论文标题
BigDetection：用于改进对象检测器预训练的大规模基准
BigDetection: A Large-scale Benchmark for Improved Object Detector Pre-training
论文作者
论文摘要
近年来已经引入了多个数据集和对象检测的开放挑战。为了构建更通用和强大的对象检测系统，在本文中，我们构建了一个称为BigDetection的新的大规模基准。我们的目标是简单地利用经过精心设计的原理利用现有数据集（LVI，OpenImages和Object365）的培训数据，并策划更大的数据集以改进检测器预训练。具体而言，我们生成了一种新的分类法，该分类法统一了来自不同来源的异质标签空间。我们的BigDetection数据集具有600个对象类别，并包含340万以上36m边界框的培训图像。在多个维度上，它比以前的基准要大得多，后者既提供机遇和挑战。广泛的实验证明了其有效性是评估不同对象检测方法的新基准，及其作为预训练数据集的有效性。
Multiple datasets and open challenges for object detection have been introduced in recent years. To build more general and powerful object detection systems, in this paper, we construct a new large-scale benchmark termed BigDetection. Our goal is to simply leverage the training data from existing datasets (LVIS, OpenImages and Object365) with carefully designed principles, and curate a larger dataset for improved detector pre-training. Specifically, we generate a new taxonomy which unifies the heterogeneous label spaces from different sources. Our BigDetection dataset has 600 object categories and contains over 3.4M training images with 36M bounding boxes. It is much larger in multiple dimensions than previous benchmarks, which offers both opportunities and challenges. Extensive experiments demonstrate its validity as a new benchmark for evaluating different object detection methods, and its effectiveness as a pre-training dataset.
