Awesome GitHub RepositoriesDataset Configuration Utilities

This project is a modular research toolkit designed for developing, training, and evaluating deep learning models for object detection, segmentation, and video instance tracking. It provides a flexible training engine that manages complex neural network execution, including distributed training, custom lifecycle hooks, and weight optimization. The framework is built around a hierarchical configuration system that allows users to define architectures, data pipelines, and training hyperparameters through composable, inheritable files. The project distinguishes itself through its highly modular

The project provides tools to configure panoptic segmentation datasets by mapping void categories and encoding instance-category identifiers for accurate evaluation across training and testing scenarios.