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.

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.