Computer Vision News - January 2023

5 Feature Extraction Layers in ConvNets Convolutional Blocks It does this by passing them through long chains of convolutional blocks which perform this extraction. These convolutional blocks are stacks of Conv2D and MaxPool2D layers, whose role in fea- ture extraction we learned about in the last few lessons. Each block represents a round of extraction, and by composing these blocks the convnet can combine and recombine the features produced, growing them and shaping them to better fit the problem at hand. The deep structure of modern convnets is what allows this sophisticated feature engineering and has been largely responsible for their superior per- formance. Example - Design a Convnet Let's see how to define a deep convolutional network capable of engineering complex fea- tures. In this example, we'll create a Keras Sequence model and then train it on our Cars dataset. Step 1 - Load Data This hidden cell loads the data. # Imports import os, warnings import matplotlib.pyplot as plt from matplotlib import gridspec