diff --git a/detect.py b/detect.py
index 0b6875e55..14cdf96ca 100644
--- a/detect.py
+++ b/detect.py
@@ -38,7 +38,7 @@ from utils.torch_utils import select_device, time_sync
 @torch.no_grad()
 def run(weights=ROOT / 'yolov5s.pt',  # model.pt path(s)
         source=ROOT / 'data/images',  # file/dir/URL/glob, 0 for webcam
-        imgsz=640,  # inference size (pixels)
+        imgsz=(640, 640),  # inference size (height, width)
         conf_thres=0.25,  # confidence threshold
         iou_thres=0.45,  # NMS IOU threshold
         max_det=1000,  # maximum detections per image
diff --git a/hubconf.py b/hubconf.py
index e407677b3..f8f34401e 100644
--- a/hubconf.py
+++ b/hubconf.py
@@ -139,5 +139,6 @@ if __name__ == '__main__':
             np.zeros((320, 640, 3))]  # numpy
 
     results = model(imgs)  # batched inference
-    results.print()
-    results.save()
+    rl = results.tolist()
+
+    print(rl[0].pandas().xyxy[0])
\ No newline at end of file
diff --git a/models/common.py b/models/common.py
index b39017378..c2edff4d3 100644
--- a/models/common.py
+++ b/models/common.py
@@ -525,7 +525,7 @@ class AutoShape(nn.Module):
 
 class Detections:
     # YOLOv5 detections class for inference results
-    def __init__(self, imgs, pred, files, times=None, names=None, shape=None):
+    def __init__(self, imgs, pred, files, times=(0, 0, 0, 0), names=None, shape=None):
         super().__init__()
         d = pred[0].device  # device
         gn = [torch.tensor([*(im.shape[i] for i in [1, 0, 1, 0]), 1, 1], device=d) for im in imgs]  # normalizations
@@ -533,6 +533,7 @@ class Detections:
         self.pred = pred  # list of tensors pred[0] = (xyxy, conf, cls)
         self.names = names  # class names
         self.files = files  # image filenames
+        self.times = times  # profiling times
         self.xyxy = pred  # xyxy pixels
         self.xywh = [xyxy2xywh(x) for x in pred]  # xywh pixels
         self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)]  # xyxy normalized
@@ -612,10 +613,11 @@ class Detections:
 
     def tolist(self):
         # return a list of Detections objects, i.e. 'for result in results.tolist():'
-        x = [Detections([self.imgs[i]], [self.pred[i]], names=self.names, shape=self.s) for i in range(self.n)]
-        for d in x:
-            for k in ['imgs', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']:
-                setattr(d, k, getattr(d, k)[0])  # pop out of list
+        r = range(self.n)  # iterable
+        x = [Detections([self.imgs[i]], [self.pred[i]], [self.files[i]], self.times, self.names, self.s) for i in r]
+        # for d in x:
+        #    for k in ['imgs', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']:
+        #        setattr(d, k, getattr(d, k)[0])  # pop out of list
         return x
 
     def __len__(self):