Tag Archives: TensorFlow Lite

New Coral products for 2020

Posted by Billy Rutledge, Director Google Research, Coral Team

More and more industries are beginning to recognize the value of local AI, where the speed of local inference allows considerable savings on bandwidth and cloud compute costs, and keeping data local preserves user privacy.

Last year, we launched Coral, our platform of hardware components and software tools that make it easy to prototype and scale local AI products. Our product portfolio includes the Coral Dev Board, USB Accelerator, and PCIe Accelerators, all now available in 36 countries.

Since our release, we’ve been excited by the diverse range of applications already built on Coral across a broad set of industries that range from healthcare to agriculture to smart cities. And for 2020, we’re excited to announce new additions to the Coral platform that will expand the possibilities even further.

First up is the Coral Accelerator Module, an easy to integrate multi-chip package that encapsulates the Edge TPU ASIC. The module exposes both PCIe and USB interfaces and can easily integrate into custom PCB designs. We’ve been working closely with Murata to produce the module and you can see a demo at CES 2020 by visiting their booth at the Las Vegas Convention Center, Tech East, Central Plaza, CP-18. The Coral Accelerator Module will be available in the first half of 2020.

Coral Accelerator Module, a new multi-chip module with Google Edge TPU

Coral Accelerator Module, a new multi-chip module with Google Edge TPU

Next, we’re announcing the Coral Dev Board Mini, which provides a smaller form-factor, lower-power, and lower-cost alternative to the Coral Dev Board. The Mini combines the new Coral Accelerator Module with the MediaTek 8167s SoC to create a board that excels at 720P video encoding/decoding and computer vision use cases. The board will be on display during CES 2020 at the MediaTek showcase located in the Venetian, Tech West, Level 3. The Coral Dev Board Mini will be available in the first half of 2020.

We're also offering new variations to the Coral System-on-Module, now available with 2GB and 4GB LPDDR4 RAM in addition to the original 1GB LPDDR4 configuration. We’ll be showcasing how the SoM can be used in smart city, manufacturing, and healthcare applications, as well as a few new SoC and MCU explorations we’ve been working on with the NXP team at CES 2020 in their pavilion located at the Las Vegas Convention Center, Tech East, Central Plaza, CP-18.

Finally, Asus has chosen the Coral SOM as the base to their Tinker Edge T product, a maker friendly single-board computer that features a rich set of I/O interfaces, multiple camera connectors, programmable LEDs, and color-coded GPIO header. The Tinker Edge T board will be available soon -- more details can be found here from Asus.

Come visit Coral at CES Jan 7-10 in Las Vegas:

  • NXP exhibit (LVCC, Tech East, Central Plaza, CP-18)
  • Mediatek exhibit (Venetian, Tech West, Level 3)
  • Murata exhibit (LVCC, South Hall 2, MP26061)

And, as always, we are always looking for ways to improve the platform, so keep reaching out to us at [email protected]

Object Detection and Tracking using MediaPipe

Posted by Ming Guang Yong, Product Manager for MediaPipe

MediaPipe in 2019

MediaPipe is a framework for building cross platform multimodal applied ML pipelines that consist of fast ML inference, classic computer vision, and media processing (e.g. video decoding). MediaPipe was open sourced at CVPR in June 2019 as v0.5.0. Since our first open source version, we have released various ML pipeline examples like

In this blog, we will introduce another MediaPipe example: Object Detection and Tracking. We first describe our newly released box tracking solution, then we explain how it can be connected with Object Detection to provide an Object Detection and Tracking system.

Box Tracking in MediaPipe

In MediaPipe v0.6.7.1, we are excited to release a box tracking solution, that has been powering real-time tracking in Motion Stills, YouTube’s privacy blur, and Google Lens for several years and that is leveraging classic computer vision approaches. Pairing tracking with ML inference results in valuable and efficient pipelines. In this blog, we pair box tracking with object detection to create an object detection and tracking pipeline. With tracking, this pipeline offers several advantages over running detection per frame:

  • It provides instance based tracking, i.e. the object ID is maintained across frames.
  • Detection does not have to run every frame. This enables running heavier detection models that are more accurate while keeping the pipeline lightweight and real-time on mobile devices.
  • Object localization is temporally consistent with the help of tracking, meaning less jitter is observable across frames.

Our general box tracking solution consumes image frames from a video or camera stream, and starting box positions with timestamps, indicating 2D regions of interest to track, and computes the tracked box positions for each frame. In this specific use case, the starting box positions come from object detection, but the starting position can also be provided manually by the user or another system. Our solution consists of three main components: a motion analysis component, a flow packager component, and a box tracking component. Each component is encapsulated as a MediaPipe calculator, and the box tracking solution as a whole is represented as a MediaPipe subgraph shown below.

Visualization of Tracking State for Each Box

MediaPipe Box Tracking Subgraph

The MotionAnalysis calculator extracts features (e.g. high-gradient corners) across the image, tracks those features over time, classifies them into foreground and background features, and estimates both local motion vectors and the global motion model. The FlowPackager calculator packs the estimated motion metadata into an efficient format. The BoxTracker calculator takes this motion metadata from the FlowPackager calculator and the position of starting boxes, and tracks the boxes over time. Using solely the motion data (without the need for the RGB frames) produced by the MotionAnalysis calculator, the BoxTracker calculator tracks individual objects or regions while discriminating from others. To track an input region, we first use the motion data corresponding to this region and employ iteratively reweighted least squares (IRLS) fitting a parametric model to the region’s weighted motion vectors. Each region has a tracking state including its prior, mean velocity, set of inlier and outlier feature IDs, and the region centroid. See the figure below for a visualization of the tracking state, with green arrows indicating motion vectors of inliers, and red arrows indicating motion vectors of outliers. Note that by only relying on feature IDs we implicitly capture the region’s appearance, since each feature’s patch intensity stays roughly constant over time. Additionally, by decomposing a region’s motion into that of the camera motion and the individual object motion, we can even track featureless regions.

Visualization of Tracking State for Each Box

An advantage of our architecture is that by separating motion analysis into a dedicated MediaPipe calculator and tracking features over the whole image, we enable great flexibility and constant computation independent of the number of regions tracked! By not having to rely on the RGB frames during tracking, our tracking solution provides the flexibility to cache the metadata across a batch of frame. Caching enables tracking of regions both backwards and forwards in time; or even sync directly to a specified timestamp for tracking with random access.

Object Detection and Tracking

A MediaPipe example graph for object detection and tracking is shown below. It consists of 4 compute nodes: a PacketResampler calculator, an ObjectDetection subgraph released previously in the MediaPipe object detection example, an ObjectTracking subgraph that wraps around the BoxTracking subgraph discussed above, and a Renderer subgraph that draws the visualization.

MediaPipe Example Graph for Object Detection and Tracking. Boxes in purple are subgraphs.

In general, the ObjectDetection subgraph (which performs ML model inference internally) runs only upon request, e.g. at an arbitrary frame rate or triggered by specific signals. More specifically, in this example PacketResampler temporally subsamples the incoming video frames to 0.5 fps before they are passed into ObjectDetection. This frame rate can be configured differently as an option in PacketResampler.

The ObjectTracking subgraph runs in real-time on every incoming frame to track the detected objects. It expands the BoxTracking subgraph described above with additional functionality: when new detections arrive it uses IoU (Intersection over Union) to associate the current tracked objects/boxes with new detections to remove obsolete or duplicated boxes.

A sample result of this object detection and tracking example can be found below. The left image is the result of running object detection per frame. The right image is the result of running object detection and tracking. Note that the result with tracking is much more stable with less temporal jitter. It also maintains object IDs across frames.

Comparison Between Object Detection Per Frame and Object Detection and Tracking

Follow MediaPipe

This is our first Google Developer blog post for MediaPipe. We look forward to publishing new blog posts related to new MediaPipe ML pipeline examples and features. Please follow the MediaPipe tag on the Google Developer blog and Google Developer twitter account (@googledevs)

Acknowledgements

We would like to thank Fan Zhang, Genzhi Ye, Jiuqiang Tang, Jianing Wei, Chuo-Ling Chang, Ming Guang Yong, and Matthias Grundman for building the object detection and tracking solution in MediaPipe and contributing to this blog post.

Coral updates: Project tutorials, a downloadable compiler, and a new distributor

Posted by Vikram Tank (Product Manager), Coral Team

coral hardware

We’re committed to evolving Coral to make it even easier to build systems with on-device AI. Our team is constantly working on new product features, and content that helps ML practitioners, engineers, and prototypers create the next generation of hardware.

To improve our toolchain, we're making the Edge TPU Compiler available to users as a downloadable binary. The binary works on Debian-based Linux systems, allowing for better integration into custom workflows. Instructions on downloading and using the binary are on the Coral site.

We’re also adding a new section to the Coral site that showcases example projects you can build with your Coral board. For instance, Teachable Machine is a project that guides you through building a machine that can quickly learn to recognize new objects by re-training a vision classification model directly on your device. Minigo shows you how to create an implementation of AlphaGo Zero and run it on the Coral Dev Board or USB Accelerator.

Our distributor network is growing as well: Arrow will soon sell Coral products.

Updates from Coral: A new compiler and much more

Posted by Vikram Tank (Product Manager), Coral Team

Coral has been public for about a month now, and we’ve heard some great feedback about our products. As we evolve the Coral platform, we’re making our products easier to use and exposing more powerful tools for building devices with on-device AI.

Today, we're updating the Edge TPU model compiler to remove the restrictions around specific architectures, allowing you to submit any model architecture that you want. This greatly increases the variety of models that you can run on the Coral platform. Just be sure to review the TensorFlow ops supported on Edge TPU and model design requirements to take full advantage of the Edge TPU at runtime.

We're also releasing a new version of Mendel OS (3.0 Chef) for the Dev Board with a new board management tool called Mendel Development Tool (MDT).

To help with the developer workflow, our new C++ API works with the TensorFlow Lite C++ API so you can execute inferences on an Edge TPU. In addition, both the Python and C++ APIs now allow you to run multiple models in parallel, using multiple Edge TPU devices.

In addition to these updates, we’re adding new capabilities to Coral with the release of the Environmental Sensor Board. It’s an accessory board for the Coral Dev Platform (and Raspberry Pi) that brings sensor input to your models. It has integrated light, temperature, humidity, and barometric sensors, and the ability to add more sensors via it's four Grove connectors. The secure element on-board also allows for easy communication with the Google Cloud IOT Core.

The team has also been working with partners to help them evaluate whether Coral is the right fit for their products. We’re excited that Oivi has chosen us to be the base platform of their new handheld AI-camera. This product will help prevent blindness among diabetes patients by providing early, automated detection of diabetic retinopathy. Anders Eikenes, CEO of Oivi, says “Oivi is dedicated towards providing patient-centric eye care for everyone - including emerging markets. We were honoured to be selected by Google to participate in their Coral alpha program, and are looking forward to our continued cooperation. The Coral platform gives us the ability to run our screening ML models inside a handheld device; greatly expanding the access and ease of diabetic retinopathy screening.”

Finally, we’re expanding our distributor network to make it easier to get Coral boards into your hands around the world. This month, Seeed and NXP will begin to sell Coral products, in addition to Mouser.

We're excited to keep evolving the Coral platform, please keep sending us feedback at [email protected].

You can see the full release notes on Coral site.

Introducing Coral: Our platform for development with local AI

Posted by Billy Rutledge (Director) and Vikram Tank (Product Mgr), Coral Team

AI can be beneficial for everyone, especially when we all explore, learn, and build together. To that end, Google's been developing tools like TensorFlow and AutoML to ensure that everyone has access to build with AI. Today, we're expanding the ways that people can build out their ideas and products by introducing Coral into public beta.

Coral is a platform for building intelligent devices with local AI.

Coral offers a complete local AI toolkit that makes it easy to grow your ideas from prototype to production. It includes hardware components, software tools, and content that help you create, train and run neural networks (NNs) locally, on your device. Because we focus on accelerating NN's locally, our products offer speedy neural network performance and increased privacy — all in power-efficient packages. To help you bring your ideas to market, Coral components are designed for fast prototyping and easy scaling to production lines.

Our first hardware components feature the new Edge TPU, a small ASIC designed by Google that provides high-performance ML inferencing for low-power devices. For example, it can execute state-of-the-art mobile vision models such as MobileNet V2 at 100+ fps, in a power efficient manner.

Coral Camera Module, Dev Board and USB Accelerator

For new product development, the Coral Dev Board is a fully integrated system designed as a system on module (SoM) attached to a carrier board. The SoM brings the powerful NXP iMX8M SoC together with our Edge TPU coprocessor (as well as Wi-Fi, Bluetooth, RAM, and eMMC memory). To make prototyping computer vision applications easier, we also offer a Camera that connects to the Dev Board over a MIPI interface.

To add the Edge TPU to an existing design, the Coral USB Accelerator allows for easy integration into any Linux system (including Raspberry Pi boards) over USB 2.0 and 3.0. PCIe versions are coming soon, and will snap into M.2 or mini-PCIe expansion slots.

When you're ready to scale to production we offer the SOM from the Dev Board and PCIe versions of the Accelerator for volume purchase. To further support your integrations, we'll be releasing the baseboard schematics for those who want to build custom carrier boards.

Our software tools are based around TensorFlow and TensorFlow Lite. TF Lite models must be quantized and then compiled with our toolchain to run directly on the Edge TPU. To help get you started, we're sharing over a dozen pre-trained, pre-compiled models that work with Coral boards out of the box, as well as software tools to let you re-train them.

For those building connected devices with Coral, our products can be used with Google Cloud IoT. Google Cloud IoT combines cloud services with an on-device software stack to allow for managed edge computing with machine learning capabilities.

Coral products are available today, along with product documentation, datasheets and sample code at g.co/coral. We hope you try our products during this public beta, and look forward to sharing more with you at our official launch.

