add all-at-once edition of FCN-8s for PASCAL VOC

This net is fine-tuned from VGG-16 all-at-once instead of in stages.
All-at-once learning is faster and less tedious but gives an
ever-so-slightly less accurate model.

README.md

@@ -13,15 +13,17 @@ Please ask Caffe and FCN usage questions on the [caffe-users mailing list](https
 These models are compatible with `BVLC/caffe:master` @ 8c66fa5 with the merge of PRs BVLC/caffe#3613 and BVLC/caffe#3570.
 The code and models here are available under the same license as Caffe (BSD-2) and the Caffe-bundled models (that is, unrestricted use; see the [BVLC model license](http://caffe.berkeleyvision.org/model_zoo.html#bvlc-model-license)).
 
-PASCAL VOC models: trained online with high momentum for a ~5 point boost in mean intersection-over-union over the original models.
+**PASCAL VOC models**: trained online with high momentum for a ~5 point boost in mean intersection-over-union over the original models.
 These models are trained using extra data from [Hariharan et al.](http://www.cs.berkeley.edu/~bharath2/codes/SBD/download.html), but excluding SBD val.
 FCN-32s is fine-tuned from the [ILSVRC-trained VGG-16 model](https://github.com/BVLC/caffe/wiki/Model-Zoo#models-used-by-the-vgg-team-in-ilsvrc-2014), and the finer striders are then fine-tuned in turn.
+The "at-once" FCN-8s is fine-tuned from VGG-16 all-at-once by scaling the skip connections to better condition optimization.
 
 * [FCN-32s PASCAL](tree/master/fcn32s): single stream, 32 pixel prediction stride version, scoring 63.6 mIU on seg11valid
 * [FCN-16s PASCAL](tree/master/fcn16s): two stream, 16 pixel prediction stride version, scoring 65.0 mIU on seg11valid
 * [FCN-8s PASCAL](tree/master/fcn8s): three stream, 8 pixel prediction stride version, scoring 65.5 mIU on seg11valid and 67.2 mIU on seg12test
+* [FCN-8s PASCAL at-once](tree/master/fcn8s): all-at-once edition of the three stream, 8 pixel prediction stride version, scoring 65.4 mIU on seg11valid
 
-To reproduce the validation scores, use the [seg11valid](https://gist.github.com/shelhamer/edb330760338892d511e) split defined by the paper in footnote 7. Since SBD train and PASCAL VOC 11 segval intersect, we only evaluate on the non-intersecting set for validation purposes.
+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.
 
 **The following models have not yet been ported to master and trained with the latest settings. Check back soon.**
 

voc-fcn8s-atonce/caffemodel-url

+http://dl.caffe.berkeleyvision.org/fcn8s-atonce-pascal.caffemodel

voc-fcn8s-atonce/net.py

+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()
+    pydata_params = dict(split=split, mean=(104.00699, 116.66877, 122.67892),
+            seed=1337)
+    if split == 'train':
+        pydata_params['sbdd_dir'] = '../../data/sbdd/dataset'
+        pylayer = 'SBDDSegDataLayer'
+    else:
+        pydata_params['voc_dir'] = '../../data/pascal/VOC2011'
+        pylayer = 'VOCSegDataLayer'
+    n.data, n.label = L.Python(module='voc_layers', layer=pylayer,
+            ntop=2, param_str=str(pydata_params))
+
+    # 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 = L.Convolution(n.drop7, num_output=21, kernel_size=1, pad=0,
+        param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)])
+    n.upscore2 = L.Deconvolution(n.score_fr,
+        convolution_param=dict(num_output=21, kernel_size=4, stride=2,
+            bias_term=False),
+        param=[dict(lr_mult=0)])
+
+    # scale pool4 skip for compatibility
+    n.scale_pool4 = L.Scale(n.pool4, filler=dict(type='constant',
+        value=0.01), param=[dict(lr_mult=0)])
+    n.score_pool4 = L.Convolution(n.scale_pool4, num_output=21, kernel_size=1, pad=0,
+        param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)])
+    n.score_pool4c = crop(n.score_pool4, n.upscore2)
+    n.fuse_pool4 = L.Eltwise(n.upscore2, n.score_pool4c,
+            operation=P.Eltwise.SUM)
+    n.upscore_pool4 = L.Deconvolution(n.fuse_pool4,
+        convolution_param=dict(num_output=21, kernel_size=4, stride=2,
+            bias_term=False),
+        param=[dict(lr_mult=0)])
+
+    # scale pool3 skip for compatibility
+    n.scale_pool3 = L.Scale(n.pool3, filler=dict(type='constant',
+        value=0.0001), param=[dict(lr_mult=0)])
+    n.score_pool3 = L.Convolution(n.scale_pool3, num_output=21, kernel_size=1, pad=0,
+        param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)])
+    n.score_pool3c = crop(n.score_pool3, n.upscore_pool4)
+    n.fuse_pool3 = L.Eltwise(n.upscore_pool4, n.score_pool3c,
+            operation=P.Eltwise.SUM)
+    n.upscore8 = L.Deconvolution(n.fuse_pool3,
+        convolution_param=dict(num_output=21, kernel_size=16, stride=8,
+            bias_term=False),
+        param=[dict(lr_mult=0)])
+
+    n.score = crop(n.upscore8, n.data)
+    n.loss = L.SoftmaxWithLoss(n.score, n.label,
+            loss_param=dict(normalize=False, ignore_label=255))
+
+    return n.to_proto()
+
+def make_net():
+    with open('train.prototxt', 'w') as f:
+        f.write(str(fcn('train')))
+
+    with open('val.prototxt', 'w') as f:
+        f.write(str(fcn('seg11valid')))
+
+if __name__ == '__main__':
+    make_net()

