diff --git a/utils/utils.py b/utils/utils.py
index 7d8d7a9..9d412bc 100755
--- a/utils/utils.py
+++ b/utils/utils.py
@@ -84,15 +84,17 @@ def check_anchors(dataset, model, thr=4.0, imgsz=640):
         r = wh[:, None] / k[None]
         x = torch.min(r, 1. / r).min(2)[0]  # ratio metric
         best = x.max(1)[0]  # best_x
-        return (best > 1. / thr).float().mean()  # best possible recall
+        aat = (x > 1. / thr).float().sum(1).mean()  # anchors above threshold
+        bpr = (best > 1. / thr).float().mean()  # best possible recall
+        return bpr, aat
 
-    bpr = metric(m.anchor_grid.clone().cpu().view(-1, 2))
-    print('Best Possible Recall (BPR) = %.4f' % bpr, end='')
-    if bpr < 0.99:  # threshold to recompute
+    bpr, aat = metric(m.anchor_grid.clone().cpu().view(-1, 2))
+    print('anchors/target = %.2f, Best Possible Recall (BPR) = %.4f' % (aat, bpr), end='')
+    if bpr < 0.98:  # threshold to recompute
         print('. Attempting to generate improved anchors, please wait...' % bpr)
         na = m.anchor_grid.numel() // 2  # number of anchors
         new_anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False)
-        new_bpr = metric(new_anchors.reshape(-1, 2))
+        new_bpr = metric(new_anchors.reshape(-1, 2))[0]
         if new_bpr > bpr:  # replace anchors
             new_anchors = torch.tensor(new_anchors, device=m.anchors.device).type_as(m.anchors)
             m.anchor_grid[:] = new_anchors.clone().view_as(m.anchor_grid)  # for inference