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Preprocess image data

Deep learning Keras Image classification

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In this workflow we pre-process the image data, which we will use throughout the following example workflows. Please note: The workflow series is heavily inspired by the great blog-post of François Chollet (see https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html.) 1. Workflow 01 Preprocess image data: In this workflow we pre-process the image data, which we will use throughout the following example workflows. You can download the data from https://www.kaggle.com/c/dogs-vs-cats/data (file train.zip) and unzip it to a desired location. 2. Workflow 02 Train simple CNN 3. Workflow 03 Fine-tune VGG16 Python 4. Workflow 04 Fine-tune VGG16 In order to run the example, please make sure you have the following KNIME extensions installed: - KNIME Deep Learning - Keras Integration (Labs) - KNIME Image Processing (Community Contributions Trusted) - KNIME Image Processing - Deep Learning Extension (Community Contributions Trusted) - KNIME Image Processing - Python Extension (Community Contributions Trusted) - KNIME Streaming Execution (Labs) You also need a local Python installation that includes Keras. Please refer to https://www.knime.com/deeplearning#keras for installation recommendations and further information.

External resources

  • Keras Cats & Dogs
  • Kaggle - Dogs vs. Cats

Used extensions & nodes

Created with KNIME Analytics Platform version 4.5.1
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    KNIME Base nodes Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.1

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    KNIME Basic File System Connectors Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.1

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    KNIME Image Processing Trusted extension

    University of Konstanz / KNIME

    Version 1.8.3

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    KNIME Javasnippet Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.0

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