Tensorflow is preferred but Caffe works too. What I'm looking for are pretrained image recognition, audio recognition, etc algorithms that can be embedded into a Nifi processor, so that popular ML techniques can be accessed with a drag and drop interface :)
This is a cool idea!
I dont know of an authorative list, but it might be good to start one!
Usually the models have a name, after the project or paper that was used,
To get you started, here's the "Inception v3" model: https://storage.googleapis.com/download.tensorflow.org/model...
Maybe making a list of "Inception v3", "ImageNet" etc, then letting the user select? Most ML engineers know these by their names.
One for caffe: You can download BVLC CaffeNet Model from: http://dl.caffe.berkeleyvision.org/bvlc_reference_caffenet.c...
Do you have a target implementation in mind?