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README
Apache-2.0

PaddleX

简介

PaddleX 3.0 是飞桨精选模型的低代码开发工具,支持国内外多款主流硬件的模型训练和推理,覆盖工业、能源、金融、交通、教育等全行业,助力开发者产业实践落地。

通用图像分类 通用目标检测 通用语义分割 通用实例分割
通用OCR 通用表格识别 通用场景信息抽取 文档场景信息抽取
时序预测 时序异常检测 时序分类 多模型融合时序预测

📣 近期更新

  • 🔥🔥《PaddleX图像分类产线赋能野生动物保护》,集成多种先进图像分类模型,满足多场景多样化需求。零代码开发破解相似类别商品分类难题,释放无标注数据潜力,精度一步到位。适用电商-商品分类、制造-产品质量监控、安防-人员出没与行为分析、环保-垃圾分类、环保-动植物识别等产业实际场景。8月8日(周四)19:00直播分享图像分类全流程解决方案,解密模型选型与快速调参技法。 报名链接
  • 🔥 2024.6.27,PaddleX 3.0 Beta 本地端正式发布,支持以低代码的方式在本地端使用多种主流硬件进行产线和模型开发。
  • 🔥 2024.3.25,PaddleX 3.0 云端发布,支持在AI Studio 星河社区以零代码的方式【创建产线】使用。

🌟 特性

PaddleX 3.0 集成了飞桨生态的优势能力,覆盖 7 大场景任务,构建了 16 条模型产线,提供低代码开发模式,助力开发者在多种主流硬件上实现模型全流程开发。

  • 基础模型产线(模型丰富,场景全面): 精选 68 个优质飞桨模型,涵盖图像分类、目标检测、图像分割、OCR、文本图像版面分析、文本图像信息抽取、时序分析任务场景。
  • 特色模型产线(显著提升效率): 提供大小模型结合、大模型半监督学习和多模型融合的高效解决方案。
  • 低门槛开发模式(便捷开发与部署): 提供零代码和低代码两种开发方式。
    • 零代码开发: 用户通过图形界面(GUI)交互式提交后台训练任务,打通在线和离线部署,并支持以 API 形式调用在线服务。
    • 低代码开发: 通过统一的 API 接口实现 16 条模型产线的全流程开发,同时支持用户自定义模型流程串联。
  • 多硬件本地支持(兼容性强): 支持英伟达 GPU、昆仑芯、昇腾和寒武纪等多种硬件,纯离线使用。

⚡ 安装与快速开始

🛠️ PaddleX 3.0 覆盖的模型和模型产线

PaddleX 3.0 覆盖了 16 条产业级模型产线,其中 9 条基础产线可以直接使用本仓库离线使用,7 条特色产线可在飞桨 AI Studio 星河社区上免费使用。

产线类型 模型产线 产线模块 具体模型
基础产线 通用图像分类 图像分类 CLIP_vit_base_patch16_224
CLIP_vit_large_patch14_224
more
ConvNeXt_tiny
MobileNetV2_x0_25
MobileNetV2_x0_5
MobileNetV2_x1_0
MobileNetV2_x1_5
MobileNetV2_x2_0
MobileNetV3_large_x0_35
MobileNetV3_large_x0_5
MobileNetV3_large_x0_75
MobileNetV3_large_x1_0
MobileNetV3_large_x1_25
MobileNetV3_small_x0_35
MobileNetV3_small_x0_5
MobileNetV3_small_x0_75
MobileNetV3_small_x1_0
MobileNetV3_small_x1_25
PP-HGNet_small
PP-HGNetV2-B0
PP-HGNetV2-B4
PP-HGNetV2-B6
PP-LCNet_x0_25
PP-LCNet_x0_35
PP-LCNet_x0_5
PP-LCNet_x0_75
PP-LCNet_x1_0
PP-LCNet_x1_5
PP-LCNet_x2_0
PP-LCNet_x2_5
ResNet18
ResNet34
ResNet50
ResNet101
ResNet152
SwinTransformer_base_patch4_window7_224
基础产线 通用目标检测 目标检测 PicoDet-S
PicoDet-L
more
PP-YOLOE_plus-S
PP-YOLOE_plus-M
PP-YOLOE_plus-L
PP-YOLOE_plus-X
RT-DETR-L
RT-DETR-H
RT-DETR-X
RT-DETR-R18
RT-DETR-R50
基础产线 通用语义分割 语义分割 OCRNet_HRNet-W48
PP-LiteSeg-T
more
Deeplabv3-R50
Deeplabv3-R101
Deeplabv3_Plus-R50
Deeplabv3_Plus-R101
基础产线 通用实例分割 实例分割 Mask-RT-DETR-L
Mask-RT-DETR-H
基础产线 通用OCR 文本检测 PP-OCRv4_mobile_det
PP-OCRv4_server_det
文本识别 PP-OCRv4_mobile_rec
PP-OCRv4_server_rec
基础产线 通用表格识别 版面区域检测 PicoDet layout_1x
表格识别 SLANet
文本检测 PP-OCRv4_mobile_det
PP-OCRv4_server_det
文本识别 PP-OCRv4_mobile_rec
PP-OCRv4_server_rec
基础产线 时序预测 时序预测 DLinear
Nonstationary
TiDE
PatchTST
TimesNet
基础产线 时序异常检测 时序异常检测 DLinear_ad
Nonstationary_ad
AutoEncoder_ad
PatchTST_ad
TimesNet_ad
基础产线 时序分类 时序分类 TimesNet_cls
特色产线 大模型半监督学习-图像分类 大模型半监督学习-图像分类 CLIP_vit_base_patch16_224
MobileNetV3_small_x1_0
morePP-HGNet_small
PP-HGNetV2-B0
PP-HGNetV2-B4
PP-HGNetV2-B6
PP-LCNet_x1_0
ResNet50
SwinTransformer_base_patch4_window7_224
特色产线 大模型半监督学习-目标检测 大模型半监督学习-目标检测 PicoDet-S
PicoDet-L
more
PP-YOLOE plus-S
PP-YOLOE_plus-L
RT-DETR-H
特色产线 大模型半监督学习-OCR 文本检测 PP-OCRv4_mobile_det
PP-OCRv4_server_det
大模型半监督学习-文本识别 PP-OCRv4_mobile_rec
PP-OCRv4_server_rec
特色产线 通用场景信息抽取v2
(PP-ChatOCRv2-common)
文本识别 PP-OCRv4_mobile_rec
PP-OCRv4_server_rec
文本检测 PP-OCRv4_mobile_det
PP-OCRv4_server_det
prompt工程 -
特色产线 文档场景信息抽取v2
(PP-ChatOCRv2-doc)
版面分析 PicoDet layout_1x
文本检测 PP-OCRv4_mobile_det
PP-OCRv4_server_det
文本识别 PP-OCRv4_mobile_rec
PP-OCRv4_server_rec
表格识别 SLANet
prompt工程 -
特色产线 多模型融合时序预测v2
(PP-TSv2_forecast)
时序预测 多模型融合时序预测
特色产线 多模型融合时序异常检测v2
(PP-TSv2_anomaly)
时序异常检测 多模型融合时序异常检测

📖 零代码开发教程

📖 低代码开发教程

一、模型产线开发工具 🔥

PaddleX 3.0 模型产线开发工具支持开发者通过 6 个步骤,完成产业级落地解决方案的开发。PaddleX 3.0 支持的模型产线可以参考 PaddleX 模型产线列表

二、单模型开发工具 🚀

PaddleX 3.0 单模型开发工具支持开发者以低代码的方式快速实现模型的开发和优化,包括数据准备、模型训练/评估、模型推理的使用方法,方便低成本集成到模型产线中。PaddleX3.0 支持的模型可以参考 PaddleX 模型库

🌟 多硬件支持

PaddleX 3.0 支持在多种硬件上进行模型的开发,除了 GPU 外,当前支持的硬件还有昆仑芯昇腾寒武纪。只需添加一个配置设备的参数,即可在对应硬件上使用上述工具。使用方式详情多硬件使用

👀 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。

许可证书

本项目的发布受Apache 2.0 license许可认证。

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Low-code development tool based on PaddlePaddle(『飞桨』低代码开发工具) expand collapse
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