我是新手,所以请善待我。 self.metrics = torchmetrics.IoU(num_classes=n_classes) 我不知道如何使用jaccard。
What does this PR do? Preventing future collision with adding detection IoU metrics and in docs we say that our IoU is known as jaccard index anyway Before submitting Was this discussed/approved ...
Corrects a few issues with the type hints for iou_type: We sometimes use str and other times use Literal["bbox", "segm"] tuple[foo] means a tuple of length 1, use ... to make it a variadic tuple Note that there are several other examples of the latter issue in the codebase, I...
Renamed IoU -> Jaccard Index (#662) Renamed text WER metric: (#714) functional.wer->functional.word_error_rate WER->WordErrorRate Renamed correlation coefficient classes: (#710) MatthewsCorrcoef->MatthewsCorrCoef PearsonCorrcoef->PearsonCorrCoef SpearmanCorrcoef->SpearmanCorrCoef Renamed audio STOI metr...
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if len(gt_label_mask) == 0 or len(det_label_mask) == 0: # return None nb_iou_thrs = len(self.iou_thresholds) nb_gt = len(gt) nb_det = len(det) return { "dtMatches": torch.zeros((nb_iou_thrs, nb_det), dtype=torch.bool, device=self.device), "gtMatches": torch.zeros(...
MeanIoU class DSC class (Dice Similarity Coefficient) F1Score class RSquared class Hinge class SquaredHinge class LogCoshError class Accuracy class KLDivergence class CosineSimilarity class AUC class BinaryCrossEntropy class CategoricalCrossEntropy class ...
detection.iou import intersection_over_union # noqa: F401 from torchmetrics.functional.detection.giou import generalized_intersection_over_union from torchmetrics.functional.detection.iou import intersection_over_union __all__.append("generalized_intersection_over_union") __all__.append("intersection_...
Example code for one specific IoU, one specific box size, class, and max number of detections allowed: target_iou = 0.5 # IoU for which we want to extract the metrics idx_iou = np.where(self.iou_thresholds.numpy() == target_iou) idx_cls = 0 # first class idx_bbox_area = 0 #...
(preds: float = [N1,C] , targets: long/bool = [N2,C] , pred_boxes = [x,x,y,y], target_boxes = [x,x,y,y], pred_grouping: long = [N1], target_grouping: long = [N2], iou_cutoff = 0.5); where the values in the grouping are in the range [0, NUM_GROUPS] so only ...