mlsanityData to 13 Oct 2026

AGAR a microbial colony dataset for deep learning detection

arXiv 2108.01234 ↗First posted 3 Aug 2021cs.CV cs.AI q-bio.QMcited by 6

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Abstract (arXiv)

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/.

Key facts. Cited by 6 papers in the map (data through 13 Oct 2026). Builds on 17 of its 53 references that are in the map. First posted on arXiv 3 Aug 2021.

Builds on

17 of 53 references.

References · 17

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5 more
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Built on by

Cited by year (2026 to 13 Oct)

Citing papers per year: 2021: 2; 2022: 1; 2023: 2; 2024: 0; 2025: 0; 2026: 1.

6 papers.

  1. 2021Generation of microbial colonies dataset with deep learning style transfercited by 2
  2. 2023Light-Weight Vision Transformer with Parallel Local and Global Self-Attentioncited by 2
  3. 2023Transformer-based Detection of Microorganisms on High-Resolution Petri Dish Imagescited by 2
  4. 2021Deep neural networks approach to microbial colony detection -- a comparative analysiscited by 1
  5. 2022Self-Normalized Density Map (SNDM) for Counting Microbiological Objectscited by 1
  6. 2026Count Anythingcited by 0