diff --git a/larq_zoo/literature/meliusnet.py b/larq_zoo/literature/meliusnet.py
index 68a7c814..52e67dda 100644
--- a/larq_zoo/literature/meliusnet.py
+++ b/larq_zoo/literature/meliusnet.py
@@ -139,40 +139,51 @@ def build(self) -> tf.keras.models.Model:
             inputs=self.image_input, outputs=x, name=self.name
         )
 
+        if self.weights == "imagenet":
+            if self.include_top:
+                weights_path = self.imagenet_weights_path
+            else:
+                weights_path = self.imagenet_no_top_weights_path
+            model.load_weights(weights_path)
+        elif self.weights is not None:
+            model.load_weights(self.weights)
+
         return model
 
 
+######################
+# Concrete factories #
+######################
+
+
 @factory
 class MeliusNet22Factory(MeliusNetFactory):
     num_blocks = (4, 5, 4, 4)
     transition_features = (160, 224, 256, None)
     name = "meliusnet22"
 
-    def build(self) -> tf.keras.models.Model:
-        model = super().build()
+    @property
+    def imagenet_weights_path(self):
+        return utils.download_pretrained_model(
+            model="meliusnet22",
+            version="v0.1.0",
+            file="meliusnet22_weights.h5",
+            file_hash="bb8dda20642508bbe5e0ff95012fec450103c4b23989f4c9c9d853d67b6ff806",
+        )
 
-        # Load weights.
-        if self.weights == "imagenet":
-            # Download appropriate file
-            if self.include_top:
-                weights_path = utils.download_pretrained_model(
-                    model="meliusnet22",
-                    version="v0.1.0",
-                    file="meliusnet22_weights.h5",
-                    file_hash="51dba19f17023a9c0b92f1a422ece9b987ce55ab6916cc8a8eb31f6b5e09ba8c",
-                )
-            else:
-                weights_path = utils.download_pretrained_model(
-                    model="meliusnet22",
-                    version="v0.1.0",
-                    file="meliusnet22_weights_notop.h5",
-                    file_hash="8c029fadb78d28f1d9baa2940844244975e029f1bdc1153d3f63729acc06be3c",
-                )
-            model.load_weights(weights_path)
-        elif self.weights is not None:
-            model.load_weights(self.weights)
+    @property
+    def imagenet_no_top_weights_path(self):
+        return utils.download_pretrained_model(
+            model="meliusnet22",
+            version="v0.1.0",
+            file="meliusnet22_weights_notop.h5",
+            file_hash="9ca867806bff0c2995ff5f1ad085d1627c8dabf12bffbf0bea86eb39ab3cf724",
+        )
 
-        return model
+
+#########################
+# Functional interfaces #
+#########################
 
 
 def MeliusNet22(
@@ -193,6 +204,13 @@ def MeliusNet22(
     ```plot-altair
     /plots/meliusnet22.vg.json
     ```
+    ```summary
+    literature.MeliusNet22
+    ```
+    # ImageNet Metrics
+    | Top-1 Accuracy | Top-5 Accuracy | Parameters | Memory   |
+    | -------------- | -------------- | ---------- | -------- |
+    | 62.4 %         | 83.9 %         | 6 944 584  | 3.88 MiB |
 
     # Arguments
     input_shape: Optional shape tuple, to be specified if you would like to use a model
@@ -213,9 +231,7 @@ def MeliusNet22(
     ValueError: in case of invalid argument for `weights`, or invalid input shape.
 
     # References
-    - [Bi-Real Net: Enhancing the Performance of 1-bit CNNs With Improved
-      Representational Capability and Advanced Training
-      Algorithm](https://arxiv.org/abs/1808.00278)
+    - [MeliusNet: Can Binary Neural Networks Achieve MobileNet-level Accuracy?](https://arxiv.org/abs/2001.05936)
     """
     return MeliusNet22Factory(
         include_top=include_top,