Tag Archives: Android Developer Challenge

Android Dev Challenge Finale: Weather app

Posted by Jolanda Verhoef, Developer Relations Engineer

Let your creativity shine in the final week of the #AndroidDevChallenge! Last week we asked you to be fast, but for this final week we ask you to bring your 'A' game. Here’s the challenge:

Week #4: Weather app 🌤

Android 12 logo

Create a single-screen weather forecast app. You have until March 23rd, 23:59 PST to submit your entry.1

Your UI must be fully built in Compose. You can use fake weather data.



We will judge your submission on these four categories:

  1. Visual beauty
  2. Novelty of idea
  3. Code quality
  4. Overall execution (including accessibility)

To help implement a beautiful design, check out the Compose documentation on layouts, theming, and graphics. Think of novel uses of animations and gestures. Improve your code quality with architecture and testing. And for overall execution, make sure to read about accessibility.

Your solution must be implemented in a public GitHub repository. Make a copy of this Github repository template and follow the instructions in the README. The template contains a basic Hello World! in Compose and a continuous integration setup.

The App Submission must, at a minimum, support English language use.

This week’s prize: 5 x Google Pixel 5!

Google pixel

This week you have a chance of winning a Google Pixel 5, the ultimate 5G Google phone! We’ll be giving away one Google Pixel 5 for the winner of each of the four categories, and one for the best of the best submission.2




Help make Jetpack Compose better

Community is at the heart of Jetpack Compose and your feedback helps us build a better product:

  • File issues with Jetpack Compose on the official issue tracker.
  • Sign up to be part of the Jetpack Compose research studies.

Notes


  1. Please review the link for the full official rules associated with the entry. 

  2. If you don’t live in a country where the Pixel 5 is available, when you win we’ll instead send you an electronics gift card valued at US$699. 

Announcing the winners of the #AndroidDevChallenge, powered by on-device machine learning

Posted by Jacob Lehrbaum, Director of Developer Relations, Android

Developers like you have always played an important role in Android innovation. Over 10 years ago, when we first launched the Android SDK, we also announced the Android Developer Challenge to reward model apps and highlight new ways of solving user problems. As Android pushes the boundaries of machine learning, 5G, foldables, and more, developers continue to help shape these new frontiers. To celebrate this work, we revived the challenge in 2019, with a focus on “Helpful Innovation,” powered by on-device machine learning.

We received hundreds of creative projects, and at the end of last year, picked 10 winners who each combined a strong idea and a thirst to bring it to life. Since then, we’ve been working with those winners to help turn their ideas into reality. And today, we’re announcing the 10 winners. Some are still at the beginning of their journey but but their apps are now ready for you to download and try out! !

  • AgroDoc helps farmers diagnose plant disease and make treatment plans. [Navneet Krishna; Kochi, India]
  • AgriFarm helps farmers detect plant diseases and prevent major damage in fruits and vegetables such as tomatoes, corn and potatoes. [Balochisan, Pakistan]
  • Eskke streamlines mobile money management for people in the Congo, letting them transfer money, pay bills, buy subscriptions and essential airtime through SMS. [David Mumbere Kathoh; Goma, Democratic Republic of Congo]
  • Leepi helps students learn hand gestures and symbols for American Sign Language. [Prince Patel; Bengaluru, India]
  • MixPose is a live streaming platform that gives yoga teachers and fitness professionals the opportunity to teach, track alignment, and give feedback in real-time. [Peter Ma; San Francisco, California, USA]
  • Pathfinder could help people with visual impairments navigate complex situations by identifying and calculating the trajectories of objects moving in their path. [Colin Shelton; Addison, Texas, USA]
  • Snore & Cough helps you identify and analyze snoring and coughing, to help provide info to users seeking assistance from a medical professional. [Ethan Fan; Mountain View, California, USA]
  • Stila pairs with a wearable device, like the Fitbit wristband or a device running on Wear OS by Google to monitor and track the body’s stress levels. By monitoring stress levels over time, you have the chance to better understand and manage stress in your life. [Yingdin Wing; Munich, Germany]
  • Trashly makes recycling easier. Just point the on-device camera at an item, and through object detection, the app identifies and classifies plastic and paper cups, bags, bottles, etc. [Elvin Rakhmankulov; Chicago, Illinois, USA]
  • UnoDogs helps owners better support their pet’s wellness, providing customized information and fitness programs. [Chinmany Mishra; New Delhi, India]

