diff --git a/modeling/stitch.py b/modeling/stitch.py
index 5d86a2e..7afe411 100644
--- a/modeling/stitch.py
+++ b/modeling/stitch.py
@@ -7,8 +7,7 @@
 which takes a list of predictions from the classifier and creates TimeFrames.
 
 """
-
-
+import itertools
 import operator
 
 import yaml
@@ -20,12 +19,10 @@ class Stitcher:
 
     def __init__(self, **config):
         self.config = config
-        self.model_config = yaml.safe_load(open(config["model_config_file"]))
         self.sample_rate = config.get("sampleRate")
         self.min_frame_score = config.get("minFrameScore")
         self.min_timeframe_score = config.get("minTimeframeScore")
         self.min_frame_count = config.get("minFrameCount")
-        self.model_labels = train.pretraining_binned_label(self.model_config)
         self.stitch_labels = config.get("postbin")
         self.allow_overlap = config.get('allowOverlap')
         self.debug = False
@@ -37,7 +34,7 @@ def __str__(self):
 
     def create_timeframes(self, predictions: list) -> list:
         if self.debug:
-            print('>>> TimePoint labels:', ' '.join(self.model_labels))
+            print('>>> TimePoint labels:', ' '.join(list(itertools.chain(self.stitch_labels.values()))))
             print('>>> TimeFrame labels:', ' '.join(list(self.stitch_labels.keys())))
         timeframes = self.collect_timeframes(predictions)
         if self.debug:
@@ -57,13 +54,12 @@ def create_timeframes(self, predictions: list) -> list:
     def collect_timeframes(self, predictions: list) -> list:
         """Find sequences of frames for all labels where the score of each frame
         is at least the mininum value as defined in self.min_frame_score."""
-        labels = self.stitch_labels if self.stitch_labels is not None else self.model_labels
         if self.debug:
-            print('>>> labels', labels)
+            print('>>> labels', self.stitch_labels)
         timeframes = []
-        open_frames = {label: TimeFrame(label, self) for label in labels}
+        open_frames = {label: TimeFrame(label, self) for label in self.stitch_labels}
         for prediction in predictions:
-            for label in [label for label in labels if label != negative_label]:
+            for label in [label for label in self.stitch_labels if label != negative_label]:
                 score = self._score_for_label(label, prediction)
                 if score < self.min_frame_score:
                     # the second part checks whether there is something in the timeframe
@@ -72,7 +68,7 @@ def collect_timeframes(self, predictions: list) -> list:
                     open_frames[label] = TimeFrame(label, self)
                 else:
                     open_frames[label].add_prediction(prediction, score)
-        for label in labels:
+        for label in self.stitch_labels:
             if open_frames[label]:
                 timeframes.append(open_frames[label])
         for tf in timeframes: