Posted by Vittorio Ferrari, Research Scientist, Machine Perception
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:
|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.
 We don’t need no bounding-boxes: Training object class detectors using only human verification, Papadopoulos, Uijlings, Keller, and Ferrari, CVPR 2016