diff --git a/benchmarks/models.py b/benchmarks/models.py
index 955986937..f696150a4 100644
--- a/benchmarks/models.py
+++ b/benchmarks/models.py
@@ -5,9 +5,12 @@ class CandidateModel(models.Model):
     name = models.CharField(max_length=200)
     brain_score = models.FloatField()
     imagenet_top1 = models.FloatField()
-    v4 = models.FloatField()
-    it = models.FloatField()
+    V4 = models.FloatField()
+    IT = models.FloatField()
     behavior = models.FloatField()
+    V4_layer = models.CharField(max_length=500, default=None)
+    IT_layer = models.CharField(max_length=500, default=None)
+    behavior_layer = models.CharField(max_length=500, default=None)
     paper_link = models.CharField(max_length=200)
     paper_identifier = models.CharField(max_length=200)
 
diff --git a/benchmarks/templates/benchmarks/table.html b/benchmarks/templates/benchmarks/table.html
index c77a51f5d..69f01b0aa 100644
--- a/benchmarks/templates/benchmarks/table.html
+++ b/benchmarks/templates/benchmarks/table.html
@@ -10,7 +10,7 @@
             <th class="rotate"><p><strong>V4</strong>: <a href="http://www.jneurosci.org/content/35/39/13402.short">dicarlo.Majaj2015</a></p></th>
             <th class="rotate"><p><strong>IT</strong>: <a href="http://www.jneurosci.org/content/35/39/13402.short">dicarlo.Majaj2015</a></p></th>
             <th class="rotate"><p><strong>Behavior</strong>: <a href="https://www.biorxiv.org/content/early/2018/02/12/240614">dicarlo.Raja...2018</a></p></th>
-            <th class="rotate"><p><strong>Performance</strong>: <a href="https://ieeexplore.ieee.org/abstract/document/5206848/">Imagenet2012</a></p></th>
+            <th class="rotate"><p><strong>Classification</strong>: <a href="https://ieeexplore.ieee.org/abstract/document/5206848/">Imagenet2012</a></p></th>
         </tr>
         <!-- data -->
         <tr>
@@ -19,8 +19,8 @@
             <td class="score_cell" style="background-color: {{data.brain_score_color}}">{{data.brain_score}}</td>
             <td class="score_cell" style="background-color:#e0e1e2"></td>
             <td class="score_cell" style="background-color:#e0e1e2"></td>
-            <td class="score_cell" style="background-color:{{data.v4_color}}">{{data.v4}}</td>
-            <td class="score_cell" style="background-color:{{data.it_color}}">{{data.it}}</td>
+            <td class="score_cell" style="background-color:{{data.V4_color}}">{{data.V4}}</td>
+            <td class="score_cell" style="background-color:{{data.IT_color}}">{{data.IT}}</td>
             <td class="score_cell" style="background-color:{{data.behavior_color}}">{{data.behavior}}</td>
             <td class="score_cell" style="background-color:{{data.imagenet_top1_color}}">{{data.imagenet_top1}}</td>
         </tr>
@@ -34,9 +34,9 @@
                 <td class="score_cell" style="background-color:{{model.brain_score_color}}">{{model.brain_score}}</td>
                 <td class="score_cell" style="background-color:#e0e1e2"></td>
                 <td class="score_cell" style="background-color:#e0e1e2"></td>
-                <td class="score_cell" style="background-color:{{model.v4_color}}">{{model.v4}}</td>
-                <td class="score_cell" style="background-color:{{model.it_color}}">{{model.it}}</td>
-                <td class="score_cell" style="background-color:{{model.behavior_color}}">{{model.behavior}}</td>
+                <td title="{{model.V4_layer}}" class="score_cell" style="background-color:{{model.V4_color}}">{{model.V4}}</td>
+                <td title="{{model.IT_layer}}" class="score_cell" style="background-color:{{model.IT_color}}">{{model.IT}}</td>
+                <td title="{{model.behavior_layer}}" class="score_cell" style="background-color:{{model.behavior_color}}">{{model.behavior}}</td>
                 <td class="score_cell" style="background-color:{{model.imagenet_top1_color}}">{{model.imagenet_top1}}</td>
             </tr>
         {% endfor %}
@@ -45,6 +45,7 @@
 	
 	Model scores on brain benchmarks.
 	The more green and bright a cell, the better the model's score.
+    Hover over cells to see the model layer.
 	Scores are un-ceiled, but ceiling is factored into the coloring.
