We have learned from Chapter 5, Neural Network Architecture and Models, that each layer of a CNN is a feature vector in itself. There are two critical and interdependent parameters associated with this, as explained here:
- As we go up the CNN of the image through various convolution layers to the fully connected layer, we identify more features (semantically strong), from a simple edge to a feature of an object to a complete object. However, in doing so, the resolution of the image decreases as the feature width and height decreases while its depth increases.
- Objects of different scales (small versus large) are affected by this resolution and dimension. As the following diagram shows, a smaller object will be harder to detect at the highest layer because its features will be so blurred that the CNN will not be able to detect...