  Future large-scale surveys with high resolution imaging will provide us with
a few $10^5$ new strong galaxy-scale lenses. These strong lensing systems
however will be contained in large data amounts which are beyond the capacity
of human experts to visually classify in a unbiased way. We present a new
strong gravitational lens finder based on convolutional neural networks (CNNs).
The method was applied to the Strong Lensing challenge organised by the Bologna
Lens Factory. It achieved first and third place respectively on the space-based
data-set and the ground-based data-set. The goal was to find a fully automated
lens finder for ground-based and space-based surveys which minimizes human
inspect. We compare the results of our CNN architecture and three new
variations ("invariant" "views" and "residual") on the simulated data of the
challenge. Each method has been trained separately 5 times on 17 000 simulated
images, cross-validated using 3 000 images and then applied to a 100 000 image
test set. We used two different metrics for evaluation, the area under the
receiver operating characteristic curve (AUC) score and the recall with no
false positive ($\mathrm{Recall}_{\mathrm{0FP}}$). For ground based data our
best method achieved an AUC score of $0.977$ and a
$\mathrm{Recall}_{\mathrm{0FP}}$ of $0.50$. For space-based data our best
method achieved an AUC score of $0.940$ and a $\mathrm{Recall}_{\mathrm{0FP}}$
of $0.32$. On space-based data adding dihedral invariance to the CNN
architecture diminished the overall score but achieved a higher no
contamination recall. We found that using committees of 5 CNNs produce the best
recall at zero contamination and consistenly score better AUC than a single
CNN. We found that for every variation of our CNN lensfinder, we achieve AUC
scores close to $1$ within $6\%$.
