Tag: machinelearning

Google launches new TensorFlow Object Detection API

Google launches new TensorFlow Object Detection API

Object Detect API

Google has finally launched its new TensorFlow object detection API. This new feature will give access to researchers and developers to the same technology Google uses for its own personal operations like image search and street number identification in street view.

The company was planning to release this new feature for quite a few time and finally, it is available to open source community. The system which the tech company has released won a Microsoft’s Common Objects in Context object detection challenge last year. The company won the challenge by beating 23 teams participating in the challenge.

According to the company, it released this new system to bring general public close to AI, and also get help from developers and AI scientist to collaborate with the company and make new and innovative things using Google’s technology.

Google is not the first company offering AI technology to the general public, user and developers. Microsoft, Facebook, and Amazon have also given access to people to use their respective AI technology. Moreover, Apple in its recent WWDC has also rolled out AI technology named as CoreML for its users.

One of the main benefits which the company is offering with this new release is giving users to use this new technology on mobile phones through its object detection system. The system is based on MobileNets image recognition models which can handle and do tasks like object detection, facial recognition, and landmark recognition.

Google makes its TensorFlow artificial intelligence platform available on iOS

Google makes its TensorFlow artificial intelligence platform available on iOS

Logo of Tensor Flow

Google this week has published a new version of its TensorFlow machine learning software that adds support for iOS. Google initially teased that it was working on iOS support for TensorFlow last November, but said it was unable to give a timeline. An early version of TensorFlow version 0.9 was released yesterday on GitHub, however, and it brings iOS support.

For those unfamiliar, TensorFlow is Google’s incredibly powerful artificial intelligence software that powers many of Google’s services and initiatives, including AlphaGo. Google describes TensorFlow as “neural network” software that processes data in a way that’s similar how our brain cells process data (via CNET).

With Google adding iOS support to TensorFlow, apps will be able to integrate the smarter neural network capabilities into their apps, ultimately making them considerably smarter and capable.

At this point, it’s unclear when the final version of TensorFlow 0.9 will be released, but the early pre-release version is available now on GitHub. In the release notes, Google points out that because TensorFlow is now open source, 46 people from outside the company contributed to TensorFlow version 0.9.

In addition to adding support for iOS, TensorFlow 0.9 adds a handful of other new features and improvements, as well as plenty of smaller bug fixes and performance enhancements. You can read the full change log below and access TensorFlow on GitHub.


Major Features and Improvements

  • Python 3.5 support and binaries
  • Added iOS support
  • Added support for processing on GPUs on MacOS
  • Added makefile for better cross-platform build support (C API only)
  • fp16 support for many ops
  • Higher level functionality in contrib.{layers,losses,metrics,learn}
  • More features to Tensorboard
  • Improved support for string embedding and sparse features
  • TensorBoard now has an Audio Dashboard, with associated audio summaries.

Big Fixes and Other Changes

  • Turned on CuDNN Autotune.
  • Added support for using third-party Python optimization algorithms (contrib.opt).
  • Google Cloud Storage filesystem support.
  • HDF5 support
  • Add support for 3d convolutions and pooling.
  • Update gRPC release to 0.14.
  • Eigen version upgrade.
  • Switch to eigen thread pool
  • tf.nn.moments() now accepts a shift argument. Shifting by a good estimate of the mean improves numerical stability. Also changes the behavior of the shift argument to tf.nn.sufficient_statistics().
  • Performance improvements
  • Many bugfixes
  • Many documentation fixes
  • TensorBoard fixes: graphs with only one data point, Nan values, reload button and auto-reload, tooltips in scalar charts, run filtering, stable colors
  • Tensorboard graph visualizer now supports run metadata. Clicking on nodes while viewing a stats for a particular run will show runtime statistics, such as memory or compute usage. Unused nodes will be faded out.