AGAR a microbial colony dataset for deep learning detection

@article{Majchrowska2021AGARAM,
  title={AGAR a microbial colony dataset for deep learning detection},
  author={Sylwia Majchrowska and Jaroslaw Pawlowski and Grzegorz Guła and Tomasz Bonus and Agata Hanas and Adam Loch and Agnieszka S. Pawlak and Justyna Roszkowiak and Tomasz Golan and Zuzanna Drulis-Kawa},
  journal={ArXiv},
  year={2021},
  volume={abs/2108.01234}
}
The Annotated Germs for Automated Recognition (AGAR) dataset is an image database of microbial colonies cultured on agar plates. It contains 18 000 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 (336 442 colonies in total). This study… Expand
Deep neural networks approach to microbial colony detection - a comparative analysis
TLDR
Three well-known deep learning approaches for object detection on the AGAR dataset are compared, namely two-stage, one-stage and transformer based neural networks, and the results may serve as a benchmark for future experiments. Expand

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