Tag Archives: TensorFlow

Coral makes edge AI even more accessible in 2020

Posted by the Coral team

Coral Dev Board Mini and Accelerator Module feature Google's Edge TPU co-processor to accelerate AI at the edge.

Since we launched Coral back in March 2019, we’ve added a number of new product form factors to accommodate the many ways users are adding on-device ML to their products. We've also streamlined the ML workflow and added capabilities like model pipelining with multiple Edge TPUs for an easier and more robust developer experience. And from this, we’ve helped enable amazing use cases from smart water meters that prevent water loss with Olea Edge, to systems for improving harvest yield with Farmwave, to noise cancellation in meetings in Google’s own Series One meeting kits.

This week, we’ll begin shipping the Coral Accelerator Module, a multi-chip module that combines the Edge TPU and it’s power circuitry into a solderable package. The module exposes PCIe and USB2 interfaces, which make it even easier to integrate Coral into custom designs. Several companies are already taking advantage of the compact size and capabilities with their new products coming to market. Read more about how Gumstix, STD, Siana Systems and IEI are using our module.

And in December, we’ll begin shipping the Dev Board Mini, a smaller, more power-efficient, and value-oriented board that brings forward a more traditional, flattened single-board computer design. The Dev Board Mini pairs a Mediatek 8167 SoC with the Coral Accelerator Module over USB 2 and is a great way to evaluate the module as the center of a project or deployment.

You can see the new Dev Board Mini and Accelerator Module in action in the latest episode of Level Up, where Markku Lepisto controls his studio lights with speech commands.

To get updates on when the board will be available for purchase and other Coral news, sign up for our newsletter.

Developing for the edge, now simplified

We recently announced a new version of the Coral ML APIs and tools. This release brings the C++ API into parity with Python and makes it more modular, reusable and performant. At the same time it eliminates unnecessary abstractions and surfaces replacing them with native TensorFlow Lite APIs. This release also graduates the Model Pipelining API out of beta and introduces a new model partitioner that automatically partitions models based on profiling and up to 10x better performance.

We’ve added a pre-trained version of MobileDet — a state-of-the-art object detection model for mobile systems — into our models portfolio. We’re migrating our model-development workflow to TensorFlow 2, and we’re including a handful of updated or new models based on the TF2 Keras framework. For details, check out the full announcement on the TensorFlow blog.

We’re also excited to see great developer tools coming from our ecosystem partners. For example, PerceptiLabs offers a visual API for building TensorFlow models and recently published a new demo which trains a machine learning model to identify sign language optimized for the edge with Coral.

The MRQ design from SigFox enables prototyping at the edge for low bandwidth IoT solutions with Coral

The MRQ design from SigFox enables prototyping at the edge for low bandwidth IoT solutions with Coral

And SigFox released a radio transceiver board that stacks on either the Coral Dev Board or Dev Board Mini. This allows small data payloads to be transmitted across low power, long range radio networks for use cases like smart cities, fleet management, asset tracking, agriculture and energy. The PCB design will be offered as a free download on SigFox’s website. Google Cloud Solutions Architect Markku Lepisto will present the new design today, in the opening keynote at SigFox Connect.

Customers with a Coral edge

The tool, from Farmwave, includes custom-developed ML models, a harvester-mounted box with cameras, an in-cab display, and on- device AI acceleration from Coral.

The tool, from Farmwave, includes custom-developed ML models, a harvester-mounted box with cameras, an in-cab display, and on- device AI acceleration from Coral.

Just in time for harvest we wanted to share a story about how Farmwave is using Coral to improve the efficiency of farm equipment and reduce food waste. Traditional yield loss analysis involves hand-counting grains of corn left on the ground mid harvest. It’s a time and labor intensive task, and not feasible for farmers who measure the value of their half-million-dollar combines in minutes spent running them.

By leveraging Coral’s on-device AI capabilities, Farmwave was able to build a system that automates the count while the machine is running. Thus allowing farmers to make real-time adjustments to harvesting machines in response to conditions in the field, which can make a big difference in yield.

