  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.
