  In the realm of self-supervised learning (SSL), conventional wisdom has
gravitated towards the utility of massive, general domain datasets for
pretraining robust backbones. In this paper, we challenge this idea by
exploring if it is possible to bridge the scale between general-domain datasets
and (traditionally smaller) domain-specific datasets to reduce the current
performance gap. More specifically, we propose Precision at Scale (PaS), a
novel method for the autonomous creation of domain-specific datasets on-demand.
The modularity of the PaS pipeline enables leveraging state-of-the-art
foundational and generative models to create a collection of images of any
given size belonging to any given domain with minimal human intervention.
Extensive analysis in two complex domains, proves the superiority of PaS
datasets over existing traditional domain-specific datasets in terms of
diversity, scale, and effectiveness in training visual transformers and
convolutional neural networks. Most notably, we prove that automatically
generated domain-specific datasets lead to better pretraining than large-scale
supervised datasets such as ImageNet-1k and ImageNet-21k. Concretely, models
trained on domain-specific datasets constructed by PaS pipeline, beat
ImageNet-1k pretrained backbones by at least 12% in all the considered domains
and classification tasks and lead to better food domain performance than
supervised ImageNet-21k pretrain while being 12 times smaller. Code repository:
https://github.com/jesusmolrdv/Precision-at-Scale/
