Understanding Image Padding and Kernel Stride in Convolutional Neural Networks
Understanding Image Padding and Kernel Stride in Convolutional Neural Networks
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Convolutional Neural Networks (CNNs) are a popular deep learning architecture for image processing tasks. Two essential concepts in designing a CNN architecture are image padding and kernel stride. In this description, we delve into these concepts, explaining their significance and how they impact feature extraction.
Image padding is an essential operation that adds extra pixels around the input image in a convolutional layer to make all inputs of different shape and size equal. It is added to preserve the spatial Dimension (height and width) when convolving the image. Padding types include same and valid. When using 'same' padding, the output would have the same spatial dimensions as the input, whereas 'valid' padding leads to a reduced output image.
Kernel stride represents the number of pixels the kernel moves during the convolution operation. It defines the step size of the filter moving over the image. Typically, a kernel can have a stride of 1 or larger. When the kernel stride is 1, it moves one pixel at a time, while larger strides result in the filter moving over multiple pixels in a single step.
Interested in deepening your knowledge on Computer Vision and CNNs? Check out these resources:
[Deep Learning Specialization by Andrew Ng on Coursera](https://www.coursera.org/specializati...)
[Convolutional Neural Networks and Large Scale Deep Learning: A Review](https://arxiv.org/abs/1610.05060)
[Understanding Convolutional Neural Networks (CNN) for object detection, part1](https://machinelearningmastery.com/un...)
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