Peer-Ranked Precision: Creating a Foundational Dataset for Fine-Tuning
  Vision Models from DataSeeds' Annotated Imagery
Authors: name: Sajjad Abdoli, name: Freeman Lewin, name: Gediminas Vasiliauskas, name: Fabian Schonholz

The development of modern Artificial Intelligence (AI) models, particularly
diffusion-based models employed in computer vision and image generation tasks,
is undergoing a paradigmatic shift in development methodologies. Traditionally
dominated by a "Model Centric" approach, in which performance gains were
primarily pursued through increasingly complex model architectures and
hyperparameter optimization, the field is now recognizing a more nuanced
"Data-Centric" approach. This emergent framework foregrounds the quality,
structure, and relevance of training data as the principal driver of model
performance. To operationalize this paradigm shift, we introduce the
DataSeeds.AI sample dataset (the "DSD"), initially comprised of approximately
10,610 high-quality human peer-ranked photography images accompanied by
extensive multi-tier annotations. The DSD is a foundational computer vision
dataset designed to usher in a new standard for commercial image datasets.
Representing a small fraction of DataSeed.AI's 100 million-plus image catalog,
the DSD provides a scalable foundation necessary for robust commercial and
multimodal AI development. Through this in-depth exploratory analysis, we
document the quantitative improvements generated by the DSD on specific models
against known benchmarks and make the code and the trained models used in our
evaluation publicly available.