Paper ID: 2410.08592 • Published Oct 11, 2024
VIBES -- Vision Backbone Efficient Selection
TL;DR
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This work tackles the challenge of efficiently selecting high-performance
pre-trained vision backbones for specific target tasks. Although exhaustive
search within a finite set of backbones can solve this problem, it becomes
impractical for large datasets and backbone pools. To address this, we
introduce Vision Backbone Efficient Selection (VIBES), which aims to quickly
find well-suited backbones, potentially trading off optimality for efficiency.
We propose several simple yet effective heuristics to address VIBES and
evaluate them across four diverse computer vision datasets. Our results show
that these approaches can identify backbones that outperform those selected
from generic benchmarks, even within a limited search budget of one hour on a
single GPU. We reckon VIBES marks a paradigm shift from benchmarks to
task-specific optimization.