This model was developed on a standard UNet architecture using a Pythorch and scorder loss function to automate core detection (semantic segmentation) on the '2018 Data Science Bowl' data
The data is available on the Kaggle website.
Note : Use Python 3.7 or newer
Download the first stage training and test datasets from the website Kaggle website Run Jupiter nootbook 'quantum_test_data_explore.ipynb' to prepare working data for the developed model
The training set of images and the corresponding masks would be placed in subdirectories 'data/img_train/images' and 'data/img_train/masks', respectively Test images would be placed in a subdirectory 'data/img_test/images'
Êun the script 'unet_segmentation_1.py' Model hyperparameters can be changed by specifying them on the command line or by changing the default values ??in the script itself The intermediate results of the model training will be available in the 'images_1class' folder, and the model chtckpoint parameters in the 'saved_models_1class' folder Model test results will be available in the folder 'images_test_1class' To continue training the model from a chtckpoint, you must specify the corresponding value of the hyperparameter 'epoch' To obtain masks for the semantic segmentation of new images, you must:
- place new images in a folder 'img_test/images'
- select the tested version of the pretrained model (from the 'saved_models_1class' folder) by specifying the corresponding value of the hyperparameter 'epoch'
- set the value of the hyperparameter 'n_epochs' to 1
- the segmentation results will be available in the folder 'images_test_1class'
The model was trained on 670 images with a size of 128x128 Model parameters use approximately 50 MB of memory
Original paper by Olaf Ronneberger, Philipp Fischer, Thomas Brox: https://arxiv.org/abs/1505.04597
