Logo Classification using Modified Pretrained Machine Learning Models

This project aims to tackle the challenge of logo classification, which is crucial for combating cybercrimes like piracy and phishing involving brand logos. Utilizing advanced machine learning techniques, specifically deep learning and CNNs, this project provides a robust solution for distinguishing authentic from counterfeit logos.

Project Overview:

• Advanced CNN Model: Built a sophisticated CNN using the pretrained ResNet50V2 model as a base, fine-tuned to classify images of 10 different brand logos with an impressive accuracy of 93%-98% on the test dataset.
• Web Interface: Developed an interactive platform where users can view the classification performance and explore the details behind the high accuracy rates of the logo classification model.
• Implementation Details: Utilized TensorFlow and Python for model development, including layers of data normalization, resizing, dropout regularization, and batch normalization to enhance model stability and performance.
• Efficient Data Handling: Implemented techniques such as global max pooling and dense layers to effectively manage spatial dimensions and improve learning capabilities across the brand logo dataset.

This project demonstrates the potential of modified pretrained models in accurately classifying complex images like brand logos and offers insights into the application of machine learning in digital security and brand protection.