Posted by The Coral Team
We're always excited to share updates to our Coral platform for building edge ML applications. In this post, we have some interesting demos, interfaces, and tutorials to share, and we'll start by pointing you to an important software update for the Coral Dev Board.
If you have a Coral Dev Board or Coral SoM, please install our latest Mendel update as soon as possible to receive a critical fix to part of the SoC power configuration. To get it, just log onto your board and install the update as follows:
This will install a patch from NXP for the Dev Board / SoM's SoC, without which it's possible the SoC will overstress and the lifetime of the device could be reduced. If you recently flashed your board with the latest system image, you might already have this fix (we also updated the flashable image today), but it never hurts to fetch all updates, as shown above.
We recently published the Coral Manufacturing Demo, which demonstrates how to use a single Coral Edge TPU to simultaneously accomplish two common manufacturing use-cases: worker safety and visual inspection.
The demo is designed for two specific videos and tasks (worker keepout detection and apple quality grading) but it is designed to be easily customized with different inputs and tasks. The demo, written in C++, requires OpenGL and is primarily targeted at x86 systems which are prevalent in manufacturing gateways – although ARM Cortex-A systems, like the Coral Dev Board, are also supported.
We've been working hard to make ML acceleration with the Coral Edge TPU available for most popular systems. So we're proud to announce support for WebUSB, allowing you to use the Coral USB Accelerator directly from Chrome. To get started, check out our WebCoral demo, which builds a webpage where you can select a model and run an inference accelerated by the Edge TPU.
We recently released a new models repository that makes it easier to explore the various trained models available for the Coral platform, including image classification, object detection, semantic segmentation, pose estimation, and speech recognition. Each family page lists the various models, including details about training dataset, input size, latency, accuracy, model size, and other parameters, making it easier to select the best fit for the application at hand. Lastly, each family page includes links to training scripts and example code to help you get started. Or for an overview of all our models, you can see them all on one page.
Even with our collection of pre-trained models, it can sometimes be tricky to create a task-specific model that's compatible with our Edge TPU accelerator. To make this easier, we've released some new Google Colab tutorials that allow you to perform transfer learning for object detection, using MobileDet and EfficientDet-Lite models. You can find these and other Colabs in our GitHub Tutorials repo.
We are excited to share all that Coral has to offer as we continue to evolve our platform. Keep an eye out for more software and platform related news coming this summer. To discover more about our edge ML platform, please visit Coral.ai and share your feedback at coral-support@google.com.
Posted by HyeJung Lee and MJ You, Google ML Ecosystem Community Managers. Reviewed by Soonson Kwon, Developer Relations Program Manager.
Google Developers Experts is a community of passionate developers who love to share their knowledge with others. Many of them specialize in Machine Learning (ML). Despite many unexpected changes over the last months and reduced opportunities for various in person activities during the ongoing pandemic, their enthusiasm did not stop.
Here are some highlights of the ML GDE’s hard work during the Q1 2021 which contributed to the global ML ecosystem.
ML GDE YouTube channel
With the initiative and lead of US-based GDE Margaret Maynard-Reid, we launched the ML GDEs YouTube channel. It is a great way for GDEs to reach global audiences, collaborate as a community, create unique content and promote each other's work. It will contain all kinds of ML related topics: talks on technical topics, tutorials, interviews with another (ML) GDE, a Googler or anyone in the ML community etc. Many videos have already been uploaded, including: ML GDE’s intro from all over the world, tips for TensorFlow & GCP Certification and how to use Google Cloud Platform etc. Subscribe to the channel now!!
TensorFlow Everywhere
17 ML GDEs presented at TensorFlow Everywhere (a global community-led event series for TensorFlow and Machine Learning enthusiasts and developers around the world) hosted by local TensorFlow user groups. You can watch the recorded sessions in the TensorFlow Everywhere playlist on the ML GDE Youtube channel. Most of the sessions cover new features in Tensorflow.
International Women’s Day
Many ML GDEs participated in activities to celebrate International Women’s Day (March 8th). GDE Ruqiya Bin Safi (based in Saudi Arabia) cooperated with WTM Saudi Arabia to organize “Socialthon” - social development hackathons and gave a talk “Successful Experiences in Social Development", which reached 77K viervers live and hit 10K replays. India-based GDE Charmi Chokshi participated in GirlScript's International Women's Day event and gave a talk: “Women In Tech and How we can help the underrepresented in the challenging world”. If you’re looking for more inspiring materials, check out the “Women in AI” playlist on our ML GDE YouTube channel!
Mentoring
ML GDEs are also very active in mentoring community developers, students in the Google Developer Student Clubs and startups in the Google for Startups Accelerator program. Among many, GDE Arnaldo Gualberto (Brazil) conducted mentorship sessions for startups in the Google Fast Track program, discussing how to solve challanges using Machine Learning/Deep Learning with TensorFlow.
