In this technical report, we present our findings from the research conducted
on the Vast Vocabulary Visual Detection (V3Det) dataset for Supervised Vast
Vocabulary Visual Detection task. How to deal with complex categories and
detection boxes has become a difficulty in this track. The original supervised
detector is not suitable for this task. We have designed a series of
improvements, including adjustments to the network structure, changes to the
loss function, and design of training strategies. Our model has shown
improvement over the baseline and achieved excellent rankings on the
Leaderboard for both the Vast Vocabulary Object Detection (Supervised) track
and the Open Vocabulary Object Detection (OVD) track of the V3Det Challenge
2024.

我们在 V3Det 数据集上对监督式大规模词汇视觉检测任务进行了研究，通过调整网络结构、改变损失函数和设计训练策略等一系列改进，我们的模型在 V3Det Challenge 2024 的大规模目标检测 (Supervised) 任务和 Open Vocabulary Object Detection (OVD) 任务中取得了出色的排名。