voc-fcn8s-atonce/solve.py

+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
+val = np.loadtxt('../data/segvalid11.txt', dtype=str)
+
+for _ in range(75):
+    solver.step(4000)
+    score.seg_tests(solver, False, val, layer='score')

voc-fcn8s-atonce/solver.prototxt

+train_net: "train.prototxt"
+test_net: "val.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
+snapshot: 4000
+snapshot_prefix: "snapshot/train"
+test_initialization: false

voc-fcn8s-atonce/train.prototxt

+layer {
+  name: "data"
+  type: "Python"
+  top: "data"
+  top: "label"
+  python_param {
+    module: "voc_layers"
+    layer: "SBDDSegDataLayer"
+    param_str: "{\'sbdd_dir\': \'../../data/sbdd/dataset\', \'seed\': 1337, \'split\': \'train\', \'mean\': (104.00699, 116.66877, 122.67892)}"
+  }
+}
+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"
+  type: "Convolution"
+  bottom: "fc7"
+  top: "score_fr"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 21
+    pad: 0
+    kernel_size: 1
+  }
+}
+layer {
+  name: "upscore2"
+  type: "Deconvolution"
+  bottom: "score_fr"
+  top: "upscore2"
+  param {
+    lr_mult: 0
+  }
+  convolution_param {
+    num_output: 21
+    bias_term: false
+    kernel_size: 4
+    stride: 2
+  }
+}
+layer {
+  name: "scale_pool4"
+  type: "Scale"
+  bottom: "pool4"
+  top: "scale_pool4"
+  param {
+    lr_mult: 0
+  }
+  scale_param {
+    filler {
+      type: "constant"
+      value: 0.01
+    }
+  }
+}
+layer {
+  name: "score_pool4"
+  type: "Convolution"
+  bottom: "scale_pool4"
+  top: "score_pool4"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 21
+    pad: 0
+    kernel_size: 1
+  }
+}
+layer {
+  name: "score_pool4c"
+  type: "Crop"
+  bottom: "score_pool4"
+  bottom: "upscore2"
+  top: "score_pool4c"
+  crop_param {
+    axis: 2
+    offset: 5
+  }
+}
+layer {
+  name: "fuse_pool4"
+  type: "Eltwise"
+  bottom: "upscore2"
+  bottom: "score_pool4c"
+  top: "fuse_pool4"
+  eltwise_param {
+    operation: SUM
+  }
+}
+layer {
+  name: "upscore_pool4"
+  type: "Deconvolution"
+  bottom: "fuse_pool4"
+  top: "upscore_pool4"
+  param {
+    lr_mult: 0
+  }
+  convolution_param {
+    num_output: 21
+    bias_term: false
+    kernel_size: 4
+    stride: 2
+  }
+}
+layer {
+  name: "scale_pool3"
+  type: "Scale"
+  bottom: "pool3"
+  top: "scale_pool3"
+  param {
+    lr_mult: 0
+  }
+  scale_param {
+    filler {
+      type: "constant"
+      value: 0.0001
+    }
+  }
+}
+layer {
+  name: "score_pool3"
+  type: "Convolution"
+  bottom: "scale_pool3"
+  top: "score_pool3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 21
+    pad: 0
+    kernel_size: 1
+  }
+}
+layer {
+  name: "score_pool3c"
+  type: "Crop"
+  bottom: "score_pool3"
+  bottom: "upscore_pool4"
+  top: "score_pool3c"
+  crop_param {
+    axis: 2
+    offset: 9
+  }
+}
+layer {
+  name: "fuse_pool3"
+  type: "Eltwise"
+  bottom: "upscore_pool4"
+  bottom: "score_pool3c"
+  top: "fuse_pool3"
+  eltwise_param {
+    operation: SUM
+  }
+}
+layer {
+  name: "upscore8"
+  type: "Deconvolution"
+  bottom: "fuse_pool3"
+  top: "upscore8"
+  param {
+    lr_mult: 0
+  }
+  convolution_param {
+    num_output: 21
+    bias_term: false
+    kernel_size: 16
+    stride: 8
+  }
+}
+layer {
+  name: "score"
+  type: "Crop"
+  bottom: "upscore8"
+  bottom: "data"
+  top: "score"
+  crop_param {
+    axis: 2
+    offset: 31
+  }
+}
+layer {
+  name: "loss"
+  type: "SoftmaxWithLoss"
+  bottom: "score"
+  bottom: "label"
+  top: "loss"
+  loss_param {
+    ignore_label: 255
+    normalize: false
+  }
+}

voc-fcn8s-atonce/val.prototxt

+layer {
+  name: "data"
+  type: "Python"
+  top: "data"
+  top: "label"
+  python_param {
+    module: "voc_layers"
+    layer: "VOCSegDataLayer"
+    param_str: "{\'voc_dir\': \'../../data/pascal/VOC2011\', \'seed\': 1337, \'split\': \'seg11valid\', \'mean\': (104.00699, 116.66877, 122.67892)}"
+  }
+}
+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"
+  type: "Convolution"
+  bottom: "fc7"
+  top: "score_fr"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 21
+    pad: 0
+    kernel_size: 1
+  }
+}
+layer {
+  name: "upscore2"
+  type: "Deconvolution"
+  bottom: "score_fr"
+  top: "upscore2"
+  param {
+    lr_mult: 0
+  }
+  convolution_param {
+    num_output: 21
+    bias_term: false
+    kernel_size: 4
+    stride: 2
+  }
+}
+layer {
+  name: "scale_pool4"
+  type: "Scale"
+  bottom: "pool4"
+  top: "scale_pool4"
+  param {
+    lr_mult: 0
+  }
+  scale_param {
+    filler {
+      type: "constant"
+      value: 0.01
+    }
+  }
+}
+layer {
+  name: "score_pool4"
+  type: "Convolution"
+  bottom: "scale_pool4"
+  top: "score_pool4"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 21
+    pad: 0
+    kernel_size: 1
+  }
+}
+layer {
+  name: "score_pool4c"
+  type: "Crop"
+  bottom: "score_pool4"
+  bottom: "upscore2"
+  top: "score_pool4c"
+  crop_param {
+    axis: 2
+    offset: 5
+  }
+}
+layer {
+  name: "fuse_pool4"
+  type: "Eltwise"
+  bottom: "upscore2"
+  bottom: "score_pool4c"
+  top: "fuse_pool4"
+  eltwise_param {
+    operation: SUM
+  }
+}
+layer {
+  name: "upscore_pool4"
+  type: "Deconvolution"
+  bottom: "fuse_pool4"
+  top: "upscore_pool4"
+  param {
+    lr_mult: 0
+  }
+  convolution_param {
+    num_output: 21
+    bias_term: false
+    kernel_size: 4
+    stride: 2
+  }
+}
+layer {
+  name: "scale_pool3"
+  type: "Scale"
+  bottom: "pool3"
+  top: "scale_pool3"
+  param {
+    lr_mult: 0
+  }
+  scale_param {
+    filler {
+      type: "constant"
+      value: 0.0001
+    }
+  }
+}
+layer {
+  name: "score_pool3"
+  type: "Convolution"
+  bottom: "scale_pool3"
+  top: "score_pool3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 21
+    pad: 0
+    kernel_size: 1
+  }
+}
+layer {
+  name: "score_pool3c"
+  type: "Crop"
+  bottom: "score_pool3"
+  bottom: "upscore_pool4"
+  top: "score_pool3c"
+  crop_param {
+    axis: 2
+    offset: 9
+  }
+}
+layer {
+  name: "fuse_pool3"
+  type: "Eltwise"
+  bottom: "upscore_pool4"
+  bottom: "score_pool3c"
+  top: "fuse_pool3"
+  eltwise_param {
+    operation: SUM
+  }
+}
+layer {
+  name: "upscore8"
+  type: "Deconvolution"
+  bottom: "fuse_pool3"
+  top: "upscore8"
+  param {
+    lr_mult: 0
+  }
+  convolution_param {
+    num_output: 21
+    bias_term: false
+    kernel_size: 16
+    stride: 8
+  }
+}
+layer {
+  name: "score"
+  type: "Crop"
+  bottom: "upscore8"
+  bottom: "data"
+  top: "score"
+  crop_param {
+    axis: 2
+    offset: 31
+  }
+}
+layer {
+  name: "loss"
+  type: "SoftmaxWithLoss"
+  bottom: "score"
+  bottom: "label"
+  top: "loss"
+  loss_param {
+    ignore_label: 255
+    normalize: false
+  }
+}