Exploring Transfer Learning in Image Analysis Using Feature Extraction with Pre-Trained Models

Transfer learning has emerged as a powerful approach in image analysis, leveraging pre-trained models to enhance performance on specific tasks. This study focuses on feature extraction using pre-trained models to address challenges in image classification. We employ state-of-the-art pre-trained models, such as ResNet and VGG, as feature extractors. The models are fine-tuned on a target dataset to adapt to the specific characteristics of the problem at hand. Extracted features are then fed into a custom classifier for task-specific learning. We explore the effectiveness of transfer learning in scenarios with limited labeled data, aiming to demonstrate the model’s ability to generalize and improve performance. Our research contributes to the understanding of transfer learning’s efficacy in image analysis, providing insights into its applicability and limitations. We propose a methodology that optimizes the use of pre-trained models for feature extraction, making them adaptable to diverse image classification tasks. Experimental results showcase significant improvements in classification accuracy compared to training models from scratch, particularly when dealing with small datasets. The study highlights the potential of transfer learning in enhancing the efficiency of image analysis tasks.