Focal loss, Dice loss, Tversky, contrastive losses — implemented and visually compared
loss-functions deep-learning focal-loss dice-loss pytorch
This repository implements a complete pipeline for loss functions explained, covering data preprocessing, model training, evaluation, and deployment.
- Clean, modular PyTorch implementation
- Reproducible experiments with MLflow tracking
- Comprehensive evaluation with standard benchmarks
- ONNX export for production deployment
- Detailed documentation and usage examples
git clone https://github.com/YOUR_USERNAME/loss-functions-explained.git
cd loss-functions-explained
pip install -r requirements.txtfrom src.model import Model
from src.trainer import Trainer
from src.config import Config
config = Config.from_yaml("configs/default.yaml")
model = Model(config)
trainer = Trainer(model, config)
trainer.train()loss-functions-explained/
├── src/
│ ├── model.py # Model architecture
│ ├── dataset.py # Data loading and preprocessing
│ ├── trainer.py # Training loop
│ ├── evaluate.py # Evaluation metrics
│ └── utils.py # Helper utilities
├── configs/
│ └── default.yaml # Default configuration
├── notebooks/
│ └── exploration.ipynb
├── tests/
│ └── test_model.py
├── requirements.txt
└── README.md
| Model | Dataset | Metric | Score |
|---|---|---|---|
| Baseline | Standard | Primary | - |
| Ours | Standard | Primary | - |
# Train
python train.py --config configs/default.yaml
# Evaluate
python evaluate.py --checkpoint checkpoints/best.pth
# Export to ONNX
python export.py --checkpoint checkpoints/best.pth- Relevant papers and resources for loss functions explained
MIT