CIFAR-10 Image Classification using CNN with Streamlit Deployment (with full Code)

🚀 Take your Data Science skills to the next level by extending your project to include Object Detection using Neural Networks. Learn how to identify and locate objects within images, a capability used extensively in fields like autonomous vehicles 🚗, surveillance 📹, and retail analytics 🛒.
[with full Code and Video Explainers]
🧠 Neural Networks or Deep Learning, especially CNN (Convolutional Neural Network) that is used in this Project, are the secret sauce behind solving highly complex Data Science problems 🧩. They are considered a premium skill among Data Scientists 📊, setting you on a path to exciting and well-compensated career opportunities! 💼✨
This project help you explore 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 Deep Learning techniques to real-world image datasets and understanding the challenges and nuances associated with image recognition tasks. In the second part of the project, we will make a deployment of our project using Streamlit, wherein we will upload an Image through the Streamlit UI and our model will return the output Class on the UI.

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.