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A Lightweight Measure of Classification Difficulty from Application Dataset Characteristics
Apr 09, 2024
Despite accuracy and computation benchmarks being widely available to help choose among neural network models, these are usually trained on datasets with many classes, and do not give a precise idea of performance for applications of few (< 10) classes. The conventional procedure to predict performance is to train and test repeatedly on the different models and dataset variations of interest. However, this is computationally expensive. We propose an efficient classification difficulty measure 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. We show how this measure can help a practitioner select a computationally efficient model for a small dataset 6 to 29x faster than through repeated training and testing. We give an example of use of 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.
Data-Side Efficiencies for Lightweight Convolutional Neural Networks
Aug 24, 2023We examine how the choice of data-side attributes for two important visual tasks of image classification and object detection can aid in the choice or design of lightweight convolutional neural networks. We show by experimentation how four data attributes - number of classes, object color, image resolution, and object scale affect neural network model size and efficiency. Intra- and inter-class similarity metrics, based on metric learning, are defined to guide the evaluation of these attributes toward achieving lightweight models. Evaluations made using these metrics are shown to require 30x less computation than running full inference tests. We provide, as an example, applying the metrics and methods to choose a lightweight model for a robot path planning application and achieve computation reduction of 66% and accuracy gain of 3.5% over the pre-method model.