diff --git a/README.md b/README.md
index 122e70b821c1126f31004cc6c71322a1dbfa4066..1cbe50e75d3a33b9f099ee490baa8d66a28cfe6c 100644
--- a/README.md
+++ b/README.md
@@ -29,17 +29,26 @@ Unlike the FCN-32/16/8s models, this network is trained with gradient accumulati
 
 To reproduce the validation scores, use the [seg11valid](https://github.com/shelhamer/fcn.berkeleyvision.org/blob/master/data/pascal/seg11valid.txt) split defined by the paper in footnote 7. Since SBD train and PASCAL VOC 2011 segval intersect, we only evaluate on the non-intersecting set for validation purposes.
 
-**NYUDv2 models**: trained online with high momentum on color, depth, and HHA features (from Gupta et al. https://github.com/s-gupta/rcnn-depth):
+**NYUDv2 models**: trained online with high momentum on color, depth, and HHA features (from Gupta et al. https://github.com/s-gupta/rcnn-depth).
+These models demonstrate FCNs for multi-modal input.
 
 * [FCN-32s NYUDv2 Color](nyud-fcn32s-color): single stream, 32 pixel prediction stride net on color/BGR input
 * [FCN-32s NYUDv2 HHA](nyud-fcn32s-hha): single stream, 32 pixel prediction stride net on HHA input
 * [FCN-32s NYUDv2 Early Color-Depth](nyud-fcn32s-color-d): single stream, 32 pixel prediction stride net on early fusion of color and (log) depth for 4-channel input
 * [FCN-32s NYUDv2 Late Color-HHA](nyud-fcn32s-color-hha): single stream, 32 pixel prediction stride net by late fusion of FCN-32s NYUDv2 Color and FCN-32s NYUDv2 HHA
 
-**The following models have not yet been ported to master and trained with the latest settings. Check back soon.**
+**SIFT Flow models**: trained online with high momentum for joint semantic class and geometric class segmentation.
+These models demonstrate FCNs for multi-task output.
+
+* [FCN-32s SIFT Flow](siftflow-fcn32s): single stream stream, 32 pixel prediction stride net
+* [FCN-16s SIFT Flow](siftflow-fcn16s): two stream, 16 pixel prediction stride net
+* [FCN-8s SIFT Flow](siftflow-fcn8s): three stream, 8 pixel prediction stride net
 
-SIFT Flow model (also fine-tuned from VGG-16):
-* [FCN-16s SIFT Flow](https://gist.github.com/longjon/f35e3a101e1478f721f5#file-readme-md): two stream, 16 pixel prediction stride version
+*Note*: in this release, the evaluation of the semantic classes is not quite right at the moment due to an issue with missing classes.
+This will be corrected soon.
+The evaluation of the geometric classes is fine.
+
+**The following models have not yet been ported to master and trained with the latest settings. Check back soon.**
 
 PASCAL-Context models including architecture definition, solver configuration, and bare-bones solving script (fine-tuned from the ILSVRC-trained VGG-16 model):
 * [FCN-32s PASCAL-Context](https://gist.github.com/shelhamer/80667189b218ad570e82#file-readme-md): single stream, 32 pixel prediction stride version
diff --git a/data/sift-flow/README.md b/data/sift-flow/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..fb7428ffb1646e22b11e1df737983ae4c558b279
--- /dev/null
+++ b/data/sift-flow/README.md
@@ -0,0 +1,16 @@
+# SIFT Flow
+
+SIFT Flow is a semantic segmentation dataset with two labelings:
+
+- semantic classes, such as "cat" or "dog"
+- geometric classes, consisting of "horizontal, vertical, and sky"
+
+Refer to `classes.txt` for the listing of classes in model output order.
+Refer to `../siftflow_layers.py` for the Python data layer for this dataset.
+
+Note that the dataset has a number of issues, including unannotated images and missing classes from the test set.
+The provided splits exclude the unannotated images.
+As noted in the paper, care must be taken for proper evalution by excluding the missing classes.
+
+Download the dataset:
+http://www.cs.unc.edu/~jtighe/Papers/ECCV10/siftflow/SiftFlowDataset.zip
diff --git a/data/sift-flow/classes.txt b/data/sift-flow/classes.txt
new file mode 100644
index 0000000000000000000000000000000000000000..eb980fc5dbb899b9ec2db8732f6b2eeb5a0eddfd
--- /dev/null
+++ b/data/sift-flow/classes.txt
@@ -0,0 +1,47 @@
+Semantic and geometric segmentation classes for scenes.
+
+Semantic: 0 is void and 1–33 are classes.
+
+01 awning
+02 balcony
+03 bird
+04 boat
+05 bridge
+06 building
+07 bus
+08 car
+09 cow
+10 crosswalk
+11 desert
+12 door
+13 fence
+14 field
+15 grass
+16 moon
+17 mountain
+18 person
+19 plant
+20 pole
+21 river
+22 road
+23 rock
+24 sand
+25 sea
+26 sidewalk
+27 sign
+28 sky
+29 staircase
+30 streetlight
+31 sun
+32 tree
+33 window
+
+Geometric: -1 is void and 1–3 are classes.
+
+01 sky
+02 horizontal
+03 vertical
+
+N.B. Three classes (cow, desert, and moon) are absent from the test set, so
+they are excluded from evaluation. The highway_bost181 and street_urb506 images
+are missing annotations so these are likewise excluded from evaluation.
diff --git a/data/sift-flow/test.txt b/data/sift-flow/test.txt
new file mode 100644
index 0000000000000000000000000000000000000000..7009acb59866193809b26e8bf6c355aa2d8d3d1f
--- /dev/null
+++ b/data/sift-flow/test.txt
@@ -0,0 +1,200 @@
+coast_natu975
+insidecity_art947
+insidecity_urb781
+highway_bost374
+coast_n203085
+insidecity_a223049
+mountain_nat116
+street_art861
+mountain_land188
+street_par177
+opencountry_natu524
+forest_natu29
+highway_gre37
+street_bost77
+insidecity_art1125
+street_urb521
+highway_bost178
+street_art760
+street_urb885
+insidecity_art829
+coast_natu804
+mountain_sharp44
+coast_natu649
+opencountry_land691
+insidecity_hous35
+tallbuilding_art1719
+mountain_n736026
+mountain_moun41
+insidecity_urban992
+opencountry_land295
+tallbuilding_art527
+highway_art238
+forest_for114
+coast_land296
+tallbuilding_sky7
+mountain_n44009
+tallbuilding_art1316
+forest_nat717
+highway_bost164
+street_par29
+forest_natc52
+tallbuilding_art1004
+coast_sun14
+opencountry_land206
+opencountry_land364
+mountain_n219015
+highway_a836030
+forest_nat324
+opencountry_land493
+insidecity_art1598
+street_street27
+insidecity_a48009
+coast_cdmc889
+street_gre295
+tallbuilding_a538076
+street_boston378
+highway_urb759
+street_par151
+tallbuilding_urban1003
+tallbuilding_urban16
+highway_bost151
+opencountry_nat965
+highway_gre661
+forest_for42
+opencountry_n18002
+insidecity_art646
+highway_gre55
+coast_n295051
+forest_bost103
+highway_n480036
+mountain_land4
+forest_nat130
+coast_nat643
+insidecity_urb250
+street_gre11
+street_boston271
+opencountry_n490003
+mountain_nat762
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+insidecity_hous109
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+opencountry_land638
+opencountry_natu563
+tallbuilding_urb733
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+opencountry_nat615
+coast_nat901
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+tallbuilding_art425
+coast_bea3
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+insidecity_art690
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+highway_bost320
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+highway_par23
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+highway_gre413
+tallbuilding_a279033
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+opencountry_nat423
+mountain_land143
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+insidecity_hous79
+street_par118
+highway_bost293
+mountain_n213021
+opencountry_nat802
+coast_n384099
+opencountry_natu998
+mountain_n344042
+coast_nat1265
+forest_text44
+forest_for84
+insidecity_a807066
+opencountry_nat1117
+coast_sun42
+insidecity_par180
+opencountry_land923
+highway_art580
+street_art1328
+coast_cdmc838
+opencountry_land660
+opencountry_cdmc354
+coast_natu825
+opencountry_natu38
+mountain_nat30
+coast_n199066
+forest_text124
+forest_land222
+tallbuilding_city56
+tallbuilding_city22
+opencountry_fie36
+mountain_ski24
+coast_cdmc997
+insidecity_boston232
+opencountry_land575
+opencountry_land797
+insidecity_urb362
+forest_nat1033
+mountain_nat891
+street_hexp3
+tallbuilding_art1474
+tallbuilding_urban73
+opencountry_natu852
+mountain_nat1008
+coast_nat294
+mountain_sharp20
+opencountry_fie14
+mountain_land275
+forest_land760
+coast_land374
+mountain_nat426
+highway_gre141
\ No newline at end of file
diff --git a/data/sift-flow/trainval.txt b/data/sift-flow/trainval.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c79a73d5f7b088f137367c9ffaec08bbf146b957
--- /dev/null
+++ b/data/sift-flow/trainval.txt
@@ -0,0 +1,2486 @@
+forest_for121
+tallbuilding_a812069
+street_bost64
+coast_land111
+mountain_land131
+mountain_n199074
+coast_n18001
+mountain_n213094
+opencountry_land692
+highway_gre459
+insidecity_urb308
+insidecity_urb301
+tallbuilding_urban22
+opencountry_nat1010
+street_boston18
+mountain_sharp18
+insidecity_art626
+forest_natu26
+insidecity_gre102
+opencountry_land625
+mountain_moun44
+mountain_n347077
+forest_land854
+tallbuilding_a562064
+tallbuilding_urban596
+tallbuilding_city14
+street_urb848
+forest_nat221
+opencountry_land351
+highway_n480001
+insidecity_boston57
+mountain_nat1003
+mountain_nat122
+opencountry_nat873
+forest_text42
+tallbuilding_art1015
+highway_bost321
+tallbuilding_art1357
+tallbuilding_a805044
+opencountry_tell56
+mountain_land879
+tallbuilding_art426
+tallbuilding_urban1057
+coast_osun54
+forest_nat209
+insidecity_a463022
+coast_natu808
+street_par150
+mountain_land280
+street_bost65
+tallbuilding_art1750
+coast_n122078
+highway_gre481
+street_boston274
+mountain_land716
+coast_cdmc954
+mountain_land130
+insidecity_urb121
+coast_n203071
+opencountry_land631
+tallbuilding_city8
+coast_n291008
+opencountry_nat1078
+opencountry_land507
+opencountry_art582
+opencountry_land658
+forest_nat315
+tallbuilding_a487092
+tallbuilding_sky3
+street_street110
+coast_cdmc862
+highway_nat542
+street_par3
+forest_land861
+coast_natu908
+opencountry_n18011
+coast_bea1
+coast_natu515
+coast_nat566
+mountain_ski72
+mountain_land880
+opencountry_nat932
+opencountry_nat823
+forest_bost98
+highway_art568
+insidecity_urb725
+opencountry_natu544
+mountain_nat1114
+coast_sun56
+forest_for102
+mountain_land150
+street_par101
+coast_cdmc900
+street_par12
+insidecity_par173
+street_urb265
+highway_bost325
+coast_cdmc845
+street_par165
+highway_gre42
+opencountry_nat457
+tallbuilding_art473
+highway_bost403
+opencountry_nat750
+highway_gre40
+mountain_sharp60
+mountain_sharp67
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+opencountry_natu841
+opencountry_natu179
+opencountry_n371054
+forest_text46
+mountain_nat78
+street_par153
+coast_n486055
+highway_urb720
+highway_art1673
+highway_art874
+highway_bost334
+highway_gre48
+street_par64
+coast_sun34
+highway_bost311
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+mountain_land479
+opencountry_natu519
+mountain_sharp34
+forest_natu866
+forest_bost100
+coast_land258
+highway_gre684
+street_street47
+mountain_n219008
