In 2016, we introduced Open Images, a collaborative release of ~9 million images annotated with labels spanning thousands of object categories. Since then we have rolled out several updates, culminating with Open Images V4 in 2018. In total, that release included 15.4M bounding-boxes for 600 object categories, making it the largest existing dataset with object location annotations, as well as over 300k visual relationship annotations.

Today we are happy to announce Open Images V5, which adds segmentation masks to the set of annotations, along with the second Open Images Challenge, which will feature a new instance segmentation track based on this data.

Open Images V5
Open Images V5 features segmentation masks for 2.8 million object instances in 350 categories. Unlike bounding-boxes, which only identify regions in which an object is located, segmentation masks mark the outline of objects, characterizing their spatial extent to a much higher level of detail. We have put particular effort into ensuring consistent annotations across different objects (e.g., all cat masks include their tail; bags carried by camels or persons are included in their mask). Importantly, these masks cover a broader range of object categories and a larger total number of instances than any previous dataset.

Example masks on the training set of Open Images V5. These have been produced by our interactive segmentation process. The first example also shows a bounding box, for comparison. From left to right, top to bottom: Tea and cake at the Fitzwilliam Museum by Tim Regan, Pilota II by Euskal kultur erakundea Institut culturel basque, Rheas by Dag Peak, Wuxi science park, 1995 by Gary Stevens, Cat Cafe Shinjuku calico by Ari Helminen, and Untitled by Todd Huffman. All images used under CC BY 2.0 license.

The segmentation masks on the training set (2.68M) have been produced by our state-of-the-art interactive segmentation process, where professional human annotators iteratively correct the output of a segmentation neural network. This is more efficient than manual drawing alone, while at the same time delivering accurate masks (intersection-over-union 84%). Additionally, we release 99k masks on the validation and test sets, which have been annotated manually with a strong focus on quality. These are near-perfect and capture even fine details of complex object boundaries (e.g. spiky flowers and thin structures in man-made objects). Both our training and validation+test annotations offer more accurate object boundaries than the polygon annotations provided by most existing datasets.

Example masks on the validation and test sets of Open Images V5, drawn completely manually. From left to right: thistle flowers by sophie, still life with ax by liz west, Fischkutter KOŁ-180 in Kolobrzeg (PL) by zeesenboot. All images used under CC BY 2.0 license.

In addition to the masks, we also added 6.4M new human-verified image-level labels, reaching a total of 36.5M over nearly 20,000 categories. Finally, we improved annotation density for 600 object categories on the validation and test sets, adding more than 400k bounding boxes to match the density in the training set. This ensures more precise evaluation of object detection models.

Open Images Challenge 2019
In conjunction with this release, we are also introducing the second Open Images Challenge, to be held at the 2019 International Conference on Computer Vision (ICCV 2019). This Challenge will have a new instance segmentation track based on the data above. Moreover, as in the 2018 edition, it will also feature a large-scale object detection track (500 categories with 12.2M training bounding-boxes), and a visual relationship detection track for detecting pairs of objects in particular relations (329 relationship triplets with 375k training samples, e.g., “woman playing guitar” or “beer on table”).

The training set with all annotations is available now. The test set has the same 100k images as the 2018 Challenge and will be launched again on June 3rd, 2019 by Kaggle. The evaluation servers will open on June 3rd for the object detection and visual relationship tracks, and on July 1st for the instance segmentation track. The deadline for submission of results is October 1st, 2019.

We hope that the exceptionally large and diverse training set will inspire research into more advanced instance segmentation models. The extremely accurate ground-truth masks we provide rewards subtle improvements in the output segmentations, and thus will encourage the development of higher-quality models that deliver precise boundaries. Finally, having a single dataset with unified annotations for image classification, object detection, visual relationship detection, and instance segmentation will enable researchers to study these tasks jointly and stimulate progress towards genuine scene understanding.