diff --git a/detect.py b/detect.py
index 15582a5d..d655e63d 100644
--- a/detect.py
+++ b/detect.py
@@ -102,7 +102,7 @@ def detect(save_img=False):
             pred = apply_classifier(pred, modelc, img, im0s)
 
         # Process detections
-        for i, det in enumerate(pred):  # detections per image
+        for i, det in enumerate(pred):  # detections for image i
             if webcam:  # batch_size >= 1
                 p, s, im0 = path[i], '%g: ' % i, im0s[i]
             else:
@@ -110,6 +110,7 @@ def detect(save_img=False):
 
             save_path = str(Path(out) / Path(p).name)
             s += '%gx%g ' % img.shape[2:]  # print string
+            gn = torch.tensor(im0s.shape)[[1, 0, 1, 0]]  #  normalization gain whwh
             if det is not None and len(det):
                 # Rescale boxes from img_size to im0 size
                 det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
@@ -122,8 +123,9 @@ def detect(save_img=False):
                 # Write results
                 for *xyxy, conf, cls in det:
                     if save_txt:  # Write to file
+                        xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
                         with open(save_path + '.txt', 'a') as file:
-                            file.write(('%g ' * 6 + '\n') % (*xyxy, cls, conf))
+                            file.write(('%g ' * 5 + '\n') % (cls, *xywh))  # label format
 
                     if save_img or view_img:  # Add bbox to image
                         label = '%s %.2f' % (names[int(cls)], conf)