This paper addresses the problem of ranking pre-trained models for object
detection and image classification. Selecting the best pre-trained model by
fine-tuning is an expensive and time-consuming task. Previous works have
proposed transferability estimation based on features extracted by the
pre-trained models. We argue that quantifying whether the target dataset is
in-distribution (IND) or out-of-distribution (OOD) for the pre-trained model is
an important factor in the transferability estimation. To this end, we propose
ETran, an energy-based transferability assessment metric, which includes three
scores: 1) energy score, 2) classification score, and 3) regression score. We
use energy-based models to determine whether the target dataset is OOD or IND
for the pre-trained model. In contrast to the prior works, ETran is applicable
to a wide range of tasks including classification, regression, and object
detection (classification+regression). This is the first work that proposes
transferability estimation for object detection task. Our extensive experiments
on four benchmarks and two tasks show that ETran outperforms previous works on
object detection and classification benchmarks by an average of 21% and 12%,
respectively, and achieves SOTA in transferability assessment.

本文提出了一个基于能量的可转移性评估度量 (ETran)，用于解决目标检测和图像分类中预训练模型的排名问题。ETran 通过能量得分、分类得分和回归得分来判断目标数据集对预训练模型是否为带内（IND）或带外（OOD）数据，并在分类、回归和目标检测任务上都适用，是首个提出目标检测任务的可转移性评估的研究。在四个基准测试和两个任务上的广泛实验证明，ETran 在目标检测和分类基准测试上的平均性能分别比先前的方法提高了 21% 和 12%，并在转移性评估方面达到了最新的成果。