 
 {% else %}
diff --git a/benchmarks/views/index.py b/benchmarks/views/index.py
index 993ad8a5b..f6cddd346 100644
--- a/benchmarks/views/index.py
+++ b/benchmarks/views/index.py
@@ -24,7 +24,7 @@ def view(request):
     is_ie = _is_ie(user_agent)
     models = CandidateModel.objects.order_by('-brain_score')
     data = {}
-    for field in ['brain_score', 'v4', 'it', 'behavior', 'imagenet_top1']:
+    for field in ['brain_score', 'V4', 'IT', 'behavior', 'imagenet_top1']:
         ceiling = ceilings[field] if field in ceilings else None
         values = [getattr(model, field) for model in models]
         min_value, max_value = min(values), max(values)
diff --git a/static/benchmarks/fixture.json b/static/benchmarks/fixture.json
index cd3d2a6fc..0be71ea0a 100644
--- a/static/benchmarks/fixture.json
+++ b/static/benchmarks/fixture.json
@@ -1,795 +1 @@
-[
-  {
-    "model": "benchmarks.CandidateModel",
-    "fields": {
-      "brain_score": 0.5217353744688654,
-      "name": "mobilenet_v2_1.0_192",
-      "imagenet_top1": 70.7,
-      "v4": 0.6007362204669524,
-      "it": 0.5953208059396436,
-      "behavior": 0.36914909700000004,
-      "paper_link": "https://arxiv.org/abs/1704.04861",
-      "paper_identifier": "Howard et al., 2017"
-    }
-  },
-  {
-    "model": "benchmarks.CandidateModel",
-    "fields": {
-      "brain_score": 0.4646110976683948,
-      "name": "mobilenet_v2_0.35_96",
-      "imagenet_top1": 45.5,
-      "v4": 0.6100306977228721,
-      "it": 0.5398343502823122,
-      "behavior": 0.243968245,
-      "paper_link": "https://arxiv.org/abs/1704.04861",
-      "paper_identifier": "Howard et al., 2017"
-    }
-  },
-  {
-    "model": "benchmarks.CandidateModel",
-    "fields": {
-      "brain_score": 0.5306009873034647,
-      "name": "resnet-18",
-      "imagenet_top1": 69.76,
-      "v4": 0.6446804237088892,
-      "it": 0.5832847542015049,
-      "behavior": 0.363837784,
-      "paper_link": "https://arxiv.org/abs/1512.03385",
-      "paper_identifier": "He et al., 2015"
-    }
-  },
-  {
-    "model": "benchmarks.CandidateModel",
-    "fields": {
-      "brain_score": 0.5227190065017765,
-      "name": "inception_v3",
-      "imagenet_top1": 78.0,
-      "v4": 0.6464528424645819,
-      "it": 0.5868147690407477,
-      "behavior": 0.334889408,
-      "paper_link": "https://arxiv.org/abs/1512.00567",
-      "paper_identifier": "Szegedy et al., 2015"
-    }
-  },
-  {
-    "model": "benchmarks.CandidateModel",
-    "fields": {
-      "brain_score": 0.5206882299337857,
-      "name": "vgg-16",
-      "imagenet_top1": 71.5,
-      "v4": 0.668700545664949,
-      "it": 0.5721637481364082,
-      "behavior": 0.321200396,
-      "paper_link": "https://arxiv.org/abs/1409.1556",
-      "paper_identifier": "Simonyan et al., 2014"
-    }
-  },
-  {
-    "model": "benchmarks.CandidateModel",
-    "fields": {
-      "brain_score": 0.532011665602014,
-      "name": "inception_v2",
-      "imagenet_top1": 73.9,
-      "v4": 0.6458807285927486,
-      "it": 0.5932801192132933,
-      "behavior": 0.356874149,
-      "paper_link": "https://arxiv.org/abs/1512.00567",
-      "paper_identifier": "Szegedy et al., 2015"
-    }
-  },
-  {
-    "model": "benchmarks.CandidateModel",
-    "fields": {
-      "brain_score": 0.5020381570814525,
-      "name": "mobilenet_v1_1.0_128",
-      "imagenet_top1": 65.2,
-      "v4": 0.6233937182693591,
-      "it": 0.5748052599749987,
-      "behavior": 0.307915493,
-      "paper_link": "https://arxiv.org/abs/1704.04861",
-      "paper_identifier": "Howard et al., 2017"
-    }
-  },
-  {
-    "model": "benchmarks.CandidateModel",
-    "fields": {
-      "brain_score": 0.5187666275535253,