Making on-device machine learning more accessible, with ML Kit and TensorFlow Lite

Increasingly, machine learning is becoming a more accessible tool to developers with limited to no background in the technology. In fact, for most of the winners of the Android Developer Challenge, this was their first foray into machine learning. That’s thanks in part to two key offerings from Google, which bring on-device machine learning into reach for millions of developers around the world.

The first is ML Kit. ML Kit brings Google’s on-device machine learning technologies to mobile app developers, so they can build customized and interactive experiences into their apps. This includes tools such as language translation, text recognition, object detection and more. Eskke, for instance, uses offline text recognition and barcode scanning from ML Kit so users can scan the QR code at a mobile money kiosk and quickly withdraw money. And MixPose uses ML Kit's forthcoming Pose detection API to detect each user’s yoga positions and movements, so teachers can provide feedback.

The other Google resource that many of the Android Dev Challenge winners used was TensorFlow Lite. This powerful machine learning framework can help run machine learning models on Android, iOS and IoT devices that would never normally be able to support them. Its set of tools can be used for all kinds of powerful neural network-related applications, from image detection to speech recognition, bringing the latest cutting-edge technology to the devices we carry around with us wherever we go. Trashly, for instance, uses a custom TensorFlow Lite model to report if an object is recyclable and how to recycle it.

Helpful innovation, such as the 10 winning apps in the Android Developer Challenge, has the potential to change the way we access, use, and interpret information, making it available when we need it, where we need it most. By working with these developers focused on helpful innovation, we hope to inspire the next wave of developers to unlock what’s possible with this new technology.

#11WeeksOfAndroid Week 2 Machine Learning with Android logo head

What’s next in Android Machine Learning week?

As we kick off the second week of #11WeeksOfAndroid, focused on Machine Learning, we will highlight new tools and resources available to Android developers. Here’s a taste of the rest of this week:

  • Tuesday - ML Kit, the turnkey ML SDK went through a major overhaul with its new on-device offering this month. Check out the substantial improvement in developer usability, CameraX support and where the platform is going next.
  • Wednesday - Custom Models. When prepackaged SDK doesn’t quite satisfy your need, tools from Android Studio, TensorFlow Lite and ML Kit might just be the answer. Aside from individual offerings, we will also highlight how they can be used together.
  • Thursday - ML design. Learn some best practices for making ML product decisions from the People + AI Guidebook. We will go behind the scenes of the Read Along app, an on-device ML app that helps grow universal literacy. Bring your whole team because everyone, including UXers, engineers, and product managers are invited!

On Tuesday and Wednesday, we will also have a “codelab of the day” so get your Android Studio 4.1 beta today, block off an hour in your schedule and take this ML journey with us!

*The apps presented here are the projects of the developers individually, and not Google.

Announcing the winners of the #AndroidDevChallenge, powered by on-device machine learning

Posted by Jacob Lehrbaum, Director of Developer Relations, Android

Developers like you have always played an important role in Android innovation. Over 10 years ago, when we first launched the Android SDK, we also announced the Android Developer Challenge to reward model apps and highlight new ways of solving user problems. As Android pushes the boundaries of machine learning, 5G, foldables, and more, developers continue to help shape these new frontiers. To celebrate this work, we revived the challenge in 2019, with a focus on “Helpful Innovation,” powered by on-device machine learning.

