I don't relish saying this but the, "Not Hotdog" app does not cut the mustard in even the most rudimentary of tests: https://twitter.com/iotmpls/status/8793811

I don't relish saying this but the, "Not Hotdog" app does not cut the mustard in even the most rudimentary of tests: https://twitter.com/iotmpls/status/879381125541613568/photo/1 https://twitter.com/iotmpls/status/879381125541613568/photo/... Probably only 20% of the world's hot dogs are just a basic hot dog with mustard on it. Once you move past one or two condiments, the domain of hot dogs identification along with fixings gets confusing from a computer vision standpoint. Pinterest's similar images function is able to identify hotdogs with single condiments fairly well: https://www.pinterest.com/pin/268175352794006376/visual-search/?x=0&y=0&w=564&h=564 https://www.pinterest.com/pin/268175352794006376/visual-sear... They appear to be using deep CNN's. https://labs.pinterest.com/assets/paper/visual_search_at_pinterest.pdf https://labs.pinterest.com/assets/paper/visual_search_at_pin... Having embedded tensorflow for on-site identification is all well and good for immediacy and cost, but if I can't really properly identify whether something is a hotdog vs. a long skinny thing with a mustard squiggle, what good does that do me? What would be the next step up in your mind? I ask this as someone who is sincerely interested in building low cost, fun, projects.