diff --git a/src/coastseg/zoo_model.py b/src/coastseg/zoo_model.py
index cb48f0a..6a23e3c 100644
--- a/src/coastseg/zoo_model.py
+++ b/src/coastseg/zoo_model.py
@@ -608,6 +608,8 @@ def set_settings(self, **kwargs):
             "use_GPU": "0",
             "implementation": "BEST",
             "model_type": "global_segformer_RGB_4class_14036903",
+            "local_model_path": "", # local path to the directory containing the model
+            "use_local_model": True,  # Use local model (not one from zeneodo)
             "otsu": False,
             "tta": False,
             "cloud_thresh": 0.5,  # threshold on maximum cloud cover
@@ -995,6 +997,39 @@ def postprocess_data(
         file_utilities.move_files(outputs_path, session_path, delete_src=True)
         session.save(session.path)
 
+    def get_weights_directory(self,model_implementation:str, model_id: str) -> str:
+        """
+        Retrieves the directory path where the model weights are stored.
+        This method determines whether to use a local model path or to download the model
+        from a remote source based on the settings provided. If the local model path is 
+        specified and exists, it will use that path. Otherwise, it will create a directory 
+        for the model and download the weights.
+        Args:
+            model_implementation (str): The implementation type of the model either 'BEST' or 'ENSEMBLE'
+            model_id (str): The identifier for the model. This is the zenodo ID located at the end of the URL
+        Returns:
+            str: The directory path where the model weights are stored.
+        Raises:
+            FileNotFoundError: If the local model path is specified but does not exist.
+        """
+
+        USE_LOCAL_MODEL = self.settings.get("use_local_model", False)
+        LOCAL_MODEL_PATH = self.settings.get("local_model_path", "")
+
+        if USE_LOCAL_MODEL and not os.path.exists(LOCAL_MODEL_PATH):
+            raise FileNotFoundError(f"The local model path does not exist at {LOCAL_MODEL_PATH}")
+
+        # check if a local model should be loaded or not
+        if USE_LOCAL_MODEL == False or LOCAL_MODEL_PATH == "":
+            # create the model directory & download the model
+            weights_directory = self.get_model_directory(model_id)
+            self.download_model(model_implementation, model_id, weights_directory)
+        else:
+            # load the model from the local model path
+            weights_directory = LOCAL_MODEL_PATH
+
+        return weights_directory
+
     def prepare_model(self, model_implementation: str, model_id: str):
         """
         Prepares the model for use by downloading the required files and loading the model.
@@ -1003,12 +1038,10 @@ def prepare_model(self, model_implementation: str, model_id: str):
             model_implementation (str): The model implementation either 'BEST' or 'ENSEMBLE'
             model_id (str): The ID of the model.
         """
-        self.clear_zoo_model()
-        # create the model directory
-        self.weights_directory = self.get_model_directory(model_id)
+        # weights_directory is the directory that contains the model weights, the model card json files and the BEST_MODEL.txt file
+        self.weights_directory = self.get_weights_directory(model_implementation, model_id)
         logger.info(f"self.weights_directory:{self.weights_directory}")
 
-        self.download_model(model_implementation, model_id, self.weights_directory)
         weights_list = self.get_weights_list(model_implementation)
 
         # Load the model from the config files
@@ -1096,6 +1129,7 @@ def run_model(
             logger.info(f"use_tta: {use_tta}")
 
             print(f"Running model {model_name}")
+            # print(f"self.settings: {self.settings}")
             self.prepare_model(model_implementation, model_name)
 
             # create a session