From e0ba8a622ed93a11aac112ee5307dbca07ae1ff9 Mon Sep 17 00:00:00 2001 From: gaohuang Date: Thu, 11 May 2017 16:06:42 -0400 Subject: [PATCH] readme --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index bee1bb0..3221606 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,7 @@ This implements the DenseNet architecture introduced in [Densely Connected Convolutional Network](http://arxiv.org/abs/1608.06993).The original Torch implementation can be found at https://github.com/liuzhuang13/DenseNet, and please find more details about DenseNet there. The only difference here is that we write a customed container "DenseLayer.lua" to implement the dense connections in a more memory efficient way. This leads to **~25% reduction** in memory consumption during training, while keeps the accuracy and training time the same. -##Usage +## Usage 0. Install Torch ResNet (https://github.com/facebook/fb.resnet.torch) following the instructions there. To reduce memory consumption, we recommend to install the [optnet](https://github.com/fmassa/optimize-net) package. 1. Add the files ```densenet_lite.lua``` and ```DenseLayer.lua``` to the folder models/; 2. Insert ```require 'models/DenseLayer``` at Line.89 of ```models/init.lua```, if you need to use multiple GPUs;