We received hundreds of creative projects, and at the end of last year, picked 10 winners who each combined a strong idea and a thirst to bring it to life. Since then, we’ve been working with those winners to help turn their ideas into reality. And today, we’re announcing the 10 winners. Some are still at the beginning of their journey but but their apps are now ready for you to download and try out! !

  • AgroDoc helps farmers diagnose plant disease and make treatment plans. [Navneet Krishna; Kochi, India]
  • AgriFarm helps farmers detect plant diseases and prevent major damage in fruits and vegetables such as tomatoes, corn and potatoes. [Balochisan, Pakistan]
  • Eskke streamlines mobile money management for people in the Congo, letting them transfer money, pay bills, buy subscriptions and essential airtime through SMS. [David Mumbere Kathoh; Goma, Democratic Republic of Congo]
  • Leepi helps students learn hand gestures and symbols for American Sign Language. [Prince Patel; Bengaluru, India]
  • MixPose is a live streaming platform that gives yoga teachers and fitness professionals the opportunity to teach, track alignment, and give feedback in real-time. [Peter Ma; San Francisco, California, USA]
  • Pathfinder could help people with visual impairments navigate complex situations by identifying and calculating the trajectories of objects moving in their path. [Colin Shelton; Addison, Texas, USA]
  • Snore & Cough helps you identify and analyze snoring and coughing, to help provide info to users seeking assistance from a medical professional. [Ethan Fan; Mountain View, California, USA]
  • Stila pairs with a wearable device, like the Fitbit wristband or a device running on Wear OS by Google to monitor and track the body’s stress levels. By monitoring stress levels over time, you have the chance to better understand and manage stress in your life. [Yingdin Wing; Munich, Germany]
  • Trashly makes recycling easier. Just point the on-device camera at an item, and through object detection, the app identifies and classifies plastic and paper cups, bags, bottles, etc. [Elvin Rakhmankulov; Chicago, Illinois, USA]
  • UnoDogs helps owners better support their pet’s wellness, providing customized information and fitness programs. [Chinmany Mishra; New Delhi, India]

Making on-device machine learning more accessible, with ML Kit and TensorFlow Lite

Increasingly, machine learning is becoming a more accessible tool to developers with limited to no background in the technology. In fact, for most of the winners of the Android Developer Challenge, this was their first foray into machine learning. That’s thanks in part to two key offerings from Google, which bring on-device machine learning into reach for millions of developers around the world.

The first is ML Kit. ML Kit brings Google’s on-device machine learning technologies to mobile app developers, so they can build customized and interactive experiences into their apps. This includes tools such as language translation, text recognition, object detection and more. Eskke, for instance, uses offline text recognition and barcode scanning from ML Kit so users can scan the QR code at a mobile money kiosk and quickly withdraw money. And MixPose uses ML Kit's forthcoming Pose detection API to detect each user’s yoga positions and movements, so teachers can provide feedback.

The other Google resource that many of the Android Dev Challenge winners used was TensorFlow Lite. This powerful machine learning framework can help run machine learning models on Android, iOS and IoT devices that would never normally be able to support them. Its set of tools can be used for all kinds of powerful neural network-related applications, from image detection to speech recognition, bringing the latest cutting-edge technology to the devices we carry around with us wherever we go. Trashly, for instance, uses a custom TensorFlow Lite model to report if an object is recyclable and how to recycle it.

Helpful innovation, such as the 10 winning apps in the Android Developer Challenge, has the potential to change the way we access, use, and interpret information, making it available when we need it, where we need it most. By working with these developers focused on helpful innovation, we hope to inspire the next wave of developers to unlock what’s possible with this new technology.

#11WeeksOfAndroid Week 2 Machine Learning with Android logo head

What’s next in Android Machine Learning week?

