diff --git a/src/__pycache__/detector.cpython-310.pyc b/src/__pycache__/detector.cpython-310.pyc
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diff --git a/src/__pycache__/objectifier.cpython-310.pyc b/src/__pycache__/objectifier.cpython-310.pyc
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diff --git a/src/detector.py b/src/detector.py
old mode 100755
new mode 100644
index 5c61aed..26567be
--- a/src/detector.py
+++ b/src/detector.py
@@ -85,129 +85,3 @@ class Detector:
         out_file = output_filename if output_filename else self.output_filename(filename)
         cv.imwrite(out_file, marked)
         return AnalyzedImage(filename, detections, str(out_file))
-
-
-
-
-# net = cv.dnn.readNet("/home/niten/Projects/yolo/yolov3.weights", "/home/niten/Projects/yolo/yolov3.cfg")
-
-# classes = []
-
-# with open("/home/niten/Projects/yolo/coco.names") as f:
-#     classes = [line.strip() for line in f.readlines()]
-
-# layer_names = net.getLayerNames()
-# output_layer = [layer_names[i - 1] for i in net.getUnconnectedOutLayers()]
-# colors = np.random.uniform(0, 255, size=(len(classes), 3))
-
-# def scale_int(o, s, m):
-#     return (m / s) * o
-
-# def scale_box(orig, scaled, box):
-#     o_h, o_w, _ = orig.shape
-#     s_h, s_w, _ = scaled.shape
-#     x, y, w, h = box
-#     return [scale_int(o_w, s_w, x),
-#             scale_int(o_h, s_h, y),
-#             scale_int(o_w, o_h, w),
-#             scale_int(o_h, s_h, h)]
-
-# tmpdir = Path(tempfile.mkdtemp())
-
-# def detect_objects(filename):
-#     simplename = path.splitext(path.basename(filename))[0]
-#     out_filename = tmpdir / ("processed_" + simplename + ".png")
-
-#     orig = cv.imread(str(filename))
-#     img = cv.imread(str(filename))
-#     # img = cv.resize(img, None, fx=0.4, fy=0.4)
-#     height, width, channel = img.shape
-#     # TODO: Change scale factor?
-#     blob = cv.dnn.blobFromImage(img, 0.00392, (416, 416), (0, 0, 0), True, crop=False)
-#     net.setInput(blob)
-#     outs = net.forward(output_layer)
-
-#     class_ids = []
-#     confidences = []
-#     boxes = []
-
-#     detections = []
-
-#     for out in outs:
-#         for detection in out:
-#             scores = detection[5:]
-#             class_id = np.argmax(scores)
-#             confidence = scores[class_id]
-#             if confidence > 0.6:
-#                 center_x = int(detection[0] * width)
-#                 center_y = int(detection[1] * height)
-#                 w = int(detection[2] * width)
-#                 h = int(detection[3] * height)
-#                 x = int(center_x - w/2)
-#                 y = int(center_y - h/2)
-#                 boxes.append([x, y, w, h])
-#                 confidences.append(float(confidence))
-#                 class_ids.append(class_id)
-
-#     indexes = cv.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
-
-#     font = cv.FONT_HERSHEY_PLAIN
-#     for i in range(len(boxes)):
-#         if i in indexes:
-#             label = str(classes[class_ids[i]])
-#             color = colors[i]
-#             scaled_box = scale_box(orig, img, boxes[i])
-#             x, y, w, h = [int(n) for n in scaled_box]
-#             detections.append(Detection(label, confidences[i], scaled_box))
-#             # cv.rectangle(out, (x, y), (x + w, y + h), color, 2)
-#             # cv.putText(out, label, (x, y + 30), font, 3, color, 3)
-
-#     #cv.imwrite(str(out_filename), out)
-#     marked = cv.imread(filename)
-#     for detection in detections:
-#         x, y, w, h = [int(n) for n in detection.box]
-#         cv.rectangle(marked, (x,y), (x + w, y + h), (255,255,255,0), 2)
-#         cv.putText(marked, detection.label, (x, y + 30), font, 3, (255,255,255,0), 1)
-
-#     cv.imwrite(str(out_filename), marked)
-#     return AnalyzedImage(filename, detections, str(out_filename))
-
-# # cv.imshow("IMG", img)
-# # cv.waitKey(0)
-# # cv.destroyAllWindows()
-
-# for filename in sys.argv[1:]:
-#     print(filename + ":")
-#     output = detect_objects(filename)
-#     print("  OUTPUT: " + str(output.outfile))
-#     for detection in output.detections:
-#         print("  " + detection.label +
-#               " (" + str(detection.confidence) + ")" +
-#               " [" +
-#               str(detection.box[0]) + ", " +
-#               str(detection.box[1]) + ", " +
-#               str(detection.box[2]) + ", " +
-#               str(detection.box[3]) +
-#               "]")
-
-
-# classes = []
-
-# with open("/home/niten/Projects/yolo/coco.names") as f:
-#     classes = [line.strip() for line in f.readlines()]
-
-# detector = Detector("/home/niten/Projects/yolo/yolov3.weights", "/home/niten/Projects/yolo/yolov3.cfg", classes, Path(tempfile.mkdtemp()))
-
-# for filename in sys.argv[1:]:
-#     print(filename + ":")
-#     output = detector.detect_objects(filename)
-#     print("  OUTPUT: " + str(output.outfile))
-#     for detection in output.detections:
-#         print("  " + detection.label +
-#               " (" + str(detection.confidence) + ")" +
-#               " [" +
-#               str(detection.box[0]) + ", " +
-#               str(detection.box[1]) + ", " +
-#               str(detection.box[2]) + ", " +
-#               str(detection.box[3]) +
-#               "]")