  Image cropping aims to improve the composition as well as aesthetic quality
of an image by removing extraneous content from it. Most of the existing image
cropping databases provide only one or several human-annotated bounding boxes
as the groundtruths, which can hardly reflect the non-uniqueness and
flexibility of image cropping in practice. The employed evaluation metrics such
as intersection-over-union cannot reliably reflect the real performance of a
cropping model, either. This work revisits the problem of image cropping, and
presents a grid anchor based formulation by considering the special properties
and requirements (e.g., local redundancy, content preservation, aspect ratio)
of image cropping. Our formulation reduces the searching space of candidate
crops from millions to no more than ninety. Consequently, a grid anchor based
cropping benchmark is constructed, where all crops of each image are annotated
and more reliable evaluation metrics are defined. To meet the practical demands
of robust performance and high efficiency, we also design an effective and
lightweight cropping model. By simultaneously considering the region of
interest and region of discard, and leveraging multi-scale information, our
model can robustly output visually pleasing crops for images of different
scenes. With less than 2.5M parameters, our model runs at a speed of 200 FPS on
one single GTX 1080Ti GPU and 12 FPS on one i7-6800K CPU. The code is available
at: \url{https://github.com/HuiZeng/Grid-Anchor-based-Image-Cropping-Pytorch}.
