We're really excited to finally share this with you all! This is the first of a series of demos that we're working to release this week, and we're hoping you'll keep us to that promise :)
Sorry if it doesn't work on your computer! There's still a few glitches and browser compatibility problems that we need to iron out, and we're collecting some telemetry data with LogRocket (https://logrocket.com/) to help us do so (so you all know what kind of data is being collected).
We'll open source the library under an MIT license once we finish writing up the API docs, and fixing these bugs.
Extremely impressed. Keep it up!
Now, as you mention, you can run it in a few seconds on your phone, or in my case, on my Chromebook, right in the browser, with zero installation. Truly amazing.
It looks like it (like keras-js) is only for inference (running already-trained models) and not for training. Is this correct?
Are the operations or memory required for training very different?
TensorFire is useful in situations where you want to perform inference, but you don't want to ship user-supplied data to your servers, either because you would run out of bandwidth, you would run out of compute power, or your users want to keep their data private.
The download link failed, as others have noted.
Thanks so much for sharing this!
It should only take five minutes or so to apply.
We will probably release it under an MIT license.
It would be good if you had a comparative benchmark on the website.
The network being run is defined here https://github.com/lengstrom/fast-style-transfer/blob/master...
This post provides a pretty good explanation of what's happening: https://shafeentejani.github.io/2017-01-03/fast-style-transf...
There's a sequence of 9x9 and 3x3 convolutions that transforms that one big input image into a bunch of smaller images. They're processed by a sequence of residual convolutions. Finally, these tiny tiles are merged together back into a stylized image of the same size as the original input with a few deconvolution operations.
Steps (disclaimer: I´m not related to the creators, so this is just what I understand it does)
1.- You upload your image
2.- Select an image to be the origin of the style
3.- Downloading Model: downloads a trained (on style
transfering) deep neural net
4.- Colorful artifacts: the model is applied to your image. Probably the artifacts are a visualization of the network weights being transformed to WebGL shaders, or just a simple visualization of the internal hidden steps of the transformation
5.- You get your image with the style applied
That said, well done, very impressive project!
could you elaborate on this statement ?. What kinds of architectures does this hold true for ?.
This only makes it more impressive when people do cool computational stuff in WebGL, but I'd wish there were some easier ways for non-experts in shader programming to do some calculations in WebGL.
Amazing and scary, this WebGL thing is.
iMac 2011, latest OS
Edit: worked on MacBookAir
I object that qualifier, if you don't mind :)
Same with iPhones, usually stop using because lithium batteries have a 4/5 years life span.
I wanted to download the resulting image but got a "Failed - Network" error :(
Quick question: is the code compiled from js to webgl in browser as well, or do I need to compile beforehand?
I see this as a great way to learn and teach AI without having to bring a large toolchain.
Edit : it seems it is just a runtime for now for Tensorflow models!
>> framebuffer configuration not supported, status = undefined
And, as everyone else mentioned already: f*ing wow!
(Also, somehow I had a feeling before even reading that this project was by the people who made Project Naptha etc. Have you written/talked about this anywhere earlier?)
> You can learn more about TensorFire and what makes it fast (spoiler: WebGL)
Does this mean that using a GPU in a browser through WebGL yields the same speed than a desktop CPU?
If so that's amazingly clever!
Tensorflow should add a WebGL backend that runs in NodeJS.
Happening in both Firefox and Chrome on Ubuntu. What exactly am I missing here?
Windows7, Firefox 54(64bit)
You can also sign up for the mailing list if you'd like us to email you when the repo goes live!
i'm running 55.0b13 (64-bit) firefox on windows 10 and clicking on that demo froze the browser, froze my box - hard reboot.
whatever you're doing some of it's wrong. bad wrong.