.. _sphx_glr_auto_tutorials_Classification:

.. _Classification_examples:

Classification
--------------

Tutorials for modeling uncertainty in classification tasks.



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    <div class="sphx-glr-thumbcontainer" tooltip="In this tutorial, we will train a LeNet classifier on the MNIST dataset using TorchUncertainty. You will discover two of the core tools from TorchUncertainty, namely">

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  .. image:: /auto_tutorials/Classification/images/thumb/sphx_glr_tutorial_classification_thumb.png
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  :ref:`sphx_glr_auto_tutorials_Classification_tutorial_classification.py`

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      <div class="sphx-glr-thumbnail-title">Training a LeNet for Image Classification with TorchUncertainty</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="In this tutorial, we explore how to assess a model&#x27;s robustness when faced with distribution shifts. Specifically, we will:">

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  .. image:: /auto_tutorials/Classification/images/thumb/sphx_glr_tutorial_distribution_shift_thumb.png
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  :ref:`sphx_glr_auto_tutorials_Classification_tutorial_distribution_shift.py`

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      <div class="sphx-glr-thumbnail-title">Evaluating Model Performance Under Distribution Shift with TorchUncertainty</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="This tutorial aims to provide an introductory overview of Deep Evidential Classification (DEC) using a practical example. We demonstrate an application of DEC by tackling the toy-problem of fitting the MNIST dataset using a Multi-Layer Perceptron (MLP) neural network model. The output of the MLP is modeled as a Dirichlet distribution. The MLP is trained by minimizing the DEC loss function, composed of a Bayesian risk square error loss and a regularization term based on KL Divergence.">

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  .. image:: /auto_tutorials/Classification/images/thumb/sphx_glr_tutorial_evidential_classification_thumb.png
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  :ref:`sphx_glr_auto_tutorials_Classification_tutorial_evidential_classification.py`

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      <div class="sphx-glr-thumbnail-title">Deep Evidential Classification on a Toy Example</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="This tutorial demonstrates how to perform OOD detection using TorchUncertainty&#x27;s ClassificationRoutine with a ResNet18 model trained on CIFAR-10, evaluating its performance with SVHN as the OOD dataset.">

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  .. image:: /auto_tutorials/Classification/images/thumb/sphx_glr_tutorial_ood_detection_thumb.png
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  :ref:`sphx_glr_auto_tutorials_Classification_tutorial_ood_detection.py`

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      <div class="sphx-glr-thumbnail-title">Out-of-distribution detection with TorchUncertainty</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="This tutorial is heavily inspired by PyTorch&#x27;s Training a Classifier tutorial.">

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  .. image:: /auto_tutorials/Classification/images/thumb/sphx_glr_tutorial_pe_cifar10_thumb.png
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  :ref:`sphx_glr_auto_tutorials_Classification_tutorial_pe_cifar10.py`

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      <div class="sphx-glr-thumbnail-title">From a Standard Classifier to a Packed-Ensemble</div>
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    </div>


.. toctree::
   :hidden:

   /auto_tutorials/Classification/tutorial_classification
   /auto_tutorials/Classification/tutorial_distribution_shift
   /auto_tutorials/Classification/tutorial_evidential_classification
   /auto_tutorials/Classification/tutorial_ood_detection
   /auto_tutorials/Classification/tutorial_pe_cifar10