diff --git a/README.md b/README.md
index 95fb31f1..ba8ce50a 100644
--- a/README.md
+++ b/README.md
@@ -163,7 +163,13 @@ global_step = global_step_t.eval()
 
 ### Running Tests
 
-Use `tox` to run the test suite on all supported environments.
+Use `tox` to run the test suite in both Python 2 and Python 3 environments.
+
+To also run slower integration tests (marked with `pytest.mark.slow`), specify the `--run-slow` option for pytest, which can be passed through `tox` like so:
+
+```
+tox -- --run-slow
+```
 
 To run tests only for a specific module, pass a folder to `tox`:
 `tox tests/misc/io`
diff --git a/lucid/modelzoo/caffe_models/AlexNet.py b/lucid/modelzoo/caffe_models/AlexNet.py
new file mode 100644
index 00000000..72d2d42a
--- /dev/null
+++ b/lucid/modelzoo/caffe_models/AlexNet.py
@@ -0,0 +1,76 @@
+# Copyright 2018 The Lucid Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+from __future__ import absolute_import, division, print_function
+from lucid.modelzoo.vision_base import Model, IMAGENET_MEAN_BGR
+
+
+class AlexNet_caffe(Model):
+  """Original AlexNet weights ported to TF.
+
+  AlexNet is the breakthrough vision model from Krizhevsky, et al (2012):
+  https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf
+  This implementation is a caffe re-implementation:
+  http://www.cs.toronto.edu/~guerzhoy/tf_alexnet/
+  It was converted to TensorFlow by this GitHub project:
+  https://github.com/huanzhang12/tensorflow-alexnet-model
+  It appears the parameters are the actual original parameters.
+  """
+
+  # The authors of code to convert AlexNet to TF host weights at
+  # http://jaina.cs.ucdavis.edu/datasets/adv/imagenet/alexnet_frozen.pb
+  # but it seems more polite and reliable to host our own.
+  model_path  = 'gs://modelzoo/AlexNet.pb'
+  labels_path = 'gs://modelzoo/ImageNet_labels_caffe.txt'
+  image_shape = [227, 227, 3]
+  is_BGR = True
+  image_value_range = (-IMAGENET_MEAN_BGR, 255-IMAGENET_MEAN_BGR)
+  input_name = 'Placeholder'
+
+  # TODO - Sanity check this graph and layers
+  layers = [
+     {'type': 'conv', 'name': 'concat_2', 'size': 256},
+     {'type': 'conv', 'name': 'conv5_1', 'size': 256},
+     {'type': 'dense', 'name': 'Relu', 'size': 4096},
+     {'type': 'dense', 'name': 'Relu_1', 'size': 4096},
+     {'type': 'dense', 'name': 'Softmax', 'size': 1000},
+   ]
+
+
+class AlexNet_caffe_Places365(Model):
+  """AlexNet re-implementation trained on Places365.
+  
+  This model is a reimplementation of AlexNet
+  https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf
+  trained on the MIT Places365 dataset, retrieved here:
+  https://github.com/CSAILVision/places365
+  and then ported to TensorFlow using caffe-tensorflow.
+  """
+
+  model_path  = 'gs://modelzoo/AlexNet_caffe_places365.pb'
+  labels_path = 'gs://modelzoo/InceptionV1_caffe_places365-labels.txt'
+  image_shape = [227, 227, 3]
+  is_BGR = True
+  image_value_range = (-IMAGENET_MEAN_BGR, 255-IMAGENET_MEAN_BGR)
+  input_name = 'input'
+
+  # TODO - Sanity check this graph and layers
+  layers = [
+    {'type': 'conv', 'name': 'conv5/concat', 'size': 256} ,
+    {'type': 'conv', 'name': 'conv5/conv5', 'size': 256} ,
+    {'type': 'dense', 'name': 'fc6/fc6', 'size': 4096} ,
+    {'type': 'dense', 'name': 'fc7/fc7', 'size': 4096} ,
+    {'type': 'dense', 'name': 'prob', 'size': 365} ,
+   ]
diff --git a/lucid/modelzoo/caffe_models/InceptionV1.py b/lucid/modelzoo/caffe_models/InceptionV1.py
new file mode 100644
index 00000000..3d0b3715
--- /dev/null
+++ b/lucid/modelzoo/caffe_models/InceptionV1.py
@@ -0,0 +1,118 @@
+# Copyright 2018 The Lucid Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+from __future__ import absolute_import, division, print_function
+from lucid.modelzoo.vision_base import Model, IMAGENET_MEAN_BGR
+
+
+class InceptionV1_caffe(Model):
+  """InceptionV1 (or 'GoogLeNet') as reimplemented in caffe.
+
+  This model is a reimplementation of GoogLeNet:
+  https://www.cs.unc.edu/~wliu/papers/GoogLeNet.pdf
+  reimplemented in caffe by BVLC / Sergio Guadarrama:
+  https://github.com/BVLC/caffe/tree/master/models/bvlc_googlenet
+  and then ported to TensorFlow using caffe-tensorflow.
+  """
+  model_path = 'gs://modelzoo/InceptionV1_caffe.pb'
+  labels_path = 'gs://modelzoo/InceptionV1_caffe-labels.txt'
+  image_shape = [224, 224, 3]
+  is_BGR = True
+  image_value_range = (-IMAGENET_MEAN_BGR, 255-IMAGENET_MEAN_BGR)
+  input_name = 'data'
+
+  layers = [
+     {'type': 'conv', 'name': 'conv1_7x7_s2/conv1_7x7_s2', 'size': 64},
+     {'type': 'conv', 'name': 'conv2_3x3_reduce/conv2_3x3_reduce', 'size': 64},
+     {'type': 'conv', 'name': 'conv2_3x3/conv2_3x3', 'size': 192},
+     {'type': 'conv', 'name': 'inception_3a_output', 'size': 256},
+     {'type': 'conv', 'name': 'inception_3b_output', 'size': 480},
+     {'type': 'conv', 'name': 'inception_4a_output', 'size': 512},
+     {'type': 'conv', 'name': 'inception_4b_output', 'size': 512},
+     {'type': 'conv', 'name': 'inception_4c_output', 'size': 512},
+     {'type': 'conv', 'name': 'inception_4d_output', 'size': 528},
+     {'type': 'conv', 'name': 'inception_4e_output', 'size': 832},
+     {'type': 'conv', 'name': 'inception_5a_output', 'size': 832},
+     {'type': 'conv', 'name': 'inception_5b_output', 'size': 1024},
+     {'type': 'dense', 'name': 'prob', 'size': 1000},
+   ]
+
+
+class InceptionV1_caffe_Places205(Model):
+  """InceptionV1 (or 'GoogLeNet') trained on Places205.
+
+  This model is a caffe reimplementation of GoogLeNet:
+  https://www.cs.unc.edu/~wliu/papers/GoogLeNet.pdf
+  trained on the MIT Places205 dataset, retrieved here:
+  http://places.csail.mit.edu/downloadCNN.html
+  and then ported to TensorFlow using caffe-tensorflow.
+  """
+  model_path = 'gs://modelzoo/InceptionV1_caffe_places205.pb'
+  labels_path = 'gs://modelzoo/InceptionV1_caffe_places205-labels.txt'
+  image_shape = [224, 224, 3]
+  # range based on emperical testing
+  image_value_range = (-1,1)
+  input_name = 'data'
+
+  layers = [
+     {'type': 'conv', 'name': 'conv1_7x7_s2/conv1_7x7_s2', 'size': 64},
+     {'type': 'conv', 'name': 'conv2_3x3_reduce/conv2_3x3_reduce', 'size': 64},
+     {'type': 'conv', 'name': 'conv2_3x3/conv2_3x3', 'size': 192},
+     {'type': 'conv', 'name': 'inception_3a_output', 'size': 256},
+     {'type': 'conv', 'name': 'inception_3b_output', 'size': 480},
+     {'type': 'conv', 'name': 'inception_4a_output', 'size': 512},
+     {'type': 'conv', 'name': 'inception_4b_output', 'size': 512},
+     {'type': 'conv', 'name': 'inception_4c_output', 'size': 512},
+     {'type': 'conv', 'name': 'inception_4d_output', 'size': 528},
+     {'type': 'conv', 'name': 'inception_4e_output', 'size': 832},
+     {'type': 'conv', 'name': 'inception_5a_output', 'size': 832},
+     {'type': 'conv', 'name': 'inception_5b_output', 'size': 1024},
+     {'type': 'dense', 'name': 'prob', 'size': 205},
+   ]
+
+
+class InceptionV1_caffe_Places365(Model):
+  """InceptionV1 (or 'GoogLeNet') trained on Places365.
+
+  This model is a caffe reimplementation of GoogLeNet:
+  https://www.cs.unc.edu/~wliu/papers/GoogLeNet.pdf
+  trained on the MIT Places365 dataset, retrieved here:
+  https://github.com/CSAILVision/places365
+  and then ported to TensorFlow using caffe-tensorflow.
+  """
+  model_path = 'gs://modelzoo/InceptionV1_caffe_places365.pb'
+  # TODO - check labels match predictions
+  labels_path = 'gs://modelzoo/InceptionV1_caffe_places365-labels.txt'
+  image_shape = [224, 224, 3]
+  # What is the correct input range???
+  is_BGR = True
+  image_value_range = (-IMAGENET_MEAN_BGR, 255-IMAGENET_MEAN_BGR)
+  input_name = 'data'
+
+  layers = [
+     {'type': 'conv', 'name': 'conv1_7x7_s2/conv1_7x7_s2', 'size': 64},
+     {'type': 'conv', 'name': 'conv2_3x3_reduce/conv2_3x3_reduce', 'size': 64},
+     {'type': 'conv', 'name': 'conv2_3x3/conv2_3x3', 'size': 192},
+     {'type': 'conv', 'name': 'inception_3a_output', 'size': 256},
+     {'type': 'conv', 'name': 'inception_3b_output', 'size': 480},
+     {'type': 'conv', 'name': 'inception_4a_output', 'size': 512},
+     {'type': 'conv', 'name': 'inception_4b_output', 'size': 512},
+     {'type': 'conv', 'name': 'inception_4c_output', 'size': 512},
+     {'type': 'conv', 'name': 'inception_4d_output', 'size': 528},
+     {'type': 'conv', 'name': 'inception_4e_output', 'size': 832},
+     {'type': 'conv', 'name': 'inception_5a_output', 'size': 832},
+     {'type': 'conv', 'name': 'inception_5b_output', 'size': 1024},
+     {'type': 'dense', 'name': 'prob', 'size': 365},
+   ]
diff --git a/lucid/modelzoo/caffe_models/README.md b/lucid/modelzoo/caffe_models/README.md
new file mode 100644
index 00000000..4deb83f1
--- /dev/null
+++ b/lucid/modelzoo/caffe_models/README.md
@@ -0,0 +1,3 @@
+Models imported from caffe.
+
+See `lucid/scripts/import_caffe/` for details on how to import more.
diff --git a/lucid/modelzoo/caffe_models/__init__.py b/lucid/modelzoo/caffe_models/__init__.py
new file mode 100644
index 00000000..a6934359
--- /dev/null
+++ b/lucid/modelzoo/caffe_models/__init__.py
@@ -0,0 +1,2 @@
+from lucid.modelzoo.caffe_models.InceptionV1 import *
+from lucid.modelzoo.caffe_models.others import *
diff --git a/lucid/modelzoo/caffe_models/others.py b/lucid/modelzoo/caffe_models/others.py
new file mode 100644
index 00000000..b043a30b
--- /dev/null
+++ b/lucid/modelzoo/caffe_models/others.py
@@ -0,0 +1,118 @@
+# Copyright 2018 The Lucid Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+from __future__ import absolute_import, division, print_function
+from lucid.modelzoo.vision_base import Model, IMAGENET_MEAN_BGR
+
+class CaffeNet_caffe(Model):
+  """CaffeNet (AlexNet variant included in Caffe)
+  
+  CaffeNet is a slight variant on AlexNet, described here:
+  https://github.com/BVLC/caffe/tree/master/models/bvlc_reference_caffenet
+  """
+
+  model_path  = 'gs://modelzoo/CaffeNet_caffe.pb'
+  labels_path = 'gs://modelzoo/ImageNet_labels_caffe.txt'
+  image_shape = [227, 227, 3]
+  is_BGR = True
+  image_value_range = (-IMAGENET_MEAN_BGR, 255-IMAGENET_MEAN_BGR)
+  input_name = 'data'
+
+  layers = [
+    {'type': 'conv', 'name': 'conv5/concat', 'size': 256} ,
+    {'type': 'conv', 'name': 'conv5/conv5', 'size': 256} ,
+    {'type': 'dense', 'name': 'fc6/fc6', 'size': 4096} ,
+    {'type': 'dense', 'name': 'fc7/fc7', 'size': 4096} ,
+    {'type': 'dense', 'name': 'prob', 'size': 1000} ,
+   ]
+
+
+class VGG16_caffe(Model):
+  """VGG16 model used in ImageNet ILSVRC-2014, ported from caffe.
+
+  VGG16 was introduced by Simonyan & Zisserman (2014):
+  https://arxiv.org/pdf/1409.1556.pdf
+  http://www.robots.ox.ac.uk/~vgg/research/very_deep/
+  as the Oxford Visual Geometry Group's submission for the ImageNet ILSVRC-2014
+  contest. We download their caffe trained model from
+  https://gist.github.com/ksimonyan/211839e770f7b538e2d8#file-readme-md
+  and convert it with caffe-tensorflow.
+  """
+  model_path = 'gs://modelzoo/VGG16_caffe.pb'
+  labels_path = 'gs://modelzoo/InceptionV1_caffe-labels.txt'
+  image_shape = [224, 224, 3]
+  is_BGR = True
+  image_value_range = (-IMAGENET_MEAN_BGR, 255-IMAGENET_MEAN_BGR)
+  input_name = 'input'
+
+  layers = [
+     {'type': 'conv', 'name': 'conv1_1/conv1_1', 'size': 64},
+     {'type': 'conv', 'name': 'conv1_2/conv1_2', 'size': 64},
+     {'type': 'conv', 'name': 'conv2_1/conv2_1', 'size': 128},
+     {'type': 'conv', 'name': 'conv2_2/conv2_2', 'size': 128},
+     {'type': 'conv', 'name': 'conv3_1/conv3_1', 'size': 256},
+     {'type': 'conv', 'name': 'conv3_2/conv3_2', 'size': 256},
+     {'type': 'conv', 'name': 'conv3_3/conv3_3', 'size': 256},
+     {'type': 'conv', 'name': 'conv4_1/conv4_1', 'size': 512},
+     {'type': 'conv', 'name': 'conv4_2/conv4_2', 'size': 512},
+     {'type': 'conv', 'name': 'conv4_3/conv4_3', 'size': 512},
+     {'type': 'conv', 'name': 'conv5_1/conv5_1', 'size': 512},
+     {'type': 'conv', 'name': 'conv5_2/conv5_2', 'size': 512},
+     {'type': 'conv', 'name': 'conv5_3/conv5_3', 'size': 512},
+     {'type': 'dense', 'name': 'fc6/fc6', 'size': 4096},
+     {'type': 'dense', 'name': 'fc7/fc7', 'size': 4096},
+     {'type': 'dense', 'name': 'prob', 'size': 1000},
+   ]
+
+
+class VGG19_caffe(Model):
+  """VGG16 model used in ImageNet ILSVRC-2014, ported from caffe.
+
+  VGG19 was introduced by Simonyan & Zisserman (2014):
+  https://arxiv.org/pdf/1409.1556.pdf
+  http://www.robots.ox.ac.uk/~vgg/research/very_deep/
+  as the Oxford Visual Geometry Group's submission for the ImageNet ILSVRC-2014
+  contest. We download their caffe trained model from
+  https://gist.github.com/ksimonyan/3785162f95cd2d5fee77#file-readme-md
+  and convert it with caffe-tensorflow.
+  """
+  model_path = 'gs://modelzoo/VGG19_caffe.pb'
+  labels_path = 'gs://modelzoo/InceptionV1_caffe-labels.txt'
+  image_shape = [224, 224, 3]
+  is_BGR = True
+  image_value_range = (-IMAGENET_MEAN_BGR, 255-IMAGENET_MEAN_BGR)
+  input_name = 'input'
+
+  layers = [
+     {'type': 'conv', 'name': 'conv1_1/conv1_1', 'size': 64},
+     {'type': 'conv', 'name': 'conv1_2/conv1_2', 'size': 64},
+     {'type': 'conv', 'name': 'conv2_1/conv2_1', 'size': 128},
+     {'type': 'conv', 'name': 'conv2_2/conv2_2', 'size': 128},
+     {'type': 'conv', 'name': 'conv3_1/conv3_1', 'size': 256},
+     {'type': 'conv', 'name': 'conv3_2/conv3_2', 'size': 256},
+     {'type': 'conv', 'name': 'conv3_3/conv3_3', 'size': 256},
+     {'type': 'conv', 'name': 'conv3_4/conv3_4', 'size': 256},
+     {'type': 'conv', 'name': 'conv4_1/conv4_1', 'size': 512},
+     {'type': 'conv', 'name': 'conv4_2/conv4_2', 'size': 512},
+     {'type': 'conv', 'name': 'conv4_3/conv4_3', 'size': 512},
+     {'type': 'conv', 'name': 'conv4_4/conv4_4', 'size': 512},
+     {'type': 'conv', 'name': 'conv5_1/conv5_1', 'size': 512},
+     {'type': 'conv', 'name': 'conv5_2/conv5_2', 'size': 512},
+     {'type': 'conv', 'name': 'conv5_3/conv5_3', 'size': 512},
+     {'type': 'conv', 'name': 'conv5_4/conv5_4', 'size': 512},
+     {'type': 'dense', 'name': 'fc6/fc6', 'size': 4096},
+     {'type': 'dense', 'name': 'fc7/fc7', 'size': 4096},
+     {'type': 'dense', 'name': 'prob', 'size': 1000},
+   ]
diff --git a/lucid/modelzoo/nets_factory.py b/lucid/modelzoo/nets_factory.py
new file mode 100644
index 00000000..a47b7c91
--- /dev/null
+++ b/lucid/modelzoo/nets_factory.py
@@ -0,0 +1,68 @@
+# Copyright 2018 The Lucid Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Contains a factory function for accessing models.
+
+You can either use the provided `get_model` function, or directly access the
+`models_map` variable containing a dictionary from a model name to its class.
+"""
+
+from __future__ import absolute_import, division, print_function
+
+import inspect
+import tensorflow as tf
+
+from lucid.modelzoo import vision_models
+from lucid.modelzoo import vision_base
+
+
+def _generate_models_map():
+    base_classes = inspect.getmembers(vision_base, inspect.isclass)
+
+    list_all_models = []
+    list_all_models += inspect.getmembers(vision_models, inspect.isclass)
+
+    list_filtered = filter(lambda c: c not in base_classes, list_all_models)
+    return dict(list_filtered)
+
+
+models_map = _generate_models_map()
+
+
+def get_model(name):
+    """Returns a model instance such as `model = vision_models.InceptionV1()`.
