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--- |
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task_categories: |
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- object-detection |
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language: |
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- en |
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pretty_name: Object Detection for Olfactory References (ODOR) Dataset |
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size_categories: |
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- 1K<n<10K |
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tags: |
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- fine grained detection |
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- small object detection |
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- art |
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- smell |
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- olfaction |
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- computational humanities |
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license: cc-by-4.0 |
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--- |
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# The Object Detection for Olfactory References (ODOR) Dataset |
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<!-- Provide a quick summary of the dataset. --> |
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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. |
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Existing datasets provide instance-level annotations on artworks but are generally biased towards the image centre and limited with regard to detailed object classes. |
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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. |
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It has challenging dataset properties, such as a detailed set of categories, dense and overlapping objects, and spatial distribution over the whole image canvas. |
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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. |
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You can download the dataset using Hugging Face: |
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```python |
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from datasets import load_dataset |
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ds = load_dataset("mathiaszinnen/odor") |
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``` |
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This dataset has received funding from the Odeuropa EU H2020 project under grant agreement No. 101004469. |
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## Citation |
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<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> |
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