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README
Apache-2.0
 
OpenMMLab 官网 HOT      OpenMMLab 开放平台 TRY IT OUT
 

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介绍

MMDeploy 是 OpenMMLab 模型部署工具箱,为各算法库提供统一的部署体验。基于 MMDeploy,开发者可以轻松从训练 repo 生成指定硬件所需 SDK,省去大量适配时间。

架构简析

特性简介

支持超多 OpenMMLab 算法库

支持多种推理后端

支持的设备平台和推理引擎如下表所示。benchmark请参考这里

Device / Platform Linux Windows macOS Android
x86_64 CPU Build StatusONNXRuntime
Build Statuspplnn
Build Statusncnn
Build StatusLibTorch
Build StatusOpenVINO
Build StatusTVM
Build StatusONNXRuntime
Build StatusOpenVINO
- -
ARM CPU Build Statusncnn - - Build Statusncnn
RISC-V Build Statusncnn - - -
NVIDIA GPU Build StatusONNXRuntime
Build StatusTensorRT
Build Statuspplnn
Build StatusLibTorch
Build StatusTVM
Build StatusONNXRuntime
Build StatusTensorRT
Build Statuspplnn
- -
NVIDIA Jetson Build StatusTensorRT Build StatusTensorRT - -
Huawei ascend310 Build StatusCANN - - -
Rockchip Build StatusRKNN - - -
Apple M1 - - Build StatusCoreML -
Adreno GPU - - - Build StatusSNPE
Build Statusncnn
Hexagon DSP - - - Build StatusSNPE

SDK 可高度定制化

  • Transform 数据预处理
  • Net 推理
  • Module 后处理

中文文档

新人解说

基准与模型库

基准和支持的模型列表可以在基准模型列表中获得。

贡献指南

我们感谢所有的贡献者为改进和提升 MMDeploy 所作出的努力。请参考贡献指南来了解参与项目贡献的相关指引。

致谢

  • OpenPPL: 高性能推理框架底层库
  • OpenVINO: AI 推理优化和部署框架
  • ncnn: 为手机端极致优化的高性能神经网络前向计算框架

引用

如果您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 MMDeploy:

@misc{=mmdeploy,
    title={OpenMMLab's Model Deployment Toolbox.},
    author={MMDeploy Contributors},
    howpublished = {\url{https://github.com/open-mmlab/mmdeploy}},
    year={2021}
}

开源许可证

该项目采用 Apache 2.0 开源许可证

OpenMMLab 的其他项目

  • 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
  • MMClassification: OpenMMLab 图像分类工具箱
  • MMDetection: OpenMMLab 目标检测工具箱
  • MMDetection3D: OpenMMLab 新一代通用 3D 目标检测平台
  • MMYOLO: OpenMMLab YOLO 系列工具箱和基准测试
  • MMRotate: OpenMMLab 旋转框检测工具箱与测试基准
  • 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
  • MMEditing: OpenMMLab 图像视频编辑工具箱
  • MMGeneration: OpenMMLab 图片视频生成模型工具箱
  • MMDeploy: OpenMMLab 模型部署框架

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,加入 OpenMMLab 团队的 官方交流 QQ 群,或添加微信小助手”OpenMMLabwx“加入官方交流微信群。

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 📘,等您来撩 💗,OpenMMLab 社区期待您的加入 👬

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简介

全面支持 OpenMMLab 算法库在多种云、边端设备中部署,并提供高度可扩展的 SDK 开发框架,打通模型部署“最后一公里”。 展开 收起
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