Related Projects

Projects Using Caffe

Fast R-CNN is a fast framework for object detection with deep ConvNets. Fast R-CNN

• trains state-of-the-art models, like VGG16, 9x faster than traditional R-CNN and 3x faster than SPPnet,
• runs 200x faster than R-CNN and 10x faster than SPPnet at test-time,
• has a significantly higher mAP on PASCAL VOC than both R-CNN and SPPnet,
• and is written in Python and C++/Caffe.

Please take a look at @rbgirshick work at https://github.com/rbgirshick/fast-rcnn

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Expresso is a Python-based GUI for designing, training and using CNNs. Expresso uses Caffe as its backend CNN framework. Some of its salient features :

• A convenient wizard-like interface to contextually guide the user during common scenarios such as data import, design and training of CNNs
• A smart-edit interface makes net creation easy and quick.
• Deep networks are color-coded and informatively presented
• Support for training external classifier (SVM) using deep features (i.e. features extracted by passing image data through pre-trained CNN and tapping output at layer(s) of CNN).
• Data Visualization

Visit the project page for installation details and links to text/video tutorials.

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Deep Visualization Toolbox is an open source software tool that lets you probe deep networks by feeding them an image (or a live webcam feed) and watching the reaction of every unit. You can also select individual units to view pre-rendered visualizations of what that neuron “wants to see most”.

Visit the deep-vis project page and the @yosinski project at https://github.com/yosinski/deep-visualization-toolbox

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Caffe with Spearmint integrates the deep learning framework with spearmint bayesian hyperparamter optimization to automate parameter search.

Visit the @kuz project at https://github.com/kuz/caffe-with-spearmint

Other Useful Components

• Fast object proposal LPO by @philkr https://github.com/philkr/lpo
• Look here for other object proposal other then selective search.