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PaddlePaddle End-to-End Development Toolkit（『飞桨』深度学习全流程开发工具）

Home Page: https://paddlex.readthedocs.io

License: Apache License 2.0

Shell 0.27% C++ 6.69% Python 84.68% C 0.04% Java 1.02% C# 1.19% Cuda 0.05% Makefile 0.01% HTML 4.67% CMake 1.35% Batchfile 0.02%

paddlex's Introduction

PaddleX

PaddleX -- 飞桨全流程开发工具，以低代码的形式支持开发者快速实现产业实际项目落地

近期动态

2021.12.10 PaddleX发布2.1.0版本

  • 新增超轻量分类模型PPLCNet，在Intel CPU上，单张图像预测速度约5ms，ImageNet-1K数据集上Top1识别准确率达到80.82%，超越ResNet152的模型效果 欢迎体验
  • 新增轻量级检测特色模型PP-PicoDet，第一个在1M参数量之内mAP(0.5:0.95)超越30+(输入416像素时)，网络预测在ARM CPU下可达150FPS 欢迎体验
  • 升级PaddleX Restful API，支持飞桨动态图开发模式 欢迎体验
  • 新增检测模型负样本训练策略 欢迎体验
  • 新增python轻量级服务化部署 欢迎体验

详情内容请参考版本更新文档。

产品介绍

🤗 PaddleX 集成飞桨智能视觉领域图像分类、目标检测、语义分割、实例分割任务能力，将深度学习开发全流程从数据准备、模型训练与优化到多端部署端到端打通，并提供统一任务API接口及图形化开发界面Demo。开发者无需分别安装不同套件，以低代码的形式即可快速完成飞桨全流程开发。

🏭 PaddleX 经过质检、安防、巡检、遥感、零售、医疗等十多个行业实际应用场景验证，沉淀产业实际经验，并提供丰富的案例实践教程，全程助力开发者产业实践落地。

安装与快速体验

PaddleX提供了图像化开发界面、本地API、Restful-API三种开发模式。用户可根据自己的需求选择任意一种开始体验

产业级应用示例

PaddleX 使用文档

本文档介绍了PaddleX从数据准备、模型训练到模型剪裁量化，及最终部署的全流程使用方法。

1. 数据准备

2. 模型训练/评估/预测

3. 模型压缩

4. 模型部署

5. 附录

常见问题汇总

交流与反馈

🤗 贡献代码🤗

我们非常欢迎您为PaddleX贡献代码或者提供使用建议。如果您可以修复某个issue或者增加一个新功能，欢迎给我们提交Pull Requests。

开发者贡献项目

paddlex's People

Contributors

flyingqianmm avatar will-jl944 avatar jiangjiajun avatar larastustu avatar rollroll90 avatar sunahong1993 avatar syyxsxx avatar channingss avatar joey12300 avatar mamingjie-china avatar zeyuchen avatar lailuboy avatar heliqi avatar yzl19940819 avatar cjh3020889729 avatar wuyefeilin avatar holyseven avatar linhandev avatar furao123 avatar chliang avatar an1018 avatar kyanaww avatar xifeng-lin avatar shenyuhan avatar guojiahuiemily avatar heqinwill avatar angel-yi avatar ucsk avatar lutaochu avatar nqzhang avatar

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