super-gradients/src/super_gradients/recipes/cifar10_resnet.yaml

# Cifar10 Classification Training:
# Reaches ~94.9 Accuracy after 250 Epochs
# Instructions:
# running from the command line, set the PYTHONPATH environment variable: (Replace "YOUR_LOCAL_PATH" with the path to the downloaded repo):
#   export PYTHONPATH="YOUR_LOCAL_PATH"/super_gradients/
# Then:
#   python train_from_recipe_example/train_from_recipe.py --config-name=cifar10_resnet

defaults:
  - training_hyperparams: cifar10_resnet_train_params
  - dataset_params: cifar10_dataset_params
  - arch_params: resnet18_cifar_arch_params
  - checkpoint_params: default_checkpoint_params

dataset_interface:
  cifar_10:
    dataset_params: ${dataset_params}

data_loader_num_workers: 8

load_checkpoint: False
checkpoint_params:
  load_checkpoint: ${load_checkpoint}

model_checkpoints_location: local
ckpt_root_dir:

sg_model:
  _target_: super_gradients.SgModel
  experiment_name: ${experiment_name}
  model_checkpoints_location: ${model_checkpoints_location}
  multi_gpu: Off

architecture: resnet18_cifar