upload mmpretrain

CITATION.cff

+cff-version: 1.2.0
+message: "If you use this software, please cite it as below."
+title: "OpenMMLab's Pre-training Toolbox and Benchmark"
+authors:
+  - name: "MMPreTrain Contributors"
+version: 0.15.0
+date-released: 2023-04-06
+repository-code: "https://github.com/open-mmlab/mmpretrain"
+license: Apache-2.0

CONTRIBUTING.md

+# Contributing to MMPreTrain
+
+- [Contributing to MMPreTrain](#contributing-to-mmpretrain)
+  - [Workflow](#workflow)
+  - [Code style](#code-style)
+    - [Python](#python)
+    - [C++ and CUDA](#c-and-cuda)
+  - [Pre-commit Hook](#pre-commit-hook)
+
+Thanks for your interest in contributing to MMPreTrain! All kinds of contributions are welcome, including but not limited to the following.
+
+- Fix typo or bugs
+- Add documentation or translate the documentation into other languages
+- Add new features and components
+
+## Workflow
+
+We recommend the potential contributors follow this workflow for contribution.
+
+1. Fork and pull the latest MMPreTrain repository, follow [get started](https://mmpretrain.readthedocs.io/en/latest/get_started.html) to setup the environment.
+2. Checkout a new branch (**do not use the master or dev branch** for PRs)
+
+```bash
+git checkout -b xxxx # xxxx is the name of new branch
+```
+
+3. Edit the related files follow the code style mentioned below
+4. Use [pre-commit hook](https://pre-commit.com/) to check and format your changes.
+5. Commit your changes
+6. Create a PR with related information
+
+## Code style
+
+### Python
+
+We adopt [PEP8](https://www.python.org/dev/peps/pep-0008/) as the preferred code style.
+
+We use the following tools for linting and formatting:
+
+- [flake8](https://github.com/PyCQA/flake8): A wrapper around some linter tools.
+- [isort](https://github.com/timothycrosley/isort): A Python utility to sort imports.
+- [yapf](https://github.com/google/yapf): A formatter for Python files.
+- [codespell](https://github.com/codespell-project/codespell): A Python utility to fix common misspellings in text files.
+- [mdformat](https://github.com/executablebooks/mdformat): Mdformat is an opinionated Markdown formatter that can be used to enforce a consistent style in Markdown files.
+- [docformatter](https://github.com/myint/docformatter): A formatter to format docstring.
+
+Style configurations of yapf and isort can be found in [setup.cfg](https://github.com/open-mmlab/mmpretrain/blob/main/setup.cfg).
+
+### C++ and CUDA
+
+We follow the [Google C++ Style Guide](https://google.github.io/styleguide/cppguide.html).
+
+## Pre-commit Hook
+
+We use [pre-commit hook](https://pre-commit.com/) that checks and formats for `flake8`, `yapf`, `isort`, `trailing whitespaces`, `markdown files`,
+fixes `end-of-files`, `double-quoted-strings`, `python-encoding-pragma`, `mixed-line-ending`, sorts `requirments.txt` automatically on every commit.
+The config for a pre-commit hook is stored in [.pre-commit-config](https://github.com/open-mmlab/mmpretrain/blob/main/.pre-commit-config.yaml).
+
+After you clone the repository, you will need to install initialize pre-commit hook.
+
+```shell
+pip install -U pre-commit
+```
+
+From the repository folder
+
+```shell
+pre-commit install
+```
+
+After this on every commit check code linters and formatter will be enforced.
+
+> Before you create a PR, make sure that your code lints and is formatted by yapf.

LICENSE

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+   APPENDIX: How to apply the Apache License to your work.
+
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MANIFEST.in

+include requirements/*.txt
+include mmpretrain/.mim/model-index.yml
+include mmpretrain/.mim/dataset-index.yml
+recursive-include mmpretrain/.mim/configs *.py *.yml
+recursive-include mmpretrain/.mim/tools *.py *.sh