+    In the future may be expanded to filter by additional criteria, such as
+    architecture, dataset, and task the model was trained on.
+    Args:
+      name: The name of the model, as given by the class name in vision_models.
+    Returns:
+      An instantiated Model class with the requested model. Users still need to
+      manually `load_graphdef` on the return value, and manually import this
+      model's graph into their current graph.
+    Raises:
+      ValueError: If network `name` is not recognized.
+    """
+    if name not in models_map:
+        candidates = filter(lambda key: name in key, models_map.keys())
+        candidates_string = ", ".join(candidates)
+        raise ValueError(
+            "No network named {}. Did you mean one of {}?".format(
+                name, candidates_string
+            )
+        )
+
+    model_class = models_map[name]
+    model = model_class()
+    return model
diff --git a/lucid/modelzoo/other_models/InceptionV1.py b/lucid/modelzoo/other_models/InceptionV1.py
new file mode 100644
index 00000000..01b3e672
--- /dev/null
+++ b/lucid/modelzoo/other_models/InceptionV1.py
@@ -0,0 +1,79 @@
+# Copyright 2018 The Lucid Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+from __future__ import absolute_import, division, print_function
+import tensorflow as tf
+from lucid.modelzoo.vision_base import Model
+
+
+def _populate_inception_bottlenecks(scope):
+  """Add Inception bottlenecks and their pre-Relu versions to the graph."""
+  graph = tf.get_default_graph()
+  for op in graph.get_operations():
+    if op.name.startswith(scope+'/') and 'Concat' in op.type:
+      name = op.name.split('/')[1]
+      pre_relus = []
+      for tower in op.inputs[1:]:
+        if tower.op.type == 'Relu':
+          tower = tower.op.inputs[0]
+        pre_relus.append(tower)
+      concat_name = scope + '/' + name + '_pre_relu'
+      _ = tf.concat(pre_relus, -1, name=concat_name)
+
+
+class InceptionV1(Model):
+  """InceptionV1 (or 'GoogLeNet')
+
+  This is a (re?)implementation of InceptionV1
+  https://www.cs.unc.edu/~wliu/papers/GoogLeNet.pdf
+  The weights were trained at Google and released in an early TensorFlow
+  tutorial. It is possible the parameters are the original weights
+  (trained in TensorFlow's predecessor), but we haven't been able to
+  confirm this.
+  
+  As far as we can tell, it is exactly the same as the model described in
+  the original paper, where as the slim and caffe implementations have 
+  minor implementation differences (such as eliding the heads).
+  """
+  model_path = 'gs://modelzoo/InceptionV1.pb'
+  labels_path = 'gs://modelzoo/InceptionV1-labels.txt'
+  image_shape = [224, 224, 3]
+  image_value_range = (-117, 255-117)
+  input_name = 'input:0'
+
+  def post_import(self, scope):
+    _populate_inception_bottlenecks(scope)
+
+  layers = [
+     {'type': 'conv', 'name': 'conv2d0', 'size': 64},
+     {'type': 'conv', 'name': 'conv2d1', 'size': 64},
+     {'type': 'conv', 'name': 'conv2d2', 'size': 192},
+     {'type': 'conv', 'name': 'mixed3a', 'size': 256},
+     {'type': 'conv', 'name': 'mixed3b', 'size': 480},
+     {'type': 'conv', 'name': 'mixed4a', 'size': 508},
+     {'type': 'conv', 'name': 'mixed4b', 'size': 512},
+     {'type': 'conv', 'name': 'mixed4c', 'size': 512},
+     {'type': 'conv', 'name': 'mixed4d', 'size': 528},
+     {'type': 'conv', 'name': 'mixed4e', 'size': 832},
+     {'type': 'conv', 'name': 'mixed5a', 'size': 832},
+     {'type': 'conv', 'name': 'mixed5b', 'size': 1024},
+     {'type': 'conv', 'name': 'head0_bottleneck', 'size': 128},
+     {'type': 'dense', 'name': 'nn0', 'size': 1024},
+     {'type': 'dense', 'name': 'softmax0', 'size': 1008},
+     {'type': 'conv', 'name': 'head1_bottleneck', 'size': 128},
+     {'type': 'dense', 'name': 'nn1', 'size': 1024},
+     {'type': 'dense', 'name': 'softmax1', 'size': 1008},
+     {'type': 'dense', 'name': 'softmax2', 'size': 1008},
+   ]
diff --git a/lucid/modelzoo/other_models/__init__.py b/lucid/modelzoo/other_models/__init__.py
new file mode 100644
index 00000000..4c95045d
--- /dev/null
+++ b/lucid/modelzoo/other_models/__init__.py
@@ -0,0 +1 @@
+from lucid.modelzoo.other_models.InceptionV1 import InceptionV1
diff --git a/lucid/modelzoo/slim_models/Inception.py b/lucid/modelzoo/slim_models/Inception.py
new file mode 100644
index 00000000..25cd7029
--- /dev/null
+++ b/lucid/modelzoo/slim_models/Inception.py
@@ -0,0 +1,284 @@
+# Copyright 2018 The Lucid Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+from __future__ import absolute_import, division, print_function
+from lucid.modelzoo.vision_base import Model, IMAGENET_MEAN
+
+
+class InceptionV1_slim(Model):
+  """InceptionV1 as implemented by the TensorFlow slim framework.
+
+  InceptionV1 was introduced by Szegedy, et al (2014):
+  https://arxiv.org/pdf/1409.4842v1.pdf
+  This function provides the pre-trained reimplementation from TF slim:
+  https://github.com/tensorflow/models/tree/master/research/slim
+  corresponding to the name "inception_v1".
+  """
+
+  model_path  = 'gs://modelzoo/InceptionV1_slim.pb'
+  labels_path = 'gs://modelzoo/InceptionV1-labels.txt' #TODO
+  image_shape = [224, 224, 3]
+  # inpute range taken from:
+  # https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/inception_preprocessing.py#L280
+  image_value_range = (-1, 1)
+  input_name = 'input'
+
+  layers = [
+     {'type': 'conv', 'name': 'InceptionV1/InceptionV1/Conv2d_1a_7x7/Relu', 'size': 64},
+     {'type': 'conv', 'name': 'InceptionV1/InceptionV1/Conv2d_2b_1x1/Relu', 'size': 64},
+     {'type': 'conv', 'name': 'InceptionV1/InceptionV1/Conv2d_2c_3x3/Relu', 'size': 192},
+     {'type': 'conv', 'name': 'InceptionV1/InceptionV1/Mixed_3b/concat', 'size': 256},
+     {'type': 'conv', 'name': 'InceptionV1/InceptionV1/Mixed_3c/concat', 'size': 480},
+     {'type': 'conv', 'name': 'InceptionV1/InceptionV1/Mixed_4b/concat', 'size': 512},
+     {'type': 'conv', 'name': 'InceptionV1/InceptionV1/Mixed_4c/concat', 'size': 512},
+     {'type': 'conv', 'name': 'InceptionV1/InceptionV1/Mixed_4d/concat', 'size': 512},
+     {'type': 'conv', 'name': 'InceptionV1/InceptionV1/Mixed_4e/concat', 'size': 528},
+     {'type': 'conv', 'name': 'InceptionV1/InceptionV1/Mixed_4f/concat', 'size': 832},
+     {'type': 'conv', 'name': 'InceptionV1/InceptionV1/Mixed_5b/concat', 'size': 832},
+     {'type': 'conv', 'name': 'InceptionV1/InceptionV1/Mixed_5c/concat', 'size': 1024},
+     {'type': 'dense', 'name': 'InceptionV1/Logits/Predictions/Softmax', 'size': 1001},
+   ]
+
+
+class InceptionV2_slim(Model):
+  """InceptionV2 as implemented by the TensorFlow slim framework.
+
+  InceptionV2 was introduced by Ioffe & Szegedy (2015):
+  https://arxiv.org/pdf/1502.03167.pdf
+  This function provides the pre-trained reimplementation from TF slim:
+  https://github.com/tensorflow/models/tree/master/research/slim
+  corresponding to the name "inception_v2".
+  """
+
+  model_path  = 'gs://modelzoo/InceptionV2_slim.pb'
+  labels_path = 'gs://modelzoo/InceptionV1-labels.txt' #TODO
+  image_shape = [224, 224, 3]
+  # inpute range taken from:
+  # https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/inception_preprocessing.py#L280
+  image_value_range = (-1, 1)
+  input_name = 'input'
+
+  layers = [
+     {'type': 'conv', 'name': 'InceptionV2/InceptionV2/Conv2d_1a_7x7/Relu', 'size': 64},
+     {'type': 'conv', 'name': 'InceptionV2/InceptionV2/Conv2d_2b_1x1/Relu', 'size': 64},
+     {'type': 'conv', 'name': 'InceptionV2/InceptionV2/Conv2d_2c_3x3/Relu', 'size': 192},
+     {'type': 'conv', 'name': 'InceptionV2/InceptionV2/Mixed_3b/concat', 'size': 256},
+     {'type': 'conv', 'name': 'InceptionV2/InceptionV2/Mixed_3c/concat', 'size': 320},
+     {'type': 'conv', 'name': 'InceptionV2/InceptionV2/Mixed_4a/concat', 'size': 576},
+     {'type': 'conv', 'name': 'InceptionV2/InceptionV2/Mixed_4b/concat', 'size': 576},
+     {'type': 'conv', 'name': 'InceptionV2/InceptionV2/Mixed_4c/concat', 'size': 576},
+     {'type': 'conv', 'name': 'InceptionV2/InceptionV2/Mixed_4d/concat', 'size': 576},
+     {'type': 'conv', 'name': 'InceptionV2/InceptionV2/Mixed_4e/concat', 'size': 576},
+     {'type': 'conv', 'name': 'InceptionV2/InceptionV2/Mixed_5a/concat', 'size': 1024},
+     {'type': 'conv', 'name': 'InceptionV2/InceptionV2/Mixed_5b/concat', 'size': 1024},
+     {'type': 'conv', 'name': 'InceptionV2/InceptionV2/Mixed_5c/concat', 'size': 1024},
+     {'type': 'dense', 'name': 'InceptionV2/Predictions/Softmax', 'size': 1001},
+   ]
+
+
+class InceptionV3_slim(Model):
+  """InceptionV3 as implemented by the TensorFlow slim framework.
+
+  InceptionV3 was introduced by Szegedy, et al (2015)
+  https://arxiv.org/pdf/1512.00567.pdf
+  This function provides the pre-trained reimplementation from TF slim:
+  https://github.com/tensorflow/models/tree/master/research/slim
+  corresponding to the name "inception_v3".
+  """
+
+  model_path  = 'gs://modelzoo/InceptionV3_slim.pb'
+  labels_path = 'gs://modelzoo/InceptionV1-labels.txt'
+  image_shape = [299, 299, 3]
+  # inpute range taken from:
+  # https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/inception_preprocessing.py#L280
+  image_value_range = (-1, 1)
+  input_name = 'input'
+
+  layers = [
+     {'type': 'conv', 'name': 'InceptionV3/InceptionV3/Conv2d_1a_3x3/Relu', 'size': 32},
+     {'type': 'conv', 'name': 'InceptionV3/InceptionV3/Conv2d_2a_3x3/Relu', 'size': 32},
+     {'type': 'conv', 'name': 'InceptionV3/InceptionV3/Conv2d_2b_3x3/Relu', 'size': 64},
+     {'type': 'conv', 'name': 'InceptionV3/InceptionV3/Conv2d_3b_1x1/Relu', 'size': 80},
+     {'type': 'conv', 'name': 'InceptionV3/InceptionV3/Conv2d_4a_3x3/Relu', 'size': 192},
+     {'type': 'conv', 'name': 'InceptionV3/InceptionV3/Mixed_5b/concat', 'size': 256},
+     {'type': 'conv', 'name': 'InceptionV3/InceptionV3/Mixed_5c/concat', 'size': 288},
+     {'type': 'conv', 'name': 'InceptionV3/InceptionV3/Mixed_5d/concat', 'size': 288},
+     {'type': 'conv', 'name': 'InceptionV3/InceptionV3/Mixed_6a/concat', 'size': 768},
+     {'type': 'conv', 'name': 'InceptionV3/InceptionV3/Mixed_6b/concat', 'size': 768},
+     {'type': 'conv', 'name': 'InceptionV3/InceptionV3/Mixed_6c/concat', 'size': 768},
+     {'type': 'conv', 'name': 'InceptionV3/InceptionV3/Mixed_6d/concat', 'size': 768},
+     {'type': 'conv', 'name': 'InceptionV3/InceptionV3/Mixed_6e/concat', 'size': 768},
+     {'type': 'conv', 'name': 'InceptionV3/InceptionV3/Mixed_7a/concat', 'size': 1280},
+     {'type': 'conv', 'name': 'InceptionV3/InceptionV3/Mixed_7b/concat', 'size': 2048},
+     {'type': 'conv', 'name': 'InceptionV3/InceptionV3/Mixed_7c/concat', 'size': 2048},
+     {'type': 'dense', 'name': 'InceptionV3/Predictions/Softmax', 'size': 1001},
+   ]
+
+
+class InceptionV4_slim(Model):
+  """InceptionV4 as implemented by the TensorFlow slim framework.
+
+  InceptionV4 was introduced by Szegedy, et al (2016):
+  https://arxiv.org/pdf/1602.07261.pdf
+  This function provides the pre-trained reimplementation from TF slim:
+  https://github.com/tensorflow/models/tree/master/research/slim
+  corresponding to the name "inception_v4".
+  """
+
+  model_path  = 'gs://modelzoo/InceptionV4_slim.pb'
+  labels_path = 'gs://modelzoo/InceptionV1-labels.txt' #TODO
+  image_shape = [299, 299, 3]
+  # inpute range taken from:
+  # https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/inception_preprocessing.py#L280
+  image_value_range = (-1, 1)
+  input_name = 'input'
+
+  layers = [
+     {'type': 'conv', 'name': 'InceptionV4/InceptionV4/Conv2d_1a_3x3/Relu', 'size': 32},
+     {'type': 'conv', 'name': 'InceptionV4/InceptionV4/Conv2d_2a_3x3/Relu', 'size': 32},
+     {'type': 'conv', 'name': 'InceptionV4/InceptionV4/Conv2d_2b_3x3/Relu', 'size': 64},
+     {'type': 'conv', 'name': 'InceptionV4/InceptionV4/Mixed_3a/concat', 'size': 160},
+     {'type': 'conv', 'name': 'InceptionV4/InceptionV4/Mixed_4a/concat', 'size': 192},
+     {'type': 'conv', 'name': 'InceptionV4/InceptionV4/Mixed_5a/concat', 'size': 384},
+     {'type': 'conv', 'name': 'InceptionV4/InceptionV4/Mixed_5b/concat', 'size': 384},
+     {'type': 'conv', 'name': 'InceptionV4/InceptionV4/Mixed_5c/concat', 'size': 384},
+     {'type': 'conv', 'name': 'InceptionV4/InceptionV4/Mixed_5d/concat', 'size': 384},
+     {'type': 'conv', 'name': 'InceptionV4/InceptionV4/Mixed_5e/concat', 'size': 384},
+     {'type': 'conv', 'name': 'InceptionV4/InceptionV4/Mixed_6a/concat', 'size': 1024},
+     {'type': 'conv', 'name': 'InceptionV4/InceptionV4/Mixed_6b/concat', 'size': 1024},
+     {'type': 'conv', 'name': 'InceptionV4/InceptionV4/Mixed_6c/concat', 'size': 1024},
+     {'type': 'conv', 'name': 'InceptionV4/InceptionV4/Mixed_6d/concat', 'size': 1024},
+     {'type': 'conv', 'name': 'InceptionV4/InceptionV4/Mixed_6e/concat', 'size': 1024},
+     {'type': 'conv', 'name': 'InceptionV4/InceptionV4/Mixed_6f/concat', 'size': 1024},
+     {'type': 'conv', 'name': 'InceptionV4/InceptionV4/Mixed_6g/concat', 'size': 1024},
+     {'type': 'conv', 'name': 'InceptionV4/InceptionV4/Mixed_6h/concat', 'size': 1024},
+     {'type': 'conv', 'name': 'InceptionV4/InceptionV4/Mixed_7a/concat', 'size': 1536},
+     {'type': 'conv', 'name': 'InceptionV4/InceptionV4/Mixed_7b/concat', 'size': 1536},
+     {'type': 'conv', 'name': 'InceptionV4/InceptionV4/Mixed_7c/concat', 'size': 1536},
+     {'type': 'conv', 'name': 'InceptionV4/InceptionV4/Mixed_7d/concat', 'size': 1536},
+     {'type': 'dense', 'name': 'InceptionV4/Logits/Predictions', 'size': 1001},
+   ]
+
+
+class InceptionResnetV2_slim(Model):
+  """InceptionResnetV2 as implemented by the TensorFlow slim framework.
+
+  InceptionResnetV2 was introduced in this paper by Szegedy, et al (2016):
+  https://arxiv.org/pdf/1602.07261.pdf
+  This function provides the pre-trained reimplementation from TF slim:
+  https://github.com/tensorflow/models/tree/master/research/slim
+  corresponding to the name "inception_resnet_v2".
+  """
+
+  model_path  = 'gs://modelzoo/InceptionResnetV2_slim.pb'
+  labels_path = 'gs://modelzoo/InceptionV1-labels.txt' #TODO
+  image_shape = [299, 299, 3]
+  # inpute range taken from:
+  # https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/inception_preprocessing.py#L280
+  image_value_range = (-1, 1)
+  input_name = 'input'
+
+  # TODO: understand this graph, see if we can delete some add or relu nodes from layers
+  layers = [