-      "name": "mobilenet_v1_0.75_224",
-      "imagenet_top1": 68.4,
-      "v4": 0.6182351815781211,
-      "it": 0.5921477470824549,
-      "behavior": 0.345916954,
-      "paper_link": "https://arxiv.org/abs/1704.04861",
-      "paper_identifier": "Howard et al., 2017"
-    }
-  },
-  {
-    "model": "benchmarks.CandidateModel",
-    "fields": {
-      "brain_score": 0.500760679951449,
-      "name": "mobilenet_v2_1.0_128",
-      "imagenet_top1": 65.3,
-      "v4": 0.6013834704518015,
-      "it": 0.5906935774025454,
-      "behavior": 0.310204992,
-      "paper_link": "https://arxiv.org/abs/1704.04861",
-      "paper_identifier": "Howard et al., 2017"
-    }
-  },
-  {
-    "model": "benchmarks.CandidateModel",
-    "fields": {
-      "brain_score": 0.5487414620874401,
-      "name": "densenet-169",
-      "imagenet_top1": 75.9,
-      "v4": 0.6626766557636573,
-      "it": 0.6056243364986629,
-      "behavior": 0.37792339399999997,
-      "paper_link": "https://arxiv.org/abs/1608.06993",
-      "paper_identifier": "Huang et al., 2016"
-    }
-  },
-  {
-    "model": "benchmarks.CandidateModel",
-    "fields": {
-      "brain_score": 0.49356660102846933,
-      "name": "mobilenet_v2_0.75_96",
-      "imagenet_top1": 58.8,
-      "v4": 0.6198976637276123,
-      "it": 0.5751869263577956,
-      "behavior": 0.28561521300000003,
-      "paper_link": "https://arxiv.org/abs/1704.04861",
-      "paper_identifier": "Howard et al., 2017"
-    }
-  },
-  {
-    "model": "benchmarks.CandidateModel",
-    "fields": {
-      "brain_score": 0.47605477497622334,
-      "name": "mobilenet_v2_0.5_96",
-      "imagenet_top1": 51.2,
-      "v4": 0.6112784459562504,
-      "it": 0.5549635639724196,
-      "behavior": 0.261922315,
-      "paper_link": "https://arxiv.org/abs/1704.04861",
-      "paper_identifier": "Howard et al., 2017"
-    }
-  },
-  {
-    "model": "benchmarks.CandidateModel",
-    "fields": {
-      "brain_score": 0.49969347940523606,
-      "name": "mobilenet_v2_0.35_160",
-      "imagenet_top1": 55.7,
-      "v4": 0.6192730727327902,
-      "it": 0.5767506564829178,
-      "behavior": 0.303056709,
-      "paper_link": "https://arxiv.org/abs/1704.04861",
-      "paper_identifier": "Howard et al., 2017"
-    }
-  },
-  {
-    "model": "benchmarks.CandidateModel",
-    "fields": {
-      "brain_score": 0.5069069037399538,
-      "name": "mobilenet_v2_0.35_224",
-      "imagenet_top1": 60.3,
-      "v4": 0.6267804129098336,
-      "it": 0.579557621310028,
-      "behavior": 0.314382677,
-      "paper_link": "https://arxiv.org/abs/1704.04861",
-      "paper_identifier": "Howard et al., 2017"
-    }
-  },
-  {
-    "model": "benchmarks.CandidateModel",
-    "fields": {
-      "brain_score": 0.5250789918863783,
-      "name": "vgg-19",
-      "imagenet_top1": 71.1,
-      "v4": 0.671551450092943,
-      "it": 0.5660890105661919,
-      "behavior": 0.337596515,
-      "paper_link": "https://arxiv.org/abs/1409.1556",
-      "paper_identifier": "Simonyan et al., 2014"
-    }
-  },
-  {
-    "model": "benchmarks.CandidateModel",
-    "fields": {
-      "brain_score": 0.5126088622267507,
-      "name": "mobilenet_v2_1.0_160",
-      "imagenet_top1": 68.8,
-      "v4": 0.6016305845991481,
-      "it": 0.5992523990811044,
-      "behavior": 0.336943603,
-      "paper_link": "https://arxiv.org/abs/1704.04861",
-      "paper_identifier": "Howard et al., 2017"
-    }
-  },
-  {
-    "model": "benchmarks.CandidateModel",
-    "fields": {
-      "brain_score": 0.48806023528760584,
-      "name": "alexnet",
-      "imagenet_top1": 57.70000000000001,
-      "v4": 0.6307502640060862,
-      "it": 0.5889010168567312,
