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 13,029 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 245k 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.

翻译：近期在现实世界中检测任意物体的进展，均基于词汇相对受限的目标检测数据集进行训练与评估。为促进更通用的视觉目标检测发展，我们提出V3Det——一个包含海量图像精标注边界框的大规模词汇视觉检测数据集。V3Det具有以下显著特性：1）广袤词汇：其真实世界图像中包含来自13,029个类别的物体边界框，规模是现有大规模词汇目标检测数据集（如LVIS）的10倍；2）层级化类别组织：V3Det通过层级化类别树组织其广袤词汇，该树状结构标注了类别间的包含关系，鼓励在广袤与开放词汇目标检测中探索类别关联；3）丰富标注：V3Det包含245k张图像中精准标注的物体，以及由人类专家与智能聊天机器人编写的每个类别的专业描述。通过提供广阔的探索空间，V3Det在广袤与开放词汇目标检测领域构建了全面的基准评测，为未来研究带来新观察、实践与洞见。该数据集有望成为构建更通用视觉感知系统的基石性数据集。