Kura Sushi designed their intelligent QA system using a Raspberry Pi paired with the Coral USB Accelerator

Kura Sushi designed their intelligent QA system using a Raspberry Pi paired with the Coral USB Accelerator

Kura Revolving Sushi Bar in Japan has always been committed to the highest standards of health and safety for its customers. Known for their tech forward approach, Kura has dabbled in sushi making robots, an automated prize machine called Bikkura-pon, and a patented dome-shaped dish cover, aptly dubbed Mr. Fresh. But most recently, Kura has used Coral to develop an AI powered system that not only facilitates efficiency for better customer experiences, but also enables better tracking to prevent foodborne illnesses.

Making AI more accessible

While this year has presented the world with many obstacles, we’ve been impressed by the new ideas and innovations coming forward through technology. By providing the necessary tools and technology for edge AI, we strive to empower society to create affordable, adaptable, and intelligent systems.

We are excited to share all that Coral has to offer as we evolve our platform. For a list of worldwide distributors, system integrators and partners, visit the Coral partnerships page.

Please visit Coral.ai to discover more about our edge ML platform and share your feedback at [email protected]. To receive future Coral updates directly in your inbox, sign up for our newsletter.

Mitigating Unfair Bias in ML Models with the MinDiff Framework

The responsible research and development of machine learning (ML) can play a pivotal role in helping to solve a wide variety of societal challenges. At Google, our research reflects our AI Principles, from helping to protect patients from medication errors and improving flood forecasting models, to presenting methods that tackle unfair bias in products, such as Google Translate, and providing resources for other researchers to do the same.

One broad category for applying ML responsibly is the task of classification — systems that sort data into labeled categories. At Google, such models are used throughout our products to enforce policies, ranging from the detection of hate speech to age-appropriate content filtering. While these classifiers serve vital functions, it is also essential that they are built in ways that minimize unfair biases for users.

Today, we are announcing the release of MinDiff, a new regularization technique available in the TF Model Remediation library for effectively and efficiently mitigating unfair biases when training ML models. In this post, we discuss the research behind this technique and explain how it addresses the practical constraints and requirements we’ve observed when incorporating it in Google’s products.

Unfair Biases in Classifiers
To illustrate how MinDiff can be used, consider an example of a product policy classifier that is tasked with identifying and removing text comments that could be considered toxic. One challenge is to make sure that the classifier is not unfairly biased against submissions from a particular group of users, which could result in incorrect removal of content from these groups.

The academic community has laid a solid theoretical foundation for ML fairness, offering a breadth of perspectives on what unfair bias means and on the tensions between different frameworks for evaluating fairness. One of the most common metrics is equality of opportunity, which, in our example, means measuring and seeking to minimize the difference in false positive rate (FPR) across groups. In the example above, this means that the classifier should not be more likely to incorrectly remove safe comments from one group than another. Similarly, the classifier’s false negative rate should be equal between groups. That is, the classifier should not miss toxic comments against one group more than it does for another.

When the end goal is to improve products, it’s important to be able to scale unfair bias mitigation to many models. However, this poses a number of challenges:

  • Sparse demographic data: The original work on equality of opportunity proposed a post-processing approach to the problem, which consisted of assigning each user group a different classifier threshold at serving time to offset biases of the model. However, in practice this is often not possible for many reasons, such as privacy policies. For example, demographics are often collected by users self-identifying and opting in, but while some users will choose to do this, others may choose to opt-out or delete data. Even for in-process solutions (i.e., methods that change how a model is trained) one needs to assume that most data will not have associated demographics, and thus needs to make efficient use of the few examples for which demographics are known.
  • Ease of Use: In order for any technique to be adopted broadly, it should be easy to incorporate into existing model architectures, and not be highly sensitive to hyperparameters. While an early approach to incorporating ML fairness principles into applications utilized adversarial learning, we found that it too frequently caused models to degenerate during training, which made it difficult for product teams to iterate and made new product teams wary.
  • Quality: The method for removing unfair biases should also reduce the overall classification performance (e.g., accuracy) as little as possible. Because any decrease in accuracy caused by the mitigation approach could result in the moderation model allowing more toxic comments, striking the right balance is crucial.

