Open Images is a dataset of ~9M images annotated with image-level labels, object bounding boxes, object segmentation masks, and visual relationships. It contains a total of 16M bounding boxes for 600 object classes on 1.9M images, making it the largest existing dataset with object location annotations. The boxes have been largely manually drawn by professional annotators to ensure accuracy and consistency. The images are very diverse and often contain complex scenes with several objects (8.3 per image on average). Open Images also offers visual relationship annotations, indicating pairs of objects in particular relations (e.g. “woman playing guitar”, “beer on table”). In total it has 329 relationship triplets with 391,073 samples. In V5 we added segmentation masks for 2.8M object instances in 350 classes. Segmentation masks mark the outline of objects, which characterizes their spatial extent to a much higher level of detail. Finally, the dataset is annotated with 36.5M image-level labels spanning 19,969 classes.
We believe that having a single dataset with unified annotations for image classification, object detection, visual relationship detection, and instance segmentation will enable to study these tasks jointly and stimulate progress towards genuine scene understanding.
5,851,536 boxes on 600 categories
2,785,498 instance segmentations on 350 categories
36,464,560 image-level labels on 19,959 categories
391,073 relationship annotations of 329 relationships
Extension – 478,000 crowdsourced images with 6,000+ categories
Company Type: Enterprise
Region: US & Canada
Industry Category: Visual Imaging
Data set: Open Images V5
Toolkit: Download and visualize single or multiple classes from the huge Open Images v4 dataset