https://github.com/hwalsuklee/how-far-can-we-go-with-mnist
A collection of codes for 'how far can we go with MNIST' challenge
https://github.com/hwalsuklee/how-far-can-we-go-with-mnist
competition convnet data-augmentation ensemble highway-network mnist mnist-classification mnist-model resnet rnn tf-kr vgg
Last synced: 6 months ago
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A collection of codes for 'how far can we go with MNIST' challenge
- Host: GitHub
- URL: https://github.com/hwalsuklee/how-far-can-we-go-with-mnist
- Owner: hwalsuklee
- Created: 2017-05-06T15:01:49.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2017-05-07T13:14:42.000Z (over 8 years ago)
- Last Synced: 2025-03-24T07:56:03.411Z (7 months ago)
- Topics: competition, convnet, data-augmentation, ensemble, highway-network, mnist, mnist-classification, mnist-model, resnet, rnn, tf-kr, vgg
- Size: 136 KB
- Stars: 61
- Watchers: 3
- Forks: 13
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# How far can we go with MNIST??
A collection of implementations for 'how far can we go with MNIST' challenge, which has been held in [TF-KR](https://www.facebook.com/groups/TensorFlowKR) at April 2017.
## List of Implementations
### Kyung Mo Kweon
* Test error : 0.20%
* Features : keras, esemble of 3 models (small VGG, small Resnet, very small VGG)
* https://github.com/kkweon/mnist-competition
### Junbum Cha
* Test error : 0.24%
* Features : tensorflow, ensemble of 3 models (VGG-like with batch size 64/128, resnet 32layers), best accuracy with a single model is 99.74%, data augmentation (rotation, shift, zoom)
* https://github.com/khanrc/mnist
### Jehoon Shin
* Test error : 0.26%
* Features : tensorflow, ensemble of 5 models obtained with different hyper-params and same architecture (4 conv-layers, 1 fc-layer), best accuracy with a single model is 0.9968
* https://github.com/zeran4/mnist_trial_and_error/blob/master/lab-11-5-1-mnist_cnn_ensemble_layers_tensorflow-kr.py
### Owen Song
* Test error : 0.28%
* Features : keras (theano-base), ensemble of 5 models obtained with different hyper-params and same architecture (6 conv-layers), data augmentation (elastic distortion)
* https://nbviewer.jupyter.org/gist/woniesong92/dba0b16405ef8c4174db2082f914dd80
### Kiru Park
* Test error : 0.30%
* Features : tflearn, ensemble of 11 models (5 conv-nets, 3 highway-nets, 3 rnn), weights for ensemble are also trained, data augmentation (shift, rotation, blur)
* https://github.com/kirumang/mnist_kr
### Mintae Kim
* Test error : 0.35%
* Features : keras, ensemble of 3 models obtained with different filter size and same architecture (VGG-like), best accuracy with a single model is 0.9959, data augmentation (shift, rotation)
* (original) https://github.com/kimmintae/MNIST/blob/master/MNIST%20Competition/mnist_competition.ipynb
* (updated, 0.20% test error) https://github.com/kimmintae/MNIST/blob/master/MNIST%20Competiton_9980/mnist_competition_9980_Final.ipynb
### Juyoung Lee
* Test error : 0.37%
* Features : tensorflow, a single model (conv3-conv3-conv3-pool-conv5-conv-conv5-conv5-conv7-conv7-fc-fc-fc-fc), data augmentation (elastic transform)
* https://github.com/uptown/TF-Mnist
### Hyungchan Kim
* Test error : 0.38%
* Features : tensorflow, a single model (conv5-conv5-conv5-conv7)
* https://github.com/kozistr/MNIST-Competition
### Taekang Woo
* Test error : 0.43%
* Features : tensorflow, a single model (resnet with number of residual units 4)
* https://github.com/tkwoo/MNISTclassification
### Hc Chae
* Test error : 0.46%
* Features : tensorflow, ensemble of 5 models obtained with same hyper-params and same architecture (VGG-like), best accuracy with a single model is 0.9935, data augmentation (scale, rotation)
* https://github.com/chaeso/dnn-study
### Junhyun Lee
* Test error : 0.47%
* Features : tensorflow, ensemble of 2 models (different conv-net architectures), data augmentation (vertical/horizontal flip)
* https://github.com/LeeJunHyun/LeeJunHyun
### Sungsub Woo
* Test error : 0.48%
* Features : keras, ensemble of 50 models obtained with same hyper-params and same architecture (3 conv-layers, 1 fc-layer), data augmentation ([infmnist](http://leon.bottou.org/projects/infimnist))
* https://github.com/sungchi/mnist/
### Byeongki Jeong
* Test error : 0.59%
* Features : keras, a single model (conv5-conv5-pool-conv5-conv3-pool-conv3-conv3-pool-conv3-conv3-pool-fc-fc)
* https://github.com/ByeongkiJeong/MostAccurableMNIST_keras
### Sungho Park
* Test error : 0.64%
* Features : keras, a single model (conv3-pool-conv3-pool-conv3-pool-fc)
* https://github.com/bart99/tensorflow/blob/master/mnist/mnist5.py
### Wonseok Jeon
* Test error : 0.70%
* Features : tensorflow, a single model (conv5-pool-conv5-pool-fc)
* (original) https://github.com/wsjeon/DeepMNISTforExperts
* (updated) https://www.dropbox.com/sh/pnlyb3fqgghm3i6/AAARrOAdOhRa54YeyfqvfBfKa?dl=0
### Byungsun Bae
* Test error : 2.21%
* Features : tensorflow, a single model (9 fc layers with skip connection), gradient-clipping
* https://github.com/ByungSunBae/BBSProject/blob/master/mnist_competition_dev_simple_fnl.py
### Hyun Seok Jeong
* Test error : 7.16%
* Features : tensorflow, a single model (conv3-pool-conv3-pool-conv3-pool-fc-fc)
* https://gist.github.com/nicewook/44a57e24e46dd531681f973b433b7fd0
### Sung Kim
* Test error : 11.20%
* Features : tensorflow, logistic regression
* https://github.com/hunkim/DeepLearningZeroToAll/blob/master/lab-07-4-mnist_introduction.py
## Acknowledgements
* You may need to download uploaded models *manually*, especially for the case that models are stored with Git LFS.
* Here are reference results for MNIST : http://rodrigob.github.io/are_we_there_yet/build/classification_datasets_results.html
