.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "checks_gallery/vision/performance/plot_mean_average_recall_report.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_mean_average_recall_report.py>`
        to download the full example code

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

.. _sphx_glr_checks_gallery_vision_performance_plot_mean_average_recall_report.py:


Mean Average Recall Report
**************************

This notebooks provides an overview for using and understanding the mean average recall report 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>`__
* `Define a condition <#define-a-condition>`__

What is the purpose of the check?
=================================
The Mean Average Recall Report evaluates the
`mAR metric <https://manalelaidouni.github.io/Evaluating-Object-Detection-Models-Guide-to-Performance-Metrics.html>`__
on the given model and data, and returns the mAR values per bounding box size category (small, medium, large).
This check only works on the Object Detection task.

.. GENERATED FROM PYTHON SOURCE LINES 24-26

Imports
-------

.. GENERATED FROM PYTHON SOURCE LINES 26-32

.. code-block:: default


    import numpy as np

    from deepchecks.vision.checks.performance import MeanAverageRecallReport
    from deepchecks.vision.datasets.detection import coco








.. GENERATED FROM PYTHON SOURCE LINES 33-40

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>`__.

For the label formatter - our dataset returns exactly the accepted format, so our
formatting function is the simple `lambda x: x` function.

.. GENERATED FROM PYTHON SOURCE LINES 40-45

.. code-block:: default


    yolo = coco.load_model(pretrained=True)

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








.. GENERATED FROM PYTHON SOURCE LINES 46-48

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

.. GENERATED FROM PYTHON SOURCE LINES 48-53

.. code-block:: default


    check = MeanAverageRecallReport()
    result = check.run(test_ds, yolo)
    result





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

 Out:

 .. code-block:: none


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.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">

    <script
        src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.6/require.min.js"
        integrity="sha512-c3Nl8+7g4LMSTdrm621y7kf9v3SDPnhxLNhcjFJbKECVnmZHTdo+IRO05sNLTH/D3vA6u1X32ehoLC7WFVdheg=="
        crossorigin="anonymous"
        referrerpolicy="no-referrer">
    </script>

    <script type="text/javascript">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>
    <script type="text/javascript">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: "STIX-Web"}});}</script>
    <script type="text/javascript">
        if (typeof require !== 'undefined') {
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    </script>
    <h4><b>Mean Average Recall Report</b></h4><p>Summarize mean average recall metrics on a dataset and model per detections and area range.</p><h5><b>Additional Outputs</b></h5><style type="text/css">
    #T_1f48e table {
      text-align: left;
      white-space: pre-wrap;
    }
    #T_1f48e thead {
      text-align: left;
      white-space: pre-wrap;
    }
    #T_1f48e tbody {
      text-align: left;
      white-space: pre-wrap;
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    #T_1f48e th {
      text-align: left;
      white-space: pre-wrap;
    }
    #T_1f48e td {
      text-align: left;
      white-space: pre-wrap;
    }
    </style>
    <table id="T_1f48e">
      <thead>
        <tr>
          <th class="blank level0" >&nbsp;</th>
          <th id="T_1f48e_level0_col0" class="col_heading level0 col0" >AR@1 (%)</th>
          <th id="T_1f48e_level0_col1" class="col_heading level0 col1" >AR@10 (%)</th>
          <th id="T_1f48e_level0_col2" class="col_heading level0 col2" >AR@100 (%)</th>
        </tr>
        <tr>
          <th class="index_name level0" >Area size</th>
          <th class="blank col0" >&nbsp;</th>
          <th class="blank col1" >&nbsp;</th>
          <th class="blank col2" >&nbsp;</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th id="T_1f48e_level0_row0" class="row_heading level0 row0" >All</th>
          <td id="T_1f48e_row0_col0" class="data row0 col0" >0.33</td>
          <td id="T_1f48e_row0_col1" class="data row0 col1" >0.42</td>
          <td id="T_1f48e_row0_col2" class="data row0 col2" >0.43</td>
        </tr>
        <tr>
          <th id="T_1f48e_level0_row1" class="row_heading level0 row1" >Small (area < 32^2)</th>
          <td id="T_1f48e_row1_col0" class="data row1 col0" >0.10</td>
          <td id="T_1f48e_row1_col1" class="data row1 col1" >0.22</td>
          <td id="T_1f48e_row1_col2" class="data row1 col2" >0.22</td>
        </tr>
        <tr>
          <th id="T_1f48e_level0_row2" class="row_heading level0 row2" >Medium (32^2 < area < 96^2)</th>
          <td id="T_1f48e_row2_col0" class="data row2 col0" >0.33</td>
          <td id="T_1f48e_row2_col1" class="data row2 col1" >0.42</td>
          <td id="T_1f48e_row2_col2" class="data row2 col2" >0.42</td>
        </tr>
        <tr>
          <th id="T_1f48e_level0_row3" class="row_heading level0 row3" >Large (area < 96^2)</th>
          <td id="T_1f48e_row3_col0" class="data row3 col0" >0.48</td>
          <td id="T_1f48e_row3_col1" class="data row3 col1" >0.54</td>
          <td id="T_1f48e_row3_col2" class="data row3 col2" >0.55</td>
        </tr>
      </tbody>
    </table>

