Dice系数 Dice实际计算 Dice Loss Dice Loss存在两个问题需要注意: mIoU MloU定义与单个IoU理解 MloU计算 F-score 和 Dice Loss结果不一致原因及分析 关键词 mDice,mIoU,mFscore Motivation MMSeg中IoUMetric 中有三个评价指标,对三个指标加强理解并进行介绍 config文件中验证阶段可以在tensorboard中显示的评价指标有...
naive_dice: Union[bool, None] = False, avg_factor: Union[int, None] = None, ignore_index: Union[int, None] = 255) -> float: """Calculate dice loss, there are two forms of dice loss is supported: - the one proposed in `V-Net: Fully Convolutional Neural ...
loss[loss_decode.loss_name] = loss_decode( mask_point_preds, mask_point_targets, avg_factor=num_total_masks) else: assert False, "Only support 'CrossEntropyLoss' and" \ " 'DiceLoss' in mask loss" else: assert False, "Only support for 'loss_cls' and 'loss_mask'" losse...
if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set 'avg_non_ignore=True'.
https://github.com/JunMa11/SegLoss/blob/master/losses_pytorch/dice_loss.py#L333 (Apache-2.0 License)""" import torch import torch.nn as nn import torch.nn.functional as F from ..builder import LOSSES from .utils import get_class_weight, weighted_loss @weighted_loss def tversky...
loss[loss_decode.loss_name] = loss_decode( mask_point_preds, mask_point_targets, avg_factor=num_total_masks) else: assert False, "Only support 'CrossEntropyLoss' and" \ " 'DiceLoss' in mask loss" else: assert False, "Only support for 'loss_cls' and 'loss_mask'" losse...
https://github.com/JunMa11/SegLoss/blob/master/losses_pytorch/dice_loss.py#L333 (Apache-2.0 License)""" importtorch importtorch.nnasnn importtorch.nn.functionalasF from..builderimportLOSSES from.utilsimportget_class_weight,weighted_loss