README_zh-CN.md

+<div align="center">
+
+<img src="resources/mmpt-logo.png" width="600"/>
+  <div>&nbsp;</div>
+  <div align="center">
+    <b><font size="5">OpenMMLab 官网</font></b>
+    <sup>
+      <a href="https://openmmlab.com">
+        <i><font size="4">HOT</font></i>
+      </a>
+    </sup>
+    &nbsp;&nbsp;&nbsp;&nbsp;
+    <b><font size="5">OpenMMLab 开放平台</font></b>
+    <sup>
+      <a href="https://platform.openmmlab.com">
+        <i><font size="4">TRY IT OUT</font></i>
+      </a>
+    </sup>
+  </div>
+  <div>&nbsp;</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/zh_CN/latest/)
+[![Build Status](https://github.com/open-mmlab/mmpretrain/workflows/build/badge.svg)](https://github.com/open-mmlab/mmpretrain/actions)
+[![codecov](https://codecov.io/gh/open-mmlab/mmpretrain/branch/main/graph/badge.svg)](https://codecov.io/gh/open-mmlab/mmpretrain)
+[![license](https://img.shields.io/github/license/open-mmlab/mmpretrain.svg)](https://github.com/open-mmlab/mmpretrain/blob/main/LICENSE)
+[![open issues](https://isitmaintained.com/badge/open/open-mmlab/mmpretrain.svg)](https://github.com/open-mmlab/mmpretrain/issues)
+[![issue resolution](https://isitmaintained.com/badge/resolution/open-mmlab/mmpretrain.svg)](https://github.com/open-mmlab/mmpretrain/issues)
+
+[📘 中文文档](https://mmpretrain.readthedocs.io/zh_CN/latest/) |
+[🛠️ 安装教程](https://mmpretrain.readthedocs.io/zh_CN/latest/get_started.html) |
+[👀 模型库](https://mmpretrain.readthedocs.io/zh_CN/latest/modelzoo_statistics.html) |
+[🆕 更新日志](https://mmpretrain.readthedocs.io/zh_CN/latest/notes/changelog.html) |
+[🤔 报告问题](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](/README.md) | 简体中文
+
+</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>
+  <img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
+  <a href="https://discord.gg/raweFPmdzG" style="text-decoration:none;">
+    <img src="https://user-images.githubusercontent.com/25839884/218347213-c080267f-cbb6-443e-8532-8e1ed9a58ea9.png" width="3%" alt="" /></a>
+  <img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
+  <a href="https://twitter.com/OpenMMLab" style="text-decoration:none;">
+    <img src="https://user-images.githubusercontent.com/25839884/218346637-d30c8a0f-3eba-4699-8131-512fb06d46db.png" width="3%" alt="" /></a>
+  <img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
+  <a href="https://www.youtube.com/openmmlab" style="text-decoration:none;">
+    <img src="https://user-images.githubusercontent.com/25839884/218346691-ceb2116a-465a-40af-8424-9f30d2348ca9.png" width="3%" alt="" /></a>
+  <img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
+  <a href="https://space.bilibili.com/1293512903" style="text-decoration:none;">
+    <img src="https://user-images.githubusercontent.com/25839884/219026751-d7d14cce-a7c9-4e82-9942-8375fca65b99.png" width="3%" alt="" /></a>
+  <img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
+  <a href="https://www.zhihu.com/people/openmmlab" style="text-decoration:none;">
+    <img src="https://user-images.githubusercontent.com/25839884/219026120-ba71e48b-6e94-4bd4-b4e9-b7d175b5e362.png" width="3%" alt="" /></a>
+</div>
+
+## Introduction
+
+MMPreTrain 是一款基于 PyTorch 的开源深度学习预训练工具箱，是 [OpenMMLab](https://openmmlab.com/) 项目的成员之一
+
+`主分支`代码目前支持 PyTorch 1.8 以上的版本。
+
+### 主要特性
+
+- 支持多样的主干网络与预训练模型
+- 支持多种训练策略（有监督学习，无监督学习，多模态学习等）
+- 提供多种训练技巧
+- 大量的训练配置文件
+- 高效率和高可扩展性
+- 功能强大的工具箱，有助于模型分析和实验
+- 支持多种开箱即用的推理任务
+  - 图像分类
+  - 图像描述（Image Caption）
+  - 视觉问答（Visual Question Answering）
+  - 视觉定位（Visual Grounding）
+  - 检索（图搜图，图搜文，文搜图）
+
+https://github.com/open-mmlab/mmpretrain/assets/26739999/e4dcd3a2-f895-4d1b-a351-fbc74a04e904
+
+## 更新日志
+
+🌟 2024/01/04 发布了 v1.2.0 版本
+
+- 支持了 LLaVA 1.5
+- 实现了一个 RAM 模型的 gradio 推理例程
+
+🌟 2023/10/12 发布了 v1.1.0 版本
+
+- 支持 Mini-GPT4 训练并提供一个基于 Baichuan-7B 的中文模型
+- 支持基于 CLIP 的零样本分类。
+
+🌟 2023/7/4 发布了 v1.0.0 版本
+
+- 支持更多**多模态**算法的推理, 例如 [**LLaVA**](./configs/llava/), [**MiniGPT-4**](./configs/minigpt4), [**Otter**](./configs/otter/) 等。
+- 支持约 **10 个多模态**数据集!
+- 添加自监督学习算法 [**iTPN**](./configs/itpn/), [**SparK**](./configs/spark/)。
+- 提供[新配置文件](./mmpretrain/configs/)和 [DeepSpeed/FSDP](./configs/mae/benchmarks/) 的样例。这是[新配置文件](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta) 和 [DeepSpeed/FSDP with FlexibleRunner](https://mmengine.readthedocs.io/en/latest/api/generated/mmengine.runner.FlexibleRunner.html#mmengine.runner.FlexibleRunner) 的文档链接。
+
+🌟 从 MMClassification 升级到 MMPreTrain
+
+- 整合来自 MMSelfSup 的自监督学习算法，例如 `MAE`, `BEiT` 等
+- 支持了 **RIFormer**，简单但有效的视觉主干网络，却移除了 token mixer
+- 重构数据管道可视化
+- 支持了 **LeViT**, **XCiT**, **ViG**, **ConvNeXt-V2**, **EVA**, **RevViT**, **EfficientnetV2**, **CLIP**, **TinyViT** 和 **MixMIM** 等骨干网络结构
+
+这个版本引入一个全新的，可扩展性强的训练和测试引擎，但目前仍在开发中。欢迎根据 [文档](https://mmpretrain.readthedocs.io/zh_CN/latest/) 进行试用。
