diff --git a/best_int8.tflite b/best_int8.tflite
index fbd9672..f1f9f3d 100644
Binary files a/best_int8.tflite and b/best_int8.tflite differ
diff --git a/quantisierung.py b/quantisierung.py
index eb3f6b0..338b238 100644
--- a/quantisierung.py
+++ b/quantisierung.py
@@ -30,7 +30,7 @@ converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
 
 # Je nach Ziel-Hardware (z. B. NAO): INT8 in/out oder UINT8
 converter.inference_input_type = tf.uint8
-converter.inference_output_type = tf.uint8
+converter.inference_output_type = tf.float32
 
 # --- Konvertieren ---
 quant_model = converter.convert()
diff --git a/test_tflite.py b/test_tflite.py
index 4045642..83a642e 100644
--- a/test_tflite.py
+++ b/test_tflite.py
@@ -1,4 +1,7 @@
 import tensorflow as tf
 
-interpreter = tf.lite.Interpreter(model_path="yolo_training/NAO_detector/weights/best_saved_model/best_float16.tflite")
-interpreter.allocate_tensors()
\ No newline at end of file
+interpreter = tf.lite.Interpreter(model_path="best_int8.tflite")
+interpreter.allocate_tensors()
+input_details = interpreter.get_input_details()
+output_details = interpreter.get_output_details()
+print(input_details[0]['dtype'], output_details[0]['dtype'])
\ No newline at end of file