Smelly, dense, and spreaded: The Object Detection for Olfactory References (ODOR) dataset

TL;DR

The ODOR dataset introduces a large, detailed collection of annotated artwork images with dense, overlapping objects across many categories, aiming to advance object detection in cultural heritage contexts.

Contribution

This paper presents the ODOR dataset, a comprehensive collection of artwork images with extensive annotations, addressing gaps in existing datasets for fine-grained, dense object detection in art.

Findings

The dataset contains 38,116 annotations across 4712 images.

It features dense, overlapping objects with a wide spatial distribution.

Baseline models show significant challenges due to dataset complexity.

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.