Most publications that aim to optimize neural networks for CBIR, train and test their models on domain specific datasets. It is therefore unclear, if those networks can be used as a general-purpose image feature extractor. After analyzing popular image retrieval test sets we decided to manually curate GPR1200, an easy to use and accessible but challenging benchmark dataset with 1200 categories and 10 class examples. Classes and images were manually selected from six publicly available datasets of different image areas, ensuring high class diversity and clean class boundaries.
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Oxford5K is the Oxford Buildings Dataset, which contains 5062 images collected from Flickr. It offers a set of 55 queries for 11 landmark buildings, five for each landmark.
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This dataset consists of 3,710 flood images, annotated by domain experts regarding their relevance with respect to three tasks (determining the flooded area, inundation depth, water pollution).
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The PS-Battles dataset is gathered from a large community of image manipulation enthusiasts and provides a basis for media derivation and manipulation detection in the visual domain. The dataset consists of 102'028 images grouped into 11'142 subsets, each containing the original image as well as a varying number of manipulated derivatives.
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