Implementing active ML for an image classification project

In this section, we will guide you through the implementation of active ML techniques for an image classification project. Image classification has various applications in computer vision, ranging from identifying products for an e-commerce website to detecting patterns of deforestation on geospatial tiles. However, creating accurate image classifiers requires extensive datasets of labeled images, which can be expensive and time-consuming to gather, as mentioned in Chapter 1, Introducing Active Machine Learning. Active ML offers a solution to this labeling bottleneck by interactively requesting the oracle to label only the most informative examples.

We will build an image classification model that will be capable of accurately classifying various images obtained from the CIFAR-10 dataset. This dataset is widely recognized in the field of computer vision and contains a diverse collection of 60,000 images, each belonging to...