An Update to Open Images - Now with Bounding-Boxes

Last year we introduced Open Images, a collaborative release of ~9 million images annotated with labels spanning over 6000 object categories, designed to be a useful dataset for machine learning research. The initial release featured image-level labels automatically produced by a computer vision model similar to Google Cloud Vision API, for all 9M images in the training set, and a validation set of 167K images with 1.2M human-verified image-level labels.

Today, we introduce an update to Open Images, which contains the addition of a total of ~2M bounding-boxes to the existing dataset, along with several million additional image-level labels. Details include:

• 1.2M bounding-boxes around objects for 600 categories on the training set. These have been produced semi-automatically by an enhanced version of the technique outlined in [1], and are all human-verified.
• Complete bounding-box annotation for all object instances of the 600 categories on the validation set, all manually drawn (830K boxes). The bounding-box annotations in the training and validations sets will enable research on object detection on this dataset. The 600 categories offer a broader range than those in the ILSVRC and COCO detection challenges, and include new objects such as fedora hat and snowman.
• 4.3M human-verified image-level labels on the training set (over all categories). This will enable large-scale experiments on object classification, based on a clean training set with reliable labels.

Annotated images from the Open Images dataset. Left: FAMILY MAKING A SNOWMAN by mwvchamber. Right: STANZA STUDENTI.S.S. ANNUNZIATA by ersupalermo. Both images used under CC BY 2.0 license. See more examples here.

We hope that this update to Open Images will stimulate the broader research community to experiment with object classification and detection models, and facilitate the development and evaluation of new techniques.

References
[1] We don't need no bounding-boxes: Training object class detectors using only human verification, Papadopoulos, Uijlings, Keller, and Ferrari, CVPR 2016