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<Product_Observational xmlns="http://pds.nasa.gov/pds4/pds/v1"
                       xmlns:ml="http://pds.nasa.gov/pds4/ml/v1"
                       xmlns:pds="http://pds.nasa.gov/pds4/pds/v1"
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                       xsi:schemaLocation="http://pds.nasa.gov/pds4/pds/v1
                                           https://pds.nasa.gov/pds4/pds/v1/PDS4_PDS_1H00.xsd
                                           http://pds.nasa.gov/pds4/ml/v1
                                           https://pds.nasa.gov/pds4/ml/v1/PDS4_ML_1H00_1000.xsd">

  <Identification_Area>
    <logical_identifier>urn:nasa:pds:mslnet:data:mslnet_predicted_classes</logical_identifier>
    <version_id>1.0</version_id>
    <title>MSLNet Predicted Class Set</title>
    <information_model_version>1.17.0.0</information_model_version>
    <product_class>Product_Observational</product_class>
    <Citation_Information>
      <author_list>Kiri Wagstaff, Steven Lu, Kevin Grimes, Jesse Cai,
      Raymond Francis, and Jake Lee.</author_list>
      <publication_year>2020</publication_year>
      <description>MSL Class Predictions generated from analysis of
      the MSL surface data set, version 2. The five columns in this
      file contain image file names, predicted class in integer
      representation, predicted class in string representation,
      predicted class probability, and version of the classifier that
      made the prediction.</description>
    </Citation_Information>
    <Modification_History>
      <Modification_Detail>
        <modification_date>2022-01-07</modification_date>
        <version_id>1.0</version_id>
        <description>Initial version.</description>
      </Modification_Detail>
    </Modification_History>
  </Identification_Area>

  <Observation_Area>
    <comment>The MSL Class Predictions were generated from analysis of
    MSL surface data, specifically, surface images from Mastcam and
    MAHLI.</comment>

    <Time_Coordinates>
      <start_date_time>2012-08-07Z</start_date_time>
      <stop_date_time>2019-07-27Z</stop_date_time>
    </Time_Coordinates>

    <Primary_Result_Summary>
      <purpose>Science</purpose>
      <processing_level>Derived</processing_level>
      <description>MSL image class predictions capture the semantic
      content of images and can be used to enable content-based search
      for relevant images.</description>
    </Primary_Result_Summary>

    <Investigation_Area>
      <name>Mars Science Laboratory</name>
      <type>Mission</type>
      <Internal_Reference>
        <lid_reference>urn:nasa:pds:context:investigation:mission.mars_science_laboratory</lid_reference>
        <reference_type>data_to_investigation</reference_type>
      </Internal_Reference>
    </Investigation_Area>

    <Observing_System>
      <Observing_System_Component>
        <name>Mast Camera</name>
        <type>Instrument</type>
        <Internal_Reference>
          <lid_reference>urn:nasa:pds:context:instrument:mast_left.msl</lid_reference>
          <reference_type>is_instrument</reference_type>
        </Internal_Reference>
      </Observing_System_Component>
      <Observing_System_Component>
        <name>Mast Camera</name>
        <type>Instrument</type>
        <Internal_Reference>
          <lid_reference>urn:nasa:pds:context:instrument:mast_right.msl</lid_reference>
          <reference_type>is_instrument</reference_type>
        </Internal_Reference>
      </Observing_System_Component>
      <Observing_System_Component>
        <name>Mars Hand Lens Imager Camera</name>
        <type>Instrument</type>
        <Internal_Reference>
          <lid_reference>urn:nasa:pds:context:instrument:mahli.msl</lid_reference>
          <reference_type>is_instrument</reference_type>
        </Internal_Reference>
      </Observing_System_Component>
      <Observing_System_Component>
        <name>Curiosity Rover</name>
        <type>Host</type>
        <Internal_Reference>
          <lid_reference>urn:nasa:pds:context:instrument_host:spacecraft.msl</lid_reference>
          <reference_type>is_instrument_host</reference_type>
        </Internal_Reference>
      </Observing_System_Component>
    </Observing_System>

    <Target_Identification>
      <name>Mars</name>
      <type>Planet</type>
      <Internal_Reference>
        <lid_reference>urn:nasa:pds:context:target:planet.mars</lid_reference>
        <reference_type>data_to_target</reference_type>
      </Internal_Reference>
    </Target_Identification>

