Introduction [Video]

The CIFAR-10 image classification project is a fundamental exercise in the field of computer vision and machine learning. CIFAR-10 stands for “Canadian Institute for Advanced Research – 10,” and it refers to a widely used dataset consisting of 60,000 labeled images belonging to ten different classes. Each image in the dataset has dimensions of 32×32 pixels and is categorized into one of the following classes: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, and truck.

The goal of the CIFAR-10 project is to build and train machine learning models, particularly convolutional neural networks (CNNs), to accurately classify these images into their respective classes. This project serves as an entry point for exploring various aspects of image classification, including data preprocessing, model architecture design, hyperparameter tuning, training, evaluation, and deployment. It’s a foundational exercise for individuals looking to gain hands-on experience in applying machine learning techniques to real-world image datasets and understanding the challenges and nuances associated with image recognition tasks. Through this project, practitioners can develop essential skills in feature extraction, model interpretation, and performance analysis while contributing to their understanding of the broader field of computer vision.

We will also build a simple UI with Streamlit and deploy the model using this UI. Through this UI, we will be able to pass an Image and the Model will return the class of the Image on the UI.