+
+同时，新版本中存在一些与旧版本不兼容的修改。请查看 [迁移文档](https://mmpretrain.readthedocs.io/zh_CN/latest/migration.html) 来详细了解这些变动。
+
+发布历史和更新细节请参考 [更新日志](https://mmpretrain.readthedocs.io/zh_CN/latest/notes/changelog.html)。
+
+## 安装
+
+以下是安装的简要步骤：
+
+```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
+pip3 install openmim
+git clone https://github.com/open-mmlab/mmpretrain.git
+cd mmpretrain
+mim install -e .
+```
+
+更详细的步骤请参考 [安装指南](https://mmpretrain.readthedocs.io/zh_CN/latest/get_started.html) 进行安装。
+
+如果需要多模态模型，请使用如下方式安装额外的依赖：
+
+```shell
+mim install -e ".[multimodal]"
+```
+
+## 基础教程
+
+我们为新用户提供了一系列基础教程：
+
+- [学习配置文件](https://mmpretrain.readthedocs.io/zh_CN/latest/user_guides/config.html)
+- [准备数据集](https://mmpretrain.readthedocs.io/zh_CN/latest/user_guides/dataset_prepare.html)
+- [使用现有模型推理](https://mmpretrain.readthedocs.io/zh_CN/latest/user_guides/inference.html)
+- [训练](https://mmpretrain.readthedocs.io/zh_CN/latest/user_guides/train.html)
+- [测试](https://mmpretrain.readthedocs.io/zh_CN/latest/user_guides/test.html)
+- [下游任务](https://mmpretrain.readthedocs.io/zh_CN/latest/user_guides/downstream.html)
+
+关于更多的信息，请查阅我们的 [相关文档](https://mmpretrain.readthedocs.io/zh_CN/latest/)。
+
+## 模型库
+
+相关结果和模型可在 [模型库](https://mmpretrain.readthedocs.io/zh_CN/latest/modelzoo_statistics.html) 中获得。
+
+<div align="center">
+  <b>概览</b>
+</div>
+<table align="center">
+  <tbody>
+    <tr align="center" valign="bottom">
+      <td>
+        <b>支持的主干网络</b>
+      </td>
+      <td>
+        <b>自监督学习</b>
+      </td>
+      <td>
+        <b>多模态算法</b>
+      </td>
+      <td>
+        <b>其它</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/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>
+      图像检索任务：
+        <ul>
+        <li><a href="configs/arcface">ArcFace (CVPR'2019)</a></li>
+        </ul>
+      训练和测试 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>
+
+## 参与贡献
+
+我们非常欢迎任何有助于提升 MMPreTrain 的贡献，请参考 [贡献指南](https://mmpretrain.readthedocs.io/zh_CN/latest/notes/contribution_guide.html) 来了解如何参与贡献。
+
+## 致谢
+
+MMPreTrain 是一款由不同学校和公司共同贡献的开源项目。我们感谢所有为项目提供算法复现和新功能支持的贡献者，以及提供宝贵反馈的用户。
+我们希望该工具箱和基准测试可以为社区提供灵活的代码工具，供用户复现现有算法并开发自己的新模型，从而不断为开源社区提供贡献。
+
+## 引用
+
+如果你在研究中使用了本项目的代码或者性能基准，请参考如下 bibtex 引用 MMPreTrain。
+
+```BibTeX
+@misc{2023mmpretrain,
+    title={OpenMMLab's Pre-training Toolbox and Benchmark},
+    author={MMPreTrain Contributors},
+    howpublished = {\url{https://github.com/open-mmlab/mmpretrain}},
+    year={2023}
+}
+```
+
+## 许可证
+
+该项目开源自 [Apache 2.0 license](LICENSE).
+
+## OpenMMLab 的其他项目
+
+- [MMEngine](https://github.com/open-mmlab/mmengine): OpenMMLab 深度学习模型训练基础库
+- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab 计算机视觉基础库
+- [MIM](https://github.com/open-mmlab/mim): MIM 是 OpenMMlab 项目、算法、模型的统一入口
+- [MMEval](https://github.com/open-mmlab/mmeval): 统一开放的跨框架算法评测库
+- [MMPreTrain](https://github.com/open-mmlab/mmpretrain): OpenMMLab 深度学习预训练工具箱
+- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab 目标检测工具箱
+- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab 新一代通用 3D 目标检测平台
+- [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab 旋转框检测工具箱与测试基准
+- [MMYOLO](https://github.com/open-mmlab/mmyolo): OpenMMLab YOLO 系列工具箱与测试基准
+- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab 语义分割工具箱
+- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab 全流程文字检测识别理解工具包
+- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab 姿态估计工具箱
+- [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 人体参数化模型工具箱与测试基准
+- [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab 自监督学习工具箱与测试基准
+- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab 模型压缩工具箱与测试基准
+- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab 少样本学习工具箱与测试基准
+- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab 新一代视频理解工具箱
+- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab 一体化视频目标感知平台
+- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab 光流估计工具箱与测试基准
+- [MMagic](https://github.com/open-mmlab/mmagic): OpenMMLab 新一代人工智能内容生成（AIGC）工具箱
+- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab 图片视频生成模型工具箱
+- [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab 模型部署框架
+- [Playground](https://github.com/open-mmlab/playground): 收集和展示 OpenMMLab 相关的前沿、有趣的社区项目
+
+## 欢迎加入 OpenMMLab 社区
+
+扫描下方的二维码可关注 OpenMMLab 团队的 [知乎官方账号](https://www.zhihu.com/people/openmmlab)，扫描下方微信二维码添加喵喵好友，进入 MMPretrain 微信交流社群。【加好友申请格式：研究方向+地区+学校/公司+姓名】
+
+<div align="center">
+<img src="./resources/zhihu_qrcode.jpg" height="400"/> <img src="./resources/miaomiao_qrcode.jpg" height="400"/>
+</div>
+
+我们会在 OpenMMLab 社区为大家
+
+- 📢 分享 AI 框架的前沿核心技术
+- 💻 解读 PyTorch 常用模块源码
+- 📰 发布 OpenMMLab 的相关新闻
+- 🚀 介绍 OpenMMLab 开发的前沿算法
+- 🏃 获取更高效的问题答疑和意见反馈
+- 🔥 提供与各行各业开发者充分交流的平台
+
+干货满满 📘，等你来撩 💗，OpenMMLab 社区期待您的加入 👬

