论文标题
幻想：一种资源，可以使图像的半自动开采大规模挖掘
ImagiFilter: A resource to enable the semi-automatic mining of images at scale
论文作者
论文摘要
从Web自动收集的数据集（半）可以轻松地扩展到数百万个条目，但是数据集的有用性与示例的清洁和高质量直接相关。在本文中，我们描述并公开释放图像数据集以及旨在（半）自动从网络中获得的非常大图像收集中的不良图像过滤的模型。我们的数据集专注于摄影和/或自然图像，这是计算机视觉研究中非常常见的用例。我们为粗略预测，即摄影与非摄影和较小的细粒度预测任务提供注释，在这些任务中，我们将非光学类别进一步分为五个类：地图，图纸，图形，图形，图标和草图。持有验证数据的结果表明，具有记忆足迹降低的模型体系结构在粗略预测上的准确性超过96％。我们最佳模型在可用的最严格的细粒度分类任务上实现了88％的精度。数据集和预算模型可在以下网址提供：https：//github.com/houda96/imagi-filter。
Datasets (semi-)automatically collected from the web can easily scale to millions of entries, but a dataset's usefulness is directly related to how clean and high-quality its examples are. In this paper, we describe and publicly release an image dataset along with pretrained models designed to (semi-)automatically filter out undesirable images from very large image collections, possibly obtained from the web. Our dataset focusses on photographic and/or natural images, a very common use-case in computer vision research. We provide annotations for coarse prediction, i.e. photographic vs. non-photographic, and smaller fine-grained prediction tasks where we further break down the non-photographic class into five classes: maps, drawings, graphs, icons, and sketches. Results on held out validation data show that a model architecture with reduced memory footprint achieves over 96% accuracy on coarse-prediction. Our best model achieves 88% accuracy on the hardest fine-grained classification task available. Dataset and pretrained models are available at: https://github.com/houda96/imagi-filter.
