Object detection is a core task in computer vision. Over the years, the development of numerous models has significantly enhanced performance. However, these conventional models are usually limited by the data on which they were trained and by the category logic they define. With the recent rise of Language-Visual Models, new methods have emerged that are not restricted to these fixed categories. Despite their flexibility, such Open Vocabulary detection models still fall short in accuracy compared to traditional models with fixed classes. At the same time, more accurate data-specific models face challenges when there is a need to extend classes or merge different datasets for training. The latter often cannot be combined due to different logics or conflicting class definitions, making it difficult to improve a model without compromising its performance. In this paper, we introduce CerberusDet, a framework with a multi-headed model designed for handling multiple object detection tasks. Proposed model is built on the YOLO architecture and efficiently shares visual features from both backbone and neck components, while maintaining separate task heads. This approach allows CerberusDet to perform very efficiently while still delivering optimal results. We evaluated the model on the PASCAL VOC dataset and additional categories from the Objects365 dataset to demonstrate its abilities. CerberusDet achieved results comparable to state-of-the-art data-specific models with 36% less inference time. The more tasks are trained together, the more efficient the proposed model becomes compared to running individual models sequentially. The training and inference code, as well as the model, are available as open-source (https://github.com/ai-forever/CerberusDet).

翻译：目标检测是计算机视觉的核心任务。多年来，众多模型的发展显著提升了检测性能。然而，这些传统模型通常受限于其训练数据及所定义的类别逻辑。随着语言-视觉模型的兴起，出现了不受固定类别限制的新方法。尽管这类开放词汇检测模型具有灵活性，但其准确率仍不及具有固定类别的传统模型。同时，更精确的特定数据模型在需要扩展类别或合并不同数据集进行训练时面临挑战。由于逻辑差异或类别定义冲突，不同数据集往往无法合并，这使得在不牺牲性能的情况下改进模型变得困难。本文提出CerberusDet，这是一个专为处理多目标检测任务设计的多头模型框架。该模型基于YOLO架构构建，能高效共享来自骨干网络和颈部结构的视觉特征，同时保持独立的任务头。这种方法使CerberusDet在保持最优结果的同时实现高效运行。我们在PASCAL VOC数据集及Objects365数据集的附加类别上评估了该模型，以验证其能力。CerberusDet在推理时间减少36%的情况下，取得了与最先进的特定数据模型相当的结果。联合训练的任务越多，该模型相比顺序运行独立模型的效率优势越显著。训练与推理代码以及模型均已开源（https://github.com/ai-forever/CerberusDet）。