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

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

.. _sphx_glr_checks_gallery_vision_performance_plot_image_segment_performance.py:


Image Segment Performance
*************************

This notebooks provides an overview for using and understanding image segment performance check.

**Structure:**

* `Why the image segment performance is important? <#why-segment-performance-is-important>`__
* `Run the check <#run-the-check>`__
* `Define a condition <#define-a-condition>`__

Why segment performance is important?
=====================================
The check helps to detect segments of your data that are under-performing based on
the basic properties of the image. For example, by default the check would show how
the performance depends on brightness, area and other such properties. Identifying
your models' weak segments might help to address specific issues and improve the
overall performance of the model.

.. GENERATED FROM PYTHON SOURCE LINES 24-26

Run the check
=============

.. GENERATED FROM PYTHON SOURCE LINES 26-36

.. code-block:: default


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

    coco_data = coco.load_dataset(train=False, object_type='VisionData')
    model = coco.load_model()

    result = ImageSegmentPerformance().run(coco_data, model)
    result





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.. raw:: html

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    <h4><b>Image Segment Performance</b></h4><p>Segment the data by various properties of the image, and compare the performance of the segments.</p><h5><b>Additional Outputs</b></h5><div>                            <div id="8e125d80-2a14-4e8f-938a-4e4b81ac3d58" class="plotly-graph-div" style="height:525px; width:100%;"></div>            <script type="text/javascript">                require(["plotly"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById("8e125d80-2a14-4e8f-938a-4e4b81ac3d58")) {                    Plotly.newPlot(                        "8e125d80-2a14-4e8f-938a-4e4b81ac3d58",                        [{"alignmentgroup":"True","customdata":[[13],[8],[13],[30]],"hovertemplate":"Metric=Average Precision<br>Property=Area<br>Range=%{x}<br>Value=%{y}<br>Number of samples=%{customdata[0]}<extra></extra>","legendgroup":"Average Precision","marker":{"color":"rgb(102, 197, 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    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 37-41

Observe the check’s output
--------------------------
The check segmented the data by different properties and calculated the metrics for each
segment. As the value of result we return all the information on the different segments:

.. GENERATED FROM PYTHON SOURCE LINES 41-45

.. code-block:: default


    print(f'Properties: {result.value.keys()}')
    print(f'brightness bins: {result.value["Brightness"]}')





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

 Out:

 .. code-block:: none

    Properties: dict_keys(['Aspect Ratio', 'Area', 'Brightness', 'RMS Contrast', 'Mean Red Relative Intensity', 'Mean Green Relative Intensity', 'Mean Blue Relative Intensity'])
    brightness bins: [{'start': -inf, 'stop': 0.3283433811152742, 'count': 13, 'metrics': {'Average Precision': 0.4544554455445543, 'Average Recall': 0.4666666666666667}, 'display_range': '(-inf, 0.33)'}, {'start': 0.3283433811152742, 'stop': 0.4030219196204722, 'count': 13, 'metrics': {'Average Precision': 0.3029702970297029, 'Average Recall': 0.3}, 'display_range': '[0.33, 0.4)'}, {'start': 0.4030219196204722, 'stop': 0.4620466695414141, 'count': 12, 'metrics': {'Average Precision': 0.20495049504950488, 'Average Recall': 0.2}, 'display_range': '[0.4, 0.46)'}, {'start': 0.4620466695414141, 'stop': 0.5060984084251257, 'count': 13, 'metrics': {'Average Precision': 0.4544554455445543, 'Average Recall': 0.45}, 'display_range': '[0.46, 0.51)'}, {'start': 0.5060984084251257, 'stop': inf, 'count': 13, 'metrics': {'Average Precision': 0.2524752475247524, 'Average Recall': 0.25}, 'display_range': '[0.51, inf)'}]




.. GENERATED FROM PYTHON SOURCE LINES 46-55

Define a condition
------------------
The check has a default condition which can be defined. The condition calculates for
each property & metric the mean score and then looks at the ratio between the lowest
segment score and the mean score. If this ratio is less than defined threshold, the
condition fails.

The purpose of the condition is to catch properties segments that are significantly
worse than the mean - which might indicate a problem.

.. GENERATED FROM PYTHON SOURCE LINES 55-59

.. code-block:: default


    check = ImageSegmentPerformance().add_condition_score_from_mean_ratio_not_less_than(0.5)
    check.run(coco_data, model)





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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">
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    <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">
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    <h4><b>Image Segment Performance</b></h4><p>Segment the data by various properties of the image, and compare the performance of the segments.</p><h5><b>Conditions Summary</b></h5><style type="text/css">
    #T_fa8ad table {
      text-align: left;
      white-space: pre-wrap;
    }
    #T_fa8ad thead {
      text-align: left;
      white-space: pre-wrap;
    }
    #T_fa8ad tbody {
      text-align: left;
      white-space: pre-wrap;
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    #T_fa8ad th {
      text-align: left;
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    #T_fa8ad td {
      text-align: left;
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    <table id="T_fa8ad">
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        <tr>
          <th id="T_fa8ad_level0_col0" class="col_heading level0 col0" >Status</th>
          <th id="T_fa8ad_level0_col1" class="col_heading level0 col1" >Condition</th>
          <th id="T_fa8ad_level0_col2" class="col_heading level0 col2" >More Info</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <td id="T_fa8ad_row0_col0" class="data row0 col0" ><div style="color: red;text-align: center">✖</div></td>
          <td id="T_fa8ad_row0_col1" class="data row0 col1" >No segment with ratio between score to mean less than 50%</td>
          <td id="T_fa8ad_row0_col2" class="data row0 col2" >Properties with failed segments: Area: {'Range': '(-inf, 243,100)', 'Metric': 'Average Precision', 'Ratio': 0.37}, Mean Blue Relative Intensity: {'Range': '[0.28, 0.32)', 'Metric': 'Average Precision', 'Ratio': 0.35}, Mean Red Relative Intensity: {'Range': '[0.33, 0.34)', 'Metric': 'Average Recall', 'Ratio': 0.19}</td>
        </tr>
      </tbody>
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.. GENERATED FROM PYTHON SOURCE LINES 60-62

In this case the condition identified under-performing segments in the
properties: mean_blue_relative_intensity, brightness, aspect_ratio, mean_red_relative_intensity


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   **Total running time of the script:** ( 0 minutes  42.137 seconds)


.. _sphx_glr_download_checks_gallery_vision_performance_plot_image_segment_performance.py:


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

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



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

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


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