diff --git a/train.py b/train.py
index ff13f1e256ec..444316ca4c08 100644
--- a/train.py
+++ b/train.py
@@ -284,7 +284,16 @@ def train(hyp, opt, device, callbacks):  # hyp is path/to/hyp.yaml or hyp dictio
     model.hyp = hyp  # attach hyperparameters to model
     model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc  # attach class weights
     model.names = names
-
+    
+    # Add tensor hooks for gradient inspection
+    if opt.use_tensor_hooks: 
+        LOGGER.info('Add tensor hooks...')
+        for name, params in model.named_parameters():
+            if params is not None:
+                params.register_hook(lambda grad: torch.nan_to_num(grad, nan=0., neginf=0., posinf=0.))
+                params.retain_grad()
+    LOGGER.info(f'Use gradient clipping with max_norm={opt.clip_grad}')
+    
     # Start training
     t0 = time.time()
     nw = max(round(hyp['warmup_epochs'] * nb), 100)  # number of warmup iterations, max(3 epochs, 100 iterations)
@@ -361,6 +370,8 @@ def train(hyp, opt, device, callbacks):  # hyp is path/to/hyp.yaml or hyp dictio
 
             # Optimize
             if ni - last_opt_step >= accumulate:
+                scaler.unscale_(optimizer)
+                torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=opt.clip_grad)
                 scaler.step(optimizer)  # optimizer.step
                 scaler.update()
                 optimizer.zero_grad()
@@ -506,7 +517,9 @@ def parse_opt(known=False):
     parser.add_argument('--save-period', type=int, default=-1, help='Save checkpoint every x epochs (disabled if < 1)')
     parser.add_argument('--seed', type=int, default=0, help='Global training seed')
     parser.add_argument('--local_rank', type=int, default=-1, help='Automatic DDP Multi-GPU argument, do not modify')
-
+    parser.add_argument('--clip_grad', type=int, default=10, help='Max norm for gradient clipping')
+    parser.add_argument('--use_tensor_hooks', action='store_true', help='Use tensor hooks to remove infs and nans')
+    
     # Weights & Biases arguments
     parser.add_argument('--entity', default=None, help='W&B: Entity')
     parser.add_argument('--upload_dataset', nargs='?', const=True, default=False, help='W&B: Upload data, "val" option')