Problem and data domain
In this context, we will utilize CNNs to tackle the object recognition task using the CIFAR-10 dataset. CNNs are particularly effective for image-related problems due to their ability to automatically learn hierarchical features from raw pixel data. By training a CNN model on the CIFAR-10 dataset, we aim to develop a robust system capable of accurately classifying objects into one of the ten predefined categories. This model can be applied in various domains, such as image-based search engines, automated surveillance systems, and quality control in manufacturing.
Dataset overview
The CIFAR-10 dataset comprises 60,000 color images, divided into 10 classes, with 6,000 images per class. Each image has dimensions of 32x32 pixels and is represented in RGB format. The dataset is split into a training set of 50,000 images and a test set of 10,000 images.
Features in the dataset include:
- Image data: Color images of various objects, each represented as a 3-dimensional array containing pixel intensities for red, green, and blue channels. These images serve as input data for training the CNN model.
- Label: The class label assigned to each image, representing the category of the depicted object. The labels range from 0 to 9, corresponding to classes such as airplane, automobile, bird, cat, deer, dog, frog, horse, ship, and truck.
By analyzing the CIFAR-10 dataset and its associated labels, our goal is to train a CNN model capable of accurately identifying objects depicted in images. This predictive model can then be deployed in real-world applications to automate object recognition tasks, improve decision-making processes, and enhance overall efficiency in diverse industries.
Figure 15.1: CIFAR-10 dataset
