diff --git a/hubconf.py b/hubconf.py
index 2a34813310e8..47eee4477725 100644
--- a/hubconf.py
+++ b/hubconf.py
@@ -133,9 +133,14 @@ def custom(path_or_model='path/to/model.pt', autoshape=True):
     # model = custom(path_or_model='path/to/model.pt')  # custom example
 
     # Verify inference
+    import numpy as np
     from PIL import Image
 
-    imgs = [Image.open(x) for x in Path('data/images').glob('*.jpg')]
-    results = model(imgs)
+    imgs = [Image.open('data/images/bus.jpg'),  # PIL
+            'data/images/zidane.jpg',  # filename
+            'https://github.com/ultralytics/yolov5/raw/master/data/images/bus.jpg',  # URI
+            np.zeros((640, 480, 3))]  # numpy
+
+    results = model(imgs)  # batched inference
     results.print()
     results.save()
diff --git a/models/common.py b/models/common.py
index f24ea7885668..e8e5ff1eb2c1 100644
--- a/models/common.py
+++ b/models/common.py
@@ -254,7 +254,7 @@ def display(self, pprint=False, show=False, save=False, render=False, save_dir='
                     n = (pred[:, -1] == c).sum()  # detections per class
                     str += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, "  # add to string
                 if show or save or render:
-                    img = Image.fromarray(img) if isinstance(img, np.ndarray) else img  # from np
+                    img = Image.fromarray(img.astype(np.uint8)) if isinstance(img, np.ndarray) else img  # from np
                     for *box, conf, cls in pred:  # xyxy, confidence, class
                         # str += '%s %.2f, ' % (names[int(cls)], conf)  # label
                         ImageDraw.Draw(img).rectangle(box, width=4, outline=colors[int(cls) % 10])  # plot