• Version
• Download 17
• File Size 254.08 KB
• File Count 1
• Create Date 25 December 2024
• Last Updated 25 December 2024

DOG BREED CLASSIFICATION USING TRANSFER LEARNING

Ms.GOPIKA P, Ms. MUTHULAKSHMI M, Ms. NIVETHA A Ms. SUJATHA N (MENTOR)

ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING

SRI SHAKTHI INSTITUTE OF ENGINEERING AND TECHNOLOGY , COIMBATORE.

Abstract - “Dog Breed Classification using Transfer Learning “ the project focus on accurately classifying dog breeds is a challenging computer vision task due to the subtle differences between breeds and the high variability within breeds. This project addresses these complexities using transfer learning with the Inception V3 model, a state-of-the-art convolutional neural network (CNN). Traditional machine learning approaches often struggle with subtle inter-breed differences and require large labeled datasets and significant computational resources. Training deep learning models from scratch is typically infeasible, which makes transfer learning an ideal solution. By using a pre-trained Inception V3 model, this approach leverages learned features from large-scale datasets like ImageNet and fine-tunes them for dog breed classification, reducing trainingtime while enhancing accuracy. The fine- tuned Inception V3 model undergoes transfer learningwhere initial layers remain frozen to retain general features, and fine-tuning optimizes the later layers for specific breed classification. To make the solution accessible and user-friendly, a Flask web application is developed. Users can upload dog images and receive breed predictions in real time, with the backend integrating the trained model to process the images, extract features, and output predictions. The application is scalable and practical for diverse deployment scenarios, such as pet identification, educational purposes, or veterinary applications. This project not only highlights the efficiency of transfer learning in reducing training time but also demonstrates how AI can be applied effectively in real-world challenges. Combining advanced deep learning with a simple web interface bridges the gap between complex machine learning models and practical applications, emphasizing accuracy, efficiency, and accessibility in pet care and animal research.

Key Words: Inception v3, fine-tuning , CNN , flask