Plain-Det: A Plain Multi-Dataset Object Detector

• URL: http://arxiv.org/abs/2407.10083v1
• Date: Sun, 14 Jul 2024 05:18:06 GMT
• Title: Plain-Det: A Plain Multi-Dataset Object Detector
• Authors: Cheng Shi, Yuchen Zhu, Sibei Yang,
• Abstract summary: Plain-Det offers flexibility to accommodate new datasets, in performance across diverse datasets, and training efficiency. We conduct extensive experiments on 13 downstream datasets and Plain-Det demonstrates strong generalization capability.
• Score: 22.848784430833835
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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

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