1st Place in ICCV 2023 Workshop Challenge Track 1 on Resource Efficient Deep Learning for Computer Vision: Budgeted Model Training Challenge

• URL: http://arxiv.org/abs/2311.11470v1
• Date: Wed, 9 Aug 2023 05:38:18 GMT
• Title: 1st Place in ICCV 2023 Workshop Challenge Track 1 on Resource Efficient Deep Learning for Computer Vision: Budgeted Model Training Challenge
• Authors: Youngjun Kwak, Seonghun Jeong, Yunseung Lee, Changick Kim
• Abstract summary: We describe a resource-aware backbone search framework composed of profile and instantiation phases. We employ multi-resolution ensembles to boost inference accuracy on limited resources. 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)
• Score: 15.213786895534225
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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).

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