amyliuser's comments

General purpose computer vision APIs are good if you're looking for breadth of concepts across many categories. For example, if you're Shutterstock and you're trying to make images searchable with very widely used, generally accepted concepts like "flower" and "car" then a general model would be good enough for you.

Custom computer vision models are good if you're looking for depth in certain categories. For example, if you're a gardening app and you want to take a pic of a flower and be able to recognize different species of flowers, then custom training is required.

There are some options with computer vision API companies where they will let you do custom model training. IBM will do custom training as a service for $$$$ but if you don't want to pay like crazy, Clarifai has a free (to a certain point) offering that lets you train a custom image recognition model on your own https://developer.clarifai.com/guide/train#train

Oh and shameless plug here, if you want to try video recognition, the Clarifai API already does it: http://clarifai.com/

Hi Nick! I think every startup considers "what if Google launches into direct competition with me" at some point. We've definitely been preparing for this day (oh, I work at Clarifai). From a sheer technology standpoint, Google is the big gorilla in the room. But, computational resources and data are basically commodities. What matters is what you do with the data - how you train the models (just like different media companies, each machine learning company has its own editorial voice when it comes to tagging), custom training and domain models, the level of service you provide to developers, the integrations you have with other services, etc. These are all things we feel like we do better, and we'll continue to make our API the most developer-friendly one out there. In the immortal words of Coach Taylor, "Clear eyes, full hearts, can't lose." :D