  Counting microbial colonies is a fundamental task in microbiology and has
many applications in numerous industry branches. Despite this, current studies
towards automatic microbial counting using artificial intelligence are hardly
comparable due to the lack of unified methodology and the availability of large
datasets. The recently introduced AGAR dataset is the answer to the second
need, but the research carried out is still not exhaustive. To tackle this
problem, we compared the performance of three well-known deep learning
approaches for object detection on the AGAR dataset, namely two-stage,
one-stage and transformer based neural networks. The achieved results may serve
as a benchmark for future experiments.
