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Posts Tagged ‘tensorflow’

Google Releases Android O Developer Preview 2, Announces Android Go for Low-End Devices, TensorFlow Lite

May 18th, 2017 No comments

After the first Android O developer preview released in March, Google has just released the second developer preview during Google I/O 2017, which on top of features like PiP (picture-in-picture), notifications channels, autofill, and others found in the first preview, adds notifications dots, a new Android TV home screen, smart text selection, and soon TensorFlow Lite. Google also introduced Android Go project optimized for devices with 512 to 1GB RAM.

Notifications dots (aka Notification Badges) are small dots that show on the top right of app icons – in supported launchers – in case a notification is available. You can then long press the icon to check out the notifications for the app, and dismiss or act on notifications. The feature can be disabled in the settings.

Android TV “O” also gets a new launcher that allegedly “makes it easy to find, preview, and watch content provided by apps”. The launcher is customizable as users can control the channels that appear on the homescreen. Developers will be able to create channels using the new TvProvider support library APIs.

I found text selection in Android to be awkward and frustrating most of the big time, but Android O brings improvements on that front with “Smart Text Selection” leveraging on-device machine learning to copy/paste, to let Android recognize entities like addresses, URLs, telephone numbers, and email addresses.

TensorFlow is an open source machine learning library that for example allows image recognition. Android O will now support TensorFlow Lite specifically designed to be fast and lightweight for embedded use cases. The company is also working on a new Neural Network API to accelerate computation, and both plan for release in a future maintenance update of Android O later this year.

Finally, Android Go project targets devices with 1GB or less of memory, and including optimization to the operating system itself, as well as optimization to apps such as YouTube, Chrome, and Gboard to make them use less memory, storage space, and mobile data. The Play Store will also highlight apps with low resources requirements on such devices, but still provide access to the full catalog. “Android Go” will ship in 2018 for all Android devices with 1GB or less of memory.

You can test Android O developer preview 2 by joining the Android O beta program if you own a Nexus 5X, 6P, Nexus Player, Pixel, Pixel XL, or Pixel C device.

GPU Accelerated Object Recognition on Raspberry Pi 3 & Raspberry Pi Zero

April 30th, 2017 5 comments

You’ve probably already seen one or more object recognition demos, where a system equipped with a camera detects the type of object using deep learning algorithms either locally or in the cloud. It’s for example used in autonomous cars to detect pedestrian, pets, other cars and so on. Kochi Nakamura and his team have developed software based on GoogleNet deep neural network with a a 1000-class image classification model running on Raspberry Pi Zero and Raspberry Pi 3 and leveraging the VideoCore IV GPU found in Broadcom BCM283x processor in order to detect objects faster than with the CPU, more exactly about 3 times faster than using the four Cortex A53 cores in RPi 3.

They just connected a battery, a display, and the official Raspberry Pi camera to the Raspberry Pi boards to be able to recognize various objects and animals.

The first demo is with Raspberry Pi Zero.

and the second demo is on the Raspberry Pi 3 board using a better display.

Source code? Not yet, but he is thinking about it, and when/if it is released it will probably be found on his github account, where there is already py-videocore Python library for GPGPU on Raspberry Pi, which was very likely used in the demos above. They may also have used TensorFlow image recognition tutorials as a starting point, and/or instructions to install Tensorflow on Raspberry Pi.

If you are interested in Deep Learning, there’s a good list of resources with links to research papers, software framework & applications, tutorials, etc… on Github’s .