paddleclas


Namepaddleclas JSON
Version 2.6.0 PyPI version JSON
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home_pagehttps://github.com/PaddlePaddle/PaddleClas
SummaryA treasure chest for visual recognition powered by PaddlePaddle.
upload_time2024-11-05 11:17:27
maintainerNone
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authorNone
requires_python>=3.8
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keywords image-classification image-recognition pretrained-models knowledge-distillation product-recognition autoaugment cutmix randaugment gridmask deit repvgg swin-transformer image-retrieval-system
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            简体中文 | [English](README_en.md)

# 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-ShiTuV2图像识别系统效果展示             |                PULC实用图像分类模型效果展示                 |
| :----------------------------------------------------: | :---------------------------------------------------------: |
| <img src="./docs/images/shituv2.gif"  width = "450" /> | <img src="./docs/images/class_simple.gif"  width = "600" /> |


## 📣 近期更新

- **🔥2024.11.5 添加图像分类和图像检索领域低代码全流程开发能力**:
  *  飞桨低代码开发工具PaddleX，依托于PaddleClas的先进技术，支持了图像分类和图像检索领域的**低代码全流程**开发能力：
     * 🎨 [**模型丰富一键调用**](docs/zh_CN/paddlex/quick_start.md)：将通用图像分类、图像多标签分类、通用图像识别、人脸识别涉及的**98个模型**整合为6条模型产线，通过极简的**Python API一键调用**，快速体验模型效果。此外，同一套API，也支持目标检测、图像分割、文本图像智能分析、通用OCR、时序预测等共计**200+模型**，形成20+单功能模块，方便开发者进行**模型组合使用**。
     * 🚀 [**提高效率降低门槛**](docs/zh_CN/paddlex/overview.md)：提供基于**统一命令**和**图形界面**两种方式，实现模型简洁高效的使用、组合与定制。支持**高性能推理、服务化部署和端侧部署**等多种部署方式。此外，对于各种主流硬件如**英伟达GPU、昆仑芯、昇腾、寒武纪和海光**等，进行模型开发时，都可以**无缝切换**。
  * 新增图像分类算法[**MobileNetV4、StarNet、FasterNet**](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/cv_modules/image_classification.md)
  * 新增服务端图像识别模型（图像特征）[**PP-ShiTuV2_rec_CLIP_vit_base、PP-ShiTuV2_rec_CLIP_vit_large**](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/cv_modules/image_feature.md)
  * 新增多标签图像分类模型[**CLIP_vit_base_patch16_448_ML、PP-HGNetV2-B0_ML、PP-HGNetV2-B4_ML、PP-HGNetV2-B6_ML、PP-LCNet_x1_0_ML、ResNet50_ML**](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/cv_modules/ml_classification.md)
  * 新增人脸识别模型[**MobileFaceNet、ResNet50_face**](https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/module_usage/tutorials/cv_modules/face_recognition.md)，新增[人脸识别端到端系统](https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/pipeline_usage/tutorials/cv_pipelines/face_recognition.md)。

- 2022.9.13 发布超轻量图像识别系统[PP-ShiTuV2](docs/zh_CN/models/PP-ShiTu/README.md)：
  - recall1精度提升8个点，覆盖商品识别、垃圾分类、航拍场景等[20+识别场景](docs/zh_CN/deployment/PP-ShiTu/application_scenarios.md)，
  - 新增[库管理工具](./deploy/shitu_index_manager/)，[Android Demo](./docs/zh_CN/quick_start/quick_start_recognition.md)全新体验。

- [more](docs/zh_CN/version_history.md)


## 🌟 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](docs/zh_CN/models/ImageNet1k/PP-HGNet.md)、[PP-LCNetv2](docs/zh_CN/models/ImageNet1k/PP-LCNetV2.md)、 [PP-LCNet](docs/zh_CN/models/ImageNet1k/PP-LCNet.md)和[SSLD半监督知识蒸馏方案](docs/zh_CN/training/advanced/ssld.md)等模型，在此基础上打造[PULC超轻量图像分类方案](docs/zh_CN/quick_start/PULC.md)和[PP-ShiTu图像识别系统](./docs/zh_CN/quick_start/quick_start_recognition.md)。

<div align="center">
  <img src="https://user-images.githubusercontent.com/50011306/198961573-06a1a78d-7669-4061-aba5-79e9a2fc84dc.png"/>
</div>

> 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](docs/zh_CN/paddlex/quick_start.md)

- [🔥 一键调用98个PaddleClas核心模型](docs/zh_CN/paddlex/quick_start.md)
- PULC超轻量图像分类方案快速体验：[点击这里](docs/zh_CN/quick_start/PULC.md)
- PP-ShiTu图像识别快速体验：[点击这里](./docs/zh_CN/quick_start/quick_start_recognition.md)
- PP-ShiTuV2 Android Demo APP，可扫描如下二维码，下载体验

<div align="center">
<img src="./docs/images/quick_start/android_demo/PPShiTu_qrcode.png"  width = "170" height = "170" />
</div>

## 🔥 [低代码全流程开发](docs/zh_CN/paddlex/overview.md)


## 🛠️ 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](./docs/zh_CN/models/PULC/model_list.md) |
| 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](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/pretrain/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_pretrained.pdparams)  / [推理模型](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer.tar)<br />识别模型：[预训练模型](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/pretrain/PPShiTuV2/general_PPLCNetV2_base_pretrained_v1.0.pdparams)  / [推理模型](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/PP-ShiTuV2/general_PPLCNetV2_base_pretrained_v1.0_infer.tar) |
| 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](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x1_0_pretrained.pdparams)  / [推理模型](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x1_0_infer.tar) |
| PP-LCNetV2 轻量骨干网络     | 针对Intel CPU设备，适配OpenVINO      | PPLCNetV2_base：[预训练模型](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNetV2_base_pretrained.pdparams)  / [推理模型](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNetV2_base_infer.tar) |
| 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](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNet_small_pretrained.pdparams)  / [推理模型](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPHGNet_small_infer.tar) |