configs/_base_/datasets/cifar100_bs16.py

+# dataset settings
+dataset_type = 'CIFAR100'
+data_preprocessor = dict(
+    num_classes=100,
+    # RGB format normalization parameters
+    mean=[129.304, 124.070, 112.434],
+    std=[68.170, 65.392, 70.418],
+    # loaded images are already RGB format
+    to_rgb=False)
+
+train_pipeline = [
+    dict(type='RandomCrop', crop_size=32, padding=4),
+    dict(type='RandomFlip', prob=0.5, direction='horizontal'),
+    dict(type='PackInputs'),
+]
+
+test_pipeline = [
+    dict(type='PackInputs'),
+]
+
+train_dataloader = dict(
+    batch_size=16,
+    num_workers=2,
+    dataset=dict(
+        type=dataset_type,
+        data_root='data/cifar100',
+        split='train',
+        pipeline=train_pipeline),
+    sampler=dict(type='DefaultSampler', shuffle=True),
+)
+
+val_dataloader = dict(
+    batch_size=16,
+    num_workers=2,
+    dataset=dict(
+        type=dataset_type,
+        data_root='data/cifar100/',
+        split='test',
+        pipeline=test_pipeline),
+    sampler=dict(type='DefaultSampler', shuffle=False),
+)
+val_evaluator = dict(type='Accuracy', topk=(1, ))
+
+test_dataloader = val_dataloader
+test_evaluator = val_evaluator

configs/_base_/datasets/cifar10_bs16.py

+# dataset settings
+dataset_type = 'CIFAR10'
+data_preprocessor = dict(
+    num_classes=10,
+    # RGB format normalization parameters
+    mean=[125.307, 122.961, 113.8575],
+    std=[51.5865, 50.847, 51.255],
+    # loaded images are already RGB format
+    to_rgb=False)
+
+train_pipeline = [
+    dict(type='RandomCrop', crop_size=32, padding=4),
+    dict(type='RandomFlip', prob=0.5, direction='horizontal'),
+    dict(type='PackInputs'),
+]
+
+test_pipeline = [
+    dict(type='PackInputs'),
+]
+
+train_dataloader = dict(
+    batch_size=16,
+    num_workers=2,
+    dataset=dict(
+        type=dataset_type,
+        data_root='data/cifar10',
+        split='train',
+        pipeline=train_pipeline),
+    sampler=dict(type='DefaultSampler', shuffle=True),
+)
+
+val_dataloader = dict(
+    batch_size=16,
+    num_workers=2,
+    dataset=dict(
+        type=dataset_type,
+        data_root='data/cifar10/',
+        split='test',
+        pipeline=test_pipeline),
+    sampler=dict(type='DefaultSampler', shuffle=False),
+)
+val_evaluator = dict(type='Accuracy', topk=(1, ))
+
+test_dataloader = val_dataloader
+test_evaluator = val_evaluator

configs/_base_/datasets/coco_caption.py

+# data settings
+# coco caption annotations can be grabbed from LAVIS repo
+# https://github.com/salesforce/LAVIS/blob/main/lavis/configs/datasets/coco/defaults_cap.yaml
+data_preprocessor = dict(
+    type='MultiModalDataPreprocessor',
+    mean=[122.770938, 116.7460125, 104.09373615],
+    std=[68.5005327, 66.6321579, 70.32316305],
+    to_rgb=True,
+)
+
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='RandomResizedCrop',
+        scale=384,
+        interpolation='bicubic',
+        backend='pillow'),
+    dict(type='RandomFlip', prob=0.5, direction='horizontal'),
+    dict(type='CleanCaption', keys='gt_caption'),
+    dict(
+        type='PackInputs',
+        algorithm_keys=['gt_caption'],
+        meta_keys=['image_id'],
+    ),
+]
+
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='Resize',
+        scale=(384, 384),
+        interpolation='bicubic',
+        backend='pillow'),
+    dict(type='PackInputs', meta_keys=['image_id']),
+]
+
+train_dataloader = dict(
+    batch_size=32,
+    num_workers=5,
+    dataset=dict(
+        type='COCOCaption',
+        data_root='data/coco',
+        ann_file='annotations/coco_karpathy_train.json',
+        pipeline=train_pipeline),
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    persistent_workers=True,
+    drop_last=True,
+)
+
+val_dataloader = dict(
+    batch_size=16,
+    num_workers=5,
+    dataset=dict(
+        type='COCOCaption',
+        data_root='data/coco',
+        ann_file='annotations/coco_karpathy_val.json',
+        pipeline=test_pipeline,
+    ),
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    persistent_workers=True,
+)
+
+val_evaluator = dict(
+    type='COCOCaption',
+    ann_file='data/coco/annotations/coco_karpathy_val_gt.json',
+)
+
+# # If you want standard test, please manually configure the test dataset
+test_dataloader = val_dataloader
+test_evaluator = val_evaluator

configs/_base_/datasets/coco_okvqa.py

+# data settings
+
+data_preprocessor = dict(
+    mean=[122.770938, 116.7460125, 104.09373615],
+    std=[68.5005327, 66.6321579, 70.32316305],
+    to_rgb=True,
+)
+
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='RandomResizedCrop',
+        scale=384,
+        interpolation='bicubic',
+        backend='pillow'),
+    dict(
+        type='PackInputs',
+        algorithm_keys=['question', 'gt_answer', 'gt_answer_weight'],
+        meta_keys=['question_id', 'image_id'],
+    ),
+]
+
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='Resize',
+        scale=(480, 480),
+        interpolation='bicubic',
+        backend='pillow'),
+    dict(
+        type='CleanCaption',
+        keys=['question'],
+    ),
+    dict(
+        type='PackInputs',
+        algorithm_keys=['question', 'gt_answer', 'gt_answer_weight'],
+        meta_keys=['question_id', 'image_id'],
+    ),
+]
+
+train_dataloader = dict(
+    batch_size=16,
+    num_workers=8,
+    dataset=dict(
+        type='COCOVQA',
+        data_root='data/coco',
+        data_prefix='train2014',
+        question_file=
+        'annotations/okvqa_OpenEnded_mscoco_train2014_questions.json',
+        ann_file='annotations/okvqa_mscoco_train2014_annotations.json',
+        pipeline=train_pipeline),
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    persistent_workers=True,
+    drop_last=True,
+)
+
+val_dataloader = dict(
+    batch_size=16,
+    num_workers=8,
+    dataset=dict(
+        type='COCOVQA',
+        data_root='data/coco',
+        data_prefix='val2014',
+        question_file=
+        'annotations/okvqa_OpenEnded_mscoco_val2014_questions.json',
+        ann_file='annotations/okvqa_mscoco_val2014_annotations.json',
+        pipeline=test_pipeline),
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    persistent_workers=True,
+)
+
+val_evaluator = dict(type='VQAAcc')
+
+test_dataloader = val_dataloader
+test_evaluator = val_evaluator

