Introduction to PaddleSeg Project
PaddleSeg stands as a high-performance image segmentation toolkit from Baidu's deep learning platform PaddlePaddle. It offers an end-to-end pipeline for developing image segmentation applications, covering everything from training to deployment.
Latest Developments
October 1, 2024
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Low-Code Development in Semantic Segmentation:
- PaddleX, leveraging PaddleSeg, now supports low-code development in image segmentation. This includes:
- Rich Model Invocation: Integration of 19 models for semantic segmentation and anomaly detection into two model pipelines. This allows easy, one-click engagement using a simplified Python API. The same API supports over 200 models across various domains like image classification, object detection, and more, enabling versatile model combinations.
- Efficiency and Accessibility: Offers simple and efficient model usage via unified command line interfaces and graphical interfaces. Supports various deployment options, including high-performance deployment, service-oriented deployment, and edge deployment. Seamless switching among popular hardware like Nvidia GPUs and other chips is supported during model development.
- PaddleX, leveraging PaddleSeg, now supports low-code development in image segmentation. This includes:
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New Algorithm: Introduced the image anomaly detection algorithm, SFTPM.
October 29, 2023
- PaddleSeg 2.9 Release: Highlights of this release include:
- Multi-Label Segmentation: Added support for this by providing data conversion codes and result visualization for semantic segmentation models.
- MobileSAM: A lightweight vision model enhancing fast SAM inference.
- Quantization-Aware Distillation Training Compression: This feature increases inference speed for models like PP-LiteSeg and OCRNet by adding quantization training compression.
Overview
PaddleSeg, built on PaddlePaddle, incorporates over 45 model algorithms and 140+ pre-trained models. It supports configuration-driven and API-based development, facilitating the entire pipeline from data annotation to model deployment. It offers capabilities in semantic segmentation, interactive segmentation, matting, and panoptic segmentation, proving beneficial across various fields like healthcare and remote sensing.
Features
- High Accuracy: Tracks cutting-edge segmentation technologies, providing over 45 main segmentation networks and 150+ high-quality pre-trained models, often outperforming other open-source implementations.
- High Performance: Utilizes multi-process asynchronous I/O and parallel training strategies, reducing model training overhead significantly.
- Modular Design: Separation of data preparation, model, backbone network, and loss functions, allowing developers to configure versatile setups tailored to specific needs.
- Comprehensive Pipeline: It covers data annotation, model development, training, compression, and deployment in a streamlined process validated through practical application scenarios.
Getting Started
The quick start guides and low-code development resources provide step-by-step instructions for leveraging PaddleSeg's capabilities efficiently.
Product Matrix
PaddleSeg's product offerings include a wide range of models like semantic segmentation models (e.g., PP-LiteSeg, OCRNet), backbone networks (e.g., HRNet, ResNet), and loss functions (e.g., Cross Entropy Loss, Dice Loss). It supports extensive datasets ranging from ADE20K to STARE and offers data augmentation methods like flipping and resizing.
PaddleSeg positions itself as a robust tool for developers aiming to craft precise and efficient image segmentation solutions, backed by comprehensive resources, cutting-edge technology, and a supportive community.