Training and test images of E. coli cells for object detection and classification using YOLOv2, as well as a trained YOLOv2 model. Additional information can be found on this github wiki. The example shows a bright field image of live E. coli cells and the respective annotation for specific growth stages. Training and test dataset Data type: Paired microscopy images (bright field) and annotations in PASCAL VOC format Microscopy data type: 2D bright field images recorded at 1 min interval Microscope: Nikon Eclipse Ti-E equipped with an Apo TIRF 1.49NA 100x oil immersion objective Cell type: E. coli MG1655 wild type strain (CGSC #6300). File format: .png (8-bit) Image size: 256 x 256 px² (158 nm / pixel), 100/15 individual frames (training/test dataset) 1024 x 1024 px² (79 nm / pixel), 9 regions of interest with 80 frames @ 1 min time interval (live-cell time series) Image preprocessing: Raw images were recorded in 16-bit mode (image size 512x512 px² @ 158 nm/px). 256 x 256 px² patches were extracted from individual frames and converted into 8-bit .png images after adjusting brightness and contrast. Annotation was performed online using LabelImg (https://github.com/tzutalin/labelImg). YOLOv2 model The YOLOv2 model was generated using the ZeroCostDL4Mic platform (Chamier et al., 2021). It was trained from scratch for 97 epochs on 100 manually annotated images (image dimensions: (256, 256)) with a batch size of 8 and a custom loss function combining MSE and crossentropy losses, using the YOLOv2 ZeroCostDL4Mic notebook (v 1.12.1). Key python packages used include tensorflow (v 0.1.12), Keras (v 2.3.1), numpy (v 1.19.5), cuda (v 11.0.221). The training was accelerated using a Tesla T4 GPU and data were augmented by a factor of 4 using flipping and rotation. The model weights can be used with the ZeroCostDL4Mic YOLOv2 notebook. Author(s): Christoph Spahn1,2, Mike Heilemann1,3 Contact email: christoph.spahn@mpi-marburg.mpg.de Affiliation(s): 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