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

.. only:: html

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

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

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

.. _sphx_glr_checks_gallery_vision_performance_plot_confusion_matrix.py:


Confusion Matrix
****************

This notebooks provides an overview for using and understanding the confusion matrix check.

**Structure:**

* `What is the purpose of the check? <#what-is-the-purpose-of-the-check>`__
* `Generate data & model <#generate-data-and-model>`__
* `Run the check <#run-the-check>`__

What is the purpose of the check? 
=================================
The confusion matrix check outputs a confusion matrix for both classification problems
and object detection problems. In object detection problems, some predictions do not
overlap on any label and can be classified as not found in the confusion matrix.

.. GENERATED FROM PYTHON SOURCE LINES 22-26

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 26-32

.. code-block:: default


    from deepchecks.vision.datasets.detection import coco

    yolo = coco.load_model(pretrained=True)
    train_ds = coco.load_dataset(object_type='VisionData')





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

 Out:

 .. code-block:: none

    Downloading https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5s.pt to yolov5s.pt...

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.. GENERATED FROM PYTHON SOURCE LINES 33-35

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

.. GENERATED FROM PYTHON SOURCE LINES 35-40

.. code-block:: default


    from deepchecks.vision.checks.performance import ConfusionMatrixReport

    check = ConfusionMatrixReport(categories_to_display=10)
    check.run(train_ds, yolo)




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

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 .. code-block:: none


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

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    <h4><b>Confusion Matrix</b></h4><p>Calculate the confusion matrix of the model on the given dataset.</p><h5><b>Additional Outputs</b></h5><div>Showing 10 of 80 classes:</div><div>"No overlapping" categories are labels and prediction which did not have a matching label/prediction.<br>For example a predictions that did not have a sufficiently overlapping label bounding box will appear under "No overlapping" category in the True Value axis (y-axis).</div><div>                            <div id="01e75ebc-25dc-4919-8d5e-92a3c6a2e87c" class="plotly-graph-div" style="height:600px; width:600px;"></div>            <script type="text/javascript">                require(["plotly"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById("01e75ebc-25dc-4919-8d5e-92a3c6a2e87c")) {                    Plotly.newPlot(                        "01e75ebc-25dc-4919-8d5e-92a3c6a2e87c",                        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.. _sphx_glr_download_checks_gallery_vision_performance_plot_confusion_matrix.py:


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