diff --git a/detect.py b/detect.py
index c10f8d8c..7510b138 100644
--- a/detect.py
+++ b/detect.py
@@ -75,6 +75,7 @@ def detect(
             det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
 
             # Print results to screen
+            print('%gx%g ' % img.shape[2:], end='')  # print image size
             for c in det[:, -1].unique():
                 n = (det[:, -1] == c).sum()
                 print('%g %ss' % (n, classes[int(c)]), end=', ')
diff --git a/utils/datasets.py b/utils/datasets.py
index 9450fbc6..6b406916 100755
--- a/utils/datasets.py
+++ b/utils/datasets.py
@@ -75,7 +75,6 @@ class LoadImages:  # for inference
 
         # Padded resize
         img, _, _, _ = letterbox(img0, new_shape=self.height)
-        print('%gx%g ' % img.shape[:2], end='')  # print image size
 
         # Normalize RGB
         img = img[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB
@@ -114,6 +113,7 @@ class LoadWebcam:  # for inference
         assert ret_val, 'Webcam Error'
         img_path = 'webcam_%g.jpg' % self.count
         img0 = cv2.flip(img0, 1)  # flip left-right
+        print('webcam %g: ' % self.count, end='')
 
         # Padded resize
         img, _, _, _ = letterbox(img0, new_shape=self.height)
@@ -144,7 +144,7 @@ class LoadImagesAndLabels(Dataset):  # for training/testing
             for x in self.img_files]
 
         # Rectangular Training  https://github.com/ultralytics/yolov3/issues/232
-        self.train_rectangular = False
+        self.train_rectangular = True
         if self.train_rectangular:
             bi = np.floor(np.arange(n) / batch_size).astype(np.int)  # batch index
             nb = bi[-1] + 1  # number of batches