Abstract: The foundation of this project lies in building a complete image classification system powered by Convolutional Neural Networks (CNNs), developed efficiently using TensorFlow and Keras. This approach aims to automate one of the most vital tasks in computer vision — categorizing visual data — with strong accuracy and reliability. The workflow begins with dataset preprocessing, which prepares the input images by normalizing pixel values to a fixed scale and resizing them into a consistent tensor shape, ensuring the CNN receives standardized data. The next key phase is data augmentation, where techniques such as image rotation, flipping, and scaling are applied to artificially expand the dataset. These transformations enhance model generalization and help minimize overfitting. Once the model achieves the desired performance, it is deployed as an interactive web app using Streamlit. This deployment converts the complex deep learning model into a user-friendly interface, enabling real-time image predictions and showcasing the high accuracy and efficiency of the developed system.
Keywords: Image Classification, Convolutional Neural Network, CNN, TensorFlow, Keras, Data Augmentation, Deep Learning, Streamlit, Python
Downloads:
|
DOI:
10.17148/IJARCCE.2025.141002
[1] Janaki K B, Suraj Jagadeesh, Tushar V Aradhyamath, Jishnu A, Vishnu R, "Image Classification using Convolutional Neural Networks (CNNs)," International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI: 10.17148/IJARCCE.2025.141002
