Technology Category
Computer Vision: Diffusion Models for VisionMachine Learning: Deep Neural Architectures and Foundation ModelsHumans and AI: Other Foundations of Human Computation & AI
Peer-Ranked Precision: Creating a Foundational Dataset for Fine-Tuning Vision Models from DataSeeds' Annotated Imagery
🤖 AI Summary
This work addresses the prevalent issues of heterogeneous quality and absence of rigorous evaluation criteria in commercial image datasets. To this end, we introduce DSD—the first high-quality, vision-foundation dataset explicitly designed for data-driven paradigms. DSD comprises 10,610 images rigorously ranked via crowdsourced peer comparison, accompanied by hierarchical semantic annotations (including fine-grained objects, attributes, and relations). We propose a novel pairwise-comparison-based quantitative quality assessment mechanism and a multi-granularity annotation modeling framework. Extensive fine-tuning experiments on state-of-the-art vision models—including diffusion models—demonstrate that DSD significantly enhances generation fidelity and controllability. All code, models, and dataset are publicly released. Moreover, DSD supports scalable training protocols adaptable to billion-scale image corpora, thereby establishing a new “quality-first” benchmark for industrial-grade visual data.
Technology Category
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
Creating high-quality dataset for vision model fine-tuning
Shifting from model-centric to data-centric AI development
Evaluating dataset impact on model performance benchmarks
Innovation
Methods, ideas, or system contributions that make the work stand out.
Peer-ranked high-quality photography dataset
Multi-tier annotated foundational vision dataset
Data-centric approach for model performance
🔎 Similar Papers
No similar papers found.