> 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

- 基于PP-ShiTuV2的生鲜品自助结算： [点击这里](./docs/zh_CN/samples/Fresh_Food_Recogniiton/README.md)
- 基于PULC人员出入视频管理： [点击这里](./docs/zh_CN/samples/Personnel_Access/README.md)
- 基于PP-ShiTu 的智慧商超商品识别：[点击这里](./docs/zh_CN/samples/Goods_Recognition/README.md)
- 基于PP-ShiTu电梯内电瓶车入室识别：[点击这里](./docs/zh_CN/samples//Electromobile_In_Elevator_Detection/README.md)

## 📖 文档教程
- [环境准备](docs/zh_CN/installation.md)
- [PP-ShiTuV2图像识别系统介绍](docs/zh_CN/models/PP-ShiTu/README.md)
  - [图像识别快速体验](docs/zh_CN/quick_start/quick_start_recognition.md)
  - [20+应用场景库](docs/zh_CN/deployment/PP-ShiTu/application_scenarios.md)
  - 子模块算法介绍及模型训练
    - [主体检测](docs/zh_CN/training/PP-ShiTu/mainbody_detection.md)
    - [特征提取模型](docs/zh_CN/training/PP-ShiTu/feature_extraction.md)
    - [向量检索](docs/zh_CN/deployment/PP-ShiTu/vector_search.md)
    - [哈希编码](docs/zh_CN/training/PP-ShiTu/deep_hashing.md)
  - PipeLine 推理部署
    - [基于python预测引擎推理](docs/zh_CN/deployment/PP-ShiTu/python.md)
    - [基于C++预测引擎推理](docs/zh_CN/deployment/PP-ShiTu/cpp.md)
    - [服务化部署](docs/zh_CN/deployment/PP-ShiTu/paddle_serving.md)
    - [端侧部署](docs/zh_CN/deployment/PP-ShiTu/paddle_lite.md)
    - [库管理工具](docs/zh_CN/deployment/PP-ShiTu/gallery_manager.md)
- [PULC超轻量图像分类实用方案](docs/zh_CN/training/PULC.md)
  - [超轻量图像分类快速体验](docs/zh_CN/quick_start/PULC.md)
  - [超轻量图像分类模型库](docs/zh_CN/models/PULC/model_list.md)
    - [PULC有人/无人分类模型](docs/zh_CN/models/PULC/PULC_person_exists.md)
    - [PULC人体属性识别模型](docs/zh_CN/models/PULC/PULC_person_attribute.md)
    - [PULC佩戴安全帽分类模型](docs/zh_CN/models/PULC/PULC_safety_helmet.md)
    - [PULC交通标志分类模型](docs/zh_CN/models/PULC/PULC_traffic_sign.md)
    - [PULC车辆属性识别模型](docs/zh_CN/models/PULC/PULC_vehicle_attribute.md)
    - [PULC有车/无车分类模型](docs/zh_CN/models/PULC/PULC_car_exists.md)
    - [PULC含文字图像方向分类模型](docs/zh_CN/models/PULC/PULC_text_image_orientation.md)
    - [PULC文本行方向分类模型](docs/zh_CN/models/PULC/PULC_textline_orientation.md)
    - [PULC语种分类模型](docs/zh_CN/models/PULC/PULC_language_classification.md)
    - [PULC表格属性识别模型](docs/zh_CN/models/PULC/PULC_table_attribute.md)
    - [PULC有无广告码分类模型](docs/zh_CN/models/PULC/PULC_code_exists.md)
    - [PULC清晰度评估模型](docs/zh_CN/models/PULC/PULC_clarity_assessment.md)
    - [PULC图像方向分类模型](docs/zh_CN/models/PULC/PULC_image_orientation.md)
  - [模型训练](docs/zh_CN/training/PULC.md)
  - 推理部署
    - [基于python预测引擎推理](docs/zh_CN/deployment/image_classification/python.md#1)
    - [基于C++预测引擎推理](docs/zh_CN/deployment/image_classification/cpp/linux.md)
    - [服务化部署](docs/zh_CN/deployment/image_classification/paddle_serving.md)
    - [端侧部署](docs/zh_CN/deployment/image_classification/paddle_lite.md)
    - [Paddle2ONNX模型转化与预测](docs/zh_CN/deployment/image_classification/paddle2onnx.md)
  - [模型压缩](deploy/slim/README.md)
- PP系列骨干网络模型
  - [PP-HGNet](docs/zh_CN/models/ImageNet1k/PP-HGNet.md)
  - [PP-LCNetv2](docs/zh_CN/models/ImageNet1k/PP-LCNetV2.md)
  - [PP-LCNet](docs/zh_CN/models/ImageNet1k/PP-LCNet.md)
- [SSLD半监督知识蒸馏方案](docs/zh_CN/training/advanced/ssld.md)
- 前沿算法
  - [骨干网络和预训练模型库](docs/zh_CN/models/ImageNet1k/model_list.md)
  - [度量学习](docs/zh_CN/algorithm_introduction/metric_learning.md)
    - [ReID](./docs/zh_CN/algorithm_introduction/ReID.md)
  - [模型压缩](docs/zh_CN/algorithm_introduction/prune_quantization.md)
  - [模型蒸馏](./docs/zh_CN/training/advanced/knowledge_distillation.md)
  - [数据增强](docs/zh_CN/training/config_description/data_augmentation.md)
- [产业实用范例库](docs/zh_CN/samples)
- [30分钟快速体验图像分类](docs/zh_CN/quick_start/quick_start_classification_new_user.md)
- FAQ
  - [图像识别精选问题](docs/zh_CN/FAQ/faq_2021_s2.md)
  - [图像分类精选问题](docs/zh_CN/FAQ/faq_selected_30.md)
  - [图像分类FAQ第一季](docs/zh_CN/FAQ/faq_2020_s1.md)
  - [图像分类FAQ第二季](docs/zh_CN/FAQ/faq_2021_s1.md)
  - [图像分类FAQ第三季](docs/zh_CN/FAQ/faq_2022_s1.md)
- [社区贡献指南](docs/zh_CN/community/how_to_contribute.md)
- [许可证书](#许可证书)
- [贡献代码](#贡献代码)

<a name="图像识别系统介绍"></a>

## PP-ShiTuV2图像识别系统

<div align="center">
<img src="./docs/images/structure.jpg"  width = "800" />
</div>


