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Utilities for filtering and modifying dataset class subsets and metadata.
Distinguishing note: Focuses on runtime modification of dataset class lists and filtering.
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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
Allows modifying dataset metadata to train on specific subsets of classes.