Off-the-shelf services like the Google Vision are trained to identify objects in general, like car, vehicle, and road in the image below.
But many of the journalism projects we're encountering in the Quartz AI Studio benefit from custom-built models that identify very specific items. I recently heard Meredith Broussard call this kind of work "artisanal AI," which cracked me up and also fits nicely.
So as an experiment, and as part of my summer training program, I trained an artisanal model to identify between the three objects at the top of this page from the Quartz offices: A Bevi water dispenser, a coffee urn, and a Quartz Creative arcade game (don't you wish you had one of those?!)
I also made a little website where my colleagues and I can test the model. You can, too — though you'll have to come visit to get the best experience!
The results
The model is 100% accurate at identifying the images I fed it — which probably is not all that surprising. It's based on an existing model called resnet34, which was trained on the ImageNet data set to distinguish between thousands of things. Using a technique called transfer learning, I taught that base model to use all of its existing power to distinguish between just three objects.
