CURE-OR: Challenging Unreal and Real Environment for Object Recognition

As one of the research directions at OLIVES Lab @ Georgia Tech, we focus on the robustness of data-driven algorithms under diverse challenging conditions where trained models can possibly be depolyed. To achieve this goal, we introduced a large-sacle (1.M images) object recognition dataset (CURE-OR) which is among the most comprehensive datasets with controlled synthetic challenging conditions. In CURE-OR dataset, there are 1,000,000 images of 100 objects with varying size, color, and texture, captured with multiple devices in different setups. The majority of images in the dataset were acquired with smartphones and tested with off-the-shelf applications to benchmark the recognition performance of devices and applications that are used in our daily lives. Please refer to our GitHub page for code, papers, and more information.

作为佐治亚理工学院OLIVES实验室的研究方向之一，我们专注于在多样化的严峻条件下数据驱动算法的鲁棒性，在这些条件下训练的模型可能被部署。为实现此目标，我们引入了一个大规模（1.M图像）物体识别数据集（CURE-OR），该数据集是包含受控合成严峻条件中最全面的数据集之一。在CURE-OR数据集中，包含100个不同大小、颜色和纹理的物体的100万张图像，这些图像由多台设备在不同配置下捕获。数据集的大部分图像均使用智能手机获取，并通过市售应用进行测试，以评估我们日常生活中使用的设备和应用的认识性能。请参阅我们的GitHub页面以获取代码、论文及更多信息。