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https://github.com/nagadomi/kaggle-cifar10-torch7
Code for Kaggle-CIFAR10 competition. 5th place.
https://github.com/nagadomi/kaggle-cifar10-torch7
kaggle
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Code for Kaggle-CIFAR10 competition. 5th place.
- Host: GitHub
- URL: https://github.com/nagadomi/kaggle-cifar10-torch7
- Owner: nagadomi
- License: mit
- Created: 2014-08-22T06:30:30.000Z (about 10 years ago)
- Default Branch: cuda-convnet2
- Last Pushed: 2016-03-01T04:09:19.000Z (over 8 years ago)
- Last Synced: 2024-05-22T07:47:46.769Z (4 months ago)
- Topics: kaggle
- Language: Lua
- Homepage:
- Size: 190 KB
- Stars: 246
- Watchers: 22
- Forks: 79
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 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](http://torch.ch/) latest
- [cuda-convnet2.torch](https://github.com/soumith/cuda-convnet2.torch)
## Installation
(This document is outdated. See: [Getting started with Torch](http://torch.ch/docs/getting-started.html))
Install CUDA (on Ubuntu 14.04):
apt-get install nvidia-331
apt-get install nvidia-cuda-toolkit
Install Torch7 (see [Torch (easy) install](https://github.com/torch/ezinstall)):
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](http://www.kaggle.com/c/cifar-10/data) 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
### Model Averaging
Training with different seed parameter for each nodes.
(same model, same data, different initial weights, different training order)
th train.lua -seed 11
th train.lua -seed 12
...
th train.lua -seed 16
Mount the `models` directory for each nodes. for example, `ec2/node1`, `ec2/node2`, .., `ec2/node6`.
Edit the path of model file in `predict_averaging.lua`.
Run the prediction command.
th predict_averaging.lua
### Network In Network
`./nin_model.lua` is an implementation of Network In Network [3].
This model gives score of 0.92400.
My NIN implementation is 2-layer NIN. Its differ from [mavenlin's implementation](https://gist.github.com/mavenlin/e56253735ef32c3c296d).
I tried to implement the mavenlin's 3-layer NIN. However, I did not get good result.
My implementation of 3-layer NIN is [here](https://gist.github.com/nagadomi/15849fb2711de78c6bf6).
### Bug
`global_contrast_normalization` in `./lib/preprocessing.lua` is incorrect implementation (This function is just z-score). but I was using this implementation in the competition.
## Figure
data augmentation + preprocessing
## References
- [1] Karen Simonyan, Andrew Zisserman, "Very Deep Convolutional Networks for Large-Scale Image Recognition", [link](http://arxiv.org/abs/1409.1556)
- [2] Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton, "ImageNet Classification with Deep Convolutional Neural Networks", [link](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks)
- [3] Min Lin, Qiang Chen, Shuicheng Yan, "Network In Network", [link](http://arxiv.org/abs/1312.4400)
- [4] R. Collobert, K. Kavukcuoglu, C. Farabet, "Torch7: A Matlab-like Environment for Machine Learning"