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Unified programming interfaces for interacting with and managing machine learning models.
Distinguishing note: Provides a high-level abstraction layer for model interaction, distinct from the underlying inference engine implementation.
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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 provides a unified high-level interface for object detection inference that supports pre-trained models, custom configurations, and automatic weight management for various input types.