I am testing out knime and am trying to do a binary image classification, wanting to use the AutoML component. According to the AI assistant, I need a table with an image column and a target label column. So I create those and then use this table as an input for the AutoML component.
I am unfamiliar with the AutoML node, but I suspect you need to do some preprocessing and data prep to ensure your dataset is in a proper place before any Machine Learning or Deep Learning flow.
The above screenshot is an example of a simple end-to-end flow that does the following:
read in a directory of images
ensures they are all the same size
takes the list of files and makes it a table
extract the label from the file path (images in the same folder share a label)
Flatten out the dataset such that each pixel in the image is a column
Apply a model to the data
This is a very simple approach, one I use with my MBA students to understand how ML can be applied to images. Other approaches work on the image directly; you may consider reviewing Convolution Neural Nets (CNN).
One last point. I enjoy using the AI feature in KNIME, but I have found that it can “hallucinate” solutions that seem plausible but require more work to get a workflow running as desired. Other times it will suggest nodes that do not exist.
Thanks for the response and the workflow, I will have a look at it.
I tried to find a more extensive documentation of the AutoML component (AutoML – KNIME Community Hub) regarding the supported input data types, but this does not seem available. Given the available options in the “Type Selection”-view for feature selection, numeric, string and date/time are probably the supported input data types:
I agree that the solution from the AI feature that I got is probably a hallucination, as the links it provided also do not contain relevant information.