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README
Apache-2.0

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。

近期更新

特性

  • PP-ShiTu轻量图像识别系统:集成了目标检测、特征学习、图像检索等模块,广泛适用于各类图像识别任务。cpu上0.2s即可完成在10w+库的图像识别。

  • PP-LCNet轻量级CPU骨干网络:专门为CPU设备打造轻量级骨干网络,速度、精度均远超竞品。

  • 丰富的预训练模型库:提供了36个系列共175个ImageNet预训练模型,其中7个精选系列模型支持结构快速修改。

  • 全面易用的特征学习组件:集成arcmargin, triplet loss等12度量学习方法,通过配置文件即可随意组合切换。

  • SSLD知识蒸馏:14个分类预训练模型,精度普遍提升3%以上;其中ResNet50_vd模型在ImageNet-1k数据集上的Top-1精度达到了84.0%, Res2Net200_vd预训练模型Top-1精度高达85.1%。

欢迎加入技术交流群

  • 您可以扫描下面的QQ/微信二维码(添加小助手微信并回复“C”),加入PaddleClas微信交流群,获得更高效的问题答疑,与各行各业开发者充分交流,期待您的加入。

快速体验

PP-ShiTu图像识别快速体验:点击这里

文档教程

PP-ShiTu图像识别系统介绍

PP-ShiTu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8个方面,采用多种策略,对各个模块的模型进行优化,最终得到在CPU上仅0.2s即可完成10w+库的图像识别的系统。更多细节请参考PP-ShiTu技术方案

PP-ShiTu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

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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简介

飞桨图像分类套件PaddleClas是飞桨为工业界和学术界所准备的一个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 展开 收起
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