MinDiff Framework
We iteratively developed the MinDiff framework over the previous few years to meet these design requirements. Because demographic information is so rarely known, we utilize in-process approaches in which the model’s training objective is augmented with an objective specifically focused on removing biases. This new objective is then optimized over the small sample of data with known demographic information. To improve ease of use, we switched from adversarial training to a regularization framework, which penalizes statistical dependency between its predictions and demographic information for non-harmful examples. This encourages the model to equalize error rates across groups, e.g., classifying non-harmful examples as toxic.

There are several ways to encode this dependency between predictions and demographic information. Our initial MinDiff implementation minimized the correlation between the predictions and the demographic group, which essentially optimized for the average and variance of predictions to be equal across groups, even if the distributions still differ afterward. We have since improved MinDiff further by considering the maximum mean discrepancy (MMD) loss, which is closer to optimizing for the distribution of predictions to be independent of demographics. We have found that this approach is better able to both remove biases and maintain model accuracy.

MinDiff with MMD better closes the FPR gap with less decrease in accuracy
(on an academic benchmark dataset).

To date we have launched modeling improvements across several classifiers at Google that moderate content quality. We went through multiple iterations to develop a robust, responsible, and scalable approach, solving research challenges and enabling broad adoption.

Gaps in error rates of classifiers is an important set of unfair biases to address, but not the only one that arises in ML applications. For ML researchers and practitioners, we hope this work can further advance research toward addressing even broader classes of unfair biases and the development of approaches that can be used in practical applications. In addition, we hope that the release of the MinDiff library and the associated demos and documentation, along with the tools and experience shared here, can help practitioners improve their models and products.

Acknowledgements
This research effort on ML Fairness in classification was jointly led with Jilin Chen, Shuo Chen, Ed H. Chi, Tulsee Doshi, and Hai Qian. Further, this work was pursued in collaboration with Jonathan Bischof, Qiuwen Chen, Pierre Kreitmann, and Christine Luu. The MinDiff infrastructure was also developed in collaboration with Nick Blumm, James Chen, Thomas Greenspan, Christina Greer, Lichan Hong, Manasi Joshi, Maciej Kula, Summer Misherghi, Dan Nanas, Sean O'Keefe, Mahesh Sathiamoorthy, Catherina Xu, and Zhe Zhao. (All names are listed in alphabetical order of last names.)

Source: Google AI Blog


Peer Bonus Experiences: Building tiny models for the ML community with TensorFlow

Almost all the current state-of-the-art machine learning (ML) models take quite a lot of disk space. This makes them particularly inefficient in production situations. A bulky machine learning model can be exposed as a REST API and hosted on cloud services, but that same bulk may lead to hefty infrastructure costs. And some applications may need to operate in low-bandwidth environments, making cloud-hosted models less practical.

In a perfect world, your models would live alongside your application, saving data transfer costs and complying with any regulatory requirements restricting what data can be sent to the cloud. But storing multi-gigabyte models on today’s devices just isn’t practical. The field of on-device ML is dedicated to the development of tools and techniques to produce tiny—yet high performing!—ML models. Progress has been slow, but steady!

There has never been a better time to learn about on-device ML and successfully apply it in your own projects. With frameworks like TensorFlow Lite, you have an exceptional toolset to optimize your bulky models while retaining as much performance as possible. TensorFlow Lite also makes it very easy for mobile application developers to integrate ML models with tools like metadata and ML Model Binding, Android codegen, and others.

What is TensorFlow Lite?

“TensorFlow Lite is a production ready, cross-platform framework for deploying ML on mobile devices and embedded systems.” - TensorFlow Youtube

TensorFlow Lite provides first-class support for Native Android and iOS-based integrations (with many additional features, such as delegates). TensorFlow Lite also supports other tiny computing platforms, such as microcontrollers. TensorFlow Lite’s optimization APIs produce world-class, fast, and well-performing machine learning models.

