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.
Get this workflow from the following link: Download
03_Fine-tune_VGG16_Python consists of the following 15 nodes(s):
03_Fine-tune_VGG16_Python contains nodes provided by the following 3 plugin(s):
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