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Utilities for loading and managing pre-trained model weights.
Distinguishing note: Focuses on the initialization of models from external weight sources.
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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
Supports initializing models before finetuning by specifying a URL or local path to pre-trained weights.