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Utilities for inspecting, parsing, and visualizing machine learning model structures without executing the underlying code.
Distinguishing note: Focuses on static analysis and structural parsing of model files rather than model training or inference execution.
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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
Computes floating-point operations and parameter counts to estimate model complexity.