.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "checks_gallery/vision/model_evaluation/plot_class_performance.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        Click :ref:`here <sphx_glr_download_checks_gallery_vision_model_evaluation_plot_class_performance.py>`
        to download the full example code

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

.. _sphx_glr_checks_gallery_vision_model_evaluation_plot_class_performance.py:


Class Performance
***********************

This notebooks provides an overview for using and understanding the class performance check.

**Structure:**

* `What is the purpose of the check? <#what-is-the-purpose-of-the-check>`__
* `Classification <#classification-performance-report>`__

  - `Generate data & model <#generate-data-and-model>`__
  - `Run the check <#run-the-check>`__

* `Object Detection <#object-detection-class-performance>`__

  - `Generate data & model <#id1>`__
  - `Run the check <#id2>`__

What Is the Purpose of the Check?
=================================
The class performance check evaluates several metrics on the given model and data
and returns all of the results in a single check.
The check uses the following default metrics:

=================  ====================
Task Type          Property name
=================  ====================
Classification     Precision
Classification     Recall
Object Detection   `Average Precision <https://manalelaidouni.github.io/Evaluating-Object-Detection-Models-Guide-to-Performance-Metrics.html>`__
Object Detection   `Average Recall <https://manalelaidouni.github.io/Evaluating-Object-Detection-Models-Guide-to-Performance-Metrics.html>`__
=================  ====================

In addition to the default metrics, the check supports custom metrics that
should be implemented using the `torch.ignite.Metric
<https://pytorch.org/ignite/metrics.html#how-to-create-a-custom-metric>`__ API.
These can be passed as a list using the alternative_metrics parameter of the
check, which will override the default metrics.

.. GENERATED FROM PYTHON SOURCE LINES 45-47

Imports
-------

.. GENERATED FROM PYTHON SOURCE LINES 47-51

.. code-block:: default


    from deepchecks.vision.checks import ClassPerformance
    from deepchecks.vision.datasets.classification import mnist








.. GENERATED FROM PYTHON SOURCE LINES 52-56

Classification Performance Report
=================================
Generate data and model:
------------------------

.. GENERATED FROM PYTHON SOURCE LINES 56-62

.. code-block:: default



    mnist_model = mnist.load_model()
    train_ds = mnist.load_dataset(train=True, object_type='VisionData')
    test_ds = mnist.load_dataset(train=False, object_type='VisionData')








.. GENERATED FROM PYTHON SOURCE LINES 63-65

Run the check
-------------

.. GENERATED FROM PYTHON SOURCE LINES 65-69

.. code-block:: default


    check = ClassPerformance()
    check.run(train_ds, test_ds, mnist_model)





.. rst-class:: sphx-glr-script-out

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.. GENERATED FROM PYTHON SOURCE LINES 70-76

Object Detection Class Performance
==================================
For object detection tasks - the default metric that is being calculated it the
Average Precision. The definition of the Average Precision is identical to how
the COCO dataset defined it - mean of the average precision per class, over
the range [0.5, 0.95, 0.05] of IoU thresholds.

.. GENERATED FROM PYTHON SOURCE LINES 76-79

.. code-block:: default


    from deepchecks.vision.datasets.detection import coco








.. GENERATED FROM PYTHON SOURCE LINES 80-84

Generate Data and Model
-----------------------
We generate a sample dataset of 128 images from the `COCO dataset <https://cocodataset.org/#home>`__,
and using the `YOLOv5 model <https://github.com/ultralytics/yolov5>`__.

.. GENERATED FROM PYTHON SOURCE LINES 84-90

.. code-block:: default


    yolo = coco.load_model(pretrained=True)

    train_ds = coco.load_dataset(train=True, object_type='VisionData')
    test_ds = coco.load_dataset(train=False, object_type='VisionData')








.. GENERATED FROM PYTHON SOURCE LINES 91-93

Run the check
-------------

.. GENERATED FROM PYTHON SOURCE LINES 93-97

.. code-block:: default


    check = ClassPerformance(show_only='best')
    check.run(train_ds, test_ds, yolo)





.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none


    Validating Input:
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    Ingesting Batches - Train Dataset:
    |     | 0/2 [00:00<?, ? Batch/s]

    Ingesting Batches - Train Dataset:
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    Ingesting Batches - Train Dataset:
    |#####| 2/2 [00:17<00:00,  8.97s/ Batch]

    Ingesting Batches - Train Dataset:
    |#####| 2/2 [00:17<00:00,  8.97s/ Batch]


    Ingesting Batches - Test Dataset:
    |     | 0/2 [00:00<?, ? Batch/s]


    Ingesting Batches - Test Dataset:
    |##5  | 1/2 [00:08<00:08,  8.91s/ Batch]


    Ingesting Batches - Test Dataset:
    |#####| 2/2 [00:17<00:00,  8.84s/ Batch]


    Ingesting Batches - Test Dataset:
    |#####| 2/2 [00:17<00:00,  8.84s/ Batch]



    Computing Check:
    |     | 0/1 [00:00<?, ? Check/s]



    Computing Check:
    |#####| 1/1 [00:00<00:00,  1.42 Check/s]



    Computing Check:
    |#####| 1/1 [00:00<00:00,  1.42 Check/s]
                                                     

                                                                               


                                                                              



                                                             

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.. GENERATED FROM PYTHON SOURCE LINES 98-103

Define a Condition
==================
We can also define a condition to validate that our model performance is above a certain threshold.
The condition is defined as a function that takes the results of the check as input and
returns a ConditionResult object.

.. GENERATED FROM PYTHON SOURCE LINES 103-109

.. code-block:: default


    check = ClassPerformance(show_only='worst')
    check.add_condition_test_performance_not_less_than(0.2)
    result = check.run(train_ds, test_ds, yolo)
    result





.. rst-class:: sphx-glr-script-out

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          "model_module_version": "1.2.0",
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    </script>
    <script type="application/vnd.jupyter.widget-view+json">
    {"version_major": 2, "version_minor": 0, "model_id": "5c04a8935abd4c0da564937b7954484c"}
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    </body>
    </html>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 110-111

We detected that for several classes our model performance is below the threshold.


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 1 minutes  53.046 seconds)


.. _sphx_glr_download_checks_gallery_vision_model_evaluation_plot_class_performance.py:


.. only :: html

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



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

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



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

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


.. only:: html

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

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