AGAR a microbial colony dataset for deep learning detection

• URL: http://arxiv.org/abs/2108.01234v1
• Date: Tue, 3 Aug 2021 01:26:41 GMT
• Title: AGAR a microbial colony dataset for deep learning detection
• Authors: Sylwia Majchrowska, Jaros{\l}aw Paw{\l}owski, Grzegorz Gu{\l}a, Tomasz Bonus, Agata Hanas, Adam Loch, Agnieszka Pawlak, Justyna Roszkowiak, Tomasz Golan, and Zuzanna Drulis-Kawa
• Abstract summary: The Annotated Germs for Automated Recognition dataset is an image database of microbial colonies cultured on agar plates. 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 was evaluated on the AGAR dataset.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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/.

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