A Transfer Learning Evaluation of Deep Neural Networks for Image Classification

2026-05-12

AI Summary

This study addresses the challenge of selecting suitable ImageNet-pretrained models for image classification tasks in target domains by introducing a multidimensional evaluation framework. The authors systematically fine-tune the output layers and general parameters of eleven pretrained models across five diverse datasets, evaluating their performance under both single-run and multiple-run training settings. Through comprehensive assessment of accuracy, accuracy density, training time, and model size, the work quantifies the cross-domain transferability differences among pretrained models, revealing consistent patterns in how model characteristics align with task-specific requirements. These findings provide empirical evidence and practical guidelines for informed model selection in real-world applications.

Abstract

Transfer learning is a machine learning technique that uses previously acquired knowledge from a source domain to enhance learning in a target domain by reusing learned weights. This technique is ubiquitous because of its great advantages in achieving high performance while saving training time, memory, and effort in network design. In this paper, we investigate how to select the best pre-trained model that meets the target domain requirements for image classification tasks. In our study, we refined the output layers and general network parameters to apply the knowledge of eleven image processing models, pre-trained on ImageNet, to five different target domain datasets. We measured the accuracy, accuracy density, training time, and model size to evaluate the pre-trained models both in training sessions in one episode and with ten episodes.

Problem

• transfer learning
• image classification
• pre-trained models
• model selection
• target domain

Innovation

• transfer learning
• pre-trained models
• image classification
• model evaluation
• fine-tuning