Minor update

README.md

@@ -23,7 +23,7 @@ towhee.glob('./towhee.jpg') \
 
 | Image | `boxes` | `classes` | `scores` |
 | ----- | ------- | --------- | -------- |
-| ![](example.jpg) | array([2645.9973 , 1200.3245 , 3176.163  , 2722.6785 ]) | array([0]) | array([0.9998573]) |
+| ![](example.jpg) | `array([2645.9973, 1200.3245, 3176.163, 2722.6785])` | `array([0])` | `array([0.9998573])` |
 
 
 
@@ -64,4 +64,4 @@ This operator takes an image as input. It first detects the objects appeared in
 
 **Return**: `List[numpy.ndarray[4], ...], List[str], numpy.ndarray`
 
-The return value is a tuple of `(boxes, classes, scores)`. `boxes` is a list of bounding boxes. Each bounding box is represented as a 1-dimensional numpy array consisting of the top-left and the bottom-right corners, i.e. `numpy.ndarray([x1, y1, x2, y2])`. `classes` is a list of prediction labels for each bounding box. `*scores*` is a list of confidence scores corresponding to each class and bounding box.
+The return value is a tuple of `(boxes, classes, scores)`. `boxes` is a list of bounding boxes. Each bounding box is represented as a 1-dimensional numpy array consisting of the top-left and the bottom-right corners, i.e. `numpy.ndarray([x1, y1, x2, y2])`. `classes` is a list of prediction labels for each bounding box. `scores` is a list of confidence scores corresponding to each class and bounding box.