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Name: ZeroCostDL4Mic - YoloV2 example training and test dataset (see our Wiki for details) Data type: 2D grayscale .png images with corresponding bounding box annotations in .xml PASCAL Voc format. Microscopy data type: Phase contrast microscopy data (brightfield) Microscope: Inverted Zeiss Axio zoom widefield microscope equipped with an AxioCam MRm camera, an EL Plan-Neofluar 20 × /0.5 NA objective (Carl Zeiss), with a heated chamber (37 °C) and a CO2 controller (5%). Cell type: MDA-MB-231 cells migrating on cell-derived matrices generated by fibroblasts. File format: .png (8-bit) Image size: 1388 x 1040 px (323 nm) Author(s): Guillaume Jacquemet1,2,3, Lucas von Chamier4,5 Contact email: lucas.chamier.13@ucl.ac.uk and guillaume.jacquemet@abo.fi Affiliation(s): 1) Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, 20520 Turku, Finland 2) Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku 3) ORCID: 0000-0002-9286-920X 4) MRC-Laboratory for Molecular Cell Biology. University College London, London, UK 5) ORCID: 0000-0002-9243-912X Associated publications: Jacquemet et al 2016. DOI: 10.1038/ncomms13297 Funding bodies: G.J. was supported by grants awarded by the Academy of Finland, the Sigrid Juselius Foundation and Åbo Akademi University Research Foundation (CoE CellMech) and by Drug Discovery and Diagnostics strategic funding to Åbo Akademi University.
Microscopy, Object detection, Deep learning, YoloV2, Classification
Microscopy, Object detection, Deep learning, YoloV2, Classification
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