As we kick off the second week of #11WeeksOfAndroid, focused on Machine Learning, we will highlight new tools and resources available to Android developers. Here’s a taste of the rest of this week:

  • Tuesday - ML Kit, the turnkey ML SDK went through a major overhaul with its new on-device offering this month. Check out the substantial improvement in developer usability, CameraX support and where the platform is going next.
  • Wednesday - Custom Models. When prepackaged SDK doesn’t quite satisfy your need, tools from Android Studio, TensorFlow Lite and ML Kit might just be the answer. Aside from individual offerings, we will also highlight how they can be used together.
  • Thursday - ML design. Learn some best practices for making ML product decisions from the People + AI Guidebook. We will go behind the scenes of the Read Along app, an on-device ML app that helps grow universal literacy. Bring your whole team because everyone, including UXers, engineers, and product managers are invited!

On Tuesday and Wednesday, we will also have a “codelab of the day” so get your Android Studio 4.1 beta today, block off an hour in your schedule and take this ML journey with us!

*The apps presented here are the projects of the developers individually, and not Google.

#AndroidDevChallenge: today is the last day to apply!

Dev Challenge banner with Android logo

Today is the last day to apply for the Android Developer Challenge! And to spark your imagination, we wanted to take a look at one of the original Android Developer Challenge winners, from over 10 years ago. Meet Maurizio Leo:

Maurizio and team have been working on Android for a while now. In fact, he was one of the winners of the original Android Developer Challenge, which launched with the start of Android over ten years ago. Their app, which won 3rd place worldwide at the time, has gone on to be downloaded over 30 million times!

If you’ve got a great idea that can help users get things done, we want to hear! We’ll pick 10 concepts and provide expertise and guidance to those developers to help in their plans to bring their ideas to fruition, in part from this amazing set of experts we’ve assembled. And once the app is ready, we’ll help showcase it in front of the billions of users on Google Play, through a collection and more. You can read more about all of the prizes here.

There’s still time to submit your idea before the deadline today! Submitting your idea is as simple as creating a repository on GitHub, telling us what you’d build and how we can help (we’ve included all of the materials here), and then officially submitting your repository here. Ideas can be in a concept phase to something that’s already complete; we can’t wait to hear what you come up with, and to work with you on bringing helpful innovation powered by machine learning to more and more users!

Our panel of experts for the #AndroidDevChallenge (apply by Dec. 2)

Just a little over a week left to finish your submission for the Android Developer Challenge, due December 2! Technology is enabling us to create a whole new era of helpful innovation by helping people get things done more quickly and surfacing patterns that would be difficult to detect using traditional methods. Ultimately, this helpful innovation is enabling us to live better, more productive, and safer lives.

Earlier this week, we highlighted the type of helpful innovation ideas powered by machine learning which are the sort of examples we’re looking for, to help inspire you. Today, we wanted to share the names of the panel of experts we’ve assembled to help bring your projects to life as part of the Android Developer Challenge. These experts will be making the final decision on the 10 finalists of the Android Developer Challenge, and if you’re selected as one of those finalists, we plan to have you meet them when we bring you to Google HQ for a bootcamp next year:

