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📘 中文文档 | 🛠️ 安装教程 | 👀 模型库 | 🆕 更新日志 | 🤔 报告问题

English | 简体中文

Introduction

MMPreTrain 是一款基于 PyTorch 的开源深度学习预训练工具箱,是 OpenMMLab 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

🌟 从 MMClassification 升级到 MMPreTrain

  • 整合来自 MMSelfSup 的自监督学习算法,例如 MAE, BEiT
  • 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, TinyViTMixMIM 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 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 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

安装

以下是安装的简要步骤:

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 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:

mim install -e ".[multimodal]"

基础教程

我们为新用户提供了一系列基础教程:

关于更多的信息,请查阅我们的 相关文档

模型库

相关结果和模型可在 模型库 中获得。

概览
支持的主干网络 自监督学习 多模态算法 其它
图像检索任务: 训练和测试 Tips:

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 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。

@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.

OpenMMLab 的其他项目

  • MMEngine: OpenMMLab 深度学习模型训练基础库
  • MMCV: OpenMMLab 计算机视觉基础库
  • MIM: MIM 是 OpenMMlab 项目、算法、模型的统一入口
  • MMEval: 统一开放的跨框架算法评测库
  • MMPreTrain: OpenMMLab 深度学习预训练工具箱
  • MMDetection: OpenMMLab 目标检测工具箱
  • MMDetection3D: OpenMMLab 新一代通用 3D 目标检测平台
  • MMRotate: OpenMMLab 旋转框检测工具箱与测试基准
  • MMYOLO: OpenMMLab YOLO 系列工具箱与测试基准
  • MMSegmentation: OpenMMLab 语义分割工具箱
  • MMOCR: OpenMMLab 全流程文字检测识别理解工具包
  • MMPose: OpenMMLab 姿态估计工具箱
  • MMHuman3D: OpenMMLab 人体参数化模型工具箱与测试基准
  • MMSelfSup: OpenMMLab 自监督学习工具箱与测试基准
  • MMRazor: OpenMMLab 模型压缩工具箱与测试基准
  • MMFewShot: OpenMMLab 少样本学习工具箱与测试基准
  • MMAction2: OpenMMLab 新一代视频理解工具箱
  • MMTracking: OpenMMLab 一体化视频目标感知平台
  • MMFlow: OpenMMLab 光流估计工具箱与测试基准
  • MMagic: OpenMMLab 新一代人工智能内容生成(AIGC)工具箱
  • MMGeneration: OpenMMLab 图片视频生成模型工具箱
  • MMDeploy: OpenMMLab 模型部署框架
  • 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,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】

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 👬

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