -83,7 +101,7 @@ MMSelfSup 依赖 [PyTorch](https://pytorch.org/), [MMCV](https://github.com/open
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## 快速入门
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请参考 [准备数据](docs/zh_cn/prepare_data.md) 准备数据集和 [入门指南](docs/zh_cn/get_started.md) 获取 MMSelfSup 的基本使用方法.
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请参考 [准备数据](docs/zh_cn/prepare_data.md) 准备数据集, [入门指南](docs/zh_cn/get_started.md) 获取 MMSelfSup 的基本使用方法和 [基准测试](docs/zh_cn/tutorials/6_benchmarks.md) 来运行下游任务。
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我们也提供了更加全面的教程，包括:
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@ -93,7 +111,6 @@ MMSelfSup 依赖 [PyTorch](https://pytorch.org/), [MMCV](https://github.com/open
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- [添加新模块](docs/zh_cn/tutorials/3_new_module.md)
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- [自定义流程](docs/zh_cn/tutorials/4_schedule.md)
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- [自定义运行](docs/zh_cn/tutorials/5_runtime.md)
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- [基准测试](docs/zh_cn/tutorials/6_benchmarks.md)
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另外，我们提供了 [colab 教程](https://github.com/open-mmlab/mmselfsup/blob/master/demo/mmselfsup_colab_tutorial.ipynb)。
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@ -105,23 +122,23 @@ MMSelfSup 依赖 [PyTorch](https://pytorch.org/), [MMCV](https://github.com/open
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目前已支持的算法:
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- [x] [Relative Location (ICCV'2015)](https://arxiv.org/abs/1505.05192)
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- [x] [Rotation Prediction (ICLR'2018)](https://arxiv.org/abs/1803.07728)
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- [x] [DeepCLuster (ECCV'2018)](https://arxiv.org/abs/1807.05520)
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- [x] [NPID (CVPR'2018)](https://arxiv.org/abs/1805.01978)
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- [x] [ODC (CVPR'2020)](https://arxiv.org/abs/2006.10645)
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- [x] [MoCo v1 (CVPR'2020)](https://arxiv.org/abs/1911.05722)
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- [x] [SimCLR (ICML'2020)](https://arxiv.org/abs/2002.05709)
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- [x] [MoCo v2 (ArXiv'2020)](https://arxiv.org/abs/2003.04297)
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- [x] [BYOL (NeurIPS'2020)](https://arxiv.org/abs/2006.07733)
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- [x] [SwAV (NeurIPS'2020)](https://arxiv.org/abs/2006.09882)
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- [x] [DenseCL (CVPR'2021)](https://arxiv.org/abs/2011.09157)
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- [x] [SimSiam (CVPR'2021)](https://arxiv.org/abs/2011.10566)
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- [x] [Barlow Twins (ICML'2021)](https://arxiv.org/abs/2103.03230)
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- [x] [MoCo v3 (ICCV'2021)](https://arxiv.org/abs/2104.02057)
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- [x] [MAE (CVPR'2022)](https://arxiv.org/abs/2111.06377)
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- [x] [SimMIM (CVPR'2022)](https://arxiv.org/abs/2111.09886)
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- [x] [CAE (ArXiv'2022)](https://arxiv.org/abs/2202.03026)
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- [x] [Relative Location (ICCV'2015)](https://github.com/open-mmlab/mmselfsup/tree/master/configs/selfsup/relative_loc)
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- [x] [Rotation Prediction (ICLR'2018)](https://github.com/open-mmlab/mmselfsup/tree/master/configs/selfsup/rotation_pred)
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- [x] [DeepCluster (ECCV'2018)](https://github.com/open-mmlab/mmselfsup/tree/master/configs/selfsup/deepcluster)
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- [x] [NPID (CVPR'2018)](https://github.com/open-mmlab/mmselfsup/tree/master/configs/selfsup/npid)
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- [x] [ODC (CVPR'2020)](https://github.com/open-mmlab/mmselfsup/tree/master/configs/selfsup/odc)
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- [x] [MoCo v1 (CVPR'2020)](https://github.com/open-mmlab/mmselfsup/tree/master/configs/selfsup/mocov1)
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- [x] [SimCLR (ICML'2020)](https://github.com/open-mmlab/mmselfsup/tree/master/configs/selfsup/simclr)
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- [x] [MoCo v2 (ArXiv'2020)](https://github.com/open-mmlab/mmselfsup/tree/master/configs/selfsup/byol)
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- [x] [BYOL (NeurIPS'2020)](https://github.com/open-mmlab/mmselfsup/tree/master/configs/selfsup/mocov2)
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- [x] [SwAV (NeurIPS'2020)](https://github.com/open-mmlab/mmselfsup/tree/master/configs/selfsup/swav)
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- [x] [DenseCL (CVPR'2021)](https://github.com/open-mmlab/mmselfsup/tree/master/configs/selfsup/densecl)
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- [x] [SimSiam (CVPR'2021)](https://github.com/open-mmlab/mmselfsup/tree/master/configs/selfsup/simsiam)
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- [x] [Barlow Twins (ICML'2021)](https://github.com/open-mmlab/mmselfsup/tree/master/configs/selfsup/barlowtwins)
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- [x] [MoCo v3 (ICCV'2021)](https://github.com/open-mmlab/mmselfsup/tree/master/configs/selfsup/mocov3)
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- [x] [MAE (CVPR'2022)](https://github.com/open-mmlab/mmselfsup/tree/master/configs/selfsup/mae)
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- [x] [SimMIM (CVPR'2022)](https://github.com/open-mmlab/mmselfsup/tree/master/configs/selfsup/simmim)
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- [x] [CAE (ArXiv'2022)](https://github.com/open-mmlab/mmselfsup/tree/master/configs/selfsup/cae)
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更多的算法实现已经在我们的计划中。
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