TensorFlow
Meanwhile in Europe, GDEs Alexia Audevart (based in France) and Luca Massaron (based in Italy) released “Machine Learning using TensorFlow Cookbook”. It provides simple and effective ideas to successfully use TensorFlow 2.x in computer vision, NLP and tabular data projects. Additionally, Luca published the second edition of the Machine Learning For Dummies book, first published in 2015. Her latest edition is enhanced with product updates and the principal is a larger share of pages devoted to discussion of Deep Learning and TensorFlow / Keras usage.
On top of her women-in-tech related activities, Ruqiya Bin Safi is also running a “Welcome to Deep Learning Course and Orientation” monthly workshop throughout 2021. The course aims to help participants gain foundational knowledge of deep learning algorithms and get practical experience in building neural networks in TensorFlow.
Nepal-based GDE Kshitiz Rimal gave a talk “TensorFlow Project Showcase: Cash Recognition for Visually Impaired" on his project which uses TensorFlow, Google Cloud AutoML and edge computing technologies to create a solution for the visually impaired community in Nepal.
On the other side of the world, in Canada, GDE Tanmay Bakshi presented a talk “Machine Learning-powered Pipelines to Augment Human Specialists” during TensorFlow Everywhere NA. It covered the world of NLP through Deep Learning, how it's historically been done, the Transformer revolution, and how using the TensorFlow & Keras to implement use cases ranging from small-scale name generation to large-scale Amazon review quality ranking.
Google Cloud Platform
We have been equally busy on the GCP side as well. In the US, GDE Srivatsan Srinivasan created a series of videos called “Artificial Intelligence on Google Cloud Platform”, with one of the episodes, "Google Cloud Products and Professional Machine Learning Engineer Certification Deep Dive", getting over 3,000 views.
Korean GDE Chansung Park contributed to TensorFlow User Group Korea with his “Machine Learning Pipeline (CI/CD for ML Products in GCP)” analysis, focused on about machine learning pipeline in Google Cloud Platform.
Last but not least, GDE Gad Benram based in Israel wrote an article on “Seven Tips for Forecasting Cloud Costs”, where he explains how to build and deploy ML models for time series forecasting with Google Cloud Run. It is linked with his solution of building a cloud-spend control system that helps users more-easily analyze their cloud costs.
If you want to know more about the Google Experts community and all their global open-source ML contributions, visit the GDE Directory and connect with GDEs on Twitter and LinkedIn. You can also meet them virtually on the ML GDE’s YouTube Channel!
Posted by Jennifer Kohl, Global Program Manager, Google Developer Groups
Irem presenting at a Google Developer Group event
We recently caught up with Irem Komurcu, a TensorFlow developer and researcher at Istanbul Technical University in Turkey. Irem has been a long-serving member of Google Developer Groups (GDG) Düzce and also serves as a Women Techmakers (WTM) ambassador. Her work with TensorFlow has received several accolades, including being named a Hamdi Ulukaya Girişimi fellow. As one one of twenty-four young entrepreneurs selected, she was flown to New York City last year to learn more about business and receive professional development.
With all this experience to share, we wanted you to hear how she approaches pursuing a career in tech, hones her TensorFlow skills with the GDG community, and thinks about how upcoming programmers can best position themselves for success. Check out the full interview below for more.
I first became interested in tech when I was in high school and went on to study computer engineering. At university, I had an eye-opening experience when I traveled from Turkey to the Google Developer Day event in India. It was here where I observed various code languages, products, and projects that were new to me.
In particular, I saw TensorFlow in action for the first time. Watching the powerful machine learning tool truly sparked my interest in deep learning and project development.
I have studied many different aspects of Tensorflow and ML. My first work was on voice recognition and deep learning. However, I am now working as a computer vision researcher conducting various segmentation, object detection, and classification processes with Tensorflow. In my free time, I write various articles about best practices and strategies to leverage TensorFlow in ML.
I kicked off my studies on deep learning on tensorflow.org. It’s a basic first step, but a powerful one. There were so many blogs, codes, examples, and tutorials for me to dive into. Both the Google Developer Group and TensorFlow communities also offered chances to bounce questions and ideas off other developers as I learned.
Between these technical resources and the person-to-person support, I was lucky to start working with the GDG community while also taking the first steps of my career. There were so many opportunities to meet people and grow all around.
I love being in a large community with technology-oriented people. GDG is a network of professionals who support each other, and that enables people to develop. I am continuously sharing my knowledge with other programmers as they simultaneously mentor me. The chance for us to collaborate together is truly fulfilling.