  • Dave Burke is Vice President of Engineering at Google where he leads engineering for the Android platform. Android is the largest mobile platform and ecosystem in the world, with over 2 billion active devices spanning smartphones, tablets, wearables, auto, TV, and IOT. Dave joined Google UK in 2007, becoming an engineering site lead and later moving to California in 2011. Prior to Google, Dave co-founded and was CTO of an internet/telecoms voice startup and helped define related Web and Internet standards.
  • Stephanie Cuthbertson is Senior Director of Developer PM, DevRel and UX for Android. She previously worked on Google’s Search & Ads businesses, as well as a range of developer tools used by Google employees internally. Prior to Google, she was at AWS where she led the product management team for Storage, including Amazon S3. Before AWS, she spent 10 years working on Visual Studio and developer tools.
  • Brahim Elbouchikhi is a Director of Product Management on the Android team. On Android, Brahim is responsible for developer and consumer facing ML and Camera products including CameraX and ML Kit. Prior to Android, Brahim led Daydream’s software team. Brahim was also a founding PM of the Google Play store where he led monetization, search, and discovery.
  • Yossi Matias is Vice President, Engineering, at Google. He is leading efforts in Search (Google Autocomplete, Search Live Results, Google Trends), Conversational AI (Google Duplex, Call Screen, Live Caption, Live Relay, Recorder, Pronunciation), and other Research initiatives. Yossi is the founding Head of Google's R&D Center in Israel, and the founding executive lead of Google for Startup Campus Tel Aviv and of Launchpad. He is the lead of Crisis Response and co-lead of Google’s AI for Social Good. In addition to his experience as an executive and entrepreneur, Yossi has a rich record of scientific research, published extensively, and has dozens of patents on his name. Yossi is a recipient of the Godel Prize and is an ACM Fellow.
  • Sarah Sirajuddin is an engineering director working on TensorFlow at Google. She leads the teams working on on-device machine learning, TensorFlow Extended, and efforts around training models for the best accuracy and performance with Google’s cutting-edge infrastructure, including TensorFlow and tensor processing units (TPUs).

If you’ve got a great idea that can help users get things done, we want to hear! We’ll pick 10 concepts and provide expertise and guidance to those developers to help in their plans to bring their ideas to fruition, in part from this amazing set of experts we’ve assembled. And once the app is ready, we’ll help showcase it in front of the billions of users on Google Play, through a collection and more. You can read more about all of the prizes here.

There’s still time to submit your idea before the December 2 deadline. Submitting your idea is as simple as creating a repository on GitHub, telling us what you’d build and how we can help (we’ve included all of the materials here), and then officially submitting your repository here. Ideas can be in a concept phase to something that’s already complete; we can’t wait to hear what you come up with, and to work with you on bringing helpful innovation powered by machine learning to more and more users!

Android Developer Challenge: here’s what we’re looking for! (Apply by Dec. 2)

Last month, we kicked off the next Android Developer Challenge, and asked you to submit your ideas focused on helpful innovation, powered by on-device machine learning. But what exactly do we mean when we say helpful innovation? We’re glad you asked! We rounded up a few of Google’s on-device machine learning offerings, together with some great recent examples of this technology in action, to help inspire your submission. Don’t forget, submit your idea by December 2!

Using machine learning to tackle Fall Armyworm

Take Nazirini Siraji. When she and a team of developers noticed a crop-pest threatening the livelihood of Ugandan farmers, they taught themselves TensorFlow to combat this pest. They collected training data from nearby fields in the form of images. With TensorFlow, they re-trained a MobileNet, a technique known as transfer learning and then used the TensorFlow Converter to generate a TensorFlow Lite FlatBuffer file which they deployed in an Android app. With the app, a farmer can snap a picture of their crop and the image frame is analysed to look for Fall armyworm damage. Depending on the results from this phase, a suggestion of a possible solution is given. It’s pretty cool!

Helping doctors detect respiratory diseases using machine learning

Tambua Health is helping doctors determine the likelihood of respiratory diseases by turning any smartphone into a powerful non-invasive screening tool. They developed an app using TensorFlow Lite that can help doctors analyze lung sounds for the presence of abnormal sounds like wheezes, crackles, stridor, and other adventitious sounds.

adidas uses machine learning to make the shopping experience easier

Even brands are tapping the power of machine learning. Take adidas, who recently launched a new “Bring It to Me” experience for their London store. Shoppers can use Visual Lookup to scan products on their phones while they are in the store, and the app lets them check stock and request their size without the need for queues. Under the hood, ML Kit is helping power the experience. It’s another way machine learning is helping users get things done more quickly.