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](docs/zh_CN/models/PP-ShiTu/README.md)。

<a name="识别效果展示"></a>

## PP-ShiTuV2图像识别系统效果展示

- 瓶装饮料识别

<div align="center">
<img src="docs/images/drink_demo.gif">
</div>


- 商品识别

<div align="center">
<img src="https://user-images.githubusercontent.com/18028216/122769644-51604f80-d2d7-11eb-8290-c53b12a5c1f6.gif"  width = "400" />
</div>


- 动漫人物识别

<div align="center">
<img src="https://user-images.githubusercontent.com/18028216/122769746-6b019700-d2d7-11eb-86df-f1d710999ba6.gif"  width = "400" />
</div>


- logo识别

<div align="center">
<img src="https://user-images.githubusercontent.com/18028216/122769837-7fde2a80-d2d7-11eb-9b69-04140e9d785f.gif"  width = "400" />
</div>



- 车辆识别

<div align="center">
<img src="https://user-images.githubusercontent.com/18028216/122769916-8ec4dd00-d2d7-11eb-8c60-42d89e25030c.gif"  width = "400" />
</div>



<a name="PULC超轻量图像分类方案"></a>

## PULC超轻量图像分类方案
<div align="center">
<img src="https://user-images.githubusercontent.com/19523330/173011854-b10fcd7a-b799-4dfd-a1cf-9504952a3c44.png"  width = "800" />
</div>
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。

<a name="分类效果展示"></a>

## PULC实用图像分类模型效果展示
<div align="center">
<img src="docs/images/classification.gif">
</div>


<a name="许可证书"></a>

## 许可证书
本项目的发布受<a href="https://github.com/PaddlePaddle/PaddleCLS/blob/master/LICENSE">Apache 2.0 license</a>许可认证。


<a name="贡献代码"></a>
## 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](docs/zh_CN/community/how_to_contribute.md)。

- 非常感谢[nblib](https://github.com/nblib)修正了PaddleClas中RandErasing的数据增广配置文件。
- 非常感谢[chenpy228](https://github.com/chenpy228)修正了PaddleClas文档中的部分错别字。
- 非常感谢[jm12138](https://github.com/jm12138)为PaddleClas添加ViT，DeiT系列模型和RepVGG系列模型。

