yolov8Total LossVal Loss
时间: 2023-09-15 19:19:21 浏览: 49
根据引用中的代码,在train.py文件中,total loss被定义为'metrics/acc'。而根据引用中的代码,在与tensorboard相关的utils/loggers/__init__.py文件中,'metrics/acc'被添加到了self.keys列表中。因此,yolov8的total loss是'metrics/acc'。
对于val loss,根据引用中的代码,在train.py文件中,val loss被定义为'val/total_loss'。而根据引用中的代码,在与tensorboard相关的utils/loggers/__init__.py文件中,'val/total_loss'被添加到了self.keys列表中。因此,yolov8的val loss是'val/total_loss'。<span class="em">1</span><span class="em">2</span><span class="em">3</span><span class="em">4</span>
相关问题
yolov8 total_loss
根据引用中提到的代码,本文只展示了yolov5代码的部分改动,因此无法提供关于yolov8 total_loss的信息。在引用和引用中的代码中,我们可以看到对total_loss进行了计算和更新的操作,但这仅适用于yolov5模型。对于yolov8模型,我们无法确定其具体的实现方式和计算方法。如果您对yolov8模型的total_loss感兴趣,建议参考yolov8的官方文档或相关的研究论文以获取更详细的信息。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
- *1* *2* *3* [yolov5可视化total_loss和acc](https://blog.csdn.net/qq_43353981/article/details/128507651)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_1"}}] [.reference_item style="max-width: 100%"]
[ .reference_list ]
yolov5中compute loss函数源码
以下是YOLOv5中的compute_loss函数源代码:
```
def compute_loss(pred, targets, model):
"""
Compute loss for YOLOv5.
Args:
pred: (tensor) predictions of the model, shape [batch_size, num_anchors*(num_classes+5), grid_size, grid_size]
targets: (tensor) targets of the model, shape [batch_size, num_targets, 6], where the last dimension represents
[class_id, x, y, w, h, scale], where (x, y) is the center of the object, (w, h) is the width and height
of the object, and scale is the scale factor (usually 1).
model: (nn.Module) YOLOv5 model
Returns:
loss: (float) total loss
loss_items: (dict) a dictionary of loss items
"""
# Constants
lcls, lbox, lobj = torch.zeros(1, device=pred.device), torch.zeros(1, device=pred.device), torch.zeros(1, device=pred.device)
tcls, tbox, indices, anchors = build_targets(pred, targets, model)
num_samples = len(indices)
# Compute losses
if num_samples > 0:
# Classification loss
lcls = F.binary_cross_entropy_with_logits(pred[..., 5:5 + model.num_classes], tcls, reduction='sum') / num_samples
# Box regression loss
lbox = F.mse_loss(pred[..., :4], tbox, reduction='sum') / num_samples
# Objectness loss
obj = pred[..., 4:5].sigmoid()
lobj = F.binary_cross_entropy(obj, indices[..., 0].float(), reduction='sum') / num_samples
indices = indices[obj > 0.5]
obj = obj[obj > 0.5]
boxes = pred[..., :4][obj > 0.5]
tbox = tbox[obj > 0.5]
lbox += F.mse_loss(boxes, tbox, reduction='sum') / num_samples
# Weighted sum of losses
loss = lobj * model.lambda_obj + lcls * model.lambda_cls + lbox * model.lambda_box
loss_items = {'loss': loss, 'obj': lobj, 'cls': lcls, 'box': lbox}
return loss, loss_items
```
这个函数计算YOLOv5的总损失,包括分类损失,框框回归损失和目标性损失。同时,它还会返回一个字典,其中包含每个损失项的值。