diff --git a/default_params.json b/default_params.json
index 4e4d482..c18637b 100644
--- a/default_params.json
+++ b/default_params.json
@@ -1,22 +1,22 @@
 {
-  "name": "Ensemble Learning 256 with aux",
+  "name": "EfficientNetB0 3D 256",
   "seed": 7171,
+  "ensemble": true,
   "data": {
     "dirname": "data/orig",
     "val_size": 0.2,
     "batch_size": 1,
-    "volume_size": [256, 256, 64],
-    "seq_type": "ALL"
+    "volume_size": [256, 256, 64]
   },
   "model": {
-    "name": "ensemble_model",
-    "use_aux": true
+    "name": "efficientnet",
+    "variant": "B0"
   },
   "train": {
     "lr": 1e-4,
-    "decay_steps": 1000,
+    "decay_steps": 100000,
     "decay_rate": 0.96,
     "epoch": 100
   },
-  "desc": "Ensemble learning experiment with 256x256x64 voxel size, with aux out."
+  "desc": "EfficientNetB0 3D experiment with 256x256x64 voxel size."
 }
diff --git a/models/__init__.py b/models/__init__.py
index 34ec76e..dfce67c 100644
--- a/models/__init__.py
+++ b/models/__init__.py
@@ -9,4 +9,5 @@ def find_model(model_name):
 
 # Import exposed model functions
 from models.baseline import baseline
+from models.efficientnet import efficientnet
 from models.ensemble import ensemble_model
diff --git a/models/efficientnet.py b/models/efficientnet.py
new file mode 100644
index 0000000..6eeee57
--- /dev/null
+++ b/models/efficientnet.py
@@ -0,0 +1,41 @@
+from tensorflow import keras
+import efficientnet_3D.tfkeras as efn
+
+def efficientnet(input_shape, n_class=2, variant='B0', **kwargs):
+    """EfficientNet model from the paper https://arxiv.org/abs/1905.11946
+    
+    Args:
+      input_shape: Shape of the input tensor, not including the `batch_size` dimension.
+      n_class: The number of class to be predicted.
+      variant: The variant of efficient net. Can be `B0`~`B7` for both 2D and 3D.
+        `L2` is available for 3D version only.
+    
+    Returns:
+      A `keras.Model` instance.
+    """
+    default_effnet_params = {
+        'include_top': True,
+        'weights': None,
+        'input_shape': input_shape,
+        'pooling': None,
+        'classes': n_class,
+    }
+    try:
+        if len(input_shape) == 4:
+            effnet_layer = getattr(efn, f'EfficientNet{variant}')(**default_effnet_params)
+            modal = '3D'
+        elif len(input_shape) == 3:
+            effnet_layer = getattr(keras.applications, f'EfficientNet{variant}')(
+                classifier_activation='softmax', **default_effnet_params
+            )
+            modal = '2D'
+        else:
+            raise ValueError('input_shape is expected as an array ranked 3 or 4')
+    except AttributeError:
+        print(f'No EfficientNet variation found for {variant}')
+        raise SystemExit
+
+    inputs = keras.Input(input_shape)
+    outputs = effnet_layer(inputs)
+
+    return keras.Model(inputs, outputs, name=f'efficientnet_{modal}_{variant}')
diff --git a/setup/requirements.txt b/setup/requirements.txt
index 7b9cc5c..878eafb 100644
--- a/setup/requirements.txt
+++ b/setup/requirements.txt
@@ -1,5 +1,7 @@
+efficientnet-3D==1.0.1
 imageio==2.9.0
 jupyter
+Keras-Applications==1.0.8
 matplotlib==3.3.2
 nibabel==3.2.1
 pandas==1.1.4