Raw data

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    "platform": null,
    "description": "\u7b80\u4f53\u4e2d\u6587 | [English](README_en.md)\n\n# PaddleClas\n\n## \u7b80\u4ecb\n\n\u98de\u6868\u56fe\u50cf\u8bc6\u522b\u5957\u4ef6PaddleClas\u662f\u98de\u6868\u4e3a\u5de5\u4e1a\u754c\u548c\u5b66\u672f\u754c\u6240\u51c6\u5907\u7684\u4e00\u4e2a\u56fe\u50cf\u8bc6\u522b\u548c\u56fe\u50cf\u5206\u7c7b\u4efb\u52a1\u7684\u5de5\u5177\u96c6\uff0c\u52a9\u529b\u4f7f\u7528\u8005\u8bad\u7ec3\u51fa\u66f4\u597d\u7684\u89c6\u89c9\u6a21\u578b\u548c\u5e94\u7528\u843d\u5730\u3002\n\n|             PP-ShiTuV2\u56fe\u50cf\u8bc6\u522b\u7cfb\u7edf\u6548\u679c\u5c55\u793a             |                PULC\u5b9e\u7528\u56fe\u50cf\u5206\u7c7b\u6a21\u578b\u6548\u679c\u5c55\u793a                 |\n| :----------------------------------------------------: | :---------------------------------------------------------: |\n| <img src=\"./docs/images/shituv2.gif\"  width = \"450\" /> | <img src=\"./docs/images/class_simple.gif\"  width = \"600\" /> |\n\n\n## \ud83d\udce3 \u8fd1\u671f\u66f4\u65b0\n\n- **\ud83d\udd252024.11.5 \u6dfb\u52a0\u56fe\u50cf\u5206\u7c7b\u548c\u56fe\u50cf\u68c0\u7d22\u9886\u57df\u4f4e\u4ee3\u7801\u5168\u6d41\u7a0b\u5f00\u53d1\u80fd\u529b**:\n  *  \u98de\u6868\u4f4e\u4ee3\u7801\u5f00\u53d1\u5de5\u5177PaddleX\uff0c\u4f9d\u6258\u4e8ePaddleClas\u7684\u5148\u8fdb\u6280\u672f\uff0c\u652f\u6301\u4e86\u56fe\u50cf\u5206\u7c7b\u548c\u56fe\u50cf\u68c0\u7d22\u9886\u57df\u7684**\u4f4e\u4ee3\u7801\u5168\u6d41\u7a0b**\u5f00\u53d1\u80fd\u529b\uff1a\n     * \ud83c\udfa8 [**\u6a21\u578b\u4e30\u5bcc\u4e00\u952e\u8c03\u7528**](docs/zh_CN/paddlex/quick_start.md)\uff1a\u5c06\u901a\u7528\u56fe\u50cf\u5206\u7c7b\u3001\u56fe\u50cf\u591a\u6807\u7b7e\u5206\u7c7b\u3001\u901a\u7528\u56fe\u50cf\u8bc6\u522b\u3001\u4eba\u8138\u8bc6\u522b\u6d89\u53ca\u7684**98\u4e2a\u6a21\u578b**\u6574\u5408\u4e3a6\u6761\u6a21\u578b\u4ea7\u7ebf\uff0c\u901a\u8fc7\u6781\u7b80\u7684**Python API\u4e00\u952e\u8c03\u7528**\uff0c\u5feb\u901f\u4f53\u9a8c\u6a21\u578b\u6548\u679c\u3002\u6b64\u5916\uff0c\u540c\u4e00\u5957API\uff0c\u4e5f\u652f\u6301\u76ee\u6807\u68c0\u6d4b\u3001\u56fe\u50cf\u5206\u5272\u3001\u6587\u672c\u56fe\u50cf\u667a\u80fd\u5206\u6790\u3001\u901a\u7528OCR\u3001\u65f6\u5e8f\u9884\u6d4b\u7b49\u5171\u8ba1**200+\u6a21\u578b**\uff0c\u5f62\u621020+\u5355\u529f\u80fd\u6a21\u5757\uff0c\u65b9\u4fbf\u5f00\u53d1\u8005\u8fdb\u884c**\u6a21\u578b\u7ec4\u5408\u4f7f\u7528**\u3002\n     * \ud83d\ude80 [**\u63d0\u9ad8\u6548\u7387\u964d\u4f4e\u95e8\u69db**](docs/zh_CN/paddlex/overview.md)\uff1a\u63d0\u4f9b\u57fa\u4e8e**\u7edf\u4e00\u547d\u4ee4**\u548c**\u56fe\u5f62\u754c\u9762**\u4e24\u79cd\u65b9\u5f0f\uff0c\u5b9e\u73b0\u6a21\u578b\u7b80\u6d01\u9ad8\u6548\u7684\u4f7f\u7528\u3001\u7ec4\u5408\u4e0e\u5b9a\u5236\u3002\u652f\u6301**\u9ad8\u6027\u80fd\u63a8\u7406\u3001\u670d\u52a1\u5316\u90e8\u7f72\u548c\u7aef\u4fa7\u90e8\u7f72**\u7b49\u591a\u79cd\u90e8\u7f72\u65b9\u5f0f\u3002\u6b64\u5916\uff0c\u5bf9\u4e8e\u5404\u79cd\u4e3b\u6d41\u786c\u4ef6\u5982**\u82f1\u4f1f\u8fbeGPU\u3001\u6606\u4ed1\u82af\u3001\u6607\u817e\u3001\u5bd2\u6b66\u7eaa\u548c\u6d77\u5149**\u7b49\uff0c\u8fdb\u884c\u6a21\u578b\u5f00\u53d1\u65f6\uff0c\u90fd\u53ef\u4ee5**\u65e0\u7f1d\u5207\u6362**\u3002\n  * \u65b0\u589e\u56fe\u50cf\u5206\u7c7b\u7b97\u6cd5[**MobileNetV4\u3001StarNet\u3001FasterNet**](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/cv_modules/image_classification.md)\n  * \u65b0\u589e\u670d\u52a1\u7aef\u56fe\u50cf\u8bc6\u522b\u6a21\u578b\uff08\u56fe\u50cf\u7279\u5f81\uff09[**PP-ShiTuV2_rec_CLIP_vit_base\u3001PP-ShiTuV2_rec_CLIP_vit_large**](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/cv_modules/image_feature.md)\n  * \u65b0\u589e\u591a\u6807\u7b7e\u56fe\u50cf\u5206\u7c7b\u6a21\u578b[**CLIP_vit_base_patch16_448_ML\u3001PP-HGNetV2-B0_ML\u3001PP-HGNetV2-B4_ML\u3001PP-HGNetV2-B6_ML\u3001PP-LCNet_x1_0_ML\u3001ResNet50_ML**](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/cv_modules/ml_classification.md)\n  * \u65b0\u589e\u4eba\u8138\u8bc6\u522b\u6a21\u578b[**MobileFaceNet\u3001ResNet50_face**](https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/module_usage/tutorials/cv_modules/face_recognition.md)\uff0c\u65b0\u589e[\u4eba\u8138\u8bc6\u522b\u7aef\u5230\u7aef\u7cfb\u7edf](https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/pipeline_usage/tutorials/cv_pipelines/face_recognition.md)\u3002\n\n- 