The ODOR dataset contributes 38,116 fine-grained object annotations over 4,712 artworks, benchmarked with five detector families, to stress-test object detection on dense, occluded, and off-centre objects in historical images.
Transfer Learning for Olfactory Object Detection
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We investigate the effect of style and category similarity in multiple datasets used for object detection pretraining. We find that including an additional stage of object-detection pretraining can increase the detection performance considerably. While our experiments suggest that style similarities between pre-training and target datasets are less important than matching categories, further experiments are needed to verify this hypothesis.
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Smelly, dense, and spreaded: The Object Detection for Olfactory References (ODOR) dataset
The ODOR dataset contributes 38,116 fine-grained object annotations over 4,712 artworks, benchmarked with five detector families, to stress-test object detection on dense, occluded, and off-centre objects in historical images.