PaddleSeg is a comprehensive, modular image segmentation toolkit built on PaddlePaddle. It provides a rich collection of state-of-the-art models for semantic, panoptic, medical, and interactive segmentation, as well as matting. The architecture is configuration-driven with a component registration system (manager.py) that allows easy extension of models, backbones, datasets, transforms, losses, and optimizers. Key entry points include training, evaluation, prediction, and export scripts under tools/, with extensive deployment support for C++, Android, FastDeploy, and serving. The codebase is actively maintained with recent commits and demonstrates solid engineering practices, though some contrib modules exhibit duplication and experimental code. It is primarily a software library/framework for segmentation tasks, not a data research pipeline.
MetaSeg is a Python library that packages Facebook's Segment Anything Model (SAM) into an easy-to-use interface for automatic and manual image/video segmentation. It provides high-level predictors (SegAutoMaskPredictor, SegManualMaskPredictor) that handle model loading, inference, and output visualization. The library integrates with SAHI for sliced inference and FalAI for cloud GPU execution, and includes a Gradio web application. The codebase is primarily a thin orchestration layer over the official SAM implementation, with additional utility functions for data loading, video processing, and model download. It is designed for developers who want to quickly apply SAM without deep model internals.