This repository consists of the code I used to create my submission file for ISIC-2019 Challenge.
Brief summary of the method can be found in method.pdf.
In order to run the code first you need to download the data and place the files inside /data. The data folder should include 'ISIC_2019_Training_GroundTruth.csv', 'ISIC_2019_Training_Input' and 'ISIC_2019_Test_Input'.
See Data_exploration.ipynb for more info on the data.
We will save the bottleneck features obtained from the pretrained model as tfrecords. For this run
python create_tfrecords.py
Default validation data size is 2000 and pretrained model base model is densenet201. Use the commandline arguments -vs and -bm to change the validation size and the base model. See the code for the available pretrained models.
This will create a folder /tfrecords under /data.
To train a model use with particular hyperparameters use training.py
python training.py -md logs -ep 20 -bs 32 -bm densenet201
The arguments for training.py are:
- -md --model_directory: The directory to save the model checkpoints and tensorboard file. Default is /logs
- -ep --epochs: Number of epochs. Default is 10.
- -bs --batch_size: Batch size. Default is 32.
- -bm --base_model: The pretrained model used for transfer learning. Default is densenet201.
- -rg --regularization: The parameter for L2 regularization. Default is 0.
In order to run training with multiple hyperparameter configurations use hyperparameter_tuning.py
python hyperparameter_tuning.py
You can change the possible values of different hyperparameters in hyperparameter_tuning.py.
In order to get the predictions for the test data, first create tfrecords from test images using test_tfrecords.py:
python test_tfrecords.py
In the notebook 'create-submission.ipynb', we load a trained model, create the predictions and submission.csv. See the notebook for the details.