Google Open Images V5

by Alina Kuznetsova,Hassan Rom,Neil Alldrin,Jasper Uijlings,Ivan Krasin,Jordi Pont-Tuset,Shahab Kamali,Stefan Popov,Matteo Malloci,Alexander Kolesnikov,Tom Duerig,Vittorio FerrariPublic Domain

Google Open Images V5

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. 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.

Dataset Attributes

Label SVG
TasksDetection, Segmentation
Label SVG
CategoriesHumans, Animals, Vehicles
Label SVG
SensorRGB Camera