Data-Side Efficiencies for Lightweight Convolutional Neural Networks
Bryan Bo Cao Lawrence O'Gorman Michael Coss Shubham Jain
We 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.