Training Workflow

From a high-level point of view, the entire training process consists of the following steps:

Initialization

1. Initialize the parameters for distributed training. This step is only applicable if args.distributed is True.
2. Initialize a value as the seed. This ensures that the results are reproducible.
3. Create the desired model architecture with the timm.create_model function.
4. Initialize the configuration for datasets based on the default setting of the model. Each model has its own setting, which typically