Implements a centralized registry system for models, datasets, and post-processing functions. Allows dynamic registration and retrieval by name, decoupling configuration from implementation. This pattern enables easy extension with new components without modifying core code, demonstrating strong plugin architecture principles.

Contains utilities for distributed training setup, seed synchronization, checkpoint management, and a MetricLogger class that handles automatic synchronization of metrics across processes. This component solves the common problem of metric aggregation in distributed training and can be extracted for use in any PyTorch distributed project.

Defines a base dataset class with standardized methods for image loading, edge mask generation, transform application, and train/val/test splitting. Includes built-in support for albumentations and caching. This abstraction provides a reusable foundation for any image-based dataset, reducing boilerplate in dataset implementation.

Establishes an abstract base class for evaluation metrics with update and compute methods. Specific implementations (like AUC) handle distributed aggregation of predictions and labels across GPUs. This framework ensures consistent metric computation regardless of hardware setup and allows easy addition of new metrics.

Provides a wrapper for data loaders that applies common image corruptions (Gaussian blur, noise, JPEG compression) on-the-fly for robustness evaluation. This component enables systematic testing of model resilience to real-world distortions without modifying the original data pipeline, demonstrating a clean wrapper pattern for robustness assessment.