diff --git a/utils/utils.py b/utils/utils.py
index 7069181..dd12201 100755
--- a/utils/utils.py
+++ b/utils/utils.py
@@ -719,7 +719,7 @@ def kmean_anchors(path='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen=10
         return x, x.max(1)[0]  # x, best_x
 
     def fitness(k):  # mutation fitness
-        _, best = metric(k)
+        _, best = metric(torch.tensor(k, dtype=torch.float32))
         return (best * (best > thr).float()).mean()  # fitness
 
     def print_results(k):
@@ -743,8 +743,8 @@ def kmean_anchors(path='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen=10
 
     # Get label wh
     shapes = img_size * dataset.shapes / dataset.shapes.max(1, keepdims=True)
-    wh = torch.tensor(np.concatenate([l[:, 3:5] * s for s, l in zip(shapes, dataset.labels)])).float()  # wh
-    wh = wh[(wh > 2.0).all(1)].numpy()  # filter > 2 pixels
+    wh = np.concatenate([l[:, 3:5] * s for s, l in zip(shapes, dataset.labels)])  # wh
+    wh = wh[(wh > 2.0).all(1)]  # filter > 2 pixels
 
     # Kmeans calculation
     from scipy.cluster.vq import kmeans
@@ -752,7 +752,7 @@ def kmean_anchors(path='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen=10
     s = wh.std(0)  # sigmas for whitening
     k, dist = kmeans(wh / s, n, iter=30)  # points, mean distance
     k *= s
-    wh = torch.tensor(wh)
+    wh = torch.tensor(wh, dtype=torch.float32)
     k = print_results(k)
 
     # Plot
@@ -771,7 +771,7 @@ def kmean_anchors(path='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen=10
     # Evolve
     npr = np.random
     f, sh, mp, s = fitness(k), k.shape, 0.9, 0.1  # fitness, generations, mutation prob, sigma
-    for _ in tqdm(range(gen), desc='Evolving anchors with Genetic Algorithm:'):
+    for _ in tqdm(range(gen), desc='Evolving anchors with Genetic Algorithm'):
         v = np.ones(sh)
         while (v == 1).all():  # mutate until a change occurs (prevent duplicates)
             v = ((npr.random(sh) < mp) * npr.random() * npr.randn(*sh) * s + 1).clip(0.3, 3.0)