Tag Archives: dataset

Announcing TensorFlow r1.4

Posted by the TensorFlow Team

TensorFlow release 1.4 is now public - and this is a big one! So we're happy to announce a number of new and exciting features we hope everyone will enjoy.


In 1.4, Keras has graduated from tf.contrib.keras to core package tf.keras. Keras is a hugely popular machine learning framework, consisting of high-level APIs to minimize the time between your ideas and working implementations. Keras integrates smoothly with other core TensorFlow functionality, including the Estimator API. In fact, you may construct an Estimator directly from any Keras model by calling the tf.keras.estimator.model_to_estimatorfunction. With Keras now in TensorFlow core, you can rely on it for your production workflows.

To get started with Keras, please read:

To get started with Estimators, please read:


We're pleased to announce that the Dataset API has graduated to core package tf.data(from tf.contrib.data). The 1.4 version of the Dataset API also adds support for Python generators. We strongly recommend using the Dataset API to create input pipelines for TensorFlow models because:

  • The Dataset API provides more functionality than the older APIs (feed_dict or the queue-based pipelines).
  • The Dataset API performs better.
  • The Dataset API is cleaner and easier to use.

We're going to focus future development on the Dataset API rather than the older APIs.

To get started with Datasets, please read:

Distributed Training & Evaluation for Estimators

Release 1.4 also introduces the utility function tf.estimator.train_and_evaluate, which simplifies training, evaluation, and exporting Estimator models. This function enables distributed execution for training and evaluation, while still supporting local execution.

Other Enhancements

Beyond the features called out in this announcement, 1.4 also introduces a number of additional enhancements, which are described in the Release Notes.

Installing TensorFlow 1.4

TensorFlow release 1.4 is now available using standard pipinstallation.

# Note: the following command will overwrite any existing TensorFlow
# installation.
$ pip install --ignore-installed --upgrade tensorflow
# Use pip for Python 2.7
# Use pip3 instead of pip for Python 3.x

We've updated the documentation on tensorflow.org to 1.4.

TensorFlow depends on contributors for enhancements. A big thank you to everyonehelping out developing TensorFlow! Don't hesitate to join the community and become a contributor by developing the source code on GitHub or helping out answering questions on Stack Overflow.

We hope you enjoy all the features in this release.

Happy TensorFlow Coding!

Introducing the Open Images Dataset

Originally posted on the Google Research Blog

In the last few years, advances in machine learning have enabled Computer Vision to progress rapidly, allowing for systems that can automatically caption images to apps that can create natural language replies in response to shared photos. Much of this progress can be attributed to publicly available image datasets, such as ImageNet and COCO for supervised learning, and YFCC100M for unsupervised learning.

Today, we introduce Open Images, a dataset consisting of ~9 million URLs to images that have been annotated with labels spanning over 6000 categories. We tried to make the dataset as practical as possible: the labels cover more real-life entities than the 1000 ImageNet classes, there are enough images to train a deep neural network from scratch and the images are listed as having a Creative Commons Attribution license*.

The image-level annotations have been populated automatically with a vision model similar to Google Cloud Vision API. For the validation set, we had human raters verify these automated labels to find and remove false positives. On average, each image has about 8 labels assigned. Here are some examples:
Annotated images form the Open Images dataset. Left: Ghost Arches by Kevin Krejci. Right: Some Silverware by J B. Both images used under CC BY 2.0 license
We have trained an Inception v3 model based on Open Images annotations alone, and the model is good enough to be used for fine-tuning applications as well as for other things, like DeepDream or artistic style transfer which require a well developed hierarchy of filters. We hope to improve the quality of the annotations in Open Images the coming months, and therefore the quality of models which can be trained.

The dataset is a product of a collaboration between Google, CMU and Cornell universities, and there are a number of research papers built on top of the Open Images dataset in the works. It is our hope that datasets like Open Images and the recently released YouTube-8M will be useful tools for the machine learning community.

By Ivan Krasin and Tom Duerig, Software Engineers

* While we tried to identify images that are licensed under a Creative Commons Attribution license, we make no representations or warranties regarding the license status of each image and you should verify the license for each image yourself.