deploy: 2d37c5d

.buildinfo

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+# Sphinx build info version 1
+# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: 2f1ad2d617d37d9178fb14b1227cada6
+tags: 645f666f9bcd5a90fca523b33c5a78b7

_sources/api.rst.txt

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+.. _API documentation:
+
+API documentation
+=================
+
+Main routine
+------------
+.. autofunction:: thetis.thetis
+
+MLflow format
+-------------
+.. autofunction:: thetis.thetis_mlflow
+
+Tensorboard format
+------------------
+Coming soon

_sources/aspects.rst.txt

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+.. _Background:
+
+Key evaluation aspects
+======================
+
+In this section, we give a detailed overview of the key evaluation aspects that are covered by Thetis.
+
+.. toctree::
+   :maxdepth: 1
+
+   evaluation-aspects/uncertainty
+   evaluation-aspects/performance
+   evaluation-aspects/data_evaluation
+   evaluation-aspects/fairness
+   evaluation-aspects/robustness

_sources/configuration.rst.txt

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+.. _Configuration:
+
+Configuration
+=============
+
+Thetis needs a YAML configuration file that specifies several aspects, e.g., the task, available classes, requested
+evaluation aspects, etc. `Download an exemplary configuration file <https://efs-techhub.com/efs-portfolio/loesungen/thetis>`__ or copy/paste the
+following configuration file. An explanation for each configuration aspect can be found below.
+
+
+Example configuration file
+--------------------------
+
+An exemplary YAML configuration for Thetis must have the following form:
+
+.. code-block:: yaml
+
+   # meta data of model predictions and dataset
+   meta:
+
+     model:
+       name: "<model name>"
+       revision: "<model revision>"
+
+     dataset:
+       name: "<dataset name>"
+       revision: "r1"
+
+
+   # Examination task. Can be one of: "classification" (binary/multi-class classification),
+   # "detection" (image-based object detection) or "regression"
+   task: "classification"
+
+   # Language of the final report. Can be one of: "en", "de"
+   language: "en"
+
+   # Task-specific settings. Required and available fields depend on the selected task.
+   task_settings:
+
+     # List of distinct classes that can occur within the dataset (can only be set for classification or
+     # object detection). If specified then this parameter cannot be empty.
+     distinct_classes: ["no person", "person"]
+
+     # In binary classification (when 'distinct_classes' has length of 2), you must specify a positive label out of
+     # the list of available classes. This is important since you only give a single "confidence" for each prediction,
+     # targeting the probability of the positive class. May only be specified for binary classification.
+     binary_positive_label: "person"
+
+     # Bounding-box format. Can be one of: "xyxy" (xmin, ymin, xmax, ymax), "xywh" (xmin, ymin, width, height),
+     # or "cxcywh" (center x, center y, width, height).
+     detection_bbox_format: "xyxy"
+
+     # List with IoU scores used for object detection evaluation
+     # Note: the IoU score "0.5" is always active for the evaluation. You can specify more IoU scores if you want
+     detection_bbox_ious: [0.75]
+
+     # String with bounding box matching strategy. Must be one of: "exclusive", "max".
+     detection_bbox_matching: "exclusive"
+
+     # Set to true if the bounding boxes are also inferred with a separate variance score (currently not supported)
+     detection_bbox_probabilistic: false
+
+     # In detection mode, it is possible to set a confidence threshold
+     # to discard blurry predictions with low confidence
+     detection_confidence_thr: 0.2
+
+     # In detection mode it is possible to specify a tolerance zone outside image bounds within which clipping is applied. The boxes within these zones are 
+     # clipped to the image dimensions. For boxes outside the specified tolerance, an error is raised instead.
+     detection_bbox_clipping: 20%
+
+   # Settings for the data evaluation routine
+   data_evaluation:
+     examine: true
+
+   # Settings for the AI baseline performance evaluation (which should be always performed!)
+   performance:
+     examine: true
+
+   # Settings for the evaluation of confidence calibration
+   uncertainty:
+     examine: true
+
+     # Number of bins used for ECE calculation, required for classification and detection evaluation
+     ece_bins : 20
+
+     # During ECE/D-ECE computation, bins with a number of samples less than this threshold are ignored
+     # Required for classification and detection evaluation
+     ece_sample_threshold: 10
+
+     # Number of bins used for D-ECE calculation (object detection), required for detection evaluation
+     dece_bins: 5
+
+   # Settings for the evaluation of model fairness
+   fairness:
+     examine: true
+
+     # Specify sensitive attributes that are used for fairness evaluation. For each of these attributes,
+     # you need to specify the classes for which the attributes are actually valid (out of the labels
+     # within 'distinct_classes' list). You can also leave it empty or type "all" to mark validity for all classes.
+     sensitive_attributes:
+       gender: ["no person", "person"]
+       age: "all"
+
+
+General application settings
+----------------------------
+
+In the following, we give a detailed overview about all possible general configuration settings.
+
+.. list-table:: Meta information settings describing the customer information, model properties, and used dataset.
+   :widths: 35 10 55
+   :header-rows: 1
+
+   * - Key/Specifier
+     - Dtype
+     - Description
+   * - :code:`meta/model/name`
+     - string
+     - Name of the AI model used to generate predictions.
+   * - :code:`meta/model/revision`
+     - string
+     - Revision of the AI model used to generate predictions.
+   * - :code:`meta/dataset/name`
+     - string
+     - Name of the dataset holding the ground truth information.
+   * - :code:`meta/dataset/revision`
+     - string
+     - Revision of the dataset holding the ground truth information.
+
+
+.. list-table:: General application settings
