Tag Archives: Custom Model

Full spectrum of on-device machine learning tools on Android

Posted by Hoi Lam, Android Machine Learning



This blog post is part of a weekly series for #11WeeksOfAndroid. Each week we’re diving into a key area of Android so you don’t miss anything. Throughout this week, we covered various aspects of Android on-device machine learning (ML). Whichever stage of development be it starting out or an established app; whatever role you play in design, product and engineering; whatever your skill level from beginner to experts, we have a wide range of ML tools for you.

Design - ML as a differentiator

“Focus on the user and all else will follow” is a Google mantra that becomes even more relevant in our machine learning age. Our Design Advocate, Di Dang, highlighted the importance of finding the unique intersection of user problems and ML strengths. Too often, teams are so keen on the idea of machine learning that they lose sight of their user needs.



Di outlined how the People + AI Guidebook can help you make ML product decisions and used the example of the Read Along app to illustrate topics like precision and recall, which are unique to ML design and development. Check out her interview with the Read Along team together with your team for more inspiration.

New ML Kit fully focused on on-device

When you decide that on-device machine learning is the solution, the easiest way to implement it will be through turnkey SDKs like ML Kit. Sophisticated Google-trained models and processing pipelines are offered through an easy to use interface in Kotlin / Java. ML Kit is designed and built for on-device ML: it works offline, offers enhanced privacy, unlocks high performance for real-time use cases and it is free. We recently made ML Kit a standalone SDK and it no longer requires a Firebase account. Just one line in your build.gradle file and you can start bringing ML functionality into your app.



The team has also added new functionalities such as Jetpack lifecycle support and the option to use the face contour models via Google Play Services saving as much as 20MB in app size. Another much anticipated addition is the support for swapping Google models with your own for both Image Labeling as well as Object Detection and Tracking. This provides one of the easiest ways to add TensorFlow Lite models to your applications without interacting with ByteArray!

Customise with TensorFlow Lite and Android tools

If the base model provided by ML Kit doesn’t quite fit the bill, what should developers do? The first port of call should be TensorFlow Hub where ready-to-use TensorFlow Lite models from both Google and the wider community can be downloaded. From 100,000 US Supermarket products to tomato plant diseases classifiers, the choice is yours.



In addition to Firebase AutoML Vision Edge, you can also build your own model using TensorFlow Model Maker (image classification / text classification) with just a few lines of Python. Once you have a TensorFlow Lite model from either TensorFlow Hub, or the Model Maker, you can easily integrate it with your Android app using ML Kit Image Labelling or Object Detection and Tracking. If you prefer an open source solution, Android Studio 4.1 beta introduces ML model binding that helps wrap around the TensorFlow Lite model with an easy to use Kotlin / Java wrapper. Adding a custom model to your Android app has never been easier. Check out this blog for more details.

Time for on-device ML is now

From the examples of the Android Developer Challenge winners, it is obvious that on-device machine learning has come of age and ML functionalities once reserved for the cloud or supercomputers are now available on your Android phone. Take a step forward with us by trying out our codelabs of the day:

Also checkout the ML Week learning pathway and take the quiz to get your very own ML badge.

Android on-device machine learning is a rapidly evolving platform, if you have any enhancement requests or feedback on how it could be improved, please let us know together with your use-case (TensorFlow Lite / ML Kit). Time for on-device ML is now.

Resources

You can find the entire playlist of #11WeeksOfAndroid video content here, and learn more about each week here. We’ll continue to spotlight new areas each week, so keep an eye out and follow us on Twitter and YouTube. Thanks so much for letting us be a part of this experience with you!

New tools for finding, training, and using custom machine learning models on Android

Posted by Hoi Lam, Android Machine Learning

Yesterday, we talked about turnkey machine learning (ML) solutions with ML Kit. But what if that doesn’t completely address your needs and you need to tweak it a little? Today, we will discuss how to find alternative models, and how to train and use custom ML models in your Android app.

Find alternative ML models

Crop disease models from the wider research community available on tfhub.dev

If the turnkey ML solutions don't suit your needs, TensorFlow Hub should be your first port of call. It is a repository of ML models from Google and the wider research community. The models on the site are ready for use in the cloud, in a web-browser or in an app on-device. For Android developers, the most exciting models are the TensorFlow Lite (TFLite) models that are optimized for mobile.