The number of women supported in science, technology, engineering, and mathematics (STEM) is low in Turkey. To address this, I partner with Women Techmakers (WTM) to give educational talks on TensorFlow and machine learning to women who want to learn how to code in my country. So many women are interested in ML, but just need a friendly, familiar face to help them get started. With WTM, I’ve already given over 30 talks to women in STEM.
Keep researching new things. Read everything you can get your eyes on. Technology has been developing rapidly, and it is necessary to make sure your mind can keep up with the pace. That’s why I recommend communities like GDG that help make sure you’re up to date on the newest trends and learnings.
Want to work with other developers like Irem? Then find the right Google Developer Developer Group for you, here.
Posted by Kanstantsin Sokal, Software Engineer, MediaPipe team
Earlier this year, the MediaPipe Team released the Face Mesh solution, which estimates the approximate 3D face shape via 468 landmarks in real-time on mobile devices. In this blog, we introduce a new face transform estimation module that establishes a researcher- and developer-friendly semantic API useful for determining the 3D face pose and attaching virtual objects (like glasses, hats or masks) to a face.
The new module establishes a metric 3D space and uses the landmark screen positions to estimate common 3D face primitives, including a face pose transformation matrix and a triangular face mesh. Under the hood, a lightweight statistical analysis method called Procrustes Analysis is employed to drive a robust, performant and portable logic. The analysis runs on CPU and has a minimal speed/memory footprint on top of the original Face Mesh solution.
Figure 1: An example of virtual mask and glasses effects, based on the MediaPipe Face Mesh solution.
The MediaPipe Face Landmark Model performs a single-camera face landmark detection in the screen coordinate space: the X- and Y- coordinates are normalized screen coordinates, while the Z coordinate is relative and is scaled as the X coordinate under the weak perspective projection camera model. While this format is well-suited for some applications, it does not directly enable crucial features like aligning a virtual 3D object with a detected face.
The newly introduced module moves away from the screen coordinate space towards a metric 3D space and provides the necessary primitives to handle a detected face as a regular 3D object. By design, you'll be able to use a perspective camera to project the final 3D scene back into the screen coordinate space with a guarantee that the face landmark positions are not changed.
The Metric 3D space established within the new module is a right-handed orthonormal metric 3D coordinate space. Within the space, there is a virtual perspective camera located at the space origin and pointed in the negative direction of the Z-axis. It is assumed that the input camera frames are observed by exactly this virtual camera and therefore its parameters are later used to convert the screen landmark coordinates back into the Metric 3D space. The virtual camera parameters can be set freely, however for better results it is advised to set them as close to the real physical camera parameters as possible.
Figure 2: A visualization of multiple key elements in the metric 3D space. Created in Cinema 4D
The Canonical Face Model is a static 3D model of a human face, which follows the 3D face landmark topology of the MediaPipe Face Landmark Model. The model bears two important functions:
The face transform estimation pipeline is a key component, responsible for estimating face transform data within the Metric 3D space. On each frame, the following steps are executed in the given order:
The Effect Renderer is a component, which serves as a working example of a face effect renderer. It targets the OpenGL ES 2.0 API to enable a real-time performance on mobile devices and supports the following rendering modes:
In both rendering modes, the face mesh is first rendered as an occluder straight into the depth buffer. This step helps to create a more believable effect via hiding invisible elements behind the face surface.
Figure 3: An example of face effects rendered by the Face Effect Renderer.
The face transform estimation module is available as a part of the MediaPipe Face Mesh solution. It comes with face effect application examples, available as graphs and mobile apps on Android or iOS. If you wish to go beyond examples, the module contains generic calculators and subgraphs - those can be flexibly applied to solve specific use cases in any MediaPipe graph. For more information, please visit our documentation.
We look forward to publishing more blog posts related to new MediaPipe pipeline examples and features. Please follow the MediaPipe label on Google Developers Blog and Google Developers twitter account (@googledevs).
We would like to thank Chuo-Ling Chang, Ming Guang Yong, Jiuqiang Tang, Gregory Karpiak, Siarhei Kazakou, Matsvei Zhdanovich and Matthias Grundman for contributing to this blog post.
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
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
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 coral-support@google.com.
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.
Posted by Vikram Sharma, Software Engineering Intern; Jianing Wei, Staff Software Engineer; Tyler Mullen, Senior Software Engineer
Today, we are excited to release the Instant Motion Tracking solution in MediaPipe. It is built upon the MediaPipe Box Tracking solution we released previously. With Instant Motion Tracking, you can easily place fun virtual 2D and 3D content on static or moving surfaces, allowing them to seamlessly interact with the real world. This technology also powered MotionStills AR. Along with the library, we are releasing an open source Android application to showcase its capabilities. In this application, a user simply taps the camera viewfinder in order to place virtual 3D objects and GIF animations, augmenting the real-world environment.
