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De Simeis Theo authoredDe Simeis Theo authored
Introduction
This tutorial aims to create a comprehensive Python program for image classification. It involves the development and testing of two classification models: k-nearest neighbors (KNN) and neural networks (NN). The experimentation will be conducted using the CIFAR-10 image database, which comprises 60,000 color images of dimensions 32x32 distributed across 10 classes (e.g., plane, car, bird, cat, ...).
To access the CIFAR-10 database and obtain the necessary data, you can visit https://www.cs.toronto.edu/~kriz/cifar.html, where instructions on data retrieval are provided. Consequently, the project is structured into three files:
- read_cifar
- knn
- mlp
KNN
The result obtained from the k-nearest neighbors (KNN) is presented as the accuracy, showcasing how it varies with changes in the value of k, ranging from 1 to 20.
NN
The output from the neural networks (NN) illustrates the progression of learning accuracy over different learning epochs.