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Techniques used to improve training efficiency, such as subsetting or distributed processing.
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YOLOv5 is a comprehensive computer vision framework designed for end-to-end deep learning, specializing in real-time object detection, image classification, and instance segmentation. It provides a unified toolkit that manages the entire lifecycle of a model, from initial dataset configuration and hyperparameter tuning
Reduces computational overhead by training on representative data subsets to accelerate initial development cycles.