2022.9.13 \u53d1\u5e03\u8d85\u8f7b\u91cf\u56fe\u50cf\u8bc6\u522b\u7cfb\u7edf[PP-ShiTuV2](docs/zh_CN/models/PP-ShiTu/README.md)\uff1a\n  - recall1\u7cbe\u5ea6\u63d0\u53478\u4e2a\u70b9\uff0c\u8986\u76d6\u5546\u54c1\u8bc6\u522b\u3001\u5783\u573e\u5206\u7c7b\u3001\u822a\u62cd\u573a\u666f\u7b49[20+\u8bc6\u522b\u573a\u666f](docs/zh_CN/deployment/PP-ShiTu/application_scenarios.md)\uff0c\n  - \u65b0\u589e[\u5e93\u7ba1\u7406\u5de5\u5177](./deploy/shitu_index_manager/)\uff0c[Android Demo](./docs/zh_CN/quick_start/quick_start_recognition.md)\u5168\u65b0\u4f53\u9a8c\u3002\n\n- [more](docs/zh_CN/version_history.md)\n\n\n## \ud83c\udf1f \u7279\u6027\n\nPaddleClas\u652f\u6301\u591a\u79cd\u524d\u6cbf\u56fe\u50cf\u5206\u7c7b\u3001\u8bc6\u522b\u76f8\u5173\u7b97\u6cd5\uff0c\u53d1\u5e03\u4ea7\u4e1a\u7ea7\u7279\u8272\u9aa8\u5e72\u7f51\u7edc[PP-HGNet](docs/zh_CN/models/ImageNet1k/PP-HGNet.md)\u3001[PP-LCNetv2](docs/zh_CN/models/ImageNet1k/PP-LCNetV2.md)\u3001 [PP-LCNet](docs/zh_CN/models/ImageNet1k/PP-LCNet.md)\u548c[SSLD\u534a\u76d1\u7763\u77e5\u8bc6\u84b8\u998f\u65b9\u6848](docs/zh_CN/training/advanced/ssld.md)\u7b49\u6a21\u578b\uff0c\u5728\u6b64\u57fa\u7840\u4e0a\u6253\u9020[PULC\u8d85\u8f7b\u91cf\u56fe\u50cf\u5206\u7c7b\u65b9\u6848](docs/zh_CN/quick_start/PULC.md)\u548c[PP-ShiTu\u56fe\u50cf\u8bc6\u522b\u7cfb\u7edf](./docs/zh_CN/quick_start/quick_start_recognition.md)\u3002\n\n<div align=\"center\">\n  <img src=\"https://user-images.githubusercontent.com/50011306/198961573-06a1a78d-7669-4061-aba5-79e9a2fc84dc.png\"/>\n</div>\n\n> \u4e0a\u8ff0\u5185\u5bb9\u7684\u4f7f\u7528\u65b9\u6cd5\u5efa\u8bae\u4ece\u6587\u6863\u6559\u7a0b\u4e2d\u7684\u5feb\u901f\u5f00\u59cb\u4f53\u9a8c\n\n\n## \u26a1 [\u5feb\u901f\u5f00\u59cb](docs/zh_CN/paddlex/quick_start.md)\n\n- [\ud83d\udd25 \u4e00\u952e\u8c03\u752898\u4e2aPaddleClas\u6838\u5fc3\u6a21\u578b](docs/zh_CN/paddlex/quick_start.md)\n- PULC\u8d85\u8f7b\u91cf\u56fe\u50cf\u5206\u7c7b\u65b9\u6848\u5feb\u901f\u4f53\u9a8c\uff1a[\u70b9\u51fb\u8fd9\u91cc](docs/zh_CN/quick_start/PULC.md)\n- PP-ShiTu\u56fe\u50cf\u8bc6\u522b\u5feb\u901f\u4f53\u9a8c\uff1a[\u70b9\u51fb\u8fd9\u91cc](./docs/zh_CN/quick_start/quick_start_recognition.md)\n- PP-ShiTuV2 Android Demo APP\uff0c\u53ef\u626b\u63cf\u5982\u4e0b\u4e8c\u7ef4\u7801\uff0c\u4e0b\u8f7d\u4f53\u9a8c\n\n<div align=\"center\">\n<img src=\"./docs/images/quick_start/android_demo/PPShiTu_qrcode.png\"  width = \"170\" height = \"170\" />\n</div>\n\n## \ud83d\udd25 [\u4f4e\u4ee3\u7801\u5168\u6d41\u7a0b\u5f00\u53d1](docs/zh_CN/paddlex/overview.md)\n\n\n## \ud83d\udee0\ufe0f PP\u7cfb\u5217\u6a21\u578b\u5217\u8868\n\n| \u6a21\u578b\u7b80\u4ecb                    | \u5e94\u7528\u573a\u666f                             | \u6a21\u578b\u4e0b\u8f7d\u94fe\u63a5                                                 |\n| --------------------------- | ------------------------------------ | ------------------------------------------------------------ |\n| PULC \u8d85\u8f7b\u91cf\u56fe\u50cf\u5206\u7c7b\u65b9\u6848     | \u56fa\u5b9a\u56fe\u50cf\u7c7b\u522b\u5206\u7c7b\u65b9\u6848                 | \u4eba\u4f53\u3001\u8f66\u8f86\u3001\u6587\u5b57\u76f8\u51739\u5927\u6a21\u578b\uff1a[\u6a21\u578b\u5e93\u8fde\u63a5](./docs/zh_CN/models/PULC/model_list.md) |\n| PP-ShituV2 \u8f7b\u91cf\u56fe\u50cf\u8bc6\u522b\u7cfb\u7edf | \u9488\u5bf9\u573a\u666f\u6570\u636e\u7c7b\u522b\u9891\u7e41\u53d8\u52a8\u3001\u7c7b\u522b\u6570\u636e\u591a | \u4e3b\u4f53\u68c0\u6d4b\u6a21\u578b\uff1a[\u9884\u8bad\u7ec3\u6a21\u578b](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/pretrain/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_pretrained.pdparams)  / [\u63a8\u7406\u6a21\u578b](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer.tar)<br />\u8bc6\u522b\u6a21\u578b\uff1a[\u9884\u8bad\u7ec3\u6a21\u578b](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/pretrain/PPShiTuV2/general_PPLCNetV2_base_pretrained_v1.0.pdparams)  / [\u63a8\u7406\u6a21\u578b](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/PP-ShiTuV2/general_PPLCNetV2_base_pretrained_v1.0_infer.tar) |\n| PP-LCNet \u8f7b\u91cf\u9aa8\u5e72\u7f51\u7edc       | \u9488\u5bf9Intel CPU\u8bbe\u5907\u53caMKLDNN\u52a0\u901f\u5e93\u5b9a\u5236  | PPLCNet_x1_0\uff1a[\u9884\u8bad\u7ec3\u6a21\u578b](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x1_0_pretrained.pdparams)  / [\u63a8\u7406\u6a21\u578b](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x1_0_infer.tar) |\n| PP-LCNetV2 \u8f7b\u91cf\u9aa8\u5e72\u7f51\u7edc     | \u9488\u5bf9Intel CPU\u8bbe\u5907\uff0c\u9002\u914dOpenVINO      | PPLCNetV2_base\uff1a[\u9884\u8bad\u7ec3\u6a21\u578b](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNetV2_base_pretrained.pdparams)  / [\u63a8\u7406\u6a21\u578b](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNetV2_base_infer.tar) |\n| PP-HGNet \u9ad8\u7cbe\u5ea6\u9aa8\u5e72\u7f51\u7edc     | GPU\u8bbe\u5907\u4e0a\u76f8\u540c\u63a8\u7406\u65f6\u95f4\u7cbe\u5ea6\u66f4\u9ad8        | PPHGNet_small\uff1a[\u9884\u8bad\u7ec3\u6a21\u578b](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNet_small_pretrained.pdparams)  / [\u63a8\u7406\u6a21\u578b](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPHGNet_small_infer.tar) |\n\n> \u5168\u90e8\u6a21\u578b\u4e0b\u8f7d\u94fe\u63a5\u53ef\u67e5\u770b \u6587\u6863\u6559\u7a0b \u4e2d\u7684\u5404\u6a21\u578b\u4ecb\u7ecd\n\n### \u4ea7\u4e1a\u8303\u4f8b\n\n- \u57fa\u4e8ePP-ShiTuV2\u7684\u751f\u9c9c\u54c1\u81ea\u52a9\u7ed3\u7b97\uff1a [\u70b9\u51fb\u8fd9\u91cc](./docs/zh_CN/samples/Fresh_Food_Recogniiton/README.md)\n- \u57fa\u4e8ePULC\u4eba\u5458\u51fa\u5165\u89c6\u9891\u7ba1\u7406\uff1a [\u70b9\u51fb\u8fd9\u91cc](./docs/zh_CN/samples/Personnel_Access/README.md)\n- \u57fa\u4e8ePP-ShiTu \u7684\u667a\u6167\u5546\u8d85\u5546\u54c1\u8bc6\u522b\uff1a[\u70b9\u51fb\u8fd9\u91cc](./docs/zh_CN/samples/Goods_Recognition/README.md)\n- \u57fa\u4e8ePP-ShiTu\u7535\u68af\u5185\u7535\u74f6\u8f66\u5165\u5ba4\u8bc6\u522b\uff1a[\u70b9\u51fb\u8fd9\u91cc](./docs/zh_CN/samples//Electromobile_In_Elevator_Detection/README.md)\n\n## \ud83d\udcd6 \u6587\u6863\u6559\u7a0b\n- [\u73af\u5883\u51c6\u5907](docs/zh_CN/installation.md)\n- [PP-ShiTuV2\u56fe\u50cf\u8bc6\u522b\u7cfb\u7edf\u4ecb\u7ecd](docs/zh_CN/models/PP-ShiTu/README.md)\n  - [\u56fe\u50cf\u8bc6\u522b\u5feb\u901f\u4f53\u9a8c](docs/zh_CN/quick_start/quick_start_recognition.md)\n  - [20+\u5e94\u7528\u573a\u666f\u5e93](docs/zh_CN/deployment/PP-ShiTu/application_scenarios.md)\n  - \u5b50\u6a21\u5757\u7b97\u6cd5\u4ecb\u7ecd\u53ca\u6a21\u578b\u8bad\u7ec3\n    - [\u4e3b\u4f53\u68c0\u6d4b](docs/zh_CN/training/PP-ShiTu/mainbody_detection.md)\n    - [\u7279\u5f81\u63d0\u53d6\u6a21\u578b](docs/zh_CN/training/PP-ShiTu/feature_extraction.md)\n    - [\u5411\u91cf\u68c0\u7d22](docs/zh_CN/deployment/PP-ShiTu/vector_search.md)\n    - [\u54c8\u5e0c\u7f16\u7801](docs/zh_CN/training/PP-ShiTu/deep_hashing.md)\n  - PipeLine \u63a8\u7406\u90e8\u7f72\n    - [\u57fa\u4e8epython\u9884\u6d4b\u5f15\u64ce\u63a8\u7406](docs/zh_CN/deployment/PP-ShiTu/python.md)\n    - [\u57fa\u4e8eC++\u9884\u6d4b\u5f15\u64ce\u63a8\u7406](docs/zh_CN/deployment/PP-ShiTu/cpp.md)\n    - [\u670d\u52a1\u5316\u90e8\u7f72](docs/zh_CN/deployment/PP-ShiTu/paddle_serving.md)\n    - [\u7aef\u4fa7\u90e8\u7f72](docs/zh_CN/deployment/PP-ShiTu/paddle_lite.md)\n    - [\u5e93\u7ba1\u7406\u5de5\u5177](docs/zh_CN/deployment/PP-ShiTu/gallery_manager.md)\n- [PULC\u8d85\u8f7b\u91cf\u56fe\u50cf\u5206\u7c7b\u5b9e\u7528\u65b9\u6848](docs/zh_CN/training/PULC.md)\n  - [\u8d85\u8f7b\u91cf\u56fe\u50cf\u5206\u7c7b\u5feb\u901f\u4f53\u9a8c](docs/zh_CN/quick_start/PULC.md)\n  - [\u8d85\u8f7b\u91cf\u56fe\u50cf\u5206\u7c7b\u6a21\u578b\u5e93](docs/zh_CN/models/PULC/model_list.md)\n    - [PULC\u6709\u4eba/\u65e0\u4eba\u5206\u7c7b\u6a21\u578b](docs/zh_CN/models/PULC/PULC_person_exists.md)\n    - [PULC\u4eba\u4f53\u5c5e\u6027\u8bc6\u522b\u6a21\u578b](docs/zh_CN/models/PULC/PULC_person_attribute.md)\n    - [PULC\u4f69\u6234\u5b89\u5168\u5e3d\u5206\u7c7b\u6a21\u578b](docs/zh_CN/models/PULC/PULC_safety_helmet.md)\n    - [PULC\u4ea4\u901a\u6807\u5fd7\u5206\u7c7b\u6a21\u578b](docs/zh_CN/models/PULC/PULC_traffic_sign.md)\n    - [PULC\u8f66\u8f86\u5c5e\u6027\u8bc6\u522b\u6a21\u578b](docs/zh_CN/models/PULC/PULC_vehicle_attribute.md)\n    - [PULC\u6709\u8f66/\u65e0\u8f66\u5206\u7c7b\u6a21\u578b](docs/zh_CN/models/PULC/PULC_car_exists.md)\n    - [PULC\u542b\u6587\u5b57\u56fe\u50cf\u65b9\u5411\u5206\u7c7b\u6a21\u578b](docs/zh_CN/models/PULC/PULC_text_image_orientation.md)\n    - [PULC\u6587\u672c\u884c\u65b9\u5411\u5206\u7c7b\u6a21\u578b](docs/zh_CN/models/PULC/PULC_textline_orientation.md)\n    - [PULC\u8bed\u79cd\u5206\u7c7b\u6a21\u578b](docs/zh_CN/models/PULC/PULC_language_classification.md)\n    - [PULC\u8868\u683c\u5c5e\u6027\u8bc6\u522b\u6a21\u578b](docs/zh_CN/models/PULC/PULC_table_attribute.md)\n    - [PULC\u6709\u65e0\u5e7f\u544a\u7801\u5206\u7c7b\u6a21\u578b](docs/zh_CN/models/PULC/PULC_code_exists.md)\n    - [PULC\u6e05\u6670\u5ea6\u8bc4\u4f30\u6a21\u578b](docs/zh_CN/models/PULC/PULC_clarity_assessment.md)\n    - [PULC\u56fe\u50cf\u65b9\u5411\u5206\u7c7b\u6a21\u578b](docs/zh_CN/models/PULC/PULC_image_orientation.md)\n  - [\u6a21\u578b\u8bad\u7ec3](docs/zh_CN/training/PULC.md)\n  - \u63a8\u7406\u90e8\u7f72\n    - [\u57fa\u4e8epython\u9884\u6d4b\u5f15\u64ce\u63a8\u7406](docs/zh_CN/deployment/image_classification/python.md#1)\n    - [\u57fa\u4e8eC++\u9884\u6d4b\u5f15\u64ce\u63a8\u7406](docs/zh_CN/deployment/image_classification/cpp/linux.md)\n    - [\u670d\u52a1\u5316\u90e8\u7f72](docs/zh_CN/deployment/image_classification/paddle_serving.md)\n    - [\u7aef\u4fa7\u90e8\u7f72](docs/zh_CN/deployment/image_classification/paddle_lite.md)\n    - [Paddle2ONNX\u6a21\u578b\u8f6c\u5316\u4e0e\u9884\u6d4b](docs/zh_CN/deployment/image_classification/paddle2onnx.md)\n  - [\u6a21\u578b\u538b\u7f29](deploy/slim/README.md)\n- PP\u7cfb\u5217\u9aa8\u5e72\u7f51\u7edc\u6a21\u578b\n  - [PP-HGNet](docs/zh_CN/models/ImageNet1k/PP-HGNet.md)\n  - [PP-LCNetv2](docs/zh_CN/models/ImageNet1k/PP-LCNetV2.md)\n  - [PP-LCNet](docs/zh_CN/models/ImageNet1k/PP-LCNet.md)\n- [SSLD\u534a\u76d1\u7763\u77e5\u8bc6\u84b8\u998f\u65b9\u6848](docs/zh_CN/training/advanced/ssld.md)\n- \u524d\u6cbf\u7b97\u6cd5\n  - [\u9aa8\u5e72\u7f51\u7edc\u548c\u9884\u8bad\u7ec3\u6a21\u578b\u5e93](docs/zh_CN/models/ImageNet1k/model_list.md)\n  - [\u5ea6\u91cf\u5b66\u4e60](docs/zh_CN/algorithm_introduction/metric_learning.md)\n    - [ReID](./docs/zh_CN/algorithm_introduction/ReID.md)\n  - [\u6a21\u578b\u538b\u7f29](docs/zh_CN/algorithm_introduction/prune_quantization.md)\n  - [\u6a21\u578b\u84b8\u998f](./docs/zh_CN/training/advanced/knowledge_distillation.md)\n  - [\u6570\u636e\u589e\u5f3a](docs/zh_CN/training/config_description/data_augmentation.md)\n- [\u4ea7\u4e1a\u5b9e\u7528\u8303\u4f8b\u5e93](docs/zh_CN/samples)\n- [30\u5206\u949f\u5feb\u901f\u4f53\u9a8c\u56fe\u50cf\u5206\u7c7b](docs/zh_CN/quick_start/quick_start_classification_new_user.md)\n- FAQ\n  - [\u56fe\u50cf\u8bc6\u522b\u7cbe\u9009\u95ee\u9898](docs/zh_CN/FAQ/faq_2021_s2.md)\n  - [\u56fe\u50cf\u5206\u7c7b\u7cbe\u9009\u95ee\u9898](docs/zh_CN/FAQ/faq_selected_30.md)\n  - [\u56fe\u50cf\u5206\u7c7bFAQ\u7b2c\u4e00\u5b63](docs/zh_CN/FAQ/faq_2020_s1.md)\n  - [\u56fe\u50cf\u5206\u7c7bFAQ\u7b2c\u4e8c\u5b63](docs/zh_CN/FAQ/faq_2021_s1.md)\n  - [\u56fe\u50cf\u5206\u7c7bFAQ\u7b2c\u4e09\u5b63](docs/zh_CN/FAQ/faq_2022_s1.md)\n- [\u793e\u533a\u8d21\u732e\u6307\u5357](docs/zh_CN/community/how_to_contribute.md)\n- [\u8bb8\u53ef\u8bc1\u4e66](#\u8bb8\u53ef\u8bc1\u4e66)\n- [\u8d21\u732e\u4ee3\u7801](#\u8d21\u732e\u4ee3\u7801)\n\n<a name=\"\u56fe\u50cf\u8bc6\u522b\u7cfb\u7edf\u4ecb\u7ecd\"></a>\n\n## PP-ShiTuV2\u56fe\u50cf\u8bc6\u522b\u7cfb\u7edf\n\n<div align=\"center\">\n<img src=\"./docs/images/structure.jpg\"  width = \"800\" />\n</div>\n\n\nPP-ShiTuV2\u662f\u4e00\u4e2a\u5b9e\u7528\u7684\u8f7b\u91cf\u7ea7\u901a\u7528\u56fe\u50cf\u8bc6\u522b\u7cfb\u7edf\uff0c\u4e3b\u8981\u7531\u4e3b\u4f53\u68c0\u6d4b\u3001\u7279\u5f81\u5b66\u4e60\u548c\u5411\u91cf\u68c0\u7d22\u4e09\u4e2a\u6a21\u5757\u7ec4\u6210\u3002\u8be5\u7cfb\u7edf\u4ece\u9aa8\u5e72\u7f51\u7edc\u9009\u62e9\u548c\u8c03\u6574\u3001\u635f\u5931\u51fd\u6570\u7684\u9009\u62e9\u3001\u6570\u636e\u589e\u5f3a\u3001\u5b66\u4e60\u7387\u53d8\u6362\u7b56\u7565\u3001\u6b63\u5219\u5316\u53c2\u6570\u9009\u62e9\u3001\u9884\u8bad\u7ec3\u6a21\u578b\u4f7f\u7528\u4ee5\u53ca\u6a21\u578b\u88c1\u526a\u91cf\u5316\u591a\u4e2a\u65b9\u9762\uff0c\u91c7\u7528\u591a\u79cd\u7b56\u7565\uff0c\u5bf9\u5404\u4e2a\u6a21\u5757\u7684\u6a21\u578b\u8fdb\u884c\u4f18\u5316\uff0cPP-ShiTuV2\u76f8\u6bd4V1\uff0cRecall1\u63d0\u5347\u8fd18\u4e2a\u70b9\u3002\u66f4\u591a\u7ec6\u8282\u8bf7\u53c2\u8003[PP-ShiTuV2\u8be6\u7ec6\u4ecb\u7ecd](docs/zh_CN/models/PP-ShiTu/README.md)\u3002\n\n<a