Object Detection (2D)#
Experimental Feature
This pre-built workflow is an experimental feature. Experimental features are under active development and may occasionally undergo API-breaking changes.
Object Detection (OD) is a computer vision task that aims to classify and locate objects of interest presented in an image. So, it can be viewed as a combination of localization and classification tasks.
This pre-built workflow is prepared for a 2D Object Detection problem and here is an example of using this workflow on the COCO dataset.
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Example: Object Detection (2D) ↗
2D Object Detection using the COCO dataset
TestSample
#
GroundTruth
#
Bases: GroundTruth
Ground truth type for the pre-built 2D Object Detection workflow.
bboxes: List[Union[LabeledBoundingBox, LabeledPolygon]]
instance-attribute
#
The ground truth LabeledBoundingBoxes or
LabeledPolygons associated with an image.
ignored_bboxes: List[Union[LabeledBoundingBox, LabeledPolygon]] = dataclasses.field(default_factory=list)
class-attribute
instance-attribute
#
The ground truth LabeledBoundingBoxes or
LabeledPolygons to be ignored in evaluation associated with an
image.
Inference
#
Bases: Inference
Inference type for the pre-built 2D Object Detection workflow.
bboxes: List[Union[ScoredLabeledBoundingBox, ScoredLabeledPolygon]]
instance-attribute
#
The inference ScoredLabeledBoundingBoxes or
ScoredLabeledPolygons associated with an image.
ignored: bool = False
class-attribute
instance-attribute
#
Whether the image (and its associated inference bboxes) should be ignored in evaluating the results of the model.
ThresholdConfiguration
#
Bases: EvaluatorConfiguration
Confidence and IoU ↗ threshold configuration for the pre-built 2D Object Detection workflow. Specify a confidence and IoU threshold to apply to all classes.
threshold_strategy: Union[Literal['F1-Optimal'], float] = 'F1-Optimal'
class-attribute
instance-attribute
#
The confidence threshold strategy. It can either be a fixed confidence threshold such as 0.3 or 0.75, or
the F1-optimal threshold by default.
iou_threshold: float = 0.5
class-attribute
instance-attribute
#
The IoU ↗ threshold, defaulting to 0.5.
min_confidence_score: float = 0.0
class-attribute
instance-attribute
#
The minimum confidence score to consider for the evaluation. This is usually set to reduce noise by excluding inferences with low confidence score.
multiclass: Optional[bool] = None
class-attribute
instance-attribute
#
Optional flag to choose whether the SingleClass or Multiclass evaluator is run. If unset, the evaluator is determined based on the number of distinct labels in test data.
ObjectDetectionEvaluator(configurations=None)
#
Bases: Evaluator
This ObjectDetectionEvaluator transforms inferences into metrics for the object detection workflow for a
single class or multiple classes.
When a ThresholdConfiguration is
configured to use an F1-Optimal threshold strategy, the evaluator requires that the first test case retrieved for
a test suite contains the complete sample set.
For additional functionality, see the associated base class documentation.