    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 54-57

Observe the check’s output
--------------------------
The result value is a dataframe that has the average recall score per each area range and IoU.

.. GENERATED FROM PYTHON SOURCE LINES 57-60

.. code-block:: default


    result.value






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
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    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>AR@1 (%)</th>
          <th>AR@10 (%)</th>
          <th>AR@100 (%)</th>
        </tr>
        <tr>
          <th>Area size</th>
          <th></th>
          <th></th>
          <th></th>
        </tr>
      </thead>
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          <td>0.330552</td>
          <td>0.423444</td>
          <td>0.429179</td>
        </tr>
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          <th>Small (area &lt; 32^2)</th>
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          <td>0.220594</td>
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          <th>Medium (32^2 &lt; area &lt; 96^2)</th>
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          <td>0.417392</td>
          <td>0.423844</td>
        </tr>
        <tr>
          <th>Large (area &lt; 96^2)</th>
          <td>0.481611</td>
          <td>0.544408</td>
          <td>0.549963</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 61-65

Define a condition 
==================
We can define a condition that checks whether our model's average recall score is not less than
a given threshold

.. GENERATED FROM PYTHON SOURCE LINES 65-69

.. code-block:: default


    check = MeanAverageRecallReport().add_condition_test_average_recall_not_less_than(0.4)
    result = check.run(test_ds, yolo)
    result.show(show_additional_outputs=False)




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

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 .. code-block:: none


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.. raw:: html

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    <!--
        ~ ----------------------------------------------------------------------------
        ~ Copyright (C) 2021-2022 Deepchecks (https://www.deepchecks.com)
        ~
        ~ This file is part of Deepchecks.
        ~ Deepchecks is distributed under the terms of the GNU Affero General
        ~ Public License (version 3 or later).
        ~ You should have received a copy of the GNU Affero General Public License
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            "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_8cf34 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_8cf34 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_8cf34 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_8cf34 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_8cf34 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_8cf34\">\n  <thead>\n    <tr>\n      <th id=\"T_8cf34_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_8cf34_level0_col1\" class=\"col_heading level0 col1\" >Condition</th>\n      <th id=\"T_8cf34_level0_col2\" class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_8cf34_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_8cf34_row0_col1\" class=\"data row0 col1\" >Scores are not less than 0.4</td>\n      <td id=\"T_8cf34_row0_col2\" class=\"data row0 col2\" >Found scores below threshold:\n{'All': {'AR@1 (%)': '0.331'}, 'Small (area < 32^2)': {'AR@10 (%)': '0.221'}}</td>\n    </tr>\n  </tbody>\n</table>\n"
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          "model_module": "@jupyter-widgets/controls",
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        "2d055db3fcee4371a7c3b90751ac0a01": {
          "model_name": "LayoutModel",
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "state": {}
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    }
    </script>
    <script type="application/vnd.jupyter.widget-view+json">
    {"version_major": 2, "version_minor": 0, "model_id": "9fb0b49259e44f34ac2df495d451345e"}
    </script>

    </body>
    </html>
    </div>
    <br />
    <br />


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

   **Total running time of the script:** ( 0 minutes  35.458 seconds)


.. _sphx_glr_download_checks_gallery_vision_performance_plot_mean_average_recall_report.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_mean_average_recall_report.py <plot_mean_average_recall_report.py>`



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

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


.. only:: html

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

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