See MGNLPER-17 for background, user stories and business benefit.

So far, we're using a provided model from the dl4j zoo with pre-trained weights for the ImageNet-1000 dataset, which is not a good fit for general-purpose image recognition (see MGNLPER-17 for details).

Since there seem to be no pre-trained networks available that are a better fit, we should train our own. However, complete training from scratch should not be necessary; just doing transfer learning by "fine-tuning" an existing pre-trained model should be enough: Only replace the output layer with one that fits our new number of classes, then freeze all other layers and train on e.g. ImageNet data. Documentation about transfer learning with dl4j

TBD: Classes (labels) we want to support