Preprocess image data

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.


This is a companion discussion topic for the original entry at https://kni.me/w/TU3nzztzzheDuKls