Different learning rate in Warmup

Pichenze commented 9 months ago

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Question

On warmup processing, most weight's learning rate start from 0, but bias's learning rate start from 0.1 which is higher than base learning rate 0.01. I would like to know the reason of this setting.

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glenn-jocher commented 9 months ago

@Pichenze hello! Thanks for your question regarding the learning rate settings during the warmup phase in YOLOv5.

The reason for having a higher initial learning rate for biases during warmup is to help the model learn faster in the early stages of training. Biases tend to require less data to fit appropriately and can be adjusted more aggressively without as much risk of overfitting. This is especially true in the case of batch normalization layers, where the biases can be more important for the initial adjustments.

The warmup phase is designed to stabilize the training, and starting with a higher learning rate for biases can contribute to a more robust and quicker convergence. It's a common practice in training deep neural networks to use different learning rates for different types of parameters.

I hope this clarifies your question! If you need more detailed information, please refer to our documentation or consider exploring the discussions and issues further for insights from the community. Happy training! 😊

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sriram-dsl commented 6 months ago

@glenn-jocher can we train head of the model at different learning rate and body at different learning rate is it possible

glenn-jocher commented 6 months ago

Hello! Yes, it is indeed possible to train different parts of the YOLOv5 model (like the head and the body) with different learning rates. This technique is often used to fine-tune specific parts of a model more carefully.

A practical way to achieve this in PyTorch (which YOLOv5 uses) is by defining separate parameter groups in the optimizer and assigning different learning rates to these groups. Here's a simplified example:

from torch import optim

model = ... # Your YOLOv5 model
optimizer = optim.SGD([
    {'params': model.head.parameters(), 'lr': 0.01},  # Higher learning rate for the head
    {'params': model.body.parameters(), 'lr': 0.001}, # Lower learning rate for the body
], lr=1e-3, momentum=0.9, weight_decay=0.0005)

This way, during training, the head and body parts of your model will be updated using their respective learning rates. Adjust the parameters and learning rates as necessary for your specific use case.

Happy training! 😊

sriram-dsl commented 6 months ago

thanks @glenn-jocher can you please tell me where do i need to update these parameters is it in train.py https://github.com/ultralytics/yolov5/blob/b599ae42d9adb8bcb96a1de6ad093436aac9fe6b/train.py#L217 here

  # Optimizer
    nbs = 64  # nominal batch size
    accumulate = max(round(nbs / batch_size), 1)  # accumulate loss before optimizing
    hyp["weight_decay"] *= batch_size * accumulate / nbs  # scale weight_decay
    optimizer = smart_optimizer(model, opt.optimizer, hyp["lr0"], hyp["momentum"], hyp["weight_decay"])

it wiil be


from torch import optim
....

  # Optimizer
    nbs = 64  # nominal batch size
    accumulate = max(round(nbs / batch_size), 1)  # accumulate loss before optimizing
    hyp["weight_decay"] *= batch_size * accumulate / nbs  # scale weight_decay
    optimizer = optim.SGD(model, opt.optimizer, [
    {'params': model.head.parameters(), 'lr0': 0.01},  # Higher learning rate for the head
    {'params': model.body.parameters(), 'lr0': 0.001}, # Lower learning rate for the body
], hyp["momentum"], hyp["weight_decay"])

or else in hyp_scratch_low.yaml and pass this with --hyp tag or something else

glenn-jocher commented 6 months ago

@sriram-dsl hi there! You're on the right track. To customize the learning rates for different parts of the model, you will indeed need to adjust the parameters within the train.py file. However, your current code snippet needs a slight correction to properly configure the optimizer.

You don’t need to modify the hyp_scratch_low.yaml for this purpose. Instead, directly modifying the optimizer in train.py is the correct approach. Here’s how you can adjust your code snippet:

from torch import optim

# Optimizer
nbs = 64  # nominal batch size
accumulate = max(round(nbs / batch_size), 1)  # accumulate loss before optimizing
hyp["weight_decay"] *= batch_size * accumulate / nbs  # scale weight_decay

# Assuming model.head and model.body are the correct paths to your model's components
optimizer = optim.SGD([
    {'params': model.module.head.parameters(), 'lr': hyp["lr0"] * 10},  # Adjust head LR
    {'params': model.module.body.parameters(), 'lr': hyp["lr0"]}  # Base LR for body
], lr=hyp["lr0"], momentum=hyp["momentum"], weight_decay=hyp["weight_decay"])

Make sure to replace model.module.head.parameters() and model.module.body.parameters() with the correct references to head and body parameters in your model.

Note: This customization might require additional adjustments based on your exact model structure and desired configuration.

Happy coding! 😊

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