Skip to content
master
Switch branches/tags
Code

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
 
 
 
 
src
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Build Status

CaffeJS

This repo is a proof of concept for porting Caffe models to the browser using a modified version of ConvNetJS (by Andrej Karpathy). It aims to help beginners to dive into Deep Neural Networks by using only a browser. Try out the ImageNet Classification using GoogleNet or the DeepDream entirely in your browser!

This work is pre-alpha and based on ConvNetJS (which is alpha), so you can imagine how much I need your help!

What's possible with CaffeJS

  • Playing around with Convolutional Neural Nets in the browser
  • Loading pretrained Deep Neural Nets entirely in JavaScript
  • Running forward and backward passes through Deep Neural Nets
  • Visualize model structure, activations and filters
  • All of this without installing any software (also running on your mobile devices)

How to run CaffeJS

Check out the project page hosted on Github which includes samples with a pretrained GoogLeNet. To run other Nets (like AlexNet, VGG or ResNet) one has to clone the repo on the local machine and download the additional model weights.

What's left to do

  • Debug and fix remaining issues with SoftMax layer
  • Implement AVE pooling backward pass
  • Implement more layers (Eltwise, Scale, BatchNorm) for ResNet
  • Evaluate weight extraction directly from *.caffemodel file (without converting to intermediate binary format)
  • Nice documentation
  • More samples (Selfie Net, Gender- and AgeNet, Facial Expression Recognition, Segmentation, etc.)
  • Write unit tests
  • Implement FilterDrawer to visualize filters
  • Auto-scale the filters and activations in the visualizations to a meaningful output dimension (seriously, 1x1 px filters are super small)

License

The software is provided under MIT license.

About

Run Caffe models in the browser using ConvNetJS

Resources

License

Releases

No releases published

Packages

No packages published