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/.

翻译：近期在现实世界中检测任意物体的研究，通常基于词汇量相对受限的目标检测数据集进行训练和评估。为促进更通用的视觉目标检测发展，我们提出了V3Det——一个包含大规模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）大规模词汇：其真实世界图像包含来自13,204个类别的物体边界框，词汇量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的大规模词汇通过层级化类别树组织，标注了类别间的包含关系，推动了大词汇及开放词汇目标检测中类别关系的探索。3）丰富标注：V3Det包含243k张图像中精确标注的物体，以及由人类专家和强大聊天机器人撰写的每个类别的专业描述。通过提供广阔的探索空间，V3Det能在大规模及开放词汇目标检测上实现广泛基准测试，为未来研究带来新观察、实践与见解。它有望成为构建更通用视觉感知系统的基石数据集。V3Det访问地址：https://v3det.openxlab.org.cn/。