DeepBacs – Escherichia coli antibiotic phenotyping object detection dataset and YOLOv2 model

Training and test images of <em>E. coli</em> cells treated with different antibiotics for antibiotic phenotyping using YOLOv2 object detection. Additional information can be found on this github wiki. Example images show predictions of drug-treated <em>E. coli</em> cells. <strong>Training and test dataset</strong> <strong>Data type</strong>: Paired microscopy images (confocal fluorescence) and manual annotations <strong>Microscopy data type</strong>: Confocal fluorescence images of fixed <em>E. coli</em> cells stained for membrane (Nile Red) and DNA (DAPI) paired with annotations in PASCAL VOC format <strong>Microscope</strong>: Zeiss LSM710 confocal microscope with a Plan-Apo 63x oil objective (1.4 NA) <strong>Cell type</strong>: Chemically fixed <em>E. coli</em> NO34 cells (MreB-sfGFPsw, kindly provided by Zemer Gitai) (untreated or drug-treated); <strong>File format</strong>: .png (RGB) <strong>Image size</strong>: 400 x 400 px² (Pixel size: 84 nm) <strong>YOLOv2 model</strong> The YOLOv2 model was generated using the ZeroCostDL4Mic platform (Chamier et al., 2021). It was trained from scratch for 97 epochs on 153 manually annotated images (image dimensions: (400, 400, 3)) with a batch size of 16 and a custom loss function combining MSE and crossentropy losses, using the YOLOv2 ZeroCostDL4Mic notebook (v 1.12) (von Chamier &amp; Laine et al., 2020). Key python packages used include tensorflow (v0.1.12), Keras (v 2.3.1), numpy (v 1.19.5), cuda (v 10.1.243). The training was accelerated using a Tesla P100GPU and data was augmented by a factor of 8 using rotation and flipping. The model weights can be used with the ZeroCostDL4Mic YOLOv2 notebook. <strong>Author(s)</strong>: Christoph Spahn^1,2, Mike Heilemann^1,3 <strong>Contact email</strong>: [e-mail] <strong>Affiliation(s)</strong>: 1) Institute of Physical and Theoretical Chemistry, Max-von-Laue Str. 7, Goethe-University Frankfurt, 60439 Frankfurt, Germany 2) ORCID: 0000-0001-9886-2263 3) ORCID: 0000-0002-9821-3578