We present Open Images V4, a dataset of 9.2M images with unified annotations
for image classification, object detection and visual relationship detection.
The images have a Creative Commons Attribution license that allows to share and
adapt the material, and they have been collected from Flickr without a
predefined list of class names or tags, leading to natural class statistics and
avoiding an initial design bias. Open Images V4 offers large scale across
several dimensions: 30.1M image-level labels for 19.8k concepts, 15.4M bounding
boxes for 600 object classes, and 375k visual relationship annotations
involving 57 classes. For object detection in particular, we provide 15x more
bounding boxes than the next largest datasets (15.4M boxes on 1.9M images). The
images often show complex scenes with several objects (8 annotated objects per
image on average). We annotated visual relationships between them, which
support visual relationship detection, an emerging task that requires
structured reasoning. We provide in-depth comprehensive statistics about the
dataset, we validate the quality of the annotations, we study how the
performance of several modern models evolves with increasing amounts of
training data, and we demonstrate two applications made possible by having
unified annotations of multiple types coexisting in the same images. We hope
that the scale, quality, and variety of Open Images V4 will foster further
research and innovation even beyond the areas of image classification, object
detection, and visual relationship detection.

Open Images V4 是一份有关图像分类、物体检测和视觉关系检测的数据集，包括了 9.2M 幅图片，其中包含了 30.1M 个图像级标签，15.4M 个边界框，以及 375k 个涉及 57 类的视觉关系注释。