diff --git a/train.py b/train.py
index 131260dca4..9c180acdd8 100755
--- a/train.py
+++ b/train.py
@@ -63,6 +63,12 @@
 except ImportError as e:
     has_functorch = False
 
+try:
+    from sklearn.metrics import precision_score, recall_score, f1_score
+    has_sklearn = True
+except ImportError:
+    has_sklearn = False
+
 has_compile = hasattr(torch, 'compile')
 
 
@@ -400,6 +406,10 @@
                    help='wandb tags')
 group.add_argument('--wandb-resume-id', default='', type=str, metavar='ID',
                    help='If resuming a run, the id of the run in wandb')
+group.add_argument('--metrics-avg', type=str, default=None,
+                   choices=['micro', 'macro', 'weighted'],
+                   help='Enable precision, recall, F1-score calculation and specify the averaging method. '
+                        'Requires scikit-learn. (default: None)')
 
 # NaFlex scheduled loader arguments
 group.add_argument('--naflex-loader', action='store_true', default=False,
@@ -1318,6 +1328,15 @@ def validate(
     top1_m = utils.AverageMeter()
     top5_m = utils.AverageMeter()
 
+    if args.metrics_avg:
+        if not has_sklearn:
+            _logger.warning(
+                "scikit-learn not installed, disabling extra metrics. Please install with 'pip install scikit-learn'.")
+            args.metrics_avg = None
+        else:
+            all_preds = []
+            all_targets = []
+
     model.eval()
 
     end = time.time()
@@ -1345,6 +1364,10 @@ def validate(
                 loss = loss_fn(output, target)
             acc1, acc5 = utils.accuracy(output, target, topk=(1, 5))
 
+            if args.metrics_avg:
+                all_preds.append(torch.argmax(output, dim=1).cpu())
+                all_targets.append(target.cpu())
+
             if args.distributed:
                 reduced_loss = utils.reduce_tensor(loss.data, args.world_size)
                 acc1 = utils.reduce_tensor(acc1, args.world_size)
@@ -1374,7 +1397,31 @@ def validate(
                     f'Acc@5: {top5_m.val:>7.3f} ({top5_m.avg:>7.3f})'
                 )
 
-    metrics = OrderedDict([('loss', losses_m.avg), ('top1', top1_m.avg), ('top5', top5_m.avg)])
+        metrics = OrderedDict([('loss', losses_m.avg), ('top1', top1_m.avg), ('top5', top5_m.avg)])
+
+        if args.metrics_avg:
+            all_preds = torch.cat(all_preds)
+            all_targets = torch.cat(all_targets)
+
+            if args.distributed:
+                # Gather list of tensors from all processes
+                pred_list = [torch.zeros_like(all_preds) for _ in range(args.world_size)]
+                target_list = [torch.zeros_like(all_targets) for _ in range(args.world_size)]
+                torch.distributed.all_gather(pred_list, all_preds)
+                torch.distributed.all_gather(target_list, all_targets)
+
+                if utils.is_primary(args):
+                    all_preds = torch.cat(pred_list)
+                    all_targets = torch.cat(target_list)
+
+            if utils.is_primary(args):
+                precision = precision_score(all_targets.numpy(), all_preds.numpy(), average=args.metrics_avg, zero_division=0)
+                recall = recall_score(all_targets.numpy(), all_preds.numpy(), average=args.metrics_avg, zero_division=0)
+                f1 = f1_score(all_targets.numpy(), all_preds.numpy(), average=args.metrics_avg, zero_division=0)
+
+                metrics[f'{args.metrics_avg}_precision'] = round(precision, 4)
+                metrics[f'{args.metrics_avg}_recall'] = round(recall, 4)
+                metrics[f'{args.metrics_avg}_f1_score'] = round(f1, 4)
 
     return metrics