Update inference default to multi_label=False (#2252)

* Update inference default to multi_label=False

* bug fix

* Update plots.py

* Update plots.py

utils/general.py

@@ -390,11 +390,12 @@ def wh_iou(wh1, wh2):
     return inter / (wh1.prod(2) + wh2.prod(2) - inter)  # iou = inter / (area1 + area2 - inter)
 
 
-def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, labels=()):
-    """Performs Non-Maximum Suppression (NMS) on inference results
+def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, multi_label=False,
+                        labels=()):
+    """Runs Non-Maximum Suppression (NMS) on inference results
 
     Returns:
-         detections with shape: nx6 (x1, y1, x2, y2, conf, cls)
+         list of detections, on (n,6) tensor per image [xyxy, conf, cls]
     """
 
     nc = prediction.shape[2] - 5  # number of classes
@@ -406,7 +407,7 @@ def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=Non
     max_nms = 30000  # maximum number of boxes into torchvision.ops.nms()
     time_limit = 10.0  # seconds to quit after
     redundant = True  # require redundant detections
-    multi_label = nc > 1  # multiple labels per box (adds 0.5ms/img)
+    multi_label &= nc > 1  # multiple labels per box (adds 0.5ms/img)
     merge = False  # use merge-NMS
 
     t = time.time()

utils/plots.py

@@ -54,7 +54,7 @@ def butter_lowpass_filtfilt(data, cutoff=1500, fs=50000, order=5):
     return filtfilt(b, a, data)  # forward-backward filter
 
 
-def plot_one_box(x, img, color=None, label=None, line_thickness=None):
+def plot_one_box(x, img, color=None, label=None, line_thickness=3):
     # Plots one bounding box on image img
     tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1  # line/font thickness
     color = color or [random.randint(0, 255) for _ in range(3)]