diff --git a/nobrainer/models/__init__.py b/nobrainer/models/__init__.py
index 5e0092b5..8dd75b44 100644
--- a/nobrainer/models/__init__.py
+++ b/nobrainer/models/__init__.py
@@ -1,3 +1,5 @@
+from pprint import pprint
+
 from .attention_unet import attention_unet
 from .attention_unet_with_inception import attention_unet_with_inception
 from .autoencoder import autoencoder
@@ -10,6 +12,22 @@
 from .unet import unet
 from .unetr import unetr
 
+__all__ = ["get", "list_available_models"]
+
+_models = {
+    "highresnet": highresnet,
+    "meshnet": meshnet,
+    "unet": unet,
+    "autoencoder": autoencoder,
+    "progressivegan": progressivegan,
+    "progressiveae": progressiveae,
+    "dcgan": dcgan,
+    "attention_unet": attention_unet,
+    "attention_unet_with_inception": attention_unet_with_inception,
+    "unetr": unetr,
+    "variational_meshnet": variational_meshnet,
+}
+
 
 def get(name):
     """Return callable that creates a particular `tf.keras.Model`.
@@ -25,24 +43,18 @@ def get(name):
     if not isinstance(name, str):
         raise ValueError("Model name must be a string.")
 
-    models = {
-        "highresnet": highresnet,
-        "meshnet": meshnet,
-        "unet": unet,
-        "autoencoder": autoencoder,
-        "progressivegan": progressivegan,
-        "progressiveae": progressiveae,
-        "dcgan": dcgan,
-        "attention_unet": attention_unet,
-        "attention_unet_with_inception": attention_unet_with_inception,
-        "unetr": unetr,
-        "variational_meshnet": variational_meshnet,
-    }
-
     try:
-        return models[name.lower()]
+        return _models[name.lower()]
     except KeyError:
-        avail = ", ".join(models.keys())
+        avail = ", ".join(_models.keys())
         raise ValueError(
             "Unknown model: '{}'. Available models are {}.".format(name, avail)
         )
+
+
+def available_models():
+    return list(_models)
+
+def list_available_models():
+    pprint(available_models())
+
diff --git a/nobrainer/processing/segmentation.py b/nobrainer/processing/segmentation.py
index c04ee906..f5a2f951 100644
--- a/nobrainer/processing/segmentation.py
+++ b/nobrainer/processing/segmentation.py
@@ -5,6 +5,7 @@
 
 from .base import BaseEstimator
 from .. import losses, metrics
+from ..models import available_models, list_available_models
 
 logging.getLogger().setLevel(logging.INFO)
 
@@ -15,20 +16,38 @@ class Segmentation(BaseEstimator):
     state_variables = ["block_shape_", "volume_shape_", "scalar_labels_"]
 
     def __init__(
-        self, base_model, model_args=None, checkpoint_filepath=None, multi_gpu=True
+        self, base_model=None, model_args=None, checkpoint_filepath=None, multi_gpu=True
     ):
         super().__init__(checkpoint_filepath=checkpoint_filepath, multi_gpu=multi_gpu)
 
-        if not isinstance(base_model, str):
+        if base_model is None:
+            print(
+                "No model specified. Please specify one using the 'add_model' method."
+            )
+            self.base_model = None
+        elif not isinstance(base_model, str):
             self.base_model = base_model.__name__
         else:
             self.base_model = base_model
+
+        if self.base_model and self.base_model not in available_models():
+            raise ValueError(
+                "Unknown model: '{}'. Available models are {}.".format(
+                    self.base_model, available_models()
+                )
+            )
+
         self.model_ = None
         self.model_args = model_args or {}
         self.block_shape_ = None
         self.volume_shape_ = None
         self.scalar_labels_ = None
 
+    def add_model(self, base_model, model_args=None):
+        """Add a segmentation model"""
+        self.base_model = base_model
+        self.model_args = model_args or {}
+
     def fit(
         self,
         dataset_train,
@@ -72,7 +91,7 @@ def _compile():
                 metrics=metrics,
             )
 
-        if self.model is None:
+        if self.model_ is None:
             mod = importlib.import_module("..models", "nobrainer.processing")
             base_model = getattr(mod, self.base_model)
             if batch_size % self.strategy.num_replicas_in_sync:
@@ -97,9 +116,9 @@ def _compile():
             epochs=epochs,
             steps_per_epoch=dataset_train.get_steps_per_epoch(),
             validation_data=dataset_validate.dataset if dataset_validate else None,
-            validation_steps=dataset_validate.get_steps_per_epoch()
-            if dataset_validate
-            else None,
+            validation_steps=(
+                dataset_validate.get_steps_per_epoch() if dataset_validate else None
+            ),
             callbacks=callbacks,
             verbose=verbose,
         )
@@ -119,3 +138,6 @@ def predict(self, x, batch_size=1, normalizer=None):
             batch_size=batch_size,
             normalizer=normalizer,
         )
+
+    def list_available_models(self):
+        list_available_models()