Mis on spatial pyramid pooling?

Spatial Pyramid Pooling (SPP) is a technique in computer vision that allows extracting fixed-size features from input images of different sizes without the need for resizing them beforehand.

The idea is that instead of simple global pooling (e.g., max pooling), a hierarchy of pooling with different window sizes is applied, dividing the image into multiple levels (pyramid), for example, 1x1, 2x2, 4x4, etc. At each level, maxima are taken over the respective regions, and all results are combined into a single fixed-length vector.

This allows neural networks to work with images of arbitrary size, preserving spatial information at different scales.

Example: if you have an image, you can divide it into 4 squares (2x2), take the maximum in each, then into 16 squares (4x4), and so on, and then combine all these values into one vector.

SPP is often used in CNN architectures to improve flexibility and recognition quality.