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.