Image Classification using Transfer Learning

Abstract

Image classification, a fundamental task
in computer vision, plays a pivotal role in various
applications, including object recognition, medical
imaging, and autonomous vehicles. However, training
deep neural networks from scratch for image
classification often requires vast amounts of labeled
data and computational resources. Transfer learning
offers a practical solution to this challenge by
leveraging pre-trained models and transferring
knowledge from one task to another. This paper
investigates the application of transfer learning in
image classification tasks. By fine-tuning pre-trained
convolutional neural networks (CNNs) on new datasets,
we demonstrate the effectiveness of transfer learning in
achieving high classification accuracy with limited
labeled data. We explore different transfer learning
strategies, including feature extraction and fine-tuning
of entire networks, and evaluate their performance on
benchmark datasets. Through experimentation, we
highlight the advantages of transfer learning in
reducing training time, alleviating the need for
extensive labeled data, and improving generalization
performance. We also discuss practical considerations
and best practices for selecting pre-trained models,
adapting them to new tasks, and fine-tuning
hyperparameters.