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

AGAR a microbial colony dataset for deep learning detection