In addition to key vision models such as MobileNet and EfficientNet, the repository also boast models powered by the latest research such as:

Many of these solutions were previously only available in the cloud, as the models are too large and too power intensive to run on-device. Today, you can run them on Android on-device, offline and live.

Train your own custom model

Besides the large repository of base models, developers can also train their own models. Developer-friendly tools are available for many common use cases. In addition to Firebase’s AutoML Vision Edge, the TensorFlow team launched TensorFlow Lite Model Maker earlier this year to give developers more choices over the base model that support more use cases. TensorFlow Lite Model Maker currently supports two common ML tasks:

The TensorFlow Lite Model Maker can run on your own developer machine or in Google Colab online machine learning notebooks. Going forward, the team plans to improve the existing offerings and to add new use cases.

Using custom model in your Android app

New TFLite Model import screen in Android Studio 4.1 beta

Once you have selected a model or trained your model there are new easy-to-use tools to help you integrate them into your Android app without having to convert everything into ByteArrays. The first new tool is ML Model binding with Android Studio 4.1. This lets developers import any TFLite model, read the input / output signature of the model, and use it with just a few lines of code that calls the open source TensorFlow Lite Android Support Library.

Another way to implement a TensorFlow Lite model is via ML Kit. Starting in June, ML Kit no longer requires a Firebase project for on-device functionality. In addition, the image classification and object detection and tracking (ODT) APIs support custom models. The latter ODT offering is especially useful in use-cases where you need to separate out objects from a busy scene.

So how should you choose between these three solutions? If you are trying to detect a product on a busy supermarket shelf, ML Kit object detection and tracking can help your user select a specific product for processing. The API then performs image classification on just the part of the image that contains the product, which results in better detection performance. On the other hand, if the scene or the object you are trying to detect takes up most of the input image, for example, a landmark such as Big Ben, using ML Model binding or the ML Kit image classification API might be more appropriate.

TensorFlow Hub bird detection model with ML Kit Object Detection & Tracking AP

Two examples of how these tools can fit together

Here are some resources to help you get started:

Customizing your model is easier than ever

Finding, building and using custom models on Android has never been easier. As both Android and TensorFlow teams increase the coverage of machine learning use cases, please let us know how we can improve these tools for your use cases by filing an enhancement request with TensorFlow Lite or ML Kit.

Tomorrow, we will take a step back and focus on how to appropriately use and design for a machine learning first Android app. The content will be appropriate for the entire development team, so bring your product manager and designers along. See you next time.

On-device machine learning solutions with ML Kit, now even easier to use

Posted by Christiaan Prins, Product Manager, ML Kit and Shiyu Hu, Tech Lead Manager, ML Kit

ML Kit logo

Two years ago at I/O 2018 we introduced ML Kit, making it easier for mobile developers to integrate machine learning into your apps. Today, more than 25,000 applications on Android and iOS make use of ML Kit’s features. Now, we are introducing some changes that will make it even easier to use ML Kit. In addition, we have a new feature and a set of improvements we’d like to discuss.

A new ML Kit SDK, fully focused on on-device ML

ML Kit API Overview

ML Kit's APIs are built to help you tackle common challenges in the Vision and Natural Language domains. We make it easy to recognize text, scan barcodes, track and classify objects in real-time, do translation of text, and more.

The original version of ML Kit was tightly integrated with Firebase, and we heard from many of you that you wanted more flexibility when implementing it in your apps. As a result, we are now making all the on-device APIs available in a new standalone ML Kit SDK that no longer requires a Firebase project. You can still use both ML Kit and Firebase to get the best of both products if you choose to.

With this change, ML Kit is now fully focused on on-device machine learning, giving you access to the unique benefits that on-device versus cloud ML offers:

  • It’s fast, unlocking real-time use cases- since processing happens on the device, there is no network latency. This means, we can do inference on a stream of images / video or multiple times a second on text strings.
  • Works offline - you can rely on our APIs even when the network is spotty or your app’s end-user is in an area without connectivity.
  • Privacy is retained: since all processing is performed locally, there is no need to send sensitive user data over the network to a server.

