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Kaggle CIFAR-10

Code for CIFAR-10 competition. http://www.kaggle.com/c/cifar-10

Summary

Description
Model Very Deep Convolutional Networks with 3x3 kernel [1]
Data Augmentation cropping, horizontal reflection [2] and scaling. see lib/data_augmentation.lua
Preprocessing Global Contrast Normalization (GCN) and ZCA whitening. see lib/preprocessing.lua
Training Time 20 hours on GTX760.
Prediction Time 2.5 hours on GTX760.
Result 0.93320 (single model). 0.94150 (average 6 models)

Neural Network Configurations

Layer type Parameters
input size: 24x24, channel: 3
convolution kernel: 3x3, channel: 64, padding: 1
relu
convolution kernel: 3x3, channel: 64, padding: 1
relu
max pooling kernel: 2x2, stride: 2
dropout rate: 0.25
convolution kernel: 3x3, channel: 128, padding: 1
relu
convolution kernel: 3x3, channel: 128, padding: 1
relu
max pooling kernel: 2x2, stride: 2
dropout rate: 0.25
convolution kernel: 3x3, channel: 256, padding: 1
relu
convolution kernel: 3x3, channel: 256, padding: 1
relu
convolution kernel: 3x3, channel: 256, padding: 1
relu
convolution kernel: 3x3, channel: 256, padding: 1
relu
max pooling kernel: 2x2, stride: 2
dropout rate: 0.25
linear channel: 1024
relu
dropout rate: 0.5
linear channel: 1024
relu
dropout rate: 0.5
linear channel: 10
softmax

Developer Environment

  • Ubuntu 14.04
  • 15GB RAM (This codebase can run on g2.2xlarge!)
  • CUDA (GTX760 or more higher GPU)
  • Torch7 latest
  • cuda-convnet2.torch

Installation

(This document is outdated. See: Getting started with Torch)

Install CUDA (on Ubuntu 14.04):

apt-get install nvidia-331
apt-get install nvidia-cuda-toolkit

Install Torch7 (see Torch (easy) install):

curl -s https://raw.githubusercontent.com/torch/ezinstall/master/install-all | bash

Install(or upgrade) dependency packages:

luarocks install torch
luarocks install nn
luarocks install cutorch
luarocks install cunn
luarocks install https://raw.githubusercontent.com/soumith/cuda-convnet2.torch/master/ccn2-scm-1.rockspec

Checking CUDA environment

th cuda_test.lua

Please check your Torch7/CUDA environment when this code fails.

Convert dataset

Place the data files into a subfolder ./data.

ls ./data
test  train  trainLabels.csv
  • th convert_data.lua

Local testing

th validate.lua

dataset:

train test
1-40000 40001-50000

Generating the submission.txt

th train.lua
th predict.lua

MISC