loss[1] *= self.hyp.pose / batch_size # pose gain loss[2] *= self.hyp.kobj / batch_size # kobj gain loss[3] *= self.hyp.cls # cls gain loss[4] *= self.hyp.dfl # dfl gain return loss.sum() * batch_size, loss.detach() 总结 YOLO是一个为目标检测任务而知名的框架。除了在...
loss[0]*=self.hyp.box # box gain loss[1]*=self.hyp.pose/batch_size # pose gain loss[2]*=self.hyp.kobj/batch_size # kobj gain loss[3]*=self.hyp.cls # cls gain loss[4]*=self.hyp.dfl # dfl gainreturnloss.sum()*batch_size,loss.detach() 1. 2. 3. 4. 5. 6. 7. 总结 ...
是train.py文件中这个类下面的代码“class Loss:” 首先我们可以看到对于cls. loss, YOLOv8的作者,没有使用varifocal loss,(尽管前面在loss.py作者定义了varifocal loss),而是使用了BCE loss 然后bbox loss 是 CIoU和DFL 然后这三个loss加权平均得到最终的loss # cls loss # loss[1] = self.varifocal_loss(pr...
classv8DetectionLoss:"""Criterion class for computing training losses."""def__init__(self,model,tal_topk=10):# model must be de-paralleled"""Initializes v8DetectionLoss with the model, defining model-related properties and BCE loss function."""device=next(model.parameters()).device# get mod...
loss = torch.zeros(3, device=self.device) # box, cls, dfl feats = preds[1] if isinstance(preds, tuple) else preds pred_distri, pred_scores = torch.cat([xi.view(feats[0].shape[0], self.no, -1) for xi in feats], 2).split( ...
loss[0] *= self.hyp.box # box gain loss[1] *= self.hyp.cls # cls gain loss[2] *= self.hyp.dfl # dfl gain return loss.sum() * batch_size, loss.detach() # loss(box, cls, dfl) 1. 2. 3. 4. 5. 6. 7. 8. 9. ...
Loss 1) YOLOv8抛弃了以往的IOU匹配或者单边比例的分配方式,而是使用了Task-Aligned Assigner正负样本匹配方式。2)并引入了 Distribution Focal Loss(DFL) 04 Train 训练的数据增强部分引入了 YOLOX 中的最后 10 epoch 关闭 Mosiac 增强的操作,可以有效地提升精度 ...
另外2个loss,是obj loss,可以理解成是否存在目标,代表每个grid前景的置信度。cls loss是类别的loss。 obj loss: # 由于feature map尺寸不同,对不同目标检测难度也不高, # 一般小物体检测难度大一点,给不同特征图不同的权重,让模型更偏重于关注的目标,一般大特征图权重大一点,这样对小目标惩罚多一些; ...
cls 0.5 cls loss gain (scale with pixels) dfl 1.5 dfl loss gain fl_gamma 0.0 focal loss gamma (efficientDet default gamma=1.5) label_smoothing 0.0 label smoothing (fraction) nbs 64 nominal batch size overlap_mask True masks should overlap during training (segment train only) mask_ratio 4 ma...
self.loss_names = 'box_loss', 'cls_loss', 'dfl_loss' return v8.detect.DetectionValidator(self.test_loader, save_dir=self.save_dir, args=copy(self.args)) def criterion(self, preds, batch): if not hasattr(self, 'compute_loss'): ...