同儕評級精準度：基於DataSeeds標註圖像構建視覺模型微調的基礎數據集

摘要

現代人工智慧（AI）模型的發展，尤其是應用於電腦視覺和圖像生成任務的擴散模型，正在經歷方法論上的範式轉變。傳統上以「模型為中心」的方法為主導，該方法主要通過日益複雜的模型架構和超參數優化來追求性能提升，而如今該領域正逐漸認識到一種更為細緻的「數據為中心」方法。這一新興框架將訓練數據的質量、結構和相關性置於模型性能提升的核心位置。為實現這一範式轉變，我們引入了DataSeeds.AI樣本數據集（簡稱「DSD」），該數據集最初包含約10,610張經過人類同行評級的高質量攝影圖像，並配備了廣泛的多層次註釋。DSD是一個基礎性的電腦視覺數據集，旨在為商業圖像數據集樹立新標準。作為DataSeed.AI超過1億張圖像目錄中的一小部分，DSD為穩健的商業和多模態AI開發提供了可擴展的基礎。通過這項深入的探索性分析，我們記錄了DSD在特定模型上相對於已知基準的定量改進，並公開了評估中使用的代碼和訓練模型。

English

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.

同儕評級精準度：基於DataSeeds標註圖像構建視覺模型微調的基礎數據集

Peer-Ranked Precision: Creating a Foundational Dataset for Fine-Tuning Vision Models from DataSeeds' Annotated Imagery

摘要

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