Enhancing Fine-Grained Image Classification with Attention Mechanisms and Transfer Learning: A Case Study on the Stanford Dogs Dataset: ENHANCING FINE-GRAINED IMAGE CLASSIFICATION

Jay Vishal Mehta

doi:10.58445/rars.2355

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Jay Vishal Mehta Polygence

DOI:

https://doi.org/10.58445/rars.2355

Keywords:

computer vision, deep learning, transfer learning, attention mechanisms, fine-grained classification

Abstract

This research explores enhancing fine-grained image classification using transfer learning and attention mechanisms. The study applies a ResNet50 architecture pretrained on ImageNet and augmented with Convolutional Block Attention Modules (CBAM) to the Stanford Dogs dataset. Results show significant improvements in classification accuracy, with the model achieving 86.92% accuracy on the test set. This performance gain demonstrates the effectiveness of combining transfer learning and attention mechanisms in overcoming challenges in fine-grained image classification, paving the way for more accurate systems in domains requiring detailed visual analysis.

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Enhancing Fine-Grained Image Classification with Attention Mechanisms and Transfer Learning: A Case Study on the Stanford Dogs Dataset