1st Place in ICCV 2023 Workshop Challenge Track 1 on Resource Efficient
  Deep Learning for Computer Vision: Budgeted Model Training Challenge
Youngjun Kwak Seonghun Jeong Yunseung Lee Changick Kim
The budgeted model training challenge aims to train an efficient
classification model under resource limitations. To tackle this task in
ImageNet-100, we describe a simple yet effective resource-aware backbone search
framework composed of profile and instantiation phases. In addition, we employ
multi-resolution ensembles to boost inference accuracy on limited resources.
The profile phase obeys time and memory constraints to determine the models'
optimal batch-size, max epochs, and automatic mixed precision (AMP). And the
instantiation phase trains models with the determined parameters from the
profile phase. For improving intra-domain generalizations, the multi-resolution
ensembles are formed by two-resolution images with randomly applied flips. We
present a comprehensive analysis with expensive experiments. Based on our
approach, we win first place in International Conference on Computer Vision
(ICCV) 2023 Workshop Challenge Track 1 on Resource Efficient Deep Learning for
Computer Vision (RCV).