DINO

t-temp.

s-temp.

72.3
71.7
72.2
71.9
71.8
71.8

72.4
66.2
72.4
72.5
68.5
72.5

Table 3: Hyperparameter selection using the common supervised linear probe strategy
(ImageNet oracle), RankMe and α-ReQ. OOD indicates the average performance over
iNaturalist18, Places 205, Sun397, EuroSat, StanfordCars, CIFAR-10, CIFAR-100, Pascal
VOC2007, CLEVR-cnt and FOOD101. Without any label, optimization or parameters,
RankMe recovers most of the performance obtained by using ImageNet validation set,
highlighting its strength as a hyperparameter selection tool. From Garrido et al. [2022a]
A Cookbook of Self-Supervised Learning