cleanup

models/common.py

@@ -176,13 +176,13 @@ class Detections:
     # detections class for YOLOv5 inference results
     def __init__(self, imgs, pred, names=None):
         super(Detections, self).__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
         self.imgs = imgs  # list of images as numpy arrays
         self.pred = pred  # list of tensors pred[0] = (xyxy, conf, cls)
         self.names = names  # class names
         self.xyxy = pred  # xyxy pixels
         self.xywh = [xyxy2xywh(x) for x in pred]  # xywh pixels
-        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
         self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)]  # xyxy normalized
         self.xywhn = [x / g for x, g in zip(self.xywh, gn)]  # xywh normalized
         self.n = len(self.pred)

utils/general.py

@@ -258,7 +258,7 @@ 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=0.1, iou_thres=0.6, classes=None, agnostic=False, labels=()):
+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
 
     Returns: