.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/benchmarks/interface/midog_openmibood.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_benchmarks_interface_midog_openmibood.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_benchmarks_interface_midog_openmibood.py:


OpenMIBOOD - MIDOG (Microscopy / Mitosis)
==========================================

Reproduces the MIDOG benchmark from
*OpenMIBOOD: Open Medical Imaging Benchmarks for Out-Of-Distribution Detection*
(CVPR 2025).

.. note::
    The MIDOG data must be prepared first by following the
    `OpenMIBOOD setup guide <https://github.com/remic-othr/OpenMIBOOD>`_.
    The ``root`` argument should point at the directory whose subfolders match
    the relative paths in the bundled image list files (e.g. ``1a/017/...``).

.. GENERATED FROM PYTHON SOURCE LINES 15-30

.. code-block:: Python
   :lineno-start: 16


    import pandas as pd  # additional dependency, used here for convenience
    import torch
    from torchvision import transforms
    from torchvision.models import resnet50

    from pytorch_ood.benchmark import MIDOG_OpenMIBOOD
    from pytorch_ood.detector import MaxSoftmax
    from pytorch_ood.utils import fix_random_seed

    fix_random_seed(123)

    device = "cuda:0" if torch.cuda.is_available() else "cpu"
    loader_kwargs = {"batch_size": 64, "num_workers": 8}


.. GENERATED FROM PYTHON SOURCE LINES 31-33

Replace this with the OpenMIBOOD pretrained MIDOG classifier (3 classes).
See https://zenodo.org/records/14982267

.. GENERATED FROM PYTHON SOURCE LINES 33-43

.. code-block:: Python
   :lineno-start: 33

    model = resnet50(num_classes=3).eval().to(device)

    trans = transforms.Compose(
        [
            transforms.Resize((224, 224)),
            transforms.ToTensor(),
            transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
        ]
    )


.. GENERATED FROM PYTHON SOURCE LINES 44-48

.. code-block:: Python
   :lineno-start: 44

    detectors = {
        "MSP": MaxSoftmax(model),
    }


.. GENERATED FROM PYTHON SOURCE LINES 49-63

.. code-block:: Python
   :lineno-start: 49

    benchmark = MIDOG_OpenMIBOOD(root="data/openmibood/midog", transform=trans)

    results = []
    with torch.no_grad():
        for detector_name, detector in detectors.items():
            print(f"> Evaluating {detector_name}")
            res = benchmark.evaluate(detector, loader_kwargs=loader_kwargs, device=device)
            for r in res:
                r.update({"Detector": detector_name})
            results += res

    df = pd.DataFrame(results)
    print((df.set_index(["Dataset", "Detector"]) * 100).to_csv(float_format="%.2f"))


.. GENERATED FROM PYTHON SOURCE LINES 64-69

This produces a table with rows for the following splits, in this order:

* cs-ID:   ``midog_csid_1b``, ``midog_csid_1c`` (other scanners, same task)
* near-OOD: ``midog_2`` ... ``midog_7`` (other scanner/staining domains)
* far-OOD:  ``midog_ccagt`` (cervical cells), ``midog_fnac2019`` (fine-needle aspirate)


.. _sphx_glr_download_auto_examples_benchmarks_interface_midog_openmibood.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: midog_openmibood.ipynb <midog_openmibood.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: midog_openmibood.py <midog_openmibood.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: midog_openmibood.zip <midog_openmibood.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_