Plain-Det: A Plain Multi-Dataset Object Detector
<|reference_start|>Plain-Det: A Plain Multi-Dataset Object Detector: Recent advancements in large-scale foundational models have sparked widespread interest in training highly proficient large vision models. A common consensus revolves around the necessity of aggregating extensive, high-quality annotated data. However, given the inherent challenges in annotating dense tasks in computer vision, such as object detection and segmentation, a practical strategy is to combine and leverage all available data for training purposes. In this work, we propose Plain-Det, which offers flexibility to accommodate new datasets, robustness in performance across diverse datasets, training efficiency, and compatibility with various detection architectures. We utilize Def-DETR, with the assistance of Plain-Det, to achieve a mAP of 51.9 on COCO, matching the current state-of-the-art detectors. We conduct extensive experiments on 13 downstream datasets and Plain-Det demonstrates strong generalization capability. Code is release at https://github.com/ChengShiest/Plain-Det<|reference_end|>
@article{shi2024plain-det:,
    title={Plain-Det: A Plain Multi-Dataset Object Detector},
    author={Cheng Shi, Yuchen Zhu and Sibei Yang},
    journal={arXiv preprint arXiv:2407.10083},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.10083},
    primaryClass={cs.CV}
}