  We introduce an effective strategy to generate an annotated synthetic dataset
of microbiological images of Petri dishes that can be used to train deep
learning models in a fully supervised fashion. The developed generator employs
traditional computer vision algorithms together with a neural style transfer
method for data augmentation. We show that the method is able to synthesize a
dataset of realistic looking images that can be used to train a neural network
model capable of localising, segmenting, and classifying five different
microbial species. Our method requires significantly fewer resources to obtain
a useful dataset than collecting and labeling a whole large set of real images
with annotations. We show that starting with only 100 real images, we can
generate data to train a detector that achieves comparable results (detection
mAP = 0.416, and counting MAE = 4.49) to the same detector but trained on a
real, several dozen times bigger dataset (mAP = 0.520, MAE = 4.31), containing
over 7k images. We prove the usefulness of the method in microbe detection and
segmentation, but we expect that it is general and flexible and can also be
applicable in other domains of science and industry to detect various objects.
