diff --git a/README.md b/README.md
index af810f0a..11d57391 100644
--- a/README.md
+++ b/README.md
@@ -119,6 +119,14 @@ datamint-upload /path/to/dicoms \
```
Use `--ai-model` when uploaded segmentation files should be linked to an existing deployed model by name. `--segmentation_names` accepts YAML mappings and ITK-SNAP label export CSV/TXT files.
+### Scaffold a New Project
+
+```bash
+datamint-init
+```
+
+Generates a numbered set of scripts (`01_upload_data.py` through `06_deploy.py`) for your task (detection, segmentation, or classification). Run it once in a new directory and follow the scripts in order.
+
### Configuration Management
```bash
diff --git a/docs/source/getting_started.rst b/docs/source/getting_started.rst
index b821ac60..7d4f5d24 100644
--- a/docs/source/getting_started.rst
+++ b/docs/source/getting_started.rst
@@ -1,32 +1,23 @@
-Getting Started with Datamint Python API
+Quick Start
=========================================
This guide will help you set up and start using the Datamint Python API for your medical imaging projects.
-Prerequisites
-=============
-
-- **Python 3.10 or later** (earlier versions are not supported)
-- **pip** or **conda** for package management
-- A **Datamint account** with an API key (get one at `app.datamint.io `_)
-
Installation
============
-Datamint requires Python 3.10+.
-Install/update Datamint and its dependencies using pip
+Datamint requires Python 3.10+ and a `Datamint account `_ with an API key.
.. code-block:: bash
pip install -U datamint
-We recommend that you install Datamint in a dedicated virtualenv, to avoid conflicting with your system packages.
-You can do this by running:
+We recommend a dedicated virtualenv to avoid conflicting with your system packages:
.. code-block:: bash
python3 -m venv datamint-env
- source datamint-env/bin/activate # In Windows, run datamint-env\Scripts\activate.bat
+ source datamint-env/bin/activate # Windows: datamint-env\Scripts\activate.bat
pip install -U datamint
Verify your installation
@@ -35,10 +26,49 @@ Verify your installation
.. code-block:: bash
python -c "import datamint; print(datamint.__version__)"
- datamint-config --help
.. include:: setup_api_key.rst
+Scaffold your first project
+===========================
+
+``datamint-init`` generates a ready-to-run set of numbered scripts tailored to your task
+(detection, segmentation, or classification):
+
+.. code-block:: bash
+
+ datamint-init
+
+It asks for a project name and task type, then writes six scripts into a new directory
+(upload data, explore, build a dataset, train, evaluate, and deploy), so you can follow
+them in order without writing boilerplate.
+
+Your first API call
+===================
+
+Once installed and configured, verify everything works end-to-end:
+
+.. code-block:: python
+
+ from datamint import Api
+
+ api = Api()
+ for project in api.projects.get_all():
+ print(project.name)
+
+.. tip::
+
+ Want to see full end-to-end examples? Browse our :doc:`tutorial notebooks ` —
+ they cover real datasets, training workflows, and deployment from scratch.
+
+Next Steps
+----------
+
+- Master the command-line interface: :ref:`command_line_tools`
+- Check out our Python API documentation: :ref:`client_python_api`
+- Our PyTorch, Lightning and MLflow integration: :ref:`pytorch_integration`
+- Use the built-in Trainer API and custom model integration patterns: :ref:`trainer_api`
+
Troubleshooting
---------------
@@ -66,14 +96,3 @@ Troubleshooting
.. code-block:: bash
datamint-config
-
-Next Steps
-----------
-
-Now that you have the basics set up, explore these advanced topics:
-
-- Master the command-line interface: :ref:`command_line_tools`
-- Check out our Python API documentation: :ref:`client_python_api`
-- Our PyTorch, Lightning and MLflow integration: :ref:`pytorch_integration`
-- Use the built-in Trainer API and custom model integration patterns: :ref:`trainer_api`
-- Browse tutorial notebooks: :doc:`tutorials`
diff --git a/docs/source/tutorials.rst b/docs/source/tutorials.rst
index f7819b9e..b3930121 100644
--- a/docs/source/tutorials.rst
+++ b/docs/source/tutorials.rst
@@ -1,33 +1,53 @@
Tutorials
=========
-This section lists the various tutorial notebooks available in the `datamint-python-api GitHub repository `_. You can run these Jupyter Notebooks locally to learn how to use the Datamint Python API in different scenarios.
