diff --git a/detect.py b/detect.py
index 2e9ed61d..3e74c6aa 100755
--- a/detect.py
+++ b/detect.py
@@ -7,6 +7,7 @@ from utils.utils import *
 
 from utils import torch_utils
 
+
 def detect(
         net_config_path,
         data_config_path,
@@ -68,7 +69,8 @@ def detect(
             # cv2.imwrite('zidane_416.jpg', 255 * img.transpose((1, 2, 0))[:, :, ::-1])  # letterboxed
             img = torch.from_numpy(img).unsqueeze(0).to(device)
             if ONNX_EXPORT:
-                pred = torch.onnx._export(model, img, 'weights/model.onnx', verbose=True); return  # ONNX export
+                pred = torch.onnx._export(model, img, 'weights/model.onnx', verbose=True);
+                return  # ONNX export
             pred = model(img)
             pred = pred[pred[:, :, 4] > conf_thres]
 
@@ -90,18 +92,17 @@ def detect(
     for img_i, (path, detections) in enumerate(zip(imgs, img_detections)):
         print("image %g: '%s'" % (img_i, path))
 
-        if save_images:
-            img = cv2.imread(path)
-
-        # The amount of padding that was added
-        pad_x = max(img.shape[0] - img.shape[1], 0) * (img_size / max(img.shape))
-        pad_y = max(img.shape[1] - img.shape[0], 0) * (img_size / max(img.shape))
-        # Image height and width after padding is removed
-        unpad_h = img_size - pad_y
-        unpad_w = img_size - pad_x
-
         # Draw bounding boxes and labels of detections
         if detections is not None:
+            img = cv2.imread(path)
+
+            # The amount of padding that was added
+            pad_x = max(img.shape[0] - img.shape[1], 0) * (img_size / max(img.shape))
+            pad_y = max(img.shape[1] - img.shape[0], 0) * (img_size / max(img.shape))
+            # Image height and width after padding is removed
+            unpad_h = img_size - pad_y
+            unpad_w = img_size - pad_x
+
             unique_classes = detections[:, -1].cpu().unique()
             bbox_colors = random.sample(color_list, len(unique_classes))
 
@@ -136,9 +137,9 @@ def detect(
                     color = bbox_colors[int(np.where(unique_classes == int(cls_pred))[0])]
                     plot_one_box([x1, y1, x2, y2], img, label=label, color=color)
 
-        if save_images:
-            # Save generated image with detections
-            cv2.imwrite(results_img_path.replace('.bmp', '.jpg').replace('.tif', '.jpg'), img)
+            if save_images:
+                # Save generated image with detections
+                cv2.imwrite(results_img_path.replace('.bmp', '.jpg').replace('.tif', '.jpg'), img)
 
     if platform == 'darwin':  # MacOS (local)
         os.system('open ' + output)