diff --git a/src/brevitas_examples/imagenet_classification/a2q/a2q_train_models.py b/src/brevitas_examples/imagenet_classification/a2q/a2q_train_models.py
index 6d2cba60e..97a214c9a 100644
--- a/src/brevitas_examples/imagenet_classification/a2q/a2q_train_models.py
+++ b/src/brevitas_examples/imagenet_classification/a2q/a2q_train_models.py
@@ -91,7 +91,12 @@
     default=False,
     help="If true, save torch model to specified save path.")
 parser.add_argument(
-    "--apply-bias-corr",
+    "--apply-act-calibration",
+    action="store_true",
+    default=False,
+    help="If true, apply activation calibration to the quantized model.")
+parser.add_argument(
+    "--apply-bias-correction",
     action="store_true",
     default=False,
     help="If true, apply bias correction to the quantized model.")
@@ -137,7 +142,7 @@
     model = utils.get_model_by_name(
         args.model_name, init_from_float_checkpoint=args.from_float_checkpoint)
     criterion = nn.CrossEntropyLoss()
-    optimizer = optim.Adam(
+    optimizer = optim.SGD(
         utils.filter_params(model.named_parameters(), args.weight_decay),
         lr=args.lr_init,
         weight_decay=args.weight_decay)
@@ -146,9 +151,14 @@
     # Calibrate the quant model on the calibration dataset
     if args.apply_ep_init:
         print("Applying EP-init:")
-        utils.apply_ep_init(model, random_inp)
+        model = utils.apply_ep_init(model, random_inp)
+
+    # Calibrate the quant model on the calibration dataset
+    if args.apply_act_calibration:
+        print("Applying activation calibration:")
+        utils.apply_act_calibrate(calibloader, model)
 
-    if args.apply_bias_corr:
+    if args.apply_bias_correction:
         print("Applying bias correction:")
         utils.apply_bias_correction(calibloader, model)
 
@@ -173,7 +183,7 @@
 
     model.load_state_dict(best_weights)
     top_1, top_5, loss = utils.evaluate_topk_accuracies(testloader, model, criterion)
-    print(f"Final top_1={top_1:.1%}, top_5={top_5:.1%}, loss={loss:.3f}")
+    print(f"Final: top_1={top_1:.1%}, top_5={top_5:.1%}, loss={loss:.3f}")
 
     # save checkpoint
     os.makedirs(args.save_path, exist_ok=True)
diff --git a/src/brevitas_examples/imagenet_classification/a2q/ep_init.py b/src/brevitas_examples/imagenet_classification/a2q/ep_init.py
index 564d0fae2..026f78af8 100644
--- a/src/brevitas_examples/imagenet_classification/a2q/ep_init.py
+++ b/src/brevitas_examples/imagenet_classification/a2q/ep_init.py
@@ -127,3 +127,5 @@ def register_upper_bound(module: AccumulatorAwareParameterPreScaling, inp, outpu
 
     for hook in hook_list:
         hook.remove()
+
+    return model
diff --git a/src/brevitas_examples/imagenet_classification/a2q/utils.py b/src/brevitas_examples/imagenet_classification/a2q/utils.py
index c7352fdb2..eaa4c200d 100644
--- a/src/brevitas_examples/imagenet_classification/a2q/utils.py
+++ b/src/brevitas_examples/imagenet_classification/a2q/utils.py
@@ -20,6 +20,7 @@
 from brevitas.core.scaling.pre_scaling import AccumulatorAwareParameterPreScaling
 from brevitas.function import abs_binary_sign_grad
 from brevitas.graph.calibrate import bias_correction_mode
+from brevitas.graph.calibrate import calibration_mode
 
 from .ep_init import apply_ep_init
 from .quant import *
@@ -28,11 +29,12 @@
 
 __all__ = [
     "apply_ep_init",
+    "apply_act_calibrate",
+    "apply_bias_correction",
     "get_model_by_name",
     "filter_params",
     "create_calibration_dataloader",
     "get_cifar10_dataloaders",
-    "apply_bias_correction",
     "train_for_epoch",
     "evaluate_topk_accuracies"]
 
@@ -239,6 +241,18 @@ def get_cifar10_dataloaders(
     return trainloader, testloader
 
 
+def apply_act_calibrate(calib_loader, model):
+    model.eval()
+    dtype = next(model.parameters()).dtype
+    device = next(model.parameters()).device
+    with torch.no_grad():
+        with calibration_mode(model):
+            for images, _ in tqdm(calib_loader):
+                images = images.to(device)
+                images = images.to(dtype)
+                model(images)
+
+
 def apply_bias_correction(calib_loader, model: nn.Module):
     model.eval()
     dtype = next(model.parameters()).dtype