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.

翻译：本文研究了目标检测和图像分类中预训练模型排序的问题。通过微调选择最优预训练模型是一项昂贵且耗时的任务。先前工作提出了基于预训练模型提取特征的迁移性估计方法。我们认为，量化目标数据集对于预训练模型属于分布内（IND）还是分布外（OOD）是迁移性估计的重要因素。为此，我们提出ETran——一种基于能量的迁移性评估指标，包含三个分数：1）能量分数，2）分类分数，3）回归分数。我们利用基于能量的模型判断目标数据集对预训练模型是否为OOD或IND。与先前工作不同，ETran适用于分类、回归和目标检测（分类+回归）等多种任务。这是首个提出目标检测任务迁移性估计的工作。我们在四个基准数据集和两个任务上的大量实验表明，ETran在目标检测和分类基准上分别平均优于先前工作21%和12%，并达到了迁移性评估的最先进水平（SOTA）。