Experiment 2 model performance.

Performance is shown for ecoset-trained models from Experiment 1 that were fine-tuned with ImageNet images for 1000-way object categorization. ImageNet contains labels at different category levels, with some images labeled at the basic-level and others labeled at the subordinate-level. Fine-tuning was run only with non-blurred ImageNet images. Each plotted point is an average across 10 runs of an otherwise identical model with different random seeds. Shaded error bars reflect mean ± SEM across these 10 trials. Dots along the top of each plot indicate time points at which a linear mixed effects model over a sliding temporal window revealed a significant effect of the specified pairwise condition comparison, in either direction (FDR corrected, ɑ = 0.05). (A) ImageNet validation set accuracy. Light gray corresponds to models trained with ecoset in the non-blurred condition, blue to models trained with ecoset in the linear-blur condition, green to models trained with ecoset in the nonlinear-blur condition, and dark gray corresponds to new models with no pre-training (that is, starting from scratch). Left and right plots show averages for models trained using either grayscale images or color images, respectively. Accuracy was temporally smoothed to reduce noise. (B) Estimated learning rate computed as slope of accuracy over time. Colors correspond to models as in (A). Only pairwise comparisons between different pre-trained models are shown; all pre-trained models performed significantly better than the no-pre-training models at all time points.