configs/_base_/datasets/coco_retrieval.py

+# data settings
+# Here are the links to download the annotations for coco retrieval for conveniency # noqa
+# https://download.openmmlab.com/mmclassification/datasets/coco_retrieval/caption_karpathy_train2014.json
+# https://download.openmmlab.com/mmclassification/datasets/coco_retrieval/caption_karpathy_val2014.json
+# https://download.openmmlab.com/mmclassification/datasets/coco_retrieval/caption_karpathy_test2014.json
+data_preprocessor = dict(
+    type='MultiModalDataPreprocessor',
+    mean=[122.770938, 116.7460125, 104.09373615],
+    std=[68.5005327, 66.6321579, 70.32316305],
+    to_rgb=True,
+)
+
+rand_increasing_policies = [
+    dict(type='AutoContrast'),
+    dict(type='Equalize'),
+    dict(type='Rotate', magnitude_key='angle', magnitude_range=(0, 30)),
+    dict(
+        type='Brightness', magnitude_key='magnitude',
+        magnitude_range=(0, 0.0)),
+    dict(type='Sharpness', magnitude_key='magnitude', magnitude_range=(0, 0)),
+    dict(
+        type='Shear',
+        magnitude_key='magnitude',
+        magnitude_range=(0, 0.3),
+        direction='horizontal'),
+    dict(
+        type='Shear',
+        magnitude_key='magnitude',
+        magnitude_range=(0, 0.3),
+        direction='vertical'),
+]
+
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='RandomResizedCrop',
+        scale=384,
+        crop_ratio_range=(0.5, 1.0),
+        interpolation='bicubic'),
+    dict(type='RandomFlip', prob=0.5, direction='horizontal'),
+    dict(
+        type='RandAugment',
+        policies=rand_increasing_policies,
+        num_policies=2,
+        magnitude_level=5),
+    dict(type='CleanCaption', keys='text'),
+    dict(
+        type='PackInputs',
+        algorithm_keys=['text', 'is_matched'],
+        meta_keys=['image_id']),
+]
+
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='Resize',
+        scale=(384, 384),
+        interpolation='bicubic',
+        backend='pillow'),
+    dict(type='CleanCaption', keys='text'),
+    dict(
+        type='PackInputs',
+        algorithm_keys=['text', 'gt_text_id', 'gt_image_id'],
+        meta_keys=['image_id']),
+]
+
+train_dataloader = dict(
+    batch_size=32,
+    num_workers=16,
+    dataset=dict(
+        type='COCORetrieval',
+        data_root='data/coco',
+        ann_file='annotations/caption_karpathy_train2014.json',
+        pipeline=train_pipeline),
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    persistent_workers=True,
+    drop_last=True,
+)
+
+val_dataloader = dict(
+    batch_size=64,
+    num_workers=16,
+    dataset=dict(
+        type='COCORetrieval',
+        data_root='data/coco',
+        ann_file='annotations/caption_karpathy_val2014.json',
+        pipeline=test_pipeline,
+        # This is required for evaluation
+        test_mode=True,
+    ),
+    sampler=dict(type='SequentialSampler', subsample_type='sequential'),
+    persistent_workers=True,
+)
+
+val_evaluator = dict(type='RetrievalRecall', topk=(1, 5, 10))
+
+# If you want standard test, please manually configure the test dataset
+test_dataloader = val_dataloader
+test_evaluator = val_evaluator

configs/_base_/datasets/coco_vg_vqa.py

+# data settings
+data_preprocessor = dict(
+    type='MultiModalDataPreprocessor',
+    mean=[122.770938, 116.7460125, 104.09373615],
+    std=[68.5005327, 66.6321579, 70.32316305],
+    to_rgb=True,
+)
+
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='RandomResizedCrop',
+        scale=(480, 480),
+        crop_ratio_range=(0.5, 1.0),
+        interpolation='bicubic',
+        backend='pillow'),
+    dict(type='RandomFlip', prob=0.5, direction='horizontal'),
+    dict(
+        type='RandAugment',
+        policies='simple_increasing',  # slightly different from LAVIS
+        num_policies=2,
+        magnitude_level=5),
+    dict(type='CleanCaption', keys=['question', 'gt_answer']),
+    dict(
+        type='PackInputs',
+        algorithm_keys=['question', 'gt_answer', 'gt_answer_weight']),
+]
+
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='Resize',
+        scale=(480, 480),
+        interpolation='bicubic',
+        backend='pillow'),
+    dict(type='CleanCaption', keys=['question']),
+    dict(
+        type='PackInputs',
+        algorithm_keys=['question'],
+        meta_keys=['question_id']),
+]
+
+train_dataloader = dict(
+    batch_size=32,
+    num_workers=8,
+    dataset=dict(
+        type='ConcatDataset',
+        datasets=[
+            # VQAv2 train
+            dict(
+                type='COCOVQA',
+                data_root='data/coco',
+                data_prefix='train2014',
+                question_file=
+                'annotations/v2_OpenEnded_mscoco_train2014_questions.json',
+                ann_file='annotations/v2_mscoco_train2014_annotations.json',
+                pipeline=train_pipeline,
+            ),
+            # VQAv2 val
+            dict(
+                type='COCOVQA',
+                data_root='data/coco',
+                data_prefix='val2014',
+                question_file=
+                'annotations/v2_OpenEnded_mscoco_val2014_questions.json',
+                ann_file='annotations/v2_mscoco_val2014_annotations.json',
+                pipeline=train_pipeline,
+            ),
+            # Visual Genome
+            dict(
+                type='VisualGenomeQA',
+                data_root='visual_genome',
+                data_prefix='image',
+                ann_file='question_answers.json',
+                pipeline=train_pipeline,
+            )
+        ]),
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    persistent_workers=True,
+    drop_last=True,
+)
+
+test_dataloader = dict(
+    batch_size=32,
+    num_workers=8,
+    dataset=dict(
+        type='COCOVQA',
+        data_root='data/coco',
+        data_prefix='test2015',
+        question_file=
+        'annotations/v2_OpenEnded_mscoco_test2015_questions.json',  # noqa: E501
+        pipeline=test_pipeline,
+    ),
+    sampler=dict(type='DefaultSampler', shuffle=False),
+)
+test_evaluator = dict(type='ReportVQA', file_path='vqa_test.json')

