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 ...
以YOLOV8s-pose为例可以新建脚本,用下面的代码实现转换,没有ultralytics库的用pip install ultralytics from ultralytics import YOLO model = YOLO("yolov8s-pose.pt") success = model.export(format="onnx", simplify=True) # export the model to onnx format assert success print("转换成功") onnx ...
# warmup_bias_lr: 0.1 # warmup initial bias lr # box: 7.5 # box loss gain # cls: 0.5 # cls loss gain (scale with pixels) # dfl: 1.5 # dfl loss gain # pose: 12.0 # pose loss gain # kobj: 1.0 # keypoint obj loss gain # label_smoothing: 0.0 # label smoothing (fraction) ...
loss[0], loss[2] = self.bbox_loss( pred_distri, pred_bboxes, anchor_points, target_bboxes, target_scores, target_scores_sum, fg_mask )# 损失值乘以超参数中的各自增益系数loss[0] *= self.hyp.box# box gainloss[1] *= self.hyp.cls# cls gainloss[2] *= self.hyp.dfl# dfl gain...
v8SegmentationLoss和v8PoseLoss类则是专门为分割和姿态估计任务设计的损失计算类,分别扩展了v8DetectionLoss类,增加了对分割掩码和关键点的处理。最后,v8ClassificationLoss类用于计算分类任务的损失,使用交叉熵损失函数来评估模型的分类性能。整体来看,这个文件通过定义多种损失函数,为YOLO模型的训练提供了灵活的损失计算...
self.conv = Conv(c1 * 4, c2, k, s, p, g, act=act)# self.contract = Contract(gain=2)def forward(self, x):""" Applies convolution to concatenated tensor and returns the output. Input shape is (b,c,w,h) and output shape is (b,4c,w/2,h/2). ...
target_bboxes, target_scores, target_scores_sum, fg_mask ) else: loss[0] += (pred_angle * 0).sum() 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...
34. pose 35. kobj 36. label_smoothing 标签平滑 37. nbs 38. overlap_mask 39. mask_ratio 40. dropout 41. val YOLO又更新了,已经来到了YOLOv8。 全部参数表 首先罗列一下官网提供的全部参数。 https://docs.ultralytics.com/modes/train/
Pose,RepC3,RepConv,RTDETRDecoder,Segment)from ultralytics.utils importDEFAULT_CFG_DICT,DEFAULT_CFG_KEYS,LOGGER,colorstr,emojis,yaml_load from ultralytics.utils.checks import check_requirements,check_suffix,check_yaml from ultralytics.utils.loss import v8ClassificationLoss,v8DetectionLoss,v8PoseLoss,...