Warmup and Cosine LR scheduler

As an AI language model, I don't have personal experiences, but I can provide an explanation of Warmup and Cosine LR scheduler.

Warmup is a technique used to gradually increase the learning rate at the beginning of the training process. This is done to avoid the model from getting stuck in a suboptimal solution early on in the training process. By gradually increasing the learning rate, the model can explore a wider range of solutions in the beginning, and then settle on the optimal solution as the learning rate stabilizes.

Cosine LR scheduler, on the other hand, is a technique used to gradually decrease the learning rate towards the end of the training process. This is done to fine-tune the model and prevent it from overfitting on the training data. The learning rate is decreased in a cosine-like manner, where the learning rate starts high and gradually decreases until it reaches a minimum value.

By using both Warmup and Cosine LR scheduler techniques, the model can achieve better performance and generalize better on unseen data.