+forest_land245
+opencountry_land550
+forest_text47
+coast_natu815
+highway_bost149
+opencountry_fie35
+tallbuilding_art1413
+opencountry_natu652
+coast_nat202
+insidecity_gre262
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+tallbuilding_urban1158
+mountain_nat316
+forest_nat623
+forest_nat619
+street_par16
+tallbuilding_art350
+insidecity_city21
+coast_n344048
+street_hexp17
+mountain_land16
+insidecity_art642
+insidecity_boston282
+coast_natu25
+opencountry_land835
+coast_sun22
+tallbuilding_art945
+forest_nat1266
+highway_bost176
+opencountry_land560
+tallbuilding_art936
+insidecity_urba2
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+opencountry_land388
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+tallbuilding_art528
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+coast_bea27
+coast_nat174
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+mountain_land28
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+insidecity_art669
+mountain_cdmc184
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+forest_for96
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+insidecity_urb612
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+coast_sun32
+forest_nat347
+mountain_sharp63
+coast_sun7
+highway_art489
+mountain_nat1207
+mountain_n44001
+highway_n480023
+forest_cdmc293
+insidecity_art681
+street_boston1
+street_art549
+coast_nat700
+opencountry_fie15
+tallbuilding_urb907
+forest_cdmc344
+tallbuilding_art1353
+coast_cdmc871
+forest_nat145
+highway_bost161
+coast_land112
+street_gre2
+coast_nat850
+forest_for110
+coast_natu473
+tallbuilding_art1351
+insidecity_urb482
+coast_land371
+forest_natu161
+mountain_n199075
+mountain_nat346
+highway_nat533
+coast_sun8
+insidecity_par129
+mountain_moun32
+coast_natu638
+forest_land314
+mountain_natu697
+mountain_land17
+insidecity_boston149
+mountain_natu756
+street_bost111
+insidecity_urb595
+tallbuilding_a248018
+opencountry_n676082
+opencountry_nat1034
+street_art798
+highway_gre144
+forest_natu869
+opencountry_land345
+tallbuilding_art106
+tallbuilding_art101
+forest_nat449
+opencountry_natu181
+highway_gre659
+mountain_nat59
+coast_nat285
+highway_urb710
+mountain_nat693
+tallbuilding_sky10
+mountain_moun38
+mountain_natu763
+highway_bost152
+mountain_nat1234
+insidecity_a807036
+tallbuilding_art392
+street_urb510
+coast_land368
+coast_land261
+mountain_n632073
+coast_n203015
+insidecity_hous40
+mountain_sharp10
+insidecity_urb108
+opencountry_land693
+mountain_land153
+highway_gre685
+mountain_natu460
+insidecity_bost44
+forest_cdmc414
+coast_n384026
+forest_nat452
+tallbuilding_a806037
+street_gre124
+forest_natu871
+opencountry_land269
+highway_bost299
+street_gre91
+mountain_nat67
+coast_bea29
+mountain_nat1017
+insidecity_art787
+coast_natu829
+forest_for65
+forest_for28
+coast_natu814
+coast_cdmc923
+tallbuilding_a244040
+insidecity_art661
+insidecity_a79054
+mountain_natu655
+coast_sun11
+tallbuilding_urb911
+coast_land309
+opencountry_land965
+street_art970
+opencountry_fie50
+coast_cdmc951
+street_urb305
+street_par14
+highway_art890
+opencountry_land709
+insidecity_art920
+tallbuilding_art457
+opencountry_natu583
+street_art862
+street_urban996
+highway_natu787
+mountain_sharp53
+coast_nat684
+coast_nat707
+opencountry_land233
+highway_bost170
+coast_sun28
+mountain_n213035
+opencountry_natu739
+opencountry_land202
\ No newline at end of file
diff --git a/siftflow-fcn16s/caffemodel-url b/siftflow-fcn16s/caffemodel-url
new file mode 100644
index 0000000000000000000000000000000000000000..4934b11d62ef136c80eda813abced1339033b818
--- /dev/null
+++ b/siftflow-fcn16s/caffemodel-url
@@ -0,0 +1 @@
+http://dl.caffe.berkeleyvision.org/siftflow-fcn16s-heavy.caffemodel
diff --git a/siftflow-fcn16s/net.py b/siftflow-fcn16s/net.py
new file mode 100644
index 0000000000000000000000000000000000000000..b3250ceb09fecc75040aa5d85e030309669a40d6
--- /dev/null
+++ b/siftflow-fcn16s/net.py
@@ -0,0 +1,105 @@
+import caffe
+from caffe import layers as L, params as P
+from caffe.coord_map import crop
+
+def conv_relu(bottom, nout, ks=3, stride=1, pad=1):
+    conv = L.Convolution(bottom, kernel_size=ks, stride=stride,
+        num_output=nout, pad=pad,
+        param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)])
+    return conv, L.ReLU(conv, in_place=True)
+
+def max_pool(bottom, ks=2, stride=2):
+    return L.Pooling(bottom, pool=P.Pooling.MAX, kernel_size=ks, stride=stride)
+
+def fcn(split):
+    n = caffe.NetSpec()
+    n.data, n.sem, n.geo = L.Python(module='siftflow_layers',
+            layer='SIFTFlowSegDataLayer', ntop=3,
+            param_str=str(dict(siftflow_dir='../data/sift-flow',
+                split=split, seed=1337)))
+
+    # the base net
+    n.conv1_1, n.relu1_1 = conv_relu(n.data, 64, pad=100)
+    n.conv1_2, n.relu1_2 = conv_relu(n.relu1_1, 64)
+    n.pool1 = max_pool(n.relu1_2)
+
+    n.conv2_1, n.relu2_1 = conv_relu(n.pool1, 128)
+    n.conv2_2, n.relu2_2 = conv_relu(n.relu2_1, 128)
+    n.pool2 = max_pool(n.relu2_2)
+
+    n.conv3_1, n.relu3_1 = conv_relu(n.pool2, 256)
+    n.conv3_2, n.relu3_2 = conv_relu(n.relu3_1, 256)
+    n.conv3_3, n.relu3_3 = conv_relu(n.relu3_2, 256)
+    n.pool3 = max_pool(n.relu3_3)
+
+    n.conv4_1, n.relu4_1 = conv_relu(n.pool3, 512)
+    n.conv4_2, n.relu4_2 = conv_relu(n.relu4_1, 512)
+    n.conv4_3, n.relu4_3 = conv_relu(n.relu4_2, 512)
+    n.pool4 = max_pool(n.relu4_3)
+
+    n.conv5_1, n.relu5_1 = conv_relu(n.pool4, 512)
+    n.conv5_2, n.relu5_2 = conv_relu(n.relu5_1, 512)
+    n.conv5_3, n.relu5_3 = conv_relu(n.relu5_2, 512)
+    n.pool5 = max_pool(n.relu5_3)
+
+    # fully conv
+    n.fc6, n.relu6 = conv_relu(n.pool5, 4096, ks=7, pad=0)
+    n.drop6 = L.Dropout(n.relu6, dropout_ratio=0.5, in_place=True)
+    n.fc7, n.relu7 = conv_relu(n.drop6, 4096, ks=1, pad=0)
+    n.drop7 = L.Dropout(n.relu7, dropout_ratio=0.5, in_place=True)
+
+    n.score_fr_sem = L.Convolution(n.drop7, num_output=33, kernel_size=1, pad=0,
+        param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)])
+    n.upscore2_sem = L.Deconvolution(n.score_fr_sem,
+        convolution_param=dict(num_output=33, kernel_size=4, stride=2,
+            bias_term=False),
+        param=[dict(lr_mult=0)])
+
+    n.score_pool4_sem = L.Convolution(n.pool4, num_output=33, kernel_size=1, pad=0,
+        param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)])
+    n.score_pool4_semc = crop(n.score_pool4_sem, n.upscore2_sem)
+    n.fuse_pool4_sem = L.Eltwise(n.upscore2_sem, n.score_pool4_semc,
+            operation=P.Eltwise.SUM)
+    n.upscore16_sem = L.Deconvolution(n.fuse_pool4_sem,
+        convolution_param=dict(num_output=33, kernel_size=32, stride=16,
+            bias_term=False),
+        param=[dict(lr_mult=0)])
+
+    n.score_sem = crop(n.upscore16_sem, n.data)
+    # loss to make score happy (o.w. loss_sem)
+    n.loss = L.SoftmaxWithLoss(n.score_sem, n.sem,
+            loss_param=dict(normalize=False, ignore_label=255))
+
+    n.score_fr_geo = L.Convolution(n.drop7, num_output=3, kernel_size=1, pad=0,
+        param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)])
+
+    n.upscore2_geo = L.Deconvolution(n.score_fr_geo,
+        convolution_param=dict(num_output=3, kernel_size=4, stride=2,
+            bias_term=False),
+        param=[dict(lr_mult=0)])
+
+    n.score_pool4_geo = L.Convolution(n.pool4, num_output=3, kernel_size=1, pad=0,
+        param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)])
+    n.score_pool4_geoc = crop(n.score_pool4_geo, n.upscore2_geo)
+    n.fuse_pool4_geo = L.Eltwise(n.upscore2_geo, n.score_pool4_geoc,
+            operation=P.Eltwise.SUM)
+    n.upscore16_geo = L.Deconvolution(n.fuse_pool4_geo,
+        convolution_param=dict(num_output=3, kernel_size=32, stride=16,
+            bias_term=False),
+        param=[dict(lr_mult=0)])
+
+    n.score_geo = crop(n.upscore16_geo, n.data)
+    n.loss_geo = L.SoftmaxWithLoss(n.score_geo, n.geo,
+            loss_param=dict(normalize=False, ignore_label=255))
+
+    return n.to_proto()
+
+def make_net():
+    with open('trainval.prototxt', 'w') as f:
+        f.write(str(fcn('trainval')))
+
+    with open('test.prototxt', 'w') as f:
+        f.write(str(fcn('test')))
+
+if __name__ == '__main__':
+    make_net()
diff --git a/siftflow-fcn16s/solve.py b/siftflow-fcn16s/solve.py
new file mode 100644
index 0000000000000000000000000000000000000000..36a679f1cd858e81d9ee360976c7fe43e757e640
--- /dev/null
+++ b/siftflow-fcn16s/solve.py
@@ -0,0 +1,31 @@
+import caffe
+import surgery, score
+
+import numpy as np
+import os
+
+import setproctitle
+setproctitle.setproctitle(os.path.basename(os.getcwd()))
+
+weights = '../siftflow-fcn32s/siftflow-fcn32s.caffemodel'
+
+# init
+caffe.set_device(int(sys.argv[1]))
+caffe.set_mode_gpu()
+
+solver = caffe.SGDSolver('solver.prototxt')
+solver.net.copy_from(weights)
+
+# surgeries
+interp_layers = [k for k in solver.net.params.keys() if 'up' in k]
+surgery.interp(solver.net, interp_layers)
+
+# scoring
+test = np.loadtxt('../data/sift-flow/test.txt', dtype=str)
+
+for _ in range(50):
+    solver.step(2000)
+    # N.B. metrics on the semantic labels are off b.c. of missing classes;
+    # score manually from the histogram instead for proper evaluation
+    score.seg_tests(solver, False, test, layer='score_sem', gt='sem')
+    score.seg_tests(solver, False, test, layer='score_geo', gt='geo')
diff --git a/siftflow-fcn16s/solver.prototxt b/siftflow-fcn16s/solver.prototxt
new file mode 100644
index 0000000000000000000000000000000000000000..09aff3378dca8db28fc768884e919edc345f9126
--- /dev/null
+++ b/siftflow-fcn16s/solver.prototxt
@@ -0,0 +1,17 @@
+train_net: "trainval.prototxt"
+test_net: "test.prototxt"
+test_iter: 1111
+# make test net, but don't invoke it from the solver itself
+test_interval: 999999999
+display: 20
+average_loss: 20
+lr_policy: "fixed"
+# lr for unnormalized softmax
+base_lr: 1e-12
+# high momentum
+momentum: 0.99
+# no gradient accumulation
+iter_size: 1
+max_iter: 300000
+weight_decay: 0.0005
+test_initialization: false
diff --git a/siftflow-fcn16s/test.prototxt b/siftflow-fcn16s/test.prototxt
new file mode 100644
index 0000000000000000000000000000000000000000..5ae3ecf006c77bf19dba368dccbd128ab2544df6
--- /dev/null
+++ b/siftflow-fcn16s/test.prototxt
@@ -0,0 +1,697 @@
+layer {
+  name: "data"
+  type: "Python"
+  top: "data"
+  top: "sem"
+  top: "geo"
+  python_param {
+    module: "siftflow_layers"
+    layer: "SIFTFlowSegDataLayer"
+    param_str: "{\'siftflow_dir\': \'../../data/sift-flow\', \'seed\': 1337, \'split\': \'test\'}"
+  }
+}
+layer {
+  name: "conv1_1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1_1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    pad: 100