Venturing into TensorFlow Lite

Last year, I started playing around with TensorFlow Lite while developing projects for Raspberry Pi for Computer Vision, using the official documentation and this course to fuel my initial learning. Following this interest, I decided to join a voluntary working group focused on creating sample applications, writing out tutorials, and creating tiny models. This working group consists of individuals from different backgrounds passionate about teaching on-device machine learning to others. The group is coordinated by Khanh LeViet (TensorFlow Lite team) and Hoi Lam (Android ML team). This is by far one of the most active working groups I have ever seen. And, back in our starting days, Khanh proposed a few different state-of-art machine learning models that were great fits for on-device machine learning:

These ideas were enough for us to start spinning up Jupyter notebooks and VSCode. After months of work, we now have strong collaborations between machine learning GDEs and a bunch of different TensorFlow Lite models, sample applications, and tutorials for the community to learn from. Our collaborations have been fueled by the power of open source and all the tiny models that we have built together are available on TensorFlow Hub. There are numerous open source applications that we have built that demonstrate how to use these models.
The Cartoonizer model cartoonizes uploaded images

Margaret and I co-authored an end-to-end tutorial that was published from the official TensorFlow blog and published the TensorFlow Lite models on TensorFlow Hub. So far, the response we have received for this work has been truly mesmerizing. I’ve also shared my experiences with TensorFlow Lite in these blog posts and conference talks:

A Tale of Model Quantization in TF Lite
Plunging into Model Pruning in Deep Learning
A few good stuff in TF Lite
Doing more with TF Lite
Model Optimization 101

The power of collaboration

The working group is a tremendous opportunity for machine learning GDEs, Googlers, and passionate community individuals to collaborate and learn. We get to learn together, create together, and celebrate the joy of teaching others. I am immensely thankful, grateful, and humbled to be a part of this group. Lastly, I would like to wholeheartedly thank Khanh for being a pillar of support to us and for nominating me for the Google Open Source Peer Bonus Award.

By Sayak Paul, PyImageSearch—Guest Author

Announcing DevFest 2020

Posted by Jennifer Kohl, Program Manager, Developer Community Programs

DevFest Image

On October 16-18, thousands of developers from all over the world are coming together for DevFest 2020, the largest virtual weekend of community-led learning on Google technologies.

As people around the world continue to adapt to spending more time at home, developers yearn for community now more than ever. In years past, DevFest was a series of in-person events over a season. For 2020, the community is coming together in a whole new way – virtually – over one weekend to keep developers connected when they may want it the most.

The speakers

The magic of DevFest comes from the people who organize and speak at the events - developers with various backgrounds and skill levels, all with their own unique perspectives. In different parts of the world, you can find a DevFest session in many local languages. DevFest speakers are made up of various types of technologists, including kid developers , self-taught programmers from rural areas , and CEOs and CTOs of startups. DevFest also features a wide range of speakers from Google, Women Techmakers, Google Developer Experts, and more. Together, these friendly faces, with many different perspectives, create a unique and rich developer conference.

The sessions and their mission

Hosted by Google Developer Groups, this year’s sessions include technical talks and workshops from the community, and a keynote from Google Developers. Through these events, developers will learn how Google technologies help them develop, learn, and build together.

Sessions will cover multiple technologies, such as Android, Google Cloud Platform, Machine Learning with TensorFlow, Web.dev, Firebase, Google Assistant, and Flutter.


At our core, Google Developers believes community-led developer events like these are an integral part of the advancement of technology in the world.

For this reason, Google Developers supports the community-led efforts of Google Developer Groups and their annual tentpole event, DevFest. Google provides esteemed speakers from the company and custom technical content produced by developers at Google. The impact of DevFest is really driven by the grassroots, passionate GDG community organizers who volunteer their time. Google Developers is proud to support them.

