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models
README.md
data.lua
datasets.lua
main.lua
model.lua
parse.lua
train.lua

README.md

This project prvides the Torch solution for the paper Recurrent Convolutional Neural Network for object recognition. The code is heavily inspired by facebook/fb.resnet.torch.

#Requirements

  1. A GPU machine with Torch and its cudnn bindings. See Installing Torch.

  2. Download Torch version cifar10 and svhn datasets, and put them to rcnn/data/cifar/ and rcnn/data/svhn/, respectively.

#How to use Run main.lua with options to train RCNN models.

An cifar10 example with error rate 4.59% is:

CUDA_VISIBLE_DEVICES=0,1 th main.lua -dataset cifar10 -model rcl3_large -nGPU 2 -nThreads 4 -lr 0.1 -nChunks 100 -batchSize 64

An svhn example with error rate 1.5% is:

CUDA_VISIBLE_DEVICES=0,1,2,3 th main.lua -dataset svhn -model rcl3 -nGPU 4 -nThreads 8 -lr 0.1 -nChunks 100 -batchSize 256

To see all options and their default value, run:

th main.lua -help

#Code introduction

  1. main.lua: Overall procedure to run the code.

  2. dataset.lua: Prepare mini-batchs from specified datasets, including possible data augmentation.

  3. data.lua: Initiate the dataset and setup multi-thread data loaders.

  4. model.lua: Initiate the network models. Model files are placed in rcnn/models/.

  5. train.lua: Train and test network models.

  6. parse.lua: Parse the input options.