Naturally, you still get access to Google’s on-device models and processing pipelines, all accessible through easy-to-use APIs, and offered at no cost.

All ML Kit resources can now be found on our new website where we made it a lot easier to access sample apps, API reference docs and our community channels that are there to help you if you have questions.

Object detection & tracking gif Text recognition + Language ID + Translate gif

What does this mean if I already use ML Kit today?

If you are using ML Kit for Firebase’s on-device APIs in your app today, we recommend you to migrate to the new standalone ML Kit SDK to benefit from new features and updates. For more information and step-by-step instructions to update your app, please follow our Migration guide. The cloud-based APIs, model deployment and AutoML Vision Edge remain available through Firebase Machine Learning.

Shrink your app footprint with Google Play Services

Apart from making ML Kit easier to use, developers also asked if we can ship ML Kit through Google Play Services resulting in a smaller app footprint and the model can be reused between apps. Apart from Barcode scanning and Text recognition, we have now added Face detection / contour (model size: 20MB) to the list of APIs that support this functionality. 

// Face detection / Face contour model
// Delivered via Google Play Services outside your app's APK…
implementation 'com.google.android.gms:play-services-mlkit-face-detection:16.0.0'

// …or bundled with your app's APK
implementation 'com.google.mlkit:face-detection:16.0.0'

Jetpack Lifecycle / CameraX support

Android Jetpack Lifecycle support has been added to all APIs. Developers can use addObserver to automatically manage teardown of ML Kit APIs as the app goes through screen rotation or closure by the user / system. This makes CameraX integration easier. With this release, we are also recommending that developers adopt CameraX in their apps due to the ease of integration and image quality improvements (compared to Camera1) on a wide range of devices. 

// ML Kit now supports Lifecycle
val recognizer = TextRecognizer.newInstance()
lifecycle.addObserver(recognizer)

// ...

// Just like CameraX
val camera = cameraProvider.bindToLifecycle( /* lifecycleOwner= */this,
    cameraSelector, previewUseCase, analysisUseCase)

For an overview of all recent changes, check out the release notes for the new SDK.

Codelab of the day - ML Kit x CameraX

To help you get started with the new ML Kit and its support for CameraX, we have created this code lab to Recognize, Identify Language and Translate text. If you have any questions regarding this code lab, please raise them at StackOverflow and tag it with [google-mlkit]. Our team will monitor this.

screenshot of app running

Early access program

Through our early access program, developers have an opportunity to partner with the ML Kit team and get access to upcoming features. Two new APIs are now available as part of this program:

  • Entity Extraction - Detect entities in text & make them actionable. We have support for phone numbers, addresses, payment numbers, tracking numbers, date/time and more.
  • Pose Detection - Low-latency pose detection supporting 33 skeletal points, including hands and feet tracking.

If you are interested, head over to our early access page for details.

pose detection on man jumping rope

Tomorrow - Support for custom models

ML Kit's turn-key solutions are built to help you take common challenges. However, if you needed to have a more tailored solution, one that required custom models, you typically needed to build an implementation from scratch. To help, we are now providing the option to swap out the default Google models with a custom TensorFlow Lite model. We’re starting with the Image Labeling and Object Detection and Tracking APIs, that now support custom image classification models.

Tomorrow, we will dive a bit deeper into how to find or train a TensorFlow Lite model and use it either with ML Kit, or with Android Studio’s new ML binding functionality.

On-device machine learning solutions with ML Kit, now even easier to use

Posted by Christiaan Prins, Product Manager, ML Kit and Shiyu Hu, Tech Lead Manager, ML Kit

ML Kit logo

Two years ago at I/O 2018 we introduced ML Kit, making it easier for mobile developers to integrate machine learning into your apps. Today, more than 25,000 applications on Android and iOS make use of ML Kit’s features. Now, we are introducing some changes that will make it even easier to use ML Kit. In addition, we have a new feature and a set of improvements we’d like to discuss.

A new ML Kit SDK, fully focused on on-device ML

ML Kit API Overview

ML Kit's APIs are built to help you tackle common challenges in the Vision and Natural Language domains. We make it easy to recognize text, scan barcodes, track and classify objects in real-time, do translation of text, and more.

