Technically no. You can't upload your own training dataset, and train the 
AI to produce *your *labels. 

Would need to deploy your own implementation - possibly 
via https://opensource.google.com/projects/tensorflow - deployed on GCE

https://www.tensorflow.org/tutorials/image_recognition

https://cloud.google.com/solutions/running-distributed-tensorflow-on-compute-engine



... but have had reasonable success building a *second *layer on top of the 
API, to produce custom labels. Not sure what the proper name is, some sort 
of Meta-AI.


Basically

1) Run all your existing images though the Cloud Vision API, and gather the 
feature labels it produces. 

2) You now have a mapping of 'labels' to your own 'value' - although 
somewhat fuzzy. Will have a long list of labels for each value. Store these 
in a database

3) To 'search' images by your "value". first look at the image(s) with that 
value, and gather all the labels. THen look for the images with the most 
similar labels. 

4) Can also do 'vision lookup' by uploading a image to the Cloud Vision 
API, and retrieveing a list of labels. Then search your database looking 
for the images with the most 'similar' labels. Take the 'value from the 
best match(es).


It doesnt matter that what the labels Google AI is detecting on the images, 
even if nothing like your 'values', just that they similar 'looking'. Say 
you have a value of 'animal'. The vision api may detect those images as a 
mixture of differnt animals (cow/dog etc), but you have a mapping back and 
forth, via your 'training' dataset.  

Not an AI expert, so probably messing up the terminology, but the labels 
that Google's AI produces become almost like a set of hidden neurons on 
your *final *Meta-AI

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