diff --git a/detect.py b/detect.py
index 63feaa6ad..5935367d3 100644
--- a/detect.py
+++ b/detect.py
@@ -81,12 +81,12 @@ def detect(save_img=False):
         # Process detections
         for i, det in enumerate(pred):  # detections per image
             if webcam:  # batch_size >= 1
-                p, s, im0 = Path(path[i]), '%g: ' % i, im0s[i].copy()
+                p, s, im0, frame = Path(path[i]), '%g: ' % i, im0s[i].copy(), dataset.count
             else:
-                p, s, im0 = Path(path), '', im0s
+                p, s, im0, frame = Path(path), '', im0s, getattr(dataset, 'frame', 0)
 
             save_path = str(save_dir / p.name)
-            txt_path = str(save_dir / 'labels' / p.stem) + ('_%g' % dataset.frame if dataset.mode == 'video' else '')
+            txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}')
             s += '%gx%g ' % img.shape[2:]  # print string
             gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
             if len(det):
@@ -96,7 +96,7 @@ def detect(save_img=False):
                 # Print results
                 for c in det[:, -1].unique():
                     n = (det[:, -1] == c).sum()  # detections per class
-                    s += '%g %ss, ' % (n, names[int(c)])  # add to string
+                    s += f'{n} {names[int(c)]}s, '  # add to string
 
                 # Write results
                 for *xyxy, conf, cls in reversed(det):
@@ -107,11 +107,11 @@ def detect(save_img=False):
                             f.write(('%g ' * len(line)).rstrip() % line + '\n')
 
                     if save_img or view_img:  # Add bbox to image
-                        label = '%s %.2f' % (names[int(cls)], conf)
+                        label = f'{names[int(cls)]} {conf:.2f}'
                         plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3)
 
             # Print time (inference + NMS)
-            print('%sDone. (%.3fs)' % (s, t2 - t1))
+            print(f'{s}Done. ({t2 - t1:.3f}s)')
 
             # Stream results
             if view_img:
@@ -121,9 +121,9 @@ def detect(save_img=False):
 
             # Save results (image with detections)
             if save_img:
-                if dataset.mode == 'images':
+                if dataset.mode == 'image':
                     cv2.imwrite(save_path, im0)
-                else:
+                else:  # 'video'
                     if vid_path != save_path:  # new video
                         vid_path = save_path
                         if isinstance(vid_writer, cv2.VideoWriter):
@@ -140,7 +140,7 @@ def detect(save_img=False):
         s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
         print(f"Results saved to {save_dir}{s}")
 
-    print('Done. (%.3fs)' % (time.time() - t0))
+    print(f'Done. ({time.time() - t0:.3f}s)')
 
 
 if __name__ == '__main__':
diff --git a/utils/datasets.py b/utils/datasets.py
index dc3c8fb74..7d10d1c0d 100755
--- a/utils/datasets.py
+++ b/utils/datasets.py
@@ -138,7 +138,7 @@ class LoadImages:  # for inference
         self.files = images + videos
         self.nf = ni + nv  # number of files
         self.video_flag = [False] * ni + [True] * nv
-        self.mode = 'images'
+        self.mode = 'image'
         if any(videos):
             self.new_video(videos[0])  # new video
         else:
@@ -256,7 +256,7 @@ class LoadWebcam:  # for inference
 
 class LoadStreams:  # multiple IP or RTSP cameras
     def __init__(self, sources='streams.txt', img_size=640):
-        self.mode = 'images'
+        self.mode = 'stream'
         self.img_size = img_size
 
         if os.path.isfile(sources):