diff --git a/utils/metrics.py b/utils/metrics.py
index 43b723e7079..e17747b703f 100644
--- a/utils/metrics.py
+++ b/utils/metrics.py
@@ -90,7 +90,7 @@ def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names
     p, r, f1 = p[:, i], r[:, i], f1[:, i]
     tp = (r * nt).round()  # true positives
     fp = (tp / (p + eps) - tp).round()  # false positives
-    return tp, fp, p, r, f1, ap, unique_classes.astype('int32')
+    return tp, fp, p, r, f1, ap, unique_classes.astype(int)
 
 
 def compute_ap(recall, precision):
@@ -156,7 +156,7 @@ def process_batch(self, detections, labels):
             matches = np.zeros((0, 3))
 
         n = matches.shape[0] > 0
-        m0, m1, _ = matches.transpose().astype(np.int16)
+        m0, m1, _ = matches.transpose().astype(int)
         for i, gc in enumerate(gt_classes):
             j = m0 == i
             if n and sum(j) == 1:
diff --git a/val.py b/val.py
index d886cf302df..42b0e1813e4 100644
--- a/val.py
+++ b/val.py
@@ -79,16 +79,17 @@ def process_batch(detections, labels, iouv):
     """
     correct = torch.zeros(detections.shape[0], iouv.shape[0], dtype=torch.bool, device=iouv.device)
     iou = box_iou(labels[:, 1:], detections[:, :4])
-    x = torch.where((iou >= iouv[0]) & (labels[:, 0:1] == detections[:, 5]))  # IoU above threshold and classes match
-    if x[0].shape[0]:
-        matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy()  # [label, detection, iou]
-        if x[0].shape[0] > 1:
-            matches = matches[matches[:, 2].argsort()[::-1]]
-            matches = matches[np.unique(matches[:, 1], return_index=True)[1]]
-            # matches = matches[matches[:, 2].argsort()[::-1]]
-            matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
-        matches = torch.from_numpy(matches).to(iouv.device)
-        correct[matches[:, 1].long()] = matches[:, 2:3] >= iouv
+    correct_class = labels[:, 0:1] == detections[:, 5]
+    for i in range(len(iouv)):
+        x = torch.where((iou >= iouv[i]) & correct_class)  # IoU > threshold and classes match
+        if x[0].shape[0]:
+            matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy()  # [label, detect, iou]
+            if x[0].shape[0] > 1:
+                matches = matches[matches[:, 2].argsort()[::-1]]
+                matches = matches[np.unique(matches[:, 1], return_index=True)[1]]
+                # matches = matches[matches[:, 2].argsort()[::-1]]
+                matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
+            correct[matches[:, 1].astype(int), i] = True
     return correct
 
 
@@ -265,7 +266,7 @@ def run(
         tp, fp, p, r, f1, ap, ap_class = ap_per_class(*stats, plot=plots, save_dir=save_dir, names=names)
         ap50, ap = ap[:, 0], ap.mean(1)  # AP@0.5, AP@0.5:0.95
         mp, mr, map50, map = p.mean(), r.mean(), ap50.mean(), ap.mean()
-        nt = np.bincount(stats[3].astype(np.int64), minlength=nc)  # number of targets per class
+        nt = np.bincount(stats[3].astype(int), minlength=nc)  # number of targets per class
     else:
         nt = torch.zeros(1)