Announcing Core ML support in TensorFlow Lite

Posted by The TensorFlow Team

On November 14th, we announcedthe developer preview of TensorFlow Lite, TensorFlow's lightweight solution for mobile and embedded devices.

Today, in collaboration with Apple, we are happy to announce support for Core ML! With this announcement, iOS developers can leverage the strengths of Core ML for deploying TensorFlow models. In addition, TensorFlow Lite will continue to support cross-platform deployment, including iOS, through the TensorFlow Lite format (.tflite) as described in the original announcement.

Support for Core ML is provided through a tool that takes a TensorFlow model and converts it to the Core ML Model Format (.mlmodel).

For more information, check out the TensorFlow Lite documentation pages, and the Core ML converter. The pypi pip installable package is available here: https://pypi.python.org/pypi/tfcoreml/0.1.0.

Stay tuned for more updates.

Happy TensorFlow Lite coding!

Announcing TensorFlow Lite

Posted by the TensorFlow team
Today, we're happy to announce the developer preview of TensorFlow Lite, TensorFlow’s lightweight solution for mobile and embedded devices! TensorFlow has always run on many platforms, from racks of servers to tiny IoT devices, but as the adoption of machine learning models has grown exponentially over the last few years, so has the need to deploy them on mobile and embedded devices. TensorFlow Lite enables low-latency inference of on-device machine learning models.

It is designed from scratch to be:
  • Lightweight Enables inference of on-device machine learning models with a small binary size and fast initialization/startup
  • Cross-platform A runtime designed to run on many different platforms, starting with Android and iOS
  • Fast Optimized for mobile devices, including dramatically improved model loading times, and supporting hardware acceleration
More and more mobile devices today incorporate purpose-built custom hardware to process ML workloads more efficiently. TensorFlow Lite supports the Android Neural Networks API to take advantage of these new accelerators as they come available.
TensorFlow Lite falls back to optimized CPU execution when accelerator hardware is not available, which ensures your models can still run fast on a large set of devices.

Architecture

The following diagram shows the architectural design of TensorFlow Lite:
The individual components are:
  • TensorFlow Model: A trained TensorFlow model saved on disk.
  • TensorFlow Lite Converter: A program that converts the model to the TensorFlow Lite file format.
  • TensorFlow Lite Model File: A model file format based on FlatBuffers, that has been optimized for maximum speed and minimum size.
The TensorFlow Lite Model File is then deployed within a Mobile App, where:
  • Java API: A convenience wrapper around the C++ API on Android
  • C++ API: Loads the TensorFlow Lite Model File and invokes the Interpreter. The same library is available on both Android and iOS
  • Interpreter: Executes the model using a set of operators. The interpreter supports selective operator loading; without operators it is only 70KB, and 300KB with all the operators loaded. This is a significant reduction from the 1.5M required by TensorFlow Mobile (with a normal set of operators).
  • On select Android devices, the Interpreter will use the Android Neural Networks API for hardware acceleration, or default to CPU execution if none are available.
Developers can also implement custom kernels using the C++ API, that can be used by the Interpreter.

Models

TensorFlow Lite already has support for a number of models that have been trained and optimized for mobile:
  • MobileNet: A class of vision models able to identify across 1000 different object classes, specifically designed for efficient execution on mobile and embedded devices
  • Inception v3: An image recognition model, similar in functionality to MobileNet, that offers higher accuracy but also has a larger size
  • Smart Reply: An on-device conversational model that provides one-touch replies to incoming conversational chat messages. First-party and third-party messaging apps use this feature on Android Wear.
Inception v3 and MobileNets have been trained on the ImageNet dataset. You can easily retrain these on your own image datasets through transfer learning.

What About TensorFlow Mobile?

As you may know, TensorFlow already supports mobile and embedded deployment of models through the TensorFlow Mobile API. Going forward, TensorFlow Lite should be seen as the evolution of TensorFlow Mobile, and as it matures it will become the recommended solution for deploying models on mobile and embedded devices. With this announcement, TensorFlow Lite is made available as a developer preview, and TensorFlow Mobile is still there to support production apps.
The scope of TensorFlow Lite is large and still under active development. With this developer preview, we have intentionally started with a constrained platform to ensure performance on some of the most important common models. We plan to prioritize future functional expansion based on the needs of our users. The goals for our continued development are to simplify the developer experience, and enable model deployment for a range of mobile and embedded devices.
We are excited that developers are getting their hands on TensorFlow Lite. We plan to support and address our external community with the same intensity as the rest of the TensorFlow project. We can't wait to see what you can do with TensorFlow Lite.
For more information, check out the TensorFlow Lite documentation pages.
Stay tuned for more updates.
Happy TensorFlow Lite coding!