The original version of ML Kit was tightly integrated with Firebase, and we heard from many of you that you wanted more flexibility when implementing it in your apps. As a result, we are now making all the on-device APIs available in a new standalone ML Kit SDK that no longer requires a Firebase project. You can still use both ML Kit and Firebase to get the best of both products if you choose to.

With this change, ML Kit is now fully focused on on-device machine learning, giving you access to the unique benefits that on-device versus cloud ML offers:

  • It’s fast, unlocking real-time use cases- since processing happens on the device, there is no network latency. This means, we can do inference on a stream of images / video or multiple times a second on text strings.
  • Works offline - you can rely on our APIs even when the network is spotty or your app’s end-user is in an area without connectivity.
  • Privacy is retained: since all processing is performed locally, there is no need to send sensitive user data over the network to a server.

Naturally, you still get access to Google’s on-device models and processing pipelines, all accessible through easy-to-use APIs, and offered at no cost.

All ML Kit resources can now be found on our new website where we made it a lot easier to access sample apps, API reference docs and our community channels that are there to help you if you have questions.

Object detection & tracking gif Text recognition + Language ID + Translate gif

What does this mean if I already use ML Kit today?

If you are using ML Kit for Firebase’s on-device APIs in your app today, we recommend you to migrate to the new standalone ML Kit SDK to benefit from new features and updates. For more information and step-by-step instructions to update your app, please follow our Migration guide. The cloud-based APIs, model deployment and AutoML Vision Edge remain available through Firebase Machine Learning.

Shrink your app footprint with Google Play Services

Apart from making ML Kit easier to use, developers also asked if we can ship ML Kit through Google Play Services resulting in a smaller app footprint and the model can be reused between apps. Apart from Barcode scanning and Text recognition, we have now added Face detection / contour (model size: 20MB) to the list of APIs that support this functionality. 

// Face detection / Face contour model
// Delivered via Google Play Services outside your app's APK…
implementation 'com.google.android.gms:play-services-mlkit-face-detection:16.0.0'

// …or bundled with your app's APK
implementation 'com.google.mlkit:face-detection:16.0.0'

Jetpack Lifecycle / CameraX support

Android Jetpack Lifecycle support has been added to all APIs. Developers can use addObserver to automatically manage teardown of ML Kit APIs as the app goes through screen rotation or closure by the user / system. This makes CameraX integration easier. With this release, we are also recommending that developers adopt CameraX in their apps due to the ease of integration and image quality improvements (compared to Camera1) on a wide range of devices. 

// ML Kit now supports Lifecycle
val recognizer = TextRecognizer.newInstance()
lifecycle.addObserver(recognizer)

// ...

// Just like CameraX
val camera = cameraProvider.bindToLifecycle( /* lifecycleOwner= */this,
    cameraSelector, previewUseCase, analysisUseCase)

For an overview of all recent changes, check out the release notes for the new SDK.

Codelab of the day - ML Kit x CameraX

To help you get started with the new ML Kit and its support for CameraX, we have created this code lab to Recognize, Identify Language and Translate text. If you have any questions regarding this code lab, please raise them at StackOverflow and tag it with [google-mlkit]. Our team will monitor this.

screenshot of app running

Early access program

Through our early access program, developers have an opportunity to partner with the ML Kit team and get access to upcoming features. Two new APIs are now available as part of this program:

  • Entity Extraction - Detect entities in text & make them actionable. We have support for phone numbers, addresses, payment numbers, tracking numbers, date/time and more.
  • Pose Detection - Low-latency pose detection supporting 33 skeletal points, including hands and feet tracking.

If you are interested, head over to our early access page for details.

pose detection on man jumping rope

Tomorrow - Support for custom models

ML Kit's turn-key solutions are built to help you take common challenges. However, if you needed to have a more tailored solution, one that required custom models, you typically needed to build an implementation from scratch. To help, we are now providing the option to swap out the default Google models with a custom TensorFlow Lite model. We’re starting with the Image Labeling and Object Detection and Tracking APIs, that now support custom image classification models.

Tomorrow, we will dive a bit deeper into how to find or train a TensorFlow Lite model and use it either with ML Kit, or with Android Studio’s new ML binding functionality.