Tutorials
Hands-on examples for every step of the Datamint workflow, from your first upload to a deployed model. The same notebooks live in the notebooks/ directory of the GitHub repository if you want to run them yourself.
Getting Started
Beginner
Upload resources to a project and explore data already there.
Annotations
Beginner
Import classification annotations and geometry (boxes, lines) to resources.
Datasets
Intermediate
Build PyTorch-ready datasets, split them safely, and import already-labeled data.
Experiment Tracking
Intermediate
Log runs and register models to MLflow through Datamint.
Deployment
Intermediate
Deploy registered or external models, validate them, and run predictions.
End-to-End Examples
Advanced
Complete pipelines from data upload to deployment on public datasets.
Evaluating
Intermediate
Compare models against a dataset with Evaluator.