This repository is for practice of implementing well-known network architectures and ensembling methods, including the followings:
- Mobilenet - [structure] [training progress]
- VGG16 - [structure] [training progress]
- Resnet164 - [structure] [training progress]
- WideResnet28-10 - [structure] [training progress]
- Unweighted average
- Majority voting
- Super Learner - [structure]
- Channel-wise normalization of input images: substracted by mean and divided by std
- Data augmentation: rotation, width shift, height shift, shearing, zooming
- MacOS High Sierra 10.13.1 for implementation / Ubuntu 14.04 for training
- Python 3.6.3
- Keras 2.1.2 (Tensorflow backend)
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.
*All pre-trained weights should be stored in './models'.
python evaluate.py [options]$ 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: 2The training can be executed by the following command. Every model has the same options.
$ python vgg16.py [options]$ 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)├── 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- Very Deep Convolutional Networks for Large-Scale Image Recognition
- Deep Residual Learning for Image Recognition
- Identity Mappings in Deep Residual Networks
- Wide Residual Networks
- The Relative Performance of Ensemble Methods with Deep Convolutional Neural Networks for Image Classification
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent
![[structure]](images/MobileNet.png){kind=link}
![[training progress]](images/MobileNet_9968_plot.png){kind=link}
![[structure]](images/VGG16.png){kind=link}
![[training progress]](images/VGG16_9968_plot.png){kind=link}
![[structure]](images/ResNet164.png){kind=link}
![[training progress]](images/ResNet164_9970_plot.png){kind=link}
![[structure]](images/WideResNet28_10.png){kind=link}
![[training progress]](images/WideResNet28_10_9976_plot.png){kind=link}
![[structure]](images/SuperLearner.png){kind=link}