configs/_base_/datasets/coco_vqa.py

+# data settings
+
+data_preprocessor = dict(
+    mean=[122.770938, 116.7460125, 104.09373615],
+    std=[68.5005327, 66.6321579, 70.32316305],
+    to_rgb=True,
+)
+
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='RandomResizedCrop',
+        scale=384,
+        interpolation='bicubic',
+        backend='pillow'),
+    dict(
+        type='PackInputs',
+        algorithm_keys=['question', 'gt_answer', 'gt_answer_weight'],
+        meta_keys=['question_id', 'image_id'],
+    ),
+]
+
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='Resize',
+        scale=(480, 480),
+        interpolation='bicubic',
+        backend='pillow'),
+    dict(
+        type='CleanCaption',
+        keys=['question'],
+    ),
+    dict(
+        type='PackInputs',
+        algorithm_keys=['question', 'gt_answer', 'gt_answer_weight'],
+        meta_keys=['question_id', 'image_id'],
+    ),
+]
+
+train_dataloader = dict(
+    batch_size=16,
+    num_workers=8,
+    dataset=dict(
+        type='COCOVQA',
+        data_root='data/coco',
+        data_prefix='train2014',
+        question_file=
+        'annotations/v2_OpenEnded_mscoco_train2014_questions.json',
+        ann_file='annotations/v2_mscoco_train2014_annotations.json',
+        pipeline=train_pipeline),
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    persistent_workers=True,
+    drop_last=True,
+)
+
+val_dataloader = dict(
+    batch_size=16,
+    num_workers=8,
+    dataset=dict(
+        type='COCOVQA',
+        data_root='data/coco',
+        data_prefix='val2014',
+        question_file='annotations/v2_OpenEnded_mscoco_val2014_questions.json',
+        ann_file='annotations/v2_mscoco_val2014_annotations.json',
+        pipeline=test_pipeline),
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    persistent_workers=True,
+)
+val_evaluator = dict(type='VQAAcc')
+
+test_dataloader = dict(
+    batch_size=16,
+    num_workers=8,
+    dataset=dict(
+        type='COCOVQA',
+        data_root='data/coco',
+        data_prefix='test2015',
+        question_file=  # noqa: E251
+        'annotations/v2_OpenEnded_mscoco_test2015_questions.json',
+        pipeline=test_pipeline),
+    sampler=dict(type='DefaultSampler', shuffle=False),
+)
+test_evaluator = dict(type='ReportVQA', file_path='vqa_test.json')

configs/_base_/datasets/cub_bs8_384.py

+# dataset settings
+dataset_type = 'CUB'
+data_preprocessor = dict(
+    num_classes=200,
+    # RGB format normalization parameters
+    mean=[123.675, 116.28, 103.53],
+    std=[58.395, 57.12, 57.375],
+    # convert image from BGR to RGB
+    to_rgb=True,
+)
+
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='Resize', scale=510),
+    dict(type='RandomCrop', crop_size=384),
+    dict(type='RandomFlip', prob=0.5, direction='horizontal'),
+    dict(type='PackInputs'),
+]
+
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='Resize', scale=510),
+    dict(type='CenterCrop', crop_size=384),
+    dict(type='PackInputs'),
+]
+
+train_dataloader = dict(
+    batch_size=8,
+    num_workers=2,
+    dataset=dict(
+        type=dataset_type,
+        data_root='data/CUB_200_2011',
+        split='train',
+        pipeline=train_pipeline),
+    sampler=dict(type='DefaultSampler', shuffle=True),
+)
+
+val_dataloader = dict(
+    batch_size=8,
+    num_workers=2,
+    dataset=dict(
+        type=dataset_type,
+        data_root='data/CUB_200_2011',
+        split='test',
+        pipeline=test_pipeline),
+    sampler=dict(type='DefaultSampler', shuffle=False),
+)
+val_evaluator = dict(type='Accuracy', topk=(1, ))
+
+test_dataloader = val_dataloader
+test_evaluator = val_evaluator

configs/_base_/datasets/cub_bs8_448.py

+# dataset settings
+dataset_type = 'CUB'
+data_preprocessor = dict(
+    num_classes=200,
+    mean=[123.675, 116.28, 103.53],
+    std=[58.395, 57.12, 57.375],
+    # convert image from BGR to RGB
+    to_rgb=True,
+)
+
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='Resize', scale=600),
+    dict(type='RandomCrop', crop_size=448),
+    dict(type='RandomFlip', prob=0.5, direction='horizontal'),
+    dict(type='PackInputs'),
+]
+
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='Resize', scale=600),
+    dict(type='CenterCrop', crop_size=448),
+    dict(type='PackInputs'),
+]
+
+train_dataloader = dict(
+    batch_size=8,
+    num_workers=2,
+    dataset=dict(
+        type=dataset_type,
+        data_root='data/CUB_200_2011',
+        split='train',
+        pipeline=train_pipeline),
+    sampler=dict(type='DefaultSampler', shuffle=True),
+)
+
+val_dataloader = dict(
+    batch_size=8,
+    num_workers=2,
+    dataset=dict(
+        type=dataset_type,
+        data_root='data/CUB_200_2011',
+        split='test',
+        pipeline=test_pipeline),
+    sampler=dict(type='DefaultSampler', shuffle=False),
+)
+val_evaluator = dict(type='Accuracy', topk=(1, ))
+
+test_dataloader = val_dataloader
+test_evaluator = val_evaluator

