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Tools for organizing, storing, and managing ground truth and metadata for machine learning datasets.
Distinguishing note: Focuses on detection-specific data sample management rather than generic data storage.
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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
Provides a unified interface for managing detection data samples, ground truth, and metadata during training and inference.