CerberusDet: Unified Multi-Dataset Object Detection

TL;DR

CerberusDet is a multi-task object detection framework based on YOLO that efficiently handles multiple datasets and categories, achieving state-of-the-art results with reduced inference time and improved scalability.

Contribution

It introduces a multi-headed YOLO-based model capable of multi-dataset object detection, addressing dataset merging and class extension challenges.

Findings

Achieved state-of-the-art results on PASCAL VOC and Objects365 datasets.

Reduced inference time by 36% compared to traditional models.

Training multiple tasks together improves efficiency over sequential models.

Abstract

Conventional object detection 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 Objects365 dataset to demonstrate its abilities. CerberusDet achieved state-of-the-art results 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).