The attendees

During DevFest 2019, 138,000+ developers participated across 500+ DevFests in 100 countries. While 2020 is a very different year for events around the world, GDG chapters are galvanizing their communities to come together virtually for this global moment. The excitement for DevFest continues as more people seek new opportunities to meet and collaborate with like-minded, community-oriented developers in our local towns and regions.

Join the conversation on social media with #DevFest.

Sign up for DevFest at goo.gle/devfest.





Still curious? Check out these popular talks from DevFest 2019 events around the world...

Doubling down on the edge with Coral’s new accelerator

Posted by The Coral Team

Coral image

Moving into the fall, the Coral platform continues to grow with the release of the M.2 Accelerator with Dual Edge TPU. Its first application is in Google’s Series One room kits where it helps to remove interruptions and makes the audio clearer for better video meetings. To help even more folks build products with Coral intelligence, we’re dropping the prices on several of our products. And for those folks that are looking to level up their at home video production, we’re sharing a demo of a pose based AI director to make multi-camera video easier to make.

Coral M.2 Accelerator with Dual Edge TPU

The newest addition to our product family brings two Edge TPU co-processors to systems in an M.2 E-key form factor. While the design requires a dual bus PCIe M.2 slot, it brings enhanced ML performance (8 TOPS) to tasks such as running two models in parallel or pipelining one large model across both Edge TPUs.

The ability to scale across multiple edge accelerators isn’t limited to only two Edge TPUs. As edge computing expands to local data centers, cell towers, and gateways, multi-Edge TPU configurations will be required to help process increasingly sophisticated ML models. Coral allows the use of a single toolchain to create models for one or more Edge TPUs that can address many different future configurations.

A great example of how the Coral M.2 Accelerator with Dual Edge TPU is being used is in the Series One meeting room kits for Google Meet.

The new Series One room kits for Google Meet run smarter with Coral intelligence

Coral image

Google’s new Series One room kits use our Coral M.2 Accelerator with Dual Edge TPU to bring enhanced audio clarity to video meetings. TrueVoice®, a multi-channel noise cancellation technology, minimizes distractions to ensure every voice is heard with up to 44 channels of echo and noise cancellation, making distracting sounds like snacking or typing on a keyboard a concern of the past.

Enabling the clearest possible communication in challenging environments was the target for the Google Meet hardware team. The consideration of what makes a challenging environment was not limited to unusually noisy environments, such as lunchrooms doubling as conference rooms. Any conference room can present challenging acoustics that make it difficult for all participants to be heard.

The secret to clarity without expensive and cumbersome equipment is to use virtual audio channels and AI driven sound isolation. Read more about how Coral was used to enhance and future-proof the innovative design.

Expanding the AI edge

Earlier this year, we reduced the prices of our prototyping devices and sensors. We are excited to share further price drops on more of our products. Our System-on-Module is now available for $99.99, and our Mini PCIe Accelerator, M.2 Accelerator A+E Key, and M.2 Accelerator B+M key are now available at $24.99. We hope this lower price will make our edge AI more accessible to more creative minds around the world. Later, this month our SoM offering will also expand to include 2 and 4GB RAM options.

Multi-cam with AI

Coral image

As we expand our platform and product family, we continue to keep new edge AI use cases in mind. We are continually inspired by our developer community’s experimentation and implementations. When recently faced with the challenges of multicam video production from home, Markku Lepistö, Solutions Architect at Google Cloud, created this real-time pose-based multicam tool he so aptly dubbed, AI Director.

We love seeing such unique implementations of on-device ML and invite you to share your own projects and feedback at [email protected].

For a list of worldwide distributors, system integrators and partners, visit the Coral partnerships page. Please visit Coral.ai to discover more about our edge ML platform.

Doubling down on the edge with Coral’s new accelerator

Posted by The Coral Team

Coral image

Moving into the fall, the Coral platform continues to grow with the release of the M.2 Accelerator with Dual Edge TPU. Its first application is in Google’s Series One room kits where it helps to remove interruptions and makes the audio clearer for better video meetings. To help even more folks build products with Coral intelligence, we’re dropping the prices on several of our products. And for those folks that are looking to level up their at home video production, we’re sharing a demo of a pose based AI director to make multi-camera video easier to make.