configs/_base_/datasets/flickr30k_caption.py

+# data settings
+
+data_preprocessor = dict(
+    type='MultiModalDataPreprocessor',
+    mean=[122.770938, 116.7460125, 104.09373615],
+    std=[68.5005327, 66.6321579, 70.32316305],
+    to_rgb=True,
+)
+
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='RandomResizedCrop',
+        scale=384,
+        interpolation='bicubic',
+        backend='pillow'),
+    dict(type='RandomFlip', prob=0.5, direction='horizontal'),
+    dict(type='CleanCaption', keys='gt_caption'),
+    dict(
+        type='PackInputs',
+        algorithm_keys=['gt_caption'],
+        meta_keys=['image_id'],
+    ),
+]
+
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='Resize',
+        scale=(384, 384),
+        interpolation='bicubic',
+        backend='pillow'),
+    dict(type='PackInputs', meta_keys=['image_id']),
+]
+
+train_dataloader = dict(
+    batch_size=32,
+    num_workers=5,
+    dataset=dict(
+        type='Flickr30kCaption',
+        data_root='data/flickr30k',
+        ann_file='annotations/dataset_flickr30k.json',
+        data_prefix='images',
+        split='train',
+        pipeline=train_pipeline),
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    persistent_workers=True,
+    drop_last=True,
+)
+
+val_dataloader = dict(
+    batch_size=16,
+    num_workers=5,
+    dataset=dict(
+        type='Flickr30kCaption',
+        data_root='data/flickr30k',
+        ann_file='annotations/dataset_flickr30k.json',
+        data_prefix='images',
+        split='val',
+        pipeline=test_pipeline,
+    ),
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    persistent_workers=True,
+)
+
+# refer tools/dataset_converters/convert_flickr30k_ann.py
+val_evaluator = dict(
+    type='COCOCaption',
+    ann_file='data/flickr30k_val_gt.json',
+)
+
+# # If you want standard test, please manually configure the test dataset
+test_dataloader = dict(
+    batch_size=16,
+    num_workers=5,
+    dataset=dict(
+        type='Flickr30kCaption',
+        data_root='data/flickr30k',
+        ann_file='annotations/dataset_flickr30k.json',
+        data_prefix='images',
+        split='test',
+        pipeline=test_pipeline,
+    ),
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    persistent_workers=True,
+)
+
+# refer tools/dataset_converters/convert_flickr30k_ann.py
+test_evaluator = dict(
+    type='COCOCaption',
+    ann_file='data/flickr30k_test_gt.json',
+)

configs/_base_/datasets/flickr30k_retrieval.py

+# data settings
+data_preprocessor = dict(
+    type='MultiModalDataPreprocessor',
+    mean=[122.770938, 116.7460125, 104.09373615],
+    std=[68.5005327, 66.6321579, 70.32316305],
+    to_rgb=True,
+)
+
+rand_increasing_policies = [
+    dict(type='AutoContrast'),
+    dict(type='Equalize'),
+    dict(type='Rotate', magnitude_key='angle', magnitude_range=(0, 30)),
+    dict(
+        type='Brightness', magnitude_key='magnitude',
+        magnitude_range=(0, 0.0)),
+    dict(type='Sharpness', magnitude_key='magnitude', magnitude_range=(0, 0)),
+    dict(
+        type='Shear',
+        magnitude_key='magnitude',
+        magnitude_range=(0, 0.3),
+        direction='horizontal'),
+    dict(
+        type='Shear',
+        magnitude_key='magnitude',
+        magnitude_range=(0, 0.3),
+        direction='vertical'),
+]
+
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='RandomResizedCrop',
+        scale=384,
+        crop_ratio_range=(0.5, 1.0),
+        interpolation='bicubic'),
+    dict(type='RandomFlip', prob=0.5, direction='horizontal'),
+    dict(
+        type='RandAugment',
+        policies=rand_increasing_policies,
+        num_policies=2,
+        magnitude_level=5),
+    dict(type='CleanCaption', keys='text'),
+    dict(
+        type='PackInputs',
+        algorithm_keys=['text', 'is_matched'],
+        meta_keys=['image_id']),
+]
+
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='Resize',
+        scale=(384, 384),
+        interpolation='bicubic',
+        backend='pillow'),
+    dict(type='CleanCaption', keys='text'),
+    dict(
+        type='PackInputs',
+        algorithm_keys=['text', 'gt_text_id', 'gt_image_id'],
+        meta_keys=['image_id']),
+]
+
+train_dataloader = dict(
+    batch_size=32,
+    num_workers=16,
+    dataset=dict(
+        type='Flickr30kRetrieval',
+        data_root='data/flickr30k',
+        ann_file='annotations/dataset_flickr30k.json',
+        data_prefix='images',
+        split='train',
+        pipeline=train_pipeline),
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    persistent_workers=True,
+    drop_last=True,
+)
+
+val_dataloader = dict(
+    batch_size=64,
+    num_workers=16,
+    dataset=dict(
+        type='Flickr30kRetrieval',
+        data_root='data/flickr30k',
+        ann_file='annotations/dataset_flickr30k.json',
+        data_prefix='images',
+        split='val',
+        pipeline=test_pipeline,
+        test_mode=True,  # This is required for evaluation
+    ),
+    sampler=dict(type='SequentialSampler', subsample_type='sequential'),
+    persistent_workers=True,
+)
+
+val_evaluator = dict(type='RetrievalRecall', topk=(1, 5, 10))
+
+# If you want standard test, please manually configure the test dataset
+test_dataloader = dict(
+    batch_size=64,
+    num_workers=16,
+    dataset=dict(
+        type='Flickr30kRetrieval',
+        data_root='data/flickr30k',
+        ann_file='annotations/dataset_flickr30k.json',
+        data_prefix='images',
+        split='test',
+        pipeline=test_pipeline,
+        test_mode=True,  # This is required for evaluation
+    ),
+    sampler=dict(type='SequentialSampler', subsample_type='sequential'),
+    persistent_workers=True,
+)
+test_evaluator = val_evaluator

