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Table 8 Classification accuracies of Ours (FractalDB-1k / 10k), Scratch, DeepCluster-10k (DC-10k), ImageNet-100/1k and Places-30/365 pre-trained models on representative pre-training datasets

From: Pre-Training Without Natural Images

Method Pre-train Img Type C10 C100 IN1k P365 VOC12 OG
Scratch 87.6 62.7 76.1 49.9 58.9 1.1
DC-10k Natural Self-supervision 89.9 66.9 66.2 51.5 67.5 15.2
Places-30 Natural Supervision 90.1 67.8 69.1 69.5 6.4
Places-365 Natural Supervision 94.2 76.9 71.4 78.6 10.5
ImageNet-100 Natural Supervision 91.3 70.6 49.7 72.0 12.3
ImageNet-1k Natural Supervision 96.8 84.6 50.3 85.8 17.5
FractalDB-1k Formula Formula-supervision 93.4 75.7 70.3 49.5 58.9 20.9
FractalDB-10k Formula Formula-supervision 94.1 77.3 71.5 50.8 73.6 29.2
  1. We show the types of pre-trained image (Pre-train Img; which includes {Natural Image (Natural), Formula-driven Image (Formula)}) and Supervision types (Type; which includes {Self-supervision, Supervision, Formula-supervision}). We employed CIFAR-10 (C10), CIFAR-100 (C100), ImageNet-1k (IN1k), Places-365 (P365), classification set of Pascal VOC 2012 (VOC12) and Omniglot (OG) datasets. The bold and underlined values show the best scores, and bold values indicate the second best scores