The code consists of two Python (3) files:
run.py: The ML pipeline. Fits the model and outputs the predictions for the submission dataset (submission_test.csv).helpers.py: Definition of all the auxiliary methods (e.g. image manipulation).
Keras (>= 2.0.9) + TensorFlow backend, OpenCV and imutils.
Install dependencies using pip:
pip install imutils opencv-python keras tensorflow-gpu
The user simply needs to Python3-execute the run.py file.
Note: All the above mentioned .py files needs to be in the same folder. This folder needs to contain a subfolder called 'data' with the training and submission folders "extracted as is" from Kaggle.
Note: Running the code requires quite some memory. Having (at least) 40GB of RAM is highly recommended.
The model was trained on a single p2.8xlarge (AWS) instance in around 1 hour. On a laptop we expect the training time to be around 72 hours. We ran our run.py with all training data in multi-gpu mode (disabled on the deliverable). The data augmentation is very memory hungry, taking a considerable amount of memory; at least 128GB of RAM are required to train the model with the full dataset.
Aimee Montero, Alfonso Peterssen, Philipp Chervet