  Efficient algorithms are being developed to search for strong gravitational
lens systems owing to increasing large imaging surveys. Neural networks have
been successfully used to discover galaxy-scale lens systems in imaging surveys
such as the Kilo Degree Survey, Hyper-Suprime Cam (HSC) Survey and Dark Energy
Survey over the last few years. Thus, it has become imperative to understand
how some of these networks compare, their strengths and the role of the
training datasets as most of the networks make use of supervised learning
algorithms. In this work, we present the first-of-its-kind systematic
comparison and benchmarking of networks from four teams that have analysed the
HSC Survey data. Each team has designed their training samples and developed
neural networks independently but coordinated apriori in reserving specific
datasets strictly for test purposes. The test sample consists of mock lenses,
real (candidate) lenses and real non-lenses gathered from various sources to
benchmark and characterise the performance of each of the network. While each
team's network performed much better on their own constructed test samples
compared to those from others, all networks performed comparable on the test
sample with real (candidate) lenses and non-lenses. We also investigate the
impact of swapping the training samples amongst the teams while retaining the
same network architecture. We find that this resulted in improved performance
for some networks. These results have direct implications on measures to be
taken for lens searches with upcoming imaging surveys such as the Rubin-Legacy
Survey of Space and Time, Roman and Euclid.
