these datasets varies significantly in size, list of labeled categories and types of images. ImageNet was created to capture a large number of object categories, many of which are fine-grained. SUN focuses on labeling scene types and the objects that commonly occur in them. Finally, PASCAL VOC’s primary application is object detection in natural images. [MS COCO]{}is designed for the detection and segmentation of objects occurring in their natural context.

The number of instances per category for all 91 categories is shown in Fig. \[fig:dataanalysis\](a). A summary of the datasets showing the number of object categories and the number of instances per category is shown in Fig. \[fig:dataanalysis\](d). While [MS COCO]{}has fewer categories than ImageNet and SUN, it has more instances per category which we hypothesize will be useful for learning complex models capable of precise localization. In comparison to PASCAL VOC, [MS COCO]{}has both more categories and instances.

An important property of our dataset is we strive to find non-iconic images containing objects in their natural context. The amount of contextual information present in an image can be estimated by examining the average number of object categories and instances per image, Fig. \[fig:dataanalysis\](b, c). For ImageNet we plot the 