diff --git a/models/autoencoder_config.json b/pretrained_models/default/autoencoder_config.json
similarity index 100%
rename from models/autoencoder_config.json
rename to pretrained_models/default/autoencoder_config.json
diff --git a/models/best_validated_encoder.pt b/pretrained_models/default/best_validated_encoder.pt
similarity index 100%
rename from models/best_validated_encoder.pt
rename to pretrained_models/default/best_validated_encoder.pt
diff --git a/src/maggotuba/models/train_model.py b/src/maggotuba/models/train_model.py
index 0401cd76cba4377ddff3fd0856fe30646ebc5b28..5527b97429f2933f1afcdd178838bed557a12199 100644
--- a/src/maggotuba/models/train_model.py
+++ b/src/maggotuba/models/train_model.py
@@ -7,7 +7,7 @@ import torch
 import os
 import glob
 
-def train_model(backend):
+def train_model(backend, pretrained_model_instance="default"):
     # make_dataset generated or moved the larva_dataset file into data/interim/{instance}/
     #larva_dataset_file = backend.list_interim_files("larva_dataset_*.hdf5") # recursive
     larva_dataset_file = glob.glob(str(backend.interim_data_dir() / "larva_dataset_*.hdf5")) # not recursive (faster)
@@ -16,7 +16,7 @@ def train_model(backend):
     nlabels = len(dataset.labels)
     assert 0 < nlabels
     # copy the pretrained model into the model instance directory
-    pretrained_autoencoder_dir = backend.model_dir() / ".."
+    pretrained_autoencoder_dir = backend.model_dir() / "pretrained_models" / pretrained_model_instance
     config_file = None
     for file in pretrained_autoencoder_dir.iterdir():
         if not file.is_file():
@@ -28,9 +28,9 @@ def train_model(backend):
             dir = backend.model_dir().relative_to(backend.project_dir)
             config["log_dir"] = str(dir)
             # optional updates?
-            config["project_dir"] = config["exp_folder"] = str(dir)
-            config["exp_name"] = backend.model_instance
-            config["config"] = str(dir / os.path.basename(config["config"]))
+            #config["project_dir"] = config["exp_folder"] = str(dir)
+            #config["exp_name"] = backend.model_instance
+            #config["config"] = str(dir / os.path.basename(config["config"]))
             with open(str(dst), "w") as f:
                 json.dump(config, f, indent=2)
             assert config_file is None