diff --git a/tutorials/mct_model_garden/models_keras/nanodet/nanodet_keras_model.py b/tutorials/mct_model_garden/models_keras/nanodet/nanodet_keras_model.py
index 66181b234..c1cad1284 100644
--- a/tutorials/mct_model_garden/models_keras/nanodet/nanodet_keras_model.py
+++ b/tutorials/mct_model_garden/models_keras/nanodet/nanodet_keras_model.py
@@ -290,15 +290,16 @@ def nanodet_generate_anchors(batch_size, featmap_sizes, strides):
         anchors_list.append(anchors)
     return np.concatenate(anchors_list, axis=1, dtype=float)
 
-def nanodet_plus_head(n, feat_channels=128):
+def nanodet_plus_head(n, feat_channels=128, num_classes=80):
+    feat_out = num_classes + 32
     h = n
     for idx in range(4):
         h[idx] = depthwise_conv_module(n[idx], out_channels=feat_channels, stride=1, name_prefix='head.cls_convs.' + str(idx) + '.0')
         h[idx] = depthwise_conv_module(h[idx], out_channels=feat_channels, stride=1, name_prefix='head.cls_convs.' + str(idx) + '.1')
-        h[idx] = Conv2D(112, 1, name='head.gfl_cls.' + str(idx))(h[idx])
+        h[idx] = Conv2D(feat_out, 1, name='head.gfl_cls.' + str(idx))(h[idx])
     return h
 
-def nanodet_box_decoding(h, res):
+def nanodet_box_decoding(h, res, num_classes=80):
     strides = [8, 16, 32, 64]
     batch_size = 1
     featmap_sizes = [(np.ceil(res / stride), np.ceil(res / stride)) for stride in strides]
@@ -309,7 +310,7 @@ def nanodet_box_decoding(h, res):
     h_bbox = []
     for idx in range(4):
         # Split to 80 classes and 4 * 8 bounding boxes regression
-        cls, regr = tf.split(h[idx], [80, 32],-1)
+        cls, regr = tf.split(h[idx], [num_classes, 32],-1)
         ndet = cls.shape[1] * cls.shape[2]
 
         # Distributed Focal loss integral
@@ -323,7 +324,7 @@ def nanodet_box_decoding(h, res):
         bbox = tf.stack([bbox1, bbox0, bbox3, bbox2], -1)
         bbox = tf.expand_dims(bbox,2)
 
-        cls = tf.reshape(cls, [-1, ndet, 80])
+        cls = tf.reshape(cls, [-1, ndet, num_classes])
         h_cls.append(cls)
         h_bbox.append(bbox)
     classes = Concatenate(axis=1, name='bb_dec_class')([h_cls[0], h_cls[1], h_cls[2], h_cls[3]])
@@ -332,24 +333,25 @@ def nanodet_box_decoding(h, res):
     return classes, boxes
 
 # Nanodet-Plus model definition
-def nanodet_plus_m(input_shape, scale_factor, bottleneck_ratio, feat_channels):
+def nanodet_plus_m(input_shape, scale_factor, bottleneck_ratio, feat_channels, num_classes=80):
     """
     Create the Nanodet-Plus object detection model.
 
     Args:
         input_shape (tuple): The shape of input images (height, width, channels).
-        scale_factor (float): Scale factor for ShuffleNetV2 backbone.
-        bottleneck_ratio (float): Bottleneck ratio for ShuffleNetV2 backbone.
+        scale_factor (float): Scale factor for the ShuffleNetV2 backbone.
+        bottleneck_ratio (float): Bottleneck ratio for the ShuffleNetV2 backbone.
         feat_channels (int): Number of feature channels.
+        num_classes (int): Number of output classes.
 
     Returns:
         tf.keras.Model: The Nanodet-Plus model.
 
     Configuration options:
-        nanodet-plus-m-1.5x-416:  input_shape = (416,416,3), scale_factor=1.5, feat_channels=128
-        nanodet-plus-m-1.5x-320:  input_shape = (320,320,3), scale_factor=1.5, feat_channels=128
-        nanodet-plus-m-416:  input_shape = (416,416,3), scale_factor=1.0, feat_channels=96
-        nanodet-plus-m-320:  input_shape = (320,320,3), scale_factor=1.0, feat_channels=96
+        nanodet-plus-m-1.5x-416:  input_shape = (416,416,3), scale_factor=1.5, bottleneck_ratio=0.5, feat_channels=128, num_classes=80
+        nanodet-plus-m-1.5x-320:  input_shape = (320,320,3), scale_factor=1.5, bottleneck_ratio=0.5, feat_channels=128, num_classes=80
+        nanodet-plus-m-416:  input_shape = (416,416,3), scale_factor=1.0, bottleneck_ratio=0.5, feat_channels=96, num_classes=80
+        nanodet-plus-m-320:  input_shape = (320,320,3), scale_factor=1.0, bottleneck_ratio=0.5, feat_channels=96, num_classes=80
 
     """
     # Nanodet backbone
@@ -359,7 +361,7 @@ def nanodet_plus_m(input_shape, scale_factor, bottleneck_ratio, feat_channels):
     x = nanodet_ghostpan(x, out_channels=feat_channels, res=input_shape[0])
 
     # Nanodet head
-    x = nanodet_plus_head(x, feat_channels=feat_channels)
+    x = nanodet_plus_head(x, feat_channels=feat_channels, num_classes=num_classes)
 
     # Define Keras model
     return Model(inputs, x, name=f'Nanodet_plus_m_{scale_factor}x_{input_shape[0]}')