One size does not fit all in evaluating model selection scores for image classification.

ABSTRACT: Selecting pretrained models for image classification often involves computationally intensive finetuning. This study addresses a research gap in the standardized evaluation of transferability scores, which could simplify model selection by ranking pretrained models without exhaustive finetuning. The motivation is to reduce the computational burden of model selection through a consistent approach that guides practitioners in balancing accuracy and efficiency across tasks. This study evaluates 14 transferability scores on 11 benchmark datasets. It includes both Convolutional Neural Network (CNN) and Vision Transformer (ViT) models and ensures consistency in experimental conditions to counter the variability in previous research. Key findings reveal significant variability in score effectiven

SUBMITTER: Abou Baker N 

PROVIDER: S-EPMC11618499 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature