OwensLab/CommunityForensics-Eval

社区法医学数据集（Community Forensics）旨在解决现有生成图像检测数据集中模型多样性有限的问题。该数据集从4803个生成模型中收集了270万张图像，比已知的最广泛的数据集多了大约34倍。数据集包含与RAISE、COCO、FFHQ和LAION数据集配对的生成图像，并用于训练和评估伪造图像检测方法。数据集以Parquet数据帧的格式存储，每个数据实例包含图像名称、格式、分辨率、模式、图像数据、模型名称、NSFW标志、提示、真实来源、子集、分割、标签和架构等信息。数据集具有非商业研究和教育用途的CC-BY-NC-SA-4.0许可证。

The Community Forensics dataset aims to address the limited model diversity issue in existing datasets for generated image detection. The dataset collects 2.7 million images from 4803 generator models, approximately 34 times more generators than the most extensive previous dataset that we are aware of. It includes generated images paired with RAISE, COCO, FFHQ, and LAION datasets, and is used for training and evaluating fake image detection methods. The dataset is stored in the format of Parquet data frames, with each data instance containing image name, format, resolution, mode, image data, model name, NSFW flag, prompt, real source, subset, split, label, and architecture information. The dataset is released under the CC-BY-NC-SA-4.0 license for non-commercial research and educational purposes.