Building a custom machine learning model on Android with Tensorflow Lite

The mobile app market is fast-evolving. When the statement “there is an app for everything” made by Apple’s then-CEO came forth, it was not taken very seriously. However, the way the mobile apps are being consumed, the new truth is that not only do you have an app for everything, you have apps that tend to incorporate the latest trends, boosting your business growth. 

Machine learning is a trend that you cannot miss out on when developing an Android mobile app for the digital era. Remember Iron Man’s assistant Jarvis? Well, with machine learning taking off the way it has, you won’t be surprised when you have a personal assistant very similar to Jarvis in real life. For now, you have Siri who understands you, uses neural networks to study your patterns, and enables personalized settings just for you.

From recommendations on Amazon that are personalized for you to responses from brands, machine learning is taking the mobile app browsing experience to another level for users.

However, despite all the good things that you hear about machine learning, there are a few gaps that exist between mobile app development and the kind of machine learning you have incorporated. 

Tensorflow Lite aims to close this gap, making machine learning easy to incorporate.

What’s more is that you don’t even need to be a Tensorflow Lite expert or a machine learning pro to incorporate this trend to your Android or iOS app. 

Want to know how things work around Tensorflow? Here are a few things that might help you. 

The first question is, how to add machine learning to your mobile app? The all-new ML Kit provided by Firebase, which contains a series of APIs, is an easy and effective method of adding machine learning capabilities to your app. The APIs range from face detection to image detection and labeling. In fact, some of the APIs can be accessed in offline mode as well.

SEE ALSO: Reasons why machine learning can prove beneficial for your organization

However, the ML Kit cannot make a specific differentiation. For instance, it cannot help the app recognize the different types of the same product. The combination of ML kit and TensorFlow Lite, which is used for lightweight mobile and embedded devices, works for your mobile app development. 

The idea behind using this combination is to streamline the app development process, improve the capabilities of the mobile app, and finally speed up the app.

Customization of ML apps with TensorFlow Lite

There are a few steps that you need to follow to customize the Android app solution using TensorFlow lite.

The first thing you need to learn is that training a TensorFlow model can take up a long time. However, with the transfer learning method, you can use an already trained model, retain its learning and use it to develop your application. This is a shorter and easier way. 

Step #1

This is where you take into account the prerequisites for the application, and make sure they are taken care of. Along with TensorFlow Lite, you should also make sure that PILLOW is installed to run the application.

pip install –upgrade “tensorflow==1.7.*”
pip install PILLOW

You can even use the GitHub code and replicate it, in case the installation does not work using the code mentioned above. 

Step #2

The next step is to gather data. For instance, if you want to segregate between multiple TVs based on the brand and their size, then you need a training model that can help transfer the data to the application. You need to download the dataset from a source that is reliable. Make sure you have enough training data available, which will help you make a meaningful analysis.

Step #3

This is the step where you convert the available data into highly qualified images that the app can connect with. You will need to adopt a particular architecture model in order to convert the data into images that can be fed into the apps.

The two most popular architectures include MobileNet_2.0 and Inception V3.

You can easily get the script required for retraining the existing model using the two architecture on GitHub. You can convert the models into images that can connect using these codes.

Now that you have retrained the model and tested it with a specific image size, you need to move to the next step.

Step #4

This step is where you convert your model into TensorFlow lite using the tflite_convert command. The converter will ably optimize the TensorFlow graphs that you obtained in the earlier steps to a mobile version. Along with the mobile-optimized version, you will also get a few labels that are stored in a .txt file.

With the TOCO converter, you don’t need to build your image for Tensorflow directly from the source. The Firebase console directly helps you optimize the file.

Step #5

This is the step where your trained model is incorporated into the machine learning program. You need to enter the Android folder from Android Studio to build your project. Here, you will need to enter the image classifier, and update two of the fields within the class with the TensorFlow Lite optimized file. The two fields are MODEL_PATH and LABEL_PATH.

Once these steps have been followed perfectly, your trained model is ready to learn, and your application is ready to work along these lines to segregate a specific bunch of devices based on their sizes. 

Some use cases of machine learning applications

• With machine learning incorporated in your applications, you can know all about your customers – from what they want to what they can afford and how they feel about your products. This will help offer a personalized experience to them.
• The intuitiveness of machine learning applications helps with advanced search options. It gives out results that are relevant to you, and are based on its understanding of your needs and behavior.
• An increase in customer engagement as a result of a deep understanding of what you do, and how you connect and communicate with an app helps in delivering the recommendations. You get communications and results that are in sync with your preferences. 

SEE ALSO: Reproduce machine learning results with pre-trained models in PyTorch Hub

Summing up

Machine learning is indeed the future of mobile app development. Until now you have used it to recommend common elements like facial recognition, object recognition, etc. With TensorFlow, you can easily go for specific searches and recommendations, as the objective of this model is to go specific with information.

From segregating the brands into specifics to segregating the products into niches, the model helps retrain your machine learning app with specific images. If you want to improve the user experience of your Android application, then TensorFlow Lite is your best bet as it is optimized for mobile and enhances the speed of your applications. 

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