Exploring new strategies for comparing deep-learning models

Samantha J. Butler; Stanton R. Price; Xian Mae D. Hadia; Steven R. Price; Samantha C. Carley

doi:10.1117/12.2663401

12 June 2023 Exploring new strategies for comparing deep-learning models

Samantha J. Butler, Stanton R. Price, Xian Mae D. Hadia, Steven R. Price, Samantha C. Carley

Proceedings Volume 12538, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications V; 125381K (2023) https://doi.org/10.1117/12.2663401
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Conference Poster

Abstract

With numerous technologies, seeking to utilize deep learning-based object detection algorithms, there is an increased need for an innovative approach to compare one model to another. Often, models are compared one of two over-arching ways: performance metrics or through statistical measures on the dataset. One common approach for training an object detector for a new problem is to transfer learn a model, often initially trained extensively on the ImageNet dataset; however, why one feature backbone was selected over another is overlooked at times. Additionally, while whether it was trained on ImageNet, COCO, or some other benchmark dataset is noted, it is not necessarily considered by many practitioners outside the deep learning research community seeking to implement a state-of-the-art detector for their specific problem. This article proposes new strategies for comparing deep learning models that are associated with the same task, e.g., object detection.

Citation Download Citation

Samantha J. Butler, Stanton R. Price, Xian Mae D. Hadia, Steven R. Price, and Samantha C. Carley "Exploring new strategies for comparing deep-learning models", Proc. SPIE 12538, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications V, 125381K (12 June 2023); https://doi.org/10.1117/12.2663401

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