Imaginative and prescient Basis Fashions (VFMs) pretrained on huge datasets exhibit spectacular efficiency on numerous downstream duties, particularly with restricted labeled goal knowledge. Nonetheless, as a result of their excessive inference compute value, these fashions can’t be deployed for a lot of real-world functions. Motivated by this, we ask the next essential query, “How can we leverage the data from a big VFM to coach a small task-specific mannequin for a brand new goal process with restricted labeled coaching knowledge?”, and suggest a easy task-oriented data switch strategy as a extremely efficient answer to this drawback. Our experimental outcomes on 5 goal duties present that the proposed strategy outperforms task-agnostic VFM distillation, web-scale CLIP pretraining, supervised ImageNet pretraining, and self-supervised DINO pretraining by as much as 11.6%, 22.1%, 13.7%, and 29.8%, respectively. Moreover, the proposed strategy additionally demonstrates as much as 9x, 4x and 15x discount in pretraining compute value when in comparison with task-agnostic VFM distillation, ImageNet pretraining and DINO pretraining, respectively, whereas outperforming them. We additionally present that the dataset used for transferring data has a major impact on the ultimate goal process efficiency, and introduce a retrieval-augmented data switch technique that makes use of web-scale picture retrieval to curate efficient switch units.