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Conv2d_1a_3x3/Relu', 'size': 32},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Conv2d_2a_3x3/Relu', 'size': 32},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Conv2d_2b_3x3/Relu', 'size': 64},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Conv2d_3b_1x1/Relu', 'size': 80},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Conv2d_4a_3x3/Relu', 'size': 192},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Mixed_5b/concat', 'size': 320},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat/block35_1/add', 'size': 320},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat/block35_1/Relu', 'size': 320},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat/block35_2/add', 'size': 320},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat/block35_2/Relu', 'size': 320},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat/block35_3/add', 'size': 320},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat/block35_3/Relu', 'size': 320},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat/block35_4/add', 'size': 320},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat/block35_4/Relu', 'size': 320},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat/block35_5/add', 'size': 320},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat/block35_5/Relu', 'size': 320},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat/block35_6/add', 'size': 320},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat/block35_6/Relu', 'size': 320},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat/block35_7/add', 'size': 320},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat/block35_7/Relu', 'size': 320},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat/block35_8/add', 'size': 320},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat/block35_8/Relu', 'size': 320},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat/block35_9/add', 'size': 320},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat/block35_9/Relu', 'size': 320},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat/block35_10/add', 'size': 320},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat/block35_10/Relu', 'size': 320},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Mixed_6a/concat', 'size': 1088},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat_1/block17_1/add', 'size': 1088},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat_1/block17_1/Relu', 'size': 1088},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat_1/block17_2/add', 'size': 1088},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat_1/block17_2/Relu', 'size': 1088},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat_1/block17_3/add', 'size': 1088},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat_1/block17_3/Relu', 'size': 1088},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat_1/block17_4/add', 'size': 1088},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat_1/block17_4/Relu', 'size': 1088},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat_1/block17_5/add', 'size': 1088},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat_1/block17_5/Relu', 'size': 1088},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat_1/block17_6/add', 'size': 1088},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat_1/block17_6/Relu', 'size': 1088},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat_1/block17_7/add', 'size': 1088},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat_1/block17_7/Relu', 'size': 1088},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat_1/block17_8/add', 'size': 1088},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat_1/block17_8/Relu', 'size': 1088},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat_1/block17_9/add', 'size': 1088},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat_1/block17_9/Relu', 'size': 1088},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat_1/block17_10/add', 'size': 1088},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat_1/block17_10/Relu', 'size': 1088},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat_1/block17_11/add', 'size': 1088},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat_1/block17_11/Relu', 'size': 1088},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat_1/block17_12/add', 'size': 1088},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat_1/block17_12/Relu', 'size': 1088},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat_1/block17_13/add', 'size': 1088},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat_1/block17_13/Relu', 'size': 1088},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat_1/block17_14/add', 'size': 1088},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat_1/block17_14/Relu', 'size': 1088},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat_1/block17_15/add', 'size': 1088},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat_1/block17_15/Relu', 'size': 1088},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat_1/block17_16/add', 'size': 1088},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat_1/block17_16/Relu', 'size': 1088},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat_1/block17_17/add', 'size': 1088},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat_1/block17_17/Relu', 'size': 1088},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat_1/block17_18/add', 'size': 1088},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat_1/block17_18/Relu', 'size': 1088},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat_1/block17_19/add', 'size': 1088},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat_1/block17_19/Relu', 'size': 1088},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat_1/block17_20/add', 'size': 1088},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat_1/block17_20/Relu', 'size': 1088},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Mixed_7a/concat', 'size': 2080},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat_2/block8_1/add', 'size': 2080},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat_2/block8_1/Relu', 'size': 2080},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat_2/block8_2/add', 'size': 2080},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat_2/block8_2/Relu', 'size': 2080},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat_2/block8_3/add', 'size': 2080},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat_2/block8_3/Relu', 'size': 2080},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat_2/block8_4/add', 'size': 2080},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat_2/block8_4/Relu', 'size': 2080},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat_2/block8_5/add', 'size': 2080},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat_2/block8_5/Relu', 'size': 2080},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat_2/block8_6/add', 'size': 2080},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat_2/block8_6/Relu', 'size': 2080},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat_2/block8_7/add', 'size': 2080},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat_2/block8_7/Relu', 'size': 2080},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat_2/block8_8/add', 'size': 2080},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat_2/block8_8/Relu', 'size': 2080},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat_2/block8_9/add', 'size': 2080},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Repeat_2/block8_9/Relu', 'size': 2080},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Block8/add', 'size': 2080},
+     {'type': 'conv', 'name': 'InceptionResnetV2/InceptionResnetV2/Conv2d_7b_1x1/Relu', 'size': 1536},
+     {'type': 'dense', 'name': 'InceptionResnetV2/Logits/Predictions', 'size': 1001},
+   ]
diff --git a/lucid/modelzoo/slim_models/README.md b/lucid/modelzoo/slim_models/README.md
new file mode 100644
index 00000000..258cf90f
--- /dev/null
+++ b/lucid/modelzoo/slim_models/README.md
@@ -0,0 +1,3 @@
+Models imported from tf-slim's [pre-trained models](https://github.com/tensorflow/models/tree/master/research/slim#pre-trained-models).
+
+See `lucid/scripts/import_slim/` for details on how to import more.
diff --git a/lucid/modelzoo/slim_models/ResNetV1.py b/lucid/modelzoo/slim_models/ResNetV1.py
new file mode 100644
index 00000000..8d659048
--- /dev/null
+++ b/lucid/modelzoo/slim_models/ResNetV1.py
@@ -0,0 +1,280 @@
+# Copyright 2018 The Lucid Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+from __future__ import absolute_import, division, print_function
+from lucid.modelzoo.vision_base import Model, IMAGENET_MEAN
+
+
+class ResnetV1_50_slim(Model):
+  """ResnetV150 as implemented by the TensorFlow slim framework.
+  
+  This function provides the pre-trained reimplementation from TF slim:
+  https://github.com/tensorflow/models/tree/master/research/slim
+  """
+  
+  model_path  = 'gs://modelzoo/ResnetV1_50_slim.pb'
+  labels_path = 'gs://modelzoo/InceptionV1-labels.txt' #TODO
+  image_shape = [224, 224, 3]
+  # inpute range taken from:
+  # https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/inception_preprocessing.py#L280
+  image_value_range = (-1, 1)
+  input_name = 'input'
+  
+  layers = [
+     {'type': 'conv', 'name': 'resnet_v1_50/conv1/Relu', 'size': 64} ,
+     {'type': 'conv', 'name': 'resnet_v1_50/block1/unit_1/bottleneck_v1/add', 'size': 256} ,
+     {'type': 'conv', 'name': 'resnet_v1_50/block1/unit_1/bottleneck_v1/Relu', 'size': 256} ,
+     {'type': 'conv', 'name': 'resnet_v1_50/block1/unit_2/bottleneck_v1/add', 'size': 256} ,
+     {'type': 'conv', 'name': 'resnet_v1_50/block1/unit_2/bottleneck_v1/Relu', 'size': 256} ,
+     {'type': 'conv', 'name': 'resnet_v1_50/block1/unit_3/bottleneck_v1/add', 'size': 256} ,
+     {'type': 'conv', 'name': 'resnet_v1_50/block1/unit_3/bottleneck_v1/Relu', 'size': 256} ,
+     {'type': 'conv', 'name': 'resnet_v1_50/block2/unit_1/bottleneck_v1/add', 'size': 512} ,
+     {'type': 'conv', 'name': 'resnet_v1_50/block2/unit_1/bottleneck_v1/Relu', 'size': 512} ,
+     {'type': 'conv', 'name': 'resnet_v1_50/block2/unit_2/bottleneck_v1/add', 'size': 512} ,
+     {'type': 'conv', 'name': 'resnet_v1_50/block2/unit_2/bottleneck_v1/Relu', 'size': 512} ,
+     {'type': 'conv', 'name': 'resnet_v1_50/block2/unit_3/bottleneck_v1/add', 'size': 512} ,
+     {'type': 'conv', 'name': 'resnet_v1_50/block2/unit_3/bottleneck_v1/Relu', 'size': 512} ,
+     {'type': 'conv', 'name': 'resnet_v1_50/block2/unit_4/bottleneck_v1/add', 'size': 512} ,
+     {'type': 'conv', 'name': 'resnet_v1_50/block2/unit_4/bottleneck_v1/Relu', 'size': 512} ,
+     {'type': 'conv', 'name': 'resnet_v1_50/block3/unit_1/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_50/block3/unit_1/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_50/block3/unit_2/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_50/block3/unit_2/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_50/block3/unit_3/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_50/block3/unit_3/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_50/block3/unit_4/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_50/block3/unit_4/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_50/block3/unit_5/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_50/block3/unit_5/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_50/block3/unit_6/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_50/block3/unit_6/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_50/block4/unit_1/bottleneck_v1/add', 'size': 2048} ,
+     {'type': 'conv', 'name': 'resnet_v1_50/block4/unit_1/bottleneck_v1/Relu', 'size': 2048} ,
+     {'type': 'conv', 'name': 'resnet_v1_50/block4/unit_2/bottleneck_v1/add', 'size': 2048} ,
+     {'type': 'conv', 'name': 'resnet_v1_50/block4/unit_2/bottleneck_v1/Relu', 'size': 2048} ,
+     {'type': 'conv', 'name': 'resnet_v1_50/block4/unit_3/bottleneck_v1/add', 'size': 2048} ,
+     {'type': 'conv', 'name': 'resnet_v1_50/block4/unit_3/bottleneck_v1/Relu', 'size': 2048} ,
+     {'type': 'dense', 'name': 'resnet_v1_50/predictions/Softmax', 'size': 1000} ,
+   ]
+
+
+class ResnetV1_101_slim(Model):
+  """ResnetV1101 as implemented by the TensorFlow slim framework.
+  
+  This function provides the pre-trained reimplementation from TF slim:
+  https://github.com/tensorflow/models/tree/master/research/slim
+  """
+  
+  model_path  = 'gs://modelzoo/ResnetV1_101_slim.pb'
+  labels_path = 'gs://modelzoo/InceptionV1-labels.txt' #TODO
+  image_shape = [224, 224, 3]
+  # inpute range taken from:
+  # https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/inception_preprocessing.py#L280
+  image_value_range = (-1, 1)
+  input_name = 'input'
+  
+  layers = [
+     {'type': 'conv', 'name': 'resnet_v1_101/conv1/Relu', 'size': 64} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block1/unit_1/bottleneck_v1/add', 'size': 256} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block1/unit_1/bottleneck_v1/Relu', 'size': 256} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block1/unit_2/bottleneck_v1/add', 'size': 256} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block1/unit_2/bottleneck_v1/Relu', 'size': 256} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block1/unit_3/bottleneck_v1/add', 'size': 256} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block1/unit_3/bottleneck_v1/Relu', 'size': 256} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block2/unit_1/bottleneck_v1/add', 'size': 512} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block2/unit_1/bottleneck_v1/Relu', 'size': 512} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block2/unit_2/bottleneck_v1/add', 'size': 512} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block2/unit_2/bottleneck_v1/Relu', 'size': 512} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block2/unit_3/bottleneck_v1/add', 'size': 512} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block2/unit_3/bottleneck_v1/Relu', 'size': 512} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block2/unit_4/bottleneck_v1/add', 'size': 512} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block2/unit_4/bottleneck_v1/Relu', 'size': 512} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_1/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_1/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_2/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_2/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_3/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_3/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_4/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_4/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_5/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_5/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_6/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_6/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_7/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_7/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_8/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_8/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_9/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_9/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_10/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_10/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_11/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_11/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_12/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_12/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_13/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_13/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_14/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_14/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_15/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_15/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_16/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_16/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_17/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_17/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_18/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_18/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_19/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_19/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_20/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_20/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_21/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_21/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_22/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_22/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_23/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block3/unit_23/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block4/unit_1/bottleneck_v1/add', 'size': 2048} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block4/unit_1/bottleneck_v1/Relu', 'size': 2048} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block4/unit_2/bottleneck_v1/add', 'size': 2048} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block4/unit_2/bottleneck_v1/Relu', 'size': 2048} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block4/unit_3/bottleneck_v1/add', 'size': 2048} ,
