属性错误:模块“torchmetrics”没有属性“IoU”问题描述 投票:0回答:1我是新手,请善待我。 我不知道如何使用jaccard。attributeerror 1个回答 0投票 从torchmetrics 导入 JaccardIndex self.metrics = JaccardIndex(task='multiclass', num_classes=n_classes)最新问题“内容多-少”Nuxt/Vue 组件不起作用 回到编...
When computing the IoU on the validation step, sometimes in the first few epochs the code run fine without any error however after a few epoch there's an error saying "Expected all tensors to be on the same device, but found at least two devices, cpu and cuda:0! (when checking argume...
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 ...
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 #...
Bugfix for empty preds or target in iou scores What does this PR do? Fixes#2805 When either preds or target have empty boxes the score for that pair should be 0. Currently instead it is a empty tensor which then are not counted towards the global average making the scores too high....
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(...
(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 ...
🚀 Feature just found this package and seems we have not yet all Image metrics :] some inspiration came from https://github.com/andrewekhalel/sewar but we aim on own implementation with torch Alternatives Mean Squared Error (MSE) Root Mea...
torchmetrics/classification/iou.py 93.75% <66.66%> (-6.25%) ⬇️ torchmetrics/classification/matthews_corrcoef.py 94.73% <66.66%> (-5.27%) ⬇️ torchmetrics/classification/precision_recall.py 88.88% <66.66%> (-4.45%) ⬇️ ...chmetrics/classification/precision_recall_curve.py 96.42%...