MULTI CLASS IMAGE CLASSIFICATION USING MACHINE LEARNING
Abstract
ABSTRACT
This project aims to design and implement an efficient multi-class image classification system capable of categorizing images into multiple predefined categories. Leveraging advanced deep learning techniques, the system utilizes convolutional neural networks (CNNs) with transfer learning and data augmentation to extract features and classify images with high accuracy. Trained on a large dataset of images, the system is optimized for performance, scalability, and robustness. The system's performance is evaluated using metrics such as accuracy, precision, and F1-score. With potential applications in image retrieval, object detection, and real-world use cases, the outcome of this project is expected to be a reliable and efficient image classification system. The system can be utilized in various fields, including computer vision, robotics, and artificial intelligence.
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