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YouTubeDaily Dose of Data Science
YOLO's biggest shift in a decade!
Traditional YOLO models generate multiple overlapping bounding boxes per object during inference. A single person in frame might produce 15 predictions. That redundancy helps during training, but at inference time you only want one box per object. So a separate post-processing step called Non-Maximum Suppression runs after the model finishes ...
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