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!

• 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)

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.

• Getting Started
• User Guide
• Development Guide
• Many cool examples and demos, like ImageNet Classification, DeepDream, etc.

• 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)

The software is provided under MIT license.