The benefits of on-device machine learning

Running machine learning on a user’s device comes with a number of benefits. First, you reduce the amount of data you send to your server, enhancing user privacy. And because it runs on device, it can also work offline - perfect for inaccessible areas such as the middle of a rainforest, a desert or the London Underground. Last but not least, the most exciting aspect of running your model on device is low latency and this can enable all kinds of new user experiences. Machine learning is not just for automating tasks, it can work alongside your users and give them super powers too!

At Google, we offer a number of different technologies to help you take advantage of this:

  • ML Kit offers a turnkey SDK to help you tackle tasks with powerful Google Machine Learning models
  • The TensorFlow Lite Framework lets you take a custom model and optimise it to run it on Android
  • There’s also the infrastructure of Firebase / Google Cloud, which can help you train on-device models using AutoML Vision Edge for specific model types or give you the raw processing power to train your own model

If you’ve got a great idea that can help users get things done, we want to hear from you! We’ll pick 10 concepts and provide expertise and guidance to those developers to help in their plans to bring their ideas to fruition. And once the app is ready, we’ll help showcase it in front of the billions of users on Google Play, through a collection and more. You can read more about all of the prizes here.

There’s still time to submit your idea before the December 2 deadline. We can’t wait to hear what you come up with, and to work with you on bringing helpful innovation powered by on-device machine learning to more and more users!

Android Developer Challenge: helpful innovation, powered by On-Device Machine Learning + you!

Posted by The Android team

Android Developer Challenge banner

Developers like you have always played an important role in shaping the direction of Android, fueling the wave of Android innovation. It’s the reason that when we first launched the SDK for Android 10+ years ago, we simultaneously announced the Android Developer Challenge: a way to help reward model apps and show us what user problems you wanted to solve. As Android continues to push the boundaries into emerging areas like ML, 5G, foldables and more, we need your help to bring to life the consumer experiences that will define these new frontiers.

So we’re bringing back the Android Developer Challenge and asking you to help us unlock new experiences on Android, and help inspire other developers around these emerging technologies.

As we kick off this challenge, the first area we’ll be focusing on is On-Device Machine Learning. At Google, we’re big believers in how this new technology can open up a world of helpful innovation so you can get things done in ways you never thought possible. Take Live Captions: for the almost 500 million people who are deaf and hard of hearing, Live Captions bring content to life and is exactly the type of machine learning-powered innovation we expect to see more of someday, and with your help we can turn someday into today!

Bringing your idea to life in front of billions of eyes

Got an idea? Whether it’s still a concept or ready for users, tell us how you could use Google’s help, and how it supports the mission of using machine learning to help people get something done. Join the #AndroidDeveloperChallenge topic on GitHub, and share your idea as a repository under this topic. Don’t forget to come back here and officially submit your concept.

We’ll pick 10 concepts and provide expertise and guidance to those developers to help in their plans to bring their ideas to fruition. And once the app is ready, we’ll help showcase it in front of the billions of users on Google Play, through a collection and more. Here’s what those 10 developers will get:

Expertise and development support bootcamp: We’ll work with you to provide expertise and guidance to help in your plans to bring your app from concept to reality, including:

  • An all-expenses paid, working session with a panel of experts at Google HQ in Mountain View, CA
  • Google engineer mentorship at the bootcamp, providing guidance and technical expertise on how to help your plans to bring your app to fruition

Exposure and street cred! Once your idea is ready for prime-time, we’ll help you get users, and celebrate you to the broader Android community, including:

  • A collection on Google Play where we’ll feature your app (apps must be ready for Google Play on May 1, and must meet Google’s minimum quality requirements)
  • Tickets to Google I/O 2020
  • And we’ll celebrate these experiences to the broader Android developer community on developers.android.com. We might even showcase you at Google I/O, in places like the sandbox, sessions, perhaps even a keynote!

Helpful innovation is an important investment area for us on the Android team, and On-Device Machine Learning has played a critical role in powering new features in the last several releases of Android. We’re just beginning to scratch the surface, and we can’t wait to see what you come up with!