A detailed description of the approach can be found on http://deepsense.io/deep-learning-right-whale-recognition-kaggle.

• a bunch of Python libraries (try pip install -r requirements.txt)
• Nvidia GPU and cuDNN v2 (or newer)

• manual annotations for bounding boxes: data/all_bbox.json
• manual annotations for bonnet-tip and blowhead: data/points1.json and data/points2.json
• manual annotations for continuous/broken classification of callosity patterns: data/new_conn.csv (and its older and poorer version data/old_conn.json)

As reproducing everything from scratch is very time consuming, we include results of head localization and head alignment steps, as well as weights for some of the models.

• all_bbox.json and all_bbox_test.json - results of head localization
• indygo_annos_all.json - results of head alignment

• models/head_localizer.3c - a sample model for head localization
• models/head_aligner.3c - a sample model for head alignment
• models/model1_params.3c - one of the classifiers used in the final submission
• models/model2_params.3c - one of the classifiers used in the final submission
• models/model3_params.3c - one of the classifiers used in the final submission

1. Set path to original images in init.sh
2. (Optional - not needed if you only care about using final classifiers) Use convert_25.sh input_path output_path to produce downsized images, set path in init.sh.
3. Run source init.sh

The scripts print tons of debug info to stdout, you may prefer to redirect their output.

./whales/model1_predict.sh
./whales/model2_predict.sh
./whales/model3_predict.sh

python whales/blender.py model1_predict/submit1.csv model2_predict/submit1.csv model3_predict/submit1.csv submission.csv

The score on private LB should be around 0.6.

Note that the classifiers were trained on the provided annotations. This means that if you train a new head localizer/aligner, compute new json files with annotations, and feed it to one of the provided models, it may achieve worse results. To avoid this, either retrain the model on new data or train it from scratch.

Depending on the GPU used, each step will take several hours (and training all final classifiers even more).

./whales/head_localizer_fit.sh
./whales/head_localizer_predict_train.sh
./whales/head_localizer_predict_test.sh

This will create json files with coordinates of bounding boxes: hl_pred_test/test_bbox.json and hl_pred_train/train_bbox.json.

./whales/head_aligner_fit.sh
./whales/head_aligner_predict_train.sh
./whales/head_aligner_predict_test.sh

This will create json files with coordinates of bonnet-tip and blowhead: ha_predict_test/test_indygo.json and ha_predict_train/train_indygo.json.

During the contest we were tampering with the learning rate manually. The scripts try to mimick this, but may not work that well.

./whales/net1_fit_step1.sh
./whales/net1_fit_step2.sh

./whales/net2_fit_step1.sh
./whales/net2_fit_step2.sh

./whales/net3_fit_step1.sh
./whales/net3_fit_step2.sh
./whales/net3_fit_step3.sh

./whales/net1_predict.sh
./whales/net2_predict.sh
./whales/net3_predict.sh

python whales/blender.py net1_predict/submit1.csv net2_predict/submit1.csv net3_predict/submit1.csv submission.csv