diff --git a/.vscode/launch.json b/.vscode/launch.json
index dafa255..409d752 100644
--- a/.vscode/launch.json
+++ b/.vscode/launch.json
@@ -5,7 +5,8 @@
     "version": "0.2.0",
     "configurations": [
         {
-            "name": "Python: Module",
+            // "name": "Python: Module",
+            "name": "Debug Wyzely Detect",
             "type": "python",
             "request": "launch",
             "module": "wyzely_detect",
diff --git a/deepface-test.ipynb b/deepface-test.ipynb
index aba1306..da7eeb5 100644
--- a/deepface-test.ipynb
+++ b/deepface-test.ipynb
@@ -36,12 +36,12 @@
     "# cv2.imwrite(str(uuid_path), frame)\n",
     "# dfs = DeepFace.find(img_path=str(uuid_path), db_path = \"faces\")\n",
     "# Don't throw an error if no face is detected (enforce_detection=False)\n",
-    "dfs = DeepFace.find(frame, db_path = \"faces\", enforce_detection=False)\n",
+    "dfs = DeepFace.find(frame, db_path = \"faces\", enforce_detection=False, silent=False, model_name=\"ArcFace\", detector_backend=\"opencv\")\n",
     "# Get the identity of the person\n",
     "for i, pd_dataframe in enumerate(dfs):\n",
     "    # Sort the dataframe by confidence\n",
     "    # inplace=True means that the dataframe is modified so we don't need to assign it to a new variable\n",
-    "    pd_dataframe.sort_values(by=['VGG-Face_cosine'], inplace=True, ascending=False)\n",
+    "    # pd_dataframe.sort_values(by=['model_name=\"ArcFace\", detector_backend=\"opencv\")'], inplace=True, ascending=False)\n",
     "    print(f'On dataframe {i}')\n",
     "    print(pd_dataframe)\n",
     "    # Get the most likely identity\n",
@@ -49,7 +49,7 @@
     "    # We could use Path to get the parent directory of the image to use as the identity\n",
     "    print(f'Most likely identity: {Path(pd_dataframe.iloc[0][\"identity\"]).parent.name}')\n",
     "    # Get the most likely identity's confidence\n",
-    "    print(f'Confidence: {pd_dataframe.iloc[0][\"VGG-Face_cosine\"]}')\n",
+    "    print(f'Confidence: {pd_dataframe.iloc[0][\"model_name=\"ArcFace\", detector_backend=\"opencv\")\"]}')\n",
     "\n",
     "# uuid_path.unlink()"
    ]
@@ -67,7 +67,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "DeepFace.stream(db_path=\"faces\")"
+    "DeepFace.stream(db_path=\"faces\", model_name=\"ArcFace\", detector_backend=\"opencv\")"
    ]
   }
  ],
diff --git a/pyproject.toml b/pyproject.toml
index a5dc457..9a95876 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "wyzely-detect"
-version = "0.1.8"
+version = "0.1.0"
 description = "Recognize faces/objects in a video stream (from a webcam or a security camera) and send notifications to your devices"
 authors = ["slashtechno <77907286+slashtechno@users.noreply.github.com>"]
 license = "MIT"
diff --git a/wyzely_detect/__main__.py b/wyzely_detect/__main__.py
index 2166a24..6b126e3 100644
--- a/wyzely_detect/__main__.py
+++ b/wyzely_detect/__main__.py
@@ -33,6 +33,8 @@ def main():
     else:
         print("No .env file found")
 
+    # TODO: If possible, move the argparse stuff to a separate file
+    # It's taking up too many lines in this file
     argparser = argparse.ArgumentParser(
         prog="Wyzely Detect",
         description="Recognize faces/objects in a video stream (from a webcam or a security camera) and send notifications to your devices", # noqa: E501
@@ -87,7 +89,7 @@ def main():
         if "FACES_DIRECTORY" in os.environ and os.environ["FACES_DIRECTORY"] != ""
         else "faces",
         type=str,
-        help="The directory to store the faces. Should contain 1 subdirectory of images per person",
+        help="The directory to store the faces. Can either contain images or subdirectories with images, the latter being the preferred method", # noqa: E501
     )
     argparser.add_argument(
         "--detect-object",
@@ -118,7 +120,7 @@ def main():
     # Defaults for the stuff here and down are already set in notify.py.
     # Setting them here just means that argparse will display the default values as defualt
     # TODO: Perhaps just remove the default parameter and just add to the help message that the default is set is x
-    # TODO: Make ntfy optional in ntfy.py
+    # TODO: Make ntfy optional in ntfy.py. Currently, unless there is a local or LAN instance of ntfy, this can't run offline
     notifcation_services = argparser.add_argument_group("Notification Services")
     notifcation_services.add_argument(
         "--ntfy-url",
@@ -198,6 +200,10 @@ def main():
         # view_frame = cv2.resize(frame, (0, 0), fx=args.view_scale, fy=args.view_scale)
 
