Noodle on this: Machine learning that can identify ramen by shop

technicalstudio | April 1, 2018 | 0 | Tech News

There are casual ramen fans and then there are ramen lovers. There are people who are all tonkatsu all the time, and others who swear by tsukemen. And then there’s machine learning, which—based on a recent case study out of Japan—might be the biggest ramen aficionado of them all.

Recently, data scientist Kenji Doi used machine learning models and AutoML Vision to classify bowls of ramen and identify the exact shop each bowl is made at, out of 41 ramen shops, with 95 percent accuracy. Sounds crazy (also delicious), especially when you see what these bowls look like:

Ramen bowls made at three different Ramen Jiro shops.
Ramen bowls made at three different Ramen Jiro shops

With 41 locations around Tokyo, Ramen Jiro is one of the most popular restaurant franchises in Japan, because of its generous portions of toppings, noodles and soup served at low prices. They serve the same basic menu at each shop, and as you can see above, it’s almost impossible for a human (especially if you’re new to Ramen Jiro) to tell what shop each bowl is made at.

But Kenji thought deep learning could discern the minute details that make one shop’s bowl of ramen different from the next. He had already built a machine learning model to classify ramen, but wanted to see if AutoML Vision could do it more efficiently.

AutoML Vision creates customized ML models automatically—to identify animals in the wild, or recognize types of products to improve an online store, or in this case classify ramen. You don’t have to be a data scientist to know how to use it—all you need to do is upload well-labeled images and then click a button. In Kenji’s case, he compiled a set of 48,000 photos of bowls of soup from Ramen Jiro locations, along with labels for each shop, and uploaded them to AutoML Vision. The model took about 24 hours to train, all automatically (although a less accurate, “basic” mode had a model ready in just 18 minutes).

This article was sourced from Google Blog

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