+The notebooks below are available in the `notebooks/ directory `_
+of the GitHub repository. Run them locally to learn how to use the Datamint Python API across different scenarios.
-Data Management
+Getting Started
---------------
-* `upload_data.ipynb `_: A comprehensive guide on uploading data to Datamint.
-* `exploring_data_tutorial.ipynb `_: Learn how to explore and query resources in Datamint.
-* `project_scoped_splits_tutorial.ipynb `_: Assign project-scoped train/val/test splits, inspect split records, and replay historical split snapshots in datasets.
-* `volume_dataset_tutorial.ipynb `_: Tutorial on working with volume datasets in Datamint.
+* `01_upload_data.ipynb `_: Upload images, DICOMs, and other resources to a Datamint project.
+* `02_explore_data.ipynb `_: Query and explore resources already in a project.
Annotations
-----------
-* `upload_annotations.ipynb `_: Guide on how to import and manage simple annotations like image or frame categories.
-* `geometry_annotations.ipynb `_: Covers integrating and uploading lines, bounding boxes, and other geometry annotations.
+* `01_upload_annotations.ipynb `_: Import and manage image-level and frame-level classification annotations.
+* `02_geometry_annotations.ipynb `_: Upload bounding boxes, lines, and other geometry annotations.
-Machine Learning & Deployment
------------------------------
+Datasets
+--------
-* `mlflow_manual_logging.ipynb `_: Explains how to log models and experiments manually to MLflow via Datamint.
-* `deploy_model_demo.ipynb `_: Basic demonstration on deploying a Datamint model.
-* `external_model_deployment_tutorial.ipynb `_: Tutorial for adapting and deploying an externally-trained model in Datamint.
+* `01_project_scoped_splits.ipynb `_: Assign project-scoped train/val/test splits, inspect split records, and replay historical snapshots.
+* `02_patient_wise_splits.ipynb `_: Split datasets by patient to avoid data leakage between train and test sets.
+* `03_build_dataset.ipynb `_: Build and configure a PyTorch dataset from a Datamint project.
+* `04_volume_dataset.ipynb `_: Work with 3D volume datasets (NIfTI, DICOM series).
-Use Cases & End-to-End Examples
--------------------------------
+Experiment Tracking
+-------------------
-These notebooks provide complete, end-to-end workflows located in the `use_cases directory `_:
+* `01_mlflow_manual_logging.ipynb `_: Log models and experiments manually to MLflow via Datamint.
-* `fracatlas_classification.ipynb `_: End-to-end classification pipeline for the FracAtlas dataset.
-* `segmentation_2d_trainer_BUSI_tutorial.ipynb `_: Train a 2D segmentation model on the BUSI dataset with ``UNetPPTrainer`` and see how to plug a custom external segmentation model,.
+Deployment
+----------
+
+* `01_deploy_registered_model.ipynb `_: Deploy a model registered in MLflow as a managed Datamint endpoint.
+* `02_deploy_external_model.ipynb `_: Adapt and deploy an externally-trained model in Datamint.
+* `03_validate_model.ipynb `_: Validate a model before promoting it to production.
+
+End-to-End Examples
+-------------------
+
+Complete workflows from data upload to deployment.
+
+**Slice-based (2D)**
+
+* `01_fracatlas_classification.ipynb `_: End-to-end classification pipeline on the FracAtlas dataset.
+* `02_busi_segmentation.ipynb `_: Train a 2D segmentation model on the BUSI dataset with ``UNetPPTrainer``, including custom model integration.
+* `03_bccd_detection.ipynb `_: Object detection pipeline on the BCCD blood cell dataset.
+
+**Full 3D**
+
+* `01_synapse_unetrpp.ipynb `_: Volumetric segmentation on the Synapse dataset using UNETR++.
+* `02_synapse_nnunet.ipynb `_: Volumetric segmentation on the Synapse dataset using nnUNet.