+   :widths: 35 10 55
+   :header-rows: 1
+
+   * - Key/Specifier
+     - Dtype
+     - Description
+   * - :code:`task`
+     - string
+     - Selection of the examination task. Can be one of: "classification" (binary/multi-class classification), "detection" (image-based object detection).
+   * - :code:`language`
+     - string
+     - Language of the final evaluation report. Can be one of: "en" (US English), "de" (German).
+   * - :code:`task_settings/distinct_classes`
+     - list of int or string
+     - List of distinct classes that can occur within the dataset. Only to be provided in case of Classification or Detection
+   * - :code:`task_settings/binary_positive_label`
+     - int or string
+     - In binary classification (when 'distinct_classes' has length of 2), you must specify a positive label out of
+       the list of available classes. This is important since you only give a single "confidence" for each prediction,
+       targeting the probability of the positive class.
+   * - :code:`task_settings/detection_bbox_format`
+     - string
+     - Bounding-box format of the provided boxes in object detection mode. Can be one of: "xyxy" (xmin, ymin, xmax, ymax),
+       "xywh" (xmin, ymin, width, height), or "cxcywh" (center x, center y, width, height).
+   * - :code:`task_settings/detection_bbox_ious`
+     - list of float
+     - List with IoU scores (in [0, 1] interval) used for object detection evaluation.
+       Note: the IoU score "0.5" is always active for the evaluation. You can specify more IoU scores if you want.
+   * - :code:`task_settings/detection_bbox_matching`
+     - string
+     - String with bounding box matching strategy within object detection evaluation. The strategy of matching the predicted bounding boxes
+       with the ground truth ones must be either "exclusive," where each prediction and each ground truth are assigned to at most a single counterpart,
+       or "max," with maximum/non-exclusive bounding box matching, where each ground truth object may have multiple predictions assigned to it.
+       The default is "exclusive".
+   * - :code:`task_settings/detection_bbox_probabilistic`
+     - boolean
+     - Currently not used.
+   * - :code:`task_settings/detection_confidence_thr`
+     - float
+     - In detection mode, it is possible to set a confidence threshold (in [0, 1] interval) to discard blurry predictions with low confidence.
+   * - :code:`task_settings/detection_bbox_clipping`
+     - int
+     - In detection mode, it is possible to specify a tolerance zone outside the image in case of boxes that are out of image bounds.
+       This can be ommitted, in which case no clipping is applied and an error is raised if a box is out of image bounds.
+       Alternatively, it can be set to relative(relative to image width and height)% ([0-100]%) or absolute values in px ([int]px). 
+       These specify the dimensions outside the image, such that if any boxes extend into this tolerance zone, they will get clipped to the image dimensions. 
+       If boxes exceed these tolerance zones no clipping will be applied, an error will be raised instead.
+
+Configuration of safety evaluation
+----------------------------------
+
+.. list-table:: Configuration settings for dataset evaluation.
+   :widths: 35 10 55
+   :header-rows: 1
+
+   * - Key/Specifier
+     - Dtype
+     - Description
+   * - :code:`data_evaluation/examine`
+     - boolean
+     - Enables/disables the data evaluation for the final rating & reporting.
+
+.. list-table:: Configuration settings for AI performance evaluation.
+   :widths: 35 10 55
+   :header-rows: 1
+
+   * - Key/Specifier
+     - Dtype
+     - Description
+   * - :code:`performance/examine`
+     - boolean
+     - Enables/disables the AI performance evaluation (e.g., accuracy, mAP, precision, recall, etc.) for the final reporting.
+
+.. list-table:: Configuration settings for uncertainty evaluation (uncertainty calibration).
+   :widths: 35 10 55
+   :header-rows: 1
+
+   * - Key/Specifier
+     - Dtype
+     - Description
+   * - :code:`uncertainty/examine`
+     - boolean
+     - Enables/disables the uncertainty evaluation (uncertainty calibration, e.g., computation of the Expected Calibration Error (ECE)) for the final rating & reporting.
+   * - :code:`uncertainty/ece_bins`
+     - int
+     - Number of bins used for the computation of the Expected Calibration Error (ECE), Maximum Calibration Error (MCE),
+       and the respective reliability diagrams. The default value is 20.
+   * - :code:`uncertainty/ece_sample_threshold`
+     - int
+     - Sample threshold used for the computation of the ECE, MCE, D-ECE, and the respective reliability diagrams to discard
+       bins with an amount of samples below this threshold. Discarding bins with only a small amount of samples is
+       recommended to stabilize the ECE/MCE computations. The default value is 10.
+   * - :code:`uncertainty/dece_bins`
+     - int
+     - Number of bins used for the computation of the Decetion Expected Calibration Error (D-ECE) (object detection only)
+       and the respective reliability diagrams. The D-ECE is the counterpart of the ECE for position-dependent calibration
+       evaluation of object detection tasks. The default value is 5.
+
+.. list-table:: Configuration settings for AI fairness evaluation.
+   :widths: 35 10 55
+   :header-rows: 1
+
+   * - Key/Specifier
+     - Dtype
+     - Description
+   * - :code:`fairness/examine`
+     - boolean
+     - Enables/disables the AI fairness evaluation for the final rating & reporting.
+   * - :code:`fairness/sensitive_attributes/<label name>`
+     - optional string or list of int/string
+     - Specify one or multiple sensitive attributes (e.g., gender or age) that are used for fairness evaluation.
+       The value of this entry is a list of target classes (given by "distinct_classes" parameter) for which the
+       sensitive attribute is valid. For example, if "distinct_classes" specifies labels "person" and "car", a
+       sensitive attribute for "gender" might only be valid for target label "person". If the attribute is valid for
+       all specified target labels, you can also leave the value empty or pass "all".