+     {'type': 'conv', 'name': 'resnet_v1_101/block4/unit_3/bottleneck_v1/Relu', 'size': 2048} ,
+     {'type': 'dense', 'name': 'resnet_v1_101/predictions/Softmax', 'size': 1000} ,
+   ]
+
+
+
+class ResnetV1_152_slim(Model):
+  """ResnetV1152 as implemented by the TensorFlow slim framework.
+  
+  This function provides the pre-trained reimplementation from TF slim:
+  https://github.com/tensorflow/models/tree/master/research/slim
+  """
+  
+  model_path  = 'gs://modelzoo/ResnetV1_152_slim.pb'
+  labels_path = 'gs://modelzoo/InceptionV1-labels.txt' #TODO
+  image_shape = [224, 224, 3]
+  # inpute range taken from:
+  # https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/inception_preprocessing.py#L280
+  image_value_range = (-1, 1)
+  input_name = 'input'
+  
+  layers = [
+     {'type': 'conv', 'name': 'resnet_v1_152/conv1/Relu', 'size': 64} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block1/unit_1/bottleneck_v1/add', 'size': 256} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block1/unit_1/bottleneck_v1/Relu', 'size': 256} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block1/unit_2/bottleneck_v1/add', 'size': 256} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block1/unit_2/bottleneck_v1/Relu', 'size': 256} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block1/unit_3/bottleneck_v1/add', 'size': 256} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block1/unit_3/bottleneck_v1/Relu', 'size': 256} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block2/unit_1/bottleneck_v1/add', 'size': 512} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block2/unit_1/bottleneck_v1/Relu', 'size': 512} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block2/unit_2/bottleneck_v1/add', 'size': 512} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block2/unit_2/bottleneck_v1/Relu', 'size': 512} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block2/unit_3/bottleneck_v1/add', 'size': 512} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block2/unit_3/bottleneck_v1/Relu', 'size': 512} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block2/unit_4/bottleneck_v1/add', 'size': 512} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block2/unit_4/bottleneck_v1/Relu', 'size': 512} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block2/unit_5/bottleneck_v1/add', 'size': 512} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block2/unit_5/bottleneck_v1/Relu', 'size': 512} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block2/unit_6/bottleneck_v1/add', 'size': 512} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block2/unit_6/bottleneck_v1/Relu', 'size': 512} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block2/unit_7/bottleneck_v1/add', 'size': 512} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block2/unit_7/bottleneck_v1/Relu', 'size': 512} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block2/unit_8/bottleneck_v1/add', 'size': 512} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block2/unit_8/bottleneck_v1/Relu', 'size': 512} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_1/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_1/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_2/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_2/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_3/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_3/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_4/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_4/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_5/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_5/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_6/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_6/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_7/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_7/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_8/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_8/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_9/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_9/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_10/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_10/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_11/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_11/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_12/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_12/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_13/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_13/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_14/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_14/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_15/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_15/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_16/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_16/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_17/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_17/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_18/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_18/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_19/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_19/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_20/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_20/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_21/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_21/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_22/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_22/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_23/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_23/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_24/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_24/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_25/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_25/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_26/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_26/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_27/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_27/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_28/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_28/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_29/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_29/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_30/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_30/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_31/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_31/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_32/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_32/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_33/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_33/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_34/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_34/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_35/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_35/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_36/bottleneck_v1/add', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block3/unit_36/bottleneck_v1/Relu', 'size': 1024} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block4/unit_1/bottleneck_v1/add', 'size': 2048} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block4/unit_1/bottleneck_v1/Relu', 'size': 2048} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block4/unit_2/bottleneck_v1/add', 'size': 2048} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block4/unit_2/bottleneck_v1/Relu', 'size': 2048} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block4/unit_3/bottleneck_v1/add', 'size': 2048} ,
+     {'type': 'conv', 'name': 'resnet_v1_152/block4/unit_3/bottleneck_v1/Relu', 'size': 2048} ,
+     {'type': 'dense', 'name': 'resnet_v1_152/predictions/Softmax', 'size': 1000} ,
+   ]
+  
diff --git a/lucid/modelzoo/slim_models/ResNetV2.py b/lucid/modelzoo/slim_models/ResNetV2.py
new file mode 100644
index 00000000..661706ba
--- /dev/null
+++ b/lucid/modelzoo/slim_models/ResNetV2.py
@@ -0,0 +1,200 @@
+# Copyright 2018 The Lucid Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+from __future__ import absolute_import, division, print_function
+from lucid.modelzoo.vision_base import Model, IMAGENET_MEAN
+
+
+class ResnetV2_50_slim(Model):
+  """ResnetV2_50 as implemented by the TensorFlow slim framework.
+
+  ResnetV2_50 was introduced by He, et al (2016):
+  https://arxiv.org/pdf/1603.05027.pdf
+  This function provides the pre-trained reimplementation from TF slim:
+  https://github.com/tensorflow/models/tree/master/research/slim
+  corresponding to the name "resnet_v2_50".
+  """
+
+  model_path  = 'gs://modelzoo/ResnetV2_50_slim.pb'
+  labels_path = 'gs://modelzoo/InceptionV1-labels.txt' #TODO
+  image_shape = [224, 224, 3]
+  # inpute range taken from:
+  # https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/inception_preprocessing.py#L280
+  image_value_range = (-1, 1)
+  input_name = 'input'
+
+  layers = [
+     {'type': 'conv', 'name': 'resnet_v2_50/block1/unit_1/bottleneck_v2/preact/Relu', 'size': 64},
+     {'type': 'conv', 'name': 'resnet_v2_50/block1/unit_1/bottleneck_v2/add', 'size': 256},
+     {'type': 'conv', 'name': 'resnet_v2_50/block1/unit_2/bottleneck_v2/add', 'size': 256},
+     {'type': 'conv', 'name': 'resnet_v2_50/block1/unit_3/bottleneck_v2/add', 'size': 256},
+     {'type': 'conv', 'name': 'resnet_v2_50/block2/unit_1/bottleneck_v2/preact/Relu', 'size': 256},
+     {'type': 'conv', 'name': 'resnet_v2_50/block2/unit_1/bottleneck_v2/add', 'size': 512},
+     {'type': 'conv', 'name': 'resnet_v2_50/block2/unit_2/bottleneck_v2/add', 'size': 512},
+     {'type': 'conv', 'name': 'resnet_v2_50/block2/unit_3/bottleneck_v2/add', 'size': 512},
+     {'type': 'conv', 'name': 'resnet_v2_50/block2/unit_4/bottleneck_v2/add', 'size': 512},
+     {'type': 'conv', 'name': 'resnet_v2_50/block3/unit_1/bottleneck_v2/preact/Relu', 'size': 512},
+     {'type': 'conv', 'name': 'resnet_v2_50/block3/unit_1/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_50/block3/unit_2/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_50/block3/unit_3/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_50/block3/unit_4/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_50/block3/unit_5/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_50/block3/unit_6/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_50/block4/unit_1/bottleneck_v2/preact/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_50/block4/unit_1/bottleneck_v2/add', 'size': 2048},
+     {'type': 'conv', 'name': 'resnet_v2_50/block4/unit_2/bottleneck_v2/add', 'size': 2048},
+     {'type': 'conv', 'name': 'resnet_v2_50/block4/unit_3/bottleneck_v2/add', 'size': 2048},
+     {'type': 'conv', 'name': 'resnet_v2_50/postnorm/Relu', 'size': 2048},
+     {'type': 'dense', 'name': 'resnet_v2_50/predictions/Softmax', 'size': 1001},
+   ]
+
+
+class ResnetV2_101_slim(Model):
+  """ResnetV2_101 as implemented by the TensorFlow slim framework.
+
+  ResnetV2_101 was introduced by He, et al (2016):
+  https://arxiv.org/pdf/1603.05027.pdf
+  This function provides the pre-trained reimplementation from TF slim:
+  https://github.com/tensorflow/models/tree/master/research/slim
+  corresponding to the name "resnet_v2_101".
+  """
+
+  model_path  = 'gs://modelzoo/ResnetV2_101_slim.pb'
+  labels_path = 'gs://modelzoo/InceptionV1-labels.txt' #TODO
+  image_shape = [224, 224, 3]
+  # inpute range taken from:
+  # https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/inception_preprocessing.py#L280
+  image_value_range = (-1, 1)
+  input_name = 'input'
+
+  layers = [
+     {'type': 'conv', 'name': 'resnet_v2_101/block1/unit_1/bottleneck_v2/preact/Relu', 'size': 64},
+     {'type': 'conv', 'name': 'resnet_v2_101/block1/unit_1/bottleneck_v2/add', 'size': 256},
+     {'type': 'conv', 'name': 'resnet_v2_101/block1/unit_2/bottleneck_v2/add', 'size': 256},
+     {'type': 'conv', 'name': 'resnet_v2_101/block1/unit_3/bottleneck_v2/add', 'size': 256},
+     {'type': 'conv', 'name': 'resnet_v2_101/block2/unit_1/bottleneck_v2/preact/Relu', 'size': 256},
+     {'type': 'conv', 'name': 'resnet_v2_101/block2/unit_1/bottleneck_v2/add', 'size': 512},
+     {'type': 'conv', 'name': 'resnet_v2_101/block2/unit_2/bottleneck_v2/add', 'size': 512},
+     {'type': 'conv', 'name': 'resnet_v2_101/block2/unit_3/bottleneck_v2/add', 'size': 512},
+     {'type': 'conv', 'name': 'resnet_v2_101/block2/unit_4/bottleneck_v2/add', 'size': 512},
+     {'type': 'conv', 'name': 'resnet_v2_101/block3/unit_1/bottleneck_v2/preact/Relu', 'size': 512},
+     {'type': 'conv', 'name': 'resnet_v2_101/block3/unit_1/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_101/block3/unit_2/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_101/block3/unit_3/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_101/block3/unit_4/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_101/block3/unit_5/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_101/block3/unit_6/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_101/block3/unit_7/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_101/block3/unit_8/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_101/block3/unit_9/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_101/block3/unit_10/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_101/block3/unit_11/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_101/block3/unit_12/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_101/block3/unit_13/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_101/block3/unit_14/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_101/block3/unit_15/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_101/block3/unit_16/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_101/block3/unit_17/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_101/block3/unit_18/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_101/block3/unit_19/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_101/block3/unit_20/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_101/block3/unit_21/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_101/block3/unit_22/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_101/block3/unit_23/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_101/block4/unit_1/bottleneck_v2/preact/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_101/block4/unit_1/bottleneck_v2/add', 'size': 2048},
