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Getting Started with Image Classification with PyTorch

Training Workflow

Understand the complete training workflow for building image classification models with PyTorch. Explore initialization steps, data augmentation strategies, optimizer configuration, distributed training, and evaluation techniques to effectively train and fine-tune models.

We'll cover the following...

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':
...