A Lightweight Measure of Classification Difficulty from Application Dataset Characteristics

• URL: http://arxiv.org/abs/2404.05981v2
• Date: Tue, 29 Oct 2024 22:22:13 GMT
• Title: A Lightweight Measure of Classification Difficulty from Application Dataset Characteristics
• Authors: Bryan Bo Cao, Abhinav Sharma, Lawrence O'Gorman, Michael Coss, Shubham Jain,
• Abstract summary: We propose an efficient cosine similarity-based classification difficulty measure S. It is calculated from the number of classes and intra- and inter-class similarity metrics of the dataset. We show how a practitioner can use this measure to help select an efficient model 6 to 29x faster than through repeated training and testing.
• Score: 4.220363193932374
• License:
• Abstract: Although accuracy and computation benchmarks are widely available to help choose among neural network models, these are usually trained on datasets with many classes, and do not give a good idea of performance for few (< 10) classes. The conventional procedure to predict performance involves repeated training and testing on the different models and dataset variations. We propose an efficient cosine similarity-based classification difficulty measure S that is calculated from the number of classes and intra- and inter-class similarity metrics of the dataset. After a single stage of training and testing per model family, relative performance for different datasets and models of the same family can be predicted by comparing difficulty measures - without further training and testing. Our proposed method is verified by extensive experiments on 8 CNN and ViT models and 7 datasets. Results show that S is highly correlated to model accuracy with correlation coefficient |r| = 0.796, outperforming the baseline Euclidean distance at |r| = 0.66. We show how a practitioner can use this measure to help select an efficient model 6 to 29x faster than through repeated training and testing. We also describe using the measure for an industrial application in which options are identified to select a model 42% smaller than the baseline YOLOv5-nano model, and if class merging from 3 to 2 classes meets requirements, 85% smaller.

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