update model description

trapdata/ml/models/classification.py

@@ -105,26 +105,20 @@ def forward(self, x):
 
  return x
 
-class Resnet50Classifier_KG(InferenceBaseClass):
+class Resnet50Classifier_Turing(InferenceBaseClass):
  # function to run the Turing models
- logger.info("KG: Resnet50Classifier_KG")
+ logger.info("KG: Resnet50Classifier_Turing")
  input_size = 300
 
  def get_model(self):
  num_classes = len(self.category_map)
- logger.info(f"KG: num_classes {num_classes}")
-
  model = Resnet50(num_classes=num_classes)
  model = model.to(self.device)
-
- # # state_dict = torch.hub.load_state_dict_from_url(weights_url)
  checkpoint = torch.load(self.weights, map_location=self.device)
-
- # # The model state dict is nested in some checkpoints, and not in others
+ # The model state dict is nested in some checkpoints, and not in others
  state_dict = checkpoint.get("model_state_dict") or checkpoint
 
  model.load_state_dict(state_dict)
- # model.load_state_dict(state_dict)
  model.eval()
  return model
 
@@ -306,11 +300,11 @@ class QuebecVermontMothSpeciesClassifierMixedResolution(
  )
 
 class TuringCostaRicaSpeciesClassifier(
- SpeciesClassifier, Resnet50Classifier_KG
+ SpeciesClassifier, Resnet50Classifier_Turing
 ):
  name = "Turing Costa Rica Species Classifier"
  description = (
- "Trained on 13th May 2024 by Turing team using Resnet50 model."
+ "Trained on 4th June 2024 by Turing team using Resnet50 model."
  )
  weights_path = (
  "turing-costarica_v03_resnet50_2024-06-04-16-17_state.pt"
@@ -321,7 +315,7 @@ class TuringCostaRicaSpeciesClassifier(
 
 
 class TuringUKSpeciesClassifier(
- SpeciesClassifier, Resnet50Classifier_KG
+ SpeciesClassifier, Resnet50Classifier_Turing
 ):
  name = "Turing UK Species Classifier"
  description = (