The meaning of the x/lr0, x/lr1 and x/lr2 graphs output by weights&biases

[gomsis]

What do the x/lr0, x/lr1 and x/lr2 graphs displayed mean when viewing the yolov5 study results in weightS&biases?
I hope you will be able to provide the information.

[glenn-jocher]

@gomsis 3 different learning rates based on 3 parameter groups here:

yolov5/train.py

Lines 153 to 162 in 2611477

	g = [], [], [] # optimizer parameter groups
	bn = tuple(v for k, v in nn.__dict__.items() if 'Norm' in k) # normalization layers, i.e. BatchNorm2d()
	for v in model.modules():
	if hasattr(v, 'bias') and isinstance(v.bias, nn.Parameter): # bias
	g[2].append(v.bias)
	if isinstance(v, bn): # weight (no decay)
	g[1].append(v.weight)
	elif hasattr(v, 'weight') and isinstance(v.weight, nn.Parameter): # weight (with decay)
	g[0].append(v.weight)

[gomsis]

So this calculates the learning rate.Thank you for telling me.
For example,In the graph in this image, the learning rate is decreasing as the number of steps increases.
What does this mean?
Thank you for your cooperation.

[glenn-jocher]

@gomsis YOLOv5 🚀 Learning Rate (LR) schedulers follow predefined LR curves for the fixed number of --epochs defined at training start (default=300), and are designed to fall to a minimum LR on the final epoch for best training results.

[gomsis]

Thank you very much.
I understood very well.