    <Discipline_Area>
      <ml:Machine_Learning>
        <ml:Trained_Machine_Learning_Model>
          <ml:trained_model_version_id>2.0</ml:trained_model_version_id>
          <ml:trained_model_name>MSLNet</ml:trained_model_name>
          <ml:Machine_Learning_Algorithm>
            <ml:algorithm_learning_style>Supervised_Learning</ml:algorithm_learning_style>
            <ml:algorithm_type>Convolutional_Neural_Network_Classifier</ml:algorithm_type>
            <ml:algorithm_name>Fine-tuned AlexNet (ImageNet) with
            Temperature Scaling Calibration</ml:algorithm_name>
            <pds:External_Reference>
              <pds:doi>10.5555/2999134</pds:doi>
              <pds:reference_text>Krizhevsky, A., Sutsekver, I., and
            Hinton, G.E., "Imagenet classification with deep
            convolutional neural networks," Advances in Neural
            Information Processing Systems 25, p., 1097-1105, 2012.
              </pds:reference_text>
              <pds:description>AlexNet citation</pds:description>
            </pds:External_Reference>
          </ml:Machine_Learning_Algorithm>
          <ml:Training_Set>
            <ml:data_set_version_id>1.0</ml:data_set_version_id>
            <ml:data_set_size>2000</ml:data_set_size>
          </ml:Training_Set>
          <ml:Validation_Set>
            <ml:data_set_version_id>1.0</ml:data_set_version_id>
            <ml:data_set_size>300</ml:data_set_size>
          </ml:Validation_Set>
          <ml:Test_Set>
            <ml:data_set_version_id>1.0</ml:data_set_version_id>
            <ml:data_set_size>600</ml:data_set_size>
          </ml:Test_Set>
          <ml:Test_Performance>
            <ml:performance_measure>Test accuracy</ml:performance_measure>
            <ml:performance_score>0.745</ml:performance_score>
          </ml:Test_Performance>
          <ml:Test_Performance>
            <ml:performance_measure>Test accuracy with abstention below
            0.9 confidence</ml:performance_measure>
            <ml:performance_score>0.903</ml:performance_score>
          </ml:Test_Performance>
          <ml:Test_Performance>
            <ml:performance_measure>Test abstention rate with 0.9
            confidence threshold</ml:performance_measure>
            <ml:performance_score>0.518</ml:performance_score>
          </ml:Test_Performance>
        </ml:Trained_Machine_Learning_Model>
      </ml:Machine_Learning>
    </Discipline_Area>

  </Observation_Area>
  <File_Area_Observational>
    <File>
      <file_name>mslnet_predicted_classes.csv</file_name>
      <creation_date_time>2022-01-07T15:10:00.000Z</creation_date_time>
      <file_size unit="byte">8719123</file_size>
      <records>136967</records>
    </File>
    <Table_Delimited>
      <name>MSL Surface Data Class Predictions</name>
      <local_identifier>TABLE</local_identifier>
      <offset unit="byte">0</offset>
      <parsing_standard_id>PDS DSV 1</parsing_standard_id>
      <records>136966</records>
      <record_delimiter>Carriage-Return Line-Feed</record_delimiter>
      <field_delimiter>Comma</field_delimiter>
      <Record_Delimited>
        <fields>5</fields>
        <groups>0</groups>
        <maximum_record_length unit="byte">73</maximum_record_length>
        <Field_Delimited>
          <name>image_filename</name>
          <field_number>1</field_number>
          <data_type>ASCII_File_Name</data_type>
          <maximum_field_length unit="byte">34</maximum_field_length>
          <description>This field contains image file names from the
          Mars Surface Data Set.</description>
        </Field_Delimited>
        <Field_Delimited>
          <name>predicted_class_id</name>
          <field_number>2</field_number>
          <data_type>ASCII_NonNegative_Integer</data_type>
          <maximum_field_length unit="byte">3</maximum_field_length>
          <description>This field contains the associated predicted
          class of an image file in integer form.</description>
        </Field_Delimited>
        <Field_Delimited>
          <name>predicted_class_string</name>
          <field_number>3</field_number>
          <data_type>ASCII_String</data_type>
          <maximum_field_length unit="byte">21</maximum_field_length>
          <description>This field contains the associated predicted
          class of an image file in string representation.
          </description>
        </Field_Delimited>
        <Field_Delimited>
          <name>predicted_probability</name>
          <field_number>4</field_number>
          <data_type>ASCII_Real</data_type>
          <maximum_field_length unit="byte">6</maximum_field_length>
          <description>This field contains the predicted probability
          for classes.</description>
        </Field_Delimited>
        <Field_Delimited>
          <name>classifier_version</name>
          <field_number>5</field_number>
          <data_type>ASCII_String</data_type>
          <maximum_field_length unit="byte">3</maximum_field_length>
          <description>This field contains the version number of the
          classifier used to predict the class.</description>
        </Field_Delimited>
      </Record_Delimited>
    </Table_Delimited>
  </File_Area_Observational>
</Product_Observational>
