Transfer Learning and Fine-tuned CNN Architecture for Dog Breed Classification

This study presents a comprehensive approach to dog breed classification, leveraging transfer learning algorithms and Convolutional Neural Networks (CNNs). The dataset utilized for training and evaluation is sourced from the Dog Breed Identification Dataset, encompassing 120 distinct breeds. A pre-trained CNN model is employed, and through the application of transfer learning, the proposed models achieve an impressive accuracy rate of 99%. The primary objective is to enable users to submit images of dogs, with the model accurately identifying the specific breed from the diverse dataset. The research underscores the efficacy of transfer learning and pretrained models in achieving exceptional accuracy for the nuanced task of dog breed classification, contributing to the advancements in image recognition within the context of diverse canine breeds.