configuration.py

import transformers
from transformers import PretrainedConfig
# create a config class
class EffNetPlantDiseaseConfig(PretrainedConfig):
  # tells hugging face the model family type
  # could be used to register class with AutoConfig
  model_type = "effnetv2_s_plant_disease"

  def __init__(self,
               num_classes=38,
               image_size=224,
               dropout_rate = 0.2,
               class_names = None,
               **kwargs):
    super().__init__(**kwargs)
    # assign inputs 
    self.num_classes = num_classes
    self.image_size = 224 # unused but will keep just in case
    self.dropout_rate = dropout_rate
    # create class dictionaries from the label (image class) names 
    if class_names:
      self.id2label = {str(i): name for i, name in enumerate(class_names)}
      self.label2id = {name: str(i) for i, name in enumerate(class_names)}
    else:
      self.id2label = {str(i): f"class_{i}" for i in range(num_classes)}
      self.label2id = {f"class_{i}": str(i) for i in range(num_classes)}