--- a
+++ b/model/model.py
@@ -0,0 +1,67 @@
+from tensorflow.keras import backend as K
+from tensorflow.keras.applications.densenet import DenseNet121
+from tensorflow.keras.applications.densenet import DenseNet169
+# Model Imports
+from tensorflow.keras.applications.resnet_v2 import ResNet50V2
+from tensorflow.keras.layers import Dense, GlobalAveragePooling2D, Multiply, Concatenate, Input
+from tensorflow.keras.models import Model
+
+
+# Parallelize Custom Block
+def parallelize_block(tensor, ratio=128):
+    nb_channel = K.int_shape(tensor)[-1]
+
+    # Averaging
+    x = GlobalAveragePooling2D()(tensor)
+    x = Dense(nb_channel // ratio, activation='relu')(x)
+    x = Dense(nb_channel, activation='sigmoid')(x)
+
+    # Weighting
+    x = Multiply()([tensor, x])
+    return x
+
+
+# MAIN MODEL
+def ParallelXNet(n_classes, tau=128, input_shape=(320, 320, 3), weights='imagenet', output_activation="sigmoid"):
+    # Combined Input
+    combinedInput = Input(batch_shape=(None, None, None, 3))
+
+    # Base models preoaded with imagenet weights
+    resnetV2_m = ResNet50V2(weights=weights, include_top=False,
+                            input_shape=input_shape, input_tensor=combinedInput)
+    densenet121_m = DenseNet121(weights=weights, include_top=False,
+                                input_shape=input_shape, input_tensor=combinedInput)
+    densenet169_m = DenseNet169(weights=weights, include_top=False,
+                                input_shape=input_shape, input_tensor=combinedInput)
+
+    for layer in resnetV2_m.layers:
+        layer._name = layer.name + str("_0")
+
+    for layer in densenet121_m.layers:
+        layer._name = layer.name + str("_1")
+
+    for layer in densenet169_m.layers:
+        layer._name = layer.name + str("_2")
+
+    base_model_output = [
+        resnetV2_m.output,
+        densenet121_m.output,
+        densenet169_m.output
+    ]
+
+    # Concatenating the models
+    c_model = Concatenate()(base_model_output)
+
+    # Passing via parallelize block
+    par_c_model = parallelize_block(
+        tensor=c_model,
+        ratio=tau
+    )
+
+    # Final Average Pooling layer
+    x = GlobalAveragePooling2D()(par_c_model)
+
+    # Final Dense Layer
+    detection_output = Dense(n_classes, activation=output_activation)(x)
+
+    return Model(inputs=combinedInput, outputs=detection_output)