diff --git a/models.py b/models.py
index ca5f07d2..2bb8719b 100755
--- a/models.py
+++ b/models.py
@@ -170,8 +170,8 @@ class YOLOLayer(nn.Module):
                 # lconf = k * BCEWithLogitsLoss(pred_conf[mask], mask[mask].float())
                 lconf = k * BCEWithLogitsLoss(pred_conf, mask.float())
 
-                lcls = k * CrossEntropyLoss(pred_cls[mask], torch.argmax(tcls, 1))
-                # lcls = k * BCEWithLogitsLoss(pred_cls[mask], tcls.float())
+                # lcls = k * CrossEntropyLoss(pred_cls[mask], torch.argmax(tcls, 1))
+                lcls = k * BCEWithLogitsLoss(pred_cls[mask], tcls.float())
             else:
                 lx, ly, lw, lh, lcls, lconf = FT([0]), FT([0]), FT([0]), FT([0]), FT([0]), FT([0])
 
diff --git a/test.py b/test.py
index b88faf0a..5ab46bb8 100644
--- a/test.py
+++ b/test.py
@@ -7,7 +7,7 @@ parser = argparse.ArgumentParser()
 parser.add_argument('-batch_size', type=int, default=32, help='size of each image batch')
 parser.add_argument('-cfg', type=str, default='cfg/yolov3.cfg', help='path to model config file')
 parser.add_argument('-data_config_path', type=str, default='cfg/coco.data', help='path to data config file')
-parser.add_argument('-weights_path', type=str, default='checkpoints/yolov3.pt', help='path to weights file')
+parser.add_argument('-weights_path', type=str, default='checkpoints/latest.pt', help='path to weights file')
 parser.add_argument('-class_path', type=str, default='data/coco.names', help='path to class label file')
 parser.add_argument('-iou_thres', type=float, default=0.5, help='iou threshold required to qualify as detected')
 parser.add_argument('-conf_thres', type=float, default=0.5, help='object confidence threshold')
diff --git a/utils/utils.py b/utils/utils.py
index 08692c5f..4c677dfa 100755
--- a/utils/utils.py
+++ b/utils/utils.py
@@ -264,8 +264,8 @@ def build_targets(pred_boxes, pred_conf, pred_cls, target, anchor_wh, nA, nC, nG
         ty[b, a, gj, gi] = gy - gj.float()
 
         # Width and height (yolo method)
-        tw[b, a, gj, gi] = torch.log(gw / anchor_wh[a, 0] + 1e-16)
-        th[b, a, gj, gi] = torch.log(gh / anchor_wh[a, 1] + 1e-16)
+        tw[b, a, gj, gi] = torch.log(gw / anchor_wh[a, 0])
+        th[b, a, gj, gi] = torch.log(gh / anchor_wh[a, 1])
 
         # Width and height (power method)
         # tw[b, a, gj, gi] = torch.sqrt(gw / anchor_wh[a, 0]) / 2