Instead of creating our own network architecture as in the previous workflow "Train simple CNN", in this workflow we use the pre-trained network architecture VGG16. 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 Preprocessing 2. Workflow 02 Trains simple CNN 3. Workflow 03 Fine-tune VGG16 Python: Instead of creating our own network architecture as in the previous workflow "Train simple CNN", in this workflow we use the pre-trained network architecture VGG16. (https://keras.io/applications/#vgg16, released by VGG (http://www.robots.ox.ac.uk/~vgg/research/very_deep/) at Oxford under the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/). 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) 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.