  The Annotated Germs for Automated Recognition (AGAR) dataset is an image
database of microbial colonies cultured on agar plates. It contains 18000
photos of five different microorganisms as single or mixed cultures, taken
under diverse lighting conditions with two different cameras. All the images
are classified into "countable", "uncountable", and "empty", with the
"countable" class labeled by microbiologists with colony location and species
identification (336442 colonies in total). This study describes the dataset
itself and the process of its development. In the second part, the performance
of selected deep neural network architectures for object detection, namely
Faster R-CNN and Cascade R-CNN, was evaluated on the AGAR dataset. The results
confirmed the great potential of deep learning methods to automate the process
of microbe localization and classification based on Petri dish photos.
Moreover, AGAR is the first publicly available dataset of this kind and size
and will facilitate the future development of machine learning models. The data
used in these studies can be found at https://agar.neurosys.com/.
