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Stride in a convolutional layer determines how the filter moves across the input image, affecting the output feature map size, computation speed, and detail in feature extraction. Increasing stride reduces output size and computation but may lead to loss of important spatial information and patterns. It is advised not to set stride greater than filter size to avoid skipping important regions and losing spatial continuity, which can degrade model performance.

Author

arundhathib31

Language

EN