ImagiFilter: A resource to enable the semi-automatic mining of images at scale
This work addresses the need for clean, high-quality image datasets in computer vision research, particularly for photographic images, though it is incremental as it builds on existing filtering methods.
The paper tackles the problem of filtering undesirable images from large web-collected datasets by introducing ImagiFilter, a resource with a dataset and pretrained models for semi-automatic mining, achieving over 96% accuracy on coarse prediction and 88% accuracy on fine-grained classification.
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.