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- 1. Statoil/C-CORE Iceberg Classifier Challenge Ship or iceberg, can you decide from space?
- 2. Who we are? Kirill Zhdanovich Inovia, Spotify, Maps.me, Wargaming
- 4. Competition mechanics ● Data ● Model ● Submission ● Evaluation ● Leaderboard
- 5. Real world application vs Competitions ● Business problem ● Problem formalization ● Data collecting ● Data processing ● Modelling ● Evaluate model in production ● Deploy
- 6. Real world application vs Competitions Aspect Real life Competition Problem formalization Y N Choice of target metric Y N Inference Speed Y N Data collecting Y Y/N Model complexity Y Y/N Target metric value Y Y
- 8. Kaggle ● Biggest prize - 1.500.000$ ● Biggest amount of participants - 5.169 ● Community(May 2016): ○ 536.000 kagglers ○ 194 country
- 9. Famous kagglers Fchollet (François Chollet) - creator of Keras Goodfellow (Ian Goodfellow) - author of Deep Learning book H2o.ai - industry leader in ml platforms, company created from kagglers DeepMind - some team member are famous kagglers(1, 2)
- 10. Why to participate ● Learning - getting job in ML after winning Kaggle competition ● Interesting challenges ● Meeting new people in ML/DS community ● Winning money
- 11. Where to start ● Kaggle ● Coursera: How to Win a Data Science Competition ● Deep learning with python
- 12. Who we are? Andrii Sydorchuk Sana Labs, Spotify, Google
- 13. Statoil/C-CORE Iceberg Classifier Challenge
- 14. Problem description ● Drifting icebergs present threats to navigation ● Companies use aerial reconnaissance and shore-based support to monitor environmental conditions and assess risks from icebergs ● In remote areas with particularly harsh weather, these methods are not feasible, and the only viable monitoring option is via satellite ● Build an algorithm that automatically identifies if a remotely sensed target is a ship or iceberg
- 15. Background ● The Sentinel-1 satellite constellation is used to monitor land and ocean ● The C-Band radar can "see" through darkness, rain, cloud and even fog ● An object will appear as a bright spot because it reflects more radar energy than its surroundings
- 16. Background
- 17. Data & evaluation ● band1, band2 - 75x75 sensor data, radar backscatter produced from different polarizations ● incidence angle - the incidence angle of which the image was taken ● 1604 images in the train set (753 icebergs, 851 ships) ● 8424 images in the test set ● test set contains synthetic data ● evaluation - log loss between the predicted values and the ground truth
- 18. Data samples
- 19. Data samples
- 20. Data samples
- 21. Data samples
- 22. High level analysis Working with images ● Convolutional neural networks ● Pretrained models (Xception, ResNet, VGG16) ● Data augmentation Working with incidence angle ● Additional input to neural network ● Feature engineering
- 23. Initial approach ● Train custom CNN for the image + angle data ● Tune hyperparameters ● Tune data augmentation parameters
- 25. Final approach Train image data Test image data CNN ensemble CNN predictions Incidence angle data features per inc. angle key KNN predictions LightGBM classifier Final predictions CNN predictions
- 26. Final approach 1. Ensemble of CNNs with similar architecture and data augmentation 2. Take subset of CNNs that gives best score on cross validation 3. Group predictions from the previous step by incidence angle a. For each group calculate mean, median, number of samples 4. Run KNN regressor on predictions from step 2) by incidence angle 5. Train LightGBM on the features from steps 2), 3), 4)
- 27. Questions? Andrii Sydorchuk - sydorchuk.andriy@gmail.com Kirill Zhdanovich - kzhdanovich@gmail.com Competition link - https://www.kaggle.com/c/statoil-iceberg-classifier-challenge Source code - https://github.com/asydorchuk/kaggle/tree/master/statoil