.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/benchmarks/interface/cifar10_odin.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_cifar10_odin.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_benchmarks_interface_cifar10_odin.py:


ODIN - CIFAR10
==================

Reproduces the ODIN benchmark for OOD detection, from the paper
*Enhancing the reliability of out-of-distribution image detection in neural networks*.

.. GENERATED FROM PYTHON SOURCE LINES 11-24

.. code-block:: Python
   :lineno-start: 11

    import pandas as pd  # additional dependency, used here for convenience
    import torch

    from pytorch_ood.benchmark import CIFAR10_ODIN
    from pytorch_ood.detector import ODIN, MaxSoftmax
    from pytorch_ood.model import WideResNet
    from pytorch_ood.utils import fix_random_seed

    fix_random_seed(123)

    device = "cuda:0"
    loader_kwargs = {"batch_size": 64, "num_workers": 12}


.. GENERATED FROM PYTHON SOURCE LINES 25-29

.. code-block:: Python
   :lineno-start: 25

    model = WideResNet(num_classes=10, pretrained="cifar10-pt").eval().to(device)
    trans = WideResNet.transform_for("cifar10-pt")
    norm_std = WideResNet.norm_std_for("cifar10-pt")


.. GENERATED FROM PYTHON SOURCE LINES 30-35

.. code-block:: Python
   :lineno-start: 30

    detectors = {
        "MSP": MaxSoftmax(model),
        "ODIN": ODIN(model, eps=0.002, norm_std=norm_std),
    }


.. GENERATED FROM PYTHON SOURCE LINES 36-50

.. code-block:: Python
   :lineno-start: 36

    results = []
    benchmark = CIFAR10_ODIN(root="data", transform=trans)

    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 51-80

This produces a table with the following output:

+--------------------+----------+-------+---------+----------+----------+
| Dataset            | Detector | AUROC | AUPR-IN | AUPR-OUT | FPR95TPR |
+====================+==========+=======+=========+==========+==========+
| TinyImageNetCrop   | MSP      | 94.59 | 93.10   | 95.77    | 17.18    |
+--------------------+----------+-------+---------+----------+----------+
| TinyImageNetResize | MSP      | 88.22 | 86.01   | 89.25    | 42.56    |
+--------------------+----------+-------+---------+----------+----------+
| LSUNResize         | MSP      | 91.45 | 89.46   | 92.65    | 29.06    |
+--------------------+----------+-------+---------+----------+----------+
| LSUNCrop           | MSP      | 96.49 | 95.69   | 97.20    | 12.49    |
+--------------------+----------+-------+---------+----------+----------+
| Uniform            | MSP      | 87.22 | 29.65   | 98.60    | 32.50    |
+--------------------+----------+-------+---------+----------+----------+
| Gaussian           | MSP      | 90.00 | 34.68   | 98.96    | 24.75    |
+--------------------+----------+-------+---------+----------+----------+
| TinyImageNetCrop   | ODIN     | 96.78 | 96.46   | 97.10    | 14.17    |
+--------------------+----------+-------+---------+----------+----------+
| TinyImageNetResize | ODIN     | 91.44 | 91.31   | 91.45    | 38.83    |
+--------------------+----------+-------+---------+----------+----------+
| LSUNResize         | ODIN     | 94.66 | 94.48   | 94.80    | 26.25    |
+--------------------+----------+-------+---------+----------+----------+
| LSUNCrop           | ODIN     | 98.10 | 98.11   | 98.16    | 9.37     |
+--------------------+----------+-------+---------+----------+----------+
| Uniform            | ODIN     | 95.94 | 73.27   | 99.57    | 17.16    |
+--------------------+----------+-------+---------+----------+----------+
| Gaussian           | ODIN     | 97.02 | 80.50   | 99.67    | 12.89    |
+--------------------+----------+-------+---------+----------+----------+


.. _sphx_glr_download_auto_examples_benchmarks_interface_cifar10_odin.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: cifar10_odin.ipynb <cifar10_odin.ipynb>`

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

      :download:`Download Python source code: cifar10_odin.py <cifar10_odin.py>`


.. only:: html

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

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