diff --git a/frigate/detectors/yolo_utils.py b/frigate/detectors/yolo_utils.py
index 43445b79f..79586890e 100644
--- a/frigate/detectors/yolo_utils.py
+++ b/frigate/detectors/yolo_utils.py
@@ -6,16 +6,15 @@ import cv2
 logger = logging.getLogger(__name__)
 
 def yolov8_preprocess(tensor_input, model_input_shape):
-  # tensor_input must be nhwc
-  assert tensor_input.shape[3] == 3
-  if tuple(tensor_input.shape[1:3]) != tuple(model_input_shape[2:4]):
-    logger.warn(f"yolov8_preprocess: tensor_input.shape {tensor_input.shape} and model_input_shape {model_input_shape} do not match!")
-  # cv2.dnn.blobFromImage is faster than numpying it
-  return cv2.dnn.blobFromImage(tensor_input[0], 1.0 / 255, (model_input_shape[3], model_input_shape[2]), None, swapRB=False)
+    # tensor_input must be nhwc
+    assert tensor_input.shape[3] == 3
+    if tuple(tensor_input.shape[1:3]) != tuple(model_input_shape[2:4]):
+        logger.warn(f"yolov8_preprocess: tensor_input.shape {tensor_input.shape} and model_input_shape {model_input_shape} do not match!")
+    # cv2.dnn.blobFromImage is faster than numpying it
+    return cv2.dnn.blobFromImage(tensor_input[0], 1.0 / 255, (model_input_shape[3], model_input_shape[2]), None, swapRB=False)
 
 def yolov8_postprocess(model_input_shape, tensor_output, box_count = 20):
     model_box_count = tensor_output.shape[2]
-    model_class_count = tensor_output.shape[1] - 4
     probs = tensor_output[0, 4:, :]
     all_ids = np.argmax(probs, axis=0)
     all_confidences = probs.T[np.arange(model_box_count), all_ids]