  Large scale imaging surveys will increase the number of galaxy-scale strong
lensing candidates by maybe three orders of magnitudes beyond the number known
today. Finding these rare objects will require picking them out of at least
tens of millions of images and deriving scientific results from them will
require quantifying the efficiency and bias of any search method. To achieve
these objectives automated methods must be developed. Because gravitational
lenses are rare objects reducing false positives will be particularly
important. We present a description and results of an open gravitational lens
finding challenge. Participants were asked to classify 100,000 candidate
objects as to whether they were gravitational lenses or not with the goal of
developing better automated methods for finding lenses in large data sets. A
variety of methods were used including visual inspection, arc and ring finders,
support vector machines (SVM) and convolutional neural networks (CNN). We find
that many of the methods will be easily fast enough to analyse the anticipated
data flow. In test data, several methods are able to identify upwards of half
the lenses after applying some thresholds on the lens characteristics such as
lensed image brightness, size or contrast with the lens galaxy without making a
single false-positive identification. This is significantly better than direct
inspection by humans was able to do. (abridged)
