diff --git a/utils/autoanchor.py b/utils/autoanchor.py
index f222220..ac17c6c 100644
--- a/utils/autoanchor.py
+++ b/utils/autoanchor.py
@@ -10,6 +10,7 @@ import torch
 import yaml
 from tqdm import tqdm
 
+from utils import TryExcept
 from utils.general import LOGGER, colorstr
 
 PREFIX = colorstr('AutoAnchor: ')
@@ -25,6 +26,7 @@ def check_anchor_order(m):
         m.anchors[:] = m.anchors.flip(0)
 
 
+@TryExcept(f'{PREFIX}ERROR:')
 def check_anchors(dataset, model, thr=4.0, imgsz=640):
     # Check anchor fit to data, recompute if necessary
     m = model.module.model[-1] if hasattr(model, 'module') else model.model[-1]  # Detect()
@@ -49,10 +51,7 @@ def check_anchors(dataset, model, thr=4.0, imgsz=640):
     else:
         LOGGER.info(f'{s}Anchors are a poor fit to dataset ⚠️, attempting to improve...')
         na = m.anchors.numel() // 2  # number of anchors
-        try:
-            anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False)
-        except Exception as e:
-            LOGGER.info(f'{PREFIX}ERROR: {e}')
+        anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False)
         new_bpr = metric(anchors)[0]
         if new_bpr > bpr:  # replace anchors
             anchors = torch.tensor(anchors, device=m.anchors.device).type_as(m.anchors)
@@ -124,7 +123,7 @@ def kmean_anchors(dataset='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen
     i = (wh0 < 3.0).any(1).sum()
     if i:
         LOGGER.info(f'{PREFIX}WARNING: Extremely small objects found: {i} of {len(wh0)} labels are < 3 pixels in size')
-    wh = wh0[(wh0 >= 2.0).any(1)]  # filter > 2 pixels
+    wh = wh0[(wh0 >= 2.0).any(1)].astype(np.float32)  # filter > 2 pixels
     # wh = wh * (npr.rand(wh.shape[0], 1) * 0.9 + 0.1)  # multiply by random scale 0-1
 
     # Kmeans init
@@ -167,4 +166,4 @@ def kmean_anchors(dataset='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen
             if verbose:
                 print_results(k, verbose)
 
-    return print_results(k)
+    return print_results(k).astype(np.float32)