Quick-Tune-Tool: A Practical Tool and its User Guide for Automatically Finetuning Pretrained Models

Pretrained models have become essential tools for machine learning practitioners across various domains including image classification, segmentation, and natural language processing. However, the complexity of selecting the appropriate pretrained model and finetuning strategy remains a significant challenge. In this paper, we present Quick-Tune-Tool, an automated solution to guide practitioners in selecting and finetuning pretrained models. Leveraging the Quick-Tune algorithm, Quick-Tune-Tool abstracts intricate research-level code into a user-friendly tool. Our contributions include the release of Quick-Tune-Tool, a detailed architectural overview, a user guide for image classification, and empirical evaluations. In experiments on four vision dataset, our results underscore the effectiveness and practicality of Quick-Tune-Tool for automating model selection and finetuning.