[MUSIC PLAYING]
SANDEEP GUPTA: Hi.
My name is Sandeep Gupta.
And I'm a product manager
in the TensorFlow team.
I'm going to talk to you
about TensorFlow Hub.
I am presenting
this work on behalf
of my colleague Gus and our
Europe-based TF Hub team.
So when you want to apply
ML to solve a problem,
you first need a suitable model.
And now, every day,
powerful new models
are published in research
papers or in blog posts.
So let's say you read
about one of these,
and you want to see
how great it is,
and you want to try
it on your problem.
So you search for more details.
And you might find the model
code repository somewhere.
Sometimes the pretrained
model is right there.
Sometimes it is stored
on some other storage.
Sometimes you may need to
download and run a script
to get access to the model.
So as you do this, you
have many questions.
How do I use this model?
Is it safe?
How was it trained?
What was the data?
Am I using the correct version?
This is where TensorFlow
Hub comes in to help you.
So TFHub.dev is the place for
all your TensorFlow model needs
to easily find the latest
ready-to-use models
with documentation, code
snippets, and much, much more.
So TensorFlow Hub's rich
repository of models
covers a wide range of
machine learning tasks for all
of your common ML needs.
For example, in
image-related tasks,
we have models for image
classification, object
detection, image augmentation,
and also image generation,
such as for slide style
transfers and more.
For text, we have
state-of-the-art models like
Bert and Albert.
We have universal
sentence encoders
and many more embeddings
that can support
a wide range of natural
language understanding tasks,
such as question and
answering, text classification,
semantic analysis,
and many more.
We also have
video-related models,
which can help with
video action recognition,
such as gestures, and
also video generation.
And now we have recently
added audio models
for things like pitch detection.
So we invested a lot
of energy in making
models in TensorFlow
Hub be easily
reusable for composing new
models for transfer learning
for your problem.
With one line of
code, models can
be put into your TensorFlow
2 code for retraining
with your own data.
Now, this works whether you are
using the high level DF.keras
API or the low level APIs.
This can also be used in
your training pipelines
through TensorFlow Extended.
Recently, we have added
support for models
that are ready to
deploy on all platforms
where you use TensorFlow.
These pretrained
models have been
prepared for a wide range
of environments, which run
across TensorFlow's ecosystem.
For example, you can
use TensorFlow GS models
for web and node-based
environments.
You can use
TensorFlow Lite models
for your mobile and
embedded devices.
In TensorFlow Hub, you can also
discover ready-to-use models
for the Coral edge TPU devices.
These devices combine
TensorFlow Lite models
with a fast and
efficient accelerator,
which helps companies create
models that perform really
fast inference on the edge.
You can learn more about
this platform at Coral.ai.
So today, we have
more than 1,000 models
available with
documentation, code snippets.
And for some of them,
there are also simple demos
that you can try interactively.
These models can be easily
found by searching or exploring
the TensorFlow Hub repository.
Now, many TensorFlow
Hub models also
have an interactive
Colab notebook
that links directly to
the model page, which
lets you play with the
models with code examples
right from your browser.
And this is all hosted on
Google's infrastructure.
So you can be getting started
with nothing to install.
So now that we have seen
what TensorFlow Hub is,
let's take a look at
a couple of examples
and see how it can help
you solve your problems.
So the first example I'll show
you is about style transfer.
So here, let's take a look
at how TensorFlow Hub can
do artistic style transfer that
can work on arbitrary painting
styles using generative models.
So let's say you have an image
of this yellow labrador shown
here.
And you would like to
imagine it in the style
of your favorite painter.
So you can find a style
transfer model on TF Hub
as shown at this URL.
And then you import the
TensorFlow Hub model.
And you can download
this model from the URL
with these lines
of code shown here.
And now you have the model is
ready for calling inference
on your image.
And you'll get back the
stylized image as shown here.
You can get more
details of this example
at the URL shown on the bottom.
The second example
we'll take a look at
has to do with text
classification.
So let's look at how
you can use a model
with a layer from TensorFlow
Hub that you can use and train
your own model.
So imagine you
want to participate
in a Kaggle competition that's
related to text classification.
Now for text
problems, you usually
start with a text
embedding, which
is a way of converting
raw text into a more
useful structured
numeric representation
that a neural network
model can take in.
Now training your
own embeddings can
take a lot of time and data.
The good news is that TF
Hub has multiple embeddings
in many languages that
are ready for your use.
So in TensorFlow
Hub, you can pull
any of these
pretrained embeddings
with one line of code.
Here, we are importing
one of these embeddings
as a Keras model layer, as you
see with that last line there.
And now this Keras
model layer can
be incorporated in the rest
of your TensorFlow 2 model
training code using
standard Keras
APIs by adding additional
layers and then calling
the model training and the
compile and fit functions.
So you can see how easy it is
to build a powerful custom text
model on top of a pretrained
embedding directly from TF Hub.
Another thing I
wanted to highlight
is that TF Hub also helps
bring these models to life
in a very interactive way.
So some of our publishers
have created custom components
that highlight the amazing
work of these models, which
you can try out directly in
the browser on your own image
or on your own audio clip
without having to download
or install anything.
Before I close, I
want to show you
some of the recent
improvements and additions
on TensorFlowHub.dev.
So first, as our model
collection on TF Hub has grown,
we have greatly improved the
search and discovery feature
on TensorFlow Hub
to make it easy
for you to find the
model that you need.
So you can filter by model type.
You can filter by model
formats or deployment targets.
For example, if you
need a model to run
on mobile or in the
browser, you can find them
easily and quickly.
You can also use these
filters to specifically find
models that are fine-tunable
on your own data.
Also, we have done a
lot of work to support
the variety of TensorFlow
deployment formats.
So for TensorFlow Lite, we now
support additional metadata
along with the TF
Lite model file.
This metadata stores useful
information about the model,
such as its version number,
its input, output, and also
its class labels,
et cetera, which
makes managing these models
in your mobile applications
much, much easier.
For TensorFlow.js, we
are excited to announce
two new models today for
face tracking and hand
tracking, which are built
by our media pipe team.
These models enable some
really cool interactive web
applications.
And in future, we will be adding
more text models for web use
cases.
Lastly, TensorFlow
Hub is powered
by the TensorFlow community.
When we first launched
TensorFlow Hub,
we used it as a platform for
sharing Google authored models.
But now, we are beginning to
share models from many more
publishers, such as Microsoft,
the Metropolitan Museum,
NVIDIA, and many, many more.
For example, very
recently, we completed
a TensorFlow 2.0 Kaggle
question answering
challenge competition.
And we are happy to
announce that Kaggle
has published all of the winners
models on TensorFlow Hub.
Now with over 1,000
state-of-the-art models from
an increasing number
of organizations,
you can find models that cover
a much wider range of machine
learning tasks and data sets.
So to everyone who has helped
contribute models to TF Hub,
a big thank you.
If you are interested in
publishing your models,
we are now accepting
submissions.
We are in the early stages of
building out our third party
model collection.
And our focus is still on
adding high quality models
with strong documentation.
And we are very
interested in helping
people share the usable
pieces of ML models
with the wider world.
So if you would like
to share your models,
please visit the link shown
here or find it on our website.
So that's it about TF Hub.
Thank you so much for watching.
And with that, I would
like to hand it over
to Gal, who will tell us
more about Tensor Board.
Thank you.
[MUSIC PLAYING]
