have been created [@Dalal; @Dollar2012PAMI]. The Caltech Pedestrian Dataset [@Dollar2012PAMI] contains 350,000 labeled instances with bounding boxes.

For the detection of basic object categories, a multi-year effort from 2005 to 2012 was devoted to the creation and maintenance of a series of benchmark datasets that were widely adopted. The PASCAL VOC [@PASCAL] datasets contained 20 object categories spread over 11,000 images. Over 27,000 object instance bounding boxes were labeled, of which almost 7,000 had detailed segmentations. Recently, a detection challenge has been created from 200 object categories using a subset of 400,000 images from ImageNet [@ILSVRCanalysis_ICCV2013]. An impressive 350,000 objects have been labeled using bounding boxes.

Since the detection of many objects such as sunglasses, cellphones or chairs is highly dependent on contextual information, it is important that detection datasets contain objects in their natural environments. In our dataset we strive to collect images rich in contextual information. The use of bounding boxes also limits the accuracy for which detection algorithms may be evaluated. We propose the use of fully segmented instances to enable more accurate detector evaluation.

[**Semantic scene labeling** ]{} The task of labeling semantic objects in a scene requires that each pixel of an image be labeled 