The Object Detection for Olfactory References (ODOR) Dataset.

Real-world applications of computer vision in the humanities require algorithms to be robust against artistic abstraction, peripheral objects, and subtle differences between fine-grained target classes. Existing datasets provide instance-level annotations on artworks but are generally biased towards the image centre and limited with regard to detailed object classes. The proposed ODOR dataset fills this gap, offering 38,116 object-level annotations across 4,712 images, spanning an extensive set of 139 fine-grained categories. Conducting a statistical analysis, we showcase challenging dataset properties, such as a detailed set of categories, dense and overlapping objects, and spatial distribution over the whole image canvas. Furthermore, we provide an extensive baseline analysis for object detection models and highlight the challenging properties of the dataset through a set of secondary studies. Inspiring further research on artwork object detection and broader visual cultural heritage studies, the dataset challenges researchers to explore the intersection of object recognition and smell perception. How to use The annotations are provided in COCO JSON format. To represent the two-level hierarchy of the object classes, we make use of the supercategory field in the categories array as defined by COCO. In addition to the object-level annotations, we provide an additional CSV file with image-level metadata, which includes content-related fields, such as Iconclass codes [72 , 73]) or image descriptions, as well as formal annotations, such as artist, license, or creation year. For the sake of license compliance, we do not publish the images directly (although most of the images are public domain). Instead, we provide links to their source collections in the metadata file (meta.csv) and a python script to download the artwork images (download_images.py).计算机视觉在人文领域的实际应用，要求算法能够抵御艺术抽象、边缘物体以及细粒度目标类别间细微差异带来的干扰。现有数据集虽可对艺术作品提供实例级标注，但普遍存在偏向图像中心区域的偏差，且细粒度物体类别覆盖范围有限。本次提出的ODOR数据集填补了这一空白：该数据集涵盖4712张图像，共包含38116个物体级标注，覆盖139个丰富的细粒度类别。通过统计分析，我们展示了该数据集的多项挑战性特性：涵盖丰富细粒度类别、存在密集且重叠的物体、空间分布覆盖整张图像画布。此外，我们针对目标检测模型开展了全面的基准分析，并通过一系列辅助研究凸显了该数据集的挑战性特性。该数据集旨在推动艺术作品目标检测及更广泛的视觉文化遗产研究领域的进一步探索，并激励研究者探索目标识别与嗅觉感知的交叉研究方向。 使用说明 标注文件采用COCO（Common Objects in Context）JSON格式存储。为体现物体类别的两级层级结构，我们沿用了COCO标准中类别数组内的超类别字段。除物体级标注外，我们还提供了一份包含图像级元数据的额外CSV文件，其中涵盖内容相关字段（如Iconclass编码[72,73]或图像描述），以及正式标注信息（如创作者、授权协议或创作年份）。 出于授权合规性考虑，我们未直接发布图像文件（尽管多数图像已进入公有领域）。取而代之的是，我们在元数据文件（meta.csv）中提供了其来源馆藏的链接，并附带了用于下载艺术作品图像的Python脚本（download_images.py）。