+     {'type': 'conv', 'name': 'resnet_v2_101/block4/unit_2/bottleneck_v2/add', 'size': 2048},
+     {'type': 'conv', 'name': 'resnet_v2_101/block4/unit_3/bottleneck_v2/add', 'size': 2048},
+     {'type': 'conv', 'name': 'resnet_v2_101/postnorm/Relu', 'size': 2048},
+     {'type': 'dense', 'name': 'resnet_v2_101/predictions/Softmax', 'size': 1001},
+   ]
+
+
+class ResnetV2_152_slim(Model):
+  """ResnetV2_152 as implemented by the TensorFlow slim framework.
+
+  ResnetV2_152 was introduced by He, et al (2016):
+  https://arxiv.org/pdf/1603.05027.pdf
+  This function provides the pre-trained reimplementation from TF slim:
+  https://github.com/tensorflow/models/tree/master/research/slim
+  corresponding to the name "resnet_v2_152".
+  """
+
+  model_path  = 'gs://modelzoo/ResnetV2_152_slim.pb'
+  labels_path = 'gs://modelzoo/InceptionV1-labels.txt' #TODO
+  image_shape = [224, 224, 3]
+  # inpute range taken from:
+  # https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/inception_preprocessing.py#L280
+  image_value_range = (-1, 1)
+  input_name = 'input'
+
+  layers = [
+     {'type': 'conv', 'name': 'resnet_v2_152/block1/unit_1/bottleneck_v2/preact/Relu', 'size': 64},
+     {'type': 'conv', 'name': 'resnet_v2_152/block1/unit_1/bottleneck_v2/add', 'size': 256},
+     {'type': 'conv', 'name': 'resnet_v2_152/block1/unit_2/bottleneck_v2/add', 'size': 256},
+     {'type': 'conv', 'name': 'resnet_v2_152/block1/unit_3/bottleneck_v2/add', 'size': 256},
+     {'type': 'conv', 'name': 'resnet_v2_152/block2/unit_1/bottleneck_v2/preact/Relu', 'size': 256},
+     {'type': 'conv', 'name': 'resnet_v2_152/block2/unit_1/bottleneck_v2/add', 'size': 512},
+     {'type': 'conv', 'name': 'resnet_v2_152/block2/unit_2/bottleneck_v2/add', 'size': 512},
+     {'type': 'conv', 'name': 'resnet_v2_152/block2/unit_3/bottleneck_v2/add', 'size': 512},
+     {'type': 'conv', 'name': 'resnet_v2_152/block2/unit_4/bottleneck_v2/add', 'size': 512},
+     {'type': 'conv', 'name': 'resnet_v2_152/block2/unit_5/bottleneck_v2/add', 'size': 512},
+     {'type': 'conv', 'name': 'resnet_v2_152/block2/unit_6/bottleneck_v2/add', 'size': 512},
+     {'type': 'conv', 'name': 'resnet_v2_152/block2/unit_7/bottleneck_v2/add', 'size': 512},
+     {'type': 'conv', 'name': 'resnet_v2_152/block2/unit_8/bottleneck_v2/add', 'size': 512},
+     {'type': 'conv', 'name': 'resnet_v2_152/block3/unit_1/bottleneck_v2/preact/Relu', 'size': 512},
+     {'type': 'conv', 'name': 'resnet_v2_152/block3/unit_1/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_152/block3/unit_2/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_152/block3/unit_3/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_152/block3/unit_4/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_152/block3/unit_5/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_152/block3/unit_6/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_152/block3/unit_7/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_152/block3/unit_8/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_152/block3/unit_9/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_152/block3/unit_10/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_152/block3/unit_11/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_152/block3/unit_12/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_152/block3/unit_13/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_152/block3/unit_14/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_152/block3/unit_15/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_152/block3/unit_16/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_152/block3/unit_17/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_152/block3/unit_18/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_152/block3/unit_19/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_152/block3/unit_20/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_152/block3/unit_21/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_152/block3/unit_22/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_152/block3/unit_23/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_152/block3/unit_24/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_152/block3/unit_25/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_152/block3/unit_26/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_152/block3/unit_27/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_152/block3/unit_28/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_152/block3/unit_29/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_152/block3/unit_30/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_152/block3/unit_31/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_152/block3/unit_32/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_152/block3/unit_33/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_152/block3/unit_34/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_152/block3/unit_35/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_152/block3/unit_36/bottleneck_v2/add', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_152/block4/unit_1/bottleneck_v2/preact/Relu', 'size': 1024},
+     {'type': 'conv', 'name': 'resnet_v2_152/block4/unit_1/bottleneck_v2/add', 'size': 2048},
+     {'type': 'conv', 'name': 'resnet_v2_152/block4/unit_2/bottleneck_v2/add', 'size': 2048},
+     {'type': 'conv', 'name': 'resnet_v2_152/block4/unit_3/bottleneck_v2/add', 'size': 2048},
+     {'type': 'conv', 'name': 'resnet_v2_152/postnorm/Relu', 'size': 2048},
+     {'type': 'dense', 'name': 'resnet_v2_152/predictions/Softmax', 'size': 1001},
+   ]
diff --git a/lucid/modelzoo/slim_models/__init__.py b/lucid/modelzoo/slim_models/__init__.py
new file mode 100644
index 00000000..c6f5ac7b
--- /dev/null
+++ b/lucid/modelzoo/slim_models/__init__.py
@@ -0,0 +1,4 @@
+from lucid.modelzoo.slim_models.Inception import *
+from lucid.modelzoo.slim_models.ResNetV1 import *
+from lucid.modelzoo.slim_models.ResNetV2 import *
+from lucid.modelzoo.slim_models.others import *
diff --git a/lucid/modelzoo/slim_models/others.py b/lucid/modelzoo/slim_models/others.py
new file mode 100644
index 00000000..6738c5c4
--- /dev/null
+++ b/lucid/modelzoo/slim_models/others.py
@@ -0,0 +1,213 @@
+# Copyright 2018 The Lucid Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+from __future__ import absolute_import, division, print_function
+from lucid.modelzoo.vision_base import Model, IMAGENET_MEAN
+
+
+class VGG16_slim(Model):
+  """VGG16 as implemented by the TensorFlow slim framework.
+  
+  VGG16 was introduced by Simonyan & Zisserman (2014):
+  https://arxiv.org/pdf/1409.1556.pdf
+  This function provides the pre-trained reimplementation from TF slim:
+  https://github.com/tensorflow/models/tree/master/research/slim
+  We believe the weights were actually trained in caffe and ported.
+  """
+  
+  model_path  = 'gs://modelzoo/VGG16_slim.pb'
+  labels_path = 'gs://modelzoo/InceptionV1-labels.txt' #TODO
+  image_shape = [224, 224, 3]
+  # inpute range taken from:
+  # https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/inception_preprocessing.py#L280
+  image_value_range = (-1, 1)
+  input_name = 'input'
+  
+  layers = [
+     {'type': 'conv', 'name': 'vgg_16/conv1/conv1_1/Relu', 'size': 64} ,
+     {'type': 'conv', 'name': 'vgg_16/conv1/conv1_2/Relu', 'size': 64} ,
+     {'type': 'conv', 'name': 'vgg_16/conv2/conv2_1/Relu', 'size': 128} ,
+     {'type': 'conv', 'name': 'vgg_16/conv2/conv2_2/Relu', 'size': 128} ,
+     {'type': 'conv', 'name': 'vgg_16/conv3/conv3_1/Relu', 'size': 256} ,
+     {'type': 'conv', 'name': 'vgg_16/conv3/conv3_2/Relu', 'size': 256} ,
+     {'type': 'conv', 'name': 'vgg_16/conv3/conv3_3/Relu', 'size': 256} ,
+     {'type': 'conv', 'name': 'vgg_16/conv4/conv4_1/Relu', 'size': 512} ,
+     {'type': 'conv', 'name': 'vgg_16/conv4/conv4_2/Relu', 'size': 512} ,
+     {'type': 'conv', 'name': 'vgg_16/conv4/conv4_3/Relu', 'size': 512} ,
+     {'type': 'conv', 'name': 'vgg_16/conv5/conv5_1/Relu', 'size': 512} ,
+     {'type': 'conv', 'name': 'vgg_16/conv5/conv5_2/Relu', 'size': 512} ,
+     {'type': 'conv', 'name': 'vgg_16/conv5/conv5_3/Relu', 'size': 512} ,
+     {'type': 'conv', 'name': 'vgg_16/fc6/Relu', 'size': 4096} ,
+     {'type': 'conv', 'name': 'vgg_16/fc7/Relu', 'size': 4096} ,
+   ]
+
+
+class VGG19_slim(Model):
+  """VGG19 as implemented by the TensorFlow slim framework.
+  
+  VGG19 was introduced by Simonyan & Zisserman (2014):
+  https://arxiv.org/pdf/1409.1556.pdf
+  This function provides the pre-trained reimplementation from TF slim:
+  https://github.com/tensorflow/models/tree/master/research/slim
+  We believe the weights were actually trained in caffe and ported.
+  """
+  
+  model_path  = 'gs://modelzoo/VGG19_slim.pb'
+  labels_path = 'gs://modelzoo/InceptionV1-labels.txt' #TODO
+  image_shape = [224, 224, 3]
+  # inpute range taken from:
+  # https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/inception_preprocessing.py#L280
+  image_value_range = (-1, 1)
+  input_name = 'input'
+  
+  layers = [
+     {'type': 'conv', 'name': 'vgg_19/conv1/conv1_1/Relu', 'size': 64} ,
+     {'type': 'conv', 'name': 'vgg_19/conv1/conv1_2/Relu', 'size': 64} ,
+     {'type': 'conv', 'name': 'vgg_19/conv2/conv2_1/Relu', 'size': 128} ,
+     {'type': 'conv', 'name': 'vgg_19/conv2/conv2_2/Relu', 'size': 128} ,
+     {'type': 'conv', 'name': 'vgg_19/conv3/conv3_1/Relu', 'size': 256} ,
+     {'type': 'conv', 'name': 'vgg_19/conv3/conv3_2/Relu', 'size': 256} ,
+     {'type': 'conv', 'name': 'vgg_19/conv3/conv3_3/Relu', 'size': 256} ,
+     {'type': 'conv', 'name': 'vgg_19/conv3/conv3_4/Relu', 'size': 256} ,
+     {'type': 'conv', 'name': 'vgg_19/conv4/conv4_1/Relu', 'size': 512} ,
+     {'type': 'conv', 'name': 'vgg_19/conv4/conv4_2/Relu', 'size': 512} ,
+     {'type': 'conv', 'name': 'vgg_19/conv4/conv4_3/Relu', 'size': 512} ,
+     {'type': 'conv', 'name': 'vgg_19/conv4/conv4_4/Relu', 'size': 512} ,
+     {'type': 'conv', 'name': 'vgg_19/conv5/conv5_1/Relu', 'size': 512} ,
+     {'type': 'conv', 'name': 'vgg_19/conv5/conv5_2/Relu', 'size': 512} ,
+     {'type': 'conv', 'name': 'vgg_19/conv5/conv5_3/Relu', 'size': 512} ,
+     {'type': 'conv', 'name': 'vgg_19/conv5/conv5_4/Relu', 'size': 512} ,
+     {'type': 'conv', 'name': 'vgg_19/fc6/Relu', 'size': 4096} ,
+     {'type': 'conv', 'name': 'vgg_19/fc7/Relu', 'size': 4096} ,
+   ]
+
+
+class MobilenetV1_slim(Model):
+  """MobilenetV1 as implemented by the TensorFlow slim framework.
+  
+  This function provides the pre-trained reimplementation from TF slim:
+  https://github.com/tensorflow/models/tree/master/research/slim
+  """
+  
+  model_path  = 'gs://modelzoo/MobilenetV1_slim.pb'
+  labels_path = 'gs://modelzoo/InceptionV1-labels.txt' #TODO
+  image_shape = [224, 224, 3]
+  # inpute range taken from:
+  # https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/inception_preprocessing.py#L280
+  image_value_range = (-1, 1)
+  input_name = 'input'
+  layers = []
+
+
+class MobilenetV1_050_slim(Model):
+  """MobilenetV1050 as implemented by the TensorFlow slim framework.
+  
+  This function provides the pre-trained reimplementation from TF slim:
+  https://github.com/tensorflow/models/tree/master/research/slim
+  """
+  
+  model_path  = 'gs://modelzoo/MobilenetV1050_slim.pb'
+  labels_path = 'gs://modelzoo/InceptionV1-labels.txt' #TODO
+  image_shape = [224, 224, 3]
+  # inpute range taken from:
+  # https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/inception_preprocessing.py#L280
+  image_value_range = (-1, 1)
+  input_name = 'input'
+  layers = []
+
+
+class MobilenetV1_025_slim(Model):
+  """MobilenetV1025 as implemented by the TensorFlow slim framework.
+  
+  This function provides the pre-trained reimplementation from TF slim:
+  https://github.com/tensorflow/models/tree/master/research/slim
+  """
+  
+  model_path  = 'gs://modelzoo/MobilenetV1025_slim.pb'
+  labels_path = 'gs://modelzoo/InceptionV1-labels.txt' #TODO
+  image_shape = [224, 224, 3]
+  # inpute range taken from:
+  # https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/inception_preprocessing.py#L280
+  image_value_range = (-1, 1)
+  input_name = 'input'
+  layers = []
+
+
+class NasnetMobile_slim(Model):
+  """NasnetMobile as implemented by the TensorFlow slim framework.
+  
+  This function provides the pre-trained reimplementation from TF slim:
+  https://github.com/tensorflow/models/tree/master/research/slim
+  """
+  
+  model_path  = 'gs://modelzoo/NasnetMobile_slim.pb'
+  labels_path = 'gs://modelzoo/InceptionV1-labels.txt' #TODO
+  image_shape = [224, 224, 3]
+  # inpute range taken from:
+  # https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/inception_preprocessing.py#L280
+  image_value_range = (-1, 1)
+  input_name = 'input'
+  layers = []
+
+
+class NasnetLarge_slim(Model):
+  """NasnetLarge as implemented by the TensorFlow slim framework.
+  
+  This function provides the pre-trained reimplementation from TF slim:
+  https://github.com/tensorflow/models/tree/master/research/slim
+  """
+  
+  model_path  = 'gs://modelzoo/NasnetLarge_slim.pb'
+  labels_path = 'gs://modelzoo/InceptionV1-labels.txt' #TODO
+  image_shape = [331, 331, 3]
+  # inpute range taken from:
+  # https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/inception_preprocessing.py#L280
+  image_value_range = (-1, 1)
+  input_name = 'input'
+  layers = []
+
+
+class PnasnetLarge_slim(Model):
+  """PnasnetLarge as implemented by the TensorFlow slim framework.
+  
+  This function provides the pre-trained reimplementation from TF slim:
+  https://github.com/tensorflow/models/tree/master/research/slim
+  """
+  
+  model_path  = 'gs://modelzoo/PnasnetLarge_slim.pb'
+  labels_path = 'gs://modelzoo/InceptionV1-labels.txt' #TODO
+  image_shape = [331, 331, 3]
+  # inpute range taken from:
+  # https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/inception_preprocessing.py#L280
+  image_value_range = (-1, 1)
+  input_name = 'input'
+  layers = []
+
+
+class PnasnetMobile_slim(Model):
+  """PnasnetMobile as implemented by the TensorFlow slim framework.
+  
+  This function provides the pre-trained reimplementation from TF slim:
+  https://github.com/tensorflow/models/tree/master/research/slim
+  """
+  
+  model_path  = 'gs://modelzoo/PnasnetMobile_slim.pb'
+  labels_path = 'gs://modelzoo/InceptionV1-labels.txt' #TODO
+  image_shape = [224, 224, 3]
+  # inpute range taken from:
+  # https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/inception_preprocessing.py#L280
+  image_value_range = (-1, 1)
+  input_name = 'input'
+  layers = []
diff --git a/lucid/modelzoo/vision_base.py b/lucid/modelzoo/vision_base.py
index 0c2667d9..070e9e87 100644
--- a/lucid/modelzoo/vision_base.py
+++ b/lucid/modelzoo/vision_base.py
@@ -17,9 +17,14 @@
 from os import path
 
