loss[1] = self.bce(pred_scores,target_scores).sum()/target_scores_sum 其中预测pred_scores: b x 8400 x cls_num; target_scores: b x 8400 x cls_num, 相当于对于每个box,其cls_num个分类都视为二分类,并进行交叉熵运算。 (2) 边框回归损失 边框回归,采用的是DFL Loss + CIOU Loss target_b...
target_bboxes[bx8400x4] (target_bboxes需要缩放到特征图尺度,即除以相应的 stride),在计算损失时,分别与预测的pred_bboxes计算Ciou Loss, 同时与pred_regs(预 测的anchors中心点到各边的距离)计算回归DFL Loss。 c. 训练mask值:fg_mask [bx8400],对8400个anchor进行正负样本标记,计算损失过程 中通过fg_...
box_loss:边界框损失(Bounding Box Loss),用于衡量模型预测的边界框与真实边界框之间的差异。在YOLOv8中,通常使用GIoU(Generalized Intersection over Union)或CIoU(Complete Intersection over Union)等损失函数来计算边界框损失,以优化模型的定位精度。 cls_loss:分类损失(Classification Loss),用于衡量模型预测的物体类别...
・Is it correct to understand that the Box_loss of the output results after training is CIOU? ・It is my understanding that CIOU + DFL constitute the loss for the Bbox. Could you clarify how DFL specifically contributes to reducing the Bbox loss?
Search before asking I have searched the YOLOv8 issues and discussions and found no similar questions. Question Hello, I have been looking into the v8DetectionLoss module and I noticed something unexpected in the DFL implementation. Base...