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Train VGG16 for binary image-classification

Classification Deep-learning Keras Vgg Fiji
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Train a deep-learning model for image-classification into 2 target categories. The training is done by transfer learning of a VGG-16 base pretrained on the ImageNet dataset, completed by freshly initialized fully connected classification layers. Category ground-truth annotations should be done in Fiji using the Qualitative-Annotation plugins, see reference below. If you use this workflow please cite : Thomas LSV, Schaefer F and Gehrig J. Fiji plugins for qualitative image annotations: routine analysis and application to image classification [version 1; peer review: awaiting peer review]. F1000Research 2020, 9:1248 doi: 10.12688/f1000research.26872.1

External resources

  • Online Documentation (GitHub)
  • Example dataset
  • Original workflow
  • YouTube tutorial
  • Publication

Used extensions & nodes

Created with KNIME Analytics Platform version 4.2.3
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    KNIME Base nodes Trusted extension

    KNIME AG, Zurich, Switzerland

    Versions 4.1.3, 4.2.2, 4.2.3

    knime
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    KNIME Deep Learning - Keras Integration Trusted extension

    KNIME AG, Zurich, Switzerland

    Versions 4.1.0, 4.2.1

    knime
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    KNIME Expressions Trusted extension

    KNIME AG, Zurich, Switzerland

    Versions 4.2.1, 4.2.2

    knime
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    KNIME Image Processing Trusted extension

    University of Konstanz / KNIME

    Version 1.8.3

    bioml-konstanz
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    KNIME JavaScript Views Trusted extension

    KNIME AG, Zurich, Switzerland

    Versions 4.2.1, 4.2.3

    knime
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    KNIME JavaScript Views (Labs) Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.2.0

    knime
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    KNIME Javasnippet Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.2.0

    knime
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    KNIME Math Expression (JEP) Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.2.2

    knime
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    KNIME Quick Forms Trusted extension

    KNIME AG, Zurich, Switzerland

    Versions 4.2.1, 4.2.3

    knime
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