Interactively visualizing timm learning rate schedulers
Are you a PyTorch Image Models (timm
) user? Want an easier way to explore learning rate scheduler parameters? I've created a lightweight interactive dashboard to help you visualize LR schedules clearly.
Here's an example using SGDR with warm restarts:
It can be hard to understand how some parameters affect the schedule without visualizing them. For instance, see how decay_rate
and k_decay
affect the Poly scheduler:
Or the non-obvious ways warm-up can affect an LR schedule:
(1) Vanilla cosine scheduler:
(2) Adding warm-up:
(3) Adding step_on_epochs=False
:
(4) Adding warmup_prefix=True
:
Check it out on GitHub. Stars appreciated! timm-lr-scheduler-explorer
Special thanks to PyTorch vision legend Ross Wightman for tirelessly maintaining timm.