Converting TF models to CoreML, an iOS-friendly format
While TensorFlow Lite seems to be a natural choice for Android software engineers, on iOS, it doesn’t necessarily have to be the same. In 2017, when iOS 11 was released, Apple announced Core ML, a new framework that speeds up AI-related operations. If you are fresh in machine learning on mobile, Core ML will simplify things a lot when adding a model to your app (literally drag-and-drop setup). It also comes with some domain-specific frameworks – Vision (computer vision algorithms for face, rectangles or text detection, image classification, etc.), and Natural Language. Core ML and Vision give us a possibility to run inference process with the use of custom machine learning model. And those models may come from machine learning frameworks like TensorFlow. In this article, we will see how to convert TensorFlow model to CoreML format and how to compare models side by side.
Building TensorFlow Lite models and deploying them on mobile applications is getting simpler over time. But even with easier to implement libraries and APIs, there are still at least three major steps to accomplish:
Build TensorFlow model,
Convert it to TensorFlow Lite model,
Implement in on the mobile app.
There is a set of information that needs to be passed between those steps – model input/output shape, values format, etc. If you know them (e.g. thanks to visualizing techniques and tools described in this blog post), there is another problem, many software engineers struggle with.
Why the model implemented on a mobile app works differently than its counterpart in a python environment?
In this post, we will try to visualize differences between TensorFlow, TensorFlow Lite and quantized TensorFlow Lite (with post-training quantization) models. This should help us with early models debugging when something goes really wrong. Here, we will focus only on TensorFlow side. It’s worth to remember, that it doesn’t cover mobile app implementation correctness (e.g. bitmap preprocessing and data transformation). This will be described in one of the future posts.
What to know before implementing TFLite model in mobile app
In previous posts, either about building a machine learning model or using transfer learning to retrain existing one, we could look closer at their architecture directly in the code. But what if we get *.tflite model from an external source? How do we know how to handle it properly? In this blog post, we’ll look closer at what we can do to get enough knowledge for plugging-in TensorFlow Lite image classification model into Android application.
TensorFlow Lite classification model for GTSRB dataset
This post is a part of a series about building Machine Learning solutions in mobile apps. In the previous article, we started from building simple MNIST classification model on top of TensorFlow Lite. That post is also a good place to start if you are looking for some hints about how to set up your very first environment (local with Docker or remote with Colaboratory).
Let’s continue with basics. If you spent some time exploring the Internet for Machine Learning <-> mobile solutions, for sure you found “TensorFlow for Poets” code labs. If not, those are places where you should start your journey with building a more complex solution for apps vision intelligence.
Those code labs are focused on building very first working solution that can be launched directly on your mobile device. And here, we’ll build something very similar, with some additional explanation that can be helpful with understanding TensorFlow Lite a little bit better.
So what are code labs and this article about? They all show how to build a convolutional neural network that is optimized for mobile devices, with a little effort required for defining the structure of the Machine Learning model. Instead of building it from scratch, we’ll use a technique called Transfer Learning and retrain MobileNet for our needs.