Plain-Det: A Plain Multi-Dataset Object Detector
Cheng Shi, Yuchen Zhu, Sibei Yang

TL;DR
Plain-Det is a versatile multi-dataset object detection framework that enhances training efficiency and robustness, achieving state-of-the-art results on COCO and demonstrating strong generalization across 13 datasets.
Contribution
It introduces a flexible, robust, and efficient multi-dataset object detector compatible with various architectures, improving performance and generalization in dense vision tasks.
Findings
Achieves 51.9 mAP on COCO using Def-DETR with Plain-Det
Demonstrates strong generalization across 13 datasets
Compatible with various detection architectures
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsImage Processing and 3D Reconstruction · Advanced Neural Network Applications
