AveragePrecisionRecall#
- class AveragePrecisionRecall[source]#
Abstract class to calculate average precision and recall for various vision tasks.
- Parameters
- max_dets: Union[List[int], Tuple[int]], default: [1, 10, 100]
Maximum number of detections per class.
- area_range: tuple, default: (32**2, 96**2)
Slices for small/medium/large buckets.
- return_option: str, default: ‘ap’
ap: ap only, ar: ar only, None: all (not ignite compliant).
- average: str, default: none
The method for averaging over the classes. If none, returns the result per class.
- iou_range: Tuple[float, float, float], default: (0.5, 0.95, 10)
The closed range of the iou values (min, max (including), number of points)
- __init__(*args, max_dets: Union[List[int], Tuple[int]] = (1, 10, 100), area_range: Tuple = (1024, 9216), return_option: Optional[str] = 'ap', average: str = 'none', iou_range: Tuple[float, float, float] = (0.5, 0.95, 10), **kwargs)[source]#
- __new__(*args, **kwargs)#
Attributes
Methods
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Attaches current metric to provided engine. |
Get a single result from group_class_detection_label and return a matrix of IoUs. |
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Helper method to compute metric's value and put into the engine. |
Compute metric value. |
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Detaches current metric from the engine and no metric's computation is done during the run. |
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Get the value of a result by the filtering values. |
Get the mean value of the classes scores and the result values. |
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Get detections object of single image and should return confidence for each detection. |
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Get detection object of single image and should return area for each detection. |
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Get labels object of single image and should return area for each label. |
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Group detection and labels in dict of format {class_id: {'detected' [...], 'ground_truth': [...]}}. |
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Checks if current metric is attached to provided engine. |
Helper method to update metric's computation. |
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Reset metric state. |
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Helper method to start data gathering for metric's computation. |
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Update metric with batch of samples. |