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Mechanisms for extending and overriding base model configurations.
Distinguishing note: Specifically addresses the reuse of configuration files to avoid redundancy.
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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 building model structures and training schedules by inheriting existing files to avoid writing configurations from scratch.