Creating a CIFAR-10 image classifier

The model we are going to create will classify images from a dataset called Canadian Institute for Advanced Research, 10 classes (CIFAR-10). It contains 60,000 32x32 red, green, blue (RGB) colored images, classified into 10 different classes. It is a collection of images that is commonly used to train ML and computer vision algorithms.

Here are the classes in the dataset:

• airplane
• automobile
• bird
• cat
• deer
• dog
• frog
• horse
• ship
• truck

In the next screenshot, you can see some random image samples found in the CIFAR-10 dataset:

Figure 4.6 – CIFAR-10 image samples

This a problem considered already solved. It is relatively easy to achieve a classification accuracy close to 80%. For better performance, we must use deep learning CNNs with which a classification precision greater than 90% can be achieved in the test dataset. Let's see how to implement it with...