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  • CatsDogsClassification
  • 03_Fine-tune_ResNet50
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Fine-tune VGG16

Deep learning Keras Image classification Cat Dog

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In this workflow we are fine-tuning a ResNet50 (https://keras.io/api/applications/resnet/#resnet50-function). First, we download the network from Keras using the DL Python Network Creator node. To implement the fine-tuning approach, we freeze the parameters of all network layers except for batch normalization layers. This is done to allow the normalization to adapt to the new data. Finally, we add a new trainable network head to output cat and dog probabilities (the original network head is already removed when loading the model from Keras).

Used extensions & nodes

Created with KNIME Analytics Platform version 4.2.0
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    KNIME Base nodes Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.2.0

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    KNIME Deep Learning - Keras Integration Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.2.0

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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 4.2.0

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