         results = model(run_frame, verbose=False)
+
+        path_to_faces = Path(args.faces_directory)
+        path_to_faces_exists = path_to_faces.is_dir() 
+
         for i, r in enumerate(results):
             # list of dicts with each dict containing a label, x1, y1, x2, y2
             plot_boxes = []
@@ -205,20 +211,24 @@ def main():
             # The following is stuff for people
             # This is still in the for loop as each result, no matter if anything is detected, will be present.
             # Thus, there will always be one result (r)
-            # TODO: Make it so this only runs if the faces directory is not empty
-            if face_details := utils.recognize_face(
-                path_to_directory=Path(args.faces_directory), run_frame=run_frame
-            ):
-                plot_boxes.append(face_details)
-                objects_and_peoples = notify.thing_detected(
-                    thing_name=face_details["label"],
-                    objects_and_peoples=objects_and_peoples,
-                    detection_type="peoples",
-                    detection_window=args.detection_window,
-                    detection_duration=args.detection_duration,
-                    notification_window=args.notification_window,
-                    ntfy_url=args.ntfy_url,
-                )
+
+            # Only run if path_to_faces exists
+            # May be better to check every iteration, but this also works
+            if path_to_faces_exists:
+                if face_details := utils.recognize_face(
+                    path_to_directory=path_to_faces,
+                    run_frame=run_frame
+                ):
+                    plot_boxes.append(face_details)
+                    objects_and_peoples = notify.thing_detected(
+                        thing_name=face_details["label"],
+                        objects_and_peoples=objects_and_peoples,
+                        detection_type="peoples",
+                        detection_window=args.detection_window,
+                        detection_duration=args.detection_duration,
+                        notification_window=args.notification_window,
+                        ntfy_url=args.ntfy_url,
+                    )
 
             # The following is stuff for objects
             # Setup dictionary of object names
diff --git a/wyzely_detect/utils/utils.py b/wyzely_detect/utils/utils.py
index ebbca11..22d369e 100644
--- a/wyzely_detect/utils/utils.py
+++ b/wyzely_detect/utils/utils.py
@@ -92,38 +92,53 @@ def recognize_face(
     """
     global first_face_try
 
-    # If it's the first time the function is being run, remove representations_vgg_face.pkl, if it exists
+    # If it's the first time the function is being run, remove representations_arcface.pkl, if it exists
     if first_face_try:
         try:
-            Path("representations_vgg_face.pkl").unlink()
-            print("Removing representations_vgg_face.pkl")
+            path_to_directory.joinpath("representations_arcface.pkl").unlink()
+            print("Removing representations_arcface.pkl")
         except FileNotFoundError:
-            pass
+            print("representations_arcface.pkl does not exist")
         first_face_try = False
 
+    # For debugging
+    # if path_to_directory.joinpath("representations_arcface.pkl").exists():
+    #     print("representations_arcface.pkl exists")
+    # else:
+    #     print("representations_arcface.pkl does not exist")
+
     # face_dataframes is a vanilla list of dataframes
-    face_dataframes = []
+    # It seems face_dataframes is empty if the face database (directory) doesn't exist. Seems to work if it's empty though
+    # This line is here to prevent a crash if that happens. However, there is a check in __main__ so it shouldn't happen
+    face_dataframes = [] 
     try:
         face_dataframes = DeepFace.find(
             run_frame,
             db_path=str(path_to_directory),
             enforce_detection=True,
             silent=True,
-        )
+            model_name="ArcFace", detector_backend="opencv"
+            )
     except ValueError as e:
         if (
             str(e)
-            == "Face could not be detected. Please confirm that the picture is a face photo or consider to set enforce_detection param to False."
+            == "Face could not be detected. Please confirm that the picture is a face photo or consider to set enforce_detection param to False." # noqa: E501
         ):
+            # print("No faces recognized") # For debugging
             return None
+        else:
+            raise e
     # Iteate over the dataframes
     for df in face_dataframes:
         # The last row is the highest confidence
         # So we can just grab the path from there
         # iloc = Integer LOCation
         path_to_image = Path(df.iloc[-1]["identity"])
-        # Get the name of the parent directory
-        label = path_to_image.parent.name
+        # If the parent name is the same as the path to the database, then set label to the image name instead of the parent directory name
+        if path_to_image.parent == Path(path_to_directory):
+            label = path_to_image.name
+        else:
+            label = path_to_image.parent.name
         # Return the coordinates of the box in xyxy format, rather than xywh
         # This is because YOLO uses xyxy, and that's how plot_label expects
         # Also, xyxy is just the top left and bottom right corners of the box
@@ -135,7 +150,7 @@ def recognize_face(
         }
         # After some brief testing, it seems positve matches are > 0.3
         # I have not seen any false positives, so there is no threashold yet
-        distance = df.iloc[-1]["VGG-Face_cosine"]
+        distance = df.iloc[-1]["ArcFace_cosine"]
         # if 0.5 < distance < 0.7:
         # label = "Unknown"
         to_return = dict(label=label, **coordinates)