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Automated optimizers that use genetic algorithms to find optimal model or tracker configurations.
Distinct from Evolutionary Algorithms: Specifically a tool for tuning hyperparameters, not a general library of evolutionary algorithm implementations.
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Boxmot is a multi-object tracking framework designed to follow multiple objects across video frames using motion and appearance algorithms to maintain consistent identities. It functions as a system for tracking objects with specific orientations using rotated bounding boxes and corresponding intersection-over-union computations. The project includes a re-identification model optimizer that converts neural networks into formats for hardware-accelerated execution. It also features an evolutionary hyperparameter tuner that iteratively mutates tracker settings to maximize accuracy for specific d
Provides an automated optimizer that uses evolutionary algorithms to find the most accurate tracker settings.
MergeKit is a toolkit for combining multiple pre-trained large language models into a single entity using algorithmic blending. It provides a specialized system for parameter interpolation and weight extraction to unify model capabilities. The project distinguishes itself through an evolutionary merge optimizer that tunes parameters based on quantitative evaluation metrics. It also features a mixture of experts orchestrator capable of converting dense models into sparse architectures and a tokenizer alignment tool for transplanting embeddings between different models. The toolkit covers a br
Tunes merge parameters automatically using evolutionary algorithms to maximize evaluation scores.