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<h1 style="color: rgb(34, 61, 96); font-family: Cabin, Arial, sans-serif; font-weight: normal; line-height: 1.3em; font-size: 39px;"> AGAR: a microbial colony detection SDK and a dataset for accurate deep learning bacteria detection </h1>
Sylwia Majchrowska (1, 2), Jarosław Pawłowski (1, 2), Grzegorz Guła (1, 3), Tomasz Bonus (1), Agata Hanas (1), Adam Loch (1), Agnieszka Pawlak (1), Justyna Roszkowiak (3), Tomasz Golan (1), Zuzanna Drulis-Kawa (1, 3)

(1) NeuroSYS Research
(2) Wroclaw University of Science and Technology
(3) University of Wroclaw

The Annotated Germs for Automated Recognition (AGAR) dataset is an image database of microbial colonies cultured on an agar plate. It contains 18000 photos of five different microorganisms, taken under diverse lighting conditions with two different cameras. 

All images are classified into countable, uncountable, and empty, with the former being labeled by microbiologists with colony location and species identification (336 442 colonies in total). This study describes the dataset itself and the process of its development. Furthermore, the performance of selected deep neural network architectures for object detection, namely Faster R-CNN and Cascade R-CNN, have been tested on the AGAR dataset as well. 

The results proved the great potential of deep learning methods to automate the process of microbes localization and classification based on Petri dish photos. Moreover, the AGAR is the first publicly available dataset of this kind and size serving for future development of machine learning models.

AGAR dataset
The AGAR dataset is freely available for academic research under Creative Common Attribution-NonCommercial 2.0 Generic license. 
If you use this dataset for your research, please cite the following paper:
      title={AGAR a microbial colony dataset for deep learning detection}, 
      author={Sylwia Majchrowska and Jarosław Pawłowski and Grzegorz Guła and Tomasz Bonus and Agata Hanas and Adam Loch and Agnieszka Pawlak and Justyna Roszkowiak and Tomasz Golan and Zuzanna Drulis-Kawa},
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Project “Development of a new method for detection and identifying bacterial colonies using artificial neural networks and machine learning algorithms” is co-financed from European Union funds under the European Regional Development Funds as part of the Smart Growth Operational Program. Project implemented as part of the National Centre for Research and Development: Fast Track.

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