Motivation

When Google released its TensorFlow Object Detection API, I was really excited and decided to build something using the API. I thought a real time object detection iOS (or Android) app would be awesome.

Why would I want to recognize objects in real time?

Of course, you can host a remote API that detects objects in a photo. But sometimes you don’t want to wait for the latency, and sometimes you don’t have reliable Internet access. When you want to know what objects are in front of you (and where) immediately, detecting objects on your camera’s screen could be useful.

Ok … but why sushi? 🍣🍣

Because everyone likes sushi (this is what my colleague told me 😋)! In all seriousness, it would be awesome if an app could enable people to access expert knowledge instantaneously.

In my case, types of sushi. Some sushi look similar, and some look totally different. These subtle similarities and differences would represent a good test case to measure the capability of a real-time object detector.

Train your own object detection model

After reading a few blog posts, I was able to train my own object detection model. I won’t explain the details of this step in this article. But please check out my GitHub repository and/or the official documentation for more details.

Export trained model