Coral M.2 Accelerator with Dual Edge TPU

The newest addition to our product family brings two Edge TPU co-processors to systems in an M.2 E-key form factor. While the design requires a dual bus PCIe M.2 slot, it brings enhanced ML performance (8 TOPS) to tasks such as running two models in parallel or pipelining one large model across both Edge TPUs.

The ability to scale across multiple edge accelerators isn’t limited to only two Edge TPUs. As edge computing expands to local data centers, cell towers, and gateways, multi-Edge TPU configurations will be required to help process increasingly sophisticated ML models. Coral allows the use of a single toolchain to create models for one or more Edge TPUs that can address many different future configurations.

A great example of how the Coral M.2 Accelerator with Dual Edge TPU is being used is in the Series One meeting room kits for Google Meet.

The new Series One room kits for Google Meet run smarter with Coral intelligence

Coral image

Google’s new Series One room kits use our Coral M.2 Accelerator with Dual Edge TPU to bring enhanced audio clarity to video meetings. TrueVoice®, a multi-channel noise cancellation technology, minimizes distractions to ensure every voice is heard with up to 44 channels of echo and noise cancellation, making distracting sounds like snacking or typing on a keyboard a concern of the past.

Enabling the clearest possible communication in challenging environments was the target for the Google Meet hardware team. The consideration of what makes a challenging environment was not limited to unusually noisy environments, such as lunchrooms doubling as conference rooms. Any conference room can present challenging acoustics that make it difficult for all participants to be heard.

The secret to clarity without expensive and cumbersome equipment is to use virtual audio channels and AI driven sound isolation. Read more about how Coral was used to enhance and future-proof the innovative design.

Expanding the AI edge

Earlier this year, we reduced the prices of our prototyping devices and sensors. We are excited to share further price drops on more of our products. Our System-on-Module is now available for $99.99, and our Mini PCIe Accelerator, M.2 Accelerator A+E Key, and M.2 Accelerator B+M key are now available at $24.99. We hope this lower price will make our edge AI more accessible to more creative minds around the world. Later, this month our SoM offering will also expand to include 2 and 4GB RAM options.

Multi-cam with AI

Coral image

As we expand our platform and product family, we continue to keep new edge AI use cases in mind. We are continually inspired by our developer community’s experimentation and implementations. When recently faced with the challenges of multicam video production from home, Markku Lepistö, Solutions Architect at Google Cloud, created this real-time pose-based multicam tool he so aptly dubbed, AI Director.

We love seeing such unique implementations of on-device ML and invite you to share your own projects and feedback at [email protected].

For a list of worldwide distributors, system integrators and partners, visit the Coral partnerships page. Please visit Coral.ai to discover more about our edge ML platform.

Introducing TensorFlow Recorder

When training computer vision machine learning models, data loading can often be a performance bottleneck, causing your GPU or TPU resources to be underutilized while waiting for data to be loaded into the model. Storing your dataset in the efficient TensorFlow Record (TFRecord) format is a great way to solve these problems, but creating TFRecords can unfortunately often require a great deal of complex code.

Last week we open sourced the TensorFlow Recorder project (also known as TFRecorder), which makes it possible for data scientists, data engineers, or AI/ML engineers to create image based TFRecords with just a few lines of code. Using TFRecords is incredibly important for creating efficient TensorFlow ML pipelines, but until now they haven’t been so easy to create. Before TFRecorder, in order to create TFRecords at scale you would have had to write a data pipeline that parsed your structured data, loaded images from storage, and serialized the results into the TFRecord format. TFRecorder allows you to write TFRecords directly from a Pandas dataframe or CSV without writing any complicated code.

You can see an example of TFRecoder below, but first let’s talk about some of the specific advantages of TFRecords.

How TFRecords Can Help

Using the TFRecord file format allows you to store your data in sets of files, each containing a sequence of protocol buffers serialized as a binary record that can be read very efficiently, which will help reduce the data loading bottleneck mentioned above.

