This repository contains the work of the AI for an Eye team for Kaggle's Diabetic Retinopathy Detection competition. This README.md file contains some information on how to run the algorithms correctly.

1. python train.py (use train/test splits that are present in the data/processed folder! this is important!)

   This will train the model and put the model, kappa plot and best weights in the models/<MODEL_ID> folder.

2. python optimize_threshold.py <MODEL_ID>

   This needs the model, best weights, validation split and true labels.

   It outputs the optimal thresholds for the validation set to models/<MODEL_ID>/optimal_thresholds.

3. python predict.py <MODEL_ID>

   This needs the test set images, sample submission, model, best weights and optimal thresholds.

   It outputs the predictions for the test set to models/<MODEL_ID>/submission.csv.

1. python train.py (use train/test splits that are present in the data/processed folder! this is important!)

   This will train the model and put the model, kappa plot and best weights in the models/<MODEL_ID> folder. Do this step for all models you wish to include in the ensemble.

2. python predict.py --validation <MODEL_ID>

   This predicts the validation set. Do this for each model in the ensemble. Outputs to models/<MODEL_ID>/raw_predictions_validation.csv.

3. python predict.py --train <MODEL_ID>

4. python ensemble_train.py <OUTPUT> [<MODEL_IDS>]

   This computes optimal weights for the various models in the ensemble. Outputs weights to ensembles/<OUTPUT>. It requires the training labels and validation prediction files raw_predictions_validation.csv in each models/<MODEL_ID> folder.

5. python predict.py --raw <MODEL_ID>

6. python ensemble_predict.py <ENSEMBLE_ID> [<MODEL_IDS>]

   This uses the raw_predictions_validation.csv files of each <MODEL_ID> in conjuction with the ensembles/<ENSEMBLE_ID> to compute the raw predictions for the complete ensemble on the validation set. Saved to ensembles/<ENSEMBLE_ID>.csv.