.. _sphx_glr_auto_tutorials_Segmentation:

.. _Segmentation_examples:

Segmentation
---------------------------

Tutorials for modeling uncertainty in Segmentation tasks.



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    <div class="sphx-glr-thumbnails">

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    <div class="sphx-glr-thumbcontainer" tooltip="This tutorial demonstrates how to train a segmentation model on the MUAD dataset using TorchUncertainty. MUAD is a synthetic dataset designed for evaluating autonomous driving under diverse uncertainties. It includes 10,413 images across training, validation, and test sets, featuring adverse weather, lighting conditions, and out-of-distribution (OOD) objects. The dataset supports tasks like semantic segmentation, depth estimation, and object detection.">

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  .. image:: /auto_tutorials/Segmentation/images/thumb/sphx_glr_tutorial_muad_deep_en_thumb.png
    :alt:

  :ref:`sphx_glr_auto_tutorials_Segmentation_tutorial_muad_deep_en.py`

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      <div class="sphx-glr-thumbnail-title">Deep ensembles Segmentation Tutorial using Muad Dataset</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This tutorial demonstrates how to train a segmentation model on the MUAD dataset using TorchUncertainty. MUAD is a synthetic dataset designed for evaluating autonomous driving under diverse uncertainties. It includes 10,413 images across training, validation, and test sets, featuring adverse weather, lighting conditions, and out-of-distribution (OOD) objects. The dataset supports tasks like semantic segmentation, depth estimation, and object detection.">

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  .. image:: /auto_tutorials/Segmentation/images/thumb/sphx_glr_tutorial_muad_packed_thumb.png
    :alt:

  :ref:`sphx_glr_auto_tutorials_Segmentation_tutorial_muad_packed.py`

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      <div class="sphx-glr-thumbnail-title">Packed ensembles Segmentation Tutorial using Muad Dataset</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This tutorial demonstrates how to train a segmentation model on the MUAD dataset using TorchUncertainty. MUAD is a synthetic dataset designed for evaluating autonomous driving under diverse uncertainties. It includes 10,413 images across training, validation, and test sets, featuring adverse weather, lighting conditions, and out-of-distribution (OOD) objects. The dataset supports tasks like semantic segmentation, depth estimation, and object detection.">

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  .. image:: /auto_tutorials/Segmentation/images/thumb/sphx_glr_tutorial_muad_seg_thumb.png
    :alt:

  :ref:`sphx_glr_auto_tutorials_Segmentation_tutorial_muad_seg.py`

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      <div class="sphx-glr-thumbnail-title">Segmentation Tutorial using Muad Dataset</div>
    </div>


.. thumbnail-parent-div-close

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    </div>


.. toctree::
   :hidden:

   /auto_tutorials/Segmentation/tutorial_muad_deep_en
   /auto_tutorials/Segmentation/tutorial_muad_packed
   /auto_tutorials/Segmentation/tutorial_muad_seg