 import tensorflow as tf
+import numpy as np
+
 from lucid.modelzoo.util import load_text_labels, load_graphdef, forget_xy
 from lucid.misc.io import load
 
+IMAGENET_MEAN = np.array([123.68, 116.779, 103.939])
+IMAGENET_MEAN_BGR = np.flip(IMAGENET_MEAN, 0)
+
 class Model(object):
   """Base pretrained model importer."""
 
@@ -49,6 +54,8 @@ def create_input(self, t_input=None, forget_xy_shape=True):
       t_prep_input = tf.expand_dims(t_prep_input, 0)
     if forget_xy_shape:
       t_prep_input = forget_xy(t_prep_input)
+    if hasattr(self, "is_BGR") and self.is_BGR == True:
+      t_prep_input = tf.reverse(t_prep_input, [-1])
     lo, hi = self.image_value_range
     t_prep_input = lo + t_prep_input * (hi-lo)
     return t_input, t_prep_input
diff --git a/lucid/modelzoo/vision_models.py b/lucid/modelzoo/vision_models.py
index 1b439a64..ca496956 100644
--- a/lucid/modelzoo/vision_models.py
+++ b/lucid/modelzoo/vision_models.py
@@ -15,31 +15,6 @@
 
 from __future__ import absolute_import, division, print_function
 
-import tensorflow as tf
-from lucid.modelzoo.vision_base import Model
-
-
-def populate_inception_bottlenecks(scope):
-  """Add Inception bottlenecks and their pre-Relu versions to the graph."""
-  graph = tf.get_default_graph()
-  for op in graph.get_operations():
-    if op.name.startswith(scope+'/') and 'Concat' in op.type:
-      name = op.name.split('/')[1]
-      pre_relus = []
-      for tower in op.inputs[1:]:
-        if tower.op.type == 'Relu':
-          tower = tower.op.inputs[0]
-        pre_relus.append(tower)
-      concat_name = scope + '/' + name + '_pre_relu'
-      _ = tf.concat(pre_relus, -1, name=concat_name)
-
-
-class InceptionV1(Model):
-  model_path = 'gs://modelzoo/InceptionV1.pb'
-  labels_path = 'gs://modelzoo/InceptionV1-labels.txt'
-  image_shape = [224, 224, 3]
-  image_value_range = (-117, 255-117)
-  input_name = 'input:0'
-
-  def post_import(self, scope):
-    populate_inception_bottlenecks(scope)
+from lucid.modelzoo.caffe_models import *
+from lucid.modelzoo.slim_models import *
+from lucid.modelzoo.other_models import *
diff --git a/lucid/scratch/scripts/get_model_layers.py b/lucid/scratch/scripts/get_model_layers.py
new file mode 100644
index 00000000..052739db
--- /dev/null
+++ b/lucid/scratch/scripts/get_model_layers.py
@@ -0,0 +1,67 @@
+import lucid.optvis.render as render
+
+
+def predecessors(graphdef, name):
+  node_map = {}
+  for n in graphdef.node:
+    node_map[n.name] = n
+    
+  seen = []
+  def get_predecessors(node_name):
+    if node_name not in node_map: return []
+    node = node_map[node_name]
+    preds = []
+    for inp in node.input:
+      if inp in seen: continue
+      seen.append(inp)
+      preds.append(inp)
+      inp_preds = get_predecessors(inp)
+      preds += inp_preds
+    return list(set(preds))
+  
+  return get_predecessors(name)
+
+
+def get_branch_nodes(graphdef, concat_node):
+  branch_head_names = concat_node.input[:]
+  branch_head_preds = [predecessors(graphdef, name) + [name] for name in branch_head_names]
+  uniqs = []
+  for n in range(len(branch_head_preds)):
+    pres_preds = branch_head_preds[n]
+    other_preds = branch_head_preds[:n] + branch_head_preds[n+1:]
+    uniqs += [name for name in pres_preds
+             if not any(name in others for others in other_preds)]
+  return uniqs
+  
+  
+def propose_layers(graphdef):
+  concats = [node for node in graphdef.node
+             if node.op in ["Concat", "ConcatV2", "Add"]]
+  branch_nodes = []
+  for node in concats:
+    branch_nodes += get_branch_nodes(graphdef, node)
+  
+  layer_proposals = [node for node in graphdef.node
+             if node.op in ["Relu", "Concat", "ConcatV2", "Softmax", "Add"] and node.name not in branch_nodes]
+
+  return layer_proposals
+  
+  
+def propose_layers_with_shapes(model):
+  proposed_layers = propose_layers(model.graph_def)
+  with tf.Graph().as_default(), tf.Session() as sess:
+    t_input = tf.placeholder(tf.float32, [1] + model.image_shape)
+    T = render.import_model(model, t_input, t_input)
+    t_shapes = [tf.shape(T(node.name))[1:] for node in proposed_layers]
+    shapes = sess.run(t_shapes, {t_input: np.zeros([1] + model.image_shape)})
+  return zip(proposed_layers, shapes)
+  
+  
+def print_model_layer_code(model):
+  print("layers = [")
+  for node, shape in propose_layers_with_shapes(model):
+      layer_type = "conv" if len(shape) == 3 else "dense"
+      name = node.name.encode("ascii", "replace")
+      layer = {"name": name, "type": layer_type, "size": shape[-1]}
+      print("  ", layer, ",")
+  print(" ]")
diff --git a/lucid/scratch/scripts/import_caffe/import_model.sh b/lucid/scratch/scripts/import_caffe/import_model.sh
new file mode 100644
index 00000000..914bf4f3
--- /dev/null
+++ b/lucid/scratch/scripts/import_caffe/import_model.sh
@@ -0,0 +1,41 @@
+#!/bin/sh
+
+# Config
+MODEL_NAME="VGG_ILSVRC_16_layers"
+PROTO_URL="https://gist.githubusercontent.com/ksimonyan/211839e770f7b538e2d8/raw/ded9363bd93ec0c770134f4e387d8aaaaa2407ce/VGG_ILSVRC_16_layers_deploy.prototxt"
+CAFFE_URL="http://www.robots.ox.ac.uk/~vgg/software/very_deep/caffe/VGG_ILSVRC_16_layers.caffemodel"
+
+# Enter virtualenv (for old TF version, etc)
+cd workspace/
+. bin/activate
+
+# Fetch caffe model files
+echo ""
+echo "----------------------------------------"
+echo "Fetching models"
+echo "----------------------------------------"
+curl -sS ${PROTO_URL} --output ${MODEL_NAME}.prototxt
+curl -sS ${CAFFE_URL} --output ${MODEL_NAME}.caffemodel
+  
+# Upgrade prototxt, in case it's for an old unsupported version of caffe.
+echo ""
+echo "----------------------------------------"
+echo "Attempting to upgrade proto file format"
+echo "----------------------------------------"
+upgrade_net_proto_text ${MODEL_NAME}.prototxt ${MODEL_NAME}_upgraded.prototxt
+
+# caffe-tensorflow will fail if .tmp/ already exists
+rm -rf .tmp/
+
+# Run caffe-tensorflow conversion script
+echo ""
+echo "----------------------------------------"
+echo "Attempting to convert to tensorflow"
+echo "----------------------------------------"
+python caffe-tensorflow/convert.py           \
+  --caffemodel ${MODEL_NAME}.caffemodel      \
+  --standalone-output-path ${MODEL_NAME}.pb  \
+  ${MODEL_NAME}_upgraded.prototxt
+
+echo "Frozen tensflow graph:"
+echo "  workspace/${MODEL_NAME}.pb"
diff --git a/lucid/scratch/scripts/import_caffe/make_workspace.sh b/lucid/scratch/scripts/import_caffe/make_workspace.sh
new file mode 100644
index 00000000..720d2d18
--- /dev/null
+++ b/lucid/scratch/scripts/import_caffe/make_workspace.sh
@@ -0,0 +1,12 @@
+#!/bin/sh
+# Run this script once to create the workspace for us to export models
+# from caffe. This is necessary because caffe-tensorflow needs old tf versions.
+
+# Create and enter virtualenv
+virtualenv workspace
+cd workspace
+. bin/activate
+
+# Install virtualenv dependencies
+pip install tensorflow==1.2
+git clone https://github.com/linkfluence/caffe-tensorflow
diff --git a/lucid/scratch/scripts/import_caffe/readme.md b/lucid/scratch/scripts/import_caffe/readme.md
new file mode 100644
index 00000000..bf634ff0
--- /dev/null
+++ b/lucid/scratch/scripts/import_caffe/readme.md
@@ -0,0 +1,14 @@
+
+
+One way to import caffe models is [caffe-tensorflow](https://github.com/ethereon/caffe-tensorflow) which now has a `--standalone-output-path` for directly crating frozen models (see ethereon/caffe-tensorflow#76).
+
+Getting this to work reliably takes a number of tricks, including using an old tensorflow version, running a caffe model update script, and manually cleaning up temp files. The included scripts should handle all of this for you.
+
+(1) Install global dependencies:
+  ```sh
+  apt install -y caffe-cpu
+  pip install virtualenv
+  ```
+(2) Run `make_workspace.sh` (in this directory) to create isolated workspace
+(3) Edit the "Config" section in `import_model.sh`
+(4) Run `import_model.sh` (in this directory) to import specified model
diff --git a/lucid/scratch/scripts/import_slim/import_models.ipy b/lucid/scratch/scripts/import_slim/import_models.ipy
new file mode 100644
index 00000000..9e8d9589
--- /dev/null
+++ b/lucid/scratch/scripts/import_slim/import_models.ipy
@@ -0,0 +1,101 @@
+from lucid.modelzoo.vision_base import load_graphdef
+
+
+path=!pwd
+path=path[0]
+!rm -rf exports/
+!mkdir exports
+!mkdir frozen
+
+model_defs = open("model_defs.py", "w")
+
+def get_model_info(graphdef_path):
+    graphdef = load_graphdef(graphdef_path)
+    info = {}
+    
+    inp_node = graphdef.node[0]
+    info['input_name' ] = inp_node.name
+    info['input_shape'] = [int(dim.size) for dim in inp_node.attr['shape'].shape.dim]
+    
+    info['out_name' ] = graphdef.node[-1].name
+    return info
+
+
+with open("weight_urls.txt") as wf, open("names.txt") as nf:
+  for weight_url, name, in zip(wf, nf)[::1]:
+    weight_url, name = weight_url[:-1], name[:-1]
+    
+    print ""
+    print ""
+    print ""
+    print ""
+    print "## ", name
+    
+    export_path = path + "/exports/" + name
+    !mkdir {export_path}
+    %cd {export_path}
+    
+    # Deal with checkpoints
+    print ""
+    print "Fetching checkpoint (weights)..."
+    !curl -sS {weight_url} --output {name}.tgz
+    !tar xf {name}.tgz
+    ckpt  = !find . | grep "ckpt$"
+    ckpt2 = !find . | grep ".ckpt.index"
+    ckpt = (ckpt+ckpt2)[0]
+    if ".index" in ckpt: ckpt = ckpt.replace(".index", "")
+    !find .
+    print ">", ckpt
+    
+    print ""
+    print "Exporting inference graph...."
+    if "vgg" not in name and "resnet_v1" not in name:
+      !echo "{path}/models/research/slim/export_inference_graph.py --alsologtostderr --model_name={name} --output_file=model.pb"
+      !{path}/tf.sh {path}/models/research/slim/export_inference_graph.py --alsologtostderr --model_name={name} --output_file=model.pb
+    else:
+      !echo "{path}/models/research/slim/export_inference_graph.py --alsologtostderr --model_name={name} --output_file=model.pb --labels_offset=1"
+      !{path}/tf.sh {path}/models/research/slim/export_inference_graph.py --alsologtostderr --model_name={name} --output_file=model.pb --labels_offset=1
+    
+    
+    info = get_model_info("model.pb")
+    out_name = info["out_name"]
+    input_name = info["input_name"]
+    input_shape = info["input_shape"]
+    
+    pretty_name = name
+    pretty_name = pretty_name.replace("vgg", "VGG")
+    pretty_name = pretty_name.replace("-", "_")
+    pieces = pretty_name.split("_")
+    for i in range(len(pieces)):
+      pieces[i] = pieces[i][0:1].upper() + pieces[i][1:]
+    pretty_name = "".join(pieces)
+    
+    print ""
+    print "Freezing variables into graph...."
+    !echo "{path}/freeze_graph.py --input_graph=model.pb --input_checkpoint={ckpt} --input_binary=true --output_graph=frozen.pb --output_node_names={out_name}"
+    !{path}/tf.sh {path}/freeze_graph.py --input_graph=model.pb --input_checkpoint={ckpt} --input_binary=true --output_graph=frozen.pb --output_node_names={out_name}
+    !cp frozen.pb {path}/frozen/{pretty_name}_slim.pb
+
+    print ""
+    
+
+    
+    model_defs.write("""\n\n
+class {pretty_name}_slim(Model):
+  \"\"\"{pretty_name} as implemented by the TensorFlow slim framework.
+  
+  This function provides the pre-trained reimplementation from TF slim:
+  https://github.com/tensorflow/models/tree/master/research/slim
+  \"\"\"
+  
+  model_path  = 'gs://modelzoo/{pretty_name}_slim.pb'
+  labels_path = 'gs://modelzoo/InceptionV1-labels.txt' #TODO
+  image_shape = {input_shape}
+  # inpute range taken from:
+  # https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/inception_preprocessing.py#L280
+  image_value_range = (-1, 1)
+  input_name = '{input_name}'
+  """.format(**locals())
+      )
+
+model_defs.close()
diff --git a/lucid/scratch/scripts/import_slim/parse_readme_table.sh b/lucid/scratch/scripts/import_slim/parse_readme_table.sh
new file mode 100644
index 00000000..2700289e
--- /dev/null
+++ b/lucid/scratch/scripts/import_slim/parse_readme_table.sh
@@ -0,0 +1,17 @@
+
+# Extract list of urls for checkpoints
+cat slim_readme_table.md          \
+  | sed "s/[|\\(\\)]/\n/g"        \
+  | grep download.tensorflow.org  \
+  > url_list.txt
+
+# Create proposed list of model names (will need to be modified)
+cat url_list.txt                                      \
+  | sed "s/\\//\n/g"                                  \
+  | grep "gz$"                                        \
+  | sed "s/_201[0-9]_[0-9][0-9]_[0-9][0-9].tar.gz//g" \
+  | sed "s/_[0-9][0-9]_[0-9][0-9]_201[0-9].tar.gz//g" \
+  > names.txt
+
+# TODO - make sure to sanity check the output of this script before
+# running the next one.
diff --git a/lucid/scratch/scripts/import_slim/readme.md b/lucid/scratch/scripts/import_slim/readme.md
new file mode 100644
index 00000000..71cdaa9e
--- /dev/null
+++ b/lucid/scratch/scripts/import_slim/readme.md
@@ -0,0 +1,12 @@
+# Importing models from tf-slim
+
+(1) Install dependencies *into this directory*:
+  ```sh
+  git clone https://github.com/tensorflow/models.git
+  wget https://raw.githubusercontent.com/tensorflow/tensorflow/master/tensorflow/python/tools/freeze_graph.py
+  ```
+(2) Save latest table of slim models and checkpoints as `slim_readme_table.md`
+(3) Run `parse_readme_table.sh` (from within this directory)
+(4) Sanity check the output (`url_list.txt` and `names.txt`)
+(5) Run `import_models.ipy` (from within this directory)
+(6) See `frozen/*.pb` and `model_defs.py`
diff --git a/lucid/scratch/scripts/import_slim/slim_readme_table.md b/lucid/scratch/scripts/import_slim/slim_readme_table.md
new file mode 100644
index 00000000..fb466eb8
--- /dev/null
+++ b/lucid/scratch/scripts/import_slim/slim_readme_table.md
@@ -0,0 +1,23 @@
+[Inception V1](http://arxiv.org/abs/1409.4842v1)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/inception_v1.py)|[inception_v1_2016_08_28.tar.gz](http://download.tensorflow.org/models/inception_v1_2016_08_28.tar.gz)|69.8|89.6|
+[Inception V2](http://arxiv.org/abs/1502.03167)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/inception_v2.py)|[inception_v2_2016_08_28.tar.gz](http://download.tensorflow.org/models/inception_v2_2016_08_28.tar.gz)|73.9|91.8|
+[Inception V3](http://arxiv.org/abs/1512.00567)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/inception_v3.py)|[inception_v3_2016_08_28.tar.gz](http://download.tensorflow.org/models/inception_v3_2016_08_28.tar.gz)|78.0|93.9|
+[Inception V4](http://arxiv.org/abs/1602.07261)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/inception_v4.py)|[inception_v4_2016_09_09.tar.gz](http://download.tensorflow.org/models/inception_v4_2016_09_09.tar.gz)|80.2|95.2|
+[Inception-ResNet-v2](http://arxiv.org/abs/1602.07261)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/inception_resnet_v2.py)|[inception_resnet_v2_2016_08_30.tar.gz](http://download.tensorflow.org/models/inception_resnet_v2_2016_08_30.tar.gz)|80.4|95.3|
+[ResNet V1 50](https://arxiv.org/abs/1512.03385)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/resnet_v1.py)|[resnet_v1_50_2016_08_28.tar.gz](http://download.tensorflow.org/models/resnet_v1_50_2016_08_28.tar.gz)|75.2|92.2|
+[ResNet V1 101](https://arxiv.org/abs/1512.03385)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/resnet_v1.py)|[resnet_v1_101_2016_08_28.tar.gz](http://download.tensorflow.org/models/resnet_v1_101_2016_08_28.tar.gz)|76.4|92.9|
+[ResNet V1 152](https://arxiv.org/abs/1512.03385)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/resnet_v1.py)|[resnet_v1_152_2016_08_28.tar.gz](http://download.tensorflow.org/models/resnet_v1_152_2016_08_28.tar.gz)|76.8|93.2|
+[ResNet V2 50](https://arxiv.org/abs/1603.05027)^|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/resnet_v2.py)|[resnet_v2_50_2017_04_14.tar.gz](http://download.tensorflow.org/models/resnet_v2_50_2017_04_14.tar.gz)|75.6|92.8|
+[ResNet V2 101](https://arxiv.org/abs/1603.05027)^|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/resnet_v2.py)|[resnet_v2_101_2017_04_14.tar.gz](http://download.tensorflow.org/models/resnet_v2_101_2017_04_14.tar.gz)|77.0|93.7|
+[ResNet V2 152](https://arxiv.org/abs/1603.05027)^|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/resnet_v2.py)|[resnet_v2_152_2017_04_14.tar.gz](http://download.tensorflow.org/models/resnet_v2_152_2017_04_14.tar.gz)|77.8|94.1|
+[ResNet V2 200](https://arxiv.org/abs/1603.05027)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/resnet_v2.py)|[TBA]()|79.9\*|95.2\*|
+[VGG 16](http://arxiv.org/abs/1409.1556.pdf)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/vgg.py)|[vgg_16_2016_08_28.tar.gz](http://download.tensorflow.org/models/vgg_16_2016_08_28.tar.gz)|71.5|89.8|
+[VGG 19](http://arxiv.org/abs/1409.1556.pdf)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/vgg.py)|[vgg_19_2016_08_28.tar.gz](http://download.tensorflow.org/models/vgg_19_2016_08_28.tar.gz)|71.1|89.8|
+[MobileNet_v1_1.0_224](https://arxiv.org/pdf/1704.04861.pdf)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.py)|[mobilenet_v1_1.0_224.tgz](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_224.tgz)|70.9|89.9|
+[MobileNet_v1_0.50_160](https://arxiv.org/pdf/1704.04861.pdf)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.py)|[mobilenet_v1_0.50_160.tgz](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.5_160.tgz)|59.1|81.9|
+[MobileNet_v1_0.25_128](https://arxiv.org/pdf/1704.04861.pdf)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.py)|[mobilenet_v1_0.25_128.tgz](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.25_128.tgz)|41.5|66.3|
+[MobileNet_v2_1.4_224^*](https://arxiv.org/abs/1801.04381)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet_v2.py)| [mobilenet_v2_1.4_224.tgz](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_1.4_224.tgz) | 74.9 | 92.5|
+[MobileNet_v2_1.0_224^*](https://arxiv.org/abs/1801.04381)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet_v2.py)| [mobilenet_v2_1.0_224.tgz](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_1.0_224.tgz) | 71.9 | 91.0
+[NASNet-A_Mobile_224](https://arxiv.org/abs/1707.07012)#|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/nasnet/nasnet.py)|[nasnet-a_mobile_04_10_2017.tar.gz](https://storage.googleapis.com/download.tensorflow.org/models/nasnet-a_mobile_04_10_2017.tar.gz)|74.0|91.6|
+[NASNet-A_Large_331](https://arxiv.org/abs/1707.07012)#|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/nasnet/nasnet.py)|[nasnet-a_large_04_10_2017.tar.gz](https://storage.googleapis.com/download.tensorflow.org/models/nasnet-a_large_04_10_2017.tar.gz)|82.7|96.2|
+[PNASNet-5_Large_331](https://arxiv.org/abs/1712.00559)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/nasnet/pnasnet.py)|[pnasnet-5_large_2017_12_13.tar.gz](https://storage.googleapis.com/download.tensorflow.org/models/pnasnet-5_large_2017_12_13.tar.gz)|82.9|96.2|
+[PNASNet-5_Mobile_224](https://arxiv.org/abs/1712.00559)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/nasnet/pnasnet.py)|[pnasnet-5_mobile_2017_12_13.tar.gz](https://storage.googleapis.com/download.tensorflow.org/models/pnasnet-5_mobile_2017_12_13.tar.gz)|74.2|91.9|
diff --git a/tests/conftest.py b/tests/conftest.py
index 8591d03b..7abb38be 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -8,3 +8,19 @@ def inceptionv1():
     model = InceptionV1()
     model.load_graphdef()
     return model
+
+
+def pytest_addoption(parser):
+    parser.addoption(
+        "--run-slow", action="store_true", default=False, help="run slow tests"
+    )
+
+
+def pytest_collection_modifyitems(config, items):
+    if config.getoption("--run-slow"):
+        return
+    else:
+        skip_slow = pytest.mark.skip(reason="need --runslow option to run")
+        for item in items:
+            if "slow" in item.keywords:
+                item.add_marker(skip_slow)
diff --git a/tests/misc/test_channel_reducer.py b/tests/misc/test_channel_reducer.py
index bb8031b3..8e6f06ab 100644
--- a/tests/misc/test_channel_reducer.py
+++ b/tests/misc/test_channel_reducer.py
@@ -7,14 +7,14 @@
 
