nnU-Net v2 is a self-configuring deep learning framework for medical image segmentation that automates preprocessing, network architecture selection, training, and inference. It ingests raw medical images in various formats (NIfTI, TIFF, DICOM via SimpleITK) and produces segmentation masks. The system is organized as a modular monolith: experiment planning extracts dataset fingerprints to determine optimal preprocessing and network topology; a preprocessing pipeline handles resampling, normalization, and cropping; training uses a configurable base trainer with many variants (including 3D/2D U-Nets, residual encoders, and transformer-based Primus architectures); inference employs sliding-window with Gaussian weighting and test-time augmentation. Key entry points are CLI commands (nnUNetv2_plan_and_preprocess, nnUNetv2_train, nnUNetv2_predict) and the programmatic nnUNetPredictor. The codebase demonstrates high craftsmanship in its checkpoint system (safetensors with backward compatibility), pluggable image I/O, and configuration management via plans files, though it carries some technical debt from legacy .pth support and a proliferation of trainer variant subclasses.
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.