同行评审精度：基于DataSeeds标注图像构建视觉模型微调的基础数据集

摘要

现代人工智能（AI）模型的发展，尤其是应用于计算机视觉和图像生成任务的基于扩散的模型，正在经历方法论上的范式转变。传统上，这一领域主要采用“模型中心”方法，即通过日益复杂的模型架构和超参数优化来追求性能提升。然而，当前领域正逐渐认识到一种更为精细的“数据中心”方法。这一新兴框架将训练数据的质量、结构和相关性视为模型性能的主要驱动力。为了实践这一范式转变，我们引入了DataSeeds.AI样本数据集（简称“DSD”），该数据集最初包含约10,610张经过人类同行排名的高质量摄影图像，并附有详尽的多层次注释。DSD作为基础计算机视觉数据集，旨在为商业图像数据集树立新标准。作为DataSeed.AI超过1亿张图像目录中的一小部分，DSD为稳健的商业和多模态AI开发提供了可扩展的基础。通过深入的探索性分析，我们记录了DSD在特定模型上相对于已知基准的定量改进，并公开了评估中使用的代码和训练模型。

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.

同行评审精度：基于DataSeeds标注图像构建视觉模型微调的基础数据集

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

摘要

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