name=\"\u8bc6\u522b\u6548\u679c\u5c55\u793a\"></a>\n\n## PP-ShiTuV2\u56fe\u50cf\u8bc6\u522b\u7cfb\u7edf\u6548\u679c\u5c55\u793a\n\n- \u74f6\u88c5\u996e\u6599\u8bc6\u522b\n\n<div align=\"center\">\n<img src=\"docs/images/drink_demo.gif\">\n</div>\n\n\n- \u5546\u54c1\u8bc6\u522b\n\n<div align=\"center\">\n<img src=\"https://user-images.githubusercontent.com/18028216/122769644-51604f80-d2d7-11eb-8290-c53b12a5c1f6.gif\"  width = \"400\" />\n</div>\n\n\n- \u52a8\u6f2b\u4eba\u7269\u8bc6\u522b\n\n<div align=\"center\">\n<img src=\"https://user-images.githubusercontent.com/18028216/122769746-6b019700-d2d7-11eb-86df-f1d710999ba6.gif\"  width = \"400\" />\n</div>\n\n\n- logo\u8bc6\u522b\n\n<div align=\"center\">\n<img src=\"https://user-images.githubusercontent.com/18028216/122769837-7fde2a80-d2d7-11eb-9b69-04140e9d785f.gif\"  width = \"400\" />\n</div>\n\n\n\n- \u8f66\u8f86\u8bc6\u522b\n\n<div align=\"center\">\n<img src=\"https://user-images.githubusercontent.com/18028216/122769916-8ec4dd00-d2d7-11eb-8c60-42d89e25030c.gif\"  width = \"400\" />\n</div>\n\n\n\n<a name=\"PULC\u8d85\u8f7b\u91cf\u56fe\u50cf\u5206\u7c7b\u65b9\u6848\"></a>\n\n## PULC\u8d85\u8f7b\u91cf\u56fe\u50cf\u5206\u7c7b\u65b9\u6848\n<div align=\"center\">\n<img src=\"https://user-images.githubusercontent.com/19523330/173011854-b10fcd7a-b799-4dfd-a1cf-9504952a3c44.png\"  width = \"800\" />\n</div>\nPULC\u878d\u5408\u4e86\u9aa8\u5e72\u7f51\u7edc\u3001\u6570\u636e\u589e\u5e7f\u3001\u84b8\u998f\u7b49\u591a\u79cd\u524d\u6cbf\u7b97\u6cd5\uff0c\u53ef\u4ee5\u81ea\u52a8\u8bad\u7ec3\u5f97\u5230\u8f7b\u91cf\u4e14\u9ad8\u7cbe\u5ea6\u7684\u56fe\u50cf\u5206\u7c7b\u6a21\u578b\u3002\nPaddleClas\u63d0\u4f9b\u4e86\u8986\u76d6\u4eba\u3001\u8f66\u3001OCR\u573a\u666f\u4e5d\u5927\u5e38\u89c1\u4efb\u52a1\u7684\u5206\u7c7b\u6a21\u578b\uff0cCPU\u63a8\u74063ms\uff0c\u7cbe\u5ea6\u6bd4\u80a9SwinTransformer\u3002\n\n<a name=\"\u5206\u7c7b\u6548\u679c\u5c55\u793a\"></a>\n\n## PULC\u5b9e\u7528\u56fe\u50cf\u5206\u7c7b\u6a21\u578b\u6548\u679c\u5c55\u793a\n<div align=\"center\">\n<img src=\"docs/images/classification.gif\">\n</div>\n\n\n<a name=\"\u8bb8\u53ef\u8bc1\u4e66\"></a>\n\n## \u8bb8\u53ef\u8bc1\u4e66\n\u672c\u9879\u76ee\u7684\u53d1\u5e03\u53d7<a href=\"https://github.com/PaddlePaddle/PaddleCLS/blob/master/LICENSE\">Apache 2.0 license</a>\u8bb8\u53ef\u8ba4\u8bc1\u3002\n\n\n<a name=\"\u8d21\u732e\u4ee3\u7801\"></a>\n## \u8d21\u732e\u4ee3\u7801\n\u6211\u4eec\u975e\u5e38\u6b22\u8fce\u4f60\u4e3aPaddleClas\u8d21\u732e\u4ee3\u7801\uff0c\u4e5f\u5341\u5206\u611f\u8c22\u4f60\u7684\u53cd\u9988\u3002\n\u5982\u679c\u60f3\u4e3aPaddleCLas\u8d21\u732e\u4ee3\u7801\uff0c\u53ef\u4ee5\u53c2\u8003[\u8d21\u732e\u6307\u5357](docs/zh_CN/community/how_to_contribute.md)\u3002\n\n- \u975e\u5e38\u611f\u8c22[nblib](https://github.com/nblib)\u4fee\u6b63\u4e86PaddleClas\u4e2dRandErasing\u7684\u6570\u636e\u589e\u5e7f\u914d\u7f6e\u6587\u4ef6\u3002\n- \u975e\u5e38\u611f\u8c22[chenpy228](https://github.com/chenpy228)\u4fee\u6b63\u4e86PaddleClas\u6587\u6863\u4e2d\u7684\u90e8\u5206\u9519\u522b\u5b57\u3002\n- \u975e\u5e38\u611f\u8c22[jm12138](https://github.com/jm12138)\u4e3aPaddleClas\u6dfb\u52a0ViT\uff0cDeiT\u7cfb\u5217\u6a21\u578b\u548cRepVGG\u7cfb\u5217\u6a21\u578b\u3002\n",
    "bugtrack_url": null,
    "license": "Apache License Version 2.0, January 2004 http://www.apache.org/licenses/  TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION  1. Definitions.  \"License\" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.  \"Licensor\" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License.  \"Legal Entity\" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, \"control\" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity.  \"You\" (or \"Your\") shall mean an individual or Legal Entity exercising permissions granted by this License.  \"Source\" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files.  \"Object\" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types.  \"Work\" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below).  \"Derivative Works\" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof.  \"Contribution\" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. 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Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form.  3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. 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