In AI and machine learning, especially in deep learning and computer vision, the term “stride” refers to the step size with which a filter or kernel moves across an input, such as an image or a feature map, during operations like convolution or pooling. Think of stride as how many units you move the window each time you slide it over your data. If the stride is set to 1, the filter examines every possible position, moving one pixel at a time. If the stride is 2, it skips every other position, moving two pixels at a time, and so on.
Stride is a key parameter in convolutional neural networks (CNNs). When a convolutional layer processes an input image, its filters (small weight matrices) scan over the image to extract features. The stride determines how much the filter shifts for each step. A smaller stride (like 1) leads to more overlap between filter applications and results in larger output feature maps. A larger stride (like 2 or more) makes the filter jump farther, reducing the size of the output feature maps, which can help decrease the computational load and memory usage.
For example, suppose you have a 5×5 image and a 3×3 filter. With a stride of 1, the filter moves one pixel at a time, resulting in a 3×3 output. With a stride of 2, the filter moves two pixels at a time, producing a smaller 2×2 output. This reduction in size is sometimes called “downsampling” and helps neural networks focus on the most essential features.
Stride is also used in pooling layers, like max pooling or average pooling. Here, it works the same way: the stride determines how far the pooling window moves each time. Adjusting the stride lets you control the spatial dimensions of the data as it moves through the network. For instance, increasing stride in pooling layers can help reduce overfitting and speed up training by making the network less sensitive to small variations and by reducing the number of parameters.
Choosing the right stride value is important for balancing information preservation and computational efficiency. A small stride preserves more information but increases the computational cost, while a larger stride reduces the amount of information captured but speeds up processing. Stride is often chosen together with other hyperparameters like filter size and padding to achieve the desired output shape and performance.
Understanding stride is essential for designing efficient neural network architectures, particularly in scenarios like image classification, object detection, and other tasks that rely on spatial data. By fine-tuning stride settings, practitioners can control the granularity of feature extraction and the depth of subsequent layers, directly influencing model performance and resource usage.
