Image_dynamism_classifier

The “Image Dynamism Classifier" workflow has three important parts. 1. The red surrounded area includes the deep learning model developed with ResNet50 and using more than 8,000 annotated images. 2. The blue area allows the workflow to use python packages through the “Conda Environment Propagation” node for predicting the classification of new images using the “Keras Network executor”. The last 3 nodes in the blue area allow column renaming and saving the results in an excel file on the user’s local machine. 3. The three yellow annotations allow the user to use a sample of pictures that we provide, a list of pictures from a user folder, or a column with a list of URLS. Each of these can be connected to the Keras Network Executor for Analysis.

This is an evolving product and so far reached an average accuracy of 81.4% on a 10-fold cross validated sample.

External resources

• hub.knime.com/-/spaces/-/latest/~NX2Z6xRC66PS4b3o/