BCEcls = nn.BCEWithLogitsLoss(pos_weight=flow.tensor([h["cls_pw"]], device=device)) BCEobj = nn.BCEWithLogitsLoss(pos_weight=flow.tensor([h["obj_pw"]], device=device)) # 标签平滑 eps=0代表不做标签平滑-> cp=1 cn=0 / eps!=0代表做标签平滑 # cp代表正样本的标签值 cn代表负样本...
BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device)) BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device)) # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3 self.cp, self.cn = smooth_BCE(eps=h....
giou:GIoU损失收益 cls:类别损失收益 cls_pw:类别交叉熵损失正类权重 obj:是否有物体损失收益 obj_pw:是否有物体交叉熵正类权重 iou_t:iou阈值 anchor_t:多尺度anchor阈值 fl_gamma:focal loss gamma系数 hsv_h:色调Hue,增强系数 hsv_s:饱和度Saturation,增强系数 hsv_v:明度Value,增强系数 degrees:图片旋转角...
BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.Tensor([h['cls_pw']])).to(device) BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.Tensor([h['obj_pw']])).to(device) # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3 cp, cn = smooth_BCE(eps=0.0) # Focal loss ...
cls_pw:类别交叉熵损失正类权重 obj:是否有物体损失收益 obj_pw:是否有物体交叉熵正类权重 iou_t:iou阈值 anchor_t:多尺度anchor阈值 fl_gamma:focal loss gamma系数 hsv_h:色调Hue,增强系数 hsv_s:饱和度Saturation,增强系数 hsv_v:明度Value,增强系数 ...
hyperparameters: lr0=0.01, lrf=0.1, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=...
hyperparameters: lr0=0.01, lrf=0.1, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=...
BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.Tensor([h['cls_pw']])).to(device) BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.Tensor([h['obj_pw']])).to(device) # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3 ...
(fractions ok)warmup_momentum:0.8# warmup initial momentumwarmup_bias_lr:0.1# warmup initial bias lrbox:0.05# box loss gaincls:0.5# cls loss gaincls_pw:1.0# cls BCELoss positive_weightobj:1.0# obj loss gain (scale with pixels)obj_pw:1.0# obj BCELoss positive_weightiou_t:0.20# IoU ...
分类损失(cls_loss):该损失用于判断模型是否能够准确地识别出图像中的对象,并将其分类到正确的类别中。 置信度损失(obj_loss):该损失用于衡量模型预测的框(即包含对象的矩形)与真实框之间的差异。 边界框损失(box_loss):该损失用于衡量模型预测的边界框与真实边界框之间的差异,这有助于确保模型能够准确地定位对象...