Quick Start¶

This guide gets you from zero to a trained model. We'll train a calibration status classifier using the NuScenes dataset.

1. Setup Dataset¶

Download the NuScenes full dataset (v1.0) from the official website after registration. After the download, confirm that the dataset is located at $AUTOWARE_ML_DATA_PATH/nuscenes.

2. Launch the Container¶

cd ~/autoware-ml
./docker/run.sh

3. Generate Dataset Info Files¶

Autoware-ML needs preprocessed info files that index the dataset:

autoware-ml create-dataset \
    --dataset nuscenes \
    --task calibration_status \
    --root-path data/nuscenes \
    --out-dir data/nuscenes/info \
    --version v1.0-trainval

This creates pickle files for train/val splits.

4. Train the Model¶

autoware-ml train --config-name calibration_status/resnet18_nuscenes

Training progress appears in your terminal. Checkpoints are saved automatically.

5. Monitor with MLflow¶

autoware-ml mlflow-ui --port 5000

Open http://localhost:5000 to view loss curves, metrics, and hyperparameters.

6. Export for Deployment¶

autoware-ml deploy \
    --config-name calibration_status/resnet18_nuscenes \
    +checkpoint=mlruns/<date>/<time>/checkpoints/best.ckpt

This generates ONNX and TensorRT files.