  Recent advances in detecting arbitrary objects in the real world are trained
and evaluated on object detection datasets with a relatively restricted
vocabulary. To facilitate the development of more general visual object
detection, we propose V3Det, a vast vocabulary visual detection dataset with
precisely annotated bounding boxes on massive images. V3Det has several
appealing properties: 1) Vast Vocabulary: It contains bounding boxes of objects
from 13,204 categories on real-world images, which is 10 times larger than the
existing large vocabulary object detection dataset, e.g., LVIS. 2) Hierarchical
Category Organization: The vast vocabulary of V3Det is organized by a
hierarchical category tree which annotates the inclusion relationship among
categories, encouraging the exploration of category relationships in vast and
open vocabulary object detection. 3) Rich Annotations: V3Det comprises
precisely annotated objects in 243k images and professional descriptions of
each category written by human experts and a powerful chatbot. By offering a
vast exploration space, V3Det enables extensive benchmarks on both vast and
open vocabulary object detection, leading to new observations, practices, and
insights for future research. It has the potential to serve as a cornerstone
dataset for developing more general visual perception systems. V3Det is
available at https://v3det.openxlab.org.cn/.
