Training and test images of E. coli cells for artificial labeling of membranes in brightfield images using fnet or CARE, as well as trained models for prediction of super-resolution membranes. Additional information can be found on this github wiki. Example image shows an E. coli bright field image and PAINT membrane image predicted by the neural network (scale bar is 1 µm). Training and testing dataset Data type: Paired bright field and super-resolution images Microscopy data type: Bright field and fluorescence microscopy (widefield and point accumulation for imaging in nanoscale topography (PAINT) images) Microscope: Nikon Eclipse Ti-E equipped with an Apo TIRF 1.49NA 100x oil immersion objective Cell type: E. coli K12 strain derivatives File format: .tif (8-bit) Image size: 512x512 px2 with different pixel sizes: 1x tube lens: 158 nm (raw) and 19.75 nm (8x upscaled for PAINT images) 1.5x tube lens: 106 nm (raw) (widefield fluorescence only) fnet model (PAINT membrane images) The fnet 2D model was generated using the ZeroCostDL4Mic platform (Chamier et al., 2021). It was trained for 200,000 steps on 33 paired images (image dimensions: (512 x 512 px²), patch size: (128 x 128 px²)) with a batch size of 4, a learning rate of 0.0004, 10% validation split and 4x data augmentation (flipping and rotation). Model weights can be used with the ZeroCostDL4Mic fnet 2D notebook. CARE model (PAINT membrane images): The CARE 2D model was generated using the ZeroCostDL4Mic platform (Chamier et al., 2021). It was trained for 300 epochs (100 steps/epoch) on 33 paired images (image dimensions: 512 x 512 px², patch size: 256 x 256 px²) with a batch size of 4, a learning rate of 0.0004, 90/10% train/validation split and 4x data augmentation (flipping and rotation). Model weights can be used with the ZeroCostDL4Mic CARE 2D notebook or the CSBDeep Fiji plugin. 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