Data loading performance can be further improved by implementing prefetching and parallel interleave along with using the TFRecord format. Prefetching reduces the time of each model training step(s) by fetching the data for the next training step while your model is executing training on the current step. Parallel interleave allows you to read from multiple TFRecords shards (pieces of a TFRecord file) and apply preprocessing of those interleaved data streams. This reduces the latency required to read a training batch and is especially helpful when reading data from the network.

Using TensorFlow Recorder

Creating a TFRecord using TFRecorder requires only a few lines of code. Here’s how it works.
import pandas as pd
import tfrecorder
df = pd.read_csv(...)
df.tensorflow.to_tfrecord(output_dir="gs://my/bucket")

TFRecorder currently expects data to be in the same format as Google AutoML Vision.

This format looks like a pandas dataframe or CSV formatted as:
splitimage_urilabel
TRAIN
gs://my/bucket/image1.jpgcat

Where:
  • split can take on the values TRAIN, VALIDATION, and TEST
  • image_uri specifies a local or google cloud storage location for the image file.
  • label can be either a text-based label that will be integerized or an integer
In the future, we hope to extend TensorFlow Recorder to work with data in any format.

While this example would work well to convert a few thousand images into TFRecords, it probably wouldn’t scale well if you have millions of images. To scale up to huge datasets, TensorFlow Recorder provides connectivity with Google Cloud Dataflow, which is a serverless Apache Beam pipeline runner. Scaling up to DataFlow requires only a little bit more configuration.
df.tensorflow.to_tfrecord(
output_dir="gs://my/bucket",
runner="DataFlowRunner",
project="my-project",
region="us-central1)

What’s next?

We’d love for you to try out TensorFlow Recorder. You can get it from GitHub or simply pip install tfrecorder. Tensorflow Recorder is very new and we’d greatly appreciate your feedback, suggestions, and pull requests.

By Mike Bernico and Carlos Ezequiel, Google Cloud AI Engineers

Calling everyone across the country to participate in a one-of-a-kind AI musical experience for India’s Independence Day

We all remember standing to attention during our daily school assembly, and that unmistakable sense of pride when singing the strains of ‘Jana Gana Mana’. And it’s something we all remember singing with fervour -- whether we were the ones who were comfortable only belting out our favorite songs in the shower, or whether we were capable of giving professional singers a run for their money. No matter which category you belong to, we’ve got something special for you.


We invite you to participate in a unique AI experiment, which involves two key ingredients: the most cutting-edge AI work we’re doing in music, and… your voice! We are bringing together these two elements to produce a song that you would know all too well -- the Indian national anthem. All you need to do is sing the national anthem, then using the power of AI your voice will get converted into one of three traditional Indian instruments -- the shehnai, sarangi, or bansuri -- effectively rendering your performance of the national anthem in the instrument of your choice.
Taking part in this experiment is simple. Using a smartphone, head over to g.co/SoundsofIndia and you will see an interactive web app that steps you through the process. You will first be able to hear the national anthem, giving you a sense of the pitch and tempo. Next, you’ll see a screen with the lyrics of the national anthem, which get highlighted to help you sing in rhythm -- much like you would with a karaoke track. After you’ve sung, pick your favourite Indian instrument and in a few moments you’ll have your own version of the national anthem -- as sung by you but in the sound of your favourite Indian instrument -- downloaded and ready to share. Finally, you can join scores of others and submit your rendition to this experiment.


Note that the computation for this experience runs completely in your browser and on-device using TensorFlow, and no personally identifiable information is collected or stored. We can’t wait to bring to you the culmination of this experience, so look out for something very special coming your way on 15th August 2020 -- the 73rd anniversary of India’s Independence.


We look forward to your joining us in creating a one-of-a-kind cultural experience that is inspired by tradition, and powered by AI.

Posted by Sanjay Gupta, Vice President and Country Manager, Google India

Summer updates from Coral

Posted by the Coral Team

Summer has arrived along with a number of Coral updates. We're happy to announce a new partnership with balena that helps customers build, manage, and deploy IoT applications at scale on Coral devices. In addition, we've released a series of updates to expand platform compatibility, make development easier, and improve the ML capabilities of our devices.

