English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The budgeted model training challenge aims to train an efficientclassification model under resource limitations. To tackle this task inImageNet-100, we describe a simple yet effective resource-aware backbone searchframework composed of profile and instantiation phases. In addition, we employmulti-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 theinstantiation phase trains models with the determined parameters from theprofile phase. For improving intra-domain generalizations, the multi-resolutionensembles are formed by two-resolution images with randomly applied flips. Wepresent a comprehensive analysis with expensive experiments. Based on ourapproach, we win first place in International Conference on Computer Vision(ICCV) 2023 Workshop Challenge Track 1 on Resource Efficient Deep Learning forComputer Vision (RCV).

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