Precision at scale: Domain-specific datasets on-demand
Jesús M. Rodríguez-de-Vera, Imanol G. Estepa, Ignacio Sarasúa, Bhalaji Nagarajan, Petia I. Radeva · Pattern Recognition · 2025
• Precision at Scale (PaS) automatically creates domain-specific datasets on-demand. • It leverages LLMs, VLMs, and generative models for data collection and curation. • PaS includes a task-agnostic framework for assessing dataset diversity. • Training on PaS datasets outperform model pretraining on large-scale general-domain datasets. • PaS datasets efficiently fine-tune SoTA VLMs in specialized domains. Recent self-supervised learning methods rely on massive general-domain datasets for robust model pretraining. However, these datasets may lack specificity required in specialized domains. Collecting large, supervised datasets to compensate for this limitation is also cumbersome. This raises a key question: Can automatically crafted domain-specific datasets serve as efficient and effective SSL pretrainers, performing comparable to—or even surpassing—much larger state-of-the-art general-domain datasets? To address this challenge, we propose Precision at Scale (PaS) , a novel modular pipeline for automatic creation of domain-specific datasets on-demand. PaS leverages Large Language Models (LLMs) and Vision-Language Models (VLMs) through three distinct phases: Concept Generation, where LLMs identify relevant domain concepts; Image Collection, utilizing VLMs and Generative models to gather appropriate images; Data Curation, ensuring quality and relevance by eliminating unrelated or redundant images. We conduct extensive experiments across three complex domains — food, insects, and birds — proving that PaS datasets compete and often surpass existing domain-specific datasets in diversity, scale, and effectiveness as pretrainers. Models pretrained on PaS datasets outperform those trained on large-scale general-domain datasets (ImageNet-1K) by up to 21 % and surpass same-scale domain-specific datasets by 6.7 % across classification tasks. Notably, despite being an order of magnitude smaller, PaS datasets outperform ImageNet-21K pretraining, with improvements of 3.3 % in fine-tuning and 9.5 % in few-shot learning, and showing superior performance on specialized dense tasks. Furthermore, by efficiently fine-tuning pretrained VLMs like CLIP and SigLIP using low-rank methods, we achieve performance gains (+4.2 % over CLIP) in specialized domains with minimal overhead, demonstrating the versatility of PaS datasets.