Syntesis images noise detection using CNN approach
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6 anni fa | |
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img_train | 6 anni fa | |
img_validation | 6 anni fa | |
modules | 6 anni fa | |
.gitignore | 6 anni fa | |
README.md | 6 anni fa | |
RESULTS.md | 6 anni fa | |
classification_cnn_keras.py | 6 anni fa | |
classification_cnn_keras_cross_validation.py | 6 anni fa | |
classification_cnn_keras_svd.py | 6 anni fa | |
config.json | 6 anni fa | |
generate_dataset.py | 6 anni fa | |
requirements.txt | 6 anni fa | |
run.sh | 6 anni fa |
pip install -r requirements.txt
Generate dataset (run only once time or clean data folder before) :
python generate_dataset.py
It will split scenes and generate all data you need for your neural network. You can specify the number of sub images you want in the script by modifying _NUMBER_SUBIMAGES variable or using parameter.
python generate_dataset.py --nb xxxx
There are 3 kinds of Neural Networks :
After your built your neural network in classification_cnn_keras.py, you just have to run it :
python classification_cnn_keras_svd.py --directory xxxx --output xxxxx --batch_size xx --epochs xx --img xx (or --image_width xx --img_height xx)
A config file in json is available and keeps in memory all image sizes available.
This project contains modules :
All these modules will be enhanced during development of the project
This git project uses git-flow implementation. You are free to contribute to it.