CNN For Image Classification | Image Classification Project [with full Code and Video Explainers]

The CNN for Image Classification project is a pivotal endeavor in the realm of computer vision and machine learning. It uses the CIFAR-10 dataset, short for “Canadian Institute for Advanced Research – 10,” which comprises of 60,000 labeled images in ten distinct classes. Each of these images is confined to the dimensions of 32×32 pixels and is categorized: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, and truck.

The primary objective of the CNN for Image Classification project is to construct and train machine learning models, specifically convolutional neural networks (CNNs), with the aim of classifying these images. It serves as an entry point for delving into image classification, image data preprocessing, model architecture design, hyperparameter optimization, training, evaluation, and deployment. It helps you with hands-on in applying machine learning techniques to image datasets, along with gaining a comprehension of the intricacies associated with image recognition tasks. Through this Image Classification Project, you can nurture your expertise in feature extraction, model explication, and performance appraisal, all while enriching your understanding of the broader field of computer vision.

You will also undertake the deployment of our CNN for Image Classification model using the Python Streamlit Library. You will upload images through the Streamlit user interface which will invoke your CNN model in classifying these uploaded images, and the results will be presented on the Streamlit user interface, contributing to a user-friendly and interactive experience.