PaddlePaddle/PaddleClas PaddlePaddle/PaddleClas

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    Python
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    Apache License 2.0
  • Created over 4 years ago
  • Updated about 2 months ago
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PaddlePaddle/PaddleClas
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Repository Details

A treasure chest for visual classification and recognition powered by PaddlePaddle

简体中文 | English

PaddleClas

简介

飞桨图像识别套件PaddleClas是飞桨为工业界和学术界所准备的一个图像识别和图像分类任务的工具集，助力使用者训练出更好的视觉模型和应用落地。

PP-ShiTu图像识别系统应用范围

PULC实用图像分类模型效果展示

📣 近期更新

  • 🔥2023.3.16 PaddleClas集成了高性能、全场景模型部署方案FastDeploy，欢迎参考指南试用（注意使用develop分支）。

  • 💥 直播回放：PaddleClas研发团队详解PP-ShituV2优化策略与真实产业应用。微信扫描下方二维码，关注公众号并填写问卷后进入官方交流群，获取直播回放与20G重磅图像分类学习大礼包（内含20+数据集、4个垂类模型、70+前沿论文集合）

🌟 特性

PaddleClas支持多种前沿图像分类、识别相关算法，发布产业级特色骨干网络PP-HGNet、PP-LCNetv2、 PP-LCNet和SSLD半监督知识蒸馏方案等模型，在此基础上打造PULC超轻量图像分类方案和PP-ShiTu图像识别系统。

上述内容的使用方法建议从文档教程中的快速开始体验

⚡ 快速开始

  • PULC超轻量图像分类方案快速体验：点击这里
  • PP-ShiTu图像识别快速体验：点击这里
  • PP-ShiTuV2 Android Demo APP，可扫描如下二维码，下载体验

📖 技术交流合作

  • 飞桨AI套件（PaddleX）—— 精选产业实用模型的一站式开发平台。包含如下特点：

    • 【丰富的算法库】PaddleX提供36个精选模型，覆盖了10大AI任务场景，并附带模型推理benchmark数据。开发者可以根据业务需求轻松选择合适的模型，并进行开发和部署。
    • 【简便的开发方式】PaddleX支持无代码和低代码开发，只需四个简单步骤，即可完成全流程AI开发，包括数据处理、模型训练、验证和部署。
    • 【高效的模型部署】PaddleX平台提供强大的部署能力，支持快速实现产业级部署，实现跨平台、跨硬件的部署。
    • 【多样的硬件支持】PaddleX不仅能够在AI Studio云端使用，还支持在Windows本地环境使用，并正在积极扩展支持Linux版本、昆仑芯版本、昇腾版本和寒武纪版本。
    • 【共赢的合作机会】除了提供便捷的AI应用开发工具，PaddleX还为企业提供商业收益机会，共同探索更多商业空间，实现共同成长和共赢。

  • 作为一款高效的开发神器，PaddleX值得每一位开发者拥有。

  • PaddleX官网地址：https://www.paddlepaddle.org.cn/paddle/PaddleX

  • 微信扫描下面二维码添加运营同学，并回复【PaddleX】，运营同学会邀请您加入官方交流群，获得更高效的问题答疑。

飞桨AI套件【PaddleX】技术交流群二维码

👫 开源社区

  • 📑项目合作： 如果您是企业开发者且有明确的图像分类应用需求，填写问卷后可免费与官方团队展开不同层次的合作。
  • 👫加入社区： 微信扫描二维码并填写问卷之后，加入交流群领取20G重磅图像分类学习大礼包，内含
    • 20+场景数据库，包括各类商品、动植物、航拍图像等数据集
    • 场景应用模型集合：包括人员出入管理、生鲜品识别、商品识别等
    • 70+前沿图像分类与识别论文、历次发版课程视频、PPT与优质社区项目等

🛠️ PP系列模型列表

模型简介 应用场景 模型下载链接
PULC 超轻量图像分类方案 固定图像类别分类方案 人体、车辆、文字相关9大模型：模型库连接
PP-ShituV2 轻量图像识别系统 针对场景数据类别频繁变动、类别数据多 主体检测模型：预训练模型 / 推理模型
识别模型：预训练模型 / 推理模型
PP-LCNet 轻量骨干网络 针对Intel CPU设备及MKLDNN加速库定制 PPLCNet_x1_0：预训练模型 / 推理模型
PP-LCNetV2 轻量骨干网络 针对Intel CPU设备，适配OpenVINO PPLCNetV2_base：预训练模型 / 推理模型
PP-HGNet 高精度骨干网络 GPU设备上相同推理时间精度更高 PPHGNet_small：预训练模型 / 推理模型

全部模型下载链接可查看 文档教程 中的各模型介绍

产业范例

📖 文档教程

PP-ShiTuV2图像识别系统

PP-ShiTuV2是一个实用的轻量级通用图像识别系统，主要由主体检测、特征学习和向量检索三个模块组成。该系统从骨干网络选择和调整、损失函数的选择、数据增强、学习率变换策略、正则化参数选择、预训练模型使用以及模型裁剪量化多个方面，采用多种策略，对各个模块的模型进行优化，PP-ShiTuV2相比V1，Recall1提升近8个点。更多细节请参考PP-ShiTuV2详细介绍。

PP-ShiTuV2图像识别系统效果展示

  • 瓶装饮料识别
  • 商品识别
  • 动漫人物识别
  • logo识别
  • 车辆识别

PULC超轻量图像分类方案

PULC融合了骨干网络、数据增广、蒸馏等多种前沿算法，可以自动训练得到轻量且高精度的图像分类模型。 PaddleClas提供了覆盖人、车、OCR场景九大常见任务的分类模型，CPU推理3ms，精度比肩SwinTransformer。

PULC实用图像分类模型效果展示

许可证书

本项目的发布受Apache 2.0 license许可认证。

贡献代码

我们非常欢迎你为PaddleClas贡献代码，也十分感谢你的反馈。 如果想为PaddleClas贡献代码，可以参考贡献指南。

  • 非常感谢nblib修正了PaddleClas中RandErasing的数据增广配置文件。
  • 非常感谢chenpy228修正了PaddleClas文档中的部分错别字。
  • 非常感谢jm12138为PaddleClas添加ViT，DeiT系列模型和RepVGG系列模型。

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