yolov5 regress updates to yolov3

utils/utils.py

@@ -365,9 +365,8 @@ def compute_loss(p, targets, model):  # predictions, targets, model
 
         nb = b.shape[0]  # number of targets
         if nb:
-            nt += nb
+            nt += nb  # cumulative targets
             ps = pi[b, a, gj, gi]  # prediction subset corresponding to targets
-            # ps[:, 2:4] = torch.sigmoid(ps[:, 2:4])  # wh power loss (uncomment)
 
             # GIoU
             pxy = torch.sigmoid(ps[:, 0:2])
@@ -408,7 +407,6 @@ def compute_loss(p, targets, model):  # predictions, targets, model
 
 def build_targets(p, targets, model):
     # Build targets for compute_loss(), input targets(image,class,x,y,w,h)
-
     nt = targets.shape[0]
     tcls, tbox, indices, anch = [], [], [], []
     gain = torch.ones(6, device=targets.device)  # normalized to gridspace gain
@@ -647,7 +645,7 @@ def coco_single_class_labels(path='../coco/labels/train2014/', label_class=43):
             shutil.copyfile(src=img_file, dst='new/images/' + Path(file).name.replace('txt', 'jpg'))  # copy images
 
 
-def kmean_anchors(path='./data/coco64.txt', n=9, img_size=(320, 1024), thr=0.20, gen=1000):
+def kmean_anchors(path='./data/coco64.txt', n=9, img_size=(640, 640), thr=0.20, gen=1000):
     # Creates kmeans anchors for use in *.cfg files: from utils.utils import *; _ = kmean_anchors()
     # n: number of anchors
     # img_size: (min, max) image size used for multi-scale training (can be same values)