# A Transfer Learning Evaluation of Deep Neural Networks for Image Classification Source: [A Transfer Learning Evaluation of Deep Neural Networks for Image Classification]() Collection index: [llms.txt]() Data updated: 2026-05-15T05:00:04.680Z Authors: Nermeen Abou Baker, Nico Zengeler, Uwe Handmann Published: 2026-05-12T11:40:49.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()