Package Health Score

85 / 100

security
No known security issues
popularity
Popular
maintenance
Healthy
community
Sustainable

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Popularity

Popular

GitHub Stars: 5.52K
Forks: 1.18K
Contributors: 100

Direct Usage Popularity

Based on project statistics from the GitHub repository for the PyPI package paddleclas, we found that it has been starred 5,522 times.

Security

No known security issues

Security and license risk for latest version

Release Date: Nov 5, 2024
Direct Vulnerabilities: 0
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0
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0
M
0
L
Indirect Vulnerabilities: 0
C
0
H
0
M
0
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License Risk: 0
H
0
M
0
L

All security vulnerabilities belong to production dependencies of direct and indirect packages.

License

Apache-2.0

Security Policy

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Maintenance

Healthy

Commit Frequency

Open Issues: 260
Open PR: 40
Last Release: 2 months ago
Last Commit: 5 days ago

Further analysis of the maintenance status of paddleclas based on released PyPI versions cadence, the repository activity, and other data points determined that its maintenance is Healthy.

We found that paddleclas demonstrates a positive version release cadence with at least one new version released in the past 3 months.

As a healthy sign for on-going project maintenance, we found that the GitHub repository had at least 1 pull request or issue interacted with by the community.

Community

Sustainable

Readme: Yes
Contributing.md: No
Code of Conduct: No
Contributors: 100
Funding: No

With more than 10 contributors for the paddleclas repository, this is possibly a sign for a growing and inviting community.

We found a way for you to contribute to the project! Looks like paddleclas is missing a Code of Conduct.

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Package

Python Versions Compatibility: >=3.8

Age: 4 years
Latest Release: 2 months ago
Dependencies: 5 Direct
Versions: 16
Maintainers: 3
Wheels: OS Independent

Readme

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-ShiTuV2图像识别系统效果展示	PULC实用图像分类模型效果展示

📣 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：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：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
- 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
- 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
- 新增人脸识别模型MobileFaceNet、ResNet50_face，新增人脸识别端到端系统。
2022.9.13 发布超轻量图像识别系统PP-ShiTuV2：
- 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，
- 新增库管理工具，Android Demo全新体验。
more

🌟 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

🔥 一键调用98个PaddleClas核心模型
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

🛠️ 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

基于PP-ShiTuV2的生鲜品自助结算：点击这里
基于PULC人员出入视频管理：点击这里
基于PP-ShiTu 的智慧商超商品识别：点击这里
基于PP-ShiTu电梯内电瓶车入室识别：点击这里

📖 文档教程

环境准备
PP-ShiTuV2图像识别系统介绍
- 图像识别快速体验
- 20+应用场景库
- 子模块算法介绍及模型训练
- PipeLine 推理部署
PULC超轻量图像分类实用方案
PP系列骨干网络模型
SSLD半监督知识蒸馏方案
前沿算法
产业实用范例库
30分钟快速体验图像分类
FAQ
社区贡献指南
许可证书
贡献代码

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。

paddleclas dependencies

easydict faiss-cpu prettytable tqdm ujson

FAQs

What is paddleclas?

A treasure chest for visual recognition powered by PaddlePaddle. Visit Snyk Advisor to see a full health score report for paddleclas, including popularity, security, maintenance & community analysis.

Is paddleclas popular?

The python package paddleclas receives a total of 18,548 weekly downloads. As such, paddleclas popularity was classified as a popular. Visit the popularity section on Snyk Advisor to see the full health analysis.

Is paddleclas well maintained?

We found that paddleclas demonstrated a healthy version release cadence and project activity. It has a community of 100 open source contributors collaborating on the project. See the full package health analysis to learn more about the package maintenance status.

Is paddleclas safe to use?

The python package paddleclas was scanned for known vulnerabilities and missing license, and no issues were found. Thus the package was deemed as safe to use. See the full health analysis review.

Last updated on 19 December-2024, at 12:36 (UTC).

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