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Utilities for organizing, downloading, and preparing datasets for machine learning workflows.
Distinguishing note: Focuses on the operational management and symlinking of datasets rather than data storage or database management.
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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
The project supports organizing tracking and segmentation datasets by downloading them from official sources and symlinking them into the project directory structure.