# Smelly, dense, and spreaded: The Object Detection for Olfactory References (ODOR) dataset Source: [Smelly, dense, and spreaded: The Object Detection for Olfactory References (ODOR) dataset]() Collection index: [llms.txt]() Data updated: 2025-10-26T05:09:30.174Z Authors: Mathias Zinnen, Prathmesh Madhu, Inger Leemans, Peter Bell, Azhar Hussian, Hang Tran, Ali H\\"urriyeto\\u{g}lu, Andreas Maier, Vincent Christlein Published: 2025-07-11T07:58:21.000Z ## Abstract 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 4712 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()