# EfficientAD quick-start configuration.
# Keep normalize=true: EfficientAD's teacher uses ImageNet preprocessing.
dataset_path: "./dataset"
class_name: "bottle"
resize: [224, 224]
crop_size: null
normalize: true
norm_mean: [0.485, 0.456, 0.406]
norm_std: [0.229, 0.224, 0.225]

algorithm: "efficientad"
efficientad_model_size: "s"
efficientad_lr: 0.0001
efficientad_weight_decay: 0.00001
efficientad_epochs: 1
efficientad_pretrained_teacher: true
# The threshold is calibrated from normal training scores.
efficientad_threshold_quantile: 0.995

model_data_path: "./distributions"
output_model: "model.pt"
model: "model.pt"
batch_size: 1
device: "cpu"
run_name: "efficientad_exp"
log_level: "INFO"

img_path: "./dataset/bottle/test"
thresh: null

# Visualization/evaluation/export defaults.
enable_visualization: true
save_visualizations: true
viz_output_dir: "./visualizations/"
format: "onnx"
opset: 18
precision: "fp32"
dynamic_batch: true