Training Workflow
Learn about the training workflow in the Pytorch Image Model.
We'll cover the following...
From a high-level point of view, the entire training process consists of the following steps:
Initialization
- Initialize the parameters for distributed training. This step is only applicable if
args.distributed
isTrue
. - Initialize a value as the seed. This ensures that the results are reproducible.
- Create the desired model architecture with the
timm.create_model
function. - 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':
...