Project Description
Image classification is a fundamental problem in computer vision with applications spanning healthcare, security, agriculture, and autonomous systems. This project focuses on the design and implementation of an image classification system using machine learning techniques. The proposed system utilizes a Convolutional Neural Network (CNN) and Vision Transformation algorithms to automatically learn and extract relevant features from input images and classify them into predefined categories. The model is trained on a labeled image dataset, where preprocessing techniques such as image resizing and normalization are applied to improve learning efficiency and accuracy. The performance of the model is evaluated using standard metrics such as accuracy and validation loss. Experimental results demonstrate that the system is capable of effectively distinguishing between different image classes, highlighting the effectiveness of deep learning approaches for image classification tasks. This project provides a scalable and efficient framework that can be extended to more complex datasets and real-world applications.