Back to articles
Article
Volume: 35 | Article ID: HPCI-243
Image
Towards real-time formula driven dataset feed for large scale deep learning training
  DOI :  10.2352/EI.2023.35.11.HPCI-243  Published OnlineJanuary 2023
Abstract
Abstract

Recently, a new deep learning architecture, the Vision Transformer, has emerged as the new standard for classification tasks, overtaking the conventional Convolutional Neural Network (CNN) models. However, these state-of-the-art models require large amounts of data, typically over 100 million images, to achieve optimal performance through transfer learning. This requirement is met by using proprietary datasets like JFT-300M or 3B, which are not publicly available. To overcome these challenges and address privacy concerns, Formula-Driven Supervised Learning (FDSL) has been introduced. FDSL trains deep learning models using synthetic images generated from mathematical formulas, such as Fractals and Radial Contour images. The main objective of this approach is to reduce the I/O bottleneck that occurs during training with large datasets. Our implementation of FDSL generates instances in real-time during training, and uses a custom data loader based on EGL (Native Platform Graphics Interface) for fast rendering via shaders. The evaluation of our custom data loader on the FractalDB-100k dataset comprising 100 million images revealed a loading time that is three times faster compared to the PyTorch Vision loader.

Subject Areas :
Views 94
Downloads 33
 articleview.views 94
 articleview.downloads 33
  Cite this article 

Edgar Josafat Martinez-Noriega, Rio Yokota, "Towards real-time formula driven dataset feed for large scale deep learning trainingin Electronic Imaging,  2023,  pp 243-1 - 243-6,  https://doi.org/10.2352/EI.2023.35.11.HPCI-243

 Copy citation
  Copyright statement 
Copyright © 2023, Society for Imaging Science and Technology 2023
ei
Electronic Imaging
2470-1173
2470-1173
Society for Imaging Science and Technology
IS&T 7003 Kilworth Lane, Springfield, VA 22151 USA