Metrics¶
We provide a few common metrics for object detection
Metrics for object detection tasks.
-
mira.metrics.
classification_metrics
(true_collection, pred_collection)[source]¶ Compute precision/recall/f1 for each class.
-
mira.metrics.
crop_error_examples
(true_collection, pred_collection, threshold=0.3, iou_threshold=0.1)[source]¶ Get crops of true positives, false negatives, and false positives.
- Parameters
true_collection (
SceneCollection
) – A collection of the ground truth scenes.pred_collection (
SceneCollection
) – A collection of the predicted scenes.threshold – The score threshold for selecting annotations from predicted scenes.
iou_threhsold – The IoU threshold for counting a box as a true positive.
- Return type
List
[Dict
[str
,List
[Annotation
]]]- Returns
A list of dicts with “tps”, “fps”, and “fns” with the same length of the input collections. The values in each dict are crops from the original image.
-
mira.metrics.
mAP
(true_collection, pred_collection, iou_threshold=0.5)[source]¶ Compute mAP (mean average precision) for a pair of scene collections.
- Parameters
true_collection (
SceneCollection
) – The true scene collectionpred_collection (
SceneCollection
) – The predicted scene collectioniou_threshold (
float
) – The threshold for detection
- Return type
Dict
[str
,float
]- Returns
mAP class scores
-
mira.metrics.
mIOU
(true_collection, pred_collection, threshold=0.5)[source]¶ Compute mIOU for two scene collections
- Return type
Dict
[str
,float
]
-
mira.metrics.
precision_recall_curve
(true_collection, pred_collection, iou_threshold=0.5)[source]¶ Compute the precision-recall curve for each of the classes.
- Parameters
true_collection (
SceneCollection
) – The true scene collectionpred_collection (
SceneCollection
) – The predicted scene collectioniou_threshold (
float
) – The threshold for detection
- Return type
Dict
[str
,ndarray
]- Returns
A dict with category names as keys and array of shape (Ni, 3) which is the precision, recall, and score for each of the predicted boxes for the category.