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Utilities for semi-automatic labeling using model-assisted inference.
Distinguishing note: Focuses on iterative labeling workflows using pre-trained model backends.
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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
Automates object detection annotation by connecting pre-trained models to annotation platforms.