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+ + + + + + + + + + + + + + +ODMetrics main class implementation.
+ + + +ODMetrics
+
+
+ODMetrics class.
+Compute the Mean-Average-Precision (mAP) and Mean-Average-Recall (mAR) +for Object Detection.
+ +src/od_metrics/od_metrics.py
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+1035 +1036 +1037 +1038 +1039 +1040 +1041 +1042 +1043 +1044 +1045 +1046 |
|
__init__(iou_thresholds=_Missing, recall_thresholds=_Missing, max_detection_thresholds=_Missing, area_ranges=_Missing, class_metrics=False, box_format='xywh')
+
+Initialize.
+ + + +Parameters:
+Name | +Type | +Description | +Default | +
---|---|---|---|
iou_thresholds |
+
+ float | list[float] | ndarray | type[_Missing]
+ |
+
+
+
+ IoU thresholds.
+If not specified ( |
+
+ _Missing
+ |
+
recall_thresholds |
+
+ float | list[float] | ndarray | type[_Missing]
+ |
+
+
+
+ Recall thresholds.
+If not specified ( |
+
+ _Missing
+ |
+
max_detection_thresholds |
+
+ int | list[int] | ndarray | None | type[_Missing]
+ |
+
+
+
+ Thresholds on maximum detections per image.
+If not specified ( |
+
+ _Missing
+ |
+
area_ranges |
+
+ dict[str, list[float] | ndarray] | None | type[_Missing]
+ |
+
+
+
+ Area ranges.
+If not specified, the default (COCO) is used and corresponds to:
+ |
+
+ _Missing
+ |
+
class_metrics |
+
+ bool
+ |
+
+
+
+ Option to enable per-class metrics (See |
+
+ False
+ |
+
box_format |
+
+ Literal['xyxy', 'xywh', 'cxcywh']
+ |
+
+
+
+ Bounding box format.
+Supported formats are: |
+
+ 'xywh'
+ |
+
Returns:
+Type | +Description | +
---|---|
+ None
+ |
+
+
+
+
+ |
+
src/od_metrics/od_metrics.py
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|
compute(y_true, y_pred, extended_summary=False)
+
+Compute metrics.
+ + + +Parameters:
+Name | +Type | +Description | +Default | +
---|---|---|---|
y_true |
+
+ list[dict]
+ |
+
+
+
+ A list consisting of dictionaries each containing +the key-values: each dictionary corresponds to the ground truth +of a single image. +Parameters that should be provided per dict: +
|
+ + required + | +
y_pred |
+
+ list[dict]
+ |
+
+
+
+ A list consisting of dictionaries each containing +the key-values: each dictionary corresponds to the predictions +of a single image. +Parameters that should be provided per dict: +
|
+ + required + | +
extended_summary |
+
+ bool
+ |
+
+
+
+ Option to enable extended summary with additional metrics
+including
|
+
+ False
+ |
+
Returns:
+Type | +Description | +
---|---|
+ dict[str, float | int | list[int] | dict | ndarray | partial]
+ |
+
+
+
+ The format of the output string metric ids is defined as: +
If a field is |
+
src/od_metrics/od_metrics.py
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|
iou(y_true, y_pred, iscrowd=None)
+
+Calculate IoU between bounding boxes.
+Single bounding boxes must be in "xywh"
format, i.e.
+ [xmin, ymin, width, height]
The standard iou of a ground truth y_true
and detected
+y_pred
object is:
+ +
+ +For crowd
regions, COCO use a modified criteria:
+ If a y_true
object is marked as iscrowd
, it is permissible
+ for a detected object y_pred
to match any subregion of the y_true
.
+ Choosing y_true'
in the crowd y_true
that best matches the y_pred
+ can be done using:
+
+ Since by definition:
+
+ computing:
+
+For crowd regions in ground truth, this modified criteria for IoU
+is applied.
Raises:
+Type | +Description | +
---|---|
+ ValueError
+ |
+
+
+
+ If |
+
Parameters:
+Name | +Type | +Description | +Default | +
---|---|---|---|
y_true |
+
+ ndarray | list
+ |
+
+
+
+
|
+ + required + | +
y_pred |
+
+ ndarray | list
+ |
+
+
+
+
|
+ + required + | +
iscrowd |
+
+ ndarray | list[bool] | list[int] | None
+ |
+
+
+
+ Whether |
+
+ None
+ |
+
Returns:
+Type | +Description | +
---|---|
+ ndarray
+ |
+
+
+
+ IoU vector of shape |
+
src/od_metrics/od_metrics.py
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