PaddleSeg — Easy-to-use Image Segmentation Library with Pre-trained Models

Overview

PaddleSeg is an end-to-end image segmentation toolkit based on PaddlePaddle, offering a wide range of practical tasks in Semantic Segmentation, Interactive Segmentation, Panoptic Segmentation, Image Matting, 3D Segmentation, etc. It features 45+ model algorithms and 140+ pre-trained models, supporting both configuration-driven and API-based development methods.

Key Features

High Accuracy: Provides 45+ mainstream segmentation networks and 150+ high-quality pre-trained models, tracking the forefront of academic segmentation technology.
High Performance: Utilizes multi-process asynchronous I/O, multi-card parallel training, and evaluation to accelerate the training process and reduce costs.
Modular Design: Decouples data preparation, segmentation models, backbone networks, loss functions, etc., allowing developers to assemble diverse configurations to meet different performance and accuracy requirements.
End-to-End Workflow: Facilitates the entire process from data annotation to model deployment, enabling one-stop development.

Use Cases

Medical Imaging: Automating the segmentation of medical images for diagnosis and treatment planning.
Industrial Applications: Segmenting images for quality control, automation, and safety in manufacturing.
Remote Sensing: Analyzing satellite imagery for environmental monitoring and urban planning.
Entertainment: Creating special effects and enhancing visual content in media production.

Advantages

Extensive Model Zoo: Offers a rich selection of pre-trained models for various segmentation tasks.
Low-code Development: Supports low-code, full-process development capabilities, reducing the barrier to entry for developers.
Cross-platform Support: Compatible with multiple operating systems, including Linux, Windows, and macOS.

Limitations / Considerations

Complexity for Beginners: The extensive feature set might be overwhelming for those new to image segmentation.
Hardware Requirements: High-performance segmentation may require significant computational resources.

MMSegmentation: An open-source image segmentation toolbox based on PyTorch, known for its modular design and extensive model support.
DeepLab: A well-known deep learning model for semantic image segmentation, with TensorFlow implementation.
TensorFlow Segmentation: TensorFlow's official models for image segmentation tasks, providing a range of pre-trained models and training utilities.

Basic Information

GitHub: PaddleSeg
Stars: 9,178
License: Unknown
Last Commit: 2025-09-25

📊 Project Information

Project Name: PaddleSeg
GitHub URL: https://github.com/PaddlePaddle/PaddleSeg
Programming Language: Python
⭐ Stars: 9,178
🍴 Forks: 1,705
📅 Created: 2019-08-26
🔄 Last Updated: 2025-09-25

🏷️ Project Topics

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📚 Documentation

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PaddleSeg

Project Description