lbcnn.torch-master.zip_CNN_LBC_LBP CNN_becomeg53_torch

# Local Binary Convolutional Neural Networks (LBCNN) Torch implementation of CVPR'17 - Local Binary Convolutional Neural Networks http://xujuefei.com/lbcnn.html *** ### Paper Download [https://arxiv.org/pdf/1608.06049.pdf](https://arxiv.org/pdf/1608.06049.pdf) *** ### People [Felix Juefei Xu](http://xujuefei.com) [Vishnu Naresh Boddeti](http://vishnu.boddeti.net) Marios Savvides **Carnegie Mellon University** and **Michigan State University** *** ### Code [LBCNN (Torch) on Github](https://github.com/juefeix/lbcnn.torch) *** ### Blog (coming soon) [Understanding Local Binary Convolutional Neural Networks (LBCNN)](https://github.com/juefeix/lbcnn.torch) *** ## Abstract We propose **local binary convolution (LBC)**, an efficient alternative to convolutional layers in standard convolutional neural networks (CNN). The design principles of LBC are motivated by local binary patterns (LBP). The LBC layer comprises of a set of fixed sparse pre-defined binary convolutional filters that are not updated during the training process, a non-linear activation function and a set of learnable linear weights. The linear weights combine the activated filter responses to approximate the corresponding activated filter responses of a standard convolutional layer. The LBC layer affords significant parameter savings, 9x to 169x in the number of learnable parameters compared to a standard convolutional layer. Furthermore, the sparse and binary nature of the weights also results in up to 9x to 169x savings in model size compared to a standard convolutional layer. We demonstrate both theoretically and experimentally that our local binary convolution layer is a good approximation of a standard convolutional layer. Empirically, CNNs with LBC layers, called **local binary convolutional neural networks (LBCNN)**, achieves performance parity with regular CNNs on a range of visual datasets (MNIST, SVHN, CIFAR-10, and ImageNet) while enjoying significant computational savings. *** ## Overview <img src="http://xujuefei.com/lbcnn_image/01_LBP_3_5.png" width="300"><img src="http://xujuefei.com/lbcnn_image/02_LBP.png" width="520"> We draw inspiration from local binary patterns that have been very successfully used for facial analysis. <img src="http://xujuefei.com/lbcnn_image/03_LBCNN_CNN.png" width="820"> Our LBCNN module is designed to approximate a fully learnable dense CNN module. <img src="http://xujuefei.com/lbcnn_image/04_sparsity_2.png" width="260"><img src="http://xujuefei.com/lbcnn_image/04_sparsity_4.png" width="260"><img src="http://xujuefei.com/lbcnn_image/04_sparsity_9.png" width="260"> Binary convolutional kernels with different sparsity levels. <img src="http://xujuefei.com/lbcnn_image/05_theory.png" width="820"> *** ## Contributions * Convolutional kernels inspired by local binary patterns. * Convolutional neural network architecture with **non-mutable randomized sparse binary convolutional kernels**. * Lightweight CNN with massive computational and memory savings. *** ## References * Felix Juefei-Xu, Vishnu Naresh Boddeti, and Marios Savvides, [**Local Binary Convolutional Neural Networks**](http://xujuefei.com/felix_cvpr17_lbcnn.pdf), * To appear in *IEEE Computer Vision and Pattern Recognition (CVPR), 2017*. (Spotlight Oral Presentation) ``` @inproceedings{juefei-xu2017lbcnn, title={{Local Binary Convolutional Neural Networks}}, author={Felix Juefei-Xu and Vishnu Naresh Boddeti and Marios Savvides}, booktitle={IEEE Computer Vision and Pattern Recognition (CVPR)}, month={July}, year={2017} } ``` *** ## Implementations The code base is built upon [fb.resnet.torch](https://github.com/facebook/fb.resnet.torch). ### Requirements See the [installation instructions](INSTALL.md) for a step-by-step guide. - Install [Torch](http://torch.ch/docs/getting-started.html) on a machine with CUDA GPU - Install [cuDNN v4 or v5](https://developer.nvidia.com/cudnn) and the Torch [cuDNN bindings](https://github.com/soumith/cudnn.torch/tree/R4) - ~~Download the [ImageNet](http://image-net.org/download-images) dataset and [move validation images](https://github.com/facebook/fb.resnet.torch/blob/master/INSTALL.md#download-the-imagenet-dataset) to labeled subfolders~~ If you already have Torch installed, update `nn`, `cunn`, and `cudnn`. ### Training Recipes The `numChannels` parameter corresponds to the `output channels` in the paper. * MNIST *CNN* ```bash th main.lua -netType resnet-dense-felix -dataset mnist -data '/media/Freya/juefeix/LBCNN' -save '/media/Freya/juefeix/LBCNN-Weights' -numChannels 16 -batchSize 10 -depth 75 -full 128 ``` *LBCNN (~99.5% after 80 epochs)* ```bash th main.lua -netType resnet-binary-felix -dataset mnist -data '/media/Freya/juefeix/LBCNN' -save '/media/Freya/juefeix/LBCNN-Weights' -numChannels 16 -batchSize 10 -depth 75 -full 128 -sparsity 0.5 ``` * SVHN *CNN* ```bash th main.lua -netType resnet-dense-felix -dataset svhn -data '/media/Freya/juefeix/LBCNN' -save '/media/Freya/juefeix/LBCNN-Weights' -numChannels 16 -batchSize 10 -depth 40 -full 512 ``` *LBCNN (~94.5% after 80 epochs)* ```bash th main.lua -netType resnet-binary-felix -dataset svhn -data '/media/Freya/juefeix/LBCNN' -save '/media/Freya/juefeix/LBCNN-Weights' -numChannels 16 -batchSize 10 -depth 40 -full 512 -sparsity 0.9 ``` * CIFAR-10 *CNN* ```bash th main.lua -netType resnet-dense-felix -dataset cifar10 -data '/media/Caesar/juefeix/LBCNN' -save '/media/Caesar/juefeix/LBCNN-Weights' -numChannels 384 -numWeights 704 -batchSize 5 -depth 50 -full 512 ``` *LBCNN (~93% after 80 epochs)* ```bash th main.lua -netType resnet-binary-felix -dataset cifar10 -data '/media/Caesar/juefeix/LBCNN' -save '/media/Caesar/juefeix/LBCNN-Weights' -numChannels 384 -numWeights 704 -batchSize 5 -depth 50 -full 512 -sparsity 0.001 ```