Complementary datasets to COCO for object detection
This work addresses the problem of dataset limitations for object detection researchers, but it is incremental as it builds upon existing datasets like COCO and OpenImages.
The authors introduced two complementary datasets, COCO_OI and ObjectNet_D, to address the saturation of performance on the COCO dataset for object detection, with COCO_OI providing 1,418,978 training bounding boxes over 380,111 images and ObjectNet_D testing generalization ability.
For nearly a decade, the COCO dataset has been the central test bed of research in object detection. According to the recent benchmarks, however, it seems that performance on this dataset has started to saturate. One possible reason can be that perhaps it is not large enough for training deep models. To address this limitation, here we introduce two complementary datasets to COCO: i) COCO_OI, composed of images from COCO and OpenImages (from their 80 classes in common) with 1,418,978 training bounding boxes over 380,111 images, and 41,893 validation bounding boxes over 18,299 images, and ii) ObjectNet_D containing objects in daily life situations (originally created for object recognition known as ObjectNet; 29 categories in common with COCO). The latter can be used to test the generalization ability of object detectors. We evaluate some models on these datasets and pinpoint the source of errors. We encourage the community to utilize these datasets for training and testing object detection models. Code and data is available at https://github.com/aliborji/COCO_OI.