-      "behavior": 0.244529425,
-      "paper_link": "https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks",
-      "paper_identifier": "Krizhevsky et al., 2012"
-    }
-  },
-  {
-    "model": "benchmarks.CandidateModel",
-    "fields": {
-      "brain_score": 0.5280237194563391,
-      "name": "inception_resnet_v2",
-      "imagenet_top1": 80.4,
-      "v4": 0.6387641495345012,
-      "it": 0.5929272748345162,
-      "behavior": 0.352379734,
-      "paper_link": "https://arxiv.org/abs/1602.07261",
-      "paper_identifier": "Szegedy et al., 2016"
-    }
-  },
-  {
-    "model": "benchmarks.CandidateModel",
-    "fields": {
-      "brain_score": 0.5440640275860259,
-      "name": "cornet_s",
-      "imagenet_top1": 74.7,
-      "v4": 0.6499003878538285,
-      "it": 0.600291694904249,
-      "behavior": 0.382,
-      "paper_link": "https://www.biorxiv.org/content/early/2018/09/04/408385",
-      "paper_identifier": "Kubilius et al., 2018"
-    }
-  },
-  {
-    "model": "benchmarks.CandidateModel",
-    "fields": {
-      "brain_score": 0.49773232279233,
-      "name": "mobilenet_v1_0.75_128",
-      "imagenet_top1": 62.1,
-      "v4": 0.6362228976421445,
-      "it": 0.5726266917348455,
-      "behavior": 0.284347379,
-      "paper_link": "https://arxiv.org/abs/1704.04861",
-      "paper_identifier": "Howard et al., 2017"
-    }
-  },
-  {
-    "model": "benchmarks.CandidateModel",
-    "fields": {
-      "brain_score": 0.47885161140051685,
-      "name": "mobilenet_v1_0.5_128",
-      "imagenet_top1": 56.3,
-      "v4": 0.6232164401633926,
-      "it": 0.557578116038158,
-      "behavior": 0.255760278,
-      "paper_link": "https://arxiv.org/abs/1704.04861",
-      "paper_identifier": "Howard et al., 2017"
-    }
-  },
-  {
-    "model": "benchmarks.CandidateModel",
-    "fields": {
-      "brain_score": 0.5249899800165727,
-      "name": "mobilenet_v2_1.4_224",
-      "imagenet_top1": 75.0,
-      "v4": 0.6261989224531371,
-      "it": 0.6004430095965811,
-      "behavior": 0.34832800799999997,
-      "paper_link": "https://arxiv.org/abs/1704.04861",
-      "paper_identifier": "Howard et al., 2017"
-    }
-  },
-  {
-    "model": "benchmarks.CandidateModel",
-    "fields": {
-      "brain_score": 0.5173816087579006,
-      "name": "mobilenet_v1_0.75_192",
-      "imagenet_top1": 67.2,
-      "v4": 0.6203011166422991,
-      "it": 0.5917856146314027,
-      "behavior": 0.340058095,
-      "paper_link": "https://arxiv.org/abs/1704.04861",
-      "paper_identifier": "Howard et al., 2017"
-    }
-  },
-  {
-    "model": "benchmarks.CandidateModel",
-    "fields": {
-      "brain_score": 0.47193117738354723,
-      "name": "mobilenet_v2_0.35_128",
-      "imagenet_top1": 50.8,
-      "v4": 0.6131275470112251,
-      "it": 0.5496334461394164,
-      "behavior": 0.253032539,
-      "paper_link": "https://arxiv.org/abs/1704.04861",
-      "paper_identifier": "Howard et al., 2017"
-    }
-  },
-  {
-    "model": "benchmarks.CandidateModel",
-    "fields": {
-      "brain_score": 0.4589068480394265,
-      "name": "mobilenet_v1_0.25_160",
-      "imagenet_top1": 45.5,
-      "v4": 0.6050937330697697,
-      "it": 0.5439472240485099,
-      "behavior": 0.227679587,
-      "paper_link": "https://arxiv.org/abs/1704.04861",
-      "paper_identifier": "Howard et al., 2017"
-    }
-  },
-  {
-    "model": "benchmarks.CandidateModel",
-    "fields": {
-      "brain_score": 0.5233702249822885,
-      "name": "mobilenet_v2_1.3_224",
-      "imagenet_top1": 74.4,
-      "v4": 0.6191526051031234,
-      "it": 0.595228586843742,
-      "behavior": 0.35572948299999996,
-      "paper_link": "https://arxiv.org/abs/1704.04861",
