to the larger number of training examples, CIFAR-10 and CIFAR-100 [@krizhevsky2009learning] offered 10 and 100 categories from a dataset of tiny $32 \times 32$ images [@torralba200880]. While these datasets contained up to 60,000 images and hundreds of categories, they still only captured a small fraction of our visual world.

Recently, ImageNet [@Imagenet] made a striking departure from the incremental increase in dataset sizes. They proposed the creation of a dataset containing 22k categories with 500-1000 images each. Unlike previous datasets containing entry-level categories [@ordonezlarge], such as “dog” or “chair,” like [@torralba200880], ImageNet used the WordNet Hierarchy [@wordnet] to obtain both entry-level and fine-grained [@Birds200] categories. Currently, the ImageNet dataset contains over 14 million labeled images and has enabled significant advances in image classification [@Hinton; @GirshickDDM13; @OverFeat].

[**Object detection** ]{} Detecting an object entails both stating that an object belonging to a specified class is present, and localizing it in the image. The location of an object is typically represented by a bounding box, Fig. \[fig:teaser\](b). Early algorithms focused on face detection [@hjelmaas2001face] using various ad hoc datasets. Later, more realistic and challenging face detection datasets were created [@LFWTech]. Another popular challenge is the detection of pedestrians for which several datasets 