Training Workflow

Learn about the training workflow in the Pytorch Image Model.

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 contains the following information:
{
  'url': '',
  'num_classes': 1000,
  'input_size': (3, 224, 224),
  'pool_size': (7, 7),
  'crop_pct':
...