Instant Motion Tracking in MediaPipe
The Instant Motion Tracking solution provides the capability to seamlessly place virtual content on static or motion surfaces in the real world. To achieve that, we provide the six degrees of freedom tracking with relative scale in the form of rotation and translation matrices. This tracking information is then used in the rendering system to overlay virtual content on camera streams to create immersive AR experiences.
The core concept behind Instant Motion Tracking is to decouple the camera’s translation and rotation estimation, treating them instead as independent optimization problems. This approach enables AR tracking across devices and platforms without initialization or calibration. We do this by first finding the 3D camera translation using only the visual signals from the camera. This involves estimating the target region's apparent 2D translation and relative scale across frames. The process can be illustrated with a simple pinhole camera model, relating translation and scale of an object in the image plane to the final 3D translation.
By finding the change in relative size of our tracked region from view position V1 to V2, we can estimate the relative change in distance from the camera.
Next, we obtain the device’s 3D rotation from its built-in IMU (Inertial Measurement Unit) sensor. By combining this translation and rotation data, we can track a target region with six degrees of freedom at relative scale. This information allows for the placement of virtual content on any system with a camera and IMU functionality, and is calibration free. For more details on Instant Motion Tracking, please refer to our paper.
A diagram of Instant Motion Tracking pipeline is shown below, consisting of four major components: a Sticker Manager module, a Region Tracking module, a Matrices Manager module, and lastly a Rendering System. Each of the components consists of MediaPipe calculators or subgraphs.
Diagram of Instant Motion Tracking Pipeline
The Sticker Manager accepts sticker data from the application and produces initial anchors (tracked region information) based on user taps, and user gesture controls for every sticker object. Initial anchors are then sent to our Region Tracking module to generate tracked anchors. The Matrices Manager combines this data with our device’s rotation matrix to produce six degrees-of-freedom poses as model matrices. After integrating any user-specified transforms like asset scaling, our final poses are forwarded to the Rendering System to render all virtual objects overlaid on the camera frame to produce the output AR frame.
The Instant Motion Tracking solution is easy to use by leveraging the MediaPipe cross-platform framework. With camera frames, device rotation matrix, and anchor positions (screen coordinates) as input, the MediaPipe graph produces AR renderings for each frame, providing engaging experiences. If you wish to integrate this Instant Motion Tracking library with your system or application, please visit our documentation to build your own AR experiences on any device with IMU functionality and a camera sensor.
Instant Motion Tracking solution allows bringing both 3D stickers and GIF animations into Augmented Reality experiences. GIFs are rendered on flat 3D billboards placed in the world, introducing fun and immersive experiences with animated content blended into the real environment.Try it for yourself!
Demonstration of GIF placement in 3D
MediaPipe Instant Motion Tracking is already helping PixelShift.AI, a startup applying cutting-edge vision technologies to facilitate video content creation, to track virtual characters seamlessly in the view-finder for a realistic experience. Building upon Instant Motion Tracking’s high-quality pose estimation, PixelShift.AI enables VTubers to create mixed reality experiences with web technologies. The product is going to be released to the broader VTuber community later this year.
Instant Motion Tracking helps PixelShift.AI create mixed reality experiences
We would like to thank Vikram Sharma, Jianing Wei, Tyler Mullen, Chuo-Ling Chang, Ming Guang Yong, Jiuqiang Tang, Siarhei Kazakou, Genzhi Ye, Camillo Lugaresi, Buck Bourdon, and Matthias Grundman for their contributions to this release.
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.
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.
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.
We’ve also made a number of new updates to our ML tools:
sudo apt-get update && sudo apt-get install edgetpu
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.
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.
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.
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 coral-support@google.com.
Posted by Priyanka Vergadia, Developer Advocate
Google Cloud is a cloud computing platform that can be used to build and deploy applications. It allows you to take advantage of the flexibility of development while scaling the infrastructure as needed.
I'm often asked by developers to provide a list of Google Cloud architectures that help to get started on the cloud journey. Last month, I decided to start a mini-series on Twitter called “#13DaysOfGCP" where I shared the most common use cases on Google Cloud. I have compiled the list of all 13 architectures in this post. Some of the topics covered are hybrid cloud, mobile app backends, microservices, serverless, CICD and more. If you were not able to catch it, or if you missed a few days, here we bring to you the summary!
Series kickoff #13DaysOfGCP
Day 1
Day 2
Day 3
Day 4
Day 5
Day 6
Day 7
Day 8
Day 9
Day 10
Day 11
Day 12
Day 13
Wrap up!
We hope you enjoy this list of the most common reference architectures. Please let us know your thoughts in the comments below!