-      "paper_identifier": "Howard et al., 2017"
-    }
-  },
-  {
-    "model": "benchmarks.CandidateModel",
-    "fields": {
-      "brain_score": 0.5325125625060104,
-      "name": "xception",
-      "imagenet_top1": 79.0,
-      "v4": 0.6706893521061001,
-      "it": 0.5654023284119313,
-      "behavior": 0.361446007,
-      "paper_link": "https://arxiv.org/abs/1610.02357",
-      "paper_identifier": "Chollet et al., 2016"
-    }
-  },
-  {
-    "model": "benchmarks.CandidateModel",
-    "fields": {
-      "brain_score": 0.4778991493601561,
-      "name": "mobilenet_v1_0.25_224",
-      "imagenet_top1": 49.8,
-      "v4": 0.6190783067458023,
-      "it": 0.5744915843346659,
-      "behavior": 0.240127557,
-      "paper_link": "https://arxiv.org/abs/1704.04861",
-      "paper_identifier": "Howard et al., 2017"
-    }
-  },
-  {
-    "model": "benchmarks.CandidateModel",
-    "fields": {
-      "brain_score": 0.4951332480894446,
-      "name": "mobilenet_v1_0.5_160",
-      "imagenet_top1": 59.099999999999994,
-      "v4": 0.6208973825034729,
-      "it": 0.5757949537648608,
-      "behavior": 0.288707408,
-      "paper_link": "https://arxiv.org/abs/1704.04861",
-      "paper_identifier": "Howard et al., 2017"
-    }
-  },
-  {
-    "model": "benchmarks.CandidateModel",
-    "fields": {
-      "brain_score": 0.503485415960625,
-      "name": "mobilenet_v1_0.5_192",
-      "imagenet_top1": 61.7,
-      "v4": 0.6138104005085991,
-      "it": 0.5784046333732759,
-      "behavior": 0.318241214,
-      "paper_link": "https://arxiv.org/abs/1704.04861",
-      "paper_identifier": "Howard et al., 2017"
-    }
-  },
-  {
-    "model": "benchmarks.CandidateModel",
-    "fields": {
-      "brain_score": 0.5159973664121874,
-      "name": "mobilenet_v1_1.0_192",
-      "imagenet_top1": 70.0,
-      "v4": 0.6288517923284525,
-      "it": 0.5942274659081096,
-      "behavior": 0.324912841,
-      "paper_link": "https://arxiv.org/abs/1704.04861",
-      "paper_identifier": "Howard et al., 2017"
-    }
-  },
-  {
-    "model": "benchmarks.CandidateModel",
-    "fields": {
-      "brain_score": 0.5410803485897608,
-      "name": "densenet-201",
-      "imagenet_top1": 77.2,
-      "v4": 0.6547249948543018,
-      "it": 0.6007032069149808,
-      "behavior": 0.36781284399999997,
-      "paper_link": "https://arxiv.org/abs/1608.06993",
-      "paper_identifier": "Huang et al., 2016"
-    }
-  },
-  {
-    "model": "benchmarks.CandidateModel",
-    "fields": {
-      "brain_score": 0.4994227018945037,
-      "name": "mobilenet_v2_0.35_192",
-      "imagenet_top1": 58.20000000000001,
-      "v4": 0.6290821304589146,
-      "it": 0.5791477622245969,
-      "behavior": 0.290038213,
-      "paper_link": "https://arxiv.org/abs/1704.04861",
-      "paper_identifier": "Howard et al., 2017"
-    }
-  },
-  {
-    "model": "benchmarks.CandidateModel",
-    "fields": {
-      "brain_score": 0.5422500350240155,
-      "name": "resnet-101_v2",
-      "imagenet_top1": 77.37,
-      "v4": 0.6525502989170329,
-      "it": 0.5847226311550136,
-      "behavior": 0.389477175,
-      "paper_link": "https://arxiv.org/abs/1512.03385",
-      "paper_identifier": "He et al., 2015"
-    }
-  },
-  {
-    "model": "benchmarks.CandidateModel",
-    "fields": {
-      "brain_score": 0.5016913996130937,
-      "name": "mobilenet_v1_0.75_160",
-      "imagenet_top1": 65.3,
-      "v4": 0.6231800016285648,
-      "it": 0.5808747372107165,
-      "behavior": 0.30101946,
-      "paper_link": "https://arxiv.org/abs/1704.04861",
-      "paper_identifier": "Howard et al., 2017"
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