diff --git a/py/src/config.json b/py/src/config.json
new file mode 100644
index 00000000..420250c6
--- /dev/null
+++ b/py/src/config.json
@@ -0,0 +1,7 @@
+{
+  "device_id": "ai-spy",
+  "server_ip": "10.44.67.2",
+  "stream_port": 8000,
+  "model_path": "./model/edgetpu.tflite",
+  "label_path": "./model/labelmap.pbtxt"
+}
diff --git a/py/src/main.py b/py/src/main.py
index ee164c4c..3dbe205b 100644
--- a/py/src/main.py
+++ b/py/src/main.py
@@ -1,7 +1,7 @@
 from config.Config import ConfigStore, LocalConfig, RemoteConfig
 from config.ConfigSource import FileConfigSource, NTConfigSource
 from pipeline.Capture import CVCapture
-from pipeline.Detector import Detector, draw_bbox
+from pipeline.Detector import Detector
 from output.StreamServer import StreamServer
 
 import time
@@ -40,5 +40,5 @@
 
         objs = detector.run_inference(image)
 
-        image = draw_bbox(image, objs)
+        image = detector.draw_bbox(image, objs)
         stream_server.set_frame(image)
diff --git a/py/src/pipeline/Detector.py b/py/src/pipeline/Detector.py
index d85f9512..26c7f2a1 100644
--- a/py/src/pipeline/Detector.py
+++ b/py/src/pipeline/Detector.py
@@ -11,15 +11,6 @@
 import cv2 as cv
 
 
-def draw_bbox(image: cv.Mat, objs: list[Object]) -> cv.Mat:
-    for obj in objs:
-        cv.rectangle(image,
-                     (obj.bbox.xmin.item(), obj.bbox.ymin.item()),
-                     (obj.bbox.xmax.item(), obj.bbox.ymax.item()),
-                     (0, 255, 0))
-    return image
-
-
 class Detector:
     """Runs a tflite model on the coral to detect objects"""
     _labels: Dict[int, str]
@@ -46,3 +37,20 @@ def run_inference(self, image: cv.Mat) -> list[Object]:
         run_inference(self._interpreter, image)
         objs = get_objects(self._interpreter, self._config.remote_config.detection_threshold)[:self._config.remote_config.max_targets]
         return objs
+
+    def draw_bbox(self, image: cv.Mat, objs: list[Object]) -> cv.Mat:
+        height, width, channels = image.shape
+        scale_x, scale_y = width / self._inference_size[0], height / self._inference_size[1]
+        for obj in objs:
+            bbox = obj.bbox.scale(scale_x, scale_y)
+            x0, y0 = int(bbox.xmin), int(bbox.ymin)
+            x1, y1 = int(bbox.xmax), int(bbox.ymax)
+
+            percent = int(100 * obj.score)
+            label = '{}% {}'.format(percent, self._labels.get(obj.id, obj.id))
+
+            image = cv.rectangle(image, (x0, y0), (x1, y1), (0, 255, 0), 2)
+            image = cv.putText(image, label, (x0, y0+30),
+                               cv.FONT_HERSHEY_SIMPLEX, 1.0, (255, 0, 0), 2)
+
+        return image