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu1_1"
+  type: "ReLU"
+  bottom: "conv1_1"
+  top: "conv1_1"
+}
+layer {
+  name: "conv1_2"
+  type: "Convolution"
+  bottom: "conv1_1"
+  top: "conv1_2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu1_2"
+  type: "ReLU"
+  bottom: "conv1_2"
+  top: "conv1_2"
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "conv1_2"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv2_1"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2_1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu2_1"
+  type: "ReLU"
+  bottom: "conv2_1"
+  top: "conv2_1"
+}
+layer {
+  name: "conv2_2"
+  type: "Convolution"
+  bottom: "conv2_1"
+  top: "conv2_2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu2_2"
+  type: "ReLU"
+  bottom: "conv2_2"
+  top: "conv2_2"
+}
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "conv2_2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv3_1"
+  type: "Convolution"
+  bottom: "pool2"
+  top: "conv3_1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu3_1"
+  type: "ReLU"
+  bottom: "conv3_1"
+  top: "conv3_1"
+}
+layer {
+  name: "conv3_2"
+  type: "Convolution"
+  bottom: "conv3_1"
+  top: "conv3_2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu3_2"
+  type: "ReLU"
+  bottom: "conv3_2"
+  top: "conv3_2"
+}
+layer {
+  name: "conv3_3"
+  type: "Convolution"
+  bottom: "conv3_2"
+  top: "conv3_3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu3_3"
+  type: "ReLU"
+  bottom: "conv3_3"
+  top: "conv3_3"
+}
+layer {
+  name: "pool3"
+  type: "Pooling"
+  bottom: "conv3_3"
+  top: "pool3"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv4_1"
+  type: "Convolution"
+  bottom: "pool3"
+  top: "conv4_1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu4_1"
+  type: "ReLU"
+  bottom: "conv4_1"
+  top: "conv4_1"
+}
+layer {
+  name: "conv4_2"
+  type: "Convolution"
+  bottom: "conv4_1"
+  top: "conv4_2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu4_2"
+  type: "ReLU"
+  bottom: "conv4_2"
+  top: "conv4_2"
+}
+layer {
+  name: "conv4_3"
+  type: "Convolution"
+  bottom: "conv4_2"
+  top: "conv4_3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu4_3"
+  type: "ReLU"
+  bottom: "conv4_3"
+  top: "conv4_3"
+}
+layer {
+  name: "pool4"
+  type: "Pooling"
+  bottom: "conv4_3"
+  top: "pool4"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv5_1"
+  type: "Convolution"
+  bottom: "pool4"
+  top: "conv5_1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu5_1"
+  type: "ReLU"
+  bottom: "conv5_1"
+  top: "conv5_1"
+}
+layer {
+  name: "conv5_2"
+  type: "Convolution"
+  bottom: "conv5_1"
+  top: "conv5_2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu5_2"
+  type: "ReLU"
+  bottom: "conv5_2"
+  top: "conv5_2"
+}
+layer {
+  name: "conv5_3"
+  type: "Convolution"
+  bottom: "conv5_2"
+  top: "conv5_3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu5_3"
+  type: "ReLU"
+  bottom: "conv5_3"
+  top: "conv5_3"
+}
+layer {
+  name: "pool5"
+  type: "Pooling"
+  bottom: "conv5_3"
+  top: "pool5"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "fc6"
+  type: "Convolution"
+  bottom: "pool5"
+  top: "fc6"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 4096
+    pad: 0
+    kernel_size: 7
+    stride: 1
+  }
+}
+layer {
+  name: "relu6"
+  type: "ReLU"
+  bottom: "fc6"
+  top: "fc6"
+}
+layer {
+  name: "drop6"
+  type: "Dropout"
+  bottom: "fc6"
+  top: "fc6"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc7"
+  type: "Convolution"
+  bottom: "fc6"
+  top: "fc7"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 4096
+    pad: 0
+    kernel_size: 1
+    stride: 1
+  }
+}
+layer {
+  name: "relu7"
+  type: "ReLU"
+  bottom: "fc7"
+  top: "fc7"
+}
+layer {
+  name: "drop7"
+  type: "Dropout"
+  bottom: "fc7"
+  top: "fc7"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "score_fr_sem"
+  type: "Convolution"
+  bottom: "fc7"
+  top: "score_fr_sem"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 33
+    pad: 0
+    kernel_size: 1
+  }
+}
+layer {
+  name: "upscore2_sem"
+  type: "Deconvolution"
+  bottom: "score_fr_sem"
+  top: "upscore2_sem"
+  param {
+    lr_mult: 0
+  }
+  convolution_param {
+    num_output: 33
+    bias_term: false
+    kernel_size: 4
+    stride: 2
+  }
+}
+layer {
+  name: "score_pool4_sem"
+  type: "Convolution"
+  bottom: "pool4"
+  top: "score_pool4_sem"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 33
+    pad: 0
+    kernel_size: 1
+  }
+}
+layer {
+  name: "score_pool4_semc"
+  type: "Crop"
+  bottom: "score_pool4_sem"
+  bottom: "upscore2_sem"
+  top: "score_pool4_semc"
+  crop_param {
+    axis: 2
+    offset: 5
+  }
+}
+layer {
+  name: "fuse_pool4_sem"
+  type: "Eltwise"
+  bottom: "upscore2_sem"
+  bottom: "score_pool4_semc"
+  top: "fuse_pool4_sem"
+  eltwise_param {
+    operation: SUM
+  }
+}
+layer {
+  name: "upscore16_sem"
+  type: "Deconvolution"
+  bottom: "fuse_pool4_sem"
+  top: "upscore16_sem"
+  param {
+    lr_mult: 0
+  }
+  convolution_param {
+    num_output: 33
+    bias_term: false
+    kernel_size: 32
+    stride: 16
+  }
+}
+layer {
+  name: "score_sem"
+  type: "Crop"
+  bottom: "upscore16_sem"
+  bottom: "data"
+  top: "score_sem"
+  crop_param {
+    axis: 2
+    offset: 27
+  }
+}
+layer {
+  name: "loss"
+  type: "SoftmaxWithLoss"
+  bottom: "score_sem"
+  bottom: "sem"
+  top: "loss"
+  loss_param {
+    ignore_label: 255
+    normalize: false
+  }
+}
+layer {
+  name: "score_fr_geo"
+  type: "Convolution"
+  bottom: "fc7"
+  top: "score_fr_geo"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 3
+    pad: 0
+    kernel_size: 1
+  }
+}
+layer {
+  name: "upscore2_geo"
+  type: "Deconvolution"
+  bottom: "score_fr_geo"
+  top: "upscore2_geo"
+  param {
+    lr_mult: 0
+  }
+  convolution_param {
+    num_output: 3
+    bias_term: false
+    kernel_size: 4
+    stride: 2
+  }
+}
+layer {
+  name: "score_pool4_geo"
+  type: "Convolution"
+  bottom: "pool4"
+  top: "score_pool4_geo"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 3
+    pad: 0
+    kernel_size: 1
+  }
+}
+layer {
+  name: "score_pool4_geoc"
+  type: "Crop"
+  bottom: "score_pool4_geo"
+  bottom: "upscore2_geo"
+  top: "score_pool4_geoc"
+  crop_param {
+    axis: 2
+    offset: 5
+  }
+}
+layer {
+  name: "fuse_pool4_geo"
+  type: "Eltwise"
+  bottom: "upscore2_geo"
+  bottom: "score_pool4_geoc"
+  top: "fuse_pool4_geo"
+  eltwise_param {
+    operation: SUM
+  }
+}
+layer {
+  name: "upscore16_geo"
+  type: "Deconvolution"
+  bottom: "fuse_pool4_geo"
+  top: "upscore16_geo"
+  param {
+    lr_mult: 0
+  }
+  convolution_param {
+    num_output: 3
+    bias_term: false
+    kernel_size: 32
+    stride: 16
+  }
+}
+layer {
+  name: "score_geo"
+  type: "Crop"
+  bottom: "upscore16_geo"
+  bottom: "data"
+  top: "score_geo"
+  crop_param {
+    axis: 2
+    offset: 27
+  }
+}
+layer {
+  name: "loss_geo"
+  type: "SoftmaxWithLoss"
+  bottom: "score_geo"
+  bottom: "geo"
+  top: "loss_geo"
+  loss_param {
+    ignore_label: 255
+    normalize: false
+  }
+}
diff --git a/siftflow-fcn16s/trainval.prototxt b/siftflow-fcn16s/trainval.prototxt
new file mode 100644
index 0000000000000000000000000000000000000000..f79cdfda09a366fd9e76a0c058a706c788b64af3
--- /dev/null
+++ b/siftflow-fcn16s/trainval.prototxt
@@ -0,0 +1,697 @@
+layer {
+  name: "data"
+  type: "Python"
+  top: "data"
+  top: "sem"
+  top: "geo"
+  python_param {
+    module: "siftflow_layers"
+    layer: "SIFTFlowSegDataLayer"
+    param_str: "{\'siftflow_dir\': \'../../data/sift-flow\', \'seed\': 1337, \'split\': \'trainval\'}"
+  }
+}
+layer {
+  name: "conv1_1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1_1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    pad: 100
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu1_1"
+  type: "ReLU"
+  bottom: "conv1_1"
+  top: "conv1_1"
+}
+layer {
+  name: "conv1_2"
+  type: "Convolution"
+  bottom: "conv1_1"
+  top: "conv1_2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu1_2"
+  type: "ReLU"
+  bottom: "conv1_2"
+  top: "conv1_2"
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "conv1_2"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv2_1"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2_1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu2_1"
+  type: "ReLU"
+  bottom: "conv2_1"
+  top: "conv2_1"
+}
+layer {
+  name: "conv2_2"
+  type: "Convolution"
+  bottom: "conv2_1"
+  top: "conv2_2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu2_2"
+  type: "ReLU"
+  bottom: "conv2_2"
+  top: "conv2_2"
+}
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "conv2_2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv3_1"
+  type: "Convolution"
+  bottom: "pool2"
+  top: "conv3_1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu3_1"
+  type: "ReLU"
+  bottom: "conv3_1"
+  top: "conv3_1"
+}
+layer {
+  name: "conv3_2"
+  type: "Convolution"
+  bottom: "conv3_1"
+  top: "conv3_2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu3_2"
+  type: "ReLU"
+  bottom: "conv3_2"
+  top: "conv3_2"
+}
+layer {
+  name: "conv3_3"
+  type: "Convolution"
+  bottom: "conv3_2"
+  top: "conv3_3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu3_3"
+  type: "ReLU"
+  bottom: "conv3_3"
+  top: "conv3_3"
+}
+layer {
+  name: "pool3"
+  type: "Pooling"
+  bottom: "conv3_3"
+  top: "pool3"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv4_1"
+  type: "Convolution"
+  bottom: "pool3"
+  top: "conv4_1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu4_1"
+  type: "ReLU"
+  bottom: "conv4_1"
+  top: "conv4_1"
+}
+layer {
+  name: "conv4_2"
+  type: "Convolution"
+  bottom: "conv4_1"
+  top: "conv4_2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu4_2"
+  type: "ReLU"
+  bottom: "conv4_2"
+  top: "conv4_2"
+}
+layer {
+  name: "conv4_3"
+  type: "Convolution"
+  bottom: "conv4_2"
+  top: "conv4_3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu4_3"
+  type: "ReLU"
+  bottom: "conv4_3"
+  top: "conv4_3"
+}
+layer {
+  name: "pool4"
+  type: "Pooling"
+  bottom: "conv4_3"
+  top: "pool4"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv5_1"
+  type: "Convolution"
+  bottom: "pool4"
+  top: "conv5_1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu5_1"
+  type: "ReLU"
+  bottom: "conv5_1"
+  top: "conv5_1"
+}
+layer {
+  name: "conv5_2"
+  type: "Convolution"
+  bottom: "conv5_1"
+  top: "conv5_2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu5_2"
+  type: "ReLU"
+  bottom: "conv5_2"
+  top: "conv5_2"
+}
+layer {
+  name: "conv5_3"
+  type: "Convolution"
+  bottom: "conv5_2"
+  top: "conv5_3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu5_3"
+  type: "ReLU"
+  bottom: "conv5_3"
+  top: "conv5_3"
+}
+layer {
+  name: "pool5"
+  type: "Pooling"
+  bottom: "conv5_3"
+  top: "pool5"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "fc6"
+  type: "Convolution"
+  bottom: "pool5"
+  top: "fc6"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 4096
+    pad: 0
+    kernel_size: 7
+    stride: 1
+  }
+}
+layer {
+  name: "relu6"
+  type: "ReLU"
+  bottom: "fc6"
+  top: "fc6"
+}
+layer {
+  name: "drop6"
+  type: "Dropout"
+  bottom: "fc6"
+  top: "fc6"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc7"
+  type: "Convolution"
+  bottom: "fc6"
+  top: "fc7"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 4096
+    pad: 0
+    kernel_size: 1
+    stride: 1
+  }
+}
+layer {
+  name: "relu7"
+  type: "ReLU"
+  bottom: "fc7"
+  top: "fc7"
+}
+layer {
+  name: "drop7"
+  type: "Dropout"
+  bottom: "fc7"
+  top: "fc7"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "score_fr_sem"
+  type: "Convolution"
+  bottom: "fc7"
+  top: "score_fr_sem"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 33
+    pad: 0
+    kernel_size: 1
+  }
+}
+layer {
+  name: "upscore2_sem"
+  type: "Deconvolution"
+  bottom: "score_fr_sem"
+  top: "upscore2_sem"
+  param {
+    lr_mult: 0
+  }
+  convolution_param {
+    num_output: 33
+    bias_term: false
+    kernel_size: 4
+    stride: 2
+  }
+}
+layer {
+  name: "score_pool4_sem"
+  type: "Convolution"
+  bottom: "pool4"
+  top: "score_pool4_sem"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 33
+    pad: 0
+    kernel_size: 1
+  }
+}
+layer {
+  name: "score_pool4_semc"
+  type: "Crop"
+  bottom: "score_pool4_sem"
+  bottom: "upscore2_sem"
+  top: "score_pool4_semc"
+  crop_param {
+    axis: 2
+    offset: 5
+  }
+}
+layer {
+  name: "fuse_pool4_sem"
+  type: "Eltwise"
+  bottom: "upscore2_sem"
+  bottom: "score_pool4_semc"
+  top: "fuse_pool4_sem"
+  eltwise_param {
+    operation: SUM
+  }
+}
+layer {
+  name: "upscore16_sem"
+  type: "Deconvolution"
+  bottom: "fuse_pool4_sem"
+  top: "upscore16_sem"
+  param {
+    lr_mult: 0
+  }
+  convolution_param {
+    num_output: 33
+    bias_term: false
+    kernel_size: 32
+    stride: 16
+  }
+}
+layer {
+  name: "score_sem"
+  type: "Crop"
+  bottom: "upscore16_sem"
+  bottom: "data"
+  top: "score_sem"
+  crop_param {
+    axis: 2
+    offset: 27
+  }
+}
+layer {
+  name: "loss"
+  type: "SoftmaxWithLoss"
+  bottom: "score_sem"
+  bottom: "sem"
+  top: "loss"
+  loss_param {
+    ignore_label: 255
+    normalize: false
+  }
+}
+layer {
+  name: "score_fr_geo"
+  type: "Convolution"
+  bottom: "fc7"
+  top: "score_fr_geo"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 3
+    pad: 0
+    kernel_size: 1
+  }
+}
+layer {
+  name: "upscore2_geo"
+  type: "Deconvolution"
+  bottom: "score_fr_geo"
+  top: "upscore2_geo"
+  param {
+    lr_mult: 0
+  }
+  convolution_param {
+    num_output: 3
+    bias_term: false
+    kernel_size: 4
+    stride: 2
+  }
+}
+layer {
+  name: "score_pool4_geo"
+  type: "Convolution"
+  bottom: "pool4"
+  top: "score_pool4_geo"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 3
+    pad: 0
+    kernel_size: 1
+  }
+}
+layer {
+  name: "score_pool4_geoc"
+  type: "Crop"
+  bottom: "score_pool4_geo"
+  bottom: "upscore2_geo"
+  top: "score_pool4_geoc"
+  crop_param {
+    axis: 2
+    offset: 5
+  }
+}
+layer {
+  name: "fuse_pool4_geo"
+  type: "Eltwise"
+  bottom: "upscore2_geo"
+  bottom: "score_pool4_geoc"
+  top: "fuse_pool4_geo"
+  eltwise_param {
+    operation: SUM
+  }
+}
+layer {
+  name: "upscore16_geo"
+  type: "Deconvolution"
+  bottom: "fuse_pool4_geo"
+  top: "upscore16_geo"
+  param {
+    lr_mult: 0
+  }
+  convolution_param {
+    num_output: 3
+    bias_term: false
+    kernel_size: 32
+    stride: 16
+  }
+}
+layer {
+  name: "score_geo"
+  type: "Crop"
+  bottom: "upscore16_geo"
+  bottom: "data"
+  top: "score_geo"
+  crop_param {
+    axis: 2
+    offset: 27
+  }
+}
+layer {
+  name: "loss_geo"
+  type: "SoftmaxWithLoss"
+  bottom: "score_geo"
+  bottom: "geo"
+  top: "loss_geo"
+  loss_param {
+    ignore_label: 255
+    normalize: false
+  }
+}
diff --git a/siftflow-fcn32s/caffemodel-url b/siftflow-fcn32s/caffemodel-url
new file mode 100644
index 0000000000000000000000000000000000000000..3fe4172d63ec8ace4e0946312934843718c6a58a
--- /dev/null
+++ b/siftflow-fcn32s/caffemodel-url
@@ -0,0 +1 @@
+http://dl.caffe.berkeleyvision.org/siftflow-fcn32s-heavy.caffemodel
diff --git a/siftflow-fcn32s/net.py b/siftflow-fcn32s/net.py
new file mode 100644
index 0000000000000000000000000000000000000000..c7c04225d487a547252dd3397655c93ee37364cc
--- /dev/null
+++ b/siftflow-fcn32s/net.py
@@ -0,0 +1,82 @@
+import caffe
+from caffe import layers as L, params as P
+from caffe.coord_map import crop
+
+def conv_relu(bottom, nout, ks=3, stride=1, pad=1):
+    conv = L.Convolution(bottom, kernel_size=ks, stride=stride,
+        num_output=nout, pad=pad,
+        param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)])
+    return conv, L.ReLU(conv, in_place=True)
+
+def max_pool(bottom, ks=2, stride=2):
+    return L.Pooling(bottom, pool=P.Pooling.MAX, kernel_size=ks, stride=stride)
+
+def fcn(split):
+    n = caffe.NetSpec()
+    n.data, n.sem, n.geo = L.Python(module='siftflow_layers',
+            layer='SIFTFlowSegDataLayer', ntop=3,
+            param_str=str(dict(siftflow_dir='../data/sift-flow',
+                split=split, seed=1337)))
+
+    # the base net
+    n.conv1_1, n.relu1_1 = conv_relu(n.data, 64, pad=100)
+    n.conv1_2, n.relu1_2 = conv_relu(n.relu1_1, 64)
+    n.pool1 = max_pool(n.relu1_2)
+
+    n.conv2_1, n.relu2_1 = conv_relu(n.pool1, 128)
+    n.conv2_2, n.relu2_2 = conv_relu(n.relu2_1, 128)
+    n.pool2 = max_pool(n.relu2_2)
+
+    n.conv3_1, n.relu3_1 = conv_relu(n.pool2, 256)
+    n.conv3_2, n.relu3_2 = conv_relu(n.relu3_1, 256)
+    n.conv3_3, n.relu3_3 = conv_relu(n.relu3_2, 256)
+    n.pool3 = max_pool(n.relu3_3)
+
+    n.conv4_1, n.relu4_1 = conv_relu(n.pool3, 512)
+    n.conv4_2, n.relu4_2 = conv_relu(n.relu4_1, 512)
+    n.conv4_3, n.relu4_3 = conv_relu(n.relu4_2, 512)
+    n.pool4 = max_pool(n.relu4_3)
+
+    n.conv5_1, n.relu5_1 = conv_relu(n.pool4, 512)
+    n.conv5_2, n.relu5_2 = conv_relu(n.relu5_1, 512)
+    n.conv5_3, n.relu5_3 = conv_relu(n.relu5_2, 512)
+    n.pool5 = max_pool(n.relu5_3)
+
+    # fully conv
+    n.fc6, n.relu6 = conv_relu(n.pool5, 4096, ks=7, pad=0)
+    n.drop6 = L.Dropout(n.relu6, dropout_ratio=0.5, in_place=True)
+    n.fc7, n.relu7 = conv_relu(n.drop6, 4096, ks=1, pad=0)
+    n.drop7 = L.Dropout(n.relu7, dropout_ratio=0.5, in_place=True)
+
+    n.score_fr_sem = L.Convolution(n.drop7, num_output=33, kernel_size=1, pad=0,
+        param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)])
+    n.upscore_sem = L.Deconvolution(n.score_fr_sem,
+        convolution_param=dict(num_output=33, kernel_size=64, stride=32,
+            bias_term=False),
+        param=[dict(lr_mult=0)])
+    n.score_sem = crop(n.upscore_sem, n.data)
+    # loss to make score happy (o.w. loss_sem)
+    n.loss = L.SoftmaxWithLoss(n.score_sem, n.sem,
+            loss_param=dict(normalize=False, ignore_label=255))
+
+    n.score_fr_geo = L.Convolution(n.drop7, num_output=3, kernel_size=1, pad=0,
+        param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)])
+    n.upscore_geo = L.Deconvolution(n.score_fr_geo,
+        convolution_param=dict(num_output=3, kernel_size=64, stride=32,
+            bias_term=False),
+        param=[dict(lr_mult=0)])
+    n.score_geo = crop(n.upscore_geo, n.data)
+    n.loss_geo = L.SoftmaxWithLoss(n.score_geo, n.geo,
+            loss_param=dict(normalize=False, ignore_label=255))
+
+    return n.to_proto()
+
+def make_net():
+    with open('trainval.prototxt', 'w') as f:
+        f.write(str(fcn('trainval')))
+
+    with open('test.prototxt', 'w') as f:
+        f.write(str(fcn('test')))
+
+if __name__ == '__main__':
+    make_net()
diff --git a/siftflow-fcn32s/solve.py b/siftflow-fcn32s/solve.py
new file mode 100644
index 0000000000000000000000000000000000000000..35ea7e94eac6b49dd992ac5b6454f2d3f7f5f50e
--- /dev/null
+++ b/siftflow-fcn32s/solve.py
@@ -0,0 +1,31 @@
+import caffe
+import surgery, score
+
+import numpy as np
+import os
+
+import setproctitle
+setproctitle.setproctitle(os.path.basename(os.getcwd()))
+
+weights = '../vgg16fc.caffemodel'
+
+# init
+caffe.set_device(int(sys.argv[1]))
+caffe.set_mode_gpu()
+
+solver = caffe.SGDSolver('solver.prototxt')
+solver.net.copy_from(weights)
+
+# surgeries
+interp_layers = [k for k in solver.net.params.keys() if 'up' in k]
+surgery.interp(solver.net, interp_layers)
+
+# scoring
+test = np.loadtxt('../data/sift-flow/test.txt', dtype=str)
+
+for _ in range(50):
+    solver.step(2000)
+    # N.B. metrics on the semantic labels are off b.c. of missing classes;
+    # score manually from the histogram instead for proper evaluation
+    score.seg_tests(solver, False, test, layer='score_sem', gt='sem')
+    score.seg_tests(solver, False, test, layer='score_geo', gt='geo')
diff --git a/siftflow-fcn32s/solver.prototxt b/siftflow-fcn32s/solver.prototxt
new file mode 100644
index 0000000000000000000000000000000000000000..ca72e5e32d065baf9c7d5957fee49f56e84e505b
--- /dev/null
+++ b/siftflow-fcn32s/solver.prototxt
@@ -0,0 +1,17 @@
+train_net: "trainval.prototxt"
+test_net: "test.prototxt"
+test_iter: 1111
+# make test net, but don't invoke it from the solver itself
+test_interval: 999999999
+display: 20
+average_loss: 20
+lr_policy: "fixed"
+# lr for unnormalized softmax
+base_lr: 1e-10
+# high momentum
+momentum: 0.99
+# no gradient accumulation
+iter_size: 1
+max_iter: 300000
+weight_decay: 0.0005
+test_initialization: false
diff --git a/siftflow-fcn32s/solver.prototxt.orig b/siftflow-fcn32s/solver.prototxt.orig
new file mode 100644
index 0000000000000000000000000000000000000000..ca72e5e32d065baf9c7d5957fee49f56e84e505b
--- /dev/null
+++ b/siftflow-fcn32s/solver.prototxt.orig
@@ -0,0 +1,17 @@
+train_net: "trainval.prototxt"
+test_net: "test.prototxt"
+test_iter: 1111
+# make test net, but don't invoke it from the solver itself
+test_interval: 999999999
+display: 20
+average_loss: 20
+lr_policy: "fixed"
+# lr for unnormalized softmax
+base_lr: 1e-10
+# high momentum
+momentum: 0.99
+# no gradient accumulation
+iter_size: 1
+max_iter: 300000
+weight_decay: 0.0005
+test_initialization: false
diff --git a/siftflow-fcn32s/test.prototxt b/siftflow-fcn32s/test.prototxt
new file mode 100644
index 0000000000000000000000000000000000000000..ced00099337347baf3f4d125e9bf8c127fa25157
--- /dev/null
+++ b/siftflow-fcn32s/test.prototxt
@@ -0,0 +1,587 @@
+layer {
+  name: "data"
+  type: "Python"
+  top: "data"
+  top: "sem"
+  top: "geo"
+  python_param {
+    module: "siftflow_layers"
+    layer: "SIFTFlowSegDataLayer"
+    param_str: "{\'siftflow_dir\': \'../../data/sift-flow\', \'seed\': 1337, \'split\': \'test\'}"
+  }
+}
+layer {
+  name: "conv1_1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1_1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    pad: 100
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu1_1"
+  type: "ReLU"
+  bottom: "conv1_1"
+  top: "conv1_1"
+}
+layer {
+  name: "conv1_2"
+  type: "Convolution"
+  bottom: "conv1_1"
+  top: "conv1_2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu1_2"
+  type: "ReLU"
+  bottom: "conv1_2"
+  top: "conv1_2"
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "conv1_2"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv2_1"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2_1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu2_1"
+  type: "ReLU"
+  bottom: "conv2_1"
+  top: "conv2_1"
+}
+layer {
+  name: "conv2_2"
+  type: "Convolution"
+  bottom: "conv2_1"
+  top: "conv2_2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu2_2"
+  type: "ReLU"
+  bottom: "conv2_2"
+  top: "conv2_2"
+}
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "conv2_2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv3_1"
+  type: "Convolution"
+  bottom: "pool2"
+  top: "conv3_1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu3_1"
+  type: "ReLU"
+  bottom: "conv3_1"
+  top: "conv3_1"
+}
+layer {
+  name: "conv3_2"
+  type: "Convolution"
+  bottom: "conv3_1"
+  top: "conv3_2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu3_2"
+  type: "ReLU"
+  bottom: "conv3_2"
+  top: "conv3_2"
+}
+layer {
+  name: "conv3_3"
+  type: "Convolution"
+  bottom: "conv3_2"
+  top: "conv3_3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu3_3"
+  type: "ReLU"
+  bottom: "conv3_3"
+  top: "conv3_3"
+}
+layer {
+  name: "pool3"
+  type: "Pooling"
+  bottom: "conv3_3"
+  top: "pool3"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv4_1"
+  type: "Convolution"
+  bottom: "pool3"
+  top: "conv4_1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu4_1"
+  type: "ReLU"
+  bottom: "conv4_1"
+  top: "conv4_1"
+}
+layer {
+  name: "conv4_2"
+  type: "Convolution"
+  bottom: "conv4_1"
+  top: "conv4_2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu4_2"
+  type: "ReLU"
+  bottom: "conv4_2"
+  top: "conv4_2"
+}
+layer {
+  name: "conv4_3"
+  type: "Convolution"
+  bottom: "conv4_2"
+  top: "conv4_3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu4_3"
+  type: "ReLU"
+  bottom: "conv4_3"
+  top: "conv4_3"
+}
+layer {
+  name: "pool4"
+  type: "Pooling"
+  bottom: "conv4_3"
+  top: "pool4"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv5_1"
+  type: "Convolution"
+  bottom: "pool4"
+  top: "conv5_1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu5_1"
+  type: "ReLU"
+  bottom: "conv5_1"
+  top: "conv5_1"
+}
+layer {
+  name: "conv5_2"
+  type: "Convolution"
+  bottom: "conv5_1"
+  top: "conv5_2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu5_2"
+  type: "ReLU"
+  bottom: "conv5_2"
+  top: "conv5_2"
+}
+layer {
+  name: "conv5_3"
+  type: "Convolution"
+  bottom: "conv5_2"
+  top: "conv5_3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu5_3"
+  type: "ReLU"
+  bottom: "conv5_3"
+  top: "conv5_3"
+}
+layer {
+  name: "pool5"
+  type: "Pooling"
+  bottom: "conv5_3"
+  top: "pool5"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "fc6"
+  type: "Convolution"
+  bottom: "pool5"
+  top: "fc6"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 4096
+    pad: 0
+    kernel_size: 7
+    stride: 1
+  }
+}
+layer {
+  name: "relu6"
+  type: "ReLU"
+  bottom: "fc6"
+  top: "fc6"
+}
+layer {
+  name: "drop6"
+  type: "Dropout"
+  bottom: "fc6"
+  top: "fc6"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc7"
+  type: "Convolution"
+  bottom: "fc6"
+  top: "fc7"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 4096
+    pad: 0
+    kernel_size: 1
+    stride: 1
+  }
+}
+layer {
+  name: "relu7"
+  type: "ReLU"
+  bottom: "fc7"
+  top: "fc7"
+}
+layer {
+  name: "drop7"
+  type: "Dropout"
+  bottom: "fc7"
+  top: "fc7"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "score_fr_sem"
+  type: "Convolution"
+  bottom: "fc7"
+  top: "score_fr_sem"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 33
+    pad: 0
+    kernel_size: 1
+  }
+}
+layer {
+  name: "upscore_sem"
+  type: "Deconvolution"
+  bottom: "score_fr_sem"
+  top: "upscore_sem"
+  param {
+    lr_mult: 0
+  }
+  convolution_param {
+    num_output: 33
+    bias_term: false
+    kernel_size: 64
+    stride: 32
+  }
+}
+layer {
+  name: "score_sem"
+  type: "Crop"
+  bottom: "upscore_sem"
+  bottom: "data"
+  top: "score_sem"
+  crop_param {
+    axis: 2
+    offset: 19
+  }
+}
+layer {
+  name: "loss"
+  type: "SoftmaxWithLoss"
+  bottom: "score_sem"
+  bottom: "sem"
+  top: "loss"
+  loss_param {
+    ignore_label: 255
+    normalize: false
+  }
+}
+layer {
+  name: "score_fr_geo"
+  type: "Convolution"
+  bottom: "fc7"
+  top: "score_fr_geo"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 3
+    pad: 0
+    kernel_size: 1
+  }
+}
+layer {
+  name: "upscore_geo"
+  type: "Deconvolution"
+  bottom: "score_fr_geo"
+  top: "upscore_geo"
+  param {
+    lr_mult: 0
+  }
+  convolution_param {
+    num_output: 3
+    bias_term: false
+    kernel_size: 64
+    stride: 32
+  }
+}
+layer {
+  name: "score_geo"
+  type: "Crop"
+  bottom: "upscore_geo"
+  bottom: "data"
+  top: "score_geo"
+  crop_param {
+    axis: 2
+    offset: 19
+  }
+}
+layer {
+  name: "loss_geo"
+  type: "SoftmaxWithLoss"
+  bottom: "score_geo"
+  bottom: "geo"
+  top: "loss_geo"
+  loss_param {
+    ignore_label: 255
+    normalize: false
+  }
+}
diff --git a/siftflow-fcn32s/trainval.prototxt b/siftflow-fcn32s/trainval.prototxt
new file mode 100644
index 0000000000000000000000000000000000000000..57e17dda954be178ce91241332832c4384076106
--- /dev/null
+++ b/siftflow-fcn32s/trainval.prototxt
@@ -0,0 +1,587 @@
+layer {
+  name: "data"
+  type: "Python"
+  top: "data"
+  top: "sem"
+  top: "geo"
+  python_param {
+    module: "siftflow_layers"
+    layer: "SIFTFlowSegDataLayer"
+    param_str: "{\'siftflow_dir\': \'../../data/sift-flow\', \'seed\': 1337, \'split\': \'trainval\'}"
+  }
+}
+layer {
+  name: "conv1_1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1_1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    pad: 100
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu1_1"
+  type: "ReLU"
+  bottom: "conv1_1"
+  top: "conv1_1"
+}
+layer {
+  name: "conv1_2"
+  type: "Convolution"
+  bottom: "conv1_1"
+  top: "conv1_2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu1_2"
+  type: "ReLU"
+  bottom: "conv1_2"
+  top: "conv1_2"
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "conv1_2"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv2_1"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2_1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu2_1"
+  type: "ReLU"
+  bottom: "conv2_1"
+  top: "conv2_1"
+}
+layer {
+  name: "conv2_2"
+  type: "Convolution"
+  bottom: "conv2_1"
+  top: "conv2_2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu2_2"
+  type: "ReLU"
+  bottom: "conv2_2"
+  top: "conv2_2"
+}
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "conv2_2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv3_1"
+  type: "Convolution"
+  bottom: "pool2"
+  top: "conv3_1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu3_1"
+  type: "ReLU"
+  bottom: "conv3_1"
+  top: "conv3_1"
+}
+layer {
+  name: "conv3_2"
+  type: "Convolution"
+  bottom: "conv3_1"
+  top: "conv3_2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu3_2"
+  type: "ReLU"
+  bottom: "conv3_2"
+  top: "conv3_2"
+}
+layer {
+  name: "conv3_3"
+  type: "Convolution"
+  bottom: "conv3_2"
+  top: "conv3_3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu3_3"
+  type: "ReLU"
+  bottom: "conv3_3"
+  top: "conv3_3"
+}
+layer {
+  name: "pool3"
+  type: "Pooling"
+  bottom: "conv3_3"
+  top: "pool3"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv4_1"
+  type: "Convolution"
+  bottom: "pool3"
+  top: "conv4_1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu4_1"
+  type: "ReLU"
+  bottom: "conv4_1"
+  top: "conv4_1"
+}
+layer {
+  name: "conv4_2"
+  type: "Convolution"
+  bottom: "conv4_1"
+  top: "conv4_2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu4_2"
+  type: "ReLU"
+  bottom: "conv4_2"
+  top: "conv4_2"
+}
+layer {
+  name: "conv4_3"
+  type: "Convolution"
+  bottom: "conv4_2"
+  top: "conv4_3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu4_3"
+  type: "ReLU"
+  bottom: "conv4_3"
+  top: "conv4_3"
+}
+layer {
+  name: "pool4"
+  type: "Pooling"
+  bottom: "conv4_3"
+  top: "pool4"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv5_1"
+  type: "Convolution"
+  bottom: "pool4"
+  top: "conv5_1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu5_1"
+  type: "ReLU"
+  bottom: "conv5_1"
+  top: "conv5_1"
+}
+layer {
+  name: "conv5_2"
+  type: "Convolution"
+  bottom: "conv5_1"
+  top: "conv5_2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu5_2"
+  type: "ReLU"
+  bottom: "conv5_2"
+  top: "conv5_2"
+}
+layer {
+  name: "conv5_3"
+  type: "Convolution"
+  bottom: "conv5_2"
+  top: "conv5_3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu5_3"
+  type: "ReLU"
+  bottom: "conv5_3"
+  top: "conv5_3"
+}
+layer {
+  name: "pool5"
+  type: "Pooling"
+  bottom: "conv5_3"
+  top: "pool5"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "fc6"
+  type: "Convolution"
+  bottom: "pool5"
+  top: "fc6"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 4096
+    pad: 0
+    kernel_size: 7
+    stride: 1
+  }
+}
+layer {
+  name: "relu6"
+  type: "ReLU"
+  bottom: "fc6"
+  top: "fc6"
+}
+layer {
+  name: "drop6"
+  type: "Dropout"
+  bottom: "fc6"
+  top: "fc6"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc7"
+  type: "Convolution"
+  bottom: "fc6"
+  top: "fc7"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 4096
+    pad: 0
+    kernel_size: 1
+    stride: 1
+  }
+}
+layer {
+  name: "relu7"
+  type: "ReLU"
+  bottom: "fc7"
+  top: "fc7"
+}
+layer {
+  name: "drop7"
+  type: "Dropout"
+  bottom: "fc7"
+  top: "fc7"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "score_fr_sem"
+  type: "Convolution"
+  bottom: "fc7"
+  top: "score_fr_sem"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 33
+    pad: 0
+    kernel_size: 1
+  }
+}
+layer {
+  name: "upscore_sem"
+  type: "Deconvolution"
+  bottom: "score_fr_sem"
+  top: "upscore_sem"
+  param {
+    lr_mult: 0
+  }
+  convolution_param {
+    num_output: 33
+    bias_term: false
+    kernel_size: 64
+    stride: 32
+  }
+}
+layer {
+  name: "score_sem"
+  type: "Crop"
+  bottom: "upscore_sem"
+  bottom: "data"
+  top: "score_sem"
+  crop_param {
+    axis: 2
+    offset: 19
+  }
+}
+layer {
+  name: "loss"
+  type: "SoftmaxWithLoss"
+  bottom: "score_sem"
+  bottom: "sem"
+  top: "loss"
+  loss_param {
+    ignore_label: 255
+    normalize: false
+  }
+}
+layer {
+  name: "score_fr_geo"
+  type: "Convolution"
+  bottom: "fc7"
+  top: "score_fr_geo"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 3
+    pad: 0
+    kernel_size: 1
+  }
+}
+layer {
+  name: "upscore_geo"
+  type: "Deconvolution"
+  bottom: "score_fr_geo"
+  top: "upscore_geo"
+  param {
+    lr_mult: 0
+  }
+  convolution_param {
+    num_output: 3
+    bias_term: false
+    kernel_size: 64
+    stride: 32
+  }
+}
+layer {
+  name: "score_geo"
+  type: "Crop"
+  bottom: "upscore_geo"
+  bottom: "data"
+  top: "score_geo"
+  crop_param {
+    axis: 2
+    offset: 19
+  }
+}
+layer {
+  name: "loss_geo"
+  type: "SoftmaxWithLoss"
+  bottom: "score_geo"
+  bottom: "geo"
+  top: "loss_geo"
+  loss_param {
+    ignore_label: 255
+    normalize: false
+  }
+}
diff --git a/siftflow-fcn8s/caffemodel-url b/siftflow-fcn8s/caffemodel-url
new file mode 100644
index 0000000000000000000000000000000000000000..c83d54b0529bcbad7439d1be9113ac2eb1502767
--- /dev/null
+++ b/siftflow-fcn8s/caffemodel-url
@@ -0,0 +1 @@
+http://dl.caffe.berkeleyvision.org/siftflow-fcn8s-heavy.caffemodel
diff --git a/siftflow-fcn8s/net.py b/siftflow-fcn8s/net.py
new file mode 100644
index 0000000000000000000000000000000000000000..1309080b955b52c5735ba9c5455f6f7c1dbcf2ba
--- /dev/null
+++ b/siftflow-fcn8s/net.py
@@ -0,0 +1,127 @@
+import caffe
+from caffe import layers as L, params as P
+from caffe.coord_map import crop
+
+def conv_relu(bottom, nout, ks=3, stride=1, pad=1):
+    conv = L.Convolution(bottom, kernel_size=ks, stride=stride,
+        num_output=nout, pad=pad,
+        param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)])
+    return conv, L.ReLU(conv, in_place=True)
+
+def max_pool(bottom, ks=2, stride=2):
+    return L.Pooling(bottom, pool=P.Pooling.MAX, kernel_size=ks, stride=stride)
+
+def fcn(split):
+    n = caffe.NetSpec()
+    n.data, n.sem, n.geo = L.Python(module='siftflow_layers',
+            layer='SIFTFlowSegDataLayer', ntop=3,
+            param_str=str(dict(siftflow_dir='../data/sift-flow',
+                split=split, seed=1337)))
+
+    # the base net
+    n.conv1_1, n.relu1_1 = conv_relu(n.data, 64, pad=100)
+    n.conv1_2, n.relu1_2 = conv_relu(n.relu1_1, 64)
+    n.pool1 = max_pool(n.relu1_2)
+
+    n.conv2_1, n.relu2_1 = conv_relu(n.pool1, 128)
+    n.conv2_2, n.relu2_2 = conv_relu(n.relu2_1, 128)
+    n.pool2 = max_pool(n.relu2_2)
+
+    n.conv3_1, n.relu3_1 = conv_relu(n.pool2, 256)
+    n.conv3_2, n.relu3_2 = conv_relu(n.relu3_1, 256)
+    n.conv3_3, n.relu3_3 = conv_relu(n.relu3_2, 256)
+    n.pool3 = max_pool(n.relu3_3)
+
+    n.conv4_1, n.relu4_1 = conv_relu(n.pool3, 512)
+    n.conv4_2, n.relu4_2 = conv_relu(n.relu4_1, 512)
+    n.conv4_3, n.relu4_3 = conv_relu(n.relu4_2, 512)
+    n.pool4 = max_pool(n.relu4_3)
+
+    n.conv5_1, n.relu5_1 = conv_relu(n.pool4, 512)
+    n.conv5_2, n.relu5_2 = conv_relu(n.relu5_1, 512)
+    n.conv5_3, n.relu5_3 = conv_relu(n.relu5_2, 512)
+    n.pool5 = max_pool(n.relu5_3)
+
+    # fully conv
+    n.fc6, n.relu6 = conv_relu(n.pool5, 4096, ks=7, pad=0)
+    n.drop6 = L.Dropout(n.relu6, dropout_ratio=0.5, in_place=True)
+    n.fc7, n.relu7 = conv_relu(n.drop6, 4096, ks=1, pad=0)
+    n.drop7 = L.Dropout(n.relu7, dropout_ratio=0.5, in_place=True)
+
+    n.score_fr_sem = L.Convolution(n.drop7, num_output=33, kernel_size=1, pad=0,
+        param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)])
+    n.upscore2_sem = L.Deconvolution(n.score_fr_sem,
+        convolution_param=dict(num_output=33, kernel_size=4, stride=2,
+            bias_term=False),
+        param=[dict(lr_mult=0)])
+
+    n.score_pool4_sem = L.Convolution(n.pool4, num_output=33, kernel_size=1, pad=0,
+        param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)])
+    n.score_pool4_semc = crop(n.score_pool4_sem, n.upscore2_sem)
+    n.fuse_pool4_sem = L.Eltwise(n.upscore2_sem, n.score_pool4_semc,
+            operation=P.Eltwise.SUM)
+    n.upscore_pool4_sem  = L.Deconvolution(n.fuse_pool4_sem,
+        convolution_param=dict(num_output=33, kernel_size=4, stride=2,
+            bias_term=False),
+        param=[dict(lr_mult=0)])
+
+    n.score_pool3_sem = L.Convolution(n.pool3, num_output=33, kernel_size=1,
+            pad=0, param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2,
+                decay_mult=0)])
+    n.score_pool3_semc = crop(n.score_pool3_sem, n.upscore_pool4_sem)
+    n.fuse_pool3_sem = L.Eltwise(n.upscore_pool4_sem, n.score_pool3_semc,
+            operation=P.Eltwise.SUM)
+    n.upscore8_sem = L.Deconvolution(n.fuse_pool3_sem,
+        convolution_param=dict(num_output=33, kernel_size=16, stride=8,
+            bias_term=False),
+        param=[dict(lr_mult=0)])
+
+    n.score_sem = crop(n.upscore8_sem, n.data)
+    # loss to make score happy (o.w. loss_sem)
+    n.loss = L.SoftmaxWithLoss(n.score_sem, n.sem,
+            loss_param=dict(normalize=False, ignore_label=255))
+
+    n.score_fr_geo = L.Convolution(n.drop7, num_output=3, kernel_size=1, pad=0,
+        param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)])
+
+    n.upscore2_geo = L.Deconvolution(n.score_fr_geo,
+        convolution_param=dict(num_output=3, kernel_size=4, stride=2,
+            bias_term=False),
+        param=[dict(lr_mult=0)])
+
+    n.score_pool4_geo = L.Convolution(n.pool4, num_output=3, kernel_size=1, pad=0,
+        param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)])
+    n.score_pool4_geoc = crop(n.score_pool4_geo, n.upscore2_geo)
+    n.fuse_pool4_geo = L.Eltwise(n.upscore2_geo, n.score_pool4_geoc,
+            operation=P.Eltwise.SUM)
+    n.upscore_pool4_geo  = L.Deconvolution(n.fuse_pool4_geo,
+        convolution_param=dict(num_output=3, kernel_size=4, stride=2,
+            bias_term=False),
+        param=[dict(lr_mult=0)])
+
+    n.score_pool3_geo = L.Convolution(n.pool3, num_output=3, kernel_size=1,
+            pad=0, param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2,
+                decay_mult=0)])
+    n.score_pool3_geoc = crop(n.score_pool3_geo, n.upscore_pool4_geo)
+    n.fuse_pool3_geo = L.Eltwise(n.upscore_pool4_geo, n.score_pool3_geoc,
+            operation=P.Eltwise.SUM)
+    n.upscore8_geo = L.Deconvolution(n.fuse_pool3_geo,
+        convolution_param=dict(num_output=3, kernel_size=16, stride=8,
+            bias_term=False),
+        param=[dict(lr_mult=0)])
+
+    n.score_geo = crop(n.upscore8_geo, n.data)
+    n.loss_geo = L.SoftmaxWithLoss(n.score_geo, n.geo,
+            loss_param=dict(normalize=False, ignore_label=255))
+
+    return n.to_proto()
+
+def make_net():
+    with open('trainval.prototxt', 'w') as f:
+        f.write(str(fcn('trainval')))
+
+    with open('test.prototxt', 'w') as f:
+        f.write(str(fcn('test')))
+
+if __name__ == '__main__':
+    make_net()
diff --git a/siftflow-fcn8s/solve.py b/siftflow-fcn8s/solve.py
new file mode 100644
index 0000000000000000000000000000000000000000..8de67e14e080faae4d75f3a856058a6ded2e8f5d
--- /dev/null
+++ b/siftflow-fcn8s/solve.py
@@ -0,0 +1,31 @@
+import caffe
+import surgery, score
+
+import numpy as np
+import os
+
+import setproctitle
+setproctitle.setproctitle(os.path.basename(os.getcwd()))
+
+weights = '../siftflow-fcn16s/siftflow-fcn16s.caffemodel'
+
+# init
+caffe.set_device(int(sys.argv[1]))
+caffe.set_mode_gpu()
+
+solver = caffe.SGDSolver('solver.prototxt')
+solver.net.copy_from(weights)
+
+# surgeries
+interp_layers = [k for k in solver.net.params.keys() if 'up' in k]
+surgery.interp(solver.net, interp_layers)
+
+# scoring
+test = np.loadtxt('../data/sift-flow/test.txt', dtype=str)
+
+for _ in range(50):
+    solver.step(2000)
+    # N.B. metrics on the semantic labels are off b.c. of missing classes;
+    # score manually from the histogram instead for proper evaluation
+    score.seg_tests(solver, False, test, layer='score_sem', gt='sem')
+    score.seg_tests(solver, False, test, layer='score_geo', gt='geo')
diff --git a/siftflow-fcn8s/solver.prototxt b/siftflow-fcn8s/solver.prototxt
new file mode 100644
index 0000000000000000000000000000000000000000..09aff3378dca8db28fc768884e919edc345f9126
--- /dev/null
+++ b/siftflow-fcn8s/solver.prototxt
@@ -0,0 +1,17 @@
+train_net: "trainval.prototxt"
+test_net: "test.prototxt"
+test_iter: 1111
+# make test net, but don't invoke it from the solver itself
+test_interval: 999999999
+display: 20
+average_loss: 20
+lr_policy: "fixed"
+# lr for unnormalized softmax
+base_lr: 1e-12
+# high momentum
+momentum: 0.99
+# no gradient accumulation
+iter_size: 1
+max_iter: 300000
+weight_decay: 0.0005
+test_initialization: false
diff --git a/siftflow-fcn8s/test.prototxt b/siftflow-fcn8s/test.prototxt
new file mode 100644
index 0000000000000000000000000000000000000000..d9acc78c587ac3302f471923e98ae6718ed7cabe
--- /dev/null
+++ b/siftflow-fcn8s/test.prototxt
@@ -0,0 +1,807 @@
+layer {
+  name: "data"
+  type: "Python"
+  top: "data"
+  top: "sem"
+  top: "geo"
+  python_param {
+    module: "siftflow_layers"
+    layer: "SIFTFlowSegDataLayer"
+    param_str: "{\'siftflow_dir\': \'../../data/sift-flow\', \'seed\': 1337, \'split\': \'test\'}"
+  }
+}
+layer {
+  name: "conv1_1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1_1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    pad: 100
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu1_1"
+  type: "ReLU"
+  bottom: "conv1_1"
+  top: "conv1_1"
+}
+layer {
+  name: "conv1_2"
+  type: "Convolution"
+  bottom: "conv1_1"
+  top: "conv1_2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu1_2"
+  type: "ReLU"
+  bottom: "conv1_2"
+  top: "conv1_2"
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "conv1_2"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv2_1"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2_1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu2_1"
+  type: "ReLU"
+  bottom: "conv2_1"
+  top: "conv2_1"
+}
+layer {
+  name: "conv2_2"
+  type: "Convolution"
+  bottom: "conv2_1"
+  top: "conv2_2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu2_2"
+  type: "ReLU"
+  bottom: "conv2_2"
+  top: "conv2_2"
+}
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "conv2_2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv3_1"
+  type: "Convolution"
+  bottom: "pool2"
+  top: "conv3_1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu3_1"
+  type: "ReLU"
+  bottom: "conv3_1"
+  top: "conv3_1"
+}
+layer {
+  name: "conv3_2"
+  type: "Convolution"
+  bottom: "conv3_1"
+  top: "conv3_2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu3_2"
+  type: "ReLU"
+  bottom: "conv3_2"
+  top: "conv3_2"
+}
+layer {
+  name: "conv3_3"
+  type: "Convolution"
+  bottom: "conv3_2"
+  top: "conv3_3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu3_3"
+  type: "ReLU"
+  bottom: "conv3_3"
+  top: "conv3_3"
+}
+layer {
+  name: "pool3"
+  type: "Pooling"
+  bottom: "conv3_3"
+  top: "pool3"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv4_1"
+  type: "Convolution"
+  bottom: "pool3"
+  top: "conv4_1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu4_1"
+  type: "ReLU"
+  bottom: "conv4_1"
+  top: "conv4_1"
+}
+layer {
+  name: "conv4_2"
+  type: "Convolution"
+  bottom: "conv4_1"
+  top: "conv4_2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu4_2"
+  type: "ReLU"
+  bottom: "conv4_2"
+  top: "conv4_2"
+}
+layer {
+  name: "conv4_3"
+  type: "Convolution"
+  bottom: "conv4_2"
+  top: "conv4_3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu4_3"
+  type: "ReLU"
+  bottom: "conv4_3"
+  top: "conv4_3"
+}
+layer {
+  name: "pool4"
+  type: "Pooling"
+  bottom: "conv4_3"
+  top: "pool4"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv5_1"
+  type: "Convolution"
+  bottom: "pool4"
+  top: "conv5_1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu5_1"
+  type: "ReLU"
+  bottom: "conv5_1"
+  top: "conv5_1"
+}
+layer {
+  name: "conv5_2"
+  type: "Convolution"
+  bottom: "conv5_1"
+  top: "conv5_2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu5_2"
+  type: "ReLU"
+  bottom: "conv5_2"
+  top: "conv5_2"
+}
+layer {
+  name: "conv5_3"
+  type: "Convolution"
+  bottom: "conv5_2"
+  top: "conv5_3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu5_3"
+  type: "ReLU"
+  bottom: "conv5_3"
+  top: "conv5_3"
+}
+layer {
+  name: "pool5"
+  type: "Pooling"
+  bottom: "conv5_3"
+  top: "pool5"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "fc6"
+  type: "Convolution"
+  bottom: "pool5"
+  top: "fc6"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 4096
+    pad: 0
+    kernel_size: 7
+    stride: 1
+  }
+}
+layer {
+  name: "relu6"
+  type: "ReLU"
+  bottom: "fc6"
+  top: "fc6"
+}
+layer {
+  name: "drop6"
+  type: "Dropout"
+  bottom: "fc6"
+  top: "fc6"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc7"
+  type: "Convolution"
+  bottom: "fc6"
+  top: "fc7"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 4096
+    pad: 0
+    kernel_size: 1
+    stride: 1
+  }
+}
+layer {
+  name: "relu7"
+  type: "ReLU"
+  bottom: "fc7"
+  top: "fc7"
+}
+layer {
+  name: "drop7"
+  type: "Dropout"
+  bottom: "fc7"
+  top: "fc7"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "score_fr_sem"
+  type: "Convolution"
+  bottom: "fc7"
+  top: "score_fr_sem"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 33
+    pad: 0
+    kernel_size: 1
+  }
+}
+layer {
+  name: "upscore2_sem"
+  type: "Deconvolution"
+  bottom: "score_fr_sem"
+  top: "upscore2_sem"
+  param {
+    lr_mult: 0
+  }
+  convolution_param {
+    num_output: 33
+    bias_term: false
+    kernel_size: 4
+    stride: 2
+  }
+}
+layer {
+  name: "score_pool4_sem"
+  type: "Convolution"
+  bottom: "pool4"
+  top: "score_pool4_sem"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 33
+    pad: 0
+    kernel_size: 1
+  }
+}
+layer {
+  name: "score_pool4_semc"
+  type: "Crop"
+  bottom: "score_pool4_sem"
+  bottom: "upscore2_sem"
+  top: "score_pool4_semc"
+  crop_param {
+    axis: 2
+    offset: 5
+  }
+}
+layer {
+  name: "fuse_pool4_sem"
+  type: "Eltwise"
+  bottom: "upscore2_sem"
+  bottom: "score_pool4_semc"
+  top: "fuse_pool4_sem"
+  eltwise_param {
+    operation: SUM
+  }
+}
+layer {
+  name: "upscore_pool4_sem"
+  type: "Deconvolution"
+  bottom: "fuse_pool4_sem"
+  top: "upscore_pool4_sem"
+  param {
+    lr_mult: 0
+  }
+  convolution_param {
+    num_output: 33
+    bias_term: false
+    kernel_size: 4
+    stride: 2
+  }
+}
+layer {
+  name: "score_pool3_sem"
+  type: "Convolution"
+  bottom: "pool3"
+  top: "score_pool3_sem"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 33
+    pad: 0
+    kernel_size: 1
+  }
+}
+layer {
+  name: "score_pool3_semc"
+  type: "Crop"
+  bottom: "score_pool3_sem"
+  bottom: "upscore_pool4_sem"
+  top: "score_pool3_semc"
+  crop_param {
+    axis: 2
+    offset: 9
+  }
+}
+layer {
+  name: "fuse_pool3_sem"
+  type: "Eltwise"
+  bottom: "upscore_pool4_sem"
+  bottom: "score_pool3_semc"
+  top: "fuse_pool3_sem"
+  eltwise_param {
+    operation: SUM
+  }
+}
+layer {
+  name: "upscore8_sem"
+  type: "Deconvolution"
+  bottom: "fuse_pool3_sem"
+  top: "upscore8_sem"
+  param {
+    lr_mult: 0
+  }
+  convolution_param {
+    num_output: 33
+    bias_term: false
+    kernel_size: 16
+    stride: 8
+  }
+}
+layer {
+  name: "score_sem"
+  type: "Crop"
+  bottom: "upscore8_sem"
+  bottom: "data"
+  top: "score_sem"
+  crop_param {
+    axis: 2
+    offset: 31
+  }
+}
+layer {
+  name: "loss"
+  type: "SoftmaxWithLoss"
+  bottom: "score_sem"
+  bottom: "sem"
+  top: "loss"
+  loss_param {
+    ignore_label: 255
+    normalize: false
+  }
+}
+layer {
+  name: "score_fr_geo"
+  type: "Convolution"
+  bottom: "fc7"
+  top: "score_fr_geo"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 3
+    pad: 0
+    kernel_size: 1
+  }
+}
+layer {
+  name: "upscore2_geo"
+  type: "Deconvolution"
+  bottom: "score_fr_geo"
+  top: "upscore2_geo"
+  param {
+    lr_mult: 0
+  }
+  convolution_param {
+    num_output: 3
+    bias_term: false
+    kernel_size: 4
+    stride: 2
+  }
+}
+layer {
+  name: "score_pool4_geo"
+  type: "Convolution"
+  bottom: "pool4"
+  top: "score_pool4_geo"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 3
+    pad: 0
+    kernel_size: 1
+  }
+}
+layer {
+  name: "score_pool4_geoc"
+  type: "Crop"
+  bottom: "score_pool4_geo"
+  bottom: "upscore2_geo"
+  top: "score_pool4_geoc"
+  crop_param {
+    axis: 2
+    offset: 5
+  }
+}
+layer {
+  name: "fuse_pool4_geo"
+  type: "Eltwise"
+  bottom: "upscore2_geo"
+  bottom: "score_pool4_geoc"
+  top: "fuse_pool4_geo"
+  eltwise_param {
+    operation: SUM
+  }
+}
+layer {
+  name: "upscore_pool4_geo"
+  type: "Deconvolution"
+  bottom: "fuse_pool4_geo"
+  top: "upscore_pool4_geo"
+  param {
+    lr_mult: 0
+  }
+  convolution_param {
+    num_output: 3
+    bias_term: false
+    kernel_size: 4
+    stride: 2
+  }
+}
+layer {
+  name: "score_pool3_geo"
+  type: "Convolution"
+  bottom: "pool3"
+  top: "score_pool3_geo"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 3
+    pad: 0
+    kernel_size: 1
+  }
+}
+layer {
+  name: "score_pool3_geoc"
+  type: "Crop"
+  bottom: "score_pool3_geo"
+  bottom: "upscore_pool4_geo"
+  top: "score_pool3_geoc"
+  crop_param {
+    axis: 2
+    offset: 9
+  }
+}
+layer {
+  name: "fuse_pool3_geo"
+  type: "Eltwise"
+  bottom: "upscore_pool4_geo"
+  bottom: "score_pool3_geoc"
+  top: "fuse_pool3_geo"
+  eltwise_param {
+    operation: SUM
+  }
+}
+layer {
+  name: "upscore8_geo"
+  type: "Deconvolution"
+  bottom: "fuse_pool3_geo"
+  top: "upscore8_geo"
+  param {
+    lr_mult: 0
+  }
+  convolution_param {
+    num_output: 3
+    bias_term: false
+    kernel_size: 16
+    stride: 8
+  }
+}
+layer {
+  name: "score_geo"
+  type: "Crop"
+  bottom: "upscore8_geo"
+  bottom: "data"
+  top: "score_geo"
+  crop_param {
+    axis: 2
+    offset: 31
+  }
+}
+layer {
+  name: "loss_geo"
+  type: "SoftmaxWithLoss"
+  bottom: "score_geo"
+  bottom: "geo"
+  top: "loss_geo"
+  loss_param {
+    ignore_label: 255
+    normalize: false
+  }
+}
diff --git a/siftflow-fcn8s/trainval.prototxt b/siftflow-fcn8s/trainval.prototxt
new file mode 100644
index 0000000000000000000000000000000000000000..73479cb4b85dded7d08a8754334e8b54cd45ac87
--- /dev/null
+++ b/siftflow-fcn8s/trainval.prototxt
@@ -0,0 +1,807 @@
+layer {
+  name: "data"
+  type: "Python"
+  top: "data"
+  top: "sem"
+  top: "geo"
+  python_param {
+    module: "siftflow_layers"
+    layer: "SIFTFlowSegDataLayer"
+    param_str: "{\'siftflow_dir\': \'../../data/sift-flow\', \'seed\': 1337, \'split\': \'trainval\'}"
+  }
+}
+layer {
+  name: "conv1_1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1_1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    pad: 100
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu1_1"
+  type: "ReLU"
+  bottom: "conv1_1"
+  top: "conv1_1"
+}
+layer {
+  name: "conv1_2"
+  type: "Convolution"
+  bottom: "conv1_1"
+  top: "conv1_2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu1_2"
+  type: "ReLU"
+  bottom: "conv1_2"
+  top: "conv1_2"
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "conv1_2"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv2_1"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2_1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu2_1"
+  type: "ReLU"
+  bottom: "conv2_1"
+  top: "conv2_1"
+}
+layer {
+  name: "conv2_2"
+  type: "Convolution"
+  bottom: "conv2_1"
+  top: "conv2_2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu2_2"
+  type: "ReLU"
+  bottom: "conv2_2"
+  top: "conv2_2"
+}
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "conv2_2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv3_1"
+  type: "Convolution"
+  bottom: "pool2"
+  top: "conv3_1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu3_1"
+  type: "ReLU"
+  bottom: "conv3_1"
+  top: "conv3_1"
+}
+layer {
+  name: "conv3_2"
+  type: "Convolution"
+  bottom: "conv3_1"
+  top: "conv3_2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu3_2"
+  type: "ReLU"
+  bottom: "conv3_2"
+  top: "conv3_2"
+}
+layer {
+  name: "conv3_3"
+  type: "Convolution"
+  bottom: "conv3_2"
+  top: "conv3_3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu3_3"
+  type: "ReLU"
+  bottom: "conv3_3"
+  top: "conv3_3"
+}
+layer {
+  name: "pool3"
+  type: "Pooling"
+  bottom: "conv3_3"
+  top: "pool3"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv4_1"
+  type: "Convolution"
+  bottom: "pool3"
+  top: "conv4_1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu4_1"
+  type: "ReLU"
+  bottom: "conv4_1"
+  top: "conv4_1"
+}
+layer {
+  name: "conv4_2"
+  type: "Convolution"
+  bottom: "conv4_1"
+  top: "conv4_2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu4_2"
+  type: "ReLU"
+  bottom: "conv4_2"
+  top: "conv4_2"
+}
+layer {
+  name: "conv4_3"
+  type: "Convolution"
+  bottom: "conv4_2"
+  top: "conv4_3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu4_3"
+  type: "ReLU"
+  bottom: "conv4_3"
+  top: "conv4_3"
+}
+layer {
+  name: "pool4"
+  type: "Pooling"
+  bottom: "conv4_3"
+  top: "pool4"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv5_1"
+  type: "Convolution"
+  bottom: "pool4"
+  top: "conv5_1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu5_1"
+  type: "ReLU"
+  bottom: "conv5_1"
+  top: "conv5_1"
+}
+layer {
+  name: "conv5_2"
+  type: "Convolution"
+  bottom: "conv5_1"
+  top: "conv5_2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu5_2"
+  type: "ReLU"
+  bottom: "conv5_2"
+  top: "conv5_2"
+}
+layer {
+  name: "conv5_3"
+  type: "Convolution"
+  bottom: "conv5_2"
+  top: "conv5_3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu5_3"
+  type: "ReLU"
+  bottom: "conv5_3"
+  top: "conv5_3"
+}
+layer {
+  name: "pool5"
+  type: "Pooling"
+  bottom: "conv5_3"
+  top: "pool5"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "fc6"
+  type: "Convolution"
+  bottom: "pool5"
+  top: "fc6"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 4096
+    pad: 0
+    kernel_size: 7
+    stride: 1
+  }
+}
+layer {
+  name: "relu6"
+  type: "ReLU"
+  bottom: "fc6"
+  top: "fc6"
+}
+layer {
+  name: "drop6"
+  type: "Dropout"
+  bottom: "fc6"
+  top: "fc6"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc7"
+  type: "Convolution"
+  bottom: "fc6"
+  top: "fc7"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 4096
+    pad: 0
+    kernel_size: 1
+    stride: 1
+  }
+}
+layer {
+  name: "relu7"
+  type: "ReLU"
+  bottom: "fc7"
+  top: "fc7"
+}
+layer {
+  name: "drop7"
+  type: "Dropout"
+  bottom: "fc7"
+  top: "fc7"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "score_fr_sem"
+  type: "Convolution"
+  bottom: "fc7"
+  top: "score_fr_sem"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 33
+    pad: 0
+    kernel_size: 1
+  }
+}
+layer {
+  name: "upscore2_sem"
+  type: "Deconvolution"
+  bottom: "score_fr_sem"
+  top: "upscore2_sem"
+  param {
+    lr_mult: 0
+  }
+  convolution_param {
+    num_output: 33
+    bias_term: false
+    kernel_size: 4
+    stride: 2
+  }
+}
+layer {
+  name: "score_pool4_sem"
+  type: "Convolution"
+  bottom: "pool4"
+  top: "score_pool4_sem"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 33
+    pad: 0
+    kernel_size: 1
+  }
+}
+layer {
+  name: "score_pool4_semc"
+  type: "Crop"
+  bottom: "score_pool4_sem"
+  bottom: "upscore2_sem"
+  top: "score_pool4_semc"
+  crop_param {
+    axis: 2
+    offset: 5
+  }
+}
+layer {
+  name: "fuse_pool4_sem"
+  type: "Eltwise"
+  bottom: "upscore2_sem"
+  bottom: "score_pool4_semc"
+  top: "fuse_pool4_sem"
+  eltwise_param {
+    operation: SUM
+  }
+}
+layer {
+  name: "upscore_pool4_sem"
+  type: "Deconvolution"
+  bottom: "fuse_pool4_sem"
+  top: "upscore_pool4_sem"
+  param {
+    lr_mult: 0
+  }
+  convolution_param {
+    num_output: 33
+    bias_term: false
+    kernel_size: 4
+    stride: 2
+  }
+}
+layer {
+  name: "score_pool3_sem"
+  type: "Convolution"
+  bottom: "pool3"
+  top: "score_pool3_sem"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 33
+    pad: 0
+    kernel_size: 1
+  }
+}
+layer {
+  name: "score_pool3_semc"
+  type: "Crop"
+  bottom: "score_pool3_sem"
+  bottom: "upscore_pool4_sem"
+  top: "score_pool3_semc"
+  crop_param {
+    axis: 2
+    offset: 9
+  }
+}
+layer {
+  name: "fuse_pool3_sem"
+  type: "Eltwise"
+  bottom: "upscore_pool4_sem"
+  bottom: "score_pool3_semc"
+  top: "fuse_pool3_sem"
+  eltwise_param {
+    operation: SUM
+  }
+}
+layer {
+  name: "upscore8_sem"
+  type: "Deconvolution"
+  bottom: "fuse_pool3_sem"
+  top: "upscore8_sem"
+  param {
+    lr_mult: 0
+  }
+  convolution_param {
+    num_output: 33
+    bias_term: false
+    kernel_size: 16
+    stride: 8
+  }
+}
+layer {
+  name: "score_sem"
+  type: "Crop"
+  bottom: "upscore8_sem"
+  bottom: "data"
+  top: "score_sem"
+  crop_param {
+    axis: 2
+    offset: 31
+  }
+}
+layer {
+  name: "loss"
+  type: "SoftmaxWithLoss"
+  bottom: "score_sem"
+  bottom: "sem"
+  top: "loss"
+  loss_param {
+    ignore_label: 255
+    normalize: false
+  }
+}
+layer {
+  name: "score_fr_geo"
+  type: "Convolution"
+  bottom: "fc7"
+  top: "score_fr_geo"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 3
+    pad: 0
+    kernel_size: 1
+  }
+}
+layer {
+  name: "upscore2_geo"
+  type: "Deconvolution"
+  bottom: "score_fr_geo"
+  top: "upscore2_geo"
+  param {
+    lr_mult: 0
+  }
+  convolution_param {
+    num_output: 3
+    bias_term: false
+    kernel_size: 4
+    stride: 2
+  }
+}
+layer {
+  name: "score_pool4_geo"
+  type: "Convolution"
+  bottom: "pool4"
+  top: "score_pool4_geo"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 3
+    pad: 0
+    kernel_size: 1
+  }
+}
+layer {
+  name: "score_pool4_geoc"
+  type: "Crop"
+  bottom: "score_pool4_geo"
+  bottom: "upscore2_geo"
+  top: "score_pool4_geoc"
+  crop_param {
+    axis: 2
+    offset: 5
+  }
+}
+layer {
+  name: "fuse_pool4_geo"
+  type: "Eltwise"
+  bottom: "upscore2_geo"
+  bottom: "score_pool4_geoc"
+  top: "fuse_pool4_geo"
+  eltwise_param {
+    operation: SUM
+  }
+}
+layer {
+  name: "upscore_pool4_geo"
+  type: "Deconvolution"
+  bottom: "fuse_pool4_geo"
+  top: "upscore_pool4_geo"
+  param {
+    lr_mult: 0
+  }
+  convolution_param {
+    num_output: 3
+    bias_term: false
+    kernel_size: 4
+    stride: 2
+  }
+}
+layer {
+  name: "score_pool3_geo"
+  type: "Convolution"
+  bottom: "pool3"
+  top: "score_pool3_geo"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 3
+    pad: 0
+    kernel_size: 1
+  }
+}
+layer {
+  name: "score_pool3_geoc"
+  type: "Crop"
+  bottom: "score_pool3_geo"
+  bottom: "upscore_pool4_geo"
+  top: "score_pool3_geoc"
+  crop_param {
+    axis: 2
+    offset: 9
+  }
+}
+layer {
+  name: "fuse_pool3_geo"
+  type: "Eltwise"
+  bottom: "upscore_pool4_geo"
+  bottom: "score_pool3_geoc"
+  top: "fuse_pool3_geo"
+  eltwise_param {
+    operation: SUM
+  }
+}
+layer {
+  name: "upscore8_geo"
+  type: "Deconvolution"
+  bottom: "fuse_pool3_geo"
+  top: "upscore8_geo"
+  param {
+    lr_mult: 0
+  }
+  convolution_param {
+    num_output: 3
+    bias_term: false
+    kernel_size: 16
+    stride: 8
+  }
+}
+layer {
+  name: "score_geo"
+  type: "Crop"
+  bottom: "upscore8_geo"
+  bottom: "data"
+  top: "score_geo"
+  crop_param {
+    axis: 2
+    offset: 31
+  }
+}
+layer {
+  name: "loss_geo"
+  type: "SoftmaxWithLoss"
+  bottom: "score_geo"
+  bottom: "geo"
+  top: "loss_geo"
+  loss_param {
+    ignore_label: 255
+    normalize: false
+  }
+}
diff --git a/siftflow_layers.py b/siftflow_layers.py
new file mode 100644
index 0000000000000000000000000000000000000000..5f7c60f12f42f3c367ed4443d4f6f29504dfe616
--- /dev/null
+++ b/siftflow_layers.py
@@ -0,0 +1,122 @@
+import caffe
+
+import numpy as np
+from PIL import Image
+import scipy.io
+
+import random
+
+class SIFTFlowSegDataLayer(caffe.Layer):
+    """
+    Load (input image, label image) pairs from SIFT Flow
+    one-at-a-time while reshaping the net to preserve dimensions.
+
+    This data layer has three tops:
+
+    1. the data, pre-processed
+    2. the semantic labels 0-32 and void 255
+    3. the geometric labels 0-2 and void 255
+
+    Use this to feed data to a fully convolutional network.
+    """
+
+    def setup(self, bottom, top):
+        """
+        Setup data layer according to parameters:
+
+        - siftflow_dir: path to SIFT Flow dir
+        - split: train / val / test
+        - randomize: load in random order (default: True)
+        - seed: seed for randomization (default: None / current time)
+
+        for semantic segmentation of object and geometric classes.
+
+        example: params = dict(siftflow_dir="/path/to/siftflow", split="val")
+        """
+        # config
+        params = eval(self.param_str)
+        self.siftflow_dir = params['siftflow_dir']
+        self.split = params['split']
+        self.mean = np.array((114.578, 115.294, 108.353), dtype=np.float32)
+        self.random = params.get('randomize', True)
+        self.seed = params.get('seed', None)
+
+        # three tops: data, semantic, geometric
+        if len(top) != 3:
+            raise Exception("Need to define three tops: data, semantic label, and geometric label.")
+        # data layers have no bottoms
+        if len(bottom) != 0:
+            raise Exception("Do not define a bottom.")
+
+        # load indices for images and labels
+        split_f  = '{}/{}.txt'.format(self.siftflow_dir, self.split)
+        self.indices = open(split_f, 'r').read().splitlines()
+        self.idx = 0
+
+        # make eval deterministic
+        if 'train' not in self.split:
+            self.random = False
+
+        # randomization: seed and pick
+        if self.random:
+            random.seed(self.seed)
+            self.idx = random.randint(0, len(self.indices)-1)
+
+    def reshape(self, bottom, top):
+        # load image + label image pair
+        self.data = self.load_image(self.indices[self.idx])
+        self.label_semantic = self.load_label(self.indices[self.idx], label_type='semantic')
+        self.label_geometric = self.load_label(self.indices[self.idx], label_type='geometric')
+        # reshape tops to fit (leading 1 is for batch dimension)
+        top[0].reshape(1, *self.data.shape)
+        top[1].reshape(1, *self.label_semantic.shape)
+        top[2].reshape(1, *self.label_geometric.shape)
+
+    def forward(self, bottom, top):
+        # assign output
+        top[0].data[...] = self.data
+        top[1].data[...] = self.label_semantic
+        top[2].data[...] = self.label_geometric
+
+        # pick next input
+        if self.random:
+            self.idx = random.randint(0, len(self.indices)-1)
+        else:
+            self.idx += 1
+            if self.idx == len(self.indices):
+                self.idx = 0
+
+    def backward(self, top, propagate_down, bottom):
+        pass
+
+    def load_image(self, idx):
+        """
+        Load input image and preprocess for Caffe:
+        - cast to float
+        - switch channels RGB -> BGR
+        - subtract mean
+        - transpose to channel x height x width order
+        """
+        im = Image.open('{}/Images/spatial_envelope_256x256_static_8outdoorcategories/{}.jpg'.format(self.siftflow_dir, idx))
+        in_ = np.array(im, dtype=np.float32)
+        in_ = in_[:,:,::-1]
+        in_ -= self.mean
+        in_ = in_.transpose((2,0,1))
+        return in_
+
+    def load_label(self, idx, label_type=None):
+        """
+        Load label image as 1 x height x width integer array of label indices.
+        The leading singleton dimension is required by the loss.
+        """
+        if label_type == 'semantic':
+            label = scipy.io.loadmat('{}/SemanticLabels/spatial_envelope_256x256_static_8outdoorcategories/{}.mat'.format(self.siftflow_dir, idx))['S']
+        elif label_type == 'geometric':
+            label = scipy.io.loadmat('{}/GeoLabels/spatial_envelope_256x256_static_8outdoorcategories/{}.mat'.format(self.siftflow_dir, idx))['S']
+            label[label == -1] = 0
+        else:
+            raise Exception("Unknown label type: {}. Pick semantic or geometric.".format(label_type))
+        label = label.astype(np.uint8)
+        label -= 1  # rotate labels so classes start at 0, void is 255
+        label = label[np.newaxis, ...]
+        return label.copy()