Smelly, dense, and spreaded: The Object Detection for Olfactory References (ODOR) dataset
Journal:
arXiv
Published Date:
Jul 11, 2025
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.