configs/_base_/datasets/gqa.py

+# data settings
+
+data_preprocessor = dict(
+    mean=[122.770938, 116.7460125, 104.09373615],
+    std=[68.5005327, 66.6321579, 70.32316305],
+    to_rgb=True,
+)
+
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='RandomResizedCrop',
+        scale=384,
+        interpolation='bicubic',
+        backend='pillow'),
+    dict(
+        type='PackInputs',
+        algorithm_keys=['question', 'gt_answer', 'gt_answer_weight'],
+        meta_keys=['question_id', 'image_id'],
+    ),
+]
+
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='Resize',
+        scale=(480, 480),
+        interpolation='bicubic',
+        backend='pillow'),
+    dict(
+        type='CleanCaption',
+        keys=['question'],
+    ),
+    dict(
+        type='PackInputs',
+        algorithm_keys=['question', 'gt_answer', 'gt_answer_weight'],
+        meta_keys=['question_id', 'image_id'],
+    ),
+]
+
+train_dataloader = dict(
+    batch_size=16,
+    num_workers=8,
+    dataset=dict(
+        type='GQA',
+        data_root='data/gqa',
+        data_prefix='images',
+        ann_file='annotations/train_balanced_questions.json',
+        pipeline=test_pipeline),
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    persistent_workers=True,
+    drop_last=True,
+)
+
+val_dataloader = dict(
+    batch_size=16,
+    num_workers=8,
+    dataset=dict(
+        type='GQA',
+        data_root='data/gqa',
+        data_prefix='images',
+        ann_file='annotations/testdev_balanced_questions.json',
+        pipeline=test_pipeline),
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    persistent_workers=True,
+)
+val_evaluator = dict(type='GQAAcc')
+
+test_dataloader = dict(
+    batch_size=16,
+    num_workers=8,
+    dataset=dict(
+        type='GQA',
+        data_root='data/gqa',
+        data_prefix='images',
+        ann_file='annotations/testdev_balanced_questions.json',
+        pipeline=test_pipeline),
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    persistent_workers=True,
+)
+test_evaluator = val_evaluator

configs/_base_/datasets/imagenet21k_bs128.py

+# dataset settings
+dataset_type = 'ImageNet21k'
+data_preprocessor = dict(
+    num_classes=21842,
+    # RGB format normalization parameters
+    mean=[123.675, 116.28, 103.53],
+    std=[58.395, 57.12, 57.375],
+    # convert image from BGR to RGB
+    to_rgb=True,
+)
+
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='RandomResizedCrop', scale=224),
+    dict(type='RandomFlip', prob=0.5, direction='horizontal'),
+    dict(type='PackInputs'),
+]
+
+train_dataloader = dict(
+    batch_size=128,
+    num_workers=5,
+    dataset=dict(
+        type=dataset_type,
+        data_root='data/imagenet21k',
+        split='train',
+        pipeline=train_pipeline),
+    sampler=dict(type='DefaultSampler', shuffle=True),
+)

configs/_base_/datasets/imagenet_bs128_mbv3.py

+# dataset settings
+dataset_type = 'ImageNet'
+data_preprocessor = dict(
+    num_classes=1000,
+    # RGB format normalization parameters
+    mean=[123.675, 116.28, 103.53],
+    std=[58.395, 57.12, 57.375],
+    # convert image from BGR to RGB
+    to_rgb=True,
+)
+
+bgr_mean = data_preprocessor['mean'][::-1]
+bgr_std = data_preprocessor['std'][::-1]
+
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='RandomResizedCrop', scale=224, backend='pillow'),
+    dict(type='RandomFlip', prob=0.5, direction='horizontal'),
+    dict(
+        type='AutoAugment',
+        policies='imagenet',
+        hparams=dict(pad_val=[round(x) for x in bgr_mean])),
+    dict(
+        type='RandomErasing',
+        erase_prob=0.2,
+        mode='rand',
+        min_area_ratio=0.02,
+        max_area_ratio=1 / 3,
+        fill_color=bgr_mean,
+        fill_std=bgr_std),
+    dict(type='PackInputs'),
+]
+
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='ResizeEdge', scale=256, edge='short', backend='pillow'),
+    dict(type='CenterCrop', crop_size=224),
+    dict(type='PackInputs'),
+]
+
+train_dataloader = dict(
+    batch_size=128,
+    num_workers=5,
+    dataset=dict(
+        type=dataset_type,
+        data_root='data/imagenet',
+        split='train',
+        pipeline=train_pipeline),
+    sampler=dict(type='DefaultSampler', shuffle=True),
+)
+
+val_dataloader = dict(
+    batch_size=128,
+    num_workers=5,
+    dataset=dict(
+        type=dataset_type,
+        data_root='data/imagenet',
+        split='val',
+        pipeline=test_pipeline),
+    sampler=dict(type='DefaultSampler', shuffle=False),
+)
+val_evaluator = dict(type='Accuracy', topk=(1, 5))
+
+# If you want standard test, please manually configure the test dataset
+test_dataloader = val_dataloader
+test_evaluator = val_evaluator