论文标题
与可可的补充数据集以进行对象检测
Complementary datasets to COCO for object detection
论文作者
论文摘要
近十年来，可可数据集一直是对象检测中研究床的中央测试床。但是，根据最近的基准测试，该数据集的性能似乎已经开始饱和。一个可能的原因是，也许它不足以训练深层模型。 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用于对象识别为objectnet；后者可用于测试对象检测器的概括能力。我们在这些数据集上评估了一些模型，并查明错误的源头。我们鼓励社区利用这些数据集进行培训和测试对象检测模型。代码和数据可在https://github.com/aliborji/coco_oi上找到。
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.