Open-source Edge TPU runtime now available on GitHub

First up, our Edge TPU runtime is now open-source and available on GitHub, including scripts and instructions for building the library for Linux and Windows. Customers running a platform that is not officially supported by Coral, including ARMv7 and RISC-V can now compile the Edge TPU runtime themselves and start experimenting. An open source runtime is easier to integrate into your customized build pipeline, enabling support for creating Yocto-based images as well as other distributions.

Windows drivers now available for the Mini PCIe and M.2 accelerators

Coral customers can now also use the Mini PCIe and M.2 accelerators on the Microsoft Windows platform. New Windows drivers for these products complement the previously released Windows drivers for the USB accelerator and make it possible to start prototyping with the Coral USB Accelerator on Windows and then to move into production with our Mini PCIe and M.2 products.

New fresh bits on the Coral ML software stack

We’ve also made a number of new updates to our ML tools:

  • The Edge TPU compiler is now version 14.1. It can be updated by running sudo apt-get update && sudo apt-get install edgetpu, or follow the instructions here
  • Our new Model Pipelining API allows you to divide your model across multiple Edge TPUs. The C++ version is currently in beta and the source is on GitHub
  • New embedding extractor models for EfficientNet, for use with on-device backpropagation. Embedding extractor models are compiled with the last fully-connected layer removed, allowing you to retrain for classification. Previously, only Inception and MobileNet were available and now retraining can also be done on EfficientNet
  • New Colab notebooks to retrain a classification model with TensorFlow 2.0 and build C++ examples

Balena partners with Coral to enable AI at the edge

We are excited to share that the Balena fleet management platform now supports Coral products!

Companies running a fleet of ML-enabled devices on the edge need to keep their systems up-to-date with the latest security patches in order to protect data, model IP and hardware from being compromised. Additionally, ML applications benefit from being consistently retrained to recognize new use cases with maximum accuracy. Coral + balena together, bring simplicity and ease to the provisioning, deployment, updating, and monitoring of your ML project at the edge, moving early prototyping seamlessly towards production environments with many thousands of devices.

Read more about all the benefits of Coral devices combined with balena container technology or get started deploying container images to your Coral fleet with this demo project.

New version of Mendel Linux

Mendel Linux (5.0 release Eagle) is now available for the Coral Dev Board and SoM and includes a more stable package repository that provides a smoother updating experience. It also brings compatibility improvements and a new version of the GPU driver.

New models

Last but not least, we’ve recently released BodyPix, a Google person-segmentation model that was previously only available for TensorFlow.JS, as a Coral model. This enables real-time privacy preserving understanding of where people (and body parts) are on a camera frame. We first demoed this at CES 2020 and it was one of our most popular demos. Using BodyPix we can remove people from the frame, display only their outline, and aggregate over time to see heat maps of population flow.

Here are two possible applications of BodyPix: Body-part segmentation and anonymous population flow. Both are running on the Dev Board.

We’re excited to add BodyPix to the portfolio of projects the community is using to extend our models far beyond our demos—including tackling today’s biggest challenges. For example, Neuralet has taken our MobileNet V2 SSD Detection model and used it to implement Smart Social Distancing. Using the bounding box of person detection, they can compute a region for safe distancing and let a user know if social distance isn’t being maintained. The best part is this is done without any sort of facial recognition or tracking, with Coral we can accomplish this in real-time in a privacy preserving manner.

We can’t wait to see more projects that the community can make with BodyPix. Beyond anonymous population flow there’s endless possibilities with background and body part manipulation. Let us know what you come up with at our community channels, including GitHub and StackOverflow.

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We are excited to share all that Coral has to offer as we continue to evolve our platform. For a list of worldwide distributors, system integrators and partners, including balena, visit the Coral partnerships page. Please visit Coral.ai to discover more about our edge ML platform and share your feedback at [email protected].

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!