Training Arguments

There are over 100 arguments available in the training script of the PyTorch Image Model.

These parameters can be organized into the following categories:

• Dataset
• Model
• Optimizer
• Learning rate
• Augmentation and regularization
• Batch normalization
• Model exponential moving average
• Miscellaneous

Dataset

The training script accepts the following arguments that are related to the datasets:

• data_dir: This is the path to datasets.
• dataset: This is the dataset type. If it’s not specified, it defaults to ImageFolder/ImageTar.
• train-split: This specifies whether to split the datasets into train segments.
• val-split: This specifies whether to split the datasets into validation segments.
• dataset-download: This allows us to download datasets for supported torch and TFDS datasets.
• class-map: This is the class-to-idx mapping file path.

Model

We can specify the following arguments to configure our model:

• model: This is the name of the model to train (default is resnet50).
• pretrained: This specifies whether to start with a pretrained version of the specified network if available.
• initial-checkpoint: We use this checkpoint to initialize the model.
• resume: This specifies whether to resume the full model and optimizer state from a checkpoint.
• no-resume-opt: This prevents the resumption of the optimizer state when resuming model.
• num-classes: This is the total number of label classes.
• gp: This is the type of global pool. It accepts fast, avg, max, avgmax, or avgmaxc.
• img-size: This is the patch size of the image.
• input-size: This is the dimensions of the input image (D H W). For example, we can use --input-size 3 224 224 for input of 224 x

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