 
 def test_channel_reducer_trivial():
-  array = np.zeros((10,10,10), dtype=np.float32)
-  for d in range(array.shape[-1]):
-    array[:,:,d] = np.eye(10,10)
+    array = np.zeros((10, 10, 10))
+    for d in range(array.shape[-1]):
+        array[:, :, d] = np.eye(10, 10)
 
-  channel_reducer = ChannelReducer(n_features=2)
-  channel_reducer.fit(array)
-  reduced = channel_reducer.transform(array)
+    channel_reducer = ChannelReducer(n_features=2)
+    channel_reducer.fit(array)
+    reduced = channel_reducer.transform(array)
 
-  assert reduced.shape == (10,10,2)
-  assert (reduced[:,:,0] == array[:,:,0]).all()
-  assert (reduced[:,:,1] == 0).all()
+    assert reduced.shape == (10, 10, 2)
+    # the hope here is that this was reduced to use only the first channel
+    assert np.sum(reduced[:, :, 1]) == 0
diff --git a/tests/modelzoo/test_InceptionV1.py b/tests/modelzoo/test_InceptionV1.py
new file mode 100644
index 00000000..ccac0ab8
--- /dev/null
+++ b/tests/modelzoo/test_InceptionV1.py
@@ -0,0 +1,42 @@
+from __future__ import absolute_import, division, print_function
+
+import pytest
+
+import tensorflow as tf
+from lucid.modelzoo.vision_models import InceptionV1
+
+important_layer_names = [
+    "mixed3a",
+    "mixed3b",
+    "mixed4a",
+    "mixed4b",
+    "mixed4c",
+    "mixed4d",
+    "mixed4e",
+    "mixed5a",
+    "mixed5b",
+]
+
+
+@pytest.mark.slow
+def test_InceptionV1_model_download():
+    model = InceptionV1()
+    model.load_graphdef()
+    assert model.graph_def is not None
+
+
+@pytest.mark.slow
+def test_InceptionV1_graph_import():
+    model = InceptionV1()
+    model.load_graphdef()
+    model.import_graph()
+    nodes = tf.get_default_graph().as_graph_def().node
+    node_names = set(node.name for node in nodes)
+    for layer_name in important_layer_names:
+        assert "import/" + layer_name + "_pre_relu" in node_names
+
+
+def test_InceptionV1_labels():
+    model = InceptionV1()
+    assert model.labels is not None
+    assert model.labels[0] == "dummy"
diff --git a/tests/modelzoo/test_nets_factory.py b/tests/modelzoo/test_nets_factory.py
new file mode 100644
index 00000000..44a15136
--- /dev/null
+++ b/tests/modelzoo/test_nets_factory.py
@@ -0,0 +1,42 @@
+# Copyright 2018 The Lucid Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import pytest
+import tensorflow as tf
+
+from lucid.modelzoo.nets_factory import get_model, models_map
+from lucid.modelzoo.vision_models import InceptionV1
+from lucid.modelzoo.vision_base import Model, SerializedModel, FrozenGraphModel
+
+def test_models_map():
+  assert len(models_map) > 1
+  assert Model.__name__ not in models_map
+  assert SerializedModel.__name__ not in models_map
+  assert FrozenGraphModel.__name__ not in models_map
+  assert InceptionV1.__name__ in models_map
+
+def test_get_model():
+  model = get_model("InceptionV1")
+  assert model is not None
+  assert type(model) == InceptionV1
+
+def test_get_model_fuzzy_feedback():
+  with pytest.raises(ValueError) as excinfo:
+    _ = get_model("InceptionV2")
+  assert "InceptionV2_slim" in str(excinfo.value)
diff --git a/tests/modelzoo/test_vision_models.py b/tests/modelzoo/test_vision_models.py
index a972b4d7..42df8933 100644
--- a/tests/modelzoo/test_vision_models.py
+++ b/tests/modelzoo/test_vision_models.py
@@ -1,30 +1,22 @@
 from __future__ import absolute_import, division, print_function
 
 import pytest
-
 import tensorflow as tf
-from lucid.modelzoo.vision_models import InceptionV1
-
-important_layer_names = [
-  "mixed3a", "mixed3b", "mixed4a", "mixed4b", "mixed4c", "mixed4d", "mixed4e",
-  "mixed5a", "mixed5b"
-]
 
-def test_InceptionV1_model_download():
-  model = InceptionV1()
-  model.load_graphdef()
-  assert model.graph_def is not None
+from lucid.modelzoo.nets_factory import models_map, get_model
 
-def test_InceptionV1_graph_import():
-  model = InceptionV1()
-  model.load_graphdef()
-  model.import_graph()
-  nodes = tf.get_default_graph().as_graph_def().node
-  node_names = set(node.name for node in nodes)
-  for layer_name in important_layer_names:
-    assert "import/"+layer_name+"_pre_relu" in node_names
 
-def test_InceptionV1_labels():
-  model = InceptionV1()
-  assert model.labels is not None
-  assert model.labels[0] == "dummy"
+@pytest.mark.slow
+@pytest.mark.parametrize("name,model_class", models_map.items())
+def test_model_layers_shapes(name, model_class):
+    scope = "TestLucidModelzoo"
+    model = model_class()
+    model.load_graphdef()
+    with tf.Graph().as_default() as graph:
+        model.import_graph(scope=scope)
+        for layer in model.layers:
+            name, declared_size = (layer[key] for key in ("name", "size"))
+            imported_name = "{}/{}:0".format(scope, name)
+            tensor = graph.get_tensor_by_name(imported_name)
+            actual_size = tensor.shape[-1]
+            assert int(actual_size) == int(declared_size)
diff --git a/tests/optvis/param/test_cppn.py b/tests/optvis/param/test_cppn.py
index 7502e6ea..497034fa 100644
--- a/tests/optvis/param/test_cppn.py
+++ b/tests/optvis/param/test_cppn.py
@@ -12,23 +12,34 @@
 
 log = logging.getLogger(__name__)
 
+
+@pytest.mark.slow
 def test_cppn_fits_xor():
 
-  with tf.Graph().as_default(), tf.Session() as sess:
-    cppn_param = cppn(16, num_output_channels=1)[0]
-
-    def xor_objective(a):
-      return -(tf.square(a[0, 0])      + tf.square(a[-1, -1]) +
-               tf.square(1.0-a[-1, 0]) + tf.square(1.0-a[0, -1]))
-
-    loss_t = xor_objective(cppn_param)
-    optimizer = tf.train.AdamOptimizer(0.01)
-    objective = optimizer.minimize(loss_t)
-    tf.global_variables_initializer().run()
-    loss = loss_t.eval()
-    for i in range(100):
-      _, vis = sess.run([objective, cppn_param])
-      close_enough = vis[ 0, 0] > .99 and vis[-1, -1] > .99 and \
-                     vis[-1, 0] < .01 and vis[ 0, -1] < .01
-      if close_enough: return
-    assert False, "fitting XOR took more than 100 steps, failing test"
+    with tf.Graph().as_default(), tf.Session() as sess:
+        cppn_param = cppn(16, num_output_channels=1)[0]
+
+        def xor_objective(a):
+            return -(
+                tf.square(a[0, 0])
+                + tf.square(a[-1, -1])
+                + tf.square(1.0 - a[-1, 0])
+                + tf.square(1.0 - a[0, -1])
+            )
+
+        loss_t = xor_objective(cppn_param)
+        optimizer = tf.train.AdamOptimizer(0.01)
+        objective = optimizer.minimize(loss_t)
+        tf.global_variables_initializer().run()
+        loss = loss_t.eval()
+        for i in range(100):
+            _, vis = sess.run([objective, cppn_param])
+            close_enough = (
+                vis[0, 0] > .99
+                and vis[-1, -1] > .99
+                and vis[-1, 0] < .01
+                and vis[0, -1] < .01
+            )
+            if close_enough:
+                return
+        assert False, "fitting XOR took more than 100 steps, failing test"
diff --git a/tests/optvis/test_integration.py b/tests/optvis/test_integration.py
index e49efaca..8a5b6154 100644
--- a/tests/optvis/test_integration.py
+++ b/tests/optvis/test_integration.py
@@ -6,6 +6,7 @@
 from lucid.optvis import objectives, param, render, transform
 
 
+@pytest.mark.slow
 @pytest.mark.parametrize("decorrelate", [True, False])
 @pytest.mark.parametrize("fft", [True, False])
 def test_integration(decorrelate, fft, inceptionv1):
diff --git a/tox.ini b/tox.ini
index ae112e63..0faac0d7 100644
--- a/tox.ini
+++ b/tox.ini
@@ -5,8 +5,8 @@ envlist = py{27,36}
 deps =
   tensorflow
   .[test]
-commands = coverage run --source lucid -m py.test
+commands = coverage run --source lucid -m py.test {posargs}
 
 [pytest]
-addopts = --verbose
+addopts = --verbose --run-slow
 testpaths = ./tests/