kaggle-whale-tail

25th solution for the Kagge Competition https://www.kaggle.com/c/humpback-whale-identification

Env

Look at requirements.sh. There are listed all requirements. After creating new conda evv, you can just run sh requirements.sh

Data

Download data to data folder. Then apply script data_cropping.py for train and test dataset.

Training

For training first of all change path in main file. There are two variable to change: train_df and train_folder. You can also change batch-size (default = 16, which need 2x 11 GB). You can then just run

python main_cosine_bb_final.py <folder_name> There would be saved two checkpoints. The last one and with best-acc. As this script learn on entire dataset, I'm using last one (checkpoint_base).

Submission

Here the code is for only 'without-new-whale'. To create the submission, firstly change folder to data in the file.

Then eval_bb_se.py <path_to_checkpoint> <encoder_npy_file> <X_test>

For a 'new-whale', I have just found threshold which gave 28%-29%.