in detail.

The Microsoft Common Objects in COntext ([MS COCO]{}) dataset contains 91 common object categories with 82 of them having more than 5,000 labeled instances, Fig. \[fig:exampleimages\]. In total the dataset has 2,500,000 labeled instances in 328,000 images. In contrast to the popular ImageNet dataset [@Imagenet], COCO has fewer categories but more instances per category. This can aid in learning detailed object models capable of precise 2D localization. The dataset is also significantly larger in number of instances per category than the PASCAL VOC [@PASCAL] and SUN [@SUN] datasets. Additionally, a critical distinction between our dataset and others is the number of labeled instances per image which may aid in learning contextual information, Fig. \[fig:dataanalysis\]. [MS COCO]{}contains considerably more object instances per image (7.7) as compared to ImageNet (3.0) and PASCAL (2.3). In contrast, the SUN dataset, which contains significant contextual information, has over 17 objects and “stuff” per image but considerably fewer object instances overall.

An abridged version of this work appeared in [@eccv].

Related Work
============

![image](figs/iconic){width="\textwidth"}

Throughout the history of computer vision research datasets have played a critical role. They not only provide a means to train and evaluate algorithms, they drive research in new and 