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 ODOR dataset fills this gap, offering 38,116 object-level annotations across 4,712 images, spanning an extensive set of 139 fine-grained categories.

It has challenging dataset properties, such as a detailed set of categories, dense and overlapping objects, and spatial distribution over the whole image canvas.

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.

You can download the dataset using Hugging Face:

from datasets import load_dataset
ds = load_dataset("mathiaszinnen/odor")

This dataset has received funding from the Odeuropa EU H2020 project under grant agreement No. 101004469.