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Getting Started

Train your first skin lesion classification model in 5 minutes.

1. Setup

git clone https://github.com/Aryan-Mi/dtu-vibe-ops-02476.git
cd dtu-vibe-ops-02476
uv sync

2. Get Data

uv run dvc pull

3. Train a Model

# Train EfficientNet (recommended)
uv run python -m mlops_project.train model=efficientnet

# Or train with smaller subset for testing
uv run python -m mlops_project.train model=efficientnet data.subsample_percentage=0.1

4. Run Inference

# Start the API server
uv run python -m mlops_project.api

# Test with curl
curl -X POST "http://localhost:8000/predict" -F "file=@image.jpg"

Configuration Options

Training is configured via Hydra. Override any parameter:

# Change model
uv run python -m mlops_project.train model=resnet
uv run python -m mlops_project.train model=baseline

# Change hyperparameters
uv run python -m mlops_project.train model.learning_rate=0.001 trainer.max_epochs=50

# Enable W&B logging
uv run python -m mlops_project.train wandb.enabled=true

Next Steps