Utilities for easy use of custom losses in CatBoost, LightGBM, XGBoost.
Contains the examples which covers how to incrementally train, how to implement learning_rate scheduler, and how to implement custom objective and evaluation function in case of lightgbm/xgboost models.
Hierarchical classification of marine life using deep learning. The project leverages PyTorch and EfficientNet to predict the most confident taxonomic level from image data, with a custom loss function and fallback inference for improved taxonomic accuracy.
custom loss functions
Deep-Learning approach for generating Fair and Accurate Input Representation for crime rate estimation in continuous protected attributes and continuous targets.
Facial Landmark Detection Using Knowledge Distillation-Based Neural Networks
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