Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/ranpy13/digit-reco
A simple machine learning model to identify handwritten digits in image form, built using keras and mnist dataset.
https://github.com/ranpy13/digit-reco
Last synced: 6 days ago
JSON representation
A simple machine learning model to identify handwritten digits in image form, built using keras and mnist dataset.
- Host: GitHub
- URL: https://github.com/ranpy13/digit-reco
- Owner: ranpy13
- Created: 2023-12-19T15:59:50.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2023-12-19T16:19:02.000Z (11 months ago)
- Last Synced: 2024-02-26T13:01:25.236Z (9 months ago)
- Language: Python
- Size: 17.1 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Handwritten digit recognition with MNIST and Keras
This repository is for practice of implementing well-known network architectures and ensembling methods, including the followings:
#### Architectures
- [Mobilenet](https://arxiv.org/abs/1704.04861) - [[structure]](images/MobileNet.png) [[training progress]](images/MobileNet_9968_plot.png)
- [VGG16](https://arxiv.org/abs/1409.1556) - [[structure]](images/VGG16.png) [[training progress]](images/VGG16_9968_plot.png)
- [Resnet164](https://arxiv.org/abs/1603.05027) - [[structure]](images/ResNet164.png) [[training progress]](images/ResNet164_9970_plot.png)
- [WideResnet28-10](https://arxiv.org/abs/1603.05027) - [[structure]](images/WideResNet28_10.png) [[training progress]](images/WideResNet28_10_9976_plot.png)
#### Ensembling methods
- Unweighted average
- Majority voting
- [Super Learner](https://arxiv.org/abs/1704.01664) - [[structure]](images/SuperLearner.png)
#### Others
- Channel-wise normalization of input images: substracted by mean and divided by std
- Data augmentation: rotation, width shift, height shift, shearing, zooming
## Environment
- Windows 11 23H2 for implementation
- Ubuntu 23.10 for training
- Python 3.12.1
- Keras 3.0.1 (Tensorflow backend)
## Evaluation
The best single model and the best ensemble method achieve **99.76%** and **99.77%** on the test set respectively.
| **Model** | **On the validation set** | **On the test set** |
|:-------------------:|:---------------------------:|:-------------------:|
| Mobilenet | 99.63% | 99.68% |
| VGG16 | 99.61% | 99.68% |
| Resnet164 | **99.72%** | 99.70% |
| WideResnet28-10 | **99.72%** | **99.76%** |
| **Ensemble (all)** | **On the validation set** | **On the test set** |
|:-------------------:|:---------------------------:|:-------------------:|
| Unweighted average | 99.70% | 99.75% |
| Majority voting | 99.71% | 99.76% |
| Super Learner | **99.73%** | **99.77%** |
In order to run the evaluation, it requires pre-trained weights for each model, which can be downloaded [here](https://drive.google.com/drive/folders/1kBvOL019Gcx00vwUM1BhtaoWQDlfPPim?usp=sharing).
**\*All pre-trained weights should be stored in './models'.**
#### How to run
```bash
python evaluate.py [options]
```
#### Options
```bash
$ python evaluate.py --help
usage: evaluate.py [-h] [--dataset DATASET]
optional arguments:
-h, --help show this help message and exit
--dataset DATASET training set: 0, validation set: 1, test set: 2
```
## Training
The training can be executed by the following command. Every model has the same options.
#### How to run
```bash
$ python vgg16.py [options]
```
#### Options
```bash
$ python vgg16.py --help
usage: vgg16.py [-h] [--epochs EPOCHS] [--batch_size BATCH_SIZE]
[--path_for_weights PATH_FOR_WEIGHTS]
[--path_for_image PATH_FOR_IMAGE]
[--path_for_plot PATH_FOR_PLOT]
[--data_augmentation DATA_AUGMENTATION]
[--save_model_and_weights SAVE_MODEL_AND_WEIGHTS]
[--load_weights LOAD_WEIGHTS]
[--plot_training_progress PLOT_TRAINING_PROGRESS]
[--save_model_to_image SAVE_MODEL_TO_IMAGE]
optional arguments:
-h, --help show this help message and exit
--epochs EPOCHS How many epochs you need to run (default: 10)
--batch_size BATCH_SIZE
The number of images in a batch (default: 64)
--path_for_weights PATH_FOR_WEIGHTS
The path from where the weights will be saved or
loaded (default: ./models/VGG16.h5)
--path_for_image PATH_FOR_IMAGE
The path from where the model image will be saved
(default: ./images/VGG16.png)
--path_for_plot PATH_FOR_PLOT
The path from where the training progress will be
plotted (default: ./images/VGG16_plot.png)
--data_augmentation DATA_AUGMENTATION
0: No, 1: Yes (default: 1)
--save_model_and_weights SAVE_MODEL_AND_WEIGHTS
0: No, 1: Yes (default: 1)
--load_weights LOAD_WEIGHTS
0: No, 1: Yes (default: 0)
--plot_training_progress PLOT_TRAINING_PROGRESS
0: No, 1: Yes (default: 1)
--save_model_to_image SAVE_MODEL_TO_IMAGE
0: No, 1: Yes (default: 1)
```
## File descriptions
```bash
├── images/ # model architectures and training progresses
├── predictions/ # prediction results to be used for fast inference
├── models/ # model weights (not included in this repo)
├── README.md
├── base_model.py # base model interface
├── evaluate.py # for evaluation
├── utils.py # helper functions
├── mobilenet.py
├── vgg16.py
├── resnet164.py
├── wide_resnet_28_10.py
└── super_learner.py
```
## References
#### Papers
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://arxiv.org/abs/1409.1556)
- [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385)
- [Identity Mappings in Deep Residual Networks](https://arxiv.org/abs/1603.05027)
- [Wide Residual Networks](https://arxiv.org/abs/1605.07146)
- [The Relative Performance of Ensemble Methods with Deep Convolutional Neural Networks for Image Classification](https://arxiv.org/abs/1704.01664)
#### Implementation
- [ResNet Author's Implementation](https://github.com/KaimingHe/resnet-1k-layers/blob/master/resnet-pre-act.lua)
- [WideResNet Author's implementation](https://github.com/szagoruyko/wide-residual-networks)
- [MobileNet in Keras](https://github.com/keras-team/keras/blob/master/keras/applications/mobilenet.py)
- [How far can we go with MNIST??](https://github.com/hwalsuklee/how-far-can-we-go-with-MNIST)
#### Others
- [Global weight decay in keras? - Stackoverflow](https://stackoverflow.com/questions/41260042/global-weight-decay-in-keras)
- [Best up to date result on MNIST dataset - Kaggle](https://www.kaggle.com/c/digit-recognizer/discussion/23999#138390)
- [Batch Normalization before or after ReLU? - Reddit](https://www.reddit.com/r/MachineLearning/comments/67gonq/d_batch_normalization_before_or_after_relu/)
- [Depth-wise Conv2D - Tensorflow Document](https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d)
- [A Complete Tutorial on Ridge and Lasso Regression in Python](https://www.analyticsvidhya.com/blog/2016/01/complete-tutorial-ridge-lasso-regression-python/)