Add PyTorch Hub results.save(labels=False) option (ultralytics#7129)

Resolves ultralytics#388 (comment)

models/common.py

@@ -131,7 +131,7 @@ def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, nu
         c_ = int(c2 * e)  # hidden channels
         self.cv1 = Conv(c1, c_, 1, 1)
         self.cv2 = Conv(c1, c_, 1, 1)
-        self.cv3 = Conv(2 * c_, c2, 1)  # act=FReLU(c2)
+        self.cv3 = Conv(2 * c_, c2, 1)  # optional act=FReLU(c2)
         self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))
         # self.m = nn.Sequential(*(CrossConv(c_, c_, 3, 1, g, 1.0, shortcut) for _ in range(n)))
 
@@ -589,7 +589,7 @@ def __init__(self, imgs, pred, files, times=(0, 0, 0, 0), names=None, shape=None
         self.t = tuple((times[i + 1] - times[i]) * 1000 / self.n for i in range(3))  # timestamps (ms)
         self.s = shape  # inference BCHW shape
 
-    def display(self, pprint=False, show=False, save=False, crop=False, render=False, save_dir=Path('')):
+    def display(self, pprint=False, show=False, save=False, crop=False, render=False, labels=True, save_dir=Path('')):
         crops = []
         for i, (im, pred) in enumerate(zip(self.imgs, self.pred)):
             s = f'image {i + 1}/{len(self.pred)}: {im.shape[0]}x{im.shape[1]} '  # string
@@ -606,7 +606,7 @@ def display(self, pprint=False, show=False, save=False, crop=False, render=False
                             crops.append({'box': box, 'conf': conf, 'cls': cls, 'label': label,
                                           'im': save_one_box(box, im, file=file, save=save)})
                         else:  # all others
-                            annotator.box_label(box, label, color=colors(cls))
+                            annotator.box_label(box, label if labels else '', color=colors(cls))
                     im = annotator.im
             else:
                 s += '(no detections)'
@@ -633,19 +633,19 @@ def print(self):
         LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {tuple(self.s)}' %
                     self.t)
 
-    def show(self):
-        self.display(show=True)  # show results
+    def show(self, labels=True):
+        self.display(show=True, labels=labels)  # show results
 
-    def save(self, save_dir='runs/detect/exp'):
+    def save(self, labels=True, save_dir='runs/detect/exp'):
         save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/detect/exp', mkdir=True)  # increment save_dir
-        self.display(save=True, save_dir=save_dir)  # save results
+        self.display(save=True, labels=labels, save_dir=save_dir)  # save results
 
     def crop(self, save=True, save_dir='runs/detect/exp'):
         save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/detect/exp', mkdir=True) if save else None
         return self.display(crop=True, save=save, save_dir=save_dir)  # crop results
 
-    def render(self):
-        self.display(render=True)  # render results
+    def render(self, labels=True):
+        self.display(render=True, labels=labels)  # render results
         return self.imgs
 
     def pandas(self):